Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sentence = \"At eight o'clock on Thursday morning Arthur didn't feel very good.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tokens = nltk.word_tokenize(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['At',\n",
       " 'eight',\n",
       " \"o'clock\",\n",
       " 'on',\n",
       " 'Thursday',\n",
       " 'morning',\n",
       " 'Arthur',\n",
       " 'did',\n",
       " \"n't\",\n",
       " 'feel',\n",
       " 'very',\n",
       " 'good',\n",
       " '.']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tagged = nltk.pos_tag(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('At', 'IN'),\n",
       " ('eight', 'CD'),\n",
       " (\"o'clock\", 'NN'),\n",
       " ('on', 'IN'),\n",
       " ('Thursday', 'NNP'),\n",
       " ('morning', 'NN'),\n",
       " ('Arthur', 'NNP'),\n",
       " ('did', 'VBD'),\n",
       " (\"n't\", 'RB'),\n",
       " ('feel', 'VB'),\n",
       " ('very', 'RB'),\n",
       " ('good', 'JJ'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import email"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "msg = email.message_from_file(open('corpus/spam_2/00224.1b3430b101a8a8b22493c4948fcbe9cc', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'multipart/related'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msg.get_content_type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<email.message.Message instance at 0x106c1e998>]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msg.get_payload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "msg.get('Content-Transfer-Encoding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "None\n",
      "base64\n"
     ]
    }
   ],
   "source": [
    "for part in msg.walk():\n",
    "    print part.get('Content-Transfer-Encoding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "for part in msg.walk():\n",
    "    if part.get_content_type().lower() == 'text/plain':\n",
    "        print part.get('Content-Transfer-Encoding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from HTMLParser import HTMLParser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a subclass and override the handler methods\n",
    "class MyHTMLParser(HTMLParser):\n",
    "    \n",
    "    _contents = []\n",
    "    \n",
    "    def handle_data(self, data):\n",
    "        data = data.strip()\n",
    "        data = data.replace('\\r', '')\n",
    "        data = data.replace('\\n', '')\n",
    "        if data:\n",
    "            self._contents.append(data)\n",
    "            \n",
    "    def get_body(self):\n",
    "        return ' '.join(self._contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parser = MyHTMLParser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "parser.feed(msg.get_payload())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Congratulations! You Get a Free Handheld Organizer! Dear Friend, I have your Personal Digital Organizer. It's FREE, but I need to know where to send it. Click                Here and complete the form. Organize your life and keep track of            appointments, names and numbers with this modern digital organizer.            Store up to 100 important text notes and 100 names/numbers.            Features easy to use, touch-screen technology, 10 digit calculator,            currency and metric converters, alarm clock and password            protection. Plus you can try all of our money-saving benefits            FREE for 30 days. Act            Now! You also get a full subscription to HOME Magazine at no additional cost! This offer is risk-free , registration only takes a minute and is completely secure.It's that easy. Your satisfaction is guaranteed because our credibility is on the line. P.S.:This offer is only valid for the next 48 hours, so Act Now! Copyright 2002, American Homeowners Association (AHA). All rights reserved. You are receiving this mailing because you are amember of SendGreatOffers.com and subscribed as:JM@NETNOTEINC.COMTo unsubscribe Click Here (http://admanmail.com/subscription.asp?em=JM@NETNOTEINC.COM =SGO)or reply to this email with REMOVE in the subject line - you mustalso include the body of this message to be unsubscribed. Any correspondence aboutthe products/services should be directed tothe company in the ad.%EM%JM@NETNOTEINC.COM%/EM%\""
