PredireReussiteSolution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/EmmanuelADAM/IntelligenceArtificiellePython/blob/master/PredireReussiteSolution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3qF8xmpAdACr"
   },
   "source": [
    "# Prédire la réussite des études\n",
    "\n",
    "Dans cet exercice, il s'agit d'étudier un ensemble de données sur la performance d'étudiantes et d'étudiants aux hautes études en fonction du contexte local, familliale et personnel.\n",
    "\n",
    "Le but est de créer un réseau de neurones capable de prédire la réussite d'une personne en fonction de ces données.\n",
    "\n",
    "Le jeux de données (dataset) est issu du site [kaggle](https://www.kaggle.com/); il comporte 145 exemples, composés de 20 critères, et d'une sortie (le niveau de réussite).\n",
    "\n",
    "Voici le fichier à télécharger : [student_prediction_td.csv](https://github.com/EmmanuelADAM/IntelligenceArtificiellePython/blob/master/student_prediction_td.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Les données représentant les personnes sont les suivantes : \n",
    " - **Student Age** (1: 18-21, 2: 22-25, 3: above 26)\n",
    " - **Sex** (1: female, 2: male)\n",
    " - **Graduated high-school type:** (1: private, 2: state, 3: other)\n",
    " - **Scholarship type:** (1: None, 2: 25%, 3: 50%, 4: 75%, 5: Full)\n",
    " - **Additional work:** (1: Yes, 2: No)\n",
    " - **Regular artistic or sports activity:** (1: Yes, 2: No)\n",
    " - **Do you have a partner:** (1: Yes, 2: No)\n",
    " - **Transportation to the university:** (1: Bus, 2: Private car/taxi, 3: bicycle, 4: Other)\n",
    " - **Accommodation type in Cyprus:** (1: rental, 2: dormitory, 3: with family, 4: Other)\n",
    " - **Mother's education:** (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.)\n",
    " - **Father's education:** (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.)\n",
    " - **Number of sisters/brothers (if available):** (1: 1, 2:, 2, 3: 3, 4: 4, 5: 5 or above)\n",
    " - **Parental status:** (1: married, 2: divorced, 3: died - one of them or both)\n",
    " - **Weekly study hours:** (1: None, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: more than 20 hours)\n",
    " - **Reading frequency (non-scientific books/journals):** (1: None, 2: Sometimes, 3: Often)\n",
    " - **Attendance to classes** (1: always, 2: sometimes, 3: never)\n",
    " - **Preparation to midterm exams 1:** (1: alone, 2: with friends, 3: not applicable)\n",
    " - **Preparation to midterm exams 2:** (1: closest date to the exam, 2: regularly during the semester, 3: never)\n",
    " - **Taking notes in classes:** (1: never, 2: sometimes, 3: always)\n",
    " - **Listening in classes:** (1: never, 2: sometimes, 3: always)\n",
    "\n",
    "La sortie est codée ainsi : \n",
    " - **OUTPUT Grade** (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JZpJB9iOdRS3"
   },
   "source": [
    "---\n",
    "## Importer les librairies\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "import numpy as np\n",
    "#pour les graphiques\n",
    "import matplotlib.pyplot as plt\n",
    "#SI bug plus loin lors du dessin des graphiques, ajouter ces 2 lignes\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XN8WXXdTdXx0"
   },
   "source": [
    "---\n",
    "\n",
    "### Définir les entrées et sorties attendues\n",
    "\n",
    "Les entrées et sorties correspondent valeurs du tableau csv, normalisées sur [0,1].\n",
    "\n",
    "Ici, les entrées d'apprentissage sont consituées de 70% des exemples. \n",
    "\n",
    "Les entrées et sorties de validations sont donc constitués des 30% restants\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "A1mjGN2Bb5Ki",
    "tags": []
   },
   "outputs": [],
   "source": [
    "#transformer les données en entrées et sorties\n",
    "# attention, il vaut mieux rester entre 0 & 1, surtout pour la sortie !\n",
    "\n",
    "original = np.array([\n",
    "[2, 2, 3, 3, 1, 2, 2, 1, 1, 1, 2, 3, 1, 3, 2, 1, 1, 1, 3, 2, 1], \n",
    "[2, 2, 3, 3, 1, 2, 2, 1, 1, 2, 3, 2, 1, 2, 2, 1, 1, 1, 3, 2, 1], \n",
    "[2, 2, 2, 3, 2, 2, 2, 4, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1], \n",
    "[1, 1, 1, 3, 1, 2, 1, 1, 2, 1, 2, 5, 1, 3, 1, 1, 1, 2, 3, 2, 1], \n",
    "[2, 2, 1, 3, 2, 2, 1, 1, 4, 3, 3, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1], \n",
    "[2, 2, 2, 3, 2, 2, 2, 1, 1, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2], \n",
    "[1, 2, 2, 4, 2, 2, 2, 1, 3, 1, 3, 1, 1, 2, 2, 2, 1, 1, 3, 3, 5], \n",
    "[1, 1, 2, 3, 1, 1, 1, 2, 3, 4, 3, 1, 1, 1, 2, 1, 3, 1, 3, 2, 2], \n",
    "[2, 1, 3, 3, 2, 1, 1, 1, 3, 2, 4, 2, 1, 1, 2, 1, 1, 1, 3, 2, 5], \n",
    "[2, 1, 2, 3, 2, 2, 1, 4, 2, 1, 2, 3, 1, 2, 2, 2, 1, 1, 2, 2, 0], \n",
    "[1, 1, 1, 3, 2, 2, 2, 2, 3, 3, 4, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2], \n",
    "[1, 1, 1, 4, 1, 1, 2, 2, 3, 5, 5, 1, 1, 3, 3, 1, 3, 2, 3, 1, 0], \n",
    "[1, 1, 1, 4, 2, 2, 2, 1, 1, 3, 5, 4, 2, 3, 2, 1, 1, 1, 2, 2, 0], \n",
    "[2, 1, 2, 5, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 3, 2, 1], \n",
    "[3, 2, 2, 4, 1, 1, 2, 4, 2, 3, 1, 2, 1, 2, 2, 1, 1, 1, 2, 3, 2], \n",
    "[2, 2, 2, 3, 2, 2, 2, 1, 2, 4, 4, 2, 1, 2, 2, 2, 1, 1, 3, 2, 2], \n",
    "[1, 1, 2, 5, 2, 1, 2, 1, 1, 2, 2, 4, 1, 2, 2, 2, 1, 1, 3, 2, 1], \n",
    "[2, 2, 2, 3, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2], \n",
    "[1, 1, 2, 4, 2, 2, 2, 1, 1, 2, 2, 5, 1, 5, 3, 2, 1, 1, 3, 1, 2], \n",
    "[1, 2, 1, 3, 2, 2, 1, 2, 2, 3, 3, 3, 1, 4, 2, 1, 1, 1, 3, 2, 3], \n",
    "[1, 2, 2, 5, 1, 2, 1, 4, 2, 3, 3, 3, 2, 4, 2, 1, 1, 2, 3, 1, 1], \n",
    "[1, 2, 2, 5, 2, 2, 1, 4, 2, 2, 2, 4, 1, 3, 2, 1, 1, 1, 3, 1, 1], \n",
    "[2, 2, 2, 3, 1, 2, 1, 1, 1, 1, 1, 4, 2, 3, 3, 1, 1, 2, 3, 1, 3], \n",
    "[3, 2, 2, 2, 1, 1, 2, 1, 1, 1, 4, 3, 1, 3, 2, 2, 1, 1, 3, 2, 1], \n",
    "[2, 2, 2, 3, 2, 2, 2, 1, 1, 3, 1, 3, 1, 1, 2, 1, 1, 1, 2, 1, 2], \n",
    "[2, 2, 2, 3, 2, 2, 1, 1, 2, 1, 4, 3, 1, 2, 2, 1, 1, 1, 2, 1, 3], \n",
