From 042585b87d28ff478e66f954a61a9f852ed5cee1 Mon Sep 17 00:00:00 2001 From: Vo Van Nghia Date: Fri, 18 Feb 2022 10:59:57 +0100 Subject: [PATCH] notebook(tp): add notebook and dataset --- notebook/ozone/dep_seuil.dat | 1042 ++++++ notebook/ozone/python.ipynb | 2709 +++++++++++++++ notebook/ozone/r.ipynb | 6011 ++++++++++++++++++++++++++++++++++ 3 files changed, 9762 insertions(+) create mode 100644 notebook/ozone/dep_seuil.dat create mode 100644 notebook/ozone/python.ipynb create mode 100644 notebook/ozone/r.ipynb diff --git a/notebook/ozone/dep_seuil.dat b/notebook/ozone/dep_seuil.dat new file mode 100644 index 0000000..4e1b889 --- /dev/null +++ b/notebook/ozone/dep_seuil.dat @@ -0,0 +1,1042 @@ +"JOUR","O3obs","MOCAGE","TEMPE","RMH2O","NO2","NO","STATION","VentMOD","VentANG" +1,91,93.2,21.5,0.00847,1.602,0.424,Aix,9.5,-0.6435 +1,100,104.6,20.2,0.00881,2.121,0.531,Aix,8.01,-0.04996 +0,82,103.6,17.4,0.00951,1.657,0.467,Aix,9.3771,-0.12832 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+0,174,132.8,31.4,0.00948,2.243,0.346,Als,3.55106,0.56457 +0,164,134,29.3,0.00779,1.466,0.226,Als,4.24382,0.60107 +0,196,138.6,30.7,0.00846,1.584,0.233,Als,4.87032,1.23606 +0,179,141.8,32.6,0.01084,1.308,0.175,Als,3.19061,1.00887 +1,84,107.1,23.9,0.01372,1.519,0.276,Als,4.81041,0.75599 +1,156,143.8,27.3,0.01092,2.171,0.322,Als,2.40416,0.7854 +0,152,148.8,29.8,0.0106,1.636,0.22,Als,1.3,-1.17601 +0,215,156.3,30.8,0.01082,2.578,0.338,Als,4.51885,1.13684 +0,78,113.8,27.6,0.01196,1.337,0.232,Als,4.30116,0.30705 +0,94,84.7,23.1,0.011,0.945,0.219,Als,5.02892,0.30288 +0,73,78.3,21.1,0.00831,0.932,0.248,Als,6.23939,0.37753 +1,59,52.5,20.7,0.01222,1.577,0.535,Als,3.11127,0.7854 +1,99,157.4,25.9,0.01228,3.235,0.446,Als,1.14018,0.90975 +0,53,101,17.4,0.01132,1.505,0.288,Als,8.15414,0.5846 +0,44,71.5,19.8,0.01089,1.07,0.292,Als,5.34509,0.304 +0,48,89.1,16.7,0.00853,1.514,0.359,Als,5.004,-0.03998 +0,63,88.4,17,0.00891,1.385,0.333,Als,1.64012,0.6557 +1,117,121.4,21.1,0.00995,1.758,0.294,Als,5,0.9273 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\n", + "\"INSA\"/ \n", + "\n", + "\"Wikistat\"/\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [Scénarios d'Apprentissage Statistique](https://github.com/wikistat/Apprentissage)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adaptation Statistique d'un Modèle de Prévision du Pic d'Ozone en \"Python\"/ avec \"Scikit-learn\"/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Résumé**: Exploration puis modélisation de données climatiques en utilisant Python et la librairie [Scikit-learn](http://scikit-learn.org/stable/#). L'objectif est de prévoir pour le lendemain un possible dépassement d'un seuil de concentration en ozone à partir d'une prévision déterministe sur un maillage grossier et de variables climatiques locales. Estimation par différentes méthodes: régression [logistique](http://wikistat.fr/pdf/st-m-app-rlogit.pdf), [k plus proches voisins](http://wikistat.fr/pdf/st-m-app-add.pdf), [arbre de décision](http://wikistat.fr/pdf/st-m-app-cart.pdf), [agrégation de modèle](http://wikistat.fr/pdf/st-m-app-agreg.pdf), [SVM](http://wikistat.fr/pdf/st-m-app-svm.pdf). Comparaison des [erreurs de prévision](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf) sur un échantillon test puis des courbes ROC. Itération sur plusieurs échantillons tests pour analyser la distribution de l'erreur de prévision. Ce calepin vient compléter l'[étude faite avec R](http://www.math.univ-toulouse.fr/~besse/Wikistat/Notebooks/Notebook-R-Ozone.html) pour en comparer les deux approches." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Avertissement** \n", + "\n", + "* Ce calepin complète [celui en R](https://github.com/wikistat/Apprentissage/blob/master/Pic-ozone/Apprent-R-Ozone.ipynb) afin de comparer les performances respectives des deux environnements: complétude des résultats et efficacité du code. Les explications sont plus sommaires dans ce tutoriel qui est en principe exécuté *après* ou parallèlement à celui réalisé en R. \n", + "* Comme pour R il est *découpé en 5 séances* de travaux dirigés *syncronisées* avec le cours d'apprentissage automatique. \n", + "* Réfléchir aux réponses aux questions marquées **Q** issues du sujet d'examen.\n", + "* Toutes les options n'ont pas été testées et certaines sont posées en **exercice**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'objectif, sur ces données, est d'améliorer la prévision déterministe (MOCAGE), calculée par les services de MétéoFrance, de la concentration d'ozone dans certaines stations de prélèvement. Il s'agit d'un problème dit d'*adaptation statistique* ou post-traitement d'une prévision locale de modèles à trop grande échelle en s'aidant d'autre variables également gérées par MétéoFrance, mais à plus petite échelle (température, force du vent...). \n", + "\n", + "La question posée reste: quelle est la meilleure stratégie pour prévoir l'occurrence d'un pic de pollution. \n", + "\n", + "Comme avec R différentes méthodes sont testées : régression logistique, k plus proches voisins, arbre de décision, random forest, SVM. De façon générale on suppose que l'utilisateur dispose d'une installation python à jour. Le calepin a été testé avec la version 3.8.\n", + "\n", + "**Question subsidiaire** quand préférer R ou Python ? Python conduit a des résultats (conclusions) identiques à ceux de R, moins complets pour leur interprétation, mais plus rapidement. Il s'agit des principales différences entre R pour \"statisticien\" et python pour \"informaticien\", on perd en interprétabilité mais on gagne en vitesse d'exécution. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prise en compte des données" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données ont été extraites et mises en forme par le service concerné de Météo France. Elles sont décrites par les variables suivantes:\n", + "\n", + "\n", + "* **JOUR** Le type de jour ; férié (1) ou pas (0) ;\n", + "* **O3obs** La concentration d'ozone effectivement observée le lendemain à 17h locales correspondant souvent au maximum de pollution observée ;\n", + "* **MOCAGE** Prévision de cette pollution obtenue par un modèle déterministe de mécanique des fluides (équation de Navier et Stockes);\n", + "* **TEMPE** Température prévue par MétéoFrance pour le lendemain 17h ;\n", + "* **RMH2O** Rapport d'humidité ;\n", + "* **NO2** Concentration en dioxyde d'azote ;\n", + "* **NO** Concentration en monoxyde d'azote ;\n", + "* **STATION** Lieu de l'observation : Aix-en-Provence, Rambouillet, Munchhausen, Cadarache et Plan de Cuques ;\n", + "* **VentMOD** Force du vent ;\n", + "* **VentANG** Orientation du vent. \n", + "\n", + "Ce sont des données \"propres\", sans trous, bien codées et de petites tailles. Elles présentent avant tout un caractère pédagogique.\n", + "\n", + "Il est choisi ici de lire les données avec la librairie `pandas` pour bénéficier de la classe DataFrame. Ce n'est pas nécessaire pour l'objectif de prévision car les variables qualitatives ainsi construites ne peuvent être utilisées pour l'interprétation des modèles obtenus dans `scikit-learn` qui ne reconnaît pas la classe DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:10.756181Z", + "start_time": "2019-11-18T09:19:10.033317Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Lecture des données\n", + "## Charger les données ou les lire directement en précisant le chemin\n", + "path = \"\"\n", + "ozone = pd.read_csv(path + \"dep_seuil.dat\", sep=\",\", header=0)\n", + "# Vérification du contenu\n", + "ozone.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ce qui suit permet d'affecter le bon type aux variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:10.784429Z", + "start_time": "2019-11-18T09:19:10.762200Z" + } + }, + "outputs": [], + "source": [ + "ozone[\"STATION\"] = pd.Categorical(ozone[\"STATION\"], ordered=False)\n", + "ozone[\"JOUR\"] = pd.Categorical(ozone[\"JOUR\"], ordered=False)\n", + "ozone[\"O3obs\"] = pd.DataFrame(ozone[\"O3obs\"], dtype=float)\n", + "ozone.dtypes\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:10.823473Z", + "start_time": "2019-11-18T09:19:10.787257Z" + } + }, + "outputs": [], + "source": [ + "ozone.describe()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Même si les données ne présentent pas de défauts particuliers, une étude exploratoire préliminaire est indispensable afin de s'assurer le leur bonne cohérence, proposer d'éventuelles transformations et analyser les structures de corrélations ou plus généralement de liaisons entre les variables, de groupes des individus ou observations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unidimensionnelle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:10.936092Z", + "start_time": "2019-11-18T09:19:10.826112Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:11.125737Z", + "start_time": "2019-11-18T09:19:10.937377Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "ozone[\"O3obs\"].hist()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:11.262897Z", + "start_time": "2019-11-18T09:19:11.128038Z" + } + }, + "outputs": [], + "source": [ + "ozone[\"MOCAGE\"].hist()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice** Traiter ainsi toutes les variables. Ceci suggère des transformations pour une meilleure utilisation des modèles linéaires. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:11.275823Z", + "start_time": "2019-11-18T09:19:11.264575Z" + } + }, + "outputs": [], + "source": [ + "from math import sqrt, log\n", + "\n", + "ozone[\"SRMH2O\"] = ozone[\"RMH2O\"].map(lambda x: sqrt(x))\n", + "ozone[\"LNO2\"] = ozone[\"NO2\"].map(lambda x: log(x))\n", + "ozone[\"LNO\"] = ozone[\"NO\"].map(lambda x: log(x))\n", + "del ozone[\"RMH2O\"]\n", + "del ozone[\"NO2\"]\n", + "del ozone[\"NO\"]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice** Vérifier l'opportunité de ces transformations (histogrammes des nouvelles variables)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Retirer les variables initiales et construire ci-dessous la variable \"dépassement de seuil\" pour obtenir le fichier qui sera effectivement utilisé." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:11.297416Z", + "start_time": "2019-11-18T09:19:11.279311Z" + } + }, + "outputs": [], + "source": [ + "ozone[\"DepSeuil\"] = ozone[\"O3obs\"].map(lambda x: x > 150)\n", + "ozone.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploration multidimensionnelle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:16.123259Z", + "start_time": "2019-11-18T09:19:11.299583Z" + } + }, + "outputs": [], + "source": [ + "# scatter plot matrix des variables quantitatives\n", + "from pandas.plotting import scatter_matrix\n", + "\n", + "scatter_matrix(\n", + " ozone[[\"O3obs\", \"MOCAGE\", \"TEMPE\", \"VentMOD\", \"VentANG\", \"SRMH2O\", \"LNO2\", \"LNO\"]],\n", + " alpha=0.2,\n", + " figsize=(15, 15),\n", + " diagonal=\"kde\",\n", + ")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Commenter les relations entre les variables prises 2 à 2." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Analyse en composantes principales](http://wikistat.fr/pdf/st-m-explo-acp.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:16.608748Z", + "start_time": "2019-11-18T09:19:16.124724Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.preprocessing import scale\n", + "\n", + "# réduction des variables\n", + "# X=scale(ozone[[\"O3obs\",\"MOCAGE\",\"TEMPE\",\"VentMOD\",\"VentANG\",\"SRMH2O\",\"LNO2\",\"LNO\"]])\n", + "X = scale(ozone[[\"MOCAGE\", \"TEMPE\", \"VentMOD\", \"VentANG\", \"SRMH2O\", \"LNO2\", \"LNO\"]])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tous les résultats numétriques classiques sont fournis par l'[implémentation](http://scikit-learn.org/stable/modules/decomposition.html) de scikit-learn mais des efforts sont à produire pour construire les graphiques usuels généralement automatiquement produits par des librairies dédiées comme [FactoMineR](http://factominer.free.fr/) de R.\n", + "\n", + "Les commandes suivantes permettent de réaliser une analyse en composantes principales sur les seules variables quantitatives. Par ailleurs la variable à modéliser (O3obs, concentration observée) n'est pas utilisée." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:16.792776Z", + "start_time": "2019-11-18T09:19:16.610421Z" + } + }, + "outputs": [], + "source": [ + "pca = PCA()\n", + "## Estimation, calcul des composantes principales\n", + "C = pca.fit(X).transform(X)\n", + "## Décroissance de la variance expliquée\n", + "plt.plot(pca.explained_variance_ratio_)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:17.039276Z", + "start_time": "2019-11-18T09:19:16.798186Z" + } + }, + "outputs": [], + "source": [ + "## distribution des composantes principales\n", + "plt.boxplot(C[:, 0:20])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Commenter ces résultats: quel choix de la dimension? \n", + "\n", + "**Q** Présence de valeurs atypiques." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:19.969368Z", + "start_time": "2019-11-18T09:19:17.040897Z" + } + }, + "outputs": [], + "source": [ + "## Repésentation des individus\n", + "plt.figure(figsize=(5, 5))\n", + "for i, j, nom in zip(C[:, 0], C[:, 1], ozone[\"DepSeuil\"]):\n", + " color = \"red\" if nom else \"blue\"\n", + " plt.plot(i, j, \"o\", color=color)\n", + "plt.axis((-4, 6, -4, 6))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:20.137406Z", + "start_time": "2019-11-18T09:19:19.973057Z" + } + }, + "outputs": [], + "source": [ + "## coordonnées et représentation des variables\n", + "coord1 = pca.components_[0] * np.sqrt(pca.explained_variance_[0])\n", + "coord2 = pca.components_[1] * np.sqrt(pca.explained_variance_[1])\n", + "fig = plt.figure(figsize=(5, 5))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "for i, j, nom in zip(\n", + " coord1,\n", + " coord2,\n", + " ozone[[\"MOCAGE\", \"TEMPE\", \"VentMOD\", \"VentANG\", \"SRMH2O\", \"LNO2\", \"LNO\"]].columns,\n", + "):\n", + " plt.text(i, j, nom)\n", + " plt.arrow(0, 0, i, j, color=\"black\")\n", + "plt.axis((-1.2, 1.2, -1.2, 1.2))\n", + "# cercle\n", + "c = plt.Circle((0, 0), radius=1, color=\"gray\", fill=False)\n", + "ax.add_patch(c)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Commenter la structure de corrélation des variables.\n", + "\n", + "**Q** L'objectif est de définir une surface séparant les deux classes. Une discriminaiton linéaire (hyperplan) semble-t-elle possible? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ce n'est pas utile ici mais une classification non supervisée est facile à obtenir à titre illustratif, par exemple en 4 classes, par l'algorithme k-means:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:20.327504Z", + "start_time": "2019-11-18T09:19:20.138931Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "clust = KMeans(n_clusters=4)\n", + "clust.fit(X)\n", + "classe = clust.labels_\n", + "print(classe)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:20.591749Z", + "start_time": "2019-11-18T09:19:20.329120Z" + } + }, + "outputs": [], + "source": [ + "## Repésentation des individus dans les coordonnées de l'acp.\n", + "plt.figure(figsize=(10, 8))\n", + "plt.scatter(C[:, 0], C[:, 1], c=classe)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modélisations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La recherche d'une meilleure méthode de prévision suit généralement le protocole suivant dont la première étape est déja réalisée.\n", + "\n", + "\n", + "1. Etape descriptive préliminaire uni et multidimensionnelle visant à repérer les incohérences, les variables non significatives ou de distribution exotique, les individus non concernés ou atypiques... et à étudier les structures des données. Ce peut être aussi la longue étape de construction de variables, attributs ou *features* spécifiques des données. \n", + "2. Procéder à un tirage aléatoire d'un échantillon *test* qui ne sera utilisé que lors de la *dernière étape* de comparaison des méthodes.\n", + "3. La partie restante est l'échantillon d'*apprentissage* pour l'estimation des paramètres des modèles.\n", + "4. Pour chacune des méthodes, optimiser la complexité des modèles en minimisant une estimation \"sans biais\" de l'erreur de prévision, par exemple par [*validation croisée*](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf).\n", + " - Variables et interactions à prendre en compte dans la régression linéaire ou logistique;\n", + " - variables et méthode pour l'analyse discriminante;\n", + " - nombre de feuilles dans l'arbre de régression ou de classification;\n", + " - architecture (nombre de neurones, pénalisation) du perceptron;\n", + " - algorithme d'agrégation, \n", + " - noyau et pénalisation des SVMs.\n", + "5. Comparaison des qualités de prévision sur la base du taux de mal classés pour le seul échantillon test qui est resté à l'écart de tout effort ou \"acharnement\" pour l'optimisation des modèles.\n", + "\n", + "**Remarques**\n", + "* En cas d'échantillon relativement \"petit\" il est recommandé d'itérer la procédure de découpage apprentissage / test ([validation croisée *Monte Carlo*](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf)), afin de réduire la variance (moyenne) des estimations des erreurs de prévision.\n", + "* *Attention*: ne pas \"tricher\" en modifiant le modèle obtenu lors de l'étape précédente afin d'améliorer le résultat sur l'échantillon test !\n", + "* Le critère utilisé dépend du problème : erreur quadratique, taux de mauvais classement, AUC (aire sous la courbe ROC), indice de Pierce, *log loss function*...\n", + "* L'étape \"choix\" de la meilleure méthode peut être remplacée par une combinaisons de prévision comme c'est souvent le cas dans les soutions \"gagnantes\" mais lourdes du site [kaggle](https://www.kaggle.com/competitions)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extraction des échantillons apprentissage et test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transformation des données pour l'apprentissage. \n", + "\n", + "**Q** Pourquoi les variables qualitatives sont-elles transformées en paquets d'indicatrices ou *dummy variables*?\n", + "\n", + "**Q** Pourquoi le type data frame est transformé en une matrice. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:20.610161Z", + "start_time": "2019-11-18T09:19:20.594438Z" + } + }, + "outputs": [], + "source": [ + "ozone.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:20.645052Z", + "start_time": "2019-11-18T09:19:20.611990Z" + } + }, + "outputs": [], + "source": [ + "# Variables explicatives\n", + "ozoneDum = pd.get_dummies(ozone[[\"JOUR\", \"STATION\"]])\n", + "del ozoneDum[\"JOUR_0\"]\n", + "ozoneQuant = ozone[[\"MOCAGE\", \"TEMPE\", \"VentMOD\", \"VentANG\", \"SRMH2O\", \"LNO2\", \"LNO\"]]\n", + "dfC = pd.concat([ozoneDum, ozoneQuant], axis=1)\n", + "dfC.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:20.652330Z", + "start_time": "2019-11-18T09:19:20.647878Z" + } + }, + "outputs": [], + "source": [ + "# variable à expliquer binaire\n", + "Yb = ozone[\"DepSeuil\"].map(lambda x: int(x))\n", + "# variable à expliquer réelle\n", + "Yr = ozone[\"O3obs\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:20.824319Z", + "start_time": "2019-11-18T09:19:20.653924Z" + } + }, + "outputs": [], + "source": [ + "Yr.hist()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extractions des échantillons d'apprentissage et test pour les deux types de modèles. Comme le générateur est initalisé de façon identique, ce sont les mêmes échantillons dans les deux cas." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:20.838969Z", + "start_time": "2019-11-18T09:19:20.825953Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, Yb_train, Yb_test = train_test_split(\n", + " dfC, Yb, test_size=200, random_state=11\n", + ")\n", + "X_train, X_test, Yr_train, Yr_test = train_test_split(\n", + " dfC, Yr, test_size=200, random_state=11\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'étape suivante est une étape de standardisation des données ou normalisation. Les variables sont divisées par leur écart-type. Ce n'est pas utile dans le cas d'un modèle linéaire élémentaire car la solution est identique mais indispensbale pour beaucoup d'autres méthodes non linéaires (SVM, réseaux de neurones, modèles avec pénalisation). Cette étape est donc concrètement systématiquement exécutée pour éviter des soucis. *Attention*, les mêmes paramètres (moyennes, écarts-types) estimés sur l'échantillon d'apprentissage sont utilisés pour normaliser l'échantillon test. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:20.849578Z", + "start_time": "2019-11-18T09:19:20.840871Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# L'algorithme ds réseaux de neurones nécessite éventuellement une normalisation\n", + "# des variables explicatives avec les commandes ci-dessous\n", + "scaler = StandardScaler()\n", + "scaler.fit(X_train)\n", + "Xr_train = scaler.transform(X_train)\n", + "# Meme transformation sur le test\n", + "Xr_test = scaler.transform(X_test)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modèles linéaires" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les fonctions de modéles linéaires et linéaires généralisées sont limitées dans [Scikit-learn](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) et sans sorties numériques (tests) détaillées qui sont à rechercher dans une autre librairie ([StatsModels](http://statsmodels.sourceforge.net/stable/examples/notebooks/generated/glm.html)). Dans les deux cas, les stratégies classiques (forward, backward, stepwise, Furnival et Wilson) de sélection de variables par optimisation d'un critère (Cp, AIC, BIC) ne semblent pas disponibles, même si AIC et BIC sont présents dans scikit-learn, et le type DataFrame (package *pandas*) n'est pas reconnu.\n", + "\n", + "La façon efficace de procéder est donc d'introduire une [pénalisation Lasso](http://wikistat.fr/pdf/st-m-app-select.pdf) pour opérer une sélection de variables ou plutôt la sélection de variables quantitatives et d'indicatrices des modalités de celles qualitatives mais sans analyse fine des interactions comme cela est possible avec R.\n", + "\n", + "**Q** Quel autre type de pénalisation est aussi utilisée en régression?\n", + "\n", + "**Q** Quelle la méthode qui combine les deux?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A titre de comparaison, on trace la prévision de la concentration de l'échantillon test par la seule valeur du modèle *Mocage* ainsi que les résidus à ce modèle fonction de la valeur prédite (Mocage)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:21.112432Z", + "start_time": "2019-11-18T09:19:20.851395Z" + } + }, + "outputs": [], + "source": [ + "plt.plot(X_train[\"MOCAGE\"], Yr_train, \"o\")\n", + "plt.xlabel(\"Mocage\")\n", + "plt.ylabel(\"O3 observee\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:21.118987Z", + "start_time": "2019-11-18T09:19:21.114624Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.metrics import r2_score\n", + "\n", + "print(\"R2=\", r2_score(Yr_train, X_train[\"MOCAGE\"]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:21.259504Z", + "start_time": "2019-11-18T09:19:21.121039Z" + } + }, + "outputs": [], + "source": [ + "plt.plot(X_test[\"MOCAGE\"], Yr_test, \"o\")\n", + "plt.xlabel(\"Mocage\")\n", + "plt.ylabel(\"O3 observee\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:21.436658Z", + "start_time": "2019-11-18T09:19:21.261005Z" + } + }, + "outputs": [], + "source": [ + "plt.plot(X_test[\"MOCAGE\"], X_test[\"MOCAGE\"] - Yr_test, \"o\")\n", + "plt.xlabel(\"Mocage\")\n", + "plt.ylabel(\"Residus\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Commenter la qualité de ces résidus." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:21.445464Z", + "start_time": "2019-11-18T09:19:21.439951Z" + } + }, + "outputs": [], + "source": [ + "# Erreur quadratique moyenne\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "print(\"MSE=\", mean_squared_error(X_test[\"MOCAGE\"], Yr_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:21.454376Z", + "start_time": "2019-11-18T09:19:21.447037Z" + } + }, + "outputs": [], + "source": [ + "# Le coefficient de détermination\n", + "# peut être négatif en prévision avec un mauvais modèle,\n", + "# est nul si la prévision est constante égale à la moyennne\n", + "from sklearn.metrics import r2_score\n", + "\n", + "print(\"R2=\", r2_score(Yr_test, X_test[\"MOCAGE\"]))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### [Régression linéaire](http://wikistat.fr/pdf/st-m-app-select.pdf) ou modèle gaussien" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparer cette prévision déterministe (équation de Navier et Stockes) par l'adaptation statistique la plus élémentaire. Il s'agit d'une régression avec choix de modèle par régularisation avec une pénalisation lasso. \n", + "\n", + "**Q** Quelles est la valeur par défaut du paramètre de pénalisation Lasso?." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:21.465143Z", + "start_time": "2019-11-18T09:19:21.457333Z" + } + }, + "outputs": [], + "source": [ + "from sklearn import linear_model\n", + "\n", + "regLasso = linear_model.Lasso()\n", + "regLasso.fit(Xr_train, Yr_train)\n", + "prev = regLasso.predict(Xr_test)\n", + "print(\"MSE=\", mean_squared_error(Yr_test, prev))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:21.471807Z", + "start_time": "2019-11-18T09:19:21.466586Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.metrics import r2_score\n", + "\n", + "print(\"R2=\", r2_score(Yr_test, prev))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le paramètre de pénalisation lasso est optimisé par validation croisée." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:23.608300Z", + "start_time": "2019-11-18T09:19:21.473424Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "# grille de valeurs du paramètre alpha à optimiser\n", + "param = [{\"alpha\": [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 1]}]\n", + "regLasso = GridSearchCV(linear_model.Lasso(), param, cv=5, n_jobs=-1)\n", + "regLassOpt = regLasso.fit(Xr_train, Yr_train)\n", + "# paramètre optimal\n", + "regLassOpt.best_params_[\"alpha\"]\n", + "print(\n", + " \"Meilleur R2 = %f, Meilleur paramètre = %s\"\n", + " % (regLassOpt.best_score_, regLassOpt.best_params_)\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Quelle validation croisée est exécutée?\n", + "\n", + "Prévision avec la valeur optimale de `alpha` puis calcul et tracé des résidus." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:23.616625Z", + "start_time": "2019-11-18T09:19:23.610359Z" + } + }, + "outputs": [], + "source": [ + "prev = regLassOpt.predict(Xr_test)\n", + "print(\"MSE=\", mean_squared_error(prev, Yr_test))\n", + "print(\"R2=\", r2_score(Yr_test, prev))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:23.773914Z", + "start_time": "2019-11-18T09:19:23.618387Z" + } + }, + "outputs": [], + "source": [ + "plt.plot(prev, Yr_test, \"o\")\n", + "plt.xlabel(\"O3 Prédite\")\n", + "plt.ylabel(\"O3 observee\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:23.955256Z", + "start_time": "2019-11-18T09:19:23.778464Z" + } + }, + "outputs": [], + "source": [ + "plt.plot(prev, Yr_test - prev, \"o\")\n", + "plt.xlabel(\"Prédites\")\n", + "plt.ylabel(\"Résidus\")\n", + "plt.hlines(0, 40, 220)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Comparer ces résidus avec ceux précédents (mocage) et noter l'amélioration. \n", + "\n", + "**Q** Commenter la forme du nuage et donc la validité du modèle. \n", + "\n", + "L'interprétation nécessite de connaître les valeurs des coefficients du modèle alors que l'objet `regLassOpt` issu de `GridSearchCV` ne retient pas les paramètres estimés. Il faut donc le ré-estimer avec la valeur optimale du paramètre de pénalisation si l'on souhaite afficher ces coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:23.966794Z", + "start_time": "2019-11-18T09:19:23.959069Z" + } + }, + "outputs": [], + "source": [ + "# Coefficients\n", + "regLasso = linear_model.Lasso(alpha=regLassOpt.best_params_[\"alpha\"])\n", + "model_lasso = regLasso.fit(Xr_train, Yr_train)\n", + "model_lasso.coef_\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:23.973385Z", + "start_time": "2019-11-18T09:19:23.968424Z" + } + }, + "outputs": [], + "source": [ + "coef = pd.Series(model_lasso.coef_, index=X_train.columns)\n", + "print(\n", + " \"Lasso conserve \"\n", + " + str(sum(coef != 0))\n", + " + \" variables et en supprime \"\n", + " + str(sum(coef == 0))\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:24.238690Z", + "start_time": "2019-11-18T09:19:23.974918Z" + } + }, + "outputs": [], + "source": [ + "imp_coef = coef.sort_values()\n", + "plt.rcParams[\"figure.figsize\"] = (8.0, 10.0)\n", + "imp_coef.plot(kind=\"barh\")\n", + "plt.title(\"Coefficients du modèle lasso\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Noter les conséquences de la pénalisation; interpréter l'effet de chaque variable sur la concentration en ozone.\n", + "\n", + "C'est ici qu'apparaît une insuffisance de la librairie python. Il faudrait construire \"à la main\" ou utiliser la librairie *Statsmodels* pour afficher les statistiques des tests et p-valeurs. Même avec ces compléments, la prise en compte des interactions et de leur sélection ne sont pas prévues. De plus l'interprétation est compliquée par l'éclatement de chaque variable qualitative en paquets d'indicatrices. C'est encore compréhensible avec peu de variables mais devient rapidement inexploitable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le graphe quivant permet d'identifier les bonnes et mauvaises prévisions de dépassement du seuil légal, ici fixé à $ 150 \\mu g $." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:24.418626Z", + "start_time": "2019-11-18T09:19:24.240884Z" + } + }, + "outputs": [], + "source": [ + "plt.plot(prev, Yr_test, \"o\")\n", + "plt.xlabel(\"Valeurs prédites\")\n", + "plt.ylabel(\"O3 observée\")\n", + "plt.hlines(150, 50, 300)\n", + "plt.vlines(150, 0, 300)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:24.443895Z", + "start_time": "2019-11-18T09:19:24.420117Z" + } + }, + "outputs": [], + "source": [ + "# Dénombrement des erreurs par\n", + "# matrice de confusion\n", + "table = pd.crosstab(prev > 150, Yr_test > 150)\n", + "print(table)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Observer l'asymétrie de cette matrice. A quoi est-elle due au moins en partie ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Scikit-learn* propose d'autres procédures d'optimisation du paramètre de régularisation lasso par validation croisée en régression; `lassoCV` utilise un algorithme de *coordinate descent*, sans calcul de dérivée puisque la norme *l1* n'est pas dérivable, tandis que `lassoLarsCV` est basée sur l'algorithme de *least angle regression*. Ces fonctions permettent de tracer également les *chemins de régularisation*. Voici l'exemple de `lassoCV` qui offre plus d'options." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:24.818339Z", + "start_time": "2019-11-18T09:19:24.446157Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LassoCV, LassoLarsCV\n", + "\n", + "model = LassoCV(\n", + " cv=5, alphas=np.array(range(1, 50, 1)) / 20.0, n_jobs=-1, random_state=13\n", + ").fit(Xr_train, Yr_train)\n", + "m_log_alphas = -np.log10(model.alphas_)\n", + "\n", + "plt.figure()\n", + "# ymin, ymax = 2300, 3800\n", + "plt.plot(m_log_alphas, model.mse_path_, \":\")\n", + "plt.plot(\n", + " m_log_alphas, model.mse_path_.mean(axis=-1), \"k\", label=\"MSE moyen\", linewidth=2\n", + ")\n", + "plt.axvline(\n", + " -np.log10(model.alpha_), linestyle=\"--\", color=\"k\", label=\"alpha: optimal par VC\"\n", + ")\n", + "\n", + "plt.legend()\n", + "\n", + "plt.xlabel(\"-log(alpha)\")\n", + "plt.ylabel(\"MSE\")\n", + "plt.title(\"MSE de chaque validation: coordinate descent \")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Vérifier que c'est bien la même valeur optimale que celle précédemment trouvée.\n", + "\n", + "Tracés des chemins de régularisation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:25.069976Z", + "start_time": "2019-11-18T09:19:24.819818Z" + } + }, + "outputs": [], + "source": [ + "from itertools import cycle\n", + "\n", + "from sklearn.linear_model import lasso_path\n", + "\n", + "alphas_lasso, coefs_lasso, _ = lasso_path(\n", + " Xr_train,\n", + " Yr_train,\n", + " alphas=np.array(range(1, 50, 1)) / 20.0,\n", + ")\n", + "\n", + "\n", + "plt.figure()\n", + "ax = plt.gca()\n", + "\n", + "styles = cycle([\"-\", \"--\", \"-.\", \":\"])\n", + "\n", + "neg_log_alphas_lasso = -np.log10(alphas_lasso)\n", + "for coef_l, s in zip(coefs_lasso, styles):\n", + " l1 = plt.plot(neg_log_alphas_lasso, coef_l, linestyle=s, c=\"b\")\n", + "plt.xlabel(\"-Log(alpha)\")\n", + "plt.ylabel(\"Coefficients\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### [Régression logistique](http://wikistat.fr/pdf/st-m-app-rlogit.pdf) ou modèle binomial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La même démarche est déroulée mais en modélisant directement la variable binaire Yb de dépassement ou non du seuil. Il s'agit d'une régression logistique avec toujours une pénalisation Lasso pour opérer une sélection de variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:25.074229Z", + "start_time": "2019-11-18T09:19:25.071680Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:25.292166Z", + "start_time": "2019-11-18T09:19:25.080838Z" + } + }, + "outputs": [], + "source": [ + "# Optimisation du paramètre de pénalisation\n", + "# grille de valeurs\n", + "param = [{\"C\": [1, 1.2, 1.5, 1.7, 2, 3, 4]}]\n", + "logit = GridSearchCV(\n", + " LogisticRegression(penalty=\"l1\", solver=\"liblinear\"), param, cv=5, n_jobs=-1\n", + ")\n", + "logitOpt = logit.fit(Xr_train, Yb_train) # GridSearchCV est lui même un estimateur\n", + "# paramètre optimal\n", + "logitOpt.best_params_[\"C\"]\n", + "print(\n", + " \"Meilleur score = %f, Meilleur paramètre = %s\"\n", + " % (1.0 - logitOpt.best_score_, logitOpt.best_params_)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:25.301641Z", + "start_time": "2019-11-18T09:19:25.295698Z" + } + }, + "outputs": [], + "source": [ + "# erreur sur l'échantillon test\n", + "1 - logitOpt.score(Xr_test, Yb_test)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le modèle \"optimal\" obtenu est utilisé pour prédire l'échantillon test et estimer ainsi, sans biais, une erreur de prévision. \n", + "\n", + "La matrice de confusion croise les dépassements de seuils prédits avec ceux effectivement observés. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:25.324130Z", + "start_time": "2019-11-18T09:19:25.303337Z" + } + }, + "outputs": [], + "source": [ + "# Prévision\n", + "y_chap = logitOpt.predict(Xr_test)\n", + "# matrice de confusion\n", + "table = pd.crosstab(y_chap, Yb_test)\n", + "print(table)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'interprétation du modèle est basée sur les valeurs des coefficients avec les mêmes difficultés ou restrictions que pour la régression. Attention, `GridSearch` ne retient pas les coefficients, il faut les ré-estimer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:25.342364Z", + "start_time": "2019-11-18T09:19:25.326204Z" + } + }, + "outputs": [], + "source": [ + "# Coefficients\n", + "logitLasso = LogisticRegression(\n", + " penalty=\"l1\", C=logitOpt.best_params_[\"C\"], solver=\"liblinear\"\n", + ")\n", + "logitCoef = logitLasso.fit(Xr_train, Yb_train).coef_\n", + "print(logitCoef[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:25.352838Z", + "start_time": "2019-11-18T09:19:25.345009Z" + } + }, + "outputs": [], + "source": [ + "coef = pd.Series(logitCoef[0], index=X_train.columns)\n", + "print(\n", + " \"Lasso conserve \"\n", + " + str(sum(coef != 0))\n", + " + \" variables et en supprime \"\n", + " + str(sum(coef == 0))\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:25.597658Z", + "start_time": "2019-11-18T09:19:25.354622Z" + } + }, + "outputs": [], + "source": [ + "imp_coef = coef.sort_values()\n", + "plt.rcParams[\"figure.figsize\"] = (6.0, 6.0)\n", + "imp_coef.plot(kind=\"barh\")\n", + "plt.title(\"Coefficients du modèle lasso\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Interpréter l'effet des variables retenues." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:25.803353Z", + "start_time": "2019-11-18T09:19:25.599427Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.metrics import roc_curve\n", + "\n", + "probas_ = (\n", + " LogisticRegression(penalty=\"l1\", solver=\"liblinear\", C=logitOpt.best_params_[\"C\"])\n", + " .fit(X_train, Yb_train)\n", + " .predict_proba(X_test)\n", + ")\n", + "fpr, tpr, thresholds = roc_curve(Yb_test, probas_[:, 1])\n", + "plt.plot(fpr, tpr, lw=1)\n", + "plt.xlabel(\"Taux de faux positifs\")\n", + "plt.ylabel(\"Taux de vrais positifs\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Commenter la courbe ROC à propos du choix de la valeur seuil." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [K plus proches voisins](http://wikistat.fr/pdf/st-m-app-add.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici un cas d'application d'analyses discriminantes [non paramétriques](http://scikit-learn.org/stable/modules/neighbors.html), celles [paramétriques](http://scikit-learn.org/stable/modules/lda_qda.html) (gaussienes) linéaires et quadratiques sont également présentes dans *scikit-learn* mais laissées en exercice.\n", + "\n", + "Le paramètre de compléxité ($k$) est optimisé sur une grille prédéfinie en minimisant l'erreur estimée par validation croisée; scikit-learn propose de nombreuses options de validation croisée. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:26.282117Z", + "start_time": "2019-11-18T09:19:25.805488Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "# Optimisation de k\n", + "# grille de valeurs\n", + "param_grid = [{\"n_neighbors\": list(range(1, 15))}]\n", + "knn = GridSearchCV(KNeighborsClassifier(), param_grid, cv=5, n_jobs=-1)\n", + "knnOpt = knn.fit(Xr_train, Yb_train) # GridSearchCV est lui même un estimateur\n", + "# paramètre optimal\n", + "knnOpt.best_params_[\"n_neighbors\"]\n", + "print(\n", + " \"Meilleur score = %f, Meilleur paramètre = %s\"\n", + " % (1.0 - knnOpt.best_score_, knnOpt.best_params_)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:26.301358Z", + "start_time": "2019-11-18T09:19:26.284307Z" + } + }, + "outputs": [], + "source": [ + "# Estimation de l'erreur de prévision sur l'échantillon test\n", + "1 - knnOpt.score(Xr_test, Yb_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:26.352100Z", + "start_time": "2019-11-18T09:19:26.304330Z" + } + }, + "outputs": [], + "source": [ + "# Prévision de l'échantillon test\n", + "y_chap = knnOpt.predict(Xr_test)\n", + "# matrice de confusion\n", + "table = pd.crosstab(y_chap, Yb_test)\n", + "print(table)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice** Compléter les résultats en utilisant la fonction [KNeighborsRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) pour modéliser la concentration; optimiser $k$, calculer la prévision de l'échantillon test, tracer le graphe des résidus, calculer le MSE sur l'échantillon test." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Arbre binaire de décision](http://wikistat.fr/pdf/st-m-app-cart.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les [arbres binaires de décision](http://scikit-learn.org/stable/modules/tree.html) : discrimination ou régression, sont bien implémentés dans *scikit-learn* mais avec une insuffisance pour leur élagage. Ce n'est pas une *pénalisation* de la *complexité*, et donc précisément le nombre de feuilles qui est optimisé, mais la profondeur globale de l'arbre au risque d'élaguer, à une profondeur donnée, des feuilles importantes ou de conserver des feuilles ambigües.\n", + "\n", + "Comme précédemment, la validation croisée permet d'optimiser le paramètre sur une grille." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:26.625493Z", + "start_time": "2019-11-18T09:19:26.354381Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "# Optimisation de la profondeur de l'arbre\n", + "param = [{\"max_depth\": list(range(2, 10))}]\n", + "tree = GridSearchCV(DecisionTreeClassifier(), param, cv=10, n_jobs=-1)\n", + "treeOpt = tree.fit(Xr_train, Yb_train)\n", + "# paramètre optimal\n", + "print(\n", + " \"Meilleur score = %f, Meilleur paramètre = %s\"\n", + " % (1.0 - treeOpt.best_score_, treeOpt.best_params_)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:26.632813Z", + "start_time": "2019-11-18T09:19:26.627275Z" + } + }, + "outputs": [], + "source": [ + "# Estimation de l'erreur de prévision\n", + "1 - treeOpt.score(Xr_test, Yb_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:26.664280Z", + "start_time": "2019-11-18T09:19:26.634973Z" + } + }, + "outputs": [], + "source": [ + "# prévision de l'échantillon test\n", + "y_chap = treeOpt.predict(Xr_test)\n", + "# matrice de confusion\n", + "table = pd.crosstab(y_chap, Yb_test)\n", + "print(table)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Autre difficulté dans la représentation d'un arbre de décision binaire. Le logiciel conseillé (Graphviz) semble délicat d'installation et d'utilisation pour un néophyte. Il est possible de lister la construction des noeuds avec quelques [lignes de commande.](http://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:26.806240Z", + "start_time": "2019-11-18T09:19:26.666103Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.tree import export_graphviz\n", + "from sklearn.externals.six import StringIO\n", + "import pydotplus\n", + "\n", + "treeG = DecisionTreeClassifier(max_depth=treeOpt.best_params_[\"max_depth\"])\n", + "treeG.fit(Xr_train, Yb_train)\n", + "dot_data = StringIO()\n", + "export_graphviz(treeG, out_file=dot_data)\n", + "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n", + "graph.write_png(\"treeOpt.png\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:26.819123Z", + "start_time": "2019-11-18T09:19:26.808625Z" + } + }, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "\n", + "Image(filename=\"treeOpt.png\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que dire de l'interprétation de l'arbre? Comparer les rôles des variables avec le modèle logit.\n", + "\n", + "**Exercice** Compléter les résultats en utilisant la fonction [DecisionTreeRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html) pour modéliser concentration; optimiser la profondeur, calculer la prévision de l'échantillon test, tracer les résidus, calculer le MSE sur l'échantillon test." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Réseau de neurones](http://wikistat.fr/pdf/st-m-app-rn.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les réseaux neuronaux (perceptron multicouche) ne sont présents dans le package `Scikit-learn` qu'à partir de la version 0.18. Les méthodes *profondes* (*deep learning*) nécessitent l'installation des librairies [*theano*](http://deeplearning.net/software/theano/) et [*Lasagne*](http://lasagne.readthedocs.io/en/latest/index.html) ou [*theano*](http://deeplearning.net/software/theano/), [*TensorFlow*](https://www.tensorflow.org/versions/r0.11/get_started/os_setup.html) et [*Keras*](https://keras.io/). Ces dernières sont nettement plus complexes à installer, surtout sous Windows. Elles feront l'objet d'un autre tutoriel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:26.835311Z", + "start_time": "2019-11-18T09:19:26.821031Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Définition des paramètres dont le nombre de neurones et `alpha` qui règle la régularisation par défaut 10-5. Le nombre de neurones est optimisé mais ce peut être `alpha` avec un nombre grand de neurones. Le nombre max d'itérations par défaut (200) semble insuffisant. Il est fixé à 500." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:38.412805Z", + "start_time": "2019-11-18T09:19:26.836996Z" + } + }, + "outputs": [], + "source": [ + "param_grid = [{\"hidden_layer_sizes\": list([(5,), (6,), (7,), (8,)])}]\n", + "nnet = GridSearchCV(MLPClassifier(max_iter=500), param_grid, cv=10, n_jobs=-1)\n", + "nnetOpt = nnet.fit(Xr_train, Yb_train)\n", + "# paramètre optimal\n", + "print(\n", + " \"Meilleur score = %f, Meilleur paramètre = %s\"\n", + " % (1.0 - nnetOpt.best_score_, nnetOpt.best_params_)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:38.422719Z", + "start_time": "2019-11-18T09:19:38.414606Z" + } + }, + "outputs": [], + "source": [ + "# Estimation de l'erreur de prévision sur le test\n", + "1 - nnetOpt.score(Xr_test, Yb_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:38.505699Z", + "start_time": "2019-11-18T09:19:38.424800Z" + } + }, + "outputs": [], + "source": [ + "# prévision de l'échantillon test\n", + "y_chap = nnetOpt.predict(Xr_test)\n", + "# matrice de confusion\n", + "table = pd.crosstab(y_chap, Yb_test)\n", + "print(table)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice** Remplacer ensuite la fonction MLPClassifier par celle [MLPRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) de régression. Optimiser le paramètre, calculer la prévision, les résidus, le MSE." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Forêts aléatoires](http://wikistat.fr/pdf/st-m-app-agreg.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La librairie *randomForest* de R utilise le programme historique développé par [Breiman et Cutler](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_software.htm)(2001) et interfacé par [Liaw et Wiener](https://cran.r-project.org/web/packages/randomForest/randomForest.pdf). Cette interface est toujours mise à jour mais il n'est pas sûr que le programme original continue d'évoluer depuis 2004. Pour des tailles importantes d'échantillons, quelques milliers, cette implémentation atteint des temps d'exécution rédhibitoires (cf. cet [exemple](https://github.com/wikistat/Ateliers-Big-Data/blob/master/2-MNIST/Atelier-MNIST-R.ipynb)) au contraire de celle en Python dont gestion mémoire et capacité de parallélisation ont été finement optimisées par [Louppe et al.](http://fr.slideshare.net/glouppe/accelerating-random-forests-in-scikitlearn)(2014). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "De même que le boosting, deux fonctions de forêt sont proposés dans [scikit-learn](http://scikit-learn.org/stable/modules/ensemble.html) ; une pour la régression et une pour la classification ainsi qu'une version \"plus aléatoire\". Par rapport à la version originale de R, moins d'options sont proposées mais l'utilisation de base est très similaire avec le même jeu de paramètres.\n", + "\n", + "**Q20** Identifier les paramètres, les valeurs par défaut." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:39.568151Z", + "start_time": "2019-11-18T09:19:38.507445Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# définition des paramètres\n", + "forest = RandomForestClassifier(\n", + " n_estimators=500,\n", + " criterion=\"gini\",\n", + " max_depth=None,\n", + " min_samples_split=2,\n", + " min_samples_leaf=1,\n", + " max_features=\"auto\",\n", + " max_leaf_nodes=None,\n", + " bootstrap=True,\n", + " oob_score=True,\n", + ")\n", + "# apprentissage\n", + "rfFit = forest.fit(Xr_train, Yb_train)\n", + "print(1 - rfFit.oob_score_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparer l'erreur out-of-bag ci-dessus avec celle sur l'échantillon test." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:39.621139Z", + "start_time": "2019-11-18T09:19:39.570086Z" + } + }, + "outputs": [], + "source": [ + "# erreur de prévision sur le test\n", + "1 - rfFit.score(Xr_test, Yb_test)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optimisation par validation croisée du nombre de variables tirés aléatoirement lors de la construction de chaque noeud. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:43.165576Z", + "start_time": "2019-11-18T09:19:39.622794Z" + } + }, + "outputs": [], + "source": [ + "param = [{\"max_features\": list(range(2, 10, 1))}]\n", + "rf = GridSearchCV(RandomForestClassifier(n_estimators=100), param, cv=5, n_jobs=-1)\n", + "rfOpt = rf.fit(Xr_train, Yb_train)\n", + "# paramètre optimal\n", + "print(\n", + " \"Meilleur score = %f, Meilleur paramètre = %s\"\n", + " % (1.0 - rfOpt.best_score_, rfOpt.best_params_)\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plusieurs exécutions, rendues aléatoires par la validation croisée, peuvent conduire à des valeurs \"optimales\" différentes de ce paramètre sans pour autant nuire à la qualité de prévision sur l'échantillon test." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:43.182784Z", + "start_time": "2019-11-18T09:19:43.167657Z" + } + }, + "outputs": [], + "source": [ + "# erreur de prévision sur le test\n", + "1 - rfOpt.score(Xr_test, Yb_test)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice** Tester différentes valeurs de *min_samples_split* de celle trouvée optimale. Conclusion sur la sensibilité de l'optimisation de ce paramètre ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:43.291927Z", + "start_time": "2019-11-18T09:19:43.184872Z" + } + }, + "outputs": [], + "source": [ + "# prévision\n", + "y_chap = rfFit.predict(Xr_test)\n", + "# matrice de confusion\n", + "table = pd.crosstab(y_chap, Yb_test)\n", + "print(table)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comme avec R, il est possible de calculer un indicateur d'importance des variables pour aider à une forme d'interprétation. Celui-ci dépend de la position de la variable dans l'arbre et correspond donc au *mean decrease in Gini index* de R plutôt qu'au *mean descrease in accuracy*. La forêt doit être réestimée car GridSearch ne connaît pas le paramètre d'importance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:43.502454Z", + "start_time": "2019-11-18T09:19:43.293577Z" + } + }, + "outputs": [], + "source": [ + "rf = RandomForestClassifier(n_estimators=100, max_features=2)\n", + "rfFit = rf.fit(Xr_train, Yb_train)\n", + "# Importance décroissante des variables\n", + "importances = rfFit.feature_importances_\n", + "indices = np.argsort(importances)[::-1]\n", + "for f in range(Xr_train.shape[1]):\n", + " print(dfC.columns[indices[f]], importances[indices[f]])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:43.693998Z", + "start_time": "2019-11-18T09:19:43.504115Z" + } + }, + "outputs": [], + "source": [ + "# Graphe des importances\n", + "plt.figure()\n", + "plt.title(\"Importances des variables\")\n", + "plt.bar(range(Xr_train.shape[1]), importances[indices])\n", + "plt.xticks(range(Xr_train.shape[1]), indices)\n", + "plt.xlim([-1, Xr_train.shape[1]])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Comparer les importances des variables et les sélections opérées précédemment. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice** Remplacer ensuite la fonction RandomForestClassifier par celle [RandomForestRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) de régression. Optimiser le paramètre, calculer la prévision, les résidus, le MSE.\n", + "\n", + "**Exercice** Expérimenter également le boosting sur ces données en exécutant la fonction [GradientBoostingClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier) opérant l'agorithme de *gradient tree boosting*. \n", + "\n", + "**Remarque:** Une version \"améliorée\" de *boosting* mieux paralélisée et incluant d'autres paramètres (pénalisation), est proposé dans le package: [`XGBoost`](https://xgboost.readthedocs.io/en/latest/build.html#python-package-installation) qui peut être utilisé à partir de Python mais aussi R, Julia ou Java. Nénamoins le choix est fait d'arrêter l'acharnement sur ces données; `XGBoost` est testé en python sur un autre jeu de données. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [*Support Vector Machine*](http://wikistat.fr/pdf/st-m-app-svm.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "De nombreux paramètres sont associés à cette méthode. La liste est à consulter dans la [documentation](http://scikit-learn.org/stable/modules/svm.html) en ligne.\n", + "\n", + "L'optimisation de la pénalisation (paramètre C) est recherchée sur une grille par validation croisée. Remarque: il serait nécessaire d'optimiser également la valeur du coefficient *gamma* lié au noyau gaussien (\"écart-type\").\n", + "\n", + "Il est souvent nécessaire de normaliser des données avant d'opérer les SVM." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:44.153231Z", + "start_time": "2019-11-18T09:19:43.696057Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.svm import SVC\n", + "\n", + "param = [{\"C\": [0.4, 0.5, 0.6, 0.8, 1, 1.4]}]\n", + "svm = GridSearchCV(SVC(), param, cv=10, n_jobs=-1)\n", + "svmOpt = svm.fit(Xr_train, Yb_train)\n", + "# paramètre optimal\n", + "print(\n", + " \"Meilleur score = %f, Meilleur paramètre = %s\"\n", + " % (1.0 - svmOpt.best_score_, svmOpt.best_params_)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:44.166775Z", + "start_time": "2019-11-18T09:19:44.155679Z" + } + }, + "outputs": [], + "source": [ + "# erreur de prévision sur le test\n", + "1 - svmOpt.score(Xr_test, Yb_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:44.191961Z", + "start_time": "2019-11-18T09:19:44.170189Z" + } + }, + "outputs": [], + "source": [ + "# prévision de l'échantillon test\n", + "y_chap = svmOpt.predict(Xr_test)\n", + "# matrice de confusion\n", + "table = pd.crosstab(y_chap, Yb_test)\n", + "print(table)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice** Comme précédemment, remplacer ensuite la fonction SVC par celle [SVR](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR) de régression. Optimiser le paramètre, calculer la prévision, les résidus; le MSE." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Synthèse: comparaison des méthodes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Courbes ROC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dans toute méthode, la prévision de dépassement ou non est associée au choix d'un seuil qui est par défaut 0.5. L'optimisaiton de ce seuil dépend des coûts respectifs associés aux faux positifs et aux faux négatifs qui ne sont pas nécessairement égaux. La courbe ROC permet de représenter l'influence de ce seuil sur les taux de faux positifs et vrais positifs. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:19:44.198684Z", + "start_time": "2019-11-18T09:19:44.193534Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.metrics import roc_curve\n", + "\n", + "listMethod = [\n", + " [\"RF\", rfOpt],\n", + " [\"NN\", nnetOpt],\n", + " [\"Tree\", treeOpt],\n", + " [\"K-nn\", knnOpt],\n", + " [\"Logit\", logitOpt],\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:20:02.322746Z", + "start_time": "2019-11-18T09:19:44.200268Z" + } + }, + "outputs": [], + "source": [ + "for method in enumerate(listMethod):\n", + " probas_ = method[1][1].fit(Xr_train, Yb_train).predict_proba(Xr_test)\n", + " fpr, tpr, thresholds = roc_curve(Yb_test, probas_[:, 1])\n", + " plt.plot(fpr, tpr, lw=1, label=\"%s\" % method[1][0])\n", + "plt.xlabel(\"Taux de faux positifs\")\n", + "plt.ylabel(\"Taux de vrais positifs\")\n", + "plt.legend(loc=\"best\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q22** Le critère d'AUC (aire sous la courbe) permet-il d'ordonner les courbes et donc les méthodes? \n", + "\n", + "C'est à un taux de faux positif admissible et donc à valeur de seuil fixé qu'il faut choisir la méthode d'apprentissage à privilégier. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Itération sur plusieurs échantillons de test ([validation croisée *Monte Carlo*](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'échantillon test est de taille modeste et donc l'estimation de l'erreur de prévision peut présenter une variance importante. Celle-ci est réduite en opérant une forme de validation croisée (*Monte Carlo*) en tirant plusieurs couples d'échantillon apprentissage et test pour itérer les traitements précédents. Les données sont normalisées pour toutes les méthodes car les autres que SVM et NN ne sont pas affectées.\n", + "\n", + "Les fonctionnalités de scikit-learn se prètent bien à l'automatisation de ces traitements enchaînant extraction d'échantillons, estimation de plusieurs modèles, optimisation de leurs paramètres et estimation de l'erreur de prévision sur le test.\n", + "\n", + "Le code est compact et d'exécution efficace car bien parallélisé par les fonctions utilisées." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T14:51:22.438772Z", + "start_time": "2019-07-03T14:50:38.969063Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.utils import check_random_state\n", + "import time\n", + "\n", + "check_random_state(13)\n", + "tps0 = time.perf_counter()\n", + "# définition des estimateurs\n", + "logit = LogisticRegression(penalty=\"l1\", solver=\"liblinear\")\n", + "knn = KNeighborsClassifier()\n", + "tree = DecisionTreeClassifier()\n", + "nnet = MLPClassifier(max_iter=600)\n", + "rf = RandomForestClassifier(n_estimators=100)\n", + "svm = SVC()\n", + "# Nombre d'itérations\n", + "B = 3 # pour exécuter après le test, mettre plutôt B=30\n", + "# définition des grilles de paramètres\n", + "listMethGrid = [\n", + " [svm, {\"C\": [0.4, 0.5, 0.6, 0.8, 1, 1.4]}],\n", + " [rf, {\"max_features\": list(range(2, 10, 2))}],\n", + " [nnet, {\"hidden_layer_sizes\": list([(5,), (6,), (7,), (8,)])}],\n", + " [tree, {\"max_depth\": list(range(2, 10))}],\n", + " [knn, {\"n_neighbors\": list(range(1, 15))}],\n", + " [logit, {\"C\": [0.5, 1, 5, 10, 12, 15, 30]}],\n", + "]\n", + "# Initialisation à 0 des erreurs pour chaque méthode (colonne) et chaque itération (ligne)\n", + "arrayErreur = np.empty((B, 6))\n", + "for i in range(B): # itérations sur B échantillons test\n", + " # extraction apprentissage et test\n", + " X_train, X_test, Yb_train, Yb_test = train_test_split(dfC, Yb, test_size=200)\n", + " scaler = StandardScaler()\n", + " scaler.fit(X_train)\n", + " X_train = scaler.transform(X_train)\n", + " # Meme transformation sur le test\n", + " X_test = scaler.transform(X_test)\n", + " # optimisation de chaque méthode et calcul de l'erreur sur le test\n", + " for j, (method, grid_list) in enumerate(listMethGrid):\n", + " methodGrid = GridSearchCV(method, grid_list, cv=10, n_jobs=-1, iid=\"TRUE\").fit(\n", + " X_train, Yb_train\n", + " )\n", + " methodOpt = methodGrid.best_estimator_\n", + " methFit = methodOpt.fit(X_train, Yb_train)\n", + " arrayErreur[i, j] = 1 - methFit.score(X_test, Yb_test)\n", + "tps1 = time.perf_counter()\n", + "print(\"Temps execution en mn :\", (tps1 - tps0))\n", + "dataframeErreur = pd.DataFrame(\n", + " arrayErreur, columns=[\"SVM\", \"RF\", \"NN\", \"Tree\", \"Knn\", \"Logit\"]\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T14:51:28.031583Z", + "start_time": "2019-07-03T14:51:27.827667Z" + } + }, + "outputs": [], + "source": [ + "# Distribution des erreurs de prévisions\n", + "# Les SVM présentant des erreurs atypiques sont laissés de côté.\n", + "dataframeErreur[[\"SVM\", \"RF\", \"NN\", \"Tree\", \"Knn\", \"Logit\"]].boxplot(return_type=\"dict\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T14:51:46.326851Z", + "start_time": "2019-07-03T14:51:46.320143Z" + } + }, + "outputs": [], + "source": [ + "# Moyennes\n", + "dataframeErreur.mean()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Conclusion sur l'apprentissage\n", + "**Q** Quel méthode retenir? Est-ce cohérent avec les résultats e R?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cet exemple, traité en R puis en Python, résume bien l'intérêt et le contexte des méthodes d'apprentissage.\n", + "* Par rapport à la *base line* : prévision MOCAGE présentant un taux moyen d'erreur de 30%, un modèle statistique élémentaire améliore très sensiblement le résultat.\n", + "* Une méthode plus sophistiquée, ici *SVM* ou *random forest* apporte une amélioration statistiquement significative mais assez faible au prix de l'interprétation fine des résultats fournie par une régression logistique.\n", + "* Python, outil d'*apprentissage machine*, est plus efficace que R pour les simulations.\n", + "* En revanche, R, outil d'*apprentissage statistique*, permet la sélection et l'interprétation des variables et de leurs **interactions** pour un modèle de régression linéaire ou logistique classique. La prise en compte d'interactions (modèle quadratique) améliore sensiblement la qualité des prévisions.\n", + "* Les forêts aléatoires et les SVM font mieux sur cet exemple, c'est souvent le cas comme avec le *boosting*, mais d'autres exemples mettent en avant d'autres méthodes: neurones pour une modélisation physique, SVM pour du criblage virtuelle de molécules, régression PLS pour la spectrométrie en proche infra-rouge (NIR)... pas de règle générale.\n", + "* Jupyter est un support pédagogique efficace pour des analyses sans développement volumineux de code.\n", + "* Avant d'éventuellement passer à [Julia](http://julialang.org/), R et Python sont à l'usage très complémentaires.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 5\n", + "**Remarque** Il est possible d'exécuter directement l'*épisode 5* sans passer par toutes les étapes de classification supervisée. Il suffit d'exécuter jusqu'à la *section 4.1* de l'*épidode 1*, phase exploratoire et préparation des échantillons, afin de construire les données utilisées dans les sections 12 et 13 d'imputation des données manquantes et de détection d'atypiques." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Gestion des données manquantes](http://wikistat.fr/pdf/st-m-app-idm.pdf)\n", + "Les vraies données sont le plus souvent mitées par l'absence de données, conséquences d'erreurs de saisie, de pannes de capteurs... Les librairies de Python (`pandas`) offrent des choix rudimentaires pour faire des imputations de données manquantes quand celles-ci le sont de façon complètement aléatoire. \n", + "\n", + "Le [calepin R](https://github.com/wikistat/Apprentissage/blob/master/Pic-ozone/Apprent-R-Ozone.ipynb) d'analyse de ces mêmes données propose une comparaison assez détaillée de deux stratégiées afin d'évaluer leurs performances respectives. \n", + "\n", + "La **première stratégie** commence par imputer les données manquantes en les prévoyant par l'algorithme `missForest`. Une fois les données manquantes imputées, différentes méthodes de prévision sont utilisables comme précédemment. Deux sont exécutées: forêts aléatoires et *extrem gradient boosting*.\n", + "\n", + "La **deuxième stratégie** évite l'étape d'imputation en exécutant directement un algorithme de prévision tolérant des données manquantes. Peu le fond, c'est le cas de `XGBoost`.\n", + "\n", + "Sur ces données, mais sans gros effort d'optimisation de `XGBoost`, la première stratégie enchaînant `missForest` puis `randomForest` conduit à de meilleurs résultats. Seule celle-ci est employée dans ce calepin mais, bien évidemment, l'exécution de `xgboost` sans imputation préalable est une option également possible en Python.\n", + "\n", + "Bien moins de méthodes sont proposées en Python, `SCikit-learn` ne proposant que des imputations basiques par la moyenne ou la médiane comme dans `pandas`. Néanmoins une imputation par prévision utilisant *k*-nn, ou des forêts aléatoires (Missforest) est disponible dans la librairie `missingpy`.\n", + "\n", + "Les commandes ci-dessous font appel aux fichiers suivants:\n", + "- `X` données complètes initiales \n", + "- `Xna` les données avec des trous, \n", + "- `XnaImp` les données avec imputations \n", + "\n", + "\n", + "### Préparation des trous dans `ozone`\n", + "Les données initiales de la base `ozone` sont reprises. Seule la variable à expliquer de dépassement de seuil est conservée. La première opération consiste à générer aléatoirement un certain taux de données manquantes par la fonction définie ci-dessous." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T14:54:34.117257Z", + "start_time": "2019-07-03T14:54:34.110315Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import numpy.ma as ma\n", + "import random\n", + "\n", + "\n", + "def input_nan(x, tx):\n", + " \"\"\"\n", + " x : a 2D matrix of float dtype\n", + " tx: the rate of nan value to put in the matrix\n", + " \"\"\"\n", + " n_total = x.shape[0] * x.shape[1]\n", + " mask = np.array([random.random() for _ in range(n_total)]).reshape(x.shape) < tx\n", + " mx = ma.masked_array(x, mask=mask, fill_value=np.nan)\n", + " return mx.filled()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T14:54:35.714762Z", + "start_time": "2019-07-03T14:54:35.707142Z" + } + }, + "outputs": [], + "source": [ + "# données initiales avec\n", + "X = dfC\n", + "# Génération de 10% de valeurs manquantes\n", + "Xna = input_nan(X, 0.1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imputation par `missForest`\n", + "Le même algorithme que celui présent dans la librairie de R `MissForest` est implémenté dans la librairie `Scikit-learn`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from missingpy import MissForest\n", + "\n", + "imputer = MissForest()\n", + "XnaImp = imputer.fit_transform(Xna)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Séparation des échantillons\n", + "Des cas sont consiédérés: les données sans données manquantes et les données après imputation des données manquantes. Les mêmes échantillons sont considérés en utilisant la même initialisation du générateur." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:08:59.148966Z", + "start_time": "2019-07-03T15:08:59.139158Z" + } + }, + "outputs": [], + "source": [ + "# Données sans trous\n", + "X_train, X_test, Yb_train, Yb_test = train_test_split(\n", + " X, Yb, test_size=200, random_state=11\n", + ")\n", + "XnaImp_train, XnaImp_test, Yb_train, Yr_test = train_test_split(\n", + " XnaImp, Yb, test_size=200, random_state=11\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prévision par forêt aléatoire\n", + "Prévision du dépassement d'ozone sans données manquantes et avec données manquantes imputées. Comparaison des erreurs de prévision sur l'échantillon test. Les valeurs par défaut des paramètres sont conservées. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:09:03.941566Z", + "start_time": "2019-07-03T15:09:03.362895Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# prévision sans trous\n", + "forest = RandomForestClassifier(n_estimators=500)\n", + "# apprentissage\n", + "rfFit = forest.fit(X_train, Yb_train)\n", + "# erreur de prévision\n", + "# erreur de prévision sur le test\n", + "1 - rfFit.score(X_test, Yb_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:09:05.304552Z", + "start_time": "2019-07-03T15:09:04.733321Z" + } + }, + "outputs": [], + "source": [ + "# prévision avec trous imputés\n", + "forest = RandomForestClassifier(n_estimators=500)\n", + "# apprentissage\n", + "rfFit = forest.fit(XnaImp_train, Yb_train)\n", + "# erreur de prévision\n", + "# erreur de prévision sur le test\n", + "1 - rfFit.score(XnaImp_test, Yb_test)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que dire de la qualité de prévision avec 10% de trous\n", + "\n", + "**Exercice** Faire varier le taux de trous et étudier la dégradation de la prévision.\n", + "\n", + "**Exercice** Comparer avec une approche directe de la prévision avec `XGBoost` sans imputation préalable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 5 bis\n", + "**Remarque** Il est possible d'exécuter directement l'*épisode 5* sans passer par toutes les étapes de classification supervisée. Il suffit d'exécuter jusqu'à la *section 4.1* de l'*épidode 1*, phase exploratoire et préparation des échantillons, afin de construire les données utilisées dans les sections 6 et 7 d'imputation des données manquantes et de détection d'atypiques.\n", + "## Détection d'observations atypiques\n", + "\n", + "Le [calepin R](https://github.com/wikistat/Apprentissage/blob/master/Pic-ozone/Apprent-R-Ozone.ipynb) d'analyse de ces mêmes données propose une comparaison assez détaillée des scores de détection des anomalies. Comme dans R, `Scikit-learn` propose des fonctions en Pyhton de détection d'atypiques multidimensionnels. Les principales sont *LOF* et *Isolation Forest* dont les résultats sont comparés ci-dessous.\n", + "\n", + "\n", + "### *Local Outlier Factor*\n", + "Les données sont restreintes aux seules variables quantitatives explicatives.\n", + "\n", + "**Q** Quel est le rôle du paramètre *k* ci-dessous?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:09:09.064105Z", + "start_time": "2019-07-03T15:09:09.039201Z" + } + }, + "outputs": [], + "source": [ + "ozone.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:09:10.225913Z", + "start_time": "2019-07-03T15:09:10.210390Z" + } + }, + "outputs": [], + "source": [ + "ozoneR = ozone[[\"MOCAGE\", \"TEMPE\", \"VentMOD\", \"VentANG\", \"SRMH2O\", \"LNO2\", \"LNO\"]]\n", + "ozoneR.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:09:11.500912Z", + "start_time": "2019-07-03T15:09:11.349314Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.neighbors import LocalOutlierFactor\n", + "\n", + "clf = LocalOutlierFactor(n_neighbors=20, contamination=0.05) # choix de n_n par défaut\n", + "scoreLOF = clf.fit_predict(ozoneR)\n", + "scoreAtyp = -clf._decision_function(ozoneR) # opposé du LOF\n", + "plt.boxplot(scoreAtyp)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Comment se comporte le *LOF* en fonction de *k*?\n", + "\n", + "**Q** Quel taux d'observations par défaut sont considérées comme atypiques?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:09:13.463557Z", + "start_time": "2019-07-03T15:09:13.446299Z" + } + }, + "outputs": [], + "source": [ + "atypLofInd = clf.fit_predict(X)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'analyse en composante principale est utilisée pour représenter les observations atypiques." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:09:17.797466Z", + "start_time": "2019-07-03T15:09:15.737146Z" + } + }, + "outputs": [], + "source": [ + "## Repésentation des atypiques\n", + "plt.figure(figsize=(5, 5))\n", + "for i, j, nom in zip(C[:, 0], C[:, 1], atypLofInd):\n", + " color = \"red\" if nom != 1 else \"blue\"\n", + " plt.plot(i, j, \"o\", color=color)\n", + "plt.axis((-4, 6, -4, 6))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *OCC SVM*\n", + "**Q** Quels sont les paramètres de cette fonction." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:09:20.975581Z", + "start_time": "2019-07-03T15:09:20.760546Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.svm import OneClassSVM\n", + "\n", + "clf = OneClassSVM(nu=0.1, gamma=0.01)\n", + "scoreSVM = clf.fit(ozoneR)\n", + "scoreAtypSVM = clf._decision_function(ozoneR)\n", + "plt.boxplot(scoreAtypSVM)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Quel taux d'atypiques par défaut?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:09:22.970638Z", + "start_time": "2019-07-03T15:09:22.960893Z" + } + }, + "outputs": [], + "source": [ + "atypSVMInd = clf.predict(ozoneR)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:09:26.678417Z", + "start_time": "2019-07-03T15:09:24.607060Z" + } + }, + "outputs": [], + "source": [ + "## Repésentation des atypiques\n", + "plt.figure(figsize=(5, 5))\n", + "for i, j, nom in zip(C[:, 0], C[:, 1], atypSVMInd):\n", + " color = \"red\" if nom != 1 else \"blue\"\n", + " plt.plot(i, j, \"o\", color=color)\n", + "plt.axis((-4, 6, -4, 6))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Isolation forest*\n", + "**Q** Comment se mesure l\"atypicité\" d'une observation dans le cas d'*isolation forest*?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:10:04.393891Z", + "start_time": "2019-07-03T15:10:04.070051Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import IsolationForest\n", + "\n", + "clf = IsolationForest(max_samples=1000, contamination=0.05, behaviour=\"new\")\n", + "scoreIF = clf.fit(ozoneR)\n", + "scoreAtypIF = clf.decision_function(ozoneR)\n", + "plt.boxplot(scoreAtypIF)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T15:10:09.667586Z", + "start_time": "2019-07-03T15:10:07.527164Z" + } + }, + "outputs": [], + "source": [ + "atypIFInd = clf.predict(ozoneR)\n", + "## Repésentation des atypiques\n", + "plt.figure(figsize=(5, 5))\n", + "for i, j, nom in zip(C[:, 0], C[:, 1], atypIFInd):\n", + " color = \"red\" if nom != 1 else \"blue\"\n", + " plt.plot(i, j, \"o\", color=color)\n", + "plt.axis((-4, 6, -4, 6))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Q** Les observations définies comme des anomalies se retrouve-t-elles généralement d'une approche à l'autre?\n", + "\n", + "**Remarques**\n", + "\n", + "- la littérature sur la détection d'anomalies ou de nouveautés multidimensionnelles est vaste et fort peu consensuelle. Ceci est encore renforcé par le fait qu'il est difficile de définir un critère efficace de mesure de la qualité d'une détection. Voir à ce sujet l'[article](http://www.dbs.ifi.lmu.de/research/outlier-evaluation/) de Campos et al. (2016). Il importe donc, en fonctin du cas et des données traitées, de pouvoir disposer d'une \"vérité terrain\": quelle méthode est le pllus susceptible de retrouver des anomalies identifiées en tant que telle?\n", + "- Conrairement à la librairie originale `randomForest` de R, il ne semble pas exister de librairie proposant la détection d'anomalies relativemement à la construction d'un modèle de prévision *y=f(X)* par forêt aléatoire. Il importe de suivre l'évolution des librairies en cours de développement." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "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 + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": true, + "toc_cell": false, + "toc_position": { + "height": "333.133px", + "left": "528px", + "top": "179.283px", + "width": "231.05px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebook/ozone/r.ipynb b/notebook/ozone/r.ipynb new file mode 100644 index 0000000..7032915 --- /dev/null +++ b/notebook/ozone/r.ipynb @@ -0,0 +1,6011 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\"INSA\"/ \n", + "\n", + "\"Wikistat\"/\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [Scénarios d'Apprentissage Statistique](https://github.com/wikistat/Apprentissage)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adaptation Statistique d'un Modèle de Prévision du Pic d'Ozone avec \"R\"/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Résumé**: Exploration puis modélisation de données climatiques en utilisant R. L'objectif est de prévoir pour le lendemain un possible dépassement d'un seuil de concentration en ozone à partir d'une prévision déterministe sur un maillage grossier et de variables climatiques locales. Estimation par différentes méthodes : régression [linéaire](http://wikistat.fr/pdf/st-m-app-select.pdf) ou [logistique](http://wikistat.fr/pdf/st-m-app-rlogit.pdf), [analyse discriminante](http://wikistat.fr/pdf/st-m-app-add.pdf), [arbre de décision](http://wikistat.fr/pdf/st-m-app-cart.pdf), [réseau de neurones](http://wikistat.fr/pdf/st-m-app-rn.pdf), [agrégation de modèle](http://wikistat.fr/pdf/st-m-app-agreg.pdf), [SVM](http://wikistat.fr/pdf/st-m-app-svm.pdf). Comparaison des [erreurs de prévision](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf) sur un échantillon test puis des courbes ROC. Industrialisaiton avec le package `caret` et itération sur plusieurs échantillons tests pour analyser la distribution de l'erreur de prévision." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Avertissement** \n", + "\n", + "* Ce tutoriel est découpé en 5 séances / épisodes de travaux dirigés syncronisées avec le cours d'apprentissage machine. \n", + "* Réfléchir aux réponses aux questions marquées **Q** issues du sujet d'examen.\n", + "* Ce calepin est complété par celui en Python (à faire _après_, ou en parallèle) afin de comparer les performances respectives des deux environnements. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'objectif, sur ces données, est d'améliorer la prévision déterministe (MOCAGE), calculée par les services de MétéoFrance, de la concentration d'ozone dans certaines stations de prélèvement. Il s'agit d'un problème dit d'*adaptation statistique* d'une prévision locale de modèles à trop grande échelle en s'aidant d'autres variables également gérées par MétéoFrance, mais à plus petite échelle (température, force du vent...). C'est une première façon de concevoir de l'l'*IA hybride* entre un modèle déterministe et un algorithme d'apprentissage automatique. Plus précisément, deux variables peuvent être prévues : soit la concentration quantitative d'ozone, soit le dépassement (qualitatif) d'un certain seuil fixé à 150 $\\mu g$. Dans chaque cas, deux approches sont considérées : soit prévoir la *concentration quantitative* puis en déduire l'éventuel dépassement ou bien prévoir directement le *dépassement*. Dans le premier cas, il s'agit d'abord d'une *régression* tandis que dans le deuxième il s'agit d'un problème de *discrimination* à deux classes ou de régression logistique. \n", + "\n", + "La question posée est donc: quelles sont les meilleures méthodes et stratégies pour prévoir la concentration d'ozone du lendemain d'une part et l'occurrence d'un pic de pollution d'autre part.\n", + "\n", + "On se propose de tester différentes méthodes : régression [logistique](http://wikistat.fr/pdf/st-m-app-rlogit.pdf), [analyse discriminante](http://wikistat.fr/pdf/st-m-app-add.pdf), [réseau de neurones](http://wikistat.fr/pdf/st-m-app-rn.pdf), [arbre de décision](http://wikistat.fr/pdf/st-m-app-cart.pdf), [agrégation d'arbres](http://wikistat.fr/pdf/st-m-app-agreg.pdf) (bagging, boosting, random forest), [SVM](http://wikistat.fr/pdf/st-m-app-svm.pdf). L'objectif final, à ne pas perdre de vue, est la comparaison de ces méthodes afin de déterminer la plus efficace pour répondre au problème de prévision. Ceci passe par la mise en place d'un protocole très strict afin de s'assurer d'un minimum d'objectivité pour cette comparaison.\n", + "\n", + "Toutes les opérations sont réalisées dans R avec l'appui de bibliothèques complémentaires éventuellement à télécharger (`corrplot, FactoMineR, glmnet, ROCR, mlbench, MASS, boot, class, e1071, rpart, partykit, nnet, ipred, gbm, randomForest, caret, doParallel, xgboost, missForest, Rlof, dbscan, kernlab`). \n", + "\n", + "Python (consulter le [calepin](https://github.com/wikistat/Apprentissage/blob/master/Pic-ozone/Apprent-Python-Ozone.ipynb)) conduit à des résultats comparables mais moins complets pour leur interprétation. En particulier, l'absence du type *DataFrame* dans la librairie scikit-learn n'autorise pas une sélection fine des variables dans les modèles statistiques usuels. En revanche, l'exécution de la validation croisée Monte Carlo est plus rapide en python." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prise en charge des données" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données ont été extraites et mises en forme par le service concerné de Météo France. Elles sont décrites par les variables suivantes :\n", + "\n", + "* **JOUR** : type de jour ; férié (1) ou pas (0) ;\n", + "* **O3obs** : concentration d'ozone effectivement observée le lendemain à 17h locales correspondant souvent au maximum de pollution observée ;\n", + "* **MOCAGE** : prévision de cette pollution obtenue par un modèle déterministe de mécanique des fluides (équation de Navier et Stockes);\n", + "* **TEMPE** : température prévue par MétéoFrance pour le lendemain 17h ;\n", + "* **RMH2O** : rapport d'humidité ;\n", + "* **NO2** : concentration en dioxyde d'azote ;\n", + "* **NO** : concentration en monoxyde d'azote ;\n", + "* **STATION** : lieu de l'observation : Aix-en-Provence, Rambouillet, Munchhausen, Cadarache et Plan de Cuques ;\n", + "* **VentMOD** : force du vent ;\n", + "* **VentANG** : orientation du vent. \n", + "\n", + "Ce sont des données \"propres\", sans trous, bien codées et de petites tailles. Elles présentent donc avant tout un caractère pédagogique car permettant de décliner puis comparer toutes les approches de régression et classification supervisée.\n", + "\n", + "**Attention**: Même si les données sont de qualité, une étude exploratoire préalable est toujours nécessaire pour se familiariser avec les données et les préparer à la phase de modélisation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-22T09:48:06.646161Z", + "start_time": "2019-11-22T09:48:06.591Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + " JOUR O3obs MOCAGE TEMPE \n", + " Min. :0.0000 Min. : 19.0 Min. : 46.4 Min. :10.40 \n", + " 1st Qu.:0.0000 1st Qu.: 87.0 1st Qu.: 97.5 1st Qu.:20.20 \n", + " Median :0.0000 Median :109.0 Median :125.6 Median :23.80 \n", + " Mean :0.3045 Mean :115.4 Mean :127.2 Mean :23.88 \n", + " 3rd Qu.:1.0000 3rd Qu.:135.0 3rd Qu.:153.6 3rd Qu.:27.60 \n", + " Max. :1.0000 Max. :319.0 Max. :284.7 Max. :38.00 \n", + " RMH2O NO2 NO STATION \n", + " Min. :0.00285 Min. : 0.258 Min. :0.0010 Length:1041 \n", + " 1st Qu.:0.00763 1st Qu.: 1.248 1st Qu.:0.2360 Class :character \n", + " Median :0.00985 Median : 2.109 Median :0.3880 Mode :character \n", + " Mean :0.01025 Mean : 3.505 Mean :0.6574 \n", + " 3rd Qu.:0.01244 3rd Qu.: 4.062 3rd Qu.:0.7440 \n", + " Max. :0.02753 Max. :44.396 Max. :9.4290 \n", + " VentMOD VentANG \n", + " Min. : 0.1414 Min. :-1.5708 \n", + " 1st Qu.: 3.9623 1st Qu.:-0.3948 \n", + " Median : 5.5973 Median : 0.2783 \n", + " Mean : 5.9072 Mean : 0.1631 \n", + " 3rd Qu.: 7.1063 3rd Qu.: 0.6926 \n", + " Max. :19.8910 Max. : 1.5708 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Lecture des données\n", + "# path=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/data/\"\n", + "path <- \"\"\n", + "ozone <- read.table(paste(path, \"dep_seuil.dat\", sep = \"\"),\n", + " sep = \",\", header = TRUE)\n", + "# Vérification du contenu\n", + "summary(ozone)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:37.832339Z", + "start_time": "2019-11-18T09:21:59.889Z" + } + }, + "outputs": [], + "source": [ + "# Changement du type des variables qualitatives en facteur\n", + "ozone[, \"JOUR\"] <- as.factor(ozone[, \"JOUR\"])\n", + "ozone[, \"STATION\"] <- as.factor(ozone[, \"STATION\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploration élémentaire" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remarquer le type des variables. Il est nécessaire d'en étudier la distribution. Noter la symétrie ou non de celles-ci ." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:37.891238Z", + "start_time": "2019-11-18T09:22:00.279Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Histogram of ozone[, \"NO2\"]”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "par(mfrow = c(1, 2))\n", + "options(repr.plot.width = 8, repr.plot.height = 4)\n", + "hist(ozone[, \"O3obs\"])\n", + "hist(ozone[, \"NO2\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:37.909444Z", + "start_time": "2019-11-18T09:22:00.286Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Histogram of ozone[, \"MOCAGE\"]”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Histogram of ozone[, \"TEMPE\"]”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Histogram of ozone[, \"RMH2O\"]”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Histogram of ozone[, \"NO\"]”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Histogram of ozone[, \"VentMOD\"]”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+3iIAeWvE98BrqjsZ3wNXVq/y7+YAcKNxe2Jt37iFPbGAwmn8vtB3syMWUCiND1jm+XAzAHjhNeAPH0+Q8fqBr7cJQJ68BrxC0vh6mwDkyWt6O2dLm6sXG2SYfpBx6bb5c5NmVTX2NiJu7pvrzkuFvsHx5f2585EOvf5qrqGe98DOgCfLfs9zIJ7GHDXNjQ43F/oGx1cjXvx+eFLpVV82/CHWcwTc5Iy54FU3BhGwZ41591p3a4tj1hMwshBwaBr38dOao8r/k4CRiYBD08jPj/deJASMTAQcmkZ/AbRyydO5BxBw00PAoQn+G1wCbnoIODQEDP8RcGgIGP4j4NAQMPxHwKHxGvBP2qfJMZKAmx4CDo3XgN+6pKW0HZiUYyQBNz0EHBrvL6GflIl5jSPgpoeAQ9OI98B9CRj1I+DQNCLgs6bkNYyAmx4CDg2fQsN/BBwaAob/CDg0BAz/EXBoCBj+I+DQEDD8R8ChIWD4j4BDQ8DwHwGHhoDhPwIODQHDfwQcGgKG/wg4NAQM/xFwaAgY/iPg0BAw/EfAoSFg+I+AQ0PA8B8Bh4aA4T8CDg0Bw38EHBoChv8IODTBBFzz2MNJNxJwk0PAoQkm4PcrD05qK/sCmQPRRcCh4SU08vDoXFe6EHBYCBh5mNpnmhstCDgsBIw8TD3LVZHtCDgsBIw8EHBUETDyQMBRRcDIAwFHFQEjDwQcVQSMPBBwVBEw8kDAUUXAyAMBRxUBIw8EHFUEjDwQcFQRMPJAwFFFwMgDAUcVASMPBBxVBIw8EHBUETDyQMBR1biAd2zaXNvQGAIuAgQcVY0I+NWZnUWkrEv16pzDCLgIEHBUeQ94QYlUDpswYXhXkTm5xhFwESDgqPIc8J0y9mVraf2ZsjTHQAIuAgQcVZ4DrupXk1isO35EjoEEXAQIOKo8B1wxK7V8dbscAwm4CBBwVHl/Bu5/ILk8mmfgIkfAUdWI98Dj11lLb86QW3IMJOAiQMBR5f1T6Hki3UZOmjyql8jsuhzjCLgIEHBUNeJ74DXVnYzvgSurV+UcRsBFgICjqnF7Ym3fuIU9sZoCAo4qdqVEHgg4qtiVEnkg4KhiV0rkgYCjil0pkQcCjqqAdqXctS3pSQKOPwKOqmB2pXy7VBz2eZ0DUUHAURXQrpTrXkxazjNw/BFwVLErJfJAwFHFrpTIQ6QCvn2uKxe8G9CdEgnsSok8RCrgfoOnuVF+f0B3SiSwKyXyEK2Af+Bq7Yc0mYBX7AhiBgIuAgQcVc6ApXzqw3t9n4GAiwABR5Uz4DtPKJU2Zz/+pb8zEHARCDbgnuMXu3EIAaekvwfecofecIfzn2nwja0LBFwEgg241WED3Cgh4JSsD7G23DGqVCq/80ID1/tJ+zQ5RhJwEQg44MtdDW9OwCnZn0K/srCXsf9j30dzXu+tS1pK24FJOUYScBEg4KhKD7jmme/0EKmc99RLl7cp+Wfuaz4pE/OagYCLAAFHlTPgR885WOSI//ibuV/Vy3JlA1ftS8BNBgFHVdrXSPLVhWsTJ3Z0urWBq541Ja8ZCLgIEHBUOQNe8k4QMxBwESDgqEp/D7zhaf3g52/4OgMBFwECjqq0gL9TMlI/bFZyea7/XeQWARcBAo4qZ8DLpeoJ/eiPo+UeH2cg4CJAwFHlDHj0kdZelDUDhvo4AwEXAQKOKmfA7S+wF+a39XEGAi4CBBxVzoD7j7cXTunr4wwEXAQIOKqcAc8t+615/Mey2T7OQMBFgICjyhnwZz1lzKK7F59acugWH2cg4Ciq2+bKqQQcUWlfI31wjvn7nE953c8ZCDiKLhN3CDiiMv430ier/2flh/7OQMBRNPuEh9w4mIAjqnG/1C4fBBxFsye7iuBQAo6otIAfmT7G5uMMBBxFBFwknAHfLdKmk8XHGQg4igi4SDgD/kpF7j/V7Q0BRxEBFwlHwHUtLg5iBgIOxe4XXTmVgIuDI+B9JZcFMQMBh+IKl98LEXBxcL6EPqHn5wHMULwBb7rZ1a8zXvyHIG/MJSc850ZXAi4OzoA/GDToobe3mnycoXgD/q+Wrn6d8WHHBnljLhnj6lHdnYCLQ9r/RmqdfIHVyLVuHjkkqZ/sa+TaournPV09kC4nYAUC9s6Z6pyURq51z9LUC8cLi/YZmIBVCDg07InlHQGrEHBoMgLes+55v2cgYBsBqxCwd2kBvz+1uf729wdnbfJzBgK2EbAKAXvnDHhzN6kaLdqt0mWzjzMQsI2AVQjYO2fAF8kvtfv0M1aUzfdxBgK2EbAKAXvnDLjHaM0MWJt0pI8zELCNgFUI2DtnwK0vsAO+sLWPMxCwjYBVCNg7Z8DDvm4HPHiIjzMQsI2AVQjYO2fAi+TGWiPgRXKVjzMQsI2AVQjYO2fAB0ZJn+Nk/hAZ9IWPMxCwjYBVCNi7tO+B9y/rLiIdr93p5wwEbCNgFQL2LnNXyl2vfebzDARsI2AVAvaOfaG9I2AVAg6NM+CzU3ycgYBt84942JXXXN0YAlZpOgEn/zdw2z4+zkDAthPKKtxoebKrG0PAKk0n4H2mrStHtHrCxxkI2DZqoKvhF3zD1Y0hYJWmE3DCnn4dv/RvBgK2EbAKAXtX74dYV8hG/2YgYBsBqxCwd/UG/J2Wtf7NQMA2AlYhYO/qCbju2XZH+zgDAdsIWIWAvXMG3MbSUmSFjzMQsI2AVQjYO2fAE20zf+vnDARsI2AVAvaucXti7di0ucF3ywRsI2AVAvauEQG/OrOziJR1qc79Nw0J2EbAKgTsnTPgrmlGNnDNBSVSOWzChOFdRXL+HngCthGwCgF75wx4XhcpOXxI1xLpOVI3JfcV75SxL1tL68+UpTkGErCNgFUI2DtnwH8t/ea/9KM3xnZ5v+ErVvWrSSzWHT8ix0ACthGwCgF75wz41F57zeO9vac1fMWKWanlq9vlGEjANgJWIWDvnAEfNste+HbXhq9Y1f9Acnk0z8B5IGAVAvYu8/dCm8ZUNnzFO2X8OmvpzRlyS46BBGwjYBUC9s4Z8PSSx8zj35VOyuOa80S6jZw0eVQvkdl1OcYRsI2AVQjYO2fA73csPeOePy4/o7TV2nyuuqa6k/E9cGX1qpzDCNhGwCoE7F3ajhyvnGT+Qo6BK/O99vaNW+rdE4s/8F0PAlYhYO8y9sRa/8jSXz7v6v8SfvzS7uwzN48cktRP9jXmBkYYAasQcGi8/4Hv92f9TNP+eYxI6akf5hrHS2gbAasEG3B5ryFuVG0I6OESDM9/4PutjvKf2oZWJd+cd4JUbssxkIBtBKwSbMClEy5zo/njAT1cguH5D3xPK7mrTju99Gl98UFZkGMgAdsIWCXggG92NbxVfAN29Qe+DxumH3Qdby6P+UqOgQRsI2AVAvbO8x/4bjtDPzj0PHP5/IocAwnYRsAqBOyd5z/wPfrwHZo28WhjF47agaNyDCRgGwGrELB3nv/A97Mthj+vrWlz1QHti4vkthwDCdhGwCoE7J33P/D9QDPpdnxv6TS0QmbnGkfANpcBn9j8YDdaErBC0wnY5R/4/uDyw439tsrH/SnnMAK2uQx48JFL3TicgBWaTsDu/8D3rg/f/aih/bYI2OY24OGuhvcjYIUmE/Dun/+NP/DtBgGrEHBo0j6FPiuIGQjYRsAqBOydM+D5h2wNYAYCthGwCgF75wy45oJBD721c7fBxxkI2EbAKgTsnTPgzp3LxObjDARsI2AVAvbOmersFB9nIGAbAasQsHeJgBfcG9QMBGwjYBUC9i4RsJxtHC7P+UdSvCFgGwGrELB36QHP9vPNr42AbQSsQsDeEbB3BKxCwKEhYO8IWIWAQ0PA3hGwCgGHhoC9I2AVAg4NAXtHwCoEHJpkwD2m63rJdIuPMxCwjYBVCNi7ZMDpfJyBgG0ErELA3iVSfTGdjzMQsI2AVQjYuwDe9GYgYBsBqxCwdwTsHQGrEHBoCNg7AlYh4NAQsHcErELAoSFg7whYhYBDE1DA61IfaC8nYAsBqxCwd8EE/Hap80vlfYHMUXgErELAoQnoGXjXtqQneQa2ELAKAXvHe2DvCFiFgENDwA5rhw9xozsBKxBwaAjY4f7Wl7nRn4AVCDg0BOxw/yGuftSnELACAYeGgB0IWIWAo4qAHQhYhYCjioAdCFiFgKOKgB0IWIWAo4qAHQhYhYCjioAdCFiFgKOKgB0IWIWAo4qAHQhYhYCjioAdCFiFgKOKgB0IWIWAo4qAHQhYhYCjioAdCFiFgKOKgB0IWIWAo4qAHQhYhYCjioAdCFilCQXcfNxcNy7cWtjHLAE7ELBKEwpYRk5zQ/5S2McsATsQsEpTCvhON6PXEnCEELAKASvEPOAdmzbXNjSGgG0ErELA3jUi4FdndhaRsi7Vq3MOK2TA229d7EY1ASsQsEKMA15QIpXDJkwY3lVkTq5xhQz4idIBbnQgYAUCVohvwHfK2JetpfVnytIcAwsZ8OOtXP3seAZWIWCF+AZc1a8msVh3/IgcAwnYRsAqsQ74iW1u7Pb7Ie454IpZqeWr2+UYSMA2AlaJccBrxJ2y93x+iHt/Bu5/ILk82vszcO3/PuzKM65uJAGrELCKu4Bflh8+5MI9stbVA7hhjXgPPH6dtfTmDLklx8DcAa+XthUutC49kGNlWQhYhYBV3AZ8r5vhq6MTsDZPpNvISZNH9RKZXZdjXO6A18pqN3fAvVKTY2VZCFiFgFWaTMDamupOxov6yupVOYcRsI2AVQjYu8btibV945Z698R6v/LgpLayL8caCFiFgFUI2KExAX/8hl3Tp5syLql5LPWx043+PgP//UUXlhGwAgGrNJmA1xwt0nmFuTgu11p8fQm90OWn9gSsQMAqTSXgt8tLx0wolzuN5fACvkb+8pwL5xGwAgGrNJWAp5f8QdM+6VP+hhZuwK+4GT6fgBUIWKWpBNxrrHH4ZqtTNQLODwGrELB3ngNua/0XpOuMnbkJOB8ErELA3nkOeOQA82h3t6/sJ+C8ELAKAXvnOeCrZIH5/e4TMv0LAs4HAasQsHeeA/7ieGk70Vi4TrocQsB5IGAVAvbO+/fA26/sb72KXtFPCDgPBKxCwN758Vsp695bmeNSArYRsAoBe1foXytLwCoErELADgTsQMAqBKxAwASsQMAqBOxAwA4ErELACgRMwAoErELADgTsQMAqBKxAwASsQMAqBOxAwA4ErELACgRMwAoErELADgTsQMAqBKxAwASsQMAqBOxAwA4ErELACgRMwAoErELADgTsQMAqBKxAwASsQMAqBOxAwA4ErELACgRMwAoErELADgTsQMAqBKwQ84B3bNpc718ndCJgGwGrELB3jQj41Zmdjb8P3KV6dc5hBGwjYBUC9s57wAtKpHLYhAnDu4rMyTWOgG0ErELA3nkO+E4Z+7K1tP5MWZpjIAHbCFiFgL3zHHBVv5rEYt3xIzIu3DZ/btLkBgKePM2FwXK6m+EDytyMntan3NXw7m1dDa882NXwToe6Gt6+i6vhbXq4Gt6qj6vhLY5yNbzZ0a6Glw52NbxkmKvhMtLN6NNltJvhk6MTcMWs1PLV7TIudAY8qyrXanYtmOvGrMGuhp99rKvh1cNdDT+jytXwqaNcDZ882tXwiSe7Gj5unKvhJ090Nfykya6Gj5rqanjVGa6GD692NfzYs10NHzzT1fAFu7wGp+D9Gbj/geTy6MxnYAChaMR74PHrrKU3Z8gtft0cAG54/xR6nki3kZMmj+olMrvOx1sEIG+N+B54TXUn43vgyupV/t0cAG40bk+s7Ru3NLgnFoDABL8vNIDAEDAQYwQMxFhUA+4rQIz1DSmUqAZ88swXw3dTpwJM+mLZTwsw6amnFmDSn5YVYNIXO91UgElnnhxSKFEN+JQrCjDp/ZUFmFRr9nQBJp09uwCTPt2sAJNqlfcXYNIrTglpIgJ2IOBgEbD/CNiBgINFwP4jYAcCDhYB+4+AHQg4WATsPwJ2IOBgEbD/CNiBgINFwP4jYAcCDhYB+4+AHQg4WATsPwJ2IOBgEbD/ohrwlGsKMOmjPQowqXbQswWYdO7cAkz67EEFmFTr8WgBJr1mSkgTRTXgT3YWYNKaDwowqfZuIX4h0bZtBZi07t0CTKp9UNPwGN/t/CSkiaIaMIA8EDAQYwQMxBgBAzFGwECMETAQYwQMxBgBAzFGwECMETAQYwQMxBgBAzFGwECMETAQYwQMxFgEA37rJ01nVgSnID/R0CeNYMCXtHee6mr9sbdrw51V++mIdiN+GvCUmXMEv6lZW1Wcm2kpwE+0AA/e6AX8VEvnfbC35PATDfeEOqs2T/rN7CsLAp0yc47gNzVrq4pzMy0F+IkW4sEbtYDP6ifivA/WyY0FmHWNjKvRar5Z8mqAc2bNEfimZs1YnJtpKsRPtCAP3qgFPGXixLbO++BReaQAs1bLWv3wJZkZ4JxZcwS+qVkzFudmmgrxEy3IgzdqAesGOu+DH8k/7rv+rtdCnrVTV/OosnOAE2bNEfimZs1YnJuZEPpPNGvSMDY16gF/Ww4RkdKLA//Ngs5Zt8sI83iYBPe7MbPnCHpTs2Yszs1MCvsnmjVpKJsa9YBHyvR1u1YfKzeHOetGmWQeT5BNgc2XPUfQm5o1Y3FuZlLYP9GsSUPZ1IgEvGeZ7nFrOe0++MszxuGnB7epDXHWLTLZPJ4gmwObNHuOQDdVq2erAt1M1aSBb2ZSaD9R1aShbGpEAv7I+LpsmrU8sH325dNkQ4iz1paNMo+HlwVwz9uTKucIZlO1erYq0M1UTZoQ2GYmhfYTVU2aEOimRiRgp/rugwsk6E890mat7G0edesS4ISqOYLb1KwZi3MzE0L/iWZNagt0UyMe8Gv9rzKPh7cM+jOPgelfOrypH66X6gAnzJwj+E3N2qri3MyE0H+imZOGsqnRDfjLrdv1lz7dWv1DX75HAv9bXM5ZtVVytqbVnSl/DXBCxxwhbWrmjEW6mQmh/0QzJw1lU6Mb8Eo5Rj9c1aH5lAtHyFHbQ51Vmy0nXT1Kzgt0xtQcYW1q5oxFupm28FEKy8cAAAXhSURBVH+imZOGsalRD1j74NyBbYZe90XIs9bdXFVRdWuwM6bmCGtTs2Yszs20hf8TzZo0hE2NYMAA8kXAQIwRMBBjBAzEGAEDMUbAQIwRMBBjBAzEGAEDMUbAQIwRMBBjBAzEGAEDMUbAQIwRMBBjBAzEGAEDMUbAQIwRMBBjBAzEGAEDMUbAQIwRMBBjBAzEGAEDMUbAQIwRMBBjBAzEGAEDMUbAQIwRMBBjBAzEGAEDMUbARWqMSMbP9rU3tHfX16yW4+3Tf5FxDa3kLCn50FxYJINqrLP6dzKPNpw3pOLQ4679zLHm/9BnXO/PjUfeCLhIjSlfsiT9nPKu2kj5qK57yb+t05fJ8vqv+lTPx6yFvW1ElppLi0Rusc6zAr6mmRw67uTO0vaZ1JpXLRlHwKEj4CI1pn3mOVZm2vfkx9bpns231X/V38p91sJDMl6+bi4tkpKD3jeXzICvl8P/v35U81/N272ZWrM+ioDDRsBFYX/WOdkB9xiiTW2xX3tFRpgnX5ZTFGtJBnyaPD9I3jGWFskFMtE8zwj49WYdP7ZG3C7fSa2ZgAuAgKNvx8VHtxnyvb36UrmY+qSdOaf9xhNLWgy8xxh64KbhbXou2KzVF/Dpc7WbhurHR1nva6+Te53jU2sx3j3LVv2sz1v21J9rf2QMXiRPT5BfG0tGwPPlDnude8+/2rFmAg4fAUfeR0fIiPMHy8BdmnbrYt0MGZV25pzWg3peuqBCHtWfQkfJ0LknSvcP6gv4vt9qz9+mH98gxqH2lRafO8en1vKnS2Tuin36iF/IVdo6+aoxeJGsfO+gLjs1K+DuJXvqXTMBh4+AI2++LNMPvy83WCe3927/TtqZc2TQdk1bLdM1bZncqJ97r5xeX8AJb8lw/XCDTEob71hL4iX0N2Wdph0pr2tmwNpi8+WyHvA+6V7/mgk4fAQcdV+2GFinH+3rfLh5sm5iyf+mnzlHHjIuaDNGf2rsU2ssHtdiT46AtWNFf8ZdbFTqGO9Yix3wx2UDNOPfiIWaFXDNwLKXzIC3SJW5ns7m63lHswQcPgKOurdkgXk8VXYbRzfor2vTz5wjbxuLncZou+W4+wyj9afOHAHfJks0bVj5zrTxqbUkAr5DFumH/5CjNCtg7bmSobVGwLXl1r8lF5199tnHEHBhEXDUrTI70nMR4xubJ0tPOpBx5hzzEycjvdck4W+5Av536de1TSVTtLTxqbUkAh6RuHCtHbB2vtxuvgc+Rj5PrOtSAi4sAo66DXKxeTxNdmra+x27fJx5Ziq9rXJR8mo5AtZGy3t3yINa2visgDeWdL7UMFKuTgS87dCKfxsBn5f4KlnTqgi4sAg46r5sfrRxtL9LZ037Ykjz57LOdKTXcah56a0/yB3wf8vNJ7UyXpA7xmcFfItcaV74gvROBKz9SqYZAb9XfvCn1poe4z1wgRFw5F1gfut6jfzQ+dTnONOR3jVyk2Z8qjwjEfCXW7fXs8ZtLfo1m6ZpaePTAjb2sfya8dLZ0EP+mQhY+4a0MPbEuk56G/tQ1t3droKAC4uAI29zTzlx/jA5erf2uBx26xLDdseZzvR2DpQh8yeXddmUCHilDKxvlaeKPGIcO8Y71vKUDL1xzxsywB58hXw3GfCbLcUIuPamZtJ1wtjOMvv3BFxYBBx9n180qPXgK7/QtLsTHyu97TjTmZ72xfcHH9TnwtSeWIqAH5DW1q4YqfGOtXxxTscO2xaaXxEbXpRudYmAtYVi/W+kF751eIvDTntK2zV7U2q1BBw+Ai5S9nvgrUNCnJOAw0fARcoO+KlZIc5JwOEj4CJlBfz3Ee+GOCcBh4+Ai1T2b+QIGr+RoxAIuEg9vCTzN3IEbZU+49ZwpwQBA3FGwECMETAQYwQMxBgBAzFGwECMETAQYwQMxBgBAzFGwECMETAQYwQMxBgBAzFGwECMETAQYwQMxBgBAzFGwECMETAQYwQMxBgBAzFGwECMETAQYwQMxBgBAzFGwECM/R9H0i5+YWjh2wAAAABJRU5ErkJggg==", + "text/plain": [ + "Plot with title “Histogram of ozone[, \"VentANG\"]”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Même chose pour les autres variables\n", + " hist(ozone[,\"MOCAGE\"]);hist(ozone[,\"TEMPE\"]);hist(ozone[,\"RMH2O\"])\n", + "#\n", + "hist(ozone[,\"NO\"]);hist(ozone[,\"VentMOD\"]);hist(ozone[,\"VentANG\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Des transformations sont proposées pour rendre certaines distributions plus symétriques et ainsi plus \"gaussiennes\". C'est nécessaire pour certaines méthodes à venir de modélisation (linéaires), par pour toutes (arbres)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:37.928577Z", + "start_time": "2019-11-18T09:22:00.483Z" + } + }, + "outputs": [], + "source": [ + "ozone[, \"SRMH2O\"] <- sqrt(ozone[, \"RMH2O\"])\n", + "ozone[, \"LNO2\"] <- log(ozone[, \"NO2\"])\n", + "ozone[, \"LNO\"] <- log(ozone[, \"NO\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Histogram of ozone[, \"SRMH2O\"]”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Histogram of ozone[, \"LNO2\"]”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Histogram of ozone[, \"LNO\"]”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "hist(ozone[,\"SRMH2O\"]);hist(ozone[,\"LNO2\"]);hist(ozone[,\"LNO\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vérifier l'opportunité de ces transformations puis retirer les variables initiales et construire la variable \"dépassement de seuil\" pour obtenir le fichier qui sera effectivement utilisé.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:37.955724Z", + "start_time": "2019-11-18T09:22:00.688Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + " JOUR O3obs MOCAGE TEMPE STATION \n", + " 0:724 Min. : 19.0 Min. : 46.4 Min. :10.40 Aix:199 \n", + " 1:317 1st Qu.: 87.0 1st Qu.: 97.5 1st Qu.:20.20 Als:222 \n", + " Median :109.0 Median :125.6 Median :23.80 Cad:202 \n", + " Mean :115.4 Mean :127.2 Mean :23.88 Pla:208 \n", + " 3rd Qu.:135.0 3rd Qu.:153.6 3rd Qu.:27.60 Ram:210 \n", + " Max. :319.0 Max. :284.7 Max. :38.00 \n", + " VentMOD VentANG SRMH2O LNO2 \n", + " Min. : 0.1414 Min. :-1.5708 Min. :0.05339 Min. :-1.3548 \n", + " 1st Qu.: 3.9623 1st Qu.:-0.3948 1st Qu.:0.08735 1st Qu.: 0.2215 \n", + " Median : 5.5973 Median : 0.2783 Median :0.09925 Median : 0.7462 \n", + " Mean : 5.9072 Mean : 0.1631 Mean :0.09957 Mean : 0.8440 \n", + " 3rd Qu.: 7.1063 3rd Qu.: 0.6926 3rd Qu.:0.11153 3rd Qu.: 1.4017 \n", + " Max. :19.8910 Max. : 1.5708 Max. :0.16592 Max. : 3.7931 \n", + " LNO DepSeuil \n", + " Min. :-6.9078 FALSE:863 \n", + " 1st Qu.:-1.4439 TRUE :178 \n", + " Median :-0.9467 \n", + " Mean :-0.8399 \n", + " 3rd Qu.:-0.2957 \n", + " Max. : 2.2438 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ozone <- ozone[, c(1:4, 8:13)]\n", + "ozone[, \"DepSeuil\"] <- as.factor(ozone[, \"O3obs\"] > 150)\n", + "summary(ozone)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:39.491279Z", + "start_time": "2019-11-18T09:22:00.697Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width = 8, repr.plot.height = 8)\n", + "pairs(ozone[, c(2:4, 6:10)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que dire sur les relations des varibles 2 à 2 ?\n", + "\n", + "**Q** Compléter en visualisant les corrélations avec la fonction 'corrplot' (package `corrplot`). Quelle est la limite de ce type de diagnostic numérique : quel type de corrélation est mesuré ? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les commandes suivantes permettent de réaliser une [analyse en composantes principales](http://wikistat.fr/pdf/st-m-explo-acp.pdf) sur les seules variables quantitatives. Par ailleurs la variable à modéliser (O3obs, concentration observée) n'est pas utilisée." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:39.559229Z", + "start_time": "2019-11-18T09:22:00.921Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Coordinates of individuals”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ACP réduite\n", + "# Décroissance des valeurs propres\n", + "library(FactoMineR)\n", + "acp <- PCA(ozone[, c(11,2:4, 6:10)], scale.unit = TRUE,\n", + " graph = FALSE, quali.sup = 1, quanti.sup = 2, ncp = 7)\n", + "options(repr.plot.width = 8, repr.plot.height = 4)\n", + "par(mfrow = c(1, 2))\n", + "barplot(acp$eig[, 2], ylab = \"Percentage\", main = \"Proportion of inertia\")\n", + "boxplot(acp$ind$coord, main = \"Coordinates of individuals\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PCA package:FactoMineR R Documentation\n", + "\n", + "_\bP_\br_\bi_\bn_\bc_\bi_\bp_\ba_\bl _\bC_\bo_\bm_\bp_\bo_\bn_\be_\bn_\bt _\bA_\bn_\ba_\bl_\by_\bs_\bi_\bs (_\bP_\bC_\bA)\n", + "\n", + "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n", + "\n", + " Performs Principal Component Analysis (PCA) with supplementary\n", + " individuals, supplementary quantitative variables and\n", + " supplementary categorical variables.\n", + " Missing values are replaced by the column mean.\n", + "\n", + "_\bU_\bs_\ba_\bg_\be:\n", + "\n", + " PCA(X, scale.unit = TRUE, ncp = 5, ind.sup = NULL, \n", + " quanti.sup = NULL, quali.sup = NULL, row.w = NULL, \n", + " col.w = NULL, graph = TRUE, axes = c(1,2))\n", + " \n", + "_\bA_\br_\bg_\bu_\bm_\be_\bn_\bt_\bs:\n", + "\n", + " X: a data frame with _n_ rows (individuals) and _p_ columns\n", + " (numeric variables)\n", + "\n", + " ncp: number of dimensions kept in the results (by default 5)\n", + "\n", + "scale.unit: a boolean, if TRUE (value set by default) then data are\n", + " scaled to unit variance\n", + "\n", + " ind.sup: a vector indicating the indexes of the supplementary\n", + " individuals\n", + "\n", + "quanti.sup: a vector indicating the indexes of the quantitative\n", + " supplementary variables\n", + "\n", + "quali.sup: a vector indicating the indexes of the categorical\n", + " supplementary variables\n", + "\n", + " row.w: an optional row weights (by default, a vector of 1 for\n", + " uniform row weights); the weights are given only for the\n", + " active individuals\n", + "\n", + " col.w: an optional column weights (by default, uniform column\n", + " weights); the weights are given only for the active variables\n", + "\n", + " graph: boolean, if TRUE a graph is displayed\n", + "\n", + " axes: a length 2 vector specifying the components to plot\n", + "\n", + "_\bV_\ba_\bl_\bu_\be:\n", + "\n", + " Returns a list including:\n", + "\n", + " eig: a matrix containing all the eigenvalues, the percentage of\n", + " variance and the cumulative percentage of variance\n", + "\n", + " var: a list of matrices containing all the results for the active\n", + " variables (coordinates, correlation between variables and\n", + " axes, square cosine, contributions)\n", + "\n", + " ind: a list of matrices containing all the results for the active\n", + " individuals (coordinates, square cosine, contributions)\n", + "\n", + " ind.sup: a list of matrices containing all the results for the\n", + " supplementary individuals (coordinates, square cosine)\n", + "\n", + "quanti.sup: a list of matrices containing all the results for the\n", + " supplementary quantitative variables (coordinates,\n", + " correlation between variables and axes)\n", + "\n", + "quali.sup: a list of matrices containing all the results for the\n", + " supplementary categorical variables (coordinates of each\n", + " categories of each variables, v.test which is a criterion\n", + " with a Normal distribution, and eta2 which is the square\n", + " correlation corefficient between a qualitative variable and a\n", + " dimension)\n", + " Returns the individuals factor map and the variables factor map.\n", + " The plots may be improved using the argument autolab, modifying\n", + " the size of the labels or selecting some elements thanks to the\n", + " ‘plot.PCA’ function.\n", + "\n", + "_\bA_\bu_\bt_\bh_\bo_\br(_\bs):\n", + "\n", + " Francois Husson ,\n", + " Jeremy Mazet\n", + "\n", + "_\bR_\be_\bf_\be_\br_\be_\bn_\bc_\be_\bs:\n", + "\n", + " Husson, F., Le, S. and Pages, J. (2010). Exploratory Multivariate\n", + " Analysis by Example Using R, _Chapman and Hall_.\n", + "\n", + "_\bS_\be_\be _\bA_\bl_\bs_\bo:\n", + "\n", + " ‘print.PCA’, ‘summary.PCA’, ‘plot.PCA’, ‘dimdesc’,\n", + " Video showing how to perform PCA with FactoMineR\n", + "\n", + "_\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs:\n", + "\n", + " data(decathlon)\n", + " res.pca <- PCA(decathlon, quanti.sup = 11:12, quali.sup=13)\n", + " ## plot of the eigenvalues\n", + " ## barplot(res.pca$eig[,1],main=\"Eigenvalues\",names.arg=1:nrow(res.pca$eig))\n", + " summary(res.pca)\n", + " plot(res.pca,choix=\"ind\",habillage=13)\n", + " dimdesc(res.pca, axes = 1:2)\n", + " ## To draw ellipses around the categories of the 13th variable (which is categorical)\n", + " plotellipses(res.pca,13)\n", + " \n", + " ## Not run:\n", + " \n", + " ## Graphical interface\n", + " require(Factoshiny)\n", + " res <- Factoshiny(decathlon)\n", + " \n", + " ## Example with missing data\n", + " ## use package missMDA\n", + " require(missMDA)\n", + " data(orange)\n", + " nb <- estim_ncpPCA(orange,ncp.min=0,ncp.max=5,method.cv=\"Kfold\",nbsim=50)\n", + " imputed <- imputePCA(orange,ncp=nb$ncp)\n", + " res.pca <- PCA(imputed$completeObs)\n", + " ## End(Not run)\n", + " " + ] + } + ], + "source": [ + "help(PCA)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:39.767167Z", + "start_time": "2019-11-18T09:22:00.926Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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qaqIOhIfa2lox/GMfHh4eGBh448YNMzMzUcdCJbZvzMLCQm1tbTH8B1Js71h5ebmCgoKcnFw7tpmSksJisdqxQfCLgAQaCKm+vv7s2bMnTpzIzc2lHDp79qykpKSTk9OmTZs6P4vFSUhIKCgodPJFWSxW51+UH/X19fLy8mIYm4SEBIPBkJWVFXUgVFwuVyS/Qvxgs9liGFh9ff3379/F86aJ7RtTRkZGQUFBDBNosb1jdXV18vLy7dsXIJ597UD8QQINhHHz5s3169fn5eVJSkqOHDnSxsamX79+qqqqjY2NxcXF8fHxz549u379+o0bN9zc3Hx8fLS0tEQdMgAAAABA+4AEGgjD2dlZV1f3wIED8+fP5zlUg8vlRkVFnT17NigoSE9Pb+fOnZ0fJAAAAABAR4AEGgjj3Llzbm5uLQxdlZCQcHR0dHR0TE1NLSoq6szYAAAAAAA6FCTQQBjLly/nWc5kMmNiYths9qhRo/DJ+CYmJiYmJp0bHQCgs82fP3/AgAFGRkaiDgQAADoDJNCg3WRnZ48bNy47Oxsh1Ldv38ePH+vr64s6KABAZzAzM9PW1u7atauoAwEAgM4gduvCgh+Xu7v758+fd+/evW/fvi9fvqxbt07UEQEAAAAAtD/ogQbCYDKZ9fX1lN6mV69e2djY7NixAyEUHR398uVLEUUHAAAAANCBoAcaCKO0tFRfX9/b27u+vp4o1NXVTUlJyc7Ozs3NTUpK6tGjhwgjBAAAAADoIJBAA2H06tXrzp07t2/fNjAw8Pf353A4CKEdO3ZUVVUZGhrq6+uXlZX9/fffog4TAAAAAKD9wRAOIKRRo0a9e/fuxo0b27ZtO3LkiI+Pj6ura5cuXYKCghgMxsKFCx0dHUUdIwAAAABA+4MeaNAmM2fOTEtLW7169dKlS0eOHKmsrHzlypWQkBDIngH4pYSFhW3ZsgVfhAcAAH56kEAD4RUXFwcEBAQGBjo7O2dnZ48ePdrBwcHJySkjI0PUoQEAOtW7d++Cg4Nh1yQAwC8CEmggpMTERGNj4yVLlqxYscLExCQrK8vLyyszM1NDQ8Pc3HzVqlXfv38XdYwAAAAAAO0PEmggJA8Pj6qqqoCAgMuXL9fW1q5fvx4hpKOjc+7cucTExO/fvxsYGIg6RgAAAACA9geTCIGQEhISbGxsFi1ahBC6dOnS27dviUP9+/e/devWq1evRBcdAAAAAEBHgQQaCMnQ0PD9+/cxMTGSkpJv376l9zfb2tqKJDAAAAAAgA4FQziAkA4cOMDhcEaOHGljY9PY2Ojj4yPqiAAAAAAAOgP0QAMh2dvbx8XFhYSEYBg2d+5cMzMzUUcEABCZ0aNHs1gsXV1dUQcCAACdARJoILwBAwYMHjw4KSkpPDxcTk7O0NAQL2cymXv27EEIeXt7izRAAEAnmThx4tChQ7t37y7qQAAAoDNAAg2EVFVVhXdC40+9vLz8/f3nz5+PEKqvr8dHdEACDQAAAICfD4yBBkLy8vLCs2cDAwN9fX0Wi7Vo0aKnT5+KOi4AAAAAgI4FCTQQ0t27dxFCUVFRGRkZWVlZJ06c4HA4rq6uhYWFog4NAAAAAKADQQINhJSfn6+oqDh27Fj8qbu7u6enZ3Fx8axZs+rr60UbGwAAAABAx4EEGgipT58+dXV1fn5+NTU1eMmePXtsbW1fvnxpb28v2tgAAAAAADoOJNBASK6urgghd3d3DQ2NW7duIYSkpKRCQ0M1NTU/ffok6ugAAJ0qOTn57t27ZWVlog4EAAA6AyTQQEhbtmxxc3NjMBhNTU1MJhMv1NXVjY6O7tevn2hjAwB0ssuXL69YsQL+eQYA/CJgGTsgJBkZmcDAwF27dqWmpg4YMIAoNzU1TU9Pj46OTktLE2F4AAAAAAAdBBJoIDw2m/3u3bukpKSEhAQXFxdiI5XGxkZxWM+Oy+V28hUxDOv8i/IDwzDxjI3L5TIYDDEMTGzvGBLXXzMMwxBCXC5XPGMTw6hw+FtA1FFQie0d64g3Jt5mOzYIfhGQQAMhifNGKvgnbOcvBsJiscRzBRI2m93Y2CjqKHhgsVhIFP/qtKqpqYnNZovnT1M8f804HA5CqLGxUQxjE887hhDCMKy+vl4ME2ixvWMcDqexsbF9810OhwMJNBACJNBASOSNVLhcbnZ29qJFi3r06DF69GhRh4YYDIaEhISiomInX5fNZnf+RfnR0NAgJyenoKAg6kCoGhoaGAyGrKysqAOhwjCMwWCI50+Tw+GIYWBSUlIIIXl5eTGMTWzfmOXl5YqKimKYQIvtHWMymXJycvLy8u3YppSUlIQEzAcDAoNfGiAk2EgFAAAAAL8mSKCBkGAjFQAAwcDAYOTIkWpqaqIOBAAAOgMk0EBIsJEKAICwfPnya9euGRsbizoQAADoDJBAAyHBRioAAAAA+DVBAg2EBBupAAAAAODXBAk0EBK+kUpOTs69e/dsbGyIcnwjlUePHh07dkyE4f1kvnz5Mnv27N69e2tra0+fPv3NmzfN1Tx06NDZs2d5Hrp48aK5ubmCgkL//v3379+PLyEHAAAAAEFBAg3aRE9Pb+LEiX369CEXSkpKOjo6enh4iCqqn0xycrKZmdn9+/fHjx8/e/bs+Pj4sWPHPnr0iF6zqqrKx8cnNTWVfmjv3r1ubm4qKiru7u66urrbt29fs2ZNx8cOAAAA/IRgHWjQ/mJiYm7evIkQgk7odrFy5Uo2m52QkGBkZIQQ2rFjh7W19YYNG8aNG0fUKS8vj42N3bt3b3l5Ob2FuLg4b2/vP/74w8/PDy8ZPXr0+fPnN23aBONtAAAAAEFBAg3aX1JS0vHjxxEk0O2hpKTkzZs3y5cvx7NnhJC6urqnp+eSJUtevXpla2uLECorK9PQ0GihkQsXLsjJye3du5coOXv27MOHD2EUB2gvZWVlX7586dKli5ycnKhjAQCADgdDOED7k5GRUVNTgxVh2wW+pAmln9jExAQhFBMTgz9VVVWNiYmJiYm5c+cOvQUMw+7fvz9ixAjyT8TIyGjdunUDBgzowNDBr8THx8fGxiYhIUHUgQAAQGeABBq0v2XLllVUVFRUVIg6kJ9B9+7dEUJ5eXnkwi9fvpALpaSkbG1tbW1tra2t6S0UFhYymcwePXpcv3595MiRKioqZmZmXl5e0P0MAAAACAcSaNAmJSUl+fn5+OOKiorg4GBPT88rV65ActZe+vTp06tXr5CQkIKCArykrq7Ox8cHf8BPC9XV1QihZ8+ezZkzp1u3bqtWrVJRUdm5c+fUqVM7LmxAUVNTs2HDhl69eqmqqo4fPz4pKUnUEQEAABAejIEGQqqrq3NzcwsLC0MIOTg4BAcH29jYZGdn40ePHj365MkTFRUVkcb4M2AwGKdOnZo2bdqgQYNcXV0lJSVv3brFYDAQQtLS0vy0gP8zk5ubGx4ePmXKFLxw6dKl/v7+t27dmjFjRscFD3AYhs2fP58YYPPo0aPY2NjExEQ9PT3RBgYAAEA40AMNhOTj44NnzwihyMhIU1PT7OxsBQUFfK5bfHz8vn37RBrgz2Py5MkvXrwYOHDg5cuXw8PDJ0yYcPXqVYQQn/+fKCgoIISsrKyI7Bkh5OnpiRB69uxZh0QM/ld6ejpleHpFRcW5c+dEFQ8AAIA2ggQaCOn69esIoR07dnz48GH58uXFxcXa2tp5eXnp6eknT55ECN29e1fUMf48bG1to6Ojy8vLs7KyTp8+jY8vNzAw4Ofc7t27MxiM3r17kwt79eqFECKGhYAOlZ6eTi/8+PFj50cCAACgXUACDYSUl5enoKCwY8cOc3PzI0eOIIRcXFy6du2KEJo5cyZCKDc3V7QR/jQCAwNv3bpFLgkPD5eUlJw0aRI/p8vLy1tYWFByuMzMTIQQsTQe6FB9+/alF+rr63d+JAAAANoFJNBASFpaWkwmMy4uDiGkpKQUFBQ0b948/BC+iwrexwnaLjQ01NnZ+b///sOfJicnh4SETJ06tWfPnny2sGjRouTk5CtXruBPORzOzp07GQzG5MmTOyRi8L9MTU1HjRpFLlFUVFy8eLGo4ukIhw4d+vbtm42NjagDAQCAzgAJNBCSk5MTQmjkyJETJ05ECC1cuNDS0hIh5OLi8scffyCEpk2bJtoIfxpeXl5SUlLjxo3bvHnzkiVL7OzsunXrhvf688nJycnW1nbevHkuLi7btm0bNmzYzZs3161bN2zYsI4LGxAkJCSuX78+Z84cGRkZhJC5ufndu3eNjY1FHRcAAAAhQQINhLRnzx47OzsWi4UPBiCUlJQghEaNGvXXX3+JKLSfjZWV1ZMnTwwNDc+dOxceHj5p0qRXr1716dOH/xZkZWXv3Lmzbt269PT0U6dOSUtLBwUFwT6RnUlLSyskJKSurq62tvbDhw92dnaijggAAIDwYBk7ICRFRcUnT568ffu2tLSUXL5ixYotW7aMGzcOX2oNtAtbW1t+Vszo1q0bhmE8D8nLy0PGLHJSUlJSUvCpC34AFy9ePHr0aGZmZq9evRYsWLBp0ybyupnZ2dl///33q1evKisrjY2N161b5+rqShzlcDjy8vL03QDYbLakpGQnvQAAOhh8lAPhcTic/Pz8pKSkpKQkFxcXQ0NDhJCrqyuTydy2bRtCyNvbW9QxAgD4VVNT4+fnl5ycrKGh4ebmNmTIEFFHBETD29v7wIEDI0aMcHd3j4uL2759+5cvX4iFF3NycoYMGcLhcFxdXbt06fLw4cPZs2dnZ2dv374dr5CXl8disSwtLSkzZaFXBfxUMACEUllZOXToUOIXSVpa+tKlS/ghok9aVLHV19e/fPmy869bWVnZ+RflR2lpaV1dnaij4KG+vr6hoUHUUfBQU1NTVlYm6ih4q6qq6ohmy8rKKOOCQkJC+D+dxWJ9+/atIwJrO7F9Y+bl5XG5XFFHQfXmzRuE0B9//EGU4COOMjMz8adLly5FCL1//x5/ymazra2tpaSkiA+ZyMhIhFBERES7x1ZcXMxkMtu3zbi4uPLy8vZtE/wKYAw0EJKXlxe+BIeBgYG+vj6LxVq0aNHTp09FHdcPLzs7+9KlS9euXSssLBR1LOAXsnfv3pycHHLJqlWr6N/Cg5+en5+fnJzc3r17iZKzZ88eP36c+GVIS0vT0NAgOlAkJSXHjRvHZrMzMjLwEnxLWj4XqgfgBwUJNBASvk9KVFRURkZGVlbWiRMn8G/0IO1ri8OHD5uYmCxcuHD27NmGhob4joMAdIJ3795RSqqrq9PS0vg8fceOHcbGxrGxse0dF+hUGIbdvn3b1tZWTU2NKDQyMlq3bt2AAQPwp4MHDy4rK/v06RNR4fXr13Jycv3798efZmdnS0pKamho3Lhx49SpU8+ePWtoaOjMVwFAJ4AEGggpPz9fUVFx7Nix+FN3d3dPT8/i4uJZs2bV19eLNrYf1Nu3bzdt2tTY2Ig/ra2tXbZsWV5enmijAr8InjvDq6qq8nl6fX19VVUVm81u16BAZyssLKyrq9PV1b1+/frIkSNVVFTMzMy8vLzI30Vs2rRJX1/fzs5u586dx48fd3BwePbs2bFjx+Tk5PAKeALdv39/FxeXNWvWjB492szM7P379yJ6TQB0CEiggZD69OlTV1fn5+dXU1ODl+zZs8fW1vbly5f29vaije0H9fjxY0pJXV3d8+fPRRIM+NVMmTKFUmJmZkbZAV7k3r17t3XrVg8Pjxs3bmDNLDgD2qK6uhoh9Pz58zlz5nTr1m3VqlUqKio7d+6cOnUqUadnz55ubm7fv3/38vL6888/o6KihgwZQq6QnZ3d1NS0ZMmSz58/l5SUnDt3rqCgYPr06cQfCwB+ApBAAyHhixa5u7traGjgG01LSUmFhoZqamqSv9oD/GMymfTCurq6zo8E/IJWrVq1bNky4qmhoeG1a9dEGA/d0aNHhw0b5uPj4+vr6+LiMm3aNC6XK+qgfjZ4T/OXL19u3boVFhZ24MCBmJiYJUuWPHz4ECwrtOsAACAASURBVP+cRwht2bLlr7/+WrduXW5ubmVlZVhYWG5u7ogRI8rKyvAKhw8fjomJ8fHx6dOnj4aGxvLly/fu3VtQUBAQECCyFwZAe4MEGghpy5Ytbm5uDAajqamJyPx0dXWjo6P79esn2th+UDy3QYa9kUHnqK6udnR03LVr1969e6Oiov777z9izKs4yMvLwxfHJERERISEhPB5enFx8b59+5YsWeLj40NZuh6QKSgoIIQsLS3J30h4enoihPCl6AsKCo4ePero6Hj8+HE9PT1VVVUnJ6ezZ8/m5OScPn0ar29vb29ra0tudubMmQih5OTkznodAHQ4WAcaCElGRiYwMHDXrl2pqankP7Smpqbp6enR0dH8Tz8CuKlTp7q4uFy/fp0o8fT0NDMzE2FI4Bfx+vVrJyen79+/408XLFgwZswY0YZEERsbS0wPIMTExMyfP7/Vc9PT08ePH19ZWYk/PXTo0Js3b/B16wFF9+7dGQyGnp4eubBXr14IoYKCAoRQamoqh8MhZr/gHBwcEEJJSUnNNauhoYEQgqmE4GcCPdCgTfT09CZOnEhZPlZSUtLR0dHDw0NUUf24rl27FhYWtnLlynXr1kVGRsJONKATsNns2bNnE9kzQujSpUvnz58XYUh0srKy9EJi1hqByWTu2rVryJAhAwcOXL16dVFREUJow4YNRPaMECovL1+7dm2HRvvjkpeXHzp0KGUYXmZmJkLIyMgIIdSjRw+EEDFaA4d36uOHEhMTLS0tAwMDyRU+fvyIEDIxMenY6AHoTKJeiBr8kAYPHvzvv/+2ugXA169fPTw8Dh482DlREWAjFQrYSEVQv9RGKjw7DqdPny5QIzk5OQ8ePKipqWnf2AjFxcX0dULu3btHrsPlcidNmkSuYGhoWFFRQc+zlZWVORxOB4XKP/HcSAUfqUxso8Nms52cnBgMxps3b/Cnurq6GhoaX79+xStwuVx8a5Xw8HAMw5hMpqqqau/evYl3UFNT05QpU2RkZNLT09sYG2ykAsQHDOEAwigtLf3999/79evn5ubm7OyM90wQamtrnz17dunSpTt37rBYrH/++UdUcQIAWsVz3UmeU1pb0KNHDxkZGSUlpXYKikpTUzMoKGjhwoXESg6bN2+eOHEiuc6bN2/u3btHLsnIyAgODpaRkaEMHpCTk5OQgC9geZs9e/Y///wzb96827dv9+vXLzIyMi4uzsPDY9iwYQghSUnJgICASZMmmZqaurq6qqqqRkdHv3//3tXVFR82LS8v7+fnt2TJEjMzM1dXVwaD8fDhw5SUFG9vb8pfCgB+aJBAA2F8/Phx3759R48e/euvv/7666+uXbv269dPTU2tsbGxuLj406dPHA4HITRkyJCTJ08OHz5c1PECAJplamqqrKxMWWJsxIgRooqHUFJSkpmZ2atXL3xswIwZM4YPHx4VFVVbWztixIiBAwdS6qekpNAbSUlJGTt2LLGCBG7ChAkdF/aPTk5O7tatWz4+PtHR0Y8ePTIxMcH/dSEqODo6JiQk7Nix4+7du1VVVQMGDDh37hx5CZd58+YZGBgcOHDgxo0bdXV1FhYWBw8ehHsOfjai7gIHP7Di4mIvLy/6VH0lJaXp06dHRkaKKjAYwkEBQzgE9UsN4cAwLCQkhDzIePDgwYJ+Uc5isb59+9Ze8bDZ7NWrVxPxODk5VVdXN1e5oKBg9erVQ4YMsbCwoP+N27t3b1ZW1qBBg4gSS0tLMfnKXjyHcGBi/FEGQziA+IAeaCA8TU3Nv//++++//y4sLMzMzCwrK5OXl+/WrZupqamUFPxqAfDDmDNnjrm5eUhISHl5+dChQxcuXCgtLS3CeA4cOECsiYYQunnzpoqKCs9VhMvKyiwtLb99+8azHSUlJRcXFw0Njffv30dHR2dlZRkaGo4ZMwbGbwAA2giyHNAOdHR0dHR0RB0FAICH9+/fe3t7Z2Rk6OnpeXh44CuO0ZmYmOzfv7+TY2sOfQ+X69ev//PPP5KSkpRyX1/f5rLn3r17nzt3zsDAoKqqCl8ayNHRsUPCBQD8eiCBBgCAn1ZMTMzIkSPxx6mpqffv3w8NDXVxcRFtVK0ir6mHq6urq66uVldXp5TzXELk8uXLtra2vXv3ZjAYHRXiz6KxsTE9PV1eXl5fX5/+/wkAoDnwNRYAAPy0tm/fTinBd5Xjcrm1tbXteKFNmzbZ2Ni4uLiEhIS0fYdt+mjmPn360LNnhFD37t3phQMHDuzTpw9kz626detW7969LSwsjIyMTE1N4+LiRB0RAD8MSKABAODHxmazz507N3PmTFdX14sXL5LzV/rmyTk5OcuWLVNWVlZWVu7Xr9/NmzfbHsD69euPHz/+5cuXGzduzJs3z8nJCcOwtjTo7e1NWbz5yJEjPGu6urpSSszNzekLdAC6tLS0uXPnEp39Hz9+nDFjRlVVlWijAuBHAQk0AAD8wDAMGzZs2MqVK8PCwkJDQ93c3Mi7W9M7aKWlpS9cuIAv85ydne3s7Pzs2bO2BJCUlHTs2DFyye3bt2/fvt2WNocMGfL27dt58+ZZWVnNnDnz5cuXM2bM4Flz1KhR/v7+ROf08OHDw8LCYBIzP27dukVZAvzr168vXrwQVTwA/FgggQbCY7PZ169f3759+969ezMyMohyJpO5devWrVu3ijA2AH4Ra9asiY+PJ5dcuXKFSIPIyTSOxWJRSs6cOSPcpTkcTk5OztOnT+mHXr9+LVybCKGkpCQ7OztLS8ubN2/26tXr6NGjLS9KvXjx4oKCgsTExJycnNevX/fr10/oS/9SCgoK6IXNzcgEAFDAv+lASFVVVfb29sSYOS8vL39/f/yvdX19vY+PD0LI29tblCEC8AsICgqiF8bGxv72228Ioc2bN5eUlJw8eZLFYjEYjMmTJ0dERFAqZ2VlCXHdS5curV+/vqysjOdRZWVlIdpECBUWFjo6OhYXFyOEWCxWWFhYdnb2mzdvyMtU00lKSn7+/DknJ6dPnz5TpkwR7Rp8Pwpzc3N6IXnBbABAC6AHGgjJy8sLz54NDAz09fVZLNaiRYt49kUBAFoVHh5ua2urra09fPhw/sclFxQU8Nxzu0uXLvgDCQmJI0eOlJaWJiYmlpaWBgcH05dApu+F1KonT54sXLiwuewZtWGrv2vXruHZMyExMbHlcQVFRUUWFhZOTk4bN250cnIyNzenL+IB6BYsWGBqakoucXZ2tra2FlU8APxYIIEGQrp79y5CKCoqKiMjIysr68SJExwOx9XVtbCwUNShAfCDuXXr1rRp016/fl1UVPT27VtnZ+eQkBDiaF5eXkRERGxsLJvNppyoqakpIyNDKZSUlKQs9qyiomJhYdGlSxcVFRXyDn8IIQkJiWnTpgka8MWLF5s7JCcn5+vra2lpKWibuMzMTHoheYQYnbu7e1paGvH048ePa9asEe7qvxQ5ObkXL154enpaWVmNGjXqyJEjV65cEXVQAPwwYAgHEFJ+fr6iouLYsWPxp+7u7gUFBT4+PrNmzRLiU7ipqSkgICAxMbGqqsrAwGDx4sV9+vSh1Pn333/J31ZLSkreunWrDa8AAJEpLy/ncrkaGhr4Uy8vL0oFLy+vuXPnYhi2YcMGYoqemZlZWFhYt27diGrS0tJz5syhjOJYsWJFz549m7v02rVr//nnn8bGRvwpl8v19PQcP368QIMu8vLy6IW//fbbrFmzpkyZ0sLVW2VoaEgv7N+/f3P1MQyLjo6mFEZFRWEYBsvYtUpNTQ0G2gEgHOiBBkLq06dPXV2dn59fTU0NXrJnzx5bW9uXL1/a29sL2tqBAwdiY2MXL168e/duKSmpXbt20RepLSoqGjx48K7/s2PHjnZ4GQB0ruTkZGtr665du2pqalpYWMTGxmIY9vHjR0q1zMzMxsbGgIAA8gIXycnJs2fPptQ8efLk/Pnz8WRRVlZ227Ztp06daiGAsLAwInvGff78WdDBV/R14hgMxunTp1evXt2W7BkhNHfuXMrKIdbW1vh4bp4wDKNPi2Sz2W1fixoAAFoACTQQEr78qru7u4aGBt4TLCUlFRoaqqmp+enTJ4GaKi0tff/+vYeHh7W1tZGR0ZYtW5hMJn1J/6Kiov79+w/+PzDZBfxwKisrJ0+eHBsbiz9NSkqaPHlyUVGRnp4epWb37t1lZWXpi8HFx8fn5+eTS5SUlC5dulReXp6SklJZWblv3z7y0dLS0sePH798+RJfsKy2tvbAgQP0wL58+SLQC9mwYYOamhq5ZO3atTw3OhGUpqZmdHT0xIkTFRUV1dXVFy5ceOfOnRYmBUpISNDX6BgxYgRsqgcA6FCQQAMhbdmyxc3NjcFgNDU1EdOYdHV1o6OjBV1Gqrq6ul+/fsRXt7KysnJycpWVlZRqRUVF2traDQ0NRJ83AD+WR48eUdLfkpKS27dvL1u2jFJzxYoVCKHy8nJ6I5RCDMOCgoIcHR0dHR2nT59OXj/u3LlzvXv3Hjdu3G+//WZoaPjixYtjx47x3CmDMpmsVb1793779u2sWbP09fUtLS39/PzwhXfaRf/+/e/du1dbW1teXh4UFEQessKTn5+ftrY28bRbt24t98EDAEDbwRhoICQZGZnAwMBdu3alpqaSZ/Gbmpqmp6dHR0eTp/W0rG/fvkePHiWevn//vqqqysTEhFwHw7CioqK7d+8eO3YMw7CePXuuWbOGfF0mk0l8k9vU1IRhGOV76k7AZrM7/6L84HA4LBZLDGOjf/kuJlgsFofDafc7lp2dzbPQy8ururr62LFjtbW1CgoKa9eu3bBhQ2Nj46BBgygLKisqKhoYGJADO3bsGLHmekFBwfPnzyMjIy0tLWNjY1euXElU+/r1q7Ozc9++fekBSElJWVlZCfpie/fuTZ5KyOFwuFyuSH7HdHV1k5OTg4ODP3/+3Ldv37lz56qqqpIjEds3Jv4xJYZjtcX2juEfZfSVZNqCy+W2ceNM8GtiwO8NEB8YhkVGRp47d27cuHHLly8nHyorK1uxYsWECROcnJzYbHZgYOCHDx9Onz6tqqqKV/j8+XNFRQX+mMFg0FPwTsBiscRzAdqmpiZJSUkx/FKbw+EwGIz2/XPYLvBBtPQFLtooMjJy3rx5lMJTp045OzsjhDgcTnFxsaamJrGRXllZmb29PXnDi6NHj7q4uBC/Zlwu18DAgDJhwMHBITg4eP/+/b6+vvxEpaqq2vIyF/zAs2d5efk2ttMRxPaNyWQy5eXlxTCBFts71tjYKCUl1b4fZVlZWQYGBpqamu3YJvgVQA80EBdFRUXHjh3Lzc1dunQpfRHZrl27hoWFEU/Xrl07f/78+Pj4MWPG4CXkrrWGhoa4uDgtLa1OCJusqqqKSOjFSllZmby8vIKCgqgDoWpoaGAwGC3vkSEStbW1TU1NxGrK7UVFRYVSgq85Iycnhz/V0dEhH9XS0vrvv/+OHz8eHx/frVu3RYsWjRw5srq6mmgnNzeXPt02MzNTS0uroaGBz6gmTpzY9jcLm80uLi7u/DcdP8T2jZmfn6+lpSWGCbTY3rGSkhIlJaX2/T8tPz8f9n4HQoBfGiAWMjIyduzYMWTIkK1bt/LzwS0rK6upqUkfJw2AONu4cSOlRFdXl8ieeerSpQt9kTuCjo6OjIxMU1MTubB3794IoaFDh547d67VkLp27cpnR3XLnj59+uzZs9WrV+vq6gp0YnV19d69e+/du9fU1DRmzJjdu3eTBzS3l+fPnz948IDL5Y4ZM2b8+PH8n8jlcjMyMsrLy01MTMQzpwQAiITYfXMKfkEcDsfb29ve3n7Tpk3N/Yl69erVH3/8UV1djT9lMpnFxcW9evXqxDABaJPq6mr6LiHJycltaVNWVnb+/PmUQnxK4sKFC4cPH95qC7a2tkJ8eX3v3r0JEyaYmpq6urriL+H+/fv79+8XdDUPDoczffr0Q4cOpaWlZWVlnT9/fvTo0XV1dYLG07I9e/bY2dkdOHDg0KFDEyZMwCdo8iMrK2vYsGEDBgywtbXV1dU9fvx4+wYGAPhxQQINRC8xMbG8vNzY2DiFBF9qIDo6+sGDBwghMzOz6urqo0ePfvjwITU11dvbu2fPnoMHDxZ17ADwS1FRkT6Kpu1jHnx9fZcvX45/B62mpubr6ztnzhyEkLS09NOnT48cOTJ9+vRZs2atX7+e5+kC7Z+CCwwMnDx58sOHD1NSUkJDQ4cNG5aQkMCzZqup8PPnzylLUKenpwu9H15JScmyZcu0tLTU1NSmTZuG/7uSmJh45MgRcrXz589HRUW12hqHw5k5c+b79+/xp3V1dX/++Sf+cQQAADCEA4je169fMQyjLIO1YsWKSZMmPXv2rK6ubsKECcrKykePHr1w4cKRI0ckJSUHDx68efNmMZx8Bn4d0dHRERERDQ0No0aNcnV1bXUkq6Sk5IwZM4KDg8mFM2fObGMYioqK586dO3HiRFFRka6uLnl+lays7Pr16/HUGcOw/Pz8GzduUE6fNGkSQojNZufn52tra/MzutTT05P8tL6+fseOHQYGBkQJi8Xas2ePn59fRUVFr169du3atWjRIp5Npaam0gtTUlJajYGOzWZPmzbtzZs3+NPw8PCEhIQPHz68evWKXpmy31NlZeX79++5XK6lpSUx8P3Tp08fPnygnHjt2jX6DA0AwK8IA6BtWCyWr6/vjBkz7GlEFVJ9ff3Lly87/7qVlZWdf1F+lJaW1tXViToKHurr6xsaGkQdBQ81NTVlZWUtVPj777/JH6RTp07FF8NqWUVFxcSJE4mzFi5ciC+5SJeZmenl5eXu7h4YGEipU1VVJdBrITt48KCSkhIRwLp16zAMO3ToEF4oISGxZMmSmpqaFlogLwlC6NGjh4eHB0Lo1atXGIZRMmyE0PXr13m2Rt8pBiF09OhRIV7akydP6E0dPHjw/Pnz9PJ9+/YRJ968eZNImlVVVa9cuYKX03cIRwjp6Oh07dpVQ0Nj7ty5+H/+bZGXl8fPr03nE9uPsuLiYiaT2b5txsXFlZeXt2+b4FcACTRoqw0bNtD/zOBEFRIk0BSQQAuq5QSavvM2Qig0NJTPxj99+vTo0aPc3NzmKoSHh5NnFg4aNKi2tpY42pYEGsOw6urq8PDwixcvfvz4EcOwoKAgygtZtGhRC6c3NTXRV02xtrYmEmgOh0MfFjJixIjmgqEsTa2mpvblyxchXtfp06fpPxRTU1Nvb2/6V1X4DuoYhuXk5CgqKpIPycvLp6enYxhWUlLS8nJpxsbGbXxbQQItKEiggfiAb8BBW+EDFvv167d8+fKV/0vUoQHQId69e0cv5DlUgCdDQ0NHR0f69t04Npu9ePFi8iJ0iYmJ7bXPX25urru7+5YtW86ePRsVFYUvqU6pExIS0sImGtLS0i4uLpRC8kTG4uJi+l6hWVlZPFtTVlaOiIiwsbHBnxobG0dERAg3OZiyAiBx3a1bt3K5XKJEVlb2wIEDlpaW+NOoqCjKQO36+vqHDx8ihDQ0NLZt29bCFdPS0sLDw4UIFQDwE4Ax0KCt8M3kIiMj8cWzAPjp8VxRm9KRKbT09PTS0lJK4YsXL9recmFhobW1dXFxMf70zZs36enp9HUzmpqaCgsLW3g7+/n5sVisa9euIYRkZGQ2bdq0evXqoKCgyZMnd+3atVu3bqqqqpQNw/v3799ca8bGxq9evSovL29qaqqsrAwICDh37pypqenKlSvpy2a3wNraml5YX19PKVm/fv3mzZuJp8TuS2TEZuleXl4mJibBwcH//fcfzwVG0tPT+Y8QAPAzgR5o0FZjx45FCOXn54s6EAA6yYgRI+ijFHjOLSsuLl61apWhoaGJicnmzZuJdRhbwHP7w3bZa8bX15fInnGnTp3S19enVFNRUWm5D1hFReXq1avFxcUJCQmlpaV79+5lMBjz588/d+6ckZERg8EgZ6g4eglFly5dEhMTLSwsDh06FBwcvGXLloEDBxYVFTVXn8PhXLp0afny5X/++efz588RQjo6Oi2k6YSvX7+SnxJd0WTkXHzWrFnh4eHNLTlPv3sAgF+FqMeQgB8ePtG+a9euixcv9vxfogoJxkBTwBhoQbU6ifDmzZtE/6iMjIy3tzelApfLDQoKovRVjxo1is1mt3xpDodDGRaMEDp8+DBRQegx0OT5i4Rjx45RdnI5duyYEI2zWKxv377hj5uampYuXaqhoSEtLW1ubn779m1+WqDvwLJ06VKeNdlstoODA7nm/v37MQx7//69uro6UdizZ0/66924cSOlNXzVP8KMGTMo45IpOTdBT0+voqJC4DtFAmOgBQVjoIH4gAQatJW/v39zC3iJKiRIoCkggRZUqwk0hmFFRUWhoaEXL17MysqiHz127BjPN8X9+/dbvfq7d+/Iu/HNmDGDxWIRR5tLoCsrK6Oiop48edLcMho8NxBJSUl58+bNhAkTevbsOXz48MuXLwuX0hEJdHl5+aBBg4j29fX1W5guScjLy6PHZmJiwrMyfdw2QignJwfDsOLi4mPHjm3YsCEgIKCsrIw+EOXNmzeU1vDObFdXVxcXF39/fw6HQ39p9EE7ampq+CzMtmg5ga6pqdm5c+fYsWOnTp0aGBhID6xlVVVVBw4cmDdv3qZNmwQNVWw/yiCBBuIDEmjQViYmJgghGRmZwYMHD/1fogoJEmgKSKAFxU8CTXf79m1nZ+fRo0dv3LiRvFocmaamZkZGRqtNVVVVXbt2zdfXNyYmhn6IXv/atWtE56umpuadO3fodV6+fEkJxtraWtC0rDlEAv37779TruLo6Njq6WVlZfT/w62trXlWXr58Of3GXrt2jV4zPj6eGNehqqrq7+8v3Ktbu3Yt5XLEandt0UICXV9fb2ZmRr7iihUr+G+5qKiI3AEvKyt79+5d/k8X248ySKCB+IAEGrQV3jdDLAslDiCBpoAEWlBCJND79+/nmTHTNZcX8omeQKenp1M2QFFWVua5GFxwcDCxcbe9vX1eXl5bIiHDE+jXr1/T82BZWdnGxsYWzv327dusWbPoi83t2bMHr1BdXX379u2goKCUlBQMw/Al8ygiIiKaC+zly5evXr36+vXru3fvPn/+LMSra2ho2LBhA36TtbS0zpw5I0QjdC0k0H5+fvTXmJqaymfL9P8xdHR0+P9uQWw/yiCBBuIDJhGCtsK7SYTYEBiAn0ZVVdWOHTv4rPzu3Tv6Ohtt8fDhQ8pyEzU1NZGRkfSac+fOLSgoSE9PLy4ujoyM5DlKWDgNDQ0bN260tbXFMIxyiMPhcDic5k5samqaOnVqaGgoebE5hJCTk9OWLVsQQvv27evSpcv06dPd3NwGDhy4atUq+nxNdXV1Q0PDmzdvXr16NSUl5caNG2fOnHn79i1CSEpKytTU9MWLFwYGBtbW1n379rWzs2tuWHNzZGVlDx8+XFNTU1RUVFRU1AlrdPLcHb25LdPpiB0ZCYWFhbm5uW0LCgDw/4Nl7EBbzZo1KzY2duHChcuWLSPP4EEIOTk5iSoqADpTcnIym83mv35NTY2GhkZ7Xb2srIzPQoSQlJSUkZFRe12asHbt2qtXr/I8ZGZmJi8vX15efurUqY8fP/bo0WPp0qWGhob40ZiYmPj4eMopHh4e+CDyf//996+//iIfOnv27PDhw3fu3Ll79268RF1d/Y8//rCyssLXzmMwGEQS7+LicuXKldu3b2/dupVo4fnz53PmzBF0ZcC6urpdu3ZduXKloqLC0tLywIEDw4YNE6gFgWhpafFZyBPPEUTNDSsCAAhD1F3g4Icnhr9dMISDAoZwCErQIRw89ybU09PjOb+2e/fubVl7gT6E486dO/SrPH36VOhLMJlMQX+ZKUt5kL179y4/P79bt25Eiays7OPHj/ETz549Sz9l2bJl+FHyfETCrFmzMAzLyMi4ePHijRs3UlNTW/gG7Pjx45MnT6aX5+fnC/QCZ82aRT5dUVGxQycR0jfr6dmzZ3V1NZ8t7927l3K6QAOHxPajDIZwAPEBQzgAAKCtDA0Nzc3NKYVbtmy5evVqQEAAZTrXP//809zCNXyKjIzctm3brl27YmNjEUJTpkyZNGkSucKsWbPs7OyKioqOHDmyfv16f39/8taGLfj8+fO4ceOUlJTU1NSGDBlCHwnAE5PJbK59Y2NjKyurrVu3khd1bmxsJEZBEF3RZMTMP56jDvDOdQMDgwULFjg7O6emptL3PiQ8fPiQ53rSBQUFzZ1Cl5eXFxoaSi6pq6s7deoU/y0IysrKKiAgQFVVFX9qYGBw8+ZN/kfKbd682dnZmXhqZGR0+fLl9o8SgF+ZqDN4ANof9EBTQA+0oISYRJiRkTF48GD8c1VOTs7CwoL4mB0+fPiOHTvmzp27efPmtndbLly4kPwZvm/fPgzDWCzWmTNnnJycnJ2d8bXY3r17R6RfCCEDA4PS0tKWW2YymcbGxuTG1dXV+ZxoqKam1txfmRs3bvTr149e/v37dwzDmpqaKJsIamtrFxUV4c3y3F8Qf8mEgICAFv7GjRo1avHixZRCGRkZgd4Rjx8/prdsb2/Pfws8tboOdFVV1cuXL+Pj45uamohCNpudnZ3NzxLUCQkJgYGBjx49ankSJ53YfpRBDzQQH5BAg58QJNAUkEALSrhl7DgcTlJS0tOnT93d3SnJ1tSpU9slsAcPHtAzubS0NHrNgQMHUqo1ty8J4eHDh/TGDx48yE9gPIdJ4GbMmEEfiSEhIUGsV/39+/cFCxaoqanJy8uPGzeOvNYEpd8XIaSjo0NJoa5fv97cpRFCf/31V2pqapcuXciFPj4+/LwoQnZ2Nr3lVatWtXBKWVlZqwN1hNhIJSgoqGvXrngAkydPLigoEOh0PontRxkk0EB8wCRCIAxfX1+E0LJlyxQUFPDHPK1bt64TgwJAxCQkJMzMTn1z4AAAIABJREFUzBobG+l7/j148KChoaGFgcJ8oq/ljBCKiYkZMGAAuaS8vDwlJYVSDd/yugWfP3/ms5AO72M2MzNLTk6mHCooKJg0aVJiYiK5cPTo0cSctm7dul28eBEhxOVyKYvZubi41NfXb9++/du3b3Jyco6OjlevXiWv2ffhw4fZs2c3F5W5ubmnpyebzf7w4cO+ffsSExM1NDQWL14s6Pzmvn37TpkyJSIigiiRl5fnuRw1QujkyZNeXl6lpaXKyspr167duXOntLS0QJdrzpMnT9zc3Iind+/enT179tOnT9s4IggAIAxRZ/Dgh4T/8uDfwIrhbxf0QFNAD7SghOuBxt28eZPn2wF/v7QRz8XyAgICKNUqKyvpSZW+vn7LvXdPnz6lN37ixAl+AsPXZo6OjqYniytXrmxsbJw+fTpRYmZmxnOZ6hbU19fzLKRvAI4Qcnd3X7ly5fnz5/GhC+3yxqyoqFi2bJmSkhKDwTA3N4+MjORZjT6eZNu2bc29+wTqgS4oKOjevTv9xWZnZwv5kponth9l0AMNxAdMIgTC6NGjR48ePfC+oh7NE3WYAIhAWloavVBVVZW8DIXQxo4dSymRl5cfNWoU/XKWlpaUwuzsbFVV1T59+ujp6Y0ePfrevXuUCiNHjqQ0paenN3/+fH4C69Kli56enpqaGrG6HFG+bds2GRmZW7duJSQkXLx48cmTJ/Hx8b169eKnWQK9876iosLCwuLbt2/0yhMmTDhz5syyZctkZGQEukoL1NTUzp8/X11dXVdX9+HDB3t7e0qFuLg4Ozu7pUuXUsq9vb0VFRUNDQ1v3Lgh9NVZLNaMGTN4Tnz88uWL0M0CAIQn6gwegPYHPdAU0AMtqLb0QAcHB9M/adeuXdtesW3evJloVk5O7vz58zyrpaWltbpPyvXr1/HKcXFxLi4ugwYNmjx5spOTU+/evbt16+bq6pqTk8NnVMRW3hiGXb9+ffz48YMHD7aysho8eLCFhYW7u3txcXGbX/r/WLVqVXOvi7JjXye8MbOzs1VUVFr9gzt//vybN2+y2Wz8LD57oG/cuEHsH0nBYDDa/cZiYvxRBj3QQHxAAg3aqrS0NDY29vbt27Gxsa1O8+8ckEBTQALdMn9/fyMjI2lpaSMjowsXLmBtS6DLy8spmauGhkZJSUl7RVtVVZWUlOTr63vmzJmsrKwWatbW1l6+fLmFXlh5efmsrCz6uOrw8HB+IklNTd28efPChQuPHTtWVVVFJNA4FxcXcpsGBgbErMF2QZ8liVNTU6NkpZ3wxvT09Gw1eyZYWVnV1tZi/CXQiYmJLQyd9/Dw6IiXI7YfZZBAA/EBCTQQUkNDw5kzZ/T09Cgf6L169Tp9+rRosyJIoCkggW6Bv78/5Xf4/PnzbUmgMQz777//bG1t8daGDRuWmJhYVFSUlJTULj8F+kYqLSgpKWk5mbOwsCBCJfTv37/Vlv/9919ZWVniFH19ffJiIDw3nT5+/LgwL7gZPPdYkZKSOn/+fFxcHHnMdCe8MWfMmNHyfabYuHEjxl8CTd5DkcLW1pbFYnXEyxHbjzJIoIH4gDHQQBiNjY3W1tarVq2iD7/Ly8tbvXq1tbV1Y2OjSGIDQCCHDx+mlBw6dKiNbQ4cODAmJqaqqqqysjIiImLnzp3dunUzNzfX1NQ8cOBAGxsXiIaGho6OTgsVPnz48OHDB0rhp0+fmExmC2dxudzly5eT3+PZ2dn45ts4+locCCF825f24uDgQCnp0qWLlpbW8uXLhw4d2rdvX/og747Dc6HrFhbHePLkCZ8t8xzkjRBSU1MLDg6WkoKltAAQDUiggTB2796dlJSEEBo1atSFCxeePXuWnJz87NmzgICA0aNHI4SSkpL27Nkj6jABaAWXy83MzKQUZmdns1istjeuoqKiqqq6ePHi8PBwvITJZHp6el65cqXtjfNv//79LVdQVFSklKirq5OXiqPLyMgoLS2lFL5//554zHNxDJ7d0vfu3bOyslJUVDQyMjpx4gSXy23uotHR0WPGjNHV1bWxsbl+/frOnTuHDBlCHNXR0WGz2cQ0u8LCQnwMdwuvoh0tXbqUWJUP5+DggK9MwhPW4vpFZKampvTCadOmxcfH9+7dW5AYAQDtStRd4OCHhE/wX7JkCc+jK1asQAhZW1t3clQEGMJBAUM4WmBgYED5VOzbt28bh3AQKioq6N2Q+F4hiYmJgu4PhxNoCAfuzp079vb2hoaG6urq/PxdWLNmTcsN5uXl0c+ytbUlKtTV1SkoKFAqMBgMyv559B3+9uzZQ65QXV0dExPz9u1b8hrMuKlTpxKPpaWl58yZQw8Jz8iDg4O3bNni7e2dnp4u6K3jX0xMjJWVlaSkpLKy8uLFi0tLS5uamvbt20cf54YQ2rBhA9bMEA7KqIyqqip9fX3yufPmzeu4V4ET248yGMIBxAck0EAY+La98fHxPI/indPq6uqdHBUBEmgKSKBb4OfnR0lufH192yuBTk1NpSdPxKy+7t27R0RECNqmEAk0EYyRkRElGPoYgN9++42fHIXeM7pz505yBfp/Jgih//77j1yHvhicsrIyh8PBj4aFhRG77rW6IB3PwQxbt24dP3488VRWVjYkJES4u0e4evWqo6PjoEGDFi9e/PnzZ8rRhoYGck7MYrFsbGwoUQ0ZMgSfT0lOoAsLC+fMmaOsrCwrKzt27NikpCSikaKiojVr1uCj1Y8ePUre1ruDiO1HGSTQQHxAAg2Egf8ZaG5jCOK73U6OigAJNAUk0C3gcrnHjx/Ht6jQ0dE5duwYl8ttlwS6oaHB39+/5VGqioqKGRkZAjUrXALNZrONjY0pV6cPI0YIubu7E2clJyfPnj170KBBU6ZMefz4MbnBlJSUvn37EmfhqfmrV6+ICvQuYWlpaUqmwnO1eHyPlU+fPtH7sAW1bNkySomSkhKlF5zw9evXVtcR2rdvH7k1NTW13NzcFuoHBQXRo4qLi8OPEgl0U1OTlZUVuY6mpmYHbdPND7H9KIMEGogPGAMNAPilMRiMdevWffv2ra6urqCgwMPDo102Ri4pKTEzM1uyZAmbzW6hWl1d3Z07d9p+uValp6fTd3ipr6+n15SUlMQfJCQkWFlZXb16NTExMSIiwtHRMSQkhKhmYmKSmpp67969gICAhISECRMmUNr566+/KKOrWSyWmZlZRkYGUUIZnIAQUlBQwP+ZuXfvXssTGekoC9uNGjWqurqaUqe2tjY+Pp5S+OTJEz09vR49emhoaKirqx8/fpxn+0wmc9euXeSSysrKloeY06/Fs/D169eUGZYlJSU8FxQHAIgJmMALhDdx4kSe36u2ywQsADqZoP2dtbW1x48ff/funaqqqqur6+TJk8lHN23aRM4UEULy8vImJiZxcXGUdm7dupWcnKynp7d8+fJWtz4RGn0MMUJIUlJSQUGBkqc6ODiwWKyTJ0/u27evoaGBfGjTpk1z585FCH348CEpKUlHR8fe3h7/ELh06RKl8QEDBrx7927q1KmfP38mCr9+/bp06dIXL17gT9esWfP8+XPyWStXrsT77IuLiwV9jZcuXXr27NmDBw+4XK6Dg8O6devo+wIi2mCP7OzsqVOn1tXV4U8rKyv//PPPxsbGLVu2UE78+PEj/cONvoYJGTEEhUxDQ4NSQvlVwX369KmFlgEAIibqLnDwQxLz3y4YwkEBQzgE1eoQjpqamv79+5N/23fv3k2uQJ86Jikp+fDhwxbeL4qKirGxsa3GJsQQjurqap7TB7dv337p0iXyPh3r1q3DMGzWrFnNBfn161fyUUNDQ3xmHr7iBHkIB47exywpKUn+bQwICMAHcqiqqm7dupWYWMlz42u8d1xGRsbDw2PlypXkQ15eXvQXHhgYSGmhS5culP1cvL296ReSlZWlL7HMc0W56dOnt3DnExISKNugaGpqEgNFiCEcT58+pbd88OBBPn62HUJsP8pgCAcQH9ADDYRBnpcDwC/oxIkT6enp5JKdO3cuX75cW1sbf0ofB8JgMEaMGDF16lRiVTuKurq61atXkxeDay8JCQkVFRWUQiUlpY0bN6qpqY0YMeLhw4d1dXWjRo2ytLRMSEgIDQ3l2Y6cnNzFixfJRzMyMubOnUvuVs/Nzd22bdurV6/k5eV///13epcthmHkteoWLVq0aNGi6upqylbYU6dOHThwYEpKCrmQw+FISkqGhIQ4OzsjhFxcXCIjI6WlpSdOnGhtbY0QSklJwVfb6NGjh7u7+8KFC6Ojo4mxEMrKyhcvXqSsN8dzRZHGxsbMzMwBAwaQC7t37z569GhKsjt9+vTPnz/37t1bQoI6JDI3NzcqKmrs2LFPnjzBR8v07Nnz0qVL9G5pW1tba2vrd+/eESVaWlrz5s2jBwYAEBeizuABaH/QA00BPdCCarUHmufOc/fu3SMqLF68mHJ05MiRGIY1NjYeOnTIzs7OxsaGvtaypKRkqx1sQvRAR0ZG0qP9/fffeVYOCAho7u/FggULfvvtN3p5YWEh3gN9//59yrxA+jRBfha4rK6uHjp0aHNhSElJkXc9JMTHx1O6e4ODgzEMe/DgwZQpU9TV1RkMhrKysqKioqKi4oQJE/BG6MuwIIQYDAbPt3NBQQEx81JBQYFYaURbWzs0NJRcMyIigvzzNTU1ffr0KeWHS1mFY968eSoqKrKysvb29snJya3epY4jth9l0AMNxAck0OAnBAk0BSTQgmo1geY5uPbt27dEhbKyMvKcNl1d3U+fPlEaoQ/zUFBQaGFzZi6XGxAQMHjw4O7du48bNw4fL5GTk/Pnn39OmTJl7dq1mZmZPE8sLy+n75Zy+vRpnpUfPHjAM22dOXNmVVUVzw20nz17ho+N/uOPP/4fe2ceF9P+//HPTNNeSqTSnhZtCEmIhMiScCVr9n2XyJp9iXS52Ym4dF1cogjZt5RKi25FivbSopmWaWbO749zv+d3vp/PmWlKmOv7eT780XzO53zO55yZOV7nPe/P641upSd2a2trQ052jEgoQULC6IWMLmTU09MjCGLLli2Mg+jp6ZWUlHz58kVHRwfaNHr0aAnT+/TpU0JCAr2MCwBASUmJ+gA0NDSgYeadO3dC40jjA02nvLw8IiLi2LFjSUlJTV7Dr0Fmb2VYQGNkByygMT8hWEBDYAHdXJoU0GiZaDMzM+hc+Hz+pUuXAgMDw8LCoLxbkgULFkCD9OvXj8vlijvo3r17IcV27tw5+tpHJSWl58+fM+4bHh6uqKhI9Rw2bJhAIGDs+eXLF6jEnaKiYmxsLLl1xIgR0JyplcTKysqMYWNFRcUePXrMmzfvt99+Q5VKQ0PDnj17evbsaWVlNWPGDNLDrkuXLug4dExNTTMyMgiCqKmpefnypZeXl5mZGeUfQqe4uBjVxxTBwcEEQRQWFjo6OlJZN6NGjWrSz450u4dYtGgRuZWx5uLQoUOhQRgFtDju3btHF+Xz58+XcscWILO3MiygMbIDFtCYnxAsoCGwgG4u0vhA79q1i5KkFhYWiYmJLTiKq6srJLMMDAzQpXgEQQiFQnV1dagz6ufg6Ogo7nAZGRk7duzw8/O7cuWKZN2WlJRESVgdHZ0rV66Q7RcvXoQOJ73ln5WVFeNTxLRp0+jdOnbsWFZWRjrZNYmWlpbkCbBYrM+fP0voMHfuXGomPB4vOTlZnL09BKOryciRI8mtjPJ6+PDh0CDSC2gej4c+BpAJKt8Cmb2VYQGNkR3wIkIMBoNpCWvXrp0xY0ZiYqKmpmaPHj2aLJWHoqam9uDBg6CgIH9/f6oxPz/f29s7KysLstXLy8urqamBRqioqIBakpOTGxoaFBUVs7KyXr9+raqqKi8vTxBEjx49OnfuvG7dOmkm1q1bt6SkpJycHB6PZ21tTZ0aujshtSdPZmZmSEgIn88XCASDBw92c3MDAGRlZUH+d4WFhZs3by4pKZFmTPT00ekpKyvr6OiIG9DS0pL6W0VFpWvXrtIcFwBga2uLNlJPHdbW1np6ekVFRfStjGVrpCQpKQk9hdu3b5OZMwCA3NzcvXv3pqWl6evrL1iwgDFVHYPBtCY/WMBjMN8AHIGGwBHo5tLcSoQ1NTWXL18+cuSIuAwKCcybNw+9M1MpExSfP39GixrSszJINDU1RSIRmkOsrKwcEhLS3LnRQYuSfA1kBsKVK1fQTYz5G2gRcmkwMjIiCGLr1q2MW7+y2t+sWbPoo3Xo0KGoqIja+uDBA7p14KhRo9DkZukj0IzLQMeNG0duzcrKgn6d+MqK5TJ7K8MRaIzsgAU05icEC2gILKCbS7MEdGJior6+PqVdSO826Y/FaLpsbW1NH2T//v3oKkAAgLu7O9Qyffp0CRXsHj9+LG4aOTk5gYGBCxYsOHz4MKlRysrKtm3bNnXq1PXr1+fl5YlEIg0NDWhAcRkU2tra4uZAMWjQIDQpBQDQu3dvtHHfvn2M1UYkQy6UrKioCA4ONjQ0ZLFYampqCgoK8vLyrq6uycnJ0r9NKHw+f9++fT179rSwsJg6dWpOTg7UobS09MSJEzt37rx79y7jCNIL6M+fP6OeLQcOHCC3enl5QZu0tbWlz65GkdlbGRbQGNkBC2jMTwgW0BBYQDcX6QW0SCRCg6PNKoERHBzMKP7OnDlDdrh8+TK6VVNT89dff+VyufRVfYMHD66qqvLx8REnKFeuXMk4h5iYGLo+s7KySkpKoidYKykpPXr0aPHixdCA9vb24o7VMpSUlH799Ve0/f79+806lqam5vHjx8mzo76Y5DNJfn7+4sWLXV1dvb29Y2JipH+nmktKSsrgwYMVFRXV1dUnTZqERrubtYjwxIkT9BPs168fn88nCCI/P59x9WRubm6LZy6ztzIsoDGyAxbQmJ8QLKAhsIBuLtILaHqdago3Nzfpj1VXV4f62QGapQOj53R+fj41QkpKytWrV6lVjMOHD5egLOXk5FxdXenr+UQiET2CToJWEORwOOHh4bNmzSIrhtBjz5JX8jHKO0Y6duxILnAcN24cvd3ExGTlypVSDkKipqZWV1dHniD9i5mfnw9Fx0NDQ6V/s6QHdf9wdHSk6iySNEtAEwSRmJi4du3aBQsWnD17ljJRmTp1KuM1l2Dn0iQyeyvDAhojO8CVkzAYDAYjPWSFOWkaxaGkpAT505FQnmWMFaTpC9Ts7e3HjBlDOTSTNfnEIRQKHz58SF9klpubix4iLy8PahEIBHPnzl28eHFxcbG+vj5BWz5ISFxKyLjejhEOh/PhwweCILy8vOjp3bm5ueLi9OLgcrkPHz5E27dv315WVkZvWb16NZ/Pb9bg0nDhwgVo2V98fPzTp0+/ZkwHB4ddu3YdPnx42rRp1GPJy5cv0Z6DBw9mzPnBYDCtBRbQGAwG03KsrKxQLzkXF5dmDeLm5gYNoqSkNHr0aPJvdFGdvLy8jY2NuNFWrVpFr+HCSFJS0m+//UbqZqh6H4lQKEQb6+vrL1++XFRUhApusuKgqakpuhdUEFsCHz9+9PPzW7Vq1dy5cxsaGqTcSxyMahV1aK6trX379u1XHguFMWM7MzOz1Q+E5pGzWKwjR460+oEwGAwdLKAxGAym5cjJyZ06dYoeLrW1tV2/fn2zBmnfvv2lS5eoqtfq6uqhoaHdu3cnX65btw6KJgYGBkImd3RUVVVfvXp14MABtIw2nSVLllhZWUVEROjp6aEefOKCyteuXWO04yAdQgIDA9FNenp6dLe4Jjl48CAawu8AOpgBM+gfB0hyYkU9toGY1Y0dOnSQfnpSQlX5piO9lwhBEO/fv3/9+jWPx5PcEy1tM3DgQMYnGQwG05r8yPwRDObbgHOgIXAOdHNpro1dZmbmxo0b582bd+TIEfSMzpw5Y2trq6ysbG9vf+7cOXGD1NbWPn78+M6dO/SMzMbGRnqFag6H4+/vL2XiLGO9cQhVVdXCwkI045nNZjOWFQQAvHjxAnWEcHZ2BgAwrv9zc3Pj8Xj0gt4toAbUEICA/i0G8KJGChUVFeoq0b+YZ86cgXr269dP+jdaehhzoMllfxTicqCzs7MpK5I2bdocPXpUwoEaGhroWfJdunQhqzl+DTJ7K8M50BjZAQtozE8IFtAQWEA3l+YKaAmcPn0aUmzh4eFN7lVVVbVs2TIjIyNUqurp6UlpkxcTEyONNj1y5AjqG92nTx+hUIj61gEAjh8/fvz4cXqLvb39okWLAABQDXASNpv98OFDgUCwb98+Jyen3r17o/bVTdIIGn/1/fXLly8bN24kW26D26vBasbOqqqqSUlJ9ItJvyzr1q2Tl5cnezo7O3+93BRHamrqkCFDSBeOyZMn012iSRgFdGNjY7du3aAzunfvnuRjJSYmnj179v79+82yUBSHzN7KsIDGyA5YQGN+QrCAhsACurm0ooBGf7W3sbGRvItIJBo2bJgENZmSktLkcYVC4bVr14YNG9ZkiURra2sul4vmbYtb/Efawz158mTy5MleXl4hISF1dXWoBKfj4OBAn9uqVaskTwmCxWIJ2UL+y3/Ct2vXruVwOKSAdnBw2LZtm5eXl6en57Jlyw4ePHj9+nXoQ4V+MUtKSu7fv5+WlvY1ZskQsbGx06dPHzlyZGBgIP2IjY2N4o7CKKBTUlLQKzB79uzWmqc0SH8rEwqF7969S0xMbHVdywgW0BjZAZfyxvyEiEQioVBYXV39nY9bX1//nY8oJeQPx42NjT96IjACgQDI5HVraGholY+QQCB49+4d1JiVlVVRUSHO3C09PX3btm23b9+WMGxVVZXkudXX148ZM+bFixfkS3l5eRMTk+zsbMbOGRkZJSUlkZGRBw8e3Lx5M30mjP2FQuHp