13 - TNG - Naive-Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Palabras clave:\n",
    "\n",
    "Bayes, Naive Bayes Multinomial y Gaussiano, Probabilidad, Probabilidad condicional, Likelihood, Verosimilitud, Clasificación, Tokenización, Stemming, Lemmatización, Distribución, Stop Words, NLTK, Count Vectorizer, Vocabulario, sklearn, dataframe, pandas, sparse matrix, NLP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes\n",
    "\n",
    "En el reciente caso las probabilidades de ocurrencia de H o T quedaban multiplicadas ya que se asume independencia entre una tirada y otra de la moneda.\n",
    "Naive Bayes es una técnica estadística que consiste en repetir el método anterior en problemas cuyos sucesos no son independientes, pero suponiendo independencia.\n",
    "Pongamos como ejemplo la clasificación de artículos periodísticos. En este caso podríamos estimar la probabilidad de ocurrencia de cada palabra según la categoría a la que pertenece el artículo.\n",
    "\n",
    "Veamos un ejemplo concreto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Loading the data set - training data.\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "twenty_train = fetch_20newsgroups(subset='train', shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'filenames', 'target_names', 'target', 'DESCR', 'description'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11314"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(twenty_train.data) #Cantidad de artículos periodísticos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11314"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(twenty_train[\"target\"]) #Cantidad de clasificaciones. Debería coincidir con la cantidad de artículos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 4, 4, ..., 3, 1, 8])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train[\"target\"] #Clasificaciones de los artículos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism',\n",
       " 'comp.graphics',\n",
       " 'comp.os.ms-windows.misc',\n",
       " 'comp.sys.ibm.pc.hardware',\n",
       " 'comp.sys.mac.hardware',\n",
       " 'comp.windows.x',\n",
       " 'misc.forsale',\n",
       " 'rec.autos',\n",
       " 'rec.motorcycles',\n",
       " 'rec.sport.baseball',\n",
       " 'rec.sport.hockey',\n",
       " 'sci.crypt',\n",
       " 'sci.electronics',\n",
       " 'sci.med',\n",
       " 'sci.space',\n",
       " 'soc.religion.christian',\n",
       " 'talk.politics.guns',\n",
       " 'talk.politics.mideast',\n",
       " 'talk.politics.misc',\n",
       " 'talk.religion.misc']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train[\"target_names\"] #Referencia de las clasificaciones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(twenty_train[\"target_names\"]) #Cantidad de categorías"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"From: lerxst@wam.umd.edu (where's my thing)\\nSubject: WHAT car is this!?\\nNntp-Posting-Host: rac3.wam.umd.edu\\nOrganization: University of Maryland, College Park\\nLines: 15\\n\\n I was wondering if anyone out there could enlighten me on this car I saw\\nthe other day. It was a 2-door sports car, looked to be from the late 60s/\\nearly 70s. It was called a Bricklin. The doors were really small. In addition,\\nthe front bumper was separate from the rest of the body. This is \\nall I know. If anyone can tellme a model name, engine specs, years\\nof production, where this car is made, history, or whatever info you\\nhave on this funky looking car, please e-mail.\\n\\nThanks,\\n- IL\\n   ---- brought to you by your neighborhood Lerxst ----\\n\\n\\n\\n\\n\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.data[0] # Primer artículo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.target[0] #Código de clasificación del primer artículo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'rec.autos'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.target_names[7] #Clasificación del primer artículo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En resumen, tenemos un dataset que contiene 11314 artículos periodísticos, clasificados en 20 tipos.\n",
    "Ahora vamos a armar un bag of words y a filtrar un poco el vocabulario.\n",
    "\n",
    "Vamos a aplicar el siguiente procesamiento:\n",
    "\n",
    "- Tokenization (nltk)\n",
    "- Lemmatization (nltk)\n",
    "- Stop Words (nltk)\n",
    "- Stemming (nltk)\n",
    "- Filtrado de palabras\n",
    "- Obtención del vocabulario (countvectorizer)\n",
    "- Estimación de la probabilidad de ocurrencia de cada palabra para cada categoría\n",
    "- Estimación de la probabilidad de cada categoría\n",
    "\n",
    "Mas info en:\n",
    "http://text-processing.com/demo/stem/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to /home/cselmo/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /home/cselmo/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to /home/cselmo/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "\n",
      "Artículo tokenizado:\n",
      "['From', ':', 'lerxst', '@', 'wam.umd.edu', '(', 'where', \"'s\", 'my', 'thing', ')', 'Subject', ':', 'WHAT', 'car', 'is', 'this', '!', '?', 'Nntp-Posting-Host', ':', 'rac3.wam.umd.edu', 'Organization', ':', 'University', 'of', 'Maryland', ',', 'College', 'Park', 'Lines', ':', '15', 'I', 'was', 'wondering', 'if', 'anyone', 'out', 'there', 'could', 'enlighten', 'me', 'on', 'this', 'car', 'I', 'saw', 'the', 'other', 'day', '.', 'It', 'was', 'a', '2-door', 'sports', 'car', ',', 'looked', 'to', 'be', 'from', 'the', 'late', '60s/', 'early', '70s', '.', 'It', 'was', 'called', 'a', 'Bricklin', '.', 'The', 'doors', 'were', 'really', 'small', '.', 'In', 'addition', ',', 'the', 'front', 'bumper', 'was', 'separate', 'from', 'the', 'rest', 'of', 'the', 'body', '.', 'This', 'is', 'all', 'I', 'know', '.', 'If', 'anyone', 'can', 'tellme', 'a', 'model', 'name', ',', 'engine', 'specs', ',', 'years', 'of', 'production', ',', 'where', 'this', 'car', 'is', 'made', ',', 'history', ',', 'or', 'whatever', 'info', 'you', 'have', 'on', 'this', 'funky', 'looking', 'car', ',', 'please', 'e-mail', '.', 'Thanks', ',', '-', 'IL', '--', '--', 'brought', 'to', 'you', 'by', 'your', 'neighborhood', 'Lerxst', '--', '--']\n",
      "\n",
      "Lematización\n",
      "['From', ':', 'lerxst', '@', 'wam.umd.edu', '(', 'where', \"'s\", 'my', 'thing', ')', 'Subject', ':', 'WHAT', 'car', 'be', 'this', '!', '?', 'Nntp-Posting-Host', ':', 'rac3.wam.umd.edu', 'Organization', ':', 'University', 'of', 'Maryland', ',', 'College', 'Park', 'Lines', ':', '15', 'I', 'be', 'wonder', 'if', 'anyone', 'out', 'there', 'could', 'enlighten', 'me', 'on', 'this', 'car', 'I', 'saw', 'the', 'other', 'day', '.', 'It', 'be', 'a', '2-door', 'sport', 'car', ',', 'look', 'to', 'be', 'from', 'the', 'late', '60s/', 'early', '70s', '.', 'It', 'be', 'call', 'a', 'Bricklin', '.', 'The', 'doors', 'be', 'really', 'small', '.', 'In', 'addition', ',', 'the', 'front', 'bumper', 'be', 'separate', 'from', 'the', 'rest', 'of', 'the', 'body', '.', 'This', 'be', 'all', 'I', 'know', '.', 'If', 'anyone', 'can', 'tellme', 'a', 'model', 'name', ',', 'engine', 'specs', ',', 'years', 'of', 'production', ',', 'where', 'this', 'car', 'be', 'make', ',', 'history', ',', 'or', 'whatever', 'info', 'you', 'have', 'on', 'this', 'funky', 'look', 'car', ',', 'please', 'e-mail', '.', 'Thanks', ',', '-', 'IL', '--', '--', 'bring', 'to', 'you', 'by', 'your', 'neighborhood', 'Lerxst', '--', '--']\n",
      "\n",
      "Eliminando stopwords:\n",
      "['From', ':', 'lerxst', '@', 'wam.umd.edu', '(', \"'s\", 'thing', ')', 'Subject', ':', 'WHAT', 'car', '!', '?', 'Nntp-Posting-Host', ':', 'rac3.wam.umd.edu', 'Organization', ':', 'University', 'Maryland', ',', 'College', 'Park', 'Lines', ':', '15', 'I', 'wonder', 'anyone', 'could', 'enlighten', 'car', 'I', 'saw', 'day', '.', 'It', '2-door', 'sport', 'car', ',', 'look', 'late', '60s/', 'early', '70s', '.', 'It', 'call', 'Bricklin', '.', 'The', 'doors', 'really', 'small', '.', 'In', 'addition', ',', 'front', 'bumper', 'separate', 'rest', 'body', '.', 'This', 'I', 'know', '.', 'If', 'anyone', 'tellme', 'model', 'name', ',', 'engine', 'specs', ',', 'years', 'production', ',', 'car', 'make', ',', 'history', ',', 'whatever', 'info', 'funky', 'look', 'car', ',', 'please', 'e-mail', '.', 'Thanks', ',', '-', 'IL', '--', '--', 'bring', 'neighborhood', 'Lerxst', '--', '--']\n",
      "\n",
      "Aplicando stemming\n",
      "['from', ':', 'lerxst', '@', 'wam.umd.edu', '(', \"'s\", 'thing', ')', 'subject', ':', 'what', 'car', '!', '?', 'nntp-posting-host', ':', 'rac3.wam.umd.edu', 'organ', ':', 'univers', 'maryland', ',', 'colleg', 'park', 'line', ':', '15', 'I', 'wonder', 'anyon', 'could', 'enlighten', 'car', 'I', 'saw', 'day', '.', 'It', '2-door', 'sport', 'car', ',', 'look', 'late', '60s/', 'earli', '70', '.', 'It', 'call', 'bricklin', '.', 'the', 'door', 'realli', 'small', '.', 'In', 'addit', ',', 'front', 'bumper', 'separ', 'rest', 'bodi', '.', 'thi', 'I', 'know', '.', 'If', 'anyon', 'tellm', 'model', 'name', ',', 'engin', 'spec', ',', 'year', 'product', ',', 'car', 'make', ',', 'histori', ',', 'whatev', 'info', 'funki', 'look', 'car', ',', 'pleas', 'e-mail', '.', 'thank', ',', '-', 'IL', '--', '--', 'bring', 'neighborhood', 'lerxst', '--', '--']\n",
      "\n",
      "Filtrando no-palabras:\n",
      "['from', 'lerxst', 'thing', 'subject', 'what', 'car', 'organ', 'univers', 'maryland', 'colleg', 'park', 'line', 'I', 'wonder', 'anyon', 'could', 'enlighten', 'car', 'I', 'saw', 'day', 'It', 'sport', 'car', 'look', 'late', 'earli', 'It', 'call', 'bricklin', 'the', 'door', 'realli', 'small', 'In', 'addit', 'front', 'bumper', 'separ', 'rest', 'bodi', 'thi', 'I', 'know', 'If', 'anyon', 'tellm', 'model', 'name', 'engin', 'spec', 'year', 'product', 'car', 'make', 'histori', 'whatev', 'info', 'funki', 'look', 'car', 'pleas', 'thank', 'IL', 'bring', 'neighborhood', 'lerxst']\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('wordnet')\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "stemmer = PorterStemmer()\n",
    "#for art in twenty_train.data:\n",
    "art=twenty_train.data[0]\n",
    "tok=word_tokenize(art)\n",
    "print(\"\\nArtículo tokenizado:\")\n",
    "print(tok)\n",
    "lem=[lemmatizer.lemmatize(x,pos='v') for x in tok]\n",
    "print(\"\\nLematización\")\n",
    "print(lem)\n",
    "stop = [x for x in lem if x not in stopwords.words('english')]\n",
    "print(\"\\nEliminando stopwords:\")\n",
    "print(stop)\n",
    "stem=[stemmer.stem(x) for x in stop]\n",
    "print(\"\\nAplicando stemming\")\n",
    "print(stem)\n",
    "alpha=[x for x in stem if x.isalpha()]\n",
    "print(\"\\nFiltrando no-palabras:\")\n",
    "print(alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "#Procesando todos los artículos:\n",
    "articulos_filtrados=list()\n",
    "for idx in range(len(twenty_train.data)):\n",
    "    if idx%100==0:\n",
    "        print(idx)\n",
    "    art=twenty_train.data[idx]\n",
    "    tok=word_tokenize(art)\n",
    "    lem=[lemmatizer.lemmatize(x,pos='v') for x in tok]\n",
    "    stop = [x for x in lem if x not in stopwords.words('english')]\n",
    "    stem=[stemmer.stem(x) for x in stop]\n",
    "    alpha=[x for x in stem if x.isalpha()]\n",