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parser.get_body()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os import path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__all__',\n",
       " '__builtins__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '_joinrealpath',\n",
       " '_unicode',\n",
       " '_uvarprog',\n",
       " '_varprog',\n",
       " 'abspath',\n",
       " 'altsep',\n",
       " 'basename',\n",
       " 'commonprefix',\n",
       " 'curdir',\n",
       " 'defpath',\n",
       " 'devnull',\n",
       " 'dirname',\n",
       " 'exists',\n",
       " 'expanduser',\n",
       " 'expandvars',\n",
       " 'extsep',\n",
       " 'genericpath',\n",
       " 'getatime',\n",
       " 'getctime',\n",
       " 'getmtime',\n",
       " 'getsize',\n",
       " 'isabs',\n",
       " 'isdir',\n",
       " 'isfile',\n",
       " 'islink',\n",
       " 'ismount',\n",
       " 'join',\n",
       " 'lexists',\n",
       " 'normcase',\n",
       " 'normpath',\n",
       " 'os',\n",
       " 'pardir',\n",
       " 'pathsep',\n",
       " 'realpath',\n",
       " 'relpath',\n",
       " 'samefile',\n",
       " 'sameopenfile',\n",
       " 'samestat',\n",
       " 'sep',\n",
       " 'split',\n",
       " 'splitdrive',\n",
       " 'splitext',\n",
       " 'stat',\n",
       " 'supports_unicode_filenames',\n",
       " 'sys',\n",
       " 'walk',\n",
       " 'warnings']"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('/Users/apple/Documents/github_repos/spam_email_classifier/corpus', ['easy_ham'], ['20021010_easy_ham.tar.bz2', '20021010_hard_ham.tar.bz2', '20021010_spam.tar.bz2', '20030228_easy_ham.tar.bz2', '20030228_easy_ham_2.tar.bz2', '20030228_hard_ham.tar.bz2', '20030228_spam.tar.bz2', '20030228_spam_2.tar.bz2'])\n",
      "('/Users/apple/Documents/github_repos/spam_email_classifier/corpus/easy_ham', [], ['0001.ea7e79d3153e7469e7a9c3e0af6a357e', '0002.b3120c4bcbf3101e661161ee7efcb8bf', '0003.acfc5ad94bbd27118a0d8685d18c89dd', '0004.e8d5727378ddde5c3be181df593f1712', '0005.8c3b9e9c0f3f183ddaf7592a11b99957', '0006.ee8b0dba12856155222be180ba122058', '0007.c75188382f64b090022fa3b095b020b0', '0008.20bc0b4ba2d99aae1c7098069f611a9b', '0009.435ae292d75abb1ca492dcc2d5cf1570', '0010.4996141de3f21e858c22f88231a9f463', '0011.07b11073b53634cff892a7988289a72e', '0012.d354b2d2f24d1036caf1374dd94f4c94', '0013.ff597adee000d073ae72200b0af00cd1', '0014.532e0a17d0674ba7a9baa7b0afe5fb52', '0015.a9ff8d7550759f6ab62cc200bdf156e7', '0016.d82758030e304d41fb3f4ebbb7d9dd91', '0017.d81093a2182fc9135df6d9158a8ebfd6', '0018.ba70ecbeea6f427b951067f34e23bae6', '0019.a8a1b2767e83b3be653e4af0148e1897', '0020.ef397cef16f8041242e3b6560e168053', '0021.f8e73bdba7277d967af4d561f0402129', '0022.7241da4491c49b50c0470a3638ee35c4', '0023.b4a61a2990263e8825246e41a8d78399', '0024.771af861a302951df7630ec4ff1965a2', '0025.64d0382de54b9e4c5b4200173133b8d7', '0026.6baf1aea162ccb9a6e9f142c0715ceb4', '0027.11da06d9130a188bf0ffb2060881dbe9', '0028.54cf7aa229456fb33194b3a12a713e3e', '0029.7119e865bff73aca46681d96a451cb60', '0030.77828e31de08ebb58b583688b87524cc', '0031.af5b387661e1f58d3787ac41139106e5', '0032.f07a3f2152f2bedfc9492459c171012c', '0033.e3fd617544226dc06abf36c95a9a2d11', '0034.4be53f8dac3bd651ace83a38b6313c45', '0035.50b257408356cfcfce8cf2afe9fd7959', '0036.7ca216a105267375948e13b460d1fb8f', '0037.0c57a93c0241775d406efecf43ba19cf', '0038.f2ffa1197c969d067be4d01c26fa9b5e', '0039.5bf1ec6602c4657bac8d566604572ad5', '0040.930593a7e616570a2b63f2d774847316', '0041.af4e0891f17484c10c032f713bce43de', '0042.7ef6c72f88de15d72b1c0d9c164ccb78', '0043.2ea45c42b1ea15bc214723e142b81127', '0044.f1db2c76854ee58bc73d0c85ca6a86d2', '0045.910fa2e79f96c646f1b6c987c65cbe3f', '0046.05e8a863eaf20f90374d0a15427d9a16', '0047.5c3e049737a2813d4ac6f13f02362fb1', '0048.6dbad96d78f9dd6100a4ad2a8b8086b6', '0049.bda43370915afa1f557f7edab6913e04', '0050.fd9291b33ecd99af26da03a7d4152dc2', '0051.9281