M2_SETI/D3/TP/TP_SETI_MLP/TP2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP2 Régression\n",
    "\n",
    "On souhaite faire de la modélisation sur des problématiques de régression. Comment construire un bon modèle et surtout un modèle optimale par rapport au jeu de données et à la famille de modèle utilisés.\n",
    "\n",
    "## 1. Modèles polynomiaux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fonction de génération\n",
    "\n",
    "def genere_exemple_dim1(xmin, xmax, NbFx, sigma):\n",
    "    x = np.arange(xmin, xmax, (xmax-xmin)/NbFx)\n",
    "    y = np.sin(-np.pi + 2*x*np.pi)+np.random.normal(loc=0, scale=sigma, size=x.size)\n",
    "    return x.reshape(-1,1), y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fonction EQM\n",
    "def getMSE(x, y, reg):\n",
    "    return sum(pow(reg.predict(x)-y,2))/x.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fonctions plot\n",
    "\n",
    "def plot_model(Xa, Ya, Xt, Yt, reg, degre, nameFig):\n",
    "    Ypred = reg.predict(Xt)\n",
    "    plt.plot(Xa[:,1], Ya, '*r')\n",
    "    plt.plot(Xt[:,1], Yt, '-b')\n",
    "    plt.plot(Xt[:,1], Ypred, '-r')\n",
    "    plt.grid()\n",
    "    plt.savefig(nameFig+'.jpg', dpi=200)\n",
    "    plt.legend([\"Sortie bruitée\", \"Sortie réelle\", \"Modèle prédit\"])\n",
    "    plt.title(\"Prédiction du modèle à degré %02d\" % (degre))\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "\n",
    "def plot_error_profile(L_error_app, L_error_test, nameFig):\n",
    "    plt.semilogy(range(1, len(L_error_app)+1), L_error_app, '-r')\n",
    "    plt.semilogy(range(1, len(L_error_test)+1), L_error_test, '-b')\n",
    "    plt.grid()\n",
    "    plt.savefig(nameFig+'.jpg', dpi=200)\n",
    "    plt.title(\"Profil d'erreur\")\n",
    "    plt.ylabel(\"erreur\")\n",
    "    plt.xlabel(\"degré du modèle\")\n",
    "    plt.legend([\"EQMT\",\"EQMA\"])\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "\n",
    "def plot_confusion(Xt, Yt, reg, nameFig):\n",
    "    plt.plot(Yt, reg.predict(Xt), '.b')\n",
    "    plt.plot(Yt, Yt, '-r')\n",
    "    plt.savefig(nameFig+'.jpg', dpi=200)\n",
    "    plt.legend([\"Y prédit\",\"Y désiré\"])\n",
    "    plt.title(\"Graphe de confusion pour le meilleur modèle\")\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Création du script principal pour l'exploration des exemples et du bruit sur les données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def main(degreMax=12, NbEx=20, sigma=0.2, ens=200):\n",
    "    xmin = 0\n",
    "    xmax = 1.2\n",
    "    xapp, yapp = genere_exemple_dim1(xmin, xmax, NbEx, sigma)\n",
    "    xtest, ytest = genere_exemple_dim1(xmin, xmax, ens, 0)\n",
    "\n",
    "    L_error_app = []\n",
    "    L_error_test = []\n",
    "\n",
    "    for i in range(1, degreMax+1):\n",
    "        print(\"Degre = \", i)\n",
    "\n",
    "        # Transformation des données d'entrée des basses d'app et de test\n",
    "        poly = PolynomialFeatures(i)\n",
    "        Xa = poly.fit_transform(xapp)\n",
    "        Xt = poly.fit_transform(xtest)\n",
    "        # print(xapp)\n",
    "\n",
    "        # Création du modèle linéaire\n",
    "        reg = LinearRegression().fit(Xa,yapp)\n",
    "\n",
    "        # Estimation des erreurs d'apprentissage et de test\n",
    "        L_error_app.append(getMSE(Xa,yapp,reg))\n",
    "        L_error_test.append(getMSE(Xt,ytest,reg))\n",
    "\n",
    "        plot_model(Xa, yapp, Xt, ytest, reg, i, \"Model_%02d\"%i)\n",
    "    \n",
    "    # Determiner le degré optimal\n",
    "    best = np.argmin(L_error_test)+1\n",
    "    print('Meilleur modèle -> degre =', best)\n",
    "    plot_error_profile(L_error_app, L_error_test, 'Profil_Err_App_Test')\n",
    "    \n",
    "    # Création du modèle final optimal\n",
    "    poly = PolynomialFeatures(best)\n",
    "    Xa = poly.fit_transform(xapp)\n",
    "    Xt = poly.fit_transform(xtest)\n",
    "\n",
    "    reg = LinearRegression().fit(Xa,yapp)\n",
    "    plot_confusion(Xt, ytest, reg, 'Confusion')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  1\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  2\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  3\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABLfklEQVR4nO2dd3hURdfAfyeBEDpKE0QBKSq9qogIAUSKgvjSEUGQKjZeC34WUEHwVVHUgCJgpSmCoBSVJtJESugC0qRJCSAEAqTM98fZxCVskk02W5Kd3/PcZ/feO3fmzJZz5545c44YY7BYLBZLzifE3wJYLBaLxTdYhW+xWCxBglX4FovFEiRYhW+xWCxBglX4FovFEiRYhW+xWCxBglX4QYSINBSR30Xk2jTKfCYiIxzvG4nIzky29ZGIvJxZWTPQThMROeTtdjKKiJQTESMiuRz7ISLypYgMcVG2l4isyGQ714rIbhGp5amM3kJEiolIlIjU82Y7lvSxCj8bIiL7RSRWRGJE5JhDSRdI55obgDeANsaYU+60Y4z51RhzsxvyXKWwjDEDjDGvu9NOMGCMSQR6AbeJSMssrPcU0BUY723FnRlEJDfwOTDIGLMujXJ5RGSyiJwVkb+db4wiUkVE1onIace2SESq+EL+nIZV+NmX+40xBYA6QD3gpZQFnBWAMeagMaaxMea4D2W0OGGMSTDGdDHGLMzietcBI4FKWVlvZhElBMAYE2eMaWOMWZXOZcNR+csCEcBzTjfGI0AH4FqgGDAXmO4N2XM6VuFnc4wxh4EFQDUAxyP6YyKyG9jtOHaf45H6jIisEpEaSdeLSG0R2SAi50RkBhDudO4Kc4mI3CAis0TkhIhEi8iHInIr8BHQwPHEccZRNtk05NjvKyJ/isgpEZkrIqWdzhkRGeAwTZwRkUgREVf9FZG8jrpPi8h2oH6K80ZEKjrtXyFHirK9RGSliLzraHeviNzpOH5QRI6LSE+n8oVF5AtH/w+IyEtJik1EQkXkbRE5KSJ7gTYp2iosIpNE5KiIHBaRN0QkNBW5bhGRnx2f1U4R6eSqnKPsIyKyA5gGzBOR/mmUzaiMI5JkdFz7juPafSIyWK40WS0TkZEishK4ANyUkX4APYHXjTGnjTE7gE/QJyKMMWeMMfuNhgUQIAGomGpNltQxxtgtm23AfqC54/0NwDb0zwJggJ/R0VBeoDZwHLgdCEX/WPuBPEAYcAB4GsiNjqLigBGOupoAhxzvQ4FNwLtAfvTGcJfjXC9gRQoZP3OqpylwEn0ayQN8ACx3KmuAH4AiwI3ACaBlKn0fDfzq6N8NwNYkGZ3qquhKDhd19QLigUcc/RsB/AVEOuRsAZwDCjjKfwHMAQoC5YBdQB/HuQHAHw6ZrgWWOmTJ5Tg/G5gIFABKAuuAx1J+fo7P9qBDplyO7+8kUCWVPrQBKqCKsDGqbOukUtYdGT92yFACWAv0d7p2O1AGuAZYlOLaZY7PrqpD7sLu9sNRnwFKOh3rAGxJUe6M4/tKBF7y9/8wO25+F8BumfjSVGHHOP4AB4BxQF7HOQM0dSo7HsfNwOnYTodyuBt9XBanc6twrfAboIo4lwt5khWW07HPnOqZBPzP6VwB9MZSzknmu5zOfw0MTaXve3G6GQD98Ezh73bar+5C8UQDtdAbwmVnhQX0B5Y53i8BBjida5GkEFEFfxnI53S+m9O1yZ8f0Bn4NYWcHwPD3PxtfAc8mcq59GS8lPQ7cpzvCix1ura/07nmXK3wX3M673Y/0BuQAcKdjt0D7HdRNj8wCJ2L8vt/MbttATfJY3GbB4wxi1I5d9DpfVmgp4g87nQsDCiN/skOG8c/ycGBVOq8AThgjInPhKylgQ1JO8aYGBGJBq5Hb14AfzuVv4DeFFKry7l/qcnrLsec3sc65Et5rABqO86dor0DaB/Sk6ssOgLf4GSpyg2cdiFPWeD2JNOYg1zAl66EF5FWwDCgMmqizQdscVXWDRlzA0edZAxxKp/yWuf3ro5lpB8xjtdCwEWn9+dSFjTGnBeRj4ATInKrsXNSGcIq/JyJswI/CIw0xoxMWUhEGgPXi4g4Kf0bgT0u6jwI3CgiuVwo/fRCrh5BFUBSu/mBosDhdK5zxVH+NWMlyevMBVTpJXEdkBVumyfRp5KyqGkjqe2kPiTJhdO5JA6idufqxpi4dNo5CPxijLknPYFEJA/wLfAwMMcYEyci36E3F1ekJ+MloFgqN/WjqDkniRtclEn5u3OrH8aY0yJyFKiJmiNxvN+WyiVJN7brUXOlxU3spG3O5xNggIjcLkp+EWkjIgWB1ahN9AkRyS0iDwK3pVLPWvRPP9pRR7iINHScOwaUEZGwVK6dBjwiIrUcSuoN4DdjzP5M9Odr4AURuUZEygCPpzgfBXRzTDK2RE1XHmOMSXC0PVJECopIWWAI8JWTXE+ISBkRuQYY6nTtUeBH4D3HxGiIiFRw3HBT8gNQWUR6OL6T3CJSX3RyPCVh6FzDCSDeMdpvkUY30pPxJ+AdESnkQsavgSdF5HoRKQI8n+YHlrF+gM6PvOT4Xm8B+qLmOETkHlHnglARKQSMQZ+OdqQjgyUFVuHncIy67PUFPkT/JH/yr/fDZeBBx/4p1O46K5V6EoD7Ue+Iv9BRc2fH6SXoaOxvETnp4tpFwMvoaPQoOsnYJZNdehU1RexDFVRKE8GTDjnPAN1Rm3ZW8ThwHp1HWAFMBSY7zn2CKvVNqPkq5ef4MPp/24Z+DzOBUikbMMacQ5V2F/TJ6G/gTVSxuyr7BKqMT6PzAnPTkN8dGcPQJ5iUMn6Cft6bgY3AfHSwkOCqoYz0w8Ew9MnyAPAL8Jb51321CDpo+MdRpgI6j3PRRT2WNJArzbcWi8WSPo6niY+MMWXTLWwJGOwI32KxpIvo+ofWIpJLRK5HR+Sz/S2XJWPYEb7FYkkXEcmHmlpuQT2X5qHun2f9KpglQ1iFb7FYLEGCNelYLBZLkBDQfvjFihUz5cqVy9S158+fJ3/+/FkrkJ+wfQlMbF8Ck2Dvy/r1608aY4q7OhfQCr9cuXKsW5dqRNU0WbZsGU2aNMlagfyE7UtgYvsSmAR7X0Qk1dXn1qRjsVgsQYJV+BaLxRIkWIVvsVgsQUJA2/BdERcXx6FDh7h4Me1V1YULF2bHjpwRaiO79CU8PJwyZcqQO3duf4tisVhckO0U/qFDhyhYsCDlypVDXCdFAuDcuXMULFjQh5J5j+zQF2MM0dHRHDp0iPLly/tbHIvF4oJsZ9K5ePEiRYsWTVPZW3yPiFC0aNF0n7wswNGj0Lgx/P13+mUtliwk2yl8wCr7AMV+L27y+uuwYgW89pq/JbEEGdlS4Vt8w7x589i8ebO/xcg55M0LIjB+PCQm6quIHrdYfIBV+Jlg5MiRVK1alRo1alCrVi1+++23DF3/2WefceTIkeT9Rx99lO3bt6dxxZU0adIk0wvSkmjdujVnzpzhzJkzjBs37qrzCxcu5JdffqF69eoetWNxYu9e6NYN8jkScuXLB927w759/pXLEjQEh8LPQpvp6tWr+eGHH9iwYQObN29m0aJF3HCDq2xvrklISLhK4U+cOJEqVap4LJsz8fFpp56dP38+RYoUSVXht2zZkv/973/WTJOVlCoFhQrBxYsQHq6vhQrBddf5WzJLkBAcCj8LbaZHjx6lWLFi5MmjiXuKFStG6dKlAVi8eDG1a9emevXq9O7dm0uXLgEaIuL555+nTp06TJs2jXXr1tG9e3dq1apFbGzsFSP2n376iQYNGlCnTh06duxITEyMSzm+/PJLatWqRbVq1Vi7di0Aw4cPp0ePHjRs2JAePXrw2WefMXjw4ORr7rvvPpYtW5Ys08mTJxk6dCh79uyhVq1aPPvsswC89dZb1K9fnxo1ajBs2LDk67/66ituu+02atWqRf/+/UlIcJnsyJIWx47BgAGwZo2+2olbiw/J2QrfCzbTFi1acPDgQSpXrsygQYP45ZdfAPUe6tWrFzNmzGDLli3Ex8czfvz45OuKFi3Khg0beOihh6hXrx5TpkwhKiqKvE6ynDx5khE
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  4\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  5\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  6\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  7\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  8\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  9\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  10\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  11\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  12\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meilleur modèle -> degre = 6\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main(degreMax=12, NbEx=20, sigma=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On observe qu'à partir d'un certain moment (degré du modèle supérieur au degré sur la fenêtre observée) on observe que le modèle va venir faire la régression sur le bruit, c'est-à-dire que la précision devient trop grand pour approximer la fonction observée et le modèle va venir prendre en compte les petites variations dûes au bruit. On observe le degré optimal grâce à la courbe du profil d'erreur lorsque l'EQMA commence à remonter. On observe également l'effet du bruit et son écart à la fonction prédite grâce au graphique de confusion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut observer l'impact du nombre d'exemple sur le profil d'erreur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABMfklEQVR4nO2deZyN1RvAv8+MGTP2LMlSlpDIMJYi2bIkFSmyZEslW0kpkX2JSvm1MC0SpVBSaVcyCanQlJBIFMY2iLGOmfP749wZd65779y5+3K+n8/93OU973mfc9/3fd7nPOc5zxGlFAaDwWAIf6ICLYDBYDAY/INR+AaDwRAhGIVvMBgMEYJR+AaDwRAhGIVvMBgMEYJR+AaDwRAhGIUfQYhIUxH5WURKOikzT0SmWD43E5Ftbh7rFREZ666s+ThOSxHZ4+vj5BcRqSwiSkQKWL5HicjbIvKInbL9RGS1m8cpKSLbRaSepzL6ChEpLSIpItLQl8cx5I1R+CGIiOwSkdMiki4iByxKukge+1wOPAXcopQ64spxlFLfK6WuckGeixSWUmqgUmqyK8eJBJRSWUA/4FoRae/Feo8APYAkXytudxCRGGA+MFgptd5JuYIiMldEjovIfusHo4jEisgSy3WvRKSl7yUPT4zCD11uU0oVAeoDDYExtgWsFYBS6l+lVAul1EE/ymiwQimVqZTqrpT60sv1rgemAtW9Wa+7iCYKQCmVoZS6RSm1No/dJqDlrwS0Ah63eTCuBnoB+30gcsRgFH6Io5TaC3wBXANgsYCGiMh2YLvlt1stXepjIrJWRBKy9xeRRBHZKCInRGQxEGe1LZe7REQuF5GlInJIRNJE5GURuRp4BWhi6XEcs5TNcQ1Zvt8vIjtE5IiILBOR8lbblIgMtLgmjonILBERe+0VkXhL3UdFZAvQyGa7EpFqVt9zyWFTtp+IrBGRmZbj7hSR6y2//ysiB0Wkr1X54iLylqX9u0VkTLZiE5FoEZkhIodFZCdwi82xiovIGyKSKiJ7ReQpEYl2IFdNEfna8l9tE5G77JWzlL1HRLYCC4HPROQBJ2XzK+OUbBkt+z5n2fdvERkquV1WySIyVUTWAKeAqvlpB9AXmKyUOqqU2gq8ju4RoZQ6p5T6n1JqNZDppA5DHhiFH+KIdtV0AH6x+vl24DqglogkAnOBB4BSwKvAMksXOhb4CHgbKAm8D9zp4DjRwKfAbqAyUAFYZLk5BwI/KKWKKKVK2Nn3RmAacBdQzlLHIptit6KVd4Kl3E0OmjweuNLyugmtKDzhOuA39H/zrkWuRkA1tEX5slxwl70EFAeqAi2APsA9lm33W9qQiO5xdbE5zjxAoa3Y+kA79P+WCxEpDHxtkeVSoDswW0RqOZD/oOW4xSyyzBSR+g7KuiLjeUvbEy0y3me1781APYv8t9upvzcwACgKHHK1HSJyCfq6+NXq51+B2g7aYXAXpZR5hdgL2AWkA8fQynM2EG/ZpoAbrcomoS0n6/23oRVWc2AfIFbb1gJTLJ9bAnssn5ugb+ICduTpB6y2+W2eVT1vAM9YbSsCZACVrWS+wWr7e8ATDtq+E2hv9X1AtoxWdVWzJ4cDubdbfa9j2b+s1W9paCUXDZwDalltewBItnz+Fhhota2dpa4CQFnLvoWstve02jfn/wO6Ad/byPkqMN7Fa+MjYJiDbXnJeDb7OrJs7wGstNr3AattbbL3tXxPBiZZbXe5HcDllrrirH5rC+yyU3YP0DLQ92CovoJukMfgMrcrpb5xsO1fq8+VgL4i8qDVb7FAefRNtldZ7iQLux3UeTmwWyl13g1ZywMbs78opdJFJA3dS9hl+dnaN3sK/VBwVJd1+xzJ6yoHrD6ftshn+1sRoDQQY3O83eg25CVXJUCAjVaeqhjgqB15KgHXZbvGLBRA98IuQkRuRvd6aqB77IWATfbKuiBjDJBqJWOUVXnbfa0/2/stP+1It7wXA85YfT5hrxEG9zEKPzyxVuD/AlOVUlNtC4lIC6CCiIiV0r8C+MtOnf8CV4hIATtKP6+Uq/vQCiD7uIXRLpS9eexnj1T0w2ezlbzWnEIrvWwuQ1uFnnIY3SupBGyxOnZ2G7LlwmpbNv+ifc91lFIZeRznX+A7pVTbvAQSkYLAB2jX0sdKqQwR+Qj9cLFHXjKeBUo7eKinAhWtvl9up4ztdedSO5RSR0UkFaiLdgNh+bzZ8V4GdzA+/PDndWCgiFwnmsIicouIFAV+QPtsHxKRGBG5A7jWQT0/oW/66ZY64kSkqWXbAaCiZUzAHguBe0SknkVJPQX8qJTa5UZ73gNGicglIlIReNBmewrQ0zLI2B7tuvIYpVSm5dhTRaSoiFQCHgEWWMn1kIhUtPikn7DaNxX4CvifZWA0SkSutDxwbfkUqCEivS3nJEZEGokeHLclFiiIdrWdt1j77Zw0Iy8ZlwPPiUgxOzK+BwwTkQoiUgIY6fQPy187AN4CxljOa030mMG87I2WMafsgIJYy/Xn6MFmcIBR+GGO0iF79wMvo10IO7CKfgDusHw/gva7LnVQTyZwG3pA7x+01dzNsvlbtDW2X0QO29n3G2As2hpNRQ+4dnezSRPRroi/0QrK1kUwzCLnMeButE/bWzwInESPI6xGD0jOtWx7Ha3Uf0W7r2z/xz7o+20z+jwsQQ9U5kIpdQKttLuje0b7gafRit1e2YfQyvgoelxgmRP5XZExFt2DsZXxdfT//Rs6QOBztLFgN2omP+2wMB7ds9wNfAc8q3KHr25Du9cqWNpwGqteo8E1JLf71mAwGPLG0pt4RSlllG4IYSx8g8GQJ6LnP3QQkQIiUgFtkX8YaLkM+cNY+AaDIU9EpBDa1VIT7U75DB3+eTygghnyhVH4BoPBECEYl47BYDBECEEbh1+6dGlVuXJlt/c/efIkhQsX9p5AAcS0JTgxbQlOIr0tGzZsOKyUKmNvW9Aq/MqVK7N+vcNsqnmSnJxMy5YtvSdQADFtCU5MW4KTSG+LiDicfW5cOgaDwRAhGIVvMBgMEYJR+AaDwRAhBK0P3x4ZGRns2bOHM2fO5Fm2ePHibN261Q9S+Z5QaUtcXBwVK1YkJiYm0KIYDAY7hJTC37NnD0WLFqVy5crklTfpxIkTFC1a1E+S+ZZQaItSirS0NPbs2UOVKlUCLY7BYLBDSLl0zpw5Q6lSpfJU9gb/IyKUKlXKpd6XIUhITYUWLWC/WSY2UggphQ8YZR/EmHMTYkyeDKtXw6RJgZbE4CdCyqVj8B+fffYZl19+OQkJCXkXNoQW8fFg3RNLSqJlUhLExcHp04GTy+BzQs7CDzRTp06ldu3aJCQkUK9ePX788cd87T9v3jz27duX8/2+++5jy5YtTvbITcuWLT2akAbQoUMHjh07xrFjx5g9e/ZF27/88ku+++476tSp49FxDEHKzp3QsycUsiwMVqgQB9q0gb//DqxcBp8T/grfi37KH374gU8//ZSNGzfy22+/8c0333D55fZWerNPZmbmRQp/zpw51KpVy2PZrDl/3vmys59//jklSpRwqPDbt2/PM888Y1w04Uq5clCsmLby4+LgzBnOFyoEl12mtxvfftgS/grfi37K1NRUSpcuTcGCetGe0qVLU758eQBWrFhBYmIiderUoX///pw9exbQKSJGjhxJ/fr1WbhwIevXr+fuu++mXr16nD59OpfFvnz5cpo0aUL9+vXp2rUr6enpduV4++23qVevHtdccw0//fQTABMmTKB37940bdqU3r17M2/ePIYOHZqzz6233kpycnKOTIcPH+aJJ57gr7/+ol69ejz22GMAPPvsszRq1IiEhATGjx+fs/+CBQu49tprqVevHg888ACZmXYXOjKECgcOwMCBsG4dDBxI7FGr9dSNbz9sCVuFX6RMGRCBpCTIytLvItp/6Sbt2rXj33//pUaNGgwePJjvvvsO0NFD/fr1Y/HixWzatInz58+TlJSUs1+pUqXYuHEjvXr1omHDhrzzzjukpKQQbyXL4cOHmTJlCt988w0bN26kYcOGPP/883blOHXqFCkpKcyePZv+/fv
