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{
 "cells": [
  {
   "cell_type": "code",
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   "execution_count": 1,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "import numpy as np\n",
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    "from matplotlib import pyplot as plt\n",
    "from sklearn import datasets\n",
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    "# Des datasets comme discuté au téléhphone @Théophile"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
      "text/plain": [
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       "((506, 13), (506,))"
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      ]
     },
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     "execution_count": 4,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Boston dataset (Régression)\n",
    "# https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html\n",
    "# Prix des maisons à Boston (cf le site pour les variables)\n",
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    "\n",
    "boston = datasets.load_boston()\n",
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    "X, y = boston[\"data\"], boston[\"target\"]\n",
    "X.shape, y.shape"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a13aa97b8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JHwHofM1fJXWCxiyh99eZJfQGJfy/AY6XdLSkKYwe1LmryQYkidHXuMMR8YMm5x4rIq6IiNkRMYfRx+GBiGh0bxcR24DNkk7o3DUfeKLJHjo2AadIOrjz7zOf/h0EvQs4v/P9+cDP+9HEmCX0zkovoRcRA/EHOJPRo5fPAt/qw/x/yuhLjd8Cj3b+nNnnx+TPgbv7NPfHgXbn8fgZcGif+vgn4ElgHXAT8L4G5ryV0WMMrzP6LOgC4EOMHuV/pvN1Rp/6WM/o8bG3fkd/NNH6/oSfWaUG5Wm/mTXM4TerlMNvVimH36xSDr9ZpRx+s0o5/GaVcvjNKvV/xfwjZnkF3OYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_mat = np.corrcoef(X.T)\n",
    "plt.imshow(corr_mat, cmap='hot', interpolation='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ty = y.reshape(-1,1)\n",
    "ty.shape\n",
    "dataset = np.append(X,ty, axis= 1)\n",
    "#plt.imshow(corr_mat, cmap='hot', interpolation='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Colormap cold is not recognized. Possible values are: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, viridis, viridis_r, winter, winter_r",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-db232998f101>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcorr_mat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorrcoef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorr_mat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cold'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nearest'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3203\u001b[0m                         \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3204\u001b[0m                         \u001b[0mimlim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3205\u001b[0;31m                         **kwargs)\n\u001b[0m\u001b[1;32m   3206\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3207\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1853\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1855\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1856\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1857\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5483\u001b[0m         im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent,\n\u001b[1;32m   5484\u001b[0m                               \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5485\u001b[0;31m                               resample=resample, **kwargs)\n\u001b[0m\u001b[1;32m   5486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5487\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ax, cmap, norm, interpolation, origin, extent, filternorm, filterrad, resample, **kwargs)\u001b[0m\n\u001b[1;32m    822\u001b[0m             \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    823\u001b[0m             \u001b[0mresample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 824\u001b[0;31m             \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    825\u001b[0m         )\n\u001b[1;32m    826\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ax, cmap, norm, interpolation, origin, filternorm, filterrad, resample, **kwargs)\u001b[0m\n\u001b[1;32m    226\u001b[0m         \"\"\"\n\u001b[1;32m    227\u001b[0m         \u001b[0mmartist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mArtist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m         \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mScalarMappable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mouseover\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0morigin\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/matplotlib/cm.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, norm, cmap)\u001b[0m\n\u001b[1;32m    201\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0;31m#: The Colormap instance of this ScalarMappable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m         \u001b[0;31m#: The last colorbar associated with this ScalarMappable. May be None.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/matplotlib/cm.py\u001b[0m in \u001b[0;36mget_cmap\u001b[0;34m(name, lut)\u001b[0m\n\u001b[1;32m    166\u001b[0m         raise ValueError(\n\u001b[1;32m    167\u001b[0m             \u001b[0;34m\"Colormap %s is not recognized. Possible values are: %s\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m             % (name, ', '.join(sorted(cmap_d))))\n\u001b[0m\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Colormap cold is not recognized. Possible values are: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, viridis, viridis_r, winter, winter_r"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_mat = np.corrcoef(dataset.T)\n",
    "plt.imshow(corr_mat, cmap='cold', interpolation='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "concatenate((a1, a2, ...), axis=0, out=None)\n",
       "\n",
       "Join a sequence of arrays along an existing axis.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "a1, a2, ... : sequence of array_like\n",
       "    The arrays must have the same shape, except in the dimension\n",
       "    corresponding to `axis` (the first, by default).\n",
       "axis : int, optional\n",
       "    The axis along which the arrays will be joined.  If axis is None,\n",
       "    arrays are flattened before use.  Default is 0.\n",
       "out : ndarray, optional\n",
       "    If provided, the destination to place the result. The shape must be\n",
       "    correct, matching that of what concatenate would have returned if no\n",
       "    out argument were specified.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "res : ndarray\n",
       "    The concatenated array.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "ma.concatenate : Concatenate function that preserves input masks.\n",
       "array_split : Split an array into multiple sub-arrays of equal or\n",
       "              near-equal size.\n",
       "split : Split array into a list of multiple sub-arrays of equal size.\n",
       "hsplit : Split array into multiple sub-arrays horizontally (column wise)\n",
       "vsplit : Split array into multiple sub-arrays vertically (row wise)\n",
       "dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).\n",
       "stack : Stack a sequence of arrays along a new axis.\n",
       "hstack : Stack arrays in sequence horizontally (column wise)\n",
       "vstack : Stack arrays in sequence vertically (row wise)\n",
       "dstack : Stack arrays in sequence depth wise (along third dimension)\n",
       "\n",
       "Notes\n",
       "-----\n",
       "When one or more of the arrays to be concatenated is a MaskedArray,\n",
       "this function will return a MaskedArray object instead of an ndarray,\n",
       "but the input masks are *not* preserved. In cases where a MaskedArray\n",
