{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "GZGbpCAWQ_Xt"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score, f1_score\n",
        "from sklearn.metrics import confusion_matrix, classification_report, f1_score, roc_curve, roc_auc_score, accuracy_score\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "data_folder_path = '/content/drive/'\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bU0jbRKDRDzq",
        "outputId": "c6a2616f-45b5-4bd2-984a-a8f2a34b3d6c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/dataset/GNN_Dataset/patientdata.csv\")  # Replace with the actual file path"
      ],
      "metadata": {
        "id": "-Xvr6QcWRHpR"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the label column\n",
        "label_counts = data['label_class'].value_counts()\n",
        "label_counts.plot(kind='bar')\n",
        "plt.xlabel('Labels')\n",
        "plt.ylabel('Counts')\n",
        "plt.title('Label Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "AfO8hME9RKCB",
        "outputId": "11714f43-4984-433a-ba4e-dd41445ecb37"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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s7GyzxtfX16Gm9Pmf1Vw4f+Hryqspz4wZM+Tt7W0+AgIC/vB4AQBA1VVpQ1V6errmzZunhIQE2Ww2Z7dzRSZMmKCCggLzkZWV5eyWAABABam0oeqrr75Sbm6umjRpIldXV7m6uurQoUN65pln1KxZM0mSn5+fcnNzHV5XXFysvLw8+fn5mTU5OTkONaXP/6zmwvkLX1deTXnc3d3l5eXl8AAAANenShuqHnnkEe3cuVMZGRnmw9/fX88995y++OILSVJISIjy8/OVnp5uvm7dunUqKSlRcHCwWbNx40YVFRWZNcnJyWrZsqXq1q1r1qSkpDjsPzk5WSEhIZKkwMBA+fn5OdTY7XalpaWZNQAAoHpzdebOT506pe+//958fuDAAWVkZKhevXpq0qSJ6tev71Bfs2ZN+fn5qWXLlpKk1q1b65577tHw4cMVHx+voqIiRUdHKzIy0lx+YdCgQZo2bZqioqL0/PPPa/fu3Zo3b57mzJljbnf06NG68847NWvWLIWHh+vjjz/Wtm3bzGUXbDabxowZo5deekktWrRQYGCgpkyZIn9//zLfVgQAANWTU0PVtm3b1KtXL/N5TEyMJGno0KFKSEi4pG0sXLhQ0dHR6t27t1xcXNS/f3/FxcWZ897e3lqzZo1GjhypLl26qEGDBoqNjXVYy+r222/XokWLNHnyZE2cOFEtWrRQYmKi2rVrZ9aMGzdOhYWFGjFihPLz89W9e3clJSXJw8PjKt8FAABwPag061RVB6xTheqiOq5jU51xflcv1fH8vi7WqQIAAKgqCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFnBqqNm7cqPvuu0/+/v6y2WxKTEw054qKivT888+rffv2ql27tvz9/TVkyBAdPXrUYRt5eXkaPHiwvLy85OPjo6ioKJ06dcqhZufOnerRo4c8PDwUEBCgmTNnlull6dKlatWqlTw8PNS+fXutXr3aYd4wDMXGxqpRo0by9PRUaGio9u/fb92bAQAAqjSnhqrCwkJ17NhR8+fPLzN3+vRpbd++XVOmTNH27dv16aefat++fbr//vsd6gYPHqw9e/YoOTlZK1eu1MaNGzVixAhz3m63q0+fPmratKnS09P1+uuva+rUqXrrrbfMms2bN+vhhx9WVFSUvvnmG0VERCgiIkK7d+82a2bOnKm4uDjFx8crLS1NtWvXVlhYmM6cOVMB7wwAAKhqbIZhGM5uQpJsNpuWL1+uiIiIi9Zs3bpVt912mw4dOqQmTZpo7969atOmjbZu3aqgoCBJUlJSku69914dOXJE/v7+WrBggSZNmqTs7Gy5ublJksaPH6/ExERlZmZKkgYOHKjCwkKtXLnS3Fe3bt3UqVMnxcfHyzAM+fv765lnntGzzz4rSSooKJCvr68SEhIUGRl5Scdot9vl7e2tgoICeXl5XcnbVGU1G7/K2S3gGjr4arizW8A1xPldvVTH8/tSf35XqXuqCgoKZLPZ5OPjI0lKTU2Vj4+PGagkKTQ0VC4uLkpLSzNrevbsaQYqSQoLC9O+fft04sQJsyY0NNRhX2FhYUpNTZUkHThwQNnZ2Q413t7eCg4ONmsAAED15ursBi7VmTNn9Pzzz+vhhx82U2J2drYaNmzoUOfq6qp69eopOzvbrAkMDHSo8fX1Nefq1q2r7Oxsc+zCmgu3ceHryqspz9mzZ3X27Fnzud1uv+TjBQAAVUuVuFJVVFSkhx56SIZhaMGCBc5u55LNmDFD3t7e5iMgIMDZLQEAgApS6UNVaaA6dOiQkpOTHT7L9PPzU25urkN9cXGx8vLy5OfnZ9bk5OQ41JQ+/7OaC+cvfF15NeWZMGGCCgoKzEdWVtYlHzcAAKhaKnWoKg1U+/fv19q1a1W/fn2H+ZCQEOXn5ys9Pd0cW7dunUpKShQcHGzWbNy4UUVFRWZNcnKyWrZsqbp165o1KSkpDttOTk5WSEiIJCkwMFB+fn4ONXa7XWlpaWZNedzd3eXl5eXwAAAA1yenhqpTp04pIyNDGRkZkn67ITwjI0OHDx9WUVGRBgwYoG3btmnhwoU6f/68srOzlZ2drXPnzkmSWrdurXvuuUfDhw/Xli1btGnTJkVHRysyMlL+/v6SpEGDBsnNzU1RUVHas2ePFi9erHnz5ikmJsbsY/To0UpKStKsWbOUmZmpqVOnatu2bYqOjpb02zcTx4wZo5deekkrVqzQrl27NGTIEPn7+//htxUBAED14dQb1bdt26ZevXqZz0uDztChQzV16lStWLFCktSpUyeH161fv1533XWXJGnhwoWKjo5W79695eLiov79+ysuLs6s9fb21po1azRy5Eh16dJFDRo0UGxsrMNaVrfffrsWLVqkyZMna+LEiWrRooUSExPVrl07s2bcuHEqLCzUiBEjlJ+fr+7duyspKUkeHh5Wvy0AAKAKqjTrVFUHrFOF6qI6rmNTnXF+Vy/V8fy+LtepAgAAqKwIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABZwaqjauHGj7rvvPvn7+8tmsykxMdFh3jAMxcbGqlGjRvL09FRoaKj279/vUJOXl6fBgwfLy8tLPj4+ioqK0qlTpxxqdu7cqR49esjDw0MBAQGaOXNmmV6WLl2qVq1aycPDQ+3bt9fq1asvuxcAAFB9OTVUFRYWqmPHjpo/f3658zNnzlRcXJzi4+OVlpam2rVrKywsTGfOnDFrBg8erD179ig5OVkrV67Uxo0bNWLECHPebrerT58+atq0qdLT0/X6669r6tSpeuutt8yazZs36+GHH1ZUVJS++eYbRUREKCIiQrt3776sXgAAQPVlMwzDcHYTkmSz2bR8+XJFRERI+u3KkL+/v5555hk9++yzkqSCggL5+voqISFBkZGR2rt3r9q0aaOtW7cqKChIkpSUlKR7771XR44ckb+/vxYsWKBJkyYpOztbbm5ukqTx48crMTFRmZmZkqSBAweqsLBQK1euNPvp1q2bOnXqpPj4+Evq5VLY7XZ5e3uroKBAXl5elrxvVUWz8auc3QKuoYOvhju7BVxDnN/VS3U8vy/153elvafqwIEDys7OVmhoqDnm7e2t4OBgpaamSpJSU1Pl4+NjBipJCg0NlYuLi9LS0syanj17moFKksLCwrRv3z6dOHHCrLlwP6U1pfu5lF7Kc/bsWdntdocHAAC4PlXaUJWdnS1J8vX1dRj39fU157Kzs9WwYUOHeVdXV9WrV8+hprxtXLiPi9VcOP9nvZRnxowZ8vb2Nh8BAQF/ctQAAKCqqrSh6nowYcIEFRQUmI+srCxntwQAACpIpQ1Vfn5+kqScnByH8ZycHHPOz89Pubm5DvPFxcXKy8tzqClvGxfu42I1F87/WS/lcXd3l5eXl8MDAABcnyptqAoMDJSfn59SUlLMMbvdrrS0NIWEhEiSQkJClJ+fr/T0dLNm3bp1KikpUXBwsFmzceNGFRUVmTXJyclq2bKl6tata9ZcuJ/SmtL9XEovAACgenNqqDp16pQyMjKUkZEh6bcbwjMyMnT48GHZbDaNGTNGL730klasWKFdu3ZpyJAh8vf3N78h2Lp1a91zzz0aPny4tmzZok2bNik6OlqRkZHy9/eXJA0aNEhubm6KiorSnj17tHjxYs2bN08xMTFmH6NHj1ZSUpJmzZqlzMxMTZ06Vdu2bVN0dLQkXVIvAACgenN15s63bdumXr16mc9Lg87QoUOVkJCgcePGqbCwUCNGjFB+fr66d++upKQkeXh4mK9ZuHChoqOj1bt3b7m4uKh///6Ki4sz5729vbVmzRqNHDlSXbp0UYMGDRQbG+uwltXtt9+uRYsWafLkyZo4caJatGihxMREtWvXzqy5lF4AAED1VWnWqaoOWKcK1UV1XMemOuP8rl6q4/ld5depAgAAqEoIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGCBKwpV27dv165du8znn332mSIiIjRx4kSdO3fOsuYAAACqiisKVU888YS+++47SdKPP/6oyMhI1apVS0uXLtW4ceMsbRAAAKAquKJQ9d1336lTp06SpKVLl6pnz55atGiREhIS9Mknn1jZHwAAQJVwRaHKMAyVlJRIktauXat7771XkhQQEKCff/7Zuu4AAACqiCsKVUFBQXrppZf04YcfasOGDQoPD5ckHThwQL6+vpY2CAAAUBVcUaiaM2eOtm/frujoaE2aNEnNmzeXJC1btky33367pQ0CAABUBa5X8qKOHTs6fPuv1Ouvvy5X1yvaJAAAQJV2RVeqbr75Zv3yyy9lxs+cOaO//OUvV90UAABAVXNFoergwYM6f/58mfGzZ8/qyJEjV90UAABAVXNZoWrFihVasWKFJOmLL74wn69YsULLly/Xiy++qMDAQMuaO3/+vKZMmaLAwEB5enrqlltu0YsvvijDMMwawzAUGxurRo0aydPTU6Ghodq/f7/DdvLy8jR48GB5eXnJx8dHUVFROnXqlEPNzp071aNHD3l4eCggIEAzZ84s08/SpUvVqlUreXh4qH379lq9erVlxwoAAKq2y7oBKiIiQpJks9k0dOhQh7maNWuqWbNmmjVrlmXNvfbaa1qwYIHef/99tW3bVtu2bdOwYcPk7e2tp59+WpI0c+ZMxcXF6f3331dgYKCmTJmisLAwffvtt/Lw8JAkDR48WMeOHVNycrKKioo0bNgwjRgxQosWLZIk2e129enTR6GhoYqPj9euXbv02GOPycfHRyNGjJAkbd68WQ8//LBmzJihfv36adGiRYqIiND27dvVrl07y44ZAABUTTbjwss+lygwMFBbt25VgwYNKqInU79+/eTr66v//Oc/5lj//v3l6empjz76SIZhyN/fX88884yeffZZSVJBQYF8fX2VkJCgyMhI7d27V23atNHWrVsVFBQkSUpKStK9996rI0eOyN/fXwsWLNCkSZOUnZ0tNzc3SdL48eOVmJiozMxMSdLAgQNVWFiolStXmr1069ZNnTp1Unx8/CUdj91ul7e3twoKCuTl5WXJe1RVNBu/ytkt4Bo6+Gq4s1vANcT5Xb1Ux/P7Un9+X9E9VQcOHKjwQCVJt99+u1JSUsw/ibNjxw7973//U9++fc0+srOzFRoaar7G29tbwcHBSk1NlSSlpqbKx8fHDFSSFBoaKhcXF6WlpZk1PXv2NAOVJIWFhWnfvn06ceKEWXPhfkprSvdTnrNnz8putzs8AADA9emK1z9ISUlRSkqKcnNzzdXVS7377rtX3Zj029Uiu92uVq1aqUaNGjp//rxefvllDR48WJKUnZ0tSWUWHPX19TXnsrOz1bBhQ4d5V1dX1atXz6Hm9/eClW4zOztbdevWVXZ29h/upzwzZszQtGnTLvewAQBAFXRFV6qmTZumPn36KCUlRT///LNOnDjh8LDKkiVLtHDhQi1atEjbt2/X+++/r3/+8596//33LdtHRZowYYIKCgrMR1ZWlrNbAgAAFeSKrlTFx8crISFBjzzyiNX9OHjuuec0fvx4RUZGSpLat2+vQ4cOacaMGRo6dKj8/PwkSTk5OWrUqJH5upycHPMPPvv5+Sk3N9dhu8XFxcrLyzNf7+fnp5ycHIea0ud/VlM6Xx53d3e5u7tf7mEDAIAq6IquVJ07d+6a/Dma06dPy8XFscUaNWqYHzcGBgbKz89PKSkp5rzdbldaWppCQkIkSSEhIcrPz1d6erpZs27dOpWUlCg4ONis2bhxo4qKisya5ORktWzZUnXr1jVrLtxPaU3pfgAAQPV2RaHq8ccfN5cjqEj33XefXn75Za1atUoHDx7U8uXLNXv2bD3wwAOSflvaYcyYMXrppZe0YsUK7dq1S0OGDJG/v7+5/EPr1q11zz33aPjw4dqyZYs2bdqk6OhoRUZGyt/fX5I0aNAgubm5KSoqSnv27NHixYs1b948xcTEmL2MHj1aSUlJmjVrljIzMzV16lRt27ZN0dHRFf4+AACAyu+KPv47c+aM3nrrLa1du1YdOnRQzZo1HeZnz55tSXNvvPGGpkyZon/84x/Kzc2Vv7+/nnjiCcXGxpo148aNU2FhoUaMGKH8/Hx1795dSUlJ5hpVkrRw4UJFR0erd+/ecnFxUf/+/RUXF2fOe3t7a82aNRo5cqS6dOmiBg0aKDY21lyjSvrtm4iLFi3S5MmTNXHiRLVo0UKJiYmsUQUAACRd4TpVvXr1uvgGbTatW7fuqpq6XrFOFaqL6riOTXXG+V29VMfz+1J/fl/Rlar169dfcWMAAADXoyu6pwoAAACOruhKVa9evWSz2S46z8d/AACgurmiUFW6BlSpoqIiZWRkaPfu3WX+0DIAAEB1cEWhas6cOeWOT506VadOnbqqhgAAAKoiS++p+vvf/27Z3/0DAACoSiwNVampqQ7rQwEAAFQXV/Tx34MPPujw3DAMHTt2TNu2bdOUKVMsaQwAAKAquaJQ5e3t7fDcxcVFLVu21PTp09WnTx9LGgMAAKhKrihUvffee1b3AQAAUKVdUagqlZ6err1790qS2rZtq86dO1vSFAAAQFVzRaEqNzdXkZGR+vLLL+Xj4yNJys/PV69evfTxxx/rxhtvtLJHAACASu+Kvv03atQonTx5Unv27FFeXp7y8vK0e/du2e12Pf3001b3CAAAUOld0ZWqpKQkrV27Vq1btzbH2rRpo/nz53OjOgAAqJau6EpVSUmJatasWWa8Zs2aKikpueqmAAAAqporClV33323Ro8eraNHj5pjP/30k8aOHavevXtb1hwAAEBVcUWh6s0335TdblezZs10yy236JZbblFgYKDsdrveeOMNq3sEAACo9K7onqqAgABt375da9euVWZmpiSpdevWCg0NtbQ5AACAquKyrlStW7dObdq0kd1ul81m01//+leNGjVKo0aNUteuXdW2bVt99dVXFdUrAABApXVZoWru3LkaPny4vLy8ysx5e3vriSee0OzZsy1rDgAAoKq4rFC1Y8cO3XPPPRed79Onj9LT06+6KQAAgKrmskJVTk5OuUsplHJ1ddXx48evuikAAICq5rJCVePGjbV79+6Lzu/cuVONGjW66qYAAACqmssKVffee6+mTJmiM2fOlJn79ddf9cILL6hfv36WNQcAAFBVXNaSCpMnT9ann36qv/zlL4qOjlbLli0lSZmZmZo/f77Onz+vSZMmVUijAAAAldllhSpfX19t3rxZTz31lCZMmCDDMCRJNptNYWFhmj9/vnx9fSukUQAAgMrsshf/bNq0qVavXq0TJ07o+++/l2EYatGiherWrVsR/QEAAFQJV7SiuiTVrVtXXbt2tbIXAACAKuuK/vYfAAAAHBGqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALVPpQ9dNPP+nvf/+76tevL09PT7Vv317btm0z5w3DUGxsrBo1aiRPT0+FhoZq//79DtvIy8vT4MGD5eXlJR8fH0VFRenUqVMONTt37lSPHj3k4eGhgIAAzZw5s0wvS5cuVatWreTh4aH27dtr9erVFXPQAACgyqnUoerEiRO64447VLNmTX3++ef69ttvNWvWLIe/Mzhz5kzFxcUpPj5eaWlpql27tsLCwnTmzBmzZvDgwdqzZ4+Sk5O1cuVKbdy4USNGjDDn7Xa7+vTpo6ZNmyo9PV2vv/66pk6dqrfeesus2bx5sx5++GFFRUXpm2++UUREhCIiIrR79+5r82YAAIBKzWYYhuHsJi5m/Pjx2rRpk7766qty5w3DkL+/v5555hk9++yzkqSCggL5+voqISFBkZGR2rt3r9q0aaOtW7cqKChIkpSUlKR7771XR44ckb+/vxYsWKBJkyYpOztbbm5u5r4TExOVmZkpSRo4cKAKCwu1cuVKc//dunVTp06dFB8ff0nHY7fb5e3trYKCAnl5eV3x+1IVNRu/ytkt4Bo6+Gq4s1vANcT5Xb1Ux/P7Un9+V+orVStWrFBQUJD+3//7f2rYsKE6d+6st99+25w/cOCAsrOzFRoaao55e3srODhYqampkqTU1FT5+PiYgUqSQkND5eLiorS0NLOmZ8+eZqCSpLCwMO3bt08nTpwway7cT2lN6X4AAED1VqlD1Y8//qgFCxaoRYsW+uKLL/TUU0/p6aef1vvvvy9Jys7OliT5+vo6vM7X19ecy87OVsOGDR3mXV1dVa9ePYea8rZx4T4uVlM6X56zZ8/Kbrc7PAAAwPXJ1dkN/JGSkhIFBQXplVdekSR17txZu3fvVnx8vIYOHerk7v7cjBkzNG3aNGe3AQAAroFKfaWqUaNGatOmjcNY69atdfjwYUmSn5+fJCknJ8ehJicnx5zz8/NTbm6uw3xxcbHy8vIcasrbxoX7uFhN6Xx5JkyYoIKCAvORlZX15wcNAACqpEodqu644w7t27fPYey7775T06ZNJUmBgYHy8/NTSkqKOW+325WWlqaQkBBJUkhIiPLz85Wenm7WrFu3TiUlJQoODjZrNm7cqKKiIrMmOTlZLVu2NL9pGBIS4rCf0prS/ZTH3d1dXl5eDg8AAHB9qtShauzYsfr666/1yiuv6Pvvv9eiRYv01ltvaeTIkZIkm82mMWPG6KWXXtKKFSu0a9cuDRkyRP7+/oqIiJD025Wte+65R8OHD9eWLVu0adMmRUdHKzIyUv7+/pKkQYMGyc3NTVFRUdqzZ48WL16sefPmKSYmxuxl9OjRSkpK0qxZs5SZmampU6dq27Ztio6OvubvCwAAqHwq9T1VXbt21fLlyzVhwgRNnz5dgYGBmjt3rgYPHmzWjBs3ToWFhRoxYoTy8/PVvXt3JSUlycPDw6xZuHChoqOj1bt3b7m4uKh///6Ki4sz5729vbVmzRqNHDlSXbp0UYMGDRQbG+uwltXtt9+uRYsWafLkyZo4caJatGihxMREtWvX7tq8GQAAoFKr1OtUXW9YpwrVRXVcx6Y64/yuXqrj+X1drFMFAABQVRCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALFClQtWrr74qm82mMWPGmGNnzpzRyJEjVb9+fd1www3q37+/cnJyHF53+PBhhYeHq1atWmrYsKGee+45FRcXO9R8+eWXuvXWW+Xu7q7mzZsrISGhzP7nz5+vZs2aycPDQ8HBwdqyZUtFHCYAAKiCqkyo2rp1q/7973+rQ4cODuNjx47V//3f/2np0qXasGGDjh49qgcffNCcP3/+vMLDw3Xu3Dlt3rxZ77//vhISEhQbG2vWHDhwQOHh4erVq5cyMjI0ZswYPf744/riiy/MmsWLFysmJkYvvPCCtm/fro4dOyosLEy5ubkVf/AAAKDSqxKh6tSpUxo8eLDefvtt1a1b1xwvKCjQf/7zH82ePVt33323unTpovfee0+bN2/W119/LUlas2aNvv32W3300Ufq1KmT+vbtqxdffFHz58/XuXPnJEnx8fEKDAzUrFmz1Lp1a0VHR2vAgAGaM2eOua/Zs2dr+PDhGjZsmNq0aaP4+HjVqlVL77777rV9MwAAQKVUJULVyJEjFR4ertDQUIfx9PR0FRUVOYy3atVKTZo0UWpqqiQpNTVV7du3l6+vr1kTFhYmu92uPXv2mDW/33ZYWJi5jXPnzik9Pd2hxsXFRaGhoWZNec6ePSu73e7wAAAA1ydXZzfwZz7++GNt375dW7duLTOXnZ0tNzc3+fj4OIz7+voqOzvbrLkwUJXOl879UY3dbtevv/6qEydO6Pz58+XWZGZmXrT3GTNmaNq0aZd2oAAAoEqr1FeqsrKyNHr0aC1cuFAeHh7ObueyTZgwQQUFBeYjKyvL2S0BAIAKUqlDVXp6unJzc3XrrbfK1dVVrq6u2rBhg+Li4uTq6ipfX1+dO3dO+fn5Dq/LycmRn5+fJMnPz6/MtwFLn/9ZjZeXlzw9PdWgQQPVqFGj3JrSbZTH3d1dXl5eDg8AAHB9qtShqnfv3tq1a5cyMjLMR1BQkAYPHmz+d82aNZWSkmK+Zt++fTp8+LBCQkIkSSEhIdq1a5fDt/SSk5Pl5eWlNm3amDUXbqO0pnQbbm5u6tKli0NNSUmJUlJSzBoAAFC9Vep7qurUqaN27do5jNWuXVv169c3x6OiohQTE6N69erJy8tLo0aNUkhIiLp16yZJ6tOnj9q0aaNHHnlEM2fOVHZ2tiZPnqyRI0fK3d1dkvTkk0/qzTff1Lhx4/TYY49p3bp1WrJkiVatWmXuNyYmRkOHDlVQUJBuu+02zZ07V4WFhRo2bNg1ejcAAEBlVqlD1aWYM2eOXFxc1L9/f509e1ZhYWH617/+Zc7XqFFDK1eu1FNPPaWQkBDVrl1bQ4cO1fTp082awMBArVq1SmPHjtW8efN000036Z133lFYWJhZM3DgQB0/flyxsbHKzs5Wp06dlJSUVObmdQAAUD3ZDMMwnN1EdWG32+Xt7a2CgoJqd39Vs/Gr/rwI142Dr4Y7uwVcQ5zf1Ut1PL8v9ed3pb6nCgAAoKogVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABao1KFqxowZ6tq1q+rUqaOGDRsqIiJC+/btc6g5c+aMRo4cqfr16+uGG25Q//79lZOT41Bz+PBhhYeHq1atWmrYsKGee+45FRcXO9R8+eWXuvXWW+Xu7q7mzZsrISGhTD/z589Xs2bN5OHhoeDgYG3ZssXyYwYAAFVTpQ5VGzZs0MiRI/X1118rOTlZRUVF6tOnjwoLC82asWPH6v/+7/+0dOlSbdiwQUePHtWDDz5ozp8/f17h4eE6d+6cNm/erPfff18JCQmKjY01aw4cOKDw8HD16tVLGRkZGjNmjB5//HF98cUXZs3ixYsVExOjF154Qdu3b1fHjh0VFham3Nzca/NmAACASs1mGIbh7CYu1fHjx9WwYUNt2LBBPXv2VEFBgW688UYtWrRIAwYMkCRlZmaqdevWSk1NVbdu3fT555+rX79+Onr0qHx9fSVJ8fHxev7553X8+HG5ubnp+eef16pVq7R7925zX5GRkcrPz1dSUpIkKTg4WF27dtWbb74pSSopKVFAQIBGjRql8ePHX1L/drtd3t7eKigokJeXl5VvTaXXbPwqZ7eAa+jgq+HObgHXEOd39VIdz+9L/fldqa9U/V5BQYEkqV69epKk9PR0FRUVKTQ01Kxp1aqVmjRpotTUVElSamqq2rdvbwYqSQoLC5PdbteePXvMmgu3UVpTuo1z584pPT3docbFxUWhoaFmTXnOnj0ru93u8AAAANenKhOqSkpKNGbMGN1xxx1q166dJCk7O1tubm7y8fFxqPX19VV2drZZc2GgKp0vnfujGrvdrl9//VU///yzzp8/X25N6TbKM2PGDHl7e5uPgICAyz9wAABQJVSZUDVy5Ejt3r1bH3/8sbNbuWQTJkxQQUGB+cjKynJ2SwAAoIK4OruBSxEdHa2VK1dq48aNuummm8xxPz8/nTt3Tvn5+Q5Xq3JycuTn52fW/P5beqXfDryw5vffGMzJyZGXl5c8PT1Vo0YN1ahRo9ya0m2Ux93dXe7u7pd/wAAAoMqp1FeqDMNQdHS0li9frnXr1ikwMNBhvkuXLqpZs6ZSUlLMsX379unw4cMKCQmRJIWEhGjXrl0O39JLTk6Wl5eX2rRpY9ZcuI3SmtJtuLm5qUuXLg41JSUlSklJMWsAAED1VqmvVI0cOVKLFi3SZ599pjp16pj3L3l7e8vT01Pe3t6KiopSTEyM6tWrJy8vL40aNUohISHq1q2bJKlPnz5q06aNHnnkEc2cOVPZ2dmaPHmyRo4caV5FevLJJ/Xmm29q3Lhxeuyxx7Ru3TotWbJEq1b9/99oiYmJ0dChQxUUFKTbbrtNc+fOVWFhoYYNG3bt3xgAAFDpVOpQtWDBAknSXXfd5TD+3nvv6dFHH5UkzZkzRy4uLurfv7/Onj2rsLAw/etf/zJra9SooZUrV+qpp55SSEiIateuraFDh2r69OlmTWBgoFatWqWxY8dq3rx5uummm/TOO+8oLCzMrBk4cKCOHz+u2NhYZWdnq1OnTkpKSipz8zoAAKieqtQ6VVUd61ShuqiO69hUZ5zf1Ut1PL+vy3WqAAAAKitCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFC1WWaP3++mjVrJg8PDwUHB2vLli3ObgkAAFQChKrLsHjxYsXExOiFF17Q9u3b1bFjR4WFhSk3N9fZrQEAACcjVF2G2bNna/jw4Ro2bJjatGmj+Ph41apVS++++66zWwMAAE5GqLpE586dU3p6ukJDQ80xFxcXhYaGKjU11YmdAQCAysDV2Q1UFT///LPOnz8vX19fh3FfX19lZmaW+5qzZ8/q7Nmz5vOCggJJkt1ur7hGK6mSs6ed3QKuoer4//HqjPO7eqmO53fpMRuG8Yd1hKoKNGPGDE2bNq3MeEBAgBO6Aa4d77nO7gBARanO5/fJkyfl7e190XlC1SVq0KCBatSooZycHIfxnJwc+fn5lfuaCRMmKCYmxnxeUlKivLw81a9fXzabrUL7hfPZ7XYFBAQoKytLXl5ezm4HgIU4v6sXwzB08uRJ+fv7/2EdoeoSubm5qUuXLkpJSVFERISk30JSSkqKoqOjy32Nu7u73N3dHcZ8fHwquFNUNl5eXvyjC1ynOL+rjz+6QlWKUHUZYmJiNHToUAUFBem2227T3LlzVVhYqGHDhjm7NQAA4GSEqsswcOBAHT9+XLGxscrOzlanTp2UlJRU5uZ1AABQ/RCqLlN0dPRFP+4DLuTu7q4XXnihzEfAAKo+zm+Ux2b82fcDAQAA8KdY/BMAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAIArlJ+f7+wWUIkQqgAAuASvvfaaFi9ebD5/6KGHVL9+fTVu3Fg7duxwYmeoLAhVQAWy2+1KTEzU3r17nd0KgKsUHx+vgIAASVJycrKSk5P1+eefq2/fvnruueec3B0qAxb/BCz00EMPqWfPnoqOjtavv/6qoKAgHTx4UIZh6OOPP1b//v2d3SKAK5SdnW2GqpUrV+qhhx5Snz591KxZMwUHBzu5O1QGXKkCLLRx40b16NFDkrR8+XIZhqH8/HzFxcXppZdecnJ3AK5G3bp1lZWVJUlKSkpSaGioJMkwDJ0/f96ZraGSIFQBFiooKFC9evUk/faPbv/+/VWrVi2Fh4dr//79Tu4OwNV48MEHNWjQIP31r3/VL7/8or59+0qSvvnmGzVv3tzJ3aEy4OM/wEIBAQFKTU1VvXr1lJSUpI8//liSdOLECXl4eDi5OwBXY86cOWrWrJmysrI0c+ZM3XDDDZKkY8eO6R//+IeTu0NlwN/+Ayz0r3/9S6NHj9YNN9ygJk2a6JtvvpGLi4veeOMNffrpp1q/fr2zWwQAVBBCFWCxbdu2KSsrS3/961/N32RXrVolHx8f3XHHHU7uDsDV+PDDD/Xvf/9bP/74o1JTU9W0aVPNnTtXgYGB+tvf/ubs9uBk3FMFWCwoKEjh4eH66aefVFxcLEkKDw8nUAFV3IIFCxQTE6O+ffsqPz/fvDndx8dHc+fOdW5zqBQIVYCFTp8+raioKNWqVUtt27bV4cOHJUmjRo3Sq6++6uTuAFyNN954Q2+//bYmTZqkGjVqmONBQUHatWuXEztDZUGoAiw0YcIE7dixQ19++aXDjemhoaEOKzEDqHoOHDigzp07lxl3d3dXYWGhEzpCZUOoAiyUmJioN998U927d5fNZjPH27Ztqx9++MGJnQG4WoGBgcrIyCgznpSUpNatW1/7hlDpsKQCYKHjx4+rYcOGZcYLCwsdQhaAqicmJkYjR47UmTNnZBiGtmzZov/+97+aMWOG3nnnHWe3h0qAUAVYKCgoSKtWrdKoUaMkyQxS77zzjkJCQpzZGoCr9Pjjj8vT01OTJ0/W6dOnNWjQIPn7+2vevHmKjIx0dnuoBAhVgIVeeeUV9e3bV99++62Ki4s1b948ffvtt9q8ebM2bNjg7PYAXKHi4mItWrRIYWFhGjx4sE6fPq1Tp06Ve2Ua1Rf3VAEW6t69u3bs2KHi4mK1b99ea9asUcOGDZWamqouXbo4uz0AV8jV1VVPPvmkzpw5I0mqVasWgQplcKUKsEhRUZGeeOIJTZkyRW+//baz2wFgsdtuu03ffPONmjZt6uxWUEmxojpgIW9vb2VkZCgwMNDZrQCw2JIlSzRhwgSNHTtWXbp0Ue3atR3mO3To4KTOUFkQqgALDR06VJ06ddLYsWOd3QoAi7m4lL1jxmazyTAM2Ww2c4V1VF98/AdYqEWLFpo+fbo2bdpU7m+yTz/9tJM6A3C1Dhw44OwWUMlxpQqw0B997Gez2fTjjz9ew24AANcSoQoAgEvwwQcf/OH8kCFDrlEnqKwIVQAAXIK6des6PC8qKtLp06fl5uamWrVqKS8vz0mdobLgnirAQo899tgfzr/77rvXqBMAVjtx4kSZsf379+upp57Sc88954SOUNkQqgAL/f4f3aKiIu3evVv5+fm6++67ndQVgIrSokULvfrqq/r73/+uzMxMZ7cDJyNUARZavnx5mbGSkhI99dRTuuWWW5zQEYCK5urqqqNHjzq7DVQC3FMFXAP79u3TXXfdpWPHjjm7FQBXaMWKFQ7PDcPQsWPH9OabbyogIECff/65kzpDZcGVKuAa+OGHH1RcXOzsNgBchYiICIfnNptNN954o+6++27NmjXLOU2hUiFUARaKiYlxeF76m+yqVas0dOhQJ3UFwAolJSXObgGVHB//ARbq1auXw3MXFxfzN9nHHntMrq78HgNUVRs3blSrVq3UsGFDh/GioiKlpqaqZ8+eTuoMlQWhCgCAS+Di4iJfX18tX75c3bp1M8dzcnLk7+/P3/6Dyv51SABXbPr06Vq3bl2Z8cLCQk2fPt0JHQGwUmRkpHr37q2EhASHca5PQOJKFWApFxcX1axZUzNmzHC4v4rfZIGqr0aNGjp27Jj+97//aciQIRoxYoRmzZql3Nxczm9I4koVYLkPPvhAr7zyioYNG6Zz5845ux0AFim9BvHggw/qq6++0rJly9S3b1/l5+c7tzFUGoQqwGK9evVSWlqa0tLSdNdddyk3N9fZLQGwWOfOnbVlyxbl5+erd+/ezm4HlQShCrCQzWaTJN1yyy36+uuv5eXlpS5dumjbtm1O7gzA1Ro6dKg8PT3N535+ftqwYYN69+6tJk2aOLEzVBbcUwVYyMXFRdnZ2eZXrktKSjRmzBgtWLBAJSUl3HMBANcxFs0BLPTee+/J29vbfO7i4qK4uDh17txZGzdudGJnAK7Ezp071a5dO7m4uGjnzp1/WNuhQ4dr1BUqK65UAQBwERdefXZxcZHNZnNYPqH0uc1m40o0uFIFXK24uDiNGDFCHh4eiouLu2idzWbTqFGjrmFnAK7WgQMHdOONN5r/DfwRrlQBVykwMFDbtm1T/fr1FRgYeNE6m82mH3/88Rp2BgC4lghVAABcog8//FDx8fE6cOCAUlNT1bRpU82dO1eBgYH629/+5uz24GQsqQBUgHPnzmnfvn0qLi52disALLJgwQLFxMTo3nvvVX5+vnkPlY+Pj+bOnevc5lApEKoAC50+fVpRUVGqVauW2rZtq8OHD0uSRo0apVdffdXJ3QG4Gm+88YbefvttTZo0STVq1DDHg4KCtGvXLid2hsqCUAVYaMKECdqxY4e+/PJLeXh4mOOhoaFavHixEzsDcLUOHDigzp07lxl3d3