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABTKUlEQVR4nO2dZ5hTRReA39ldYOkoTXoREEGagICogBQREUVBQKWISpNuAwsdQfATERBUREClKKJgRwREEFCaVJFel16Xtm2+Hye7ZLNJNtkkmzbv89wn5c6dO5N7c+7MmVOU1hqDwWAwhD4R/m6AwWAwGDIHI/ANBoMhTDAC32AwGMIEI/ANBoMhTDAC32AwGMIEI/ANBoMhTDACP4xQStVXSv2tlLrZSZmZSqlRlvf3KqV2ZfBc05RSb2a0rW6cp6FS6oivz+MuSqnSSimtlIqyfI5QSn2mlBpop2wXpdSqDJ7nZqXUbqVUdU/b6CuUUgWUUpuVUrV8eR5D+hiBH4QopQ4opa4qpWKVUicsQjpXOseUAN4CHtJan3XlPFrrP7TWt7nQnjQCS2vdQ2s90pXzhANa6ySgC3CXUqq5F+s9C3QApvpacGcEpVQWYBbQS2u93km5bEqpGUqpi0qp49YPRqVUXaXUr0qps0qpU0qpr5RSRTKj/aGGEfjBy8Na61zAnUAt4A3bAtYCQGt9WGvdQGt9MhPbaLBCa52otW6vtf7Zy/WuB0YD5b1Zb0ZRQgSA1jpea/2Q1vrPdA4bhrS/FNAIeMXqwXgT8BFQ2rL/EvCpD5oe8hiBH+RorY8CPwF3AFim6C8opXYDuy3ftbRMqc8rpf5USlVNPl4pVUMptVEpdUkpNR+IttqXSl2ilCqhlFpoGWWdUUpNVkrdDkwD6llmHOctZVNUQ5bPzyul9lhGaYuVUkWt9mmlVA+LauK8UmqKUkrZ669SKrul7nNKqR1AbZv9WilVzupzqnbYlO2ilFqtlJpgOe8+pdTdlu8PK6VOKqU6W5XPq5Saben/QaXUG8mCTSkVqZR6Ryl1Wim1D3jI5lx5lVKfKKVilFJHlVJvKaUiHbSrotWIdpdS6gl75Sxln1FK7QTmAj8opbo7KetuG0clt9Fy7P8sx+5XSvVWqVVWK5RSo5VSq4ErQFl3+gF0BkZqrc9prXcCHyMzIrTWP2mtv9JaX9RaXwEmA/Wd1GVwgBH4QY4SVU0LYJPV148CdYBKSqkawAygO5Af+BBYbJlCZwW+BT4Dbga+Ah53cJ5I4HvgIDLSKgbMs/w5ewBrtNa5tNb57Bx7PzAGeAIoYqljnk2xlojwrmop94CDLg8FbrVsDyCCwhPqAFuQ32aOpV21gXLA08BkdUNdNgnIC5QFGgCdgGcs+5639KEGMuNqY3OemYBGRrF3As2Q3y0VSqmcwK+WthQC2gMfKKUqOWj/Sct581jaMkEpdaeDsq60McHS9xqWNj5ndeyDQHVL+x+1U39HoBuQGzjlaj+UUjch98U/Vl//A1R20I/7gO0O9hmcobU2W5BtwAEgFjiPCM8PgOyWfRq436rsVGTkZH38LkRg3QccA5TVvj+BUZb3DYEjlvf1kD9xlJ32dAFW2Xw306qeT4BxVvtyAfFAaas232O1/0tgkIO+7wOaW33ultxGq7rK2WuHg3bvtvpcxXJ8YavvziBCLhKIAypZ7esOrLC8Xwb0sNrXzFJXFFDYcmwOq/1PWh2b8vsB7YA/bNr5ITDUxXvjW6Cfg33ptfF68n1k2d8BWG51bHerfU2Sj7V8XgGMsNrvcj+AEpa6oq2+awocsFO2KnAWuNff/8Ng3AJukcfgMo9qrZc62HfY6n0poLNSqo/Vd1mBosif7Ki2/JMsHHRQZwngoNY6IQNtLQpsTP6gtY5VSp1BZgkHLF8ftyp/BXkoOKrLun+O2usqJ6zeX7W0z/a7XEABIIvN+Q4ifUivXaUABWy00lRlAc7ZaU8poE6yasxCFDILS4NS6kFk1lMBmbHnALbaK+tCG7MAMVZtjLAqb3us9Xt737nTj1jLax7gmtX7S9aFLKq6n5AH2h926jGkgxH4oYm1AD8MjNZaj7YtpJRqABRTSikroV8S2GunzsNASaVUlB2hn17I1WOIAEg+b05EhXI0nePsEYM8fJKn9CVt9l9BhF4ytwDeMNs8jcxKSgE7rM6d3IfkdmG1L5nDQCJQRWsdn855DgO/a62bptcgpVQ24GtEtbRIax2vlPoWebjYI702XgcKOHioxwDFrT6XsFPG9r5zqR9a63NKqRigGqIGwvI+RW2jlCoFLEVmq3Yffob0MTr80OdjoIdSqo4SciqlHlJK5QbWIDrbvkqpLEqpx4C7HNTzF/KnH2upI1oplbxwdgIoblkTsMdc4BmlVHWLkHoLWKe1PpCB/nwJDFZK3aSUKg70sdm/GXjSssjYHFFdeYzWOtFy7tFKqdwWATQQ+NyqXX2VUsUtOulBVsfGAL8A71kWRiOUUrdaHri2fA9UUEp1tFyTLEqp2koWx23JCmRDVG0JltF+MyfdSK+NS4D/KaXy2Gnjl0A/pVQxpVQ+4FWnP5h7/QCYDbxhua4VkTWDmQBKqWKISmmy1npaOuc1OMEI/BBHi8ne84hlwzlgDzesH+KAxyyfzyJ614UO6kkEHkYW9A4ho+Z2lt3LkNHYcaXUaTvHLgXeREajMciCa/sMdmk4oorYjwgo29FeP0s7zwNPITptb9EHuIysI6xCFiRnWPZ9jAj1fxD1le3v2An5v21HrsMCZKEyFVrrS4jQbo/MjI4DbyOC3V7ZvogwPoesCyx20n5X2pgVmcHYtvFj5PfeghgI/IgMFhLtncidflgYiswsDwK/A+P1DfPV55CF8mFKLMFilVKxDuoxOEGlVt8aDAZD+lhmE9O01qXSLWwIGMwI32AwpIsS/4cWSqkoi4plKPCNv9tlcA8zwjcYDOmilMqBqFoqIpZLPyDWMhf92jCDWxiBbzAYDGGCUekYDAZDmBCwdvgFChTQpUuXzvDxly9fJmfOnN5rkB8xfQlMTF8Ck3Dvy4YNG05rrQva2xewAr906dKsX+8wmmq6rFixgoYNG3qvQX7E9CUwMX0JTMK9L0oph97nRqVjMBgMYYIR+AaDwRAmGIFvMBgMYULA6vDtER8fz5EjR7h27Vq6ZfPmzcvOnTszoVW+J1j6Eh0dTfHixcmSJYu/m2IwGOwQVAL/yJEj5M6dm9KlS6PsJ0RK4dKlS+TOnTuTWuZbgqEvWmvOnDnDkSNHKFOmjL+bYzAY7BBUKp1r166RP3/+dIW9IfNRSpE/f36XZl+GACEmBho0gOPH0y9rCAmCSuADRtgHMObaBBkjR8KqVTBihL9bYsgkgkqlY8g8fvjhB0qUKEHVqlXTL2wILrJnB+uZ2NSpNJw6FaKj4epV/7XL4HOCboTvb0aPHk3lypWpWrUq1atXZ926dW4dP3PmTI4dO5by+bnnnmPHjh1OjkhNw4YNPXJIA2jRogXnz5/n/PnzfPDBB2n2//zzz/z+++9UqVLFo/MYApR9++DJJyGHJTFYjhycaNIE9u+Xz0bVE7KEvsD34s27Zs0avv/+ezZu3MiWLVtYunQpJUrYy/Rmn8TExDQCf/r06VSqVMnjtlmTkOA87eyPP/5Ivnz5HAr85s2bM27cOKOiCVWKFIE8eWSUHx0N166RkCMH3HKL7DeqnpAl9AW+F2/emJgYChQoQLZskrSnQIECFC1aFIDffvuNGjVqUKVKFbp27cr169cBCRHx6quvcueddzJ37lzWr1/PU089RfXq1bl69WqqEfuSJUuoV68ed955J23btiU21n5Sn88++4zq1atzxx138NdffwEwbNgwOnbsSP369enYsSMzZ86kd+/eKce0bNmSFStWpLTp9OnTDBo0iL1791K9enVefvllAMaPH0/t2rWpWrUqQ4cOTTn+888/56677qJ69ep0796dxES7iY4MwcKJE9CjB6xdCz16kPXcOVH1KAVTp0JSkrwqJd8bQoKQFfi5Chb0+s3brFkzDh8+TIUKFejVqxe///47INZDXbp
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  3\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  4\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  5\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  6\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABQ1UlEQVR4nO2dZ3hURReA35OQkITepHdBOoGAgoCAKCIiRVGKUmxIFfADEZXeBUFUiogUEQEFRLALggjYQEOvIkgJLYAQElLn+zEbWMJuskk22Tbv89wnu/fOnXsm9+65Z86cOSNKKQwGg8Hg/fi5WgCDwWAwZA9G4RsMBoOPYBS+wWAw+AhG4RsMBoOPYBS+wWAw+AhG4RsMBoOPYBS+DyEijUTkDxEpmEqZRSIy3vK5iYgczOC15orIiIzKmo7rNBORk1l9nfQiIuVERIlIDst3PxFZIiIv2yjbU0S2ZPA6BUXksIiEZlbGrEJECotIuIjUy8rrGNLGKHwPRESOiUiMiESJyFmLks6dxjmlgYnAI0qpi45cRyn1s1LqLgfkuU1hKaV6K6XGOXIdX0AplQT0BO4WkVZOrPci0AWYk9WKOyOISACwGOirlNqeSrmcIrJARK6IyJmUL0YRCRGR2SJyQUT+E5HNWS27N+J2D4jBYR5VSq0XkZLAd8AbwKvWBUQkh1IqAUApdQJomv1iGpJRSiUCnbOg3u0iMgGoBOx3dv3pRUQEEKVUklIqHnjEgdNGo+UvCxQDNorIPqXUt5bj89D6qipwEQh1tty+gLHwPRyl1CngG6AGgKWL3k9EDgOHLfvaWLrUl0Vkm4jUSj5fROqIyJ8iclVEVgBBVsducZeISGkRWS0i50UkUkTeE5GqwFygoaXHcdlS9oZryPL9BRE5IiIXRWStiJSwOqZEpLfFNXFZRGZZlMZtiEiwpe5LIrIPqJ/iuBKRO62+3yJHirI9RWSriMywXPeoiNxr2X9CRM6JSA+r8vlE5CNL+4+LyBsi4mc55i8i0ywW6FFSKDnLuR+KSISInBKRiSLib0euKiLyg+V/dVBEnrRVzlL2GRHZDywDvhKRF1Mpm14ZxyfLaDn3Lcu5/4hIf7nVZbVJRCaIyFYgGqiQnnYAPYBxSqlLSqn9wAfoHhEiUgVoC/RSSp1XSiUqpXakUpfBDkbheziiXTWtgb+sdrcH7gGqiUgdYAHwIlAIeB9Ya+lCBwJrgCVAQeAz4HE71/EHvgSOA+WAksByy4+zN/CLUiq3Uiq/jXPvByYBTwLFLXUsT1GsDVp517KUe8hOk0cBFS3bQ2hFkRnuAXah/zefWOSqD9wJPA28JzfdZe8C+YAK6N5Sd+AZy7EXLG2oA9QDOqa4ziJAoa3YukBL9P/tFkQkF/CDRZY70D2C2SJSzY785yzXzWuRZYaI1LVT1hEZEyxtr2OR8Xmrcx9GW9Z10c9YSroBvYA8wHlH2yEiBdDPxU6r3TuB6pbPd6OfmTGWF85uEbH5nBrSQCllNg/bgGNAFHAZ/UOYDQRbjingfquyc9CWk/X5B9EK6z7gNLr7nXxsGzDe8rkZcNLyuSH6R5zDhjw9gS0p9i2yqudD4E2rY7mBeKCclcyNrY5/Crxqp+1HgVZW33sly2hV15225LAj92Gr7zUt5xe12heJVnL+QBxQzerYi8Amy+cfgd5Wx1pa6soBFLWcG2J1vKvVuTf+f0An4OcUcr4PjHLw2VgDDLRzLC0ZY5OfI8vxLsBGq3NftDr2QPK5lu+bgLFWxx1uB1DaUleQ1b4HgWOWz69Zjo8GAtHPbhRQ1dW/RU/bjA/fc2mvlFpv59gJq89lgR4iMsBqXyBQAv0jOqUsvyoLx+3UWRo4rixjAumkBPBn8helVJSIRKJ7Cccsu89YlY9GvxTs1WXdPnvyOspZq88xFvlS7ssNFAYCUlzvOLoNaclVFhDgTytPVQBwyYY8ZYF7kl1jFnKge2G3ISIPo3s9ldE99hBgt62yDsgYAERYyehnVT7ludafbe1LTzuiLH/zAtetPl+1fI5BGwjjLc/fTyKyEf3CcvmYhSdhFL53Yq3ATwATlFITUhYSkaZASRERK6VfBvjbRp0ngDJiNRBs53q2OI1WAMnXzYV2oZxK4zxbRKBfPnut5LUmGq30kikGOCNs8wJa6ZQF9lldO7kNyXJhdSyZE0AiUFPpQczUOAH8pJR6MC2BRCQnsArtWvpCKRUvImvQLxdbpCVjLFDYzks9Aihl9b20jTIpnzuH2qGUuiQiEUBttBsIy+fke7wrjWsZHMT48L2fD4DeInKPaHKJyCMikgf4Be2zfUlEAkTkMbS/1Ba/o3/0ky11BIlII8uxs0Apy5iALZYBz4hIqEVJTQR+U0ody0B7PgWGi0gBESkFDEhxPBzoahlkbIWTIpOUjrD5FJggInlEpCzwMvCxlVwviUgpi0/6VatzI9CRVG9bBkb9RKSi5YWbki+ByiLSzXJPAkSkvujB8ZQEAjnRrrYEi7XfMpVmpCXj98BbIpLXhoyfAgNFpKSI5AeGpfoPS187AD4C3rDc1yroMYNFlmObgX/R9z2H5blrjv6fGtKBUfhejtKxzy8A76FdCEewRD8opeKAxyzfL6L9rqvt1JMIPIoe0PsXbTV3shz+EW2NnRGRCzbOXQ+MQFujEegB14yGJ45BuyL+QSuolC6CgRY5LwNPoX3azmIAcA09jrAFPSC5wHLsA7QC2ol2X6X8P3ZH/972ou/DSvRA5S0opa6ilXZndM/oDDAFrdhtlX0JrYwvoccF1qYivyMyBqJ7MCll/AD9/96FDhD4Gm0sJNq6UHraYWEUumd5HPgJmKosIZmWXlE7dHDCfxZZuiulDqTSVoMN5Fb3rcFgMKSNpTcxVylVNs3CBrfBWPgGgyFNRM9/aG1xqZREW+Sfu1ouQ/owFr7BYEgTEQlBu1qqoKNmvkKHf15xqWCGdGEUvsFgMPgIxqVjMBgMPoLbxuEXLlxYlStXLsPnX7