       "is expected as input, use the ma.concatenate function from the masked\n",
       "array module instead.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> a = np.array([[1, 2], [3, 4]])\n",
       ">>> b = np.array([[5, 6]])\n",
       ">>> np.concatenate((a, b), axis=0)\n",
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])\n",
       ">>> np.concatenate((a, b.T), axis=1)\n",
       "array([[1, 2, 5],\n",
       "       [3, 4, 6]])\n",
       ">>> np.concatenate((a, b), axis=None)\n",
       "array([1, 2, 3, 4, 5, 6])\n",
       "\n",
       "This function will not preserve masking of MaskedArray inputs.\n",
       "\n",
       ">>> a = np.ma.arange(3)\n",
       ">>> a[1] = np.ma.masked\n",
       ">>> b = np.arange(2, 5)\n",
       ">>> a\n",
       "masked_array(data = [0 -- 2],\n",
       "             mask = [False  True False],\n",
       "       fill_value = 999999)\n",
       ">>> b\n",
       "array([2, 3, 4])\n",
       ">>> np.concatenate([a, b])\n",
       "masked_array(data = [0 1 2 2 3 4],\n",
       "             mask = False,\n",
       "       fill_value = 999999)\n",
       ">>> np.ma.concatenate([a, b])\n",
       "masked_array(data = [0 -- 2 2 3 4],\n",
       "             mask = [False  True False False False False],\n",
       "       fill_value = 999999)\n",
       "\u001b[0;31mType:\u001b[0m      builtin_function_or_method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.concatenate?"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 22,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((569, 30), (569,))"
      ]
     },
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     "execution_count": 22,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "# Breast cancer dataset (classification)\n",
    "# https://scikit-learn.org/stable/datasets/index.html#breast-cancer-wisconsin-diagnostic-dataset\n",
    "# Données sur les tumeurs : cette tumeur est-elle cancéreuse ?\n",
    "breast = load_breast_cancer()\n",
    "X, y = breast[\"data\"], breast[\"target\"]\n",
    "X.shape, y.shape"
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   ]
  },
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  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fAcAC6AuRZTK9VLZr36s2u7m1DtvWLcD+yGVsaq3HisZqbVGOg2f7TJGvz7WfNr4vo9OIVcrCIZSWhI1Nz7JwCC31leBgiFy8kWKP9TqeTsKz8/KQ8TrEYCQssxsjq8/lwiKAvVeOk7ati6B1U9IwW6ieVPsOTaQ61gn1EAO+smVp1vqXDSjydJLiZqnsR/Dbta9uOgoPEqelvzCJnB84Z2nTVu3kndEhPP3yMZP7oXjQl9RX4WTvjZRzlYQY7p5bhc7LQzjZe8OIQG2dN8PQ6ADgrTN9ePKhZgDA57/7Pg6e7cd4nLvKkeM0/nJqBgDYsKQWz2xpSWnDze/TEYni3a6rxuuycMjWK8dJ23azerIqaRgkuuvf1d6JF9/sQpwDe9/rRpwDsqdGSYhhPK733VCVgnxYZaQLCfZJitPD69Urw237artA4iHae+g8dmxYlCK87NrTpQYWK4Hn2k+jMzpkOh6AkaAMSDzk8oPOACxrmI6nNy7BwbN9hhlERKAOjoyZhG2cA98+0AUGQNb7dTlyVCFkNz77DnXj4Nl+47u6CF4vNmxZYAHA2uZZtr+jk7atrrYAdyUNg0RNNveFTyzEW2f6TBN1TCO/dUI9zifMSMXiDUSCfZLidBN79cpw277ariAW59j9ZhdWNFZrzRpeHjrRPzlgSKwM5HOOxzkaZpRjZuUU1FaWpUxcuvOp5hsOs0aoQyeEnXLjyOgEsRcbtnyuMANa582w7a8bM5nshaSLtM0U4r44ev66Sbv+9oEux9/BCtmeLidaA1K9rgqF8M6dO7N+0j179uzcvn171s87memIRPG9X32M8TjHotpKLKqtREt9FWZOLcWODYtSbtzxOMf+SNSk4YzHOWZOLUXb0jmO57NqX263LBwCBzceSA5g5tRSjMc5nn75GI50X8P+SBQt9VXJzUTr/ro5/3ico/3kZcSkaOvBO+O4emsUn1w+F597sMn2+4tqKzEyHseHfUMY0dhqBWXhEP7yk3djUW0lAOB7v/oYR7qvGdf/cf8QDp7tw6qmGjy4aJbRvnycoKI0jK8+vNRoS6CO44yKUkybUpJynLiW4bEYjp6/hhgHTvbeQEt9lfZY+TttS+dYHiMmIfEbfXL5XPzr9Qtt23SLeq+K9/73f4xg91sf4ej56+gfupO2IFeZMbUUH/YN4TvvnMNH/bcQvXkHc6sr8B9e/8B0D/7kaC+++fNTuHzzDtY2zw7o7N74xje+cWnnzp17nI6jmqeTgHTrSspmASFA3OZXd2pXuBy+feYKTvXeRBwwwtf3Ry4bZhTAXW1PL+fed6gbZy4PovfGiPF+mAG7P7fatV28LBzC3BlT0DMwjDgSvvVb7qnH7Moy24hX1cb7qeVz8ccrG1JcMMvCoRTzly5Aat+hbsO/3uq3FW6OQY7ps6+d9Fx/1Q1WNVplE1o2qK4owfXhceP18obpJg+lp9qaLc2GmYRqnhIG6bqeiWMSG2/BKQCqqUQIsdZ5M1J8xoO2dQq7b0ckih0/PGL4LceSdla7cVHNSBeujxhC3W5/QDZtvPFBFL3XJyaU109cwi9PXcZ4so6pVeSjXe3V0dhEymWdbT/d5GN2ZMoerbtXewZuZ1WoAzAJ9RCAM9KkCAD7I5dzItjdQjVPJwEiFB/w/lDrCmfo8FLjE9Cnxx0cGUtxyctUYMvm1jrs2LDI07jI6XjlYJZYnDuWqtvcmqip+ujKu1I+Exq8CNQS+wHyWFrlolm/uNZIkwxM5HjXfQ8AGqrLDTtyuoiVw7Z1C0xploNATXlcVV6KM9HBQNpOlziAO+PmiWXRnKrcdMYlpLEXObpQfLcRhQBMbnIAcPBsv1EcQv6O1yhDqwhRWQvUueQF6Xv/zJYWrGistkwYpssLIzRqNQOi06Qgt/ep5XPx+olLKceUhUM4dv6aMS7yWFrlydncWoe1zbOMzJGqm56sWYcYEL15B73XRzxthKvX4TfDph1idSMybMrupfnE8Oi480E5hAR7kaOG4ttplqqAfmBhTcpDNR7nKcv9oKIMvURP+k10JX+u2oZ1eWxkIS7OazUp6M6l9nvB7GnY/WYXYjwh0JfUV+HM5UGTHVfWzO0m561rmrTuhmJMt61bYJwrzv2FzGcrolRcj0xDdTlqppXhZO/NAA2D6SErOPmYE4dMMUWGahJRl7Z2mqX60IrvOOF0Dp2ZRg0GESYeYbLQPSBW5ggVIUi//+tuPP3ysRTzkPz5jh8ewa72TtvzvHrsgum1m7469fuZLS3Y/bnVeOLBJrzw+Cqsml+dMonqskTqJmchvFvqKrUrssGRsRSfbqd0AlZmNS/3U7rosmxWlIax84+W4edfWo8vtjXnPNR9PM4Nc5ndvZYrSLAXEbqbTGjBbmyh6kO7dU3iO20ttSgJJR4lXbCM3Tmsbnwn27D4bjqTlC7kXW5HFZS73+wynVvt26UbIwhJkkTXVztU27wobi1PDPIxItJVTmzmNHG+9M45dEaH8NI751L6Zte2ipOg8nI/pYtaDBxIJGMT51rRWA2WY8keSubtcatsZBvyYy8iVH9p4XMufJI/6ruV4h8sY+W7/ejKBtw9dzpmTi3Fkxb5SnR+z8LN7vLNO9o+HT1/DR9fvQ0AiHGOM5dvon5GBRbVVhqRhUfPX0f7yctYOne6pS+77Pf8Ud8tvNvVj4vXRxDjCRfNnoFhHD1/3fBHbpo1Da+fuATh6csBDI2M4dGVDca1/DJyGVcG7xifywpvjLv35xftyX7kR7sH0BkdwqFzV7VxBU8+1Iz/5Q9bjLF0ijmw+t11v6vatopTW6I9Ox93v7SfuozfXDCneviwbwit82ZgUW0l/vyVY4gm76lc8cW2ZnzuwSZTPEFFaRg7NizK2LgA7v3YycaeIeyKMmTKDmfngubWPm0VBu41PNzJzU5ogiLBFmDO66LzxhF9sEt9II4vC4ewrGEGrt26Y7gXyqkIdmxYhBcPdBmpAIQWLtqeXTnF8trssixaMTgyZnKtFJunVtWiVOw+c+N66Pb3C8KN0e+9rkvQFufA82+cwb5D3TjZm1LHJ2e4idLNBaSxZwA1Km94LJYSxZaJWd1Os3OjiQWJGkXZUleJv3p0uSlfzNkrQwiHGOpnTMHgyLipbzeHxwxtHgAWzJ5maNRW54lxbkSVxjjH1VujuCH5I8sa1drm2Tjec920YpDHZNqUEiNKNZFTJoQY5wmf9YeaUT+j3Hb1ozIe5/in31xM2fQT1zse55bt6SIxZZw0ei/4bUu999O51xfVVmLp3Ol4t6sPw2MTwr1v8A7OSfdELrl4/Tb+53ULAWR+BSNDGnsOUe1uuqIMmZrZrTQzq9wk6Wgabr6nnk92XVR92JfUVWHg1liKligiKnV2fd15hF1caHpyEQVdmtrWeTNMBaZl2+7m1jq88Pgqk+vnt944i2tDd3Cu/5a2yIfdWPUPjWo9OYSvtl0mTLcrLTG28mu7PtnlgUn3/gzSQwq41xRxmmtPGJmL10ewq70zb4OUSGPPAKrd7U/ua8TJ3htZs8PpUDUxAGlpVlYamZdcNOr4fPXhpfjk8rkYGhnDXTMrsPyuRCKwpQ52ffW6nnyoGQ0zK3D0/DXImTIqSsPGakFcw/d+9TE+vDJkWhXcPbcqxTYtNLGfHO3FP/32EgbvjOPslSEjqMhu9SOP1dkr5sjF5Q3T8fCyeuzYsAhnrwxarqbcrrTcasryce0nL+Po+WuWOWbSIR2bs9WKRGjuM6eWoufabZP2ng8cPX8NrXOnZ/VZzqrGzhh7GMC3AIQBfIdz/n8G0W6horO7ufV5znS/xLnVCkJuNSsrLwCdVmlnrxd2dBnhuywHz7hNfSBrrHaauprvRdj4dfbkXe2dRmGPV49e0J7brXeOTJgBT29cYro2K7u2W5u3W03Zb9ZOJ7zanOXf4/u/7sanls/Ftx9flZKz/9j5axi4ZR/dm21iSZdH+d4SOX9yXRA7iJqnYQAvANgM4AKAf2aM/YxzHvHbdiGjCiU/y9tMkO4mme57boSKbvkvBPnbZ/pw97wZnicaXZuqaWZedYXpO6pga2upRWPNVFSVl5rMGLvaO41yeZ3RLsyuLDO101QzFRtaal2bowQir4x6b1gJQ/UzQJ9KVv1d1ELdAp0roTxBB6F8eLnX1cnv9ROX8MW9R3Hg9JWUDfGSEEOcc1jUycgKwu01ngws6xm4bTgCqM4Cuaz16ju7I2PsQQA7Oedbkq+/BgCc8//D6juU3TE/CMrG7hRmvqu906j+Lj4/eLbPlB0wxICS0IT2bJUMS+6DsL+qWSftsh5aZQ9U31OzITZUlyN68w7G44nN1Bc/e1+KpmYVLbvvUDf6hka1Od+94DTOssaoi5QFUrMyAhNZNa2+46Zf6U4I8gQqUDMryqhZFnNBW4t5D6iiNIwHFtaY9msEQWYmBdxndwwiQKkBQI/0+kLyPbVD2xljhxljh/v68sOJf7LjNnJSRvcQOwUo7X6zK6X6+/rFtUYCLiChAa1tnoUnHmwyhIxdNJ9dcrLNrXVorJlqfC5rpLq+6gKaykrMj8a9jTOxfvFstLXUpgh1p8jD9z4awMneG3jvowHX46zDKRhG/J5yMjX1OFVjX94wHc8/ttL2O3b4jbzUpbj4/WZzgJigLBzC0xuXYFmDfaGQoAizxPgsb5huBOiVSJFq8v0FpEZp57IKUxA2dl0MWMoygHO+B8AeIKGxB3BeIsvoPDSAieW7TjM5eLbPFM4eDjFjUtjxULNJkxfabLr2f9Uua2eakAVzz8BtU8FqoYmVhBjqpk/BvY0zDdOA6Kc4V8/Abdu+pmum0l2b3E87oWFnZlMF6cr5M41zpmOac7o+p2tTzVXCxv75776v1YABYH7NVG2t2qCJcWB0PI5NrfXJwuIc43GOA519KfszW9c0YeuapuKxsSOhoTdKr+8CcDGAdok8Q6fZqtXp1RtZzkoYZjDsyx2RKAZHxrBjw6KUh8CN/X/rmiaTO2TrvBkpk44w+cimCbl+K2DOCS+W2EKgjMc5Nt5dZ1yv7rqdNmCdrsWNO6O64dvWUutYuk0u8WZni5ezRHrd9BRjW1EaTjsozuq8cmIzwWgsjm+9cTYrQl3QGR1C15WulFw78v6MunrNB4IQ7P8MYDFjbAGAXgCfAbA1gHaJPEMVCkBqdXpgQoMHzFkJRTEKJ1uxGyGzubUOX/jEQsNrRWdKEGYmeQUge4I8sLDG9L54SHWZEuUUw/J1j8biWN4w3dDs3AougRuNft+h7pR+Hu+5bmRslIWmOrZWeX2sNmrdrozkc+j2Q0Q6CTcrL915Zc8p2ZZ9bSj7qQTE/St7W8krzHzEt2DnnI8zxv4MQDsS7o4vcc5P+e4ZkXfoPDRkIagG2siCU85KmG4Qi5orXmjh5wfOYdu6BZaao847ZXgshv6hOynfsXLFlGmdN8OksXdeHsJoLI5z/R8hcvFGygNvJzCt8qybr7nfeF0WDqGqvDRhwuIT1yLG0M3Y+vXQUs8xODJmMsO5SSfhZmUg+ik2oAGgoqwEvZpc9kFRW1mGG8PjpnQG8uSVD2YWNwTix845fx3A60G0ReQHVg+fKhRkQW+V9lcVtumYJwBYThrDYzFELt5wdBmU