第四次作业.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopword_set = set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "    with open(\"D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA/stopwords.txt\", 'r', encoding=\"utf-8\") as stopwords:\n",
    "        for stopword in stopwords:\n",
    "            stopword_set.add(stopword.strip(\"\\n\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'［①②］',\n",
       " '［②①］',\n",
       " '［①⑥］',\n",
       " '互',\n",
       " '为',\n",
       " '可好',\n",
       " '儿',\n",
       " '不特',\n",
       " '得出',\n",
       " '出于',\n",
       " '︿',\n",
       " '反而',\n",
       " '内',\n",
       " '趁便',\n",
       " '因着',\n",
       " '－－',\n",
       " 'ｆ］',\n",
       " '—',\n",
       " '故而',\n",
       " '依据',\n",
       " '平素',\n",
       " '长期以来',\n",
       " '＜±',\n",
       " '近几年来',\n",
       " '几经',\n",
       " '......',\n",
       " '呜',\n",
       " '按说',\n",
       " '动辄',\n",
       " '哎呀',\n",
       " '前后',\n",
       " '方面',\n",
       " '普遍',\n",
       " '千万',\n",
       " '单单',\n",
       " '却',\n",
       " '当中',\n",
       " '有所',\n",
       " '其',\n",
       " '以及',\n",
       " '何必',\n",
       " '连连',\n",
       " '那么些',\n",
       " '［③ｄ］',\n",
       " '4',\n",
       " '凡是',\n",
       " '每逢',\n",
       " '密切',\n",
       " '［③①］',\n",
       " '蛮',\n",
       " '若夫',\n",
       " '恰逢',\n",
       " '反倒是',\n",
       " '的确',\n",
       " '③',\n",
       " '欢迎',\n",
       " '.数',\n",
       " '遇到',\n",
       " '掌握',\n",
       " '切莫',\n",
       " '加以',\n",
       " '这么些',\n",
       " '只有',\n",
       " '每每',\n",
       " '倘若',\n",
       " './',\n",
       " '从',\n",
       " '举凡',\n",
       " '呼啦',\n",
       " '一次',\n",
       " '常常',\n",
       " '好的',\n",
       " '进而',\n",
       " '嘻',\n",
       " '9',\n",
       " '借此',\n",
       " '到目前为止',\n",
       " '何况',\n",
       " '除此',\n",
       " '·',\n",
       " '逢',\n",
       " '至今',\n",
       " '一片',\n",
       " '哎哟',\n",
       " '地',\n",
       " '深入',\n",
       " '率然',\n",
       " '不可',\n",
       " '需要',\n",
       " '吧哒',\n",
       " '由',\n",
       " '由是',\n",
       " '取道',\n",
       " '再者说',\n",
       " '反过来',\n",
       " '这样',\n",
       " '亲身',\n",
       " '别的',\n",
       " '啥',\n",
       " '策略地',\n",
       " '方便',\n",
       " '截然',\n",
       " '２',\n",
       " '白白',\n",
       " '屡屡',\n",
       " '急匆匆',\n",
       " '即如',\n",
       " '然则',\n",
       " '独',\n",
       " '三番两次',\n",
       " '■',\n",
       " '到了儿',\n",
       " '距',\n",
       " '为着',\n",
       " '挨着',\n",
       " '极了',\n",
       " '自个儿',\n",
       " '几乎',\n",
       " '来看',\n",
       " '注意',\n",
       " '时候',\n",
       " '他',\n",
       " '哪样',\n",
       " '联系',\n",
       " '结合',\n",
       " '迅速',\n",
       " '伟大',\n",
       " '虽然',\n",
       " '其余',\n",
       " '哗',\n",
       " '重要',\n",
       " '么',\n",
       " '造成',\n",
       " '长话短说',\n",
       " '哪里',\n",
       " '挨门逐户',\n",
       " '别是',\n",
       " '起见',\n",
       " '莫',\n",
       " '」',\n",
       " '切不可',\n",
       " '］［',\n",
       " '先生',\n",
       " '嘛',\n",
       " '遭到',\n",
       " '问题',\n",
       " '嘿',\n",
       " 'Ａ',\n",
       " '别管',\n",
       " '换言之',\n",
       " '尔后',\n",
       " '且不说',\n",
       " '依',\n",
       " '既是',\n",
       " '还要',\n",
       " '开始',\n",
       " '例如',\n",
       " '而且',\n",
       " '严重',\n",
       " '我是',\n",
       " '不限',\n",
       " '决非',\n",
       " '即或',\n",
       " '特殊',\n",
       " 'ａ］',\n",
       " '传',\n",
       " '它的',\n",
       " '之后',\n",
       " '～',\n",
       " '仍然',\n",
       " '/',\n",
       " '－',\n",
       " '这么样',\n",
       " '俺们',\n",
       " '挨门挨户',\n",
       " '巩固',\n",
       " '＜λ',\n",
       " '／',\n",
       " '［②④',\n",
       " '七',\n",
       " '［⑤ｂ］',\n",
       " '所谓',\n",
       " '猛然间',\n",
       " '梆',\n",
       " '起头',\n",
       " '倍加',\n",
       " '$',\n",
       " '倘',\n",
       " '大批',\n",
       " '如同',\n",
       " '坚决',\n",
       " '随后',\n",
       " '这里',\n",
       " '故此',\n",
       " '加强',\n",
       " '二话不说',\n",
       " '过来',\n",
       " '怪不得',\n",
       " '所',\n",
       " '~~~~',\n",
       " '个',\n",
       " '较比',\n",
       " '固',\n",
       " '。',\n",
       " '不一',\n",
       " '以上',\n",
       " '&',\n",
       " '随著',\n",
       " '大不了',\n",
       " '如期',\n",
       " '为主',\n",
       " '极度',\n",
       " 'φ．',\n",
       " '不仅',\n",
       " '广大',\n",
       " '［①ｈ］',\n",
       " '继之',\n",
