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": 1,
   "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": 2,
   "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": 3,
   "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": 4,
   "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": 5,
   "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": 6,
   "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": "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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": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABJnklEQVR4nO2de5yNVffAv2suxjAuheROoYhxD1FG7gopQohUCJX6VXR5o1Lp8qYbSiq3QqHySiUyiUhocpvknmsYxDCDmdm/P/aZ6Rhnruc6M+v7+ZzPOc+z97P32nPmrGc/a6+9lhhjUBRFUfI/Qf4WQFEURfENqvAVRVEKCKrwFUVRCgiq8BVFUQoIqvAVRVEKCKrwFUVRCgiq8AsQItJCRH4VkcszqTNNRMY5Pt8oItty2dd7IvKf3Mqag36iRGS/t/vJKSJSVUSMiIQ4joNEZKaIPOqi7kARWZnLfi4Xke0iUt9dGb2FiJQWkRgRaezNfpSsUYWfBxGRPSKSICLxIvK3Q0lHZHFNJeAl4BZjzPHs9GOM+ckYc0025LlEYRljhhpjXshOPwUBY0wKMBC4XkQ6erDd40AfYLK3FXduEJFQYDowzBizLpN6d4rIzyJyVkSiXZRPEZFtIpIiIgO9J3H+RhV+3qWLMSYCaAg0Bp5JX8FZARhj9hljWhljjvhQRsUJY0yyMaa3MeZbD7e7DngRqOHJdnOLWIIAjDEXjDG3GGN+zuKy48CbwPgMyn8HhgEbPCZoAUQVfh7HGHMA+AaoA+B4RB8uItuB7Y5ztzoeqU86ZlGRqdeLSAMR2SAip0VkLlDYqewic4mIVBKRBSJyVETiRORdEakFvAc0dzxxnHTUTTMNOY7vF5EdInJcRBaKSHmnMiMiQx2miZMiMlFExNV4RSTc0fYJEdkKNElXbkSkutPxRXKkqztQRFaJyARHv7tE5AbH+X0ickREBjjVLyEiMxzj3ysiz6QqNhEJFpHXReSYiOwCbknXVwkR+VBEDonIARF5SUSCM5DrWhH53vG32iYid7qq56h7j4jEArOBr0VkSCZ1cyrjuFQZHdf+13HtbhEZIRebrKJF5EURWQWcBa7KyTiMMUuNMZ8BBzMon2iMWQYkZtSGkjWq8PM4DlNNZ+A3p9O3AU2B2iLSAPgIGAKUAt4HFopImIgUAr4EZgKXA58Dd2TQTzCwCNgLVAUqAHOMMbHAUGC1MSbCGFPSxbU3Ay8DdwLlHG3MSVftVqzyjnTU65DBkMcAVzteHYABGdTLLk2Bjdi/zacOuZoA1YF+wLvyr7nsHaAEcBXQCrgbuMdRdr9jDA2wT1w90vUzDTDYWXhDoD3273YRIlIU+N4hyxVAb2CSiNTOQP4jjn6LO2SZICINM6ibHRmTHGNv4JDxPqdrOwH1HfLf5qL9/sBgoBhwNIfjUHyBMUZfeewF7AHigZNY5TkJCHeUGeBmp7qTgRfSXb8Nq7Buws6oxKnsZ2Cc43MUsN/xuTn2RxziQp6BwMp056Y5tfMh8KpTWQRwAajqJHNLp/LPgNEZjH0X0NHpeHCqjE5tVXclRwZyb3c6ruu4vqzTuTiskgsGzgO1ncqGANGOzz8AQ53K2jvaCgHKOq4t4lR+l9O1aX8/oBfwUzo53wfGZPN/40vg4QzKspLxXOr/kaO8D7Dc6dohTmVtU691HEcDzzuV52oc2BtMdCblK4GB/vjd5YdXwC3yKNnmNmPM0gzK9jl9rgIMEJEHnc4VAspjf7AHjOOX5GBvBm1WAvYaY5JyIWt5nGyvxph4EYnDPiXscZw+7FT/LPamkFFbzuPLSN7s8rfT5wSHfOnPRQClgdB0/e3FjiEruaoAAmxwslSFAidcyFMFaJpqGnMQgn0KuwQR6YR96qmJfWIvAmxyVTcbMoYCh5xkDHKqn/5a58+uzuVoHIpvUIWfP3FW4PuAF40xL6avJCKtgAoiIk5KvzKw00Wb+4DKIhLiQulnFXL1IFYBpPZbFGtCOZDFda44hL35bHGS15mzWKWXypWAJ9w2j2GfSqoAW536Th1Dqlw4laWyD0gG6hpjLmTRzz7gR2NMu6wEEpEwYD7WtPSVMeaCiHyJvbm4IisZzwGlM7ipHwIqOh1XclEn/f9dtsah+A614ed/PgCGikhTsRQVkVtEpBiwGmuzfUhEQkXkduD6DNpZi/3Rj3e0UVhEWjjK/gYqOtYEXDEbuEdE6juU1EvAL8aYPbkYz2fAkyJymYhUBB5MVx4D3OVYZOyINV25jTEm2dH3iyJSTESqAI8Cs5zkekhEKorIZcBop2sPAd8BbzoWRoNE5GrHDTc9i4CaItLf8Z2EikgTsYvj6SkEhGFNbUmO2X77TIaRlYxLgP+KSHEXMn4GPCwiFUSkJDAq0z9YzsaRuihcGDsJDXL8f4U6lRdylAsQ6ihX/ZVD9A+WzzHWZe9+4F2sCWEH1maMMeY8cLvj+DjW7rogg3aSgS7YBb2/sLPmXo7iH7Az7sMicszFtUuB/2Bno4ewC669czmk57CmiN1YBZXeRPCwQ86TQF+sTdtTPAicwa4jrMQuSH7kKPsAq9R/x5qv0v8d78b+3rZgv4d52AXsizDGnMYq7d7YJ6PDwCtYxe6q7kNYZXwCuy6wMBP5syNjIewTTHoZP8D+vTdiHQQWYycLya46ysk4HPTHms8mAzc6Pn/gVL7Ece4GYIrj802ZjFVxgVxsvlUURckax9PEe8aYKllWVgIGneEripIlYvc/dBaREBGpgF0o/sLfcik5Q2f4iqJkiYgUAX4ErsWaU77Gun+e8qtgSo5Qha8oilJAcNuk41gtXysiv4vIFhF5zkWdMBGZK3Zr/S8iUtXdfhVFUZSc4Qk//HPYnZ3xDjeqlSLyjTFmjVOde4ETxpjqItIbu1rfy1VjqZQuXdpUrVo110KdOXOGokWL5vr6QELHEpjoWAKTgj6W9evXHzPGlHFZ6Mltu9gNLxuApunOfwc0d3wOwW5ikczaatSokXGH5cuXu3V9IKFjCUx0LIFJQR8LsM5koFc9YsN3BNZaj/XRnmiMGZWufDM2/sl+x/FOx03hWLp6g7GxUShbtmyjOXPSx9fKPvHx8UREZBoiPs+gYwlMdCyBSUEfS+vWrdcbY1wnm8noTpCbF1ASWA7USXd+M1DR6Xgndgu3zvCzgY4lMNGxBCYFfSxkMsP3qB++MeakQ+Gnz+hzAEfsDUf87BLYKISKoiiKj3B70VZEygAXjDEnRSQcaIddlHVmITZu+WpsDO4fHHeiHHHhwgX2799PYmLWORBKlChBbGxsTrsISPLKWAoXLkzFihUJDQ3NurKiKD7HE1465YDpDjt+EPCZMWaRiDyPfbRYiI2HPlNEdmBjtuQqjsr+/fspVqwYVatWRVwnRErj9OnTFCtWLDfdBBx5YSzGGOLi4ti/fz/VqlXztziKorjAbYVvjNmIzY6T/vyzTp8TgZ7u9pWYmJgtZa/4HhGhVKlSHD161N+iKIqSAXkulo4q+8BFvxtFCWzynMJXfMPXX3/Nxo0b/S2GohQ8ZsyADz7Iul4uUIWfQ1588UWuu+46IiMjqV+/Pr/88kuOrp82bRoHDx5MO77vvvvYunVrJldcTFRUFOvWrctRn+np3LkzJ0+e5OTJk0yaNOmS8m+//ZYff/yRunXrutWPoii5YOZM+PhjrzSd/xX+oUPQqhUcPpx13SxYvXo1ixYtYsOGDWzcuJGlS5dSqZKrTG+uSU5OvkThT506ldq1a7stmzNJSZmnnV28eDElS5bMUOF37NiRV199VU00iuIPEhMhPNwrTed/hf/CC7ByJTz/vNtNHTp0iNKlSxMWZpP2lC5dmvLlywOwbNkyGjRoQN26dRk0aBDnzp0DoGrVqowaNYqGDRsye/Zs1q1bR9++falfvz4JCQkXzdiXLFlC8+bNadiwIT179iQ+Pt6lHDNnzqR+/frUqVOHtWvXAjB27Fj69+9PixYt6N+/P9OmTWPEiBFp19x6661ER0enyXTs2DFGjx7Nzp07qV+/Po8//jgAr732Gk2aNCEyMpIxY8akXT9r1iyuv/5
