Nping_n_grams.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:44:48.189532Z",
     "start_time": "2019-05-27T14:44:45.405334Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting editdistance\n",
      "  Downloading https://files.pythonhosted.org/packages/09/c0/e3ee9a2344d333c1b6e66022297dad3ac20199e092f168bd53e8af966d29/editdistance-0.5.3-cp36-cp36m-win_amd64.whl\n",
      "Installing collected packages: editdistance\n",
      "Successfully installed editdistance-0.5.3\n"
     ]
    }
   ],
   "source": [
    "!pip install editdistance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:44:56.182680Z",
     "start_time": "2019-05-27T14:44:56.176863Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import editdistance\n",
    "n_grams = 3\n",
    "word_in_doc = 1002\n",
    "max_range = 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> จำนวนไฟล์ทั้งหมด"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:48:50.058527Z",
     "start_time": "2019-05-27T14:48:50.032486Z"
    }
   },
   "outputs": [],
   "source": [
    "folder = 'all_document'\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "onlyfiles = [folder+'/'+f for f in listdir(folder) if isfile(join(folder, f))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>ลบช่องว่างออก"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:48:51.569983Z",
     "start_time": "2019-05-27T14:48:51.567087Z"
    }
   },
   "outputs": [],
   "source": [
    "def clean_document(text): #ลบช่องว่าง\n",
    "    text = \"\".join(text.split())\n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> ตัด 1002 character และตัด n grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:48:52.575600Z",
     "start_time": "2019-05-27T14:48:52.570773Z"
    }
   },
   "outputs": [],
   "source": [
    "def word2ngrams(text, n_grams,word_in_doc): #ตัดตัวอักษร n grams    \n",
    "    #token character\n",
    "    count = 0\n",
    "    word_in_doc=[]\n",
    "    char = ''\n",
    "    for i in text:\n",
    "        if count <= 1001:\n",
    "            char+=i\n",
    "            count+=1\n",
    "        else:\n",
    "            word_in_doc.append(char)\n",
    "            char = ''\n",
    "            char+=i\n",
    "            count=0\n",
    "            \n",
    "    n_grams_word = []\n",
    "    for word in word_in_doc:      \n",
    "        word_grams = [\"\".join(j) for j in zip(*[word[i:] for i in range(n_grams)])]\n",
    "        n_grams_word.append(word_grams)\n",
    "    \n",
    "    return n_grams_word,word_in_doc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>นับจำนวนคำทั้งหมด"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:48:54.058386Z",
     "start_time": "2019-05-27T14:48:54.054500Z"
    }
   },
   "outputs": [],
   "source": [
    "def count_ngrams(doc_token_word): #นับจำนวน n grams\n",
    "    ngram_character_freq_list = []\n",
    "    \n",
    "    for ngrams_output in doc_token_word[0]:\n",
    "        characte_ngrams = list(set(ngrams_output)) #เอาคำซ้ำออก\n",
    "        count_word_dup=[] #นับจำนวนคำ\n",
    "        \n",
    "        for w in characte_ngrams:\n",
    "            count_word_dup.append(ngrams_output.count(w))\n",
    "            \n",
    "        ngram_character_freq = dict(zip(characte_ngrams, count_word_dup))\n",
    "        #print(ngram_character_freq)\n",
    "        ngram_character_freq_list.append(ngram_character_freq)\n",
    "        \n",
    "    return ngram_character_freq_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> เรียงฟังก์ชั่นนี้อันเดียว ก็จะเรียกทุกอันเอง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:48:58.081867Z",
     "start_time": "2019-05-27T14:48:58.077040Z"
    }
   },
   "outputs": [],
   "source": [
    "def read_file(file_names):\n",
    "    word_in_file=[]\n",
    "    count_word = []\n",
    "    for name in file_names:\n",
    "        print('now file : ',name)\n",
    "        word = open(name,encoding='utf-8').read()\n",
    "        word = clean_document(word) # ลบ space ออก\n",
    "        ngrams_word = word2ngrams(word[:],n_grams,word_in_doc) # ตัดให้เหลือ n grams\n",
    "        ngrams_items_count = count_ngrams(ngrams_word) #count_ngrams -> max_min_index\n",
    "       \n",
    "        count_word.append(len(ngrams_word[:][:]))\n",
    "        word_in_file.append(ngrams_items_count)\n",
    "        \n",
    "        #print('we use {} grams and have {} not word unique with {} unique word'.format(n_grams,len(ngrams_word),len(ngrams_items_count)))\n",
    "    return word_in_file,count_word\n",
