a1

Assignment_1_intro_word_vectors/Gensim Doc2vec.ipynb

@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gensim进阶教程：训练Doc2vec\n",
+    "Doc2vec是Mikolov在word2vec基础上提出的另一个用于计算长文本向量的工具。它的工作原理与word2vec极为相似——只是将长文本作为一个特殊的token id引入训练语料中。\n",
+    "\n",
+    "在Gensim中，doc2vec也是继承于word2vec的一个子类。因此，无论是API的参数接口还是调用文本向量的方式，doc2vec与word2vec都极为相似。\n",
+    "\n",
+    "主要的区别是在对输入数据的预处理上。Doc2vec接受一个由LabeledSentence对象组成的迭代器作为其构造函数的输入参数。其中，LabeledSentence是Gensim内建的一个类，它接受两个List作为其初始化的参数：word list和label list。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "data = [\"I love machine learning. Its awesome.\",\n",
+    "        \"I love coding in python\",\n",
+    "        \"I love building chatbots\",\n",
+    "        \"they chat amagingly well\"]\n",
+    "tagged_data = [TaggedDocument(words=word_tokenize(_d.lower()), tags=['SENT_%s' %str(i)]) for i, _d in enumerate(data)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model = Doc2Vec(size=100,\n",
+    "                alpha=0.01, \n",
+    "                min_count=2,\n",
+    "                dm =1)\n",
+    "model.build_vocab(tagged_data)\n",
+    "for epoch in range(10):\n",
+    "    print('iteration {0}'.format(epoch))\n",
+    "    model.train(tagged_data,\n",
+    "                total_examples=model.corpus_count,\n",
+    "                epochs=model.iter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model[\"SENT_0\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.docvecs.most_similar(0)"
+   ]
+  },
+  {
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Assignment_1_intro_word_vectors/python review.ipynb

