Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([[0,0],[1,1],[2,2],[3,3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8668208>,\n",
       " <matplotlib.lines.Line2D at 0x8668358>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 0.5])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a20be17f0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x[:,0],y,'*k')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=datasets.load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(442, 10)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.03807591, -0.00188202,  0.08529891, -0.08906294,  0.00538306,\n",
       "       -0.09269548, -0.04547248,  0.06350368,  0.04170844, -0.07090025,\n",
       "       -0.09632802,  0.02717829,  0.01628068,  0.00538306,  0.04534098,\n",
       "       -0.05273755, -0.00551455,  0.07076875, -0.0382074 , -0.02730979,\n",
       "       -0.04910502, -0.0854304 , -0.0854304 ,  0.04534098, -0.06363517,\n",
       "       -0.06726771, -0.10722563, -0.02367725,  0.05260606,  0.06713621,\n",
       "       -0.06000263, -0.02367725,  0.03444337,  0.03081083,  0.01628068,\n",
       "        0.04897352,  0.01264814, -0.00914709, -0.00188202, -0.00188202,\n",
       "        0.00538306, -0.09996055, -0.06000263,  0.01991321,  0.04534098,\n",
       "        0.02717829, -0.05637009, -0.07816532,  0.06713621, -0.04183994,\n",
       "        0.03444337,  0.05987114, -0.05273755, -0.00914709, -0.04910502,\n",
       "       -0.04183994, -0.04183994, -0.02730979,  0.04170844,  0.06350368,\n",
       "       -0.07090025, -0.04183994, -0.02730979, -0.03457486,  0.06713621,\n",
       "       -0.04547248, -0.00914709,  0.04170844,  0.03807591,  0.01628068,\n",
       "       -0.00188202, -0.00188202,  0.06350368,  0.01264814,  0.01264814,\n",
       "       -0.00914709, -0.03094232, -0.09632802,  0.00538306, -0.10359309,\n",
       "        0.07076875,  0.01264814, -0.01641217, -0.0382074 ,  0.00175052,\n",
       "        0.04534098, -0.07090025,  0.04534098, -0.05273755, -0.04547248,\n",
       "        0.01264814,  0.04534098, -0.02004471, -0.04910502, -0.07816532,\n",
       "       -0.07090025,  0.0562386 , -0.02730979,  0.00175052, -0.00188202,\n",
       "        0.01628068,  0.01628068, -0.09269548,  0.05987114, -0.02730979,\n",
       "        0.02354575, -0.09632802,  0.02717829,  0.01991321,  0.03807591,\n",
       "        0.04170844,  0.01991321, -0.0854304 ,  0.01991321,  0.02354575,\n",
       "       -0.03094232,  0.04897352,  0.05987114, -0.05637009,  0.01628068,\n",
       "       -0.04910502,  0.06350368,  0.04897352,  0.00538306, -0.00551455,\n",
       "       -0.00551455, -0.08906294,  0.03444337, -0.05273755,  0.0090156 ,\n",
       "       -0.06363517, -0.09632802,  0.01628068, -0.04183994, -0.07453279,\n",
       "       -0.00551455, -0.09269548,  0.00538306,  0.03444337,  0.02354575,\n",
       "        0.04170844, -0.02730979,  0.04170844, -0.03094232,  0.03081083,\n",
       "       -0.04183994, -0.03094232, -0.05637009, -0.06000263, -0.04910502,\n",
       "        0.02354575,  0.00175052, -0.03457486,  0.04170844,  0.06713621,\n",
       "       -0.02730979, -0.01641217, -0.00188202, -0.01277963, -0.00551455,\n",
       "       -0.00914709, -0.04547248, -0.05273755,  0.01628068,  0.04534098,\n",
       "       -0.04183994, -0.05637009,  0.07076875,  0.00175052, -0.00188202,\n",
       "        0.02354575, -0.02004471,  0.04170844, -0.06363517,  0.01628068,\n",
       "        0.06713621,  0.04534098,  0.04897352,  0.04170844, -0.02367725,\n",
       "       -0.0382074 ,  0.04897352,  0.04534098,  0.04534098,  0.01628068,\n",
       "       -0.07453279, -0.08179786, -0.06726771,  0.00538306, -0.00188202,\n",
       "        0.0090156 , -0.00551455,  0.0562386 ,  0.0090156 , -0.06726771,\n",
       "        0.02717829, -0.02367725,  0.04897352, -0.05273755,  0.04170844,\n",
       "        0.0562386 , -0.03457486,  0.08166637, -0.00188202,  0.11072668,\n",
       "       -0.03094232,  0.00175052,  0.0090156 ,  0.03081083,  0.03807591,\n",
