diff --git a/Dijkstra/Annexe_Dijkstra_Dictionnaire.ipynb b/Dijkstra/Annexe_Dijkstra_Dictionnaire.ipynb new file mode 100755 index 0000000..db778d8 --- /dev/null +++ b/Dijkstra/Annexe_Dijkstra_Dictionnaire.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Annexe optionnelle — Dijkstra avec une structure de données en dictionnaire\n", + "\n", + "### 2ème mise en œuvre de l'algorithme de Dijkstra\n", + "VERSION ELEVE\n", + "\n", + "> **Cette annexe est facultative**, pour les étudiantes à l'aise en Python qui veulent aller plus loin après la Séance 7. Elle reprend le même algorithme que la Séance 7, mais avec une structure de données en dictionnaire plutôt qu'en liste — un bon exercice de transfert, mais pas un prérequis pour la suite du cours." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conditions de réalisation :\n", + "- soit en complétant le notebook fourni soit sous la forme d'un fichier exécutable .py." + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1- Présentation\n", + " Lors de l'activité précédente vous avez abouti à l'implémentation de l'algorithme de Dijkstra en vous appuyant sur une structure de données sous forme de liste.\n", + "Pour rappel la structure du graphe utilisé:\n", + "![image.png](attachment:image.png)\n", + "Cette fois ci je vous demande de construire une solution dans laquelle le graphe serait représenté sous la forme d'un dictionnaire dans lequel les clés serait les sommets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2- Représentation du graphe par un dictionnaire\n", + " ### Travail 1 : définir la structure de donnée\n", + "Définir la structure de donnée pour représenter le graphe sous forme d’un dictionnaire. \n", + " L'idée est de créer un dictionnaire dans lequel chaque sommet serait une clé. Et pour chaque clé on aurait à nouveau un dictionnaire contenant les sommets adjacents et la distance depuis le sommet précédent.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#création du dictionnaire du graphe pondéré pour la recherche du plus court chemin\n", + "graph = {\n", + "'sommet': {'s_voisin1': distance, 's_voisin2': distance},\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vous avez la possibilité de revenir vers moi pour valider votre solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3- Implémentation de l'algorithme de Dijkstra\n", + " ### Travail 2 : implémenter votre solution\n", + " Votre solution sera documentée et s'appuiera sur le travail réalisé en classe.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def initialisation(s_debut):\n", + " \"\"\"\n", + " initialisation des variables permettant de parcourir le graphe\n", + " Parameters\n", + " ----------\n", + " depart : string\n", + " sommet de depart pour le parcours du graphe.\n", + " Returns\n", + " -------\n", + " E_calcul : dict\n", + " chemin en cours de calcul: poids et sommet précédent\n", + " E_calcul = {s_debut:[poids,prédécesseur]}\n", + " la distance au sommet de depart est nulle\n", + " E_sommets : dict\n", + " on met dans le dictionnaire provisoire les sommets adjacents \n", + " et leur poids par rapport au point de départ\n", + " E_sommets = {s_voisin1: [poids,depart],...}\n", + " \"\"\"\n", + " assert type(s_debut) ... , \" s_debut doit être un caractère \"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def Maj_poids(s_voisin,s_mini,poids, E_sommets):\n", + " \"\"\"\n", + " Mise à jour du poids et du prédécesseur:\n", + " si le sommet est nouveau : mettre à jour poids et prédecesseur\n", + " si le sommet est déjà découvert: mettre à jour uniquement le poids\n", + " \n", + " Parameters\n", + " ----------\n", + " s_voisin : str\n", + " sommet voisin\n", + " s_mini : str\n", + " sommet de poids mini\n", + " poids : int\n", + " poids du chemin le plus court \n", + " E_sommets : dict\n", + " dictionnaire des chemins en cours d'exploration'\n", + "\n", + " Returns\n", + " -------\n", + " E_sommets : dict\n", + " dictionnaire des chemins en cours d'exploration mise à jour\n", + " avec le poids et le sommet de poids mini\n", + "\n", + " \"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def Dijkstra(graphe, s_debut):\n", + " \"\"\"\n", + " Cette fonction implémente l'algorithme de dijkstra\n", + " Parameters\n", + " ----------\n", + " graphe : dict\n", + " DESCRIPTION. description du graphe\n", + " s_debut : str\n", + " DESCRIPTION. le sommet de départ\n", + "\n", + " Returns\n", + " -------\n", + " calcul: dict\n", + " le résultat de l'algorithme de dijkstra\n", + "\n", + " \"\"\"\n", + " assert type(s_debut) == str, \" s_debut doit être un caractère \"\n", + " assert type(graphe) == dict, \"graphe doit être un dictionnaire\"\n", + " \n", + " \n", + " #phase d'initialisation des données\n", + " E_calcul,E_sommets=initialisation(s_debut)\n", + " \n", + " #tant que provisoire non vide\n", + " while E_sommets!= {}: \n", + " #recherche de la distance la plus faible\n", + " s_mini=min(E_sommets, key=E_sommets.get)\n", + " #---------------------Partie à compléter\n", + " \n", + " \n", + " \n", + " #------------------------------------- \n", + " #fin while \n", + " return E_calcul " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail 3: La vérification de votre implémentation\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def routage(calcul,depart,arrivee): \n", + " \"\"\"\n", + " Cette fonction donne le routage d'un sommet A au sommet B à partir\n", + " du résultat obtenu par l'algorithme de dijkstra\n", + "\n", + " Parameters\n", + " ----------\n", + " calcul : dict\n", + " résultat de l'algo de dijkstra\n", + " depart : str\n", + " sommet de départ\n", + " arrivee : string\n", + " sommet d'arrivée\n", + "\n", + " Returns\n", + " -------\n", + " routage : list\n", + " le routage de A à B\n", + " distance : int\n", + " la distance\n", + "\n", + " \"\"\"\n", + " \n", + " routage = [arrivee]\n", + " distance=calcul[arrivee][0]\n", + " #création de la liste de routage\n", + " while routage[0]!= depart: \n", + " for key in calcul:\n", + " if key==routage[0]:\n", + " routage.insert(0,calcul[routage[0]][1]) \n", + " return routage,distance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resultat = Dijkstra(graph,\"A\")\n", + "routage, distance= routage(resultat,\"A\",\"G\")\n", + "print(\"le plus court chemin est: \", routage)\n", + "print(\"la distance parcourue : \", distance)" + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Dijkstra/Annexe_Dijkstra_Dictionnaire_Corrige.ipynb b/Dijkstra/Annexe_Dijkstra_Dictionnaire_Corrige.ipynb new file mode 100755 index 0000000..f267736 --- /dev/null +++ b/Dijkstra/Annexe_Dijkstra_Dictionnaire_Corrige.ipynb @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Annexe optionnelle — Dijkstra avec une structure de données en dictionnaire\n", + "\n", + "### 2ème mise en œuvre de l'algorithme de Dijkstra\n", + "VERSION corrigée\n", + "\n", + "> **Cette annexe est facultative**, pour les étudiantes à l'aise en Python qui veulent aller plus loin après la Séance 7. Elle reprend le même algorithme que la Séance 7, mais avec une structure de données en dictionnaire plutôt qu'en liste — un bon exercice de transfert, mais pas un prérequis pour la suite du cours." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conditions de réalisation de l'évaluation:\n", + "- Travail en binome.\n", + "- A rendre dans un délai de 15 jours après la séance.\n", + "- soit en complétant le notebook fourni soit sous la forme d'un fichier exécutable .py." + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1- Présentation\n", + " Lors de l'activité précédente vous avez abouti à l'implémentation de l'algorithme de Dijkstra en vous appuyant sur une structure de données sous forme de liste.\n", + "Le graphe :\n", + "![image.png](attachment:image.png)\n", + "Cette fois ci je vous demande de construire une solution dans laquelle le graphe serait représenté sous la forme d'un dictionnaire dans lequel les clés serait les sommets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2- Représentation du graphe par un dictionnaire\n", + " ### Travail 1 : définir la structure de donnée\n", + "Définir la structure de donnée pour représenter le graphe sous forme d’un dictionnaire. \n", + " L'idée est de créer un dictionnaire dans lequel chaque sommet serait une clé. Et pour chaque clé on aurait à nouveau un dictionnaire contenant les sommets adjacents et la distance depuis le sommet précédent.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#création du dictionnaire du graphe pondéré pour la recherche du plus court chemin\n", + "graph = {\n", + "'A': {'B': 4, 'C': 2},\n", + "'B': {'A': 4, 'C': 6, 'E':5},\n", + "'C': {'A': 2, 'B': 6, 'D': 3, 'H' : 5},\n", + "'D': {'C': 3, 'H': 1, 'G': 4, 'F': 3},\n", + "'E': {'B': 5, 'F': 2},\n", + "'F': {'E': 2, 'D': 3, 'G': 7},\n", + "'G': {'F': 7, 'D': 4, 'H': 10},\n", + "'H': {'C': 5, 'D': 1, 'G': 10},\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vous avez la possibilité de revenir vers l’enseignant pour valider votre solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3- Implémentation de l'algorithme de Dijkstra\n", + " ### Travail 2 : implémenter votre solution\n", + " Votre solution sera documentée et s'appuiera sur le travail réalisé en classe.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def initialisation(s_debut):\n", + " \"\"\"\n", + " initialisation des variables permettant de parcourir le graphe\n", + " Parameters\n", + " ----------\n", + " depart : string\n", + " sommet de depart pour le parcours du graphe.\n", + " Returns\n", + " -------\n", + " E_calcul : dict\n", + " chemin en cours de calcul: poids et sommet précédent\n", + " E_calcul = {s_debut:[poids,prédécesseur]}\n", + " la distance au sommet de depart est nulle\n", + " E_sommets : dict\n", + " on met dans le dictionnaire provisoire les sommets adjacents \n", + " et leur poids par rapport au point de départ\n", + " E_sommets = {s_voisin1: [poids,depart],...}\n", + " \"\"\"\n", + " assert type(s_debut) == str, \" s_debut doit être un caractère \"\n", + " \n", + " E_calcul = dict()\n", + " E_calcul = {s_debut:[0,s_debut]}\n", + " \n", + " E_sommets=dict()\n", + " for suivant in graph[s_debut]:\n", + " #chemins en cours d'exploration : sommet: poids, précédent\n", + " E_sommets[suivant]=[graph[s_debut][suivant],s_debut]\n", + "\n", + " return E_calcul,E_sommets" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def Maj_poids(s_voisin,s_mini,poids, E_sommets):\n", + " \"\"\"\n", + " Mise à jour du poids et du prédécesseur\n", + " \n", + " Parameters\n", + " ----------\n", + " s_voisin : str\n", + " sommet voisin\n", + " s_mini : str\n", + " sommet de poids mini\n", + " poids : int\n", + " poids du chemin le plus court \n", + " E_sommets : dict\n", + " dictionnaire des chemins en cours d'exploration'\n", + "\n", + " Returns\n", + " -------\n", + " E_sommets : dict\n", + " dictionnaire des chemins en cours d'exploration mise à jour\n", + " avec le poids et le sommet de poids mini\n", + "\n", + " \"\"\"\n", + " #si le sommet est nouveau\n", + " #mettre àjour poids et prédecesseur\n", + " if s_voisin in E_sommets: \n", + " d=poids + graph[s_mini][s_voisin]\n", + " if d< E_sommets[s_voisin][0]:\n", + " #mémoriser son prédécesseur et le poids depuis le début\n", + " E_sommets[s_voisin] = [d,s_mini]\n", + " else:\n", + " #si le sommet est déjà découvert mettre à jour le poids\n", + " E_sommets[s_voisin]=[poids + graph[s_mini][s_voisin],s_mini]\n", + " return E_sommets" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def Dijkstra(graphe, s_debut):\n", + " \"\"\"\n", + " Cette fonction implémente l'algorithme de dijkstra\n", + " Parameters\n", + " ----------\n", + " graphe : dict\n", + " DESCRIPTION. description du graphe\n", + " s_debut : str\n", + " DESCRIPTION. le sommet de départ\n", + "\n", + " Returns\n", + " -------\n", + " calcul: dict\n", + " le résultat de l'algorithme de dijkstra\n", + "\n", + " \"\"\"\n", + " assert type(s_debut) == str, \" s_debut doit être un caractère \"\n", + " assert type(graphe) == dict, \"graphe doit être un dictionnaire\"\n", + " \n", + " \n", + " #phase d'initialisation des données\n", + " E_calcul,E_sommets=initialisation(s_debut)\n", + " \n", + " #tant que provisoire non vide\n", + " while E_sommets!= {}: \n", + " #recherche de la distance la plus faible\n", + " s_mini=min(E_sommets, key=E_sommets.get) \n", + " #ajout du sommet de valeur minimum aux sommets explorés\n", + " E_calcul[s_mini]=E_sommets[s_mini]\n", + " poids = E_sommets[s_mini][0]\n", + " \n", + " #suppression du sommet des chemins en cours d'exploration\n", + " del E_sommets[s_mini] \n", + " \n", + " #pour chaque sommet voisin de x\n", + " for s in graphe[s_mini]:\n", + " if s not in E_calcul:\n", + " Maj_poids(s, s_mini, poids,E_sommets) \n", + " \n", + " print(\"calcul \", E_calcul)\n", + " return E_calcul" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail 3: La vérification de votre implémentation\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def routage(calcul,depart,arrivee): \n", + " \"\"\"\n", + " Cette fonction donne le routage d'un point A à B à partir\n", + " du résultat obtenu par l'algo de dijkstra\n", + "\n", + " Parameters\n", + " ----------\n", + " calcul : Dict\n", + " résultat de l'algo de dijkstra\n", + " depart : string\n", + " départ\n", + " arrivee : string\n", + " arrivée\n", + "\n", + " Returns\n", + " -------\n", + " routage : list\n", + " le routage de A à B\n", + " distance : int\n", + " la distance\n", + "\n", + " \"\"\"\n", + " \n", + " routage = [arrivee]\n", + " distance=calcul[arrivee][0]\n", + " #création de la liste de routage\n", + " while routage[0]!= depart: \n", + " for key in calcul:\n", + " if key==routage[0]:\n", + " routage.insert(0,calcul[routage[0]][1]) \n", + " return routage,distance" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calcul {'A': [0, 'A'], 'C': [2, 'A'], 'B': [4, 'A'], 'D': [5, 'C'], 'H': [6, 'D'], 'F': [8, 'D'], 'E': [9, 'B'], 'G': [9, 'D']}\n", + "le plus court chemin est: ['A', 'C', 'D', 'G']\n", + "la distance parcourue : 9\n" + ] + } + ], + "source": [ + "resultat = Dijkstra(graph,\"A\")\n", + "routage, distance= routage(resultat,\"A\",\"G\")\n", + "print(\"le plus court chemin est: \", routage)\n", + "print(\"la distance parcourue : \", distance)" + ] + }, + { + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Dijkstra/Annexe_Dijkstra_Preuve_Complexite.ipynb b/Dijkstra/Annexe_Dijkstra_Preuve_Complexite.ipynb new file mode 100644 index 0000000..e61a251 --- /dev/null +++ b/Dijkstra/Annexe_Dijkstra_Preuve_Complexite.ipynb @@ -0,0 +1,128 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Annexe optionnelle — Preuve et complexité de l'algorithme de Dijkstra\n", + "VERSION ELEVE\n", + "\n", + "> **Cette annexe est facultative.** Elle prolonge la Séance 6 pour les étudiantes à l'aise et curieuses d'aller plus loin dans la formalisation (preuve d'algorithme, complexité). Elle n'est pas nécessaire pour suivre la Séance 7 (implémentation) ni pour l'évaluation." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3- Preuve de l'algorithme de Dijkstra\n", + " #### Rappel de cours sur la preuve d'un algorithme: \n", + " réaliser la preuve (appelée aussi \"correction totale\"$*$) d’un algorithme, par deux vérifications :\n", + " • Prouver (vérif.1) sa terminaison .\n", + " • Prouver (vérif.2) sa correction (appelée aussi \"correction partielle\"$*$).\n", + " #### Terminaison (vérif.1) : \n", + "Consiste à vérifier que les calculs effectués par l’algorithme s’arrêtent bien.\n", + "Notamment, lorsqu’une boucle conditionnelle est effectuée par l’algorithme, il est primordial de\n", + "vérifier que l’on sort bien de cette boucle, en particulier si cette boucle est non bornée. Dans ce dernier cas, identifier un variant de boucle (quantité entière positive ou nulle) qui doit décroitre à chaque itération." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 3, \"preuve\" de l'algorithme de Dijkstra: terminaison\n", + " Montrer la terminaison de cet algorithme en raisonnant sur son écriture (proposée au Taf2). Pour cela, répondre au QCM2 ci-dessous." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$QCM2:$ Dans l'écriture de l'algorithme de Dijkstra proposée au Taf2, pour montrer sa terminaison, on peut raisonner comme suit:\n", + " $Rep1:$ La boucle conditionnelle 'Pour' porte sur le parcours des sommets voisins, donc la variable $s_{voisin}$ est le variant de boucle. A chaque itération, le plus proche sommet voisin est mis à jour. Donc quand tous les sommets ont été parcourus, l'algorithme donnera le plus court chemin. \n", + " $Rep2:$ Dans cet algorithme, il y a une boucle conditionnelle non bornée, donc cela suffit pour montrer sa terminaison.\n", + " $Rep3:$ La boucle conditionnelle non bornée 'Tant que' porte sur la condition du parcours exhaustif des sommets du graphe pondéré, donc la variable $E_{calculés}$ est le variant de boucle. A chaque itération, le sommet actuel de poids minimal est ajouté à $E_{calculés}$. Donc pour $n$ sommets, et en autant d'itérations, tous les sommets auront été parcourus et l'ensemble $E_{calculés}$ sera rempli. \n", + " $Rep4:$ La boucle conditionnelle non bornée 'Tant que' porte sur la condition du parcours exhaustif des sommets du graphe pondéré, donc la variable $E_{sommet}$ est le variant de boucle. A chaque itération, le sommet actuel de poids minimal est retiré de $E_{sommet}$. Donc pour $n$ sommets, et en autant d'itérations, tous les sommets auront été parcourus et l'ensemble $E_{sommet}$ sera vide. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Suite du rappel de cours sur la preuve d'un algorithme, Correction (vérif.2) : \n", + " Consiste à prouver que l’algorithme aboutit au résultat escompté en identifiant un \"invariant de boucle\". Cet invariant doit posséder une propriété vérifiable avant l’entrée dans la boucle, lors d'une itération n de la boucle, ainsi qu'à l'itération n+1 et enfin amène au résultat escompté à la sortie de la boucle." + ] + }, + { + "attachments": { + "Algo_Dijkstra_f_secondaires_f2_non_comment%C3%A9es.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 4, \"preuve\" de l'algorithme de Dijkstra: correction\n", + "L'algorithme détaillé des trois fonctions secondaires, de l'algorithme de Dijkstra proposé au Taf2 vous est donné ci-dessous. \n", + " Vous admettrez que $poids[s_{voisin}] ≥ poids[s_{mini} ] + Distance(s_{mini} , s_{voisin}) )$ est un invariant de boucle dans cet algorithme et qu'il est vérifiable à chaque itération de la boucle principale dans l'algorithme de Dijkstra .\n", + " 1- Que signifie l'affirmation (démontrée) ci-dessus pour la correction de cet algorithme?\n", + " 2- Conclure sur la preuve de l'algorithme de Dijkstra.\n", + " ![Algo_Dijkstra_f_secondaires_f2_non_comment%C3%A9es.png](attachment:Algo_Dijkstra_f_secondaires_f2_non_comment%C3%A9es.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4- Complexité de l'algorithme de Dijkstra\n", + " ### Rappel de cours, complexité (temporelle) d'un algorithme\n", + "Le calcul de la complexité d’un algorithme permet de mesurer sa performance. Il en existe deux types :\n", + " - complexité spatiale : permet de quantifier l’utilisation de la mémoire\n", + " - complexité temporelle : permet de quantifier la vitesse d’exécution\n", + " Réaliser un calcul de complexité temporelle d'un algorithme revient à compter le nombre d’opérations élémentaires (affectation, calcul arithmétique ou logique, comparaison…) effectuées par cet algorithme. On calculera le plus souvent la complexité dans le pire des cas, car elle est la plus pertinente. \n", + " Pour simplifier on s'interresse à l’ordre de grandeur (asymptotique), noté O (« grand O ») du calcul exact exhaustif (qui peut être complexe) consistant à négliger le nombre d'instructions éxécutées en 1 tour de boucle (en le ramenant à 1) devant n (nombre de données à traiter) tours de boucle (soit n tours dans une boucle 'Tant que').\n", + " Pour repère,quelques valeurs: \n", + " pour un nombre n de données à traiter, l'ordre de grandeur de la complexité d'une boucle simple est O(n), de k boucles consécutives est k x O(n) et pour deux boucles imbriquées est O(n x n) = O(n²) ." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 5, complexité (temporelle) de l'algorithme de Dijkstra\n", + " Déterminer l'ordre de grandeur de complexité de la version proposée de l’algorithme de Dijkstra en portant attention à l'agencement des boucles dans cet algorithme. Pour cela, répondre au QCM3 suivant." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$QCM3:$ Dans l'écriture de l'algorithme de Dijkstra proposée au Taf2, la fonction principale contient des boucles et pour déterminer sa complexité (en ordre de grandeur), on raisonne comme suit:\n", + " $Rep1:$ Une boucle non bornée de complexité $O(n-1)≈ O(n)$ et une boucle bornée de complexité $O(n-2)≈ O(n)$ , toutes deux consécutives d'ou une complexité $2×O(n)$.\n", + " $Rep2:$ Deux boucles bornées imbriquées, d'ou une complexité $O(n × n) = O(n²)$.\n", + " $Rep3:$ Une boucle 'Pour' de complexité $O(n-1)≈ O(n)$ imbriquée dans une boucle 'Tant que' de complexité $O(n-1)≈ O(n)$, d'ou une complexité $O(n × n) = O(n²)$.\n", + " $Rep4:$ Une boucle 'Tant que' de complexité $O(n-1)≈ O(n)$ consécutive à une boucle 'Pour' de complexité $O(n-1)≈ O(n)$, d'ou une complexité $2×O(n)$" + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Dijkstra/Annexe_Dijkstra_Preuve_Complexite_Corrige.ipynb b/Dijkstra/Annexe_Dijkstra_Preuve_Complexite_Corrige.ipynb new file mode 100644 index 0000000..5f8f050 --- /dev/null +++ b/Dijkstra/Annexe_Dijkstra_Preuve_Complexite_Corrige.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Annexe optionnelle — Preuve et complexité de l'algorithme de Dijkstra\n", + "VERSION corrigée\n", + "\n", + "> **Cette annexe est facultative.** Elle prolonge la Séance 6 pour les étudiantes à l'aise et curieuses d'aller plus loin dans la formalisation (preuve d'algorithme, complexité). Elle n'est pas nécessaire pour suivre la Séance 7 (implémentation) ni pour l'évaluation." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3- Preuve de l'algorithme de Dijkstra\n", + " #### Rappel de cours sur la preuve d'un algorithme: \n", + " réaliser la preuve (appelée aussi \"correction totale\"$*$) d’un algorithme, par deux vérifications :\n", + " • Prouver (vérif.1) sa terminaison .\n", + " • Prouver (vérif.2) sa correction (appelée aussi \"correction partielle\"$*$).\n", + " #### Terminaison (vérif.1) : \n", + "Consiste à vérifier que les calculs effectués par l’algorithme s’arrêtent bien.\n", + "Notamment, lorsqu’une boucle conditionnelle est effectuée par l’algorithme, il est primordial de\n", + "vérifier que l’on sort bien de cette boucle, en particulier si cette boucle est non bornée. Dans ce dernier cas, identifier un variant de boucle (quantité entière positive ou nulle) qui doit décroitre à chaque itération." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 3, \"preuve\" de l'algorithme de Dijkstra: terminaison\n", + " Montrer la terminaison de cet algorithme en raisonnant sur son écriture (proposée au Taf2). Pour cela, répondre au QCM2 ci-dessous." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$QCM2:$ Dans l'écriture de l'algorithme de Dijkstra proposée au Taf2, pour montrer sa terminaison, on peut raisonner comme suit:\n", + " $Rep1:$ La boucle conditionnelle 'Pour' porte sur le parcours des sommets voisins, donc la variable $s_{voisin}$ est le variant de boucle. A chaque itération, le plus proche sommet voisin est mis à jour. Donc quand tous les sommets ont été parcourus, l'algorithme donnera le plus court chemin. \n", + " $Rep2:$ Dans cet algorithme, il y a une boucle conditionnelle non bornée, donc cela suffit pour montrer sa terminaison.\n", + " $Rep3:$ La boucle conditionnelle non bornée 'Tant que' porte sur la condition du parcours exhaustif des sommets du graphe pondéré, donc la variable $E_{calculés}$ est le variant de boucle. A chaque itération, le sommet actuel de poids minimal est ajouté à $E_{calculés}$. Donc pour $n$ sommets, et en autant d'itérations, tous les sommets auront été parcourus et l'ensemble $E_{calculés}$ sera rempli. \n", + " $Rep4:$ La boucle conditionnelle non bornée 'Tant que' porte sur la condition du parcours exhaustif des sommets du graphe pondéré, donc la variable $E_{sommet}$ est le variant de boucle. A chaque itération, le sommet actuel de poids minimal est retiré de $E_{sommet}$. Donc pour $n$ sommets, et en autant d'itérations, tous les sommets auront été parcourus et l'ensemble $E_{sommet}$ sera vide. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf3 :** \n", + " La bonne réponse:\n", + " $Rep4:$ La boucle conditionnelle non bornée 'Tant que' porte sur la condition du parcours exhaustif des sommets du graphe pondéré, donc la variable $E_{sommet}$ est le variant de boucle. A chaque itération, le sommet actuel de poids minimal est retiré de $E_{sommet}$. Donc pour $n$ sommets, et en autant d'itérations, tous les sommets auront été parcourus et l'ensemble $E_{sommet}$ sera vide. \n", + " Explications complémentaires: \n", + " L'algorithme de Dijkstra dans sa fonction principale (ligne2) contient une boucle non bornée \"Tant que\" qui est le parcours exhaustif des sommets du graphe pondéré. \n", + " Sa condition d'arrêt est lorsqu'il n'y a plus de sommets à explorer dans le graphe.\n", + " Le \"variant de boucle\" est $E_{sommets}$ (ligne2), ensemble duquel est retiré (ligne4) le sommet de poids minimal actuel à chaque tour de boucle.\n", + " Cet ensemble, au bout d'un nombre fini d'itérations (correspondant à $n$ sommets) sur la boucle \"Tant que\" sera vide; ce qui montre la terminaison de cet algorithme.\n", + " référence sitographique: cours Bloc2 correction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Suite du rappel de cours sur la preuve d'un algorithme, Correction (vérif.2) : \n", + " Consiste à prouver que l’algorithme aboutit au résultat escompté en identifiant un \"invariant de boucle\". Cet invariant doit posséder une propriété vérifiable avant l’entrée dans la boucle, lors d'une itération n de la boucle, ainsi qu'à l'itération n+1 et enfin amène au résultat escompté à la sortie de la boucle." + ] + }, + { + "attachments": { + "Algo_Dijkstra_f_secondaires_f2_non_comment%C3%A9es.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 4, \"preuve\" de l'algorithme de Dijkstra: correction\n", + "L'algorithme détaillé des trois fonctions secondaires, de l'algorithme de Dijkstra proposé au Taf2 vous est donné ci-dessous. \n", + " Vous admettrez que $poids[s_{voisin}] ≥ poids[s_{mini} ] + Distance(s_{mini} , s_{voisin}) )$ est un invariant de boucle dans cet algorithme et qu'il est vérifiable à chaque itération de la boucle principale dans l'algorithme de Dijkstra .\n", + " 1- Que signifie l'affirmation (démontrée) ci-dessus pour la correction de cet algorithme?\n", + " 2- Conclure sur la preuve de l'algorithme de Dijkstra.\n", + " ![Algo_Dijkstra_f_secondaires_f2_non_comment%C3%A9es.png](attachment:Algo_Dijkstra_f_secondaires_f2_non_comment%C3%A9es.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf4 :** \n", + "1- L'affirmation \" $poids[s_{voisin}] ≥ poids[s_{mini} ] + Distance(s_{mini} , s_{voisin}) )$ est un invariant de boucle dans cet algorithme et qu'il est vérifiable à chaque itération de la boucle principale de l'algorithme de Dijkstra\" signifie que la correction de l'algoritme de Dijkstra est vérifiée.\n", + " 2- Conclusion: puisque la terminaison (Taf3) et la correction (ci-dessus) sont vérifiées, alors la preuve de l'algorithme de Dijkstra est faite.\n", + " référence sitographique: \n", + "https://interstices.info/le-plus-court-chemin/?hlText=bellman\n", + "https://perso.liris.cnrs.fr/christine.solnon/supportAlgoGraphes.pdf\n", + "https://www.enseignement.polytechnique.fr/informatique/INF431/X12-2013-2014/inf431-poly.pdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4- Complexité de l'algorithme de Dijkstra\n", + " ### Rappel de cours, complexité (temporelle) d'un algorithme\n", + "Le calcul de la complexité d’un algorithme permet de mesurer sa performance. Il en existe deux types :\n", + " - complexité spatiale : permet de quantifier l’utilisation de la mémoire\n", + " - complexité temporelle : permet de quantifier la vitesse d’exécution\n", + " Réaliser un calcul de complexité temporelle d'un algorithme revient à compter le nombre d’opérations élémentaires (affectation, calcul arithmétique ou logique, comparaison…) effectuées par cet algorithme. On calculera le plus souvent la complexité dans le pire des cas, car elle est la plus pertinente. \n", + " Pour simplifier, on s'interresse à l’ordre de grandeur (asymptotique), noté O (« grand O ») du calcul exact exhaustif (qui peut être complexe) consistant à négliger le nombre d'instructions éxécutées en 1 tour de boucle (en le ramenant à 1) devant n (nombre de données à traiter) tours de boucle (soit n tours dans une boucle 'Tant que').\n", + " Pour repère,quelques valeurs: \n", + " pour un nombre n de données à traiter, l'ordre de grandeur de la complexité d'une boucle simple est O(n), de k boucles consécutives est k x O(n) et pour deux boucles imbriquées est O(n x n) = O(n²) ." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 5, complexité (temporelle) de l'algorithme de Dijkstra\n", + " Déterminer l'ordre de grandeur de complexité de la version proposée de l’algorithme de Dijkstra en portant attention à l'agencement des boucles dans cet algorithme. Pour cela, répondre au QCM3 suivant.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$QCM3:$ Dans l'écriture de l'algorithme de Dijkstra proposée au Taf2, la fonction principale contient des boucles et pour déterminer sa complexité (en ordre de grandeur), on raisonne comme suit:\n", + " $Rep1:$ Une boucle non bornée de complexité $O(n-1)≈ O(n)$ et une boucle bornée de complexité $O(n-2)≈ O(n)$ , toutes deux consécutives d'ou une complexité $2×O(n)$.\n", + " $Rep2:$ Deux boucles bornées imbriquées, d'ou une complexité $O(n × n) = O(n²)$.\n", + " $Rep3:$ Une boucle 'Pour' de complexité $O(n-1)≈ O(n)$ imbriquée dans une boucle 'Tant que' de complexité $O(n-1)≈ O(n)$, d'ou une complexité $O(n × n) = O(n²)$.\n", + " $Rep4:$ Une boucle 'Tant que' de complexité $O(n-1)≈ O(n)$ consécutive à une boucle 'Pour' de complexité $O(n-1)≈ O(n)$, d'ou une complexité $2×O(n)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf5 :** \n", + " La bonne réponse:\n", + " $Rep3:$ Une boucle 'Pour' de complexité $O(n-1)≈ O(n)$ imbriquée dans une boucle 'Tant que' de complexité $O(n-1)≈ O(n)$, d'ou une complexité $O(n × n) = O(n²)$.\n", + " Explications:\n", + " ##### boucle 'Tant que' : \n", + " Pour $n$ sommets, l’algorithme de Dijkstra nécessite au plus $n–1$ étapes pour parcourir les sommets du graphe directement accessibles (au plus $n-1$) à partir du sommet de départ . $O(n-1)≈ O(n)$.\n", + " ##### boucle 'Pour' :\n", + " Pour chaque sommet de $S_{calculés}$, la boucle 'Pour' itère sur les $n-2$ (au plus) sommets directement accessibles du sommet en cours d'évaluation. $O(n-2)≈ O(n)$.\n", + " ##### imbriquation des 2 boucles:\n", + " Comme la 2ème boucle 'Pour' est imbriquée dans la 1ère boucle 'Tant que', le coût d'une telle structure de boucle est $O(n × n) = O(n²)$\n", + " A noter que cette complexité peut être réduite en changeant la structure de donnée, ce qui pourra faire un objet d'étude en terminale .\n", + " référence sitographique: cours Bloc2 correction" + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Dijkstra/Seance_6_Dijkstra_1_RecapForceBrute.ipynb b/Dijkstra/Seance_6_Dijkstra_1_RecapForceBrute.ipynb new file mode 100755 index 0000000..96118f4 --- /dev/null +++ b/Dijkstra/Seance_6_Dijkstra_1_RecapForceBrute.ipynb @@ -0,0 +1,420 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calcul du chemin le plus court" + ] + }, + { + "cell_type": "markdown", + "source": "## Séance 6 (1/2) — Graphes pondérés et algorithme de Dijkstra : recap force brute\n\nJusqu'ici, nos graphes indiquaient seulement l'existence d'un lien entre deux individus (Séances 2 et 3). Mais en sociologie, toutes les relations ne se valent pas : deux personnes peuvent se croiser une fois par an ou se parler tous les jours. On peut représenter cette **intensité** par un **poids** sur chaque arête (voir FONDAMENTAUX.md, §2.3) — par exemple le temps qu'il faut pour transmettre une information d'une personne à l'autre, ou l'inverse de la fréquence de contact.\n\n**Question sociologique posée par cette séance :** dans un réseau où chaque lien a un coût (temps, distance, effort), quel est le chemin le *moins coûteux* entre deux individus ? C'est exactement le problème que résout l'**algorithme de Dijkstra**, que nous découvrirons juste après cette activité de recap (méthode \"force brute\").\n\nL'exercice ci-dessous l'illustre sur un cas logistique concret (circuits courts en Bretagne) : le raisonnement — trouver le chemin de poids minimal dans un graphe pondéré — est rigoureusement le même que pour calculer, par exemple, le canal de diffusion le plus rapide d'une information dans un réseau social pondéré par la force des liens.\n\n> Pour aller plus loin : la preuve formelle de l'algorithme de Dijkstra (terminaison, invariant de boucle) et l'analyse de sa complexité sont regroupées dans une **annexe optionnelle** (`Annexe_Dijkstra_Preuve_Complexite.ipynb`), à explorer si vous êtes à l'aise et que le temps le permet.", + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lors de cette activité, vous allez travailler sur un algorithme permettant la détermination du chemin le plus court entre deux points d'un graphe. Vous allez réinvestir vos notions de programmation et d'algorithme (définition, correction et optimisation)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Énoncé" + ] + }, + { + "attachments": { + "graphe.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un site web propose une plateforme reliant producteurs et consommateurs en Bretagne. Leur objectif étant de proposer des circuits courts et ainsi de contribuer au développement écologique et économique de la région. \n", + " Afin d’aider les consommateurs a préparer leurs courses, les responsables du site web souhaitent développer un algorithme qui optimise leur trajet pour l’achat des différent vivres. \n", + " En cochant les cases des produits que le consommateur souhaite acheter (fruits, légumes, produits carnés, produits laitiers, produits de la pêche,…) l’algorithme proposera le chemin le plus court pour faire l’ensemble des achats. \n", + " Regardons l’exemple ci-dessous :\n", + "![graphe.png](attachment:graphe.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1\n", + "Rechercher le chemin le plus court entre le point A et le point G. Notez les différentes étapes, ainsi que la distance parcouru en total." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On représente ce graphe dans un premier temps sous forme de tableau:\n", + " | | A | B | C | D | E | F | G | H |\n", + "|:- |:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n", + "|A|0|4|2|-|-|-|-|-|\n", + "|B|4|0|6|-|5|-|-|-|\n", + "|C|2|6|0|3|-|-|-|5|\n", + "|D|-|-|3|0|-|3|4|1|\n", + "|E|-|5|-|-|0|2|-|-|\n", + "|F|-|-|-|3|2|0|7|-|\n", + "|G|-|-|-|4|-|7|0|10|\n", + "|H|-|-|5|1|-|-|10|0|\n", + " Puis on décide de modéliser ce graphe sous Python à l'aide d'une liste de listes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Déclaration de notre graphe sous forme de liste de liste\n", + "graphe = [\n", + " [0 ,4 ,2 ,99,99,99,99,99],\n", + " [4 ,0 ,6 ,99,5 ,99,99,99],\n", + " [2 ,6 ,0 ,3 ,99,99,5 ,99],\n", + " [99,99,3 ,0 ,99,3 ,4 ,1 ],\n", + " [99,5 ,99,99,0 ,2 ,99,99],\n", + " [99,99,99,3 ,2 ,0 ,7 ,99],\n", + " [99,99,99,4 ,99,7 ,0 ,10],\n", + " [99,99,5 ,1 ,99,99,10,0 ]\n", + "]\n", + "\n", + "graphe # Vérification de notre sortie" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " 1. Quel choix a fait le développeur pour indiquer la non existence d'un arc entre deux sommets ?\n", + " 2. Ce choix vous semble t-il judicieux ? Argumentez.\n", + " 3. Que proposeriez-vous au développeur ? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calcul de la distance entre deux sommets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le calcul de la distance entre deux noeuds se fait à l'aide d'une fonction intitulé distance dont le code est donne ci dessous:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def distance(départ, arrivée) -> int:\n", + " # on vérifie que les valeurs de départ et arrivée sont bien des entiers\n", + " assert type(départ) == int, \"la valeur départ n'est pas un entier\"\n", + " assert type(arrivée) == int, \"la valeur départ n'est pas un entier\"\n", + " assert graphe[départ][arrivée] != 99, \"les noeuds ne sont pas adjacent\"\n", + " \n", + " return(graphe[départ][arrivée])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " 1. Que renvoie l'instruction $distance(2,3)$ ?\n", + " 1. 6\n", + " 2. \"La valeur n'est pas un entier\"\n", + " 3. 3\n", + " 4. 99\n", + " 2. Démonstration de la correction de cette fonction. \n", + " - Dans un premier temps montrez que la fonction se termine (pour rappel cela consiste a verifier que les calculs effectuées par l'algorithme s'arretent bien):\n", + " 1. Il n'y a aucune boucle dans la fonction, elle se termine forcément\n", + " 2. La fonction ne vérifie pas tout les cas possibles, elle ne se termine jamais dans certains cas\n", + " 3. Une fonction ne se termine que lorsqu'il y a une boucle \"while\"\n", + " - Dans un deuxieme temps montrer la correction partielle (pour rappel: l'algorithme donne bien le bon résultat)\n", + " 1. Pour chaque couple de sommets la fonction retourne bien la distance demandée\n", + " 2. Le développeur n'a pas prévu le cas où l'on passe le même sommet en arrivée et départ\n", + " 3. L'algorithme ne donne pas le bon résultat.\n", + " 3. Quel est le niveau de complexité de cette fonction ? QCM\n", + " 1. Il s'agit du parcours séquentiel d'un tableau, $n \\log(n)$\n", + " 2. $1$\n", + " 3. $n^2$\n", + " 4. $0$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Création d'une liste de sommets adjacents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Maintenant que nous savons déterminer la distance entre deux noeuds, on a besoin de connaitre les différents parcours entre un point de départ et un point d'arrivée. On crée d'abord une fonction qui retourne la liste des noeuds adjacents par rapport a un noeud de référence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def determine_adjacents(sommet) -> list:\n", + " #************************************************************************\n", + " # A partir d'un noeud en entrée, la fonction donne la liste des noeuds adjacents\n", + " # Entrée : Le noeud de départ\n", + " # Sortie : la liste des noeuds adjacents\n", + " #************************************************************************\n", + " # On sélectionne la ligne contenant les noeuds adjacent par rapport a notre point de départ\n", + " ligne = graphe[sommet]\n", + "\n", + " # on détermine le nombre de noeuds adjacents pour ce point\n", + " nb_adjacent = len(ligne)-ligne.count(99)-ligne.count(0)\n", + "\n", + " # Puis on crée la variable parcours sous forme de liste de listes\n", + " liste_adjacents = [[] for i in range(0,nb_adjacent)]\n", + "\n", + " a = 0\n", + " for i in range(0,len(ligne)):\n", + " if (ligne[i] != 0 and ligne[i] != 99):\n", + " liste_adjacents[a].append(sommet)\n", + " liste_adjacents[a].append(i)\n", + " a += 1\n", + " \n", + " # On retourne la liste des sommet adjacents\n", + " return(liste_adjacents)\n", + "\n", + "#****************************************************************************\n", + "# Ajouter votre code ici pour l'exécution de la fonction ci-dessous" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " 1. Faites fonctionner la fonction $determine$_$adjacents$ pour l'ensemble des sommets du graphe, imprimez le résultat.\n", + " 2. Vérifier qu'il n'y ait pas d'erreur dans la transcription du graph. Le corriger le cas échéant." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Composition d'une liste de listes avec l'ensemble des chemins possibles entre un point de départ et un point d'arrivée." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour commencer cette exercice, nous allons d'abord nous intéresser au nombre de noeuds adjacents par rapport a notre point de départ, puis, à partir de cette liste, nous allons bâtir l'ensemble des chemins possibles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def recursive2(liste) -> list:\n", + " #************************************************************************\n", + " # La fonction récursive établit la liste exhaustive des chemins possibles\n", + " # Entrée : La liste de chemins déja établie\n", + " # Sortie : la liste de chemins jusqu'au noeuds suivants\n", + " #************************************************************************\n", + " \n", + " sommets_adjacents = []\n", + " # On détermine le nombre d'arcs adjacents aux noeuds\n", + " for i in range(0,len(liste)): # on boucle dans notre liste existante\n", + " for element in determine_adjacents(liste[i][-1]): # pour chaque sommet dans la liste des adjacents\n", + " if element not in sommets_adjacents: # si le sommet n'est pas encore dans notre liste\n", + " sommets_adjacents.append(element) # alors on le rajoute\n", + " \n", + " # On identifie les chemins qu'on doit créer, en ignorant les chemins:\n", + " # - où on revient sur un noeud déja visité\n", + " # - déja existant dans la liste\n", + " # Puis on renseigne les chemins possibles\n", + "\n", + " entrees_a_supprimer = [] # On va garder en mémoire les entrées a supprimer\n", + " for i in range(0,len(liste)) : # on boucle dans notre liste existante\n", + " if liste[i][-1] != arrivée: # on ignore les chemins menant déja au point d'arrivée\n", + " for j in range(0,len(sommets_adjacents)): # on boucle dans la liste qu'on vient d'obtenir\n", + " if (sommets_adjacents[j][0] == liste[i][-1] and\n", + " sommets_adjacents[j][-1] != liste[i][0] and\n", + " sommets_adjacents[j][-1] not in liste[i]\n", + " ):\n", + " liste.append(liste[i] + sommets_adjacents[j][1:len(sommets_adjacents)])\n", + " entrees_a_supprimer.append(i) # On va garder en mémoire les entrées a supprimer\n", + " \n", + " for i in range(0,len(entrees_a_supprimer)): # avant de retourner notre résultat on supprime les entrées superflus\n", + " del liste[entrees_a_supprimer[i]-i]\n", + " return(liste)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En étudiant la fonction $recursive2$ ci-dessus, répondez aux questions suivantes:\n", + " 1. Identifier la ligne de code qui ajoute les chemins possible a la liste.\n", + " 2. Ecrivez le commentaire de cette ligne de code en langage naturel ou pseudocode.\n", + " 3. Quel est le coût de cette fonction ?\n", + " 1. Il s'agit du parcours séquentiel d'un tableau, le coût est linéaire ;\n", + " 2. Il s'agit d'un tri par insertion, au pire le coût de cette fonction est quadratique ($n^2$) ;\n", + " 3. Il s'agit d'un succession d'instructions simple, le cout est de 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Programme principal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": "#************************************************************************\n# Transcription du graphe sous forme de liste de listes\n#************************************************************************\n\ngraphe = [\n [0 ,4 ,2 ,99,99,99,99,99],\n [4 ,0 ,6 ,99,5 ,99,99,99],\n [2 ,6 ,0 ,3 ,99,99,5 ,99],\n [99,99,3 ,0 ,99,3 ,4 ,1 ],\n [99,5 ,99,99,0 ,2 ,99,99],\n [99,99,99,3 ,2 ,0 ,7 ,99],\n [99,99,99,4 ,99,7 ,0 ,10],\n [99,99,5 ,1 ,99,99,10,0 ]\n ]\n\ndef distance(Noeud_A, Noeud_B) -> int:\n #************************************************************************\n # Donne la distance entre deux noeuds adjacents Noeud_A et Noeud_B\n # Entrée : Le noeud de départ\n # Sortie : la liste des noeuds adjacents\n #************************************************************************\n # on vérifie que les valeurs de départ et arrivée sont bien des entiers\n assert type(Noeud_A) == int, \"la valeur départ n'est pas un entier\"\n assert type(Noeud_B) == int, \"la valeur départ n'est pas un entier\"\n assert graphe[Noeud_A][Noeud_B] != 99, \"les noeuds ne sont pas adjacent\"\n \n # On retourne la distance entre les deux noeuds\n return(graphe[Noeud_A][Noeud_B])\n\n\ndef determine_adjacents(noeud) -> list:\n #************************************************************************\n # A partir d'un noeud en entrée, la fonction donne la liste des noeuds adjacents\n # Entrée : Le noeud de départ\n # Sortie : la liste des noeuds adjacents\n #************************************************************************\n # On sélectionne la ligne contenant les noeuds adjacent par rapport a notre point de départ\n ligne = graphe[noeud]\n\n # on détermine le nombre de noeuds adjacents pour ce point\n nb_adjacent = len(ligne)-ligne.count(99)-ligne.count(0)\n\n # Puis on crée la variable parcours sous forme de liste de listes\n liste_adjacents = [[] for i in range(0,nb_adjacent)]\n\n a = 0\n for i in range(0,len(ligne)):\n if (ligne[i] != 0 and ligne[i] != 99):\n liste_adjacents[a].append(noeud)\n liste_adjacents[a].append(i)\n a += 1\n \n # On retourne la liste des noeuds adjacents\n return(liste_adjacents)\n\ndef recursive2(liste) -> list:\n #************************************************************************\n # La fonction récursive établit la liste exhaustive des chemins possibles\n # Entrée : La liste de chemins déja établie\n # Sortie : la liste de chemins jusqu'au noeuds suivants\n #************************************************************************\n \n sommets_adjacents = []\n # On détermine le nombre d'arcs adjacents aux noeuds\n for i in range(0,len(liste)): # on boucle dans notre liste existante\n for element in determine_adjacents(liste[i][-1]): # pour chaque sommet dans la liste des adjacents\n if element not in sommets_adjacents: # si le sommets n'est pas encore dans notre liste\n sommets_adjacents.append(element) # alors on le rajoute\n \n # On identifie les chemins qu'on doit créer, en ignorant les chemins:\n # - où on revient sur un noeud déja visité\n # - déja existant dans la liste\n # Puis on renseigne les chemins possibles\n\n entrees_a_supprimer = [] # On va garder en mémoire les entrées a supprimer\n for i in range(0,len(liste)) : # on boucle dans notre liste existante\n if liste[i][-1] != arrivée: # on ignore les chemins menant déja au point d'arrivée\n for j in range(0,len(sommets_adjacents)): # on boucle dans la liste qu'on vient d'obtenir\n if (sommets_adjacents[j][0] == liste[i][-1] and\n sommets_adjacents[j][-1] != liste[i][0] and\n sommets_adjacents[j][-1] not in liste[i]\n ):\n liste.append(liste[i] + sommets_adjacents[j][1:len(sommets_adjacents)])\n entrees_a_supprimer.append(i) # On va garder en mémoire les entrées a supprimer\n \n for i in range(0,len(entrees_a_supprimer)): # avant de retourner notre résultat on supprime les entrées superflus\n del liste[entrees_a_supprimer[i]-i]\n return(liste)\n\n\n#********************************************************************************\n# Début du programme principal\n#********************************************************************************\n\n# on définit un point de départ et d'arrivée\ndépart = 0\narrivée = 6\n\n\n# On détermine le nombre d'arcs partant de notre sommet départ\nliste_arcs_départs = determine_adjacents(départ)\n\n\n# Construction de la liste de chemins possibles\nfor i in range(0,len(graphe)):\n liste_arcs_départs = recursive2(liste_arcs_départs)\n print(liste_arcs_départs)\n\n# On imprime le nombre de chemins et les chemins possibles\nprint(\"Il y a\",len(liste_arcs_départs),\"chemins possibles:\",liste_arcs_départs)\n\n# On identifie le chemin le plus court\nthis_distance = 0\ncourt_distance = 9999\ncourt_chemin =[]\n\nfor element in liste_arcs_départs:\n this_distance = 0\n for i in range(0,len(element)-1):\n this_distance += distance(element[i],element[i+1])\n if this_distance < court_distance:\n court_distance = this_distance\n court_chemin = element\n \n# Ecrivez votre réponse à la question 6.1 ici" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Le programme ci dessous n'affiche pas le résultat attendu. Modifiez le pour qu'on vous affiche le chemin le plus court, et la distance a parcourir.\n", + "2. Implementez la fonction $timeit$ et $memit$ afin de connaître le temps d'exécution et la mémoire occupé par la fonction \"recursive2\"\n", + "3. Est-ce que les résultats confirment votre réponse à la question 5.3 ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pip install memory_profiler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext memory_profiler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Déclaration des variables en fonction des itérations\n", + "chemins_it1 = [[0, 1], [0, 2]]\n", + "chemins_it3 = [[0, 1, 2, 3], [0, 1, 2, 7], [0, 1, 4, 5], [0, 2, 1, 4], [0, 2, 3, 5], [0, 2, 3, 6], [0, 2, 3, 7], [0, 2, 7, 3], [0, 2, 7, 6]]\n", + "chemins_it5 = [[0, 2, 3, 6], [0, 2, 7, 6], [0, 1, 2, 3, 6], [0, 1, 2, 7, 6], [0, 1, 4, 5, 6], [0, 2, 3, 5, 6], [0, 2, 3, 7, 6], [0, 2, 7, 3, 6], [0, 1, 2, 3, 5, 4], [0, 1, 2, 3, 5, 6], [0, 1, 2, 3, 7, 6], [0, 1, 2, 7, 3, 5], [0, 1, 2, 7, 3, 6], [0, 1, 4, 5, 3, 2], [0, 1, 4, 5, 3, 6], [0, 1, 4, 5, 3, 7], [0, 2, 1, 4, 5, 3], [0, 2, 1, 4, 5, 6], [0, 2, 3, 5, 4, 1], [0, 2, 7, 3, 5, 4], [0, 2, 7, 3, 5, 6]]\n", + " \n", + "# Time and memory Profile\n", + "# # Ecrivez votre réponse à la question 6.2 ici" + ] + } + ], + "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": "3.7.6" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/Dijkstra/Seance_6_Dijkstra_1_RecapForceBrute_Corrige.ipynb b/Dijkstra/Seance_6_Dijkstra_1_RecapForceBrute_Corrige.ipynb new file mode 100755 index 0000000..f05bfbd --- /dev/null +++ b/Dijkstra/Seance_6_Dijkstra_1_RecapForceBrute_Corrige.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mini- projet : algorithmes de parcours de graphes pondérés\n", + " ### 1ème partie: à la recherche du plus court chemin\n", + "VERSION corrigée" + ] + }, + { + "cell_type": "markdown", + "source": "## Séance 6 (1/2) — Graphes pondérés et algorithme de Dijkstra : recap force brute\n\nJusqu'ici, nos graphes indiquaient seulement l'existence d'un lien entre deux individus (Séances 2 et 3). Mais en sociologie, toutes les relations ne se valent pas : deux personnes peuvent se croiser une fois par an ou se parler tous les jours. On peut représenter cette **intensité** par un **poids** sur chaque arête (voir FONDAMENTAUX.md, §2.3) — par exemple le temps qu'il faut pour transmettre une information d'une personne à l'autre, ou l'inverse de la fréquence de contact.\n\n**Question sociologique posée par cette séance :** dans un réseau où chaque lien a un coût (temps, distance, effort), quel est le chemin le *moins coûteux* entre deux individus ? C'est exactement le problème que résout l'**algorithme de Dijkstra**, que nous découvrirons juste après cette activité de recap (méthode \"force brute\").\n\nL'exercice ci-dessous l'illustre sur un cas logistique concret (circuits courts en Bretagne) : le raisonnement — trouver le chemin de poids minimal dans un graphe pondéré — est rigoureusement le même que pour calculer, par exemple, le canal de diffusion le plus rapide d'une information dans un réseau social pondéré par la force des liens.\n\n> Pour aller plus loin : la preuve formelle de l'algorithme de Dijkstra (terminaison, invariant de boucle) et l'analyse de sa complexité sont regroupées dans une **annexe optionnelle** (`Annexe_Dijkstra_Preuve_Complexite_Corrige.ipynb`), à explorer si vous êtes à l'aise et que le temps le permet.", + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lors de cette activité, vous allez travailler sur un algorithme permettant la détermination du chemin le plus court entre deux points d'un graphe. Vous allez réinvestir vos notions de programmation et d'algorithme (définition, correction et optimisation)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Énoncé" + ] + }, + { + "attachments": { + "graphe.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un site web propose une plateforme reliant producteurs et consommateurs en Bretagne. Leur objectif étant de proposer des circuits courts et ainsi de contribuer au développement écologique et économique de la région. \n", + " Afin d’aider les consommateurs a préparer leurs courses, les responsables du site web souhaitent développer un algorithme qui optimise leur trajet pour l’achat des différent vivres. \n", + " En cochant les cases des produits que le consommateur souhaite acheter (fruits, légumes, produits carnés, produits laitiers, produits de la pêche,…) l’algorithme proposera le chemin le plus court pour faire l’ensemble des achats. \n", + " Regardons l’exemple ci-dessous :\n", + "![graphe.png](attachment:graphe.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1\n", + "Rechercher le chemin le plus court entre le point A et le point G. Notez les différentes étapes, ainsi que la distance parcouru en total." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Réponse 1\n", + "Le chemin le plus court entre le point A et le point G est A,C,D,G, la distance parcouru est de 9" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On représente ce graphe dans un premier temps sous forme de tableau:\n", + " | | A | B | C | D | E | F | G | H |\n", + "|:- |:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n", + "|A|0|4|2|-|-|-|-|-|\n", + "|B|4|0|6|-|5|-|-|-|\n", + "|C|2|6|0|3|-|-|-|5|\n", + "|D|-|-|3|0|-|3|4|1|\n", + "|E|-|5|-|-|0|2|-|-|\n", + "|F|-|-|-|3|2|0|7|-|\n", + "|G|-|-|-|4|-|7|0|10|\n", + "|H|-|-|5|1|-|-|10|0|\n", + " Puis on décide de modéliser ce graphe sous Python à l'aide d'une liste de listes:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": {}, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Déclaration de notre graphe sous forme de liste de liste\n", + "graphe = [\n", + " [0 ,4 ,2 ,99,99,99,99,99],\n", + " [4 ,0 ,6 ,99,5 ,99,99,99],\n", + " [2 ,6 ,0 ,3 ,99,99,5 ,99],\n", + " [99,99,3 ,0 ,99,3 ,4 ,1 ],\n", + " [99,5 ,99,99,0 ,2 ,99,99],\n", + " [99,99,99,3 ,2 ,0 ,7 ,99],\n", + " [99,99,99,4 ,99,7 ,0 ,10],\n", + " [99,99,5 ,1 ,99,99,10,0 ]\n", + "]\n", + "\n", + "graphe # Vérification de notre sortie" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " 1. Quel choix a fait le développeur pour indiquer la non existence d'un arc entre deux sommets ?\n", + " 2. Ce choix vous semble t-il judicieux ? Argumentez.\n", + " 3. Que proposeriez-vous au développeur ? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Réponse 2\n", + "1. Le développeur a choisi d'utiliser le nombre 99 pour indiquer la non existence d'un arc entre deux sommets.\n", + "2. Dans le contexte de ce graphe ce choix peut être défendu, car la somme de toutes les distances de ce graphe ne dépasse pas 52. Le choix de ce codage \"en dur\" empêche toutefois l'évolution du programme vers des graphes plus étendues.\n", + "3. Le développeur pourrait calculer la somme des distances de l'ensemble du graphe et l'utiliser cette valeur pour indiquer qu'aucun arc n'existe." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calcul de la distance entre deux sommets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le calcul de la distance entre deux noeuds se fait à l'aide d'une fonction intitulé distance dont le code est donne ci dessous:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def distance(départ, arrivée) -> int:\n", + " # on vérifie que les valeurs de départ et arrivée sont bien des entiers\n", + " assert type(départ) == int, \"la valeur départ n'est pas un entier\"\n", + " assert type(arrivée) == int, \"la valeur départ n'est pas un entier\"\n", + " assert graphe[départ][arrivée] != 99, \"les noeuds ne sont pas adjacent\"\n", + " \n", + " return(graphe[départ][arrivée])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " 1. Que renvoie l'instruction $distance(2,3)$ ?\n", + " 1. 6\n", + " 2. \"La valeur n'est pas un entier\"\n", + " 3. 3\n", + " 4. 99\n", + " 2. Démonstration de la correction de cette fonction. \n", + " - Dans un premier temps montrez que la fonction se termine (pour rappel cela consiste a verifier que les calculs effectuées par l'algorithme s'arretent bien):\n", + " 1. Il n'y a aucune boucle dans la fonction, elle se termine forcément\n", + " 2. La fonction ne vérifie pas tout les cas possibles, elle ne se termine jamais dans certains cas\n", + " 3. Une fonction ne se termine que lorsqu'il y a une boucle \"while\"\n", + " - Dans un deuxieme temps montrer la correction partielle (pour rappel: l'algorithme donne bien le bon résultat)\n", + " 1. Pour chaque couple de sommets la fonction retourne bien la distance demandée\n", + " 2. Le développeur n'a pas prévu le cas où l'on passe le même sommet en arrivée et départ\n", + " 3. L'algorithme ne donne pas le bon résultat.\n", + " 3. Quel est le niveau de complexité de cette fonction ?\n", + " 1. Il s'agit du parcours séquentiel d'un tableau, $n \\log(n)$\n", + " 2. $1$\n", + " 3. $n^2$\n", + " 4. $0$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Réponse 3\n", + "1. (C) L'instruction $distance(2,3)$ renvoie la valeur 3. Il s'agit de la valeur dans la troisième ligne, quatrième colonne.\n", + "2. (A) La fonction se termine car il n'y a aucune boucle dans la fonction, elle se termine forcément par le renvoi d'une valeur ou d'un message d'erreur. Comme il s'agit d'un simple renvoi d'une valeur dans la liste de listes, la fonction retournera le bon résultat.\n", + "3. (B) Le niveau de complexité de cette fonction est de 1, il s'agit d'un simple renvoi de valeur.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Création d'une liste de sommets adjacents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Maintenant que nous savons déterminer la distance entre deux noeuds, on a besoin de connaitre les différents parcours entre un point de départ et un point d'arrivée. On crée d'abord une fonction qui retourne la liste des noeuds adjacents par rapport à un noeud de référence." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Les sommets adjacents au sommet A sont :\n", + "A , B\n", + "A , C\n", + "Les sommets adjacents au sommet B sont :\n", + "B , A\n", + "B , C\n", + "B , E\n", + "Les sommets adjacents au sommet C sont :\n", + "C , A\n", + "C , B\n", + "C , D\n", + "C , G\n", + "Les sommets adjacents au sommet D sont :\n", + "D , C\n", + "D , F\n", + "D , G\n", + "D , H\n", + "Les sommets adjacents au sommet E sont :\n", + "E , B\n", + "E , F\n", + "Les sommets adjacents au sommet F sont :\n", + "F , D\n", + "F , E\n", + "F , G\n", + "Les sommets adjacents au sommet G sont :\n", + "G , D\n", + "G , F\n", + "G , H\n", + "Les sommets adjacents au sommet H sont :\n", + "H , C\n", + "H , D\n", + "H , G\n" + ] + } + ], + "source": [ + "def determine_adjacents(sommet) -> list:\n", + " #************************************************************************\n", + " # A partir d'un noeud en entrée, la fonction donne la liste des noeuds adjacents\n", + " # Entrée : Le noeud de départ\n", + " # Sortie : la liste des noeuds adjacents\n", + " #************************************************************************\n", + " # On sélectionne la ligne contenant les noeuds adjacent par rapport a notre point de départ\n", + " ligne = graphe[sommet]\n", + "\n", + " # on détermine le nombre de noeuds adjacents pour ce point\n", + " nb_adjacent = len(ligne)-ligne.count(99)-ligne.count(0)\n", + "\n", + " # Puis on crée la variable parcours sous forme de liste de listes\n", + " liste_adjacents = [[] for i in range(0,nb_adjacent)]\n", + "\n", + " a = 0\n", + " for i in range(0,len(ligne)):\n", + " if (ligne[i] != 0 and ligne[i] != 99):\n", + " liste_adjacents[a].append(sommet)\n", + " liste_adjacents[a].append(i)\n", + " a += 1\n", + " \n", + " # On retourne la liste des sommet adjacents\n", + " return(liste_adjacents)\n", + "\n", + "#****************************************************************************\n", + "# Proposition de code dans le cadre de la correction\n", + "\n", + "for i in range (0,len(graphe)):\n", + " adjacents = determine_adjacents(i)\n", + " print(\"Les sommets adjacents au sommet\", chr(65+i), \"sont :\")\n", + " for j in range(0,len(adjacents)):\n", + " print(chr(adjacents[j][0]+65),\",\",chr(adjacents[j][1]+65))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " 1. Faites fonctionner la fonction $determine\\_adjacents$ pour l'ensemble des sommets du graphe, imprimez le résultat.\n", + " 2. Vérifier qu'il n'y ait pas d'erreur dans la transcription du graphe. Le corriger le cas échéant." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Réponse 4\n", + "1. Le code est rajouté ci dessus\n", + "2. L'impression des sommets adjacents au sommet C indique une erreur. En effet, sur le graphe, les sommets C et G ne sont pas adjacents. En vérifiant la définition de graphe on remarque en troisième liste une inversion des deux dernieres valeurs par rapport à la matrice initiale de l'énoncé. On l'a corrigée dans le programme principal ci-dessous." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Composition d'une liste de listes avec l'ensemble des chemins possibles entre un point de départ et un point d'arrivée." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour commencer cet exercice, nous allons d'abord nous intéresser au nombre de noeuds adjacents par rapport à notre point de départ, puis, à partir de cette liste, nous allons bâtir l'ensemble des chemins possibles." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def recursive2(liste) -> list:\n", + " #************************************************************************\n", + " # La fonction récursive établit la liste exhaustive des chemins possibles\n", + " # Entrée : La liste de chemins déja établie\n", + " # Sortie : la liste de chemins jusqu'au noeuds suivants\n", + " #************************************************************************\n", + " \n", + " sommets_adjacents = []\n", + " # On détermine le nombre d'arcs adjacents aux noeuds\n", + " for i in range(0,len(liste)): # on boucle dans notre liste existante\n", + " for element in determine_adjacents(liste[i][-1]): # pour chaque sommet dans la liste des adjacents\n", + " if element not in sommets_adjacents: # si le sommet n'est pas encore dans notre liste\n", + " sommets_adjacents.append(element) # alors on le rajoute\n", + " \n", + " # On identifie les chemins qu'on doit créer, en ignorant les chemins:\n", + " # - où on revient sur un noeud déja visité\n", + " # - déja existant dans la liste\n", + " # Puis on renseigne les chemins possibles\n", + "\n", + " entrees_a_supprimer = [] # On va garder en mémoire les entrées a supprimer\n", + " for i in range(0,len(liste)) : # on boucle dans notre liste existante\n", + " if liste[i][-1] != arrivée: # on ignore les chemins menant déja au point d'arrivée\n", + " for j in range(0,len(sommets_adjacents)): # on boucle dans la liste qu'on vient d'obtenir\n", + " if (sommets_adjacents[j][0] == liste[i][-1] and\n", + " sommets_adjacents[j][-1] != liste[i][0] and\n", + " sommets_adjacents[j][-1] not in liste[i]\n", + " ):\n", + " liste.append(liste[i] + sommets_adjacents[j][1:len(sommets_adjacents)])\n", + " entrees_a_supprimer.append(i) # On va garder en mémoire les entrées a supprimer\n", + " \n", + " for i in range(0,len(entrees_a_supprimer)): # avant de retourner notre résultat on supprime les entrées superflus\n", + " del liste[entrees_a_supprimer[i]-i]\n", + " return(liste)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En étudiant la fonction $recursive2$ ci-dessus, répondez aux questions suivantes:\n", + " 1. Identifier la ligne de code qui ajoute les chemins possible a la liste.\n", + " 2. Ecrivez le commentaire de cette ligne de code en langage naturel ou pseudocode.\n", + " 3. Quel est le coût de cette fonction ?\n", + " 1. Il s'agit du parcours séquentiel d'un tableau, le coût est linéaire ;\n", + " 2. Il s'agit d'un tri par insertion, au pire le coût de cette fonction est quadratique ($n^2$) ;\n", + " 3. Il s'agit d'un succession d'instructions simple, le cout est de 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Réponse 5\n", + "1. la ligne ``` liste.append(liste[i] + sommets_adjacents[j][1:len(sommets_adjacents)])``` permet l'ajout des chemins possibles\n", + "2. On rajoute à notre liste l'index actuel + le sommet adjacent\n", + "3. (A) Il s'agit du parcours séquentiel d'un tableau, le coût est linéaire et dépend de la taille de la liste passée en entrée;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Programme principal" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Il y a 19 chemins possibles: [[0, 2, 3, 6], [0, 2, 7, 6], [0, 1, 2, 3, 6], [0, 1, 2, 7, 6], [0, 1, 4, 5, 6], [0, 2, 3, 5, 6], [0, 2, 3, 7, 6], [0, 2, 7, 3, 6], [0, 1, 2, 3, 5, 6], [0, 1, 2, 3, 7, 6], [0, 1, 2, 7, 3, 6], [0, 1, 4, 5, 3, 6], [0, 2, 1, 4, 5, 6], [0, 2, 7, 3, 5, 6], [0, 1, 2, 7, 3, 5, 6], [0, 1, 4, 5, 3, 7, 6], [0, 2, 1, 4, 5, 3, 6], [0, 1, 4, 5, 3, 2, 7, 6], [0, 2, 1, 4, 5, 3, 7, 6]]\n", + "Le chemin le plus court est: [0, 2, 3, 6]\n", + "La distance a parcourir est: 9\n" + ] + } + ], + "source": [ + "#************************************************************************\n", + "# Transcription du graphe sous forme de liste de listes\n", + "#************************************************************************\n", + "\n", + "graphe = [\n", + " [0 ,4 ,2 ,99,99,99,99,99],\n", + " [4 ,0 ,6 ,99,5 ,99,99,99],\n", + " [2 ,6 ,0 ,3 ,99,99,99,5 ],\n", + " [99,99,3 ,0 ,99,3 ,4 ,1 ],\n", + " [99,5 ,99,99,0 ,2 ,99,99],\n", + " [99,99,99,3 ,2 ,0 ,7 ,99],\n", + " [99,99,99,4 ,99,7 ,0 ,10],\n", + " [99,99,5 ,1 ,99,99,10,0 ]\n", + " ]\n", + "\n", + "def distance(Noeud_A, Noeud_B) -> int:\n", + " #************************************************************************\n", + " # Donne la distance entre deux noeuds adjacents Noeud_A et Noeud_B\n", + " # Entrée : Le noeud de départ\n", + " # Sortie : la liste des noeuds adjacents\n", + " #************************************************************************\n", + " # on vérifie que les valeurs de départ et arrivée sont bien des entiers\n", + " assert type(Noeud_A) == int, \"la valeur départ n'est pas un entier\"\n", + " assert type(Noeud_B) == int, \"la valeur départ n'est pas un entier\"\n", + " assert graphe[Noeud_A][Noeud_B] != 99, \"les noeuds ne sont pas adjacent\"\n", + " \n", + " # On retourne la distance entre les deux noeuds\n", + " return(graphe[Noeud_A][Noeud_B])\n", + "\n", + "\n", + "def determine_adjacents(noeud) -> list:\n", + " #************************************************************************\n", + " # A partir d'un noeud en entrée, la fonction donne la liste des noeuds adjacents\n", + " # Entrée : Le noeud de départ\n", + " # Sortie : la liste des noeuds adjacents\n", + " #************************************************************************\n", + " # On sélectionne la ligne contenant les noeuds adjacent par rapport a notre point de départ\n", + " ligne = graphe[noeud]\n", + "\n", + " # on détermine le nombre de noeuds adjacents pour ce point\n", + " nb_adjacent = len(ligne)-ligne.count(99)-ligne.count(0)\n", + "\n", + " # Puis on crée la variable parcours sous forme de liste de listes\n", + " liste_adjacents = [[] for i in range(0,nb_adjacent)]\n", + "\n", + " a = 0\n", + " for i in range(0,len(ligne)):\n", + " if (ligne[i] != 0 and ligne[i] != 99):\n", + " liste_adjacents[a].append(noeud)\n", + " liste_adjacents[a].append(i)\n", + " a += 1\n", + " \n", + " # On retourne la liste des noeuds adjacents\n", + " return(liste_adjacents)\n", + "\n", + "def recursive2(liste) -> list:\n", + " #************************************************************************\n", + " # La fonction récursive établit la liste exhaustive des chemins possibles\n", + " # Entrée : La liste de chemins déja établie\n", + " # Sortie : la liste de chemins jusqu'au noeuds suivants\n", + " #************************************************************************\n", + " \n", + " sommets_adjacents = []\n", + " # On détermine le nombre d'arcs adjacents aux noeuds\n", + " for i in range(0,len(liste)): # on boucle dans notre liste existante\n", + " for element in determine_adjacents(liste[i][-1]): # pour chaque sommet dans la liste des adjacents\n", + " if element not in sommets_adjacents: # si le sommets n'est pas encore dans notre liste\n", + " sommets_adjacents.append(element) # alors on le rajoute\n", + " \n", + " # On identifie les chemins qu'on doit créer, en ignorant les chemins:\n", + " # - où on revient sur un noeud déja visité\n", + " # - déja existant dans la liste\n", + " # Puis on renseigne les chemins possibles\n", + "\n", + " entrees_a_supprimer = [] # On va garder en mémoire les entrées a supprimer\n", + " for i in range(0,len(liste)) : # on boucle dans notre liste existante\n", + " if liste[i][-1] != arrivée: # on ignore les chemins menant déja au point d'arrivée\n", + " for j in range(0,len(sommets_adjacents)): # on boucle dans la liste qu'on vient d'obtenir\n", + " if (sommets_adjacents[j][0] == liste[i][-1] and\n", + " sommets_adjacents[j][-1] != liste[i][0] and\n", + " sommets_adjacents[j][-1] not in liste[i]\n", + " ):\n", + " liste.append(liste[i] + sommets_adjacents[j][1:len(sommets_adjacents)])\n", + " entrees_a_supprimer.append(i) # On va garder en mémoire les entrées a supprimer\n", + " \n", + " for i in range(0,len(entrees_a_supprimer)): # avant de retourner notre résultat on supprime les entrées superflus\n", + " del liste[entrees_a_supprimer[i]-i]\n", + " return(liste)\n", + "\n", + "\n", + "#********************************************************************************\n", + "# Début du programme principal\n", + "#********************************************************************************\n", + "\n", + "# on définit un point de départ et d'arrivée\n", + "départ = 0\n", + "arrivée = 6\n", + "\n", + "\n", + "# On détermine le nombre d'arcs partant de notre sommet départ\n", + "liste_arcs_départs = determine_adjacents(départ)\n", + "\n", + "\n", + "# Construction de la liste de chemins possibles\n", + "for i in range(0,len(graphe)):\n", + " liste_arcs_départs = recursive2(liste_arcs_départs)\n", + "\n", + "# On imprime le nombre de chemins et les chemins possibles\n", + "print(\"Il y a\",len(liste_arcs_départs),\"chemins possibles:\",liste_arcs_départs)\n", + "\n", + "# On identifie le chemin le plus court\n", + "this_distance = 0\n", + "court_distance = 9999\n", + "court_chemin =[]\n", + "\n", + "for element in liste_arcs_départs:\n", + " this_distance = 0\n", + " for i in range(0,len(element)-1):\n", + " this_distance += distance(element[i],element[i+1])\n", + " if this_distance < court_distance:\n", + " court_distance = this_distance\n", + " court_chemin = element\n", + " \n", + "# Ecrivez votre réponse à la question 6.1 ici\n", + "print(\"Le chemin le plus court est:\", court_chemin)\n", + "print(\"La distance a parcourir est:\", court_distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Le programme ci dessous n'affiche pas le résultat attendu. Modifiez le pour qu'on vous affiche le chemin le plus court, et la distance à parcourir. (Il n'est pas attendu d'afficher le résultat sous la forme A,B,C, vous pouvez utiliser les valeurs numériques.)\n", + "2. Implementez la fonction $timeit$ et $memit$ afin de connaître le temps d'exécution et la mémoire occupé par la fonction \"recursive2\"\n", + "3. Est-ce que les résultats confirment votre réponse à la question 5.3 ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Réponse 6\n", + "1. Le programme est modifié pour afficher le chemin le plus court et la distance parcouru\n", + "2. Les fonctions timeit et memit sont implémentés ci dessous\n", + "3. Le temps nécessaire à l'exécution ne varie que légèrement en fonction de la taille de la liste, il s'agit donc bien d'une complexité linéaire." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting memory_profiler\n", + " Downloading memory_profiler-0.58.0.tar.gz (36 kB)\n", + "Collecting psutil\n", + " Downloading psutil-5.8.0-cp37-cp37m-win_amd64.whl (244 kB)\n", + "Installing collected packages: psutil, memory-profiler\n", + " Running setup.py install for memory-profiler: started\n", + " Running setup.py install for memory-profiler: finished with status 'done'\n", + "Successfully installed memory-profiler-0.58.0 psutil-5.8.0\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: You are using pip version 20.0.2; however, version 21.0.1 is available.\n", + "You should consider upgrading via the 'C:\\EduPython666\\App\\Python\\python.exe -m pip install --upgrade pip' command.\n" + ] + } + ], + "source": [ + "pip install memory_profiler" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext memory_profiler" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Itération 1\n", + "87.7 µs ± 828 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n", + "peak memory: 51.88 MiB, increment: 0.90 MiB\n", + "Itération 3\n", + "85.7 µs ± 859 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n", + "peak memory: 51.88 MiB, increment: 0.00 MiB\n", + "Itération 5\n", + "86.9 µs ± 1.29 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n", + "peak memory: 51.92 MiB, increment: 0.04 MiB\n" + ] + } + ], + "source": [ + "# Déclaration des variables en fonction des itérations (1, 3 et 5)\n", + "chemins_it1 = [[0, 1], [0, 2]]\n", + "chemins_it3 = [[0, 1, 2, 3], [0, 1, 2, 7], [0, 1, 4, 5], [0, 2, 1, 4], [0, 2, 3, 5], [0, 2, 3, 6], [0, 2, 3, 7], [0, 2, 7, 3], [0, 2, 7, 6]]\n", + "chemins_it5 = [[0, 2, 3, 6], [0, 2, 7, 6], [0, 1, 2, 3, 6], [0, 1, 2, 7, 6], [0, 1, 4, 5, 6], [0, 2, 3, 5, 6], [0, 2, 3, 7, 6], [0, 2, 7, 3, 6], [0, 1, 2, 3, 5, 4], [0, 1, 2, 3, 5, 6], [0, 1, 2, 3, 7, 6], [0, 1, 2, 7, 3, 5], [0, 1, 2, 7, 3, 6], [0, 1, 4, 5, 3, 2], [0, 1, 4, 5, 3, 6], [0, 1, 4, 5, 3, 7], [0, 2, 1, 4, 5, 3], [0, 2, 1, 4, 5, 6], [0, 2, 3, 5, 4, 1], [0, 2, 7, 3, 5, 4], [0, 2, 7, 3, 5, 6]]\n", + " \n", + "# Time and memory Profile\n", + "# # Ecrivez votre réponse à la question 6.2 ici\n", + "print(\"Itération 1\")\n", + "%timeit recursive2(chemins_it1)\n", + "%memit recursive2(chemins_it1)\n", + "\n", + "print(\"Itération 3\")\n", + "%timeit recursive2(chemins_it3)\n", + "%memit recursive2(chemins_it3)\n", + "\n", + "print(\"Itération 5\")\n", + "%timeit recursive2(chemins_it5)\n", + "%memit recursive2(chemins_it5)" + ] + }, + { + "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": "3.7.6" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/Dijkstra/Seance_6_Dijkstra_1_test.py b/Dijkstra/Seance_6_Dijkstra_1_test.py new file mode 100755 index 0000000..632ccc8 --- /dev/null +++ b/Dijkstra/Seance_6_Dijkstra_1_test.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python +#-*-coding:UTF-8-*- + +#************************************************************************ +# Transcription du graphe sous forme de liste de listes +#************************************************************************ + +graphe = [ + [0 ,4 ,2 ,99,99,99,99,99], + [4 ,0 ,6 ,99,5 ,99,99,99], + [2 ,6 ,0 ,3 ,99,99,99,5 ], + [99,99,3 ,0 ,99,3 ,4 ,1 ], + [99,5 ,99,99,0 ,2 ,99,99], + [99,99,99,3 ,2 ,0 ,7 ,99], + [99,99,99,4 ,99,7 ,0 ,10], + [99,99,5 ,1 ,99,99,10,0 ] + ] + +def distance(Noeud_A, Noeud_B) -> int: + #************************************************************************ + # Donne la distance entre deux noeuds adjacents Noeud_A et Noeud_B + # Entrée : Le noeud de départ + # Sortie : la liste des noeuds adjacents + #************************************************************************ + # on vérifie que les valeurs de départ et arrivée sont bien des entiers + assert type(Noeud_A) == int, "la valeur départ n'est pas un entier" + assert type(Noeud_B) == int, "la valeur départ n'est pas un entier" + assert graphe[Noeud_A][Noeud_B] != 99, "les noeuds ne sont pas adjacent" + + # On retourne la distance entre les deux noeuds + return(graphe[Noeud_A][Noeud_B]) + + +def determine_adjacents(noeud) -> list: + #************************************************************************ + # A partir d'un noeud en entrée, la fonction donne la liste des noeuds adjacents + # Entrée : Le noeud de départ + # Sortie : la liste des noeuds adjacents + #************************************************************************ + # On sélectionne la ligne contenant les noeuds adjacent par rapport a notre point de départ + ligne = graphe[noeud] + + # on détermine le nombre de noeuds adjacents pour ce point + nb_adjacent = len(ligne)-ligne.count(99)-ligne.count(0) + + # Puis on crée la variable parcours sous forme de liste de listes + liste_adjacents = [[] for i in range(0,nb_adjacent)] + + a = 0 + for i in range(0,len(ligne)): + if (ligne[i] != 0 and ligne[i] != 99): + liste_adjacents[a].append(noeud) + liste_adjacents[a].append(i) + a += 1 + + # On retourne la liste des noeuds adjacents + return(liste_adjacents) + +def recursive2(liste) -> list: + #************************************************************************ + # La fonction récursive établit la liste exhaustive des chemins possibles + # Entrée : La liste de chemins déja établie + # Sortie : la liste de chemins jusqu'au noeuds suivants + #************************************************************************ + + sommets_adjacents = [] + # On détermine le nombre d'arcs adjacents aux noeuds + for i in range(0,len(liste)): # on boucle dans notre liste existante + for element in determine_adjacents(liste[i][-1]): # pour chaque sommet dans la liste des adjacents + if element not in sommets_adjacents: # si le sommets n'est pas encore dans notre liste + sommets_adjacents.append(element) # alors on le rajoute + + # On identifie les chemins qu'on doit créer, en ignorant les chemins: + # - où on revient sur un noeud déja visité + # - déja existant dans la liste + # Puis on renseigne les chemins possibles + + entrees_a_supprimer = [] # On va garder en mémoire les entrées a supprimer + for i in range(0,len(liste)) : # on boucle dans notre liste existante + if liste[i][-1] != arrivée: # on ignore les chemins menant déja au point d'arrivée + for j in range(0,len(sommets_adjacents)): # on boucle dans la liste qu'on vient d'obtenir + if (sommets_adjacents[j][0] == liste[i][-1] and + sommets_adjacents[j][-1] != liste[i][0] and + sommets_adjacents[j][-1] not in liste[i] + ): + liste.append(liste[i] + sommets_adjacents[j][1:len(sommets_adjacents)]) + entrees_a_supprimer.append(i) # On va garder en mémoire les entrées a supprimer + + for i in range(0,len(entrees_a_supprimer)): # avant de retourner notre résultat on supprime les entrées superflus + del liste[entrees_a_supprimer[i]-i] + return(liste) + + +#******************************************************************************** +# Début du programme principal +#******************************************************************************** + +# on définit un point de départ et d'arrivée +départ = 0 +arrivée = 6 + + +# On détermine le nombre d'arcs partant de notre sommet départ +liste_arcs_départs = determine_adjacents(départ) + + +# Construction de la liste de chemins possibles +for i in range(0,len(graphe)): + liste_arcs_départs = recursive2(liste_arcs_départs) + +# On imprime le nombre de chemins et les chemins possibles +print("Il y a",len(liste_arcs_départs),"chemins possibles:",liste_arcs_départs) + +# On identifie le chemin le plus court +this_distance = 0 +court_distance = 9999 +court_chemin =[] + +for element in liste_arcs_départs: + this_distance = 0 + for i in range(0,len(element)-1): + this_distance += distance(element[i],element[i+1]) + if this_distance < court_distance: + court_distance = this_distance + court_chemin = element + +# Ecrivez votre réponse à la question 6.1 ici +print("Le chemin le plus court est:", court_chemin) +print("La distance a parcourir est:", court_distance) + +# Déclaration des variables en fonction des itérations (1, 3 et 5) +chemins_it1 = [[0, 1], [0, 2]] +chemins_it3 = [[0, 1, 2, 3], [0, 1, 2, 7], [0, 1, 4, 5], [0, 2, 1, 4], [0, 2, 3, 5], [0, 2, 3, 6], [0, 2, 3, 7], [0, 2, 7, 3], [0, 2, 7, 6]] +chemins_it5 = [[0, 2, 3, 6], [0, 2, 7, 6], [0, 1, 2, 3, 6], [0, 1, 2, 7, 6], [0, 1, 4, 5, 6], [0, 2, 3, 5, 6], [0, 2, 3, 7, 6], [0, 2, 7, 3, 6], [0, 1, 2, 3, 5, 4], [0, 1, 2, 3, 5, 6], [0, 1, 2, 3, 7, 6], [0, 1, 2, 7, 3, 5], [0, 1, 2, 7, 3, 6], [0, 1, 4, 5, 3, 2], [0, 1, 4, 5, 3, 6], [0, 1, 4, 5, 3, 7], [0, 2, 1, 4, 5, 3], [0, 2, 1, 4, 5, 6], [0, 2, 3, 5, 4, 1], [0, 2, 7, 3, 5, 4], [0, 2, 7, 3, 5, 6]] diff --git a/Dijkstra/Seance_6_Dijkstra_2_Intuition.ipynb b/Dijkstra/Seance_6_Dijkstra_2_Intuition.ipynb new file mode 100644 index 0000000..234d225 --- /dev/null +++ b/Dijkstra/Seance_6_Dijkstra_2_Intuition.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Séance 6 (2/2) — Algorithme de Dijkstra : intuition et pseudo-code\n", + "VERSION ELEVE\n", + "\n", + "> Pour aller plus loin : la preuve formelle de terminaison/correction et l'analyse de complexité (anciennement dans cette activité) sont désormais dans une **annexe optionnelle** (`Annexe_Dijkstra_Preuve_Complexite.ipynb`). L'implémentation en Python se fait en Séance 7 (`Seance_7_Dijkstra_Implementation.ipynb`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Conditions de réalisation :\n", + "- Un ordinateur par élève équipé pour lire une video avec casque ou oreillette, et Jupyter Notebook.\n", + "- Seul le travail à faire \"Taf1\" sera réalisé en binôme. Les autres travaux le seront en individuel.