diff --git a/.gitignore b/.gitignore
index adbb97d2d3137fe76f2d8e88a55e1c2b285a6cd6..13ecb9ca2712d5a1dd984163925d8b259fc03409 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
-data/
\ No newline at end of file
+data/
+test.py
diff --git a/hello.ipynb b/hello.ipynb
index 7c8a38d39870a27d06afe79b4a8bca79a249cc9b..65066ef1f89a2df58cd74df3c79df7f0f1dcfd13 100644
--- a/hello.ipynb
+++ b/hello.ipynb
@@ -4,19 +4,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Image Classification\n",
+    "<h1> Image Classification</h1>\n",
     "<p><a href=\"https://gitlab.ec-lyon.fr/edelland/mod_4_6-td1\">Ennoncé</a>.</p>\n",
     "\n",
-    "## Introduction\n",
-    "<p>Le but de ce TD est d'appliquer les méthodes de classification vues en cours. Pour cela nous travaillerons sur la base de donnée CIFAR-10 sur laquelle nous appliquerons d'abord la classification par k-plus proches voisins avec une distance euclidienne puis nous utiliserons une classification par réseaux de neuronne.\n",
+    "<h2> Introduction</h2>\n",
+    "<p>Le but de ce TD est d'appliquer les méthodes de classification vues en cours. Pour cela nous travaillerons sur la base de donnée <a href=\"https://www.cs.toronto.edu/~kriz/cifar.html\">CIFAR-10</a> sur laquelle nous appliquerons d'abord la classification par k-plus proches voisins avec une distance euclidienne puis nous utiliserons une classification par réseaux de neuronne.\n",
     "Pour chacune de ces méthodes nous regarderons le taux de réussite.</p>\n",
     "\n",
-    "## Importation des données"
+    "<h2> Importation des données</h2>\n",
+    "<p>On importe les bibliothèque dont nous aurons besoin pour importer et traiter les fichiers "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 66,
    "metadata": {
     "dotnet_interactive": {
      "language": "csharp"
@@ -32,9 +33,16 @@
     "import os"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p>Nous récupérons ces données et développons des fonctions qui permettrons, à partir des données, de faire des listes d'images d'entrainement et de test. Nos récupérons également les listes de labels de classe associé à chaque image pour attribuer un label à une image test et enfin comparer au label réel de l'image</p>"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,9 +91,16 @@
     "    return data_train, labels_train, data_test, labels_test"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p>Pour vérifier le fonctionnement de ces fonction nous les appliquons et pour une liste de 10 images vérifions que les images d'entrainnement de de test sont bien prise aléatoirement dans la base de donnée.</p>"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +108,8 @@
      "output_type": "stream",
      "text": [
       "[6 9 9 4 1 1 2 7 8 3]\n",
-      "[9 6 7 4 8 1 3 1 9] [2]\n"
+      "[8 2 6 9 7 1 3 4 9] [1]\n",
+      "[8 3 9 1 1 4 7 6 9] [2]\n"
      ]
     }
    ],
@@ -108,14 +124,21 @@
     "    print(l_1,l_2)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p>Pour vérifier la bonne association entre images et labels, l'algorithme suivant affiche les labels et images associé et annonce si cette image servira pour l'entrainement du modèle ou pour le test.</p>"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x500 with 10 Axes>"
       ]
@@ -158,9 +181,74 @@
     "    affichage(d_1, l_1, d_2, l_2)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## k-plus proches voisins\n",
+    "\n",
+    "<p>Dans cette partie nous allons développer un algorithme de classification par k plus proche voisins choisit par une distance euclidienne.</p>\n",
+    "<p>Pour faire cela nous commencons par écrire la fonction de calcule de distance. Cette fonction va prendre en entré les images d'entrainnement et de test et pour chaque image de test il va mesurer sa distance avec chaque image d'entrainement. En entrant le resultat dans une matrice nous avons une image de test associé à chaque ligne et une image d'entrainement associé à chaque colonne</p>\n",
+    "<p>Pour une base de donnée de N image d'entrainement et M image de test nous avons la matrice de distance en sortie suivante :</p>\n",
+    "<table>\n",
+    " <thead>\n",
+    "   <tr>\n",
+    "    <th align=\"center\"><b></b></th>\n",
+    "    <th align=\"center\"><b>Image entrainement 1</b></th>\n",