06fXrFlTXl4OAMjLy3N2dpZsZJGamlpaWkoFnjdu3FhYWPjHH3+IRCIAAIvFIsT7eDg7O4eFhbHt2LUNtbXVtQCAtWvXTp8+XdNH06K3RcDOANJWDzp9+ocK+oDV19crKSmRKeatVV7x2LFja9asIf++efNmWFjYo0ePNDU1Je8lEomqq6shE0D0AwMA+Pjx4/e8oUn5lczKypo3b15SUhIAQFNTc+fOnZMmTfqmE+Pz+Twer3VdU8gnnFYcEPO/wo9W8BhM64Mj0BA4At1cWjECjS7nsrCwENf56dOnTaY3tG/fHkqlRWFczCeB9PR0giCkVD979+6FjDusrKxevHjRp08fcbtoaWmhodaGhoY3b97k5+dHRkZSnn2MGBoaiuRExOt/dqytrX3z5k3dgDqCqV5NdXX1ypUrTU1NO3ToMHbs2MzMTOqLeenSJXJtn6ampr+/P2UU/ZVwuVz0XVuzZk2TOzJGoAsLC9ErsG7dulaZqpRIcyurr69H/VWePn36TSeGI9AY2QG7cGAwGMw3BK3eh7ZQrF69WrJ9m4qKypEjR6gUXnE8ePBA+hnKy8uTeSYfP35ssrOysnJubi4UoczMzKyvr58+fbq4vX755RcqzpqcnLx9+/Z169bdv3+/S5cu+vr6o0aNys7Ovnnz5qxZsxh3//Tpk1Ao/Pz5MwDg6NGjurq6Xbt2ffTo0dGjRyG/aoIgfHx8goODP3z4UFpaevXq1QEDBpSWlgIAYmJivL29yTB8VVXV3r17xRVnyc3NHTNmjJaWVseOHefPn08e9+PHj+Hh4WFhYWjpnJSUFPRde/XqlbirIRk9Pb3Vq/8rt1tfX3/FihVoz3fv3nl5eWlqanbo0GHWrFnkaX433rx5k5GRATX+8ccf33MOGMyP5EcreAym9cERaAgcgW4urRiBFolEe/bsIW2etbW19+3bJy4jViQSoUsGSZSVlefOnRsUFJSXl1ddXd3kQel1UppEWVmZ3GvixIlNdnZ3dzc3N0fbHRwc5s+fv3DhQnSTp6cnVfgQsumwtLTs2rVrv379/P39w8LCJETf60E9ry2vVq/2PXhP/qsFtSvByl69egmFQurEExIS0H03btxIEMSoUaOgdg6Hg4Yzq6qq2rZtS+/WvXv348ePU2+NoqLigQMH6Lv8/fff6EF/+eWXJt8mcS4cIpEoLCxsyJAhPXv2XLx4Mbr6kCCIz58/Q8YmTk5OTf40ISXS3Mr++usv9KzHjRvXKhMQB45AY2QHnAONwWAw3xAWi+Xv7+/v7//ly5c2bdpQ7Tk5OWSceODAgWZmZmRPbW1tNAzcrVu3gwcPUov8pEnbHThw4OPHj6WcYV1dXUREhI+PD1p9EOXOnTuM7UlJSUlJSW3atLlz505paamGhoaxsXFBQUGnTp0oR+S8vDx/f3/6XlS1kSZL9PUD/Vb/svrjx48xRf9vLZIAEqpeVWVlZXXu3JlsQWOi4D/lS9AUcIFA8OHDB6gkzerVqysrK+ktiYmJixYtopYQNDQ0rFixon///pRRt5qaWvv27cmMcIqxY8dKPiMJsFis6dOnS4joAwAiIiI+ffpEb4mLi3v48OGQIUNafNxmgdb3AQCg/iEYzM8KFtAYDAbzPaCr59DQ0FWrVpG/+ysqKi5atMjExKS+vr53796QgJ47d+6xY8ckDFteXv769WsOh9OrVy+qKN3atWtv3bqFZhFoaGgwrkWbM2dOr1690tLSWnZqFF++fDl8+DAVm7S3t6dvffHiRYvrCyaAhJXRK9u3b/8GvIE2FRQUUAKasYAIaa5nZWUFhYrl5eXJRxc6jN5/6ALcM2fOkAI6Nze3e/fudM0tJyfn7+8vTTj/a2AMe2dkZHw3AW1mZrZw4cLDhw9TLcbGxhLSkzCYn40fHQLHYFofnMIBgVM4mksrpnCgpKWlMVbPJqEXHfT29kbfOHoKx+nTpynRrK2tHRUVRW0SCoV+fn70bGl9fX0J/xdwOJwmU6ulQV5ePjk5mfHEQ0JCvn58CDabXVpaSh2Cz+f36NGD3kFNTe3169cEQTx69Aja18/PD5qRj11ZAAAgAElEQVRhcXGxOHcUFHd395qamsmTJ0Pt7du3l9IdT1wKhzQwXszo6OiWjQYh5a1MKBQePXp00KBBPXv2XLp0aUlJSascXQI4hQMjO2ABjfkJwQIaAgvo5vJNBfTBgwclKzNjY+O7d++KK/BBCeg3b95AQlxDQ6OwsJDeOT8//8SJE4cOHYqKimpSEaLasUkPaUYUFBTWrl2LCh16ScUWQ9YMpyDNLh4/fjxy5Ehra+vhw4dHRESMGjWKPBc7O7uHDx+SX0wejzdjxgxNTU02m92hQ4cdO3agGcNHjhxp1mQWLFjAWJ37w4cP0nwSWiaghUJhbW1tUVERVIHczs6utXxFZPZWhgU0RnbALhwYDAbzXWnSZDcvL09NTa26urqgoEBCt6ioKGio6urq+/fv01v09fVnz57t4+OzZMmSJicmFAqhlqCgIEZHvHgQjxbWpv418BuO7z5+2uk0YAH6v+hb0V/AlySQNA1Mo4YKASEEIJyAE318NpttZGR0D9wTAZEm+MdNeQ1YQwCiUdBIP9buPbsBC7j0d0m7mZaRkREdHe3j4zN79mwul1tRUZGamjpgwAAAQF1dXe/evcPCwqqqqkQiUWlpKZ/PRyPuHz58aPIq0YmMjNTS0oIa2Ww22tgqVFRUzJw5U11dXVVVddiwYdu2bXNxcZGTk1NQUBg9evTNmzerq6vnzp1rZmZmbm6+ePFi0j8Eg8F8C3AONAaDwXxX+vbt22QfNze3uro6AICLi8uIESOEQmG3bt08PDzoRTegtW4kFRUVaOOqVatQ8zVGNmzY8PTp0/T0dEVFRUtLS6FQuGrVKmtra19fX7pYvw/uPwX/LPtzAk7OwDkSROaAfw7BBdwKUKGcqgwAALMAoUY8f/48Pj6eAziGwHAkGHkWnP0IPj4ED8nd+YCfDJKhmeR/zHcEjpkgswpUkS2OwBEAcFb1bFudtnl5eSKRSE9Pz9XVNTY2Nr8kPxfkUvsGBAR4enrSw/OHDx9OTU2lj79ly5Y5c+ZAaS2osbFkqqurvby8qII1JIMHD6bnu7cWBEFMnjyZqrDz5s2b5cuXv3z50srKis1my8vL19XV0RPZQ0NDX7169ezZs1bJzMFgMDA/OgSOwbQ+OIUDAqdwNJdvmsJBEMSyZctacLsePHhwQ0MDlcJx6dIltE9sbCx6OAMDAykPkZ6eHhsbS3fT09XVlVzbJRyEE4AwB7C3XSpI5Svxjx89DoVjA0EgAYjtYDsAQAEo1IP6l+AlOqwNsCEAEQbCqJaP4GMBKNDR0aF309bWRvNM2Gw2/XNVVVU1YcIE9BDXr1+HLhSXy6XWI0qDm5ubUCicM2cO1eLs7FxQUCDlx+D27dve3t62trZDhw6NjIyU3Jn0EoFYvHgx1eH3339HO1y7dk3KydCR2VsZTuHAyA44hQODwWC+NyEhIXfu3PHz85s6daqEBYUQ9+7d27dvH/Vy8ODBaBXrBQsW+Pv7Q7kfZMl0iI4dO0ItdnZ2NjY2c+bMIYPfJMXFxZKtM3qBXpWg8h34rwLUakDNBtjk6+bPnT8XCoo/B88BAPWgHgDgABwUgWIciEOH7Q16AwBIbc3hcHSBriEwLDQsLCkpoXcrKyujllFStGvXjhL9BQUFV65cYUxmQBMtVFVV//jjD8iNW0NDg/HE1dTUQkJC2Gz28ePHP3z4EBkZmZiY+OzZM/TCMpKYmOjl5XXp0qX09PSYmBhPT89Tp05J6P/+/Xu0kV73++3bt2gHcfXYMRjMV4IFNAaDwfwAhgwZEhQUFB4enpWVBUVVJRAbG0v9nZaWJhKJoA5ZWVlBQUE2Njb0gCWZBwxRWFhIlnchsbe3f/bs2bVr16RM9iDRBJqWwPK13GvID64H6MEG7AQ2Q1mTHqAHACABJID/VskQZFY0uen+/fvpZ9IBAI0OsJ0cAAC1oquoqNi5cycA4PTp05aWlrNmzbp37x7Ux9jYGPLrIHny5An9+QEAwGj85+Pj8/btW8qnz8TEZNSoUQ4ODvQcG8ls2rQJSmEPCAgQ17mgoGDz5s1oOz1ebmxsjHZg9PXDYDBfDxbQGAwG8yMxNDT08vKSsjN9nR9UR4POly9fPDw8eDwe+fLAgQOMQqq8vNzMzGzOnDkpKSkpKSnXr18fM2ZMk3PgcDhTp05t27atoqJiP6V+LMCym2nn6ekJANDV1SX7jDUYCwB40vAE2tcW2C4Hy5+D5zEgBvxHJYuLQPMALw2kKSgoODg4aL3TAgAoujAkk1haWkK1A4VC4fr160+fPr148eLa2lp0FzMzsz///JOx7mN8fDzaCI3v4uJy/vx5qBBgc0lJSYFaysrKGJeNlpSU2NnZoRNTV1enl34cPXo09CRmaGjYKs4nGAyGgR+dQ4LBtD44BxoC50A3FyoHOjw8vHPnzhwOx8zM7NChQy127ZVMYWEhJT0lExgYWF1dXVdXJ40wmj59enl5+ebNmydMmLB48WIJqQVBQUFCoVB674i4uDhy5u9mviMAMUtnFqkmnzx58u7du/z8fNE4EZnBHASCyH8hIOQmuFkLal+BV8bgn1hpDsgpBaXo+GpATQAED8ADAMA/dbPdxZp+7AP70BEAAIz1zGfNmvX48WNxn7pDhw5BNnkkYWFh3t7e2trahoaGixYtapWUWScnJ+goioqKDQ0NaE+ofCPFgwcPoJ4JCQk9e/Ykt/bp0yclJaVlc5PZWxnOgcbIDtiFA4PBYJi5ePHitGn/GK7l5OQsWbKksbFxxYoVrX4gPT291NTUkJCQpKQkPT29ESNGREVFvXr1Sk5OLiMjg0pB7tOnz5o1a/h8/tatW2/dutXksOHh4dHR0aWl/yhUCdkFW7Zs8fLyYnTw0NfXR8OiUVFRCQkJCQkJo8NGdwKdbpbcLAElAIDg4ODPnz/X19dHvonUATrTwXR0wEPgUB7IAwB0AB1MgelNcBPt0xP0lANyNbY1McEx7u7uAAAQD4AuAD7g+vXrkNncn+BPxpOqqalBG42MjKii6BDJycnLly9Hvfx69uw5adIkyYW1m4VIJIqMjKTnz5B4e3szGm8nJ8MWJSTt2rWDWnr06BEfH19RUcFmszU1NVtlthgMhpkfreAxmNYHR6AhcAS6uZARaDRHVk9P7zvPJD8/Pzg4OCAg4NKlS0KhkCCICxcuqKiotPr/BbGxsdLn71I9C0Eh3T+ORAfoEIC4AW7QG9uANhPBRAIQNaBGDshpaGh4Ak8CEOvBenR80vK56ux/vlDZBAEIwpcQJyUZYbQ6uXfvnrhL7erqivbv0aNHcXFxK76hPB7P2dkZvZI+Pj70GpN0fH190YnJy8t/u++OzN7KcAQaIzvgHGgMBoNhBjUOKyoq+vLly/ecg76+/ooVK3bu3Dl+/Hg2m3316tVJkyYx5vWi8ld6QQwA2LBhA7oaTxwEQQAADICBHtB7BV5BW3uBXgAAqP0L+HIRXEwEiapA9fH9x5aWlg7AAfxnNSFEf9BfCISRZZEAgMTExOchzwEAhCMhvaeEvb39nj17FixYQG+0sLCwsbFh7C8SiSA7Z5JRo0ZJv8RTGrZt20Y/EEEQmpqab9++vXjxojj36IkTJ6KNGzZskGwviMFgvilYQGMwGAwz5uawt7Guru7X18hITU1dtGjR6NGj161bV1JSUlJSsmLFin79+nl6ejJaO9PZsWOHuE2kqKXTLOX34sULRqM0CTAKZaqdcWlgB9Chuk11n4F9HBwcyEzoGgAnWhgBI3fgHgki3xS9GTFiRI8ePeJD4wEAM47MkPLijxgxIj4+XlFR8eDBg/RS29nZ2YMGDaLWVtKprKxkdOtjNDD5Gh48eAC1VFVVOTk5QXVe6AwdOjQ0NJT62UFNTW337t0bNmxo3YlhMJjm8YMj4BjMNwCncEDgFI7mQqZwnDlzBrph7tq16ytHjoqKogcO27ZtC8nczZs3i9tXJBI1WVXO1NRUXV3dwsIiKCho06ZNrfQfBTO7wW4CEP0BvFYvBsSIgKgtaAu16wN9AhAF3QoIgvj48eNGlY0EIC6Ci3JA7v/nD0xTQSoXcHuAHr179yYbn4Fn9aBeASh4enp269ZN3HwUFBSGDRv2+PFj6opFR0ej3cLCwhgvr56eHtTTwMDgK99uFEdHR8bJ9+vXT/KOXC43Pj4+JyfnG61kpSOztzKcwoGRHfAiQgwGg2HG19eXz+fv2LEjLy9PV1d35cqVq1atIjdFR0cnJCR06NDB09NTysIZJAsXLqRHOtFy3Nu3b1+6dClpiCEUClNTU6urq+3t7bW0tFgsVpNZGV5eXsHBweTfjY2Nb9++vXz5MtrN2dmZMWOhWfQCvURAlAgS6Y0swHIEjtkguxLAp9YNdAMAdLDvAAAwNDSclzivtHepT5WPm4pbXZ+6uNS4NiVtBoPBPMDzBJ7JcsnCl0IAAAdwHIADD/B2gB1yUXKjR49OrErMzc0lx6wCVTvAP1H52NjYfv360Y/ImPJx7dq1Xr16obkcO3bsmDlzJr3lwoULkq9AdXX1/v374+PjNTQ0Jk2aRHr5SaZ3796MTnmvXr3i8/mMiwhJVFVVKYcNDAbz4/nRCh6DaX1wBBoCR6CbC1TKu66ujvo7OzubHjNmsVgjRowoLy+XZlioip447t+/TxBEWloaVadDWVl5//79dXV1TQro8PBw6KBJSUm2trb0Pjt27KiqqrKzs2vmfxf/BRuwv4AvKSAFCopbAAsCEOEgHOqvoqKyt+9eAhCEEW1yXwhiI0E4EIQ6IWwvfK/7fr/mfssOlnJy/x+TdgAO4gzsCECQftIkZ8+ehc79zz+ZDToAAN7e3nw+H+p/9epVV1dXU1PT4cOHP3v2TPK7WV1dDSX5bN26tcnPAN25mY6amhq5SFQWkNlbGY5AY2QHLKAxPyFYQENgAd1cIAFNwePxGA2be/Xq1djY2OSwPB6P0WYY4u3bt3w+39raGmqPjo7W1taWsKOjoyOjkbBAIDhz5sysWbOWLl368OFDstHS0rLJmairq0+ZMsXQ0BCqJEJHTU2tyXHmzp0r5ZXncrmQ3GcEvTgAgFmzZlFnR1JdXS2hFN/GjRuho+fk5MycOdPa2trKysrX1zc3N1fCVAMDA6EBORxOSUmJ5BMUl8Ixbtw4KS/Rd0Bmb2VYQGNkByygMT8hWEBDYAHdXMQJ6MjISHFq7NatW+JGEwgEr169ioyMzM3NbbIAioODg1AoTEpKQjfNnDlz5cqVjHtpaWlt2LDhy5cv0p+jlBHoJrOuhw4dyljSj8LAwEAaJ7icnJyoqKhffvlFmlkBAOhRajojRoygZwmnp6f36dOHsWeXLl3oE/Dz82Oz/2thva6urgRBPGrUKHTM27dvSz5NxlWJtra2rWuW95XI7K0MC2iM7IBdODAYzP8cX758efny5bt37wjEuUIy7969E7fp7du3jO0fPnxwdHTs1auXp6eniYmJrq5ur169qK0TJ07cu3cvZbDQq1ev5cuXL1iwgFEol5aW7ty509fXF03kqKio0NDQUFdXl/5chgwZIk23xsZGyR2UlZVVVVWhxokTJy5fvtzHx2fHjh1paWmS/UCEQuHMmTPNzMxGjBjBmLEtbi/G842Kitq37/9rE9rY2Fy/fv3JE7ioOPjvBPQzZ87s27dPJBLROxQXF//222/iJoBWQiEbnzx54urqqqWl1blz5/379wsEAnoH9AnK1NT0zZs3rWuWh8Fgvjk/WsFjMK0PjkBD4Ag0nV27dikpKZE3QGdn55ycHLSPuAh0VFSUuHvpX3/9Re/58uVLNzc3fX19NMMhNDQ0Li7u8uXLb9++JTtXVVU9ffo0IyNj165dEm7Xa9eu/eOPP/bv33/v3j1qOSOFoqJidna29NeBy+VCS+5aheHDh6elpUk+dHZ2NrlWMjw8vLGxUYI3n2Q6d+4cGhqKtltaWpIHEgqFGzZsIN9u9KnDycmJy+WSPf+pd4jg5eVVUlISERFx6tSpx48fBwcHr1279sKFC42NjTdu3IA6W1pavnz5kvp0kaxevZp+7o2NjfQou6mpaZOX6/sjs7cyHIHGyA5YQGN+QrCAhsACmsLPzw8SPT179kQXb4kT0A0NDd27d0dllrm5eU1NDdXt0aNHzIoPAADA4MGDoWGTk5PHjRtnbm4uYY1gx44dO3ToQH+J9jl79iyfzz948KCdnZ2GhoaBgcG8efPKysoIgigrK3v8+DEZdKcQiUS3b9/ev3//6tWrJWQ5Nwt5efmTJ08GBQWdO3eOMaXkr7/+okvMXr16WVhYtOxYSkpK48aNQ9u1tbXJY+3evVvyCIaGhvHx8d7e3uI6jBw5UkNDA23v2bMnl8vdtWsXZUpobW2dkpIyYcIEqCeHw0G/fa9evTp58mRYWJhsfjFl9laGBTRGdsACGvMTggU0BBbQJElJSYyJs3///TfUU5yAJgiiuLh4xowZqqqqZL4sm812c3PLzMyk95GsCB0cHKBZScghNjAwGDhw4Nq1aw0MDCSMSXLixAnUnc3c3NzR0ZE68UGDBkHptlVVVfv27UOrxnw9enp6ycnJ9GNdv34dfQugkO3XY2trSx4OXSiJpnQzZmKQKCoqSlgiuXbtWoIgSkpKbt++TZrQEQTRtWtXtGdSUhLjZ+njx4/fwdS5BcjsrQwLaIzsgHOgMRjM/wp//fWXUChE24uLi6UfREdH5/Tp01wuVygUVlRUVFZWxsbGQkKNcilmhJ4DDQDYsmVLXV2duM4ODg7t2rW7detWfn6+5IkpKCg8ePAATcV+9+5dfHw8deKxsbFTp04l/05PTz9w4ICpqamfn5+E9O4WU1RUNG3aNOrl69evx48fj74FjE81xsbGLbY9JiuwEASRl5cHbUJTusvLyxkHadOmzfLly7lcrrijPHz4EADQoUOHoUOHOjo6ktIcNf1gs9kSnEAwGMy/FCygMRjM/wpFRUVoI5vN7tKlS3OHysvLGzt2rIGBgY6OzuDBg7OysuhbJThX6Orqbt68mfz73bt3I0aMuH79uoQD3bhx4/Lly2/evGlySqNHj05ISJBm8nfv3p04ceKUKVPs7OxWrlyJFnMhUVNT6927t4uLC710IgmLxerTp8/evXslhG9JUlJSKPfrkydP8vl8tA8asFdQUIiJiXn27JmTkxO9feHChWiOBAr5PPPo0SNqdSYFei7iOH78uIODg4QOjLp//vz5UMukSZMYk0AwGMy/mx8dAsdgWh+cwgHxb0/hqKioePToUWJiojReyxI4fPgweg+cP38+2lNCCgdBEDweD/IhlpOTO3z4MNUBtSpjs9nDhg0LCAggM5IJgqiuru7UqZOEm7OCgkKzhNedO3eksXaWHspTmdHsorq6miCI5ORkemY2I58+fSLHEef7ERISsnjxYuqlgoLCr7/+Su7S0NBw6NChCRMmzJkzJyYmhiCIsrKyJmceHx8fFhbGuElK7xEAgJ+f34cPHyR0EFd0/fz582S+jaKi4oIFC+jJ8RA4haO54BQOjOyABTTmJwQLaIh/tYA+dOgQ5ZJmbW0N5dQ2i9raWqq2H0n//v0Ze0oW0FeuXGFUVFFRUWSHz58/Q2vyTp48CQ2yadMmccrMzc3t0qVLjFbQ4rC2thaJRPPmzZN+lyZxcXHZs2cPuSQR2tSxY8cXL16EhIScOnWquLj42rVrS5cuhUyUSczMzKhTXrJkCdph2LBhAoGAIIjXr1/v3bv3wIEDlDmJOBjXcVJwOJzq6mpNTU2oXUdH5/r169nZ2VKevpeXV3l5ubhliK6urp8+fVqwYIGNjY2Dg8OmTZugr1h5eXmTz3tYQDcXLKAxsgMW0JifECygIf69Avr+/fuQcOnUqRO9sHZzqa6uXr9+ff/+/YcMGXL48GFSuqFIFtDiRNWoUaOoPlwud/v27R4eHnPnzk1ISIBGKC4uVlBQYBxk4sSJZHC3sbFRcoESClNTUzLMWVlZyVii7ytBM1Loadxt2rSJiopqbGxkNAZxd3d///49edZZWVlQMNvd3Z3xCpeXl7969aq0tBRqJyVpenq6sbGxhAlHRESgjeTKQsarampqyvh2aGlpxcfHP3nyZNWqVWPHju3SpUvbtm319fUXLlxYVVVlZWVF7+zh4dFcNYwFdHPBAhojO2ABjfkJwQIa4t8roOm/7FM8ffr0W0+sZRFoyvyBkZs3b44cObJnz56+vr7bt29nHGHHjh30XaZMmcLYjcLOzu7cuXPUYwCPx+vcuTO9gzR1tqVBQUHBwsKCw+GYm5vT1wWStGvXrqKiYsGCBeJ2nzBhAvnYk5CQMHjw4DZt2piamq5fvx4VQ42NjQsXLiTTi1ks1tSpU2trawUCwZ49ezp27MhisSwtLc+fP19bW3vlyhVxbicvXrxADQEHDhxIHgKd/549e2JiYhgfAOzs7AiCSElJgdKpR4wYgXZu7m1HZgV0ampqQEDA5MmTAwMDmyxO/j3BAhojO2ABjfkJwQIa4t8roBkNeqGSJd+Cly9fHjlyJCYmhnF6aA40yfjx48UNeOzYMXpPceHno0eP0veqqqoaPXo0Y095efmrV69CR7l48SLaU01NTUtLS4LDNIm+vr7kDufPnyeP0qNHD3RrbGxsXFychN3JQomOjo7r16/fuHHj3r17o6OjKel/6dKlbt26qaur6+rqQjsuXrx4y5YtUOOff/5JEARjErm5ublQKBw8eDDUTiWpV1dXjx07lmzkcDhLly4ljcB5PJ6ZmRk6YGlp6eTJk6FGxnyV0NDQZn3MZFNAv3z5kh6k19LSysrK+tGT+gcsoDGyAxbQmJ8QLKAh/r0Ces+ePahMycvL+3ZTEgqFvr6+1LE6derEWCguLy8PivUqKSnFx8eLG1PK5YAeHh5nzpx5+fIldCxGd2E0HZzy92gW+vr6cXFxf//9t+RJhoeHEwQhEAg4HA669datWwUFBW3atGnWobt27VpcXIyGhOloaWlpaWlBjX369BEKhWhZbDab/ebNG4IgCgoK+vbtSzZyOJwVK1ZAUvXTp0/Pnj0rLCykZypDKfIkZWVlqLs2Izdu3GjWh002BTSaYj5y5MgfPal/wAIaIztgGzsMBiO7LFy4ENKp/v7+RkZG3+6IoaGhZ8+epV6+f//ex8cH7WZkZJSamhocHGxtba2lpeXq6nr37l1xvsW5ubnV1dVQI6PV3a1bt6ZPn967d+9hw4ZR5tBGRkaMnVGPtiYNMRgpKCjg8/lWVlYZGRkBAQFjxoxBw95ycnJpaWkLFy7s3bu3QCCAtrLZbFtb26SkJG1t7WYd+s2bN8OGDQsPD5fQp6KioqKiAmpMSkpSV1e/desWFFkPCgoiTQlLS0upa66mpiYUCi9dulRQUED1LC8vX7NmjZGRkZqa2tixYz9+/AgAcHNzgw7UtWtXf39/1F2bxWJB6TFmZmaurq5NnbGsU1dXh3om3r17V5xbNgbzv8uPVvAYTOuDI9AQ/94INEEQ1dXVmzZtGjRo0NixYyMiIr71lIYOHYreJz98+PA1Y9bU1KC/+DcZk161ahW5e1lZGaqVjYyM0ODlgQMHWvYfwYkTJ6Ch5syZI/3udnZ2M2fObNmhGXMhICgbFnFoa2sPGDDg4sWLISEhJiYmcnJyjGFyFRWVU6dOEQRRWFgIaf0uXbrU1dV9+fKF/hSko6OzaNEixiOOHz/+7t27VMqHo6Njampqcz8YMhiBFggE6IcNADBkyBBZmCqOQGNkByygMT8hWEBD/KsF9HemT58+qHpISUlp7jhFRUVkbWcSLy8vyRIQpXPnzuS+f/31F7p1zpw50BHr6uoke1NIICAggBqnurr677//bmhoYMyfYWTbtm2MyQ+tBeqjB9G1a1eCIIKCgpocSllZ+e+//2Z80rh9+zZBEAKB4OrVq1u2bDl58mRVVVW3bt3QnpMnTya/7CKRKCcnp7CwsPkfNIKQSQFNEMSYMWMYLx1a8f77gwU0RnbAKRwYDAbz/5BVoOloamo2yxsuLCxMV1dXT09PVVV13rx5NTU1AIATJ044Ozs3aybkjgAAxkqBCgoKXC43Li4uOTmZzKnYvn07WrlaSnbt2nXhwoXAwEBLS0tNTc3OnTtraWmhHoIoZAbFxo0b0SSHVsTGxiYoKMjAwIDNZjM6b+Tk5AAA9u3b1+RQdXV1sbGxjHXLSYtoOTm5MWPGbNq0adasWRoaGoypC8HBweQPCCwWy9TUVE9Pj9pUVlYWFhZ24MCBJ0+eUI2NjY1oDo/McvToUcZc9m9R7B2D+feCBTQGg8H8Pxs2bDA3N6deKioqHjt2jDEfgE5lZSWptG7evDlz5kyyeHVjY+Px48cXLlwIAGjfvj1UlbpJyFh4RUXF06dP0a1CodDY2Lh3794ODg42NjavXr26e/dus8aH8PX13bJlS3Z2NkEQAAAejxcTEyOhP4vFUlZWJjuT8/mao0MjQy1Dhgzx8/P79OkTn8/Pysoi6/zRsbW1LSkpoWqGS4bL5fJ4PLS9trZWJBJBjajliJGRkbhc8/v371tZWc2cOXPlypX9+/f39vYuKiry9vZWVVXV1NS0sbGJjo6OjY39448/MjMzpZnq11BdXb1kyRIdHR1VVdVBgwYlJiZKuWOHDh3oPt8U38JiHIP5F/OjQ+AYTOuDUzggcApHs+ByuTt27Bg/fvyKFStISwcJpKamUkHrLl26uLi4QPdYNpv95csXQnz5FUbYbPbhw4eLi4sZndp69uyppKREb1FQUIBqH7YKEszvoMWdX0P79u0tLCzEHWv8+PGkzRwF5AkIALh586b0+d87d+5UVFRk3KSnp/fx40f6sTIyMqDFgteuXWP8JDQ0NKAefPSHMfDf13PBggV5eXnfKIVDJBINHz6cfmhNTU3pU/lR1xd5eXlZyDbBKRwY2QELaMxPCBbQEFhAN5l90wEAACAASURBVBfJhVQoqqqqoLRjRhVIplBnZ2dLs2AOEj1o44YNGzZs2CDN7i0z5ZCG/v37L1u2DG2nZD2jobI4AgMD79y5g7ZPnDgxNjaW8cqfO3euR48e7dq1c3Z2vnnzJiFF0RkK9CGHjoGBAXSs3NzcxYsXu7m5+fr6vnr1StyH4fXr19KfMsmePXu+kSpNS0tDD0fPdJdM+/bt0d1loaIKFtAY2QGncGAwGEwLuXv3LpR2TPwnpYFCQUHB0tISAGBubh4cHEzW2JOSqqoqtNHGxqasrKzJfTkczvbt2yH/CtKyTfoJkEBPBTo6OufOnWOUofb29hYWFv369QsICGgy74Wc5LJly9avX5+cnIxuzczMFJc2MGXKlISEhPLy8ufPn5NFAaV/WigqKpKwNT8/H5qMsbHxoUOHYmNjz5w54+joKG7HxsZGKSdAce/evebuIiWMKSLS542giebt2rX7ds9jGMy/ESygMRgMpoXk5uY22cff359KGFi2bFl+fn5wcHBzQ9F0YmJipMnWaNOmzbJly+jJvgYGBmTxQrSziYmJhKGgXUpKSmbOnOnl5TVw4EB6u7y8fHx8fHZ29tOnT+fMmePg4CB5hg8fPqysrAwJCeFwOGjyAwAgMTHR0dExNDR006ZNp0+fZkxcppD+waDJqiipqalSDkXH3t6e0a5bAl++fGnBgaQBSh0hEVf5HGX58uVQy9q1a792ThjMT8YPjoBjMN8AnMIBgVM4mouUKRzR0dHibq3q6ur29vYHDx6kqlVTvHz58utv3XTnB0bQNN9u3bqhReYAAKqqqk+ePCHD5FIiJyfH5XLr6+uDgoLc3d09PDzQgLScnNyMGTPIRwU5OTlIXM6ePZt+TYqLixnTBuiwWCxdXd2NGzfW1dXR933//v38+fMHDBhga2vb5Mw7der06dMnU1NTCX2uX79eVlbWgo9Nk3Z7EAsWLPhGKRxCoRAqCqOmpiZ9Re6qqqq//vqre/fuSkpKlpaWoaGhUBr6jwKncGBkByygMTKBSCQ6f/78rFmzZsyYcerUKVRzSNmHBAtoCCygm4uUArqxsZHRNxoAIKHmS1ZWlvQaS0dHR11dXZz8gta30UEzKBjD3hoaGn/++SdBEA0NDX/99Zf0TtLFxcX0k+rXrx/a59mzZ9euXZs7d+7mzZt///33QYMGaWhomJmZBQYGUiK4srJyxIgRSkpKLBZLysD8/PnzqeOmp6c3WWaFRFtbe9asWQUFBQRBFBcXo36FJAoKCuQfjo6OzfX/XrVqFTqggYGBmZmZlpYWtDLPwsIiPT39263MKysr8/X1VVVVlZOTc3R0fPr0qfT7yuytDAtojOyABTRGJoiIiJg0adLz589fvXo1bdo0slpYC/qQYAENgQV0c5FSQBMEUVFR8csvv0CaSU1NjRRqjAiFQunLjuzatSs3N5dxNeHSpUsZi8YBAGxtbSkhSMGYlLxr164TJ05s27bt+vXrQqHw06dP0ujRTp06QSc1YcIEtJu3tzf1t6Ki4sGDB6n+9fX1iYmJkZGRqCddk7DZ7OrqanIcT0/PJvsrKSlt3rwZkqp//vkn2hPKUDc2Nm7Wl7q0tJTxkSYxMZHsEBcXt3DhQm9v7z179nC53O9QSEUoFEIBe2mQ2VsZFtAY2QELaMyPRyAQTJs2jSwDRhDEo0ePJkyYAN30pelDgQU0BBbQzUV6AU2yefNmSi2pqan98ccfEjqLRCIPDw8pxeL8+fObVSXbxcXl8uXLklMU6NDt8LS0tDp27GhhYSF5uZiiouK9e/egk9q7dy/UDc1nUFJSIp3UoqKidHR0pJwhI/Hx8eRxJehveXn5ZcuWpaSkFBUVoe8Cj8eDkqHbtWuHDnL58mXpPwaEGEGP/hwhFArfvXsXHR3N5XKbNf73QWZvZVhAY2QHvIgQ8+PJz8+vrKykChZ07969traWLC3WrD4YzPentrZ24cKFu3btAgCoqanNmDEjKyuLHnmlqKmpOXTo0NKlSydOnHjr1i0px6+rq3vw4IH08zEwMFBWVv7w4YOU/evr66m/KyoqCgsLs7OzS0tL+/fvj3a2srL65Zdfbt++PWjQIHr748eP/f396S1dunRBk1vq6+ufPXv24cOHCRMmSFP0BA2ik7BYLGqRnLa2trjdNTQ0xowZY29vr6ysjG5VUVG5ffv2hAkTNDQ01NXVx44dy1hunXJZycrKOnny5NmzZz99+iRhzoy/LUCe2ZmZmU5OTubm5sOHDzcwMDh16pSEATEYjGzStM0QBvOtqaioYLFYWlpa5Es1NTVFRUWofLE0fSjq6upKS0uPHz8Otc+ZMwe16b148SJVM5nC09MTtQW4f/8+WszWyckJLTqQnp7+7NkzqNHY2Hjo0KFQY3l5+dWrV6FGJSWladOmAYRTp06h9d5IBQA13rx5s7CwkHrJ4/Hk5eXd3d3RhWLx8fFJSUlQo7W1Nbom7NOnT6js09LSQhMY+Hz+mTNn0PnPmDEDtSm4cuUKl8uFGklhATU+evQI9eHq0aMHWisuMzPz0aNHUKOBgQFUWgIAUFVVdenSJahRXl5+/Pjx1MuCgoJjx47l5ubW1NT07dsXqnL85MmT8+fPk39zudywsDAPDw91dfWPHz/Su1VWVh44cEDKUnl0XFxcHj9+LH3/hw8fSkiMlp6nT58qKSnR5TWLxcrMzMzMzLx8+bKLi8ukSZPYbLa9vX16enpAQAC0e35+PqPuZ7PZN2/eRN9xOsrKym/fvq2qqtLU1BwyZAj6pXN2dqbeNQ8PD/QDTFJeXj569Ojt27cLhUJlZWVnZ2dI3RoaGq5fv55abId+Z8F/BPHu3bs3bdpEGtXJy8tPnjyZLMwuJydXX1+fmZlpaGg4bdo0gUAwffr0kJAQyDBk3759Li4uEydOVFdX5/P548aNS09PJzdVVVXNnj3777//pltkuLm5oTYacXFxb968gRptbW379u0LNebl5aFVJNu3b48aldTX14eHh6NnjX6pAQARERGobcjIkSM7duwINT548ICsi07H0dERNWbJyMig1zwnMTIyGjZsGNRYUVFx+fLlmpoaJSUl6jaiqKjo6+uLTjUsLAx1FfT29kZToaKjo3k8Xtu2bb9FHSLMzw0W0JgfT01NjaKiIn39kLKyMnSnbrJPWlpaaWkp9TI3Nxdd0OPu7o668Pr7++fn50ON7dq1Q4vZHjp06Nq1a1BjQEAAJetFIhE5patXr27atAk9OuqflZaWNm/ePPTokEEYyaJFixoaGqDGzp07o7/X79q16/nz51BjcHAwGoo7f/78wYMHocbJkyejvmb3799Hp9q5c2e0QvWXL1/QngAAFxcXNTW1lJSUZ8+esVisAQMGdO7cOTAwEP2/9ty5cwMGDIAajx49GhERATWuWLECzTeIjIyEAqIAgP79+6PRwXfv3qFTVVVVJUUVj8dLT08fP348JfjQDwCaWHzw4EEFBYX79++Dr8bV1XXQoEF37tyRPqJcVFR04sSJrz+0SCSCntYImpndkydPSN1jYWGBvn0AgIqKioqKCqhRSUlJT0+PUafSqa+vl5OTIxMqbt26FRUVdf/+/bi4OOrx4/nz59TH++rVq5MmTbpw4QLjUGQ5a/LvjRs3ovrp8uXLW7duFTcTZ2dnCwuL69ev058QGhsbz5w5Qz4iysnJUVdpzZo1BEFwOBzGb9n58+ft7e0NDAxSUlIo9Uyxb98++suDBw+i4fCzZ88eOXIEapw+fbqRkRHUeO/ePfRTbWdnh5pYf/78mfGr6urqipoGBgQEoL6NERER6E8NoaGhV65cgRpXr16NGq1cu3Zt3bp1UKObmxtqqJKRkYFOVUNDAzIbIVmyZAk6fwsLC/SxZM+ePX369Gnfvn2zSv9gMAALaIwsoKqq2tDQQBAEFR6uq6uDVjI12YeecFlZWVlVVYXW+zU2NkYj0Hv37kUj0H379kUj0EuWLEFTV52cnAwNDcm/q6uryWDw2LFj0RRPY2NjqieFsrIyOk8lJSW0JwCANJOCGrt27YpGoAMCAhgj0OiwU6ZMQf+vsra2Rnu6ubmhU9XS0kJ78vl8tCcAwNzcfOvWrdu3bydf7ty5c8eOHYGBgWg80tXVFY1Az58/H32u6NGjBzoBT09P9JoYGBigPdXV1dGpysvLt2vXjs/na2lpjR07FpqetbU15ZJbVlaGFgUsLCwMDQ2lFtXdvHnz1q1bAoEASI2Tk5Ojo2OPHj24XG5ISEiXLl3evHlDRd/V1dVXrVp17ty59+/fixsBCh6LQ1lZua6uTtxWyZVBTExMvL290dRncbBYLBaLNXDgQMZUYwoOh/Pbb7/R36lly5aRVQ8Zw4p9+/YdM2bM7t27bWxsGAPbS5Ys6dSpExmBRj8Av/zyi76+PvWypqbmxo0b796909DQGDly5Pr169u0aXPu3Dl0WF9f37///jsuLo5qIR8wBAIBej9hsVhBQUF2dnbq6uqMJWPYbPbatWspCxQ3Nzd0qr6+vt26dYMabW1t0Z6DBw9GP9Xt27dHe2prazN+Vdu3b0/FBSh27dqFRqBdXFzQCPSiRYvc3d2hRkdHR3QCXl5e6OfByMgI7amqqnrs2DE0As14qzx06BD6UXFwcECfoNasWcPj8aQ3n8Fg/p8fmoGNwRAEQbx//37UqFHUMo7a2tpRo0ZBBlLS9KHAiwghZGQRIRoUBwAkJCT86HkxQC4izM3NRZ+41NTUKOcEgUCA+mAMGDCA2jp//vwW3JYDAgLy8vLowq59+/Y7d+50cnIipYO2tva2bdskODfLy8tv3ry5e/futra2s2fPFqcPli5d2uJ8D3l5+aVLl7ZsX8mQBiYNDQ3Sew+LWw6hqKhYWlr6NV9MLpfLWHPx8OHDkqvPQKiqqoaEhBAEIa74jre3d4sn2erI7K0MLyLEyA54ESHmx2NsbKyhoUElMiYnJysrK0NFs6Tpg5FxHj58iDaiGZCyA6l4oEZlZWVKVcvJyaEWGdnZ2WQodN26dUePHm3BcU+ePLl8+fKCggKqpby8fM+ePXFxcWRcraysbOPGjUOHDmVc9wYAMDExCQwMfP36dWxsrI2NjZeXl4eHB5S/JCcnN2vWrIULF7ZghgCAxsbGw4cPo+0sFotxDSU6Qzs7OzQnXkVFJT8/38XFRVVVVV1dfeLEiZIrb0tm3759ElYZovD5/Bs3boSGhj569IggiMbGxkGDBjF+RPv27StujSMjPB5v+fLlUVFRxsbGkydPRju8fftW+tEwGMwPB6dwYH48cnJyw4cPP3/+vL6+PpvNDgsLc3d3J921YmNj+Xw++X+/uD6YfwuoGBXXKCOkpKSgjVByPPqjcGFh4YsXL5ydnffv39+y45aXl6MLB6urq6GWQ4cOycnJderUicPhQMsrx40bBwB4+fLlsGHD0B1JhEIhutSsWaB5KfLy8s+ePevZs2dVVdWdO3ck7Mvn81NTU8PDw6FFYBMmTPDw8CDzpwUCQURERE5OztOnTyVXyUaTgwEAY8eOXbx4sbQnA0BOTo6bmxtlu6Grq7tmzRp6kgYFh8MRCARDhgxpVk0cAEBERMSIESNCQ0MvXLgAffKlz8Hl8/nN0u4YDOZbgCPQGJnAx8dn4MCBQUFBu3btcnJymjFjBtn+8OHDu3fvSu6D+bfg6uqKNjL+Pi4jMJYU2bZtG/0lY3y0oKAgIyMDTVhHQTNNAQAcDkeyXqQQCoXv37/PzMyEUsYPHDhw/vz5yZMni1PPJFVVVajdAYSampqPj4+UxtLjx493dHRksVi///67j48PucLS2NgYTXSxtrYGAEybNu3XX38lL4KiouKyZcvMzMyg1YevXr168eIF4+Fu3bo1atSoLl26MD6r3Lp1S7KIh5g8eTKlngEAxcXFGzduZOwpEAg2b96sq6srOZ8bhfy0aGhoTJkyBdoEZfvcvn1769athw4douxcuFzusmXL2rZtq6Ki4uDgQN0YMRjMj+GHJpBgMN8EnAMNISM50ARBQH5nW7du/W6FVHJyck6ePHn8+PHs7Gxok0gkgupZkDnQkH0vAEBPTw8qHYd6mAAAkpOTpXGs09HRYSz+3KlTp6lTpzbnRt5CIiMjCYIQV86QQvqfes6ePUsQRG5u7m+//bZjx46YmBiyiMmoUaOgnhs3bqRf/4KCAj6fTxAEY9WYEydOoG/oyZMnm5yPtrZ2bW0t9MWsr6+Pj4+/efPm8+fPq6qqnj17dv369YyMDCkLiZNAMeDhw4cPHTpUV1dXcl7ZmDFjyMnU1NTMnz+fHKRjx47h4eHU9AQCAb0ai4qKyvXr1wmC8PHxoQ+lpKT07dYPyOytDOdAY2QHLKAxPyFYQEPIjoAmCCIuLm7Hjh27du1KTEz8bpUIjxw5QqlARUXF/fv3k+01NTVLliwhF9JZWlpevXqVas/Ly0MVlbm5OTQyl8slg6kUU6ZMITdJU2V6yZIl6DrFw4cPl5eXo/7irYuJiUlUVBRBEFJGlweDwXfBXerfPda9WHbsaXCa6mBubv758+c///yT7uPm4+MjEolQTxVHR0fGd2r37t3oodGvs0gkYgzeo7x48YL+xXz+/Dl98R918cUlRUiZLCEvL19WVkYegsyfEUe7du3u3r1L9szJySkuLoZODc0sb9u2LeMqyZkzZzbnS9AMZPZWhgU0RnbAAhrzE4IFNIRMCWg630dAZ2RkoDHUxMREgiDQX9IfPnxIEERNTU1OTg4qbVEBTRBERUWFn5+fo6PjwIEDf/3118bGRoIgcnJyZs+eraenRw6CDkUyevTonTt30pX6mDFjCILgcrlo2Z2vh81mQ3kp5ubmqDk6I+vB+rfg7Rqwhvy3QX7DGdUztaxacuuwYcPi4+MrKyuhQjMAAMaVlGpqaoxvVlFREWTs3a9fP/KS0hHnZYHy5MkT6otZVVWFeq5JZufOnVIuQ4yJiSGPQi6vHDly5NChQ6dMmdK9e3eoJ5vNJnOgP378CP2gQRAEvYgPRWhoKNrYr1+/ln4nmkBmb2VYQGNkByygMT8hWEBD/C8L6KKiIrQAGwBgz549lZWVqK795ZdfiP+kcKC2u126dDE0NCQrrcTHx9MP1NjYmJycHBcXx+VyU1JS6DpVQm7AwoULCYL49OnTzp0716xZQ8p6giAWLVokbhdGpMyZ/hrWg/WRIJLe0hv05gGet7d3ZWVlY2NjQUHBxYsX0R2nTp2KFrvp1q1bQ0ODQCBA37LU1NShQ4eqqKi0bdvW09MzLS0N7cPj8aTR/UpKSu/evaO+mNHR0c06ZScnJ4FAwOPxGAt0Q8TFxTF+AhnfSgUFhcTEREYBzVgF8Pr162jjrFmzmvdlkBqZvZVhAY2RHfAiQgwG89MSFxdnZWWFFksHANTX179//55APEBu3LhBWSucOnWKniegrq6ekpLy6dMnHo93//79fv36mZubGxgYjBs37tKlSzY2Nt26dXNycjI2Np46dSq9EJpIJBI3Q7I0jIGBQUBAwO7du6lax2ghNwm0adOme/fuzUrhbUUuXbpEneyhQ4fQDgRBoG4YZCEkVVXVsWPHQmXP7ezsoqOj58+fX1NTExkZaWdn5+3tDZXwUFFREWfhR6e+vt7BwYGqgE0vVsqIvb29qakpm81WUlKaPHnyjRs35OTkVFRU0LpIECYmJuJSbtC6KgAAPp9PVYCHQKsFtW3bdtCgQVBavLKycrM8RjAYTCvzoxU8BtP64Ag0xP9sBBotskjx4MGD6upqRtHZu3dvMgJNEER5eXlISIifn5/kknstFq9hYWEEQVRVVe3cuXPSpEkrV65MS0traGj4GjX8jZQ0GYGmx33JCDQA4M6dO42Njfn5+WgBSADAyZMnBQLB1q1byaeRDh06qKur0zvY29vX1dXR3ziotDUAwNfXF3pzP3/+jJaGbteuHeQzCADo0aMHuQujLyGd1atXEwSBZowEBQVBPbW0tKj0aBMTE+jnCIIgYmNjR48e3b17d3HLCl1dXRkj0AKBgL7mUllZ+dq1awRB8Hg8f3//Dh06yMvLOzk5kblG3wiZvZXhCDRGdsACGvMTggU0xP+mgC4vLxenkxYsWED2Yfx5ncViffz4kRTQFLdv35asvVrG/Pnzi4uL6WvsFBUVUfnYXLp37+7k5ATl+w4fPrxTp04tHlNcCgcAIDs7mxTQaAI0h8Oh69Hq6mpG3xJyOSMFmjesrKyM6lpdXV10KDQth8PhUKprzpw54k7QxMQEetMpGhsbx4wZQ/XU09OLj4/Py8u7cuVKbGwspP4JgoiIiBB3FIo+ffowCuiGhoYhQ4ZQ3bS0tF68eEHvIH2BxhYjs7cyLKAxsgMupILBYH5OOBwOi8Ui/jtJo3379r///ru7uzv5cv/+/ZcuXSorK6P3IQiCTDCgN36N9JTAp0+fNm3alJ+fT7U0NDRs3779K4dt164daYF89+7d27dvi0Qid3d3Dw+PBw8eDBs2jM/nt2xYc2C+BqyhXhoBIwDAkCFDOnXqJBQKWSzWgAEDbty4Qd9l6NChpBs0SZs2bd69e4eOnJ2dTX8JJXUAAOrq6ioqKqBcamNj4+LiYqgngaTlcDgcag5Hjx51dnY+evTou3fv5OTkrK2ttbS0BAKBo6PjsmXLGCPo5AhXr1599uxZYmKijo6Oh4cHGUQ3MjJi7O/v78/YTsfQ0JCxPSgoiO7xXFFRMWnSpPfv31MPBmw2u7y8/O3bt+3atbOxsRG3PhWDwXxTsIDGYDA/JxoaGr169YIqyY0bN+7atWt+fn5KSkpeXl4rV6708vI6ceIEvY+pqamGhgZZNJuiU6dOHh4et27davF8lJSU6uvrocaGhga01l1VVVWLj0ISFxdna2vr4eGhra1dWVmpq6ubkJBw/PhxgUCwevXq8vLyJ0+e5OXl0RO1zczMGL3SKJ6D5wPAgMFgMACAzWaLRCIWi/XS7OXvv//OYrHOnTt39erVVatWvXnzhpK/2trav/32GzSOpaUlOjjdcvvgwYNQLRUAgI6ODlr0cfbs2YyVAiFcXV2pRZZsNnvGjBktK8PUt2/fvn37NtmtoqICfQBAEVdCKDY2Fmr58OHD+/fvzc3NyZdbtmzZuXMn+RTk5OQUERFBN+bDYDDfiR8cAcdgvgE4hQPifzOFgyCIjIwMeoywd+/eUMHkQYMGjRo1im5hoaioSPaRl5cfOXKkn5/flClT1q1bl5eX9/nz5xkzZrS4gPygQYNQ44g1a9b069cP7Uy3Um51qOC6mpragAEDli5d+vTp05SUlIEDBxoaGvbs2fP06dNQhW0IR0fH8ePHb926lfrte/ny5QCAZ8+eJSUl0evzDRgwAPrsoZK6c+fODQ0N5Nb379+Lu8JKSkqbN2+Gch5QVQ3Rrl273bt3o+kf3w6BQMBYw5JOt27d6urqGFM4GD8PGRkZ5NYLFy5Am5ycnFr9FGT2VoZTODCyAxbQmJ8QLKAh/mcFNEEQPB4vIiJi37590dHRBw4ckCxrbGxsxK3AU1FRefnyJUEQAoEALQsiDZs3b540aRK9RV9fv6ysjLF0CABg+PDhgwcPdnR0FKcRpfRvloyCgkJSUtL+/fvpoykqKkpeNEmho6Pz8eNHgiag0TV8fn5+9HcEvXrOzs7U1vDwcMlHPHjwIH00tMYhI7169Wp14SUBqGogHRUVlfXr15OVLxkF9Pr166FdOnbsSOU9M3oy5ubmtu78ZfZWhgU0RnbAAhrzE4IFNMT/soCmM2vWLGnElji6d+9OjnPnzh1xEWJ0FR2Juro63TLCwMBg1apVJSUlBEE0Nja6urqiu4wcOZIgiMDAQHHzkZeXt7CwUFNToycZt4DevXujjeJyvtHKfBMmTCD+I6BjYmLQlFw1NTWyTDdBEJWVleiYWlpa1Ht07tw5ybNVUFAIDg6mBGVycrKU0fotW7ZAnwcej8doRP2VpKenQzYj3bt3pz+f6Onp5efnE2IENI/Hc3R0pDqrqqrevHmTrIhOEARjDok4C+oWI7O3MiygMbID9oHGYDD/K6DlPJrFmzdvyKThIUOGpKWlrV+/HlWuTk5OaWlpx44d++2330aMGEEuZDQ0NJSXl6e7guTn5//5558nT55saGjgcDiMxhQGBgYikUhCJLixsTE7O5vL5QoEgq85L8Y04ry8PHEHhVqeP39O/U3qUagDl8sNDg4m/27Tpg36jEFPs+nZs6dkGz4+n79y5cpOnTp5enpevHixS5cu8fHxkyZN6tq1q56enoQdnzx5Qv394MGDrl27kkbUU6dOhVaRfiXLly+HvJ/T09OFQiH1sqioKCAgQNzuKioqz58///333/38/DZt2uTi4uLp6amnp2diYnL9+nXUn0RRUdHOzq4V54/BYKTiRyt4DKb1wRFoiJ84Al1eXr5mzRp3d/eJEydStZQZqampMTU1/Zq7pby8PPnLOwVqthAdHU0dTpwBMB3S3lggEKCObL6+vqhhxRqwhgAE47/X4DXVzQSY1IN6AhC7AUN+yEVwkQCECTARNysFoLBFdcvzNs9zQS4P8N6Ct8EgWAtoqaqqUs8MEmaCDu7u7k5dtCVLlkCH+/XXX6mt27Ztk3zFIAICAqh90eQHOsOHDye7paenq6io0DcNHDiwtbzhRCKRuF8h6JA14Rkj0BRCobB///70vZSUlG7fvg1VdQkODm6VmdOR2VsZjkBjZAfswoHBYP6tlJWVOTg4FBQUkC8vXrwYEhKybNkyxs4HDx788OHD1xyONAO+ffs2FSLdvn27mprakSNHSkpKrKystm7d6uHhQW4aOXIkZM3GyNmzZwMDA01MTOgRSpI7d+6sWLGC9LugGh2BIwDgMDjMB7AVXSJIpP7eDXYrAkUAQCfAkInhCBzLQFkuyGWckj2wvwQuWdVaVdhVXE29ygKs7DjiMQAAIABJREFU7qD7CrDCA3g48hwtbSzfvn1Ln4l6O/XPnz+T+7JYLGtr6zdv30CDE7Sw9N69ez98+BAdHS0SiRQVFdesWaOpqenq6lpSUtKtWzdp/Cvo7Nq1a+nSpeTjx9SpU/fv349anZBQ3oVhYWG1tbX0TQ8ePEhPT5emXneTsFgsdXV1qG4iCpTjUV5eHh8fz+FwevXqRVnpZWZmPn78mN6tvr4+MjIyOTl57969CQkJOjo606dPHzFixNdPG4PBNJsfreD/j73zjmti6fr4BJAOCogIivQOFrAjomLFggW5CKKIvVwFe0HFBqJe8doVGzZs2DtiBSx0aSrSFQSkhpJAYN8/9nn22XdmEwKCRpzvxz/I7JmZs5vs+svkzDkYTMuDV6Ah2uoK9PLly6EHmoSExMuXLxmN6YUwKJydncmgXhaLhZYthFQOyZ07d9DBoSQPpMQUkjt37tTW1vLbEQhFieSAnK/gq+ABRyqMJABxHVyvBJV0VQ0AaNeunY6iDgGIu+Au2UJGmFAG0kA6BaR8k/xW87qGXpd7H9hHAGImmDlr1ixSaJKeCBk84OfnR16ZhoaGv/7663/TSUs7OTnRLZuxM/LBgwfk4OHh4fxsJk+eTIU7jxs3DjW4fv26kJ+6goKCc+fOHTp0KDY29tu3b+idNX/+fGhwNHZo48aNxH9XoE+fPk190jp27Hj79m1ynPv376N+jhkzhj5XRUVFa9RVEdlHGV6BxogOOAYag8H8rsTExEAtPB5v8ODBW7duJV8SBHHv3r1du3YFBQXJy8ujIyxfvry4uDg+Pv779+9xcXFr164lf9zv0KHD1q1bGcthxMbGoo2QzBWg5FC0tbXbtWtnamrKeJQe36zZTlMTaL4FzJmPBw4cSAZMb2Fv4QHeWrA2F+RCK9B1dXVGFUYAgHfgHdlCEISJiQmVeG6q+FQTYKJ2Vk26vzQ9XcYNcAMAoA7UTUxMYmNjX155qQk0QT/AWBUFYujQodRXnfPnz1++fJk6xOFwrly5QjdGV+IbhYp+OXbsGHp027ZtDx8+DAkJoaQ5Y7prExMTYea6d++eoaGhm5vb4sWLLS0tO3furKCg4ODgkJeXR9ns2bPHzs6Oejlw4MCHDx/Sv2k4ODh4e3uTfycmJi5cuJCKmf7+/fv06dPJH1UYPxLU17yQkBA9PT1FRUV5efkFCxZAUdcYDKbV+dUKHoNpefAKNERbXYGeMmUKvydbbGxsTU0NPYQUTQanp6eHzs7j8fLz8ysqKoqLi48fP46OfOzYMcq4rKwsJCQkKCiIStNLsmbNGrQj4944bW1tLy8vX1/fkydPNvq4PjPlDAGInEU56PhjxozJz88fOXKkM3AmAHEQHAQAPAaPCUB0BB3plhvBRgIQo8FodHxJScnEYYkEIIjz/zk7anufHJAzA2aa8prp6ekEQRC3CQIQJ/RPCHa4b9++t27doof5enh4NHqaTcLc3Jxa/h8wYABqEBERQb8xq6qq0EXuTp06CfN5KysrU1ZWZnRjwIABUEKPt2/fnjt3LiIigjz9urq6sLCw06dPb926dfHixevWrYuJicnJyWFMYnj27FlyEHd3d3q7srIymTTw+fPnUBcyF0pLIbKPMrwCjREdsIDGtEGwgIZoqwIaLSpBsW/fPnRLWadOnaSkpMi/DQwM4uLi+I3MZrOLi4tzcnKg2s6KiopbtmxZsWJFYGDggwcPVFVVqUP0bMcrV65EXYL2fkFIS0v7+vpOnjyZn0QDAFw2vEwAoupOFUEQjx8/nj179qRJk1avXv3161dyXsNuhlkgqxyUqwJVAMAJcIIARD/Qjz7IHXCHAIQKUGGcYonMEgIQhBJBeBNEFBEfG9+rVy/ykI6OzrVr17KyshoaGj5M/UAAYrjYcAFnBP6biY9OywpoS0vLDx8+UIOj9QVZLFZxcTH9xvz06RM6TpcuXRr9sH38+BFK4w3B7+NUW1sbFhZ28eLFuLg4W1tbepedO3euXbsWHWrfvn1kXw6Hs337dlNTUw0NjUmTJqWkpJDt06ZNQ3sVFRU1ehZCIrKPMiygMaIDFtCYNggW0BBtVUATBOHt7c0YNfvvv/8ypjeOi4u7d+9eZGQkVfoOgsfjHTx4sF+/fkZGRu7u7mfPnqUiGdTU1OgJFtB5Q0JCyEEYK7ZAq4koHTp0qKmpEVDtJUI+gjnrxcz/OH9C/wQBiPVgPWm/GWwmADEN/D+xVQAKPgO+cRfiQDx9QToh95+RG5Qacgfk3v3r7rH9xwYOHEjayMrKPgKP+OXf2AP2UKOtXr0aurxomudGk1gzxt6Qbly9epU+eEJCApQTesGCBcT/vzF5PB71JYrO+vXrBXzM7ty5w9iLzo0bN9COnz59EhAcIiUldfr0abT99evXAj/1BFqtBgBAFvppEUT2UYYFNEZ0wAIa0wbBAhqiDQtogiDu3buHiomEhITevXuj7WjNtvj4+ClTphgbG9vZ2V29ehVKsqaqqpqVlRUdHX3v3j2oDDjK7NmzyTHfv38PHZKWlk5KSvr3339JCc5isTQ0NNAR3r59W1lZaWRkxDh+CSj5xvqWNi6N8CT+378wgiAIooCok63LBbky4D8i0gN4EIDwBt7UCFpAiwDERcB35R4A8OrVK6KUIK4T+ZPz08XTSVmcDJKVwf+WxktAST7IDwAB6D9qwVtJSSk7Oxu62g0NDfR1XBkZmePHj1P7OxUUFLy8vOg55lRUVGJiYqjYdJTQ0FCCICorK9+/f19SUhIREWFraysvL6+jo7Nlyxby00W/MQsLC/lJ4cTERPTT9ejRI8Ya7CifPn1CuzMqXTr379+nMreQeHh40D+co0ePVlJS0tHRWbt2LXUXz5gxAxqHxWK14PNHZB9lWEBjRAcsoDFtECygIdq2gCYIYvPmzXQx4e/vTzDl6NDR0YE6ClPHbtWqVWfOnBGm3B09DvXAgQOUUJOWlj506BDZXltb+/Hjx/LycgcHB3SEmJgYgiCKi4vR6fSBPgGIIBCkrq5OjypuaGiIioq6efNmmXMZAYhv5t982/uuAWvWgDVnwBkCEKfB/9Y4HYEjAQhP4MnvFNq3b3/jxo3c3Nza2loyO0dP0PMNeEMAwgt40T05A87wG4TFYtnY2DDqUZKwsLBt27bt27cvIyODbCkuLk5KSiJ/FsjOzl6/fr2rq+uWLVsKCwtJg+TkZENDQ3SuCRMmeHt7k5eaxWK5urpWVFRA09FvzFu3bvFzmx7dTnLjxg1+xhCurq7oaRYUFDTa8fnz53V1dcePH3dycpo2bdrFixepNzczMxMK3JeRkbG1tX348CH6uV2yZAmXyz1w4ICrq+uCBQuePn3K7+ILg8g+yrCAxogOWEBj2iBYQEO0eQFNEERcXNyuXbv27NlD6baKiooePXpQCkNBQQHNcMeY2w7C0tJSyGLR+/fvpw+ekZFx4sSJEydOUDKRDj1JHIm6ujpZ8ppMkAwddQEuBCAWg8UAAGrA/Px8cuecGTDjAR5jTMVL8L9cwrvALgIQA8FA1Hl9oH8D3JgEJgEAxMXFXV1dqUN9QB8CEEfBUboni8AixovAYrEOHDjQIu8pREJCAjodWoBm1qxZUEf6jQkl/aBz7tw5qKOAJH1qamr9+/fv2rWrqanp1q1ba2pqUIcZ463pSElJoXKfYtWqVfw63r17Nzw83NbWVkFBQU9Pb/v27RUVFfQC4ACAXbt2NftSi+yjDAtojOiABTSmDYIFNMSfIKAZqa2tPXv27MqVK/fs2ZOXl4ca6OvrC5Y4gE/2DBRra2tS/tIJCwvbu3fv2bNnqf+hy8rKvLy89PX1tbS0tLS0qO7KysrPnj2jOqJVqclMzH1BXwAAtS47atQo8uhD8JAAhA2wkZCQoOINJIAED/DoeaOfg+d1oI6K8aAzHAwnALEJbEIPkaEgjsCR7klvwBAhQ7JixYrWeDdramrQgGklJSWoRVpamv5GNDQ0hIWFXb16NSEhgSCI7OxsxhAOOTk5MsEFRV1dnYDIDSqztQDq6+s7duzIbwQAgJGRkYBKhIz5qkn69OkDGe/btw+ykZCQOHjw4Lt375p2lQmCEOFHGRbQGNEBC2hMGwQLaIg/VkA3CpQVAQDAYrEEKB46PXv2JIsFenh4nDhxgkqm9vHjx7t37yYkJNAFUMeOHSMiIng83pAhQ+iDSEtLjx49+tixY/QUCg0NDejOuQgQwQVcKSBlZWVFEERBQQEVBDIajCYAcQFcQJ3MAlkNoIFUzBJiEtXi1e8l4Phsks6gMwGIFJCiAP5T1IOUj2PB2FJQ+gQ8EQfilCccwJEEkvyuDJp8o6Xw8vKC5qInQqGg0pIUFRVZW1tT7RMmTKipqdm7dy9kLy8vf+nSJXQ6eiZsOpMnT4ZK5/AjJCSE31Uix4EENJfLpdLhLV26lF9HWVlZaCJnZ2d+xiNGjGCz2U26ziL7KMMCGiM6YAGNaYNgAQ2BBTQ/0OzLQhZG9vb2hvL+EgRRU1Pj6OjIr4uWllZYWBjjoZ49e9JjAEpLSyEDCSBRDaqjQJSBgUFMTExDQwNVqkMciCeBpEpQ2RUwqL3n4DkBCDNgBgAwA2YEINhd2LvBbugfWZf7H/APAYjv4Ps5cG4v2HtZ4nICSCAA8Qg8IvPiUZ4Ug2J0kN1g9wawAbTaCjRBEFwud9u2bZqamu3atbOysnrw4MHo0XBCazKpc3l5uY+PDxrgQfr27t27devWzZo1a/HixRcvXmT8dYIgiI0bN0Ldx48f39RkFwkJCUuXLu3SpQv67sjJyQUFBZFmsbGxgwYNkpCQkJaWdnR0/PLli4AAfVNTU2iWefPmMVqSLFq0qEk+i+yjDAtojOiABTSmDYIFNAQW0ALYvn07KVNYLJazs3NJSQlawRta5nRzcyMIIjk5edeuXdu2bXv+/Dk5FGP6ZzqoIKMICAigewXl6OgFehGAeGv1NisrKzU1NTMzkzq0ACygp66DIPcRTgATAACzwCx+uecGgAEAADEg5gJcXoKX+SC/ElQmiideAVfGgf+to7NYLNITfv8egUcKCgqM+ShaiXfv3klLS9NP+eTJk2w2m18mE319feEHr6ur8/LyateuHQCgXbt2K1asEHLhGSU9PZ0q90hHWlo6ISHh69evULCHpaUll8t99OgRYxY86NNCEMTt27cZz5ekW7duTfJWZB9lWEBjRAcsoDFtECygIf40AV1fX3/8+HErK6suXbqMGTPm7du3jbqRmJhI/SeKLub5+vo+f/581apVy5Ytu337NkEQhw4dkpT8XwzD3LlzCYLQ09MDAjl48CC/Q9OmTaO7FBgYKGAcevC08EhKSv6njiBBpKen9+vXT4CxqqoqOouYmBj9rKFDAIDu3bs3msO4xYmJiZkyZYqRkdGAAQPIBd0dO3bwOy8VFZWmjl9dXZ2amvrjuq24uJgx0GLHjh27d+9G2588eUJ2LCoqWr58Ofk9QV5efuvWrYyR0xs3buT37rRv375JrorsowwLaIzogAU0pg2CBTTEnyagt23bRlcPMjIysbGx5KHc3FxXV1c1NbUuXbrMnj27oKAA7V5ZWenk5ET2lZCQWLZsGRStkZmZCa16AgBu3boFlS2EUFVVLSsrY8zFBph+ZL906VKnTp0EDNhUfH19yZG/fPnCuLlNQkJi0KBB5AK8mJgYWjpRTEyMX4w42R4REfGD793jx4/t7e3Nzc0dHR2joqIYbWprawMDAxcuXDhv3rzx48f369eve/fu1Dtib29vb2/P7yLY29v/oIf8yMzMnDt3bv/+/R0cHO7evctoc+jQIdQlT0/P+fPno+1HjhyBzjonJwcNHKKTkZERHBxsYGAADTV69GhG+6qqKsYBRfZRhgU0RnTAAhrTBsECGuKPEtC1tbWounV0dCQIgs1mQ/rVysqKsSQhm83++PHju3fvGP9nZSwh7unpScUlo8jIyJCi6uPHj4MHD0YNHj9+jE7EWAumeZiZmUVGRpLDrlixAjWQkpKaNm0aGr4iJGJiYmPGjKEX1m4Gly5dglxCFXl1dXWjl4Ux4BgAoKiomJqaKtiH0tLS5cuXm5mZmZmZeXp6Cimt0tPToa9PBw8eRM3evHmDenXx4sV//vkHbadnZRGS3NzchQsXQplJ2rdvj551aGiohYUFAEBaWnrmzJnfv3+nHxXZRxkW0BjRAQtoTBsEC2iItiSgKyoqgoODAwIC+JWKSE1NRbWIsbExQRBnzpxBD926dQsdhM1mFxcX8/MhODgYHcfLyys+Pp5eME9KSmr9+vWenp5+fn6ZmZn0EXx8fKjseCwWiwyqRhGQyKx5jBs3jsvljhgxomWHBQAMGjSIyn1RVlYWHBw8YsQIdXV1XV1dT0/P0tJSYd5cbW1taFgjIyPIZvv27c1wT05ObuXKlV++fBHsQG1tLRTZ0rt3b35V3+nMnDkTnRFNa4ha2tra8ni8wsJCKHHhwIEDoXjr+vr6rKwsAXmjb968iX517N69e35+PmSJ1jwfNWoUPSxEZB9lWEBjRAcsoDFtECygIdqMgI6NjaXvrhs5ciTavbKyEk3fS/6EvWbNGlRa7dy5E51IsIDOzs5G0yOQC8zp6elLliwZNWrU/Pnzk5KSGLunp6fLyclBYgsqtvLu3buZM2eamZmhDgsPWQIaapw7d66VldWPDEtCT8ksKyv7/PlzUkCfOnUKXcYeNmxYfX294De3rKyMcSIohF1AeAadpUuXUmpSX18/Li5O8OwkjKUHr1692mhHeskeCsYPQH19/ZkzZxwdHR0cHHx8fKgPcGpq6tixY+Xl5VVUVGbPnk2l+iY5deoUuQeRxWJNnTqVnvSQpKKiAipbSMJisVB1yJggj/7rgcg+yrCAxogOWEBj2iBYQEO0GQFtamoK/a/v4+ODmtGr6JFcunRp165djAkNrly5go4gWEATBBEYGEivx7F48WLhz+Lw4cOoG/SAVwHlpkUHKBi6d+/e2dnZ0dHR6CIoCVoGEqK+vp4xa9v69evpZpMnT27UN0lJyerq6oKCgocPH759+xYSowKAoudJNm7c2KjnjOmoobgIlJycHAGFVCgePnwIjYxGcj9//pzf1UBrqTB+CaHHbYvsowwLaIzoAFd1wmAwGNHk69evKSkpUOPjx483b94MNR45ckRSUvLs2bP19fXKysrbt28PDAxkzMGsp6eHJhIWhjlz5gwePPj+/fs1NTW2trYDBzIUx+ZHcXGx4MZly5Y1w6XWoEOHDvwWhgmCoL+Mjo6OiopKSEjgcDiM9ikpKTY2NlBjdHT05cuXS0tL+/Xr5+7u3r9//2fPnkE2RUVF9JejRo26fv26YLd9fHxkZGRkZGTISo3l5eWC7Sk0NTXRxkZznpw9exZyEgBga2vLmLcOAJCfn3/z5s3CwkJtbW03N7dGa/egoUf379/Pz8+nR33U19cz9hUTE0P3rRoZGd2/fx9qZPx6icFg+IEFNAaD+T3gcrlCNiooKJw6derw4cOFhYVdu3a9e/cuo3pWUVEhV/Ka54+hoSG/lBqCYdwD16dPH/KP4uLirKys5rnU4pSVlfXt21dCQiIyMrJR44yMjMLCQn5HdXV1oZbAwEAqY+DJkydPnDgxa9YsVED37duX/nLu3LmvX79GNWW3bt3k5OQ0NDRmz54toCyfYOzt7dXU1AoKCqiWTp06NRqJHh4ejjaiUdEkoaGhU6ZMYbPZ5MsLFy7cu3ePTDXNj+zsbMZGuoC2tLRUUFCghqWYN28emhxm8eLFJ0+erKiooFqcnJzQNwiDwQhA7Fc7gMFgMEKho6ODZldgzGhBIi0t3a1bNzExsbi4OEaD4uLiAwcO9OnTB5UdrcqoUaOoKtwkkyZNojb2KSgo0INDfjnv3r0rKCgQJvO0vr4+v+hqMzOzQYMG0VvKysqghfZ3794xBqlDPxGwWCwjIyNKccrKym7evDknJyc7OzslJeXJkyfTpk0Tvh47hKqq6p07d8j0FKTbt2/fRtP5QTC+X4y96urq3Nzc6J+30NDQffv2CR4fDYUXExODwpk6dOhw/PhxevyMtLT0xo0bGQfX09N7+vSpnZ2dnJycpqbm6tWrT506JdgHDAYD86tjSDCYlgfHQEP8LjHQ2dnZJ0+ePHr0aEpKCqN9aGgoPc2FmZlZeXl5o7MwxhzT2bZtG9Sl0RjoH4TH4x09enTKlClTpkw5duwYlIt36tSpLfuc79q1q7u7O6M8FRINDQ1ra2vqpYGBAZRJunfv3nfv3i0oKEDX18XExNBUJ0+fPhVy6vDwcHrHiIgIyEBWVjY7O1vA1W7Gjfn169dGU3ZQ3Lx5E3Kpffv2jJ+fhIQE9AT5JWmm+PTpE7Qvc82aNYyWaWlp/v7+69evv337toDo6rS0NAcHh/bt26uqqnp4eKDZ0EX2UYZjoDGiAw7hwGAwIsGZM2cWLVpUU1MDAJCSkvL29vb29oZshg8fnpqaeuHChby8vJ49e7q5ufErvUbH3t6e8ddtiqioqB90vknU1dXt3bv3xIkTRUVFPXv2NDU1hdKGHDlypLS09MmTJy0ynbS0tI2NjaWl5cyZM9XU1FatWsUvXlYAeXl57u7uAQEBSUlJ3759Ky0traioSEhIeP/+vbS09MSJEzMyMsaNG8disdTV1aGr3dDQEBAQMGHChMrKSnl5ebIRykPCDxaLZWxsTG95/PgxZFNdXe3u7l5RUaGsrDxjxgxXV9dmr0BTQKXUBePg4ODl5RUQEEC+VFRUDAoKUlZWRi0JpnghxkY6BgYGkZGRmzZtio2NVVVVnTVrFmPhFQCAvr7+6tWrBY9WUlIybNiw3Nxc8uWpU6eSk5NfvXolOIwEg8HA/GoFj8G0PHgFGkL0V6AzMzPRDAzQ0uOPcO/ePQGSaNasWZB9q65Ae3l50WeXkZGJiYlBzZKTkz09PX/k8W5oaEhfsFdXV8/KykpMTJw6daquri6/dBn86NWrV2lp6ezZs+mNW7duJfhUlqEjJSVF7qhTU1MLCAggCKKmpoZfuRM6rq6u0GVZtWqV4C7e3t5Ql59zY8bHxx86dCgoKAjNu0xRW1uLVpf09/dPS0ubOnVqly5dDA0N169fX1lZydi9sLAwJibmB0+HsRoiVMdHZB9leAUaIzpgAY1pg2ABDSH6AjooKAj9T33Tpk0tO9e7d+8iIyP19fWhidDCy60noBnTVLu4uJBHS0pKvLy8evToYWlpuWHDhsTERLoCBkjyOAG4urra2tpCjQ4ODpQndXV1N2/epDYvNgrqNklSUhK0z69RDhw48OzZMwsLC/rpmJubQ2ZKSkpolkPGVM2Qn5AeEqkb89GjR9QyPADAzs4uNze3c+fO9FNwcHCAAjDYbLaLiwt5udq1a7dixYpG82rz4++//0Yv2r///ku3EakrRgcLaIzogAU0pg2CBTSE6Avo48ePo/+p8wv0/EHi4+ONjIzIKaSkpPz8/FAbfgKax+OdOnVq1qxZCxcuZCy+3SixsbHomXbp0oXD4XA4nF69etHbhw4devz4cSraQUZGxsvLS8gthosXL0YtlZSU6MosIyOjqevQKEePHmUs4SEAFotFr8MCAHB1dS0pKaFnNZGTk3v+/Dl6ARsaGpycnASPv3//fvoirqjdmF++fNm/f//GjRtPnTpVX1/PmHw6Pj6e3mXOnDmQwa5du5o3O+O2wvv379NtRO2KUWABjREdsIDGtEGwgIYQfQGdlJSE/qeOLgy3FFwu982bN/fv3//27RujAaOArq+vHzlyJN3DDRs2NHXq0tJSxlXkESNGnDt3jp8ilJGRcXd3J7e1vX79eu7cucOGDVuwYIGAfG0XLlxA44zV1NTq6+upTWMnT55EO0pKSnbr1k14YX3q1Cl0BboZiUTCwsJqamqOHj26ePHiHTt25OTk8LuGDQ0N165dW7RokZeXl52dHb8B9fT0bt++TYjwjUkWUnFxcUGdv3DhAmVWX19PX7Qm6dGjR/Mmzc/Ph8JIzM3Na2pq6DYie8WwgMaIDlhAY9ogWEBDiL6AJgjCx8eH/p/6zJkzf6FjjAKaUeB+/PixqYOjhRJJHB0d+WlBErKmND3xSG1tLb9e1tbWQ4YMgRrNzMzImBAVFZV9+/ZR+97o9O/fv6GhIS8vz9nZmV/YBoWMjEx6evqlS5eg9p07d8bHx7u4uKDKjx9qamqQjKMoKCgICwuLi4uD0pUQBHHv3j3Bwz579kxkb0xSQK9fvx51OzIykjIrKSlBDTp27NjseePi4gYPHiwhISEpKTlx4sSsrCzIQGSvGBbQGNEBC2hMGwQLaIjfQkATBBEeHr527doVK1a03tpzaWmpMGaMAnrx4sWojgkKCiKPVlVV7dy5c9KkSR4eHoKjO9hsNmPQ8NixYwVrwbFjxxL/X0CvXbtWgL2GhoaBgQH1Et2+tmHDBsaO//zzD0EQEydOFOyPtLT0iRMnSE/Gjx8vJibGYrG6deu2d+9eHo/X6Omg0FUjxdatW6n17B49enz48AEyOHDgAJTljY6Dg4PI3pikgE5JSUG/qEAxFd26dYMMhg8f/oOzczicuro6xkMie8WwgMaIDlhAY9ogWEBD/C4CulU5dOgQWdtCUVFxw4YNtbW1AowZBTRjHuXr168TBFFVVQXtgfP39yd75eTkzJo1y9TUtH///gEBAeS8x44dQ4fauXMnFBkMYWFhQdAEdH19faOBFidOnAgODt65c+f+/fsVFRWho3379vXw8EB7aWtrC4gnAQDMnTv32LFjmZmZ1MWpq6v7+vUrtUIsTOVClEePHkHX/Nq1a5BN9+7d0f1zlZWVUHQNhYmJicjemKSAZiw0SO0rJYHW+KWlpd++fdt6jonsFcMCGiM64DzQmDYIQRD19fU/ubwcAIDL5f78SYWhrq4OANCM7L+tTV0tghxuAAAgAElEQVRdHYvFqq2tbe2JgoODqfXjioqKHTt2cDiczZs387Pncrk8Hg96N4cMGeLv709vad++fc+ePdls9t69e6Ewbm9vb7IeysCBA799+0Y2vnnzJj4+/sCBAzY2NlCyZEVFRXJSAWdhbGzMZrOpj1leXh6HwxF84vPmzSMIQkxMjPHdT0tLW7p0KVqFLjc3lzHZGQBAT09v+vTpU6ZM0dLSYrFY1CnU19fX19dXV1eTL6OjowU7hiIlJWVkZARdczQ45P3797GxsdQ2UApbW1s0RTQAQFdX9xfemHV1dRkZGbKyspqamujRhoYGNpv98eNH9FBGRgbdZ3t7+9u3bx8+fDg3N9fY2NjLy8vExKT1TkqUH2XV1dWCb5OmwuPxGhoaWnBAzJ/Cr1bwGEzLg1egIfAKdL9+/aBHX4cOHQSUauOXhcPf358KJ1BWVqZCTSZNmoQ+Xe/cuTNs2DC0PSMjgyCIhw8fdu3alWzp2rXr48ePGY0p5OTkkpOTif+/Ai1MHRkByMrK7tmzB23v2LEjY9psRUVFKl23iopKx44dTU1N/fz8uFwuuQJNXagHDx401ZlFixb9888/586do99HjHsEe/fu7efnB61EVldXd+/eHTV++vTpr7oxr1y5QoXN9OnTJzU1FTIgV6BLSkrQfaULFy78JT6TiOyjDK9AY0QHLKAxbRAsoCGwgFZVVUWlVWFhIT97AXmgs7KygoODb9y4Qf9PlzEQYtq0aYxK8caNG2QvDocTExNz8eLF69evf/z4ceDAgaixioqKvr7+lClT3r9/T/aix0AzJnBoEm5ubmjjwoULUTUvIAX1woULIQFdWVkpZDE/XV3dBQsW0AuAq6mpRUVFkeMIqKtnbW0NhfBWVVXt2LHD0NBQWlpaTEzM3NycLCEu4Masra1NTk5OSUkRHNLTDGJiYqAAG2NjY2iXJCmgCYJYuXIl3bJ9+/bkt6xfhcg+yrCAxogOWEBj2iBYQENgAT1o0CBIfqmqqgqwb2ohlVu3bkHja2tr86uNTNUdzMnJ6dmzJ9VuYWGBGpPpzOrr69+8eRMSEpKcnEwX0Dwez9ramjJu167d4MGDu3XrxlhHmhFbW1t7e3t6S7du3aqrq0NDQyFLMzMzAePk5eXRBTRBEH5+fqgZGocNAFi0aBHUoq2tffHixdDQ0K9fvzIGP5CQF6eurm7//v0jRowYMmTI5s2b2Ww22Uh5wu/GDAsL09bWJofS1dVlTDvdbBgj5l++fEm3oQQ0j8c7fPhw37599fT0nJyc0LVqktTU1LNnz966dYv+GWgNRPZRhgU0RnTAAhrTBsECGgILaDTZ2d69ewXYN6MSoY+PDxXdoaOjc/nyZUbNZ2lpSWq7p0+fosmSIeGro6PDZrPz8vLoWTsmT54MLZe+efPG39//wIEDaWlpBEEUFBQwRgMzoq+vX11dvW3btj59+pibm9vY2MydO3f//v0VFRUPHz4cMGBA+/btzc3Njx07xrhAThEWFgYJ6Pfv36NmjOlH0IBmCj09vZcvX65Zs4aeToRixYoVBEH89ddf9EYrKysul0v3hPHGzM3Nheq/SEhIUDlVfpzp06ejDl+8eJFuQwloYVixYgU1TufOnV+8eMFolpeXd+zYMT8/v9DQ0GY7L7KPMiygMaIDFtCYNggW0BBYQBMEcevWrT59+sjJyZmYmBw9elSwcGleKe/c3NybN28+e/aspqaGsWR3x44dyZ/mv3//jlY5AQCYmJgsWbKEnotDU1MTjabYvHkzowPHjh0jg1WkpaXV1dUZJSlKSEgIQRCJiYl0+d6lSxeolMnChQsFDBIZGQkJaIIgZs2aRbcxMzODYhWosxYwsomJSUFBARUvTsff3//Nmzdo+4EDB+hrtIw3JmPxSwDAvn37du3atWvXrri4uKZ+AOhA+01JyCh2CuEF9NWrV6Gh1NXV0Zv64cOH9DV+BwcHNHO2MIjsowwLaIzogAU0pg2CBTQEFtBNpXkCGmLu3LmQ6KHyQ58+fZpRvXXq1Kl///5QIxp83LNnT3S6K1euQGampqZdunRRUlLS1tY2Nja2s7NDgyUAAG5ubgRB0ENBSIyMjIqKiqjxc3NzVVRUGN0GACgqKioqKkK51Xg83tGjR0eOHGljY7N+/fqysrLY2FgoMtjAwMDLy4vfsCQTJkxAG2VlZVNTU48ePcqv16BBg0jBSr8xGxoaTp8+3atXL8YvMBBbtmxp9rtfVlamo6NDH23GjBmQjfACGvoqQhIeHk634XA4aKz/gQMHmud8M3r9BLCAxogOWEBj2iBYQEP8yQK6uLg4Ozu7qb1aREBXV1evWLGCXBE0MDC4cuUKdWjZsmWMim3MmDGCU0GTaGlpodOhCSskJSWhyxsWFoaOZm9vz+FwGCO21dXV6dXOc3JyFixY0K9fP1NTU0bHIiIiGr0sly9fpnJTWFlZJSUlsdlseiw4SseOHaEWFot1+fJlgiBu3LghoKO2tnZ5eTn9xty/f78AexRqO2Mz+Pbt25IlS3r27GltbU2lAKcjvIAmUyJCPHjwgG4TFRWF2kycOLEZnovsowwLaIzoINakRwkGg8H8LqSnpw8ZMkRFRUVLS0tLS6vRms+CefHihZub2+jRo1etWlVQUCBMFxkZmT179pSXl1dWVn769ImugaysrFB7cXHxnTt3ooEfqKRGs/IBADIzM6GW2traL1++0Ft69uyJCuV+/fqJi4szVu3Oz8/ftm0b9VJTU/PIkSNv3rzhl0JbmHy6Tk5Oubm579+/T09Pj4qKMjMzk5eX9/T05LfxUV5eXkwM/q+qXbt2Dg4OAABbW1sBwSpZWVlQOPj27dsb9ZDOs2fPmmRPR01N7cCBA3FxceHh4Z6envw2lQrDgAEDoBYpKSl66hLAJ9G7CGZ/x2DaBlhAYzAY0SIqKmr58uXu7u6HDh3icrnNG4TL5U6cOPHFixfky5ycnKlTp0K1ToTn1KlTQ4YMOX/+/KNHj/bs2WNhYfH161dhfHj8+PG5c+cyMjKgQ46OjvLy8lDj0aNHu3fvbmtrC7Xb2dnRtxsqKSnt3LkTnQ6NJJaTk6OyTJAoKytDyTGMjIy8vLwkJCT4ZaG+dOlSYWEh9bKiouLFixfl5eWMxgsWLBCmAIekpKSFhYWuri4ZnXLp0iV3d/eSkhJG48OHDw8ZMgRqHDhwIHlNlJSUQkJC9PX1+c1F/17x/ft3+rkIA0EQTbJvJRYtWgR9a9qzZw+0MN+9e3clJSWo49ChQ1vdOQzmz+RXL4FjMC0PDuGA+I1COKCtXT169Gie5+Hh4ejjbt26dUJ2p4dw1NXVocnX5syZI3iEpKQkPT09yt7NzQ0qQP3mzRtKAElISHh7e5PtWVlZdDloZWVVWlqamJi4ZMkSBweHdevW8ctx9urVK8jJ7du3QzbZ2dnv3r27ceOGu7v7hAkTduzYQSZ9IwgiJyeH7jAdGxsbci/axYsXKYnGbz3177//FvIiU0ArqXTOnj1LEEReXh49nrhz584fP36kj1BXV5eQkLBp0yZ0hHv37lE3Zn19PfpWamhokPHQCgoKaPdWrZjdpCwctbW1x44dc3d3X7ZsWWRkJKPNzZs3qUo3AIDhw4cLzm+dmpq6cuVKFxcXX1/f0tJSql1kH2U4hAMjOmABjWmDYAEN8bsI6PLycvp//yTbtm1rxsgXL15ExZCLi4uQ3ekCOiUlBR2KcRsfHTSp8+TJkyGbmpqasLCwGzdu5Obm0ts5HM61a9f++eefO3fuUFkUsrKyJk2aJC0t3a5du2HDhiUmJpLtdXV1AQEBVlZW2tragwcP7t27t7KysoWFxeHDh+mSvaCgYPTo0aQnUlJSPj4+qM/fvn2DMrtRREdHJyUlQe8OY7i2sbGxkBeZglG5kuNXVlaSNtXV1SdPnly9evXRo0f5ZUGuqqqCluEHDBhQV1dHvzHpyeBITp06xePx8vPzGxoaoNCUjRs3NvVcmkSTBLSQZGZm7t69e926ddevXxc8+L179+i/bHTp0iU/P588JLKPMiygMaIDFtCYNggW0BC/i4B++fIlqqLs7e2bMXJCQgI6lJ+fn5Dd6QKaMeJ5+PDhArpnZ2czKsLz588341wIgqiurjY3N6cPpa6uTlZSXLp0Kb2dXxG7sWPHQs6cPn2absDhcATs5AsODmaMG0EzhOjp6TX17Hr16sU4qfC/GFDk5eV5eHhoaWnp6ur+/fff5JtIvzG5XO7KlSvJbwLKysoBAQHQCFFRUb6+vjt27Hj37l1TZ28qTRLQERERY8eONTQ0tLOzIyss/iBoZsDZs2eTh0T2UYYFNEZ0wAIa0wbBAhridxHQsbGxqIpycnJq3uBOTk70cTQ1NYVPrAFl4UALGR45cgTtVVtbu3fvXmtra2NjY0ZF2K1bt+ady927d9HRDh48WFRUhErYJUuWQN3LyspQM0tLS7rN2bNnGX0mGTBgAGMuObRet4eHR1PPLigoCB324MGDzcthjILemDweLy8vr8VXf5uK8AIa/W75gzVfcnJy0HfTxMSEPCqyjzIsoDGiA95EiMFgRAUzMzMtLS2ocdy4cc0bLSgoaNu2bb169dLX1581a1ZkZKTwBa4hLly4QC+h5+npOX/+fNRs9uzZy5cvj4iI+PDhA+M4OTk5/LbfCSYtLQ1t/PTpU0pKCoHscktMTIRayFR0UGNsbCz55YR8yVg4kOL169e3b99G27du3UqP+tDU1KRn7RCSGTNmBAYGkvsd1dXV/f39c3NzFy9ezJgYpKWQl5dHv1SgEAQREhKyatUqX1/f1NTU1vNHMGjakw0bNvzIgIynz1hoHYPBMPNr9TsG0xrgFWiI32UFmiCIN2/e0H9ZRhdTfw5oHuj6+vrY2NgHDx5A8coUycnJwjxy37x50wx/GHPwHT58GE1dBwCwsLCAutfV1fGrG0IFcuzbt69R5yFFS8aUFxQUuLm5UeVRtLS0+FWZbpRWSgpOvzGLi4vd3d3J2F8jI6O7d+8K6FhXVzdy5EjqfKWkpMhNjT8Cvcy48CvQaCZsAMAPpipHS+dQ+w0uXLhgb2/fu3dvDw+P9PT0H5mlZcEr0BjRAQtoTBsEC2iI30hAEwRRWVl59+7ds2fPksurv4RmFFJh3LaI8v3792b4w+FwoEBhMiKloaHBzMwMneXDhw/QCAcOHGD0hyxDSBBEVlYWtADJuEdwzZo1Y8aMmTBhwvHjx8kQi+TkZGhzoaqqKr2EofDk5eWlp6dD6Up+HPqNCcWCS0tLCyiVcujQIej05eTk+Imturq6U6dOLVq0aP369e/fv0cNLly4YGJiIiYmpqKisnbt2pqaGuEFNBqerqio+IMXKj09nV4Qx9HRkUzZ8e+//0ITff78+UcmakGwgMaIDlhAY9ogWEBD/F4CWhRohoCmck4LYNq0ac12KS8vb/r06R06dJCTkxs/fvynT5/Idnd3d3QiKEC2tLR0yZIljMkuevfufeHCBTKvRVhYmK6uLtlubm7ep08fyFhMTOz48eMGBgYsFktTU3PPnj319fVQYmkSetlFfiQmJu7atcvX1zcyMjI5OZkKkunWrdv9+/ebfaFQqBszKysLdXXu3Ln8Ojo7O6P2oaGhqCWHw6HnaZaUlKSvVT9+/BiNTVqwYIEAAR0bG2tnZycvL6+hoeHl5YXu4GyRH2dqa2ufPHkSFBQUFxdHtcjKykJzzZw588fnahGwgMaIDlhAY9ogWEBDYAHdVJohoGtqaoyMjOiyQ1paes+ePWpqaqT0NDY2pnLP/QhQEre1a9eiIu/GjRvk0eLi4hcvXqCbICE6d+4cExNDEER9ff3nz58zMzMJghg/fjxkRg8EJ/H19R08eDA64IEDB/j5f/PmTU9Pz2HDhtEzSUPp8+Tk5Pilu0Z5+PDh4MGD1dXVBwwYwCjcqRuTsZL5sGHD+I08ffp01P7Zs2eopb+/P2QmLy9P5thOTExEJSkAQFxc/MOHDw0NDfX19dBdkJGR0b59e7qxg4ODt7c3GXkiJiY2d+5cfjoyMzNz2bJl48aNW7x4MfpDRKMwRiJ17969qeO0ElhAY0QHLKAxbRAsoCGwgG4qzRDQBEGkpqZSq7ZqampXr1719vamCxFlZeWcnJwf9A0S0Gh+BiUlJTKCYsOGDZKSkqgeYgTK3xwSEoIKPmp9moLftrPXr18zOu/q6iqkP1u2bBHmaqDR4VB6PoJ2Y0KFzUkWL17Mb/AzZ85AxsrKyhUVFaglY4qSly9fEgSxatUqfud46dIlV1dXWVlZCQmJ/v37U/HxjF+KUlNTa2pqkpOTGR0giY+Pp4t1KSmppj4Ji4uL0c2Fo0ePbtIgrQcW0BjRAQtoTBsEC2gILKCbSvMENElWVlZKSkptbW15eTmaR+LHf3mnC+jS0tJDhw4NHTqUWs3t2LEjGf9w8uRJfrqNH9nZ2dTIAwYMaGp3ik6dOiUnJ6Oe37lzR/hBGq31SAIVuAYAqKurP3z4MDw8PDIykrxW9Btz5syZdGN5eXmoqCGdhoaGGTNmUMaKior8Nh1CORNJgoODCwoKpkyZwniCYmJiVlZW9BYqh/fEiRNR++vXrzd6NdB67L169RLmMtKhCu5Q/PjWyZYCC2iM6MCwRwSDwWAwzYaKdo2Kiqqvr4eOxsfHN2/YioqKuLg4KSkpHR0dct03JSVl6NChhYWFpIGamtquXbsmTJhAhkNcvny5qVPU1tZSf+fn5wvTpV27dnV1dVBjYWHhkCFDEhISkpKSbty4weFwbGxsZsyYERERIbwz3bt3F8YMDTnIz8+nJKCsrOz27ds9PDyoo0eOHOnWrdv58+eLi4v79Omzc+dOQ0NDfoOzWKygoKAFCxa8fv1aUVFx3LhxnTt3Rs2SkpLQ0jksFmvatGkAAAMDA8bBLSwsYmJi6C3l5eVBQUE+Pj6MNdX5jUMnKioKann//n1NTQ1a4FMAZ86ccXV1JcNdpKWl169f7+bmJnx3DOZP4VcreAym5cEr0BB4Bbqp/MgKNAXjljVnZ+dmDHXx4kUlJSVyBA0NjSdPnhBM5V3omxR1dHSa9H9B165dqQ1tHA6HUcOhoKnQKIYMGUJ/OWLEiE2bNgnpjL6+voBABYpv374Jk7r40qVLzbjmQhIfH9+oPGXMZwKF95CQmQFTUlKgtIN2dnbC5Nzo1q0bNGDzknWUlZVlZWW9fftW1J5peAUaIzrgQioYDAbTKmhpaaEb7Bj3pQkmOTl59uzZpaWl5Mu8vDwnJ6fc3Ny3b99Cls+fP6f+puz50aVLF+pvOTm5M2fOUMGvK1asSE9PF9xdXl5+9+7dV69eJXdJotCdAf9NXsFvtKVLl27bts3S0tLExGThwoXh4eGMOUPoNDQ0/PXXXxUVFYLNAAA3btxo1KbZbNy4saamht6CymUej9epUyd6i7u7u4ODAzoauQ/VxMTk3r17vXr1YrFYMjIyM2bMuHTpkphY4/9fo7EfEydOFKYjipaWVt++faG9jBgMhgKHcGAwGExrERwc7O7uHhoaCgBQVFT08/OD8hALw927dyGJVlJS8vz5c3SzFxVyzeVy2Ww2dFRMTKyhoQEAICUltXbt2vXr11+8eDEuLk5RUVFNTS0tLU1FRaVnz56VlZVHjhxp1KsZM2asXLkSABAXF+fm5saY4AKiqKjI19d3y5YtXC4XACAtLT1x4kQLC4thw4ZpaWktW7YsKSmprq5OVlb28+fP/HQ5xYcPH4RJHQgA+PbtmzBmzSMhIQFq4fF4qNn9+/ffv38fGhoqLi5ub29P/hBhbW1ND2tRUVGZPXs2+betrW1sbGxNTY2kpKTwFRl37tyZlZVF1Yy0s7ODkjpjMJiWAgtoDAaDaS00NDQeP36cl5dXXFxsYGBAletrEkVFRWhjWVnZoEGDnj59Sm8cPnw4+YeUlJSysjLUUVtbOzw8PD8/38DAgFzfdXd3V1NTc3FxKSsrI22WLFkye/ZsUmcLprq6mvxDXV3d0dFRGAGtoKCwbt26KVOmvHjxQlJS0s7Ojqw6yePxbGxs3rx5Q5rFxMSMGTMmLi5OcBgJYxVGRnr06CGkZTPQ0NDIycmht1BfVCgkJSXNzMysrKxmzZpFNbJYrKNHjx46dOjatWvV1dXW1tZ79uyh/ywAAGhS7DJpf+vWreTk5LS0NB0dnVY9cQzmDweHcGAwGEzroqGhYWFh0Tz1DACAcjWQ9O7d+/jx4/SYV3Nz8z179lAvyR1sdFxdXdXV1S0tLanoiIqKiunTp1PqGQBw8ODB9+/fC/OjP13baWhooAbo+ZKr74aGhnPnzp05cyZVsz0mJoZSzyRsNvvs2bOCHTAxMWnUSdK3pUuXCmPZPFxcXKCWsWPHQvEnPj4+jO9++/btDx8+XFRUVFVV9fjxYyH3TTaKmZnZxIkTsXrGYFoVLKAxGAxGpHFycoJiqV1dXQcMGKCnp5eamnru3Llt27ZdvXo1Li5ORUWFsvH396dSsImJic2fP3/jxo3QyG/evCkpKYEaX758iVY3RCOSx4wZQ/09ePDgjh070o/KyMjs3btXWVmZfCklJbVz505bW1vGE2SMt/78+TOjMYWuri4kXpWUlM6fP+/i4tKjRw8DA4O+fft6enrGxsZCvv043759+/vvv/v27TtixAgZGZk1a9ZQ+bYdHR3PnTsXHR09ffp0U1PTYcOGXbhwgTGvMwaD+a3BIRwYDAYj0oiLiz9+/PjgwYNhYWFk5MPixYvJQ7Kysvx2JUpLSwcFBe3evTsjI8PAwICurSnu3r2LNubn51+9elVJSenEiRPl5eUmJiZ+fn7i4uJubm7UWvWqVavodQo7dOhw69YtNze3jIwMAICqqurhw4cdHR2dnZ1fvHhRU1MzYMAAbW1tficIVXAkMTY25mdPceLECQMDA7IU+YABA3x9fc3MzNBaLeXl5Y0OJTwlJSV9+/bNzc0lXz558sTT07OwsDAtLa1r165knrv27dufO3euBSfFYDCiBkvAtmgM5jeFw+FER0c3Wr64xSkvLxfNTevFxcUyMjKM9YR/LRwOh8VikQWKW4NPnz5FRUUpKSkNHjxYXl5e+I6VlZW1tbXUAqpIUVFRIUzuNgBAfn7+5s2bnz9/LisrO378+LVr10LJ0WbMmIHqvBkzZgQFBZF/V1dXUx+boqKisLCwiooKa2trMzMzqBePx8vLyysvL+dyuWZmZk0K3iUIYsyYMY8ePaJaVFVVExIS1NXVhR9EAC17Y/r4+GzZsgVqzM3NpSJShIfshW4G/eWI7KOsqKhIXl6+qaHhgomJidHV1aXSRGIwQoJXoDEYTNtk1apVVEywhobGlStXBCQtbnuw2WxbW9u0tDTyZUJCQkxMzL179+hyDU0bDACYPHky9Tf9S5eqqqqzs7OAGSUkJCwsLJrhKovFunz58qZNm0JCQthsto2Nza5du5qhnq9evbpv377s7GwjI6N169ZRWypbFsZSOPHx8U0V0ARBJCUlxcfHm5ubNzVpNwaD+eXgGGgMBtMGuXbtGn1HXV5enrOzM5QMrm1z7tw5Sj2TPHjwIDIykt6Cpg1WV1fnF6ncGhQVFYWEhAQHB5eWlv77779fvnwpLy+/e/euqalpU4c6ffq0k5NTZGTk169fnz59OmLEiMePH7eGz4zKnnEbpQDy8vKsra3t7e0nTJigq6s7Z84ctGglBoMRZbCAxmAwbZB79+5BLV++fGl2Ge3fkaSkJLQxMTGR/rJ3795nzpyhfqzX1dUNCQkhK4H/BK5fv25gYODo6Oji4mJsbEz/wiOApKSkDRs2LF68+NSpU/Qq4ps3b4Ys0UCLFgFdhrewsGjq0vvs2bNfv35NvTx58uQ///zTAs5hMJifBRbQGAymDcJYoA6tLSLi5Ofn8/P5+vXrPXv2lJGRMTQ0PHjwIJq5WVNTE+2lpaUFtcycOTMnJ+fFixdRUVEpKSkDBgxohp+FhYUbN250d3dfuHBhXFycMF3y8/Pd3d2p7X1cLnfVqlU3b950cXFRVVVVU1Nzd3fPz8+Hel24cMHKysrX1/fw4cOzZ88eOHAgh8MBAFRUVFC7+igYv0L8OLa2toGBgdTXjL59+4aEhLRr1074ESorK9HV8VYtl4jBYFqeX1pIHINpFWpqal69evXz5y0rK/v5kwrD9+/fq6qqfrUXDNTU1HA4nNYYeffu3dCzTkpK6vv370J2Z7PZxcXFreGYkNy5c4cSu/b29jk5OdSh8vJyqtQchb+/PzTC58+fodxzOjo6R44c2bt378uXLymz2trawMDAuXPnrl69Ojo6uhmu5uTkQHnirl+/3mivy5cvo/8fUcngSLp3715TU0N1qaqqQrPp7dixgyCIhoYGdBOYhYUF2bE1bsyqqqro6OjPnz/X19c3tS+q9QEA+vr6Le5ksxHZR1lhYWF1dXXLjhkdHV1SUtKyY2L+BLCAxrRBsICG+AMFdE1NTe/evekCZf/+/cJ3JwX0tWvXBg0apKmpOXz48KdPn7aGn4zEx8dDeQb69OlTV1dHHi0vL7exsYHkl4qKCjrOkydPqGJ+pqamdPXp5ORUX1/P4XD69etHHycwMLCp3lLZpik6d+4swL6ystLX19fS0hIVkSghISFURyiAm2T06NHkUbKuOJ1///2XPATdmF+/fj137lxgYODHjx+berItBZntjo6Li8uvcgZFZB9lWEBjRAcsoDFtECygIf5AAU0QBJfLPXTokJub25IlSyIiIprUl81mHzx4EJI4oaGhreQqxIoVK1ClSC0Pl5eXq6qqogYFBQXoUPX19bt37x45ciS0uAsA6NmzJxqwISMjU1pa2qiH9HeNMWFzVlYWY0c2my1MgmcKcoGZhDE4ZNKkSeRRLpfr6elJnqacnNzWrVsbGhrIQ/QbMzg4mMrlJykpuZOFJIoAACAASURBVG3bNn7nWFxcHBoaGh4e3uKKjSCIa9eu0c9CWVk5MzOzxWdpNiL7KMMCGiM6YAGNaYNgAQ3xZwroH4HNZqNZyaytrX/O7I6OjqhSvHbtGnm0vLy8f//+0NEOHTpQepHOnDlzhFerJE+ePOHnWFVV1cqVK5WUlMTExMzMzG7fvk0QBKrCWSwWv3uhqRv7Ll26RPXlcrnom3Ls2DH6+FwuNysri1qtJ6Gc+fLlC5QJGwDw4sUL1M/AwEAqcbimpiajzQ8SFRXl7Ow8evTolStX5uXltfj4P4LIPsqwgMaIDngTIQaDwcCw2ewvX75Aja20KQ2FMYmbubk59ffff/8NHV2yZAlajyMxMfHEiRNNnV1AwZ2///57z549paWlDQ0NycnJEyZMCA8Pd3BwgMyGDRvGrwzHu3fvhPdET0/P3t6eeikpKRkcHNypUyeqZebMmXPnzqV3kZSU1NLSkpBgLnHw6tWrqqoqqJFewIUkKipq7ty5lZWV5Mvc3NypU6eWlpYK77kw9O7de9euXffv39+9e3dLlYzBYDA/DSygMRgMBkZeXh7N5oamsGglFi1apKamRm+ZNm0avd61i4tLYGAgmWdDWVnZx8cHTeIGAIiNjW3q1BoaGj179mQ8VFFRcebMGajxwIEDK1eudHd3p1rI1Hj8xhdc701KSsrKykpcXFxCQmLkyJEPHjyAdg0OGjTo48ePly9fPnz4cFRU1JkzZ5pUxo8xETjaeOfOHailsLDw1atXwk+EwWDaPLgSIQaDwcCwWCxXV9dDhw7RG2fPnv1zZldTU4uIiPD29o6MjFRUVHRyclq1ahVkM2fOnDlz5lRWVgooUc4YKi2YOXPm8KuTnJ6ejibLS0tLExcXP336NEEQQUFBgYGBHh4eYmJ8l2YcHBzOnz9Pb+nWrZudnV1mZqahoeHy5cuNjIy4XC6LxUKDtkk6dOjg5OTUxNP6D4xJ+hISEu7fv09f6v7+/TtqxtiIwWD+WLCAxmAwGAa8vb3FxMSOHj1aV1cnKyu7Zs0aNHCi9dDT0wsODm7UTIB6BgBYW1traGjk5eVRLRISEt27dxewMv3582d+h/T19cXExCANTa2Ljx49ur6+fujQoQLUMwDA0dFxw4YNu3fvrq2tJce8evUqtOYtJSUlYIQfwdjY2MfHx8fHh9749OnTp0+f2tjY/PXXX9OmTVNWVrayskL79unTp5W8wmAwvyM4hAODwWAYkJSU3L9/P5vN/vz5c1lZ2aZNm5oULSAKtG/fPiQkhMpk16FDhxMnTkRFRc2aNYtfl7KyMn6HFBQU5s+fT2+Rlpb29PQk/3Z0dPT396fmEsD27ds/f/5848aN58+fJyUl8YsYaSU2b9788uXLYcOGQe2vXr1asmSJoaFhXFzcjBkzoG2aCxcubGqtQQwG07bBK9AYDAbDFykpKWFEocjSv3//lJSUxMTE6urqHj16KCoq3rlzp2PHjkuWLOnYseOOHTvo1bABnyAHioCAgPr6+rNnz3K5XFlZ2Xnz5jVvXVZTU5OxUOLPwcbGBgoxpyguLp41a1Z8fPyzZ88OHjz44sULGRkZBwcHFxeXn+zkj5Obm/v06dPa2lpbW1tDQ8Nf7Q4G09bAAhqDwWDaMpKSkmRMAkEQf/3115UrV6hDvXv3jo6Opl6am5t7eXkJGCojI+PChQtk9eyqqqqAgAAFBYWmZqYTBQTsZXz//n1JSYmysvLKlSvR4iwtQllZWW5uro6OTmsMTnL27NkFCxaQ+yOlpKQ2bdq0fv361psOg/kDwSEcGAwG80dw5coVunoGAERHRw8dOnThwoXOzs4BAQHv3r1D0yTT2blzJ5QGzs/PjzG1RTPg8XgvXry4fPlycnJyiwwogAkTJgg4KjiM+0eoqanx8PBQVlbu3r27srKyn58fui/zx8nKyqLUMwCAy+Vu2LCBsY4jBoNpNngFGiMS1NbWnjp1Ki4urry83MDAwMPDA12euX79Oj09lri4+I0bN36qlxjM7wxjIrZnz57t2rULKnvODzQTdl1dXWpqqpB1uQEAVVVVXC5XWVkZas/IyHBwcKDGnz59+pkzZ8TFxYUctqmMGjXKz8/Px8eHy+VCh6ysrNAMhi3FqlWrTp8+Tf5dV1d35MgRbW3t1atXt+wsr169Qr/VPH78eODAgS07EQbzJ4NXoDEigb+//7t37zw8PLZs2SIhIeHj40NVMaAoKCiwtLT0+S+bNm36Ja5iML8p7dq1Y2xftmxZcHAwQRCNjtCtWzchG1EyMzNHjRqloKCgoqJiYWHx8uVL+tEZM2bQ1fn58+f/+ecfYYZtNmvXrs3IyAgODjYwMKAa1dXVKYHb4hAEAaXwAwCgLT+OkOmuMRjMj4AFNObX8/3796ioKE9Pz379+hkZGa1Zs6a6upoemklSUFBgbGxs+V969er1S7zFYH5TRowYwdgeGRnp4uLi7Ozc6Aj0gikkEyZM6NixIwAgLS3t1atXBQUFjB05HM6ECRMeP35MyvSkpKTx48dnZGSQR0tKStAAg9u3bzfqzw+ioaHh7OycmpoaEhKyZcuWU6dOffz4kV7xsWVhs9nl5eVQY25ubotPxLjSbGNj0+ITYTB/MlhAY349FRUV+vr61D5xKSkpaWlpNJ1WQUFB586dORwOm83+6T5iML899vb2bm5u/I5euXLl/v37gkdwcHA4fPgwFeHg5ORErtey2exp06Y5Ozurq6ubm5uj40RGRkLhHxUVFVSi6/LycnT9W0BCvZZFXFx88uTJmzZtmjVrFlT4sGVRVFREi1n26NGjGUPV1tb6+fkZGxt36tRp5MiRUVFR9KPm5ubQD3Rubm7jxo1rxkQYDIYfWEBjfj26urp79+6VlpYmX0ZFRZWXl5uZmdFtCIIoKCi4e/fuX3/95erqunjx4tTU1F/hLAbTWmRlZfn7+69cufLChQs8Hq9JfRMTE0ePHq2oqKihobF06VJ0pZPk7Nmz27dv51dr8PXr141OtHDhwqKiorS0tPLy8suXL5PRzPPmzUtISAAAEASRnJw8ZcqUN2/e0HtlZmaiQ1Er0Nra2mheOcEJ9X4OGRkZQUFB58+fb6l14p07d0It27Zta8Y4Xl5e69ev//jxY1FRUWho6NChQ1NSUugGW7Zsefny5erVq728vG7fvn327FkAAEEQWVlZxcXFzfYfg8FQsISJe8Ngfg4EQYSGhh47dmzUqFHz5s2jHyouLp4/f/6YMWOmTJnC4/FOnz4dHx9/+PDh9u3bkwZpaWnUfwzi4uKVlZUmJiY/2X8ejychIYobc+vq6sTExFpvS1azIVMQtF7Sg2bD4/EIguAXNNwahIWFzZkzh8wQBwDo1avX9evXqW+VkG/Qxyw/P9/Ozq60tJRqGTp06IULF/hVfqmoqAgICDh69CjUvnHjxkWLFgnvM4/Hu3v3bnR09MmTJ6FDU6dO3b9/P/UyKioKTXzh4+NDVWZ5+PDh/PnzyfKEAIDOnTs/evSoU6dOwjsj2M9m3JiBgYHbt28nXZKWlvb19Z02bdqPO/P8+fPjx49/+fJFT09vwYIFffv2bWqBnrKyMlNTU+j/bhcXF8FR448fP167dm1+fj4AYMCAAXv37tXW1uZnLLKPstraWnFx8ZZ9lKWnpxsaGjaj7j3mDwcLaMwvIDIyklqJOXLkSJcuXQAABQUFAQEBWVlZM2fOHDNmjOARuFyum5vbggULqIpi1dXVVEmI2traDx8+9O3bt9XOgJnKykrBpZV/FeXl5WRgzK92BIZUJ5KSkr/aEZjq6moej6eoqPhzpuPxeNra2t+/f6c3ent7e3t7o8ZVVVVQsrnt27dv374dMnv79q2A8ICsrCxLS8vq6mqqRVpaOjw8XPgI4IqKiuHDh79//57xaL9+/V68eEG9bGhoGD9+fFhYGNXStWvXd+/e0dNxJCYmnj59+tu3b2ZmZosWLRKQqrmpNOPGjIuLg5bAZWRkoqKi9PX1W8orAEBBQUGnTp2aKqDfvHkzZMgQqBG64BCJiYmDBw+m7yM0NzePiIjgVzVdZB9lpaWlsrKyLVvs/f3793p6emhmGAxGMKL4FRPT5unXr9+lS5fIv8lfkz99+rRp0yYrK6t169ZRi8oCkJKSUlVVpUdJysrKUn9zOBwWi9WyD1lh4HA4P39SYRAXF2/Xrp0I+kYQxC95pxqlrq6OIIif5lhaWhqkngEA4eHhjA5wuVyo/fPnz6hZenq6gO+QRkZGJ0+enD9/fkVFBQBAVlZ23759ZL0VIdm+fTs/9QwAMDExgZy8fv361q1bb968yeFwbGxsfH191dXV6Qa9e/cWMpteU2nGjQklCQEA1NTUREREQKFlPwj54W+qgGYsK6ivry/gHENCQqAsHElJSYmJidbW1oz2f9SjTExMrKlvAQYDsIDG/BLExcXpere+vt7Pz2/48OFz5szh1yUiIuLixYt+fn7komB1dXVhYaGQ+bMwGBGH8edy4QNIGIuN07OzMeLs7GxnZ/fq1auGhgZra2tIzjbKs2fPBBylYjMoFBUV9+zZs2fPnibN8qugr81TQEVkfhXq6uoTJ068efMmvRGKeYPIyclBG7Ozs/kJaAwG0ygiF3qI+QOJi4srKSkxNTVNolFSUgIACAsLe/DgAQCge/fuFRUVe/fujY+PT05O9vPz09TUFL58AwYjyhgaGqL5GUaOHClkd3d3dyh9xKBBg4TJ86iqqjp58mRHR8emqmcAgIBFOwMDg/79+wvoW11dvWXLlr59+1pZWa1evfqnJdwQHsZMcIMGDfr5njBy+vRpd3d3MvZJS0vr6tWrgwcPFmDPuHBuYWHRWv5hMH8AeAUa8+v58uULQRDQ/vT58+ePHTv2+fPnVVVVY8aMUVBQ2Lt374kTJ/755x9xcXFLS8vVq1eL4OYzDKYZiImJXbx40cHBgQrkGDdunKenp5Dd9fX1Hzx44OnpGRsbKykpOXny5L1797b2ntGhQ4cyhnBIS0vTK4aiNDQ0ODg4PHnyhHwZGxsbFhYWGRkpUjEDI0eOdHd3p5+Il5dXnz59fr4nVVVV169fz8nJMTY2dnBwIH+s6NChw+nTp48fP15eXk7m4RbM/Pnzjxw58uXLF6rlr7/+wgIag/kR8CZCTBuEw+FER0f//OWi8vJyYQK4fz7FxcUyMjL0sBkR4VdFqzdKZWVlbW3tT95XVFpaeufOnaKiIktLy6FDh/Izq6io4Le7kc1my8rK/px0K2w2e+jQoTExMeRLWVnZXr169evXb9GiRYwhJRShoaHo4vq5c+emT58u/OwEQQQFBQUFBRUVFVlZWW3atEnApM2+MW/duvXkyRNxcfExY8aMGjWqGSMAAHJzc8vLyw0MDNDPeW5ubteuXQWs5aelpdnZ2VFJ9CwsLJ4/f968j2Vubu7mzZvDw8MVFBSmTJmyfPlyAbuKRfZRVlRUJC8vzy8PY/OIiYnR1dVtwU2rmD8FAoNpc9TU1Lx69ernz1tWVvbzJxWG79+/V1VV/WovGKipqeFwOL/aCwbYbHZxcfGv9oIZsuwInYaGhn///VdTUxMAoKOjc/To0Z/jCY/Hu3bt2ubNmwMDA4uKir5+/SpML8Yw6JUrVwo/7/fv3zdu3Ejv3qFDh6ysLH72v+rGzMjIoOr/KSkpnTx5EjLIyclpaGgQMAIamOHh4dGaLv8HkX2UFRYWVldXt+yY0dHRJSUlLTsm5k8Ah3BgMBjM783+/fupeI/MzMwFCxaIi4sL2JLbUoiLi0+ZMmXKlCkAAB6PV1hYKEwvMm0lRNeuXYXpGxAQsGPHDrQUSFlZmb+//+HDh4UZ5OdQX18/depUaoW+tLR09uzZurq6aAY6fnA4HLS0DT0VIAaD+YXgEFIMBoP5vdm3bx/UEhAQ8Es8EYYRI0ZAexYVFRUnTpzYaMejR48uX76cXyE9shSikPB4vL179xoZGSkqKlpbW7eGKv3w4QOlnikuXrwo/AgNDQ1kpSE69fX1P+oZBoNpCbCAxmAwmN8YLpebnZ0NNaalpaHaS0RQUVG5efOmqakp+VJHR+f69etoEhKUI0eOCDgq5Bo2iY+Pz4oVKz59+sRmsyMjI8eNGxcRESF8d2Gg79gT3MgPWVlZNJO3gOB4DAbzM8ECGoPBYH5jpKSkUPVpaGjIL0fNt2/f1q9fP2nSpKVLl6ampraUGzU1NeXl5TweTxjjvn37JiUlpaenf/r0idwnJ0yvzMxMAUdnzJghlKMA1NXVQXHYHA5n165dQnYXEsYcFz179mzSIIGBgfR65gYGBr9LIm0Mps2DBTQGg8H83ixfvrzRFpLPnz+bmJj4+fndvHnzwIEDvXr1Cg0NbREfNm3aZGpq+u7dO8FmcXFxgYGB165dq6io0NXVNTAwED5niLGxMWO7oqLioUOHxo4dK+Q4mZmZXC4XamzB7xIkGhoaUCLCzp07C5+akMTMzOzjx4+HDx9es2bN6dOn379/T9fTGAzmF4I3EWIwGMzvzZIlS8TExPbs2ZOdna2rq7t27VoPDw9GyzVr1tCrlnC53MWLF3/69OknOEkQxLx5806cOEG+VFNTu3btWpNyTZIL5/SWXbt2jR492tDQsEmZEDU1NSUkJKDFcsGp95rH/7V353FNHW3fwCcJWyCyIwiCoKBSQVpQQXBDKoh71ccirftebdVWrbvcuLcVtVYUtah1tyhUkeJCxY2iCKIi4oJYK0jY9yVkef+Y586bJywSiEkIv+8ffs5MJudc53gIV4Y5Mzt27HB0dDx16lRRUZGHh8e6detakP4aGhouWLBA7rEBQCshgQYAaNsYDMbChQsXLlwoEAia7tCt30P84sWLwsJCExOTDxkgIYQcOXJEnD0TQrhcbkBAQGZmZvNz33HjxkVGRm7ZsiUjI8POzu7bb7+dNm1aCyJhs9kzZ848cOCAZOVXX33Vgl01jclkzp07t+lFtgGgjUICDQCgJt47HMLAwEDqOTZNTU09Pb0PGdT/unz5slRNdnb248eP+/Tp0/ydjBs3rjnzdbzXrl27dHV1Dx48WFlZ2blz5y1btowePbr1uwWA9gMJNABAezF27NgnT55I1gwfPryJFenkqKqqqpmVCsBms7dv3z516lSBQPDxxx/T9bEBAJoPDxECALQX69evp+ueUB4eHlIjGeSltrZ269atn3zySdeuXb/44otXr155enpKtdHT0/vkk08+xNHf648//rC1tXV1de3bt6+zs3NSUpJSwgCAtgtfuwEA2gttbe2IiIi0tLSnT5/a2Nj069ePwWB8iAPNmTPn2LFjdDsrKysuLu7evXuRkZHiQdhaWlp79+7t0KHDhzh60zIyMgIDA8Wd3xkZGZ999llaWpqhoWEL9nbmzJmdO3e+efPGwcFh9erVfn5+cg0WAFQUEmgAgPbFycnJyclJvvsMDg6eO3eug4MDIeTly5fi7JnicrmHDh26ffv2r7/+evTo0cLCwm7duunq6so3hmaKjIyUGjqSnZ0dHx/fgtHV4eHhs2bNotvv3r27efNmTEyMv7+/fAIFABWGIRwAANBabDbbwMCADiaWGmZNPX78WCQS/frrr4mJiS9evIiNjZ00adJ3332n8EjJu3fv6lfm5OS0YFfBwcHvrQEAtYQEGgAA5MnGxqZ+pa2tbXh4+P379yUrQ0JCFDMLtSQXF5f6lS0YjV1RUVF/EfW0tLQWhgUAbQoSaAAAkCdnZ+e+fftK1ujo6EyZMkUqe6YarPygvvzyS6kltSdMmNC/f39Z98PhcExNTaUq7ezsWhUcALQRSKABAECeNDQ0zp07J36crnPnzqdOnXJ1dTU2Nq7fWAFruEjR1ta+cePG6tWrPTw8hgwZsmPHjlOnTrVsV7Nnz5aqmTNnjkx7yMzMDAwM7NKli4GBQf/+/U+fPi3V4J9//pk8ebKtra2FhcW4ceP+/vvvloUKAPKFhwgBAEDOrK2tY2NjS0pKysrKrK2t6VwfY8eO/fHHHyWbderUqQVdv62nr6+/efPm1u8nODiYz+fv2bOntraWw+GsWrVq0aJFzX/7v//+O2LECIFAEBAQYGxsHBsbO3ny5MzMzDVr1tAGjx49GjhwICFk8uTJbDY7IiLCx8cnMjISc30AKB1DJBIpOwYAOaupqbl///6AAQMUfNzS0lIDAwMFH7Q5CgsL2Wy2siY9aEJNTQ2DwWj+Ys4KU1FRwePxGuwxVbqysjJ9fX1lRyGNz+fn5eVZWlo23Wzv3r0rVqygk2AYGBgMHz587Nixn3/+OZP5Af8c+qF/MOvq6nJzcy0tLd+7EqSUwMDAU6dOJSUl0eUYBQKBl5dXcnJyaWkp/Wn19PR8+PBhSkpKjx49CCHFxcXu7u5aWlofeqS1yn6U5efnczgcNpstx30mJyd37drVyMhIjvuE9gBDOAAAoLV2797t7+//6NGjppstXLjw9evXq1at0tTULC0tPXPmTGBgoLe3N4/HU0ycH4Kmpqa1tbWs2TMh5Pnz56ampuLFzFkslp+fH5/Ppw9W5ufn//33319++SXNngkhRkZGK1eufPLkyZ07d+QYPwC0ABJoAABorbdv3z569KiiouK9LXV1dffu3VtXVyeuuXnz5p49ez5kdCrK2dm5sLDw2bNn4pqEhAQdHZ2ePXsSQmi9vb295Ft69epFCLl9+7ZiIwUAaUigAQBAcR4+fFhWViZVefPmTaUEo1zz5s3r1q3bkCFDNmzYsGvXrmHDhsXHx+/cuVNHR4cQQsfDvHnzRvItdOI8qUpFeu9Tj1wud86cOS4uLhwOx9nZeePGjTU1NVJtjh496uLioqur27Nnzy1btkh+mwJoK5BAAwCA4mhqajazUtUUFxcvWbKke/fuDg4OCxYsyMvLa+UOLS0tp02blpubGxwcvHTp0mvXrrm5uY0ZM4a+amdnZ2Njc+LECfEiL5WVldu2baMbrTx0y2RlZbm5uV28eNHX13f+/PlVVVWTJ0+WfBwzKyvL09PzyJEjtra2X3/9NYfDWb9+/ZgxYySfttqwYcP06dP19fW//vprKyurNWvWyPTkJYCqEAGonerq6lu3bin+uCUlJYo/aHMUFBRUVlYqO4oGVFdX19TUKDuKBpSXlxcWFio7ioaVlpYqO4QGLFmyhBBy586d97asrq62sLCQ+k0UFhb24WKTyw8mj8fz8PCQjNnZ2bm6uro1+5w/fz4hZPHixa9fvy4pKYmIiDA3N7ezsysoKKANLl68yGQyO3bs+M033yxdutTW1pbOMz179uzWn1ETGrtidNq+pKQkWuTz+e7u7hoaGuKPl6lTpxJCTpw4QYtCoZAudX727FlaQ6fhW7hwoXifQ4YMIYS8ePGiOYHl5eVVVVW17KQac//+/aKiIvnuE9oD9EADAIDi6OjonDhxQnLSg8DAwPoTKquay5cvJyYmStY8fvw4IiKiibfcvXt3//79ERER5eXl9V/Nyck5ePCgr6/vrl276IiICRMm7N+/PysrKzQ0lLYZNWrUzZs3nZycjh07duHCBX9/fzpltbKmYUlPT2/iqUdCSGxsrIuLS2BgIC0yGIxVq1YRQm7dukVrfvnlFx0dnU2bNon3uX///l27dmEUB7Q5mAcaAAAUaujQoc+fP4+JiSkqKnJ3d1fKVNCyevLkSf3KxqaTEwqFgYGBZ86cocVOnTr98ccfUqszPnnyRCAQDB06VLJy2LBhhJCHDx+Ka7y8vOLi4sTF2NhYQoiDg0MLT6N1XF1d//7772fPnoknBpF86pHP55uYmEidEZ0CnH6FEIlEUVFRgwcPNjQ0FDfo0aOHeG8AbQgSaAAAkEFqauqKFSsSExMNDQ3/53/+JygoqEOHDnPnzvXw8KBzRDSHqakp/XN/W2FjY1O/0tbWtsHGv/zyizh7JoS8e/cuICDgxYsXktNdd+7cmRBSVFQk+caCggLxS4SQw4cPGxoafvbZZ+IGFy5cYLFYI0eObPGJtMby5ctjY2OHDBkyd+5cIyOjS5cuxcfH79mzhz71qKGhkZ6eLvWW33//nRBCR7+8e/eusrLS2tr67Nmze/bsefjwoa2t7cSJE+m0hoo/HYBWUfYYEgD5wxhoKRgDLSuMgW5MVlaWZPchIWTcuHEikaiuri47O1uJgTVBLj+YRUVF4ryWMjMze/fuXYONR4wYUf+37dOnTyXb8Pl8CwsLU1PTt2/f0hqhUEiHsly4cIHW+Pn5MZnMR48e0eLDhw/19fU/++yz1p9O0xq7YkKhUHL0BSHE3d29if/38PBwFovl4OBABy4/ffqUEGJnZ8disSZMmLBixQovLy9CyPDhw5sZGMZAg+rAGGgAALXF5XL5fL4cdxgWFlZSUiJZExUVlZGRIcdDqCba4dqvXz9adHFxiY6Orv80JEWXWmy6ksVi/fTTTyUlJc7Ozl999dWqVavc3d0PHToUEBAwevRo2iY4OFhDQ8PPz2/FihWzZs0aMmSIubn5jh075HpmMvj+++/Xrl0r+dTj69evBwwYUFhYKNXy33//nTRp0syZMx0cHGJjY+nagXSgc1ZWVmRkZERExPbt22/fvj1r1qzY2NjIyEglnA9AKyCBBgBQQwcOHOjYsaOFhQWHw1m0aFGDKV0LNJgr055Ftde7d++7d+8WFBTk5eWlpqaKk+n6aMeqJCMjIycnJ6nKQYMGJScnDx48ODo6OjQ0lMlkhoWFnTx5UtygX79+f/31V/fu3cPCwi5cuDBy5Mg7d+7QiTgULycnJyQkpOmnHqnw8PBevXr98ccfy5cvT0lJ6dq1K62n65N7eHiIvyEQQlauXEkIiY+PV9yZAMgDxkADAKibiIiIefPm0e3a2tq9e/fyeLwDBw60fs/dunWrXym1Wp56MzExeW+bVatWXbx4Ubywuba29sGDB7W0tOq3dHZ2brrz/SBpiQAAHTxJREFU1cvLS0WSS/rUo4+Pj2Rl/ace58+fHxYW5uPjs3//fqkbw9LSksFgSH0BoIPLxXNdA7QV6IEGAFA39XPlI0eO1F8QrgVmzpxJ+xHFvL29m//sYDuhp6eXlJQUFhY2b968NWvWpKamTpgwQdlBtRYdAi41WkPqqceff/45LCzs22+/vXr1av2vVWw2u0+fPlJ/r3jx4gUhBBNxQJuDBBoAQN1kZWVJ1dTV1f3777+t3/NHH30UHR3t4uLCYDC0tbW//PLLM2fOSE4uAZSWltbcuXP379+/adMmOstbW9e9e3crK6vw8PDs7GxaIxKJ6DOFtFtaIBDs2LHDxsbmhx9+oLPX1bdgwYLU1FTxMBWBQLBhwwYGgzFq1CiFnASA3GAIBwCAunF0dHz58qVkja6urrzGznp7e6emplZVVWlpaWlo/O8vkYiIiEuXLgUFBTU4xgPUAIvFCg8PHzlypLOzc0BAgIGBQVxcXFJSkvipx4yMjDdv3nTv3r3+sjje3t501sLJkycfPnz4yy+/jIqKsre3v3r16v3795csWSK1yiOA6kMCDQCgblauXHnx4kXJmlWrVomTXbmQGshx9+7d48ePL1iwAAm03MXHxx86dCg/P9/Z2Xn58uXm5ubKisTX1zclJWX9+vXR0dGlpaWOjo5hYWFz5syhr75+/ZoQ8vz5c/HChGI6Ojo0gdbR0bl8+fLq1avj4uIuX77cq1evI0eOTJs2TbHnASAHSKABANSNp6dnfHx8UFBQWlpap06dFixYIH6mENqWX3/9Vdyhe+XKld9+++3BgwdWVlbKiqeJpx5HjhwpEoneuwc2m71z5055xwWgaEigAQDU0ODBg69fv67sKKBV+Hz+0qVLJWvy8/ODg4PDwsKUFRIAUEigAQAAVNHz58/Ly8ulKu/fv08IEYlEFy5cuHPnDofDGTduXO/evZURIED7hQQaAABAFTU457SpqalQKJwwYUJUVBSt2bx58+7du+fPny/3APLz85lMZnOmvgZobzD3EAAAgCoyNzcfMGCAVOXEiRNPnDghzp4JITweb+nSpe/evZPjoRMTE52dnTt27Ghqauru7v748WM57hxADSCBBgCA1vL29l64cKESH25TVydOnHB3d6fbGhoa33333ezZs2/evCnVrKam5u7du/I6aG5u7ujRo9PS0mjx3r17o0aNKikpkdf+AdQAhnAAAEBrjRgxok+fPpaWlsoORN3Y2Nj8/fffjx494nK5Tk5O9ApramrWb9lgZctcunSJLjEo9ubNmytXrkyaNElehwBo69ADDQAAoLoYDIaLi4uvr6/4+8mnn34q1UZfX1/cUd16DS5aSad5BgAKCTQAAEBbMn78+IULF4qLenp64eHhpqam8tp/g2uPOzk5yWv/AGoAQzhADYlEIoFAUH/6pw+ttrZW8Qdtjrq6OkKIQCBQdiDS6urqGAwGj8dTdiDSamtr+Xy+av5vquZtJhAIlPJD15h3795lZmZaWVnZ2dmp5hUjhAiFwvLycgaD0YL3bt26ddKkSYmJibq6usOGDbO0tJTjOfr7+3/00Ufp6eniGg8Pj/79+yv9MtbV1VVVVfH5fDnuk8/nC4VCOe4Q2gkk0KCGGAwGi8XS09NT8HEFAoHiD9octbW1Ojo6bDZb2YFIq6mpYTAY2trayg6kAQwGQzX/N4VCoQoGxufzq6qqVCEwgUDw9ddfi5ca8ff3Dw0NlWPvrByVlJTo6em1LIEmhAwYMKD+HB1yIRAI4uLi/vOf/1y7do3FYo0YMWLt2rUGBgYf4lgyqa6uZrPZOjo6ctwni8Vq8X8BtGdIoEFtMZmKHqHEYDAUf9DmYDAYqhkbk8lUzcBU9ooRVb3NaEiqENi2bdskF+r7888/p0yZMmPGDEdHx/79+ysxsAbRHwFlRyGNwWBYWFjs27dP2YFI+xA/mHSfctwhtBPK/7ADAIC2LjEx8fjx41wuV9mBkIiICKma27dvz5o1y9PT09/fv6amRilRAYCaQQINAACtde7cue+//z4zM1PZgZAmkvjY2NigoCAFxgIAagsJNAAAqA9XV9cmXo2JiVFYJHKUmZnJaISDg4O4WU1NzfLly3v16mVmZjZq1KiUlJT6+wkMDOzSpYuBgUH//v1Pnz6t2PMAUB8YAw0AAOpj69at169fr6ysbPDViooKBccjFxwOJyAgQKpSKBSeO3fO2tqaFgUCgbe3971790aPHj1kyJDIyMiBAwdevnxZ/KBhVlaWm5ubQCAICAgwNjaOjY2dPHlyZmbmmjVrFHoyAGoBCTQAAKgPJyen1NTUH3/8MT09/eHDh1Izr3l5eSkrsNYwNzc/deqUVGVYWFhUVNSePXto8cKFC4mJiZs2baIJ8Xfffde3b9+NGzdevnyZNtiyZUtpaWlSUlKfPn1o0cvLKygoaOnSpbq6ugo8GwB1gCEcAACgVuzt7cPCwm7dunXx4kXJ2RstLS23b9+uxMDkKDc3d+XKlWvXru3Vqxet2bNnD5vN/u6772ixa9eugYGBV65cycjIoDXp6emmpqY0eyaEsFgsPz8/Pp///PlzxccP0NYhgQYAAPU0ePDgJ0+eLFu2bOrUqdu3b09PTxevht3WLV682NLScuXKlbQoEAgSEhIGDhwoOUeyr68vIeTWrVu06OrqWlhY+OzZM3GDhIQEHR2dBtcdBICmYQgHAAC0loODw8CBAw0NDZUdiDQ7OzsVWQREjhISEs6ePRsREaGpqUlrsrOza2trO3fuLNnMysqKEPLq1StaXL58eWxs7JAhQ+bOnWtkZHTp0qX4+Pg9e/bId10SgHYCCTQAALTW3Llzx40bpzb9uypu3bp1Li4u48ePF9fQhyONjIwkmxkbGxOJ5yatra2nT5++du3a4OBgWuPu7j5mzBgFBQ2gXjCEAwAAoM24fv36X3/9tX79esn187S0tAghUivqiUQiycrvv/9+7dq1ixcvfv36dUlJSURExOvXrwcMGFBYWKjA8AHUBBJoAACANmP37t1mZmZSPccWFhaEkOLiYslKWuzUqRMhJCcnJyQkxNfXd9euXXQe6AkTJuzfvz8rKys0NFSB4QOoCSTQAAAAbUNeXl5MTExAQICGxv8ZgcnhcPT09N69eydZmZubSwih42qePHkiEAh8fHwkGwwbNowQ8vDhww8eN4DaQQINAADQNpw4caKuru6LL76o/5Knp+etW7fq6urENXFxcYQQDw8PQgh9vlBqtEZBQYH4JQCQCRJoAACAtiE6OlpfX79v3771X5o5c2Z5efmvv/5Ki1wu99SpUwMHDuzRowchpHv37lZWVuHh4dnZ2bSBSCTatGkTIUSqWxoAmgOzcAAAQGsVFhb+888/xsbGmBPtw6mpqUlISBg0aBCT2UDn1/jx44cOHbpkyZInT55YWloeO3asoqJi27Zt9FUWixUeHj5y5EhnZ+eAgAADA4O4uLikpKSAgIDRo0cr9jwA1AF6oAEAoLW2bdvm6emZkpKi7EDUWUJCQk1NTf/+/Rt8VUtLKzo6ev78+X/99VdISIiDg8O1a9c8PT3FDXx9fVNSUgYPHhwdHR0aGspkMsPCwk6ePKmo8AHUCnqgAQAA2oChQ4fSmekaw2azd+3a1UQDZ2fnyMhIeccF0B6hBxoAAAAAQAZIoAEAAAAAZIAEGgAAAABABkigAQAAAABkgAQaAABai81mGxgYSC2PBwCgrpBAAwBAawUHB6enp/fr10/ZgQAAKAISaAAAAAAAGSCBBgAAAACQARJoAAAAAAAZIIEGAAAAAJABEmgAAAAAABkggQYAAAAAkAESaAAAaK3169d/9NFH9+7dU3YgAACKgAQaAABaq7q6urS0lM/nKzsQAABFQAINAAAAACADJNAAAAAAADJAAg0AAAAAIAMk0AAAAAAAMkACDQAAAAAgAw1lBwAAAG3e4sWL/fz8evfurexAAAAUAQk0AAC0VufOnbW0tDgcjrIDAQBQBAzhAAAAAACQARJoAAAAAAAZIIEGAAAAAJABEmgAAAAAABkggQYAAAAAkAFm4QCVcP78+SNHjoiLLBYrMjJSqo1IJDp58uT169eFQuGAAQOmTZvGYrEUGiUANOLYsWPnz5//6aefevTooexYAAA+OCTQoBK4XK6rq+uYMWNokcFg1G9z9uzZmJiYRYsWaWho/PLLL4SQmTNnKjRKAGjEo0ePoqOjV61apexAAAAUAQk0qAQul9uzZ09XV9fGGggEgpiYmKlTp/bv358QMmvWrNDQ0MDAQB0dHQWGCQAAAIAx0KAauFyuhYVFTU1NeXl5gw3evn1bXFzs5uZGi66urlVVVa9evVJgjAAAAACEoAcaVIFIJOJyudHR0Tt37hSJRNbW1osWLXJ0dJRsU1RUxGAwjI2NaZHD4WhraxcXFysjXgAAAGjXkECD8hUVFTGZTEdHx7Vr1/L5/MOHD2/atCk0NNTAwEDcpry8XFtbm8n8/38zYbPZZWVl4mJubm5lZSXdFggEAoGgtLRUYadA1dTUKPiIzcTj8UQiUV1dnbIDkcbn84lKXrfa2lql3ELNUVtbKxKJlB2FNB6PRwipqKhQwYumgjcYJRQKS0tLG3zkQ7lU9orxeLzKykp6s8lLXV2dUCiU4w6hnUACDUqQkJCwbds2ur1v3z4rK6uIiAjxq998882UKVOSk5OHDh0qrtTT06N5g/iXTXV1tZ6enriBhoaGpqYm3WYymXV1ddnZ2R/8TP6vuro6cQwqhcfjsVgsFZy0RCAQMBgMye9FKoLP5wuFQi0tLWUH0gA+n6+hoXIf3d26dZs4caJIJFL8z917qewPZlVVlfhrv0pR2StWW1uroaEh34+y6upqOe4N2g+V+xSG9sDd3f306dN0m81mS72qra1tZmZWUlIiWWlkZCQSiUpKSoyMjAgh1dXVtbW1dJsyNTUVbwsEAqUki6qZ2RBCCgoKdHR0dHV1lR2INNrxo4IJdEVFBY/H43A4yg6kAap5m3388cedO3e2srJSwYummleMEFJSUmJmZqaCPdAqe8Wqq6u1tLTq/9ZoDQ6Ho4I3Lag+VfwJAbXHYrEkk7k7d+6cPHly69at+vr6hJCqqqq8vDwbGxvJt3Tp0sXAwODBgwe0Wzo1NZXNZjs4ODS2f6m3t3NlZWVGRkbm5ubKDqTNyM7OLisrw13UfB07dkxOTnZyclJ2IG3Jy5cvbWxsVDCBVllFRUUmJiaS3SUAyqJyHT/QDvXu3busrCwkJCQ1NfXJkydbt261tramU9rFxcX9+eefhBAWizVixIjjx48/e/bsxYsXhw8f9vX1xRx2AAAAoHjogQbl69ChQ0hIyKFDh3bs2MFisVxdXVesWEH/rB8fH19ZWenv708ICQgI4PP5P/74o1Ao9PLymjFjhrIDBwAAgPYICTSoBDMzswbXMNu4caN4m8FgTJkyZcqUKQqMCwAAAEAaQwWnQwIAAAAAUFkYAw0AAAAAIAMk0AAAAAAAMkACDQAAAAAgAzxECKBuzp8/f+TIEXGRxWJFRkZKtRGJRCdPnrx+/bpQKBwwYMC0adNUcJ1CReLxeOHh4Q8ePCgtLXVwcJg5c6adnZ1Um+ZcWLXXnDsHd5cU3F2ywocYqD4k0ADqhsvlurq6jhkzhhYbXKbh7NmzMTExixYt0tDQ+OWXXwghM2fOVGiUKmb79u1ZWVnz5s0zNDQ8ffp0UFDQ3r17pdYna86FVXvNuXNwd0nB3SUrfIhBGyACAPWyYcOGU6dONdGAz+dPnTo1NjaWFm/cuPH5559XV1crJDpVlJ+fP3r06IcPH9JidXX1xIkTr1+/LtXsvRdW7TXnzsHdJQV3VwvgQwxUH8ZAA6gbLpdrYWFRU1NTXl7eYIO3b98WFxe7ubnRoqura1VV1atXrxQYo2opKyuzt7fv3r07LWpra+vo6JSUlEg1e++FVXvNuXNwd0nB3dUC+BAD1YchHABqRSQScbnc6OjonTt3ikQia2vrRYsWOTo6SrYpKipiMBjGxsa0yOFwtLW1i4uLlRGvSujatWtISIi4mJSUVFpa2qtXL8k2zbmwaq85dw7uLim4u2SFDzFoE9ADDaBWioqKmEymo6Pj0aNHw8PDbW1tN23aVFpaKtmmvLxcW1ubLpZOsdnssrIyhQerckQi0ZUrV7Zv3z5q1CgHBwfJl5pzYdVec+4c3F2Nwd3VTPgQgzYBPdAAbVtCQsK2bdvo9r59+6ysrCIiIsSvfvPNN1OmTElOTh46dKi4Uk9Pr7a2ViQSiR/Nqa6u1tPTU2TYylX/ohFCuFzuzp07X79+PXv2bH9/f6m3mJiYvPfCqr3m3Dm4uxqEu6v5mnM1cJuB0iGBBmjb3N3dT58+TbfZbLbUq9ra2mZmZlIDLo2MjEQiUUlJiZGRESGkurq6traWbrcT9S/a8+fP169f7+bmtmrVKgMDg/fuocELq/aac+fg7qoPd1dr4EMMVBOGcAC0bSwWS/e/GAzGnTt3Fi5cKP5TZlVVVV5eno2NjeRbunTpYmBg8ODBA1pMTU1ls9lSf1NWb1IXTSAQbN269dNPP12+fHlj+U1zLqzaa86dg7tLCu4uWeFDDNoEVlBQkLJjAAC5MTIyOn/+/LNnzwwNDQsLC/fv389ms6dNm8ZgMOLi4p49e+bg4MBkMmtra6Oionr27FlcXLx3797Bgwf369dP2bErTUpKyuXLlz/77LPS0tK8/9LQ0GCz2eKL1sSFVXb4itPEnYO7qzG4u2SFDzFoExgikUjZMQCAPOXn5x86dCg9PZ3FYrm6us6YMaNDhw6EkHXr1lVWVtIJAUQi0fHjx2/cuCEUCr28vGbMmCH5OE57ExUVFR4eLlU5b968kSNHSl60xi5su9LYnYO7qzG4u1oAH2Kg+pBAAwAAAADIAF/XAAAAAABkgAQaAAAAAEAGSKABAAAAAGSABBoAAAAAQAZIoAEAAAAAZIAEGgAAAABABkigAQAAAABkgAQaAABAdfn7+zMkGBsbe3h4rFmzpra2VtzG2tqawWDs3r1bwbFVVVV16dLFzc1NwceVyZs3b6qqqsTFAwcOMBiM8+fPKzEkUANIoAEAANqM4uLiu3fvbtmyxd3dXTKHlrtjx4598sknt27daqLN5s2b37x5s3btWlqsrKxctmyZra0tm812dHTcsGGDZOZ69uzZQYMGGRoaDhw48MCBAy0IID4+nlFPZmYmISQuLq5Pnz76+vre3t6pqanit9y4caN3797V1dXimunTp1tZWX377beSlQAyEwEAAICqGj58OCHE1dU1Nzc3Nzc3OTl51qxZ9Df4vn37aJu8vLzc3NzKykp5HTQuLs7ExIQQEh0d3VibvLw8HR0de3t7oVAoEomEQqG3tzcNTFdXl25MnDiRNt67d6/US5s2bZI1gH379tVPY16+fPn48WMtLS02mz1w4EANDQ0TExMul0vf0q9fvy1btkjtfPv27YSQkJCQll0cAJFIhB5oAAAAVaelpWVubm5ubu7q6nro0KHBgwcTQrZt20ZfNTMzMzc3F+emrREYGNi1a1cfH5/CwsKmW4aFhdXU1EyfPp3BYBBCEhMTr1+/rqmpmZiYWFZWtm7dOkJIREREfn5+bW1tUFAQIeSHH36oqKg4evQoISQ4OLioqEimADIyMgghV65cyZVga2t7/PhxHo935cqVmzdvhoWFFRYWRkdHE0J+//33t2/fLlmyRGo/U6ZMYbFYP//8s0AgaNFFAsAQDgAAgLZmypQphJB//vmHZplSY6Dp2Ibdu3d7eXnp6ek5OzsfPny4pKRk8uTJZmZmVlZWy5cvbyx3zMnJYbFYXbt2fW8MJ0+eJIRMnDiRFgsKCuzt7T/99FN3d3cWizVhwgRaX11dffny5fz8fB0dnaVLlzIYjKlTp5qbm/N4vAsXLsgUAE2g3d3dzSWwWCyhUEgIYbFYhBAtLS1CCJ/Pr6urW716dXBwMJvNltpPp06dPD09X79+nZiY+N7TBGgQEmgAAIA2xt7enm68evWqsTbffvttQkJCVVVVWlrarFmzXFxcTp8+XVBQkJOT89NPP4WGhjb4rvj4+BcvXqSnpzcdQG5u7tOnT01MTHr06EFrRo8e/eLFi5iYGEJIRUUFHW4xbNgwGxubZ8+eEULMzc01NDRo486dOxNCsrOzZQogIyODw+EsXrzY2NjYxsZm7ty5JSUlhJAvvvhCU1PTz8/Pz89v9uzZxsbGY8aMCQsL09TUnD59eoPxe3p6EkL++uuvpk8ToDFIoAEAANoYY2NjupGTk9NYGzc3t/T09Lt372ppaYlEosrKyhs3brx69Yom39evX29NAM+fPycSebyko0ePdujQISwsrGPHjsePHyeElJeXE0I4HI64TYcOHQghBQUFzT9iVVXVmzdv6AgQTU3Nf//99+DBgz4+PgKBwMXFJTo62t7ePiEhwd3d/erVq3p6ehs3bty2bRvtlq6PRk7PAqAFkEADAAC0MeLRw5aWlo21mTNnjqOjY79+/ZycnAghY8eOHTRokJ2d3dChQ8l/k9oWo0NHDA0N67/00Ucfff311+bm5nl5eZ6ensXFxeKOZzEmk0kIoUMvmn/EsWPHjh8//unTp1wu99q1a0wmMyUl5dy5c4QQX1/flJSU8vLyGzduuLq6/vjjj927dx8zZsyZM2ecnJwMDQ1HjRpF5+ugDAwMCCH5+fmynzoAIUigAQAA2hzxyI0mBiuLe6lpttqxY0fJYivRgcUNzqPXt2/fn3/+OS0tTVdXNzMz89KlS/TQkik73e7UqVPzj2htbR0ZGXnu3Dk6aMTHx6dPnz6EkOTkZKmWubm5ISEhP/zww9WrVwMCAt6+fevk5BQTE+Pn5yeeuo5GXn94NEAzIYEGAABoY+jQCBsbGzrXm+KZm5sTQvLy8sQ1p0+f9vb2njRpEi2amppaW1sTQtLS0uhGTk6OeFpo2hncRPd5fY8ePTpz5kxsbKy4hsfjEUL09PSkWm7YsMHPz69///5HjhxhMpnJycm3b98OCgrKzMwUzypN+54tLCxkOmsAMem/qgAAAICqqauro6MmsrOz9+3bR59+W7lypbLicXZ25nA4z549q6iooIObDQwM4uPjCSGhoaETJ048e/YsfXawd+/ePj4+hoaGJSUlmzdvXrZs2cGDB4uKiths9qhRowghBw8e5PF4Li4uAwYMaOKIKSkpM2bMYDKZFy5cGDRo0OnTp+mCKQMHDpRslpGR8dtvv9GXRCIRg8GgA0jov3w+nza7f/8++e+jhAAtoeyJqAEAAKBRdCGV+lxcXGpqamgbOqnFrl27aJE2iIiIoEU61GHlypW0OH/+fELIp59+2sRBa2pq6E6aWEhl3LhxhJCLFy/SokAgGDRokFSQbm5uPB5PJBKFhobS6aLF1q5dS99IB1IvWbKk6QDKy8t79uwptf8vvviiflTz58+n27S72tTU1MfHh8Vi2dnZVVRU0FAtLS1ZLFZOTk4TFwGgCRjCAQAA0GYYGhr27dt39erVd+/e1dbWVmIkX331FSHkt99+o0Umk3np0qXly5f36NGDzWb37Nlz2bJlcXFxmpqahJAFCxYcP3588ODB+vr6ffv2DQ0N3bhxo0yH43A48fHx8+bNs7Oz09PTc3Nz+/nnn8VHpxISEq5evbphwwZa9PPzO3HihJmZWVJSkp+f35UrV+h4j2vXruXk5EyYMEGmQdgAkhii/35VBQAAAGg+Nze3tLS0rKwsmUYzK9348eOjoqLu3btH++YBWgA90AAAANASe/bsqaur++mnn5QdiAzS09OjoqKmT5+O7BlaAw8RAgAAQEt4enrSJwKVHYgMcnNzN2zYsGDBAmUHAm0bhnAAAAAAAMgAQzgAAAAAAGSABBoAAAAAQAZIoAEAAAAAZIAEGgAAAABABkigAQAAAABkgAQaAAAAAEAGSKABAAAAAGSABBoAAAAAQAb/D8Mc0N+yHhxtAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(acp, choix = \"varcor\")\n", + "plot(acp, choix = \"ind\", select = \"contrib 5\", unselect = 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que sont ces graphiques?