t2jVy5cjlPIBdi2uKemLa4J77elh07dlxQShWxdcxtFX65cuXYvt1uNtU02bRpE82aNXOeQC7EtMU9MW1xT3y9LSJid/a5cekYDAaDj2AUvsFgMPgIRuEbDAaDj+C2PnxbxMfHc/LkSa5fv55m2Xz58rF/v3ck0vOUtgQFBVGqVCkCAgJcLYrBYLCBRyn8kydPkidPHsqVK4fYXhDpBlevXiVPnjzZJFnW4gltUUoRGRnJyZMnKV++vKvFMRgMNvAol87169cpVKhQmsrekP2ICIUKFXKo92VwEyIioGlTOHMm7bIGr8CjFD5glL0bY+6NhzFuHGzZAmPHuloSQzbhUS4dQ/bx1VdfUbp0aWrVqpV2YYNnERwM1j2xOXNoNmcOBAVBTIzr5DJkOR5n4buaCRMmUL16dWrVqkVoaCi//fZbus5ftGgRp0+fvvH9+eefZ9++famccSvNmjXL1IQ0gNatW3P58mUuX77M7Nmzbzv+7bff8tNPP1GzZs1MXcfgphw9Cl27QohlYbCQEM4+8AD8849r5TJkOd6v8J3op/zll1/48ssv+fPPP9m1axfr16+ndGlbK73ZJjEx8TaFP3/+fKpVq5Zp2axJSEh92dmvv/6a/Pnz21X4rVq14s033zQuGm+leHHIm1db+UFBcP06CSEhUKyYPm58+16L9yt8J/opIyIiKFy4MDlz6kV7ChcuTIkSJQDYsGEDderUoWbNmjz77LPExsYCOkXEsGHDqFu3LsuWLWP79u089dRThIaGEhMTc4vF/v3339OwYUPq1q3LE088QVRUlE05lixZQmhoKDVq1OD3338HYPTo0XTr1o1GjRrRrVs3Fi1aRP/+/W+c06ZNGzZt2nRDpgsXLvDqq6/y999/ExoaytChQwGYOnUq9evXp1atWowaNerG+R9//DF33303oaGhvPjiiyQm2lzoyOApnD0LvXvDr79C794EXrJaT9349r0Wr1X4uYsUARGYMweSkvRfEe2/zCAtW7bkxIkTVK5cmb59+/LTTz8BOnqoZ8+erFixgt27d5OQkMCcOXNunFeoUCH
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  7\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  8\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  9\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  10\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  11\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABR/ElEQVR4nO2dZ3hURReA30kjoYYm0iRUqSF0EOkISBFRkCIIitJF4FMBRQhVEAQVIUiTJk1AQUUFFKQJSCD0DqEltFBDC0nm+zGbsCy7ySbZZNu8z3Of7O6dO/dM7r3nnjlz5oyQUqLRaDQa18fD3gJoNBqNJmPQCl+j0WjcBK3wNRqNxk3QCl+j0WjcBK3wNRqNxk3QCl+j0WjcBK3w3QghRG0hxH9CiFxJlJknhBhj+FxHCHEsleeaIYT4LLWypuA89YUQF9L7PClFCBEghJBCCC/Ddw8hxEIhxCAzZbsJIbam8jy5hBAnhBBBaZUxvRBC5BFChAkhqqbneTTJoxW+EyKECBdC3BdCRAshLhuUdNZkjikMjANaSCmvW3MeKeUWKeXzVsjzlMKSUvaSUo625jzugJQyHugGVBdCNLNhvdeBjkBIeivu1CCE8AbmA32klLuTKPeGEGK7EOKeEGKTmf0zhRDHhBDxQohu6Sexa6MVvvPSSkqZFagMVAWGmRYwVgBSyvNSynpSyisZKKPGCCllnJSyg5TyDxvXuxsYC5S0Zb2pRSg8AKSUj6SULaSU25M57DrwFTDewv59QB9gj80EdUO0wndypJQXgd+B8gCGLnpfIcQJ4ITht5aGLvVNgxUVmHC8EKKSEGKPEOKOEGIZ4Gu07wl3iRCisBBilRDiqhAiSgjxrRCiDDADqGXocdw0lE10DRm+vyeEOCmEuC6EWCOEKGC0TwohehlcEzeFENOEEMJce4UQfoa6bwghDgPVTPZLIUQJo+9PyGFStpsQYpsQYorhvKeFEC8Yfj8vhLgihOhqVD6HEGKBof1nhRDDEhSbEMJTCDFJCHFNCHEaaGFyrhxCiDlCiEghxEUhxDghhKcFuUoLIdYb/lfHhBBvmCtnKPu2EOIIsAT4TQjRM4myKZVxTIKMhmO/NBx7RgjRTzzpstokhBgrhNgG3AOKpaQdUsoNUsrlQISF/dOklH8BDyzVoUkerfCdHIOrpjmw1+jnV4EaQFkhRCVgLtATyA18B6wRQmQSQvgAPwMLgVzAj8DrFs7jCfwKnAUCgILAUinlEaAX8K+UMquU0t/MsQ2Bz4E3gPyGOpaaFGuJUt6BhnJNLTR5BFDcsDUFulooZy01gP2o/81ig1zVgBJAZ+Bb8dhdNhXIARQD6gFvAW8b9r1naEMlVI+rrcl55gESZYVXBpqg/m9PIITIAqw3yPIM0AGYLoQoa0H+K4bzZjfIMkUIUdlCWWtkjDW0vZJBxneNjn0ZCDLI/6qZ+rsAPYBswNUUtkOTEUgp9eZkGxAORAM3UcpzOuBn2CeBhkZlQ4DRJscfQymsuiiLShjt2w6MMXyuD1wwfK6Feoi9zMjTDdhq8ts8o3rmAF8Y7csKPAICjGR+0Wj/cmCIhbafBpoZfe+RIKNRXSXMyWFB7hNG3ysYjs9n9FsUSsl5AjFAWaN9PYFNhs9/A72M9jUx1OUF5DMcm9lofyejYxP/f0B7YIuJnN8BI6y8N34GPrCwLzkZHybcR4b9HYGNRsf2NNrXOOFYw/dNwCij/alqB+oFsymJ/VuBbvZ47lxhc7hBHo3VvCql3GBh33mjz0WArkKI941+8wEKoB7Yi9LwJBk4a6HOwsBZKWVsKmQtgJHvVUoZLYSIQvUSwg0/XzIqfw/1UrBUl3H7LMlrLZeNPt83yGf6W1YgD+Btcr6zqDYkJ1cRQAB7jDxV3sANM/IUAWokuMYMeKF6YU8hhHgZ1espheqxZwYOmCtrhYzeQKSRjB5G5U2PNf5s7rcUtUOTMWiF75oYK/DzwFgp5VjTQkKIekBBIYQwUvrPAafM1HkeeE4I4WVG6SeXcjUCpQASzpsF5UK5mMxx5ohEvXwOGclrzD2U0kvgWcAWYZvXUL2SIsBho3MntCFBLoz2JXAeiAMqSCkfJXOe88A/UsqXkhNICJEJWIlyLa2WUj4SQvyMermYIzkZHwJ5LLzUI4FCRt8Lmyljet9Z1Q5NxqF9+K7PLKCXEKKGUGQRQrQQQmQD/kX5bPsLIbyFEK8B1S3Uswv10I831OErhKht2HcZKGQYEzDHEuBtIUSQQUmNA3ZKKcNT0Z7lwFAhRE4hRCHgfZP9YUAnwyBjM5TrKs1IKeMM5x4rhMgmhCgCDAIWGcnVXwhRSAiRExhidGwk8CfwlWFg1EMIUdzwwjXlV6CUEKKL4Zp4CyGqCTU4booPkAnlaos1WPtNkmhGcjKuA74UQmQ3I+Ny4AMhREEhhD8wOMl/WMrakTAo7IsyQj0M95e30X4fw34BeBv2a/2VQvQ/zMWRKmTvPeBblAvhJMpnjJQyBnjN8P06yu+6ykI9cUAr1IDeOZTV3N6w+2+UxX1JCHHNzLEbgM9Q1mgkasC1QyqbNBLlijiDUlCmLoIPDHLeBN5E+bRtxfvAXdQ4wlbUgORcw75ZKKW+D+W+Mv0/voV63g6hrsMK1AD2E0gp76CUdgdUz+gSMAGl2M2V7Y9SxjdQ4wJrkpDfGhl9UD0YUxlnof7f+1EBAmtRxkKcuROlpB0GuqDcZyFAHcPnWUb71xl+ewGYafhcN4m2aswgnnTfajQaTfIYehMzpJRFki2scRi0ha/RaJJFqPkPzYUQXkKIgqiB4p/sLZcmZWgLX6PRJIsQIjPwD1Aa5U75DRX+eduugmlShFb4Go1G4yZol45Go9G4CQ4bh58nTx4ZEBCQ6uPv3r1LlixZbCeQHdFtcUx0WxwTd29LaGjoNSllXnP7HFbhBwQEsHu3xWyqybJp0ybq169vO4HsiG6LY6Lb4pi4e1uEEBZnn2uXjkaj0bgJWuFrNBqNm6AVvkaj0bgJDuvDN8ejR4+4cOECDx4kvwZCjhw5OHLkSAZIlf44S1t8fX0pVKgQ3t7eyRfWaDQZjlMp/AsXLpAtWzYCAgIQ5hdESuTOnTtky5YtgyRLX5yhLVJKoqKiuHDhAkWLFrW3OBqNxgxO5dJ58OABuXPnTlbZazIeIQS5c+e2qvelcRAiI6FePbh0KfmyGpfAqRQ+oJW9A6OvjZMxejRs3QqjRtlbEk0G4VQuHU3G8dtvv1G4cGECAwOTL6xxLvz8wLgnFhJC/ZAQ8PWF+/ftJ5cm3XE6C9/ejB07lnLlyhEYGEhQUBA7d+5M0fHz5s0jIiIi8fu7777L4cOHkzjiSerXr5+mCWkAzZs35+bNm9y8eZPp06c/tf+PP/7gn3/+oUKFCmk6j8ZBOX0aOnWCzIaFwTJn5nLjxnDmjH3l0qQ7rq/wbein/Pfff/n111/Zs2cP+/fvZ8OGDRQubG6lN/PExcU9pfBnz55N2bJl0yybMbGxSS87u3btWvz9/S0q/GbNmvHFF19oF42rkj8/ZM+urHxfX3jwgNjMmeHZZ9V+7dt3WVxf4dvQTxkZGUmePHnIlEkt2pMnTx4KFCgAwF9//UWlSpWoUKEC77zzDg8fPgRUiojBgwdTuXJllixZwu7du3nzzTcJCgri/v37T1js69ato1atWlSuXJl27doRHR1tVo6FCxcSFBRE+fLl2bVrFwDBwcF06dKF2rVr06VLF+bNm0e/fv0Sj2nZsiWbNm1KlOnatWsMGTKEU6dOERQUxEcffQTAxIkTqVatGoGBgYwYMSLx+EWLFlG9enWCgoLo2bMncXFmFzrSOAuXL0OvXrBjB/Tqhc8No/XUtW/fZXFZhZ81b14QAkJCID5e/RVC+S9TSZMmTTh//jylSpWiT58+/PPPP4CKHurWrRvLli3jwIEDxMbGEhISknhc7ty52bNnD507d6Zq1ar88MMPhIWF4Wcky7Vr1xgzZgwbNmxgz549VK1alcmTJ5uV4969e4SFhTF9+nTeeeedxN8PHz7Mhg0bWLJkiVXtGT9+PMWLFycsLIyJEye
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  12\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meilleur modèle -> degre = 7\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main(degreMax=12, NbEx=100, sigma=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va pouvoir mieux approximer les observations, c'est-à-dire diminuer l'erreur pour le degré optimal mais on aura toujours le cas d'EQMA qui remonte."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'évolution de l'écart-type va venir augmenter l'erreur globale sur le profil d'erreur en diminuant la précision des mesures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  1\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  2\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  3\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  4\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  5\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  6\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  7\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  8\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  9\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  10\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  11\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  12\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meilleur modèle -> degre = 7\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main(degreMax=12, NbEx=20, sigma=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  1\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  