       "        0.0090156 ,  0.09256398,  0.06713621,  0.00175052,  0.03081083,\n",
       "        0.02717829,  0.01264814,  0.07440129, -0.04183994, -0.08906294,\n",
       "        0.02354575, -0.04547248, -0.02367725, -0.09996055, -0.02730979,\n",
       "        0.03081083, -0.10359309,  0.06713621, -0.05273755, -0.02730979,\n",
       "       -0.0382074 ,  0.0090156 ,  0.01264814,  0.06713621,  0.04534098,\n",
       "        0.06713621,  0.02717829,  0.0562386 ,  0.03444337,  0.02354575,\n",
       "        0.04897352,  0.03081083, -0.10359309,  0.01628068, -0.06000263,\n",
       "       -0.02730979,  0.04170844, -0.08179786, -0.04183994, -0.01277963,\n",
       "        0.06713621, -0.05273755,  0.00538306,  0.08166637,  0.03081083,\n",
       "        0.00175052, -0.04910502, -0.02730979,  0.07803383,  0.01264814,\n",
       "        0.04170844,  0.04897352, -0.01641217, -0.07453279,  0.03444337,\n",
       "       -0.03457486, -0.05273755,  0.05987114,  0.06350368,  0.0090156 ,\n",
       "        0.00538306,  0.03807591,  0.01264814,  0.07440129,  0.01628068,\n",
       "       -0.00551455,  0.01264814, -0.03457486,  0.06713621,  0.03807591,\n",
       "        0.0090156 , -0.09269548,  0.07076875, -0.01641217,  0.04170844,\n",
       "        0.01264814, -0.0382074 ,  0.04534098,  0.07076875, -0.07453279,\n",
       "        0.05987114,  0.07440129,  0.0090156 , -0.07090025,  0.02354575,\n",
       "       -0.05273755,  0.06713621,  0.00175052,  0.02354575,  0.03807591,\n",
       "        0.01628068, -0.00188202,  0.01264814,  0.07440129,  0.04170844,\n",
       "       -0.00914709,  0.0090156 ,  0.06713621,  0.00175052, -0.00914709,\n",
       "       -0.00551455,  0.09619652, -0.07453279,  0.05987114, -0.02367725,\n",
       "        0.0090156 ,  0.01628068,  0.01991321,  0.08893144,  0.01991321,\n",
       "       -0.02367725,  0.09619652,  0.02354575,  0.07076875,  0.03081083,\n",
       "       -0.00188202,  0.04534098,  0.07440129, -0.0382074 , -0.01277963,\n",
       "        0.0090156 ,  0.08166637,  0.03081083,  0.02717829, -0.06000263,\n",
       "        0.00538306, -0.02004471,  0.01991321, -0.06363517,  0.02717829,\n",
       "       -0.01641217,  0.03081083,  0.0562386 , -0.02004471, -0.10722563,\n",
       "        0.08166637,  0.00538306,  0.03807591,  0.03081083,  0.00175052,\n",
       "       -0.02730979, -0.0854304 ,  0.01264814, -0.05273755, -0.02367725,\n",
       "       -0.07453279, -0.00551455, -0.06000263, -0.02004471,  0.03807591,\n",
       "        0.01628068,  0.04170844,  0.01991321, -0.04910502,  0.00175052,\n",
       "        0.03444337, -0.04547248, -0.00914709, -0.01641217, -0.00914709,\n",
       "        0.01991321,  0.05260606, -0.02730979, -0.07453279, -0.10722563,\n",
       "        0.04534098, -0.00188202,  0.01991321,  0.01628068, -0.00188202,\n",
       "        0.01628068, -0.07090025,  0.04897352,  0.00538306,  0.03444337,\n",
       "        0.02354575,  0.01991321, -0.04547248,  0.05260606, -0.00551455,\n",
       "        0.0090156 , -0.02367725, -0.04183994, -0.07453279,  0.03444337,\n",
       "       -0.06000263, -0.0854304 ,  0.05260606,  0.01264814,  0.05987114,\n",
       "       -0.02367725,  0.01628068,  0.11072668, -0.02004471, -0.01641217,\n",
       "        0.04897352, -0.05637009,  0.02717829,  0.06350368, -0.05273755,\n",
       "       -0.00914709,  0.00538306,  0.07440129, -0.05273755,  0.08166637,\n",
       "       -0.00551455, -0.02730979, -0.05273755,  0.0090156 , -0.02004471,\n",
       "        0.02354575,  0.03807591, -0.07816532,  0.0090156 ,  0.00175052,\n",
       "       -0.07816532,  0.03081083, -0.03457486,  0.04897352, -0.04183994,\n",
       "       -0.00914709,  0.07076875,  0.0090156 , -0.02730979,  0.01628068,\n",
       "       -0.01277963, -0.05637009,  0.04170844, -0.00551455,  0.04170844,\n",
       "       -0.04547248, -0.04547248])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a213ae518>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x[:,0],'*k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