\n", + "\n", + "**Q** Que dire du choix de la dimension, des valeurs atypiques?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que dire de la structure de corrélation des variables ? Est-elle intuitive ?\n", + "\n", + "Même graphe en coloriant les dépassement de seuil." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:39.847382Z", + "start_time": "2019-11-18T09:22:01.315Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(acp, choix = \"ind\", habillage = 1,\n", + " select = \"contrib 5\", unselect = 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'objectif est donc de définir une surface séparant les deux classes. \n", + "\n", + "**Q** Une discrimination linéaire (hyperplan) semble-t-elle possible? \n", + "\n", + "Ce n'est pas utile ici mais une classification non supervisée est facile à obtenir. Par exemple en 2 classes, par l'algorithme k-means. Donne t-elle la même information ?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:39.912435Z", + "start_time": "2019-11-18T09:22:01.509Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "km.ozone <- kmeans(ozone[, c(3:4, 6:10)], centers = 2)\n", + "# Représentation dans les coordonnées de l'acp\n", + "acp2 <- PCA(cbind(coul = as.factor(km.ozone$cluster),\n", + " ozone[, c(11, 3:4, 6:10)]), scale.unit = TRUE,\n", + " graph = FALSE, quali.sup = 1:2, ncp = 7)\n", + "plot(acp2, choix = \"ind\", habillage = \"coul\",\n", + " select = \"contrib 3\", unselect = 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Protocole de comparaison" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stratégie" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La recherche d'une meilleure méthode de prévision suit le protocole suivant.\n", + "\n", + "1. Étape descriptive préliminaire uni et multidimensionnelle visant à repérer les incohérences, les variables non significatives ou de distribution exotique, les individus non concernés ou atypiques... et à étudier les structures des données. Ce peut être aussi la longue étape de construction de variables, attributs ou *features* spécifiques des données. \n", + "2. Procéder à un tirage aléatoire d'un échantillon *test* qui ne sera utilisé que lors de la *dernière étape* de comparaison des méthodes.\n", + "3. La partie restante est l'échantillon d'*apprentissage* pour l'estimation des paramètres des modèles.\n", + "4. Pour chacune des méthodes, optimiser la complexité des modèles en minimisant une estimation \"sans biais\" de l'erreur de prévision, par exemple par [*validation croisée*](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf):\n", + " - Variables et interactions à prendre en compte dans la régression linéaire ou logistique;\n", + " - variables et méthode pour l'analyse discriminante;\n", + " - nombre de feuilles dans l'arbre de régression ou de classification;\n", + " - architecture (nombre de neurones, pénalisation) du perceptron;\n", + " - algorithme d'agrégation, \n", + " - noyau et pénalisation des SVMs.\n", + "5. Comparaison des qualités de prévision sur la base du taux de mal classés pour le seul échantillon test qui est resté à l'écart de tout effort ou \"acharnement\" pour l'optimisation des modèles.\n", + "\n", + "**Remarques**\n", + "* En cas d'échantillon relativement \"petit\" il est recommandé d'itérer la procédure de découpage apprentissage / test, afin de réduire la variance (moyenne) des estimations des erreurs de prévision.\n", + "\n", + "**Q** Commenta appelle-t-on cette procédure spécifique de validation croisée?\n", + "\n", + "* *Attention*: ne pas \"tricher\" en modifiant le modèle obtenu lors de l'étape précédente afin d'améliorer le résultat sur l'échantillon test!\n", + "* Le critère utilisé dépend du problème : erreur quadratique, taux de mauvais classement, entropie, AUC (aire sous la courbe ROC), indice de Pierce, *log loss function*..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extraction des échantillons" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les commandes ci-dessous réalisent l'extraction du sous-ensemble des données d'apprentissage et de test. \n", + "\n", + "Utiliser trois chiffres au hasard, et **remplacer** \"111\" ci-dessous, comme initialisation du générateur de nombres aléatoires. Attention, chaque participant tire un échantillon différent ; il est donc \"normal\" de ne pas obtenir les mêmes modèles, les mêmes résultats." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:39.945357Z", + "start_time": "2019-11-18T09:22:02.486Z" + } + }, + "outputs": [], + "source": [ + "set.seed(956) # initialisation du générateur\n", + "# Extraction des échantillons\n", + "test.ratio <- .2 # part de l'échantillon test\n", + "npop <- nrow(ozone) # nombre de lignes dans les données\n", + "nvar <- ncol(ozone) # nombre de colonnes\n", + "# taille de l'échantillon test\n", + "ntest <- ceiling(npop * test.ratio) \n", + "# indices de l'échantillon test\n", + "testi <- sample(1:npop, ntest)\n", + "# indices de l'échantillon d'apprentissage\n", + "appri <- setdiff(1:npop, testi) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Construction des échantillons pour la régression: prévision de la concentration en ozone." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:39.976151Z", + "start_time": "2019-11-18T09:22:02.695Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'data.frame':\t832 obs. of 10 variables:\n", + " $ JOUR : Factor w/ 2 levels \"0\",\"1\": 2 2 1 1 1 1 2 2 1 1 ...\n", + " $ O3obs : int 91 100 82 94 150 164 135 121 118 48 ...\n", + " $ MOCAGE : num 93.2 104.6 103.6 94.8 114.3 ...\n", + " $ TEMPE : num 21.5 20.2 17.4 18.8 23.6 26.6 23.5 23.3 22.2 14.3 ...\n", + " $ STATION: Factor w/ 5 levels \"Aix\",\"Als\",\"Cad\",..: 1 1 1 1 1 1 1 1 1 1 ...\n", + " $ VentMOD: num 9.5 8.01 9.38 9.46 6.31 ...\n", + " $ VentANG: num -0.6435 -0.05 -0.1283 -0.3452 0.0634 ...\n", + " $ SRMH2O : num 0.092 0.0939 0.0975 0.0925 0.1087 ...\n", + " $ LNO2 : num 0.471 0.752 0.505 0.854 1.671 ...\n", + " $ LNO : num -0.858 -0.633 -0.761 -0.355 0.295 ...\n", + "'data.frame':\t209 obs. of 10 variables:\n", + " $ JOUR : Factor w/ 2 levels \"0\",\"1\": 1 2 1 1 1 2 2 1 1 1 ...\n", + " $ O3obs : int 146 111 152 112 117 97 107 194 145 180 ...\n", + " $ MOCAGE : num 145 137 185 152 163 ...\n", + " $ TEMPE : num 21 22.2 18.2 31.9 25 23.5 20 30.9 26 29.5 ...\n", + " $ STATION: Factor w/ 5 levels \"Aix\",\"Als\",\"Cad\",..: 3 4 4 3 4 3 1 3 5 1 ...\n", + " $ VentMOD: num 4.98 11.81 2.98 8.06 6.18 ...\n", + " $ VentANG: num 0.182 -0.494 0.88 -0.124 -1.172 ...\n", + " $ SRMH2O : num 0.0812 0.0688 0.1127 0.0886 0.0991 ...\n", + " $ LNO2 : num 2.446 1.335 -0.243 0.667 -0.112 ...\n", + " $ LNO : num 0.346 -0.297 -2.323 -1.355 -2.017 ...\n" + ] + } + ], + "source": [ + "# construction de l'échantillon d'apprentissage\n", + "datappr <- ozone[appri, -11] \n", + "# construction de l'échantillon test\n", + "datestr <- ozone[testi, -11] \n", + "# vérification\n", + "str(datappr)\n", + "str(datestr)\n", + "#summary(datappr) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Construction des échantillons pour la discrimination: prévision de dépassement." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:40.000864Z", + "start_time": "2019-11-18T09:22:02.905Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'data.frame':\t832 obs. of 10 variables:\n", + " $ JOUR : Factor w/ 2 levels \"0\",\"1\": 2 2 1 1 1 1 2 2 1 1 ...\n", + " $ MOCAGE : num 93.2 104.6 103.6 94.8 114.3 ...\n", + " $ TEMPE : num 21.5 20.2 17.4 18.8 23.6 26.6 23.5 23.3 22.2 14.3 ...\n", + " $ STATION : Factor w/ 5 levels \"Aix\",\"Als\",\"Cad\",..: 1 1 1 1 1 1 1 1 1 1 ...\n", + " $ VentMOD : num 9.5 8.01 9.38 9.46 6.31 ...\n", + " $ VentANG : num -0.6435 -0.05 -0.1283 -0.3452 0.0634 ...\n", + " $ SRMH2O : num 0.092 0.0939 0.0975 0.0925 0.1087 ...\n", + " $ LNO2 : num 0.471 0.752 0.505 0.854 1.671 ...\n", + " $ LNO : num -0.858 -0.633 -0.761 -0.355 0.295 ...\n", + " $ DepSeuil: Factor w/ 2 levels \"FALSE\",\"TRUE\": 1 1 1 1 1 2 1 1 1 1 ...\n", + "'data.frame':\t209 obs. of 10 variables:\n", + " $ JOUR : Factor w/ 2 levels \"0\",\"1\": 1 2 1 1 1 2 2 1 1 1 ...\n", + " $ MOCAGE : num 145 137 185 152 163 ...\n", + " $ TEMPE : num 21 22.2 18.2 31.9 25 23.5 20 30.9 26 29.5 ...\n", + " $ STATION : Factor w/ 5 levels \"Aix\",\"Als\",\"Cad\",..: 3 4 4 3 4 3 1 3 5 1 ...\n", + " $ VentMOD : num 4.98 11.81 2.98 8.06 6.18 ...\n", + " $ VentANG : num 0.182 -0.494 0.88 -0.124 -1.172 ...\n", + " $ SRMH2O : num 0.0812 0.0688 0.1127 0.0886 0.0991 ...\n", + " $ LNO2 : num 2.446 1.335 -0.243 0.667 -0.112 ...\n", + " $ LNO : num 0.346 -0.297 -2.323 -1.355 -2.017 ...\n", + " $ DepSeuil: Factor w/ 2 levels \"FALSE\",\"TRUE\": 1 1 2 1 1 1 1 2 1 2 ...\n" + ] + } + ], + "source": [ + "# construction de l'échantillon d'apprentissage\n", + "datappq <- ozone[appri,-2]\n", + "# construction de l'échantillon test \n", + "datestq <- ozone[testi,-2] \n", + "\n", + "# vérification\n", + "str(datappq)\n", + "str(datestq)\n", + "#summary(datappq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, avant de passer aux différents algorithmes, définissons une fonction traçant le graphe des résidus avec des couleurs et des échelles fixes sur les axes. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "options(repr.plot.width = 8, repr.plot.height = 4)\n", + "# Définition d'une fonction pour un graphe coloré et des échelles fixes sur les\n", + "# axes\n", + "plot.res <- function(x, y, titre = \"titre\") {\n", + " plot(x, y, col = \"blue\", xlim = c(0, 250), ylim = c(-100, 100), ylab = \"Résidus\", \n", + " xlab = \"Valeurs prédites\", main = titre, pch = 20)\n", + " # points(x2, y, col='red')\n", + " abline(h = 0, col = \"green\")\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Prévision par modèle gaussien](http://wikistat.fr/pdf/st-m-app-select.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le premier modèle à tester est un simple modèle de régression linéaire mais, comme certaines variables sont qualitatives, il s'agit d'une analyse de covariance. D'autre part, on s'intéresse à savoir si des interactions sont à prendre en compte. Le modèle devient alors polynomial d'ordre 2 ou quadratique." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modèle linéaire" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sans sélection de variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le modèle de régression linéaire simple intégre des variables qualitatives; c'est dans ce cas une *analyse de covariance* estimée par la fonction `aov` mieux adaptée à ce modèle." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:40.107560Z", + "start_time": "2019-11-18T09:22:03.699Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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1qr8fPHjQv39/1YeNr69vixYt5D+63CfzeCYmJmru0iypsHPnTou+/Fjdu3Bfmc9dTkRuy8tG5r62HBn5+2pGZC1cuFAzYnjDhg07duyIi4uLi4vTzN2moXrXm5Cbbwo/Pz9NDj1nzhzVmApmXrBgwZYtWySVbTmkGppRHNOnT1f9M4yKitJcYJdDbxPrmh08ePDdu3eJ6OXLl/369VM14uHhYeIbkX3/u1r3YKHgyvXLFgHszPRCKqprgFRmz56tKtT9rKpXr96AAQMaNGigGdSomXk0OTlZ99qgsmXL+vj46L59jC2kIolBZndVqlRRlbi5uUVGRvbu3btjx47FihVTFQYGBiYmJsqvJpxwIDk5OTg4WBNM5cqVmzRp4uXlpSlZsWKF2Ue0b98+Tf0HDx6YeGqsmIVDTl9yjueDBw90R6nWrVtXtQCbpiQ0NNSKeOQ/m0Jynjv5kVt00GS+bKwOW/6RER5wmfvev39fc85VoVA0atRIc86SiKKiojQN+vr6qgorVKjw/vvv//vvv8a6dtSbQqVs2bKGV79p5vGw5cWm8vLlS93HQkR//vmn5l7bX2zCoyq/Wck80K6urlWrVtVdFWXMmDEmOpJ/iGx5/QMIIYEGp2c6gX706JHm3qFDh2rKJ06c6ObmRgb69u2blZWlqWa4ZpiHh4fmlJXMBFpmdxcvXtSsUCBRqFAhzceezGrGVu06ffq08PdohUIxceJE4aJreS2Blnk8NRPualSqVElzQs7d3V01kbClCbTM3oVkPncyI7fooMns2pawZR4ZY69MmUd1+/btukOHNRo2bKibA/Xs2VP3XtMrEebmmyItLU14PrtTp06av3UnwrP6xaahu8Zh6dKls7Ozde+18cVm7KjKbFaTQPv6+uouXqPSvXv3pKQk0x3JPES2vP4BhJBAg9Mzu5S3ZoJVV1dXzeILzHzo0KE+ffqEhoZ6eXlVrVq1e/fuf/31l+Hu169fHzBgQLVq1fz9/Tt27Hjw4MElS5aoGpSfQMvs7vnz5/PmzYuMjKxUqZKXl1dAQEB4ePioUaPu3btnaTVjHzbMnJ6ePnv27DfeeCM4OLho0aKvvPLKoEGDDJcgzssJtJzjmZ2dvWDBgjp16hQpUiQ8PHzMmDEvXrzYtm2bpsFRo0ZZGo/83o2R89zJjNzSgybz1WV12DKPjIlXpsyj+vDhw1GjRjVv3tzPzy8oKOi1115bsWKFJC98/PjxO++8ExQU5OXlVb169fPnz5vuOtfeFCpbtmzp2bNnxYoVAwIC2rdvv379et2r6yQzSVv9YlOJiYmRvHJ02fhiYyNHVWazmgS6WbNmFy9e7NOnT/Xq1QsVKlSrVq358+dLFjex8ZVjy+sfwJAL/zcrEADINHny5OnTpxNRVFTUTz/95OhwAAAAIFfhIkIAo95///2qVatWrVo1IiIiNTVVVZiVlaUZdVevXj3HRQcAAACOgQQawKhq1apdu3bt2rVrx44de/vtt/fu3fvbb7+1b99etTKWj49P7969HR0jAAAA5DYM4QAwSqlUvvXWW5s2bTK8q0iRIuvWrevSpUvuRwUAAACOhQQawIzff/99wYIFV69evXXrVrFixSpWrNikSZOxY8faazFhAAAAcC5IoAEAAAAALIAx0AAAAAAAFkACDQAAAABgASTQAAAAAAAWQAINAAAAAGABJNAAAAAAABZAAg0AAAAAYAEk0AAAAAAAFkACDQAAAABgASTQAAAAAAAWQAINAAAAAGABJNAAAAAAABZAAg0AAAAAYAEk0AAAAAAAFkACDQAAAABgASTQAAAAAAAWQAINAAAAAGABJNAAAAAAABZAAg0AAAAAYAEk0AAAAAAAFkACDQAAAABgASTQAAAAAAAWQAINAAAAAGABJNAAAAAAABZAAg0AAAAAYAEk0AAAAAAAFkACDQAAAABgASTQAAAAAAAWQAINAAAAAGABd0cHYKXExMSXL1+6urqWLFnS1RVfAwAAAAAglzhZ6hkXF/fOO+8EBQX5+PiULVu2dOnSHh4eZcuW7d2796FDhxwdHQAAAADkfy7M7OgY5Bo5cuSiRYuYOSgoqHz58v7+/kT07Nmzu3fv3r17l4gGDx68YsUKR4cJAAAAAPmZ0wzhWLx48cKFC9u2bTtr1qywsDDJvefPn58+ffrKlStr1KgxZswYh0QIAAAAAAWB05yBbtq06dOnT+Pi4tzdxUk/M7do0UKpVB48eDCXYwMAAACAgsNpzkDHxcV17drVWPZMRC4uLs2bN1+0aFFO9B4bG5uVlZUTLQMAAACAkLu7e926dR0dhYDTJNChoaHHjh3Lzs52c3MzVufIkSOhoaF27/rEiRMNGza0e7MAAAAAYNrx48cbNGjg6CiknCaB7tOnz4gRIzp16vTll1/Wrl1bcu+VK1emTp26f//+2bNn273rjIwMIkpPT/fw8LB74wAAAABgKCMjw9PTU5WG5TVOk0C/9957586dW7p06e7du8uVK1ehQoXixYu7uLgkJCTcuXPnxo0bRNS/f/+PPvrI0ZECAAAAQH7mNAk0ES1ZsmTYsGGzZ8/+7bffNFcKurm5BQYGRkdHDxs2rEWLFo6NEAAAAADyPWdKoImoXr1669evJ6Lnz5+/fPlSoVAEBgZiJUIAgNyXmUm//UY3blBICLVqRfhPDAAFh5Ml0Bqurq6urq7OMgcfAEA+c/s2tW9PFy6ob9avT7/+SgEBDo0JACC3ONkZAyzlDQCQFwwapM2eiejkSRoxwnHRAADkLmc6A627lPcrr7yiu5T3hg0bNmzYgKW8AQByQUoK7d8vLdy9m5RKDOQAgALBaRJoLOUNAJBHvHxJ2dnSwtRUysigQoUcERAAQO4q6Et5JyQkTJw40fQqg3fu3Nm9ezfmgQYA0AgOplu39ErCwujUKQdFAwD5kWoe6EOHDjVp0sTRsUg5zY9tcXFxERERZpfyjouLs3vXSUlJ9N9yKgAAQESLFpHusrAKBS1Y4LhoAAByl9MM4cihpbz9/PwWLVpkus6yZcv+/vtvi5oFAMjfOnakf/6hBQvo5k2qWpVGj6aaNR0dEwBAbnGaM9B9+vS5dOlSp06dzp07Z3jvlStX+vTps3///s6dO+d+bAAABVB4OH33HR04QCtWIHsGgILFac5AYylvAAAomFJTiZkKF3Z0HADwH6c5A01ES5YsOX36dHR0dGpq6sGDB7dv375t27ZDhw6lpaVFR0cfOHBg9erVLi4ujg4TAADAPs6doxYtyNubihal5s0pNtbRAQEAETnRGWgVLOUNAAAFxOPH1LYtxcerbx48SG3bUmwslSzp0LAAwLnOQOvy9fUtV65cqVKlkD0DAEC+tGmTNntWefiQfvzRQdEAgA5knwAAAHnR5ctyCwEglyGBBgAAyIuqVpVbCAC5DAk0AACY8vQpzZlDw4fT7Nn0+LGjoylIoqKkw51LlKBevRwUDQDocI6LCBcuXDhp0iSZlRMSEnI0GACAgiMujlq0oGfP1De/+IIOHKA6dRwaU4FRsiTt3k0jRtCRI0REr7xCixZRUJCjwwIAZ0mg27Vrd/Xq1WXLlqWnpxctWrRChQqOjggAoEAYOlSbPRNRQgINHkz//OO4gAqYsDA6fJgSE4mIihVzdDQA8B/nSKCrVKkyf/78Dh06tGvXrkWLFjt27HB0RAAA+V9qKh0/Li08dYqSksjb2xEBFVRInQHyGmcaA922bduQkBBHRwEAUNBhxSoAKOCcKYEmooYNGyoUCkdHAQBQIHh5UUSEtLBhQypSxBHRAADkGc4xhENj3bp1jg4BAKAAWb6cWrTQTr4REEArVzo0IACAPMDJEmgAAMhNNWrQ5cu0Zg39+y9VrEj9+lHx4o6OCQDA0ZBAAwCAKX5+9L//OToIAIC8xMnGQAMAAAAAOBYSaAAAAAAACyCBBgCAvI6Zbt6ky5cpK8vRoTgCMz165OggAEAHEmgAAMjTTpygunWpYkWqXp0qVKCYGEcHlIuysmjSJPLxoZIlydeXZs4kpdLRMQEALiIEAIC87OlT6tyZ7t9X37x/n6Kj6dgxqlvXoWHlls8+o88/V//94gVNnEiurjRunENjsgoz/fgj7d5NGRnUsiUNHEjuSEDAmeEMNAAA5F179mizZ5X0dPrhBwdFk7uUSlq4UFq4YIEjQrFZ//4UHU1r1tDGjTRsGLVpQ9nZjo4JwAZIoAEAIO+6eVNuYf7z8CElJkoLHzygly8dEY0N9u+nNWvMlAA4FyTQAACQd9WsKbcw/ylZkooVkxaWKkVFizoiGhscPiy3EMBZIIEGAIC8q317Cg/XKylRgoYOdVA0ucvVld5/X1r4wQeOCMU2hQvLLQRwFkigAQAg7/L0pF27qH9/8venokWpQwfav59Kl3Z0WLll6lSaOFF9ytnHh2bMoE8+cXRMlnvtNcElg23aOCIUADvBRbAAAJCnlSpFq1c7OggHcXen6dNp2jR6/JgCAx0djbXq1qXZs2nsWO003qNHU8eODo0JwDZIoAEAAPI0Fxcnzp5VRo+mdu1o3z7KyKBXX6X69e3Z+J079Ouv9PIlNW5MjRvbs2UAY5BAAwBAwXL3Ls2eTbGxVLIk9e9PHTo4OqCCoUYNqlHD/s1u2ECDB1NKivpmv3703Xfk4mL/jgB0IYEGAIAC5N9/qX59ev5cfXPTJpo7lz780KExgbXu36chQ7TZMxGtWUORkTRokONigoIBFxECAEABMmGCNntWGT+eXrxwUDRgm4MHKTlZWrhnjyNCgQIGCTQAABQgx49LSzIy6MwZR4QCNktKEhQaptQAdocEGgAKrps36ZdfKDaWmB0dCuQWX19B4bNntH497dtHaWm5HhDYICJCbiGAfSGBBoCCKDOT3nmHKlakjh2pXj1q3pzu3nV0TJAr3nhDWuLtTd26UZ8+1Lo11apFp0/nXjBKJW3aRGPH0hdf0OXLuddvvlGzJn30kV5JrVo0ZoyDooGCBBcRAkBBNG0arVmjvXnoEPXtS/v3Oy4gyC0TJtCZM7Rtm/qmj4/eAOh//6WePenCBVIocjyS9HRq04b++kt987PPaMUK6tcvx/vNZ+bMoWbNKCaGEhOpcWMaMQJrHEJuQAINAAXRTz9JSw4coEePnH62XTBLoaCtW+nYMTp7lgIDadYsOnZMr8K1a3T+PNWrl+ORzJ2rzZ6JKCODhg+ndu3wIrRY587UubOjg4ACBkM4AKAgio+XW+iMMJDXrFdeoSFDqHNnevZMcO+TJ7kRg+EvHikp0mweAPImJNAAUBDVrSst8fSk6tUdEYr9pKXR+PEUEEBeXlS1Kq1d6+iAchcz3b0rnpbBBMMl8dzccuP0M5F2XWtd2dm50TUA2AgJNAAURDNnkrv+ELapU8nT00HR2Mno0TRrFj19SkR07Rr160ebNtmn5QsXaPNmOnhQnPPlBWvXUlAQlStHxYpRly50/77cHadPp6JF9UomTqSAALsHKBAZKS3x9KRXXsmNrgHARkigAaAgioykP/+kDh0oOJiaNqW1a2nsWEfHZJvERFq+XFo4d66tzWZmUnQ01apFUVHUvDmFh9O1a7a2aXd791K/fvTwIRERM23bRlFRck/lVqlCZ87QoEHUoAG1b08bN9KUKTkZq45PP6WwMO1NNzf6+msKCsql3gHAFriIEAAKqCZNaNcuRwdhP1eukFIpLbx0ydZmp02jjRu1N8+do7feohMnLGskK4u+/54OHqRChejNN6l9e1ujkli2TFpy+DCdPauXnppQqRKtXGm+2qNHNGcOnTpF/v7Up48drlorXJiOHaPvvqMTJ8jPj3r0oAYNbG0TAHIHEmgAgPygUiVycZGuCFO5sq3N/vyztOTkSbp1iypUkNtCVha1aqWdbmLpUho9mr7+2rIw/vyTDh8mHx9q354qVpTee/26YJdr1+Qm0HLEx1NYmPokNxFt2kRTp9LkybY2q1DQkCE0ZIit7QBALsMQDgCA/KB4cYqKkuAGKW4AACAASURBVBYOG2Zrs48fCwofPbKgheXL9SZrI6JvvqGTJ+XurlRSdDS9+iqNH08jRlDNmoKLI6tVE+xo30tCp0zRZs8q06bln2lbAMBSSKABAPKJ5cupf39ydSUi8vamL7+0QwIdHi4t8fCg0FALWjh0SG6h0MqVemNI0tLo3Xely0b+73/SdU/atrUsSLP++Udakp1twdcAAMhnkEADAOQTPj60ejUlJdG//1JCgvqyyPR08VlkmT7/XDo5yYwZ5OVlQQseHoJC+ROe/PabtCQlhQ4e1Ctp0oS2bqXQUHJxIW9vGjyYNmwgFxcLgjTL11duIQAUBEigAQDyFS8vqliR3N0pPp6iosjbmwIDqWJF2rLFmtbCw+n4cYqOptBQatOGNm2ijz+2rIXXX5eWKBTUooXc3VNTzRcqlRQURLNn06VLlJhIK1aQn59lQZrVsaO0JChIcHoeJDZtok6dqHFjGjaMbt50dDQA9oOLCAEA8qHsbIqK0o6UuHmT3nqL/viDmje3uKnatWn9eusj6d2b9u6lNWvUN93dae5cCwYoCydLadxY+/fNm9SzJx0/TkTk6kqDB9PixeTmJqtxZvrpJ/r9d3JxoTZtqFs3o+etR4+mEyfoxx/VNwMDaeNGKlxY7qMomKZNo88+U/999Cht3EinTtnhwlaAvMCFJddsO4nExMSXL1+6urqWLFnS1TVnz6MvW7bs3Xffffnypbe3d452BABgL8ePU6NG0sLoaJtSYVv8+ScdPEientSxI9WoYcGOqanUuDHFxmpLxo+nmTO1NyMipMtff/45jRtnvmVm6tmTNm/WlvTpQ+vWmdrln3/o9Gny96fWrcnHR1b8BdaTJxQUJF15x4GvQHBGGRkZnp6ehw4datKkiaNjkXKyM9BxcXFz5szZu3fvgwcPVCVubm6lSpWKjIwcMWJE06ZNHRseFChKJcXE0MmT5OdHXbtSlSqODghAx9WrgsIrV3I9jv+0aGHBsA1dXl509CgtW0ZHjlCxYtS1K7VvT9nZ6nPMN29Ks2ci+uknWQn01q162TMR/fAD9e5NHToY3aVRI8HXEhA6c0awbiUuu4R8w5nGQI8cObJOnTpr1qxxcXF55ZVXOnTo0KFDh4YNG7q4uGzYsKFZs2ZDMJcm5Jb0dGrZknr0oFmzaOxYCg2lDRscHRPkef/+S4cPU0JCbvQVEiIoFE73lvcVKkT/+x9t3EjLl1N8PIWEkEJBFSrQV1/RvXuC+jJnl5Nciagif3oQMM3fX24hgDNymgR68eLFCxcubNOmzalTp+7fv3/06NFdu3bt2rXryJEjd+7ciYuL69Wr18qVK7+2dHZ+AKvMnq03tW16Og0dSk+fOi4gyNvu3KGWLalyZWralEqVonHjpCue2F1YGEVG6pUoFDRiRM52aoXr1ykmhv7+mzIyzFdetYoGDaKrV4mZbt+mjz6iXbsEw53r1pXVtXB6EGGhA925QzExtG8fJSU5OhQLhYYKhju/+aYjQgHIAU6TQP/www/VqlXbuXNnmGhpqVq1am3YsKF58+Y/G66aBWCDjAw6fZpOnaK0NL3yP/6Q1kxKEswUC0BEzBQdTQcOqG9mZNAXX9DixTnbqZsbbdpEvXurM8KQENqyhfLUMEKlkt59l0JCqFs3ioykOnXo3Dkzu3zxhbRkwQIaP16vRKGg6dNlBdCqlaDwtddk7Zs7pkyhypWpWzdq3ZpCQgT/dvIyhYJ++omCg7UlffpYPIULQJ7lNAl0XFxcRESEu7vRQdsuLi7NmzePi4vLzaggf9uzhypXpvBwql+fKlakbdu0dxmO7SOizMxcCw2cyY0bgoEBpi9Ws4vAQPrhB0pOphcv6PJl6tQpx3u0yIIFtGwZKZXqm5cvU1SUqTdRRoZgye7kZHrnHVq1iho3pooVqVMnOnRI7jDliAhq2FCvZPx4a2YpySExMTR1qvaAxMdTr15O9jNXeDhduEC//krff09nz9K6dXJnRwHI+5zmIsLQ0NBjx45lZ2e7GX//HTlyJNS+a09BAXbzJkVF0cuX6psPHlB0NJ06pZ5+q3lz6QBKT09cXQRid+7ILcwJ7u5UrFgu9WURw98LL1+muDgS/cpIRBQfTwEB0kVhPD2pXDkaOJC6d6dChcyvz6JUUmws3b9PoaE0apR68juVQoXo7bctfRA5yPD4PHlCf/1FXbs6IhpreXlR27aODgIgBzjNGeg+ffpcunSpU6dO50Q/8l25cqVPnz779+/v3Llz7scG+dK2bdrsWSU1VbsUxfjxVLu29i43N/r6aypVKvfCAyeiWiHPsDAvS02lKVOoZk0qU4a6dqXz5+3fxaNHcgufP6cuXSg4WLCkYr9+dPgw1a1Lvr7k7U0dOtCNG9p7MzMpNpZOn1YPsL51ixo3pvBweuMNqliRtm7VayotjebOtekR2Zdw/Ujh8QGA3Oc0Z6Dfe++9c+fOLV26dPfu3eXKlatQoULx4sVdXFwSEhLu3Llz48YNIurfv/9HH33k6EghnzB91tDbm44fp2+/pRMnyN+foqKkvwUDaPj70wcf0Pz52hKFgiZNclxAMvTtq/26uHUr/fEHnT5NlSrZs4uwMLp8Wa/E1VV8/d977+kNoNJUHjiQhg+n5s0pOZmIKCuLdu+mN96g48epcGHau5cGD1a/Z0uXpqVLadYs7YUKwos4z5617SHZVVgY7dkjLcTahwB5BTuV06dPR0dHBwQEaOJ3c3MLCgqKjo4+cOBADnW6dOlSInr58mUOtQ9503ffMZF0W7jQ0WGBc8rI4DlzuEYN9vfnV19l3X9XDx/yqlU8dy7/8UeOh5GSIqva6dOCF/+wYXYO5vJl9vbW6+KTTwTVUlNZoZAG4+/Pqn/JH34oCHXbNr5xg4sV0yv08BDUlGw1apgK+NEj/vVX/vtvuYfRRo8fc5kyeuG99VZu9AuQd6SnpxPRoUOHHB2IgNOcgVapV6/e+vXriej58+cvX75UKBSBgYG2rESYkJAwceLELOEVYf+5ePGi1e2D8+rZk+bOJd2rUqtUoX79HBcQODOFgj76iAx/Idu9m6Kj6cUL9c033qCYGDJ+sbSV0tJo6lRavpyePaPKlWnyZDOvZOGJWLNTZFgqJIROnqTp0+n0aSpZkvr2FUf18KHgysKEBFIoiIwsDXPlCt24QYmJeoVypsnTXNFo6OuvaeJESk0lIipbltasoZYtzTdoi4AAOn6cpk/XriCTB2chBCiwnCyB1vD19fX19VUqldevX8/MzAwJCTExQQeAFby8aN8+mjiR9u4lpZJee40+/5yKFnV0WJCPJCZS377a7JmIdu6kuXPp00/t3NHo0bR0qfrv69fpnXeoUCHq2dNo/bJl5RbaKCSE1q41U6d8efL1pefPpTuqrhcUrhcTEkJ//mlNPMbe4Hv30ocfam/evUs9e9KFC1SihDW9mMBMGzbQvn1ERK1aUe/eOT7dIQBYydGnwOWaOHHiqlWrNDczMzO//PJLb29v1aPw9PQcOnTo8+fPc6JrDOEAgJzw+++CUQSvvqpXJy2N16zhceN40SJ+8sSaXl68YFdXaS+NGpnaJTmZK1eW7rJ1qzW928X8+dJgfvpJfdeFC1y4sHQYRnIyr15tfsCG4TZqlDiAIUMElX/80c4PU6nkHj30uujWjZVKO/cC4ETy8hAOp0mgiehVnQ+WkSNHEpGfn1+PHj2GDRsWERFBRDVr1kxLS7N710igASAnbN8uSMsaNtRWePiQQ0K0dxUvzkePWtzL8eOCXooVM7NXbCzXrq2uXLgwz51rcb92pFTy999z/fpcogQ3acLbtund+8cfHBrKROzmxm3b8vXrzMzJyVytmuCBu7hwRARXqsSdOvGECezmpr2renU2dhKmUydBU1Wr8pkz9nyY27YJevn5Z3t2AeBc8nIC7ZTDHs6fP79w4cJGjRr98ssv/v7+qsJvv/120KBBn3/++dSpUx0bHgCAHA0akEIhHeAbEaH9e/RovTG+z57R22/T1auW9VK5Mrm4SCedMFxjWaJOHTp9mi5coBcvKDSUfH0t69S+XFyoXz+j47ZbtqRz5yghgby8qFAhdWHhwrR/P1WvLh0JXbs2HTmivdmtG23cSI8fU/36NGgQeXmJu6hXj3bskBZevUqtWlFsLJUpY82DMrR/v6Bw8WInm/gZoIBwmnmgdR0+fJiZ582bp8meiWjgwIFNmzbdvXu3AwMDgHwjNpa2bKGTJ8XznekyW8GYoCCaMUOvpEIF+uwz7c3ff5fucu0a3bplWS9+foLhzu++a35HNzeqXZuaNbND9pyQQAcO0MmTsq7kM+HxY9q7l44do7Q06V1+ftrsWSUoiH7+We+KTA8PWrhQr054OM2eTatX0/vvG82eieiDD8RZ8tOntHKlZQ/BhGfPBIW601oDQN7hlAn03bt3iai27joWRKqSS5cuOSIiAMg/Xr6kdu2oXj3q0YMaNKDISPHqFZmZ9PnnVL48KRQUGko//WRNX2PH0m+/0aBB9OabNG0axcaSzmkB8brWVqwYv3w5DRigXkW5aFGaPZuGDrUmWussWEDlylHLltSgAdWsqXcC2CJz5lCFCtS2LUVEUM2adPiw+V1ataIzZ2jYMGrbloYPp9hYK1fqDgigo0epfn3BXcZmaVIq6eZN6bWPJmRmUsWKgnLVZCMSzNa8DADAnhw9hkQu0hkDvXbtWiKKi4uT1OnSpUvt2rXt3jXGQAMUKIMGScehdu4sqGY4A/HmzXaOpHVraReFC1t/VVlqKt+4wVlZdg3RnD17pA8hKIifPbO4nZ07Be0kJORAxMatWycYozx+vKDmDz9wyZLqCq1b882bppq9fJnbtmWFQm9AtmYbPlyv8r17/NZb7O3NCgU3bcrHjjEzp6by3LncpQv37Mlr1+K6Q8g/8vIYaGc6A33lypUZM2Zs3ry5WrVqJUqUmKH/2+fx48d37drVEMvBQW7JyqIHD2xqITvbTqHkbZmZdPs2mZxsPQ9hpp9/lhb+8ot0zEBKit7KgipffGHnYHTWjNL2K5z5WI5ChSg4WH0e+t49OnqUnj61KTw5fvxRWhIfb80cc4Yn+OPj6a+/rIzKOu3bS+fyK1yY3n5bWm3/fnrnHXr4UH3zt9+oa1ejY1eeP6e2bWnPHsrMFPxDKFGCJk7U3kxPp06daONGSkqizEw6dIhat6aLF6l5c/roI9q6lX76ifr2pQEDrH+MACCT0yTQ5cqVi4+PnzRpUlRUVKNGjR4/frxx48b9/11zMW7cuBYtWhQrVmzKlCkODRMKhORkGjmSvL0pKIgCAgSJlFmbN1NoKHl6UunSNGUKpafnQJR5QHo6jR5N3t5UoQL5+NCkSU7wnSEtTW9iZpXMTOn41CtXBF8JLlywczDXrwsKT5+2qc2EBOrWjcqWpcaNqWRJGjFC8EDs+DTduyco/PNPOnFCkFNmZtKFCxQXJxifcP++oJ34eHuEKFvx4rRjh3YgR3Aw/fwz1aghrbZypfSQnj5Nx4+L24yJoZs3pYXVq1PjxjRyJJ05Q6VLa8t//51OndKrmZhII0fSiRN6hd9/TwcPyng8AGADp0mgb9++nZycfPbs2c2bN8+aNWvgwIHNmjXTLJ6ydevWgICALVu2lCtXzrFxQkEwciQtXKjOep8+pVGjLLuQaMcOioqi8+cpO5vi42nqVBozJocidbBPPqF589R5UkoKzZhBs2Y5OiZzvLwEKVHJknp5DBEFB5OLi7SacAyrLUqVEhQGBdnU5rBhFBOj/js7mxYvpmnT1DeVSpo/nypWJIWCqlShZcts6kilbl1B4bx51LAh1aihN4553z4KCaFatah2bapcmSQXhNerJ2hHWJij6tWjVauof39q3Zqio6lOHUGdf/8VFAq/CxGR8LKdJk3o8GFasED6qhOOtxa2cOyYuDsAsBtHjyGxj/Pnz2dnZ+dQ4xgDDboSEwXLUtSta0ELTZoI5qbNmVWAHCkrS7rCBRGXLu3osGTYtUsa9tq1gmpRUdJqX39t50jWrpV2ERzMycnWN5iUJBhoGxysvnfWLOldixbZ+hDu3+cSJQRDe1VbqVL89Ckz882bXKyY3l1FivDVq3rtBAbqVeja1dbYrLBli94B9PHhS5ekdfr3FzzSf/4RN7h8uaDy55+LK8fECCprpuvW3ZYts+ejBnAUjIHOcTVr1nR1zSePBfK469dJqZQWWjQ1r+FpJGbxaST7svtAkeRk+v57mjKF1qyh1FTpvffvU0qKoNCwZp6SlUVff61XMmCAYJwrES1fTtHR6vPQhQrRpEk0apSdg3n7bRo/XjsPQ/nytHYtFS5sfYP37gmGZ9y9S9nZxExffim9y/ZR3UFBdOQIRUdTcDD5+UnvffCADhwgItq6VTphc3IyLV5Mv/1Gd++q2zl6lPr2pSpVKDycpk+n9evlxhAfT4cOiQeBWESppGHD9A7gixc0erS02gcfqJcZ13jtNfEMHkTUtav0JwVfX+rdW1y5VSvpBN6enoIRz56e1LKluAUAsBtHZ/BOAGegQdfLl4Iz0PXqWdBCeLjgjNHDhzkWMfO6dVy1KhOxvz+PG8epqXZo8+pVLldOG3+FCvzvv3oVsrO5aFHpwyxf3g5d56gFC6Qxu7vzxYt6de7e5bt31X8nJHBcnH0OqTG3bnGXLuzurv6xom5d/uUXK5vKzBT8LFCnDjPzvXvik8S//GK3iTvatxe0/3//x8w8erTRs9Surjx0qJUxJCdznz7s4qI+dNHRnJRkffwXLwrC8/MT1PzjD65fn93cuFgxHjTIzBrsp0/zK69onwvT59ouXND+hFW+PG/fzsw8Zow2nkKFeNUq6x8jQJ6Sl89AI4E2Dwl0vvfoEY8dy6+/zr168Y4d5usPGSL9EF292oLuliyR7i6cJc1etmyRdjdsmKn6Bw5wu3ZcqRK3bMlbthitFhkpbfb116V1Jk2S1pk/3w6PKEf16CFIklauZGY+epSjotjHR11YowYfPJgbIb37riCkjz+2srVp06RNqZ7lrCwuUkScwoaFab8w2OLTTwWN//knM/OqVUYTaNU2Y4Y1PQ4fLm1nyBDr4799WxBYmTJG66elWdD4s2dm8mxdT55Ip8Y7c4YXLeKVK/nWLQs6BcjjkEA7NyTQ+du9e9qUSLVNn25ml5QUHjNGnW2UKsWLF1vc6YwZ2hOBvXrl7OnniAjpR76JIde//CKtvHSpoFpKivqEqO7m4cHp6XrVMjN5xgz1ENjSpXnevBycodbwIoiUFGva6dlTkCStWsWLFglGD/v58Z07tsduyiefGM0pjx61uLVnzzg0VNuCm5teYvr++0b7atOGmXn/fu7ShevW5R491DMQWyQ+XjqOuX179UsiKYmrVTOVQHt5cY8e/McfFnSnVErHVROxt7dNJ9Rr1pQ2OGCA9t6MDF68mHv25H79eONGzMcMYCsk0M4NCXT+VqOG9BPR3Z3v3ze/o1JpzXoQGunpfOAAd+/ORYuyhwdHRvKJE9a3ZoKvryAdMZb91K0rrenrK8gDnj9X/yyuu7m5Gb2+zZbr3kxLSeHx47lUKXZ357p1eft2fvaMhwzhokXZ1ZVr17Z4tMPixdLHpVDw0aPs6SlO7L75xnybBw9y27ZcujQ3aMDLlglyfWO2bjWVU86ZY9lDY9GPJ2XLahPK1FR+913xch5ubvz999KSPXssDuD6de7dm4ODOTSUJ07UG1Bx9y737cslS7Kfn6lHLbygUyghQdzC48cWh61x+jQHBWmbCgvT/hPIzOTmzaW59eLF3Lo1N27MH35oU78ABRMSaOeGBDofi40Vf8Tu3JnjXaelcViYNFW9ccPWZhMTWfJSrV9f8ACFPxZnZAjOKxNJBzerGF77X7++rcHLER+vd8J+wADpl5+GDaXp7+HDFrSflcUdOuiliV99JZiXQ7O9/76ZBg8eZIVCb5eJE+UGY5jv6m5ffWXB41KpVEnQzvnzenWePRPn0Jql9TRbaKjFAciRnCx+Haq24sUtOLNbtqx091KlbA0vMZHXrOFZs3jrVr2T2UuXmnqyiLhChdxeOjGXZWTwF19w5crs5cWNGvGuXY4OCJxfXk6gMXMFFGjGVjfw8cnxrvfula6I8fy5TTPvnjxJERFUrBgVK0bNmtHZs+ryYcOkNXv0IH9/QQsKhWDpOzc3CgwUVF6+XG86iCJFaOlSaZ379+nsWbtNu3HwINWqRUFBVLIkhYXR8eP08CGtXq1XJytL+pxmZopXumEW9+LmRjt30v/9H7VrR1FRtHcvjRkjnVRBl+Gk0RKffSZdFmTWLEpKMrOXiuGSLrpatJDViC7DBUoMC/38BNMbBwZql9bTuHAhR+ZUKVxYb/k9iWfPxBMtC02dar7EUkWLUt++9Omn1LmzellHlSNHzOx465Z0gheLnD9PnTtTiRJUuTJ99JF00pK8YOxY+vRTun6dUlPpn3+oY0f67TdHxwSQcxydwTsBnIHOxwwvsCNiLy+bLtWX6csvBV1bfTXhvXvS2XYVCg4N5ZEj+dEj/vJL9vZmInZ15b59TZ0GGzlSGlKnTkYr37zJn37KvXrxuHF8+7beXbdvc6tW6haKFLFmsIHErVvSsSiBgbx5s5lzfqqtenW9po4f51df5UKFuHhxHjSIHz2S9vXhh9pTsL6+HBPDz5+LR8KUL29+GE9AgGBHmcOX5841+qAmT5Z97HRERwtO6GZkSKsdOCA9B7xmjeCssI+P9GSwUsl//83ffsv79pkaahwXxxs28P79gq5VsrO5d2/xiXAXF8uGTm3YwA0asJ8f16/P69ZZsKOlDC9YNNzat7ey8Vu3pCNb2rSxYCxQLnj+XDA9UWSko8MCJ5eXz0AjgTYPCXQ+9vChIDGaMCE3uhYmf126WHzp2507PGIEBwcb/cyuUoUTEzkzk69fNz8WOSWFu3fX7vvqq9Zc4JiVJbhy8YcfzOyVmMgrVvCECbxqleALjPD7xvTpshLowEBtO9euSS8sa9KEMzO1FX76Sbp7sWJ8/z7//LPeBHCurty+PV++bP5oGA4rJ5J+3zAmJUU6VKZmTZ40iY8ckbW7obt3uVQpbWsKBf/8s7jmqVPcuzc3asQ9eqgnymjTRvoo+vXT2yUhQW8QcJ06goeZkcFvvaWtU726dIpAlY0bjT6bwpzs8WM+coQfPLD4gNiRcJUTyda3r5WNC6f5+/tvuz4A2xw5IojQ39/RYYGTQwLt3JBA5287d3Lx4tqs6N13c6nfFy/EWW9wMJ85I7eRW7e0wZvYZs+2LLYrV3jHDj571tLHpBYXJ4ihdWtTu1y8qHdtVrly0oHXQ4cK2hwzhlu3Nv/wPTy0ZzpHjRJU2LdP21HfvoIKGzYwM9+6xfPn8+TJHBOjl3Ob9sUX0tYsmjU8KYmnTePWrblLF16xwtaTjpcu6Z0R9/GR+2LbsEE6q3fLltK5XN55R/pIW7WStjNlirRO7dqCMc0tW4qfytq1pdcJpKXxoEHqc5+qmZ4d+K/6gw+0oSoUgim3N2+2suXXXxccDSvm/8k59+8LIgwPd3RY4OSQQDs3JND53tOnvHkzf/cdX7iQq/2eO8eNGgk+dapVk5snCRcNNtyio3PqIdy4wbNm8Qcf8KpV2llvhZfcVa1qqh3D5c1ff52zs3nxYq5Zk7299RZt0WwrVvDDh9yli/pmkSI8dapgehAPD21sbdsK2lmwQBvJG2/YOVNRjUbQbc3NTZ2R574WLaQPTc6ln9eusZeXdMf9+/XqKJWC73KurpyYqFdNeD7+yhVthaNH+d13BaknEc+eLfjeoruGiOaV1qEDT5jgmFkvjh/nuXN54UK+fJl/+UX7dcXdnceNs77ZgQMFByQXrnW2iOFvFLrvLAArIIF2bkigwawHD/jgQSunBDacWZZI/Lu2RmYmr1vHo0YJJkYQbqNGWfewzNixQy+vql5dPTj1xg1BDF26GG0nKUkw2tXTk6dONfWggoO1g7mfPeMLF9SzUOvOc6zamjXT9iVclER39Rxhp8eP23SgdKf1UG2+vhacw7aXtDT28BDkuC9emNnRcGo/Ih4zRq9Oerp0shHVpjuKIzVV/IPJhg08aBA3acK1awu+/2g24ftLOMRctZUsyffu2XzUbJOQwDt28Pr1fO2aTe38/rv00VWoYP6Jy2UPH2q/f3p68oQJmAkbbIUE2rkhgQYT0tJ4wADt1TPduxtdo8QYw5m2iEzNvJaSIj5vbWJTjWG1r7Q0Qe6iWePw7bf1yhUKU8N2ExIEaZO7u+Csp7c3e3iwpyd36KB32lLX0aPqKyZVm58fx8Vp7z1yRJrnVami96N/YqJ62XPNprtShnX8/QVPim5UucPqBNpw8UIyGADNogkTdeeMu3xZPImeu7vRObZ1N+GvKElJphJuuzx3eceyZdoln0JD+dQpRwdkxP37fOqU9JcHAOvk5QQa09gB2GTCBFq9mpRK9c0tW2jECMtaaNhQWuLpKZhHTGPWLPrnH6P3li9P69dTiRLqm4UL04IFFBlpWUhyxMXRkyfSwgMH1H8sX04TJlCFClSoEDVpQnv2UESE0aZ8fSk0VFpYq5ZgirS0NDpyhGbMoMhIevZM3Norr9ClSzR1KvXvTzNn0uXLVKuW9t6ICFq7lkqW1Fbeto28vbUVihalf/6hCROoZUvq1IlWraKVK41GLpNu+6YL7UWppHXraNgwGjWK9u1TF3p6CqYprFCBlEqaOpU6d6YhQ2j/fkFr9esLChs0kJZ88w25u+uVLFig/fudd8TTz5UvT+npJh4KFS9Oo0fT8uWCu4oUoeBgU/sKH46TGjqU7t2jf/6hixfpzBkKC3N0QEYEBVFYGBUt6ug4AHKaFUm3Uqm8detWumTR3vwLZ6DBBMNBFLojbuW4fFl6eda8eabqS1Y7093850S12wAAIABJREFU/dXzoyUm8r59/Msv0jk0lEretIkHDeJ+/Xj1apvWND5+XBBASIiVrR09ql4aXbUVLcqHDwvGdRQpolc4cqSV3WVl8eXLNv2+n5DAe/bwzp2CWfAkLl/m8HDpA6lRg5k5JYW//ZY/+YQXLeKnT60PRiIrS3rN2SefMBsZKhMSIv0NxHBtRaWS27fXq1O7tni6mNhY7tePIyK4Vy+9OSKePhWcKlYo+Kef9H4uMNyqVDHzYL/7ztTurq4cGyv3uF26xEuX8rJlRn/cAIBclpfPQMtKoA8cODBw4MAbN24w8927d0NDQ4nI09Nz0qRJygIwxAkJNBiTmiqY+pSIb90yv+/ZszxiBHfqxGPH8pEj/N573LQp9+jBu3eb2bFpU1MZw/TppvaVXHTYrp35qxUvX+Zu3TgggCtU4BEjtFPwpqUJxrMOHqzd8c4dy67iunOHJ07kt9/mzz5TL6Xes6f5X/a3b7egC3vZtEn72IsU4aVLjdbctk0waqJMGT5zhu/e5YoV9b782Gst92XLBAfq5Ek+dUqcYkpKPDwET1x6Os+Zw6+9xs2a8YQJglEfP/7Ir73G1apxly6CIeM3bwq6LlSIs7LEl4dqNs2gIBM2bOCwMPb2Fq9f+NZbsg7azJna3RUKiyeuKciePMFYZ8gpzp1A796929XVlYji4uKYOTo6mohat27doEEDItrgqIvJcxESaDAhJET6ge3ra/7jZPt2vQ97b28+d05uj+PHm0o43N2Nnsvct09Qf+1aU33Fx3NgoF79pk21V79t3ao3ejU4WL1C+N69XLmyurBRI/EpwJQUHjeOS5dmhYLr1xd8bcjKkjXHiPAkdFISX7wo63eA9HSeOZPLl2d3d65Th7/5hj/9lAcO5Pnzjc6ZffWqdI4Id3fxwigZGYLRz82aqYdcd+0qvatWLfMByyEZgK7aFizg58/NjBjWbL/+almPkgVfFAo2/LzTnaZQcyiY+b33jIZRpIj5mct1zZwpaETOUT14UPA+OnbMgq4LIKWSv/pKfSGEtzd/+ikXmJ+lIfc4dwLdvHnzwoUL//zzz0qlMjk52cvL69VXX2Xm9PT0kiVLNm/ePOeDdDAk0GDChg3Sj17DX8AllEpBMtGyJf/0E3/8Mc+axZcumdo9KYnr1TOV/fzxh3hH4aVgxua9Tkzkr74STzr2yy/aOroJoqsrr1rFFy9K88ty5QRLx/XrJ81XDhzQqyBzkZQhQ/T2Sk7moUPVZ1U9PPiTT8wMUxFOC63aKldWfx+Q+L//E1TWnaHsxQueOZN79hSfQS9fnplZqRQvbahZCiQpiceP5xo1uFw57tnTskEFwi8eqpn4DFciFG4WXXWalsaFCklbaNFCWm3rVr0BJF5efPIkM/PLl/zaa+IwChfmyZP511+NzliSmMhr1vCsWbx1K2dl8cmTgkbatjX/ECZNEuxo+sccWLBAesRGj3Z0TJDvOHcCXbx48Q4dOqj+3rdvHxGtWrVKdbNXr16Bumt85VNIoMG0LVu4USP28+OwMF692vzp53//FXxa6+YWHh78/femWsjI4Jkz9ZaU092MDfqcPVtQWTjDXXy8qR/WNb9u16ghvcvdnT/+WLCLZAnlO3cEdf77N6MmnN3PcFu9Wm8vw/VWpk0zehhfvhSPwNFswvEDEycKag4cqL734UMzYxIqVWJmTk4Wj/1VzdRmOOY4IMCCEdtr1giel7g4Tkri+fO5Vi3z56ErVOC9e+V2d+aMoIVixQQ1T57kwYO5bVseOZKvX9e76+BBXrWKy5cXxxMaKhgWdeqU3hfR8HB+9Ijr1JHua/o3FhXh96ixY+UegYKpShXpEfP0NLo2O4B1nDuB9vHx6d27t+rvyZMnE9GV/06GDB48uEiRIjkYXd6ABBrs6+FD83lh4cKy1tCeMEG6Y9WqRk/XCddi2LVLfW9GBn/9NTdrxmFhgnEputvGjczMw4aJ723cWFDYsqXerFt79gjqBAfrRauZscvE1qiR3gnm9HTBhGjlyhk9esLrIHW3mjUFe/38s6DmwoXqe4cMMdNmhw4cHs5uboLr+SpWVDfy99+CHT/+2PjrQJ9Sqbdctpsbz5nDt2+byewlm7e33KmL4+MFu7u6ctGi3KmTBefOnzwxFY/hooaG398GDuQrV7STPBYqZOrrky7D35GIeMsWuZEXQJmZ4i+fV686OjLIX5w7gW7UqFHp0qXT09MzMjKqV69euXJlVXlGRkaVKlVqCj9h8hck0GB3YWHmMxg5l8dlZur9KF+pEp8+La65ebOgi6FD1fc+fCheV9xw8/Xlu3cFY0Y1W7t24nI3N/7qK3V3V64IKrRsqRdwZKT5YHr00NtFeGrfxUUwNFP1HcN0xkbEdeoIjmRWljS2QoX422/V91arZqrBSpWMznns5aUde7NokaCCnKEIuvbs4QkTeOZMPn2aL13i0qVlPb+62+efy+ooOdnUha1ly4pHwhgyPUTbzY2TkrSVb90S98XM2dl8+TIfPKheZ2frVg4LYw8PDg7mmTPF43Szs6VrwnfogAvjzNBc56DZPD0xDBrszLkT6O+//56IqlWrFhwcTETTp09n5v3790dGRhLR1KlTcz5IB0MCDXYXF6c3d5juDG6aLSbGTCOaD/izZ3nNGt67l1NTxTW3bBGffYyJ4awsTkoS/PBtYgsO5k8/NXpvx45Gx0UoFOrzkUqlYE1pyU/tBw6IJ1XQ3WrX5kePeO1aXrCAjxzhrCzBkZTMg/b33xwRwQoF+/ryoEHSkRKSzdgKjklJglWLv/uOmcUjmz/4gD/6iL/7jgcPFtzbrh1PnMg3bmjb37pVUE0zSsRS169zsWIWPL+aTfP9ypgDBzgsjF1c2N3d1JNl9qoAZs7K4u++M7WsIJF6bhaVCxcEFfz8pM0aripv7DnNzOTFi7l7d+7enZcudcA6kU5n3jzB6xzAvpw7gWbmmTNnBgQEuLu7d+vWLSUlhZknTpxIRJ07dy4IaSUSaMgJSUn8ww/8xRccEyMYieHpqZcu6Hr2jIcPZx8fdnPjoCCeNMnMh/2UKUYzkvBwVijkzsyguxkb4yH8JqC7ac7U3r/PHTuqC729ee5cQeR//slt2nDp0tywIU+eLGgtIkIvYX3rLUG1FSu0DZ47J13dMCKCe/RQ/+3hobdIYViYeDW1jAxev15w2VzlypyWJhiY4eKiXelQOIe3boQqCQlcpoy02qZNpp7lO3d4yBCuXZsbN+a5c/VOBA4aZPHzq9pMz0d+/rz0alFjm+RCT0NZWdITwIab6uyy7i6GM5xIhtGzaM5HV1d7LpJ34wavXMnLlvHFi3Zr01kolfzll+o3oJcXf/SRZfPfA8jh9Am0SqbOp/TVq1evS64Byb+QQIN9ZWbypUt6v2snJ+sN6nBz40WLxPtmZ3OrVtKcoF49/v137tePW7fmUaPUF6KpPHggSOls34RnHBs0ML/jypV6D+fJE46L4/R0vn2bV6zg+fP5n3/EDzwrSzC6WrIADZFeSufqyu+/r9eI4SWGRLxuHT99yufOcUoK37rFc+bw2LH8ww/iryWPHgmG3tJ/ibLw4kjdBHrAAEGFzZsFHR09Kv2J3M9POlGJxsOH0itKe/XS3mvseTG9gHa5cmbWdvngA7mvFrNDQVatEuyl+zuGmxtv3Srda/Nmvde2jw9fviytI/xBwF7z0y1cqD2Gbm5cAH6OFYuPt2lJJgAT8kkCXWAhgQY7WrJE+6Heti3fvq0uz8jg5ct56FD+5BPBOhQaR46Yz1eKFdNmErt3y81yLNpcXXnWLL0c+t13zV8/R8T16gkm6Vu3Ti/x1V2NRdfdu9qT1v7+pmag002q4uO1LQhPAAcEmF9NRqNPH6N9lSzJzFyhgrRcteigyqFDgi8e9eqJ5y4YPlxas0wZcVovnPnk+HG+epVXrxasg2hiK1SIixXjLl30rgb74w/+8ktetkzvu1nbtrIalHMxovDy1sGDeeBAbtaM337b6NeqM2f4vff4zTd53Di9J1pDOOGj4eW5Fy7wkiW8dKkgBTcmNlbv9wrVZmwGSQCwTl5OoN3NrvXdt29f0xXWrl0rb9VwgIJu+3YaPlx7c88eioqiQ4fIzY0UChoyhIYMMdPChQvme0lMpI8/pm3biIi8vQUVfH0pMpK2bzfVSKdOtGOH0XsVCrp9m/bvp+PHKSODIiOpcWOaNs18bGfOUIcOFBdHXl7qkjt3aOhQSknR1lm5kiIjyfAfT5kytHMnPX9OZ89SUhJdvGi+uxcv6K+/qGdP9c2KFenvv6V1njwhb2+KjqbPP6eSJc00uG+f0bsaNaIRIygsjG7d0ha6udE332hvNmlC7drRzp16O545Q19/TZ98Im3wjz+kJffu0cWLVLu2tPz0aUE8M2bQrl2UlWU0YEP+/nTmDJUtqy3JzqaoKIqJUd8sUoTWrKFu3YiIqlalPXvMNFipEi1bRpUrm6nm4SEo/PZb+vhjWrXK1I5169KiRaYqDBxIH3ygV/LGGxQYqFcyYwZNmULZ2URE7u7UsSMNGkTt2pFCYarlvXspM1NauHs3tWxpai8AyD/Mptgm9i1atGgVyRU6+RHOQIO9dOokOB9mbN4MiTNneOJEwfJ1wq1UKWbmFy8EV8oT8ezZ4kk5VJufHy9Zwsw8fbqZ4R9Vq6oHJyQm8ujR7Ocn9zRngwbapVXWrRNUiIrifft4717pCiwZGdIVWMxu9evzggXqMcHCtRh1T74GB3Pv3oKhC7dv848/8ubN4sn13Nz0LgnV3WrV4rt39ZoSXrZYtCinpEg7NTyZTST+dcLEeXHDTTj/tLs7v/YanzkjbdnwQrFixdQLfZ8+bXQQSOXKHBfH16+rT+3v3ctNmrCPD1erxnPnCk6im3g1rl8veLzyKZU8aZJ2wHq3btJVyv/6S9xv9eqCmad1CddeMZw4/PlzPn7cgjm8AUBXXj4DbT6BTjPw5MmTffv2NW3a1MvLa5dmFtn8Cwk02EtoqOBDV850s0uWWDaUOSSEmcUzykVFcVYWJyaKF62YNUtv9q5z53j+fJ4/nzdvFlzWRv8Nb+3WzYLYVFt0tLqLpUsF92p+HPfx0VtTxvQy5ia2iAh1Dm16mjnVVqiQ3srqX32lzRQNB2D4+4tXqNFsr72m91SOGCGuduSI9Env3Vtax8WFz54VvDyEU1Mbbu+9x//8w126CO4yttqIMN3fsUN97y+/cNWq6sA8PLQHRHdU9x9/SA/amDGCjoTzkxBxVJQ4MNNSU3nqVA4J4YAAbtOG//qLY2OlqbOK4fW7mu311011sXOnYBfNNbLMnJ3NY8dqX8mdOvGjR9Y8FoCCzLkTaGOSk5OrVavm7++fkd+XHkICDfYSFSX40DV7/f69e2au95LMLEHEo0bxs2fi6eT27+fERL5yhf+fvfOOi+Js4vjccYACNqzYUGzYCyoKgr3EQhRbMGqs0ViiidFEgxp7bIk1BqNJJPYSYzex965RsaCiqAh2Aenlfu8ft7m9fe652zua4Lvfz/MH9+w8zzO7t3qzs/PMXL/O1hLr3t1kNDDX6iJC5cq4fdsi641p+fIJgb/cOnaGTaPByZOCGlyHuinvL9OWLgWAa9csehTRBy4beygNk5bY2WHPHvTubW4qlUri0r5wgZ/25NAh9ppHRHCibI1LZOuYOlX+pHRVRX76ifNd6GPxGYyzDRJh0yaJzPPn+OILVqZMGSxZgvR0tGzJHlKrER3NWWviRM5aLVrwFTNP376SSfLnlzwRvXgh+vvNbIXUaBAfb3IJrRYffiiR9/GRONcXLGAnNE4SkgtJScGtWyZTACko5DC52YBWZzj2w8HBwc/P79WrV0+fPs3wJAoK/1eMG8cGVnbtSu7uMqPOnqXkZLbTw4NGj6YxY2jDBtq7VxLW2aIFjR9P69aRVsuZbcYMKliQqlalpk3p00/pzBkKDKTJk2n/ftq6ldS8/xJevKC//uLr9vy5RWHZxiQlUXQ0EVHdumyUKkNaGrVoQT16UEgIRUVxBIKCyMZGfsUzZ4iIatemqVPlhW/fFtQLCmIPAdSxIwUE0Fdf0bVr1LEj/6IZyj9/Ln5s2JDatGFlVCoKD6dbt4RIXB0FCkg+6s9CdzMkJkr6u3SROSMiatyYiGjYMCGIWYedHS1fTuXK8Yd4ebE9NjbCPHrUalq8mBV78oQ+/5wWLqSQEPaQVsu5Z7RaqliRo0CDBnzFzHD3Lq1dK+lJTKQxY+jMGdqxg6pWpeLFycmJOnWi8HBq0sTkPGlpktB8BpWKtm2joCDy9yc/P/rxRzp4kDQGu4rWrGGH7N1LL15YfTo5yZo15OJC1atT6dLk40N3775rhRQUcjOZsb7HjBljb2+fbvkO9ryJ4oFWyEIOHEDDhrC1RbFiGDsWMTHyQ4KDOe4x3fvl9HSMHy/6KatXx9atkhfHZrynurZ6tbwCZ8+a9NIVLy7vQua2okXx999iiPPWrfj4Y3z4oTn3sEYDd3eO9zQ5Gbt3i1mBCxbkxwPok3ukp2PECJPVXvTt2TMEBvIPTZ0quT4rV5qbx8lJSPJ14QI6d0b58qhXz2TZkVq1xJj4mBiOknZ2+PJLIdy8ShWsWycIX78uczoffigJzvnnH0yfjh9+MFdqOzoaf/7JljA0Lo59+LDJRYsU4ecAYeLCnz/n59orVQpPn8rfnwzbtpnUh7n/a9dGfDzatOEL/1d111IiIiT/nLnFay5etPp0coxDh1hta9Y0WZtJQSFnyM0e6Awa0Fqt9tixY4UKFarDrXX7fqEY0ApZjuVpU8+e5W/OmzkTABYuZPstifE1bJ6ewkJRUTh2DNz07tHRJoe3aIE1a/ix3Za0IkWwebNFC+macfmS778Xh8fHC+/cz5zhmJ5MIZLwcH72NP1CnTubPKoPAtbx+rW5kiK60JGLF8UoYfOtQgUxb7Sx9cnke6b/KlampnKKdXt6om1bdOqE5cutLq23dy9KlBDmsbFBzZoYMgR//82RvHXL3OlMn872MEHh4IU2FSyIzz7LyN67sDDMnWvFHfj330INwhYtJPeMRoP9+y1ddOtWMY6ofXuhqKSXF7uWrhp5aiqWLEGdOihRAi1b4sgRq88xm2DiXnQt96in8P9J3jagnXjY29vrHNi/68rXvtcoBrTCO4QJU9Y1b2+h6FfdulbYClwDrnhxpKZi2DDRemjfnpMol1sJXNaJSzyTl2n584tO0Nu3Uby4FWdEhK5dOU5K492Tvr4AJC5YADExGanC2L49GylubCMSwdUVbdrgzz8FGVNuTm7bt08YdeWKpFhMkSIchZs1E4SPHJEkCalXjx9qbAlRUZwSJLod4wkJrC2eni4pA8S0t2/x9dfiK5F27dj42rQ0TmIQjYaTlsQ8Wi1GjbLonjRsy5aJM9y6heHD0bo1hg7F1auWrnvqFPvCp25dJCVxUrBPmAAAX3/NnumxY9adaTZhXLWRpNsiFRRynrxtQHc2Qf/+/f8yrg31PqIY0Ao5RkwMdu5EcDBu3gSABw84P2mFC4sObGdni6wEf3+Tm8x8fTmHOnWCVou1a9GxI5o0QY0a1hkl+mZnh+BgmR2QRFi8GAC++MJq60fXVCoMHiwax5GRHBl3dzRvDnt7ODtjyBAxIYOvr3Vr6R9dDGE2kxmelB4Lq17r2sqVwihmIxo3vqVYMXGVp0+xaBFatUKFCihfHt27CzeShWi1OHkSc+bwDeLOndGggZBwo3t37NiByZMxYQL++gsBASYfRXQ1UN68wdmz/MRw8fH887I2Z8WKFRm5eQ4csG4VY7jVJXWO29270bgxHB1RtaqQv48blmPskn8ncAshnT37rtVS+P8mbxvQCooBrZAzHDkivqBXqzF8OP/leNGi4hAL7b/z502m2p06FW5ubKdKhdGjM2KOMO2zzwBgwgQZMT8/ThoHa9uPPwrX5PRpzlHGvPPxER5C3rxB7dpWrGIYcKJn8GCOpD46WYfsU4Rh06W0e/DAIge5PghHR79+kqOOjpg0Cb/8IoRDJCWZDB96/RrNmplbKGM14QMCsGCBTDBGnTrsqAoVzMlzaduWs3rHjuZ0a9CA8zhkLdzylr/8whfm3pzFi8uv8uQJAgMREIBvvsH9+5nVmUtICPuY17atFUU6FRSyA8WAztsoBrRCDhATg5Il2V/WoCBOp5+fOOrAAfao4Rt/XVOrkZCAmzdNmhEZCGOwsBUpAq0W8fEYMECILihWzIrhlSpJTqdAAXMual3qawDR0RadkWFw58WLmD0bQUEyLnB/fzYIRMeqVaxkoUJsoIKLi6Vn7eqK48cBXh5obvvtN3GVGzdMiuXPjypVYGOD/PnRqxe7jQ8mQmCzqhUoYK7M9dGjkl2VNjbYvVvm38vbt5gwARUrokgRtG+Py5f54UzBweyV191Rtrbw95dUJpfl+nVMn44vv8TGjRKzkuu4PXGCP8njxxzh+vVllr56VfIPIX9+4Q7Jck6fho8P7O1RrBhGjmTLGCko5Dx5z4Auaw05rHHOoxjQCjnAP/9wflk7d8auXZLY5eLFWf/T33+jaVMUKgR3d/zwA374gZ1k0CBBsnt3S80dU0k8MtCOHJFYMJ07o2ZNS8eqVGjdGqVKoVw5fk4Dw+bgIF4TM8l99W35cs63YJy02NcXvXrBzw+//sqxntPS8OABPDzYUZ9/zkp++aX8yRp+XLiQn7/CsGk0+OEHySobNlh6bRnnq1bLL7Kob8a5xq1tZcqYc2dev45Bg+Djg/795VNVaLVsUU8nJ041H5UKDx/i4UMMGIAqVVC/Pr77DvHxePsW1lYvWLlSYuL7+Ah1eQBcvcoG+nt7m9uyaZxae8ECmdWNo5OrVbNOfwWFPEreM6CbSXF1ddVtGSxevHj9+vUrVKigVquJqFmzZsOHD89hjXMexYBWyAG4po+XFwDcvo0JE9CnD2bO5FSZZtBqMWuW6GYbMUIsBvH2LcaPR9my8u/iTZWFM29gGWcLsbfn7FwcMwZffQUPD0sDuC1sjRohLg7btyMoCCdOYNYsMYkEty1cCAB372LkSHTogGHDcO0awsMl9n2dOqheXTzBOXMQEYGVKzF/Po4dw8yZHH+/rrVsyX4v8fEyARJM02j4md0M27hx7CozZ1qxhH6rIoDERHN3xfDh8POzTnlHR06/VQHZZrh4kTN5796slT9mTNYs9+QJZy+srganjsOHUb8+1Go4OKBvX3YP7p49aNMGlSqhXTv88w8iIsTnNI0GX3whEyaRnMzf/sstrKig8J6R9wxoQ+7fv1+qVCkfH58LFy4Ydnbq1MnJyemKPmfp+4tiQCvkAHfvcn4jjR2ZFpKWhkePRCcZA9eSc3dHiRJo0AArV+LtW0vL+2k0qFYN/fsjNBSzZlk0RKVCo0bYu9e6xBSybf58SbHxLl3kw0WaNZOEJms02LcPqanYvx8//4x9+zgpUCwMZa5cmXPZtVrs3o05c+DpmQXnW7IkoqIACPE5uv+fTNUJ5zZ91DiAK1f4pdqJ4O0NWObU17cffuDfP4blADPD779zJmceZlq1yrL4XW5i6XbtWLHERM6Ka9eyA3V1zu/exfHjFhnBaWmcHahqtUUp5BUU8jp524Du2bNn+fLljc3HxMTESpUq9ejRI3sUy0UoBrRCzjBmjOQ30skJU6ZY4bSLjYWFN+nYsRyDoF8/hIeLMqdOSWyg7t2xcCG+/x6HD2PCBLi5oWhRlC8vBA07OOCLL8QCH5Y0jQa9emXKgqxfH/nzQ61GhQro0CFrwk4qVhSvwNGjGZ+na1dz15+bE9DyplajQwcsXYrjx/HFF8KJq9X49FOMGmXFPPps1mbMbnd3ITX47NnyE9rawt0dP/8MrRYBAexRXcJEAOnpWL0aTZuiYkV06SKk6TDk2TP8/DOmTcOOHXwj2EzpFsN27hz/+j95grFj0aIFevUScvOZh7sBV1fJSBbjxxJHR6tjizt1Yidhdo4qKLyv5G0DukyZMv379+ceGjRokIuLS1arlOtQDGiFnEGrRXAwOnaUuE41Gn60riEXLwpOTZUK3t7yKWyfPePU49D9tIeEiGJxcdi7F8HBuHGDM8knn2TKCiRCpUqZGq5zzycny4c6WNX0WaXXrcvgDHZ2YhTv69di1gutFrdv4/hx63ZSWtWME2CbatWq4eVLnDjBpiXWtxo1sHevGCt86pT8nEePirdHZKTkOcHeXtwXOGWKZJStrWDpXriAMWPQpYskLbSXlxiDpCchAVWryuszYwbnvn34kI0dmjtX5t9LRATnzYNxRUZjnj/nK+bvLz/WkEePJKlySpeWea4OD8eECejWDV99xS+NpKCQV8jzBnQrE2kq27Rp4+rqmsUa5T4UA1ohJ1m9mmOQ3b1rUv7JE7b4iIuLfA7dqCiMHs0pKG38YppLXFwGk5oZNu6+tAkTzFXlYNrff2PTpsyqYdjUatGLf+1aRmYoUwanTwPA2rUoXx5EsLfHkCG4fFmM3DDO9VGkCOrVywL98+dnH2yaNUPXrhg4EN99hwoVhM4WLbBhA/8hyrAxtRvHjTMn7ObGWrpv3+KHHzB4MAIDxRs4NpZz55Qpg1atTM781VecOzAkRNxkWaAAp+wL/ZdFkYFJ80cEW1v5rQXLlknU9vS0qMZ1Sgq/kFAGysQkJiI4GFOmYPVqmRdN589LQj7y5TOZEkRBIfeTtw1of39/tVptXDNlx44darVaCeFQUMhauMnLzNQDM67mTYQVK+QX4ia2K1TIIiVDQrLA2jPO0EeEoCB+xjfuPqrx4zFihKXLWRJe0ry55DQ/+sjqk1q8GBcvcsx6MzkuMlY+xlTr1w9ubihbFq1a4eBByekkJGDhQowahYkTOc9Oxm3kSPZ71wXwBASwp1OtGizcDsNNhGy+1a3Lnyo9HXfv4vwVqEv4AAAgAElEQVR5xMXxU7sYBnnr4da6Zy4Ul8uXMXEiRo7EmjU4eRKtWqFQIbi5ITDQnDXcuzf/pLLPMWycE6ZmzexaS0Ehu8nNBrSG5Jg9e/aBAwf8/f179OjRvn370qVLR0VF7d+/f+vWrU5OTrNmzZKdQUFBwXJSUizt1HHnjqWdDAULWtqpJyyM7tyhihWpUiWytaXUVPlVzNCrFy1dKunJl49at6Zp0zjCpi5LQoL8QuXL05QptGcPbd/OHnJwEGdwcaHffpMc/e03qlqVgoLo2TP5VYioWDEaM4aISKViD8XEsD1ubtSkCT18SKdOWTS5JajV9Mcfwt8REVSkCLVuLXyMiqJmzej+fStm02rZnpYtydeXKldmT6ddO6pXz6I5XVysUEBHUhK/X62mypWFv/396cYN9mi3bpxRhQtb2slQvz7Vr09EdPUqNWkiaBUTQzNnUlgYrV/PHzVmDO3YwZ5CgQJUoYL8ihkgIYH+/ZftvHmToqMtOkcFBQXLUctKVKtW7e+///bw8Ni8efPgwYM/+OCDQYMGbd682dPTc//+/VWrVs0BLY2JjY198uRJVFSU1vj/eAWFHGf/fvr8cxo+nDZuJCBTUzVrxun08jIpz/0nWLUqpaXR4sVUsyY5O5OPDx04QEQUH0+zZ1PHjtSzJx0+TLVrswM/+IC/Snw89ehBlStTx45UvTqVLJlZ69nBgb77jr79lmxshB5HR1q5kipVomLFLJ3E25saN5YXe/2aPvqIc7JEEvs7Kor8/WnUKBoxgoYMoSVLSKulmzcttZ6J6OVL4Q9L7gGtlj77jN68sXRyS2D+O9y2jU6fFv4eM8Y665mIfH05naGhFB7OduruLkuoUIE8Pa1Tw8zNr+frr9nvd84c+i//qoROndgeV1f+vWGKuXNZg3jDBs4jq1ZLAwaQlxfnAWDqVFLL//ZmBFtb0hi5xWxsyM4uW5ZTUPi/xnJn9cWLF9etW7dgwYJNmza9q+x1169f79+/f6lSpfT629jYlClTJiAg4OTJk9m0qBLCofDsGUaMQI0aqFcP33yD2FjJUabqdZcumcqflZLC1k2YOJEjlpoqFMKIjGRjoAsWxPHj7OYwjQZ79rBlq6tUkXxUqbBzJ2ctrRbt25t7yV62rExALbc0oC4LxK1b+OUXrFsn1sYzjlLltg8/hFaLe/csCoE4fhwRESha1LrgAVOZ3XJVK1ECKhVcXcWU1YZt2TLhqlq7c7FuXX7ZxfPnOcKOjhg5UpJY2gwPHqBRI0vVcHTEJ59gxw75aRMTsXQp+vXDqFHmCvWlpKBrV3H+0qWFwumWw639vm0bK2YcW6VWo149rF7Nv7BZhfE/VSYwSUEhD5GbQzjyUinvUaNGqVQqInJxcfH09OzYsWPHjh2bNGlStmxZnTE9ZMiQ7FhXMaD/z3n7ljU0DSuNnTzJ+TX9449MrZiail9+wcCBGDECBw6wR+/fh58f7Oyg0cDbGxcv4tIlNG0qUcDGhmNWVqwob68Y/9ampqJdO5lRQUFITsbmzZg9GzNmcAS4+wWDg/mnz80aZjxhrVoICMDAgRbZYU2aYP9+HD+Odu3g4IAiRazYqph7mo2NmK1PpULVqli7FkOHolYtNG3Kt0o3bwYArdaKDIO6FhjI/3YSEyVZMphmYe2S9HR8843MmTKPZEOHWjSzhZw5g59/xrZtlmZ+NKRzZ47CujIJ8fFifUdfX46Yqcx6WcjDh5KUHa6uuHcv2xdVUMgm8p4BrbNHIyIiYMGbyJxRdPny5UTUvn37y5cvGx8NCQnp3bs3ES3UlRfLUhQD+v+cH3/k/BDqHU7ff885+umn2aVMXBy7C6pIETx8iL//ljeJuPvwmFa0KLviggXyowzzJCQkcGw1booJU6m4tFr06GGdtWdhK1AAq1cLq6xZky1LZGtzdYWHB0qVEirRmEpHaPht6vOxWFVKkIj/LkIHt46Jvlno0E1PlyQCt7dH48YoVgyFC6NFCyxZwpn5yBGLZs5uNm9mFatbF+fOwcsLajVsbNCqFW7e5O9r3Ls3JzRMTMT69Zg+HWvXWp3uQ0EhV5GbDWgVeCZyt27diGjVqlVFixbt2bOneQN6y5YtskZ25vH29n716lVISIjGOMKLiIgANG/eXKvVnjx50vJp37x5ExgYmJaWZkbm1q1bJ1xPDPx1oK2trXVKK7wXHD3KCXBs0IAaNiQiunaNzp5lj9aowQ9ltpz0dLp5k54+JRsbKl9e3CwVFkaHDrHCHh6UmkrXrsnMaW9PyckyMkWLUvfukp7Nmyk6WmaUry+5u4sfw8Pp0CFKTxc+FipEH3xAu3ZRfLwoY3yJ0tLo+nWKihJO2c6OIiMpOpqiomRWtwq1mrp2pWLFKD6etmwxtzszl6PRUOXKdPs2269WC5HQDg7UsiWVKSP0v31Lf/1FiYkWTW5vT23a0IMHlJREzs5UqxbZ2VFSEoWG0tu3VKAAFS1KDx7Q48cUF8eObdKE6tSxaJXoaNq9WwxDz5+ftFpKTiYnJ3JyoqdPWfmGDalBA4tmzm6uXaOLF0n3u1G8OBUtSnfvijc8ETk5UcmSFBYmGaVS0ccfk4MDJSbSpUsUGUlqNZUtSw0aKDHKCgp8tFrtqv6rTo045WXJZoichW+MbjfYrJ4z9rEsISEh3bp1M2U9E5FKpfLx8dE5qhUUshAHB3OdpUtzjuqtloyh1dKuXfT8ufDx3j168oSaNycivi0bHU358slPW7o0PXgg6VGp2O1uxlsSDa1eLk5OVLGipMfVlYoXFw2gmBjas4fq16fYWHr+nOzsyM2NXSg9nXbuFDfhPXxIbm7Upg1FR9PWrZx0EBlGq6WQEIqOppcvycZGNDczQLFi5OTE2VGnQ2dEyj7VmMfentLSJJaZnrQ0unuX09+oERUuTBoNlSwp2U9WoAD17k23blF0NDk60suX9OiRyXWTk2nPHuHv+/fpxg1ycqIXLySKde5MRHTrFjvWku1xMTF05gw9fiy5/fTGfVwcxy4nEvebvnPq1KHq1Sk6mpKT6eBByZXRERdHNWrQ48eSJ7R69cjBgVJTadcu8R/y69f09Cn5+WXXtkIFBYXsIgNea61W+/Dhw+Tk5Cz2hpvFy8vL3d09TV/Ui0fLli29vb2zfGklhOP/nPPn2TLRTk548EAUmD5dcnTgwMyuuHQp5+Wvbpfs2rWcQ4GBWL+e029cscKwOJyjI2bMQIkSwkeVCn37stubEhJkEgaXKiUpXsi9IPpmZjMT95X94cMAsHhxFhRtMWzMGbm5oUMHmSW4R3Ux3CtXcmJjGjdGaipSUzFsmBiMXrEiv6xG1jbufrs7d7BtGx4+xJw5KFMGKhU/2bZVrVEjTrprjQb//itze79+LZSYsbYZF/1+5zRrZlLb8ePx8CGGDYOnJzp2xIYNwj8u7r/uTZve9ZkoKORKcnMIh0UG9NGjRwcNGvTgwQMAERERtWrVIiJ7e/vJkydrs3U7sQE61/IHH3xw7do146OhoaF9+vQhonnz5mX50ooBrbB6NQoWFH7qSpTghIdeuICZMzF5Mg4dyoLluLVUFiwAgIcPOcbc9OlIT0fHjhZZIXXr4qefsGYN7t1DjRqSQyoVSpfG55/jzRts3Ihq1fjZM5j2/fd4/Bj9+6N8ebi5YcQIfq0KXVu1in/KffvyZ05N5W/Gyto2eLC8jOGzBxE++ADp6UhNFcv7Gbb588VTe/IER44gNBRRURZdz8y0EiXYonqPH5v7OjLT1Gq8fi0pfGhjg65dMX06du82l2jip5+sXsvGBtOmZf4flhUcOYLOnVG9Ojp2NFlmJTnZ3L6ClSv5o7g3GzfTjoKCQt42oPft26dWq4koJCQEQEBAABG1bdu2YcOGRLRhw4bsV1Jg+PDhOq95uXLlmjVr5ufn9+GHH/r6+lb87/3xgAEDssOgVwxoBQCvX+PAARw9mpFt+9by6aecn9iffwbA3yzYuTMApKfj11/Rrx8GDEDDhubMEd3uvT//NClQp44Vxo2HB5vfg3HYGzZTpcJHjuQIN20qlr/OQKtXDwMHmlPG0Ba01p47cAAJCWjRgi9gnDLi/Hn50tmZb23asOtyc9tlVatWDdev49AhTJ2KAQMkFaSbN2eLXb95g8uX8fIlRo2yaPISJXDiBGbMwJw5uHgRN27gwAE8fiyZMz4eP/2EUaMwZw57KDNs384qo0tmwpCayk8vo1P+xQv+5JMnc+SXLs0y5RUU3ifytgHt4+Pj4ODw559/arXa+Pj4/Pnzt2jRAkBycnLJkiV9fHyyX0mRK1euBAQEFDMotGBjY+Pi4hIQEHD06NFsWlQxoBVyGGPT1s4OoaEAsHEj59fXy4udgZv1Qt90qQBmz84aK6pwYSuEW7Tgn/LevZlVw94eNWrAxQV162LUKMEJKhuCkuFWvTrGjDF5dOpU7N6NoCAhIXFamhgqY6qp1XB2hr19prRSqfDmjXhVHz7MlnM3bO7uSElBXBwnz7Q+EV5yMj77THhzolLJPN3p2+TJwvCICPEthFqNoUOFJJKRkRL3v6Nj1rz/AVC5MqtM+fJ8yQ4d+MovWmRy8suXWb+1kxPu388azRUU3jPytgHt7OzcsWNH3d8HDx4kotX/JYLq3bt3iRIlslE707x58+bRo0dRUVHpmSlZYRmKAa2Q8xgaZ/b24uvgu3c5v9aff84O5waB6NujRwAvG1fGWsmSnE5TIcVVqiA4mH2/r9Xi66+tdgMzzfgpAsDbt1kcP61vunAX7iEbG0mMb6tW2LZNZra6dbFiBZ49w759KFDApJhajbp1Zaa6dEk8/VOnsuXcjVc8cYLT7+mJ27fRqxcKFWIPyT4naDR4+lQ4i5Yt2aO6cI6PPmL7y5bNgholMTH8SJuXLznC4eF8JzQv26pIcLD42FmypLmMgQoK/+fkZgNaft9venp64cKFdX8fP36ciHx8fHQfCxQoEC+7RT97KFy4cLly5UqVKqVWti4rvI8sWkSXL9PSpRQURLdu0dChQn/lyjR2rERSrRYS6hkybhzZ25uc/NQpIqL27cnNLQtUbdeO0zllCjVsSCoV23/3LvXvT4GBwse4OPr2WypfnubOzWyqjYIF2R6AbGzIoG6pgLFWGcDenl694h8qWVKS4OLwYZo+XWa2q1fps8+oalVKT6fbt2npUvrmG/qvQpSIVktXr8pMZZjepEQJGeEs4elTTrVqIoqNpSZNaPNmiolhD6WkUKFC5uZMS6NLl+jlSzp3jo4cYY9u3EhEdPQo2x8RQffuWa44H0dHcnRkO/Pl49xgROTqSj//zHY2aCBTG7xfP3r4kA4fphMnKCyMunTJhLoKCgrvClkTu3HjxqVLl05OTk5JSXF3d69UqZKuPyUlpXLlyjVq1MhmE//do3igFXIVFy+yXlXuK+CjR+HlxQ9g6N9fkLl5M7Nb9D75BNeusQ5FBweEhwNAWhpGj+YUrlOpsGwZ0tLQqlWW+UGdnREfL5zXq1cYPBhOTlCrM+vYNtX8/DiV/9RqrFjBEdbvQJVtRYrg9WvhLG7dYktgyjYHB0md+fT07IpgMfwqIyKwbh1nIW4lEcubmehtOzsA/JhyXaRTJjHe0tqrlzn5+fPh6ChI+vgo8RgKCllG3vZAjxw5MjIysk6dOlWrVr19+/aAAQOI6OjRo23atLl3756u/p+Cwv8VWi1dvky7dtH9++9g9e3b2cTAcXFi1l49zZvTqVO0eDFnhsePadcuun2bqlenY8coOppmzMiIJjY2VLgwlShBs2ZJ+hMS6PffiYgiI+nWLU5OX4BGjaK+fenw4Yysy+X1a7p8mYhIq6WPPqLVqykujrTaTDm2fXyoQgVOf/HiFBREc+cSk5h+2TJ+AR3LX9S9eUPnzgl/z53Lz/RshoQEql1bTEusVtN/W69FuK8mTGfYl2H0aPr4Y/r4Y2KqUVWowE+gbiEqFSfDtJ6UFDp1SsiMbkiZMmLJocywdCm1aSN+bNmSVqwwJ//VV/T0KV26ROHhtH8/mxZdQUHh/cQSK3vWrFnFihXTaDT+/v4JCQkAAgMDiejDDz/8f/DLKh5oBUPCw9G4seia6t8fKSk5qgA3DdakSXzhixfNOfn8/BAXBwCdO8u4A9VqzJtnMmmXcUirjQ1evULTpjLOy6z1hu7ZAwBXrmTNbDY2qFMHgYF8zevUwfr1OHMGfn5wd0ebNhg9GrVqmUzLYLkvecsWAHjxIuOaDxsm3gCJifj0U8ENb2ODESNEX2nmW8eOnJwSdnaYNg2xsdlVj13Xvv8ekZGSWHMHB5P55jLGlSvYtk0mmlmPVosffhCya5cogTlzYLZogYKCgkXkZg+0FYVUUnU7nwEAd+/eDQsLywZ9ciOKAa1giLc3+1s+ZUqOKsCtOcJNs6Wjf38ZY+v2bRljxcYGK1YAQGgonJ0tNXF+/z0b7SfjplajfHnky5epoIXGjdGvnxXyf/whXOTly2Uk27WzaEIbGyH6hbstz7hxK7PUqsXeAzExuH4dT54gMhINGpib0MkJo0dbGnOycye8vDj9R44AwNat1l18Fxd89BFGjMCXX8oL//gjAMTFYdkyjByJWbOErbHvioULWQ1zOHG1gsJ7SW42oK3YgZeSknL9+vWzZ88SUeXKld2yZP+RgkKeIjJS2IFnSA5Xux80iKpVk/Q0aUJdu3Ikjx+nWrUoOJiIqFgx8vbmFEPesoUuXZJZ0d5eeF1esSK/ljiX5GR5mfz5LZ1Nj6ktgFotPXpESUlsLIEhNjbm9tVpNPTzz/K79AzRh77IbhO0cLdzYCC5utKNG3T2rIxk4cI0axb9/TfnkHFd95cv6csvqUwZKl2anj41VxM7Lo6mTqWdO+m/reMiBQpIPhYqRNev87cP6r767t1p1ixRGdm9m1FR1Lw5LV8uv6lOo6FGjWjiRGrRgoKDydmZPv+cypWTGZWtLFzI9vz4o6RQuY43b2juXBowgCZPpgcPckY1BQWF7MESKzs8PNzf39/W1lY/ZMqUKR9//HFEREQ22/e5AsUDraCHGxFRpEhOq/HsGUaORPXqqFMH48cjOpojc+8emxCtTBmO8mo1du2Sd/h16iRM6+ZmkTexYEGEhcnIMCVOuMEP3DCPgQMxcCAmT8bYsXB1tUifzZtx/jxevUJsLL79Fo0b813pW7da58BWq5GcjOfP5SVNZdPr0QNTp6JzZ/Trh927AWDcOItS7/XsCQDx8Zw9mkxZu4QEK/bz2dkhKQkAnj/Hzz9j6lT88QcuX8arV0hMRGAgG8ZjHJqSL5+khsjz5zh4EBcvIipKJqSHCIMHA0BqKvuex/BLsbHBrFmoX18i0KRJTkdSGRIbyz+dyEiJWFgYihcXj+bPn8UxJwoK7x+52QPNN6BfGPz/FxkZWa5cOSLy8vJq2bKlzoCeP38+EZUpUyaS+R/ifUQxoBX0JCRw4oBbtnzXavGYOpXzi26cY7h6dcTECLGbZlrx4sK0P/5okR02Zw4ANhzCMCGGYdU6vQ1knBLE2Dokgrc3FizA0qUYP55fSZs55bVrOdenTRuO8G+/oWxZi05Q1+zt8ewZ0tP5eppv+fMLzwxVq2LrVkEr2YzR+rZ4MQD88gvb7+yMoCCsXw+9f4NbvdJU8/Mzd1ONHcsZwlyxoUOxcaMQiMLw4IHJ5Nm6NmGCIPn8OQYMQKFC0GjEf3GFCmHIEFy7hlWrOGPXr7fuH0jWYpwNvUABMFUKOnViZUzVZ1FQUNCR9wzoOnXq6G3okSNHElFwcDCAtWvX6p3Wv//+u42NzYgRI3JG0XeIYkArGMIU8LO1xenT71onHtxaKn5+bI+uiMOxY6Jlw837VrkyADx6hMuXUakSR0DvKlapoP9fISEB334LV1fkywcvLxw6hNBQbNmCAwf4+w4/+UTicq5USaYijCVt+HD+9Zk4kSN84wYmTLBufgcHLF+Ozz7LrJ7//INHj/iuYo2G7W/WTHC4mglo1tX8u3lTPj5b3xo0QFSUuZvKuKYJEbp0Qc2aqFQJDRuKz0V2dpg7VzJWq8X69WjWDMWLo2JF9O3LeRY9cUIy5M4ddstjgQJYuZIfov3VVxn4V5JlGD+vjhsnEdBqOQVliPDgwbtRWEEhT5D3DGiNRlOnTp2XL18CcHV1bfmfh83QgAbg5+dXpUqVHNDy3aIY0AqGaLVYuxa+vqhcGX5+OH/+XStkgmnTOL/W69Zh5060bo1KldChg6T08du32L8f69cjNBQ1arADP/zQZLCEjQ1rCTVtinv3MHQoPDzQrh2n9KCFFbYDAxEVxU/3a3kzVVT5zRtUrCiRHDUKAJKTMWCAdRlCNBocOoT27TOlp6+vjBu7bl189hkGDsSqVUhNxY4d6N+fDYMxbgUKYN06S3U4d469SnfuYNUqBAcLW/SMi/+Zb4cPi1PNnSs5ZGuLKVPEVyL29vjhBwC4exdff40+fTBtGifFB5kOhpk3L5P/YjJFWhomTRJeKdjbY9w4JCezMkWLctTOkkDI5GQcOoT16xESkgWzKSjkHvKeAX3lypX69es3bNgQgKOj47D/siIxBvRnn33m6OiYA1q+WxQDWiEv8vAhihSR/FRXriypsmGGa9ck5nKDBuaicitX5nQytuD48ewS3CokTE+pUgDw4gUmTLA09pppLi549gxnzmD9ely8yOrw+jUmTUKrVvD3x9q1Eiv/yRMcPYqZMy1daPx41KqVEQ31zTimxbhdvowHD7BsGXx8rJh58GCxcLT5xlQhmTZNfM7Jnx+LFlkaca5vX3whTJWUxHnn0Lw5nj3Dtm3YtAmPHwPAP/9IxGQrfhtK3rhh4b+MbCQ1FQ8fmozGNs7rV61aFiwaEoKqVcU5+/SBQcYsBYW8Td4zoAGkpqYuWrQIgKenZ+PGjXWdjAHdoEEDDw+P7FbxnaMY0Aq5h5QUzJ+P6tXh7AxfX4mHz5gLF9C0KWxsYGuLjh1x754VCyUkYPt2LF+Oo0dljFfjuGrjplYLFpKeS5ckb+dNmUq6NNUAFiyQmd+4M39+HD0q2bjWujWio6HV4t49nDnD33xpeAVq17bUgOvTJ7MlDy1xyY8ebTLPtJlmodVbpYokZvfgQfmLLOun/+QTYbbLlzlHCxWSXPCDB62wmJkvmhvmntuIjJSEPzk7c1z+1pKeznlfNGNGVqiroJALyJMGtJ6ZM2cS0YwZM9LT0w0NaF3/RGa/9/uIYkAr5B6YXVwaDY4flxmSmJipBAWRkfKGlyVWji7Y2pDHjzFpEnr3ho8P33x0chJt3H//NWdicss+f/01unRhO/39xdTF+fKZMzW++84KG27hwoyYtoaN+37f2FLMwMwlSsjLFCokeOj15T8sScYs25YsMXcXGfpfjx3jf7+WPJboCujkCRIT8euvmDABS5ZIcpVkmJs3ORdE79eKi8OECShTBvb28PLC0aNZsKKCQk6Stw3otLQ0X19fIqpcuXLTpk2JaMSIER4eHkRUu3btxMTEHNDy3aIY0Aq5hFevOD6/tm05kuHhmDsX48ZhzRoZ6/nGDfTpg9q10a4dvxpLcLA528XGBkOHspG43IKFprZa7t5tbv4uXcTIikWL+CG/NjY4eRKenpLOMmUQFcWRN45FCQ7mK9a6taVmYrVqiItD165W25c667B4caxahaFD2aPMd+3ubvX8umbJE07p0ihfHoULQ6VCpUpYuZJf8NKqVqcO9L8PISGcW7d/f/Fqt23Ln6RwYfTrh9KlUaYM6tXjCDg7v8sEdpkhIgLHjvHTlVjO8eOca6JP7tGzp6Tfzg4XLmRecQWFnCNvG9AAkpOTFy1aVL58eX326KJFiwYGBsZaGFCZx1EMaIVcAvfH0sWFFdu1SxJQW6cOYmL4E169ypay06WfMyQggLOoSiWxh4oVE+Otq1blOC9dXWHqWdvYcGRa79549UoQ/vdfSSZdnSZNmgh/29igZElUrYohQ4Sqe5bYedwnEEB+U6C9PerVw9ixePkSAJ4+5Vt4ZtqyZXj8WHhCiIkRT0RnAy1YgHLlhPPq3h0//WTd5Hqb6YsvMjKwb195mSJFJEXgCxbEkCFo0wbNm2PyZEnAvXGhPiKMH481a+DtDTc3fklF+i/ddVISNm3ip+42rFueV0hMRL9+4r+gDz/EmzcZnCo6muO579YNAEJDTV5PBYW8Qp43oPW8ffv2xo0br/S/Zv8fKAa0Qi7hwQPOL+J/OxQEEhM5wQC6/BLGGCemtbUFc6c3bsxZ1LgmS4sWuHgRN24gNRVarcQsdnU1F+vZrZu8oebrK4QWWJImuUcPjB+PoCC8fSuf35oI7u6iMjExYhwwkzWC25jggbQ07N+PpUsRFGRRuEXnzti6FVu3Csnj0tOxfz9+/BFbtuDOHWzciM2bcfWq8OwRFmZRgRWm9e6NN28yUt48f342wXOHDoJBr2tqNRo2xPTp2LMHCxZg9Wo8fSpchzdvsGcPNm/Gw4dCT2AgZ4mGDWV0qFABUVG4d4+/UZUIGg0uXTJ5a+VajB9pAgIyPhuz1dXBQdhPuX0754rVrJlVJ6GgkBO8PwY0Q0pKyvfff59VquRaFANaIfdgnH5h6VKJwLlzVvxqcu3Lv/6SyPTqxTdcmB5bW9bHfOsW1q/HP/8gIcHcGVmY6eLIEQCYM8cKE7BsWSxeLC/WowcArFsn1GRxcMDw4YiJQUqKfBSHfpOcMbt3i+amKfeqvulyNl++LIxdvFgc4uCAVauE/hkzJKNKlZLJ3dG0qeAdtza5teESbdvCzw8//YTUVMTGYuFCdm9lpUqS7Zi7domPcLa2QpT5jh2cyc0/D7i7CzURzaQcMV/2xZjUVCxZgvr1UaoUOnTAmTNC/40bmDUL48Zh0ya2+kl2YByVbmsrnGzG2LwZHTqgfn307y+mUrlyhXPF9CVFFRTyBHnSgMilZTAAACAASURBVD5//nzr1q2LFi2aL18+T0/PAwcOAIiNjV20aNGAAQO6devWunXrsmXLWlgMPE+jGNAKuYeICLRqJfwW2tnhm2/YFMunT3N+NatX589Wpw5HuHBhSVzm3r2sQM2anK1darWlOfIYYmJM+hcN2/LlgDWF+nStTRscOgQ/P9SujS5dOGG4ajUuX+bEYXfvDgBaLbZvx5AhJuc3Ff6hIy0NoaEIC0NaGlautEjhihUxfz77fGJnh+vXhTkPHsSwYQgIwKJFGD6cM4OrKzZvxu+/48QJ8d5ITcXIkRyDVTaNNBEWLhTOJTwc8fG4e5cj8803AJCcjOPHOVlZ9uyBVosOHdi7yPwX9+QJAMTGmttHaG3u53Hj2NM/dw6rV0uug68vJ4VzFpKYyD8jvbc+q0hN5SSL1Ne8VFDIE+Q9A/rKlSs2Nja6cOf8+fMTkUajOXr0aKNGjUhKjRo1cljjnEcxoBVyG/fv4/RpvH4tfExKEr288fGcgmeffsqfx1SiCcPdXQCCgsREws2a4e5dTrBvvXoZP51XrzB+PJo2RevW+Ogjfi4zXbBEXJylST/oP/u4Rg0MHIgHD/iPFrpMatyy3rrSIYA529c4v7UZvv3WUp0ttBS9vfkzfPkl1q/HiROsM/X1a6xYIZaPcXOzyP3/0UdYtEi4AdRq/qItWmDGDJPu8KFDASA5GYsWoX17tGmDWbNw547JFQcNEnU2E8hube7nly85T1CtW3OCbWbPtmLaDFCtGrti4cLsk3CWEB4uPm8XKCCUf1dQyEPkPQO6a9euRPTll19GR0cDCA0Nbdy4sZOTExGNGjXq+vXrT58+jYyMTDD/avZ9QTGgFXItN2+idWtoNLCxgbe3EAOwaZPEo1a5svAe35iUFH5xZuPn4uRkhISIVdPOnpVYSw4OOHs2y07q2DHWV1q8OLp1Q48emDDBoqRsxs3ZGZcucfobNgQgCe3VtwMHBH3WruXPaWuLK1dY5ePjzcUArF/P3wkn23QuXobu3WVGeXiw6bcBpKfj3j3cvSuElc+ZI5N9WW+BmWnmE2br9rTpePUK8+dj2DDMmcPxj+qNWkO4eawLFeLkfj5yBLNnY+lShIVxLteRI5x5uNkD27bFixc4fVrwgptCq8WaNejSBb6++OorK9LSbd7MrqjP95cdREbi+vVMhYgoKLwr8p4B7erq6u7urjV4Ij579iwR1alTJ6cUy0UoBrRC7uT1a9byK14ckZEAcOsWvv0WQ4di6VKZEGRuzHSzZvKrP3yIb75Br1745pusf/u8dq2YbcPCKnokF5Dw2WecTZPz5wOQpL/Qt2XLBGXOnDFZMaRDBwB4+xbz5qFFCzg7Q6WCoyMGDuQ8tMTHo1gxS8+Fadw37xs2yA9kjNEXLxAYiE6dMHiwmEE8Kgp79uDoUVy6xAnX5ubYZprxplLDNnOmsNDNm5Jv09GRf2EHDpTovGcPG9NSvTqb0lirlZQZt7fHb7+xl4sbfMJ9oVGmjPgeoEcPkwV3Ro6UjCpXzuSTqjE7dsDLCyVKoGFD/PFHtrifFRTeA/KeAa1Wq7vrYgD/Iz4+noh69+6dI1rlLhQDWiF38ssvnN9+nUVoOUlJHA/f3LnZo7E1pKTg5k3+OZpqkyaxJcQNW+3aePFC9NrqLaSaNTn1VogweLCgyQ8/mJxTrUZ4uBgUYdjatBFc0RcuYNkyzJmDkiUtPRHGLVqnDho3RqFCqFULQUESD/fkyTJTqVSi/RcRwfrvf/qJvezr1onX0MYGkybx1TZM9d25s7ntgJUqiVkUjUMXuM24vua//7J3qZMT7twRBYzvk/z5xSAcHa9fcx6xunfnpy03bNwUGdwKJlaF9CgoKMiSmw1oNfHQarW60Gc9Dg4ORGRra8uVV1BQyHlCQy3tNIO9PW3aRGXLij19+tC4cZlSzAzx8XT5MoWHU3IyzZ9PTZtS7dr06acUEcFK2tpS9eoUFmbF5D170u3b9N131K4d5+jdu6RW09atdP8+ubiQViv037hBf//NkU9KorQ0+vVXWrXK5IpaLU2eTA8ecA4dPEjXrtHAgdSoEY0aRRMn0rNnFp2FRkMbNtCMGdSyJbVuTf360bVrdP48xcRQSAgNG0YVK1LfvnTlChHR9OmUL5+52QCaMkX4e9Ikev5ccvTLLyk2VtLTpw/duUPr19OqVXTjBs2aRe7unGlTUsjfnxYvpiNH6PZtSk/nyDg707hxdO4cFSxIRPTokUU35/Dh1LIl25mWRg8fSnri4mjhQvHjP/+wQxIT6cQJSc/Zs5SayooB9MMP9N+WHyIiOztWZssWSkxkOy9e5Ch/4QKnU0FB4b1E864VUFBQyCBVqljaaR5PT7p9m44coZcvqV49qlcv86rx+fFHmjyZ4uOJiIoVo5cvhf6QENq9m/79l0qUYIc4OVk6+ZAhguZTp1JcHJUqJSykJymJ/v6bAgLo1CmKipIcSknhTHjtGlWuzNptDIUL0/XrJo+OGkWnTsmobWtLc+bQnj104gSlpVGFCrRwIbVtS23bUmAgEVGdOuyQR49o3TrauJH++YdatqR8+SgpydwSS5aQtzf16kXHjrGHkpLo33/J11fS6eJCAQHix3HjOAOJaN8+2rSJ9u2je/f466an0/z5pFIJH8+dM6eknk8+4XSGhHA6Da889wownfr7jekcOZKaNKGtWyk6mjw8aPhwViYtjaKiyM1N0lmkCGc2Z2dOp4KCwnsJ3wOtoKCQ+/H3p9KlJT3OzhLTx3IcHalzZxowIBut523b6MsvRaOWsWaiouiHHzij2rUjjenH/Hz5yNOTOnWiX3+loCCxf9IkSkjgyN+/T0R09y7nkN7O03P9uoz1TETz5plzAMtaz0Q0ejR16kSjR9OxY/TsGT14QP7+4tH0dLp1iz8wPZ3GjiWVipo1k19lyxbatIkePeIc0j+iALRxI7VuTdWrU48egoebiLp0oXXrONcnMZHu3zf3iiAhQeLxBeT1rF6d6tfn9BuUweV3enlxBJo0kXz08ODI6Do9PGjOHFqxgoYMoWrVWBknJ6pQge308qKiRdnOLl04SygoKLyXKAa0gkJepVgx2ruXmjYVPjZoQHv2ULly71Qn06xZIyOgt9gMcXeXmEFqNfXtSz17UvPmNGYMhYXR2bO0ezeVK0ft2lGZMtS0KY0YQUuX8s21GjWIiGMhERFAEydS+/YyStraUsmS5OZG7drRzp00dCi1aSMzxAy9e1NEBFWvTv7+5O1NnTuzNq6NDfuMZMjNm5SYSEuWSDz33OeNyEgaNoxzTcqWpVq1hL/nzaOAADp8mG7fpm3bqEkTOn9eONSnD7m6smPValKpqHp1k+qVLEmrV9Pjx8LHxo05Mvb24hNIvXq0dSvZ23PEmjThvFrp10/8e+xY1vKeOFH4unWkpVFcHPv9urjQN9+w006dyvZMnkxqo59KZ2dav56KFRN7RoygAQM4yisoKLyXqMD7nVGpVC4uLnXr1jXs3L9/v3EnEe3bty8bFcwFBAUFDR8+/O3bt06Wv05WUMhBYmJIq+W/U8491K1L166ZE+jXj4KD2U5/f9q+Xfyo0dCxY6y7ce9e6tRJXoF69ejsWbK3p7dvqWRJTlTrrl107hzNnGluko0bqXdvSU9SErVvT8ePyytgSP78dP48BQfT/PmSfl9fNl5i8mSTKhUoQNHRpFbTmzf0yy908yaVK0deXtSxIyvZqxdt3sx22tjQkSPk40NElJhIhQuzoSytW9PBg8LfX3xBixaxMzg60urVtHChudhfBwf64w/y96fHj/mO5CdPKCyMihShGjUkdmpUFC1YQFevUokSNHAglS5N/fvT5ctERE5ONH48BQZK5JOTaeVKOn2aChSgbt3ogw/EQ6Gh1KuXcPupVFSmDJUuTY0b06RJ5OLCUenPP2nOHAoNJVdXGj2ahg7lOOB1REfTkSMUG0uNGknsdQUFhSwhJSXF3t7+1KlTXtzXTO8W7tbCzM/wPqFk4VBQyDx9+sjkOti0iR0SGsoRK1wY//4rEatfXz6xQ5MmQoI/HUxFOiI4OuL5c8yda26SfPk4aZUBaLXYuRPffYfevS3KMlGxIsaPN5n67dkzyeQpKRg+nJ/mok8fPHuGCxfEkjo6+vaViDk748ABfn4MPRcvcgScncUTNJWzuUABhISgb18ULAiNBiVLomxZVqZQIbx6hdRUTsmS0qX5d0t4OJswe9EipKfjyBE0biz0lCyJdesAICoKw4ejRg00aIDJkxEXJ5kqPR1167LrWlvCUEFB4Z2Qm7Nw8M3fe9aQwxrnPIoBraCQef79l80xzHx0cUHnzihVCuXL49NP8ewZ/vqLb7SVLy+mZktLs6getb8/nj8XlUlORosW4lE7O6Eqx5UrbMphfdNosHKlzDmaSXhn2LiVQfRNX7XbkBcvsGOHpF6Jpye6dhV1GzUKqani2S1YgEaNUK0aevTAb7/h3DmULs0uNGKEOP/jxxxN9PV0uBmU9W3HDkEsJQUAWrfmyOzbBwCjR7P906bxr6Tx45a9PZ4/Zx+WNBrs2sWmEWzVSpLp79Ytjj6NG8t8lbkKrRYbNmDQIAwYgOBgc5V6FBTeM/KeAa1giGJAKygYc/s2+vZFnTpo2xbr11tUCeL0abRogQIFULEiJk2Cr685s6x+fb5bVNe2bBGn5dYRNG6enoKFp0OrxZ9/YuJEzJ0rZhS+fh1ubuIQtRoBAfj6a8yZY1HVaG5VGuPGLdata7a2EiUZdC7Y337DiRMcE3PKFImwVouvvhKfBypWlCTJrl9fTM8MICYGBQuyE06eLBw9dszc6WzYIFmXW+t7+3YASEzEqFGCSvb2+OYboRqiMZUqcSZZtozTaVxVnv4r/K6DW4CwfHn5bzP3EBAgUd7PTym8ovD/gmJA520UA1pBQU9yMlatQkAA6/c15Uo0RVKSfAGLDRv41hhJS72MH2+R2UqEvXvNqfT6NcqXl8g7OuLuXevO6/PPZXTgmob6ZmPDRonExuLpU3aVhASOp5wxCoOCWIEqVbBkCQIDsXmz6K7WMXgwK1yqlGjKv3pl0ui3sWHrZht/Hba2YhF4AElJuHcPycnmLmODBpy1ZszgdHJrcZcuDRcXfPABzp7lK+/nJ/tNyhMdjcOHceoU4uOzYDZT7NnDOcHNm7NxRQWF3ENuNqCVLBwKCgqWEhdHjRvTkCG0YQNbk2LGDIqOZuWjoykoiCZNot9+YzftpaXxq28YcuMGbdpElStzDtWoQQCtXEm1atGKFZyEYlxu3jR3dPduNglGfDz98YdFM+tZvJi6dmU7bWyoXDnq0IEWLqTTpzkpHfSkp4u5L8LCqF07KliQSpWiSpVo715RLCKC0tLYsRER9PPPYrkW412Dd++SlxfNmEE9e1JyMoWEiFVUduxghZ8/F78yXUkULpMniwmSU1IoPZ1692ZTwcyeTWXKiB/t7alSJU69kpcvae5cGjqU5syh5s3ZoxUqUOvWHAVKluR0RkZSVBTt20e+vhQWRhMnSo7my0czZvBPx3J+/ZXKl6dWrcjbm6pU4dfiyRJOn+Z0WpIhUUFBIXt51xZ8HkDxQCso6Pj6a3Pe06NHJcLXrkkKR1eowJZW9vCQcdbqYo4fPkThwpJ+Dw8kJ3M2/LVsKTPh1q3mzm7KFM6QPn1krkl6OlavRosWqFEDffvi1i1OLESRIpJ37swbeabt2gUA8fGoUUPSb2+Py5eFGdLS4ODAH16oEP75By9fcoKedZOnpODzzwUHtlqNIUMQHc2P/A4PF3VOS8OyZfDwgIsLqlVDy5YYMgT79wtHL12Cjw80GtHXq1KhcmUMG4Zjx9gr9uwZBg9G8eIoWBCdOgmxMSEhkl2DBQuiWTOJR/nsWaSno1Ur9pr8+qvJsHVd69QJ6en49Vf4+qJaNfTsiatXZb5TWc6eZd/AFCrE32AKmIvJsQSu3/3rrzM1p4JCXiE3e6AVA1oexYBWUNDRpIk5S+XmTYmw8Vv4zp0lAps3Q6UyOZuzM548ESSvXEGbNsifH0WKoH9/REUhPR0FCrBDqlZF1aomJyxfHm/emDu79es5o6ZPl7kmTMSCoyN/dcNEGU+fso8E+ubkJGx25L64L1cO8+YhKQkAZs40eaaFC6NIEU6/SoVHjzBsGNs/fDhf7XPnLLkp8OgRfzkizJrFCicno2FDiUzx4njyRGIu61q9ejhxAsuXY8sWxMYKw1++xODBKFgQNjbw8MDhwwDw008mL7vuomWYK1fQqRNKlECVKpg8WQzV4EYN/fILOzw4GFWqQKVCqVKYNk0masUUp09z1jpwgCP59i2bgURBIa+jGNB5G8WAVlDQ4eVl0kypWVOyIezVK45x7OQkkTFO16D3X1asiCNH2NUNnbj373N0UKvx6JHE7W1ojV25wk545gxmzMD06Th5EgBiY1GlimSUszPrNWeIjOTE19rbsz2lSklGBQfzr2G+fOKGvMWLTV7qli2Rno70dCxbBnd3c1sSmTZyJN6+5WTEs7dnM6LoTc+EBNmbAlOnmlzR0RF9+ki+yp07OWLffstJpaJSiblWjNE9RehZvtykDk2ayJ8Cl9BQybZLInTvLhz6+GPOQsw2gA0bWIEvvrB06ePH8dVXGDYMf/yB9HRMmyb51iZMYOUvX0bTplCpoFLB25vN86igkHdRDOi8jWJAKyjomDSJb6O4u7NJKp494xuI+tfZ6emsdaJru3bh2jX5t96JiRyTq0wZ7N3LmXPAAM4MTDjK2LEAcO8eOnWCrS1sbODlhQsXZNTYt4+zXPHibM+iRZJREybw7bz790WZ/fvNmcKGe8hq1pQ3nR0dsXQpUlOxaRNfoHJlfr/u0cI8lmS/1ntn58zhHO3WjfPUoVaLjueUFPz4I9q0ga8vvv1Wkj9ER5cuJpdevFj+FLgYb6wkEsI/5s3jHPrrL8lw4/TkGo1FDyTTpklGtWiB1FT8+y/mz8fcuZx7MiqKfWgsVYrNJq6gkEfJzQa0solQQUHBUr79lho1Ej/a2tLnn9PRo3TtGluGrUQJqlqVHd6kCdnaCn+r1fyizY0aUe3aopgp8uWjXr3Yzv796fZtjvDTp2zP8eM0d66kZ9EiOnCAKlWi3bspPp7i4+nUKWrYUEYN7g626tVp6VKqWJFUKnJzo+XL6fPPJQLcgnwdO1LFiuLHFi2oQQOT6168KP5tSX0uV1caNYo0GoqI4BzVaGjgQP7AyEj5ybm7PBnGjqXkZEETY9zcOGfh4UEFChARAdSjB33xBR08SMeP06xZ5OVFCQkSYf3WSWMMr6pVhIRwOq9fJyL69FN22mbNJOUwAbp1ix2blkZ371JsLB0/TqdPU3w8Z/6bN2n6dEnP0aO0YgXVrUtffUUTJnDuyU2b6PlzSc/Tp7Rli8nzMs/Fi/TNNzR8OK1ezW4UVlBQMEQxoBUUFCzFwYFOn6a1a2nsWJo1i65fp8WLqXlzvr27ahU5OIgfCxemwYOpUydycyNfX9q4kdq1Y4fUrcs3SbksX049ewp/q9U0fDhNm0b79nEkjU35w4c5YocOCX/Y2vKNe2Nq1aJq1djO7t1p1Ci6f59SUyksjEaMYAtB+/lR4cKSHnt7thq5vT3t3Ek9e5KNDWddZ2fx75kz2awXGg0r36SJ8Ef16pzZmjalr7+mmjU5h+rW5XQakpJCzZpJvmgu8fF04wYRUfv2VLq05JCdHfXpQ0FBVKqU2FmsGK1eLfx98CDt3CkZcuMGBQVJeurXN7m0TvLkSZo7l5Yto3v3ZFTVU7Ysp1N3qQsVopMnaehQqlKFatWi8eNpzx7JZdc9OzGo1XT+PFWoQM2bk7c3Va5Mu3ezMqdOcbLTmC8Uf+eOpZ2yLF9OTZrQ3LkUFERDhlDTppyK9woKCgLv2gWeB1BCOBQUMkZ4OAID0a8fpk/Hn3+y2RKmT5eEH5QujWvXrF4iMhJnzuDlSwC4cYPzYt3GhjMtNxblyy8zco7XrqFaNTHq4LPPLCoUd+gQWz9Po8Ho0ZzYlVOn2Otma8sGucbEYNYs9OqFESNw9ixbcMTFBVFRgmRSEru5094eoaEAcO8euy9z0CBzp6DVYto0oTq3SsXZ08m0+vXRqxeOH8eFC6hVSww20KdGiYnBsmUYOxaLF0v2XM6ezZnt448lyjx8aHIjo7s7PvlE/Ghnx9ntx2X7dnaqatUsisHQsWgRO/yDD9jc505OkrgdAKtXc06hd2/rFiLCsmWW6qknIoITSDN1qtXzKChkIbk5hEMxoOVRDGgFhczj6cn+Njs4ICEBW7Zgxgz8/rsY8Jph1q7lWBL6etSGcGOX3d1NZiIDEBqKb7/FkCFYtIjNdZCcjBMnsH07W1LEPCkpnHx23PRkixeLlk3+/AgKkp/82DEMGQI/P0ydilevJIeePMFHH8HRERoNvLxw9qx46NYt9OwJNzc0bIgFC2Qi0ZcsYZU3zt/HbRs2QKtFWBhu3jS5xJs3GD8eHh5o3BidO3MmGT2aHRIWhm7dOJLG2WDs7Vmz1RQLF4r5PerXR0iIRaN0aLX47jvxAeOTT/ix70uXSkbducMJ7jf/jWdVDPS2bRz1WrWyeh4FhSxEMaCznpiYmIiIiMjIyHRLvD2ZQzGgFRQyiVYrWBJMs6Q+to7wcNGNaooDBzhLdOvGF+7fnyPcvDlfePt2ie+wQoUs2KSVlsZJvlasGF/40SNs2ICNGxERgdu38eefOHfOIle3KdLTM5hVTU/t2haZy8ateHGZStQJCezkxplGuHnctFp2N6FGg7ZtOTr89pulpxkTg9Oncft2Bq92cjJCQ6H79eDuSpw4kR3y44+SnBt+fvJLZ0kWjr/+4qjXpk1GplJQyCpyswGdx2KgQ0JCPvnkExcXl0KFCpUtW7Z06dJ2dnZly5bt06fPKaU0k4JCbkWlkkS4muk05p9/yM2NKlQgFxfy8KArV0xKenpy4la7d+cLr1kjKY+n49gxzna0lBQaPJhSUsSe8HCaMEFec2OSkmjNGpo0iVasoDt3ONvIXr6kt285A8uVo48+In9/mjCB3N3J3588PalRI3rwwGod0tPpwQNKTubUAjQlr9Vy+u/ft2i4cdnFFy/o4UNJT1KSZL/amjXCXj09Wi3lzy/8bWtLs2dTmzactVQqCg6mYcOE3YfVq9PWrVSwIEfS8tDeggWpaVOqVs1c/Ugz2NlR1ark5ERkIqDcuHPsWLp4kWbOpK+/pp07accO+aXr16fTpyk2lt6+pZMn5SPXuXh6ihdZT8uWGZlKQeH/gndtwVvBqFGjVCoVEbm4uHh6enbs2LFjx45NmjQp+99v5pAhQ7JjXcUDraCQeYyrGLZtKz/q1i225F6ZMkLEM5czZ1Chguh9NHbvGcINOTBOF33pEkfMzc2Cc5YSGSnJFmeqlsr69SZnCAxkha1NcjxvnhCsrFajf3+ZyjLXr6NdO9jbw9ER3brhwQOkpuLWLdy+jdRUth4KEb8sjvFFNkzwfOYMGjeGWg07O3TpIkRWDB3KmUethlqNokUxc6b8aWq1YtGThQs5s72TTMlv37KFfho1ymydwqzlt98kASS+vpl9TaGgkElyswc6zxjQy5cvJ6L27dtf1lezNSAkJKR3795EtHDhwixfWjGgFRQyT3Iy+vQRf5ubNRMLDZrB2GQkQnCwuSGJiTh2DDt3mgto1uHjw85sb89W6ADw778cHapUkVeeoUcPvsXMNDN+AHd3jnxEhKUKrFzJju3Rw6Tw06coWVIiXLYsypcX/q5YkVP5/OOP2V1oNWpINvDpWosWwhJhYax5Xb064uPRr5/MJfr0U4svOpCUhEaNJMMztls0S3j6FMOHo0oVVK+OL76Q7JXMJdy8idmz8dVX2Lw5UzFCCgpZgmJAZwFeXl7VqlVLTU01JaDVan18fLy9vbN8acWAVlDIKsLCsHs3rl6ViYLVY7zNjrIoM8CZM6hTh515zhyOZEoKa0paa8MB0Grh7GyRAd2/Pzs2Ph6TJ6NmTX7RwUuXLNXBw4Mz3JQ7//vvZfQsWBCLFqFaNajVKFUK332H5GTs2ydUc1Sp0K4d7t1DTAyaNxdH1a6N8HBhCW4ulK1b5Q1osrjMuI7kZCxdit69MWQIdu60YqCCgsK7JTcb0Eb5QnMrISEh3bp10xgnOP0PlUrl4+Ojc1QrKCjoefGC5s6lCxeoSBHq3Zs++ohNS5yTuLlx8uMS0dOntG4dRURQzZr08cdiLCY3bzG30ypCQ6ltW4qLE3sKFqQlS6h/f46wrS0FB5O/vxiyXKMGff+9dSsClpalaNaMHfjRR7RrF1/Y3p4tYcPl5k36/Xc2sFhHWBgVLcp2pqXRyZMyc8bGklZLt29TWpqYArlDB7pzh168oPz5hcBfIjpyhE6fpjt3qHx5at5cFA4N5UwbGkqOjvJndPo0NW5MRATQjRsUGUnu7vwKNURkZ0ejRtGoUfLTKigoKFhInjGga9Wqde7cufT0dBtuXQEiIjpz5kytWrVyUisFhVzO8+dUrx5FRQkfd+ygS5dowYKMT5iURD/+SH/9RUlJ5OVFU6aQi4vMkNev6eBBev2aGjUiDw+OwIkT1LkzxcYKH2fPptOnhf2FgwfTkiX08qUoXKMGdemScf11LFsmsZ6JKDaWKlQw+WjRrh3dvk0bN9LTp1S7NgUEEEA//SQ8lvTsSU2byqyoVpO3N+3fL+ksVYqSkig6Wuxp04YGDZLInDtn0nomohkzKF8+maW3baOAAL75rlJxCsHcuEEBAXxrm0G3F9DYrVG8OLuKtzd5e7NiVapw5qxShUqXpp9/lllad9YRERQQINj6ajUNGkQrVnD0UVBQUMh63rUL3FJ0ruUPPvjgGq/WQmhoaJ8+fYho3rx5Wb60EsKhkHf5/HPO6+979zI4m1bLJuUtn0os5AAAIABJREFUV04mjvOff1C0qCjfrx8bWKnVitv+9M2wcsTVq2jdWtjK1qMHHv6vvTuPi7Lc/z/+GVZlEWVzA9JcEFSwFEVwS1wIt+xohqmZK1lqj2N12shOejyW/U5ppmlpmQsqhpVpiye1477vmuCCimmpYCoii9y/P8Yvy3Azw43IPSOv54M/mGvuueYzXN325ua6r+tsOYsvKipK5ccyZ05ZX37jRrF11uztlY8+svyqlJRiP4pq1ZT//vfu7IJ+/ZShQ5VFi1Rmnc6bp1Kqq6sSFaWsWFHqey1bpoSGKi4uSrNmpndhmp+Ikp2tBAWVaaqJiDJ/fll/YqqOHzddyO/hh5W//lJyc1Wmpxf9cnBQjh1TFKXY5BDj1z//eU8l6e7ECeWHH8p/hgIPGGuewmEzAVpRlLi4OGPo9/f379ChQ9++ffv169epU6eGDRsa24cPH55fxpmVWhCgYbvat1fJH2aCl3mbNqn09s47pR5/7Zri42N6vMnOEadPq/Tp62vaVW5uWadNl8WoUSpv+uOPZX15ycm7Tk5lupnvyhVl6lRl6FDljTfKuuvK99+rlFpynnRRX31lOft6eiqvvaZkZZm+ds8e9eNbtFDq1y/W0qRJBex988svhXdGdumiHD9+tz07W/nPf0z3BzF+2dkpM2cqiqJcvKjyrMm+OdnZyurVyocfKt99p5R+B41VyMhQYmIKP8jf/ma6Xw9QBRGgK8z+/ftjY2O9vb0LrqDb29vXrVs3NjZ206ZN9+lNCdCwXb16qYSMn38uZ28ffqjSW79+pR4fH69yfHR0sWNSUlSOKW0/kYpScn/swMDChc8s6tRJpeakpPtS6l9/KX5+pu+1bp25lxhv4zPz1bBhqa/97juV4x0clMWLldRU5emnFS8vxctLGTy4Yv4UYPTnn8pff5k2rlmjUkmzZoXbAaouL+jhUdhDamqxZeNatLC8F0+53b59ryu+lbxfduzYCioOsFnWHKBtbCOVVq1aLVu27PLlyxkZGefOnbt48WJOTs7vv/++bNmyzp07610dYHV69zZt8fW9e/dVOdSuXdZGERk7VqZMUWn/669iDxs1UtnQpE0b+eknOXasHDWWSUSELF5cWHlkpHz7rbi4lPXlqluQODpWTG0matSQpCRp2vTuQ1dX+egjefzxUo+/fVtOnrTQp+ouJEaqe3Dk5cnQoXL4sCQkyJUrcuWKLF1a6h175eDjo7Ldyd69KkeGh0vz5ne/DwpS+ZkXrX/kSElOLnx45Ij8318xK9Lhw/LYY+LmJq6uEhUlR4+Wp5OcHElKMm1cuVIU5d4LBHBf2FiALlCzZk1/f39fX99Tp04dO3YsLy9P74oAazR2rIwaVfjQ11cSEsTDo5y9de0qnp6mjapb/W3dKvPnq3diEt8NBvnyy2JboFWvLj/+KNHR0ry5PPaYXLpUzmrNe/ppuXBBkpPl0iXZskXlXjozunY1bXFzk3btKrC6YsLC5MgR2bdPfv1V0tJk4kRzB1erVuqvNEYhIfL++6U+GxAg48apPzVrVhlqrThF/tCo3li9urz9drFnHRxk6tS732dlya+/mr58/Xq5c6cCa5TLl6VnT9m0SfLyJC9PNmyQnj2L3fNaRunpkp1t2njtmty6VSFlAqh4NhOg4+PjFy5cWPAwLy/v/fff9/DwaNq0afPmzd3c3MaOHfuXyaUtoMozGOSzz+TQIVm4UJKSJDlZJfyVXe3asnx54bIb1arJjBnSo4fKkdu2qffg5ydvvWXa2K2bHD8u77wjo0dLVFSxbZY3bZLnnit/webZ20uTJhbipqqXX5aoqMKHzs4yf77p0hMVy9FRHnlEOnWSmjUtHzx8uGlLkyYSGCghIfLuu7J3r4VOZs5Uz9Cqq87dPz16qCww0qOHvPOOBAZK7doSEyPR0bJwobRrJ/7+Eh0tmzZJx453j7x1S0peV8nOVsmpInL1qsyfL++8I0lJ2hJ2QkLhEjdGFy7IypUaejCqU0flv8NGjcq0oh8AXRgUG/kTkcFg6NKly8aNG40PJ0yY8PHHH9eqVSsqKsrLy+vgwYM7duwIDg7et2+fs7Nz2bvNyMh46623zF/APn78+ObNm2/cuOFWsK4pUIVlZsquXZKVJa1bl5o+P/5YJkwwbXzkEfnvf1WuYRsdPixTp8rq1aYLrhkMkp5epuBYmRRF1qyR3bulZk3p3199cWu95ObKxIkyf77cuSMGg9jbF4uSS5bIM89Y6CE1Vf7v3uxC3brJ+vUVXKp5y5bJ88/fXd/Q2Vn+9S/ZvVtWrCg8wMVF9uwpdV3wpk0lJaVYS6tWsn+/6WFbt0rfvpKefvfho4/Khg1l/SvNCy/InDmmjRMmyMyZZXp5UQsXysiRxVoSE2XAAM39AA+SnJwcZ2fnrVu3RkRE6F2LKZu5Al3U0aNHZ8+e3bZt25SUlMTExE8//XT79u0LFiw4duzYtGnT9K4OeMC5uspjj0lMjLlrt127qsxPjY8vNT0fPCht28rKlSrLFSuK6UU+a2AwSN++MmWKTJpUqen50iUZMULq1RMfHxk0SE6fVjnG0VHmzJHLl2X3bunUyfRC7IsvWr7IeuWKymrKRTciOX9eYmPF01M8PKRfP9OcWlEGD5ZTpyQpSVaskJMn5fHHi6VnEbl1S957r9SXz5lT7FM4Ocns2abH5OfL4MGF6VlE9u2T114ra4WlLWVdDiNGSFKSdOwofn7y2GOybh3pGbBuet/FWFYi0qVLF+P38+fPF5Ft27aZHBMZGRkWFlbhb80qHEA5fPhhsZUunn/e3MEmy0sX/XJxsfYFyCrKiRNKbKzStKnSrp0yc6bKp87KKrb+tIji769cvWquTw8PlR+pcRFlM0JDTV/StGnhszdvFq49Z/yqX1+5fLlcn1mL5ctVPkvr1uZecuSIEhenREcr48YpJ06oHHD8uEqfjRqVtaQLFxRv72Kv9fU1Xetj7Vpl0CAlKkqZNEm5dKmsPQNQrHsVDpvcsiktLU1EWrZsadLesmXLpUuX6lERAFMvvSQ9e8rPP8vt29K5s4SHmzu45B/WC7z7bpXYW+70aQkLK9yOcedOOXpU5s0rdszq1aYbBJ4/L198IZMmldptjRqmy54YG824dk0OHTJtTE2V7Gwxzo9LSpLffiv27IULsmiRuTIqhL+/SqOfn7mXNG8uc+eaO+D27bI2qqpXT9aulXHj7q4Z0qaNfPrp3U00jd57r/B69i+/yKJFsn+/hZoB2ASbnMLRpEkTETlr3Ea2iEuXLjVo0ECHggCoCQqSiRPlH/+wkJ6llLXwHnlEliyRv//9fpRmdf71r8L0bDR/vumCdEeOqLxQtbFATIxpS2ioyrqBRdnZqWxpbjAUNqouL3j/1hws0Lq1ynTnIUPuqc/gYJXp9SV3HTejbVvZs0fS0yU9XXbvLrZZ/ZUrEh9f7OCSLQBslC0F6OTk5KlTp65atSowMNDHx2dqwXpFIiKye/futWvXhoWF6VUegHIrOd2zVSvZu1eeeUYlzD2QDhyw3Ki69PJDD5nrdsaMYnGwQQNZtsxCJTVqyKOPmjZGRhaufq16maISrl04O8vXXxcmVBcXef/9e50o7OQk8+aJvX1hi6+vfPCB5n5q1ZJatUwb9+9XmdO/a5fmzgFYIZtZhSMgICAtLc2k2g0bNjz22GMi8vrrr8+cOdPFxWX//v3+qn/nuwfz5s2Li4tjFQ7g/rlzR8aMkYKVKkNCJDGxcPeQqqBXL1m3zrRxy5Zi8ff336Vly2J3vFWrJnv3SnCwuZ4VRTZulKNHxc9PoqOLLbldmsOHpWvXwvWM69SRzZulceO7Dy9elJCQYqsdu7rKvn2VNF75+ZKSIunpEhxc/hXNTRw6JF98IRcvSvPm8sILpd7qqtWuXSqrg7dvX+oijwBMWPMqHDYztfDcuXNZWVknT55MTk5OSUlJSUlJTk52+L+pkd988423t/fixYsrPD0DqAT29rJggbzxhhw+LHXrSuvWVWLec1EDB5oG6EaNTK8E16snSUkycqScOnX34Zw5FtKziBgM0rWrtvW/W7aUEydkyRI5c0aaNJEhQ+5Om1YUWb5cPvtMXFzE3V1u3BAReeghmTev8n7bsbPTtutNWYSEyIcfVnCfxm79/CQtrVhjr14V/0YAKp/NXIE279ixY82aNbOzuy8zUrgCDaASvPaa/L//d3fVucBASUiQRx5ROSwvT1JSJDdXmjVT31S8qJwccXCQivqnsegtcSLi4CDLl8sTTxSbAoGitm6VAQMKd9Ps319WrLhfu74DDx5rvgJtS3OgzQgODr5P6RkAKsf06ZKaKt9/Lzt2yKFD6ulZRBwcJChIQkIspOedOyUyUlxdxd1dBg2SCxfutbysLNOts/PyZO5c0rM5kZFy4oQkJsqcObJ9uyQlkZ6BB4Rt/5V07dq1X375ZWJiot6FAEAFqF/fwhIZZXTqlPTocXdZj7w8WblSkpNlxw7Rsk+rqWPHJCfHtNHM+oOVT1Fk2TL5+mu5dk3Cw+WVV1Ru7Kt8NWqwJQrwALLtAH3y5MlVq1bpXQUAWJf5800XxTtwQNavl969y9+n6lKDRdc81t2ECYV7DW7cKAkJsm+fuQxdsLI1AGjFtAcAeNAcP67SaLL7iVZ+firreVfgtdWbN2XyZImIkIgImTxZbt7U9vJjx0x36k5NlfffVzlSUWTWLAkIkGrVpH59mTFD8vPLXzaAqsm2r0ADAEoqWHKuqEaN7rXbZctk4MC7u+6JyJAh8tZb99qnUW6udOsmO3fefbh9u/z0k2zerGHG8O7dZW2cNUteeunu97//Lq++Kjk58uabWksGUKVxBRoAHjTPPms6OaFRI+nW7V67bdhQdu2SXbvkm28kJUUWL66wW+KWLy9Mz0Y7d8ry5Rp6UJ2qUXKXQRGZMcO05YMP5IFYjwpA5bHtAD169OhLBesDAQBERCQ0VL7+unB3wA4dZM0acXevgJ7t7CQsTPr1U7/IXW779pW1sTQRESoZuuSc7xs3VBYkuXZNKvz/JCdOyLRpMmmSJCTInTsV3DkA3dn2FA4XFxcXFxe9qwAAq9Orl/TqJWlp4uJSYVvr3T8+PmVtLI23tyxZIkOHFu7UGBcnzz5repi7u/j6yp9/Fmt0c1O/RbLcliyRkSMLFy2ZNUs2bCjTHpAAbIVtX4EGAJjh52cD6VlEevUynQ3i6Kh5076YGElOlpUr5bPP5NAhmTtXDAaVw8aONW0ZPbrC9poRkatXJS6u2JJ/O3bIv/9dYf0DsAa2fQUaAPAACA2VefNkwoS7i2+4ucmsWRIaqrkfLy8ZONDCMW+/LdnZMnOmZGeLo6M8/3wFp9vduyUz07Rx06aKfAsAuiNAAwD099xz0quX7NghIhIeLr6+ZXpVTo58/rls3iyurtKnj/TrZ/klDg7y3nsyZYqcOyf+/hW/FLTqonislAc8YAjQAACr4OsrfftqOD4nRzp3vpu5RWTBApkwQWbOLNNrnZwq+D7IAmFhUr26ZGUVa+zU6b68FwC9MAcaAGCTPv20MD0bzZpVuEy1Xnx8ZNYssbcvbGnVinWmgQcNV6ABADZp2zb1xtatK72U4kaNkrZtZcUKyciQ1q1l2LAKWzAbgJUgQAMAbJLqwnDVqlV6HWpCQiQkRO8iANw3TOEAANik7t1VGl9/XZYurfRSAFQxBGgAgE0aPFhGjDBtvHpVhg+XjRv1KAhAlUGABgDYqgULZO5c08a8PPn8cz2qAVBlEKABADbMxUWl8fTpSq8DQFVCgAYA2LCgIJXGZs0qvQ4AVQkBGgBgw1q3lqioYi3OzjJxok7VAKgaCNAAABtmZycrV8qoUeLhIQ4O0qaN/PijtGqld1kAHmisAw0AsG2envLZZ/LZZ5KTI05OelcDoArgCjQA4AFBegZQOQjQAAAAgAYEaAAAAEADAjQAAACgAQEaAAAA0IAADQAAAGjAMnYAcH/9+qvs2yeenhITIz4+elcDALhnBGgAuF/y8uTJJ2XNmrsPa9aU5culZ09dawIA3DOmcADA/fLBB4XpWUSuXZOhQ+XGDf0KAgBUBFsN0NevX79w4cLFixfz8/P1rgUA1P34o2nL5cuyd68epQAAKo6NBegjR448++yzdevW9fDw8PPzq1evnpOTk5+f3+DBg7du3ap3dQBQTGamSuPNm5VeBwCgQtlSgB4/fnxISMhXX31lMBjatWsXExMTExMTFhZmMBgSEhI6dOgwevRovWsEgELh4aYtjo7Spo0epViBlBRJSpLNmyU3V+9SAODe2MxNhHPmzJk9e3bPnj3//e9/P/LIIybPHj16dMqUKZ9//nlQUNDf//53XSoEABOTJ8u338r584UtU6dKnTr6FaSTO3dkzBhZuPDuw2bNZNUqad5c15oA4B4YFEXRu4YyiYyMvHr16pEjRxwc1EO/oiidO3fOz8/fsmVLxb71vHnz4uLibty44ebmVrE9A3jgXbsmM2fK3r3i7S2xsdK9u94F6WHGDHn11WItwcFy6JDY2+tUEABbkJOT4+zsvHXr1oiICL1rMWUzV6CPHDnSv3//0tKziBgMho4dO37yySeVWRUAmFezpkyerHcRektKMm05dkyOH5cWLfSoBgDumc3MgW7RosXOnTvv3Llj5pjt27e34N9jALAyf/5Z1kYAsAk2E6CfeeaZ3377rU+fPocPHy75bHJy8jPPPLNx48Z+/fpVfm0AADNK3Lci9vYSGqpHKQBQEWxmCse4ceMOHz786aef/vDDD/7+/g899JCnp6fBYMjIyDh//vyZM2dEZPjw4S+//LLelQIAipkyRX74QW7dKmx5/XXx8tKvIAC4NzYToEVk7ty5Y8eOff/999evX19wp6C9vb2vr29sbOzYsWM7d+6sb4UAgJKCgmTvXvnXv+TAAaldW559VoYM0bsmALgHthSgRaRVq1bLli0TkWvXrt24ccPR0dHX19fOzmYmogBA1dSsmSxerHcRAFBBbDV62tnZ2dnZ2coafAAAAHhg2FiAZitvAAAA6MuWpnCMHz/+k08+URSlbt267dq18/LyEpH09PS0tLSEhISEhIRRo0Z99tlnepcJAACAB5nNBGi28gYAAIA1sJkpHEuXLg0MDPz+++9LpmcRad68eUJCQseOHZNKbngFAAAAVBybCdBHjhwJDw+3uJX3kSNHKrMqAAAAVDU2E6DZyhsAAADWwGYCNFt5AwAAwBrYzE2E92kr74yMjLfeeisvL8/MMcePH7+n0gEAAPAAsZkALfpt5e3m5iYiTk5O96NzAAAA2BaDjW7mV5lbeW/bti0yMjI7O5sMDQAAUDlycnKcnZ23bt0aERGhdy2mbOkKdFE1a9asWbOm3lUAAACgyrGZmwgBAAAAa0CABgAAADQgQAMAAAAa2MYc6NmzZ8fHx5fx4IyMjPtaDAAAAKoy2wjQ0dHRKSkp8+bNy87Odnd3f+ihh/SuCAAAAFWUbQToxo0bz5w5MyYmJjo6unPnzmvWrNG7IgAAAFRRthGgjXr27Nm0adPKf1/j8s/Ozs6V/9YAAABVmXXuwmFLAVpEwsLCbt26Vclv2qZNmwMHDpjf7rt8UlJSYmNjP/30U1dX1wrvHJVj1KhRL774YqtWrfQuBOU0ffr0xo0bDxgwQO9CUE6rVq06efLka6+9pnchKKcDBw7Mnj37888/17sQlFNmZmZcXFxCQkKTJk0qvHMHB4fQ0NAK7/be2epOhA+GQ4cOhYaGXr161dPTU+9aUE5ubm4rVqzo1auX3oWgnLp37x4eHj5lyhS9C0E5xcfH79ixY/369XoXgnJau3btoEGDbt68qXchKKf09HQvL6+DBw+GhIToXUvlYRk7AAAAQAMCNAAAAKCBbQfotWvXDhw4UO8qAAAAUIXYdoA+efLkqlWr9K4CAAAAVYhtB2gAAACgkhGgAQAAAA0I0AAAAIAGth2gR48efenSJb2rAAAAQBViYzsRmnBxcXFxcdG7CgAAAFQhtn0F2tY5OTkZDAZHR0e9C0H5OTk5OTk56V0Fys/JyYlz0KY5OjpyDto0/hW1dY6OjgaDoaoNIlt56+z06dMPP/yw3lWg/FJTUwMCAuzs+F3UVv3xxx9ubm6urq56F4JyyszMvHnzZu3atfUuBOWUn59/7ty5Bg0a6F0Iyq8KhhkCNAAAAKABl80AAAAADQjQAAAAgAYEaAAAAEADAjQAAACgAQEaAAAA0IAADQAAAGhAgAYAAAA0IEADAAAAGhCgAQAAAA0I0AAAAIAGBGgAAABAAwI0AAAAoAEBGgAAANCAAA0AAABoQIAG8CA7efLk7Nmz9a4C94RBBGBtCNC6mTt3bocOHWrWrNmhQ4e5c+fqXQ4s8Pf3N5QQHx9f9BjG1Ap9/PHHJsNUwOJ4MaBWorRB5Ky0ctnZ2W+++WanTp08PDwaNWo0ePDgU6dOmRzDaWjNLI5glT4HFeghLi5ORAIDA4cNG9a0aVMRefHFF/UuCqW6deuWwWCoV69el+IWLFhQcAxjaoV+/vlnZ2fnmjVrlnzK4ngxoFaitEHkrLRy165d69ixo4gEBwePGjWqR48eBoOhevXq+/fvLziG09CaWRzBKn4OEqB1sH//fhGJjo7Ozc1VFCU3N9f43+Xhw4f1Lg3qDh06JCJTpkwp7QDG1No888wzgYGBxssEJbOXxfFiQK2B+UHkrLRyr7/+uoi88MILBS1r1661s7MLDQ01PuQ0tHIWR7CKn4MEaB3ExsaKyMGDBwta9u7dKyLDhg3TsSqYsWrVKhFJTEws7QDG1Nr079+/d+/evXv3dnd3L5m9LI4XA2oNzA8iZ6WVa9asmbu7++3bt4s2duvWTUT++OMPhdPQ6lkcwSp+DhoURamYuSAoMx8fn2rVqp0/f75oY7169RRFuXjxol5VwYzp06e//vrru3btSk5OTklJ8fPzi4iICA4OLjiAMbVaLVu2TEtLy8jIKNpocbwYUKuiOoiclVauefPmDz/88Jo1a4o29urVa926db/99ltgYCCnoZWzOIJV/RzULbpXVcb/B0RGRpq0t2vXTkSuX7+uS1Uwb8SIESLi4+NTcOLY2dmNHz/e+GcpxtSatWjRwuTipcXxYkCtTclBVDgrbdCff/5ZrVq12rVr5+bmchraoqIjqFT5c5BVOCrbjRs3RMTLy8uk3dhy/fp1HWqCJcnJySISFRV16NChGzdubNmypXXr1h9//PF//vMfYUxtjcXxYkBtAmelbUlOTo6IiLh9+/b06dMdHBw4DW2OyQhKlT8HCdCVzdHRUUQMBoPqs3Z2jIg1mjZt2oYNGxISElq2bOnm5hYZGblu3bpatWpNmTIlPz+fMbUtFseLAbUJnJW2IjMzc/Lkya1atUpLS5s9e/bw4cOF09CmqI6gVPlz0EHvAqocX19fe3t7k8l8IpKenm5vb1+7dm1dqoJ5xqV8ivL29o6Kilq1atWpU6caNWrEmNqQspyDDKj146y0CT/88ENcXNy5c+d69+79wQcfFCyrwmloK0obQany56DN/wZgc+zs7Hx9fdPS0kzaL1y4UKdOnQfgd7Kqw/h3qNzcXMbUtlgcLwbUdnFWWpXJkyfHxMS4u7v/+uuva9asKZq9OA1tgpkRLE3VOQdt/gPYoi5dupw+fdo4ecjo6NGj58+f79Spk45VoTTHjh0LCgp64403TNoPHjzo7OxsXBmeMbUtFseLAbVynJXWb9GiRe++++7TTz+9b98+1Z85p6GVMz+CnIOswqGDTZs2iciQIUOMD/Pz8wcNGiQimzdv1rcwqLpz546/v3/16tV37dpV0LhgwQIRGTNmjPEhY2q1VBdwsDheDKhVKTmInJVWLj8/PzAwsH79+llZWaUdw2lozSyOIOcgAVofxjn4Xbt2feONN4y/io0cOVLvolCqTZs2eXp6Ojo69u/f//nnn4+MjBSRoKCgjIyMgmMYU+ukGqCVMowXA2o9SvstiLPSap05c0ZEfHx8otVcvnzZeBinodUqywhW8XOQAK2P/Pz89957LyIiokaNGhERETNmzNC7Ilhw9uzZ5557rkWLFm5ubm3atImPjzf5vZwxtU6lBWiL48WAWo/SBpGz0mr98ssvZv70nZaWZjyM09BqlXEEq/I5yE6EAAAAgAbcRAgAAABoQIAGAAAANCBAAwAAABoQoAEAAAANCNAAAACABgRoAAAAQAMCNAAAAKABARoAAADQgAANAAAAaECABgAAADQgQAMAAAAaEKABAAAADQjQAAAAgAYEaAAAAEADAjQAAACgAQEaAAAA0IAADQAAAGhAgAYAAAA0IEADAAAAGhCgAQAAAA0I0AAAAIAGBGgAAABAAwI0AAAAoAEBGgAAANCAAA0AAABoQIAGAAAANCBAAwAAABoQoAEAAAANCNAAAACABgRoAAAAQAMCNAAAAKABARoAAADQgAANAMVs3brVYDB06tRJ9dnNmzcbDIbHH3/cYj9Dhw41GAzZ2dkVXaBuDhw44OjoGBIScuXKFfNHduzY0d/f3/h9//79DQbD/a8OACoPARoAiomIiAgICNiyZcvvv/9e8tnVq1eLyFNPPVXpdeksLy9v5MiRwcHBGzZs8Pb2Ll8n69evb9iw4TfffFOxtQFAJSNAA0AxBoPh6aefVhRl1apVJZ9dvXq1o6PjE088UfmF6euTTz6xs7MrR3r+/PPP09LSjN/funUrNTU1MzPzPhQIAJWHAA0ApgYPHiwiK1euNGnfv39/ampqjx49atWqpUddhe4xgyqKkpubq+ldJk6cuHv3bi8vL63v5eXlVb9+fa2vAgBrRoAGAFOhoaFBQUHbtm0ruHRqZDJ/49y5c8OGDQsODq5evXpAQMCAAQMOHjxYWp937tyZNm1a+/bt3d3dGzZsOH78+IsXLxadpUHOAAAI10lEQVQ826dPH3d396LHZ2dnGwyGoUOHGh8+99xzdevWzcvLGz9+vLu7+5IlS4ztixcvbt++fa1atby9vTt37vzTTz+Z+Vw+Pj6jR4/+4osv6tSp4+Tk9NBDDw0aNOj06dMFB6i+i/nKRSQ5OXnAgAH+/v5+fn5PPfVUampq0WcHDhxonAPdvXt345X7IUOGGAyGq1evlqVzTR8QACoHARoAVMTGxiqKkpiYWLQxKSnJycmpX79+InLs2LHmzZuvXLkyODh49OjRoaGh3377bdeuXVVnTufk5HTt2vXNN9/My8sbPHhwgwYNZs+eHR4efu7cOU1VvfjiiytWrOjTp0/Lli1FZNq0acOGDTt79uzjjz/etm3bPXv2xMTE/O9//zPTw4YNG0aOHOnq6vrss882aNAgMTGxTZs2+/btK+1dLFa+ffv2sLCwpKSkxo0bd+3adceOHe3btz9//nzJt37llVcmTJggImPGjPnyyy/d3Nwsdl6ODwgAlUEBAJSQkpIiIuHh4QUtycnJItK3b1/jw/Hjx4vI2rVrCw745JNPROSrr74yPhwyZIiI3L59W1GUjz76SESmTJlScPCiRYtE5G9/+5vxYe/evd3c3IoWcPv2bREZMmSI8eHw4cPt7e1btmx55cqVgmO8vb0DAwONb6EoSlJSkoiMGDGitA9lnL78+OOPZ2VlGVsSEhJEJCoqqrR3sVh527Zt7ezsVq9ebXx4/fr1Dh06iIifn5+xZcCAAQX/rzHePrhkyZIydq71AwJA5SBAA4C6sLAwETl79qzx4fTp04uGv19//XXJkiV37twpOH7dunUi8tFHHxkfFg3QAQEBjRs3Lnqwoijt27d3cnLKzMxUyhagRWTFihUFB+Tk5Dg4ODRs2DAnJ8fYkp+ff+TIkTNnzpT2iby9ve3s7JKTk4s29urVS0ROnDih+i7mK9+zZ4+IDBw4sOizu3btKmOANt95OT4gAFQOh/t+iRsAbFNsbOzu3bsTExMnTZokIqtXr65WrVrfvn2NzxoXis7Ozk5OTk5NTT1+/PiCBQtU+8nMzDx37lz79u2Nl3sLVKtWLScn59SpU8b5GGXx6KOPFnzv6OjYp0+f1atXh4aGPvXUU506dQoPD2/evLn5HgICApo0aVK0JTo6eu3atcnJyU2bNjV5F4uVnzhxwthD0WfDwsJ8fHwsfpay/FjK8QEBoBIQoAFA3aBBg15++eWVK1dOmjTpwoULu3bteuKJJwpu9bt169bEiROXLl2alZXl4ODw8MMPN23a1DjNw8TZs2dFZPv27du3by/57M2bN8teUu3atYs+XLp06YwZMxYtWvTPf/5TRFxcXJ544okZM2bUq1evtB7q1Klj0mJcIqPobOyCd7FYufGGv5Jv5+fnd/nyZfOfpSw/lnJ8QACoBNxECADq6tWr17lz5127dqWmpn7zzTeKogwaNKjg2SeffHLBggUvvfTSoUOHbt++feLEibfeeku1H2MefeGFF1T/Dti+fXvVV6kGazu7Yv9oV69e/e233zZeCV6wYEFYWNiyZcuio6MVRSntQ/3xxx8mLZcuXZLiwbrgXSxWbtxu0GTdDBHJyMgorYACZfmxlOMDAkAlIEADQKliY2NFZOXKlUlJSdWrV+/du7ex/a+//vrll1+efPLJadOmtWzZ0t7eXkSuX7+u2omXl5eXl9fOnTtN2j/44IPJkycXPMzNzS2aC82siGd08uTJ+Pj4TZs2iUjTpk1HjBixadOm7t27Hz58WHURDKOzZ8+eOnWqaMvPP/8sIoGBgeWo3Djrw2RpuTNnzpRldRGLnZfvAwJAJSBAA0CpBgwY4OTktHDhwv/973+9evVydXU1tt+5cycvL+/atWsFR6anp0+dOlVE8vPzS/YTFxe3Z8+eadOmFbR89dVXr7zyysmTJ40PPT09s7Ozt2zZYnyYlZVVNFursrOzmzp16uTJkwu2RMnNzc3IyHB2di45T6NAfn7+xIkTs7OzjQ+//vrrb7/9NjIysrS5xeYrb9WqVXh4eGJi4nfffWd89vbt2+PHj1f9IRTIyckpS+fl+4AAUBkq5FZEAHhQ9enTx/ivZWJiYtH2nj17ikj79u3feOONMWPGeHt7R0VFiUhISMj333+vFF+F4/r16y1atBCR1q1bjxs3rl+/fvb29vXr109LSzP2ZtyixcPD46WXXnr11VcDAwOrV6/u7u5usgrHzZs3i9ZgvKOxcePGY8aMefrpp42xMj4+vrTP4u3t7efn5+np2bhx45EjR3br1s1gMHh4eOzcubO0d7FY+bZt22rUqGFnZ9e9e/eRI0c2atTIzc2tQ4cOqqtwGK92t2nTZsqUKZmZmRY71/oBAaByEKABwBzjGhGurq7G9eYKXL16NS4uzs/Pr0aNGh07dly0aJGiKOPGjfPw8Bg1apRSPEAripKVlfWPf/zj0UcfdXFxady48fPPP//7778X7fCLL75o0aKFs7OziHh6en7//feNGzc2H6CvX78+efLkoKAgV1dXb2/vyMjIpUuX5ufnl/ZZvL29u3TpkpKS0q9fvzp16vj5+Q0YMCAlJaXgANV3sVj5iRMnBgwYEBAQUKdOnSeffHLfvn3Gn4zx2aIBOisra+jQoV5eXp6enunp6RY71/oBAaByGBRuxQAAq5Gfn5+Wlla3bl1HR8cK79zHx6dFixYbN26s8J4BoEphGTsAsCJ2dnYBAQF6VwEAMIebCAEAAAANCNAAUFX4+vp6enrqXQUA2DzmQAMAAAAacAUaAAAA0IAADQAAAGhAgAYAAAA0IEADAAAAGhCgAQAAAA0I0AAAAIAGBGgAAABAAwI0AAAAoAEBGgAAANCAAA0AAABoQIAGAAAANCBAAwAAABoQoAEAAAANCNAAAACABgRoAAAAQAMCNAAAAKABARoAAADQgAANAAAAaECABgAAADQgQAMAAAAaEKABAAAADQjQAAAAgAYEaAAAAEADAjQAAACgAQEaAAAA0IAADQAAAGjw/wE2e+/xnDEh2gAAAABJRU5ErkJggg==", + "text/plain": [ + "Plot with title “Régression linéaire sans sélection de variables”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# estimation du modèle sans interaction\n", + "reg.lm <- aov(O3obs ~ . , data = datappr)\n", + "\n", + "# Extraction des résidus et des valeurs ajustées de ce modèle\n", + "res.lm <- reg.lm$residuals\n", + "fit.lm <- reg.lm$fitted.values\n", + "\n", + "# Graphe des résidus. \n", + "plot.res(fit.lm,res.lm,\"Régression linéaire sans sélection de variables\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que dire de la distribution de ces résidus?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The residuals \"bounce randomly\" around the 0 line and the residuals roughly form a \"horizontal band\" around the 0 line." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** La forme du nuage renseigne sur les hypothèses de linéarité du modèle et d'homoscédasticité. Que dire de la validité de ce modèle?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The observation above suggests that the assumption that the relationship is linear is reasonable and the variances of the error terms are equal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apprécier néanmoins sa significativité par la commande suivante." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:40.123527Z", + "start_time": "2019-11-18T09:22:03.902Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "JOUR 1 387 387 0.495 0.481750 \n", + "MOCAGE 1 476033 476033 609.252 < 2e-16 ***\n", + "TEMPE 1 227286 227286 290.893 < 2e-16 ***\n", + "STATION 4 15348 3837 4.911 0.000645 ***\n", + "VentMOD 1 8864 8864 11.345 0.000792 ***\n", + "VentANG 1 9450 9450 12.094 0.000532 ***\n", + "SRMH2O 1 2969 2969 3.800 0.051603 . \n", + "LNO2 1 4525 4525 5.791 0.016329 * \n", + "LNO 1 11732 11732 15.015 0.000115 ***\n", + "Residuals 819 639917 781 \n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(reg.lm)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
(Intercept)
-25.4416841303498
JOUR1
1.74830638286088
MOCAGE
0.406757206374541
TEMPE
4.2427645232671
STATIONAls
5.7943917589543
STATIONCad
12.678262273577
STATIONPla
22.6684937638938
STATIONRam
6.92312535900016
VentMOD
-0.986351533776573
VentANG
4.01703423955161
SRMH2O
121.966074045563
LNO2
-16.1558687812782
LNO
19.2571237848165
\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[(Intercept)] -25.4416841303498\n", + "\\item[JOUR1] 1.74830638286088\n", + "\\item[MOCAGE] 0.406757206374541\n", + "\\item[TEMPE] 4.2427645232671\n", + "\\item[STATIONAls] 5.7943917589543\n", + "\\item[STATIONCad] 12.678262273577\n", + "\\item[STATIONPla] 22.6684937638938\n", + "\\item[STATIONRam] 6.92312535900016\n", + "\\item[VentMOD] -0.986351533776573\n", + "\\item[VentANG] 4.01703423955161\n", + "\\item[SRMH2O] 121.966074045563\n", + "\\item[LNO2] -16.1558687812782\n", + "\\item[LNO] 19.2571237848165\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "(Intercept)\n", + ": -25.4416841303498JOUR1\n", + ": 1.74830638286088MOCAGE\n", + ": 0.406757206374541TEMPE\n", + ": 4.2427645232671STATIONAls\n", + ": 5.7943917589543STATIONCad\n", + ": 12.678262273577STATIONPla\n", + ": 22.6684937638938STATIONRam\n", + ": 6.92312535900016VentMOD\n", + ": -0.986351533776573VentANG\n", + ": 4.01703423955161SRMH2O\n", + ": 121.966074045563LNO2\n", + ": -16.1558687812782LNO\n", + ": 19.2571237848165\n", + "\n" + ], + "text/plain": [ + "(Intercept) JOUR1 MOCAGE TEMPE STATIONAls STATIONCad \n", + "-25.4416841 1.7483064 0.4067572 4.2427645 5.7943918 12.6782623 \n", + " STATIONPla STATIONRam VentMOD VentANG SRMH2O LNO2 \n", + " 22.6684938 6.9231254 -0.9863515 4.0170342 121.9660740 -16.1558688 \n", + " LNO \n", + " 19.2571238 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "coef(reg.lm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Ce premier modèle est comparé avec celui de la seule prévision déterministe MOCAGE. Qu'en conclure?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:40.226000Z", + "start_time": "2019-11-18T09:22:04.102Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “Linéaire, sans sélection”" + ] + }, + "metadata": { + "image/png": { + "height": 240, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Graphe des résidus du modèle déterministe MOCAGE\n", + "par(mfrow = c(1, 2))\n", + "plot.res(datappr[, \"MOCAGE\"],\n", + " datappr[, \"MOCAGE\"] - datappr[, \"O3obs\"], \"linéaire, MOCAGE seul\")\n", + "plot.res(fit.lm, res.lm, \"Linéaire, sans sélection\")\n", + "par(mfrow = c(1, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We see that with only MOCAGE, the variance of the residus increases when the predicted values increase so we couldn't simplify our model this way." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sélection de variable par régularisation L1 (LASSO)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading required package: Matrix\n", + "\n", + "Loaded glmnet 4.1-3\n", + "\n" + ] + }, + { + "data": { + "image/png": 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raWUhhNBqJOOtaMbLTZKEJIRw0wp3jSSEkCThrZeFEO5ayU0jyZJwDuWpkzUq26cYAACUsPXr1zsfNGzY8NKlS64tJpfZbN62bZu/v39kZOSd+gwfPnznzp0HDhwowYDj9OnTZrO5VatWzqdxcXEhISGNGjXK7dC0adPg4OAtW7aU1B0B5KWagGPIkCGjR4/u3bv37NmzGzZsmO9qYmLiW2+9tWPHDmdK+pAbPXr0XVtQdBr5bytufNQf/mTZlJxb2YqiCGdKkhuXOBSRYXXceqAIIRShZFgUIYRDiIxbIU6GVXE+MtsUq0MRQlhvxT2pZkduXpNh/TPG+SsMUsTNW7mMyabY7jZdRq+RnBmKt5sk/pzhIuUuUzLqJK0sdLJk0EniVrbiphHuGilvbqLXSLlroDx0kk6WcmeveOgkrSy0slRwpRUAAMBtnTt3zmq11qlTp5A+zqu//vpridzRbDYnJCSMGTNGq9XOmDFDCJGWlnb9+vV27drl61mlSpX9+/dnZGTcdmoJgPuhmoBj1KhRx44dW7x48aZNmypXrhwaGurr6ytJUmpq6sWLF8+fPy+EiIqKmjBhgqsrBe6XUScJ8dcf8+XcXVjLn2wOxWRTxK1pL84pJ3ZFZFodQohMm2J3/BmgKELctDiEEKYcxeYQDoeSYVOEEFlWxa4o100Oc45zHIeSm6rcilTutEwpL+cSHi+9JEl/zlhxbrziqZM0svDUybIsPHWSRpaMOkknC2+9rNNIBq3k4XzqJmtl4amT3bSSm4b1PgAAlE5paWlCiCpVqhTSJzQ0VAhx5syZ4t1i8+bNUoFd5jw9PePi4jp16iSEyMjIEEI4T37My9ly8+ZNAg6gxKkm4BBCLFq0aPjw4e+9915cXNyPP/7obNRoNIGBgYMHDx4+fHjHjh1dWyFQWulkycdNEg9kgkxuRJKdo1jtf01dMeUoNrtisTvblewcxepQzDbF6hDmHMVmV5wdMi1KilmYbH+2OCew3LQouRlNPhpJeOplN41w00qeOlmnEV562bm0x6iTtbLwdpP1sjBoJQ+drNMI71tXPXSy/tZTd63Egh0AAIrBZDItXbo092lYWFhERMT9D+vj4yOEuHjxYiF9kpKShBDFThnyHRNrs9lOnDhx7NixiRMnbt261cvLS6fTCSEKhiBOssznBqDkqSngEEKEh4evXr1aCJGWlpaRkaHT6QIDA/ntAJQm0q1Tfv6JMMU59yTDquQ4lEyr4pxCkmVTbA6RYXE4E5OsHMVmFzetDptdSbcolzPtOQ6RbnHkJiY2x9/2c8nlXFbjzEq89LJOFp562Tl5xEMn+bjJRq3koZMMOslbL3vqJQ+d5KGVvG49dmMyCQCg7MnIyBg3blzu0/79+xcx4EhOTjaZTBUqVHBzu80nhmrVqul0upMnTxYygnNxSs2aNe+x5D8VPCZWCPHyyy8vXLjws88+Gzt2bGBgoEajSU1Nzdfnxo0bGo0mKCioePcFUAiVBRxCiGvXrqWmptaoUaNcuXL5Ll2/ft1isVSqVMklhQF4+Bm0kkErlUh04pwS4jzM6KbFYbUr5j+zEiXD8md0kmlTsnMUk025aXFczrA7O2RYHZlWxWTLvx7HucTGWy8Z9bKHTvLUSZ56yUsve+gko04y/hWLyM6nPm6yUScZ9SQjAAAVCwoKUpR73mpdCDFq1Kh169bt3r37kUcecbZYrdbcq0ajsVOnTnFxcd98880TTzxx2xE++eQTIUSvXr2cT729vYUQ6enpBXuazWar1Vrwr4+Chg8fvnDhQucxsbIsBwYGFtx4NSkpqUKFCvyPFvgnqCngOHz48NChQ48ePSqEqFChwqxZs4YOHZq3w7PPPrt58+bi/YoEgHviXLZzKyu54+lOhXBuYpJhVUw2JcummGyOdItisjlMOUqmVcm0KRkWR5ZNSTE7smyODKuSaVVMNkemLf9aG60sPPWyl17y0sueOsmol4w62VMveetlZ0pi1EnebrKPm+ztJvm4yc5kpAR+BAAAuI4zbkhOTs5tuXLlihCiYsWKzqezZ8/evn37+PHju3TpYjQa8335unXr4uLiGjRo0L17d2dLeHi4EGLfvn0OhyNf+rBz506Hw9G0adO7VhUYGCjypCSdOnWKjo5OTEysVauWs+X48eMXL14cPHjwPX/DAIpANQHH2bNn27RpY7VaH3vsMb1ev3379qioqKysrFGjRrm6NAAoDo0kfNzk4k0nSbc4smxKllXJtClZNsdNi5JhdWTZlCybkmV1ZNiUDIvjSqb9zw5WR7pVuZlnWY3z7CFvvezjJvm4yd5u+R/46GVvtz9jES89aQgA4KHjzCNWrVrVr18/IURWVtbKlSuFEM2aNXN2aNKkyejRoz/88MNmzZpFR0c3adLE2W632997771p06a5u7svWbJEo/nzvxTly5fv2bPn//73v7Fjx86ePdvDw8PZHh8fP3z4cFmW8/1v9bYcDoe4tb2oEGL48OHR0dEzZsxwLmZRFMV5wAp/wgD/ENUEHFOmTLFYLN99912PHj2EEMnJyW3btnUmsrVr13Z1dQDwQBUjGVGESLc4blqUdIsj3eJItyg3cx9YHanZjt/S7c5Lzj65XyhLwpl0/BmC6KW8T33d5XLucnl32dddLm+QyUIAAA/G888//+GHH8bExLRt27ZBgwY7duw4c+bMoEGDWrZsmdtn3rx5/v7+M2bMaNq0aWhoaHh4eEpKSnx8fFZWVkhIyKefftq2bdu8Yy5btqxdu3YLFixYvXp18+bNfXx8zp8///PPP0uS9M4773Tu3PmuVTnnlZw8edJms+l0uo4dO0ZFRS1btuzy5cutW7f+8ccfd+3a9a9//St3WQ2AkqWagGP//v3dunVzphtCiICAgO+++y48PPy111775ptvXFsbADz8JCHKucnl3EQRF9TkJh03rY505wPLXw8uZTjTECUt25GbhkhClDfIvu5yeXe5nLvs5y77GfLEH+6yn0Eub5A9tMQgAID7ZTAY9uzZM3Xq1Li4uFWrVoWFhc2aNWv8+PF5+2i12qlTp0ZERLz//vu7du3avHlzQEBAmzZtunTpMnbsWIPBkG/MgICAhISExYsXr1+//vjx46mpqdWrVx88ePDEiRMbNWpUxKrq1at34sSJ+fPnT5w4UQjx2Wef1a1bd+PGjQsWLGjQoMGcOXMmTJhQUj8EAPlIatmxwtvb+6mnnsp7iJQQYtq0aTNmzNi1a1f79u2FED169LjXPTguXbrUvXt3s9lcSJ+srKyrV6+aTKaCvwQBAHaHuJHtSMt23Mh2pGY7UsyO1FuP07Id182OG2ZHmsWRnfPnL2d3reSMPPwMsq9BLu8ul3fL85jJIABKl0uXLlWuXPn06dNhYWGurgUASoDFYunateusWbPyzYF6GKhmBkfjxo1/+umnfI0TJ05ctmzZyJEjf/nlF71eX4xhAwICxo8fb7PZCumza9euVatW2e32YowPAKWeRhYBHnKAx112gzflKKlmR4r5z+wjNdtxw+xIMTsu3rQfzbY5n6bdmgwiS8LPIPu6y0FGjZ9B9jfIAUaNn7sc4CEHeGicaQgJCAAAAPJSTcDRvn37mTNnjhkzZu7cubmHXRuNxsWLF0dERAwdOvTzzz8vxrBubm7Dhg0rvI+iKKtWrSrG4ACAXB5aycNLU8mrsAUyzskgzvjjmsmeYnZcNzmumexn0nJ+SrJeNztSzPYchxBCaGTh5y77e2gCPGQ/g1zBqAnwkCt6avw9NMFG2d+g0XD6HgAAQBmjmoBj2rRpP/7444IFC5YvX96xY8fY2Fhne8+ePadOnTpjxozdu3fnPfsaAKA6RZkM4pz3cd38ZwKSbHIkm+xHrtmuZtmvZtkzrIoQQpaEv0EOMmoCPTQVPOVAD02wpybAQ65g1AQa5XJuhB8AAAClkGoCDnd392+++Wb27Nlff/31uXPn8l56++23a9SoMXPmzFOnTrmqPADAg+FrkH0Ncs07XDXnKFcy7ckmx5VMe7LZ8Uem/ZrJfjIl52qW/ZrJYbUrQgh3rVTJUxNk1FTwlCt5aip4aioYNRU9NcGeGs7EBQAAUC/VBBxCiHLlys2cOXPmzJkFLw0dOvS55577/fffz549++ALAwA8JAxaqXo5bfVyt7+aYnZcM9mvZDquZNr/yLJfybQfuGL9I8vxR6bdYleEEB46qaKnpqKnpoJRE+ypqeSlaRqkq1ZOTe+VAAAAZVbp+dAmSVLVqlWrVq3q6kIAAA8pP4PsZ5Dr+t3mUorZ4Yw8LmfanZHHT0mWCzftV7PsQUZN20r61hX1bSq5Fb6HCAAAAFyo9AQcAAAUmzP7qO+vy9d+8ab90B/WQ39Y3z+UOfGH9EAPuXmwvl0ltw5V3Cp6EnYAAAA8RAg4AAC4o8remsrehn61DEKIizftey5Z9iRZ5hzImLwrvbK3pl2IW/MK+raV9EFGwg4AAAAXI+AAAKBIKntrBtXzGFTPw6GIkym2vZet+5Isb/6YnmVVavlqHwlx61HDvUmQ3tVlAgAAlFHqCDgWLFgwderUInZOTU39R4sBAJRxsiTq+evq+ev+1chod4hj1237kiw7L1g+P5ZV2Vvbp6Z7n5qGqj7qeIcFAAAoNdTx8at79+6nT59esmSJxWLx8vIKDQ11dUUAAAghhEYW4YG68EDdiCaeV7Psm85lrz9l/vBQZlh5bWQtQ2RtjwAP2dU1AgAAlAnqCDjCwsI++OCDnj17du/evWPHjrGxsa6uCACA/IKMmqiGxqiGxtM3cv53zhx9wjTvQEbrSm79ahker+buoZNcXSAAAEBppo6Aw+nxxx+vVauWq6sAAOAuavpqx/p6jWnm9ctV6//OZr/z080pu9I7h7r1rWXoWNldy5QOAACAf4CaAg4hRIsWLUwmk6urAADg7mRJNK+gb15B/3orr22/WzYmmkd/n1bOXeoVZuhXy1DwSFoAAADcD5UFHCtXrnR1CQAA3Bt3rRRRwz2ihntatuPbs9kbT5uXHc2q668bWMejT013bzdmdAAAAJQAlQUcAACoVzl3+Zn6Hs/U9/gtPWfjafOSw5n/t/fmY1XdBtX1aBvixhYdAAAA94P/GgEA8KBV9dGObe61c0jgJz3KCyGG/e9G1+jkJfGZKWaHq0sDAABQK2ZwAADgGhpJtAtxaxfidjXL/nWiOfqE6T8HM9tX1ver5fF4dXcNMzoAAADuBQEHAAAuFmTUDG/i+WK4594ky5e/ml7Zlur3k6ZvLcPT9TxCvDSurg4AAEAdCDgAAHgoyLcmdFw3O9afMq351fzJ4cyOVdyequvROdRdZkIHAABAoQg4AAB4uPgb5JfCPV8M9zxw2brmV9O/49ICjfKzDYwD63h46ck5AAAAbo+AAwCAh5EkRKuK+lYV9W8+4lifaP70SNb7BzOeqGmIamCs6cvbNwAAQH6cogIAwEPN202Oamj8YUjg+4+Vu3jT3v2r5IFfp/zvbLZdcXVlAAAADxP+BQQAgApoJNEl1L1LqPvx67bVJ0wTtqfNPaB5qo5hcD0Pbzf+XQEAAMAMDgAAVKW+v+7dDj67ngl8qo5heYLpkZXXJu9KP5uW4+q6AAAAXIyAAwAA9fE3yMObeO4cEjCrU7lTKbbua5Kf+/bGtt+zWbYCoMxas2aNJElTp07N137kyJFhw4ZVq1bNYDD4+vq2bNnynXfeMZlM+bpt3LhRkqTXXnvttoMbDIbKlSs7H589e1YqQKvVVqtWLTIy8ujRo7cd4cyZMwsWLLi/bxHAXbBEBQAAtdLJUs8a7j1ruB+8Yl1+LGvk96k1y+teCjf2qmHQ8C8MAA+ExWJ5++23d+/efeTIEX9//1atWs2YMaNGjRqurksIIRwOx5w5c6ZNm2a1WoODgzt37pySknL06NGDBw+uWLFi5cqVLVq0KPbgwcHBXbp0yX1qMpmOHz++YcOG2NjYvXv3Nm/ePF//jz766Isvvnj55ZeLfUK19NcAACAASURBVEcAd0XAAQCA6rUI1rcI1l/OtH9+NGvKzvT/HMx4KdzzydoGNw3HygL4B6Wnp/fu3Xv37t316tUbOHDghQsXvvzyy6+//vqnn34KDw93dXXitddemz9/fo0aNVavXt2yZUtnY05OzqxZs95888127dodPny4Xr16xRu8cePGK1asyNuiKMqECRPmz58/adKkrVu35r0UFxe3ZMkSg8FQvHsBKCL+vwMAQClR0VMzua337mcC+9UyzNuf0X7ltQ8OZdy0OFxdF4BSa/bs2bt37x49evTx48eXLl36/ffff/vttxaLJSoqytWliYSEhA8//LB69erx8fG56YYQQqvVTpkyJTo62mazjRkzpgTvKEnStGnThBCHDx/ObXzmmWfq1KnTrVs3i8VSgvcCcFsEHAAAlCrl3OWxzb12PxM4qqnnV7+a26+69vaem8kmYg4AJW/Dhg1eXl7z5s3LbenZs2fnzp2PHDly7do1FxYmhHj99ddzcnLmzJnj5eVV8OqAAQM6duy4ffv2LVu2lOBNfXx8DAZDVlZWbovJZKpZs2avXr1uWwaAkkXAAQBAKeShk6IaGn8YEvDmIz67L1o6r7729p6bVzLtrq4LQKkiy3LHjh3d3NzyNur1eiFEamqqi4oSQgiz2bxt2zZ/f//IyMg79Rk+fLgQ4sCBAyV439OnT5vN5saNG+e2rF+/PjY2NjY2NjQ0tARvBOC22IMDAIBSSydL/WoZ+tQ07LiQ/eGhzFXHs3qHGUY29axRjg8AAErA8ePH87UkJydv3749KCjItfuMnjt3zmq11qlTp5A+zqu//vpridzRbDYnJCSMGTNGq9XOmDGjRMYEcK/4fAMAQCknS6JLqHuXUPc9lyzvH8rsvia5UxW3Mc28GgXqXF0agFIlMTExIiIiOzt70aJFWq0r/9BIS0sTQlSpUqWQPs4pFWfOnCneLTZv3ixJ+Tdy9vT0jIuL69SpU/HGBHCfCDgAACgr2oW4tQtx23/Zuig+M3L99U6hbq+08KrvT8wB4H5lZWW99957c+bMURRlwYIFRd9k1GQyLV26NPdpWFhYRETE/dfj4+MjhLh48WIhfZKSkoQQxd4aI98xsTab7cSJE8eOHZs4ceLWrVvZcQNwCQIOAADKllYV9a0q+iYk2z48lNln3fXHq7u/0sIrrDwfCQAU06ZNm0aMGHHhwoVevXrNnTu3du3aRf/ajIyMcePG5T7t379/EQOO5ORkk8lUoUKFfDuAOFWrVk2n0508ebKQEZyLU2rWrFn0avMqeEysEOLll19euHDhZ599Nnbs2OINC+B+sMkoAABlUYMA3X97lF8X6W+1Kz2+Sh4Tl/p7eo6riwKgPtOnT+/Zs6eXl9fOnTtjY2PvKd0QQgQFBSl5rF27tohfOGrUqKpVqx48eDC3xWq15j42Go2dOnVKTk7+5ptv7jTCJ598IoTo1auX86m3t7cQIj09vWBPs9lstVrLlSt316qcG5fmPSYWwINEwAEAQNkVHqhb2sM3uo9fitnRbU3y5F3pV7M4aQVAUS1fvvztt98eNGjQL7/80qFDhwd5a2fckJycnNty5coVIUTFihWdT2fPnq3RaMaPH5/30NZc69ati4uLa9CgQffu3Z0t4eHhQoh9+/Y5HPnP1d65c6fD4WjatOldqwoMDBR3SEkAPAAEHAAAlHXNK+hXP+H3WU/fY9dsHVclT96VnmzK//keAPJRFGXmzJmVKlX6/PPPnUfDPkjOPGLVqlXOp1lZWStXrhRCNGvWzNnSpEmT0aNHnzlzplmzZvHx8blfaLfbZ86cOXjwYHd39yVLlmg0Gmd7+fLle/bseezYsbFjx5pMptz+8fHxw4cPl2V56NChd63KGY5kZGSUzDcJ4B6x4BYAAAghRLsQt4393bb/nj3/QGbn1deebWgcEW70duN/IQBu7/fffz916lRAQEC/fv0KXl2xYoW/v/8/d/fnn3/+ww8/jImJadu2bYMGDXbs2HHmzJlBgwa1bNkyt8+8efP8/f1nzJjRtGnT0NDQ8PDwlJSU+Pj4rKyskJCQTz/9tG3btnnHXLZsWbt27RYsWLB69ermzZv7+PicP3/+559/liTpnXfe6dy5812rcs4rOXnypM1m0+nYwhl40Ag4AADAnyQhuoS6P1rFffO57Dn7b645YXquoce/Gnl66vMfhQgA586dE0IkJydv3ry54FWLxfKP3t1gMOzZs2fq1KlxcXGrVq0KCwubNWvW+PHj8/bRarVTp06NiIh4//33d+3atXnz5oCAgDZt2nTp0mXs2LEGgyHfmAEBAQkJCYsXL16/fv3x48dTU1OrV68+ePDgiRMnNmrUqIhV1atX78SJE/Pnz584cWKJfbcAikZSFMXVNTzslixZMmLEiIyMDE9PT1fXAgDAA5LjEOtOmj78OdNqV15sbIxqZHTTEHMA9+zSpUuVK1c+ffp0WFiYq2sBgBJgsVi6du06a9asfHOgHgbMOwUAALehlcWgeh7bBge8FG7875Gsbl8m/+9stquLAgAAuCMCDgAAcEcGrfRSuOfOpwMjwgzjt6cN2phy/LrN1UUBAADcBgEHAAC4C0+99Horr+2DA0K8NH1jro+JS72cyWmyAADg4ULAAQAAiiTYUzO3c7mv+vpfybQ//mXyB4cyLHZ28gIAAA8LAg4AAHAPmgTpvurr/3YHn+gTps6rkzckmgk5AADAw4CAAwAA3BtZEv1qGbY/HTiwrmHyzvT+66/HX7W6uigAAFDWEXAAAIDi8NBKY5t7bRscUK2cduDXKWzMAQAAXIuAAwAAFJ9zY46Vvf1+S2djDgAA4EoEHAAA4H61qqjf+KT/lHbeq4+bHv8yefO5bFdXBAAAyhwCDgAAUAJkSTxV12Pb4MAeNQxjt6a+sOlGUgYrVgAAwINDwAEAAEqMp16a2Nrr+6cCsnOUx9ckf3Aow+ZgxQoAAHgQCDgAAEAJq+qjXdHbb0YHnxUJpr4xKfFXba6uCAAAlH4EHAAAoORJQvSrZdj8VEBdP+3Ar69P3pWeaWUqBwAA+AcRcAAAgH+Kv0F2nrFy4LK165fXNiSaXV0RAAAotQg4AADAP6tVRX1sf/9B9Tz+3w/pbD4KAAD+IQQcAADgH+eulcY299o00N98a/PRHIerawIAAKULAQcAAHhAqpXTruztN6ODzxcJpr4x1w9fY/NRAABQYgg4AADAg+PcfPT7pwLq+GkHbGDzUQAAUGIIOAAAwIPm3Hz0056+ey5aeq5N3pdkdXVFAABA9Qg4AACAa3So7LbpqYBHq7g9+23KOz/dzM5hKgcAACg+Ag4AAOAyBq30VnufZRG+m85m94m5fiyZXTkAAEAxEXAAAAAXaxfitvmpgObB+ic3XH9vf4bNwVQOAABwzwg4AACA63nppXc7+HzwWPmvfjX1jUk5mcJUDgAAcG8IOAAAwMOiR3X3zU8FhHhpItenLInPZCYHAAAoOgIOAADwEPE3yEu6l3+3o8/CXzIHbUy5cNPu6ooAAIA6EHAAAICHTr9ahv8NDNDKove65C9PmFxdDoCH0VtvvSVJkiRJu3fvvm2HN954w9lh//79+S4dOXJk2LBh1apVMxgMvr6+LVu2fOedd0ymO/622bdvX1RUVLNmzTw9PatVq/b444/HxsbeqfMzzzwjSZIsy5cuXbpTn9OnT7/wwgvNmzf38fEJCgpq27bt1KlTb9y4ka/bzz//LN1Z5cqV7zQ+UDZpXV0AAADAbYR4aVY94bf8WNZbe27G/ZY9s1O5QA/+MQM8dM6fPz958uSDBw9evny5evXqPXr0mDx5so+Pz4OsYd26de3bty/YHhMTU7DR4XDMmTNn2rRpVqs1ODi4c+fOKSkpR48ePXjw4IoVK1auXNmiRYt8/adPnz5z5kxFUerUqdO1a9cLFy5s3759y5YtQ4YMWblyZb7xzWbzxo0bhRCKonz11VevvvpqwRqmTJkye/bsnJycwMDAtm3b2u32Y8eOvfPOOx988MHGjRsfffTRfP1DQ0Nv+w2WL1++0B8MUPYouJvFixcLITIyMlxdCAAAZVFiiq332uQWy/7Ycs7s6lqAe3bx4kUhxOnTp11dyD/i9OnTRqNRq9V27tx5xIgRrVq1EkLUr1/fbH4Qr9Y333xTCOHl5VWpUiWHw5Hv6rFjx4QQ3t7eQoh9+/bltjsThxo1auzfvz+30WazzZgxQ6PR6HS648eP5x3H+bdA06ZNf/vtt9zGxMTENm3aCCEWLlyY775r1qwRQvTo0UMI0bJly4JlT58+XQhRsWLFbdu25S1gyZIlOp3Ox8fn1KlTue2HDh0SQgwaNOgefi7APyw7O7t9+/Z79uxxdSG3wX9CAADAQ62mrzYm0m9gHY/RW1In70zPzmHrUeBhMWnSJJPJtH79+m3bti1atGjfvn2vvPLK8ePHP/nkkwdWQ+/evZOSkvbu3ZuvPSYmRpKknj175m1MSEj48MMPq1evHh8f37Jly9x2rVY7ZcqU6Ohom802ZsyY3PaUlJQ33ngjNDR09+7doaGhue01a9Zct26dVqtdsGBBvvtGR0cLIaZNm9awYcMDBw6cO3cu79WTJ0++++67fn5+8fHxnTt3zlvASy+9NG/evPT09I8//riYPwugzCPgAAAADzudLE1o5fVlH79dFy1Pbkg5n5bj6ooACCHEjz/+2LRp0969e+e2DBs2TAjxyy+/PLAa+vbtq9Fo1q5dm689JiamdevWISEheRtff/31nJycOXPmeHl5FRxqwIABHTt2dC4/cba89957N27cmDRpkoeHR77OFStWHDt2bI0aNZKTk3Mb09PTN23aVLVq1datW0dGRgohvvrqq7xf9dFHH+Xk5Lz11luBgYEFC3jhhRdefPFFo9FY1G8ewN8RcAAAAHVoWkEf298/2FPuE3M99ozZ1eUAZZ3D4Zg6dapznUiuq1evCiHCwsIeWBmBgYGdOnWKiYlRlL+md50+ffrYsWP9+/fP29NsNm/bts3f398ZPdzW8OHDhRAHDhxwPv3++++FEAMHDrxt57lz58bGxgYEBOS2bNiwwWKxDB48WAjx5JNPCiG+/PLLvF/y7bffSpLkjIEKMhgM//3vf999993CvmEAd8YmowAAQDXKuctLe/guP5Y1YXvazguWdzr4uGslVxcFlFGyLI8ePdr52Gw2p6amHjlyZOzYsUFBQQMGDHiQlfTv33/kyJEHDhxwbgIihFi3bp0QIjIycuHChbndzp07Z7Va69SpU8hQzqu//vqr82liYqK3t7evr28RK3GuT3EGHA0bNqxZs+aRI0dOnjzpHNZisVy4cKFKlSoF54MUbv/+/bfNRB599NHnnnvunoYCSjcCDgAAoCaSEFENjfX9dWO3pg34OmVB13KhPnyeAVzs1VdfdW7GaTQad+3aVbNmzQd59379+o0ePXrt2rW5AUdMTEyzZs2qVq2at1taWpoQokqVKoUM5dxo48yZM0IIi8ViNpsL75/XtWvXtm3bVq9evYYNGzpbIiMjZ8+evWbNGufGoqmpqUKIfKtmhBDBwcF//PFH3paEhIT69evnPj1//vz58+cL3tHd3Z2AA8iLJSoAAEB9WgTrvxvg72+Qn4i5/t1ZlqsALjZixIg1a9Y4t89s27at85zUojCZTB/k8d133xXj7kFBQR06dHDO2hBC/P777z///HO+9SlCCOfhtc5zbe4kKSlJCOHcocPNzU2v1+eLHgqxdu1au93+9NNP57Y4V6k4z1URQgQGBrq7u//222/5vvDJJ5985pbw8PCCI9/pFJVFixYVsTagjOA/HgAAQJXKu8ufRfguP5b1yra0n5Ksbz7irZNZrgK4RuPGjRs3biyEiIqKqlOnzujRo/v06VOUL8zIyBg3blzu0/79+0dERBSjgP79+7/88ssHDx5s0aJFTEyMuBUu5FWtWjWdTnfy5MlCxnEuTsmdgVKtWrVTp05dv37d39+/YOevv/561apVQ4cO7dWrl7i1PmXKlClTpkzJN+bRo0cbNWoky3KdOnUOHz6cnp7uTFuc8h7F8sorrxw+fPhevnUAf2EGBwAAUCvncpUVvfy2/5bdf0PKxZt2V1cElCFnz55dsmRJQkJC3saKFSs2b948KSnJuRzjroKCgvJOSSh4GEoRRUZGyrLsnMQRExPj3P8iXx+j0dipU6fk5ORvvvnmTuM4D7h1BhZCiC5duog8UzDyWb169bp168qXLy+EuHjx4k8//VShQoVxf/fII4/kHaFZs2ZCiOXLl9+pgNz9TQEUAwEHAABQt1YV9d8NCCjvLvdal7zpXLarywHKiqtXr44YMWLp0qX52pOTkz09PfPOUHgAgoOD27Ztu3bt2qSkpL179xZcn+I0e/ZsjUYzfvz4rKysglfXrVsXFxfXoEGD7t27O1smTpxoNBrffffdjIyMfJ3Pnz8fGxtrNBpbtGghhPjyyy8VRYmKivrP382dO1fkOUtlypQp7u7ub7755vXr1wsW8PXXX//000/F/RkAIOAAAADq52uQP4/wHdXEc2xc6tt7btocyt2/BsD9adGiRWBg4PLly8+dO5fbuGbNmoSEhC5dusjyg/5DY8CAAefPn586daqiKAXXpzg1adJk9OjRZ86cadasWXx8fG673W6fOXPm4MGD3d3dlyxZotFonO1VqlSZPn36lStXWrduferUqdz+Z8+effTRR7Ozs//v//5Pr9eLv5+fklerVq1CQ0PPnTt36NAhIUTVqlVfe+211NTUVq1a7dixI7eboiiffvppVFSUt7d3yfw4gDKJPTgAAEBpIAkxvIln40D9uG2p8VetH3YtX9lL4+qigNJMp9N99NFHgwYNatiwYc+ePQMDA3/99dcdO3YEBQXlPZz1gYmMjBw3btznn39eu3btvEeQ5DNv3jx/f/8ZM2Y0bdo0NDQ0PDw8JSUlPj4+KysrJCTk008/bdu2bd7+r7zySmJi4ieffFK3bt3atWvXq1fvxIkTzo08Bg4cOGbMGCHEqVOn4uPj69Wr16hRo4J3HDhw4Jw5c7788svmzZsLId588013d/fp06d37tw5JCSkUaNGdrv9yJEjf/zxR1RU1JNPPtm7d+98I9zpmFghxKRJk2rXrn2PPyqg1CLgAAAApUfrSvqNT/qP25rWL+b6/C7lOlR2c3VFQGk2cODAgICA9957b8eOHWazuWbNmq+++uqUKVOc21I8YCEhIa1bt967d++dpm84abXaqVOnRkREvP/++7t27dq8eXNAQECbNm26dOkyduxYg8FQsP/SpUsHDRq0ePHiEydObNq0qVKlShERESNHjszdD9W5AqXg9A2np556as6cOV999dWcOXMkSZJl+Y033ujSpcu8efP27NmzdevW8uXLt2nTZtSoUV27ds3MzIyKiipXrlzeEe50TKwQIioqioADyCUpCnM472LJkiUjRozIyMjw9PR0dS0AAODu7A4x90DGp0czJ7X2fr6R0dXloEy7dOlS5cqVT58+HRYW5upaAKAEWCyWrl27zpo1K990p4cBMzgAAEBpo5HFxNZeDQK0E3ekJyTbZnbycdNwgiwAAKUcm4wCAIDSKaKG4au+fgeuWJ+JvXHd7HB1OQAA4J9FwAEAAEqtev66mH5+OQ6lz7rrCck2V5cDAAD+QQQcAACgNAsyar7s49emkn7wNylbzme7uhwAAPBPIeAAAAClnJtGmtO53MtNPV/ekvrBoQz2VwcAoFRik1EAAFD6SUIMb+JZy1c3blvq2dSc2Y+WM2jZdhQAgFKFGRwAAKCseDTUbU0fvyPXbAO/TrmcaXd1OQAAoCQRcAAAgDKkjp/u6yf9vfRS/w0pR66x7SgAAKUHAQcAAChbyrvLy3v5tq/sNnhjyoZEs6vLAQAAJYM9OAAAQJmjk6XZnXyaBOom/ZB2OjVnQksvmR05AABQOQIOAABQRg2q51HBUzNua+r5tJx5nct56Ag5AABQMZaoAACAsqtTFbd1/fxP3ch5+puUFLPD1eUAAIDiI+AAAABlWlh57fpIP60sPbnh+m/pOa4uBwAAFBMBBwAAKOvKuclf9PatXk47aGPKiescrQIAgCoRcAAAAAgPrbS0u2+nKu5PbUzZfdHi6nIAAMA9I+AAAAAQQgiNLGZ28nmmvvHFTanfneX4WAAAVIZTVAAAAP4kCTGxtVeQUX5la9qVTMcLjY2urggAABQVAQcAAMDfRDU0lnOTJ/6QdiPb8VorLw6PBQBAFQg4AAAA8utbyxDgIY/8PjXZZJ/VsZyGRb0AADz0eLsGAAC4jXYhbit7+/1wwTLy+1RzjuLqcgAAwF0QcAAAANxeo0BdTD//M2k5z3yTkprtcHU5AACgMAQcAAAAd1TFW7Omj5/VIQZtTLmSaXd1OQAA4I4IOAAAAAoT4CGv6u3r6y4/uSHlZIrN1eUAAIDbI+AAAAC4C283eXkv3+YVdE9/c+PQH1ZXlwMAAG6DgAMAAODu9BrpP4+Vj6jhHvXtjZ0XLK4uB3gYZWZmLl++/NKlSw/gXiNHjpQKVbNmTSHEzz//XEifypUrO0d76623nC27d+++7e3eeOMNZ4f9+/c7W44cOVJwwEqVKnXt2nXbtm25X1jEAgCUCI6JBQAAKBKNJGZ08CnvLo/4PnVht/KdQ91cXRHwcBkzZsyyZcu+/fbbkJCQf/peLVu2zMzMzH26efPm69ev9+/f393d3dkSFBSUezU0NLR9+/YFBylfvny+lnXr1t22Z0xMzG3LqFKlSocOHZyPbTZbYmLi1q1bt27d+umnnz7//PPFKADA/SDgAAAAuAevtvQy6qRR36d+1LVc12ruri4HeFisW7du2bJlD+x2w4YNGzZsWO7TNm3aXL9+ffHixX5+fgU7t2nTZsWKFXcd08vLKyYm5v3335ckKW97QkJCYmKit7f3zZs3831Jy5Yt84381VdfPfXUUxMmTHj66adz05YiFgDgPrFEBQAA4N4Mb+L5SgvPMXFpceezXV0L8FBISkoaPny4p6enqwu5L717905KStq7d2++9piYGEmSevbsWZRBBg4c+Mgjj6SmpiYmJv4DNQIoDAEHAADAPRvexPPVlp5j4tK2kHGgzFMU5bnnnvPx8fn3v//t6lruS9++fTUazdq1a/O1x8TEtG7duujrbipVqiSESEpKKuH6ANwNAQcAAEBxvBTuOb6l18txqbFnzK6uBXClefPm/fDDD1988YW3t7era7kvgYGBnTp1iomJURQlt/H06dPHjh3r379/EQex2WwHDx4UQoSFhf0jVQK4M/bgAAAAKKYXw41CEuO3pymKeKKmwdXlAC5w+PDhyZMnT5w48ZFHHtmzZ4+ry7m9/fv3592wI9ejjz763HPP5W3p37//yJEjDxw40KpVK2fLunXrhBCRkZELFy4s/C52u/306dPTpk07d+5co0aNqlevXowCANwPAg4AAIDie7GxURJiwo40RYg+ZBwoY8xm85AhQ+rVq/fmm2+6upbCnD9//vz58wXb3d3d8+UL/fr1Gz169Nq1a3MDjpiYmGbNmlWtWvW2I69bty7fjqRCiAoVKqxcuVKj0RSjAAD3g4ADAADgvrzQ2ChJ4rXtaYoi+tYi40AZ8tprr507d+7QoUN6vb54I5hMpqVLl+Y+DQsLi4iIKKHq/jJo0KDo6Oii9AwKCurQocO6devmzp0rhPj9999//vnnmTNn3ql/3mNihRBGo7F+/fpDhw7Nt1qn6AUAuB8EHAAAAPfrX42MkhCv70gTgowDZcW2bdsWLlz4n//8p379+sUeJCMjY9y4cblP+/fv/08EHPekf//+L7/88sGDB1u0aBETEyOEePLJJ+/UueAxsQBcSK2bjN68eTMpKenKlSsOh8PVtQAAAIjnGxn/Xxvv13ekbUhkz1GUCYcPHxZCvPLKK9ItkyZNEkL06tVLkqRPP/20KIMEBQUpeRQ8weTBi4yMlGXZufVGTExMw4YNa9as6eqiABSJymZwJCQkzJkzZ8uWLX/88YezRaPRVKhQoUOHDqNHj27Xrp1rywMAAGXZsEZGN6008Yc0IUQ/5nGgtGvcuPGIESPytsTHx+/fv79Hjx6hoaF16tRxVWH3KTg4uG3btmvXrv33v/+9d+/eh3x7EQB5qSngGDNmzMKFCxVFCQ4ObtWqlZ+fnxDixo0bly5dio6Ojo6OfuGFF/Iu4QMAAHjAnq7nIQkx8Yc0RRGRtck4UJo99thjjz32WN6W2bNn79+/f/To0S5fZnKfBgwYMHbs2KlTpyqKUsj6FAAPG9UEHB9//PGCBQsef/zxmTNnNmnSJN/V48ePz5gx45NPPqlbt+6rr77qkgoBAACEEIPreUhCTNqZpgjxJBkH8BC40ymtQohJkybVrl07X2NkZOS4ceM+//zz2rVr388OI8UuAEDxqCbgWLVqVe3atb/99lut9jY1169fPzo6+vLly+vXryfgAAAArjWonoeQxP/7Ic1iV56u5+HqcoCy7k6ntAohoqKiCuYLISEhrVu33rt3b0lN37jXAgAUj6QoiqtrKBIfH59+/fotW7askD6TJ09euHBhWlpayd56yZIlI0aMyMjI8PT0LNmRAQBAKRZ9wjR9d/rbHXwG1SXjKLsuXbpUuXLl06dPh4WFuboWACgBFoula9eus2bNatu2ratryU81MzgaNGiwf/9+u92u0Wju1Gfv3r0NGjR4kFUBAADcyeB6HlpZTN6VbrIpzzcyurocAABKOdUcEztkyJCTJ0/27t372LFjBa8mJiYOGTJkx44dffr0efC1AQAA3NaAOh7/6VJ+9r6b7+3PcHUtAACUcqqZwTFq1Khjx44tXrx406ZNlStXDg0N9fX1lSQpNTX14sWLziVtUVFREyZMcHWlAAAAf4mo4W7UvOn5rQAAIABJREFU+Y7ekmqyOaY/4iO5uh4AAEor1QQcQohFixYNHz78vffei4uL+/HHH52NGo0mMDBw8ODBw4cP79ixo2srBAAAKKhTFbfPI3xf3HQj06rM7lROo5oZtAAAqImaAg4hRHh4+OrVq4UQaWlpGRkZOp0uMDBQlvmYAAAAHmotg/Ure/sN++7Gq9vT5nUup+XDCwAAJU2t766yLMuyrJYjYAAAABoG6L7s43fwinXE9zeyc/gMAwBACVNZwJGQkDB06NDg4GAfH5+QkJCKFSvq9fqQkJCnn356z549rq4OAACgMGHltWv6+p1JzXn+fzeybGQcAACUJDUFHGPGjGnUqNEXX3whSVKrVq169uzZs2fPFi1aSJIUHR39yCOPvPjii66uEQAAoDCVvTSrn/C7luV4JjYlLdvh6nIAACg9VBNwfPzxxwsWLOjWrdsvv/xy+fL/Z+8+w6K61reBrz0MZQaQImKJVAHpaIwI2BAsiA0BC9gVwRqM7SQeQI0FObaEo4lEiA2liggqKC0nJhHUiDSpItKMCghSB5jh/bD/Z14OEJrKMHD/PuSCtfde695ELuVhldLExMTbt2/fvn37wYMHRUVF6enpy5Yt8/X1PXXqlKCTAgAAAHRmlJRIoM3QJi5ZHlH+upYr6DgAAAADhNBsMnr16tWxY8feunWLyewgs56eXkBAQGlpaVhY2M6dO7vfLY/H+/XXX5ubmzu5JzMzs8dxAQAAAP6eAotxdaH8hjvvVkZWXJ4vP1JKRNCJAAAAhJ7QFDjS09MXL17cYXWDRlHU1KlTz54926NuX758uXTp0s4LHBwOhxCCDU0BAADgI5IRZ1xeIL8p+t3S8PIrC+RVZYTmX2UAAAD9k9AsUdHX109KSuJyO5vG+eDBA319/R51q6am9ubNm4pO0cteKIr6oBcAAAAA+F9sJuU7V05vmOiy8PKs8iZBxwEAABBuQlPgWLFiRVZW1oIFC9LS0tpfzcnJWbFiRUJCwqJFi/o+GwAAAEDviIlQZ2bJTRol5hhRkfwaNQ4AAIDeE5rJkFu2bElLSzt37lxUVJSSkpKKioq8vDxFUe/evSsqKnrx4gUhZO3atbt37xZ0UgAAAIAeYDLI6Zly//xP1drb5T9by08YISboRAAAAEJJaAochJAff/zRxcXlX//6V0xMzG+//UY3ioiIKCoqOjg4uLi4TJ8+XbAJAQAAAHpBhCKe5jLiImT9nYpL84eOUxQVdCIAAADhI0wFDkLIuHHjrl27RgiprKysrq4WFRVVVFRkMIRmoQ0AAABAhyhCDkyV4bWQtbfKL88faogaBwAAQA8JWYGDT1ZWVlZWVtApAAAAAD4aipBvp8lwW8ia2xX+C+T1FFDjAAAA6AHMfQAAAADoLyhCDk2TMVcWX3u7Ireis2PsAQAAoA0UOAAAAAD6ERGKnLCQNR0lviKyPO8dahwAAADdhQIHAAAAQP8iQpFTlrLjFMXW3KoofM8VdBwAAADhIBwFjjNnzsh1m6DDAgAAAHwoJoOcnSOro8B0jCgvqkaNAwAAoGvCscmolZVVbm6uj48Ph8ORlpZWUVERdCIAAACAT0uUQZ2ZJecUVbEiojxg4dDPpEUEnQgAAKBfE44Ch4aGxvfff29tbW1lZTV9+vTIyEhBJwIAAAD45CSY1Pm58hvuVKy+VRGwaKgiWzjm3gIAAAiEMP01OWfOHC0tLUGnAAAAAOg7LCblay0/jM1YEVH+to4n6DgAAAD9lzAVOAghEydOFBXFmfAAAAAwiLCZ1Pm58lJi1NrbFZUNqHEAAAB0TMgKHP7+/mFhYYJOAQAAANCnpMWoS/PkRSiy6lZFJQc1DoD/78GDB3Z2dtra2iwWa+TIkRYWFn5+fjze/32bbN68meqUpqYmv6uVK1dSFMVgMIqLi3v6OEVRampq7eOlpKSsW7dOTU2NxWLJy8sbGxsfPny4rq6uzW1HjhyhKMrQ0LC5uYPDoXV0dIYNG9a+vX1gvlWrVlEUxeFwuvVFBBgohGMPDgAAAIBBbog44+I8ecfIcqc7FZfmD5UUpQSdCIAQQpSUlNr/dO3m5nbo0KE+GN3T03Pfvn0SEhIzZ840Nzd//fr1/fv3ExISrl+/HhkZKSIiYmxsXFNTw78/Ojq6rKzM3t5eQkKCbhk+fDj9QX19/c2bNwkhLS0twcHBO3fuJIR0//H2eDze8ePHPTw8Ghsb6cpLeXl5amrqo0ePrly54u/vP3HixDaPpKWlnT59es+ePd159w4DAwxyKHAAAAAACAd5FuPqgqGOEeVrb1dcmifPRo0DBK2+vr6kpGTUqFFtdsrrcC7DR5ecnOzm5qajoxMXFzdy5Ei6saamZu3atdevXz958uTevXvXrVu3bt06/iOmpqZlZWXnzp0bOnRom94iIyNramrmzp0bFRUVFBRE1wu6/3h7e/bsOXXq1JgxY65du2ZsbEw3Njc3Hzt27MCBA5MnT3769Kmurm7rRyiKOnDgwNKlS7tzamSHgQEGOSFbogIAAAAwmA1lMS7Nl39bx3WOrmhobhF0HBjs8vLyWlpaNm/enPC/1q9f3wejx8bG8ni8ffv28asbhBApKSlfX18Gg3H58uUe9RYQEEAI8fDwMDAwePjwYX5+/odkS09P9/b2VldXT05O5lc3CCFMJtPNzS0gIKCpqWn79u1tnnJ2dq6rq9u2bVvfBwYYGFDgAAAAABAmIyRFri4Y+rKKuzOukosSBwhUTk4OIURbW1sgo5eWlhJCWlrafhvIysp6e3tv2rSp+11VVVVFRUWpqqqamJjY2toSQoKDgz8k2969e5ubm48fPy4tLd3+6pIlS6ZPnx4fH3/v3r3W7fb29tbW1rdu3epy28GeBr5y5YqpqamcnJyCgsL06dPv3r3b83cCEAIocAAAAAAImc+kRS7Mk08qbdx/v0rQWWBQy83NJYSoqKhcvXr1wIEDvr6+z54967PR6ZkRe/bsCQoKarOb5tatW7s5D4J248YNDofj4OBACLGzsyOEBAYG9jpYfX19XFycgoICXXrokIuLCyHk4cOHrRspijp79iybzf7yyy+rq6s/VuCjR4+uXr365cuXc+fONTY2fvz4sbW19a+//trT9wLo/1DgAAAAABA+GnLMc1ZyYdn1Pz2t6fpugE+DLnDMmzdv5cqVBw8e3Lhxo4GBwZdfftnhUSAf3bJly+zt7V+/fr18+fJhw4YtWrTI29u7dxUWerkHXS8wMDDQ1NRMSUnJysrqXbD8/PzGxsbOJ7bQVzMzM9u0q6qqenh4lJSUuLu7f6zAp0+fHjt27IsXL65du3bnzh1/f38ej3fp0qUevRSAUECBAwAAAEAoTRwp5j1L9sTD6hs59YLOAoMUvUTF0tIyNTW1urr6t99+mzBhwr///e9Tp071wegMBiMkJCQ2NtbJyUlBQSEiIsLV1VVPT09dXf3UqVP8k2K79ObNm7i4OF1dXQMDA7qFnnkRFBTUu2CVlZWEEGVl5U7uobcRzcvLa39p165d+vr6Z86cefLkyYcHbmpqqqysbGxsZDD+70c/Gxub9PT0zgsoAEIKBQ4AAAAAYTVTVWL/ZJmvf6n8rZjT9d0AH9vRo0fj4+MDAgIMDAykpKQmT558584dOTm5Q4cOdbO+UFdX930rt2/f7mkGS0vL8+fP5+fn5+fn+/r6Lly4sLi4eNeuXatXr+5mDyEhIVwu19HRkd9CL/rodYFDRkaGEFJUVNTJPSUlJYSQDnfoYDKZPj4+PB7PxcWlwy9jjwKLioouWLDgxYsXRkZGBw4ciI+Pr6+v19PTU1VV7dlbAQgDFDgAAAAAhNgKPfYafcmt9949K2sSdBYYdKZOnTpjxozWLQoKCpaWljU1Nc+fP+9OD9XV1TtauXjxYvdH53K5rX/+V1NT27Bhw82bN5OTkxUVFa9evfrnn392px96uYebmxv1X/TuHpmZmampqd3P0zqJqKho5ytc6MUpmpqaHV41MzNzcnJ6/Pjx2bNnPzzw1atXDx48yOFwDh48aGlpOWzYsBUrVtBbtAIMMExBBwAAAACAD/KN2ZCKBp5T1LsQm6GfSYsIOg4MdkOHDiWENDV1q+I2fPjw9segdAePx2Oz2ePGjUtKSmpzSU9Pz8nJ6ejRoxkZGRMmTOi8n6Kioj/++GPEiBHLly9v3f748ePffvstKCjI0NCwp9kkJSXNzc1jYmIiIiIWLlzY4T2+vr6EkPnz5/9dJ15eXjdv3nRzc6NnZ3xIYBaL5eHh4eHhkZOT89tvv12+fPnatWtpaWkpKSkURfX07QD6MxQ4AAAAAIQbRYinucyGO+/W36kIthkqI44putAXnj17Zmdnt3jx4qNHj7ZuT0lJERcX19LS+qSjMxgMTU3NtLS0oqIiJSWlNlcLCwsJIbq6ul32ExgY2NLSsnbtWk9Pz9btSUlJJiYmgYGBR44c6UU8Ly+v+Pj4Xbt2WVpaSkpKtrkaGhoaExOjr69vZWX1dz3IycmdPHly1apVrq6uHxI4Ly/v0qVLlpaW5ubmWlpaWlpa69evnz17dkxMTFFRUecbhQAIHfz9BwAAACD0RBnUD7PlmAxqU/S7Rm5vfh8O0FPa2tq1tbXffffdo0eP+I0///xzYmLimjVrmMxP/pvUbdu21dfX29jYtFkMEh4eHhgYqKGhMX78+C47aX0cSWuTJk1SUVHJz89//PhxL7KNHz9+69ateXl5EyZMSE5O5rdzuVxPT08HBwcJCQkfHx8Rkc6mXK1cudLS0jI0NDQ/P7/XgRkMxuHDh/fv38+fU9PU1PTu3TtxcfERI0b04tUA+jMUOAAAAAAGAikx6mdruaJq7u74Sh5KHPDpMRiMK1eusFisyZMn29rabtmyZcqUKRs2bNDR0fHy8uqDAM7OzmvXrn3y5ImRkdG4cePs7e1tbGx0dHQWL17MZrPDw8M7Lx8QQrKzs5OTk3V1dTtch7J06VJCSGBgYO/inTx58ttvv83Pz//8889VVVVtbGymTp0qIyOzb9++ESNG3Lx508zMrMtOfvjhB3Fx8cbGxl4HVldXX7hw4a+//qqrq+vi4uLg4KCsrPz48eO9e/eKiYn17tUA+i0UOAAAAAAGiOGSIhes5e8XN3olvhd0FhgUpk+fnpycvHLlytzc3CtXrnA4HHd39ydPnsjKyvbB6AwG48KFCxEREdbW1m/fvo2MjExMTJSRkXF3d3/+/Lmenl6XPdC1gPazIWjLli0jhAQHB/dulxAmk+nu7p6YmLhq1SpCSHR0dEFBgampqaenZ05OzuzZs7vTiZaW1jfffPOBgf39/ffv3y8qKnr16tXY2NgxY8bQ24724qUA+jmqd9+ug4qPj8+mTZuqq6ulpKQEnQUAAACgC0mljWtvV+ydJL3OsO3Kf+h7xcXFSkpKubm5Ghoags4CAPARcDicWbNmHTt2rDuzkPoYZnAAAAAADCiTRokdnyHjmfg+Kr9B0FkAAAD6Dk5RAQAAABho5muwSqq5X8VWys6TN/0My+wBAGBQwAwOAAAAgAHIZbyUox57892K7IpmQWcBAADoCyhwAAAAAAxMbmZDJo8WX3+74lUNV9BZAAAAPjkUOAAAAAAGJgZFTlrIjpIWcY5+V9uEfeUBAGCAQ4EDAAAAYMCSYFLnreTqmlp2xVeiwgEAAAMbChwAAAAAA5msBOP8XLnEEs6/H9cIOgsAAMAnhAIHAAAAwACnLss8YSF75s/qO89xcCwAAAxYKHAAAAAADHwzVSW+/EL6H79U5uBQFQAAGKBQ4AAAAAAYFLZOkJqhLO4cVfGugSfoLAAAAB8fChwAAAAAgwJFyDFzWbYotSO2kosdRwEAYMBBgQMAAABgsGCLUj/Nlc8oazqe9F7QWQAAAD4yFDgAAAAABpHR0iLes2R/Tq29nl0v6CwAAAAfEwocAAAAAIOL2Wfi/zAZ4v5rVeqbJkFnAQAA+GhQ4AAAAAAYdDYYSi7UZG299668HhuOAgDAAIECBwAAAMBgdHDqkGFsxua775p42HEUAAAGAhQ4AAAAAAYjcRHqnJV8cTX38O/YcBQAAAYCFDgAAAAABilFNuPMLNngrPrAZ3WCzgIAAPChUOAAAAAAGLw+HyF2eJrM/t+qHr5qFHQWAACAD4ICBwAAAMCgZjeWtUSbvf3eu79quYLOAgAA0HsocAAAAAAMdgemyIyRYzpHvWtoxoajAAAgrFDgAAAAABjsmAziPUuuooHn9muVoLMAAAD0EgocAAAAAEAUWIyfrOSi8xsuptUKOgsAAEBvoMABAAAAAIQQoqsgemiazNEH72/l1Qs6CwAAQI+hwAEAAAAA/2exFusbkyE74yr9M3BwLAiHBw8e2NnZaWtrs1iskSNHWlhY+Pn58Xg8+urmzZupTmlqavK7WrlyJUVRDAajuLi4p49TFKWmptY+XkpKyrp169TU1Fgslry8vLGx8eHDh+vq2n5/HTlyhKIoQ0PD5ubm9p3o6OgMGzasTWNiYuLatWsnTJggJSWlpqY2Z86cyMjIXn0J/8fUqVOVlJQ+vB8AgWAKOgAAAAAA9CPrDCXlWYy9CZWlNdy9k6QFHQeEwP379w8ePPjo0SNJScmpU6cePXp0zJgxfTO0p6fnvn37JCQkZs6caW5u/vr16/v37yckJFy/fj0yMlJERMTY2LimpoZ/f3R0dFlZmb29vYSEBN0yfPhw+oP6+vqbN28SQlpaWoKDg3fu3EkI6f7j7fF4vOPHj3t4eDQ2NtKVl/Ly8tTU1EePHl25csXf33/ixIltHklLSzt9+vSePXs6f2sej7d//35PT8+WlhZtbe1Zs2YVFhbGx8ffu3dvxYoV/v7+3fzqAQxALdCVc+fOEUKqq6sFHQQAAACgj8QVNOief7X/fiWXJ+goQq6oqIgQkpubK+ggn0pgYKCYmNioUaMcHR0XLVokIiIydOjQly9f9sHQT548YTAYOjo6paWl/Mbq6mo7OztCiJeXV/tHTExMCCFlZWXtLwUFBRFC5s6dSwgxNjbucMROHieEqKqqtm6hSyRjxoxJSkriNzY1NR06dEhERERUVDQjI4PffvjwYUIIRVFsNrugoKBN59ra2goKCvxP6R9PPv/889Z35uTkmJqaEkLOnj3bYfhumjJlyujRoz+kBxjwGhoapk6d+vvvvws6SAewRAUAAAAA2rJQEfdfMDQyt2FXfGUzT9BpoL8qLCxcuXLl+PHjnz17dvXq1fDw8HPnzpWXl3t4ePTB6LGxsTweb9++fSNHjuQ3SklJ+fr6MhiMy5cv96i3gIAAQoiHh4eBgcHDhw/z8/M/JFt6erq3t7e6unpycrKxsTG/nclkurm5BQQENDU1bd++vc1Tzs7OdXV127Zt66Tn8vLyffv2qaio3L9/X0VFhd+uqakZGhrKZDLPnDnzIckBhBoKHAAAAADQgfHDRQMWDU0qbVx/p6KuqUXQcaA/+v7775ubm7/77jsZGRm6ZcOGDadPn6ZnOnxqpaWlhJCWlrZ/OGVlZb29vTdt2tT9rqqqqqKiolRVVU1MTGxtbQkhwcHBH5Jt7969zc3Nx48fl5buYJ3XkiVLpk+fTi8qad1ub29vbW1969atsLCwv+v5X//6V0VFxddff81ms9tcGjVqlKur65gxY96+fUu3FBYWrl69WldXl8ViKSsr29vbp6SktH4kJyfH3t5eSUlp9OjRS5cuLSgo6N37AvQTKHAAAAAAQMe05JlBNkOLq7krI8srGzCRA9oKDAxUUlJqXc6gKGrHjh09Ki70Gj0zYs+ePUFBQRwOp/WlrVu3dj4Poo0bN25wOBwHBwdCCL3CJTAwsNfB6uvr4+LiFBQU6FpJh1xcXAghDx8+bN1IUdTZs2fZbPaXX35ZXV3d4YN3794lhCxdurTDqydOnIiMjKR3JH327Jmenl5wcLCuru7GjRuNjIxu3rxpYWFBF4YIIQ8ePJg4cWJYWJiGhoaFhUViYqKpqSm9qApASKHAAQAAAAB/S0laJGjR0EYuWXaz/HUtV9BxoB+pqakpLS1VVVVNSUlZuHDh8OHDlZWVlyxZkpeX1zcBli1bZm9v//r16+XLlw8bNmzRokXe3t7Pnj3rRVf0+hS6wGFgYKCpqZmSkpKVldW7YPn5+Y2Njdra2p3cQ1/NzMxs066qqurh4VFSUuLu7t7hgzk5OUOGDJGXl+8yxrlz52pqasLCwkJDQ729vSMjI7///vuKioq4uDj6hh07dtA3JCQkXL58OSMjQ0ND4+XLl916SYB+CQUOAAAAAOjMMDbj2kJ5GXHG0vDygqoOzrCEwamyspIQUlpaOmXKlIKCgvnz5+vp6YWFhRkZGT1+/LgPAjAYjJCQkNjYWCcnJwUFhYiICFdXVz09PXV19VOnTvFPiu3Smzdv4uLidHV1DQwM6BZ65gW97Wgv0F8ZZWXlTu6ht8/osBi0a9cufX39M2fOPHnypM0lDodTX1/fes+RTtjb2/v7+1tZWfFb6INsKyoqCCF//vnnw4cP7ezsbGxs6KvS0tKnTp3qTs8A/RaOiQUAAACALgwRZ1yaL7/13rtl4eUX5snrKogKOhEIXlNTEyHk+fPnX3/99dGjRymKIoTExsbOnj3b2dm5/Q/nHaqrqzt//jz/Uw0NjXnz5vUohqWlpaWlJSHkxYsX8fHxERERUVFRu3btevLkSTcPTA0JCeFyuY6OjvwWOzs7Ly+voKCg/fv39ygMjd6RpPO1HiUlJYSQDnfoYDKZPj4+U6ZMcXFxSUpKYjD+/++kxcXFxcTE/vrrr+7EmDZtGiGEw+Hk5OQUFBRkZmb6+fnxr2ZnZxNCWpc/CCETJ06kl7cACCnM4AAAAACArrGY1Hkr+enK4isiKx7/1SjoOCB49CaXQ4cOPXz4MF3dIITMnDlz1qxZycnJb9686U4n1dXVO1q5ePFi9wNwudzW0zTU1NQ2bNhw8+bN5ORkRUXFq1ev/vnnn93ph16f4ubmRv0XvbtHZmZmampq9/O0TiIqKtr5Chd6cYqmpmaHV83MzJycnB4/fnz27Nn2nVdVVZWVlXX4YHh4+JIlS27dukUIqaur27hxo5ycnKGhoa2trZ+fn5aWFv/OV69eEUJGjRrVpofRo0d38XoA/RgKHAAAAADQLSIM4jVD1n4sa1Vkxb0XDYKOAwI2bNgwCQkJNTU1ERGR1u3q6uqEkOLi4u50Mnz48JZWQkJCujk6j8djs9mmpqbtL+np6Tk5ORFCMjIyuuynqKjojz/+GDFixI7/NWXKFNLbVSqSkpLm5uZv376NiIj4u3t8fX0JIfPnz/+7G7y8vBQVFd3c3Ph7gtLo6Sp/F+zatWuhoaFycnKEELqosWPHjtTU1IaGhuzsbDc3N/6dSkpK5L9ljtbevXvX5QsC9FsocAAAAABAd1GE/NNsyI4vpLbFvAvJqhN0HBAkBoNhbm6ek5PT0PA/1a7MzEwGgzF27NhPPbqmpmZaWlqHK0EKCwsJIbq6ul32ExgY2NLSsnbt2tP/68SJE+QDzlLx8vISERHZtWtXbW1t+6uhoaExMTH6+vptVoi0Jicnd/Lkyffv37u6urZu/8c//iEpKXnkyJH2x6y8ePEiMjJSUlJy4sSJVVVVcXFxtra2R48eNTAwoItQ79+/599Mz+agz2Rp3QP9pQMQUihwAAAAAEDPuIyXcjMb8s//VF1K6+CHNxg8vvrqq/fv3+/evZu/VCQ4OPg///mPtbW1pKTkpx5927Zt9fX1NjY2bRaDhIeHBwYGamhojB8/vstOWp+f0tqkSZNUVFTy8/N7t2Hq+PHjt27dmpeXN2HChOTkZH47l8v19PR0cHCQkJDw8fFpM/mljZUrV1paWoaGhubn5/MblZWV9+/f/+rVKxMTE3ofDdrz589nzJjR0NBw9OhRMTExLpfb3NxMb3dKq6ioOHz4MCGE/p81btw4ExOTkJAQ/jSThoaG7du3d39zVoB+CJuMAgAAAECPrdaXlBVn7EmofF3H2zNJmhJ0HhCI2bNnr1279uzZs7/++qupqemLFy9iYmJGjhz5448/9sHozs7OSUlJFy9eNDIy0tHR0dDQaG5uzs7OzsrKGjJkSHh4eOflA0JIdnZ2cnKyrq6uoaFh+6tLly49fvx4YGDgF1980Yt4J0+eVFBQOHTo0Oeff66iojJu3Ljy8vLk5OTa2trRo0f7+fmZmZl12ckPP/xgaGjI4XBaN3711Vc5OTm+vr46Ojpjx47V1dV99uwZXeVZunTp9u3bCSHy8vJz5sy5e/eumZnZjBkzysrK6ANuCCEXL17U0tKaN2/eqVOnrKysFi9ebGlpqays/Msvv7x+/Zo+E6cX7wvQH2AGBwAAAAD0xkJN1oV58tcyarffe8fhtgg6DgjGhQsXTpw4ISMjExAQUFJSsm3btoyMjL7ZqJLBYFy4cCEiIsLa2vrt27eRkZGJiYkyMjLu7u7Pnz/X09Prsgd6BUr76Ru0ZcuWEUKCg4NbWnrzx5vJZLq7uycmJq5atYoQEh0dXVBQYGpq6unpmZOTM3v27O50oqWl9c0337Tv+fz587GxsXZ2dgwGIyoqqrm5ed68ebdu3QoKCuJv+Hrt2rVNmzYVFRWdOXMmMzPz5MmTsbGxW7ZsefnyZXh4OCHE1NT00aNHtra22dnZt2/fNjIy+vXXX/X19XvxsgD9BNW7b9dBxcfHZ9OmTdXV1VJSUoLOAgAAANC/ZFc0r79doSorcm6OvLQYZnK0VVxcrKSklJubq6GhIegsAAAfAYfDmTVr1rFjx7ozC6mPYQYHAAAAAPTeWHlmmO3QyoaWpeFlr2q4go4DAACDFwocAAAAAPBBhkuKXFsoLyvOsLtRnlneJOg4AAAwSKHAAQAAAAAfSkaccWm+/MSRYo4RFUmljYKOAwAAgxEKHACBdhCLAAAgAElEQVQAAADwEYiJUN/NlF2uw15zqyIyr17QcQAAYNDBMbEAAAAA8HFQhPzDRHq4JGNXXGV+ZbPrF9KCTgQAAIMIChwAAAAA8DGtNZAcISmyM66yitPiZjaEgZNVAACgT6DAAQAAAAAfmZW6hJyE/Ka7717VcE9bykowUeQAAIBPDntwAAAAAMDHN2mUWPCioelvm1ZGlr9r4Ak6DgAADHwocAAAAADAJ6Epz7xuq8DhkuU3y0uquYKOAwAAAxwKHAAAAADwqSiyGUGLho6SErG/UZZR1iToOAAAMJChwAEAAAAAnxBblPpprtykUeKOEeW/FHIEHQcAAAYsFDgAAAAA4NMSZVCnZ8quNZB0jq44n1Ir6DgAADAw4RQVAAAAAPjkKEK+mig9Vl50b0JlxtumY+YyOFoFAAA+LszgAAAAAIA+Yj1GIthm6J9/Na6MLH9bh6NVAADgY0KBAwAAAAD6jq6C6HVbhZYWsuh6WdpbbDsKAAAfDQocAAAAANCnFNmMgEVDJ48WWxZefjO3XtBxAABggMAeHAAAAADQ18REqOMzZPUUavfEV2ZXNO82lmZgRw4AAPgwKHAAAAAAgGCsNZBUk2G6xr4rrGr+l4UsG9uOAgDAB8ASFQAAAAAQmOnK4sE2Culvm5beKC+p5go6DgAACDEUOAAAAABAkLTkmeF2CrIS1KLrZQ9fNQo6DgAACCsUOAAAAABAwGQlGJfmDV2gwVpzq+J6NrYdBQCA3sAeHAAAAAAgeCIMsn/KkLHyzH3/qcwoa/qn2RAR7MgBAAA9gQIHAAAAAPQXy3XZSkNEtsdUllRzf5wjh6NVAACg+7BEBQAAAAD6kcmjxUMWD/21iJNQ2CDoLAAAIExQ4AAAAACA/mWMLHOBhoRfSq2ggwAAgDBBgQMAAAAA+h0nI6mHpY0pb5oEHQT6uwcPHtjZ2Wlra7NYrJEjR1pYWPj5+fF4PPrq5s2bqU5pamryu1q5ciVFUQwGo7i4uKePUxSlpqbWPl5KSsq6devU1NRYLJa8vLyxsfHhw4fr6ura3HbkyBGKogwNDZubm9t3oqOjM2zYMH6H7WN89tlns2bNiouL4z+yatUqiqI4HE4vv6wAwgl7cAAAAABAv6Mlz5yqJH4htfa7mbKCzgIde/369YgRI/7uqq+v74YNGz51Bk9Pz3379klISMycOdPc3Pz169f3799PSEi4fv16ZGSkiIiIsbFxTU0N//7o6OiysjJ7e3sJCQm6Zfjw4fQH9fX1N2/eJIS0tLQEBwfv3LmTENL9x9vj8XjHjx/38PBobGykKy/l5eWpqamPHj26cuWKv7//xIkT2zySlpZ2+vTpPXv2dPniysrK06ZNoz9uamrKycmJjY2NjY318/Nbv359l48DDFQocAAAAABAf7TBSHL9nYo91dKfSYsIOgt0QExMzNzcvH37ixcvXr582clP/h9LcnKym5ubjo5OXFzcyJEj6caampq1a9dev3795MmTe/fuXbdu3bp16/iPmJqalpWVnTt3bujQoW16i4yMrKmpmTt3blRUVFBQEF3g6P7j7e3Zs+fUqVNjxoy5du2asbEx3djc3Hzs2LEDBw5Mnjz56dOnurq6rR+hKOrAgQNLly5VUVHpvHNjY+MrV660bgkODl62bNnu3bsdHR359ReAwQZLVAAAAACgP5oyWnysvOilNOzE0U/JyckltBMZGUlRlI2Nzfz58z91gNjYWB6Pt2/fPn51gxAiJSXl6+vLYDAuX77co94CAgIIIR4eHgYGBg8fPszPz/+QbOnp6d7e3urq6snJyfzqBiGEyWS6ubkFBAQ0NTVt3769zVPOzs51dXXbtm3rxYhLly6dMmXKu3fvcnJyPiQ5gFBDgQMAAAAA+qn1hpKBmXXVjS2CDgLdtXv37rq6up9++qkPxiotLSWEtLS0/eMhKyvr7e29adOm7ndVVVUVFRWlqqpqYmJia2tLCAkODv6QbHv37m1ubj5+/Li0tHT7q0uWLJk+fXp8fPy9e/dat9vb21tbW9+6dSssLKwXg3722WeEkJKSkvaXCgsLV69eraury2KxlJWV7e3tU1JSejEEQD+HAgcAAAAA9FMLNFjSYozAzLY7MkL/FBsb6+Pjc+7cOf6OmJ8UPTNiz549QUFBbXbT3Lp1a4/mQdy4cYPD4Tg4OBBC7OzsCCGBgYG9DlZfXx8XF6egoEDXSjrk4uJCCHn48GHrRoqizp49y2azv/zyy+rq6h4N2tTU9OjRI0KIhoZGm0vPnj3T09MLDg7W1dXduHGjkZHRzZs3LSws6AoRwECCAgcAAAAA9FNMBlmlz76QWtvME3QU6EpTU9PmzZunTZu2ePHivhlx2bJl9vb2r1+/Xr58+bBhwxYtWuTt7f3s2bNedEWvT6ELHAYGBpqamikpKVlZWb0Llp+f39jYqK2t3ck99NXMzMw27aqqqh4eHiUlJe7u7t0cjsvlZmVlrVixIj8/39DQUF1dvc0N586dq6mpCQsLCw0N9fb2joyM/P777ysqKlqfugIwMKDAAQAAAAD9l4Muu7qRd+d5vaCDQBfOnTuXl5fn5eXVZyMyGIyQkJDY2FgnJycFBYWIiAhXV1c9PT11dfVTp07xT4rt0ps3b+Li4nR1dQ0MDOgWeuZFUFBQ74JVVlYSQpSVlTu5h95GNC8vr/2lXbt26evrnzlz5smTJ3/3eGhoKP+MWCaTqaOjExISMmLECH9/fxGRtpvy2tvb+/v7W1lZ8VvoE20rKip68loAQgCnqAAAAABA/yUjzliizf7pae1CTZags8Dfev/+/bfffrto0SITE5MePVhXV3f+/Hn+pxoaGvPmzetRD5aWlpaWloSQFy9exMfHR0REREVF7dq168mTJ/7+/t3pISQkhMvlOjo68lvs7Oy8vLyCgoL279/fozA0GRkZQkhRUVEn99A7ZXS4QweTyfTx8ZkyZYqLi0tSUhKD0cHvpFsfE0sIkZSU1NPTW7NmzZAhQ9rfTN/J4XBycnIKCgoyMzP9/Px6+E4AwgEFDgAAAADo19YbSvpn1CaWNJp8JiboLNCxn3/+uaysbOvWrT19sLq6eseOHfxP7e3tu1/g4HK5FEXxf/5XU1PbsGHDhg0bMjIyLCwsrl69+tVXX02YMKHLfuj1KW5ubm5ubq3bMzMzU1NTDQ0Nu/sy/6WmpiYqKtr5Chd6cYqmpmaHV83MzJycnM6fP3/27Nn2h62Qjo6J7URdXZ2rq+vVq1fr6+uZTKa6urqWlhYOW4EBCUtUAAAAAKBfGy0tMltVwje1RtBB4G/99NNPysrK9EyKHhk+fHhLKyEhId18kMfjsdlsU1PT9pf09PScnJwIIRkZGV32U1RU9Mcff4wYMWLH/5oyZQrp7SoVSUlJc3Pzt2/fRkRE/N09vr6+hJBODtP18vJSVFR0c3P78K1AbW1t/fz8duzYkZqa2tDQkJ2d3aaUAzBgYAYHAAAAAPR3zuOkbMPK8t41a8jhn6/9zv379zMzM93d3TtcTPGJMBgMTU3NtLS0oqIiJSWlNlcLCwsJIbq6ul32ExgY2NLSsnbtWk9Pz9btSUlJJiYmgYGBR44c6UU8Ly+v+Pj4Xbt2WVpaSkpKtrkaGhoaExOjr6/fel+MNuTk5E6ePLlq1SpXV9deBOCrqqqKi4uztbU9evQov/H9+/cf0idAv4UZHAAAAADQ3xkqin4+QuxCaq2gg0AHwsLCCCFz5szp43G3bdtWX19vY2PTZjFIeHh4YGCghobG+PHju+yk9fkprU2aNElFRSU/P//x48e9yDZ+/PitW7fm5eVNmDAhOTmZ387lcj09PR0cHCQkJHx8fNpvCNraypUrLS0tQ0ND8/Pze5GBP2JzczO97ymtoqLi8OHDhJDu78MKICxQAgcAAAAAIeBkJOkaW7ljovQwNn5F17/cvXtXQkLiiy++6ONxnZ2dk5KSLl68aGRkpKOjo6Gh0dzcnJ2dnZWVNWTIkPDw8M7LB4SQ7Ozs5ORkXV3dDjfaWLp06fHjxwMDA3v3aidPnlRQUDh06NDnn3+uoqIybty48vLy5OTk2tra0aNH+/n5mZmZddnJDz/8YGhoyOFwehGAJi8vP2fOnLt375qZmc2YMaOsrCwsLMzIyIgQcvHiRS0trZ7u6grQn+GvBwAAAAAQAjNVJUZJiVzNwCSO/qW4uDgzM9PY2FhcXLyPh2YwGBcuXIiIiLC2tn779m1kZGRiYqKMjIy7u/vz58/19PS67CEwMJB0NH2DtmzZMkJIcHBwS0tLL+IxmUx3d/fExMRVq1YRQqKjowsKCkxNTT09PXNycmbPnt2dTrS0tL755ptejN7atWvXNm3aVFRUdObMmczMzJMnT8bGxm7ZsuXly5fh4eEf2DlAv0L17tt1UPHx8dm0aVN1dbWUlJSgswAAAAAMXv4ZdacfVt9fpchmUoLO0l3FxcVKSkq5ubkaGhqCzgIA8BFwOJxZs2YdO3asO7OQ+hhmcAAAAACAcLAby6IoEp5TL+ggAADQH6HAAQAAAADCgcWkHHXZfim1PExBBgCAdlDgAAAAAAChscZA8lUNN/5lg6CDAABAv4MCBwAAAAAIjaEsxkJNll8KthoFAIC2UOAAAAAAAGGycZzko1eNT980CToIAAD0LyhwAAAAAIAwGSPLnKYs/jMmcQAAwP9CgQMAAAAAhIyTkWR0fn3Re66ggwAAQD+CAgcAAAAACBmzz8THDhW9nI5JHAAA8P+hwAEAAAAAwme9oWRQZt17Dk/QQQAAoL9AgQMAAAAAhM8CDdYQcUZQZr2ggwAAQH8hrAWO9+/fl5SUvHr1isdD2R4AAABg0GEyyGp99oW02iZei6CzAABAvyBkBY709PQ1a9aMHDlSRkZm9OjRo0aNEhMTGz16tKOj4++//y7odAAAAADQdxx1JWubeLfzGgQdBAAA+gVhKnBs377d0NDw8uXLFEVNmjTJ2tra2tp64sSJFEUFBARMmTJl48aNgs4IAAAAAH1ESoxaos0+n1KLKRwAAEAIYQo6QHf98MMPZ86cmTNnjqen5/jx49tczcjIOHTokK+vr46Ozs6dOwWSEAAAAAD62DoDycvptYkljaafiQk6CwAACJjQzOC4evXq2LFjb9261b66QQjR09MLCAiYOnVqWFhY32cDAAAAAIH4TFrESk3CN6VG0EEAAEDwhKbAkZ6ebmJiwmT+7ZQTiqKmTp2anp7el6kAAAAAQLDWGUr9p5BT+J4r6CAAACBgQlPg0NfXT0pK4nI7+6vrwYMH+vr6fRYJAAAAAARu/HBRHQXRwMw6QQcBAAABE5oCx4oVK7KyshYsWJCWltb+ak5OzooVKxISEhYtWtT32QAAAABAgJbrsIMz6xq52GwUAGBQE5pNRrds2ZKWlnbu3LmoqCglJSUVFRV5eXmKot69e1dUVPTixQtCyNq1a3fv3i3opAAAAADQp2y0WF6J72MLONZjJASdBQAABEZoChyEkB9//NHFxeVf//pXTEzMb7/9RjeKiIgoKio6ODi4uLhMnz5dsAkBAAAAoO9JilLzNVgBz+pQ4AAAGMyEqcBBCBk3bty1a9cIIZWVldXV1aKiooqKigxG7xfa/PXXX+vXr29qaurknpKSEkJISwsmPQIAAAD0Uw667MXXy15UNqvJCtm/bwEA4GMR1r8AZGVlZWVlCSFVVVW5ubmqqqoKCgq96EdaWtrY2LihoaGTe0RERDIzMymK6mVWAAAAAPjEDIaJ6g0TDcqq+9pkiKCzAACAYAhTgaOqqurUqVOpqanGxsbbtm2TlpY+efKkh4dHXV0dIcTY2PjSpUva2to96lNSUvLAgQOd3+Pj43P37t1exwYAAACAPuCgwz7xsHrnRGkxEfxeCgBgMBKaAkdFRYWxsfHz588JIeHh4fHx8atXr969e7empqa5uXlJSUl0dLSpqWl2draioqKgwwIAAABAX1uoyfJMfH/vRcN8DZagswAAgAAIzTGxhw8ffv78+XfffVdSUnLp0qVffvllw4YN8+bNS09P/+mnn27fvn379u2qqqr9+/cLOikAAAAACABblFqgwbr2rE7QQaBPPXjwwM7OTltbm8VijRw50sLCws/Pj8fj0Vc3b95MdUpTU5Pf1cqVKymKYjAYxcXFPX2coig1NbX28VJSUtatW6empsZiseTl5Y2NjQ8fPkzPQG/tyJEjFEUZGho2Nze370RHR2fYsGH0x8+fP28fg8lkqqmp2drapqamftiXE0C4Cc0MjqioqBkzZri6uhJCVq9efefOnaCgoKNHj4qJidE3WFlZWVhY3L9/X6AxAQAAAEBgVuix54eU5b1r1pATmn/lCruKioojR45ER0cXFBSoqqpaWVm5ubnJycn1zeienp779u2TkJCYOXOmubn569ev79+/n5CQcP369cjISBEREWNj45qaGv790dHRZWVl9vb2EhL/d+DO8OHD6Q/q6+tv3rxJCGlpaQkODt65cychpPuPt8fj8Y4fP+7h4dHY2EhXXsrLy1NTUx89enTlyhV/f/+JEye2eSQtLe306dN79uzp8sVHjhxpaWnJ/7Suri4jI+PGjRuRkZEPHjz44osvuuwBYGBqERJsNtvFxYX/qYeHByGkrq6u9T2bN2+WlJT86EOfO3eOEFJdXf3RewYAAACAj8vm+tsjv1cJOsX/V1RURAjJzc0VdJBPoqKiQl1dnRBibm7u7Ow8ffp0QoiGhkZlZWUfjP7kyRMGg6Gjo1NaWspvrK6utrOzI4R4eXm1f8TExIQQUlZW1v5SUFAQIWTu3LmEEGNj4w5H7ORxQoiqqmrrFrpEMmbMmKSkJH5jU1PToUOHREREREVFMzIy+O2HDx8mhFAUxWazCwoK2nSura2toKBAf5yXl0cIsbKyanMPj8ejR7S0tOwwPMDH0tDQMHXq1N9//13QQTogNEtUlJWVc3Jy+J/SH9Pf3nzPnz9XVVXt42AAAAAA0H8s12Ffz6nncFsEHWRQOHHiRH5+/tmzZxMSEnx8fH755ZfvvvsuLy/v9OnTfTB6bGwsj8fbt2/fyJEj+Y1SUlK+vr4MBuPy5cs96i0gIIAQ4uHhYWBg8PDhw/z8/A/Jlp6e7u3tra6unpycbGxszG9nMplubm4BAQFNTU3bt29v85Szs3NdXd22bdt6MSJFUfTvgJ8+fdqLx+vr63vxFEB/IzQFjrlz5yYkJPz4449v374NCAgIDQ1lMpnu7u5NTU30DTExMTExMVOmTBFsTgAAAAAQoAWarGZeS3R+g6CDDAopKSmEkCVLlvBb6I/p9k+ttLSUENLS0raYJSsr6+3tvWnTpu53VVVVFRUVpaqqamJiYmtrSwgJDg7+kGx79+5tbm4+fvy4tLR0+6tLliyZPn16fHz8vXv3Wrfb29tbW1vfunUrLCysF4PKyMiwWKza2lp+S2Fh4erVq3V1dVkslrKysr29fev/NRs3bpSTk0tMTNTS0mKz2YqKira2tq9fv3779q2Tk5OmpuaQIUMsLCywrwcIEaEpcLi5uamrq2/ZskVRUdHR0XHq1Knnzp27efOmkZHRli1bbGxs5s6dKy0t3eWZrwAAAAAwgLGZ1CJNViC2Gu0T9NyE2NhYfktcXBy/vW9G37NnT1BQEIfDaX1p69atPZoHcePGDQ6H4+DgQAihV7gEBgb2Olh9fX1cXJyCggJdK+mQi4sLIeThw4etGymKOnv2LJvN/vLLL6urq3s6bm5ubn19vZGREf3ps2fP9PT0goODdXV1N27caGRkdPPmTQsLC7owxI86b968oUOH7tu3z8DA4MaNG1ZWVtOnT3/06NGSJUumTJmSkJBgY2PT4danAP2Q0Gy/JC8v//jx4+PHj6emphobG7u6usrIyLx58+bbb7/NzMwkhBgZGfn7+48YMULQSQEAAABAkBx12fNCynIrmjXlhebfukLK1dU1ISFhzZo1kZGRWlpaOTk5ISEhM2fO7N0ii55atmxZWFhYaGjo8uXLpaWlZ8yYYWlpOXPmTF1d3Z52Ra9PoQscBgYGmpqaKSkpWVlZ2travQiWn5/f2NjY+bP0VfoHmdZUVVU9PDy+/vprd3f37777rpsj1tfXp6enb9++nclkHjp0iG48d+5cTU3N7du3ra2t6ZYffvhh69atcXFxq1atols4HM6iRYsCAgIYDAYhRFlZ+enTp9OnT7937x59mIO1tXVUVFRubq6Ojk533x9AcIRmBgchRE5O7ujRo7du3fLw8JCRkSGEfPPNNyUlJX/88UdJScnTp0/19fUFnREAAAAABEx7qOg4RdGgLEzi+ORkZGRWrVrV0tISEBBw8ODBgIAAiqLWrFnT4bqMj47BYISEhMTGxjo5OSkoKERERLi6uurp6amrq586dYp/UmyX3rx5ExcXp6ura2BgQLfQMy/obUd7obKykhCirKzcyT0qKiqk3ZaCtF27dunr6585c+bJkyd/93h0dHTrY2LZbLaxsXFGRkZMTMysWbPoe+zt7f39/a2srPhP0QfZVlRUtO7q66+/pqsbhJB58+YRQnbv3s0/qpLeOLbNIwD9ltBXteXl5U1NTQWdAgAAAAD6EQdd9pEH1buMpVlMStBZBrJjx4598803CxcuPHTokIaGRm5u7j//+c9Vq1a9efOGPtGjS3V1defPn+d/qqGhQf+M3X2Wlpb0gakvXryIj4+PiIiIioratWvXkydP/P39u9NDSEgIl8t1dHTkt9jZ2Xl5eQUFBe3fv79HYWj072LpA3T+TklJCSGkw0oQk8n08fGZMmWKi4tLUlISv/rQWptjYpuamp49e5aWlvaPf/wjNjaW7nbatGmEEA6Hk5OTU1BQkJmZ6efn174rJSUl/seysrKEkDFjxvBb+AfiAggFYZrBAQAAAADQHfM1WBQh2Gr0k6qoqDh48KCOjk5oaKihoSGbzTYyMgoLC9PU1HR3d3///n13Oqmurt7RysWLF7sfgMvltp6moaamtmHDhps3byYnJysqKl69evXPP//sTj/0+hQ3Nzf+hAh6d4/MzMze7a+ppqYmKiqalZXVyT304hRNTc0Or5qZmTk5OT1+/Pjs2bMd3mBkZHSllcDAwNTU1K1btz58+PDnn3+m76mrq6O3ETU0NLS1tfXz89PS0mrfFUW1LQJ2WFIBEAr4swsAAAAAA40Ek1qkyQrAVqOfUnZ2dkNDg7m5uaioKL9RTExs+vTpdXV1OTk53elk+PDhLa2EhIR0c3Qej8dmszucyq2np+fk5EQIycjI6LKfoqKiP/74Y8SIETv+F304Y+9WqUhKSpqbm799+zYiIuLv7vH19SWEzJ8//+9u8PLyUlRUdHNza70naOfojUv5x8TSRY0dO3akpqY2NDRkZ2e7ubn14DUAhJDQL1EBAAAAAGhvhR7bKuhtTkWzFrYa/TToXSTa//j96tUr/tVPh8FgaGpqpqWlFRUVtV5kQSssLCSEdGe30cDAwJaWlrVr13p6erZuT0pKMjExCQwMPHLkSC/ieXl5xcfH79q1y9LSUlJSss3V0NDQmJgYfX391htktCEnJ3fy5MlVq1a5urp2c1BFRUVCSFVVFf3fuLg4W1vbo0eP8m/o5rQaAOGFGRwAAAAAMABpyDHHjxDDJI5PZ9SoUUZGRrdu3YqJieE3RkdHR0VFTZw4cdiwYZ86wLZt2+rr621sbNosBgkPDw8MDNTQ0Bg/fnyXnbQ+P6W1SZMmqaio5OfnP378uBfZxo8fv3Xr1ry8vAkTJiQnJ/PbuVyup6eng4ODhISEj4+PiIhIJ52sXLnS0tIyNDQ0Pz+/O4PSC3bo82W5XG5zczO93SmtoqLi8OHD/NsABiTUswEAAABgYHLUZR/8rWqvCbYa/VSuXLkyefLkOXPmzJ49W11dPTc3Ny4ubsiQIZcvX+6D0Z2dnZOSki5evGhkZKSjo6OhodHc3JydnZ2VlTVkyJDw8PDOyweEkOzs7OTkZF1dXUNDw/ZXly5devz48cDAwC+++KIX8U6ePKmgoHDo0KHPP/9cRUVl3Lhx5eXlycnJtbW1o0eP9vPzMzMz67KTH374wdDQkMPhdGdEeovQrKyspqYmeXn5OXPm3L1718zMbMaMGWVlZWFhYUZGRoSQixcvamlp9XQzVwChgBkcAAAAADAwzVWXEKGoO8+x1einYmBgkJ2d7eTkVFRUdOnSpdLSUmdn5+zsbG1t7T4YncFgXLhwISIiwtra+u3bt5GRkYmJiTIyMu7u7s+fP9fT0+uyh8DAQNLR9A3asmXLCCHBwcEtLS29iMdkMt3d3RMTE1etWkUIiY6OLigoMDU19fT0zMnJmT17dnc60dLS+uabb7o5IovF0tXVLS4uPnXqFCHk2rVrmzZtKioqOnPmTGZm5smTJ2NjY7ds2fLy5cvw8PBevBFA/0f17tt1UPHx8dm0aVN1dbWUlJSgswAAAABAD3z7+/vUN42hixUEFaC4uFhJSSk3N1dDQ0NQGQAAPiIOhzNr1qxjx451ZxZSH8MMDgAAAAAYsBx12U9fNz0raxJ0EAAA+ORQ4AAAAACAAUtDjjlhhFhwFrYaBQAY+FDgAAAAAICBzEGXHZ5TX9eMddkAAAMcChwAAAAAMJBZj5EQFaFu59ULOggAAHxaKHAAAAAAwEAmJkLZaLICnmGVCgDAAIcCBwAAAAAMcA667NQ3TRnYahQAYEBDgQMAAAAABjh1WabxKLFATOIAABjQUOAAAAAAgIHPQZcdnltf24StRgEABiwUOAAAAABg4LNSl2AxqUhsNQoAMHChwAEAAAAAA58og7LVYl3NwCoVAIABCwUOAAAAABgUHPUkM8ua0t9iq1EAgIEJBQ4AAAAAGBSUh4hM+kwM58UCAAxUKHAAAAAAwGDhqMuOzKuva8ZWowAAAxAKHHxXugQAACAASURBVAAAAAAwWMxUlRBhUPfyGwQdBAAAPj4UOAAAAABgsBAXoazVJcJzcZYKAMAAhAIHAAAAAAwiNlqs34s5f9VyBR0EAAA+MhQ4AAAAAGAQ+WKk2GhpkQhM4gAAGHBQ4AAAAACAQYQixEaLdT0bBQ4AgIEGBQ4AAAAAGFwWa7Gfv2t+VtYk6CAAAPAxocABAAAAAIOL8hCRz0eI3cjBJA4AgAEFBQ4AAAAAGHQWa7Fu5tY38wSdAwAAPh4UOAAAAABg0JmvwaptarlfxBF0EAAA+GhQ4AAAAACAQUdajLJQEccqFQCAgQQFDgAAAAAYjGy0WLEFDe85WKYCADBAoMABAAAAAIORuZKEtBh1J79B0EEAAODjQIEDAAAAAAYjEQaZp8EKxyoV4fT7779TFDVt2rQOr96/f5+iqLlz536KoQ8ePEhRFEVR9+/f7/CGffv20TckJSW1uZSSkrJu3To1NTUWiyUvL29sbHz48OG6uro2t9Fv15qoqKiqqqqzs/Nff/31KV4KYGBAgQMAAAAABqnFWqzHrxoL33MFHUSIVVZWfvnllwYGBjIyMubm5t99913fjGtmZqasrPzbb7+Vlpa2v3rjxg1CyNKlSz98oJiYGDU1tfDw8PaXQkNDO3zk+vXr7Rt5PJ6Xl5exsfHFixc5HI6FhYWWllZ6erq7u/v48eMfPXrU/hE1NbWV/zVjxoyamprz589PmDDhwYMHH/hSAAMVChwAAAAAMEgZDBPVlGdiEkevFRcX6+np/fvf/x4xYsTy5csrKiq++uqr9evX98HQFEUtX768paWlwyrDjRs3REVFbWxsPnygurq6goKC2traNu3S0tLXr19vaWlp056enp6TkzNkyJA27Xv27Pn666+VlJSSkpJKS0tv376dmJj4/v37Q4cOPX/+fPLkyc+ePWvzyOTJk6/8171798rKyk6dOvX69Wtra+uqqqoPfzWAgQcFDgAAAAAYvBZpsm7k1LX9IRW6Z/PmzaWlpcHBwTExMT4+Pk+fPt2yZcuFCxfu3r3bB6M7OjoSQoKDg9u0JycnFxQUzJ49W05OrkcdNjY2dv/mBQsWlJSUtJ9Mcf36dYqirK2tWzemp6d7e3urq6snJycbGxvz25lMppubW0BAQFNT0/bt27sc9Kuvvtq/f39lZeX333/f/agAgwcKHAAAAAAweC3WYpVUc5P/6sFPtkCrra29c+eOubn5kiVL6BYGg3HixAlpaenTp0/3QQAjIyMdHZ0//vijuLi4dXub9SlcLvfo0aOmpqbS0tJqamrbt29/9eoV/+aNGzfKyckVFRXNmDFDQkJCXFzcwMDg559/pq/OmjWLngaycuVKiqLKy8v5D9rY2IiIiISEhLRJdf36dRMTk9GjR7du3Lt3b3Nz8/Hjx6Wlpdu/yJIlS6ZPnx4fH3/v3r0u33rr1q1sNvvMmTNd3gkwCKHAAQAAAACD13BJEZPPxG9glUrPZWVl8Xg8HR2d1o0sFktLSyshIYHL7YudTRwcHFpaWtpUGcLCwsTExBYtWkQIaWxstLCw+Oc//9nc3Ozo6KiqqnrmzBkTE5PCwkL+/U1NTfPmzSsoKHB1dXV2di4sLNywYQO9j8aePXu+/PJLQoizs/PFixelpKT4TykqKpqbm7dZpZKbm5uWlmZvb986T319fVxcnIKCgq2t7d+9iIuLCyHk4cOHXb6yvLz8hAkT3r59W1lZ2eXNAIMNChwAAAAAMKgt1mJF5tU3NGOdSs/QkxRevHjRupHL5b58+bKxsbH1LIlPx8HBgfzvKpXc3NyMjAwrKysZGRlCyI8//vjrr78eOnTo0aNHPj4+CQkJly5dKiws3LlzJ/8Ren+N5OTk06dP//vf/75z5w757wais2fPtrCwIIRMmzZtzZo14uLirUe3t7cvKipqXZWgn2pTyMjPz29sbNTW1u7kReirmZmZ3XlrJSUl0u4rDwAEBQ4AAAAAGOTmqEtwW0hCIUfQQYTM8OHDDQ0N7927l5CQwG88cOBAWVkZIaSmpqYPMmhoaEycODExMZE/IyMsLIy0Wp9y6tQpDQ2Nffv28R9ZvXq1qalpZGRk68NZ3dzcZGVl6Y8nT54sJSVFv0XnFi9ezGAwWs8fuX79+oQJE1RVVVvfRk+1UFZW7qQrFRUVQkheXl6XgxJCFBUVCSEdHh8DMMgxBR0AAAAAAECQ2ExqjppEeE79XHUJQWcRMufPnzc3N581a5a1tfWoUaMePnyYm5urrq6en58vJibWnR7q6urOnz/P/1RDQ2PevHk9yuDg4PDo0aOQkJBdu3YRQm7cuCEhIbFw4UJCSG1tbWFhoampaUBAQOtHJCQkGhsbnz9/bmBgQLdMmDChzQ3dGXr48OHTpk0LDQ09ceIEIeTl/2Pv7uNqvh//j79OnU5FIXRFVJRyUcSEXLUa8yn7IJnNGL5MbS5yuZkVJmah2WdL1sdl9tkHQwwbK9cXSzEx5SJJSa6vU5xK5/fH+fzOp09Xp1Cnc3rc/zrn9b44z+P20UfPvV6vd2bmn3/+uXjx4hKnKeeSZGVlVXCr7OxsIUSZO3SUdvfuXSGEtbV1ZU4G6hQKDgAAANR1Q9oYj/3twb1nRU2NmeBcBe7u7mfOnJkzZ05CQsKJEye6du0aHR09efLk9PR05W/1auXk5EydOlX11t/fv6oFx/Dhw2fOnPnzzz/PmDEjOzs7MTFx8ODByqYgMzNTCBEfH1/6WSfif+eYqKZvVJW/v/+kSZNOnjzZtWtX5bYdQ4cOLXGOvb29gYHBxYsXK7iPcnGKo6NjZT5U2ZW0atXq5TIDOoyf4AAAAKjrejQ3tKin/2saW41WWZs2bbZu3ZqVlXXnzp1ff/3VxcUlIyOjSZMmTZo0qczllpaWimJKP5RErWbNmvXt2zcxMTEjI2PHjh0KhWL48OGqmwshJk6cqChLjx49qvpZpfn5+enp6Sm33ti2bZuLi0vpkqJ+/fqenp53797duXNnefdZvXq1EGLgwIFqP/HRo0enT582Nzd/6VIG0GEUHAAAAKjr9CTi747GPEulqtauXfvDDz8Uf4xIYmJiZmamcu/PGqPaajQmJsbY2FhVEyh7loSEhBLnL1u2bN68ea/lo62trT08PLZs2ZKdnR0fH1/i+SkqYWFh+vr6M2bMUG5oWsLWrVvj4uI6dOgwYMAAtZ8YGRn59OnTSZMmvWp0QBdRcAAAAADCr43xubsFqQ8KNR1Emxw8ePDjjz/+8ccflW+fPn06Y8YMfX39adOm1WQMf39/mUy2du3aI0eO+Pr61q9fX3UoMDDw1KlTX331lWpkw4YNs2bNquR2nir5+fnlHRo2bNjVq1dDQkIUCkXp9SlKbm5uEydOTEtL69KlS1JSkmr8xYsXixcvfv/9942MjKKiovT19SuOsWLFinnz5jVq1CgoKKhK+YE6gj04AAAAAOFgJu1gbvDL5WezulVqo0cIIRYsWPDrr7+OHTt21apVrVu3Pnjw4M2bN9euXVvD20OYmZm9/fbbu3btEkKo1qcoffbZZ7/88ssXX3wRExPTrVu37Ozs3bt3N2/efMmSJZW8eb169YQQkZGR2dnZ06dPV74tzs/Pb+rUqevWrXNycmrfvn159wkPD2/atGloaGjnzp1tbW07dep0//79pKSk3NxcGxubNWvWeHh4lLjkjz/+GDt2rPL1nTt3Tp8+fevWLWtr661bt1ZyixOgrmEGBwAAACCEEEPaGG9PffZCof5MKNnb2yckJPj5+aWlpW3bts3Ozm7v3r0ffvhhzScZMWKEEKJ+/fo+Pj7Fx01NTU+ePPnZZ58pFIr169enpKRMmDDh5MmTzZs3r+Sde/fuPWrUqKtXry5fvlwuL+NZwjY2Nt27dxdlbS9anFQqDQkJOXHixKhRo4QQe/fuzcjI6NGjx+LFi1NTU/v371/6kvT09PX/X2xsrIGBwbhx406fPl26CgGgJCm+ZA5lioqKCgwMzMnJMTEx0XQWAAAAVJcHz4p6/Hh7rU/jnjaGr+ue169fb9GixeXLlx0cHF7XPQFAg+Ryeb9+/b7++uta2LUxgwMAAAAQQojGxnp9Wxix1SgAaCkKDgAAAOA/Brcx/j39eV4Bc5wBQPtQcAAAAAD/0c/eUCaV/H71uaaDAACqjIIDAAAA+A8DPcnfWhntYJUKAGghCg4AAADgv4a0Mf4jW37z6QtNBwEAVA0FBwAAAPBfXaxktg2kOy8ziQMAtAwFBwAAAPA//u5oHMMqFQDQNhQcAAAAwP/wczK+8rAw+W6BpoMAAKqAggMAAAD4Hzam+l2sZNuZxAEAWoWCAwAAAChpSBvjnWnPCos0nQMAUGkUHAAAAEBJvg7GeQWKw1nPNR0EAFBZFBwAAABASaYyibet4Q5WqQCA9qDgAAAAAMrwd0fj/Rny3AKFpoMAACqFggMAAAAoQ58WhjJ9sT+DVSoAoB0oOAAAAIAyyPQlb9kZ/ZZOwQEA2oGCAwAAACjb31obHbkmf5rPKhUA0AIUHAAAAEDZ+rQwNJJKDmQyiQMAtAAFBwAAAFA2Az2Jl60hq1QAQCtQcAAAAADlYpUKAGgLCg4AAACgXKxSAQBtQcEBAAAAlItVKgCgLSg4AAAAgIr4tDY+zCoVAKj1KDgAAACAivRuITOWSvazSgUAajcKDgAAAKAi/1mlcoWCAwBqNQoOAAAAQA2f1sZHsuQ5rFIBgFqMggMAAABQo08LQ2OepVKhtLS0iIgITaf4r+PHj0skkj59+pR59OjRoxKJ5G9/+1t1xxg5cqREItHT07t+/XqJQ4sWLZJIJK6uroWFhaUvbNu2rbm5eYnBy5cvjx8//o033mjYsKGlpaWHh0dISMiDBw+qKz2gbSg4AAAAADWkeoJVKhX7/vvvQ0JCyjy0cuXKXr16NWrUqFevXitXrqyZPB4eHi1btjx27NiNGzdKH92+fbsQ4t133331D4qLi7O3t9+xY0fpQ8+ePfvll1+EEAqF4ueffy7z8nPnzi1fvrwyHxQcHNyuXbs1a9ZkZWV5eHh07Njx6tWrCxcutLOzO3jw4Kt8BUBnUHAAAAAA6rFKpQJxcXFRUVFlHvr4448/+eSTe/fuDRo06O7du5988snkyZNrIJJEInnvvfcUCsXWrVtLH92+fbuBgcHgwYNf/YPy8vIyMjJyc3NLH9q1a9fTp0+V80Q2b95cXs758+dnZmZW/Cnz589ftGiRhYXF/v37b9++vWfPntjY2KysrKioqOfPnw8ZMiQ1NfXVvwug7Sg4AAAAAPVYpVKmkSNHOjs79+/fXy6Xlz565syZH374YcCAAcnJydHR0SkpKf3791+xYkVycnINZBsxYoQQovTUiaSkpIyMjP79+5uZmVXphvn5+VU6f+PGjUKIuXPnuri4JCYmpqenlz5nwoQJeXl5kyZNquA+Fy9eXLRoUZMmTZKSkry8vFTjUql0woQJ4eHhjx8/joyMrFI2QCdRcAAAAADqSfWEtx2rVErKy8tzdHQcOHCgqalp6aNLliwRQoSFhUmlUiGEVCpdvHixQqFYunRpDWTr2LFj27Zt//jjjxL7X5RYn/LixYuvvvqqR48epqam9vb2kydPvnnzpurkjz76yMzMLCsr68033zQyMjI0NHRxcVm7dq3yaL9+/ZTTQJR7bdy/f1914ePHj/fs2WNnZ9e9e3c/Pz9RVtUihPD39/fx8dm9e3dMTEx5X+T7778vLCz88ssvLSwsSh8dP378Rx99VL9+/Ur/wQA6i4IDAAAAqBSfVqxSKSkmJmbXrl27du2ytbUtfTQuLs7GxsbV1VU10rlzZ2tr69jY2JqJ9/777ysUii1bthQfjImJkclkgwYNEkLk5+d7eXl98cUXhYWFI0aMsLOzi4iI6N69+7Vr11TnFxQU+Pr6ZmRkBAUFTZgw4dq1a+PGjdu2bZsQYtasWVOmTBFCTJgwYf369SYmJqqrtm/fLpfL33//fSHE0KFDhRCbNm0qnVAikaxYsaJevXpTpkzJyckp81vs3r1bIpGMHTu2zKPGxsb//Oc/Fy1aVMU/G0AHUXAAAAAAldK7hWE9qWR/BpM4KuXRo0f37t0rXXy0bNny1q1b5f0y/3op+4XiUycuX76ckpIyYMCAhg0bCiFWrlx55MiR0NDQkydPRkVFHTx4MDo6+tq1a9OnT1ddotxfIykpafny5d9///1vv/0mhFBu7dG/f3/lmpE+ffqMHj3a0NBQdZVyfYoygIuLi6Oj49mzZy9evFg6pJ2d3dy5c7Ozs8vcpVUul1+7dq1Fixb16tV7LX8mgA6j4AAAAAAqRaonvOwM96RTcFSKssJo0qRJiXHlyJMnT2ogg4ODQ9euXU+cOKGakaFcCaJan/LNN984ODjMmTNHdcmHH37