Authorship attribution of ghost written novels.

Assignment_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Can a ghost writer mimic another author?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fiction literature, it is not uncommon for authors to write under more than one name or to write as another author.  Some examples of this are:\n",
    "* 'Tom Clancy' books written by Tom Clancy as well as other authors including Mark Greaner, Grant Blackwood etc\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|<img src=http://www.tomclancy.com/books/true-faith-and-allegiance-hc300.jpg width='100px'>|<img src=http://www.tomclancy.com/books/tom-clancy-duty-and-honor-hc300.jpg width='100px'>|\n",
    "\n",
    "* 'Clive Cussler' books written by Clive Cussler as well as other authors including Boyd Morrison, Justin Scott etc\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|<img src=http://clive-cussler-books.com/wp-content/uploads/2016/05/053116_Emperors-Revenge-Oregon-Files-Clive-Cussler-Novels_199x300.jpg width='100px'>|<img src=http://clive-cussler-books.com/wp-content/uploads/2016/05/030116_The-Gangster-Isaac-Bell-Clive-Cussler-Adventure-Novels_199x300.jpg width='100px'>|\n",
    "\n",
    "* J.K Rowling writting under the pseudonym Robert Galbraith\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|<img src=http://vignette3.wikia.nocookie.net/harrypotter/images/7/7b/Harry01english.jpg/revision/latest?cb=20150208225304 width='100px'>|<img src=https://static.independent.co.uk/s3fs-public/styles/story_medium/public/thumbnails/image/2013/07/14/22/9-calling.jpg width='112px'>|\n",
    "\n",
    "* Stephen King writing under the pseudonym Richard Bachman\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|<img src=https://images-na.ssl-images-amazon.com/images/I/513H0tH7t-L._SX301_BO1,204,203,200_.jpg width='100px'>|<img src=https://room435.files.wordpress.com/2011/03/2running-man.jpg width='100px'>|\n",
    "\n",
    "\n",
    "This notebook contains the code use to test whether an author writing under another autors name (e.g. Mark Greaney writing as Tom Clancy) can write in the style of another author, or if they retain their own language style."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Autorship attribution is the task of identifying the autor of a document.  Authors typically have their own unique writing style and their works can often be characterised by identifying elements or features of these styles.  There are numerous features which can be used to help classify a text including [1](http://www.icsd.aegean.gr/lecturers/stamatatos/papers/survey.pdf):\n",
    "\n",
    "* lexical features (e.g. average word length, sentence length, vocabulary richness, word frequencies, common errors\n",
    "* character features (e.g. character types, character n-grams etc)\n",
    "* Syntactic features (e.g. parts-of-speech, sentence structure etc)\n",
    "* Semantic features (e.g. function words, synonym use etc)\n",
    "\n",
    "A well known example of authorship attribution is that of determining the authorship of the federalist papers [a collection of 85 articles and essays written (under the pseudonym Publius) by Alexander Hamilton, James Madison, and John Jay promoting the ratification of the United States Constitution](https://en.wikipedia.org/wiki/The_Federalist_Papers).\n",
    "\n",
    "More recently authorship attribution is often used to identify who wrote blog posts or social media comments, and whether one person is writing under multiple pseudonyms in order for it to appear more people are commenting on a subject than may otherwise be the case.  This is known as [astroturfing](https://en.wikipedia.org/wiki/Astroturfing).\n",
    "\n",
    "Authorship attribution methods have proven successful in identifying authors from text, typically achieving accuracy levels in excess of 80 per cent.\n",
    "\n",
    "But what about professional authors?  Is a professional author able to mimic the style of another author well enough that they can pass as the other author?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to test whether an author can mimic the style of another author, we need three samples of text:\n",
    "1. text written by the base 'headline' author e.g. Tom Clancy\n",
    "2. text written by the 'ghost' author, under their name e.g. an original Mark Greaney text\n",
    "3. text written by the 'ghost' author under the name of the 'headline' author.\n",
    "\n",
    "This groups of samples then enables us to:\n",
    "* train a model to classify texts written by the 'headline' author and the 'ghost' author\n",
    "* use the model to predict the author of text written by the 'ghost' author under the name of the 'headline' author.\n",
    "\n",
    "\n",
    "<img src='./classifier.png'>\n",
    "\n",
    "If the ghost author is able to mimic the style of the headline author, their ghost writing work should be classified as that of the headline writer and not as their own writing.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Obtaining sufficient text resource for this type of analysis is challenging, not only finding appropriate canditates for the three text types, but also obtaining the text once candidates are identified.\n",
    "\n",
    "For this analysis, text versions of several Tom Clancy, Tom Clancy (written by Mark Greaney) and Mark Greaney novels were obtained using bit torrent sources.  \n",
    "\n",
    "Given the source of the texts, their accuracy couldn't be completeley validated, although a scan of the books used appeared to be accurate representations of the various works and, all of the books contained non-author written information in the form of publishers notes, comments about the books, and other scanning artifacts (it is assumed these books were originally scanned from hard copies).\n",
    "\n",
    "While initial cleaning of the books was tested using automated processes including regex filters, there was too much variation for this to be completed successfully.  Consequently a manual cleanup step for each book was completed including:\n",
    "* removal of publishers comments, tables of comments, comments about the books, character references etc\n",
    "* marking of chapter breaks to facilitate computational text splitting by chapters \n",
    "* removal of scanning artifacts\n",
    "\n",
    "Once prelimary cleaning was completed, the data was further processed in python below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages and initial configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import NuSVC\n",
    "import sys\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.pipeline import FeatureUnion\n",
    " \n",
    "from sklearn import cross_validation\n",
    "from sklearn import metrics\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import math\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "params = {'legend.fontsize': '8',\n",
    "         'axes.labelsize': '8',\n",
    "         'axes.titlesize':'8',\n",
    "         'xtick.labelsize':'8',\n",
    "         'ytick.labelsize':'8'}\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "STD_COLORS = {\n",
    "          'khg':     '#00A8EC',\n",
    "          '1':       '#e41a1c',\n",
    "          '2':       '#377eb8',\n",
    "          '3':       '#4daf4a',\n",
    "          '4':       '#984ea3',\n",
    "          '5':       '#ff7f00',\n",
    "          '6':       '#ffff33',\n",
    "          '7':       '#1b9e77',\n",
    "          '8':       '#d95f02',\n",
    "          '9':       '#7570b3'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scrape Tom clancy biblography from the Tom Clancy website\n",
    "The bibliographic order of Tom Clancy books is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>published</th>\n",
       "      <th>series</th>\n",
       "      <th>coauthor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom Clancy True Faith and Allegiance</td>\n",
       "      <td>2016</td>\n",
       "      <td>Jack Ryan Jr</td>\n",
       "      <td>Grant Blackwood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tom Clancy Duty and Honor</td>\n",
       "      <td>2016</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td>Mark Greaney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tom Clancy Commander in Chief</td>\n",
       "      <td>2015</td>\n",
       "      <td>Jack Ryan Jr</td>\n",
       "      <td>Grant Blackwood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tom Clancy Under Fire</td>\n",
       "      <td>2015</td>\n",
       "      <td>Jack Ryan Jr</td>\n",
       "      <td>Mark Greaney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tom Clancy Full Force and Effect</td>\n",
       "      <td>2014</td>\n",
       "      <td>Jack Ryan Jr</td>\n",
       "      <td>Mark Greaney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tom Clancy Support and Defend</td>\n",
       "      <td>2014</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td>Mark Greaney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Command Authority</td>\n",
       "      <td>2013</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td>Mark Greaney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Threat Vector</td>\n",
       "      <td>2012</td>\n",
       "      <td>Other</td>\n",
       "      <td>Peter Telep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Locked On</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jack Ryan Jr</td>\n",
       "      <td>Mark Greaney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Against All Enemies</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jack Ryan Jr</td>\n",
       "      <td>Grant Blackwood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Dead or Alive</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jack Ryan Jr</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>The Teeth Of The Tiger</td>\n",
       "      <td>2003</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Red Rabbit</td>\n",
       "      <td>2002</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>The Bear and the Dragon</td>\n",
       "      <td>2000</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Rainbow Six</td>\n",
       "      <td>1998</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Executive Orders</td>\n",
       "      <td>1996</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Debt of Honor</td>\n",
       "      <td>1994</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Without Remorse</td>\n",
       "      <td>1993</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>The Sum of All Fears</td>\n",
       "      <td>1991</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Clear and Present Danger</td>\n",
       "      <td>1989</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>The Cardinal of the Kremlin</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Patriot Games</td>\n",
       "      <td>1987</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Red Storm Rising</td>\n",
       "      <td>1986</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>1984</td>\n",
       "      <td>Jack Ryan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     title published        series  \\\n",
       "0   Tom Clancy True Faith and Allegiance        2016  Jack Ryan Jr   \n",
       "2              Tom Clancy Duty and Honor        2016     Jack Ryan   \n",
       "4          Tom Clancy Commander in Chief        2015  Jack Ryan Jr   \n",
       "6                  Tom Clancy Under Fire        2015  Jack Ryan Jr   \n",
       "8       Tom Clancy Full Force and Effect        2014  Jack Ryan Jr   \n",
       "10         Tom Clancy Support and Defend        2014     Jack Ryan   \n",
       "12                     Command Authority        2013     Jack Ryan   \n",
       "14                         Threat Vector        2012         Other   \n",
       "16                             Locked On        2011  Jack Ryan Jr   \n",
       "18                   Against All Enemies        2011  Jack Ryan Jr   \n",
       "20                         Dead or Alive        2010  Jack Ryan Jr   \n",
       "22                The Teeth Of The Tiger        2003     Jack Ryan   \n",
       "24                            Red Rabbit        2002     Jack Ryan   \n",
       "26               The Bear and the Dragon        2000     Jack Ryan   \n",
       "28                           Rainbow Six        1998     Jack Ryan   \n",
       "30                      Executive Orders        1996     Jack Ryan   \n",
       "32                         Debt of Honor        1994     Jack Ryan   \n",
       "34                       Without Remorse        1993     Jack Ryan   \n",
       "36                  The Sum of All Fears        1991     Jack Ryan   \n",
       "38              Clear and Present Danger        1989     Jack Ryan   \n",
       "40           The Cardinal of the Kremlin        1988     Jack Ryan   \n",
       "42                         Patriot Games        1987     Jack Ryan   \n",
       "44                      Red Storm Rising        1986     Jack Ryan   \n",
       "46              The Hunt for Red October        1984     Jack Ryan   \n",
       "\n",
       "           coauthor  \n",
       "0   Grant Blackwood  \n",
       "2      Mark Greaney  \n",
       "4   Grant Blackwood  \n",
       "6      Mark Greaney  \n",
       "8      Mark Greaney  \n",
       "10     Mark Greaney  \n",
       "12     Mark Greaney  \n",
       "14      Peter Telep  \n",
       "16     Mark Greaney  \n",
       "18  Grant Blackwood  \n",
       "20                   \n",
       "22                   \n",
       "24                   \n",
       "26                   \n",
       "28                   \n",
       "30                   \n",
       "32                   \n",
       "34                   \n",
       "36                   \n",
       "38                   \n",
       "40                   \n",
       "42                   \n",
       "44                   \n",
       "46                   "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_html(\"\"\"http://www.tomclancy.com/tom_clancy_books_fiction.php\"\"\")\n",
    "df_ = df[2][pd.notnull(df[2][0])].iloc[:,0]\n",
    "dfBooks = pd.DataFrame()\n",
    "dfBooks['title'] = df_.apply(lambda x: x.split(\"Original\")[0])\n",
    "dfBooks['published'] = df_.apply(lambda x: x.split(\"Original\")[1][18:24])\n",
    "dfBooks['series'] = \"Jack Ryan\"\n",
    "dfBooks.loc[[0, 4, 6, 8, 14, 16, 18, 20], 'series'] = 'Jack Ryan Jr'\n",
    "dfBooks.loc[14,'series'] = \"Other\"\n",
    "dfBooks['coauthor'] = \"\"\n",
    "dfBooks.loc[[2, 6, 8, 10, 12, 16 ], 'coauthor'] = \"Mark Greaney\"\n",
    "dfBooks.loc[[0, 4, 18 ], 'coauthor'] = \"Grant Blackwood\"\n",
    "dfBooks.loc[[14 ], 'coauthor'] = \"Peter Telep\"\n",
    "dfBooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The oldest nine books written by Tom Clancy were found in text format and used in this analysis.  These included:\n",
    "* The Hunt for Red October\n",
    "* Red storm rising\n",
    "* Patriot Games\n",
    "* The Cardinal of the Kremlin\n",
    "* Clear and Present Danger\n",
    "* The Sum of All Fears\n",
    "* Without Remorse\n",
    "* Debt of Honor\n",
    "* Executive Orders\n",
    "\n",
    "Two books written by Mark Greaney, under the Tom Clancy brand were found in text format.  These are:\n",
    "* Commander-in -chief\n",
    "* Support and defend\n",
    "\n",
    "Several books witten by Mark Greaney under his own name were found in text format:\n",
    "* Back blast - A Gray man novel\n",
    "* Ballistic\n",
    "* Dead Eye"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean book text\n",
    "\n",
    "As outlined previously, the books were relatively inconsistent and contained various amounts of additional material (e.g. introductions, publisher comments etc).  These inconsistencies were removed manually.\n",
    "\n",
    "For each book, chapters have been marked with \"[SPLIT_POINT]\" for use in determining where book breaks are.\n",
    "\n",
    "The following three lists contain the file paths for the three groups of books."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clancy_books = [r\"./Tom Clancy_clean/The Hunt for Red October - Tom Clancy.txt\",\n",
    "               r\"./Tom Clancy_clean/Red storm rising - Tom Clancy.txt\",\n",
    "               r'./Tom Clancy_clean/Patriot Games - Tom Clancy.txt',\n",
    "               r'./Tom Clancy_clean/5 The Cardinal of the Kremlin - Tom Clancy.txt',\n",
    "               r'./Tom Clancy_clean/6 Clear and Present Danger - Tom Clancy.txt',\n",
    "               r'./Tom Clancy_clean/7 The Sum of All Fears - Tom Clancy.txt',\n",
    "               r'./Tom Clancy_clean/1 Without Remorse - Tom Clancy.txt',\n",
    "               r'./Tom Clancy_clean/8 Debt of Honor - Tom Clancy.txt',\n",
    "               r'./Tom Clancy_clean/Executive Orders - Tom Clancy.txt']\n",
    "\n",
    "clancy_greaney_books = [r'./Tom Clancy - Mark Greaney_clean/Tom Clancy Support and Defend - Mark Greaney.txt',\n",
    "                        r'./Tom Clancy - Mark Greaney_clean/Commander-In-Chief - Mark Greaney.txt']\n",
    "\n",
    "greaney_books = [r'./Mark Greaney_clean/Back Blast_ A Gray Man Novel - Mark Greaney.txt',\n",
    "                 r'./Mark Greaney_clean/Ballistic - Mark Greaney.txt',\n",
    "                 r'./Mark Greaney_clean/Dead Eye - Mark Greaney.txt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save duplication, the following function was written to read in the books and complete some intition pre-processing:\n",
    "* open a book from the filesystem and read it in\n",