dXYWGhEzpCZUOoAiyUmJioN998U927dzdXV5ektm3b6ocffnBiZwCuVmBgoDIyMsqMJyUlqXXr1te+IVQ6LKkAWOj48ePmauoXKiwsdAhZAKqemJgYjRw5UmfOnJFhGNqyZYv++9//asaMGXrnnXec3R4qAUIVYKGgoCCtWrXKXI+qNEi98847CgkJcWZrAK7S448/Lk9PT02ePFmnT5/WoEGD1LhxY82bN0+RkZHObg+VAKEKsNArr7yivn376ttvv1VxcbHmzZunb7/9Vps3b9aGDRuc3R6Aq/Drr7/qgQce0ODBg3X69Gnt3r1bmzZt0k033eTs1lBJcE8VYKHu3bsrIyNDxcXFat++vdasWaOGDRsqNTVVXbp0cXZ7AK7C3/72N33wwQeSfls25f7779fs2bMVERGhBQsWOLk7VAYs/gkAwCVo0KCBNmzYoLZt2+qdd97RG2+8oW+++UaffPKJYmNjtXfvXme3CCfjShVgoe3btzusV/PZZ58pIiJCEydO1Llz55zYGYCrdfr0adWpU0eStGbNGj344INycXFRt27ddOjQISd3h8qAUAVY6IknntB3330nSfrxxx81cOBA1apVS0uXLtW4ceOc3B2Aq9G8eXMlJiYqKytLX3zxhfr06SNJys3NlZeXl5O7Q2VAqAIs9N1336lTp06SpKVLl+rOO+/UokWLlJCQoE8++cS5zQG4KrGxsXr22WfVrFkzBQcHm9/oXbNmTbmLgqL64dt/gIUMw1BJSYkkae3aterXr58kKSAgQD///LMzWwNwlQYMGKDu3bvr2LFj6tixozneu3dvPfDAA07sDJUFN6oDFrr77rsVEBCg0NBQRUVF6dtvv1Xz5s21YcMGDR06VAcPHnR2iwCACsLHf4CF5s6dq+3btys6OlqTJk1S8+bNJUnLli3T7bff7uTuAAAViStVwDVw5swZ1ahRQzVr1nR2KwCACsI9VUAFSE9PN9esadOmjW699VYndwQAqGiEKsBCubm5GjhwoDZs2CAfHx9JUn5+vnr16qWPP/5YN954o3MbBABUGO6pAiw0atQonTp1Snv27FFeXp7y8vK0e/du2e12Pf30085uDwBQgbinCrCQt7e31q5dq65duzqMb9myRX369FF+fr5zGgMAVDiuVAEWKikpKfdm9Jo1a5rrVwEArk+EKsBCd999t0aPHq2jR4+aYz/99JPGjh2r3r17O7EzAEBF4+M/wEJZWVm6//77tWfPHgUEBEiSDh8+rPbt22vFihW66aabnNwhAKCiEKoAixmGoZSUFHNJhdatWys0NNTJXQEAKhqhCrBYSkqKUlJSlJubW+Y+qnfffddJXQEAKhrrVAEWmjZtmqZPn66goCA1atRINpvN2S0BAK4RrlQBFmrUqJFmzpypRx55xNmtAACuMb79B1jo3Llz/OFkAKimCFWAhR5//HEtWrTI2W0AAJyAe6oAC505c0ZvvfWW1q5dqw4dOpRZCHT27NlO6gwAUNG4pwqwUK9evS46Z7PZtG7dumvYDQDgWiJUAQAAWIB7qgAAACxAqAIAALAAoQoAAMAChCoAuEwJCQny8fG56u3YbDYlJiZe9XYAVA6EKgDV0qOPPqqIiAhntwHgOkKoAgAAsAChCgB+Z/bs2Wrfvr1q166tgIAA/eMf/9CpU6fK1CUmJqpFixby8PBQWFiYsrKyHOY/++wz3XrrrfLw8NDNN9+sadOmqbi4uNx9njt3TtHR0WrUqJE8PDzUtGlTzZgxo0KOD0DFIFQBwO+4uLgoLi5Oe/bs0fvvv69169Zp3LhxDjWnT5/Wyy+/rA8++ECbNm1Sfn6+IiMjzfmvvvpKQ4YM0ejRo/Xtt9/q3//+txISEvTyyy+Xu8+4uDitWLFCS5Ys0b59+7Rw4UI1a9asIg8TgMVY/BNAtfToo48qPz//km4UX7ZsmZ588kn9/PPPkn67UX3YsGH6+uuvFRwcLEnKzMxU69atlZaWpttuu02hoaHq3bu3JkyYYG7no48+0rhx43T06FFJv92ovnz5ckVEROjpp5/Wnj17tHbtWtlsNusPGECF40oVAPzO2rVr1bt3bzVu3Fh16tTRI488ol9++UWnT582a1xdXdW1a1fzeatWreTj46O9e/dKknbs2KHp06frhhtuMB/Dhw/XsWPHHLZT6tFHH1VGRoZatmypp59+WmvWrKn4AwVgKUIVAFzg4MGD6tevnzp06KBPPvlE6enpmj9/vqTf7nu6VKdOndK0adOUkZFhPnbt2qX9+/fLw8OjTP2tt96qAwcO6MUXX9Svv/6qhx56SAMGDLDsuABUPFdnNwAAlUl6erpKSko0a9Ysubj89nvnkiVLytQVFxdr27Ztuu222yRJ+/btU35+vlq3bi3pt5C0b98+NW/e/JL37eXlpYEDB2rgwIEaMGCA7rnnHuXl5alevXoWHBmAikaoAlBtFRQUKCMjw2GsQYMGKioq0htvvKH77rtPmzZtUnx8fJnX1qxZU6NGjVJcXJxcXV0VHR2tbt26mSErNjZW/fr1U5MmTTRgwAC5uLhox44d2r17t1566aUy25s9e7YaNWqkzp07y8XFRUuXLpWfn58li4wCuDb4+A9AtfXll1+qc+fODo8PP/xQs2fP1muvvaZ27dpp4cKF5S5tUKtWLT3//PMaNGiQ7rjjDt1www1avHixOR8WFqaVK1dqzZo16tq1q7p166Y5c+aoadOm5fZSp04dzZw5U0FBQeratasOHjyo1atXm1fLAFR+fPsPAADAAvwKBAAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWOD/AxrurDbc232MAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features and labels\n",
        "X = data.drop(\"label_class\", axis=1)\n",
        "y = data[\"label_class\"]"
      ],
      "metadata": {
        "id": "t5E2ABJlRMAU"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert labels to numerical values\n",
        "label_encoder = LabelEncoder()\n",
        "y_encoded = label_encoder.fit_transform(y)"
      ],
      "metadata": {
        "id": "rrM3RXftRMmx"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "APRrePVjROio"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize the feature data\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "bFmNq32bRQlm"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the data to TensorFlow tensors\n",
        "X_train_tensor = tf.constant(X_train_scaled, dtype=tf.float32)\n",
        "y_train_tensor = tf.constant(y_train, dtype=tf.int32)\n",
        "X_test_tensor = tf.constant(X_test_scaled, dtype=tf.float32)\n",
        "y_test_tensor = tf.constant(y_test, dtype=tf.int32)"
      ],
      "metadata": {
        "id": "GNq1BWL8RTqE"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "class SimpleRNNModel(tf.keras.Model):\n",
        "    def __init__(self, num_classes):\n",
        "        super(SimpleRNNModel, self).__init__()\n",
        "        self.rnn_layer = tf.keras.layers.SimpleRNN(32, activation='relu')\n",