\n", + "- Une version papier de ce notebook pour les travaux à faire \"en débranché\".\n", + "- A partir du Taf6, le travail est à faire directement sur ce notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1- Présentation\n", + " Lors des cours et activités précédentes, vous avez étudié le fonctionnement, l'usage et les limites de plusieurs algorithmes constituant une base de travail pour résoudre des problèmes de tri et de recherche.\n", + " Certains problèmes consistent à chercher entre deux points donnés le parcours qui a une \"longueur\" (durée, coût, distance) minimum.\n", + " Ces problèmes se ramènent à la recherche d'une chaîne ou d'un chemin de plus faible pondération entre deux sommets d'un graphe pondéré (les pondérations des arêtes étant toutes positives). On parlera de plus courte distance (ou de plus court chemin) en interprétant les pondérations comme des distances entre les sommets. \n", + " Classifions maintenant ces problème. On peut en distinguer trois types :\n", + " 1- plus court chemin entre deux sommets donnés;\n", + " 2- plus courts chemins d’origine fixée mais vers n’importe quelle extrémité ;\n", + " 3- plus courts chemins de n’importe quelle origine vers n’importe quelle extrémité.\n", + " Pour la 1ère catégorie, si le nombre de chemins possibles entre le point de départ et le point d'arrivée est faible, il suffira de calculer la longueur cumulée de chacun des chemins et de les comparer. \n", + " Remarque: une telle méthode, exhaustive, devient rapidement couteuse en temps si le nombre de chemins possibles est grand, surtout pour les 2ème et 3ème catégorie." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Trouver les chemins issus d’un sommet : l’algorithme de Dijkstra\n", + " Parmi les nombreux algorithmes traitant de \"chemin de valeur additive minimale\", celui de Dijkstra repose sur le principe d’« exploration à partir du meilleur », c’est à dire du meilleur prédécesseur visité." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Description sommaire de l'algorithme de Dijkstra:\n", + "•- Initialisation:\n", + " * Associer à chaque sommet du graphe une distance infinie par rapport au sommet de départ, \n", + " sauf pour le sommet de départ qui prend la valeur 0.\n", + " •- Répèter de façon itérative les opérations suivantes jusqu'au dernier sommet du graphe à explorer :\n", + " * Sélectionner à chaque itération le sommet du graphe qui a la distance la plus petite, \n", + " remarque: démarrer avec le sommet de départ.\n", + " * Pour chacun des voisins de ce sommet:\n", + " * Calculer la distance cumulée qui le sépare du départ en passant par ce sommet sélectionné.\n", + " * Si cette distance est plus petite qu'une valeur précédente mémorisée:\n", + " garder cette dernière distance cumulée en mémoire ainsi que le sommet voisin sélectionné. \n", + " •- Résultat:\n", + " * En refaisant le meilleur parcours mémorisé à l'envers à partir d'un sommet autre que celui de départ,on obtient le trajet entre ces deux sommets.\n", + " * Associée au trajet , on obtient la dernière plus courte distance cumulée du plus court chemin qui sépare le dernier sommet exploré du sommet de départ. \n" + ] + }, + { + "attachments": { + "tableau_Dijkstra_exercice_incomplet.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 1 (en binôme), Tableau des plus courts chemins\n", + "1- En exploitant la description sommaire de l'algorithme de Dijkstra ci-dessus et de la vidéo https://www.youtube.com/watch?v=rI-Rc7eF4iw, essayez de retrouver votre résultat intuitif du chemin le plus court entre les sommets A et G de la 1ère partie de cette évaluation et que que vous aviez confirmé avec la méthode \"naïve\", dite aussi \"de force brute\". Pour cela, vous pourrez compléter un tableau adapté à notre problème (un modèle, 1ère ligne complétée vous est proposé ci-dessous).\n", + " 2- Existe t'il une variable contenant la distance cumulée de ce plus court chemin? Justifier. \n", + " ![tableau_Dijkstra_exercice_incomplet.png](attachment:tableau_Dijkstra_exercice_incomplet.png)" + ] + }, + { + "attachments": { + "Algo_Dijkstra_f_principale.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2- Algorithme de Dijkstra en pseudo-code\n", + " ### Travail à faire 2, algorithme et variables\n", + " Ci-dessous, vous est proposé une écriture possible de l'algorithme de Dijkstra:\n", + "![Algo_Dijkstra_f_principale.png](attachment:Algo_Dijkstra_f_principale.png)\n", + " 1- Dans cet algorithme, identifier la variable qui donnera le plus court chemin entre le sommet de départ et le dernier sommet visité du graphe lorsque le traitement sera terminé.\n", + " 2- Dans cet algorithme, existe t'il une variable contenant la distance de ce plus court chemin? Justifier.\n", + " 3- Catégorisation de l'algorithme de Dijkstra: répondre au QCM1 ci-dessous." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$QCM1:$ Dans l'écriture de l'algorithme de Dijkstra proposée ci-dessus, on peut remarquer que:\n", + " $Rep1:$ La recherche, à chaque itération, du plus proche sommet voisin signifie que cet algorithme fait partie de la famille des 'algorithmes des k plus proches voisins'.\n", + " $Rep2:$ La recherche d’une solution optimale à un moment donné sans retour en arrière, indique qu'il s'agit d'une forme d'algorithme 'glouton'.\n", + " $Rep3:$ Le fait qu'à chaque itération, l'algorithme sélectionne 'le sommet non encore traité dont la distance à l'origine est minimale', c'est à dire le meilleur, est un élément clé pour montrer qu'il s'agit d'un algorithme glouton. \n", + " $Rep4:$ La résolution des problèmes du \"rendu de monnaie\" et du \"plus court chemin\" peuvent être envisagées par des approches dites 'gloutonnes' " + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Dijkstra/Seance_6_Dijkstra_2_Intuition_Corrige.ipynb b/Dijkstra/Seance_6_Dijkstra_2_Intuition_Corrige.ipynb new file mode 100644 index 0000000..04d01d2 --- /dev/null +++ b/Dijkstra/Seance_6_Dijkstra_2_Intuition_Corrige.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Séance 6 (2/2) — Algorithme de Dijkstra : intuition et pseudo-code\n", + "VERSION corrigée\n", + "\n", + "> Pour aller plus loin : la preuve formelle de terminaison/correction et l'analyse de complexité (anciennement dans cette activité) sont désormais dans une **annexe optionnelle** (`Annexe_Dijkstra_Preuve_Complexite_Corrige.ipynb`). L'implémentation en Python se fait en Séance 7 (`Seance_7_Dijkstra_Implementation_Corrige.ipynb`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Conditions de réalisation de l'évaluation:\n", + "- Un ordinateur par élève équipé pour lire une video avec casque ou oreillette, et Jupyter Notebook.\n", + "- Seul le travail à faire \"Taf1\" sera réalisé en binôme. Les autres travaux le seront en individuel.\n", + "- Une version papier de ce notebook pour les travaux à faire \"en débranché\".\n", + "- A partir du Taf6, le travail est à faire directement sur ce notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1- Présentation\n", + " Lors des cours et activités précédentes, vous avez étudié le fonctionnement, l'usage et les limites de plusieurs algorithmes constituant une base de travail pour résoudre des problèmes de tri et de recherche.\n", + " Certains problèmes consistent à chercher entre deux points donnés le parcours qui a une \"longueur\" (durée, coût, distance) minimum.\n", + " Ces problèmes se ramènent à la recherche d'une chaîne ou d'un chemin de plus faible pondération entre deux sommets d'un graphe pondéré (les pondérations des arêtes étant toutes positives). On parlera de plus courte distance (ou de plus court chemin) en interprétant les pondérations comme des distances entre les sommets. \n", + " Classifions maintenant ces problèmes. On peut en distinguer trois types :\n", + " 1- plus court chemin entre deux sommets donnés;\n", + " 2- plus courts chemins d’origine fixée mais vers n’importe quelle extrémité ;\n", + " 3- plus courts chemins de n’importe quelle origine vers n’importe quelle extrémité.\n", + " Pour la 1ère catégorie, si le nombre de chemins possibles entre le point de départ et le point d'arrivée est faible, il suffira de calculer la longueur cumulée de chacun des chemins et de les comparer. \n", + " Remarque: une telle méthode, exhaustive, devient rapidement couteuse en temps si le nombre de chemins possibles est grand, surtout pour les 2ème et 3ème catégorie." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Trouver les chemins issus d’un sommet : l’algorithme de Dijkstra\n", + " Parmi les nombreux algorithmes traitant de \"chemin de valeur additive minimale\", celui de Dijkstra repose sur le principe d’« exploration à partir du meilleur », c’est à dire du meilleur prédécesseur visité." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Description sommaire de l'algorithme de Dijkstra:\n", + "•- Initialisation:\n", + " * Associer à chaque sommet du graphe une distance infinie par rapport au sommet de départ, \n", + " sauf pour le sommet de départ qui prend la valeur 0.\n", + " •- Répèter de façon itérative les opérations suivantes jusqu'au dernier sommet du graphe à explorer :\n", + " * Sélectionner à chaque itération le sommet du graphe qui a la distance la plus petite, \n", + " remarque: démarrer avec le sommet de départ.\n", + " * Pour chacun des voisins de ce sommet:\n", + " * Calculer la distance cumulée qui le sépare du départ en passant par ce sommet sélectionné.\n", + " * Si cette distance est plus petite qu'une valeur précédente mémorisée:\n", + " garder cette dernière distance cumulée en mémoire ainsi que le sommet voisin sélectionné. \n", + " •- Résultat:\n", + " * En refaisant le meilleur parcours mémorisé à l'envers à partir d'un sommet autre que celui de départ,on obtient le trajet entre ces deux sommets.\n", + " * Associée au trajet , on obtient la dernière plus courte distance cumulée du plus court chemin qui sépare le dernier sommet exploré du sommet de départ. \n" + ] + }, + { + "attachments": { + "tableau_Dijkstra_exercice_incomplet.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 1 (en binôme), Tableau des plus courts chemins\n", + "1- En exploitant la description sommaire de l'algorithme de Dijkstra ci-dessus et de la vidéo https://www.youtube.com/watch?v=rI-Rc7eF4iw, essayez de retrouver votre résultat intuitif du chemin le plus court entre les sommets A et G de la 1ère partie de cette évaluation et que que vous aviez confirmé avec la méthode \"naïve\", dite aussi \"de force brute\". Pour cela, vous pourrez compléter un tableau adapté à notre problème (un modèle, 1ère ligne complétée vous est proposé ci-dessous).\n", + "![tableau_Dijkstra_exercice_incomplet.png](attachment:tableau_Dijkstra_exercice_incomplet.png)\n", + " 2- Le tableau ainsi réalisé à l'aide de la vidéo peut il donner plusieurs \"plus court chemins\" en partant du sommet 'A'? Expliquer." + ] + }, + { + "attachments": { + "tableau_Dijkstra_exercice.png": { + "image/png": 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BXZ2YE6L9j4Hv2TcWT6ep09KGboBSt3Dp0N6JmuOFoj9k9+uSXy24Pbc5k23SafjEaZM/82xB3WjiW5pREZ/Tzx8mxSw7R2dZqefHUnPYEfoO7cn/jW8z5342+9pHY34xk94fc51/fgycGS65DA9Ys+n1udCsOOY9nGPqM6nMX8Qsrr7zZwTv8E15xr4PIYQQkhHJa3Gt5QQssKZ+jfbw0+/YrHKET1c40v+v4qpNlVWQqusnlb/KznBO4/f/+y0zzfkHUte6avTWj0D/+rqsiyKkqBouost58EMbiXtBNFVtU4eOAyf8sONaRHbZ8wpLlOr/17Ru5pJzzBFqFh1G/Xgisnzwtf35ox0T20g8jmvSbfaRF6XjrPev/p7VmWpeY6la9f3u+NNb86kTbuvfw0piv6zb06g7UCrdD5Q76TEK3p5fwEzLSVPvsfVV2Zi01phfyaT90sKZaEfvwhckkCMBXnp+nYWOspIgTD/91n956FGPuz+Vj1fJS+z7EEIIIRmRvBbXlvDZKjpYM/z8YiqbVYG80N/a0c/zu/xG10OqrIJUXT+p4tXMWzPp4BJA/7MzyRXVbaSrddFq8NbaYn4lu4EQqpwS+Y85YMpQUqLOLJW92uDyUl8/ffYqMVPE0zGxcXJxMNUo1W8h68YE2z6HUqkbUgFfW6nkJT71D3yTr2Xp6NbaWquiHg4gyogKehKWlK9l5dLexYL6bYU5aan5Gkb6/A/vL8xJTclRNTDWLT2BZin5qa+Dn8dkKTdt4eZiRU+kVyfK7P9JgVt7GbbysuxR50/ByYmw7Hj/tPAAwaugjCiSiOq300aDelBckqAgZ3PEeeZpQDYLIYQQqjd1UxcqzE17l6Wk21SyglEBUVZKSo6yTlNDTTpIqrIKUnX9pKpXRdmp77IKmxgY6apX8WmqqXVJqsFba0zO6qIIyS+uRHTVKBPRsbmcQsIV7/i7JIwZaOxGNvEsVt6ZhICSkTMHmrQbZtqJ7KvygR9CCCFUJ/BaLFu4/xGSUt3dSEG1FZ2bPD9kn+3V6cz032wuKudW6nM2BeCT9ITsLrLTnG/MWRHmjbPeIYQQQgihxgnb6GSJxCF/RP53IPYGuw1gytcP9Nhoqq6H96UqFJYdf/NdyOXkwPIzpw8363y602J2AyGEEPpoeC2WLdz/CElJQdrolJvoaZNVEwMdNergl38klhvxYK3bzQUl4Zyrru1+tzkJA/Y18KwD3OKkZTHDZgCJ3NIHHfHruW6e3VAnbUvmpbY61Gw+CCGEEEIINSoK0kZHE4tBmRMR6rjHG73j7rAbdOPSN82HlBntA+9LSS8sO57sTy/LHiTeY7MQQgihj4bXYtnC/Y+QlBTpOTpuhHOSSCwX6LHxdKfFOHjjxyCB3AonrwrDucQ8wYgHa2++C2G3EUIIIYQQUiyK1EbHGczjc8sdx1YxTiPu/zqxOeL8/JB9JEFiZrLDMXJGCCEkPbwWyxbuf4SkhBGdnML9XyeWhB5Z+/Iku0HHdZtcJrvq2rLbCCGEUOXwWixbuP8RklI1ER2qPX0+jGwBgcnwJInNQTJhrAFeTtDbmt0k7sbD3meQLmQ3EUIIIYQQkkvS3NTAiK4eaPJgqB21kERUBsz3ZfORDJG47qtW0L34WbsCMXiHwcmX7CZCCCGEEELypw4iOu+gDcwmkkZBUdHVrLfnMuMEonwmpwVfZ2HTlnoqPGZTel6uC8lP3P9161Ve1qH0yFfCTJLWVFbdYNGhiqLBIpAt3P8yh0UgW7j/5QGWgmzh/pc5pgikiShQfZC+47EijXUpYw9zUxfEPzqUFsmEc01V+QuNW642bVuLcA7Vkxbq2qRElpi46KmqeenbYNEghBBCCCGuw4iuGnSzWwK7UYm3Be/XJD/fkPwiRUQ9mkWihSmG9lstO3bUMGTegORK2yb6G8zb99M2Y7cRQgghhBDiLIzoqnE0PYpNVe5ERmxwbhpJ8JSVR+tZb7XohNGCnNNUVmVTCCGEEEIIcRlGdFV5lZd1MTNeTamavfSVfnMSy3XUMCSx3Gi9ZjwcVIazmFZWhBBCCCGEuAIjukoVFBXtTX1NErzqIjo9Fd5G8/YLjWszAgqSH28L3i9+G7j93UtS9GwWQgghhBBC8g0jukpdzIyPzs8mCT0VNSanCk1V+WwKcdb21Jc5YtGt7KQ1Sc9Igs1FCCGEEEJIjmFEV7EUkfBERgy7QSsoKjohiFmV+JTdRgpnrpGjjZoWSbwQZqxOfEZNYcd14sICkZhNI4QQamzEImHW+/d5eCFASMEp4Hx0hW8fb5tx+kmyWM2i9ZTtY92NaxO1ksiNVOuZ9Fpzt4zCgj2pr5mHrGYaOfTSMmFeqj+cm4OlINr/r0XXwtMLme+TEiir8Pk6psb2Xdx6j3JppsONewckbl+f8oIZ5waSc2GVn/d/K+hXOKQw81mgz/FHAXcj49MLqQy+tmXrFh2HdB84uJkud0aE4dghkJ98fenhM4HZxW27yio8Hl9Hy8jG3KFzm24Dmps04d7tMw7OBCV6e/LoH3ujMkUVXNeUNGy8to/vaabCbss9rs7EVfZYkKCs3WHVzCmdmrCbXMDNUhALY8JvePvfvxMRGfeeDeaa6Fi5NG/Tu0OfYY7mGpw5HXFr/wsDz6yY/yBZ2/nr/V+4G5XbyQXJl7/Z6R0stv16+uIJptX3/pIPTBHgfHSy0pjnoxMlXvb1i32fJ8zLinh09loaXaWtmVvZSSXhHLE3LWJNUggTzpmraUjTCbMRyot89ig8PS05M51e0pIFKbGJEQ+eXv7j4KIhu848K76oyDeektIS41ZsxG6sAet6Mj1vOUOU8Wj91jlfHj11/hUbzhHCrLiHT04v3zJ3ks/LLDYP1TFh0kPftynF3//0ZMG7+JS40KigS/eOr9j+Te+Nfx1/k413yetdfvyNkJi3GcWlUGpJi05IysJKSf0reyxILInx0QkF7NtQPclPf7R5+5xhew4dDXldEs4R7zPfPAy6sG7PgsF7fSKwFOqFKD05TpCX+yYpTVjRqUYkTI4SvM/JTJAsF4TqiMJFdPkpfmfp6ePotoio008Ta3jiEhQWHBVEsxu0V8JM8pOnrDxO33adWbu2TfSZfFQacwPB1Ovgos3nl2w++/26/ZOnfOmgS/IyX3kvuhrxnnqZE2YaOQzSsaBSmrzVic84MwCmOPvp+u3rj7zJA1CybDVs+bS1F1bsubFsw4GJE8c7m6hC3rOHflHkRVQPitgDwGzKtLX/fvvbvwvXHJ61aN3IUcPsDJVIXSrh9q9/rFgfllGLO0yoRphisO6zxJuUQqnl99Mzh9rjtCX1r/hYMBk/Yfn+2Ssll4MLv+lP9WxH9SX/ne/izesPRGaR6p1Vy8GLJ6/x+fnIkw3ej3/d5j1t+vQOjk0B0sNuPsR7ewgpGkWL6PIjn9wh4Ziy9dhvHKimtFcP/WJqNsTF0fQogSif3Shmrqax1aLTMF1LnJmgOqo6pkamVkamzUyaubXqt3DyD7Po9q63gXfCuRRLfGXQHLzDSCJHLFqV+IwLo1+Ks+7996d3Ckmpdxy65vjkcSOcbCy0tQx0LVxbD/x26oYL08YMbWlvhDXa+sUzaGrVwsy6hbltazu3Ad0+Wzlr038TBtiTM21R3D+HdlyoTa8BVGN8LVN7UgqlFitbXb7i9UqRY+pmFg5uto6SS1tzIw52P+aOvIg9+3feoPqVNB36+cYTU770amVr2kSV7HIVdSMnJ8+Z41Ze+GnJfI/urbj/lDhCqDQFO7fmx/z3JIn8VW06d/+kiwvVVz/5zsXEsvFZ5YLfp9/KJr+grLf5uWuTQu7nUNVlVBM80y5ORlQi+20q16qyJKKjg7rRetYciOQLUny3PaT6CjdxmrKqh02567WqidPI1aO6mXPmISKFoWbRZsJfX7hTrdV5gVuuhecw2QghVJcK4x4d2pdIEiquwxYtbW+qzmSXpqbfdsLQIa1xdG6EFI1iRXS5cbcuC8gf5TjC0dDIrncX6nyWdPFxjHT9/QqKivbQE9BVKDo/e0tK2Jy4hyTkw/nKpFckKqAjOXVdTQ42b3qHrTV3a4CBcD5eflSgbziV0P+0byfujP3QSCgbu3h9bUkdAO+CrzzJZTIRQqjuFMSdvRNO9UkyGPStuyU+749QI6NQEV1uyIOAFADV5p5ddJVB02mEE9VQkRh0K0yq/n4nMmKqfWKKvGH7u5dz4h+czYjDKcuqJ34f4fM8nSS0W3RxqvCGobxj5jOQe2LBk1D6+VGd9gPN8e6r/FFt6tHRhkoIX95P4chzmQgh7ihIC7xOdyNq1rEn/dAJQqhRUaSILjf8ZEgWAK9D5zb0oLFabTu56ZB1ZsDp2GrvikfnZ1/MjGc3ADSVVVvydcnSVsNgtJ41sywzbcMsOyw7D9O1JO9h340+EAnik+Mik+IiE2OCQnx+2fHbsQwSz7kvGuqqp2BdfOVKfjIdOIOSoaM1JyNnhaeib96c7n8seJUmfT9wVEuZic9uPntw/cPy8G6sAIf3QwrsfUoYXYXRbtvcCAM6GcuJuPbQ90xA2eW/0DhODZ6NuEWBKtmCiBt33wOothzRgp38TKeZp4cmWWf5+odTw1VWRU1JZYmxy0aLDt42Pciy17oLE7wtMW41Wq8ZszAxHlnYf4MqkHh88u/fjvzt25G/L5q4/8DJuDxQshj56Wd99DD8rU/i7ES6b7GWjjZey+WTqoYh3dwrzsnNw9FR6ltiwJ5vD2xc+GHZMPvwxVcY0jWo2H17loxZ//2HZcPi6RfwOdJ6Is7OENC9kbQttSSutqK4owd/HFu6FCYc9nmNx0K9yry3+d+dK46XXX65EoyDjKJ6ozARnVgQ4B9MLhV8h94dNYv/qiZ2n7pQ4VdW6I1H1VxGzHlNSKhGfrLbqLZ0rU0s7ajF3EpXk3qeqyj+1N8Lhx26+lpBWiYKioreFsjbVAxFYhE9vY2SCj5CJ++UVHDE3Hqn2tSpi2MbiaVt7zaOTfHgaFipybEvEySWt9EPopKxz3H9KCosZKY4K32CKUgPeRURXroUgoNuPyvbb+lhbiqbQnVASd/O0ta53OJkjHN3oPqjKBFdYdbTE69IxMDv0snZ4MMf1aRVx05NyTov+OQrAU7o2BAsx+7+dv3J78my8fyyvf4rVi/taEYuL8nP9i64wqEp6SojKCxYlfR0aUKQnE1Sp8TToG/Lvs+pcF5TKV3MjMdRf+pL4ft0uhKloquBY+jXu+ZdZ279+oftH5YlG4e2b4r7vUEZe32xZOf0HyWWpYc/74jzudYPpSYaTIf73ORciUf8m7T8bvbqA8XzAf45sBmTLXGaJ9eypYnBG5Jf4Gjedcd06B9z1xydX3bZP66XGfsOhOqcglzhxEmh159QPZmU0wKPLD36V8ny6z16uAjID/B7+g5DugbH024xavR3C+hR/mL9LwVzPqR7mPvulTAzRyzakBwqT8GPiq4VPV9BQfqblNoM2EP+ojXJzw+lRe5JfcVmVeeFMONVHvYgkZY4MyWGri8ZOOjz6JxaIF85UkwnBDEPc1M5M/E9aqz4Vs1cOju0llhauRjg7Yx6oqyhb2lAJdKfvc2U6NetomfawrV4PsBWTZuU6yDAU1JhTiaH0iMFhdgbEyGuUoyTa2HKLf9w+hSWGxx8+/wjiSUwhLnrJIq8fgdb6WRCtWmXVqZU4n3MqxyuP0DUT9usn445SUTnZ0sf/NQ/nrGrCd2lLOXJo4xa7GQ1JZUMemL9W9lJkkMEVaGgSLw0IeioIBqb9aQgzgwMiqL2UxP7zka1HruGp6RkodrkhCB2Q/KLOXEPp8T6rUp8ygR4WBVDqFHjG7dtSd8sCn345G0NLgJ6Kryv9JuThECUT4I6JhMhxDkKEdEVpD4484asVVx6T1s9blaZZcXgTlQ3D3H4qRe1ar1AdUZZVRGeHyIXvxZ8ahBVEvxUOB+9LCjrtHVrQfW7LHr9zwMpJ2CUREKFhcbOzPCt/wiiXwipucqrxlOizh5nBW8WJTzBxrpqvI+/uiucCrkMWvdt/VEP6/bV/tBrJ0csIiXFBHgz3vjPiAtgWvCC36djgIdQ46Ks2eqzllRXjaI3p3eGZdTkBnZXzaZdtYxJ4n52Mva9RIijFCGiK4gJvknNrazWdmJPz6EdepZZhncf9indc/95wIM4DOkaXkHCjWC676u2vaOWAgxNQIKfuUaOTPCzJ+21nIySomzi8ml/uuNlzPWdeyKzy1/ORZkv/r35IL7SQ6CpKn+hcUuesrJILN7yLqzakKCpKtvU9DY/FxvrqpKfdn/twZPUvW/VltP7OGozubVkzmvSUcOQ3ShNIMoPzk0jAd6apBAmwDuUFomFglDjoKzdpf/wllSlLuO/v/84EFPBVaByX+k311OlBkrGvpcIcZQCRHQF8T4PqV5iTVr0alcyyqUkNatP2tO9/t7evJKCJ6qGVJD6NmD7gV//SiRpJeceA10UZKo0EvzMMHIgiQKxeH3KC7moNCtrtp0zvCPVdlgUs3f70u+vBoRlMd928fv0V5cu/+H166pfrt6LquoIaMnXHadHzYNNYoMtKaFMppTOCt7MiX8Q/J6eFg8RhXkZsTGP/72wYczaLWep3aLff8zMUUYSA4vX0ig9azZVCXM1DWr+TJM2Xxk05+HAmgg1Emqmn/wyrA01Z1Peiy1bFv3v/M1Hqe+LO2CK0hIenwlKqORipafCKzn5Y99LhLiI+xHd+4Q7/1Gj7vLdO7aUGOVSklpz1+70AE/x54Pi6QlbUL2J2zXgOy/XhczyZZ8Nm3aGCUg4Z93xf+t7WSnQVGkdNQwH6ViQxNv83KPpUUymbKmYuc38c3BrKqgTJ1zz2eS14kvX7ye4f/95l5+XLrni97oAjBw72lVTBuSPYrrfvBBmHEqr6rpOwlo2VYxUBdYkhWx/9zJH3Hgbw2N///UL5vvf/ofpn275/ZcbD6NJlUrTeeKk1b+0b1rDeO6oILr8CCg2aloVNtMxgdxGiw4bzduP1mvWqOdieXn2mw7sWUhi+WV/EF4AkCLj2XZdsH+kO/WsN6T63dwx9ddJHZZM7btiqvvC8b3X/775Gd2fXsPMtIILQS8tk7Ya1OAq2PcSIS7ifERXEB/2hGoB0mg/yq7S3kxqTbuMoCrf8CbkaQJ2vKwXPGNL8/JdKlX5TZ1b9ls4dfOxMd0tPr5xQr6M07cldWiSuJgZL82DZ/VPWaN17yVn5kz1crJihwgvzBNSd2hVTay7TPti3anx3c2q7/c609Ch5O+qxSRFt7KTFr593Oga69R0bW3Knk6VNbTNWjn2nDL6x/9+XDrPxaiGY1yS2Pis4M2qxGflg7pPda3YFI2UFwZyNFUDZ+PKr2oiZtZGVL+UVNXpeEFNXRkbiBucMt+h27yTPyya39nFmr7pVpSf/S4rmzmFaDdtObjvjP3fzuha8VmCnPyZBwqw72XtKPHU6IoOT02lou++sqo6fRVQ5Vf4MkIfRamokg5jSnRfHe+gDcymHBPnZeS8V+Lr6PCqDE8L36fl5Cmr6+ipcyKK9XJdSH5yYf8rLGmKgFS1F78NJNXrYbqWbJa8EGUnpKYkZwuL1HQtTUyN1Gr0tX9b8H5pQlCOWNRWw2CJcSs2t5w5cQ+rGEO/l5YJCXr1VGo5Vj8eAiSiY4beaarKX2vuxtS0SpAvXnR+NpPuqmU818iRSdchLALZ4u7+F7/PzcpT0dJTV4AHpzl9FIgyUuOj0gXZIuUmTfQsTCxMq588gpxz9qS9Hq3bTE4ualzb/1SNNJ+noa1RyXe/QJiZVdREr0nVFVa5whRBZcECqm9MOCbN/ufOd6pSyuq62nrVhHOEShMDHT2OhHOIK0hVe4tlR/kL5whVLTMT27Z2zq5W5jUM5whzXpOvDJoP07OqIpwjeMpV/WKqsS7+kdwMB8o9M40cSFRMEiRsXp34rExfVsmn6XhQ6oYvjoaCZEu5iYauQoRzXKeqa9jM1b5td6fW7ZtZSRHOEeScs9G8vVxe1DiBqpFWGs4RPL6OAZfCOcQh+LVC6KOUaTlRGFQLG/2gfBX0lD+0vw3Ssdhq2ZH8XGbahixLTFxG61l/omNOohGc26DWSFDHPNkSnZ9dJqjrqGFoo6bFpEvGHWVsSQndkPyiMT/NiBCqtfLPSCOE5B9GdAihWuIps3ciecrKuipqi98GXs1OINFFS75u2yb6o/WaMUsL9Y8bsL9x+7Zpy5Kgbsu7cMn2t5JmOg2J2wpnM+Ie5qaSZWlisJxMrYEQQgiheoURHUKolkra6PppmZEoLkcsKhCLqx4hE9UUT0mJBHVk95J0cG7a+pQXTD7RUcOQmUJKsqG4l5YJMwM+M09gLca2QQghhBC3YESHEKolJpDgKSt/qmvVQl2bGVKfhBDyMfKn4iBB3ULjlkwfS3oO8Rgmn/hUh3rcRU/lw1jkeiq8ZSZt+ulQ45eTGHtD8gvJ9yOEEEJI8WBEh1BdEhQWHEqLbCSPMGnQvS47ahgxA1pOMWzBjJWCzXR1jgTPC42dm6rySVz3CT0RIqOftrmeqhpPqdSZnESAUwzsphjaM8VxQhC75V04PlaHEKoFcurA6ekQkn/VzF6AEKoBS21Y1xM0eXAjFrY8YTMVgz4fRraAAjEces7mEEPtYEprmO8LUcWNcl+1ot5G7AgGH7mYeF2hGGtATgG1SCKlEJAAybnspiRHA1jSGfTocVPis2H9ww8lhRBC1epsBnPbURe1RbchPI3NRAg1rEYyewFCciMui60x97YGFyM6S1GQwIBEDmQhoV0JElqQWEIySPAOA0EelfByoioBqG6RsK1MOEdcjq4gk0FqYCTeDnlHpS20qK8lQghJj5zPmTM5ievUcEIKhORXNW10OKWgrOD+l7naFUFQRpTbzQUk4apr69dzHV9ifH9OK/m7vCx7HG1PJQif5EBTdT3ylzKbjM0R5+eH7COJeXZDN7lMZjJrAQ+BOkRKhJSgb7fV7LZ0sAhkC/e/PMBSIGcPclYnieWOY1c4eTGZDQb3v8wVFwGzhRoavfulOgQwopNTuP9lrtZFUHL9W9Pyy8UtRjKZCmDEg7VnEgJIIqrfThsNY5IQigsqDFlJ7EfiB76KWqDHRietDw991QgeAjKHRSBbuP/lAZYCOc87X58dnZv8kaf02sH9L3PFRcBsoYZG736pDgHsdYlQHSOBnClfnyRWhh8jV0EmUwEsdxzLJP6I/I9JVNYCyTTNkahPUJDD5KD645/+EvczQqiekPP8frc5JCEszJ8ZvIPJRAjJG4zoEKpj5Pq3vc10kiDXP6b/oWJw1bVlOlgeiL0hFFfy4BbNw8jldKfFgR4b3fUd2CxUP7zj73reWzru8caqSwQhhGqNnNInWvcmiZvvQsj5n8lECMkVjOgQqnvDzTqThSTOJAQwPRUVwzfNh5CfgoKcai/q5M9XmGcI5RaJ4g6+8RUW5vskPandvYNJgVtnPt2J0SBCqGprWn6px9MkiSWhRxLzBEwmQkh+YESHUL3Y3nYGn573mVz/FKbG7GXZg+lQSgIJJgfJEImZj7Zf4KRNTTK+I8pnR/RlJl9K5P0kMif/0PPeUqyiIYSqYKquR4I6kkgUpi95cZjJRAjJD4zoEKoX5Pq3yH6EjYbxJpfJCtNaRf4QL4vuJOGfFq5Ijwhylx5P85L7Uube+fyQff7pL5l8abjrOzDRIClNt5sLavRvEUKNzQybAe4GjuSiNpa+CiAkzwoTYdNCmL8eEkVsjvTyXsPy2fDjPshiM7iBy2Nd5qfDmyQo0gArS1BXtNAUx3eSuY8vAqZpTsE6H958F+J5bym5qG9vO2OgsRubWw/wEJAeUygkYcrXD/TYaKqux+RXS1CQMylwK9MxmK+itsllMqm0MS8RWASyhftfHmApSErME+jxNBvyoob7X+aKi4DZ4oyAb8F9A+iNhpfHoGlFIUJuOuQogYEelJ9mMf81dHeEh8qwLQz+Z8dmygq9+6U6BLgYCOXD9fXQ3RrUDcDeGVo0Az4fOn8Bl9+wr1dACH98CsZ6oKkJ2vpg3w8eZrOvIFRvyGVPwcI5wsPIJarfTrLUaziHaoQUCjO+aKIwfdyjDUymNEjl7HSnxcwopsxAdvhYHUKoMqbqeop3UUMcEnMEHA1BSwdcJsLbQjazPHEK/LaHSkxYWDacyw6Fn0aBuRpoGoCxPqiqQYfRcLJ0DxW15rBwIIAI1m+HXDaPAzjXRpcLW0fD3EvsVilGsOseTKtobL0cP3DsCvHsFmXMBTg2iE3LJbwvJXNYBHVFUJDDTORAggc2Swq4/2vK897Sm+9CSKIWEwH7JAeSUJCZBcHdwJGUFKm6YRHIFu5/eYClIFu4/2WuuAiYLZnKh986wqKndFoJ/ngFcytpQIvdC/ZTocABgoKhLZ/NJJKvQr9B8JTuh6miCU3yIZu5gdkEfrkDP7Sn07S0C2A3BARGcOUV9JO210u9oHe/VIcA19roHv4iEc6pQgsXsFBnt+AdzJ4KEfnslqTAQ6XCOcJnL6SySYRQvWKmXFewYT/l0NEOC5lxa/6I/K+mM9QNNHYL9NjIzE7hnxaOIx8ghBCSK3kvYT8dzlHNxEWw9yQJ8SoignM7gURqDl7gKBHOFSbC7LF0OGcP+4IgLxuyhBB6HFzJr3sPP46HxxLtcQZdoS8J5N7BrrtsjvzjVERXmADLNrNpcISr8fDyGcQmwJJWbF7+Hdj9jE1/kAUHTrPJkr838wrcTmHTCKH6tKbll4o37KccMlXXO9p+AYnK/HquY8ZKqREbDWPyD70sezhpWzJ9OBFCCCE58eIfCANQ6Qzr+lObT/fDyzz6hdJEb+DQIyoxZDBIBHTwYif8m041Bq0/D5Pa0o/PKYPTZ3BqK1BNQ2Gw4Tb9PoY+eHWh1tcOAflHnMCpiC75OlwvjqBH/gZ9jamEsj7M/ZEuDdq5y2Vj9oyHcC6JTinDyp/p0J7Ihj03QcykEWog0bnJjTCkIZHGPLuhJBGWFbcjyofJRPXBw8gl0GOjk5YFu11DfHo6BL8ea2sRECKEGiEc9Bg1kFw4/De17jIVxk0HLZIKg39eUDllJN+BoCIAPejvyOZQhHCC/udag+ErJzqnmO1o6E9f8a6fILHBBx0+pX4KbkIwR4a85FRE99yHakalqMCITkyK0rQLuLBJiPGHMr2NHuwHpjVOuSOM/xy6FT/Ue2MPJGNI95EK0x8fXDS6q6OFob6Jnaun16KdN6JyyuxVcW70rb0/ju/TwdHCQN+4Wdt+U9ZdjBayLzYagoKcJaFHnG/M2Rxxns2qExwpgeWOY5kOgSvDjynW1GcKeAhwLJzDk5DMYRHImkxKgLmo2V6dXscXNU7CY6DeZT+BY7FUh8spA8C0F/TToTL/PlzByCVPL9CxQgtwpsI+ljgVrrymEq2HgSGd84Ee9KOH4Eh+AikSo60YuYIVWaWAbxSTIfeKKlH1q7IgKtrmRj4WvZgU3cxisymCorF6xS+5FUWJ2GxK2oeXOv9VVCAqOuBR/E61okPx7Lvkj/zt//JEb89Os2c+KKh8GP61aY+5B4PSC6l3FAqCDn3Tsyn7AjkUi+8g6PbfGiqkf4n8Yj4pu/HR3hfmm/pMgjPD+efHJAjT2dyPxKkS2B9znfz5ZJn3bC+bVSXmc7IbcgoPAVlT6BJgPie7IbcU/CDgQCnIqgTS87OZixr5SS5wbG5dYz4ouyGnGsmFQMbL9YnUx1DrDfGF1OaFMfTHMoc7WaXeViSE5XR4ZjoNsiTys/2Amn0VYOqtD5kly8XR9GsWcC9bIj8JPFSpbI9/JDIbfGGwhVGlSt8k/a9oKDlF3zYjH4temhc9ec9mU7KL5loWv2RfFCTxUuq5Il0mX7nor0gqJ25/EY/JgaJe+0mcKJ/kb/+Xk3lrmhn5jEpO/zsVmUPOaZmvr/35Pw8T5pOb9vvh4OGlA6g3AKjaD/vxsN9bctoqzIk4PbcVdS7TGvpvEn2mk1v0R6/LIjj91r9GIU21ZFgCm16fWx56lPxF7LZ0XH3nkz+fxLSBAvpgrBL9wfEQkCX6o7NFQIp7e5QPk5Yfil0C9AeX70NA8Q8C+S8FWZYAuRAwFzVyfmCz6hr9yfFCIEv0RydFINMlDbyoXj7Q728opHPSfcCA/mATr5d95+AmVL7rH1AgkZ92FjTo9y9/+iGzZPGfR7+mAxfSJfKzYIY5lW3zLeSUZDb4wmALo0qVvkn6X9FQsoqmmJCPRS+ORc/IIVHifdEPdsUvWRf5k0Oq2OnhbL5yp6LIAipHFFfUi8dmqnYripbTkE7+9n9ZOQ8WNiMfUWPIqXdsDqUw89mhme0lOm2Z9l92MUaysEiUPYJqLTee7JvJ5sgn5vOzG3WkRiFNdWRZAjZXviZ/yMQnW9ht6filhTOX/+EBa9isyjGfnt2QS43nECi5GTEjeEf93YyvOQUvAebTsxtySuEPArkvBZmWADkbOF2fTc4Mddn3pDTm47MbcqnRXAhkubw7TaItao4B78TiTAFMpBs99b0gTeKdBZHgSmVDnxMfMsnyzpt9zOy3sFL5zBK8jH5NHU6nSuTnwa8tqWztEZBektngC4MtjCoxfyDnFP+JJejpGmjiDztAnAx7rrLpDhPAim49VTGFCV3pLACRH1ysYl5yVBWxMJMaZEhNU4Peryxlbfuuvbs7lwwvpGnt1NLOqGTgGgpPW4/aLimmxoQZQlBYmL8y/BiT8xFkWQLM0/DMo3HSc9d38LLsQRJnEgKYmdM4jROHgHf83ZlPd7IbtaXH02QertsR5eN5b6ncPAmJJyGZwyKQNZmWAF+Zt8Z5PEmQi9q6V6eYzMYGj4F6J4bruyCT7MQ+4FnSdVUXJg6j1ukX4ZbEbGSiDGDGMTGUCKcJMQkO6ESFu5uZ840o9aoqNKXb9bJTigfxkHNsZFdO1a/KgmTXSrvSvS5ziuZbF79kXxRY/FLC0aImTKZS0TaJJpG4fR86XnbYUkQ33ckb+dv/ZYni9vekRpnR+3R/RB6dU5j16vzPI+3pXuQ8p7HfTe/O1vetB/90/Gk60xqaF3V4JNVU3mTA0QTudDmrQ8MD1jDNHR/dTCezEkgQpjN/Qi162pB/a3Pl66Nxd9jtyjGfnN2QS/J/CJACYkrqUtITNqsmmM/OpKNykpgWZrKY+kzySwtn8mVKwU9CzCdnN+SUwl8H5L4U5KAE3G8vYs4MddH3pCzmo7MbcknhjwHms5O1rBZRPPSm5j+C4adL5WffBbpTJHxyjO2KSZYcf7CmM7+89uGdZEk9CfTvgBUV9bp8+B39mi5clOx1WQSH6QkMoBP78J5MFgZbGFWq9E3S/4qGIixa6Ug+Fr2YFt2SHBkls+hLw+KX2hfFMIdLYdHhPsWZUKRnUmRqyi4m+h/ywa0onDkG5Yv87f9yCtN95zDT9Subtunh0dXFnDqpEaa9v//3FdX1tSDR97cxTsxBBMpNHTv27OFmQd/FUm2/8kk282vkFv2p674IyDWPufiR+jGbVVuyKgFSuWf+hHp9sIr+zHgIfBQSP/PPj6n1l43+1B+K4H1hvtejDUzRk18rD4/VKfZJiP7M8n0IKP51QP5LQfYlUHJRk6Y7fU3RHxovBLJEf2pSBDJboncBvbOg6xiYMEFi+ZyN6KjhUkTsm98/Als6c6zPh99AFsEVYOLq6XdK5TOLLzMDqzUE5EjkF8KejnR+V0gqyWzwhcEWRpUqfZP0v6Lh/N2TfCx64RUdS2YzicKEoq5K7Ev6nxcJ6ExRXJFn8fNyVS/rQul/IF/kcf+Xlxd3cflwJ23mwwLwm/WasvZseHap+00Fiff2LhrV3pi+XUUomXSauNU/lQm75RnzcdmNOjUjeAdz/avpyCLlyaQEfFOeMZ+/di0/UmI+LLsht+T+EJj3bG+tv2zMx2U3iq15eZL5hWSRh8fqFPgkxHxYdkOOKfR1gBOlIPsSmPhkC3NOqPNmOubTshtyS6GPAebjkrVslgLYyoRVVVCFXdHs+/NCoRWd1/+UxC8pAuFToIfAhN7epfKppRAOdKNfcy/dFpcHv9FTo/EHlXpUr4EXBlsYVVIi/7FvL02J7lVa2auy4T8PuvzBpqffhh3UAzkUgQ80/4Sd1L3bPrg9iXr+MXYv2E+Vqutrq1/g8Q8f5iiXD/K4/ysjzkuNixOoGDWz0GXuo1RInJce9yZdtamluS57p0rO1V8RJOYJbK9OFxbme1n2ONp+AZv7ERq4BA7E3pgUuJUkQvtsq/Vk1tXCQ6BOCMUF5MuWKEx31bUN9NjI5kqnsiI4kxBAvgCCAmrqz3l2Q5mnQ2VLIU9CXDoEFLQICO6UgixLoOSiVucnBLwQyFxxETBbDS0/HDo7QRBA58Uwy5nNLFGYAIsXQzJAx61wfzbdlJcCHmZwqxDa/QkPZkFx/AyQBkOs4EIuNavBq130BOUl0sGrORwTgO0iCFnLDolJyYFvnGBLHJjPhJd/Qenn8hoOvfulOgQ4FdFlXAO7fsA8Aan7KTw9Cdak+HJhQz/49j6dqwo7XsF0G1LKsLMHzPCjMy3gXjh0LV0W+S+gXSt4zmw4wtNgaC1fIR23ruUKqV6LYO2rUzYaxl4W3dltTpkfso+ZUvb90ON8ZbZ/SZ3DQ6CukMIiRUYSpzstHm7WmcmURhVFEJYdP+LBWlKBI1Eix2Yk5w48BOQBloKUyHmGXNRqdIaRBu5/mSsuAmaroYX8DK2XAgmnTkTCKGM284Nc+L4V/B4N4AphAeBIYuQcmO0If8aXC8MKYX9vmHwboClcDINPqGcYWfF/g9N4yAb49gH8LtkemAr9zeFqPnTYAf7TJYLDhkXvfqkOAU6NdanbBSY1Z9MZ56CNG3z2OXi0KQ7nAMy+gs/oRyJFMbDXn84iAd0waFuuwqFmB5/Tg5JSwuHfMDaJUINY3GIkR8M5gtTmyU8nbcv6C+dQHfKy7MFXoW4Gr6yDEVZZTloWfj3WXuqyDMM5hBAxz25onYdzqLETgvdBaq3VB3oa0TllaMDn1GCrAEHw70s60QTc6eneE4JAIKZzGCoweAH9KF0KTJsD4blMLqT7w8RZVDjXpDfMcmMzGcJYiMinEu1bySycqxFuzV6gCUv2gCO7ARkhcOIo3IpgN5VbwuHfwID+i2L+g4fF4eyQcRW1larD8C/YJPHPMfK9QQhJIygjivysq/6WJD70T2fOxKhemKrrzbAZQBKk4JiyqxMklqu/PrcIIYQaudyncPg1leg7CZpWEq84j2HDgoPedEVeGdz6UptF4RBGPRnwgfFg2DaGSsT/A05G0NYdOrUE4y5wLRPABLbvAdvSnWXTnkIMWfGhb0ncId+4FdEBGHjC7ZswuqR5rVj7r+DeHehjyG6+uscmwB4mlg66S9iPhA7Ff37MXUiXjOURQhUTigumN+s/0KTdAONKjqyaWBHm7XZzwczgHew2qh+LWoxkEn9E/sckEEIIIXkWeRliycoQvu7JZFRA3RGmtKMSr89CDN2kZtsPrMgqDa6VuVesCp8fgpOLoBmJ3N7D0wB4GAoiAIdh8N9DmMAMkSkh6D8oBFBpB50kumjKM049Rycp7SU8fA6p70HTBNp0AFtdNp9VCOnvqGhd1wg0Km8sFQvhnQAKlUDHEDSreJBVBrDvuMxhETSAkkfyNrlMnmc3lMlk4P6vW5/4r/ZJejLRuvd+tzlsVnVqVwQHYm84aVu66zPjiqHaw0NAHmApyBbuf5krLgJmq2EVQto7UNIF/ZKp2iskgpQUUNYpnlU8B/7nCH/FQ8tfIXAJVDD+TB6EB0FEMihpg2NbaM5OF1haNky1h71J0GEr+DFjrsgIvfulOgQ4G9EpOtz/ModF0AAS8wTO12cLCnJM+fpR/XZKPpiH+79uRecm6/E0a/TYWy2KwD/9pee9pSRB4kbuPikqJ/AQkAdYCrKF+1/miouA2eKGBwuh80aA1hD6CIonAqyZ7Dvg0BMSSsZblB1690t1CHCt1yVCiogENjffhbAbjYmput5yx7EkkShMX/LiMJOJ6oONhnEDjGLinxYuLMwny7hHG5gBNhFCjVB0bjIzhhZCDc91KrQlq2dwLJzJqDH/PZAAoD0IhtPjLXICRnQIydjaV6dsr04f8WAtievYrMZkhu1AV12qA/uO6MuNcw8oknl2Q492WMgMrbk54rznvaXMtHUIocZj5tOd5KKGN+mQrKg5wA/DqMTundQ4ljUlToRNJ6nE5EVgwp04CSM6hGTMSctCWJhPKr7rXp1isxoTvjKPaaYjOwFbdRSAl0V3vx5rbTSomYNuvgtxu7mgDgfYRAjJP6b//JmEADz2kWyowKe/whc9oFkGJNPDpdTI+zcALaHvHPiuE5vDCfgcnZzC/S9zDVkETK2Xr6IW1W+nqboem9uYjHiwllz+SSLQYyPTZIeHgMx9TBEICnJImTJ9ickX+2j7BThXVU3hISAPsBRqITo32fbqdJIgR/3pTouZzNrB/S9zxUXAbKGGRu9+qQ4BbKNDSPZKGqnkvJmOXKfZVF0je4DpqofNdIpBj6fp2201M34p+WKT6G5H9GXmJYSQYrPRMGbu4JxJCMCn6RBqGBjRISR75OIn/8+SCcUFzjfm2F6dfiD2BptVd8ifz8yCTf4v+DSdwtjkMpl5rI4EeAPrYgJDhBAnMLcpiZ14KwehBlFNr0uEUAPpbAZL6G5p5yNg7zM6S86UfMI1ARCQQGfVKX0+uBjBnTh2E9UHspO9nMBBH069arhdbasLaioQnsZuIoQag5+7U6f0AjFMvAQ5BWwmQqjmsNclQtxBYqSoDCox0La62TRlhFybGSHv2ETdShdiOFfv8gthgA0VYpGgrsGQLzaGcwg1NucjqJ88ZejNnQHgEeKsatrofH19mU3UwDw9PclPfBpYhhr+gewzCQEjHqwliXl2Qze5TGYy5Yft1enRuckeRi6+3VazWfUJH4ivJ8wwPO4Gjn49qC9bFbAIZAv3vzzAUqg1obiAXDUShemmfP2EAbV8QBr3v8wVFwGzhRoavfulOgSwjQ4heVHyNJ1/+ksmR1bKzyFGYjlmWJRehq2YHMRRpHZFftbfIDfSI99zUudjNxBCioWvzPum+RCSIEEdM5QxQqj+YESHkBzZ5DL5dKfF1Tae1LfNEefL1PhLrsc4Bj3XOWlZkJ+kjiXbaCosO97z3tIutxfJQ2yJEKoPE617exi5HO2wEC8cCNU3jOgQkiPk4icPVz5Tvv6IB2sla/xnEx+QnySfaUVE3NWsSVMmEZYly6cW1706JSzMD8qIcru5gJm2DiGkYEzV9Xy7rfay6M5uI4TqDUZ0CKGybDSMSVV7ZvAOZlNQkMN0BG3QAegttWFJZ+yrU+ectC2ZhGwbx/a7zWFmqyPfLs97S3G2OoQQQqjWMKJDCJVFIjry80DsDWbqOZ/kQGFhPkkMM+1EvVz/qObBTZ7Q2Wx+yD581Kpu8ZV5TKL805INrGS2OpKeGbxj5tOdWNYIIYRQLWBEhxAqi3nUiiCV7KCMKBLOmfL1Sc17oEk7Jp8Rlh1PFnajTlFRx0mqVTA6N3lHlA+TieoEE67LCS+L7n491jIfiRS0572lOL88Qgihj1IIvptg1ny4nshm1EAeeC+H2T9CcBabwRUY0SHEZflZkCFk03WKqWSTWG7Eg7XDzTonDNgX6LGRBFokxDoQe2NS4Fazy5Pnh+yrx/Dg1CsQ5JH1yvBjWMuvD3IyJImrri35ankYuZC0f1q4280FMh/rFSGEkDwT5YGYTVYg5xFMXgDbD0C2CptTRlVVJyVIugR//gqzDoGIzeIGjOjQRxGn+i7uZWuoo6VjZNt75aMqenGJYo54ORpqa+kYu0w88baQzUVVInVu7/i77MYH+XB3D4zpCvqqoK4Dek2AZwpDF0Jw1WGPCI54gaE26BjDxBNQXQlIPm1F4jfyMf6I/M/5xhzbq9PJJgnqSEX8dKfFJV346l5+IRx6TtaCgpx1r04xeejj6fE0lzuOJQsTRMkD8pEudVk2w3YgSZPiJptMPkIIIcQQpcChZdC3FfCVgMcHFWUwd4HxP4F/mZuTYrj6G0QDWE2AvuxAYDQpq05qMHYhkIvQ/fXwJJfN4wScYVxOcWSG8ax7C1y7b4pkNjSHnX9zZgg111UF8sN+6+i86CmdVmr/xyv/uXaq9IbckvnEppsjzi8JPUISUf12mqrrMZlkn8OPnvDrY3ZLkqor3PSFbiXvLC0/DDo6Q3EJwCt/qLIEZj7dWUV3x4Em7eo3nCve/66+84Myovgqan491uIwmw2s4Q+BHdGXSTiHI+MxZH4KQgSWQl3xT3958I3vgKauNRrPGfe/zBUXAbMlM/HnYMTn8LDCdgNNWHYVVnZhtwpjobc93C6ApUGwqi2bWbOqUxqMtYPjAvjyChzqx+bJCr37pToEsI0O1Z7w2ZbZmyNBybq95F2QCuW9PL6fCiaUqBCg6PHek6+pgTZQVWw0jIWF+WQp1UKV/xbOkXOSJczaBAGRkCmAYHKeo9vTREEwbTNU1pHg5XE6nGPODY/h5GsqUbmSMe7La4BwrsQml8nkJ9kJK8OPMTlIgc2wGYDhHEKKRygu+MRv1Y4onz8i/2OzEJJaylXoPYwO51Rg2BLwCYTEVIgLhVNbYLA9QA7sP0Z+sGLOwZ0CAAcY7cjmEDWrOhnAV32p9fldJLjjDIzoUG2JIg9+83NQEdjN+2NSdW0nwhf/HAojh2Lnn9f11yDbT/cff0k9IoWqMNysM9MqtSP68ocHydQsYckuiHwFf86DTragrQtthsI//0FL+tVQb4iocMcK4Z9D1Lrzz0CXAOw/DlWWQMngKGU0ZDhHlEzQdyYhAGctQwghLiKXDOZMTk7j+Fw0qhFxKnz/JVBPV2vB+kdw5lcY4AomBmDhBCPmwH/PwWc1DOkM6sy7ReBzCIoArIeAPZ/JotS06tTJC5oACK6BfzqbI/8wokO1U5h4ZtFiXyEYT9iyuCMdIlQhN/jw3xEAql2mfjFueh8tkhN26J8X9TKgh2JZ7jiW/CzdTKcJn08DW4kTFcF3hBH2dCoVkipq/cwNBroEYOoXML0PlRN2CKosgQqHPGngcI5BdgIzwP38kH1MDmpssAqIENeNLW5+90l6wiQQkkb4XjiQRCUG/QnfuNJZktRgwE/w1zhSv6GIk+F4EJXo0h9KVU1rWHUy6ADUg+YCOBHMZHAARnSoNsTpt5bPOyEA7cEbVg8wqu5blP3kwLFYAF63KQMsTXtN7adD8iL+PhzMqUdOZaLiZroKKFHPCFPUgV9RcTw5AHQJwABL6DUV6BKAw1WdqMpHdDIJ5wiyB2bYDCA/mR6YcqYw/fHBRaO7OloY6pvYuXp6Ldp5IyqnzBBc4tzoW3t/HN+ng6OFgb5xs7b9pqy7GI03NKQVnZvsdnPBzKc72e0ysARkDotA1jhRAh5GLsy9uWNv7zE5CgSPgXojBO+9dMICfhzFhm1VyHwKwfS0pr2d6e2qVV51UjGCrlZU4q5v1f2Z5AiOjCKn5HtklJxHP3bo+GsYz33j01vznVQT9ncxn/yg0pFRMm5MatHnQIpa70NRV780V06/OLb54OMCMJ9xJ3x7d6rBTj7JyQPZZxICRjxYSxLz7IZWHtKkwhBruJALyl0g+g5YlRmvNwMmtYADKdD7EFz9EpTTYWxz6plf8xkQvh0qLwGzy5MThWyHAy/LHvvd5jRkOCe5/4XigoaPJKVQmHBuZs9hu+lHElVUoJAdQLRpj7nrt64c31ZPGcQZwX8vn7vwj9spzEs8ZSigL/O6/bf6n5vtxPYTkU9ycgh43lvKdLglNcLTnRaXGglToUtATvZ/NRT8IOBAKXCoBMY93ugdd4fEdQkD9kk5pC0XjoJGciFgthpaYRR0bg6PodoKC+vZCmizEsAUbr+CHtW+u8qq01FP+PwmqHpAnC+YsHkyQO9+qQ6B6lpXEConP2zXnN/DAJy+2/q1E3XHrWrp93efJWcxfq8p/Uyp75t+18mDDcj67cm9DzLod6AqSNVM9/Yy+NItnp3Hg1m56VfS7wNdAjClH33E68PkwVT+25NQZQmUNNNNtO59tP0CGcZUchnOUUO9Lp9BruJKTv87FZkjEokyX1/7838eJpByZ8sEV+eBPx46smyQs+tX5Cquaj/sx8N+b4VF+QU5EafntlKGjCtLvj+fTF/TGxjnejBubzuDmUuDxHVuNxcEZUQx+QQ3S0ChYBHIGpdKYJhpJ/JTWJh/JiGAyVEEeAzUp9woiKETdt2qD+eIyEf0yhQsSnewrFDVVScrJ+qnKBpiOdKhDCM6VEOi2GMLl/sXgMXX275tJ8U9ttRbuy+mAzTxmNLHmPm66blP/pQaRzHl7O773HnkVIYqeppOUhZsWArUGccMVhX3JZd0azfQJQB9ijtSuk8GugRg930mo0JMREfCuf1uc5gcJCk37NyVBACNwb+uHGGrAaCibddn1rbrr54dmtleExKv/jrhy9WXE8C0/7KLESFnfh7vbqZOTrkazYcvXzNMByA74NKLKiZwrDfjHm1QOjti3OON7Lbcc9Ky8OtBTXNP0tG5yV3uLC6epJGjJaBIsAhkjVMlQI5ipuPl5RT6USeFgMdAvSpIA+bGs5kRvaqaCGLjqTXPEPSq7aBZXdXJ0JpepUIyR8Zmx4gO1Yg45fJP317MAr1Rm1Z46Ff/9REnXd91ORNAs89Uz6bFb9ftOHGYKVmnX9x9K5XJQlUoaabzT6dGeypNDHdWwEZ6SsBx28CzXKdXcRLsukwl+kwFiRIAugTg4m5ytqqMo6Y5hnNVEAszqe71apoakpcCZW37rr27O5fcHtS0dmppZ0Qu4R/wtPWobRn1YmHauISFXJo/RI+nebrT4pJbGyQopYfJ4WgJKBIsAlnjVAnwi0e8PJMQIBTTTztxHx4D9aqokIRpFJ7k7q2MCJKyqLWmIZRrciutuqoTocNM4ZQNWRz5qmJEh2oiy+/XuYeTge+55rdh5ZuoyytMvLLzei6Adr8pPdkWOop2h4kjzMk68/Ku60nY30AKm1wmkxqtXw/qgbpSYo/BZ3Rji/1s2Dq8gnNY4hWgSwCm9JQ43LVh4ghqnXkZrtNjSFUEw7mqNWne1YEHILj81+lIJjwSZ7/+75dRrR0/++ORkOc09rvp3fUh58EWL0fnIUv/fSZgnq7Ijz67leoF26RdfyepHiSpS4KCHLKQBNOPkVtWOHmVPEe3OeK85721Rt0cuVYCCoaLB4Fi4VoJDGhKDVYoLMz3TwtncrgOj4F6pazOTkuQmk2vqiaGXDr6UlGvJryptupEqDLxeBG850hEhyOjyCm5HBklP3xjt5YLH4mdltz1/b5Vyb2noqR/+jn97zFoDDr24u/+ejwtXU32XkphzG4P+6/vikCv65hhLZowmbSCuKv/XH8LQA2XcuVLcymCwwbHgQeyU33Bsx88KwTdAeB3Bj7cDixRCLs94Ou7AHowZhg1u0qJgjj45zqV6H0IrnxZ3e0sGah6//unv3TXd2A3ZEUsuDmvg+fWCHLRMW3TzUkr/eXDkLfUqd+09/dbdy4fba8hSrq5ae7Mn46HURd65aaO7Z2NRRF+gfEiUG2/8sGtZW4NfSUvGWiHhEbMzfIqyOchEJYdT/6EsKw4voraRZehp/t+yakSqAEOnII4eRDUjNyXAsdKIDFPcCD2hoeRi5QncA4cBYp+DBQXAbPV0HL8wLErxAM4/QzBP0I1QzcIYXFLWBcFemMh2ht02dyyqq860eL3guVUKnEkCb6oYDqnBkLvfqkOAYzo5JRcRnSpxz1Mxt5i7i9Vof2W1/5z7EhQJ4rc1tV+zsMq/wbV7rte35zWTA5DOnm/kGQ8gMG94J4Q1DrBzcvQRY/NlySKhK72UF0JwOubIH8lUNn+JxWCmcE7SGQiTUxS7/LjL/06e8HGM2F0Tw/gN+v1xcwF388e4qD54QahKOn+oU0b/9p/5nEyffAomXSa8NOWDTM7GzT8Tp8fsm9zxHmSSB90pNqx5uT2EBAU5EwK3DrA2G2GzQCulUANyPspqJgCFwHBhVJQ5BLgxlGg0MdAcREwWw1NnAQDrOFaPih3htd3wbbqvpf58Fs7WPQc1PpDwmWgxuArR5qqEyP8d3D6npQmnHkLwyrqltkw6N0v1SGAEZ2cksuILuvm9A6fHoql7j2VIs4T5tMfVEWdz1Mx6LvD/8yXViqQH76+s9N3QaDWefG2Wc6l+o8ThQnnFi8+mQxKHbe+vj+7uTQ9pBuWXF9IckJgZHe4kgGqbeDyNejNdPcuJ3w9OH0HoAaLt0G5EoCEc7D4JPlDYetrmN2czZQble1/Upt3vjEnUZhuyteP6rdTLobBFOelxsUJVIyaWehW8UUW56XHvUlXbWpprlv9ELH1pMudxf5p4TYaxmTXsVmV40ZdisadEqgBDu1/QiGLgOBOKShmCXDpKFDQY6C4CJitBlcI+3rDlNtU8qdHsLo9nVk5bw8YdwugHUQ9AJtysbKUVSeG3zfQdQuAOdx9Cd1k14xK736pDgGM6OSUfM9HV5q44vno8kJ+dmu9NBQ0h5yIPDvK+MOdKlbuo+9bdfw9GsD197CAbx3l7tQmvxeSvAj4vCucSgZwgPO3YAg9xkkF8uBnN6BLACLPQkUlAK06Al0CEPBtdb0ZGloV+39zxHl6YAzq8cJ5dkOZTFQtobhA/+J4YWG+lE9IcqkupYhw/8sDLAXZwv0vc8VFwGzJQOoFcBxCDeKm4gq+N6BHueayxFvwdxrMGUHVYu7Phm5/VhyGSVt1KnayP4y+CtABYvzBWnYNqfTul+oQKFfJQ6huCEO9D4aStVafST2NKvqeabh8Pr4FlQja9+9LaqgoJI3CRPhfX/qcZAX/+EB/A8jPL7XQ/TkowlCgSwD6TIJKSgCKSwA4VQIzbAcyg38uCT3CudnVZCgoI4oZ4rKzzB9BRAghhKRgOAD+oIdyKwyC3u7wyylIYiosYngTACu9wM4DVv9Dz0MA0NydnocgAcJLVw2krzqxhPA4glqbtQe57xvOwogO1Y/cp4cPvyZr7b6TepUMml8a33nMF45UIvSgd6iQzkLVij0Be6Pp1Bv4vDmoq5dddIdACv3608NAlwBM6lXJgc6HMV/QiVDwpmM/juAr86qbow9VwCfpCZPwMHJhEgpGUJDjdnPBzXch7DZCCCGuU4Vx+2FdPyopegk/jQJTPvC1QF0FrN1hxTEqlhswgp1/XN8N7MmqCHzD6O1i0ledGOI0uENPbd62r1Qzm8sDjOjQR1NWViMHFgCvCa/k+5QXeflCLFkbDv26pyGTVZ6649gp7aj25Ndnr8ZQTQeoenzDas4ubMt8Hly+QK0Nh0LlJQAkLqJLAM5eBU6VwHCzzgNN2pHEjujLzARrqFoZIuompo2GsZOWBZOjYMY93ki+DJ73ljKjvyCEEFIAyrrw/SW4vwMGtQa6ygJ5OXSdRR3cx8LfQfDP5+wU4eq2MNCKSty/BpItBdJWnYplBlGDYYIKjOrE5sg/fI5OTnHpOToiPyM5TaRhZKj14XHgwty0d1lKuk31+VXeNhBlpaTkKOs0NdSUt2ZtznffL8yFd1mg2xSqKwFIyaGm0pSzEqh2/5O6u9vNBSRBQrtL7kuZTFQ1QUFOdG4y02e1Wpw7BEh4Pz9kH9Oz1Muyx363OXIxcE5tcf4UpBCwFOpJWHY8OYeTc1HVN5hw/8tccREwW7KXnwYRMZCSBZom4NwCNMpVcO79D7r/BdASngdCy9oOEHBrKnjsBaUO8NoPZDt0H737pToEsI0O1QU1XWNTyXCOUNEwMDGpJpwjVLWbmpnKXzinCFQ0wMSkmnCOUNUGM1N5C+ekQaoCE617k4RP0pMzCQFMJqqaHk9TynCOi2bYDPDtttqUTz047x13x/PeUnzMEiE5RMI55+uzxz3aUNIVHCEpqRmAsxv07AntHSsI5wi3CdCMrF7A+Ugmo+ay4ehFat1lKjSTaThXIxjRIYS4ak3LL/kq1C24W6nPmRzUyLnrOwR6bGSiVv+0cLebC/zTXzIvIYTkhJOWBTMfJp66UZ3TcIWZbanE/mNQuzHfBP5wMoEahWD+cODQ3W6M6BBCXGWqrrfGebxvt9WbXCazWajRI98Kv57rvCx7kHSiMN3z3tId0ZeZlxBCcsLdgBoYDZ+CRnVPDSb8ADoA4bvhUTabVwNi8NkE7wCsJ8NAEzaPEzCiQwhx2Dy7oYo6ciOqNb4y72j7BUycLyzMnxm8AyuOCMmVznrU3DnRucnYNRrVOdNPYc0X0L4ZvEpmc2rgPbwE6NAX/viOM6NcMjCiQwghpIBItO/bbbUeT5MkFPjpQYS4qOSQ9E8LZxII1Rk+zDoCj+7BxOZsRg1owrIL8PAqDOfamNAY0SGEEFJMHkYuoX22rWn5JbuNEJIPJX0rAgSvmARC6GNUM3sBQgghbrPUhp+7Q0ACnHwJydSUdAghJHsHPgE9dbgbD+sfsjkIoYrg7AUIIdTodbegqk0DbNhNhBCSB3FZ1E8Lbj2shJCcwhnG5RTHZhhXRHgIyFbtDoGgjKidMVe2t5nObiMA5xtzwrLiXHVtAz02slnSUeC5fYXigiUvDi93HMsMoS6fFHj/cwiWQv2Z+XTnjigfvora+yHH2KxycP/LXHERMFuoodG7X6pDANvoEEIK4kDsDbebC0gVYXPEeTar0fNPf0nCOZIYZtqJyUHEpMCt5EvS5c7isOx4Ngsh1LAcNc1tNIzd9R0EBTlsFkKotjCiQwgpiOFmnU35+iSx7vVpobiAyWzkjsXfZRITrXszCUS+G4nCdJIgsW6X24vOJAQw+QihhjTPbmhUv53MgLRsFkKotjCiQwgpCFItWGQ/giRIfX1leKXdeBoPErp40xGdh5GLjYYxk4n4yjxSiSS1SZIWFOSMeLB2RZg38xJCCCHERRjRIYQUxwzbgU7aliSxOeI8Tlx7810I0xg11qI7k4NKbHKZvN9tDl9FjaRJ/E/iOuz6hRBCiKMwokMIKQ6+Mm+N83iSEBbmzw/Zx2Q2WgffUOP6kKDFCyO6iky07u3bbTXTU/dMQgA+VocQQoijMKJDCCmU4WadmblrvePuBGVEMZmNkKAgh3lCbKCxGz6mUhl3fYdAj43uBo4kzTxWh0EdQgghzsGIDiGkaDa5TGYSjbmZLjo32VRdjySwy2XVyF7y7bZ6hu1Akh5o0s5Jy4LJRwghxFGJvrBwFqy/zm7WyGtvmD0b9gWzm1yBER1CSNG46tp6WfYgiZvvQuRxJEOxKE8kZtP1huwEZhy54Wad2SxUCb4yb3ub6Uc7LNzvNofNQgihetUgF4JGKgfWT4aN2yEgm82omSTY/idMnwURIjaDEzCiQx8n1Rd62YKOFhjZwspHbGaFYo6AoyFo6YDLRHhbyGYiVD82uUzmq6iRYIYENmxWHcoOWObpYG1ZASunkXsj89m3lSJKeXBo2cS+rYz5Sio8Pk9FWcvcpff4nw74J9fnNcPDyIWEK+wGqpKXRXfcVwg1pKCMqCWhRzzvLeXeQFa5gas97a3YE39FrFr0XvWYffMHsrkQcFpeOIxzAytLsKQXKyto7gCd+8Kc3+ApNfJXBVKuwp5oACtY2JfNKa+K2mvzsTBQE0T3YfsTNocTlCqbhpyZJN7Xl3qwHjU8T09P8lOaSeJlKgsWuMKmSHZr2Hk4M4RNl5UPv3WERU/ptBL88Qrm2tFp+YWHgGx9/CEQnZtcT0P2i+P3dLacVskNjGYLH7xY31GD3WKI4s99P+LzTQ8rHEtR033ZVZ+VXXTZTfnBHAJyfxZSWLj/5QGWQr06kxAw4sFakvDruc5d34HJlCS/+1/w3wiroWeqbAJSH3haeGk4u0Fw+0LAbMlA+hkwHwFCdqs0A/j1EizqVLp5qhD29oapt8FhKQSvAj6bW1p1tdcLY2HIcTD6El4dAurpBdmhd79UhwC20aGP8GwLbI4EJWtoymZUKu8l7KfDOeoOeBHsPUlCPITqVf3PwNZi/uFzZfx37eySdqXDOXHK1fm9h9FXcRW7YUv2+ARGJqYmx4XeO7Vl/mB7HuT47z8WiuPmyy+cghyhelIyaBMzzwqX6PXe7HP+9KkKnNgxlekZ4uhBDbnEwgtBbZFQholm1tyH16/hdTgE3YXtC8GUZKXBD17woPRuE8XAzjtUwmt0JeGcFLXXrl9Rgdy783A3jc2Rf9hGJ6c40EYnioT+rcBXCPNPw70R8KDKNrrAH6DdGlDpDL/rwoIrAE7wLAhc1NlX5RIeArIlz4dAcRtdp31v/SaZVX1fTJx6cUqrwQeSALQ81985t9BVi32BkR9/+bcfz9qt3DKumSqbJTewdYKYH7Jvc8T5GbYDqX68DdsnE/e/PMBSqFfRucm2V6eTxH63OROtezOZkji4/4VPV7i1XRkG6n0OvLo8wUqFzlSACwGzJQNpdBtdHsDuOJgqMXDV09XQdhmV+N892NaVzqJF/QV2/4MiawgIhU6l768ypKq9psCgZnDpPXx2AY4PYvNkgt79Uh0C2EaHaqcQziyiDgjjCbC4I5tXqVw4/De17jIVxk0H6kQWBv+8oHIQUmz54Xt/pK7ioDXozwPflLmKE2oWA3468JccXsURkZgnOBB7gyR2RPlw8lEfhOQbM8U/QUI7JsF5gjsb/woja6Mx333KhnN4IagX9gOgGZ0IT6RXDDHcOU616el1AceKwjlpa68G8Ck1CxLcPAFZdIb8w4gO1Ur6LZh3AkAbNqwGo+q+RdlP4Fgs1eFyygAw7QX9dKjMvw+TQA8hxSYM894bRCUsJvw4yrohrtaCghzFqRtJozD98cFFo7s6Whjqm9i5enot2nkjKqfMAHLi3Ohbe38c36eDo4WBvnGztv2mrLsYXfFjGaWYquv59VznpG1J0v5p4V1uL2rMMxxWqj6LAEmDuyXAzLCiQApjT605mkISLb6e30ufyZPFhaAxKCoAZjQZE7pSycqEC/SsAy16080H5Uhbe1UBV7rdL+UuROXROXIPIzpUCzmw/n8QD+C+EsZYsXlVeLAX3gKo9YD+FgCGMJWa9wlij8GT2g0ri1AtNXgbS2HCnQsvqYT5UK82DTPJ98rwY7ZXp3/iv1ooLmCzFFhhwrmZnTpM/O2k38u3GVnJkcE3j/02o09zW49vDgUL6BqtOCP48DxPG1uPqb/+fePxy7fZmSmxT6/tWzzYddi2MCmu005aFn491jIzQJBQucudxd7xd5mXEKX+iwBVTTFKIOY9FQZxnjBk7zrffABetwVTW5c8wyWDC4HiE8PDo1Q9FPRgjBuTRcl7A0H0dd7VmQRl5dSk9tq8M1DBdxQ850jnDIzoUM2F7YLfw6hn4bZ+DWyPiSqkw+6z1LrXFDClv29dJ4MBWb2FvQ+oTYTqX1BGlOe9pZ/4rWK368ajxV1szE2MTcws7Vy6fDJh8farkaVvjOdGPYyh1sp23VpUeLuwjpEojok3EoXpjWEg/qx7y2fsfg1KTv87FZkjEokyX1/7838eJpByZ8sEV+eBPx46smyQs+tXf9xOUbUf9uNhv7fCovyCnIjTc1spQ8aVJd+fTy7TjlEhPZ7m6U6L59kNJWlhYf64Rxsa88z1ZTRMEaDKcb4ESgZHUQDpNzf8RYVuesO+GyXRg7KhLwSKKikSQkOpJfg+bJ0JQ/6kMj/bAgMN6ZdpuZHwhk64SDxxV6JGtVcNK6BGVxPBk1gmQ95hRIdqSBQLC5dDAcDX26CdFCfi1FtwMR2gCUzpw37d9NzhU3p4obO7SbiHUAM4+Mb35rsQEtcxj0V9JGVVPp/6MouTY94kJKckJ8ZHPvf3ObRuVn87x9Fbgz+0PRekvcmg1jpmRpUNuVWnziQEMEPGfdO8snlEFElu2LkrCeS6O/jXlSNsNQBUtO36zNp2/dWzQzPba0Li1V8nfLn6cgKY9l92MSLkzM/j3c3USeFpNB++fM0wHYDsgEsvpB9abpPL5KMdFjKP/WyOOO95b6mgAAema9AiQBXgfgkoTkQnijm59tg7krCeuLBvU4nqdUNfCBTVTz2hZUtqce0Gc3cB+eI6T4NVI0vFZoJYeE9WPLAo15+3prVXNUN23oIIjjzHgBEdqhExXP4JLmaB3ihY4SHF10cM13dBJoBmH/AsGSRWFyYOo9bpF+FWKp2DUP1a0/JLUz71UMOS0CN10B3RZPTxQD+/wLCY5PTM9KTYsIALu38a7UxdVuJPzx2y4Fo6e9e7qLCA7uevzGuYJyf+iPyP/CR/qZdlDyZHoYmFmVSXMTVNDcndq6xt37V3d+eSmpOmtVNLO6NSA+vytPWo7ZqO3uZl0d2vx1pmVgwS2ilS20JtNXQRoLKwBOSGMGTP77fyAZTaz51ROmBo6AuBojJ1gFatqMXJHvTpXRm6G5ydYMdz+mVaBjX+DPnGg2aZPpc1rb0CqOoAM7RKCkeGRsGIDtVElh/MPUwqM7DmNzCroItyWYWJsPM6leg3BYwlvmwdJoI5WWXCruvkMEOovvGVeYvsR5BEojB9ZfgxJvMj8M3auLu7Olo31dPWM7Zy7DRo6up/A+4ua0MNMxx3YNWpuEL6bcrqWnQlKic1u/6/50EZUf5p4SRBAo/G0OUSoEnzrg7k7xRc/ut0JDPBpTj79X+/jGrt+Nkfj4Q8p7HfTe+uDzkPtng5Og9Z+u8zAVMq+dFnt55NIf+8XX+nmsZkrrq2gR4bJ1r3Ptp+AZvVqMmgCFAp3C+Bb5oPWe44dphpJ3abq9J812+nelxqDvr28xale/Q16IVAga2+ASEh1BL6CtJywG8XOJJ6ZRzMHAkls7bnMUPuqQCvdHxT09oroaxKz6BMjhaq1Y8DMKJD0suH3fMgEsBpPgw3AIGAXTIygblGFORSmznM4EO0uAtwm77IJB+FiRM/LP/7i34Z4PpOSGT+MUL1a4btQFIdJ4nNEefrZYgU7fYzFvag7hsWPLvygul5qWZiT3fwz4t+nsDUtuoR00BHLGoxkkkoOhWz4Stn2JHa7LlJLZq17enZrbWVQYuhP516XWja+/t/Xzz2/m2Hb6jvb2Oc1CD2ws9j2hiaOXXq1bNd8xZfnkoD1faL1wxlHu2tGT2e5n63OdhAR5NNEaAPuF8C8+yGrnDyYgYf4i5R9Im1x6k+R2ZffDe47CSlDXohaCzUwH0anP2dDmNewhY/JhdUmWC6EESSwXPNa6+EWMSOpcn+TrmH51IkvSw4F0itw9aAmT7oFy8GTvCYfv3iWGpTyx0imKNABOd3swfE/eNw8KDE8g81+iWRfxsuxNEphOoXX5m33HEsSQgL82cG72Ay65SyhoU1PYry+4z3zGWiSfOudF9MeHn+Znzpi0UdIzEqMyYKqRgp3IDglVLW81h/6+Ly4U7a4sSnd27eD3mr0qzXlLVnw19fXTfanuowo2ri8d2xZ7H39i4a1d5YKSX84e07gfGFJp0mbr175Uc3DMo+GhaBrGEJyAPhs93rb1Pd+VvNmuOuzeR90HAXgsbGZgC0oBPBT9nKpiY18B5ALuRKNhbUtPZKE2XTj+QB6HFkOBulyqYhZyaJ9/X1ZTZRA/P09CQ/pZkkvgFlwfQOcKj8oD9iEDK3nVSAzwODvuB/BqxUID8cOjtBEEDnxTDLmX6DhMIEWLwYkgE6boX7s+kxYuULHgKyVU+HgOe9pTffhZCEX8917voOTGYdESf+3dd6vG8BmH19++XOHtRFoDB2X2/7KdSV3vGnR09Wt2d65deDta9OLXlxmCR8u632