2\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  3\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABLyUlEQVR4nO2dd3hUVdPAf0MChg7SpChgp/cmXRAQeUUUFFQEUakq6quC5RNUVBTLqzT7K74goCiCFQRBKSKCRqogIkiT3gKhJJnvj7OJS9hNNslmd5PM73nus3vvPfecOVvmnjtnzoyoKoZhGEbuJ1+4BTAMwzBCgyl8wzCMPIIpfMMwjDyCKXzDMIw8gil8wzCMPIIpfMMwjDyCKfw8hIg0F5GfROTcNMq8JyKjPO9bisiGTLb1uoj8X2ZlzUA7bURke3a3k1FEpIqIqIhEe/bzicj/ROQBH2X7isjiTLZzroj8LiJ1sypjdiEipUUkVkQaZmc7RvqYws+BiMgWEYkXkTgR2e1R0kXSueZ84FngGlU9EEg7qrpIVS8LQJ6zFJaqDlTVpwNpJy+gqklAX6CxiHQKYr0HgF7AxOxW3JlBRPIDk4DBqroijXLniMi7InJERP72vjGKSHURWSEiBz3bPBGpHgr5cxum8HMu/1LVIkB9oCHweOoC3gpAVbepamtV3RNCGQ0vVDVRVXuq6tdBrncF8AxwSTDrzSziyAegqqdV9RpVXZrOZSNx8lcG2gIPe90YdwLdgXOB0sBsYFp2yJ7bMYWfw1HVHcBXQE0AzyP6EBH5Hfjdc6yL55H6kIgsFZHaydeLSD0R+VlEjorIdCDG69wZ5hIROV9EPhGRvSKyX0TGiUg14HWgmeeJ45CnbIppyLN/l4hsEpEDIjJbRCp4nVMRGegxTRwSkfEiIr76KyIFPXUfFJF1QKNU51VELvbaP0OOVGX7isgSEXnF0+5mEbnCc3ybiOwRkT5e5YuLyPue/m8VkceTFZuIRInIiyKyT0Q2A9ekaqu4iLwjIrtEZIeIPCsiUX7kulxEvvF8VhtE5EZf5TxlbxeR9cBU4AsRGZBG2YzKOCpZRs+1L3mu/VNE7pYzTVYLReQZEVkCHAcuzEg/gD7A06p6UFXXA2/hnohQ1UOqukVdWAABEoGL/dZk+EdVbcthG7AFaO95fz6wFvdnAVDgG9xoqCBQD9gDNAGicH+sLcA5QAFgK3A/kB83ijoNjPLU1QbY7nkfBfwKvAIUxt0YWnjO9QUWp5LxPa96rgT24Z5GzgHGAt97lVXgc6AEcAGwF+jkp++jgUWe/p0PrEmW0auui33J4aOuvkACcLunf6OAv4DxHjk7AEeBIp7y7wOzgKJAFWAjcIfn3EDgN49M5wILPLJEe87PBN4GigDlgBXAkNSfn+ez3eaRKdrz/e0DqvvpwzXARThF2BqnbOv7KRuIjG94ZCgLLAcGeF27DqgElATmpbp2oeezq+GRu3ig/fDUp0A5r2PdgdWpyh3yfF9JwOPh/h/mxC3sAtiWiS/NKew4zx9gKzABKOg5p8CVXmUn4rkZeB3b4FEOrXCPy+J1bim+FX4znCKO9iFPisLyOvaeVz3vAC94nSuCu7FU8ZK5hdf5D4Hhfvq+Ga+bAdCfrCn83732a/lQPPuBurgbwilvhQUMABZ63n8LDPQ61yFZIeIU/CmgkNf5m72uTfn8gJuARankfAMYEeBv41NgqJ9z6cl4Mvl35DnfC1jgde0Ar3PtOVvhP+V1PuB+4G5ACsR4HbsK2OKjbGFgMG4uKuz/xZy2RdwkjxEw16nqPD/ntnm9rwz0EZF7vI4VACrg/mQ71PNP8rDVT53nA1tVNSETslYAfk7eUdU4EdkPVMTdvAD+9ip/HHdT8FeXd//8yRsou73ex3vkS32sCM52nD9Ve1txfUhPrsq4EfjPXpaq/MBBH/JUBpokm8Y8RAP/8yW8iFwNjAAuxZloCwGrfZUNQMb8wC4vGfN5lU99rfd7X8cy0o84z2sx4ITX+6OpC6rqMRF5HdgrItXU5qQyhCn83Im3At8GPKOqz6QuJCKtgYoiIl5K/wLgDx91bgMuEJFoH0o/vZCrO3EKILndwkApYEc61/liF/+YsZLl9eY4Tuklcx4QDLfNfbinkso400Zy28l9SJYLr3PJbMPZnWup6ul02tkGfKeqV6UnkIicA3wM3AbMUtXTIvIp7ubii/RkPAmU9nNT34Uz5yRzvo8yqX93AfVDVQ+KyC6gDs4cief9Wj+XJN/YKuLMlUaA2KRt7uctYKCINBFHYRG5RkSKAj/gbKL3ikh+EbkeaOynnuW4P/1oTx0xItLcc243UElECvi5dipwu4jU9SipZ4EfVXVLJvrzIfCIiJQUkUrAPanOxwI3eyYZO+FMV1lGVRM9bT8jIkVFpDLwADDZS657RaSSiJQEhntduwuYA/zHMzGaT0Qu8txwU/M5cKmI9PZ8J/lFpJG4yfHUFMDNNewFEjyj/Q5pdCM9GecCL4lIMR8yfggMFZGKIlICGJbmB5axfoCbH3nc871eDtyFM8chIleJcy6IEpFiwMu4p6P16chgpMIUfi5HncveXcA43J9kE/94P5wCrvfsH8DZXT/xU08i8C+cd8RfuFHzTZ7T3+JGY3+LyD4f184D/g83Gt2Fm2TsmckuPYkzRfyJU1CpTQRDPXIeAm7B2bSDxT3AMdw8wmLgA+Bdz7m3cEr9V5z5KvXneBvu/7YW9z3MAMqnbkBVj+KUdk/ck9HfwPM4xe6r7L04ZXwQNy8wOw35A5GxAO4JJrWMb+E+71XAL8CXuMFCoq+GMtIPDyNwT5Zbge+AMfqP+2oJ3KDhsKfMRbh5nBM+6jHSQM403xqGYaSP52nidVWtnG5hI2KwEb5hGOkibv1DZxGJFpGKuBH5zHDLZWQMG+EbhpEuIlIIZ2q5HOe59AXO/fNIWAUzMoQpfMMwjDyCmXQMwzDyCBHth1+6dGmtUqVKpq49duwYhQsXDq5AYSK39CW39AOsL5GK9QVWrly5T1XL+DoX0Qq/SpUqrFjhN6JqmixcuJA2bdoEV6AwkVv6klv6AdaXSMX6AiLid/W5mXQMwzDyCKbwDcMw8gim8A3DMPIIEW3D98Xp06fZvn07J06kvaq6ePHirF+fO0Jt5JS+xMTEUKlSJfLnzx9uUQzD8EGOU/jbt2+naNGiVKlSBfGdFAmAo0ePUrRo0RBKln3khL6oKvv372f79u1UrVo13OIYhuGDHGfSOXHiBKVKlUpT2RuhR0QoVapUuk9eBrBrF7RuDX//nX5ZwwgiOU7hA6bsIxT7XgLk6adh8WJ46qlwS2LkMXKkwjdCwxdffMGqVavCLUbuoWBBEIGJEyEpyb2KuOOGEQJM4WeCZ555hho1alC7dm3q1q3Ljz/+mKHr33vvPXbu3Jmyf+edd7Ju3bo0rjiTNm3aZHpBWjKdO3fm0KFDHDp0iAkTJpx1/uuvv+a7776jVq1aWWrH8GLzZrj5ZijkSchVqBDccgv8+Wd45TLyDHlD4QfRZvrDDz/w+eef8/PPP7Nq1SrmzZvH+ef7yvbmm8TExLMU/ttvv0316tWzLJs3CQlpp5798ssvKVGihF+F36lTJ1544QUz0wST8uWhWDE4cQJiYtxrsWJw3nnhlszII+QNhR9Em+muXbsoXbo055zjEveULl2aChUqADB//nzq1atHrVq16NevHydPngRciIhhw4ZRv359pk6dyooVK7jllluoW7cu8fHxZ4zY586dS7Nmzahfvz49evQgLi7Opxz/+9//qFu3LjVr1mT58uUAjBw5kt69e9O8eXN69+7Ne++9x913351yTZcuXVi4cGGKTPv27WP48OH88ccf1K1bl4ceegiAMWPG0KhRI2rXrs2IESNSrp88eTKNGzembt26DBgwgMREn8mOjLTYvRsGDoRly9yrTdwaISR3K/xssJl26NCBbdu2cemllzJ48GC+++47wHkP9e3bl+nTp7N69WoSEhKYOHFiynWlSpXi559/5tZbb6Vhw4ZMmTKF2NhYCnrJsm/fPkaNGsW8efP
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  4\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  5\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  6\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  7\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  8\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  9\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  10\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  11\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEICAYAAAB25L6yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA7h0lEQVR4nO3dd3hUxfrA8e+bhBQg1ADSi4BKDU1EVECKiFhQEVCaXkUU67161Z9eQcSOIiqiiIgKAooKKNhQohQFaSKCSAtCgAChJqSS+f0xu3EJKZuyuyfh/TzPeTa7p81kN29m3zNnRowxKKWUcq6gQBdAKaVU3jRQK6WUw2mgVkoph9NArZRSDqeBWimlHE4DtVJKOZwG6hJARDqLyK8iUiWPbaaLyDjXz5eKyJZCnustEflfYctagPN0FZE9vj5PQYlIAxExIhLieh4kIh+KyL9z2Ha4iCwr5HmqiMhWEYkuahl9RUSiRGS9iLT35XlU/jRQ+5GIxIpIsogkiki8K7iWz2efusCzwFXGmMPenMcYs9QYc54X5Tkj0BhjRhpjnvbmPGcDY0wmMBy4UER6F+NxDwODgMm+DriFISJlgPeBu40xq/PY7iYRWSEiJ0UkJof1U0Rki4hkishw35W4dNNA7X9XG2PKA22B9sAT2Tfw/MM1xuw2xnQxxhzwYxmVB2PMKWPMQGPM18V83NXAM0CT4jxuYYkVBGCMSTfGXGWMWZHPboeBV4Hnc1n/G3A3sLbYCnoW0kAdIMaYOOAroAWA66vsKBHZCmx1vdbX9dXzqKvV0sq9v4i0EZG1InJCROYA4R7rTksriEhdEflMRA6KSIKIvCEiFwBvAZ1cLfyjrm2zUiiu53eIyDYROSwiC0Sklsc6IyIjXV/hj4rIJBGRnOorIhGuYx8RkU1Ah2zrjYg09nh+WjmybTtcRJaLyATXeXeIyMWu13eLyAERGeaxfUUR+cBV/10i8oQ7IIlIsIiMF5FDIrIDuCrbuSqKyLsisk9E4kTkWREJzqVc54vId67f1RYRuSmn7Vzb3ioim4FZwEIRuTOPbQtaxnHuMrr2fdm1704RuUdOT+3EiMgzIrIcOAk0Kkg9jDGLjTEfA3tzWT/JGPM9kJLbMVT+NFAHiCul0QdY5/HydUBHoJmItAGmAXcCVYG3gQUiEiYiocA84EOgCvAJcEMu5wkGvgR2AQ2A2sBsY8xmYCTwszGmvDGmUg77Xg48B9wE1HQdY3a2zfpig24r13ZX5FLl0cC5ruUKYFgu23mrI7AB+7v5yFWuDkBjYDDwhvyTVnodqAg0AroAQ4FbXevucNWhDfYbzo3ZzjMdMNhWb1ugF/b3dhoRKQd85ypLdWAg8KaINMul/Adc563gKssEEWmby7belDHDVfc2rjLe7rHvlUC0q/zX5XD8IcAIIBI4WMB6KH8wxujipwWIBRKBo9ig9yYQ4VpngMs9tp0MPJ1t/y3YQHMZtgUjHutWAONcP3cF9rh+7oT94wvJoTzDgWXZXpvucZx3gRc91pUH0oEGHmW+xGP9x8CjudR9B9Db4/kIdxk9jtU4p3LkUu6tHs9buvav4fFaAjY4BQNpQDOPdXcCMa6ffwBGeqzr5TpWCFDDtW9Zj/U3e+yb9fsDBgBLs5XzbWC0l5+NecD9uazLr4yp7s+Ra/0gYInHvnd6rOvh3tf1PAYY67G+UPXA/mOIyWP9MmB4IP7uSsPiuIsYZ4HrjDGLc1m32+Pn+sAwEbnX47VQoBb2Dy3OuP4CXHblcsy6wC5jTEYhyloLj9yiMSZRRBKwrfJY18v7PbY/iQ3muR3Ls365lddb8R4/J7vKl/218kAUUCbb+XZh65BfueoDAqz1yOiUAY7kUJ76QEd3CsklBPut5wwiciX2W0ZT7DfbssDvOW3rRRnLAPs8yhjksX32fT1/zum1AtVD+YcGamfxDLy7gWeMMc9k30hEugC1RUQ8gnU9YHsOx9wN1BORkByCdX5DJ+7F/uG6z1sOm2qIy2e/nOzD/tP4w6O8nk5ig5XbOUBxdN87hP0WUB/Y5HFudx3c5cJjndtu4BTQ0hiTns95dgM/GmN65lcgEQkDPsWmYOYbY9JFZB72n0JO8itjKhCVyz/jfUAdj+d1c9gm++fOq3oo/9EctXO9A4wUkY5ilRORq0QkEvgZm5O8T0TKiMj1wIW5HGcV9o/1edcxwkWks2tdPFDHlfPOySzgVhGJdgWXZ4GVxpjYQtTnY+AxEaksInWAe7OtXw/c7Lr41Rub4ikyY8wp17mfEZFIEakP/BuY4VGu+0SkjohUBh712Hcf8A3wquuCXZCInOv6R5ndl0BTERniek/KiEgHsRdtswsFwrApqQxX67pXHtXIr4zfAi+LSIUcyvgxcL+I1BaRSsAjef7CClYP98XKcGyjL8j1+SrjsT7UtV6AMq71