67cCwAeXBnH33CrMrpySIoxln3o5L/1oLI7BkTHjXD0Dt01FLrz4hRveQhZ51jsiUez82UlTjdQl9VWmvDOAOXeN1dgGmavI6fdTXRjlWIJ0irIAZgWiqaYC3QPDvq5BJsQSm/clIYb/8C9/N7EaSu79iN8lXyNMraCUAkQKu9o7tct8Haqglx94eUNJ50XjxTwh/pb/lxOIvdt1FVvXWLuWiX7Km3LjcY4TvTdNG6L7DnXjTHTQcYKStUnVFuy0ChHneauzz0hCFotzRC7eMB2jar1AQqPsHxo1vbdl2VzbsXXa9PYq6J1+P7fpJNyu1tTvLKytxJXBUWPFFOfcNPl5QXafFDnWAX3ZQ7+TYzYLcpBgn2S4qSykLvPViklWWD3wbu2qAiuNUJ00AKSUhANSBZYcQarblBPXKGy5MlYTlDyOOluwlVeJlcAGzBkmdVGrYpN495tmv281eEodW6+b3m4EkN3vZyeyLI19AAAgAElEQVT4vQbG6byB1N8DSOxDvH2mL2WTUxBiwMPL5mJ/JGr8xiUhhk8smYOuK7eMSaJn4DZa581Imch1VbW8mGTSXamkCwn2SYQc1LPv0HmsXzw7xQShhuQDqSlt7XCy37oVGjrBoHvPysa/971u7HioGQBMEaRPtTVrBTGAFKGupiNQfdflh/S7n7/f1bVZpRkQ5xcarCwAQwDuaZiOpzcusXQftcPNprfdtaUjgKzuAy/eN3JgW0mIoaG6HI+uvEurMKjjorJhSW2KG+V4UhuX7wdhSlMFt+qC63ZFK/cvnX2ldCHBPomQg3qEP65qD9ZtNKoFktPFi0nAylNCFkAHz/aZHkD54YlxYPdbH6J+ujmv+v7IZTyzpSVF6wZg0tjLwiGTXVjuo9VD6mZTUg0Qqq0sw7XbYxiPc1NR782tyVqlScHWdeUWAPPvI7uP2qFuCMubv6rWnA0B5GacjMC2pLAej3P0Xh/BS++cw4rGaq35R87hXhJiCDGG0Vgc4RBD67xEUJOuPqz4TUdjEyY/dUPYNO6Sh4zbMUo3hUe6kGCf5Kg3ppWbmd5bwzmoxk4guvGDt2rXyl1y73vdhjCIxTlqppUZhTYAYFNrvbbNza2JNL1C+ImVjG4yUvPRiHwhbvquBghdHRpFKKSL8TMX55BdONMt7CBvCFuZEvwIoCBtyFYauJ2nj/r7GS6hcW6aEHReT07XLa801JQNbsbIy0olCApKsGdz86EYkYN6BFY3sU5TFbgNqlHNPqrtErA2CdhhpzGr0axPb1yC4z3XDRu78G6wugb1OnW+2OsX1+KBhTXoH7qDzstDnjxh1i+uxd5D5w2BHQcQT/6trozsgom83v9OLoqCdAVQ0DZk1RQVCjEjFbBVgjPd3oLOJRQwJ6Db8VAzntnS4ip2QryfTrbWdH63dCkYwZ7tzYdiRGg1bsOerW5Et0E1OrOPrCkCsDQJ2E3idtrVM1taUh66za11RmCUEAhuQuHVTU7hQy7eT2dJvrm1DlvuqTdK48nIphhxbFBanhdNPIiJw68JR712cQ5ZW3aSA1bXrDPZCW0+H4V0OhSMYM/25kOxEsQNqQbVCA1Knix0DI8lqknJG5I6weXkgZCOwNO1aRXQBFhvckYu3pgQCnFuG1xkherFIljbPMt2X8EPViYIlXRXxZmwIavXrtvEtJMDVvfJ+sW1KSa7YpMnBSPYs735UChk2zzVEYma0gS0LZ1jaFCCV97vQUt9pRH4ISMKVsvCWjUJqJO4MK3YFfHQpayVj9eZIqwmlX2HutE/dMfkJw9MeM3IE4JXtzdAv0GtC/+3I53f/XjPdbx9th+xONeajvysirNlQ/YqB6w24VWTXbHJE9/52NMh3XzsZGM3Y7WJmMlxUvN5t9RVmvKW6xBZEuVNTFnTtRMw4jiBLr+1m+Od8tOIduQamyUhhrvnVhkpA+T87enYoOWNPdGGWOV4CVV3cy267+z4wWHbsVR/26BziQdFUPd3IcoTt/nYC0ZjB/LfrpVtrCI0M7kXoXqElJWEUjRblfE4NxWstrNNq26Mdh4IutS5MZ7II82BlA1HJ1NEwuVt4jrG4xwr5880/Medgq7UfsnBTPKE8W7XVbzw+CpDaHrVlNON3nTyfy+UVXGQ5qlilScFJdgJM7oHMdN7Eao75Inem0Yq2dZ5MxC5eCPF80ZkRBTCun9o1LR5KHy7rTRRnQeCVe1SICHUwwwpuVcAfV1VeTxlX2ixmelFAOiEtDphqN4vXn8zLwJYl17Xyv892y55ROYgwV7AWD2IQWhddstUYbMWAR0ilazsSiiEiRD0skugWFkInLI+OrkhitzYF68PG2ahGE/1GXcSoMJrSPVl94LuHFYThrgWtwU05H66EcBu0uvq2iaBXvgUtWAvFBuan37qPAesNgWtfNKdvFJ0pgE7rVH0qSMSxf7I5ZQSdVbfPXXRXMuyf2g0xV/ZKiWsWjhDJyCdUuSqfRcTkBdtXSekrSYMqwIabs7nRgC79V23ux6vQWjZolCe7VxRUJunXkhngykXZKOfdpusuvfdbqKpIfmqXVkngK02eHVFjYUt3q5vaj4XN0FVctEPq2vRFby2EySqkJarNFl9z26crSZcN8JM5yGUToFqN5vNuXjGCuXZzgRFuXnqhULxe89GP63OYfW+WxuurN3q7MqyUBcCGJgo5yZPFvsjl1Pa122wqjk7RIoAXT1TqzEQKXLFd4AJQb730Hm0zq0yjYub1Ae6Kkdyu7rvWY2zm3z0VsIsHfOLDjf3ZTr3bhCadqE827kktRS4Bxhjuxhjpxljv2WM/ZQxVh1Ux/yyfnGtEbaezzv82ein1Tms3hfmnCcebEo7c53athDqVhXt1Rwu9zXNtOxb29I5ABIC+m/e/ghP7T2qbdNqDETBavEdWSjH4hynkhvC4tziuuTrk+mIRPFu11XjtYhQ1aUjkBHj3NZSa9QotRpPKw8oFflaZPOLunp59rWTlmOljpfVfen13hWTjtNv5UShPNu5xK/G3gHga8nyeH8N4GsA/q3/bvmnUHb4s9FPq3PI71eVl5psyl420XSap+6cdlGDwiwi53SxMke0n5zwqJG9Tey0N7k/ctUj0R/ZBTOORBSoqIEK2Kc+eK79tKkfLfWVKUFb4RBLyewoUD111OOqykuxorHacRWlTjAlIWYkKAO8heS7uS+93rtBadqF8mznksBs7IyxPwbwrzjnjzsdmw0b+2QhiKVtEDZLtxttfs+j2qVDDCgJhUwBRG7MAWo/jvdcx7cPdIEjIRBf/Ox9AMyFue02mQUVpWE8sLDGmDhkdP3T2dkBaG3vTmOcMjZITFJi9SFS2Mr59t0EIQUZEDRZbeNBkQsb+zYAf2fToe0AtgPA/PnzAzzt5CWoxGhBaFJuNPwgNC01HP/hZXPxxysbPLWp68fxnusQ4m48zvHTY704cPqKaWztUh8A5n0EoYWrwVj7DnWbvGOs7OxOWR11wlbdf4hJWSMFXvPbBJl8jzTt7OEo2Blj+wHoklh/nXP+WvKYrwMYB7DXqh3O+R4Ae4CExp5WbwkTQS1tsxlx6NdPenOruQDF/kgUw6Pjafmcy6ibtwk3TfvMjeq46ZKbyaaPsnAIcjFtEYHqtlqUwC7lsNV5ARirGi8bqkFvVJKffHZwFOyc8012nzPG/hTApwFs5LnwnZzEBCWQC02TkgtQjMbinvKh72rvxKvHLiB68w7GpcRim1rr0RmdcLccleLv7XzercZNFmArGquNItlyvhwRgWrnT6/DTtiq57UzJbmhUNIMEGZ8mWIYYw8jsVm6gXN+O5guEW4JUiAXkialmmMAd9qkzldefE+YWfa+9zGuD48bn6t+8ipux00toA2k5ndXTR1Wtu103FH9BMC5vccoaCh/8Gtj/y8ApgDoYIwBwHuc8x2+e0W4ppAEshecUhq4Kd+n8uqxC9r3