diff --git a/机器学习竞赛实战_优胜解决方案/常用特征构建方法/.ipynb_checkpoints/文本特征处理-checkpoint.ipynb b/机器学习竞赛实战_优胜解决方案/常用特征构建方法/.ipynb_checkpoints/文本特征处理-checkpoint.ipynb
index 9292cd8..54946b6 100644
--- a/机器学习竞赛实战_优胜解决方案/常用特征构建方法/.ipynb_checkpoints/文本特征处理-checkpoint.ipynb
+++ b/机器学习竞赛实战_优胜解决方案/常用特征构建方法/.ipynb_checkpoints/文本特征处理-checkpoint.ipynb
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -140,20 +140,196 @@
      "text": [
       "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "nltk.download()"
+    "nltk.download()  # 下载失败的用这个方法https://blog.csdn.net/qq_37891889/article/details/104418106"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*** Introductory Examples for the NLTK Book ***\n",
+      "Loading text1, ..., text9 and sent1, ..., sent9\n",
+      "Type the name of the text or sentence to view it.\n",
+      "Type: 'texts()' or 'sents()' to list the materials.\n",
+      "text1: Moby Dick by Herman Melville 1851\n",
+      "text2: Sense and Sensibility by Jane Austen 1811\n",
+      "text3: The Book of Genesis\n",
+      "text4: Inaugural Address Corpus\n",
+      "text5: Chat Corpus\n",
+      "text6: Monty Python and the Holy Grail\n",
+      "text7: Wall Street Journal\n",
+      "text8: Personals Corpus\n",
+      "text9: The Man Who Was Thursday by G . K . Chesterton 1908\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nltk.book import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', 'should', 'now']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 词频与停用词\n",
+    "wpt = nltk.WordPunctTokenizer()\n",
+    "stop_words = nltk.corpus.stopwords.words('english')\n",
+    "print(stop_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "停用词：\n",
+    "\n",
+    "这里面除了天气和动物信息，其它都基本没用，如i me my等等这些词，这些相当于停用词"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# 词频与停用词"
+    "def normalize_document(doc):\n",
+    "    # 预处理\n",
+    "    doc = re.sub(r'[^a-zA-Z0-9\\s]', '', doc, re.I)  # 去掉多余字符\n",
+    "    doc = doc.lower()  # 统一转小写\n",
+    "    doc = doc.strip()  # 去空格\n",
+    "    # 分词，切分提取全部词\n",
+    "    tokens = wpt.tokenize(doc)  \n",
+    "    # 查找停用词，并过滤\n",
+    "    filtered_tokens = [token for token in tokens if token not in stop_words]\n",
+    "    # 拼接所有的词\n",
+    "    doc = ' '.join(filtered_tokens)\n",
+    "    return doc\n",
+    "\n",
+    "\n",
+    "normalize_corpus = np.vectorize(normalize_document)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['sky blue beautiful', 'love blue beautiful sky',\n",
+       "       'quick brown fox jumps lazy dog', 'brown fox quick blue dog lazy',\n",
+       "       'sky blue sky beautiful today', 'dog layz brown fox quick'],\n",
+       "      dtype='<U30')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm_corpus = normalize_corpus(corpus)\n",
+    "norm_corpus  # 处理完成的结果"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 词袋模型\n",
+    "将所有词语装进一个袋子里,不考虑其词法和语序的问题,即每个词语都是独立的。"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['sky blue beautiful' 'love blue beautiful sky'\n",
+      " 'quick brown fox jumps lazy dog' 'brown fox quick blue dog lazy'\n",
+      " 'sky blue sky beautiful today' 'dog layz brown fox quick']\n",
+      "['beautiful', 'blue', 'brown', 'dog', 'fox', 'jumps', 'layz', 'lazy', 'love', 'quick', 'sky', 'today']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
+       "       [1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n",
+       "       [0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0],\n",
+       "       [0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0],\n",
+       "       [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1],\n",
+       "       [0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "print(norm_corpus)\n",
+    "# 利用语句中的词，构建成一个词汇表。\n",
+    "# min_df表示词频率小于多少不会被当做关键词，max_df则相反\n",
+    "# 直接API文档https://scikit-learn.org/stable/modules/classes.html，搜CountVectorizer\n",
+    "cv = CountVectorizer(min_df=0., max_df=1.)\n",
+    "cv.fit(norm_corpus)\n",
+    "print(cv.get_feature_names())\n",
+    "# 构建向量表\n",
+    "cv_matrix = cv.fit_transform(norm_corpus)\n",
+    "cv_matrix = cv_matrix.toarray()\n",
+    "cv_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如上数据中，在词汇表中，是否出现过，有则在相应位置标记为1，有两个则标记为2。\n",
+    "\n",
+    "如：第一句话'sky blue beautiful'，在词汇表有第一个词一次，第二个词一次，导数第二个词一次，那么下面的向量表则是[1,1,...,1,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {