From 32d8ee01c4ac9903131bfc9269fe6baae4a4a7db Mon Sep 17 00:00:00 2001
From: Sucio <esteban.cosserat@gmail.com>
Date: Sun, 29 Oct 2023 20:12:06 +0100
Subject: [PATCH] neurone_part

---
 Rapport.ipynb | 362 +++++++++++++++++++++++++++++++++++++++++++++-----
 mlp.py        | 117 ++++++++++++++++
 test.py       |  39 +++---
 3 files changed, 468 insertions(+), 50 deletions(-)
 create mode 100644 mlp.py

diff --git a/Rapport.ipynb b/Rapport.ipynb
index a58b331..5147942 100644
--- a/Rapport.ipynb
+++ b/Rapport.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {
     "dotnet_interactive": {
      "language": "csharp"
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,16 +98,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[6 9 9 4 1 1 2 7 8 3]\n",
-      "[2 4 9 6 8 1 7 1 3] [9]\n",
-      "[4 1 1 3 2 6 7 8 9] [9]\n"
+     "ename": "ValueError",
+     "evalue": "all the input array dimensions except for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 3071 and the array at index 1 has size 3072",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Utilisateur\\Documents\\GitHub\\image-classification\\Rapport.ipynb Cell 8\u001b[0m line \u001b[0;36m3\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X10sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m__name__\u001b[39m \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m__main__\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X10sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m     \u001b[39m#read_cifar_batch(\"data/cifar-10-batches-py/data_batch_1\")\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X10sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m     d, l \u001b[39m=\u001b[39m read_cifar(\u001b[39m\"\u001b[39m\u001b[39mdata/cifar-10-batches-py\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X10sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     \u001b[39mprint\u001b[39m(l[\u001b[39m0\u001b[39m:\u001b[39m10\u001b[39m])\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X10sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     d_1, l_1, d_1, l_2 \u001b[39m=\u001b[39m split_dataset(d[:\u001b[39m10\u001b[39m,:], l[:\u001b[39m10\u001b[39m], \u001b[39m0.9\u001b[39m)\n",
+      "\u001b[1;32mc:\\Users\\Utilisateur\\Documents\\GitHub\\image-classification\\Rapport.ipynb Cell 8\u001b[0m line \u001b[0;36m8\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X10sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m         batch_path \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(path_folder, filename)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X10sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m         d, l \u001b[39m=\u001b[39m read_cifar_batch(batch_path)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X10sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m         data \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mconcatenate((data, d), axis\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X10sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m         labels \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mconcatenate((labels, l), axis\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X10sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39mreturn\u001b[39;00m(data,labels)\n",
+      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mconcatenate\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 3071 and the array at index 1 has size 3072"
      ]
     }
    ],
@@ -116,10 +120,10 @@
     "    #read_cifar_batch(\"data/cifar-10-batches-py/data_batch_1\")\n",
     "    d, l = read_cifar(\"data/cifar-10-batches-py\")\n",
     "    print(l[0:10])\n",
-    "    d_1, l_1, d_2, l_2 = split_dataset(d[:10,:], l[:10], 0.9)\n",
+    "    d_1, l_1, d_1, l_2 = split_dataset(d[:10,:], l[:10], 0.9)\n",
     "    print(l_1,l_2)\n",
-    "    d_1, l_1, d_2, l_2 = split_dataset(d[:10,:], l[:10], 0.9)\n",
-    "    print(l_1,l_2)"
+    "    d_1, l_1, d_2, l_1 = split_dataset(d[:10,:], l[:10], 0.9)\n",
+    "    print(l_1,l_1)"
    ]
   },
   {
@@ -131,12 +135,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x500 with 10 Axes>"
       ]
@@ -245,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -310,12 +314,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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JCejatSsmT54MAGjXrh2cnJzQvXt3vPXWW/Dz86u0jVKphFKplH4HiIiIqF6y2MiNQqFAeHg4UlJSjMpTUlIQHR1d5TaFhYWwsTEOWS6XAygd8SEiIiKy6GGp+Ph4LF26FMnJycjIyMCkSZOgVqsNh5mmTp2KESNGGOo//PDD+Pbbb5GUlIQzZ87gjz/+wPjx4/Gf//wH/v7+ltoNIiIiqkcsdlgKAIYMGYKcnBzMnj0bGo0GYWFh2LRpE4KCggAAGo3GaM2bUaNGIT8/HwsXLsTLL78MNzc39O7dG++8846ldoGIiIjqGZn4lx3PycvLg0qlQm5uLlxdXS0dDhEREZnAnO9vi58tRURERCQlJjdERERkVZjcEBERkVVhckNERERWhckNERERWRUmN0RERGRVmNwQERGRVWFyQ0RERFaFyQ0RERFZFSY3REREZFWY3BAREZFVYXJDREREVoXJDREREVkVJjdERERkVZjcEBERkVVhckNERERWxbYmG/3666/49ddfkZ2dDb1eb/RccnKyJIERERER1YTZyc2sWbMwe/ZsREREwM/PDzKZrC7iIiIiIqoRs5ObTz/9FCtWrMDw4cPrIh4iIiKiWjF7zo1Wq0V0dHRdxEJERERUa2YnN2PHjsWXX35ZF7EQERER1ZrZh6Vu3bqFxYsX45dffkG7du1gZ2dn9Py8efMkC46IiIjIXGYnN4cPH0aHDh0AAH/++afRc5xcTERERJZmdnKzdevWuoiDiIiISBK1WsTvwoULuHjxolSxEBEREdWa2cmNXq/H7NmzoVKpEBQUhMDAQLi5ueHNN9+stKAfERER0b1mdnIzbdo0LFy4EHPnzkVaWhoOHjyIOXPm4OOPP8b06dPNDiAxMREhISGwt7dHeHg4duzYUW3dUaNGQSaTVbqFhoaa3S8RERFZJ5kQQpizgb+/Pz799FM88sgjRuXff/89XnjhBbMOU61evRrDhw9HYmIiunbtikWLFmHp0qU4duwYAgMDK9XPzc3FzZs3DY9LSkrQvn17vPTSS5g5c6ZJfebl5UGlUiE3Nxeurq4mx0pERESWY873t9kjN1evXkXr1q0rlbdu3RpXr141q6158+ZhzJgxGDt2LNq0aYP58+cjICAASUlJVdZXqVTw9fU13A4cOIBr167h6aefNnc3iIiIyEqZndy0b98eCxcurFS+cOFCtG/f3uR2tFotUlNTERMTY1QeExODXbt2mdTGsmXL0LdvXwQFBVVbp6ioCHl5eUY3IiIisl5mnwr+7rvv4qGHHsIvv/yCqKgoyGQy7Nq1C5mZmdi0aZPJ7Vy5cgU6nQ4+Pj5G5T4+PsjKyrrr9hqNBj/99NNdV0tOSEjArFmzTI6LiIiIGjazR2569uyJEydOYNCgQbh+/TquXr2KwYMH4/jx4+jevbvZAVRc+E8IYdJigCtWrICbmxtiY2PvWG/q1KnIzc013DIzM82OkYiIiBoOs0dugNJJxW+//XatOvb09IRcLq80SpOdnV1pNKciIQSSk5MxfPhwKBSKO9ZVKpVQKpW1ipWIiIgaDpOSm8OHDyMsLAw2NjY4fPjwHeu2a9fOpI4VCgXCw8ORkpKCQYMGGcpTUlIwcODAO267fft2nDp1CmPGjDGpLyIiIvr3MCm56dChA7KysuDt7Y0OHTpAJpOhqjPIZTIZdDqdyZ3Hx8dj+PDhiIiIQFRUFBYvXgy1Wo24uDgApYeULl68iM8//9xou2XLliEyMhJhYWEm90VERET/DiYlN2fPnoWXl5fhvlSGDBmCnJwczJ49GxqNBmFhYdi0aZPh7CeNRgO1Wm20TW5uLtatW4cFCxZIFgcRERFZD7MX8fv9998RHR0NW1vjvKikpAS7du1Cjx49JA1QalzEj4iIqOGp00X8evXqVeVifbm5uejVq5e5zRERERFJyuzkprpTtXNycuDk5CRJUEREREQ1ZfKp4IMHDwZQOml41KhRRqdX63Q6HD58GNHR0dJHSERERGQGk5MblUoFoHTkxsXFBQ4ODobnFAoFunTpgmeeeUb6CImIiIjMYHJys3z5cgBAcHAwXnnlFR6CIiIionrJ7LOlGjqeLUVERNTwmPP9XaPLL6xduxbffPMN1Go1tFqt0XMHDx6sSZNEREREkjD7bKmPPvoITz/9NLy9vZGWlob//Oc/8PDwwJkzZzBgwIC6iJGIiIjIZGYnN4mJiVi8eDEWLlwIhUKBV199FSkpKRg/fjxyc3PrIkYiIiIik5md3KjVasMp3w4ODsjPzwcADB8+HF999ZW00RERERGZyezkxtfXFzk5OQCAoKAg7NmzB0DpNaf+ZXOTiYiIqB4yO7np3bs3fvjhBwDAmDFjMGnSJDzwwAMYMmQIBg0aJHmAREREROYw+1RwvV4PvV5vuHDmN998g507d6J58+aIi4uDQqGok0ClwlPBiYiIGh5zvr+5zg0RERHVe3V6VfDly5djzZo1lcrXrFmDzz77zNzmiIiIiCRldnIzd+5ceHp6Vir39vbGnDlzJAmKiIiIqKbMTm7Onz+PkJCQSuVBQUFQq9WSBEVERERUU2YnN97e3jh8+HCl8kOHDsHDw0OSoIiIiIhqyuzkZujQoRg/fjy2bt0KnU4HnU6H3377DRMmTMDQoUPrIkYiIiIik5l94cy33noL58+fR58+fQyng+v1eowYMYJzboiIiMjianwq+IkTJ3Do0CE4ODjgvvvuQ1BQkNSx1QmeCk5ERNTwmPP9bfbITZmWLVuiZcuWNd2ciIiIqE6YlNzEx8fjzTffhJOTE+Lj4+9Yd968eZIERkRERFQTJiU3aWlpKC4uBgAcPHgQMpmsynrVlRMRERHdKyYlNwsWLDAc39q2bVtdxkNERERUKyadCt6xY0dcuXIFANC0aVPk5OTUaVBERERENWVScuPm5oazZ88CAM6dOwe9Xi9ZAImJiQgJCYG9vT3Cw8OxY8eOO9YvKirCtGnTEBQUBKVSiWbNmiE5OVmyeIiIiKhhM+mw1KOPPoqePXvCz88PMpkMERERkMvlVdY9c+aMyZ2vXr0aEydORGJiIrp27YpFixZhwIABOHbsGAIDA6vc5vHHH8elS5ewbNkyNG/eHNnZ2SgpKTG5TyIiIrJuJq9z8/PPP+PUqVMYP348Zs+eDRcXlyrrTZgwweTOIyMj0alTJyQlJRnK2rRpg9jYWCQkJFQZw9ChQ3HmzBm4u7ub1EdRURGKiooMj/Py8hAQEMB1boiIiBqQOlnnpn///gCA1NRUTJgwodrkxlRarRapqamYMmWKUXlMTAx27dpV5TYbNmxAREQE3n33XaxcuRJOTk545JFH8Oabb8LBwaHKbRISEjBr1qxaxUpEREQNh9mL+C1fvlySjq9cuQKdTgcfHx+jch8fH2RlZVW5zZkzZ7Bz507Y29vju+++w5UrV/DCCy/g6tWr1c67mTp1qtHaPGUjN0RERGSdTEpuBg8ejBUrVsDV1RWDBw++Y91vv/3WrAAqro0jhKh2vRy9Xg+ZTIZVq1ZBpVIBKF008L///S8++eSTKkdvlEollEqlWTERERFRw2VScqNSqQwJR1lSUVuenp6Qy+WVRmmys7MrjeaU8fPzQ+PGjY1iaNOmDYQQuHDhAlq0aCFJbERERNRwmZTclD8UJdVhKYVCgfDwcKSkpGDQoEGG8pSUFAwcOLDKbbp27Yo1a9agoKAAzs7OAEov4GljY4MmTZpIEhcRERE1bCatc1NX4uPjsXTpUiQnJyMjIwOTJk2CWq1GXFwcgNL5MiNGjDDUHzZsGDw8PPD000/j2LFj+P333zF58mSMHj262gnFRERE9O9i0shNx44dTb5u1MGDB03ufMiQIcjJycHs2bOh0WgQFhaGTZs2ISgoCACg0WigVqsN9Z2dnZGSkoKXXnoJERER8PDwwOOPP4633nrL5D6JiIjIupm0zo05p1LPmDGjVgHVNXPOkyciIqL6wZzvb5MX8bMWTG6IiIgaHnO+vy0654aIiIhIamYv4qfT6fDhhx/im2++gVqthlarNXr+6tWrkgVHREREZC6zR25mzZqFefPm4fHHH0dubi7i4+MxePBg2NjYYObMmXUQIhEREZHpzE5uVq1ahSVLluCVV16Bra0tnnjiCSxduhRvvPEG9uzZUxcxEhEREZnM7OQmKysL9913H4DSU7Nzc3MBAP/3f/+HjRs3ShsdERERkZnMTm6aNGkCjUYDAGjevDm2bNkCANi/fz+v4UREREQWZ3ZyM2jQIPz6668AgAkTJmD69Olo0aIFRowYgdGjR0seIBEREZE5ar3Ozd69e/HHH3+gefPmeOSRR6SKq85wnRsiIqKGx5zvb7NPBa8oMjISkZGRtW2GiIiISBJmH5ZKSEhAcnJypfLk5GS88847kgRFREREVFNmJzeLFi1C69atK5WHhobi008/lSQoIiIiopqq0angfn5+lcq9vLwMZ1ERERERWYrZyU1AQAD++OOPSuV//PEH/P39JQmKiIiIqKbMnlA8duxYTJw4EcXFxejduzcA4Ndff8Wrr76Kl19+WfIAiYiIiMxhdnLz6quv4urVq3jhhRcMF820t7fHa6+9hqlTp0oeIBEREZE5arzOTUFBATIyMuDg4IAWLVo0mNWJuc4NERFRw3NP1rlxdnZG586da7o5ERERUZ0we0IxERERUX3G5IaIiIisCpMbIiIisipMboiIiMiq1Ci5WblyJbp27Qp/f3+cP38eADB//nx8//33kgZHREREZC6zk5ukpCTEx8fjwQcfxPXr16HT6QAAbm5umD9/vtTxEREREZnF7OTm448/xpIlSzBt2jTI5XJDeUREBI4cOSJpcERERETmMju5OXv2LDp27FipXKlU4saNG5IERURERFRTZic3ISEhSE9Pr1T+008/oW3btmYHkJiYiJCQENjb2yM8PBw7duyotu62bdsgk8kq3f766y+z+yUiIiLrZPYKxZMnT8a4ceNw69YtCCGwb98+fPXVV0hISMDSpUvNamv16tWYOHEiEhMT0bVrVyxatAgDBgzAsWPHEBgYWO12x48fN1p62cvLy9zdICIiIitVo2tLLVmyBG+99RYyMzMBAI0bN8bMmTMxZswYs9qJjIxEp06dkJSUZChr06YNYmNjkZCQUKn+tm3b0KtXL1y7dg1ubm4m9VFUVISioiLD47y8PAQEBPDaUkRERA2IOdeWqtGp4M888wzOnz+P7OxsZGVlITMz0+zERqvVIjU1FTExMUblMTEx2LVr1x237dixI/z8/NCnTx9s3br1jnUTEhKgUqkMt4CAALPiJCIiooalVov4eXp6wtvbu0bbXrlyBTqdDj4+PkblPj4+yMrKqnIbPz8/LF68GOvWrcO3336LVq1aoU+fPvj999+r7Wfq1KnIzc013MpGm4iIiMg6mT3n5tKlS3jllVfw66+/Ijs7GxWPapWte2MqmUxm9FgIUamsTKtWrdCqVSvD46ioKGRmZuL9999Hjx49qtxGqVRCqVSaFRMRERE1XGYnN6NGjYJarcb06dPh5+dXbSJyN56enpDL5ZVGabKzsyuN5txJly5d8MUXX9QoBiIiIrI+Zic3O3fuxI4dO9ChQ4dadaxQKBAeHo6UlBQMGjTIUJ6SkoKBAwea3E5aWhr8/PxqFQsRERFZD7OTm4CAgEqHomoqPj4ew4cPR0REBKKiorB48WKo1WrExcUBKJ0vc/HiRXz++ecASq9fFRwcjNDQUGi1WnzxxRdYt24d1q1bJ0k8RERE1PCZndzMnz8fU6ZMwaJFixAcHFyrzocMGYKcnBzMnj0bGo0GYWFh2LRpE4KCggAAGo0GarXaUF+r1eKVV17BxYsX4eDggNDQUGzcuBEPPvhgreIgIiIi62H2OjeNGjVCYWEhSkpK4OjoCDs7O6Pnr169KmmAUjPnPHkiIiKqH8z5/q7RyA0RERFRfWV2cjNy5Mi6iIOIiIhIEmYnNwCg1+tx6tQpZGdnQ6/XGz1X3XozRERERPeC2cnNnj17MGzYMJw/f77SWVMymczsRfyIiIiIpGR2chMXF4eIiAhs3LixVov4EREREdUFs5ObkydPYu3atWjevHldxENERERUK2ZfODMyMhKnTp2qi1iIiIiIas3skZuXXnoJL7/8MrKysnDfffdVWuemXbt2kgVHREREZC6zF/Gzsak82COTyQxX867vE4q5iB8REVHDU6eL+J09e7bGgRERERHVNbOTm7LrPhERERHVRzVaxO/06dOYP38+MjIyIJPJ0KZNG0yYMAHNmjWTOj4iIiIis5h9ttTmzZvRtm1b7Nu3D+3atUNYWBj27t2L0NBQpKSk1EWMRERERCYze0Jxx44d0a9fP8ydO9eofMqUKdiyZQsOHjwoaYBS44RiIiKihsec72+zR24yMjIwZsyYSuWjR4/GsWPHzG2OiIiISFJmJzdeXl5IT0+vVJ6eng5vb28pYiIiIiKqMbMnFD/zzDN49tlncebMGURHR0Mmk2Hnzp1455138PLLL9dFjEREREQmM3vOjRAC8+fPxwcffIC///4bAODv74/Jkydj/Pjx9f5CmpxzQ0RE1PCY8/1tdnJTXn5+PgDAxcWlpk3cc0xuiIiIGp46XaG4THZ2No4fPw6ZTIZWrVrBy8urpk0RERERScbsCcV5eXkYPnw4/P390bNnT/To0QP+/v546qmnkJubWxcxEhEREZnM7ORm7Nix2Lt3LzZu3Ijr168jNzcXP/74Iw4cOIBnnnmmLmIkIiIiMpnZc26cnJywefNmdOvWzah8x44d6N+/P27cuCFpgFLjnBsiIqKGp04X8fPw8IBKpapUrlKp0KhRI3ObIyIiIpKU2cnN//73P8THx0Oj0RjKsrKyMHnyZEyfPl3S4IiIiIjMZdLZUh07djRav+bkyZMICgpCYGAgAECtVkOpVOLy5ct47rnn6iZSIiIiIhOYlNzExsbWWQCJiYl47733oNFoEBoaivnz56N79+533e6PP/5Az549ERYWVuXlIIiIiOjfqVaL+NXW6tWrMXz4cCQmJqJr165YtGgRli5dimPHjhlGhaqSm5uLTp06oXnz5rh06ZJZyQ0nFBMRETU892yF4tqKjIxEp06dkJSUZChr06YNYmNjkZCQUO12Q4cORYsWLSCXy7F+/fo7JjdFRUUoKioyPM7Ly0NAQACTGyIiogakTs+WkopWq0VqaipiYmKMymNiYrBr165qt1u+fDlOnz6NGTNmmNRPQkICVCqV4RYQEFCruImIiKh+s1hyc+XKFeh0Ovj4+BiV+/j4ICsrq8ptTp48iSlTpmDVqlWwtTXtyhFTp05Fbm6u4ZaZmVnr2ImIiKj+qvG1paRS8SriQogqryyu0+kwbNgwzJo1Cy1btjS5faVSCaVSWes4iYiIqGGocXKj1Wpx9uxZNGvWzORRlPI8PT0hl8srjdJkZ2dXGs0BSq9AfuDAAaSlpeHFF18EAOj1egghYGtriy1btqB379412xkiIiKyGmYfliosLMSYMWPg6OiI0NBQqNVqAMD48eMxd+5ck9tRKBQIDw9HSkqKUXlKSgqio6Mr1Xd1dcWRI0eQnp5uuMXFxaFVq1ZIT09HZGSkubtCREREVsjs5Gbq1Kk4dOgQtm3bBnt7e0N53759sXr1arPaio+Px9KlS5GcnIyMjAxMmjQJarUacXFxhr5GjBhRGqiNDcLCwoxu3t7esLe3R1hYGJycnMzdFSIiIrJCZh9PWr9+PVavXo0uXboYzY1p27YtTp8+bVZbQ4YMQU5ODmbPng2NRoOwsDBs2rQJQUFBAACNRmMYGSIiIiIyhdnr3Dg6OuLPP/9E06ZN4eLigkOHDqFp06Y4dOgQevTogdzc3LqKVRJcxI+IiKjhqdN1bjp37oyNGzcaHpeN3ixZsgRRUVHmNkdEREQkKbMPSyUkJKB///44duwYSkpKsGDBAhw9ehS7d+/G9u3b6yJGIiIiIpOZPXITHR2NP/74A4WFhWjWrBm2bNkCHx8f7N69G+Hh4XURIxEREZHJLHptKUvgnBsiIqKGx5zvb5MOS+Xl5ZncORMGIiIisiSTkhs3