       " '致',\n",
       " '朝',\n",
       " '否则',\n",
       " '充其极',\n",
       " '三天两头',\n",
       " '℃',\n",
       " '引起',\n",
       " '从优',\n",
       " '全部',\n",
       " '近年来',\n",
       " '表明',\n",
       " '因此',\n",
       " '不但',\n",
       " ')',\n",
       " 'Ψ',\n",
       " '贼死',\n",
       " '趁早',\n",
       " '过',\n",
       " '以至',\n",
       " '您们',\n",
       " '其实',\n",
       " '”',\n",
       " '不管',\n",
       " '哗啦',\n",
       " '正巧',\n",
       " 'Ⅲ',\n",
       " '完全',\n",
       " '即便',\n",
       " '临到',\n",
       " '快要',\n",
       " '［④ｄ］',\n",
       " '认真',\n",
       " '敢于',\n",
       " '并非',\n",
       " '分期',\n",
       " '挨个',\n",
       " '高低',\n",
       " '为止',\n",
       " '岂非',\n",
       " '明确',\n",
       " '也',\n",
       " '咚',\n",
       " '非但',\n",
       " '如次',\n",
       " ':',\n",
       " '［③ｃ］',\n",
       " '既往',\n",
       " '据此',\n",
       " '？',\n",
       " '只是',\n",
       " '｝＞',\n",
       " '饱',\n",
       " '怎么',\n",
       " '与',\n",
       " '似乎',\n",
       " '’',\n",
       " '［②ｂ］',\n",
       " '所有',\n",
       " '8',\n",
       " '及至',\n",
       " '竟而',\n",
       " '尽管',\n",
       " '没奈何',\n",
       " '继后',\n",
       " '不亦乐乎',\n",
       " '［①⑤］',\n",
       " '呼哧',\n",
       " '连日',\n",
       " '加之',\n",
       " '恐怕',\n",
       " '到头来',\n",
       " '她的',\n",
       " '弹指之间',\n",
       " '总的说来',\n",
       " '这般',\n",
       " '她',\n",
       " '庶几',\n",
       " 'sup',\n",
       " '何时',\n",
       " '而外',\n",
       " '不得了',\n",
       " '认为',\n",
       " '曾经',\n",
       " '按时',\n",
       " '从古到今',\n",
       " '更',\n",
       " '正是',\n",
       " '［③ｈ］',\n",
       " '莫不然',\n",
       " '大力',\n",
       " '运用',\n",
       " '必将',\n",
       " '相应',\n",
       " '极大',\n",
       " '冒',\n",
       " '｛－',\n",
       " '已经',\n",
       " '全年',\n",
       " '于是',\n",
       " '】',\n",
       " '忽地',\n",
       " '赶早不赶晚',\n",
       " '一则通过',\n",
       " '长线',\n",
       " '共总',\n",
       " '［②ｊ］',\n",
       " '从轻',\n",
       " '［⑦］',\n",
       " '〕',\n",
       " '比照',\n",
       " '趁着',\n",
       " '便于',\n",
       " '藉以',\n",
       " '多么',\n",
       " '待',\n",
       " '着',\n",
       " '这就是说',\n",
       " '［③］',\n",
       " '或',\n",
       " '尽心竭力',\n",
       " '乘隙',\n",
       " '该',\n",
       " '那时',\n",
       " '当地',\n",
       " '略微',\n",
       " '慢说',\n",
       " '一直',\n",
       " '啊哈',\n",
       " '②ｃ',\n",
       " '路经',\n",
       " 'φ',\n",
       " '经',\n",
       " '只',\n",
       " '遵照',\n",
       " '}',\n",
       " '猛然',\n",
       " '惟其',\n",
       " '自后',\n",
       " '嘎登',\n",
       " '以故',\n",
       " '直接',\n",
       " '不妨',\n",
       " '这',\n",
       " '［④ａ］',\n",
       " '至于',\n",
       " '［②ｄ］',\n",
       " '什么',\n",
       " '毫无例外',\n",
       " '哪边',\n",
       " '以下',\n",
       " '难道',\n",
       " '乘机',\n",
       " '明显',\n",
       " '朝着',\n",
       " '取得',\n",
       " '管',\n",
       " '说明',\n",
       " '要是',\n",
       " '甚而',\n",
       " '呆呆地',\n",
       " '譬如',\n",
       " '不拘',\n",
       " '然後',\n",
       " '＝｛',\n",
       " '陡然',\n",
       " '大',\n",
       " '啦',\n",
       " '＿',\n",
       " '成年累月',\n",
       " '哇',\n",
       " '长此下去',\n",
       " '８',\n",
       " '几时',\n",
       " '好象',\n",
       " 'exp',\n",
       " '碰巧',\n",
       " '不怕',\n",
       " '亲口',\n",
       " '暗中',\n",
       " '何苦',\n",
       " '近来',\n",
       " '呵',\n",
       " '趁势',\n",
       " '各级',\n",
       " '呜呼',\n",
       " '曾',\n",
       " '容易',\n",
       " '-',\n",
       " '仅仅',\n",
       " '绝不',\n",
       " '基本',\n",
       " '∈［',\n",
       " '［①ｉ］',\n",
       " '有',\n",
       " '它是',\n",
       " '不得已',\n",
       " '进行',\n",
       " '显然',\n",
       " '；',\n",
       " '从而',\n",
       " '连同',\n",
       " '您是',\n",
       " '哪儿',\n",
       " '被',\n",
       " '矣哉',\n",
       " '有力',\n",
       " '为什么',\n",
       " '嘿嘿',\n",
       " '“',\n",
       " '尽量',\n",
       " '％',\n",
       " '最後',\n",
       " '起先',\n",
       " '［⑤ｄ］',\n",
       " '实现',\n",
       " '确定',\n",
       " '趁',\n",
       " '起首',\n",
       " '那里',\n",
       " '之所以',\n",
       " '意思',\n",
       " '即',\n",
       " '两者',\n",
       " '非得',\n",
       " '遵循',\n",
       " '是以',\n",
       " '那么样',\n",
       " '极',\n",
       " '刚巧',\n",
       " '∪φ∈',\n",
       " '左右',\n",
       " '一方面',\n",
       " '＜Δ',\n",
       " '若果',\n",
       " '呢',\n",
       " '都',\n",
       " '反映',\n",
       " '任凭',\n",
       " '随着',\n",