    "    #print(word)\n",
    "    #print(ngrams_word[:500])\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> ทำความสะอาดไฟล์ -> ตัดอักษร N grams -> นับจำนวนคำ <br>\n",
    "จะได้ dict ->คำ+จำนวนคำ <br>\n",
    "และ จำนวนคำทั้งหมด <br>\n",
    "จะใช้กี่เอกสารก็ได้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:01.637388Z",
     "start_time": "2019-05-27T14:48:59.684403Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now file :  all_document/-LoveDonut-1614684.txt\n",
      "now file :  all_document/-LoveDonut-1633979.txt\n"
     ]
    }
   ],
   "source": [
    "word_in_file,count_word = read_file(onlyfiles[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:01.643065Z",
     "start_time": "2019-05-27T14:49:01.638239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_in_file[1][2]['นอก'] #ตำแหน่งแรกคือ ตำแหน่งของ doc ตำแหน่งที่ 2 คือ sub doc (ที่มี 1002 คำ) อันสุดท้ายเอาไว้หาว่าคำนั้นๆมีจำนวนความถี่เท่าไหร่"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:01.663336Z",
     "start_time": "2019-05-27T14:49:01.644030Z"
    }
   },
   "outputs": [
    {
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       " 'นใน': 1,\n",
       " 'รือ': 1,\n",
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       " 'ขวา': 1,\n",
       " 'ย์ม': 1,\n",
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       " '่าน': 1,\n",
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       " 'ีเพ': 1,\n",
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       " 'ร์พ': 1,\n",
       " '4คน': 1,\n",
       " 'สดี': 1,\n",
       " 'นพี': 1,\n",
       " 'นทั': 1,\n",
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       " '่มเ': 1,\n",
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       " 'ทั้': 1,\n",
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       " 'นัก': 2,\n",
       " '้งห': 1,\n",
       " 'พรา': 1,\n",
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       " 'าได': 1,\n",
       " 'หลั': 1,\n",
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       " 'ใจแ': 1,\n",
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       " 'ศาส': 1,\n",
       " 'หน้': 1,\n",
       " 'โคร': 1,\n",
       " 'เหอ': 1,\n",
       " '#$$': 1,\n",
       " 'โยค': 1,\n",
       " 'กลั': 1,\n",
       " 'าแล': 1,\n",
       " '่~”': 1,\n",
       " 'ไรส': 1,\n",
       " 'ดีจ': 1,\n",
       " 'ยกไ': 1,\n",
       " 'าไป': 1,\n",
       " '้จน': 1,\n",
       " '่อน': 4,\n",
       " 'ด๊า': 1,\n",
       " 'ี้จ': 1,\n",
       " 'บ!”': 1,\n",
       " 'จนถ': 1,\n",
       " 'เมื': 1,\n",
       " '^&”': 1,\n",
       " 'รื่': 1,\n",
       " 'ิคค': 2,\n",
       " 'แล้': 4,\n",
       " '้งแ': 1,\n",
       " 'าทุ': 1,\n",
       " 'รัน': 3,\n",
       " 'เค้': 1,\n",
       " 'บกล': 1,\n",
       " 'ของ': 2,\n",
       " 'บ@#': 1,\n",
       " 'อนน': 2,\n",
       " 'นด้': 1,\n",
       " 'ละณ': 1,\n",
       " 'ปี2': 1,\n",
       " '่หอ': 1,\n",
       " 'จะม': 2,\n",
       " 'ยคเ': 1,\n",
       " '้ว่': 1,\n",
       " 'าดี': 1,\n",
       " 'วัน': 1,\n",
       " 'วะ)': 1,\n",
       " 'ซรั': 3,\n",
       " 'ิทข': 1,\n",
       " 'จะท': 1,\n",
       " 'งผู': 1,\n",
       " 'ีคน': 1,\n",
       " 'งเช': 1,\n",
       " 'ังว': 1,\n",
       " '่แล': 1,\n",
       " '็กว': 1,\n",
       " '$%^': 1,\n",
       " '่อค': 1,\n",
       " 'มอง': 1,\n",
       " 'ห้อ': 2,\n",
       " 'ราด': 1,\n",
       " 'งหร': 1,\n",
       " 'ิ๊ง': 1,\n",
       " 'นชา': 1,\n",
       " 'ไรล': 1,\n",
       " 'ชาต': 1,\n",
       " 'ร่ว': 1,\n",
       " '’เล': 1,\n",
       " 'ลัย': 2,\n",
       " 'รมศ': 2,\n",
       " 'ะวา': 1,\n",
       " 'นาน': 1,\n",
       " 'ปคน': 1,\n",
       " 'างจ': 1,\n",
       " 'าทั': 1}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_in_file[1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:02.033240Z",
     "start_time": "2019-05-27T14:49:02.021679Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ทำห': 1,\n",
       " 'กษา': 1,\n",
       " 'ั่ว': 1,\n",
       " '่จะ': 2,\n",
       " 'สัน': 1,\n",
       " 'ย!น': 1,\n",
       " 'กเล': 1,\n",
       " 'นออ': 1,\n",
       " '2แล': 1,\n",
       " 'เวล': 1,\n",
       " 'กวิ': 1,\n",
       " 'นกไ': 1,\n",
       " 'ครอ': 2,\n",
       " 'รับ': 2,\n",
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       " 'ดิน': 1,\n",
       " 'ด็ก': 1,\n",
       " '์น่': 1,\n",
       " 'เสี': 2,\n",
       " 'เลื': 1,\n",
       " 'แผน': 2,\n",
       " 'นึ่': 4,\n",
       " 'ในห': 1,\n",
       " 'นั่': 1,\n",
       " 'เกื': 1,\n",
