Text Mining.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 文字探勘實作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 產生 2-gram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "那我\n",
      "我們\n",
      "們酸\n",
      "酸民\n",
      "民婉\n",
      "婉君\n",
      "君也\n",
      "也可\n",
      "可以\n",
      "以報\n",
      "報名\n",
      "名嗎\n"
     ]
    }
   ],
   "source": [
    "input_sentence='那我們酸民婉君也可以報名嗎'\n",
    "\n",
    "sentence  = input_sentence.decode('utf-8')\n",
    "\n",
    "for i in range(0, len(sentence) - 2 + 1):\n",
    "    print sentence[i:i+2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 產生 3-gram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "那我們\n",
      "我們酸\n",
      "們酸民\n",
      "酸民婉\n",
      "民婉君\n",
      "婉君也\n",
      "君也可\n",
      "也可以\n",
      "可以報\n",
      "以報名\n",
      "報名嗎\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, len(sentence) -3 + 1):\n",
    "    print sentence[i:i+3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建立 n-gram 函式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ngram(input_sentence, n = 2):\n",
    "    word_dic = {}\n",
    "    sentence  = input_sentence.decode('utf-8')\n",
    "    for i in range(0, len(sentence) - n + 1):\n",
    "        if sentence[i:i+n] not in word_dic:\n",
    "            word_dic[sentence[i:i+n]] = 1\n",
    "        else:\n",
    "            word_dic[sentence[i:i+n]] = word_dic[sentence[i:i+n]] + 1\n",
    "    return word_dic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 測試 bigram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "婉君 報名 可以 名嗎 君也 也可 民婉 酸民 以報\n"
     ]
    }
   ],
   "source": [
    "for word in ngram('酸民婉君也可以報名嗎'):\n",
    "    print word,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 測試 trigram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "酸民婉\n",
      "君也可\n",
      "名嗎?\n",
      "可以報\n",
      "也可以\n",
      "們酸民\n",
      "以報名\n",
      "民婉君\n",
      "報名嗎\n",
      "那我們\n",
      "婉君也\n",
      "我們酸\n"
     ]
    }
   ],
   "source": [
    "for word in ngram('那我們酸民婉君也可以報名嗎?' ,n=3):\n",
    "    print word"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 移除標點符號"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence = '123?'\n",
    "skip_word = '.?'\n",
    "len([word for word in sentence if word in skip_word]) > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "skip_dic = '?.,、　「！】？：《」》～【'.decode('utf-8')\n",
    "def ngram(input_sentence, n = 2, skip_word= skip_dic):\n",
    "    word_dic = {}\n",
    "    sentence  = input_sentence.decode('utf-8')\n",
    "    for i in range(0, len(sentence) - n + 1):\n",
    "        invalid = len([word for word in sentence[i:i+n] if word in skip_word])\n",
    "        if sentence[i:i+n] not in word_dic and invalid ==0:\n",
    "            word_dic[sentence[i:i+n]] = 1\n",
    "        elif invalid ==0:\n",
    "            word_dic[sentence[i:i+n]] = word_dic[sentence[i:i+n]] + 1\n",
    "    return word_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我們\n",
      "婉君\n",
      "報名\n",
      "們酸\n",
      "可以\n",
      "名嗎\n",
      "君也\n",
      "也可\n",
      "那我\n",
      "酸民\n",
      "以報\n"
     ]
    }
   ],
   "source": [
    "for word in ngram('那我們酸民、婉君也可以報名嗎?'):\n",
    "    print word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "db = sqlite3.connect('news.db') \n",
    "cur = db.cursor()\n",
    "cur.execute('select * from news_entry')\n",
    "allNews = cur.fetchall()\n",
    "dic = {}\n",
    "for rec in allNews:\n",
    "    ngram_dic = ngram(rec[4].encode('utf-8'))\n",
    "    for ele in ngram_dic:\n",
    "        if ele not in dic:\n",
    "            dic[ele] = ngram_dic[ele]\n",
    "        else:\n",
    "            dic[ele] = dic[ele] + ngram_dic[ele]\n",
    "cur.close()\n",
    "db.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "搶先 3\n",
      "五月 3\n",
      "八日 3\n",
      "二十 3\n",
      "十八 3\n",
      "月二 3\n",
      "天找 2\n",
      "頭條 2\n",
      "警消 2\n",
      "山難 2\n",
      "先報 2\n",
      "報頭 2\n",
      "劈腿 2\n",
      "條搶 2\n",
      "各報 2\n"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "words_freq = sorted(dic.iteritems(),key=operator.itemgetter(1),reverse=True)\n",
    "    \n",
    "for word in words_freq:\n",
    "    if word[1] >= 2:\n",
    "        print word[0], word[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 長詞優先演算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何依標點符號切開字詞"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3年前就讀高雄應用大學二年級的陳顯佳\n",
      "為閃避跨越雙黃線來車\n",
      "撞上路邊\n",
      "摔斷頸椎\n",
      "頸部以下全癱\n",
      "嘴被插管\n",
      "父母每天去看他\n",
      "他都意識清醒\n",
      "眨眼流淚\n",
      "17天後他因頸椎受損抑制呼吸\n",
      "醫師用盡強力針與電擊都無效\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "delimiter = \"，|。|、\"  \n",
    "text = '3年前就讀高雄應用大學二年級的陳顯佳，為閃避跨越雙黃線來車，撞上路邊、摔斷頸椎，頸部以下全癱，嘴被插管，父母每天去看他，他都意識清醒、眨眼流淚，17天後他因頸椎受損抑制呼吸，醫師用盡強力針與電擊都無效，'\n",
    "for i in re.split(delimiter, text):\n",
    "    print i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 測試切除效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def splitSentense(text, delimiter):\n",
    "    return re.split(delimiter, text)\n",
    "    \n",
    "delimiter = \"，|。|、|（|）|／|《|》|】|【|「|」|；|：\".decode('utf-8')  \n",
    "db = sqlite3.connect('news.db') \n",
    "cur = db.cursor()\n",
    "cur.execute('select * from news_entry')\n",
    "allNews = cur.fetchall()\n",
    "sentenceAry = []\n",
    "for rec in allNews:\n",
    "    text = rec[5]\n",
    "    sentenceAry += splitSentense(text,delimiter)\n",
    "cur.close()\n",
    "db.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "台南市區昨出現大型\n",
      "尋人啟事\n",
      "廣告牆\n",
      "看板用紅色大字寫\n",
      "市長賴清德與台南市民意已失聯\n",
      "\n",
      "\n",
      "第142天\n"
     ]
    }
   ],
   "source": [
    "for sentence in sentenceAry[0:8]:\n",
    "    print sentence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 移除關鍵字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "台南市區昨出現大型\n",
      "台南市區昨出現大型\n"
     ]
    }
   ],
   "source": [
    "def removeKey(text, keyword):\n",
    "    textAry= text\n",
    "    for key in keyword:\n",
    "        textAry = ''.join(textAry.split(key.decode('utf-8')))\n",
    "    return textAry\n",
    "print sentenceAry[0]\n",
    "print removeKey(sentenceAry[0], ['民進黨', '後天'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'\\u5c07\\u516c\\u5e03'"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = '民進黨後天將公布'.decode('utf-8')\n",
    "removeKey(a, ['民進黨', '後天'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 修改ngram 處理函式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ngram(input_sentence, n = 2):\n",
    "    word_dic = {}\n",
    "    sentence  = input_sentence\n",
    "    for i in range(0, len(sentence) - n + 1):        \n",
    "        if sentence[i:i+n] not in word_dic:\n",
    "            word_dic[sentence[i:i+n]] = 1\n",
    "        else:\n",
    "            word_dic[sentence[i:i+n]] = word_dic[sentence[i:i+n]] + 1\n",
    "    return word_dic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 依長詞優先產生關鍵字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "報導 18\n",
      "表示 17\n",
      "景氣 15\n",
      "美國 14\n",
      "台灣 14\n",
      "一個 13\n",
      "萬元 13\n",
      "警方 13\n",
      "綜合 13\n",
      "藍燈 13\n",
      "國賠 12\n",
      "綜合報導 12\n",
      "時間 12\n",
      "26 11\n",
      "難民 11\n",
      "外電報導 10\n",
      "搜救 10\n",
      "山難搜救 10\n",
      "夜店 10\n",
      "今年 10\n",
      "合外電報 10\n",
      "襲擊 10\n",
      "手機 10\n",
      "男子 10\n",
      "生產 10\n",
      "綜合外電 10\n",
      "今日出版 9\n",
      "逮捕 9\n",
      "日出版的 9\n",
      "以色列 9\n"
     ]
    }
   ],
   "source": [
    "keywords=[]        \n",
    "ret_terms={}\n",
    "words_freq    = []\n",
    "for term_length in range(4,1,-1):\n",
    "    word_dic = {}\n",
    "    for sentence in sentenceAry:\n",
    "        text_list = removeKey(sentence,keywords)        \n",
    "        ngram_words = ngram(text_list,term_length) \n",
    "        for word in ngram_words:\n",
    "            if word not in word_dic:\n",
    "                word_dic[word] = 1\n",
    "            else:\n",
    "                word_dic[word] += ngram_words[word]   \n",
    "    for word in word_dic:\n",
    "        if word_dic[word] >= 5:\n",
    "            keywords.append(word.encode('utf-8'))            \n",
    "            ret_terms.update({word:word_dic[word]})\n",
    "\n",
    "sorted_terms = sorted(ret_terms.iteritems(),key=operator.itemgetter(1),reverse=True) \n",
    "for term in sorted_terms[0:30]:\n",
