regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "331\n",
      "[ 4.34703147e+05  1.78907252e-02  2.28909992e+04 -1.99427168e+01] [ 4.49901532e+05  1.76663163e-02  2.43997347e+04 -1.48954702e+01]\n",
      "[25398.066083757287] [23706.49722759024]\n",
      "[25380.584122141387] [23692.037113908496]\n",
      "[25363.408290334763] [23677.833401703752]\n",
      "[25346.533227642652] [23663.881544558266]\n",
      "[25329.953667242386] [23650.17707666972]\n",
      "[25313.664434539587] [23636.71561142178]\n",
      "[25297.660445553152] [23623.492839979997]\n",
      "[25281.936705328477] [23610.504529912607]\n",
      "[25266.488306378535] [23597.746523835787]\n",
      "[25251.310427152184] [23585.214738082934]\n",
      "[25236.398330529344] [23572.905161397535]\n",
      "[25221.747362342507] [23560.813853649215]\n",
      "[25207.352949924145] [23548.936944572564]\n",
      "[25193.21060067953] [23537.270632528307]\n",
      "[25179.31590068459] [23525.811183286452]\n",
      "[25165.664513308267] [23514.55492883102]\n",
      "[25152.252177859053] [23503.49826618596]\n",
      "[25139.07470825517] [23492.63765626187]\n",
      "[25126.127991718095] [23481.969622723205]\n",
      "[25113.407987488896] [23471.490750875535]\n",
      "[25100.910725567126] [23461.19768657255]\n",
      "[25088.63230547173] [23451.087135142432]\n",
      "[25076.56889502368] [23441.155860333307]\n",
      "[25064.716729149943] [23431.400683277327]\n",
      "[25053.07210870836] [23421.81848147317]\n",
      "[25041.631399333117] [23412.406187786582]\n",
      "[25030.391030300452] [23403.160789468617]\n",
      "[25019.34749341419] [23394.07932719129]\n",
      "[25008.49734191081] [23385.15889410035]\n",
      "[24997.837189383714] [23376.396634884826]\n",
      "[24987.363708726265] [23367.789744863083]\n",
      "[24977.073631093404] [23359.335469085083]\n",
      "[24966.963744881417] [23351.031101450553]\n",
      "[24957.030894725558] [23342.87398384282]\n",
      "[24947.271980515252] [23334.86150527795]\n",
      "[24937.683956426525] [23326.99110106904]\n",
      "[24928.263829971373] [23319.260252005286]\n",
      "[24919.008661063803] [23311.6664835456]\n",
      "[24909.9155611022] [23304.207365026567]\n",
      "[24900.981692067773] [23296.880508884402]\n",
      "[24892.2042656388] [23289.68356989075]\n",
      "[24883.58054232036] [23282.61424440197]\n",
      "[24875.107830589335] [23275.670269621813]\n",
      "[24866.783486054355] [23268.849422877094]\n",
      "[24858.60491063046] [23262.14952090627]\n",
      "[24850.569551728244] [23255.568419160598]\n",
      "[24842.67490145714] [23249.104011117684]\n",
      "[24834.918495842725] [23242.754227607238]\n",
      "[24827.297914057668] [23236.51703614873]\n",
      "[24819.810777666196] [23230.390440300842]\n",
      "[24812.45474988175] [23224.37247902243]\n",
      "[24805.227534837668] [23218.461226044816]\n",
      "[24798.126876870625] [23212.654789255215]\n",
      "[24791.15055981663] [23206.951310091103]\n",
      "[24784.296406319343] [23201.34896294531]\n",
      "[24777.5622771505] [23195.845954581662]\n",
      "[24770.94607054226] [23190.440523561003]\n",
      "[24764.445721531218] [23185.130939677372]\n",
      "[24758.059201313918] [23179.915503404198]\n",
      "[24751.784516613647] [23174.792545350283]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#读取工作簿和工作簿中的工作表\n",