hReK8MyRhf+m1nrT+Xe1d6ZUY3K6BjlJGQA0zCjHkvoqbF3TZKl929m2vQpbvzZyN/cYFcLJL3wJds55c1AdIQiBGyHhVRvtiEQRvXlH+5mcU0bNLyO/ljX+zmgXzvXfwrcfX6Vtc1d7p7EPUKLUNH101V2mScGpyo9VXhsnzd5NO0GR66AhWi2Y8RWgRBBecRMc4zYYx8vDfPBsn7FpCQAsKWtVTd8u+EXdYP3FiUva6+iIRI0iygBM5wXMk4VVMJjOl12HU9BPtoJ51AAw2X8+0wQV+FRMkGAnsobbB1AVRqL0nny814dZbfOLDzVrI2vtom7V6FgOaCedg2fNuc9DDKZIVlW4bm6tS4kOtVs5qOdyE92qux43k6xbxHnaWhLXdqCzL2tC1q0iMJko2lwxROZId9nrdrlu5bZnV+pu36Fu2754sUtb2ZSf2dKC9z66iiPd14z3dJr0+sUTaXpDAJ58qNlzVXu7DVJ5/E2+60zfH931ZMImLn4Xkc45WyYZ8txJhTR2whN+lr06s4CV1ii02MGRMa02tn5xraEFAwmfcKe+6DRjL3REohhyqUkLSsIhrGis9nxuK01bHX8gUVAkHGKIceCld865+k0ypeXmIo+L3apkskIaO+EJnabsVhNVtWZgImPhK+/3GKlu3bj2bW6tw9rmWUbovlqVKF3cpP0VWAmuhNYaN/q187WT2Heo25SHXT6H1TnlDeJnXzuJqvJS7I9c1gpkq1KDVvjVcu36nIuYiGL1DkuXos3HHjTFtOvu51rUvOMAPOVpkVFzmwDWxa2dhG0QuUfs2nPKA2/VjkxZOIQvfGKhKUf6tnULbHOmW7UFwMgrv6KxOq1xSPc+8DLuxfTc5ANuc8WEd+7cmYXumNmzZ8/O7du3Z/286SJu5CPd17A/EkVLfRUW1Vbmultp4fdaFtVWoqW+CjOnlmJGRSk+7LsFIOH5MXNqqZFS1w3jyZQAsteIrp1FtZVoWzonpZ9yX3Q1PO3oiETxvV99jPE4N9r93q8+Nuznaj/kvlaUhvFXjy63PJ/o15nLN3H11qjxfoxz3BwexeWk2+V4PPW1eu1yn1Q4B0723sAnl8/FJ5fP9TwOVuNqh8hmaddn+dhieW7yhW984xuXdu7cucfpODLFuCDXPrpBEmTCr45I1DKdrdt20gk00vXFCV0d1tFY3LRxqDNPyBqnFxOD+PypvUdNtU43tdbj/MCEhq6+Vq9dF2UrkngB5sjZTN+TXsxRQHE9N4UGCXYJvyHchUCQ12JnT3W7BA8i7N2JL+49il+cuASd0VEWOHZ7AFYZHu3Y3DpR67RvaBS1lWVY0VidMmZ2HjOqh9DgyFhKybts3Y9W2Sytfq9iem4KDbKxJ3GyGxaTrdDOZl1sebd1+WFk7PrntvarE5kYD7t6s26+4/b8TrVg3aQaKJbnJh+Y9DVPveIlhDuXyCYFkXoVSC0k7VT5RnxHvA7SrzmfluCvHtXnh5ELTgetcXZEoqac6+mMh5vf0Mvvls7vq/uOV4+XfHluJhsk2JPk87JRV3Ve8Mr7PQBg2ItlLwvxWs297VR82q8wzpex7IhEER0054e5r2km7pk33TLPuSy00nHd64hETXb1d7uu4gufWIiK0rDr8fAihN3+bl4DuqzazoYtn/APCfYkufK/dUJ+yNWyZkBqzU/Vz1kkorKL2hTXnA3bezZR88PUTCuz9BqxK17hpf+yDzuQ+H0GR8Z81Qa1ixVw+7vJ0bDARECXXV+yNUGTuSZ4KPJUwm9kYiaQq86LsmYy6g+4aE6VEfkXZqmBK4A+YROQiGBsqavEtnULAgkvz/VYytcJAAO3Ri2jZYOKxFQjYsvCIUNguR0P9fd5t+uqZaSv26hLEdAlEAFddmQjopMSeGUG0thziGqL1ZkG5KrzIsDl7TNXcKL3JoBEsWKZ2ZVl2jwratSm7GZ4oLPPOM9oLI7zA+eMMPhCRlznc+2n0RkdAuC+zF262qnsCQPof1e3/Ra53EV0rVXf3a4qtq5p8uyemmkbeT7txxQTgQh2xthXAOwCUMs57w+izWJDF0au2mJfeHyV6abed6jbtKyvrUq4y6npYwXiYZUfRitXOmGSEQmbVJOO+oBlcrmcybZFe7I3h06gBWk+CkIYBhUroLaZDyYymXzZjyk2fLs7MsYaAXwHwFIA97kR7Pno7phJdO5uB8/2pYTTy+50u9o78eKBrhSNvKI0jLalc/D6iUvGe59aPhezK8tSPGTkv3UP8Rf3HjW1UxJiRmSlLvGUWzc3p5WI09hkasmfTwLNC4XcdzcU+/UFSTbdHf9vAF8F8FoAbRU8Os38ufbT2s1KeTNL2GJFG7vf+jBFqIvvz64sw1NtzSkl2mQhqXrL6Pzy20+ZNf8pJSE0VJZhoRL2bbVc1j2QblYibtp2GlevFLLbXSH33Q3Ffn25wG8x6z8C0Ms5/w1jzOnY7QC2A8D8+fP9nDZnOAkX1bNCdj0UyOYS1RYLJIJiegZup3i/CEIMxvfl8mrqBKKaVp5rPw3A7MOunuPWaAy3BobRPTCMN8/0Ydm86Xh64xJUlZcaHjlyqL3Oi0TnFSLOLc4rj59TGH/QPvbyeJGWSBQrjoKdMbYfQL3mo68D+HcA/tDNiTjnewDsARKmGA99zAvcCBdV+3z1qDnHhxqCLWsqatbEsnDIJCAF98ybnqItA0jJ4aHSGR3CU3uPGtqzVak1AefAid6bePKHRxBizPDIER4zz752Uqtp69oV5wZSVxCq3Ve+FjsfezHe6URbZmKimIzQ5Ji/OAp2zvkm3fuMseUAFgAQ2vpdAI4yxu7nnOt39woYNyYD1bwSHbxjCOiK0rA2r4Z4OORK9qOxONpaatFYMxVV5aX4m7c/wmgsjrJwCE9vXJIolPxmF2Ic2PteN+6eN8NWqAtGY3EjMMWpQIQg4QeemIdjfKKwhNWml1W7dpuz8gSnmzDUc1WVl/qKtiRPDP/Q5JjfpG2K4ZyfAGDk6mSMfQxgdbF6xVgJMlVrkYs/jMc52lpmGwJaDuEX39Vp6RWlYdOmo+zZAiARdJRc88SSqVu90BGJ4uj5657HQOcyqWpsqsYuNmTV3O12gTTqOKvnSjfaUhxHnhj+ockxvyE/dpfoBJlOa2mdNwNvn+1HTBJm/UOj2PteN2IclpqjrKULQSOq5sgpAZ597aSl/V1QXVGCRXOqcKz7mmkDNsSAmyPj+ML37T2SGqrLMTwWw8CtCe1bl8lPt+kVuWieZO6eW4WV82e6TlZlNWGo53IbbWlVfSnf3P4EhWLeoMkxv6Hsjj5Qs/+1tdQaPschAKGktqoi3BqtXP2s8l4//9hKAMCOHx6xFO527pRW1Ewrxc3hcUOzfuHxVQBgWk0sqa9CbWWZo/viv/jPB43gKQBY3jAdP//Self9ANwLtqCPyxRezp9PWTHdkOuxnYxQdscsoGotAAxhHAcQ1wjfcNKrBbDWHNW816JdkYRpyz31Jv9zQQhA29I5RtRpCKmRqTqu3R6D6tMk+vb8G2dwsvemYe5xcl+cXTlF+9pNnU87u60uQZcbYZJLVzqvduhCM2+Qm2L+QoLdBzqPDqGxyzZlQYgBOx5qdjRnyBOGQPi5d0SiOHD6irY/cQDtpy4bbomNNRXoHhh2vA7OYRShEBusYnI4dfGmqUCFVdFoIXgrykoQYkCcJ/q8dU2TrRuom03NQt2o8yqoybxBBAUJdhfYLTlVl8UHFtYAmPBLTwjJfozHOUpCIaxorHY8n5gw5O8CwPGe66bsjTrkpF/TK0rBMKytHGTHW2f6YGXGLwkx9Azcxq72TsOe3jpvhuG5I7MpOTaqp4uagdJpU7PQNFmBV0Gdz7Z/orAgwe5AOoUMZK8WOXWsrO06VcDRfVe4OAoYYCu0P7g0mNY1Wwn1yilhjI5zHOjsMzx/AODNzj5tP35x4hI