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       " '่าท': 1,\n",
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       " 'ุญแ': 1,\n",
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       " 'ครด': 1,\n",
       " 'ี่ช': 1,\n",
       " 'ปีห': 1,\n",
       " 'ี่จ': 1,\n",
       " 'อรุ': 1,\n",
       " 'ไอด': 1,\n",
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       " 'ีหน': 1,\n",
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       " '่มา': 1,\n",
       " 'การ': 1,\n",
       " '่อพ': 1,\n",
       " '่อก': 1,\n",
       " '่าม': 1,\n",
       " '่นน': 2,\n",
       " 'งนี': 1,\n",
       " '่าน': 1,\n",
       " 'ุกๆ': 4,\n",
       " '้อย': 1,\n",
       " '้นท': 1,\n",
       " 'น้า': 1,\n",
       " 'อาจ': 2,\n",
       " 'มือ': 2,\n",
       " '้งา': 1,\n",
       " 'ทำใ': 1,\n",
       " 'ญแก': 1,\n",
       " 'กคน': 2,\n",
       " 'ารอ': 1,\n",
       " 'กลา': 1,\n",
       " 'ีเพ': 1,\n",
       " 'อโห': 1,\n",
       " 'สดี': 1,\n",
       " 'นพี': 6,\n",
       " 'พศช': 1,\n",
       " '์แบ': 1,\n",
       " 'ย”พ': 1,\n",
       " 'นๆแ': 1,\n",
       " 'มัธ': 1,\n",
       " 'าม“': 1,\n",
       " '“สว': 1,\n",
       " '3ก็': 1,\n",
       " 'ยัง': 1,\n",
       " 'ดอล': 1,\n",
       " '่าพ': 1,\n",
       " 'าฉั': 1,\n",
       " '้วย': 2,\n",
       " 'นห้': 1,\n",
       " '่าร': 1,\n",
       " 'างเ': 3,\n",
       " 'าพว': 1,\n",
       " 'นัก': 1,\n",
       " 'นน้': 2,\n",
       " 'เลิ': 1,\n",
       " 'พรา': 2,\n",
       " 'ต่ใ': 1,\n",
       " 'กคล': 1,\n",
       " 'ั้น': 3,\n",
       " 'ังไ': 1,\n",
       " 'พวก': 2,\n",
       " 'ว๊า': 1,\n",
       " '“เข': 1,\n",
       " 'ฉะน': 1,\n",
       " 'หน้': 1,\n",
       " 'อ่ย': 3,\n",
       " 'นส่': 1,\n",
       " 'งชั': 1,\n",
       " '่อน': 2,\n",
       " 'ารั': 1,\n",
       " 'อนด': 1,\n",
       " 'ข่า': 1,\n",
       " 'หนุ': 1,\n",
       " 'วโม': 1,\n",
       " 'องย': 1,\n",
       " 'ต่จ': 1,\n",
       " 'รื่': 1,\n",
       " 'ยมแ': 1,\n",
       " 'แล้': 2,\n",
       " 'รี๊': 1,\n",
       " '้งแ': 1,\n",
       " '่ะต': 1,\n",
       " 'ปเล': 1,\n",
       " 'ิมเ': 1,\n",
       " 'ใจเ': 1,\n",
       " '..ร': 1,\n",
       " 'เค้': 1,\n",
       " '้!.': 1,\n",
       " 'ของ': 2,\n",
       " 'าทุ': 1,\n",
       " 'ากิ': 1,\n",
       " '่งท': 2,\n",
       " 'รัน': 1,\n",
       " 'นแบ': 1,\n",
       " 'นเด': 5,\n",
       " '้าว': 1,\n",
       " 'ียบ': 1,\n",
       " 'ปี2': 3,\n",
       " 'ปเก': 1,\n",
       " 'อกร': 1,\n",
       " '้าจ': 1,\n",
       " 'าดี': 1,\n",
       " 'เพศ': 1,\n",
       " 'ง่า': 1,\n",
       " '๊ดด': 1,\n",
       " '“เร': 1,\n",
       " 'ซรั': 1,\n",
       " 'เอง': 1,\n",
       " 'เที': 1,\n",
       " 'อพี': 1,\n",
       " 'ส่ว': 1,\n",
       " 'เก่': 1,\n",
       " 'วเล': 1,\n",
       " 'ก่ง': 1,\n",
       " 'ห้อ': 2,\n",
       " '็กว': 1,\n",
       " '่แล': 1,\n",
       " 'ชื่': 1,\n",
       " '็นห': 1,\n",
       " 'ี๊ด': 1,\n",
       " 'แก่': 1,\n",
       " 'าก่': 1,\n",
       " 'กจะ': 1,\n",
       " 'งโห': 1,\n",
       " '่มั': 5,\n",
       " 'ยท่': 1,\n",
       " 'งอา': 1,\n",
       " 'า”ผ': 1,\n",
       " 'กริ': 1,\n",
       " 'รุ่': 8}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_in_file[1][3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> สร้าง global unique word เพื่อหา idf // ตอนนี้ไม่ได้ใช้ idf แล้ว ใช้แค่ tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-05T08:47:52.385195Z",
     "start_time": "2019-06-05T08:47:52.381249Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"''\\ndef create_global_unique_word(word_in_file):\\n    global_word = []\\n    for doc in word_in_file:\\n        for sub_doc in doc:\\n            global_word = set(global_word).union(set(sub_doc.keys()))\\n    return global_word\\n\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "def create_global_unique_word(word_in_file):\n",
    "    global_word = []\n",
    "    for doc in word_in_file:\n",
    "        for sub_doc in doc:\n",
    "            global_word = set(global_word).union(set(sub_doc.keys()))\n",
    "    return global_word\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:04.256166Z",
     "start_time": "2019-05-27T14:49:04.253270Z"
    }
   },
   "outputs": [],
   "source": [
    "#global_word = create_global_unique_word(word_in_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> ทำให้ dict ของทุกๆไฟล์มีคำทุกคำของทุกๆเอกสาร // ถ้าใช้ idf ถึงจะรัน"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:05.395368Z",
     "start_time": "2019-05-27T14:49:05.390513Z"
    }