    "* split the book into chapters using the manual chapter markings\n",
    "* remove new-line characters\n",
    "* remove empty chapters if any were erroneously created\n",
    "* remove unnecessary white spare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_book(path):\n",
    "    \"\"\"\n",
    "    Read in a book, split it into chapters and clean up the text\n",
    "    \"\"\"\n",
    "    chapters = []\n",
    "    \n",
    "    with open(path, 'r') as f:\n",
    "\n",
    "        # split the book into lines\n",
    "        book = f.read()\n",
    "        \n",
    "        #Split the book into chapters\n",
    "        split_chapters =book.split(\"[SPLIT_POINT]\")\n",
    "\n",
    "        # add the chapters to the chapter object\n",
    "        chapter_count = 0\n",
    "        for chapter in split_chapters:\n",
    "            chapters.append(chapter.replace('\\n', ' '))                                \n",
    "\n",
    "    chapters = list(filter(None, chapters)) # remove empty chapters\n",
    "    chapters = list(filter(lambda chapter: chapter.strip(), chapters)) # remove items that are only white space\n",
    "    \n",
    "    return chapters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read books\n",
    "\n",
    "For ease of handling, and to facilitate analysis, the chapters of the books were stored in a pandas dataframe.  The data frame is initially created and then each of the three groups of books read in and the chapters stored in the dataframe along with metadata such as author, book title, the chapter number.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfBooks = pd.DataFrame(columns=['author', 'title', 'chapter_num', 'chapter_text'])\n",
    "chapters =[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tom Clancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading The Hunt for Red October.  [18 chapters]\n",
      "Loading Red storm rising.  [44 chapters]\n",
      "Loading Patriot Games.  [27 chapters]\n",
      "Loading 5 The Cardinal of the Kremlin.  [29 chapters]\n",
      "Loading 6 Clear and Present Danger.  [31 chapters]\n",
      "Loading 7 The Sum of All Fears.  [44 chapters]\n",
      "Loading 1 Without Remorse.  [38 chapters]\n",
      "Loading 8 Debt of Honor.  [48 chapters]\n",
      "Loading Executive Orders.  [49 chapters]\n"
     ]
    }
   ],
   "source": [
    "author = 'Clancy'\n",
    "for title in clancy_books:\n",
    "    book = read_book(title)\n",
    "    clean_title = title.replace('./Tom Clancy_clean/', '').replace(' - Tom Clancy.txt', '')\n",
    "    print('Loading {0}.  [{1} chapters]'.format(clean_title, len(book)))\n",
    "    for i, chapter in enumerate(book):\n",
    "        chapters.append(chapter)\n",
    "        df_ = pd.DataFrame({'author':author, 'title':clean_title, 'chapter_num':i, 'chapter_text':chapter}, index=[0])\n",
    "        dfBooks = pd.concat([dfBooks, df_])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mark Greaney"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Back Blast_ A Gray Man Novel.  [79 chapters]\n",
      "Loading Ballistic.  [57 chapters]\n",
      "Loading Dead Eye.  [58 chapters]\n"
     ]
    }
   ],
   "source": [
    "author = 'Greaney'\n",
    "for title in greaney_books:\n",
    "    book = read_book(title)\n",
    "    clean_title = title.replace('./Mark Greaney_clean/', '').replace(' - Mark Greaney.txt', '')\n",
    "    print('Loading {0}.  [{1} chapters]'.format(clean_title, len(book)))\n",
    "    chapters.append([author, clean_title, len(book)])\n",
    "    for chapter in book:\n",
    "        chapters.append(chapter)\n",
    "        df_ = pd.DataFrame({'author':author, 'title':clean_title, 'chapter_num':i, 'chapter_text':chapter}, index=[0])\n",
    "        dfBooks = pd.concat([dfBooks, df_])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ghost written book (Mark Greaney writing as Tom Clancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Tom Clancy Support and Defend.  [51 chapters]\n",
      "Loading Commander-In-Chief.  [82 chapters]\n"
     ]
    }
   ],
   "source": [
    "author = 'Ghost'\n",
    "for title in clancy_greaney_books:\n",
    "    book = read_book(title)\n",
    "    clean_title = title.replace('./Tom Clancy - Mark Greaney_clean/', '').replace(' - Mark Greaney.txt', '')\n",
    "    print('Loading {0}.  [{1} chapters]'.format(clean_title, len(book)))\n",
    "    chapters.append([author, clean_title, len(book)])\n",
    "    for chapter in book:\n",
    "        chapters.append(chapter)\n",
    "        df_ = pd.DataFrame({'author':author, 'title':clean_title, 'chapter_num':i, 'chapter_text':chapter}, index=[0])\n",
    "        dfBooks = pd.concat([dfBooks, df_])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the chapters are stored in a dataframe, we can easily add columns to represent parameters such as the number of words in each chapter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>chapter_num</th>\n",
       "      <th>chapter_text</th>\n",
       "      <th>title</th>\n",
       "      <th>chapter_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Clancy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>THE FIRST DAY  FRIDAY, 3 DECEMBER    The Red ...</td>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>5603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clancy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>THE SECOND DAY  SATURDAY, 4 DECEMBER    The R...</td>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>3643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clancy</td>\n",
       "      <td>2.0</td>\n",
       "      <td>THE THIRD DAY  SUNDAY, 5 DECEMBER    The Red ...</td>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>6575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clancy</td>\n",
       "      <td>3.0</td>\n",
       "      <td>THE FOURTH DAY  MONDAY, 6 DECEMBER    CIA Hea...</td>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>8394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clancy</td>\n",
       "      <td>4.0</td>\n",
       "      <td>THE FIFTH DAY  TUESDAY, 7 DECEMBER    Moscow ...</td>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>8936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   author  chapter_num                                       chapter_text  \\\n",
       "0  Clancy          0.0   THE FIRST DAY  FRIDAY, 3 DECEMBER    The Red ...   \n",
       "1  Clancy          1.0   THE SECOND DAY  SATURDAY, 4 DECEMBER    The R...   \n",
       "2  Clancy          2.0   THE THIRD DAY  SUNDAY, 5 DECEMBER    The Red ...   \n",
       "3  Clancy          3.0   THE FOURTH DAY  MONDAY, 6 DECEMBER    CIA Hea...   \n",
       "4  Clancy          4.0   THE FIFTH DAY  TUESDAY, 7 DECEMBER    Moscow ...   \n",
       "\n",
       "                      title  chapter_words  \n",
       "0  The Hunt for Red October           5603  \n",
       "1  The Hunt for Red October           3643  \n",
       "2  The Hunt for Red October           6575  \n",
       "3  The Hunt for Red October           8394  \n",
       "4  The Hunt for Red October           8936  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfBooks.reset_index(inplace=True)\n",
    "dfBooks.drop('index', axis=1, inplace=True)\n",
    "dfBooks['chapter_words'] = dfBooks.chapter_text.apply(lambda x: len(x.split(' ')))\n",
    "dfBooks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, with infomration like th enumber of words per chapter, we can look at bar charts of the number of words per chapter for each book, to see how the books compare with each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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5upGzHWOKZ/Ha44oXfYztmqfBDbhmZmZmZmZm1naSlgIfBU6MiJ/lQlvo8KSQ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DcijgM+DnxB0u+R3U726Yi4Px12DbBc0pPAPuDSiNiZYiuAk4BNZJ2010bEowARsTZdMD+Sznlr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\nVVBmvjUzv56ZH6D5tYb3AG9dbznFiNnfBP4FZ0b1AlwD3FLUdT/wGPCaIu3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O/Djwosyc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc751ca6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns_to_plot = dfBooks.title.unique()\n",
    "plot_columns = 4\n",
    "plot_rows =math.ceil(len(columns_to_plot)/plot_columns)\n",
    "fig, ax = plt.subplots(plot_rows, plot_columns, figsize=(15,20))\n",
    "fig.subplots_adjust(hspace=0.6) # adjust vertical spacing between plots\n",
    "# iterate through columns and create each chart\n",
    "for i, column in enumerate(columns_to_plot):\n",
    "  \n",
    "    # split the title into four-word long lines\n",
    "    title = column.split(' ')\n",
    "    title = [' '.join(title[i:i+4]) for i in range(0, len(title), 4)]\n",
    "    title = '\\n'.join(title)\n",
    "\n",
    "    df_ = dfBooks.loc[dfBooks.title==column, 'chapter_words']\n",
    "    x_ticks = [j for j in range(0, len(df_.index))]\n",
    "    x_label_count = 1 if len(x_ticks) < 10 else math.ceil(len(x_ticks)/10)\n",
    "    ax[int(i/plot_columns), i%plot_columns].bar(x_ticks, df_.values)\n",
    "    ax[int(i/plot_rows), i%plot_columns].set_title(title)\n",
    "\n",
    "    x_ticks = [x + (x_ticks[1] - x_ticks[0])*0.4 for x in x_ticks]\n",
    "    ax[int(i/plot_rows), i%plot_columns].set_xticks(x_ticks[::x_label_count]);\n",
    "    ax[int(i/plot_rows), i%plot_columns].set_xticklabels([j for j in range(0, len(df_.index))][::x_label_count], rotation=90);\n",
    "    ax[int(i/plot_rows), 0].set_ylabel('Words per chapter')\n",
    "    ax[plot_rows-1, i%plot_columns].set_xlabel('Chapter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inital analysis\n",
    "### Distribution of chapters per book\n",
    "\n",
    "First, we compared the number of chapters per book for the three groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>num_chapters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>author</th>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">Clancy</th>\n",
       "      <th>1 Without Remorse</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 The Cardinal of the Kremlin</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 Clear and Present Danger</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 The Sum of All Fears</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 Debt of Honor</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive Orders</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patriot Games</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red storm rising</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Hunt for Red October</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ghost</th>\n",
       "      <th>Commander-In-Chief</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Clancy Support and Defend</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Greaney</th>\n",
       "      <th>Back Blast_ A Gray Man Novel</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ballistic</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dead Eye</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       num_chapters\n",
       "author  title                                      \n",
       "Clancy  1 Without Remorse                        38\n",
       "        5 The Cardinal of the Kremlin            29\n",
       "        6 Clear and Present Danger               31\n",
       "        7 The Sum of All Fears                   44\n",
       "        8 Debt of Honor                          48\n",
       "        Executive Orders                         49\n",
       "        Patriot Games                            27\n",
       "        Red storm rising                         44\n",
       "        The Hunt for Red October                 18\n",
       "Ghost   Commander-In-Chief                       82\n",
       "        Tom Clancy Support and Defend            51\n",
       "Greaney Back Blast_ A Gray Man Novel             79\n",
       "        Ballistic                                57\n",
       "        Dead Eye                                 58"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfAveChapt = dfBooks.groupby(['author','title'])['author', 'chapter_words']\n",
    "ave_chapters = pd.DataFrame(dfAveChapt.count()['author'])\n",
    "ave_chapters.rename(columns={'author':'num_chapters'}, inplace=True)\n",
    "ave_chapters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the table above tha there is quite a range in chapters per book.\n",
    "\n",
    "### Distribution of words per book\n",
    "Next, we can look at the number of words in each book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>book_words</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>author</th>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">Clancy</th>\n",
       "      <th>1 Without Remorse</th>\n",
       "      <td>260058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 The Cardinal of the Kremlin</th>\n",
       "      <td>206940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 Clear and Present Danger</th>\n",
       "      <td>274880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 The Sum of All Fears</th>\n",
       "      <td>344539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 Debt of Honor</th>\n",
       "      <td>348569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive Orders</th>\n",
       "      <td>501404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patriot Games</th>\n",
       "      <td>221008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red storm rising</th>\n",
       "      <td>272783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Hunt for Red October</th>\n",
       "      <td>169744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ghost</th>\n",
       "      <th>Commander-In-Chief</th>\n",
       "      <td>202462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Clancy Support and Defend</th>\n",
       "      <td>136067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Greaney</th>\n",
       "      <th>Back Blast_ A Gray Man Novel</th>\n",
       "      <td>187546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ballistic</th>\n",
       "      <td>142331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dead Eye</th>\n",
       "      <td>148378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       book_words\n",
       "author  title                                    \n",
       "Clancy  1 Without Remorse                  260058\n",
       "        5 The Cardinal of the Kremlin      206940\n",
       "        6 Clear and Present Danger         274880\n",
       "        7 The Sum of All Fears             344539\n",
       "        8 Debt of Honor                    348569\n",
       "        Executive Orders                   501404\n",
       "        Patriot Games                      221008\n",
       "        Red storm rising                   272783\n",
       "        The Hunt for Red October           169744\n",
       "Ghost   Commander-In-Chief                 202462\n",
       "        Tom Clancy Support and Defend      136067\n",
       "Greaney Back Blast_ A Gray Man Novel       187546\n",
       "        Ballistic                          142331\n",
       "        Dead Eye                           148378"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_words = pd.DataFrame(dfAveChapt.sum()['chapter_words'] )\n",
    "book_words.rename(columns={'chapter_words':'book_words'}, inplace=True)\n",
    "book_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, there is quite a distribution of words in each book with actual Tom Clancy books typically longer than the ghost author written books.  This will be shown graphically below\n",
    "\n",
    "### Distribution of average words per chapter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Ave words per chapter</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>author</th>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">Clancy</th>\n",
       "      <th>1 Without Remorse</th>\n",
       "      <td>6844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 The Cardinal of the Kremlin</th>\n",
       "      <td>7136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 Clear and Present Danger</th>\n",
       "      <td>8867.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 The Sum of All Fears</th>\n",
       "      <td>7830.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 Debt of Honor</th>\n",
       "      <td>7262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive Orders</th>\n",
       "      <td>10233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patriot Games</th>\n",
       "      <td>8185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red storm rising</th>\n",
       "      <td>6200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Hunt for Red October</th>\n",
       "      <td>9430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ghost</th>\n",
       "      <th>Commander-In-Chief</th>\n",
       "      <td>2469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Clancy Support and Defend</th>\n",
       "      <td>2668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Greaney</th>\n",
       "      <th>Back Blast_ A Gray Man Novel</th>\n",
       "      <td>2374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ballistic</th>\n",
       "      <td>2497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dead Eye</th>\n",
       "      <td>2558.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Ave words per chapter\n",
       "author  title                                               \n",
       "Clancy  1 Without Remorse                             6844.0\n",