       "        69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
       "        68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n",
       "        87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n",
       "       259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n",
       "       128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n",
       "       150.,  97., 160., 178.,  48., 270., 202., 111.,  85.,  42., 170.,\n",
       "       200., 252., 113., 143.,  51.,  52., 210.,  65., 141.,  55., 134.,\n",
       "        42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n",
       "        83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n",
       "       104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
       "       173., 180.,  84., 121., 161.,  99., 109., 115., 268., 274., 158.,\n",
       "       107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n",
       "        60., 174., 259., 178., 128.,  96., 126., 288.,  88., 292.,  71.,\n",
       "       197., 186.,  25.,  84.,  96., 195.,  53., 217., 172., 131., 214.,\n",
       "        59.,  70., 220., 268., 152.,  47.,  74., 295., 101., 151., 127.,\n",
       "       237., 225.,  81., 151., 107.,  64., 138., 185., 265., 101., 137.,\n",
       "       143., 141.,  79., 292., 178.,  91., 116.,  86., 122.,  72., 129.,\n",
       "       142.,  90., 158.,  39., 196., 222., 277.,  99., 196., 202., 155.,\n",
       "        77., 191.,  70.,  73.,  49.,  65., 263., 248., 296., 214., 185.,\n",
       "        78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n",
       "       154., 259.,  90., 246., 124.,  67.,  72., 257., 262., 275., 177.,\n",
       "        71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
       "       150., 310., 153., 346.,  63.,  89.,  50.,  39., 103., 308., 116.,\n",
       "       145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n",
       "        94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
       "        60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n",
       "        31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
       "       114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
       "       191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
       "       244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n",
       "       263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n",
       "        77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
       "        58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
       "       140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
       "       219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
       "        43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
       "       140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
       "        84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
       "        94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
       "       220.,  57.])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5177494254132934"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([206.11706979,  68.07234761, 176.88406035, 166.91796559,\n",
       "       128.45984241, 106.34908972,  73.89417947, 118.85378669,\n",
       "       158.81033076, 213.58408893,  97.07853583,  95.1016223 ,\n",
       "       115.06673301, 164.67605023, 103.07517946, 177.17236996,\n",
       "       211.75953205, 182.84424343, 147.99987605, 124.01702527,\n",
       "       120.33094632,  85.80377894, 113.11286302, 252.44934852,\n",
       "       165.48821056, 147.72187623,  97.12824075, 179.09342974,\n",
       "       129.05497324, 184.78138552, 158.71515746,  69.47588393,\n",
       "       261.50255826, 112.81897436,  78.37194762,  87.66624129,\n",
       "       207.92460213, 157.87686037, 240.84370686, 136.93372685,\n",
       "       153.48187659,  74.15703284, 145.63105805,  77.8280105 ,\n",