    "[2, 2, 2, 3, 2, 1, 1, 1, 1, 4, 4, 4, 1, 2, 3, 1, 1, 1, 3, 3, 1], \n",
    "[1, 2, 1, 3, 1, 2, 2, 1, 1, 1, 1, 4, 1, 3, 2, 2, 1, 1, 3, 1, 1], \n",
    "[3, 2, 2, 3, 2, 2, 1, 4, 2, 2, 2, 4, 1, 2, 2, 1, 1, 1, 3, 2, 3], \n",
    "[2, 2, 3, 4, 2, 2, 2, 4, 2, 3, 3, 2, 1, 2, 2, 1, 1, 1, 3, 2, 5], \n",
    "[2, 2, 2, 5, 1, 1, 1, 1, 2, 1, 1, 5, 1, 2, 2, 1, 2, 1, 2, 3, 5], \n",
    "[3, 2, 2, 3, 1, 2, 2, 1, 2, 1, 2, 4, 1, 2, 2, 1, 1, 1, 3, 3, 3], \n",
    "[2, 1, 2, 3, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1], \n",
    "[2, 1, 2, 3, 1, 2, 1, 1, 1, 1, 2, 5, 1, 2, 1, 1, 1, 1, 1, 3, 2], \n",
    "[1, 2, 1, 3, 2, 2, 1, 1, 1, 3, 3, 3, 1, 2, 1, 2, 1, 1, 2, 2, 2], \n",
    "[1, 2, 1, 4, 2, 2, 2, 1, 1, 1, 2, 5, 1, 2, 2, 2, 1, 1, 2, 1, 1], \n",
    "[2, 2, 3, 4, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 2], \n",
    "[2, 2, 2, 3, 1, 1, 1, 2, 3, 3, 3, 1, 1, 2, 2, 1, 1, 1, 3, 2, 1], \n",
    "[2, 2, 2, 5, 2, 2, 2, 1, 1, 3, 3, 1, 1, 3, 2, 1, 2, 1, 2, 2, 2], \n",
    "[2, 1, 2, 3, 2, 2, 1, 1, 2, 1, 3, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1], \n",
    "[1, 2, 1, 3, 2, 2, 2, 1, 1, 2, 3, 1, 1, 1, 2, 1, 1, 1, 3, 1, 1], \n",
    "[3, 2, 2, 3, 1, 2, 2, 1, 2, 1, 4, 2, 1, 3, 2, 2, 1, 1, 3, 2, 1], \n",
    "[2, 2, 2, 3, 2, 1, 2, 4, 2, 4, 4, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1], \n",
    "[1, 2, 2, 3, 2, 2, 1, 1, 1, 2, 3, 3, 1, 2, 2, 2, 1, 1, 3, 1, 4], \n",
    "[2, 2, 3, 3, 2, 2, 1, 1, 1, 1, 3, 2, 1, 2, 2, 1, 2, 1, 3, 2, 1], \n",
    "[1, 2, 2, 3, 2, 2, 1, 1, 1, 2, 3, 5, 1, 3, 2, 1, 1, 1, 2, 2, 3], \n",
    "[2, 2, 2, 3, 2, 2, 1, 1, 1, 1, 2, 3, 1, 2, 2, 1, 1, 1, 2, 2, 5], \n",
    "[2, 2, 2, 3, 2, 2, 1, 1, 2, 4, 3, 1, 1, 2, 2, 2, 1, 1, 2, 1, 3], \n",
    "[1, 2, 2, 3, 2, 1, 1, 1, 2, 3, 3, 3, 1, 1, 1, 1, 1, 1, 3, 2, 1], \n",
    "[1, 2, 1, 4, 2, 2, 2, 4, 1, 2, 3, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2], \n",
    "[2, 2, 2, 3, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1], \n",
    "[2, 1, 3, 3, 1, 1, 2, 1, 1, 1, 2, 5, 1, 1, 2, 2, 1, 1, 2, 3, 4], \n",
    "[2, 1, 2, 3, 1, 2, 2, 1, 1, 1, 3, 4, 1, 3, 1, 1, 1, 2, 2, 2, 1], \n",
    "[2, 1, 2, 3, 2, 1, 2, 1, 1, 1, 1, 5, 1, 1, 2, 2, 1, 1, 2, 2, 5], \n",
    "[2, 2, 2, 3, 2, 2, 2, 4, 2, 1, 4, 2, 1, 2, 1, 1, 1, 2, 3, 1, 3], \n",
    "[3, 2, 2, 3, 1, 2, 1, 1, 2, 1, 1, 5, 3, 1, 2, 1, 3, 3, 1, 3, 3], \n",
    "[2, 2, 2, 3, 2, 1, 2, 1, 1, 1, 3, 5, 1, 4, 2, 1, 1, 2, 3, 2, 5], \n",
    "[2, 2, 2, 3, 1, 1, 2, 1, 1, 4, 2, 3, 1, 2, 3, 1, 1, 1, 3, 2, 4], \n",
    "[3, 2, 2, 3, 2, 2, 1, 1, 1, 1, 3, 5, 2, 3, 2, 1, 1, 1, 3, 2, 3], \n",
    "[2, 2, 2, 3, 2, 1, 1, 1, 1, 4, 4, 3, 1, 3, 2, 1, 1, 1, 3, 3, 5], \n",
    "[2, 1, 2, 3, 2, 2, 2, 2, 1, 6, 1, 3, 1, 1, 2, 1, 1, 1, 1, 3, 2], \n",
    "[1, 2, 3, 3, 2, 1, 2, 1, 2, 1, 3, 4, 1, 3, 1, 2, 1, 1, 3, 2, 5], \n",
    "[2, 2, 2, 3, 2, 2, 2, 4, 2, 2, 3, 2, 1, 3, 2, 1, 1, 1, 2, 3, 3], \n",
    "[2, 2, 2, 4, 2, 2, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 2, 2, 5], \n",
    "[2, 2, 3, 5, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 1, 3, 2, 3], \n",
    "[1, 2, 2, 3, 2, 2, 1, 1, 1, 2, 4, 3, 1, 1, 1, 2, 2, 1, 2, 1, 2], \n",
    "[2, 2, 2, 3, 2, 2, 1, 1, 1, 3, 2, 5, 1, 2, 2, 1, 1, 1, 3, 2, 5], \n",
    "[2, 2, 3, 3, 1, 1, 2, 1, 2, 3, 4, 4, 2, 2, 2, 2, 1, 1, 2, 2, 1], \n",
    "[2, 1, 2, 4, 1, 2, 2, 1, 1, 2, 3, 5, 3, 1, 2, 2, 1, 1, 2, 2, 5], \n",
    "[2, 1, 2, 4, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 3, 2, 5], \n",
    "[1, 2, 2, 4, 2, 1, 1, 1, 1, 4, 3, 1, 2, 2, 2, 1, 1, 1, 2, 2, 7], \n",
    "[1, 1, 3, 4, 2, 2, 2, 1, 3, 1, 3, 5, 1, 2, 2, 1, 1, 1, 2, 3, 6], \n",
    "[1, 2, 2, 3, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 3, 1, 1, 1, 2, 2, 6], \n",
    "[2, 2, 2, 4, 2, 2, 2, 1, 2, 1, 3, 4, 1, 3, 1, 1, 1, 2, 3, 2, 6], \n",
    "[1, 2, 2, 4, 2, 2, 2, 1, 1, 2, 2, 3, 1, 2, 2, 1, 2, 1, 3, 2, 7], \n",
    "[1, 2, 2, 4, 2, 1, 2, 1, 3, 1, 5, 4, 1, 3, 3, 1, 1, 1, 2, 2, 7], \n",
    "[2, 2, 1, 2, 2, 1, 2, 1, 1, 5, 4, 3, 1, 2, 3, 1, 3, 2, 2, 1, 4], \n",
    "[1, 2, 1, 2, 2, 2, 1, 2, 2, 6, 5, 2, 1, 2, 2, 2, 3, 1, 2, 2, 7], \n",
    "[2, 1, 2, 4, 1, 1, 2, 1, 1, 1, 3, 4, 1, 2, 3, 2, 1, 1, 3, 3, 4], \n",
    "[2, 2, 2, 4, 2, 2, 2, 1, 1, 1, 3, 4, 1, 1, 2, 1, 1, 1, 3, 3, 3], \n",
    "[2, 1, 2, 3, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 3, 4], \n",
    "[3, 2, 2, 3, 1, 2, 2, 1, 2, 1, 2, 4, 1, 2, 2, 1, 1, 1, 3, 3, 3], \n",
    "[2, 2, 2, 4, 1, 2, 1, 1, 1, 3, 1, 3, 1, 2, 2, 1, 1, 1, 3, 3, 7], \n",
    "[2, 2, 3, 3, 2, 2, 2, 1, 1, 4, 4, 2, 1, 1, 2, 1, 3, 1, 3, 2, 7], \n",
    "[3, 2, 3, 3, 1, 2, 1, 1, 2, 4, 2, 5, 3, 1, 2, 1, 3, 3, 3, 3, 7], \n",
    "[1, 2, 2, 5, 2, 2, 2, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 3, 2, 4], \n",
    "[2, 2, 2, 4, 2, 2, 2, 4, 2, 3, 3, 2, 1, 2, 2, 1, 1, 1, 3, 2, 5], \n",
    "[2, 2, 2, 3, 2, 1, 2, 1, 1, 3, 4, 2, 1, 2, 2, 1, 2, 1, 2, 2, 6], \n",
    "[1, 2, 2, 4, 2, 1, 1, 1, 1, 4, 4, 1, 1, 2, 2, 1, 1, 2, 3, 1, 6], \n",
    "[2, 2, 2, 3, 2, 2, 2, 1, 1, 3, 5, 3, 1, 2, 2, 2, 1, 1, 3, 1, 6], \n",
    "[2, 1, 2, 3, 2, 1, 1, 2, 3, 3, 3, 1, 1, 2, 2, 1, 1, 1, 2, 3, 6], \n",
    "[2, 2, 2, 5, 1, 1, 1, 1, 2, 4, 1, 5, 1, 2, 2, 1, 1, 1, 2, 3, 6], \n",
    "[1, 2, 2, 3, 2, 2, 2, 1, 1, 3, 4, 4, 1, 3, 2, 1, 1, 1, 3, 2, 7], \n",
    "[1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 4, 1, 5, 2, 1, 1, 1, 3, 3, 4], \n",
    "[2, 2, 2, 3, 2, 2, 1, 1, 1, 3, 2, 5, 1, 2, 2, 1, 1, 1, 3, 2, 6], \n",
    "[1, 2, 3, 5, 1, 1, 1, 2, 3, 3, 4, 1, 1, 2, 2, 1, 1, 1, 3, 2, 5], \n",
    "[1, 2, 2, 4, 2, 1, 1, 2, 3, 2, 2, 3, 1, 2, 3, 1, 1, 1, 3, 2, 7], \n",
    "[1, 2, 2, 4, 1, 2, 2, 1, 3, 3, 3, 4, 1, 2, 2, 1, 1, 1, 3, 2, 6], \n",
    "[1, 2, 2, 4, 2, 1, 2, 4, 2, 2, 4, 2, 1, 2, 3, 1, 1, 1, 3, 1, 7], \n",
    "[2, 2, 2, 3, 2, 2, 2, 4, 2, 3, 4, 2, 1, 3, 2, 1, 1, 1, 3, 2, 7], \n",
    "[1, 2, 2, 4, 2, 2, 2, 2, 3, 4, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 6], \n",
    "[1, 2, 2, 4, 2, 2, 1, 1, 3, 2, 3, 1, 1, 2, 2, 1, 1, 1, 3, 3, 7], \n",