    "    print term[0], term[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用jieba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大巨蛋\n",
      "案對\n",
      "市府\n",
      "同仁\n",
      "下\n",
      "封口令\n",
      "？\n",
      "　\n",
      "柯P\n",
      "否認\n"
     ]
    }
   ],
   "source": [
    "import jieba\n",
    "\n",
    "sentence = \"大巨蛋案對市府同仁下封口令？　柯P否認\"\n",
    "words = jieba.cut(sentence, cut_all=False)\n",
    "for word in words:\n",
    "    print word"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用jieba 分詞"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full Mode: 大巨蛋/ 巨蛋/ 案/ 對/ 市府/ 同仁/ 下/ 封口/ 封口令/ 口令/ / / / 柯/ P/ 否/ 認\n",
      "Default Mode: 大巨蛋/ 案對/ 市府/ 同仁/ 下/ 封口令/ ？/ 　/ 柯P/ 否認\n"
     ]
    }
   ],
   "source": [
    "#encoding=utf-8\n",
    "import jieba\n",
    "\n",
    "seg_list = jieba.cut(\"大巨蛋案對市府同仁下封口令？　柯P否認\", cut_all=True)\n",
    "print \"Full Mode:\", \"/ \".join(seg_list) \n",
    "\n",
    "seg_list = jieba.cut(\"大巨蛋案對市府同仁下封口令？　柯P否認\", cut_all=False)\n",
    "print \"Default Mode:\", \"/ \".join(seg_list) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用自訂辭典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import jieba\n",
    "\n",
    "jieba.load_userdict(\"userdict.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 新增或刪除字詞"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import jieba\n",
    "jieba.add_word('柯P',100, 'nr')\n",
    "jieba.del_word('柯P')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 表列詞性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大巨蛋 ns\n",
      "案 ng\n",
      "對 p\n",
      "市府 n\n",
      "同仁 nr\n",
      "下 f\n",
      "封口令 n\n",
      "？ x\n",
      "　x\n",
      "柯P nr\n",
      "否認 v\n"
     ]
    }
   ],
   "source": [
    "import jieba.posseg as pseg\n",
    "words = pseg.cut(\"大巨蛋案對市府同仁下封口令？　柯P否認\")\n",
    "for w in words:\n",
    "    print w.word, w.flag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 列出關鍵字所在"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大巨蛋 0 3\n",
      "案對 3 5\n",
      "市府 5 7\n",
      "同仁 7 9\n",
      "下 9 10\n",
      "封口令 10 13\n",
      "？ 13 14\n",
      "　14 15\n",
      "柯P 15 17\n",
      "否認 17 19\n"
     ]
    }
   ],
   "source": [
    "words = jieba.tokenize(unicode(sentence, 'utf-8'))\n",
    "\n",
    "for tw in words:\n",
    "    print tw[0], tw[1], tw[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 依詞性切出關鍵詞"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "柯P\n"
     ]
    }
   ],
   "source": [
    "import jieba.analyse\n",
    "tags = jieba.analyse.extract_tags(sentence, 1, allowPOS = ['nr'])\n",
    "print \",\".join(tags)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 列出斷詞位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大巨蛋 0 3\n",
      "案對 3 5\n",
      "市府 5 7\n",
      "同仁 7 9\n",
      "下 9 10\n",
      "封口令 10 13\n",
      "？ 13 14\n",
      "　14 15\n",
      "柯P 15 17\n",
      "否認 17 19\n"
     ]
    }
   ],
   "source": [
    "words = jieba.tokenize(unicode(sentence, 'utf-8'))\n",
    "\n",
    "for tw in words:\n",
    "    print tw[0], tw[1], tw[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 表列關鍵詞"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "歐洲/電商/組織/包裹/物流/一來/領域/遞送/緊密/Europe/市場/順暢/根據/決電商/服務/集團/總共結合/障礙/積極/目標\n"
     ]
    }
   ],
   "source": [
    "import jieba.analyse\n",
    "sentence = \"根據歐洲電商組織(Ecommerce Europe) 最新消息得知，\\\n",
    "該集團積極讓歐洲的包裹遞送服務與電商領域有更緊密結合。如此一來，\\\n",
    "便可解決電商物流的障礙。此組織總共結合歐洲 16 國的電商協會，\\\n",
    "目標整合歐洲的包裹物流市場，讓商品跨境更為順暢\"\n",
    "tags = jieba.analyse.extract_tags(sentence)\n",
    "print '/'.join(tags)\n",
    "\n",
    "jieba.analyse.set_stop_words(\"stopword.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成文字雲"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "import jieba\n",
    "section = requests.get('http://www.appledaily.com.tw/realtimenews/section/new/1')\n",
    "soup = BeautifulSoup(section.text)\n",
    "domain = 'http://www.appledaily.com.tw'\n",
    "worddic = {}\n",
    "for news in soup.select('.rtddt'):    \n",
    "    main = news.select('h1')[0].text\n",
    "    m = re.match('(.*)\\((\\d+)\\)',main)\n",
    "    if m:\n",
    "        title = m.group(1)\n",
    "        popularity = int(m.group(2))\n",
    "    for wd in jieba.cut(title):\n",
    "        if wd not in worddic:\n",
    "            worddic[wd] = 1\n",
    "        else:\n",
    "            worddic[wd] = worddic[wd] + 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SXkhFVUmHCBW53H6EaTDq2bJrOVKpzG2R0B5MJgPFpflNft+4YwlTx96IVuN6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\n6DmwDgB5WDQlP30Licr/iqGxmPtaafn0BUISMonICU5lzHRj1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d7Gd4of9WrsB3WvTNmr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\nt7fTltU3v/7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bNTnpFB/bQ8HBHTSU5uM3eioj7/g/fBMmIZK0nciIpDICEmcw/aUP2l1/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sc5NccdZpliX1/Evr5YSkrJnno3XbatR+znKJE3Hj5C6fsfEfLATKdrTdk55M2aQ+ic2fiN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\n1FphOMCfJ1wdPn1lUVzfQ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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pytagcloud import create_tag_image, make_tags\n",
    "from IPython.display import Image\n",
    "from operator import itemgetter\n",
    "swd = sorted(worddic.iteritems(), key=itemgetter(1), reverse=True)\n",
    "swd = [w for w in swd[1:50] if w[0] not in '【】...'.decode('utf-8')]\n",
    "\n",
    "tags = make_tags(swd, maxsize=120)\n",
    "create_tag_image(tags, 'wc.png', size=(600,400), fontname='SimHei')\n",
    "Image(filename='wc.png') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "import jieba\n",
    "section = requests.get('http://www.appledaily.com.tw/realtimenews/section/new/1')\n",
    "soup = BeautifulSoup(section.text)\n",
    "domain = 'http://www.appledaily.com.tw'\n",
    "worddic = {}\n",
    "for news in soup.select('.rtddt'):    \n",
    "    main = news.select('h1')[0].text\n",
    "    m = re.match('(.*)\\((\\d+)\\)',main)\n",
    "    if m:\n",
    "        title = m.group(1)\n",
    "        popularity = int(m.group(2))\n",
    "    for wd in jieba.analyse.extract_tags(title):\n",
    "        if wd not in worddic:\n",
    "            worddic[wd] = 1\n",
    "        else:\n",
    "            worddic[wd] = worddic[wd] + 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用pytagcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3jKCenVz22c0Wtj87n9iJw0icMeacXofAxcGTObmYHHYm+fsz2s+PtsTKt+t07KurY4iP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\nJoW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im7p1Hlu0+IWH4hc5uObcE5XmkgrMen2/YywGI22V1bRV1RCUGDuh9cEm7swGQdkH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\nMZjM4GT7qhBvFQ/OnMg90zL5/NR5fvDxFn60bCapDuqwStbtpuHUeQD2Pv8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hi9cu6zlP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0CX002Pyx3XHELLoRoOvPQBOWfOR5cZ7mfk6uzBZemO+j6XpXvUn2AVGilP3LSFrOkGllw3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\n7oKOXA3dfIUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pytagcloud import create_tag_image, make_tags\n",
    "from IPython.display import Image\n",
    "from operator import itemgetter\n",
    "swd = sorted(worddic.iteritems(), key=itemgetter(1), reverse=True)\n",
    "swd = [w for w in swd[1:50]]\n",
    "\n",
    "tags = make_tags(swd, maxsize=120)\n",
    "create_tag_image(tags, 'wc.png', size=(600,400), fontname='SimHei')\n",
    "Image(filename='wc.png') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(u'\\u516c\\u5eab', 7), (u'\\u66f4\\u65b0', 5), (u'\\u4e16\\u8db3', 2), (u'\\u8cc7\\u683c\\u8cfd', 2), (u'\\u61b2\\u6539', 2), (u'\\u4eba\\u6c11', 2), (u'\\u838e\\u6728', 1), (u'\\u756b\\u6280', 1), (u'\\u4e3b\\u4efb', 1), (u'\\u51b7\\u4e0d\\u9632', 1), (u'\\u5b89\\u6253', 1), (u'\\u52fe\\u5973', 1), (u'\\u653f\\u9ee8', 1), (u'\\u570b\\u6c11\\u9ee8\\u5834', 1), (u'\\u5c48\\u4e2d\\u6046', 1), (u'24', 1), (u'\\u544a\\u767d', 1), (u'\\u771f\\u60c5', 1), (u'\\u6700\\u5927', 1), (u'23', 1), (u'\\u9451\\u5b9a', 1), (u'\\u827e\\u6bd4', 1), (u'\\u8102\\u80aa', 1), (u'\\u6c34\\u9f8d\\u982d', 1), (u'\\u9b27\\u5287', 1), (u'\\u6843\\u733f', 1), (u'\\u6700\\u5f37', 1), (u'\\u7d10\\u7d04', 1), (u'\\u6316\\u51fa', 1), (u'\\u8354\\u679d', 1), (u'\\u8f38\\u5f97', 1), (u'\\u6731\\u9394\\u57fa\\u611b\\u5c07', 1), (u'\\u963f\\u59b9', 1), (u'\\u7aef\\u5348', 1), (u'\\u7206\\u68da', 1), (u'\\u5b78\\u6821', 1), (u'12', 1), (u'\\u52d5\\u756b', 1), (u'\\u6253\\u5361', 1), (u'\\u5c0f\\u6f32', 1), (u'Sam', 1), (u'\\u6545\\u969c', 1), (u'\\u5343\\u842c', 1), (u'\\u932f\\u8aa4', 1), (u'\\u99d5\\u99db\\u56de', 1), (u'\\u6d77\\u57df', 1), (u'\\u5973\\u7ae5', 1), (u'FDA', 1), (u'\\u5b9a\\u6027', 1)]\n"
