TP4_algoGlouton_p1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP4 : algorithmes gloutons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*suite de la partie pdf sur le rendu de monnaie*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activité 2 : organiser une conférence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objectif : Des conférenciers sont invités à présenter leurs exposés dans une salle. Mais leurs disponibilités ne leur permettent d’intervenir qu’à des horaires bien définis. Le problème est de construire un planning d’occupation de la salle avec le plus grand nombre de conférenciers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remarque : dans la suite du texte, les conférenciers seront désignés par les symboles C<sub>i</sub> où i est un entier naturel positif. Leurs horaires possibles seront donnés sous formes d'intervalles de type \\[hd ; hf[ où hd et hf désignent respectivement les heures de début et de fin. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### premier exemple :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il y a 4 conférenciers avec les horaires suivants : C1 ∶\\[3,4[ C2 ∶[0,1[ C3 ∶[2,3[ C4 ∶[1,2[ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut visualiser les horaires sur un graphique grâce au code suivant :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZqqoqpaWl6dSpU6qsrNSzzz6rRx991Ndj+UR1dbXmzJmj48ePy9/fX5mZmYqOjvb1WD5z4cIFPfnkk8rOzr7jN4cjRoxQeHi4JCkqKkqZmZk3PbZZb3x//THIgwcPKisrS2vWrGnOEVqUDRs2qKCgQMHBwb4exacKCgoUERGhRYsW6eLFi0pISLhjw/7ee+9JkvLy8vThhx8qMzPzjv0zUlVVpYyMDLVu3drXo/hcRUWFJNneADbrV57aeQzyThIdHa2VK1f6egyfGzp0qF544QXvx/7+/j6cxrfi4+M1b948SVJxcbE6dOjg44l8Z8GCBUpMTFSnTp18PYrPffzxx/riiy+UnJyspKQkHTx4sMHjmzXsN3sM8k41ZMgQnhaSFBoaqrCwMJWWlmrq1KmaNm2ar0fyqYCAAKWkpGjevHkaMmSIr8fxifz8fEVGRno3gne61q1ba+LEiXr99df10ksvaebMmQ22s1nDzmOQuJnTp08rKSlJw4cP1xNPPOHrcXxuwYIFevfdd5Wenq6ysjJfj9Pstm/frn379sntdsvj8SglJUXnzp3z9Vg+ExMTo5///OeyLEsxMTGKiIhocD2aNew8Bon6nD9/XsnJyZo1a5ZGjhzp63F8aseOHVq3bp0kKTg4WJZl3ZG3prZs2aLNmzcrJydHLpdLCxYsUMeOHX09ls9s27bN+9YsZ86cUWlpaYPr0azb5ccee0x79+5VYmKi9zFIYO3atbp8+bJWr16t1atXS6p7YflOfNFs8ODBSk1N1fjx43Xt2jWlpaUpKCjI12PBx0aOHKnU1FSNHTtWlmVp/vz5Dd7taNbHHQEAzuP92AHAMIQdAAxD2AHAMIQdAAxD2AHAMIQdLVZJSYnmz5+vy5cv+3SO/Px8LV68uMnO5/F49Pvf/77Jzgd8E1/2iRYrMjJSaWlpvh6jyblcLrlcLl+PAYMRdjS78vJypaamqri4WFVVVUpPT9fx48e1fft21dTUaOrUqbp06ZI2btwoPz8/9erVSzNnztTKlStVVFSkCxcuqLi4WKmpqerXr5/279+vZcuWyd/fX127dtXLL7+siooKzZ49W1euXNHFixc1atQojRs3Tm63W3PnzlVsbKxyc3N1/vx5DRo0SCkpKdq6daveeecd7dmzRytWrLhh5kOHDik5OVklJSUaO3asxowZo71792r58uUKCgpSRESE5s+fL4/Ho8WLFyswMFCjR49W69attWXLFu95VqxYoU8++UR5eXlatmyZBg4cqO7du6t79+5KTk5Wenq6KioqFBQUpHnz5ikyMlIvvPCCSktLVV5erlmzZqlPnz7N/Z8M3zGEHc0uLy9P99xzj5YtW6YjR45o3759atOmjdq0aaM1a9bo0qVLGjdunLZv367g4GDNmjVLe/fulSS1atVKr732mvbu3avs7Gw98sgjSk9P1x/+8Ae1b99ey5cv15///Gc9+OCDevzxxzV48GCdOXNGbrdb48aNq3eeBx98UCNHjtRvf/tbFRUVadOmTf9zTEBAgF5//XWdOnVKkyZN0ujRo5Wenq7c3FzdddddevPNN7VmzRoNGDBAFRUV+tOf/iSp7qtq169fr+DgYGVkZOif//yn7rrrLu95T58+rfz8fLVr107Tpk2T2+1W//799cEHH2jx4sWaPHmyzp8/r40bN+rChQs6ceJE0/8HgXEIO5rdsWPH9LOf/UyS1KNHD/Xo0UP5+fmKiYmRJH322WcqKSnRpEmTJElXr17VyZMnJcl7C+Puu+9WZWWlSkpKdPbsWe87QpaXl6tv377q37+/3nzzTf3tb39TWFhYve+E9/Uvuk5MTNSqVas0ZcqUG96B9CsPPPCALMtSx44dVV5erosXLyosLMwb6d69e2vp0qUaMGCA9/OQpPbt2yslJUWhoaE6duyY4uLibjhvu3