N7cqL4lQFZ1OV6uAiIiIiGrDpORm69athvvnzp3DlClTMGrUKMPZUbt378Znn32GhISEuomSiIiIyERmz7np06cPxo4diyeeeMKo/Msvv8TixYuxbds2KeOTHOfcEBERNTx1us7N7t27ERERUak8IiIC+/btM7c5IiIiIkmZndwEBATg008/rVS+aNEiBAQESBIUERERUU2ZvYjfhx9+iEcffRSbN29Gly5dAAB79uzB6dOnsW7dOskDJCIiIjKH2SM3Dz74IE6ePImBAwfi6tWryMnJwcCBA3HixAk8+OCDdREjERERkcm4iB8RERHVe3U6oZiIiIioPmNyQ0RERFaFyQ0RERFZFSY3REREZFXMTm6OHj1a7XM///xzrYIhIiIiqi2zk5uIiAh8/PHHRmVFRUV48cUXMWjQIMkCIyIiIqoJs5ObVatWYdasWRgwYACysrKQnp6Ojh074rfffsMff/xRFzESERERmczs5Gbw4ME4fPgwSkpKEBYWhqioKNx///1ITU1Fp06d6iJGIiIiIpPVaEKxTqeDVquFTqeDTqeDr68vlEql1LERERERmc3s5Obrr79Gu3btoFKpcOLECWzcuBGLFy9G9+7dcebMmbqIscHYejwbf2XlWToMIiKifzWzk5sxY8Zgzpw52LBhA7y8vPDAAw/gyJEjaNy4MTp06FAHITYM6ZnXEbcyFY99uht7z+RYOhwiIqJ/LbOTm4MHD+L55583KmvUqBG++eYbfPLJJ5IF1tAEezjivsYq5N8qwfDkffj5T42lQyIiIvpX4oUzJXSrWIeXvkpDyrFLkMmA2QPDMLxLkKR9EBER/RuZ8/1ta27jo0ePvuPzycnJZrWXmJiI9957DxqNBqGhoZg/fz66d+9eZd2dO3fitddew19//YXCwkIEBQXhueeew6RJk8zqs67Y28mR9GQnTP/+KL7ap8b09X/ict4tTHqgJWQymaXDIyKCXi+QX1SC/FvFKNTq4KS0RSNHOzjYyfl3iqyG2cnNtWvXjB4XFxfjzz//xPXr19G7d2+z2lq9ejUmTpyIxMREdO3aFYsWLcKAAQNw7NgxBAYGVqrv5OSEF198Ee3atYOTkxN27tyJ5557Dk5OTnj22WfN3ZU6YSu3wZxBYfBxVWL+Lyfx0W+nkJ1fhLdiw2Ar59UuGoqiEh1yC4txrbAY1wq1uF6oLXe/2PD4eqEWQgAtfFzQ2rfs5gqVo52ld8Ek2hI9zufcwKnsAlwuKEIjRwU8nZXwdC79qXKwg41N/f3CK9bpcfWGFlcKinClQIucgiLcKCqBl4s9Grs5wN/NHu5OCqv50tbrBQq0Jci/VZqclP+Zd7MYebeqfq7sft6tEhQUlVTZtsLWBo0c7dDIUQE3w08FGjnawc3R7vb9sselP1UOdnX6d00IgVvFehRqS1Co1d2+leDm7fs3yt0v0etRohfQ6QR0QkCnF6WPy91K9PrSnxXr6Ep/6kXZNvrSOvp/6un0Au5Oitu/V6W3xrdvvip7KGz5970+keSwlF6vxwsvvICmTZvi1VdfNXm7yMhIdOrUCUlJSYayNm3aIDY2FgkJCSa1MXjwYDg5OWHlypVVPl9UVISioiLD47y8PAQEBNTJYamKVu09j+nr/4ReAH3b+ODjJzrCQSGv0z7JmF4vkH+rBNcKtYbE5NrtxCS3QsJS/mehVlerfn1d7dHazwWtyiU8zbycLfYH8FaxDqcvF+BUdgFOXrr9Mzsf53MKUaKv/k+ArY0M7k63Ex4XJTydFKU/nRXwcFIa7ns6K+HupICdBF90N7W628nKPwlL2f2K5dcKi+/anr2djeGLyF9V9sVkj8aN/vliUtre+8+lTi9wrbB0n3Ju79vl/CLk3NDiSn4Rrt7QIq9ccpJ3sxgF2hJINZFAYWsDJ4UcBUUlKNbVvFFXe1s0clLAzcGuXDJ0OxFyKk2AtCV63Cy+nZwU3U5Uiv+5f7NYhxtG93W4qS1BYbFOsv2tSzIZ4OWsRONGDuV+1+xL79/+PVM52FlNkm0p5hyWkmzOzfHjx3H//fdDozFtIq1Wq4WjoyPWrFljdNmGCRMmID09Hdu3b79rG2lpaRgwYADeeustjB07tso6M2fOxKxZsyqV34vkBgA2H83C+K/SUFSiR3hQIywbGQE3R0Wd9/tvIIRA3s0SXLheiL+v38Lf12/i7+s3ceH2z7+v38Tl/CLc4bv7jmxkgFv5/2LL/fFu5PRPebFOjxOX8vGXJh9/ZeXj4vWbVbZnayNDUy8ntPZ1/Sfp8XOFv8pesj96+beKbycuBTh9++ep7AJkXius9kvCSSFHc29n+Krsca2wuDR5yC9C3q2q/8O/EzdHO8PIj4ezEl7l7ns6K+GklOPajWLk3Cjt43KF5CWnoAg3zEws5bcTMA8nBbxclHBUyJGdX4SL124iO7/o7g0A8HJRwt/NAU1uJz7l/zP3d3NAI0fTvpiKSnSGRCWnQIvLt/etfFlZgnb1hrbGv5sKuQ1c7G1v3+zgYm8L19s/XQw//ylzdaj8XFlCJ4TADa0O126US/Jv3h6dvGE8cll+BDO/Br8ftWFvZwNHhS0c7ORwUsrhoLCF4+379nZyKOQ2kNvIYCuXlf60sYGNrPxjWeljGxnk8oqPbUp/ymQV2pAZ2pBBhisFRfj7+i1cLPc35+L1mygq0d81fkeF3Oh3qvHt37Oyx74qe0n+ObBmFkluNm3ahJEjR+Ly5csm1f/777/RuHFj/PHHH4iOjjaUz5kzB5999hmOHz9e7bZNmjTB5cuXUVJSgpkzZ2L69OnV1rXkyE2Z/eeuYsyK/ci7VYLm3s74bPR/0NjN4Z703ZAV6/S4lHcLF6/dxN+5N2//UblZ+vh28mLqF6GTQm6cqNz+afgv08kObg7lyxVwsbet0SGZvFvFOJFVmugcz8rHX1l5+Csrv9ovAxd7W7Tycbk90uOKNr4uaOnrAlf76g9tXb2hxclL+Th1uXQk5vTtn1l5t6rdxs3RDi28ndHc2wXNvZ1v33eGXzXJlbZEfzsJ0eLK7WSk6pEULa7eqHkSWRWFrU2FxKjscJkSHs6K0udclPBwKn2vqnufikp0uJRbhIu3f1+q+nmr+O5fTA52ckPS09jNAZ7OSuTeLDYedSkoqtEXfiNDQli6b57OSni5lI6EqSokJWXJir2d5UeAS3R65N4srpT0GCVCN4qRe7MYClsbOCrkcFDI4aSwhaNCDsfbPx0Ut5MVu9LH5e87KuWGhEZeTw+PCiGQc0Nr+Jt0sSzpMfzduokrBdq7tiOTAT4u9nBztIODQm70GlV6vRS2hjrl71esb85IsRCi3OiaDoXFJf/c15YYRtTKHyK8qS3BDa3u9qFB4/tujgqsez767h2boU4nFMfHxxs9FkJAo9Fg48aNGDlypLnNVfqjKoS4639IO3bsQEFBAfbs2YMpU6agefPmeOKJJ6qsq1QqLb56cudgd6x9Phojk/fhVHYBHk3chc9G/wetfF0sGpel5d0qNkpULpb7T+jv6zdxKe+WSV+YHk6K0uHg8occbg8H+7iW/rG4l4cdXO3tEBHsjohgd0OZEAKa3FuGROd4VulIz+nLBci/VYID56/hwHnj+WyN3RzQ2rf00Ja7kwJnr9wwjMjk3Kj+j6WPq/J28uKCZuWSGA8z554obG3gp3KAn+ruibhOL3C9UFvp0FHpl/8/9wuKSuB+e15P2Re5p4sSXuVGdzydFXBW2koymqW0lSPQwxGBHo5VPi+EwLXCYuOE5/aXUtnv4+X8Itws1uH05Rs4ffnGXfu0tZH9s2/lk7Fy+1xW5u6kaLBz8WzlNvBwVsLD+d+9Or1MJjO81+2auFVZ51axDprcW4a/dxWT7L+v34JWp0dW3q07/oNiLlsbmVFCWZYEleiFYa5S+WRFSh5Odz9kXJfMHrnp1auX0WMbGxt4eXmhd+/eGD16NGxtTcuXpDgsBQBvvfUWVq5ceceRnvLq8lTwu/n7+k2MTN6Hk9kFcLG3xdIREYhs6nFPYzCXTn87my8yntBX8f7NCpP7yt+v7rk7zfUoo5DbwM/NvsIkPns0dnM0/CddH/6LrSltiR5nrhQYDmkdv538aHLv/geuSSMHQ+JSlsg093aGyqFhTGZuKG4V65CVWz7xvoUrBUVwc7SDh2EOkvEkbM6tIHPo9f+M/uTfKjH6e2nqSElhub+3Wt3dRyPv5J9Df3I42tneHkErHVFzqnC/tI4cjkrjUSNnpS3CGqskeoVK1enIzdatW2scWHkKhQLh4eFISUkxSm5SUlIwcOBAk9sRQhgddqrP/N0csCYuCmM/O4AD569hePI+fDS0A/qH+Vk6NAClr+Wu0zn4dPtpHP07DzeKSkw6llwbjRztKhyHdjBMwvN3s4enk7Jen61TWwpbG7T2dUVrX+MPam5hMY5f+ueQ1vVCLUI8ndDi9iGlpl5OcFSY/fGlGrC3kyPY0wnBnk6WDoWslI2NDF4upYcjpVCs09/xH0tbG1np4b7bhwEd7f65b28rt4q/uRb96xgfH4/hw4cjIiICUVFRWLx4MdRqNeLi4gAAU6dOxcWLF/H5558DAD755BMEBgaidevWAErXvXn//ffx0ksvWWwfzOXmqMAXYyMNi/09v+qgxRf7E0Jg24nL+PjXkziovl5lHZkMcLQrncRXekz89vFepa3hvqOydIKf4b5Cfvs/AFtDdm+4r5BD5WDHL+hqqBzt8J8Qd/wnxP3ulYmIyrGT20DlYPOvHsWt0TfL2rVr8c0330CtVkOrNT72f/DgQZPbGTJkCHJycjB79mxoNBqEhYVh06ZNCAoq/aLXaDRQq9WG+nq9HlOnTsXZs2dha2uLZs2aYe7cuXjuuedqshsWU18W+9PrBVIyLmHhb6dw5GIugNKRhCc6B+CxiIDbyUfpEKO9nQ2H2omIqEEwe87NRx99hGnTpmHkyJFYsmQJnn76aZw+fRr79+/HuHHj8Pbbb9dVrJKw5JybioQQWPDrScz/5SQAYGjngHuy2J9OL7DpiAafbD2Fv7LyAZQeY32qSyCe6d4U3q72ddo/ERGRuer0VPDWrVtjxowZeOKJJ+Di4oJDhw6hadOmeOONN3D16lUsXLiwVsHXtfqU3JS5V4v9lej02HDob3yy9ZThrA9npS1GRgdhdNeQf/1ZD0REVH/VaXLj6OiIjIwMBAUFwdvbGykpKWjfvj1OnjyJLl26ICcnp1bB17X6mNwAdbvYn7ZEj28PXkDittNQXy0EAKgc7DC6awhGRQc3mEsFEBHRv5c5399mH//w9fU1JDBBQUHYs2cPAODs2bP4l11gXFL9Qn3xxdhIuNrbIvX8Nfz3093VrnRrqlvFOny++xzuf28rpnx7BOqrhXB3UuDV/q2w87VemNC3BRMbIiKyOmZPKO7duzd++OEHdOrUCWPGjMGkSZOwdu1aHDhwAIMHD66LGP81pFrsr1Bbgi/3qrHo9zO4fHv5eW8XJZ7t0RTDIgN5hhIREVk1sw9L6fV66PV6w2J933zzDXbu3InmzZsjLi4OCkX9vm5SfT0sVV5NF/vLv1WMlXvOY9mOs4YVbP1V9nj+/mZ4LCKgQS92R0RE/251OudGrVYjICCgyssmZGZmIjAw0PyI76GGkNwAwPVCrWGxP4WtzR0X+8stLMbyXWex/I9zyL1ZuuR1oLsjXri/GQZ3amKxK1ETERFJpU6TG7lcDo1GA29vb6PynJwceHt7Q6eT9voUUmsoyQ1QOmembLE/mQyVFvu7ekOLpTvO4PPd51FQVHrBvqZeTnixV3M80t6/wV6zhoiIqKI6vfxCdRe2LCgogL0910eRUnWL/T3VJQhLdpzBF3vUuFlcmky29nXBi72bY0CYX729ei4REdG9YHJyU3Y1cJlMhunTp8PR8Z+r7Op0OuzduxcdOnSQPMB/O1u5DeYMCoOPqxLzfzmJj347hU+2nYbu9kUn72uswku9m6NvGx+ruB4IERFRbZmc3KSlpQEoHbk5cuSI0cRhhUKB9u3b45VXXpE+QoJMJsPEvi3h5aLE9PV/QqcXCA9qhJd6N0fPll68LAIREVE5Zs+5efrpp7FgwYJ6P1+lOg1pzk1VjlzIhVanQ6fARkxqiIjoX6NOJxQ3dA09uSEiIvo3qtMViomIiIjqMyY3REREZFWY3BAREZFVYXJDREREVoXJDREREVkVJjdERERkVZjcEBERkVVhckNERERWhckNERERWRUmN0RERGRVmNwQERGRVWFyQ0RERFaFyQ0RERFZFSY3REREZFUsntwkJiYiJCQE9vb2CA8Px44dO6qt++233+KBBx6Al5cXXF1dERUVhc2bN9/DaImIiKi+s2hys3r1akycOBHTpk1DWloaunfvjgEDBkCtVldZ//fff8cDDzyATZs2ITU1Fb169cLDDz+MtLS0exw5ERER1VcyIYSwVOeRkZHo1KkTkpKSDGVt2rRBbGwsEhISTGojNDQUQ4YMwRtvvFHl80VFRSgqKjI8zsvLQ0BAAHJzc+Hq6lq7HSAiIqJ7Ii8vDyqVyqTvb4uN3Gi1WqSmpiImJsaoPCYmBrt27TKpDb1ej/z8fLi7u1dbJyEhASqVynALCAioVdxERERUv1ksubly5Qp0Oh18fHyMyn18fJCVlWVSGx988AFu3LiBxx9/vNo6U6dORW5uruGWmZlZq7iJiIiofrO1dAAymczosRCiUllVvvrqK8ycORPff/89vL29q62nVCqhVCprHScRERE1DBZLbjw9PSGXyyuN0mRnZ1cazalo9erVGDNmDNasWYO+ffvWZZhERETUwFjssJRCoUB4eDhSUlKMylNSUhAdHV3tdl999RVGjRqFL7/8Eg899FBdh0lEREQNjEUPS8XHx2P48OGIiIhAVFQUFi9eDLVajbi4OACl82UuXryIzz//HEBpYjNixAgsWLAAXbp0MYz6ODg4QKVSWWw/iIiIqP6waHIzZMgQ5OTkYPbs2dBoNAgLC8OmTZsQFBQEANBoNEZr3ixatAglJSUYN24cxo0bZygfOXIkVqxYca/DJyIionrIouvcWII558kTERFR/dAg1rkhIiIiqgtMboiIiMiqMLkhIiIiq8LkhoiIiKwKkxsiIiKyKkxuiIiIyKowuSEiIiKrwuSGiIiIrAqTGyIiIrIqTG6IiIjIqjC5ISIiIqvC5IaIiIisCpMbIiIisipMboiIiMiqMLkhIiIiq8LkhoiIiKwKkxsiIiKyKkxuiIiIyKowuSEiIiKrwuSGiIiIrAqTGyIiIrIqTG6IiIjIqjC5ISIiIqvC5IaIiIisCpMbIiIisipMboiIiMiqWDy5SUxMREhICOzt7REeHo4dO3ZUW1ej0WDYsGFo1aoVbGxsMHHixHsXKBERETUIFk1uVq9ejYkTJ2LatGlIS0tD9+7dMWDAAKjV6irrFxUVwcvLC9OmTUP79u3vcbRERETUEMiEEMJSnUdGRqJTp05ISkoylLVp0waxsbFISEi447b3338/OnTogPnz59+xXlFREYqKigyP8/LyEBAQgNzcXLi6utYqfiIiIro38vLyoFKpTPr+ttjIjVarRWpqKmJiYozKY2JisGvXLsn6SUhIgEqlMtwCAgIka5uIiIjqH4slN1euXIFOp4OPj49RuY+PD7KysiTrZ+rUqcjNzTXcMjMzJWubiIiI6h9bSwcgk8mMHgshKpXVhlKphFKplKw9IiIiqt8sNnLj6ekJuVxeaZQmOzu70mgOERERkaksltwoFAqEh4cjJSXFqDwlJQXR0dEWioqIiIgaOoseloqPj8fw4cMRERGBqKgoLF68GGq1GnFxcQBK58tcvHgRn3/+uWGb9PR0AEBBQQEuX76M9PR0KBQKtG3b1hK7QERERPWMRZObIUOGICcnB7Nnz4ZGo0FYWBg2bdqEoKAgAKWL9lVc86Zjx46G+6mpqfjyyy8RFBSEc+fO3cvQiYiIqJ6y6Do3lmDOefJERERUPzSIdW6IiIiI6gKTGyIiIrIqTG6IiIjIqjC5ISIiIqvC5IaIiIisCpMbIiIisipMboiIiMiqMLkhIiIiq8LkhoiIiKwKkxsiIiKyKkxuiIiIyKowuSEiIiKrwuSGiIiIrAqTGyIiIrIqTG6IiIjIqjC5ISIiIqvC5IaIiIisCpMbIiIisipMboiIiMiqMLkhIiIiq8LkhoiIiKwKkxsiIiKyKkxuiIiIyKowuSEiIiKrwuSGiIiIrAqTGyIiIrIqFk9uEhMTERISAnt7e4SHh2PHjh13rL99+3aEh4fD3t4eTZs2xaeffnqPIiUiIqKGwKLJzerVqzFx4kRMmzYNaWlp6N69OwYMGAC1Wl1l/bNnz+LBBx9E9+7dkZaWhtdffx3jx4/HunXr7nHkREREVF/JhBDCUp1HRkaiU6dOSEpKMpS1adMGsbGxSEhIqFT/tddew4YNG5CRkWEoi4uLw6FDh7B79+4q+ygqKkJRUZHhcW5uLgIDA5GZmQlXV1cJ94aIiIjqSl5eHgICAnD9+nWoVKo71rW9RzFVotVqkZqaiilTphiVx8TEYNeuXVVus3v3bsTExBiV9evXD8uWLUNxcTHs7OwqbZOQkIBZs2ZVKg8ICKhF9ERERGQJ+fn59Te5uXLlCnQ6HXx8fIzKfXx8kJWVVeU2WVlZVdYvKSnBlStX4OfnV2mbqVOnIj4+3vBYr9fj6tWr8PDwgEwmk2BP/lGWVdblqBD7YB/sw/r7uFf9sA/20ZD6EEIgPz8f/v7+d61rseSmTMUEQwhxx6SjqvpVlZdRKpVQKpVGZW5ubjWI1HSurq51fsiLfbAP9mH9fdyrftgH+2gofdxtxKaMxSYUe3p6Qi6XVxqlyc7OrjQ6U8bX17fK+ra2tvDw8KizWImIiKjhsFhyo1AoEB4ejpSUFKPylJQUREdHV7lNVFRUpfpbtmxBRERElfNtiIiI6N/HoqeCx8fHY+nSpUhOTkZGRgYmTZoEtVqNuLg4AKXzZUaMGGGoHxcXh/PnzyM+Ph4ZGRlITk7GsmXL8Morr1hqF4wolUrMmDGj0mEw9sE+2Af7qI/9sA/20VD7uBuLngoOlC7i9+6770Kj0SAsLAwffvghevToAQAYNWoUzp07h23bthnqb9++HZMmTcLRo0fh7++P1157zZAMEREREVk8uSEiIiKSksUvv0BEREQkJSY3REREZFWY3BAREZFVYXJDREREVoXJjQR+//13PPzww/D394dMJsP69esl7yMhIQGdO3eGi4sLvL29ERsbi+PHj0vaR1JSEtq1a2dYVTIqKgo//fSTpH2Ul5CQAJlMhokTJ0ra7syZMyGTyYxuvr6+kvYBABcvXsRTTz0FDw8PODo6okOHDkhNTZWs/eDg4Er7IZPJMG7cOMn6KCkpwf/+9z+EhITAwcEBTZs2xezZs6HX6yXrAyi9FszEiRMRFBQEBwcHREdHY//+/TVu726fOSEEZs6cCX9/fzg4OOD+++/H0aNHJe3j22+/Rb9+/eDp6QmZTIb09HRJ96O4uBivvfYa7rvvPjg5OcHf3x8jRozA33//Lel+zJw5E61bt4aTkxMaNWqEvn37Yu/evZL2Ud5zzz0HmUyG+fPnS9rHqFGjKn1WunTpIvl+ZGRk4JFHHoFKpYKLiwu6dOkCtVotWR9VfeZlMhnee+89yfooKCjAiy++iCZNmsDBwQFt2rQxuoC1VP1cunQJo0aNgr+/PxwdHdG/f3+cPHnS7H5qgsmNBG7cuIH27dtj4cKFddbH9u3bMW7cOOzZswcpKSkoKSlBTEwMbty4IVkfTZo0wdy5c3HgwAEcOHAAvXv3xsCBA83+UjDF/v37sXjxYrRr107ytgEgNDQUGo3GcDty5Iik7V+7dg1du3aFnZ0dfvrpJxw7dgwffPCBpJf22L9/v9E+lC1g+dhjj0nWxzvvvINPP/0UCxcuREZGBt5991289957+PjjjyXrAwDGjh2LlJQUrFy5EkeOHEFMTAz69u2Lixcv1qi9u33m3n33XcybNw8LFy7E/v374evriwceeAD5+fmS9XHjxg107doVc+fOrdE+3K2PwsJCHDx4ENOnT8fBgwfx7bff4sSJE3jkkUck6wMAWrZsiYULF+LIkSPYuXMngoODERMTg8uXL0vWR5n169dj7969Jl0bqCZ99O/f3+gzs2nTJkn7OH36NLp164bWrVtj27ZtOHToEKZPnw57e3vJ+igfv0ajQXJyMmQyGR599FHJ+pg0aRJ+/vlnfPHFF4Y15l566SV8//33Jvdxt36EEIiNjcWZM2fw/fffIy0tDUFBQejbt6+k31vVEiQpAOK7776r836ys7MFALF9+/Y67adRo0Zi6dKlkraZn58vWrRoIVJSUkTPnj3FhAkTJG1/xowZon379pK2WdFrr70munXrVqd9VDRhwgTRrFkzodfrJWvzoYceEqNHjzYqGzx4sHjqqack66OwsFDI5XLx448/GpW3b99eTJs2rdbtV/zM6fV64evrK+bOnWsou3XrllCpVOLTTz+VpI/yzp49KwCItLS0GrVtSh9l9u3bJwCI8+fP11kfubm5AoD45ZdfJO3jwoULonHjxuLPP/8UQUFB4sMPP6xR+9X1MXLkSDFw4MAat2lKH0OGDJH0s2HK+zFw4EDRu3dvSfsIDQ0Vs2fPNirr1KmT+N///idZP8ePHxcAxJ9//mkoKykpEe7u7mLJkiU17sdUHLlpoHJzcwEA7u7uddK+TqfD119/jRs3biAqKkrStseNG4eHHnoIffv2lbTd8k6ePAl/f3+EhIRg6NChOHPmjKTtb9iwAREREXjsscfg7e2Njh07YsmSJZL2UZ5Wq8UXX3yB0aNHS3o1+27duuHXX3/FiRMnAACHDh3Czp078eCDD0rWR0lJCXQ6XaX/bh0cHLBz507J+ilz9uxZZGVlISYmxlCmVCrRs2dP7Nq1S/L+7qXc3FzIZLI6u/ivVqvF4sWLoVKp0L59e8na1ev1GD58OCZPnozQ0FDJ2q1o27Zt8Pb2RsuWLfHMM88gOztbsrb1ej02btyIli1bol+/fvD29kZkZGSdTEMoc+nSJWzcuBFjxoyRtN1u3bphw4YNuHjxIoQQ2Lp1K06cOIF+/fpJ1kdRUREAGH3u5XI5FApFnXzuK2Jy0wAJIRAfH49u3bohLCxM0raPHDkCZ2dnKJVKxMXF4bvvvkPbtm0la//rr7/GwYMHkZCQIFmbFUVGRuLzzz/H5s2bsWTJEmRlZSE6Oho5OTmS9XHmzBkkJSWhRYsW2Lx5M+Li4jB+/Hh8/vnnkvVR3vr163H9+nWMGjVK0nZfe+01PPHEE2jdujXs7OzQsWNHTJw4EU888YRkfbi4uCAqKgpvvvkm/v77b+h0OnzxxRfYu3cvNBqNZP2UKbu4bsUL8Pr4+FS68G5DcuvWLUyZMgXDhg2T/ErLP/74I5ydnWFvb48PP/wQKSkp8PT0lKz9d955B7a2thg/frxkbVY0YMAArFq1Cr/99hs++OAD7N+/H7179zZ8ydZWdnY2CgoKMHfuXPTv3x9btmzBoEGDMHjwYGzfvl2SPir67LPP4OLigsGDB0va7kcffYS2bduiSZMmUCgU6N+/PxITE9GtWzfJ+mjdujWCgoIwdepUXLt2DVqtFnPnzkVWVladfO4rsq3zHkhyL774Ig4fPlwn2W+rVq2Qnp6O69evY926dRg5ciS2b98uSYKTmZmJCRMmYMuWLWYdozbXgAEDDPfvu+8+REVFoVmzZvjss88QHx8vSR96vR4RERGYM2cOAKBjx444evQokpKSjK6HJpVly5ZhwIABNZqrcCerV6/GF198gS+//BKhoaFIT0/HxIkT4e/vj5EjR0rWz8qVKzF69Gg0btwYcrkcnTp1wrBhw3Dw4EHJ+qio4giXEELSUa97qbi4GEOHDoVer0diYqLk7ffq1Qvp6em4cuUKlixZgscffxx79+6Ft7d3rdtOTU3FggULcPDgwTp9/YcMGWK4HxYWhoiICAQFBWHjxo2SJAdlk+wHDhyISZMmAQA6dOiAXbt24dNPP0XPnj1r3UdFycnJePLJJyX/e/nRRx9hz5492LBhA4KCgvD777/jhRdegJ+fn2Qj6nZ2dli3bh3GjBkDd3d3yOVy9O3b1+jvc13iyE0D89JLL2HDhg3YunUrmjRpInn7CoUCzZs3R0REBBISEtC+fXssWLBAkrZTU1ORnZ2N8PBw2NrawtbWFtu3b8dHH30EW1tb6HQ6SfqpyMnJCffdd5+ks/T9/PwqJXxt2rQx66wJU50/fx6//PILxo4dK3nbkydPxpQpUzB06FDcd999GD58OCZNmiT5yFqzZs2wfft2FBQUIDMzE/v27UNxcTFCQkIk7QeA4cy4iqM02dnZlUZzGoLi4mI8/vjjOHv2LFJSUiQftQFKPyPNmzdHly5dsGzZMtja2mLZsmWStL1jxw5kZ2cjMDDQ8Lk/f/48Xn75ZQQHB0vSR1X8/PwQFBQk2efe09MTtra29+xzv2PHDhw/flzyz/3Nmzfx+uuvY968eXj44YfRrl07vPjiixgyZAjef/99SfsKDw83/LOs0Wjw888/Iycnp04+9xUxuWkghBB48cUX8e233+K33367J78cZf1KNazbp08fHDlyBOnp6YZbREQEnnzySaSnp0Mul0vST0VFRUXIyMiAn5+fZG127dq10qn4J06cQFBQkGR9lFm+fDm8vb3x0EMPSd52YWEhbGyM/wzI5XLJTwUv4+TkBD8/P1y7dg2bN2/GwIEDJe8jJCQEvr6+hrPLgNK5JNu3b0d0dLTk/dWlssTm5MmT+OWXX+Dh4XFP+pXycz98+HAcPnzY6HPv7++PyZMnY/PmzZL0UZWcnBxkZmZK9rlXKBTo3LnzPfvcL1u2DOHh4ZLOfQJKf6eKi4vv6edepVLBy8sLJ0+exIEDB+rkc18RD0tJoKCgAKdOnTI8Pnv2LNLT0+Hu7o7AwEBJ+hg3bhy+/PJLfP/993BxcTH8V6pSqeDg4CBJH6+//joGDBiAgIAA5Ofn4+uvv8a2bdvw888/S9K+i4tLpTlCTk5O8PDwkHTu0CuvvIKHH34YgYGByM7OxltvvYW8vDxJD7NMmjQJ0dHRmDNnDh5//HHs27cPixcvxuLFiyXrAygdCl++fDlGjhwJW1vpP64PP/ww3n77bQQGBiI0NBRpaWmYN28eRo8eLWk/mzdvhhACrVq1wqlTpzB58mS0atUKTz/9dI3au9tnbuLEiZgzZw5atGiBFi1aYM6cOXB0dMSwYcMk6+Pq1atQq9WGdWfKvvR8fX1NXlfpTn34+/vjv//9Lw4ePIgff/wROp3O8Ll3d3eHQqGodR8eHh54++238cgjj8DPzw85OTlITEzEhQsXzFpy4G6vVcWkzM7ODr6+vmjVqpUkfbi7u2PmzJl49NFH4efnh3PnzuH111+Hp6cnBg0aJNl+TJ48GUOGDEGPHj3Qq1cv/Pzzz/jhhx+wbds2yfoAgLy8PKxZswYffPCBye2a00fPnj0xefJkODg4ICgoCNu3b8fnn3+OefPmSdrPmjVr4OXlhcDAQBw5cgQTJkxAbGys0WT/OlPn52P9C2zdulUAqHQbOXKkZH1U1T4AsXz5csn6GD16tAgKChIKhUJ4eXmJPn36iC1btkjWflXq4lTwIUOGCD8/P2FnZyf8/f3F4MGDxdGjRyXtQwghfvjhBxEWFiaUSqVo3bq1WLx4seR9bN68WQAQx48fl7xtIYTIy8sTEyZMEIGBgcLe3l40bdpUTJs2TRQVFUnaz+rVq0XTpk2FQqEQvr6+Yty4ceL69es1bu9unzm9Xi9mzJghfH19hVKpFD169BBHjhyRtI/ly5dX+fyMGTMk6aPsFPOqblu3bpWkj5s3b4pBgwYJf39/oVAohJ+fn3jkkUfEvn37JH2tKqrJqeB36qOwsFDExMQILy8vYWdnJwIDA8XIkSOFWq2WfD+WLVsmmjdvLuzt7UX79u3F+vXrJe9j0aJFwsHBocafkbv1odFoxKhRo4S/v7+wt7cXrVq1Eh988IHZy0zcrZ8FCxaIJk2aGN6T//3vf5L/bamOTAghapwZEREREdUznHNDREREVoXJDREREVkVJjdERERkVZjcEBERkVVhckNERERWhckNERERWRUmN0RERGRVmNwQEVGDs2DBAuzevfuebUcNC5MbonJkMhnWr19vsf7vv/9+TJw4sU77OHfuHGQyGdLT0+u0H0uYOXMmOnToYOkwKjHnNbfm96eMKe/TqFGjEBsbW+Vz8+bNw7fffotOnTqZ1W9Nt6OGh9eWIiKqYwEBAdBoNPD09JS0bkP1yiuv4KWXXqrRtnv27MHKlSuxdetWKJXKOt+OGiYmN0S1VFxcDDs7O0uH0eBZ8+sol8tNvpimOXUbKmdnZzg7O9do2y5duiAtLe2u9YQQ0Ol0hgvOmrodWQcelqJ65/7778f48ePx6quvwt3dHb6+vpg5c6ZRHbVajYEDB8LZ2Rmurq54/PHHcenSJcPzZcPeycnJCAwMhLOzM55//nnodDq8++678PX1hbe3N95+++1K/Ws0GgwYMAAODg4ICQnBmjVrDM+VHTL45ptvcP/998Pe3h5ffPEFAGD58uVo06YN7O3t0bp1ayQmJt5xP2/cuIERI0bA2dkZfn5+VV4BWKvV4tVXX0Xjxo3h5OSEyMjIu16BWCaTISkpqdp9qGjFihVwc3MzKlu/fj1kMpnh8aFDh9CrVy+4uLjA1dUV4eHhOHDgQI1jqO511Ov1mD17Npo0aQKlUokOHTpUuir9hQsXMHToULi7u8PJyQkRERHYu3evUZ2VK1ciODgYKpUKQ4cORX5+vuE5IQTeffddNG3aFA4ODmjfvj3Wrl1b7b5MnToVXbp0qVTerl07zJgxAwDuGnfFQ03Xrl3Dk08+CS8vLzg4OKBFixZYvnx5lXW3bdsGmUyGX3/9FREREXB0dER0dLThKuQ1eX+uX7+OZ599Fj4+PrC3t0dYWBh+/PFHw/Pr1q1DaGgolEolgoODK/1uBgcHY86cORg9ejRcXFwQGBiIxYsXG9W50/tU8bCUTqdDfHw83Nzc4OHhgVdffRUVL3t4t/et7HXavHkzIiIioFQqsWPHjrtud6f3ghqwe3J5TiIz9OzZU7i6uoqZM2eKEydOiM8++0zIZDLDFcr1er3o2LGj6Natmzhw4IDYs2eP6NSpk+jZs6ehjRkzZghnZ2fx3//+Vxw9elRs2LBBKBQK0a9fP/HSSy+Jv/76SyQnJwsAYvfu3YbtAAgPDw+xZMkScfz4cfG///1PyOVycezYMSGEMFypOTg4WKxbt06cOXNGXLx4USxevFj4+fkZytatWyfc3d3FihUrqt3P559/XjRp0kRs2bJFHD58WPzf//2fcHZ2NrpK+rBhw0R0dLT4/fffxalTp8R7770nlEqlOHHiRLXtmroPaWlpQojSq1urVCqjNr777jtR/s9DaGioeOqpp0RGRoY4ceKE+Oabb0R6enqtY6j4Os6bN0+4urqKr776Svz111/i1VdfFXZ2dob9zc/PF02bNhXdu3cXO3bsECdPnhSrV68Wu3btMnrfBw8eLI4cOSJ+//134evrK15//XVDbK+//rpo3bq1+Pnnn8Xp06fF8uXLhVKpFNu2batyX44cOSIAiFOnThnK/vzzT6Ortd8t7oqv+bhx40SHDh3E/v37xdmzZ0VKSorYsGFDlXXLrrwcGRkptm3bJo4ePSq6d+8uoqOja/T+6HQ60aVLFxEaGiq2bNkiTp8+LX744QexadMmIYQQBw4cEDY2NmL27Nni+PHjYvny5cLBwUEsX77c0EZQUJBwd3cXn3zyiTh58qRISEgQNjY2IiMjw+T3qX379ob23nnnHaFSqcTatWvFsWPHxJgxY4SLi4sYOHCgye9b2evUrl07sWXLFnHq1Clx5cqVu253p/eCGi4mN1Tv9OzZU3Tr1s2orHPnzuK1114TQgixZcsWIZfLhVqtNjx/9OhRAUDs27dPCFH6x9PR0VHk5eUZ6vTr108EBwcLnU5nKGvVqpVISEgwPAYg4uLijPqOjIwUzz//vBDiny+e+fPnG9UJCAgQX375pVHZm2++KaKioqrcx/z8fKFQKMTXX39tKMvJyREODg6G5ObUqVNCJpOJixcvGm3bp08fMXXq1CrbNWcfzEluXFxc7pio1TSGiq+jv7+/ePvtt43KOnfuLF544QUhhBCLFi0SLi4uIicnp8p+q3rfJ0+eLCIjI4UQQhQUFAh7e3vDl2yZMWPGiCeeeKLa/WnXrp2YPXu24fHUqVNF586dTY674mv+8MMPi6effrrKvqpLbn755RdDnY0bNwoA4ubNm0II896fzZs3CxsbG0NiVtGwYcPEAw88YFQ2efJk0bZtW8PjoKAg8dRTTxke6/V64e3tLZKSkoQQpr1P5ZMbPz8/MXfuXMPj4uJi0aRJE0NyY8r7VvY6rV+/3vC8Kdvd6b2ghotzbqheateundFjPz8/ZGdnAwAyMjIQEBCAgIAAw/Nt27aFm5sbMjIy0LlzZwClQ+cuLi6GOj4+PpDL5bCxsTEqK2u3TFRUVKXHFc9ciYiIMNy/fPkyMjMzMWbMGDzzzDOG8pKSEqhUqir37/Tp09BqtUZ9ubu7o1WrVobHBw8ehBACLVu2NNq2qKgIHh4eVbZrzj6YIz4+HmPHjsXKlSvRt29fPPbYY2jWrFmtYyj/Oubl5eHvv/9G165djep07doVhw4dAgCkp6ejY8eOcHd3r7bfiu97+d+dY8eO4datW3jggQeMttFqtejYsWO1bT755JNITk7G9OnTIYTAV199ZTirzZS4K3r++efx6KOP4uDBg4iJiUFsbCyio6Or7R8w/kz4+fkBALKzsxEYGGjW+5Oeno4mTZpU+r0qk5GRgYEDB1bal/nz50On00Eul1eKRyaTwdfX1/A6m/I+lcnNzYVGozH6fbG1tUVERITh0JQ571v53ylTtqvJe0H1H5MbqpcqTiyVyWTQ6/UASo+9l58PUqZieVVt3KndO6nYn5OTk+F+2fZLlixBZGSkUb2yL4KqYr0bvV4PuVyO1NTUSu3UZDJmVa8ZANjY2FSKp7i42OjxzJkzMWzYMGzcuBE//fQTZsyYga+//hqDBg2qVQzlX8fq6pR/Xx0cHO7ax53e47KfGzduROPGjY3q3ekMmmHDhmHKlCk4ePAgbt68iczMTAwdOtTkuCsaMGAAzp8/j40bN+KXX35Bnz59MG7cOLz//vsm7VdZu2X7Y877c7fXsKq4q/p9vdPrbMr7ZA5z3reqPpt32q4m7wXVf5xQTA1O27ZtoVarkZmZaSg7duwYcnNz0aZNm1q3v2fPnkqPW7duXW19Hx8fNG7cGGfOnEHz5s2NbiEhIVVu07x5c9jZ2Rn1de3aNZw4ccLwuGPHjtDpdMjOzq7U7t3OpjFnH7y8vJCfn48bN24Yyqoa5WnZsiUmTZqELVu2YPDgwXeddGnu6+jq6gp/f3/s3LnTqHzXrl2G97Vdu3ZIT0/H1atX79h3ddq2bQulUgm1Wl3pNS0/ElhRkyZN0KNHD6xatQqrVq1C37594ePjY3LcVfHy8sKoUaPwxRdfYP78+ZUm5JrL1PenXbt2uHDhgtHvWnlt27atcl9atmxZbbJeVR+mvk8qlQp+fn5Gvy8lJSVITU01iqkm75up20n9XpDlceSGGpy+ffuiXbt2ePLJJzF//nyUlJTghRdeQM+ePY2GpGtqzZo1iIiIQLdu3bBq1Srs27cPy5Ytu+M2M2fOxPjx4+Hq6ooBAwagqKgIBw4cwLVr1xAfH1+pvrOzM8aMGYPJkyfDw8MDPj4+mDZtmtEhs5YtW+LJJ5/EiBEj8MEHH6Bjx464cuUKfvvtN9x333148MEHJdmHyMhIODo64vXXX8dLL72Effv2YcWKFYbnb968icmTJ+O///0vQkJCcOHCBezfvx+PPvqo5K/j5MmTMWPGDDRr1gwdOnTA8uXLkZ6ejlWrVgEAnnjiCcyZMwexsbFISEiAn58f0tLS4O/vX+kwWFVcXFzwyiuvYNKkSdDr9ejWrRvy8vKwa9cuODs7Y+TIkdVu++STT2LmzJnQarX48MMPzYq7ojfeeAPh4eEIDQ1FUVERfvzxxxon5ua+Pz179kSPHj3w6KOPYt68eWjevDn++usvyGQy9O/fHy+//DI6d+6MN998E0OGDMHu3buxcOHCu579V56579OECRMwd+5ctGjRAm3atMG8efNw/fp1w/M1fd9M2U7K94LqEQvN9SGqVs+ePY3OGBJCiIEDB4qRI0caHp8/f1488sgjwsnJSbi4uIjHHntMZGVlGZ6vOGFRCCFGjhxpdPZFVX0BEJ988ol44IEHhFKpFEFBQeKrr74yPF9xsmd5q1atEh06dBAKhUI0atRI9OjRQ3z77bfV7md+fr546qmnhKOjo/Dx8RHvvvtupXi0Wq144403RHBwsLCzsxO+vr5i0KBB4vDhw9W2W5N9+O6770Tz5s2Fvb29+L//+z+xePFiw4TioqIiMXToUBEQECAUCoXw9/cXL774omEyq1QxCFF6Js+sWbNE48aNhZ2dnWjfvr346aefjOqcO3dOPProo8LV1VU4OjqKiIgIsXfvXiFE1e/7hx9+KIKCggyP9Xq9WLBggWjVqpWws7MTXl5eol+/fmL79u3V7o8QQly7dk0olUrh6Ogo8vPzzYq74v6++eabok2bNsLBwUG4u7uLgQMHijNnzlRZt2yi7LVr1wztpaWlCQDi7NmzNXp/cnJyxNNPPy08PDyEvb29CAsLEz/++KPh+bVr14q2bdsKOzs7ERgYKN577z2j7YOCgsSHH35oVNa+fXsxY8YMw2Nz3qfi4mIxYcIE4erqKtzc3ER8fLwYMWKE0ef1bu9bVa+TKdvd6b2ghksmhAkH/4mowZDJZPjuu++qXbr+3xIDEf17cc4NERERWRUmN0RERGRVeFiKiIiIrApHboiIiMiqMLkhIiIiq8LkhoiIiKwKkxsiIiKyKkxuiIiIyKowuSEiIiKrwuSGiIiIrAqTGyIiIrIq/w/v3Q3R+RqRzwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -353,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -391,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -414,13 +418,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<p>On remarque une chute à k=2, cette chute est due à l'introduction d'un nouveau label parmi les choix possibles. L'algorithme développé ne traite pas la situation où dans ses k plus proches voisins, deux labels apparaissent le même nombre de fois, et il choisit naturellement le plus petit des deux.</p>\n",
-    "<p>Pour réduire les erreurs lorsque deux labels apparaissent le même nombre de fois parmi les k plus proches voisins, l'algorithme ci-dessous choisira celui des deux dont la somme des distances est la plus petite.</p>"
+    "On remarque une chute à k=2, cette chute est due à l'introduction d'un nouveau label parmi les choix possibles. L'algorithme développé ne traite pas la situation où dans ses k plus proches voisins, deux labels apparaissent le même nombre de fois, et il choisit naturellement le plus petit des deux.\n",
+    "\n",
+    "Pour réduire les erreurs lorsque deux labels apparaissent le même nombre de fois parmi les k plus proches voisins, l'algorithme ci-dessous choisira celui des deux dont la somme des distances est la plus petite."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,12 +458,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -498,10 +503,303 @@
    "source": [
     "On remarque que la nouvelle méthode améliore sensiblement la précédente et ne présente pas de chute à k=2.\n",
     "\n",
-    "<h3>Résultat</h3>\n",
-    "<p>Finalement, on constate que la méthode des k plus proches voisins a une efficacité d'environ 30% sur la base de données CIFAR-10, quel que soit le nombre de plus proches voisins choisis. Cela représente une amélioration par rapport à un choix aléatoire qui aurait un taux d'environ 10% compte tenu des 10 classes, mais reste relativement faible.\n",
-    "<h2>Réseaux de Neurones Artificiels</h2>\n",
-    "<p> </p>"
+    "### Résultat\n",
+    "Finalement, on constate que la méthode des k plus proches voisins a une efficacité d'environ 30% sur la base de données CIFAR-10, quel que soit le nombre de plus proches voisins choisis. Cela représente une amélioration par rapport à un choix aléatoire qui aurait un taux d'environ 10% compte tenu des 10 classes, mais reste relativement faible.\n",
+    "## Réseaux de Neurones Artificiels\n",
+    "$ Z^{L+1}=W^{L+1}A^{L}+B^{L+1}$ and $A^{L+1}=σ(Z^{L+1}) $\n",
+    "\n",
+    "$C = \\frac{1}{N_{out}}\\sum_{i=1}^{N_{out}} (\\hat{y_i} - y_i)^2$\n",
+    "### 1\n",
+    "$\\begin{matrix}\n",
+    "\\sigma(x)=\\frac{1}{1+e^{-x}}\n",
+    "\\\\ \n",
+    "\\Rightarrow \\sigma'(x)=\\frac{-e^{-x}}{-(1+e^{-x})^2}\n",
+    "\\\\\n",
+    "\\Rightarrow \\sigma'(x)=\\frac{1}{1+e^{-x}}(\\frac{1+e^{-x}-1}{1+e^{-x}})\n",
+    "\\\\\n",
+    "\\Rightarrow \\sigma'(x)=\\sigma(\\frac{1+e^{-x}}{1+e^{-x}}-\\frac{1}{1+e^{-x}})\n",
+    "\\\\\n",
+    "\\Rightarrow \\sigma'(x)=\\sigma(1-\\sigma)\n",
+    "\\end{matrix}$\n",
+    "### 2\n",
+    "$\\begin{matrix}\n",
+    "\\frac{\\partial C}{\\partial A^{(2)}} ?\n",
+    "\\\\ \n",
+    "\\Rightarrow \\frac{\\partial C}{\\partial a_i^{(2)}}=\\frac{1}{N_{out}}\\sum_{i=1}^{N_{out}} (a_i^{(2)} - y_i)^2\n",
+    "\\\\\n",
+    "\\Rightarrow \\frac{\\partial C}{\\partial a_i^{(2)}}=\\frac{2}{N_{out}} (a_i^{(2)} - y_i)\n",
+    "\\\\\n",
+    "\\Rightarrow \\frac{\\partial C}{\\partial A^{(2)}}=\\frac{2}{N_{out}} (A^{(2)} - Y)\n",
+    "\\end{matrix}$\n",
+    "### 3 \n",
+    "$\\begin{matrix}\n",
+    "\\frac{\\partial C}{\\partial Z^{(2)}}=\\frac{\\partial C}{\\partial A^{(2)}}\\frac{\\partial A^{(2)}}{\\partial Z^{(2)}}\n",
+    "\\\\ \n",
+    "A^{(2)}=\\sigma (Z^{(2)})\n",
+    "\\\\\n",
+    "\\frac{\\partial A^{(2)}}{\\partial Z^{(2)}}=A^{(2)}(1-A^{(2)})\n",
+    "\\\\\n",
+    "\\Rightarrow \\frac{\\partial C}{\\partial Z^{(2)}}=\\frac{\\partial C}{\\partial A^{(2)}}[A^{(2)}(1-A^{(2)})]\n",
+    "\\end{matrix}$\n",
+    "### 4\n",
+    "$\\begin{matrix}\n",
+    "\\frac{\\partial C}{\\partial W^{(2)}}=\\frac{\\partial C}{\\partial Z^{(2)}}\\frac{\\partial Z^{(2)}}{\\partial W^{(2)}}\n",
+    "\\\\ \n",
+    "Z^{(2)}=W^{(2)}A^{(1)}+B^{(2)}\n",
+    "\\\\\n",
+    "\\frac{\\partial Z^{(2)}}{\\partial W^{(2)}}=A^{(1)}\n",
+    "\\\\\n",
+    "\\Rightarrow \\frac{\\partial C}{\\partial W^{(2)}}=\\frac{\\partial C}{\\partial Z^{(2)}}A^{(1)}\\end{matrix}$\n",
+    "### 5\n",
+    "$\\begin{matrix}\n",
+    "\\frac{\\partial C}{\\partial B^{(2)}}=\\frac{\\partial C}{\\partial Z^{(2)}}\\frac{\\partial Z^{(2)}}{\\partial B^{(2)}}\n",
+    "\\\\ \n",
+    "Z^{(2)}=W^{(2)}A^{(1)}+B^{(2)}\n",
+    "\\\\\n",
+    "\\frac{\\partial Z^{(2)}}{\\partial B^{(2)}}=1\n",
+    "\\\\\n",
+    "\\Rightarrow \\frac{\\partial C}{\\partial B^{(2)}}=\\frac{\\partial C}{\\partial Z^{(2)}}\n",
+    "\\end{matrix}\n",
+    "$\n",
+    "### 6\n",
+    "$\\begin{matrix}\n",
+    "\\frac{\\partial C}{\\partial A^{(1)}}=\\frac{\\partial C}{\\partial Z^{(2)}}\\frac{\\partial Z^{(2)}}{\\partial A^{(1)}}\n",
+    "\\\\ \n",
+    "Z^{(2)}=W^{(2)}A^{(1)}+B^{(2)}\n",
+    "\\\\\n",
+    "\\frac{\\partial Z^{(2)}}{\\partial A^{(1)}}=W^{(2)}\n",
+    "\\\\\n",
+    "\\Rightarrow \\frac{\\partial C}{\\partial A^{(1)}}=\\frac{\\partial C}{\\partial Z^{(2)}}W^{(2)}\n",
+    "\\end{matrix}\n",
+    "$\n",
+    "### 7\n",
+    "$\\begin{matrix}\n",
+    "\\frac{\\partial C}{\\partial Z^{(1)}}=\\frac{\\partial C}{\\partial A^{(1)}}\\frac{\\partial A^{(1)}}{\\partial Z^{(1)}}\n",
+    "\\\\ \n",
+    "A^{(1)}=\\sigma (Z^{(1)})\n",
+    "\\\\\n",
+    "\\frac{\\partial A^{(1)}}{\\partial Z^{(1)}}=A^{(1)}(1-A^{(1)})\n",
