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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<center>\n",
+    "<h1> Projet 2 : Problèmes aux moindres carrés </h1>\n",
+    "<h1> Année 2020-2021 - IENM2 </h1>\n",
+    "<h1> Nom :  </h1>\n",
+    "<h1> Prénom:  </h1>   \n",
+    "<h1> Date de remise : vendredi 22 janvier à 18h00? </h1>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Algorithme de Gauss-Newton - Objectifs "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background:MistyRose\">\n",
+    "<br>\n",
+    "L'objectif est de programmer les méthodes de Gauss-Newton (voir page 58 du polycopié) pour un problème aux moindres carrés\n",
+    "$$\n",
+    "({\\cal P})\\left\\{ \n",
+    "\\begin{array}{l}\n",
+    "Min\\;\\;f(\\beta)=(1/2)||r(\\beta)||^2 \\\\ \n",
+    "\\beta \\in \\mathbb{R}^p\n",
+    "\\end{array}\n",
+    "\\right. \n",
+    "$$\n",
+    "où $r$ est la fonction résidus\n",
+    "$$ \n",
+    "\\begin{array}{cc}\n",
+    "    r:\\mathbb{R}^p  & \\rightarrow & {\\mathbb{R}^n} \\\\\n",
+    "            {\\beta} & \\rightarrow & r(\\beta).\n",
+    " \\end{array}\n",
+    " $$\n",
+    "    \n",
+    "L'objectif est d'utiliser un code générique pour l'algorithme de Gauss-Newton et pour l'algorithme de Newton pour la  résolution du problème aux moindres carrés.\n",
+    "    \n",
+    "### On prendra comme critères d'arrêt\n",
+    "  \n",
+    "On note $\\beta^{(0)}$ le point initial et $\\beta^{(k)}$ l'itéré courant. On pose aussi `Tol_rel` et `Tol_abs`$=\\sqrt{\\varepsilon_{mach}}$, les tolérances relatives et absolue pour les tests d'arrêt.\n",
+    "\n",
+    "($\\varepsilon_{mach}=$`numpy.finfo(float).eps` en python).\n",
+    "    \n",
+    "Les tests d'arrêt seront les suivants~:\n",
+    "\n",
+    "* $\\|\\nabla f(\\beta^{(k+1)})\\|$ petit~: $\\|\\nabla f(\\beta^{(k+1)})\\| \\leq \\max($ `Tol_rel` $\\|\\nabla f(\\beta^{(0)})\\|,$ `Tol_abs` $)$;\n",
+    "* Evolution de $f(\\beta^{(k+1)})$ petit~: $|f(\\beta^{(k+1)}) - f(\\beta^{(k)})| \\leq \\max($ `Tol_rel` $|f(\\beta^{(k)})|,$ `Tol_abs` $)$\n",
+    "* Evolution du pas $\\delta^{(k)}=\\beta^{(k+1)}-\\beta^{(k)}$ petit~: $\\|\\beta^{(k+1)}-\\beta^{(k)}\\| \\leq \\max($ `Tol_rel` $\\|\\beta^{(k)}\\|,$ `Tol_abs` $)$\n",
+    "* Le nombre d'itérations maximal est atteint.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import des bibliothèques Python nécessaires"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import numpy as np\n",
+    "from numpy import linalg\n",
+    "import matplotlib.pylab as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D  \n",
+    "from matplotlib import cm\n",
+    "from matplotlib.ticker import LinearLocator, FormatStrFormatter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Algorithme de Gauss-Newton"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background:LightGrey\">\n",
+    "\n",
+    "- Coder l'algorithme de Gauss-Newton. \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def GN_ref(r,Jr,beta0,option):\n",
+    "    \"\"\"\n",
+    "    #******************************************************************************\n",
+    "    # Joseph Gergaud                                                              *\n",
+    "    # Novembre 2017                                                               *\n",
+    "    # Université de Toulouse, INP-ENSEEIHT                                        *\n",
+    "    #******************************************************************************\n",
+    "    #\n",
+    "    # GN_ref résout par l'algorithme de Gauss-Newton les problèmes aux moindres carrés\n",