     ]
    }
   ],
   "source": [
    "from pytagcloud import create_tag_image, make_tags\n",
    "from IPython.display import Image\n",
    "from operator import itemgetter\n",
    "swd = sorted(worddic.iteritems(), key=itemgetter(1), reverse=True)\n",
    "swd = [w for w in swd[1:50]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用 tagcanvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <head>\n",
       "    <title>Word Cloud</title>\n",
       "    <script src=\"tagcanvas.min.js\" type=\"text/javascript\"></script>\n",
       "  </head>\n",
       "  <body>\n",
       "    <h1>Word Cloud</h1>\n",
       "    <div id=\"myCanvasContainer\">\n",
       "      <canvas width=\"300\" height=\"300\" id=\"myCanvas\">\n",
       "        <p>Anything in here will be replaced on browsers that support the canvas element</p>\n",
       "      </canvas>\n",
       "    </div>\n",
       "    <div id=\"tags\"><ul id=\"weightTags\"><li><a data-weight=\"10\" style=\"font-size: 7ex\">公庫</a></li><li><a data-weight=\"10\" style=\"font-size: 5ex\">更新</a></li><li><a data-weight=\"10\" style=\"font-size: 2ex\">世足</a></li><li><a data-weight=\"10\" style=\"font-size: 2ex\">資格賽</a></li><li><a data-weight=\"10\" style=\"font-size: 2ex\">憲改</a></li><li><a data-weight=\"10\" style=\"font-size: 2ex\">人民</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">莎木</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">畫技</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">主任</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">冷不防</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">安打</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">勾女</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">政黨</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">國民黨場</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">屈中恆</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">24</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">告白</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">真情</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">最大</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">23</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">鑑定</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">艾比</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">脂肪</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">水龍頭</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">鬧劇</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">桃猿</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">最強</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">紐約</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">挖出</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">荔枝</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">輸得</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">朱鎔基愛將</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">阿妹</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">端午</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">爆棚</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">學校</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">12</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">動畫</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">打卡</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">小漲</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">Sam</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">故障</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">千萬</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">錯誤</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">駕駛回</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">海域</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">女童</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">FDA</a></li><li><a data-weight=\"10\" style=\"font-size: 1ex\">定性</a></li></ul></div>\n",
       "  </body>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$.getScript(\"tagcanvas.min.js\", function () {\n",
       "\n",
       "TagCanvas.Start('myCanvas','weightTags', {\n",
       " textFont: null,\n",
       " textColour: null,\n",
       " weight: true,\n",
       " weightMode: 'both',\n",
       " textFont: 'Impact,\"Arial Black\",sans-serif',\n",
       " /* more options */\n",
       " \n",
       "});\n",
       "$('#tags').css('display','None');\n",
       "});\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML \n",
    "\n",
    "page= '''\n",
    "  <head>\n",
    "    <title>Word Cloud</title>\n",
    "    <script src=\"tagcanvas.min.js\" type=\"text/javascript\"></script>\n",
    "  </head>\n",
    "  <body>\n",
    "    <h1>Word Cloud</h1>\n",
    "    <div id=\"myCanvasContainer\">\n",
    "      <canvas width=\"300\" height=\"300\" id=\"myCanvas\">\n",
    "        <p>Anything in here will be replaced on browsers that support the canvas element</p>\n",
    "      </canvas>\n",
    "    </div>\n",
    "    <div id=\"tags\"><ul id=\"weightTags\">%s</ul></div>\n",
    "  </body>\n",
    "'''\n",
    "\n",
    "js = '''\n",
    "TagCanvas.Start('myCanvas','weightTags', {\n",
    " textFont: null,\n",
    " textColour: null,\n",
    " weight: true,\n",
    " weightMode: 'both',\n",
    " textFont: 'Impact,\"Arial Black\",sans-serif',\n",
    " /* more options */\n",
    " \n",
    "});\n",
    "$('#tags').css('display','None');\n",
    "'''\n",
    "\n",
    "import IPython\n",
    "from IPython.core.display import display_html, display_javascript, Javascript\n",
    "\n",
    "js_libs = ['tagcanvas.min.js']\n",
    "li_str = ''\n",
    "for li in swd:\n",
    "    li_str += '<li><a data-weight=\"10\" style=\"font-size: %dex\">%s</a></li>'%(li[1] , li[0])\n",
    "    \n",
    "display_html(IPython.core.display.HTML(data=page%(li_str)))\n",
    "display_javascript( Javascript(data=js, lib= js_libs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import jieba\n",
    "ary = ['【更新】柯P：洪智坤洩漏公文案還沒看到公文　今處理',\n",
    "'留洪智坤 柯：殘障求職不易',\n",
    "'人事處議處洪智坤　柯P：不清楚議處結果']\n",
    "corpus = []\n",
    "for title in ary:\n",
    "    corpus.append(' '.join(jieba.cut(title)))\n",
    "\n",
    "#jieba.add_word('洪智坤',100, 'nr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【 更新 】 柯P ： 洪智坤 洩漏 公文 案還 沒 看到 公文 　 今處理 留 洪智坤   柯 ： 殘障 求職 不易 人事 處議 處 洪智坤 　 柯P ： 不 清楚 議處 結果\n"
     ]
    }
   ],
   "source": [
    "for ele in corpus:\n",
    "    print ele,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不易 人事 今處理 公文 更新 柯p 案還 殘障 求職 洩漏 洪智坤 清楚 看到 結果 處議 議處 [[0 1 0]\n",
      " [0 0 1]\n",
      " [1 0 0]\n",
      " [2 0 0]\n",
      " [1 0 0]\n",
      " [1 0 1]\n",
      " [1 0 0]\n",
      " [0 1 0]\n",
      " [0 1 0]\n",
      " [1 0 0]\n",
      " [1 1 1]\n",
      " [0 0 1]\n",
      " [1 0 0]\n",
      " [0 0 1]\n",
      " [0 0 1]\n",
      " [0 0 1]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer() \n",
    "X = vectorizer.fit_transform(corpus)\n",
    "\n",
    "word = vectorizer.get_feature_names() \n",
    "for w in word:\n",
    "    print w,\n",
    "    \n",
    "print X.transpose().toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.          0.          0.31738473  0.63476946  0.31738473  0.24137927\n",
      "   0.31738473  0.          0.          0.31738473  0.18745253  0.\n",
      "   0.31738473  0.          0.          0.        ]\n",
      " [ 0.54645401  0.          0.          0.          0.          0.          0.\n",
      "   0.54645401  0.54645401  0.          0.32274454  0.          0.          0.\n",
      "   0.          0.        ]\n",
      " [ 0.          0.41074684  0.          0.          0.          0.31238356\n",
      "   0.          0.          0.          0.          0.2425937   0.41074684\n",
      "   0.          0.41074684  0.41074684  0.41074684]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "transformer = TfidfTransformer()\n",
    "tfidf = transformer.fit_transform(X)\n",
    "weight = tfidf.toarray()    \n",
    "print weight\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 計算Cosine Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.          0.06049928  0.12087772]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "cosine_similarities = linear_kernel(tfidf[0], tfidf).flatten()\n",
    "print cosine_similarities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 列出最相關文章"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【更新】柯P：洪智坤洩漏公文案還沒看到公文　今處理\n",