       " '一何',\n",
       " '初',\n",
       " '⑩',\n",
       " '后',\n",
       " '一起',\n",
       " '不如',\n",
       " '除非',\n",
       " '弗',\n",
       " '暗地里',\n",
       " '同时',\n",
       " '［⑩］',\n",
       " '使得',\n",
       " '限制',\n",
       " '替',\n",
       " '谁料',\n",
       " '不断',\n",
       " '但',\n",
       " '知道',\n",
       " '不外',\n",
       " '正值',\n",
       " '此中',\n",
       " '一面',\n",
       " '极端',\n",
       " '连袂',\n",
       " '突出',\n",
       " '常言说得好',\n",
       " '起来',\n",
       " '够瞧的',\n",
       " '加入',\n",
       " '不是',\n",
       " '满足',\n",
       " '从新',\n",
       " '凑巧',\n",
       " '以',\n",
       " '云尔',\n",
       " '从中',\n",
       " '得了',\n",
       " '自己',\n",
       " '诸如',\n",
       " '究竟',\n",
       " '心里',\n",
       " '综上所述',\n",
       " '每年',\n",
       " '［①ａ］',\n",
       " '以免',\n",
       " '＜＜',\n",
       " '亲手',\n",
       " '人家',\n",
       " '是',\n",
       " '当儿',\n",
       " '而是',\n",
       " '完成',\n",
       " '无论',\n",
       " '莫非',\n",
       " '反过来说',\n",
       " '这儿',\n",
       " '漫说',\n",
       " '能否',\n",
       " '〔',\n",
       " '应用',\n",
       " '然',\n",
       " '〕〔',\n",
       " '基于',\n",
       " '自',\n",
       " '暗自',\n",
       " '｝',\n",
       " '他们',\n",
       " '如上',\n",
       " '敞开儿',\n",
       " '上升',\n",
       " '进去',\n",
       " '举行',\n",
       " '历',\n",
       " '的',\n",
       " '先后',\n",
       " '看来',\n",
       " '＋ξ',\n",
       " '到处',\n",
       " '某个',\n",
       " '咱',\n",
       " '安全',\n",
       " '还',\n",
       " '接着',\n",
       " '总的来说',\n",
       " '相当',\n",
       " '什么样',\n",
       " '?',\n",
       " '窃',\n",
       " '更加',\n",
       " '独自',\n",
       " '假如',\n",
       " '不尽',\n",
       " '粗',\n",
       " '相对而言',\n",
       " '人们',\n",
       " '咧',\n",
       " '彻夜',\n",
       " '九',\n",
       " '｛',\n",
       " '召开',\n",
       " '哎',\n",
       " '归根结底',\n",
       " '相对',\n",
       " '顷刻间',\n",
       " '以外',\n",
       " '不成',\n",
       " '千',\n",
       " '谁知',\n",
       " '［①⑦］',\n",
       " '应当',\n",
       " '到头',\n",
       " '今',\n",
       " '实际',\n",
       " '并不是',\n",
       " '比如',\n",
       " '㈧',\n",
       " '上下',\n",
       " '可见',\n",
       " '于是乎',\n",
       " '——',\n",
       " '总而言之',\n",
       " '不但...而且',\n",
       " '出来',\n",
       " '非徒',\n",
       " '当庭',\n",
       " '种',\n",
       " '顺着',\n",
       " '奋勇',\n",
       " '假若',\n",
       " '［②Ｂ］',\n",
       " '以期',\n",
       " '几番',\n",
       " '奇',\n",
       " '纯',\n",
       " '满',\n",
       " '及',\n",
       " '［②ｅ］',\n",
       " '何止',\n",
       " '连',\n",
       " '哟',\n",
       " '相等',\n",
       " '喔唷',\n",
       " '［－',\n",
       " '川流不息',\n",
       " '反之则',\n",
       " '后者',\n",
       " '那些',\n",
       " '相信',\n",
       " '隔日',\n",
       " '０：２',\n",
       " '怎么样',\n",
       " '过去',\n",
       " '［⑥］',\n",
       " '乎',\n",
       " '从速',\n",
       " '在于',\n",
       " '纵然',\n",
       " '各',\n",
       " '大体',\n",
       " '最高',\n",
       " '进来',\n",
       " '构成',\n",
       " '范围',\n",
       " '那么',\n",
       " '不常',\n",
       " '４',\n",
       " '并没',\n",
       " '互相',\n",
       " '则甚',\n",
       " '不然',\n",
       " '忽然',\n",
       " '甚么',\n",
       " '不择手段',\n",
       " '略',\n",
       " '大张旗鼓',\n",
       " '没',\n",
       " '针对',\n",
       " '！',\n",
       " '特点',\n",
       " '怎么办',\n",
       " '从事',\n",
       " '等',\n",
       " '正如',\n",
       " '［②ｃ］',\n",
       " '＝（',\n",
       " '这些',\n",
       " '下列',\n",
       " '白',\n",
       " '７',\n",
       " '不足',\n",
       " '［④］',\n",
       " '要求',\n",
       " '作为',\n",
       " '那个',\n",
       " '尽管如此',\n",
       " '或曰',\n",
       " '打',\n",
       " '谁',\n",
       " '进步',\n",
       " '瑟瑟',\n",
       " '却不',\n",
       " '据',\n",
       " '重大',\n",
       " '叮咚',\n",
       " '不消',\n",
       " '仍旧',\n",
       " '只怕',\n",
       " '直到',\n",
       " '诸位',\n",
       " '若非',\n",
       " '任',\n",
       " '啊呀',\n",
       " '<',\n",
       " '日臻',\n",
       " '恰巧',\n",
       " '当着',\n",
       " '砰',\n",
       " '它们',\n",
       " '宁',\n",
       " '你的',\n",
       " '有及',\n",
       " '默默地',\n",
       " '牢牢',\n",
       " '有效',\n",
       " '比',\n",
       " '不仅...而且',\n",
       " '兼之',\n",
       " '使',\n",
       " '越是',\n",
       " '背地里',\n",
       " '绝对',\n",
       " '当时',\n",
       " '啐',\n",
       " '哼唷',\n",
       " '对比',\n",
       " '刚好',\n",
       " '动不动',\n",
       " '多年来',\n",
       " '哪个',\n",