+    "    # Min 0.5||r(beta)||^2\n",
+    "    # beta \\in R^p\n",
+    "    #\n",
+    "    # Paramètres en entrés\n",
+    "    # --------------------\n",
+    "    # r      : fonction qui code les résidus\n",
+    "    #          r : \\IR^p --> \\IR^n\n",
+    "    # Jr     : fonction qui code la matrice jacobienne\n",
+    "    #          Jr : \\IR^p --> real(n,p)\n",
+    "    # beta0  : point de départ\n",
+    "    #          real(p)\n",
+    "    # option[0] : Tol_abs, tolérance absolue\n",
+    "    #             float\n",
+    "    # option[1] : Tol_rel, tolérance relative\n",
+    "    #             float\n",
+    "    # option[2] : nitimax, nombre d'itérations maximum\n",
+    "    #             integer\n",
+    "    #\n",
+    "    # Paramètres en sortie\n",
+    "    # --------------------\n",
+    "    # beta      : beta\n",
+    "    #             real(p)\n",
+    "    # norm_gradf_beta : ||gradient f(beta)||\n",
+    "    #                   float\n",
+    "    # f_beta    : f(beta)\n",
+    "    #             float\n",
+    "    # res       : r(beta)\n",
+    "    #             float(n)\n",
+    "    # norm_delta : ||delta||\n",
+    "    #              float\n",
+    "    # exitflag   : indicateur de sortie\n",
+    "    #              =1 si premier test d'arrêt vrai\n",
+    "    #              =2 si deuxième test d'arrêt vrai\n",
+    "    #              =3 si troisième test d'arrêt vrai\n",
+    "    #              =4 si quatrième test d'arrêt vrai\n",
+    "    #      \n",
+    "    # ---------------------------------------------------------------------------------\n",
+    "    #\n",
+    "    \"\"\"\n",
+    "    # Initialisation\n",
+    "    beta0   = beta0[:]\n",
+    "    Tol_abs = option[0]\n",
+    "    Tol_rel = option[1]\n",
+    "    nbitmax = option[2]\n",
+    "    \n",
+    "    # beta, norm_gradf_beta, f_beta, res, norm_delta, k, exitflag\n",
+    "    \n",
+    "    beta    = beta0[:]\n",
+    "    res       = r(beta)\n",
+    "    Jres      = Jr(beta)\n",
+    "    norm_gradf_beta = np.linalg.norm(np.dot(Jres.T,res))\n",
+    "    f_beta    = np.dot(res.T,res)\n",
+    "    norm_delta = 0\n",
+    "    k         = 0\n",
+    "    exitflag  = 0\n",
+    "    \n",
+    "    # Inserer vos lignes de code ici \n",
+    "    #\n",
+    "    \n",
+    "    \n",
+    "      \n",
+    "    return (beta, norm_gradf_beta, f_beta, res, norm_delta, k, exitflag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Application: datation au carbone radioactif $^{14}C$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background:MistyRose\">\n",
+    "<br>\n",
+    " Nous allons ici voir un exemple d'application de l'algorithme de Gauss-Newton.\n",
+    "\n",
+    "Le carbone radioactif $^{14}C$ est produit dans\n",
+    "l'atmosphère par l'effet des rayons cosmiques sur l'azote\n",
+    "atmosphérique. Il est oxydé en $^{14}CO_{2}$ et absorbé sous\n",
+    "cette forme par les organismes vivants qui, par suite, contiennent un\n",
+    "certain pourcentage de carbone radioactif relativement aux carbones $^{12}C$\n",
+    "et $^{13}C\\,$ qui sont stables. On suppose que la production de carbone $%\n",
+    "^{14}C$ atmosphérique est demeurée constante durant les derniers\n",
+    "millénaires. On suppose d'autre part que, lorsqu'un organisme meurt, ses\n",
+    "échanges avec l'atmosphère cessent et que la radioactivité due\n",
+    "au carbone $^{14}C\\,$ décroit suivant la loi exponentielle suivante: \n",
+    "\n",
+    "$$\n",
+    "A(t)=A_{0}e^{-\\lambda t}\n",
+    "$$\n",
+    "\n",
+    "où $\\lambda$ est une constante positive, $t$ représente le\n",