       "        5 The Cardinal of the Kremlin                 7136.0\n",
       "        6 Clear and Present Danger                    8867.0\n",
       "        7 The Sum of All Fears                        7830.0\n",
       "        8 Debt of Honor                               7262.0\n",
       "        Executive Orders                             10233.0\n",
       "        Patriot Games                                 8185.0\n",
       "        Red storm rising                              6200.0\n",
       "        The Hunt for Red October                      9430.0\n",
       "Ghost   Commander-In-Chief                            2469.0\n",
       "        Tom Clancy Support and Defend                 2668.0\n",
       "Greaney Back Blast_ A Gray Man Novel                  2374.0\n",
       "        Ballistic                                     2497.0\n",
       "        Dead Eye                                      2558.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ave_chapter_words = pd.DataFrame(round(dfAveChapt.sum()['chapter_words'] / dfAveChapt.count()['author'],0))\n",
    "ave_chapter_words.columns=['Ave words per chapter']\n",
    "ave_chapter_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the number of words per chapter is considerably longer for Tom Clancy books compared to books written by the ghost author."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can  see these changes better by plotting the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc74fd3ec18>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/lNnHXWWfHpT386xowZE5deemmHPP0n33yjRo2Ka665JmpqaqK6ujr+4z/+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\n1Pw/GvU7r2g76LEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc74c0c58d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3,3,figsize=(14,20), facecolor='white')\n",
    "fig.subplots_adjust(hspace=0.)\n",
    "for i, author in enumerate(['Clancy', 'Ghost', 'Greaney']):\n",
    "\n",
    "    # chapters in book\n",
    "    df_ = ave_chapters.loc[author]\n",
    "    ax[0,i].bar([x for x in range(len(df_.index))], df_['num_chapters'])\n",
    "    ax[0,i].set_xticks([x+0.4 for x in range(len(df_.index))])\n",
    "    ax[0,i].set_xticklabels(df_.index, rotation = 90)\n",
    "    df_ = book_words.loc[(author)]\n",
    "    ax[1,i].bar([x for x in range(len(df_.index))], df_['book_words'])\n",
    "    df_ = ave_chapter_words.loc[(author)]\n",
    "    ax[2,i].bar([x for x in range(len(df_.index))], df_['Ave words per chapter'])\n",
    "\n",
    "ax[0,0].set_ylim([0,100]); ax[0,1].set_ylim([0,100]); ax[0,2].set_ylim([0,100]);\n",
    "ax[1,0].set_ylim([0,500000]); ax[1,1].set_ylim([0,500000]); ax[1,2].set_ylim([0,500000]);\n",
    "ax[2,0].set_ylim([0,12000]); ax[2,1].set_ylim([0,12000]); ax[2,2].set_ylim([0,12000]);\n",
    "\n",
    "ax[0,0].set_ylabel('Number of chapters per book')\n",
    "ax[1,0].set_ylabel('Number of words per book')\n",
    "ax[2,0].set_ylabel('Ave. words per chapter per book')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 Without Remorse</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5 The Cardinal of the Kremlin</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6 Clear and Present Danger</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7 The Sum of All Fears</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8 Debt of Honor</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Executive Orders</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Patriot Games</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Red storm rising</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Commander-In-Chief</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tom Clancy Support and Defend</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Back Blast_ A Gray Man Novel</td>\n",
       "      <td>Greaney</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ballistic</td>\n",
       "      <td>Greaney</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dead Eye</td>\n",
       "      <td>Greaney</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            title   author  words\n",
       "0               1 Without Remorse   Clancy     38\n",
       "1   5 The Cardinal of the Kremlin   Clancy     29\n",
       "2      6 Clear and Present Danger   Clancy     31\n",
       "3          7 The Sum of All Fears   Clancy     44\n",
       "4                 8 Debt of Honor   Clancy     48\n",
       "5                Executive Orders   Clancy     49\n",
       "6                   Patriot Games   Clancy     27\n",
       "7                Red storm rising   Clancy     44\n",
       "8        The Hunt for Red October   Clancy     18\n",
       "9              Commander-In-Chief    Ghost     82\n",
       "10  Tom Clancy Support and Defend    Ghost     51\n",
       "11   Back Blast_ A Gray Man Novel  Greaney     79\n",
       "12                      Ballistic  Greaney     57\n",
       "13                       Dead Eye  Greaney     58"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ = pd.DataFrame()\n",
    "df_['title'] = ave_chapters.index.get_level_values(level='title')\n",
    "df_['author'] = ave_chapters.index.get_level_values(level='author')\n",
    "df_['words'] = ave_chapters.num_chapters.values\n",
    "df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LF0qSunfvrtTUVN1xxx2SpL/85S+67rrrzCoLAAAAABAATGt2t23b5vl9v379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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7509ae588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "book_chapters = [len(dfBooks.loc[dfBooks.author == 'Clancy', 'author']),\n",
    "                len(dfBooks.loc[dfBooks.author == 'Ghost', 'author']),\n",
    "                len(dfBooks.loc[dfBooks.author == 'Greaney', 'author'])]\n",
    "\n",
    "s1,f1 = [0,book_chapters[0]]\n",
    "s2,f2 = [book_chapters[0], book_chapters[0] + book_chapters[1]]\n",
    "s3,f3 = [book_chapters[0] + book_chapters[1], book_chapters[0] + book_chapters[1] + book_chapters[2] ]\n",
    "\n",
    "fig, ax = plt.subplots(3,1,figsize=(10,6), facecolor='white', sharex=True)\n",
    "fig.subplots_adjust(hspace=.6)\n",
    "for i, author in enumerate(['Clancy', 'Ghost', 'Greaney']):\n",
    "\n",
    "    # chapters in book\n",
    "    df_ = pd.DataFrame()\n",
    "    df_['title'] = ave_chapters.index.get_level_values(level='title')\n",
    "    df_['author'] = ave_chapters.index.get_level_values(level='author')\n",
    "    df_['chapters'] = ave_chapters.num_chapters.values\n",
    "    df_['book_words'] = book_words.book_words.values\n",
    "    df_['chapter_words'] = ave_chapter_words['Ave words per chapter'].values\n",
    "    df_['color'] = STD_COLORS['1']\n",
    "    df_.loc[df_.author == 'Ghost', 'color'] = STD_COLORS['2']\n",
    "    df_.loc[df_.author == 'Greaney', 'color'] = STD_COLORS['3']\n",
    "    \n",
    "    # Chapters per book\n",
    "    ax[0].bar(df_.index, df_.chapters, color=df_.color, label = df_.author.values)\n",
    "    ax[0].set_xticks([x+0.4 for x in range(len(df_.index))])\n",
    "    \n",
    "    # words in book\n",
    "    ax[1].bar(df_.index, df_.book_words, color=df_.color, label = df_.author.values)\n",
    "    \n",
    "    # words per chapter\n",
    "    ax[2].bar(df_.index, df_.chapter_words, color=df_.color, label = df_.author.values)\n",
    "    ax[2].set_xticklabels(df_.title, rotation = 90)\n",
    "\n",
    "ax[0].set_ylim([0,100]);\n",
    "ax[1].set_ylim([0,500000])\n",
    "ax[2].set_ylim([0,12000])\n",
    "\n",
    "ax[0].set_ylabel('Number of chapters per book')\n",
    "ax[1].set_ylabel('Number of words per book')\n",
    "ax[2].set_ylabel('Ave. words per chapter per book')\n",
    "\n",
    "ax[0].set_axis_bgcolor('white')\n",
    "ax[1].set_axis_bgcolor('white')\n",
    "ax[2].set_axis_bgcolor('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simplistically we could use syntactic features like average words per chapter or total words in each book to classify these books, and determine who wrote them.  But where is the fun in that.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lexical analysis\n",
    "\n",
    "For our first model we'll use lexical analysis and kmeans clustering to model our books\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "sentence_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n",
    "word_tokenizer = nltk.tokenize.RegexpTokenizer(r'\\w+')\n",
    "from scipy.cluster.vq import whiten\n",
    "from sklearn.cluster import KMeans\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a series of lexical parameters:\n",
    "* words per sentence\n",
    "* average deviation of sentence length\n",
    "* lexical diverstity (the number of unique words / the total number of words)\n",
    "* chapter length\n",
    "* commas per sentence\n",
    "* semicolons per sentence\n",
    "* colons per sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create lexical and punctuation feature vectors for each chapter\n",
    "num_chapters = len(dfBooks.index)\n",
    "fv_lexical = np.zeros((num_chapters, 4), np.float64)\n",
    "fv_punct = np.zeros((num_chapters, 3), np.float64)\n",
    "\n",
    "for e, ch_text in enumerate(dfBooks.chapter_text):\n",
    "\n",
    "    tokens = nltk.word_tokenize(ch_text.lower())      # includes punctuation\n",
    "    words = word_tokenizer.tokenize(ch_text.lower())  # excludes punctuation\n",
    "    sentences = sentence_tokenizer.tokenize(ch_text)  # including punctuation\n",
    "    \n",
    "    vocab = set(words)                                # unique set of all words used\n",
    "    \n",
    "    words_per_sentence = np.array([len(word_tokenizer.tokenize(s)) for s in sentences])\n",
    "    \n",
    "    # Lexical features\n",
    "    \n",
    "    # average number of words per sentence\n",
    "    fv_lexical[e, 0] = words_per_sentence.mean()\n",
    "    \n",
    "    # sentence length variation\n",
    "    fv_lexical[e, 1] = words_per_sentence.std()\n",
    "    \n",
    "    # lexical diversity\n",
    "    fv_lexical[e, 2] = len(vocab) / float(len(words))\n",
    "    \n",
    "    # chapter length\n",
    "    fv_lexical[e, 3] = len(ch_text)\n",
    "    \n",
    "    \n",
    "    # Punctuation features\n",
    "    \n",
    "    # commas per sentence\n",
    "    fv_punct[e, 0] = tokens.count(',') / float(len(sentences))\n",
    "    \n",
    "    # semicolons per sentence\n",
    "    fv_punct[e, 1] = tokens.count(';') / float(len(sentences))\n",
    "    \n",
    "    # colons per sentence\n",
    "    fv_punct[e, 2] = tokens.count(':') / float(len(sentences))\n",
    "    \n",
    "    \n",
    "# apply whitening to decorrelate the features\n",
    "fv_lexical = whiten(fv_lexical)\n",
    "fv_punct = whiten(fv_punct)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visaluse these parameters for each of the authors below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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6CpdccgkWLlwIAMjMzMT69etDrqetrQ133HEHPvzwQ2RkZKCoqAgvv/yyrDCJ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\ngJq8jEbf8tdfIlmoKZpTJZiqqmKSyPWXZHFfWM8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7480c4cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(15,15), facecolor='white', nrows=4, ncols=1, sharex=True)\n",
    "\n",
    "ax_a = axes[0]\n",
    "ax_b = axes[1]\n",
    "ax_c = axes[2]\n",
    "ax_d = axes[3]\n",
    "\n",
    "x_ticks = [i for i in range(len(dfBooks.index))]\n",
    "\n",
    "\n",
    "s1,f1 = [0,book_chapters[0]]\n",
    "s2,f2 = [book_chapters[0], book_chapters[0] + book_chapters[1]]\n",
    "s3,f3 = [book_chapters[0] + book_chapters[1], book_chapters[0] + book_chapters[1] + book_chapters[2] ]\n",
    "\n",
    "ax_a.bar(x_ticks[s1:f1], fv_lexical[s1:f1, 0], color=STD_COLORS['1'], label = 'Clancy')\n",
    "ax_a.bar(x_ticks[s2:f2], fv_lexical[s2:f2, 0], color=STD_COLORS['2'], label = 'Ghost')     \n",
    "ax_a.bar(x_ticks[s3:f3], fv_lexical[s3:f3, 0], color=STD_COLORS['3'], label = 'Greaney')\n",
    "\n",
    "ax_b.bar(x_ticks[s1:f1], fv_lexical[s1:f1, 1], color=STD_COLORS['1'], label = 'Clancy')\n",
    "ax_b.bar(x_ticks[s2:f2], fv_lexical[s2:f2, 1], color=STD_COLORS['2'], label = 'Ghost')     \n",
    "ax_b.bar(x_ticks[s3:f3], fv_lexical[s3:f3, 1], color=STD_COLORS['3'], label = 'Greaney')\n",
    "\n",
    "ax_c.bar(x_ticks[s1:f1], fv_lexical[s1:f1, 2], color=STD_COLORS['1'], label = 'Clancy')\n",
    "ax_c.bar(x_ticks[s2:f2], fv_lexical[s2:f2, 2], color=STD_COLORS['2'], label = 'Ghost')     \n",
    "ax_c.bar(x_ticks[s3:f3], fv_lexical[s3:f3, 2], color=STD_COLORS['3'], label = 'Greaney')\n",
    "\n",
    "ax_d.bar(x_ticks[s1:f1], fv_lexical[s1:f1, 3], color=STD_COLORS['1'], label = 'Clancy')\n",
    "ax_d.bar(x_ticks[s2:f2], fv_lexical[s2:f2, 3], color=STD_COLORS['2'], label = 'Ghost')     \n",
    "ax_d.bar(x_ticks[s3:f3], fv_lexical[s3:f3, 3], color=STD_COLORS['3'], label = 'Greaney')\n",
    "\n",
    "titles = ['Average words per sentence', 'Std Dev words per sentence', ' Lexical diversity', 'Chapter length']\n",
    "axes = [ax_a, ax_b, ax_c, ax_d]\n",
    "legend_loc = ['upper left', 'upper left', 'upper left', 'upper left']\n",
    "axes_grid = [True, True, True, True]\n",
    "ax_data = zip(axes, legend_loc, axes_grid)\n",
    "for axis, legend_loc, grid in ax_data:\n",
    "    handles, labels = axis.get_legend_handles_labels()\n",
    "    axis.legend(handles, labels, loc=legend_loc, frameon=False)\n",
    "    axis.grid(grid)\n",
    "    axis.set_title(titles[axes.index(axis)])\n",
    "    axis.set_ylabel(titles[axes.index(axis)])\n",
    "\n",
    "ax_a.set_axis_bgcolor('white')\n",
    "ax_c.set_axis_bgcolor('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the above that:\n",
    "* Mark Greaney tends to average longer sentences\n",
    "* has a more diverse vocabulary\n",
    "* writes shorter chapters.\n",
    "\n",
    "All good for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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rJ3bf2H0Mxr71zV75g6HINvHqtVJ0GyU6znjbxr5nxNodDOum3+zS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\n9GqssVBsRMSESRNi6eqlRc83YdKEiIhYunpp1dbY1/4bGxs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7433f4e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(15,15), facecolor='white', nrows=3, ncols=1, sharex=True)\n",
    "\n",
    "ax_a = axes[0]\n",
    "ax_b = axes[1]\n",
    "ax_c = axes[2]\n",
    "\n",
    "x_ticks = [i for i in range(len(dfBooks.index))]\n",
    "\n",
    "ax_a.bar(x_ticks[s1:f1], fv_punct[s1:f1, 0], color=STD_COLORS['1'], label = 'Clancy')\n",
    "ax_a.bar(x_ticks[s2:f2], fv_punct[s2:f2, 0], color=STD_COLORS['2'], label = 'Ghost')\n",
    "ax_a.bar(x_ticks[s3:f3], fv_punct[s3:f3, 0], color=STD_COLORS['3'], label = 'Greaney')\n",
    "\n",
    "ax_b.bar(x_ticks[s1:f1], fv_punct[s1:f1, 1], color=STD_COLORS['1'], label = 'Clancy')\n",
    "ax_b.bar(x_ticks[s2:f2], fv_punct[s2:f2, 1], color=STD_COLORS['2'], label = 'Ghost')\n",
    "ax_b.bar(x_ticks[s3:f3], fv_punct[s3:f3, 1], color=STD_COLORS['3'], label = 'Greaney')\n",
    "\n",
    "ax_c.bar(x_ticks[s1:f1], fv_punct[s1:f1, 2], color=STD_COLORS['1'], label = 'Clancy')\n",
    "ax_c.bar(x_ticks[s2:f2], fv_punct[s2:f2, 2], color=STD_COLORS['2'], label = 'Ghost')\n",
    "ax_c.bar(x_ticks[s3:f3], fv_punct[s3:f3, 2], color=STD_COLORS['3'], label = 'Greaney')\n",
    "\n",
    "titles = ['Commas per sentence', 'Semicolons per sentence', ' Colons per sentence']\n",
    "axes = [ax_a, ax_b, ax_c]\n",
    "legend_loc = ['upper left', 'upper left', 'upper left']\n",
    "axes_grid = [True, True, True]\n",
    "ax_data = zip(axes, legend_loc, axes_grid)\n",
    "for axis, legend_loc, grid in ax_data:\n",
    "    handles, labels = axis.get_legend_handles_labels()\n",
    "    axis.legend(handles, labels, loc=legend_loc, frameon=False)\n",
    "    axis.grid(grid)\n",
    "    axis.set_title(titles[axes.index(axis)])\n",
    "    axis.set_ylabel(titles[axes.index(axis)])\n",
    "\n",
    "ax_a.set_axis_bgcolor('white')\n",
    "ax_c.set_axis_bgcolor('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, when it comes to punctuatoin differences, things aren't so clear.\n",
    "* commas per sentence are pretty similar and Tom Clancy started to be more like Mark Greaney in his most recent books\n",
    "* Mark Greaney seems to prefer semicolons, although their use if extremely variable\n",
    "* Tom Clancy seems to prefer colons.\n",
    "\n",
    "The differences between authors don't appear as clear cut from punctuation features.\n",
    "\n",
    "We'll use the Scikit learn KMeans class to cluster the text.  Bwlow are two functions:\n",
    "* the first is the actual kmeans model instantiation\n",
    "* the second is function which call the kmeans model with a set of features and labels we pass in, and which returns a confusion matirx, the accuracy and the model.  This was we can pass in various sets of parameters, get performance metrics and capture the model for use in predicting the author of the Ghost written text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def PredictAuthors(fvs):\n",
    "    km = KMeans(n_clusters=2, init='k-means++', n_init=10, verbose=0, random_state=3425)\n",
    "    km.fit(fvs)    \n",
    "    return km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cluster(feature_sets, names):\n",
    "    model = [PredictAuthors(fvs) for fvs in feature_sets]\n",
    "    classifications = [m.labels_ for m in model]\n",
    "    df = pd.DataFrame(classifications, index=names).T\n",
    "    if len(names) == 1:\n",
    "        df['score'] = df.iloc[:,0]\n",
    "    else:\n",