       " '有的是',\n",
       " '本人',\n",
       " '５：０',\n",
       " '难道说',\n",
       " '下',\n",
       " '３',\n",
       " '即若',\n",
       " '归',\n",
       " '此处',\n",
       " '千万千万',\n",
       " '此后',\n",
       " '有利',\n",
       " '方能',\n",
       " '亦',\n",
       " '自身',\n",
       " '看',\n",
       " '『',\n",
       " '不必',\n",
       " '保险',\n",
       " '不问',\n",
       " '……',\n",
       " '向',\n",
       " '这时',\n",
       " '不单',\n",
       " '尽如人意',\n",
       " '一旦',\n",
       " '［⑤ａ］',\n",
       " '这边',\n",
       " '主要',\n",
       " '之',\n",
       " '［①］',\n",
       " '宣布',\n",
       " '不同',\n",
       " '要不',\n",
       " '如是',\n",
       " '通常',\n",
       " '不比',\n",
       " '以後',\n",
       " '［③ａ］',\n",
       " '各地',\n",
       " 'ｅ］',\n",
       " '就',\n",
       " '前者',\n",
       " '来得及',\n",
       " '哪天',\n",
       " '或许',\n",
       " '只消',\n",
       " '有时',\n",
       " '始而',\n",
       " '活',\n",
       " '正在',\n",
       " '不怎么',\n",
       " '截至',\n",
       " '规定',\n",
       " '正常',\n",
       " '由此',\n",
       " '今後',\n",
       " '有点',\n",
       " '不只',\n",
       " '产生',\n",
       " '某某',\n",
       " '反应',\n",
       " '决定',\n",
       " '愤然',\n",
       " '好',\n",
       " '尔等',\n",
       " '对',\n",
       " '一致',\n",
       " '但凡',\n",
       " '呸',\n",
       " '累次',\n",
       " '决不',\n",
       " '\\\\',\n",
       " '.',\n",
       " '老是',\n",
       " '抑或',\n",
       " '对方',\n",
       " '随',\n",
       " '立地',\n",
       " '同样',\n",
       " '你是',\n",
       " '原来',\n",
       " '立即',\n",
       " '…',\n",
       " 'μ',\n",
       " '冲',\n",
       " '矣乎',\n",
       " ']',\n",
       " '全面',\n",
       " '［②ｆ］',\n",
       " '甫',\n",
       " '是不是',\n",
       " '呗',\n",
       " '岂',\n",
       " '中小',\n",
       " '［①Ｃ］',\n",
       " '进入',\n",
       " '打从',\n",
       " '顷刻之间',\n",
       " '传闻',\n",
       " '哪年',\n",
       " '｜',\n",
       " 'Δ',\n",
       " '如',\n",
       " '传说',\n",
       " '如常',\n",
       " '!',\n",
       " '串行',\n",
       " '》），',\n",
       " '［①⑨］',\n",
       " '很少',\n",
       " '为此',\n",
       " '部分',\n",
       " '后面',\n",
       " '根据',\n",
       " '5',\n",
       " '不得不',\n",
       " '不管怎样',\n",
       " '不经意',\n",
       " '他的',\n",
       " '行动',\n",
       " '再次',\n",
       " '嘘',\n",
       " '［②ｉ］',\n",
       " 'A',\n",
       " '~',\n",
       " '开外',\n",
       " '［⑤ｆ］',\n",
       " '迫于',\n",
       " '０',\n",
       " '光',\n",
       " '屡次',\n",
       " '复杂',\n",
       " '背靠背',\n",
       " '乘胜',\n",
       " '这么点儿',\n",
       " '个人',\n",
       " '与其说',\n",
       " '中间',\n",
       " '从此以后',\n",
       " '并不',\n",
       " '总是',\n",
       " '日渐',\n",
       " '大凡',\n",
       " '每时每刻',\n",
       " '［②ｇ］',\n",
       " '看出',\n",
       " '［④ｂ］',\n",
       " '对应',\n",
       " '别',\n",
       " '周围',\n",
       " '当真',\n",
       " '＝☆',\n",
       " '各个',\n",
       " '当然',\n",
       " '因了',\n",
       " '或者',\n",
       " '反之亦然',\n",
       " '反倒',\n",
       " '强调',\n",
       " '专门',\n",
       " '彻底',\n",
       " '如果',\n",
       " '不仅仅',\n",
       " '所幸',\n",
       " '...',\n",
       " '．',\n",
       " '刚才',\n",
       " '绝非',\n",
       " '有着',\n",
       " '用',\n",
       " '宁愿',\n",
       " '１２％',\n",
       " '理当',\n",
       " '每',\n",
       " '+',\n",
       " '它们的',\n",
       " '还是',\n",
       " '处处',\n",
       " '丰富',\n",
       " '全身心',\n",
       " '使用',\n",
       " '如今',\n",
       " '［②⑥］',\n",
       " '请勿',\n",
       " '果然',\n",
       " '而论',\n",
       " '比方',\n",
       " '才能',\n",
       " '一定',\n",
       " '唉',\n",
       " '前面',\n",
       " '会',\n",
       " '按理',\n",
       " '就地',\n",
       " '→',\n",
       " '对待',\n",
       " '怎麽',\n",
       " '以便',\n",
       " '来不及',\n",
       " '只要',\n",
       " '后来',\n",
       " '′｜',\n",
       " '甚至',\n",
       " '啊哟',\n",
       " '尽',\n",
       " '［⑤］',\n",
       " '这麽',\n",
       " '［③ｂ］',\n",
       " '非常',\n",
       " '老',\n",
       " '了',\n",
       " '您',\n",
       " '最',\n",
       " '惯常',\n",
       " '喏',\n",
       " '倘或',\n",
       " '些',\n",
       " '就此',\n",
       " '因为',\n",
       " '当口儿',\n",
       " '在下',\n",
       " '论',\n",
       " '‘',\n",
       " '此地',\n",
       " 'В',\n",
       " '不惟',\n",
       " '～±',\n",
       " '最好',\n",
       " '大略',\n",
       " '怎',\n",
       " '然而',\n",
       " '什麽',\n",
       " '鄙人',\n",
       " '从不',\n",
       " '真是',\n",
       " '呐',\n",
       " '、',\n",