GncKSH9hDmVs1607gDewX7W3YXOiGGPSgOtdzw9j84qf5XKcU8DV2AtNf2NbqQNcq3/AtnD3i8ihHPZdDPwP2/rbh70QOLCQVXoK+5V9JzawZP8qfb+rnEeBW7A52+JyL5CEzZMvw14om+Za9w42GP+GTfNk/z0Oxf6d/IF9H+ZiL6yexhhzAhtsB2K/iewHXsAG5Jy2vQ8bRI9g894L8ii/N2UMxX5jyF7Gd7C/7w3YC9eLsP/kT+V0ooLUw2UINs00GbjU9fM7Huu/db12MTDF9fNledRV5UBOT3MqpUozV+v9LWNM/Xw3Vo6hLWqlSjGx/df7iEiIiNTGXsD8PNDlUgWjLWqlSjERKQv8CJyPTTssxHYDPB7QgqkC0UCtlFIOp6kPpZRyOJ/0o46KijINGjQo1L5JSUmUK1eueAsUIKWlLqWlHqB1cSqtC6xZs+aQMaZajit9cbtju3btTGEtWbKk0Ps6TWmpS2mphzFaF6fSuhgDrDa5xFRNfSillMNpoFZKKYfTQK2UUg7nt0GZ0tPT2bNnDykpeY8fXrFiRTZv3uynUvlWSalLeHg4derUoUyZMvlvrJTyO78F6j179hAZGUmDBg2QnCcBAeDEiRNERkb6q1g+VRLqYowhISGBPXv20LBhw0AXRymVA7+lPlJSUqhatWqeQVr5n4hQtWrVfL/pKKUCx685ag3SzqTvi1LOphcTS6GFCxeyYcOGQBdDqbPLggXw4os+ObRXgVpEHhSRP0Rko4jMcg0EXuI888wzNG/enFatWhEdHc3KlSsLtP/06dPZu/efyZZvv/12Nm3alMcep+vatSurV68u0Dmz69OnD0ePHuXo0aO8+eabZ6z/+uuv+fHHH2nZsmWRzqOUKqAvvoBXX/XJofMN1K6hEe8D2htjWmAnCy3s4PEFs28fdOkC+/fnv20+fv75Z7788kvWrl3Lhg0bWLx4MXXr5jQrUc5OnTp1RqCeOnUqzZoV7+TMGRl5T224aNEiKlWqlGug7t27Ny+++KKmM5Tyt+RkiIjwyaG9TX2EABEiEoKd125vPtsXj6efhmXLYOzYIh9q3759REVFERZmJ6qIioqiVq1aAHz//fe0adOGli1bctttt5GamgpAgwYNeOSRR2jbti2zZs1i9erV3HLLLURHR5OcnHxaC/nbb7+lU6dOtG3blv79+5OYmJhjOT788EOio6Np0aIFq1atAmDMmDEMGTKEzp07M2TIEKZPn84999yTtU/fvn2JiYnJKtOhQ4d49NFH2b59O9HR0Tz88MMAvPTSS3To0IFWrVoxevTorP1nzJjBhRdeSHR0NHfeeSenTuU4uYdSqihOnvRZoM63e54xJk5ExmOncUoGvjXGfJt9OxEZAYwAqFGjRlZgcatYsSInTpzIt0CnTp3ChIcjrmAJwOTJMHkyJiyMxIMH8z1GTjp16sSYMWNo3LgxXbt25YYbbuCSSy4hJSWFYcOGsWDBApo0acKIESOYMGECo0aNwhhD+fLl+fHHHwF4++23GTduHG3btiUjI4NTp06RlJREbGwsTz31FJ9//jnlypVjwoQJPPfcczz88MOn1fnUqVMcO3aMpUuXsnz5coYPH87KlStJTU1l48aNfPPNN0R
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degre =  12\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meilleur modèle -> degre = 5\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main(degreMax=12, NbEx=20, sigma=0.2, ens=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Modélisation par réseaux de neurones MLP\n",
    "\n",
    "On souhaite à nouveau trouver un modèle de prédiction à partir des observations. Cette fois-ci, on n'utilise pas de solution unique mais plutôt une convergence de solution sur une moyenne de plusieurs initialisation du réseau pour chaque niveau de complexité. On aurra cette fois, un problème de surapprentissage qui sera observé lorsque l'on entraîne notre modèle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import cm\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
    "from sklearn.neural_network import MLPRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sinus_cardinal(x): \n",
    "    A = np.array([[1, 1], [-2, 1]])\n",
    "    b = np.array([0.2, -0.3])\n",
    "\n",
    "    x = -np.pi + 2*x*np.pi\n",
    "    z = A.dot(x + b)\n",
    "    h = np.sqrt(np.transpose(z).dot(z))\n",
    "    if np.abs(h) < 0.001:\n",
    "        y = 1\n",
    "    else:\n",
    "        y = np.sin(h)/h\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_surf(figName, regr=None):\n",
    "    step_v = 0.005\n",
    "\n",
    "    x1v = np.arange(0,1,step_v)\n",
    "    x2v = np.arange(0,1,step_v)\n",
    "    Xv, Yv = np.meshgrid(x1v, x2v)\n",
    "\n",
    "    R = np.zeros(Xv.shape)\n",
    "    for i,x1 in enumerate(x1v):\n",
    "        for j,x2 in enumerate(x2v):\n",
    "            if not regr:\n",
    "                R[i,j] = sinus_cardinal(np.array([x1, x2]))\n",
    "            else:\n",
    "                R[i,j] = regr.predict(np.array([[x1,x2]]))[0]\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = plt.axes(projection='3d')\n",
    "\n",
    "    # Plot the surface\n",
    "    surf = ax.plot_surface(Xv, Yv, R, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n",
    "\n",
    "    ax.set_zlim(-1.01, 1.01)\n",
    "    ax.zaxis.set_major_locator(LinearLocator(10))\n",
    "    ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
    "\n",
    "    fig.colorbar(surf, shrink=0.5, aspect=5)\n",
    "    plt.savefig(figName+'.png', dpi=300)\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def main(fileData):\n",
    "    # Chargement des données d'entrainement\n",
    "    with open(fileData, 'r') as f:\n",
    "        f.readline()\n",
    "        Xapp = np.loadtxt(f, delimiter = \";\", skiprows=1)\n",
    "    \n",
    "    Yapp = []\n",
    "    # print(sinus_cardinal(Xapp[0]))\n",
    "    for x in Xapp:\n",
    "        # print(x)\n",
    "        Yapp.append(sinus_cardinal(x))\n",
    "\n",
    "\n",
    "    regr = MLPRegressor(\n",
    "            hidden_layer_sizes=(32,32,32),\n",
    "            max_iter=1000,\n",
    "            verbose=True,\n",
    "            random_state=1,\n",
    "            tol=1e-6\n",
    "        ).fit(Xapp,Yapp)\n",
    "    \n",
    "    plot_surf(\"mlp_test\")\n",
    "\n",
    "    plot_surf(\"mlp_pred\", regr=regr)\n",
    "    plot_confusion(Xapp, Yapp, regr, 'Confusion')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fileData = \"sinc_dim2_input.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.05740163\n",
      "Iteration 2, loss = 0.05266336\n",
      "Iteration 3, loss = 0.04870684\n",
      "Iteration 4, loss = 0.04554897\n",
      "Iteration 5, loss = 0.04298842\n",
      "Iteration 6, loss = 0.04102289\n",
      "Iteration 7, loss = 0.03967706\n",
      "Iteration 8, loss = 0.03884706\n",
      "Iteration 9, loss = 0.03842922\n",
      "Iteration 10, loss = 0.03827398\n",
      "Iteration 11, loss = 0.03823175\n",
      "Iteration 12, loss = 0.03823855\n",
      "Iteration 13, loss = 0.03821542\n",
      "Iteration 14, loss = 0.03812472\n",
      "Iteration 15, loss = 0.03796829\n",
      "Iteration 16, loss = 0.03777099\n",
      "Iteration 17, loss = 0.03753403\n",
      "Iteration 18, loss = 0.03725668\n",
      "Iteration 19, loss = 0.03698537\n",
      "Iteration 20, loss = 0.03670326\n",
      "Iteration 21, loss = 0.03646273\n",
      "Iteration 22, loss = 0.03628747\n",
      "Iteration 23, loss = 0.03616490\n",
      "Iteration 24, loss = 0.03607125\n",
      "Iteration 25, loss = 0.03598384\n",
      "Iteration 26, loss = 0.03590608\n",
      "Iteration 27, loss = 0.03583750\n",
      "Iteration 28, loss = 0.03578035\n",
      "Iteration 29, loss = 0.03572684\n",
      "Iteration 30, loss = 0.03567354\n",
      "Iteration 31, loss = 0.03560571\n",
      "Iteration 32, loss = 0.03552475\n",
      "Iteration 33, loss = 0.03543328\n",
      "Iteration 34, loss = 0.03532988\n",
      "Iteration 35, loss = 0.03521193\n",
      "Iteration 36, loss = 0.03508008\n",
      "Iteration 37, loss = 0.03493347\n",
      "Iteration 38, loss = 0.03477282\n",
      "Iteration 39, loss = 0.03460402\n",
      "Iteration 40, loss = 0.03444860\n",
      "Iteration 41, loss = 0.03430615\n",
      "Iteration 42, loss = 0.03418290\n",
      "Iteration 43, loss = 0.03407200\n",
      "Iteration 44, loss = 0.03397403\n",