    "        df['score'] = df.mean(axis=1).apply(int)\n",
    "\n",
    "    df.loc[s1:f1, 'actual'] = 0\n",
    "    df.loc[f1:, 'actual'] = 1\n",
    "    df['actual'].apply(int)\n",
    "    \n",
    "    cm = confusion_matrix(df['score'], df['actual'])\n",
    "    accuracy = accuracy_score(df['score'], df['actual'])\n",
    "    return cm, accuracy, model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To build the model, we only want to use Tom Clancy and Mark Greaney book features.  We will then use the model to predict the Ghost text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fv_lexical_model = []\n",
    "fv_lexical_model.extend(fv_lexical[:f1, :])\n",
    "fv_lexical_model.extend(fv_lexical[s3:, :])\n",
    "\n",
    "fv_punct_model = []\n",
    "fv_punct_model.extend(fv_punct[:f1, :])\n",
    "fv_punct_model.extend(fv_punct[s3:, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll just try with the lexical word and sentence features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.908045977011\n"
     ]
    },
    {
     "data": {
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e27ptJwGfiYjtgdcCX6npphgzB2Mzq7SBzDjvZ3TX0BbAwRHxY4CIeAi4kaxvGbJMePua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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc743445748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm, accuracy, model = cluster([fv_lexical_model], ['Lexical'])\n",
    "pl.matshow(cm, cmap=plt.cm.Blues)\n",
    "pl.title('Confusion matrix')\n",
    "pl.colorbar()\n",
    "pl.ylabel('True label')\n",
    "pl.xlabel('Predicted label')\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us a pretty clean classification with an accuracy of ~91%.\n",
    "\n",
    "If we use this model to predict who wrote the Ghost texts we see that most of the labels are'1' which implies Mark Greaney.\n",
    "\n",
    "So, the Tom Clancy novels written by Mark Greaney are being classified as Mark Greaney."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model[0].predict(fv_lexical[s2:f2, :]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about it we just use the punctuation features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.258015267176\n"
     ]
    },
    {
     "data": {
      "image/png": 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7d46ybzlZNT0uTsZmVm8dslCQ+4zNzBLgytjMaq1H2ats21Q5GZtZrXXKqm1OxmZWa17P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\nm5klwMnYzCwBTsZmZgn4//k4SVS10A4lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc742c18908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm, accuracy, model = cluster([fv_punct],['Punct'])\n",
    "pl.matshow(cm, cmap=plt.cm.Blues)\n",
    "pl.title('Confusion matrix')\n",
    "pl.colorbar()\n",
    "pl.ylabel('True label')\n",
    "pl.xlabel('Predicted label')\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We saw in the overview of the features above that the discrimination of texts based on punctuation use wasnt clear and, classification based on these is understandably bad."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about if we combine lexical and punctuation features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.670229007634\n"
     ]
    },
    {
     "data": {
      "image/png": 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OxGjHI2KvlkVkZlaCKNGB19ZIxqdozfiEtkZhZjZGWZtx0bUp2hzMOBRKxhFxzfDPkvqB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\n4YzFcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7433f4ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm, accuracy, model = cluster([fv_lexical, fv_punct],['Lexical', 'Punct'])\n",
    "pl.matshow(cm, cmap=plt.cm.Blues)\n",
    "pl.title('Confusion matrix')\n",
    "pl.colorbar()\n",
    "pl.ylabel('True label')\n",
    "pl.xlabel('Predicted label')\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still not as good as word/sentence based features alone.\n",
    "\n",
    "We could try and construct more features like n-grams etc and use them in the clustering model, but where it the fun in that.\n",
    "\n",
    "Instead we'll try a different tack and use n-grams and character grams "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[0].predict(fv_lexical[s2:f2, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support vector machine with n-gram vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to train the model on books by Clancy and Greaney, and we'll then use the model to see how it predicts books written by Greaney under the Tom Clancy brand.\n",
    "\n",
    "For the model we'll use a support vector machine and determine features from the books based on both word and character n-grams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 328 chapters from Tom Clancy books and 194 chapters from Mark Greaney books\n",
      "There are 133 chapters from Tom Clancy books written by Mark Greaney\n"
     ]
    }
   ],
   "source": [
    "print('There are {0} chapters from Tom Clancy books and {1} chapters from Mark Greaney books'.\n",
    "      format(len(dfBooks[dfBooks.author=='Clancy']),\n",
    "             len(dfBooks[dfBooks.author=='Greaney'])))\n",
    "\n",
    "print('There are {0} chapters from Tom Clancy books written by Mark Greaney'.\n",
    "      format(len(dfBooks[dfBooks.author=='Ghost'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll create an array of dicts where each dict is a chapter from a book labeled with the author who wrote it.  \n",
    "\n",
    "We want to train the model on books written by Tom Clancy, and books Mark Greaney wrote under his own name.  So, we'll exclude 'ghost' written books from the model fitting process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trainset = []\n",
    "df_ = dfBooks.loc[(dfBooks.author != 'Ghost'), :]\n",
    "for author, chapter in zip(df_.author.values, df_.chapter_text.values):\n",
    "    trainset.append({\"label\":author,\n",
    "                     \"text\":chapter})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training instances:  522\n",
      "Number of training classes:  2\n"
     ]
    }
   ],
   "source": [
    "corpus = []\n",
    "classes = []\n",
    "\n",
    "for item in trainset:\n",
    "    corpus.append( item['text'])\n",
    "    classes.append( item['label'])\n",
    "    \n",
    "print(\"Number of training instances: \", len(classes))\n",
    "print(\"Number of training classes: \", len(set(classes)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit learn includes various vecotriser methods.\n",
    "\n",
    "Ultimately we'll use the Tfidf vectoriser, but to see how the vecotriser works, we can look a simple count vectorisers first.\n",
    "\n",
    "We'll specify two vectorisers:\n",
    "* one based on bi-grams  for words\n",
    "* one based on 2-grams for characters.\n",
    "\n",
    "Once we've defined the vecotrisers, we pass the chapter text to them to generate features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<522x2000 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 961464 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_count = CountVectorizer( analyzer='word', ngram_range=(2,2), max_features=2000)\n",
    "char_count = CountVectorizer( analyzer='char', ngram_range=(2,3), min_df=0, max_features=2000)\n",
    "\n",
    "word_count.fit_transform(corpus)\n",
    "char_count.fit_transform(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hell of': 547,\n",
       " 'must have': 877,\n",
       " 'and was': 97,\n",
       " 'our country': 1015,\n",
       " 'series of': 1109,\n",
       " 'his men': 601,\n",
       " 'over and': 1029,\n",
       " 'the tv': 1467,\n",
       " 'than he': 1175,\n",
       " 'but they': 225,\n",
       " 'it you': 742,\n",
       " 'not in': 902,\n",
       " 'one at': 983,\n",
       " 'were not': 1865,\n",
       " 'all three': 33,\n",
       " 'for me': 378,\n",
       " 'door to': 324,\n",
       " 'the door': 1287,\n",
       " 'piece of': 1044,\n",
       " 'would take': 1951,\n",
       " 'ten minutes': 1174,\n",
       " 'well as': 1851,\n",
       " 'she had': 1118,\n",
       " 'that if': 1190,\n",
       " 'the guy': 1322,\n",
       " 'in it': 661,\n",
       " 'comrade general': 265,\n",
       " 'the eyes': 1299,\n",
       " 'man in': 833,\n",
       " 'of his': 932,\n",
       " 'right hand': 1066,\n",
       " 'down at': 326,\n",
       " 'in their': 671,\n",
       " 'he learned': 507,\n",
       " 'the soldiers': 1437,\n",
       " 'for what': 392,\n",
       " 'and he': 57,\n",
       " 'lots of': 820,\n",
       " 'the archer': 1234,\n",
       " 'he not': 516,\n",
       " 'he be': 482,\n",
       " 'all right': 29,\n",
       " 'in front': 658,\n",
       " 'at the': 159,\n",
       " 'his chair': 584,\n",
       " 'and other': 74,\n",
       " 'do so': 305,\n",
       " 'how are': 622,\n",
       " 'he looked': 510,\n",
       " 'as possible': 134,\n",
       " 'as they': 138,\n",
       " 'and her': 59,\n",
       " 'to himself': 1639,\n",
       " 'all this': 32,\n",
       " 'to put': 1661,\n",
       " 'the soviet': 1442,\n",
       " 'below the': 204,\n",
       " 'told the': 1694,\n",
       " 'his back': 580,\n",
       " 'care of': 249,\n",
       " 'down to': 330,\n",
       " 'think we': 1561,\n",
       " 'it at': 702,\n",
       " 'the president': 1402,\n",
       " 'could see': 273,\n",
       " 'things were': 1554,\n",
       " 'the house': 1329,\n",
       " 'down and': 325,\n",
       " 'as she': 135,\n",
       " 'her eyes': 549,\n",
       " 'to what': 1686,\n",
       " 'walked out': 1760,\n",
       " 'worry about': 1945,\n",
       " 'time the': 1606,\n",
       " 'they saw': 1542,\n",
       " 'way of': 1826,\n",
       " 'his own': 605,\n",
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       " 'he have': 500,\n",
       " ...}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_count.vocabulary_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most frequent bi-grams are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('of the', 16376),\n",
       " ('in the', 12028),\n",
       " ('on the', 9361),\n",
       " ('to the', 9316),\n",
       " ('it was', 7915),\n",
       " ('and the', 5486),\n",
       " ('at the', 5218),\n",
       " ('he was', 4913),\n",
       " ('to be', 4812),\n",
       " ('for the', 4718)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_dist = Counter(dict(zip(word_count.get_feature_names(), word_count.fit_transform(corpus).sum(axis=0).A1)))\n",
    "freq_dist.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       " 'ek': 653,\n",
       " 'lis': 1098,\n",
       " 'st': 1688,\n",
       " 'nce': 1222,\n",
       " 'top': 1783,\n",
       " 'sio': 1661,\n",
       " 'son': 1677,\n",
       " 'sou': 1679,\n",
       " 'day': 535,\n",
       " 'hro': 891,\n",
       " 'yin': 1969,\n",
       " 'ed ': 629,\n",
       " 't t': 1724,\n",
       " 'ked': 1032,\n",
       " '’s': 1979,\n",
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       " 'ake': 321,\n",
       " 'hot': 886,\n",
       " 'ls': 1119,\n",
       " 'hem': 861,\n",
       " 'k ': 1017,\n",
       " ',\" ': 233,\n",
       " 'sec': 1640,\n",
       " ' n': 109,\n",
       " 'rve': 1593,\n",
       " \"ou'\": 1406,\n",
       " 'y b': 1938,\n",
       " 'ki': 1036,\n",
       " 'wh': 1911,\n",
       " 'cas': 454,\n",
       " 'jus': 1016,\n",
       " 'red': 1524,\n",
       " 'ng ': 1247,\n",
       " 'ery': 703,\n",
       " 'spe': 1681,\n",
       " ...}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_count.vocabulary_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most frequent 2-grams are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('e ', 514532),\n",
       " (' t', 495167),\n",
       " ('th', 401375),\n",
       " ('he', 375386),\n",
       " (' th', 332571),\n",
       " (' a', 307209),\n",
       " ('d ', 294314),\n",
       " ('t ', 289473),\n",
       " ('the', 263816),\n",
       " ('s ', 262057)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_dist = Counter(dict(zip(char_count.get_feature_names(), char_count.fit_transform(corpus).sum(axis=0).A1)))\n",
    "freq_dist.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While word freqencies are ok, what we really want is term document frequencies to adjust for differences in chapter and/or book lengths.\n",
    "\n",
    "The [Sci-kit Learn Tfidfvectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html) methods convert a collection of raw documents to a matrix of TF-IDF features, which is equivalent to using CountVectorizer followed by TfidfTransformer.\n",
    "\n",
    "* The 'analyzer' parameters analyzer specifies whether the feature should be made of word or character n-grams.\n",
    "* The 'ngram_range specifies the lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used.\n",
    "* The 'max_df' parameter specifies the threshold frequency below which words are ignored\n",
    "* The 'max_features' (int) parameter limits the vocabulary's features to the top n features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "word_vector = TfidfVectorizer( analyzer='word', ngram_range=(2,2), max_features=2000, norm='l2')\n",
    "char_vector = TfidfVectorizer( analyzer='char', ngram_range=(2,3), min_df=0, max_features=2000, norm='l2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit learn also supports an easy way to combine features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vectoriser =FeatureUnion([ (\"chars\", char_vector),\n",
    "                            (\"words\", word_vector)\n",
    "                          ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we vectorise the chapters (our corpus) using the vectoriser we defined above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of freatures:  4000\n",
      "The shape of the vectorised corpus matrix is (522, 4000)\n",
      "The shape of the vectorised class array is (522,)\n"
     ]
    }
   ],
   "source": [
    "matrix = vectoriser.fit_transform(corpus)\n",
    "print(\"Number of freatures: \", len(vectoriser.get_feature_names()))\n",
    "X = matrix.toarray()\n",
    "y = np.asarray(classes)\n",
    "print('The shape of the vectorised corpus matrix is {0}'.format(X.shape))\n",
    "print('The shape of the vectorised class array is {0}'.format(y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In our corpus we have 655 chapters in total and 522 chapters excluding the ghost written Tom Clancy books.\n"
     ]
    }
   ],
   "source": [
    "print('In our corpus we have {0} chapters in total and {1} chapters excluding the ghost written Tom Clancy books.'\n",
    "      .format(len(dfBooks.index), len(dfBooks.loc[dfBooks.author != 'Ghost', 'author'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we now have a matrix contianing a row and 4,000 features in columns for each chapter\n",
    "\n",
    "Next we define the model we want to use.  We'll use a support vector machine classifier (LinearSVC in Scikit learn speak)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = LinearSVC(loss='hinge', dual=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we train the model, we'll split the feature matrix into test and train sets.  The default size if 75% train, 25% test\n",
    "\n",
    "Then we fit the model to using the training data, and see how well it predicts the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85  0]\n",
      " [ 0 46]]\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)  #defualt test size is 25%\n",
    "y_pred = model.fit(X_train, y_train).predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualise the accuracy of the prediction using a confusion matirx."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f9e0f3e5748>"
      ]
     },
     "execution_count": 600,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U8CBwVESEpKspvjT2ZPAzCPjziLhhhOe1mjU9EVaFhxQmntQ/n98GPjp4V1JJ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\nmdXECdfMrCb/H9X9TVaecwLmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9df5dcbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.matshow(cm, cmap=plt.cm.Blues)\n",
    "pl.title('Confusion matrix')\n",
    "pl.colorbar()\n",
    "pl.ylabel('True label')\n",
    "pl.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confusion matrix shows that most of the chapters of text are accurately predicted\n",
    "\n",
    "The actual classification accuracy is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 601,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can perform cross-validation as well to see ifthe predictions are consistent.  In this case we'll do a ten cycle cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.98113208  0.98113208  1.          1.          1.          1.          1.\n",
      "  1.          1.          1.        ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_validation.cross_val_score(estimator=model, \n",
    "                                         X=matrix.toarray(),\n",
    "                                         y=np.asarray(classes),\n",
    "                                         cv=10)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-fold cross validation results: mean score =  0.996226415094 std= 0.00754716981132 , num folds = 10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f9e135fc080>"
      ]
     },
     "execution_count": 603,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ukTRf0pWStk/7NpN0uaSFku6QtF/uuEmSzpO0KB17YJnrcDI2s1ob6qYo+irojIjYOSL2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\nKsDJ2MysAv4/hb0jvMAsx8EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9de16ce908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print( \"10-fold cross validation results:\", \"mean score = \", scores.mean(), \"std=\", scores.std(), \", num folds =\", len(scores))\n",