+    "    <th align=\"center\"><b>...</b></th>\n",
+    "    <th align=\"center\"><b>Image entrainement n</b></th>\n",
+    "    <th align=\"center\"><b>...</b></th>\n",
+    "    <th align=\"center\"><b>Image entrainement N</b></th>\n",
+    " </tr>\n",
+    "</thead>\n",
+    "<tbody>\n",
+    "  <tr>\n",
+    "    <th align=\"center\"><b>Image test 1</b></th>\n",
+    "    <td align=\"center\">dist(im_e1,im_t1)</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">dist(im_en,im_t1)</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">dist(im_eN,im_t1)</td>\n",
+    " </tr>\n",
+    " <tr>\n",
+    "    <th align=\"center\"><b>...</b></th>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    " </tr>\n",
+    " <tr>\n",
+    "    <th align=\"center\"><b>Image test m</b></th>\n",
+    "    <td align=\"center\">dist(im_e1,im_tm)</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">dist(im_en,im_tm)</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">dist(im_eN,im_tm)</td>\n",
+    " </tr>\n",
+    " <tr>\n",
+    "    <th align=\"center\"><b>...</b></th>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    " </tr>\n",
+    " <tr>\n",
+    "    <th align=\"center\"><b>Image test M</b></th>\n",
+    "    <td align=\"center\">dist(im_e1,im_tM)</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">dist(im_en,im_tM)</td>\n",
+    "    <td align=\"center\">...</td>\n",
+    "    <td align=\"center\">dist(im_eN,im_tM)</td>\n",
+    " </tr>\n",
+    "</tbody>\n",
+    "</table>\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,9 +261,17 @@
     "    return(np.array(dist_mat))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p>Avec cette matrice des distances nous allons chercher pour chaque ligne (image de test) les k plus petites valeurs de distance et allons récupérer les labels des images d'entrainement associé à ces valeurs. Enfin nous comptons les lables les plus fréquents dans cette liste pour associé un label à l'image de test.</p>\n",
+    "<p>La fonction knn_predict prend donc en entrée la matrice de distance, la liste des labels d'image d'entrainement et le nombre de k plus proches voisins concidéré et donne en sortie le label associé</p>"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,9 +285,16 @@
     "    return (resultat)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p>Une dernière fonction va appeler les 2 dernière et comparer le résultat du modèle avec les vrais labels des images de test. En sortie nous aurons le taux de réussite du modèle, c'est à dire le rapport entre le nombre de classe correctement attribué et le nombre de classes testés</p>"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,25 +304,27 @@
     "    return(np.sum(labels_test == res) / len(labels_test))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p>Pour visualiser ce modèle nous tracons, pour l'entièreté de la base de donnée (60000 images), les taux de réussite en fonction du nombre de k voisins choisit</p>"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\Utilisateur\\Documents\\GitHub\\image-classification\\hello.ipynb Cell 11\u001b[0m line \u001b[0;36m5\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/hello.ipynb#X13sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m d, l \u001b[39m=\u001b[39m read_cifar_batch(\u001b[39m\"\u001b[39m\u001b[39mdata/cifar-10-batches-py/data_batch_1\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/hello.ipynb#X13sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m d_train, l_train, d_test, l_test \u001b[39m=\u001b[39m split_dataset(d, l, \u001b[39m0.9\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/hello.ipynb#X13sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m dist_matrice \u001b[39m=\u001b[39m distance_matrix(d_train, d_test)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/hello.ipynb#X13sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m y \u001b[39m=\u001b[39m []\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/hello.ipynb#X13sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mfor\u001b[39;00m knn \u001b[39min\u001b[39;00m x:\n",