    "    articulos_filtrados.append(\" \".join(alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from bmdelan brian man delaney subject brain tumor treatment thank bmdelan organ univers chicago line there peopl respond request info treatment astrocytoma email I could thank directli prob sean debra sharon So I think I publicli thank everyon thank I sure glad I accident hit rn instead rm I tri delet file last septemb hmmm\n"
     ]
    }
   ],
   "source": [
    "print(articulos_filtrados[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Salvado del procesamiento a disco:\n",
    "import pickle\n",
    "\n",
    "with open('art_filt.txt', 'wb') as fp:\n",
    "    pickle.dump(articulos_filtrados, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "with open ('art_filt.txt', 'rb') as fp:\n",
    "    itemlist = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from ab allen B subject Re tiff philosoph signific organ purdu univers line In articl prestonm martin preston write whi use PD C librari tiff file It take good minut start use app I certainli use whenev I tiff usual work well that point I philosoph oppos complex thi complex lead program poor tiff writer make bizarr file program inabl load tiff imag though save cours gener inabl interchang imag differ environ despit fact think understand tiff As say go It I worri asshol I big troubl misus abus tiff year I chalk immens unnecessari complex format In word tiff spec appendix G page capit emphasi mine the problem sort success tiff design power flexibl expens simplic It take fair amount effort handl option current defin specif probabl NO applic doe A complet job current way sure abl import tiff imag sinc mani applic If program wors applic ca read everi tiff imag mean I might deal whi would I want imag trap format I neither anyon agre anyon cours ab\n"
     ]
    }
   ],
   "source": [
    "print(itemlist[16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.8, max_features=None, min_df=10,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "        tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting features from articles\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "count_vect = CountVectorizer(max_df=0.8,min_df=10)\n",
    "count_vect.fit(itemlist) #Aprende el vocabulario y le asigna un código a cada palabra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'utter': 8595,\n",
       " 'narr': 5346,\n",
       " 'historian': 3670,\n",
       " 'revel': 6785,\n",
       " 'asid': 474,\n",
       " 'wors': 9006,\n",
       " 'attempt': 533,\n",
       " 'debunk': 2012,\n",
       " 'initi': 4004,\n",
       " 'bibl': 801,\n",
       " 'human': 3783,\n",
       " 'guess': 3437,\n",
       " 'faith': 2840,\n",
       " 'relev': 6674,\n",
       " 'inher': 3998,\n",
       " 'metaphys': 5024,\n",
       " 'multipl': 5286,\n",
       " 'cod': 1492,\n",
       " 'absolut': 25,\n",
       " 'theolog': 8094,\n",
       " 'no': 5485,\n",
       " 'found': 3083,\n",
       " 'parent': 5868,\n",
       " 'child': 1346,\n",
       " 'never': 5428,\n",
       " 'swear': 7897,\n",
       " 'assum': 495,\n",
       " 'simpli': 7344,\n",
       " 'now': 5536,\n",
       " 'trooper': 8342,\n",
       " 'pub': 6393,\n",
       " 'bar': 647,\n",
       " 'children': 1349,\n",
       " 'disobey': 2269,\n",
       " 'inappropri': 3914,\n",
       " 'quit': 6469,\n",
       " 'happi': 3516,\n",
       " 'anim': 329,\n",
       " 'analog': 300,\n",
       " 'water': 8820,\n",
       " 'type': 8401,\n",
       " 'incident': 3922,\n",
       " 'young': 9106,\n",
       " 'direct': 2216,\n",
       " 'older': 5652,\n",
       " 'learn': 4547,\n",
       " 'on': 5670,\n",
       " 'religion': 6682,\n",
       " 'oh': 5637,\n",
       " 'sea': 7117,\n",
       " 'fish': 2976,\n",
       " 'cri': 1832,\n",
       " 'swim': 7905,\n",
       " 'rod': 6897,\n",
       " 'hewlett': 3638,\n",
       " 'packard': 5813,\n",
       " 'fort': 3073,\n",
       " 'collin': 1521,\n",
       " 'co': 1480,\n",
       " 'regard': 6644,\n",
       " 'ms': 5265,\n",
       " 'east': 2475,\n",
       " 'harmoni': 3532,\n",
       " 'rd': 6545,\n",
       " 'space': 7530,\n",
       " 'station': 7659,\n",
       " 'redesign': 6615,\n",
       " 'jsc': 4300,\n",
       " 'altern': 252,\n",
       " 'nasa': 5348,\n",
       " 'lewi': 4599,\n",
       " 'research': 6738,\n",
       " 'center': 1261,\n",
       " 'cleveland': 1444,\n",
       " 'ohio': 5639,\n",
       " 'vnew': 8740,\n",
       " 'kjenk': 4414,\n",
       " 'descript': 2121,\n",
       " 'extern': 2807,\n",
       " 'tank': 7960,\n",
       " 'option': 5703,\n",
       " 'ssf': 7617,\n",
       " 'mark': 4865,\n",
       " 'propos': 6356,\n",
       " 'shea': 7249,\n",
       " 'committe': 1561,\n",
       " 'crystal': 1875,\n",
       " 'citi': 1401,\n",
       " 'receiv': 6582,\n",
       " 'orbit': 5708,\n",
       " 'yo': 9103,\n",
       " 'ken': 4365,\n",
       " 'let': 4589,\n",
       " 'both': 934,\n",
       " 'edit': 2498,\n",
       " 'york': 9104,\n",
       " 'panel': 5841,\n",
       " 'giant': 3273,\n",
       " 'fuel': 3147,\n",
       " 'build': 1048,\n",
       " 'wing': 8940,\n",
       " 'tail': 7944,\n",
       " 'remov': 6695,\n",
       " 'current': 1902,\n",
       " 'three': 8144,\n",
       " 'advisori': 119,\n",
       " 'meet': 4970,\n",
       " 'yesterday': 9098,\n",
       " 'low': 4731,\n",
       " 'modular': 5171,\n",
       " 'approach': 389,\n",
       " 'studi': 7768,\n",
       " 'team': 7990,\n",
       " 'reston': 6762,\n",
       " 'locat': 4686,\n",
       " 'respect': 6755,\n",
       " 'key': 4377,\n",
       " 'featur': 2895,\n",
       " 'bu': 1031,\n",
       " 'develop': 2153,\n",
       " 'lockhe': 4689,\n",
       " 'qualifi': 6439,\n",
       " 'st': 7619,\n",
       " 'provid': 6377,\n",
       " 'propuls': 6359,\n",
       " 'manag': 4831,\n",
       " 'air': 178,\n",
       " 'forc': 3049,\n",
       " 'capabl': 1167,\n",
       " 'obtain': 5606,\n",
       " 'flight': 3002,\n",
       " 'solar': 7479,\n",
       " 'array': 445,\n",
       " 'vehicl': 8647,\n",
       " 'fli': 3000,\n",
       " 'arrow': 450,\n",
       " 'optim': 5701,\n",
       " 'environ': 2642,\n",
       " 'mission': 5126,\n",
       " 'sourc': 7520,\n",
       " 'tend': 8038,\n",
       " 'oppos': 5695,\n",
       " 'old': 5651,\n",
       " 'sexist': 7213,\n",
       " 'achiev': 57,\n",
       " 'common': 1563,\n",
       " 'modul': 5170,\n",
       " 'modifi': 5169,\n",
       " 'lab': 4471,\n",
       " 'dock': 2323,\n",
       " 'port': 6177,\n",
       " 'intern': 4078,\n",
       " 'partner': 5891,\n",
       " 'place': 6095,\n",
       " 'nod': 5489,\n",
       " 'habit': 3465,\n",
       " 'esa': 2678,\n",
       " 'perman': 5985,\n",
       " 'presenc': 6251,\n",
       " 'assur': 497,\n",
       " 'return': 6780,\n",
       " 'freedom': 3113,\n",
       " 'deriv': 2117,\n",
       " 'mike': 5075,\n",
       " 'alot': 243,\n",
       " 'assembl': 485,\n",
       " 'inclin': 3923,\n",
       " 'occur': 5615,\n",
       " 'six': 7367,\n",
       " 'phase': 6027,\n",
       " 'transfer': 8285,\n",
       " 'visit': 8728,\n",
       " 'adopt': 106,\n",
       " 'languag': 4494,\n",
       " 'deploy': 2106,\n",
       " 'sometim': 7499,\n",
       " 'toler': 8213,\n",
       " 'thermal': 8107,\n",
       " 'radiat': 6487,\n",
       " 'after': 141,\n",
       " 'more': 5218,\n",
       " 'finish': 2960,\n",
       " 'most': 5234,\n",
       " 'except': 2753,\n",
       " 'major': 4821,\n",
       " 'singl': 7357,\n",
       " 'core': 1745,\n",
       " 'ft': 3142,\n",
       " 'diamet': 2171,\n",
       " 'cylind': 1923,\n",
       " 'slap': 7390,\n",
       " 'nose': 5519,\n",
       " 'cone': 1636,\n",
       " 'attach': 530,\n",
       " 'regular': 6655,\n",
       " 'solid': 7486,\n",
       " 'rocket': 6894,\n",
       " 'complet': 1588,\n",
       " 'grind': 3416,\n",
       " 'integr': 4052,\n",
       " 'mount': 5247,\n",
       " 'protect': 6365,\n",
       " 'debri': 2008,\n",
       " 'centerlin': 1262,\n",
       " 'pressur': 6258,\n",
       " 'volum': 8748,\n",
       " 'approxim': 393,\n",
       " 'cubic': 1886,\n",
       " 'feet': 2906,\n",
       " 'floor': 3008,\n",
       " 'grace': 3367,\n",
       " 'degrad': 2063,\n",
       " 'failur': 2834,\n",
       " 'channel': 1294,\n",
       " 'loop': 4704,\n",
       " 'dual': 2416,\n",
       " 'environment': 2643,\n",
       " 'thru': 8156,\n",
       " 'johnni': 4265,\n",
       " 'lee': 4556,\n",
       " 'move': 5252,\n",
       " 'sale': 7012,\n",
       " 'ub': 8411,\n",
       " 'forsal': 3071,\n",
       " 'behalf': 730,\n",
       " 'brother': 1013,\n",
       " 'offer': 5631,\n",
       " 'black': 843,\n",
       " 'portabl': 6178,\n",
       " 'purchas': 6405,\n",
       " 'cassett': 1215,\n",
       " 'player': 6116,\n",
       " 'equal': 2656,\n",
       " 'high': 3649,\n",
       " 'deck': 2026,\n",
       " 'sound': 7517,\n",
       " 'bet': 781,\n",
       " 'zoom': 9135,\n",
       " 'microscop': 5059,\n",
       " 'made': 4796,\n",
       " 'japan': 4199,\n",
       " 'includ': 3924,\n",
       " 'accessori': 40,\n",
       " 'hair': 3474,\n",
       " 'head': 3567,\n",
       " 'ask': 475,\n",
       " 'bag': 622,\n",
       " 'leather': 4550,\n",
       " 'brand': 966,\n",
       " 'extra': 2810,\n",
       " 'wide': 8914,\n",
       " 'angl': 326,\n",
       " 'spray': 7597,\n",
       " 'steam': 7670,\n",
       " 'dri': 2395,\n",
       " 'iron': 4140,\n",
       " 'contact': 1692,\n",
       " 'repli': 6718,\n",
       " 'expedit': 2775,\n",
       " 'lastli': 4508,\n",
       " 'mathew': 4902,\n",
       " 'polit': 6154,\n",
       " 'atheist': 514,\n",
       " 'manti': 4848,\n",
       " 'consult': 1688,\n",
       " 'uk': 8426,\n",
       " 'rusnew': 6969,\n",
       " 'keith': 4360,\n",
       " 'ryan': 6982,\n",
       " 'almost': 239,\n",
       " 'painless': 5823,\n",
       " 'method': 5027,\n",
       " 'insert': 4023,\n",
       " 'form': 3063,\n",
       " 'ethnic': 2709,\n",
       " 'minor': 5104,\n",
       " 'group': 3422,\n",
       " 'mutil': 5312,\n",
       " 'extermin': 2806,\n",
       " 'unusu': 8542,\n",
       " 'constitut': 1683,\n",
       " 'punish': 6404,\n",
       " 'reciev': 6586,\n",
       " 'trial': 8319,\n",
       " 'standard': 7640,\n",
       " 'happen': 3515,\n",
       " 'japanes': 4200,\n",
       " 'citizen': 1402,\n",
       " 'war': 8799,\n",
       " 'ii': 3846,\n",
       " 'prepar': 6246,\n",
       " 'concentr': 1613,\n",
       " 'camp': 1142,\n",
       " 'short': 7283,\n",
       " 'step': 7680,\n",
       " 'nazi': 5361,\n",
       " 'origin': 5719,\n",
       " 'imprison': 3902,\n",
       " 'solut': 7489,\n",
       " 'dream': 2391,\n",
       " 'partli': 5890,\n",
       " 'afford': 135,\n",
       " 'devast': 2152,\n",
       " 'caus': 1233,\n",
       " 'goer': 3330,\n",
       " 'total': 8241,\n",
       " 'those': 8136,\n",
       " 'gener': 3234,\n",
       " 'die': 2181,\n",
       " 'diseas': 2257,\n",
       " 'ab': 3,\n",
       " 'allen': 227,\n",
       " 'tiff': 8172,\n",
       " 'philosoph': 6039,\n",
       " 'signific': 7326,\n",
       " 'martin': 4880,\n",
       " 'preston': 6260,\n",
       " 'whi': 8894,\n",
       " 'pd': 5940,\n",
       " 'librari': 4613,\n",
       " 'minut': 5107,\n",
       " 'app': 371,\n",