MKInRv1ozCEgZ2ehionzUuPiBCpGzSx0qziViPPS496kqza1NNetn3uugoKcOmy749D+J+SkCOocd0pBMUuAE/tfnHZhfIshR9NA3XPfq6uTSuYVL9FgF4L6UFwEzJYMpJ0B8xGQB7A7DqaWHsEy7zm0dYFwANct8HAOVZFM8QazcVTL259RMMuGfVtNa6+MHD9w6gpxADPvwl/d2MyGR+9+qQ4BbKND0tOGneHw/n3ZJScamA7ww85Qm/Hn2QPi5b9UOAea8N18+OqrssukOTCBPtoe7oUI7IeAGsgml8lMou6b6fKjTu26R92l1XEf3JK9p6diOez7MXR/m/A1U3+5UzxiigRR4q0dG05Hf9QhIBQXMF0uXXVt6yqc4xZldUNrO7sqa7KEsrq+tX3z+qrJbo4473xjjn86Pe9U4yMPRdC4YQnIiijm+NoTaSRh+Nn3w8uHc0TDXAgaoZenqXCO6OzKtgtoWIMpnQih5qorVsPaK0P4lmp0INpKMe+yPMCIDtUTIXgfpNZafaCnEZ1ThgZ8Pp5OBMG/jbQOhBoeiXlm2FJz3AdlRJGFyayZ3KC1n41ZtP9ubO6Hy7Io9dGe6YNm00+Ntpz7Y9+S6XGUDQf8+scIqh9IYdCvvd3H/nIqKImZ0lec+ybg6EqvdnYeM1f/E8o8zV1LfGXefrc5w806T7cZwGahhkW+S0tCjyQK0z3vLd0RfZnNRQgpvNzgXevvUvfy7L+e50GNqFyBBrkQNCp5SXByBQxYSqVV2sPs4oF1mjQHezq2Cwr/2HH3Yh8DdUU3g1ZMT065hxEdqh+5T+HwayrRdxJIzssiyXkMONKJg94kAESoYSx3HOtl2SO0zzZmoJQay094fPXf3yb3aKZpYNO6U7ce3Tu1bmZg1HHagZdFAKbDd5z4sVSfGlXrcfsvretHXRJEL0/8NMrNlK/E19JSV9G0dv98xbFnuWA2YESrj+2nP9DY7XSnxTMwopMR8l1iBlNlntLEKcgRahzEqdd/3xlBEqpd5k9tW/l0cw1zIVBs0yypLojMwjeF0SshgYQxDnDoX3ApnppDWR/62lOJcF9qwrraE8PTO9Sa3xYcOVIsGNGhj6asDOp0Q3UTiQHTIy8D1WXZEL7uyWRUQN0RprSjEq/PQgx2NkANxFRd72j7BU5apfvjS0+n+08bZg9oSa7MGTEhD+/fvfcwJDaLfP1tPWf8ee/piekfJoFiKet2+v5S2P0dcwa1bkr3iIe8nBzqC69u5T72p7+DXvzzuXXVPaUQB6xw8iJBNfMcHU5BjlDj8O7Gn2fTydp03I9jbao8j+OFoJb4FuBYfsfwwKY9zNwArwLhc8l7s+rQj+qFA2n34WV1TQUV1l5ZmfDfM2rdbhRwpIkOR0aRV3I5Mkrl8jMgTQRGhhIDnBRC2jtQ0gX9yu9ZUUSQkgLKOmAodwNu4SEgW/J/COSnRT4PeRUvKODpGFk6tnE205DiDll+WmxETHxKVpGmSXPnFqbS/BOZ4c6YEHIkKCNqxIO10bnU8xc2GsYkxqtlUzDuf/mApVDfxj3emChMH2DstriiaVfkf/+L8zPepeWpGxjX5NlELl4ImC0OyLkHjt0hHuDX57CkJZtZmQpqr7TsW2DvAUlKsPU1zG7OZsoEvfulOgQwopNTHIvoFBEeArKFh4DMYV22dgQFOSSoY4ZU5auoHW2/oHZzbeH+lwdYCvWtyX9jhYX5M2wHbm8znc2SgPtf5oqLgNniglxY2BI2xkDrtfBoEdRuEKA7M6DnTlDtCq9ug01Fo900GHr3S3UIYK9LhBBCqM7o8TR9u62eZzeUpElVdUnoESYfIVQeOUbITxM1XWYToY+lAVNnUutn+yGcGX+mpgSw5yS1HjQfrGUaztUIRnQIIYRQHdvkMnm/2xxTvv4ld3o4NoRQOYl5AjaFUN1xmADDdADCYecjNqdGEn3g5DsAa1g0kEthEkZ0CKHG7kxCANNHjls2R5wf93hjo50ATf5NtO4d1W+njYYxu40QKo1poCNM+ZWM+49QzamYwq9roEd7yHjF5tTIm5fQsgPM+QM6cWrwUYzoEEKN2if+q0c8WDs/ZJ9QTE0pxBXk0657fdo77g758Nz65I0KX7n8GGoIIRYzhhBhqq7HJBCqEy1nwe1HcHgiu1kjHZfBg4ewZXjZ4VLkHEZ0CKFGjZnGICgjakeUD5PDCT5JTxKF1KDZi+xHYNiAEOKikl6X2EaH0EeqZqxLhBBScGoqsKs/6KmDIA++vgL5hWy+nNvkCba6UCCmPnM6ztDPKT0sobMZbHnCmS8bQvVkoC3MaEslyHksOZfOQgiVhWNdIoRQdUit+iT9KBoJ6r6sbvIaOUFiObIQPlEYznEMKbi57aC7BazrWd10nQgpOnLWZWA4h9DHwfno5BQzGRfufxnCIpCthpyPTigu6HJ7UVBGFF9FLarfTvl/omPEg7VnEgJIImHg/vr7tDgTVH2Izk0mxUe+bCRtytc/3Wmxu74D81IZuP/lAZZCvRr3eKN33B1yICQM2MdmlYb7X+aKi4DZQg2N3v1SHQLYRocQauz4yrzljmNJQliYPzN4B5MptxLzBEw4N9ysMw4nwDk2GsZ+Pdd5WfYg6URhuue9pQdibzAvIdTYLLIfscllMvnJbiOEagsjOoQQoqKjgSbtSIIES0z7idxa9+oUk2CiUMQ5fGXe0fYLSm4iTArcOj+k4gYKhBSbq67tPLuhzHT8CKGPgREdQghR1jiPZxIzn+5kErIlKMhhUxJI5o7oyyRBakJkYTIRF61w8jrdabEeT5OkN0ec97y3tMISRwghhKqFER1CCFFIgDTRujdJOGlZyMMMb9G5yeX745FKv4eRC0lgA50CGG7W2a/nOidtS5K++S5kUuBWJh8hhBCqEYzoEEKItabll4EeG/e7zZGHGd70eJrzQ/aVzNfEsNEwvuS+NLTPNhIMsFmIy5y0LPx6rB1o0o6voka+fmwuQgghVBMY0SGEEMtUXU9+ujKa8vUFBTkVttsws6IjxUBCdxKlk7gOixUhhOpKYSJsWgjz10OiiM2R3mtvmD0b9gWzm5yAER1CCDUssQhEYjZdOb4yj6+i5pP0BMdCrFqhUJCakp5b82u2XMGnIhFqXKS7EKBae7QeFmyEAwGgUotYJwm2/wnTZ0EEd64sGNEhhFANZQeApwNYWlawOI2EyHz2baWI4MEhmNgXjPmgwgOeCmiZQ+/xcMCfvFIZZnKC8n0vESHOePr3D6M6WPJVm+gbGRto8tTMO3y2/Gxk2QnX88J3j3OztbJkWVk1a+7QunPfz+b8dvJpeiH7JoRQQ4vOTWZTXJQbCJ72Zc//kotVC1j1mH3zB7W5EDRqebB7HNhaFe9XK7CxA5cOMPgL+Hk/RFY+mJQ4BX7bQyUmLISmJbFONizzBGuJUipZrJxgbyT7LqL5WBioCaL7sP0JmyP/cIZxOYXTW8scFoFsNeQM4zUWvwcsp7HpsprBgxfQUYPdYoji4fsRsOkhu1mG+zLwWQm67JYkt5sLmKkUBpq0u+S+lMmUJCjIIW8QigsGGruxWXVHnuf2FcV4T+097iBzAdYxtdQSJrwV0OGZTp9N98/Na/WhANLPDDIfcalsnMcw6PPrpZOLOunK5b1Nav+7GJFTEDMWDpIJnOG6/jDnNy/LHkfbL2CzypHf/S/4D6yGkgihKgNPw6XhbJqo7YVAtoqLgNlqcOkwyBwqOYEDaMGE32DTdNAvdw6P3Qv2U6HAAYKCoS2fzRTHQ2dLeMRulbXwAazvyKaJC2NhyHEw+hJeHQIZTvxK736pDgFso0MIodppAYfPwbnSy7Wz0K50OCdOgfm92au43TDY4wORiZAcB/dOwfzBwAPw3w+hFd9pZIa2J0r6XpIQ7ua7kBVh3iMerLW9Ol3/4vg/Iv9rdDX+/Nc7v5pIhXM817n/hmenJ7yJT8+OOv99R3WAzOvfTtoeJtFMWsTWRVqvuR/+mgh/EXT33PaF/UxJXtr1H7xWPZDXOQOMNeDbjp/4r2bmq0BIwYRlx5Of8jAMVW3o9Qaf83DqVAXLjqnsezwc2QTxEReCRq34BO7wAwQ+hadP4Yk/XPSGZZPBikQw2XBwFvRYAMlluluI4NxOKCD/ygsci8M5SfMPl710/3cNllBT0n7Q9SsqkHt3Hu6msTlyDtvo5BQ2EMkcFoFsyUMbnU9y4PyQfWucx5cdWJJto+sEb/3ArOr7YmK4OAUGH6CSnuvh3ELQorNLxF+GH8/Cyi3QTJXNkTDu8UbvuDtMmkR3ZCnTT2me3dBNLpPZjbomt3fHcx8sdO68MRZ43f8M9Z1lV7LjRFG7+jpNv5UPlnPuh23pwkbDaWc+MR/hkwcddscFTLUoKS3h09Ud2i57DmDxv3vh27qyb5YnSj2tYGEHJj3DdiApaK7WfbkM2+jqCQnnnK/PJok1Lb9c3GIkk1keB/e/EFa4wcowUO8Dry6DlQqd+VEXAtmScRtdGnxiDuQE3nYzPPoGJPeOMBq++xS2PaPSZNeemfDhVVEUdLWDh0WwIAA2dGIziZI2un1vYZIZm1mpFBjUDC69h88uwPFBbF7Do3e/VIcAttEhhFAFSIVjxIO1YVlxK8OPsVm1kB8OP9JXca1BcOCbsldxwmIAHPirsqu4ZA1eUJBTJpzb3nZG/YVzciz/za0rsWSt2uXrEbaSO07VdsTcvlQDadzF/15V1kunGN9+wIBmVCI5PDGPzpE7d+JglR/TTrsjyucTv1XkO8C8ghDXkVMrk1C0IV4Fd+CvMCox5rvicO5jLwSoQnwb2OwDnxlS6QtL4F4WnUtLvgNBJALSg/4SraQ1ZgCf0t1fbp4Aid8tvzCiQwihCpB6xgybASQRlBG1OeI8k1ljYd4QRCcm/AjWNb5aN2vSlE2VxldRO91pMfPxGp/8lIhUaq1vZ6VV5hKm37afPbWOuReaSWdUoahASA9FoG2iU1ztkj9PkiSnIC95rlK+FKY/PrhodFdHC0N9EztXT69FO29E5ZQZw0+cG31r74/j+3RwtDDQN27Wtt+UdRejq4u6kZS4WAIl32Tm660oCuHUGkghiRYwvxeTRfm4CwGqjIo5/LqUjmQS4E+2Owvl6QWqyyUpBOfywbP0VMC1K7VOuQtRcnrbrxSM6BBCqGKLWow05euTxLrXp4Vi6gJRQ4Vw5wKdMAevNnSiZkjkxqYkkI/k12NtI55hXJnXhK4SvRfklB2qUtXA1rIJWYsSX6dUfQUWCx4ePUc9xaPXe4ybNpMnl5gpyJnijs5N7nJn8ZmEAOYluVCYcG5mpw4Tfzvp9/JtRlZyZPDNY7/N6NPc1uObQ8ECOqgQZwQfnudpY+sx9de/bzx++TY7MyX26bV9iwe7DtsWxoV6kpzjaAkEZ0aTn+QUp1BtdMIQWEc/qdFtAbQueX7rYy8EqAo2nwIzLJjfZWD7MOSBPx0/m7qCwcfdrmveme7JGQXPuTDaNEZ0CCFUMVN1vUX2I0giUZi+5MVhJlPCI+hiAybGYGYJLl1gwmK4GgmlboznwsMYaq1sBy1qc6uQmb1AkpO2JanfN+65y/iWLhbUkwXZD6+8KNsLUVlDl65G5aZmlR0LXJgUGRZKeRF8/9zWmf2G/BkHYPbZlt8HGsr5hVCPp3m60+LljmNJWliYP+LB2to3Gte1rHvLZ+x+DUpO/zsVmSMSiTJfX/vzfx4mkHJnywRX54E/HjqybJCz61d/3E5RtR/242G/t8Ki/IKciNNzWylDxpUl359PLtOUhGqIqyXADIuiaF0ub26Al2SlB9+Nknjk62MvBKgKqk2hA/1EXOJTsqNpufDoDbU2dYGKRkWhLO4C5iZgYgZ2LvDJBNh+Fco2atM0rMCYrETwhOroL+8wokMIoUrNsB3IhE87oi9/mBROlU+fO8UQ8waSUyAxHp77w6F10N8ORm+VGM+6AN5kUGsds0ovLFUqGeuS4WHkQsI5Gw3qEtOIKZt4ju9M3XmN2zZt0amS+efEuTE3d/608lI6vVFQbqa5kJ96tmpJaeXabdjcXY9yQMV52oZVI60raAeVRyucvI52WMhXUSOL3IT0uWHnriSQes/gX1eOsNUAUNG26zNr2/VXzw7NbK8JiVd/nfDl6ssJYNp/2cWIkDM/j3c3U6fC7ubDl68ZpkOC8oBL5YJyVCPcLAGhuIB5jk6hbk6JYmAt/dC19UToK9lh/mMvBKgqPLCip3wQZUEufdoXCSD+PZUwtCg1kgqhrAp8Ou5JjoGEZEhOhMjn4HMIZvUHx9EQXG4uCjVDdt6CCC5MnYgRHUIIVYqvzCtpG5kfso/JBJPREOgHgWGQnA7pSRAWALt/Amc6Mjg9FxZcK26pK4ICuqWotkMUSkZ0E617X+qyrEyM1zipNp+wdWkHsk/FL/4cZadjbOfs4tTcVFPTxnPGtntM1M3TVKMHCJOgZOrQitbSyd5Sn7rQF4bu/tzZ6bMdz9k7u3LPy6I7Cem3t5kuN/NViIWZVK89NU0NyZqTsrZ9197dnUsqr5rWTi3tjEgk8QFPW4/axiEkPxY3S6BkWJS2OjZMQhGE7IFb+eRMA3NnQKnz9MdeCFDV2O+wEijTZ31RBjuQiWH5q6UJHA8Ev0CISYbMdIgNgwu7YbQz9Ur8aRiyANJLt9Sp6gAzGVEKF4ZGwYgOIYSqMtysM/MUk3fcneKn+fnQxh1cHaGpHugZg2MnmLoaAu5CG/p6cmAVxDEtRMqgRVeiclJL98aUVklz3GKHUfvd5uDg9cW0Oiy99nD/NwMcdQEKUiLDnodHJYFF+xELNvw6mL6lqmthULbprf3qG09DKM9DX71Jy0n02zXJkVwB407NHLnqIWcailx1bUlsz27IXpPmXR3Il1Jw+a/TkcwcgOLs1//9Mqq142d/PBLynMZ+N727PuQ82OLl6Dxk6b/PmFngIT/67NazKeSft+vvhLcoPgo3S6BkWBR3g48Zi1CupMH67dRacxB83oLOKfGxFwJUFREk0OGWmgFo0jGNOI8eFgVAvaILplkbcHcF66agrQdWjjBoKvwbAMvoxxvjDsAp9lYDS1mVmimQyKcb/eQcRnQIIVSN5Y5jmUFKPjTTlafdHhb2oBIFz+AF03tDDezpkZXzoiFBYs5rqTFd7Egst8Z5PJuFGMq6bSdu9gkTiLKT46Ki499lFbyPe3Tq9y+ss6ldr23XwrBMd5sy1Ezcp+04+3t76hr4cvcWP7pTFKopFbPhK2fYkYDi3KQWzdr29OzW2sqgxdCfTr0uNO39/b8vHnv/tsM31Pe3MU5qEHvh5zFtDM2cOvXq2a55iy9PpYFq+8VrhppiLeSjcLMEJlr3Lhp22rfbasXpdRl9Ao7TY/B+8V25SUo/9kKAqiAWQFAClTBvy/ZpJWEYMx5KftmHqSuhDTMW0v0zC+DKCyaLJRaRgJGiyoXO+XguRQihapBqxwybAV6WPY52WMhmVUAZLKzpxHt4z9wIbwJd6f4c8BJuUsMA1JSput7pTovlqU1G7qhoNrWwaWZuqEUHcDmhN0KpC7Bj35ZSjF+pZjNgGH0zPS34aYqU1355FZYdX6vhWD+Wsp7H+lsXlw930hYnPr1z837IW5VmvaasPRv++uq60fZUhyVVE4/vjj2Lvbd30aj2xkop4Q9v3wmMLzTpNHHr3Ss/umEL3cficAl4GLkoSr8DIexeTzcMtYI57kyWhI+9EKAqJN+BQLrl070P20NSWZNNpEvdn17DAnToREbptjhRNrmcU/S4MKINRnQIIVS9TS6Tj7ZfUH7wSQliaogUigEYMTf0lKHrOLrThhi27Cseh6tmBhozIzOj6onT7u49Rz3A7urV27xGg1ar8OR3SjopROcme95bSpYPg/c0IDWLT1acDhUI38W8fh0nKHgffXPPok8dmP5PxVRNuk5ee+JRUr4wLeZVRLxAmBiwf3bnjxxZHLGwBGQt7TrseEUlPBeCc6nHFWl1cCFAFcuF7avpoMsEvi4OpdUMwYj+Yr9LAObearXeJ7KP3jUzolfFCtKAOaWWyZdPGNEhhFBdyI+CXfeohI47tCy+oWc5DMbQ/W3C18Avdyp4iEKUCDs2QDR2xflo4gy/dfOPvAPQ/GShl700fWSEL0//G04lzDq7Nq26k6Z8Wxl+LFGY7p8W7nZzgX86NXp6w1NWN7S2s7PQrXo/KqvrW9s3N9flQhcmrsESkBURHF8LaSRhCN8PZzv8lYEXgvqQB8dmwyq6n6THMuhGj3hJ0QAXU2qdGAJSzaGfD6d20S2sOjC4JZPFEr4FZpDLtlb0Sr5hRIcQQjWRGwSfjYH9dyFX4rKc+gimD4Lb9PV47o/kys5SNoRf/wADkiqEX3vD2F8gKIl5BXLfwNGV0M4OZq6GULxtWxOixAc3HsRK7H9xdti/3w0c8BuJz9S6rVr/mUV1TQ95ScEnV4wasPQZSau0nzO7E6dnidredgYzeA+J6zzvLfWOv8vkI4TqXW4wrKePOPuvwUOfzioHLwR1qBCSX8P5nTDSFbz2UxnmY+HA1/DhJkUTcLen1glBwMyxz8qFtZ/Bov0QK7GbRamwZzrMvk2lW86FviXXblrsYxLuAZhBK6rs5J1SUSVD1yopUYO2+frSk9+jBufp6Ul+4v6XISwC2WL2f2UnKFkSXAKbQUCNpKELLg6gpwaCNxBSPP/o8B1wdHrpSYfE8GA9fLKIvonLUAfNIsgpvh1rNgb8/wZruWskYq4C8lgESUe6mn7pB9rN2rZu3lQDsuJfPA5Noh6E47Wec+ryxiFmkvsy7cwn5iN8qBHeK6LsMOmwz47PbeWwyaKm+39FmPfKcHo6LIB5dkM3uUxm0uhjyO9R0DjI/f4Xw/nP4VNy3KnCn+EwqzmbXQGuXwiYrQaXBp+YQ6UncIBe38Oh1VBmWtHnP4PLUuoBiGvR0KfkmWoBfGYDJ+hRsJq5gIUeFAggLITtb2k6HG4chQ8zfhBiONgLJt4F/kCIvQSSMww2JHr3S3UIYBsdQgjVhE532DAbWhoAiepCHsLde2w4Z+sJf96DE2XCOUIZOn0PYfdhziBoSp+bIY+9ilu5w09/w4t/5PAqLtd0230+wk4dsmKC7/teu+YbQIVzem1HLT0R4r+ldDhH8C3aOZbfvTw9m/ZDZm648Cpwj1yGc7WwwsnrdKfFzIyFmyPOj3iwVlCA03cjVK/ewZ9nqbXpOBhb9dx6eCGoFT60KzfDhao2OHSECT/AtQi4sa5sOEfY9gOqm2QaXJPshK4DP22AAXS/ypgQuH8XHtLhXBNbmPEnPD1ROpwjMuE/qhcHtBtFN6/KPWyjk1PYQCRzWASyJb9tdCXSIiHkFQgKQMcIHNuAGTO8VtXyITYC4lOgSBOaO4OpNP9EZuT87nhh1psXIa/i3uUUaRjZtGzrZKahYHcoa7f/w7LjSSzHzODspG1JYjwnLQvmJVQLct9GxD0rwrxddW3JUjLfZhU4sP/zMyAtDwyMJbr9VYuLFwJmiyNy4H+O8Fc8tPwVApeULZn8NHgeAvEC4OmApSM4m1XcupV9C+w9IEkJtr6G2VU0vtYzevdLdQhgRCenMJyQOSwC2eJARKfosC4rW7Xe/4KCHBLU3XwXQtIkomMesUO1g0dB3QrKiHK7uYAkNrlMnmc3lMmsAu5/mSsuAmaLMx4shM4bAVpD6CNwqlU3jDszoOdOUO0Kr26DjexGhaV3v1SHAPa6RAghhBSHHk/Tt9vqGbYDFzuMwnAOyZWSsVg9jFyYBEL1wXUqtCWrZ3CMHtC4xgSw5yS1HjQfrDkyyQdGdAghhJCi2d5m+hrn8ewGQvIhgI7o+CpqTtqWTA5C9UHNAX4YRiV274RsOqdGEn3g5DsAa1g0kDOREkZ0CCGEEEKo3jFtdO76DnxlaspthOqLCnz6K3zRA5plQHLxeKLSe/MSWnaAOX8Ah2a2wYgOIYQQQgjVL0FBDjNmj7tBueELEapr/JZw5DbcOwzNa/4cXcdl8OAhbBkOHBp+FCM6hBBCqFEgVWq3mwt2RF9mtxFqQMxoPURnvRZMAiFUVzCiQwghhBqFSYFbgzKiZgbvmPl0p1BcwOYi1CDId49J4LAoCNW5amYvQAghhJCC6GwGc9uBJv0IU8g7WBMAORjXoYayvCu4GUN8NvzvGpuDEJICzl6AEEIIoWIBCbDoNlWlJlyMYJMn2OrSLyBU/xz0qZ/hafQGQqgu4Qzjcgqnt5Y5LALZwv0vczjJu2zV39zKgoKcSYFbzyQEkDRfRe1o+wU4bV1lcIbruiIUF2yOOB+Q/nKClaf03zfc/zJXXATMFmpo9O6X6hDANjqEEEKoEdHjaZ7utHixwyiSFhbmj3iwdkWYN/MSQvWEr8xb3GIk+eLh7QOE6gNGdAghhFCjs8Z5/NEOC/kq1MDeK8OPlYxDiBBCiHMwokMIIYQaIy+L7n491tpoGM+zG4rDDyKEEHdhRIcQQgg1Uq66toEeG9e0/JLdRgghxEEY0SGEEEKNlx5Pk69Mz2eAEEKImzCiQwghhBBCCCmORF9YOAvWX2c3a+S1N8yeDfuC2U1OwIgOIYQQhxUKBakp6bkidhPVlfkh+xLzBOxGpcT52WnJKek5uP9lhQsHQFh2PJtSXKLcjPQsYSG7hWQtB9ZPho3bIYCefbMyolxIz4IKSi0Jtv8J02dBBHfObBjRIYQQ4h5xxtO/fxjVwZKv2kTfyNhAk6dm3uGz5WcjhezrxfLCd49zs7WyZFlZNWvu0Lpz38/m/HbyaTpWvypDwrnNEefdbi4Iyohis0oRZ4edWjW+h52uirq2oYmxgRZPxajVgJnb7iSVrgBl353frqmufguvE4liNktSfvifn1jr6Vr2Xv+8bMGhqtX2ALC2sXN06dBz8Bdzft7vG5lTUanUsejcZOfrs80uT2amQFQshSkBBxaP7tRMW4mnqWeg00RVSdOq/YhllxI+HAeyLwEOy74L7ZqCfgs4kcjmlJIPf34CerrQez2U+eKnXIU90QBWsLAvmyMpJQAWj4Zm2sDTBAMdUFUCq/aw7BKUlFrzsTBQE0T3YfsTNkf+YUSHEEKIY0Qx3pPbtR2/5tTj+DzQMbU011OBgoTHJ1YNdxuy+Xku+y5abujpM0HRcfGsuLjYqFchD66f2LZodFv7AWseZGCFqhyhuIDUwkkiUZje5c5i7/i7TH4xcarv4u5tRi3/+25kpoqBtYOjvYUWyXxxZcecnu0nHHsjEdTlvwt78S5T8DosPreiHS3KjAp9k5EZHx4p4M6tcDnwEQfAm5jIl88f37n4z7alk3vbmbabuP1Rev0eA8zEGOS7ZKNhzOQoCHH6/V/6O7lPWnfyYWw2aDS1sDQ34ENu3JMz67c9yGLfJA8lwGH57+DFOxC8hvhS3+piIogKhYxMCI/8EIxRCuHcJsgAcJgIrppsHksM938BJ3dYdxLoUgNLc+ADxD2B9dugpNSUTWHWYCpxcBtU21FBTmBEhxBCiFPyX+/8auLBSACe69x/w7PTE97Ep2dHnf++ozpA5vVvJ20Py2ffSRRBURG1br3mfvhrIvxF0N1z2xf2MyV5add/8Fr1IId6GUngK/NOd1q83HEsSQsL88c92jA/ZB/zEiXn4S+Tfw8uAOVWs09GZKbGhIe9istID9rtZUFejP9n/i/+VfZzQh+rVgeAww8+gU+J4Cf+ty56/7lssqcVqQFmBx+c5d5jgU9yPTZX30p9Tn7q8TRddW2ZHIWQH7nv84E/3UgDsBn1+7XonKzkuDfxqe/zkp9dPLBtXrsPcYTsS6DREcXAzjtUwms0Fa1JitwHA38CutTgWjRkJcObeHifB88uwrZ5IBn9df0K9ADenYe75N1cgBEdQgghLskN2v7b7TxSne2++cSG0Q6a9HWMbzPkl2NbeqkBFD7c/Nfj8lGaupG1vR3h4Ny229AZ689dXtWKyo46diQYQ7oKrXDyInEdqYiT9OaI8yMerBUUULtK+PLs2WiybvrVlp9HNteg3koqE3ptJ29e35+qPSXc9I0hxYPqS+0OgCbGLVxaE23cOvf8ZOyslXtvvIw4Obu1KvkHz/8YO/lITL01kjJtdIoVzoE46fy33/pkAVhMOHX/2Ld9mmmw9Wm1pi6fTJjcz4qaub802ZVAo/PGBx6RKNoaBtuzOQxxEnz7LdUQZzEB7h+DPs2KoyA1cPkEJvcj6w/0O0GXJgACOOTP5sg5jOgQQghxSP6bW1diyVq1y9cjbEltqISq7Yi5fakAI+7if6+qeyqLbz9gQDMqkRyeiOFHZYabdfbruc5J25KkzyQEdLmzOCw7viDtDd0NSc/aqNTtb2VNMysdKiEWYXNDPaqbA4Dg24zc7PP3Z4YkmXlhyS/3PvQTrEPRuclMD95ehvQtFAUhijq+9kwGgPbQDWs+NVNhc2uqQUqg8RHDneNAAjq9LuDI3nFiRR0HutRgwxqovtQM4FMXan3zxIfemPIMIzqEEEIckp8SkUqt9e2stMpcwvTb9qNvycbcC82kM6pQVCCkb4lrm+jUtj7WKDhpWfj1WOthRFVtwrLiutxeFG3U3Ix65fX5SxGSwXDOi4u+VNW9abdezdSZLFQP6ugAoKmYD/91aQfqtyT8++eddCazTjENdATzFVIQhfHXjjymYoaB0/vXOp6j1X8JND6ZcIGedaBFb9CiM1iFcO0IHekNhP70KawaKuDalVqn3IUoLtz2w4gOIYQQhyjzmtANE+8FOWWbglQNbC2bkLUo8XVK1VdgseDh0XPUgOp6vce4aTN5qBJ6PE3fbqvn2Q0laVIvb91q0vx+GiQmDlw8dPqhMKZ/X37Mv9+M+y0SQLPvqh+769J5qF5IeQAwOdVSs/l0ohuVEPhdDq+H/sfMQ3R8FTV3A0cmRxFkPb8cSkIDJaeBrh/9Xa/vEmh08t5AEN2LwNWZBGUSsuByKLV2GghSllrzzkAda1HwnAujo2BEhxBCiEP4li4WSmSd/fDKi7L1H2UNXbojYG5qVtlnUoRJkWGhlBfB989tndlvyJ9xAGafbfl9oCFeCKWxyWXy6U6L97vNAVWbCQf+melEdlvkwQktXYYs3vL7tG6tx+yNBN1eyy96T7Mv/wgRvHt48sC+8g5feJ7BvgNJScoDgN6WgmrTth3oBovEpzEVDif4cXySA8lPd30HvjKPyVEAotSXkdQONrQ3fHN2/dzPPFpbG2mrq/F1TR26jFyw635yjZ6Iq+cSUBAPT8K+feWWw1D+BJIbCW/ohAs1VNMHolSgSw3sDeHsevjMA6yNQF0NdE2hy0jYdb/0aJk0DSughmcVwROqn7O8wwsZQgghDlE28Rzfmbr1Grdt2qJTJdNviXNjbu78aeUlut+SuKDck1whP/Vs1ZLSyrXbsLm7HuWAivO0DatGWlcQfqCKDTfrzAyUomo+bJt/4P6ZnXSgKPrCum++3/MoCxxmnX1xbUVPwwo7ocX//f20KeXNWOnDlaHB5UYtD4BK8Qys6BYLUVZabh0/ABmUEZUopD6QYj1EB/lp8XQc8e7I8C4jv9t64lbIm9Ts/IK8zKRX/qc3Te/WZuz+CInhRqtTjyWgOP7+HqZMKbfMgPInEEEsvCcrHljoMRms/DRqPgPiyHAY+R2cuAVvUiG/ADKTwP80TO8GY/dDmVJTM6SGuyQiqP7k8g4jOoQQQlyi2nzC1qUdeKTa+uLPUXY6xnbOLk7NTTU1bTxnbLvHXN55mmpUK4YkJVOHVrSWTvaW+lRXmsLQ3Z87O322o/T0XUgq4ozXD/wev6Sf1lJm9vXLvyaM/Obw8+wKp9ZStnBpX4F2TtSoEKhGpDsA6LVUmLH1ySGiypZknUnMEzDD6gw0acfkKAZxXjZb9ddr57V037WQJGFhUVFB2vOzK/pTX+ikU3PmnHgrfXBWfyWgOCxcoH37cks7KH8CyUiiV5qgWfrmkjivOGDTA6+lcC0EhIVQVADPzwJdanBqDpx4S7+hmKoOMEOrpHBhaBSM6BBCCHGLVoel1x7u/2aAoy5AQUpk2PPwqCSwaD9iwYZfB9O3VHUtDMrWaNuvvvE0hPI89NWbtJxEv12THMkVMO7UzJGrHuLTKzUiDN//hVuHabseCCwGLTv3OuO/R3/+NMgaQBDw11duPRZdTylfl2393Vn/R+XdOTSRHnEU1Yg0BwD9RmmIBAl0dVXNwJCZCKHuDDR2C+29NarfTnd9BzZLISgpq9CRl8H4EzeOrprUp5WxOtlzqvotP136z4Fx1K7PuXHwntSzmNVjCSiO787Co0flljtQ/gSSx9yiUwFe6Z2ppAxMvDz+BBxdBX1aAV1q0PJT+OcA0KUGB+/R7yimrApMX+F8qtVP3uF3ByGEENco67aduNknTCDKTo6Lio5/l1XwPu7Rqd+/sM6mJrfWtmthKDmse3lqJu7Tdpz9vT11DXy5e4sfPswlvay7PwyZ7B0DvHZLbgafW3k//8zguKvZa394un2ECQkwgtaPmXM+qcKGOlRnqj8AmPdVTyx4GZRAJczbWpeZjLmO2GhQzyIpEhX2cUVRbh7bulZM2bDLqA7USK95USEJUswgQan/EmhcVJm7eYUgKn0WUtFgZxvPLTdslmEXoEsNokJAstTEIvbhOvZ3yjeM6BBCCHGVimZTC5tm5oZadACXE3ojlLoAO/ZtKcX4lWo2A4a1oBJpwU9TajSUQWMmTjy/8q/XJOH044HlvQpEgh1RPmRrc6TP964uO2dQYxGknfrjajKGdA3iYw4Amjj5zrFAqrD03fvYl568C1WGZ2BNN4Vmxifllv2iq+oa0bsxPydPumMAS6COaTLt07lQ5qFEngH7UFx8EpQrNSgutVIvibLpR/IA9EpNgyCnMKJDCCGkCMRpd/eeox5gd/XqbV7h8ByVUeHV6O2N2vsov1DqFrdRpx426mCqrlcyBblP0rPvBnZQs9SGgoSo1AL63agB1eYAyA3cvvoyVWk1Gfm1O1PfRdVS0XdwMaIS0fdelXkKV5z7NpZ+utSoeVOpZmXEEqhrhtb0pAX5kFB6UkYVfSguNRLulSLOheJSA8lSK0gD5sHUZvQ/lHMY0SGEEOI+cYbfuvlH3gFofrLQq6Lx88sRvjz9bziVMOvs2rTqTpqohJIqn36yJCM6NpO+BS45BfkrUMpf1xO6WDQzUJyh6jmi5gcA5EUfmz161QuS4nssW9xNyjm6ENnHLQd1oCb+S7rkHVhqzAxR7Pl9D6jjwn5gV9Pqo2osgXqgYQ2mdCKEmnFUgiYM6kCtky5B6VKD2PNAlxoM7FpqCjvhW2AGuWxrRa/kG0Z0CCGEOEWU+ODGg1iJ7k7i7LB/vxs44DcSn6l1W7X+M4vqqlJ5ScEnV4wasPQZSau0nzO7Exf61MgHjRYDelD3qwtuL1lwKDSHLgR6CvKVM7XpWo8mDxbpPEk/TqVRvfjIA6AwJ/l1wPmdS0a6OnntjyYZ5mP3HPhauiAQ0ZSbes76VJ8k3u6csexSAttnW5R885cv59/KB1D3WDjZpfJn4rAE6lOT5mBP36ELCi/du1IZPGcBXWowYxkUlxok34Qv51PDYKp7wGTqxtQHsY/p4THNoJX0Iw3JDkZ0CCGEOCX12rw+nZtp6tm4duvdr19v95bm+s5jNvrnAK/1nJP/zm1ZYWenR9MsVZSK8U1dR6+8mECugQ6TDv270EWq7lGIpuexauMoaqiLxOOTW5q06Dxg5NhxY0f279R8f5+tsOUJ0N0t/3h1bNzjjUIx9r2sD7U6AILn2fGYb7+qlkkL909nrD0dlgdg1Ov7M36HvmiGjdQ1otx0wJoNVExX9GLzICtLl669e3d1sTDzXHE3l8RnY3btm9y83B7FEmgYyvrQ155KhPtCmVGMmw6ADZ9SiRebwcoSuvaGri5g5gl0qcGufVCq1MTw9A615rcFR3yODiGEEKpjuu0+H2GnDlkxwfd9r13zDQhNEoFe21FLT4T4bxliVqZixLdo51i+rsTTs2k/ZOaGC68C93xui7fGa0TV5st/Hl1Z+3lHEyXIiXxw5fRx7+Onrz6MEYJ+W72J65xWm/Kp++DRuVR/JWU1DWr3KvGbqFY00xZ5mQ4/1DTKjDSOqvDRB4CqtqlDx4ETfthxLSL6xrph9TDL/qTArZ/4r94RfZndVjxqtpP+uX9kroeFEhQmPffz9fV7niwG/Xaf/37j8d9fSZ5UZFMCCkJZDegTCDQpfxYnlKH4BCIRz6hDv4HUOu0+vCwz3qgaTPoHjswFutTAzxf8nlPteO0+hxuP4Stb9l2sTPiP6sUB7UbRcxvIPaWi4okNy1BSok6+vr6+zCZqYJ6enuQn7n8ZwiKQLdz/MscUQWXXCJkrzHrzIuRV3LucIg0jm5Ztncw0FCwkYK7Ccrv/KfnpUc9DIhIEuWI1PVM759b2zFgQiXmCmcE7tredYaquRwoqNy31vZoBOxpjeflZKRmFOoZ61NxQ8kd+S0G+DwDbq9NJSO9h5OLbbTWbVSvyfxSIhYmhT0KiU/PUDKxburayULgZ5YqLgNmSgdw0eK8GhpW1kuVDSgboGNKTyxXLuQeO3SEe4NfnsKQlmylJLITQJxCdCmoG0NIVLDTZfEnZt8DeA5KUYOtrmN2czWx49O6X6hDAiE5OYXVW5rAIZAv3v8zJeUSn8DgQ0TUCWAq1QGI5EtGRxCaXyfPshjKZtYP7X+ZkHtHVRi4sbAkbY6D1Wni0iG7lq7k7M6DnTlDtCq9ug031o9zUF3r3S3UIKNq9BIQQQgghJCtnEgKYBDMCKkINTQOmzqTWz/ZDeLn5xKUigD0nqfWg+WAtu3CuRjCiQwghhFBDwLFSGoPLKUHkpylf31W3zJNJCDUQhwkwTAcgHHY+YnNqJNEHTr4DsIZFAzkTKWFEhxBCCKF6tznivNvNBWHZZWaJQgqFBO0334WQxEBjNyYHoYanYgq/roEe7SHjFZtTI29eQssOMOcP4NDMNhjRIYQQQqh+BWVELQk9EpYV1+X2opJeeUjxkHBOWEhN4jUAIzokUy1nwe1HcHgiu1kjHZfBg4ewZThUMqCTPMKIDiGEEEL1y1XXlhkkQ1CQM+LB2hVh3kw+UjBnEx8wCWyjQ6ghVTPWJUIIIYRQ3ehhCXPbATP5XEACNSN5Dj5Zp1h29QdjDQh5Bz/dZXMQQh8Hx7pECCGEkNy4Ewff34LkXCrd2Qx+7k7V/pHC0OezBUoiOoRQA8L56OQUTsYlc1gEsoX7X+ZwPjrZUuCZuBLzBCMerPVPCydpPZ7m6U6L5XaYe5wPraaE4gKfpCeuurY2GsZs1kfAuqjMFV8ImC3U0Jgek9hGhxBCCCH5Yqqu59tt9QzbgSQtKMiZFLiVyUcKgK/MG27WuU7COYSQ9DCiQwghhFCDIvX+7W2mb3KZrMfTvNRlGZuLEEKoVjCiQwghhJAMzLMbGtVvp5OWBbuNEEKoVjCiQwghhJBs6PE02RRCCKHawogOIYQQQgghhLgKIzqEEEIIyZEzCQGbI86zGwghVBOJvrBwFqy/zm7WQB54L4fZP0JwFpvBIRjRIYQQ4rBCoSA1JT1XxG6ihiWG7DRISYe62/9h2fGTArfOD9k37vFGoRjnH6+OfBwAB2JvkCLzSQ5ktxGSlRxYPxk2boeAbDajjPxsSE6BnAqPGCVIugR//gqzDtXhKa2BYESHEEKIe8QZT//+YVQHS75qE30jYwNNnpp5h8+Wn40Usq8XywvfPc7N1sqSZWXVrLlD6859P5vz28mn6YXsm1BNiSHsFIzvAboqoG0IxgbAU4FWA2DbnbL1oLvzoakutPCCRDGbIyk/HD6xBl1LWP+czaExD9d5x93pcntRdG4yk4lKqe0BYG1j5+jSoefgL+b8vN83MqeiUqmNg298N0ecnxm8g91GqA7lwe5xYGsFlpbllmYw6wb7LkbKVdgTDWAFC/uyOYzsMFg1Hux0QV0bTIxBiwdGrWDmNkiSPGWpwdiFQM4+99fDk1w2jyswokMIIcQxohjvye3ajl9z6nF8HuiYWprrqUBBwuMTq4a7Ddn8vNSFODf09Jmg6Lh4VlxcbNSrkAfXT2xbNLqt/YA1DzLqqkrbiIjBdzG0GQV/34VMFbB2AHsLKvPFFZjTEyYcKxXUvQuDd5nwOgxyK9rRokwIfQOZ8RApYHMAnLQsAj02MnOOB2VEud1ccPNdCPMSYn3EAfAmJvLl88d3Lv6zbenk3nam7SZuf5T+sceAoCCHKaOBJu2YHITqUi6cPgPRcRAfX26JhaBY9l2UQji3CTIAHCaCq8SgS6m+0L0NLP8bIjPBwBoc7UGLZL6AHXOg/QR4I3HKMu0Hg/UAomHbPTaHKzCiQwghxCn5r3d+NfFgJADPde6/4dnpCW/i07Ojzn/fUR0g8/q3k7aH5bPvJIqgqIhat15zP/w1Ef4i6O657Qv7mZK8tOs/eK16kEO9jKSX8xAm/w4FAK1mQ0QmxITDqzhIDwIvehKCf+aDfyW9naSmx9P07bZ6nt1QkibRwif+q3dEX2ZeQrU8ABx+8Al8SgQ/8b910fvPZZM9rUgNMDv44Cz3Hgt8kj+qufpMQgCTGGbaiUkgVJeKv8X28+H2Xbgrsdy7D8fG0O+hiWJg5x0q4TUa+HQOJQd+mQzBBaDcCk5GQGoMhL2CjHTY7UW9GP8P/OJPv41hAF/RjXvnd0EancEVGNEhhBDiktyg7b/dziPV2e6bT2wY7aBJX8f4NkN+OballxpA4cPNfz0uH6WpG1nb2xEOzm27DZ2x/tzlVa2o7KhjR4IxpKuZl2chmqyawpafobkGkwd6bWHzeroOlQC+MUzeR9rkMnl72xl8FTVhYf7M4B0zn+7Ex+ootTsAmhi3cGlNtHHr3POTsbNW7r3xMuLk7Naq5B88/2Ps5CMxH/HY0OWUIPKTlBTTsopQPdFsBl26QTeJpWsXsCo+CRFvfOARif2sYbA9m0MIX8JZ6pQFX22Bkc3pLBL/6MHkzdCfDvtu+gI5oEp08oImAIJr4J/O5nACRnQIIYQ4JP/NrStULxvVLl+PsCX10RKqtiPm9qWu7XEX/3tV9mmisvj2AwY0oxLJ4YmS13JUvbQ39EoPjD7cBKdomoEOnRDV2fOJM2wG+HZbbcrXJ+kdUT6TArcy+Y1Z3RwABN9m5Gafvz8zJMnMC0t+uVfL0f1ImM200Q00duMr85hMhGRADHeOAwno9LqAo0SYV5AGTK9uayN6VUxZE6zoU5a49O0Mgw5A3ZkQwIlgJoMbMKJDCCHEIfkpEanUWt/OSqvMJUy/bT/6xmzMvdBMOqMKRQVC+iqubaKjQmcgaZm3pFev4VIEnSj24iJQg5g0hV50rFxH3PUd/HqsddW15auoLbIfweY2YnV0ANBUzIf/urQD9VsS/v3zTu0aJG6+CxEWUt08scslkrFMuEDHYC16U4/JlVA3BzM6cf5Sqba4nBfgS4+71K0XqNM5DBUj6GpFJe6WbruTcxjRIYQQ4hBlXhO6YeK9IKdsU5Cqga1lE7IWJb5OqfpCLBY8PHouniT0eo9x02bykJRajIN+GtSjLYuHwqEwNjPmXxj3G5Xouwq669JZdcZGw9iv5zrfbqtJXMdmNWJSHgBMTrXUbD6d6EYlBH6Xw2vV//hs4gMmgcOiINnKewNBdGOcqzNI3qdTawHz+1GJwMUw/RAwX/P8GPhmHEQCaPaFH7vTWSU0obMdtY66zTbucQJGdAghhDiEb+lioUTW2Q+vvChbA1XW0KU7AuamZpV9KkiYFBkWSnkRfP/c1pn9hvwZB2D22ZbfBxrihbBmVG3gwD/gRHZbJExoCUMWw+/ToPUYqnLUazl4TwM19o0S3sHJA7BvX9nl8AVqWDop8JV57voO7EbjJuUBQG9LQbVp2w50A0bi05haDdfuk/SE/HQ3cDRV12NyEJKJ3EhgeoS70IM0faAKEw7ATCcqeXACuAyBLb9Dt9awNxJ0e8FFb7Avd8qyot8sioZY7sxhgBcyhBBCHKJs4jm+M3UDNm7btEWnSqbfEufG3Nz508pLdM8xcUG5J7lCfurZqiWllWu3YXN3PcoBFedpG1aNtK4g/EDVMR8G/oEwsxPVUndhHXy/B0gEMessXFsBhhV2Yo2H76fBlClllxkruXQPXD7U8gCoFM/Aim5TFWWl5db4AcigjChmwsABTV2ZHITqz9O10MEVXCWW9v3gfvHtC0EsvCcrHliUu7egag7b/GH/TOpR3+gL8M338CgLHGbBi2vQk3qStCxDa3qVCskS48bKOYzoEEIIcYlq8wlbl3bgkWrriz9H2ekY2zm7ODU31dS08Zyx7R4TH/A01ahWDElKpg6taC2d7C31qW5rhaG7P3d2+mxH6em7kFTE8PoBPH5Jp4srEn9NgG8OQ3aFk5spg0t7aF9uaUffCf8I/ukvw7Kp7rONiHQHAL2WCjMuPDlEVJXLHjXVctW1DfTYuNxxrJdlDzYLoXpTlAjBwaWWJzcgsvgMnpFErzRBs6LbShn/b+9O4KIo+ziA/7mRQw5BEARRFJRQQFEx7yvvvCo1K83UtDc17fV8U0vL1C6z8ijvsjTPNE3LRMUDT8ALVE4FQZRL7mt55wJ2l12YZZdj9Pf9lDv7sLsz88zzPDP/OZ4nki5cJf4BU76g311Ho2bRLVWDrTS0516yKFM63esiogMAAGmx8F984vLWWQM8rYgKH0dH3LoT84icO4yc89UKdmxYIitnW+Uj2g7LT16/yboVfu9BanbShR/f9mT2gPH7p49adrlazw89v/Jo63jyn0KX0mnwEorMoNt7abAr2zfcureo+3x6XPFST1v6I5iuXFH+L2gHadGLSmxO8qALy7qcmV82HtpzQkwF4D4oRlF6IneNw9i2ET8QgoaYoO7j1mNbWyjd6Aagey3ep2Mn6ITcf4HBNJKPvojy+dDOgIwqFOQ7W8nPn368RM6D6VAkZdymjwaz6RfXkV93+rfCY6eGfD++JZSLiA4AAKCm6Fv5TFxzLCK9KCs5PiY24UlmYW78lf1fjHfNYs+3Wrq3aiTfrXtFxg4BUzb88UUHdh9496e1F8Q9zAWcs4to0i723qaFp+jQJ+RuQW1G0+FbtJ7riDL0S5pxmFReqNO1XQln0wuzmf9GXlq58t5+IfW5UHUF4D9XNVn63dBEdsLJx1VxNAqA+sayJfXtq/Bfr45UdiLCkD+PV0xFiu1P5lkaOoniiNovpLBDNMydLNvQ8sN0fT05EBWG0msz6JHiV4pKu9YyrnxPUp8gogMAAKkyMLd3dmvm1MiC2+1mh58MZ3tE8eznJaL/SmO3AcNbsROpYdcfazG88vNFlkSfrGMnWv+PlvYs71FO34KmbaF3HNnp/d9Scm2EdAtajeKHIGemF97+edzVr5+7Ici1qQAcWXLQ7hB2Y9kE9G0pN4QXgOSY81emc0jhgVAZHf6EIpmJ1rRtqdxzvvrUdhrtfIedTN1P/3DDGJTJSuVeTMlCOiMsIqIDAIBngSz17OZD7G7Zd2wfJ5Xdc6hjYKTRx59ruTEUzp2+7tRdYQgnljUN8WFfCxMppZYiK34Icmsjc2Z6V3xQlzPz+Y46nkfVqQA5IeuXH2d7k3AYNTUAXVWCpDVy5U4xFVCi/HCMuXQhnH2160Ruyk0W+Q3hmrFCiuEGeSyTGs+92JKddLrOQkQHAADSJ8u4sGr2L0+IzAd9OLZiZ9Qq5N09sOcOO9Gks6+9dG6tqWN6hsSftI69T8qPyxVRTBL7amBPtrV3ZjvAxiOk19f8UHWhGTFdghYEp/FdtjxXNK8AlB+7+/1Xlt1mpkx7LVnQVcejCALUMjNX4m4SoJvynSXpkSnXGmXE0tMKT/imxnBjiBtQM8UnTx9yQSA5kYt0bkVGRAcAAJJSlHTp5KX7OeX39cmyIvbMHThgNROfGXdd9uWrzlVdoMh/FLbv49EDFt9gpg06zHi/kwWfDlUya0Xd7diJMwtpR7jc83IyCllHy8PYyW5vkX2tHl3wQ5Dz3S0m5aX1Prd42/2T/J+eRVpWgOLs5MiLhzcuHOXbeuzWWCbBacymbVPFBYEA9VeDFtSSOzcXekeuZTKjAVw/rIVnaM4Oyi7/A2WE0Mzl7IRxN+pR2r0KK4+uRrGvTTqQrXTu30BEBwAAkpJy4oO+nZuZW7v5du3Tv3+fAC8nmzavfR2cTUZtZ+zbM9Orwp01rCtTmhrolTJ19H3lk6OJzD7Q4+0dez70VvkNUMmaln1NjZmJJJrkRa0606gxNGYUdWpB7Wexg8tZ9qEf3qRav+Zpqm/0W4c5Sz3HMNN5xQX8fZjPqGpVgLAP3I340m9o4dAq4OVpKw9E5BPZ9Zx38MKO8c003mAr7+3fEHv8+b3HFeoffRvq15KduBNI8v0X91pGo9kmi36fRA6taMAoGjeGXupEju3pL7bJolU/kJtcDZClUlAcO+HTjyR0sg8RHQAASIpV+9dHuptQZlzY+cATJwIvhj8qImuf0Yv33gxeO7SJ0qGpqXN7z4pHq0bWbh2GTv/qyL2QTa83x8UJzbi9SVf+ptc7soM6RV+iA7/T7wfoMnMEZEOjllDoEXpB7kYlY663DT1TMlQ11pm+sfAwnplu7tL8uPXYA50WfOM9aUSTzkLSM0jrCmBo6ejRceCERRtORMWeXDW8WqPsr7q3f3rYhunXNwrvAWqOPplzDYVpA2EoOdVMqP9A9jX1PN0tHXqfYehGv16hla+Tgx5lR9PfB2jX7/TPZWI+4jOKDoTSBy8In+Q9DaUbxeytmKM7CSmSoFdSOrSkEj09NtMCAwP5t1DLevfuzfyL/K9D2AR1C/lf5/hNoG4fUeeKMx/cvnkv/kl2iZmdm5dP6yZmz9gZSn4vXG/zn1WQRrduUmI6yYzJ0Z3atqzQVwqjmFJTyNiW+N4YKyrIpIxiamRdP88w19+tUKcV4NSTm73PLWYm1vtMm+Y2gE+sCTgWrXOlOwL+XZ0pzqGUXLJtVMXl/+xz5NmNEohW3KKFXkJiuQKKuUVRiZQjI2tHatOW7FVd0D49mXptJj1/irxALSqfX83jaoCoJgjX6AAAQHoMLF3adukzaNiwwX27eD1z4Zw0GNuwo/MOHkZDB5C/ynCOYUC2jdWGcwxjS7Kvp+FcvVanFeD441B+YmBjP34CoEYZmFHjqsI5hrkfjWnGTvx2mAnfKjCm5n7UbzC9PJR6+KsO5yiLfjvKvnaZTJrfjFyX0IgCAADAMy4pP12YAq0dTLzI/Otr1dzNjHs+CaCeMKPJ09nXG1vpTuko4RpJD6Z9iezDdbNHlA+3KQmI6AAAAOBZll6Y3fvc4pGXVjITQhJUV0RWQkQmO1zXcEdJPWYEzwePCTS8IdEd2nhFSNGAjI59Q0+IXCfRQAchTSoQ0QEAAMCzbPbNLUwQcjDxIhPXoXtGLfEX6BjPdPczIFUGjrTic+regTLuCSkayKW7RP796Nu5UurlkoeIDgAAAJ5ln3u9GWDryUyEZsT4nZrzXA5BrjN/JF1i/nUza8yP6g5Q33i9R2eu0M8ThbcaMKclR+jyPzTCWUiQEER0AAAA8CxzNLEO7LqcH4KcvwNzQ+xx/k+gkaT89ODUO8wEn5kAUE8gogMAAIBnHD8E+edebzLTecUF08M2zL65hf8TiFd2y+UAe19+AgDqgyrGowMAAAB4dnRuQjPbkzk3pvnNJ/T5Rcou5P4AIjD5xmRgewf68rKQAgA1TMx4dIjoAAAA4HnS3IoWdqbGZuz0V1coiO25EQCgftJBRIdx+usKP0g/8r8OYRPULeR/neM3gZgdCdQE7IVrVFZJ/uKcP1sa2P/HtIeQpApqQd1CLahzpVWAfwe1jb++JqYJwnN0AAAA8Hyx0DNZZT6i8nAOAEAqENEBAADAc8eYDIQpAACJQ0QHAAAAAAAgVYjoAAAAAFgFVDw7e19oEfpKAQApQUQHAAAAwFqV+w8Tzs3P+eNQwQ0hCYjyZIWzb24JzYgR3gNAPYOIDgAAAIDVRt+B+begpOib3JPf5AWSMZ61Yx17dG1N1GG/U3M2xB4XkgDqt+Ik+uZDmv0lJRUJKeLlR9LS9+l/WyhTSJAARHQAACBtsoKs1OTHadma77cBFL1i4rfcbKiFngkzfSj/Oi3pIoxFXm8V56WnPE7LqdnC/0fSJX5iRJPO/ARAXZEVUEY65RULb9W58iXN+Zq2XSQDlbFOMaWnUFqO8E6JHtFf62nFu7QjSkip/xDRAQCAJMmyIvYve6O7u5WBiWUjh8a2FkYGdi8MmP590CPFo9v8Oz+N82vu0lSFZh3eO5kufAyA083I/QeLMY76Ddk33nb0Te/6eLehLOP6zkWj/ZuaGjawsWtsa25k7OT/6tI/ovOEv5dSKv0urm7unt7+PYaMn/Hp1sDobJnwsSocTLzI/NvLztvRxJpPAahVMrp7lGaNopa2ZGBC1jbUwJAsm1KfcfTdUcqqUI5lj2n1JnZiwodkLx/ryOj6ThrtT6aGZGNHtuZk7ESvLiWlimPcgj4cSFREX64nNUFfvYOIDgAApEeWErigW7vRS3eejX5qYOvq4dnS2YJJvP33hhk9OkzY/UAuqMsJP3AwNDY+QYX7IaH3c4WPAZRy1bf5yeJ1X8Om7JvGZl2CFvAhTX1RFLdrUnufNz7ffzUhnxo6NnWyNqDCxKt7l43wG7rmlsIRqFLpj38QF3331tWgo79+v3hSH3fH9hPXX0mrIqw79eRmemE2MzHGuRufAlCb8u/T/D7kOYTWHqCoNCGRkZVAgbto5hByH0a3FUOy+EN0OIPIg972FVJYRbRrEvm8QfuvUj6RY1PiKg7tXUZ+Q0mh4uhT//fImih2O52TyEk/RHQAACA52Zc/m/RFWCHpv/D+vqinKXF3Iu7FZ6SF/jTWmfljwq+zPwvO4j/IKKGSEva15ey9Z84qOHc+ZPdr7HNTAEr4IcjpeCwznVdcMPLSyuC0u/yf6lpB5Ma3Jm6PJjLynbnnTlZa4oOEtKyYw/M6mhA9/fe/b6+PKBA+ySgt/R6LjoVcZ4RdCz59dNcPSyb1dmGOALPCtr8X0H3OseTK7mA7/jiUnxjY2I+fAKg1+ZH0eidafZqdbjmMvj9E99OpuISKMinkL1o6gZyIko9SYBL3aV4RHdpIhUypH0uepkIaI3IjTdzOTvjOpDtZlPiA0rLo8DziKg69vZ7kKg7Zvkj9mJDuCf14Vkip5xDRAQCA1OTd/eMP9lDb/q21n45qYcYn6lv7TFrz5UvsDjzxVGBcPp9azryZX5euCl7s4uNihv0gqMYOQb4+lDaEMdNjm3YPsPHg0+tYTuj61WeY0m3Ubc3er17xMOcKsKnb0M92r+1pTFR8ec26q+wVNUUNGrfybsto59e5x6Ax732y+eTdqH3vtzVkvnDr2zGTfolT/yDervgg5l9fq+ZuZo35FIBakk0rRtH+R+zkhF/oxiH6zzBysWKjFwML8h1IH2+j6Pv0xVzq2Ij7PKfoAe24wk4MHULlAV0OrV/NXpoz6kZ7vyIPcy7RlIZ+Rmt7spOX15BCxbGhsV3Y1xM7SO66YP2FPRkAAEhNYeoD7k4Ya1c7uTOwzD7NvIkL9/iTrKiqx+YBxDkWc6HHqq1+M4S3da3gwem/7zOvhl2mjmzOBGRlDJuPnNmPPbsRf/TPe8qP06lg6jZqzbGdr7IHwk+PLPzsnOpe/SKyEmJzkpmJ4Y6d+BSAWhO9nT7nhhHp9g1tHC8XnskxcaH/rqZOlsJbRnIQhZYwuwd6yVNIYRQ8IK7iUJeppFhxaORM4ioO/XmPTxL4v8z+m36KwqTQ5SUiOgAAkBoTJ68m7Gvk4b+i5K/FZd8+Gsgefdp37dmM7a0QQAcCbDxM9etLj5cFj6NS2FcbdxcLpWM4G5/+LdnXuHPhT7mEqhg4jVix2J/9lcQ9PwSpvBBR9gDhQIf2/ARALcmjbd+yN09SC/p6KntvpEjXj3DfakVtLPgEVsFj4ioOubsoBz82PsRVHDoXzr2UsvMlF+blMQVKYSBGRHQAACA1xq3Gze5vRlQSsmDYuzsi+FtlCuL2zBq3OprIvN+y/3Wz4tIAnjH6Rg24Cwy56dnK16ENbZs3bcC8FiVFPuZTqmTs9vJE7uG49AvH71S8V7N03AJHU5v6ctMpPDcKYmgf9+yq50RqJ9xbL0I+BXMPfjr6kq3ccJL6RsRfmUuvUM4NbYmrOJQUyb0vZd6C3LnvnLnFva/fENEBAIDkGLpN2Pbr9NbMPix6+wQv76EL1n4xpWvb1zZHk1XPpUd3TWlpLHwQoGbkyQrXRB0W3tQi06beznrMa9blv28rH5rqm1lx96XlpIi+S8zQ3sefu96ddD1OVTft69u9u8Bj9KwWQ4X3ALUl+y7x18a6dNPgAh3l0JUH7Kujt8JdmqZNias4dPlvqlBxqLTicG/LmFFr7tHR2GsSGMMAER0AAEiQodPw74NDtk7v1JBKYo+smjVv05VM8njvj9snPu7RSO7MbLnrK4f4+8rz69B/0XkpPCAB9dDC2z/Pvrml97nFfM/+tUbfofcbndkCHv/9lPn7y4bRkuXEndr40Sd/cbdOygrFP0ZqZOvCXc8uykzNUfEtX6vmn7d5Y0GrUcJ7gNryNIH4sWXc5XvkyafvXyU/X/It+8+Puo6jW6U1oSidErivNXIWLsrx9B3oDW54/Pjvaf5+KnvSNCeONn5EpRWHSypjTK7c+IspUQrdYNZPiOgAAECSZBmRly5cvcs9MaTPnXylu+smjJr1862K482ySpIiwhSEXjt5Nloqw8dCfZInKzz15CYzwfzrd2pOrQ5BbthiwneL/Y2YCnD7h9HuDRu7t/Fu3cLR3Nyt97TvhaGzjMw1uEbNj25ApGco1CKAeqGoNIoykA9W8ujiSQoNo7Cy/0Lp/C4KThX+XpRB/Gm6RnxvlmUMacJ3xFUc+mE0NWxMbbyphSOZu9G076m04nAvZQzJnrvbM+sx92Be/YaIDgAApCfvztbxfv5TfryU7jx4yaHIjIzbez8a7EqUfnHdW37d5//7uOLlhhbv7zx2QkFg8M8j7YW/Aohnqm8U2HX5iCbsOf/YnOTe5xbX5hDkFv6LT1zeOmuApxVR4ePoiFt3Yh6Rc4eRc75aMYS7pGDlbMt9UIyi9ETuANjYthE/EAJA/WBuS/w5hgT5TnusaE0QnTtLZ7n//vpUSC49MUGyfCH6MqnQmZGFP524TLMGML/BVByKuEVcxaE5X1FpxeE+V0afTLkfKSmgwirG4a97qL0AACA1mWcXDZ20K46M2i88FXbok2HuFpZtRi8/fOv6+pEOzL469MvXZhx+pLwHtmwZ0FdRr47NcBAL1WNtZH6g04IP3Icx0+mF2SMvrfw4Yhf/p5qnb+Uzcc2xiPSirOT4mNiEJ5mFufFX9n8x3jWLHVnf0r2V3OBclZOl3w1NZCecfFxV9g0PUEcsmhF/u+XFMJI/Q9fIi17sSl25/zq2Uo5k9A2Jv+u+QNUQi1Y+tOYYpRdRcjzFJlBmIcVfoS/GE1dxyL0V96EyMsrnf8SQDOv9ngK7MgAAkBZZ0uFP1rGdkrX+37alPcsfmtO3aDtty853HJnJ1P3f/pNc70+qguR94z3pN/8PTQ3Yuxw/ubN73NWv85SfxalJBub2zm7NnBpZcA8MZYefDGcPQD37eckNzlUpWXLQ7hC2otgE9G0pvjtBgJpn1oY6cGXy2jaKVT8CvhJ9c25wOaK0Su6oNyB7Z2rmRKUVh7iKQ/282H/LFVEq90ieqTXVl9FL1ENEBwAA0pIbcyGcHYXOrlN3N+Uu0Kz9hviwaYWJMSn1/8kHeAaMde4W2HW5o6kNM70rPqjLmflJ+fxTObVLlnp28yF2NEbfsX2cVPYNVFFOyPrlx9ljVodRUwO4+84A6gt9O5o2hJ2QXaJlx0jkGTrjRmTHFf8niQpX9tSS0dnNxFUc6uPEJ5UqpHiuKts2o/rfezIiOgAAkBY9Q/7hhozY+0+Vd9lFqTFJbLRnYN/Mtv6fVYVnQ4CNR0ivr32tmjPT1kbmjia1HxzJMi6smv3LEyLzQR+OFTd4R37s7vdfWXabmTLttWRBV4URHEMzYrbdP1nL3XgCKNCnl5aSP3cZbcfrtDpYXFBnRt7sfRqUdLO8Q8tKZFyg2b+wE4M+JOWKk0fhbKhHTj4KAyHUT4joAABAWsxaDehux7wWnlk4Z0d4dvleXpYRsm7m8jBmyrjbWz3ssYeDWsNEcRd6rPrAfdiBTguEpJpSlHTp5KX7OXLlPitiz9yBA1bfYQp+12Vfvupc6RW64uzkyIuHNy4c5dt67NZYJsFpzKZtU5WOZb+N/vPtkO+aHJ9UqzeRAigyeYF+XkMNmalMWtiFBs+l09HlV94e36Atv1cI8xpQQEv2NTGU0hX/lnSJLt2X+7yMIvbQwAHEVRz68lXhAbwyefcpiutvs8MLyn+qh7C/AwAAibHutezr0ewz80m/T/JyaNV5wKgx48aMeqlTC8f2s/5KJ7Lss+qHN93kxyICqHGm+kbfeE+yVu4BXedSTnzQt3Mzc2s33659+vfvE+DlZNPmta+Ds8mo7Yx9e2Z6qRyMOewDdyM9jqGFQ6uAl6etPBCRT2TXc97BCzvGN1OuLHzXnQE2HsxK8SkAdaL1dApaR825Xi+Pf0m93MnYgpycyFyPGrejefu4DzUil7Jqp09+/djXkjsUoXiN+cQH1LkZWbtR1z7Uvw95OVGb14irOLRvD1WsOKnXKY55MaV+nnxCvYaIDgAApMbQ7c1fr/y98vWODnqUHX3p7wO/7/r9wD+X4/LIxmfUkgOhRz54ofwmGX1jc3ZXrWfawBDjbYH0WbV/faS7CWXGhZ0PPHEi8GL4oyKy9hm9eO/N4LVDmyjFZqbO7T2VwzVDS0ePjgMnLNpwIir25Krhrsr3aJ56cpO/33KMczc+BaDO6FO76XQzkr6YTB7crcGybEpMJL7fE2tPevMjCrpOA+TudG7en1yYl1Q6cZdPELR/nbiKQ+cD6UQghT9ivk+jF9PNYBraRPiMvNA/2euBBu2pk/jRQOqOXknZCA6K9PTYHV9gYCD/FmpZ7969mX+R/3UIm6BuIf/rHL8J1O0j6o2CtJhbN6MS03NkxtaO7m3atrRXcYWiOCc1JdfYVugRUBqwF64PdFULkvLTdfxwXXHmg9s378U/yS4xs3Pz8mndxEx3p+inX9+4IeYYM5E4cGtdPBNYDrWgzpVWAf5d3UuNoYhYysgncxtq7kUuKnt1zab/eNK6BPJaQSELFTs1KaYHt+lePGWXkJ0b+bQmtRUniya3pM2PyP87uvA+1dW+g6sBopogXKMDAADpMrZp7te93+BhLw8d0MNfZTjHMDCzbSypcA6eJWuiDrf5930dD0FuYOnStkufQcOGDe7bxUuX4Rzj2KNrzL8Btp51G84BVGTbnF7sTYMGUo/OasI5hjlNGMO+3v6NorkH4coZkEtb6jOIhg2mLl7qwzkmoAuho4/YkegmD62zcE4jiOgAAAAAakREVsLsm1v4IchX3tsvpNZjwWl3Y3PYDv6GO3biUwAkx3cy+TAvN2j3HT5BY8GbKJHIcjCNcBVS6jlEdAAAAAA1orWFc9kQ5Atv/1zbQ5BXZWE413G7HP4CHWNEk878BIDkGHvQouHsxE8bKYtL0Ygsib7h+lyZNJ8cJBIqIaIDAAAAqCljnbtd6L7SzYztnZUfgpy/CFYfhGbEKF05/CPpEvMvs7RMLMqnAEiPAb28gsZ3p2YZlKx046UIuQ+IvKjfDJornQvViOgAAAAAapCvVfOQXl/3svNmppkgqkvQguA0xW746gizYAtv/7wr4Sz/lgk1mcVjJsY27c6nAEiUqRf9cobO/UwtlDtzrZp5Rzpyif5ZS87SefwaER0AAABAzbI2Mg/sunxa84HMdFJeWu9zizfEHuf/VIc6W7di/n075LtTT24yE6YGxlv9Zoxo0rniQ3QYahygPkNEBwAAAFAb1rd7d73PNCZwyisu2P4gsM7DJF+r5sy/zMKMvLQyIivB0cR6omufA50WBNh48B+IzUleE3WYiT+ZKJR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mXlvozvywQefPvn6Je2C89Sc38oS/1aBqVvq6VFs5VfzkyEQH9vfJoveXIZlCapn8+GPLJ0z/NbbGDmbVHzDnxux7vy3fPVHDIdt0ugQSLA9Qc3R3ja44I+rcn7u3b9q0bfdfl+Mqu2c4P+lG4MFdO3bs+uPUjaR8IZFk+akJcfcT0riTN7LcxwlxnITU/NKfKkqPuRFyIzqtwq3QxZn3b4aERabInfcpzoy7GXorgVuM4rRb/+zatn1vYLjSV8UvshxZ9oNbIdejUtXMjF29kGO/b2N+83jYo7K1Y7Fdj10NCX+Yo2pGRWnR16+GRaYqrVyVi1j1ihZnxgT/uWvb5q2/Hjx1U3GJ5FQjL2TJQb+HljBHhl1e8izvAiU/7eH9uPjH2ez5oBJ2myptRfW0LUEqsNti91YV20KBuBmLy0dWdTZ0tbd0QZX1poyu15PDLlZYxKM8/teKUm+fOvjL5i07D52PVnvrviwn4frpw79v3/TTlp0HT99+XPF8rdpSXXUrwal6DgqzUF9pRc5PpYKUWycP7Ny6bdfRywlVPihTjfpXFL199uc3mApo3O2bIxvHt1R5w4+Jy4D/rv6wk6XwlmPr/7I385J+am9YJp9SFTGroknzrILC9pBlx54/tHPL1l+PXH6gUIvyH145+tvWzT8fPBeVWUkeVZ2bKppxTVU5E53vsFTRcVsoYrV0saWqvxetiia1rhqVrlxO2M87o4gMu0weP+7dvmwvzxE7fr2tyRNxIlqpCqpd6euQ9jklTsGdzf/b9oiZsBj8w7ZZvkodbzNZ5jzgo23rxjWrg45fTd1GrTm281X2DMvTIws/Oyey3a2aFMsD1CQhslNJ5DW64vTQTVMDuNOBZYzch392Mkn5pFjhw7+XDW/FXnkvY+ox8vNTj4pKsi/OcRWSFJVdABJuUjbs/bvidZ+SpycncncQd/yx/NzqSa6br+bzL8Yem9+pNNww6rsnhf+7+EVWJnSdRZ03J5TeEV02s8vJN7a/688+pFKqycDPglKEz+WGLWnFppkN3pVY+tVSeWFLW7N/c3jnVOmZJXGLWPmKFib+s3Qge8G/XIO2U/fFK2zNaudF2l/cAznUcWNc2Sfzbizz5H5Bidvcy+xWVHPaWCcliFM2gw3hN3ZM76RuW5QSOWNR+VhGww2t3ZbutWJ6FfWGVSPryWEqH7tY3l9EpIX/NqsbO13KuM0bG68rnijNvrd38UgfG+EDpaw6TN4eIX9yUW2pflBlKyFyDiIrbdWtkmq5936d0UV+Iaz8p+24dGi8qmt01a1/uaGL+YeRWsy7VNmyqJB1YZYL+81WS8KqPFVe1aqULqQmzbMKZdvj0qOwTZPayT0H07DT7EMJTAnMivhtRoDcgph3mHngfoWSKTI3VTTjGlyjEzcTXe6wVNFxW6hxQ6TFlqr+XrQyYosqQ2QxqURm0DQn9ks9tzIb8ckfIxuyP+E6O1hUVRTbSlWgRaWvoDDhyLJ3Xnvt7SUHYjV8ik8zWuWUBpj9Lp83zv85p333odVRxQFz/r01/tzOVYu7I5TosjzAs0DriK44PXhpZ+5T5Nxj/KxFSxbNGt+LPyjUe2FeUFp5Q1z06MiU5twHycKj5/AxY0f282HbcCL7twOf5t/78RWfVu5uQiNr7Ni8JauVzys/3uPbm5R9/di9l173XUo7SBX3VQg7M8chY324PZ5Roya2RmTE71vFL7IK6nf7jfq92oG9mYns2vYaNKBLc+FsidXwnfH8J7Mvz+fW36Tvzw8V51FaL23G/cXXdNGLWNmK5kes6WrMJtp2HDNryfJPFk4fHeCsT7bj/ym/d0yLvMi7vpRbaMcpZ8oP2wvv7xzn26pl88b803N69txmbOXz6uZIdiuqyD2dlSDuI6UzsPFrXem2YImdsZh8VKDBhtZ6S/dc+3UV9abm1pMlLFbTUW905I5DrVp3HzysfwdHdprReNy+xLIcL7z3DddfDjVo3nXEhOkzZ05/c6CXcOLQc8Gl8r2w2lL9sIpWQvQcSmdRRaWtulVSoejhvjfYQxj2C80CBg4bEMD/rLEDf5gqH9FVv/7l3/7ci/ue5zLN72B69Gsv9kS1Ya9fk4QU1USsSunW1aR5VqF0e/R9hdsehk39+w3s1UZ4yMq091d7lvVkz7nrNfHrM6i3j7A9jLp8EyG/GcTnpjA7hYZIRZqqj4meic52WKrouC3UvCHSakupz+oq96LqaFBUq1/pyqX/y53JMu6zg4tJU4/wZzedpgVVuNmvAvGtlDKtKr2Sorj1HbjfohdWhqttzLSnTU5poij6e359dP7LolV1wFxYuohNZ17QScypy/IAzwRtI7rsywu5Q1Tz3l+FllejzJBVXblv+n5+q7SgpZ+c3IRNMmq/KPBJaduan3BixcvuLd8R9kGVPrFSjR0kx+m1n248ZdqSwqePUnKZV/GLrIr6fRHLyHfGvmj+LFth/J43+OPatitv8w1mbih/Esm49/YE+d1TbshHLdl0+7f/5dda/CJWsqKll4pe+FSushcmXT55PbVs5trkRerBIdyu1/fbiqUjZf9LbGxg0EvpiERF7um2BIneFuJnLCYflYje0LrZ0pXXmxpcT4bcYhl6v/vbHeFEYXb4hiH8kZT7fO7qLKvo4YHpw9/54q+obHaZeYWxO4ZzH2w8qbQRqGJdK1tb0XPQpKBo+Bzd0zPTnbmfaPHuQfaKBaMo9eK3I/lEBtNOcakMLepf6sHBXPWzn3iyfK3EEk6bk9t/Kz2zK2ZVSsuGbiI6lon/3CMJXOazYUtTIZUtX+/ti+Hyozj1zFz+VgCFFdAgN0tnJx+qqUhTkSR+JrraYami47ZQk9WS+9lqb6lKsppR9dJWpEFR1aLSlUk9Opa9wmbaf6dwG0baMf5SoP3E0qZdPfGtlDKtKr2Sovs/dmR/i73BQtOuNjWgVU5p4um/E7gr3/rdf678NFXNqfKAOTPoXa7aGvbapTh6bzXpsjzAM0HLiC7tr3HcrQPtv1Q4V8oU3bN8A9t2Nf+H4qRdA7my576g7ABPBZ1HdMZdVl1XnJ/oRVatkn2R/gsfnnwitwcuvPOlD5tu1He3UH3zbn7KnVIx6rml/ECiJPfaIva5YXKaLpxb0mARK1nRpye5Fs6o969Jag4LtMqLwui1vuxHjPrurXhnkPiITsclSPy2ED1jEflYgcgNraMtXWm9qdH1lFssz5nHSm/34hRGfsuOZV12w61a6X8LC7hOTPVlaRZhqZyDBgVFw/mlHeNn5jDphMLoAPlR20bxF1GYdkpI06L+FcX+0J79BLVeflPuADTv9nevdPL1kePr9+LY727yx8dl8m+v4Mqn5cg/KxnCQNSqlOaojiI6fe+5p+TusCsdBoWp9tP+lLsjrjh+azfucRj/DWW3fGuSm6Wzk2vGVaVVTNJgJjraYamg67ZQo9Uq+9nqb6nKslrM0lakQVHVotKVeXKAu3ewwcBdZW2lcCmKbMYerd4ddapaKSXaVXplRclB38+bPu2/a04mqjm804GayCnVUvb0YasbWb/2l04jRQ1UecCcd+tT7qEL6qB+I2tAt+UBngXcMVe1Zd86fCaNeW09cqAbd79WGQuPXh5sOx57IZIbyDbz+sHgXObVbdRor9KnBGpBqwXrZ7ZVmJ/4RdaY38xZPRrJZahhk/Z+7DFy4ZOHT/kn3E1avfI2GwYVnt/6d0JpV7J54Xt2RTGvrmMmtOce5q3OIlZcUWrg1smdWZrCwPkfbApNV/XUt1Z5UZTxiHu817yRudjxCVSoqRJU5bYQP+Oq87EicRtaV1u6MjW7nuU6zPlvP+FWW56hy0uj27ATcUE3Kv01I5um3C1buWlcbzryNFxXdSqZQ6UFhU/RTM69f86z+W038A2FJ5fIuMWI9wZwB/JltKl/JUXcwMlMwGCgz/d0xsl7ePHkpdAwOaEh53ftCFYap8CwoT2XrVmPMwu5BFU0WRWd8Zsxo5tt+fYwbNyOv5nwhakf9HcoL1/61h4+XK926Q9ShTXQJjdFq+mZiCvxOm4Lq7da1d9SlRKxtBVpUFR1sAVlj/798TjTOpj3ndzbvnRRrTpOHM5eTUw7+tPpFD5JM5W1UgJNK33ljbiBfbf/rFq3/otZvR3Z1a4JNZNTqpUUF3KZo29UU2ujA/zoBswGNJTfgNWl2/IAzwK5prMasuNuJLKv9zaM6eyrqM+882xhy3qSyTZPxRnR99KZV30XP1ftj9DEa+hsy523KSd6kXVAv4EVdya1ILdAqEvG7iMn+TN1r/DC1uPx/Fzybu3eFc28uo9/04fPmuosYsUVJcNmr3/xoTezgR/setfP0aPfu6sP3spQ+JpWeSHLz+F20AYmRloUolorQcrbQvy6V52PKoja0Lra0pWp4fWshLFDm+ZslpekxD6R78VNlpdw6cDGlYtmvPPm2FdGjRw5du6+eDadvWGA+0A5Dde1lAZzqEiuoHDvNZT/MJztbY1c/N2qLKba1D99c1tzbif+NCFN7kDZqueaoOBzZwV/fcpdRmdvxOBey+gbmnIZW1KQW6h2N6/JqtQYfVMLxeNugZ4Jn84c0wgroE1uilbTMxFV4nXdFupmtcRvKc1UXNqKNCiq2q9qcdLfG/9lYj7L/u/0aFy+77P0nziSvZX56fEf/30kYk2r0UppV+lrn5Y5lXr0TXdrSwU2nlMC1fQTqW9iwV0gy05R28lyZTSbWTUVpSdyP2hs28hc9VGTRoshtfIANU91sRKpOC+T77C4ODHyjpLIR3qmphZNuvXh+lMtzknnOqs1tmhQnUM03RG/yLqgx1U3eYZuL0/pYshU7eAtf3HDaOfc/P33GObV881xXvxsdbaI+tbdPz9z8cf3ejnrUX7Uvz/OH+nt4PXqV+dThP2VdjPSNzTmTsQWV3NHzanFEqS4LTRZ96ryUSURG7o2CmONr2cl9Bs05H64ILusq/+8qN/+E9CkaedR0xZ+/v2WX3bvO3Dw4OFT0exxlI5oP4eKlVYTsrxMrrN1Eyuzqi5da7X59S2aeXHdBSZcDHsst4EMG3l1frGroGOrhqobeFlRPp8jhsaGancBGqxKPVALlamWZlIlHbeF9WW11BFRIcUXVe1XtTj+yI9n2LM9Bsm/zZ0o5z/r7nIfyPl3499VDOZczVZKu0pf67TNqeKclMSMLAXpCcmZanLJ2KEld9tufuytxGqcjNNsZtUjS78byp1OcPJxVVPENFoMiZUHqAVabWsDE3NTrrX13xiVnatC5sMzH/uz3RsbmJhx+56CTP7CTvXwLXuJjGuRq0f8ItcQg6ZDpnRj8qL44tYj94so59Zu7ji/7duveXJnmHS7iAY2/lN+CIx9Erpv1eQeTZjW7u7e//Z79cdIrsXTbkb65rbc6dCctJzK916Vqe0SVEbDda80H1WrekPrdEurUfPrqV5xVgp391IDG3P+PpiM03P7v77ucjo5DVyw5URY7JPM3Pz8/PTguW7cn3Wg5udQFT0Tc+6KRG5qVlUHA9ptfrM2AztwFfDatsOxmh53FGWlsnftkam1hfqYQINV4XDrol3zrIVaqEwaz6SGckTHbWEt5V1NEl9UtV7VorjDP53jZpJ+/vftCn799yH3kYIzPx4RbsxQqfqtlFaVvrZpnVP2rxzNYR8Nkpf1x8vc04YVNWjxYhuuENw9fCpB87zRbGbVIksO2h3CtgU2AX1bqrmWrNliSKo8QG3QLno3a+rB3uJOj27FcwcIahlYN3NhS57s/pWYyu9RL1VxJ6hvbMZW2JKcdOUQQpPLyaIXuaYYNBkwpZcps9CXt/0Zl3Fz955YJrHDO6Nbca0RS+eLaGjrM2reT4F3Lq8IYGaSG7h29z3ujKZWMzJu5GrHng8teJL4tPohXQ2WoMpVZ91V5KPwF1Wq3tA1URiV642O1lOjQX55OTEX77JfM3Vr48Ctctrpb7ayQa3H4iMHPn+7b7tmjSxMjY2NjbR4pEBpbWtgDoqqPjQ3cWjJ5Xf8tbgK+V0iU2yotNr8+na9p3HdicoufbbsWHLVSyavMDWevW2PbJvZlZdGZZqsiq6aZy3UQGWqSJOZ1FiO6LotrKW8q0EaFFUtV7Ugav/my8wPGnde8KMQnsjZsnI0e82k6NxPh+PUHmBr0UppVelrmQ5ySiP6di+OC2DPdMgurt0Sppt6oVM5IeuXH2fLnMOoqQEKT3tWm5TKA9QK7SI6c69B/uw97g/2/xaWxSep0bDdYB/2+P/+nl+uKd0TnPfw5t10YZenb2BixDZrqp4ONrJrxp2piD59W67PAlnm5e+X7FV68L8S4he5pug79Jvaz5zZ01zZvvf4L3vimD10l8nD3cof562hRdS39Bv3hjc79TSev+tauxmZuXpz/Uon3Uzg7gGqFp2XILGqv+4K+cilqFHlhtbdllZfb3S0nlVeC7i6+rOjCjfQZF3fuYs9bjHwG9Ge230VP02Iz2ZeTVt2aCZ/y0lBxhMND63Ura3u5qCoslZJWQP3nu3Y/E75e9fVDD6JI8sI/mbhHsXyot3m12/00tKF/mxperTj9bGrg9M02J/nPQxPZl+dfFzU3P3D0GRVdNU8a0Fnlakymsyk5nJEx21hLeVdDdKgqGq3qvl392wJZV7NX5o7+523Knh7xoIJ7IW2ksub90epubFBq1ZKm0qvgiw/KzNP49Iiig5ySkMGTYfPe4278fLO55M/C1KRNUVJpzd8dSBWN7PTTH7s7vdfWXabmTLttWRBV2H4Rq3puDyA5ImK6IrzMjPSFfHtgH7jfnNeZw/r73/7xvs/R2TLF6e8B2d3fLXhvNC/joHL8JmD2A7+4r+fMOvXu8IZlKKUSz9ODnD3n3q8dA/XwKEF2zNV/pV9Z7kzDrLyh3DMPfp1YitC+t6FX5zmn+3Jiz28sH+vjy5ocAVB/CLXGP3GvacMYLv0vfrpnJ/uM3v+7lOGuMgd5+tmEfPvbn538oo/o8q/nh/1175w5tWgWXsXdp+m5YwatAhoyS51YugdjXtGLKP7EiSS6BlXno98ihpVbWgdbWmW2nqjo/XkykulojYO7/HOlusZ3NcLEg4vmPANE8OS5ZCZL7twz7YYWDVrxmZG3uV9QcnCUUTx46AVr76yOYl/J5qatdXhHBRV0iop07frMWUQG8Em/fTu//5M5E8/58X8Me+lvp9cUYqLtd38Ji/M+nnNS+waZwYu7OIzeO7W09FlnToUPL5xeMvvd1V+P+/+Ve4oqkmHF2y5baOSJquiq+ZZC7qrTJXQZCY1lyM6bgtrKe9qkAZFVatVzQvftZ1tEi36vt3DTtWxk5n3629wo3qGbtnD3aFQkZatVLUrfQUZge+2MLVs2MD1rSMpOt+4usgpTek3GrDi25FsS10cuqJPwJjP9oc+4n9ZlvPg4m+fjG3v3mv68l/Da/H6XXF2cuTFwxsXjvJtPXYre5OO05hN26a2VH9jhMZ0Vx7gmSDcqqtS6fAaqjhMPs2NqVX06Oh/uPE6GZbuAQNGvjb2tZEDXmzN1+KW/wspGwQjP/KnoaW3Azdo+kKHjr7u3Ll7IvsJ/5aNj5hyeIzwIRuPDh1a25u6zThfOrx+1uWPXuD/Rg1cfTq1579v5DNpKjfIh4rhfeSHGyojfpFVqGQknQozywr+gDvkb7NCeXBUYdhNjulLOx/Kjb/DEb2I6lc08+x0bhBhU7fOg155/Y0xQ7u4cg9fkNu0E+XDCGmTF3k3l3P5bvvGCeXhLTUYYVy3JUiTbSFuxuLyUZ2qNrQutjSnknpTo+tZulgNvdy4j5s4e/szs2cnGTZDf4osL/rpZ2a24NMdOo2YOGXSq71bsZGiadMmbFjR+tMKgxirWVf1ayt6DqIKipDCqKxVqiAvYm1vIQLWa9zav7NfC3Z/SwYvTJjWjr3Ux7RTwie1q3+c4sywda81Z39WoG/eqEmTRgpPaTR6/ZjCIF3FCdu6sflhOnBPJUN8sUSsSnnuiW+eVVC3PbLOv88NEvbCKsVBwrKvzG/OpiuMdig+N1XNTkWaqo9psMl0tMNSRcdtoQ4aIk22lMis5qjfiyrQoKhWu9JlX5zjyn1pxAGlIQbL5V3/mB9SXf3viG+l1KhOpa+gMGoNN9AfkccnN6qao6Z0lFOaK06/uKo/G9SVMTHnn7HkNXltZ1yNjb5X2QEzw67nvINxlZXh6tJJeYBnQqURXfalRe3kLynIc55+VhgluaQoJXjdlK5OSid7jZ39R/9vb7RCU5Ed8esHvZ25dlNg0XrYwt0RWXJHjIUPD8/hTm3yTLp/d6+8EuTH7Z/TVa6+2nacsik0/dGu3sxi6gVsii/9ncwzU9jTcAbdtlU8hmaJX2RlKn5Z7cxyry3kRpT2/fqeciPy9PR0rsVjDhFfPZisahnFLWIlK5od8fPM3sJRuaCBx9CPDim3KdXPi2xhX9tiwRWlFjnt8Aj2FLLpoAOKY4iqWVzdlSANt4WYGYvNR9Wq3tDab2lOpfWm5taz7EBs/a0rGya2k+tSwKHr+7/cVgp7cu/tfK+zXP01dOk39/frp2ezWdT2i7IjwarWtZK1FTkHUQVFSGFVmrsVFKde/HZsG7lbquwDpm4OTX9ycDCTxrZTwuc41a9/ZbKjjnwxua9H+fIJrD17v/nRlqAEpV9JO/IK+1GDF38qH/NZnSpXRT73xDbPKqjbHtmX+P4i2q9VHLS39HDQY0mYQtsjMjdVzU5FmpqlEr/JdLXDUkXHbaG2DZEmW0p8Vqtf2go0KarVqnR5N5Zxa9Lo9aMpQpIK+eGr23MH197qRyoX20pVRsNKX1Hq0XFc0201Yk/Z6N86orucqo6i5PMbZgxuay8X4jBNtkvAmI92hqZV2eJpQdUBs6Glo0fHgRMWbTgRJX+Yq3talwd4Fugx/wtbXlv5KZHXb9xLelpk1NDBrbW3h6OZ/M5GTl7S7ZDb99Nllk6tvL2bWSm1qpyClMiwW3GZ+vat/LxdlEfuKEi9e+3avVR9ew+/9i1t2Boky09/kkFWdtYmZR8tzk55nG1o29iqsgvcohdZnopfVjuz4pzUJ5l61vY25QtWpigr5UlmcQNbOysVfyxT5SJWsaIFqZHXb8Y8yiy2cPBo59uCyy6VqpMX2ef+49ltXQJ5rbgVstBLYQGKMh8/yTO1s7dUmmFli6uTElSdbSFixqLzsSJxG1rrLc2qtN7UyHpmnpzQvO+OFPbU+sWpLgb5SdeDQx4UWDT19GvraqF6dYsyYkKvRTwqsHDx7uDtzC5CcXZqSoGZnY1p2RdErGslaytmDtUoKFXkrrL8R7cuh8ZmmTp6+Pi0sOYykqkVqcUN5dupMtWpf8oKUmMiImITM/INzG0cm3u1cbFU1bhmnZ7cstfmR3r+30VeeL+FqIKsyaqIap5VULc92M2RpW9d4duyvLTHzK/ay2/TMlXnpqrZqUirrCCK3WS622GpoOu2UMRqqftZTbaUJlld6dIq06jWaVrp+CVRU+bKMTN8nK3f0L6Ruar6V0pUK1U1cZVepeLspPvJJfauTSxEf0UsneZUNRWk3o+KS3icWWLu0KJNq+o0qFKkRXkA6dNhRAfPn5xLH3p1/jqO2q4MvzK/tYbHIvAMUIrohFSov7KCpnn02Jho+OKGe2fedcMWAwAAeAY8H6ctoIaY+U6ezt6Mf2Pr7ju10/8BAGghPXjTvkQiy8GzR7ginAMAAHg2IKIDbRh7TFg0vCHRnZ82XpFit9cAzxNZ0rFv9j0hcp00f6ADGn8AAIBnBHbqoBUDx5dXfD6+e4dmGfe4Ea7g+aLfwNqSeWlg29BY4UF0qJdyH9wlL/9+M76d24ntuggAAACeCXiODgC0I5ORPs4NAQAAANQNRHQAAAAAAABShTPrAAAAAAAAUoWIDgAAAAAAQJqI/g8uY5mLXuj3ewAAAABJRU5ErkJggg==" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf1 :** \n", + " 1- en appliquant l'algorithme de Dijkstra avec la méthode du tableau, le plus court chemin est A-C-D-G pour une valeur de 9.\n", + "Lors de l'évaluation, prévoir de donner le corrigé aux élèves au bout de 15'.\n", + "![tableau_Dijkstra_exercice.png](attachment:tableau_Dijkstra_exercice.png)\n", + " 2- L'application de l'algorithme par la méthode du tableau d'exploration fournis les plus courts chemins du point de départ choisi vers chacun des autres sommets. Il suffit de faire la même lecture inversée du tableau à partir d'un de ces sommets jusqu'au sommet de départ. \n", + " référence sitographique: http://fred.boissac.free.fr/AnimsJS/DariushGraphes/index.html" + ] + }, + { + "attachments": { + "Algo_Dijkstra_f_principale.