    "[1, 2, 2, 3, 2, 2, 1, 1, 2, 3, 1, 1, 2, 2, 2, 1, 1, 1, 3, 1, 7], \n",
    "[1, 2, 1, 4, 2, 2, 1, 1, 2, 1, 3, 4, 1, 2, 3, 1, 1, 1, 2, 2, 7], \n",
    "[1, 1, 2, 3, 2, 2, 2, 2, 2, 4, 4, 1, 1, 2, 2, 1, 1, 1, 2, 3, 3], \n",
    "[1, 2, 2, 4, 1, 1, 2, 1, 1, 2, 2, 4, 1, 2, 3, 2, 1, 2, 2, 1, 7], \n",
    "[1, 2, 2, 4, 2, 1, 2, 1, 2, 2, 2, 1, 1, 3, 2, 1, 3, 2, 2, 2, 7], \n",
    "[1, 1, 2, 4, 1, 1, 2, 1, 3, 1, 3, 5, 1, 2, 2, 1, 2, 1, 2, 3, 6], \n",
    "[2, 1, 1, 5, 2, 1, 2, 2, 1, 1, 2, 1, 3, 3, 3, 2, 1, 1, 2, 3, 6], \n",
    "[1, 2, 1, 3, 2, 1, 1, 2, 2, 4, 4, 2, 3, 3, 2, 1, 2, 2, 3, 2, 7], \n",
    "[2, 2, 2, 3, 2, 2, 2, 4, 2, 4, 4, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2], \n",
    "[1, 1, 1, 5, 2, 1, 2, 1, 2, 3, 3, 1, 1, 1, 2, 1, 1, 1, 3, 1, 2], \n",
    "[2, 1, 3, 3, 1, 2, 2, 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 1, 3, 3, 2], \n",
    "[2, 1, 3, 3, 2, 2, 2, 4, 2, 4, 4, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1], \n",
    "[2, 1, 2, 5, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2], \n",
    "[2, 1, 2, 5, 1, 2, 1, 1, 1, 1, 1, 2, 1, 3, 3, 1, 2, 2, 3, 2, 1], \n",
    "[2, 1, 2, 5, 2, 2, 2, 1, 1, 1, 1, 5, 1, 3, 3, 1, 1, 1, 3, 3, 1], \n",
    "[3, 1, 1, 3, 2, 1, 2, 1, 2, 4, 4, 1, 1, 2, 2, 2, 1, 1, 2, 3, 1], \n",
    "[1, 2, 2, 5, 2, 1, 1, 4, 2, 1, 2, 3, 1, 1, 2, 1, 3, 1, 3, 3, 1], \n",
    "[2, 1, 2, 4, 2, 1, 2, 1, 2, 3, 3, 2, 3, 5, 2, 1, 1, 2, 2, 2, 2], \n",
    "[2, 1, 1, 3, 1, 1, 1, 2, 3, 3, 3, 2, 1, 1, 1, 2, 2, 1, 3, 3, 1], \n",
    "[2, 1, 2, 3, 1, 1, 1, 2, 1, 1, 1, 5, 3, 5, 3, 1, 2, 1, 1, 1, 0], \n",
    "[1, 2, 2, 5, 2, 1, 2, 1, 1, 3, 3, 2, 1, 2, 1, 1, 2, 1, 3, 2, 2], \n",
    "[2, 1, 3, 3, 2, 2, 2, 1, 1, 3, 2, 4, 1, 2, 2, 1, 1, 1, 3, 3, 1], \n",
    "[1, 1, 2, 4, 1, 1, 1, 1, 3, 2, 3, 2, 1, 4, 2, 1, 2, 2, 3, 3, 3], \n",
    "[1, 1, 2, 5, 1, 1, 2, 1, 3, 1, 1, 5, 1, 3, 2, 1, 1, 2, 3, 2, 2], \n",
    "[1, 1, 1, 4, 1, 1, 1, 2, 3, 4, 6, 2, 1, 4, 2, 1, 2, 1, 3, 3, 3], \n",
    "[1, 1, 2, 4, 2, 2, 2, 4, 3, 3, 2, 2, 1, 2, 1, 1, 1, 1, 3, 2, 1], \n",
    "[1, 1, 2, 4, 2, 1, 1, 4, 3, 3, 4, 2, 1, 2, 1, 1, 1, 1, 3, 2, 0], \n",
    "[1, 1, 2, 3, 1, 1, 2, 1, 1, 1, 1, 3, 1, 4, 2, 1, 2, 2, 3, 2, 3], \n",
    "[1, 1, 2, 3, 1, 1, 2, 1, 1, 1, 2, 3, 1, 4, 2, 1, 1, 2, 3, 1, 1], \n",
    "[1, 1, 1, 5, 2, 1, 2, 2, 2, 4, 3, 1, 1, 4, 1, 1, 1, 2, 3, 2, 4], \n",
    "[1, 1, 1, 5, 2, 1, 2, 1, 2, 1, 2, 3, 1, 2, 2, 1, 2, 1, 3, 2, 3], \n",
    "[1, 1, 2, 5, 2, 2, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3], \n",
    "[1, 1, 2, 4, 2, 1, 2, 2, 3, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1], \n",
    "[2, 1, 2, 3, 1, 1, 2, 4, 2, 3, 3, 1, 1, 2, 3, 1, 1, 1, 3, 3, 2], \n",
    "[1, 1, 2, 3, 1, 1, 1, 2, 3, 3, 3, 3, 1, 2, 2, 1, 3, 1, 3, 2, 0], \n",
    "[1, 1, 1, 5, 2, 1, 2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 3, 1, 2], \n",
    "[1, 1, 2, 4, 1, 1, 1, 2, 3, 3, 1, 1, 2, 2, 2, 1, 2, 1, 3, 2, 0], \n",
    "[1, 1, 2, 4, 1, 2, 1, 2, 3, 3, 3, 1, 1, 1, 1, 2, 2, 1, 2, 3, 0], \n",
    "[2, 1, 2, 3, 1, 1, 2, 1, 2, 1, 2, 2, 2, 3, 3, 1, 1, 1, 2, 1, 5], \n",
    "[1, 1, 2, 4, 2, 2, 2, 4, 2, 1, 1, 5, 1, 3, 2, 2, 1, 1, 3, 2, 5], \n",
    "[1, 1, 1, 4, 2, 2, 2, 1, 1, 3, 4, 4, 1, 2, 2, 1, 1, 1, 3, 3, 1], \n",
    "[2, 1, 2, 4, 1, 1, 1, 2, 3, 4, 4, 1, 1, 2, 2, 1, 2, 1, 2, 1, 4], \n",
    "[1, 1, 1, 5, 2, 2, 2, 1, 1, 3, 1, 5, 1, 3, 1, 1, 2, 1, 3, 2, 3]]\n",
    "                   , float)\n",
    "nb_lignes = original.shape[0]\n",
    "nb_val = original.shape[1]-1\n",
    "\n",
    "maxis = np.max(original, axis=0)\n",
    "\n",
    "tableau = original/maxis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#on prend un pourcentage de lignes pour apprendre, le reste pour valider\n",
    "#il faut mélanger les lignes pour pouvoir avoir des exemples d'apprentissage\n",
    "# et de validation homogènes (autrement, les exemples de tests seraient tous des exemples négatifs (non dangereux))\n",
    "#np.random.shuffle(tableau)\n",
    "\n",
    "#on décide que 70% des lignes sont des lignes d'apprentissage\n",
    "nb_lignes_app = nb_lignes * 70 // 100\n",
    "nb_lignes_val = nb_lignes - nb_lignes_app\n",
    "\n",
    "#on remplit les entrees et sorties d'apprentissage\n",
    "entrees_app = tableau[0:nb_lignes_app, 0:nb_val]\n",
    "sorties_app = tableau[0:nb_lignes_app, nb_val:nb_val+3]\n",
    "\n",
    "#on remplit les entrees et sorties de validation\n",
    "entrees_val = tableau[nb_lignes_app:nb_lignes, 0:nb_val]\n",
    "sorties_val = tableau[nb_lignes_app:nb_lignes, nb_val:nb_val+1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SC-1MnShdwe0",
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "## Choisir le modèle de réseau\n",
    "- ici les couches sont séquentielles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-09 16:08:22.889639: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Jo5Ej8kkd8nh",
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "### Définir l'architecture du réseau\n",
    "Choisissez la struture du réseau, le nb de couches cachées, etc.\n",
    "- une première couche composée de \n",
    "  - autant de neurones en entrée que de valeurs, plus le neurone BIAS (ou non)\n",
    "  - x neurones en sortie \n",
    "- une dernirèe couche composée de\n",
    "  - y neurones en entrée (ceux de la couche précédente) et \n",
    "  - de 1 neurone en sortie (niveau / 7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#une premiere couche constituée d'autant de neurones en entrée que de mots clés, 6 en sortie...\n",
    "model.add(Dense(10, use_bias=True, input_shape=(nb_val,), activation='tanh'))\n",
    "\n",
    "#model.add(Dense(12, use_bias=True, activation='sigmoid'))\n",
    "#model.add(Dense(10, use_bias=True, activation='sigmoid'))\n",
    "#model.add(Dense(8,  use_bias=True, activation='sigmoid'))\n",
    "#model.add(Dense(5,  use_bias=True, activation='sigmoid'))\n",
    "#model.add(Dense(3,  use_bias=True, activation='sigmoid'))\n",
    "\n",
    "#une derniere couche constituée de 1 neurone en sortie, \n",