+    "temps en année, et $A(t)$ est la radioactivité exprimée en nombre\n",
+    "de désintégrations par minute et par gramme de carbone. \n",
+    "\n",
+    "On\n",
+    "désire estimer les paramètres $A_{0}$ et $\\lambda $ par la\n",
+    "méthode des moindres carrés. Pour cela on analyse les troncs (le\n",
+    "bois est un tissu mort) de très vieux arbres \"Sequoia gigantea\" et \n",
+    "\"Pinus aristaca\". Par un prélèvement effectué sur le tronc,\n",
+    "on peut obtenir:\n",
+    "\n",
+    "- son âge $t$ en année, en comptant le nombre des anneaux de\n",
+    "croissance,\n",
+    "\n",
+    "- sa radioactivité $A$ en mesurant le nombre de\n",
+    "désintégration.\n",
+    "\n",
+    "Voici les données correspondantes:\n",
+    "$$\n",
+    "\\begin{array}{||c|ccccccc||}\\hline\\hline\n",
+    "t & 500 & 1000 & 2000 & 3000 & 4000 & 5000 & 6300 \\\\ \\hline \n",
+    "A & 14.5 & 13.5 & 12.0 & 10.8 & 9.9 & 8.9 & 8.0\\\\ \\hline\\hline\n",
+    "\\end{array}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#\n",
+    "# Donnees experimentales concernant les ages et radioactivites declarees \n",
+    "# comme variables globales.\n",
+    "#\n",
+    "global Ti,Ai\n",
+    "\n",
+    "Ti = np.array([500,  1000, 2000, 3000, 4000, 5000, 6300])\n",
+    "Ai = np.array([14.5, 13.5, 12.0, 10.8, 9.9,  8.9,  8.0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background:LightGrey\">\n",
+    "    \n",
+    "- Ecrire en respectant les interfaces d'appel les fonctions qui codent respectivement les fonctions residu $r(\\beta)$ et jacobienne $J_r(\\beta)$ dans ce cadre."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def residu_C14(beta):\n",
+    "    \"\"\"\n",
+    "    Fonction residu pour le probleme Carbone 14.\n",
+    "    % Input:\n",
+    "    % ------\n",
+    "    % x : vecteur des paramètres\n",
+    "    %        float(p)\n",
+    "    %\n",
+    "    % Output:\n",
+    "    % -------\n",
+    "    % r : vecteur residu \\IR^n\n",
+    "    \"\"\"\n",
+    "    # Inserer vos lignes de code ici\n",
+    "    \n",
+    "    r = np.zeros((np.shape(Ai)[0],))\n",
+    "    \n",
+    "    return r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-4.9877 -4.4516 -3.8127 -3.3918 -3.1968 -2.8347 -2.6741]\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "\nNot equal to tolerance rtol=0, atol=0.0001\n\nMismatched elements: 7 / 7 (100%)\nMax absolute difference: 4.9877\nMax relative difference: 1.\n x: array([0., 0., 0., 0., 0., 0., 0.])\n y: array([4.9877, 4.4516, 3.8127, 3.3918, 3.1968, 2.8347, 2.6741])",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-52da801ef0e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4.9877\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4.4516\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3.8127\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3.3918\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3.1968\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.8347\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.6741\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_beta0\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_allclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_beta0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-4\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# atol + rtol * abs(desired).