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2- Algorithme de Dijkstra en pseudo-code\n", + " ### Travail à faire 2, algorithme et variables\n", + " Ci-dessous, vous est proposé une écriture possible de l'algorithme de Dijkstra:\n", + " ![Algo_Dijkstra_f_principale.png](attachment:Algo_Dijkstra_f_principale.png)\n", + "1- Dans cet algorithme, identifier la variable qui donnera le plus court chemin entre le sommet de départ et le dernier sommet visité du graphe lorsque le traitement sera terminé.\n", + " 2- Dans cet algorithme, existe t'il une variable contenant la distance de ce plus court chemin? Justifier.\n", + " 3- Catégorisation de l'algorithme de Dijkstra: répondre au QCM1 ci-dessous.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$QCM1:$ Dans l'écriture de l'algorithme de Dijkstra proposée ci-dessus, on peut remarquer que:\n", + " $Rep1:$ La recherche, à chaque itération, du plus proche sommet voisin signifie que cet algorithme fait partie de la famille des 'algorithmes des k plus proches voisins'.\n", + " $Rep2:$ La recherche d’une solution optimale à un moment donné sans retour en arrière, indique qu'il s'agit d'une forme d'algorithme 'glouton'.\n", + " $Rep3:$ Le fait qu'à chaque itération, l'algorithme sélectionne 'le sommet non encore traité dont la distance à l'origine est minimale', c'est à dire le meilleur, est un élément clé pour montrer qu'il s'agit d'un algorithme glouton. \n", + " $Rep4:$ La résolution des problèmes du \"rendu de monnaie\" et du \"plus court chemin\" peuvent être envisagées par des approches dites 'gloutonnes' " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf2-1 :** \n", + " En fin de traitement par l'algorithme proposé, la variable $E_{calculés}$ donnera le plus court chemin.\n", + " **Réponse Taf2-2 :**\n", + "A chaque itération, la variable $Poids[s_{voisin}]$ contient le poids (distance cumulée) du plus court chemin à ce sommet voisin. Ce poids est définitif lorsque ce sommet est ajouté à l'ensemble $E_{calculés}$.\n", + " **Réponse Taf2-3 :**\n", + " Les bonnes réponses: \n", + " $Rep2:$ La recherche d’une solution optimale à un moment donné sans retour en arrière, indique qu'il s'agit d'une forme d'algorithme 'glouton'.\n", + " $Rep3:$ Le fait qu'à chaque itération, l'algorithme sélectionne 'le sommet non encore traité dont la distance à l'origine est minimale', c'est à dire le meilleur, est un élément clé pour montrer qu'il s'agit d'un algorithme glouton. \n", + " $Rep4:$ La résolution des problèmes du \"rendu de monnaie\" et du \"plus court chemin\" peuvent être envisagées par des approches dites 'gloutonnes' \n", + " référence sitographique: cours du Bloc2 et http://fred.boissac.free.fr/AnimsJS/DariushGraphes/index.html et https://www.supinfo.com/cours/2ADS/chapitres/06-algorithmes-gloutons" + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Dijkstra/Seance_7_Dijkstra_Implementation.ipynb b/Dijkstra/Seance_7_Dijkstra_Implementation.ipynb new file mode 100644 index 0000000..c3eb1aa --- /dev/null +++ b/Dijkstra/Seance_7_Dijkstra_Implementation.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Séance 7 — Implémentation de l'algorithme de Dijkstra\n", + "VERSION ELEVE\n", + "\n", + "On reprend ici l'intuition et le pseudo-code vus en Séance 6 pour les traduire en Python, sur le même graphe (circuits courts en Bretagne)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5- Implémentation de l'algorithme de Dijkstra\n", + " ### Travail à faire 6: Les données à traiter\n", + "Le graphe sera sous forme d'une liste de liste, comme dans la 1ère partie \"méthode par force brute\". \n", + " 1- Reprenez cette liste et insérez la ci-dessous. \n", + " 2- La fonction 'Dijkstra' est documentée (en rouge), complétez les trois assertions permettant de s'assurer de l'intégrité des données à traiter. Puis tester ces insertions avec 'dijkstra(\"A\",0)', puis 'dijkstra(Graphe,\"A\")', puis 'dijkstra(Graphe,8)' et enfin 'dijkstra(Graphe,0) qui est la bonne écriture d'appel pour cette fonction.\n", + " 3- Expliquer pourquoi '0' est saisi pour le paramètre 's-debut'?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# la structure de données pour le Graphe est la liste de liste de la 1ère partie: \n", + "Graphe = " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " \"\"\" fonction: calculer les plus courts chemins à partir d'un sommet de départ vers chacun des autres sommets\n", + " paramètres :\n", + " 'Graphe', un graphe sous forme d'une liste de liste,\n", + " 's_debut' un sommet de départ.\n", + " renvoie:\n", + " 'E_calcules', liste des sommets rangés dans l'ordre d'exploration,\n", + " 'poids', liste des poids de chaque sommet rangés dans l'ordre d'exploration,\n", + " 'predecesseurs', liste des sommets prédécesseur de chaque sommet rangés dans l'ordre d'exploration,\n", + " \"\"\"\n", + " assert type(Graphe) ... , \"Graphe doit être de type liste\"\n", + " assert type(s_debut) ... , \" s_debut doit être un entier \"\n", + " assert s_debut in [ ... ], \"s_debut doit être dans la plage d'indice de Graphe\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 7: Initialisation de l'algorithme\n", + " En reprenant les éléments de l'algorithme fournis au Taf2 et au Taf4, compléter les deux lignes manquantes de la partie \"Algo: initialisation\" du script, ci-dessous." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " #Algo: Initialisation\n", + " infini=float(\"inf\") # définition d'une valeur infinie\n", + " predecesseurs = [-1 for sommet in range(len(Graphe))] # initialisation des prédecesseurs à non parcouru (-1)\n", + " predecesseurs[s_debut] = 0 # sauf le sommet de départ qui est le prédécesseur de lui-même\n", + " ... # initialisation des poids à l'infini\n", + " ... # sauf le sommet de départ de poids nul\n", + " E_sommets = [i for i in range(len(Graphe))] # initialisation de l'ensemble des sommets du graphe\n", + " E_calcules = [] # création de la liste des calculés, vide au départ" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 8: Partie \"Mise à jour, poids et prédécesseur du plus proche voisin\" de l'algorithme\n", + " En reprenant les éléments de l'algorithme fournis au Taf2 et au Taf4, complétez l'implémentation de la structure conditionnelle de la partie \"Algo: Mise à jour, poids et prédécesseur du plus proche voisin\" du script de la boucle principale, ci-dessous. \n", + " Pour cela, répondez au QCM4 ci-dessous, puis placez la structure de code choisie dans le script de la boucle principale situé plus bas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$QCM4:$ Parmi les quatres extraits de script ci-dessous, un seul convient:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Réponse1\n", + "if poids[s_voisin] > poids[s_mini] + Graphe[s_mini][s_voisin] and Graphe[s_mini][s_voisin] = 0:\n", + " poids[s_voisin] = poids[s_mini] - Graphe[s_mini][s_voisin] \n", + " predecesseurs[s_voisin] = s_mini " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Réponse2\n", + "if poids[s_voisin] < poids[s_mini] + Graphe[s_mini][s_voisin] and Graphe[s_mini][s_voisin] != 0:\n", + " poids[s_voisin] = poids[s_mini] + Graphe[s_mini][s_voisin] \n", + " predecesseurs[s_mini] = s_voisin " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Réponse3\n", + "if poids[s_voisin] > poids[s_mini] + Graphe[s_mini][s_voisin] and Graphe[s_mini][s_voisin] != 0:\n", + " poids[s_voisin] = poids[s_mini] + Graphe[s_mini][s_voisin] \n", + " predecesseurs[s_voisin] = s_mini " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Réponse4\n", + "if poids[s_voisin] > poids[s_mini] + Graphe[s_mini][s_voisin] or Graphe[s_mini][s_voisin] != 0:\n", + " poids[s_mini] = poids[s_voisin] + Graphe[s_mini][s_voisin] \n", + " predecesseurs[s_voisin] = s_mini " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "#Algo: boucle principale\n while E_sommets: # Tant que tous les sommets ne sont pas définitivement calculés:\n\n # Algo: recherche d'un sommet de distance minimale\n poids_local = infini #\n s_mini = -1 # \n for s in E_sommets: # \n if poids[s] < poids_local: # \n poids_local = poids[s] # \n s_mini = s # \n #----------> Algo: fin de recherche d'un sommet de distance minimale\n\n E_sommets.remove(s_mini) # retrait du dernier sommet calculé à l'ensemble des sommets non définitivement calculés\n E_calcules.append(s_mini) # ajout du dernier sommet calculé à la liste des sommets définitivement calculés\n for s_voisin in E_sommets: # Pour chaque sommet voisin du dernier sommet calculé :\n\"\"\" Partie du script à compléter ci-dessous à partir du choix effectué au QCM4\"\"\"\n #Algo: mise à jour poids et prédécesseur du plus proche voisin\n ... # si le chemin st plus court et il y a existance d'un arc:\n ... # mise à jour du poids du plus proche voisin\n ... # mise à jour du prédécesseur du plus proche voisin\n #----------> fin de mise à jour du poids et du prédécesseur du plus proche voisin\n#---------->fin de boucle principale" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 9: Partie \"Recherche d’un sommet de distance minimale \" de l'algorithme\n", + " En reprenant les éléments de l'algorithme fournis au Taf2 et au Taf4, commenter chaque ligne du script proposé pour la partie \"Algo: Recherche d’un sommet de distance minimale\" de façon à bien expliquer la solution implémentée pour obtenir le sommet \"actuel\" de poids minimal ." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "#Algo: boucle principale\n while E_sommets: # Tant que tous les sommets ne sont pas définitivement calculés:\n\n # Algo: recherche d'un sommet de distance minimale\n poids_local = infini #\n s_mini = -1 # \n for s in E_sommets: # \n if poids[s] < poids_local: # \n poids_local = poids[s] # \n s_mini = s # \n #----------> Algo: fin de recherche d'un sommet de distance minimale\n\n E_sommets.remove(s_mini) # retrait du dernier sommet calculé à l'ensemble des sommets non définitivement calculés\n E_calcules.append(s_mini) # ajout du dernier sommet calculé à la liste des sommets définitivement calculés\n for s_voisin in E_sommets: # Pour chaque sommet voisin du dernier sommet calculé :\n\n #Algo: mise à jour poids et prédécesseur du plus proche voisin\n if ... and Graphe[s_mini][s_voisin] !=0: # si le chemin st plus court et il y a existance d'un arc:\n ... # mise à jour du poids du plus proche voisin\n ... # mise à jour du prédécesseur du plus proche voisin\n #----------> fin de mise à jour du poids et du prédécesseur du plus proche voisin\n#---------->fin de boucle principale" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 10: la fonction Dijkstra complète\n", + " Reconstituer ci dessous le script complet de la fonction 'dijkstra' et en effectuer le test. Si le test n'est pas probant, recherchez d'éventuelles erreurs flagrantes, et, si nécessaire appelez l'enseignant pour débloquer la situation. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " \n", + "\n", + "# le sommet s_debut choisi ici est \"A\", d'indice 0 dans E_sommets\n", + "dijkstra(Graphe,0)== ([0, 2, 1, 3, 7, 5, 4, 6], [0, 4, 2, 5, 9, 8, 9, 6], [0, 0, 0, 2, 1, 3, 3, 3])" + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Dijkstra/Seance_7_Dijkstra_Implementation_Corrige.ipynb b/Dijkstra/Seance_7_Dijkstra_Implementation_Corrige.ipynb new file mode 100644 index 0000000..9c8d9b8 --- /dev/null +++ b/Dijkstra/Seance_7_Dijkstra_Implementation_Corrige.ipynb @@ -0,0 +1,685 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Séance 7 — Implémentation de l'algorithme de Dijkstra\n", + "VERSION corrigée\n", + "\n", + "On reprend ici l'intuition et le pseudo-code vus en Séance 6 pour les traduire en Python, sur le même graphe (circuits courts en Bretagne)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5- Implémentation de l'algorithme de Dijkstra\n", + " ### Travail à faire 6: Les données à traiter\n", + "Le graphe sera sous forme d'une liste de liste, comme dans la 1ère partie \"méthode par force brute\". \n", + " 1- Reprenez cette liste et insérez la ci-dessous. \n", + " 2- La fonction 'Dijkstra' est documentée (en rouge), complétez les trois assertions permettant de s'assurer de l'intégrité des données à traiter. Puis tester ces insertions avec 'dijkstra(\"A\",0)', puis 'dijkstra(Graphe,\"A\")', puis 'dijkstra(Graphe,8)' et enfin 'dijkstra(Graphe,0) qui est la bonne écriture d'appel pour cette fonction.\n", + " 3- Expliquer pourquoi '0' est saisi pour le paramètre 's-debut'?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# la structure de données pour le Graphe est la liste de liste de la 1ère partie: \n", + "Graphe = " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " \"\"\" fonction: calculer les plus courts chemins à partir d'un sommet de départ vers chacun des autres sommets\n", + " paramètres :\n", + " 'Graphe', un graphe sous forme d'une liste de liste,\n", + " 's_debut' un sommet de départ.\n", + " renvoie:\n", + " 'E_calcules', liste des sommets rangés dans l'ordre d'exploration,\n", + " 'poids', liste des poids de chaque sommet rangés dans l'ordre d'exploration,\n", + " 'predecesseurs', liste des sommets prédécesseur de chaque sommet rangés dans l'ordre d'exploration,\n", + " \"\"\"\n", + " assert type(Graphe) ... , \"Graphe doit être de type liste\"\n", + " assert type(s_debut) ... , \" s_debut doit être un entier \"\n", + " assert s_debut in [ ... ], \"s_debut doit être dans la plage d'indice de Graphe\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf6-1 :** il suffit de recopier la lsite de liste de la 1ère partie de l'évaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# la structure de données pour le Graphe est la liste de liste de la 1ère partie: \n", + "Graphe = [[0, 4, 2, 0, 0, 0, 0, 0 ],\n", + " [4, 0, 6, 0, 5, 0, 0, 0 ],\n", + " [2, 6, 0, 3, 0, 0, 0, 5 ],\n", + " [0, 0, 3, 0, 0, 3, 4, 1 ],\n", + " [0, 5, 0, 0, 0, 2, 0, 0 ],\n", + " [0, 0, 0, 3, 2, 0, 7, 0 ],\n", + " [0, 0, 0, 4, 0, 7, 0, 10],\n", + " [0, 0, 5, 1, 0, 0, 10, 0]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf6-2 :** \n", + " Les trois premieres vérifications mettent en évidence le retour de chacune des trois assertions ( 'Graphe' n'est pas de type 'list'; 's_debut' n'est pas de type 'int'; 's_debut' n'est pas un entier entre 0 et 7, puisque 'Graphe' est une liste de 8 éléments). La quatrième vérification ne crée pas d'AssertionError." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "Graphe doit être de type liste", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0ms_debut\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"s_debut doit être dans la plage d'indice de Graphe\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mdijkstra\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"A\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mdijkstra\u001b[1;34m(Graphe, s_debut)\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;34m'predecesseurs'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mliste\u001b[0m \u001b[0mdes\u001b[0m \u001b[0msommets\u001b[0m \u001b[0mprédécesseur\u001b[0m \u001b[0mde\u001b[0m \u001b[0mchaque\u001b[0m \u001b[0msommet\u001b[0m \u001b[0mrangés\u001b[0m \u001b[0mdans\u001b[0m \u001b[0ml\u001b[0m\u001b[1;34m'ordre d'\u001b[0m\u001b[0mexploration\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \"\"\"\n\u001b[1;32m---> 11\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Graphe doit être de type liste\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 12\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms_debut\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\" s_debut doit être un entier \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0ms_debut\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"s_debut doit être dans la plage d'indice de Graphe\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAssertionError\u001b[0m: Graphe doit être de type liste" + ] + } + ], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " \"\"\" fonction: calculer les plus courts chemins à partir d'un sommet de départ vers chacun des autres sommets\n", + " paramètres :\n", + " 'Graphe', un graphe sous forme d'une liste de liste,\n", + " 's_debut' un sommet de départ.\n", + " renvoie:\n", + " 'E_calcules', liste des sommets rangés dans l'ordre d'obtention des plus courts chemins,\n", + " 'poids', liste des poids de chaque sommet rangés dans l'ordre d'obtention des plus courts chemins,\n", + " 'predecesseurs', liste des sommets \"prédécesseur de chaque sommet\" rangés dans l'ordre d'obtention des plus courts chemins,\n", + " \"\"\"\n", + " assert type(Graphe) == list, \"Graphe doit être de type liste\"\n", + " assert type(s_debut) == int, \" s_debut doit être un entier \"\n", + " assert s_debut in [i for i in range(len(Graphe))], \"s_debut doit être dans la plage d'indice de Graphe\"\n", + " \n", + "dijkstra(\"A\",0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": " s_debut doit être un entier ", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0ms_debut\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"s_debut doit être dans la plage d'indice de Graphe\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mdijkstra\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"A\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mdijkstra\u001b[1;34m(Graphe, s_debut)\u001b[0m\n\u001b[0;32m 10\u001b[0m \"\"\"\n\u001b[0;32m 11\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Graphe doit être de type liste\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms_debut\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\" s_debut doit être un entier \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 13\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0ms_debut\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"s_debut doit être dans la plage d'indice de Graphe\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAssertionError\u001b[0m: s_debut doit être un entier " + ] + } + ], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " \"\"\" fonction: calculer les plus courts chemins à partir d'un sommet de départ vers chacun des autres sommets\n", + " paramètres :\n", + " 'Graphe', un graphe sous forme d'une liste de liste,\n", + " 's_debut' un sommet de départ.\n", + " renvoie:\n", + " 'E_calcules', liste des sommets rangés dans l'ordre d'exploration,\n", + " 'poids', liste des poids de chaque sommet rangés dans l'ordre d'exploration,\n", + " 'predecesseurs', liste des sommets prédécesseur de chaque sommet rangés dans l'ordre d'exploration,\n", + " \"\"\"\n", + " assert type(Graphe) == list, \"Graphe doit être de type liste\"\n", + " assert type(s_debut) == int, \" s_debut doit être un entier \"\n", + " assert s_debut in [i for i in range(len(Graphe))], \"s_debut doit être dans la plage d'indice de Graphe\"\n", + " \n", + "dijkstra(Graphe,\"A\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "s_debut doit être dans la plage d'indice de Graphe", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0ms_debut\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"s_debut doit être dans la plage d'indice de Graphe\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mdijkstra\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mdijkstra\u001b[1;34m(Graphe, s_debut)\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Graphe doit être de type liste\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms_debut\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\" s_debut doit être un entier \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0ms_debut\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"s_debut doit être dans la plage d'indice de Graphe\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mdijkstra\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGraphe\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAssertionError\u001b[0m: s_debut doit être dans la plage d'indice de Graphe" + ] + } + ], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " \"\"\" fonction: calculer les plus courts chemins à partir d'un sommet de départ vers chacun des autres sommets\n", + " paramètres :\n", + " 'Graphe', un graphe sous forme d'une liste de liste,\n", + " 's_debut' un sommet de départ.\n", + " renvoie:\n", + " 'E_calcules', liste des sommets rangés dans l'ordre d'exploration,\n", + " 'poids', liste des poids de chaque sommet rangés dans l'ordre d'exploration,\n", + " 'predecesseurs', liste des sommets prédécesseur de chaque sommet rangés dans l'ordre d'exploration,\n", + " \"\"\"\n", + " assert type(Graphe) == list, \"Graphe doit être de type liste\"\n", + " assert type(s_debut) == int, \" s_debut doit être un entier \"\n", + " assert s_debut in [i for i in range(len(Graphe))], \"s_debut doit être dans la plage d'indice de Graphe\"\n", + " \n", + "dijkstra(Graphe,8)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " \"\"\" fonction: calculer les plus courts chemins à partir d'un sommet de départ vers chacun des autres sommets\n", + " paramètres :\n", + " 'Graphe', un graphe sous forme d'une liste de liste,\n", + " 's_debut' un sommet de départ.\n", + " renvoie:\n", + " 'E_calcules', liste des sommets rangés dans l'ordre d'exploration,\n", + " 'poids', liste des poids de chaque sommet rangés dans l'ordre d'exploration,\n", + " 'predecesseurs', liste des sommets prédécesseur de chaque sommet rangés dans l'ordre d'exploration,\n", + " \"\"\"\n", + " assert type(Graphe) == list, \"Graphe doit être de type liste\"\n", + " assert type(s_debut) == int, \" s_debut doit être un entier \"\n", + " assert s_debut in [i for i in range(len(Graphe))], \"s_debut doit être dans la plage d'indice de Graphe\"\n", + " \n", + "dijkstra(Graphe,0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf6-3 :** \n", + " La quatrième vérification n'ayant pas créé d'AssertionError, elle valide à priori l'appel de la fonction 'dijkstra' avec les bons paramètres. La valeur '0' correspond au premier sommet du graphe dans la structure de liste de liste qui est la liste [0, 4, 2, 0, 0, 0, 0, 0 ] indiquant bien que le sommet A a un arc de distance 4 avec B et un arc avec C de distance 2." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 7: Initialisation de l'algorithme\n", + " En reprenant les éléments de l'algorithme fournis au Taf2 et au Taf4, compléter les deux lignes manquantes de la partie \"Algo: initialisation\" du script, ci-dessous." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " #Algo: Initialisation\n", + " infini=float(\"inf\") # définition d'une valeur infinie\n", + " predecesseurs = [-1 for sommet in range(len(Graphe))] # initialisation des prédecesseurs à non parcouru (-1)\n", + " predecesseurs[s_debut] = 0 # sauf le sommet de départ qui est le prédécesseur de lui-même\n", + " ... # initialisation des poids à l'infini\n", + " ... # sauf le sommet de départ de poids nul\n", + " E_sommets = [i for i in range(len(Graphe))] # initialisation de l'ensemble des sommets du graphe\n", + " E_calcules = [] # création de la liste des calculés, vide au départ" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf7 :** \n", + " Les deux lignes manquantes sont de la même forme que les deux précédentes en remplaçant la variable 'predecesseurs' par la variable 'poids' de type liste, et la valeur '-1' par 'infini' ." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + "#Algo: Initialisation\n", + " infini=float(\"inf\") # définition d'une valeur infinie\n", + " predecesseurs = [-1 for sommet in range(len(Graphe))] # initialisation des prédecesseurs à non parcouru (-1)\n", + " predecesseurs[s_debut] = 0 # sauf le sommet de départ qui est le prédécesseur de lui-même\n", + " poids = [infini for sommet in range(len(Graphe))] # initialisation des poids à l'infini\n", + " poids[s_debut] = 0 # sauf le sommet de départ de poids nul\n", + " E_sommets = [i for i in range(len(Graphe))] # initialisation de l'ensemble des sommets du graphe\n", + " E_calcules = [] # création de la liste des calculés, vide au départ" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 8: Partie \"Mise à jour, poids et prédécesseur du plus proche voisin\" de l'algorithme\n", + " En reprenant les éléments de l'algorithme fournis au Taf2 et au Taf4, complétez l'implémentation de la structure conditionnelle de la partie \"Algo: Mise à jour, poids et prédécesseur du plus proche voisin\" du script de la boucle principale, ci-dessous. \n", + " Pour cela, répondez au QCM4 ci-dessous, puis placez la structure de code choisie dans le script de la boucle principale situé plus bas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$QCM4:$ Parmi les quatres extraits de script ci-dessous, un seul convient:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Réponse1\n", + "if poids[s_voisin] > poids[s_mini] + Graphe[s_mini][s_voisin] and Graphe[s_mini][s_voisin] = 0:\n", + " poids[s_voisin] = poids[s_mini] - Graphe[s_mini][s_voisin] \n", + " predecesseurs[s_voisin] = s_mini " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Réponse2\n", + "if poids[s_voisin] < poids[s_mini] + Graphe[s_mini][s_voisin] and Graphe[s_mini][s_voisin] != 0:\n", + " poids[s_voisin] = poids[s_mini] + Graphe[s_mini][s_voisin] \n", + " predecesseurs[s_mini] = s_voisin " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Réponse3\n", + "if poids[s_voisin] > poids[s_mini] + Graphe[s_mini][s_voisin] and Graphe[s_mini][s_voisin] != 0:\n", + " poids[s_voisin] = poids[s_mini] + Graphe[s_mini][s_voisin] \n", + " predecesseurs[s_voisin] = s_mini " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Réponse4\n", + "if poids[s_voisin] > poids[s_mini] + Graphe[s_mini][s_voisin] or Graphe[s_mini][s_voisin] != 0:\n", + " poids[s_mini] = poids[s_voisin] + Graphe[s_mini][s_voisin] \n", + " predecesseurs[s_voisin] = s_mini " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Algo: boucle principale\n", + " while E_sommets: # Tant que tous les sommets ne sont pas définitivement calculés:\n", + "\n", + " # Algo: recherche d'un sommet de distance minimale\n", + " poids_local = infini #\n", + " s_mini = -1 # \n", + " for s in E_sommets: # \n", + " if poids[s] < poids_local: # \n", + " poids_local = poids[s] # \n", + " s_mini = s # \n", + " #----------> Algo: fin de recherche d'un sommet de distance minimale\n", + "\n", + " E_sommets.remove(s_mini) # retrait du dernier sommet calculé à l'ensemble des sommets non définitivement calculés\n", + " E_calcules.append(s_mini) # ajout du dernier sommet calculé à la liste des sommets définitivement calculés\n", + " for s_voisin in E_sommets: # Pour chaque sommet voisin du dernier sommet calculé :\n", + "\"\"\" Partie du script à compléter ci-dessous à partir du choix effectué au QCM4\"\"\"\n", + " #Algo: mise à jour poids et prédécesseur du plus proche voisin\n", + " ... # si le chemin st plus court et il y a existance d'un arc:\n", + " ... # mise à jour du poids du plus proche voisin\n", + " ... # mise à jour du prédécesseur du plus proche voisin\n", + " #----------> fin de mise à jour du poids et du prédécesseur du plus proche voisin\n", + "#---------->fin de boucle principale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf8 :** \n", + " La bonne réponse au QCM4 est la 3. D'ou le script complété ci-dessous." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Algo: boucle principale\n", + " while E_sommets: # Tant que tous les sommets ne sont pas définitivement calculés:\n", + "\n", + " # Algo: recherche d'un sommet de distance minimale\n", + " poids_local = infini #\n", + " s_mini = -1 # \n", + " for s in E_sommets: # \n", + " if poids[s] < poids_local: # \n", + " poids_local = poids[s] # \n", + " s_mini = s # \n", + " #----------> Algo: fin de recherche d'un sommet de distance minimale\n", + "\n", + " E_sommets.remove(s_mini) # retrait du dernier sommet calculé à l'ensemble des sommets non définitivement calculés\n", + " E_calcules.append(s_mini) # ajout du dernier sommet calculé à la liste des sommets définitivement calculés\n", + " for s_voisin in E_sommets: # Pour chaque sommet voisin du dernier sommet calculé :\n", + "\"\"\" Partie du script complété ci-dessous à partir de la réponse 3 du QCM4\"\"\"\n", + " #Algo: mise à jour poids et prédécesseur du plus proche voisin\n", + " if poids[s_voisin] > poids[s_mini] + Graphe[s_mini][s_voisin] and Graphe[s_mini][s_voisin] !=0: # si le chemin est plus court et il y a existance d'un arc:\n", + " poids[s_voisin] = poids[s_mini] + Graphe[s_mini][s_voisin] # mise à jour du poids du plus proche voisin\n", + " predecesseurs[s_voisin] = s_mini # mise à jour du prédécesseur du plus proche voisin\n", + " #----------> fin de mise à jour du poids et du prédécesseur du plus proche voisin\n", + "#---------->fin de boucle principale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 9: Partie \"Recherche d’un sommet de distance minimale \" de l'algorithme\n", + " En reprenant les éléments de l'algorithme fournis au Taf2 et au Taf4, commenter chaque ligne du script proposé pour la partie \"Algo: Recherche d’un sommet de distance minimale\" de façon à bien expliquer la solution implémentée pour obtenir le sommet \"actuel\" de poids minimal ." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Algo: boucle principale\n", + " while E_sommets: # Tant que tous les sommets ne sont pas définitivement calculés:\n", + "\n", + " # Algo: recherche d'un sommet de distance minimale\n", + " poids_local = infini #\n", + " s_mini = -1 # \n", + " for s in E_sommets: # \n", + " if poids[s] < poids_local: # \n", + " poids_local = poids[s] # \n", + " s_mini = s # \n", + " #----------> Algo: fin de recherche d'un sommet de distance minimale\n", + "\n", + " E_sommets.remove(s_mini) # retrait du dernier sommet calculé à l'ensemble des sommets non définitivement calculés\n", + " E_calcules.append(s_mini) # ajout du dernier sommet calculé à la liste des sommets définitivement calculés\n", + " for s_voisin in E_sommets: # Pour chaque sommet voisin du dernier sommet calculé :\n", + "\n", + " #Algo: mise à jour poids et prédécesseur du plus proche voisin\n", + " if ... and Graphe[s_mini][s_voisin] !=0: # si le chemin st plus court et il y a existance d'un arc:\n", + " ... # mise à jour du poids du plus proche voisin\n", + " ... # mise à jour du prédécesseur du plus proche voisin\n", + " #----------> fin de mise à jour du poids et du prédécesseur du plus proche voisin\n", + "#---------->fin de boucle principale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf9 :** \n", + " Les commentaires doivent mettre en évidence le rôle et l'initialisation des variables locales à cette partie du script, les conditions de la boucle fort et du test conditionnel ." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Algo: boucle principale\n", + " while E_sommets: # Tant que tous les sommets ne sont pas définitivement calculés:\n", + "\n", + " # Algo: recherche d'un sommet de distance minimale\n", + " poids_local = infini # initialisation du poids en cours de calcul à une valeur infinie\n", + " s_mini = -1 # initialisation du sommet de poids minimal à 'non défini'\n", + " for s in E_sommets: # Pour chaque sommet non définitivement calculés:\n", + " if poids[s] < poids_local: # Si le poids du sommet actuel est inférieur au poids local:\n", + " poids_local = poids[s] # le poids local est remplacé par la valeur (inférieure) du sommet actuel\n", + " s_mini = s # le sommet de poids minimal est le sommet actuel\n", + " #----------> Algo: fin de recherche d'un sommet de distance minimale\n", + "\n", + " E_sommets.remove(s_mini) # retrait du dernier sommet calculé à l'ensemble des sommets non définitivement calculés\n", + " E_calcules.append(s_mini) # ajout du dernier sommet calculé à la liste des sommets définitivement calculés\n", + " for s_voisin in E_sommets: # Pour chaque sommet voisin du dernier sommet calculé :\n", + "\n", + " #Algo: mise à jour poids et prédécesseur du plus proche voisin\n", + " if poids[s_voisin] > poids[s_mini] + Graphe[s_mini][s_voisin] and Graphe[s_mini][s_voisin] !=0: # si le chemin est plus court et il y a existance d'un arc:\n", + " poids[s_voisin] = poids[s_mini] + Graphe[s_mini][s_voisin] # mise à jour du poids du plus proche voisin\n", + " predecesseurs[s_voisin] = s_mini # mise à jour du prédécesseur du plus proche voisin\n", + " #----------> fin de mise à jour du poids et du prédécesseur du plus proche voisin\n", + "#---------->fin de boucle principale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Travail à faire 10: la fonction Dijkstra complète\n", + " Reconstituer ci dessous le script complet de la fonction 'dijkstra' et en effectuer le test. Si le test n'est pas probant, recherchez d'éventuelles erreurs flagrantes, et, si nécessaire appelez l'enseignant pour débloquer la situation. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " \n", + "\n", + "# le sommet s_debut choisi ici est \"A\", d'indice 0 dans E_sommets\n", + "dijkstra(Graphe,0)== ([0, 2, 1, 3, 7, 5, 4, 6], [0, 4, 2, 5, 9, 8, 9, 6], [0, 0, 0, 2, 1, 3, 3, 3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Réponse Taf10 :** \n", + " La présence de la documentation de la fonction et des assertions est un plus dans la reconstitution de la fonction.\n", + "L'accompagnement au bon fonctionnement du programme final se fera au prix de malus suivant le niveau d'aide apportée et le type d'erreur corrigé." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def dijkstra(Graphe, s_debut):\n", + " \"\"\" fonction: calculer les plus courts chemins à partir d'un sommet de départ vers chacun des autres sommets\n", + " paramètres :\n", + " 'Graphe', un graphe sous forme d'une liste de liste,\n", + " 's_debut' un sommet de départ.\n", + " renvoie:\n", + " 'E_calcules', liste des sommets rangés dans l'ordre d'exploration,\n", + " 'poids', liste des poids de chaque sommet rangés dans l'ordre d'exploration,\n", + " 'predecesseurs', liste des sommets prédécesseur de chaque sommet rangés dans l'ordre d'exploration,\n", + " \"\"\"\n", + " assert type(Graphe) == list, \"Graphe doit être de type liste\"\n", + " assert type(s_debut) == int, \" s_debut doit être un entier \"\n", + " assert s_debut in [i for i in range(len(Graphe))], \"s_debut doit être dans la plage d'indice de Graphe\"\n", + " \n", + " #Algo: Initialisation\n", + " infini=float(\"inf\") # définition d'une valeur infinie\n", + " predecesseurs = [-1 for sommet in range(len(Graphe))] # initialisation des prédecesseurs à non parcouru (-1)\n", + " predecesseurs[s_debut] = 0 # sauf le sommet de départ qui est le prédécesseur de lui-même\n", + " poids = [infini for sommet in range(len(Graphe))] # initialisation des poids à l'infini\n", + " poids[s_debut] = 0 # sauf le sommet de départ de poids nul\n", + " E_sommets = [i for i in range(len(Graphe))] # initialisation de l'ensemble des sommets du graphe\n", + " E_calcules = [] # création de la liste des calculés, vide au départ\n", + " \n", + " #Algo: boucle principale\n", + " while E_sommets: # Tant que tous les sommets ne sont pas définitivement calculés:\n", + "\n", + " # Algo: recherche d'un sommet de distance minimale\n", + " poids_local = infini # initialisation du poids en cours de calcul à une valeur infinie\n", + " s_mini = -1 # initialisation du sommet de poids minimal à 'non défini'\n", + " for s in E_sommets: # Pour chaque sommet non définitivement calculés:\n", + " if poids[s] < poids_local: # Si le poids du sommet actuel est inférieur au poids local:\n", + " poids_local = poids[s] # le poids local est remplacé par la valeur (inférieure) du sommet actuel\n", + " s_mini = s # le sommet de poids minimal est le sommet actuel\n", + " #----------> Algo: fin de recherche d'un sommet de distance minimale\n", + "\n", + " E_sommets.remove(s_mini) # retrait du dernier sommet calculé à l'ensemble des sommets non définitivement calculés\n", + " E_calcules.append(s_mini) # ajout du dernier sommet calculé à la liste des sommets définitivement calculés\n", + " for s_voisin in E_sommets: # Pour chaque sommet voisin du dernier sommet calculé :\n", + "\n", + " #Algo: mise à jour poids et prédécesseur du plus proche voisin\n", + " if poids[s_voisin] > poids[s_mini] + Graphe[s_mini][s_voisin] and Graphe[s_mini][s_voisin] !=0: # si le chemin est plus court et il y a existance d'un arc:\n", + " poids[s_voisin] = poids[s_mini] + Graphe[s_mini][s_voisin] # mise à jour du poids du plus proche voisin\n", + " predecesseurs[s_voisin] = s_mini # mise à jour du prédécesseur du plus proche voisin\n", + " #----------> fin de mise à jour du poids et du prédécesseur du plus proche voisin\n", + " #---------->fin de boucle principale\n", + " return E_calcules, poids, predecesseurs\n", + " \n", + " # le sommet s_debut choisi ici est \"A\", d'indice 0 dans E_sommets\n", + "dijkstra(Graphe,0)== ([0, 2, 1, 3, 7, 5, 4, 6], [0, 4, 2, 5, 9, 8, 9, 6], [0, 0, 0, 2, 1, 3, 3, 3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optionnel, Travail à faire 11: Complexité expérimentale\n", + " En utilisant le script complet de la fonction 'dijkstra', effectuer la mesure de temps d'éxécution dans plusieurs situations pour évaluer expérimentalement le coût de cet algorithme. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "from random import randint\n\ndef dijkstra(Graphe, s_debut):\n \"\"\" fonction: calculer les plus courts chemins à partir d'un sommet de départ vers chacun des autres sommets\n paramètres :\n 'Graphe', un graphe sous forme d'une liste de liste,\n 's_debut' un sommet de départ.\n renvoie:\n 'E_calcules', liste des sommets rangés dans l'ordre d'exploration,\n 'poids', liste des poids de chaque sommet rangés dans l'ordre d'exploration,\n 'predecesseurs', liste des sommets prédécesseur de chaque sommet rangés dans l'ordre d'exploration,\n \"\"\"\n assert type(Graphe) == list, \"Graphe doit être de type liste\"\n assert type(s_debut) == int, \" s_debut doit être un entier \"\n assert s_debut in [i for i in range(len(Graphe))], \"s_debut doit être dans la plage d'indice de Graphe\"\n\n#Algo: Initialisation\n infini=float(\"inf\") # définition d'une valeur infinie\n predecesseurs = [-1 for sommet in range(len(Graphe))] # initialisation des prédecesseurs à non parcouru (-1)\n predecesseurs[s_debut] = 0 # sauf le sommet de départ qui est le prédécesseur de lui-même\n poids = [infini for sommet in range(len(Graphe))] # initialisation des poids à l'infini\n poids[s_debut] = 0 # sauf le sommet de départ de poids nul\n E_sommets = [i for i in range(len(Graphe))] # initialisation de l'ensemble des sommets du graphe\n E_calcules = [] # création de la liste des calculés, vide au départ\n#---------->fin d'initialisation\n#Algo: boucle principale\n while E_sommets: # Tant que tous les sommets ne sont pas définitivement calculés:\n\n # Algo: recherche d'un sommet de distance minimale\n poids_local = infini # initialisation du poids en cours de calcul à une valeur infinie\n s_mini = -1 # initialisation du sommet de poids minimal à 'non défini'\n for s in E_sommets: # Pour chaque sommet non définitivement calculés:\n if poids[s] < poids_local: # Si le poids du sommet actuel est inférieur au poids local:\n poids_local = poids[s] # le poids local est remplacé par la valeur (inférieure) du sommet actuel\n s_mini = s # le sommet de poids minimal est le sommet actuel\n\n #----------> Algo: fin de recherche d'un sommet de distance minimale\n\n E_sommets.remove(s_mini) # retrait du dernier sommet calculé à l'ensemble des sommets non définitivement calculés\n E_calcules.append(s_mini) # ajout du dernier sommet calculé à la liste des sommets définitivement calculés\n for s_voisin in E_sommets: # Pour chaque sommet voisin du dernier sommet calculé :\n\n #Algo: mise à jour poids et prédécesseur du plus proche voisin\n if poids[s_voisin] > poids[s_mini] + Graphe[s_mini][s_voisin] and Graphe[s_mini][s_voisin] !=0: # si le chemin est plus court et il y a existance d'un arc:\n poids[s_voisin] = poids[s_mini] + Graphe[s_mini][s_voisin] # mise à jour du poids du plus proche voisin\n predecesseurs[s_voisin] = s_mini # mise à jour du prédécesseur du plus proche voisin\n #----------> fin de mise à jour du poids et du prédécesseur du plus proche voisin\n#---------->fin de boucle principale\n\n return E_calcules, poids, predecesseurs\n\ndef matrice(i,j): # fonction qui génère une liste de 'i' liste de 'j' éléments \n # avec des valeurs entières aléatoires entre 0 et 20\n return [[randint(0,20) for q in range(0,j)] for p in range(0,i)]\n\nfor n_elt in range(6,20,1): # test de durée d'éxécution de la fonction 'dijkstra' pour des graphes de 6 à 20 sommets\n M = matrice(n_elt, n_elt)\n %timeit dijkstra(M,0)" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Constatation et interrogation:\n", + "\n", + "En doublant le nombre $n$ de sommets du graphe, on constate approximativement un doublement du temps d'éxécution $T$ de cette implémentation de l'algorithme de Dijkstra. Pour les étudiantes ayant fait l'annexe optionnelle sur la complexité théorique : ceci n'est pas en accord avec le calcul théorique qui y est fait. Cherchez l'erreur (?)