    "\n",
    "model.fit( X= matrix.toarray(), y= np.asarray(classes) )\n",
    "  \n",
    "predicted = model.predict(matrix.toarray())\n",
    "cm = confusion_matrix(classes, predicted)\n",
    "pl.matshow(cm, cmap=plt.cm.Blues)\n",
    "pl.title('Confusion matrix')\n",
    "pl.colorbar()\n",
    "pl.ylabel('True label')\n",
    "pl.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the trained model which appears to accurately predict whether text is written by Tom Clancy or by Mark Greaney, we can use it to test text written by Mark Greaney under the Tom Clancy name to see if he is able to mimic Tom Clancy.\n",
    "\n",
    "To do this, we:\n",
    "* exctract the text with the author of 'Ghost'\n",
    "* transform the text using the same vecotriser we used to build the model, so that we create the same features, and then use the model to predict which author wrote the text.\n",
    "\n",
    "If Mark Greaney can mimic Tom Clancy, the model should predict 'Tom Clancy', if he can't the model will predict Mark Greaney"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ghost_set = []\n",
    "df_ = dfBooks.loc[(dfBooks.author == 'Ghost'), :]\n",
    "for author, chapter in zip(df_.author.values, df_.chapter_text.values):\n",
    "    ghost_set.append({\"label\":author,\n",
    "                     \"text\":chapter})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ghost_corpus = []\n",
    "ghost_classes = []\n",
    "\n",
    "for item in ghost_set:\n",
    "    ghost_corpus.append( item['text'])\n",
    "    ghost_classes.append( item['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of freatures:  4000\n"
     ]
    }
   ],
   "source": [
    "ghost_matrix = vectoriser.transform(ghost_corpus)\n",
    "print(\"Number of freatures: \", len(vectoriser.get_feature_names()))\n",
    "X_ghost = ghost_matrix.toarray()\n",
    "y_ghost = np.asarray(ghost_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Clancy', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Clancy', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Clancy', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Clancy', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Clancy',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Clancy',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney'], \n",
       "      dtype='<U7')"
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghost_predict = model.predict(X_ghost)\n",
    "ghost_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Clancy': 6, 'Greaney': 127})"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(ghost_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the 133 chapters written by Greaney under the Tom Clancy brand, only 6 are classified by our model as written by Clancy.\n",
      "This is only 4.51%\n"
     ]
    }
   ],
   "source": [
    "print('For the {0} chapters written by Greaney under the Tom Clancy brand, only {1} are classified by our model as written by Clancy.\\n\\\n",
    "This is only {2:0.2f}%'.format(len(ghost_predict), Counter(ghost_predict)['Clancy'], 100.0*Counter(ghost_predict)['Clancy']/len(ghost_predict)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it appears that Mark Greaney isn't able to mimic Tom Clancy very well.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As as test, to make sure we are getting the results we think we are, we can re-vectorise Mark Greaney's won books, independent of the original model vectorisation, and then see if the model accurately predicts that they are Greaney chapters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temp_set = []\n",
    "df_ = dfBooks.loc[(dfBooks.author == 'Greaney'), :]\n",
    "for author, chapter in zip(df_.author.values, df_.chapter_text.values):\n",
    "    temp_set.append({\"label\":author,\n",
    "                     \"text\":chapter})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Greaney'"
      ]
     },
     "execution_count": 611,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_corpus = []\n",
    "temp_classes = []\n",
    "\n",
    "for item in temp_set:\n",
    "    temp_corpus.append( item['text'])\n",
    "    temp_classes.append( item['label'])\n",
    "temp_classes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of freatures:  4000\n"
     ]
    }
   ],
   "source": [
    "temp_matrix = vectoriser.transform(temp_corpus)\n",
    "print(\"Number of freatures: \", len(vectoriser.get_feature_names()))\n",
    "X_temp = temp_matrix.toarray()\n",
    "y_temp = np.asarray(temp_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney', 'Greaney',\n",
       "       'Greaney', 'Greaney'], \n",
       "      dtype='<U7')"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_predict = model.predict(X_temp)\n",
    "temp_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying 1 features\n",
      "Accuracy: 64.8854961832061\n",
      "Trying 2 features\n",
      "Accuracy: 71.7557251908397\n",
      "Trying 3 features\n",
      "Accuracy: 72.51908396946564\n",
      "Trying 5 features\n",
      "Accuracy: 91.6030534351145\n",
      "Trying 10 features\n",
      "Accuracy: 93.12977099236642\n",
      "Trying 20 features\n",
      "Accuracy: 98.47328244274809\n",
      "Trying 50 features\n",
      "Accuracy: 100.0\n"
     ]
    }
   ],
   "source": [
    "accuracy_range = []\n",
    "features_list = [1, 2, 3, 5, 10, 20, 50]\n",
    "for features in features_list:\n",
    "    print('Trying {0} features'.format(features))\n",
    "    word_vector = TfidfVectorizer( analyzer='word', ngram_range=(2,2), max_features=features, norm='l2')\n",
    "    char_vector = TfidfVectorizer( analyzer='char', ngram_range=(2,3), min_df=0, max_features=features, norm='l2')\n",
    "\n",
    "    vectoriser =FeatureUnion([ (\"chars\", char_vector),\n",
    "                                (\"words\", word_vector)\n",
    "                              ])\n",
    "\n",
    "    matrix = vectoriser.fit_transform(corpus)\n",
    "\n",
    "    X = matrix.toarray()\n",
    "    y = np.asarray(classes)\n",
    "    model = LinearSVC(loss='hinge', dual=True)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)  #defualt test size is 25%\n",
    "    y_pred = model.fit(X_train, y_train).predict(X_test)\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    accuracy_range.append(acc)\n",
    "    print(\"Accuracy: {0}\".format(acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1),\n",
       " <matplotlib.text.Text at 0x7f9de255a198>,\n",
       " (0, 50),\n",
       " <matplotlib.text.Text at 0x7f9e07447860>,\n",
       " None]"
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DAODyRmiKgWZ/UH/5pDmifViKR5OGeG2oCAAAxBqhKQberGmSL2BGtM/LSZPLATcPBgAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dffe54b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4), facecolor='white')\n",
    "ax.plot(features_list, accuracy_range)\n",
    "ax.set(xlim=[0,50],\n",
    "       ylim=[0,1],\n",
    "       xlabel='Features',\n",
    "       ylabel='Accuracy',\n",
    "       axis_bgcolor = 'white')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And it does.\n",
    "\n",
    "So what this tells us is that Mark Greaney isn't able mimic Tom Clancy.\n",
    "\n",
    "However, fans of Tom Clancy books written by Mark Greaney seem happy anyway.\n",
    "<img src=Mark%20Greaney%20ratings.png>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Date</th>\n",
       "      <th>Author</th>\n",
       "      <th>Brand author</th>\n",
       "      <th>Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Back Blast</td>\n",
       "      <td>2016</td>\n",
       "      <td>Mark Greaney</td>\n",
       "      <td>Mark Greaney</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Gray Man</td>\n",
       "      <td>2009</td>\n",
       "      <td>Mark Greaney</td>\n",
       "      <td>Mark Greaney</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Commander in Chief</td>\n",
       "      <td>2016</td>\n",
       "      <td>Mark Greaney</td>\n",
       "      <td>Tom Clancy</td>\n",
       "      <td>1358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>On Target</td>\n",
       "      <td>2010</td>\n",
       "      <td>Mark Greaney</td>\n",
       "      <td>Mark Greaney</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dead Eye</td>\n",
       "      <td>2013</td>\n",
       "      <td>Mark Greaney</td>\n",
       "      <td>Mark Greaney</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Title  Date        Author  Brand author  Reviews\n",
       "0         Back Blast   2016  Mark Greaney  Mark Greaney      566\n",
       "1        The Gray Man  2009  Mark Greaney  Mark Greaney      695\n",
       "2  Commander in Chief  2016  Mark Greaney    Tom Clancy     1358\n",
       "3           On Target  2010  Mark Greaney  Mark Greaney      356\n",
       "4            Dead Eye  2013  Mark Greaney  Mark Greaney      436"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./ratings.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9df507ac50>"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e5ccff+TChQuMGTOmzLXfeecdFixYQGpqKoaGhowdO1Z9bM+ePYwaNYpJkyZx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\nRKSw/wfcXYwmCmv6rQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e07d69c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4), facecolor='white')\n",
    "\n",
    "df_ = df[df['Brand author'] == 'Mark Greaney']\n",
    "ax.bar([x for x in df_.Date], df_.Reviews, color=STD_COLORS['1'], width=0.3, label = 'Mark Greaney')\n",
    "\n",
    "df_ = df[(df['Brand author'] == 'Tom Clancy') & (df['Author'] == 'Tom Clancy')]\n",
    "ax.bar([x+0.33 for x in df_.Date], df_.Reviews, color=STD_COLORS['2'], width=0.3, label = 'Tom Clancy')\n",
    "\n",
    "df_ = df[(df['Brand author'] == 'Tom Clancy') & (df['Author'] != 'Tom Clancy')]\n",
    "ax.bar([x+0.33 for x in df_.Date], df_.Reviews, color=STD_COLORS['3'], width=0.3, label = 'Mark Greaney as Tom Clancy')\n",
    "\n",
    "ax.set(xlim=[1985, 2016],\n",
    "       xlabel = 'Published year',\n",
    "       ylabel = 'Amazon reviews', \n",
    "       axis_bgcolor='white');\n",
    "ax.legend(loc=\"upper left\", frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "word_vector = TfidfVectorizer( analyzer='word', ngram_range=(2,2), max_features=10, norm='l2')\n",
    "char_vector = TfidfVectorizer( analyzer='char', ngram_range=(2,3), min_df=0, max_features=10, norm='l2')\n",
    "\n",
    "W = word_vector.fit_transform(corpus)\n",
    "C = char_vector.fit_transform(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>author</th>\n",
       "      <th>title</th>\n",
       "      <th>chapter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.256175</td>\n",
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       "      <td>0.278174</td>\n",
       "      <td>0.164055</td>\n",
       "      <td>0.304013</td>\n",
       "      <td>0.241084</td>\n",
       "      <td>0.606864</td>\n",
       "      <td>0.340429</td>\n",
       "      <td>0.154746</td>\n",
       "      <td>0.394181</td>\n",
       "      <td>...</td>\n",
       "      <td>0.273267</td>\n",
       "      <td>0.451733</td>\n",
       "      <td>0.317862</td>\n",
       "      <td>0.278427</td>\n",
       "      <td>0.205424</td>\n",
       "      <td>0.342532</td>\n",
       "      <td>0.243378</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.070314</td>\n",
       "      <td>0.162195</td>\n",
       "      <td>0.285049</td>\n",
       "      <td>0.140092</td>\n",
       "      <td>0.389408</td>\n",
       "      <td>0.285049</td>\n",
       "      <td>0.617290</td>\n",
       "      <td>0.298352</td>\n",
       "      <td>0.173437</td>\n",
       "      <td>0.367203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.274962</td>\n",
       "      <td>0.451541</td>\n",
       "      <td>0.316757</td>\n",
       "      <td>0.239309</td>\n",
       "      <td>0.228035</td>\n",
       "      <td>0.327755</td>\n",
       "      <td>0.239034</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.236902</td>\n",
       "      <td>0.179093</td>\n",
       "      <td>0.155356</td>\n",
       "      <td>0.277644</td>\n",
       "      <td>0.381339</td>\n",
       "      <td>0.225972</td>\n",
       "      <td>0.720442</td>\n",
       "      <td>0.177389</td>\n",
       "      <td>0.147313</td>\n",
       "      <td>0.204680</td>\n",
       "      <td>...</td>\n",
       "      <td>0.295998</td>\n",
       "      <td>0.442149</td>\n",
       "      <td>0.322033</td>\n",
       "      <td>0.298231</td>\n",
       "      <td>0.211084</td>\n",
       "      <td>0.340057</td>\n",
       "      <td>0.235517</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.149937</td>\n",
       "      <td>0.296453</td>\n",
       "      <td>0.121567</td>\n",
       "      <td>0.209111</td>\n",
       "      <td>0.366339</td>\n",
       "      <td>0.243134</td>\n",
       "      <td>0.614274</td>\n",
       "      <td>0.411085</td>\n",
       "      <td>0.221901</td>\n",
       "      <td>0.205542</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251296</td>\n",
       "      <td>0.448230</td>\n",
       "      <td>0.299277</td>\n",
       "      <td>0.260463</td>\n",
       "      <td>0.250260</td>\n",
       "      <td>0.346912</td>\n",
       "      <td>0.214016</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.220454</td>\n",
       "      <td>0.160586</td>\n",
       "      <td>0.152870</td>\n",
       "      <td>0.115585</td>\n",
       "      <td>0.464924</td>\n",
       "      <td>0.317500</td>\n",
       "      <td>0.622487</td>\n",
       "      <td>0.340839</td>\n",
       "      <td>0.147186</td>\n",
       "      <td>0.215865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250328</td>\n",
       "      <td>0.438147</td>\n",
       "      <td>0.300455</td>\n",
       "      <td>0.270469</td>\n",
       "      <td>0.262784</td>\n",
       "      <td>0.345113</td>\n",
       "      <td>0.228759</td>\n",
       "      <td>Clancy</td>\n",
       "      <td>The Hunt for Red October</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.256175  0.126626  0.278174  0.164055  0.304013  0.241084  0.606864   \n",
       "1  0.070314  0.162195  0.285049  0.140092  0.389408  0.285049  0.617290   \n",
       "2  0.236902  0.179093  0.155356  0.277644  0.381339  0.225972  0.720442   \n",
       "3  0.149937  0.296453  0.121567  0.209111  0.366339  0.243134  0.614274   \n",
       "4  0.220454  0.160586  0.152870  0.115585  0.464924  0.317500  0.622487   \n",
       "\n",
       "          7         8         9   ...           13        14        15  \\\n",
       "0  0.340429  0.154746  0.394181   ...     0.273267  0.451733  0.317862   \n",
       "1  0.298352  0.173437  0.367203   ...     0.274962  0.451541  0.316757   \n",
       "2  0.177389  0.147313  0.204680   ...     0.295998  0.442149  0.322033   \n",
       "3  0.411085  0.221901  0.205542   ...     0.251296  0.448230  0.299277   \n",
       "4  0.340839  0.147186  0.215865   ...     0.250328  0.438147  0.300455   \n",
       "\n",
       "         16        17        18        19  author                     title  \\\n",
       "0  0.278427  0.205424  0.342532  0.243378  Clancy  The Hunt for Red October   \n",
       "1  0.239309  0.228035  0.327755  0.239034  Clancy  The Hunt for Red October   \n",
       "2  0.298231  0.211084  0.340057  0.235517  Clancy  The Hunt for Red October   \n",
       "3  0.260463  0.250260  0.346912  0.214016  Clancy  The Hunt for Red October   \n",
       "4  0.270469  0.262784  0.345113  0.228759  Clancy  The Hunt for Red October   \n",
       "\n",
       "   chapter  \n",
       "0      0.0  \n",
       "1      1.0  \n",
       "2      2.0  \n",
       "3      3.0  \n",
       "4      4.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 666,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = W.toarray()\n",
    "T = np.hstack((W.toarray(), C.toarray()))\n",
    "dfTfidf = pd.DataFrame(T)\n",
    "dfTfidf['author'] = dfBooks.loc[dfBooks.author != 'Ghost', 'author'].values\n",
    "dfTfidf['title'] = dfBooks.loc[dfBooks.author != 'Ghost', 'title'].values\n",
    "dfTfidf['chapter'] = dfBooks.loc[dfBooks.author != 'Ghost', 'chapter_num'].values\n",
    "dfTfidf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def freq_plot(df, gram, title):\n",
    "    fig, ax = plt.subplots(figsize=(6,4), facecolor='white')\n",
    "\n",
    "    df_ = df[df.author == 'Clancy']\n",
    "    ax.bar(df_.index, df_[gram], color='r', label='Clancy')\n",
    "#     df_ = df[df.author == 'Ghost']\n",
    "#     ax.bar(df_.index, df_[gram], color='b', label='Ghost')\n",
    "    df_ = df[df.author == 'Greaney']\n",
    "    ax.bar(df_.index, df_[gram], color='g', label='Greaney')\n",
    "    ax.set(axis_bgcolor='white',\n",
    "           xlabel = 'Chapter',\n",
    "           ylabel = 'Tfidf norm. freq',\n",
    "           title = title )\n",