        "        self.dense_output = tf.keras.layers.Dense(num_classes, activation='softmax')\n",
        "\n",
        "    def call(self, inputs):\n",
        "        expanded_inputs = tf.expand_dims(inputs, axis=-1)\n",
        "        x = self.rnn_layer(expanded_inputs)\n",
        "        output = self.dense_output(x)\n",
        "        return output"
      ],
      "metadata": {
        "id": "Z36-M2gnRUq3"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = len(label_encoder.classes_)\n",
        "model = SimpleRNNModel(num_classes)\n",
        "loss_object = tf.keras.losses.SparseCategoricalCrossentropy()\n",
        "optimizer = tf.keras.optimizers.Adam()\n",
        "epochs = 10\n",
        "batch_size = 32\n",
        "steps_per_epoch = len(X_train_scaled) // batch_size\n",
        "train_loss_history = []\n",
        "test_loss_history = []\n",
        "train_accuracy_history = []\n",
        "test_accuracy_history = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    for step in range(steps_per_epoch):\n",
        "        start = step * batch_size\n",
        "        end = (step + 1) * batch_size\n",
        "        with tf.GradientTape() as tape:\n",
        "            predictions = model(X_train_tensor[start:end])\n",
        "            loss = loss_object(y_train_tensor[start:end], predictions)\n",
        "\n",
        "        gradients = tape.gradient(loss, model.trainable_variables)\n",
        "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
        "\n",
        "    train_loss = loss_object(y_train_tensor, model(X_train_tensor))\n",
        "    test_loss = loss_object(y_test_tensor, model(X_test_tensor))\n",
        "    train_loss_history.append(train_loss.numpy())\n",
        "    test_loss_history.append(test_loss.numpy())\n",
        "\n",
        "    y_pred_train = np.argmax(model(X_train_tensor).numpy(), axis=1)\n",
        "    y_pred_test = np.argmax(model(X_test_tensor).numpy(), axis=1)\n",
        "\n",
        "    train_accuracy = accuracy_score(y_train, y_pred_train)\n",
        "    test_accuracy = accuracy_score(y_test, y_pred_test)\n",
        "\n",
        "    train_accuracy_history.append(train_accuracy)\n",
        "    test_accuracy_history.append(test_accuracy)\n",
        "\n",
        "    print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.4f}, Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEWU4xeGRpWM",
        "outputId": "d2172c90-bf82-4e82-9a19-29072ddaaef1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10, Train Loss: 0.3483, Train Accuracy: 0.8511, Test Loss: 0.3510, Test Accuracy: 0.8488\n",
            "Epoch 2/10, Train Loss: 0.3393, Train Accuracy: 0.8561, Test Loss: 0.3424, Test Accuracy: 0.8543\n",
            "Epoch 3/10, Train Loss: 0.3394, Train Accuracy: 0.8545, Test Loss: 0.3412, Test Accuracy: 0.8532\n",
            "Epoch 4/10, Train Loss: 0.3347, Train Accuracy: 0.8568, Test Loss: 0.3383, Test Accuracy: 0.8556\n",
            "Epoch 5/10, Train Loss: 0.3233, Train Accuracy: 0.8642, Test Loss: 0.3274, Test Accuracy: 0.8630\n",
            "Epoch 6/10, Train Loss: 0.3135, Train Accuracy: 0.8717, Test Loss: 0.3187, Test Accuracy: 0.8708\n",
            "Epoch 7/10, Train Loss: 0.3223, Train Accuracy: 0.8644, Test Loss: 0.3269, Test Accuracy: 0.8625\n",
            "Epoch 8/10, Train Loss: 0.3191, Train Accuracy: 0.8670, Test Loss: 0.3243, Test Accuracy: 0.8659\n",
            "Epoch 9/10, Train Loss: 0.3141, Train Accuracy: 0.8699, Test Loss: 0.3206, Test Accuracy: 0.8673\n",
            "Epoch 10/10, Train Loss: 0.3093, Train Accuracy: 0.8724, Test Loss: 0.3154, Test Accuracy: 0.8700\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "y_pred_train_prob = model(X_train_tensor).numpy()\n",
        "y_pred_test_prob = model(X_test_tensor).numpy()\n",
        "\n",
        "# Convert probabilities to class indices\n",
        "y_pred_train = np.argmax(y_pred_train_prob, axis=1)\n",
        "y_pred_test = np.argmax(y_pred_test_prob, axis=1)\n"
      ],
      "metadata": {
        "id": "h2sB1lkPSB8y"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# F1 Score\n",
        "f1_train = f1_score(y_train, y_pred_train, average='weighted')\n",
        "f1_test = f1_score(y_test, y_pred_test, average='weighted')\n",
        "\n",