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 100 graphes de 6 sommets pour une durée de 0.015600100000000339 secondes.\n", + " 100 graphes de 7 sommets pour une durée de 0.015600100000000339 secondes.\n", + " 100 graphes de 8 sommets pour une durée de 0.031200200000000677 secondes.\n", + " 100 graphes de 9 sommets pour une durée de 0.046800300000001016 secondes.\n", + " 100 graphes de 10 sommets pour une durée de 0.031200200000000677 secondes.\n", + " 100 graphes de 11 sommets pour une durée de 0.031200200000000677 secondes.\n", + " 100 graphes de 12 sommets pour une durée de 0.046800300000001016 secondes.\n", + " 100 graphes de 13 sommets pour une durée de 0.062400400000001355 secondes.\n", + " 100 graphes de 14 sommets pour une durée de 0.046800300000001016 secondes.\n", + " 100 graphes de 15 sommets pour une durée de 0.062400400000001355 secondes.\n", + " 100 graphes de 16 sommets pour une durée de 0.0780005000000017 secondes.\n", + " 100 graphes de 17 sommets pour une durée de 0.09360060000000203 secondes.\n", + " 100 graphes de 18 sommets pour une durée de 0.09360060000000203 secondes.\n", + " 100 graphes de 19 sommets pour une durée de 0.10920070000000237 secondes.\n" + ] + } + ], + "source": [ + "def matrice(i,j):\n", + " return [[randint(0,20) for q in range(0,j)] for p in range(0,i)]\n", + "\n", + "#import timeit\n", + "import time\n", + "for n_elt in range(6,20,1):\n", + " t1 = time.process_time()\n", + " for i in range(100):\n", + " M = matrice(n_elt, n_elt)\n", + " dijkstra(M, 0)\n", + " t2 = time.process_time()\n", + " print(\" 100 graphes de \", n_elt,\" sommets pour une durée de \", t2-t1, \" secondes.\")" + ] + }, + { + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Exercices_1.md b/Exercices_1.md new file mode 100644 index 0000000..0eb8dd5 --- /dev/null +++ b/Exercices_1.md @@ -0,0 +1,59 @@ +# Exercices sur les Algorithmes des Graphes + +Ces exercices sont destinés aux étudiants en sociologie quantitative pour découvrir les bases des algorithmes des graphes et leur application dans l'analyse des réseaux sociaux. + +## Exercice 1 : Représentation d’un réseau social avec des graphes + +**Objectif** : Initier les étudiants à la modélisation de réseaux à l’aide de graphes. + +### Énoncé +Imaginez un groupe de personnes et leurs relations d’amitié, où chaque personne est représentée par un nœud, et chaque relation d'amitié par une arête (lien) entre deux nœuds. Construisez un graphe représentant les relations suivantes : +- Alice est amie avec Bob et Carla. +- Bob est ami avec Alice, Carla et Dan. +- Carla est amie avec Alice, Bob et Eve. +- Dan est ami avec Bob et Eve. +- Eve est amie avec Carla et Dan. + +1. Représentez ce réseau sous forme de graphe en dessinant les nœuds et les arêtes. +2. Utilisez une **matrice d’adjacence** pour représenter ce réseau (matrice où chaque ligne et colonne représente une personne et où les cases contiennent 1 si deux personnes sont amies, 0 sinon). +3. Utilisez une **liste d’adjacence** pour représenter le même réseau (liste où chaque personne est associée à une liste de ses amis). + +**Question** : Quelle représentation (matrice d’adjacence ou liste d’adjacence) serait, selon vous, la plus adaptée pour analyser des réseaux sociaux de grande taille ? Expliquez pourquoi. + +--- + +## Exercice 2 : Recherche de chemins dans un graphe + +**Objectif** : Introduire la recherche de chemins dans un graphe et sa pertinence dans la sociologie quantitative. + +### Énoncé +Dans le réseau social précédent, vous devez trouver le plus court chemin entre **Alice** et **Eve**, c’est-à-dire le nombre minimum d’étapes (arêtes) pour aller de l’une à l’autre. + +1. Tracez le graphe du réseau. +2. Utilisez une **recherche en largeur (BFS)** pour trouver le chemin le plus court entre **Alice** et **Eve**. +3. Énumérez le chemin que vous avez trouvé et précisez le nombre d’étapes nécessaires. + +**Question** : En sociologie, pourquoi serait-il pertinent de savoir qu'un individu peut rejoindre un autre en un nombre minimum d'étapes dans un réseau social ? + +--- + +## Exercice 3 : Identification de communautés dans un réseau + +**Objectif** : Faire réfléchir les étudiants à la détection de communautés dans un réseau. + +### Énoncé +Dans le réseau social présenté dans le premier exercice, imaginez que les relations ne sont pas toutes égales en force : certaines amitiés sont plus fortes que d’autres, ce qui peut influencer la formation de groupes ou de communautés. Une manière simple de détecter les communautés est de voir si certaines personnes ont plus d’amis en commun entre elles qu’avec les autres. + +1. Identifiez les groupes de personnes qui ont plus de connexions entre elles qu'avec les autres (essayez d’isoler les sous-groupes formés de relations plus fortes). +2. Proposez une méthode pour diviser le graphe en communautés et expliquez pourquoi certaines personnes sont plus susceptibles de former une communauté. + +**Question** : En quoi cette notion de communauté peut-elle être utile pour analyser des phénomènes sociaux dans les études quantitatives ? + +--- + + + + + + + diff --git a/Exercices_2.md b/Exercices_2.md new file mode 100644 index 0000000..7fb0f03 --- /dev/null +++ b/Exercices_2.md @@ -0,0 +1,102 @@ +## Exercices Socio et algorithmes des graphes + + + +### Exercice 1 : Questions de cours + +1.1 Qu'est-ce qu'un graphe non orienté ? Donnez un exemple d'application pratique. + +1.2 Quelle est la différence entre une matrice d'adjacence et une liste d'adjacence ? + +1.3 Dans quel cas serait-il préférable d'utiliser une liste d'adjacence au lieu d'une matrice d'adjacence ? + +--- + +### Exercice 2. Matrices et Listes d'Adjacence + +Considérez le graphe suivant : + +- A est connecté à B et C +- B est connecté à A, C et D +- C est connecté à A, B et E +- D est connecté à B et E +- E est connecté à C et D + +### 2.1 Matrice d'adjacence + +Complétez la matrice d'adjacence pour ce graphe. + +| | A | B | C | D | E | +| ---- | ---- | ---- | ---- | ---- | ---- | +| A | 0 | 1 | 1 | 0 | 0 | +| B | 1 | 0 | 1 | 1 | 0 | +| C | 1 | 1 | 0 | 0 | 1 | +| D | 0 | 1 | 0 | 0 | 1 | +| E | 0 | 0 | 1 | 1 | 0 | + +### 2.2 Liste d'adjacence + +Écrivez la liste d'adjacence correspondante. + +--- + +### Exercice 3 : Degré des sommets et centralité +**Objectif** : Comprendre l’importance des sommets dans un réseau social. + +1. **Degré d’un sommet** : + - Calculez le degré de chaque sommet dans le graphe donné (A, B, C, D, E). + - Quel sommet a le plus haut degré ? Que peut-on en déduire dans un contexte sociologique ? + +2. **Centralité de degré** : + - Expliquez pourquoi un sommet ayant un haut degré peut être considéré comme central dans un réseau social. + - Donnez un exemple d’application (ex. influence sur les réseaux sociaux, diffusion d’informations). + +--- + +### Exercice 4 : Graphes pondérés et applications +**Objectif** : Introduire la notion de poids sur les arêtes pour modéliser des relations plus complexes. + +1. **Ajout de poids** : + Imaginez que les relations d’amitié entre les individus du graphe ont des intensités différentes. Attribuez un poids à chaque arête pour refléter cette intensité : + - A ↔ B : 2 + - A ↔ C : 3 + - B ↔ C : 1 + - B ↔ D : 4 + - C ↔ E : 2 + - D ↔ E : 1 + +2. **Distance minimale** : + - Trouvez le chemin de coût minimal (somme des poids) entre le sommet A et le sommet E en utilisant l’algorithme de Dijkstra. + - Expliquez l’intérêt de l’algorithme dans des contextes réels (ex. optimisation des trajets). + +--- + +### Exercice 5 : Représentation alternative des graphes +**Objectif** : Explorer une autre représentation des graphes : les graphes orientés. + +1. **Transformation en graphe orienté** : + - Transformez le graphe initial (non orienté) en un graphe orienté en choisissant une direction pour chaque arête. + - Justifiez les directions choisies dans un contexte de réseaux sociaux (ex. influence d’une personne sur une autre). + +2. **Conséquences sur les chemins** : + - Quels chemins sont encore possibles entre A et E après l'orientation des arêtes ? + - Expliquez en quoi les graphes orientés peuvent être utiles pour modéliser des hiérarchies ou des flux d’informations. + +--- + +### Exercice 6 : Graphes bipartis +**Objectif** : Introduire la notion de graphes bipartis et leur utilité. + +1. **Construction d’un graphe biparti** : + Imaginez que le graphe initial représente des personnes (A, B, C, D, E) et des événements auxquels elles participent (E1, E2). + Voici les participations : + - A participe à E1 et E2. + - B participe à E1. + - C participe à E2. + - D participe à E2. + - E participe à E1 et E2. + + Représentez ce graphe biparti sous forme de matrice d’adjacence. + +2. **Application sociologique** : + Expliquez en quoi les graphes bipartis peuvent être utilisés pour analyser des réseaux sociaux ou des collaborations (ex. analyse de co-participation à des projets). diff --git a/README.md b/README.md index b0f54e4..7667e41 100644 --- a/README.md +++ b/README.md @@ -21,40 +21,27 @@ Comprendre et utiliser les graphes pour analyser les **réseaux sociaux** et les | **Jupyter Notebook** | `pip install notebook` puis `jupyter notebook` | | **Google Colab** | [colab.research.google.com](https://colab.research.google.com) | -## Programme +## Programme — 12 séances (24h) -### 1. Introduction contextuelle +Aligné sur le programme officiel de la maquette (BCC5, EC "Algorithme des graphes"). Une séance tampon (11) est explicitement réservée pour absorber le retard selon le niveau réel de la promo plutôt que de préparer un second parcours en parallèle, notamment s'il y a du retard sur Python. -Les graphes en sciences sociales : modélisation des relations, réseaux sociaux, exemples Facebook/Twitter. +| # | Séance | Statut | +|---|--------|--------| +| 1 | Définitions, typologie des graphes, prise en main de NetworkX | Fait | +| 2 | Représentations : matrice et liste d'adjacence, matrice/liste d'incidence | Fait | +| 3 | Mesures statistiques avancées (clustering, distribution des degrés...) | Fait | +| 4 | Parcours BFS/DFS | Fait | +| 5 | Composantes connexes + diffusion d'information (BFS) | Fait | +| 6-7 | Graphes pondérés et algorithme de Dijkstra (allégé) | Fait | +| 8 | Centralité (degré, proximité, intermédiarité) | Fait | +| 9 | Coloration de graphes | Fait | +| 10 | Page-rank et propagation d'étiquettes (label propagation) | Fait | +| 11 | Séance tampon / rattrapage | réservée, pas de contenu fixe | +| 12 | Révisions + partiel blanc | Fait | -### 2. Types de graphes +### Applications sociologiques (fil rouge de tout le cours) -| Type | Description | Exemple | -|------|-------------|---------| -| **Non dirigé** | Relations réciproques | Amitié Facebook | -| **Dirigé** | Relations orientées | Followers Twitter | -| **Pondéré** | Force des relations | Fréquence d'interaction | -| **Biparti** | Deux types d'entités | Individus ↔ Opinions | - -### 3. Concepts fondamentaux - -- Sommets et arêtes -- Degré d'un sommet -- Chemins et cycles -- Connexité - -### 4. Algorithmes - -| Algorithme | Usage | -|------------|-------| -| **Parcours en largeur (BFS)** | Plus court chemin, exploration | -| **Parcours en profondeur (DFS)** | Détection de cycles, composantes | -| **Dijkstra** | Plus court chemin pondéré | -| **Centralité** | Identifier les nœuds influents | - -### 5. Applications sociologiques - -- Analyse des réseaux sociaux +- Analyse des réseaux sociaux, identification des individus influents - Détection de communautés - Étude des inégalités d'accès aux ressources - Diffusion des idées et innovations @@ -62,20 +49,30 @@ Les graphes en sciences sociales : modélisation des relations, réseaux sociaux ## Structure du dépôt ``` -├── FONDAMENTAUX.md # Concepts de base -├── Djikstra/ # Algorithme de Dijkstra -├── Exercices/ # Exercices pratiques -├── Corrige1/, 2/, 3/ # Corrigés -├── copies/ # Travaux étudiants -└── assets/ # Images et schémas +├── COURS.md # Chapitre 1 : introduction contextuelle +├── FONDAMENTAUX.md # Chapitre 2 : concepts de base +├── Seance_1.ipynb / Seance_1_TP.ipynb # Séance 1 : prise en main de NetworkX +├── Seance_2_Representations.ipynb # Séance 2 : matrice/liste d'adjacence, incidence +├── Seance_3_Mesures_Statistiques.ipynb # Séance 3 : mesures statistiques avancées +├── Seance_4_BFS_DFS.ipynb # Séance 4 : parcours & distances (BFS/DFS) +├── Seance_5_Composantes_Diffusion.ipynb (+ Corrigé) # Séance 5 : composantes connexes & diffusion +├── Dijkstra/ # Séance 6 (recap + intuition), Séance 7 (implémentation), annexes optionnelles (preuve/complexité, version dictionnaire) +├── Seance_8_Centralite.ipynb # Séance 8 : centralité +├── Seance_9_Coloration.ipynb # Séance 9 : coloration de graphes +├── Seance_10_PageRank_LabelPropagation.ipynb # Séance 10 : page-rank & label propagation +├── Exercices_1.md, Exercices_2.md # Exercices complémentaires +├── Seance_12_Partiel.md / Seance_12_Partiel_Corrige.md # Partiel blanc et son corrigé +├── Exercices/, copies/ # Travaux et rendus d'étudiants +└── assets/ # Images et schémas ``` ## Ressources -- 📖 [Introduction contextuelle (cours)](COURS.md) -- 📄 [Cours complet (PDF)](Cours.pdf) -- 📄 [Algorithmes des graphes (PDF)](coursAlgoGraphes.pdf) -- 📝 [Exercices](Exercices.md) +- [Introduction contextuelle (cours)](COURS.md) +- [Concepts fondamentaux (cours)](FONDAMENTAUX.md) +- [Exercices — série 1](Exercices_1.md) +- [Exercices — série 2](Exercices_2.md) +- [Partiel blanc](Seance_12_Partiel.md) / [Corrigé](Seance_12_Partiel_Corrige.md) ## Licence diff --git a/Seance_1.ipynb b/Seance_1.ipynb new file mode 100644 index 0000000..afeeb36 --- /dev/null +++ b/Seance_1.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Séance 1 — Rappels & prise en main de NetworkX\n", + "\n", + "**Objectifs pédagogiques**\n", + "- Réviser les définitions : nœuds, arêtes, graphes dirigés / non-dirigés / pondérés.\n", + "- Prendre en main `networkx` pour créer un graphe et le visualiser.\n", + "- Lire des mesures simples (degrés).\n", + "\n", + "**Consignes**\n", + "- Exécutez cellule par cellule.\n", + "- Commentez en 2-3 phrases ce que vous observez." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "# Installation (si nécessaire) — peut être ignorée si networkx est déjà présent\n", + "# %pip install networkx matplotlib\n", + "\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) Créer un premier graphe non dirigé" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "# TODO: Compléter la liste d'arêtes pour former votre mini-réseau social\n", + "G = nx.Graph()\n", + "G.add_edges_from([\n", + " (\"Alice\", \"Bob\"),\n", + " (\"Bob\", \"Claire\"),\n", + " (\"Alice\", \"David\"),\n", + " # (\"Claire\", \"David\"), # décommentez pour ajouter un lien\n", + "])\n", + "\n", + "print(\"Nœuds :\", list(G.nodes()))\n", + "print(\"Arêtes:\", list(G.edges()))\n", + "print(\"Degrés:\", dict(G.degree()))\n", + "\n", + "plt.figure()\n", + "nx.draw(G, with_labels=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) Questions\n", + "1. Quel nœud a le plus grand degré ?\n", + "2. Que se passe-t-il si vous ajoutez l'arête ('Claire','David') ?\n", + "3. Donnez une interprétation sociologique rapide (qui est au centre ?)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Seance_10_PageRank_LabelPropagation.ipynb b/Seance_10_PageRank_LabelPropagation.ipynb new file mode 100644 index 0000000..c11cbdf --- /dev/null +++ b/Seance_10_PageRank_LabelPropagation.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 🔍 Séance 10 — Page-rank et propagation d'étiquettes\n", + "\n", + "## Objectifs pédagogiques\n", + "À la fin de cette séance, vous serez capables de :\n", + "- comprendre l'intuition de l'algorithme du **page-rank** et le calculer avec `networkx` ;\n", + "- comprendre le principe de la **propagation d'étiquettes** (label propagation) pour détecter des communautés ;\n", + "- relier ces deux algorithmes au reste du cours (centralité, diffusion par BFS).\n", + "\n", + "## Introduction\n", + "En Séance 8, on a vu trois façons de mesurer l'importance d'un individu : le degré, la proximité, l'intermédiarité. Le **page-rank** en propose une quatrième, plus subtile : être suivi par une personne elle-même très suivie compte plus que d'être suivi par dix personnes obscures. C'est l'algorithme qui a fait le succès de Google pour classer les pages web (une page est importante si des pages elles-mêmes importantes pointent vers elle).\n", + "\n", + "La **propagation d'étiquettes**, elle, répond à une tout autre question : comment détecter automatiquement des **communautés** dans un réseau, sans les connaître à l'avance ? On retrouve ici l'idée de diffusion vue en Séance 5, mais appliquée différemment : au lieu de propager une information depuis une seule source, chaque personne adopte peu à peu l'étiquette majoritaire de son entourage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 1 — Page-rank\n", + "\n", + "Le principe intuitif est celui du **\"surfeur aléatoire\"** : imaginez une personne qui clique aléatoirement sur les liens d'une page web à l'autre, indéfiniment. Le page-rank d'une page est la probabilité qu'à un instant donné, le surfeur se trouve sur cette page. Une page reçoit un score d'autant plus élevé qu'elle est pointée par des pages elles-mêmes très visitées.\n", + "\n", + "Transposé à un réseau social, cela devient : *qui suit qui* est une arête **orientée**, et le page-rank d'une personne dépend de la quantité **et de la qualité** de celles et ceux qui la suivent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Un réseau de \"qui suit qui\"" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Graphe orienté : une arête A -> B signifie \"A suit B\"\n", + "G = nx.DiGraph()\n", + "G.add_edges_from([\n", + " (\"Bob\", \"Alice\"),\n", + " (\"David\", \"Alice\"),\n", + " (\"Emma\", \"Alice\"),\n", + " (\"Alice\", \"Chloé\"), # Alice, très suivie, suit aussi Chloé\n", + "])\n", + "\n", + "plt.figure()\n", + "pos = nx.spring_layout(G, seed=0)\n", + "nx.draw(G, pos, with_labels=True, node_color=\"lightblue\", node_size=1200, arrows=True, arrowsize=20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculer le page-rank\n", + "\n", + "NetworkX calcule directement le page-rank de chaque sommet.\n", + "\n", + "> Si vous obtenez `ModuleNotFoundError: No module named 'scipy'`, exécutez d'abord `%pip install scipy` dans une cellule, puis relancez." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "pr = nx.pagerank(G)\n", + "for personne, score in sorted(pr.items(), key=lambda x: -x[1]):\n", + " print(f\"{personne} : {round(score, 3)}\")\n", + "\n", + "print(\"\\nPour comparaison, degré entrant (nombre de personnes qui suivent) :\")\n", + "for personne, deg in sorted(G.in_degree(), key=lambda x: -x[1]):\n", + " print(f\"{personne} : {deg}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interprétation :** Alice a 3 personnes qui la suivent (Bob, David, Emma), mais Chloé n'en a qu'**une seule** — Alice elle-même. Pourtant, le page-rank de Chloé dépasse celui d'Alice ! C'est exactement l'idée du \"surfeur aléatoire\" : être suivi par une seule personne très centrale (Alice, qui concentre le trafic entrant du réseau) rapporte plus que d'être suivi par plusieurs personnes elles-mêmes peu suivies.\n", + "\n", + "**Questions :**\n", + "1. Le classement par page-rank est-il le même que le classement par simple degré entrant ? Que montre cette différence ?\n", + "2. Si Bob, David et Emma se mettaient aussi à suivre Chloé (en plus d'Alice), que deviendrait le page-rank de Chloé ?\n", + "3. En quoi ce principe rejoint-il la centralité d'intermédiarité vue en Séance 8 (une position stratégique compte plus qu'un grand nombre de liens) ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 2 — Propagation d'étiquettes (détection de communautés)\n", + "\n", + "**Principe :** au départ, chaque personne a sa propre étiquette (son nom). À chaque étape, chaque personne adopte l'étiquette **la plus fréquente parmi ses voisin·es**. On répète jusqu'à ce que plus personne ne change d'étiquette. Les personnes qui finissent avec la même étiquette forment une **communauté** détectée automatiquement — sans qu'on ait eu besoin de la définir à l'avance.\n", + "\n", + "On reprend le réseau à 7 personnes des Séances 5 et 8." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "from networkx.algorithms.community import asyn_lpa_communities\n", + "\n", + "H = nx.Graph()\n", + "H.add_edges_from([\n", + " ('Alice', 'Bob'), ('Alice', 'Emma'),\n", + " ('Bob', 'Chloé'), ('Bob', 'Félix'),\n", + " ('Chloé', 'David'), ('Chloé', 'Gaël')\n", + "])\n", + "\n", + "communautes = list(asyn_lpa_communities(H, seed=3))\n", + "for i, communaute in enumerate(communautes):\n", + " print(f\"Communauté {i+1} : {sorted(communaute)}\")\n", + "\n", + "# Visualisation avec une couleur par communauté détectée\n", + "palette = [\"lightblue\", \"lightgreen\", \"lightcoral\", \"khaki\"]\n", + "couleur_par_personne = {}\n", + "for i, communaute in enumerate(communautes):\n", + " for personne in communaute:\n", + " couleur_par_personne[personne] = palette[i]\n", + "\n", + "plt.figure()\n", + "pos = nx.spring_layout(H, seed=0)\n", + "nx.draw(H, pos, with_labels=True, node_size=1200,\n", + " node_color=[couleur_par_personne[n] for n in H.nodes()])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interprétation :** l'algorithme retrouve automatiquement deux groupes : {Alice, Bob, Emma, Félix} d'un côté, {Chloé, David, Gaël} de l'autre — sans qu'on lui ait jamais dit où étaient les frontières. Remarquez que **Bob** se retrouve dans le groupe d'Alice alors qu'en Séance 8 il avait la plus forte centralité d'intermédiarité avec Chloé : la propagation d'étiquettes doit \"trancher\", même pour les personnes-ponts qui, dans la réalité, appartiennent un peu aux deux groupes à la fois.\n", + "\n", + "**Questions :**\n", + "1. Réexécutez la cellule en changeant le `seed`. Le résultat change-t-il ? Que cela vous apprend-il sur la fiabilité de cet algorithme sur de petits réseaux ?\n", + "2. Bob et Chloé, identifié·es comme \"ponts\" en Séance 5-8, se retrouvent chacun·e dans un groupe différent ici. Est-ce cohérent avec leur rôle de pont ? Pourquoi la méthode a-t-elle du mal à les classer ?\n", + "3. Sur un réseau de plusieurs milliers de personnes, pourquoi la détection automatique de communautés est-elle plus utile qu'une lecture \"à l'œil\" du graphe ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Synthèse\n", + "\n", + "| Algorithme | Question posée | Ce qu'il donne |\n", + "|:------------|:------------------|:------------------|\n", + "| Page-rank | Qui est important, en tenant compte de la qualité de ses connexions ? | Un score d'importance par personne |\n", + "| Propagation d'étiquettes | Quels sous-groupes se dessinent naturellement dans ce réseau ? | Une partition du réseau en communautés |\n", + "\n", + "## Pour clore le cours\n", + "Ce cours a parcouru tout le programme officiel de graphes : définitions et représentations (Séances 1-3), parcours et distances (Séances 4-5), graphes pondérés (Séances 6-7), et enfin ces mesures plus avancées (Séances 8-10). Un même fil rouge — un petit réseau social imaginaire — a servi de terrain d'expérimentation du début à la fin.\n", + "\n", + "**Questions de synthèse générale :**\n", + "1. Parmi tous les algorithmes vus ce semestre (BFS, DFS, Dijkstra, centralité, coloration, page-rank, label propagation), lequel vous semble le plus directement utile pour un mémoire ou une enquête de sociologie ?\n", + "2. Choisissez un phénomène social qui vous intéresse (mobilisation collective, diffusion d'une mode, ségrégation résidentielle...). Quel(s) outil(s) de ce cours utiliseriez-vous pour l'étudier, et pourquoi ?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Seance_12_Partiel.md b/Seance_12_Partiel.md new file mode 100644 index 0000000..d8908a7 --- /dev/null +++ b/Seance_12_Partiel.md @@ -0,0 +1,88 @@ +# Partiel L3 Sociologie Quantitative - Analyse des Graphes + +## Introduction et Rappels + +Ce partiel a pour objectif d'évaluer vos compétences sur les notions fondamentales des graphes et leurs applications sociologiques. +Vous devez répondre aux questions en suivant les consignes. + +### Définitions utiles : +- **Graphe** : Un ensemble de sommets reliés par des arêtes (ou arcs pour les graphes orientés). +- **Chemin** : Une suite de sommets connectés par des arêtes. +- **Cycle** : Un chemin qui revient au sommet de départ. +- **Matrice d'adjacence** : Représentation tabulaire des connexions entre sommets. +- **Liste d'adjacence** : Représentation où chaque sommet est associé à ses voisins. +- **BFS (parcours en largeur)** : Algorithme pour explorer un graphe niveau par niveau. +- **Algorithme de Dijkstra** : Méthode pour trouver le chemin de coût minimal dans un graphe pondéré. + +--- + +## Exercice 1 : Représentation des Graphes (7 points) + +### Étude de cas : +Un réseau universitaire connecte les départements suivants : +- A (Sociologie) est connecté à B (Psychologie) et C (Histoire). +- B est connecté à A, C, et D (Philosophie). +- C est connecté à A, B, et E (Anthropologie). +- D est connecté à B et E. +- E est connecté à C et D. + +### Consignes : +1. Représentez ce graphe sous forme de **matrice d'adjacence**. (3 points) +2. Représentez ce graphe sous forme de **liste d'adjacence**. (3 points) +3. Justifiez si la liste ou la matrice est la plus adaptée pour analyser un réseau universitaire. (1 point) + +--- + +## Exercice 2 : Recherche de Chemins (8 points) + +### Étude de cas : +Dans un graphe représentant un réseau social : +- A (Alice) est amie avec B (Bob) et C (Carla). +- B est ami avec A, C, et D (Dan). +- C est amie avec A, B, et E (Eve). +- D est ami avec B et E. +- E est amie avec C et D. + +### Consignes : +1. Effectuez un **parcours en largeur (BFS)** depuis Alice. Listez les sommets dans l’ordre de leur visite. (4 points) +2. Trouvez le chemin le plus court entre Alice et Eve en utilisant BFS. (2 points) +3. Expliquez en quoi ce type d’analyse peut être utile pour étudier des dynamiques sociales. (2 points) + +--- + +## Exercice 3 : Optimisation Logistique avec Dijkstra (10 points) + +### Étude de cas : +Une entreprise veut optimiser ses livraisons entre ses dépôts. Le graphe pondéré suivant représente les coûts (en minutes) entre les dépôts : +- A ↔ B : 10 +- A ↔ C : 15 +- B ↔ C : 5 +- B ↔ D : 20 +- C ↔ D : 10 +- C ↔ E : 30 +- D ↔ E : 10 + +### Consignes : +1. Représentez ce graphe sous forme de **matrice d'adjacence pondérée**. (3 points) +2. Utilisez l’**algorithme de Dijkstra** pour trouver le chemin de coût minimal entre A et E. Montrez toutes les étapes. (5 points) +3. Expliquez en quoi cet algorithme est utile dans des problématiques logistiques. (2 points) + +--- + +## Exercice 4 : Analyse des Communautés (10 points) + +### Étude de cas : +On analyse la participation des étudiants à des projets collaboratifs : +- A participe aux projets P1 et P2. +- B participe aux projets P1 et P3. +- C participe aux projets P2 et P4. +- D participe aux projets P3 et P4. +- E participe aux projets P1 et P4. + +### Consignes : +1. Représentez ce graphe biparti sous forme de **matrice d'adjacence**. (3 points) +2. Identifiez les groupes d’étudiants qui collaborent régulièrement en analysant leurs participations communes. (4 points) +3. Expliquez comment cette analyse peut aider à améliorer la collaboration dans un cadre universitaire. (3 points) + +--- + diff --git a/Seance_12_Partiel_Corrige.md b/Seance_12_Partiel_Corrige.md new file mode 100644 index 0000000..1c7f2c5 --- /dev/null +++ b/Seance_12_Partiel_Corrige.md @@ -0,0 +1,136 @@ +# Corrigé + +--- + +# Exercice 1 – Lecture d’un réseau + +## 1. Sous-groupes naturels +- Sous-groupe principal : {Alice, Bob, Chloé, David, Félix, Gaël} +- Sous-groupe annexe : {Alice, Emma} + +## 2. Sommets centraux +Les plus centraux sont Bob, Chloé ou David (2 liens chacun + position intermédiaire). +On peut aussi accepter Alice (3 liens mais position en bordure d’un des sous-groupes). + +## 3. Ponts +- Bob relie Alice/Emma au reste. +- Chloé relie Bob/Alice au segment David/Félix/Gaël. +- Félix relie David/Chloé à Gaël. + +Tous ces sommets sont argumentables comme « ponts ». + +## 4. Individus isolés ou périphériques +- Pas d’isolés. +- Individus périphériques : Emma et Gaël (degré 1). + +--- + +# Exercice 2 – Matrice d’adjacence + +Ordre des sommets : Alice, Bob, Chloé, David, Félix, Gaël, Emma. + +## 1. Matrice complète + +| | A | B | C | D | F | G | E | +| ----- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | +| Alice | 0 | 1 | 0 | 0 | 0 | 0 | 1 | +| Bob | 1 | 0 | 1 | 0 | 0 | 0 | 0 | +| Chloé | 0 | 1 | 0 | 1 | 0 | 0 | 0 | +| David | 0 | 0 | 1 | 0 | 1 | 0 | 0 | +| Félix | 0 | 0 | 0 | 1 | 0 | 1 | 0 | +| Gaël | 0 | 0 | 0 | 0 | 1 | 0 | 0 | +| Emma | 1 | 0 | 0 | 0 | 0 | 0 | 0 | + +## 2. Matrice symétrique ? +Oui : relations **non orientées**, donc si i connaît j, alors j connaît i. + +## 3. Signification d’un 1 en (i, j) +Il existe une relation entre les individus i et j. + +## 4. Degrés +- Alice : 2 +- Bob : 2 +- Chloé : 2 +- David : 2 +- Félix : 2 +- Gaël : 1 +- Emma : 1 + +--- + +# Exercice 3 – BFS depuis Alice + +## 1. Niveaux BFS (3 pts) + +- Niveau 0 : Alice +- Niveau 1 : Bob, Emma +- Niveau 2 : Chloé +- Niveau 3 : David +- Niveau 4 : Félix +- Niveau 5 : Gaël + +Ordre BFS possible : +**Alice → Bob → Emma → Chloé → David → Félix → Gaël** + +## 2. Premiers / Derniers +Premiers : Bob, Emma +Dernier : Gaël + +## 3. Sens sociologique de distance 2 +Deux relais nécessaires → relation indirecte, moins immédiate, moins proche. + +## 4. Ce que montre le BFS +La dynamique de **diffusion** dans un réseau. + +--- + +# Exercice 4 – DFS depuis Alice + +## 1. Ordre DFS possible +Exemple : +**Alice → Bob → Chloé → David → Félix → Gaël → Emma** + +Toute variante respectant la logique « profondeur d’abord » est correcte. + +## 2. Arbre DFS +Forme linéaire typique : +Alice → Bob → Chloé → David → Félix → Gaël +Retour arrière → Emma + +## 3. Comparaison BFS / DFS +- BFS : exploration horizontale, diffusion collective. +- DFS : exploration verticale, suit une piste jusqu’au bout. +- Les arbres sont très différents dans leur forme. + +## 4. Exemple sociologique pertinent +Exemples acceptés : +- chaîne d’influence +- filiation (arbre généalogique) +- enquête suivant une piste unique +- propagation ciblée + +--- + +# Exercice 5 – Composantes connexes avec Hugo + +## 1. Graphe connexe ? +Oui : Hugo est relié à Félix → tout est atteignable. + +## 2. Nombre de composantes +1 seule composante. + +(Si Hugo n’avait aucun lien : 2 composantes.) + +## 3. Interprétation sociologique +Hugo est **mal intégré**, dépend d’un seul contact (Félix). +Risque d’isolement informationnel ou social. + +## 4. Arêtes pour mieux intégrer Hugo +Exemples : +- Hugo — David +- Hugo — Bob +- Hugo — Chloé + +Justification : Augmente son degré, le relie à plusieurs sous-groupes, diminue sa dépendance à Félix. + +--- \ No newline at end of file diff --git a/Seance_1_TP.ipynb b/Seance_1_TP.ipynb new file mode 100644 index 0000000..c38b765 --- /dev/null +++ b/Seance_1_TP.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Séance 1 TP — Rappels & prise en main de NetworkX\n\n## Objectifs pédagogiques\n- Réviser les définitions : nœuds, arêtes, graphes dirigés / non-dirigés / pondérés.\n- Prendre en main `networkx` pour créer un graphe et le visualiser.\n- Explorer des propriétés simples (degré, nombre de sommets et d'arêtes).\n- Introduire graphes dirigés et pondérés.\n\n## Contexte sociologique\nLes graphes servent à modéliser les relations sociales :\n- Graphe non dirigé : relations symétriques (amitié, collaboration).\n- Graphe dirigé : relations asymétriques (qui suit qui sur Twitter).\n- Graphe pondéré : intensité des relations (fréquence de contact)." + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %pip install networkx matplotlib\n", + "\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercice 1 : Créer un graphe non dirigé" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Construisons un mini-réseau social\n", + "G = nx.Graph()\n", + "G.add_edges_from([\n", + " (\"Alice\", \"Bob\"),\n", + " (\"Bob\", \"Claire\"),\n", + " (\"Alice\", \"David\"),\n", + " (\"Claire\", \"David\"),\n", + "])\n", + "\n", + "print(\"Nœuds :\", list(G.nodes()))\n", + "print(\"Arêtes:\", list(G.edges()))\n", + "\n", + "plt.figure()\n", + "nx.draw(G, with_labels=True, node_color=\"lightblue\", node_size=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions :**\n", + "1. Qui a le plus de voisins / voisines (ami-e-s) ?\n", + "2. Quelles sont les relations réciproques ?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercice 2 : Explorer les propriétés du graphe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Nombre de sommets :\", G.number_of_nodes())\n", + "print(\"Nombre d'arêtes :\", G.number_of_edges())\n", + "print(\"Degrés de chaque sommet :\", dict(G.degree()))\n", + "print(\"Degré moyen :\", sum(dict(G.degree()).values())/G.number_of_nodes())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question :** Quel est le degré moyen et comment l’interpréter sociologiquement ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercice 3 : Graphe dirigé" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DG = nx.DiGraph()\n", + "DG.add_edges_from([\n", + " (\"Alice\", \"Bob\"),\n", + " (\"Bob\", \"Claire\"),\n", + " (\"Claire\", \"Alice\")\n", + "])\n", + "\n", + "plt.figure()\n", + "nx.draw(DG, with_labels=True, node_color=\"lightgreen\", node_size=1000, arrows=True)\n", + "plt.show()\n", + "\n", + "print(\"Degré sortant :\", dict(DG.out_degree()))\n", + "print(\"Degré entrant :\", dict(DG.in_degree()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions :**\n", + "1. Quelle différence avec le graphe non dirigé ?\n", + "2. Que représentent les degrés entrants et sortants sociologiquement (ex : followers) ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercice 4 : Graphe pondéré" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "WG = nx.Graph()\n", + "WG.add_edge(\"Alice\", \"Bob\", weight=5) # forte relation\n", + "WG.add_edge(\"Alice\", \"Claire\", weight=1) # relation faible\n", + "\n", + "print(\"Arêtes avec poids :\", WG.edges(data=True))\n", + "\n", + "# Dessin avec poids visibles\n", + "pos = nx.spring_layout(WG)\n", + "nx.draw(WG, pos, with_labels=True, node_color=\"lightcoral\", node_size=1000)\n", + "labels = nx.get_edge_attributes(WG, \"weight\")\n", + "nx.draw_networkx_edge_labels(WG, pos, edge_labels=labels)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions :**\n", + "1. Comment interpréter le poids d’une relation en sociologie ?\n", + "2. Donnez un exemple concret (fréquence de discussions, intensité d’une amitié)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.x" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/Seance_2_Representations.ipynb b/Seance_2_Representations.ipynb new file mode 100644 index 0000000..73d1045 --- /dev/null +++ b/Seance_2_Representations.ipynb @@ -0,0 +1,419 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 🧮 Séance 2 — Représenter un graphe : matrice, liste d'adjacence, incidence\n", + "\n", + "## Objectifs pédagogiques\n", + "À la fin de cette séance, vous serez capables de :\n", + "- représenter un graphe à l'aide d'une **matrice d'adjacence** et d'une **liste d'adjacence** ;\n", + "- représenter un graphe à l'aide d'une **matrice/liste d'incidence** (sommets × arêtes) ;\n", + "- choisir la représentation la plus adaptée selon la taille et la densité d'un réseau ;\n", + "- interpréter les liens sociaux à partir de ces représentations ;\n", + "- comprendre la **symétrie**, le **degré** et les **liens indirects** dans un réseau ;\n", + "- construire et explorer ces représentations avec **Python**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "En sociologie, on cherche souvent à représenter **les relations entre individus** : qui connaît qui, qui travaille avec qui, qui échange le plus, etc.\n", + "\n", + "Un **graphe** permet de modéliser ces interactions :\n", + "- **nœuds (ou sommets)** → les individus\n", + "- **arêtes (ou liens)** → les relations entre eux\n", + "\n", + "Mais il existe plusieurs manières, plus mathématiques, de représenter ces relations : la **matrice d'adjacence**, la **liste d'adjacence**, et la **matrice/liste d'incidence**. Chacune a ses avantages selon la taille du réseau et ce qu'on veut en faire." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Définition\n", + "Pour un graphe $G = (V, E)$ à *n* sommets, la **matrice d'adjacence** $A$ est une matrice carrée *n × n* où :\n", + "\n", + "$$A_{ij} = \\begin{cases}1 & \\text{si une arête relie le sommet } i \\text{ au sommet } j \\\\0 & \\text{sinon}\\end{cases}$$\n", + "\n", + "- Dans un **graphe non dirigé**, $A$ est **symétrique** : $A_{ij} = A_{ji}$\n", + "- Dans un **graphe dirigé**, $A$ peut être **asymétrique** : $A_{ij} = 1$ ne signifie pas forcément $A_{ji} = 1$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 1 — Représentation papier\n", + "### 1. Graphe de départ\n", + "Voici un petit réseau d'amitié entre cinq personnes :\n", + "\n", + "```\n", + "Alice — Bob — Claire\n", + " │ │\n", + " David Emma\n", + "```\n", + "\n", + "**Consigne :**\n", + "1. Listez les sommets : `Alice, Bob, Claire, David, Emma`.\n", + "2. Complétez la matrice d'adjacence correspondante :\n", + "\n", + "| | Alice | Bob | Claire | David | Emma |\n", + "|:------|:------:|:---:|:------:|:------:|:----:|\n", + "| Alice | 0 | | | | |\n", + "| Bob | | 0 | | | |\n", + "| Claire| | | 0 | | |\n", + "| David | | | | 0 | |\n", + "| Emma | | | | | 0 |\n", + "\n", + "(Remplissez avec des 1 là où il existe un lien.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Questions d'analyse\n", + "1. Quel est le **degré** de chaque individu (combien de liens possède-t-il) ?\n", + "2. Quel est l'individu le plus **central** dans ce réseau ?\n", + "3. Si on ajoute un lien entre *David* et *Emma*, comment la matrice change-t-elle ?\n", + "4. Que signifie le fait que la matrice soit **symétrique** ?\n", + "5. En sociologie, que représenterait une matrice **non symétrique** ? (donnez un exemple concret)\n", + "\n", + "**Explication :**\n", + "\n", + "- Une matrice **symétrique** signifie que les relations sont **réciproques** : si Alice est amie avec Bob, Bob l'est aussi avec Alice. Cela correspond à des relations mutuelles (amitié, collaboration, parenté, etc.).\n", + "- Une matrice **non symétrique** représente des relations **non réciproques** : par exemple, *A suit B* sur Twitter, *A cite B* dans un article scientifique, ou *A supervise B* dans une organisation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 2 — Exploration Python" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Créer et afficher la matrice" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "G = nx.Graph()\n", + "G.add_edges_from([\n", + " (\"Alice\",\"Bob\"),\n", + " (\"Bob\",\"Claire\"),\n", + " (\"Alice\",\"David\"),\n", + " (\"Bob\",\"Emma\")\n", + "])\n", + "\n", + "# Obtenir la matrice d'adjacence\n", + "A = nx.to_numpy_array(G, nodelist=G.nodes())\n", + "print(list(G.nodes()))\n", + "print(A.astype(int))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Visualiser le graphe" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "plt.figure()\n", + "nx.draw(G, with_labels=True, node_color=\"lightblue\", node_size=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Explorer la structure du réseau" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "# Degré de chaque sommet\n", + "print(\"Degré de chaque individu :\", dict(G.degree()))\n", + "\n", + "# Densité du réseau (rapport entre liens existants et liens possibles)\n", + "print(\"Densité du réseau :\", round(nx.density(G), 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Liens indirects : le carré de la matrice" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "A2 = np.linalg.matrix_power(A, 2)\n", + "print(\"A² =\\n\", A2.astype(int))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interprétation :\n", + "- Si `A²[i][j] > 0`, cela signifie qu'il existe **un ami commun** entre les nœuds *i* et *j*.\n", + "- En sociologie : cela mesure les **liens indirects**, c'est-à-dire les \"amis d'amis\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pourquoi ne met-on pas les éléments eux-mêmes au carré ?\n", + "\n", + "Quand on écrit **A²**, cela signifie **A × A**, c'est-à-dire qu'on multiplie la matrice par elle-même selon les règles de la multiplication matricielle.