    "    ax.legend(loc=\"upper right\", frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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MJhO6urpgtVqxbt06t5mNHQ4HTp8+7bIsWPdWqZvdbscXX3yB0aNHu42Bo8CwbUOHbRs6\nbNvQYdsGn9FY7dTUVPU9nYGIyDQNY8aMwZ49e9yWr1q1yuM+2hfJFhYWqrcKvTF6WjBYTwdQN0mS\n4HA4IEkS2zXI2Lahw7YNHbZt6LBtwyNYMw3wDBERUZ9WXV+N6vrqgLfxZzuKf0ywiIioT2tsb0Rj\ne2PA2/izHcU/JlhEREREQcYEi4iIiCjImGARERERBRkTLCIiIqIgi8g0DUREROQ7e3U15JMnMaKj\nA3JlJWxBmqZBOuccJDvfluKNw+HAww8/jBdffBEWiwUJCQmYNm0arr76aqxcuRIHDhwISjzxhAkW\nERFRlBONjUibOjXo5doqKgAfEqwf/ehHaGpqwt69e5Geng4A2L59O06dOhW0eaPiDW8REhERkUdH\njhzB9u3bUV5eriZXAFBUVOTymjuHw4E5c+Zg2rRpyMvLw8KFC9HW1gYAeOedd5CXl4e77roL+fn5\nyMvLw/79+9V9X3vtNUybNg35+fmwWq14//33sXbtWpSWlqrbNDc3IzMzE01NTWGodeCYYBEREZFH\n+/fvx+jRo3HOOed43c5sNuOFF17Avn37cOjQIaSnp2P9+vXq+sOHD+PWW2/Fhx9+iLvvvhu/+MUv\nAHS/Pu9HP/oRtmzZgg8//BDvv/8+LrroItx+++3YsWMHWlpaAAAbN27Etdde6/ZavGjFBIuIiIgC\nJoTA2rVrYbVaMWHCBPztb3/Dhx9+qK4fNWoUpkyZAgC45JJLcPToUQDAm2++iauvvhqjR48G0J2o\npaWlISMjA/Pnz8eGDRsAAE8//TTuvvvuMNeq9zgGi4iIiDyyWq344osv0NjY6LUXa8uWLXj77bfx\n7rvvIiUlBevXr8fOnTvV9f3791f/bTab0dXV1eOxly1bhnnz5uGiiy7CoEGDMHHixMAqE0bswSIi\nIiKPRo4ciaKiItx2221obm5Wl7/88stqLxQANDU14bzzzkNKSgpaW1tRXl7uU/lXXXUVXn/9dXz+\n+ecAgK6uLvW24NixY5GTk4OSkhIsW7YseJUKAyZYRERE5NWGDRswYcIEFBQUIC8vDxdffDHeeOMN\nnHvuueo2t9xyC2w2G3Jzc/G9730PM2bM8KnskSNHYuPGjViwYAHy8/NRWFioJlsAcMcdd8DhcKCo\nqCjo9Qol3iIkIiKKctI556D1/ffR0dGBxMREmII4D5YvzGYzVq5ciZUrV7qt+8EPfgAASE9Pxxtv\nvGG4/8yZM12eGrz44otder/mzJmDOXPmGO67c+dOLF26FGaz2adYowUTLCIioiiXPGIEbIMGobqy\nErm5uUhJSYl0SCFXV1eHyy67DAMHDsTrr78e6XD8xgSLiIiIok5WVhYqKysjHUavcQwWERERUZAx\nwSIiIiIKMiZYREREREHGBIuIwspeXQ17dXWkwyAiCikOcieisBKNjd3/GDEisoEQxZDq+mqctJ9E\nR3oHKhsqYToZnP6Rc/qfgxGDev4sdnV14de//jVefPFFJCQkIDExEcOHD8fKlSsxYcKEoMQSb5hg\nERERRbnG9kZMfW5q0MutuLUCI9BzgrV48WLY7Xbs3bsX6enpAIB//etfOHz4sFuCJcty0ObpimVs\nASIiIvKoqqoKO3bswMaNG9XkCgAuu+wyXHfddXjuuedw2WWXYf78+Zg4cSLef/99nDhxAjfccAMK\nCwsxceJEPPjgg+p+y5cvR0FBAaxWK2bNmoUvvvhCXWcymfCb3/wGBQUFGDlypMvrdqqqqjB37lwU\nFBQgPz8fv//97wEAa9euRWlpqbpdc3MzMjMz0dTUFMJW6Rl7sIiIiMijAwcOYNSoUcjIyPC4zb59\n+/Dhhx9i1KhRALpnZn/ggQfwX//1X3A4HJg7dy62b9+OoqIi/PznP8ejjz4KANi6dSt+/OMf4+9/\n/7taVlJSEvbu3YvDhw9j6tSpuOWWWwAAN910E7Zs2YIxY8agra0NhYWFKCgowO23346xY8fi0Ucf\nRXp6OjZu3Ihrr70WAwYMCGGr9CwiCVZVVRWKi4vx7bffYsCAASgvL0dubq7H7RcvXoznn38eTU1N\nava8d+9elJaWor29HUOGDMGmTZuQlZUVrioQERH1SUePHkVRURHa2tpw6aWXYubMmbj00kvV5Mpu\nt+Ott95CfX09hBAAAJvNhsOHDwMAXn/9dTz55JNobW2FLMtoVMZlOt18880Aul/0bLFY8M0336C5\nuRmffPIJbrzxRrXM06dP49NPP8XkyZMxf/58bNiwAffeey+efvppvPTSS+FqDo8ikmCVlpZiyZIl\nWLRoEbZv347i4mLs27fPcNtXXnkFiYmJkCRJXSaEwMKFC/GHP/wBM2bMwNq1a3HPPfdERYMSERHF\nk0mTJqGqqgrNzc3IyMhATk4ODhw4gOeeew47duwAAKSmpqrbCyEgSRL27t0Li8XiUlZtbS2WLVuG\niooKZGdn49ChQ5g5c6a6XpIk9O/fX/3ZZDKhq6sLQggMHDjQ5X2GWsuWLcO8efNw0UUXYdCgQZg4\ncWIwm6BXwj4Gq6GhARUVFViwYAEAoKioCLW1tS4vfVScOHECv/nNb/DYY4+pGSsAVFRUwGKxqG/q\nLi0txV/+8hd0dHSEpxJERER9xKhRo/CDH/wAt912G5qbm9XlNpvNcPuUlBTMnj0bq1evVpfV1dXh\n66+/RnNzMxITE3HBBRdACIH169e77Kv9Xa81duxYpKenu4zJOnLkiNr7NXbsWOTk5KCkpATLli3r\nbVWDKuwJVm1tLbKyslyeMBg2bBhqamrcti0pKcGjjz7q9lLLmpoaDB8+XP05NTUVGRkZOH78eOgC\nJyIi6qPKy8sxfvx4FBQUIC8vDzNmzMBbb72F//f//p/h9lu2bEFVVRXy8vIwYcIEFBUV4dSpUxg/\nfjxuvPFGjBs3DgUFBcjOznbZT3u3Svuz2WzGX//6V7z88svIz8/H+PHjcfvtt6O9vV3d9o477oDD\n4UBRUVFwK99LUTvI/Q9/+AOGDx/u0nXojaesV89ut7udQOq9trY2l/9T8MRr28qyDMDzX7/hEK9t\nGw1isW19uSZ9vW5DdX2nWdKw75Z96OzshMViCdrvsTRLms+xLl++HMuXL3dbPn78eMyfP9+lnKSk\nJJSVlblta7PZ8PDDD+Phhx9Wl91zzz3qvi0tLep2AFDtnJTYZrNh0KBBeOGFFwzLBIB//vOfbklX\nT3zNHXpDEqEs3UBDQwNGjx6NU6dOqb1YWVlZ2L17N3JyctTtFi5ciHfffRdmsxlCCHz55ZcYNmwY\nduzYgc7OTixatEh9y/bp06eRmZmpdj0qZFlGa2ury/GPHj0Kh8MRhpoSkZERzlv51ZrPKlEkdaR3\nX5OJLZ6vSV+28Wc7Cp5vv/0Wd955JzIyMrB+/XokJSX5vK/ZbHbJPQAgLS0tKPN4hb0HKzMzE1ar\nFZs2bUJxcTG2bduGoUOHulVw8+bNLj+bTCYcOnQIaWlpEEKgq6sL77zzDmbOnImysjJ8//vfd0mu\nPBk9ejR7sIKora0Nx44dQ3Z2tl8XNfUsXttWdv5h5O3J4VCL17aNBrHYtpUNPV+Tvmzjz3a9EYtt\nGy4ff/xxr/YTQoSs0yUitwjLysqwePFirF69GhkZGeqgtRUrVmDw4MEoKSlx20eSJLUrT5IkbN68\nGSUlJThz5gwuvPBCbNq0yadjJycnc4bZEEhKSnIbK0fBEW9ta3N+/qKhTvHWttEkltpWee2Mt3h9\n2caf7QIRS20b7YzudAVLRBKsMWPGYM+ePW7LV61a5XEffYZZUFCAgwcPBj02IiIiokCxK4eIiIgo\nyJhgkd/s1dWwO5/sICIiIndMsMhvorERQvdqAyIiIjqLCRYRERFRkDHBIiIiIgoyJlhEREREQcYE\ni4iIiCjImGARERERBRkTLCIiIqIgY4JFREREFGRMsIiIiIiCjAkWERERUZAxwSIiIiIKMiZYRERE\nREHGBIuIiIgoyJhgEREREQUZE6wA2aurYa+ujnQYREREFEWYYAVINDZCNDZGOgwiIiKKIkywiIiI\niIKMCRYRERFRkDHBigCO26Jg4bXUd1XXV6O6nuee7UDRiglWBHDcFgULr6W+q7G9EY3tPPdsB4pW\nTLAoILIsRzoEopBg7yARBYIJFgVECBHpEIhCgr2DRBQIJlhEREREQcYEK4p1dHREOgQiIiLqhYgk\nWFVVVZg+fTrGjh2LgoICVFZWum1z7NgxTJkyBVarFXl5ebjhhhvQ3NysrjeZTJg4cSImTZoEq9WK\n3bt3h7MKYdHZ2RnpEIiIiKgXIpJglZaWYsmSJTh8+DDuv/9+FBcXu20zePBg7N69G/v378ehQ4eQ\nlZWFlStXquslScKuXbtw4MAB7N+/H9OnTw9jDagv4WBnIiLyV9gTrIaGBlRUVGDBggUAgKKiItTW\n1uLo0aMu21ksFvTr1w8A4HA4YLPZIEmSul4IwQHWFBYc7ExERP5KCPcBa2trkZWVBZPpbG43bNgw\n1NTUICcnx2Xbzs5OTJs2DTU1NZgwYQJeffVVdZ0kSZg9ezZkWcbll1+Ohx56CMnJyT0e3263uyRq\ngVKmKbDZbEHfR0kso402fofDAQBoa2uLZEgh1ZtzHAxKm3pr20jFFohoiDke2jZa4/OlbYMpGO3g\nSxm+HieU5yXcbdsXhLKjRhJh7gbav38/FixY4DLuqqCgAGvWrMGsWbMM9+nq6sKyZcuQk5OD5cuX\nAwC++uorDBkyBG1tbSgtLUVaWhqeeuopl/1kWUZra6vLsqNHj6pJQTCMcA5Er05MDPo+WVlZqKur\n631wIaKNP1pjDKbenONwiebYPImVmKM9zo707vgSW6IzvnAJRjv4Uoavx+F5iS1ms9mtcyctLc2l\nE6i3wp5gNTQ0YPTo0Th16pRagaysLOzevdutklp79+5FSUkJDh486LbuvffeQ2lpqds6owTLbDYH\ntwfLmSiacnODvo/D4YDZbO59cCGijb+jowNHjhxBdnY2kpKSIhxZaPTmHAdDW1sbjh075rVtIxVb\nIKIh5nho28qG7vhyM6MrPl/aNpiC0Q6+lOHrcUJ5XsLdtn2BEMKt0yVYCVbYbxFmZmbCarVi06ZN\nKC4uxrZt2zB06FC35KqmpgaZmZlISkqCEAJ/+tOfMGHCBABAU1MT+vXrh6SkJMiyjK1bt2LSpEk+\nHT85OTkoDaewOctKSUkJ+j42m82vcsNFG79yYSYlJUVlrMHQm3McTN7aNtKx9UY0xRzLbWs6Gd3x\nhes7IRjt4EsZvh4nHOclnr9vw82oIyZYwp5gAUBZWRkWL16M1atXIyMjA+Xl5QCAFStWYPDgwSgp\nKcFHH32EBx54AJIkQZZlWK1WPPHEEwCAzz77DKWlpTCZTOjq6oLVasW6desiURUiIiIiNxFJsMaM\nGYM9e/a4LV+1apX677lz52Lu3LmG+xcWFhreKiQiIiKKBpzJnYiIiCjImGARERERBRkTLCKKO3yP\nJxFFGhMsIoo7ffU9ntX11aiu52udiKIBEywiojjR2N6Ixna+1okoGjDBIiIiIgoyJlhEREREQcYE\ni4iIiCjImGARERERBRkTLCIiIqIgY4JFRHHNXl0NezWnLiCi8GKCRURxTTQ2QjRy6gIiCi8mWEQU\nFOwpIiI6KyHSARCRKzVJGTSo522iiNpLNGJEZAMJMaXtk+O8nkQUGPZgEUUZX25p8bZX5LDticgX\nTLCCQDabo7JHgaivs1dXQ/TR9xL6K5rfY8iXd1MsYoLVA5/Gldjt/Is2TnAcUXwRjY0QshzpMGJC\nNL/HsK++vDsQ0Zww9xVMsHrA2wF9C883EcWDaE6Y+womWETU57HnkoiCjQkWEfV57LkkomBjgkVE\nREQUZEywiIiIiIKMCRYRERFRkDHBIoozHLBNRBR5TLCI4ky0D9gWQkQ6BKKg4SSo5ElEEqyqqipM\nnz4dY8eORUFBASorK922OXbsGKZMmQKr1Yq8vDzccMMNaG5uVtfv3bsX+fn5uOiii3DFFVegrq4u\nnFUgol6K1wSLPYd9EydBJU8ikmCVlpZiyZIlOHz4MO6//34UFxe7bTN48GDs3r0b+/fvx6FDh5CV\nlYWVK1eua2yqAAAgAElEQVQC6P6CXrhwIZ544gl89tlnuPrqq3HPPfeEuRbRj1/4ROET7T2HRBRe\nYU+wGhoaUFFRgQULFgAAioqKUFtbi6NHj7psZ7FY0K9fPwCAw+GAzWaDJEkAgIqKClgsFsyYMQNA\nd8L2l7/8hV21OuH+wmdCR0ThVF1fjc4u9iBRdAp7glVbW4usrCyYTGcPPWzYMNTU1Lht29nZiUmT\nJmHQoEGoqqrCqlWrAAA1NTUYPny4ul1qaioyMjJw/Pjx0FcgikRbQsm/4IkoEP6+P6+xvRGy6Pld\nk3wvH0VCQqQD8MZiseDAgQPo6urCsmXLUFZWhuXLlxtu6+u4DrvdrvaE+UJ2vijWZrN5Xi8EZFn2\nuI2/ZSqUnjtv673d//f1OP7SlutwOAAAbW1t6Bei44WTUZuFqh17iuFMWxuA7rb1tI0ikvH25rj+\nfF56Q/nsePp8tuna1p/z7u/yUAn2tRqs+PVt64+T9pMAgEG2QS7LPX0XyrIMgZ6/fz2Vqy0H8F53\nT9voYwvldeBP20bqeyDWhHJMqCTCPOK0oaEBo0ePxqlTp9RerKysLOzevRs5OTke99u7dy9KSkpw\n8OBBfPDBB1i0aJE6OP706dPIzMxEc3MzEhMT1X1kWUZra6tLOUePHlWTAl+McPYSVWvK1a+3CIFO\nSfK4jb9lKrKysrwO3u9pva/H8Ze2XG0MwTpeamoqTp8+HViQvWRUh1C1oz8xeNpGEcl4/T3uaFlG\nV1dXSONTrktfP5/+nHd/l4dKR3r38RJbEr0uC6S8cPMUg6fvuo70DgiTgCRLXuPuqW6+1N3X2KKh\nHaMpjmhnNpvdco+0tDSXu2y9FfYECwAuu+wyFBcXo7i4GNu2bcNvf/tb7Nu3z2WbmpoaZGZmIikp\nCUIILF++HCdOnMCmTZsghMCYMWPw7LPPYubMmfjd736Hffv24aWXXnIpwyjBMpvN/vVgOZM4U26u\n5/UdHUBiosdt/C1T4XA4YDabe73e1+P4S1tuR0cHjhw5guzsbPQ7diwox+upXqFk1GahaseeYjiT\nnY1jx44hOzsbSUlJhtsoAo1Xdt5eN114Ya9i9ue44WhP5Rry9Plsa2tzaVt/zru/y0OlsqH7eLmZ\nuV6XBVJeb+jbNhgxePpOqGyoRIejA4nmRK9x91Q3X+rua2zBakcj/rRtKOOIJ0IIt06XYCVYEblF\nWFZWhsWLF2P16tXIyMhAeXk5AGDFihUYPHgwSkpK8NFHH+GBBx6AJEmQZRlWqxVPPPEEAECSJGze\nvBklJSU4c+YMLrzwQmzatMmnYycnJ/vVcDbntikpKR7Xy5IEk8nkcRt/y1S3s9m8btPjeh+P4y9t\nucqFmZSUBATpeD3VK5SM2ixU7dhTDMoXaFJSktuxbbprONB4bc4/RAKpo6/HbQX8+rz0KhbnNdTT\n51NpW3/Ou7/LQ8V00v14Rst6U54yXmnEoBG9js/ouvUnBi1P3wmmkyZIjp6/f5VyLRaLy12Ono7b\nm9gCOQe+8qVtwxFHPDDqiAmWiCRYY8aMwZ49e9yWK4PYAWDu3LmYO3euxzIKCgpw8ODBkMRH0UN5\nKjF5RO+/6InIP43t3Q+rjEB8fe46OzsNEyyiUOBM7hTV+GQiEdFZlgEWHG/pW0/Mx6qofoowHmh7\nYDhHFBERBcIm29B5hnN/xQL2YIWYtgeGvTHRiROkUrDIZjOvJSICwASLiIkv9YphYm6381oiIgBM\nsIiIeoWJORF5wwSLiIIq2l7hREQUCX4Ncr/11lu9TtK5YcOGgAMiotjGR+GJiPzswerXrx/ee+89\n5OTkYOTIkdi3bx/69euHyZMnY/LkyaGKkYiIiCim+NWD9emnn+K9995Deno6AGDZsmWYO3cunn76\n6ZAER0RERBSL/OrBamhoUJMrAEhPT0dDQ0PQgyIiIlIcbzmuvr6HKFb41YM1ceJELF68GLfddhsA\nYOPGjZg4cWJIAos2fGULEVFkNJ9phslkirtX91B886sH69lnn0VmZibuvfde3HvvvcjMzMSzzz4b\nqtiiSl95JJuTblKkBOvpQ3t1NRx2e1DKCoXq+mr2xhD1AX71YKWmpuLRRx8NVSwUBdQkkj11FGbB\nevpQNDYC/fr5vL29uhqisxOSxRLYcYXwabt4fZEyEbnyqwertrYWc+fORX5+PgDgww8/xGOPPRaS\nwKKB8sVLRMEXLb2lorERQpYDL0eXYLGniqhv8yvBKi0txY033qh+kYwfPz6u574K1hdvvOKEkhSI\neL/t3tjeqPZWUTcmndSX+JVg1dfXY+HChTCZundLSEhAQoJfdxmpB/bqarR++mlU/GXfk0727kUF\nvmA48sL1x0as/1HDpJP6Er8SrISEBJdu8MbGRp/HHZBvRGMj0NwcdX/ZR8vtHDLAFwxHXLj+2OAf\nNb1nhhn2M9H78APFH78SrOuuuw6lpaVoaWnBs88+iyuvvBK33357qGKjKBLvt3PIHZNqiif2Tjs6\nZSaoFD5+3d/76U9/ihdeeAHNzc345z//ifvuuw8333xzqGIjogjiE6VERL3nc4LlcDjwi1/8AmvW\nrMFNN90UypiiBm9/EhERUW/4fIvQbDZj586doYwl6jDBIop+sT7wm4jik19jsK655hr8+te/xvHj\nx9HS0qL+R6SIhSfaOLYovnDgNxFFI7/GYD300EMAgF/96leQJAlCCEiSBIfDEZLgKDakpqae/cFu\nh3A4onrcDscWERFRqPnUg/XJJ58AAGRZVv9zOBzq/6lvS0tLi3QIREREUcWnBGvRokUAgO985zsh\nDYbiE2/JdWM7EBH1HT7dImxvb8fWrVtx/PhxvPrqq27r582bF/TA+pKOjo6gvOQ2WvGWXDe2AxFR\n3+FTgvXII4+grKwMDQ0Nbi93liTJ7wSrqqoKxcXF+PbbbzFgwACUl5cjNzfXZZuPP/4Yd911Fxoa\nGpCQkIBp06bhqaeeQr9+/QAAJpMJeXl5MJlMkCQJ69evx/Tp0/2KI1p0dnYiMTEx6l4urfS2JDMh\nICIi8otPCda8efMwb9483HPPPVi3bl3ABy0tLcWSJUuwaNEibN++HcXFxdi3b5/LNv3798dTTz2F\n8ePHQwiBm266CWvWrMGDDz4IoDux27VrV1yN/4m2l0uzxyU2KQ+fEFHg+HJq6i2/pmkIRnLV0NCA\niooKLFiwAABQVFSE2tpaHD161GW7UaNGYfz48QC6k6mpU6fi2LFj6nohBOep0uEYn7PC1RbR2Ob6\nz0WgMXKeKerL+IJq6i2/pmkIhtraWmRlZcFkOpvbDRs2DDU1NcjJyTHcx2az4dlnn8UjjzyiLpMk\nCbNnz4Ysy7j88svx0EMPITk5ucfj2+12//66FwKyplfJZrO5rJZlWd1Gv05d79xP1vVOKds7HI6z\n652/HGVZhnAu98ShWy+fPNld7qBBbsf2lVG8nv4tHA71KdK2tjb089AW/sahrVdv6gC4t4WnOup/\nNjqetxiMjiMfPw4AMF14odfjeozdud2ZtjYA3W3raRsAHttcrZ+XtvAWm7Ksvb3dp7mmtNeFr3XU\nHlN/PfvK7XMgyxBCuHym9O3Tpmlbf69bo8+xw+GABLiU09P+gdbV32u1Jz19DnzV5sN166lcj+sF\n3M6Psr2A5+9f/XbKdeFPXJ6+txVG15+nsgKltKmnemiFMo54EsqOGkmEuRto//79WLBgASorK9Vl\nBQUFWLNmDWbNmuW2fWdnJ/77v/8bo0aNchn/9dVXX2HIkCFoa2tDaWkp0tLS8NRTT7nsK8syWltb\nXZYdPXrU56klRssypM5OdGoSsmrdYPQRHR2wCIFOSXJbp6xX9huh6wlQts/KykJdXZ1aFgB0ShLa\nhw9HXV2dx/iU/YyOZfSzL4zi9fTv9uHD1f208evbwt84tPXqTR162s9bOxnt509Z3rb3tS6+bKe9\nlvRtPlqW0dXV5dd14K0ePV2H/m6vxKfQfw4AYIjFAgD4qofELjU1FWlpaW6fA9PQoThy4kSPn09l\ne3+uW6PPcVZWFlK//lr9vqhOTERHevd2iS2u+3ta7gttGxmVE0jZ2n0DKcfXY/izXj5HRpejy7At\nhUlAkiWvsSrbDUsfhhM1J/yKS1mn0G+j/x4OVdv5W36o44gXZrPZrXMnLS3NpROot8KeYDU0NGD0\n6NE4deqUWoGsrCzs3r3brZJdXV24/vrrMWjQIJSVlXks87333kNpaSkOHjzostwowTKbzT73YMmV\nlUBHB6D5gjXpBuNrt9GvU9c795M1SaW2LIfDAbPZfLYsAEhMhBgzBmaz2WN8yn5GxzL62RdG8brE\nnpEBnDwJJCRAjBkDh8MBu92Ofv36od+xY4Zt4W8c2nr1pg497eetnYz286csb9v7WhdluzPZ2Th2\n7Biys7ORlJRkuA0AtzbvzXXgrR49XYf+bq//HCAjo3u/88/3+7wrfyzpPwdyVhYSBgzw+Plsa2tT\n29bf69boc+xwOCB9/rlLOZUN3dvlZrru72m5L7SfDaNyAilbu28g5WjbVn/d9lSuxzarrwQk47bs\ncHQg0ZzoNVZlu+HpwzEgeYDPx9WuU+i30X8PB9J2PWlra8Pnpz6HxWLBuEHjvG4byjjiiRDCrdMl\nWAlWwLcI9+/fD6vV6vP2mZmZsFqt2LRpE4qLi7Ft2zYMHTrULblyOBy44YYbMHDgQLfkqqmpCf36\n9UNSUhJkWcbWrVsxadIkn46fnJzsc8O1AoAkuWyfkpLiso3NZILs3Ea/Tlmv7GfTHVfZ3mazqetl\nZ/JnMpkAs9mlTP1Tfcp+Rscy+tkXRvG6xN7aClkINT5FUlIS4KEt/I1DW6/e1EG/n1u7eWkn5d9S\nfb26j7cYjNZ52t7XuijbKb+ckpKSPJYFwK3NWwGXn305rrd66K/DnsroaXslvrMLnH8EXXhhjzEb\nfQaM4haShJSUFJyWJAgvn8/eXLdGn2ObzQYZUL8vUlJSYDppvL+n5b7QfjaMygmkbO2+gZSjMLpu\neyrX43oJhufQdNIEyeH5/Oq3k5zXhc/H1axTuF0Puu/hYLRdTzzVQyscccQDo46YYAk4RfvVr37l\n9z5lZWV45plnMHbsWPz2t79FeXk5AGDFihX43//9XwDA1q1b8ec//xkffPABJk2aBKvVimXLlgEA\nPvvsMxQWFmLSpEmYOHEiTp06hccffzzQqoSdv4OPRWPj2Sf7yGe9aTe2dXTy97zwQZjYxgcsKJYF\n3IP12muv+b3PmDFjsGfPHrflq1atUv9988034+abbzbcv7Cw0O12YCxSf1F4GXxMRLFFfeF5z3dV\nqQfKHIHRRpm6YcQgTmFDngV+k5GCwtNfavwLjvqyaJwGo0d2O3s/4xynbiBf+JVg/fvf/8a0adNw\n7rnnIj09HWlpaUhPTw9VbH2Kp8fgfXk8nihe8VYtEcUqv24R3nHHHfj1r3+NadOm+fRUEcUu5bU9\nkvNxeaJ4JB8/Dol/xBBRCPiVYKWnp2P+/PmhioWiiPLaHmVCi2C+fiXeX25N/pNTU2ECwn87sLk5\nql5PFWwcK0QUOX7dIiwqKsKmTZtielxQW01NpEOIScF8Gou3PUlPpKZ2/z+MtwRTncfsDdlsjqoX\ns3vCsUJEkeNXgpWbm4ulS5ciKSkJZrMZJpMp5m4ViqamSIdApIrlP1aCIZKJiv5F8erTf76w2+O6\n54uIAudXgvWTn/wEO3bsQGNjI1paWtDa2oqWlpZQxRZT/Ppypqgjm81o/fTTsP+yD1ZvntH1FxNP\n4EVTohKnT/+lWnrfU0dEvedXgjVo0CBcdtllSE9PR0pKivofIW6/nHsjWpJN2eAWkDJ4332FPbbH\n4xhcf3wC76xouSYjgQlWdKqur1bHyFF88ivBmjdvHp588knU19ejpaVF/Y/IRZCSTY/JkI+EQYKl\nDN6n0JDNZjjs9kiH4Y5/AFGU4fi4+OfXU4S//OUvAQA//vGPIUmS+mSZ/kWJRMGgf5Ixnqi9KfE2\ni7/dDsTA4G8iolDzK8GS+Zc/UVBE+2uSOA8aEVFgfL5F6HA4MG7cuFDGQhRWMTEIPEKi4Vaqv08Y\nxtr57OtPkBLFO58TLLPZjMzMTNijcXxFDIqVeXTimWhshKOlhechWvn5hGGsDernfHBE8c2vW4Sj\nRo3C9OnTcd1117lM0vfjH/846IHFK9lshtTeDpw5AwHE5fiimGK3IxhTqHJ2eiIi0vJ7DFZ+fj6+\n+OILdVmwXp/SZwTpFzpFl87OzqAkWBz7ROQZX/0TGLZfePmVYG3cuDFUcVCQcXxH6ISytypan5xU\nxjYlj4i+L2b5+HHYE/z6KgurcPVu9oXPvDKtwQgEdh321R7nYLUf+cavebC6urrw6KOP4rvf/S6+\n+93vYu3atejq6gpVbCETa4Nhe6Ovje8I5y+XWGxbo0lX/RHV45uam6M3NoTveonF6zJS2FYUDn79\n2XfffffhyJEjWLp0KSRJwrPPPosvv/wSTzzxRKjiCwn1yzgK/xonz7z91RmsW3TxymjS1XjXm6S7\nN4loX+g5IiL/+ZVgvf322/jwww9hMnV3fH3ve9+D1WoNSWCRFq1fmsorP6LxVk2oxUMS1Zdf2eIr\n9UGQAPWml6I3iWg09obE+ytY4r1+FB/8SrCEEJBlWU2whBAQIj6HbEfjlyaA7kHyDgd732KVcv5i\nXEjHsOgeBAn09mYwResfXnrx/goWf+pXXV+Nzq4o/T73kZJQDkqJzomJyZhfCdacOXPw3e9+F4sX\nLwYAPP/887j66qtDEVefIaem+jcQzt/yY6DHK5oHUJOxcPYmRtPtzaj9w4s8amxvhCxi+y0kSkLp\nLcHy5wlBPk0YHn4lWGvWrMEzzzyDV199FQAwf/58lJSUhCSwviLkvzxioMeLY+KoL+jtbS3+MiRf\n+POEIJ8mDA+/EiyTyYQ777wTd955Z6jiISKnaHn3p3z8OCTN3Fz26mo4UlOBlJQIRxZbenvbjr8M\nPYvXISoUH/xKsJqamvDMM8/gyJEjLtMzbNiwIeiBEUVapAekh+KXR69uxzY3u8zNJRobgX79gh5b\nOEVL8ko98zbujQkWRTO/Eqz58+cjMzMTl1xyCcxmc6hiol6IhxnAo27yvwAHpNurq+Foa4M5KSkq\nxpd1dHRE9HZsND09yV/MsSOWxr0pA+otCbH7PUzB41eCVVdXhzfffDPgg1ZVVaG4uBjffvstBgwY\ngPLycuTm5rps8/HHH+Ouu+5CQ0MDEhISMG3aNDz11FPo5/zLee/evSgtLUV7ezuGDBmCTZs2ISsr\nK+DYYlW0zgDuj3iYhkFLNDZ2v3OyvT0qxpcF+osq0B69aJ4MVCvqEn2KGfEwoJ6Cx68H2EaOHImm\npqaAD1paWoolS5bg8OHDuP/++1FcXOy2Tf/+/fHUU0/h008/xcGDB3H69GmsWbMGQPdfnwsXLsQT\nTzyBzz77DFdffTXuueeegOMiIi/s9phJkgIRSz0mRBS9/OrBSk5OhtVqxZw5c9C/f391+f/8z//4\nXEZDQwMqKirwxhtvAACKiopw99134+jRo8jJyVG3GzVqlPpvSZIwdepUfPLJJwCAiooKWCwWzJgx\nA0B3wvbLX/6y1395cpqA6KPc8iQiIopFfiVYubm5brfy/FVbW4usrCx1slIAGDZsGGpqalwSLC2b\nzYZnn31W7cGqqanB8OHD1fWpqanIyMjA8ePHkZ2d7XdMnCYg+ii3PImIiGKRXwnWihUrQhWHR52d\nnbjxxhsxZ84czJs3z+N2vg5alYWA5PzFbbPZupfpfgYAh8PRPZ7JOXu9QruNuq/z2LIsG6937qfd\nVhu3wzmQWtKVpQywdonTGY/NZoPD4fC6TtIt86l9tEmNbl91nXO5cDjU2Nva2tDPQ1vo21cbt3a9\ncC7Xxq4tS7+flrJOPn5cKRRCiLPtDtdzoK2LxzbwcO7d2l0Tu7c2dDu2bj91X+c22rb1GKOmDobn\nSt1EnG1XeLiOYXyt6Y+hlOfps2C0Th+TvlyX+Aw+I96uJclbfT20j9K2QghImuMp++mvG4XRtal8\nTvXHUkLSl6cty5fPpTYOo2tPWyejpyM9nQ9vxzGqu6+U69XbdeupXKM6AQCEhyc/BSDg/TtOlmUI\nnL3OjL5LvMWlP66+ffTnpKd4fDnm8Zbu77EL0y902VZpU4+fP3j/vWZUl74ulA+8SCLMj9M0NDRg\n9OjROHXqlNqLlZWVhd27d7v1YHV1deH666/HoEGDUFZWpi7/4IMPsGjRIlRWVgIATp8+jczMTDQ3\nN7vcIpRlGa2tra5lfvYZ4PzQVju3HeF8DLhas29WVhZSv/4aUmcnOqWzQ8erdbcgR3R0wOJswk5J\nMlyv7KfdVuEYNgx2Zzv0//JLl7Lanb10dXV1LsdSjpOVleV1nVKeUVyejNA8Eq3dd7Qsq1NzKMvb\nNb2IdXV1HttC377auLXr24cPR11dnUvs2rL0+2kp67Txm4YOxZETJwzPgXJO9edDOZ62ngqj+PWx\ne2tD/bH1+yn7atvXU331x9C2k/ZcadvCbjIh9euv0dXV5fU61ceRkJAAyXm7VoldaVt9GRYh4Bg2\nzG2d9hgJCQku1xJw9nOgPe8KbXlG15JSJ0/11cau3Q+A2/GUOLTXkv57wdPnVH+sw+d17zM8pfs8\ndqS7TjegLO+Jsl9ii+tnQLtcie1L25du+w8dMBQnatzPh7fj6MsOlp7KNaoTAHzd/jW6HF1u2yeY\nE9ApOiHJktcyhan7fA1LH4YTNSfcvku0x7UM6H4CsLOp02WdQt8++nMiTMJrPL60hX6dL+dGXwc7\n7BCycNnOqC59ndlsdss90tLSXO6y9ZZfPVjBkJmZCavVik2bNqG4uBjbtm3D0KFD3SrocDhwww03\nYODAgS7JFQBMnjwZXV1deOeddzBz5kyUlZXh+9//vk/jryyJiZCcCZZyu1N2Jmra258OhwOSyQRI\nkku5+lukcmUl4PwiTkxMNF7v3E+7rcJkMiEtLQ0AICUmupRlcS4fMGCAy7GU4zgcDq/rlPKM4vJE\niReAy75yZeXZdnAut6SlweFwwG63Izs7G4nHjhm2hb59tXFr11vS0jBgwAA4jh/vbnvnX1tG9dVT\n1mnjl02ms+2uOwfauuhp11lSUiC1tQEJCYbx62PvqQ1djq3bT93XuY0pORkAkJ2djaSkJJcY5cpK\nCIulOzZdO7mcK01bpKWlQTKZerxO9XEAAJyJlfKzbDJh7NixLl9Cyj4mZ7vradtF3/7K50D7GVBo\nyzO6lpQ6eaqvNnZlv46ODtjtdlgsFjUp0sahvZbcvhc8fE71x1JCUsqrbNBcF5rlPVH2038GtMuV\n2BI73b8DTZLx+fB2HH3Z/mhra8OxY8cMr1tP5SrvuDWqEwCYOkxINBt8vwt0JzNevuMqGyrR4XBe\nZ8620H+XeKu7/rzpt9Gfkw6Hb9+53trYUwzZ2dn4/NTnsFgsbvvp6yAcwi0Oo7r0ddq7SMHmU4L1\nySef4OKLLw7aQcvKyrB48WKsXr0aGRkZKC8vB9B9C3Lw4MEoKSnB1q1b8ec//xkTJkzApEmTIEkS\npk+fjvXr10OSJGzevBklJSU4c+YMLrzwQmzatMmnY5skqfuLEECKcyZqm+5nwNkNDACS5PJLJEU3\ne7XNZIKs/FVvMhmuV/bTbquQJOnsnGK6suBcro1TdsaTkpICm83mdR10y3x5CMCm/YUpSUB6OlJS\nUtCqxORcro0PQPcXqYe20LevNm6XY5rN3cdqbu6+1aIrS7+fS9w2G6T6ekgOhzoXmJAktd3dzkF6\nOkynT7udD7XtnfWU7Hb3+ujaXRu7URuq8WvOh0qzn7Kvso1yXSQlJRlfd87Y9O2kPVcK4bzOZOc6\nqb4ewNkHO9zOkSYOWZZdjqGUp7/elX0kZ7vrKcfQvjBeaX/1c+DhM+LtWlLq5Km++vYBzv7iliRJ\nXa+NQ3/dqHXQfK7U9nCeJ/2xzl4a3eWZTrrGaNade0+U/fSfAe1yJTajv7o9nQ9vx9GX3RtG162n\ncm02G+pt9XDIDlgSLC516q6E+zkGuq8lCe7Xov6YkuPsdWb0XeKt7vrzpt9Gf04kh3E8+lceeWtj\nTzEoCaskSai31Xssz1McRnXp64zudAWLTwnWokWLsH//fnznO9/Brl27Aj7omDFjsGfPHrflq1at\nUv9988034+abb/ZYRkFBAQ4ePBhwLH1Jb+aZiqYX7XqizPTsaS4wIUT3L1LtstRU4PTpMEXo/anI\nSDzFGssPdsTCC8zJf/E+h1SwXnmkJJp8hVL08ynBam9vx9atW1FXV6e+6FnL2+BzolDrad4iowQr\nFLxNwuntqUhfkh1OJaIRAy8wp8BV11cjNSEVyf2SIx1KVDFJgY8NovDwKcF65JFHUFZWhvr6ejz2\n2GMu6yRJYoIVYZwzKjqEchLOWO5x0pJTU4GWlkiHQTGgsb0R/ZJD985L/S07omDzKcGaN28e5s2b\nh3vuuQfr1q0LdUwRpfZCDBoU2UD8EKtzRinv6pMi9A7FUDxAK5vNkNrbY/qdkP7wt2dNpKZCiuEE\nSz5+3GVQPMWuQG+xmWFGdX01BqXEzu8KCi+/+hrjPbkCnMlKH3gdSDQQjY1Ac3PEkkNPCZYcyLgz\nu73X9YnFnsg+93nx83pNtUT/GEbqHXunXU3SiIz41INlMpm8jmEJ1SOOFBuSWlog2+1+ZesdBtMj\nRItQD4D31MvlqSdS6TXpK71i8STeEizeViPynU8JVmtrK4QQePzxx9HW1oY777wTQPd0C/p5TmKF\ntyeRZLMZwm53exqNjJlbWwGTCTB4jNqTaH+hbkjn37XbIQDfry9nr0ksXY+9fS8oRTc+uUbkO58S\nLGWujFdeeQUVFRXq8ocffhiTJ0/GAw88EJroQsnbk0h2O+AlAeAvj/gX5hccxJ3eTAlCRBRP/BqD\n1RFTZ/sAACAASURBVNrainrnBIUAUF9fH7IJuqJZtPe+UHjE4pipQMlmc1DqHKxyqPeq66vVW35G\nfLm92VMZkRTNsVHf4Nercn76059i4sSJuOaaawAA//jHP7By5cpQxEVeKLc3o/1Jx3js6dOOHfM0\nsWlcc97ejJpy4lx1fTU6uzphSQj++Luebvf5kmBF8y1DDkCnSPMrwSotLcX06dOxc+dOAMB9990X\n1FfokI+U25tRnmAZ3SayV1fDkZoKc3LvJw+M5KSbvem9lFNTYQrjrPEUP+J9dnPyjj1wsc3vlz2P\nHz8e48ePD0UsYRfQ4/h+iuan5sJJNDYC/QKbPDDWJt0M92t5tJQnFnu9f4ATg3IGegoWM8xo72pH\n/4T+aO9qD0mvXrRhL1xs8ynBuummm/DCCy+oL13W279/f9ADC4dwvmeP47YoGLy9jsd4B/9uxenL\nD3Ri0GhLhtX6DRwY2UAiLJS3HkPF3ml3+T9RtPMpwfrZz34GAHj88cdDGgzFH2UgeKzP4RQt495C\nPalnPE8aKpvNkOrru69FHxMs2WxG66efwpyUFFe9cLz1SBR6PiVYd999N/7zn//glVdeYZJFfgnW\nQHB/brFqb4sF7dZsiMe9BXorry9SxvP5fG35O/+Ycx8AEO3tUdMLR0SxwacEq6mpCSdOnMDOnTvV\nSUe10tPTQxIceSanpvo3x0aM8+sWq+a2WMzcmuVTdX4Lxng+ihzOCu+ZEMLr21MoNviUYF1//fUY\nMWIEzpw5g4yMDACAJEnqRcBX5YSft/Fj8TqgXj