d3f8a3faf47d09a7fafdf2caf26e', '0052.40794e238104d6b01f36b0f4d5145175', '0053.71bcb179e702d98e88fd0bc081ba0f52', '0054.a6fa82d1b26c7772829e54ec41a1fbd3', '0055.b5509ace7ed62c900779c0c51cca92a3', '0056.6ac9eb23e2ef6c7e7eabc3fb0ac3038a', '0057.be5e34dcebd922928045634015e3ed78', '0058.a7c28223050c35c3a22ac4e2b5969111', '0059.b3b1b1d95976b70d9bbd6c39c6b8d5d2', '0060.877bf70a297621b9dc3bab0095f95027', '0061.20319a885b8cf6c88c8098eafd731396', '0062.b675bdb7b9e2321dfe97e48037fe7782', '0063.7823bd01e05ee0a1dda2f73f880838eb', '0064.7160290efcb7320ac8852369a695bcaf', '0065.b72ddcc517cc317f3fc1e79c3feeca15', '0066.dcf72ba90be7570f2f7edab45b23ae36', '0067.d77af11e9b2a74048c073462efecca12', '0068.f1c604a78739e4f966253d762c972dde', '0069.7173de1d2da14306c5a20e8abda7a6e2', '0070.4f269f2d783b479971f31006fe17ce62', '0071.75403094cab986a246c1e7ce3460e827', '0072.d1c080832388ae81835ca069e2efffa3', '0073.cd29b8202a4b05db3c6f0d71b72a3260', '0074.78000652dcb19856e85ff9637f0e52dd', '0075.faa4a28fdd9a82edd6d35ebb6bae3085', '0076.f565b68778786f9b9736f779489331f0', '0077.dc5578862c0e716ee82e78b0dffbc8d2', '0078.8481bc92aa3ab9d23ca30c0eaecfc5e4', '0079.083beadce354774290a4a5bc4175366e', '0080.7c3a836baaa732cd915546442c0fef1a', '0081.9d26c2d149545a1c5d23e2cd4dca1b5f', '0082.7f7858a1a7360410ed120899504c3a25', '0083.e506d3273b3dde9ae2d340cb0197a2a0', '0084.e84ff95488fab22c26801bcd5bae337e', '0085.1a80e2b1720bd5cbeb900554a5477d0b', '0086.b96641b3e0a2cf8c238976336d45a86c', '0087.dd5f3aa35f50008bf7dfc5059ac8a239', '0088.43fa07c598e8b3d272a1a00705398e48', '0089.8b3fea141f5b2f2e5b66a9f12148b7d0', '0090.314ec4268af7a3a1974d5eab84ea65af', '0091.3bdd7b578973ee005733480a8b6c9b54', '0092.a1560d2416687b2db8204c2fa69163f2', '0093.0c71febfdf6f3acbc4d0c76b777a8530', '0094.b7bf14fae9c31d0516cfe00dd9ab068d', '0095.b51416f612ac5737e0f4a5529ce453d1', '0096.0446f3ed63b550a8622c8671d8ae9a9c', '0097.39badf2fea6bcebc640bea05ced59b59', '0098.5053f669dda8f920e5300ed327cdd986', '0099.9f54be08406e67fd8944f2f1d0fbdd90', '0100.1728f45047ff2a1601d4e3ee91f26a00', '0101.48557f7f38d947eab77aefc03d0520a3', '0102.ba8679813c4b5f424fb225f09b2fb1f2', '0103.3fc5444196f4726ee138fbabc5086ea1', '0104.cbb1681451b2525d7aeec9eeb285306d', '0105.be508e1b909bae328d4a13fe898f60fb', '0106.808c5e5ed0b801f667a34ead8221972e', '0107.2447a90f32ab7642ff8309d41c242db2', '0108.e6e9cb097a3b5e37d94a7ff29bc4412a', '0109.4ccc46c546b93015aafdfc40495f187d', '0110.0bb9a36c3037be09867c0251e0fd6a3a', '0111.e10679164e671fd9211c0303af7ee9f0', '0112.b6c2ea75f9f7efcd3d0e4c43a751479c', '0113.55a6bf6a4534d447af2060b174c0d70a', '0114.72c7d2d85d9d99ce3ce268d09147bdea', '0115.41825f367f7f1efcc31e12376d82a994', '0116.4cfce953cce07ef03a0cd55ca18053ba', '0117.fbc79c02a2c13d6c9b36f9ba38a09170', '0118.4ebb343d73a83dc935197850e507aad6', 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'2423.fa4019d241ed7886c8d138565751930e', '2424.ef94e4767df08bd382aea26ff29774bc', '2425.9cef8b6162d888cdb9d7af1f21e788f2', '2426.ba29d4b97bc1e42b7b12f678cf41d981', '2427.2e44be16705ea1d39a01befda3915eb7', '2428.d0ebfd677cd78292f198d292d4cb61c4', '2429.ba3172b8bb1849e4ef208584b4fa51dd', '2430.07f067ef6e156d06699718e8927d58d2', '2431.73b1c36d5c643ad44e91562f4063c197', '2432.16b94633e0023cece6e08a08effe7714', '2433.a058c43e5eb916e0c788dd9be2678fbb', '2434.477cbb634d8dd2ee4e697f479fa77b24', '2435.1b197d20c88b19fb1ea4c3c51bdf9980', '2436.6d5122b7964e63cbf7b656d21bb77c50', '2437.35d0ef84e97135b9bbc945193344c307', '2438.a99d09c02269744f20cb1ee2cbbe2728', '2439.c8c3aa25791710ac30aaaa49cb276f12', '2440.041d0de1683ab08bbd1c626e7d0d3beb', '2441.8b6ac53af9cab970afa13ff99ecaf8da', '2442.ba0f88eeb7387e4481fabc6cb31d1b30', '2443.2378d372705c7f64f101b68e6d7a0b5a', '2444.825099d851d45e7c2b34e197d6812323', '2445.631e9fa95e78d7f56fce09a4e6da56fd', '2446.34e896282d23f593971bf47f5bb1ab0b', '2447.e93a9792f68b12cbc322eba2353b3c8e', '2448.b0f260e47f1853702da3918968a3588e', '2449.d06b13a245cc7eb0f5bd7ae0b4f3c2f9', '2450.4eba9e73d4d29b754a60647748c9a2d9', '2451.1ccaf9bc09a2b8e339f02d21ac62f437', '2452.462af6aad417a792d50847cddb590f90', '2453.3c30230578edacb961366df72c6fd2ac', '2454.927f352b226246d7bca7badfc2d7aa50', '2455.a8172ce70c990c52ba73c2f2cb17b5f2', '2456.1b028dbafc21c1169e554a1ca612743f', '2457.5fba5a23aa428e3201526f7c978c88c6', '2458.dc9072428527f92ea8b938be335b3621', '2459.f9df87daac76e5eab06b4afa37b3921d', '2460.8a782dd603bfe327c2f7adadf590a094', '2461.20f3b1b3e7e2bd1bd6a016f9f09374d3', '2462.5ae37f6f0ec68fd1c2dbcec70b6413c9', '2463.4120f7bdd5fe52177c1d78f371b69c76', '2464.0c973fb4c6105b3cdc0ce5c786ed6b0c', '2465.2586b48935009c0fcea80ef5d89f4e20', '2466.4c6ca64a8fb037e9515adc98486a8146', '2467.5c3cff250c85a50dd75b0b9e1ffaac3f', '2468.897a44a9936163da20ab13ce60d5e153', '2469.1135462b65bc58a05522e86383b7943c', '2470.e2f55eaf9c7aed2b85c4b4ca65781d3d', '2471.9bc6c0ad8e1151925d35ad52c6667d62', '2472.2f502e69b5201834c4e02ac0ea9c6be3', '2473.2d70d3fac740804c6a280009e0418f13', '2474.e7fce1af310d8b7a2c87dfaeec71c172', '2475.8572d77868481d0fe409eedd8fa6d443', '2476.26c155346386cbeb36ea1c539b294be3', '2477.9b5f251639293269f0980399c10a70d1', '2478.d8deadfe2c1d046587d57a1b03fff346', '2479.a918c490f758e2421f35b66077f2699a', '2480.d652d7fb8b29ab26269aa5668f3e0035', '2481.03fe35f728922f8cf6d9b69771da462e', '2482.67146ca47d2cb5e4657d54c66bf68a69', '2483.9eb471e8645403dda2bd70c08b463faf', '2484.634d1d98cd2c7d4ae85bc5413c399c44', '2485.0d0140da769ebe4de6c1851fb7017352', '2486.5801c079150004024c1e2a0a21fa4521', '2487.68761726d5f3f00dca1dded48e089e68', '2488.8e2706d02807ec4fe59ebf7aaf77a1e7', '2489.b76d126a6aef85219a4ac8d6df5f4963', '2490.f03277d54faea3974942b3213f38268f', '2491.7b6a8fff32b29e04a8638dbb67cd85b9', '2492.a60a61ef3280f7b68df65d4d7cf3afd0', '2493.67725941996521d187945dfaa2f19ed4', '2494.ad5de9e5dc49e51155a6d444e772b671', '2495.8adeb962ec89dedb809a2a314b8a9fef', '2496.b8dd0beb971f9a82a8cdebf562f8dc1f', '2497.0cbb23e8858a0e33bcf2cf6fe158deb3', '2498.462ad5291e185d0288f4a70313e3cfe0', '2499.6fc4e078d141a24f3a81bc4936722fbb', '2500.231666f4f5641227a17a09ba77cd5c18', '2501.36311f6ceac129fa31540555ffe3cc97', '2502.1b59b45ca1fe56edf35ae8a398636806', '2503.43a0b463302d0c595b9829dda18245fc', '2504.9fc1898ab2a75d2d9e9810318bb4e90a', '2505.550c8b2240659bb8bf54b17edea4a96b', '2506.7acaf086619d33409b90223fe066589a', '2507.e7c3831cab3bc377bd62de9912c076f8', '2508.c2a4352b3d889e87d3493e5aff6b571c', '2509.96e57ee2a1f9741a7d09e431f59bb049', '2510.4de2705ed127d2de3f3b5cd1b8043ba6', '2511.1982adbc74d9a84a5ff8db7e7c722028', '2512.d68f3adc32f665ecaf1d096a83d78aa5', '2513.753d70e90eb4759d73d67ea6b9b12617', '2514.f2c8376ccb20e0255fd97cab426f0c3a', '2515.177e746c09892ea4c2631adcc62d73a7', '2516.030f0cecab78d582f6f5be58422acf79', '2517.2dbd54c3580b5fb8e12dea4d876348f3', '2518.9c2a50b66e91110fae3c27851e9ec1dd', '2519.4b5c7eea59a27b30bb33503d1be85950', '2520.554ec4788bbcc1ed13c453719c8d698e', '2521.8e5616c36de4145286f63b8ddd50f429', '2522.dc159c06c191406ece1b0fc98a352bbe', '2523.7b5518a502b8a309e42770179d71626c', '2524.c06019b7e085875e69ff55f43defeb83', '2525.9559a3f0d4d17d33379acdf5bf356435', '2526.7ff8826a8e943eabf68cdcbb4c29c8bc', '2527.fad44667449822628022a95a7cf83ac0', '2528.8b6dd74e9b8d04f5f1338bcec51b0b11', '2529.c3856b9d7d098309eb71e3ed1993aa31', '2530.31d1d0691296d715441ab4e2890d3780', '2531.8f40eb6fd94f9e48d56f6374ddc3427a', '2532.8b61e23f6e94d8b2303554395d0374e5', '2533.ba0ac203656046766a368dbc29cf36a6', '2534.2804e0302718cda7783689fa2a6d90d9', '2535.4d4a1eed6617e6df7c1a5d4ac7d368a2', '2536.601b538e15f39aaac0e038cb860ae762', '2537.2769f2836293c4c9105e6a89fc14fd8e', '2538.e3f596927fd5dd74ad23a0792a449241', '2539.466ecd08a54bf64e857522423a33108a', '2540.9527f844d218311f91ec4d87fd2035ca', '2541.892b976061b9ea684b9b3009d0b87dab', '2542.1a940f58e68fa8a84a3f83e30a624e8d', '2543.1fa39c8b86bc5ae84b837e8c7ae3717d', '2544.1a95794a767c776e71893fda91f59efb', '2545.6ddb98905ca38f1dc76e5266427c0574', '2546.dd6480d3f87f7d525f797d17ac1a0bc8', '2547.18ce2b2d4f757e70154b5c8cfe6e684f', '2548.b2259f104edd1ecbc9dc0327df387147', '2549.d05fe143eb28d1fb19fbdc39781d5e34', '2550.963956a866a2e00b3d5b353ecc9216ae', '2551.3b1f94418de5bd544c977b44bcc7e740'])\n"
     ]
    }
   ],
   "source": [
    "for sub_file in os.walk('/Users/apple/Documents/github_repos/spam_email_classifier/corpus'):\n",