\n", + "\n", + "On ne calcule **pas** $(A_{ij})^2$, car cela n'aurait aucun intérêt :\n", + "- $0^2 = 0$ et $1^2 = 1$, donc la matrice ne changerait pas.\n", + "- Le but du carré matriciel est de compter **les chemins de longueur 2** entre les nœuds.\n", + "\n", + "Formellement :\n", + "$$A^2_{ij} = \\sum_k A_{ik} \\times A_{kj}$$\n", + "\n", + "Cela signifie que $A^2_{ij}$ indique combien de façons il existe d'aller du nœud *i* au nœud *j* en passant par **exactement un autre nœud**.\n", + "\n", + "**Exemple :**\n", + "Si $A =$\n", + "$$\n", + "\\begin{bmatrix}\n", + "0 & 1 & 0 \\\\\n", + "1 & 0 & 1 \\\\\n", + "0 & 1 & 0\n", + "\\end{bmatrix}\n", + "$$\n", + "alors $A^2 =$\n", + "$$\n", + "\\begin{bmatrix}\n", + "1 & 0 & 1 \\\\\n", + "0 & 2 & 0 \\\\\n", + "1 & 0 & 1\n", + "\\end{bmatrix}\n", + "$$\n", + "\n", + "- $A^2_{13} = 1$ : il existe un chemin de longueur 2 entre 1 et 3 (en passant par 2).\n", + "- $A^2_{22} = 2$ : le sommet 2 a deux chemins de longueur 2 qui reviennent à lui-même.\n", + "\n", + "**Résumé :**\n", + "\n", + "| Calcul | Signification | Utilité |\n", + "|:--------|:--------------|:--------|\n", + "| $(A_{ij})^2$ | Élève chaque case au carré | Inutile (0 et 1 inchangés) |\n", + "| $A^2 = A × A$ | Produit matriciel | Donne les **liens indirects** (chemins de longueur 2) |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 3 — Liste d'adjacence\n", + "\n", + "La matrice d'adjacence a un défaut : pour un réseau de 500 personnes, elle contient 500 × 500 = 250 000 cases, même si chacune n'a que 10 amis en moyenne (la plupart des cases valent alors 0). Or les réseaux sociaux réels sont presque toujours **creux** (*sparse*) : chaque individu ne connaît qu'une petite fraction du groupe.\n", + "\n", + "La **liste d'adjacence** répond à ce problème : au lieu d'une grille, on associe à **chaque sommet la liste de ses voisins**. On ne stocke que les liens qui existent réellement.\n", + "\n", + "$$\\text{Alice} \\rightarrow [\\text{Bob}, \\text{David}]$$\n", + "$$\\text{Bob} \\rightarrow [\\text{Alice}, \\text{Claire}, \\text{Emma}]$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Construire la liste d'adjacence" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "# NetworkX construit directement la liste d'adjacence à partir du graphe G\n", + "liste_adjacence = nx.to_dict_of_lists(G)\n", + "\n", + "for personne, amis in liste_adjacence.items():\n", + " print(f\"{personne} → {amis}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Comparer matrice et liste d'adjacence\n", + "\n", + "**Questions :**\n", + "1. Combien de \"cases\" contient la matrice d'adjacence de ce petit réseau à 5 personnes ? Combien de liens contient la liste d'adjacence ?\n", + "2. Pour un réseau de 10 000 personnes où chacune a en moyenne 50 ami·es, quelle représentation prendrait le moins de mémoire ?\n", + "3. Pour répondre rapidement à la question *\"Alice et Emma sont-elles amies ?\"*, quelle représentation est la plus directe ?\n", + "4. Pour répondre à *\"quels sont les ami·es de Bob ?\"*, quelle représentation est la plus directe ?\n", + "\n", + "**En résumé :** la matrice d'adjacence est pratique pour les calculs matriciels (comme $A^2$) et les réseaux **denses**. La liste d'adjacence est plus économe en mémoire et plus rapide à parcourir pour les réseaux **creux** — c'est-à-dire la grande majorité des réseaux sociaux réels." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 4 — Matrice et liste d'incidence\n", + "\n", + "Les représentations précédentes croisent des **sommets avec des sommets** (qui est relié à qui). La **matrice d'incidence** change de point de vue : elle croise des **sommets avec des arêtes** (quel individu est concerné par quelle relation).\n", + "\n", + "Pour un graphe à *n* sommets et *m* arêtes, la matrice d'incidence $B$ est une matrice *n × m* où :\n", + "\n", + "$$B_{ie} = \\begin{cases}1 & \\text{si le sommet } i \\text{ est une extrémité de l'arête } e \\\\0 & \\text{sinon}\\end{cases}$$\n", + "\n", + "Chaque **colonne** représente donc une relation (une arête), avec exactement deux 1 : ses deux extrémités.\n", + "\n", + "Cette idée rejoint celle du **graphe biparti** vue en Introduction (COURS.md — individus ↔ opinions, individus ↔ événements) : on peut voir la matrice d'incidence comme \"qui participe à quelle relation\", exactement comme on notait \"qui participe à quel événement\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Construire la matrice d'incidence" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "# Construction manuelle de la matrice d'incidence avec numpy\n", + "# (une colonne par arête, deux 1 par colonne : les deux extrémités)\n", + "liste_aretes = list(G.edges())\n", + "sommets = list(G.nodes())\n", + "\n", + "B = np.zeros((len(sommets), len(liste_aretes)), dtype=int)\n", + "for colonne, (u, v) in enumerate(liste_aretes):\n", + " B[sommets.index(u), colonne] = 1\n", + " B[sommets.index(v), colonne] = 1\n", + "\n", + "print(\"Arêtes (colonnes) :\", liste_aretes)\n", + "print(sommets)\n", + "print(B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interprétation : chaque colonne de $B$ contient exactement deux 1, ceux des deux personnes impliquées dans cette relation. Une ligne \"pleine de 0\" signifierait un individu isolé, qui n'apparaît dans aucune relation.\n", + "\n", + "### 2. Liste d'incidence\n", + "\n", + "De la même façon qu'on a allégé la matrice d'adjacence en liste d'adjacence, on peut alléger la matrice d'incidence : au lieu d'une grille sommets × arêtes, on liste simplement, pour chaque arête, ses deux extrémités. C'est en fait exactement ce que renvoie `G.edges()` :" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "# La liste d'incidence : pour chaque arête, ses deux extrémités\n", + "for i, (u, v) in enumerate(G.edges()):\n", + " print(f\"Arête {i} : {u} — {v}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Application sociologique :** l'incidence est surtout utile quand la relation elle-même porte de l'information (sa date, son contexte, son type), plutôt que le simple fait qu'elle existe. Par exemple, une matrice d'incidence \"individus × réunions\" (chaque colonne = une réunion, chaque 1 = une personne présente) permet de repérer qui participe aux mêmes réunions que qui — c'est d'ailleurs la logique du graphe biparti vu en introduction, ramenée à une structure sommets × arêtes plutôt que sommets × sommets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Synthèse\n", + "| Concept mathématique | Interprétation sociologique |\n", + "|:----------------------|:----------------------------|\n", + "| 1 dans la matrice d'adjacence | lien direct entre deux individus |\n", + "| 0 dans la matrice d'adjacence | absence de lien |\n", + "| somme d'une ligne (matrice) / longueur d'une liste (liste d'adjacence) | degré (popularité / nombre de connexions) |\n", + "| symétrie | réciprocité des relations |\n", + "| $A^2$ | existence de relations indirectes |\n", + "| densité | cohésion du groupe |\n", + "| liste d'adjacence | représentation compacte, adaptée aux réseaux creux (la plupart des réseaux sociaux réels) |\n", + "| matrice/liste d'incidence | met en avant les relations elles-mêmes (utile si elles portent de l'information : date, contexte, type) |\n", + "\n", + "| Représentation | Taille | Adaptée à |\n", + "|:-----------------|:--------|:-----------|\n", + "| Matrice d'adjacence | *n × n* | petits réseaux, réseaux denses, calculs matriciels |\n", + "| Liste d'adjacence | proportionnelle au nombre de liens | grands réseaux creux (cas le plus fréquent) |\n", + "| Matrice/liste d'incidence | *n × m* (sommets × arêtes) | quand la relation elle-même porte de l'information |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Questions de réflexion\n", + "1. En quoi la matrice d'adjacence permet-elle de **quantifier** un réseau social ?\n", + "2. Si une matrice est très dense, que peut-on en conclure sur le **type de groupe** étudié ?\n", + "3. Comment pourriez-vous pondérer les liens dans cette matrice pour représenter l'**intensité** des relations ?\n", + "4. Vous devez analyser le réseau de tou·tes les utilisateur·rices d'un réseau social (des millions de personnes, chacune avec quelques centaines d'ami·es en moyenne). Justifiez votre choix entre matrice et liste d'adjacence.\n", + "5. Donnez un exemple de situation sociologique où la matrice d'incidence serait plus informative que la matrice d'adjacence." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Seance_3_Mesures_Statistiques.ipynb b/Seance_3_Mesures_Statistiques.ipynb new file mode 100644 index 0000000..2016064 --- /dev/null +++ b/Seance_3_Mesures_Statistiques.ipynb @@ -0,0 +1,214 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 📊 Séance 3 — Mesures statistiques d'un réseau\n", + "\n", + "## Objectifs pédagogiques\n", + "À la fin de cette séance, vous serez capables de :\n", + "- calculer et interpréter le **coefficient de clustering** d'un individu et d'un réseau ;\n", + "- étudier la **distribution des degrés** d'un réseau ;\n", + "- comprendre la notion de **transitivité** ;\n", + "- relier ces mesures à des phénomènes sociologiques (cohésion de groupe, inégalité des connexions).\n", + "\n", + "## Introduction\n", + "Jusqu'ici, nous avons mesuré des propriétés **individuelles** (le degré d'une personne) ou des **paires** (existe-t-il un lien entre deux personnes). Cette séance s'intéresse à des mesures qui caractérisent le réseau **dans son ensemble**, ou des structures locales à plusieurs personnes (des triangles d'amitié)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 1 — Coefficient de clustering\n", + "\n", + "**Question sociologique :** vos ami·es sont-iels ami·es entre eux ? Un individu dont l'entourage forme un groupe très soudé (tout le monde se connaît) n'occupe pas la même position sociale qu'un individu dont l'entourage est éclaté en plusieurs groupes qui ne se connaissent pas — ce second cas est typique d'une personne qui fait le **pont** entre plusieurs cercles (déjà rencontré dans le corrigé du partiel blanc).\n", + "\n", + "Le **coefficient de clustering local** d'un sommet mesure cela : parmi toutes les paires possibles d'ami·es de cette personne, quelle proportion sont elles-mêmes ami·es entre elles (forment un triangle fermé) ?\n", + "\n", + "$$C_i = \\frac{2 \\times \\text{nombre de triangles passant par } i}{\\deg(i) \\times (\\deg(i) - 1)}$$\n", + "\n", + "- $C_i = 1$ : tout l'entourage de $i$ se connaît (clique fermée).\n", + "- $C_i = 0$ : aucun·e ami·e de $i$ ne connaît un·e autre ami·e de $i$ ($i$ est un pur intermédiaire entre des cercles séparés)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Un réseau avec un triangle d'amitié" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# On reprend l'esprit du réseau des séances précédentes, en ajoutant un lien\n", + "# Alice-Claire pour former un triangle d'amitié fermé (Alice, Bob, Claire)\n", + "G = nx.Graph()\n", + "G.add_edges_from([\n", + " (\"Alice\", \"Bob\"),\n", + " (\"Bob\", \"Claire\"),\n", + " (\"Alice\", \"Claire\"), # ferme le triangle\n", + " (\"Bob\", \"David\"),\n", + " (\"David\", \"Emma\"),\n", + "])\n", + "\n", + "plt.figure()\n", + "nx.draw(G, with_labels=True, node_color=\"lightblue\", node_size=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Calculer le coefficient de clustering\n", + "\n", + "NetworkX calcule directement le coefficient de clustering de chaque sommet, et la moyenne sur tout le réseau." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "clustering_individuel = nx.clustering(G)\n", + "print(\"Coefficient de clustering de chaque personne :\")\n", + "for personne, c in clustering_individuel.items():\n", + " print(f\" {personne} : {round(c, 2)}\")\n", + "\n", + "print(\"\\nCoefficient de clustering moyen du réseau :\", round(nx.average_clustering(G), 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interprétation :**\n", + "- **Alice** et **Claire** ont un coefficient de 1 : leur unique paire d'ami·es communs (Bob-Claire pour Alice, Alice-Bob pour Claire) est bien reliée — elles appartiennent à un petit groupe fermé.\n", + "- **Bob** a un coefficient plus faible (≈ 0,33) : il a 3 ami·es, mais seule la paire Alice-Claire se connaît, pas David. Bob commence à jouer un rôle de **pont** entre le triangle Alice/Claire et la branche David-Emma.\n", + "- **David** a un coefficient de 0 : ses deux ami·es (Bob et Emma) ne se connaissent pas entre eux.\n", + "- **Emma**, avec un seul lien, n'a pas de paire d'ami·es à évaluer (coefficient non pertinent, NetworkX renvoie 0 par convention).\n", + "\n", + "**Questions :**\n", + "1. Si on ajoutait le lien Claire-David, comment évoluerait le coefficient de clustering de Bob ?\n", + "2. Un réseau où tout le monde a un coefficient de clustering proche de 1 ressemble-t-il plutôt à une seule grande communauté, ou à plusieurs petits groupes très soudés et peu reliés entre eux ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 2 — Distribution des degrés\n", + "\n", + "**Question sociologique :** dans un réseau, est-ce que tout le monde a à peu près le même nombre de connexions, ou est-ce qu'une minorité concentre l'essentiel des liens (quelques individus très connectés, une majorité peu connectée) ? La **distribution des degrés** répond à cette question : elle montre comment les degrés se répartissent dans l'ensemble du réseau.\n", + "\n", + "Sur notre petit réseau à 5 personnes, une distribution n'est pas très parlante. On va donc utiliser un réseau plus grand, généré automatiquement, mais qui imite un phénomène très réel : dans beaucoup de réseaux sociaux, quelques comptes concentrent énormément de connexions (célébrités, influenceur·euses) pendant que la majorité en a très peu." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "# Un réseau généré automatiquement de 30 \"individus\", où les nouvelles personnes\n", + "# ont tendance à se connecter aux personnes déjà bien connectées (comme sur un vrai réseau social)\n", + "grand_reseau = nx.barabasi_albert_graph(30, 2, seed=42)\n", + "\n", + "degres = [d for _, d in grand_reseau.degree()]\n", + "\n", + "plt.figure()\n", + "plt.hist(degres, bins=range(min(degres), max(degres) + 2), edgecolor=\"black\")\n", + "plt.xlabel(\"Degré (nombre de connexions)\")\n", + "plt.ylabel(\"Nombre d'individus\")\n", + "plt.title(\"Distribution des degrés sur un réseau de 30 personnes\")\n", + "plt.show()\n", + "\n", + "print(\"Degré minimum :\", min(degres))\n", + "print(\"Degré maximum :\", max(degres))\n", + "print(\"Degré moyen :\", round(sum(degres) / len(degres), 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interprétation :** l'histogramme montre que la majorité des individus ont un degré proche du minimum, tandis qu'une petite minorité concentre beaucoup plus de connexions que la moyenne. C'est le signe d'un réseau **inégalitaire** : quelques nœuds jouent un rôle disproportionné dans la structure du groupe.\n", + "\n", + "Nous reviendrons sur ces individus très connectés en **Séance 8 (Centralité)**, où nous verrons plusieurs façons de formaliser précisément la notion d'individu \"influent\" dans un réseau.\n", + "\n", + "**Questions :**\n", + "1. À quoi ressemblerait l'histogramme d'un réseau où tout le monde aurait exactement le même nombre d'ami·es ?\n", + "2. Donnez un exemple réel de réseau social où vous pensez observer une distribution très inégalitaire, et un exemple où elle serait plus équilibrée." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 3 — Transitivité globale\n", + "\n", + "La **transitivité** est une autre façon de mesurer la fermeture des triangles, mais à l'échelle de **tout le réseau** plutôt que personne par personne : parmi toutes les paires d'ami·es-d'ami·es du réseau (deux personnes reliées par un intermédiaire commun), quelle proportion sont elles-mêmes ami·es ?\n", + "\n", + "$$T = \\frac{3 \\times \\text{nombre de triangles dans le réseau}}{\\text{nombre de \"chemins à deux étapes\" (triades ouvertes)}}$$\n", + "\n", + "C'est une mesure proche du clustering moyen, mais elle donne plus de poids aux personnes qui ont beaucoup d'ami·es (contrairement à la moyenne des coefficients individuels, qui traite chaque personne de façon égale)." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "# On revient à notre petit réseau à 5 personnes\n", + "print(\"Transitivité globale du petit réseau :\", round(nx.transitivity(G), 2))\n", + "print(\"Coefficient de clustering moyen (rappel) :\", round(nx.average_clustering(G), 2))\n", + "\n", + "print(\"\\nTransitivité globale du grand réseau (30 personnes) :\", round(nx.transitivity(grand_reseau), 2))\n", + "print(\"Coefficient de clustering moyen du grand réseau :\", round(nx.average_clustering(grand_reseau), 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Synthèse\n", + "\n", + "| Mesure | Ce qu'elle capture | Interprétation sociologique |\n", + "|:--------|:--------------------|:------------------------------|\n", + "| Coefficient de clustering (local) | Les ami·es d'un individu se connaissent-iels entre eux ? | Individu \"au cœur\" d'un groupe soudé vs individu \"pont\" entre plusieurs cercles |\n", + "| Coefficient de clustering (moyen) | Moyenne des coefficients individuels | Niveau général de cohésion, en traitant chaque personne à égalité |\n", + "| Distribution des degrés | Répartition des degrés dans tout le réseau | Réseau égalitaire vs réseau concentré autour de quelques individus très connectés |\n", + "| Transitivité globale | Proportion de triades fermées, à l'échelle du réseau | Cohésion globale du réseau, en donnant plus de poids aux individus très connectés |\n", + "\n", + "## Questions de réflexion\n", + "1. Un réseau avec un coefficient de clustering élevé mais une distribution des degrés très inégalitaire : que pouvez-vous en déduire sur sa structure ?\n", + "2. En quoi le coefficient de clustering local est-il lié à la notion de **pont** entre communautés déjà rencontrée dans le partiel blanc ?\n", + "3. Comment ces mesures pourraient-elles aider à comparer deux réseaux sociaux différents (par exemple une promotion d'étudiant·es et un réseau professionnel) ?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Seance_4_BFS_DFS.ipynb b/Seance_4_BFS_DFS.ipynb new file mode 100644 index 0000000..baea499 --- /dev/null +++ b/Seance_4_BFS_DFS.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Séance 4 — Parcours & distances (BFS / DFS)\n\n## Objectifs pédagogiques\n- Comprendre les notions de **chemin**, **distance** et ** composantes connexes**.\n- Distinguer **BFS** (parcours en largeur) et **DFS** (parcours en profondeur).\n- Utiliser `networkx` pour calculer des plus courts chemins dans un graphe **non pondéré**.\n- Relier les distances à des interprétations sociologiques (proximité, relais, \"six degrés\").\n\n## Rappels\n- Dans un **graphe non pondéré**, un **plus court chemin** est le chemin avec le **moins d'arêtes**.\n- **BFS** explore par **couches** (distance croissante) et permet de trouver un plus court chemin.\n- **DFS** explore en **profondeur** (par branches) ; utile pour détecter des cycles/composantes mais **ne garantit pas** un plus court chemin." + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %pip install networkx matplotlib\n", + "\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "from collections import deque" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) Graphe de départ : petit réseau social" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Vous pouvez modifier/étendre ce graphe\n", + "G = nx.Graph()\n", + "G.add_edges_from([\n", + " (\"Alice\",\"Bob\"),\n", + " (\"Bob\",\"Claire\"),\n", + " (\"Claire\",\"Emma\"),\n", + " (\"Alice\",\"David\"),\n", + " (\"David\",\"Emma\"),\n", + " (\"Emma\",\"Fanny\"),\n", + " (\"Bob\",\"Gaston\"),\n", + "])\n", + "\n", + "# Visualisation simple\n", + "plt.figure()\n", + "nx.draw(G, with_labels=True)\n", + "plt.show()\n", + "\n", + "print(\"Nœuds :\", list(G.nodes()))\n", + "print(\"Arêtes:\", list(G.edges()))\n", + "print(\"Composantes connexes:\", nx.number_connected_components(G))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question :** Y a-t-il un unique groupe connecté, ou plusieurs ? Impact sociologique : des individus isolés auront des distances infinies vers le reste." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) Plus courts chemins et distances avec NetworkX" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "source, cible = \"Alice\", \"Fanny\"\n", + "chemin = nx.shortest_path(G, source=source, target=cible) # plus court chemin (non pondéré)\n", + "dist = nx.shortest_path_length(G, source=source, target=cible)\n", + "print(f\"Plus court chemin de {source} à {cible} :\", chemin)\n", + "print(f\"Distance (nombre d'arêtes) :\", dist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice 2.1** : testez 3 autres paires (par ex. `('Gaston','Fanny')`, `('Alice','Claire')`, `('David','Bob')`).\n", + " **Exercice 2.2** : si vous supprimez l'arête `('David','Emma')`, que devient la distance `Alice→Fanny` ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3) Implémenter un BFS pédagogique (pour comprendre l'ordre d'exploration)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def bfs_layers(graph, start):\n", + " \"\"\"Retourne l'ordre de visite et les couches de distance (dict: noeud -> distance)\n", + " BFS avec file d'attente (deque).\n", + " \"\"\"\n", + " visited = set([start])\n", + " dist = {start: 0}\n", + " order = []\n", + " q = deque([start])\n", + "\n", + " while q:\n", + " u = q.popleft()\n", + " order.append(u)\n", + " for v in graph.neighbors(u):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " dist[v] = dist[u] + 1\n", + " q.append(v)\n", + " return order, dist\n", + "\n", + "order, dist = bfs_layers(G, \"Alice\")\n", + "print(\"Ordre BFS depuis Alice:\", order)\n", + "print(\"Distances depuis Alice:\", dist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice 3.1** : Comparez les **distances** renvoyées par `bfs_layers` et `nx.shortest_path_length` pour 3 nœuds.\n", + " **Exercice 3.2** : Quel nœud est le plus proche (distance minimale) d'**Alice** ? Le plus lointain ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4) Implémenter un DFS (ordre d'exploration par branches)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def dfs_order(graph, start, visited=None, order=None):\n", + " if visited is None: visited = set()\n", + " if order is None: order = []\n", + " visited.add(start)\n", + " order.append(start)\n", + " for v in graph.neighbors(start):\n", + " if v not in visited:\n", + " dfs_order(graph, v, visited, order)\n", + " return order\n", + "\n", + "print(\"Ordre DFS depuis Alice:\", dfs_order(G, \"Alice\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice 4.1** : Pourquoi **l'ordre DFS** diffère-t-il souvent de l'ordre BFS ?\n", + " **Exercice 4.2** : DFS trouve-t-il un plus court chemin ? Expliquez avec un contre-exemple si possible." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5) Distances globales : diamètre & distance moyenne (si connexe)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if nx.is_connected(G):\n", + " # Diamètre = plus longue distance entre deux nœuds\n", + " d = nx.diameter(G)\n", + " # Distance moyenne (longueur moyenne des plus courts chemins)\n", + " apl = nx.average_shortest_path_length(G)\n", + " print(\"Diamètre:\", d)\n", + " print(\"Distance moyenne:\", apl)\n", + "else:\n", + " print(\"Le graphe n'est pas connexe : diamètre et distance moyenne ne sont pas définis globalement.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice 5.1** : Ajoutez/supprimez une arête et observez l'effet sur la distance moyenne. Interprétez sociologiquement (ex. apparition d'un **pont** qui réduit les distances)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6) Étude guidée : \"six degrés de séparation\" (mini-expérience)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Construisons un graphe en anneau + quelques liens longue portée pour réduire drastiquement les distances\n", + "H = nx.cycle_graph(12) # 12 individus en cercle (0..11)\n", + "H = nx.relabel_nodes(H, {i: f\"P{i}\" for i in range(12)})\n", + "H.add_edge(\"P0\",\"P6\") # raccourci longue portée\n", + "H.add_edge(\"P3\",\"P9\") # autre raccourci\n", + "\n", + "plt.figure()\n", + "nx.draw(H, with_labels=True)\n", + "plt.show()\n", + "\n", + "print(\"Connexe:\", nx.is_connected(H))\n", + "print(\"Distance moyenne:\", nx.average_shortest_path_length(H))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions**\n", + "1. Que se passe-t-il si on **retire** les liens longue portée ?\n", + "2. Pourquoi quelques liens inter-groupes peuvent-ils **réduire fortement** les distances ?\n", + "3. Donnez une interprétation en termes de diffusion d'une rumeur/idée." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7) Synthèse écrite\n", + "- Comparez BFS et DFS sur votre graphe.\n", + "- Donnez 2 exemples de distances pertinentes en sociologie (ex. accès à l'information, recrutement).\n", + "- Proposez une **hypothèse** : l'ajout d'un lien entre deux groupes réduira X ; comment le tester ?" + ] + }, + { + "cell_type": "markdown", + "id": "75d566ac", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.x" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/Seance_5_Composantes_Diffusion.ipynb b/Seance_5_Composantes_Diffusion.ipynb new file mode 100644 index 0000000..5c39ee1 --- /dev/null +++ b/Seance_5_Composantes_Diffusion.ipynb @@ -0,0 +1,263 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Séance 5 — Diffusion d'une information (BFS) et composantes connexes\n", + "\n", + "## Objectifs\n", + "- Comprendre intuitivement ce qu'est un **parcours en largeur** (BFS) et relier ce concept à la **diffusion d'une information, d'une rumeur ou d'une idée** dans un réseau social.\n", + "- Mesurer des **distances sociales** et comparer l'effet du point de départ de la diffusion.\n", + "- Comparer intuitivement le BFS au **parcours en profondeur (DFS)**.\n", + "- Identifier les **composantes connexes** d'un réseau et interpréter la position d'un individu isolé." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Introduction sociologique : une rumeur se propage\n", + "Imaginez qu'une personne — appelons-la **Alice** — partage une nouvelle dans son groupe d'ami·es.\n", + "Chacun·e la répète à ses proches, et ainsi de suite.\n", + "\n", + "L'information se diffuse **par cercles successifs** : d'abord les ami·es direct·es d'Alice, puis les ami·es des ami·es, etc.\n", + "\n", + "C'est exactement ce que fait un **parcours en largeur** : explorer un réseau *niveau par niveau*.\n", + "\n", + "→ En sociologie des réseaux, cela correspond à la **distance sociale** entre individus." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Exemple manuel : propagation d'une information\n", + "Considérons ce réseau à 7 personnes :\n", + "\n", + "```\n", + "Alice — Bob — Chloé — David\n", + " │ │ │\n", + " Emma Félix Gaël\n", + "```\n", + "\n", + "Supposons qu'Alice commence à diffuser une nouvelle.\n", + "\n", + "**Étapes :**\n", + "1. Niveau 0 : Alice\n", + "2. Niveau 1 : les personnes directement connectées à Alice → {Bob, Emma}\n", + "3. Niveau 2 : les ami·es de Bob (hors Alice) → {Chloé, Félix}\n", + "4. Niveau 3 : les ami·es de Chloé → {David, Gaël}\n", + "\n", + "Chaque *niveau* correspond à une **distance sociale** par rapport à la source." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Construire le réseau avec Python" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Création du graphe\n", + "G = nx.Graph()\n", + "G.add_edges_from([\n", + " ('Alice', 'Bob'), ('Alice', 'Emma'),\n", + " ('Bob', 'Chloé'), ('Bob', 'Félix'),\n", + " ('Chloé', 'David'), ('Chloé', 'Gaël')\n", + "])\n", + "\n", + "pos = nx.spring_layout(G, seed=0)\n", + "plt.figure(figsize=(6,4))\n", + "nx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=1000)\n", + "plt.title('Réseau social — Diffusion de la nouvelle')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Explorer le réseau en largeur depuis Alice\n", + "On peut utiliser la fonction `nx.bfs_tree()` pour construire un arbre de parcours à partir d'un point de départ." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "source = 'Alice'\n", + "T = nx.bfs_tree(G, source=source)\n", + "\n", + "plt.figure(figsize=(6,4))\n", + "nx.draw(T, with_labels=True, node_color='lightgreen', node_size=1000)\n", + "plt.title(f'Arbre BFS à partir de {source}')\n", + "plt.show()\n", + "\n", + "print('Ordre du parcours BFS :')\n", + "print(list(nx.bfs_edges(G, source)))\n", + "\n", + "distances = nx.single_source_shortest_path_length(G, source)\n", + "print('\\nDistances sociales depuis Alice :')\n", + "for k, v in distances.items():\n", + " print(f'{k} : {v}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interprétation sociologique\n", + "- Les **valeurs faibles** indiquent des individus **proches** d'Alice (accès direct à l'information).\n", + "- Les **valeurs élevées** indiquent des individus **périphériques** : ils n'apprennent la nouvelle que tardivement.\n", + "\n", + "→ Cela permet d'analyser la **vitesse de diffusion** ou la **position sociale** dans le réseau.\n", + "\n", + "**Questions :**\n", + "1. Dans quel ordre les personnes reçoivent-elles l'information ?\n", + "2. Quel est le rôle d'Alice dans cette diffusion ?\n", + "3. Quelle est la distance maximale observée ? Que signifie-t-elle socialement ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Comparaison intuitive : BFS vs DFS\n", + "Pour bien comprendre la logique du BFS (revu en Séance 4), comparons-le brièvement à l'autre stratégie : le **DFS**.\n", + "\n", + "| Stratégie | Métaphore | Manière d'explorer | Exemple |\n", + "|:-----------|:-----------|:------------------|:---------|\n", + "| BFS (largeur) | diffusion sociale | explore les cercles autour de la source | bouche-à-oreille, message collectif |\n", + "| DFS (profondeur) | exploration ciblée | suit un chemin jusqu'au bout avant de revenir | enquête, filiation, exploration hiérarchique |\n", + "\n", + "Le BFS **propagera rapidement une information**, tandis que le DFS **creusera une piste en profondeur**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Le point de départ change-t-il tout ? Recommençons depuis Chloé\n", + "Recommençons la diffusion mais cette fois **à partir de Chloé**. Observez les différences de structure et de distances." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "source2 = 'Chloé'\n", + "T2 = nx.bfs_tree(G, source=source2)\n", + "\n", + "plt.figure(figsize=(6,4))\n", + "nx.draw(T2, with_labels=True, node_color='lightcoral', node_size=1000)\n", + "plt.title(f'Arbre BFS à partir de {source2}')\n", + "plt.show()\n", + "\n", + "print('Distances sociales depuis Chloé :')\n", + "for k, v in nx.single_source_shortest_path_length(G, source2).items():\n", + " print(f'{k} : {v}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions :**\n", + "1. Quelles différences remarquez-vous par rapport au départ depuis Alice ?\n", + "2. Quelle personne semble jouer un rôle « de pont » dans la diffusion (voir la notion de coefficient de clustering, Séance 3) ?\n", + "3. Que peut-on dire de la position de Chloé dans le réseau ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Composantes connexes : un acteur isolé\n", + "\n", + "Jusqu'ici, tout le monde pouvait recevoir l'information de proche en proche. Mais que se passe-t-il si une personne n'est reliée à **personne** ?\n", + "\n", + "Ajoutons une nouvelle personne, **Hugo**, qui n'est reliée à personne dans le réseau." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "# Ajout d'une personne isolée\n", + "G.add_node('Hugo')\n", + "\n", + "plt.figure(figsize=(6,4))\n", + "pos = nx.spring_layout(G, seed=1)\n", + "nx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=1000)\n", + "plt.title('Réseau social avec une personne isolée (Hugo)')\n", + "plt.show()\n", + "\n", + "print('Composantes connexes du graphe :')\n", + "for i, comp in enumerate(nx.connected_components(G)):\n", + " print(f'Composante {i+1} : {comp}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interprétation :** une **composante connexe** est un sous-ensemble du réseau où tout le monde peut s'atteindre par une suite de liens, mais qui n'a aucun lien vers le reste du réseau. Un individu isolé forme, à lui seul, sa propre composante.\n", + "\n", + "**Questions :**\n", + "1. Hugo peut-il recevoir l'information ? Pourquoi ?\n", + "2. Que représente un acteur isolé dans un réseau social réel ?\n", + "3. Comment cette notion d'isolement peut-elle s'interpréter en sociologie (accès à l'information, exclusion sociale, marginalité) ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Pour aller plus loin (facultatif)\n", + "1. Ajoutez de nouvelles relations pour rendre le graphe plus connecté.\n", + "2. Essayez de trouver une configuration où tout le monde est relié en deux étapes maximum.\n", + "3. Reliez Hugo au reste du réseau : combien de composantes reste-t-il ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Synthèse\n", + "- Le **BFS** explore un réseau **par cercles successifs** à partir d'une source, et permet de mesurer la **distance sociale**.\n", + "- Le point de départ de la diffusion **change radicalement** l'ordre et la vitesse à laquelle l'information circule.\n", + "- Le **DFS** suit une logique d'exploration **en profondeur**, utile pour détecter des sous-groupes plutôt que pour simuler une diffusion rapide.\n", + "- Une **composante connexe** est un sous-groupe où tout le monde est atteignable, mais coupé du reste. Un individu isolé ne reçoit jamais l'information : cela peut traduire une exclusion ou une marginalisation sociale." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Seance_5_Composantes_Diffusion_Corrige.md b/Seance_5_Composantes_Diffusion_Corrige.md new file mode 100644 index 0000000..0f28c48 --- /dev/null +++ b/Seance_5_Composantes_Diffusion_Corrige.md @@ -0,0 +1,105 @@ +# Séance 5 — Diffusion d'une information (BFS) et composantes connexes +### Fiche enseignant + +## Objectifs pédagogiques +- Introduire intuitivement le concept de **parcours en largeur (BFS)** à travers la métaphore de la **diffusion d'une information** dans un réseau social. +- Faire le lien entre la **distance dans le graphe** et la **distance sociale** entre individus. +- Montrer que le **point de départ** d'une diffusion change radicalement son déroulement. +- Introduire la notion de **composantes connexes** à travers un acteur isolé. +- Comparer BFS et DFS sans mathématiques : deux logiques d'exploration du réseau. + +--- + +## Prérequis +- Avoir compris les notions de **graphe**, **sommets**, **arêtes** (Séance 1). +- Avoir manipulé BFS et DFS une première fois (Séance 4). +- Avoir manipulé un graphe simple avec `networkx` (ajout de sommets, arêtes, visualisation). + +--- + +## Durée estimée +**2h**, séance unique (fusion des anciennes séances 6 et 6bis — leur contenu se recoupait largement). + +--- + +## Matériel / outils +- Notebook `Seance_5_Composantes_Diffusion.ipynb` +- Python avec `networkx` et `matplotlib` + +--- + +## Déroulé pédagogique + +### 1. Introduction sociologique +**But :** ancrer le BFS dans une situation concrète. + +Expliquer : +> Une information (rumeur, message, idée) se propage dans un réseau social. +> Certain·es la reçoivent directement, d'autres plus tard. +> Le BFS modélise cette propagation *par cercles de proximité*. + +**Point clé à dire** : « En BFS, on explore *niveau par niveau* — comme une onde sociale. » + +### 2. Exemple manuel : propagation pas à pas +Simulation à la main sur le petit graphe (Alice — Bob/Emma — Chloé/Félix — David/Gaël) : faire remplir le tableau des niveaux avant de passer à Python. + +### 3-4. Construction du réseau et parcours BFS depuis Alice +**Résultat attendu (ordre BFS) :** +``` +[('Alice', 'Bob'), ('Alice', 'Emma'), ('Bob', 'Chloé'), ('Bob', 'Félix'), ('Chloé', 'David'), ('Chloé', 'Gaël')] +``` +**Distances depuis Alice :** Alice 0, Bob/Emma 1, Chloé/Félix 2, David/Gaël 3. + +**À dire :** la distance = nombre d'étapes pour atteindre une personne ; plus elle est grande, plus la personne est éloignée du centre du réseau. Lien sociologique : proximité sociale, vitesse d'accès à l'information. + +### 5. Comparaison BFS / DFS (à l'oral) +| Stratégie | Métaphore | Manière d'explorer | Exemple | +|:-----------|:-----------|:------------------|:---------| +| BFS (largeur) | diffusion sociale | explore les cercles autour de la source | bouche-à-oreille, message collectif | +| DFS (profondeur) | exploration ciblée | suit un chemin jusqu'au bout avant de revenir | enquête, filiation, exploration hiérarchique | + +### 6. Recommencer depuis Chloé +**Résultat attendu (exemple) :** +``` +[('Chloé', 'Bob'), ('Chloé', 'David'), ('Chloé', 'Gaël'), ('Bob', 'Alice'), ('Bob', 'Félix'), ('Alice', 'Emma')] +``` +**Analyse :** Chloé devient un nouveau centre de diffusion, la profondeur du graphe diminue (elle est plus "au milieu"). Chloé relie deux sous-groupes : c'est une **personne-pont** — à relier au coefficient de clustering vu en Séance 3 (un pont a un coefficient de clustering faible : ses ami·es ne se connaissent pas entre eux). + +**Point clé à dire :** le point de départ de la diffusion influence sa vitesse et sa portée — une info ne se propage pas pareil selon qui la lance en premier. + +### 7. Composantes connexes : Hugo l'isolé +Après `G.add_node('Hugo')` : +``` +Composante 1 : {'Alice', 'Bob', 'Chloé', 'Emma', 'Félix', 'David', 'Gaël'} +Composante 2 : {'Hugo'} +``` +**À dire :** le graphe n'est plus connexe : deux composantes distinctes. Hugo ne reçoit aucune information. + +**Lien sociologique :** un acteur isolé symbolise une **exclusion sociale** : il n'est intégré à aucun cercle relationnel. + +### 8. Discussion finale +**Questions à lancer :** +- Que se passe-t-il si un individu n'est relié à personne ? +- Si plusieurs personnes lancent l'info en même temps ? +- Qui apprend la nouvelle le plus vite ? Pourquoi ? + +**Lien avec la sociologie des réseaux :** notions de **centralité**, **vitesse de diffusion**, **position périphérique**, **effet d'isolation**. + +--- + +## Interprétations sociologiques clés à souligner +| Concept Python | Traduction sociologique | +|:----------------|:------------------------| +| Distance | Proximité ou éloignement social | +| Composante connexe | Groupe / sous-communauté | +| Sommet central | Individu influent, pivot relationnel | +| Sommet isolé | Exclusion, absence de lien social | +| BFS | Diffusion collective, propagation rapide | + +## Notes pédagogiques +- Insister sur le **lien entre structure et diffusion** : le BFS donne une première intuition du "pouvoir de connexion" dans un réseau social. +- Éviter tout formalisme algorithmique (pas de file, pas de pseudo-code) — le DFS/BFS formels ont déjà été codés en Séance 4, ici on reste sur l'interprétation sociologique. +- Valoriser les représentations graphiques et le vocabulaire sociologique (proximité, cercles, diffusion, influence). + +**Transition possible :** +Cette séance consolide le BFS (Séance 4) et introduit les composantes connexes. Le DFS ayant déjà été vu et comparé au BFS dès la Séance 2/4, ces notions sont désormais réunies : elles préparent directement les étudiantes à l'évaluation (partiel blanc), qui reprend BFS, DFS et composantes connexes dans un contexte de lecture de réseau. La séance suivante (6-7) introduit les **graphes pondérés** et l'algorithme de **Dijkstra**. diff --git a/Seance_8_Centralite.ipynb b/Seance_8_Centralite.ipynb new file mode 100644 index 0000000..bdb68c9 --- /dev/null +++ b/Seance_8_Centralite.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 🎯 Séance 8 — Centralité : qui est influent dans un réseau ?\n", + "\n", + "## Objectifs pédagogiques\n", + "À la fin de cette séance, vous serez capables de :\n", + "- calculer et interpréter la **centralité de degré**, de **proximité** et d'**intermédiarité** ;\n", + "- comprendre que ces trois mesures répondent à des questions différentes, et peuvent désigner des individus différents comme \"centraux\" ;\n", + "- relier ces mesures à des situations sociologiques concrètes (popularité, accès rapide à l'information, position de pont).\n", + "\n", + "## Introduction\n", + "En Séance 3, on a vu qu'un réseau peut être **inégalitaire** : certain·es individus concentrent beaucoup plus de connexions que d'autres. Mais \"avoir beaucoup d'ami·es\" n'est qu'une façon d'être important dans un réseau — il en existe d'autres. Cette séance formalise plusieurs notions de **centralité**, c'est-à-dire plusieurs manières de répondre à la question : *qui compte le plus dans ce réseau, et pourquoi ?