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (<ipython-input-18-da412c72d494>, line 13)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-18-da412c72d494>\"\u001b[1;36m, line \u001b[1;32m13\u001b[0m\n\u001b[1;33m    ''''\u001b[0m\n\u001b[1;37m        \n^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": [
    "'''''\n",
    "def change_word_global_dict(global_word,word_in_file):\n",
    "    complete_word = []\n",
    "    for index in range(len(word_in_file)):\n",
    "        have=0\n",
    "        have_not=0\n",
    "        for word in global_word:\n",
    "            if word not in word_in_file[index].keys():\n",
    "                ## add new items with 0 value to dict\n",
    "                word_in_file[index].update( { word: 0} )\n",
    "    return word_in_file\n",
    "        #print('Set {} have = {} have_not {}'.format(index,have,have_not))\n",
    "''''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:05.659182Z",
     "start_time": "2019-05-27T14:49:05.656313Z"
    }
   },
   "outputs": [],
   "source": [
    "#complete_word = change_word_global_dict(global_word,word_in_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> ฟังก์ชั่นหา tf-idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:06.085937Z",
     "start_time": "2019-05-27T14:49:06.082075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"''\\ndef compute_idf(complete_word,global_word):\\n    import math\\n    N = len(complete_word)\\n    \\n    idfDict = {}\\n    idfDict = dict.fromkeys(complete_word[0].keys(), 0)\\n    \\n    for doc in complete_word:\\n        for word, val in doc.items():\\n            if val > 0:\\n                idfDict[word] += 1\\n    \\n    for word, val in idfDict.items():\\n        #print('word = {} value = {}'.format(word,val))\\n        idfDict[word] = math.log10(N / float(val))\\n        \\n    return idfDict\\n\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''''\n",
    "def compute_idf(complete_word,global_word):\n",
    "    import math\n",
    "    N = len(complete_word)\n",
    "    \n",
    "    idfDict = {}\n",
    "    idfDict = dict.fromkeys(complete_word[0].keys(), 0)\n",
    "    \n",
    "    for doc in complete_word:\n",
    "        for word, val in doc.items():\n",
    "            if val > 0:\n",
    "                idfDict[word] += 1\n",
    "    \n",
    "    for word, val in idfDict.items():\n",
    "        #print('word = {} value = {}'.format(word,val))\n",
    "        idfDict[word] = math.log10(N / float(val))\n",
    "        \n",
    "    return idfDict\n",
    "'''''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-05T08:47:42.296712Z",
     "start_time": "2019-06-05T08:47:42.288817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"''\\ndef compute_tfidf(tf_ngrams, idf_ngrams):\\n    tfidf_compled = []\\n    for doc in tf_ngrams:\\n        tfidf = {}\\n        for word, val in doc.items():\\n            tfidf[word] = val*idf_ngrams[word]\\n        tfidf_compled.append(tfidf)\\n    return tfidf_compled\\n\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''''\n",
    "def compute_tfidf(tf_ngrams, idf_ngrams):\n",
    "    tfidf_compled = []\n",
    "    for doc in tf_ngrams:\n",
    "        tfidf = {}\n",
    "        for word, val in doc.items():\n",
    "            tfidf[word] = val*idf_ngrams[word]\n",
    "        tfidf_compled.append(tfidf)\n",
    "    return tfidf_compled\n",
    "'''''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:07.013045Z",
     "start_time": "2019-05-27T14:49:07.008220Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_tf(word_in_file):\n",
    "    tf_dict_list = []\n",
    "    for doc in word_in_file:\n",
    "        tf_dict_sub = []\n",
    "        for sub_doc in doc:\n",
    "            tfdict = {}\n",
    "            count_word = sum(sub_doc.values())\n",
    "            for word,count in sub_doc.items():\n",
    "                tfdict[word] = count/float(count_word)\n",
    "            tf_dict_sub.append(tfdict)   \n",
    "        tf_dict_list.append(tf_dict_sub)\n",
    "    return tf_dict_list\n",
    "    '''''\n",
    "    for ngrams_word in word_in_file:\n",
    "        for counter, number_all in count_word:\n",
    "            tfdict = {}\n",
    "            #print('len = ',number_all,'index = ',counter)\n",
    "            for word,count in ngrams_word[counter].items():\n",
    "                tfdict[word] = count/float(number_all)\n",
    "            #print(tfdict)\n",
    "            tf_dict_list.append(tfdict)\n",
    "    return tf_dict_list\n",