       " '相似',\n",
       " '比较',\n",
       " '略加',\n",
       " '有的',\n",
       " '不曾',\n",
       " '既...又',\n",
       " '甚且',\n",
       " '简直',\n",
       " '隔夜',\n",
       " '一来',\n",
       " '保管',\n",
       " '就算',\n",
       " '.日',\n",
       " '吧',\n",
       " '我的',\n",
       " '啊',\n",
       " '各自',\n",
       " '喀',\n",
       " '三',\n",
       " '不免',\n",
       " '岂止',\n",
       " '某些',\n",
       " '继续',\n",
       " '同',\n",
       " '不过',\n",
       " '宁可',\n",
       " '存在',\n",
       " '⑧',\n",
       " '很多',\n",
       " '所在',\n",
       " '以后',\n",
       " '把',\n",
       " '另行',\n",
       " '各位',\n",
       " '赶快',\n",
       " '更为',\n",
       " '照着',\n",
       " '再有',\n",
       " '彼此',\n",
       " '↑',\n",
       " '必定',\n",
       " '⑥',\n",
       " '』',\n",
       " '趁机',\n",
       " '而言',\n",
       " '转贴',\n",
       " '你',\n",
       " '即是说',\n",
       " '能够',\n",
       " '其他',\n",
       " '当头',\n",
       " '其它',\n",
       " '再说',\n",
       " '比如说',\n",
       " '不够',\n",
       " '要么',\n",
       " '老大',\n",
       " '咦',\n",
       " '［①ｇ］',\n",
       " '嗯',\n",
       " '莫不',\n",
       " '一一',\n",
       " '主张',\n",
       " '亲眼',\n",
       " '从无到有',\n",
       " '八',\n",
       " '\"',\n",
       " ...}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopword_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path = \"D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA/wiki_corpus00',\n",
       " 'D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA/wiki_corpus01',\n",
       " 'D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA/wiki_corpus02']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[os.path.join(save_path, \"wiki_corpus0%s\" % str(i)) for i in range(3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging,jieba,os,re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stopwords():\n",
    "    logging.basicConfig(format='%(asctime)s:%(levelname)s:%(message)s',level=logging.INFO)\n",
    "    #加载停用词表\n",
    "    stopword_set = set()\n",
    "    with open(\"D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA/stopwords.txt\",'r',encoding=\"utf-8\") as stopwords:\n",
    "        for stopword in stopwords:\n",
    "            stopword_set.add(stopword.strip(\"\\n\"))\n",
    "    return stopword_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用正则表达式解析文本1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_zhwiki(read_file_path,save_file_path):\n",
    "    #过滤掉<doc>\n",
    "    regex_str = \"[^<doc.*>$]|[^</doc>$]\"\n",
    "    file = open(read_file_path,\"r\",encoding=\"utf-8\")\n",
    "    #写文件\n",
    "    output = open(save_file_path,\"w+\",encoding=\"utf-8\")\n",
    "    content_line = file.readline()\n",
    "    #获取停用词表\n",
    "    stopwords = get_stopwords()\n",
    "     #定义一个字符串变量，表示一篇文章的分词结果\n",
    "    article_contents = \"\"\n",
    "    while content_line:\n",
    "        match_obj = re.match(regex_str,content_line)\n",
    "        content_line = content_line.strip(\"\\n\")\n",
    "        if len(content_line) > 0:\n",
    "            if match_obj:\n",
    "                #使用jieba进行分词\n",
    "                words = jieba.cut(content_line,cut_all=False)\n",
    "                for word in words:\n",
    "                    if word not in stopwords:\n",
    "                        article_contents += word+\" \"\n",
    "            else:\n",
    "                if len(article_contents) > 0:\n",
    "                    output.write(article_contents+\"\\n\")\n",
    "                    article_contents = \"\"\n",
    "        content_line = file.readline()\n",
    "    output.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "将维基百科语料库进行分类\n",
    "'''\n",
    "def generate_corpus():\n",
    "    zhwiki_path = \"D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA\"\n",
    "    save_path = \"D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA\"\n",
    "    for i in range(3):\n",
    "        file_path = os.path.join(zhwiki_path,str(\"wiki_zh_0%s\"%str(i)))\n",
    "        parse_zhwiki(file_path,os.path.join(save_path,\"wiki_corpus0%s\"%str(i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "2019-07-31 17:52:52,612:DEBUG:Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\C00495~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "2019-07-31 17:52:52,633:DEBUG:Loading model from cache C:\\Users\\C00495~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.851 seconds.\n",
      "2019-07-31 17:52:53,481:DEBUG:Loading model cost 0.851 seconds.\n",