       "       221.0786645 , 125.22224022, 142.60147066, 109.4926324 ,\n",
       "        73.14037106, 189.87368742, 157.93636782, 169.55816531,\n",
       "       134.18186217, 157.72356219, 139.1077439 ,  72.73252701,\n",
       "       207.8289973 ,  80.10834588, 104.08562488, 134.57807971,\n",
       "       114.23779529, 180.67760064,  61.12644508,  98.7215441 ,\n",
       "       113.79626149, 189.96141244, 148.98263155, 124.33457266,\n",
       "       114.83969622, 122.00224605,  73.91315064, 236.70948329,\n",
       "       142.31366526, 124.51427625, 150.84273716, 127.75408702,\n",
       "       191.16674356,  77.05921006, 166.82129568,  91.00741773,\n",
       "       174.75026808, 122.83488194,  63.27214662, 151.99895968,\n",
       "        53.73407848, 166.00134469,  42.65030679, 153.04135861,\n",
       "        80.54493791, 106.9048058 ,  79.94239571, 187.1634566 ,\n",
       "       192.60115666,  61.07125918, 107.40466928, 125.04038427,\n",
       "       207.72180472, 214.21749964, 123.47505642, 139.16396617,\n",
       "       168.21035724, 106.9267784 , 150.64502809, 157.92231541,\n",
       "       152.75856279, 116.22255529,  73.03090141, 155.66898717,\n",
       "       230.14278537, 143.50191007,  38.0947967 , 121.860737  ,\n",
       "       152.79569851, 207.99651918, 291.23082717, 189.17431487,\n",
       "       214.02871163, 235.18090808, 165.3872774 , 151.25000032,\n",
       "       156.57626783, 200.44154589, 219.35211772, 174.79049427,\n",
       "       169.23161767, 187.8719893 ,  57.49473392, 108.55110499,\n",
       "        92.68518048, 210.87365701, 245.47433558,  69.84529943,\n",
       "       113.0351432 ,  68.42945176, 141.69628649, 239.46177949,\n",
       "        58.3802079 , 235.47268158, 254.91986281, 253.31042713,\n",
       "       155.50813249, 230.55904185, 170.44063216, 117.99200943,\n",
       "       178.55548636, 240.07155813, 190.3398776 , 228.66100769,\n",
       "       114.24162642, 178.36570405, 209.09273631, 144.85567253,\n",
       "       200.65791056, 121.34184881, 150.50918174, 199.02165018,\n",
       "       146.2806806 , 124.02443772,  85.26036769, 235.16536625,\n",
       "        82.17255475, 231.29266191, 144.36634395, 197.04778326,\n",
       "       146.99720377,  77.18477545,  59.3728572 , 262.67891084,\n",
       "       225.12578458, 220.20506312,  46.59691745,  88.1040833 ,\n",
       "       221.77623752,  97.24900614, 164.48869956, 119.90114263,\n",
       "       157.79986195, 223.08505437,  99.5885471 , 165.84341641,\n",
       "       179.47571002,  89.83382843, 171.82492808, 158.36337775,\n",
       "       201.47857482, 186.39202728, 197.47094269,  66.57241937,\n",
       "       154.59826802, 116.18638034, 195.92074021, 128.04740268,\n",
       "        91.20285628, 140.56975398, 155.23013996, 169.70207476,\n",
       "        98.75498537, 190.1453107 , 142.5193942 , 177.26966106,\n",
       "        95.31403505,  69.0645889 , 164.16669511, 198.06460718,\n",
       "       178.26228169, 228.58801706, 160.67275473, 212.28682319,\n",
       "       222.48172067, 172.85184399, 125.27697688, 174.7240982 ,\n",
       "       152.38282657,  98.58485669,  99.73695497, 262.29658755,\n",
       "       223.73784832, 221.3425256 , 133.61497308, 145.42593933,\n",
       "        53.04259372, 141.81807792, 153.68369915, 125.21948824,\n",
       "        77.25091512, 230.26311068,  78.90849563, 105.20931175,\n",
       "       117.99633487,  99.06361032, 166.55382825, 159.34391027,\n",
       "       158.27612808, 143.05658763, 231.55938678, 176.64144413,\n",
       "       187.23572317,  65.38504165, 190.66078824, 179.74973878,\n",
       "       234.91022512, 119.15540438,  85.63464409, 100.85860205,\n",
       "       140.4174259 , 101.83836332, 120.66138775,  83.06599161,\n",
       "       234.58754656, 245.16192142, 263.26766492, 274.87431887,\n",
       "       180.67699732, 203.05474761, 254.21769367, 118.44122343,\n",