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mresidu_C14\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDonnees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/testing/_private/utils.py\u001b[0m in \u001b[0;36massert_allclose\u001b[0;34m(actual, desired, rtol, atol, equal_nan, err_msg, verbose)\u001b[0m\n\u001b[1;32m   1531\u001b[0m     \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Not equal to tolerance rtol=%g, atol=%g'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrtol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1532\u001b[0m     assert_array_compare(compare, actual, desired, err_msg=str(err_msg),\n\u001b[0;32m-> 1533\u001b[0;31m                          verbose=verbose, header=header, equal_nan=equal_nan)\n\u001b[0m\u001b[1;32m   1534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/testing/_private/utils.py\u001b[0m in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m    844\u001b[0m                                 \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m                                 names=('x', 'y'), precision=precision)\n\u001b[0;32m--> 846\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: \nNot equal to tolerance rtol=0, atol=0.0001\n\nMismatched elements: 7 / 7 (100%)\nMax absolute difference: 4.9877\nMax relative difference: 1.\n x: array([0., 0., 0., 0., 0., 0., 0.])\n y: array([4.9877, 4.4516, 3.8127, 3.3918, 3.1968, 2.8347, 2.6741])"
+     ]
+    }
+   ],
+   "source": [
+    "# Test de la fonction résidues\n",
+    "beta0 = np.array([10, 0.0001])\n",
+    "res_beta0 = residu_C14(beta0)\n",
+    "res = np.array([4.9877, 4.4516, 3.8127, 3.3918, 3.1968, 2.8347, 2.6741])\n",
+    "print(res_beta0-res)\n",
+    "np.testing.assert_allclose(res_beta0,res,rtol=0, atol=1e-4)   # atol + rtol * abs(desired).\n",
+    "residu_C14(beta0, Donnees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def jacobienne_res_C14(beta):\n",
+    "    \"\"\"\n",
+    "    Calcul de la jacobienne de la fonction objectif au point x pour le probleme Carbone 14.\n",
+    "    \n",
+    "    Entree: \n",
+    "    x : vecteur des paramètres float(p) \n",
+    "    \n",
+    "    Sortie: \n",
+    "    J: Matrice Jacobienne calculee au point x\n",
+    "    float de taille pxp\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    # Inserer vos lignes de code ici\n",
+    "    \n",
+    "    J = np.zeros((np.shape(Ai)[0],2))\n",
+    "    \n",
+    "    return J"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 0.]\n",
+      " [0. 0.]\n",
+      " [0. 0.]\n",
+      " [0. 0.]\n",
+      " [0. 0.]\n",
+      " [0. 0.]\n",
+      " [0. 0.]]\n",
+      "[[-9.51229425e-01  4.75614712e+03]\n",
+      " [-9.04837418e-01  9.04837418e+03]\n",
+      " [-8.18730753e-01  1.63746151e+04]\n",
+      " [-7.40818221e-01  2.22245466e+04]\n",
+      " [-6.70320046e-01  2.68128018e+04]\n",
+      " [-6.06530660e-01  3.03265330e+04]\n",
+      " [-5.32591801e-01  3.35532835e+04]]\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "\nNot equal to tolerance rtol=0, atol=0.0001\n\nMismatched elements: 14 / 14 (100%)\nMax absolute difference: 33553.2835\nMax relative difference: inf\n x: array([[-9.512294e-01,  4.756147e+03],\n       [-9.048374e-01,  9.048374e+03],\n       [-8.187308e-01,  1.637462e+04],...\n y: array([[0., 0.],\n       [0., 0.],\n       [0., 0.],...",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-4374dcedc9be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m  [-5.32591801e-01 , 3.35532835e+04]])\n\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msol_jac_res_C14_beta0\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mjac_res_C14_beta0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_allclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msol_jac_res_C14_beta0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mjac_res_C14_beta0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/testing/_private/utils.py\u001b[0m