        "# Accuracy\n",
        "train_accuracy = np.mean(np.equal(y_train, y_pred_train))\n",
        "test_accuracy = np.mean(np.equal(y_test, y_pred_test))\n",
        "\n",
        "# Other metrics\n",
        "print(\"Train F1 Score:\", f1_train)\n",
        "print(\"Test F1 Score:\", f1_test)\n",
        "\n",
        "# Accuracy\n",
        "print(\"Train Accuracy:\", train_accuracy)\n",
        "print(\"Test Accuracy:\", test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HXnAbH3KSEs6",
        "outputId": "36dcedcc-3200-483d-9c59-c078a41e33e4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train F1 Score: 0.8724374303546774\n",
            "Test F1 Score: 0.8699736081982974\n",
            "Train Accuracy: 0.8724459585861273\n",
            "Test Accuracy: 0.8699927916795387\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "cm_train = confusion_matrix(y_train, y_pred_train)\n",
        "cm_test = confusion_matrix(y_test, y_pred_test)\n",
        "\n",
        "# Plot Accuracy Curve\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(range(epochs), train_accuracy_history, label='Train Accuracy')\n",
        "plt.plot(range(epochs), test_accuracy_history, label='Test Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy Curve')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "x4aHisQgSIMf",
        "outputId": "1a839e4a-810c-4035-8351-53df2ac3a1e6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loss Curve\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(train_loss_history, label='Train Loss')\n",
        "plt.plot(test_loss_history, label='Test Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Loss Curve')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "ysa9duvnSKf7",
        "outputId": "5f4ae3ca-0ff4-454d-aa7a-6854fc0bcea5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report\n",
        "report_train = classification_report(y_train, y_pred_train, target_names=label_encoder.classes_)\n",
        "report_test = classification_report(y_test, y_pred_test, target_names=label_encoder.classes_)"
      ],
      "metadata": {
        "id": "HRrZZJqvSMof"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print Confusion Matrix and Classification Report\n",
        "print(\"Train Classification Report:\")\n",
        "print(report_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yczDjs7TSOsW",
        "outputId": "c2d97fc0-a5ef-49fd-de1b-d43ac7ca664a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "  noseizures       0.88      0.86      0.87    116674\n",
            "    seizures       0.87      0.88      0.87    116388\n",
            "\n",
            "    accuracy                           0.87    233062\n",
            "   macro avg       0.87      0.87      0.87    233062\n",
            "weighted avg       0.87      0.87      0.87    233062\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test Classification Report:\")\n",
        "print(report_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVg1U66-SQhZ",
        "outputId": "3eb38414-8efc-4fee-9d37-72d3c66cbdb4"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "  noseizures       0.88      0.86      0.87     28990\n",
            "    seizures       0.86      0.88      0.87     29276\n",
            "\n",
            "    accuracy                           0.87     58266\n",
            "   macro avg       0.87      0.87      0.87     58266\n",
            "weighted avg       0.87      0.87      0.87     58266\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Confusion Matrix Heatmap for Train Set\n",
        "plt.figure(figsize=(15, 10))\n",
        "sns.heatmap(cm_train, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix (Train Set)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "muhRX61CSSSF",
        "outputId": "73bd595c-e426-4f76-86ff-6159b34423d5"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Confusion Matrix Heatmap for Test Set\n",
        "plt.figure(figsize=(15, 10))\n",
        "sns.heatmap(cm_test, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix (Test Set)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "iRorQnO9SUZt",
        "outputId": "4a929981-fe6c-4228-9c89-5e2a32e74c2a"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ],