Yo0ePXbt25eXlqQaDg4MbNWqkfN2zZ08TE5N79+5V8Ll37tzZv39/u3btXFxclCPKVSrlbTU6Y8aMDh06REREnD59usShhw8fCiFsbGxKjFtbW0v+V0pKSgWRgLpAqukAAAAAgNbwaWX8SezDnHyFqUyi6Sy1nYGBgRBCIin7D0pPr1L/qTUvL2/VqlWqtw4ODr6+vlWK8f777588eXLLli0zZswQQmzfvt3IyOjvf/+7ECI3N/fatWs9evRQzrZQMTIyys/Pv3Lliqqe6NKlS4kTKv7QLVu2vHjxQrnLqdLQoUPDwsI2b948b9680udLpdKoqKhevXoFBAQkJCQU/8OxsLAwMjLKyMgoccnQoUMfP36sfJ2cnHzmzJmKIwF1AQUHAAAAUFmqVSqD2xhrOkttZ2Fhoa+vr5yAUNyDBw/09fUtLS0rc5OcnJypU6eq3vr7+1e14Bg+fPjMmTN//vnnGTNmZGdnJyYmDh48WLklqvLhrPHx8fHx8aUvfPr0qeq1avpGJSkbk+Dg4ODg4OLjFy5c+Ouvv4pvSqLi4eExfvz4VatWrVixoviTdPX09Jydnc+cOfP48WPlshqliIgI1etp06ZRcACCggMAAACoPNUqFQoOtfT09CwsLEo8wUQIkZ2dbWVlVckZHJaWlgrFK+3q2qxZs759+x48eDAjI+PXX39VKBTDhw9X3VwIMXHixOJlwavLysr6448/rKys3nvvveLjp06dOnbs2ObNm8ssOIQQYWFhv/zyS3BwsHJTUpUuXbqcOXMmOjpauaFpaYmJia8rPKDV2IMDAAAAqAKepVJ5np6e6enpqampqpGUlJSsrKw+ffrUZAzVVqMxMTHGxsYDBw5Ujjdp0qRJkyYJCQklzl+2bFmZC0kqadOmTQqFYsyYMcv/17Jly0Q5z1JRMjMzCw8Pf/LkSVBQUPHx4OBgIyOj+fPnl7nxx44dO/7444+XTgvoEgoOAAAAoAp4lkrlBQQECCFCQ0OVbxUKhfL1J598UpMx/P39ZTLZ2rVrjxw54uvrW79+fdWhwMDAU6dOffXVV6qRDRs2zJo1Ky0trUofkZ+fr3pd/PkpxXXr1s3W1jY9Pf3UqVPl3WfkyJHe3t5bt25NT09XDdrZ2c2aNevhw4fdunU7ePCgalyhUKxZs2bMmDENGjSoUlpAV7FEBQAAAKgCqZ7wZpVK5fTt23fMmDHr16+/ceNG9+7djx07duTIkXHjxvXq1asmY5iZmb399tu7du0SQqjWpyh99tlnv/zyyxdffBETE9OtW7fs7Ozdu3c3b958yZIllby58umtkZGR2dnZ06dPz8rKSkpKateuXZnrUN59992lS5du2rTpjTfeKO+GkZGRrq6ucrm8+OD8+fONjIzmzZvn5eVlY2Pj6ur64sWLs2fP3rp1a8yYMUOHDn3nnXcqGRjQYczgAAAAAKrGpzWrVCpr7dq1YWFhz58/j4iIKCwsXLp06erVq2s+hvKBJvXr1/fx8Sk+bmpqevLkyc8++0yhUKxfvz4lJWXChAknT55s3rx5Je/cu3fvUaNGXb16dfny5XK5XLkCpfT0DSVlvfLzzz9XsLFImzZtPv/88xKDenp6c+bMOXbs2LBhw4qKivbt23fmzJnu3bvHxsauW7fO09NzzJgxVd0JFdA9klfcs6cuiIqKCgwMzMnJMTEx0XQWAAAAaF5hkegWfTu4Z4Mh6iZxXL9+vUWLFpcvX3ZwcKiZbABQreRyeb9+/b7++msPDw9NZymJGRwAAABA1fxnlcoVtuEAgFqEggMAAACoMp/Wxkevs0oFAGoRCg4AAACgynrZGNaTSvbxLBUAqDUoOAAAAIAqk+qJt+yNWKUCALUHBQcAAADwMnxaGR3Jkj+WF2k6CABACAoOAAAA4OX0tDGsbyA5kCnXdBAAgBAUHAAAAMDLUa5S+e3KM00HAQAIQcEBAAAAvDSfVkZHs/JZpQIAtQEFBwAAAPCSWKUCALUHBQcAAADwklilAgC1BwUHAAAA8PJYpQIAtQQFBwAAAPDyWKUCALUEBQcAAADw8qR6oh+rVACgFqDgAAAAAF6JT2ujI1lyVqkAgGZRcAAAAACvxKO5oYlMb38Gq1QAQJMoOAAAAIBXItUT/eyM9qSzSgUANImCAwAAAHhVb9sbHb+en1eo0HQQAKi7KDgAAACAV9XTRqavJ45lsUoFADSGggMAAAB4VTJ9SU8bw/08LBYANIeCAwAAAHgNvG0ND2Q+f8EiFQDQEG0tOJ48eZKdnX3z5s2iIh7HBQAAAM3zsjV6/LzozO18TQcBgDpKywqO5OTk0aNHW1tbN2zY0MbGplmzZjKZzMbGZsSIEcePH9d0OgAAANRdTYz1OlrKWKUCAJqiTQXH5MmTXV1dN2zYIJFIunXr5uPj4+Pj07VrV4lEsnHjxl69en300UeazggAAIC6y9vWcH/Gc02nAIA6SvoS1ygUiqysLCsrK5lM9toDlScyMjIiIuLtt99evHixm5tbiaMpKSmhoaGrV69u27bt9OnTaywVAAAAoPKWndHShJyMx4V2DV/mn9kAgFdRqRkchw8fHjduXEZGhhAiOzvb1dXV1ta2QYMGc+fOVShqaBuln376ycnJaffu3aXbDSFE+/btN27c2Lt375iYmJrJAwAAAJTgYCa1ayitm6tU0tLSIiIiXuWEahIfHz906FBnZ2djY2Nra2svL681a9YU38jvypUrklKkUqm9vb2fn99ff/2lOvPLL79UHj169GiZnzVnzhzlCQkJCcqRzZs3SySSkJCQMs83MDCwt7ev6jc6e/bs2LFj7e3tjY2NGzdu7O7uvnDhwry8vKreB9A96guOvXv3enl5rV27Njc3Vwgxa9as5OTkfv36ubi4hIaGbt68ufpDCiFEcnJy9+7dpdJyu3CJRNK7d+/k5OSayQMAAACU5lVXV6l8//335f0aX8kTqsPixYs9PDx+++03R0fH0aNHd+/e/a+//ho/fvzAgQNfvHhR/Exra+uRxQwaNMjQ0HD79u1dunQ5depUidtu3bq1zI/btm3bq6SVy+VffPFFnz59GjZs2Lp16xEjRly5cqX4CUVFRWFhYe7u7uvXr5fL5V5eXm3atElOTg4JCXFzczt58uSrfDqgA9QXHF999ZWRkVFMTEy7du3y8vJ27Njh6ekZGxt7/PhxS0vLyMjIGkgphOjQoUNCQkKJH0MlxMfHd+jQoWbyAAAAAKV52xqdupn/6HndetJfXFxcVFTUq5xQHZKSkoKDg9u2bZuenr5r164ffvhh+/btGRkZQ4cO3bNnT3h4ePGTO3bs+GMx27Ztu3DhwvTp0wsLC2fPnl38TFNT023btpWeyZ6cnJyamtqgQYOXS/v48eN+/fp99dVX9+/ff/fddx0cHDZt2uTi4nLmzBnVObNmzZo9e3aLFi0SEhJu3Ljx66+/njhx4smTJ6GhoVeuXOnZs+f58+df7tMB3aC+4EhJSfH09BwyZIhEIomPj3/27NmoUaOEEDKZzNPT89KlS9UfUgghPvjgg4sXL77zzjvnzp0rfTQ1NfWDDz44ePDgoEGDaiYPAAAAUFrXZjJTQ73DWXVllcrIkSOdnZ379+8vl5f9ldWeUH327dtXVFQ0Z84ca2tr1aCJicnq1av19PQ2bNhQ8eUSiWTu3LlCiOIVgxDinXfeyc7Ojo+PL3H+tm3bJBKJj4/Py6UNCws7evToxIkTU1JSVq1a9fvvv+/evVsul48ZM0Z5QnJy8nfffdeqVaukpCR3d3fVhVKpNDg4eOPGjQUFBZMnT365Twd0g/rdj168eNGoUSPl6yNHjgghevfurXxramqqXLdSAz755JNz58798MMPe/bsadGiha2tbePGjSUSycOHD7Oysq5evSqEGDNmzMyZM2smDwAAAFCavkT0bWG4P0M+yNFY01lqQl5enqOjo6Oj4+HDh1/uhOpz48YNIUTpqRaNGjX67rvvKrOZYMOGDY2NjUv8yjN48ODNmzdv2bLFw8Oj+Pi2bdu6d+9uY2Pzcmm3b99uampafF6Jj4+Pl5fXvn377ty5Y2Fh8emnnxYWFi5dutTU1LT05cOGDevbt++BAwdiY2P79+//chkAbae+4HBycjp06FB+fr5EIvn5559bt27t6OgohCgoKDh06JCtrW31h/yPlStXBgQELFmyJC4u7tixY8pBfX19CwuL999/PyAgoG/fvjUWBgAAACiTt53hnMOPC4oUBnoSTWepdqo9/l1cXK5fv/4SJ1Qf5TSHWbNmyWSywYMHGxoaqg5NnDixMne4fPnys2fPunXrVnzQwsLC09Nz27Zt33zzjUQiUZ157ty58PDwmzdvvlxaPT29vn37Fg8phFA+tvLhw4empqb79+9v2rSpn59feXcICAg4fPhwYmIiBQfqLPUFx8SJE0ePHu3q6iqXyzMyMkJDQ4UQhw4dmjdvXlpa2pdffln9If+rU6dO//73v4UQjx49ysnJMTAwsLCw0NOr1LNgypOc5V/6TAAAIABJREFUnFzxfLlr1669yv0BAABQp/RpYSQvfJxwI7+XjaH6s1Fthg8fHhMTs3Xr1vfee8/U1PTNN9/09vZ+66232rVrp/baZ8+eJScnT548WSqVKn8DKs7f3//jjz9OTExUdR/KbUf9/PxWrFhR+m47d+4ss9wpvsNgSkpKiaN37949cOCApaVl69atL126lJ+f7+zsXEFm5dELFy6o/XaArlJfcHz44YfXr19fvnz5o0eP/Pz8ZsyYIYTYv3//kSNHBg0aNH369OoPWYZGjRopF87cuXPn+vXrTk5O9evXf4n7XLlyxdXVtTLz02rsgbgAAADQaqYyiXsz2f4MOQWHZunp6W3ZsmX//v2bNm3av3//zp07d+7cKYSwt7efNGnS1KlTi/+H0r1796qmY6iYmJjExcV5enqWGB8yZMjEiRO3bNmiKji2bdvWpUsXOzu7MpP89ddfxR83Wxmpqam+vr7Pnz9fuXKlVCp99OiREKJly5YVXKKcXJ+WllalDwJ0ifqCQwgxZ86cOXPmFBYWqp7SOnr06LFjx7Zq1ao6s5WUmZk5b9687t27BwYGCiFOnTr10UcfKbf80dPT8/X1jYyMrOqat9atWz958qSgoKCCc9avXz99+vTSP+8AAACAMnnbGq4+mzu310s+UAMqeXl5q1atUr11cHDw9fWt0h28vb29vb2FEFevXj1w4MDOnTv37NkzY8aM06dP/+tf/1KdZm1trTxNqaCg4Pz58+fOnfvss8/27dtXYtsLS0vLPn36bN26ddmyZUKIzMzMP//8c/HixeVlCA4OLj0NRAhhYGBQejA3N3fJkiVLly5VKBQRERHKTUYbNmwohMjKyqrgm2ZnZwshytyhA6gj1Bcc9+/fNzExMTQ0VLUbQggHBwchRF5enlwuNzMzq8aA/19aWlr37t3v37/fqVMnIcTly5f79Onz/Pnz/v37t2rV6sKFC7t27Tp16lRKSkpV85iYmFR8Qr169V4+NwAAAOqet+yMFhx/cul+gZp/aEKdnJycqVOnqt76+/tXvuB48eKFRCJRTdOwt7cfN27cuHHjUlJSvLy8fvrpp2nTpnXp0kV5VPmY2BJ3mDRp0ooVK9auXRsUFFTikL+//6RJk06ePNm1a9dt27YJIYYOHfoSX7CEPXv2BAYGXrt2beDAgcuWLXNyclKFNzAwuHjxYgXXKhenKDdMBOom9btXNG3adNOmTWUeCg8Pr7G/P59//vmDBw9WrVql/OHy+eefy+Xy2NjY33//feXKlYcOHdq0adPNmzeVT3ICAAAANKi5qb5zE4P9GXXlYbHVx9LSUlHMli1bKnlhUVFRvXr1evToUfpQ+/btx48fL8ra9qKEgIAAUeoxsUp+fn56enrKrTe2bdvm4uLy6r8ZzZs3z8fHx9TU9PDhw7t27VK1G0KI+vXre3p63r17V7nKpkyrV68WQgwcOPAVYwDaq9wZHDt27FA9D+mPP/4oPn1DKT8/v4K/Xa/d0aNH3d3dlT+JhBAJCQlvv/32W2+9pTph+PDhq1evPnjwYI1FAgAAAMrjbWe4P/P5IEtN56ir9PT0HB0dz507l5WV1aJFixJHlY8RULvbqIWFhRDi8ePHpQ9ZW1t7eHhs2bJlypQp8fHx8+fPf8XA0dHRCxYseO+996Kjo5UPTykhLCzswIEDM2bM8Pb2Lr3/4NatW+Pi4jp06DBgwIBXTAJor3ILjmnTpmVkZChf//Of//znP/9Z5mkffvhhdcQqLS8vr3Xr1qq3+fn5zZo1K3GOvb19YmJizeQBAAAAKuBtaxT559N7eS/Un4rqMWnSpI8//njw4ME//fRT8eeP7NixY9OmTQ4ODm5ubhXfoaioSAiRk5NT5tFhw4YFBQWFhIQoFIpXXJ+iUCgWL17cvHnzdevWldluCCHc3NwmTpz43XffdenSZePGjarwL168WLJkydy5c42MjKKiovT19V8lCaDVyi04/vnPf+bl5QkhBg8ePGXKFC8vr9Ln1KtXr3fv3tWYrpg33njj0KFDT548adCggRDC3d395MmTCoVCtfdnUVFRfHy8cocOAAAAQLNcLQws6uv/kZ2v6SB114QJExISEtavX9+xY8e2bds6ODgUFhZeunTp4sWLDRo02LFjh9ouQPncxosXLxYUFJTeENTPz2/q1Knr1q1zcnJq3779q0TNzMy8dOmSubn5kCFDSh/98ccfmzZtKoQIDw9v2rRpaGho586dbW1tO3XqdP/+/aSkpNzcXBsbmzVr1nh4eLxKDEDblVtw9OvXT/nirbfe8vX17d+/f01FKtv8+fP79ev39ttvL1++vHv37qGhob179/7iiy9CQ0P19fWfP38+c+bM5OTkb775RrM5AQAAACGERIg3Wxoeu8Y2HBqjp6e3bt06Pz+/tWvXJiYmXrhwwczMzM7OLiQkZMqUKcrKoGLGxsbt2rU7f/78N99889lnn5U4amNj07179/j4+FffXjQ9PV0Icffu3b1795Y+Kpf/539FUqk0JCTE19f322+/PXLkyN69e83NzXv06OHt7R0UFGRsbPyKMQBtJ1EoFJrOUFmbNm0aNWpUYWFhixYt7OzssrOz09PTmzZtamdnl5qa+uTJkzFjxqxbt+61f25UVFRgYGBOTo7a560AAAAAKvszn3/884XLn3a+fPmy8imEAKDt5HJ5v379vv7661o4Y0j9Y2KFEFu3bt22bdu9e/fKPBoXF/daI5Xrvffe8/Dw+Mc//rFp06ajR48qB+/du/f06VNPT89p06ZpfJoJAAAAoNKzuaH6ZxYCAF4T9QXHmjVrlM8uMTExMTIyqv5IFWnZsmV4eHh4ePjTp08fPXpUUFBQr149c3Nz1dOtAQAAgFrCSCp5w1p2SdMxAKCOUF9wLF++vEGDBr/99lvPnj1rIFAlmZiYsGAEAAAAtVxPG9lPms4AAHWEmokPCoXi8uXLo0ePrlXtBgAAAKAVetkYajoCANQVagqO/Pz8goICqbRSW3UAAAAAKM7MiJXUAFBD1PzANTQ07NOnz/bt2x8/flwzgQAAAAAAAKpKfaO8YcMGU1PT3r17//zzz1euXLlfSg2kBAAAAAAAqID6tScdO3YsKCjIzc0dPnx4mScoFIrXnQoAAAAAAKAK1Bcc/v7+NZADAAAAAADgpakvOFatWlUDOQAAAAAAAF5aFXZ1zsvLO3fu3IkTJ6ovDQAAAAAAwEuoVMGRmZk5dOjQRo0aubq69ujRQwgxb968kSNHZmdnV3M8AAAAAAAA9dQXHDdv3uzdu3dMTEzXrl3ffPNN5aCpqelPP/3UrVu3mzdvVnNCAAAAAAAANdQXHIsWLcrKytqwYcPx48fHjRunHJw5c+b69etv3bq1cOHCak4IAAAAAACghvqCY/fu3W+++eaoUaNKjI8ePdrX1zcuLq56ggEAAAAAAFSW+oLj3r17bdq0KfNQ8+bNb9y48bojAQAAAAAAVI36gqNDhw5JSUllHkpISHB2dn7dkQAAAAAAAKpGfcHxzjvvJCYmLly4sKioqPj4okWLTp8+3b9//2rLBgAAAAAAUClStWfMnj07NjY2JCQkOjra3NxcCDFx4sSEhIQ///zTxcVl7ty51R8SAAAAAACgIupncOjr68fFxX377bf5+fnx8fFCiMjIyIyMjODg4OPHjxsZGVV/SAAAAAAAgIqon8EhhJDJZEFBQUFBQU+fPr127ZqVlVXjxo2rOxkAAAAAAEAlVargUDExMWnXrl01RQEAAAAAAHg5ZS9RkUgkEokkOztb9boCNRsYAAAAAACgpLJncAwePFgIodxfw9/fv0YTAQAAAAAAVFHZBcf27dtVr7ds2VJTYQAAAAAAAF6G+qeoKF2+fHnfvn2qt1FRUZcuXaqeSAAAAAC0TFpaWkREhKZT/I+oqCiJRBIeHl7eCYsWLZJIJK6uroWFhaWPtm3b1tzcvMTg2bNnx44da29vb2xs3LhxY3d394ULF+bl5b3m6ABeSqUKjqlTpzo5OX355ZeqkUmTJrVt23bGjBkKhaLasgEAAADQDt9//31ISEjpcblc/sUXX/Tp06dhw4atW7ceMWLElStXaj5eBc6dO7d8+XK1pxUVFYWFhbm7u69fv14ul3t5ebVp0yY5OTkkJMTNze3kyZMlzq/9XxzQPeoLjnXr1v3jH//o0aPH559/rhrctWuXp6fnN998s27duuqMBwAAAKC2i4uLi4qKKj3++PHjfv36ffXVV/fv33/33XcdHBw2bdrk4uJy5syZmg9ZHolEMn/+/MzMzIpPmzVr1uzZs1u0aJGQkHDjxo1ff/31xIkTT548CQ0NvXLlSs+ePc+fP686WSu+OKB71BccP/74o6Oj46FDh3x8fFSDAwYMiI2Nbdeu3cqVK6szHgAAAIDaa+TIkc7Ozv3795fL5aWPhoWFHT16dOLEiSkpKatWrfr99993794tl8vHjBlT40nLNWHChLy8vEmTJlVwTnJy8nfffdeqVaukpCR3d3fVuFQqDQ4O3rhxY0FBweTJk1XjWvHFAd2jvuBISkry8vIyMDAoMS6VSj09PdmJAwAAAKiz8vLyHB0dBw4caGpqWvro9u3bTU1Ni++C4ePj4+Xldfbs2Tt37tRgzIr4+/v7+Pjs3r07JiamvHM+/fTTwsLCpUuXlvk1hw0b1rdv3wMHDsTGxipHtOKLA7pHfcFhZWV17dq1Mg9lZmZaW1u/7kgAAAAAtENMTMyuXbt27dpla2tb+qienl7fvn0NDQ2LD8pkMiHEw4cPayiiOhKJZMWKFfXq1ZsyZUpOTk7pE549e7Z///6mTZv6+fmVd5OAgAAhRGJiovKtVnxxQPeoLzj69OkTGxv7yy+/lBjfu3fv3r17PTw8qicYAAAAAO2WkpKya9eu4iN37949cOCApaVl69atNZWqNDs7u7lz52ZnZ5e5T2p6enp+fr6zs3MFd1AevXDhgvKttnxxQMdI1Z6xePHi2NjYwYMHv/XWW56enlZWVvfu3Tt+/Pju3bvNzc0XL15cAykBAAAAaLvU1FRfX9/nz5+vXLlSKlX/m0hNmjFjxr/+9a+IiIgPP/ywc+fOxQ89evRICNGyZcsKLldOYElLSyvzaG3+4oAuUf+3q3HjxocPHw4ODv7pp5/27dunGvf19V22bJmVlVV1xgMAAACg9XJzc5csWbJ06VKFQhEREVH5vTbz8vJWrVqleuvg4OD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w6dKl58+fOzk5ffLJJ+fPn585c6ZUWvZDWAAAAAAAAGqSmoaiadOmYWFhYWFhQgi5XG5oaFgjqQAAAAAAAKqg3BkcpdFuAAAAAACA2qkKBQcAAAAAAEDtRMEBAAAAAAC0HgUHAAAAAADQehQcAAAAAABA61FwAAAAAAAArUfBAQAAAAAAtB4FBwAAAAAA0HoUHAAAAAAAQOtRcAAAAAAAAK1HwQEAAAAAALQeBQcAAAAAANB6FBwAAAAAAEDrUXAAAAAAAACtR8EBAAAAAAC0HgUHAAAAAADQehQcAAAAAABA61FwAAAAAAAArUfBAQAAAAAAtB4FBwAAAAAA0HoUHAAAAAAAQOtRcAAAAAAAAK1HwQEAAAAAALQeBQcAAAAAANB6FBwAAAAAAEDrSTUdAAAAANBxycnJjx8/1nQKAHgN8vPzNR2hXNpacDx58iQnJ0dPT8/S0lJPj3koAAAAqI3q1asnk8mGDBmi6SAAoPu0rOBITk5eunRpbGzsrVu3lCP6+vpWVlZ9+vSZOHFiz549NRsPAAAAKK5x48YPHz6Uy+XVcfNB2+69367ee23rVcfNAaBM+fn5VlZWmk5RNm0qOCZPnrxixQqFQmFtbd2tW7cmTZoIIR48eHD9+vWNGzdu3Lhx/Pjxq1at0nRMAAAA4L/q1atXr161dBA9HfRSnhaZmZlVx80BoEywwVL/AAAgAElEQVQsUXkNIiMjIyIi3n777cWLF7u5uZU4mpKSEhoaunr16rZt206fPl0jCQEAAICa5N5M9lX8E4UQEk0nAYDaQGt2r/jpp5+cnJx2795dut0QQrRv337jxo29e/eOiYmp+WwAAABAzeveTPbgWdGVh4WaDgIAtYLWFBzJycndu3eXSsudciKRSHr37p2cnFyTqQAAAABNsTbRb26qn3iz9k4XB4CapDUFR4cOHRISEl68eFHBOfHx8R06dKixSAAAAIBmuVvLEm5QcACAEFpUcHzwwQcXL1585513zp07V/poamrqBx98cPDgwUGDBtV8NgAAAEAjujWTJd6olke0AIDW0ZpNRj/55JNz58798MMPe/bsadGiha2tbePGjSUSycOHD7Oysq5evSqEGDNmzMyZMzWdFAAAAKgh3ZoZ3sl7nPG40K6h1vzDHgCqiTb9HFy5cmVAQMCSJUvi4uKOHTumHNTX17ewsHj//fcDAgL69u2r2YQAAABATWrZQN/aRD/hRj4FBwBo2c/BTp06/fvf/xZCPHr0KCcnx8DAwMLCQk/v5RfaPHjwYNq0ac+ePavgnPT09Je+PwAAAFCtulrLTt7MH962nqaDAICGaVnBodKoUaNGjRopX69Zs8bZ2blnz54vcR+JRGJqampkZFTBOfXq8f8WAAAAqKXcrWUrTj/VdAoA0DxtLTiKGz9+fGBg4MsVHGZmZhERERWfExUVdfTo0ZeKBgAAAFQv92ay4CMvrue8sDHV13QWANAk7Sg4rl+/fvbs2QpOyMzM/PXXX5WvfX19ayQUAAAAoHmtG0kt6ukl3si3cTLWdBYA0CTtKDj2798/ZsyYCk7Ys2fPnj17lK8VCkVNZAIAAABqhzesZQk38/0oOADUbdpRcPj5+R06dGj9+vUmJiZTpkxp0KBB8aOzZ8/u1q3bkCFDNBUPAAAA0CB3a8M1f7ENB4C6TjsKDlNT03Xr1vn6+gYEBGzcuHHDhg29evVSHZ09e7abm9tnn32mwYQAAACApnRrJpt/7MXNpy+sTdiGA0Dd9fIPWK15/v7+Z8+etbe379u375w5cwoKCjSdCAAAANA8x8ZSMyO9kzfzNR0EADRJmwoOIYSNjc2+ffvCwsLCw8Pd3d1TUlI0nQgAAADQMIkQXa1lCRQcAOo2LSs4hBASiWTmzJkJCQlyufyNN9749ttvNZ0IAAAA0DB3a1niDQoOAHWa9hUcSp06dfrzzz/HjRs3bdo0TWcBAAAANKxbM1n6o8LbuS80HQQANEY7Nhktk7GxcURExJAhQ86cOdOxY0dNxwEAAAA0xrmJQQNDvVO3Cnxbs88ogDpKiwsOJW9vb29vb02nAAAAADRJTyLesDJIvCH3bW2k6SwAoBnaukQFAAAAQHHuzQzZZxRAXUbBAQAAAOiCbtaytAeF958VaToIAGgGBQcAAACgC9o3Nagvk/x5i0kcAOooCg4AAABAF+jric6WsgQeFgugrqLgAAAAAHTEG1YyZnAAqLMoOAAAAAAd4WZlcOF+wbNChaaDAIAGUHAAAAAAOqKjhUyhEMl3CzQdBAA0gIIDAAAA0BH1DSSOjQ2SbrNKBUBdRMEBAAAA6A43S4PTt5nBAaAuouAAAAAAdEdnS9lp9hkFUCdRcAAAAAC6o7OVwf1nRVk5LzQdBABqGgUHAAAAoDtsG0obG+slMYkDQN1DwQEAAADoDokQnSwMktiGA0DdQ8EBAAAA6BQ3S9lpHqQCoO6h4AAAAAB0SmdL2YX7BXmFCk0HAYAaRcEBAAAA6BRXSwMhRPJdVqkAqFsoOAAAAACdUk8qcWpswD6jAOoaCg4AAABA17hZGpxmn1EAdQwFBwAAAKBr3CxlSewzCqCOoeAAAAAAdE1nS4P7z4qynrzQdBAAqDkUHAAAAICuadlQ2thYj4fFAqhTKDgAAAAAXSMRws2CVSoA6hYKDgAAAEAHuVkZ/HmLfUYB1CEUHAAAAIAO6mwpu3S/IK9AoekgAFBDKDgAAAAAHeRiYSAk4txdJnEAqCsoOAAAAAAdVE8qcWpswD6jAOoOCg4AAABAN3W2NEhiGw4AdQYFBwAAAKCb3Cxlp2/nswkHgDqCggMAAADQTZ2tZA+fF117XKjpIABQEyg4AAAAAN3UsoF+U2O907dZpQKgTqDgAAAAAHRWJ0tZEvuMAqgbKDgAAAAAneVmaZDEDA4AdQMFBwAAAKCz3Cxll+4X5BWw0ygA3UfBAQAAAOgsVwsDIRF/3WESBwDdR8EBAAAA6CxjqcS5scFptuEAUAdQcAAAAAC6rLOVAfuMAqgLKDgAAAAAXeZmKTt9m004AOg+Cg4AAABAl3W2lD16XpT5uFDTQQCgelFwAAAAALqsRQN983p6PCwWgM6j4AAAAAB0XEcL2elbbMMBQMdRcAAAAAA6rrOlATM4AOg8Cg4AAABAx3W2kqU+KHiaz06jAHQZBQcAAACg41zMDfQkknN3mcQBQJdRcAAAAAA6zkgqcW4iPX2bbTgA6DIKDgAAAED3uVnKkig4AOg0Cg4AAABA93W2Mki6XcAmHAB0GAUHAAAAoPvcLGWPnhdlPCrUdBAAqC4UHAAAAIDuszHVt6inx8NiAegwCg4AAACgTuhkKWOfUQA6jIIDAAAAqBM6W8qYwQFAh1FwAAAAAHWCm5VB6oOCp/nsNApAN1FwAAAAAHVCh6YG+hLJX3dZpQJAN1FwAAAAAHWCkVTStqk06RarVADoJgoOAAAAoK5wY59RALqLggMAAACoKzpbGpy+XVDELhwAdBEFBwAAAFBXuFnKnsiLrj4u1HQQAHj9KDgAAACAuqK5qb5lff2kW6xSAaCDKDgAAACAOqSThUHSbfYZBaCDKDgAAACAOsTNin1GAegmCg4AAACgDulsaZD2sDAnn41GAegaCg4AAACgDnExN5DqSc7eYRIHAF1DwQEAAADUITJ9SdsmUrbhAKB7KDgAAACAusXNUnaaB6kA0DkUHAAAAEDd0tnK4MydgiJ24QCgWyg4AAAAgLrFzVL2RF6U/qhQ00EA4HWi4AAAAADqlmYm+pb19ZN4WCwA3ULBAQAAANQ5bpYG7DMKQMdQcAAAAAB1jpul7DQzOADoFgoOAAAAoM7pbGmQ9qDwsbxI00H+H3t3HidJWd8P/Lmqurq7+j7mvnb2nmVhMRwLAgICsmgMRAMiESQqAl4B8SAIiRpXjAcoCAaDmh+KRqMEEESJJIAgyhVg72N27qtnpu+r6qn6/VE9s7PDXiy729Mzn/drX/Pqrq7u+Y4jfXzm+3wfAIDDBgEHAAAAAMCCsyqmKJy+MopVKgAwfyDgAAAAAABYcFROu6ICYzgAYD5BwAEAAAAAsBAdE1NfHcMYDgCYPxBwAAAAAAAsRF1RsSFhVrsKAIDDBgEHAAAAAMBC1BVTRnJyLI85owAwTyDgAAAAAABYiJaGFBenGxMYwwEA8wQCDgAAAACAhYgzsiwiXkPAAQDzBQIOAAAAAIAFqiuqoIMDAOYNBBwAAAAAAAtUV1TZMIaAAwDmCQQcAAAAAAALVFdU6c/IZAlzRgFgPkDAAQAAAACwQC2PCM4IVqkAwPyAgAMAAAAAYIFSOV0cUjaMmdUuBADgMEDAAQAAAACwcHVFxQZ0cADAvICAAwAAAABg4cJGKgAwbyDgAAAAAABYuLpiSnfKzBl2tQsBAHizEHAAAAAAACxcKyMKIWTTOJo4AKDmIeAAAAAAAFi4PArtCIgNYwg4AKDmIeAAAAAAAFjQumIK5owCwDyAgAMAAAAAYEFbGVU2JLBTLADUPAQcAAAAAAAL2qqosm3SKJqYMwoAtQ0BBwAAAADAgtYVFZZFtkygiQMAahsCDgAAAACABc3vYs0+jjEcAFDrEHAAAAAAACx0mDMKAPMAAg4AAAAAgIVuZVTZiIADAGocAg4AAAAAgIVuVVTZPG6aVrXrAAB4ExBwAAAAAAAsdKtiSlna2ybRxAEANQwBBwAAAADAQhdxszov3zCGgAMAahgCDgAAAAAAIKuiyoYEdooFgBqGgAMAAAAAAEhXTGAjFQCoaQg4AAAAAACAdEWVTQlD2tWuAwDgUCHgqLKvfOUrHVM0TduwYUO1KwIAAACAhagrquRNuzuJVSoAUKsQcFTZjTfe2N3d3d3d/eSTT65du7arq6vaFQEAAADAQtSg87CbYZUKANQuBBxzxec///kvfOEL1a4CAAAAABaurqiyEQEHANQsBBxzwpYtWxKJxFlnnVXtQgAAAABg4eqKKujgAIDaJapdABBCyO23337DDTcc5MmlHfnR7/TZlk0IIZTwwAF+iZRR7t/zHE65j+9xjsKYvseR3efqnAq694cWlOszHvl1xXC/oJzueYQTto9HAwAAAICqWhlVfrIxbxOCt2sAUIsQcMwJjz/++G233XaQJ1tFy5w0iFWZcC0nKim7TJu2te975aVd3uNmKyttc48x2TJt2vsanG3aMisPssJDwAPi9S+kPKjQGQepxpg2o+doz+CGuRlzsd1nuit5DXMx6mbTl6ePE0K4X0z3MM2KeGbGOlRjTONTp1HmnbpMCQ8q+/8RAAAAAGrIqqhIl6z+tGzx7/1PXwAAcxkCjup7+eWXFy9erKrqQZ7v7tI7frTqiJb0hsiUSWakIjJjzsxN7KJlFWfnLjJlkL0FKTJl2nset/LSLu2+u23ZVnqPnEVmd387u2BZUydbBWkXLUKIJMQqWVahctw2bCu3eza4zEgylenY0pbp3TdZeWtWJHSQKKdsqkGGaZxNxyteTpVKBML9YrqThfsFncpZ2IyeF65zIighhE51ytCpEIf7hHOTCApSiV0EIYS5GXUx53sxlRFCmI/PaqIBAAAA2JfWgPC72GsJAwEHANQiBBzV98gjj5xzzjnVruLQzV6WEpyH/6eyZ0YkpiUzu0MWOWMrNZmsdNPYhi1zlXPsgpyOeGRWkqk4RqZMeyrOkWmTTGUpMm06mYucNORUl810A45MS2LZtmXLlEkqec1BddY4DSaU0sovaGqZElWY05PCXMzJYqibO50yzMOpSgkh3MupwoigXOeEUec3zgOCUsJ0QQVlHkZVxjTG3HxmkwsAAADUFkrIiojYmDDOX6RVuxYAgDdsHn4WrTk33nhjtUuAA6Auxl27V8eIaBVr2TsrJ23DJk5uYtlkOnmxKm0p9tQiI7tkWXlJCLGKltPkYuWlVbbIjFVLTsgiJ41KT41NZMogUw0vVl5aJcsu287j7B2j3M8JISKkkKnVQMwrqEKZhzGVURdjHk4FZTqfHhPjhC88IAij3MupQpmbUxejLupEJ1wXVKXMgwAFAADgSFkVVV4bw5xRAKhJCDgA5oPppomj3EHjtJBMd5Q465Vk1iSmbeWkZdhOA0vlNNOWWZPYpHJy0iS2LbulbdpOi4pVsOySZZUtKyf3P/bF6S5xptjyoKCCMl0wjTGNMZ1ThXG/oCrlumA6Zy7G/IJ5ONMY9wvmYczFeFCZPdUFAAAACFkZVR7YVqh2FQAAhwIBBwAcOqpU1ryIsHLAkw/F7jTEsO1Kj4lMGk5DitONYuWlXbadEbkyZdqGJScNY6hkFS2ZNu2iZeWlTJtW0drrch4eFM6oFB4QTGPUzUVQUPf0kakLHs7cTj7CmZsxn+A6Zx6O9TgAADDPrIop4wVrOCfr8RoHALWmVgOOdDqdyWQYY3V1dYzhb7AA8xSdWrdymDpTZFbaRSkz0spJq2hZGdPKSatkyZRp5aVVsGTGtLLSKllywij3Fe2CJbOmzEi7aMms+fq9hwgh3C+YmzEP50HB3FM5iM6ZmzOdc5/gfsF9nPsF8wvuEzxYOUJVPHcBAMCc0xEUHkFfGzMQcABAzamxgOO11177l3/5l9/+9rfDw8POEc55fX396aeffu2115566qnVLQ8A5jiuc6LzNzNFxVluI1OmVZRW3pJJw8pbVlHKlGnlpFWwZNa00tIqSHOsLLebMidl2rQyUqbNmdv0EEKYxphPcB/nAcGDCvcJ7ufcL3hAcL9wAhHhpCEBwZwjWFMDAABHGKdkWUTZmDDf3l7tUgAA3qBaCjg+/vGP33nnnbZtNzQ0nHTSSZFIhBAyMTHR399///3333///R/60IfuueeeapcJAPOZsyrnkDtKZMqUaVNmTCstZcaUaVOmTJkyZcaUaWllzHJfUb42dTxpOktvdn93F3OyDx6o/BNB54LCg7sP8oAQAYUHBVbQAADAIVgVExsSmDMKALWnZgKO7373u3fcccd55523fv36NWvWzLp1w4YNX/rSl77//e+vWLHiuuuuq0qFAAAH5KQPb+guTsOI0wBS+Zc0K0eSpkyZxlDOTE4FJWlz5qgRKmgl7wgrPKyIsCJCCg8rIqJUjkQUEVZEWEEUAgAA01ZGlcd3ZatdBQDAG1YzAcePf/zjZcuWPfzww0Lspeaurq77779/cHDwl7/8JQIOAJhPmJczL1caXQd5vm3YMmU4DSCmk3okDTlpmuNlc8Io9RXlyxlzwjAnDDlpTI8UYRrjoUrYwcOKiFUu705GIoqIqm80nQEAgFq0KqoMZWWiYEXdWBoJALWkZt6qvvbaaxdeeOFe0w0HpfS000678847j2ZVAABzDVWoiKoiqh7MyTJtmgnDHDfkpFFJPSYMc9wwJ4zStvz0EatgVR6c00rfR0gRUYWHFaVOFVFViamiXlWiqogpB/mtAQBgzloSFiqnGxPG6S0HG68DAMwFNRNwrFq16rnnnpNScr7PPupnn3121apVR7MqAICa5kz0cC1y7/80Ky93Zx/jhjlhmOOGnDDMCSP/QtoYLpuJspkwnHEhVFARVUVMUepdIqooMVXUu5SoImKq0uASUUXEVMrpUfn5AADgUCiMLg2LDQg4AKDW1EzA8f73v//aa69917vedeuttx5zzDGzbt26des//dM/PfHEE1/72teqUh4AwDzGPFz1cNKs7e8kmxhjZTNRNscMY7hkJgxzrGwMl4pb88Yfks4Ru2QRQgijlbyjXhVxVXGCj5iq1Kliqh+EIAABAKiqVVFlI+aMAkCtqZmA45prrnn11VfvvvvuRx99tKWlpa2tLRwOU0onJyf7+vq6u7sJIVdcccWnP/3palcKALAgUaLEVSW+v/UpMmU6SYcxVDJGy+ZY2RgqFbfmM09NGiNlc6xsG1M9IDFVqVeVBpfaqIk6VW3WlDpVaXYp9S4RVo7WjwQAsHCtjCrf/z/MGQWAGlMzAQch5K677rrqqqu+9rWv/e53v3v66aedg5zzeDz+vve976qrrjrjjDMO4WHT6bSUcj8n5PP5QykXAAD2VNlEZtk+T6g0gIyWjeGSMVw2BovGULnwWqY8VDKGy04DCNOY0uBS6l1Kk0upd6lNLqXepbS41GZNqXdRgd4PAIDDoCum9KVlqmQFXJgzCgA1o5YCDkLIcccd95Of/IQQkkwmM5mMoijxeJyxQ3/a3bFjx5IlS2zbPnw1AgDAIXLGo2orvHu91UyUjZFyub9kDpfKAyVjpFTakc8+NWkMl4yRMiGECqrUu9QWTW3RlGaX2qKpzZpzFfu/AAC8ISsigjGyKWGe3ITR0QBQM2rvDd/o6Ojk5GRnZ2cwGJx1UyKRKJVKTU1NB/9onZ2d3d3d++/guP/++2+66aZDqRUAAA4fJ/5wd+mvv8kqWsZAsdxfKvcXyz3F8kCx8H+Z1MOJcl/B2QKG61xp0VytmtKsqa2aq82ttrnVNm3/a2oAABYsF6edQbFh3EDAAQA1pJYCjpdffvnyyy9/5ZVXCCH19fVf/epXL7/88pkn/O3f/u1vfvObN9qO0dbWtv8TotHoGy0VAACOJqYxV6fH1el5/U3muFHuKxr9xVJf0egrlvtLyZfHyj1FM1EmzvzUNs3V7lZbNVe7W23T1Da3q83Ng7X0+ggAcCR0Yc4oANSamnkDt2PHjrVr15bL5be//e2qqv7+97+/4oorcrncNddcU+3SAABg7hIRRUQUcpxv1nErJ0s9hfKuYqmnUN5VKPcWs88kyz1FmTYJITwg1Da3q8Pt7tLdx+ieY3S1/QCb6QIAzDNdUeWnmzCKDgBqSc0EHDfddFOpVPr1r399/vnnE0LGxsZOOeWU66+//uyzz162bN8D6wAAAPaGebl7pe5eOXvBizlplHuK5Z5CqadY3lnI/H589Ns9Miu5X7iP0T2rfe5jdPcxPvdKL8XgPQCY17piys5nzbxhexTMbwaA2lAzAcdzzz137rnnOukGISQWi/36178+7rjjbrjhhgcffLC6tQEAwLwhQooIKZ49Oz6MoVL+pUz+5XRxUy79xERxS45yqrZo2nKvZ43Pc5zf8xa/Uodl6gAwr6yKKoSQzePG8fV4fgOA2lAzAUcikTj77LNnHlm6dOmnP/3pL33pS0899dRpp51WrcIAAGDeUxpcgQZXYF1lJJM5aRReyRZezRRezSYfTgx/vccuW0q9S1vm0ZZ4teUe11KvtsSjtmjVLRsA4M3wKLTNL15LIOAAgJpRMwHHscce+8wzz8w6+NnPfvaHP/zh1Vdf/eKLL6oqnnkBAOBoECHFd0bId0bIuWobdnFTtrAhV9ySK27NZ56aLHUX7LLFvFxb6tGWeLVlHtdSr7bUoy32YGELANSQrpiyMWFWuwoAgINVMwHHaaedtn79+o9//ONf//rXXS6Xc9Dr9d59990XXHDB5Zdf/oMf/KC6FR68QqGQTCYbGhoIIeVy+bLLLtu6dauU8t577z3hhBOqXR0AALwxVKHu1T736t2rWmzTLu8qFDfnitvyxW351GPjxe/0yqRJOVVbNW2ZV1vm0ZZ6tS5dW+bhvpp5LQaAhaYrKh7aXqx2FQAAB6tm3lTdfPPNTz/99B133PGjH/3ojDPOeOihh5zj69at+8IXvuAsVCmXy9Ut8iDdddddtm1ff/31hJCf/exnsVjsP/7jPx5++OEbb7zxd7/7XbWrAwCAN4sK6lrscS32BGYcNMfKhc250tZ8cVu+sCE3+cvRcl+REKK2aNoyr3ulV1vm1Vbq2nJEHgAwV3RFlW/8KVOStotjzigA1ICaeQuladqDDz546623PvDAAzt37px50xe/+MXOzs7169dv2bKlWuUdvPPPP//555//3Oc+51xtb29fvXo1IWTJkiVSyqqWBgAAR5CIqb6Y6jstNH1EZszi5nxxY7a4NVfYmJt8YLTcuzvy0FZ4K8HHCi8iDwCoilUxRVpk64R5TEypdi0AAAdWS2+YgsHg+vXr169f//qbLr/88g984AM9PT07duw4+oW9IY8++uidd95ZLFaa/ZzxqDfffPNPf/rTGlplAwAAbx73Ce8Jfu8J/ukjMiuLm3PFjdnillxhYy75X6Pl3iKhxNXudq/2eVbr7tU+z2qf0uSqYtkAsHAEXKzRxzckDAQcAFATaing2D9KaXt7e3t7e7ULORSXXHKJlPJb3/rWqaeeesCTyz3FxA8GmIdTQZmfU0Z5UFBGeUAQRrlfUEGZzpnKmJdTlTIPPwo/AgAAvHlc596/8Hv/Ys/IY1Ou8Eom/0om9ZvE8Dd6rLwUEcW92uc51uderXtW+1xLPBTd4wBwZHRFlY0Jo9pVAAAclPkTcNSoBx988Nhjj125cuWXv/zlhoYGy7IYO8CAfXPSyL+csYqWlTPtkm0VpFWwrKK0CpZdsvZ6F65zqnHu40znTGNMF1zn1MW4XzAPZ27GfYJ5OXUxHhDMzZiL8aDCNEY1xgOCuRjzIiUBAKgCrvOZXR62tEvb84VXsvlXMoX/y4zfN2QmyszN3F2V5g73at19jI+5sVcLABweXVHliR7MGQWA2oCAo8qef/75Z5555qtf/eqGDRt0XT9gukEI8RznW/zAcfu61cpJ27Bl2rSlLdMmsYhMGlbRsoqWTBp2ybLylkybTiwiU6YxWrZy0sqYVtGyclJmpF2UMruXaSC7UxLvVDji5dzLmc55QHCfYDrnXs58QgQFc47rgvsF93PC8KdFAIDDgHKqLfNqy7yh99Y5R4zBUv6VjBN5jNzeU9pVoIK6V+qet/g9x/u9x/u0lToVeBIGgEPUFVW++2LWtIhAcAoAcx4Cjir7zGc+c9lllx177LGEkMMyg8NpteDBN/ubtXLSKlkyZVoFaZfs3SlJyrRLlpWTMmtaecvKmnLCKPcUrawps9LKSXPScEKWParycOblXOc8KJhXcJ0zrxOLcOYVzMu5j/OAcM5hXsGDggeEs9zmTf4gAADzm9LoCjS6Au+IOldlVhZezeRfyuRfSo/e0VvcnKMKdXV69FOC+skBzxq/tsyD0BkADt4xcaUk7R1Jc1kYHxwAYK7D81QVXHvttdOXdV1/4IEHqljMvjAvZ14uwoc4UKrSD5I2Zdq0slJmpZUzZdKUWWllpZWv3GQMlayslFlTJk0rJ2VOWrk9mkeYl3O/4IFK3lG5HBTcP/XVL3hQiJDCQ4oIKVTBu3YAWNC4zvW1QX1t0LlqjJbzL6TzL6ZzL6T7bxwzE2XuF57j/d63+D1v8XnfEsC8UgDYv6ibxT1sw5iBgAMA5j48T8ERwTTGNCYibzwfsYlMmTJjypQp01NfnQvOv6RZ7i3OvGlmJsJ1zithh+AhRYQVEVJ4aCoBCQsnB+EhBQvUAWAhUOJq4Pxo4PxKf0e5p5h7MZ1/IZ37U2rsX/tlxlSbNe/JAe9JAf2kgHu1D31zAPB6XTFlQ8K4aJm72oUAABwAAg6YYyjhQcGDgrQc7D3skmVOGjJpmpOGnDDNSUNOGuakISdNc9Io9xTMSVNOGuaEIVPm9L2YxqbCDiHCMzKRkCIiU1fDioioiEIAYN5Q2zS1TQtdGCeEEMsubsln/5jMPZdK/Gt//w1bmYd7jvfrawPeEwPekwMihF0hAYAQQrqiynOD5WpXAXNBCPMAACAASURBVABwYAg4oOZRF1PqXUr9QXRZW/Z02FG54KQhE6acNAqbcjJpmhNOPmISqzJGxFmqI6KKiKoiooiwIiKKiKgiroiIKsIKjygiomCPRgCoMYxqK7zaCm/0g02EEJkx88+ns88ms8+mRu/ss/LS1eH2nhzwrPHra4OeY3VM7gBYsLqiyr2v5CwbTwMAMNch4ICFhFERUUREOZgV5zJpmuNlM2GY44Y5YZjjhjlWNsfLpV2F3Atpc9wwE2WZ3N0SImKqiCpKTFXqXSKqiOkLUVVpUEVUZRo6QQBg7uI+4Tsz7DszTAixDTv/cib3p1Tuj8mR23r6P7NVhBTvyQH9tJDvrUH3sT5EugALSldUyRv2rpS56E2PsQcAOKLwJAWwd85KGVfn/s6xTducMOS44UQhxkjZTJSN0XKpt5h7IW2Olp1deCsPqHOlwSWiqogpSr1LRFUlrir1qoiqok5V4qqzAw4AQNVRhXpP8HtP8JNrWwgh5b5i7o+p7DPJ8X8fHPiHbVwX+luD+ltDvrcG3cch7ACY/5p8PKSxjQkDAQcAzHF4kgI4dFRQJa4qcZUQ777OsfLSHC0bI2UzYRijJeeCOVIqvJY1E4YxUpqeDMK8XImrIq6KmKrUq0pcFVFVqXcpdaraook6FZ8iAKAq1BZNbdFC760jhJiJcu5P6ewfk5O/GB64aRvzcO+JAX1tQF8b1E8NUhWtagDz08qosiFhvnNxtesAANgvBBwARxbzcLXdrbbvc/C4XbKMsbIxXDbHymbCMIZK5ljZGCsXN+fNRNkYLjkLYaigIq6qLZra6FIaXWqLpjgXmjWlzoX9cQHg6BBRNbAuGlgXJYSYiXL26WTmqcnkA2ND67u5l+unBvW3hvS3hjzHo7MDYF7piiobEka1qwAAOAAEHABVRl1MbdbUZm1fJ1gFq9xfNAZLxkCx3F8yBkul7kLmqaQxWDITZUIIYVSpU9Vml9Kgqc0upVlTm1xKk8vV4hZ1KjZ9BIAjRETV4F/Fg38VJ07Y8Ydk5qnkxP3DAzdv57rQTw/6zwz7zgxry/bZ4wYAtaIrKn62KW8TgncVADCXIeAAmOuYm2lLPNoSz+tvsoqWMVgyBovl/lJ5oGgMlko9hewfkuWBSvZBORX1qtqsqU2a0uRSWzS12aU0amqzS6lz4U0KABwuIqoG3x0PvjtOCDHHjcyTk5nfT4zc0df36a1Ko8tJOnxnhpU6tdqVAsCh6IoqqZI1kJHNPowMA4C5CwEHQA1jGnMtcrsW7WX9i1W0jIFieaBk9BfLfcXyYKm0LZ/574nyYLGy5kVlapNLbdXUFk1tdattmrPMXm3WsOAFAN4MEVFCF8ZDF8YJIaXuQuaJicwTE/2f2WpOGK4Ot5N0+M8Ocz/ehADUjPag8Kl0Q8JAwAEAcxneWwDMT0xjrk6Pq3NvfR85We4rlp3go69Y7i1mnpgo9xWNoZItbcKo0qC6WjS1za22amqzprZqapvmandjfCAAvFGuDreroyl6ZROx7Pz/ZTNPTKSfmJj4yQbbsN2rdd+ZYf+ZYUwnBZj7KCHLI8rGhHFexz4X1QIAVB0CDoAFh3m5ttyrLZ+9Kt42bGOwVO4rlHuLpZ5iua+Y+3N68j9HygMlu2RRTtVWzbXI7er0uBZ7tMUeV6dbbXNjxgcAHBRGPWt8njW+uuvarIKVfSaZeWIi898TI7f1ioDwnxsJnB/1nxPhAbwzAZijOkOiOymrXQUAwP7gbQQAVFCFqm2a2va6v8zYxBgqFXfkSzsKpR350o589g/J0o68VbSoQtVWt7a4knq4Oj1ap1tp0bB7AgDsB3Mz/9lh/9lhQoiZKKcfn0g9muj95GYrJ/VTgoHzo4F10b02oAFAFbUHxIPbCtWuAgBgfxBwAMCBUOJsSes7LbT7oE3K/cXSVOpR3JFPPz5R2lWwyxZVmWuRW1vqcS32aJ0e11KPtsQjopgsCAB7IaJq+JL68CX1trRzf0qlHk0kfjjY//ltrg534B3RwLqo/tYQBgMBzAWLgnxX0sRGKgAwlyHgAIBDQokzlNT3tt3HbGkbfcXijkJpe764JVd4OTP5i5Fyf4lYNg8KbYnHtcSrLfa4lni0xR7XYg9zY9U9AFRQTvW1QX1tsOmLi0vdhdSjidSjibHv9zMv958ZDpwfDayL8SDetwBUTUdA5E17JCfrvZgzCgBzFN4oAMBhQzlV291qu5ucHZ4+aBWt0vZ8aXu+uC1f2p5P/SZR+k7enDSciERb7NGWebVlXtcyj3uFF40eAEAIcXW449e0xK9pMSeN9G/HU48k+m7Y2nPNJv2UYOCCWPDdMbUZYw4BjrZWvxCMdCcRcADA3IWAAwCOLKYx9yrdvUqfedAcN4rbcqWt+eL2fHFLLvVYotxTtKUtwoq2zKst92rLPNpyr7bUq7Zo6IUFWLBESAlfXB++uN427OwzydSjidHv9vZ/bqv3hEDwr+Khd8f3MjYIAI4MwUiLT+xMmmub8NcIAJijEHAAQBWIiKJHgvrJwekjdskqbssXt+aKm3PFzfnx+4aK2/J2yWJeri31OKmHe7nXvUpX291VrBwAqoIq1HdGyHdGqPmrS4qbcpO/Ghn/wcDAjdu05d7QRfHQe+u1JRhKCnDEdQR5d8qsdhUAAPuEgAMA5gTqmt3oYUu7vKtQ3JwvbskVt+SSD42NfH2XzEruF+5jdPcxPs8xuvsYXVupMw2zPAAWEG2Ft2HFooYbFzlJx+R/jg59pbuSdPx1nbZs9h7YAHC4dATEjiQCDgCYuxBwAMAcRTl1dXpcnZ7ABdHpg8ZQKf9SJv9yurgpN/I/E8UtOcqo2qppy72eNT7PcX5thdfVgRYPgAVhdtLxy91JR+D8mGeNr9oFAsw3HUHxeE+p2lUAAOwTAg4AqCVKgyvQ4Aqsq0QeMmnmX80UXs0WXs2kHkkMf6PHLllKXHUfo7tX+zxrfJ41fuQdAPPe7qRjc27yV6PJB6aTjrrwJfWuRXgSADg8OgKiP22Wpa1yzMcCgLkIAQcA1DAeFL7TQr7TQs5V27SLW3KF17KFV7P5lzKJewdkyhQhxXO8z3O837PG7zneh80XAOYxbbm34fMdDZ/vKG7LJ381OvHz4aGv7PSeGAhfXB/66zj2aQJ4kzqCXNqkNy0Xh/AhAgDmIjw3AcD8QQV1d+nuLp1cXDkyvaQl/1Im8f0Bc9LgAeFe4fWs8XtPCeprA0q9q6olA8ARoS3x1H+mvf4z7c7qlZFv9/R/Zqt+eijyvvrgu+MMm1wCHJI6L/cqdFfKRMABAHMTnpsAYD7bY0mLZRe35vMvpvMvZXIvphM/GrTyUml0eY73e4/3e08KeN7i5zo+9gDMK5XVK5/ryD6Xmrh/uO/6rb2f3Bx4RzT8vgb/uREq0GYP8Ma0B0R3Ula7CgCAvUPAAQALBqPacq+23Bu+tIE461k25/IvpvMvZpIPjw2t30ksoq3wek8KeE8IeE/0a0u8BJ99AOYHRvW1QX1tsPlrSzO/n5i4f2jnZa+KsBK6MB68KK6vDR74EQCAEELIoqDATrEAMGch4ACABYoK6mxMG/kAIYTYhl14LZt9Npl/KT20fme5p8h9wt3l9Z4S1E8Oek8OiLBS7ZIB4M1iGgusiwbWRc1xY/I/RyZ+Njx6V5+23Bu+uD78N/VqG2b0ABxAR5A/O1CudhUAAHuHgAMAgBBCqEI9a3zT+0qWe4u5P6Vyf0pln06O3tFnm7a2zOM9MaCfGPCeGNCWo7kDoLaJiBL7SHPsI82l7sLET4fHfzw0+MUd3hMCoffUhS6MKw2YzgOwdx0B8ZMN+WpXAQCwdwg4AAD2Qm3V1FYt9J46QohdsvIvZXLPp3LPpQa/stMYKImw4l0b0E8J6WsDnjV+qiDtAKhVrg53ZeOVTbnJX42M3d3X/7lt+on+4EV1ob+uU+qw8QrAHjqCIlGw0iXL72LVrgUAYDYEHNV333333XHHHclk8t577z3llFOqXQ4AzEZdzHtywHtygHyMEEKMoVL2j6ncM8nJXwwPfGE705hnte6sZNFPDfIAnlcBalJlHOmNi4qbcuP3D418q2fg89u8J/iDF9WF/6YOW8wCOBYFBSVkV0qujiPgAIA5B2/Eq2xgYOCb3/zms88+u3Pnzk9+8pO//e1vq10RAByA0uAKXRgPXRgnhMiszP85lX02mX02NXpnHzFt1xKPfkpQPzmgnx5Sm7GeH6D2aCu8TV9c3PSPndnnUslfjo58fdfA57c5W8wG/jKOvZZggfMqNOZhO5Pm6jhGUwHAnIOAo8oefPDBSy65xOVyrVix4tFHH612OQDwxnCd+84M+84ME0KsvMw9n87+IZl7Ntn7s2ErJ7WlXv20oP+ssO+MMA/i+RagpkxtvNK0fknmiYnJ/xzpu6GyxWzovXX+c6NMw5+vYYHqCIpd2EgFAOYkvOGusv7+/o0bN65evbpQKNx2220XXHBBtSsCgEPEPNx3esh3eogQYpt24ZVM9tlU5smJnqs2WgXLs8bnOyvsOzOsnxSgKj4XAdQMKqj/nIj/nEjr7Vb6d+MT/zmy68MbqaCh99ZF3t/oPcFf7QIBjraOgNiZlNWuAgBgLxBwVFmxWMzlcs8//3xPT8+pp546PDzMGD75ANQ8KqjneL/neH/82hZb2oVXspknJtJPTIzc1ksF9Z4U8J8Z9p0Z9hznw24sALWCuljgnbHAO2NWTib/a3T8vqEtZz+vLfFELmsIX1KPjVdg4WgPige3FapdBQDAXiDgqLJYLHbuueeqqrpkyRK/3z8xMRGNRvd/lz8WSzdPJvd6k0qpRvfyaWnWcYUQz4wYhRPiZXvcSyVUY7MfRyHU87oH1xllU5/PXJS4pk5glPpmnOxhVMz4GMcp8dI9chw3peqMx2aE6q8rAKBGUV7ZgLbuujYrJ3N/SqWfmJj81cjALdtFVPW9Neg7M+w/N4KBHQC1gnl5+NKG8KUNxkBp4mfDiR8MDNyyw3dGKPK++uBfxZkHQzpgnusI8F1J0yaI6AFgzkHAUWVnnXXWjTfeeP3112/atMkwjAOmG4SQLlW5LuCXhOQse68nWMTOvu4mSUjWtqav2jbJzLhKCMlY9oz72MQiecs2yOzHKdp22d7jYNayranTyjYpzLg1be3xLQ7ZzBhFo9Q19XI6MwfRKFUpJYRwQvSp+MZLKaeEEKIQ6p4600crNwtKPFM5i4dR5z+GmdHM9GNOn0kJ8U89jpcxvIeFN4p5d8/sKPcVM7+fSD8xMfjFnb2f2OxeqfvODgfOi+inBLGGBaAmKE2uuuva6q5ry7+USfxgoO+6LX2f3hp6T134ffX62mC1qwM4UhYFRd60R3Ky3ou3QgAwtyDgqLITTzzx5JNPPumkk4rF4n333Xcwd/Ex9g6P+0gXdtjtNRmZuXxzVi5jEZKdkY/kbNu0d8coxanLM++VtyuJjGGTwlR8MxXc2EVpl6buNZ28lG17+qGm65G2nbP3Hh7t1XQOolLirlyotMy4KHW6WrSpC66pgGb6XhqlLjLrXpVeGJVS5wF1xhghbkoVSp3vgiaXeUBt0SKXN0YubySWnX8lm/n9RPp342N39TE3950VDpwX8Z8XVeqwMyVADfCs8bWuWd5869LUbxKJewe2nvuCtsQbek88clmj2ormLJhvWv1CMNKdRMABAHMOtd/IB7mF6Xvf+95HP/rRTCaj63q1a4GjLWfb0rYJIUXbLtmEECKJPd07Mx2U5G3bsG3ngmk7d7Rk5YJtEpsQkrNsSWxS6XkhhJCsZTmRyvRN0xfylm1MPbK53/9IfYxRQjyUKrQSkXBCnTVHfsZoJRap9LBQQvyUEUK8jHJCNUpVShRKPZRSQnyMEUJ8jFJCZz7gYftfEw6ClZOZ/51M/SaRejRhDJW05d7Auqj/zLB+WogK/C4AakOpuzBx/9D4j4eMgZL/7ZHI5Y2BddFKSyHAvHD2/WN/d6z30pWeahcCAFVQLpddLtcf/vCHU045pdq1zIYODoD98VJKKCWEVHdKvmHbBdu2CMlYTkRiWYQUbNuw7fLUTU4nS8a2bLvSzOL0p1g2SVs2IXbWtCQhJdsu2bZpk7xt2cS5ieSmopa94oR4GVMo8VAnE6E+xjghPsZUQjRG3ZQplPgpE5R4KXM6ULyMKYT4GFMocVPmJCZ+zNA9EOblgXXRwLoosez8/2UzT0ykHhkbua1XhITv9FDg/GhgXQw7zgLMca4Od8ONixo+15F5Ojl+39CuD77GI0r0g03RKxqVeswihflgUZB3J7FTLADMOXiXDFADFEoVSgkhwUo+cEQ6Qp1lRM4glem1PxnLsqdaVDK2bdp2zrJLxC7attNvkrbscdvM27YTnWQt2yR2xrJnLv+ZSVDqodQZK6szphDqZdRZwqNTqtCpq4TqjCqUeisnUx9jKiVuyvxT5x+J/xHmELZ7NKkxUk4/lkg9Nt533Zaeazbpa4P+8yKBdVFtqbfaVQLAvjHqbB0tv7508hcjo3f1Da/v9p8biV/T4ntbGOMZoaZ1BMTOFAIOAJhzEHAAQIV2BBakOEtsUpZtEjtv2U6G4gQlWWsqE7EtwyZZ2yradtay+8zdV8u2nbFsY29TURghOmNOVuJh1MeYRqmbUh+jbsrclHop9THqZcxLqc6Yl1IfYzqj3qnpJzVEqVMjH2iMfKDRLlvZZ5Kpx8YTPxwcuGm7ttQbfFcs8K6Y9y1+fFgCmLO4X0SvbIpe2ZR9Njn23b7tF72strqjlzdGrmgUYaXa1QEcio6geLynVO0qAABmQ8ABAEeQh1JCqf9wLEwp2nbJttOWXbAtp38ka9sF2y5adtqyCrZdtO2sbeWmOkoylpWz7Jxt5yxrVj6iTMUfOmM6rYQgTvzhp0xn1MuYTqnOqE6ZjzEfozqlYg7EIlRlvreFfW8LN69fUuoupB5NJH85MvzNXSKi+s8Jhy6s8789jB1YAOYsfW1QXxs0hksTPxkeu6d/6Cs7A+dHY9e26CdjyxWoMR0B0Z82DctWMPIcAOYSBBwAUBucBpMAI4ewQscmJGNZWSfvsK2cZWcsK2vbucpXO2tZScvqM62sVUlJsrY1a7tlbSry0Bn1MRZgTKd0Kv5gPlaJSHyU+RjzM6of4Y2EXR3u+DUt8WtazEQ5/dvxyV+N7rzsVeZh/jPDgfOjgb+Mcx3D7QHmIqXeVXddW/yTrenHxkfv6tt6zgue43zRDzaFL65n2JMCakRHkEub9KZlJ8ZCAcBcgqckAJj/KCF+xg6hkSRtWVnLzthOOGJlLTtr2WnLythWxrKzlpUwzczUORnLzszY25gQ4qHUz1ig8o8GGAtyFmQsuOfBAGNvZnGQiKrhSxvClzbIpJl+YiL1yFjfdVt6P7HZ5yQd74wpcew1CzDnUE6dicLFbfnE9/sHvrB94ObtkcsaY1c1uzpqbzN4WGjiXu5VaHfSRMABAHMKnpIAAPZpKhZ5A39TTVtWZioTyVhW2rJSlp20rJRlpSxruGykLCtl2ZOWNTMN0Wgl6QgyFmQ0zLlzwUlDZsYi+/nWPChCF8ZDF8atgpX57/HkQ2OD/7ij77ot+qnB0IV1wYviWO0PMAdpSzzNty5tvKVz8hcjY//aP3ZXX/AvY3WfavO8pbr7dwHsDyWkPSC6k/vZhA0AoAoQcAAAHE4Hn4lYhKSmgo+UZSUtOymtpFX5t8s0p69O70fDCJnOO0KMBRmLchZiLMR4kNMgY1HGQ5y53SzwzljgnTHbtLN/SCYfGhtav7Pvhi3+cyLhv6kPrIsyD9rgAeYW5uHOLOHss8mRb/Zsftuf9ZMCsWtbQ++OEcw4gDmpIyi6sZEKAMwxCDgAAKqDERJiLLTfpgxH0bYrwYe0Jq3dIciktDaUjXHLmpR75CAarbR+RDkLrWLBY+vCN9W3vJQv/3w8/fHN5Gor8I5o5H0N/nMiVMEHJ4C5RV8b1H8eLO3Ij93d3/PhDYO3uGIfbYle2cQ0zA+GuWVRkP9xoFztKgAA9oCAAwBgrtMoree8nnOy3yUmBduelNa4ZU1WohA5aVnj0ho05WuWlVhOx24My+uCxzyTO+k36RMvf1WqdOcFweGLItZJgTrBwpzHGItyHuEsfBCxCwAcOa5OT/O/LK27oT3x/f6hr+wc+fqu6IeaYle3iBAWmsFc0R4QP9mQr3YVAAB7QMABADBPuCl1C96439UxedseaZcTF1vjk+XyI4mGh8ZXXrwtV69sfrv/ZxcFXlnmMm2bEKJQGmEsznmUV77GGI9xFuGsjvMIY3Nh01yAeU+Jqw03Lop/om383wdHv907cntv9AON8U+0qi1atUsDIIuCIlGw0iXL70ImDgBzBQIOAIAFxENphxAdgpB6lVypkyvby/3FyV+MxH42/Bc/SniO87neW2dcFB2LioSUY9Ias2RCWtsMY0JaI1Lmp1bBRBiLcB7nLMZ5I+f1gsc5b+S8jjMfuj8ADiuu8/g1LbEPNycfHhv5Vs9o1zOB8yINn+vAFFKorkVBQQjZlZKr43jaB4C5AgEHAMCCpjZrdZ9qq/tUW3FTbuI/hif+td+4ZUf9uZGuyxoD50dnDeko2PaolAlpOV/HLDki5XOl0mjeGpaybNuEEC+l9YI3cF7HeYPg9c4FzusF96LvA+BQUYWGLoyH/iqefnx85LaezW/7s/+scP1nO/RTgtUuDRYor0JjHtadMlfHsXIKAOYKBBwAAEAIIdoKb+MtnY23dOZfyiR+MLDrwxuoQkMX1UX/rslzrM85x01pmxBt+3jpGJfWsJQjUg5KOSLliCmfLZZGpByRlmHbhBAfYw2cNwrewHkD5w2CN3DeJHiMc2zrAnBQKPGfE/GfE8m/lBn5Vs/Wd7zoOz3U8A8d+lrEHFAFHQHRncRGKgAwhyDgAACAPXjW+FrXLG/658Wph8bG7x/e/NY/acu8kUsbIpc3isj+/kwX4SzCWdfrRqHahCSkHJbWsJRDphyQ5rApN5SNQWkmpEUI4YTEOG8SlUaPRs4bOG8WollwDU0fAHvjWePr+PdVDZtzw9/Yte0dL3pP8Dfc3Ok7PVTtumBhWRQU3SlZ7SoAAHZDwAEAAHvBfSJ8aUP40obitvzkz4fH7ukf/OedwXXR8Psa/OdFKH8DuQMlJMZ5jPNjXpd9lG17SMohUw5LOSDlkCk3lY3fy+KgKZ1db6OcNXPRIip5R7PgLULE0fEBQAghRFvubb+nq/769uFv7Nr+rpcQc8BR1h4UD24rVLsKAIDdEHAAAMD+aEs8DTcuqv9sR/bJycS9Azvf/4qIqeFL6qMfbHJ1uN/kg6uVNS97eTFKSKtfmn2m7DfNflO+UCo/kDNHpLQIUSht5JWwo1nwZs7bFNEuBNo9YGGqxBzXtQ9/EzEHHFUdAb4radqE4MkXAOYIBBwAAHBglFPfmWHfmWFzrDzx0+HEvw+O3NbrOy0YvbIp+JfxWbNID4soZ1GuHqfucdCw7UEpp1OPfilfyZf7TZm2LEJIPeftimgT3AlNOhTRzLmC1AMWBm3F62KOWzp9pyHmgCOoIyjypj2ak3Ve9NUBwJyAgAMAAN4AEVPjH2+Nf7w193x6/P8N9lyzqf/z22J/1xT5YJMSVw98/zdH2d3x4Zp5PGlZu0xzl2H2mLLHNB/KF3YZZsG2OSFNohJ5tCuiXYg2IRoFx5aGMF/NjDm2XfCSfqK/8R879bci5oAjos0vBCM7kwg4AGCuQMABAACHwvsXfu9f+Ju+vHjy5yOjd/YN3boreEE0dm2LfnIVdnMIMnacqh6n7pGwjEjZY5o9puwxzF2m+Wyp1GfKsm27KO0QYpEiFiuiQ4hORWkXaPSAecWJOer+vm3kWz1b173kOyPUeHOn9wR/teuC+UYw0uwT3SlzbdMRD7gBAA4GAg4AADh03CeiVzZFr2jM/O/k6Hf7tp7zguc4X/SDTeFLG5hW5T6JOs7rOD9xRquHJGTQlN2msd0wdxrmU8XSD41c2rI4Ic1CLFZEhyI6hehUlA4hdIbIA2qbe6Xefk9X3Sfahtbv3HLWn4PvijX+02JtiafadcG80hHk2CkWAOYOBBwAAPCmscqEjtLOQuKHAwM3bx/68s7wZQ2xjzSrzVq1i9uNE9IieIvgp88oalxa201jp2HuMMwNZeOhXGFESkJIPeeLFNE2tcKlDRM9oDa5j9EX/WR1/uXMwE3bN53wx+gVTfU3dhyFBWWwQCwKiJ0pBBwAMFcg4AAAgMPGtcjd9MXF9Z/pmPyP4dG7+ka/0xt8Zyx6ZZPvzHC1S9unCGcR7jrJtbvTI2vZO01jh2HuNM1ew3yxVO4xzaJtc0IaBW8VTuoh2hXeKkQz5wKpB8x5nuN8Sx5ek3liov8ftm845pnYVc31N7RzH94HwpvVHhT/3VOqdhUAABV4YQMAgMOM6zx6ZVP0g03px8fH7u7b9u6XPcfqsY+2hN9TR101MN9TZ3S1qq7ec6LHsJS9ptlryl2G2Wuaz5fKvVOpR5MQrYK3ChHnvI6zKOd1nEc5C7Ea+GFhQfGdGV7x9AmT/zU2cNO28R8N1n+2I/aRZiqQ0MGhWxQQfRnTsGwFy/oAYA5AwAEAAEcGJf5zIv5zIqWdhbHv9fXfsHXwlh3xa1qif9fEA7X36lPPef2eEz3s6TmmhpN9mBvLxoiUCcsybJsQolIa5ayO8yhjdZxHOa/jLMJ5PeIPqCJGQxfGA++Ijt3VN/TlnYl7+htu7gxdGK92WVCrOoJcWqQ3LTuDtffEDgDzD56JAADgyHItcjffurTx5s7EjwZHv9M7dGt39AON8U+0qi1zaDzHIaBTqcdJrtk3TVhWQsoR6XyV49IalnKDYQybctyyyrZNCHFRWsd5jLMGzmOc13Me56yO83rBY4xh2QscUczN6q5ri1zROHJb4skpnQAAIABJREFUz64PbRj9Tm/TPy/W11ZhCySodXEv9yq0O2ki4ACAuQDPRAAAcDQwL49f0xL7cHPy4bGR23rGVj8TfGes7u/bPMfPw60rw4yFGVuq7P3WCcsal9aQNMekNSzliCl3muYfiqUxKSctixBCCYlwVsd5fCr4aOSiSfBGweOc86P6o8B8JsJK0xcXxz7UPPilHVvPfSHwjmjzrUtdi9zVrgtqCSWkLSC6k7LahQAAEIKAAwAAjiaq0NCF8dCF8eyzyZFv9mw+48/6SYG669oD66LVLu3oceKPJcpeXoJLtj0i5Zi0hqQck3JYyhFpbSobgzI/JqVNCCckznmj4E2CN3LRwHmj4A2cNwmuoekDDonaqrXf0xX/aEv/P2zf+JZnI3/b2PiFRSKGbVbgYC0Kil3YSAUA5gYEHAAAUAX62qD+82DhlczId3p3XvqKtlKv+1hL6G/qF/i8QxelrUK07u3FuWzbw1IOmnJIygFTDkr5crn8iCmHpHRGfoQZaxS8kfNmIZoFbxaiRfAmbG0LB8fzFv/S3xyffGhs8JYdG459NnpFY/TDza4OdHPAgXUE+HOD5WpXAQBACAIOAACoIvdqX/s9XY03dY7e0dv791sGv9Idv6YlekUj82AdxmxqJfuY/cJtEzIm5eCM7GObYfy+UByQ0rBtRkic8xbBW2akHs1cRDlGnMJeBN8VC5wfnfjp8NhdfSN39AXOi8Q+2uI/K0yQksG+dQTFTzflq10FAAAhCDiqzrKsq6666umnn1ZV9Z577jnxxBOrXREAwNGmtmnN/7K0/nMdY9/rH/7aruGv7Yp9pDl2dbMI7WOIBcxACYlzHuf8uD2XFFiEjErZZ8o+0+w3Zb9pPlko9ctcQlqEEI3SFsFbhWgTok2IVsHbFVHHOT7GAhU0cllD5LKG/EuZ0e/27njPy2qLFr2iKXplE8cUSdibjoAYy1uZsu1T8RQCAFWGF6oqe+yxx/r7+zdu3Pjkk09ef/31Tz31VLUrAgCoDhFRGm7sqPtU6/iPh0a/3Tv6nd74x1riH2utxT1l5wI2tcnLCa49ko+ibfebss80nexju2E8XigOmKYkRKPUCTvaFNEuRKsQ7YLHMNV0ofKs8bXf09X05SUTPx4a/V7f8Ne6Q39TH7+6RVvhrXZpMLcsCgpCyK6UeUwMqTQAVBneNVYZ51xVVUqpx+OhWCYNAAse8/DYh5ujVzRN/nx46Kvdo9/pjX6kuf76dsQch4tG6WJFLN5zxKlp2wNS9pjmLkPuMs2NZeM3+cKQKSUhHkqdvKNNiEWKWCREhyI8eMFaMJQ6te66tvjHWpK/TiTuHdh4wh/1kwOxa1uD74ot8Ik5ME1XaczDdiYRcABA9eH9YpWdc845t99++7JlywYGBv7nf/6n2uUAAMwJVKHhSxtC762f/Pnw0Pru8R8Mxq5ujn+slfvxsnVECEqdtSqna7sPlm27z5S7TLPXNHtM86VS+ee53Li0CCGNgncI0TmVd3QKJYKhHvMaVZmz/1H+5Uzi3oGej2wYrHdFP9gUuaJRhPGZFkhHQHQnsZEKAFQf3ilW2aOPPloulx977LGnnnrqU5/61NNPP33Au2zcuPH2229//XG3261p2qyDQgifzzfzSCgUmnk1GAzO7Bzx+/18z25kn88n9pxp5/V6VXV3w/PM7/v6bwcAcMgqMcd76sbvGxr6avfY3f2xjzbHP97KfXjxOhpUSjsV0blnr0fasrpNc7thdhvmDtP8n0LJWd4SYMzp71ikiE4hFilKk0DmMQ95jvO1fnt54xcWJX4wOPq9vqGvdocvqY9+sMmzBq/+C1pHUHSnZLWrAABAwFFtv/71r6+44or29vb29vabb755cnJyVgDxeowxSqlt25OTkzOPO1ez2axhGLPukkqlLMuavprJZExzd8qey+XK5d2bexmGkc1mD/knmikQCDBWeX87K3+ZlZLouq4oyl7veMDLjLFAIOAcnA5oNE1zu92EEM653++fdZfpYpDIAMx9VGXRK5silzWM3zc0tL577Hv9dZ9si320GTutVIWfsWNV9dgZT+Bl2+42zW7DdIKPh/OFbsMs2rab0k5FLFUU5+tiRdRjnMd8IWJq/Wfa665rSz40lvjX/s2n/8mz2he5ojF8cT3arBamjoB4aHuh2lUAACDgqLbFixc/8cQTl1566fbt2wuFQjAYPOBdli9ffvfddx+F2qaZppnJZGYekVKm0+npq8VisVAo7PWmdDotZSXRnxWdlEqlfH73pmKFQqFYLE5fnRnKTF+enJxMJpO2bTvHpy/PfOTp3GevWc9+TDezeDwel8tFCFFV1ev1zrygKIqu6zMvTEck00em7+7kKdMJy3TmMt0U44RZ03cEgP2oxBzvbxj/8dDQV7pHvt1b94nW2NUtzI0ugSpTKV2mKMtmhNQWIc5utdsNc6thPJQr7DAzhm37GFuiiCWKWKIonUIsVZUww6+vhlFBnXUrpR35xI8Gh9d3D3x+W+D8aPTKJt/bsLPswtIR5N1J0yb4tQNAlSHgqLKrr776yiuvXLVqFaX0vvvum5tzRoUQr+8riUajVSnmEJTL5VwuRwixbTuZTDoHp9tYpnMWy7JSqZRz63QuM93eks/nS6XSzAvTiUxvb69zYfpkJ3mZDoZmZTf7sq9kxOl2cbpOnKjFSVUopU4i5kQzTm7inOw8lBOpuFwuj8eDGAXmB+pi0SubwhfXJ340OPKNXSPf6a37eGvsmham4XPyHMIIaRG8RfCz3JUjkpBdhulEHttN88eZXK9pSkIijC1RlaWKWKEoK1SlUwgxJ18HYf9cnZ6mLy5uvKUz++Rk4t6B7Re+rLa7ox9ojPxtg4ipB74/1L6OoMib9mhO1nnRqAUA1YSAo8o0TXvkkUec+ODf/u3f3v72t1e7onlIVdXp5TDhcLiKlTi5yXSSMp287D8ZmW6KcZpTnH6W7u5uwzCctMXJaJw2llkdNLM4yYizqMfJR/b11Tln5lcnW9F1XVXVYDDo5CavH9ECcBQwL49f0xL5QOPYXX0j3+oZu6e/8ebO8MV1hOGz8RzFCZk1zqNs2ztNc5thbjOMLWXz0XxxTEqV0iWKWKkoK1RlhaosUxQ38o7aQTn1nRn2nRk2BksTPx0e+7f+wS/vDF4QDb+vwX9ehHL8KuezVj/njHQnEXAAQJXhk0mVjY2NnXTSSY899li1C4GjYXoaSCQSOaLfaDpDmZmJOPGKs3LHiVScfOT1X/v6+pzwZdbXWWNfHE784XSL+P1+VVX9fr/TQhIKhZwcJBAIuFwuXdd1XXe5XIFAYNYJs4bdAhwMrvP6G9pjH20e/XZv76c2j97V17x+iX7qgRf6wVygUrpcUZYrCiGVNo+0ZW0zzA1lY4NR/nE2122YFiExzrtUpUtRulRltapir5aaoDS66q5rq/tUa+Z/JxP3Dux8/ysiroYvro99qFltnT0NHeYHhdFmn9iZMk9uQs8OAFQTAo4q6+npaW5urnYVMN8wxpy2oAPOrH2jnLAjm82Wy+VkMulkIplMplwup1IpZxpLOp0ulUqZTCaXy42Pj6dSKacVxTk/l8vNnMwyzYlIgsGg2+12u92hUMg5MvOC2+3Wdd3n8wWDQScu8Xq9oVBo1pBaWFC4TzT8w6LolU1D67u3rXvR///Zu/MoSY76XvS/iMiMzKy9a+uuXqZ7FqGRZkYbqyQEEpKQwcggsPEFhO1rwwNbgJ+B847t64fBNsbA5T2DJTDGxsYYIfvJCAO2jISQkdHCJonRLJJGM9PT03vta64R8f6Iqpqanp69e3qLz+lTJ6sqKyu7uru68pu/+MVrU8OffJGxxTr9I5VVJobxiw36YoMChAGgKcSznr/f9/d7/vcd54v1RiDEACE7qL6T6rsovYzqUdXCYzXDnYKOOa/4T9PFr0zPf24i9tpU8lcH4q9LqybB68+WBBlXM8UqirLSVMCxwiYmJp544ombbrrp6NGjd955580337zSe6QopyLH+5x/biKH4TSbTcdxqtXqogsyK2k2m4VCoVarNRqNVqvVaDQqlYpMWHo3KCtE4vF4LBaT2Uc0Gu3r64vFYvJGeRmLxRKJRCKRkLf0TuWjrGl6ztj0ue3p3xya+oMD+178eOqdg4N/vFVLqdhrDQsj1Mk7AAB8IV7wg32+v9fzH7Kdu2oNJsSopu2i+k5KLzP07bpuqkKwVUnvpwMfGhv44Fj9kXLxn6Yn3ves4CL+unTyl/tjN6eQoVKqdWJzXDtUVQGHoigrTAUcK2zz5s2f/OQn3/CGN/z85z+/+eabZ2dn8enORx2uBF/e3eQAGoIwXbhynC78eBehuDvuNWq0R6gjBLHOmhpGYb29jBFEe7aJEUR6NkgQipywfUU5B7Jz7fkEJZ7nybCjXq83Go1Go1Gr1WQy0mg06vV6pVKp1WqTk5Py9lqtVqvVFoyyMU1TJh3xeDzR0V2WCwsuz/tbV5ZR6IroRfdfVX+4dPT/en7fFY/3/95o9o4Rdfi0PugIycYcbwkDAARCjAdsr+c96Xn/0mx+shIggDFd20H1HTrdQfXLqK6rvGNVQRB9dV/01X3C5bWHSuX75g7/1l5EUPwX03239cduSqITPtIoa8vmhPb9I+5K74WiKBudCjhW2JVXXnnllVcCwOWXXx6NRkul0mlnJ/E4VF0hQABAyeFNXwT8uBXcQDhM9N5S9wTvTK1ac9sLXIi6d9xqZ8sgyNQQACAE0U7wEaVYhigmQYaGAID0pCQ6RlYnTOkNaDAc2wJCEOvcHjUQAgQAYR1pGADA0hAlCAAoQZYmF0DuRveplY2AUppMJs+ha2w37JAL1Wq1Wq1WKpVKpSIXZmZmusvVanXBHMl9fX2JRKJ72bXoVdWBdUVEb0he8tjLi1+dnv6TQ4V/nB784619t2VXeqeUJaYhtE3XtunaG8MhAKhzvsfzd3v+M5735XpjjrEQQpdS/XJKrzTolQZV89GuHsjA8den469P88/x+sOl8n1zh39jD6Io/rp03239sdemkKb+l69Jm+Pa0Xrgc6GrT2OKoqwc9eF7hf3RH/2Rpmkf/ehHn332WSHEmcy9enFS+9zNS9xFL+DQ9I/FJDVP5ifdq914ZOFVOxAeAwDwubD99q1Vt72pZtAOXzwm7KB9r9+zPN/iTX+RwKW7P0JA98aay88kj9ExCuntTIRg0BAK025EgnAni6GddCaiI4JRN2ExNGQSBAARijBClICMUWIGRgCmhgyCuoFOWMea+sy81sh6jTNfnzEmw45yudwNPsrlsrxaLpenpqZ6r/aOnZHDZFKpVCaTSZ0gk8mk0+lUKhUOh5fhG93QkI7SvznU9+b+2f9nfPxdewtfmhz6xEWhy6MrvV/KcolifLVpXG0a8uocY3s8/xnPe9r17240bSHGNO1Kg15l0Csp3aKrA+hVAVvtpIN9Jqh+J1++b/7QO3aTqJZ4Uzb5toHIKxKgfk5rypYEYRyO1tiWhDq+UBRlxag3oBX24Q9/+F3vetdVV10FAHffffdK7YaGId5TxR03Fty/ujqBNTzBhAAAOxA+AwCoe5wL8Dm0fA4ALV/4HLgQDU8AQCsQPhNCBjc9FS4NT3AhyjY0/IW3MyG4APnwhs8ZP9m+AABoGMI6RgBR41ghicxTTIIMrR2myIoVBO3BQVEDY4CQjjSMDA1MguRYoe4ooShFCKkMZeURQs6qVKTZbHbDjnK5XCqVisVisVjM5/NTU1O7d+8udvi+Lx9immY38shms+mO/v5+GYJIpx2/pixAEtrQn2xL//rg9EcPPnvdT/remBn684voiJrEYf3rJ6TfIjdaJgAwgMN+8KTr/cxzv1irTwYsjNBlBr2K0h1Uf4lBVafSFUdiWvLtueTbc0HRr9w3X/7XuedveZIOG323ZWO3pCOviKvRK2tCNkxCOjpUCVTAoSjKCkJCnNcghY3gi1/84nvf+956vR6JRJbvWe67774bbrghkVATHK5SPhctX0CnvMUJhMsEE9DwOADIgUIuE24gPC5sXwQCmr4QAmoeB4C6KwQIuZodCI8Jn4lWcCxG6Za9LCpmYB1DSEdyhE6MYp1AWEdm71UNWTqiGEUNTHtXPv6xF+TVUs5IrVbL5/PFYrFQKMjIo1Qq5fP5ubm5Qg/5Lo0QSveQ2Ucmk+nv7x8YGJDJyHJPP7ym1f+7PPUHB5wDrf7fG+3/3VFsqeOlDWqWsZ+53tOu96TnPef5AHAp1a806BWUXmHQHFldgf6G5c+45fvmK/823/xRFRk4ekMydlMyflOKjqkJkla1W+8t/NJF1rsvV2WJirLOeZ5nGMajjz56zTXXrPS+LKQCjtNb8oDDtu1KpZLL5eTVj370o1//+tcPHDjwve997zWvec2SPIWyRjV9EXDhBMJj4DLhBCIQ0PS4HApU97jPO1c51F3uc2j6PfcyaPjc6VxddEjPgrwjbmA5DCesY0ogQrGlIYohbmCdIEtDEYooRhGKwjoK6SisY9Vo9kLinHeTjgXZx+zs7Pz8fD6fz+fz8p1c13WZeuRyObkwMDCQzWYzmczg4KBc3tBlIFwU756d/thBbOLhT78o/gunHxKorG8tIXa73lOe95TrPe35dc7TBO+idBfV5WV8I/+9rA68yZo/rlb/o1D597w34RibLTn1bOzmFImoNGrV+cCDlShFH3+1asitKOvcag44VAnZCvjCF74ghPjQhz4EAPv27bv//vuHhoY455/97GdVwLHBhXUEgE4YInSOGD8u7/D4IllJKxAeg5rHPSbKjjhaYy4TTV/YvvC4qLmit39KV5SisI5l5BEzcFhH7S+KYxSFdBTRcUhHUYqiFIepvBdHVTJy9jDG2Ww2mz1Vj0zGmIw55ubm5ubm8vn8/Pz87Ozs888//8Mf/lDmII7jAICmaf39/YODg7lcTkYeQ0ND3cv+/v51Hn9glLo913dbdu6zRw69/ZnYjcmR/30xHVUjVjauEEKvMI1XdDp3HA3Yk6671/d/6LhfrDVcITKE7KD6Dl1/sUGvNKiahvbCw2EiE43hT7/IPWzXHy5V7y8cefdeAAhfnYjdkIzekAxdEVXdOlaJLQny4xnv9OspiqIsGxVwXGive93rfvrTn/7+7/++vPrAAw8AwEc+8pE/+7M/++lPf7qiu6asNwRD3MCduOS8znQ1PNEKRNPjTV/UPN7yRdMXLV/UXN7oLJccPl5lTV80fd7yRd0TJ1aRRCkK6TjSSUYiOgpTFNFxhKKIvIW2i0QiOopSHDVQVMdkXR90nz9CyMDAwMDAwK5du062TqVSmZ6enp2dnZqa6l7u3bt3ZmZmenq61WrJ7XTjj5GREXk5ODg4NDQ0NDS0bqbIxWGS+8MtiV/KTvzus/te+kT//7lp4ENjaipZBQBGNDKihd4IAACeEM/6/jOe/4zn32/bX6jVEcBFun6ZoV9G6S6qb9V1VT9wgRmbLWPzUPo3h3iL1f+7UnuwWPjK9NRHXqAjZuzmVOymVOzGJA6rH8tKGotr9+xvrfReKIqyoamA40K7//7777rrLnk2FQB+8IMfZDKZ66+//uMf/zjGOAiC084rWfDmf1B8QMAp+15eECEShrV/0iRMlrG1yjkIkQs0chUjbOHQ2T2GACaQMOHUrWIwIiZpD5O2A+QEYHvQ8FHTQ3YALR/sADVdaAWo6aFmAAUHNXzUcMEOUMtrr9C7QUODsAYhiqJURCiKGSJKUYRC1IAYBbkQpShKRdRAEQp6z7EqBtzdmbOy2n4xACAQgcudM1jN9/gJJ9BCkNmWymxL7YIdANBijd7oqdlszs/PF4vF9mVprlCaO3B4X/7H+VKpFAQBAJimKeeCSWfSyVxfX19fOp1OJBLyEnXObHvcDUSw6I7ZvMXFIm9c7OTfV4s1j5vSCQAAuOAOt09c2WE2A3b8msxhC9d0uMNEAATQX6HR/9h0xWdbz39192Mffnz65VOnfWqJYqojuuhdkkXC3d9ggjQTHysSodjUcPtNHgO2yLG/wd6/fQuHMGr/HhvYJKj9EBNbBJEF6/f+LROkGcRcsDJBxOjsg4UtjNQR4OlRhC6j9DLa/kHXOd/bmYb2r6r1OcYiGMk5aK+i9HKDhlRxxwWEQyR+Syp+SwoA3MN27XvF2veK4+/eC0xEr0/GfzEdf31G7z/VH6myTLYktHyL1z2hajYVRVkpKuBYYU899RRC6Oqrr963b5/jOM8///yll1566oeU/eIz9Z8tepywtvjc58cfjZxIgFgNbWIECP/EI0YAgS707vnC733+5Xh2ARAc9yzLwwAwAMIAAAggCnD8BJ5ICMIFEQILQcSxBY1zUhGkLAgABw+ES+SavYeiCHGEOEIMIYYQx3IBM4QYRgyjAKEAAUeILXlAh3q3iOAkB8gLV+YgTrPqhREDiAFsAwCwAIYhPgzHV20EIBgI0SzxRlFMHOBCOAImASYBY8w8TjAhGiEEE40QjWCCZfBBsYEAECyskiCYEKRhwOiEg0MDmRjh7kG+hABRbBBEFmxKQxrFdEEmZRILAe5NIjREKDYxwjrS2zf9T9B+JUQ/w275vVvEa5D4hC5yiGLj2ApnoMkaJ97ocLv7Ls2B2+zYKU2fe75ov59wwW1+7K4Wa3YXevMalzuMt2Mjm9tcsAXrL3iKM0EQMXE7++tmJSaxCBAAoNjQsA4AGtINbEBPVtiNbDSkU2wAgI51igwAoJhqSO/dTkiLAICGNJmwGNgkiCxIdtaEKMa9g1nmGXva8590vUds969rDSHEi6j+Ypl3GHRAdSq9gIzNVubdw5l3D3OHNx+vVP+jMPOJwxPvf9bcHu57czb+ukzoSjU/9IWzOaEBwHg12JU5i3dRRVGUJaQCjhX2gQ98AAA++MEP3nTTTfv37z9tugEAHneLXn75d23JaFinpzzbeQoI0IlHPhdA70f/1UAeJCwfA5saWsp3gxPrJgIRnM8xPEaaiduHFkywEwO+gIPHwQ/AY+By8ANwOXgBuAxcBq5r2gG2A3CY8Nmx3ygdC1MDQwOTCFMHg4CpCVMDUxMhjUR0aulgaQIgYOI0YZxECUUCA4AA4XH3FGsihOQxIQAEwj/F9jWsyVPuAsSJ9QjHVkPd1bjDFi+IIJ3IgAnmnFA0gQF3qwOYYKeoFukelvu2X5qqVKar5elaZbpenqqUJ6vl6UptrsEZRwgi6XB8KJbIReNDsXguFh+KWoM0PhzVTQ0AhBDdNM3j3qLJmi/8U7+SS+C3YOi6kev+97Wx6+NP/R9P7f+V/QILhLB1kjeBU78/IHSao3cE6NSFWgRpBjZOto7eiRVO8kCTAws6aUggfPlXRxHtxiUed+XfIgOGAGHAHHGftV98lzsIIR3pHnc5cADwuc8gAIAWa8kFn/uBXGCuAI4AudyVv8MOtwEEAOqGL6egI0oxBQCTWBiwhnUDGYDar498X+q+mciqFpmqdItW5JoWCWHABjYJ1uTrI4tWli9JyRLyWou81jIBIBDiOT/4mes95Xl/Vq6WOZedO2TecRnVdVXccUFgE3e7dTj7m+X75qr3F2b+/DAdsxKvS8dfn468sg/p6mexvKIUpS18uKICDkVRVowKOFbYLbfc8r73ve+DH/xguVx+yUteciYP2RG94q92/tNy75iirFcBh4rLKw4vO7zq8rLDK64oO7xk83KdTzq87PCyyyvOsQYiIR31mThl4T7z2FcmhJOd5ZSF4xu2icOORW4LgmB6enpiYmJ8fHxiYuLIkSMTExPjj00cPvy4bdsAkM1mN2/ePDY21ns5OjpqGGfaYtflzoljYZgI3BPSkID7rlh4o8edbgoAALAFxGu5/dX6yz/+ipc9cLX4c028GAOALzyfn7SgyeH2KZKpUwy9ae/Y6VZwuXOy7S8o/eglhLBPEi60eEssVv1nc/vE289wSNR5kj/ERlCXVwlghHBncJDgIDSkCQEAQoDgwAEQAgAhOAghOEcChODAT13VqCOKADSsIUQoogiQhnUEYJEQAqQjigBRTDWs69jAAiMAg5gUGQQRWZNCiRHCIQAwtBAAaIDjehIADGQihLPYekso8paQzkEfD/g+nzzl+f9fs/WZai2M0GUGfalBX2YYu6hOVdhxQZiXhHOXbMn94RZv3K78R6F6fyH/t5MkqsVuScVfn1EzsCyrLQntcHXxgYqKoigXgAo4VtiOHTtuvPHGG264IZlM3nnnnSu9O4qy/mkY0hZOW6fJIwSADEHkV8XhZUcUHSbnmvn5vF+2ecFmdU90NyvzjqSF0xaRC0kTZ0I4beFMmGRD2CAb5dhG07RNmzZt2rTpla985YK7Zmdnx8fHDx8+fPjw4fHx8Z/+9Kf33nvvxMSE53kY41wut7lj69atF1100bZt2zKZzIlPYWBziaYb6vE+8H/Fm/qjA6Xb5pK/2j/8Fy/SUuok5EKLdiexWWuxG5v8+Bt97i0Y7nfi4BoOfEGlki+8BW1lnJ5xOgDgcVcO35OXHnc87jPBBHABLOCBwx0hZEQiAu4FInCZAyBs1vKFL4ALAUJwgYALDoIDgEByEOC5150NAQwC4cjwcOhhFH0QLI4NXdgUPJM3LLB1pMmKKoI0HVMC7WUTmwRrCIBi08AGRoQADpFIT/uVdk3fouOMDGzIUKa3HVIIhxDC0Bkl1Lva+kbHrOzvjGR/ZyQoeNX7i9X/yB95zz4QInpjqu+2bPz1aRJVn4SX2OaEdqhyRjWPiqIoywGthgYHq9wXv/jF9773vfV6PRJZdU0HFUVZWT4XJZuXHV60edHmZYeXHF6yecFuJyOFFq+47XPLMQNnQzhl4YEwSVm4P0zSFu4Pk0wIp0M4sWFrQAA451NTUzLy6GYfBw8enJycBIB4PL6tQ0YeF1100amnzj1P9R+Uj/7ec0GMg8EyAAAgAElEQVTJH/y/t0SuSejDhjoKUgDAZi1PeHLMlMfdJqszwRzuAoAdNF1uM8Q95gJAg9U451wwT3gceCtoBkjkg6AkcJX5LcCEOxbyDdGyhI/BFYIFEHDOObCTle1ghBEgBBgBQgiQQAgR2XEcAQACxjkAIIR87p95J/Ju09zueNL2WCEAkB1zkdz8saFVchRV5+HHetb0NsSV44a6y92WQyESlssGaa9soG7DF00Ov+o2bVkO3Ob175cq35qvfCcvXB67OdX35v7469Nq+pWl8qWnm99+wf7WL6dXekcURVlGnucZhvHoo49ec801K70vC6mA4/RUwKEoyvlwmSi0+HyLFVp8tsmLNptv8XxLXvKCzRgHADAIyoRwf5hkQ7g/TPrDOBMiuTBJh3AuQsIbcui4bdsv9Dhw4MALL7wwOTkphIjFYtt6bN++ffv27X19fUv11MLjc5+bmP30OG8yACARog+bdNjUBw06bOiDJh006IipDxkkprIP5ey0hPi56z3muk+63m7P1wAupfpVBr3GMK4yqIGQrEmRY5Rs1uLAW6wpBG+xpqxwCYTvctcXns89OVSqxZpyVJFsSdtiTQbMYbbX09S2lwwjdKRTQjEQHes60hFgDWkEa0gggoiOqY51xrmONYI1IogPvoY0BCgQQbc5Tm9DXIcdK645/4a4J0tVevtGhUhYjlsi0FOx0nlUN3/Bx1W7hBBgYGD9yCD3cvSAQL5A11FyKzXeEKVRA3qa3XRjlw1S83L+vjfu/N73KrvfNbAR/2kpyoahAo61TQUciqIsHwFQaPGizeaavGDzmQbLt9hsk8vLbvwR0lAu0g47MhYeiJBsiAyE8WCUZCxCNlLxh+M4L5xgYmJCCJHNZi+99NKLL754+/btl1xyyfbt2zdt2nSejYr9Oc+fdv1p1ztqe+0Fx592vWlXuBwAcJjQYZMOGfqggUxMYho2MLYIjhCkIdKnIw2RCMEWQQYmcQ1piMQ0ZGB8unFSykZQ5/xnrvdj1/ux6z7r+TpCLzHotabxStPcpi9ZdmazFhOBzW2XOz73ZCmKz70Wa8oRQDZr+cJzueMw2xeew50WazqsZTPb5q0FjX5NbFkkZBIrhMMmsUIkHCLhMImESDhEIp0FeUskRMILpkPq6g53cpgtO9p2e+t0RzP1BiK+8LtDnHo64IhueuJz3xMuyE40nSY13fwl4O2OxQKg1Zn8qMWaxMODPxoeeWho8yNbCMNHXzL5wg0HDl5/0LcW77/TLU6RMUp3oJCOqY4pdBridvvjygmbMWCThKAnc+ltptud4FnOBmVggyBtWStZls/BSvDae/KPvTPbr4piFGX9UgHH2qYCDkVRVgoXULD5fJPNtdhck883j2Ufc01WdjgAEATpEMmFcX+YDERILkIGwjgXIf1hMhDGOt4QZ9E8zztw4MC+ffsOHTq0d+/effv27d+/v9VqUUq3bdu2Y8eOLVu2XHrppTt27LjkkktCoaWZWcOf9/xp159yvalO5GEz1mDCF6zqC0/wFuMtxl3Oagz4Iv9tSVRDBiZRgkMEUUQSOjYwtjCOaZhiHCXYItjAJKFhi+AQJjENhwkOERwmJKZhC+OQOoRYP+qc/9T1HnXcRx13PAgGCLnWNF5pGlebRhyvZBzGBbO53WJNmzVl5CEXHN5qsWaLtWzWbLJGizVbrCEXegfaWCR0igQkrEWOLfe0GlmBb9Ph9e+XCt+Yqn+nBAyMV0VCb4xat8Zdqz1JUIs1QQjRiVS6XY27XWAc1pItb2W84nKXieDYtFOd1EbOLS3zGtET0JyMTD10TLujgbrzN8MJMzf1TphtYqubLvVOwISOn2DotOOJoGc2t97JibozQ/c+r8/Fjr+d/cdfTL1i6Bxn0FMUZfVTAcfapgIORVFWJ5eJmQaba/LpBptrthdmm2yuyfKt9gh8OexlMEKGo2Q4SoZjZDiqDUfX/5gXxtjhw4f379+/f//+5557bv/+/c8++2y5XCaEbN26ddeuXTt37tyxY8euXbu2bdumacs+zEQwweuMO4zbnDeY8DirBdzh3OGsGgiP8wZjTSY8zqoBd7iwGasF3BW8GfAm5zZj9YDXmWAL/2uThIZDBJuYxDUc1kgY47BGEhqJEBwhOKyRKJHJCIkSeRcOExImqunAajYZsMcc93HXfdRxm5xfQvWrTeMaw3ipQbW1MBWLy50mazRZw2bNZtBoHZ+ANHuikFbQ7B1EY2CzN/tI6um4lujTUwk9mdCTfXrq1LMsLwneYrUHiuV/nat+t4gwxN+QSb49F7u+D5YzL5Y1KQH3PeEJwVu8BTIxEbw7W3bvvE5ymFJ7h4E7PQN/mp36FDh+MqbjpkYSxwUri44nWrCpM6Qh/fGn/njL0H9tGnhCxiUYEev4IhdZ1QKdMpZuXmNiCyPSLV2R9+qYUkR7JpA2NKTLqaBPO+u2oijLRAUca5sKOBRFWXN8LuaafLbJZhpstsmn62yyHhyts8kaswMBAAkTtyOPaDvykPGHpa2BY6dzNjc3t2/fvr179+7Zs2fPnj179+6tVCqGYVxyySU7duzY2TE6OnqeA1uWj3A5tzmr+NzmvMVYLWANxm3GG4zVmawZYdWA1QPelDcGrBrwJmMNJvuJHIOAxDUS10hUI1GNxAiOaiTWuUVejWokRkhcJ3GNRAmJa4iqkTUXFAN41vMfd9zHXPfHjmsg9DLTuME0X2kag9o6iag87nXzjuNykKBR9ovVoFzyCrWgIkevUEz79HRc60vSdFxLJPV0N/tI6Mlub44lwZus+p+F0r/M1h4oalmaelsu+fYB80Ub8Yi6t9KECdYNSrpjhYQ4Npjojx+O5aLuO68sdidF8oQro5l2kQu0Z7PuNnORI4m4YHL9dtzTuXfRyZt6ySIUig0NaTIu6Ra2yBFDsphFBisyRjGxSZAm5xWSA4UMbBKsydstbCGEe8thFEXppQKOtU0FHIqirCdFm0/W2WSdTdWDzgI7WmMuEwCQsvBIlIzEtJEYGYmRTVFtJEYGImS9TnF79OjRvXv3PvPMMzL12Ldvn23b0Wj00ksv3bVr16WXXrp169axsbGxsbFYLLbSO7sEWCVgjU72UQtYPWA1xuoBrwWszlgtYJWA1QNeD1idsXogb+ndArZwO++QXzGNJDQS17SE3l1uLyR0LaGhDTw90JIrc/6Y4z7quD903DxjW3Xt1ab5Gsu8wqDrJOo4pWpQrvqVsl+s+KWKXyr7xUo7+yjXgioAIEBxvS9FM0k9ndTTKZpJ6pkkTSf1dEyLn89Ts0pQ/sZc6e6ZxhNVc3s49fZc6tdyWloNwVjcxx+rjVeDL70uubSb7c4YLaMWmYnIgT+ypW73Lpu3uOAedwPhy7oVGcowETjcYSJwmSMfIiOYU/S+lUUiMgSRtSQUGxrWZVYi7zKxRbGhIT1EQgRpJrbkmhYJEUQsEpatamXUsrSviaKsFBVwrG0q4FAUZSPIt/hkPZiss6M1drTGJurBZI3NNBgToGEYimqbYmQkSjbFtOEYkcuxdXfsKge27N69W+Yd+/fvP3ToULPZBIBkMjnWMTo6unnzZrkcjUZXeq+XHasHvMZYLWDVIKgGrOq3oxB5Wem5pRrIoTfdx+IQIQlNS+gkoZGkriU0ktBJQtP69G4IQvo0LalrSX1ZhwCsM8/5/g8d92Hbedr1Yhi/yjJfY5mvNI3Qai0+WlaB8Ct+qeQXCl6+5BdKXqHkF4refMkvyANXHdEUTSf1dB9Np/VskqbTNJum/Uk9fVZdP5xnm8W7Z0pfmwkqQew1yeTbc4k3ZNB6H/F3tr62t/Xl3c2H3pZZ6R05O7JbioxCAu67wvW4G3BfZiI2bwkhmqwhBwTJ3MTjbiAC2XTGYS05mMjmNhPBoomJTENkVmJhi2JTx3qIhGXr2RAJ65jqiIZJRMfUwIaFQyaxKDbk0C01lY+yeqiAY21TAYeiKBtWwGGqHkzU2NF28NFerrkcABIGHouTzQltS0LbnNA2x8nmhGasu2KPfD4/Pj4+Pj5+5MgRuXD48OHx8fFWqwUAqVRKJh2bNm1KJpN9fX2Jju7yUrU1XStkYxFW8Vk5CMo+qwRBxWflgFX8oNK5veKzSiDH2nQfqPXpJKlrKV1L6iSpa32altRJSteSupaiMgQhKR2b6y1ZOx9Vzh933Icd5/u2Y3NxuUFvsazXhswBshGqOk7PZq1u2CGDj4KXL/n5kl/kgmGE+/SUDDvSNJuh/WmaTenZPj11qpPtXNR/UC7ePVP5tzw2cOK2bPJtA5GrExfw21rVHp/yfv3fi3vfNbBBulyfjOyTIitNWqwpi01avOlzOXVRU84G7XDb577DbYfZvvAd1nK56wnXYbYsRendJkb4WOqBjJAWocgwiWliK0TCJrYMYprY6l41iWVg0yIhVT+iLC0VcKxtKuBQFEVZoOryozU2UWPj1eBQJThcCQ5XWdXlGMFghGxOaJvj2ta+duSRi6y7zAMAAObn58c7jhw5cuTIkUqlUi6XK5VKpVKR8YdkGMaCyKOvr0+mIcmO7rJprr2JIc8Hdzgr+0HJD0o+K/lB0Q+KneWyHxR9VvKDkh+Ug+5MNDhM9CzVMlRL6VpK19JUy3aWU1TP6FqGbsAWqgzgadf7rm0/2HJmGduqazdY5vWmeZVB1+Uf4HnigpX8QsGbL3jzeW+u6M3nvbmCN1/xSwIEQSSlZ9I0m6LZjDHQT3PysnfyEZBDV+6dK359pvmjqrUrkrp9MPnWfi2z0YeuzDbZtV+df/B/ZLYklr1/87onB+a0WNPljssdlzk2b7nc9bjTYq32je15nW2H2zIoabFmz1TKbRQbJjYtErawFdIiFg7J4MMkllyQ0xv1zgC9FicqVi4MFXCsbSrgUBRFORMlmx+sBIfbkQc7VAkmakHAwdKQrO/YktAuSrazj/VX6LGA53ndsGPRhVJHuVyu1WrdB1qWtSDySCaTqVQqk8mkUqlUKpVOp+UC2WCn6I9LQIp+kPf8vBcUfVb0gqLvz3tBweet9gd6bGEtRbWMrvcbWlrX+w0tS7W0rvdTLUu1NNXT63ZEjADY5/kP2873HWe/5w8QcoNlvsYyX25QfUMOYDkrgQiK3nzh2NdcwZuf82YaQQ0AolosS3NZY6B9aeSyNBfVYs6BVunrM6W7Z/15L3FrJv2uoegr+2CjvtgC4LK/m/3LmxI3jqrD45UUCN9hjpzX2eGOyx2btWzWarGmzVty2eEtm9lypmeb23L0TXcLGBGZevRM7Ry25FUc7r0rRMJhLaKjjZ7ubRwq4FjbVMChKIpybhiHo/XgUIUdqgSHq8HBcvBCOSg7HCMYjpKtfdq2Pm1rQtvWp29NrMOOHmcuCAKZdHQjj974o1QqFYvFQqGQz+d7o5BkMtkNO+RCOp3OZrOZTEYu9Pf3b7T/XLzFZPYRFPyg6AcFz591/bwfFDx/xg0KflDwhC8AABGkpXUtTY9FHjlD76f6oKH3Uz1nkNh6OPk8y9jDtvN92/mx65kI3WiZvxAyrzYMlXScLZu15ryZvDs7583MuzPz7uy8N1PxSwBgkVA779Bz0UMR4z6N/guOZ5Pp3xpKvSNH4uvhF+ls3Xpv4Zcust59+UaccWat87jn8HYOcsJXo73A27fYrCk7v0oU0zCJymmeI1okRCJh+aVFw53lkBaJkKia33etUwHH2qYCDkVRlCVUdviBcnCoHLxQCV4oB4cqwVSdAUA2hLd2844+8qKknrY2buRxMr7vFwqFYkc+n5dXuzcWCoW5ublGoyHXtywrnU739/d3g4+BgYFuAiKXN9qgmKDgBQXfz3vBrOcXvCDv+bNekPf8Oc+fcYO8JwIBANjCes7Q+w09Z+gDVM8Z+oCh56g+YOgDBllrtfeOEI877n/a9oMtR0PoBsv4Bcu61jSoSjrOg8fdeW923p3pXs650yWvIECE/EjfoXj8YCIXHh676kVbX7o9QwfOqqHpmvaBBytRij7+6vOavEZZEwLhtyd4DppNVm925nhuskaT1Zus2WT17l3dUTMYkQiJRrSovAyTaFSLRbRY92pEi0ZILKJF0YYthVrdVMCxtqmAQ1EUZVm1fHGw0q7vOFgJXii3x7akLLw9pW9Pahen9O0pbdsGGNiyVBzHyefz8/Pzc3NzsvRjbm4u3zE7O1soFLpdQhKJRC6Xy2Qyg4OD/f39/f39uVwum83mcjmZjGjaGjuYPy9c+PPtsMOf9fwZN5jzvBknmPO8aTfItwtAsIX1IZPmDH3YoEOmnjPosKkPGvqgoWfpav5ArpKO5eZxd9adnnWnZp3JiYnDM+WjhXjet3wicIYODFjDA+bQgDE4YAwNGsMRbT3MP32i//cn9Z/MeHf/Umqld0RZXRxuy+yjHlQbrN4Iag1Wbwb1Bqs3gnojqNVZrRHUXe50HxLVYlEtHiGxqBaL64koiUe0aFSLx7REVIvJe1UIcuGpgGNtUwGHoijKBcY4HKoEB8rBgbK/J+8fKAeTNYYRDEbJRX3azox+UZ9+UZ+2tU9bp10ULoRmszk3Nzc7Ozs/Pz89PS3TkJmZmfn5+ZmZmbm5Odu2AQAhlMlkZN6Ry+WGhobk5cDAwPDw8MDAAKUbadC1AH/eC+Y9b9Lxp11/1vUmHH/G9aZdf9JhDQYAiGI9R+mgqQ8Zes6gIyYdMuiwqY+YenYVvVYnJh03mOb1lmmqpGOpuYftQ/fsO/jEs+VssXWDW9tRz9O5ojcvQMS0+JA5mjOHh81Ng+bIkDkaJuvh0+Y3n7c/+UTt8V/rX+kdUdakQPiNburBajW/WmfVRlCvBZV6UK0HtXpQqwdVAQIAECAZeURILK4n4loiqsXjel9MS8S0eEJPxrS4ml53yamAY21TAYeiKMqKqzh8fzF4ruQ/Vwz2F/0XyoEdiChFFyf17SltR0bfmdZflNQ1Nahl6dRqtenp6Xw+PzMzMzs7Ozc3NzU1NTMzIy9LpZJcTTb7GBkZ6e/vl5HH8PBwLpeTy2gjHS2zeuBPud6U68+43lHHn3H9adebdLxJh1UCAMAm1odNOmLSEZMOm3RTZ3nIQCvXg6YpxMO2858t+4eOSzs1HVebhko6lpbweOXfC4UvT9X/qxS6Ipr4razzxmAWpqaco9Pu0Sn7SN6bEyDiWt+QOTJobhoyNw2aI4PmyFqMPH4+77/5G4Wf/+ZARM3hoywPAaIR1OtBtcFqtaBa8yt1VqsHtYpfqgfVWlCt+KVuJUiIhONaX1SLJ44PPhJ6Kq4nYlpC1YCcLRVwrG3LGnB4nnf77bc///zzjLEvf/nLL33pS5f8KRRFUdYfJmCiGjxbCp4t+s8Wg70Ff6bBKEHbU9rOtL4zo+/KqLxjeTmOM93RTT2mpqZmZ2cnJyfr9ToAUEoHBweHh4c3bdo0NDTUu7Dhso8G84867hHbn3S9Scc76ngTjjfp+DOubPmhDxh0xKDDJh216JhpjFrGZouOmBcy+GgK8V+dpIMDXGXQaw3jWtPYTvUN9KNafva+RuFvp0r3zCIDZ949nHn3kJxZ1uPejHt0yjk67UxMORNTzlFZ5dGnpwbNkUFjJGcODRhDOXM4rvWt9DdxGnVPXPHl2W++Jb0ro86cKyvG4241qFT9ci2oVoNyza/Ugko1KNeCatUvV4Oy7JCKEYlriYSejGt9SZqKaYk+PRXXEn16Oq4n1BCYRamAY21b1oDjq1/96hNPPHHXXXd95zvf+exnP/vggw8u+VMoiqJsBDWXP18O9uT9PXn/mbx/sBwQDGNxbVdG35lpRx6qhccF02g0JiYmJicnp6amFixUq1U4PvsYHh4eHR0dHR0dGxsbHR3dUPWSIhD+jOtNOt6E4x11vEnHG3fccdubcITHASM9R40xyxizqIw8xkxjzNIHjGX9vO0I8ZTrPea6jzvuPs9PYPxy07jaMF5lGQMbbHLi5cObrPi1mfk7J/xpN/m2XPZ9I+bFC+eVcLkz40xOORNT7tEZ5+iMO1Xw5rjgIRLuNwYHzZGcMTRgDOWM4ayRW20dTF/+lbkPvCT6jh2hld4RRTmpFmtW/FIlKFf8UtUvV/xSJShV/FLFL3XjD4JITEsk9XRCTyb1dJJ2FvR0Qk+utr+7C0YFHGvbsgYc//3f/x2LxS6//PLnnnvut3/7t7///e8v+VMoiqJsQCWb7yn4z+T9vXl/T8GfqjMdo+0pbWdGvyKrX9FPtyRU/46VcWL2cfTo0SNHjoyPjzuOAwCpVGq0Y2xsbGxsbNOmTaOjo8lkcqX3/QLiwpt2vSOOe9j2jtjuuO0dtt0jjj/jAgA2Md1k0THT3BoytoWMLZaxLURHTKQt/e90kfGfuO7jrvsD251jbEQjV5vG1YZxnWWGN1INznLhovqfxfkvHK0/XIq8It7/wbH469KnSK8CEcy507Pu1Iw7NeMcld1MbdbCiGRpf84cabcvNUcGjRGLrGS48L8eqd6zr3V5Vn/rJaFbt1lhVQakrDVN1mgHH0Gp5Bcrfqnk5eVCLagAAAIU0xNJPd2nJ5N6pk9P9enJJM0k9XRST+F1nX2ogGPp1Wq1er2OMe7v78d4eas3L0APjo985CP33HPP3//931977bXL9BSKoigbWdnhz+R9Wd/x9Lw/12RRii7P0sv79Suy9IqsnlRT0q4Cs7OzR44cOXLkyMTEhIw8xsfHjxw5Ige8RKPR0dHRzZs3b9myZevWrVu2bNmyZcvmzZs31DS33OHeEdsdd7wjtjduO4ds94WWe9gWLkc6oqOWudUyuqnH1hDdZKIlKlwSAM96/qOO+6jrPul6SI5hMY1rTeNFuq7+fs5T6+n6/F0T5XvnzO3hzLuHk2/PYfNMX9SyX5x1p2bdaVnlMetOFb08ACT19KA5MtRuX7opZwxf4MjjUCW49zn73mdbDU/cOGb8j0tC1wwbKudQ1oFA+DLpKHrzZb9U8YtFv1D2i2W/WPMrAgRGuE9PpfRMmmZTNJum2ZSeSdFMimbWR8dTFXAsmT179nz6059+4IEHZmdn5S2EkIGBgVe96lV33HHHMqUDFyDg2Ldv39e+9rXnnnvu3nvvPf3anHsT08D5uT0XsiykzlquQpqGzVXUXX8pIYRD1krvhKIcZ67J9hT8PXn/Z7P+k7OeHYhsCO/M6C8ZoC/OUTWYZbUplUpHOg71cBwHITQ0NLSloxt8ZLPZld7rC4gLb9J1D7bcg7Z7qOUcbLkv2O64LVyOKDZGTRl5mBeFjBeFzIvD5z+ZiyPEj13vUcd51HEP+kEc4xcb9CUGfbFBd1C6ns9aLjN/1i18eWr+80cxxel3DWXeO6Ilz+VYKBD+nDsz7Rydlh1MnYkZZ1KACJPIoDkyam2VqceINWbiZf8H7XPxyFH3m8/b3z3kjMW1t1xs/cr2kMqUlfUqEEHZLxa9+aKXL3jzBX++6M0XvHzZLzDBEKC43peimbSeTdFMimYHjZFN1uaVrbc6ByrgWBrvf//777rrLiFELpfbtGlTKpUCgFKpNDk5OTk5CQDvete7vvSlLy358y5rwPGtb33r8ssvHx0dld/X9PT0aQtSWj/ZPf/Jv1nyPVGUjQbpOqIXNkTHy5v14LAFSzEuH2GEQ+d7ShxZJjrX8jpkGkg7u0MkRHWkn8VPs/cpmEAHmLnbCz3jW3t960hg6Ehs15ydtLVDaw1pfo74Geyf1f5sHCIIhOOt1LPPlkuH52fH52eP5OcOz82Oz8+O5+fy1QoAhE1zc3Zgc39u28DQloHctoHBrbmhdDTGWzas6Ccf7ngQBMv/LK7wGa9Tv6wHFcpKRlCmQZGyGhUckMn0pKelXD3paklPS7sk7iN8Fi8LbzmCt9cvhq3dQ/27B7O7h7JH+hJm4O+cye+anr9scn77fEFn53g+Zq0Qtt19KZYKDzTnQL+9b5Db1BjLh3ZNavHWmT8cWyac8Pbr6EEhbs8l7PlYaz7RysftSsgFgETTyNSsdNVK16x03UzVrLhNkVj8XwmmOqLaOX9feWx919x0n7WlQKxXsbk3+RMvYcWz+qeFKD3bHUC6hs54Kmuka8g4o5URxsg6/T/KM9ngaf9/YUNH+sm/a03DJ38KZNCTPXYFPgVtbFzwSlAqevm8Nyfjj6KfL3jzeW+WC56h/ZusLZuszSPW5k3W5qSeXun9PQ0VcCyBz3/+83fcccctt9zyiU984sorr1xw7969e//0T//0n//5nz/zmc988IMfXNqnXtaA4yMf+YjneX/xF3+xZ8+eN73pTS+88MKZPIo3bYC18YNblHB94aujhaUkhBAte6X3YgkIL+DeavndEC37Ar1DcsGX7ce3JIdz538QK3xfnNNPVjAubOfsHuKd3TsMb9q9r1Ad031mep+R2WeknjNSZWICgC54f9DsD5oDQbM/aOb8hryaZU1NrIfjtyVJtVYWMtqhVctzx4v5I8X8eDF/uDh/KD9/qDA3VSkJIeJWaEs6uyXdvzU7sCXdvyWd3Zrpj5pLHDuewzHYeT3d2YSJIgB/Hgez2JvF/iz255A3i4WLkAZahtN+oeeYPiBoDpOUhnVABiBLoDPOKpuY7AubP45HdodDe8MhAeIi23lpvXl5vXVFoxlZibADmwaszcaogkHjCVb5N+a8ICIvQ5FrCA4hHAJsAbYQjgC20FmN8ReuKwLWveogb9YozenlOb08r1fyermo1RjiutCyfiLrJzJ+Iuv1yQVTnPQQWnhn9/bOAf2URb/hZ/8rSKRw8Ata8ZfxTL9bO4vvRD4v56J1dv8duHsW8WJvine6NVun/1TOGLfdU6+y4Ae0gk52vgSHFsnO2ref8DaBqIZPDFAwxicEQ4u+Z+LwwooGbJlA8MJbjt8fHLKgp1AdaQQZxoJd7a1kX5DyIHr8VeOsT7ecAybYrDt1xD443jp4xEVpXOwAACAASURBVD44YR92uWOR0LA5OmptHQttHTQ3DZujGrpw/1bOhAo4lsC1115bLBb37NmjaYv/dIUQr371qznnP/zhD898s41G41Of+pTnneqD+9NPP/3d7353mQKORqNx++23Hz58GADuvPPO6667bsmfQlEURTlndiCm6myqzqYbbLrBpupsshFMOrzgM6EJRFEsjGMRFLGwYYoNNe/p2sJ5YNcqrVrZrlXserlVq9i1sue0AICaISvWF4r16cbSJB1c8C4h5MVJP2vxVRCQpcskl9eG5rWhOX0wrw3N64nacccMjAibCscUPhG2yR1DeLpwqHAM7mvQMk/yLSAkcPtLHnJoXCAhZKWIPGjAAKj9heSNqOdLjWGQMAcSCCwABKDjf5VkpcWJl+cIAddA6ILrwDUhFwQRAIAYwj4gH6Gl+4XlQFwUcVGEAaHgEFgtpzcUZbUS4sryH/zum1Z6NwBWd8CxuqKgU9izZ89tt912snQDABBC11133V133XVWm221Wk899ZRs234yU1NTALBMSVAkEvnmN7+5HFtWFEVRzsccY1+tNwuc1zmvmrxORS3Bq5zLeg95eKYDIOAOQ64vWLBGzhhsTBggkYBEAgBMAHkCkbOAuXbg2oHbclzH5oE4SX8rAdCuhBIgOudq5QeD7l3H1jnOGki9ZgZg5hJ4snOVoMB0IOog6qFoC+mBsDwcaiLTR9SDSItoHjI8sGwIe0BdsEqARPu77FwiAJA3db77U78KnRewewWOvc7K4i7gyyMwCAwCAeAlf1oOUBNAmCCdt1VFURYnMNo/pnLA01szAcfOnTt/9KMfMcbIyesMH3/88Z07d57VZrPZ7Le//e1TryOHqCzTJ5QgCN773vc+/vjjqVTq7/7u7y666KLleBZFURTlzDGAf6o3/qpaH9G0S6g+rJEdWI8iHMc4hlEU4zjGUYziGJur/+BVWUyty6/VGrVqtVqpVGq1WrlcrlQq5XK5uyAvXfdYbXkoFOrr6+vr60ueIJVK9V5dvt7kAMBtBziXQz6FHwjPE37AXR+CgLteux6ec247nUFwgjdtWdUvGBOOK9eXDxSuK3zGHQdOMopEFnK3vywNUSrbMSBKZZeB9qV23DI2KWiaHCSCTYo0gkwDEdJpTIBwWLWgVhRFUZbSmgk43vGOd9xxxx233nrrJz/5yV27di249/nnn//Yxz728MMPf+pTn1qR3TtnX/nKV1zX3b179yOPPPKhD33oW9/61krvkaIoyoa21/M/Vq6MB+wD8dg7omF1SnE1azabzWaz0WiUy+VGo9FoNJrNZqVSqdfr8mqtVpPJRa9KpdK7EUppNBqNx+OJREImF4lEYmhoSF498ZKepGFhu9+NAN5sAYCotbz5EvS0Fmrf7gfC9aDTTmvRq922SrILgMwgumP45XZORg5Kx5YBhGCDgqbJDoXdJAJHQjJlkMPpcdgChHA4hDBCloU0jE1Dti3szSyW4selKIqiKMtuzQQcv/M7v/PMM8/89V//9f333z8yMjI6OppMJhFC5XL56NGjsofFb/zGb3z4wx9e6T09O08++eQb3vAGQsj111//1re+daV3R1EUZeOqc/5Xtfrd9ebNIesL6VSKqA4Ay65Sqbiu22w2q9Vqq9WybbtcLsuFarXaaDRs227U616t7jhOs9ms1Wpey+GdZSFERKekU0cTC0f6wlHLssLhsGVZqVC4z7K2h5LhzEjcMC3DtCzLMIywpluGYZqmYRgGINIZPcFtFziDnolOeMWHfAGgwD1feAEDyHfa5QrfF64PZ9wXsDtRAo6Eeq+SiAWyx56uyXuRrsnOdu0yh85M2zKPkA9sxxCdPnw4HAIEOGSBKilSFEVRNrY1E3AAwBe+8IX3vOc9n/rUpx588MFuJ1FCSDabfdvb3vae97zn1a9+9cru4TnYtm3bQw899Na3vvWRRx4pFAqNRmNZK1oVRVGURf1ny/6zcjWC8d9kUteYxukfsNSE64mTtPfnzUWmwjnZ3EntcQoLbpQlAAtudE48Mhe8uXCbJ06R4DtO0LJ93+ece57HOQ9aDvc8zrnv+0IIz/NIwARjQRBwzhljEDBNIMYY51wIEQRBVNN7m1uFNT2CcAQgixBCKEw0fOxYXQdIAgGIAcQWfYUWcawDPwccGCAIOAFU69j0unNqYJMyjciXoNstH+kaNikARZqmGRRkH375K9Ht/9+Z8rnbor87faYsjoBO6NCNJxRFURRFuQDWUsABAFdcccXdd98NALIAVdf1bDaLz3h2tFXoPe95z+23375z584tW7bkcjnDOKNP1Usy7+PGcQ4Tia1vggthr5k5ZeWJ05XcAzmgfVktzzSxZztb6uI45+fx5yPbBJzto85latiACfc0M/AdW/n42YinEtG/vP7le3OZX/3Z3rf/bI/O+HTnrlOPBTjJ1hfJCI7deeFnASQYKIVuR0whhBCg4QBjAGCMAQDnHABcwRkAY0yIziwggjcCT+YR3cuS3eoNJgRAzXcBgFKKMaaUcoQYwbqua5pmGIamadzSqBXRdV3XddM0ia7TkKWbpq7rhmGYpqlHI1Y4ZJom6UYDHd1ShfbV46f0a4cIi92lKIqiKMoGtMYCjq5EIpFIJFZ6L5ZAKBT6xje+Ydt2oVC4/vrrdf30H85aP3lm/pNfvAD7pigbxInzqC/bMy3XuVw50n5pNtU5EX1uuqevz/qBIRN1fgqIYBQJnXp9ONsDWrRIO0MXo6/2Z/4xl76s0frH5w6PagRefnn3nPzZkg0XT3Zv70G74zgyLGg0GgDgAPcDv3vVttvFEY1GM9BJs9kMgsD3/VarJYSo1WqAUL5RY4w5juM4DmNMPrBQLtssaDQacmX35IlPOBymlFqWZZpmKBQyDCMSiei6Ho1GNU2Lx+MY476+JMY4Ho9rmhaNRiml4XA4HAqlDCMWi+m6Ho/H2zeGwydrS6EoiqIoinIhrdWAY9146KGH/uEf/uHKK6988sknb7rppjN5iHXVjsFP/z6IpZuIfCX0ftZXzk2nC/2a1S32VpQLqFwuy4WHGs07AQWA3uu0XmbX/Ez4eW5Vq1UADmDXajVZ3SDzAgCoVqtyeIVsURkEQb1eBwDP85rNJgDYti0nHa/X60EQdJ+ru6bjOPbpiqdkmgAAMmvQdV2OW5ShgwwjKKXhWJQilEskumuapmlZlnw4QkieA+jr6+s+NhaLEUJkkLEcL6yiKIqiKMqKUwHHCrvxxhvf+c533nPPPRdffPEjjzxyJg9BBNPNw8u9Y4qiKCfTPfiXuhGAVKlUukMYusf2Um9ZAWOsVqt17+qGAtATGQCA7/uyPGHBcu86vcvd7fRuf0E5g9bfn/mDP4z90psqX/1K/tOferSzza5uSYJhGKFQCABksQMAdAMCmRrIKIFS2g0mug/pbkSuKashAEBWPXRXIITEYrHeLSuKoiiKoijnRgUcK296evquu+5yHCeZTK70vijKxtI9mX+iZrPpeYu33jhZ8X/33P6JFiQCUu9h+an3inNerVYX3Nh7tN/VGy5I3bKCrgV5xIm77bpuq3Ws98Siz34OegcydOMASRYXyGVZiSCXu/UL3WVZknDi7QueolvCsGCDoVjswWjsvnB0jLP3C7b59neQX/81mS8AQCKRQGoSCkVRFEVRlDVLBRxrD+d8YmKCn33fvtNacNhzIcna7+Xb/oJzxUvixCPJc3A+r/m5fVMLjl2X9iFneCS84Kz+yZxJPf+pGw3A8XUBK2LR0/Ldk/ZnsnLvwX/XiYfi8jC+tzlRt2TgFLf0HvxLcrxD92q3HuFkW+jdvQX3LnjsinvIdv6kUqty/keJ2FvCIZVkKIqiKIqirDMq4Fh7vv3tb7/pTW9a6b1YvRacGV4S3ZLy83TiweGFefg5tADsfYhhGKee3+cMt3+Gh7tnsrXTbqr3rP7ZPvwUp/EXzRrg5IGFskoc9INPVaqPud6bw9bvxmPJtTz3lqIoiqIoinIyKuBYe2699dbDhw+fQ73DUh2lnzNZW64oinLBVDj/fK1+d735MtO4rz+zTVf/9RRFURRFUdYt9VFv7cEYj42NrfReKIqirGq+EF9vtO6s1bMEfz6TfJWppuxRFEVRFEVZ51TAsSrccccdK70LiqIo68fDtvOJSrXOxfti0XdEw4sMK1IURVEURVHWHRVwKIqiKOvHXs//i0r1ac9/WyT0/lg0qtptKIqiKIqibBgq4FAURVHWg3nG7qrV/7XRus4y/30gs0lT/+AURVEURVE2FvX5T1EURVnbHCH+qd7861p9VNf+IZt+iXF2cwYpiqIoiqIo64MKOBRFUZS1KhDiG037C7U6B/hfffE3hkNqRIqiKIqiKMqGpQIORVEUZe1hAN9qtj5fq1e5+J/R8G9EIxZCK71TiqIoiqIoykpSAYeiKIqylnCAB1r256r1OcbeHg2/OxqJqU6iiqIoiqIoigo4FEVRlLVCAPyX7XyuWh8Pgl+JhN4TjaaIijYURVEURVGUNhVwKIqiKGvA4477mWrteT+4LWz9TSaZIWSl90hRFEVRFEVZXVTAoSiKoqxqP3O9v6zWnvb8N4etu9LJfhVtKIqiKIqiKItRAYeiKIqySj3pep+r1n/iuq8NWf8+kNikqf9ZiqIoiqIoykmpD4uKoijKqvOU632uVv+x474hZN2fy6poQ1EURVEURTkt9ZFRURTl/2/vzqOrKO/Hjz+z3CU3KwkEAglbENkUrPqVJRDcEbAigi3iQkUqS1HqUulRq4UeKCjafn8gqNhCrWJbviAIRkBkUxAFDEsEIySsakjYstxt7szz++NqikElBJLJhPfr+EfuM3Pv/cRzcC5vZ+aivpBCbAgG55aWbw2Fb/LFLG2WmuniOAUAAIBq4YMjAMB+lhDrAsGXSsv2GJH+Pu+SRqntSBsAAAA4F3x8BADYKSxljj8wu7S82DTviPP9v8bJzbiNKAAAAM4dgQMAYI8TlvVGWcUb5RWqEMPiYu+Jj01UVbuHAgAAgFMROAAAde2riDmvvHxhuT9FU8ckxN8Z5/Mqit1DAQAAwNkIHACAupNvGK+Vlb/rD7bT9WcaJQ6M9XE5CgAAAC4IAgcAoC5sDoX+Vlq+IRjq7vW80ji5h9dj90QAAABoUAgcAIBaFJRyuT/wz7KKLw3jJl/Mf5o26ex22T0UAAAAGiACBwCgVhSb5r/K/W+WV0SEGBQbM7Nxcgud61EAAABQWwgcAIALLC9s/KO8/F1/MF3TRifED43zxXAPUQAAANQyAgcA4MIwpFwdCM4rq9gZDl/j9fxvSqO+MV7CBgAAAOoGgQMAcL5KTOvtCv/r5RV+afX3xUxJTmrr4vgCAACAOsUHUABAzVVejdJC00bGxw2J8/m4GgUAAAB2IHAAAM5ZmWUt8wf+r8K/O2z0jvHOaZzc0+shbAAAAMBGBA4AQHVJIT4JhRaV+1cGgrGqMsjneyGlUUudQwkAAADsx6dSAMDZHTXNpRWB/1T4D0ci13g9U5OTbojx6lyNAgAAgHqDwAEA+FGmEJ8EQ/+u8K/yB9J1/VZfzOBYX3Nds3suAAAAoCoCBwDgBxQYkcV+/+Jyf7mU18Z4X22S0p27bAAAAKAeI3AAAP6r3JI5gcD/lfu3h8OXu90PJSb098XEqZQNAAAA1HcEDgCAiEi5IRha5g98EAj6FOXWWN/k5KRLXBwjAAAA4Bh8eAWAi5cUIjcUfscfyPEHAlJmez3PpzTq4/W4uHsoAAAAnIbAAQAXowIj8q4/8I4/cDgS6eZxj0mIvzU2ppGq2j0XAAAAUEMEDgC4iJy0rJX+4BK/f1sonOnSb/XFDIr1pfOtKAAAAHA+AgcANHxBKdcGgkv8gQ2BYBNNu9HnfTIpsZPbZfdcAAAAwAVD4ACABqtCyvWB4KpAcE0g6FaUm2O8f09tfKXHzYUoAAAAaHgIHADQ0Jy0rA8CwVWB4KZgyKWIbK/3uZRGfbweN7cOBQAAQMNF4ACABuK4ZW0IBN8LBD8MhuIUpU+M58WURr3oGgAAALg4EDgAwNkOR8wPAsEVgcBnoXBTTesT4/nflEa9vR6drgEAAICLCYEDABxprxF5zx9YGwzmhY0MXesb430kMeFnHjdVAwAAABcnAgcAOEZIyk9C4bWB4Lpg8EjE7OR23RjjnZbcKNPFf8wBAABwseMzMQDUd0dNc10wtC4Q3BgMmUL8j8f9q/i4vl5vC12zezQAAACgviBwAEA9tdeIrA0E1wSDn4XCjVQ1K8YzJTkpy+uNU7kMBQAAAKiKwAEA9UhQyk3B0NpgcG0gdNQ0M136tdxcAwAAAKgGAgcA2C/fMDYGQ+uDoS2hsFuInl7PQ4nxfb3eFE21ezQAAADAGQgcAGCPEtPaGAp9FAxuCoaLTbOtS8/yekbFx13lcbv4hlcAAADgHBE4AKDuBKX8LBTeGAptCoY+DxuJqtrd6/lNQnyW19OcO4YCAAAA54HAAQC17lDE3BQMrQkGNwVDESk7uF09vJ7fJyVe4XFzCQoAAABwQRA4AKBWFEYi20Lhj4OhTcHQMctq59J7ej3DGidf7XHHcAUKAAAAcKEROADgwjCF+NIwPg2Gt4bDW0OhEtNK1bT/8bgfTUro5fWkalyBAgAAANQiAgcA1JwpxJ6wsTUU/iwc3hQMnbKsJpp2pcc9PiHhZx53pkvnVA0AAACgbhA4AODcBKXMCxvRe4V+FgoHpczQtR5ez++TEq72cK9QAAAAwB4EDgA4u68i5mfh8PZwODcU/jxsKIrS2eW6yuO+Jy72So87QeVWoQAAAIDNCBwA8ANCUuaFje3h8GehcG7YKDbNRFXt6nZfG+N9JDGhKzcKBQAAAOoZAgcAfOuoaeaFjW3h8LZQOC9shKTM0LUrPO4H4uOu9Lg7ul2cpwEAAADUWwQOABevckvmGeFdYWNn2MgNhYtMM15Vr3C7enk94xLiL3e741RO0wAAAACcgcAB4CISlHJ32NgVNnaFw7sMY78RUYRo53Jd5naNT4zv5na35XtPAAAAAGcicABoyEwhCo1IXjicZxh5YWNX2AhL2UTTOrtdt/t8V3jcnd0uL3fTAAAAAJyPwAGgQQlJ+aUR2WMYn4eNXeHwHiNiSJmua13c7utivA8lxnd2ceEJAAAA0AAROAA423HL+iJsfG4YX4SNPYZRYERMIZrrWkeX69oY7/hEdxe3qxFf4woAAAA0dAQOAA4T/a6TvLCRZxj7DONQxNSEaO3SO7tdP/f5Ml365W53ikbRAAAAAC4uBA4A9drXpllgRPYaxj4j8qURyTcMv5QJqtrR5brUrd8c4+3gdmXquov7aAAAAAAXNwIHgPrCEuJwJFJgRPZFIvuMyF7DKDAiFVK6FKWNrme69N4xngcS4jq6XM11ze5hAQAAANQvBA4A9jCkPBgxCyKRfYax14gURCKFRiQopVdR2rr0TF2/ISambYLezqVn6PQMAAAAAGdB4ABQF46aZmEkst+I7I+YBYaxP2IeiURMIeJUpa3uusSlD/DFtNP1TJeruc79MwAAAACcMwIHgAvMkPIb04reNeOQGdlrRPINo9ySmhDNdS1d1zN0rZfXe4lLT9f1dF3j5hkAAAAAzh+BA0DNGVIeipiFkciB6D+GeSAS+cY0hRCNVLWNS2+j69d6vffHx7XW9Za6xq1AAQAAANQSAgeAajGFOBKJHIiYByKR/ca3ReOriGkK4VOUVi69ta5f4XEPio1p49Jb63qiyoUmAAAAAOoOgQPA90ghik3zSMQ8YppfRcyvzMiRiHkkYh42TUNKt6K01LXWun6py3WzL6aVrrfStVSNe4ACAAAAsBmBA7hImZUhI2IeMSNffVc0vjbNsJRCiBRNbaHpLXTtUpfruhhvK11vpetp3AEUAAAAQL1E4AAauOOWVRQxvzHNr0zzG9Msinz3g2lFpFSEaKxpLTStha51cbtuivG20PXmmtZC17zcLwMAAACAcxA4gIagzLKKTOsr0ywyzW8i/20ZX5tmUEohRIyiNNe1NE1rqmk9vJ40TWumac11rbmmuQkZAAAAAJyPwAE4RmXFOGqa30TMr6M5wzS/jph+KYUQbkVppmnNNK2ZrnZzu5vGqGmalqZrzTQtgVt+AgAAAGjQCBxAPXLKskpM67hlHTXNY6Z13DKPmtbRH6oYqZraXNM6uV3Xat7o6RhNNS2F+2MAAAAAuFgROIC6U2pZxaZ1wrKKTbMyZBy3rGOmWWxaxyzLkFIIoQqRrKnJqtZEU1M0lYoBAAAAAGdF4AAumGOmdcKyjllmsWkdN7/7wbKOmWbJaf1C+bZfqE00rbGqttb1qz3uxqqWrKlNNDVF1RppKl+7CgAAAADnhMABnJ0h5QnLOmlZJywrWjFOmN9/aFnHTdMUQgihCZGsacmqmqqpjTS1ra5397iT6RcAAAAAUJsIHLiolVrWScs6aVmnLHnKsir/+XbluzMyyi0Z3V8TIklTG6lqI1WNVoxWut5IVRtpamNVTdG0ZFVN1lS+lQQAAAAA6hiBAw2NJcT3U4UsrXxoWie/HzKs757lUpREVUlU1SRVTVTVRFVto2vJbjVZU5NUNZowklU1ke8iAQAAAIB6icBhg0AgcPLkybS0NCGEZVmPPPLI+vXrS0tLX3jhhZ///Od2T1cfBaUstawyS5ZJq8yS5ZYVfVgqrfKqZ17Isv9WC+FRlERVjZaLaLxo59KiK9GQkaR9mzN8CmddAAAAAICDEThsMHv2bCnlo48+KoR45513CgsLt23btmfPnuzs7FtvvVVp0H/TLrekX1oVUvotWWpZfikrLFkhrQpLlkmrwpJ+Kf1Sln6XMMosq0zKiJSVr+BWlHhViVfUeFWJV9UEVU1QlRa66/STLxJVJUFVk1TV26D/ZQIAAAAAKhE46tott9yyZcuWiRMnRh+Wl5fff//9QogOHTrEx8cHg8GYmBhbBzw7Q8qAlOWWDEjpl7LcssqlrLAsv5R+S56q7BSWVS5l2XfrFfJ751ZEJaiqT1FiVSVWUWNVJV5RfKraWFVb61q8qsYraoKqxKvftoxo1PDQLAAAAAAAZyBw1LWcnJxZs2YFg8How+HDh0d/WLRoUdu2bWu7blhClFtWQMqQlGWWDEoZlLLMsoJVVqQVlDIsxSnLCn+7jwx92zWqVgpViDhVjVMVn6LEKqpPVaJXfDTTNZ+ixClqnKrERkOGosR/t6dPVWNJFQAAAACAC4TAYb9IJPL000+vWbNm+fLl1dk/KOXGYCgspV/KiBTl0rKkOCUtKUWZtEwpKqQ0pPRLGW0TlTkjIKVx2rUeUW5F8SpKvKp4v/1BdQvFpyqxihKnKum6Fqsonu/KhUdR4hQlRlU8Qon77uQLLgMBAAAAANiOwGGzSCRyyy23XHXVVRs2bHC5XNV5ypZQ+PfHT2pCxKmKLhSfqkQjhVdRPIoSoygJqhKraJoi4hVVFSJBVT2K8ChKgqp6vssZ0T3jVb7QFAAAAADQEBA4bPb6669nZmZOnTq1+k/J8no2t2hWeyMBAAAAAOA4BA6bffTRR8uWLVu9enX04fbt230+n70jAQAAAADgOAQOG4wbN67y57lz59o4CQAAAAAADYNq9wAAAAAAAADni8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcj8ABAAAAAAAcT7d7gBoqLS0tKytTVbVp06aqSqYBAAAAAOCi5rA0sGvXrvvuuy8tLS0xMTE9Pb158+Zutzs9Pf2uu+766KOP7J4OAAAAAADYw0lncIwfP37WrFlSyrS0tGuuuSYlJUUIcfz48cOHDy9YsGDBggUPPPDAq6++aveYAAAAAACgrjkmcLz00kszZ868+eabp06desUVV1TZmpeXN3ny5Llz53bs2PGRRx6xZUIAAAAAAGAXx1yi8sYbb1x66aXLli07s24IITp37rxgwYLevXsvWrSo7mcDAAAAAAD2ckzg2LVrV/fu3XX9R085URSld+/eu3btqsupAAAAAABAfeCYwNGlS5fNmzebpvkT+2zatKlLly51NhIAAAAAAKgnHBM4hg8fvmfPnltvvXXnzp1nbs3Pzx8+fPiaNWtuu+22up8NAAAAAADYyzE3GR07duzOnTvnzJmTk5OTkZHRqlWr5ORkRVFOnDhx6NChwsJCIcSIESMee+wxuycFAAAAAAB1zTGBQwgxe/bsBx98cPr06atWrfrwww+ji5qmpaamDhs27MEHH8zOzrZ3QgAAAAAAYAsnBQ4hRLdu3d58800hxMmTJ8vKylwuV2pqqqo65kIbAAAAAABQGxwWOIQQR48ePXHiRGZmZlJSUpVNJSUloVCoRYsWtgwGAAAAAADs4qRzH3Jzc7t27dq0adMOHTpkZGTMnz+/yg733HNPenq6LbMBAAAAAAAbOeYMjn379vXo0SMcDt9www1ut/uDDz4YMWJERUXF2LFj7R4NAAAAAADYzDGB46mnngqFQsuXL7/llluEEMXFxT179nz00Uevv/76Sy+9tMYvW1BQ0KlTp1AodNY9FUWp8bsAAAAAAIBa5ZjAsXnz5ptuuilaN4QQTZo0Wb58ebdu3R5//PGlS5fW+GXbtGmzcuXKcDj8E/vk5eVNmDDB5XLV+F0AAAAAAECtckzgKCkpuf76609fad++/WOPPTZ58uQNGzb07t27Zi+rKEqfPn1+eh+fz1ezFwcAAAAAAHXDMYGja9euGzdurLL4xBNPzJs3b8yYMdu2bXO73bX01tFX9ng8tfT6AAAAAAA4SO39Bfx8OCZw9O7de+rUqePHj3/+M+MVcQAAEOZJREFU+ecrW0NsbOycOXMGDBhw3333/f3vf6+lt77qqqtyc3MjkUgtvX699etf/zojI2PIkCF2DwKgJpYtW7Zly5Znn33W7kEA1MT+/fuffvrpV155JSYmxu5ZANTEmDFjnnrqqRtuuMHuQYALT9f1rl272j3FD1CklHbPUC3BYPCmm27asGFDfHx8dnb2O++8U7npD3/4w+TJk1u0aBEOh4uLi53yG9V/N954Y/fu3SdPnmz3IABqYtq0aYsXL/7444/tHgRATWzbtu3KK688depUQkKC3bMAqInGjRu//PLLd9xxh92DABcR1e4Bqsvr9S5dunTixIktWrQoKCg4fdOkSZPmzZsXFxdXXFxs13gAAAAAAMBGjgkcQoikpKSpU6fu3r07Ly+vyqb77rtv9+7dhYWF77//vi2zAQAAAAAAGznmHhxnpShK69atW7dubfcgAAAAAACgrjnpDI4zLV++fOjQoXZPAQAAAAAAbObswLF3796FCxfaPQUAAAAAALCZswMHAAAAAACAIHAAAAAAAIAGgMABAAAAAAAcz9mBY9SoUd98843dUwAAAAAAAJs5+2tifT6fz+eze4oGy+12u1wuu6cAUENut9vtdts9BYAacrvdqqrqurM/qgEXMw7EQN1TpJR2z4B6qqioKC4uLjY21u5BANREIBA4efJkWlqa3YMAqKGCgoK2bdvaPQWAGjpw4EB6erqmaXYPAlxECBwAAAAAAMDxnH0PDgAAAAAAAEHgAAAAAAAADQCBAwAAAAAAOB6BAwAAAAAAOB6BAwAAAAAAOB6BAwAAAAAAOB6BAwAAAAAAOB6BAwAAAAAAOB6BAwAAAAAAOB6BAwAAAAAAOB6BAwAAAAAAOB6BAwAAAAAAOB6BAwAAAAAAOB6BAwAAAAAAOB6BA9VVXl4+f/78w4cP2z0IAAAAAABVEThQXePHjx8xYsT27dvtHgRAdYVCoSeffLJPnz6JiYmZmZl33XXXvn377B4KwNnNnj07KysrKSkpKytr9uzZdo8D4Bxw8AVsROBAtSxcuHDevHl2TwHgHJw6derGG2+cMmXKsWPH7rzzznbt2r311luXXXZZbm6u3aMB+CljxowZO3ZsSUnJbbfdVlxcPHbs2PHjx9s9FIBq4eAL2IvAgbM7cuTIgw8+GBcXZ/cgAM7BtGnTNmzYMG7cuLy8vFdffXXFihXLli0LhUIjRoywezQAPyo3N3fOnDn9+vXbtWvX/Pnz8/LybrrpplmzZu3atcvu0QCcHQdfwF4EDpyFlPLee+9NTEx86KGH7J4FwDlYvHhxfHz8jBkzKlf69+9/3XXXbd++/ejRozYOBuAnTJ8+XQgxbdo0XdeFELquT506VUr53HPP2T0agLPj4AvYi8CBs5gxY8batWv/8Y9/JCQk2D0LgHOgqmp2drbH4zl90e12CyFOnDhh01AAzmLVqlXp6emXX3555crPfvaztLS0lStX2jgVgGri4AvYS7d7ANRrubm5Tz755BNPPJGVlfXRRx/ZPQ6Ac5CXl1dlpbi4+IMPPmjatGlmZqYtIwH4aSdPniwpKenVq1eV9ZYtW27evLmsrCw+Pt6WwQBUEwdfwF6cwYEfFQgEhg8f3qlTp2effdbuWQCcr/z8/J49ewaDwT//+c/RU98B1DdlZWVCiJSUlCrr0ZXS0lIbZgJwHjj4AnWMP2YQfr//1VdfrXzYrl27AQMGCCEef/zxgoKCLVu2RE+rA1A//dgf4UoVFRXTp09/7rnnpJQzZ87kPmdAveVyuYQQiqL84FZV5f9LAY7BwRewBYEDoqysbMKECZUPhwwZMmDAgNWrV8+aNevFF1/s3LmzjbMBOKsf/CNc+TAnJ2f06NEHDx4cOHDg888/f+mll9oxI4BqSU1N1TTtzAv1jx8/rmla06ZNbZkKwLni4AvYRZFS2j0D6qMZM2Y89thjP7Z17ty5I0eOrMt5ANTAM888M2nSpM6dO7/00kt9+vSxexwAZ9e8efOYmJh9+/advtiyZUvLsg4fPmzXVACqj4MvYCPO4MAP69q16+jRo09f+eyzzzZv3nzLLbe0atWqQ4cOdg0GoJrmz58/adKkX/7yl/Pnz+dCM8Ap+vbtu2DBgvz8/Pbt20dX8vLyDh06NGzYMHsHA1AdHHwBe3EGB6pr2rRpEydOXLZsWZXL+wHUQ1LKjh07lpeX79271+v12j0OgOpat25d375977777tdff10IIaUcNmzYv/71rw0bNmRlZdk9HYCfwsEXsB1ncABAA3TgwIEvvviiSZMmt99++5lbX3/99caNG9f9VADOKjs7e8SIEfPmzfvqq6+6d+/+4Ycfrl+/fuTIkdQNoP7j4AvYjsABAA1QQUGBEKK4uPi99947c2soFKrziQBU19/+9reOHTsuWbJk5syZXbp0ee65537irlgA6g8OvoDtuEQFAAAAAAA4Hl+oDgAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAAAAAHI/AAQAAvvXyyy8rijJjxowG/6ZNmjS58cYbz/VZvXv3zsjIqI15AADA+SNwAAAAAAAAxyNwAAAAAAAAxyNwAACAhiMQCNg9AgAAsAeBAwAAnIPS0tKHHnqoa9eu8fHxV1111RNPPFGlKZSUlNx///2tWrVq1arVr371q2PHjjVp0mTUqFHn86YHDx689957O3XqFBMT07JlyyFDhmzfvr1y66hRoxo1avTxxx+3b9/e5/OlpqYOHjy4qKiouLj4gQceuOSSSxISEq677rodO3ac+bK/+MUv0tPTMzIyBg8evHv37tO35ufnDxkyJCMjIz09/c4779y/f/85TQUAAOqYbvcAAADAMYqKinr16rVv375evXoNGzZs69at06dPf/fddzdt2hQXFyeE+Oabb7Kysg4cOHDzzTenpKTk5OSsW7fO7/efz5t+/vnn11xzjWEYAwcOvOGGGwoLC5csWbJmzZqdO3c2b948uk8gEBgwYED79u2HDh368ccfL168uLCwMBQKuVyuoUOH5ubm5uTkDBo0KD8/X9e//fBz+PDhrKwsl8sVfc2333571apVOTk5WVlZQohNmzb169evrKwsOzs7IyNj7dq1PXr08Hg85zQVAACoSwQOAABQXZMmTdq3b99f/vKXhx9+OLoyceLEadOmvfjii08//bQQYsqUKfv27Vu8ePGgQYOEEF9//XXPnj3PM3DMmTOnvLx8+fLl/fv3j6689NJL48aNW7169T333BNdCYVCt91224IFC1RVFUK0bNkyNzc3Ozt75cqVbrdbCNG/f/+cnJwvv/yyY8eO0afs2bNn4MCBCxcujGaLBQsW3HXXXY888sgnn3wihJgwYUJ5efmiRYuiv0hZWVn//v0//PDD9PT06k8FAADqEpeoAACAajEMY+7cuV26dHnooYcqF//4xz82a9Zszpw5QohQKPTKK6/07NkzGgWEEGlpab/97W/P832HDBnyz3/+s1+/fpUrbdq0EUIcP3789N0mTpwYrRtCiAEDBgghHnvssWjdEEJkZ2dXeYqmaX/9618rT8oYNmzYgAEDPv300x07dmzduvWTTz654447Kn+R+Pj4F154oQZTAQCAOsMZHAAAoFoOHDgQDof79u2rKErlosfj6dmz56JFiyoqKg4dOhQKhXr27Hn6s3r06HGe79unTx8hRCgUys/P379//+7du1977bUzd8vIyKj8OSkpSQiRmZlZueL1eqvs36ZNm7Zt256+0q9fv+XLl3/55ZehUCj68PStV199dZMmTc51KgAAUGcIHAAAoFqOHDkihGjWrFmV9bS0tOjWgwcPCiFOrwBCiNTU1PN8X7/f//DDD7/xxhuBQEDX9bZt27Zv3z4/P7/Kbqdnl6jKEzp+0Jm/SIsWLYQQRUVF0dumnnkrjfT09OLi4nOaCgAA1BkuUQEAANUS/Qt/UVFRlfXoSlpaWtOmTYUQJSUlp2+t8rAGBg8e/Nprr02YMGHHjh3BYPCLL7546qmnzvM1xQ/9Il9//bUQolWrVtGTQaIPT3fixInangoAANQYgQMAAFRL69atXS7XunXrTl8Mh8ObNm1q1qxZfHx8u3btVFXdvHnz6TtUeXiuTp06tXr16sGDB0+ZMuWyyy7TNE0IUVpaej6vGVVQUFDlm19XrFghhOjQoUP79u0rH1YqLCyMnqJSq1MBAIAaI3AAAIBqcblc999//44dO2bNmlW5OGnSpCNHjowZM0YIERsbO2LEiPXr17/77rvRrUVFRTNmzDifNzVNMxKJnDx5snLl+PHjf/rTn4QQlmWd5ytPmDAhHA5HH/773/9eunTpgAEDMjMzu3Xr1r179//85z9Lly6Nbg0Gg+PHj698x9qbCgAA1Bj34AAAAN/z1ltv7dq1q8riNddcM3r06GeeeWbFihW/+c1vFi5c2KlTp61bt27evPnyyy9/9NFHo7tNmTJl5cqVgwYNGjhwYHJy8rvvvtutW7eCgoLKLyupwZvefPPNK1as6Nmz57XXXltSUrJo0aKuXbsKIebNm9e+ffvoF6bUQGpq6pIlS7p06dKnT5/CwsI1a9akpqZOnz49uvWFF17o16/f7bfffv3117ds2XLt2rVFRUVZWVnRkz6Sk5NraSoAAFBjnMEBAAC+Z8uWLfPOEL0yJS0tLTc3d9y4cceOHZs/f75hGBMnTty8eXNsbGz0uU2bNt26devQoUM3bty4cePGkSNHTp06VQgRFxdX4zd98803R48efejQoZkzZ+7evXvGjBnvv//+2LFjDxw48Pbbb9f417zzzjvfe++9zMzMJUuWFBQU3HXXXVu3bu3UqVN0a48ePT799NPBgwd/8cUXy5cv79q16/r167t06VL59FqaCgAA1JgipbR7BgAA0EBs2bLF6/WeHgJycnL69+//yiuvjBo1ysbBAABAg0fgAAAAF0yvXr22bNmyd+/e6BeRSCkHDx68YsWKgwcPNm7c2O7pAABAQ8Y9OAAAwAUzceLE22677frrr7/99ttTUlJWrly5evXq3/3ud9QNAABQ2ziDAwAAXEgrV66cMmXKzp07VVXt0qXLyJEj7777bruHAgAADR+BAwAAAAAAOB7fogIAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAAByPwAEAAAAAABzv/wNzjNrvEodfqQAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 600, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "library(glmnet)\n", + "# avec des variables quantitatives seulement\n", + "reg.lasso.quanti <- glmnet(y = datappr[, 2],\n", + " x = as.matrix(datappr[, -c(1, 2, 5)]))\n", + "# avec toutes les variables, créer d'abord la matrice d'expériences \n", + "# avec 'model.matrix' (penser à retirer l'intercept du modèle)\n", + "x.mat <- model.matrix(O3obs ~ . - 1, data = datappr)\n", + "reg.lasso <- glmnet(y = datappr$O3obs, x = x.mat)\n", + "options(repr.plot.width = 12, repr.plot.height = 10)\n", + "plot(reg.lasso, xvar = \"lambda\", label = TRUE)\n", + "legend(\"topright\", \n", + " legend = paste(1:ncol(x.mat), \" - \", colnames(x.mat)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que fait la commande model.matrix ? Comment sont gérées les variables catégorielles ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`model.matrix` return a model matrix.\n", + "\n", + "Categorical variables are divided into separated columns." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que représentent les courbes ci-dessus, appelées \"chemins de régularisation\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 6 × 13 of type dbl