6IlGTgCsLh1BWYvYAcjJNFaomm46gJvMGEQQk2B3w61qnuv9VlZcm8sC82YU4B/YdOo8QY9rQfzniU83FXhJKCGC7vW/dxm26lIQYfu93akwCXWB1Fg7g+TfO4OdfWm/KJnmi9ybKwiG0tdSmbMbqBFs2NNlMbQQGKahps5JwCwl2B7wUMtCFxquZGN8604dI7w1jU1MOAJKLKoiUuyLic8s99Thw+ooxeYy72RVVKA0zjMXSE/Z3z60CAMN+LlNi4f0DJHKgA/psko01U10LqKAEZKFGohZCH4n8gQS7A261RbvQeJnIxRspniryZuO7XVcxGuszmVlifCLPupxb3CvpCvWycAidl4dwInYz5bOG6nLc2zhT66UDAI+uvMv4O9cmlUKORC2EPhL5A1VQcsHmVnfVb+TjdC6LQEKAq4M+a9oU1EwrRWV52NiAVEVw/9AoDp7tw6bW+pQqSpkkxIAl9VXavDUhlig398YH+qo3Vi6UThV5OiJRPPvaycCr6VjlnJErFuWrDb8Q+kjkDxSglCE6IlHs+MFhqEqyXAXpZO9N1x4rwie9JMTQUF3uyo0xKJY3TDcFHYn+TBiRrAmHGHZ/9j7X2mUmg3Ts2i4E+3Uh9JHILBSglCPkh2/LsrkpJoq1zbOMKkheplTZJp+uUA8zgDFrezig97S5fGPE9LqlrhIjYzFtP5pqKnDh2vBELps492Q2kGu8Bm1ysDOrFYI3SiH0kcgPSLAHiGrDFT7tgjBL+Hw7pdjNFDEOhB2mE46EW+PQnYn+9Q+NGn+XhUPY1FqPFw90ab9/T0M1Pn1vQyJRmZS/3Q26Gq9BmxyCFo5BaNGkiRNBQ4I9QFQbLpCwhw6PxRAOMezYsAiDI2MmoV4ztRTzZk4FSwpc1eQRNG72T2WhDpg1+CX1VRgcGbNMVfDhlUF8+/FVlrVW7VCLdKxtnpXXgi4ITxWvbdAkQLiBNk8DZP3iWiOVgAinF5uFuz97H57Z0pLi135zZBxf3rgYP//S+pQ8KzIlIYblDdNRM82+QEbQqBugkYs3UFVeamzkqWxqTdRkcbvhLKNuEIro3XzFajM2U22ISeD7v+7G0y8fC3xzmSgeSLBnGFXAqWltx5M2aCtCScka5xynLt40pdK1oyTEUoRyOqgKfjxZbGPbugUpk4zoa7oeLW69ZvKFIDxVvLQRxERCTA7IFBMgsilBrlYETCyh+yR7NZCYWcXDvHVNU9KPPdFGbeUU9A3dAWAd5q+jsiyM36mt9FyAww0MiYjY//rWhymbsHEOI+2BnVlhV3tnSiFuQSFtEAYREeuljVzHARCFAwn2AFHNLAfP9huaq5zbXERqMgAPL59ryg8jF7juH7pjCHYvDI3GcCoDQh1ICPZ//M1FrWdNiE3Y8K08Wna1d+KF5MZrZzTxvyrcC4kgJiIvKYgpSRjhBt+CnTH2JQB/BmAcwD9xzr/qu1cFilrvUzazyJWSljdMR+TiTcQ4cOD0Fexq7zSqJAEwF+uAu5zqKpmKTogD6B64nfJ+zbRSPHZ/k5GON8xSJzoAKSkW9kcuF7RgzzaFtKIhcocvwc4YawPwCIDf5ZzfYYzNcfpOMaMWsw4lhduKxmpTdsOBW6MmzfbFA12IA4aLpFysQ1ASYli/eDYqykrwixOX0hLcZWGGURu3GLucLwJdrhjGgMfub8LgyBjals4xcsK/9M45ADCV9tvUWm9o6sDEZitBEMHhK/KUMfb3APZwzvd7+V4xR552RKJ4/o0zONV7E3GYS9qJ7Ia68HzB8obplhGpLXWVKCsJZcQl0ikFsKCpZip6rt02hDsD8Mnlc7E/EsVoLG7ZjhzpaWdjJwjCGreRp369YpYAWM8YO8QYe4sx9ns2HdrOGDvMGDvc11e8u/mbW+uwcv5MQ9uWbc2NNVNthTqQyKE+pUT/s3RGhzLm5+5GqJeEGC7dGDHy3SxvmI49T6zG8Oi4ZY4bgRgHEuoEkXkcTTGMsf0AdOvlrye/PxPAAwB+D8DfM8YWcs0ygHO+B8AeIKGx++l0vmPlvaAreacyHueYXlGCkXFzSb0AU6unjZxiOA5g5fyZ2NxaZ2z22hEOMfQPjRoFtoU5Jp1AJoIg7HEU7JzzTVafMcaeBPCTpCB/nzEWBzAbQPGq5BaoEYE67wXx/vNvnLHUvMvCITx2f5PJnTAfhLpKwuZfa3j9qPZ5ueZrmAE7NixK2Th99dgFbe1TgiD84dcr5lUAfwDgTcbYEgBlAPp996rAsAoL1wkpkdJXFew100px713VRkWhFY3VvnKvy9jZz6eUMCyum+7Z5z0W5zjec90QzCUhhoYZ5bh3/kzMriyzLPknb5zWTCtD7/VEgjHKMU4QweFXsL8E4CXG2EkAowD+VGeGKXbUiEBRBcnKvFBVXmpyYywLh/DXf3JvSrZBAPg3PzjsSmMPMaB6aqk2MtXq6wzAv16/CAAsBbvVpMABU1Hq8ThH740R9EWiWNs8y7gG+ZqETV3Y2Fc0VpvS6FLADUEEA+VjDwBZY5dNELp84vKxoWS2xw1Lao1UA2oN0H/xnw+atHsre/vyhukAvCcRqygN44GF+lqmQKK2qq4MX0mI4e65Vfjg0qDWRdJtLvVCSWpVKP0kihu3XjEk2ANCPPg9A7dNQvKJB5vwzUeWGa+ffe2ksYEIJEww126NGVpxSYjhxc/eBwBG7VNd+L6KHM3q9RetmWbW9J02a+fXTMXlGyOOHj7qtRcqmSz+QRBeoEIbGcCN1tY6b4YROaqaFzoiUfQM3EZZOGQIRdV0Mh7neP6NM+i6csu0AnBCCH5VHjMApdL5dKh92LCkFhevD5vs+yUhYHpFqRGIJE9OgobqckRv3sF4nGckl3quoHqjRKFB2R1dYpcyVf7spXfOYdu6BSkZCsUxQptvqC63PNfArVFTCgInbd0OjkRe8ycebEJTTYXpM52/vCgG8pUtS02Tyng8MQG89M45VJWXpkw4ZeEQHl15F0IsiwVZswTVGyUKDRLsLrFLmap+NjgylpKLXD5mNBbHkroqrTYuBKRVvnOviHOsX1yLT9/bgHAyt25FaRjTpqSeI8ZhpAIQm6Ay4vrUz9Y2z8LgyJgpu2WxpJUttHTCBEGC3SV2WpsbjU5XROKFx1ehraUWbS21eKqtGW0ttUZN1OcfW4mWukrL/pSFQ7ivaSaqK0qwqHaa6YcMAbivaSYaqssR5xwHOvvwb35wGP81Wa4uzIBt6xbgsfv1hSzExNU6b4aRY10gknttXdOUcj3FrNmmUziEIHIFbZ56wM7G7sb+7vR9dYMOAJ7ae3QiqRgSaX6Fn/jm1roUL5t75k3HJ5bMMfzLrRAbm6rXDZAQym1L5+C/nbhkSkQmNlXl/qnX0xGJGpGosocPeZUQhH9o8zQD2KVMdZNO1e4Ynannm48sw9rmWYZdPg5gdmWZydNE/l6cJ8L81bqqKvLG5tMbl5hcNdc2z0LrvBnY/daHKemChalf9M9KIxebx+99NBn1jmMAAAlbSURBVGBMAH5rgxIE4R4yxeQBsrcMYDZjqCYPVZiqdVbXL641mUTCNnuZQosWm70vPL4K3/38/RgcGUPMopCG6EdVeal2M1k3QVFJN4LILqSx5xg1uKmtpdZkwkinao74jkgTHFNcHUXZPtktU9aidcnKysIhfOETC43c6qqwfq79dMp35YmISroRRPYgwZ5jVG+ZxpqpKcLbyYSjeqKI4xOfTWjH4RBDLM4Nbd7KN1ueTKrKS02FMmRk4d8ZHcLTLx9LJDnTTERU0o0gsgcJ9hzjt0Cx3ffVz7atW2AI6eM9100RsmoZO6c9AyH8d/7sZEoiL533CJV0I4jsQYI9g7jxBPFqalHbtPu+3WeqnVut1+qWvsFR4+9iijYliEKGBHuGsErlq8OtNmuXHthr235XCoDZDAQkgpRIKyeI3ENeMRkiE54