in \u001b[0;36massert_allclose\u001b[0;34m(actual, desired, rtol, atol, equal_nan, err_msg, verbose)\u001b[0m\n\u001b[1;32m   1531\u001b[0m     \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Not equal to tolerance rtol=%g, atol=%g'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrtol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1532\u001b[0m     assert_array_compare(compare, actual, desired, err_msg=str(err_msg),\n\u001b[0;32m-> 1533\u001b[0;31m                          verbose=verbose, header=header, equal_nan=equal_nan)\n\u001b[0m\u001b[1;32m   1534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/testing/_private/utils.py\u001b[0m in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m    844\u001b[0m                                 \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m                                 names=('x', 'y'), precision=precision)\n\u001b[0;32m--> 846\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: \nNot equal to tolerance rtol=0, atol=0.0001\n\nMismatched elements: 14 / 14 (100%)\nMax absolute difference: 33553.2835\nMax relative difference: inf\n x: array([[-9.512294e-01,  4.756147e+03],\n       [-9.048374e-01,  9.048374e+03],\n       [-8.187308e-01,  1.637462e+04],...\n y: array([[0., 0.],\n       [0., 0.],\n       [0., 0.],..."
+     ]
+    }
+   ],
+   "source": [
+    "# Test de la fonction jacobienne des résidus\n",
+    "beta0 = np.array([10, 0.0001])\n",
+    "jac_res_C14_beta0 = jacobienne_res_C14(beta0)\n",
+    "print(jac_res_C14_beta0)\n",
+    "sol_jac_res_C14_beta0 = np.array([[-9.51229425e-01 , 4.75614712e+03],\n",
+    " [-9.04837418e-01 , 9.04837418e+03],\n",
+    " [-8.18730753e-01 , 1.63746151e+04],\n",
+    " [-7.40818221e-01 , 2.22245466e+04],\n",
+    " [-6.70320046e-01 , 2.68128018e+04],\n",
+    " [-6.06530660e-01 , 3.03265330e+04],\n",
+    " [-5.32591801e-01 , 3.35532835e+04]])\n",
+    "print(sol_jac_res_C14_beta0-jac_res_C14_beta0)\n",
+    "np.testing.assert_allclose(sol_jac_res_C14_beta0,jac_res_C14_beta0,rtol=0, atol=1e-4) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background:LightGrey\">\n",
+    "\n",
+    "- Appliquer ensuite la méthode de Gauss-Newton pour résoudre le problème proposé. Discuter de l'influence \n",
+    "du choix de l'itéré initial sur la convergence de l'algorithme. Comment pouvez-vous expliquer ce comportement ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Algorithme de Gauss-Newton\n",
+      "Itere initial:  [1.e+01 1.e-04]\n",
+      "------------------------------------------------------------------\n",
+      "k     ||f(beta)||      f(beta)          ||s||          exitflag \n",
+      "------------------------------------------------------------------\n",
+      "0     0.000000e+00     0.000000e+00     0.000000e+00     0\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Algorithme de Gauss-Newton\n",
+      "Itere initial:  [1.5e+01 1.0e-04]\n",
+      "------------------------------------------------------------------\n",
+      "k     ||f(beta)||      f(beta)          ||s||          exitflag \n",
+      "------------------------------------------------------------------\n",
+      "0     0.000000e+00     0.000000e+00     0.000000e+00     0\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Algorithme de Gauss-Newton\n",
+      "Itere initial:  [1.5e+01 5.0e-04]\n",
+      "------------------------------------------------------------------\n",
+      "k     ||f(beta)||      f(beta)          ||s||          exitflag \n",
+      "------------------------------------------------------------------\n",