      "人事處議處洪智坤　柯P：不清楚議處結果\n"
     ]
    }
   ],
   "source": [
    "related_docs_indices = cosine_similarities.argsort()[:-3:-1]\n",
    "related_docs_indices\n",
    "for index in related_docs_indices:\n",
    "    print ary[index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析6/17 新聞內容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from C:\\Python27\\lib\\site-packages\\jieba\\dict.txt ...\n",
      "DEBUG:jieba:Building prefix dict from C:\\Python27\\lib\\site-packages\\jieba\\dict.txt ...\n",
      "Loading model from cache c:\\users\\david\\appdata\\local\\temp\\jieba.cache\n",
      "DEBUG:jieba:Loading model from cache c:\\users\\david\\appdata\\local\\temp\\jieba.cache\n",
      "Loading model cost 1.77000021935 seconds.\n",
      "DEBUG:jieba:Loading model cost 1.77000021935 seconds.\n",
      "Prefix dict has been built succesfully.\n",
      "DEBUG:jieba:Prefix dict has been built succesfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "靈匣網\n",
      "民報\n",
      "NOWnews\n",
      "蘋果日報\n",
      "中央社\n",
      "時報資訊\n",
      "中廣新聞網\n",
      "風傳媒\n",
      "新頭殼\n",
      "match生活網\n"
     ]
    }
   ],
   "source": [
    "from xml.dom import minidom\n",
    "from xml.etree import ElementTree\n",
    "import jieba.analyse\n",
    "\n",
    "f = open('1435449602.xml', 'r')\n",
    "events=ElementTree.fromstring(f.read())\n",
    "f.close()\n",
    "corpus = []\n",
    "ary= []\n",
    "src = []\n",
    "for elem in events.findall('./channel/item'):\n",
    "    guid = elem.find('guid').text\n",
    "    title = elem.find('title').text\n",
    "    description = elem.find('description').text\n",
    "    pubDate = elem.find('pubDate').text\n",
    "    source = elem.find('source').text\n",
    "    src.append(source)\n",
    "    ary.append(title)\n",
    "    corpus.append(' '.join(jieba.analyse.extract_tags(description, 20)))\n",
    "dict1 = {}\n",
    "for ele in src:    \n",
    "    dict1[ele] = ele\n",
    "for ele in dict1.keys():\n",
    "    print ele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from xml.dom import minidom\n",
    "from xml.etree import ElementTree\n",
    "import jieba.analyse\n",
    "\n",
    "f = open('1435449602.xml', 'r')\n",
    "events=ElementTree.fromstring(f.read())\n",
    "f.close()\n",
    "corpus = []\n",
    "ary= []\n",
    "for elem in events.findall('./channel/item'):\n",
    "    title = elem.find('title').text\n",
    "    description = elem.find('description').text\n",
    "    source = elem.find('source').text\n",
    "    ary.append(title)\n",
    "    corpus.append(' '.join(jieba.analyse.extract_tags(description, 20)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "br 現場 樂園 八仙 檢警 人員 新北市 了解 昨晚 空間 引發 偵辦 已經 這次 活動 粉塵 主辦 地檢署 舞台 士林\n",
      "醫院 br 病患 受傷 超過 統計 腳部 治療 50 人數 淡水 插管 台北 引發 粉塵要 院長 噴灑 樂園 相當 27\n",
      "br 櫃員機 希臘 千部 資金 銀行 債務 他們 估計 當局 現金 金供 資金持續 全國 違約 擔心 協調 明顯 業界 三間\n",
      "br 台鐵 南站 基隆 車站 新車 旅客 北站 完工 啟用 減少 鐵工局 出入口 一分 只要 千度 整建 外觀 必須 總經費\n",
      "樂園 現場 八仙 粉塵 瞬間 煉獄 新北市 舞台 爆炸 彩色 玉米粉 民眾 人間 遊客 宛如 昨晚 火海 生粉 水上 一樣\n",
      "基改 法國人 消費者 標示 食品 美食 蘇菲 法國 食材 資訊 超市 成分 東西 很難 態度 含基改 市場 巴黎 食物 基因\n",
      "因為 醫院 一場 家長 急診室 驚恐 爆炸 病患 意外 進醫院 27 現場 不斷 傷者 傷名 這起 救護車 婦女 樂園 仍待\n",
      "比賽 艾德蒙 分鐘 岩淵 加拿大 日本 勝球 Cup 真奈 節奏 場雙方 攻進 致勝 強賽 補上 挺進 頓舉行 隊主導 27 分鐘攻\n",
      "開池 蓋屋 出火 豎柱 納采 移徙 合木 安香 入宅 動土 安門 訂盟 起基 開市 冠笄 好運來 嫁娶 旺旺 拆卸 祈福\n",
      "br 金曲 江蕙 特別 辛苦 努力 謝謝 強調 機前 因為 時詢問 爺對 其實 還特別 台視 這個獎 獎到 封麥 拿獎 介紹\n",
      "br 高溫 地區 半部 影響 機會 各地 及東 天氣 指標 暫陣雨 高壓 出現 暫雷陣 過後 天數 山區 等級 炎熱 六月\n",
      "br 樂園 八仙 28 協助 生粉 27 燙傷 毛治國 傷患 洪秀柱 加強 影響 原訂 運送 活動 鹿野 決定 舉辦 觀光產業\n",
      "希臘 歐元區 協議 30 達成 救助 公投 瓦魯法 財政部長 債務 一個 逐漸 然後再 爭取 延長 計劃 英國 貨幣 拒絕 經濟\n",
      "志祥 金曲 表演 蔡依林 26 難免 壓力 台北 看到 巨蛋 舞王 問到羅志祥 台淚 嘉賓 指剛 丟掉 因為 27 獨舞 獎頒獎\n",
      "專輯 蔡依林 金曲 感動 得獎 國語 最佳 舞曲 真的 開心 26 這張 錦榮 傳訊息 領獎 問到 這麼 典禮 恭喜 台北\n",
      "br 中資 中國 企業 台灣 掛牌 外資 投資 美國 問題 認為 管理 還是 股權 資金 這些 認定 阿里巴巴 上市 持股\n",
      "樂園 新北市 八仙 人數 關園 救護車 醫及 統計 調查 暫停 受傷 嚴格 27 衛生局 28 營業 根據 日晚 性粉 導致\n",
      "大雕 行動 情侶 挪威 騰斯堡 還會 機構 宣傳 van 宣導 這樣 Eck 碎紙 顯示 Philip 對路 現身 RFSU 噴射 隨機\n",
      "高溫 天氣 半部 雖然 地區 br 影響 山區 各地 大雨 注意 局部 特別 及東 氣象局 區域 風過 有強 高壓 陸地\n",
      "德國 反應 br 核子 再生能源 關閉 已經 政策 這個 商業 一個 逐漸 2050 從現 推動 成電力 爐設 改採 巴伐利 將關閉\n",
      "br 最佳 獲得 歌王 金曲 蔡依林 專輯獎 國語 歌曲 則是 島嶼 繼續 專輯 張惠妹 首歌 天光 火器 副歌 今年 年度\n",
      "閃燃 傷亡 br color 玉米粉 周卓 發生 空間 play 靜電 爆炸 造成 火花 美國 趴時 外辦 樂園 還是 建議 活動\n",
      "br 判決 屍體 邱和順 柯洪玉蘭 他們 一個 nbsp 自白 被告 鄭新福 認定 證據 結果 因為 沒有 被判 分屍 什麼 指紋\n",
      "空姐 將展 風景靚點 成為 投給 評標 爭奇鬥妍 夏裝 佛靠 衣裝 旅遊 帥哥 人靠 金裝 腦汁 最欣賞 絞盡 空少 一票 盛夏\n",
      "希臘 br 歐元區 債權國 債權 籌碼 談判 衝擊 撙節 經濟 違約 發生 一個 ECB IMF 退歐 德國 達成 國內 當然\n",
      "新北市 br 現場 傷者 人數 人員 送醫 點多 桃園 21 家醫院 救護車 重傷 樂園 偵辦 偵訊 透過 粉塵 舉辦 調查\n",
      "br 中文系 中國 中華 文化 課綱 微調 我們 這個 文學 中文 發展 價值 這種 因為 灣社會 無法 他們 嚴時代 轉型\n",
      "訓練 br 戰場 一個 我們 進行 救護 戰爭 可以 新兵 部隊 入伍 階段 士兵 戰鬥 時間 問題 野外 互救 就是\n",
      "br 照護 護理 醫療 問題 災難 災民 慈善 醫院 發展 衛生 災區 準備 醫學 人員 理學 救援 角色 公共 感染\n",
      "br 柳丁 nbsp 推動 結合 行銷 農民 生產 收購 台灣 銷售 市場 進行 價格 產地 雲林 模式 特別 減少 方式\n",
      "br 進步 台灣 誠實 街招 左派 招牌 對策 掛著 他們 搖的 現實 政黨 不過 當然 思想 退步 退一步 老牌 浪漫\n",
      "美國 時速 系統 飛彈 防禦 研發 000 英里 亦不例外 泰晤士 大強 亟思 戰鬥機 將近 擁有 這些 騎士 軍方 競賽 機能\n",
      "div 自信心 女性 一種 這種 食指 表現 br 這類 問題 身邊 征服 做事 男性 令人尊敬 妥妥 給人 很傑 豐滿 基位\n",
      "div 享用 br 四處 財帛 印堂 部位 小富 有缺 不能 特別 豐滿 絞肉 天倉 財路 損害 之數 盡者 正而 骨起\n",
      "巴拉圭 足球 國家 巴西 奪得 決賽 球隊 世界 美洲 開始 2010 1998 出現 連續 打進 他們 賽事 奧運 四屆 2002\n",
      "div 看重 這個 選擇 塔羅牌 什麼 占卜 事業 然後憑 有興趣 規則 一張 中選擇 想著 br 直覺 東西 塔羅 不可 今生\n",
      "尼西 槍手 英國人 15 沙灘 畫面 冷血 照片 40 宣稱 瑞茲古伊 瑞茲古 25 Qayrawani 27 翻攝 確定 現場 名無辜 勝地\n",
      "希臘 歐元區 展延 債務 會議 穩定 戴松布倫 財政部長 因為 願意 確保 歐元 歐洲 風暴 階段 違約 可能 金融 表示 全力\n",
      "莫文蔚 開心 獲得 張惠妹 阿妹 最佳 歌后 金曲 原住民 還沒告訴 感謝 齊聚 迅與 Boxing 中拉 雙雙 他們 皮膚 則低 獲獎\n",
      "希臘 歐元區 經濟 歐洲 歐元 發展 衝擊 歐盟 穩定 帶來 嚴重 一體性 問題 未來 相當 債權 衰退 爬升 雪上加霜 谷底\n",
      "div 心機 安靜 這樣 選擇 選出 環境 結果 比童 現實 還是 規則 牌里 塔羅測試 殘酷 猶豫 情況 一張 想著 切記\n",
      "27 13 患者 台北 慈濟 進行 醫院 治療 全身 生命 靜脈 衛生局 28 多數 消防局 燒傷 輸液 穩定 機制 搶救\n",
      "希臘 歐元區 沙班 30 法國 結束 成員 未來 我們 命運 隨時 續留 想要 公投 並無希臘 必須 當前 明確 團結 27\n",
      "新鮮 蔬菜 瓜果 顏色 表現 水分 表皮 聲音 購買 飽滿 蛋則 不新鮮 發生 食物 流失 半球型 易腐 表面 氣溫 進水裡\n",
      "運動 夏季 心臟 養生 天氣 認為 養心 激動 炎熱 夏初 做做 心神 延年益 特別 臟氣 以養陽 可幫助 體內 拉耳 叩齒\n",
      "苦瓜 紅棗 齒莧 夏天 苦味 蒲公英 功效 出現 常吃 促進 吃些 楊梅 蘇葉 還有 蘇葉湯 殺菌 人體 身體 之食 消暑\n",
      "牛奶 吸收 食物 芝麻 100 營養素 補鈣 效果 毫克 促進 輔助 含鈣量 考慮 同時 還有 這三種 比如 毫升 咀嚼 蛋白\n",
      "考生 環境 呼吸 性鹼 酸中毒 中毒 激動 表現 症狀 緊張 代謝性 二氧化碳 酸性 預防 進行 過多 補充 營養 情緒 治療\n",
      "草莓 100 50 維他 榨汁 適量 調勻 生瘡 30 250 乾咳 飲服 10 氣血 蘋果 麻油 食欲不振 消化不良 口舌 每天\n",
      "台灣 台籍 鄭弘儀 偉大 日本 台灣人 允許 全體 紀念 想當 抗日 福星 爸爸 大屠殺 怎麼 大陸網 因為 當志願兵 只講 1942\n",
      "西瓜 中國 瓜果 紀曉嵐 認為 翡翠 形容 乾隆 活現 提筆 傳入 描寫 河圖 價值 當時 活靈 發現 對聯 文學 小販\n",
      "歷史 國民黨 閱兵 大陸 台灣 安倍 兩岸 當年 70 中華民國 抗日 和解 中日 恩仇 藉由 有關 也展 國共 抗戰 共產黨\n",
      "當晚 飯局 10 李春燕 陪酒 死亡 永州市 房間裡 餐會 2013 幹部 車裡 發生 陸媒 公室 同事 接待 中暑 醉酒 主任\n",
      "西瓜 慈禧 西瓜霜 獨到 當時 宮廷 最愛的 數十個 講究 御膳 清代 吃掉 料理 古人 挑嘴 一套 三拍 瓜瓤 百姓 喉科\n",
      "戰爭 紀念 抗戰 歷史 發生 戰敗國 網友 反省 德國 大戰 父親 大陸 國家 紐則 鄭弘儀 仍然 看待 能面 不同 感情\n",
      "老兵 抗戰 大陸 國民黨 胡葆琳 閱兵 防部 參與 邀請 這些 不過 多位 官方 一定 抗戰勝利 一個 國共 相傳 團結 參加\n",
      "幹部 能上能下 領導 官員 會議 規定 中共 強調 機制 推進 體制 試行 下台 審議 27 貪腐 嚴治黨 三嚴 汪玉凱 新規\n",
      "抗戰 八路 參加過 中華民國 老兵 防部 因為 參加 曾經 大陸 期間 領導 昭明 中共 娃娃兵 歷史 戰爭 態度 國民 貢獻\n",
      "改革 推動 許多 進行 隨著 經濟 大陸 基層 同時 文中 地方 中央 拖延 泛化 各級 2015 針對 出現 不斷 幹部\n",
      "金蟬花 中毒 出現 冬蟲 百株 真菌 一個 現象 醫院 頭暈 大陸 金蟬 絲體 夏草 嘔吐 導致 環境 感染 竹林 泡酒\n",
      "台灣 紀念 抗戰勝利 鄭弘儀 抗戰 犧牲 50 批評 65 萬人 文章 殖民 輿論 中國人 不願 忘祖 統治者 數典 新華社 寧可當\n",
      "蘇東坡 萬元 白居易 我們 部門 成長 薪俸 畢業 服務業 17 公部門 工業 人生 百姓 僱人員 無常 領先 歷史 慚諫紙 來往\n",
      "A股 中國 回歸 美國 企業 奇虎 上市 中資 市場 私有化 報導 網路 這些 暴風 尋求 10 互聯網 科技 公司 退市\n",
      "中國 美國 中國人 QDII2 外國 匯款 投資 中國民眾 不動產 海外 房地產 統計 資金 市場 顯示 購房 買家 協會 美元 成交\n",
      "貸款 銀行 Kabbage 傳統 企業 他們 自動化 機構 瑞拉 業者 美國 問題 放貸 線上 申請 審核 資金 不過 科技 中小\n",
      "太陽光 面板 太陽能 美國 發電量 發電 發電場 運作 電力 熱發 伊梵帕 技術 產生 傳統 蒸氣 結果 梵帕 鏡片 小時 計畫\n",
      "事務 英國 調查 計師 勞工階級 條件 集團 中產階級 師事務 發現 徵才 經驗 招募 進行 機會 畢業生 高貴 顯示 poshness SMCPC\n",
      "首爾 柯文 台北市 朴元淳 大眾 韓國 朴槿惠 運輸 費用 首爾市 政府 優惠 悠遊卡 調漲 不悅 捷運 市民 交通 取消 台北\n",
      "發電 電力 Plant 此技術 進行 地區 植物 根部 發展 一個 偏遠 這項 發電技術 LED 供給 荷蘭 生長 紅樹林 稻田 溼地\n",
      "26 恐怖 尼西 國內 人喪生 麥當勞 經濟部 組織 年減 24 發動 22 23 5.9 訂單 台灣 法國 攻擊 權經營 營店\n",
      "經濟 問題 台灣 成長 消費 民眾 因為 社會 低物價 雖然 四低 沒有 國際 環境 有利於 低稅 兩岸 資本 關鍵 低薪\n",
      "員警 槍械 標準 警械 使用 通緝犯 執法 情況 警政署 本案 國外 用槍 維持 國家 實務 公權力 依據 規定 相較 倒車\n",
      "國民黨 洪秀柱 勢力 總統 選舉 立委 參選 林義雄 英文 宋楚瑜 民進 這個 李登輝 主席 席次 打倒 第三 台灣 開始 沒有\n",
      "台大 學生 社會 升學 登山 募款 己所不欲 機會 同學 老師 考慮 大學 進入 弱勢 同學們 資源 這次 不應 沒有 顯然\n",
      "補票 逃票 旅客 上車 台鐵 火車 金額 車後補票 先上 查票 主動 不買票 萬元 票價 無法 手續費 10 還有 無票 自動\n",
      "論壇 雙城 柯文 共識 大陸 一五 善意 柯市 兩岸 之間 釋出 九二 大陸方 發展 沒有 進行 時刻 傳話 所樂見 問題\n",
      "民宅 消防人 傳出 延燒 木造 火勢 送醫 連棟 當店 火警 中山路 受困 救出 凌晨 灌救 有名 男子 趕緊 處便 破門架梯\n",
      "粉絲 現場 排隊 預購 茉莉 一出 第二本 小洋 開賣 面會 揮汗 場便 27 這次 燦爛 一種 新書見 過這本 周圍 希甜\n",