    "data_frame=pd.read_excel('Problem_C_Data_Wordle.xlsx')\n",
    "\n",
    "data = list(reversed(data_frame.loc[ 1: ,\"Unnamed: 4\"].tolist()))\n",
    "start=28\n",
    "data=data[start:]\n",
    "length=len(data)\n",
    "print(length)\n",
    "ydata = data\n",
    "xdata = [ i for i in range(length)]\n",
    "local_max_x=[]\n",
    "local_max_y=[]\n",
    "local_min_x=[]\n",
    "local_min_y=[]\n",
    "for i in range(length-1):\n",
    "    if(i==0):\n",
    "        if(data[i]>data[i+1]):\n",
    "            local_max_x.append(xdata[i])\n",
    "            local_max_y.append(ydata[i])\n",
    "        if(data[i]<data[i+1]):\n",
    "            local_min_x.append(xdata[i])\n",
    "            local_min_y.append(ydata[i])\n",
    "    else:\n",
    "        if(data[i]>data[i+1] and data[i]>data[i-1]):\n",
    "            local_max_x.append(xdata[i])\n",
    "            local_max_y.append(ydata[i])\n",
    "        if(data[i]<data[i+1] and data[i]<data[i-1]):\n",
    "            local_min_x.append(xdata[i])\n",
    "            local_min_y.append(ydata[i])                \n",
    "\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    " \n",
    "def func(x, a, b, c,d):\n",
    "    return a * np.exp(-(x-d)*b) + c\n",
    "\n",
    "local_min_x=np.array(local_min_x)\n",
    "local_min_y=np.array(local_min_y)\n",
    "local_max_x=np.array(local_max_x)\n",
    "local_max_y=np.array(local_max_y)\n",
    "plt.plot(xdata,ydata,'b-')\n",
    "popt_min, pcov_min = curve_fit(func, local_min_x, local_min_y,bounds = ([0,1e-4,1e+04,-150],[1e6,1,4e+04,150]))\n",
    "#popt数组中，三个值分别是待求参数a,b,c\n",
    "y_min = [func(i, popt_min[0],popt_min[1],popt_min[2],popt_min[3]) for i in xdata]\n",
    "plt.plot(xdata,y_min,'r--')\n",
    "popt_max, pcov_max = curve_fit(func, local_max_x, local_max_y,maxfev=10000,bounds = ([0,1e-4,1e+04,-150],[1e6,1,4e+04,150]))\n",
    "#popt数组中，三个值分别是待求参数a,b,c\n",
    "y_max = [func(i, popt_max[0],popt_max[1],popt_max[2],popt_max[3]) for i in xdata]\n",
    "plt.plot(xdata,y_max,'g--')\n",
    "plt.savefig(\"regression\")\n",
    "print(popt_min,popt_max)\n",
    "for k in range(60):\n",
    "    pre_data=length+k\n",
    "    y_predict_max=[func(pre_data, popt_max[0],popt_max[1],popt_max[2],popt_max[3])]\n",
    "    y_predict_min=[func(pre_data, popt_min[0],popt_min[1],popt_min[2],popt_min[3])]\n",
    "    print(y_predict_max,y_predict_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 74458], [1, 72518], [2, 88932], [3, 74412], [4, 79446], [5, 75673], [6, 77585], [7, 73225], [8, 67115], [9, 68349], [10, 70722], [11, 73933], [12, 70920], [13, 69884], [14, 66814], [15, 67909], [16, 66431], [17, 63380], [18, 62723], [19, 63188], [20, 63846], [21, 60069], [22, 56839], [23, 60969], [24, 62768], [25, 63241], [26, 61278], [27, 65431], [28, 58263], [29, 56738], [30, 58478], [31, 58991], [32, 61026], [33, 60020], [34, 55376], [35, 51958], [36, 56684], [37, 53802], [38, 59968], [39, 55989], [40, 53430], [41, 54665], [42, 47205], [43, 55359], [44, 50484], [45, 53342], [46, 47645], [47, 53111], [48, 50617], [49, 46089], [50, 50450], [51, 47986], [52, 47312], [53, 45645], [54, 44212], [55, 47248], [56, 41765], [57, 40486], [58, 42645], [59, 44578], [60, 47344], [61, 43407], [62, 42806], [63, 47094], [64, 41785], [65, 40545], [66, 46910], [67, 46246], [68, 40549], [69, 39234], [70, 38769], [71, 39611], [72, 