JOUR0JOUR1MOCAGETEMPESTATIONAlsSTATIONCadSTATIONPlaSTATIONRamVentMODVentANGSRMH2OLNO2LNO
101 93.221.500009.5000-0.643500.092032600.4712528-0.8580218
201104.620.200008.0100-0.049960.093861600.7518877-0.6329933
310103.617.400009.3771-0.128320.097519230.5050087-0.7614260
410 94.818.800009.4578-0.345160.092466210.8544153-0.3552474
610114.323.600006.3127 0.063410.108719821.6707211 0.2949059
710127.726.600004.8042 0.041640.096798761.0441561-0.5978370
\n" + ], + "text/latex": [ + "A matrix: 6 × 13 of type dbl\n", + "\\begin{tabular}{r|lllllllllllll}\n", + " & JOUR0 & JOUR1 & MOCAGE & TEMPE & STATIONAls & STATIONCad & STATIONPla & STATIONRam & VentMOD & VentANG & SRMH2O & LNO2 & LNO\\\\\n", + "\\hline\n", + "\t1 & 0 & 1 & 93.2 & 21.5 & 0 & 0 & 0 & 0 & 9.5000 & -0.64350 & 0.09203260 & 0.4712528 & -0.8580218\\\\\n", + "\t2 & 0 & 1 & 104.6 & 20.2 & 0 & 0 & 0 & 0 & 8.0100 & -0.04996 & 0.09386160 & 0.7518877 & -0.6329933\\\\\n", + "\t3 & 1 & 0 & 103.6 & 17.4 & 0 & 0 & 0 & 0 & 9.3771 & -0.12832 & 0.09751923 & 0.5050087 & -0.7614260\\\\\n", + "\t4 & 1 & 0 & 94.8 & 18.8 & 0 & 0 & 0 & 0 & 9.4578 & -0.34516 & 0.09246621 & 0.8544153 & -0.3552474\\\\\n", + "\t6 & 1 & 0 & 114.3 & 23.6 & 0 & 0 & 0 & 0 & 6.3127 & 0.06341 & 0.10871982 & 1.6707211 & 0.2949059\\\\\n", + "\t7 & 1 & 0 & 127.7 & 26.6 & 0 & 0 & 0 & 0 & 4.8042 & 0.04164 & 0.09679876 & 1.0441561 & -0.5978370\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 6 × 13 of type dbl\n", + "\n", + "| | JOUR0 | JOUR1 | MOCAGE | TEMPE | STATIONAls | STATIONCad | STATIONPla | STATIONRam | VentMOD | VentANG | SRMH2O | LNO2 | LNO |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 0 | 1 | 93.2 | 21.5 | 0 | 0 | 0 | 0 | 9.5000 | -0.64350 | 0.09203260 | 0.4712528 | -0.8580218 |\n", + "| 2 | 0 | 1 | 104.6 | 20.2 | 0 | 0 | 0 | 0 | 8.0100 | -0.04996 | 0.09386160 | 0.7518877 | -0.6329933 |\n", + "| 3 | 1 | 0 | 103.6 | 17.4 | 0 | 0 | 0 | 0 | 9.3771 | -0.12832 | 0.09751923 | 0.5050087 | -0.7614260 |\n", + "| 4 | 1 | 0 | 94.8 | 18.8 | 0 | 0 | 0 | 0 | 9.4578 | -0.34516 | 0.09246621 | 0.8544153 | -0.3552474 |\n", + "| 6 | 1 | 0 | 114.3 | 23.6 | 0 | 0 | 0 | 0 | 6.3127 | 0.06341 | 0.10871982 | 1.6707211 | 0.2949059 |\n", + "| 7 | 1 | 0 | 127.7 | 26.6 | 0 | 0 | 0 | 0 | 4.8042 | 0.04164 | 0.09679876 | 1.0441561 | -0.5978370 |\n", + "\n" + ], + "text/plain": [ + " JOUR0 JOUR1 MOCAGE TEMPE STATIONAls STATIONCad STATIONPla STATIONRam VentMOD\n", + "1 0 1 93.2 21.5 0 0 0 0 9.5000 \n", + "2 0 1 104.6 20.2 0 0 0 0 8.0100 \n", + "3 1 0 103.6 17.4 0 0 0 0 9.3771 \n", + "4 1 0 94.8 18.8 0 0 0 0 9.4578 \n", + "6 1 0 114.3 23.6 0 0 0 0 6.3127 \n", + "7 1 0 127.7 26.6 0 0 0 0 4.8042 \n", + " VentANG SRMH2O LNO2 LNO \n", + "1 -0.64350 0.09203260 0.4712528 -0.8580218\n", + "2 -0.04996 0.09386160 0.7518877 -0.6329933\n", + "3 -0.12832 0.09751923 0.5050087 -0.7614260\n", + "4 -0.34516 0.09246621 0.8544153 -0.3552474\n", + "6 0.06341 0.10871982 1.6707211 0.2949059\n", + "7 0.04164 0.09679876 1.0441561 -0.5978370" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#help(model.matrix)\n", + "head(x.mat)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 600, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# choix du paramètre de régularisation par validation croisée\n", + "reg.lasso.cv <- cv.glmnet(y = datappr[, 2], x = x.mat)\n", + "plot(reg.lasso.cv)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cv.glmnet package:glmnet R Documentation\n", + "\n", + "_\bC_\br_\bo_\bs_\bs-_\bv_\ba_\bl_\bi_\bd_\ba_\bt_\bi_\bo_\bn _\bf_\bo_\br _\bg_\bl_\bm_\bn_\be_\bt\n", + "\n", + "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n", + "\n", + " Does k-fold cross-validation for glmnet, produces a plot, and\n", + " returns a value for ‘lambda’ (and ‘gamma’ if ‘relax=TRUE’)\n", + "\n", + "_\bU_\bs_\ba_\bg_\be:\n", + "\n", + " cv.glmnet(\n", + " x,\n", + " y,\n", + " weights = NULL,\n", + " offset = NULL,\n", + " lambda = NULL,\n", + " type.measure = c(\"default\", \"mse\", \"deviance\", \"class\", \"auc\", \"mae\", \"C\"),\n", + " nfolds = 10,\n", + " foldid = NULL,\n", + " alignment = c(\"lambda\", \"fraction\"),\n", + " grouped = TRUE,\n", + " keep = FALSE,\n", + " parallel = FALSE,\n", + " gamma = c(0, 0.25, 0.5, 0.75, 1),\n", + " relax = FALSE,\n", + " trace.it = 0,\n", + " ...\n", + " )\n", + " \n", + "_\bA_\br_\bg_\bu_\bm_\be_\bn_\bt_\bs:\n", + "\n", + " x: ‘x’ matrix as in ‘glmnet’.\n", + "\n", + " y: response ‘y’ as in ‘glmnet’.\n", + "\n", + " weights: Observation weights; defaults to 1 per observation\n", + "\n", + " offset: Offset vector (matrix) as in ‘glmnet’\n", + "\n", + " lambda: Optional user-supplied lambda sequence; default is ‘NULL’,\n", + " and ‘glmnet’ chooses its own sequence. Note that this is done\n", + " for the full model (master sequence), and separately for each\n", + " fold. The fits are then alligned using the master sequence\n", + " (see the ‘allignment’ argument for additional details).\n", + " Adapting ‘lambda’ for each fold leads to better convergence.\n", + " When ‘lambda’ is supplied, the same sequence is used\n", + " everywhere, but in some GLMs can lead to convergence issues.\n", + "\n", + "type.measure: loss to use for cross-validation. Currently five options,\n", + " not all available for all models. The default is\n", + " ‘type.measure=\"deviance\"’, which uses squared-error for\n", + " gaussian models (a.k.a ‘type.measure=\"mse\"’ there), deviance\n", + " for logistic and poisson regression, and partial-likelihood\n", + " for the Cox model. ‘type.measure=\"class\"’ applies to binomial\n", + " and multinomial logistic regression only, and gives\n", + " misclassification error. ‘type.measure=\"auc\"’ is for\n", + " two-class logistic regression only, and gives area under the\n", + " ROC curve. ‘type.measure=\"mse\"’ or ‘type.measure=\"mae\"’ (mean\n", + " absolute error) can be used by all models except the ‘\"cox\"’;\n", + " they measure the deviation from the fitted mean to the\n", + " response. ‘type.measure=\"C\"’ is Harrel's concordance measure,\n", + " only available for ‘cox’ models.\n", + "\n", + " nfolds: number of folds - default is 10. Although ‘nfolds’ can be as\n", + " large as the sample size (leave-one-out CV), it is not\n", + " recommended for large datasets. Smallest value allowable is\n", + " ‘nfolds=3’\n", + "\n", + " foldid: an optional vector of values between 1 and ‘nfold’\n", + " identifying what fold each observation is in. If supplied,\n", + " ‘nfold’ can be missing.\n", + "\n", + "alignment: This is an experimental argument, designed to fix the\n", + " problems users were having with CV, with possible values\n", + " ‘\"lambda\"’ (the default) else ‘\"fraction\"’. With ‘\"lambda\"’\n", + " the ‘lambda’ values from the master fit (on all the data) are\n", + " used to line up the predictions from each of the folds. In\n", + " some cases this can give strange values, since the effective\n", + " ‘lambda’ values in each fold could be quite different. With\n", + " ‘\"fraction\"’ we line up the predictions in each fold\n", + " according to the fraction of progress along the\n", + " regularization. If in the call a ‘lambda’ argument is also\n", + " provided, ‘alignment=\"fraction\"’ is ignored (with a warning).\n", + "\n", + " grouped: This is an experimental argument, with default ‘TRUE’, and\n", + " can be ignored by most users. For all models except the\n", + " ‘\"cox\"’, this refers to computing ‘nfolds’ separate\n", + " statistics, and then using their mean and estimated standard\n", + " error to describe the CV curve. If ‘grouped=FALSE’, an error\n", + " matrix is built up at the observation level from the\n", + " predictions from the ‘nfold’ fits, and then summarized (does\n", + " not apply to ‘type.measure=\"auc\"’). For the ‘\"cox\"’ family,\n", + " ‘grouped=TRUE’ obtains the CV partial likelihood for the Kth\n", + " fold by _subtraction_; by subtracting the log partial\n", + " likelihood evaluated on the full dataset from that evaluated\n", + " on the on the (K-1)/K dataset. This makes more efficient use\n", + " of risk sets. With ‘grouped=FALSE’ the log partial likelihood\n", + " is computed only on the Kth fold\n", + "\n", + " keep: If ‘keep=TRUE’, a _prevalidated_ array is returned containing\n", + " fitted values for each observation and each value of\n", + " ‘lambda’. This means these fits are computed with this\n", + " observation and the rest of its fold omitted. The ‘foldid’\n", + " vector is also returned. Default is keep=FALSE. If\n", + " ‘relax=TRUE’, then a list of such arrays is returned, one for\n", + " each value of 'gamma'. Note: if the value 'gamma=1' is\n", + " omitted, this case is included in the list since it\n", + " corresponds to the original 'glmnet' fit.\n", + "\n", + "parallel: If ‘TRUE’, use parallel ‘foreach’ to fit each fold. Must\n", + " register parallel before hand, such as ‘doMC’ or others. See\n", + " the example below.\n", + "\n", + " gamma: The values of the parameter for mixing the relaxed fit with\n", + " the regularized fit, between 0 and 1; default is ‘gamma =\n", + " c(0, 0.25, 0.5, 0.75, 1)’\n", + "\n", + " relax: If ‘TRUE’, then CV is done with respect to the mixing\n", + " parameter ‘gamma’ as well as ‘lambda’. Default is\n", + " ‘relax=FALSE’\n", + "\n", + "trace.it: If ‘trace.it=1’, then progress bars are displayed; useful for\n", + " big models that take a long time to fit. Limited tracing if\n", + " ‘parallel=TRUE’\n", + "\n", + " ...: Other arguments that can be passed to ‘glmnet’\n", + "\n", + "_\bD_\be_\bt_\ba_\bi_\bl_\bs:\n", + "\n", + " The function runs ‘glmnet’ ‘nfolds’+1 times; the first to get the\n", + " ‘lambda’ sequence, and then the remainder to compute the fit with\n", + " each of the folds omitted. The error is accumulated, and the\n", + " average error and standard deviation over the folds is computed.\n", + " Note that ‘cv.glmnet’ does NOT search for values for ‘alpha’. A\n", + " specific value should be supplied, else ‘alpha=1’ is assumed by\n", + " default. If users would like to cross-validate ‘alpha’ as well,\n", + " they should call ‘cv.glmnet’ with a pre-computed vector ‘foldid’,\n", + " and then use this same fold vector in separate calls to\n", + " ‘cv.glmnet’ with different values of ‘alpha’. Note also that the\n", + " results of ‘cv.glmnet’ are random, since the folds are selected at\n", + " random. Users can reduce this randomness by running ‘cv.glmnet’\n", + " many times, and averaging the error curves.\n", + "\n", + " If ‘relax=TRUE’ then the values of ‘gamma’ are used to mix the\n", + " fits. If eta is the fit for lasso/elastic net, and eta_R is the\n", + " relaxed fit (with unpenalized coefficients), then a relaxed fit\n", + " mixed by gamma is\n", + "\n", + " eta(gamma)=(1-gamma)eta_R+gammaeta. \n", + " \n", + " There is practically no extra cost for having a lot of values for\n", + " ‘gamma’. However, 5 seems sufficient for most purposes. CV then\n", + " selects both ‘gamma’ and ‘lambda’.\n", + "\n", + "_\bV_\ba_\bl_\bu_\be:\n", + "\n", + " an object of class ‘\"cv.glmnet\"’ is returned, which is a list with\n", + " the ingredients of the cross-validation fit. If the object was\n", + " created with ‘relax=TRUE’ then this class has a prefix class of\n", + " ‘\"cv.relaxed\"’.\n", + "\n", + " lambda: the values of ‘lambda’ used in the fits.\n", + "\n", + " cvm: The mean cross-validated error - a vector of length\n", + " ‘length(lambda)’.\n", + "\n", + " cvsd: estimate of standard error of ‘cvm’.\n", + "\n", + " cvup: upper curve = ‘cvm+cvsd’.\n", + "\n", + " cvlo: lower curve = ‘cvm-cvsd’.\n", + "\n", + " nzero: number of non-zero coefficients at each ‘lambda’.\n", + "\n", + " name: a text string indicating type of measure (for plotting\n", + " purposes).\n", + "\n", + "glmnet.fit: a fitted glmnet object for the full data.\n", + "\n", + "lambda.min: value of ‘lambda’ that gives minimum ‘cvm’.\n", + "\n", + "lambda.1se: largest value of ‘lambda’ such that error is within 1\n", + " standard error of the minimum.\n", + "\n", + "fit.preval: if ‘keep=TRUE’, this is the array of prevalidated fits.\n", + " Some entries can be ‘NA’, if that and subsequent values of\n", + " ‘lambda’ are not reached for that fold\n", + "\n", + " foldid: if ‘keep=TRUE’, the fold assignments used\n", + "\n", + " index: a one column matrix with the indices of ‘lambda.min’ and\n", + " ‘lambda.1se’ in the sequence of coefficients, fits etc.\n", + "\n", + " relaxed: if ‘relax=TRUE’, this additional item has the CV info for\n", + " each of the mixed fits. In particular it also selects\n", + " ‘lambda, gamma’ pairs corresponding to the 1se rule, as well\n", + " as the minimum error. It also has a component ‘index’, a\n", + " two-column matrix which contains the ‘lambda’ and ‘gamma’\n", + " indices corresponding to the \"min\" and \"1se\" solutions.\n", + "\n", + "_\bA_\bu_\bt_\bh_\bo_\br(_\bs):\n", + "\n", + " Jerome Friedman, Trevor Hastie and Rob Tibshirani\n", + " Noah Simon helped develop the 'coxnet' function.\n", + " Jeffrey Wong and B. Narasimhan helped with the parallel option\n", + " Maintainer: Trevor Hastie \n", + "\n", + "_\bR_\be_\bf_\be_\br_\be_\bn_\bc_\be_\bs:\n", + "\n", + " Friedman, J., Hastie, T. and Tibshirani, R. (2008) _Regularization\n", + " Paths for Generalized Linear Models via Coordinate Descent_, \n", + " _Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010_\n", + " \n", + " Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011)\n", + " _Regularization Paths for Cox's Proportional Hazards Model via\n", + " Coordinate Descent, Journal of Statistical Software, Vol. 39(5)\n", + " 1-13_\n", + " \n", + "\n", + "_\bS_\be_\be _\bA_\bl_\bs_\bo:\n", + "\n", + " ‘glmnet’ and ‘plot’, ‘predict’, and ‘coef’ methods for\n", + " ‘\"cv.glmnet\"’ and ‘\"cv.relaxed\"’ objects.\n", + "\n", + "_\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs:\n", + "\n", + " set.seed(1010)\n", + " n = 1000\n", + " p = 100\n", + " nzc = trunc(p/10)\n", + " x = matrix(rnorm(n * p), n, p)\n", + " beta = rnorm(nzc)\n", + " fx = x[, seq(nzc)] %*% beta\n", + " eps = rnorm(n) * 5\n", + " y = drop(fx + eps)\n", + " px = exp(fx)\n", + " px = px/(1 + px)\n", + " ly = rbinom(n = length(px), prob = px, size = 1)\n", + " set.seed(1011)\n", + " cvob1 = cv.glmnet(x, y)\n", + " plot(cvob1)\n", + " coef(cvob1)\n", + " predict(cvob1, newx = x[1:5, ], s = \"lambda.min\")\n", + " title(\"Gaussian Family\", line = 2.5)\n", + " set.seed(1011)\n", + " cvob1a = cv.glmnet(x, y, type.measure = \"mae\")\n", + " plot(cvob1a)\n", + " title(\"Gaussian Family\", line = 2.5)\n", + " set.seed(1011)\n", + " par(mfrow = c(2, 2), mar = c(4.5, 4.5, 4, 1))\n", + " cvob2 = cv.glmnet(x, ly, family = \"binomial\")\n", + " plot(cvob2)\n", + " title(\"Binomial Family\", line = 2.5)\n", + " frame()\n", + " set.seed(1011)\n", + " cvob3 = cv.glmnet(x, ly, family = \"binomial\", type.measure = \"class\")\n", + " plot(cvob3)\n", + " title(\"Binomial Family\", line = 2.5)\n", + " ## Not run:\n", + " \n", + " cvob1r = cv.glmnet(x, y, relax = TRUE)\n", + " plot(cvob1r)\n", + " predict(cvob1r, newx = x[, 1:5])\n", + " set.seed(1011)\n", + " cvob3a = cv.glmnet(x, ly, family = \"binomial\", type.measure = \"auc\")\n", + " plot(cvob3a)\n", + " title(\"Binomial Family\", line = 2.5)\n", + " set.seed(1011)\n", + " mu = exp(fx/10)\n", + " y = rpois(n, mu)\n", + " cvob4 = cv.glmnet(x, y, family = \"poisson\")\n", + " plot(cvob4)\n", + " title(\"Poisson Family\", line = 2.5)\n", + " \n", + " # Multinomial\n", + " n = 500\n", + " p = 30\n", + " nzc = trunc(p/10)\n", + " x = matrix(rnorm(n * p), n, p)\n", + " beta3 = matrix(rnorm(30), 10, 3)\n", + " beta3 = rbind(beta3, matrix(0, p - 10, 3))\n", + " f3 = x %*% beta3\n", + " p3 = exp(f3)\n", + " p3 = p3/apply(p3, 1, sum)\n", + " g3 = glmnet:::rmult(p3)\n", + " set.seed(10101)\n", + " cvfit = cv.glmnet(x, g3, family = \"multinomial\")\n", + " plot(cvfit)\n", + " title(\"Multinomial Family\", line = 2.5)\n", + " # Cox\n", + " beta = rnorm(nzc)\n", + " fx = x[, seq(nzc)] %*% beta/3\n", + " hx = exp(fx)\n", + " ty = rexp(n, hx)\n", + " tcens = rbinom(n = n, prob = 0.3, size = 1) # censoring indicator\n", + " y = cbind(time = ty, status = 1 - tcens) # y=Surv(ty,1-tcens) with library(survival)\n", + " foldid = sample(rep(seq(10), length = n))\n", + " fit1_cv = cv.glmnet(x, y, family = \"cox\", foldid = foldid)\n", + " plot(fit1_cv)\n", + " title(\"Cox Family\", line = 2.5)\n", + " # Parallel\n", + " require(doMC)\n", + " registerDoMC(cores = 4)\n", + " x = matrix(rnorm(1e+05 * 100), 1e+05, 100)\n", + " y = rnorm(1e+05)\n", + " system.time(cv.glmnet(x, y))\n", + " system.time(cv.glmnet(x, y, parallel = TRUE))\n", + " ## End(Not run)\n", + " " + ] + } + ], + "source": [ + "library(glmnet)\n", + "help(cv.glmnet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que représente la courbe rouge ? Et la bande qui est autour ? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The two different values of $\\lambda$ reflect two common choices for $\\lambda$. The $\\lambda_{min}$ is the one which minimizes out-of-sample loss in CV. The $\\lambda_{1se}$ is the one which is the largest $\\lambda$ value within 1 standard error of $\\lambda_{min}$. One line of reasoning suggests using $\\lambda_{1se}$ because it hedges against overfitting by selecting a larger $\\lambda$ value than the min. Which choice is best is context-dependent.\n", + "\n", + "- The intervals estimate variance of the loss metric (red dots). They're computed using CV.\n", + "- The vertical lines show the locations of $\\lambda_{min}$ and $\\lambda_{1se}$.\n", + "- The numbers across the top are the number of nonzero coefficient estimates.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Comment sont obtenues les valeurs de log(lambda) correspondant aux lignes verticales en pointillé ?" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "'CV estimate of lambda : 4.303'" + ], + "text/latex": [ + "'CV estimate of lambda : 4.303'" + ], + "text/markdown": [ + "'CV estimate of lambda : 4.303'" + ], + "text/plain": [ + "[1] \"CV estimate of lambda : 4.303\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "14 x 1 sparse Matrix of class \"dgCMatrix\"\n", + " s1\n", + "(Intercept) 2.7883658\n", + "JOUR0 . \n", + "JOUR1 . \n", + "MOCAGE 0.3314439\n", + "TEMPE 2.9229402\n", + "STATIONAls . \n", + "STATIONCad . \n", + "STATIONPla . \n", + "STATIONRam . \n", + "VentMOD . \n", + "VentANG . \n", + "SRMH2O 0.8851589\n", + "LNO2 . \n", + "LNO . " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# valeur estimée\n", + "paste(\"CV estimate of lambda :\", round(reg.lasso.cv$lambda.1se, 3))\n", + "# modèle correspondant\n", + "coef(reg.lasso.cv, s = \"lambda.1se\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Combien restent-ils de coefficients non nuls. Vérifier sur les chemins de régularisation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 600, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width = 10, repr.plot.height = 10)\n", + "l1se <- log(reg.lasso.cv$lambda.1se)\n", + "plot(reg.lasso, xvar = \"lambda\", label = TRUE, xlim=c(l1se - 0.01, l1se + 0.01), ylim=c(0, 3))\n", + "abline(v = l1se, lty=\"dotted\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Même question en choisissant l'autre valeur de lambda retenue par glmnet, i.e. \"reg.lasso.cv$lambda.min\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "10" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "'CV estimate of lambda : 0.42'" + ], + "text/latex": [ + "'CV estimate of lambda : 0.42'" + ], + "text/markdown": [ + "'CV estimate of lambda : 0.42'" + ], + "text/plain": [ + "[1] \"CV estimate of lambda : 0.42\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "14 x 1 sparse Matrix of class \"dgCMatrix\"\n", + " s1\n", + "(Intercept) -25.2741642\n", + "JOUR0 -0.9290224\n", + "JOUR1 . \n", + "MOCAGE 0.2934772\n", + "TEMPE 3.8980416\n", + "STATIONAls . \n", + "STATIONCad 7.2718012\n", + "STATIONPla 17.4465238\n", + "STATIONRam 1.6012211\n", + "VentMOD -0.7785773\n", + "VentANG 4.1349184\n", + "SRMH2O 114.8607426\n", + "LNO2 . \n", + "LNO 2.7787037" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 600, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# NEW : \n", + "# valeur estimée\n", + "paste(\"CV estimate of lambda :\", round(reg.lasso.cv$lambda.min, 3))\n", + "# modèle correspondant\n", + "coef(reg.lasso.cv, s = \"lambda.min\")\n", + "\n", + "options(repr.plot.width = 10, repr.plot.height = 10)\n", + "lmin <- log(reg.lasso.cv$lambda.min)\n", + "plot(reg.lasso, xvar = \"lambda\", label = TRUE, xlim=c(lmin - 0.01, lmin + 0.01))\n", + "abline(v = lmin, lty=\"dotted\")" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "Plot with title “linéaire, pénalité L1”" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Extraction des valeurs ajustées et des résidus\n", + "fit.lasso <- predict(reg.lasso.cv, s = \"lambda.min\", newx = x.mat)\n", + "res.lasso <- datappr$O3obs - fit.lasso\n", + "# Graphe des résidus\n", + "options(repr.plot.width = 12, repr.plot.height = 6)\n", + "par(mfrow = c(1, 2))\n", + "plot.res(fit.lm, res.lm, \"linéaire\")\n", + "plot.res(fit.lasso, res.lasso, \"linéaire, pénalité L1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "Plot with title “Linéaire, pénalité L1, lambda 1se”" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 1080 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Extraction des valeurs ajustées et des résidus\n", + "\n", + "fit.lasso <- predict(reg.lasso.cv, s = \"lambda.min\", newx = x.mat)\n", + "res.lasso <- datappr$O3obs - fit.lasso\n", + "\n", + "fit.lasso.1se <- predict(reg.lasso.cv, s = \"lambda.1se\", newx = x.mat) # NEW\n", + "res.lasso.1se <- datappr$O3obs - fit.lasso.1se # NEW\n", + "\n", + "# Graphe des résidus\n", + "options(repr.plot.width = 18, repr.plot.height = 6)\n", + "par(mfrow = c(1, 3))\n", + "plot.res(fit.lm, res.lm, \"Linéaire, sans sélection\")\n", + "plot.res(fit.lasso, res.lasso, \"Linéaire, pénalité L1, lambda min\")\n", + "plot.res(fit.lasso.1se, res.lasso.1se, \"Linéaire, pénalité L1, lambda 1se\") # NEW" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Commenter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Calculer le critère MSE (moyenne des carrés des résidus) pour les deux modèles. Pourquoi celui obtenu par LASSO est-il moins bon ? Quel critère LASSO minimise t-il ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Estimer l'erreur de généralisation du modèle de régression linéaire simple sans sélection de variables par validation croisée. Comparer avec celle du LASSO. Qu'observez-vous?" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "'Modèle linéaire sans séletion: 769.130879193'" + ], + "text/latex": [ + "'Modèle linéaire sans séletion: 769.130879193'" + ], + "text/markdown": [ + "'Modèle linéaire sans séletion: 769.130879193'" + ], + "text/plain": [ + "[1] \"Modèle linéaire sans séletion: 769.130879193\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "'LASSO avec lambda.min: 782.345739242146'" + ], + "text/latex": [ + "'LASSO avec lambda.min: 782.345739242146'" + ], + "text/markdown": [ + "'LASSO avec lambda.min: 782.345739242146'" + ], + "text/plain": [ + "[1] \"LASSO avec lambda.min: 782.345739242146\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "'LASSO avec lambda.1se: 859.658954687572'" + ], + "text/latex": [ + "'LASSO avec lambda.1se: 859.658954687572'" + ], + "text/markdown": [ + "'LASSO avec lambda.1se: 859.658954687572'" + ], + "text/plain": [ + "[1] \"LASSO avec lambda.1se: 859.658954687572\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NEW : \n", + "paste(\"Modèle linéaire sans séletion:\",mean(res.lm^2))\n", + "paste(\"LASSO avec lambda.min:\",mean(res.lasso^2))\n", + "paste(\"LASSO avec lambda.1se:\",mean(res.lasso.1se^2))" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "859.040199699821" + ], + "text/latex": [ + "859.040199699821" + ], + "text/markdown": [ + "859.040199699821" + ], + "text/plain": [ + "[1] 859.0402" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Call: cv.glmnet(x = x.mat, y = datappr[, 2]) \n", + "\n", + "Measure: Mean-Squared Error \n", + "\n", + " Lambda Index Measure SE Nonzero\n", + "min 0.420 45 808.1 64.42 10\n", + "1se 4.303 20 868.1 79.47 3\n" + ] + } + ], + "source": [ + "# NEW\n", + "V=10 ; nV=floor(nrow(datappr)/V)\n", + "S=sample(1:nrow(datappr),replace=FALSE)\n", + "error.CV = c()\n", + "for(v in 1:V)\n", + "{ # Rq : les deux dernières obs sont tjs dans l'échantillon d'apprentissage...\n", + " datappr.learn=datappr[-c(S[(nV*(v-1)):(nV*v)]),] \n", + " datappr.valid=datappr[c(S[(nV*(v-1)):(nV*v)]),]\n", + " error.CV=c(error.CV,mean((datappr.valid$O3obs-predict(aov(O3obs ~ ., data=datappr.learn),newdata=datappr.valid))^2))\n", + "}\n", + "mean(error.CV)\n", + "\n", + "print(reg.lasso.cv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modèle quadratique" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'étude suivante met en oeuvre toutes les interactions d'ordre 2 entre les variables. Il s'agit donc d'un modèle de régression quadratique. Il est estimé avec la fonction glm qui permet une sélection automatique de modèle. La méthode descendante est utilisée mais celle pas-à-pas pourrait également l'être. Ce type de procédure n'est pas implémentée en python." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sélection de variables par critère AIC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sélection descendante: à chaque étape, chaque modèle est comparé à tous les sous-modèles possibles obtenus par suppression d'une des interactions ou une des variables, à condition qu'elle ne soit pas présente dans une interaction. La variable sélectionnée et supprimée est celle qui fait décroîre le critère considéré : AIC ou *Akaïke Information Criterion*. \n", + "\n", + "**Q** Quel autre critère, équivalent à AIC dans le cas gaussien et de variance résiduelle connue, est utilisée en régression linéaire? \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:44.291201Z", + "start_time": "2019-11-18T09:22:04.508Z" + } + }, + "outputs": [], + "source": [ + "# Estimation du modèle de toute interaction d'ordre 2\n", + "reg.glm <- glm(O3obs ~ .^2, data = datappr)\n", + "# Recherche du meilleur modèle au sens \n", + "# du critère d'Akaïke par méthode descendante\n", + "reg.glm.step <- step(reg.glm, direction = \"backward\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:44.497378Z", + "start_time": "2019-11-18T09:22:04.515Z" + } + }, + "outputs": [], + "source": [ + "# Coefficients du modèle\n", + "anova(reg.glm.step, test = \"F\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sélection de variable par régularisation L1 (LASSO)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Comparer avec un modèle quadratique avec pénalité L1\n", + "x.mat2 <- model.matrix(O3obs ~ .^2 - 1, data = datappr)\n", + "reg.lasso2.cv <- cv.glmnet(y = datappr[, \"O3obs\"], x = x.mat2)\n", + "coef(reg.lasso2.cv, s = \"lambda.1se\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:44.635351Z", + "start_time": "2019-11-18T09:22:04.520Z" + } + }, + "outputs": [], + "source": [ + "# Extraction des valeurs ajustées et des résidus\n", + "fit.glm <- reg.glm.step$fitted.values\n", + "res.glm <- reg.glm.step$residuals\n", + "fit.lasso2 <- predict(reg.lasso2.cv, s = \"lambda.min\", newx = x.mat2)\n", + "res.lasso2 <- datappr$O3obs - fit.lasso2\n", + "\n", + "# Graphe des résidus\n", + "options(repr.plot.width = 8, repr.plot.height = 8)\n", + "par(mfrow = c(2, 2))\n", + "plot.res(fit.lm, res.lm, \"linéaire\")\n", + "plot.res(fit.lasso, res.lasso, \"linéaire, pénalité L1\")\n", + "plot.res(fit.glm, res.glm, \"quadratique, backward AIC\")\n", + "plot.res(fit.lasso2, res.lasso2, \"quadratique, pénalité L1\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " On remarque que la présence de certains interactions ou variables sont pertinentes au sens du critère d'Akaïke mais pas significative au sens du test de Fisher. Cette présence dans le modèle pourrait être plus finement analysée en considérant une estimation de l'erreur par validation croisée. L'idée serait de retirer une à une les variables ou interactions les moins significatives pour voir comment se comporte la validation croisée. D'autre part, si la procédure pas-à-pas conduit à un modèle différent, l'estimation de l'erreur par validation croisée permet également d'optimiser le choix.\n", + " \n", + "Ces raffinements ne s'avèrent pas efficaces sur ces données. Le modèle obtenu par minimisaiton du critère AIC est conservé." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prévision de l'échantillon test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le modèle \"optimal\" obtenu par la méthode stepwise est utilisé pour prédire l'échantillon test et estimer ainsi, sans biais, une erreur de prévision. Deux erreurs sont estimées ; la première est celle quadratique pour la régression tandis que la deuxième est issue de la matrice de confusion qui croise les dépassements de seuils prédits avec ceux effectivement observés. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Erreur de régression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:44.652288Z", + "start_time": "2019-11-18T09:22:05.132Z" + } + }, + "outputs": [], + "source": [ + "# Calcul des prévisions pour le nomdèle quadratique backward AIC\n", + "pred.glm <- predict(reg.glm.step, newdata = datestr)\n", + "# Erreur quadratique moyenne de prévision (MSE)\n", + "sum((pred.glm - datestr[, \"O3obs\"])^2) / nrow(datestr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:44.669514Z", + "start_time": "2019-11-18T09:22:05.139Z" + } + }, + "outputs": [], + "source": [ + "# Erreur quadratique par MOCAGE\n", + "sum((datestr[,\"MOCAGE\"] - datestr[,\"O3obs\"])^2) / nrow(datestr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Erreur de classification (matrice de confusion)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:44.689237Z", + "start_time": "2019-11-18T09:22:05.144Z" + } + }, + "outputs": [], + "source": [ + "# Matrice de confusion pour la prévision du dépassement de seuil\n", + "table(pred.glm > 150, datestr[, \"O3obs\"] > 150)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:44.714261Z", + "start_time": "2019-11-18T09:22:05.150Z" + } + }, + "outputs": [], + "source": [ + "# Matrice de confusion pour la prévision du \n", + "# dépassement de seuil par MOCAGE\n", + "table(datestr[, \"MOCAGE\"] > 150, datestr[, \"O3obs\"] > 150)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Noter ces erreurs pour les comparer avec celles obtenues par les autres méthodes. Noter l'asymétrie des erreurs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Prévision par modèle binomial](http://wikistat.fr/pdf/st-m-app-rlogit.pdf)\n", + "\n", + "Plutôt que de prévoir la concentration puis le dépassement, on peut se poser la question de savoir s'il ne serait pas pertinent de prévoir directement la présence ou l'absence d'un dépassement. La variable à modéliser étant binaire, c'est la régression logistique qui va être employée. Comme pour la régression, différentes stratégies de choix de modèle peuvent être utilisées et comparées avant d'estimer l'erreur de prévision sur l'échantillon test.\n", + "\n", + "### Régression logistique sans interaction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:44.819132Z", + "start_time": "2019-11-18T09:22:05.557Z" + } + }, + "outputs": [], + "source": [ + "# estimation du modèle complet\n", + "log.lm <- glm(DepSeuil ~. , data = datappq, family = binomial)\n", + "# significativité des paramètres\n", + "anova(log.lm, test = \"Chisq\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:45.012876Z", + "start_time": "2019-11-18T09:22:05.564Z" + } + }, + "outputs": [], + "source": [ + "# Recherche d'un modèle optimal au sens d'Akaïke\n", + "log.lm.step <- step(log.lm, direction = \"backward\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:45.052862Z", + "start_time": "2019-11-18T09:22:05.570Z" + } + }, + "outputs": [], + "source": [ + "# Modèle obtenu\n", + "anova(log.lm.step, test = \"Chisq\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:45.074743Z", + "start_time": "2019-11-18T09:22:05.576Z" + } + }, + "outputs": [], + "source": [ + "# matrice de confusion de l'échantillon d'apprentissage et erreur apparente\n", + "table(log.lm.step$fitted.values > 0.5, datappq[, \"DepSeuil\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Régression logistique avec interactions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Avec autant de variables et d'interactions donc de paramètres, l'estimation du modèle complet de régression logistique rencontre des soucis et affiche des *warnings* car certaines probabilité trop bien ajustés (0 ou 1) provoquent des divisions par 0. Ici une procédure *forward* ou mieux *stepwise* de sélection des variables et interactions conduit à des résultats raisonnables. Une méthode avec pénalisation L1 peut aussi être utilisée." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.096169Z", + "start_time": "2019-11-18T09:22:05.997Z" + } + }, + "outputs": [], + "source": [ + "# régression avec le modèle minimum\n", + "log.qm <- glm(DepSeuil ~ 1, data = datappq,family = binomial)\n", + "# algorithme stepwise en précisant le plus grand \n", + "# modèle possible\n", + "log.qm.step1 <- step(log.qm, direction = \"both\",\n", + " scope = list(lower = ~1, upper = ~(JOUR + MOCAGE + TEMPE + \n", + " STATION + VentMOD + VentANG + LNO2 + LNO + SRMH2O)^2), \n", + " family=binomial)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.158081Z", + "start_time": "2019-11-18T09:22:06.003Z" + } + }, + "outputs": [], + "source": [ + "anova(log.qm.step1, test = \"Chisq\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prévision de l'échantillon test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Matrice de confusion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.179001Z", + "start_time": "2019-11-18T09:22:06.010Z" + } + }, + "outputs": [], + "source": [ + "# Prévision du modèle quadratique\n", + "pred.log <- predict(log.qm.step1, newdata = datestq, type = \"response\")\n", + "# Matrice de confusion pour la prévision du \n", + "# dépassement de seuil\n", + "table(pred.log > 0.5, datestq[, \"DepSeuil\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparer avec l'approche précédente. Mémoriser les résultats obtenus pour comparer avec les autres méthodes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### [Courbe ROC](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il est également possible de construire une courbe ROC en association de la prévision obtenue à partir d'un modèle gaussien. En effet, la variation du seuil théorique de dépassement (150) va faire varier les proportions respectives des taux de vrais et faux positifs. Cela revient encore à faire varier le seuil d'une \"proba\" pour les valeurs de prévisions divisées par 300." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.280706Z", + "start_time": "2019-11-18T09:22:06.620Z" + } + }, + "outputs": [], + "source": [ + "library(ROCR) # Librairie à charger\n", + "roclogit <- predict(log.qm.step1, newdata = datestq, type=\"response\")\n", + "predlogit <- prediction(roclogit, datestq[, \"DepSeuil\"])\n", + "perflogit <- performance(predlogit, \"tpr\", \"fpr\")\n", + "# Tracé de la courbe\n", + "plot(perflogit, col = \"blue\")\n", + "\n", + "# Calculs pour la régression\n", + "rocglm <- pred.glm / 300\n", + "predglm <- prediction(rocglm, datestq[, \"DepSeuil\"])\n", + "perfglm <- performance(predglm, \"tpr\", \"fpr\")\n", + "# tracé de la courbe et ajout au graphe précédent.\n", + "plot(perfglm, col = \"blue\",lty=2, add = TRUE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que sont sensibilité et spécificité d'une courbe ROC?\n", + "\n", + "Les résultats obtenus dépendent évidemment en plus de l'échantillonnage initial entre apprentissage et test. Dans le cas où les courbes se croisent, cela signifie qu'il n'y a pas de prévision uniformément meilleure de l'occurrence de dépassement. Cela dépend de la sensibilité ou de la spécificité retenue pour le modèle. Ceci souligne l'importance de la bonne définition du critère à utiliser pour le choix d'une \"meilleure\" méthode. Ce choix dépend directement de celui , \"politique\" ou \"économique\" de sensibilité et / ou spécificité du modèle retenu. En d'autres termes, quel taux de fausse alerte, avec des imputations économiques évidentes, est supportable au regard des dépassements non détectés et donc de la dégradation sanitaire de la population à risque ?\n", + " \n", + "C'est une fois ce choix arrêté que le statisticien peut opérer une comparaison des méthodes en présence.\n", + "\n", + "**Q** Les performances des deux approches gaussiennes et binomiales sont-elles très différentes?\n", + "\n", + "**Q** Sur le graphe ci-dessus, ajouter la courbe ROC pour le modèle déterministe MOCAGE. Qu'observez-vous?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Analyse discriminante](http://wikistat.fr/pdf/st-m-app-add.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " L'objectif est de comparer les trois méthodes d'analyses discriminantes disponibles dans R: `lda` paramétrique linéaire (homoscédasticité), `qda` paramétrique quadratique (hétéroscédasticité) sous hypothèse gaussienne et celle non-paramétrique des $k$ plus proches voisins.\n", + " \n", + "**Q** Quel critère d'affectation est utilisé en `lda`?\n", + "\n", + "**Q** Que signifient les hypothèses d'homo ou d'hétéroscédasticité?\n", + "\n", + "**Q** Quelle fonction est estimée \"non paramétriquement\" par l'algorithme des $k$ plus proches voisins?\n", + " \n", + "*Attention*, ces techniques n'acceptent par principe que des variables explicatives ou prédictives quantitatives. Néanmoins, une variable qualitative à deux modalités, par exemple le type de jour, peut être considérée comme quantitative sous la forme d'une fonction indicatrice prenant ses valeurs dans $\\{0, 1\\}$ et, de façon plus \"abusive\", une variable ordinale est considérée comme \"réelle\". Dans ce dernier cas, il ne faut pas tenter d'interpréter les fonctions de discrimination, juste considérer des erreurs de prévision. La variable *Station* n'est pas prise en compte.\n", + "\n", + "La bibliothèque standard de R (`MASS`) pour l'analyse discriminante ne propose pas de procédure automatique de choix de variable mais, dans cet exemple, les variables sont peu nombreuses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimation des modèles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.294675Z", + "start_time": "2019-11-18T09:22:07.823Z" + } + }, + "outputs": [], + "source": [ + "library(MASS) # chargement des librairies\n", + "library(class) # pour kNN" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.341174Z", + "start_time": "2019-11-18T09:22:07.829Z" + } + }, + "outputs": [], + "source": [ + "# analyse discriminante linéaire\n", + "disc.lda=lda(DepSeuil~.,data=datappq[,-4]) \n", + "# analyse discriminante quadratique \n", + "disc.qda=qda(DepSeuil~.,data=datappq[,-4]) \n", + "# k plus proches voisins\n", + "disc.knn=knn(datappq[,c(-4,-10)],datappq[,c(-4,-10)],datappq$DepSeuil,k=10) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Noter le manque d'homogénéité des commandes de R issues de librairies différentes. L'indice de colonne négatif ($-10$) permet de retirer la colonne contenant la variable à prédire de type facteur. Celle-ci est mentionnée en troisième paramètre pour les données d'apprentissage. La librairie [caret](http://topepo.github.io/caret/index.html) contourne ces difficultés en englobant toutes les librairies d'apprentissage et en homogénéisant les appels pour l'estimation et la prévision des modèles. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimation de l'erreur de prévision par validation croisée" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.362843Z", + "start_time": "2019-11-18T09:22:08.228Z" + } + }, + "outputs": [], + "source": [ + "# erreur par validation croisée en analyse discriminante linéaire\n", + "disc.lda=lda(DepSeuil~.,data=datappq[,-4],CV=T) \n", + "# estimer le taux d'erreur à partir de la matrice de confusion\n", + "table(datappq[,\"DepSeuil\"],disc.lda$class) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.385631Z", + "start_time": "2019-11-18T09:22:08.238Z" + } + }, + "outputs": [], + "source": [ + "# analyse discriminante quadratique\n", + "disc.qda=qda(DepSeuil~.,data=datappq[,-4],CV=T) \n", + "table(datappq[,\"DepSeuil\"],disc.qda$class) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour knn, le choix du nombre de voisins $k$ doit être optimisé par validation croisée mais la procédure proposée par la bibliothèque `class` est celle *leave-one-out*, donc trop coûteuse en calcul pour des gros fichiers. Il serait simple de la programmer mais une autre bibliothèque (`e1071`) propose déjà une batterie de fonctions de validation croisée pour de nombreuses techniques de discrimination. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.922719Z", + "start_time": "2019-11-18T09:22:08.551Z" + } + }, + "outputs": [], + "source": [ + "# k plus proches voisins: optimisation de k\n", + "library(e1071)\n", + "plot(tune.knn(as.matrix(datappq[,c(-4,-10)]),as.factor(datappq[,10]),k=2:20))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Quelle procédure de validation croisée est exécutée par défaut par la fonction `tune`?\n", + "\n", + "Lancer plusieurs exécutions successives de cette \"optimisation\".\n", + "\n", + "**Q** Pourquoi la valeur de $k$ optimale diffère à chaque exécution? Comment choisir k ? \n", + "\n", + "Comparer avec les erreurs précédentes estimées également par validation croisée. \n", + "\n", + "**Q** Quelle analyse discriminante retenir ? Pourquoi?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prévision de l'échantillon test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Matices de confusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les commandes suivantes calculent la matrice de confusion pour la \"meilleure\" méthode d'analyse discriminante au sens de la validation croisée. Cette \"meilleure\" méthode peut être edifférente d'un participant à l'autre." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.941352Z", + "start_time": "2019-11-18T09:22:09.176Z" + } + }, + "outputs": [], + "source": [ + "disc.lda=lda(DepSeuil~.,data=datappq[,-4]) \n", + "table(predict(disc.lda,datestq[,-4])$class,datestq[,\"DepSeuil\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A titre indicatif, voici l'estimation de l'erreur sur l'échantillon test pour la méthode des $k$ plus proches voisins." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:46.972490Z", + "start_time": "2019-11-18T09:22:09.406Z" + } + }, + "outputs": [], + "source": [ + "disc.knn=knn(as.matrix(datappq[,c(-4,-10)]),as.matrix(datestq[,c(-4,-10)]),datappq$DepSeuil,k=15)\n", + "table(disc.knn,datestq$DepSeuil)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Courbes ROC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:47.043989Z", + "start_time": "2019-11-18T09:22:09.622Z" + } + }, + "outputs": [], + "source": [ + "library(ROCR)\n", + "ROCdiscrim=predict(disc.lda,datestq[,c(-4)])$posterior[,2]\n", + "preddiscrim=prediction(ROCdiscrim,datestq$DepSeuil)\n", + "perfdiscrim=performance(preddiscrim,\"tpr\",\"fpr\")\n", + "# tracer les courbes ROC en les superposant pour mieux comparer\n", + "plot(perflogit,col=\"blue\") \n", + "plot(perfdiscrim,col=\"magenta\",add=TRUE) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Une méthode est-elle uniformément meilleure sur cet échantillon test ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Arbre de décision binaire](http://wikistat.fr/pdf/st-m-app-cart.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La librairie `rpart` est celle la plus couramment utilisée pour la construction d'arbres de décision. Deux types d'arbre peuvent être estimer selon que la variable à modéliser est la concentration d'ozone (arbre de régression) ou directement le dépassement du seuil (arbre de discrimination ou de décision). Différents paramètres contrôlent l'exécution de l'algorithme: la pénalisation minimale (`cp`) pour la construction de l'arbre maximal, le nombre minimal d'observation par noeud, le nombre de validations croisées (par défaut 10)... cf. l'aide en ligne (?rpart.control) pour plus de détails mais celle-ci n'est pas très explicite sur certains paramètres, c'est le travers des logiciels \"libres\".\n", + "\n", + "**NB.** Une séquence de valeurs de la pénalisation `cp` est associée à une séquence d'arbres emboîtés.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimation et élagage de l'arbre de régression\n", + "**Q** Quel critère est optimisé lors de la création d'un noeud? de l'arbre?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:47.088662Z", + "start_time": "2019-11-18T09:22:10.466Z" + } + }, + "outputs": [], + "source": [ + "library(rpart) # chargement de la librairie\n", + "tree.reg=rpart(O3obs~.,data=datappr,control=rpart.control(cp=0.001))\n", + "# La commande ci-dessous fournit un descriptif de l'arbre obtenu\n", + "# summary(tree.reg) \n", + "# mais un graphe est préférable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "library(rpart)\n", + "help(rpart)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(rpart.control)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:47.181644Z", + "start_time": "2019-11-18T09:22:10.473Z" + } + }, + "outputs": [], + "source": [ + "plot(tree.reg)\n", + "text(tree.reg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'arbre est illisible et présente trop de feuilles pour une bonne prévision (sur-apprentissage), il est nécessaire d'en réduire le nombre par élagage. Les commandes suivantes calculent les prévisions obtenues par validation croisée *10-fold* pour chaque arbre élagué suivant les valeurs successives du coefficient de complexité. La séquence de ces valeurs est implicitement celle fournit par `rpart`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:47.218915Z", + "start_time": "2019-11-18T09:22:10.686Z" + } + }, + "outputs": [], + "source": [ + "xmat=xpred.rpart(tree.reg)\n", + "xerr=(xmat-datappr[,\"O3obs\"])^2\n", + "CVerr=apply(xerr,2,sum)\n", + "CVerr # CP erreur" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(xpred.rpart)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chercher la valeur de `cp` correspondant à la plus petite erreur puis l'utiliser la construction del'arbre." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:47.236505Z", + "start_time": "2019-11-18T09:22:10.912Z" + } + }, + "outputs": [], + "source": [ + "as.numeric(attributes(which.min(CVerr))$names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:47.279074Z", + "start_time": "2019-11-18T09:22:10.919Z" + } + }, + "outputs": [], + "source": [ + "tree.reg=rpart(O3obs~.,data=datappr,control=rpart.control(cp=as.numeric(attributes(which.min(CVerr))$names)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La librairie `partykit` propose une construction graphique de l'arbre:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:47.731129Z", + "start_time": "2019-11-18T09:22:11.150Z" + } + }, + "outputs": [], + "source": [ + "library(partykit)\n", + "plot(as.party(tree.reg), type=\"simple\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La fenêtre est trop petite pour représenter les distributions (histogramme) de la variable cible (concentration en ozone) dans chaque feuille. \n", + "\n", + "**Q** Quelle est la variable qui contribue le plus à l'interprétation?\n", + "\n", + "Graphe des résidus" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:47.831850Z", + "start_time": "2019-11-18T09:22:11.369Z" + } + }, + "outputs": [], + "source": [ + "fit.tree=predict(tree.reg)\n", + "res.tree=fit.tree-datappr[,\"O3obs\"]\n", + "plot.res(fit.tree,res.tree)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** A quoi est due la structure particulière de ce graphe?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimation et élagage d'un arbre de discrimination" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dans le cas d'une discrimination, le critère par défaut est l'indice de concentration de Gini ; il est possible de préciser un autre critère (split=\"information\") ainsi que des poids sur les observations, une matrice de coûts de mauvais classement ainsi que des probabilités *a priori* (?rpart pour plus de détails).\n", + "\n", + "**Q** Quel autre critère d'hétérogénéité est utilisé?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:47.936071Z", + "start_time": "2019-11-18T09:22:12.009Z" + } + }, + "outputs": [], + "source": [ + "tree.dis=rpart(DepSeuil~.,data=datappq,parms=list(split=\"information\"),cp=0.001)\n", + "plot(tree.dis) \n", + "text(tree.dis) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La même procédure d'élagage par validation croisée est mise en place mais avec un expression différente de l'erreur de prévision: taux de mal classés plutôt qu'erreur quadratique." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:47.989031Z", + "start_time": "2019-11-18T09:22:12.228Z" + } + }, + "outputs": [], + "source": [ + "xmat = xpred.rpart(tree.dis)\n", + "# Comparaison des valeurs prédite et observée\n", + "xerr=datappq$DepSeuil!= (xmat>1.5) \n", + "# Calcul des estimations des taux d'erreur\n", + "CVerr=apply(xerr, 2, sum)/nrow(xerr)\n", + "CVerr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:48.011656Z", + "start_time": "2019-11-18T09:22:12.235Z" + } + }, + "outputs": [], + "source": [ + "as.numeric(attributes(which.min(CVerr))$names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:48.218396Z", + "start_time": "2019-11-18T09:22:12.241Z" + } + }, + "outputs": [], + "source": [ + "tree.dis=rpart(DepSeuil~.,data=datappq,parms=list(split=\"information\"),\n", + " cp=as.numeric(attributes(which.min(CVerr))$names))\n", + "plot(as.party(tree.dis), type=\"simple\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prévision de l'échantillon test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Différentes prévisions sont considérées assorties des erreurs estimées sur l'échantillon test. Prévision quantitative de la concentration, prévision de dépassement à partir de la prévision quantitative et directement la prévision de dépassement à partir de l'arbre de décision. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Erreur de régression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:48.248763Z", + "start_time": "2019-11-18T09:22:12.672Z" + } + }, + "outputs": [], + "source": [ + "# Calcul des prévisions\n", + "pred.treer=predict(tree.reg,newdata=datestr)\n", + "pred.treeq=predict(tree.dis,newdata=datestq,type=\"class\") \n", + "# Erreur quadratique moyenne de prévision en régression\n", + "sum((pred.treer-datestr[,\"O3obs\"])^2)/nrow(datestr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Erreur de classification (matrice de confusion)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:48.267073Z", + "start_time": "2019-11-18T09:22:12.681Z" + } + }, + "outputs": [], + "source": [ + "# Matrice de confusion pour la prévision du \n", + "# dépassement de seuil (régression)\n", + "table(pred.treer>150,datestr[,\"O3obs\"]>150)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:48.285260Z", + "start_time": "2019-11-18T09:22:12.688Z" + } + }, + "outputs": [], + "source": [ + "# Même chose pour l'arbre de discrimination\n", + "table(pred.treeq,datestq[,\"DepSeuil\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Quelle stratégie semble meilleure à ce niveau?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Courbes ROC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:48.365865Z", + "start_time": "2019-11-18T09:22:13.108Z" + } + }, + "outputs": [], + "source": [ + "ROCregtree=pred.treer/300\n", + "predregtree=prediction(ROCregtree,datestq$DepSeuil)\n", + "perfregtree=performance(predregtree,\"tpr\",\"fpr\")\n", + "ROCdistree=predict(tree.dis,newdata=datestq,type=\"prob\")[,2]\n", + "preddistree=prediction(ROCdistree,datestq$DepSeuil)\n", + "perfdistree=performance(preddistree,\"tpr\",\"fpr\")\n", + "# tracer les courbes ROC en les superposant \n", + "# pour mieux comparer\n", + "plot(perflogit,col=\"blue\")\n", + "plot(perfregtree,col=\"orange\",lty=2,add=TRUE) \n", + "plot(perfdistree,col=\"green\",add=TRUE) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparer les qualités de prévision.\n", + "\n", + "**Q** Une meilleure méthode se dégage-t-elle?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Réseau de neurones](http://wikistat.fr/pdf/st-m-app-rn.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il s'agit d'estimer un modèle de type *perceptron* avec en entrée les variables qualitatives ou quantitatives et en sortie la variable à prévoir. Des fonctions R pour l'apprentissage d'un perceptron élémentaire ont été réalisées par différents auteurs et sont accessibles sur le réseau. La librairie `nnet` de (Ripley, 1999), est limitée au perceptron à une couche. Ce n'est pas de l'*apprentissage profond* ! mais suffisant dans bien des cas. Une librairie R associée au logiciel éponyme H2O propose des réseaux à plusieurs couches et \"convolutionnels\".\n", + "\n", + "Comme pour les arbres, la variable à expliquer est soit quantitative soit qualitative ; la fonction de transfert du neurone de sortie d'un réseau doit être adaptée en conséquence. \n", + "\n", + "**Q** Quelle fonction de transfert pour le dernier neurone en régression ?\n", + "\n", + "**Q** Quelle focntion de transfert pour le dernier neuronne en discrimination binaire?\n", + "\n", + "**Q** Quid de la discrimination avec plusieurs classes?\n", + "\n", + "**Q** Quel est le choix par défaut pour les neurones de la couche cachée?\n", + "\n", + "Différentes stratégies sont proposées pour éviter le sur-apprentissage. La première conciste à optimiser le nombre de neurones sur la couche cachée. Très approximativement il est d'usage de considérer, qu'en moyenne, il faut une taille d'échantillon d'apprentissage 10 fois supérieure au nombre de poids c'est-à-dire au nombre de paramètres à estimer. On remarque qu'ici la taille de l'échantillon d'apprentissage (832) est modeste pour une application raisonnable du perceptron. Seuls des nombres restreints de neurones peuvent être considérés et sur une seule couche cachée. \n", + "\n", + "**Q** Quel est le paramètre `decay` de la fonction `nnet`?\n", + "\n", + "**Q** Indiquer une autre façon déviter le sur-apprentissage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cas de la régression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:23:48.781644Z", + "start_time": "2019-11-18T09:22:14.471Z" + } + }, + "outputs": [], + "source": [ + "library(MASS)\n", + "library(nnet)\n", + "# apprentissage\n", + "# attention au paramètre linout dans le cas de la régression\n", + "nnet.reg=nnet(O3obs~.,data=datappr,size=5,decay=1,linout=TRUE,maxit=500) \n", + "summary(nnet.reg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La commande donne la \"trace\" de l'exécution avec le comportement de la convergence mais le détail des poids de chaque entrée de chaque neurone ne constituent pas des résultats très explicites ! Contrôler le nombre de poids estimés.\n", + "\n", + "L'optimisation des paramètres nécessite encore le passage par la validation croisée. Il n'y a pas de fonction dans la librairie `nnet` permettant de le faire mais la fonction ` tune.nnet` de la librairie `e1071` est adaptée à cette démarche." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:00.187792Z", + "start_time": "2019-11-18T09:22:14.679Z" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "library(e1071)\n", + "plot(tune.nnet(O3obs~.,data=datappr,size=c(2,3,4),decay=c(1,2,3),maxit=200,linout=TRUE))\n", + "plot(tune.nnet(O3obs~.,data=datappr,size=4:5,decay=1:10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Faire éventuellement varier la grille des paramètres (zoom), noter la taille et le `decay` optimaux. Il faudrait aussi faire varier le nombre total d'itérations. Cela risque de prendre un peu de temps ! Noter également que chaque exécution donne des résultats différents... il n'est donc pas très utile d'y passer beaucoup de temps !\n", + "\n", + "Ré-estimer le modèle supposé optimal avant de tracer le graphe des résidus. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:00.436424Z", + "start_time": "2019-11-18T09:22:14.895Z" + } + }, + "outputs": [], + "source": [ + "nnet.reg=nnet(O3obs~.,data=datappr,size=3,decay=2,linout=TRUE,maxit=200)\n", + "# calcul et graphe des résidus\n", + "fit.nnetr=predict(nnet.reg,data=datappr)\n", + "res.nnetr=fit.nnetr-datappr[,\"O3obs\"]\n", + "plot.res(fit.nnetr,res.nnetr,titre=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cas de la discrimination" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:00.463554Z", + "start_time": "2019-11-18T09:22:15.102Z" + } + }, + "outputs": [], + "source": [ + "# apprentissage\n", + "nnet.dis=nnet(DepSeuil~.,data=datappq,size=5,decay=0) \n", + "summary(nnet.reg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La validation croisée est toujours nécessaire afin de tenter d'optimiser les choix en présence : nombre de neurones, `decay` et éventuellement le nombre max d'itérations. \n", + "\n", + "L'initialisation de l'apprentissage d'un réseau de neurone comme celle de l'estimation de l'erreur par validation croisée sont aléatoires. Chaque exécution donne donc des résultats différents. À ce niveau, il serait intéressant de construire un plan d'expérience à deux facteurs (ici, les paramètres de taille et `decay`) de chacun trois niveaux. Plusieurs réalisations pour chaque combinaison des niveaux suivies d'un test classique d'anova permettraient de se faire une idée plus juste de l'influence de ces facteurs sur l'erreur. \n", + "\n", + "Noter la taille et le `decay` optimaux et ré-estimer le modèle pour ces valeurs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:25.823681Z", + "start_time": "2019-11-18T09:22:15.309Z" + } + }, + "outputs": [], + "source": [ + "plot(tune.nnet(DepSeuil~.,data=datappq,size=c(3,4,5),decay=c(0,1,2),maxit=200,linout=FALSE))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:25.936811Z", + "start_time": "2019-11-18T09:22:15.315Z" + } + }, + "outputs": [], + "source": [ + "nnet.dis=nnet(DepSeuil~.,data=datappq,size=5,decay=1) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prévisions de l'échantillon test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Différentes prévisions sont considérées assorties des erreurs estimées sur l'échantillon test. Prévision quantitative de la concentration, prévision de dépassement à partir de la prévision quantitative et directement la prévision de dépassement à partir de l'arbre de décision. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Erreur de régression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:25.972074Z", + "start_time": "2019-11-18T09:22:15.713Z" + } + }, + "outputs": [], + "source": [ + "# Calcul des prévisions\n", + "pred.nnetr=predict(nnet.reg,newdata=datestr)\n", + "pred.nnetq=predict(nnet.dis,newdata=datestq) \n", + "# Erreur quadratique moyenne de prévision\n", + "sum((pred.nnetr-datestr[,\"O3obs\"])^2)/nrow(datestr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Erreur de classification (matrice de confusion)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:25.996337Z", + "start_time": "2019-11-18T09:22:15.718Z" + } + }, + "outputs": [], + "source": [ + "# Matrice de confusion pour la prévision du \n", + "# dépassement de seuil (régression)\n", + "table(pred.nnetr>150,datestr[,\"O3obs\"]>150)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:26.022088Z", + "start_time": "2019-11-18T09:22:15.725Z" + } + }, + "outputs": [], + "source": [ + "# Même chose pour la discrimination\n", + "table(pred.nnetq>0.5,datestq[,\"DepSeuil\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Courbes ROC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:26.112355Z", + "start_time": "2019-11-18T09:22:15.926Z" + } + }, + "outputs": [], + "source": [ + "library(ROCR)\n", + "rocnnetr=pred.nnetr/300\n", + "prednnetr=prediction(rocnnetr,datestq$DepSeuil)\n", + "perfnnetr=performance(prednnetr,\"tpr\",\"fpr\")\n", + "\n", + "rocnnetq=pred.nnetq\n", + "prednnetq=prediction(rocnnetq,datestq$DepSeuil)\n", + "perfnnetq=performance(prednnetq,\"tpr\",\"fpr\")\n", + "\n", + "# tracer les courbes ROC en les superposant pour mieux comparer\n", + "plot(perflogit,col=\"blue\")\n", + "plot(perfnnetr,col=\"darkgreen\",lty=2,add=TRUE) \n", + "plot(perfnnetq,col=\"darkgreen\",add=TRUE) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Une méthode semble-t-elle significativement meilleure?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Agrégation de modèles](http://wikistat.fr/pdf/st-m-app-agreg.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les sections précédentes ont permis d'expérimenter les constructions d'un modèle de prévision assorties du problème récurrent lié à l'optimisation de la complexité d'un modèle. Cette section aborde d'autres stratégies dont l'objectif est de s'affranchir de ce problème de choix, par des méthodes se montrant pas ou très peu sensibles au sur-apprentissage ; c'est le cas des algorithmes d'agrégation de modèles.\n", + "\n", + "Cette section propose de mettre en évidence la plus ou moins grande influence des paramètres de ces méthodes. \n", + "* *Random forest*: nombre d'arbres et `mtry` et intérêt des critères de Breiman permettant de mesurer l'influence des variables au sein d'une famille agrégée de modèles. \n", + "* Le *bagging*, cas particulier de forêt aléatoire, n'est pas traité;\n", + "* *Boosting*: profondeur d'arbre, nombre d'itérations ou d'arbres et coefficient de *shrinkage*.\n", + "\n", + "**Q** Quel est le paramètre `mtry` de la fonction `randomForest`?\n", + "\n", + "**Q** En quoi le bagging est un cas particulier des forêts aléatoires?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Forêts aléatoires" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le programme est disponible dans la librairie *randomForest*. Il est écrit en fortran, donc en principe efficace en terme de rapidité d'exécution, et facile à utiliser grâce à une interface avec R. La comparaison avec Python montre qu'il n'est finalement pas très efficace sans doute à cause de l'interface avec R. Les paramètres et sorties sont explicités dans l'aide en ligne.\n", + "\n", + "En R et pour des gros fichiers, privilégier la librairie `ranger` à la place de `ranfomForest`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "library(randomForest)\n", + "help(randomForest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Régression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:27.791580Z", + "start_time": "2019-11-18T09:22:17.440Z" + } + }, + "outputs": [], + "source": [ + "library(randomForest)\n", + "rf.reg=randomForest(O3obs~., data=datappr,xtest=datestr[,-2],ytest=datestr[,\"O3obs\"],\n", + " ntree=500,do.trace=50,importance=TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "attributes(rf.reg)\n", + "rf.reg$mtry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Quelles est la valeur par défaut de `mtry`?\n", + "\n", + "Relancer en faisant varier les paramètres `mtry` et `ntree` pour expérimenter leur peu d'influence sur les erreurs.\n", + "\n", + "Calcul et graphe des résidus." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:27.893162Z", + "start_time": "2019-11-18T09:22:17.662Z" + } + }, + "outputs": [], + "source": [ + "fit.rfr=rf.reg$predicted\n", + "res.rfr=fit.rfr-datappr[,\"O3obs\"]\n", + "plot.res(fit.rfr,res.rfr,titre=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Discrimination\n", + "**Q** Quelle est la valeur par défaut de `mtry`?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:28.378755Z", + "start_time": "2019-11-18T09:22:17.947Z" + } + }, + "outputs": [], + "source": [ + "rf.dis=randomForest(DepSeuil~.,data=datappq,xtest=datestq[,-10],ytest=datestq[,\n", + " \"DepSeuil\"],ntree=500,do.trace=50,importance=TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rf.dis$importance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(randomForest)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rf.dis$mtry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Commenter les erreurs, tester d'autres exécutions avec d'autres valeurs des paramètres." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Importance des variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le modèle obtenu est ininterprétable mais des coefficients estiment les contributions des variables dans leur participation à la discrimination. Comparer avec les variables sélectionnées par les autres modèles. deux critères d'importance sont proposés.\n", + "\n", + "**Q** Quelles sont les deux mesures d'importance des variables?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:28.476696Z", + "start_time": "2019-11-18T09:22:18.805Z" + } + }, + "outputs": [], + "source": [ + "sort(round(importance(rf.reg), 2)[,1], decreasing=TRUE)\n", + "sort(round(importance(rf.dis), 2)[,4], decreasing=TRUE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Boosting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Deux librairies proposent des versions relativement sophistiquées des algorithmes de *boosting* dans R. La librairie *boost* propose 4 approches : *adaboost, bagboost* et deux *logitboost*. Développées pour une problématique particulière : l'analyse des données d'expression génomique, elle n'est peut-être pas complètement adaptée aux données étudiées ; elles se limitent à des prédicteurs quantitatifs et peut fournir des résultats étranges. La librairie *gbm* lui est préférée ; elle offre aussi plusieurs versions dépendant de la fonction coût choisie. Une librairie plus récente `xgboost` intègre des fonctionnalités de parallélisation (pas sous Windows) et fait intervenir plusieurs autres paramètres.\n", + "\n", + "La variable à prévoir doit être codée numériquement (0,1) pour cette implémentation. Le nombre d'itérations, ou nombre d'arbres, est paramétré ainsi qu'un coefficient de rétrécissement (*shrinkage*).\n", + "\n", + "**Q** Comment intervient le *schrinkage* en *boosting*? \n", + "\n", + "**Q** Pour quel boosting? Ou que signifie `gbm`?\n", + "\n", + "*Attention*, par défaut, ce paramètre a une valeur très faible (0.001) et il faut un nombre important d'itérations (d'arbres) pour atteindre une estimation raisonnable. La qualité est visualisée par un graphe représentant l'évolution de l'erreur d'apprentissage. D'autre part, une procédure de validation croisée est incorporée afin d'optimiser le nombre d'arbres car la version de *boosting* considérée est (légèrement) sujette au sur-apprentissage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Régression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class(ozone$STATION)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:31.408193Z", + "start_time": "2019-11-18T09:22:19.423Z" + } + }, + "outputs": [], + "source": [ + "library(gbm)\n", + "boost.reg = gbm(O3obs ~ ., data = datappr, distribution = \"gaussian\", n.trees = 500, \n", + " cv.folds = 10, n.minobsinnode = 5, shrinkage = 0.03, verbose = FALSE)\n", + "# fixer verbose à FALSE pour éviter trop de sorties\n", + "plot(boost.reg$cv.error, type = \"l\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:31.484639Z", + "start_time": "2019-11-18T09:22:19.430Z" + } + }, + "outputs": [], + "source": [ + "# nombre optimal d'itérations par valiation croisée\n", + "best.iter=gbm.perf(boost.reg,method=\"cv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut s'assurer de l'absence d'un phénomène de sur-apprentissage critique en calculant puis traçant l'évolution de l'erreur sur l'échantillon test en fonction du nombre d'arbre dans le modèle. L'erreur reste stable autour du nombre d'arbres sélectionné et matérialisé par la ligne verticale. \n", + "\n", + "**Q** Tester ces fonctions en faisant varier le coefficient de rétrécissement.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:32.465883Z", + "start_time": "2019-11-18T09:22:19.638Z" + } + }, + "outputs": [], + "source": [ + "test=numeric()\n", + "for (i in 10:500){\n", + "pred.test=predict(boost.reg,newdata=datestr,n.trees=i)\n", + "err=sum((pred.test-datestr[,\"O3obs\"])^2)/nrow(datestr)\n", + "test=c(test,err)}\n", + "plot(10:500,test,type=\"l\")\n", + "abline(v=best.iter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Discrimination" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Attention, la variable à modéliser doit être codée $(0, 1)$ et il faut préciser un autre paramètre de distribution pour considérer le bon terme d'erreur." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:35.441056Z", + "start_time": "2019-11-18T09:22:20.049Z" + } + }, + "outputs": [], + "source": [ + "datappq2=datappq\n", + "datappq2[,\"DepSeuil\"]=as.numeric(datappq[,\"DepSeuil\"])-1\n", + "boost.dis=gbm(DepSeuil~.,data=datappq2,distribution=\"adaboost\",n.trees=500, cv.folds=10,\n", + " n.minobsinnode = 5,shrinkage=0.03,verbose=FALSE)\n", + "plot(boost.dis$cv.error,type=\"l\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:35.510693Z", + "start_time": "2019-11-18T09:22:20.056Z" + } + }, + "outputs": [], + "source": [ + "# nombre optimal d'itérations \n", + "best.ited=gbm.perf(boost.dis,method=\"cv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comme pour la régression, il est possible de faire varier le coefficient de rétrécissement en l'associant au nombre d'arbres dans le modèle.\n", + "\n", + "Calcul des résidus et graphe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:35.623088Z", + "start_time": "2019-11-18T09:22:20.260Z" + } + }, + "outputs": [], + "source": [ + "fit.boostr=boost.reg$fit\n", + "res.boostr=fit.boostr-datappr[,\"O3obs\"]\n", + "plot.res(fit.boostr,res.boostr,titre=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prévision de l'échantillon test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Erreur de régression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:28.399531Z", + "start_time": "2019-11-18T09:22:18.361Z" + } + }, + "outputs": [], + "source": [ + "# Forêts aléatoires\n", + "pred.rfr=rf.reg$test$predicted\n", + "pred.rfq=rf.dis$test$predicted\n", + "# Erreur quadratique moyenne de prévision\n", + "sum((pred.rfr-datestr[,\"O3obs\"])^2)/nrow(datestr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:35.648538Z", + "start_time": "2019-11-18T09:22:20.473Z" + } + }, + "outputs": [], + "source": [ + "# Boosting \n", + "pred.boostr=predict(boost.reg,newdata=datestr,n.trees=best.iter)\n", + "# Erreur quadratique moyenne de prévision\n", + "sum((pred.boostr-datestr[,\"O3obs\"])^2)/nrow(datestr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Erreur de classification (matrices de confusion)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:28.427113Z", + "start_time": "2019-11-18T09:22:18.371Z" + } + }, + "outputs": [], + "source": [ + "# Forêts aléatoires\n", + "# Matrice de confusion pour la prévision du \n", + "# dépassement de seuil (régression)\n", + "table(pred.rfr>150,datestr[,\"O3obs\"]>150)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:28.447982Z", + "start_time": "2019-11-18T09:22:18.379Z" + } + }, + "outputs": [], + "source": [ + "# Forêts aléatoires\n", + "# Même chose pour la discrimination\n", + "table(pred.rfq,datestq[,\"DepSeuil\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:35.683005Z", + "start_time": "2019-11-18T09:22:20.480Z" + } + }, + "outputs": [], + "source": [ + "# Boosting \n", + "# Matrice de confusion pour la prévision \n", + "# du dépassement de seuil (régression)\n", + "table(pred.boostr>150,datestr[,\"O3obs\"]>150)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:35.705652Z", + "start_time": "2019-11-18T09:22:20.487Z" + } + }, + "outputs": [], + "source": [ + "# Boosting \n", + "# Même chose pour la discrimination\n", + "pred.boostd=predict(boost.dis,newdata=datestq,n.trees=best.ited)\n", + "table(as.factor(sign(pred.boostd)),datestq[,\"DepSeuil\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Quelle stratégie d'agrégation de modèles vous semble fournir le meilleur résultat de prévision? \n", + "\n", + "**Q** Est-elle, sur ce jeu de données, plus efficace que les modèles classiques expérimentés auparavant ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Courbes ROC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:35.789943Z", + "start_time": "2019-11-18T09:22:20.890Z" + } + }, + "outputs": [], + "source": [ + "# Forêts aléatoires\n", + "rocrfr=pred.rfr/300\n", + "predrfr=prediction(rocrfr,datestq$DepSeuil)\n", + "perfrfr=performance(predrfr,\"tpr\",\"fpr\")\n", + "\n", + "# Boosting\n", + "rocbstr=pred.boostr/300\n", + "predbstr=prediction(rocbstr,datestq$DepSeuil)\n", + "perfbstr=performance(predbstr,\"tpr\",\"fpr\")\n", + "\n", + "# tracer les courbes ROC en les superposant \n", + "# pour mieux comparer\n", + "plot(perflogit,col=\"blue\")\n", + "plot(perfrfr,col=\"purple\",lty=2,add=TRUE) \n", + "plot(perfbstr,col=\"purple\",add=TRUE) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Qu'indique la comparaison des coubes ROC?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 4" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## [Séparateur à Vaste Marge (SVM)](http://wikistat.fr/pdf/st-m-app-svm.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Malgré les assurances théoriques concernant ce type d'algorithme, les résultats dépendant fortement du choix des paramètres. Nous nous limiterons d'abord au noyau gaussien (choix par défaut) ; la fonction `tune.svm` permet de tester facilement plusieurs situations en estimant la qualité de prévision par validation croisée sur une grille. Le temps d'exécution en R est un peu long... \n", + "\n", + "**Q** Le temps d'exécution pour les SVM est-il plus sensible au nombre d'observations ou au nombre de varaibles ? Pourquoi ?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Régression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bien qu'initialement développés dans le cas d'une variable binaire, les SVM ont été étendus aux problèmes de régression. L'estimation et l'optimisation du coefficient de pénalisation sont obtenues par les commandes suivantes. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:42.490229Z", + "start_time": "2019-11-18T09:22:22.305Z" + } + }, + "outputs": [], + "source": [ + "library(e1071)\n", + "svm.reg0 = svm(O3obs ~ ., data = datappr)\n", + "summary(svm.reg0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "library(e1071)\n", + "help(svm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:42.490229Z", + "start_time": "2019-11-18T09:22:22.305Z" + } + }, + "outputs": [], + "source": [ + "#set.seed(2021)\n", + "svm.reg.tune = tune.svm(O3obs ~ ., data = datappr, cost = c(1, 1.5, 2, 2.5, 3, 3.5), \n", + " gamma = seq(0.02, 0.1, by = 0.02))\n", + "plot(svm.reg.tune)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Par défaut la pénalisation (cost) vaut 1. Noter la pénalisation optimale pour le noyau considéré (gaussien). Ré-estimer le modèle supposé optimal avant de tracer le graphe des résidus. Comme précédemment, observer que plusieurs exécutions conduisent à des résultats différents et donc que l'optimisaiton de ce paramètre est pour le moins délicate.\n", + "\n", + "**Q** Quels autres noyaux sont dispnibles dans cette implémentation des SVM?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:42.711433Z", + "start_time": "2019-11-18T09:22:22.518Z" + } + }, + "outputs": [], + "source": [ + "svm.reg = svm(O3obs ~ ., data = datappr, cost = svm.reg.tune$best.parameters$cost, \n", + " gamma = svm.reg.tune$best.parameters$gamma)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "summary(svm.reg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:42.711433Z", + "start_time": "2019-11-18T09:22:22.518Z" + } + }, + "outputs": [], + "source": [ + "# calcul et graphe des résidus\n", + "fit.svmr=fit.svmr=svm.reg$fitted\n", + "res.svmr=fit.svmr-datappr[,\"O3obs\"]\n", + "plot.res(fit.svmr,res.svmr,titre=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observer l'effet ''couloir'' sur les résidus. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Qu'est-ce qui cause le rapprochement des résidus dans un \"couloir\"? Qu'observez-vous lorsque vous faîtes varier les paramètres cost et epsilon?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discrimination" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:44.944530Z", + "start_time": "2019-11-18T09:22:22.941Z" + } + }, + "outputs": [], + "source": [ + "# optimisation\n", + "svm.dis.tune = tune.svm(DepSeuil ~ ., data = datappq, cost = c(1,1.25,1.5,1.75,2), \n", + " gamma = seq(0.02, 0.1, by = 0.02))\n", + "plot(svm.dis.tune)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:45.010857Z", + "start_time": "2019-11-18T09:22:22.948Z" + } + }, + "outputs": [], + "source": [ + "# apprentissage\n", + "svm.dis.tune$best.parameters\n", + "svm.dis=svm(DepSeuil~.,data=datappq,cost = svm.reg.tune$best.parameters$cost, \n", + " gamma = svm.reg.tune$best.parameters$gamma)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prévision de l'échantillon test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Erreur de régression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:45.046687Z", + "start_time": "2019-11-18T09:22:23.162Z" + } + }, + "outputs": [], + "source": [ + "pred.svmr=predict(svm.reg,newdata=datestr)\n", + "# Erreur quadratique moyenne de prévision\n", + "sum((pred.svmr-datestr[,\"O3obs\"])^2)/nrow(datestr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Erreur de classification (matrices de confusion)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:45.064806Z", + "start_time": "2019-11-18T09:22:23.170Z" + } + }, + "outputs": [], + "source": [ + "# Matrice de confusion pour la prévision du dépassement de seuil (régression)\n", + "table(pred.svmr>150,datestr[,\"O3obs\"]>150)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:45.084456Z", + "start_time": "2019-11-18T09:22:23.176Z" + } + }, + "outputs": [], + "source": [ + "# Même chose pour la discrimination\n", + "pred.svmq=predict(svm.dis,newdata=datestq)\n", + "table(pred.svmq,datestq[,\"DepSeuil\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Courbes ROC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:45.469186Z", + "start_time": "2019-11-18T09:22:23.397Z" + } + }, + "outputs": [], + "source": [ + "rocsvmr = pred.svmr/300\n", + "predsvmr = prediction(rocsvmr, datestq$DepSeuil)\n", + "perfsvmr = performance(predsvmr, \"tpr\", \"fpr\")\n", + "# re-estimer le modèle pour obtenir des probabilités de classe plutôt que des\n", + "# classes\n", + "svm.dis = svm(DepSeuil ~ ., data = datappq, cost = 1.25, probability = TRUE)\n", + "pred.svmq = predict(svm.dis, newdata = datestq, probability = TRUE)\n", + "rocsvmq = attributes(pred.svmq)$probabilities[, 2]\n", + "predsvmq = prediction(rocsvmq, datestq$DepSeuil)\n", + "perfsvmq = performance(predsvmq, \"tpr\", \"fpr\")\n", + "# tracer les courbes ROC en les superposant pour mieux comparer\n", + "plot(perflogit, col = \"blue\")\n", + "plot(perfsvmr, col = \"red\", lty = 2, add = TRUE)\n", + "plot(perfsvmq, col = \"red\", add = TRUE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Les SVM apportent-ils une amélioration?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Industrialisation de l'apprentissage\n", + "Un avantage de R est le nombre considérables d'utilisateurs qui participent au développement des librairies. cet avantage a un revers: le manque d'homogénéité de celles-ci. Pour y remédier dans les applications d'apprentissage machine, la (méta)librairie [`caret`](https://topepo.github.io/caret/) de [Max Kuhn (2008)](https://www.jstatsoft.org/article/view/v028i05) intègre dans un même usage, une même syntaxe, l'ensemble des fonctionnalités d'apprentissage et propose une approche unifiée des procédures d'optimisation des paramètres.\n", + "\n", + "Les instructions suivantes reprennent rapidement les étapes précédentes afin d'introduire l'usage de `caret`. Elles se limitent à l'objectif de prévision de dépassement du seuil (classification). Le code pour modéliser la concentration par régression s'en déduit facilement.\n", + "\n", + "### Calcul parallèle\n", + "Par ailleurs, même sous windows, `caret` offre simplement des possibilités de parallèlisation en utilisant la package `doParallel`. Même si les algorithmes des différentes méthodes d'apprentissage ne sont pas parallélisés, les itérations des calculs de validations croiser pour l'optimisation des paramètres sont effectivement parallélisés avec un gain de temps très appréciable fonciton du nombre de processeurs. Ceci est obtenu en exécutant les commandes suivantes en supposant que 4 processeurs sont disponibles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:46.232486Z", + "start_time": "2019-11-18T09:22:23.827Z" + } + }, + "outputs": [], + "source": [ + "library(doParallel)\n", + "cl <- makeCluster(4)\n", + "registerDoParallel(cl) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Préparation des données\n", + "Les données sont celles initiales et la stratégie adoptée pour optimiser les modèles est la validation croisée. D’autres choix sont possibles (bootstrap). La librairie `caret` intègre des fonctions d’échantillonnage et de normalisation des données." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:46.260767Z", + "start_time": "2019-11-18T09:22:24.051Z" + } + }, + "outputs": [], + "source": [ + "summary(ozone)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:47.233193Z", + "start_time": "2019-11-18T09:22:24.058Z" + } + }, + "outputs": [], + "source": [ + "library(caret)\n", + "# extraction des données\n", + "# Variable cible\n", + "Y=ozone[,\"DepSeuil\"]\n", + "# Variables explicatives\n", + "X=ozone[,-c(2,11)]\n", + "# Transformation des facteurs en indicatrices pour utiliser certains algorithmes\n", + "# notamment xgboost\n", + "library(FactoMineR)\n", + "X=data.frame(tab.disjonctif(X[,c(1,4)]),X[,-c(1,4)])\n", + "summary(Y);summary(X)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "library(caret)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "??caret" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:47.393572Z", + "start_time": "2019-11-18T09:22:24.065Z" + } + }, + "outputs": [], + "source": [ + "# indices de l’échantillon d’apprentissage\n", + "xx=11 # Changer cette valeur pour personnaliser l'échantillonnage\n", + "set.seed(xx)\n", + "inTrain = createDataPartition(X[,1],p = 0.8, list = FALSE)\n", + "# Extraction des échantillons\n", + "trainDescr=X[inTrain,]\n", + "testDescr=X[-inTrain,]\n", + "testY=Y[-inTrain]\n", + "trainY=Y[inTrain]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Certaines méthodes sont sensibles à des effets de variance ou d'unité des variables. Il est préférable d'introduire une normalisation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:47.433813Z", + "start_time": "2019-11-18T09:22:24.296Z" + } + }, + "outputs": [], + "source": [ + "# Normalisation calculée sur les paramètres de l'échantillon d'apprentissage\n", + "xTrans=preProcess(trainDescr)\n", + "trainDescr=predict(xTrans,trainDescr)\n", + "# Puis appliquée également à l'échantillon test\n", + "testDescr=predict(xTrans,testDescr)\n", + "# Choix de la validation croisée\n", + "cvControl=trainControl(method=\"cv\",number=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimation des modèles\n", + "La librairie intègre beaucoup de modèles ou méthodes (233!) et celles sélectionnées ci-dessous font partie des plus utilisées. Consulter la [liste des méthodes](http://topepo.github.io/caret/available-models.html) disponibles en option de la fonction: `train`. Le choix est en principe limité également aux méthodes acceptant des variables quantitatives et qualitatives mais, en transformant préalablement les variables qualitatives en paquets d'indicatrices (*dummies*) les autres méthodes sont accessibles. Exécuter chaque blocs de commandes pour tracer séparamment chacun des graphes afin de contrôler le bon comportement\n", + "de l’optimisation du paramètre de complexité de chaque modèle.\n", + "\n", + "L'automatisation de l'optimisation de certaines méthodes comme la régression logistique est moins flexible qu’en utilisation \"manuelle\"; en particulier pour le choix de l’algorithme de sélection de variables. Il faut se montrer (très) patient pour certaines optimisations alors que d'autres sont immédiates, voire inutiles. \n", + "\n", + "Le paramètre `tuneLength` caractérise un \"effort\" d'optimisation, c'est en gros le nombre de valeurs de paramètres testées sur une grille fixée automatiquement. En prenant plus de soin et aussi plus de temps, il est possible de fixer précisément des grilles pour les valeurs du ou des paramètres optimisés pour chaque méthode. Néanmoins, comme expérimenté précédemment, il n'est pas toujours utile de passer beaucoup de temps à optimiser un paramètre. L'approche sommaire de `caret` s'avère souvent suffisante et l'optimisation d'un modèle, de sa complexité, peut être affinée après sélection de la méthode.\n", + "\n", + "**Q** Dans chaque cas, identifier la méthode, préciser les paramètres associés et noter celui ou ceux optimisés par défaut par `caret`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:53.674471Z", + "start_time": "2019-11-18T09:22:24.529Z" + } + }, + "outputs": [], + "source": [ + "#1 Régression logistique\n", + "# Attention, la régression logistique sans interaction (linéaire) est estimée ci-dessous\n", + "set.seed(2)\n", + "rlogFit = train(trainDescr, trainY,method = \"glmStepAIC\", tuneLength = 10,\n", + " trControl = cvControl, trace=FALSE)\n", + "rlogFit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:25:54.874252Z", + "start_time": "2019-11-18T09:22:24.537Z" + } + }, + "outputs": [], + "source": [ + "#2 Arbre de décision\n", + "set.seed(2)\n", + "rpartFit = train(trainDescr, trainY, method = \"rpart\", tuneLength = 10,\n", + " trControl = cvControl)\n", + "rpartFit\n", + "plot(rpartFit)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:26:06.838240Z", + "start_time": "2019-11-18T09:22:24.544Z" + } + }, + "outputs": [], + "source": [ + "#3 Réseau de neurones\n", + "set.seed(2)\n", + "nnetFit = train(trainDescr, trainY, method = \"nnet\", tuneLength = 6,\n", + " trControl = cvControl, trace=FALSE)\n", + "nnetFit\n", + "plot(nnetFit)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:26:19.223823Z", + "start_time": "2019-11-18T09:22:24.551Z" + } + }, + "outputs": [], + "source": [ + "#4 Random forest\n", + "set.seed(2)\n", + "rfFit = train(trainDescr, trainY,method = \"rf\", tuneLength = 8,\n", + " trControl = cvControl, trace=FALSE)\n", + "rfFit\n", + "plot(rfFit)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-11-18T09:26:28.217758Z", + "start_time": "2019-11-18T09:22:24.558Z" + } + }, + "outputs": [], + "source": [ + "#5 Boosting \n", + "set.seed(2)\n", + "gbmFit = train(trainDescr, trainY,method = \"gbm\", tuneLength = 8,\n", + " trControl = cvControl)\n", + "gbmFit\n", + "plot(gbmFit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comme l'algoritme *extreme gradient boosting* (approximation du gradient par décoposition de taylor et parallélisation des codes) est très présent dans les solutions des concours *Kaggle* celui-ci est testé. *Attention*, les bons résultats des concours sont obtenus au prix d'une lourde et complexe procédure d'optimisation des nombreux paramètres de cette approche; procédure rendue possible par la parallélisation avancée de la librairie [`xgboost`](https://xgboost.readthedocs.io/en/latest/) et l'utilisation de cartes graphiques (GPU). Si cet environnement n'est pas disponible l'optimisation est assez longue, même avec la parallélisation sur 4 processeurs..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:24.804Z" + } + }, + "outputs": [], + "source": [ + "#6 Extrême boosting\n", + "library(xgboost)\n", + "set.seed(2)\n", + "xgbFit = train(trainDescr, trainY,method = \"xgbTree\", tuneLength = 6,\n", + " trControl = cvControl, trace=FALSE)\n", + "xgbFit\n", + "plot(xgbFit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prévision et erreur de test\n", + "Les méthodes sélectionnées et optimisées sont ensuite appliquées à la prévision de l’échantillon test. Estimation du taux de bien classés:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:25.049Z" + } + }, + "outputs": [], + "source": [ + "models=list(logit=rlogFit,cart=rpartFit,nnet=nnetFit,rf=rfFit,gbm=gbmFit,xgb=xgbFit)\n", + "testPred=predict(models, newdata = testDescr)\n", + "# taux de bien classés\n", + "lapply(testPred,function(x)mean(x==testY))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tracer les courbes ROC pour analyser spécificité et sensibilité des différentes méthodes. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:25.288Z" + } + }, + "outputs": [], + "source": [ + "library(ROCR)\n", + "models=list(logit=rlogFit,cart=rpartFit,nnet=nnetFit,rf=rfFit,gbm=gbmFit,xgb=xgbFit)\n", + "testProb=predict(models, newdata = testDescr,type=\"prob\")\n", + "predroc=lapply(testProb,function(x)prediction(x[,1],testY==FALSE))\n", + "perfroc=lapply(predroc,\n", + "function(x)performance(x, \"tpr\", \"fpr\"))\n", + "plot(perfroc$logit,col=1)\n", + "plot(perfroc$cart,col=2,add=TRUE)\n", + "plot(perfroc$nnet,col=3,add=TRUE)\n", + "plot(perfroc$rf,col=4,add=TRUE)\n", + "plot(perfroc$gbm,col=5,add=TRUE)\n", + "plot(perfroc$xgb,col=6,add=TRUE)\n", + "legend(\"bottomright\",legend=c(\"logit\",\"CART\",\"nnet\",\"RF\",\"boost\",\"xgBoost\"),col=c(1:6),pch=\"_\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Validation croisée *Monte Carlo*](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf)\n", + "L'échantillon est de faible taille (#200), et les estimations des taux de bien classés comme le tracé des courbes ROC sont très dépendants de l’échantillon test; on peut s’interroger sur l’identité du modèle le plus performant ainsi que sur la significativité des différences observées entre les méthodes. Il est donc important d’itérer le processus (validation croisée *Monte Carlo*) sur plusieurs échantillons tests. Exécuter la fonction en annexe en choisissant les méthodes semblant les plus performantes. Attention au temps de calcul ! CART peut performant est supprimé." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:25.551Z" + } + }, + "outputs": [], + "source": [ + "# Choisir la liste des méthodes et l’effort d’optimisation\n", + "models=c(\"gbm\",\"rf\",\"nnet\",\"glmStepAIC\",\"xgbTree\")\n", + "noptim=c(6,6,6,6,6)\n", + "# Initialiser le générateur et fixer le nombre d’itérations\n", + "# Changer ces valeurs. Attention au temps de calcul! Être patient!\n", + "Niter=10 ; Init=11 \n", + "# Appel de la fonction définie en annexe\n", + "pred.ozone=pred.autom(X,Y,methodes=models,N=Niter,xinit=Init,size=noptim,type=\"prob\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:25.800Z" + } + }, + "outputs": [], + "source": [ + "# Calcul des taux de bien classés\n", + "obs=pred.ozone$obs\n", + "prev.ozone=pred.ozone$pred\n", + "res.ozone=lapply(prev.ozone,function(x)apply((x>0.5)==(obs==1),2,mean))\n", + "# Moyennes des taux de bien classés par méthode\n", + "lapply(res.ozone,mean)\n", + "# distributions des taux de bien classés\n", + "boxplot(data.frame(res.ozone))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les commandes suivandes tracent les courbes ROC moyennes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:26.055Z" + } + }, + "outputs": [], + "source": [ + "## Comparaison des méthodes par le\n", + "# tracer des courbes ROC moyennes\n", + "# Problème pas identifié avec rlogit!\n", + "predroc.ozone=lapply(prev.ozone,function(x)prediction(x,obs==1))\n", + "perfroc.ozone=lapply(predroc.ozone,function(x)performance(x,\"tpr\",\"fpr\"))\n", + "plot(perfroc.ozone$gbm,col=1,lwd=2,avg=\"vertical\")\n", + "plot(perfroc.ozone$rf,col=2,add=TRUE,lwd=2,avg=\"vertical\")\n", + "plot(perfroc.ozone$nnet,add=TRUE,col=3,lwd=1.5,avg=\"vertical\")\n", + "plot(perfroc.ozone$xgbTree,add=TRUE,col=4,lwd=1.5,avg=\"vertical\")\n", + "plot(perfroc.ozone$glmStepAIC,add=TRUE,col=5,lwd=1.5,avg=\"vertical\")\n", + "legend(\"bottomright\",legend=c(\"boost\",\"RF\", \"nnet\",\"xgBoost\",\"logit\"),col=c(1:5),pch=\"_\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Quelle méthode retenir, en fonction du taux de faux positif acceptable, pour prévoir le dépassement du seuil? Et si le comanditaire veut une solution explicable?\n", + "\n", + "La même démarche réalisée sur la prévision de concentration avant de prédire le dépassement du seuil conduit à des résutlats similaire. \n", + "\n", + "*N.B.* \n", + "* Ce n'est pas la régression logistique avec interactions (quadratique) qui a été testée dans cette dernière comparaison\n", + "* L'algorithme xgboost nécessiterait des efforts plus important d'optimisation des paramètres mais le coût de calcul s'en ressent. A tester en Python avec un accès à une carte GPU." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Épisode 5\n", + "**Remarque** Il est possible d'exécuter directement l'*épisode 5* sans passer par toutes les étapes de classification supervisée. Il suffit d'exécuter les *sections 2 et 3* de l'*épisode 1*, phase exploratoire, afin de construire les données utilisées dans les sections 13 et 14 d'imputation des données manquantes et de détection d'atypiques." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Gestion des données manquantes](http://wikistat.fr/pdf/st-m-app-idm.pdf)\n", + "\n", + "Les vraies données sont le plus souvent mitées par l'absence de données, conséquences d'erreurs de saisie, de pannes de capteurs... Les librairies de R offrent de très nombreux choix pour faire des imputations de données manquantes quand celles-ci le sont de façon complètement aléatoire. \n", + "\n", + "Plusieurs stratégies sont exécutées et comparées après avoir généré aléatoirement un pourcentage de défaillances (trous) dans les valeurs des variables explicatives.\n", + "\n", + "**Q.** Pourquoi la structure des variables explicatives incite-t-elle à exécuter l'algorithme missForest de la librairie éponyme? \n", + "\n", + "**Dans un premier temps**, nous allons comparer quelques méthodes d'imputation sur les données explicatives quantitatives : LOCF, imputation par la moyenne ou la médiane, kNN, MissForest et Amelia II.\n", + "\n", + "\n", + "**Dans un deuxième temps**, nous nous concentrerons sur la méthode Missforest et l'objectif sera d'étudier l'impact de l'imputation des données sur les performances de classification pour prédire la variable \"depassement de seuil\" en comparant deux stratégies :\n", + "\n", + "\n", + "La **première stratégie** commence par imputer les données manquantes en les prévoyant par l'algorithme MissForest. \n", + "\n", + "Une fois les données manquantes imputées, différentes méthodes de prévision sont utilisables comme précédemment. Deux sont exécutées: forêts aléatoires et *extrem gradient boosting*.\n", + "\n", + "La **deuxième stratégie** évite l'étape d'imputation en exécutant directement un algorithme de prévision tolérant des données manquantes. Peu le fond, c'est le cas de `XGBoost`.\n", + "\n", + "Attention, les commandes ci-dessous font appel à de nombreux fichiers qu'il est facile de mélanger.\n", + "- `X` données complètes initiales et `Xd` la version où les variables qualitatives sont remplacées par des indicatrices, \n", + "- `Xna` les données avec des trous, `Xdna` la version avec indicatrices,\n", + "\n", + "- `XnaImp` les données avec imputations et `XdnaImp` la version avec indicatrices.\n", + "\n", + "Le remplacement des variables qualitatives par des variables indicatrices est imposé par l'utilisation de la librairie `XGBoost` et cela ne change en rien les résultats des forêts aléatoires.\n", + "\n", + "### Préparation des trous dans `ozone`\n", + "Les données initiales de la base `ozone` sont reprises. Seule la variable à expliquer de dépassement de seuil est conservée. La librairie `missForest`propose une fonction pour générer un pourcentage fixé a priori de données manquantes dans une base." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:26.834Z" + } + }, + "outputs": [], + "source": [ + "# Variable cible\n", + "Y=ozone[,\"DepSeuil\"]\n", + "# Variables explicatives\n", + "X=ozone[,-c(2,11)]\n", + "n=nrow(X); p=ncol(X)\n", + "summary(Y); summary(X)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:26.842Z" + } + }, + "outputs": [], + "source": [ + "library(missForest)\n", + "# faire une proportion tauxNA de trous aléatoires dans X\n", + "# Données missing at random\n", + "tauxNa=0.2\n", + "set.seed(11)\n", + "Xna=prodNA(X,tauxNa)\n", + "summary(Xna)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Quel est en moyenne le nombre de données manquantes par colonne?\n", + "\n", + "### Comparaison de méthodes d'imputation sur données quantitatives ###\n", + "\n", + "On conserve seulement les variables quantitatives pour comparer diverses méthodes d'imputation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Tableau des données quantitatives\n", + "#On compare les différentes méthodes de complétion sur la variable Temperature\n", + "\n", + "Xnaquanti=Xna[,-c(1,4)]\n", + "Xquanti=X[,-c(1,4)]\n", + "ind.na=which(is.na(Xnaquanti),arr.ind=TRUE)\n", + "ind.na.Temp=which(is.na(Xnaquanti[,2]),arr.ind=TRUE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Complétion par la dernière valeur connue (LOCF) ####" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "library(zoo) # chargement de la bibliothèque\n", + "X.locf=na.locf(Xnaquanti,na.rm=FALSE)\n", + "X.locf=na.locf(X.locf,na.rm=FALSE,fromLast=TRUE) # dans l'autre sens\n", + "err.locf=(Xquanti-X.locf)[ind.na.Temp,2]\n", + "boxplot(err.locf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Complétion par la moyenne ####" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "moy=apply(Xnaquanti,2,mean,na.rm=TRUE)\n", + "X.moy=Xnaquanti\n", + "ind.na=which(is.na(X.moy),arr.ind=TRUE)\n", + "X.moy[ind.na]=moy[ind.na[,2]]\n", + "err.moy=(Xquanti-X.moy)[ind.na.Temp,2]\n", + "boxplot(data.frame(err.locf,err.moy),ylim=c(-15,15))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Complétion par la mediane ####" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "med=apply(Xnaquanti,2,median,na.rm=TRUE)\n", + "X.med=Xnaquanti\n", + "ind.na=which(is.na(X.med),arr.ind=TRUE)\n", + "X.med[ind.na]=med[ind.na[,2]]\n", + "err.med=(Xquanti-X.med)[ind.na.Temp,2]\n", + "\n", + "boxplot(data.frame(err.locf,err.moy,err.med),ylim=c(-15,15))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Complétion par k plus proches voisins (kNN) ####" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "library(VIM) # chargement de la bibliothèque\n", + "X.kNN=kNN(Xnaquanti, k=5, imp_var=FALSE)\n", + "err.kNN=(Xquanti-X.kNN)[ind.na.Temp,2]\n", + "boxplot(data.frame(err.locf,err.moy,err.med,err.kNN),ylim=c(-15,15))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Complétion avec Missforest ####" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "X.mf=missForest(Xnaquanti,xtrue=Xquanti)\n", + "err.mf=(Xquanti-X.mf$ximp)[ind.na.Temp,2]\n", + "boxplot(data.frame(err.locf,err.moy,err.med,err.kNN,err.mf),ylim=c(-15,15))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Completion avec Amelia II ####" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "library(Amelia) # chargement de la bibliothèque" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X.amelia=amelia(Xnaquanti,m=1)$imputations$imp1\n", + "err.amelia=(Xquanti-X.amelia)[ind.na.Temp,2]\n", + "boxplot(data.frame(err.locf,err.moy,err.med,err.kNN,err.mf,err.amelia),ylim=c(-15,15))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q.** Que concluez vous ? Quelle méthode vous semble la plus pertinente sur ces données ? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imputation avec MissForest et impact sur la classification ###\n", + "\n", + "On reprend ici le jeu de données complet, incluant les variables explicatives quantitatives. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Connaissant les \"vraies\" données initiales, il est possible, dans ce cas de calculer des erreurs d'imputation de `missForest`.\n", + "\n", + "**Q** Quelles sont elles? Quelle estimation de l'erreur est fournie quand les données manquantes le sont vraiment?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(missForest)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:27.103Z" + } + }, + "outputs": [], + "source": [ + "XnaImp=missForest(Xna,xtrue=X)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:27.111Z" + } + }, + "outputs": [], + "source": [ + "XnaImp$OOBerror;XnaImp$error" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vérifier que les imputations sont réalisées." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:27.381Z" + } + }, + "outputs": [], + "source": [ + "summary(XnaImp$ximp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comme précédemment, l'utilisation de `XGBoost` impose de transformer les facteurs en indicatrices." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:27.648Z" + } + }, + "outputs": [], + "source": [ + "library(FactoMineR)\n", + "# données complètes\n", + "Xd=data.frame(tab.disjonctif(X[,c(1,4)]),X[,-c(1,4)])\n", + "# données avec trous\n", + "Xdna=data.frame(tab.disjonctif(Xna[,c(1,4)]),Xna[,-c(1,4)]) \n", + "# données avec imputations\n", + "XdnaImp=data.frame(tab.disjonctif(XnaImp$ximp[,c(1,4)]),XnaImp$ximp[,-c(1,4)]) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La librairie `caret` facilite beaucoup la syntaxe pour l'exécution de `xgboost`. elle est reprise. Il faudrait sinon transformer les données sous un autre format. C'est intégré par `caret`.\n", + "\n", + "Construction des mêmes échantillons d'apprentissage et de test dans les trois cas: données initiales, manquantes, imputées." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:27.913Z" + } + }, + "outputs": [], + "source": [ + "library(caret)\n", + "# parallélisation\n", + "library(doParallel)\n", + "cl <- makeCluster(4)\n", + "registerDoParallel(cl) \n", + "# indices de l’échantillon d’apprentissage\n", + "xx=11 # Changer cette valeur pour personnaliser l'échantillonnage\n", + "set.seed(xx)\n", + "inTrain = createDataPartition(X[,1],p = 0.8, list = FALSE)\n", + "# Extraction des échantillons\n", + "trainDescr=Xd[inTrain,]\n", + "testDescr=Xd[-inTrain,]\n", + "# Les mêmes avec trous\n", + "trainDescrNA=Xdna[inTrain,]\n", + "testDescrNA=Xdna[-inTrain,]\n", + "# Les mêmes avec données manquantes imputées\n", + "trainDescrNAimp=XdnaImp[inTrain,]\n", + "testDescrNAimp=XdnaImp[-inTrain,]\n", + "testY=Y[-inTrain]\n", + "trainY=Y[inTrain]\n", + "cvControl=trainControl(method=\"cv\",number=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:27.921Z" + } + }, + "outputs": [], + "source": [ + "# prévision avec random forest sur données initiales\n", + "set.seed(2)\n", + "rfFit = train(trainDescr, trainY,method = \"rf\", tuneLength = 8,\n", + " trControl = cvControl, trace=FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:27.928Z" + } + }, + "outputs": [], + "source": [ + "# Prévision avec XGBoost sur données initiales\n", + "\n", + "#set.seed(2)\n", + "#xgbFit = train(trainDescr, trainY,method = \"xgbTree\", tuneLength = 6,\n", + " # trControl = cvControl)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Pendant que `XGBoost` tourne, réviser les [principes de cet algorithme](http://wikistat.fr/pdf/st-m-app-agreg.pdf)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:28.205Z" + } + }, + "outputs": [], + "source": [ + "# erreur de prévision sur le test avec données initiales\n", + "#models=list(rf=rfFit,xgb=xgbFit)\n", + "models=list(rf=rfFit)\n", + "testPred=predict(models, newdata = testDescr)\n", + "# taux de bien classés\n", + "lapply(testPred,function(x)mean(x==testY))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:28.212Z" + } + }, + "outputs": [], + "source": [ + "# Prévision avec random forest sur données imputées\n", + "set.seed(2)\n", + "rfFitNAimp = train(trainDescrNAimp, trainY,method = \"rf\", tuneLength = 8,\n", + " trControl = cvControl, trace=FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:28.220Z" + } + }, + "outputs": [], + "source": [ + "# Prévision avec XGBoost sur données imputées\n", + "\n", + "#xgbFitNAimp = train(trainDescrNAimp, trainY,method = \"xgbTree\", tuneLength = 6,\n", + "# trControl = cvControl)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Pendant que `XGBoost` tourne, réviser les [principes de missForest](http://wikistat.fr/pdf/st-m-app-idm.pdf)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:28.506Z" + } + }, + "outputs": [], + "source": [ + "# erreur de prévision sur le test avec données imputées\n", + "\n", + "#models=list(rfNAimp=rfFitNAimp,xgbNAimp=xgbFitNAimp)\n", + "\n", + "models=list(rfNAimp=rfFitNAimp)\n", + "testPred=predict(models, newdata = testDescrNAimp)\n", + "# taux de bien classés\n", + "lapply(testPred,function(x)mean(x==testY))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q.** Qu'en déduisez vous sur la qualité des résultats après imputation ? Augmenter le taux de données manquantes pour voir l'impact de ce taux sur la qualité de prédiction. \n", + "\n", + "**FIN DU TP**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prévision sans imputation \n", + "\n", + "La phase d'imputation est rendue obligatoire par l'usage de nombreuses méthodes qui n'acceptent pas les données manquantes. Il peut être intéressant de s'en passer car les informations reconstruites ne sont pas utilisables à d'autres fins; `XGBoost` offre cette oppotunité. Pendant qu'il tourne, [essayer de comprendre](https://arxiv.org/abs/1603.02754) les astuces mises en oeuvre pour tolérer des données manquanres." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:28.785Z" + } + }, + "outputs": [], + "source": [ + "# Prévision avec XGBoost avec données manquantes\n", + "\n", + "#xgbFitNA = train(trainDescrNA, trainY,method = \"xgbTree\", tuneLength = 6,\n", + " # trControl = cvControl)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:28.793Z" + } + }, + "outputs": [], + "source": [ + "# Erreur de prévision avec XGBoot tolérant les données manquantes.\n", + "#testPred=predict(xgbFitNA, newdata = testDescrNA)\n", + "#mean(testPred==testY)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Comparer les résultats obtenus par les différents stratégies. En tenant compte des temps de calcul, laquelle semble la plus efficace sur ces données. \n", + "\n", + "*NB* L'utilisation avancée de `XGBoost` nécessite plus de puissance de calcul afin d'affiner le réglage des nombreux paramètres.\n", + "\n", + "**Q** Qu'en serait-il en utlisant Python au lieu de R?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Détection d'observations atypiques ou anomalies\n", + "La détection d'observations *atypiques*, *anomalies* ou *outliers* nommée également *OCC* (*One Class Classification*) ou *novelty detection* est source d'une très abondante bibliographie; voir par exemple [Aggarwal 2016](http://www.charuaggarwal.net/outlierbook.pdf). A ne pas confondre avec les modèles de *valeurs extrêmes*, les valeurs atypiques dans le cas unidimensionnel sont généralement traitées en référence à des modèles paramétriques: gaussien ou autre, qui caractérisent la \"normalité\". Systématiquement et également dans le cas multidimensionnelle, la notion d'anomalie est définie relativement à un modèle et sous le contrôle d'un paramètre à \"régler\". Le modèle est paramétrique ou non, local ou global. Par example dans le cas du modèle linéaire, la distance de Cook est un indicateur de points influents ou atypique par rapport au modèle.\n", + "\n", + "R propose quelques librairies et fonctions de détection d'atypiques. \n", + "- [`outliers`](https://cran.r-project.org/web/packages/outliers/outliers.pdf) propose un ensemble de tests univariés.\n", + "- [`Rlof`]() propose une version parallélisée du calcul du score LOF (*Local Factor Outlier*). Une estimation locale de la denisité en un point est comparée à celle de ses voisins. \n", + "- [`dbscan`](https://cran.r-project.org/web/packages/dbscan/dbscan.pdf) propose en ples d'algorihtmes de classification non-superviée originaux, le calcul de `glosh` (*Global-Local Outlier Score from Hierarchies*).\n", + "- [`kernlab`](http://ftp.auckland.ac.nz/software/CRAN/doc/vignettes/kernlab/kernlab.pdf) propose une option de *One Class Classification SVM* qui cherche à séparer l'origine de l'ensemble des points; `e1071`le propose aussi mais avec des problèmes d'exécution!\n", + "- [`randomForest`](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#micro7) estime, dans le cas supervisé lorsque une variable explicative est connue, une notion de \"distance\" de chaque point avec ses voisins en considérant les co-appartenances des points aux mêmes feuilles des arbres. Dans le cas contraire, comme pour la situation d'OCC, une approche non supervisée consiste à générer tout un ensemble d' observations atypiques avant de construire un modèle prédisant pour chaque observation la variable échantillon initiale *vs.* atypique simulé. La notion précédente de \"distance\" est à nouveau utilisé comme score d'atypicité.\n", + "\n", + "Quelques cas sont considérés ici.\n", + "\n", + "Ce traitement intervient dans ce tutoriel avec une finalité essentiellement pédagogique. Il n'est pas indispensale sur ces données, relativement cohérentes alors que l'objectif poursuivit n'est pas la recherche d'une défaillance contrairement à une situation du domaine industriel: suivi de fabrication ou de fonctionnement. \n", + "\n", + "Néanmoins, sur tout jeu de données, l'étape préalable exploratoire peut inclure la recherche d'observations atypiques multidimensionnelles qui permettraient d'identifier des incohérences de mesure en complément des études unidimensionnelles de la première partie.\n", + "\n", + "Considérons quatre approches suivant des principes très différents parmi bien d'autres. Elles vont permettre d'identifier des observations atypiques avant de les représenter dans l'ACP.\n", + "### *Local Outlier Factor*\n", + "Les données sont restreintes aux seules variables quantitatives explicatives.\n", + "\n", + "**Q** Quel est le rôle du paramètre *k* ci-dessous?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:29.355Z" + } + }, + "outputs": [], + "source": [ + "library(Rlof)\n", + "ozoneR=ozone[,-c(1,2,5,11)]\n", + "atypLof=lof(ozoneR,k=c(3:7),cores=3)\n", + "options(repr.plot.width=8, repr.plot.height=6)\n", + "boxplot(atypLof)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:29.362Z" + } + }, + "outputs": [], + "source": [ + "table(atypLof[,1]>1.5,Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Comment intervient la borne 1.5? A quelles classe appartiennent majoritairement les observations jugées atypiques." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:29.650Z" + } + }, + "outputs": [], + "source": [ + "atypLofInd=which(atypLof[,1]>1.5)\n", + "coul=as.integer(ozone[,\"DepSeuil\"])+2\n", + "taille=rep(0.5,length(coul))\n", + "acp=princomp(ozoneR,cor=TRUE)\n", + "options(repr.plot.width=6, repr.plot.height=6)\n", + "coul[atypLofInd]=2\n", + "taille[atypLofInd]=.8\n", + "plot(acp$scores,col=coul, pch=17+coul-2,cex=taille)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que dire de la localisation des observations atypiques dans le plan de l'acp?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Global-Local Outlier Score from Hierarchies* \n", + "Les scores proches de 1 signalent des atypiques." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:30.220Z" + } + }, + "outputs": [], + "source": [ + "library(dbscan)\n", + "atypGlosh=glosh(as.matrix(ozoneR),k=3)\n", + "options(repr.plot.width=4, repr.plot.height=6)\n", + "boxplot(atypGlosh)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:30.226Z" + } + }, + "outputs": [], + "source": [ + "table(atypLof[,1]>1.5,atypGlosh>0.82)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que dire de ces deux critères?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:30.528Z" + } + }, + "outputs": [], + "source": [ + "atypGloshInd=which(atypGlosh>0.82)\n", + "coul=as.integer(ozone[,\"DepSeuil\"])+2\n", + "taille=rep(0.5,length(coul))\n", + "coul[atypGloshInd]=2; taille[atypGloshInd]=.8\n", + "options(repr.plot.width=6, repr.plot.height=6)\n", + "plot(acp$scores,col=coul, pch=17+coul-2,cex=taille)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *One Class Classification SVM*\n", + "**Q** Quel est le rôle du paramètre `nu`?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:30.828Z" + } + }, + "outputs": [], + "source": [ + "library(kernlab)\n", + "ozoneOcc=ksvm(x=as.matrix(ozoneR),y=NULL,type=\"one-svc\",\n", + " kernel=\"rbfdot\",nu = 0.005)\n", + "atypOcc=!fitted(ozoneOcc)\n", + "ozoneOcc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:30.835Z" + } + }, + "outputs": [], + "source": [ + "coul=as.integer(ozone[,\"DepSeuil\"])+2\n", + "taille=rep(.5,length(coul))\n", + "options(repr.plot.width=6, repr.plot.height=6)\n", + "coul[atypOcc]=2\n", + "taille[atypOcc]=0.8\n", + "plot(acp$scores,col=coul, pch=17+coul-2,cex=taille)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Même question sir la répartition des observations atypiques." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:31.130Z" + } + }, + "outputs": [], + "source": [ + "table(atypLof[,1]>1.5,atypOcc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Comment interpréter la table ci-dessus?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Anomalies au sens de *random forest*\n", + "#### Cas supervisé\n", + "La première approche prend en compte la variable explicative et considère donc les observations les plu sen en marge du modèle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2019-11-18T09:22:31.742Z" + } + }, + "outputs": [], + "source": [ + "library(randomForest)\n", + "Y=ozone[,11]\n", + "X=ozone[,-c(2,11)]\n", + "ozoneRF=randomForest(X,Y,proximity=TRUE)\n", + "atypRF=outlier(ozoneRF)\n", + "options(repr.plot.width=4, repr.plot.height=6)\n", + "boxplot(atypRF)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "atypRFInd=which(atypRF>20)\n", + "coul=as.numeric(Y)+2\n", + "options(repr.plot.width=8, repr.plot.height=6)\n", + "plot(atypRF,type=\"h\",col=coul)\n", + "legend(\"topright\",legend=levels(Y),text.col=c(3:4))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table(atypRF>20,Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que dire de la répartition des atypiques par rapport à la variable de dépassement de seuil." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coul=as.integer(ozone[,\"DepSeuil\"])+2\n", + "taille=rep(.5,length(coul))\n", + "acp=princomp(ozoneR,cor=TRUE)\n", + "options(repr.plot.width=6, repr.plot.height=6)\n", + "coul[atypRFInd]=2\n", + "taille[atypRFInd]=.8\n", + "plot(acp$scores,col=coul, pch=17+coul-2,cex=taille)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Commenter la répartition des atypiques au sens de **Random Forest** supervisée. Serait-il raisonnable de supprimer ces observations ?\n", + "\n", + "**Remarque** Si la variable à expliquer *Y* est telle que l'on soupçonne des possibles erreur de label, ce peut être une façon de les détecter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Cas non-supervisé\n", + "Moins connue, Breiman à proposé une version [non-supervisée](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#unsup) de randomForest. Elle fournit *in fine* le même type de critère mais sans faire intervenir *Y*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set.seed(11)\n", + "ozoneURF <- randomForest(x=ozoneR,y=NULL,proximity=TRUE)\n", + "atypURF=outlier(ozoneURF)\n", + "options(repr.plot.width=4, repr.plot.height=6)\n", + "boxplot(atypURF)\n", + "#MDSplot(ozoneURF, ozone$Depseuil)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "atypURFInd=which(atypURF>2.5)\n", + "coul=as.numeric(Y)+2\n", + "options(repr.plot.width=8, repr.plot.height=6)\n", + "plot(atypURF,type=\"h\",col=coul)\n", + "legend(\"topright\",legend=levels(Y),text.col=c(3:4))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "coul=as.integer(ozone[,\"DepSeuil\"])+2\n", + "taille=rep(.5,length(coul))\n", + "options(repr.plot.width=6, repr.plot.height=6)\n", + "coul[atypURFInd]=2\n", + "taille[atypURFInd]=.8\n", + "plot(acp$scores,col=coul, pch=17+coul-2,cex=taille)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table(atypURF>2.5,atypLof[,1]>1.5)\n", + "table(atypURF>2.5,atypOcc)\n", + "table(atypLof[,1]>1.5,atypURF>2.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q** Que dire sur la correspondance entre les trois stratégies de détection d'observations atypiques?\n", + "\n", + "**Q** Qu'est-ce qui psermettrait d'en choisir une parmi les trois ou parmi les très nombreuses autres disponibles dans la littérature?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Annexe: Fonction de validation croisée *Monte Carlo*\n", + "*N* réplications des estimations / prévisions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pred.autom=function(X,Y,p=1/2,methodes=c(\"knn\",\n", + "\"rf\"),size=c(10,2),xinit=11,N=10,typerr=\"cv\",\n", + "number=4,type=\"raw\") {\n", + "# Fonction de prévision de N échantillons tests\n", + "# par une liste de méthodes de régression\n", + "# ou classification (uniquement 2 classes)\n", + "# Optimisation des paramètres par validation\n", + "# croisée (défaut) ou bootstrap ou... (cf. caret)\n", + "# X : matrice ou frame des variables explicatives\n", + "# Y : variable cible quantitative ou qualitative\n", + "# p : proportion entre apprentissage et test\n", + "# methodes : liste des méthodes de rdiscrimination\n", + "# size : e grille des paramètres à optimiser\n", + "# xinit : générateur de nombres aléatoires\n", + "# N : nombre de réplications apprentissage/test\n", + "# typerr : \"cv\" ou \"boo\" ou \"oob\"\n", + "# number : nombre de répétitions CV ou bootstrap\n", + "# pred : liste des matrices de prévision\n", + "# type d’erreur\n", + "Control=trainControl(method=typerr,number=number)\n", + "# initialisation du générateur\n", + "set.seed(xinit)\n", + "# liste de matrices stockant les prévisions\n", + "# une par méthode\n", + "inTrain=createDataPartition(Y,p=p,list=FALSE)\n", + "ntest=length(Y[-inTrain])\n", + "pred=vector(\"list\",length(methodes))\n", + "names(pred)=methodes\n", + "pred=lapply(pred,function(x)x=matrix(0,\n", + "nrow=ntest,ncol=N))\n", + "obs=matrix(0,ntest,N)\n", + "set.seed(xinit)\n", + "for(i in 1:N) {\n", + "# N itérations\n", + "# indices de l’échantillon d’apprentissage\n", + "inTrain=createDataPartition(Y,p=p,list=FALSE)\n", + "# Extraction des échantillons\n", + "trainDescr=X[inTrain,]\n", + "testDescr=X[-inTrain,]\n", + "trainY=Y[inTrain]\n", + "testY=Y[-inTrain]\n", + "# stockage des observés de testY\n", + "obs[,i]=testY\n", + "# centrage et réduction des variables\n", + "xTrans=preProcess(trainDescr)\n", + "trainDescr=predict(xTrans,trainDescr)\n", + "testDescr=predict(xTrans,testDescr)\n", + "# estimation et optimisation des modèles\n", + "# pour chaque méthode de la liste\n", + "for(j in 1:length(methodes)) {\n", + "# modélisation\n", + "modFit = train(trainDescr, trainY,method = methodes[j], tuneLength = size[j],\n", + " trControl = Control)\n", + "# prévisions\n", + "if (type==\"prob\") pred[[j]][,i]=predict(modFit,\n", + "newdata = testDescr,type=type)[,1]\n", + "else pred[[j]][,i]=predict(modFit,\n", + "newdata = testDescr)\n", + "}}\n", + "list(pred=pred,obs=obs)\n", + "# résultats\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "0650c59a78a128c748f8aadfab5692cc08be5aacb695e9a8f2efcdc6dbedda40" + }, + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.1.2" + }, + "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 + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": false, + "skip_h1_title": true, + "toc_cell": false, + "toc_position": { + "height": "630.933px", + "left": "33px", + "right": "1081.6px", + "top": "107.133px", + "width": "153px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}