gQba5ubUO29YtQEtdJbatW5CWQNYFXREEkXtIY88QQWjEdm2KCFAgveCfjkjUCGI6P5BIIWC1SWpFOh47BEFkHoo8zSCZiLbc1d6J3cnUAGJDVAhoEWCk5nTXoaYPDrNEnhhKS0sQ+YvbyFNfphjG2ArG2HuMseOMscOMsfv9tFdsZCK/iBw8NDwWM1UxGo3FcaCzLyX7pA5TEFOIIaZElRIEUbj4tbH/RwDf4JyvAPBs8jWRQVS79qbW+pRMkG6Es5yxcMeGRSnRrR2RKJ597aTjBEEQRP7h18bOAUxP/j0DwEWf7U16nMw3uuChbesWIHLxBt7tumqU5HNj05c9ZlY0Vpvyv1BuF4IoXHzZ2BljdwNoR6JQTwjA73POU0vrJI7dDmA7AMyfP/++7m7tYZMaLyXYrLJBBmHTV+3vxVLijiAKncBs7Iyx/Yyxk5p/jwB4EsBfcM4bAfwFgL+1aodzvodzvppzvrq2loJYdHhxZ7Qq1xaETb+Y86oTxGTA0RTDOd9k9Rlj7PsAvpx8+SMA3wmoX5MSLy6SQblT6kw/5MZIEIWNX1PMBwCe5Jy/yRjbCOA/cs7vc/reZHF3TAcvLpJ+3Sm9mH4Igsg92Sq08QUA32KMlQAYQdKGTqSPl2RZfhNrWZlzCIIobHy5O3LO3+Gc38c5v5dzvoZzfiSojhGZh2zpBFGcUEqBSQzZ0gmiOCHBPsmhPOkEUXyQYM9TMpFnhiCIyQGl7c1DhLeKWiiaIAjCDSTY85BM5HInCGLyQII9DyFvFYIg/EA29jyEvFUIgvADCfY8hbxVCIJIFzLFEARBFBkk2AmCIIoMEuwEQRBFBgl2giCIIoMEO0EQRJFBgp0gCKLI8FVoI+2TMtYHINtFT2cD6M/yOf1SaH0utP4ChdfnQusvUHh9zuf+NnHOHSMWcyLYcwFj7LCbyiP5RKH1udD6CxRenwutv0Dh9bnQ+quDTDEEQRBFBgl2giCIImMyCfY9ue5AGhRanwutv0Dh9bnQ+gsUXp8Lrb8pTBobO0EQxGRhMmnsBEEQkwIS7ARBEEVGUQl2xth/zxg7xRiLM8ZWS+9vZowdYYydSP7/Bxbf38kY62WMHU/++1Su+pz87GuMsS7GWCdjbIvF9xcwxg4xxs4yxv6OMVaW6T5L5/47aaw+Zowdtzju4+TYH2eMHc5W/yz64uo3Zow9nBz3LsbYX2a7n1I/djHGTjPGfssY+yljrNriuJyOsdN4McamJO+XruT9+jvZ7qPSn0bG2AHG2AfJ5+/LmmMeYozdkO6VZ3PR17TgnBfNPwB3A2gB8CaA1dL7KwHMS/69DECvxfd3AvhKnvS5FcBvAEwBsADAhwDCmu//PYDPJP/eDeDJHI39/wXgWYvPPgYwO9f3h9vfGEA4Od4LAZQlf4fWHPX3DwGUJP/+awB/nW9j7Ga8AHwRwO7k358B8Hc5vg/mAliV/LsKwBlNnx8C8I+57Ge6/4pKY+ecf8A579S8f4xzfjH58hSAcsbYlOz2To9VnwE8AuAVzvkdzvk5AF0A7pcPYIwxAH8A4MfJt74H4NFM9ldHsh//A4CXs33uDHE/gC7O+Uec81EAryDxe2QdzvkvOefjyZfvAbgrF/1wwM14PYLE/Qkk7teNyfsmJ3DOL3HOjyb/HgTwAYCGXPUnaIpKsLvkTwAc45zfsfj8z5LL3pcYYzOz2TGFBgA90usLSL3xZgG4Lj34umOywXoAUc75WYvPOYBfJs1g27PYLyucfmM3Y58LtgH4hcVnuRxjN+NlHJO8X28gcf/mnKRZaCWAQ5qPH2SM/YYx9gvG2D1Z7ZgPCq40HmNsP4B6zUdf55y/5vDde5BYzv6hxSEvAvgrJB6Sv0LCvLAt/d4a502nzzptRvVNdXOML1z2/THYa+trOecXGWNzAHQwxk5zzt8Osp8ydn2Gu9844+NqOpmLMWaMfR3AOIC9Fs1kdYwV8uJeTQfGWCWAfwDw55zzm8rHR5HIzTKU3It5FcDibPcxHQpOsHPON6XzPcbYXQB+CuAJzvmHFm1HpeP/BsA/ptXJ1HbT6fMFAI3S67sAXFSO6QdQzRgrSWpBumN84dR3xlgJgH8J4D6bNi4m/7/CGPspEkv3jAkdt+Nt8xu7GfvAcDHGfwrg0wA28qTxV9NGVsdYwc14iWMuJO+ZGQAGstM9PYyxUiSE+l7O+U/Uz2VBzzl/nTH2bcbYbM55viYIM5gUppikJ8E/Afga5/xdm+PmSi//GMDJTPfNhp8B+EzSm2ABEprC+/IByYf8AIB/lXzrTwHYrloywCYApznnF3QfMsamMcaqxN9IrJZyNq4uf+N/BrA46XFUhsRm38+y0T8VxtjDAP4tgD/inN+2OCbXY+xmvH6GxP0JJO7X/89qksoGSfv+3wL4gHP+nyyOqRf7AIyx+5GQl1ez10sf5Hr3Nsh/SDyoFwDcARAF0J58/38DcAvAcenfnORn30HSGwXADwCcAPBbJG7Eubnqc/KzryPhbdAJ4JPS+69jwstnIRICvwvAjwBMyfKY/78AdijvzQPwutS/3yT/nULCvJDLe0T7G8t9Tr7+FBKeEh/mss/J37VHum+FZ0lejbFuvAB8E4kJCQDKk/dnV/J+XZjj+2AdEqag30pj+ykAO8T9DODPkuP5GyQ2rn8/l3328o9SChAEQRQZk8IUQxAEMZkgwU4QBFFkkGAnCIIoMkiwEwRBFBkk2AmCIIoMEuwEQRBFBgl2giCIIuP/B2+tAAUqdBOnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Pour la partie clustering, j'en ai parlé avec François Ozenne,\n",
    "# je pense que c'est pas grave si on prend un dataset plus simple.\n",
    "# C'est mieux d'avoir qqchose en 2D (pas besoin de faire une réduction de dimension\n",
    "# pour commencer à jour avec le dataset). Les exemples sklearns sont pas mal et intuitifs:\n",
    "# https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html\n",
    "\n",
    "n_samples = 1500\n",
    "noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5, noise=.05)[0]\n",
    "noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)[0]\n",
    "blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)[0]\n",
    "varied = datasets.make_blobs(n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state)[0]\n",
    "\n",
    "for X in [blobs, varied, noisy_circles, noisy_moons,]:\n",
    "    fig, ax = plt.subplots()\n",
    "    plt.scatter(X[:, 0], X[:, 1], s=10)\n",
    "    plt.show()"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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342
   "version": "3.6.6"
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