      "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": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEICAYAAABGaK+TAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA1N0lEQVR4nO3deXwV1dnA8d+TPSEhBMK+BWSRCBg2kaISSkVcqtW60YqgteBal9pq3y5SRaXVStUXqBuvWlGwLpVqtW5EBBQlkAImKIvBBCLZISF7ct4/5t5wCTfJzc3dRp7v5zOfu8yZmefc5blnzsw9I8YYlFJK2U9YsANQSinlHU3gSillU5rAlVLKpjSBK6WUTWkCV0opm9IErpRSNqUJ3MZEZKqIfC4i3dso86yILHLcP1NEvvRyW38Tkd97G2sHtpMuIvn+3k5HiUiKiBgRiXA8DhORv4vIHW7KzhOR9V5up7uI7BKRtM7G6C8ikiwiWSIy0Z/bUe3TBB4CRCRXRKpFpFJEDjqSbnw7ywwEHgDON8aUerIdY8zHxpiRHsRzXAIyxlxvjLnPk+2cCIwxTcA84DQRmeXD9ZYCs4Hl/k7E3hCRSOA54EZjzOY2yl0uIhtFpEpEMlrMGyEib4hIkYiUish/RKTdz6U6nibw0PFDY0w8MB6YCPyuZQHXL7QxJs8YM80YUxjAGJULY0yjMeZKY8w7Pl7vZuB+YLgv1+stsYQBGGPqjTHnG2M2trNYKfBXYLGbed2ANcBIoDfwGfCGzwI+gWgCDzHGmP3A28BoAMcu8U0isgvY5XjuAscubLmjlTPWubyIjBORLSJSISKrgRiXecd0T4jIQBF5zdESKhGR/xWRUcDfgCmOPYJyR9nmrhjH45+LyG5HC2qNiPRzmWdE5HpHV0C5iCwVEXFXXxGJday7TESygUkt5hsRGeby+Jg4WpSdJyIbRGSJY7t7ReR7jufzRKRQROa6lE8Ukecd9d8nIr9zJioRCReRh0WkWET2Aue32FaiiDwjIgUisl9EHhCR8FbiOllE3nO8Vl+KyOXuyjnKXiMiOcBLwFsisqCNsh2NcZEzRseyf3Es+7WI3CzHdhFliMj9IrIBqAKGdqQexpj3jTEvAwfczPvMGPOMMabUGFMPLAFGikiP1tan3NMEHmLE6ho5D9jq8vSPgMlAqoiMA1YAC4AewBPAGhGJFpEo4J/A34HuwD+AH7eynXDgTWAfkAL0B1YZY3KA64FPjDHxxphubpb9PvAgcDnQ17GOVS2KXYCVjMc6yp3TSpXvAU5yTOcAc1sp56nJwDas1+ZFR1yTgGHAVcD/ytHuqceBRGAoMA24GrjGMe/njjqMw9ojurTFdp4FDFYreTwwE+t1O4aIdAHec8TSC7gSWCYiqa3EX+jYbldHLEtEZHwrZT2JscFR93GOGK9zWfZcIM0R/4/crH8OMB9IAIo6WI+OOAv41hhT4oN1nViMMToFeQJygUqgHCsZLgNiHfMM8H2XssuB+1os/yVWAjoLq8UjLvM2Aosc99OBfMf9KVhfygg38cwD1rd47lmX9TwD/NllXjxQD6S4xHyGy/yXgbtbqfteYJbL4/nOGF3WNcxdHK3Evcvl8RjH8r1dnivBSlrhQB2Q6jJvAZDhuP8hcL3LvJmOdUVg7fbXAXEu83/ismzz6wdcAXzcIs4ngHs8/Gz8E7i1lXntxVjr/Bw55s8G1rosu8Bl3g+cyzoeZwD3usz3qh5YPxgZbcwfAOwHZgfju2f3KeQOkpzAfmSMeb+VeXku9wcDc0XkFpfnooB+WF/A/cbxzXDY18o6BwL7jDENXsTaD9jifGCMqRSREqxWfK7j6W9dyldhJfnW1uVav9bi9dRBl/vVjvhaPhcPJAORLba3D6sO7cU1GBBgi0vPUCRQ5iaewcBkZ1eUQwTWXtJxRORcrL2SEVh7yHHAdndlPYgxEihwiTHMpXzLZV3vu3uuQ/XwhIj0BN4FlhljXvJ2PScyTeD24JqQ84D7jTH3tywkItOA/iIiLkl8ELDHzTrzgEEiEuEmibc3ROUBrC+0c7tdsLos9reznDsFWD8mX7jE66oKK4k59QF8cZphMdZew2Ag22Xbzjo448JlnlMe0AiMMVYfblvygI+MMWe3F5CIRAOvYnXlvGGMqReRf2L9WLjTXoy1QHIrP9IFWK1fp4FuyrT83HlUD0+ISBJW8l7j7rOsPKN94PbzFHC9iEwWSxcROV9EEoBPsPo8fyEikSJyCXBaK+v5DOtLvNixjhgRmeqYdxAY4OhTd+cl4BoRSXMknQeATcaYXC/q8zLwGxFJEpEBwC0t5mcBP3EcdJuF1VXUacaYRse27xeRBBEZDNwBvOAS1y9EZIAj2dztsmwB8B/gr44DhWEicpLjB7SlN4ERIjLH8Z5EisgksQ4WtxQFRGN1bTU4WuMz26hGezG+C/xFRLq6ifFl4FYR6S8i3YC72nzBOlYP50HSGKxGYpjj8xXpmNcV6/XbYIy5293yyjOawG3GWKeY/Rz4X6xd9t1Yfa4YY+qASxyPS7H6LV9rZT2NwA+xDnB9g9WqvcIx+0OsFvG3IlLsZtn3gd9jtRYLsA5AXulllf6Itev/NVbCablLfqsjznLgp1h9wr5yC3AEqx9+PdYBuhWOeU9hJZn/YnUXtXwdr8b6/nyB9T68gnVA9xjGmAqsJHwl1p7Lt8CfsBK1u7K/wEquZVj96mvaiN+TGKOw9jBaxvgU1uu9DeuA+b+xfvwb3W2oI/VwmIPVXbUcONNx/ynHvIuxDixfI9aZTs6p5d6Xaocc212qlDoROVr7fzPGDG63sAoZ2gJX6gQk1vn354lIhIj0xzpw+nqw41Idoy1wpU5AIhIHfAScjNW98RbW6YqHgxqY6hBN4EopZVPahaKUUjYV0PPAk5OTTUpKilfLHjlyhC5duvg2oCDRuoQmrUto0rpAZmZmsTGmZ8vnA5rAU1JS2Ly51REo25SRkUF6erpvAwoSrUto0rqEJq0LiIjbfyhrF4pSStmUJnCllLIpTeBKKWVTQR/Mqr6+nvz8fGpqatosl5iYSE5OToCi8i871SUmJoYBAwYQGRkZ7FCUUi0EPYHn5+eTkJBASkoK4v6iLQBUVFSQkJAQwMj8xy51McZQUlJCfn4+Q4YMCXY4SqkWgt6FUlNTQ48ePdpM3io4RIQePXq0u3eklAqOoCdwQJN3CNP3RqnQFRIJXAXGW2+9xbZt24IdhlInlvx8+P3vYdcun69aEzhw//33c8oppzB27FjS0tLYtGlTh5Z/9tlnOXDg6MW3r7vuOrKzs9tY4ljp6ele/8HJ6bzzzqO8vJzy8nKWLVt23Px33nmHjz76iDFjxnRqO0qpDtq3DxYtgq+/9vmq7ZnACwpg2jT49tv2y7bjk08+4c0332TLli1s27aN999/n4ED3V1dyr3GxsbjEvjTTz9NaqovLtZ9VEND25eu/Pe//023bt1aTeCzZs3iz3/+s3aJKBVozmNIsbE+X7U9E/h998H69XDvvZ1eVUFBAcnJyURHWxcWSU5Opl+/fgB88MEHjBs3jjFjxnDttddSW1sLWEMC3HXXXYwfP56XXnqJzZs389Of/pS0tDSqq6uPaVG/++67TJkyhfHjx3PZZZdRWVnpNo6///3vpKWlMXr0aD777DMAFi5cyJw5c5g6dSpz5szh2Wef5eabb25e5oILLiAjI6M5puLiYu6++2727NlDWloav/rVrwB46KGHmDRpEmPHjuWee+5pXv6FF17gtNNOIy0tjQULFtDY6PZiLEqpznAm8JgYn6/aXgk8NhZEYPlyaGqybkU69cs2c+ZM8vLyGDFiBDfeeCMfffQRYJ0dM2/ePFavXs327dtpaGhg+fLlzcv16NGDLVu2cNVVVzFx4kRWrlxJVlYWsS6xFBcXs2jRIt5//322bNnCxIkTeeSRR9zGUVVVRVZWFsuWLePaa69tfj47O5v333+fl17y7KLdixcv5qSTTiIrK4uHHnqId999l127dvHZZ5+RlZVFZmYm69atIycnh9WrV7NhwwaysrIIDw9