      "br 民宿 strong 08 大鵬灣 殼島 www 濕地 小時 大橋 莊園 跨海 會館 tw 濱灣 公園 電動 水映 com 300\n",
      "洪慈庸 力量 時代 公室 事務 開始 進行 競選辦 鄉親 傾聽 未來 參選 而戰 關心 公共 立委 夥伴 發聲 輕人舉 吳大學\n",
      "電影 預計 親節 門市 預購 他們 OPEN 消費者 熱潮 即日起 推出 商品 58 熱門品 3C 喜愛的 業者 精補類 售價 營補類\n",
      "42 單位 負責 男子 爆炸案 漏夜 員工 噴灑 創意 樂園 傷害 數百人重 蘆洲 師廖俊明 28 40 依業務過 維護義務 偵辦 盧建佑\n",
      "希臘 提款 ATM 27 億歐元 補鈔 全國 希臘民眾 路透 帕拉斯 公投 呼籲 600 愈排 銀行界 正慢 700 億台幣 瀕臨 權國\n",
      "賴柏豪 李昇峰 玉璽 白珍 王靖雯 林愛蓉 李慧珠 育英 林宗 名氏 薛明娟 信彥 宋芷薇 林霆 林思庭 學宇 古澤霖 江亭蓁 吳得明 廖千輔\n",
      "希臘 加薩 ECB 基斯 德拉 德拉吉 公投 將會 穩定 緊密 及希臘 會晤 持續 決定 拉斯 表示 政府 願意 總統府 瀕臨\n",
      "br 東所 受難者 林家 白色恐怖 當年 台灣 影片 政治犯 被關 監獄 自己 政治 軍法處 葉信 爺爺 他們 社會 奶奶 世代\n",
      "晚間 全案 爆炸案 紛紛 開置 將依竊 豈料 樂園 偵訊 將其 輕重傷 27 40 王姓 發現 皮夾 翻開 物櫃 偵辦 分駐\n",
      "樂園 八仙 醫院 名傷 新北市 受傷 市長 淡水 彩色 侯友 將近 閃燃 Asia 瞬間 問傷 關園 調查 晚間 當時 27\n",
      "醫院 當時 傷患 不斷 快點 現場 彩色 聲響 救護車 滿傷 抵達 Asia 聽到 瞬間 連軍方 因為 統計 床不夠 實在 晚間\n",
      "應變 災害 開設 送醫 市府 消防 中心 爆炸案 暫時 機關 呼籲 新北 吃緊 粉塵 送急診 王鼎鈞 一級 重傷 樂園 局處\n",
      "br 蔣丙煌 醫院 金屬 發生 娛樂 樂園 轉送 傷患 加強 粉塵 放空 八仙 台大 粉末 彩色 爆炸 用途 前往 意外\n",
      "風勢 主辦 Asia Play 活動 粉塵 現場 Color 彩色 太大 色粉 點結束 傷送醫 新北 閃燃 樂園 呂忠吉未 27 每個 這次\n",
      "希臘 歐元區 債權 普拉斯 公投 債務 撙節 協商 清單 討論 財政部長 過去 過程 改革 左派 民意 接受 展延 政府 到期\n",
      "查詢 新北市 醫院 指揮 電話 樂園 受傷 八仙 超過 傷勢 28 疑因 日電 119 02 27 致電 0963257119 縣市 粉塵\n",
      "資料 一個 照片 衣服 禮服 這件 這個 那種 這次 金曲 配件 蔡依林 一樣 顯得 路線 什麼 婚紗 大展 莫文蔚 細節\n",
      "樂園 金屬 發生 彩色 八仙 粉末 28 疑因 粉塵 200 燒傷 研議 加強 蔣丙煌 娛樂 放空 爆炸 用途 引發 超過\n",
      "新北市 台北市 醫院 桃園 林口 淡水 北榮 亞東 新光 聯醫 台北 中興 北國泰 慈濟 不詳 三重市 新泰 名氏 衛部 恩主公\n",
      "粉塵 送醫 暫時 機關 呼籲 吃緊 送急診 王鼎鈞 132 重傷 分現 樂園 急診 燃燒案 如非 醫院 28 雙北 輕傷 因雙北\n",
      "醫院 粉塵 台大 病房 這次 蔣丙煌 規範 相關 放空 患者 醫療 針對開 228 特別 應有 因為 衛福部長 28 該部 研議\n",
      "單親 媽媽 下海 倒楣 扶養 這名 實在 幼兒 二名 台中 第一次 警方 業者 從事網 指停 趁著 交談 網拍 投資 幾經\n",
      "現場 民眾 噴發 噴灑 紅色 音樂 目擊者 粉塵 同時 受傷 舞台 粉末 彩色 新北 閃燃 變成 猶如人間 27 參加 驚傳\n",
      "協助 時間 關切 重傷 毛揆 樂園 署及 運送 台東 鹿野 鄉農村 40 觀光產業 傷患 嘉年華 發稿 多人輕 熱氣球 新北市 粉爆\n",
      "劉玄灝 樂園 晚間 相關 現場 八仙 指示 全案 閃燃 游儒倡 轄區 檢察長 內勤 林朝 失傷害 受傷 蘆洲 27 活動 28\n",
      "br 28 樂園 八仙 毛治國 劉玄顥 負責人 傷患 調查 檢察官 指示 意外 行政院 行程 凌晨 全力 警方 得知 密切 取消\n",
      "警察局 消防局 醫院 新北市 協助 病患 全力 警政署 爆炸案 狀況 樂園 國恩 報請 瞭解 周邊 相關 救護 偵處 家屬 請該\n",
      "醫院 傷患 蔣丙煌 轉送 台北 台大 急診 28 聯合 樂園 榮總 林口 淡水 新光 八仙 住院 前往 北市 王宗曦 叮寧\n",
      "傷者 醫療 樂園 新聞 新北市 協助 發生 八仙 意外 祈盼 總統 並動員 震驚 照顧 百人燒 舉辦 桃園 剛才 晚間 已致\n",
      "財運 羊年 其實 厚非 這幾個 發達 經濟 問的 2015 社會 風水 平時 仔細來 基礎 問題 催旺 八字 必需 借助 下面\n",
      "桃花 因為 遇到 我給 免會 這些 趨之若鶩 麻煩 那個 人來 時候 絕對 所謂 煩心 過程 支幾個 對于 很煩 未婚 女朋友\n",
      "金曲 張學友 二度 先泡 男歌手 摘金 歌王 激烈 神級 呼籲 一個 擊敗 這個 打太極 最後由 慢一點 未來 開心 03 26\n",
      "專輯 蔡依林 獎項 上台 婚姻 心情 相扣 金曲 發聲 氣度 先奪 26 美國 最終 創作 許多 記者 細節 整張 對此\n",
      "金曲 張惠妹 熱情 台受 獲得 入圍 江蕙 專輯 訪時 女歌手 彩虹 失落 坦言 台北 希望 歌后 巨蛋 阿弟仔 一直 度奪獎\n",
      "新北市 相關 醫院 協助 病患 警察局 全力 警政署 警局 爆炸案 續並 狀況 有關 國恩 動用 報請 晚間 瞭解 周邊 消防局\n",
      "br 現場 協助 樂園 八仙 消防局 傷患 市長 晚間 調查 新北市 台北市 消防 桃園 27 傷者 救護車 緊急 粉塵 停業\n",
      "希臘 歐元區 瓦魯費 展延 克斯 戴松布倫 公投 財政部長 決定 可能 債務 發展 記者 穩定 確保 否決 歐元 財長 問及 脫離\n",
      "科威特 科國 國家 報導 石油 清真寺 等級 自殺 OPEC 這輛 KNPC 組織 現正 內部 當局 國營煉 27 羈押 尋找 額外\n",
      "最佳 音樂 有限公司 演唱者 專輯 股份 專輯獎 環球 華納國際 國際 唱片 蔡依林 錄音 人獎 人員 國語 演唱 工作室 得獎 不見\n",
      "樂園 八仙 調查 朱立倫 發生 新北市 意外 嚴格 調度 遺憾 沒有 停業 關園 不過 至於 立即 台北市 消防 救治 全力\n",
      "病患 台北 傷勢 28 嚴重 開刀 救治 減輕 變化 現腔室 及民眾 轉送 湧入 樂園 輸液 現象 嚴重燒 救護車 人有 會出\n",
      "最佳 金曲 專輯 入圍 人獎 國語 本屆 張學友 徐佳瑩 歌唱 可惜 蔡依林 同樣 還贏 剛剛 遺珠 抱回 一舉 當年 作詞\n",
      "新北市 樂園 八仙 關園 新北 抵達 生粉 桃園 晚間 27 20 縣市 日晚 指揮 社會 200 救災 現場 報導 朱立倫\n",
      "專輯 開心 蔡依林 獲得 最佳 金曲 國語 本屆 贏家 項大獎 大獎 星光 大喊 想到 巨蛋 夥伴 工作 莫文蔚 加持 致詞\n",
      "英國 尼西 艾伍 事件 爆炸案 必須 卡麥隆 Ellwood 準備 屠殺 中喪生 亞海灘 27 倫敦 日倫敦 亞攻擊 Tobias 日法 名英國 許多\n",
      "樂園 八仙 傷者 市長 調查 新北市 台北市 醫療 桃園 協助 徹底 災害 親電 調度 各醫院 慰問 停業 關園 機構 朱立倫\n",
      "張惠妹 謝謝 抽到 金曲 紀錄 音樂 典禮 一個 開心 26 27 獎頒獎 什麼 領獎 專輯 發抖 這麼 二姐 台北 上台\n",
      "謝謝 開心 歌王 金曲 封后 專輯 張惠妹 這些 這麼 感言 唱片 希望 所有人 自己 可以 夥伴 男歌手 哈林 跑跳 首句\n",
      "金曲 蔡依林 專輯 最佳 26 專輯獎 開心 他們 贏家 感謝 國語 我贏 台北 巨蛋 最大 很爽 謝謝 預測 27 獎時\n",
      "樂園 新北市 八仙 晚間 傷患 現場 時間 朱立倫 發言人 協助 全力 指示 行政院 救治 爆炸 得知 214 總統 探視 趴引\n",
      "張學友 金曲 謝謝 哈林 興奮 男歌手 歌王 26 27 伍思凱 擁抱 頒獎 領獎 台灣 繼續 我會 二度 一支 台北 台上\n",
      "LOEWE Jonathan 圖案 機器 Anderson 組成 材質 各種 Puzzle 年春夏 牛皮 皮革 令人 鏡面 放進 運用 東洋 革飛行 服裝 男裝\n",
      "內湖 何必 露台 現場 設計 專案 div 樓層 協理 龔志謙 none display ad 周邊 23 28 15 公園 此區 大磊\n",
      "BV 編織 鑰匙 小物 零錢 買個 經典 球粉 萬元 實用 聞名 禮物 包款 皮革 名片 手工 或是 件套 品牌 Veneta\n",
      "蘇益良 大人物 攝影 北文 00 影展 創會 獲贈 方念華 統籌 立體 捐款 手工 展出 慈善 蔡依林 謝金燕 夢想 21 粉絲\n",
      "花園 帝后 特區 規劃 新板 中正路 商圈 ad 29 div 機能 display 28 60 none 四房 週邊 板橋 一樓 優勢\n",
      "桃園 捷運 機場 購物 A19 如火如荼 none 環球 青塘園 通車 div 萬坪 分鐘 特區 未來 display ad 人氣 國泰 10\n",
      "力克斯 時候 Ryder Kelly 確定 小孩 小朋友 自信 乾脆 衣服 穿著 造勢 會問 李詠 我們 兩件 他們 走秀 style 選好\n",
      "粉霜 氣墊 粉餅 BB 網狀氣 Jart 韓國 Dr 雪花 innisfree 戰場 感氣 LANCOME 保養 粉底液 品牌 加入 海綿 暴紅 美妝腳步\n",
      "br 最佳 有限公司 音樂 演唱者 股份 專輯獎 環球 國際 唱片 華納國際 人獎 人員 錄音 工作室 不見 給你看 佛跳牆 台語 國語\n",
      "GIVENCHY Tisci 黑珍珠 2015 Riccardo 壓軸 走秀 高級 系列 秋冬 性感 超模 男裝 2016 設計 預告 流蘇 耶穌 丹寧 引領\n",
      "剪裁 Maison Margiela 春夏 男裝 2016 Galliano 重點 西裝 合身 大衣 詮釋 發表 輕盈 搖滾 乾淨 經典 John 無論 Berluti\n",
      "Gap 徵選 Ryder Makayla 現場 小朋友 代言人 小小 爸爸 力克斯 更勝 還有 全場 風靡 20 裝好 親子 紀念禮 繽紛 童裝\n",
      "王姓 行動 性交易 警方 社維法 前來 當場將 員警 軟體 王女 店外 應召 標榜 經絡 法辦 某飯 查獲 27 麻痺症 6000\n",
      "最佳 專輯 國語 開心 繼續 領獎 金曲 蔡依林 給你看 特別 島嶼 台語 台灣 歌曲 演奏 天光 男歌手 歌王 演唱 原住民\n",
      "醫院 晚間 柯文 台北市 現場 樂園 八仙 北市 全力 醫療 協助 調度 名傷 新北市 台北 防部 EOC 區緊 27 鄧家基\n",
      "樂園 指揮部 八仙 防部 救護車 警備車 國軍三 部抵 60 nbsp 桃園 檢派 醫院 27 治療 日晚 總醫院 灼傷 記者 現場\n",
      "蔡依林 音樂 國語 演唱 最佳 金曲 獎入 舉行 日電 26 27 專輯獎 專輯 不錯 由華納國際 獲得 典禮 張茗 記者 啟事\n",
      "div 26 報導 最佳 歌曲 巨蛋 金曲 一時 登場 獎者 娛樂 這些 樂壇 音樂 27 獎頒獎 都將 專輯 相關 認識\n"
     ]
    }
   ],
   "source": [
    "for ele in corpus:\n",
    "    print ele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer() \n",
    "X = vectorizer.fit_transform(corpus)\n",
    "word = vectorizer.get_feature_names() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 10)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "n_cosine_similarities = linear_kernel(tfidf[0:10], tfidf[0:10])\n",
    "#print cosine_similarities\n",
    "print n_cosine_similarities.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0  81  80 ..., 126 124 146]\n",
      " [  0  89  88 ..., 126 116 145]\n",
      " [ 73  65  64 ..., 120 113 144]\n",
      " ..., \n",
      " [ 73 101  99 ..., 120 135 140]\n",
      " [  0  91  92 ..., 131 138 139]\n",
      " [  0 102 101 ..., 130 139 138]]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "only integer arrays with one element can be converted to an index",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-2f9b78484fff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mrelated_docs_indices\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrelated_docs_indices\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[1;32mprint\u001b[0m \u001b[0mary\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: only integer arrays with one element can be converted to an index"
     ]
    }
   ],
   "source": [
    "related_docs_indices = cosine_similarities.argsort()[:-10:-1]\n",
    "print related_docs_indices\n",
    "for index in related_docs_indices:\n",
    "    print ary[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "transformer = TfidfTransformer()\n",
    "tfidf = transformer.fit_transform(X)\n",
    "weight = tfidf.toarray() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 147)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "cosine_similarities = linear_kernel(tfidf, tfidf)\n",
    "print cosine_similarities.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 1881)\n"
     ]
    }
   ],
   "source": [
    "print tfidf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,  81,  80, ..., 126, 124, 146],\n",
       "       [  0,  89,  88, ..., 126, 116, 145],\n",
       "       [ 73,  65,  64, ..., 120, 113, 144],\n",
       "       ..., \n",
       "       [ 73, 101,  99, ..., 120, 135, 140],\n",
       "       [  0,  91,  92, ..., 131, 138, 139],\n",
       "       [  0, 102, 101, ..., 130, 139, 138]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "related_docs_indices = cosine_similarities.argsort()[:-10:-1]\n",
    "related_docs_indices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 10)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "n_cosine_similarities = linear_kernel(tfidf[0:10], tfidf[0:10])\n",
    "#print cosine_similarities\n",
    "print n_cosine_similarities.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用 HeatMap 視覺化相似程度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1345f870>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p3G43yzKXU3qoFLPZzBMpKcTabL7ybdu3s+GVjURGmkgalsTIEcN9ZYWFhaxZ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\nVAxFRCT1PgWIK/Q2TpwTcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1345f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "# pip install seaborn\n",