    "    print(sub_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os import path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'corpus'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.basename('/Users/apple/Documents/github_repos/spam_email_classifier/corpus')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'i m ok thanks '"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.sub(r'[^a-zA-Z]+', ' ', \"i'm ok, thanks!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wnl = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dog\n"
     ]
    }
   ],
   "source": [
    "print(wnl.lemmatize('dogs'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "b = np.array([4, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.inner(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.71828183,   7.3890561 ,  20.08553692])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.inner(np.transpose(a), b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c = np.transpose(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c = np.arange(12).reshape(3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(np.e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a[:] = b[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b[0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5, 6])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5, 6])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b[1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 6])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "from os import path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_dir = '/Users/apple/Documents/github_repos/spam_email_classifier/data/preprocess_spam'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "files = [f for f in listdir(file_dir) if path.isfile(path.join(file_dir, f))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0000.7b1b73cf36cf9dbc3d64e3f2ee2b91f1',\n",
       " '00001.317e78fa8ee2f54cd4890fdc09ba8176',\n",
       " '00001.7848dde101aa985090474a91ec93fcf0',\n",
       " '00002.9438920e9a55591b18e60d1ed37d992b',\n",
       " '00002.d94f1b97e48ed3b553b3508d116e6a09',\n",
       " '00003.2ee33bc6eacdb11f38d052c44819ba6c',\n",
       " '00003.590eff932f8704d8b0fcbe69d023b54d',\n",
       " '00004.bdcc075fa4beb5157b5dd6cd41d8887b',\n",
       " '00004.eac8de8d759b7e74154f142194282724',\n",
       " '00005.57696a39d7d84318ce497886896bf90d']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = [1, 2, 3]\n",
    "b = [4, 5, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a.extend(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nltk import corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AlignedCorpusReader',\n",
       " 'AlpinoCorpusReader',\n",
       " 'BNCCorpusReader',\n",
       " 'BracketParseCorpusReader',\n",
       " 'CHILDESCorpusReader',\n",
       " 'CMUDictCorpusReader',\n",
       " 'CategorizedBracketParseCorpusReader',\n",
       " 'CategorizedCorpusReader',\n",
       " 'CategorizedPlaintextCorpusReader',\n",
       " 'CategorizedSentencesCorpusReader',\n",
       " 'CategorizedTaggedCorpusReader',\n",
       " 'ChasenCorpusReader',\n",
       " 'ChunkedCorpusReader',\n",
       " 'ComparativeSentencesCorpusReader',\n",
       " 'ConllChunkCorpusReader',\n",
       " 'ConllCorpusReader',\n",
       " 'CorpusReader',\n",
       " 'CrubadanCorpusReader',\n",
       " 'DependencyCorpusReader',\n",
       " 'EuroparlCorpusReader',\n",
       " 'FramenetCorpusReader',\n",
       " 'IEERCorpusReader',\n",
       " 'IPIPANCorpusReader',\n",
       " 'IndianCorpusReader',\n",
       " 'KNBCorpusReader',\n",
       " 'LazyCorpusLoader',\n",
       " 'LinThesaurusCorpusReader',\n",
       " 'MTECorpusReader',\n",
       " 'MacMorphoCorpusReader',\n",
       " 'NKJPCorpusReader',\n",
       " 'NPSChatCorpusReader',\n",
       " 'NombankCorpusReader',\n",
       " 'NonbreakingPrefixesCorpusReader',\n",
       " 'OpinionLexiconCorpusReader',\n",
       " 'PPAttachmentCorpusReader',\n",
       " 'PanLexLiteCorpusReader',\n",
       " 'Pl196xCorpusReader',\n",
       " 'PlaintextCorpusReader',\n",
       " 'PortugueseCategorizedPlaintextCorpusReader',\n",