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### On reprend le réseau de la Séance 5\n", + "Le même réseau à 7 personnes que celui utilisé pour la diffusion en BFS :\n", + "\n", + "```\n", + "Alice — Bob — Chloé — David\n", + " │ │ │\n", + " Emma Félix Gaël\n", + "```" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "\n", + "G = nx.Graph()\n", + "G.add_edges_from([\n", + " ('Alice', 'Bob'), ('Alice', 'Emma'),\n", + " ('Bob', 'Chloé'), ('Bob', 'Félix'),\n", + " ('Chloé', 'David'), ('Chloé', 'Gaël')\n", + "])\n", + "\n", + "plt.figure(figsize=(6,4))\n", + "pos = nx.spring_layout(G, seed=0)\n", + "nx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 1 — Centralité de degré\n", + "\n", + "C'est la mesure la plus simple, déjà rencontrée : **combien de connexions directes** une personne a-t-elle ? NetworkX la normalise entre 0 et 1 en divisant le degré par le nombre maximal de connexions possibles ($n - 1$).\n", + "\n", + "$$C_{degré}(i) = \\frac{\\deg(i)}{n - 1}$$\n", + "\n", + "**Interprétation sociologique :** popularité directe, nombre de contacts immédiats." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "centralite_degre = nx.degree_centrality(G)\n", + "for personne, c in sorted(centralite_degre.items(), key=lambda x: -x[1]):\n", + " print(f\"{personne} : {round(c, 2)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 2 — Centralité de proximité (closeness)\n", + "\n", + "Une personne peut avoir peu de connexions directes, mais être malgré tout **proche de tout le monde** grâce à des chemins courts. La centralité de proximité mesure l'inverse de la distance moyenne entre une personne et toutes les autres.\n", + "\n", + "$$C_{proximité}(i) = \\frac{n - 1}{\\sum_j d(i, j)}$$\n", + "\n", + "Plus une personne est proche (en moyenne) de tout le monde, plus sa centralité de proximité est élevée.\n", + "\n", + "**Interprétation sociologique :** vitesse d'accès à l'information — une personne avec une forte proximité reçoit et diffuse l'information rapidement dans tout le réseau (voir Séances 4 et 5, BFS)." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "centralite_proximite = nx.closeness_centrality(G)\n", + "for personne, c in sorted(centralite_proximite.items(), key=lambda x: -x[1]):\n", + " print(f\"{personne} : {round(c, 2)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 3 — Centralité d'intermédiarité (betweenness)\n", + "\n", + "Certaines personnes ne sont ni très connectées, ni particulièrement proches de tout le monde en moyenne — mais elles occupent une position stratégique : de nombreux **plus courts chemins entre les autres passent par elles**. Ce sont les **ponts** entre des groupes qui, sans elles, seraient déconnectés (cette notion de pont a déjà été rencontrée en Séance 3 avec le coefficient de clustering, et en Séance 5 avec Chloé).\n", + "\n", + "$$C_{intermédiarité}(i) = \\sum_{j \\neq k \\neq i} \\frac{\\text{nombre de plus courts chemins entre } j \\text{ et } k \\text{ passant par } i}{\\text{nombre total de plus courts chemins entre } j \\text{ et } k}$$\n", + "\n", + "**Interprétation sociologique :** pouvoir de contrôle sur la circulation de l'information — une personne avec une forte intermédiarité peut filtrer, ralentir ou accélérer ce qui passe d'un groupe à l'autre." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "centralite_intermediarite = nx.betweenness_centrality(G)\n", + "for personne, c in sorted(centralite_intermediarite.items(), key=lambda x: -x[1]):\n", + " print(f\"{personne} : {round(c, 2)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 4 — Comparer les trois mesures\n", + "\n", + "**Questions :**\n", + "1. La personne la plus centrale change-t-elle selon la mesure utilisée ?\n", + "2. Bob et Chloé ont le même degré (3 connexions chacun·e). Ont-ils/elles la même centralité d'intermédiarité ? Pourquoi ?\n", + "3. Emma, Félix, David et Gaël ont tou·tes un degré de 1. Ont-iels tou·tes la même centralité de proximité ? Pourquoi ?\n", + "4. Imaginez une personne qui a un très fort coefficient de clustering (Séance 3) — a-t-elle plutôt tendance à avoir une forte ou une faible centralité d'intermédiarité ? Justifiez." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Synthèse\n", + "\n", + "| Mesure | Question qu'elle pose | Type d'individu qu'elle repère |\n", + "|:--------|:------------------------|:----------------------------------|\n", + "| Centralité de degré | Combien de connexions directes ? | La personne \"populaire\" |\n", + "| Centralité de proximité | À quelle distance moyenne de tout le monde ? | La personne qui diffuse/reçoit vite l'info |\n", + "| Centralité d'intermédiarité | Combien de plus courts chemins passent par moi ? | La personne \"pont\", qui contrôle la circulation entre groupes |\n", + "\n", + "## Questions de réflexion\n", + "1. Dans une entreprise, un individu avec une forte centralité d'intermédiarité mais un faible degré perd son emploi. Quelles conséquences cela peut-il avoir sur la circulation de l'information dans l'organisation ?\n", + "2. Sur un réseau social, qui serait plutôt \"central en degré\" (beaucoup d'abonné·es) sans être \"central en intermédiarité\" ? Donnez un exemple.\n", + "3. En quoi la centralité d'intermédiarité peut-elle représenter un **pouvoir**, y compris pour une personne peu visible dans le réseau ?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Seance_9_Coloration.ipynb b/Seance_9_Coloration.ipynb new file mode 100644 index 0000000..3cb6651 --- /dev/null +++ b/Seance_9_Coloration.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 🎨 Séance 9 — Coloration de graphes\n", + "\n", + "## Objectifs pédagogiques\n", + "À la fin de cette séance, vous serez capables de :\n", + "- comprendre le **problème de coloration** d'un graphe et la notion de **nombre chromatique** ;\n", + "- appliquer un algorithme glouton de coloration avec `networkx` ;\n", + "- reconnaître des situations sociologiques et organisationnelles modélisables par la coloration de graphe.\n", + "\n", + "## Introduction\n", + "Jusqu'ici, les arêtes de nos graphes représentaient des liens positifs (amitié, communication). La coloration de graphe part d'une idée différente : des arêtes qui représentent des **incompatibilités**, des **conflits**, des situations où deux sommets ne doivent surtout pas se retrouver \"dans la même case\".\n", + "\n", + "**Exemple concret :** vous devez organiser le planning des examens d'un semestre. Deux matières sont *en conflit* si des étudiant·es sont inscrit·es aux deux — dans ce cas, elles ne peuvent pas être programmées au même créneau. Combien de créneaux minimum faut-il pour que personne n'ait deux examens en même temps ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 1 — Le problème de coloration\n", + "\n", + "**Colorier un graphe**, c'est attribuer une couleur (ou un créneau, une catégorie...) à chaque sommet, de telle sorte que **deux sommets reliés par une arête n'aient jamais la même couleur**.\n", + "\n", + "Le **nombre chromatique** d'un graphe est le nombre minimum de couleurs nécessaires pour y parvenir. Plus un graphe a de conflits imbriqués les uns dans les autres, plus son nombre chromatique est élevé.\n", + "\n", + "**Question de réflexion préalable :** si trois matières sont toutes en conflit deux à deux (Maths-Info, Info-Socio, Maths-Socio), combien de créneaux minimum faut-il pour elles trois ? Pourquoi ne peut-on pas faire avec moins ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 2 — Un graphe de conflits d'examens\n", + "\n", + "Voici le graphe de conflits entre 5 matières :\n", + "\n", + "```\n", + " Maths — Info\n", + " │ │\n", + " Socio Anglais\n", + " \\ /\n", + " Sport\n", + "```\n", + "\n", + "(Maths-Info, Info-Anglais, Anglais-Sport, Sport-Socio, Socio-Maths sont en conflit)\n", + "\n", + "**À la main :** essayez de proposer une répartition en créneaux (couleurs) avant de passer à Python. Combien de créneaux minimum pensez-vous nécessaires ?" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "\n", + "G = nx.Graph()\n", + "G.add_edges_from([\n", + " (\"Maths\", \"Info\"),\n", + " (\"Info\", \"Anglais\"),\n", + " (\"Anglais\", \"Sport\"),\n", + " (\"Sport\", \"Socio\"),\n", + " (\"Socio\", \"Maths\"),\n", + "])\n", + "\n", + "plt.figure()\n", + "nx.draw(G, with_labels=True, node_color=\"lightblue\", node_size=1500)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 3 — Colorier automatiquement avec un algorithme glouton\n", + "\n", + "L'algorithme **glouton** de coloration parcourt les sommets un par un, et attribue à chacun **la plus petite couleur disponible** qui n'est pas déjà utilisée par l'un de ses voisins déjà colorié. C'est simple et rapide, mais il ne garantit **pas toujours** d'obtenir le nombre chromatique minimal — cela dépend de l'ordre dans lequel les sommets sont traités." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "# NetworkX propose directement un algorithme glouton de coloration\n", + "coloration = nx.greedy_color(G, strategy=\"largest_first\")\n", + "\n", + "print(\"Créneau (couleur) attribué à chaque matière :\")\n", + "for matiere, creneau in coloration.items():\n", + " print(f\" {matiere} : créneau {creneau}\")\n", + "\n", + "nb_creneaux = len(set(coloration.values()))\n", + "print(f\"\\nNombre de créneaux utilisés : {nb_creneaux}\")\n", + "\n", + "# Visualisation avec les couleurs attribuées\n", + "palette = [\"lightblue\", \"lightgreen\", \"lightcoral\", \"khaki\", \"plum\"]\n", + "couleurs_sommets = [palette[coloration[noeud]] for noeud in G.nodes()]\n", + "\n", + "plt.figure()\n", + "nx.draw(G, with_labels=True, node_color=couleurs_sommets, node_size=1500)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interprétation :** ce graphe de conflits forme un **cycle** à 5 sommets (chaque matière est en conflit avec exactement 2 autres, en boucle). Sur un cycle à un nombre **impair** de sommets, il est mathématiquement impossible de s'en sortir avec seulement 2 créneaux : en alternant 2 couleurs autour du cycle, on finit toujours par faire se toucher deux sommets de la même couleur. Il faut donc au minimum 3 créneaux.\n", + "\n", + "**Questions :**\n", + "1. Le résultat de l'algorithme glouton correspond-il à votre proposition manuelle ?\n", + "2. Si vous ajoutiez une 6ᵉ matière en conflit uniquement avec Maths, combien de créneaux seraient nécessaires ? Pourquoi ?\n", + "3. Un graphe **sans aucune arête** (aucun conflit) a quel nombre chromatique ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Synthèse\n", + "\n", + "| Concept | Signification | Exemple d'application |\n", + "|:---------|:----------------|:--------------------------|\n", + "| Sommet | Élément à catégoriser (matière, tâche, personne, fréquence radio...) | Un examen |\n", + "| Arête | Incompatibilité entre deux sommets | Deux matières partagées par les mêmes étudiant·es |\n", + "| Couleur | Catégorie/créneau attribué | Un créneau horaire |\n", + "| Nombre chromatique | Nombre minimum de catégories nécessaires | Nombre minimum de créneaux d'examens |\n", + "| Algorithme glouton | Attribue la plus petite couleur libre à chaque sommet, dans un ordre donné | Rapide, mais pas toujours optimal |\n", + "\n", + "## Questions de réflexion\n", + "1. Donnez un autre exemple sociologique ou organisationnel modélisable par la coloration de graphe (répartition en groupes de travail, attribution de bureaux, allocation de fréquences...).\n", + "2. Pourquoi un algorithme glouton, malgré sa simplicité, peut-il être un bon compromis en pratique même s'il ne garantit pas le minimum absolu ?\n", + "3. En quoi la coloration de graphe diffère-t-elle fondamentalement des mesures vues en Séance 8 (centralité) ? Que cherche-t-on à optimiser dans chaque cas ?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/TP_1.ipynb b/TP_1.ipynb deleted file mode 100644 index d9ea80f..0000000 --- a/TP_1.ipynb +++ /dev/null @@ -1,191 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Séance 1 TP — Rappels & prise en main de NetworkX\n", - "\n", - "## Objectifs pédagogiques\n", - "- Réviser les définitions : nœuds, arêtes, graphes dirigés / non-dirigés / pondérés.\n", - "- Prendre en main `networkx` pour créer un graphe et le visualiser.\n", - "- Explorer des propriétés simples (degré, nombre de sommets et d’arêtes).\n", - "- Introduire graphes dirigés et pondérés.\n", - "\n", - "## Contexte sociologique\n", - "Les graphes servent à modéliser les relations sociales :\n", - "- Graphe non dirigé : relations symétriques (amitié, collaboration).\n", - "- Graphe dirigé : relations asymétriques (qui suit qui sur Twitter).\n", - "- Graphe pondéré : intensité des relations (fréquence de contact).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# %pip install networkx matplotlib\n", - "\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercice 1 : Créer un graphe non dirigé" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Construisons un mini-réseau social\n", - "G = nx.Graph()\n", - "G.add_edges_from([\n", - " (\"Alice\", \"Bob\"),\n", - " (\"Bob\", \"Claire\"),\n", - " (\"Alice\", \"David\"),\n", - " (\"Claire\", \"David\"),\n", - "])\n", - "\n", - "print(\"Nœuds :\", list(G.nodes()))\n", - "print(\"Arêtes:\", list(G.edges()))\n", - "\n", - "plt.figure()\n", - "nx.draw(G, with_labels=True, node_color=\"lightblue\", node_size=1000)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Questions :**\n", - "1. Qui a le plus de voisins / voisines (ami-e-s) ?\n", - "2. Quelles sont les relations réciproques ?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercice 2 : Explorer les propriétés du graphe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Nombre de sommets :\", G.number_of_nodes())\n", - "print(\"Nombre d'arêtes :\", G.number_of_edges())\n", - "print(\"Degrés de chaque sommet :\", dict(G.degree()))\n", - "print(\"Degré moyen :\", sum(dict(G.degree()).values())/G.number_of_nodes())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Question :** Quel est le degré moyen et comment l’interpréter sociologiquement ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercice 3 : Graphe dirigé" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "DG = nx.DiGraph()\n", - "DG.add_edges_from([\n", - " (\"Alice\", \"Bob\"),\n", - " (\"Bob\", \"Claire\"),\n", - " (\"Claire\", \"Alice\")\n", - "])\n", - "\n", - "plt.figure()\n", - "nx.draw(DG, with_labels=True, node_color=\"lightgreen\", node_size=1000, arrows=True)\n", - "plt.show()\n", - "\n", - "print(\"Degré sortant :\", dict(DG.out_degree()))\n", - "print(\"Degré entrant :\", dict(DG.in_degree()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Questions :**\n", - "1. Quelle différence avec le graphe non dirigé ?\n", - "2. Que représentent les degrés entrants et sortants sociologiquement (ex : followers) ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercice 4 : Graphe pondéré" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "WG = nx.Graph()\n", - "WG.add_edge(\"Alice\", \"Bob\", weight=5) # forte relation\n", - "WG.add_edge(\"Alice\", \"Claire\", weight=1) # relation faible\n", - "\n", - "print(\"Arêtes avec poids :\", WG.edges(data=True))\n", - "\n", - "# Dessin avec poids visibles\n", - "pos = nx.spring_layout(WG)\n", - "nx.draw(WG, pos, with_labels=True, node_color=\"lightcoral\", node_size=1000)\n", - "labels = nx.get_edge_attributes(WG, \"weight\")\n", - "nx.draw_networkx_edge_labels(WG, pos, edge_labels=labels)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Questions :**\n", - "1. Comment interpréter le poids d’une relation en sociologie ?\n", - "2. Donnez un exemple concret (fréquence de discussions, intensité d’une amitié)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.x" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/TP_2.ipynb b/TP_2.ipynb deleted file mode 100644 index 4319fac..0000000 --- a/TP_2.ipynb +++ /dev/null @@ -1,284 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Séance 2 — Parcours & distances (BFS / DFS)\n", - "\n", - "## Objectifs pédagogiques\n", - "- Comprendre les notions de **chemin**, **distance** et ** composantes connexes**.\n", - "- Distinguer **BFS** (parcours en largeur) et **DFS** (parcours en profondeur).\n", - "- Utiliser `networkx` pour calculer des plus courts chemins dans un graphe **non pondéré**.\n", - "- Relier les distances à des interprétations sociologiques (proximité, relais, \"six degrés\").\n", - "\n", - "## Rappels\n", - "- Dans un **graphe non pondéré**, un **plus court chemin** est le chemin avec le **moins d'arêtes**.\n", - "- **BFS** explore par **couches** (distance croissante) et permet de trouver un plus court chemin.\n", - "- **DFS** explore en **profondeur** (par branches) ; utile pour détecter des cycles/composantes mais **ne garantit pas** un plus court chemin.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# %pip install networkx matplotlib\n", - "\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "from collections import deque" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1) Graphe de départ : petit réseau social" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Vous pouvez modifier/étendre ce graphe\n", - "G = nx.Graph()\n", - "G.add_edges_from([\n", - " (\"Alice\",\"Bob\"),\n", - " (\"Bob\",\"Claire\"),\n", - " (\"Claire\",\"Emma\"),\n", - " (\"Alice\",\"David\"),\n", - " (\"David\",\"Emma\"),\n", - " (\"Emma\",\"Fanny\"),\n", - " (\"Bob\",\"Gaston\"),\n", - "])\n", - "\n", - "# Visualisation simple\n", - "plt.figure()\n", - "nx.draw(G, with_labels=True)\n", - "plt.show()\n", - "\n", - "print(\"Nœuds :\", list(G.nodes()))\n", - "print(\"Arêtes:\", list(G.edges()))\n", - "print(\"Composantes connexes:\", nx.number_connected_components(G))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Question :** Y a-t-il un unique groupe connecté, ou plusieurs ? Impact sociologique : des individus isolés auront des distances infinies vers le reste." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2) Plus courts chemins et distances avec NetworkX" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "source, cible = \"Alice\", \"Fanny\"\n", - "chemin = nx.shortest_path(G, source=source, target=cible) # plus court chemin (non pondéré)\n", - "dist = nx.shortest_path_length(G, source=source, target=cible)\n", - "print(f\"Plus court chemin de {source} à {cible} :\", chemin)\n", - "print(f\"Distance (nombre d'arêtes) :\", dist)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Exercice 2.1** : testez 3 autres paires (par ex. `('Gaston','Fanny')`, `('Alice','Claire')`, `('David','Bob')`).\n", - "\n", - "**Exercice 2.2** : si vous supprimez l'arête `('David','Emma')`, que devient la distance `Alice→Fanny` ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3) Implémenter un BFS pédagogique (pour comprendre l'ordre d'exploration)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def bfs_layers(graph, start):\n", - " \"\"\"Retourne l'ordre de visite et les couches de distance (dict: noeud -> distance)\n", - " BFS avec file d'attente (deque).\n", - " \"\"\"\n", - " visited = set([start])\n", - " dist = {start: 0}\n", - " order = []\n", - " q = deque([start])\n", - "\n", - " while q:\n", - " u = q.popleft()\n", - " order.append(u)\n", - " for v in graph.neighbors(u):\n", - " if v not in visited:\n", - " visited.add(v)\n", - " dist[v] = dist[u] + 1\n", - " q.append(v)\n", - " return order, dist\n", - "\n", - "order, dist = bfs_layers(G, \"Alice\")\n", - "print(\"Ordre BFS depuis Alice:\", order)\n", - "print(\"Distances depuis Alice:\", dist)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Exercice 3.1** : Comparez les **distances** renvoyées par `bfs_layers` et `nx.shortest_path_length` pour 3 nœuds.\n", - "\n", - "**Exercice 3.2** : Quel nœud est le plus proche (distance minimale) d'**Alice** ? Le plus lointain ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4) Implémenter un DFS (ordre d'exploration par branches)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def dfs_order(graph, start, visited=None, order=None):\n", - " if visited is None: visited = set()\n", - " if order is None: order = []\n", - " visited.add(start)\n", - " order.append(start)\n", - " for v in graph.neighbors(start):\n", - " if v not in visited:\n", - " dfs_order(graph, v, visited, order)\n", - " return order\n", - "\n", - "print(\"Ordre DFS depuis Alice:\", dfs_order(G, \"Alice\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Exercice 4.1** : Pourquoi **l'ordre DFS** diffère-t-il souvent de l'ordre BFS ?\n", - "\n", - "**Exercice 4.2** : DFS trouve-t-il un plus court chemin ? Expliquez avec un contre-exemple si possible." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5) Distances globales : diamètre & distance moyenne (si connexe)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if nx.is_connected(G):\n", - " # Diamètre = plus longue distance entre deux nœuds\n", - " d = nx.diameter(G)\n", - " # Distance moyenne (longueur moyenne des plus courts chemins)\n", - " apl = nx.average_shortest_path_length(G)\n", - " print(\"Diamètre:\", d)\n", - " print(\"Distance moyenne:\", apl)\n", - "else:\n", - " print(\"Le graphe n'est pas connexe : diamètre et distance moyenne ne sont pas définis globalement.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Exercice 5.1** : Ajoutez/supprimez une arête et observez l'effet sur la distance moyenne. Interprétez sociologiquement (ex. apparition d'un **pont** qui réduit les distances)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6) Étude guidée : \"six degrés de séparation\" (mini-expérience)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Construisons un graphe en anneau + quelques liens longue portée pour réduire drastiquement les distances\n", - "H = nx.cycle_graph(12) # 12 individus en cercle (0..11)\n", - "H = nx.relabel_nodes(H, {i: f\"P{i}\" for i in range(12)})\n", - "H.add_edge(\"P0\",\"P6\") # raccourci longue portée\n", - "H.add_edge(\"P3\",\"P9\") # autre raccourci\n", - "\n", - "plt.figure()\n", - "nx.draw(H, with_labels=True)\n", - "plt.show()\n", - "\n", - "print(\"Connexe:\", nx.is_connected(H))\n", - "print(\"Distance moyenne:\", nx.average_shortest_path_length(H))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Questions**\n", - "1. Que se passe-t-il si on **retire** les liens longue portée ?\n", - "2. Pourquoi quelques liens inter-groupes peuvent-ils **réduire fortement** les distances ?\n", - "3. Donnez une interprétation en termes de diffusion d'une rumeur/idée." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7) Synthèse écrite\n", - "- Comparez BFS et DFS sur votre graphe.\n", - "- Donnez 2 exemples de distances pertinentes en sociologie (ex. accès à l'information, recrutement).\n", - "- Proposez une **hypothèse** : l'ajout d'un lien entre deux groupes réduira X ; comment le tester ?" - ] - }, - { - "cell_type": "markdown", - "id": "75d566ac", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.x" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/TP_3.ipynb b/TP_3.ipynb deleted file mode 100644 index b77b8a7..0000000 --- a/TP_3.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Activité — Les matrices d’adjacence\n\n"},{"metadata":{},"cell_type":"markdown","source":"## Introduction\nEn sociologie, on cherche souvent à représenter **les relations entre individus** : qui connaît qui, qui travaille avec qui, qui échange le plus, etc.\n\nUn **graphe** permet de modéliser ces interactions :\n- **nœuds (ou sommets)** → les individus\n- **arêtes (ou liens)** → les relations entre eux\n\nMais il existe une autre manière, plus mathématique, de représenter ces relations : la **matrice d’adjacence**.\n"},{"metadata":{},"cell_type":"markdown","source":"### Définition\nPour un graphe $G = (V, E)$ à *n* sommets, la **matrice d’adjacence** $A$ est une matrice carrée *n × n* où :\n\n$$A_{ij} = \\begin{cases}1 & \\text{si une arête relie le sommet } i \\text{ au sommet } j \\\\0 & \\text{sinon}\\end{cases}$$\n\n- Dans un **graphe non dirigé**, $A$ est **symétrique** : $A_{ij} = A_{ji}$\n- Dans un **graphe dirigé**, $A$ peut être **asymétrique** : $A_{ij} = 1$ ne signifie pas forcément $A_{ji} = 1$\n"},{"metadata":{},"cell_type":"markdown","source":"## Partie 1 — Représentation papier\n### 1. Graphe de départ\nVoici un petit réseau d’amitié entre cinq personnes :\n\n```\nAlice — Bob — Claire\n │ │\n David Emma\n```\n\n**Consigne :**\n1. Listez les sommets : `Alice, Bob, Claire, David, Emma`.\n2. Complétez la matrice d’adjacence correspondante :\n\n| | Alice | Bob | Claire | David | Emma |\n|:------|:------:|:---:|:------:|:------:|:----:|\n| Alice | 0 | | | | |\n| Bob | | 0 | | | |\n| Claire| | | 0 | | |\n| David | | | | 0 | |\n| Emma | | | | | 0 |\n\n(Remplissez avec des 1 là où il existe un lien.)\n"},{"metadata":{},"cell_type":"markdown","source":"### 2. Questions d’analyse\n1. Quel est le **degré** de chaque individu (combien de liens possède-t-il) ?\n2. Quel est l’individu le plus **central** dans ce réseau ?\n3. Si on ajoute un lien entre *David* et *Emma*, comment la matrice change-t-elle ?\n4. Que signifie le fait que la matrice soit **symétrique** ?\n5. En sociologie, que représenterait une matrice **non symétrique** ? (donnez un exemple concret)\n"},{"metadata":{},"cell_type":"markdown","source":"## Partie 2 — Exploration Python\n"},{"metadata":{"trusted":false},"cell_type":"code","source":"import networkx as nx\nimport numpy as np\nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 1. Créer et afficher la matrice"},{"metadata":{"trusted":false},"cell_type":"code","source":"G = nx.Graph()\nG.add_edges_from([\n (\"Alice\",\"Bob\"),\n (\"Bob\",\"Claire\"),\n (\"Alice\",\"David\"),\n (\"Bob\",\"Emma\")\n])\n\n# Obtenir la matrice d’adjacence\nA = nx.to_numpy_array(G, nodelist=G.nodes())\nprint(list(G.nodes()))\nprint(A.astype(int)) # version entière","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 2. Visualiser le graphe"},{"metadata":{"trusted":false},"cell_type":"code","source":"plt.figure()\nnx.draw(G, with_labels=True, node_color=\"lightblue\", node_size=1000)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 3. Explorer la structure du réseau"},{"metadata":{"trusted":false},"cell_type":"code","source":"# Degré de chaque sommet\nprint(\"Degré de chaque individu :\", dict(G.degree()))\n\n# Densité du réseau (rapport entre liens existants et liens possibles)\nprint(\"Densité du réseau :\", round(nx.density(G), 2))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 4. Liens indirects : le carré de la matrice"},{"metadata":{"trusted":false},"cell_type":"code","source":"A2 = np.linalg.matrix_power(A, 2)\nprint(\"A² =\\n\", A2.astype(int))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":" Interprétation :\n- Si `A²[i][j] > 0`, cela signifie qu’il existe **un ami commun** entre les nœuds *i* et *j*.\n- En sociologie : cela mesure les **liens indirects**, c’est-à-dire les “amis d’amis”.\n"},{"metadata":{},"cell_type":"markdown","source":"### Pourquoi ne met-on pas les éléments eux-mêmes au carré ?\n\nQuand on écrit **A²**, cela signifie **A × A**, c’est-à-dire qu’on multiplie la matrice par elle-même selon les règles de la multiplication matricielle.\n\nOn ne calcule **pas** $(A_{ij})^2$, car cela n’aurait aucun intérêt :\n- $0^2 = 0$ et $1^2 = 1$, donc la matrice ne changerait pas.\n- Le but du carré matriciel est de compter **les chemins de longueur 2** entre les nœuds.\n\nFormellement :\n$$A^2_{ij} = \\sum_k A_{ik} \\times A_{kj}$$\n\nCela signifie que $A^2_{ij}$ indique combien de façons il existe d’aller du nœud *i* au nœud *j* en passant par **exactement un autre nœud**.\n\n**Exemple :**\nSi $A =$ \n$$\n\\begin{bmatrix}\n0 & 1 & 0 \\\\\n1 & 0 & 1 \\\\\n0 & 1 & 0\n\\end{bmatrix}\n$$\nalors $A^2 =$ \n$$\n\\begin{bmatrix}\n1 & 0 & 1 \\\\\n0 & 2 & 0 \\\\\n1 & 0 & 1\n\\end{bmatrix}\n$$\n\n- $A^2_{13} = 1$ : il existe un chemin de longueur 2 entre 1 et 3 (en passant par 2).\n- $A^2_{22} = 2$ : le sommet 2 a deux chemins de longueur 2 qui reviennent à lui-même.\n\n**Résumé :**\n\n| Calcul | Signification | Utilité |\n|:--------|:--------------|:--------|\n| $(A_{ij})^2$ | Élève chaque case au carré | Inutile (0 et 1 inchangés) |\n| $A^2 = A × A$ | Produit matriciel | Donne les **liens indirects** (chemins de longueur 2) |"},{"metadata":{},"cell_type":"markdown","source":"## Synthèse\n| Concept mathématique | Interprétation sociologique |\n|:----------------------|:----------------------------|\n| 1 dans la matrice | lien direct entre deux individus |\n| 0 dans la matrice | absence de lien |\n| somme d’une ligne | degré (popularité / nombre de connexions) |\n| symétrie | réciprocité des relations |\n| $A^2$ | existence de relations indirectes |\n| densité | cohésion du groupe |\n"},{"metadata":{},"cell_type":"markdown","source":"## Questions de réflexion\n1. En quoi la matrice d’adjacence permet-elle de **quantifier** un réseau social ?\n2. Si une matrice est très dense, que peut-on en conclure sur le **type de groupe** étudié ?\n3. Comment pourriez-vous pondérer les liens dans cette matrice pour représenter l’**intensité** des relations ?"},{"metadata":{},"cell_type":"markdown","source":""}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":2} \ No newline at end of file diff --git a/TP_4.ipynb b/TP_4.ipynb deleted file mode 100644 index 7278ae6..0000000 --- a/TP_4.ipynb +++ /dev/null @@ -1,88 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": "# Séance 6 — Parcours en largeur (BFS) : comment l’information se propage dans un réseau\n\n## Objectifs\n- Comprendre intuitivement ce qu’est un **parcours en largeur** (Breadth-First Search ou BFS).\n- Relier ce concept à la **diffusion d’une information, d’une rumeur ou d’une idée** dans un réseau social.\n- Explorer un réseau avec Python et `networkx` pour visualiser cette propagation.\n- Comparer avec une autre stratégie d’exploration : le **parcours en profondeur (DFS)**.\n" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 1. Introduction sociologique : une rumeur se propage\nImaginez qu’une personne — appelons-la **Alice** — partage une nouvelle dans son groupe d’amis.\nChacun la répète à ses proches, et ainsi de suite.\n\nL’information se diffuse **par cercles successifs** : d’abord les amis directs d’Alice, puis les amis des amis, etc.\n\nC’est exactement ce que fait un **parcours en largeur** : explorer un réseau *niveau par niveau*.\n\n→ En sociologie des réseaux, cela correspond à la **distance sociale** entre individus." - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 2. Exemple manuel : propagation d’une information\nConsidérons ce réseau :\n\n```\nAlice — Bob — Chloé — David\n │ │ │\n Emma Félix Gaël\n```\n\nSupposons qu’Alice commence à diffuser une nouvelle.\n\n**Étapes :**\n1. Niveau 0 : Alice\n2. Niveau 1 : les personnes directement connectées à Alice → {Bob, Emma}\n3. Niveau 2 : les amis de Bob (hors Alice) → {Chloé, Félix}\n4. Niveau 3 : les amis de Chloé → {David, Gaël}\n\nChaque *niveau* correspond à une **distance sociale** par rapport à la source." - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 3. Visualisation avec Python\nCréons ce réseau et voyons comment le BFS le parcourt." - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": "import networkx as nx\nimport matplotlib.pyplot as plt\n\n# Création du graphe\nG = nx.Graph()\nG.add_edges_from([\n ('Alice', 'Bob'), ('Alice', 'Emma'),\n ('Bob', 'Chloé'), ('Bob', 'Félix'),\n ('Chloé', 'David'), ('Chloé', 'Gaël')\n])\n\nplt.figure(figsize=(6,4))\npos = nx.spring_layout(G, seed=0)\nnx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=1000)\nplt.title('Réseau social — Diffusion de la nouvelle')\nplt.show()" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 4. Explorer le réseau en largeur\nOn peut utiliser la fonction `nx.bfs_tree()` pour construire un arbre de parcours à partir d’un point de départ (ici, Alice)." - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": "source = 'Alice'\nT = nx.bfs_tree(G, source=source)\n\nplt.figure(figsize=(6,4))\nnx.draw(T, with_labels=True, node_color='lightgreen', node_size=1000)\nplt.title(f'Arbre BFS à partir de {source}')\nplt.show()\n\nprint('Ordre du parcours BFS :')\nprint(list(nx.bfs_edges(G, source)))" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 5. Distances sociales depuis Alice\nLe BFS permet aussi de mesurer la **distance minimale** entre Alice et chaque autre personne." - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": "distances = nx.single_source_shortest_path_length(G, source)\nprint('Distances sociales depuis Alice :')\nfor k, v in distances.items():\n print(f'{k} : {v}')" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "### Interprétation sociologique\n- Les **valeurs faibles** indiquent des individus **proches** d’Alice (accès direct à l’information).\n- Les **valeurs élevées** indiquent des individus **périphériques** : ils n’apprennent la nouvelle que tardivement.\n\n→ Cela permet d’analyser la **vitesse de diffusion** ou la **position sociale** dans le réseau." - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 6. Comparaison intuitive : BFS vs DFS\nPour bien comprendre la logique du BFS, comparons-le brièvement à une autre stratégie : le **DFS**.\n\n| Stratégie | Métaphore | Manière d’explorer | Exemple |\n|:-----------|:-----------|:------------------|:---------|\n| BFS (largeur) | diffusion sociale | explore les cercles autour de la source | bouche-à-oreille, information publique |\n| DFS (profondeur) | exploration ciblée | suit un chemin jusqu’au bout avant de revenir | enquête individuelle, relation hiérarchique |\n\nLe BFS **propagera rapidement une information**, tandis que le DFS **creusera une piste en profondeur**." - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 7. Pour aller plus loin\n- Comment interpréteriez-vous un individu qui n’est **atteint par personne** (non connexe) ?\n- Si plusieurs personnes diffusent simultanément une info, que se passe-t-il ?\n- Quels phénomènes réels le BFS peut-il modéliser ? (rumeur, contagion, propagation d’un hashtag, etc.)" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 8. Synthèse\n- Le **BFS** explore un réseau **par cercles successifs** à partir d’une source.\n- Il permet de mesurer la **distance sociale** et d’identifier les **positions centrales**.\n- Dans un graphe non connexe, certaines personnes ne reçoivent jamais l’information.\n- Le **DFS**, lui, suit une logique d’exploration **en profondeur**, utile pour détecter des **sous-groupes**.\n" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.x" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/TP_4_bis.ipynb b/TP_4_bis.ipynb deleted file mode 100644 index 23f5f41..0000000 --- a/TP_4_bis.ipynb +++ /dev/null @@ -1,117 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": "# Séance 6 bis — TP dirigé : Diffusion d’une information dans un réseau (BFS)\n\n## Objectifs\n- Manipuler concrètement un **parcours en largeur (BFS)** avec Python.\n- Visualiser la **diffusion d’une information** dans un réseau social.\n- Interpréter sociologiquement la **distance**, la **proximité** et la **position** des individus.\n\nCe TP prolonge la séance précédente sur le parcours en largeur." - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 1. Création du réseau\nCommençons par créer un petit réseau social entre 7 personnes. Nous utiliserons la bibliothèque `networkx` pour représenter et afficher le graphe." - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": "import networkx as nx\nimport matplotlib.pyplot as plt\n\n# Création d'un graphe non orienté\nG = nx.Graph()\n\n# Sommets et arêtes du réseau\nrelations = [\n ('Alice', 'Bob'), ('Alice', 'Emma'),\n ('Bob', 'Chloé'), ('Bob', 'Félix'),\n ('Chloé', 'David'), ('Chloé', 'Gaël')\n]\n\nG.add_edges_from(relations)\n\n# Affichage du graphe\npos = nx.spring_layout(G, seed=0)\nplt.figure(figsize=(6,4))\nnx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=1000)\nplt.title('Réseau social : relations entre individus')\nplt.show()" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "**Questions :**\n1. Quelle personne semble la plus « centrale » dans le graphe ?\n2. Quelle personne paraît la plus isolée ?\n3. Ce graphe est-il connexe (toutes les personnes peuvent-elles être atteintes ?)" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 2. Parcours en largeur (BFS)\nNous allons simuler la diffusion d’une information à partir d’un individu : **Alice**." - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": "source = 'Alice'\n\n# Création de l'arbre de parcours BFS depuis Alice\nT = nx.bfs_tree(G, source=source)\n\nplt.figure(figsize=(6,4))\nnx.draw(T, with_labels=True, node_color='lightgreen', node_size=1000)\nplt.title(f'Arbre BFS à partir de {source}')\nplt.show()\n\n# Affichage de l'ordre du parcours\nprint('Ordre du parcours BFS :')\nprint(list(nx.bfs_edges(G, source)))" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "**Questions :**\n1. Dans quel ordre les personnes reçoivent-elles l'information ?\n2. Quel est le rôle d'Alice dans cette diffusion ?\n3. Que se passerait-il si l'information partait de Bob plutôt que d'Alice ?" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 3. Distances sociales depuis la source\nLe BFS permet de mesurer la distance sociale entre la source et chaque personne du réseau." - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": "distances = nx.single_source_shortest_path_length(G, source)\nprint('Distances sociales depuis Alice :')\nfor k, v in distances.items():\n print(f'{k} : {v}')" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "**Interprétation :**\n- Distance = nombre d’étapes pour atteindre une personne depuis Alice.\n- Plus la distance est grande, plus la personne est éloignée socialement.\n\n**Questions :**\n1. Qui apprend la nouvelle en premier ? En dernier ?\n2. Quelle distance maximale observe-t-on dans ce réseau ?\n3. Quelle est la signification sociologique d'une distance de 3 ?" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 4. Comparaison avec un autre point de départ\nRecommençons la diffusion mais cette fois **à partir de Chloé**. Observez les différences de structure et de distances." - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": "source2 = 'Chloé'\nT2 = nx.bfs_tree(G, source=source2)\n\nplt.figure(figsize=(6,4))\nnx.draw(T2, with_labels=True, node_color='lightcoral', node_size=1000)\nplt.title(f'Arbre BFS à partir de {source2}')\nplt.show()\n\nprint('Distances sociales depuis Chloé :')\nfor k, v in nx.single_source_shortest_path_length(G, source2).items():\n print(f'{k} : {v}')" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "**Questions :**\n1. Quelles différences remarquez-vous par rapport au départ depuis Alice ?\n2. Quelle personne semble jouer un rôle « de pont » dans la diffusion ?\n3. Que peut-on dire de la position de Chloé dans le réseau ?" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 5. Expérience sociologique : un acteur isolé\nAjoutons une nouvelle personne, **Hugo**, qui n’est reliée à personne. Que se passe-t-il lors du BFS ?" - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": "# Ajout d'une personne isolée\nG.add_node('Hugo')\n\nplt.figure(figsize=(6,4))\npos = nx.spring_layout(G, seed=1)\nnx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=1000)\nplt.title('Réseau social avec une personne isolée (Hugo)')\nplt.show()\n\nprint('Composantes connexes du graphe :')\nfor i, comp in enumerate(nx.connected_components(G)):\n print(f'Composante {i+1} : {comp}')" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "**Questions :**\n1. Hugo peut-il recevoir l'information ? Pourquoi ?\n2. Que représente un acteur isolé dans un réseau social réel ?\n3. Comment cette notion d'isolement peut-elle s’interpréter en sociologie ?" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 6. Pour aller plus loin (facultatif)\n1. Ajoutez de nouvelles relations pour rendre le graphe plus connecté.\n2. Essayez de trouver une configuration où tout le monde est relié en deux étapes maximum.\n3. Testez d'autres points de départ pour le BFS et comparez les distances obtenues." - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 7. Synthèse\n- Le **BFS** permet de simuler la **diffusion d’une information** dans un réseau.\n- Les **distances** indiquent le **niveau d’accès à l’information**.\n- Les **personnes centrales** propagent plus vite, les **isolées** restent à l’écart.\n- Ces observations permettent de **lier structure du réseau et dynamique sociale**." - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.x" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/programmation/NOTEBOOK.ipynb b/programmation/NOTEBOOK.ipynb index 9a92b7f..b96b81c 100644 --- a/programmation/NOTEBOOK.ipynb +++ b/programmation/NOTEBOOK.ipynb @@ -35,7 +35,7 @@ "\n", "Il suffit de se rendre à l'url [https://notebook.basthon.fr/](https://notebook.basthon.fr/), l'interface suivante apparait :\n", "\n", - "![interface_bashton](attachment:C)" + "![interface_bashton](assets/interface_bashton.png)" ] }, {