PageRank.ipynb

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    "## PageRank的迭代算法\n",
    "***\n",
    "***\n",
    "Time: 2020-09-10<br>\n",
    "Author: dsy\n",
    "***"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
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   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PageRank(M,d,R0,iters=1000):\n",
    "    R = R0\n",
    "    n = len(M)\n",
    "    l = np.ones((n,1))\n",
    "    for each in range(iters):\n",
    "        R =  d * M .dot( R ) +  (1-d)/ n * l \n",
    "    return R"
   ]
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   "cell_type": "code",
   "execution_count": 53,
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   "source": [
    "M = np.array([\n",
    "    [0,1./2,0,0],\n",
    "    [1./3,0,0,1./2],\n",
    "    [1./3,0,1,1./2],\n",
    "    [1./3,1./2,0,0]\n",
    "])\n",
    "R0 = np.array([\n",
    "    [1./4],\n",
    "    [1./4],\n",
    "    [1./4],\n",
    "    [1./4]\n",
    "])"
   ]
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   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(4, 4)"
      ]
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     "execution_count": 54,
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   "source": [
    "M.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.10135135],\n",
       "       [0.12837838],\n",
       "       [0.64189189],\n",
       "       [0.12837838]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PageRank(M,0.8,R0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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