       " 'PropbankCorpusReader',\n",
       " 'ProsConsCorpusReader',\n",
       " 'RTECorpusReader',\n",
       " 'RegexpTokenizer',\n",
       " 'ReviewsCorpusReader',\n",
       " 'SemcorCorpusReader',\n",
       " 'SensevalCorpusReader',\n",
       " 'SentiSynset',\n",
       " 'SentiWordNetCorpusReader',\n",
       " 'SinicaTreebankCorpusReader',\n",
       " 'StringCategoryCorpusReader',\n",
       " 'SwadeshCorpusReader',\n",
       " 'SwitchboardCorpusReader',\n",
       " 'SyntaxCorpusReader',\n",
       " 'TEICorpusView',\n",
       " 'TaggedCorpusReader',\n",
       " 'TimitCorpusReader',\n",
       " 'TimitTaggedCorpusReader',\n",
       " 'ToolboxCorpusReader',\n",
       " 'TwitterCorpusReader',\n",
       " 'UdhrCorpusReader',\n",
       " 'UnicharsCorpusReader',\n",
       " 'VerbnetCorpusReader',\n",
       " 'WordListCorpusReader',\n",
       " 'WordNetCorpusReader',\n",
       " 'WordNetICCorpusReader',\n",
       " 'XMLCorpusReader',\n",
       " 'YCOECorpusReader',\n",
       " '_LazyModule__lazymodule_globals',\n",
       " '_LazyModule__lazymodule_import',\n",
       " '_LazyModule__lazymodule_init',\n",
       " '_LazyModule__lazymodule_loaded',\n",
       " '_LazyModule__lazymodule_locals',\n",
       " '_LazyModule__lazymodule_name',\n",
       " '__builtins__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__getattr__',\n",
       " '__init__',\n",
       " '__lazymodule_loaded',\n",
       " '__module__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " 'abc',\n",
       " 'alpino',\n",
       " 'brown',\n",
       " 'cess_cat',\n",
       " 'cess_esp',\n",
       " 'cmudict',\n",
       " 'comparative_sentences',\n",
       " 'comtrans',\n",
       " 'conll2000',\n",
       " 'conll2002',\n",
       " 'conll2007',\n",
       " 'crubadan',\n",
       " 'demo',\n",
       " 'dependency_treebank',\n",
       " 'find_corpus_fileids',\n",
       " 'floresta',\n",
       " 'framenet',\n",
       " 'framenet15',\n",
       " 'gazetteers',\n",
       " 'genesis',\n",
       " 'gutenberg',\n",
       " 'ieer',\n",
       " 'inaugural',\n",
       " 'indian',\n",
       " 'ipipan',\n",
       " 'jeita',\n",
       " 'knbc',\n",
       " 'lin_thesaurus',\n",
       " 'mac_morpho',\n",
       " 'machado',\n",
       " 'masc_tagged',\n",
       " 'movie_reviews',\n",
       " 'multext_east',\n",
       " 'names',\n",
       " 'nkjp',\n",
       " 'nombank',\n",
       " 'nombank_ptb',\n",
       " 'nonbreaking_prefixes',\n",
       " 'nps_chat',\n",
       " 'opinion_lexicon',\n",
       " 'panlex_lite',\n",
       " 'perluniprops',\n",
       " 'pl196x',\n",
       " 'ppattach',\n",
       " 'product_reviews_1',\n",
       " 'product_reviews_2',\n",
       " 'propbank',\n",
       " 'propbank_ptb',\n",
       " 'pros_cons',\n",
       " 'ptb',\n",
       " 'qc',\n",
       " 're',\n",
       " 'reader',\n",
       " 'reuters',\n",
       " 'rte',\n",
       " 'semcor',\n",
       " 'senseval',\n",
       " 'sentence_polarity',\n",
       " 'sentiwordnet',\n",
       " 'shakespeare',\n",
       " 'sinica_treebank',\n",
       " 'state_union',\n",
       " 'stopwords',\n",
       " 'subjectivity',\n",
       " 'swadesh',\n",
       " 'swadesh110',\n",
       " 'swadesh207',\n",
       " 'switchboard',\n",
       " 'tagged_treebank_para_block_reader',\n",
       " 'teardown_module',\n",
       " 'timit',\n",
       " 'timit_tagged',\n",
       " 'toolbox',\n",
       " 'treebank',\n",
       " 'treebank_chunk',\n",
       " 'treebank_raw',\n",
       " 'twitter_samples',\n",
       " 'udhr',\n",
       " 'udhr2',\n",
       " 'universal_treebanks',\n",
       " 'util',\n",
       " 'verbnet',\n",
       " 'webtext',\n",
       " 'wordnet',\n",
       " 'wordnet_ic',\n",
       " 'words',\n",
       " 'ycoe']"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import brown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1161192"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(brown.words())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "from os import path\n",