      "Prefix dict has been built succesfully.\n",
      "2019-07-31 17:52:53,482:DEBUG:Prefix dict has been built succesfully.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-fcac2fec906f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgenerate_corpus\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-10-760e957268b5>\u001b[0m in \u001b[0;36mgenerate_corpus\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mfile_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzhwiki_path\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"wiki_zh_0%s\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[0mparse_zhwiki\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msave_path\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"wiki_corpus0%s\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-9-4e798c6cf5da>\u001b[0m in \u001b[0;36mparse_zhwiki\u001b[1;34m(read_file_path, save_file_path)\u001b[0m\n\u001b[0;32m     22\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marticle_contents\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[0m\n\u001b[1;32m---> 24\u001b[1;33m                     \u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marticle_contents\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     25\u001b[0m                     \u001b[0marticle_contents\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m         \u001b[0mcontent_line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "generate_corpus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "合并分词后的文件\n",
    "'''\n",
    "def merge_corpus():\n",
    "    output = open(\"D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA/wiki_corpus\",\"w\",encoding=\"utf-8\")\n",
    "    input = \"D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA\"\n",
    "    for i in range(3):\n",
    "        file_path = os.path.join(input,str(\"wiki_corpus0%s\"%str(i)))\n",
    "        file = open(file_path,\"r\",encoding=\"utf-8\")\n",
    "        line = file.readline()\n",
    "        while line:\n",
    "            output.writelines(line)\n",
    "            line = file.readline()\n",
    "        file.close()\n",
    "    output.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_corpus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -m pip install --upgrade pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "from gensim.models import word2vec\n",
    " \n",
    "def main():\n",
    "    logging.basicConfig(format=\"%(asctime)s:%(levelname)s:%(message)s\",level=logging.INFO)\n",
    "    sentences = word2vec.LineSentence(\"D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA/wiki_corpus\")\n",
    "    model = word2vec.Word2Vec(sentences,size=250)\n",
    "    #保存模型\n",
    "    model.save(\"D:/AI/第4课/wikiextractor-master/wikiextractor-master/zhwiki/AA/model/wiki_corpus.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-07-31 17:56:17,361:INFO:'pattern' package not found; tag filters are not available for English\n"
     ]
    }
   ],
   "source": [
    "from gensim import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wordcloud in d:\\programdata\\anaconda3\\lib\\site-packages (1.5.0)\n",
      "Requirement already satisfied: pillow in d:\\programdata\\anaconda3\\lib\\site-packages (from wordcloud) (5.0.0)\n",
      "Requirement already satisfied: numpy>=1.6.1 in d:\\programdata\\anaconda3\\lib\\site-packages (from wordcloud) (1.14.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = r'C:\\Windows\\Fonts\\simhei.ttf'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'''\n",
    "round marks\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mask():\n",
    "    x, y = np.ogrid[:300, :300]\n",
    "    mask = (x-150) ** 2 + (y-150) ** 2 > 130 **2\n",
    "    mask = 255 * mask.astype(int)\n",
    "    return mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[255, 255, 255, ..., 255, 255, 255],\n",
       "       [255, 255, 255, ..., 255, 255, 255],\n",
       "       [255, 255, 255, ..., 255, 255, 255],\n",
       "       ...,\n",
       "       [255, 255, 255, ..., 255, 255, 255],\n",
       "       [255, 255, 255, ..., 255, 255, 255],\n",
       "       [255, 255, 255, ..., 255, 255, 255]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_mask()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# '''绘制词云'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_word_cloud(word_cloud):\n",
    "    wc = WordCloud(background_color=\"white\", mask=get_mask(), font_path=font)  # \n",