+    "\\\\\n",
+    "\\Rightarrow \\frac{\\partial C}{\\partial Z^{(1)}}=\\frac{\\partial C}{\\partial A^{(1)}}[A^{(1)}(1-A^{(1)})]\n",
+    "\\end{matrix}\n",
+    "$\n",
+    "### 8\n",
+    "$\\begin{matrix}\n",
+    "\\frac{\\partial C}{\\partial W^{(1)}}=\\frac{\\partial C}{\\partial Z^{(1)}}\\frac{\\partial Z^{(1)}}{\\partial W^{(1)}}\n",
+    "\\\\ \n",
+    "Z^{(1)}=W^{(1)}A^{(0)}+B^{(1)}\n",
+    "\\\\\n",
+    "\\frac{\\partial Z^{(1)}}{\\partial W^{(1)}}=A^{(0)}\n",
+    "\\\\\n",
+    "\\Rightarrow \\frac{\\partial C}{\\partial W^{(1)}}=\\frac{\\partial C}{\\partial Z^{(1)}}A^{(0)}\\end{matrix}$\n",
+    "### 9\n",
+    "$\\begin{matrix}\n",
+    "\\frac{\\partial C}{\\partial B^{(1)}}=\\frac{\\partial C}{\\partial Z^{(1)}}\\frac{\\partial Z^{(1)}}{\\partial B^{(1)}}\n",
+    "\\\\ \n",
+    "Z^{(1)}=W^{(1)}A^{(0)}+B^{(1)}\n",
+    "\\\\\n",
+    "\\frac{\\partial Z^{(1)}}{\\partial B^{(1)}}=1\n",
+    "\\\\\n",
+    "\\Rightarrow \\frac{\\partial C}{\\partial B^{(1)}}=\\frac{\\partial C}{\\partial Z^{(1)}}\n",
+    "\\end{matrix}\n",
+    "$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 10\n",
+    "$loss=\\frac{1}{N_{out}}\\sum_{i=1}^{N_{out}}-(y_i*log(\\hat{y_i})+(1-y_1)*log(1-\\hat{y_i}))\n",
+    "$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def learning_methode(k,dk,learning_rate):\n",
+    "    k=k-learning_rate*dk\n",
+    "    return(k)\n",
+    "\n",
+    "def learn_once_mse(w1,b1,w2,b2,data,targets,learning_rate):\n",
+    "    # Forward pass\n",
+    "    a0 = data # the data are the input of the first layer\n",
+    "    z1 = np.matmul(a0, w1) + b1  # input of the hidden layer\n",
+    "    a1 = 1 / (1 + np.exp(-z1))  # output of the hidden layer (sigmoid activation function)\n",
+    "    z2 = np.matmul(a1, w2) + b2  # input of the output layer\n",
+    "    a2 = 1 / (1 + np.exp(-z2))  # output of the output layer (sigmoid activation function)\n",
+    "    predictions = a2  # the predicted values are the outputs of the output layer\n",
+    "\n",
+    "    dc_da2=(2/data.shape[0])*(a2-targets)\n",
+    "    dc_dz2=dc_da2*(a2*(1-a2))\n",
+    "    dc_dw2=np.matmul(np.transpose(a1), dc_dz2)\n",
+    "    dc_db2=np.matmul(np.ones((1,dc_dz2.shape[0])),dc_dz2)\n",
+    "    dc_da1=np.matmul(dc_dz2,np.transpose(w2))\n",
+    "    dc_dz1=dc_da1*(a1*(1-a1))\n",
+    "    dc_dw1=np.matmul(np.transpose(a0), dc_dz1)\n",
+    "    dc_db1=np.matmul(np.ones((1,dc_dz1.shape[0])),dc_dz1)\n",
+    "\n",
+    "    w1=learning_methode(w1,dc_dw1,learning_rate)\n",
+    "    b1=learning_methode(b1,dc_db1,learning_rate)\n",
+    "    w2=learning_methode(w2,dc_dw2,learning_rate)\n",
+    "    b2=learning_methode(b2,dc_db2,learning_rate)\n",
+    "\n",
+    "    # Compute loss (MSE)\n",
+    "    # loss = np.mean(np.square(predictions - targets))\n",
+    "    # binary cross-entropy loss\n",
+    "    loss = np.mean(targets*np.log(predictions)-(1-targets)*np.log(1-predictions))\n",
+    "    return(w1,b1,w2,b2,loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def one_hot(label):\n",
+    "    nbr_classe=9\n",
+    "    mat=np.zeros((len(label),nbr_classe))\n",
+    "    for label_indexe,label_im, in enumerate(label):\n",
+    "        mat[label_indexe,label_im-1]=1\n",
+    "    return(mat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def learn_once_cross_entropy(w1,b1,w2,b2,data,labels_train,learning_rate):\n",
+    "    Y=one_hot(labels_train)\n",
+    "    w1,b1,w2,b2,loss=learn_once_mse(w1,b1,w2,b2,data,Y,learning_rate)\n",
+    "    return(w1,b1,w2,b2,loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_mlp(w1,b1,w2,b2,d_train,labels_train,learning_rate,num_epoch):\n",
+    "    train_accuracies=[]\n",
+    "    pas=len(labels_train)//num_epoch\n",
+    "    for k in range(num_epoch):\n",
+    "        partial_data=d_train[k*pas:(k+1)*pas,:]\n",
+    "        patial_label=l_train[k*pas:(k+1)*pas]\n",
+    "        w1,b1,w2,b2,loss=learn_once_cross_entropy(w1,b1,w2,b2,partial_data,patial_label,learning_rate)\n",
+    "        train_accuracies.append(loss)\n",
+    "    return (w1,b1,w2,b2,train_accuracies)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_mlp(w1,b1,w2,b2,d_test,labels_test):\n",
+    "    w1,b1,w2,b2,test_accuracy=learn_once_cross_entropy(w1,b1,w2,b2,d_test,labels_test,0)\n",
+    "    return(test_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_mlp_training(data_train, labels_train, data_test, labels_test,d_h,learning_rate,num_epoch):\n",
+    "    d_in = data_train.shape[1]  # input dimension\n",
+    "    d_out = max(labels_train)  # output dimension (number of neurons of the output layer)\n",
+    "\n",
+    "    w1 = 2 * np.random.rand(d_in, d_h) - 1  # first layer weights\n",
+    "    b1 = np.zeros((1, d_h))  # first layer biaises\n",
+    "    w2 = 2 * np.random.rand(d_h, d_out) - 1  # second layer weights\n",
+    "    b2 = np.zeros((1, d_out))  # second layer biaises\n",
+    "\n",
+    "    w1,b1,w2,b2,loss=train_mlp(w1,b1,w2,b2,data_train, labels_train,learning_rate,num_epoch)\n",
+    "    test_accuracy=test_mlp(w1,b1,w2,b2,data_test, labels_test)\n",
+    "    return(loss,test_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'read_cifar_batch' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Utilisateur\\Documents\\GitHub\\image-classification\\Rapport.ipynb Cell 40\u001b[0m line \u001b[0;36m2\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X36sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m__name__\u001b[39m \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m__main__\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X36sZmlsZQ%3D%3D?line=1'>2</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/Rapport.ipynb#X36sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m     num_epoch\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Utilisateur/Documents/GitHub/image-classification/Rapport.ipynb#X36sZmlsZQ%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;31mNameError\u001b[0m: name 'read_cifar_batch' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "if __name__ == \"__main__\":\n",
+    "    d, l = read_cifar_batch(\"data/cifar-10-batches-py/data_batch_1\")\n",
+    "    num_epoch=100\n",