    "sys.path.append('./libsvm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from svmutil import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y, x = svm_read_problem('./libsvm/heart_scale')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = svm_train(y[:200], x[:200], '-c 4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 84.2857% (59/70) (classification)\n"
     ]
    }
   ],
   "source": [
    "p_label, p_acc, p_val = svm_predict(y[200:], x[200:], m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm_save_model('./libsvm/heart_scale.model', m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = svm_load_model('./libsvm/heart_scale.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 89.2593% (240/270) (classification)\n"
     ]
    }
   ],
   "source": [
    "p_label, p_acc, p_val = svm_predict(y, x, m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ACC, MSE, SCC = evaluations(y, p_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.25925925925925"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ACC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42962962962962964"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0% (0/2) (classification)\n"
     ]
    }
   ],
   "source": [
    "y, x = [1,-1], [{1:1, 3:1}, {1:-1,3:-1}]\n",
    "prob  = svm_problem(y, x)\n",
    "param = svm_parameter('-t 0 -c 4 -b 1')\n",
    "m = svm_train(prob, param)\n",
    "p_label, p_acc, p_val = svm_predict(y, x, m, '-b 1')\n",
    "ACC, MSE, SCC = evaluations(y, p_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ACC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.zeros((4, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = [[y for y in x] for x in a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0, 0.0, 0.0, 0.0],\n",
       " [0.0, 0.0, 0.0, 0.0],\n",
       " [0.0, 0.0, 0.0, 0.0],\n",
       " [0.0, 0.0, 0.0, 0.0]]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok\n"
     ]
    }
   ],
   "source": [
    "c = 0.1\n",
    "if c:\n",
    "    print 'ok'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__delslice__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getslice__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__reversed__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__setslice__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'append',\n",
       " 'count',\n",
       " 'extend',\n",
       " 'index',\n",
       " 'insert',\n",
       " 'pop',\n",
       " 'remove',\n",
       " 'reverse',\n",
       " 'sort']"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  1.  1.]\n",
      "[[ 1.  1.  1.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "a = np.zeros((3, 3))\n",
    "b = np.ones(3)\n",
    "print b\n",
    "a[0] = b\n",
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T',\n",
       " '__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__array__',\n",
       " '__array_finalize__',\n",
       " '__array_interface__',\n",
       " '__array_prepare__',\n",
       " '__array_priority__',\n",
       " '__array_struct__',\n",
       " '__array_wrap__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__copy__',\n",
       " '__deepcopy__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__delslice__',\n",
       " '__div__',\n",
       " '__divmod__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__float__',\n",
       " '__floordiv__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getslice__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__hex__',\n",
       " '__iadd__',\n",
       " '__iand__',\n",
       " '__idiv__',\n",
       " '__ifloordiv__',\n",
       " '__ilshift__',\n",
       " '__imod__',\n",
       " '__imul__',\n",
       " '__index__',\n",
       " '__init__',\n",
       " '__int__',\n",
       " '__invert__',\n",
       " '__ior__',\n",
       " '__ipow__',\n",
       " '__irshift__',\n",
       " '__isub__',\n",
       " '__iter__',\n",
       " '__itruediv__',\n",
       " '__ixor__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__long__',\n",
       " '__lshift__',\n",
       " '__lt__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__neg__',\n",
       " '__new__',\n",
       " '__nonzero__',\n",