    "# nb neurones de la couche précédente en entrée et activation sigmoide\n",
    "model.add(Dense(1, activation='tanh'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8L4LxSBWeaF1",
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "### Compiler le  réseau\n",
    "- ici, on précise que l'algo de correction d'erreur est *'adam'*, et que l'erreur calculée est la moyenne des valeurs absolues des erreurs commises. On indique également que l'on veut voir apparaître en plus la précision de l'apprentissage (accuracy).\n",
    "\n",
    "(vous pouvez chosir un autre optimizer et un autre calcul de loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Nadam', loss='MSE', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rZ4IQ-bIdtN1"
   },
   "source": [
    "---\n",
    "\n",
    "### Entraîner le réseau \n",
    "- ici on peut le faire  'parler' (verbose=1 ou 2) si le nb de cycles d'apprentissage (epochs) est court\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 148
    },
    "colab_type": "code",
    "id": "ddTla-J_cfz8",
    "outputId": "0fd3cade-cd59-41db-c5f6-0ea22e48cac0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-09 16:08:27.732794: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n"
     ]
    }
   ],
   "source": [
    "tests = model.fit(entrees_app, sorties_app, \n",
    "                    validation_data=(entrees_val, sorties_val),\n",
    "                    epochs=500, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I0zBYIALlDYp",
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "## Dessiner l'évolution de l'erreur et de la pertinence\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Z89vptAXcuDM"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "les ensembles de valeurs récupérées lors des tests sont \n",
      "['loss', 'accuracy', 'val_loss', 'val_accuracy']\n",
      "-> [perte sur exemple d'entrainement, qualité sur exemples d'entrainement,perte sur exemple de validation, qualité sur exemples de validation]\n"
     ]
    }
   ],
   "source": [
    "history_dict = tests.history\n",
    "eval_tests = list(history_dict.keys())\n",
    "print(\"les ensembles de valeurs récupérées lors des tests sont \")\n",
    "print(eval_tests)\n",
    "print(\"-> [perte sur exemple d'entrainement, qualité sur exemples d'entrainement,perte sur exemple de validation, qualité sur exemples de validation]\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tracer l'erreur d'entrainement / l'erreur de validation\n",
    "- L'erreur d'entrainement et l'erreur de validation doivent suivre des courbes quasi parallèle.\n",
    "- L'erreur d'entrainement doit être plus faible que l'erreur de validation\n",
    "    - si les courbes divergent, si la courbe de validation s'éloigne de la courbe d'entrainement, il y a alors un sur-apprentissage (overfitting) trop adapté aux données d'entrainement, sans doute trop poussé (diminuer les epochs?).\n",
    "    - s'il y a une erreur d'entrainement trop grande, il y a sous-apprentissage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aw801d26geXMgEfHSFUVRMiruFBDBAMpZ0mUBXEzsRZj5JoBNADqkSKucYQiIffuARYuAmzfdditFUZT0gjsFxB4AVYioIhFlB9ATwIqEnEhExYiooO04F4DHAbgvWFK1akDr1sAKW/MKFnTbrRRFUdILbhMQzBwF4DUAawEcA/AbMx8hosFENBgAiKgkEQUDGA5gNBEFE1F+AKUAbCSifyGCZj0zJ8wBPCn06QOsWwdcuyYT5o4dc9utFEVR0gtunQfBzKuZuSozV2bmiba875n5e9vxZWYuy8z5mbmg7fg2M//LzPWYuQ4z12Lmj9zZzhguXgTKlEmVWylKZsDd4b7dgbXNnTp1wk0nKuexY8fGhLRwxfLly3H06NGY9Icffoi//vorRdvqbnQmNQAsWwZ4e0scpqNHgc6dgUQuzadkfDTcd+Zj9erVKJhElbOjgPjoo4/w+OOPp1DLUgcVEICE2Dh3DjBmz65eDdy549k2KamKhvtOn+G+XX3+BmvWrEH37t1j0ps2bcITTzwBwHU4bSvW0crEiRNRrVo1PP744wgMDIypM3PmTDRs2BB169bFM888g7t372L79u1YsWIF3nnnHfj6+uLUqVPo169fTEjwDRs2oF69eqhduzb69+8fEwDQ29sbY8aMiQlbftzT69S4mkGXHrckz6TetElmUC9YwDx+vByfP5+0aymJJtbszlSO963hvtNnuO+4Pn+DyMhILleuXMx9Bw8eHPO5OAunbW0zsznbOiAggGvVqsV37tzhW7duceXKlWOe8dq1azH3e//993natGnMLN/1okWLYsqMtBGyOzAwkJmZX3jhBZ4yZUrM/Yzzp0+fzgMGDHD6/SYVDfedFIoUkX2WLEDlynLs5A1IyZhouO/0Ge47IZ9/1qxZ0aFDB6xcuRLPPvssVq1ahc8++wyA83DaderUcfp5btmyBU8//TRy585t95kAwOHDhzF69GjcvHkT4eHhaN++vdNrGAQGBqJixYqoWrUqAKBv376YPn063nzzTQBA165dAQANGjSIeW5PoQICkBjRAPDqq8CHH8qxi2iMSiqQyvG+WcN9p8tw33F9/lZ69OiB6dOno3DhwmjYsCHy5cuHM2fOOA2nHReuQo7369cPy5cvR926dTF37lxsiuf3x/GEBTc+Yy8vL4/bltQGAZgjiGvXpAOqUgWwLAqiZGw03Hf6DPed0O+tVatW2LdvH2bOnBkz4nIVTtsVLVq0wLJly3Dv3j2EhYVh5cqVMWVhYWEoVaoUIiMjY9oJuA77Xb16dZw9ezbms/jpp5/QsmXLOO/vKVRAABJawwjL+9RTEqzPNhRWMj4a7jt9hvtO6Pfm5eUFf39/rFmzJkaF5Sqctivq16+PHj16wNfXF8888wyaN28eUzZ+/Hg0btwYbdu2tft+evbsicmTJ6NevXo4depUTH7OnDkxZ84cdOvWDbVr10aWLFkwePDgBHzKqQ/FN9xJT/j5+XGSfa6HDQNmzhTVUjyrVykpy7Fjx5K1LKLiWc6ePQt/f/80G7BPMXH2XyOivczs56y+2iAMpk6VvbGiXJYswHffebRJiqIonkQFhMH33wORtmUpLl8GLENCRVFck5bDfSvJQwWEgXXRjerVgT//BKKigKz6EaUGzBzvwvSKoiSdpJgT1EjtDB8fICICOH3a0y3JFOTMmROhoaFJ+gErihI/zIzQ0FDkzJkzUefp67EzfHxkf/QoYJvMoriPsmXLIjg4GCEhIZ5uiqJkWHLmzImyZcsm6hwVEM6oUgXo0EFXlkslsmXLhooVK3q6GYqiOKACwhkFCwLxTJxRFEXJ6KgNQlEURXGKCghX+PiYcZkURVEyISogXBEaCqjRVFGUTIxbBQQRdSCiQCIKIqKRTsqrE9EOInpARG9b8ssR0UYiOkZER4joDXe20ym5c+uiQYqiZGrcZqQmIi8A0wG0BRAMYA8RrWDmo5Zq1wEMBfCUw+lRAN5i5n1ElA/AXiJa73Cue8mdW9eEUBQlU+POEUQjAEHMfJqZIwD8CqCLtQIzX2XmPQAiHfIvMfM+23EYgGMAyrixrbHJk0cFhKIomRp3urmWAXDBkg4G0DixFyEibwD1AOxyUT4IwCAAMWGAU4QOHYBEzjpUFEXJSLhTQDgLrJOoWApElBfAEgBvMvNtZ3WYeQaAGYCE+05sI11iW9hdURQls+JOFVMwgHKWdFkAFxN6MhFlgwiH+czs2YVZFUVRMiHuFBB7AFQhoopElB1ATwArEnIiSVjPWQCOMfOXbmyja155xYzJpCiKkglxm4Bg5igArwFYCzEy/8bMR4hoMBENBgAiKklEwQCGAxhNRMFElB/AowBeANCaiA7Ytk7uaquLBwCOHQMOHkzV2yqKoqQV3BqLiZlXA1jtkPe95fgyRPXkyFY4t2GkHrlzy/6VV4Bt2zzaFEVRFE+gM6ldcfKk7J2NIF5/HahTBzhzJnXbpCiKkopoNFdXlCgh+zt3ZPGg7NklzQx8840cb9sGaJhqRVEyKDqCADBpErBwoUPmtGnAu+/K8QXLdI6wMPP4xg23t01RFMVTqIAAMGcOsGiRQ2auXED79nJ87pyZf/myeXzzprubpiiK4jFUQAAoXx44f95JQb16wPLlYm8wUAGhKEomQQUEgAoVXAiIQoWALl2AokXNvCtXzOObN4FLl4D792Ofe/MmcPZsyjZUURQlFVEBARlBXLnivJ/Hli3Apk1m+tlngdu3JZBfmzZA6dLAP//EPq9LFzVgK4qSrlEvJoiAyJZNBgOx+vT33pPCVq0kTQTkyyfHK2wTw0NDY1+0SBGgVi13NVlRFMXtqIAA8PzzQJ8+0vfHokIFYPt2Mz1rltghihY1XZ/u3Yt93s2bQMGCbmitoihK6qACAoCXVxyFFSoAv/0GPHwoFZctAy5etI/T5MxYvXGj7O/f17DhiqKkS9QGAZkL178/sGqVk8IKFYCoKBEKgBgrSpSwHx3E5c3kuGzpTz8BtWvLhDtFUTIus2YBK1d6uhXJQgUE5AV/3jxgl7MliSpUkP25c/KFBwTIanOlS5t1OnSIfV5W2+Bs82YzbAcA/PcfcPiw7BVFybgMHAg8+aSnW5EsVEBANEdFi9p7sMbwyCPA3r1AgwamUPD2Bnx95Xj8eODRR2Of9/PPsu/a1V6ANLYtqnf8eAq1XlEUxT2oDcJGiRL2c+BiyJ8fqF9fVEIdOsjbf/nyQHi4qJny5gVOnQIqV7Y/L08e8/j0aak7ZQpw9KjkHTsGPP64m55GURSP0707cOCAp1uRLHQEYaNCBem7nZoGtm8HChcGxoyRWEz58gGlSgHXrwO//w7873/A0qXmyWfOAE88YZ5fujRw65YYOqZOlTwdQShKxoVZ+ol0Hm1BBYSNjh2BHDmkz9+61UFQlC4tX/T48cDnn5v5RKaxeuxY2d+4IYZoQDye7t8XLygAmDBBDN5AbOO1oigZh8uXxWY5frynW5IsVEDYGDxYtEcTJgDNm4stOoby5c0FhAoVsj+xQAHZHzki60QYIw0AKF5cpI6hVrp2TfYnTwJz57rrURRF8TTGyMHoH9IpbhUQRNSBiAKJKIiIRjopr05EO4joARG97VA2m4iuEtFhd7bRIIvtk5g+XfbWCN/IkgWoUUOOHQWEMYKIjjZPNnj0UaBZM2D9eklPnQpUrSoqKUBCiteuraMJRcloGALi3XeBq1c92pTk4DYBQUReAKYD6AjAB0AvIvJxqHYdwFAAnyM2cwE48R91L0uWyN6Y9hCDEYPDlYBwxbZtwPffA40aSdrXVyLEPv00cOKEDFs+d/b4iqKkWwwBcf68qJrjg1niviVmftT16/Ji6kbcOYJoBCCImU8zcwSAXwF0sVZg5qvMvAdApOPJzLwZIkBSlc6dJfRSrGkKLVvK3nHI2KUL4qVwYTk/Z06xT1y4IEIiJETKnS1rqihK+sVqnB48OHZHvn49sGCBmV67FmjRwj6sDwB88AEwerSZvn9f/PGvXBEV9qpVoon4668UfwTAvW6uZQBYFTXBABq78X4pQpYsEpzV29uhYNAgoEkToGxZ+/x69YC//5aAfb/8Yr4tvPWWvA08+6ykCxeWLzcqSjygAHGPBTQsuKJkNKpWBQYMEEM1IJ6NVlf4774T22SvXpI2+oDy5cXhpXJlcXiZMEHy69WTOVWVKsnIoXFjCf9TvjzQr5+41LrBbd6dAsJZ6LsUjy9BRIMADAKA8uXLp8g1mzYV4d66tbk0NbJnB/z8Yle+dUvK2rUTYRAeLq6vPj7ypRoYP4CAAHN9ieBg+zJFUTIGDRoAP/wgI4AtWyS6M7O8IGbLJlqD2rXNWG3Hj8sqlgUKiK2ydWtgwwbzemPGiHqje3fRg2/eDLz0ElC3LhAUBDx44JbHcKeKKRhAOUu6LABHzX6yYeYZzOzHzH7FihVLkWtGRQHvvy9LkcbL3r1iiF66VNJ58wK9e9sLB0DeJooWBapVM+0WZcrI/sYNETSKoqR9IiJkb9gLjh+XVSdffVUcTm7dkjVkbt+WCVZPPCGRFTp2lDf+s2dl8uzvv0u/sX8/8NVXEhW6QAE5v3t36fjv3ZNr/vuvCJKpU2WS7alTwIwZcv9ChYCSJd3yqO4cQewBUIWIKgL4D0BPAM+58X4pRtassh7QiRMJqJw3r+ynTZOhnisaNjRtDhER8kMZPVqExJo1GrxPUdIDp04BVarI3Kbz58XB5IUXgEOHZKtSBahZUzQKmzaJ7TE6WlzgDayLzuzZY3o1GuTLJ4uRuXrhzZvX7HfcjNsEBDNHEdFrANYC8AIwm5mPENFgW/n3RFQSQACA/ACiiehNAD7MfJuIFgBoBaAoEQUDGMPMs9zVXkeqVUuggDD8Y50uJuGCcuUk+J+B4eGkKErqcfWqzE3ycXSudMKlS/IGv2OHvMxt2CAq4kuXREjUrCnHR4+a4f0Nm0OhQiIEgoJEwOzbJ6OCt96SEcGSJXJeyZKiXTDUUGkAt8ZiYubVAFY75H1vOb4MUT05O7eXO9sWHzdvyqjuzJl4Vg719QVefFH8nRNL27ZiDZ8wQX4QhQsnrbGKoiQeX1/p1BMyen/5ZRkxtG0rNscvv5Q4bYCokJYvl1nTFSuK2ihnTvuIz5s3i2qiWDExNAMSJXTAALFbAqYLfRoRDoDOpHbJgAGyb9tWnAVckjUrMHs2UL164m7Qvbu4pl2/Lm8OjpPsFEVxL1njeD++ehX45BNzOeH//pMOf8UKGS3062eGzTHC+X/wgbmomJ+fqV0AxHPRUWX0/PNiXL5+3ZyIm8ZQAeGC118XT7RTp9zkZGTE8ihQQDwcLl1yXu/QIQnjoShKylKyJNC+vfOy9euBUaOAnj0lffGiqJTOnRNjtGFvLFFC1EO7d0u6TBnxYFq3zu3NTw1UQLiAyDQNbN3qhhsYnky5c8sP1ZWA6NlT3kwURUlZTp+WpYHPnDHzfv1VbAS9e0u0g+3bxankyhVJ374t6oWOHUU1NWmSzHmoVUvOL1FC1E25cnnkkVIaFRBx4Osr8+ISMlM+0VgFRKlSzhejuHNH3k62bXNDAxQlkxMaKp2/db7B5MmmPbF9e3FnDAgQYVC6tNgfrPTtCwQGmsE8q1WLPZk2HaMCIg6yZJHJi2vXmnakFMOYgefrK8fOlrMzJtKl42BfipJmuX9f9kZEA2ZxXaxZU9KGXeD8eeDbb81wO5kIXVEuHnr2lLkq4eEp7HrcqZN0/B06SEyVtm1j1zEMZID8eBPjSqsomZmE/GFz5JDZzP/+K+nLl+W8qlUlXauWRGR+9FFxTc+E6AgiHpo2lflsAwfKvJcU44UXZGhbuLDEUOnbN3Ydq4CIb2WqU6dE92mtP2eO+XakZBzWrpW3WsU5v/0GtGrlPPxERATQrZsYmQcPFluBESxz1CjZGwKicGFg4UIzdlomRAVEAsifX4ImPvaYm+wRoaEy3d4x4qMhIL75Rn7IoaHiGudMWNSuLUY0g8mTZYnTf/5xQ4MVj3H3row6O3XyzP1v35Zgcjt2eOb+CWHWLAlf42gvAIBFi4DFi4F580TQNm8uLqy3bolrauXKsga9QZkycbvDZnBUQCQA65IPcUXTSDLz5smPMizMPt8QEM8/LxNvPv8cmD/fjMFipVkz2Rt6VWPFI2MVOyVjsG+f7K2jy9QkWzbgo4/MRbDcRVBQ7LyHD2U4b43FP3UqsHKlHC9YIGrYdetM3bAjb70l+xUrxINp1CgJqpc3r7ivBgWJ27kCQAVEgvnxRwmeaETxTlEMXamjJXzgQODAAZmFefCg2fk7wxg9GKOLGzdk724Dd2SkxJ9ZscK991GEXbtk37t37LLAQGDECPswLinNjBnyB9i503332LxZflPz5kn6wAFRl+7eDUycaIbIjo4Ghg0DnnxS0tOmmdeYPl0cQLZulf/C66+LgFm/XkY/TzwhUQwKFxa3VS8v9z1POkYFRAJ54QX5b3z4oRtsxYaACA2VDheQP2GBAhIlslcv+YMYP2Jr4C8Do1MwBITxlrVli6gE3BUM8OpVeevauNF1nZMn3b7yVZrhyhXgnXfMWbYpzRNPAKtXO1+FMDAQ+Owz6fjGjXPP/Y0Rqat5O4nhwgXpsMeMsV9219dX9gcOyL5BA1GXHjok6f79JbyNUT56tLRn1y5xM+3XT4RFcLCokMqWBf7v/+TzqV1b1nVREgYzZ5itQYMG7E6iopgvXmQOD0/hC69YwSxdOHONGsx37zI/9hjzoEFSXrcuc/XqzIMHM8+Z4/waxvnBwZIuUcLMs+anNCdOyPV//tl5+fXrUj5jhnvun9Z4+ml53rVrk36Na9eYf/yR+fz5xJ33yy/m9/3ii4m/7z//yL1dMWOGef2SJRN/fUemTzev166d/WfWsCFzy5bM69ebddq3Z86enfm11yTdsqXsd+1iHjZMjg8fNq9x+zbzmDHM/fsz79iR/PZmUAAEsIs+VUcQieDgQZkrk+LqV6s73rFjEt1x40YzDPBjj0nM+e+/l0bMn29/vvG2OmaMucZEtWrAa6+ZhrocOVK40TZu35a9q3gk+fObC6Ikh8OH41axJZULF5wsQJ4MjJFalmT8tc6fB/r0McM3WNm8WQLCde0au8xQURYtaoaWTyjLl4ufvzVExPbt5opogMwyNrh7N3HXd0ZgoHls2A0A8e7bs0c2q/v32rXibvrNN5L29pYJaoGBwJQpgL+/OYcBkLDZY8fKM+ioIUmogEgExpocziY9JwsfHzFyNGsmk3OMSXMvvyz7KVPkD1+0qBjlZs+2P98Y9ufMaf5x//kH+Ppr+UN1726uYpfSGALC+NM6kiWLCKtjx5J+j5s3RTUwcGDSr+GM0FBZl2P48MSd99hjZiRPR157TfbOPGgSynffyd6Z/eijj0QYW8NDhIWJCs9Q01SsmHgB8cMPsQXPokXA0KGmetAIi/3II/G7XSeEwEBxzhg82ExHRcn6KIC9EMqVS377VtXZZ5+JS2v9+mJz+OGH5LdJsUMFRCIoXlz6uwStE5EYSpQQI4ePj3RaoaHiWpcvn1mnaFExglSsGFtCGZFg33sP+OknM//uXZkHce9e4juMhGK8Mbsy8u3cKaMe40+fFAx9959/Jv0azjD8311d9/p109hvZdMm6ZR//z12WdGiMnnGiMXjKhRwXDYhY8UyZ9P3jTxDGFy/LgKrfXvTa8fbO3HOCcyyMmLz5iIMIiNlP2uW/IaMGf2GUOjRI2UMccePy8vD119Le4sVE/vNF19I+ejR5v0uXwbeeEOM8xMmyGiieHFZhrNmTeCVVyzrAysphQqIRJA1qzgL/d//pbCXYUSEqI9mzJA/SmioeFc4/gmfflqG0cYf9uOPJQb9mjWyYh0gf+IDB+SN++BBaezKleafLqVp3VpWv6pQwXm5dbJSUg3VhoBICTXBW2+JpwFgdv6u4qgUKRL3Gh1PPSWC9+BB+W4iIkRV4utrfh+tWsVud2CgvGmsXClOBI4Ynb8z4WS4Qht1ihSRzr1wYYkh9OCBTLy0Rim9eVPu4+hG3bevPMPs2dIBP3ggjhFbt8rv0KhvxCq6cUPe1ps2FSNxQgzVUVHOVYMXLohjRYMG8sc6fVqcMVbblo9p0EC8i0aNAj791H7E9v77smKb4n5cGSfS4+ZuIzUz8+7dYgurVo25dm3m6OgUuGhoqGmI27hRjIXz5jmv+9lnUu/mTfOc06eZr1wRA96IEcyrVkn+zp1yToUKzM8/nwINdcHTTzPXquW8bM0as51Jte7v2cPctas8Z3IBmL295dhqdHVV18dHnAhOnjTze/Y0z7t2jdnXV4737mXu0oW5Th37azhef8ECeweCvXsl/9NPmadNY27RQvIHD47dpnLlpKxAAebISPvrHDoUu/6dO3JdQH5XzMxHjzIfOWJ/LmD+bsaPF8OvkV+wIHNEhHyXS5YwL19u325nvPkmc+fOzNu2mQbm8eOZP/xQfqvh4cwLFzKfOyf1//47dnuUVAFxGKnj7XQhy4X+HF+9tLClhoB4+JB55UrmVq3k07t6NQUuGhFh/ilOnIi7rtG5WP/gx45JWYUKzL17iwcMYHZqjz4qDXYHxr1KlXJevmyZlB84kLTr37rFPHy4CMTkcuuWtGXCBElPmmR+hg8emPX+/pv522+Zy5dn7ttXyl991f5affrI583MPHSo1Ll4kTlLFjleuFDKnF3f6m0EMP/2m7xplCnD3KOHvH1Ur8586ZJ5zvz5zB07MufKxZwvH3OvXuKZZr3OU08xjx4t9cePZ+7WjblsWbP8hx+krHt3+b6s5/bqJd5ztWqJd9C8eZI/YoTUDw0127Jxo5T9/bfrz7pxY3mOrl1jd/wtW8auHx3N/NFHzM2bMxcubD6H4naSJSDkfKwFkD0