PredireReussiteSolutionBis.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/PredireReussiteSolutionBis.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",
    "maxis[nb_val] = 1\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 * 80 // 100\n",
    "nb_lignes_val = nb_lignes - nb_lignes_app\n",
    "\n",
    "# les premieres colonnes sont les entrees \n",
    "# la derniere colonne est la sortie\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+1]\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": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#take a array of values in [0,7] and return an array of size 8 arrays. \n",
    "# 0->[1,0,0,0,0,0,0,0], ..., 7->[0,0,0,0,0,0,0,1]\n",
    "def multiplexeur(tab):\n",
    "    nb_lignes = len(tab)\n",
    "    tabmulti = np.zeros((nb_lignes, 8), float)\n",
    "    for i in range(0, nb_lignes):\n",
    "        j = round(tab[i][0])\n",
    "        tabmulti[i][j] = 1\n",
    "    return tabmulti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorties_app = multiplexeur(sorties_app)\n",
    "sorties_val = multiplexeur(sorties_val)"
   ]
  },
  {
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-03 12:15:12.742040: 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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#une premiere couche constituée d'autant de neurones en entrée que de mots clés, 6 en sortie...\n",
    "model.add(Dense(15, use_bias=True, input_shape=(nb_val,), activation='sigmoid'))\n",
    "#une seconde couche constituée de 4 neurones en sortie (donc 8 en entree)\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(8, activation='sigmoid'))"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Nadam', loss='categorical_crossentropy', 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": 9,
   "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": [
      "2022-02-03 12:15:12.833658: 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": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAA5zUlEQVR4nO3dd3hUZfbA8e9JiHRBqmjAACI9BYKAVBugoIsVWQuIirKsiO4qrA3rioqCrOzPBV0rCjZcG4piwa50gSAdQRACSAk1JOf3x5lMAiQhQCaTZM7neeaZmXvvzLx3AnPu284rqopzzrnIFRXuAjjnnAsvDwTOORfhPBA451yE80DgnHMRzgOBc85FOA8EzjkX4TwQOOdchPNA4BwgIqtEZLeIpOW4PR3ucjlXFMqEuwDOFSMXqOqn+R0gImVUdf9B26JVNaMwCpDb+zsXal4jcC4fItJfRL4RkdEisgW4T0ReEJH/E5EPRWQncKaInCQib4lIqoisFJEhOd7jBRF5KMfzriKyNsfzVSIyTETmAztFxC/QXJHyQODc4bUFVgC1gIcD2/4ceFwZ+BZ4D5gHnAycDQwVke5H8Bl9gZ5AVa8RuKLmgcC5bO+IyNYctxsC29ep6r9Udb+q7g5s+5+qfqOqmUBLoKaqPqCq+1R1BTABuOIIPnusqq7J8f7OFRmvgjqXrffBfQQi0h9Yk8uxObedApwkIltzbIsGvjqCz87tM5wrEh4InDu83FL05ty2Blipqo3yeP1OoEKO5ycW8DOcKxLeNOTcsfsR2B7o8C0vItEi0kJE2gT2zwXOF5FqInIiMDRcBXUuNx4InMv23kHzCKYU5EWBoaMXAInASmAT8CxQJXDIy1hH8ipgGjC5kMvt3DERX5jGOecim9cInHMuwnkgcM65COeBwDnnIpwHAueci3Albh5BjRo1NC4uLtzFcM65EmXWrFmbVLVmbvtKXCCIi4tj5syZ4S6Gc86VKCKyOq993jTknHMRzgOBc85FOA8EzjkX4UpcH4ErmdLT01m7di179uwJd1GcK9XKlStHbGwsMTExBX6NBwJXJNauXUvlypWJi4tDRMJdHOdKJVVl8+bNrF27lvr16xf4dd405IrEnj17qF69ugcB50JIRKhevfoR17w9ELgi40HAudA7mv9nHgiccy7CRUwg+PFH6NgRFi4Md0mcK126du1a6JM84+Li2LRpU/BxcbNq1SpeffXV4POZM2cyZMiQMJbo2ERMIMjIgG++gV9/DXdJXHGWkZGR7/MjtX///mN6fajey2U7mu/14ECQnJzM2LFjC7NYRSpiAkHt2na/YUN4y+HC55VXXuH0008nMTGRG2+8MfgjX6lSJe69917atm3Ld999d8jz/F6X5c0336R///4A9O/fn9tuu40zzzyTYcOGHVCGhQsXBt8rPj6epUuXsmrVKlq0aBE8ZtSoUdx3332AXW3feeeddOnShaeeeuqA99q5cycDBgygTZs2JCUl8b///Q+AIUOG8MADDwDw8ccf07lzZzIzM5k1axZdunShdevWdO/enfXr1wc/49Zbb6Vz5840bdqUn376iYsvvphGjRpx9913A/bD16RJE/r160d8fDyXXnopu3btOuQ7njZtGu3bt6dVq1ZcdtllpKWlATB8+HCaNWtGfHw8f//73w953ebNm+nWrRtJSUnceOON5Fwwq2ZNS4+TlpbG2WefTatWrWjZsmXwfPMrW1xcHMOGDeP000/n9NNPZ9myZbn+jZYvX06PHj1o3bo1nTp1YvHixcHjhgwZwhlnnEGDBg148803g+fz1VdfkZiYyOjRo/niiy/o1asXAF9++SWJiYkkJiaSlJTEjh07WL9+PZ07dyYxMZEWLVrw1VdfATBo0CCSk5Np3rw5I0aMCJ7zhx9+SJMmTejYsSNDhgwJvndef/Njpqol6ta6dWs9GmlpqqA6cuRRvdwdo0WLFh3wvEuXQ2/jxtm+nTtz3//887Y/NfXQfQX5/F69eum+fftUVXXQoEH64osvqqoqoJMnTw4em/N5fq+rWLFi8DVvvPGG9uvXT1VV+/Xrpz179tT9+/cfUo6//vWv+sorr6iq6t69e3XXrl26cuVKbd68efCYxx9/XEeMGBH4nrrooEGDcj2nf/zjH/ryyy+rquoff/yhjRo10rS0NN25c6c2a9ZMP/vsMz3ttNN02bJlum/fPm3fvr1u3LhRVVUnTZqk1157bfAz7rjjDlVVHTNmjNapU0fXrVune/bs0ZNPPlk3bdqkK1euVEC//vprVVW99tpr9fHHHw++/qefftLU1FTt1KmTpqWlqarqyJEj9f7779fNmzfraaedppmZmcGyHuzmm2/W+++/X1VV33//fQU0NTX1gGPS09N127ZtqqqampqqDRs21MzMzHzLdsopp+hDDz2kqqovvvii9uzZM9e/0VlnnaVLlixRVdXvv/9ezzzzzOBxl156qWZkZOjChQu1YcOGqqr6+eefB9/r4Oe9evUKlmXHjh2anp6uo0aNCpZj//79un37dlVV3bx5c3Bbly5ddN68ebp7926NjY3VFStWqKrqFVdcEXzvvP7mBzv4/5uqKjBT8/hdjZh5BBUr2s1rBJFp+vTpzJo1izZtbD353bt3U6tWLQCio6O55JJLgsfmfJ7f6/Jz2WWXER0dfcj29u3b8/DDD7N27drgVffh9OnTJ9ft06ZN491332XUqFGADdH99ddfadq0KRMmTKBz586MHj2ahg0bsmDBAhYsWMC5554LWJNXnTp1gu914YUXAtCyZUuaN28e3NegQQPWrFlD1apVqVu3Lh06dADgqquuYuzYsQdc3X///fcsWrQoeMy+ffto3749xx9/POXKleP666+nZ8+ewavbnGbMmMHbb78NQM+ePTnhhBMOOUZVufPOO5kxYwZRUVH89ttvbAj8h86vbH379g3e33rrrcH3y/obpaWl8e2333LZZZcF9+3duzf4uHfv3kRFRdGsWbPg5+WnQ4cO3HbbbVx55ZVcfPHFxMbG0qZNGwYMGEB6ejq9e/cmMTERgNdff53x48ezf/9+1q9fz6JFi8jMzKRBgwbBeQB9+/Zl/PjxQP5/82MRMYEA4Mwzs5uIXHh98UXe+ypUyH9/jRr578+NqtKvXz8eeeSRQ/aVK1fugB/tnM/ze13OYXoHj9uuWLFiruX485//TNu2bfnggw/o3r07zz77LKeddhqZmZlH/F6qyltvvUXjxo0P2ffzzz9TvXp11q1bFzy2efPmfPfdd7m+V9myZQGIiooKPs56ntWGfvCwxIOfqyrnnnsur7322iHv/+OPPzJ9+nQmTZrE008/zWeffXbIMYcb9jhx4kRSU1OZNWsWMTExxMXFBb+r/MqW1+Os7zUzM5OqVasyd+7cXD835/ehBVjjffjw4fTs2ZMPP/yQdu3a8emnn9K5c2dmzJjBBx98wNVXX83tt99Op06dGDVqFD/99BMnnHAC/fv3Z8+ePfl+Rn5/82MRMX0EAO+9Bwc12boIcfbZZ/Pmm2+yceNGALZs2cLq1Xlm5S3Q62rXrk1KSgqZmZlMmTKlQOVYsWIFDRo0YMiQIVx44YXMnz+f2rVrs3HjRjZv3szevXt5//33C/Re3bt351//+lfwh2POnDkArF69mieeeII5c+YwdepUfvjhBxo3bkxqamowEKSnp7PwCIfQ/frrr8HXv/baa3Ts2PGA/e3ateObb74JtsPv2rWLJUuWkJaWxrZt2zj//PMZM2ZMrj+4nTt3ZuLEiQBMnTqVP/7445Bjtm3bRq1atYiJieHzzz8/4O+XX9kmT54cvG/fvv0h73v88cdTv3593njjDcB+bOfNm5fvd1G5cmV27NiR677ly5fTsmVLhg0bRnJyMosXL2b16tXUqlWLG264geuuu47Zs2ezfft2KlasSJUqVdiwYQNTp04FoEmTJqxYsYJVq1YdUH7I+29+rCIqELjI1axZMx566CG6detGfHw85557brCz9GhfN3LkSHr16sVZZ511QDNLfiZPnkyLFi1ITExk8eLFXHPNNcTExAQ7p3v16kWTJk0K9F733HMP6enpxMfH06JFC+655x5Uleuuu45Ro0Zx0kkn8dxzz3H99deTmZnJm2++ybBhw0hISCAxMZFvv/22QJ+TpWnTprz44ovEx8ezZcsWBg0adMD+mjVr8sILL9C3b1/i4+Np164dixcvZseOHfTq1Yv4+Hi6dOnC6NGjD3nvESNGMGPGDFq1asW0adOoV6/eIcdceeWVzJw5k+TkZCZOnHjA95Rf2fbu3Uvbtm156qmncv1ssNrGc889R0JCAs2bNz9sJ2x8fDxlypQhISHhkPccM2YMLVq0ICEhgfLly3PeeefxxRdfBDuP33rrLW655RYSEhJISkqiefPmDBgwINi0Vb58ef7973/To0cPOnbsSO3atalSpQqQ+9+8MEhBqjrFSXJysh7tmOVHH4XJk2H27EIulDuslJSUY27HdOGzatUqevXqxYIFC8JdlEPkV7ashaxq1KgRhpIdvbS0NCpVqoSqMnjwYBo1anRA/8bh5Pb/TURmqWpybsdHVI1g1y6YOxfS08NdEuecy9uECRNITEykefPmbNu2jRtvvDGknxdRncUnngiqkJoKJ50U7tI4V3LExcUVy9oA5F+2rHb2kubWW289ohrAsYqoGoFPKnPOuUN5IHDOuQgXUYGgXj3o1QuOPz7cJXHOueIjovoI6ta1uQTOOeeyRVSNwDlX+EKdhjoUcpb5/PPPZ+vWrYccc9999wVTOeTlnXfeYdGiRcHn9957L59++mmhlrUoRFwg6NABBgwIdylcceVpqCPPhx9+SNWqVY/qtQcHggceeIBzzjmnkEpWdEIWCESkroh8LiIpIrJQRG7J5Zg/ich8EZkrIjNFpGNu71WYMjNhzZpQf4orjjwNdclMQ53X959l6tSpXH755cHnX3zxBRdccAGQd5rnnHLWPh5++GEaN27MOeecwy+//BI8ZsKECbRp04aEhAQuueQSdu3axbfffsu7777L7bffTmJiIsuXL6d///7BVNXTp08nKSmJli1bMmDAgGAiu7i4OEaMGBFMp52V8jqs8kpLeqw3oA7QKvC4MrAEaHbQMZXInt0cDyw+3PsebRrqLL17q7ZocUxv4Y7CIWlxizgPtaehLplpqPP7/rOkp6dr3bp1g5970003Bb+X3NI85yyzqqWqTk1N1ZkzZ2qLFi10586dum3bNm3YsGHwHDdt2hT8vLvuukvHjh2rqva3fuONN4L7sp5npZL+5ZdfVFX16quv1tGjRwc/L+v148aN0+uuuy7Xv++xKDZpqFV1PbA+8HiHiKQAJwOLchyTluMlFYGQ57uoXdtWKnORxdNQl8w01AX5/suUKUOPHj147733uPTSS/nggw947LHHgNzTPMfHx+f6fX711VdcdNFFVKhQ4YDvBGDBggXcfffdbN26lbS0NLp3757re2T55ZdfqF+/PqeddhoA/fr1Y9y4cQwdOhSAiy++GIDWrVsHzzucimTUkIjEAUnAD7nsuwh4BKgF9Ax1WWrXhk2bYP9+KBNRY6aKmSLOQ62ehrpEpqHO7/vPqU+fPowbN45q1arRpk0bKleuzMqVK3NN85yfvFJh9+/fn3feeYeEhAReeOEFvjjMvz89TA63rO84Ojq6WPT9hLyzWEQqAW8BQ1V1+8H7VXWKqjYBegMP5vEeAwN9CDNTU1OPqTzt28PAgZBj3QkXATwNdclMQ13Qv1vXrl2ZPXs2EyZMCNag8krznJfOnTszZcoUdu/ezY4dO3gvx1jzHTt2UKdOHdLT04PlhLzTUTdp0oRVq1YFv4uXX36ZLl265Pv54RTSQCAiMVgQmKiq+dZ/VHUG0FBEDkkTqKrjVTVZVZOz1i89Wj16wDPP2GplLnJ4GuqSmYa6oH+36OhoevXqxdSpU4NNT3mlec5Lq1at6NOnD4mJiVxyySV06tQpuO/BBx+kbdu2nHvuuQf8fa644goef/xxkpKSWL58eXB7uXLleP7557nsssto2bIlUVFR3HTTTQX4lsMjZGmoxepYLwJbVHVoHsecCixXVRWRVsB7QKzmU6hjSUOdJSPDRg/FxBzT27gj4GmoS7binIbaHao4paHuAFwNnBUYHjpXRM4XkZtEJCs0XgIsEJG5wDigT35BoDCsWGEBIJdmTOeci0ihHDX0NZDvIqSq+ijwaKjKkJuaNS0VtSeec67ginMaanfsIm5mcaVKUL68B4JwCHFlzznH0f0/i7hAIGJDSH//PdwliSzlypVj8+bNHgycCyFVZfPmzZQrV+6IXheRI+lPPNFrBEUtNjaWtWvXcqzDf51z+StXrhyxsbFH9JqIDASedK7oxcTEUL9+/XAXwzmXi4gMBDfcEO4SOOdc8RFxfQQA6emwdq3NJXDOuUgXkYFg/Hhbrcybq51zLkIDgS9i75xz2SI6EPgQUueci9BAcOKJdu81Aueci9BA4E1DzjmXLSIDQeXK8OijUIzTgzvnXJGJyHkEInDHHeEuhXPOFQ+RUyP4/nto1gxmzwZg3TpYvDjMZXLOuWIgcgJBuXKQkgIrVwIwaBBcfnmYy+Scc8VA5ASCunXt/tdfATjpJCjASoXOOVfqRU4gqFbNFiJYswawQLBpky9i75xzkRMIRKBevWAgOPlk27xuXRjL5JxzxUDkBAKAXr2gRQsgOxD89lsYy+Occ8VAZA0fHTUq+DApCV5+GRo1CmN5nHOuGIisQAC2cj1Qq5Zw1VVhLotzzhUDkdU09OabcPzxsGoVAD/8APPmhbdIzjkXbpEVCKpUgbS0YIfxlVfCyJFhLpNzzoVZZAWCg+YSnHyydxY751xkBoIcQ0g9EDjnIl1kBYKKFW1iWY5JZb/9Fuw/ds65iBRZgQDgppugXTvAKgh799oMY+eci1SRN3z04YeDDy+6CBITbSCRc85FqpDVCESkroh8LiIpIrJQRG7J5ZgrRWR+4PatiCSEqjxBqrBtG2AZJ7p0gbJlQ/6pzjlXbIWyaWg/8DdVbQq0AwaLSLODjlkJdFHVeOBBYHwIy2OeeAKqVoW0NFTh9ddtPoFzzkWqkAUCVV2vqrMDj3cAKcDJBx3zrar+EXj6PRAbqvIEnXSS3a9Zg4itS/DCCyH/VOecK7aKpLNYROKAJCC/a+/rgKl5vH6giMwUkZmpqanHVpiD5hLUrx9cq8Y55yJSyAOBiFQC3gKGqur2PI45EwsEw3Lbr6rjVTVZVZNr1qx5bAU6aC6BBwLnXLE3bx5MnBiytw9pIBCRGCwITFTVt/M4Jh54FviTqm4OZXkAm0UmEgwEcXGwejVkZob8k51z7uh07AhXXQXz54fk7UM5akiA54AUVX0yj2PqAW8DV6vqklCV5QAxMfDQQ9C1K2A1gr174fffi+TTnXMufwfPcJ0/33KkVagAsaHpRhUN0bRaEekIfAX8DGRdb98J1ANQ1WdE5FngEmB1YP9+VU3O732Tk5N15syZhVbOzZttNOkpp0B0dKG9rXPOHbm0NGjZEq64Ah55xLb9/js8/TTceitUr37Uby0is/L6fQ1ZIAiVQgkEO3bYGpWNGxdOoZxz7mitXg3XXgt3320tFbffDk8+CV9+CQ0bZi+neIzyCwSRl2IC4L77bEqxKqr2nX/0UbgL5ZyLOF27Wkfl55/D2WfD3/5mv08nnGCzXevXh8WLQ16MyAwE9erBnj2weTMi8Pjj8MYb4S6Uc67E27PHrvALaupUeOwxGDECKleG9euhfHl47jmb+Nq7d5G0XERmIPC5BM65ULjjDrvCv/XWw6c1Tk21H/3bb7dawPbt8NprUKaMJULbssVSH4iEvNiRGQgaNrT75csBCwQrVoSxPM65kmn9ehvauWsXTJtmV/IA48blP9RzyRL74fnmmwO35/zRL4IAkCUyA8Gpp9r9EhuxetppVjnYvTuMZXLOlTxPPWU/5nPn2jj/qCh7vmGDXeEnJ8PHH8M//xn8vQFg9GjYvz+7dSLMIi8NNdgCNRMmwOmnA9CkiQXfVaugadPwFs05V0KMGAGPPgp9+sAZZ8B331lO+6zsBz/8ALNmQY8e9rx8eZg+3foFPv0Urr7a+iuLgcgcPnqQPXvsvly5Qn1b51xplZICzZrBn/9szUF5/Xi8/TZccgkkJcFPP1nNITnZ+gHmzoXmzYusyPkNH43MGgHYPIJ58+C88zwAOOeOzLRp1gz0xBP5X0FefDEsWmT9ktHR0Lo17NtnTUchmiV8NCI3EEycaD38f/wBVavyyCMWpG+/PdwFc84VS6rZHbiDB0ONGnDiiYd/3cHtzTExxSoIQKR2FoP1EAMsXQrYJL5Jk8JYHudc0du82dI6HM4991h6h7vvtpElZcrAlVeGvnxFJHIDQaNGdh/oyW/a1CbweRZS5yLEZ5/ZQlWxsfD993kft2GDJar84w9b8zwhodT9UERuIGjY0Kp5gUDQpIkNBQ5kp3bOlQa7d1tn7s8/w5gxkJ5u2/fssXbg446Ds86yrJM5rVhhtYB9+6BSJRgwwGYMJyXBf/9r/QOlSOT2EZQta3/8QNNQixa2ecGCQ/9NOFck1qyxKe5Nm2YPQXQH2rnT/u+WyfHTtX27/ch/+aX90I8ZA61aWZt+nz7w3nvZxw4ebE1BdevC1q3w5ps2qgesmWj8eLj8cujWzcaT33CDDfHMmig2e3bRnGcRi9xAAPDKK8HOnubNoVYtS0ntXNAnn9gPya232kzQpUthzhzLDV+hgo0+27QJrr/erhwXLLAfmt9/t1wxrVrZ2PLcLF5sKQT69bOrjw8+sEW0ARo0sBEn6ek2Vr1s2SI75WJh715LxFamjF2xR0XZLN62be1vceut9v01aWLb//lPmyi6dq0d/+OPNob/vffgllssWMTFWUftli2W7bNdu+wgANZJeOedcNddNtdoxoxiM84/5FS1RN1at26tzgVt3aq6ZInq9Omqe/cW7DWrVql+/LHqxImq48erPvOMakpK9v5Nm1Q/+EC1SxdVUC1fXnX7dtt34422Leetdm3V/fttf9Zrsm4iqt26Zb/3XXep3nab3VeoYMe88EL25370keoTT9hroqLsmPR02z9pkuq776quXn0s31jR2rjRvuObb1a9/HLVxx9Xzcg48JinnlJNSlJ97jl7/s47qtWrZ3+H1aurXnONamamaq9eqjExqv3723f74ouqP/+sOnu2vXbVKtWqVVUfekh1xw7VUaMO/by87NunevXVqsOGqa5dW3jfQTEBzNQ8flcje0LZb7/BO+/YIhDHsOCDC4NPP4WRI22mJlhb75YtdiX39NM2rT821q6ov/oK2rSxGuCePZbiN2sWYZabb4axY625oFo1+wmKjYW//92u0o87zo5bs8auLnfutE6lOnWgdm27+gerLfz2m23fuNGuSqtWhb/+1fafcYZNJNq9Gzp0gMmT8843n55uV8LR0VaeuDgbsRIVZc1HTZpYWoPeva0G8vDDVq3dt8+uqN9912a/9u0Ly5bZvJkNGyy1waxZNvzxkUfs3D75xGa+duhw+Bw3mzfb99SggR37n/9Yzbp79wPH1K9caZOnsv4uO3daLWrBAqtdgV2F9+1rV/Njx8J559n+4cNh4EDroP34Y6t5vf++lb1XL0vj0KyZdfJWrnxg+b7+2moOMTH5n0eEyW9CWdiv8I/0Vqg1gi+/tCuOqVNV1S4QExPtwsCF2Z49dgWYm1desb9bbKzqPffYFfWXX2bvHzBAtUkTu5quUEH1vPPsSjTL22+rzphhtYAVK+wKe+VK2/f776pPPmlX3gWtYRypzEzVtLS8zy8v27fbeQ4dqtqhg2rDhqqjR9u+JUtUK1fOroWA6tln2+eoqo4YcWBNpWZN1Tp1ssvQrp1tP/VU1eHD7Uo+y4YNqv/3f6qXXqraqZNq2bJ27KpVtv8vf7HnTZqofv11du1o61a7cv/4Y/uczEzVZcts35w5Vn6w99y9u+Dfw/79VnP6/fcj+/4iHPnUCML+w36kt0INBH/8YV/BI4+oqupLL9nThQsL7yNcHjZutB+K9etV339f9f77Ve++O3t/ixaq9erZj0S5cqoVK6red5/t27nTmhOO5McjEmRm2neyZ092U1aWX39VnTtXdd06C3pZP8xZduxQff551XPOUY2Otu/7ySdt3+TJ9h/jpJMsYAwcaFdNWfbsUf3f/1Tr1rXjKlZUnTUr/7Ju3aqanGzNMFnByoVUfoEgspuGwKrb7dvDa68xb54tXPbqq1ZbdUdowwZrmhCxL/GBB6zj9JRTrIpfsSJ8+61V2a+7zobhZRGxJIBZ47knTrSO1CVLbKWm446zoV0DB4bn3CJJSopl1ezWzTqsd+ywDvFTTsl/2OT27fDCC5ZS4eSTbfilKzY811B+EhKs7RRrcixXDmbO9EBwRD78EF5+Gd56yzIrnn02VKli7djHH29txXXr2o/9nj0WCK65xtq4Y2JsZE1S0oFtvVdeWapmbpYoTZvCM89kP69c+dB2+NwcfzwMGRK6crmQ8UCQmGg/ZHv2EFOuHImJliTQHYaqXb0/8wy89JJ1wN54Y/ZaDz172i0vXbrYzTkXdqVretzRuOUWG9UQGO1w0UXQsmWYy1Sc7duX/fjaa23Uyx132KiVf/3LZ+M5VwJ5jaBatQOe3nFHmMpRHO3aZdWjRYtsiv6HH9q2JUtsSOTkyTYMMK8JU865EuGwgUBEooEXVfWqIihPePz73zbm+777AGv12L3bhjxHtKefhmHD7HGFCtZ5mJSUXStISAhf2ZxzheawgUBVM0Skpogcp6r7Dnd8ifTTTzYV/d57UYmiUSObpT5+fLgLVoS2bbNRPF99ZT3ll11maROaNoX4eJsglTWpyjlXqhS0aWgV8I2IvAvszNqoqk+GolBFrksXG/aWkoI0b07jxvZ7GBGWLLFZnO++CxkZNuyva1fbV60aXHBBWIvnnAu9ggaCdYFbFFCAcWQlTOfOdv/ll9C8OZ06WXN4amopTwKpaikKFi+GoUNt/dVWrcJdKudcEStQIFDV+0NdkLCqX9/yysyYAX/5C5062eavv7ZRRKWWiI39Bxvn75yLSAUaPioin4vIZwffDvOauoHXpYjIQhG5JZdjmojIdyKyV0T+frQnccxELNlVIMd5crJl/S21zUNvvmlX/3v2WADwIOBcRCto01DOH+lywCXA/sO8Zj/wN1WdLSKVgVki8omqLspxzBZgCNC7gOUInRw9w2XLwqhRpXA+wcaN8H//Z6kf2rWzpiHnXMQraNPQrIM2fSMiXx7mNeuB9YHHO0QkBTgZWJTjmI3ARhHJZwpqEcvMhKioYNbgUmHxYltp6Ztv7Mf/iitgwgRLO+yci3gFCgQiknPWVRTQGjixoB8iInFAEvDDkRQux+sHAgMB6oVyxaDzz7fe4RdfJDPTFjmqUMFGT5ZoDRvaaKB777Xc9QkJh88575yLGAVtGpoFKCBYk89K4LqCvFBEKgFvAUNVdfvRFFJVxwPjwbKPHs17FEiNGtmLX0SV4YILrOvgpZdC9omhs2WLZfh89llbdGfSpHCXyDlXTBWos1hV66tqg8B9I1XtpqpfH+51IhKDBYGJqvr2sRY25Hr3th/QGTOIioJzz4Vp06y1qET59Vc45xzLBJqSEu7SOOeKuYKOGqogIneLyPjA80Yi0uswrxHgOSClxEw8y1pqb8qU4NMNG2D+/DCXq6AyMy3/T0KCLbI+ZQp07BjuUjnnirmCZh99HtgHnBF4vhZ46DCv6QBcDZwlInMDt/NF5CYRuQlARE4UkbXAbcDdIrJWRMKXwaxiRcunM2UKZGTQrZtt/uijsJXoyDz2mHUEn3aarYt73nnhLpFzrgQoaB9BQ1XtIyJ9AVR1d+CKP0+BpqPDHfM7EFvAMhSNIUNsoe+MDOrUiaZVK1vXe/jwcBcsDz//bCOB4uNtIZfYWOjTxxfuds4VWEEDwT4RKY91GCMiDYG9IStVOJ19tt0CXn/dfluLne3bLTvoY4/ZEo/TptnEsKtKb5JY51xoFDQQjAA+AuqKyESs2ad/qAoVdtu329KLF19Mw4Z1wl2aQ23ebJ3Bc+daj/azz4a7RM65Eqwg6xFEAScAFwPtsOaeW1R1U4jLFj6pqbZy2dKlMGYM//mPZaouFr+3mzfDmWda2aZOhR49wl0i51wJd9jOYlXNBP6qqptV9QNVfb9UBwGwCVjXXGPr8a5bx4YNlqp/7dpwFwxbDaxlS5vv4EHAOVcICjpq6BMR+XsgkVy1rFtISxZud99t+flHjqRvX+uPnTw5TGVJT7eVwlassE7giRMP6MdwzrljUdBAMAAYDMzAZhnPAmaGqlDFQoMG0L8//Oc/NGIpycnw6qthKEdqqg1pfewxWzzGOecK2WEDQaCPYHhgVnHOW4MiKF94PfigTcjau5erroLZs2HevCL8/LlzoU0b+O4767weOrQIP9w5FykKsmZxpogMBsLVMBI+J54I06cDcPVJ8MUXRfjZ06ZBz55Qu7YtjNCmTRF+uHMukngfQUHs2UO1IVcxZcRcEhJC/FmbAv3wHTvayKU5czwIOOdCyvsICmLjRlvG8swz2TDuTb776nBr8hyF1avhT3+Ctm1h927Lfz1qVClfNNk5VxwcSfbRyOsjyFKvni1sf8op1P7rZdQ6pyUZ06YXznuvWAHXXw9Nm8Knn8KNNwaXzHTOuaKQbyAQkTtyPL7soH3/DFWhiqX69WHmTH4YMpHK+zazYWjg9NPTYf36o3vP2bNtzsLLL1ueoJQUuOMOzxPknCtSovmsWysis1W11cGPc3teVJKTk3XmzPC1SqlC+8TdVEzbwCdL44hauRwaNYJrr4W77rJhp7m9aMECWLUKfvvNmn2uucbSRo8dC5deWkwTGjnnSgsRmaWqybntO1zTkOTxOLfnEUEEbhlens9WxPH++9iqZoMG2SSDhg0t8VvPntbmD/CXv9gVfnw8XHihHTtihNUkoqJsSKgHAedcGB2uMVrzeJzb84hx2WW2/O/y5cCFVWDcOJv5+/rrNvY/JQVq1bKD27eH446DxERo3hxOOsmGpUZHh/EMnHMu2+GahjKAndjVf3lgV9YuoJyqFnljdribhrLs3g3ly4e7FM45VzD5NQ3lWyNQVb9szUNWEPj6a7vYr1QprMVxzrmjVtB5BC4XS5dCp04wcmS4S+Kcc0fPA8ExaNTIRn2OGmUDgpxzriTyQHCMHnnEBv8MGxbukjjn3NHxQHCM6tbNHjD0+efhLo1zzh05DwSF4PbbrcN448Zwl8Q5546cJ7UpBBUqwMyZPjXAOVcyeY2gkERHWyaJ55+HSZPCXRrnnCs4rxEUosxMCwRz50Lr1jaqyDnnijuvERSi6GhLJHrccba2/JYt4S6Rc84dngeCQnbKKTB1KqxbB1dfDXv3hrtEzjmXPw8EIdCmDfz73xYQPvkk3KVxzrn8hSwQBNY3/lxEUkRkoYjckssxIiJjRWSZiMwXkSJf3yBUBg60JQh69bLn+eT2c865sApljWA/8DdVbQq0AwaLSLODjjkPaBS4DQT+L4TlKXLNAmf7+efQrRts3x7e8jjnXG5CFghUdb2qzg483gGkACcfdNifgJfUfA9UFZE6oSpTuGzdCl98Aeed58HAOVf8FEkfgYjEAUnADwftOhlYk+P5Wg4NFojIQBGZKSIzU1NTQ1bOULnoIptb8OOP0LatNRk551xxEfJAICKVgLeAoap68PVwbstdHtKarqrjVTVZVZNr1qwZimKG3CWXWMfx1q0WDBYtCneJnHPOhHRCmYjEYEFgoqq+ncsha4G6OZ7HAutCWaZw6toV5syB//wHmja1bTt3QsWKYS2Wcy7ChXLUkADPASmq+mQeh70LXBMYPdQO2Kaq60NVpuLgxBNt7XoRWLnS5h2MHOnzDZxz4RPKpqEOwNXAWSIyN3A7X0RuEpGbAsd8CKwAlgETgL+EsDzFTsWK0KUL/OMf0LgxvPKKpalwzrmilO/i9cVRcVm8vjB9+inccYc1G7VsCfPmWY3BOecKS36L1/vM4mLgnHMsjfWkSTYBLSsIPPMMrF0b3rI550o/rxEUU0uXwmmn2eOTTrIhqDffDPXrW1I755w7El4jKIEaNYLly+Gxx6B9e3j2WWjSJHs5zFWrbD6C9yk4546Vr0dQjDVoYMtggjURTZ0KZ55pz597Dh56yFJfx8ZaCotOnSzjqXPOHQlvGiqh1q6FDz6AxYth/nz48kvLbTR/vu0//3x7HBcHJ5xgt1694PLLbf/06bB5swWbOnWgXDmoVAnKls1OkOcd1s6VHvk1DXmNoISKjYUbb8x+vn+/zVrOkpwM1arB+vWwZg389BPs2ZMdCC64AHbvPvA9R4yA++6D1astqMTFQfnyVuuoUAHuvts6tpctg7vugk2brL8iNtY+f+hQSEiAJUvgww8hIwPS0y2wVKuWXd7PPrNV3E44wd7311+tKax3b9v/7beWkykjA44/HqpWhRo1LGCpwooVtr9cOahbF8qUgZiYQ9eM3rXL9nmfinP580BQSpQpYz+WWR544NBjsn74VeH996FyZetrWLPGtnXrZvtjYmDQINu3d6/9IKel2SxosEV3Zs+2H/ft2+2Hu0wZ+Oc/bf/IkbZk58GuvdZ+lF97zfo8cmrQIDsQDBqUXbPJct55FlwyMyE+3n7kcxo8GJ5+GnbssEl7VavCxo22r1Ytm6vx179aYLzqKjunqlXtO9ixA8aMscC3YgX87392btHR2cGod