42574], [73, 39667], [74, 42237], [75, 39086], [76, 43099], [77, 36769], [78, 39813], [79, 39228], [80, 39171], [81, 38384], [82, 40650], [83, 37791], [84, 37353], [85, 39250], [86, 36662], [87, 34909], [88, 38381], [89, 37229], [90, 37350], [91, 38841], [92, 36223], [93, 35516], [94, 36223], [95, 37654], [96, 37301], [97, 34198], [98, 35276], [99, 31652], [100, 35376], [101, 35105], [102, 35815], [103, 34938], [104, 33965], [105, 38245], [106, 35617], [107, 35888], [108, 33549], [109, 33700], [110, 36737], [111, 34716], [112, 31241], [113, 30214], [114, 34281], [115, 33660], [116, 35343], [117, 31903], [118, 35724], [119, 31191], [120, 32018], [121, 32733], [122, 32734], [123, 30992], [124, 31962], [125, 32172], [126, 29237], [127, 27887], [128, 29147], [129, 29497], [130, 32142], [131, 33344], [132, 37309], [133, 33418], [134, 33102], [135, 35050], [136, 31277], [137, 31976], [138, 34455], [139, 31509], [140, 32777], [141, 28994], [142, 31706], [143, 30985], [144, 31355], [145, 30477], [146, 31223], [147, 28202], [148, 30088], [149, 32288], [150, 32014], [151, 30935], [152, 32522], [153, 29026], [154, 26905], [155, 28408], [156, 26878], [157, 28575], [158, 29151], [159, 27197], [160, 28906], [161, 30403], [162, 30459], [163, 31269], [164, 28612], [165, 28322], [166, 28741], [167, 28637], [168, 29084], [169, 29279], [170, 28947], [171, 28953], [172, 30063], [173, 27609], [174, 27905], [175, 25156], [176, 24672], [177, 26498], [178, 27502], [179, 27670], [180, 29554], [181, 27330], [182, 29743], [183, 31068], [184, 26096], [185, 27213], [186, 28984], [187, 27467], [188, 25993], [189, 24660], [190, 25085], [191, 26536], [192, 27475], [193, 25576], [194, 27465], [195, 29208], [196, 24749], [197, 24991], [198, 24288], [199, 27437], [200, 26663], [201, 27705], [202, 24197], [203, 26381], [204, 25206], [205, 26051], [206, 23739], [207, 25569], [208, 22628], [209, 24646], [210, 23873], [211, 25577], [212, 23153], [213, 23509], [214, 24899], [215, 21199], [216, 23640], [217, 21157], [218, 21947], [219, 22873], [220, 24101], [221, 20824], [222, 22176], [223, 22853], [224, 22336], [225, 22166], [226, 26010], [227, 24137], [228, 22180], [229, 20490], [230, 21937], [231, 20281], [232, 15554], [233, 20011], [234, 20879], [235, 20160], [236, 20001], [237, 21204], [238, 20380]]\n"
     ]
    }
   ],
   "source": [
    "plot_data=[]\n",
    "for i in range(len(data)):\n",
    "    plot_data.append([i,data[i]])\n",
    "print(plot_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "334\n",
      "[-2.80558160e-02  1.89980125e+01 -4.21355528e+03  3.39050759e+05]\n",
      "332\n",
      "6617.361161894805\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#读取工作簿和工作簿中的工作表\n",
    "data_frame=pd.read_excel('Problem_C_Data_Wordle.xlsx')\n",
    "\n",
    "data = list(reversed(data_frame.loc[ 1: ,\"Unnamed: 4\"].tolist()))\n",
    "start=25\n",
    "data=data[start:]\n",
    "length=len(data)\n",
    "print(length)\n",
    "ydata = data\n",
    "xdata = [ i for i in range(length)]\n",
    "mean_x=[]\n",
    "mean_y=[]\n",
    "for i in range(length-1):\n",
    "    if(i!=0):\n",
    "        mean_x.append(xdata[i])\n",
    "        mean_y.append((ydata[i]+ydata[i+1]+ydata[i-1])/3)\n",
    "\n",
    "\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    " \n",
    "def func(x, a, b, c, d):\n",
    "    return a * x**3 +b*x**2+c *x+d\n",
    "\n",