      "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": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA3EklEQVR4nO3deXwUVbbA8d/JBgoom6MgIOCCighoBgjwIAiIKziiouM+KjLggo4LERfEcXQc33NFIS7gMiqKOjIOLhCN6NisioqigojsilFkz3rfH7e6u7rp7nTSW5I+388nn3RV3bp1q9I5ffvUrSoxxqCUUqrhy0h1A5RSSiWHBnyllEoTGvCVUipNaMBXSqk0oQFfKaXShAZ8pZRKExrw6xARmSQizyeo7ktE5KNE1F1bIvJXEflZRDbHUEcHEdkhIpnxbFuqicgMEflrHWjHlyKS77z2vT9FpKOIGBHJSmX74iH4WItIroh8KiKtQpRdIyJDktvC+NGAH4GInCsiC0Vkp4j85LweKyKS6rbVdyLSAfgLcLQx5qDa1mOMWWuMaWqMqYxf65SXMaarMaY41e1IJmPMEmAc8IyIZKe6PfGkAT8MEfkL8BDwD+Ag4EBgDNAPyAmzToPqZSZYB6DEGPNTqhuSag2hlxwvYqU8LhljPjbGnGaMKU91W+Ip5Qe2LhKR/YHJwFhjzCxjzHZjfWqMOd8YU+qUmyEij4vIHBHZCQwSkVOdr4PbRGSdiExy1ev9GjxaRDaKyCYRuSFo8zki8qyIbHe+Tue61m8rIq+KyBYR+V5EromwD61EZLbTjkXAoUHLjxSRuSLyi4h8IyLnRKirpYhMd9r8q4j8y7XsChFZ5dQzW0TaupYZERkjIitFZKuITHH+oYcAc4G2Tjpmhojki8j6oO36vj6LSC8RWeLsz48i8n9BxzTLdYxmO+1ZJSJXuOqbJCIvhzu+IfbbiMg1IrLaST39wxuMRCRDRG4VkR+cb3/POu8botiXSSIyS0SeF5FtwCXh2uBa/zQRWeYcx49F5Nhq2j3WOe7bReQuETnUWW+bcwxyoqlbokxhiMj+IvKU857eIDZdl+na3+ddZYP/ZsUicreI/BfYBXQOUf8aEblRRD4X+437KRE5UETecvZxnoi0cJUf7vx9tzr1H+Va1lNEPnHWmwk0jnCsPSLSI8w+Z4jIBBH5TkRKnOPasrpjlVLGGP0J+gFOAiqArGrKzQB+w/b6M7BvnHygmzN9LPAjcIZTviNggBeBJk65LcAQZ/kkYA9wCpAJ3AMscJZlAEuB27HfMDoDq4FhYdr2EvCys51jgA3AR86yJsA64FIgC+gJ/IxNr4Sq6z/ATKAFkA0MdOaf4Kx3HNAIeASY71rPAG8CzbE9+i3ASc6yfGC9q2zAtDNvjevYeIALnddNgT5BxzTLmZ4PPOb8LXo42zyhuuMbZr8N8D7Q0mn/t8DlzrI/Aaucv0NT4DXguSj3ZRJQDpzh/F33CfPe+qvzuifwE9DbaffFTn2NIrT7DWA/oCtQChQ5bd0f+Aq4OJq6Q7T7+TDH/XVgGva99TtgEXBl8Hph1i0G1jptzQKyQ+zTGmAB9pv2wU6bP3Ha3xh4D7jDKXsEsBMYin2/3uT8rXKcnx+A65xlZzl/C/ex3gLkOcfjT07bGoc4Htc6bWqHff9PA15MdfyKGLNS3YC6+ANcAGwOmvcxsBXYDQxw5s0Anq2mrgeBB5zX3jf6ka7l9wFPOa8nAfNcy44GdjuvewNrg+ouAKaH2Gam8yZ2b+dv+AP+KODDoHWmef9hgua3AaqAFiGWPQXc55pu6my3ozNtgP6u5S8DE5zX+dQs4M8H7gRaB5XxHtMsoD1QCTRzLb8HmFHd8Q3ztzM4H1DO9FigyHldhP0G6F3Wxdn3rCj2ZRKuD8Yw256BPwg9DtwVtPwbnA/eMO3u55peCtzsmv5f4MFo6g7R7r0CPjYIl+L64ALOA94PXi94XWe6GJhczfFYA5zvmn4VeNw1fTXwL+f1bcDLrmUZ2A5PPjAA2AhI0P+2+1jfHbTtb4H8EMdjBTA46H+lnGo6iqn80dxhaCVAaxHJMsZUABhj+gI4X9XdqbB17hVFpDdwL7ZXnYP95H8lqH73Oj9ge/pe7hEru4DGzlffQ7ApkK2u5ZnAhyHafwD2HzF4O16HAL2D6soCngtRV3vgF2PMryGWtcX2sgAwxuwQkRJsD2xNmP1pGqKeaFyGTbN9LSLfA3caY94M0Z5fjDHbXfN+ANxpm5DH1/t3DiH4GHpTVm0JPKY/4A9+0VhXfRGfQ4CLReRq17wcV1tC+dH1eneIae+J8trUHap92cAm8Y9nyKBm+xhN2er2yfveCvjbGGOqRGQd9n1ZCWwwToR2BP9vnCEiI13zmmG/tQQ7BHhdRKpc8yqx74ENUexP0mnAD82D7bGMwPYkIjFB0y8AjwInG2P2iMiDQOugMu2Br53XHbA9juqsA743xhweRdkt2JRU8HbcdX1gjBka5XZbikhzY8zWoGUbsW96AESkCdCK2r3ZdwL7uurKxH5wAWCMWQmc5+TQzwRmyd7D5jY6bW3mCvodatker/bAl666vH+rgH13llVgg1DbSPvi3aUatGEdttd5dw3WSWbd67D/L63DfHAG/G3xf9i41eR4VGcjrk6U2E+h9tj3gQEOFhFxBf0OwHfO63WAxxhzVxTbWQf8yRjz37i1PMH0pG0ITmC7E3hMRM4SkWbOCZoe2BxlJM2wvcw9ItIL+GOIMreJyL4i0hWbR58ZRbMWAdtF5GYR2UdEMkXkGBH5fYj2V2JzypOc7RyNzc16vQkcISIXiki28/N794ktV12bgLewx6KFU3aAs/hF4FIR6SEijbBpo4XGmDVR7E+wb7G97VPFDoW7FfvtCAARuUBEDjDGVGFTa2BTTe62rsN+Pb9HRBo7Jx8vA2K5tuFGZ7/bY3O23r/Vi8B1ItJJRJpi932mE/Ai7kstPAGMEZHeYjVx6m4WQ51xq9t5j7wL/K+I7Of8rxwqIgOdIsuAAWKvmdgfm4pMpJeBU0VksHP8/4L9QPoY25mrAK5x3stnAr1c63qPR56zH5GOx1TgbhE5BEBEDhCREYncsVhpwA/DGHMfcD32hM+Pzs804GbsGyecscBkEdmOPcH6cogyH2BPIhUB9xtj3o2iPZXAadgTkd9jT5Y+iT0JF8pV2K+4m7H54OmuurYDJwLnYntDm4G/Ez4oXYjNTX6NPVk23qlnHjZf+iqwCTsS6Nzq9iXM/v2GPXZPYntiOwH3SJeTgC9FZAd2uOy5xpjdIao6D5sj3og9kXiH087aegObA1+GPXn9lDP/aWwKbD7277EHm0eOZl9qxNhx4Vdgvzn+in3vXFLb+hJU90XYVNBXTj2zsDltjDFzsR+Un2OPZXAqLq6MMd9gz8M9gv0/OR043RhTZowpw35DvAT4BXs+6zXXukuwnYSHneWRjsdDwGzgXef/fQH2XFudJYGpLJVIItIRGxyyI+SMVR0hIgY43BizKtVtUSoetIevlFJpQgO+UkqlCU3pKKVUmtAevlJKpYk6Ow6/devWpmPHjqluhlJK1StLly792RgTfN0HUIcDfseOHVmyZEmqm6GUUvWKiPwQbpmmdJRSKk1owFdKqTShAV8ppdJEnc3hh1JeXs769evZs2dPqptS5zVu3Jh27dqRnd2gntCmlIpBvQr469evp1mzZnTs2BHRx8qGZYyhpKSE9evX06lTp1Q3RylVR8QlpSMiT4t9zNvyMMtFRB4W+8i5z0XkuNpsZ8+ePbRq1UqDfTVEhFatWuk3IaVUgHjl8Gdg72YYzsnA4c7PaOxTZWpFg3109DgppYLFJeAbY+ZjbyUazgjsowCNMWYB0FxE2sRj20op1WAYA//4ByxcmJDqkzVK52ACH2G23pkXQERGi8gSEVmyZcuWJDUt9TZu3Mhzz4V6uqBSKm28/TZkZMBNN8H48QnZRJ0almmMKTTG5Bpjcg84IOSVwSlljKF///689dZbvnmvvPIKJ50UKZsV2datW7n++usZPHhwyOVr1qzhmGO
      "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": 7,
   "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=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": 8,