2+rIf32m30XlSvbOVesaPdly+b+d3GuJPCmIVfodu2yABIdbT/8qlZbqV0boqKyay6pqVZLqVvXmrpatLDtc+ZYgClf3u63boXq1bP7R955xwLC9u32Hvv3Q6tW0L27fdawYdbsVauWfd7vv8Oll1owWb0arrzSftS3b4dt26yMgwfDLbfA669Dnz6HntPHH1ug/Nvf4MmD5slXqZJ9TgMHWm1HxGaN16plfTd9+tjnPfMMnH66lTMqygYAVKtmwde5UMqvacgDgXM5qMIff9gtI8N+0DdutESBZcrYj/zPP1utIDPTAliNGhYgMjNhwABrGtu3z2oaGzdawsFnn7WAVbmyBb+cXn0V+va1mtXo0XZMxYrQsKENIe7c2fpvnDsW3kfgXAGJ2BV6tWrZ2xo1yn6cmGi33ERFwQsv5P3eZcpY/8rPP1tNJD3dAk5y4L9mRobVhtLSbPu+fbZ90SJLUvjUU/Dgg9akVasWdOhgNaE//cn6Wpw7Wl4jcK4Y2rvXmpIWL7Z5JGXKWBrzd96BDRssYeHcuRaw1q61PooLLrAmqqQkCwx160LHjtaB75zXCJwrYcqWhZo17Zbl3HPtlmXrVgsWWR3VrVpZ3qr//teanzIyrGP85ZetWeq226yvpUkTOPVU65PxTm4HXiNwrtTJyLC+jnnzrIM+NtYmI157rfVZZP2Xr1EDvv7aMt+uXm19EzmbxFzp4jUC5yJI1nyK1q2zt513no2eSk21PFYrV1qSw6pVbf9LL9lM9XPOsc7patVslNappxZ58V0YeCBwLoJkNTedcYYNo83Sp481Nb3yis3XAOsUnzPHHqek2HyKkw9ZUdyVBp50zjnHaafBE0/YZMFNm+CXX2z2ONi2du2siemcc6z28NVXnvCwNPEagXMuKDraOpSrV89Og37CCTbb+uuvbb3tfv1s+5gxNgkvM9OGzrqSy/98zrl8lS8PXbtarqmVKy39+UsvQf/+tn/MGMtN9e9/2/wIV/J4IHDOFViZMtC8uaU7r1LFtjVvbiOOBg+2zudTT7WUHhkZYS2qOwIeCJxzx6R7d/j+e7s9+KClw2jTxpqZ9u2zZqWsWdKuePJ5BM65kHn1VRuddMIJlhG3fXtLH968ebhLFnl8HoFzLiwuv9yGnY4ebWtugw1fXb3a+h5c8eCBwDkXMmXK2Mp4559vCxJ9951Nbitf3oLBhx/aHAaf0Rxe3kfgnAu5qCibg3DPPdnZVt97D/7yF5ukdt99hy425IqOBwLnXFgMHmwzly+8EO6/30Yb3XWXT1QLBw8EzrmwELE0FpMn20zlZs0szUVUlCXG27Ah3CWMHB4InHNh17GjpdB++ml7PnOmLfX55z9nrz3tQidkgUBE/isiG0VkQR77TxCRKSIyX0R+FJEWoSqLc65kELH76tXh4ostQ+opp9haCps3h7dspVkoawQvAD3y2X8nMFdV44FrgKdCWBbnXAnSoIHNQZg929ZLGD0a3n473KUqvUIWCFR1BrAln0OaAdMDxy4G4kSkdqjK45wrebJSYX/2mdUSwGoHvXvbqmuucIRzHsE84GLgaxE5HTgFiAW8i8g5FyRii+SALazz0kvWTNS+Pbz2mi+eUxjC2Vk8EjhBROYCNwNzgFxjvIgMFJGZIjIzNTW1CIvonCtOata0YPDgg7bSWuPG0KEDpKeHu2QlW0hzDYlIHPC+qubbESwiAqwE4lV1e37Heq4h5xzAr7/CuHE27LRfPxtyun8/xMSEu2TFU365hsJWIxCRqiJyXODp9cCMwwUB55zLUq8ePPpo9kI5L70EdevaugglLJdm2IVy+OhrwHdAYxFZKyLXichNInJT4JCmwEIRWQycB9wSqrI450q/evUsq+ngwZCUZIHBZykXjKehds6VGpmZ8N//wr/+BfPnWwrsV14Jd6mKh2LZNOScc4UtKgquv96GnL76Ktxwg21/7z1LeLdpU3jLV1x5GmrnXKkTFQV9+2Y/X70aHnrIchpNneprIRzMawTOuVJv8GBrIpoxw4acTp7sHco5eSBwzpV6ItZf8PHHNhfhiissKDjjgcA5FzHOPRd+/BGmTIHOnW3b88/DypXhLVe4eSBwzkWU6GjLVSQCe/bA8OHQpo0tirN0abhLFx4eCJxzEatcOWsiSkiwyWktW9qaCJG2bKYHAudcRGvcGKZPhzVroEsXuPPOyBtm6oHAOeeAOnVsaOncuTZLed8+CwxDh4a7ZKHn8wiccy4gKsoWxQFYvtw6kWfMsKaiu+6y1dJKI68ROOdcLpo2tWBw883w4ou27kHv3pCREe6SFT4PBM45l4eYGBg71gLC4MG2jnJ0NKxYAd9+G+7SFR4PBM45dxixsTBmDFxzjT0fN84WxLniCpuXUNJ5IHDOuSN01VUwbJgls2vb1pqMvvgi3KU6eh4InHPuCCUlwciRsGGDdSJ/9JE1FwEsXAi7d4e3fEfK1yNwzrljtHu39SeUKQNdu9oooyefhHbtbFtx4OsROOdcCJUvn/2Df8MN8PPP0KmT5Tb68svin+nUA4FzzhWiK6+EX36Bhx+Gb76BHj0gJSXcpcqfBwLnnCtk9epZqoqUFEtf0ayZNRddcgn84x+wbFm4S3ggDwTOORciDRvCGWfY46VLYfFiGDUKWrWyiWo//WSpLMLNA4FzzhWBhAQbUbRkCZx/PkyYYEFiyxbb/9FHNms5HDOXPRA451wRql8fJk2y5qH33oMTT7TO5MGDrcO5fXvYv79oy+SBwDnnwiA21jqSszz4oN