5+HPYEv5/LICIvIjWdSjRP8RBpTLDig0+dIKtW\nrYLdbkdhYSFkWYYsy3A4HOr/Y1k8JiMx02vjr+bmuB4jRLFHNpvhsPd+0HU0fP/E7fcFUYT5lGDd\ndNNNAIDdu3eHNJhI6CtfLvbq6oB+EUSreK0XxYgeXqsFAMdbjnucz6g33z/RMkO58iQiERnz6X7L\nZ599Fuo4KMTidbxKvNYrnKKhFyVQykMC0fi0avOZZpj8eBF6TyIxN5JRQscnEYm88+lTz3vB3dRH\n9YnCZIjFAnR1AQjd+/viohfXboeQ+cs+VBrbG4Oa2MVj71c81okC41MP1kcffYRzzz3XbbkyEO/U\nqVNBDywqxcgraih+WGy27n9IUu+mGSCKQvHY+xWPdaLA+JRgjR07Fn/7299CHUvc009v4Qvlybl4\nmuSQ4l+wn/i0V1fD0dYGKR562yhmmWGOivFv/ojUU6LkY4LVr18/DB8+PNSxxL3eJFhoboYwmTjJ\nIcUW5boNEtHYCJw5o84VxolZKRLsnXY4EFtPznd2djLBihCfvgF7lRj0Ufbqao7TilP+vBw8VOOl\nyIljrogoyvmUYB04cCDUccQN0dioztWkvIevL/InGYkVfr0cXJcA8I8UiqR4eFKUKNZwWuwQCtZ7\n+GKRX8lIH+BvghWPCSpFTl+6TRRrY6QofkXkdXZVVVWYPn06xo4di4KCAlRWVrptY7PZMGfOHGRm\nZho+wWgymTBx4kRMmjQJVqs1LidB7Q3+Yo4PTFCJeifYU0qES7RMIEvBE5EerNLSUixZsgSLFi3C\n9u3bUVxcjH379rlsY7FY8POf/xznnnsuZs2a5VaGJEnYtWsX0tLSwhR1bOAvZqLA8J2XFAl8N2P8\nCXsPVkNDAyoqKrBgwQIAQFFREWpra3H06FGX7RITEzFr1iz15dJ6QoiIjGthDxFR9ArKwwV85yUF\nCce+9W1h/zOttrYWWVlZLq+OGDZsGGpqapCTk+NzOZIkYfbs2ZBlGZdffjkeeughJCcn97ifLAQk\nWQacCZrD4ej+Gd23JWXNOgkAhOhepvw7JQXC4YDNOQGksr36bydtWbIsQzgcanlaSgwA1LiUspR9\nZFl2KU+7n6fl2vKUuIQQatwe20f7ZJazHVzqqmkT4XDA4XDA5CzbKA5tDK1ffAF0dLit09ZXjU/T\nTvq20LexUXkuMRi0ofb86nlap21bw/OrO35P5XnbTwgBWfsidV176Jcpy43OlVFbaNtQ+xnQBdFj\nW3j77Hg6Vz2WZ9QWvratzQahq69ROynnUQgBSXfNuMSuuT6Nrk2X2HXbd4csAKH7XDlpryVPZFmG\ngFCP73A43MpS9jdap41PexxtuS7fI3A9V27Xk+aY+tgUbW1tLv/X10eNy6Be2mXazzZE9/ZuZQm4\nxWDYBjj7OdC3o1Hdte1qFLP239r2U46jrZfypgRP5Rr+7OGaUdpU/Ux42N/o/Bq1TU+/D/qCUHbU\nxGw/+JdffokhQ4agra0NpaWlWL58OZ566qke9xMmE0R7OyTnBWpvbUV/518Z1ZWVGNHRAYty8coy\nJCHQ6Vxv0exTV1cHAOr2ANTt9GV1dnSgvbUVqc7ytJTyAKC/rixln66uLnRWVSGps1PdXxuHNgaj\n8pS4TLJsON5Na4SmDkp9KysrMdoZh7JciQ8AUnH2dSv6OFxiaGhQyxRffw2zpj5KeXV1dRitayd9\nW+jb2KQ7lr5tlf08nV89T+u0battdyU+/fF7Ks/bfrIsw263oz/cvwC016N+ebXBuTJqC20bZmVl\nqZ8BLW/t5Mtnx9O56qk8o7bo8rNttfU1bHfnC8I7OzvdrhkYtJ+2bX05VmdHBzo6ADlZ7o7f0QW9\nVs215ElHegeESaCjowOVznOl7xVRPtNG64DuGFo131n6cpV1Hekdankd6R2Qk89+XyjrtMfUx6Z3\n7Ngxw/oojOqlXabE0NraqiZbLmV1dCDBnAAhXGPQx6ocCwBkIbu1o3Iso397iln7b237KcfR1ktp\nd0/lGv0sn2N8zRw7dgw4x/X1Vkb7G51fo7bp6fdBX2A2m/3q3PFH2BOsoUOHoq6uDrIsq71YNTU1\nGDZsmF/lDBkyBACQlJSEpUuXorS01Kf9JGdyBUmCyWRCWloaJOfTNbm5uZArK4GODphMpu74JOns\n0zfO5WlpaRgwYAAAqNsDcHlKR1tWYmIiLGlpkJzlaSnlAeiOQ1OWsk9iYiISOzu7/zp27q+NQxuD\nWt6JE93Hk2U1LtlkQm5urtf2kbUfOGd9lbpo20GJT/kr3GKxdL+zUheHUid9G5psNpf6KOWp9dG0\nkyUlBQkWC0zOtlCMzcgATp6ErDuWvm2VNvR0fvU8rdOeq3S7HTh5Um1fi8HxeyrP234mkwnJycmQ\n4f4uUG1b6pcbnSujtlDk5uZ2974YPWHmpZ18+ewYfR5kX8ozaIsEP9tWW1+jdjclJ8Nut3dft5pf\nVm6xaz6Pah19OFZiYiISEwGT5CzP7N6+yrWkfJcYqWyoRIejQz2+w+FAYqdrWcpn2mgd0B2D9jtL\nX66yrrKhUi2vsqESJuns94WyTntMfWyKtrY2HDt2DNnZ2UhKSnKrj8KoXtplSgxpaWkwdZggyQaf\nAwFIsuQSgz5W5VhKW+jbUTmW0b89xaz9t7b9lONo66W0u6dyjX6urK80vGays7NxtPno2e9bT/sb\nnF+jtunp90FfoL0zEWxhT7AyMzNhtVqxadMmFBcXY9u2bRg6dKjHDNJorFVTUxP69euHpKQkyLKM\nrVu3YtKkSb4FIEnql6AkSTCbzYAz0UtJSYHNZIIsSWd/qTkTMQDqcrPZjJSUFABQtwdwdjuzGVJ9\nPUzOdSaTCTCbISvHdwnHGUN3Aa5lOffRHl+/nzZm7XK5uflsAmOxdCeWkqTGrae8TsGmuXWr1Dcl\nJQWtujiU+AB0vx/PuUwfB7RtoClTPRdQqt5dnnIs7TrJbldfeOzSxqdOqe/D1B5L30ZKG3o6v3qe\n1rmcq9ZWyJpEHQbH76k8X64L+ewC1/0At2MpddSfK6O2UKSkpHTfJjCYdd1bO/ny2XE7jskE4UN5\n+nrpry39Om/lGbWtyWRSz6Ok+T7QlqdvP5PJBIvFgjM+Hqs7UVPKP1ueGWa0d7XDkmBRY/D0mQQA\n00kTJId09vq12VzaVbu/0TptfMp21fXVcMgOSJDUtkhJSYHp5NlzaTppcvm+UNZpj6mPTS8pKclt\nubYco3pplykxmM1mQAIkuJ9HWZbVeniKVTmW0hb6djSqu7ZdjWLW/lvbfspxtPXSl+PTzxIMz2VS\nUhLQfPYz4Wl/7bnRnnuj89jXybKs9iYHW0RuEZaVlWHx4sVYvXo1MjIyUF5eDgBYsWIFBg8ejJKS\nEgDAxIkT8e2336K1tRXDhg3D7Nmz8dxzz+Gzzz5DaWkpTCYTurq6YLVasW7duoDjCtrEoMpLoaOF\n8yXB3sTcPDk+1IkoWGSzGe3NzQHPaWfvtAclnkDwpcR9D6d/iIyIJFhjxozBnj173JavWrXK5eeD\nBw8a7l9YWOhxXSCUiUEjQT5+nC+yDbG+PLM+BchuB3jtUIyKxXnB4kFEJholA83NfLdaiEUygTbC\nKT8oHhk9/UbUFzHB6mM4L0v04KSwFI/43k2ibkyw+phO3uYgoihhhtlwfFB1fTU6u/hdRbGtzydY\n8vHjvRqXw54gIu+CMqs6xTV7p91wfBAH4hvj7dfY0ucTrN6OfQp1T5BsNsNhj8wTR/zF2DcF/bzb\n7UEb8xbPt534kt/oEe09Z/H8OYhHTLCiVYifWrJXV8Ne7eFLPYi/GKMFk0YfRPF5D/cvlnBeL43t\njXzKK0qw54yCKWZflRPtZLMZUnt7pMPwSHmZrdpHNmhQYOVZLBBRXN94mjdLTk2F6fTpwMsxm7uT\n7ADPfVyKo+uFiCKDCVaoxMAXtDLjvGSxBPxLVrLZzs7STSElUlOBICRY6oS4TLD6BN6GJAovJlh9\nmTMJ7Ckt4oD+2KD2SBEZ4G1IovBighUGstkMYbcH/JqNSOHUDjEi2l7RFOWSWlrUd1wSEQUbE6xw\niKLXbMjHj///9u4/OIry/gP4e3eTK5KQCBZKgASChhAhITkrUZliaP2BU5zSwQ5U0BDrEEpptX/I\ndNrOIEzLlM4XZtC24vSHobTDtOIvdKQz1FGG1gqWyI9qDEJCgk1aKEJI7kg2d/t8/7jsZm9v73K5\n7P1+v2aY4fbZfZ7PfnZz+WR37zl4c3jYiZTeXkDTbL/wm8Ymn5PoErHAyjo9PRA239JOqUmW5UAR\nQBRB+8V25OfEXtTo0xPk5uQ6Es+ECRMc6SfROA0COYm/adMcv8A4s8kshikKV/qvYFCL/X1gLNMT\nhJuNPR1YY2eBRU7iu3eaS7UvMI43zmeVevhwffylchETbjb2dJDOsVPq4y1CSi9pMP1FKkjoPGxe\nL/wAJBa+ceMd9MIPP6bkcUoNonTBAovIISl1eyHKQlT/hKuSoPEodgoUeAeS8/VZlNo4lU5qYoFF\n5JBEFliOjZVCn3ClyLyD3jE9Z5XpUvUWajzpt455ZTM18RksojSUUlfLiFKA9TsdU/FnxOln6eye\nIUv1L6zOJryClYVS/XsSY6F1dfEZoAyQir8UKb7idetTCAEpijnOFCjo9/U7NkWFlbmg0p+lK0Vp\nXMYCov9EaH4u5yqLNxZYMUrrCTsz8XmZnp6s+jSl01KlsEmVOJJNVVW4XK5kh5EQyb716R2M73Nt\nqfopRRZY8cdbhLHq6YG4kpo/OESjxcImtWTC11N1XetK+HNRChTeHqOUwQKLaIw0RYHfy093EZk/\nzdYz0BPXqzd2V2C8g96obo91XetiIUZxxwKLaKz4STxKA0KIuF/hSeSVN3OBNdr96hnoiXnm+mTh\nLb30wwKLYsKrNkSpzVp0CCGivsIzVte0a/BpvriPo0vUfiUTC6z0wwKLDKOarM7hqzYs2Iiclcyi\no1ftzfiCh2gkSSmwzp49i0WLFqG8vBy1tbVoaWkJWcfj8WDp0qWYPHkyJk2aFNJ+9OhRVFdXY+7c\nubjnnnvQ3d2diNAzWlIfrOVtNqKopdrD3Kn6XYmplifKLkkpsBobG7F+/Xq0trZi06ZNqK+vD1kn\nNzcXP/jBD/DWW2+FtAkhsGbNGjzzzDP4+OOP8cADD+CJJ55IROhERGM21rmfUu2WWKp+aXKq5Ymy\nS8ILrEuXLuH48eNYvXo1AGDFihW4cOEC2tragtZzuVyoq6tDYWFhSB/Hjx9Hbm4uFi9eDCBQsL3+\n+uv8PqYx0rq6IHgViSjukj33UyzsnukiovASXmBduHABRUVFkOXhoUtKStDZ2Rl1H52dnZg5c6bx\nOj8/H4WFhejq6nI01qzDyTqJKAzr1aB0LbD4hzglSppORR4q6h92IQL/hraRhpZpemExQpsQAn6/\nHxIQso31tTU+yabN3J9dm904Qdtpmv3yCP1Zl2uaBhFhm3D7K4SA5vcPV+kR4rPmMNxYHo8npK9I\nsUdqiyUX0fQHwDbvTscXKbfhxgp3Do50HM39RXus7M6/cP15PJ7AeZbgYxXufPfHkNvRjKVpGgKp\nEYAYzqlA6M+I3mac+wD8fv9wzizbmJdZc6uZ/kDS1zHOWxE8vjkXep/W/vSY7Ma09qdAgaffg1w5\nVw9geF3TeNb4rPtlzlWk2PXcWpcb44TZpr+/P2wu9OXmuK0xm+PT82c9Vvr25uNr3kZfH0DIOHpu\nzdvpsevvP9Z82Y2j92ltM/dnPjbZKp5/KCS8wCouLkZ3dzc0TTOuYnV2dqKkpCTqPkpKSnD+/Hnj\ndV9fH65du4Zp06aNuK0wFViapgGaBkkIDA79VZM7QpumafD29iJf0+Dz+YK2sfZhZu7Pulzvz64N\nNuOYtxunqrbLI/VnXT6oquiPsE24/dU0DV6vF/kIPUmt8VlzGC4XFy5cQNEoYo/UFksuoukPgG3e\nnY4vUm7DjTWoqsjJyRn1cTT3F+2xsjv/wvXX3tKCUlWFnOBjZdefb9w4XI8ht6MZa1BVoaqANl6D\npmnw+QM5F7LpnBEaent7jSsq5g/7FBUVobu7G2qBGrKNqg4vU1UVLS0tUAtUaOO1oKsz+jr6OHrR\nZKaqqhGDkEVIf3pM5jj0dbSJwf31qX0YGDeAwf7AbUSB4f6M8QpC47Pul74PQhYRY9eEZrtcjzFH\nybHdJlIu9G3McVtjNsen5896rPTti4qKQo6JAgVnu89C1QLLz3afDWyjDZ8H2sThc8aIfXzgPQEY\n/kCSOQa7Y2/XZu7P7gNm2UZRFMyePTsufSe8wJo8eTLcbjf27t2L+vp67N+/H8XFxWF3UAz9FW12\n2223wefz4fDhw7j77ruxe/duPPjgg1F9d5ckScYXgMqyHCjyJGl426ETMVybLMuYMGECJFkO2cb6\n2szcn3W53p9dm2wzTtB2Lpf98gj9WZe7XC7kRtgm3P7Ksozx48cbebXdbig+aw7D5WLatGmQ+vqi\njj1SWyy5iKY/ALZ5dzq+SLkNN5aR51EeR3N/0R4ru/MvXH8VFRXQWlqgJfhY2fWnTJwYU25HM5bL\n5YLLBcjS0M+wEsip6jedM1JgO9dgoK2iosJo8/v9uPHGG9FyqSVkG5fLZSzTc9tyqcVo0+nr6OPI\nqgxJC/3Z12NQ/WpIf3pM5jiMdS62hPQnS0PnbT8gQTLW1bVcMhVbYfZL3wfVr0aMXc+tdbkeIwRs\nt4mUC30bl8uFwumB5397BnqCYjbHp+fPeqz0/fb7/cbx1be57r8OV44LLgSWDyJQLOmv9dzq54xd\nbnNzcyFJUlAM1nH0OKxt5v7MxyZbma8MOi0ptwh3796NtWvXYtu2bSgsLERTUxMAYPPmzZg+fTrW\nrVsHAFiwYAH+97//obe3FyUlJViyZAn27NkDSZLwhz/8AevWrcPAwACmTZuGvXv3Rje4JBlvgsab\nqyQZV9O0EdokSYKiKNCAkG2sr4OHHe7Pulzvz64tXL/6dpBl2+WR+rP7BYEI24TbX71NDK8cNj5r\nDmPNhd0vvkT3B8A2707HFym34caSZTlwpWWUx9Hc32iOlTUP4frLy8uDR5YhUuTYx5Lb0YwVKMaG\ntpGGcyr5Q39G9La8vDyjzePxIC8vD/JlOWQbWR5epudWviwbbca6/uCfU0iBosdMlmUjBskvhfSn\nx2SOQ1/Hrr+gnxHJtK4+3uXQ+Kz7Zc5VpNj13FqX6zFqmhY+vjC50LeRZRm9g73GcnPM5vj0/FmP\nlXHOezy2x8S8zErPrXUdc26t+TKfR9ZxrG3m/szHJltpWuCqZjwkpcCaM2cO3n333ZDlW7ZsCXp9\n8uTJsH3U1tZGbCciIoonzq5OkXAmdyIiohiwwKJIWGBR3HBeLaLMw6KCKDossCh+OK8WUcZhgTV6\n/Mqe7MQCKw1oisIrQURpjL9gs1u4r+zheZHZWGClA6+XV4IorXnb27P6jwR+J152ifbLr3leZLaM\nmcmdiFKXuHKFfyQ4JJFfURNNkUChvINe+OHHlLwpyQ6FkogF1hBNUSD194+5n3T9fi4iSj69oIn0\nizmR7zFX+q8kbCyiTMNbhDqHbsOxwCKiWF3pv8KixgHp9iA+n8XKTCywiIgorHT85Z8OBZb5j3E+\ni5WZWGARUVzxU7Dpjb/848Pubkc8C8N0LJTTHZ/BIqL48nrBG+eUyVTLF57HKp4FlnfQCyBQaPX7\nxv68MY2MV7CIiJKk61oXrypkgMEkXqFVoMA74I16fV6RTBwWWBS1sdzq4W0iolA9Az0hv+zGehWD\nt4JSRyIKaO+gF4Maj3cqYoFF0RvLJy05WSpRVMZaYKXSFYpoJ9zMVHYFNGUPFlhEWYZXEylRvINe\nTjtBWYsPuRNlGz50TkQUd7yCRUSUAtovtvPZKaIMwgKLiCgFXOm/EtPzOizMiFITCyyiUdK6uvgM\nE6WMWAszO9n+UHq64ydIUwufwSIarZ4efiJyCL97M7N4B73ww2+8Dnd8nZpYk5ylTyZKqYFXsIgo\nZiywnJGqVx7CHd9kTqxJlC54BYsCH9v3eiElOxCiLJXMKw+cTZ4oPngFiwCvF+BfpERZiZNhEsUH\nCywiIiIih7HAIiIiInIYCywiIiIihyWlwDp79iwWLVqE8vJy1NbWoqWlxXa9N954AxUVFSgvL8dD\nDz2Evr4+o02WZSxYsAA1NTVwu934+9//nqjwiYgoC6TqpzspPSSlwGpsbMT69evR2tqKTZs2ob6+\nPmQdj8eDxx9/HAcOHEBrayuKioqwdetWo12SJPztb3/DBx98gObmZixatCiRu0BERDFIp6LFO+jl\nBwAoZgkvsC5duoTjx49j9erVAIAVK1bgwoULaGtrC1rv4MGDcLvdKCsrAwBs2LAB+/btM9qFEJyD\nh4gozbBooWyR8HmwLly4gKKiIsjycG1XUlKCzs5OzJ4921jW2dmJmTNnGq9nzZqF7u5uaJoGWZYh\nSRKWLFkCTdPwla98BVu3bsX48eNHDkCIwD8EijRJXxay2nCbps/aPVTU+f1+SEDQcti8Dtefdbne\nn11bpPj8fj8kTQtqH0t/scSn+f3DVbolDqfHyrb+YsltuHNwNLFbz/dUyEUq5HY0Y2mahsCPpgAE\nIGDfn12b3p/H44GmaUHt1m30YyUQOFbW9c392Y2laVrIcqPPEeKL2BYICkIKxOTxeIy+w+VCb7Pu\nU6SxYs3tSLkwxxCUk3Dx2eybXW5HahNCBHIlTD+H1tgt+bJ7bR7H7/eHPS/045LN4nmhJq0mGpWk\n4akwOzo6MGPGDFy/fh2NjY146qmn8Mtf/nLEPoSpwNI0DdA0SOF+UQ21DQ59LUTu0Ens7e1FvqbB\n5/MZywFgUFWRk5MzYn/W5Xp/dm2R4vP29mKcqhrjj7W/WOLzer3IR+hJGo+xsq2/WHIb7hwcTezW\n8z0VcpEKuR3NWIOqClUFtPFa4JebXX/Cvk0TGnp7e9Hd3Q21QIWQRdht9K+sEbKAJjSoavD65v7s\nxhonj4N30Bu0XO8zXOyR+tNEILfAUPEgAv3pz9la98e8nR67dZ8ijRVrbu3aVFVFjpIDIYJjMOck\nXHz6OuZ9M/dnZZd3Pb6WlhZoEzX4/L7gtvHDudVn0tfHC3fsVVVFb29v2PMi3PPP2URRlKCLO05K\neIFVXFwcdCUKCFytKikpCVqvpKQEhw4dMl63t7cHXfmaMWMGAOCGG27Ahg0b0NjYGNX4kiQZhZos\ny4H+TIWbztzmcrkCC1UVsixjwoQJkGQ5aDmA4dcj9Gddrvdn1xYpvgkTJkByuYzxx9pfLPHpVw2l\nBIyVbf3Fkttw5+BoYree76mQi1TI7WjGcrlccLkAWQpsI2k2/YVpk6VAfzfeeCNaLrVA9atht9GP\nlepXIUuB9yTz+ub+ZDV0rIk3TES/vz9oud7nSPHZ9SdLQ7ntByRIgBTor6KiAgBC9se8nSvXBVVT\nQ/Yp0lix5tauP5fLBQhA0qSgGABgUt4k9A32QfXbxwcArsHg3Jv7s7LLux5fRUUFWi62wKW4wuY2\nNzcXkiQZ44U79i6XCxMmTAiJzTxWtgu66uqwhBdYkydPhtvtxt69e1FfX4/9+/ejuLg4pIJcunQp\nNm7ciDNnzmDOnDl47rnnsGrVKgDA1atX8bnPfQ433HADNE3Dn/70J9TU1EQXgCQZb4LGm6vNm6m5\nTS/qtKHiTFEUaEDQcgy91jRtxP6sy/X+7NoixacoCiDLxvhj7S+W+BRFGb7wbInD6bGyrb9Ychvu\nHBxN7NbzPZbYw8WXKv3F+7wNFGND20hDxYZdfzZten+5ubmBIsEvhd1GP1aSP3CsrOub+ws3lnW5\n0ecI8UVsA4x2WZaRl5cX6PtyaHzWsaz7NJrYR2qL1J/+s2ONAQAKPlcAr98LyR8mvqHtzftm7m80\n8eXl5QHS8HEIiX3o/+bxwh17WZahKErY80I/LtlM04avQjotKbcId+/ejbVr12Lbtm0oLCxEU1MT\nAGDz5s2YPn061q1bh/z8fPzmN7/B1772Nfj9fsyfPx979uwBAHz88cdobGyELMvw+Xxwu93YtWtX\nMnaFiCgu+IXKROktKQXWnDlz8O6774Ys37JlS9DrZcuWYdmyZSHr3XHHHTh58mTc4iMiIiIa40lT\nqQAADSBJREFUC87k7iAtPz/ZIRAREVEKYIHlIMECi5LMiSJfUxQI3p5Kuq5rXWkzISc5R1VDPwRA\n6YkFFlEGcaTI93ohLHPwUOL1DPRwQs4sxGfvMgcLLCIiohTjxFcKKVDgHfA6FBGNVlpNNEpERJRq\n4nE71zs4usLIbkJT76AXg9pgXGcrp/B4BYuIiGgEkYqUVLidGyk+FljJwQKLiIhoBCxSaLRYYBER\nERE5jM9gERFRXLVfbE92CEQJxwJrDDRFgdTfn+wwiIhS2pX+K8kOYVQUKOj38b2dxoYF1lh4veBd\neSKizDLaT/AR2eEzWEREREQOY4FFRERE5DAWWEREFDdOzEhOlI74DBYREcUNn2eibMUrWEREREQO\nY4FFRERE5DAWWEREREQOY4FFRERE5DAWWEREREQOY4FFRERE5DAWWEREREQOY4FFRERE5DAWWERE\nREQOY4FFRERE5LCkFFhnz57FokWLUF5ejtraWrS0tNiu98Ybb6CiogLl5eV46KGH0NfXZ7QdPXoU\n1dXVmDt3Lu655x50d3cnKnwiIiKiiJJSYDU2NmL9+vVobW3Fpk2bUF9fH7KOx+PB448/jgMHDqC1\ntRVFRUXYunUrAEAIgTVr1uCZZ57Bxx9/jAceeABPPPFEoneDiIiIyFbCv+z50qVLOH78OA4dOgQA\nWLFiBTZu3Ii2tjbMnj3bWO/gwYNwu90oKysDAGzYsAH33Xcffv7zn+P48ePIzc3F4sWLAQQKth//\n+MdQVRUul8voQwgRMr5QlOH/A4CiAJoWup6pTciysa0Y6lfYbBdtf9blsfYnhABkOWSfEhqfEICi\nBGKRpPiOlW39Mbfx6y/OuRWyDHmoW0VSoEmh/UVqE0KEbTMvlyXZWKa/1v8fS3/mPmONT28TEJAw\nnFtrrOHisK43mtijaQvXnyzJkCRpxP6ijS/a/kaKz3w89Da9b3MuY8mFZnOeZxu7HNjVDrGQhFM9\nRam5uRmrV68Oui1YW1uL7du3o66uzli2c+dOfPLJJ3juuecAANevX0dBQQEGBgbw6quv4te//jUO\nHjxorD916lS89957mDVrlrHM5/PB4/HEfZ+IiIgoM+Tl5SEnZ+zXnzLmIfcE14lEREREYSW8wCou\nLkZ3d3fQZbnOzk6UlJQErVdSUoLz588br9vb21FUVARZlkPa+vr6cO3aNUybNi3e4RMRERGNKOEF\n1uTJk+F2u7F3714AwP79+1FcXBz0/BUALF26FB988AHOnDkDAHjuueewatUqAMBtt90Gn8+Hw4cP\nAwB2796NBx98MOj5KyIiIqJkSfgzWABw5swZrF27FpcvX0ZhYSGamppw6623YvPmzZg+fTrWrVsH\nIDBNw1NPPQW/34/58+djz549mDBhAoDANA3r1q3DwMAApk2bhr1792L69OlB42iaFvIAmyRJkEwP\ntRIREVF2EkKEPGIkyzJkeezXn5JSYBERERFlsox5yJ2IiIgoVWR8gRXtrPEU6oknnkBpaSlkWcap\nU6eM5ZcuXcIDDzyAOXPmoKqqCkeOHDHarl+/jocffhhlZWWYO3cuXnrppWSEnvIGBgbw9a9/HXPn\nzkVNTQ3uv/9+nDt3DgDz64T7778f1dXVqKmpwd13340TJ04AYG6d8sILL0CWZRw4cAAA8+qUWbNm\noaKiAjU1NXC73XjxxRcBML9OUFUV3/3udzFnzhwsWLAAjz76KIA451ZkuC9/+cvi97//vRBCiP37\n94vbb789yRGljyNHjoh///vforS0VJw8edJY/thjj4ktW7YIIYR4//33xYwZM4TP5xNCCLF161bR\n0NAghBCivb1dTJkyRXz22WeJDz7F9ff3i4MHDxqvf/GLX4i6ujohhBANDQ3M7xj19PQY/3/llVfE\nggULhBDMrRPOnz8v7rrrLnHXXXeJ1157TQjB9wSnlJaWilOnToUsZ37H7sknnxTf+973jNf//e9/\nhRDxzW1GF1gXL14UhYWFwu/3G8umTp0qzp07l8So0s+sWbOCCqz8/Hzj5BRCiNraWvHWW28JIYSY\nN2+eOHr0qNG2cuVK8dvf/jZxwaapf/7zn6K0tFQIwfw67YUXXhBut1sIwdyOlaZp4p577hHNzc2i\nrq7OKLCYV2dY32t1zO/YeDweUVBQIHp7e0Pa4pnbhH9VTiJduHDBmDtLV1JSgs7OzpBpISg6n332\nGXw+H6ZMmWIsmzlzJjo7OwEE5jSbOXOmbRuFt2vXLixfvpz5dVB9fT3efvttSJKEN998k7l1wM6d\nO/GlL30JNTU1xjLm1VmPPPIIAGDhwoX42c9+BkmSmN8xOnfuHCZNmoSf/vSn+Otf/4rx48dj8+bN\nqK6ujmtuM/4ZLKJUt23bNpw7dw7btm1LdigZZc+ePejs7MRPfvITbNq0CQC/8WEsPvzwQ7z00kv4\n0Y9+lOxQMtaRI0dw8uRJNDc346abbkJ9fT0Anrdj5fP50NHRgfnz5+P999/Hrl27sGrVKvh8vrjm\nNqMLrGhnjafoTZo0CTk5Obh48aKx7Pz580ZOZ86ciY6ODts2CvV///d/ePXVV/GXv/wF48aNY37j\n4JFHHsE777wDAMjNzWVuY3TkyBF0dHSgrKwMpaWleO+997Bu3Tr8+c9/5jnrkBkzZgAAFEXBk08+\niSNHjvA9wQElJSVQFAUPP/wwAKC6uhqzZs3C6dOn4/ueEOs9zXSxZMkS0dTUJIQQ4sUXX+RD7jGw\nPhfQ0NAgnn76aSGEEMeOHQt6KPDpp582Hgpsa2sTX/jCF8Tly5cTH3Qa2LFjh7jtttvE1atXg5Yz\nv2Nz9epV0dXVZbx+5ZVXRHFxsRCCuXVSXV2dOHDggBCCeXWCx+MJei/YsWOHuPvuu4UQzK8T7r//\nfvHmm28KIQJ5mjx5sujq6oprbjO+wGptbRV33nmnmDNnjrj99tvFv/71r2SHlDYaGxvFjBkzRG5u\nrpg6daooKysTQgQ+fXHfffeJsrIyMX/+fHH48GFjG4/HI1auXCluvvlmUV5eLvbv35+s8FPap59+\nKiRJErfccouoqakR1dXV4o477hBCML9j1dHRIRYuXCiqqqrEggULxL333mv8gcDcOmfJkiXGQ+7M\n69i1tbWJmpoasWDBAlFVVSWWL18uOjo6hBDMrxPa2trEkiVLRGVlpaiurhavvPKKECK+ueVM7kRE\nREQOy+hnsIiIiIiSgQUWERERkcNYYBERERE5jAUWERERkcNYYBERERE5jAUWERERkcNYYBERERE5\njAUWERERkcNYYBFRyvP7/diyZQsqKipQVVUFt9uN9evX47XXXkNNTY2jY3V0dOD55593tE8iyj4s\nsIgo5T322GNobm7G0aNHcerUKTQ3N+Pee+/FZ599BkmSHB2rvb0du3fvjmlbv9/vaCxElL5YYBFR\nSjt37hxeeuklNDU1oaCgwFi+YsUKzJ49G4ODg/jOd76D6upqVFZWorm5GUCg2Fm6dCkWLlyIyspK\nrFmzBtevXwcAHD58GJWVlaivr0dlZSVuv/12nDp1CgDw7W9/G2fOnIHb7cby5csBAGfPnsWyZctQ\nW1uL6upq/OpXvzLikGUZTz/9NBYuXIgf/vCHiUoLEaU4FlhElNKam5tRVlaGiRMn2ra3traioaEB\nJ06cwMaNG40iR1EU7Nu3D8eOHcPp06dRUFCAZ5991tjuo48+QkNDA06fPo1NmzZh5cqVAIDdu3ej\nvLwczc3NePXVV6FpGr75zW9i586dOHr0KP7xj3/g+eefx/Hjx42+cnNzcezYMWzfvj2OmSCidMIC\ni4jS2i233IIvfvGLAIA777wTbW1tAAAhBHbs2AG3242qqiq8+eabOHHihLHdrFmzUFdXBwD4xje+\ngf/85z/49NNPQ/pvbW3Fhx9+iFWrVqGmpgZ33XUX+vr68NFHHxnrNDQ0xHEPiSgd5SQ7ACKiSNxu\nNz755BNcuXLF9irWuHHjjP8rigKfzwcA+OMf/4h33nkHR44cQV5eHp599lm8/fbbYceRJMn2eS4h\nBG666Sbj1qPddvn5+aPdLSLKcLyCRUQp7eabb8aKFSvwrW99Cz09Pcbyl19+2bhaZefq1av4/Oc/\nj7y8PPT29qKpqSmo/fz58zh8+DAAYP/+/Zg6dSqmT5+OgoKCoHHKy8tRUFAQtP25c+dw9epVAIEC\njIjIigUWEaW83/3ud6iqqkJtbS0qKysxb948HDp0CJMmTQq7zaOPPgqPx4OKigp89atfxeLFi4Pa\nb731VjQ1NaGqqgrbt2/Hvn37AABVVVWYN28eKisrsXz5ciiKgtdffx0vv/wyqqurMX/+fDz++OPG\nA/NOf4qRiDKDJPjnFxFlmcOHD+P73/9+2Nt+RERjxStYRERERA7jFSwiIiIih/EKFhEREZHDWGAR\nEREROYwFFhEREZHDWGAREREROYwFFhEREZHDWGAREREROYwFFhEREZHDWGAREREROYwFFhEREZHD\n/h9ZYeqBHAcJ7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e0c3493c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_plot(dfTfidf, 0, 'Chapter frequency of the bi-gram \"to be\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JKFWkxTsAMh/tCzA/P76BUELSuiyLiuIbCBFRlLElinwk2tgds2KLDBFRcgup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\n/M/FmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9df5275940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_plot(dfTfidf, 1, 'Chapter frequency of the bi-gram \"it