       " 'certainli': 1273,\n",
       " 'whenev': 8887,\n",
       " 'that': 8081,\n",
       " 'complex': 1589,\n",
       " 'program': 6324,\n",
       " 'poor': 6166,\n",
       " 'bizarr': 839,\n",
       " 'inabl': 3908,\n",
       " 'load': 4681,\n",
       " 'save': 7050,\n",
       " 'interchang': 4064,\n",
       " 'despit': 2133,\n",
       " 'worri': 9005,\n",
       " 'asshol': 489,\n",
       " 'big': 806,\n",
       " 'misus': 5133,\n",
       " 'abus': 30,\n",
       " 'immens': 3877,\n",
       " 'unnecessari': 8515,\n",
       " 'format': 3065,\n",
       " 'word': 8991,\n",
       " 'page': 5819,\n",
       " 'capit': 1172,\n",
       " ...}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.vocabulary_ #Estos son los índices de cada una de las palabras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['aa', 'aaa', 'aaron', 'ab', 'abandon', 'abbrevi', 'abc', 'aberystwyth', 'abid', 'abil', 'abl', 'abnorm', 'aboard', 'abolish', 'abomin', 'abort', 'abound', 'about', 'abov', 'abpsoft', 'abraham', 'abridg', 'abroad', 'absenc', 'absent', 'absolut', 'absorb', 'abstract', 'absurd', 'abund', 'abus', 'ac', 'academ', 'academi', 'academia', 'acc', 'acceler', 'acceller', 'accept', 'access', 'accessori', 'accid', 'accident', 'accommod', 'accomod', 'accompani', 'accompli', 'accomplish', 'accord', 'accordingli', 'account', 'accumul', 'accur', 'accuraci', 'accus', 'ace', 'ach', 'achiev', 'achkar', 'acid', 'ack', 'acker', 'acknowledg', 'aclu', 'acm', 'acquaint', 'acquir', 'acquisit', 'acronym', 'across', 'act', 'action', 'activ', 'activist', 'actual', 'acut', 'ad', 'adam', 'adapt', 'adaptec', 'adaptor', 'adb', 'adc', 'adcom', 'add', 'addict', 'addit', 'addr', 'address', 'adequ', 'adher', 'adirondack', 'adjac', 'adject', 'adjust', 'adl', 'admin', 'administ', 'administr', 'admir', 'admiss', 'admit', 'admittedli', 'adob', 'adolesc', 'adolf', 'adopt', 'adress', 'adrian', 'adult', 'advanc', 'advantag', 'adventist', 'adventur', 'advers', 'advertis', 'advic', 'advis', 'advisor', 'advisori', 'advoc', 'advocaci', 'ae', 'aerial', 'aerodynam', 'aeronaut', 'aerospac', 'aesthet', 'af', 'afair', 'affair', 'affect', 'affili', 'affirm', 'afflict', 'afford', 'afifi', 'aforement', 'afraid', 'africa', 'african', 'after', 'afteral', 'aftermarket', 'afternoon', 'afterward', 'ag', 'again', 'against', 'agdam', 'age', 'agenc', 'agenda', 'agent', 'aggrav', 'aggress', 'agn', 'agnost', 'ago', 'agoni', 'agre', 'agreement', 'agress', 'agricultur', 'ah', 'ahead', 'ahem', 'ahh', 'ahl', 'ahm', 'ahold', 'ahv', 'ai', 'aiaa', 'aid', 'aiken', 'aim', 'aint', 'air', 'airbag', 'aircraft', 'airlin', 'airplan', 'airport', 'aix', 'aj', 'ak', 'aka', 'akin', 'al', 'ala', 'alabama', 'alamo', 'alan', 'alarm', 'alaska', 'albani', 'albeit', 'albert', 'alberta', 'albican', 'album', 'albuquerqu', 'alcohol', 'ale', 'alert', 'alex', 'alexand', 'alfa', 'algorithm', 'algorythm', 'ali', 'alia', 'alias', 'alic', 'alicea', 'alien', 'align', 'alik', 'alink', 'aliv', 'all', 'allah', 'allan', 'alleg', 'allegedli', 'allegheni', 'allen', 'allerg', 'allergi', 'allevi', 'alley', 'alli', 'allianc', 'alloc', 'allow', 'allud', 'almanac', 'almighti', 'almost', 'alomar', 'alon', 'along', 'alot', 'alpha', 'alphabet', 'alreadi', 'alright', 'also', 'alt', 'altar', 'alter', 'altern', 'although', 'altitud', 'alto', 'altogeth', 'aluminum', 'alung', 'alvin', 'alway', 'am', 'ama', 'amanda', 'amateur', 'amaz', 'amazingli', 'ambassador', 'ambigu', 'ambit', 'ambiti', 'ambul', 'amd', 'ame', 'amehdi', 'amen', 'amend', 'america', 'american', 'amherst', 'ami', 'amiga', 'amin', 'amino', 'amir', 'ammend', 'ammo', 'ammunit', 'amnesti', 'amolitor', 'among', 'amongst', 'amount', 'amp', 'ampl', 'amplifi', 'amsterdam', 'amus', 'an', 'ana', 'analog', 'analys', 'analysi', 'analyst', 'analyt', 'analyz', 'anarchi', 'anarchist', 'anatolia', 'ancestor', 'ancestri', 'ancient', 'and', 'anderson', 'andersson', 'andi', 'andr', 'andrea', 'andrew', 'andreychuk', 'andtbacka', 'anecdot', 'angel', 'angelino', 'angelo', 'anger', 'angl', 'angri', 'ani', 'anim', 'ankara', 'ann', 'anna', 'annal', 'annex', 'annihil', 'anniversari', 'announc', 'annoy', 'annual', 'anon', 'anonym', 'anoth', 'ansi', 'answer', 'ant', 'antenna', 'anthoni', 'antibiot', 'anticip', 'antiqu', 'anton', 'antonio', 'anwar', 'anxiou', 'anybodi', 'anyhow', 'anymor', 'anyon', 'anyth', 'anytim', 'anyway', 'anywher', 'aol', 'ap', 'apart', 'api', 'apollo', 'apolog', 'apologist', 'apostl', 'app', 'appal', 'appar', 'apparantli', 'appeal', 'appear', 'appeas', 'append', 'appl', 'applaud', 'applaus', 'applelink', 'appletalk', 'appli', 'applic', 'appoint', 'appreci', 'appressian', 'approach', 'appropri', 'approv', 'approx', 'approxim', 'apr', 'april', 'apt', 'aquina', 'ar', 'arab', 'arabia', 'arafat', 'arbitrari', 'arbitrarili', 'arbor', 'arc', 'arcad', 'archer', 'archi', 'architect', 'architectur', 'archiv', 'are', 'area', 'aren', 'arena', 'arf', 'arg', 'argc', 'argic', 'argu', 'arguabl', 'arguement', 'argument', 'argv', 'ari', 'arian', 'aris', 'aristid', 'arithmet', 'arizona', 'arkansa', 'arlington', 'arm', 'armenia', 'armenian', 'armi', 'armor', 'armori', 'armstrong', 'arnold', 'around', 'arpa', 'arra', 'arrang', 'array', 'arrest', 'arriv', 'arrog', 'arromde', 'arrow', 'arsen', 'art', 'arthur', 'artic', 'articl', 'articul', 'artifact', 'artifici', 'artilleri', 'artist', 'artwork', 'aryan', 'as', 'asap', 'asbesto', 'ascend', 'ascens', 'ascii', 'ash', 'asham', 'ashley', 'asia', 'asian', 'asid', 'ask', 'asleep', 'asp', 'aspect', 'asphalt', 'aspir', 'ass', 'assail', 'assassin', 'assault', 'assembl', 'assert', 'assess', 'asset', 'asshol', 'assign', 'assimil', 'assist', 'associ', 'assort', 'assum', 'assumpt', 'assur', 'astein', 'asteroid', 'astonish', 'astor', 'astray', 'astro', 'astronaut', 'astronom', 'astronomi', 'astrophys', 'asynchron', 'at', 'ata', 'atari', 'atf', 'atheism', 'atheist', 'athen', 'athena', 'athlet', 'ati', 'atkin', 'atla', 'atlant', 'atlanta', 'atleast', 'atm', 'atmospher', 'atom', 'aton', 'atroc', 'att', 'attach', 'attack', 'attain', 'attempt', 'attend', 'attende', 'attent', 'atterlep', 'attest', 'attitud', 'attorney', 'attract', 'attribut', 'au', 'auction', 'aucun', 'audi', 'audienc', 'audio', 'aug', 'augment', 'august', 'augustin', 'aura', 'austin', 'australia', 'australian', 'austria', 'authent', 'author', 'authorit', 'authoritarian', 'authur', 'auto', 'autocad', 'autom', 'automat', 'automobil', 'automot', 'autonomi', 'autr', 'autumn', 'av', 'avail', 'ave', 'aveng', 'avenu', 'averag', 'avg', 'aviat', 'aviv', 'avoid', 'aw', 'await', 'awar', 'award', 'away', 'awd', 'awesom', 'aweto', 'awhil', 'ax', 'axe', 'axelsson', 'axi', 'az', 'azerbaijan', 'azerbaijani', 'azeri', 'azw', 'ba', 'baalk', 'babak', 'babb', 'babe', 'babi', 'babylon', 'bach', 'back', 'backcountri', 'backdoor', 'background', 'backon', 'backup', 'backward', 'bacteri', 'bacteria', 'bad', 'badg', 'badli', 'baerga', 'baffl', 'bag', 'bailey', 'bait', 'bake', 'baker', 'baku', 'balanc', 'baldwin', 'ball', 'ballentin', 'ballist', 'balloon', 'ballot', 'baltimor', 'ban', 'band', 'bandit', 'bandwidth', 'bang', 'bank', 'bankrupt', 'banner', 'baptism', 'baptist', 'baptiz', 'bar', 'barbar', 'barbara', 'barbecu', 'barber', 'bare', 'bargain', 'bark', 'barlow', 'barn', 'baron', 'barrasso', 'barrel', 'barrett', 'barri', 'barrier', 'baru', 'base', 'basebal', 'baseman', 'basement', 'bash', 'basi', 'basic', 'basil', 'basketbal', 'bass', 'bastard', 'bat', 'batch', 'bate', 'batf', 'bath', 'bathroom', 'batman', 'battalion', 'batter', 'batteri', 'battl', 'battlefield', 'baud', 'bay', 'baylor', 'bayonet', 'bb', 'bbc', 'bc', 'bclark', 'bd', 'bdi', 'bdira', 'be', 'beach', 'beacon', 'beal', 'beam', 'bean', 'bear', 'beast', 'beastmast', 'beat', 'beauchain', 'beauti', 'beaverton', 'becaus', 'beck', 'beckman', 'becom', 'becuas', 'bed', 'bedford', 'bedroom', 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'them', 'theme', 'then', 'theodor', 'theolog', 'theologian', 'theoret', 'theori', 'theorist', 'ther', 'therapi', 'there', 'thereaft', 'therebi', 'therefor', 'therein', 'thereof', 'thermal', 'these', 'thesi', 'they', 'thi', 'thick', 'thief', 'thier', 'thigh', 'thigpen', 'thin', 'thine', 'thing', 'think', 'thinker', 'third', 'thirst', 'thirteen', 'thirti', 'tho', 'thoma', 'thomasp', 'thompson', 'thomson', 'thor', 'thorn', 'thorough', 'thoroughli', 'thorson', 'those', 'thou', 'though', 'thought', 'thousand', 'thread', 'threat', 'threaten', 'three', 'threshold', 'thrill', 'thrive', 'throat', 'throne', 'throttl', 'through', 'throughout', 'throughput', 'throw', 'thrower', 'thru', 'thrust', 'thruster', 'thu', 'thug', 'thumb', 'thunder', 'thursday', 'thwart', 'thx', 'thyagi', 'ti', 'tick', 'ticket', 'tickl', 'tie', 'tiff', 'tiger', 'tight', 'tighten', 'tightli', 'til', 'tile', 'till', 'tilt', 'tim', 'time', 'timeout', 'timer', 'timmbak', 'timmon', 'timothi', 'tin', 'tini', 'tion', 'tip', 'tire', 'tissu', 'titan', 'titl', 'tkachuk', 'tm', 'tn', 'to', 'toal', 'tobacco', 'tobia', 'tocchet', 'tod', 'todamhyp', 'today', 'todd', 'toe', 'togeth', 'toilet', 'token', 'tokyo', 'toler', 'toll', 'tom', 'tomb', 'tomh', 'tommi', 'tomorrow', 'ton', 'tone', 'toner', 'tongu', 'toni', 'tonight', 'too', 'tool', 'toolkit', 'top', 'topic', 'toplevel', 'tor', 'torah', 'torch', 'toronto', 'torqu', 'torr', 'tortur', 'toshiba', 'toss', 'total', 'totalitarian', 'touch', 'tough', 'tour', 'tourist', 'tournament', 'tout', 'tove', 'tow', 'toward', 'tower', 'town', 'toxic', 'toxin', 'toy', 'toyota', 'tp', 'tq', 'tr', 'trace', 'tracer', 'track', 'tract', 'trade', 'trader', 'tradit', 'traffic', 'tragedi', 'tragic', 'trail', 'trailer', 'train', 'trait', 'trajan', 'trajectori', 'trampl', 'tran', 'tranni', 'tranquil', 'transact', 'transceiv', 'transcend', 'transcript', 'transfer', 'transform', 'transgress', 'transient', 'transistor', 'transit', 'translat', 'transmiss', 'transmit', 'transmitt', 'transpar', 'transplant', 'transport', 'trap', 'trapdoor', 'trash', 'travel', 'travi', 'tray', 'tread', 'treason', 'treasur', 'treasuri', 'treat', 'treati', 'treatment', 'tree', 'trek', 'tremend', 'trench', 'trend', 'trent', 'trevor', 'tri', 'trial', 'triangl', 'triangul', 'tribe', 'tribun', 'trick', 'tricki', 'trickl', 'trident', 'trigger', 'trillion', 'trim', 'triniti', 'trinitron', 'trip', 'tripe', 'tripl', 'tripoli', 'triumf', 'triumph', 'trivia', 'trivial', 'troop', 'trooper', 'trophi', 'troubl', 'troy', 'truck', 'true', 'truecolor', 'trueli', 'truetyp', 'truli', 'trumpet', 'trunk', 'trust', 'trustworthi', 'truth', 'trw', 'tseng', 'tsn', 'tsr', 'tt', 'tte', 'tti', 'ttl', 'tu', 'tub', 'tube', 'tucson', 'tue', 'tuesday', 'tuff', 'tuft', 'tuinstra', 'tuition', 'tulsa', 'tune', 'tunnel', 'turbo', 'ture', 'turgeon', 'turk', 'turkey', 'turkish', 'turkiy', 'turn', 'turner', 'turpin', 'turtl', 'tutori', 'tv', 'tvtwm', 'twelv', 'twenti', 'twice', 'twin', 'twist', 'twm', 'two', 'tx', 'ty', 'type', 'typic', 'typo', 'tyranni', 'tyrant', 'tyre', 'tz', 'ua', 'uaccess', 'uart', 'ub', 'ubiquit', 'uc', 'ucbvax', 'ucc', 'uci', 'ucla', 'ucsd', 'ud', 'ug', 'ugli', 'uh', 'ui', 'uiuc', 'uj', 'uk', 'ulf', 'ultim', 'ultra', 'ultrastor', 'ultrix', 'um', 'umm', 'ummm', 'umpir', 'un', 'unabl', 'unaccept', 'unambigu', 'unanim', 'unapprov', 'unarm', 'unauthent', 'unauthor', 'unawar', 'unbeliev', 'unbias', 'unbreak', 'unc', 'uncertain', 'uncertainti', 'unchang', 'uncl', 'unclear', 'uncomfort', 