   "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": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEICAYAAABCnX+uAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABEVUlEQVR4nO2dZ3hV1dKA35UCoSMEgnSkqDQpQURQQBRRbCgoqCiiIojtevWzXK+gonKtFxWxIiIIKKJXKYog3QqIVAWkSFMg1NASkvl+zEk8hJN+ajLv8+znlL33WrPO3mf2WrNmZjkRwTAMw4hcokItgGEYhlE4TJEbhmFEOKbIDcMwIhxT5IZhGBGOKXLDMIwIxxS5YRhGhGOKvAjgnGvvnPvJOVcph2PGOOeGed6f55z7rYB1veGc+3dBZc1HPZ2cc1sDXU9+cc7Vdc6Jcy7G8znKOfeBc+5+H8f2c84tLGA9lZxz65xzLQorY6BwzsU755Y55xIDWY+RO6bIwwjn3Cbn3BHnXLJz7i+P8i2byzm1gGeA7iKyJy/1iMgCETk9D/KcpIhEZKCIPJWXeooDIpIO9APOds5182O5e4A+wKhAK+SC4JyLBd4H7hSRxTkcV9I5N9o5d8A592fWB55z7lrn3Brn3EHn3Grn3FUBFr1IYoo8/LhcRMoCrYBE4LGsB3j/sUVki4h0FJGdQZTR8EJE0kSkt4h86edyFwNPAw39WW5BcUoUgIikikh3Efk2l9OGovLXAToD/5fxwHPO1QDGAfcD5YEHgQ+dc1UD1IQiiynyMEVEtgEzgKYAnqHyYOfcOmCd57vLPEPbfc65b51zzTPOd861dM4t9fR0JgFxXvtOMFs452o556Y453Y555Kcc685584E3gDaeUYI+zzHZppoPJ9vd86td87tcc597pyr7rVPnHMDPSaCfc65kc4556u9zrlSnrL3OudWA22y7BfnXAOvzyfIkeXYfs65Rc65lz31bnDOnev5fotzbqdz7mav4ys458Z62r/ZOfdYhsJyzkU7515wzu12zm0Aumepq4Jz7l3n3A7n3Dbn3DPOuehs5DrDOfe157f6zTl3ra/jPMfe4pxbA0wApjnn7sjh2PzKOCxDRs+5L3rO3eicu8udaDqa65x72jm3CDgMnJafdgA3A0+JyF4RWQO8jY5gAGoC+0RkhijTgENA/RzKM3whIraFyQZsAi70vK8FrEL/BAACfA1UAkoBLYGdQFsgGv3DbAJKAiWAzcA/gFigJ5AKDPOU1QnY6nkfDfwCvAyUQRV+B8++fsDCLDKO8SrnAmA3OnooCbwKzPc6VoCpQEWgNrAL6JZN24cDCzztqwWszJDRq6wGvuTwUVY/4Dhwi6d9w4A/gJEeObsCB4GynuPHAv8DygF1gbXArZ59A4FfPTJVAuZ4ZInx7P8UeAcoCyQAi4HBWX8/z2+7xSNTjOf67QYaZ9OG7qhCc0BHVIm2yubYvMj4pkeGqsCPwB1e565GleopwKws5871/HZNPHJXyGs7POUJkOD1XU9ghde9Nw+4wvP+KmArUCbU/8VI20IugG1eF0MVcTKwD1XErwOlPPsEuMDr2FF4lLzXd795/vTnA9sB57XvW3wr8naogo3xIU+mIvL6boxXOe8Cz3ntK4s+MOp6ydzBa/9HwMPZtH0DXkoeGEDhFPk6r8/NfCiUJKCFR4GkeCsi4A5gruf9N8BAr31dMxQdqrhTgNJe+6/3Ojfz9wOuAxZkkfNNYEge743PgHuz2ZebjMcy7iPP/j7AHK9z7/DadyEnK/InvfbnuR3og0WAOK/vLgI2eX2+Fb3nj6MPq+6h/h9G4hZ2kygGV4nIrGz2bfF6Xwe42Tl3t9d3JYDq6J9nm3j+KR42Z1NmLWCziBwvgKzVgaUZH0Qk2TmXBNRAH0oAf3odfxhV9tmV5d2+7OTNK395vT/ikS/rd2WBeHTU4l3fZrQNuclVB+0xL/WyGMUCe33IUwdom2Gi8hADfOBLeOfcJcAQoBFqAi0NrPB1bB5kjAV2eMkY5XV81nO93/v6Lj/tSPa8lgeOer0/COCcuxB4Du1YLAVaA5875y4RkWU+yjOywRR5ZOGtmLcAT4vI01kPcs51BGo455yXMq8N/O6jzC1AbedcjA9lnltqzO3oHzuj3jJAZWBbLuf5Ygd/m5My5PXmMKrMMqiGDsMLy250FFEHNTFk1J3Rhgy58NqXwRYgDWgmIqm51LMFmCciF+UmkHOuJPAJcBPwPxFJdc59hj40fJGbjMeA+Gwe1jtQs0oGtXwck/W+y1M7RGSvc24HcBZqFsTzPuMat0BNcRleLz85535ARwXLcivf+Bub7Ixc3gYGOufaOqWMc667c64c8B06VL3HORfrnLsaODubcn5E/8zDPWXEOefae/b9BdR0zpXI5twJwC3OuRYe5fMM8IOIbCpAez4CHnHOneKcqwncnWX/MuB6z+RcN9SEVGhEJM1T99POuXLOuTqoF8U4L7nucc7VdM6dAjzsde4O4Cvgv54JxSjnXH3PgzQrU4FGzrm+nmsS65xr43RSOSslUFv+LuC4p3feNYdm5CbjTOBF51x5HzJ+BNzrnKvhnKsIPJTjD5a/doDOPzzmua5nALejZjGAn4DznMdX3jnXEjgPWJ6LDEYWTJFHKJ5ezO3Aa+hQfj0ebwARSQGu9nzeg9o1p2RTThpwOdAAndTa6jke1H66CvjTObfbx7mzgH+jvccd6ORc7wI26QnUJLARVTxZh+r3euTcB9yA2oz9xd2ot8QGYCHwITDas+9tVFn/gg7/s/6ON6H/o1XodZgMnJq1AhE5iCrj3uhI5k/gP6jC9nXsPaiS3Yva3T/PQf68yFgCHXFklfFt9PdeDvwMTEc7AWm+KspPOzwMQUeCm9GJzefF46YpIvNQ98TJzrmD6H30jIjMzKGthg/ciWZUwzCKM57e/xsiUifXg42wwXrkhlGMceq/f6lzLsZpgM4Q1F3RiCCsR24YxRjnXGnU5HEG6skzDXVzPBBSwYx8YYrcMAwjwjHTimEYRoQTEj/y+Ph4qVu3boHOPXToEGXKlPGvQCHC2hKeWFvCE2sLLFmyZLeIVMn6fUgUed26dVm8ONvMlzkyd+5cOnXq5F+BQoS1JTyxtoQn1hZwzvmMeDbTimEYRoRjitwwDCPCMUVuGIYR4YRN0qzU1FS2bt3K0aNHczyuQoUKrFmzJkhSBZZIaktcXBw1a9YkNjY21KIYhpGFsFHkW7dupVy5ctStWxfnexEZAA4ePEi5cuWCKFngiJS2iAhJSUls3bqVevXqhVocwzCyEDamlaNHj1K5cuUclbgRGpxzVK5cOdfRkhFCduyAjh3hzz9zP9YocoSNIgdMiYcxdm3CnKeegoUL4cknQy2JEQLCSpEbwWHatGksX24pn4sEpUqBczBqFKSn66tz+r1RbDBF7sXTTz9NkyZNaN68OS1atOCHH37I1/ljxoxh+/btmZ9vu+02Vq9encMZJ9KpU6cCB0plcOmll7Jv3z727dvH66+/ftL+L7/8knnz5tGsWbNC1WOECRs2wPXXQ2nP4kmlS8MNN8DGjaGVywgqka3I/WgX/O6775g6dSpLly5l+fLlzJo1i1q1fK165Zu0tLSTFPk777xD48aNCy2bN8eP57y05vTp06lYsWK2irxbt24899xzZiopKpx6KpQvD0ePQlycvpYvD9WqhVoyI4hEtiL3o11wx44dxMfHU7KkLnQSHx9P9erVAZg9ezYtW7akWbNm9O/fn2PHjgGaauChhx6iVatWTJgwgcWLF3PDDTfQokULjhw5ckIPe+bMmbRr145WrVrRq1cvkpOTfcrxwQcf0KJFC5o2bcqPP/4IwNChQ+nbty/t27enb9++jBkzhrvuuivznMsuu4y5c+dmyrR7924efvhhfv/9d1q0aMGDDz4IwPPPP0+bNm1o3rw5Q4YMyTx/3LhxnH322bRo0YI77riDtDSfi8MY4cpff8HAgfD99/pqE57FjshU5AGwC3bt2pUtW7bQqFEj7rzzTubNmweoN02/fv2YNGkSK1a