    "sns.heatmap(n_cosine_similarities, annot=True, center=0, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.09167541  0.01601732 ...,  0.0653352   0.          0.        ]\n",
      " [ 0.09167541  1.          0.01601694 ...,  0.10915177  0.0173006\n",
      "   0.01726419]\n",
      " [ 0.01601732  0.01601694  1.         ...,  0.          0.          0.        ]\n",
      " ..., \n",
      " [ 0.0653352   0.10915177  0.         ...,  1.          0.06651145\n",
      "   0.01688704]\n",
      " [ 0.          0.0173006   0.         ...,  0.06651145  1.          0.17886177]\n",
      " [ 0.          0.01726419  0.         ...,  0.01688704  0.17886177  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "n_cosine_similarities = linear_kernel(tfidf, tfidf).flatten()\n",
    "print cosine_similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "八仙樂園爆炸案 負責人等5人移送\n",
      "希臘1／3提款機錢被提光\n",
      "八里爆炸意外衛生局凌晨公布314名傷患名單 分送27\n",
      "確保銀行穩定 希臘續與ECB緊密合作\n",
      "「我不說誰說？」世新廣電畢製《東所》受難者觀影\n",
      "八仙樂園爆炸 劣賊趁亂打劫\n",
      "侯友宜探視傷者：追究責任\n",
      "八仙樂園火警  國軍加入救援\n",
      "八仙樂園爆炸案災害應變中心　1時30分一級開設\n",
      "八仙派對彩粉釀大禍  衛福部：研議加強管理\n",
      "彩色派對主辦人：風勢太大，引燃粉塵\n",
      "希臘態度強硬 歐元區耐心漸失\n",
      "八仙樂園大火  傷者查詢電話\n",
      "金曲紅毯眾星出招　個人意見獨特見解\n",
      "派對彩粉首次釀禍  蔣丙煌：研議管理\n",
      "八仙樂園火警受傷名單一覽表\n",
      "00：35統計　八仙大火重傷97人、輕傷132人\n",
      "衛福部長：北北基醫護人員全力動員\n",
      "單親媽媽第一次下海就被逮　直呼太倒楣了！\n",
      "八仙樂園粉塵瞬燃　還原失事現場影片曝光\n",
      "八仙意外毛揆取消台東熱氣球活動\n",
      "八仙樂園爆炸案 士檢派3檢察官現場指揮\n",
      "八仙樂園爆炸案 3檢察官展開調查\n",
      "八仙爆炸案   陳國恩：全力支援\n",
      "八仙大火傷患爆量  衛福調度醫院\n",
      "八仙樂園意外 洪秀柱：為傷者祈福\n",
      "提升財運的家居風水\n",
      "如何化解感情上的爛桃花\n",
      "金曲26／陳奕迅二度擊敗張學友　濕身奪歌王\n",
      "金曲26／蔡依林擒３獎大勝　淚崩再挺婚姻平權\n",
      "金曲26／張惠妹奪歌后卻失落　要世界感受彩虹力量\n",
      "陳國恩指示警方全力動員 協助救護交通順暢\n",
      "八仙樂園爆炸228人輕重傷 朱立倫：無限期停業\n",
      "希臘盼展延債務 歐元區拒絕\n",
      "清真寺爆炸凶嫌所乘車 車主遭科國羈押\n",
      "金曲26／蔡依林淚奪最佳專輯＋完整得獎名單\n",
      "八仙樂園爆炸意外 朱立倫：立即停園接受調查\n",
      "北榮33名八里傷患  2/3傷重\n",
      "僅次Jolin！徐佳瑩入圍6獎全槓被封遺珠\n",
      "八仙樂園塵爆215傷　朱立倫下令即刻停業\n",
      "金曲最風光！蔡依林紅毯全勝又獲3獎成大贏家\n",
      "突尼西亞恐攻 已知15英人遇害\n",
      "八仙樂園爆炸 朱立倫4點指示\n",
      "張惠妹3度封后  想破江蕙紀錄\n",
      "金曲26／陳奕迅稱王謝台灣　張惠妹封后秒噴淚\n",
      "蔡依林呸大贏家  金曲最佳專輯獎\n",
      "【八仙意外】馬英九、毛治國第一時間得知　指示全\n",
      "陳奕迅二度打敗歌神  金曲歌王好嗨\n",
      "LOEWE瘋玩春夏 迷上日本漫畫\n",
      "捷運內湖站2分鐘 輕質2房最熱銷\n",
      "BV萬元小皮件 小資族快下手\n",
      "大人物攝影展開展吸人潮 方念華 時尚入境\n",
      "新板＋府中雙商圈 每坪60萬起\n",
      "北台灣最宜居新都心！\n",
      "Kelly Talk：艾力克斯讓兒挑衣 訓練衣Q\n",
      "氣墊粉霜又有新品 酷夏玩美肌\n",
      "金曲獎完整得獎名單！阿妹封后 陳奕迅稱王\n",
      "男裝周加映高級訂製服 GIVENCHY 黑珍珠使壞\n",
      "Margiela大衣掛帥 Berluti熱鬧色計\n",
      "Gap徵小小代言人 艾力克斯親子秀時尚\n",
      "行動不便為生活當馬伕 首天上班就被逮\n",
      "第26屆金曲獎 陳奕迅奪歌王、阿妹封歌后\n",
      "【八仙意外】讓專業的來！柯文哲指示啟動EOC\n",
      "八仙水上樂園爆炸逾百人傷　三軍總醫院收治傷患\n",
      "金曲最佳國語專輯：呸\n",
      "《金曲26》2015金曲獎得獎名單 線上直播懶人包\n"
     ]
    }
   ],
   "source": [
    "for ele in ary:\n",
    "    print ele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0  81  80 ..., 126 124 146]\n",
      " [  0  89  88 ..., 126 116 145]\n",
      " [ 73  65  64 ..., 120 113 144]\n",
      " ..., \n",
      " [ 73 101  99 ..., 120 135 140]\n",
      " [  0  91  92 ..., 131 138 139]\n",
      " [  0 102 101 ..., 130 139 138]]\n",
      "[  0  81  80  79  78  76  75  74  72  71  70  83  68  66  65  64  63  61\n",
      "  60  59  58  57  54  67  84  85  88 140 139 138 136 135 132 131 129 127\n",
      " 123 118 117 114 107 106 105 103 101  99  97  96  92  89  53  52  73  50\n",
      "  28  27  26  25  24  23  22  21  29  51  17  12  10   8   5   4   3   2\n",
      "  18  30  19  44  34  39  49  43  37  45  46  48  47   7 141  56  77 122\n",
      "  82  36  42  86  41  91   6 144  93  16   1  11  87 143 113   9  94  69\n",
      "  33 133  40  32  35 134 130  31 108  55  15  98 112 104  38 109  95 100\n",
      " 115  90 102 120 137 128 119 110  62 125 111 116  14 121  20 142  13 145\n",
      " 126 124 146]\n",
      "[  0  89  88  85  84  83  81  80  79  78  76  75  74  72  71  70  68  67\n",
      "  66  65  64  63  62  61  60  59  90  58  92  96 140 139 138 136 135 134\n",
      " 133 131 130 129 127 123 118 117 112 108 107 106 105 104 103 101  99  98\n",
      "  97  95  57  73  55  33   4  31  30  29  28   5  27  26  34  25  23  22\n",
      "  21   8  19  18  17  10  12  24  35  32  15  45  47  44  43  48  49   2\n",
      "  46  50  40  39  52  53  54   3  37  51   7 141 115  56  77 122  82  36\n",
      "  42  86  41  91   6 102  16   1  11 120  87 143 113   9 132  69 114  94\n",
      " 125 109 100 144 128  93 111  38  13 119 137 110 146 121 124 142  14  20\n",
      " 126 116 145]\n",
      "[ 73  65  64  63  62  61  60 119  58  66 121  53  52  51  50  49  48  99\n",
      "  46  54  45  67  69  92 107 108 109 111  85  84  83  68  80  78  76  75\n",
      "  74 116  72  71  70  79  44 125  19  20  43  18  17 137  15  14  12 138\n",
      "  10   9   8 139   5   3   2 142 136  23  94 135  40  39  38  37 129  35\n",
      "  24  33  32  34 132  31  30  26 131  27   7 141 115  56  81 122  82 101\n",
      "  89  21  42  13 128 146 124 126 140  59 130  28  98 112  86 134  77  36\n",
      "  97 103  95 100 117 104  47  57  55 110 114  29  22 118 133  91  96  11\n",
      "   4 105   0 127 145  90 106 123 102  93  87  88  41   1   6  25  16 143\n",
      " 120 113 144]\n",
      "[ 73  65  64  63  62  61  60 121  58  66 125  53  52  51  50  49  48  47\n",
      "  92  54  45 119  69  99 107 108 109 110  85  84  83  68  80  78 114  76\n",
      "  74 116  72  71  70  79  44  46  20  43  19  18  17 135  15 136 137  22\n",
      "  12  10   9   8 139   5   3   2 142 138  23  94 134 129  40  39  38 131\n",
      " 132  35  24  33  32  34  30  29 133  27  26  31   7 141 115  56  81 122\n",
      "  82  89  21  42 146 145 140  59 130 111  14  77  36  37  97  95 100 118\n",
      "  13 128  57  55 124 126  75  91  86  28  41  98  25  90 101 103  88   6\n",
      "  16   4   1   0  11  93  67  96 102 104 120 105 112 144 106 127 117  87\n",
      " 123 113 143]\n",
      "[  0  99  97  96  95  93  92  91  90  89  88  87  86  84 100  83  81  80\n",
      "  79  78  77  76  75  74  71  68  67  66  65  82  64 101 103 144 143 141\n",
      " 140 139 138 136 135 134 133 131 130 129 102 127 122 120 118 117 115 114\n",
      " 113 112 108 107 106 105 104 123  63  73  61  35  34  32  31  28  62  26\n",
      "  25  24  23  22  21  36  19  16  12  11  10   8   7   6   5   4   3   2\n",
      "   1  17  37  27  52  53  56  57  50  48  55  59  47  46  45  58  54  43\n",
      "  42  39  41  40 132  60  49  72  30  85  69  51  70  15  44  33  18  98\n",
      " 111  94  13   9  29 110 119 146  38 124 109 125 116 121 137 126 145  14\n",
      " 128  20 142]\n",
      "[  0  98  97  96  95  94  92  90  89  88  85  84  83  81  80  79  78  76\n",
      "  75  74  72  70  69  68  67  66  65  64 101 104 105 106 142 140 139 138\n",
      " 137 136 135 134 133 132 131 130 129  63 127 123 121 119 118 117 116 114\n",
      " 112 111 110 109 108 107 125  62  73  60  34  33  32  61  30  29  28  27\n",
      "  26  25  24  23  22  21  20  19  18  15  14  12  10   9   8   5   4   3\n",
      "   2  35  37  31  38  52  53  54  55  51  50  49  48  47  45  44  43  46\n",
      "  57  58  59  39  40   7 115  56  77 122  82  36  42  13  41 128  91 100\n",
      "   6 144 102  93 146 145  16   1 124  11 126 120  87 143 113  99  17  71\n",
      " 103  86 141]\n",
      "[ 73 101  99  98  97  96  95  94  93  92  90  89  87  86  85  84  83  82\n",
      "  81  80  79  78  76  75 145  72  71  70  69  68 103 104 105 106 142 141\n",
      " 139 138 137 136 134 133 132 131 129 128 126 125  67 124 122 121 119 118\n",
      " 117 116 115 114 112 111 110 109 108 107 123  66 146  64  33  32  65  30\n",
      "  29  28  27  26  24  23  22  21  20  19  34  18  16  15  14  13  12  11\n",
      "  10   9   8   7   5   3   2   1  17  35  31  52  55  51  50  56  48  47\n",
      "  57  58  59  53  60  44  62  43  42  41  40  39  38  37  63  61  54  77\n",
      "  36 130  91 100  88   4   6 144 102   0 127  25 143 113  45  46  74  49\n",
      " 120 135 140]\n",
      "[  0  91  92  93  94  95  96  90  97  99 100 101 102 103 104  98 105  89\n",
      "  87 145  74  75  76  77  78  88  79  81  82  83  84  85  86  80  72 106\n",
      " 108 127 128 130 132 133 134 126 135 137 140 141 142 143 144 136 107 125\n",
      " 123 109 110 111 112 113 114 124 115 117 118 119 120 121 122 116  71  73\n",
      "  69  19  20  21  22  23  24  18  25  27  28  29  30  31  70  26  33  17\n",
      "  15   1   2   3   4   5   6  16   7   9  10  11  12  13  14   8  34  32\n",