       "       268.44988948, 104.83643442, 115.87172349, 140.45788952,\n",
       "        58.46850453, 129.83264097, 263.78452618,  45.01240356,\n",
       "       123.28697604, 131.08314499,  34.89018315, 138.35659686,\n",
       "       244.30370588,  89.95612306, 192.07094588, 164.32674962,\n",
       "       147.74783541, 191.89381753, 176.44296313, 158.34707354,\n",
       "       189.19183226, 116.58275843, 111.44622859, 117.45262547,\n",
       "       165.79457547,  97.80241129, 139.54389024,  84.17453643,\n",
       "       159.9389204 , 202.4011919 ,  80.48200416, 146.64621068,\n",
       "        79.05274311, 191.33759392, 220.67545196, 203.75145711,\n",
       "        92.87093594, 179.15570241,  81.80126162, 152.82706623,\n",
       "        76.79700486,  97.79712384, 106.83424483, 123.83477117,\n",
       "       218.13375502, 126.02077447, 206.76300555, 230.57976636,\n",
       "       122.0628518 , 135.67694517, 126.36969016, 148.49621551,\n",
       "        88.07082258, 138.95595037, 203.86570118, 172.55362727,\n",
       "       122.95773416, 213.92445645, 174.88857841, 110.07169487,\n",
       "       198.36767241, 173.24601643, 162.64946177, 193.31777358,\n",
       "       191.53802295, 284.13478714, 279.30688474, 216.0070265 ,\n",
       "       210.08517801, 216.22213925, 157.01489819, 224.06561179,\n",
       "       189.05840605, 103.56829281, 178.70442926, 111.81492124,\n",
       "       290.99913121, 182.64959461,  79.33602602,  86.33287509,\n",
       "       249.15238929, 174.51439576, 122.10645431, 146.27099383,\n",
       "       170.6555544 , 183.50018707, 163.36970989, 157.03563376,\n",
       "       144.42617093, 125.30179325, 177.50072942, 104.57821235,\n",
       "       132.1746674 ,  95.06145678, 249.9007786 ,  86.24033937,\n",
       "        62.00077469, 156.81087903, 192.3231713 , 133.85292727,\n",
       "        93.67456315, 202.49458467,  52.53953733, 174.82926235,\n",
       "       196.9141296 , 118.06646574, 235.3011088 , 165.09286707,\n",
       "       160.41863314, 162.37831419, 254.05718804, 257.23616403,\n",
       "       197.50578991, 184.06609359,  58.62043851, 194.3950396 ,\n",
       "       110.77475548, 142.20916765, 128.82725506, 180.12844365,\n",
       "       211.26415225, 169.59711427, 164.34167693, 136.2363478 ,\n",
       "       174.50905908,  74.67649224, 246.29542114, 114.14131338,\n",
       "       111.54358708, 140.02313284, 109.99647408,  91.37269237,\n",
       "       163.01389345,  75.16389857, 254.05755095,  53.47055785,\n",
       "        98.48060512, 100.66268306, 258.58885744, 170.67482041,\n",
       "        61.91866052, 182.3042492 , 171.26913027, 189.19307553,\n",
       "       187.18384852,  87.12032949, 148.37816611, 251.35898288,\n",
       "       199.69712357, 283.63722409,  50.85577124, 172.14848891,\n",
       "       204.06179478, 174.16816194, 157.93027543, 150.50201654,\n",
       "       232.9761832 , 121.5808709 , 164.54891787, 172.67742636,\n",
       "       226.78005938, 149.46967223,  99.14026374,  80.43680779,\n",
       "       140.15557121, 191.90593837, 199.27952034, 153.63210613,\n",
       "       171.80130949, 112.11314588, 162.60650576, 129.8448476 ,\n",
       "       258.02898298, 100.70869427, 115.87611124, 122.53790409,\n",
       "       218.17749233,  60.94590955, 131.09513588, 119.48417359,\n",
       "        52.60848094, 193.01802803, 101.05169913, 121.22505534,\n",
       "       211.8588945 ,  53.44819015])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a20eb39b0>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.predict(x), 'ob')\n",
    "plt.plot(y,'or')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=MLPRegressor(hidden_layer_sizes=(100,100,20),max_iter=6000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100, 100, 20), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=6000000, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5116039121156097"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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