@@ -0,0 +1,963 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python Numpy Review\n",
+    "\n",
+    "主要复习numpy\n",
+    "\n",
+    "tutor:  `chongjiujin # gmail.com`\n",
+    "\n",
+    "```\n",
+    "if you have any question in python or pytorch:\n",
+    "\n",
+    "    print(add personal weichat:flypython)\n",
+    "   ```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# List Slicing\n",
+    "\n",
+    "List elements can be accessed in convenient ways.\n",
+    "\n",
+    "Basic format: some_list[start_index:end_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3, 4, 5, 6]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers = [0, 1, 2, 3, 4, 5, 6]\n",
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[0:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 6]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[5:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3, 4, 5, 6]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Negative index wraps around\n",
+    "numbers[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 5, 6]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[-3:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Can mix and match\n",
+    "numbers[1:-10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Numpy python矩阵计算库\n",
+    "\n",
+    "\n",
+    "Optimized library for matrix and vector computation.\n",
+    "\n",
+    "用于矩阵和向量\n",
+    "\n",
+    "\n",
+    "\n",
+    "Makes use of C/C++ subroutines and memory-efficient data structures.\n",
+    "\n",
+    "底层是C/C++编译的，效率更高\n",
+    "\n",
+    "(Lots of computation can be efficiently represented as vectors.)\n",
+    "\n",
+    "**Main data type: `np.ndarray`**\n",
+    "\n",
+    "This is the data type that you will use to represent matrix/vector computations.\n",
+    "这个数据结构是用来放矩阵/向量的\n",
+    "\n",
+    "Note: constructor function is `np.array()`\n",
+    "\n",
+    " `np.array()`初始化函数\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np#导入库"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([1,2,3])#一维向量\n",
+    "x\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = np.array([[3,4,5],[6,7,8]])#二维矩阵\n",
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 1)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = np.array([[1],[2],[3]])#每个框是增加一个维度\n",
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# np.ndarray Operations 操作函数\n",
+    "\n",
+    "Reductions: `np.max`, `np.min`, `np.argmax`, `np.sum`, `np.mean`, …\n",
+    "\n",
+    "Always reduces along an axis! (Or will reduce along all axes if not specified.)\n",
+    "\n",
+    "(You can think of this as “collapsing” this axis into the function’s output.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([1,2,3])#一维向量\n",
+    "x.max()#np.max(x)\n",
+    "#x.min()\n",
+    "#x.sum()\n",
+    "#x.mean()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5],\n",
+       "       [8]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = np.array([[3,4,5],[6,7,8]])#按维度取最大值\n",
+    "#np.max(y,axis = 1)\n",
+    "np.max(y, axis = 1, keepdims = True)\n",
+    "#https://docs.scipy.org/doc/numpy/reference/generated/numpy.amax.html#numpy.amax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 基本矩阵运算\n",
+    "\n",
+    "\n",
+    "`np.dot`矩阵点乘\n",
+    "$$ np.dot(v,w)=v^T w $$\n",
+    "https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html?highlight=dot#numpy.dot\n",
+    "\n",
+    "`np.multiply` 在 np.array 中重载为元素乘法，在 np.matrix 中重载为矩阵乘法\n",
+    "\n",
+    "https://docs.scipy.org/doc/numpy/reference/generated/numpy.multiply.html\n",
+    "\n",
+    "\n",
+    "我们这里只讨论一维向量"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#np.dot点乘\n",
+    "\n",
+    "x=np.array([1,2,3])#一维向量\n",
+    "y=np.array([1,2,3])#一维向量\n",
+    "np.dot(x,y)\n",
+    "#"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(x.T*y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 4, 9])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x=np.array([1,2,3])#一维向量\n",
+    "np.multiply(x,x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Indexing  索引"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#基本同list\n",
+    "x = np.array([1,2,3])#一维向量\n",
+    "x[x > 2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 2, 1])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "index=[2,1,0]#按索引排序\n",
+    "x[index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 矩阵遍历\n",
+    "\n",
+    "有时候需要遍历矩阵里所有的向量"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3 4 5]\n",
+      " [6 7 8]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "y = np.array([[3,4,5],[6,7,8]])#二维矩阵\n",
+    "print(y)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3 4 5]\n",
+      "-----\n",
+      "[6 7 8]\n",
+      "-----\n"
+     ]
+    }
+   ],
+   "source": [
+    "#默认按第1维度遍历\n",
+    "for y1 in y:\n",
+    "    print(y1)\n",
+    "    print(\"-----\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "#按指定维度遍历\n",
+    "d1,d2= y.shape\n",
+    "print(d1,d2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 [3 6]\n",
+      "1 [4 7]\n",
+      "2 [5 8]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for d in range(d2):\n",
+    "    print(d,y[:,d])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Efficient Numpy Code\n",
+    "尽量用Numpy的特性提升效率"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array([[3,4,5],[6,7,8]])#二维矩阵\n",
+    "y = np.array([[1,2,3],[9,0,10]])#二维矩阵"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 9, 16, 25],\n",
+       "       [36, 49, 64]])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for i in range(x.shape[0]):\n",
+    "    for j in range(x.shape[1]):\n",
+    "        x[i,j] **= 2\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  81,  256,  625],\n",
+       "       [1296, 2401, 4096]])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x **= 2\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 全0 和全 1 矩阵"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.30950800e+06, 1.82888704e+08])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z=np.zeros((2,))\n",
+    "for i in range(x.shape[0]):\n",
+    "    x1=x[i]\n",
+    "    y1=y[i]\n",
+    "    z[i]=np.dot(x1,y1)\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1.],\n",
+       "       [1., 1., 1.]])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z=np.ones((2,3))\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 矩阵和常数计算以及 Broadcasting广播"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 3)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([[3,4,5],[6,7,8],[1,2,3]])#二维矩阵\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 5,  6,  7],\n",
+       "       [ 8,  9, 10],\n",
+       "       [ 3,  4,  5]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6,  8, 10],\n",
+       "       [12, 14, 16],\n",
+       "       [ 2,  4,  6]])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x*2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 1)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y=np.array([[2],[4],[8]])\n",
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 5,  6,  7],\n",
+       "       [10, 11, 12],\n",
+       "       [ 9, 10, 11]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x+y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 矩阵变换"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 3)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z=np.array([[2, 4, 8]])\n",
+    "z.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 1)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z=y.reshape(-1,1)\n",
+    "z.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 3)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z=y.T\n",
+    "z.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6, 16, 40],\n",
+       "       [12, 28, 64],\n",
+       "       [ 2,  8, 24]])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x*z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 思考题\n",
+    "y=np.array([[2],[4],[8]])\n",
+    "\n",
+    "(y + y.T)是什么\n",
+    "\n",
+    "\n",
+    "# 如果对操作有不确定，开一个jupyter notebook，测试后使用"
+   ]
+  },
+  {
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}