    "    '''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> พอได้คำในแต่ละไฟล์แล้วถึงหา tf-idf ได้ #แต่ตอนนี้ใช้แค่ TF พอ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:11.408625Z",
     "start_time": "2019-05-27T14:49:11.366029Z"
    }
   },
   "outputs": [],
   "source": [
    "tf_ngrams = compute_tf(word_in_file)  # จำนวน word ที่ปรากฏใน file นั้นๆ / จำนวนคำทั้งหมดของไฟล์นั้นๆ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:12.296691Z",
     "start_time": "2019-05-27T14:49:12.285107Z"
    }
   },
   "outputs": [
    {
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       " 'อ)พ': 0.000999000999000999,\n",
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       " 'หนน': 0.000999000999000999,\n",
       " 'รื่': 0.000999000999000999,\n",
       " '้จู': 0.000999000999000999,\n",
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       " 'วัน': 0.000999000999000999,\n",
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       " 'เอง': 0.000999000999000999,\n",
       " '้รั': 0.000999000999000999,\n",
       " 'คะค': 0.000999000999000999,\n",
       " 'ลคว': 0.000999000999000999,\n",
       " 'อก็': 0.000999000999000999,\n",
       " 'ุยอ': 0.000999000999000999,\n",
       " 'งผู': 0.001998001998001998,\n",
       " '็งี': 0.000999000999000999,\n",
       " '็นส': 0.001998001998001998,\n",
       " 'งหร': 0.000999000999000999,\n",
       " 'ี๊ด': 0.000999000999000999,\n",
       " 'แกก': 0.000999000999000999,\n",
       " 'ิ๊ง': 0.000999000999000999,\n",
       " 'กจะ': 0.002997002997002997,\n",
       " 'ไรล': 0.000999000999000999,\n",
       " 'ลัย': 0.000999000999000999,\n",
       " 'เถอ': 0.000999000999000999,\n",
       " 'างจ': 0.000999000999000999,\n",
       " 'องพ': 0.000999000999000999}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_ngrams[0][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:13.026542Z",
     "start_time": "2019-05-27T14:49:13.023673Z"
    }
   },
   "outputs": [],
   "source": [
    "#tf_ngrams = compute_tf(complete_word,count_word)  # จำนวน word ที่ปรากฏใน file นั้นๆ / จำนวนคำทั้งหมดของไฟล์นั้นๆ\n",
    "#idf_ngrams = compute_idf(complete_word,global_word) #เอา len of file_names หารด้วย จำนวน document ที่ contain word นั้นๆ\n",
    "#tfidf_compled = compute_tfidf(tf_ngrams, idf_ngrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:15.600296Z",
     "start_time": "2019-05-27T14:49:15.594499Z"
    }
   },
   "outputs": [],
   "source": [
    "def graph_ngram(max_list): #เอาไว้ดูความถี่ของคำ\n",
    "    for doc in max_list:\n",
    "        for sub_doc in doc:\n",
    "            y_value = []\n",
    "            x_items = []\n",
    "            for y in sub_doc:\n",
    "                y_value.append(y[1])\n",
    "                x_items.append(y[0])\n",
    "            #print('end 10 sentence')  \n",
    "            x_len = range(len(y_value))\n",
    "            #print('word = {} and value = {} '.format(x_items,y_value))\n",
    "            fig,ax = plt.subplots()\n",
    "            plt.bar(x_len, y_value , align='center', alpha=0.5)\n",
    "            plt.xticks(x_len, x_items,fontname='Tahoma')\n",
    "            plt.ylabel('Frequency')\n",
    "            plt.title('TF-IDF Frequency')\n",
    "            plt.grid()\n",
    "            fig.autofmt_xdate()\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:16.246343Z",
     "start_time": "2019-05-27T14:49:16.241522Z"
    }
   },
   "outputs": [],
   "source": [
    "def max_min_index(tf_doc): #หา max min\n",
    "    max_list = []\n",
    "    for doc in tf_doc:\n",
    "        max_sub_list = []\n",
    "        for sub_doc in doc:\n",
    "            word = list(sub_doc.keys())\n",
    "            value = list(sub_doc.values())\n",
    "            max_items = []\n",
    "            while len(max_items) < 500:\n",
    "                max_index = word.index(max(word))\n",
    "                #print('index {} items {} value {}'.format(max_index,characte_ngrams[max_index],count_word_dup[max_index]))\n",
    "                if(value[max_index] != 0):\n",
    "                    max_items.append([word[max_index],value[max_index]])\n",
    "                del word[max_index]\n",
    "                del value[max_index]\n",
    "            max_sub_list.append(max_items)\n",
    "        max_list.append(max_sub_list)\n",
    "    return max_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:17.708961Z",
     "start_time": "2019-05-27T14:49:16.508927Z"
    }
   },
   "outputs": [],
   "source": [