-      "\u001b[1;32mc:\\Users\\Utilisateur\\Documents\\GitHub\\image-classification\\hello.ipynb Cell 11\u001b[0m line \u001b[0;36m6\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/hello.ipynb#X13sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     dist_mat\u001b[39m.\u001b[39mappend([])\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/hello.ipynb#X13sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     \u001b[39mfor\u001b[39;00m image_train \u001b[39min\u001b[39;00m data_train:\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/hello.ipynb#X13sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m         dist_mat[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mappend(np\u001b[39m.\u001b[39msum(np\u001b[39m.\u001b[39msquare(image_train\u001b[39m-\u001b[39mimage_test)))\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/hello.ipynb#X13sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mreturn\u001b[39;00m(np\u001b[39m.\u001b[39marray(dist_mat))\n",
-      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36msum\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
-      "File \u001b[1;32mc:\\Users\\Utilisateur\\anaconda3\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:2324\u001b[0m, in \u001b[0;36msum\u001b[1;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[0;32m   2321\u001b[0m         \u001b[39mreturn\u001b[39;00m out\n\u001b[0;32m   2322\u001b[0m     \u001b[39mreturn\u001b[39;00m res\n\u001b[1;32m-> 2324\u001b[0m \u001b[39mreturn\u001b[39;00m _wrapreduction(a, np\u001b[39m.\u001b[39madd, \u001b[39m'\u001b[39m\u001b[39msum\u001b[39m\u001b[39m'\u001b[39m, axis, dtype, out, keepdims\u001b[39m=\u001b[39mkeepdims,\n\u001b[0;32m   2325\u001b[0m                       initial\u001b[39m=\u001b[39minitial, where\u001b[39m=\u001b[39mwhere)\n",
-      "File \u001b[1;32mc:\\Users\\Utilisateur\\anaconda3\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:86\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[1;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[0;32m     83\u001b[0m         \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     84\u001b[0m             \u001b[39mreturn\u001b[39;00m reduction(axis\u001b[39m=\u001b[39maxis, out\u001b[39m=\u001b[39mout, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mpasskwargs)\n\u001b[1;32m---> 86\u001b[0m \u001b[39mreturn\u001b[39;00m ufunc\u001b[39m.\u001b[39mreduce(obj, axis, dtype, out, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mpasskwargs)\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -236,10 +341,173 @@
     "    plt.plot(x, y, label='Précision')\n",
     "    plt.xlabel('nombre de plus proche voisins concidérés')\n",
     "    plt.ylabel('taux de bonne calification')\n",
-    "    plt.xticks(range(1, 20))  # Afficher des valeurs entières sur l'axe des abscisses\n",
+    "    plt.xticks(range(1, 20))\n",
+    "    plt.yticks(np.arange(0, 1.1, 0.1))\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p>Pour voir l'efficacité de cette méthode j'ai itéré l'opération 10 fois donc à chaque fois sur une nouvelle base de test de d'entrainement et j'ai tracé les 10 courbes sur le même graphe, ce qui me donner le résultat ci-dessous</p>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%script false\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    nbr_knn = 20\n",
+    "    nbr_val = 10\n",
+    "    x = range(1, nbr_knn)\n",
+    "    d, l = read_cifar_batch(\"data/cifar-10-batches-py/data_batch_1\")\n",
+    "    for essai in range(nbr_val):\n",
+    "        d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)\n",
+    "        dist_matrice = distance_matrix(d_train, d_test)\n",
+    "        y = []\n",
+    "        for knn in x:\n",
+    "            stat = 0\n",
+    "            res = knn_predict(dist_matrice, l_train, knn)\n",
+    "            y.append(np.sum(l_test == res) / len(l_test))\n",
+    "        plt.plot(x, y, label=f'Précision knn mesure {essai}')\n",
+    "    plt.xlabel('nombre de plus proche voisins concidérés')\n",
+    "    plt.ylabel('taux de bonne calification')\n",
+    "    plt.xticks(range(1, nbr_knn))\n",
+    "    plt.yticks(np.arange(0, 1.1, 0.1))\n",
     "    plt.legend()\n",
     "    plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<p>On remarque une chute à k=2, cette chute est due à un nouveau label dans les choix possible, l'algorithme développé ne traite pas le choix du label si dans ses k plus proches voisins 2 apparaissent le même nombre de fois et choisit naturellement le plus petit des deux.</p>\n",