    "# local_min_x=np.array(local_min_x)\n",
    "# local_min_y=np.array(local_min_y)\n",
    "# local_max_x=np.array(local_max_x)\n",
    "# local_max_y=np.array(local_max_y)\n",
    "import uncertainties as unc\n",
    "import uncertainties.unumpy as unp\n",
    "plt.plot(xdata,ydata,'b-')\n",
    "popt_mean, pcov_mean = curve_fit(func, mean_x[1:], mean_y[1:])\n",
    "#popt数组中，三个值分别是待求参数a,b,c\n",
    "# a, b, c ,d= unc.correlated_values(popt_mean, pcov_mean)\n",
    "y_mean= [func(i, popt_mean[0],popt_mean[1],popt_mean[2],popt_mean[3]) for i in xdata[1:]]\n",
    "plt.plot(xdata[1:],y_mean,'r--')\n",
    "nom = unp.nominal_values(y_mean)\n",
    "std = unp.std_devs(y_mean)\n",
    "print(popt_mean)\n",
    "print(mean_x[-1])\n",
    "y_predict_mean=[]\n",
    "for k in range(1,len(ydata)):\n",
    "    data_to_pred=k\n",
    "    y_predict_mean.append(func(data_to_pred, popt_mean[0],popt_mean[1],popt_mean[2],popt_mean[3]))\n",
    "\n",
    "print(y_predict_mean[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09009009009009009\n",
      "98236237.51602651\n",
      "6904.677822044624\n",
      "0.9848534250925316\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error #均方误差\n",
    "from sklearn.metrics import mean_absolute_error #平方绝对误差\n",
    "from sklearn.metrics import r2_score#R square\n",
    "#调用\n",
    "acc=0\n",
    "total=0\n",
    "y_pred=[]\n",
    "y_test=ydata[1:]\n",
    "x_test=xdata[1:]\n",
    "for i in range(len(y_test)):\n",
    "    if(y_test[i]>y_predict_mean[i]-1000 and y_test[i]<y_predict_mean[i]+1000):\n",
    "        acc+=1\n",
    "    total+=1\n",
    "    y_pred.append((y_predict_mean[i]+y_predict_mean[i])/2)\n",
    "print(acc/total)\n",
    "\n",
    "print(mean_squared_error(y_test,y_pred))\n",
    "print(mean_absolute_error(y_test,y_pred))\n",
    "print(r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.42203941e+05  1.70087621e-02  2.24766593e+04 -1.73839911e+01] [ 4.48812459e+05  1.79264491e-02  2.39280441e+04 -1.51627907e+01]\n",
      "[24226.150620661683] [22887.992199711356]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#读取工作簿和工作簿中的工作表\n",
    "data_frame=pd.read_excel('Problem_C_Data_Wordle.xlsx')\n",
    "\n",
    "data = list(reversed(data_frame.loc[ 1: ,\"Unnamed: 4\"].tolist()))\n",
    "start=25\n",
    "data=data[start:]\n",
    "length=len(data)\n",
    "#print(length)\n",
    "ydata = data\n",
    "xdata = [ i for i in range(length)]\n",
    "mean_x=[]\n",
    "mean_y=[]\n",
    "for i in range(length-3):\n",
    "    if(i!=0):\n",
    "        mean_x.append(xdata[i])\n",
    "        mean_y.append((ydata[i]+ydata[i+1]+ydata[i-1]+ydata[i+2]+ydata[i-2])/5)\n",
    "\n",
    "ydata = mean_y\n",
    "xdata = mean_x\n",
    "length=len(xdata)\n",
    "local_max_x=[]\n",
    "local_max_y=[]\n",
    "local_min_x=[]\n",
    "local_min_y=[]\n",
    "for i in range(length-1):\n",
    "    if(i==0):\n",
    "        if(data[i]>data[i+1]):\n",
    "            local_max_x.append(xdata[i])\n",
    "            local_max_y.append(ydata[i])\n",
    "        if(data[i]<data[i+1]):\n",
    "            local_min_x.append(xdata[i])\n",
    "            local_min_y.append(ydata[i])\n",
    "    else:\n",
    "        if(data[i]>data[i+1] and data[i]>data[i-1]):\n",
    "            local_max_x.append(xdata[i])\n",
    "            local_max_y.append(ydata[i])\n",