3/9JMNRX355aIriwcC55wLMxH4858hMxOefRY6doTZs23f1VfDU0/BH3+E8PN9QplzzhVP27bBmWfCnDlQt66lxY6OPrr3ym9CWTGZ8+acc+5gVapYzWDuXEthcbRB4HA8EDjnXDGXmGi3UPE+Aueci3AhCwQi8l8R2SgiC/LYX0VE3hOReSKyUESuDVVZnHPO5S2UNYIXgB757B8MLFLVBKAr8ISIHBfC8jjnnMtFyAKBqs4AtuR3CFBZRASoFDi2iKdROOecC2cfwdNAU2Ad8DNwi6pm5nagiAwUkZkiMjM1NbUoy+icc6VeOANBd2AucBKQCDwtIsfndqCqjlfVZFVNrlmzZtGV0DnnIkA4A8G1wNtqlgErgSZhLI9zzkWkcAaCX4GzAUSkNtAYWBHG8jjnXEQKWYoJEXkNGw1UA9gAjABiAFT1GRE5CRtZVAcQYKSqvlKA900FVh9lsWoAm47ytSWVn3Nk8HOODMdyzqeoaq5t6yUu19CxEJGZeeXaKK38nCODn3NkCNU5+8xi55yLcB4InHMuwkVaIBgf7gKEgZ9zZPBzjgwhOeeI6iNwzjl3qEirETjnnDuIBwLnnItwEREIRKSHiPwiIstEZHi4y1NYckv1LSLVROQTEVkauD8hx75/BL6DX0Ske3hKfWxEpK6IfC4iKYH05bcEtpfa8xaRciLyY46U7fcHtpfac84iItEiMkdE3g88L9XnLCKrRORnEZkrIjMD20J/zqpaqm9ANLAcaAAcB8wDmoW7XIV0bp2BVsCCHNseA4YHHg8HHg08bhY497JA/cB3Eh3ucziKc64DtAo8rgwsCZxbqT1vbMJlpcDjGOAHoF1pPucc534b8CrwfuB5qT5nYBVQ46BtIT/nSKgRnA4sU9UVqroPmAT8KcxlKhSae6rvPwEvBh6/CPTOsX2Squ5V1ZXAMuy7KVFUdb2qzg483gGkACdTis9bTVrgaUzgppTicwYQkVigJ/Bsjs2l+pzzEPJzjoRAcDKwJsfztYFtpVVtVV0P9qMJ1ApsL3Xfg4jEAUnYFXKpPu9AE8lcYCPwiaqW+nMGxgB3ADnT05f2c1ZgmojMEpGBgW0hP+dIWLxectkWiWNmS9X3ICKVgLeAoaq63dY3yv3QXLaVuPNW1QwgUUSqAlNEpEU+h5f4cxaRXsBGVZ0lIl0L8pJctpWocw7ooKrrRKQW8ImILM7n2EI750ioEawF6uZ4HosthlNabRCROgCB+42B7aXmexCRGCwITFTVtwObS/15A6jqVuALbBnY0nzOHYALRWQV1px7loi8Quk+Z1R1XeB+IzAFa+oJ+TlHQiD4CWgkIvUDayJfAbwb5jKF0rtAv8DjfsD/cmy/QkTKikh9oBHwYxjKd0wCS5s+B6So6pM5dpXa8xaRmoGaACJSHjgHWEwpPmdV/YeqxqpqHPZ/9jNVvYpSfM4iUlFEKmc9BroBCyiKcw53L3kR9cSfj40uWQ7cFe7yFOJ5vQasB9Kxq4PrgOrAdGBp4L5ajuPvCnwHvwDnhbv8R3nOHbHq73xshbu5gb9vqT1vIB6YEzjnBcC9ge2l9pwPOv+uZI8aKrXnjI1snBe4Lcz6rSqKc/YUE845F+EioWnIOedcPjwQOOdchPNA4JxzEc4DgXPORTgPBM45F+E8EDgXICIZgayPWbdCy1QrInE5s8Q6V5xEQooJ5wpqt6omhrsQzhU1rxE4dxiBHPGPBtYE+FFETg1sP0VEpovI/MB9vcD22iIyJbB+wDwROSPwVtEiMiGwpsC0wCxhRGSIiCwKvM+kMJ2mi2AeCJzLVv6gpqE+OfZtV9XTgaexrJgEHr+kqvHARGBsYPtY4EtVTcDWi1gY2N4IGKeqzYGtwCWB7cOBpMD73BSaU3Mubz6z2LkAEUlT1Uq5bF8FnKWqKwIJ735X1eoisgmoo6rpge3rVbWGiKQCsaq6N8d7xGHpoxsFng8DYlT1IRH5CEgD3gHe0ey1B5wrEl4jcK5gNI/HeR2Tm705HmeQ3UfXExgHtAZmiYj33bki5YHAuYLpk+P+u8Djb7HMmABXAl8HHk8HBkFwQZnj83pTEYkC6qrq59giLFWBQ2olzoWSX3k4l618YBWwLB+patYQ0rIi8gN28dQ3sG0I8F8RuR1IBa4NbL8FGC8i12FX/oOwLLG5iQZeEZEq2EIjo9XWHHCuyHgfgXOHEegjSFbVTeEui3Oh4E1DzjkX4bxG4JxzEc5rBM45F+E8EDjnXITzQOCccxHOA4FzzkU4DwTOORfh/h+bSnbQ12BeEgAAAABJRU5ErkJggg==\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": 12,
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "girl_prediction = model.predict(np.array([girl_normalisee]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.30757582 0.46907312 0.42700544 0.35384417 0.27828628 0.42280766\n",
      "  0.51303464 0.7976618 ]]\n"
     ]
    }
   ],
   "source": [
    "print(girl_prediction)"
   ]
  },
  {
   "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
}