diff --git a/Chapitre 9 - Produits scalaires et espaces euclidens/9.10-9.11 La meilleure approximation quadratique.ipynb b/Chapitre 9 - Produits scalaires et espaces euclidens/9.10-9.11 La meilleure approximation quadratique.ipynb
index 115c9ad..e95a643 100644
--- a/Chapitre 9 - Produits scalaires et espaces euclidens/9.10-9.11 La meilleure approximation quadratique.ipynb	
+++ b/Chapitre 9 - Produits scalaires et espaces euclidens/9.10-9.11 La meilleure approximation quadratique.ipynb	
@@ -1,370 +1,294 @@
 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# **Concept(s)-clé(s) et théorie**\n",
     "\n",
     "## Proposition 1\n",
     "Soient $V$ un espace euclidien et $W \\subset V$ un sous-espace vectoriel de $V$. Alors pour tout $x \\in V$ et tout $y \\in W$, on a\n",
     "\n",
     "\\begin{equation}\n",
     "||x - proj_W x|| \\leq ||x-y||\n",
     "\\end{equation}\n",
     "\n",
     "## Définition 1\n",
     "Soient $V$ un espace euclidien, $W \\subset V$ un sous-espace vectoriel de $V$ et $x \\in V$; considérez aussi le produit scalaire usuel. Alors le vecteur $proj_Wx$ est appelé la **meilleure approximation quadratique** (ou la **meilleure approximation au sens des moindres carrées**) **de $\\boldsymbol{x}$ par un vecteur dans $\\boldsymbol{W}$**."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Exercises et Examples"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "        <script type=\"text/javascript\">\n",
-       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
-       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
-       "        if (typeof require !== 'undefined') {\n",
-       "        require.undef(\"plotly\");\n",
-       "        requirejs.config({\n",
-       "            paths: {\n",
-       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
-       "            }\n",
-       "        });\n",
-       "        require(['plotly'], function(Plotly) {\n",
-       "            window._Plotly = Plotly;\n",
-       "        });\n",
-       "        }\n",
-       "        </script>\n",
-       "        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import Librairie.AL_Fct as al\n",
     "import Corrections.corrections as corrections\n",
     "import numpy as np\n",
     "import sympy as sp"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Exercice 1\n",
     "\n",
     "Soit $V = \\mathbb{R}^n$. Considérez les paires suivantes, faites par un ensemble de vecteurs $\\mathcal{S}$ générant un sous-espace vectoriel $W$ de $V$ et par un élément $v$ de $V$. Calculez la meilleure approximation au sens des moindres carrés de $v$ par un vecteur dans $W$.\n",
     "\n",
     "1. $V = \\mathbb{R}^2 \\qquad \\mathcal{S} = \\left\\{ \\begin{pmatrix}1 \\\\ -2\\end{pmatrix} \\right\\} \\qquad \\qquad \\quad \\  v = \\begin{pmatrix} -2 \\\\ 1 \\end{pmatrix}$\n",
     "2. $V = \\mathbb{R}^3 \\qquad \\mathcal{S} = \\left\\{ \\begin{pmatrix}0 \\\\ 1 \\\\ 0\\end{pmatrix}, \\begin{pmatrix} 1 \\\\ -1 \\\\ 0 \\end{pmatrix} \\right\\} \\qquad \\qquad v = \\begin{pmatrix} -3 \\\\ 2 \\\\ 1 \\end{pmatrix}$\n",
     "3. $V = \\mathbb{R}^4 \\qquad \\mathcal{S} = \\left\\{ \\begin{pmatrix}1 \\\\ 2 \\\\ -1 \\\\-2 \\end{pmatrix}, \\begin{pmatrix}0 \\\\ 1 \\\\ 0 \\\\-1 \\end{pmatrix} \\right\\} \\qquad \\quad \\ \\ \\ v = \\begin{pmatrix} 0 \\\\ -1 \\\\ 1 \\\\ -1\\end{pmatrix}$"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "case_number = 1"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "if case_number == 1:\n",
     "    S = [[1,-2]]\n",
     "    v = [-2,1]\n",
     "    dim=1\n",
     "elif case_number == 2:\n",
     "    S = [[0,1,0], [1,-1,0]]\n",
     "    v = [-3,2,1]\n",
     "    dim=2\n",
     "elif case_number == 3:\n",
     "    S = [[1,2,-1,-2], [0,1,0,-1]]\n",
     "    v = [0,-1,1,-1]\n",
     "    dim=2\n",
     "else:\n",
     "    print(f\"{case_number} n'est pas un numéro de cas valide!\" \n",
     "          f\"Numéros de cas disponibles: [1,2,3]\")\n",
     "\n",
     "step = 0\n",
     "VectorsList = [S]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Aide\n",
     "\n",
     "Pour calculer la meiileure approximation quadratique de $v$ par un vecteur dans $W$,, il peut être utile de dériver une base ortogonale (ou orthonormée) pour ce dernier. Vous pouvez utiliser la cellule suivante pour exécuter l'algorithme interactif de Gram-Schmidt.\n",
     "\n",
     "#### Instructions\n",
     "\n",
     "Pour utiliser la méthode interactive de Gram-Schmidt, procédez comme suit:\n",
     "\n",
     "1. Insérez le numéro de dossier souhaité dans la cellule suivante\n",
     "2. Exécutez la cellule appelée \"SÉLECTION DES PARAMÈTRES\" pour sélectionner le type d'opération et les coefficients nécessaires\n",
     "3. Exécutez la cellule appelée \"EXÉCUTER L'ÉTAPE DE L'ALGORITHME GRAM-SCHMIDT\" pour exécuter l'étape de l'algorithme de Gram-Schmidt avec les paramètres précédemment sélectionnés\n",
     "\n",
     "En outre:\n",
     "\n",
     "1. Vous pouvez annuler une opération en sélectionnant le bouton \"Revert\".\n",
     "\n",
     "2. Si les coefficients insérés sont incorrects, vous pouvez essayer avec de nouvelles valeurs sans effectuer une opération \"Revert\".\n",