    "max_list = max_min_index(tf_ngrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:17.724310Z",
     "start_time": "2019-05-27T14:49:17.709802Z"
    }
   },
   "outputs": [
    {
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       " ['าเฝ', 0.000999000999000999],\n",
       " ['าเจ', 0.000999000999000999],\n",
       " ['าาเ', 0.000999000999000999],\n",
       " ['าาา', 0.001998001998001998],\n",
       " ['าออ', 0.001998001998001998],\n",
       " ['าส่', 0.001998001998001998],\n",
       " ['าวว', 0.000999000999000999],\n",
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       " ['าลพ', 0.000999000999000999],\n",
       " ['าลค', 0.001998001998001998],\n",
       " ['าลก', 0.000999000999000999],\n",
       " ['ารจ', 0.000999000999000999],\n",
       " ['ายั', 0.000999000999000999],\n",
       " ['ายล', 0.000999000999000999],\n",
       " ['ายย', 0.000999000999000999],\n",
       " ['ายต', 0.000999000999000999],\n",
       " ['ายข', 0.001998001998001998],\n",
       " ['ายก', 0.000999000999000999],\n",
       " ['ามใ', 0.000999000999000999],\n",
       " ['ามา', 0.000999000999000999],\n",
       " ['ามร', 0.000999000999000999],\n",
       " ['ามจ', 0.000999000999000999],\n",
       " ['าพร', 0.000999000999000999],\n",
       " ['าบ้', 0.000999000999000999],\n",
       " ['านแ', 0.000999000999000999],\n",
       " ['านเ', 0.000999000999000999],\n",
       " ['านี', 0.000999000999000999],\n",
       " ['านห', 0.002997002997002997],\n",
       " ['านม', 0.000999000999000999],\n",
       " ['านบ', 0.000999000999000999],\n",
       " ['านท', 0.000999000999000999],\n",
       " ['านด', 0.000999000999000999],\n",
       " ['านซ', 0.000999000999000999],\n",
       " ['านฉ', 0.000999000999000999],\n",
       " ['านข', 0.000999000999000999],\n",
       " ['านก', 0.000999000999000999],\n",
       " ['าตา', 0.004995004995004995],\n",
       " ['าซั', 0.001998001998001998],\n",
       " ['าชุ', 0.000999000999000999],\n",
       " ['าฉั', 0.000999000999000999],\n",
       " ['าจะ', 0.000999000999000999],\n",
       " ['างใ', 0.000999000999000999],\n",
       " ['างแ', 0.000999000999000999],\n",
       " ['างอ', 0.000999000999000999],\n",
       " ['างร', 0.000999000999000999],\n",
       " ['างม', 0.001998001998001998],\n",
       " ['างด', 0.000999000999000999],\n",
       " ['างช', 0.000999000999000999],\n",
       " ['างค', 0.000999000999000999],\n",
       " ['าคล', 0.000999000999000999],\n",
       " ['าคร', 0.000999000999000999],\n",
       " ['ากไ', 0.000999000999000999],\n",
       " ['ากว', 0.000999000999000999],\n",
       " ['ากก', 0.001998001998001998],\n",
       " ['ั้น', 0.004995004995004995],\n",
       " ['ั้ง', 0.001998001998001998],\n",
       " ['ั่น', 0.001998001998001998],\n",
       " ['ัวใ', 0.000999000999000999],\n",
       " ['ัยน', 0.000999000999000999],\n",
       " ['ับแ', 0.000999000999000999],\n",
       " ['ับป', 0.000999000999000999],\n",
       " ['ัน“', 0.000999000999000999],\n",
       " ['ันไ', 0.001998001998001998],\n",
       " ['ันเ', 0.001998001998001998],\n",
       " ['ันอ', 0.000999000999000999],\n",
       " ['ันห', 0.000999000999000999],\n",
       " ['ันย', 0.000999000999000999],\n",
       " ['ันบ', 0.000999000999000999],\n",
       " ['ันน', 0.000999000999000999],\n",
       " ['ันท', 0.000999000999000999],\n",
       " ['ันต', 0.000999000999000999],\n",
       " ['ันช', 0.000999000999000999],\n",
       " ['ันก', 0.001998001998001998],\n",
       " ['ัดช', 0.000999000999000999],\n",
       " ['ัดง', 0.000999000999000999],\n",
       " ['ัญห', 0.000999000999000999],\n",
       " ['ัง”', 0.000999000999000999],\n",
       " ['ังไ', 0.000999000999000999],\n",
       " ['ังห', 0.000999000999000999],\n",
       " ['ังส', 0.000999000999000999],\n",
       " ['ังท', 0.000999000999000999],\n",
       " ['ังง', 0.000999000999000999],\n",
       " ['ังค', 0.000999000999000999],\n",
       " ['ักใ', 0.000999000999000999],\n",
       " ['ักแ', 0.000999000999000999],\n",
       " ['ักร', 0.000999000999000999],\n",
       " ['ักต', 0.000999000999000999],\n",
       " ['ักข', 0.000999000999000999],\n",
       " ['ะ”“', 0.001998001998001998],\n",
       " ['ะ”ฉ', 0.001998001998001998],\n",
       " ['ะ“ว', 0.000999000999000999],\n",
       " ['ะไป', 0.000999000999000999],\n",
       " ['ะโบ', 0.000999000999000999],\n",
       " ['ะเอ', 0.000999000999000999],\n",
       " ['ะเบ', 0.000999000999000999],\n",
       " ['ะเด', 0.000999000999000999],\n",
       " ['ะเง', 0.000999000999000999],\n",
       " ['ะอิ', 0.000999000999000999],\n",
       " ['ะหม', 0.000999000999000999],\n",