+      "0     0.000000e+00     0.000000e+00     0.000000e+00     0\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Algorithme de Gauss-Newton\n",
+      "Itere initial:  [1.e+01 5.e-04]\n",
+      "------------------------------------------------------------------\n",
+      "k     ||f(beta)||      f(beta)          ||s||          exitflag \n",
+      "------------------------------------------------------------------\n",
+      "0     0.000000e+00     0.000000e+00     0.000000e+00     0\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Algorithme de Gauss-Newton\n",
+      "Itere initial:  [1.5e+01 8.0e-04]\n",
+      "------------------------------------------------------------------\n",
+      "k     ||f(beta)||      f(beta)          ||s||          exitflag \n",
+      "------------------------------------------------------------------\n",
+      "0     0.000000e+00     0.000000e+00     0.000000e+00     0\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#\n",
+    "# Resolution du probleme Carbone 14 par l'algorithme de Gauss-Newton\n",
+    "#\n",
+    "\n",
+    "itmax     = 20\n",
+    "\n",
+    "#\n",
+    "# Iteres initiaux\n",
+    "#\n",
+    "\n",
+    "beta0_array = np.array([[10, 0.0001],[15, 0.0001],[15, 0.0005],[10, 0.0005],[15, 0.0008]])\n",
+    "\n",
+    "#\n",
+    "# Affichage de l'historique de convergence\n",
+    "# \n",
+    "\n",
+    "plt.figure()\n",
+    "\n",
+    "for initialisation in range(len(beta0_array)):\n",
+    "\n",
+    "    # Choix de l'itere initial\n",
+    "    beta0  = beta0_array[initialisation]\n",
+    "    \n",
+    "    # Affichage\n",
+    "    print('Algorithme de Gauss-Newton')\n",
+    "    print('Itere initial: ',beta0)\n",
+    "    print('------------------------------------------------------------------')\n",
+    "    print('k     ||f(beta)||      f(beta)          ||s||          exitflag ')\n",
+    "    print('------------------------------------------------------------------')\n",
+    "    \n",
+    "    \n",
+    "    # Choix des parametres \n",
+    "    option = np.array([math.sqrt(1.e-10), 1.e-10, itmax])\n",
+    "\n",
+    "    # Appel a l'algorithme en effectuant un nombre maximal d'iterations egal a iteration\n",
+    "    beta, norm_gradf_beta, f_beta, res, norm_delta, k, exitflag = GN_ref(residu_C14,jacobienne_res_C14,beta0,option)\n",
+    "\n",
+    "    # Affichage de l'historique de convergence\n",
+    "    string = \"   \"\n",
+    "    print(\"%d %s %4e %s %4e %s %4e %s %d\"%(k,string,norm_gradf_beta,string,f_beta,string,norm_delta,string,exitflag))\n",
+    "    \n",
+    "    #\n",
+    "    # Affichage des resultats pour chaque application de l'algorithme\n",
+    "    #    \n",
+    "    if exitflag == 1:\n",
+    "        print(\"Convergence apres %d iterations !\"%(k))\n",
+    "        print(\"Point solution trouve: \", beta)\n",
+    "        print(\"Valeur de la fonction objectif au point solution: \", f_beta)\n",
+    "    elif exitflag == 2:\n",
+    "        ## A COMPLETER\n",
+    "        print(\"Point solution courant: \", beta)\n",
+    "        print()\n",
+    "    elif exitflag == 3:\n",
+    "        ## A COMPLETER\n",
+    "        print()\n",
+    "    elif exitflag == 4:\n",
+    "        print(\"Nombre maximal d'iterations atteint !\" )\n",
+    "    print()\n",
+    "    \n",
+    "    #\n",
+    "    # Affichage de la solution en terme de radioactivite\n",
+    "    #\n",
+    "    T = np.linspace(0,Ti[-1],100)\n",
+    "    A = np.zeros(len(T))\n",
+    "    for i in range(len(T)):\n",
+    "        t = T[i]\n",