    "    wc.generate_from_frequencies(word_cloud)\n",
    "    #隐藏x轴和y轴\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(wc, interpolation=\"bilinear\")  # , interpolation=\"bilinear\"\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test():\n",
    "    logging.basicConfig(format=\"%(asctime)s:%(levelname)s:%(message)s\", level=logging.INFO)\n",
    "    ss = models.Word2Vec.load(\"D:/AI/第4课/model/wiki_corpus.model\")\n",
    "    # 输入一个词找出相似的前10个词\n",
    "    one_corpus = [\"人工智能\"]\n",
    "    result = ss.most_similar(one_corpus[0], topn=100)\n",
    "    word_cloud = dict()\n",
    "    for sim in result:\n",
    "        word_cloud[sim[0]]= sim[1]\n",
    "    draw_word_cloud(word_cloud)\n",
    "    # return word_cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-07-31 20:17:02,611:INFO:loading Word2Vec object from D:/AI/第4课/model/wiki_corpus.model\n",
      "2019-07-31 20:17:03,903:INFO:loading wv recursively from D:/AI/第4课/model/wiki_corpus.model.wv.* with mmap=None\n",
      "2019-07-31 20:17:03,903:INFO:loading vectors from D:/AI/第4课/model/wiki_corpus.model.wv.vectors.npy with mmap=None\n",
      "2019-07-31 20:17:04,195:INFO:setting ignored attribute vectors_norm to None\n",
      "2019-07-31 20:17:04,195:INFO:loading vocabulary recursively from D:/AI/第4课/model/wiki_corpus.model.vocabulary.* with mmap=None\n",
      "2019-07-31 20:17:04,199:INFO:loading trainables recursively from D:/AI/第4课/model/wiki_corpus.model.trainables.* with mmap=None\n",
      "2019-07-31 20:17:04,199:INFO:loading syn1neg from D:/AI/第4课/model/wiki_corpus.model.trainables.syn1neg.npy with mmap=None\n",
      "2019-07-31 20:17:04,492:INFO:setting ignored attribute cum_table to None\n",
      "2019-07-31 20:17:04,492:INFO:loaded D:/AI/第4课/model/wiki_corpus.model\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \n",
      "2019-07-31 20:17:05,908:INFO:precomputing L2-norms of word weight vectors\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 获取某个单次对应的词向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-07-31 17:57:20,931:INFO:loading Word2Vec object from D:/AI/第4课/model/wiki_corpus.model\n",
      "2019-07-31 17:57:22,234:INFO:loading wv recursively from D:/AI/第4课/model/wiki_corpus.model.wv.* with mmap=None\n",
      "2019-07-31 17:57:22,235:INFO:loading vectors from D:/AI/第4课/model/wiki_corpus.model.wv.vectors.npy with mmap=None\n",
      "2019-07-31 17:57:22,528:INFO:setting ignored attribute vectors_norm to None\n",
      "2019-07-31 17:57:22,529:INFO:loading vocabulary recursively from D:/AI/第4课/model/wiki_corpus.model.vocabulary.* with mmap=None\n",
      "2019-07-31 17:57:22,529:INFO:loading trainables recursively from D:/AI/第4课/model/wiki_corpus.model.trainables.* with mmap=None\n",
      "2019-07-31 17:57:22,529:INFO:loading syn1neg from D:/AI/第4课/model/wiki_corpus.model.trainables.syn1neg.npy with mmap=None\n",
      "2019-07-31 17:57:22,842:INFO:setting ignored attribute cum_table to None\n",
      "2019-07-31 17:57:22,843:INFO:loaded D:/AI/第4课/model/wiki_corpus.model\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.92715037,  0.37340653,  0.7036365 , -1.0053872 ,  0.178802  ,\n",
       "       -0.70325994, -0.22989012, -0.5972023 ,  0.7952115 ,  0.35076633,\n",
       "        0.4717583 , -0.58666325, -0.82284343,  0.46206126, -0.18627585,\n",
       "        0.65332615, -0.24379948,  0.7706841 ,  0.80552995, -0.31332815,\n",
       "       -1.1175491 , -1.7366598 ,  1.3227059 , -0.13442394, -1.1407534 ,\n",
       "       -0.26598984,  0.0930741 , -0.06712216, -1.2153015 ,  0.5320565 ,\n",
       "        2.484655  ,  1.4416156 ,  0.22363117, -0.23027188,  0.6691169 ,\n",
       "       -0.73985755, -0.55115795,  0.69387126,  1.7137287 ,  1.9085842 ,\n",
       "        0.16799086, -0.17947552, -1.1842592 ,  1.1340004 ,  0.19082463,\n",
       "       -0.48882124, -0.72009623, -0.83133554, -0.06480438,  0.0764263 ,\n",
       "       -0.314567  ,  0.74851215, -1.3036588 ,  1.5294709 , -0.31337866,\n",
       "        0.5087171 ,  0.5092004 ,  0.32408065,  0.27710614, -1.2052797 ,\n",
       "        0.3899453 ,  0.3313894 ,  0.26940075, -0.29445854, -1.0105472 ,\n",
       "        1.583133  , -0.03797611, -0.03000293,  2.41505   , -2.5477514 ,\n",
       "       -0.34906253,  0.87459207,  0.54966736, -0.4489655 ,  0.742072  ,\n",
       "        0.07124654, -1.4842881 , -1.3680931 , -0.25773156,  0.10630202,\n",
       "       -0.31519398, -0.02316765, -0.4937844 , -1.3585551 ,  0.8570287 ,\n",
       "       -0.8167963 ,  0.09998806, -0.627438  ,  0.16593161, -0.09120765,\n",
