练习.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = np.array([3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.inner(k,l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.linalg.norm(k,ord=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[3],[4]])\n",
    "a **= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.array([[3],[4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.linalg.norm(a) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2 * np.identity(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_t = np.zeros((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_t[2][2] = 3\n",
    "A_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.ones((3, 1))\n",
    "print(a.shape)\n",
    "b = np.zeros((2, 1))\n",
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.r_[a,b]\n",
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([[1,2,3],[4,5,6],[7,8,9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.33333333, -7.        , -3.        ],\n",
       "       [-3.33333333,  2.        ,  0.        ],\n",
       "       [ 5.66666667,  5.        ,  3.        ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x - np.mean(x, axis=0)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 3) :\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1, 4, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 2, 3])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 2, 0])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.argsort()[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.tofile('1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.random.random((3,3))\n",
    "eigenValues, eigenVectors = np.linalg.eig(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.15211946,  1.23604844,  0.56559231])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenValues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = eigenValues.argsort()[::-1]   \n",
    "eigenValues = eigenValues[idx]\n",
    "eigenVectors = eigenVectors[:,idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.23604844,  0.56559231, -0.15211946])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenValues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.49814536, -0.53335771, -0.88489979],\n",
       "       [-0.63654616, -0.17773285,  0.25389007],\n",
       "       [-0.58877855,  0.82700701,  0.39050249]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenVectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(x.reshape(-1,1),axis = 0)[int(x.reshape(-1,1).shape[0] * 0.3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "w,v = np.linalg.eig(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.61168440e+01, -1.11684397e+00, -8.58274334e-16])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.23197069, -0.78583024],\n",
       "       [-0.52532209, -0.08675134],\n",
       "       [-0.8186735 ,  0.61232756]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[:, 0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '2.jpg'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-092946eac2c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'2.jpg'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'local_img'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'source'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;34m'album'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '2.jpg'"
     ]
    }
   ],
   "source": [
    "from numpy import source\n",
    "import requests\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.image as mpimg\n",
    "url = 'http://buling.wudaoai.cn/retrieval'\n",
    "while True :\n",
    "    time.sleep(1)\n",
    "    img = '2.jpg'\n",
    "    files = {'local_img': open(img,'rb')}\n",
    "    params = {'source' : 'album'}\n",
    "    r = requests.post(url, files=files, params=params)\n",
    "    print(r.json()['sentence'])\n",
    "    Img = mpimg.imread(img)\n",
    "    plt.imshow(Img)\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,4,7],[2,5,8]])\n",
    "b = np.array([1.0,2.0,3.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0., -2., -4.],\n",
       "       [-1., -3., -5.]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b - a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b - np.array([1,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-23.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.sum((b - np.array([1,1,1])) * (b - a),axis=1),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 1)\n",
      "[[1]\n",
      " [2]\n",
      " [3]\n",
      " [4]\n",
      " [5]]\n"
     ]
    }
   ],
   "source": [
    "a=np.array([1,2,3,4,5])\n",
    "aa=a[:,np.newaxis]\n",
    "print(aa.shape)\n",
    "print (aa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "def sampleExp(Lambda = 2,maxCnt = 50000):\n",
    "    ys = []\n",
    "    standardXaxis = []\n",
    "    standardExp = []\n",
    "    for i in range(maxCnt):\n",
    "        u = np.random.random()\n",
    "        y = -1/Lambda*np.log(1-u) #F-1(X)\n",
    "        ys.append(y)\n",
    "    for i in range(1000):\n",
    "        t = Lambda * np.exp(-Lambda*i/100)\n",