+    "        A[i] = beta0[0]*math.exp(-beta0[1]*t)\n",
+    "        \n",
+    "    plt.figure(initialisation+1)\n",
+    "    plt.plot(Ti,Ai,'o')\n",
+    "    plt.plot(T,A)\n",
+    "    for i in range(len(T)):\n",
+    "        t = T[i]\n",
+    "        A[i] = beta[0]*math.exp(-beta[1]*t)\n",
+    "    plt.plot(T,A)\n",
+    "    plt.grid()\n",
+    "    plt.xlabel(\"Age\")\n",
+    "    plt.ylabel(\"Radioactivite\")\n",
+    "    plt.title(\"Analyse Carbone 14\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background:LightGrey\">\n",
+    "\n",
+    "- Récapituler ici les valeurs des paramètres $A_{0}$ et $\\lambda $ obtenus au final pour chacun des choix de l'itéré initial. \n",
+    "\n",
+    "- Représenter la fonction objectif pour vérifier vos résultats. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fonction objectif"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background:LightGrey\">\n",
+    "    \n",
+    "- Implémenter la fonction objectif associée au problème proposé."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def objectif(beta):\n",
+    "    \"\"\"\n",
+    "    % Input:\n",
+    "    % ------\n",
+    "    % beta : vecteur des paramètres\n",
+    "    %        float(p)\n",
+    "    %\n",
+    "    % Output:\n",
+    "    % -------\n",
+    "    % res : (1/2)||residu(beta)||^2\n",
+    "    %       float\n",
+    "    \"\"\" \n",
+    "    f = 0\n",
+    "    \n",
+    "    # Inserer vos lignes de code ici\n",
+    "    \n",
+    "    return f\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#\n",
+    "# Cette visualisation vous permet de valider vos resultats\n",
+    "#\n",
+    "#\n",
+    "# Normalement les lignes de code ne doivent pas etre modifiees dans ce qui suit\n",
+    "#\n",
+    "fig = plt.figure()\n",
+    "ax  = fig.gca(projection='3d')\n",
+    "\n",
+    "# Creation de X et Y\n",
+    "X    = np.arange(9.0, 20.0, 11./100.)\n",
+    "Y    = np.arange(0., 0.0003, 0.0003/50.)\n",
+    "X, Y = np.meshgrid(X, Y)\n",
+    "Z    = np.sqrt(X**2 + Y**2)\n",
+    "\n",
+    "# Not the smartest way to fill Z...\n",
+    "for item in range(len(X)):\n",
+    "    xs       = X[item]\n",
+    "    for itemy in range(len(Y[0])):\n",
+    "        y     = Y[item][itemy]\n",
+    "        count = 0\n",
+    "        for x in xs:\n",
+    "            Z[item][count] = objectif(np.array([x,y]))\n",
+    "            count         += 1\n",
+    "\n",
+    "# Creation de la surface\n",
+    "surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n",
+    "\n",
+    "# Legendes\n",
+    "ax.set_zlim(0., 300)\n",
+    "ax.zaxis.set_major_locator(LinearLocator(10))\n",
+    "ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
+    "\n",
+    "ax.view_init(elev=22.5, azim=45)\n",
+    "\n",
+    "# Echelles colorees\n",
+    "fig.colorbar(surf, shrink=0.5, aspect=7.5)\n",
+    "plt.xlabel('A_0')\n",
+    "plt.ylabel('lambda')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Questions bonus : algorithme de Newton"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Questions\n",
+    "<div style=\"background:LightGrey\">\n",
+    "    \n",
+    "* Dans l'algoritme de Newton, de quelles fonctions dépendant du problème a-t-on besoin?\n",
+    "    \n",
+    "* Donner les avantages et inconvénients éventuels de la méthode de Gauss-Newton par rapport à l'algorithme de Newton. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### De la même façon que pour Gauss-Newton écrivez une fonction qui code l'algorithme de Newton\n",
+    "Vous testerez votre fonction sur le même problème `Carbonne 14`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}