bt1K5dO0nSkSNHtG7dOr322muqra1VYGCg7r//fo0fP14vvviirl27Jrfb3YgVx52GsKPZxcbG6t///rfi4+N18uRJLV++XH379pWfX91r+VFRUercubOys7MVGBio/Px8uVwu7dy5U5Zl3XCudu3a6e6779bq1asVHh6uXbt2KSQkRNnZ2YqLi9O4ceP0r3/9S7t375ZUt+M/d+6cYmNj9d///tcb5oULF2rixInKz89XfHy8unbtesN16rtuaWmpzp49q06dOmn//v3q1q2bJHk/jytXrujVV1/V+++/L0l65pln9M138PjqWEne2zE9e/bU0aNHdeDAAR0+fFhXr17V+vXrdfbsWSUmJmrgwIG3sfq4ExB2NLvExESlpaXpqaeeUnV1tdLS0vTJJ594fz4yMlITJkyQ2+1WdXW17rnnHg0bNqzec/n5+Wn27NmaNGmSamtrFRoaqoULF8qyLM2dO1dvvfWWIiIi5O/vr8rKSiUlJenll19W586dvd/AYdeuXTpx4oTS09MVFxenmTNnavPmzQoMDLzp52BZll555RX9+te/lmVZatu2rTIzM2/4PMLCwtSzZ08lJCQoJCREbdq00dmzZxUVFVXvOVNSUjR37lxVVFSovLxcs2fPVrdu3bRq1Srt2LFDgYGBmjp1amOWHHcY3gQMAAzDc+wAYBjCDgCGIewAYBjCDgCGIewAYBjCDgCGIewAYJj/B08rOgQEdd/qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "## les données des conférenciers\n",
    "conf=[[3,4],[0,1],[2,3],[1,2]]\n",
    "## le graphique\n",
    "def affiche_graphe(conf) :\n",
    "    \"\"\" prend en arguments la liste conf des données des conférenciers\n",
    "        affiche le graphique \"\"\"\n",
    "    # récupération de la taille des données à afficher\n",
    "    nb_conferenciers = len(conf) # le nombre de conférenciers \n",
    "    horaires_max=[conf[i][1] for i in range(nb_conferenciers)] # les horaires de fin de chaque conférencier\n",
    "    plage_horaire=max(horaires_max) # l'horaire maximum\n",
    "    # gestion du graphique\n",
    "    plt.style.use('seaborn-whitegrid')\n",
    "    fig = plt.figure()\n",
    "    ax = plt.axes()\n",
    "    ax = ax.set(xlabel='créneaux horaires', ylabel='conférenciers')\n",
    "    plt.axis([0, plage_horaire+1, 0, nb_conferenciers+1]) #pour les graduations, de façon à voir les axes\n",
    "    for i in range(len(conf)) :\n",
    "        plt.plot(conf[i],[i+1,i+1]) # affichage des lignes conférencier par conférencier\n",
    "    plt.show() #indispensable pour l'affichage\n",
    "affiche_graphe(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**application 1 :** Compléter la fonction *ordre_conference*() suivante qui prend en argument la liste des données des conférenciers et retourne la liste des numéros des conférenciers dans leur ordre de passage.(attention au décalage indice de la liste de départ qui commence à 0 par rapport au numéro du conférencier qui commence à 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ordre_conference(conf):\n",
    "    ordre_conf=[]\n",
    "    ## à compléter\n",
    "    return ordre_conf\n",
    "\n",
    "assert ordre_conference([[3,4],[0,1],[2,3],[1,2]])==[2,4,3,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### deuxième exemple :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On considère à nouveau quatre conférenciers dont les créneaux horaires sont les suivants : <br>\n",
    "C1 ∶\\[2,4[ ; C2 ∶[0,1[ ; C3 ∶[1,3[ ;  C4 ∶[0,2[."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut visualiser les horaires sur un graphique grâce au code suivant :<br>\n",