hdR3O6wAgEEAQgJFOyqsD2AHgAYC3E3Ousy01BITB6tXy6W3ZkkIXXLqUuUoVcclbvNh8s3Lk/n3me/dEUhl/uMcfF3fANm2YGzVi/vJLyb9+Xc7p0UOu7Q7ee4/t3k4dMQTa8eOSXrJE3k6tREVJp+2Yz2zfmRYtKsI0qdSqJdcx3IdHjJD03Ln2HbhjhwbIyM1o68mTkle+vHwfw4Yx580rb8nWc3791Ty+fNm8/k8/MRcqZF5n0iTmM2fk+JtvmMeNkzpWjLZOncp86pTkGcNZ61atmpQNHx67zHB9rVvXzOvdm7lZMxk9MItgMZ7N21t+Zwa7d4tQ2rdP6ixbFvszXriQ+f33Y9+7Vy9mIun4HZ9N8SgpISD+D8AeAB8AGG5s8ZzjBeAUgEoAsgM4CMDHoU5xAA0BTLQKiISc62xLTQERFCSf3qxZKXjRzZvNP1SfPnHXvXrV/g84ahTzokXS2Y0cyZwtm/nnHj5c3jyPHWMeOFA66bfeSpk2v/oqc5Eirsv37JEOqGhReUMGZERj5cYNye/YUY6tLFli/5z//Ze0dlrVeIb64tw5+cwdcSYgJk+WN/3jx+3zv/uO+ckn5fiRR5yfax3lWdm7V8r695dOE5D5K8yizpk7V44NlQ5gqvI2bhShmi2b/X2M/4AxsgOYc+SQ/dNPm/NWSpeWvePznz9vnjdunJl//778ht5+WwSUIVgNhg8XwTtggHn+22/LvnNnqWMVwkqaISUExBhnWzznNAWw1pJ+D8B7LuqOdRAQCT7XuqWmgIiKEpX/22+n4EVDQuRPCMhbqStGjmQeO9asCzB//LFZ/s03ohYw2LFD1AtjxzJnzWqeExWV/Da/8IK8GS5YICMbZ2zfHrvDtGK8PQPMW7fal/38s9m5JUdVZXSMuXPLF2dw86bc89YtM8/aGVs72dq17YW40YmuXOlaMBjb9u327bG+/fftK52rkb52TewP+fJJp+p4rXfftf8Oo6OZa9aUdIsWcv2DB2Of17y5OXr48UfmCxecf1YzZ8rnZfw+HnnE/K1NnCjty5/fVFlZ23j9ughj49r37yft+1JSjbgERIK8mJh5nLMtntPKALhgSQfb8hJCcs5NFby8xNvuiSdS8KJFi8p8grZt444KuGyZxLC3et/kzStujkePyiSrhQvNsiZNxCPk6lVzUt2sWSkTf+bSJekaevUyw3uMHWu63j54EP+cAKtPvaOnTevW4tny0kuSTqq77q1bsq9VS7yNIiLkuhMnyvwTY00Aq1uq9cutUkXaOXOm/XVDQiQcioERNBEQD6E//pAlY41JNIB8PkOGyHGePMDcufYeS0WLiqtpWJhzL6fPPjOPz5wRbzdj/oUx6bJ6dbPOr78ClSrJ/JpnnpH79+7teuWzgQPleY3fx4wZpgttgwbiOXXrlvymRowwJ2HWqCHfd+HC5rXdNUFTSRUSFMeWiDYCYMd8Zm4d12lO8mJdI7nnEtEgAIMAoHz58gm8fMrwzjtuuGj//rLFRdWqEt8oSxbpICpWlI4hIABo0UJcLa0zUB88kDg2hgshIL7j8+eLwHC2FkVCKVZMfNkDA6WzrFTJnMz06qvAl1+acfZdYRUQji6npUrJdvSopJMqIIwJfaVLy/7OHREOhmAwyq0d9apVsm/eXD7j5cslr3Vr4O+/pezqVfup9QUKyGSzdu2kAw0Pl/DcVmG8das518FoT7t2UseYrdyrF/DLL+Ky+vLL4q5s8PXX8jmEhJgL2QweLN+xMWkye3aZI5Avn7jgbtokYa6Tsr55zZpy/qVL9u6zgLjxnj8v8z6mT88wazErQkIDnb9tOc4J4BkA8UUjCwZgXYapLICExjRI8LnMPAPADADw8/NLqABKMZYvFzfy5cuTF10hUVSpIj70b7wBDBokeXnzmrOl27WT2bjGDNUrV0y/fINu3cyQ4FYBsXmzXHf+fHPmbFz88osIpoYNpbMkkrfT7dul3FV4jPv35W09Tx7z7R6ILSCOHhXh06KFdJpGh5pYjHsYoUjCw2UrVUqErTFyyZFD3tDPnpVlJgGgbl15azaEyKOPygzsX34RgfLxx5KfOzfQpYuEZ8+aVUJzN24so47mzUWQAhLao2pVEZyhofKZ/fmnhJowBETFikDHjsDSpeb8l0ceEcFSooTziWpffWUKUkBGLwYTJ5rtTAqultvs2FE2JUOSUBXTXsu2jZmHA2gcz2l7AFQhoopElB1ATwAJjQmdnHNTldu3pa/esycVb9qqlewfPpRRQb9+0jFZ49jnyWMelyoVu0O5ft08tk5gu3RJJto564Bu3JD7OYaTLl5c9iEhoorIm9dUPd2/L2sIWFm9WibXGeqQzp2BI0fk2FHF9Ntv8kZeqJB0yEldFzhrVolt9fzzorYpVkxGEcZqYUbnny+fDA2ffVbS334rIR6MRUFmzZKZvXPmSFmvXuaktVdeEVXYmDEysevNNyX/pZfMMCG3bskM4vr1RSAUKyb5Eyfaq6FKlJA38nnzROD7+4vqpmRJ17OYs2a1V3dZKVxYPkNFSQyujBPWDUBhy1YUQHsAgQk4rxOAExCPpPdteYMBDLYdl4SMFm4DuGk7zu/q3Pi21DRSG1y9ypwnD3OlSqnspBEZKcbJPn1k8pSRZxgLHb1TrNFdhw0Tt1cjfe6cnDtmjBjADaOs4yzAhQulrFgxMSx/+KEYR405GR9/LJ41xnVv3pQ5AgUKmIbMDz6wd9E1PK0iI8XV0tG99913pS0GVrdLZjGkHj0a+/N5+FDcMCMjXX+GJUqYk95y5RIX0uPHxePo9dcl35gMdvYs84YNzr/knDmZn31WPKyio81nMwzrNWqIIf/KFXMy2rp1cq4RybdtW0m3aWO6qipKKoAU8GI6A+C0bX8SwDoAzRJybmpunhAQzKar/p9/euDm7drJzS9elLTROTmGim7QgLlDB2YvL3HLbNLErLt+vfMJTY7XMOY8GC6SxhYSIl46V66Iuy3A7O8vvv+DBpmumzlzipvtlClmBxwcLJNK3n/f+bT011+XmbzMzH5+Mvnr3j3TK8gQWo7hxv/4Q/Id3cxCQkwPnrx5md94Q9yDrc9TqpTMAQCYAwPNcy9elJDUYWGSvnNHhJPhzQSY8wgMj6vSpeUcQLx+9u+Xa9++bbanUCFzQs2yZeYkO0VJBeISEAmyQTBzxWQMUjI8Tz8t2pK5c2Pb8NyOERTqwgVRl3h7i/7cUVcfGSm2gocPZW+EEv/wQ1HrLF0a+9qHDplGUMA04DqGxy5SxLR/3L0rahpjGchOnWSdX0DUTUbETWMFvHPnxMg7c6bo2IsXl7WBDe7dMw2fWbKIKujHH8VwO2WKqW6ZONF+lbXTp2Vf0fbT/eorCfD20Ufi5bVihYTTzp8/dpC3hg3FhjNnjth7AFHJDRkiAfqOHhWPnc8+Mw3yRjDC/Pnl/Dt3xHbx338iLipVAn7+WQzXx4+b3j1Fi9qr+556KtbXoCieIkE2CCLKTUSjiWiGLV2FiPzd27T0Q86coqZ2i1dTfEydKi6NRhz8Tz8Vl0dHF9YPPhCDdni4rABnCIiKFcV+sW2b6NOrVZNgbIBpFzBwtA907CgdKJEYRJcvFwGRO7dZp0sXCSIH2OcvWyb7W7dkK1BAOn0j1LWBVUDkyyftN2wXH39sGp8HDJCgeZUqSQTbwEDprA130iNHgP37zaBvL7wgn1uZMvYuwVWqSODE0qUlKJwhgM6dM6O3GjYXI5Dftm32Hgq7d9t/dkTiMLBtmwgOdf1U0guuhhbWDcBCAO8COGxL5wJwICHnpubmKRWTlYiIFIrP5G4CAjhG9z1pUuzywoWZhwyxz+vSxVSfVKwoaqo2baSsTRuZUPX886ISKl2a+f/+TyZVGSE/hgyRWdfGNYyJdV27ii2jenVRIVk5eVJmYxv3NwLh/fabXKNHD+amTSXPsCcMGybP1bChTNoKCzNjHD18KOFIjAlnR45I2w01laONw8A6mc+oY8xWPnFCbAqA8/ATzKKO+uIL+wl5ipIGQHJVTAAqM3MPIuplEyr3iFJ8ZeZ0D7PMIypXTjQsqeb2mhQMtcb+/aLiMd7gIyLkjbdfP/vJVoC8UTdvLm/0PXqIKshwny1QQDyXnn5aPoThwyX93HPylr9zp7zZh4ZKnP9KlWToBcg8iIIFZb6G4eZ67ZqoX4yRDmCOIABzQlrLluIhFBIiqjNA5inUry8jpSJF5Lh4cRk9ZMkio6zWrUVF5OMjo5/ff5dJZK6+tAoVTHdVo47hFTR1qngc/fef6RXlSO7ciV+YSFE8TEIFRAQR5YJtshoRVYYE2FMsEInKfdIkWYjL1SJraQJjclf16jJx65tvZB2DJk2ks3a2FkHDhvbzKXbvNtVGBQuKIDBWJBs/XjrtQ4ekUwXM5SzHj5f9qFGikgoLM1djCgwU185+/eTcs2dl0le7djLhrHJlEU65csmkNV9fUTWNHSv13npLXEIBsbuMGyfzEUqVMtVwdeqIcAsIEHfgnDnlmnFBFHtFO0N4GXaapM7RUJQ0SkIFxBgAfwIoR0TzATwKoJ+7GpWeGT9e1m+ZPl1sn61amf1jmuLDD0Xf/vjj8jZftqxp0L15U4zZ770nb+yffCIjhPXrpSOvW1fqGT78gJTfvCmjhhw55O3/r7/sZ9+uW2dvkD1xQnT1R4/KyOXrr2XoZdgcAgOByZPl2u3amUbomjVFsHXqJCOUBQsk/9NPxdB98KBMfvP3l9GBv7/YLYw5AkWKyL2aNrVfDzyxVK8uAk5nDysZlHgFBBFlAVAIQFcATSBhMN5g5mtublu6JFs20VzUr2/2wSm+RGlKkDevGd9o/355+7V2+IsXS+cMyGzgvn3FEPz447HXxAZkBHHnjhiuS5cWAbF9u9ynUyepYw3/AYjaxgj/kSOHTGLz9zdVN5cuiZHamEDGLPe4ckVmVm/fbgoHQDyAKlY0DcurVpkzpydNMj2nANPgbqi5kooKByUDE6+WnJmjAbzGzKHMvIqZ/1DhEDf16kmIpOeekxdZa6ieNImvr4wMiMQTKSBAQnGcPSsqmH37pF5YmOs37ldekRnYQUGidjLCdHz6qf0MYSsVKogAePppERQlS4raZsoUKT93zt6LacoUsUOEhsrwbOpUyTdmkK9ZYx+2o3x5GRn5+5szoQ0CA2Uf15KiipLJSagZdT0RvU1E5YiosLG5tWXpHG9v8Sr18pKgoAcPerpFCaRLFzEyZ8kiHbivr+jMtm4VFVK+fM7PK1pU9Ph37oiAmDlT1s5+/nnX9/L2lv3vvwOHD8vxuHHytg+IUfviRXNkYxVOVavKXARAYiMBEhxw1y6ge3dJly8v7fr9d3H9tUZiffVVkd6uQlMoipJgAdEfwKsANgPYa9sC3NWojELLlrIWfIEC8oLNqR5KMAVo1Uresg1DiqsRxJkz5lu5ERq6TBl5eFdUrmxOUjPqTZgg+1dflVHInTuiTrLe28dHBNf778sEwenTgUaNpKxwYTFyHzpkznkICxM7h3UeB5G9h5SiKLFIqA1iJDMvjK+uEpuiRSVApzV2XrrC6LANvb4rAbF3r3mcUG+emjWBDRtkjQZDBVS+vISPnjxZ7AMvvmhOTDNGLz/+aKqwjHUHXnlFvKoKFZLzatUy72ME+DMEhqIoCSJeAcHM0UT0KmSynJIEHntMPD7feUe0H8bLbroia1Z5a7caeq088YS4mubKZc7ETghGiArDBfWff0QfZ9gdjDAdgCllHWd0A2aEWWc2hccfl2tao9YqihIvCXVzXU9Eb0OExB0jk5mvuz5FsWKE41i8WDxIp01z3demKZjFrTUqSuY4uFqFLEcOCXOdWJYuFTWT8cbv7W3aJhypUQOoXdt5G/78U/bOVFoTJsgopGfPxLdPUTIxxAlQjBPRGSfZzMyVUr5JScfPz48DAtKuaeSRR0QL8vChzEfbscPTLUog//ufGIyNIHUpSUiI+AY7ehkllnv3RDVlLMqjKEqCIKK9zOznrEyjuaYiv/wi86qyZ09nk25r1hSVT0oLB8B+7kVyyJVLhYOipDBxejER0buW424OZclYvzBz4u0tttX//c8+sGmaJ08ecUN1XA5UUZQMTXxurlal7XsOZR1SuC2Zip9/FpX+gAHAl196ujXxYBiMzzjTNCqKklGJT8VELo6dpZVEkDOnuSQCIBEq3KHBSRG++UbCWNSu7emWKIqSisQ3gmAXx87SsSCiDkQUSERBRDTSSTkR0TRb+b9EVN9S9gYRHSaiI0T0Znz3Sm907SqLm3XoIF6ht297ukVxkCuXuLEqipKpiG8EUZeIbkNGC7lsx7Cl44xyRkReAKYDaAsgGMAeIlrBzEct1ToCqGLbGgP4DkBjIqoF4CUAjQBEAPiTiFYx88lEPV0aJksWWeRNURQlrRLnCIKZvZg5PzPnY+astmMjnS2eazcCEMTMp5k5AsCvALo41OkC4EfbwkY7ARQkolIAagDYycx3mTkKwD8A0sOsgSQTECAqpps3Pd0SRVEUwZ1rnpUBcMGSDrblJaTOYQAtiKgIEeUG0AlAOWc3IaJBRBRARAEhISEp1vjU5s4dWcv+p5883RJFURTBnQLCmRHb0W7htA4zHwMwCcB6yEJFBwFEObsJM89gZj9m9iuWUj71HqBlS7FFvP++rM6pKIriadwpIIJh/9ZfFsDFhNZh5lnMXJ+ZWwC4DiDD2B9csWiRRIRo2lTW2ImM9HSLFEXJzLhTQOwBUIWIKhJRdsicihUOdVYA6GPzZmoC4BYzXwIAIipu25eHrGa3ABmcMmWATZskuvXbb0sECkVRFE+R0GB9iYaZo4joNQBrAXgBmM3MR4hosK38ewCrIfaFIAB3AbxoucQSIioCIBLAq8x8w11tTUuULSsBTQEJUHrnjkS3HjNGV7dUFCV1cZuAAABmXg0RAta87y3HDFmIyNm5zd3ZtrTOgweyVs/Zs5L28QH69PFkixRFyWy4U8WkJIMcOYCvvwZat5b0zz97tj2KomQ+3DqCUJKHv79sU6fK/IjoaJlgpyiKkhqogEgHvPmmeTxhAlCuHNC3r8eaoyhKJkEFRDpi2zYzPEfjxhI2PKt+g4qiuAlVWKQjmjaVFTpz5JCwHK1bA/fve7pViqJkVFRApCOyZJF1rFetAipXllU6r1zxdKsURcmoqIBIh7RpAwQFAStWAGFhYo/QtXwURUlpVECkc7y9ZSJd+/bAxYvA/v2ebpGiKBkFFRDpnLx5gT//BE6elFAd9eurkFAUJWVQAZEBaN8eGDcOqFoVWLwY8PX1dIsURckIkES7yBj4+flxQECAp5uRZpg4Ebh6FfjqK0+3RFGUtAoR7WVmP2dl6kWfwbh/H3jpJSAkBFi7VvLGjAEKF/ZsuxRFSX+oiimDkSMHEB4uwiFXLuDff1U4KIqSNFRAZDCIJLDfzJmyznXt2sDDhyIoFEVREoMKiAxInjzAwIESIhwAXn0VeOQRUTspiqIkFBUQmYBWrWThoWrVgMOHgeBgT7dIUZT0gAqITEC3bsCQIcCNG6Jy+vNPT7dIUZT0gHoxZQK8vIDp04F8+cSA3aoVcOqUxHNSFEVxhVtHEETUgYgCiSiIiEY6KScimmYr/5eI6lvKhhHRESI6TEQLiCinO9ua0SECJk0SQXHzJlCnDnDhgqdbpShKWsZtAoKIvABMB9ARgA+AXkTk41CtI4Aqtm0QgO9s55YBMBSAHzPXAuAFoKe72prZKFIEuHsXeOcdT7dEUZS0jDtHEI0ABDHzaWaOAPArgC4OdboA+JGFnQAKElEpW1lWALmIKCuA3AAuurGtmYqKFYGxY4GFC4Hu3cU2oSiK4og7bRBlAFiVGMEAGiegThlmDiCizwGcB3APwDpmXufsJkQ0CDL6QPny5VOo6Rmf99+X1ehGjxb108iRQL16nm6VoihpCXeOIMhJnmPgJ6d1iKgQZHRREUBpAHmI6HlnN2HmGczsx8x+xYoVS1aDMxNZs4qQWLwY+O03iQJ78qSnW6UoSlrCnQIiGEA5S7osYquJXNV5HMAZZg5h5kgASwE84sa2ZlqeeQY4cgTInVsWIhoyxIzhpChK5sadAmIPgCpEVJGIskOMzCsc6qwA0MfmzdQEwC1mvgRRLTUhotxERADaADjmxrZmanx8gC1bJNDfjBkiJB4+9HSrFEXxNG6zQTBzFBG9BmAtxAtpNjMfIaLBtvLvAawG0AlAEIC7AF60le0iosUA9gGIArAfwAx3tVURFdPly8D27UBEhKx/rShK5kbXg1Cccu0aMGWKrHd9/DgQHQ089ZSnW6UoSkqj60EoiebhQ2DyZODjjyVNJHnkzK1AUZQMiSoSFKeUKAFMnWqmjXDhzBrsT1EyCyogFJe88gpw6ZIIhwIFgNatxTZRrhxw6JCnW6coirtRAaHEScmSEgE2WzYxYBv89Zd5vGULsGFD6rdNURT3ojYIJUGULAk8eACsWQPs3An4+5tlH38MnDsHHD3qufYpipLy6AhCSRQdOwLjxskCRE8+KUubbtoE5M8vQkJRlIyDCgglSeTJA5w4AQwaJC6wu3YBc+d6ulWKoqQkKiCUJFGlihiq//kHOHsW8PaWCLH+/sDmzR5unKIoKYIKCCXJZMsGtGgBlColaicAWLUK+OormY2tKEr6RgWEkiL06SOxnObMkRDiWbIA69fLnInJk4Hs2TW+k6KkN9SLSUkxcuQA+vWT42bNgG3bAF9f4MAByTt6VFxmFUVJH+gIQklxmIGDB+X4wAGgTBk53rnTY01SFCUJqIBQUhwi4PBhMVqXLSvG7GLFgCtXpDwDxYdUlAyNRnNVUoUbN4BChUTt1LWrCI3ixT3dKkVR4ormqiMIJVUoVEj2J04AV68CQ4fqSEJR0joqIJRU5cUXgYkTgYULgdmzJe/hQ+CLL8yIsYqipA3Ui0lJdUaOBJYuBQYOlNFE/frA229L2bp1QNu2nm2foiiCjiCUVCdLFmDlSqB/f6BVK6BCBbPs009lHxUlnlChoR5poqIocLOAIKIORBRIREFENNJJORHRNFv5v0RU35ZfjYgOWLbbRPSmO9uqpC6lSgGzZgFNmwLVqwOTJgE//ggsXw6MGgUULAjUqwf06uXplipK5sVtKiYi8gIwHUBbAMEA9hDRCma2BoXuCKCKbWsM4DsAjZk5EICv5Tr/AVjmrrYqnufdd83jatUkWiwgs7Ffe00WLpo7F8iXzyPNU5RMiTtHEI0ABDHzaWaOAPArgC4OdboA+JGFnQAKElEphzptAJxiZg0mnUno2xcYMkTiPAHA9OmyDoWuYqcoqYs7jdRlAFywpIMho4T46pQBcMmS1xPAAlc3IaJBAAYBQPny5ZPRXCUtMX26TLjr2VM8nqZNE2P2G2/IetmNG5vBAhVFcQ/uFBDkJM/R8z3OOkSUHcCTAN5zdRNmngFgBiAT5RLfTCUtQrZfxuzZwNNPA888A1y/LutO7NolZa1aARs3eqyJipLhcaeACAZQzpIuC+BiIut0BLCPma+4pYVKmid3bqBHDzkuXlwEQu7ckh42DLh4Efj7b+DyZZl49847nmuromQ03GmD2AOgChFVtI0EegJY4VBnBYA+Nm+mJgBuMbNVvdQLcaiXlMxHrlzA8ePARx8Bjz8uwuOFF0Qw/PMPcPOm1Js6VTyiDIx8RVESjtsEBDNHAXgNwFoAxwD8xsxHiGgwEQ22VVsN4DSAIAAzAbxinE9EuSEeUEvd1UYlfVKtGvDBBzLz2tcXGD5c8oODJaTHpk0yupgyRfIDA2XFu7//9lCDFSWdosH6lHQPs4QSL1QIaNgQCA+X/KNHRS311lvAb79JhNm9e4FnnzVtHFeuANu3i51DUTIjcQXr01AbSrqHSCbcASIAli6VkUXhwkDRopLfvj1QubIc//EH0LmzHH/0EbBhA/DUU6bQUBRF0FAbSoaialWJ9dShg+nhVKCAuWY2AJSzuUXs2wd8+60slerrC5xz80yb+/eB27fdew9FSUlUQCgZlp49Rf107RrQqJHkvfmm2DC6dAEaNJC8c+fEnvHLL+a5M2aI7SIlad5chJWipBdUxaRkeLLafuUPH4oaad06OR42DPDzA3r3lvLjx0WgnD8PvPwyUKkScOpUyrWjTh0gIACIjpaAhYqS1lEBoWQajE65fXvZDLy9ZaW7Vq2AM2dMW8XZs7L/8Udg0SJg3jyxa4SFiW1j9mzZHzlielLFRcOGcs6lS+Y63YqSllEBoWR6HnlENkDCi5crJyONwEBg1SqJDQUARYqI19OePUBEhNg6goOlrFs3oHRpwMvL+T2io4GffpLjs2dVQCjpAx3oKoqFIkVExXT2LJAzpwiDqlVNb6iCBcXrCbBXE5UvLwsdBQVJ+upV++tevizutID7jeGKklKogFAUJxgur2PHAseOiX3iyhUZCQQEAIMGATt2iODIlUvqbtwIVKkiq+OVKCHBBCdOlDJDXdWoEVC7dvLaNnOmLKbkTrZtk9DqjoJOyVzoRDlFSQa3b0tHunWrGVm2Zk2xSxjcuCGT+ACxP5QsaZadOCHBB8+dA0aPjv9+9+6ZsaiYgdOnxS5SsGCKPE4M7duLMf/334Enn0zZaytpi7gmyukIQlGSQf78Mtpo3lxGFFu3igrqxAmgY0cgTx4ZeQAiSEqWlI6XCHjiCXG57dNHQoeEhMhIY9IksX3MmSO2DkC8ri5dErXX889L3vXrYlBv2VLSKfmuFxYm+6iolLumO4iMlBFVZKSnW5IxUQGhKClEkybAo4+KeqlKFVl3e9ky6cQ3bRJ7BiBqKkBmdFvZuVOM17//Dvj7y5rdw4eL/aJtW1m7++RJccEFxLsKkDkcgNTJkQOYP9+85pgxYmQ/fFjSEREyYS8+DC+vOnWcl1uF0d27smaHJ5QRn30m6r4FaTSk5/nznm5BMmHmDLM1aNCAFSU9EBLCvHs389ChzGfPMgPM7dpJ2aJFzHnzMhctKvl79jDnycPs5SXpCRMkPWAAc+HCknfliuwB5pMn5To3bph5U6YwBwYyZ83K3KAB83//Ja3du3Yxv/Ya8//+xxwVJXlffcVcpw7znTtxnxsdzfzMM8zz5sV/n5MnmZcsib+ev788344d8ddNbTZtkrb9+qunWxI3AALYRZ/q8U49JTcVEEp6ZeFC5kOHzPSDB7L9+6+kr1xhHjhQ/rGDBzM/8QTz4cPMP/wgeVWryv7vv6X+iRPMkyebAgJgbtjQPO7a1bxXgwbMffowR0ZKG+7fly0wUK5jEB5uf72lS5m3bjXv74yQEObVq+X4yBHz3MDAuD+P8eOl3r17cderUoX5ySfjruMpjM//9dc93ZK4iUtAqIpJUdIA3bsDtWqZ6ezZZTM8nooXl/AfAQHAN98As2YBZcuKPWLePDMabaNGosJq2VLWyMiaVeoBpl0BkBhUH30E1KsnAQ6ZgRdflPs984wY1atVExffZ54RNVL37ub5zZuL6qtZM0nnyAGsX2//TOfOAV98AXTqJN5Qs2ebZTNnuv4s/vtPbDKAOc/EGffuiVuxr2/C1GYGAQESnNFQu7kLw36ULZt77+NWXEmO9LjpCELJrERHy1u/wT//MI8Ywfz99zLS2LZN6uzdy7x5s5Rny8Z2aqnOnc10qVLMJUua6bJlY48eihRh7tiRed8+UTGVKCFqFWZRP1nrL1rE/NZbzE2aMF+9Km0x2v3PP6IOM1iyxDxvwwapM2aMPIOVBw9kdFKyJHObNjJasbJ8uai/HBk6VK7dqRPz3LnMp0/bl584wfzwYVK+BXtOnmR++eXY6rzgYFErphWgKiZFURy5fp350iURGszScd28yTx7NvOZM6Le2r2b+euvmbdvZ546VXqMy5el/n//ieqLWQSR0alPmsR84YK9gACY9+83BcP06aIaGjxYymrWFKGydi1z797mOd98I524kbYKEkMYPfusWW60bf9+SU+cGPu558+XsoIFZV++vKi8Zs9mvnWLmUhUXCkhJJxhtHX3bubz591zj8S1RwWEoihu5MEDGbEY9oi7d5m/+MJeQMyZI3XDwph9fMx8Pz8RTsxmZ1+3rnTUvXubhmiAOV8+5ho1mN94gzlXLuaICBnBGOWLFzP//DNzs2bMOXKITWXcOObu3U1ByMz8wQf2bWvXTvYffGAKjoED7Z/x999NARQUJAL0448lb/VqsQdZP4+ffhLB+cknzAcOSP7Dh/b3LVEiYZ9vVBTz+vWmgE1JVEAoipIqhIaaqq6oKFFFXbggIwHrG/mxY8wHD4oh12qIvnBBvKQWLhQ1THi4nLdlC3PFivada/v2pifVgwfMrVsz//GHGOAB5ueek1FSzpySrlyZedQoucfOnWY9gPn552VPxPz002b+xo0ihJYvNwVXZCRz9epmnZMnzeNu3ZhHjxZBBpieaIDcMyjI/hkA89nHjmWeNk2Oly6V6xhMnCh116418/bvZ27ZUrzckoPHBASADgACIWtOj3RSTgCm2cr/BVDfUlYQwGIAxyFrWjeN734qIBQl4xIdLW/kBw+KR5Srt+mVK8W76dQpSYeFMa9bx1ysmPR4NWqY54aGMn/7rRxbXYUPHhRBZe38AeZ+/eTcjz828wxhkD8/c8+ezMWLSzpvXlHVzZrFXKgQc5cuYieZPl3sHI89JvWWLzftPT16MC9bZl776FERSNWqSfrLL83ntLYhKCjpn6tHBAQALwCnAFQCkB3AQQA+DnU6AVhjExRNAOyylM0DMNB2nB1AwfjuqQJCURRm58IjIkIM0teuuT7PMOBbrzNokHT869eLjcbI9/OTDn3jRhmNhIZK2X//Sc9qHQGMGSN5VkP6zJkiOIxOvkwZ05UZEJVWdLSoyADmevVkRNWpk6jYrILL0UCfGOISEG6LxURETQGMZeb2tvR7AMDMn1jq/B+ATcy8wJYOBNAKwB2bQKnEiWigxmJSFCW1iI6WkCnO1jK/elUiAxvh32/cAF55xX7G98OHEhF4wQJg1Chg/36J7fXsszIr/+uvpd7UqXKtzp0lpIg1lhcg7tAPHiT9OeKKxeTO9SDKALhgSQcDaJyAOmUARAEIATCHiOoC2AvgDWa+43gTIhoEYBAAlC9fPsUaryiKEhdxrQpYvLh9ulCh2OFADOHx3HOyGfWMkCwGb75pHl+7JmFNduwAXnpJlrDNkSNJzU8Q7pwo50SuwnE04KpOVgD1AXzHzPUgI4qRzm7CzDOY2Y+Z/YoVK5ac9iqKoqRpihaVCYtTpgA+PhK7y1irxB24U0AEAyhnSZcFcDGBdYIBBDPzLlv+YojAUBRFUVIJdwqIPQCqEFFFIsoOoCeAFQ51VgDoQ0ITALeY+RIzXwZwgYiq2eq1AXDUjW1VFEVRHHCbDYKZo4joNQBrIR5Ns5n5CBENtpV/D2A1xJMpCMBdAC9aLvE6gPk24XLaoUxRFEVxM7qinKIoSiZGV5RTFEVREo0KCEVRFMUpKiAURVEUp6iAUBRFUZySoYzURBQC4FwSTi0K4FoKNyeto8+cOdBnzhwk55krMLPTWcYZSkAkFSIKcGXFz6joM2cO9JkzB+56ZlUxKYqiKE5RAaEoiqI4RQWEMMPTDfAA+syZA33mzIFbnlltEIqiKIpTdAShKIqiOEUFhKIoiuKUTC8giKgDEQUSURAROV2UKD1CRLOJ6CoRHbbkFSai9UR00rYvZCl7z/YZBBJRe8+0OukQUTki2khEx4joCBG9YcvPyM+ck4h2E9FB2zOPs+Vn2Gc2ICIvItpPRH/Y0hn6mYnoLBEdIqIDRBRgy3P/M7tarDozbJAw5KcAVAKQHbIOto+n25VCz9YCssjSYUveZwBG2o5HAphkO/axPXsOABVtn4mXp58hkc9bCkB923E+ACdsz5WRn5kA5LUdZwOwC0CTjPzMlmcfDuAXAH/Y0hn6mQGcBVDUIc/tz5zZRxCNAAQx82lmjgDwK4AuHm5TisDMmwFcd8juAmCe7XgegKcs+b8y8wNmPgNZn6NRarQzpWBZaGqf7TgMwDHI+uYZ+ZmZmcNtyWy2jZGBnxkAiKgsgM4AfrBkZ+hndoHbnzmzC4gyAC5Y0sG2vIxKCWa+BEiHCsBYWj1DfQ5E5A2gHuSNOkM/s03VcgDAVQDrWZbpzdDPDGAqgHcBRFvyMvozM4B1RLSXiAbZ8tz+zG5bUS6dQE7yMqPfb4b5HIgoL4AlAN5k5ttEzh5NqjrJS3fPzMwPAfgSUUEAy4ioVhzV0/0zE5E/gKvMvJeIWiXkFCd56eqZbTzKzBeJqDiA9UR0PI66KfbMmX0EEQygnCVdFsBFD7UlNbhCRKUAwLa/asvPEJ8DEWWDCIf5zLzUlp2hn9mAmW8C2ASgAzL2Mz8K4EkiOgtRCbcmop+RsZ8ZzHzRtr8KYBlEZeT2Z87sAmIPgCpEVNG29nVPACs83CZ3sgJAX9txXwC/W/J7ElEOIqoIoAqA3R5oX5IhGSrMAnCMmb+0FGXkZy5mGzmAiHIBeBzAcWTgZ2bm95i5LDN7Q/6vfzPz88jAz0xEeYgon3EMoB2Aw0iNZ/a0dd7TG4BOEI+XUwDe93R7UvC5FgC4BCAS8kYxAEARABsAnLTtC1vqv2/7DAIBdPR0+5PwvM0gw+h/ARywbZ0y+DPXAbDf9syHAXxoy8+wz+zw/K1gejFl2GeGeFketG1HjH4qNZ5ZQ20oiqIoTsnsKiZFURTFBSogFEVRFKeogFAURVGcogJCURRFcYoKCEVRFMUpKiAUJR6I6KEtiqaxpVjUXyLytkbcVZS0RGYPtaEoCeEeM/t6uhGKktroCEJRkogtRv8k25oMu4nof7b8CkS0gYj+te3L2/JLENEy2/oNB4noEdulvIhopm1Nh3W2WdEgoqFEdNR2nV899JhKJkYFhKLETy4HFVMPS9ltZm4E4BtIlFHYjn9k5joA5gOYZsufBuAfZq4LWavjiC2/CoDpzFwTwE0Az9jyRwKoZ7vOYPc8mqK4RmdSK0o8EFE4M+d1kn8WQGtmPm0LFHiZmYsQ0TUApZg50pZ/iZmLElEIgLLM/MByDW9ImO4qtvQIANmYeQIR/QkgHMByAMvZXPtBUVIFHUEoSvJgF8eu6jjjgeX4IUzbYGcA0wE0ALCXiNRmqKQqKiAUJXn0sOx32I63QyKNAkBvAFttxxsADAFiFvrJ7+qiRJQFQDlm3ghZHKcggFijGEVxJ/pGoijxk8u2apvBn8xsuLrmIKJdkJetXra8oQBmE9E7AEIAvGjLfwPADCIaABkpDIFE3HWGF4CfiagAZAGYKSxrPihKqqE2CEVJIjYbhB8zX/N0WxTFHaiKSVEURXGKjiAURVEUp+gIQlEURXGKCghFURTFKSogFEVRFKeogFAURVGcogJCURRFccr/A+A1Qnjk7D9SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "loss = history_dict[eval_tests[0]]\n",
    "val_loss = history_dict[eval_tests[2]]\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "# draw the loss evolution in blue\n",
    "plt.plot(epochs, loss, 'b--', label='erreur sur exemples d\\'apprentissage')\n",
    "# draw the accuracy evolution in blue\n",
    "plt.plot(epochs, val_loss, 'r--', label='erreur sur exemples de validation')\n",
    "plt.title('Erreur')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Erreur')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history_dict[eval_tests[1]]\n",
    "val_acc = history_dict[eval_tests[3]]\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "# draw the loss evolution in blue\n",
    "plt.plot(epochs, acc, 'b--', label='précision sur exemples d\\'apprentissage')\n",
    "# draw the accuracy evolution in blue\n",
    "plt.plot(epochs, val_acc, 'r--', label='précision sur exemples de validation')\n",
    "plt.title('précision')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('précision')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N38spzckmMMJ",
    "tags": []
   },
   "source": [
    "---\n",
    "## Utilisation du réseau\n",
    "Déterminez la probabilité de réussite :\n",
    " 1. d'une femme de 25 ans, étudiant à plein temps dans une université, n'ayant pas d'emploi, et membre d'un groupe de musique, en couple dans une location, se rendant aux cours en bus, dont les parents se sont arrêtés au lycée, ayant 1 frère, qui assiste aux 20h de cours hebdomadaires, qui révise seule et à la dernière minute, mais qui prend des notes et écoute en classe ?\n",
    " 2. de votre cas personnel ?\n",
    " \n",
    "- Quel est l'impact du niveau d'étude des parents sur la réussite d'une personne ?\n",
    "\n",
    "- Quel est l'impact de l'écoute et de la prise de notes ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "girl = np.array([2, 2, 2, 5, 2, 1, 1, 1, 1, 3, 3, 1, 1, 5, 3, 1, 2, 2, 3, 3], float)\n",
    "#girl = np.array([1, 2, 2, 4, 2, 1, 1, 1, 1, 4, 3, 1, 2, 2, 2, 1, 1, 1, 2, 2], float)\n",
    "girl_normalisee = girl/maxis[:nb_val]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "girl_prediction = model.predict(np.array([girl_normalisee]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.7613173]]\n"
     ]
    }
   ],
   "source": [
    "print(girl_prediction*maxis[nb_val])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "TestOUX.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}