      "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 = 4\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEXCAYAAACzhgONAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA2wUlEQVR4nO3debzM9f7A8dfbsR9Cdk62lKUoUbgkacMlk+VGSllzK9Jyu9Kv7d603TZr2hxC3Fahoo2otJEkrhYhJVuy7z6/P94zHMc5x5xz5jvfWd7Px2MeZ2bOzPf7/lrmPZ/t/RHnHMYYY0x2CvgdgDHGmNhmicIYY0yOLFEYY4zJkSUKY4wxObJEYYwxJkeWKIwxxuTIEoUx2RCRFiLyg4jsFJGAiLwjItcGf3ediHyci2OtFpGLvYvWGO8U9DsAYyJJRFYDFYFDwC7gHeAm59zOPBzuX8Bo59yI4OPpEYrxPgDn3H2ROJ4xXrMWhUlEHZ1zJYBzgCbA/2V+gYiE8yWpOvBdhGPLs6xiDvM68nUOYyxRmITlnPsVbVGcCSAiTkRuFJEfgB+Cz/UXkR9F5A8RmSEiVYLP/wTUAmYGu56KiMg8EekXzrlF5BoRWSMiW0TkrhO8toOILBGRP0XkUxFpmOF3q0XknyKyFNglIrWD19FXRNYCHwZf10dEVojIVhGZIyLVg8/XCL6+YIZjHrmOYBfaJyLypIhsAe4L6w/XJBVLFCZhicgpQHvg6wxPB4CmQH0RaQM8BPwNqAysAaYBOOdOBdYSbJ045/bl4rz1gaeBa4AqQFkgLfR759x9oW4nEWkEjAeuD77uGWCGiBTJcMgewF+B0sDB4HMXAPWAy0SkEzAM6AyUBxYAU8ONF/3zWIV22Q3PxftMkrBEYRLRdBH5E/gY+Ah4MMPvHnLO/eGc2wP0BMY75xYHE8GdQHMRqZHP83cFZjnn5gePezdwOJvXDgCecc597pw75JybCOwDmmV4zUjn3C/BmEPuc87tCj43MHhdK5xzB4PXe3aoVRGG35xzo5xzBzOdwxjAEoVJTAHnXGnnXHXn3A2ZPvx+yXC/CtqKACA44L0FqJrP81fJeB7n3K7gcbNSHbgt2O30ZzDBnRI8RlYxZ/VcdWBEhvf/AQjhX0dWxzfmCBu4MskmY7nk39APWQBEJBXt/vk1n+dYj3YLhY5bPHjcrPwCDHfO5dTlk1WJ54zPhY4xJfOLRKR88G5xYHvwfqUwjm/MEdaiMMlsKtBbRM4Ojgk8CHzunFudz+O+CnQQkZYiUhidZpvd/7XngIEi0lRUqoj8VURK5uJ844A7ReQMABEpJSLdAJxzm9DEd7WIpIhIH+DUvF6YSU6WKEzScs69j44fvIa2Ak4FukfguN8BNwIvBY+7FViXzWu/AvoDo4Ov+xG4LpfnewN4BJgmItuBZUC7DC/pD/wD7f46A/g0N8c3RmzjImOMMTmxFoUxxpgcWaIwxhiTI0sUxhhjcmSJwhhjTI4sURhjjMlRQi64K1eunKtRo4bfYYRl165dpKam+h2GJ+za4lciX59dW9YWLVq02TlXPqvfJWSiqFGjBl999ZXfYYRl3rx5tG7d2u8wPGHXFr8S+frs2rImImuy+511PRljjMmRJQpjjDE5skRhjDEmRwk1RiEiHYGOtWvXPu53Bw4cYN26dezduzf6geWgVKlSrFixwvPzFC1alLS0NAoVKuT5uYwxiSWhEoVzbiYws0mTJv0z/27dunWULFmSGjVqICI+RJe1HTt2ULJkbgqF5p5zji1btrBu3Tpq1qzp6bmMMYknabqe9u7dS9myZWMqSUSLiFC2bNmYa00ZY+JD0iQKICmTREgyX7sxIc7pzeROQnU9xbqUlBQaNGhw5HH37t258cYb2b9/P3fccQezZs0CoG7duowdO5Zq1aoB+iHfs2dPJk+eDMDBgwepXLkyTZs2pUuXLowYMQKA5cuXU6dOHVJSUmjbti0PP/xwlK/QmNi1axe0bw8ff3wBJUpA8eKQmnr0Z3b3c/v7IkUg0b6XWaKIomLFirFkyZJjntuxYwfDhg1jx44drFy5kpSUFNLT0+nUqROLFi2iQIECpKamsmzZMvbs2UOxYsV47733qFpVt0Pu3bs3vXv3BnSh4dy5cylXrly0L82YmHb4MPTqBR9/DFdc8SunnJLGrl2wezfH/Nyy5ej90PP79+fuXAUKHE0apUvDSy/BOed4cllRY4nCZ7t37yY9PZ2ff/6ZlJQUQD/8x48fz/vvv8+ll14KQPv27Xnrrbfo2rUrU6dOpUePHixYsMDP0I2JG3ffDa+/Dk88AY0a/Ujr1mlhv/fAAU0YmRNIxp/Z3Z80CcaPt0QRn4YMgUzf7PPt7LPhqadyfMmePXs4++yzjzy+8847qVatGtWqVeOkk0465rVNmjRh+fLlRxJF9+7d+de//kWHDh1YunQpffr0sURhTBgmT4YHH4R+/fS//kcf5e79hQpBqVJ6y61Nm+DNN2HUqPjujkrOROGTrLqeFi5cGNZ7GzZsyOrVq5k6dSrt27f3IDpjEs/ChdC3L7RuDWPGRP/DulMneOMNWLwYGjeO7rkjKTkTxQm++UdTzZo1Wbt27XHrKRYtWkSXLl2Oee3ll1/O7bffzrx589iyZUu0QzUmrqxZA4EAVKsGr74KhQtHP4YOHXTMYvr0+E4USTU9NhalpqZy7bXXcuutt3Lo0CEAXnzxRYoWLUqLFi2OeW2fPn249957j5k5ZYw53o4d0LEj7NsHM2dC2bL+xFG2LLRqpYkinlmiiKLQGEXoNnToUAAeeughihUrRp06dahatSpPPPEEb7755nFrH9LS0hg8eLAfoRsTNw4dgquuguXL4ZVXoG5df+MJBGDZMvjxR3/jyA9LFFF06NAhlixZcuQWWudQpEgRRo4cyY8//siiRYsQESZNmnTkfTt37jzuWK1btz6y7iJk9erVNjXWJL2hQ2HWLBgxAi65xO9odJwCdFA7XiXnGEUMq1SpEl9//bXfYRgTl8aPh8cegxtv1FssqFFDJ0VOnw633eZzMHlkLQpjTEL46CMYOFBbETE0XwXQ7qdPPoGNG/2OJG8sURhj4t5PP0GXLlCrFrz8MhSMsb6STp20xlSm3uK4YYnCGBPXtm3TGU6HD+sHcenSfkd0vLPOgurV43f2kyUKY0zcOngQrrwSfvhBS3RksWdZTBDR7qd334Us5qbEPEsUxpi4ddttMGcOPP20rr6OZYGArut4912/I8k9SxRRlJKScsw6itD02P379zNkyBBq165N7dq16dChA2vXrj3yPhHh6quvPvL44MGDlC9fng4dOhxz/EAgQLNmzaJzMcb4bNw4GDkSbrlF6zjFupYt4eST47P7KcaGfBKbF2XGQ/78808WLVpEiRIlWLVqFbVq1YrilRkTXe+/DzfdpPtL/Oc/fkcTnoIFdSxlxgytSBtP29dbi8JnoTLjTz755DFlxkuUKMH7779/5HWhMuPAkTLjGb3++ut07NiR7t27M23atOhdgDFR9v330K2brrieOhWC/23iQiAAW7dCvBV+TsoWhU9Vxj0tMz516lTuueceKlasSJcuXRg2bFiErsyY2PHHH1por2BBreGU6b9NzLv0UihWTFdpt2njdzThS8pE4Revyoxv2LCBH374gZYtWyIiFCpUiGXLlnHmmWdGKnRjfHfggLYk1qyBDz6AmjX9jij3ihfXBYHTp+sXy3jZoyIpE0UsrdqMRJnxl19+ma1bt1Iz+D9n+/btTJ06leHDh0fnIozxmHMwaBB8+CFMnKgDw/EqENBxiiVLoFEjv6MJj41R+CwSZcanTp3K7NmzWb16NatXr2bRokU2TmESyqhR8MwzWvCvVy+/o8mfjHtUxAtLFFHkRZnx1atXs2bNmmOmxdasWZNSpUrx+eefe39Rxnhs9mydAhsIQCI0ksuX1xZRPCWKpOx68kuoxZDRjh07jpQZHzlyJL///jvt2rVj0qRJDBgwAMi+zHjr4AqjX3/99bjfL168OLLBG+OD5ct15XWDBjBpkn4TTwSBANx6K6xapfW
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA2fElEQVR4nO2deXxU1fn/308SAi0gqwvI6q7sNkIjLkEQ3HEXq6LWSq0raluN1opYi/r1V1dapYqIWjfcaEWpoEGtQQiKyi4oQlgUo6gghJCc3x/nXnJncmdJZp953q9XXjP33HPPee6Zm88885xNjDEoiqIo2U9eqg1QFEVRkoMKvqIoSo6ggq8oipIjqOAriqLkCCr4iqIoOYIKvqIoSo6ggp9miMg4EXkqQWVfJCLvJaLspiIifxGRb0RkYwxldBORLSKSH0/bUo2ITBGRv6SBHYtFpMR5v+v5FJEeImJEpCCV9sWD4LYWkSIR+UhEOvjkXS0iw5JrYXxQwY+AiIwSkQ9EZKuIfO28v1xEJNW2ZToi0g24HjjEGLNXU8sxxqwxxrQyxtTGzzrFxRjTyxhTlmo7kokxpgK4AnhCRJql2p54oYIfBhG5Hrgf+D9gL2BP4DJgMFAY4pqs8jITTDegyhjzdaoNSTXZ4CXHC7GkXJuMMe8bY04yxtSk2pZ4kfJGTVdEpA0wHrjcGDPNGPOjsXxkjDnPGFPt5JsiIv8QkRkishUYIiInOj8HfxCRtSIyzlOu+zN4jIisF5ENIvL7oOoLRWSqiPzo/Jwu8lzfWUReFJFNIvKFiFwd5h46iMh0x455wL5B5w8SkTdF5FsRWS4iZ4cpq72IPO7Y/J2IvOI5d6mIrHTKmS4inT3njIhcJiKfichmEZno/EMPA94EOjvhmCkiUiIilUH17vr5LCIDRaTCuZ+vRORvQW1a4Gmj6Y49K0XkUk9540Tk+VDt63PfRkSuFpHPndDT/7liJCJ5IvInEfnS+fU31XluiOJexonINBF5SkR+AC4KZYPn+pNEZKHTju+LSN8Idl/utPuPInK7iOzrXPeD0waF0ZQtUYYwRKSNiDzmPNPrxIbr8j33+5Qnb/BnViYid4jI/4CfgH18yl8tIn8QkU/E/uJ+TET2FJHXnXucJSLtPPlPcT7fzU75B3vODRCRD53rngNahGnrchHpH+Ke80TkRhFZJSJVTru2j9RWKcMYo38+f8BxwE6gIEK+KcD3WK8/D/vglAB9nOO+wFfAqU7+HoABngFaOvk2AcOc8+OA7cAJQD4wAZjrnMsDFgB/xv7C2Af4HBgRwrZngeedenoD64D3nHMtgbXAxUABMAD4Bhte8SvrNeA5oB3QDDjaST/Gue5QoDnwIPCO5zoD/Adoi/XoNwHHOedKgEpP3oBjJ221p23KgQuc962AXwa1aYFz/A7wd+ez6O/UeUyk9g1x3wZ4G2jv2L8C+I1z7tfASudzaAW8BDwZ5b2MA2qAU53P9Wchnq2/OO8HAF8Dgxy7L3TKax7G7leB3YBeQDUw27G1DbAEuDCasn3sfipEu78MPIJ9tvYA5gG/Db4uxLVlwBrH1gKgmc89rQbmYn9p7+3Y/KFjfwvgLeBWJ+8BwFbgWOzz+kfnsyp0/r4ErnXOnel8Ft623gQUO+3xa8e2Fj7tcY1jUxfs8/8I8Eyq9Svk85xqA9L1Dzgf2BiU9j6wGdgGHOWkTQGmRijrPuBe5737oB/kOX838Jjzfhwwy3PuEGCb834QsCao7FLgcZ86852H2FvPX6kX/HOAd4OuecT9hwlK7wTUAe18zj0G3O05buXU28M5NsARnvPPAzc670tonOC/A9wGdAzK47ZpAdAVqAVae85PAKZEat8Qn53B+YJyji8HZjvvZ2N/AbrnDnTuvSCKexmH54sxRN1TqBehfwC3B51fjvPFG8LuwZ7jBcANnuP/B9wXTdk+djcQfKwIV+P54gLOBd4Ovi74Wue4DBgfoT1WA+d5jl8E/uE5vgp4xXl/C/C851we1uEpAY4C1gMS9L/tbes7gupeAZT4tMdSYGjQ/0oNERzFVP1p3DA0VUBHESkwxuwEMMYcDuD8VPeGw9Z6LxSRQcCdWK+6EPvN/0JQ+d5rvsR6+i7eESs/AS2cn77dsSGQzZ7z+cC7Pvbvjv1HDK7HpTswKKisAuBJn7K6At8aY77zOdcZ62UBYIzZIiJVWA9sdYj7aeVTTjRcgg2zLRORL4DbjDH/8bHnW2PMj560LwFv2Ma3fd3P2YfgNnRDVp0JbNMvqRe/aFgbOcsuugMXishVnrRCjy1+fOV5v83n2O0ob0rZfvY1AzZI/XiGPBp3j9HkjXRP7rMV8NkYY+pEZC32uawF1hlHoR2C/zdOFZEzPGmtsb9agukOvCwidZ60WuwzsC6K+0kqKvihKcd6LCOxnkQ4TNDxv4CHgOONMdtF5D6gY1CersAy5303rMcRibXAF8aY/aPIuwkbkgqux1vWHGPMsVHW215E2hpjNgedW4996AEQkZZAB5r2sG8Ffu4pKx/7xQWAMeYz4Fwnhn46ME0aDptb79ja2iP63Zpoj0tXYLGnLPezCrh359xOrAh1Dncv7i01woa1WK/zjkZck8yy12L/XzqG+OIM+Gyp/7Lx0pj2iMR6PE6U2G+hrtjnwAB7i4h4RL8bsMp5vxYoN8bcHkU9a4FfG2P+FzfLE4h22obAEbbbgL+LyJki0trpoOmPjVGGozXWy9wuIgOBX/nkuUVEfi4ivbBx9OeiMGse8KOI3CAiPxORfBHpLSKH+dhfi40pj3PqOQQbm3X5D3CAiFwgIs2cv8O8HVuesjYAr2Pbop2T9yjn9DPAxSLSX0SaY8NGHxhjVkdxP8GswHrbJ4odCvcn7K8jAETkfBHZ3RhThw2tgQ01eW1di/15PkFEWjidj5cAscxt+INz312xMVv3s3oGuFZEeopIK+y9P+cIXth7aQL/BC4TkUFiaemU3TqGMuNWtvOM/Bf4fyKym/O/sq+IHO1kWQgcJXbORBtsKDKRPA+cKCJDnfa/HvuF9D7WmdsJXO08y6cDAz3Xuu1R7NxHuPZ4GLhDRLoDiMjuIjIykTcWCyr4YTDG3A1ch+3w+cr5ewS4AfvghOJyYLyI/IjtYH3eJ88cbCfSbOAeY8x/o7CnFjgJ2xH5Bbaz9FFsJ5wfV2J/4m7ExoMf95T1IzAcGIX1hjYCdxFalC7AxiaXYTvLxjrlzMLGS18ENmBHAo2KdC8h7u97bNs9ivXEtgLekS7HAYtFZAt2uOwoY8w2n6LOxcaI12M7Em917Gwqr2Jj4AuxndePOemTsSGwd7Cfx3ZsHDmae2kUxo4LvxT7y/E77LNzUVPLS1DZo7GhoCVOOdOwMW2MMW9ivyg/wbZlcCgurhhjlmP74R7E/p+cDJxsjNlhjNmB/YV4EfAttj/rJc+1FVgn4QHnfLj2uB+YDvzX+X+fi+1rS0skMIylJBoR6YEVh2ZhYsZKmiAiBtjfGLMy1bYoSqyoh68oipIjqOAriqLkCBrSURRFyRHUw1cURckR0nocfseOHU2PHj1SbYaiKErGsGDBgm+MMcFzPoA0F/wePXpQUVGRajMURVEyBhH5MtQ5DekoiqLkCCr4iqIoOYIKvqIoSo6Q1jF8P2pqaqisrGT79u2pNiWtadGiBV26dKFZs6zZnU1RlBjJOMGvrKykdevW9OjRA9FtZX0xxlBVVUVlZSU9e/ZMtTmKoqQJcQnpiMhksVu8LQpxXkTkAbHbzX0iIoc2ta7t27fToUMHFfswiAgdOnTQX0GKogQQrxj+FOxKhqE4Htjf+RuD3VGmyajYR0bbSFGUYOIi+MaYd7DLiIZiJHYbQGOMmQu0FZFO8ahbURQlazAG/vQnuPHGhBSfrFE6exO4fVmlk9YAERkjIhUiUrFp06akGJdq1q9fz5NP+u0sqChKTmCMFfm8PLjjDrjrroRUk3bDMo0xk4wxRcaYot13950dnDKMMRxxxBG8/vrru9JeeOEFjjsuXDQrPJs3b+a6665j6NChvudXr15N7969AaioqODqq68GoKy
      "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": 9,
   "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": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABNkElEQVR4nO2dd3gU1deA35uEkNA70nuRGgioiAhIEQEFVERUpCgdpPxQuvSOqGgEFQtNQBGUTxCRJlKkGnpHkCYlgBB6kvv9cTZxE3ZTN5nd7H2fZ5/dmbkzc+7u7Jk7556itNYYDAaDIf3jY7UABoPBYEgbjMI3GAwGL8EofIPBYPASjMI3GAwGL8EofIPBYPASjMI3GAwGL8EofC9CKVVbKbVdKZUrnjZfK6XG2j7XUUodTua5ZiqlhidX1iScp55S6kxqnyepKKWKK6W0UsrPtuyjlJqrlOrvoG0HpdTGZJ4nl1LqqFIqKKUyphZKqTxKqVClVI3UPI8hYYzC90CUUieVUreVUuFKqQs2JZ0lgX2KAOOBZlrrK4k5j9b6d611uUTI84DC0lp301qPScx5vAGtdRTQAXhEKdXEhce9ArQFZqS24k4OSqkMwGygh9Z6RzztMiqlvlRKXVdK/WN/Y7S7MYXbvVJ9MJEecbsLxJBontVar1ZKFQJ+AYYBg+wbKKX8tNYRAFrr00DdtBfTEI3WOhJ4ORWOu0MpNQ4oAxx09fGTilJKAUprHaW1vg80S8RuIxH5iwEPAeuUUge01ivt2uSIvp4NycOM8D0crfVZ4GegEoBtJNRTKXUUOGpb19z2SH1NKbVZKVUlen+lVDWl1C6l1A2l1CIgwG5bLHOJUqqIUmqJUuqSUipMKfWxUuphYCZQyzbyumZrG2Masi13VkodU0pdUUotU0oVtNumlVLdbKaJa0qpEJvSeAClVKDt2FeVUgeAmnG2a6VUabvlWHLEadtBKbVJKfW+7bwnlFKP29afVkpdVEq1t2ufXSk1x9b/U0qpYUopH9s2X6XUVKXUZaXUCeIoOdu+XyilziulziqlxiulfJ3IVV4p9avtuzqslHrJUTtb245KqYPAAmC5UqprPG2TKuPYaBlt+75n2/cvpVQvFdtktV4pNU4ptQm4BZRMSj+A9