    "(attention, il dépend de la fonction présente dans la première cellule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEECAYAAAA8tB+vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAV7UlEQVR4nO3deXBV5f3H8c/JQsgGISwKhkiI0l6d2hRK+VmkIEaWcSzEYQngjRhaimgRBJomkDSKQ8IOUnaNSKBQCimN41hbqMIUrDCZAdvpFRwWJQTZAgMhZiHJ7w/0RmwSDyEnNzy8XzPMGDmc882DvHk89+TGqqmpqREAwBh+vh4AANC0CDsAGIawA4BhCDsAGIawA4BhCDsAGCbAqRMPHz5c4eHhkqSoqChlZWU5dSkAwDc4Evby8nJJUm5urhOnBwA0wJFbMZ988om+/PJLJScnKykpSQcPHnTiMgCAOlhOfOXp4cOHdejQIY0cOVInTpzQL3/5S/31r39VQMD1/0EoKCho6ksCwB2hV69e33mMI7diYmJidO+998qyLMXExCgiIkLnzp1T586db2q4O4HH45HL5fL1GC0Ca1GLtajFWtSyuyl25FbMtm3blJ2dLUk6c+aMSkpK1LFjRycuBQD4Fkd27CNGjFBqaqrGjBkjy7I0d+5c720YAICzHKltq1attGjRIidODQD4DnyBEgAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEcC/uFCxfUv39/HT161KlLAADqEODESSsrK5WRkaHWrVvXe8xn7iQnLn37KS3VZyEhvp6iZXj4/ySXy9dTALc9R8I+b948JSYmau3atfUeU1pa6sSlbzvV1dWsxVcqKivl8Xh8PUaLUFZWxlp8hbW4eU0e9ry8PEVGRqpfv34Nht21fVtTX/q25PF45GKXKom1+CbWohZrUaugoMDWcU1+j3379u3at2+f3G63PB6PUlJSdO7cuaa+DACgHk2+Y9+0aZP3n91utzIzM9WxY8emvgwAoB487ggAhnHkxdOv5ebmOnl6AEAd2LEDgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGFshf3AgQPas2ePdu/erfj4eL399ttOzwUAaCRbYV+wYIG6deumDRs2aPPmzdqyZYvTcwEAGinAzkFBQUFq3769AgIC1LFjR1VUVDR4fFVVlWbPnq3jx4/L399fWVlZio6ObpKBAQANs7VjDwsL07PPPquhQ4dq06ZN6ty5c4PHv//++5KkLVu2aMqUKcrKyrr1SQEAttjasc+YMUM1NTW67777dOTIEY0cObLB4+Pj4zVgwABJUlFRkTp06HDLg5oo/2i+Nnk2KeSzEF+P0iKUlpayFl/pE9pHLrl8PQZuU7bCPnv2bG3evFmS1KNHD3snDghQSkqK/v73v+u11177n5/3eDw3MaaZis4Xqbq6WqWlpb4epUVgLWpVtqrkz8hXysrKWIubZNXU1NR810ETJkxQbGysYmJi5Od3/e7N6NGjbV3g3LlzGjVqlN555x2FhFzfjRUUFKhXr163MLY5PB6PXC52ZhJr8U2sRS3Wopbddtrasf/oRz+SJF24cMHWxXfs2KEzZ87oV7/6lYKDg2VZlvz9/W39WgDArbEV9hdeeEH79u1TYWGhHnroIcXExDR4/KBBg5Samqpx48bp2rVrSktLU1BQUJMMDABomK2wL168WF988YWOHj2qwMBArV27VosXL673+JCQEC1btqzJhgQA2GfrcceCggLNnz9fISEhSkhIUGFhodNzAQAayVbYq6qqVF5eLsuyVFVV5X0BFQDQ8ti6FfPMM8/oqaeeUnFxsUaOHKnx48c7PBYAoLFshX3o0KH66U9/qs8++0xRUVGKjIx0ei4AQCM1GPaVK1dq8uTJeumll2RZ1g0/t2jRIkcHAwA0ToNhHzhwoCQpMTGxWYYBANy6Bl8F/f73vy9JKikp0Ycffqif/OQnWrNmjcrLy5tlOADAzbP1eMvy5cv19NNPS5KWLl2qFStWODoUAKDxbIU9ICBA7du3lySFh4fzuCMAtGC2nop56KGHNH36dMXFxenjjz/WAw884PRcAIBGsv22vbt27dKxY8c0dOhQ74uqAICWx9Y9latXr6qiokKdOnXS5cuXtWPHDqfnAgA0kq0d++TJk9WpUyfvt8T79jPtAICWw1bYa2pqtHDhQqdnAQA0AVu3Yr73ve/p0KFDqqio8P4AALRMtnbs+/fv1z/+8Q/vx5ZladeuXY4NBQBoPFthz8/PlyRdunRJbdu25R47ALRgtsJ+4MABvfzyy6qqqtKQIUPUpUsXjRw50unZAACNYOse+9KlS7Vx40Z16NBBkyZN0ubNm52eCwDQSLbC7ufnp4iICFmWpaCgIIWGhjo9FwCgkWyFPTo6WosWLdKlS5e0du1adenSxem5AACNZCvsv/vd79SlSxf16tVLwcHBmjNnjtNzAQAaydaLp5MmTVJOTo7TswAAmoCtsIeHh2vXrl3q1q2b9y17Y2JiHB0MANA4tsJeXFys9evXez+2LEsbNmxwaiYAwC2wFfbc3FxduXJFp06dUteuXXkqBgBasAbDXlNTI8uy9N5772nVqlXeL1CyLEuTJ09urhkBADehwadi1q1bJ0l68803tXXrVkVERGjy5MnauXNnswwHALh5DYa9T58+kq7fU2/VqpUsy5JlWQoODm6W4QAAN6/BsP/whz+UJPXu3VvTp0/XmTNnlJGRoR/84AfNMhwA4OY1eI/9wIED6t27t55//nl99NFHcrlc6t69O9/zFABasAZ37PPmzVNpaal+8Ytf6OGHH1ZSUpIeeeQRvtEGALRgDe7Y+/btq+HDh+uLL77QkCFDJNU+KcM32gCAlqnBsE+bNk3Tpk3TihUr9PzzzzfXTACAW2DrC5QSEhK0bt06lZeXe//dCy+84NhQAIDGs/XujlOnTlVJSYk6dOjg/QEAaJls7dhDQ0M1bdo0WyesrKxUWlqaTp06pYqKCj333HN67LHHbmlIAIB9tsJ+//3365133pHL5fJ+I+v63t0xPz9fERERWrBggS5evKiEhIS6w/7mE42f2iBt7xoouVy+HgOAQWyF3ePxyOPxeD9u6N0dhwwZosGDB3s/9vf3r/O4q6VXb2ZOY1VWVt6wtneysrIy1uIrrEUt1uLm2X53x4sXL+rkyZOKiopSZGRkvcd+/c6PJSUlmjJliqZOnVr3cc9/cPPTGuhzj0cuduySrm8gWIvrWItarEWtgoICW8fZevH03XffVWJiolavXq3Ro0frL3/5S4PHnz59WklJSRo2bJiefPJJW4MAAJqGrR37+vXrlZeXp9DQUJWUlOiZZ57RsGHD6jz2/PnzSk5OVkZGhh5++OEmHRYA8N1s7dgty/LeYgkLC1NQUFC9x65evVqXL1/WypUr5Xa75Xa7VVZW1jTTAgC+k60de3R0tLKzs/XjH/9YBQUFio6OrvfY2bNna/bs2U02IADg5tjasY8aNUpt27bVvn37lJeXp3Hjxjk9FwCgkWyFPTs7W48//rgyMjK0bds2ZWdnOz0XAKCRbIU9ICBA9913nySpa9eu8vOz9csAAD5g6x57ly5dtHjxYsXFxenjjz9Wp06dnJ4LANBItrbeWVlZioyM1O7duxUZGamsrCyn5wIANJKtHXtQUJDGjx/v8CgAgKbAzXIAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMAxhBwDDEHYAMIxjYT906JDcbrdTpwcA1CPAiZOuW7dO+fn5Cg4OduL0AIAGOBL26OhoLV++XL/5zW+cOD0MtL2gUOv3FClkzyVfj9IilJaWshZfeeQef7lcvp7i9uJI2AcPHqzCwsIGj/F4PE5c+rZTVlbGWkgqOn1F1dXVKi0t9fUoLQJrUauyojV/Rm6SI2G3w8VfwZKu/wXHWkgulxQfy1p8jf8uarEWtQoKCmwdx1MxAGAYwg4AhnEs7FFRUdq6datTpwcA1IMdOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGEIOwAYhrADgGECnDhpdXW1MjMzdfjwYbVq1Uqvvvqq7r33XicuBQD4Fkd27Dt37lRFRYX++Mc/avr06crOznbiMgCAOjiyYy8oKFC/fv0kSXFxcfrPf/5T5zG4jrWoxVrUYi1qsRY3x5Gwl5SUKCwszPuxv7+/rl27poCA65fr1auXE5cFAMihWzFhYWG6evWq9+Pq6mpv1AEAznIk7D179tSePXskSQcPHlSPHj2cuAwAoA5WTU1NTVOf9OunYo4cOaKamhrNnTtXsbGxTX0ZAEAdHAl7fXgM8n8dOnRICxcuVG5urq9H8ZnKykqlpaXp1KlTqqio0HPPPafHHnvM12P5RFVVlWbPnq3jx4/L399fWVlZio6O9vVYPnPhwgU99dRTysnJueM3h8OHD1d4eLgkKSoqSllZWfUe26w3vr/5GOTBgweVnZ2tVatWNecILcq6deuUn5+v4OBgX4/iU/n5+YqIiNCCBQt08eJFJSQk3LFhf//99yVJW7Zs0UcffaSsrKw79s9IZWWlMjIy1Lp1a1+P4nPl5eWSZHsD2KxfeWrnMcg7SXR0tJYvX+7rMXxuyJAhevHFF70f+/v7+3Aa34qPj9ecOXMkSUVFRerQoYOPJ/KdefPmKTExUZ06dfL1KD73ySef6Msvv1RycrKSkpJ08ODBBo9v1rDX9xjknWrw4ME8LSQpNDRUYWFhKikp0ZQpUzR16lRfj+RTAQEBSklJ0Zw5czR48GBfj+MTeXl5ioyM9G4E73StW7fWhAkT9MYbb+jll1/WjBkzGmxns4adxyBRn9OnTyspKUnDhg3Tk08+6etxfG7evHl67733lJ6ertLSUl+P0+y2b9+uffv2ye12y+PxKCUlRefOnfP1WD4TExOjn//857IsSzExMYqIiGhwPZo17DwGibqcP39eycnJmjlzpkaMGOHrcXxqx44dWrNmjSQpODhYlmXdkbemNm3apI0bNyo3N1cul0vz5s1Tx44dfT2Wz2zbts371ixnzpxRSUlJg+vRrNvlxx9/XHv37lViYqL3MUhg9erVunz5slauXKmVK1dKuv7C8p34otmgQYOUmpqqcePG6dq1a0pLS1NQUJCvx4KPjRgxQqmpqRozZowsy9LcuXMbvNvRrI87AgCcx/uxA4BhCDsAGIawA4BhCDsAGIawA4BhCDtarOLiYs2dO1eXL1/26Rx5eXlauHBhk53P4/Ho97//fZOdD/g2vuwTLVZkZKTS0tJ8PUaTc7lccrlcvh4DBiPsaHZlZWVKTU1VUVGRKisrlZ6eruPHj2v79u2qrq7WlClTdOnSJa1fv15+fn7q1auXZsyYoeXLl6uwsFAXLlxQUVGRUlNT1a9fP+3fv19LliyRv7+/unbtqldeeUXl5eWaNWuWrly5oosXL2rkyJEaO3as3G63MjMzFRsbq82bN+v8+fMaOHCgUlJStHXrVr377rvas2ePli1bdsPMhw4dUnJysoqLizVmzBiNHj1ae/fu1dKlSxUUFKSIiAjNnTtXHo9HCxcuVGBgoEaNGqXWrVtr06ZN3vMsW7ZMn376qbZs2aIlS5bo0UcfVffu3dW9e3clJycrPT1d5eXlCgoK0pw5cxQZGakXX3xRJSUlKisr08yZM9WnT5/m/i3DbYawo9lt2bJF99xzj5YsWaIjR45o3759atOmjdq0aaNVq1bp0qVLGjt2rLZv367g4GDNnDlTe/fulSS1atVKr7/+uvbu3aucnBw98sgjSk9P1x/+8Ae1b99eS5cu1Z///Gc9+OCDeuKJJzRo0CCdOXNGbrdbY8eOrXOeBx98UCNGjNBvf/tbFRYWasOGDf9zTEBAgN544w2dOnVKEydO1KhRo5Senq7Nmzfrrrvu0ltvvaVVq1ZpwIABKi8v15/+9CdJ17+qdu3atQoODlZGRob++c9/6q677vKe9/Tp08rLy1O7du00depUud1u9e/fXx9++KEWLlyoSZMm6fz581q/fr0uXLigEydONP1vCIxD2NHsjh07pp/97GeSpB49eqhHjx7Ky8tTTEyMJOnzzz9XcXGxJk6cKEm6evWqTp48KUneWxh33323KioqVFxcrLNnz3rfEbKsrEx9+/ZV//799dZbb+lvf/ubwsLC6nwnvG9+0XViYqJWrFihyZMn3/AOpF974IEHZFmWOnbsqLKyMl28eFFhYWHeSPfu3VuLFy/WgAEDvJ+HJLVv314pKSkKDQ3VsWPHFBcXd8N527Vrp3bt2kmSjhw5ojVr1uj1119XTU2NAgMDdf/992vcuHF66aWXdO3aNbnd7kasOO40hB3NLjY2Vv/+978VHx+vkydPaunSperbt6/8/K6/lh8VFaXOnTsrJydHgYGBysvLk8vl0s6dO2VZ1g3nateune6++26tXLlS4eHh2rVrl0JCQpSTk6O4uDiNHTtW//rXv7R7925J13f8586dU2xsrP773/96wzx//nxNmDBBeXl5io+PV9euXW+4Tl3XLSkp0dmzZ9WpUyft379f3bp1kyTv53HlyhW99tpr+uCDDyRJzz77rL79Dh5fHyvJezumZ8+eOnr0qA4cOKDDhw/r6tWrWrt2rc6ePavExEQ9+uijt7D6uBMQdjS7xMREpaWl6emnn1ZVVZXS0tL06aefen8+MjJS48ePl9vtVlVVle655x4NHTq0znP5+flp1qxZmjhxompqahQaGqr58+fLsixlZmbq7bffVkREhPz9/VVRUaGkpCS98sor6ty5s/cbOOzatUsnTpxQenq64uLiNGPGDG3cuFGBgYH1fg6WZenVV1/Vr3/9a1mWpbZt2yorK+uGzyMsLEw9e/ZUQkKCQkJC1KZNG509e1ZRUVF1njMlJUWZmZkqLy9XWVmZZs2apW7dumnFihXasWOHAgMDNWXKlMYsOe4wvAkYABiG59gBwDCEHQAMQ9gBwDCEHQAMQ9gBwDCEHQAMQ9gBwDD/D0ODQ7pU/1i2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf=[[2,4],[0,1],[1,3],[0,2]]\n",