'uncommon', 'uncompress', 'uncondit', 'unconstitut', 'uncontrol', 'unconvent', 'uncov', 'und', 'undefin', 'undeni', 'under', 'underdog', 'underestim', 'undergo', 'underground', 'underli', 'underlin', 'undermin', 'underneath', 'understand', 'undertak', 'underway', 'undesir', 'undisput', 'undo', 'undocu', 'undoubtedli', 'unemploy', 'unequivoc', 'unexpect', 'unfair', 'unfamiliar', 'unforgiven', 'unfortun', 'unfound', 'unhappi', 'uni', 'unifi', 'uniform', 'uniformli', 'unintent', 'union', 'uniqu', 'unisql', 'unit', 'uniti', 'univ', 'univers', 'universit', 'universitaet', 'unix', 'unjust', 'unknown', 'unlaw', 'unless', 'unlik', 'unlimit', 'unload', 'unlock', 'unnecessari', 'unnecessarili', 'unoc', 'unopen', 'unorgan', 'unpleas', 'unplug', 'unpopular', 'unpreced', 'unpredict', 'unread', 'unreason', 'unrecogn', 'unregist', 'unrel', 'unreli', 'unsaf', 'unsecur', 'unsign', 'unstabl', 'unsubstanti', 'unsuccess', 'unsupport', 'until', 'unto', 'untru', 'unus', 'unusu', 'unwant', 'unwil', 'unworthi', 'uo', 'up', 'upcom', 'updat', 'updatedir', 'upgrad', 'uphold', 'upi', 'upload', 'upon', 'upper', 'uppsala', 'upright', 'upris', 'upset', 'upstair', 'upto', 'upward', 'ur', 'uranium', 'uranu', 'urban', 'urbana', 'urbanachampaign', 'urg', 'urgent', 'us', 'usa', 'usabl', 'usaf', 'usag', 'usc', 'use', 'useful', 'useless', 'usenet', 'user', 'usernam', 'usmail', 'usp', 'usrobot', 'ussr', 'usual', 'ut', 'utah', 'utexa', 'util', 'utopian', 'utrecht', 'utter', 'utterli', 'utzoo', 'uucp', 'uudecod', 'uuencod', 'uunet', 'uupcb', 'uv', 'uw', 'uzi', 'va', 'vac', 'vacat', 'vaccin', 'vacuum', 'vagu', 'vain', 'valentin', 'valeri', 'valid', 'valley', 'valu', 'valuabl', 'valv', 'van', 'vancouv', 'vandal', 'vanderbi', 'vanderbyl', 'vanheyningen', 'vanish', 'vapor', 'vari', 'variabl', 'variant', 'variat', 'varieti', 'variou', 'vast', 'vastli', 'vat', 'vaux', 'vax', 'vbv', 'vcr', 'vd', 'veal', 'vector', 'vega', 'veget', 'vegetarian', 'vehicl', 'veil', 'vein', 'velasco', 'veloc', 'vendor', 'vener', 'veng', 'venom', 'vent', 'ventur', 'ventura', 'venu', 'ver', 'vera', 'verbal', 'verbatim', 'verbeek', 'verdict', 'veri', 'verif', 'verifi', 'vernon', 'vers', 'versa', 'version', 'versu', 'vertic', 'vesa', 'vessel', 'vesselin', 'vest', 'vesterman', 'vet', 'veteran', 'veto', 'vezina', 'vga', 'vh', 'vhf', 'vi', 'via', 'viabl', 'vibrat', 'vic', 'vice', 'vicin', 'viciou', 'victim', 'victor', 'victori', 'victoria', 'video', 'videotap', 'vienna', 'vietnam', 'view', 'viewer', 'viewpoint', 'vike', 'villag', 'vin', 'vinc', 'vincent', 'vintag', 'vinyl', 'viola', 'violat', 'violenc', 'violent', 'viper', 'virgilio', 'virgin', 'virginia', 'viril', 'virtu', 'virtual', 'viru', 'visa', 'visibl', 'vision', 'visit', 'visitor', 'visual', 'vital', 'vitamin', 'vl', 'vladimir', 'vladimirov', 'vlb', 'vlsi', 'vm', 'vnet', 'vnew', 'vo', 'vocal', 'voic', 'void', 'vol', 'volt', 'voltag', 'volum', 'volunt', 'voluntari', 'volvo', 'vomit', 'von', 'vote', 'voter', 'vow', 'vox', 'voyag', 'vp', 'vr', 'vram', 'vs', 'vt', 'vu', 'vulcan', 'vulner', 'vw', 'wa', 'wacko', 'waco', 'wade', 'waffl', 'wag', 'wage', 'wager', 'wagner', 'wagon', 'wail', 'wait', 'waiver', 'wake', 'wale', 'walk', 'walker', 'wall', 'walla', 'wallac', 'wallach', 'wallet', 'wallpap', 'walsh', 'walt', 'walter', 'wan', 'wander', 'wang', 'wangr', 'want', 'war', 'ward', 'ware', 'warfar', 'wari', 'warm', 'warn', 'warner', 'warp', 'warrant', 'warranti', 'warren', 'warrent', 'warrior', 'warsaw', 'wash', 'washer', 'washington', 'wast', 'watch', 'wate', 'water', 'waterloo', 'watson', 'watt', 'wave', 'waveform', 'wax', 'way', 'wayn', 'wb', 'wc', 'wcl', 'wd', 'wdstarr', 'we', 'weak', 'weaken', 'weaker', 'wealth', 'wealthi', 'weapon', 'weaponri', 'wear', 'weari', 'weather', 'weaver', 'web', 'webb', 'weber', 'webster', 'wed', 'wednesday', 'wee', 'weed', 'week', 'weekend', 'weekli', 'weeni', 'weep', 'weigh', 'weight', 'weird', 'weiss', 'weitek', 'welch', 'welcom', 'weld', 'welfar', 'well', 'wellington', 'were', 'wesley', 'west', 'western', 'wet', 'wfan', 'wh', 'whaler', 'whaley', 'wharfi', 'what', 'whatev', 'whatsoev', 'wheat', 'wheel', 'wheeli', 'when', 'whenev', 'where', 'wherea', 'wherebi', 'wherein', 'wherev', 'whether', 'whi', 'which', 'whichev', 'while', 'whine', 'whip', 'whistl', 'whit', 'white', 'whitmor', 'who', 'whoever', 'whole', 'wholesal', 'wholli', 'whore', 'whose', 'wi', 'wibbl', 'wick', 'wide', 'wider', 'widespread', 'widget', 'width', 'wiener', 'wierd', 'wife', 'wil', 'wild', 'wilder', 'will', 'willi', 'william', 'willing', 'willingli', 'wilmington', 'wilson', 'win', 'winbench', 'wind', 'window', 'windshield', 'wine', 'winfax', 'winfield', 'wing', 'wingat', 'winmark', 'winner', 'winnipeg', 'winona', 'winter', 'winword', 'wipe', 'wire', 'wirefram', 'wireless', 'wiretap', 'wisconsin', 'wisdom', 'wise', 'wish', 'wit', 'witch', 'with', 'withdraw', 'wither', 'withhold', 'within', 'without', 'withstand', 'wittgenstein', 'wive', 'wizard', 'wk', 'wlsmith', 'wm', 'wn', 'wo', 'wod', 'woe', 'wolf', 'wolv', 'woman', 'women', 'won', 'wonder', 'wong', 'wont', 'wood', 'wooden', 'woodi', 'woodward', 'woof', 'woop', 'worcest', 'word', 'wordperfect', 'work', 'workabl', 'workaround', 'worker', 'workgroup', 'workplac', 'workshop', 'workspac', 'workstat', 'world', 'worldwid', 'worm', 'worri', 'wors', 'worship', 'worshipp', 'worst', 'worth', 'worthi', 'worthless', 'worthwhil', 'would', 'wound', 'wow', 'wp', 'wq', 'wr', 'wrap', 'wrat', 'wrath', 'wreck', 'wrench', 'wright', 'wrigley', 'wrist', 'write', 'writer', 'written', 'wrong', 'wrt', 'ws', 'wt', 'wtc', 'wtm', 'wtte', 'wu', 'wuarchiv', 'ww', 'wwi', 'wwii', 'wwiz', 'xannounc', 'xavier', 'xcopyarea', 'xcreatewindow', 'xdm', 'xenix', 'xerox', 'xhost', 'xi', 'ximag', 'ximenez', 'xl', 'xlib', 'xlyx', 'xm', 'xmodmap', 'xor', 'xpert', 'xputimag', 'xserver', 'xsun', 'xt', 'xterm', 'xtian', 'xtwindow', 'xv', 'xview', 'xwd', 'xwindow', 'xx', 'xxx', 'xyz', 'ya', 'yah', 'yale', 'yamaha', 'yang', 'yank', 'yanke', 'yard', 'yassin', 'ye', 'yea', 'yeah', 'year', 'yearli', 'yeast', 'yee', 'yeh', 'yell', 'yellow', 'yep', 'yer', 'yerevan', 'yesterday', 'yet', 'yield', 'yigal', 'yn', 'yo', 'york', 'you', 'young', 'younger', 'youngster', 'youngstown', 'yount', 'your', 'youth', 'yr', 'ysebaert', 'yu', 'yugoslavia', 'yup', 'yzerman', 'zbib', 'zealand', 'zen', 'zeo', 'zephyr', 'zero', 'zeu', 'zhamnov', 'zhenghao', 'zinc', 'zionism', 'zionist', 'zip', 'zisfein', 'zone', 'zoolog', 'zoom', 'zubov', 'zurich', 'zyeh', 'zz']\n"
     ]
    }
   ],
   "source": [
    "vocabulary=count_vect.get_feature_names() #Estos nombres de las palabras seleccionadas para el vocabulario, ordenadas por orden alfabético\n",
    "print(vocabulary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bank\n",
      "641\n"
     ]
    }
   ],
   "source": [
    "print(vocabulary[641])\n",
    "print(count_vect.vocabulary_[\"bank\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "count_vect = CountVectorizer(max_df=0.8,min_df=100)\n",
    "X_train_data=count_vect.fit_transform(articulos_filtrados) #Aprende el vocabulario y le asigna un código a cada palabra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11314, 1924)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_data.shape #Para cada documento hay un vector de ocurrencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_data.toarray() #Es una sparse matrix, vamos a expandirla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_data.toarray()[0,:].argmax() #Veamos a qué palabra pertenece la máxima ocurrencia en el primer artíclo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.vocabulary_[\"car\"] #La máxima ocurrencia pertenece a \"car\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "alpha=1.0\n",
    "columns=count_vect.get_feature_names()\n",
    "df=pd.DataFrame(X_train_data.toarray(), columns=columns) # Se suma alpha a un artículo para que el log-likelihood no de -inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1924 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   abil  abl  about  absolut  abus  academ  acceler  accept  access  accid  \\\n",
       "0     0    0      0        0     0       0        0       0       0      0   \n",
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       "4     0    0      0        0     0       0        0       0       0      0   \n",
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       "   ...   ye  yeah  year  yesterday  yet  york  you  young  your  zero  \n",
       "0  ...    0     0     1          0    0     0    0      0     0     0  \n",
       "1  ...    0     0     0          0    0     0    0      0     0     0  \n",
       "2  ...    0     0     0          0    0     0    0      0     0     0  \n",
       "3  ...    0     0     0          0    0     0    0      0     0     0  \n",
       "4  ...    1     0     0          0    1     0    0      0     0     0  \n",
       "\n",
       "[5 rows x 1924 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
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   "source": [
    "df[\"target_\"]=twenty_train.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 1925 columns</p>\n",
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       "   abil  abl  about  absolut  abus  academ  acceler  accept  access  accid  \\\n",
       "0     0    0      0        0     0       0        0       0       0      0   \n",
       "1     0    0      0        0     0       0        1       0       0      0   \n",
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       "[5 rows x 1925 columns]"
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     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
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   "execution_count": 48,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de palabras en el vocabulario:\n",
      "1924\n",
      "El vector de ocurrencias para la categoría 0 cuyo largo es 1924 es:\n",
      "[ 26.  51.   3. ...,  23.  63.   4.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:alt.atheism\n",
      "[  3.87458274e-04   7.60014306e-04   4.47067239e-05 ...,   3.42751550e-04\n",
      "   9.38841202e-04   5.96089652e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: alt.atheism\n",
      "0.04242531377054976\n",
      "El vector de ocurrencias para la categoría 1 cuyo largo es 1924 es:\n",
      "[  9.  62.   5. ...,   9.  28.   5.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:comp.graphics\n",
      "[  1.74010557e-04   1.19873939e-03   9.66725315e-05 ...,   1.74010557e-04\n",
      "   5.41366176e-04   9.66725315e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: comp.graphics\n",
      "0.05161746508750221\n",
      "El vector de ocurrencias para la categoría 2 cuyo largo es 1924 es:\n",
      "[ 13.  41.   8. ...,   6.  27.   5.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:comp.os.ms-windows.misc\n",
      "[ 0.00027294  0.0008608   0.00016796 ...,  0.00012597  0.00056687\n",
      "  0.00010498]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: comp.os.ms-windows.misc\n",
      "0.0522361675799894\n",
      "El vector de ocurrencias para la categoría 3 cuyo largo es 1924 es:\n",
      "[ 12.  68.   4. ...,   4.  17.   7.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:comp.sys.ibm.pc.hardware\n",
      "[  2.43383024e-04   1.37917047e-03   8.11276747e-05 ...,   8.11276747e-05\n",