       " '__oct__',\n",
       " '__or__',\n",
       " '__pos__',\n",
       " '__pow__',\n",
       " '__radd__',\n",
       " '__rand__',\n",
       " '__rdiv__',\n",
       " '__rdivmod__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rfloordiv__',\n",
       " '__rlshift__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__ror__',\n",
       " '__rpow__',\n",
       " '__rrshift__',\n",
       " '__rshift__',\n",
       " '__rsub__',\n",
       " '__rtruediv__',\n",
       " '__rxor__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__setslice__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__truediv__',\n",
       " '__xor__',\n",
       " 'all',\n",
       " 'any',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'astype',\n",
       " 'base',\n",
       " 'byteswap',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'compress',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'copy',\n",
       " 'ctypes',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'data',\n",
       " 'diagonal',\n",
       " 'dot',\n",
       " 'dtype',\n",
       " 'dump',\n",
       " 'dumps',\n",
       " 'fill',\n",
       " 'flags',\n",
       " 'flat',\n",
       " 'flatten',\n",
       " 'getfield',\n",
       " 'imag',\n",
       " 'item',\n",
       " 'itemset',\n",
       " 'itemsize',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'min',\n",
       " 'nbytes',\n",
       " 'ndim',\n",
       " 'newbyteorder',\n",
       " 'nonzero',\n",
       " 'partition',\n",
       " 'prod',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'ravel',\n",
       " 'real',\n",
       " 'repeat',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'round',\n",
       " 'searchsorted',\n",
       " 'setfield',\n",
       " 'setflags',\n",
       " 'shape',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'squeeze',\n",
       " 'std',\n",
       " 'strides',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'tobytes',\n",
       " 'tofile',\n",
       " 'tolist',\n",
       " 'tostring',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'var',\n",
       " 'view']"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "c = np.array([b])\n",
    "print c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 'bbb', 2, 4, 'aaa']"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from random import shuffle\n",
    "l = [1, 'aaa', 4, 'bbb', 2]\n",
    "shuffle(l)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from shutil import copyfile\n",
    "copyfile('README.md', 'dst')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ccc.dat'"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.basename('/aaa/bbb/ccc.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/apple/venv/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-inf"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.zeros((3, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b = np.ones((3, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.805332786326785"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from random import uniform\n",
    "uniform(0, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.  3.  3.]\n",
      "3.0\n"
     ]
    }
   ],
   "source": [
    "a = np.ones((3,3))\n",
    "b = np.ones(3)\n",
    "print a.dot(b)\n",
    "print b.dot(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0\n"
     ]
    }
   ],
   "source": [
    "a = np.ones((3,3))\n",
    "b = a[:][1:]\n",
    "c = b*b\n",
    "print c.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "class MyIter(object):\n",
    "    def __init__(self, n):\n",
    "        self.n = n\n",
    "        self.i = 0\n",
    "        \n",
    "    def __iter__(self):\n",
    "        return self\n",
    "    \n",
    "    def next(self):\n",
    "        if self.i < self.n:\n",
    "            i = self.i\n",
    "            self.i += 1\n",
    "            return i\n",
    "        else:\n",
    "            raise StopIteration\n",
    "i = MyIter(10)\n",
    "for j in i:\n",
    "    print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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