    "affiche_graphe(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**application 2 :** <br>\n",
    "- Quelle est la particularité de ces nouveaux horaires ?\n",
    "- Modifier, au besoin,  la fonction prédédente (dans le cellule ci-dessous) afin qu'elle permette de produire le bon ordre de passage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ordre_conference(conf):\n",
    "    ordre_conf=[]\n",
    "    ## à compléter\n",
    "    return ordre_conf\n",
    "\n",
    "assert ordre_conference([[3,4],[0,1],[2,3],[1,2]])==[2,4,3,1]\n",
    "assert ordre_conference([[2,4],[0,1],[1,3],[0,2]])==[2,4,3,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### troisième exemple :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cette fois-ci, on a trois conférenciers avec les horaires suivants :<br>\n",
    "C1 ∶ \\[0,3[ C2 ∶ [1,2[ C3 ∶ [2,3["
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEECAYAAAAlEzNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAfrUlEQVR4nO3dfVRUdeIG8GcEBgeGUBTtRSeVdCNdF+W05arLi6byU44owgwoVLKVkZlULkjgYnok0kxTQdAVS01jkVw8nY6mWJ5VQwdXK5yyxcTXQkEOzkwwCPf3h6e7jfIysnNnhu7zOcdzmPudufPMF+Fh7stchSAIAoiISLZ6ODsAERE5F4uAiEjmWARERDLHIiAikjkWARGRzLEIiIhkTrIiqK2tRUhICKqqqqyWl5WVITo6GlqtFkVFRVI9PRER2chdipU2NzdjyZIl6Nmz513Ls7OzUVxcDJVKhbi4OISFhcHf31+KGEREZANJ3hHk5ORAp9OhX79+Vsurqqqg0Wjg6+sLpVKJ4OBg6PV6KSIQEZGN7P6OoKSkBH5+fhg/fjwKCgqsxoxGI3x8fMTb3t7eMBqNba6noqLC3tGIiH7zgoOD7/kxdi+C3bt3Q6FQ4NixYzAYDEhNTUVeXh78/f2hVqthMpnE+5pMJqtiuFNXXpAjGQwGBAYGOjtGp5jTvpjTvpjTfrr6B7Tdi2DHjh3i1wkJCcjKyhL3AQQEBKC6uhr19fXw8vKCXq9HUlKSvSMQEdE9kGRn8Z327t0Ls9kMrVaLtLQ0JCUlQRAEREdHo3///o6IQERE7ZC0CLZt2wbg9juBX4SHhyM8PFzKpyUionvAE8qIiGSORUBEJHMsAiIimWMREBHJHIuAiEjmWARERDLHIiAikjkWARGRzLEIiIhkjkVARCRzLAIiIpljERARyRyLgIhI5lgEREQyxyIgIpI5FgERkcyxCIiIZI5FQEQkcywCIiKZk+SaxS0tLcjIyMAPP/wANzc3ZGdnQ6PRiOOFhYUoLi6Gn58fAGDp0qUYMmSIFFGIiKgTkhTBoUOHAAC7du1CeXk5srOzkZeXJ45XVlYiJycHI0aMkOLpiYjoHkhSBBMnTkRoaCgA4MqVK+jbt6/VeGVlJQoKCnDt2jWEhobihRdekCIGERHZQCEIgiDVylNTU/HZZ5/hvffew7hx48Tl69evR3x8PNRqNebPn4+4uDiEhYVZPbaiogJeXl5SRbOLxsZG9OzZ09kxOsWc9sWc9sWc9mM2mxEcHHzvDxQkVlNTI4SGhgomk0kQBEFobW0VGhoaxPHt27cL69evv+txer1e6mj/szNnzjg7gk2Y076Y076Y0366+ntTkqOG9uzZg/z8fACASqWCQqGAm5sbAMBoNGLatGkwmUwQBAHl5eXcV0BE5ESS7COYNGkSFi9ejNmzZ+PWrVtIT0/H/v37YTabodVqkZKSgsTERCiVSowZMwYhISFSxCAiIhtIUgReXl5Yu3Ztu+NRUVGIioqS4qmJiOge8YQyIiKZYxEQEckci4CISOZYBEREMsciICKSORYBEZHMsQiIiGSORUBEJHMsAiIimWMREBHJHIuAiEjmWARERDLHIiAikjkWARGRzLEIiIhkjkVARCRzLAIiIpljERARyRyLgIhI5lgEREQyJ0kRtLS0YPHixdDpdJg9ezYuXLhgNV5WVobo6GhotVoUFRVJEYGIiGzkLsVKDx06BADYtWsXysvLkZ2djby8PABAc3MzsrOzUVxcDJVKhbi4OISFhcHf31+KKER2VVpVih2GHfCq9nJ2lE6ZzeZukfMJ7ycQiEBnx5A1SYpg4sSJCA0NBQBcuXIFffv2Fceqqqqg0Wjg6+sLAAgODoZer0dERMRd6zEYDFLEs5vGxkaXzwgwpz1duX4Fra2tMJvNzo7Sqe6Ss1nZ7PLfd6B7/P/sKkmKAADc3d2RmpqKzz77DO+995643Gg0wsfHR7zt7e0No9HY5joCA137rwSDweDyGQHmtKdABCLEEOLyOYHuMZ8Ac9pTRUVFlx4n6c7inJwc7Nu3D5mZmeJfJmq1GiaTSbyPyWSyKgYiInIsSYpgz549yM/PBwCoVCooFAq4ubkBAAICAlBdXY36+npYLBbo9XqMGjVKihhERGQDSTYNTZo0CYsXL8bs2bNx69YtpKenY//+/TCbzdBqtUhLS0NSUhIEQUB0dDT69+8vRQwiIrKBJEXg5eWFtWvXtjseHh6O8PBwKZ6aiIjuEU8oIyKSORYBEZHMsQiIiGSORUBEJHMsAiIimWMREBHJHIuAiEjmWARERDLHIiAikjkWARGRzLEIiIhkjkVARCRzLAIiIpljERARyRyLgIhI5lgEREQyxyIgIpI5FgERkcyxCIiIZM7u1yxubm5Geno6Ll++DIvFghdffBETJkwQxwsLC1FcXAw/Pz8AwNKlSzFkyBB7xyAiIhvZvQhKS0vRq1cvrFy5Ejdu3MCMGTOsiqCyshI5OTkYMWKEvZ+aiIi6QCEIgmDPFZpMJgiCALVajRs3bmDWrFk4ePCgOB4REYGhQ4fi2rVrCA0NxQsvvNDmeioqKuDl5WXPaHbX2NiInj17OjtGp5jTvpjTvpjTfsxmM4KDg+/9gYINjh8/LnzxxRfC559/LkyYMEEoLS3t9DE3b94U5syZc9d9161bJ9TW1gpNTU3Cc889J5SVlbX5eL1eb0s0pzpz5oyzI9iEOe2LOe2LOe2nq783bdpZvHLlSgwaNAgffPABdu7ciV27dnV4/6tXryIxMRHTp09HZGTkr0sHTz/9NPz8/KBUKhESEoIzZ87ce3sREZHd2FQEnp6e6NOnD9zd3eHv7w+LxdLufa9fv465c+di0aJFmDVrltWY0WjEtGnTxM1H5eXl3FdARORkNu0sVqvVePbZZxEfH48dO3bggQceaPe+GzduRENDA3Jzc5GbmwsAiImJwc8//wytVouUlBQkJiZCqVRizJgxCAkJsc8rISKiLrGpCF5//XUIgoBHHnkEZ8+eRUxMTLv3zcjIQEZGRrvjUVFRiIqKuvekREQkCZs2DWVkZOCRRx4BAAwbNgxKpVLSUERE5Dg2vSPw8vLCihUrMHjwYPTocbs7tFqtpMGIiMgxbCqCUaNGAQBqa2slDUNERI5nUxHMnz8fR48exaVLlzBy5EgMHjxY6lxEROQgNhXB6tWr8eOPP6KqqgoeHh4oKCjA6tWrpc5GREQOYNPO4oqKCrz99tvw8vLCjBkzcOnSJalzERGRg9hUBC0tLWhqaoJCoUBLS4u4w5iIiLo/mzYNPf3005g5cybq6uoQExODZ555RuJYRETkKDYVQUREBP70pz+huroaAwYMEK8lQERE3V+HRZCbm4vk5GS8+uqrUCgUVmPvvPOOpMGIiMgxOiyC8PBwAIBOp3NIGCIicrwO9/o++uijAG5/auixY8fwxz/+Efn5+WhqanJIOCIikp5Nh/+sW7cOc+bMAQCsWbMGGzZskDQUERE5jk1F4O7ujj59+gAAfHx8ePgoEdFviE1HDY0cORKvvfYagoKC8NVXX+Gxxx6TOhcRETmITUWQkZGBgwcP4ty5c4iIiBB3IhMRUfdn0zYek8kEi8WCfv36oaGhAXv27JE6FxEROYhN7wiSk5PRr18/8RKVd55TQERE3ZdNRSAIAlatWiV1FiIicgKbNg397ne/w+nTp2GxWMR/7WlubsaiRYsQHx+PWbNm4eDBg1bjZWVliI6OhlarRVFR0f+WnoiI/mc2vSM4fvw4ysrKxNsKheKuX/C/KC0tRa9evbBy5UrcuHEDM2bMwIQJEwDcLons7GwUFxdDpVIhLi4OYWFh8Pf3t8NLISKirrCpCEpLSwEA9fX18PX17XAfwZQpUzB58mTxtpubm/h1VVUVNBoNfH19AQDBwcHQ6/WIiIjoUniywamd0BzJB770dnaSTmnMpm6R07d/OBAY6OwYRHZjUxGcOHECS5cuRUtLC6ZMmYIHH3wQMTExbd7X2/v2D7LRaMSCBQuwcOFCccxoNMLHx8fqvkajsd3nNRgMNr0IZ2lsbHT5jL5XrsCntRUms8nZUTrV2k1yNjc3u/z3Hege/z8B5nQFNhXBmjVrsH37drz88suYN28e4uLi2i0CALh69SpeeuklxMfHIzIyUlyuVqthMv33B91kMlkVw50CXfyvLoPB4PIZERgIg+H/XD8nusl8ArjQTXJ2l/lkTvupqKjo0uNs2lnco0cP9OrVCwqFAp6enuJf/W25fv065s6di0WLFmHWrFlWYwEBAaiurkZ9fT0sFgv0ej1GjRrVpeBERGQfNr0j0Gg0eOedd1BfX4+CggI8+OCD7d5348aNaGhoQG5uLnJzcwEAMTEx+Pnnn6HVapGWloakpCQIgoDo6Gj079/fPq+EiIi6xKYi+Nvf/obdu3cjODgYKpUKy5Yta/e+GRkZyMjIaHc8PDycH1FBRORCbCqCefPmYcuWLVJnISIiJ7CpCHx8fHDw4EEMGjRI/AjqwYMHSxqMiIgcw6YiqKurw9atW8XbCoUCH3zwgVSZiIjIgWwqgm3btuHmzZu4fPkyBg4c2OFRQ0RE1L10WASCIEChUGDfvn3Iy8sTTyhTKBRITk52VEYiIpJQh+cRbNq0CQBQWFiIoqIi9OrVC8nJyThw4IBDwhERkfQ6LIInnngCwO19AkqlEgqFAgqFAiqVyiHhiIhIeh0WwR/+8AcAwOOPP47XXnsNP/30E5YsWYLf//73DglHRETS63AfwYkTJ/D444/jpZdeQnl5OQIDAzFkyBCeEEZE9BvS4TuCnJwcmM1m/OUvf8GYMWOQmJiIcePGdXhhGiIi6l46fEcwduxYREVF4ccff8SUKVMA/PdIovYuTENERN1Lh0WQkpKClJQUbNiwAS+99JKjMhERkQPZdELZjBkzsGnTJjQ1NYnL5s+fL1koIiJyHJuuR7Bw4UIYjUb07dtX/EdERL8NNr0j8Pb2RkpKitRZiIjICWwqgqFDh+KTTz5BYGCgeOF6fvooEdFvg01FYDAYrC7azE8fJSL67bD500dv3LiBixcvYsCAAfDz85M6FxEROYhNO4s//fRT6HQ6bNy4EVqtFv/85z+lzkVERA5iUxFs3boVJSUlyM3Nxccff2zTZqHTp08jISHhruWFhYWYOnUqEhISkJCQgHPnzt17aiIishubNg0pFArxYjRqtRqenp4d3n/Tpk0oLS1t81NKKysrkZOTgxEjRnQhLhER2ZtN7wg0Gg3eeustHDhwADk5OdBoNJ3ef926dW2OVVZWoqCgAHFxccjPz7/3xEREZFc2vSOIjY3FiRMncPToUXzyySfYvHlzh/efPHkyLl261ObY1KlTER8fD7Vajfnz5+PQoUMICwtr876/PlLJFTU2Nrp8RoA57Y057Ys5XYBgg+joaOH7778