sAYrfVVrfVB4HPkicjgSrTW5uVhL+Ak0ND2uQiwH/mzAGjgVyAXEAhUAy4CjwK+yB/rJJAR8AdOAf2ADMCLwH1grO1Y9YAzts++wG7gfSAzcmN4wratA7Axjoxf2x3nKeAyUN123o+ADXZtNfATkAMoClwCmjjp+0Tgd1v/igD7omW0O1ZpR3I4OFYHIALoaOvfWOBvIMQmZ2PgBpDF1n4O8COQFSgOHAHesG3rBhyyyZQLWGeTxc+2fSkwC8gC5Ad2AD3jfn+27/a0TSY/2+93GajgpA/NgFKAQp7gbgHVnbRNjIyf2mTIB2wDutrtewAoDOQEVsfZd73tu6tokzt7YvthO54G8tutexHYa/tc3Lb9LHAG+ArIY/X/0BNflgtgXsn40URhhwPXEIX9CRBo26aBp+zazsB2M7Bbd9imHJ4EziGP39HbNuNY4ddCFLGfA3liFJbduq/tjvMFMNluWxbkxlLcTuYn7LZ/Cwxy0vcT2N0MgC6kTOEftVuu7EDxhAFByA3hnr3CAroC622f1wLd7LY1jlaIiIK/B2Sy2/6K3b4x3x/QBvg9jpyfAiMSeW38APRxsi0hGe9GX0e27W2BdXb7drXb1pAHFf5ou+2J7gdyA9JAgN26RsBJu+ulhp2ci4FfrP4feuLL2PA9l5Za69VOtp22+1wMaK+U6m23zh8oiG3UpG3/KhunnByzCHBKJ8+GWhDYFb2gtQ5XSoUBhZCbF8A/du1vIX9yZ8ey758zeRPLBbvPt23yxV2XBciDPAXZn+8U0oeE5CqGjMB32VmqMgBXHchTDHg02jRmww+Y60h4pdQzwAigLGKizQTsddQ2ETJmAM7byehj1z7uvvafHa1LSj/Cbe/ZgDt2n2+AXC/IExHABaVUL5ucWbXWNxwcz+AEo/DTJ/YK/DQwTms9Lm4jpVRdoJBSStkp/aLAcQfHPA0UtZ8IdnI+R5xDFED0eTMDuZFH9KRynv/MWNHy2nMLUXrRPISYAVLKZeSppBhi2og+d3QfouXCbls0p4FIoLKWScz4OA38prVulJBASqmMwPfA68CPWuv7SqkfkJuLIxKS8S5iKnF0Uz+PmHOiKeKgTdzrLlH90FpfVUqdB6oi5khsn/c728X2buYgk4j5wtI/nwPdlFKPKiGzUqqZUiorsAWxYb+llMqglHoeeMTJcbYhf/qJtmMEKKVq27ZdAAorpfyd7LsA6KiUCrIpqfHAVq31yWT051tgsFIqp1KqMNA7zvZQ4BXbJGMTXOSZpMXD5ltgnFIqq1KqGNAfmGcn11tKqcJKqZzYeUxprc8jnlQf2CZGfZRSpWw33Lj8BJRVSrWz/SYZlFI1lUyOx8UfmWu4BETYRvuN4+lGQjKuAt5TSmVzIOO3QB+lVCGlVA5gYLxfWNL6ATI/Msz2u5YHOiPmOGzXbjmbTLmB6Yg57N8EZDDEwSj8dI4W3+fOwMeICeEYNu8HrfU94Hnb8hXE7rrEyXEigWeB0sjk3BlbexD77n7gH6XUZQf7rgaGI6PR88gkY3LdE0chpoi/EAUV10TQxybnNeBVxKbtKnoDN5F5hI3AN8CXtm2fI0p9N2K+ivs9vo783/Yjv8NioEDcE9hMFI2R7+ccYuqahCh2R23fQpTxVWReYFk88idGRn/kCSaujJ8j3/ce4E9gBTJYiHR0oqT0w8YI5MnyFPAbMEX/55JZEliJmHj2IU8ibePpp8EJKrb51mAwGBLG9jQxU2tdLMHGBrfBjPANBkOCKIl/aKqU8lMS7DcCceM0eBBmhG8wGBJEKZUJMbWURzyXliPun9ctFcyQJIzCNxgMBi/BmHQMBoPBS3BbP/w8efLo4sWLJ3v/mzdvkjlzZtcJZBHppR9g+uKumL64J8nty86dOy9rrfM62ua2Cr948eLs2OE0m2qCrF+/nnr16rlOIItIL/0A0xd3xfTFPUluX5RSTqPPjUnHYDAYvASj8A0Gg8FLMArfYDAYvAS3teE74v79+5w5c4Y7d+4k2DZ79uwcPGh58Z8U40n9CAgIoHDhwmTIkMFqUQwGgwM8SuGfOXOGrFmzUrx4cZTjgkgx3Lhxg6xZs6aRZKmHp/RDa01YWBhnzpyhRIkSVotjMBgc4FEmnTt37pA7d+4Elb0h7VFKkTt37kQ9fRks4vx5qFsX/vkn4baGdIlHKXzAKHs3xvw2bs6YMbBxI4webbUkBovwOIVvSBuWL1/Onj17rBbD4AoCA0EpmDEDoqLkXSlZb/AqjMJPIuPGjaNixYpUqVKFoKAgtm7dmqT9v/76a86dOxez/Oabb3LgwIF49ohNvXr1UhSQBtC0aVOuXbvGtWvX+OSTTx7YvnLlSn777TcqV66covMY3IQTJ+CVVyCTrRBYpkzw6qvw11/WymVIc9K/wneh3XLLli389NNP7Nq1iz179rB69WqKFHFU6c0xkZGRDyj8WbNmUaFChRTLZk9ERPxlZ1esWEGOHDmcKvwmTZowefJkY6JJLxQoANmywZ07EBAg79mywUMPWS2ZIY1J/wrfhXbL8+fPkydPHjJmlKI9efLkoWDBggCsWbOGatWqUblyZTp16sTdu3cBSRExcOBAqlevzoIFC9ixYwevvvoqQUFB3L59O9aIfdWqVdSqVYvq1avTunVrwsPDHcoxd+5cgoKCqFSpEtu2bQNg5MiRtGvXjtq1a9OuXTu+/vprevXqFbNP8+bNWb9+fYxMly9fZtCgQRw/fpygoCDefvttAKZMmULNmjWpUqUKI0aMiNl/3rx5PPLIIwQFBdG1a1ciIx0WOjK4KxcuQLdu8Mcf8m4mbr2SdKvws+TN63K7ZePGjTl9+jRly5alR48e/Pbbb4B4D3Xo0IFFixaxd+9eIiIimDFjRsx+uXPnZteuXbz22mvUqFGD+fPnExoaSqCdLJc
      "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": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABOVElEQVR4nO2deZyN1f/A32dmMDP2bNmyU/Z9SVkSSb6kCEVE2SmSJcoSspdkCRWylqX8Itmzk31NluwKgxjrLOf3x+eZ6Rp39jt3Pe/X677ufZ7nPOf5nLt87nk+57MorTUGg8Fg8H78XC2AwWAwGJyDUfgGg8HgIxiFbzAYDD6CUfgGg8HgIxiFbzAYDD6CUfgGg8HgIxiF70MopaorpX5XSj0WR5uZSqlh1utnlVLHknitqUqpj5IqayKuU0spdT6lr5NYlFL5lVJaKRVgbfsppb5TSvWy07atUmpzEq/zmFLquFKqbHJlTCmUUlmVUvuUUhVT8jqG+DEK3wNRSp1WSt1VSoUqpf6xlHS6eM7JC4wAXtJaX0vIdbTWm7TWxRIgzyMKS2vdSWv9SUKu4wtorSOBtkBlpVR9B/Z7DWgJTElpxZ0UlFKpgFlAF631rjjapVFKfaOUuqmU+jvmH6NS6m2l1AnrO79SKZUrpWX3RozC91z+p7VOB5QHKgIDYzawVQBa63Na65pa68tOlNFgg9Y6QmvdQmu90sH97gKGA0Uc2W9SUYIfgNY6TGv9ktZ6azynDUbkzwfUBvpE/TEqpWohk5XGwGPAX8D8FBHeyzEK38PRWl8AfgFKAli36F2VUseB49a+htYt9Q2l1FalVOmo85VS5ZRSe5RSt5RSC4FAm2MPmUuUUnmVUkuUUleUUiFKqS+VUk8BU4Fq1uzrhtU22jRkbb9jzdCuKaWW2c7QLJk7WaaJG0qpSUopZW+8Sqkgq+/rSqkjQKUYx7VSqrDN9kNyxGjbVim1RSn1mXXdU0qpp63955RSl5VSbWzaZ1RKzbbGf0YpNTBKsSml/JVSY5VSV5VSp4CXYlwro1Lqa6XUJaXUBaXUCKWUfyxyPamUWm29V8eUUq/Za2e1fUspdRRRgMuVUh3jaJtYGYdFyWidO8469y+lVDf1sMlqg1JquFJqC3AHKJiYcQBtgE+01te11keB6cgdEUBD4Aet9WGt9QPgE6CGUqpQHP0Z7GAUvoejxFTTANhrs/tloApQXClVDvgG6AhkAb4Cllm30KmBH4HvkJnTD8CrsVzHH/gZOAPkB3IDC6wfZydgm9Y6ndY6k51znwM+BV4Dclp9LIjRrCGivEtb7V6IZciDgELW4wVEUSSHKsAB5L2ZZ8lVCSgMtAK+VP+ZyyYCGYGCQE3gTeAt69g71hjKIXdcTWNcZyagkVlseaAe8r49hFIqLbDakiU70AKYrJQqHov8l63rZrBk+UwpVT6WtgmRMdwaezlLxrdtzn0RKGvJ/7Kd