+    "<p>Pour limiter les erreurs lorsque deux labels apparaissent le même nombre de fois dans les k plus proche voisin, l'algorithme ci-dessous choisira celui des deux dont la somme des distance est la plus petite</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def knn_predict2(dist, labels_train, k):\n",
+    "    resultat=[]\n",
+    "    for im in dist:\n",
+    "        dico={}\n",
+    "        kmax=np.argpartition(im, k)[:k]\n",
+    "        for indexe in kmax:\n",
+    "            if labels_train[indexe] in dico:\n",
+    "                dico[labels_train[indexe]][0]+=1\n",
+    "                dico[labels_train[indexe]][1]+=im[indexe]\n",
+    "            else:\n",
+    "                dico[labels_train[indexe]]=[1,im[indexe]]\n",
+    "        dico = sorted(dico.items(), key=lambda item: item[1][0], reverse=True)\n",
+    "        max_value = dico[0][1][0]\n",
+    "        dico = [item for item in dico if item[1][0] == max_value]\n",
+    "        if len(dico) > 1:\n",
+    "            dico = sorted(dico, key=lambda item: item[1][1])\n",
+    "        resultat.append(dico[0][0])\n",
+    "    return(resultat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p>Pour comparer les deux méthodes j'ai tracé le resultat des deux méthodes sur le même graphique avec les mêmes valeurs d'entrainement et de test</p>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if __name__ == \"__main__\":\n",
+    "    nbr_knn = 20\n",
+    "    x = range(1, nbr_knn)\n",
+    "    #d, l = read_cifar_batch(\"data/cifar-10-batches-py/data_batch_1\")\n",
+    "    #d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)\n",
+    "    #dist_matrice = distance_matrix(d_train, d_test)\n",
+    "    y1 = []\n",
+    "    y2 = []\n",
+    "    for knn in x:\n",
+    "        stat = 0\n",
+    "        res = knn_predict(dist_matrice, l_train, knn)\n",
+    "        res2 = knn_predict2(dist_matrice, l_train, knn)\n",
+    "        y1.append(np.sum(l_test == res) / len(l_test))\n",
+    "        y2.append(np.sum(l_test == res2) / len(l_test))\n",
+    "    plt.plot(x, y1, label=f'Précision knn')\n",
+    "    plt.plot(x, y2, label='Précision knn2')\n",
+    "    plt.xlabel('nombre de plus proche voisins concidérés')\n",
+    "    plt.ylabel('taux de bonne calification')\n",
+    "    plt.xticks(range(1, nbr_knn))\n",
+    "    plt.yticks(np.arange(0, 1.1, 0.1))\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img alt=\"Image\" title=\"icon\" src=\"result/comparaison_knn_0to20.png\" />\n",
+    "<p>On remarque que la nouvelle méthode majore la précédente</p>\n",
+    "<p>Ene appliquant cette nouvelle la fonction sur 10 choix différents de valeurs de test nous avons les courbes ci-dessous</p>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%script false\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    nbr_knn = 20\n",
+    "    nbr_val = 10\n",
+    "    x = range(1, nbr_knn)\n",
+    "    d, l = read_cifar_batch(\"data/cifar-10-batches-py/data_batch_1\")\n",
+    "    for essai in range(nbr_val):\n",
+    "        d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)\n",
+    "        dist_matrice = distance_matrix(d_train, d_test)\n",
+    "        y = []\n",
+    "        for knn in x:\n",
+    "            stat = 0\n",
+    "            res = knn_predict(dist_matrice, l_train, knn)\n",
+    "            res2 = knn_predict2(dist_matrice, l_train, knn)\n",
+    "            y.append(np.sum(l_test == res) / len(l_test))\n",
+    "        plt.plot(x, y, label=f'Précision knn2 mesure {essai}')\n",
+    "    plt.xlabel('nombre de plus proche voisins concidérés')\n",
+    "    plt.ylabel('taux de bonne calification')\n",
+    "    plt.xticks(range(1, nbr_knn))\n",
+    "    plt.yticks(np.arange(0, 1.1, 0.1))\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img alt=\"Image\" title=\"icon\" src=\"result/xxx\" />\n",
+    "\n",
+    "<h3>Resultat</h3>\n",
+    "<p>Finalement on remarque que la méthode des k plus proche voisins a une efficacité d'environ 30% sur la base de donnée CIFAR-10 et ce quelque soit le nombre de plus proche voisins choisit. C'est mieux qu'un choix aléatoire qui tournerai autour de 10% avec 10 classes mais ca reste assez faible. \n",