      "   3.44792617e-04   1.41973431e-04]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: comp.sys.ibm.pc.hardware\n",
      "0.05214778150963408\n",
      "El vector de ocurrencias para la categoría 4 cuyo largo es 1924 es:\n",
      "[  8.  61.  12. ...,   7.  12.   4.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:comp.sys.mac.hardware\n",
      "[  1.84331797e-04   1.40552995e-03   2.76497696e-04 ...,   1.61290323e-04\n",
      "   2.76497696e-04   9.21658986e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: comp.sys.mac.hardware\n",
      "0.05108714866537034\n",
      "El vector de ocurrencias para la categoría 5 cuyo largo es 1924 es:\n",
      "[ 14.  55.   6. ...,  22.  28.  11.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:comp.windows.x\n",
      "[  2.09568289e-04   8.23303994e-04   8.98149811e-05 ...,   3.29321598e-04\n",
      "   4.19136579e-04   1.64660799e-04]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: comp.windows.x\n",
      "0.05241293972070002\n",
      "El vector de ocurrencias para la categoría 6 cuyo largo es 1924 es:\n",
      "[  5.  12.   7. ...,   9.  22.   3.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:misc.forsale\n",
      "[  1.48306342e-04   3.55935220e-04   2.07628878e-04 ...,   2.66951415e-04\n",
      "   6.52547903e-04   8.89838049e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: misc.forsale\n",
      "0.05170585115785752\n",
      "El vector de ocurrencias para la categoría 7 cuyo largo es 1924 es:\n",
      "[  8.  32.  12. ...,  11.  30.   3.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:rec.autos\n",
      "[  1.52992924e-04   6.11971696e-04   2.29489386e-04 ...,   2.10365271e-04\n",
      "   5.73723465e-04   5.73723465e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: rec.autos\n",
      "0.05250132579105533\n",
      "El vector de ocurrencias para la categoría 8 cuyo largo es 1924 es:\n",
      "[ 13.  25.   6. ...,   9.  33.  14.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:rec.motorcycles\n",
      "[ 0.00027791  0.00053445  0.00012827 ...,  0.0001924   0.00070547\n",
      "  0.00029929]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: rec.motorcycles\n",
      "0.05285487007247658\n",
      "El vector de ocurrencias para la categoría 9 cuyo largo es 1924 es:\n",
      "[ 36.  40.  11. ...,  82.  10.   7.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:rec.sport.baseball\n",
      "[ 0.00071371  0.00079301  0.00021808 ...,  0.00162566  0.00019825\n",
      "  0.00013878]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: rec.sport.baseball\n",
      "0.052766484002121264\n",
      "El vector de ocurrencias para la categoría 10 cuyo largo es 1924 es:\n",
      "[ 14.  45.   9. ...,  34.  10.   5.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:rec.sport.hockey\n",
      "[  2.25392021e-04   7.24474354e-04   1.44894871e-04 ...,   5.47380623e-04\n",
      "   1.60994301e-04   8.04971504e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: rec.sport.hockey\n",
      "0.0530316422131872\n",
      "El vector de ocurrencias para la categoría 11 cuyo largo es 1924 es:\n",
      "[ 49.  88.  16. ...,   4.  20.  13.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:sci.crypt\n",
      "[  5.06475653e-04   9.09588928e-04   1.65379805e-04 ...,   4.13449513e-05\n",
      "   2.06724756e-04   1.34371092e-04]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: sci.crypt\n",
      "0.05258971186141064\n",
      "El vector de ocurrencias para la categoría 12 cuyo largo es 1924 es:\n",
      "[  6.  52.   3. ...,   4.  14.  22.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:sci.electronics\n",
      "[  1.22920593e-04   1.06531181e-03   6.14602966e-05 ...,   8.19470622e-05\n",
      "   2.86814718e-04   4.50708842e-04]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: sci.electronics\n",
      "0.0522361675799894\n",
      "El vector de ocurrencias para la categoría 13 cuyo largo es 1924 es:\n",
      "[ 13.  51.  11. ...,  47.  20.   3.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:sci.med\n",
      "[  1.92778231e-04   7.56283829e-04   1.63120042e-04 ...,   6.96967450e-04\n",
      "   2.96581894e-04   4.44872841e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: sci.med\n",
      "0.05250132579105533\n",
      "El vector de ocurrencias para la categoría 14 cuyo largo es 1924 es:\n",
      "[  8.  56.   6. ...,  17.  16.  16.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:sci.space\n",
      "[  1.13558937e-04   7.94912560e-04   8.51692028e-05 ...,   2.41312741e-04\n",
      "   2.27117874e-04   2.27117874e-04]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: sci.space\n",
      "0.05241293972070002\n",
      "El vector de ocurrencias para la categoría 15 cuyo largo es 1924 es:\n",
      "[ 32.  65.  10. ...,  29.  44.   1.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:soc.religion.christian\n",
      "[  3.67145103e-04   7.45763490e-04   1.14732845e-04 ...,   3.32725249e-04\n",
      "   5.04824516e-04   1.14732845e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: soc.religion.christian\n",
      "0.052943256142831886\n",
      "El vector de ocurrencias para la categoría 16 cuyo largo es 1924 es:\n",
      "[ 17.  63.   8. ...,  14.  44.   8.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:talk.politics.guns\n",
      "[  2.06248104e-04   7.64331210e-04   9.70579315e-05 ...,   1.69851380e-04\n",
      "   5.33818623e-04   9.70579315e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: talk.politics.guns\n",
      "0.048258794414000356\n",
      "El vector de ocurrencias para la categoría 17 cuyo largo es 1924 es:\n",
      "[ 10.  80.  20. ...,  81.  76.   7.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:talk.politics.mideast\n",
      "[  9.22441148e-05   7.37952919e-04   1.84488230e-04 ...,   7.47177330e-04\n",
      "   7.01055273e-04   6.45708804e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: talk.politics.mideast\n",
      "0.04984974368039597\n",
      "El vector de ocurrencias para la categoría 18 cuyo largo es 1924 es:\n",
      "[ 11.  61.   9. ...,  77.  40.   4.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:talk.politics.misc\n",
      "[  1.31006967e-04   7.26493182e-04   1.07187519e-04 ...,   9.17048770e-04\n",
      "   4.76388972e-04   4.76388972e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: talk.politics.misc\n",
      "0.041099522715220084\n",
      "El vector de ocurrencias para la categoría 19 cuyo largo es 1924 es:\n",
      "[  9.  17.   5. ...,  30.  41.   3.]\n",
      "\n",
      "Log-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:talk.religion.misc\n",
      "[  1.69840162e-04   3.20809194e-04   9.43556453e-05 ...,   5.66133872e-04\n",
      "   7.73716291e-04   5.66133872e-05]\n",
      "\n",
      "Log-probabilidad de ocurrencia de la categoría: talk.religion.misc\n",
      "0.033321548523952624\n"
     ]
    },
    {
     "data": {
      "image/png": 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5adu/2rcCU9feCcR6K/oMhAKUaRzccD1LSAn0JbRJqMP1lFJRpLd14yYMoGFS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Mi8KEJoYDRx1zUA8BGgROvRs6UR/hCvp682g7ve+Xd4/IL3ojxWmFymWIJUIhnUkYBpBmkJerrt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\nG7BAFhIiCYQSSoAS0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AYqJUBiaS7ZuABmZqeFZZ2C4dDAOtxGKPbSFr5RvQNjtIzJ9E+t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\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0GZdi/IZFN6CYmofJLOOBXz3jq/jaWXco3SSYPHAGyOQMlEekrOv5Lt50j3T3ec+K94h7\nDWm27z0V9OLW0nUAgB9f8GP8/LU/t/YBABpq21CcdwFWj+nBetVxEkCRMnf4e53afar2WKCspwhR\nIB2uwlSliNFmF4LKUYz6n27ExKBFYQSABnUCetCAIm7FvuvVmDx4CvKjXRhuuCby/MWL/hnnLblN\n/BZy/BQKlWwQxGaz/dFrfoR/OPHDMU+yh5xMEo2H8yiPzMb4wGvE3RMebUExvRDl1AxxraOs7Om0\nsnKR9Z29O5cFHUQT+LBy7Fpn5+tjq2q47joQMD7W0Z+Gy7msUZ2rht4I7yUXnq5ckmuVEID60bXf\nNaLIAkY/zpl5jtrliMIG0GP0CUVQwGOf6jU2l8tYPqrvZQSAA7ZHpIppbT6VAjfSqYu8An564U+B\n6+9DUMvkYquyRrnmwsVX9/YjVWJAURCeKzoSrVg0MYk3DzIr4Y58B07pOgWfWPMJTOu+FoWa46Bs\nyGIgjgFFx+h/jRJt7IAul3LUoqjAzb8phUMJLt2YR6qxF+8f2okTn/gdoktbp7RiMePUNWBNQw1A\nE5jYcxmrlxBg4cUYdpZhfOf1SLqVGeIVu57D8qf/oPSToJVIDeasPLM06Nu0EdO+9lVxfXZtBk2D\n+5XngEUXb9fqTnR0oG/TRjTursfmPTczbXWoubRlEBre3wKiAEWTB85G6XD0cA5EBbZsUIP1/jhW\nPP0guke7kfa5ixPF5L4Lhbl8ENbfMJwS/QaAtsMjeOu0FszLsY301JwUzt2YQHRANEXxomwS7+mR\nQE29YZkw54J9+nso4+C1S7AqacQoGt3yPgShdkfhb6I/3KLId12UGmQ9HQkPhZf248LfSdVjQgGK\nVmX12CZf/PjTmNwXBedKh1dg8sDZSHtp+KO9mNh9BdRdLhkkceYjbZi+dwA37/sx/mnTJ8K+2QUF\nt1BA36aNaL7mDRht7xXXa5yoyw+nLr9e+937859hzoMPoj6MDUIowdM0Gg8qn5LfMrdsGTJeBjUB\n+84JT8YESLtplIeWojV4HVbW6QeTxlyN9ZCQGm3QftcND0TKmMYMZ93fgO5x3QVu1ZZntN9Lds/E\nhSN6RjpOf3v41/j50/rBXRVOZ4RAhEozs1EBcevOt1nrn+OuRGnwZCwZ78cn53TJG+0StDazVCUK\nkmd8Zl43bupuxoraHGbdey9eEQVJZBP6XHBDrbOweLAJEwS4ol83j466ghiHwEIj/NF5mNh7KT7Y\n24FWH0jRKFDUkU2jPl2PuhgLD5MoCOY++rB2LUjVw0lktF5UEqVn7EvhhG3TtWupGh9999yBzEJ5\nGC1rAnm0xgcCZn2kZz2ztUwU8Fref/O0Nr2UAM8tgmYuGm+EEGAIOXzdPwMjLcuR9pkLI+f2ZeMb\n1ZXjYN9Y84mY8oDjB5g1Fg8SJDwPmHsm0MHmd7l3PQ7uuxIAkHeSWNG/AohYmFFkMjx2XQIf6tU1\noulutm4UY0kRx+3kZ5owfb/B52wWrHz+p+Ki/BIkSpP2O4QIN1+R3jlwccYjrUg7qcgobmqQc0wc\n2rU04vJgO2/fNu3ZIKBoG++X3RZAEXsmNVnEpmJUY2zGYmtPeMh39UbKAcDK53QlQTDWC1qW1taq\ngumq1jzOf/8taP/QhzBzYgfqS0dw2YIsPlu7y1o3EN2nkkme/ZATNX7bKZfI4TtL5uFDa26L3Ns9\nepb2u2/TRhTOPEPUya0vnDDuX5tqkZ7g8goHl8MDjuPgOE/nD5xaxpiyZsULzSgkWZ0JJyGskwDg\n8m2PWN9JXU59mzaib9NGdH3uswCA9c//EZd0tOMjp3wRM2pn4Egpg41DLcgUQyWgJW5Sy6mdaFnA\nDm8cuPRcV4x73+ansf7+H0ae68h34JPr/hVfXcTiN3qOh0JKDaBPQEtNaNtxoXb4X+J246xT94iQ\nA3WJaOyfl2gnhsHWWMbLVMx+5tbXw2nsRXOED3CgnEau8R4CQD5pWFhxACZG3ucKkv9YMqty1i9L\nX14REU8Gs1aVkJwVDCxHsf8s9O49G5c8/hw+MFNXrNY+vgWdDz0tfqdD976EAs4Qi9VYaVRYEAAA\nIABJREFUvR/EWhQl3ATSbjq0KNLpqvYm3LVoJtzmTrgBwfjO69FAutC3aSOrdziJEzfMxmXtct/S\nZmUVloHcoogDRS58NCbtypVETPZcTq23vBd9mzZi5XONWP9oK4JQ7qSGot7Rsk6zPk5Om4HOf/kM\n+2UY2k4c0OPFAcDiQ6qXgj7mqzoZuO4IgCS6VhfmFStDxfXMJs2fOjaBlgl9Tcx97T7kwv294cgg\n+l56WrkbnQNznFEk3ATQfQJSLSydjxXEEFZ2BIHjIY8AiZLuudDS2IO79u7HNUPMVd9zPNx22m1Y\n3KwoDxWgSGZfPgYUHaM/MblbhsTfb50W1RLxtZB22cZ5/LaNmH2QCVKn9O8FAMzZ+jwA4CV04hOp\nt6EDZax+5D5RRR99DjMCPSUvAKwbYdkbakcOA4V2kcFD1QFoXZli/hdKkzj1oV/IC4S5Q51LfwBC\nfXTFpBZyQNC1a5t2jbzlkYptURDUjjNf5/YDNqGNwLWkZgaA5mEWcPSqjlBg5NrZ0mYkaBGtEyP4\n6qlrkSwVkX15I3ooO2Q3lZhVRt0R9u9xo+wg3xCO45n4CQBgsFzArb0dQuvbm8tj5oHdKIyPAqGw\nsfxltgkVMITl9BEQGqC+xBjSiTt2wfXLyDnAe2YomV1CPjSzfzfaSABy/S/1N1aEZXWkqWJ5tmTH\niwCAhaPs3yXjL6NTEcwvwt04Dz9gfRs+jMmC1KKAEBQ27Uf90ABOL7Lv3FeW8+r9i+dh7j4G8Lm0\nVKVLjV7mkksuwdrl7CBKQPH3578FXVd8Y8p6RBcdFzPoZqToOOpLMsbTKno/6qm0qonLM5OpkQf3\nt9N3wvUpVrwogZiE4s5IjH3Blgp8djaNm7qlIHQW/Qmchqgwft5550V6lCnJdk2XAgB4zQO/Qro4\ngRND3/FVezajZfwgRrO94TOM0vtl4NOTUg5mZ1N4Y1cT0g7BO86+EfOv+BryhKJumPGi7n4ZU4WD\nyZ7CpwoJF61HDqHtyEEcf2A7sqPDYsJd6ZXEwQgAuuYzcHZBz2xc09nELt74IHD1j9A6xoSIRS0d\n6B7Yjw6frWPHIfBeYu11pVP48KxOayyQStR7YBfqJpmAYTOpnrmb8c7F23azdx6Tc6WzxEDrGSP9\nWLTqSu25daeu035zQXjaoX2oHx0CcvIQevP0FiQCsPgWABbQZ9A+wISRprRx2HgL08zPoi9gLt0g\nLi/b/oJsy9Wf8TuWo8ZNo3ZshLUNYH3p1+L+ef2/Y+84Ius4PgTyhfvPqncAPXrgTR5U+pLgO7CR\nOc8vpP8BCgddlK392XQTgNDVwfLZckaSgWSYTn35dvYcIQQXtDAgd2W9BE8veXFA3Oe084Rbkcqz\nssJdO7z3Wvpd1l9jH1s19gcthbtJMgiypfOUapdXbBlCwXPwt1uZZYIp5AfKpkmyjUjt34lsyPtV\napoeWmPSAG825ACOsfgBFfVVWg6OQ9AMxtNP2PEyu1jHLJlIbRdm001opOx+7dgEUpSBAImyHhOv\njz6HXvoiOwiEDa4fZBZw2dDKk4BgGeRevXD3Vq2Oc3azPXp1bS0Wt3UCAI7fy+SV1+Lf0X5oLzLj\nbH89gT4MnzrwLvgC1ow/jCwdRScYT2icZDyJ+IpjoargCPu3eCcDdmsb2kBbwwCz4UFOdU+to4No\no3sAAE0jusKFz6MlO17Ee2dPF7xnR7EJw34auPF+4M0PyQcmfbi7JBDvG4fY173uMiQP7MbKMPnB\n+bv/i9W/h8di4qC1j8t3DKPuyEHMDmW6dY0FXN4ezY207Lg+seYXbHkxcn/5iyzTX2GMyRRuKo+Z\n/btRGB9BIpNDcv8uvHroN6zsHsZvevbuhBeOqWdYCOa4ZefEGOg5nwHe/jRMuv6qm7Bq7PcAgHN2\nsf6fsGs/cqGcdlb5p1r5Sy65BB8841R8vm86Xme8I1+xpnUgAOC6e+Bc8HkAwHowOaRrfAwkDIJZ\nO3wYizc+Fn0OwAWt9Ti7We7xmUz0INvdqIOpqWwd8KbfIBmubfWZStSa9HB6Sk7S0x75PfKjQ6CU\nIuHqlnOAaozFuLNHHSzbIXl33bicY69pYe+QpuO4FN8GwOLeNNJ+nER/L8o5pUl017Kyc3JprHzi\nAQBAfoJZMx1Hn0IPlWv2Usp4PgEVmZRnTGwT/AIA3nDgV+Lv9MQYFm18DD10CxZQpqBKeJ5Yj8dv\n2yTKdpXZPOCv6Q0NYv7+Hfib6Xoyi/mbn8Fs5blECGw6fhntk3uRpuNIW6x0CaVCrpt+5DA8v4ya\n8VF0DXAltO521naAxcq7tLEGpzUWQFv6hDzalNb5+KJNj+OzfdNwYm0OM4YOCn6qvlANoPGBvh2T\nqB0K96xwT5m9awcAoBM7hbLwZPo75Pj+vX493vSmN0XerRLN2sP42PQ9unLdVWNjWpQ/6SKTLy/E\nf0A9MaglnTopu5pSFI8fKICi0AXuslGp0OtAABIEmNG/B7lJ1t6rnnwAJPxS9fQQVtIHwnYpsuOG\n98B192I8VPg6FPAUcFUFPZeH88VbKJMSdYVKhhN3R7OeLhp5Mfx3E6iTwNtKbxFA8Ks2Psnep8oY\nd6I/ImvsXx9QNHWQl2P036LE5mG88be34yN3MQb9T8b9VHkUp+NB9HZMx6FQS7Kzez1W3PcoFkxL\n4b1fulWUHUMakyQlfVNDzcSt+BAmJnIALtLqXjY6iLpnHxcmdxIZ1vXT1Rz4bUSIAwcEr8e38Hp8\nC8S5KaakEseDhkfi5rn4bbEXZ+/5WcwTQPdgP264/4eo3fgYaMOpcLxpoJACgi3j05nFJaBFik+v\nU93wWLlbRj6DTN0BHHAuFEzQnRjFOXQIZ9FLsH98Hr6Hk9Hevxvv/vIHsXDNqXgeQKY0jruoHNuS\n4YvdPnMOzv3c3wOEYPgIG+sFu7eir38XkmuKeCc+CQoHY6WTAADXb+jHh1/+KDDNbm2wfsOj+PCl\nH8bEiB4UkDgO0js3Y9CtAVWMe2io2bkpnD+bcAK6J/fjLnoRRkqrUOOP45a7PobFlz8unllFH8RT\nxT7NZB2QwvaJ5T/gPPpjlPzZuItehMmBN2BR3Qex7oUn8fet7wpLTx2g1zzxLFiwAJvHhiF6kmsE\nac0D+IP5pJUc4uD/4H2gINjpXwy+7dyMz2nlqgkSOo4M3v+9Qf2iCO4p/893UNe11+m6rjY/4FwZ\nee/ly5cDX9fnuplFkBP/jmf84h78V0+TWLe3PHEfDmW34Ovd/wBAWiOpMXJ+sFK6gX10dmjh07EU\nmzuAz1x+E8brfeTGFiEIZWUXQPre3Vr7DiG48Cl2YDwu4eOZDZvxje4r0fqLF/GPn7pULxuuQS14\nZzvrQ1PRQ/re3Wi4tgPnPPsQFi2+B6hlfMPbNoJ3zGirCiBK79+FOWQL+lqO4J5DtwKE4uMFgs3P\nPoFxzLWe9xuPDOG9X7oVrZnjgTbgrH3DWDrnEhBKkTg4C+W2l5AsngRkdSuvk1adjPsfkL+5YHn2\nc6G1z7lrxD1CWMhf7hLwfnwEm371FXbPDLTfzLJ8fQTvBwBcH87XFds2YsW2jfg2lkbeI/B9JCnw\n+kfvAykxoWhVagNOpReh6eMetl7Qh7voRRiclJmaTBmGNEYznJUJn68ET4O5BniHDyIxNABM08vy\nef0LrMQn/HeDhIFheP+riZflUsmb2CMEx9fmsG/dEuzb8hL47Lty0yH85OVxQOHdnucCiTTU2KA+\n1dcbNfaBi4e/j4WP9gNYqV0vDC4AoQ4orQQU6fvhhU8cwLVvXIPgvm/jbbu+jUNXP2AWF/+H4yI5\nEJPphfAYFlEhkycqCxTPHlvmQZWyGMNtA2/By/vPj7zLh/EB0bl7Hvk/OPOUD6KIqBb7VnwIALCT\n/Ku4tmr4cVzUeAdGMAvchus6/CuupXfgDw9cAQDYrLzDsoEJPHbvMIqnJLBk4Qno+cZv0Fuzi30f\nSrCxvBpv/frHseRGBp79J/4vUNOGq3Z9BjemPw0AWEl/j00Dq3FITD67IC7n0JrQqioEYEoBHi11\nATVy8n4BbxRVfat4iVYPARF1EUgXlEl4SIIwnqDwhfTvdKApoBQ/mZyP+aHRan19A1KH9mJmsYi7\n6EUYHVqJD949gJq8Phe+iUvRu/lz6Hzxdnbh+ius7wkwPnTZVz4DALjsu3cDb2R8l8/Nvh2bMXPs\nEJwwniJxCNZvYECVd965SA3sw0mje3Fx3Tfwx4GLcPM938KczS8g09GCtWvXovse0/JZ7ng02wDU\n90T6VGhowuKRp3FX/UUoHboGj907jA2Hh7DBKeEuehHGyjoAs2DBgkgdVVG2Ae605cDzwAo8jJvu\n/yFqHBfp8TYEbhHOwSeqr0tZRxxkmDarD9i/TSnkAJ3LAXIP+6UsvUoxw55exaw0P/xLpni79c4v\n4OeLe0GXLEci4VosgKS8TQFcUFyBV20fxN/ffi0WPfkkNralkMkwC6gvL+jBpfulxdw59McAgM+B\nydgP4irW1+IEXMWVctGmJzF767Mo1dSBlMv4uzXf1HpwPv0Rzqc/wLNH3oW8ywCxzzU/gqGhu7H3\nqXZceeRZjOZm4ktgoO/f/NvHAABL5m1U2mWuTIRSLNi7DVe234Z8fhClgB/i2XsmhweRqJ16f+CW\nMQTAW3ffjmkzNuDXxcvw28m5WK2VpII1vOexh/GsNyGlvIvOEEUA4Orf/Qyr7/ktZh3ZjcJ/srTv\nNAiEJV4iRpb74bLZ+P4v78JhL+pd8b26NF5z727QEG29+KFRTAz+MwAgu+MFPD77PHT0HxB705EQ\nUH4zPo+nvskUBStXrozUOxW1Dxxg58AwRmb25Q04410fAFHeQbimU7AJTJlHxkd//HHMOO8xgChc\nVVVupDIijag6W9euXYu+PtZniY84uIteBHdyJoD3sb7REt51x4cwMm85CID3fOlWEAAkFDtuA8s+\n+BT6wgYIfjw5H5ceYrIwaZ0PCgl8RkmVHVrgKIq6nroc/uPe9yDvlQC8W3uKn3+OFBdilE7Hc3QG\nHOrjpvt/iMThw+H7HC3gw8/Pf31A0TGLov8f0Ml4DAVPmoVTN4Fn/I4IgCPiGVkAElrJPDLMGGEK\nn8Tw1Z9q+kcAJUK0GUQqmOPazDe3Bw3wxuMyJHDzdUbJiRRSJf1QZwO4uoJGdAdNtqpAKAkPdfpz\nrpeMXHcojYyPeBXDXDqZSCB1cA9S/butKDWB7l+eDzJYgo1RM2HzdYzfjuMiMXIYdHxSE2BopSic\nhM8buxmsYwRGjQsanE0VItdeiUURoGdrMPswFRHXDceTIkviNQLVAEXW7qubqDBfDQ8kFosiAsB1\n9UNYXMp6qpixAjpQ9Mqg2qMjEgRIHdyrGwWbPAFEE5SdKgO1W9sL/zX5CudD1X725OGDyJWH0Yct\nSJbqkCzWo7u9TXyrynwrtNCgTmwwUK1vztHxREopHBvgFyOIxtYDEvkWvl+OHjUqxGoS9ai/LYGZ\ni6qTTNimN3IEXqj1lHWYyJWtzxW7Y6VoKu8pRtkwoQ8i+5a9v6bxe2qyEcliveTRce506vIw+uYa\nB4jq4p9S+DzzlOVdpUWRvFtxWKvN9gI1gD4UgMxoX1uI0VVr7l/adiMCUMk05+J7UAIYVkxFYcmi\n7LUIECgpU8UddWwtzJrzUwKC5/12lJw413l9vLRYfyooQKZe7wCzKDpEczic6YjtWzxVVzYZk2Ez\n7mk1Phu3tFZlwtTBvUAoA65duxYpC3DIiCoWd9bbrB/aOx/9wWmqUXA9Uw4hIHCQG+mJzXo2Fcl4\noGbrxvywfs/qvzENAriOxZFeWdwUQB3Noc/vEum2+/r60NPTU3U7lfqVGD5cQbYGsmPSkr2BW3Wq\n7osV9xk6JZDNeka1BCyx5Vx97TugGPFT2E91mZPtFJLXR3tIIvxa77XMnupNsT/b5kDCi3cZc4qT\n2OnqLnZx/Paoia9HLjdOjGHx4sWaCy3RmaVilRmNY6T5lGhxqeX1tWvXClmIj0XAYxsq2Y8pDc9K\nZhMV1ssAzSERem1UyuBpIw0cI8BqPIo0KVoKyj8pi46vhAXhoFo1fCv+PPDXRMeAoj8z8c3ctvmZ\n05EFkpUTdqolJheCbs5uLmshGh7l/CdEX+g0Nl29HgC3GtYQ6cp/6yRtAlym8O9ZG4078JuBXd0q\nmR01/oX5zUllgUX2h+rfKiBT+qbbtlUgejCWQFHkeB+ts+psTkZNBuByNJuFo82x+OfsMVV0mrKE\ncWZyYoT3SIrvOOHI+Ea6RVG0N7ZvSq0lqyXLk5YPpAqBpApBL7Y1Y/wE1+HxNY5i3RBzbUBmjKw4\n88Wii4OtTDKBlsp9DChALABUxKJoCmLfVW8rKB9dFspIhZY6gcoZ5LQqzKEyX1NxWao0ShVHfIrx\ntQlzZtazwJxoU5DV5Jwqf6jz33jp6AFWF9Ct7YHC96n42yRuUVSmUTDZXp/9b2tZDQmxl3ash35u\ntWwBaGL4CKWhPCOacRCUdReSUozCQot/URVaps6NozxsaN9Xb7YqoChs92gUHLK96p6Js3iI6OrE\ndSV+IU+vzrNs8f8R9QmdtHV2FC4ZRDNV+NORYygdpwLIY0k5HNuSorAi1cnT1RINAiRcJ7JnidlK\nqM42/Pjg+VPSK+y0toaJtOiRl6YAUsJaVJLSqdJKFYomzYWq0pKmFIFRLFqGP24pEQQihlDSjR84\nQoi131PFFop0pcrsfFORAG6NLmsK1whvlyC6Sdp3rkKGFxZFXEFOVKAoJqFMlcqMavmh6MtRylUA\n420BHJk8hisYj8b1jChj9deHEx0Div4nqbeRmXZWI0SrQNFUS0cwMU1bWIHxhXEX8in75nviTGbO\n11JTRXR/vQZocjOpEOAvfD8KIJOsPO2mj223CKNTM5RCurInpUj5bjBHL/SRNtuMB4r066rA6CB+\n0+elEgEP4Olb78eRQPhB0dOY0+5FQQXjd0zWIjcGKPIoO6RygTWfrp5Bp3xpHTdjfLv4e14bM003\nmf3RaGSJcsglJF4rYI19ACDjSSCsUrt8mwVkG6rlUGc9MxFf3lMfBb5i6k0G+qEpFchxsih7YoAi\naRm4dp4l5lkFiuZKisbFzSRd5BQ+4TguUgHT2MwoR91qeptZ4M1FnbWRe7yfgQmQEQf5lIe6TLWC\nlw3NcpDic9TyqV1xzuUWRZp4FN6ztTU13zlxpmLZSKndUukoLYqmj26PdMgvl5EwmreD0vH7gFm6\nafIgivywHiPM5YXG0ADNjOJtE/siYzi9MT7AvOhTxKIorN9S1g8o0omQJzkEx3UW4BtrW/IwozMx\nAh2tEKPIQTCFRZEeR6qrPquUjaeGbOjqHES1nzPTjC90pqkAPyvVpWWzORpBO4ZXppRNXM6fqYXo\njvE9EvgKqAK0cOUXATXAzrTgeXpfuEVRIigh6xvZohDDUw1Bf2VvU7QMKo9lKiHfPYj0SlKuLK0z\ngvAjmWfNCOew9bnKQ0Ycb5SHFc7X2L+qBRK3NNAsu/iXquLgfrSadtm3Kh7jZbmVblxbZj9fKYpj\n8Jc5rXks7DKto1kf1oX7qaf7ngnKJKoE2GMtiqK8LqCBJfj90RBB1ZMKEDxftbjmSkTicIUyqWhJ\nnPIcCT4C4r3429WVWCDyZFCqyqJILcP5T6MbVZIQ5b71jckUFkWUIltmvOWkGF4hq1JlelZnJoxr\nFDcyJ8/W5bHm/NTyjXkms1KMq7R+PqHi//p1e1xTTq4SVzYuUHizOBeG30m1KIoBw6xhFSiQhPFd\nNXlm6nmsvltjjvWrxnLuU0Edyqwr4Bh9PWoXMhG65U9kKfb/EB2LUfQ/SN96fR/+9ZZ3VtRKBxaL\nIikLxGgAHQt2bwGKxPPhPz1NOfzoLaswOr4X47sAznje+eo5uHh5F3qacpE6KlEQ+Ego7j+m69kT\nH3w1AOCXn3oALpUMoiHHhO1HP3A6vvHG2yP1nn3gXlz62a/j67dX74v+yAdOQzrhYujDD0fuCW1U\nOA6eq49rMpnEWHj/2h0ysHLcZmkCRZ7DQrc5oGgsDlqfUSlfDgPiRSyKKj/HrRQIgM+/fime+KO9\n3Bt2fBPJmRyUIOy/GCsC8xW5xiXts8B0maSDcQCNOXuAx2t3fAMo3Khdu2bnt1B2XADn4Mz+X2PU\nSeGGz9+BurAO0/XsqCyKjEwPDAiIPh8XzDqXtGuTOVHlpinoO66Dx249HZ5DUJdN4rfvWYvpDVkM\n9+/QK4kRjmaPbQ1j6rB62yf3YQRd1rKsC7b1z4CiB29Zh5bC0QG7EiiS9fK66nNJDI4WUZtJ4Pjp\n9eDJfx3XRZoWcfXOb6GjPgoCnNTbiF+/+xTMtPAOceQ0LH8IIfjF21crAsgURG3wDUEeZQwBsMlj\nSUIxqbwz1yaxoY8/jZua6/aJvRhFRrt25xtWYCAMjk0ptYLDR2tR9OqDvwYhN6G7Qbbll8vIJMRm\nAACozSQgQpeFPN91CD7dvQ07XoayoO2WKRft/QESCvokhUM5J1q8EsxX+u171mLzw/JQcuXOb6Ou\nfEQIeg4h+OPfnaaBjKINYx+LA4pUWtpdhyd3HkZAgXzKBUYYaP3vN56EwcOD2KTEr5xWl8LLWoPC\nzsJadyUB0aFUy55ODPdWN6G/33GdtfjyVcvxs3/fEH9SpkBHDfuuWX84cvuk2jFMPHYvFq++BAJq\nqZYlTlFOrHjtZKdTb4vMsiQzllWqk+ANO77BQJ+aa8L6WSBuZvUsO+UbQNHKsWcAzIvUGYTa6lRQ\nRF15AkXo8W6ibvgqrya4/71r0VGnr1PR5sBDmLF9KwCe7lnWpR7U4sT/63Z8PVTusGyxPJj1VJbE\n9rvVfdj6XDTzFqffv28dvnUrCyjLQWpVSeaFvMeUCaliARghZWoc7QFKgD5HgRSlUWJKQwsoCBgx\n71jtR9Un0TelS5ft/h7e/X+/g2zSxaKuOtzzQT2hyj9duhjvO3OeAKZVOr2vFR++bk3kuo2kS6R5\nMOV9Uq9XTlN/tFRtXdrcsFhaVbIEKmQS4Tsa8ySscn3/fVj25lvx/Bd2gjjRzLImqYo23q+ZyQlc\nuUtPtkColOumsiiy3qIU9eUj+HDrFlx92tnxBQnReESClgHiIpdO4tEPnI4XP3sHnrI89pELjsMH\nX/iN+N3TkMXmg/HNPPqB02NdTPV+c9lFn1GahbvmA63ur7LMa7K7UB6bCXWQkp4qC9ipqz6L+9+7\nFj/4JkukQYnC02NAk2wwEblGQJGneubNKS0FjU6poGJrgZ0nOuuicqnqPUODAIQGSoiAEFSrAvDR\nPGEEUDTlY39xdMyi6H+Q6usLyPuj2F3Q07jmG2RArpomjkLLSTu/g2nna4zI/Nuz07BsWh2yhVp4\nqZS+K1QCikCRq2Na8MXddVg2jdXPF6nrkKpAoukLl2i/g3IZiYxcpImsXkdDLomGXBK1zS26uXzY\nL9tBsXv+QnjUR2fkUFp5C2ypSaOQjkHwif4Hz2xV09gMAEgms+J+3pdZDerbO63VpVO6QJpNusIS\np6GDPWPjJfwTJbNhewajIkbWODf0iZ5z4qrwvrQoUgVJL+Nr1ic1/ogFZLAv9fpWFmehsy6DfNpD\nObQKKxM+lpVV3Op4cWqsr0HOZ2GmF5y0EoXyCDrqsuIQmcmzQ0B9BwNJjiYOjpeUcyaRSsVo1Amm\nLT5e9rFRao9StaE20QFOmNFgPigO2gQEqfA7JVJsQ0rXFNCUT6Euy8Z+RlOOWZOZFkVJu5A/a7Fc\nP60zZ1l7nlFSqvqEoLFrmsYvKIAFHQV0N2Q1S4CqiMeuMC53N2SRT3nobgizTygHoFxdPerbO1Fb\nHsac41dYq+1tzlsP/AtCKyNuqSghCYLuhqxVILeRzV0nlc0iXWDf0rGYkbucF4VWYERFPsLi9nOe\nftGjUc1mJumiMzyU5lIe3GS0TKIrHgC0kUd9EMdBrWJJ0Dx9BjK10dhg0S4TtNSy/qTzrLy0UNPH\nrmfOHGQC5XBmGYNsbW3k5oymnDZxZjRmNU8WAia42TSklYCilLJnqAdvvjYbckmt3WzSQ3utvs+k\n8rmwD9Udk5p7ZlYsnylI6zjpehaCcm50bXcaAMWcE082SlAEPrcAiQqnXfMWoL50GM3TZ6A+5C2L\nu2Sig2nhvjviGnsitXD1iFGVMnh5+x5vW7vJ8LvUt7VH7rW31KPGH0XKUGi3FdL6SqWO2Ls4tTQ3\noLa5JTL23KLIS2erMpBINzZqc3t6Yy7WXctBILK4VSLL0RcAkPPH0NouMzdxRdeiLnsyCrEmlIPc\nn/JwwazYuAUBQa6uHp6S0j2RCvdIC2BaM2OGtc50XgJzDZ3V8K5X9kKzV7BgvnxvLcbElXKMb5nM\nSzCzvr2j+gZFXBEg19aNXIpl7OKWsADgtjAZPOW5Yg80qSGfjL1n0vRFS9Wm1c6I61Iyp0h0dZsF\nqydC0L1ASecdAdgMEnNCtShi10pOuFc6DuauXG0+KcjJZLS1xt8llWV8MBNM4uT5nXBAMXPpCfY6\nFJkplZN8iZ9JalvaUF86bHYehxIN4WtEeVa2pVWTeyebWsP+su/W2MnGefnieRG30daZMulDtlCr\n7QEctEykUmiuSaHQYJEbAaQSnqYomQqIaK5Jafu9SXyMsvXsnJJI68paT4mZ1NvFynTUpeEWCpBr\nXn6nnunhulGWrpe1g+smTW/MSdczLUZRDB+wxHNyEhSZunrtGiFRrwZOnfPmR64l6+sj12wKVU2G\nra1DV1NexiiiFIHjoGN2VGFhUqKVxfJya2tVC44pn/tLo2MWRf+DlM7lccVnv4YjgdzE3/K172pp\n3U+9/s3Ad36rZS56+2mzceq8FhSfuA+bASw763yccP5FWD/qYHZbAUmXCY+//JlM7VxRL0EpCk3N\nlhtHp8tYfu6FmLfqFBz+FEu36pfLmLdmDV4K7gQALFh7hvW509/4Fux4eA62Be/4xs6cAAAgAElE\nQVTg3Yml1/7dR1CcGJ/SHenmO75ddb9TaYrJcQgGmQmZ87X/fDv8chk7nvktMMG0GTff8W188Y2X\nAwBaZszEDbf/G/btfAA7Dr1X1HfSq5Zq9fc255FOJeBPlLHkrPPRcuaFIL//fYR98VdqmdEL7EEE\nKGpdNqT99pJJ3PTlbwohjgMqs5p1gb9nfT+G9kuGf+PtX8dvf/Y21ibCw47F9ezG27+ObF0D7iy0\noqcph6Z8Cj952xqMH1mEicFrUJwcFX2Oi3H05q/chTtveVy7dt1nviw0yWfe/E6su+ZNuslo1zRc\n8+nbkOWbRtiGR6YW5ldcfRGe28UySyVSaSSzCUyORQ/qJ116JX71yc+y/vzzl8T1wozpAAaQqQ1w\n2+VLcefDDwAAWu74V+TbO7Dp+78HRgHiOkKoLTTX4+DOEWQLNZF2AGhBBQHAixEkTrnyOjz7hS8A\nAC77yCcRlMv4+Kc+HQ5BaGVUm8G+cSYgdX35yzh+fh+e/d1+vPTYFrikhJNnt+CsM+Zq9b7lq98F\n/tEOaqqUa2vB5K6d2rWp1tnqy6/BykuugF8uIVNTBWihEOdji7vr8APl+lH77pMop2rtnY0Vr2nB\n1meeQL4u+l1a+mbj8H89jXxbU2gdE9W4eWk7yKhS9/yFOLJvW2zXpjfmEHSPQhVp0x/9BFrPtfNC\nTg/esg74lFw3137my3AcF1+4fBmSr/o2Uk4ZDYuWYmCgDOzYEPlOHZ/8BLY/85/heySx8tIrMHvF\nSaipqwW++HNRzgTZLrn1oxjZuwWPbzmXPZuIrp26tg7gQLTPqkB29ac/D79UPgrTFzsVmluQveoq\nYBNz7fjVO5nW/pYz5uKche2Y21aj5FbkzEjnRfmOZrz1zn/HnV99R1Vt9ixaCuA3sO1/9R0dqMvW\nAft5iyHA4xCAUhn/JYbe/JW7kMpm8bsHvqJcVQCNxcdFnpl70snomPtvqGlggPaP37oK89oKor5k\nJott+w4jmwnlCCJBAp6J0QsBZvMA9O5Xz8WLE0ChqRltl1+B0fuj7qPa3Aq7WtvSghtv/xgDqY3s\n7B953w14/tmT0dfTjl1fDM1aA4rF3XX49CWL8V+/eDKsi+Cst74LLzz0oHh29euvRr5dT5cNADTc\nnxauOw2TdUn89v77UfPaC3Hot5GiAIDG5ctxZP+T/A20ezff8W3816Nyjz7h4iuQvU5mPovjeafM\na8ZQf3QPuvmr34GnxCeZ3VqDH791FfraJT+8+Y5v45c/YW0Ql+3ruQYOghCmB3Sjfa2GOr8TlXV4\n7Lj6nhm4/oaP4tGfSeuYVDqNm778Tdz9g+sASHkrf9WVmHnzW61tNHa1Y+jQQZzzjveifVZPbF9k\n2IOjfg0AwDlvfy8mx8bwiy+/hH0jQ/jYFSfh+PlnRdiIqTxqmtEFhBnVr/rk5xjvqYaUei+/5W+t\nRXo//olqu1+Retb345y3/xJzZ3SGTRv7XPjTdeQdSimm3/m1ivVSyi2no9Q2ey6Wnnmu+J3q7MDY\n3j3xlYng81GLonmLjgN+8xTS+Rqcft1bcPLrrsaXbrwqUkWqr48peQxBvqGjFRf/y9dBHIJCUwNu\n/NI3kKuNgqlvvfNuNo8+wQ7hC087Axse+A1S2RxI6Jo0q68Pp51zNR556lXas6euPxXds67Dxrvu\njtRbv3Qp6m+5Bdi4EfXZJF799dvgPPU4UjMZONo2aw5uuP3fkK9v1J67+Y5vM8V7SOvecAMCJW5U\ntpDFyOCkAGCP6+vFA89EmgchBO2K0uC/mx3rTbd9FaXJCTx//2/wx5efQ12hBgcGDmDGEqYEVdfI\nDa/+/9o78zg7qjLv/566t+6+dN/et3Rn6axk7RACBISwhQAJiygoiAxuILjA4KuAzoziAo7gMjqI\ngyIKjMvIC+MyCurndRQkJCRIAgIJEAjZOkDS+13P+0dV3VtVt6pu3aW7k87z/Xz60/fWrTp16tRz\nnnPOc57znIU4bl4Xlk6rB9CI5ps+DTz0AEgXE2764mXYvuk5eJsS0LYanX/mWRj8kbKD7tV334/b\n7/y6bX6yOaulZ9bGsERjAnjbuLoi0prEvIWz8d+LVuF3NygrSYgkBP0eDKaA4y68GCtPXYrvfVJZ\nFbL2Y5/C3de835BGXDfZ6tT30C8frWtrxwOXrcGzzQeAN3YCEJjxm18j2N0NPGqbBACg6bpr4Z87\nB5FTTsHoTsVvmZeeMTWntbUZrbrvgXDE8LtXVuMY6YTPIxGWdNVhg7ryyiPLiCQasFA3/gzX1Rvr\niYVOslNUeVfGMlt7IkIk0ZAfFGUzaYRiQUBZkgzJa+1JIQcCaJ3VhVdfNN7fCq/PB6/Ph6wpwN/S\n0EPojGTxi7fVWYsyBq3rVj2DHX99CbvU74Eg5fMlA/AFgoqhyJQuSR5EE43o3220js/rM864ERF8\nPh9Gx0bhkb1oOG0N6HGL7d7VRjofo8ekZIOJtPkKhHUWeK2T5jUNBuRgDnU9hQ5uJNFg6AwnvK8h\n6p1dlLbmqXKqLtbNMR1xoCOOx58wyYaNqIRicZz2/nmoaw7hrvsUo4tWroAyGxOy6Cw0TuspJE2E\nVe/qRdcf1uCbuLjoXD3xjjbkXyQRTr1sLl577q2iZQOStzD7op+JSTR7cFzkx5jbuRsh35U49fK5\naOyMoKFbee/ByAZgGPDInnx5r716EV7csBexRuvZF0lyF2tHP9iQfX5A5x2Vn/vVlXOkpweyP4BF\nqzuR2vkslrzxCLyhdUXxbwIRo06xo7k7hrd2AYtXd8Lb0o3HHn+25DWyPwC53NBlKpoeM+NkJ1r/\niSV49dk30VQ/ggfVXVOV+qJctPbqhZA8EogIifY4Vp4/A7NXtBal0zojjhcfBxJtEWAXDDuTSWo9\nDHmKd4UxDyL1M/VWSAS0B57Hq1DOW1v3JUx/52OO1wCKF9cu3fd4k1IHuxvCQENhkBttUApfMwBo\nOtvf2wtsLXQCZZ8f7bPnAVlNh6jGBFPF8Pp8CMYLOqUp8AoOosdoR7Or7GqsE6AgFyJtrcvPvW4x\nsupv5hhV5jKO9kwH/j4AIkJvi2L083okLDbLjuYJZjLM1tcr3n8h01LMlZEf4ZcoeFCcXfcVyDQG\nYDUuvLEPu3a8hl89riwi0CZafAEZi2btQvLVLejwbUMyFwFwua5E7NtMIkIoVhyrCwDae+uw/Jwe\nLDrF2ltDMxIBRk8VLb1ZXVYTPYXcnHnVAjz/+G40dkWA7YXfwwEPMKa0Hd6wtUeEMU6U6jnllQye\njHpi4QCOX7kMALBLExz1Hc9pjeEvOk8Wj2lmWQ5a61Bt6ZnH44VH1ZNyfQIXN/wjDqR7AKzOn3vs\nOT3omF2PQ6rNq32WUU7MfYO6SAhdHYXytTMULT+jG6ODxTGkgpFiQ7TZm0h/zwANoy/8c3ibkmrX\nSCiDfQhLz+9SxJYuLTrmD3kxCiAY8kMOBNA5NwGo6tzj8Sj9Br1HkRCoe8c7bJ/91MvmorFzF3r7\nuh3z4vcq5RMNCyC/itL9gNjjlRGKxXHWBxbi70/sQV9fm6sYhUQSzvnoIkCU2Sbpkk6YPBHXfWIJ\nUiMZePz2iZUz2A8m0pg7s+AdZH6qwlJJgfWfXIrU97dCIAdfi3OsQaedISWv17iU1+WS51l9LcD2\nMTWfSt1r0Dx/ieDxeg19T0N+7DzAidDdVtAZkXobzxvTygNfIJhPN78kjsigEwGl+xwK+rG4twvP\n2zxX45o1wPPPwyMR4o31wOmnG343pwkU6wstPxrnX78UL285AH9I804mnFf/L8jMvxQP/dKYlmFI\nVqVBoaB/jV792vJnQxwliVQjkUJ41UnAQw8YctQxpx7zTmjDzL5m/Pzz6nmxYL4aW/XT9Wg6Wr/0\nzK52mOPAKvlV8rywM47f5Y8REq0hDL4GdMxuhS8YwvmfXIqRwRRkk2f+xZ9ZbkzQoW4Wbq98iIdk\nzD73LDz23btBBMVI5ALy+RA/5xzTfV1dOqVgQ9Fko+04YBX8q+S1hY/OMYqEqcHTouhXt/Iwl8kY\n03BopAy7o7moaObOQ7NvB6YHDgCwd4m1Ix5LYVn4/2IXNGu0say9mlHBHAi24E5joNFi8FtYU+8w\nkDAfKLMh0Rpoq86L3ewVSFGadVLxTLI7Sr+suSuLlyeUy+LVXcCfdpU8z6PfeYIIM5c1Y+ayZvz+\nD6YTbYw3RITlkf8CfAsAAPNPNLmxU9EHRBMB9K3psc2T285ZuVvNa+d7ZQ9WrhwBfmGxDWgZeNSl\nEI2dEQS6YsDjVSVXMU4eRZ1zE8qg580dgGooIiq4FE1fXBgwE5Hte9EMK9qyNENwxXxf1JUicv5d\nCASlAQBKB3R64KnSaVrexvo+wtRJLLk1q1mnW+gKSbc8QaIy2h2rW9sUT/eCwoAhp99txPI5y+15\nmfS0urzQa4pqvizyMH6JgpfRjEAhqFvbzDjkSLNFHRDweoHjo/fb3Lq4DrsZTJJEOO68GSXPKx+l\nLMJ1fixfa7GsSK/PbN6Vley53WBAazIL8Vd0g1Yro4hNstrSM8AYH6RZ3oFmeYfh3BVqOWrlHm8q\ntSTInXx1zrEeGJcNCayM3o/N0gVFWajO/053C9NkkX43Pm0XzqIYRQ79jWDUly9XN0TjGWCv69OL\niNT7sXxtj+3vRfJHEnoWOgcgLoXZwNE119qQYXN11ffLB30mpQ3eDUUsSsVpISLbPrP5uNt6e/J7\n5qL/81u0jGqpWSdqly/TroPlxKjSY+jTOcSXU8JRKnm1k+RyNkZxS7wphKVnTDMcm+bfAnSucbyu\n1rue5Xe7zu8aay83hbpeOCcc92P1++YZznOzK51GNmux9MzGo8jqNVj2t4gKy0zVazpUPTw6ZIzn\n19xt5xxg36fQ68CC539l7yU//joKLUUco2iyySvpSqzPlQlswaOoutefTaeNdVSytzsW2h5y17E2\naZqq1L8pmLVZgXtUy7V5+Z6dIpY8Futv1eWEjoGZKd+b1jLilOviezgYioplwfhd86LI5SotycND\nVejXZTs+SQXbaFaKedczwDpvzoaiEoO1/EismvdQ+05UJeTcbEuqe04iqYqcq0uHKtKvhevHG9sO\nbk7Lv+l82Ox4ZbHVcfEphTpk+XS2eXE416GYSur7Ura4oqCppgskr/V5JdAb1w2Gdqc6VkH9Oxw6\nlqTNGFj9ZrXzXLkVLj/GFLoBgUWQXJs8FAxFuiRLyI2w2Q3o8KMQ4LtmkqAFTM4bvQtloBmKtEF3\n/t41GrgW56X2SRb1/8qcZNFdaZvmeFNkAMrvDqYrMOG8+6qakv1PFS5v0udNa0fc6im7eum045gT\neU9rNRi+lpr1TWpcj8aRapee5dMx9ZcKBqPSclFyjFdGvbLyKCp1/1LUpk66u5dmkKz0vWg5HTc9\nehhzeIz+jma0nawstsgsJdBGN0d7j6JiBW4yWFRINpMx1lGbnbUAGAYZlRiKqoLM28UaFa/Xa+0O\nb9c5kSwMYppHkavlfPnBVXleZPn8WHoUGRGmXzxaXAuLWEWWHKa6UP9O7N4PCftBUcn0Kxh0WBmK\nLNN2yJP1ZIuhhmsHy8iZNZM9cHVjJzIaiiq5i7FDKVnVNTdVtZJb15CcjTHZ+A71Rg5jjnOWgR6t\nDJsudHIZHkV69Jsq1AbzINKj3ch0lnP3xtLcLorTN92sdPbGm3KE0lVzpDdIax/KfE5hIY8O8mLW\nQUJUUK5GhwaH0yZL3+nkPp/XGsW3MNtOdRNUhZlzk0eRK8XrknFWjEWGIqe+pSO6ntAETh4BKBqE\n642whQ2J7bws3WGWbbf9ZkPfyTxZ7To/pnpe4WN49Mu78y6KNieT5PzzBBsDne4vLMZ0lSBMnltw\nYSjKX1GqPMoor2xWPdeFR5H9JLj7+5Xsi7uVdTWd/CRzpc1B/n6H6eBoHDkMej1HOfmlZ+U34iNj\nytr5dNqHbKbUbjOFzx6PEtMkHite++6Gg8nHsPXtPyOXzUL4CdKQuszAoSGWo0peBwYaK7Loup2t\niK7uQuQdpjgQar5GBhSDkJCNHkGeQADZrAeZjLE6aI2px+O8VS+gMxQ5WOgHR3KQW8OInaCmp3vn\nXm/pmEtabI5C+elnhYwkc8o9UkINhK2W38AhJZ5LYMy5EYvFlWV69a3KTlexnsKuEGNjpXfIG29y\nI0bjnt5TKuYYE9tZjrIx5fcxr/W2vVboZ+eSA4psDTeN4lCq33Cek0dR9KTiYNSWHkXVMMntXCqt\nLPG08h4oQm8okqji2cqkfwxyZwQx7z0AgEOHmjE6oLpuZw4gEFDKfd/+Qewc2lacQMY5RlE11F9U\n2DrYbQc3FlsEAJC9caSEojO0um7E3jhDqmEzvT8BNx23oUF1iUbaeimRf1YdGi4v3qGk6NS8F4Qp\nnYQHbyX34mWPRfkDyEYVPZn0qxXbdL3sVeL4pFJKuzY8rCwNdvTuhPHJBwaVZS25jK+ER1H5A5GA\nv4xdmlwgNyg6fGgoAU/MWT79LcpzxeKL4euKwtvsvMtNWt2dzSO72+Fpb/R1HEzuR6apIGda/R47\nUByvyZtQdEA6bZRLbbaaYu67pMJOnkwEA1XsKlUGI8OqfhtVIhNJ6iYmQ4fqMbRXKc/6C+ZYX1w2\nZgNBodw0j6JMxotcTlKNcMKw9Kz+ol4EF5W/lCt3SI2pGRbwtoSwfXAzcmnl3iMjxUHKK0V7p0ND\nyjIUb6KyKBkke7BvdCce3/9w2Uu/FSpvLM19weSQUnapkQikoBej4VE80f+IrfzKstKvrq8zBnTW\nTwq77RbI6m5hqX2JfN5Cy1sQP3cGQsFuAEBQLjOEgHrvQbX/H4i5i5VoJh9zjgjZrKzG8yrWa7HB\nQqgLr7/SUAq1xL7wRc7dygl3t9GWnqnf1Q+OS8/yy9WUc2I2Yz0p6IVfegZB6X8tfzekqRrzk/2F\nJZv2xufCs4u8J501tvqbKrdHa32igwd1sX/zS+6r88Kr2Xs9guAYRZONNlNiMeOtbQNa3269o9HA\noRXYvbkRyWQIUsphdCyAcF2hcvv9TTj22IcRDvXaX+OAhPux7eBcrG06HoFoBM/8rAFzW9LAavtq\n7fPVYeGmEdw1eiJkufyKFpGV+CwEvXtqMfEze4oPqmX85o5p2H1oBk6aYew4+gIRPLNlDZKq8cHr\n8yOTSua3Yo/XzcEL3+pBakiGHMzgtNUoItrYhAOv74RXXcbm8crICoGnNqxHNisjEBhC+ICEli8v\nU+KvAPkG4PiVj8HrjQN/mO5YBto27VqQu5NWPYFMZgj40+Kic3PiWGzZImFa4zIA98Encnh60znI\nputwneff4EvngLX295o398uYNu0qRCPz0TawHvF4oaHZ/PQ5kOUxy3KoBU9tOB+fzN6jj19qYOf/\nLMPA3iHMO6EQhHLDkxfB40njo557EfK7MGTZNE4BdQctbzRg+bslkoRNG8+F5Mki8cYAcD4Qm9aC\nXz10O7z39UKSczhttXPDHlzQCGwwHrP0KKpiKrexS+kQRhuboDkPT+QM3I7tK7B//3RM61pQ+mTd\nwMcXDEEuJ5YOgLpWZTBd39mJljOXIvlPL2LTxsuQTEbwjmgS236RxDFdSSQSJ2J538/w4x/fhrHh\nX+JEfATHrfgNfA9cgbGBF/FKz0wAW0veb8OTF0AICafhuyXOuxACwGmrgfCxrcCv1Me1eQ++gDLA\n1PTKnNmfQ0f7JQgGuyCkZdiyOYvORHEnUKYsxgD4g8V1gSQP/v7T6WjMjkJYNi2FvGzaeB6SyRDW\nrwcSW4DfN56NVCqYr/tEhKYPLHR8Zg1J8sDqLQYTdXh09w+x9OzzLK/z10WBnYAcDWk3xXEb34Yv\nlcNYQEJwutJOjox0YsvmNRgc1AbAzrIt6Zax7ti+Anv3zEaPpwmg4k0F8jgYkfTvUNHpUYyOvm7Q\nnbUgEJqDzb8/G0ODCTQ3FxtdNzx5IWTfKPzPvYjT7unCisgjCIVmwuMJoPX65cA/KxFYt2y+FKlU\nEuvWFfLdv6cLY5kwLrrgqvyxJ/96EcjGEyY2sxW/feoHeHdLYeeobNaHLU+sxqxAYcLmuQdmIpeR\ncNpqpcz3bJ+J3Qc7MfJKDmitgzbp4Y9GkBxLuiqHmBoAXtNrerR299pr3414fJmr9KplYPcM7B7o\nRN+hbUAACCKKzU+vxegBGfUHduKMd34B0ahzG++WaGMT3h4ay3uzyrqAzJpHUf/ebgwMNiGX8xb1\nAcPHtir6p0xie9vwt+dCmH9uN1o/2YeRSwaRS8p4etM5kOXaGeSIKK97AEDyVeYNRBJhe/1W7N37\nckXL12JNivGrsWtaiTOLkT1G/XNobwJbNq9Bw5gfJBHeOuYg3t5hb/A4fuVvkUq9Db+/BcMj2/Hj\nT9+AwWmLkM0W9FbAFGi9ddYcHHh9Z/67VndvuOEmZDKH8Ms7vo3+/92CU0/xIvFOZYOTKDqwvO9n\niA1kARTvAOcPh/Ptm572OfPwIoCXdyzHvr2zMHd1ZYZCrW3rmDsfLx3MYsvmevSsVer21h/NgiQJ\nXHFODJFXHgVmanFykgAmacJSp+cDkSjGTLF0ntqwHr5XXsX8GxJYteQJ5HJJbP/5ZzH05oGKblff\npujRzvkLsXfHS2ia1qNmw7590zZNap01G8etuA6BgPVkBXk8aPLdrH67yTEfQgCbNp6LjpHCfeta\njcbFBt8w3kyF8xbM/PgH2+DV9eFaZvRi38svwQmPt4R5wsFgk831YctmD5Kvj2HZYkUvaf1vuyDr\nJdGc3dhQxEw4mhuhhUdR74oTcOkXvoq23rmWlzZ0dmPPVkVJNTUWd5Y0gT7r6o+jdabRKBSLFm/T\n65Z5sX7E5TG0n/gO0NB+XNr+DBr8JTwwiBAdziIHryFGyQdnbSjpLfCeW7+GtvsVz5YP9z6JlBuP\nBD2qgqjzjWL74AzU1Ru9dzw+H4aHE5ChDBDe+6U7MHigP2+oq2ttw4XX34VYUzNGDhq3fNQ440PX\nYu9LL2L6smMBAIFoFKmBAYyNKfdKp4NY2qs28PmYSUo5hELuOo/Tl/Zh3Q03oWWG8i59vkb4fNaz\ngo3TpuO5V19H49Ie4GVgeWIXnhg+G8FgEOHR0oNujyeQlxHzQCeT8SOTqXArLBeMjUXhd5CJc67+\nCt5643V0LViUP5ZOB5FOBxFFFqjAEKmRaO8EsBGNne47hkSEkRFl5vOjn/8yAGDO8Sch1tiEeHMr\n0mPK7iJOhiKrdefWMYoqN+z0rV2P9tlz0T57HrZv3176ghqTSoXw5oFudLS76LDrBuTvvOkLiNxd\n3mB7Zt9xBt0pkci/o1Unz0fvngfRElV0Zjy+DFd+/W6MDSm7oEUis4GcF76hDBYd34emRatw9wPF\n2/EqKO8lmSw9m/qBb/0H7vzOXWU9R7heybM2yJMkP2IxxTDT2NWDrTteReOSYv0Rk5MYRGEwbYAk\nvLtuG6JyEj/HTADAWeefjpmxNPBH48ziyEghcP8S3z48PFh5QFl/KISRkeJ2oqGjC+/90p1o6u6x\nvK6xcxrwzLMGg0BkRNFhvqFsPnC9EAKDg7rdwUosE/MH9VsZezA01IC+914A7Pq5/UUul55pOt1O\nP1fLkMN7SCbDSCbD+OCXPwsAiEatDbPpdATJpHHXJBmEAwe6EY4WBgCplL130bHrL0LXgkVon23s\no/ijC7H2wx8CAFx553eR/MYKxOSCAaguGMRLB1rR59+ETcMF+Y01NmHwjTds76enZ9FSpY7PKvbS\n0drdiTISAUBDzIs9h+JIBBQZj4RlDL0aQ8u0Zlx8zccQjfYAAK765n8YDDuVMGflKrz22GP5QXai\no2Ck0doZDyQMDyt64/QPXFP0jirh+LkBzNiyAW1dilHgI3fdhwP79+MHDzyIeLx2wwmlTS3oHnKI\nf1mKC/7P53Cof39FkyJOMlaKeU2jwLDy+YOzNmBL3Zn4w2AT5jQpk55LzjoXM/tW2F4vy/WQZUX/\nx2OLccnNP8C3vneP4Zzm6TMN30+76mosOXMt/OEIZL8fX/36NwAAPl8CPl8C59/weRzq31/YeVcl\nHl8GDG4sysOVd34XgUgEt99xZ9FvF9x4C27716/ldWel+AJBXH7bN1Hf2o6777kH/f1AtEFJ76r2\np5EVhFh2rdLU5peeTfaicIUr77wLh/bvNeyUNjYWw1lXXK/sQqryvtu/hZFDB62SKMmi09egqbsH\n7bPnoXfFCWibpRj4nPqTsaZmXPblr6Ohq9uw267Gh//9h0rokDLLcWSkHud9/OP573NOOBnRxmZ8\n70Glf3RJzzMYzvihbe6h6eFL1vWh+cVC3+Liz96KQZPhzGyAkf0BXH7bN/Gde+8rK4+A0jd64bU3\ncGzdU1h69joAhf707ONOLDs9AIjUN2Dtdf+Idpvx+FSGDUWTjcOuZ0RkUDZOWCkDreJZzbhVAxHQ\nERqEut8hWoLDbq6CpK6B1isEfefRjrbeQiMd9qYRhsOMr+WtPfl8K/c3B4czKtzGru6iMtMMbXbb\nH0cTjYge5zwoCAbNhqLyDBoer4zeFSeUdY2mHANeFwHojhBaps9Ei6mDZKAKg79WXuUkoe+Aalsp\nW9Xdcl3frTu2lXeQSJLyeZrMWZFs1oV3kO7ZGzq7QJ7y5Vdf/vpSkzwetAWHDPcIxeLGuq0OTEgI\n1zq4FPHm8mfwNZx2prL+TYtJZhn8Co0Bo8EmGAkhGnPWq9U6n2nyb5Vf80SG9f1tMqC+q6Ig6Tnn\n57Eqm2A0VsIYZB8L8HAj3uw8w2+5dMtNoFQdkuQxGCC0NGW/P+8Bm2jvAIJDhus8qrdFnc/eE9pN\nudaqbtYG6zKTPB40ql4AAFDXUrke0KdpuLOVDtC1YrXqA0pEaAsOQnvWcF09UhMh/lXEBvMFQ3kv\njEqoVMZk3S6MMTmJgGm05Q+F4C8jXw0dxR5b5jrilWW0zJhVdJ5GuWWRsBFX1OwAABFqSURBVFnN\nABRvc19NA9HcM0NNQu1/qc+lrSQoTJQpcp87TAxFRf0GFXN9C0ai+b5huej7knpdW0pHO8mBtioB\nw2+WnZ9gpDApRkTomFOoHwFPFgHPSFEHeuaMZuAl5GXEHwoXy48FmlxY4hTzU/0t6kvmP+cN6BZj\nZTf4QyHMW3VKRdce6XCMoskmrrgV0sKLqkrGMVBurZeXSDIQVmeqfWpl95VQgnIw33ExNG7kASIu\nOk6yTqkkHIwEVkTUvHrU5URRo7uk5FVm90QVs1auqFMbj5Dq+rjo3cbfK73/okucf59mYVyac05l\n95oomqqw2i92KA/t3R9TXX3T4zqAZJl11HCsTV1eOPtM+xvU1dYgPF64MhT5C15/+XKwkmOX0KJ3\nFb6EVYNus0NcnYXvVP7HSsSXmb/e+N1T25hGbuKwWA6oQw4zvIZdrlTDQKwdaFPikjnKWBXUtB3S\n68o6xfuvqByiFu8u7mJ5TM+q4mPL/0H5fzgEs64x+fey9LLxDV+m1+lqey6V2o1w1hnjmKEaEzPF\neOk8dtxu5aYukbfgtVSzujf3XOV/c7HxZDyXMZO/skH2pLL0cuP37uOV/zF740u5uNpB1C3qWAQL\nLnR1uvl9i3CTzZk2NBZ7aZkNRYirnt2qjtfabhGpXTyssuk+0fh/kqhJffOp3qIuY9IBJSY8F1yg\n/Df3wbvUOFszTnF9n5JoMqHd0wqp0B+znFiTw0CgznwVY4I9iiabcCNw0x5ADgKP/UvFyVgpjXGb\n6bx5T+GzLwzc+DJQaucnrx/S9c8Dd3zHmK+b97qbifi0uu565C3AX2bQvCXvBXpOAn79Z+Cll4Co\nyTCl5V2qzNLsFqENUvxR5bk9Jvfzm/cCX/hi+Qmf/x3gvG8AXyysLzcMMK/4b2B4ELjjG+p99gGe\n8X3WirhlP3CrsnQLH/lLZWncvBfwOsQXCjeWPqdM3M++V+FR1HqMoid8Ng36Lf3Vu3xMEK4MRebn\nvGU/HHdVLAGt+zfgb7cqX2adDnxia7Ee0LPyGsU4IDsH/0XflYqh9ku3K99v2l1xHmvGzftAP/wx\nMLLLvg3QdMD99wM7XgaiLUDr7IKM/eyZ4mtu6Qdu/dL45t0tN+5Qlu56A3lZKXrWax4HvqJbMmGq\nI7Zl07NKKQePr7DhwNlfBc78YlnbCR9xnPct4MV/BYbdB/J3zS39RiNb83zg7Rcgrb4Z+ENhSUKR\nLn3PT4DcEeING+8CXj8ErPs2cMwC4MBBuIlvVgmuDEW+EJCp8btccqliHNfp5wnxqCu3zzcZ3GLc\nvAIn3wj8Ue3T37QH2LIV+NuvgfraTejUtOyjrWX1jYpk0GlywoqrHy/aHasozY89rXgTSR7g2A/k\njQO5aAfQ/3LpPI0H009y7osdSchB4FOvOG5EZMaxjC+6Bzj/LsDrB066HvjibcrxzuW1L7NYe2Hs\nbJNHcfKN+WOWHs3auJJxhA1FhwM1qDxOhqKaK0+zkSHsroEg9TxD4+Z1OQOv3TNawUwCkdo4/7n8\na8cLqwFopcYbyeOs6D1eo6eDXDsjSU3RzYCWNDzaUWpg7/acCaakRxHgrCfc1qPDAFeGIjPe6mJ6\nkGmpBupKeJYQuZMTIuN7qbEB1kmH2+p1OVDaaKjpALOHzJEiY8HiWcCi2fWAaUlAOfk3l4MkAZKz\n3pzsrZnLpUi2JEkXCq3Gz2Iq+/y+naUMAKXatsOIfJl5/ar8VBaTpOb5QY3f5yQMkI+IumXWL/o8\n+0Lj8gw1N9JNZN/IoY+Xfy59e6ozsE36cl+bOnBEyKmZUHnBnR0nPPX62ixL46E3SqWp3z3X6t0c\njhPmhyFTeHrsyGTBAhc7AqnMnKkswWpsbEQkUtzhmjdPcQ+2+m0y0BTMokWLSpxZoKenp2b37+1V\n4mAkEkbFqO0SUk6+SmH1HtvbS2+TLFe4flZDe8auLmUg3NmpuBP71KCXxxxTeRBzAGhpcTbUecwD\n8gpoaytzm1YATU3Wbs9uylxPc7OyTHHGDIe10Ta4rbtz5xaWYGh1IhZTllrNmVNwx65FWdqh1QFN\nXqyoZacnECgMsmfNsl87r6ccXVgK7VkWLnS3Q5eZUKh0J0erY27zAhjftxWa7tZ0uR6tbmt1XY+W\nbjxuHVNNw04n6uVCL6+VoL1v7X36ywzk26ruYFeqLXDSTVpZ6bF6X+Fw+TvpWNXd8UT/rqzkAijt\nvVhfX5/XjUFdUG+tfdCXjVZ3rcrQTDSqLBEqJTNamen1AqDIa0eHsjRn2rTiDQXc6g7Avk3QmD17\ntuu03KC1Gdp9tbJwIxd29c1O72jvTCtHQHmnevS6Tn9erdHyMn++w1LeKqmrO7yXhzi1lZo8u9Vj\n5dDd7eyd5LZNKofp0603X9HkvRo0fVYqLX190bcn2vPWsu9QDlpdH8/6ZqbWesxOzzuVqVn36Glo\ncHYk0PLvlIZdO2eH1nZodU8jEokcNuPhIwmadMusjuXLl4uNG4sj7x8tjI6OwufzlTVAHB4ezhsX\nzI1CLpfD2NiYq0HORFHOM46NjcHr9cJbaptElwghMDIyYjkYqKTsndDK3uPxQJIkpFIpV4OQVEoJ\n3ldJAz82NgZZlvPPMDw8bLjn6Ogo/H5/2Uug9GQyGaRSKUiSVNTJrybvGslkEh6Pp+x3nk6nkcvl\nDJ2GSuXHXG5ucCs/5nekL7ORkREEAgEMDw9DCDHunY3h4WGEQtYznbV4l3rS6TSEEEin07b3NJPN\nZpFKpQwD2Wqopo5r+bcrj1QqBSIqaeg1l2s2m0U6nS6qS3o0ubCqt3ayKoTA6OhoSd1vpxO1fAGw\nlVe3ZDIZZDIZ+Hy+itsjN3UylUrh0KFDqK+vRyaTQSAQwNjYGHK5HHw+n6UeGB0dhSzL+eeqtK10\nekfjwfDwMIjI8p6l3lEymYQkSRBCYGBgAA0NDfn6mMvlkEwmDXVOk31JkvLl6oSbsshkMnjzzTfR\n1NSEZDKZfwda+Vu9b7PudKJUO1JOWm6xqktu5cKqvmnvyUqnCCHQ39+PRCKRf0ZzG6j1QYDK5dot\n4yH/Wvutl4vDEaf6Zu6DVNK30KPJhHbPUmmVarfcot0vnU4X9SllWUYymazquTTcjlm09s3r9Ra1\nuxOti63yNVHyWutxi1Of2ak/ZtX/BuCowzScxmVanirR1VZ1rdbldSRDRJuEEMtdncuGIoZhGIZh\nGIZhGIZhmKlLOYYiXnrGMAzDMAzDMAzDMAzDAGBDEcMwDMMwDMMwDMMwDKPChiKGYRiGYRiGYRiG\nYRgGABuKGIZhGIZhGIZhGIZhGBU2FDEMwzAMwzAMwzAMwzAAqjQUEdHFRLSNiHJEtFx3/Awi2kRE\nz6r/V1efVYZhGIZhGIZhGIZhGGY88VZ5/VYAFwL4run4AQDnCSF2E9ExAH4LoKPKezEMwzAMwzAM\nwzAMwzDjSFWGIiHE8wBARObjm3VftwEIEpFfCJGs5n4MwzAMwzAMwzAMwzDM+DERMYouAvC0nZGI\niD5ERBuJaGN/f/8EZIdhGIZhGIZhGIZhGIaxoqRHERE9BqDV4qebhRAPl7h2AYDbAJxpd44Q4m4A\ndwPA8uXLRan8MAzDMAzDMAzDMAzDMONDSUOREOL0ShImok4ADwF4nxBih5trNm3adICIdlZyv8OQ\nRiixmhgGYHlgjLA8MHpYHhg9LA+MHpYHRoNlgdHD8sDocSsP3W4TrDaYtSVEVAfgVwA+LYT4i9vr\nhBBN45GfyYCINgohlpc+kzkaYHlg9LA8MHpYHhg9LA+MHpYHRoNlgdHD8sDoGQ95qCpGERFdQES7\nABwP4FdE9Fv1p2sBzALwOSLaov41V5lXhmEYhmEYhmEYhmEYZhypdtezh6AsLzMfvxXArdWkzTAM\nwzAMwzAMwzAMw0wsE7Hr2dHK3ZOdAeawguWB0cPywOhheWD0sDwwelgeGA2WBUYPywOjp+byQELw\nRmMMwzAMwzAMwzAMwzAMexQxDMMwDMMwDMMwDMMwKmwoYhiGYRiGYRiGYRiGYQCwoajmENEaInqB\niLYT0acnOz/M+ENEXUT0RyJ6joi2EdHH1eP/TERv6Hb+W6u75jOqjLxARGdNXu6Z8YCIXiWiZ9X3\nvlE9liCiR4noJfV/ve58locpChHN0emALUQ0QESfYP1w9EBE3yei/US0VXesbH1ARH2qXtlORN8k\nIproZ2Gqx0YevkpEfyeivxHRQ0RUpx7vIaJRnZ64S3cNy8MUwEYeym4fWB6mBjby8BOdLLxKRFvU\n46wfpjAO48sJ6z+woaiGEJEHwLcBnA1gPoBLiWj+5OaKmQAyAG4QQswHsBLAR3Xv/U4hxBL179cA\noP52CYAFANYA+I4qO8zU4lT1vS9Xv38awO+FEL0Afq9+Z3mY4gghXtB0AIA+ACMo7BbK+uHo4F4o\n71JPJfrg3wF8EECv+mdOkzkyuBfF7+5RAMcIIRYBeBHAZ3S/7dDpiY/ojrM8TA3uhfW7K7d9YHmY\nGtwL07sTQrxb14/4LwC/0P3M+mHqYje+nLD+AxuKassKANuFEC8LIVIA/hPA+knOEzPOCCH2CCGe\nVj8PAngeQIfDJesB/KcQIimEeAXAdiiyw0xt1gP4ofr5hwDO1x1neTg6OA1Kp26nwzksD1MMIcSf\nALxlOlyWPiCiNgAxIcRfhbILyX26a5gjCCt5EEL8TgiRUb/+FUCnUxosD1MHG/1gB+uHKY6TPKhe\nIO8C8KBTGiwPUwOH8eWE9R/YUFRbOgC8rvu+C84GA2aKQUQ9AJYCeFI9dJ3qSv59nWsgy8nURwB4\njIg2EdGH1GMtQog96ue9AFrUzywPRw+XwNjBY/1w9FKuPuhQP5uPM1OPfwDwG9336eqykv9HRCep\nx1gepj7ltA8sD0cHJwHYJ4R4SXeM9cNRgGl8OWH9BzYUMUyNIKIIFJfQTwghBqC4+c0AsATAHgBf\nm8TsMRPLKtVF+GworqIn639ULfpiUnLGTApE5AOwDsDP1EOsHxgArA+YAkR0M5TlBverh/YAmKa2\nJ9cDeICIYpOVP2bC4PaBseJSGCebWD8cBViML/OMd/+BDUW15Q0AXbrvneoxZopDRDKUSny/EOIX\nACCE2CeEyAohcgC+h8LyEZaTKY4Q4g31/34o8WhWANinun9qbsH71dNZHo4OzgbwtBBiH8D6gSlb\nH7wB43IklospBhG9H8C5AN6rdv6hLiF4U/28CcAOALPB8jClqaB9YHmY4hCRF8CFAH6iHWP9MPWx\nGl9iAvsPbCiqLU8B6CWi6ers8SUAHpnkPDHjjLpm+B4Azwsh7tAdb9OddgEAbQeDRwBcQkR+IpoO\nJajYhonKLzO+EFGYiKLaZwBnQnn3jwC4Qj3tCgAPq59ZHo4ODDOBrB+OesrSB6qb+QARrVTbnPfp\nrmGOcIhoDYBPAVgnhBjRHW/SgpES0Qwo8vAyy8PUptz2geXhqOB0AH8XQuSXELF+mNrYjS8xgf0H\nbw2eg1ERQmSI6FoAvwXgAfB9IcS2Sc4WM/6cCOByAM+SumUlgJug7Hq3BIpL4KsAPgwAQohtRPRT\nAM9BcTH/qBAiO+G5ZsaLFgAPqTtPegE8IIT4HyJ6CsBPiegqADuhBCRkeTgKUA2GZ0DVASq3s344\nOiCiBwGcAqCRiHYB+CcAX0H5+uAaKDviBKHEsNHHsWGOEGzk4TMA/AAeVduOv6o7GJ0M4PNElAaQ\nA/ARIYQW6JblYQpgIw+nVNA+sDxMAazkQQhxD4pjHAKsH6Y6duPLCes/kOrdyjAMwzAMwzAMwzAM\nwxzl8NIzhmEYhmEYhmEYhmEYBgAbihiGYRiGYRiGYRiGYRgVNhQxDMMwDMMwDMMwDMMwANhQxDAM\nwzAMwzAMwzAMw6iwoYhhGIZhGIZhGIZhGIYBwIYihmEYhmEYhmEYhmEYRoUNRQzDMAzDMAzDMAzD\nMAwA4P8DEp6VkeEoZmgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2763c3f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "# Importo el paquete para graficar\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "N=20 #Cantidad de categorías a filtrar para calcular las probabilidades condicionales\n",
    "\n",
    "p_priori=list() #Inicializo una lista para guardar los vectores de probabilidad de cada palabra, para cada categoría\n",
    "p_model=list() #Inicializo una lista para guardar la probabilidad de ocurrencia de cada categoría\n",
    "\n",
    "num_arts=df.values.shape[0] #Me quedo con la cantidad de artículos\n",
    "num_cols=df.values.shape[1]-1 #Me quedo con la cantidad de palabras en el vocabulario\n",
    "print(\"Cantidad de palabras en el vocabulario:\")\n",
    "print(num_cols)\n",
    "\n",
    "plt.figure(figsize=(20, 5)) #Inicializo el gráfico sobre el que voy a dibujar todas las distribuciones\n",
    "for i in range(20):\n",
    "    p_aux=sum(df.loc[df['target_'] == i].drop('target_',axis=1).values)+alpha #Sumo las ocurrencias para la categoría i y le sumo a todos los valores alpha, para no tener probabilidad cero\n",
    "    print(\"El vector de ocurrencias para la categoría {} cuyo largo es {} es:\".format(i,len(p_aux)))\n",
    "    print(p_aux)\n",
    "    print(\"\\nLog-probabilidad de ocurrencia de cada palabra en el vocabulario, para la categoría:{}\".format(twenty_train[\"target_names\"][i]))\n",
    "    print(p_aux/sum(p_aux))\n",
    "    p_priori.append(np.log(p_aux/sum(p_aux)))\n",
    "    print(\"\\nLog-probabilidad de ocurrencia de la categoría: {}\".format(twenty_train[\"target_names\"][i]))\n",
    "    print(df.loc[df['target_'] == i].shape[0]/num_arts)\n",
    "    p_model.append(np.log(df.loc[df['target_'] == i].shape[0]/num_arts))\n",
    "     # Tamaño del gráfico\n",
    "    plt.plot(p_priori[-1]) # Grafico\n",
    "    #plt.title(title)\n",
    "plt.show()"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "Vamos a calcular los Log-likelihood para cada categoría. Utilizaremos el artículo 56\n",
      "El artículo pertenece a la categoría 7, la cual es rec.autos\n",
      "El log-likelihood para la categoría 0 del artículo 56 es:-992.3237612544019\n",
      "El log-likelihood para la categoría 1 del artículo 56 es:-955.6678567235585\n",
      "El log-likelihood para la categoría 2 del artículo 56 es:-956.0807534413605\n",
      "El log-likelihood para la categoría 3 del artículo 56 es:-943.9567849072215\n",
      "El log-likelihood para la categoría 4 del artículo 56 es:-957.0693638790765\n",
      "El log-likelihood para la categoría 5 del artículo 56 es:-985.091487052275\n",
      "El log-likelihood para la categoría 6 del artículo 56 es:-961.1427412816205\n",
      "El log-likelihood para la categoría 7 del artículo 56 es:-883.2414924226738\n",
      "El log-likelihood para la categoría 8 del artículo 56 es:-937.6581621605908\n",
      "El log-likelihood para la categoría 9 del artículo 56 es:-1002.3202026301077\n",
      "El log-likelihood para la categoría 10 del artículo 56 es:-993.3567679610701\n",
      "El log-likelihood para la categoría 11 del artículo 56 es:-968.0981257268968\n",
      "El log-likelihood para la categoría 12 del artículo 56 es:-929.6097107835376\n",
      "El log-likelihood para la categoría 13 del artículo 56 es:-968.9175831297672\n",
      "El log-likelihood para la categoría 14 del artículo 56 es:-962.1828801859996\n",
      "El log-likelihood para la categoría 15 del artículo 56 es:-1011.4827284087149\n",
      "El log-likelihood para la categoría 16 del artículo 56 es:-950.8641689425539\n",
      "El log-likelihood para la categoría 17 del artículo 56 es:-1006.0027262382807\n",
      "El log-likelihood para la categoría 18 del artículo 56 es:-1000.6301068923655\n",
      "El log-likelihood para la categoría 19 del artículo 56 es:-976.9528375809344\n",
      "El Log-likelihood mayor pertenece a la categoría: 7\n"
     ]
    }
   ],
   "source": [
    "#Agarramos un artículo en particular y calculamos el Likelihood de cada una de las categorías\n",
    "n_art=56\n",
    "print(\"Vamos a calcular los Log-likelihood para cada categoría. Utilizaremos el artículo {}\".format(n_art))\n",
    "print(\"El artículo pertenece a la categoría {}, la cual es {}\".format(twenty_train[\"target\"][n_art], twenty_train[\"target_names\"][twenty_train[\"target\"][n_art] ] ))\n",
    "maxLogL=-float('inf')\n",
    "for i in range(20):\n",
    "    logL=np.dot((X_train_data.toarray()[n_art]),p_priori[i])+p_model[i]\n",
    "    print(\"El log-likelihood para la categoría {} del artículo {} es:{}\".format(i,n_art,logL))\n",
    "    if logL>maxLogL:\n",
    "        maxIdx=i\n",
    "        maxLogL=logL\n",
    "print(\"El Log-likelihood mayor pertenece a la categoría: {}\".format(maxIdx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La predicción para el artículo 56 es:\n",
      "[7]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "clf = MultinomialNB()\n",
    "clf.fit(X_train_data.toarray(), twenty_train[\"target\"])\n",
    "print(\"La predicción para el artículo {} es:\".format(n_art))\n",
    "print(clf.predict(X_train_data.toarray()[n_art:n_art+1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El porcentaje de artículos clasificados correctamente es: 84.51476047374933%\n"
     ]
    }
   ],
   "source": [
    "porc=sum(np.array(clf.predict(X_train_data.toarray()))==np.array(twenty_train[\"target\"]))/11314*100\n",
    "print(\"El porcentaje de artículos clasificados correctamente es: {}%\".format(porc))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicio de multinomialNB\n",
    "\n",
    "En el caso anterior, para medir la cantidad de artículos clasiicados correctamente se utilizó el mismo subconjunto del dataset que se utilizó para entrenar.\n",
    "\n",
    "Esta medida no es una medida del todo útil, ya que lo que interesa de un clasificador es su capacidad de clasificación de datos que no fueron utilizados para entrenar. Es por eso que se pide, para el clasificador entrenado con el subconjunto de training, cual es el porcentaje de artículos del subconjunto de testing clasificados correctamente. Comparar con el porcentaje anterior y explicar las diferencias.\n",
    "\n",
    "Para este ejercicio se puede utilizar el paquete multinomialNB que se encuentra dentro de naive-bayes en el paquete sklearn.\n",
    "\n",
    "** Importante: para aplicar count vectorizer al dataset de testing sin redefinir el vocabulario, se debe usar el método transform, ya que fit y fit_transform recalculan el vocabulario. **\n",
    "\n",
    "Se puede encontrar mas información del dataset en:\n",
    "\n",
    "http://scikit-learn.org/stable/datasets/twenty_newsgroups.html#newsgroups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solución"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "jupyter": {
     "outputs_hidden": true
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   "outputs": [],
   "source": []
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