      "  36  35  56  57  58  59  60  54  61  63  64  65  66  67  68  62  53  55\n",
      "  51  37  38  39  40  41  42  52  44  43  46  47  48  49  50  45 146 129\n",
      " 131 138 139]\n",
      "[  0 102 101 100  99  98  97  96  95  94  93  92  91  90  89  88  87  86\n",
      "  72 145  74  75  76  77 103  78  80  81  82  83  84  85  79 104 105 106\n",
      " 126 127 128 131 132 133 125 134 137 140 141 142 143 144 136  71 124 122\n",
      " 108 109 110 111 112 113 123 114 116 117 118 119 120 121 115  70  73  68\n",
      "  31  30  29  28  27  26  25  24  23  22  21  20  19  18  17  16  15   1\n",
      "   2   3   4   5   6  69   7   9  10  11  12  13  14   8  33  32  35  53\n",
      "  34  55  56  57  59  52  60  62  63  64  65  66  67  61  51  54  49  36\n",
      "  37  38  39  40  41  50  42 146  44  45  46  47  48  43 129 107  58 135\n",
      " 130 139 138]\n"
     ]
    }
   ],
   "source": [
    "related_docs_indices = cosine_similarities.argsort()[:-10:-1]\n",
    "print related_docs_indices\n",
    "for index in related_docs_indices:\n",
    "    print index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  0.  0. ...,  0.  0.  1.]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "n_cosine_similarities = linear_kernel(tfidf, tfidf)\n",
    "print cosine_similarities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用PCA 降維資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\numpy\\core\\_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "object of type 'PCA' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-e4a531dea3c1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mpca\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPCA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpca\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_cosine_similarities\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: object of type 'PCA' has no len()"
     ]
    }
   ],
   "source": [
    "#from sklearn import cluster\n",
    "#c = cluster.KMeans(n_cosine_similarities,3)\n",
    "#print c.n_clusters\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "a = pca.fit(n_cosine_similarities)\n",
    "print len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 2)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import cluster\n",
    "pca = PCA(n_components=2)\n",
    "pca_data = pca.fit_transform(cosine_similarities)\n",
    "\n",
    "print pca_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 147)\n"
     ]
    }
   ],
   "source": [
    "print cosine_similarities.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 1881)\n"
     ]
    }
   ],
   "source": [
    "print weight.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "param = [{'model':cluster.KMeans(n_clusters=10),\n",
    "          'title':'KMeans'}]\n",
    "c = param[0]['model']\n",
    "k_data = c.fit_predict(weight)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 檢視資料與Sum of Square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147,)\n",
      "130.21794048\n"
     ]
    }
   ],
   "source": [
    "print k_data.shape\n",
    "print c.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "台鐵新基隆車站29日正式啟用\n",
      "八仙樂園舞台大火 逾400人輕重傷\n",
      "女子世界盃足球賽  日擊敗澳洲晉4強\n",
      "大台北今高溫上探37度\n",
      "八仙樂園意外 毛揆取消視察臺東行程\n",
      "八仙樂園貼出暫停營業海報\n",
      "還是熱！西半部高溫達37度\n",
      "八仙樂園粉塵燃爆 400人傷 負責人被移送\n",
      "歐洲》高貴的徵才條件\n",
      "東北亞》大眾運輸費變貴了\n",
      "八仙樂園爆炸 劣賊趁亂打劫\n",
      "侯友宜探視傷者：追究責任\n",
      "八仙樂園火警  國軍加入救援\n",
      "八仙樂園爆炸案災害應變中心　1時30分一級開設\n",
      "八仙樂園火警受傷名單一覽表\n",
      "八仙意外毛揆取消台東熱氣球活動\n",
      "八仙樂園爆炸案 士檢派3檢察官現場指揮\n",
      "八仙樂園爆炸案 3檢察官展開調查\n",
      "八仙爆炸案   陳國恩：全力支援\n",
      "八仙樂園意外 洪秀柱：為傷者祈福\n",
      "陳國恩指示警方全力動員 協助救護交通順暢\n",
      "八仙樂園爆炸228人輕重傷 朱立倫：無限期停業\n",
      "八仙樂園爆炸意外 朱立倫：立即停園接受調查\n",
      "北榮33名八里傷患  2/3傷重\n",
      "八仙樂園塵爆215傷　朱立倫下令即刻停業\n",
      "突尼西亞恐攻 已知15英人遇害\n",
      "八仙樂園爆炸 朱立倫4點指示\n",
      "【八仙意外】馬英九、毛治國第一時間得知　指示全\n",
      "Gap徵小小代言人 艾力克斯親子秀時尚\n",
      "行動不便為生活當馬伕 首天上班就被逮\n",
      "【八仙意外】讓專業的來！柯文哲指示啟動EOC\n",
      "八仙水上樂園爆炸逾百人傷　三軍總醫院收治傷患\n"
     ]
    }
   ],
   "source": [
    "for id, i in enumerate(k_data):\n",
    "    if i == 4:\n",
    "        print ary[id]\n",
    "\n",
    "#print ary[0]\n",
    "#print ary[1]\n",
    "#print ary[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 視覺化呈現分群結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -4.82892054e-01  -1.20848312e-01]\n",
      " [ -3.31142404e-01  -1.19192847e-01]\n",
      " [  1.34610908e-01   1.86601595e-01]\n",
      " [  1.02922389e-01   1.42994038e-01]\n",
      " [ -5.06425207e-01  -9.92628015e-02]\n",
      " [  1.26330216e-01   1.61720131e-01]\n",
      " [ -3.89858868e-01  -7.33056414e-02]\n",
      " [  8.14725884e-02   1.00776437e-01]\n",
      " [  1.15130699e-01   1.42930063e-01]\n",
      " [  1.68517584e-01  -3.88867681e-02]\n",
      " [  1.03240992e-01   1.66763598e-01]\n",
      " [ -4.88734424e-01  -1.01991800e-01]\n",
      " [  1.70736604e-01   2.55362674e-01]\n",
      " [  2.08037230e-01  -3.13014231e-01]\n",
      " [  4.34740660e-01  -7.16871002e-01]\n",
      " [  1.49657977e-01   1.62001716e-01]\n",
      " [ -5.81561390e-01  -1.58725031e-01]\n",
      " [  1.04598017e-01   1.47557706e-01]\n",
      " [  1.07013227e-01   1.46104427e-01]\n",
      " [  1.14682794e-01   1.36403297e-01]\n",
      " [  4.46854478e-01  -7.19960993e-01]\n",
      " [ -1.77496879e-01   4.12482065e-02]\n",
      " [  1.17953887e-01   1.07804844e-01]\n",
      " [  1.15130699e-01   1.42930063e-01]\n",
      " [  1.17749129e-01   2.26429938e-01]\n",
      " [ -5.86662563e-01  -1.55249189e-01]\n",
      " [  1.31038638e-01   1.56424403e-01]\n",
      " [  1.03570780e-01   1.49642358e-01]\n",
      " [  3.58029321e-03   9.15344536e-02]\n",
      " [  1.20369717e-01   1.28274521e-01]\n",
      " [  1.20621544e-01   1.27867881e-01]\n",
      " [  1.13886632e-01   1.28505120e-01]\n",
      " [  1.10789071e-01   1.50721358e-01]\n",
      " [  1.10468876e-01   1.18132336e-01]\n",
      " [  1.34910849e-01   1.49936763e-01]\n",
      " [  1.26738566e-01   1.19965647e-01]\n",
      " [  6.02547786e-03   6.88270128e-02]\n",
      " [  1.13290347e-01   2.76884620e-01]\n",
      " [  3.22877972e-01  -3.32136874e-01]\n",
      " [  1.51751302e-01   2.62289590e-01]\n",
      " [  1.30767683e-01   1.63196156e-01]\n",
      " [ -1.26645263e-01  -1.26129848e-02]\n",
      " [  1.19657827e-01   1.79543135e-01]\n",
      " [  8.71583439e-02   1.48179215e-01]\n",
      " [  1.28109520e-01   1.43580812e-01]\n",
      " [  1.20479066e-01   1.57447065e-01]\n",
      " [  1.16047860e-01   1.57022284e-01]\n",
      " [  1.06611775e-01   1.57534278e-01]\n",
      " [  1.16793500e-01   1.56721598e-01]\n",
      " [  1.31519460e-01   1.48704020e-01]\n",
      " [  1.03344812e-01   1.59600880e-01]\n",
      " [  1.47624683e-01   1.68213089e-01]\n",
      " [  8.90740242e-02   1.49629117e-01]\n",
      " [  9.74864867e-02   1.41846885e-01]\n",
      " [  1.04746738e-01   1.87526345e-01]\n",
      " [  1.21952694e-01   1.55648750e-01]\n",
      " [  8.35462223e-02   1.11855053e-01]\n",
      " [  1.04885750e-01   1.76382238e-01]\n",
      " [  1.22293795e-01   1.89342738e-01]\n",
      " [  6.23414249e-02   1.37212316e-01]\n",
      " [  1.34308984e-01   1.55762424e-01]\n",
      " [  1.26358784e-01   1.66176327e-01]\n",
      " [  1.41743700e-01   1.50491690e-01]\n",
      " [  1.13363157e-01   1.67719462e-01]\n",
      " [  1.28319154e-01   1.70037921e-01]\n",
      " [  1.27298005e-01   1.59122242e-01]\n",
      " [  5.42160637e-02   1.43252905e-01]\n",
      " [  3.52000114e-02   6.81012876e-02]\n",
      " [  1.38678828e-01   1.68542979e-01]\n",
      " [  1.62699605e-01   6.89961110e-02]\n",
      " [  1.23031439e-01   1.39561423e-01]\n",
      " [  1.02678431e-01   1.47903550e-01]\n",
      " [  1.02740675e-01   1.32450518e-01]\n",
      " [  6.18726236e-02   1.34194161e-01]\n",
      " [  1.24537109e-01   1.63812357e-01]\n",
      " [  1.11449200e-01   1.88410112e-01]\n",
      " [  8.36658138e-02   1.35267584e-01]\n",
      " [  9.86090972e-04   6.64840216e-02]\n",
      " [  9.81574660e-02   1.36163565e-01]\n",
      " [  1.45258276e-01   1.31248021e-01]\n",
      " [  1.30359815e-01   1.41190497e-01]\n",
      " [ -1.02204025e-01   6.68005518e-02]\n",
      " [  9.20740555e-02   1.36143219e-01]\n",
      " [  1.10373566e-01   1.40643710e-01]\n",
      " [  1.30059049e-01   2.19547558e-01]\n",
      " [  1.29875799e-01   9.27620161e-02]\n",
      " [ -1.70872026e-01   1.11614522e-02]\n",
      " [ -7.61071717e-01  -2.28565545e-01]\n",