       " ['ะวั', 0.000999000999000999],\n",
       " ['ะมี', 0.000999000999000999],\n",
       " ['ะป้', 0.000999000999000999],\n",
       " ['ะน้', 0.000999000999000999],\n",
       " ['ะนา', 0.000999000999000999],\n",
       " ['ะนั', 0.000999000999000999],\n",
       " ['ะตอ', 0.000999000999000999],\n",
       " ['ะดู', 0.000999000999000999],\n",
       " ['ะค่', 0.000999000999000999],\n",
       " ['ะคุ', 0.000999000999000999],\n",
       " ['ะคะ', 0.002997002997002997],\n",
       " ['อ้โ', 0.000999000999000999],\n",
       " ['อไป', 0.000999000999000999],\n",
       " ['อเล', 0.000999000999000999],\n",
       " ['อิน', 0.000999000999000999],\n",
       " ['อาไ', 0.000999000999000999],\n",
       " ['อาม', 0.000999000999000999],\n",
       " ['อาช', 0.000999000999000999],\n",
       " ['ออเ', 0.000999000999000999],\n",
       " ['ออก', 0.001998001998001998],\n",
       " ['อวา', 0.000999000999000999],\n",
       " ['อย่', 0.000999000999000999],\n",
       " ['อยๆ', 0.000999000999000999],\n",
       " ['อยแ', 0.001998001998001998],\n",
       " ['อยู', 0.002997002997002997],\n",
       " ['อยั', 0.001998001998001998],\n",
       " ['อยห', 0.000999000999000999],\n",
       " ['อยด', 0.000999000999000999],\n",
       " ['อมี', 0.002997002997002997],\n",
       " ['อบม', 0.000999000999000999],\n",
       " ['อบพ', 0.000999000999000999],\n",
       " ['อบค', 0.000999000999000999],\n",
       " ['อนิ', 0.000999000999000999],\n",
       " ['อนน', 0.000999000999000999],\n",
       " ['อนข', 0.000999000999000999],\n",
       " ['องห', 0.000999000999000999],\n",
       " ['องร', 0.000999000999000999],\n",
       " ['องม', 0.000999000999000999],\n",
       " ['องฝ', 0.000999000999000999],\n",
       " ['องป', 0.000999000999000999],\n",
       " ['องบ', 0.000999000999000999],\n",
       " ['องท', 0.000999000999000999],\n",
       " ['องด', 0.000999000999000999],\n",
       " ['องช', 0.000999000999000999],\n",
       " ['องฉ', 0.003996003996003996],\n",
       " ['อคะ', 0.000999000999000999],\n",
       " ['อก‘', 0.000999000999000999],\n",
       " ['อกไ', 0.001998001998001998],\n",
       " ['อกั', 0.000999000999000999],\n",
       " ['ห้”', 0.000999000999000999],\n",
       " ['ห้“', 0.000999000999000999],\n",
       " ['ห้เ', 0.000999000999000999],\n",
       " ['ห้ป', 0.000999000999000999],\n",
       " ['ห้น', 0.000999000999000999],\n",
       " ['ห้ค', 0.000999000999000999],\n",
       " ['ห้ก', 0.000999000999000999],\n",
       " ['ห็น', 0.001998001998001998],\n",
       " ['หาแ', 0.000999000999000999],\n",
       " ['หาค', 0.000999000999000999],\n",
       " ['หัน', 0.000999000999000999],\n",
       " ['หวั', 0.000999000999000999],\n",
       " ['หวง', 0.000999000999000999],\n",
       " ['หละ', 0.000999000999000999],\n",
       " ['หรื', 0.001998001998001998],\n",
       " ['หรอ', 0.000999000999000999],\n",
       " ['หมั', 0.003996003996003996],\n",
       " ['หน้', 0.000999000999000999],\n",
       " ['หน่', 0.000999000999000999],\n",
       " ['หนู', 0.000999000999000999],\n",
       " ['หนุ', 0.000999000999000999],\n",
       " ['หนื', 0.002997002997002997],\n",
       " ['หนั', 0.000999000999000999],\n",
       " ['หงา', 0.000999000999000999],\n",
       " ['ส่ง', 0.001998001998001998],\n",
       " ['สือ', 0.000999000999000999],\n",
       " ['สึก', 0.000999000999000999],\n",
       " ['สอง', 0.000999000999000999],\n",
       " ['สมั', 0.000999000999000999],\n",
       " ['ศ)“', 0.000999000999000999],\n",
       " ['ว“เ', 0.000999000999000999],\n",
       " ['ว้า', 0.000999000999000999],\n",
       " ['ว่า', 0.001998001998001998],\n",
       " ['วให', 0.000999000999000999],\n",
       " ['วเห', 0.000999000999000999],\n",
       " ['วาม', 0.001998001998001998],\n",
       " ['วาน', 0.000999000999000999],\n",
       " ['วัน', 0.000999000999000999],\n",
       " ['วัง', 0.000999000999000999],\n",
       " ['วว่', 0.000999000999000999],\n",
       " ['วร้', 0.000999000999000999],\n",
       " ['วยน', 0.000999000999000999],\n",
       " ['วป้', 0.000999000999000999],\n",
       " ['วนา', 0.000999000999000999],\n",
       " ['วนต', 0.000999000999000999],\n",
       " ['วจร', 0.000999000999000999],\n",
       " ['วงข', 0.000999000999000999],\n",
       " ['วค่', 0.001998001998001998],\n",
       " ['วคุ', 0.000999000999000999],\n",
       " ['วก็', 0.000999000999000999],\n",
       " ['ล’แ', 0.000999000999000999],\n",
       " ['ล้ว', 0.005994005994005994],\n",
       " ['ลเอ', 0.000999000999000999],\n",
       " ['ลูก', 0.000999000999000999],\n",
       " ['ลี้', 0.000999000999000999],\n",