      "Iteration 45, loss = 0.03386732\n",
      "Iteration 46, loss = 0.03374848\n",
      "Iteration 47, loss = 0.03361640\n",
      "Iteration 48, loss = 0.03347746\n",
      "Iteration 49, loss = 0.03335331\n",
      "Iteration 50, loss = 0.03324367\n",
      "Iteration 51, loss = 0.03313346\n",
      "Iteration 52, loss = 0.03302073\n",
      "Iteration 53, loss = 0.03290221\n",
      "Iteration 54, loss = 0.03277717\n",
      "Iteration 55, loss = 0.03264278\n",
      "Iteration 56, loss = 0.03250400\n",
      "Iteration 57, loss = 0.03236950\n",
      "Iteration 58, loss = 0.03223926\n",
      "Iteration 59, loss = 0.03211255\n",
      "Iteration 60, loss = 0.03198813\n",
      "Iteration 61, loss = 0.03186576\n",
      "Iteration 62, loss = 0.03174014\n",
      "Iteration 63, loss = 0.03161073\n",
      "Iteration 64, loss = 0.03147959\n",
      "Iteration 65, loss = 0.03134529\n",
      "Iteration 66, loss = 0.03120728\n",
      "Iteration 67, loss = 0.03106766\n",
      "Iteration 68, loss = 0.03093437\n",
      "Iteration 69, loss = 0.03080154\n",
      "Iteration 70, loss = 0.03066901\n",
      "Iteration 71, loss = 0.03053476\n",
      "Iteration 72, loss = 0.03040142\n",
      "Iteration 73, loss = 0.03026495\n",
      "Iteration 74, loss = 0.03012527\n",
      "Iteration 75, loss = 0.02998402\n",
      "Iteration 76, loss = 0.02984238\n",
      "Iteration 77, loss = 0.02970261\n",
      "Iteration 78, loss = 0.02956367\n",
      "Iteration 79, loss = 0.02942443\n",
      "Iteration 80, loss = 0.02928540\n",
      "Iteration 81, loss = 0.02914197\n",
      "Iteration 82, loss = 0.02899609\n",
      "Iteration 83, loss = 0.02884932\n",
      "Iteration 84, loss = 0.02870217\n",
      "Iteration 85, loss = 0.02855378\n",
      "Iteration 86, loss = 0.02840348\n",
      "Iteration 87, loss = 0.02825170\n",
      "Iteration 88, loss = 0.02809726\n",
      "Iteration 89, loss = 0.02794295\n",
      "Iteration 90, loss = 0.02778718\n",
      "Iteration 91, loss = 0.02763034\n",
      "Iteration 92, loss = 0.02747079\n",
      "Iteration 93, loss = 0.02731023\n",
      "Iteration 94, loss = 0.02714905\n",
      "Iteration 95, loss = 0.02698684\n",
      "Iteration 96, loss = 0.02682147\n",
      "Iteration 97, loss = 0.02665309\n",
      "Iteration 98, loss = 0.02648336\n",
      "Iteration 99, loss = 0.02631360\n",
      "Iteration 100, loss = 0.02614681\n",
      "Iteration 101, loss = 0.02597884\n",
      "Iteration 102, loss = 0.02580967\n",
      "Iteration 103, loss = 0.02563606\n",
      "Iteration 104, loss = 0.02546296\n",
      "Iteration 105, loss = 0.02528971\n",
      "Iteration 106, loss = 0.02511409\n",
      "Iteration 107, loss = 0.02493485\n",
      "Iteration 108, loss = 0.02475519\n",
      "Iteration 109, loss = 0.02457515\n",
      "Iteration 110, loss = 0.02439186\n",
      "Iteration 111, loss = 0.02420914\n",
      "Iteration 112, loss = 0.02402564\n",
      "Iteration 113, loss = 0.02383851\n",
      "Iteration 114, loss = 0.02365114\n",
      "Iteration 115, loss = 0.02346107\n",
      "Iteration 116, loss = 0.02326692\n",
      "Iteration 117, loss = 0.02307226\n",
      "Iteration 118, loss = 0.02287828\n",
      "Iteration 119, loss = 0.02268393\n",
      "Iteration 120, loss = 0.02248483\n",
      "Iteration 121, loss = 0.02228224\n",
      "Iteration 122, loss = 0.02208278\n",
      "Iteration 123, loss = 0.02188281\n",
      "Iteration 124, loss = 0.02167925\n",
      "Iteration 125, loss = 0.02147705\n",
      "Iteration 126, loss = 0.02127666\n",
      "Iteration 127, loss = 0.02107473\n",
      "Iteration 128, loss = 0.02087318\n",
      "Iteration 129, loss = 0.02067031\n",
      "Iteration 130, loss = 0.02046491\n",
      "Iteration 131, loss = 0.02026048\n",
      "Iteration 132, loss = 0.02005549\n",
      "Iteration 133, loss = 0.01984929\n",
      "Iteration 134, loss = 0.01964350\n",
      "Iteration 135, loss = 0.01943632\n",
      "Iteration 136, loss = 0.01922900\n",
      "Iteration 137, loss = 0.01902228\n",
      "Iteration 138, loss = 0.01881514\n",
      "Iteration 139, loss = 0.01860667\n",
      "Iteration 140, loss = 0.01839877\n",
      "Iteration 141, loss = 0.01819112\n",
      "Iteration 142, loss = 0.01798198\n",
      "Iteration 143, loss = 0.01777239\n",
      "Iteration 144, loss = 0.01756298\n",
      "Iteration 145, loss = 0.01735301\n",
      "Iteration 146, loss = 0.01714276\n",
      "Iteration 147, loss = 0.01693119\n",
      "Iteration 148, loss = 0.01671911\n",
      "Iteration 149, loss = 0.01650618\n",
      "Iteration 150, loss = 0.01629211\n",
      "Iteration 151, loss = 0.01607746\n",
      "Iteration 152, loss = 0.01586293\n",
      "Iteration 153, loss = 0.01564690\n",
      "Iteration 154, loss = 0.01542983\n",
      "Iteration 155, loss = 0.01521276\n",
      "Iteration 156, loss = 0.01499634\n",
      "Iteration 157, loss = 0.01477966\n",
      "Iteration 158, loss = 0.01456259\n",
      "Iteration 159, loss = 0.01434836\n",
      "Iteration 160, loss = 0.01413316\n",
      "Iteration 161, loss = 0.01391742\n",
      "Iteration 162, loss = 0.01370211\n",
      "Iteration 163, loss = 0.01348695\n",
      "Iteration 164, loss = 0.01327205\n",
      "Iteration 165, loss = 0.01305722\n",
      "Iteration 166, loss = 0.01284279\n",
      "Iteration 167, loss = 0.01263141\n",
      "Iteration 168, loss = 0.01242085\n",
      "Iteration 169, loss = 0.01221132\n",
      "Iteration 170, loss = 0.01200266\n",
      "Iteration 171, loss = 0.01179582\n",
      "Iteration 172, loss = 0.01159065\n",
      "Iteration 173, loss = 0.01138843\n",
      "Iteration 174, loss = 0.01118885\n",
      "Iteration 175, loss = 0.01099218\n",
      "Iteration 176, loss = 0.01079844\n",
      "Iteration 177, loss = 0.01060582\n",
      "Iteration 178, loss = 0.01041679\n",
      "Iteration 179, loss = 0.01022925\n",
      "Iteration 180, loss = 0.01004503\n",
      "Iteration 181, loss = 0.00986393\n",
      "Iteration 182, loss = 0.00968419\n",
      "Iteration 183, loss = 0.00950732\n",
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      "Iteration 277, loss = 0.00106983\n",
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      "Iteration 333, loss = 0.00042891\n",
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      "Iteration 356, loss = 0.00033861\n",
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      "Iteration 370, loss = 0.00030017\n",
      "Iteration 371, loss = 0.00029792\n",
      "Iteration 372, loss = 0.00029572\n",
      "Iteration 373, loss = 0.00029366\n",
      "Iteration 374, loss = 0.00029167\n",
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      "Iteration 376, loss = 0.00028790\n",
      "Iteration 377, loss = 0.00028609\n",
      "Iteration 378, loss = 0.00028436\n",
      "Iteration 379, loss = 0.00028269\n",
      "Iteration 380, loss = 0.00028102\n",