    "        if(data[i]<data[i+1] and data[i]<data[i-1]):\n",
    "            local_min_x.append(xdata[i])\n",
    "            local_min_y.append(ydata[i])                \n",
    "        \n",
    "\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    " \n",
    "def func(x, a, b, c,d):\n",
    "    return a * np.exp(-(x-d)*b) + c\n",
    "\n",
    "local_min_x=np.array(local_min_x)\n",
    "local_min_y=np.array(local_min_y)\n",
    "local_max_x=np.array(local_max_x)\n",
    "local_max_y=np.array(local_max_y)\n",
    "plt.plot(xdata,ydata,'b-')\n",
    "popt_min, pcov_min = curve_fit(func, local_min_x, local_min_y,bounds = ([0,1e-4,1e+04,-150],[1e6,1,4e+04,150]))\n",
    "#popt数组中，三个值分别是待求参数a,b,c\n",
    "y_min = [func(i, popt_min[0],popt_min[1],popt_min[2],popt_min[3]) for i in xdata]\n",
    "plt.plot(xdata,y_min,'r--')\n",
    "popt_max, pcov_max = curve_fit(func, local_max_x, local_max_y,maxfev=10000,bounds = ([0,1e-4,1e+04,-150],[1e6,1,4e+04,150]))\n",
    "#popt数组中，三个值分别是待求参数a,b,c\n",
    "y_max = [func(i, popt_max[0],popt_max[1],popt_max[2],popt_max[3]) for i in xdata]\n",
    "plt.plot(xdata,y_max,'g--')\n",
    "plt.savefig(\"regression\")\n",
    "print(popt_min,popt_max)\n",
    "pre_data=418-start\n",
    "y_predict_max=[func(pre_data, popt_max[0],popt_max[1],popt_max[2],popt_max[3])]\n",
    "y_predict_min=[func(pre_data, popt_min[0],popt_min[1],popt_min[2],popt_min[3])]\n",
    "print(y_predict_max,y_predict_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "334\n",
      "[ 8.50453446e-05 -8.49752433e-02  3.12930079e+01 -5.14084225e+03\n",
      "  3.55396726e+05]\n",
      "[44686.33699609607]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#读取工作簿和工作簿中的工作表\n",
    "data_frame=pd.read_excel('Problem_C_Data_Wordle.xlsx')\n",
    "\n",
    "data = list(reversed(data_frame.loc[ 1: ,\"Unnamed: 4\"].tolist()))\n",
    "start=25\n",
    "data=data[start:]\n",
    "length=len(data)\n",
    "print(length)\n",
    "ydata = data\n",
    "xdata = [ i for i in range(length)]\n",
    "mean_x=xdata\n",
    "mean_y=ydata\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    " \n",
    "def func(x, a, b, c, d,e):\n",
    "    return a * x**4 + b*x**3 + c*x**2 + d*x + e\n",
    "\n",
    "# local_min_x=np.array(local_min_x)\n",
    "# local_min_y=np.array(local_min_y)\n",
    "# local_max_x=np.array(local_max_x)\n",
    "# local_max_y=np.array(local_max_y)\n",
    "import uncertainties as unc\n",
    "import uncertainties.unumpy as unp\n",
    "plt.plot(xdata,ydata,'b-')\n",
    "popt_mean, pcov_mean = curve_fit(func, mean_x, mean_y)\n",
    "#popt数组中，三个值分别是待求参数a,b,c\n",
    "a, b, c ,d, e= unc.correlated_values(popt_mean, pcov_mean)\n",
    "y_mean= [func(i, popt_mean[0],popt_mean[1],popt_mean[2],popt_mean[3],popt_mean[4]) for i in xdata]\n",
    "plt.plot(xdata,y_mean,'r--')\n",
    "nom = unp.nominal_values(y_mean)\n",
    "std = unp.std_devs(y_mean)\n",
    "print(popt_mean)\n",
    "data_to_pred=418-start\n",
    "y_predict_mean=[func(400, popt_mean[0],popt_mean[1],popt_mean[2],popt_mean[3],popt_mean[4])]\n",
    "\n",
    "print(y_predict_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "bdec6ca5ce638a4776cd655f71204534c127e30145c88be198b9bebdb38fc0db"
  },
  "kernelspec": {
   "display_name": "Python 3.6.5 ('snow')",
   "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": "3.6.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}