was\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lX0OoRnpde4pu3xNlJN6GMCL1KAlHb0RrR0fg4SYVQ2paeBu+ChS86BncaO0B\n0DqJOKjeDedV78PMPWiQz541RJCkhcf7x+kGEA+ONpblwM88DPN7wJtQH+OhZiK1kVa7DvR+DMdw\nodrzN1qvoa82MdLr2lOwJyqSvPxFH6kgytEbodedbGrvRnTpaVExUdhWXa3rnWfu+QWaQB/schmR\n5N6T4ov7ufns7dJwDQZ9x1yYqa270dZSc+45cEwYNtINJ0bi3vti5F4XvV7D6vpqfPnVl0EPHVLk\nMYjSKNTu/nCtO+V484ZjLSeXgMLPo068H6z+TibHnXp63nnm/niPQIGRUQInF0EGwh511/NusuZm\nj3WZ9BTqfEPZ7W5cZc6T03VrhCUKvN1p5a3XNZSAobsEZbYOG2QhwwwzquurDXeXmqNegH49543n\nG9F8oRkdckfQ14BzPSj8OJznRVAfOqF29/sbdggg0JwelwnVOgqYZ4ht4m1oJpz9dN7uKvRHCAFJ\nCv+UY28T+pVJ9ZddpjysN+rCOAQZ6nxDl2vJ13UUwSHUUIUyTGO0oahQ2TpssEPf102PiduOeoVr\nKC3YgDHc9SBX3TaIks1mCJtN0102Rr/VNNj6OeZghXK7tizLuDI+HvKpUyF3Wwa62y/cQ5vuvRve\n7ir0R68gylZdDXtyMsyJid7L8dLL56irv7tKHcG1vx6WYJYWcCk3QsPN0aK2PWJVqPOturtgA81g\nJu9X11cjOS4Zib28v7/d93UI9phQ6kb667ZBVCg9PUYV6MsXuDRpu+uArr/ARQgBpRCi6xlkHR1A\nqCtAhzCUIp86pXr9JY/yDXA3paMezrfu60UJrv31sKjs3evuQVQ0JpBHQ0+fcOwY4go1iAxm8n7j\n+Ub0SvT9/nYeSXDuXfJ3jF51I/1xTlSMsFVXBzc/yNuk7RADSpPJBBEfr+8t3xefdRZ0njo/2iUY\njrk0Rpg7400s3oavlZHPVe85R6daThlyTotjTo4sy2iRW3Cq5VS0qxQ0W4fNMPOnjD7SQep0356o\nEOnR86Enb70oeiz26ejdkpqaLvVguTGZTJCsVghJ8ujNCmk45GIvQLhmGTnPefLXVkqQ5L6IpaOX\nQse5M7ou0NpDelEAhP1aCYXec46aLzTDZDIF7DXSY4iuur4abe1t6JPQJ2A+jiCkf+/+aG1vhclu\nCvh8QkcdByQFfvKBGsnxyTjXcU7XPLWI1DzJSJdFwWMQ5UsUej7UCnV4SjabIdXXdw0tOVbNVivA\nF3k055ywLNDDAAAgAElEQVQ4z3lybyuXYMZmgx1dw43+hkr1rFMgLsOyZGiOVbUDBSF6z0vSY4iu\n8XwjLnRcwHn5vOp84k3xON/p/73tqGM4gqjW9lZIkhTWXrtAE871DmxkP58NvsriaubRxeG8niwS\nK1EbZLVrdx7BTIhDnsGu3eQ3D+chK5UP3fWVV7iW0nAvpydrvtAc1FCRv1vyQ50UrMeQoto6WDus\nqufg6Lm2k2O+ntalDvwtBeCoZ6hDb/7a1Nuq41rmIHZ0dHBSeRQxiCJVwvGlGe4vej35PH89ei59\nBJya2tyRV7hvsDBokBwLTrWcUr743CcFq13rx/3LXkuw4l4HveZ6OT9nzkhrO/mbJ6VXPf1N9nas\ne+UuWcOCsJxUHj0cLyBFUENv4ZiHE+Yvel0ft6Pz+ScnJwPnvM/tUOqt5aHGMczfddidliRovtDs\n9wvWDjtQH3x+zte0HkN9es31CuY5c1yC4ZKUlJSg91XzIGQKj6j0RFVWVmLixInIyspCcXExjhw5\n4nf/efPmwWQyocUoiwx2V921VyGKj9sJxO8HZnd9PQLxd94x2CZah7DMMAd88K5zj5VRrmktjNRD\n5c7IK75H+kHI5CkqQdSiRYuwePFiVFRU4IknnkBpaanPfd966y0kJCT06LsSvN3R5W8OTk+fpxJL\nw4PUvVXXVwcMhJwlx18aynF+zImv+S5GunXfIdj5OYGCy0gEL2aYAz6bLhaWJIjlADrWRTyIamho\nwIEDBzBnzhwAQElJCWpra3H8+HGPfb/++mv8/Oc/x+rVq3v0ReL1ji5/c3Bi8K91XV0cHjT6NePv\nThzqHtTOVXEOohx8zZ0xKsc5+wv+HPv5CwAjEbzYOmzKs+limdE/67qziAdRtbW1yMjIcFlfJDMz\nEzU1NR77Lly4EM8//zySkpIiWUXqJoz+wWL0+pE6et55pifnCezB0OsBtlqDv+r66oC9Qw6BAjWi\ncDPsxPLf//73uOqqq3D99derOk4WApIsA0J0rasBAF6+rNzTHL0CQgjY7Xavx6nJz3l7KPkpvRUX\n99G7foHqLtvtlyJtp/RotEU087MnJkI6dy6ktnV+LR1tq+zTg9s2lq9bWZYB0fX/GdsZAMAA6wCX\ndAHh0utotVpht9sBAZc0ga7rwrHdnSzLsFqtykrhznl7y89RVvP5ZtiFHWbZDKvVeqnejvODgBlm\ntHW2QUDA2mFFh9yB/r37K/s7H2N3LDwrACEJl3Sr1arUy70NHWmOejnnJ9tlQOrafsZ2Bu32dqT1\nSvPZFo52snZ01c8sm13O2VsdnNuvvbPdJd1RP+d9403xsJ63ond8b6/5Ourg/Pq5f484v0aO/J23\nO7er4xgAaGtrU9Kcrwvnstzbwnmbc3k9Xbj/WI14EDV48GDU1dVBlmWlN6qmpgaZmZku++3atQt7\n9+7F22+/rTRCXl4etm/fjvz8fJ/5d7S3A3Y74h1fWrLs8gXl4J7WcXH83STLsLW2ItnLcWryc94e\nSn6dnZ0AgHinYE/P+gWsu82GZHheiNFoi2jmZ09KgqmlJaS2dVxjjmvTdnHeVkdHh/L6xkJbGCG/\ngGVF6LrtaG+HLMeh096pbHO+UaY9tR3CJFzm9xw5cgQZGRldX5DiUpowCchCVra7a29vx5EjR9Ce\neikvR96tra0e+TnKki/r2m6GGZV1lV31RgeELJQ8zrWf8yirtbUVdXV1LucCQLluRdc3t9f6CZNb\nGwrZpe7OPzvq3mnvdDneX1u4p7mfc2ffTo8gKpj6OZ/rBdMFXOh9AR3nu3q66urqXI511MHx2ju/\nznKijNbWVrS3X3r9HflnZGQo253rLide+kw4ceKEUg/ntnAuy1dbOPKsra3FOR93/vYkZrMZw4YN\nC1v+EQ+i0tPTYbFYsHHjRpSWlmLLli0YPHiwx0m++uqrLr+bTCZ8/vnnAW//jE9IgGS3A+3tMJku\nPpbAy6R09zTHrbyyyYSUlBRIXo5Tk5/z9lDyU24xdgR5OtcvUN0TL67g7T6xPxptEWv5uafFJyVB\namsDLv4BkZiYCHH6NOJNJsCpZ8AIdTd6foHKitR1m5CQAJMJSDBfWgogOztb+flIwxG0211XlM7O\nzobdboep3QRJlpS0dns7TFJXHSTZs34JCQnIzs7GkQanL/uLeaekpCj5JfVOQltnG+JMcV371x+B\nJEtos7chIa6rLGEXLuV6KyslJQV9+/Z1OZfk+OSutj0PSJBcnsPjXD/3PE2SyaXuzj876p5gdj3e\nX1u4pzm3b1ZWFiq+qfCYcxhM/ZzP1VEHx3dO3759XY511MHx2ju/zo7jEjoSlO2O/O12u7Ldue4m\nqeu6tdlsGDJkCPr06ePRFs5l+WoLR57f6vctmM2Xeuh6KucevnCIynDe2rVrMW/ePKxYsQJpaWko\nKysDACxduhSDBg3CwoULPY6RJCm4brm4OEjt7V3PeXN84Hn5wHRPc/SKCUmC2WyG7OU4Nfk5bw8l\nP0e95Iu/h5qflrqLSxt0yc8obRvu/NzTJMcdgxevTbPZDNHa2hVAsW1j8rrtCrpk5REo8XHxLnM4\nTWdMkOySyxzQpKSkrqEWqSsQcaRJ9oufWRe3uzOZTEhKSoLpjAlmmHG+87xyvNlsVo6zddhc9nfO\nz7ks55+9lfW19WvEmS89zsZ0xoTUXqmXvpjd6ulcP/c8JUlS0gCg3loPu2xHfFy8Unf34/21hXua\nc/t2vSaex5lMJpw+dxp22e6R5qifsq9THRzn635ujjo4t6PjOnAcZzJdev0d+VutVmU7AI9jAKBP\nnz5KG0mQvJblqy0c52o2mzmfGF09ya2trWHLPypB1KhRo/Dxxx97bF++fLnPY4KOJNvaevadaUQU\ncY7ApTuVF+yDkNVyrH8V6TsOk+OT0djm+25JxzPo1D5CxRHQAq6vS7AT+n29llyFPDbwsS9ERDHM\n27IIRszTEZyEY8mGYO7SC3ROjiUV1AYvvs7H34r01H0Y9u48IqJY55iC4OitiI+L172McAVRLRf0\nfUJEoOAklKUV9OiZO9VyCnFW71+J/tLCQe2yFBQ9DKKIiMLEEURFergvFinPC4wSx/Cl2rRw1YW9\nWLGBQRQREfmkpndI7aNawtGLFgvCvXaR48HEnFgefgyiiIi6qUBBTTBf5mqezXfyzElVw1BGC6Kc\nJ4mHU7iDKD6YOHI4sZyIKIrC+egSf8+fS45P1v3LPNaHofxNeucjZsgbBlFERDpS+wy9aD1g2Gi9\nQICxA5VYexA0oH54ldRjEEVEpKPG841oPN/ILzANYjFQMTJ/PZGkDwZRREQaycnee3PMMKP5XHOE\naxNYNHt6wj0PiCgaGEQREWkkfARRRpjY67hDy1k0e3oYRFF3xCCKiChCQllQUi0jBHLUJZKvO0UW\ngygiogixddhULRngjvOsYlOorzsZF4MoIqIICuWuOCNPFDbi3X5GU11f7THESrGNQRQRUQSFEmxE\n+plqjonoejzgl7ru3OQQa/fCFcuJiGJEpBezdDzzj8/+I/KOPVFEREREGjCIIiIiItKAQRQRkU6M\n/NgSin28voyHc6KIiHTCuUMUTry+jIc9UURERAbRIregU+6MdjUoSAyiiIiIDKK1vZUPYY4hDKKI\niGIA12GKbZzP1D0xiCIiigEMomJbNB/+TOHDIIqIiIhIAwZRRERERBowiCIiIiLSgEEUERERkQYM\nooiIiIg0YBBFREREpAGDKCIi6ha4DARFGoMoIiLqFhhEUaQxiCIiIiLSgEEUERERkQYMooiIiIg0\nYBBFREREpAGDKCIiIiINGEQRERERacAgioiIiEgDBlFEREREGjCIIiIiItKAQZTByWYzREdHtKtB\nREREbuKiXQEKwGaDiHYdiIiIyAN7ooiIiIg0YBBFREREpAGDKCIiIiINGEQRERERacAgioiIiEgD\nVXfnzZ8/H5Ik+Uxft25dyBUiIiIiigWqeqJ69eqFf/3rXxg2bBiGDx+OTz75BL169cLVV1+Nq6++\nOlx1JCIiIjIcVT1RX375Jf71r38hNTUVALBkyRJMnz4dL730UlgqR0RERGRUqnqiGhoalAAKAFJT\nU9HQ0KB7pYiIiIiMTlVPVH5+PubNm4d7770XALB+/Xrk5+eHpWJERERERqaqJ+qVV15Beno6Hn30\nUTz66KNIT0/HK6+8Eq66ERERERmWqp6o5ORkPP/88yEXWllZidLSUnzzzTfo27cvysrKkJ2d7bLP\niRMnMGvWLMiyjI6ODowZMwb/+7//i7S0tJDLJyIiIgqVqp6o2tpaTJ8+HQUFBQCAQ4cOYfXq1aoL\nXbRoERYvXoyKigo88cQTKC0t9dhn0KBB+Oijj1BeXo7PP/8cGRkZWLZsmeqyiIiIiMJBVRC1aNEi\nzJ49G0IIAEBOTo7qtaEaGhpw4MABzJkzBwBQUlKC2tpaHD9+3GW/+Ph49OrVCwBgt9thtVr9rlFF\nREREFEmqhvPq6+tx1113YdWqVV0Hx8UhLk5VFqitrUVGRgZMpkvxW2ZmJmpqajBs2DCXfTs6OlBU\nVISamhrk5eVhx44dgQsQousfACEEJMc2j91c02RZVrbb7Xavx6nJz3m7kfMLVJZst1+KtJ3SjVB3\no+cXqCy2rfb8ApUVybaFAAS85+ctzZGfr7Ro5xdUWUBXuiQCHhPLbdEd2tZqtXoc05MIL+91PamK\ngOLi4lwq1NjYGNYKxsfH4+DBg+js7MSSJUuwdu1aPP74436PEU5BlCzLgCxD8lJH97SO9nYAgEmW\nYWttRbKX49Tk57zdyPkFLMtmQzI8L0Qj1N3o+QUsi22rOb+AZUWwbWVZ9vo5KAvvabKQ0dra6jMt\n2vkFKstmswG4+KUtAh8Ty23RHdq2rq7O45iexGw2e3TQ6ElVEHXbbbdh0aJFaGlpwSuvvIK1a9di\nwYIFqgocPHgw6urqIMuy0htVU1ODzMxM35WMi8O8efOwcOHCgEGUJEnKsJ/JZOoqw8swoHtaQkIC\nAEA2mZCSkgLJy3Fq8nPebuT8ApWVmJgIAB5DqUaou9HzC1QW21Z7foHKinTbSrKX/CTvaSapKz9T\nu/e0aOcXqKzExETgPCBBAqTAx8RyW3SHtu3bt6/HMT2JSw9fGKgKon70ox/htddeQ3NzM/7617/i\nsccew5133qmqwPT0dFgsFmzcuBGlpaXYsmULBg8e7BEp1tTUID09HX369IEQAm+++Sby8vICFyBJ\nyged8gHq5QPTPc0R0AlJgtlshuzlODX5OW83cn7BlCUubTBU3Y2eXzBlsW27QdtKF7/0vOXnJc2R\nn6+0aOcXVFmAR7oR6m70/IIqC9C1bZOSkjyO6UlkuatHLlyCDqLsdjuefPJJrFy5EnfccUdIha5d\nuxbz5s3DihUrkJaWhrKyMgDA0qVLMWjQICxcuBCHDx/GU089BUmSIMsyLBYLfvOb34RULhEREZFe\ngg6izGYzdu3apUuho0aNwscff+yxffny5crP06dPx/Tp03Upj4iIiEhvqpY4uPnmm/Hss8/i1KlT\naGlpUf4RERER9TSq5kQ9/fTTAID/+Z//gSRJXbcES1JYJ20RERERGVFQPVFffPEFgEu388qyDLvd\nrvxPRERE1NMEFUTdfffdAIBvf/vbYa0MERERUawIajjv/Pnz2Lx5M06dOuV11fAZM2boXjEiIiIi\nIwsqiHruueewdu1aNDQ0eDxwWJIkBlFERETU4wQVRM2YMQMzZszAI488ghdeeCHcdSIiIiIyPFVL\nHDCAIiIiIuqiKogiIiIioi4MooiIiIg0YBBFREREpEHIQVR5ebke9SAiIiKKKSEHUf/zP/+jRz2I\niIiIYkrIQdQ777yjRz2IiIiIYgrnRBERERFpoCqI+vvf/46ioiL069cPqampSElJQWpqarjqRkRE\nRGRYQa1Y7nDffffh2WefRVFREcxmc7jqRERERGR4qoKo1NRUzJo1K1x1ISIiIooZqobzSkpKsHHj\nRrS3t4erPkREREQxQVUQlZ2djQceeAB9+vSB2WyGyWTisB4RERH1SKqG8374wx9i+/btGDduHIMn\nIiIi6tFUBVEDBgzAlClTwlUXIiIiopihajhvxowZePHFF1FfX4+WlhblHxEREVFPo6on6qc//SkA\n4OGHH4YkSRBCQJIk2O32sFSOiIiIyKhUBVGyLIerHkREREQxJejhPLvdjjFjxoSzLkREREQxI+gg\nymw2Iz09HTabLZz1ISIiIooJqobzRowYgYkTJ+K2225DcnKysv3hhx/WvWJERERERqZ6TlRBQQGO\nHTumbJMkSfdKERERERmdqiBq/fr14aoHERERUUxRFUR1dnZi9erV+OCDDwAAU6dOxSOPPIK4OFXZ\nEBEREcU8VdHPY489hqqqKjzwwAOQJAmvvPIKTp48id/85jfhqh8RERGRIakKonbv3o1Dhw7BZOq6\nqe+WW26BxWIJS8WIiIiIjEzVY1+EEC4LbgohIITQvVJERERERqeqJ2ratGn43ve+h3nz5gEA/vCH\nP+Cmm24KR72IiIiIDE1VELVy5Uq8/PLL2LFjBwBg1qxZWLhwYVgqRkRERGRkqoIok8mE+++/H/ff\nf3+46kNEREQUE1QFUU1NTXj55ZdRVVWFzs5OZfu6det0rxgRERGRkakKombNmoX09HRcc801MJvN\n4aoTERERkeGpCqLq6uqwc+fOcNWFiIiIKGaoWuJg+PDhaGpqClddiIiIiGKGqp6oxMREWCwWTJs2\nDb1791a2/+pXv9K9YkRERERGpiqIys7ORnZ2drjqQkRERBQzVAVRS5cuDVc9iIiIiGKKqjlRRERE\nRNSFQRQRERGRBkEFUV988UW460FEREQUU4IKou6++24AwLe//e2wVoaIiIgoVgQ1sfz8+fPYvHkz\n6urqlIcPO5sxY4buFSMiIiIysqCCqOeeew5r165FfX09Vq9e7ZImSRKDKCIiIupxggqiZsyYgRkz\nZuCRRx7BCy+8EO46ERERERmeqrvzGEARERERdQmqJ8pkMkGSJJ/pdrtdtwoRERERxYKggqjW1lYI\nIfDrX/8abW1tuP/++wEAa9euRZ8+fVQXWllZidLSUnzzzTfo27cvysrKPB4n8+9//xsPPvggGhoa\nEBcXh6KiIvz2t79Fr169VJdHREREpLeghvOSkpKQnJyMt956C88++yyuvPJKXHnllXjmmWewbds2\n1YUuWrQIixcvRkVFBZ544gmUlpZ67NO7d2/89re/xZdffonPPvsM586dw8qVK1WXRURERBQOquZE\ntba2or6+Xvm9vr4era2tqgpsaGjAgQMHMGfOHABASUkJamtrcfz4cZf9RowYgZycHABddwCOHz8e\nJ06cUFUWERERUbioegDxj370I+Tn5+Pmm28GAPzlL3/BsmXLVBVYW1uLjIwMmEyX4rfMzEzU1NRg\n2LBhXo+xWq145ZVX2BNFREREhqEqiFq0aBEmTpyIXbt2AQAee+wxjB07NiwVc+jo6MDs2bMxbdq0\n4NajEqLrHwAhBCTHNo/dXNNkWVa22+12r8epyc95u5HzC1SWbLdf6q50SjdC3Y2eX6Cy2Lba8wtU\nViTbFgIQ8J6ftzRHfr7Sop1fUGUBXemSCHhMLLdFd2hbq9XqcUxPIry81/WkKogCgJycHGWYTYvB\ngwejrq4OsiwrvVE1NTXIzMz02LezsxO33347Bg0a5LHIpy/CKYiSZRmQZUheGtE9raO9HQBgkmXY\nWluR7OU4Nfk5bzdyfgHLstmQDM8L0Qh1N3p+Acti22rOL2BZEWxbWZa9flDLwnuaLGS0trb6TIt2\nfoHKstlsAC5+aYvAx8RyW3SHtq2rq/M4picxm80+R7n0EFQQdccdd+C1115DYWGh16UOysvLgy4w\nPT0dFosFGzduRGlpKbZs2YLBgwd7nKTdbsftt9+O/v37Y+3atUHnL0mSUkeTydQVqHmps3taQkIC\nAEA2mZCSkgLJy3Fq8nPebuT8ApWVmJgIAB6vuxHqbvT8ApXFttWeX6CyIt22kuwlP8l7mknqys/U\n7j0t2vkFKisxMRE4D0iQACnwMbHcFt2hbfv27etxTE/i0sMXBkEFUT/+8Y8BAL/+9a91KXTt2rWY\nN28eVqxYgbS0NJSVlQEAli5dikGDBmHhwoXYvHkz/vSnPyEvL08J3iZOnIg1a9b4z1ySlA865QPU\nyweme5qjV0xIEsxmM2Qvx6nJz3m7kfMLpixxaYOh6m70/IIpi23bDdpWuvil5y0/L2mO/HylRTu/\noMoCPNKNUHej5xdUWYCubZuUlORxTE8iy7LqG+DUCCqIeuihh/DPf/4Tb731li6B1KhRo/Dxxx97\nbF++fLny85133ok777wz5LKIiIiIwiGoIKqpqQlff/01du3apSy86Sw1NTUslSMiIiIyqqCCqB/8\n4AcYOnQoLly4gLS0NABdXYVCCEiSxMe+EBERUY8T1GKby5cvh81mw4QJE5S7Uex2u/I/ERERUU8T\nVBB1xx13AAA++uijsFaGiIiIKFYEFUT95z//CXc9iIiIiGJKUEGUt7WhiIiIiHqyoCaWHz58GP36\n9fPY7phYfvbsWd0rRkRERGRkQQVRWVlZePfdd8NdFyIiIqKYEVQQ1atXL1x11VXhrgsRERFRzAhq\nTlS4n4JMREREFGuCCqIOHjwY7noQERERxZSggigiIiIicsUgioiIiEgDBlFEREREGjCIIiIiItKA\nQRQRERGRBgyiiIiIiDRgEEVERESkAYMoIiIiIg0YRBERERFpwCCKiIiISAMGUUREREQaMIgiIiIi\n0oBBFBEREZEGDKKIiIiINGAQRURERKQBgygiIiIiDRhEEREREWnAIIqIiIhIAwZRRERERBowiCIi\nIiLSgEEUERERkQYMooiIiIg0YBBFREREpAGDKCIiIiINGEQRERERacAgioiIiEgDBlFEREREGjCI\nIiIiItKAQRQRERGRBgyiiIiIiDRgEEVERESkAYMoIiIiIg0YRBERERFpwCCKiIiISAMGUUREREQa\nMIgiIiIi0oBBFBEREZEGDKKIiIiINGAQRURERKQBgygiIiIiDRhEEREREWnAIIqIiIhIg6gEUZWV\nlZg4cSKysrJQXFyMI0eOeOxjtVoxbdo0pKeno1+/flGoJREREZFvUQmiFi1ahMWLF6OiogJPPPEE\nSktLPfaJj4/HT37yE3z44YdRqCERERGRfxEPohoaGnDgwAHMmTMHAFBSUoLa2locP37cZb+EhARM\nmjQJaWlpka4iERERUUARD6Jqa2uRkZEBk+lS0ZmZmaipqYl0VYiIiIg0i4t2BXQnRNc/AEIISI5t\nHru5psmyrGy32+1ej1OTn/N2I+cXqCzZbr8UaTulG6HuRs8vUFlsW+35BSorkm0LAQh4z89bmiM/\nX2nRzi+osoCudEkEPCaW26I7tK3VavU4picRXt7reop4EDV48GDU1dVBlmWlN6qmpgaZmZm65C+c\ngihZlgFZhuSlEd3TOtrbAQAmWYattRXJXo5Tk5/zdiPnF7Asmw3J8LwQjVB3o+cXsCy2reb8ApYV\nwbaVZdnrB7UsvKfJQkZra6vPtGjnF6gsm80G4OKXtgh8TCy3RXdo27q6Oo9jehKz2Yxhw4aFLf+I\nB1Hp6emwWCzYuHEjSktLsWXLFgwePNjnSQohVEWSkiRBkiQAgMlk6grULv7uzD0tISEBACCbTEhJ\nSYHk5Tg1+TlvN3J+gcpKTEwEAKVNjVR3o+cXqCy2rfb8ApUV6baVZC/5Sd7TTFJXfqZ272nRzi9Q\nWYmJicB5QIIESIGPieW26A5t27dvX49jehKXHr4wiMpw3tq1azFv3jysWLECaWlpKCsrAwAsXboU\ngwYNwsKFCwEA+fn5+Oabb9Da2orMzExMnjwZGzZs8J+5JCkfdMoHqJcPTPc0R6+YkCSYzWbIXo5T\nk5/zdiPnF0xZ4tIGQ9Xd6PkFUxbbthu0rXTxS89bfl7SHPn5Sot2fkGVBXikG6HuRs8vqLIAXds2\nKSnJ45ieRJa7euTCJSpB1KhRo/Dxxx97bF++fLnL75999lmkqkRERESkClcsJyIiItKAQRQRERGR\nBgyiiIiIiDRgEEVERESkAYMoIiIiIg0YRBERERFpwCCKiIiISAMGUUREREQaMIgiIiIi0oBBFBER\nEZEGDKKIiIiINGAQRURERKQBgygiIiIiDRhEEREREWnAIIqIiIhIAwZRRERERBowiCIiIiLSgEEU\nERERkQYMooiIiIg0YBBFREREpAGDKCIiIiINGEQRERERacAgioiIiEgDBlFEREREGjCIIiIiItKA\nQRQRERGRBgyiiIiIiDRgEEVERESkAYMoIiIiIg0YRBERERFpwCCKiIiISAMGUUREREQaMIgiIiIi\n0oBBFBEREZEGDKKIiIiINGAQRURERKQBgygiIiIiDRhEEREREWnAIIqIiIhIAwZRRERERBowiCIi\nIiLSgEEUERERkQYMooiIiIg0YBBFREREpAGDKCIiIiINGEQRERERacAgioiIiEgDBlFEREREGjCI\nIiIiItKAQRQRERGRBgyiiIiIiDRgEEVERESkAYMoIiIiIg2iEkRVVlZi4sSJyMrKQnFxMY4cOeJ1\nv7fffhvZ2dnIysrCrFmzcO7cuQjXlIiIiMi7qARRixYtwuLFi1FRUYEnnngCpaWlHvtYrVYsWLAA\nO3bsQEVFBTIyMvD0009HobZEREREnuIiXWBDQwMOHDiADz74AABQUlKChx56CMePH8ewYcOU/d57\n7z1YLBaMHDkSAPDAAw/ge9/7Hn7xi18o+wghPPIXZvOlnwHAbAZk2XM/tzRhMinbhRBd+bgdpyY/\n5+1Gzi9QWRACMJu72lqSDFV3o+cXqCy2rfb8ApUVybY1S2bIkmd+8JMmhPCZZoT8gilLQECCFNQx\nkZiRqSsAAAniSURBVKy70fMLpiw921b2cq33JN7O31vsoJUk9MwtCOXl5ZgzZ47LEF5xcTFWrlyJ\nSZMmKdt+9atf4dixY3jppZcAAG1tbUhNTcWFCxdguhjwdHZ2wmq1RrL6REREFMOSkpIQF6dPHxIn\nlhMRERFpEPEgavDgwairq3PpYqupqUFmZqbLfpmZmThx4oTye3V1NTIyMpReKCIiIqJoinhEkp6e\nDovFgo0bNwIAtmzZgsGDB7vMhwKAadOm4eDBgzh69CgA4KWXXsLs2bMjXV0iIiIiryI+JwoAjh49\ninnz5uHMmTNIS0tDWVkZxowZg6VLl2LQoEFYuHAhgK4lDh5//HHY7Xbk5ORgw4YNSElJUfKRZdlj\n0pgkSZAk1wl5RERE1PMIITwmkptMJt1GtaISRBERERHFOk4wIiIiItKgWwRRwa6ATp4eeeQRDB06\nFCaTCYcPH1a2NzQ04KabbsKoUaOQl5eHvXv3KmltbW248847MXLkSIwePRpbt26NRtUN78KFC7j1\n1lsxevRoFBYWYurUqaiqqgLA9tXD1KlTUVBQgMLCQlx//fU4dOgQALatXtavXw+TyYQdO3YAYLvq\nZciQIcjOzkZhYSEsFgvefPNNAGzfULW3t2PJkiUYNWoU8vPzMXfuXAARaFfRDUyZMkX84Q9/EEII\nsWXLFjF+/Pgo1yh27N27V/z3v/8VQ4cOFZ999pmy/Z577hHLly8XQgixf/9+ceWVV4rOzk4hhBBP\nP/20mD9/vhBCiOrqajFgwABx9uzZyFfe4M6fPy/ee+895fcXX3xRTJo0SQghxPz589m+IWpublZ+\nfuutt0R+fr4Qgm2rhxMnTohrr71WXHvttWL79u1CCH4m6GXo0KHi8OHDHtvZvqF59NFHxcMPP6z8\n/vXXXwshwt+uMR9E1dfXi7S0NGG325VtAwcOFFVVVVGsVewZMmSISxCVnJysXIRCCFFcXCw+/PBD\nIYQQY8eOFfv27VPSbr/9dvH73/8+cpWNUZ9++qkYOnSoEILtq7f169cLi8UihGDbhkqWZXHDDTeI\n8vJyMWnSJCWIYrvqw/2z1oHtq53VahWpqamitbXVIy3c7Rrxx77orba21mP9qMzMTNTU1Hgsm0DB\nOXv2LDo7OzFgwABl21VXXYWamhoAXet6XXXVVV7TyLcXXngBM2fOZPvqqLS0FLt27YIkSXj33XfZ\ntjr41a9+he985zsoLCxUtrFd9XX33XcDAIqKivDcc89BkiS2bwiqqqrQr18/PPvss9i5cycSExOx\ndOlSFBQUhL1du8WcKCKjW7FiBaqqqrBixYpoV6Vb2bBhA2pqavDMM8/giSeeAKDvc7F6mi+++AJb\nt27FU089Fe2qdFt79+7FZ599hvLycvTv3x+lpaUAeN2GorOzEydPnkROTg7279+PF154AbNnz0Zn\nZ2fY2zXmg6hgV0Cn4PXr1w9xcXGor69Xtp04cUJp06uuugonT570mkaefvnLX+JPf/oT/vKXv6B3\n795s3zC4++67sXv3bgBAfHw821ajvXv34uTJkxg5ciSGDh2Kf/3rX1i4cCHeeOMNXrM6ufLKKwEA\nZrMZjz76KPbu3cvPhBBlZmbCbDbjzjvvBAAUFBRgyJAh+Pzzz8P/eaBl/NFoJk+eLMrKyoQQQrz5\n5pucWK6B+zj9/PnzxbJly4QQQnzyyScuk/GWLVumTMY7fvy4uOKKK8SZM2ciX+kYsGrVKnH11VeL\npqYml+1s39A0NTWJU6dOKb+/9dZbYvDgwUIItq2eJk2aJHbs2CGEYLvqwWq1unwWrFq1Slx//fVC\nCLZvqKZOnSreffddIURXG6Wnp4tTp06FvV27RRBVUVEhrrnmGjFq1Cgxfvx48e9//zvaVYoZixYt\nEldeeaWIj48XAwcOFCNHjhRCdN3Z8L3vfU+MHDlS5OTkiD179ijHWK1Wcfvtt4vhw4eLrKwssWXL\nlmhV39C++uorIUmSGDFihCgsLBQFBQViwoQJQgi2b6hOnjwpioqKRF5ensjPzxc33nij8kcA21Y/\nkydPViaWs11Dd/z4cVFYWCjy8/NFXl6emDlzpjh58qQQgu0bquPHj4vJkyeL3NxcUVBQIN566y0h\nRPjblSuWExEREWkQ83OiiIiIiKKBQRQRERGRBgyiiIiIiDRgEEVERESkAYMoIiIiIg0YRBERERFp\nwCCKiIiISAMGUUREREQaMIgiIkOw2+1Yvnw5srOzkZeXB4vFgsWLF2P79u0oLCzUtayTJ0/i5Zdf\n1jVPIup5GEQRkSHcc889KC8vx759+3D48GGUl5fjxhtvxNmzZyFJkq5lVVdXY+3atZqOtdvtutaF\niGIXgygiirqqqips3boVZWVlSE1NVbaXlJRg2LBh6OjowIMPPoiCggLk5uaivLwcQFdAM23aNBQV\nFSE3Nxd33XUX2traAAB79uxBbm4uSktLkZubi/Hjx+Pw4cMAgPvvvx9Hjx6FxWLBzJkzAQCVlZWY\nPn06iouLUVBQgN/97ndKPUwmE5YtW4aioiI8+eSTkWoWIjI6nZ79R0Sk2RtvvCEKCgq8pu3evVvE\nx8eL/fv3CyGEWLt2rZg6daqSfvbsWeXn+++/X6xcuVI5zmQyiV27dilljB49WkkrLCxUjrPb7WLc\nuHGioqJCCCGEzWYTeXl54tNPPxVCCCFJknjmmWd0Olsi6i7YE0VEhjdixAiMGzcOAHDNNdfg+PHj\nAAAhBFatWgWLxYK8vDy8++67OHTokHLckCFDMGnSJADAbbfdhtOnT+Orr77yyL+iogJffPEFZs+e\njcLCQlx77bU4d+4cvvzyS2Wf+fPnh/EMiSgWxUW7AkREFosFx44dQ2NjIy677DKP9N69eys/m81m\ndHZ2AgA2bdqE3bt3Y+/evUhKSsKaNWuwa9cun+VIkuR1fpUQAv3791eGCb0dl5ycrPa0iKibY08U\nEUXd8OHDUVJSgnvvvRfNzc3K9m3btim9Tt40NTXh8ssvR1JSElpbW1FWVuaSfuLECezZswcAsGXL\nFgwcOBCDBg1CamqqSzlZWVlITU11Ob6qqgpNTU0AuoIsIiJ3DKKIyBDWrVuHvLw8FBcXIzc3F2PH\njsUHH3yAfv36+Txm7ty5sFqtyM7Oxi233ILrrrvOJX3MmDEoKytDXl4eVq5ciddeew0AkJeXh7Fj\nxyI3NxczZ86E2WzGn//8Z2zbtg0FBQXIycnBggULlEnqet8dSETdgyT4JxYRdUN79uzBD3/4Q59D\ndEREoWJPFBEREZEG7IkiIiIi0oA9UUREREQaMIgiIiIi0oBBFBEREZEGDKKIiIiINGAQRURERKQB\ngygiIiIiDRhEEREREWnAIIqIiIhIAwZRRERERBr8fwdlhHhZMfZNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9df52759b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_plot(dfTfidf, 11, 'Chapter frequency of the bi-gram \"the\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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heOqpp/DFF1/gwIEDOHv2LDZt2qSWS5KE999/H/v378e+fftQUlIS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dea534390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_plot(dfTfidf, 12, 'Chapter frequency of the bi-gram \"th\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Can a ghost writer mimic another author?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fiction literature, it is not uncommon for authors to write under more than one name or to write as another author.  Some examples of this are:\n",
    "* 'Tom Clancy' books written by Tom Clancy as well as other authors including Mark Greaner, Grant Blackwood etc\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|<img src=http://www.tomclancy.com/books/true-faith-and-allegiance-hc300.jpg width='100px'>|<img src=http://www.tomclancy.com/books/tom-clancy-duty-and-honor-hc300.jpg width='100px'>|\n",
    "\n",
    "* 'Clive Cussler' books written by Clive Cussler as well as other authors including Boyd Morrison, Justin Scott etc\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|<img src=http://clive-cussler-books.com/wp-content/uploads/2016/05/053116_Emperors-Revenge-Oregon-Files-Clive-Cussler-Novels_199x300.jpg width='100px'>|<img src=http://clive-cussler-books.com/wp-content/uploads/2016/05/030116_The-Gangster-Isaac-Bell-Clive-Cussler-Adventure-Novels_199x300.jpg width='100px'>|\n",
    "\n",
    "* J.K Rowling writting under the pseudonym Robert Galbraith\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|<img src=http://vignette3.wikia.nocookie.net/harrypotter/images/7/7b/Harry01english.jpg/revision/latest?cb=20150208225304 width='100px'>|<img src=https://static.independent.co.uk/s3fs-public/styles/story_medium/public/thumbnails/image/2013/07/14/22/9-calling.jpg width='112px'>|\n",
    "\n",
    "* Stephen King writing under the pseudonym Richard Bachman\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|<img src=https://images-na.ssl-images-amazon.com/images/I/513H0tH7t-L._SX301_BO1,204,203,200_.jpg width='100px'>|<img src=https://room435.files.wordpress.com/2011/03/2running-man.jpg width='100px'>|\n",
    "\n",
    "\n",
    "This notebook contains the code use to test whether an author writing under another autors name (e.g. Mark Greaney writing as Tom Clancy) can write in the style of another author, or if they retain their own language style."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Autorship attribution is the task of identifying the autor of a document.  Authors typically have their own unique writing style and their works can often be characterised by identifying elements or features of these styles.  There are numerous features which can be used to help classify a text including [1](http://www.icsd.aegean.gr/lecturers/stamatatos/papers/survey.pdf):\n",
    "\n",
    "* lexical features (e.g. average word length, sentence length, vocabulary richness, word frequencies, common errors\n",
    "* character features (e.g. character types, character n-grams etc)\n",
    "* Syntactic features (e.g. parts-of-speech, sentence structure etc)\n",
    "* Semantic features (e.g. function words, synonym use etc)\n",
    "\n",
    "A well known example of authorship attribution is that of determining the authorship of the federalist papers [a collection of 85 articles and essays written (under the pseudonym Publius) by Alexander Hamilton, James Madison, and John Jay promoting the ratification of the United States Constitution](https://en.wikipedia.org/wiki/The_Federalist_Papers).\n",
    "\n",
    "More recently authorship attribution is often used to identify who wrote blog posts or social media comments, and whether one person is writing