+    "\n",
+    "<h1>Réseaux de neurone artificiels</h1>\n",
+    "<p> </p>"
+   ]
   }
  ],
  "metadata": {
diff --git a/kkn.py b/kkn.py
index f58a38bdeb9977826b29f8418da40aa5e73a8f3e..8779030b412a3c7fb0e3aae23fac2f7f2b5ce376 100644
--- a/kkn.py
+++ b/kkn.py
@@ -21,6 +21,25 @@ def knn_predict(dist, labels_train, k):
         resultat.append(val[indexe])
     return (resultat)
 
+def knn_predict2(dist, labels_train, k):
+    resultat=[]
+    for im in dist:
+        dico={}
+        kmax=np.argpartition(im, k)[:k]
+        for indexe in kmax:
+            if labels_train[indexe] in dico:
+                dico[labels_train[indexe]][0]+=1
+                dico[labels_train[indexe]][1]+=im[indexe]
+            else:
+                dico[labels_train[indexe]]=[1,im[indexe]]
+        dico = sorted(dico.items(), key=lambda item: item[1][0], reverse=True)
+        max_value = dico[0][1][0]
+        dico = [item for item in dico if item[1][0] == max_value]
+        if len(dico) > 1:
+            dico = sorted(dico, key=lambda item: item[1][1])
+        resultat.append(dico[0][0])
+    return(resultat)
+
 def affichage(d_train, l_train, d_test, l_test):
     long, large = 5,4
 
@@ -65,7 +84,6 @@ def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
 
 if __name__ == "__main__":
     #d, l = read_cifar_batch("data/cifar-10-batches-py/data_batch_1")
-    t=time.time()
     nbr_knn = 20
     nbr_val = 10
     x = range(1, nbr_knn)
@@ -73,12 +91,20 @@ if __name__ == "__main__":
     for essai in range(nbr_val):
         d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)
         dist_matrice = distance_matrix(d_train, d_test)
-        y = []
+        y1 = []
+        #y2 = []
         for knn in x:
             stat = 0
             res = knn_predict(dist_matrice, l_train, knn)
-            y.append(np.sum(l_test == res) / len(l_test))
-        plt.plot(x, y)
+            res2 = knn_predict2(dist_matrice, l_train, knn)
+            y1.append(np.sum(l_test == res) / len(l_test))
+            #y2.append(np.sum(l_test == res2) / len(l_test))
+        plt.plot(x, y1, label=f'Précision knn mesure {essai}')
+        #plt.plot(x, y2, label='Précision knn2')
+    plt.xlabel('nombre de plus proche voisins concidérés')
+    plt.ylabel('taux de bonne calification')
+    plt.xticks(range(1, nbr_knn))  # Afficher des valeurs entières sur l'axe des abscisses
+    plt.yticks(np.arange(0, 1.1, 0.1))
     plt.legend()
     plt.show()
         
diff --git a/result/comparaison_knn_0to20.png b/result/comparaison_knn_0to20.png
new file mode 100644
index 0000000000000000000000000000000000000000..0202b206a237bdffa2a9345d26f90cc1e3e806bb
Binary files /dev/null and b/result/comparaison_knn_0to20.png differ
diff --git a/result/knn_1_20_valid_10test.png b/result/knn_1_20_valid_10test.png
index 79d1c2ce00fd9a38c5124b3b32fde2f43fc31039..24a832ad8f1cd2b7d4fd3d5e40822aa033cba6b7 100644
Binary files a/result/knn_1_20_valid_10test.png and b/result/knn_1_20_valid_10test.png differ
diff --git a/test.py b/test.py
index cfb0fb4417affbcbfc75fec86f9dbf5102e7f17a..ce77c9cadb212fe00cbcc827883d1d6fa3346972 100644
--- a/test.py
+++ b/test.py
@@ -1,12 +1,22 @@
 import numpy as np
 
-def count_matching_elements(vector1, vector2):
-    if len(vector1) != len(vector2):
-        raise ValueError("Les vecteurs doivent avoir la même taille.")
+label=np.array([1,2,2,3,3])
+dist=np.array([[10,25,10,42,3],[75,63,87,64,1]])
+for im in dist:
+    dico={}
+    kmax=np.argpartition(im, 3)[:3]
+    for indexe in kmax:
+        if label[indexe] in dico:
+            dico[label[indexe]][0]+=1
+            dico[label[indexe]][1]+=im[indexe]
+        else:
+            dico[label[indexe]]=[1,im[indexe]]
+    dico = sorted(dico.items(), key=lambda item: item[1][0], reverse=True)
+    
+    max_value = dico[0][1][0]
+    dico = [item for item in dico if item[1][0] == max_value]
+    print(dico)
+    if len(dico) > 1:
+        filtered_dict = sorted(dico, key=lambda item: item[1][1])
+    return(dico[0][0])
 
-    matching_elements = np.sum(vector1 == vector2)
-    return matching_elements
-
-# Exemple d'utilisation
-vector1 = np.array([1, 2, 3, 4, 5])
-vector2 = np.array([1, 0, 3, 3, 5])