      "Iteration 381, loss = 0.00027934\n",
      "Iteration 382, loss = 0.00027768\n",
      "Iteration 383, loss = 0.00027606\n",
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      "Iteration 385, loss = 0.00027295\n",
      "Iteration 386, loss = 0.00027149\n",
      "Iteration 387, loss = 0.00027004\n",
      "Iteration 388, loss = 0.00026855\n",
      "Iteration 389, loss = 0.00026701\n",
      "Iteration 390, loss = 0.00026541\n",
      "Iteration 391, loss = 0.00026383\n",
      "Iteration 392, loss = 0.00026226\n",
      "Iteration 393, loss = 0.00026068\n",
      "Iteration 394, loss = 0.00025910\n",
      "Iteration 395, loss = 0.00025756\n",
      "Iteration 396, loss = 0.00025604\n",
      "Iteration 397, loss = 0.00025451\n",
      "Iteration 398, loss = 0.00025303\n",
      "Iteration 399, loss = 0.00025155\n",
      "Iteration 400, loss = 0.00025009\n",
      "Iteration 401, loss = 0.00024865\n",
      "Iteration 402, loss = 0.00024802\n",
      "Iteration 403, loss = 0.00024715\n",
      "Iteration 404, loss = 0.00024605\n",
      "Iteration 405, loss = 0.00024479\n",
      "Iteration 406, loss = 0.00024344\n",
      "Iteration 407, loss = 0.00024209\n",
      "Iteration 408, loss = 0.00024125\n",
      "Iteration 409, loss = 0.00024027\n",
      "Iteration 410, loss = 0.00023917\n",
      "Iteration 411, loss = 0.00023796\n",
      "Iteration 412, loss = 0.00023702\n",
      "Iteration 413, loss = 0.00023613\n",
      "Iteration 414, loss = 0.00023512\n",
      "Iteration 415, loss = 0.00023395\n",
      "Iteration 416, loss = 0.00023320\n",
      "Iteration 417, loss = 0.00023240\n",
      "Iteration 418, loss = 0.00023154\n",
      "Iteration 419, loss = 0.00023064\n",
      "Iteration 420, loss = 0.00022970\n",
      "Iteration 421, loss = 0.00022873\n",
      "Iteration 422, loss = 0.00022783\n",
      "Iteration 423, loss = 0.00022703\n",
      "Iteration 424, loss = 0.00022610\n",
      "Iteration 425, loss = 0.00022524\n",
      "Iteration 426, loss = 0.00022450\n",
      "Training loss did not improve more than tol=0.000001 for 10 consecutive epochs. Stopping.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASsAAADyCAYAAAAV4uPnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAACUW0lEQVR4nO19d3hc1Zn+e+700Yx6sYplS+69GwiBhIRQTILNLjUJoSSBsCSwSdgEkl0g2ZBlN3UJbPJLSIEklGCaqSmUNAg22LIky7Zky+plNJqi6XPL+f1x59y5M3Nn5soayTbM+zzzSJq5dXTve7/znfd7P0IpRRFFFFHEyQ7uRB9AEUUUUYQeFMmqiCKKOCVQJKsiiijilECRrIoooohTAkWyKqKIIk4JFMmqiCKKOCVgzPN5UddQRBGzD3KiD+BUQDGyKqKIIk4JFMmqiCKKOCVQJKsiiijilECRrIoooohTAkWyKqKIIk4JFMmqiCKKOCVQJKsiiijilECRrIoooohTAkWyKqKIIk4JFMmqiCKKOCVQJKsiiijilECRrIoooohTAkWyKqKIIk4JFMmqiCKKOCVQJKsiiijilEA+P6siZgGSJCEWi4HjOBiNRnAcB0KKlkZFFJELRbKaQ1BKIYoieJ5HPB4H69lICIHBYIDJZCqSVxFFZEGRrOYIlFLwPA9RFOH1etHT04OSkhJUVFSgvLwcACAIAgDA5/OhoqICVqu1SF5FFJFAkazmAIIgwOPxoKSkBL29vfD5fFi+fDlisRi8Xi+GhoYgiiLKy8tRXl6OkZERWK3WlMjLaDQqryJ5FfFeBMnTPr7owT4DUEohCAIikQj27t0Lg8GAyspKtLa2guf5FMIRRRF+vx9erxcjIyOwWCyorKxEeXk5ysrKwHHJuRBGXiaTCQaDoUhepz6K/zwdKEZWswRKKeLxOCRJwuTkJILBIDZt2oTKykpoPSAYkVVWViISiaC5uRk8z8Pr9aKvrw+EECXyKisrA8/z4HkegExeLN9VJK8i3q0oktUsQBAEJT/V3d2NaDQKh8OByspKXeuzhHtpaSmqqqoAADzPw+/3w+Px4NixY+A4DuXl5aioqIDT6UQ8HkcsFgMAcBynkJfRaAQhpEheRZzyKJJVAcGGfYIgIBQKobOzEw0NDVi+fDl2796tubwWiRBCMqIvk8mE6upqVFdXA4ASdU1MTODIkSMwGAyoqKjIIC9CCARBAMdxcDqdMBgMRfIq4pREkawKBEmSlGhqdHQUAwMDWL16NUpLSzOIh5FFnnxhTphMJtTW1qK2thYAEI/H4fV6MT4+jp6eHhiNRmWmMR6PIxAIYOHChQCg6LvUOa8iijjZUSSrGUKtnRIEAQcPHgTHcdi6dSuMRmPKcnpxPERmNptRV1eHuro6AFBmGkdHR+H1epWkfHl5uRJ5xeNxAEXyKuLUQJGsZgC1dmpqagpdXV1YuHAhGhoaUpY7EUMui8WCefPmYd68eZiYmIDX64XZbMbw8DCCwSCsVquSsC8pKckgr/SEfRFFnGgUyeo4IUmSMtvX398Pl8uF9evXw26361o/HA7DbDanRF8MMx0iasFkMqG+vh719fWglCIajcLr9WJwcBDBYBB2u10hL7vdjlgsppmwL5JXEScKRbKaJiilCAaDkCQJANDZ2Qmn04mtW7fquoklSUJ3dze8Xi8kSUpJjJeWls4JERBCYLPZYLPZ0NDQAEopIpEIvF4v+vv7EQqFUtT1Vqs1hbxYaZDBYFBmG4soYrZRJKtpgGmnhoaGwPM83G43li5dipqaGl3rh8NhdHR0oLa2Fhs3bgSQnNUbGxtDd3c3zGYzBEFAMBiE0+mcEyIghMBut8Nut6OxsRGUUoTDYXi9XvT29iIcDsPhcCjkZbFYcPjwYdTU1KCsrCylrpHNNhZRRKFRVLDrBEuii6KIvXv3Ih6PY/PmzbBarbrW//Of/wyj0YhVq1YpM3Ra0oVoNIqDBw+CEIJ4PA6bzaZEXna7/biIYGJiAsFgEC0tLdNeF0hGkz6fD16vF9FoFKIoora2Fg0NDbBYLCnD1iJ5TRvFL0gHipFVHqi1U9FoFB0dHTCZTJg3b54uohJFEYcPHwbP83jf+94Hk8mUc3mr1YqSkhLU1tairKxMGZ4dO3YMoVBIiXAqKipgs9kKdZo5QQiB0+mE0+nE/PnzQSlFR0eHInqNxWJwOp1K5EUIQTQaVdYvklcRhUCRrHKAaackScL4+Dh6e3uxcuVKBINBXQnwUCiE9vZ2NDQ0wGaz5SWqdGgNz0KhkOLaEI1GFZKoqKiAxWI53lOd9nGxhL3T6YQkSQgEAvB6vTh48CB4nkdpaWkKeUUiEYWkiuRVxPGgSFYaUGunWGQkCAK2bt0Kk8mEcDis1OVlw8jICPr6+rBq1SqUlZVheHhY9/6zzQYSQuBwOOBwODB//nxIkoRgMAiPx4Ouri4IgqCQREVFxbTJcTpQHx/HcSgrK0NZWRkWLlwISZIwNTWlFGWrj0uLvNSOEkXyKiIbimSVBrV2KhgMorOzE/Pnz0dTU5NyE+WSFoiiiIMHDyrkpiVNKBQ4jkNpaSlKS0sVkmDODUNDQ5AkKcOxoZDIRiqsbpH5dDEdWvpxsaJsQgh4nofH44HZbFaItmiHU4QaRbJSQa2dGh4exvDwMNasWQOn05myHMdxinRBjWAwiI6ODjQ1NaWQmxZyEd7x6qw4jlOiKkAmCZ/Ph+HhYQQCAfh8PpSXl6OyshKlpaUwGAzT3gdDtrpGLajlGey4GKkODAyAUory8nLEYjHl2JgRYdFFtQiGIlkhs2TmwIEDMJvN2Lp1q+YNrUUmw8PD6O/v1yS36aJQolCDwYCqqiqIogin04mmpib4fD6l+JnVD7Li5+lEYNMhK63jYnY4gOxS4ff7cezYMfT392NkZCTFDoctAxSNCN/LeM+Tldp3yu/3o6urC4sWLcK8efOyrqMmE0EQ0NXVBQC6h33xeByHDh2C0WhEZWWlolWaTbCkeE1NjaILi8Vi8Pl8GB0dxeHDh2GxWBTycjgcOUmgkAp7o9GIqqoqeL1eVFZWwul05rTDYRY87LyK5PXewHuarCRJgtvtxujoKEwmE9xuNzZs2JC3ZIYNAwOBADo6OrBgwQI0Njbq2qfP58OBAwewYMECEELgdrtx9OjRlCgHKCwZZIPFYkkpfmYyiYGBAaUEJ5fGq9CkwKK16drhpBsRFl1U3514T5KVWjsVj8cxOjqKxsZGbNmyRfdQaGpqCh6PB2vXroXD4dC1z76+PoyNjWHDhg0wmUyQJCklymGzZ263G16vF5FIBBUVFSgpKZmTGy69BCebir2iomJGw8BskCRJ8/vXa4fDIsJ08orH40r0WiSvUxfvObJSa6fcbjcOHz4Mh8OBpUuX6lpfEAQcO3YM0WgU73vf+3QN33ieRyQSQTgcVmoI06UPapeE3t5emM1mEELQ19eXIgatrKzUrZqfCQghKCkpQUlJCZqamhQVu9frRXd3N3w+H44ePYqqqqqCabz0EmA2O5yRkREEAgFYLBZl2FhSUoKOjg5s2LBB8REruqiemnjPkJU6iS5JEo4cOYJQKITVq1ejv79f1zampqbQ2dmJ2tpaxGIxXUTl9/uVhP3y5ct1RW6EEFgsFtTU1ChiUEYUhw4dQjweR2lpqdJQwmw26zr+mUCtYm9ubkZbWxvq6uoQDAYVjVdZWZmipToejZckScdFHGqiB6A4SgwNDSEQCCAWi2FkZEQhL7WLKiMvNmwsktfJi/cEWamHfZFIBB0dHairq8OyZcsQjUY1ZQjp6w8MDGBkZATr1q2DIAgYHBzUtc7o6CjWr1+P9vZ23Xmo9NnAdKJgokuPx6PollgkUV5ePuvJeobS0lJUV1dj4cKFKVoqtRyhoqICZWVluiYeKKUF0YRZrVbFDicej6O9vR1GozEjF8fscIpGhKcG3vVkpR72jY2NpajKgeyaKQae59HZ2QmLxaJIGQKBQM51BEFAZ2cnTCYTtmzZojyxC5U0TxddCoIAn88Hj8eD3t7eDNuZ2UD6kC1dS8XkCKyukRC
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main(fileData=fileData)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque que le modèle est très proche des données d'apprentissage, mais comme on peut le prévoir avec la regression 2D, les bords introduisent une discontinuité de la fonction que l'on ne peut pas prédire correctement.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Nous avons pu découvrir les outils proposés par scikit learn pour la regression par des modèles polynomiaux et des modèles par réseaux de neurones. "
   ]
  }
 ],
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   "nbconvert_exporter": "python",
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