     "\n",
     "3. Les coefficients qui ne sont pas liés à l'opération sélectionnée peuvent être définis sur n'importe quelle valeur, car ils ne sont pas utilisés dans le code."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# SÉLECTION DES PARAMÈTRES\n",
     "norm_coeff, proj_coeffs, operation, step_number = al.manual_GS(dim=dim)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# EXÉCUTER L'ÉTAPE DE L'ALGORITHME GRAM-SCHMIDT\n",
     "S = al.interactive_gram_schmidt(norm_coeff, proj_coeffs,\n",
     "                                operation, step_number, \n",
     "                                S.copy(), VectorsList)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# INSÉREZ ICI LE VALEUR DE LA MEILLEURE APPROXIMATION DE v AU SENS DES MOINDRES CARRÉES DANS W\n",
     "best_appr = [0, 0]  "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "corrections.Ex1Chapitre9_10_11(best_appr, \n",
     "                               case_nb=case_number)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Exercice 2\n",
     "\n",
     "Soit $V = \\mathcal{C}\\left(I, \\mathbb{R}\\right)$, ou $I$ est un interval dans $\\mathbb{R}$. Considérez les paires suivantes, faites par un ensemble de fonctions $\\mathcal{S}$ générant un sous-espace vectoriel $W$ de $V$ et par un élément $v$ de $V$. Calculez la meilleure approximation au ses des moindres carrés de $v$ par un vecteur dans $W$.\n",
     "\n",
     "1. $\\quad \\mathcal{S} = \\left\\{ 1, x \\right\\} = \\mathbb{P}^1(\\mathbb{R}) \\qquad \\qquad \\quad  v = |x| \\qquad \\qquad \\ I = [-1,1]$\n",
     "2. $\\quad \\mathcal{S} = \\left\\{ 1, x, x^2 \\right\\} = \\mathbb{P}^2(\\mathbb{R}) \\qquad \\quad \\ \\ v = |x| \\qquad \\qquad \\ I = [-1,1]$\n",
     "3. $\\quad \\mathcal{S} = \\left\\{ 1, x, x^2 \\right\\} = \\mathbb{P}^2(\\mathbb{R}) \\qquad \\quad \\ \\ v = sin(x) \\qquad \\quad I = [-\\pi,\\pi]$\n",
     "4. $\\quad \\mathcal{S} = \\left\\{ 1, x, x^2, x^3 \\right\\} = \\mathbb{P}^3(\\mathbb{R}) \\qquad \\ v = e^x \\qquad \\qquad \\ I=[0,1]$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "case_number=1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "x = sp.Symbol('x')\n",
     "if case_number == 1:\n",
     "    S = [1+0*x, x]\n",
     "    v = sp.Abs(x)\n",
     "    int_limits = [-1,1]\n",
     "    dim=2\n",
     "elif case_number == 2:\n",
     "    S = [1+0*x, x, x**2]\n",
     "    v = sp.Abs(x)\n",
     "    int_limits = [-1,1]\n",
     "    dim=3\n",
     "elif case_number == 3:\n",
     "    S = [1+0*x, x, x**2]\n",
     "    v = sp.sin(x)\n",
     "    int_limits = [-np.pi,np.pi]\n",
     "    dim=3\n",
     "elif case_number == 4:\n",
     "    S = [1, x, x**2, x**3]\n",
     "    v = sp.exp(x)\n",
     "    int_limits = [0,1]\n",
     "    dim=4\n",
     "else:\n",
     "    print(f\"{case_number} n'est pas un numéro de cas valide!\" \n",
     "          f\"Numéros de cas disponibles: [1,2,3,4]\")\n",
     "\n",
     "step = 0\n",
     "VectorsList = [S]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Aide\n",
     "\n",
     "Pour calculer la meiileure approximation quadratique de $v$ par un vecteur dans $W$, il peut\n",
     "aider à dériver une base orthogonale (ou orthonormée) pour ce dernier. Vous pouvez utiliser la cellule suivante pour exécuter l'algorithme interactif de Gram-Schmidt pour fonctions."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# SÉLECTION DES PARAMÈTRES\n",
     "norm_coeff, proj_coeffs, operation, step_number = al.manual_GS(dim=dim)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# EXÉCUTER L'ÉTAPE DE L'ALGORITHME GRAM-SCHMIDT\n",
     "S = al.interactive_gram_schmidt_func(norm_coeff, proj_coeffs,\n",
     "                                     operation, step_number, \n",
     "                                     S.copy(), VectorsList,\n",
     "                                     int_limits=int_limits,\n",
     "                                     weight_function=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# INSÉREZ ICI LE VALEUR DE LA MEILLEURE APPROXIMATION DE v AU SENS DES MOINDRES CARRÉES DANS W\n",
     "best_appr = 1 + 0*x"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "C'est correct!"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Latex object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/latex": [
-       "La projection de $u$ sur $W$ est: 0.303963550927013*x"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Latex object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/latex": [
-       "La projection de $u$ sur $W^\\perp$ est: -0.303963550927013*x + sin(x)"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Latex object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "corrections.Ex2Chapitre9_10_11(best_appr, \n",
     "                               int_limits=int_limits, \n",
     "                               case_nb=case_number)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
    "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": "3.7.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
 }