+    "    d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)\n",
+    "\n",
+    "    loss,test_accuracy=run_mlp_training(d_train, l_train, d_test, l_test,64,0.1,num_epoch)\n",
+    "    plt.plot(range(num_epoch), loss, label='evolution de la fonction loss par epoque')\n",
+    "    plt.xlabel('epoque')\n",
+    "    plt.ylabel('loss')\n",
+    "    plt.legend()\n",
+    "    plt.show()"
    ]
   }
  ],
diff --git a/mlp.py b/mlp.py
new file mode 100644
index 0000000..ddf191f
--- /dev/null
+++ b/mlp.py
@@ -0,0 +1,117 @@
+import numpy as np
+import pickle
+from read_cifar import read_cifar_batch, split_dataset
+import matplotlib.pyplot as plt
+
+def learning_methode(k,dk,learning_rate):
+    k=k-learning_rate*dk
+    return(k)
+
+def learn_once_mse(w1,b1,w2,b2,data,targets,learning_rate):
+    # Forward pass
+    a0 = data # the data are the input of the first layer
+    z1 = np.matmul(a0, w1) + b1  # input of the hidden layer
+    a1 = 1 / (1 + np.exp(-z1))  # output of the hidden layer (sigmoid activation function)
+    z2 = np.matmul(a1, w2) + b2  # input of the output layer
+    a2 = 1 / (1 + np.exp(-z2))  # output of the output layer (sigmoid activation function)
+    predictions = a2  # the predicted values are the outputs of the output layer
+
+    #dc_da2=(2/data.shape[0])*(a2-targets)
+    dc_da2=(1/data.shape[0])*((-targets/a2)-(1-targets)/(1-a2))
+    dc_dz2=dc_da2*(a2*(1-a2))
+    dc_dw2=np.matmul(np.transpose(a1), dc_dz2)
+    dc_db2=np.matmul(np.ones((1,dc_dz2.shape[0])),dc_dz2)
+    dc_da1=np.matmul(dc_dz2,np.transpose(w2))
+    dc_dz1=dc_da1*(a1*(1-a1))
+    dc_dw1=np.matmul(np.transpose(a0), dc_dz1)
+    dc_db1=np.matmul(np.ones((1,dc_dz1.shape[0])),dc_dz1)
+
+    w1=learning_methode(w1,dc_dw1,learning_rate)
+    b1=learning_methode(b1,dc_db1,learning_rate)
+    w2=learning_methode(w2,dc_dw2,learning_rate)
+    b2=learning_methode(b2,dc_db2,learning_rate)
+
+    # Compute loss (MSE)
+    # loss = np.mean(np.square(predictions - targets))
+    # binary cross-entropy loss
+    loss = np.mean(targets*np.log(predictions)-(1-targets)*np.log(1-predictions))
+    return(w1,b1,w2,b2,loss)
+
+def one_hot(label):
+    nbr_classe=9
+    mat=np.zeros((len(label),nbr_classe))
+    for label_indexe,label_im, in enumerate(label):
+        mat[label_indexe,label_im-1]=1
+    return(mat)
+
+def learn_once_cross_entropy(w1,b1,w2,b2,data,labels_train,learning_rate):
+    Y=one_hot(labels_train)
+    w1,b1,w2,b2,loss=learn_once_mse(w1,b1,w2,b2,data,Y,learning_rate)
+    return(w1,b1,w2,b2,loss)
+
+def train_mlp(w1,b1,w2,b2,d_train,labels_train,learning_rate,num_epoch):
+    train_accuracies=[]
+    pas=len(labels_train)//num_epoch
+    for k in range(num_epoch):
+        partial_data=d_train[k*pas:(k+1)*pas,:]
+        patial_label=l_train[k*pas:(k+1)*pas]
+        w1,b1,w2,b2,loss=learn_once_cross_entropy(w1,b1,w2,b2,partial_data,patial_label,learning_rate)
+        train_accuracies.append(loss)
+    return (w1,b1,w2,b2,train_accuracies)
+
+def test_mlp(w1,b1,w2,b2,d_test,labels_test):
+    a0 = d_test # the data are the input of the first layer
+    z1 = np.matmul(a0, w1) + b1  # input of the hidden layer
+    a1 = 1 / (1 + np.exp(-z1))  # output of the hidden layer (sigmoid activation function)
+    z2 = np.matmul(a1, w2) + b2  # input of the output layer
+    a2 = 1 / (1 + np.exp(-z2))  # output of the output layer (sigmoid activation function)
+    predictions = a2  # the predicted values are the outputs of the output layer
+    prediction_2 = np.empty(predictions.shape[0], dtype=int)
+    for i, ligne in enumerate(predictions):
+        prediction_2[i] = np.argmax(ligne)+1
+    indices_egalite = np.where(prediction_2 == labels_test)[0]
+    nombre_indices = len(indices_egalite)
+    return(nombre_indices/len(labels_test))
+
+def run_mlp_training(data_train, labels_train, data_test, labels_test,d_h,learning_rate,num_epoch):
+    d_in = data_train.shape[1]  # input dimension
+    d_out = max(labels_train)  # output dimension (number of neurons of the output layer)
+
+    w1 = 2 * np.random.rand(d_in, d_h) - 1  # first layer weights
+    b1 = np.zeros((1, d_h))  # first layer biaises
+    w2 = 2 * np.random.rand(d_h, d_out) - 1  # second layer weights
+    b2 = np.zeros((1, d_out))  # second layer biaises
+
+    w1,b1,w2,b2,loss=train_mlp(w1,b1,w2,b2,data_train, labels_train,learning_rate,num_epoch)
+    test_accuracy=test_mlp(w1,b1,w2,b2,data_test, labels_test)
+    test_accuracy2=unit_test(w1,b1,w2,b2,data_test, labels_test)
+    print(test_accuracy,test_accuracy2)
+    return(loss,test_accuracy)
+
+def unit_test(w1,b1,w2,b2,data_test, labels_test):
+    pos=0
+    for indexe,image in enumerate(data_test):
+        a0 = [image] # the data are the input of the first layer
+        z1 = np.matmul(a0, w1) + b1  # input of the hidden layer
+        a1 = 1 / (1 + np.exp(-z1))  # output of the hidden layer (sigmoid activation function)
+        z2 = np.matmul(a1, w2) + b2  # input of the output layer
+        a2 = 1 / (1 + np.exp(-z2))  # output of the output layer (sigmoid activation function)
+        predictions = a2  # the predicted values are the outputs of the output layer
+        classe = np.argmax(predictions[0])+1
+        
+        if classe==labels_test[indexe]:
+            pos+=1
+    return(pos/len(labels_test))
+
+if __name__ == "__main__":
+    d, l = read_cifar_batch("data/cifar-10-batches-py/data_batch_1")
+    num_epoch=100
+    d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)
+
+    loss,test_accuracy=run_mlp_training(d_train, l_train, d_test, l_test,64,0.1,num_epoch)
+    print(test_accuracy)
+    plt.plot(range(num_epoch), loss, label='evolution de la fonction loss par epoque')
+    plt.xlabel('epoque')
+    plt.ylabel('loss')
+    plt.legend()
+    plt.show()
diff --git a/test.py b/test.py
index ce77c9c..836906a 100644
--- a/test.py
+++ b/test.py
@@ -1,22 +1,25 @@
 import numpy as np
 
-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)
+# 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])
+#     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])
+#     print(dico[0][0])
 
+K=np.array([8,4])
+dc_dw2=np.matmul(np.transpose(a1), dc_dz2)
+print(K)
\ No newline at end of file
-- 
GitLab