       " ['ลิศ', 0.000999000999000999],\n",
       " ['ลำพ', 0.000999000999000999],\n",
       " ['ลาอ', 0.001998001998001998],\n",
       " ['ลาย', 0.000999000999000999],\n",
       " ['ลาง', 0.001998001998001998],\n",
       " ['ละ“', 0.000999000999000999],\n",
       " ['ลยเ', 0.000999000999000999],\n",
       " ['ลยน', 0.000999000999000999],\n",
       " ['ลยค', 0.000999000999000999],\n",
       " ['ลพู', 0.000999000999000999],\n",
       " ['ลฝา', 0.000999000999000999],\n",
       " ['ลนา', 0.000999000999000999],\n",
       " ['ลดี', 0.000999000999000999],\n",
       " ['ลจะ', 0.000999000999000999],\n",
       " ['ลงม', 0.000999000999000999],\n",
       " ['ลคะ', 0.000999000999000999],\n",
       " ['ลคง', 0.000999000999000999],\n",
       " ['ลก็', 0.000999000999000999],\n",
       " ['ร์ไ', 0.000999000999000999],\n",
       " ['ร์ก', 0.000999000999000999],\n",
       " ['ร้า', 0.000999000999000999],\n",
       " ['รู้', 0.001998001998001998],\n",
       " ['รือ', 0.001998001998001998],\n",
       " ['รีด', 0.000999000999000999],\n",
       " ['ริง', 0.001998001998001998],\n",
       " ['ราย', 0.000999000999000999],\n",
       " ['รัว', 0.000999000999000999],\n",
       " ['รัก', 0.001998001998001998],\n",
       " ['ระอ', 0.000999000999000999],\n",
       " ['รอบ', 0.000999000999000999],\n",
       " ['รอค', 0.000999000999000999],\n",
       " ['รจั', 0.000999000999000999],\n",
       " ['ย่”', 0.000999000999000999],\n",
       " ['ย่า', 0.000999000999000999],\n",
       " ['ยๆท', 0.000999000999000999],\n",
       " ['ยแล', 0.000999000999000999],\n",
       " ['ยแย', 0.000999000999000999],\n",
       " ['ยเห', 0.000999000999000999],\n",
       " ['ยู่', 0.002997002997002997],\n",
       " ['ยาย', 0.000999000999000999],\n",
       " ['ยัง', 0.003996003996003996],\n",
       " ['ยหร', 0.000999000999000999],\n",
       " ['ยวร', 0.000999000999000999],\n",
       " ['ยวป', 0.000999000999000999],\n",
       " ['ยลง', 0.000999000999000999],\n",
       " ['ยยย', 0.001998001998001998],\n",
       " ['ยย!', 0.000999000999000999],\n",
       " ['ยบก', 0.000999000999000999],\n",
       " ['ยนี', 0.000999000999000999],\n",
       " ['ยนะ', 0.001998001998001998],\n",
       " ['ยตา', 0.000999000999000999],\n",
       " ['ยดู', 0.000999000999000999],\n",
       " ['ยงล', 0.000999000999000999],\n",
       " ['ยค่', 0.000999000999000999],\n",
       " ['ยขอ', 0.001998001998001998],\n",
       " ['ยก็', 0.000999000999000999],\n",
       " ['ย!ค', 0.000999000999000999],\n",
       " ['ม้ข', 0.000999000999000999],\n",
       " ['ม่ไ', 0.000999000999000999],\n",
       " ['ม่เ', 0.000999000999000999],\n",
       " ['ม่ร', 0.000999000999000999],\n",
       " ['ม่บ', 0.001998001998001998],\n",
       " ['ม่ต', 0.000999000999000999],\n",
       " ['ม่ข', 0.000999000999000999],\n",
       " ['มใน', 0.000999000999000999],\n",
       " ['มื่', 0.000999000999000999],\n",
       " ['มี่', 0.002997002997002997],\n",
       " ['มีแ', 0.000999000999000999],\n",
       " ['มีอ', 0.000999000999000999],\n",
       " ['มีช', 0.000999000999000999],\n",
       " ['มีค', 0.000999000999000999]]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_list[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:17.732032Z",
     "start_time": "2019-05-27T14:49:17.725247Z"
    }
   },
   "outputs": [],
   "source": [
    "#graph_ngram(max_list) #แสดงกราฟบอก max และ value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:17.740717Z",
     "start_time": "2019-05-27T14:49:17.732970Z"
    }
   },
   "outputs": [],
   "source": [
    "def edit_distance(list_A,list_B):\n",
    "    A_items = []\n",
    "    A_values = []\n",
    "    B_items = []\n",
    "    B_values = []\n",
    "    for i in list_A:\n",
    "        A_items.append(i[0])\n",
    "        A_values.append(i[1])\n",
    "    for i in list_B:\n",
    "        B_items.append(i[0])\n",
    "        B_values.append(i[1])\n",
    "    matrixAB = []\n",
    "    for a in A_items:\n",
    "        sub_matrixAB = []\n",
    "        for b in B_items:\n",
    "            sub_matrixAB.append(editdistance.eval(a,b))\n",
    "        matrixAB.append(sub_matrixAB)\n",
    "    return A_values,B_values,matrixAB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-27T14:49:17.979130Z",
     "start_time": "2019-05-27T14:49:17.741658Z"
    }
   },
   "outputs": [],
   "source": [
    "frequency_A,frequency_B,matrixAB = edit_distance(max_list[0][1],max_list[0][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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