       "       -0.12923527, -1.6455221 ,  0.35436735, -1.2397305 ,  1.3569922 ,\n",
       "        0.62536925, -0.35759893,  0.13185135, -0.6377505 ,  0.08756936,\n",
       "       -1.2676665 ,  0.05620896,  0.49728706, -0.35628802, -0.5703023 ,\n",
       "       -1.3384908 ,  0.36633337, -1.6745546 , -0.5677189 ,  0.6054513 ,\n",
       "        0.31608245, -0.14135905,  1.2052014 , -0.45156485,  0.54868805,\n",
       "        1.2350458 , -0.28922293,  1.4573281 ,  1.1546717 ,  0.8453068 ,\n",
       "        0.6343222 ,  0.86928225,  0.7909805 , -0.8832606 , -0.40911388,\n",
       "       -0.88809264, -0.3681779 ,  0.6865326 , -0.62034124,  1.3314852 ,\n",
       "       -0.9565945 ,  0.5006785 ,  1.302065  , -1.377739  ,  0.09719864,\n",
       "        1.1938517 ,  0.3100892 ,  0.4687882 , -0.23037893, -0.4988884 ,\n",
       "        2.0269334 ,  0.41625413,  0.15974194,  0.30068898,  0.6449656 ,\n",
       "       -0.16497542,  1.9152718 ,  0.13316545,  0.83635503, -1.6637782 ,\n",
       "        0.71450037,  0.15466839,  0.06384248,  0.1678372 , -0.79553616,\n",
       "        2.1284084 ,  0.18740565, -0.7405261 ,  0.8299895 , -0.8007893 ,\n",
       "        0.8897626 ,  0.9268389 ,  0.60514444, -2.0359776 ,  0.13263908,\n",
       "       -1.3007696 , -1.1637598 , -0.5341993 ,  0.04713425, -1.2458456 ,\n",
       "        0.32128543, -0.3546448 ,  1.6787508 , -0.03513904, -0.18319632,\n",
       "       -0.4863898 ,  0.3898416 , -0.7733321 , -0.5594637 , -1.2693325 ,\n",
       "        1.3027647 ,  1.8410907 ,  0.25114116, -0.8689624 , -0.5973644 ,\n",
       "        0.58659697,  1.2551639 , -0.6549125 ,  0.9669485 , -0.19314195,\n",
       "        0.78127897,  0.2537014 , -0.76670766,  0.73279434, -0.88059634,\n",
       "       -0.6241359 , -1.8911088 , -1.0446979 , -0.9817431 , -0.6476858 ,\n",
       "       -0.16858798,  0.45709425,  0.06089562, -0.22997908,  0.8868034 ,\n",
       "       -1.101709  ,  1.420577  , -0.60721946, -0.684174  ,  0.748139  ,\n",
       "       -0.48513386, -0.93921226, -0.42190158,  0.4691317 , -1.3603612 ,\n",
       "       -0.47589388, -0.54274565,  0.97763973, -0.07425226,  0.72201645,\n",
       "       -0.2448814 , -0.9799892 ,  1.1773899 ,  0.28237665,  0.16176142,\n",
       "        0.8478896 ,  0.15848929,  0.6943867 ,  0.13428394,  0.17206715,\n",
       "        0.6116789 ,  0.35751393, -0.1921896 ,  1.3066531 ,  0.62340796,\n",
       "        1.3359535 , -1.9932408 ,  0.19482128,  0.01093235, -1.0690598 ,\n",
       "       -0.3356595 , -0.6130543 ,  0.30650952, -0.89061046, -0.80874634,\n",
       "        1.5271109 , -0.02879892,  0.5446013 , -0.21509796, -0.07016123],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss = models.Word2Vec.load(\"D:/AI/第4课/model/wiki_corpus.model\")\n",
    "ss['大象']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 两个词的相似度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "two_corpus = [\"乌龟\",\"鳖\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `similarity` (Method will be removed in 4.0.0, use self.wv.similarity() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "ret = ss.similarity(two_corpus[0], two_corpus[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5326433"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  如果是相同的单词，能不能认为是1？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: Call to deprecated `similarity` (Method will be removed in 4.0.0, use self.wv.similarity() instead).\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "other_corpus = [\"百科\",\"百科\"]\n",
    "res = ss.similarity(other_corpus[0],other_corpus[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99999994"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  词云"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = r'C:\\Windows\\Fonts\\simhei.ttf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud1 = {'中文':0.1,\"英文\":0.2,\"C\":0.3,\"D\":0.4,\"E\":0.5,\"F\":0.6,\"G\":0.7,\"H\":0.8,\"I\":0.9}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "w1 = WordCloud(font_path=font, background_color=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<wordcloud.wordcloud.WordCloud at 0x246a1580668>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w1.generate_from_frequencies(cloud1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.axis(\"off\")\n",
    "plt.imshow(w1)  # , interpolation=\"bilinear\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 词云默认是不支持中文的，必须指定font中文字眼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}