XBEEQLly4IMTHx3f6mIsXLwoxMTFWy1pbW4WGhgbx9vbt24X169e3+Xi9Xm9LNKc6c+aMsyPYhDntizntizntp6u/N23aNOTu7o5HHnkEADBw4ED06GHTw+5iNBoxbdo0mEwmCIKA8vJy7isgInIymzYNPfjgg1i9ejWCgoLw1VdfoV+/fvf0JHv37oXZbIZWq0VKSgoSExOhVCoxZswYhISEdCk4ERHZh01FkJ2djZ07d+KLL75AQEAAkpOTO33MgAEDUFRUBACIjIwUl0dFRSEqKqqLcYmIyN5sKgJPT08888wzEkchIiJn6NrGfiIi+s1gERARyRyLgIhI5lgEREQyxyIgIpI5FgERkcyxCIiIZI5FQEQkcywCIiKZYxEQEckci4CISOZYBEREMsciICKSORYBEZHMsQiIiGSORUBEJHMsAiIimWMREBHJnGRFcPr0aSQkJNy1vKysDNHR0dBqteI1jYmIyHlsumbxvdq0aRNKS0uhUqmsljc3NyM7OxvFxcVQqVSIi4tDWFgY/P39pYhBREQ2kKQINBoN1q1bh7/+9a9Wy6uqqqDRaODr6wsACA4Ohl6vR0RERJvr0eYfkyKe3ZjNZngdrnd2jE4xp30xp32Ne8gNgYHOTiFvkhTB5MmTcenSpbuWG41G+Pj4iLe9vb1hNBrbXY/ZbJYint20tra6fEaAOe2NOe2r2dITBoPB2TE61djY2C1ydoUkRdAetVoNk8kk3jaZTFbFcKe9KRMcEavLDAYDArvBnzLMaV/MaV/MaT8VFRVdepxDjxoKCAhAdXU16uvrYbFYoNfrMWrUKEdGICKiOzjkHcHevXthNpuh1WqRlpaGpKQkCIKA6Oho9O/f3xERiIioHZIVwYABA8TDQyMjI8Xl4eHhCA8Pl+ppiYjoHvGEMiIimWMREBHJHIuAiEjmWARERDLHIiAikjkWARGRzLEIiIhkjkVARCRzLAIiIpljERARyRyLgIhI5lgEREQyxyIgIpI5FgERkcyxCIiIZI5FQEQkcywCIiKZYxEQEckci4CISOYkuWZxa2srsrKy8N1330GpVGL58uV4+OGHxfHly5fj5MmT8Pb2BgDk5ubCx8dHiihERNQJSYrgwIEDsFgs+Oijj3Dq1Cm89dZbyMvLE8crKyuxefNm+Pn5SfH0RER0DyTZNFRRUYHx48cDAIKCgvDNN9+IY62traiursaSJUug0+lQXFwsRQQiIrKRJO8IjEYj1Gq1eNvNzQ23bt2Cu7s7zGYz5syZg2effRYtLS1ITEzEiBEj8Oijj961HoPBIEU8u2lsbHT5jABz2htz2hdzOp8kRaBWq2EymcTbra2tcHe//VQqlQqJiYlQqVQAgCeffBLffvttm0UQGBgoRTy7MRgMLp8RYE57Y077Yk77qaio6NLjJNk0NHr0aBw+fBgAcOrUKQwbNkwcO3/+POLj49HS0oLm5macPHkSw4cPlyIGERHZQJJ3BE899RSOHDkCnU4HQRCwYsUKFBYWQqPRYMKECYiMjERsbCw8PDwwffp0DB06VIoYRERkA0mKoEePHnjzzTetlgUEBIhfP/fcc3juueekeGoiIrpHPKGMiEjmWARERDLHIiAikjkWARGRzLEIiIhkjkVARCRzLAIiIpljERARyRyLgIhI5lgEREQyxyIgIpI5FgERkcyxCIiIZI5FQEQkcywCIiKZYxEQEckci4CISOZYBEREMsciICKSORYBEZHMSVIEra2tWLJkCbRaLRISElBdXW01XlRUhJkzZyI2NhaHDh2SIgIREdnIXYqVHjhwABaLBR999BFOnTqFt956C3l5eQCAa9euYdu2bdi9ezeampoQHx+PsWPHQqlUShGFiIg6IUkRVFRUYPz48QCAoKAgfPPNN+LYV199hVGjRkGpVEKpVEKj0eDbb7/FyJEj21yPq+sOGQHmtDfmtC/mdC5JisBoNEKtVou33dzccOvWLbi7u8NoNMLHx0cc8/b2htFovGsdwcHBUkQjIqI7SLKPQK1Ww2QyibdbW1vh7u7e5pjJZLIqBiIicixJimD06NE4fPgwAODUqVMYNmyYODZy5EhUVFSgqakJN2/eRFVVldU4ERE5lkIQBMHeK21tbUVWVhbOnj0LQRCwYsUKHD58GBqNBhMmTEBRURE++ugjCIKAF154AZMnT7Z3BCIispEkRWCrXwrju+++g1KpxPLly/Hwww+L40VFRdi1axfc3d3x4osvIiwszCVzLl++HCdPnoS3tzcAIDc312mbu06fPo1Vq1Zh27ZtVsvLysqwYcMGuLu7Izo6GrGxsU7J94v2chYWFqK4uBh+fn4AgKVLl2LIkCEOz9fc3Iz09HRcvnwZFosFL774IiZMmCCOu8p8dpbTVeazpaUFGRkZ+OGHH+Dm5obs7GxoNBpx3FXms7OcrjKfv6itrcXMmTOxZcsWBAQEiMvveT4FJ9q3b5+QmpoqCIIg/Pvf/xbmzZsnjtXU1AjTpk0TmpqahIaGBvFrV8spCIKg0+mE2tpaZ0SzUlBQIEybNk2IiYmxWm6xWISJEycK9fX1QlNTkzBz5kyhpqbGSSnbzykIgvDaa68JX3/9tRNSWSsuLhaWL18uCIIg1NXVCSEhIeKYK81nRzkFwXXm87PPPhPS0tIEQRCEL7/80upnyJXms6OcguA68ykIt+ctOTlZmDRpkvCf//zHavm9zqdTzyy29TBTHx8f8TBTV8vZ2tqK6upqLFmyBDqdDsXFxU7JCAAajQbr1q27a3lVVRU0Gg18fX2hVCoRHBwMvV7vhIS3tZcTACorK1FQUIC4uDjk5+c7ONl/TZkyBa+88op4283NTfzaleazo5yA68znxIkTsWzZMgDAlStX0LdvX3HMleazo5yA68wnAOTk5ECn06Ffv35Wy7syn04tgvYOM/1lzJbDTB2ho5xmsxlz5szBypUrsXnzZnz44YdOK6zJkyeLR2f9mivNJdB+TgCYOnUqsrKy8P7776OiosJpZ557e3tDrVbDaDRiwYIFWLhwoTjmSvPZUU7AdeYTANzd3ZGamoply5ZZ7Rd0pfkE2s8JuM58lpSUwM/PT/wD9de6Mp9OLYLucphpRzlVKhUSExOhUqmgVqvx5JNPOq0I2uNKc9kRQRDw9NNPw8/PD0qlEiEhIThz5ozT8ly9ehWJiYmYPn06IiMjxeWuNp/t5XS1+QRu/xW7b98+ZGZmwmw2A3C9+QTazulK87l7924cPXoUCQkJMBgMSE1NxbVr1wB0bT6dWgTd5TDTjnKeP38