/lsDHYD0wJWEjkMplRn5Xuy32b0fKGHbzM7rkvaHaYgVrbV5eNgDOA2EAjcQ5TkZCLKOaeA5m7ZTkJmT7fnHEIVVA7gIKJtjW4Fh1utawHnrdTXkRxxgR562wOYY+2ba9PM1MNrmWDogDMhvI/MzNse/B/rFMvZTQH2b7Q5RMtr0VdieHLHIfdxmu5R1fg6bfSGIkvMHHgDFbY51BDZYr9cBnWyO1bP6CgByWOcG2xx/3ebc6PcPaA5siiHnV8CgBH43fgTejeVYfDLej/oeWcdbAuttzu1oc+z5qHOt7Q3AUJvjCR4HkNfqK9BmX13gtM21riKTgSCrn0igpat/i572cLtFHkOCeVlrvSaWY+dsXucD2iilutvsSw3kQn5kF7T1q7I4E0ufeYEzWuvwJMiaC9gTtaG1DlVKhSB3Caet3X/btL+D/CnE1pft+GKTN6H8Y/P6riVfzH3pgKxAqhjXO4OMIT658iGz0j02lqpUwHU78uQDqkSZxiwCkLuwR1BKvYjc9RRF7tiDgYP22iZAxlTAJRsZ/WzaxzzX9rW9fYkZR6j1nAG4Z/P6FoDWeo1SahCw2Nr/uXXM7byz3B2j8L0TWwV+DhiutR4es5FSqiaQWymlbJT+E8BJO32eA55QSgXYUfrxpVy9iCiAqOumRUwoF+I5zx6XkD+fwzby2nIHUXpRPI5jFMNV5K4kH3DE5tpRY4iSC5tjUZwDIoBSWuuweK5zDvhNa103PoGUUmkQJfgm8JPWOkwp9SMPmz9siU/G+0DWWP7ULwF5bLbz2mkT83uXoHFora8rpS4BZRAzENbrwzZtJgGTAJRSRREnhUPx9W14GGPD936mA52UUlWUkFYp9ZJSKj2wDbHZ9lBKpVJKvQJUjqWfnciPfqTVR6BSqrp17B8gj7UmYI/5wFtKqbKWkhoB7NBan07CeL4H+iulMiul8gDdYxzfB7xuLTLWR0xXyUZrHWFde7hSKr1SKh/QC5hjI1cPpVQeyybdz+bcS8CvwOfWwqifUqqQ9Ycbk5+Bokqp1tZnkkopVUnJ4nhMUgNpEFNbuDXbrxfHMOKTcRUwTimVwY6M3wPvKqVyK6UyAX3jfMMSNw6A2cBA63N9ElkzmAlgfddKWt/fJ4BpwASttb07JEMcGIXv5Whx2XsH+BIxIZzA8n7Q4vHwirV9DbG7Lomlnwjgf8iC3llk1tzcOrwOmY39rZS6aufcNcBHyGz0ErLg2iKJQxqCmCL+QhRUTBPBu5acN4A3EJu2o+gO3EbWETYjC5LfWMemI0p9P2K+ivk+von83g4jn8MiZKHyIbTWtxCl3QK5M/obGIUodntteyDK+DqyLrAsDvkTImNq5A4mpozTkff7AOIgsAKZLETYu1BixmExCLmzPAP8BozR/7mvBiLvdSgy8diGfJ8MiUQ9bL41GAyG+LHuJqZqrfPF29jgNpgZvsFgiBcl8Q8NlFIBSqncyIx8qavlMiQOM8M3GAzxopQKRkwtTyKeS8sR98+bLhXMkCiMwjcYDAYfwZh0DAaDwUdwWz/8rFmz6vz58yf5/Nu3b5M2bVrHCeQivGUcYMbirpixuCdJHcvu3buvaq2z2Tvmtgo/f/787NoVazbVeNmwYQO1atVynEAuwlvGAWYs7ooZi3uS1LEopWKNPjcmHYPBYPARjMI3GAwGH8EofIPBYPAR3NaGb4+wsDDOnz/PvXv34m2bMWNGjh496gSpUhZPGkdgYCB58uQhVapUrhbFYDDYwaMU/vnz50mfPj358+dH2S+IFM2tW7dInz69kyRLOTxlHFprQkJCOH/+PAUKFHC1OAaDwQ4eZdK5d+8eWbJkiVfZG5yPUoosWbIk6O7L4CIuXYKaNeHvv+Nva/BKPErhA0bZuzHms3FzPvkENm+GoUNdLYnBRXicwjc4h+XLl3PgwAFXi2FwBEFBoBRMmQKRkfKslOw3+BRG4SeS4cOHU6JECUqXLk3ZsmXZsWNHos6fOXMmFy9ejN5+++23OXLkSBxnPEytWrWSFZAG0KBBA27cuMGNGzeYPHnyI8dXrlzJb7/9RqlSpZJ1HYObcOoUvP46BFuFwIKD4Y034K+/XCuXwel4v8J3oN1y27Zt/Pzzz+zZs4cDBw6wZs0a8ua1V+nNPhEREY8o/BkzZlC8ePFky2ZLeHjcZWdXrFhBpkyZYlX49evXZ/To0cZE4y3kzAkZMsC9exAYKM8ZMsDjj7taMoOT8X6F70C75aVLl8iaNStp0kjRnqxZs5IrVy4A1q5dS7ly5ShVqhTt2rXj/v37gKSI6Nu3L+XLl2f+/Pns2rWLN954g7Jly3L37t2HZuyrVq2iWrVqlC9fnmbNmhEaGmpXju+++46yZctSsmRJdu7cCcDgwYNp3bo11atXp3Xr1sycOZNu3bpFn9OwYUM2bNgQLdPVq1fp168fJ0+epGzZsnzwwQcAjBkzhkqVKlG6dGkGDRoUff6cOXOoXLkyZcuWpWPHjkRE2C10ZHBX/vkHOnWC7dvl2Szc+iReq/DTZcvmcLtlvXr1OHfuHEWLFqVLly789ttvgHgPtW3bloULF3Lw4EHCw8OZMmVK9HlZsmR
      "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, 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": 10,
   "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": 22,
   "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": 50,
   "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": 46,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fileData = \"sinc_dim2_input.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
      "Iteration 184, loss = 0.00933157\n",
      "Iteration 185, loss = 0.00915684\n",
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      "Iteration 272, loss = 0.00119922\n",
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      "Iteration 278, loss = 0.00104678\n",
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      "Iteration 334, loss = 0.00042488\n",
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      "Iteration 340, loss = 0.00039653\n",
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      "Iteration 356, loss = 0.00033861\n",
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      "Iteration 365, loss = 0.00031223\n",
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      "Iteration 369, loss = 0.00030253\n",
      "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": "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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 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main(fileData=fileData)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.4 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4 (tags/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "2ef431f6525756fa8a44688585fa332ef3b2e5fcfe8fe75df35bbf7028a8b511"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}