    "        standardXaxis.append(i/100)\n",
    "        standardExp.append(t)\n",
    "    plt.plot(standardXaxis,standardExp,'r')\n",
    "    plt.hist(ys,1000)\n",
    "    plt.show()\n",
    "\n",
    "sampleExp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def f1(x):\n",
    "    return (0.3*np.exp(-(x-0.3)**2) + 0.7* np.exp(-(x-2.)**2/0.3))/1.2113\n",
    "x = np.arange(-4.,6.,0.01)\n",
    "plt.plot(x,f1(x),color = \"red\")\n",
    "\n",
    "size = int(1e+07)\n",
    "sigma = 1.2\n",
    "z = np.random.normal(loc = 1.4,scale = sigma, size = size)\n",
    "qz = 1/(np.sqrt(2*np.pi)*sigma)*np.exp(-0.5*(z-1.4)**2/sigma**2)\n",
    "k = 2.5\n",
    "#z = np.random.uniform(low = -4, high = 6, size = size)\n",
    "#qz = 0.1\n",
    "#k = 10\n",
    "u = np.random.uniform(low = 0, high = k*qz, size = size)\n",
    "\n",
    "pz =  0.3*np.exp(-(z-0.3)**2) + 0.7* np.exp(-(z-2.)**2/0.3)\n",
    "sample = z[pz >= u]\n",
    "plt.hist(sample,bins=150, edgecolor='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def get_cos_similar(v1, v2) :\n",
    "    num = float(np.dot(v1, v2))  # 向量点乘\n",
    "    denom = np.linalg.norm(v1) * np.linalg.norm(v2)  # 求模长的乘积\n",
    "    return 0.5 + 0.5 * (num / denom) if denom != 0 else 0\n",
    "file_img = '/Users/zhangyuyao/Library/Containers/com.tencent.xinWeChat/Data/Library/Application Support/com.tencent.xinWeChat/2.0b4.0.9/3b589b8fb0516c8e072f981ad5ce1b32/Message/MessageTemp/b8b5be39d4e1d00a0a072cb274aabcdf/Image/8291651288469_.pic_hd.jpg'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "files = {\"image\": open(file_img, \"rb\")}\n",
    "url = \"http://180.76.186.10:6175/image_query\"\n",
    "\n",
    "r = requests.post(url, files=files)\n",
    "a = r.json()['embedding']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4524454169557914\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "url = \"http://180.76.186.10:6175/text_query\"\n",
    "params = {\"text\":\"如果说到保险，你会想到什么？寿险？不对不对，这是我最近的一个作业，大概是要想一些比较新奇的保险业务，讨论可行性之类的。我觉得你平时的点子挺不错的，所以想听听你的看法。\"}\n",
    "\n",
    "r = requests.get(url, params = params)\n",
    "b = r.json()['embedding']\n",
    "\n",
    "print(get_cos_similar(a, b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"text\":\"\"}\n",
    "\n",
    "r = requests.get(url, params = params)\n",
    "b = r.json()['embedding']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2048)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array(a).reshape(1 , -1)\n",
    "b = np.array(b).reshape(1 , -1)\n",
    "c = np.r_[a, b]\n",
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.12245935],\n",
       "       [0.12245935, 1.        ]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarity(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.array([[1,2],[3,4],[5,6]])\n",
    "y = np.array([[10,10],[100,100],[1000,1000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2]],\n",
       "\n",
       "       [[3, 4]],\n",
       "\n",
       "       [[5, 6]]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs = x[:, np.newaxis, :]\n",
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  -9,   -8],\n",
       "        [ -99,  -98],\n",
       "        [-999, -998]],\n",
       "\n",
       "       [[  -7,   -6],\n",
       "        [ -97,  -96],\n",
       "        [-997, -996]],\n",
       "\n",
       "       [[  -5,   -4],\n",
       "        [ -95,  -94],\n",
       "        [-995, -994]]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs - y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  -17,  -197, -1997],\n",
       "       [  -13,  -193, -1993],\n",
       "       [   -9,  -189, -1989]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(xs - y , axis = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.07142857, 0.14285714, 0.21428571]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.array([[1],[2],[3]])\n",
    "c.shape\n",
    "np.linalg.pinv(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(8).reshape(2, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(a, keepdims = True).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.arange(9).reshape(3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3],\n",
       "       [12],\n",
       "       [21]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(axis = 1)[:,None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3],\n",
       "       [12],\n",
       "       [21]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x, axis = 1, keepdims = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  6],\n",
       "       [ 3,  8, 15],\n",
       "       [ 6, 14, 24]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x * y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.66666667, 2.        ],\n",
       "       [0.25      , 0.66666667, 1.25      ],\n",
       "       [0.28571429, 0.66666667, 1.14285714]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x * y) / np.sum(x, axis = 1, keepdims = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mysql"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.9.2 ('base')",
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