      " [ -3.72381234e-01  -3.73995019e-02]\n",
      " [ -2.16430414e-01   2.38613653e-02]\n",
      " [ -6.65550838e-01  -1.58057503e-01]\n",
      " [ -3.57659329e-01  -7.18416082e-02]\n",
      " [  1.60844315e-01   2.68748389e-01]\n",
      " [ -5.17683766e-01  -1.63789767e-01]\n",
      " [  2.12680393e-01  -1.10580519e-01]\n",
      " [ -5.66793577e-01  -1.23365988e-01]\n",
      " [ -2.07590635e-01  -4.03923198e-02]\n",
      " [ -2.32803727e-01   1.76024847e-02]\n",
      " [ -2.27629557e-01  -1.27637027e-02]\n",
      " [  1.03441596e-01   1.48470183e-01]\n",
      " [ -2.62472157e-01  -1.05282154e-01]\n",
      " [ -2.82995072e-01   1.69759381e-03]\n",
      " [ -4.83755732e-01  -1.30354601e-01]\n",
      " [ -4.54257564e-01  -6.05603655e-02]\n",
      " [ -4.35778493e-01  -5.60970704e-02]\n",
      " [ -4.17648277e-01  -1.12572381e-01]\n",
      " [ -5.90392556e-01  -1.15486546e-01]\n",
      " [  1.22104589e-01   1.72516180e-01]\n",
      " [  1.25494599e-01   1.38306169e-01]\n",
      " [  2.97032591e-01  -2.47557062e-01]\n",
      " [  2.86891022e-01  -2.64954863e-01]\n",
      " [  2.77953809e-01  -3.16394221e-01]\n",
      " [ -4.14176955e-01  -4.94349692e-02]\n",
      " [ -1.03798522e+00  -3.28391470e-01]\n",
      " [  1.57896153e-01   2.68418407e-01]\n",
      " [  7.87160598e-02   9.29550591e-02]\n",
      " [  4.20289560e-01  -7.01533111e-01]\n",
      " [ -6.73301249e-01  -1.35593801e-01]\n",
      " [ -1.78775960e-01  -1.51007027e-02]\n",
      " [  3.57098202e-01  -4.39245033e-01]\n",
      " [ -6.67274570e-01  -2.01093630e-01]\n",
      " [  4.25681282e-01  -6.07986171e-01]\n",
      " [  6.61584851e-02   9.99760903e-02]\n",
      " [ -7.41481118e-01  -1.82286972e-01]\n",
      " [  3.64038980e-01  -6.62909718e-01]\n",
      " [  3.77584075e-01  -4.55219815e-01]\n",
      " [  4.34179265e-01  -8.28678097e-01]\n",
      " [ -6.94261047e-01  -1.47177990e-01]\n",
      " [  2.85402209e-01  -4.31813746e-01]\n",
      " [  1.17913458e-01   1.48383097e-01]\n",
      " [ -7.44827198e-03   1.04705124e-01]\n",
      " [  1.21373135e-01   1.52313990e-01]\n",
      " [  1.37858894e-01   5.19276336e-02]\n",
      " [  6.44488193e-02   1.29080252e-01]\n",
      " [  8.04643335e-02   1.02011435e-01]\n",
      " [  1.36104471e-01   1.47019055e-01]\n",
      " [  1.16823600e-01   1.47536337e-01]\n",
      " [  3.36775693e-01  -5.46536314e-01]\n",
      " [  1.20369931e-01   1.56791960e-01]\n",
      " [  1.18920188e-01   1.50423784e-01]\n",
      " [  4.22348645e-02   1.24389523e-01]\n",
      " [  6.82436734e-02   9.90779172e-02]\n",
      " [  5.16531299e-01  -8.02515112e-01]\n",
      " [ -7.48069174e-01  -2.65246192e-01]\n",
      " [ -4.17670151e-01  -1.17670033e-01]\n",
      " [  3.99494275e-01  -7.72290728e-01]\n",
      " [  2.53742141e-01  -5.25182666e-01]]\n"
     ]
    }
   ],
   "source": [
    "print pca_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "string index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-59-e8961a1ced05>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mncols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpca_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpca_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'rgbyc'\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mk_data\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'title'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: string index out of range"
     ]
    },
    {
     "data": {
      "image/png": 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vkEux52bZr7Byj8HpWja7ZH4J6flJ3rlwSZD1ccH5VdW9VXX9zgszP5PkK1X15YPZJhew\n7PvvJ0kOjzFesXN8U+ZnP1gPy2Z3MvOXTZzZia2fZX5ZkPW352bZl1vauMfgdC2bXZLv7fz3nYUP\n+XxVff2ybpILWvW9t/C89yUZVfXnl3+XXMhF/N15Poo3khyvqj89mJ2y20XM7kNJPpDkl5m/nueP\nds48sibGGC/P/AfOozu/AX9JzeJegQAATbxBKABAE2EFANBEWAEANBFWAABNhBUAQBNhBQDQRFgB\nADT5H3etfoAb4wGcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d20710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=1, nrows=1,figsize=(10,5))\n",
    "axes.scatter(pca_data[:,0], pca_data[:,1], c=['rgbyc'[i] for i in k_data ], s=200)\n",
    "axes.set_title(param[0]['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'disk', u'format', u'hard', u'how', u'my', u'problems', u'to']\n",
      "[[1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [1 0]\n",
      " [0 1]\n",
      " [1 0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(min_df=1)\n",
    "content = [\"How to format my hard disk\", \" Hard disk format problems \"]\n",
    "X = vectorizer.fit_transform(content)\n",
    "print vectorizer.get_feature_names()\n",
    "print(X.toarray().transpose())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#samples: 5, #features: 25\n",
      "[u'about', u'actually', u'capabilities', u'contains', u'data', u'databases', u'images', u'imaging', u'interesting', u'is', u'it', u'learning', u'machine', u'most', u'much', u'not', u'permanently', u'post', u'provide', u'safe', u'storage', u'store', u'stuff', u'this', u'toy']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "posts = [open(os.path.join('toy/', f)).read() for f in os.listdir('toy')]\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(min_df=1)\n",
    "\n",
    "X_train = vectorizer.fit_transform(posts)\n",
    "num_samples, num_features = X_train.shape\n",
    "\n",
    "print(\"#samples: %d, #features: %d\" % (num_samples,num_features)) \n",
    "print(vectorizer.get_feature_names())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 5)\t1\n",
      "  (0, 7)\t1\n",
      "[[0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "new_post = \"imaging databases\"\n",
    "new_post_vec = vectorizer.transform([new_post])\n",
    "print new_post_vec\n",
    "print(new_post_vec.toarray())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy as sp\n",
    "def dist_raw(v1, v2):\n",
    "    delta = v1-v2\n",
    "    return sp.linalg.norm(delta.toarray())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Post 0 with dist=4.00: This is a toy post about machine learning. Actually, it contains not much interesting stuff.\n",
      "=== Post 1 with dist=1.73: Imaging databases provide storage capabilities.\n",
      "=== Post 2 with dist=2.00: Most imaging databases safe images permanently.\n",
      "=== Post 3 with dist=1.41: Imaging databases store data.\n",
      "=== Post 4 with dist=5.10: Imaging databases store data. Imaging databases store data. Imaging databases store data.\n",
      "Best post is 3 with dist=1.41\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "best_doc = None\n",
    "best_dist = sys.maxint\n",
    "best_i = None\n",
    "for i in range(0, num_samples):\n",
    "    post = posts[i]\n",
    "    if post==new_post:\n",
    "        continue\n",
    "    post_vec = X_train.getrow(i)\n",
    "    d = dist_raw(post_vec, new_post_vec)\n",
    "    print \"=== Post %i with dist=%.2f: %s\"%(i, d, post)\n",
    "    if d<best_dist:\n",
    "        best_dist = d\n",
    "        best_i = i\n",
    "print(\"Best post is %i with dist=%.2f\"%(best_i, best_dist))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dist_norm(v1, v2):\n",
    "    v1_normalized = v1/sp.linalg.norm(v1.toarray())\n",
    "    v2_normalized = v2/sp.linalg.norm(v2.toarray())\n",
    "    delta = v1_normalized - v2_normalized\n",
    "    return sp.linalg.norm(delta.toarray())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'about',\n",
       " 'above',\n",
       " 'across',\n",
       " 'after',\n",
       " 'afterwards',\n",
       " 'again',\n",
       " 'against',\n",
       " 'all',\n",
       " 'almost']"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(min_df=1, stop_words='english')\n",
    "sorted(vectorizer.get_stop_words())[0:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'imagin'"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk.stem\n",
    "s= nltk.stem.SnowballStemmer('english')\n",
    "s.stem(\"graphics\")\n",
    "\n",
    "s.stem(\"imaging\")\n",
    "s.stem(\"image\")\n",
    "s.stem(\"imagination\")\n",
    "s.stem(\"imagine\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk.stem\n",
    "english_stemmer = nltk.stem.SnowballStemmer('english')\n",
    "class StemmedCountVectorizer(CountVectorizer):\n",
    "    def build_analyzer(self):\n",
    "        analyzer = super(StemmedCountVectorizer, self).build_analyzer()\n",
    "        return lambda doc: (english_stemmer.stem(w) for w in analyzer(doc))\n",
    "vectorizer = StemmedCountVectorizer(min_df=1, stop_words='english')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}