e8fHxaGlpQXNzM06ePInhw4c7JWd7AgICUF1djfr6elgsFuj1eowaNcrZse5iNBoxbdo0mEwmCIKA8vJyjBgxwilZrl+/jrlz52LRokWYNWuW1ZgrzWdHOV1pPvfs2SNuSlGpVFAoFOJmLFeaz45yutJ87tixA9u3b8e2bdsQGBiInJwc+Pv7A+jafEpyZrGtnnrqKRw5cgQ6nU48zLSwsFA8zDQhIQHx8fEQBAEpKSnw9PR0yZyRkZGIjY2Fh4cHpk+fjqFDhzol55327t0Ls9kMrVaLtLQ0JCUlQRAEREdHo3///s6OJ/p1zpSUFCQmJkKpVGLMmDEICQlxSqaNGzeioaEBubm5yM3NBQDExMTg559/dqn57Cynq8znpEmTsHjxYsyePRu3bt1Ceno69u/f73L/PzvL6Srz2Zb/5efdqYePEhGR8/F6BEREMsciICKSORYBEZHMsQiIiGSORUBEJHMsAuq26urqsGLFCjQ0NDg1R0lJCVatWmW39RkMBqxfv95u6yPqjFPPIyD6X/j5+SE9Pd3ZMewuMDAQgYGBzo5BMsIiIJfT2NiIxYsX48qVK2hubkZmZiZ++OEH7N69G62trViwYAHq6+uxdetW9OjRA8HBwXj99dexbt06XLp0CbW1tbhy5QoWL16M8ePH4/jx43j33Xfh5uaGgQMH4s0330RTUxPeeOMN3Lx5Ezdu3EBMTAzi4+ORkJCArKwsBAQEYOfOnbh+/TrCw8ORmpqKoqIifPrppzh8+DDWrl1rlfn06dOYO3cu6urqEBcXB61WiyNHjmDNmjXw9PREr169sGLFChgMBqxatQoeHh6IjY1Fz549sWPHDnE9a9euxffff49du3bh3XffRVhYGIYMGYIhQ4Zg7ty5yMzMRFNTEzw9PbFs2TL4+fnhlVdegdFoRGNjIxYtWoQnnnjC0d8y6uZYBORydu3ahYceegjvvvsuzp49i6NHj+K+++7Dfffdh7y8PNTX1yM+Ph67d++GSqXCokWLcOTIEQCAUqnE5s2bceTIEWzZsgXjxo1DZmYmPvzwQ/Tp0wdr1qzBxx9/jOHDh2Pq1KmYNGkSfvrpJ/Es9rYMHz4cs2bNQlpaGi5duoQPPvjgrvu4u7vj73//Oy5fvoznn38esbGxyMzMxM6dO9G/f3+8//77yMvLQ2hoKJqamvCPf/wDwO0zgwsKCqBSqbBkyRL861//sjoL9OrVqygpKUHv3r2xcOFCJCQkICQkBMeOHcOqVaswb948XL9+HVu3bkVtbS3Onz9v/28I/eaxCMjlnDt3Dn/+858BAMOGDcOwYcNQUlKCwYMHAwAuXLiAuro6PP/88wBuf6jWxYsXAUDcpHL//ffDYrGgrq4ONTU14qdyNjY2YuzYsQgJCcH777+P/fv3Q61Wi58m+2u/Pulep9Nhw4YNSE5Otvok2l889thjUCgU8Pf3R2NjI27cuAG1Wi3+Un/88cexevVqhIaGiq8DAPr06YPU1FR4e3vj3LlzCAoKslpv79690bt3bwDA2bNnkZ+fj82bN0MQBHh4eGDo0KGYPXs2Xn31Vdy6dQsJCQldmHGSOxYBuZyAgAB8/fXXmDhxIi5evIg1a9Zg7Nix6NHj9rENAwYMwAMPPIAtW7bAw8MDJSUlCAwMxIEDB6BQKKzW1bt3b9x///3iVeMOHjwILy8vbNmyBUFBQYiPj8eXX36JL774AsDtdxTXrl1DQEAAzpw5I/4if/vtt5GUlISSkhJMnDgRAwcOtHqetp7XaDSipqYG/fr1w/HjxzFo0CAAEF/HzZs38d577+Hzzz8HADz77LO48xNffrkvAHHz0OjRo1FVVYUTJ07gu+++g8lkQkFBAWpqaqDT6Zx2JT/qvlgE5HJ0Oh3S09MxZ84ctLS0ID09Hd9//7047ufnh2eeeQYJCQloaWnBQw89hIiIiDbX1aNHD7zxxht4/vnnIQgCvL298fbbb0OhUCArKwt79+5Fr1694ObmBovFgsTERLz55pt44IEHxAt+HDx4EOfPn0dmZiaCgoLw+uuvY/v27fDw8Gj3NSgUCixfvhwvv/wyFAoFfH19kZ2dbfU61Go1Ro8ejRkzZsDLywv33XcfampqMGDAgDbXmZqaiqysLDQ1NaGxsRFvvPEGBg0ahA0bNmDPnj3w8PDAggULujLlJHP80DkiIpnjeQRERDLHIiAikjkWARGRzLEIiIhkjkVARCRzLAIiIpljERARydz/A562/ecSaw0sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf=[[0,3],[1,2],[2,3]]\n",
    "affiche_graphe(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**application 3 :** <br>\n",
    "- Quelle stratégie mettre en place pour déterminer un ordre de passage ?\n",
    "- Vérifier que la fonction précédente fournit encore un résultat correct ou la modifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quatrième exemple :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Et si le problème devenait cela :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf=[[0,7],[2,5],[6,8],[1,2],[5,6],[0,2],[4,7],[0,1],[3,6],[1,3],[4,5],[6,8],[0,2],[5,7],[1,4]]\n",
    "affiche_graphe(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**application 4 :** Construire un algorithme qui permette de donner un planning d'occupation de la salle de conférence avec le maximum de conférenciers intervenant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(*la suite au prochain numéro*)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}