            "image/png": 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vniQL7Wh1ySWXJEl69eq11OJq27ZtPvzwwzz77LPFsUmTJuXOO++sN+/9999f6NqOHTsmyULbsy+wxhprpGPHjrn55pvrJZAmTJiQ++67r/ieX4Vddtkl5557bq666qq0atVqsfMaNGiwUFXQsGHD8p///Kfe2IIk1KISYUtqwIABefPNN3PzzTfnkksuSZs2bdKnT5/Ffo8LLL/88tlqq63yxBNPLNHz9ttvv4wbNy733nvvQuemTp2auXPnJvm/NX0WqK2tzWabbZbk/37jJf0eRo8evcjxBestLWjZ22+//TJv3ryce+65C82dO3duveetuOKKn/n8J5988nMTqADAN5/KHQCWurZt2+a2227L/vvvn4033jiHHnpoNt1008yePTuPPvpohg0blsMOOyxJsvnmm6dPnz657rrrMnXq1Oy00055/PHHc/PNN6d3796L3Wb7izjggAMyYMCAfO9738uPf/zjzJw5M9dcc0022GCDegsKDx48OGPHjk2vXr2yzjrr5O23384vfvGLrLnmmtl+++0Xe/+LLrooPXv2TJcuXXLkkUfmo48+ypVXXplmzZp9ZrvUl1VbW5szzzzzc+ftscceGTx4cA4//PBst912ee6553LrrbdmvfXWqzevbdu2WXnllXPttdemSZMmWXHFFdO5c+esu+66SxTXmDFj8otf/CI//elPi1ua33TTTdl5551z1lln5cILL/zM6/faa6+cccYZmTZtWnEtp89z6qmn5k9/+lP22GOPHHbYYenUqVNmzJiR5557Lr///e/z+uuvp3nz5jnqqKPy/vvv59vf/nbWXHPNvPHGG7nyyivTsWPHYgVZx44d06BBg/zsZz/Lhx9+mLq6unz7299e7JpBe+21V9Zdd93sueeeadu2bWbMmJH7778/f/7zn7P11ltnzz33TJLstNNOOfbYYzNkyJCMHz8+3bt3z/LLL5+XX345w4YNy+WXX5599903ySdbnV9zzTU577zz0q5du7Ro0SLf/va3k3yyftCzzz6bvn37lvTdAADfYGXdqwuAb7R//vOfhaOPPrrQpk2bQsOGDQtNmjQpdO3atXDllVcWPv744+K8OXPmFM4555zCuuuuW1h++eULa621VmHgwIH15hQKn2yF3qtXr4We879bcC9uK/RCoVC47777CptuummhYcOGhQ033LDwm9/8ZqGt0EePHl3Ya6+9Cq1bty40bNiw0Lp168KBBx5Yb3vtRW2FXigUCvfff3+ha9euhcaNGxeaNm1a2HPPPQsvvPBCvTkLnve/W63fdNNNhSSFiRMnLvY7LRTqb4W+OIvbCv3kk08urLHGGoXGjRsXunbtWhg3btwitzC/6667Cu3bty8st9xy9d5zp512KmyyySaLfOan7zNt2rTCOuusU9hyyy0Lc+bMqTfvpJNOKtTW1hbGjRv3me8wZcqUwnLLLVe45ZZbFjtnUVuF//e//y0MHDiw0K5du0LDhg0LzZs3L2y33XaFn//854XZs2cXCoVC4fe//32he/fuhRYtWhQaNmxYWHvttQvHHntsYdKkSfXudf311xfWW2+9QoMGDT53W/Tf/va3hQMOOKDQtm3bQuPGjQuNGjUqtG/fvnDGGWcUpk2bttD86667rtCpU6dC48aNC02aNCl06NChcNpppxXeeuut4pzJkycXevXqVWjSpEkhSb13veaaaworrLDCIu8NACxbagqFJdyGAgDga3LkkUfmn//8Z/7617+WO5SKs8UWW2TnnXfOpZdeWu5QAIAyk9wBACrWm2++mQ022CCjR49O165dyx1OxRg5cmT23XffvPbaa0u0tTwA8M0kuQMAAABQxeyWBQAAAFDFJHcAAAAAqpjkDgAAAEAVk9wBAAAAqGKSOwAAAABVbLlyB/BVaLzd6eUOAQCq0tsPnFfuEACgKjWpWzZqJxpv0a/cISzWR09fVe4QymbZ+LsPAAAA4BtKcgcAAACgin0j27IAAACAr0CNGpFK5FcBAAAAqGKSOwAAAABVTFsWAAAAUJqamnJHwCKo3AEAAACoYpI7AAAAAFVMWxYAAABQGrtlVSS/CgAAAEAVk9wBAAAAqGLasgAAAIDS2C2rIqncAQAAAKhikjsAAAAAVUxbFgAAAFAau2VVJL8KAAAAQBWT3AEAAACoYtqyAAAAgNLYLasiqdwBAAAAqGKSOwAAAABVTFsWAAAAUBq7ZVUkvwoAAABAFZPcAQAAAJYpQ4YMydZbb50mTZqkRYsW6d27d1566aV6c3beeefU1NTUO4477rh6c95888306tUrK6ywQlq0aJFTTz01c+fOrTfnwQcfzJZbbpm6urq0a9cuQ4cOXSieq6++Om3atEmjRo3SuXPnPP7440v0PpI7AAAAQGlqair3WAIPPfRQ+vbtm7/97W8ZNWpU5syZk+7du2fGjBn15h199NGZNGlS8bjwwguL5+bNm5devXpl9uzZefTRR3PzzTdn6NChOfvss4tzJk6cmF69emWXXXbJ+PHjc+KJJ+aoo47KvffeW5xzxx13pH///vnpT3+ap556Kptvvnl69OiRt99+u/SfpVAoFJboG6gCjbc7vdwhAEBVevuB88odAgBUpSZ1y0btROMuPyl3CIv10bgLvvC177zzTlq0aJGHHnooO+64Y5JPKnc6duyYyy67bJHX3HPPPdljjz3y1ltvpWXLlkmSa6+9NgMGDMg777yThg0bZsCAARkxYkQmTJhQvO6AAw7I1KlTM3LkyCRJ586ds/XWW+eqq65KksyfPz9rrbVWjj/++PzkJ6V938vG330AAADAN9qsWbMybdq0esesWbNKuvbDDz9Mkqy66qr1xm+99dY0b948m266aQYOHJiZM2cWz40bNy4dOnQoJnaSpEePHpk2bVqef/754pxu3brVu2ePHj0ybty4JMns2bPz5JNP1ptTW1ubbt26FeeUQnIHAAAAKE1NbcUeQ4YMSbNmzeodQ4YM+dxXmj9/fk488cR07do1m266aXH8oIMOym9+85s88MADGThwYG655ZYccsghxfOTJ0+ul9hJUvw8efLkz5wzbdq0fPTRR3n33Xczb968Rc5ZcI9S2AodAAAAqHoDBw5M//79643V1dV97nV9+/bNhAkT8vDDD9cbP+aYY4p/7tChQ9ZYY43suuuuefXVV9O2bdulE/RSIrkDAAAAVL26urqSkjmf1q9fv9x9990ZO3Zs1lxzzc+c27lz5yTJK6+8krZt26ZVq1YL7Wo1ZcqUJEmrVq2K/++CsU/Padq0aRo3bpwGDRqkQYMGi5yz4B6l0JYFAAAAlKbcO2Itpd2yCoVC+vXrlzvvvDNjxozJuuuu+7nXjB8/PkmyxhprJEm6dOmS5557rt6uVqNGjUrTpk3Tvn374pzRo0fXu8+oUaPSpUuXJEnDhg3TqVOnenPmz5+f0aNHF+eUQuUOAAAAsEzp27dvbrvtttx1111p0qRJcX2bZs2apXHjxnn11Vdz2223Zffdd89qq62WZ599NieddFJ23HHHbLbZZkmS7t27p3379vnBD36QCy+8MJMnT86ZZ56Zvn37FiuIjjvuuFx11VU57bTTcsQRR2TMmDH53e9+lxEjRhRj6d+/f/r06ZOtttoq22yzTS677LLMmDEjhx9+eMnvI7kDAAAALFOuueaaJJ9sd/5pN910Uw477LA0bNgw999/fzHRstZaa2WfffbJmWeeWZzboEGD3H333fnhD3+YLl26ZMUVV0yfPn0yePDg4px11103I0aMyEknnZTLL788a665Zm644Yb06NGjOGf//ffPO++8k7PPPjuTJ09Ox44dM3LkyIUWWf4sNYVCofAFv4uK1Xi708sdAgBUpbcfOK/cIQBAVWpSt2ysetJ4+7PKHcJiffTwueUOoWyWjb/7AAAAAL6hJHcAAAAAqpg1dwAAAIDSLOGuVHw9VO4AAAAAVDHJHQAAAIAqpi0LAAAAKE2NGpFK5FcBAAAAqGKSOwAAAABVTFsWAAAAUBptWRXJrwIAAABQxSR3AAAAAKqYtiwAAACgNLU15Y6ARVC5AwAAAFDFJHcAAAAAqpi2LAAAAKA0dsuqSH4VAAAAgComuQMAAABQxbRlAQAAAKWpsVtWJVK5AwAAAFDFJHcAAAAAqpi2LAAAAKA0dsuqSH4VAAAAgComuQMAAABQxbRlAQAAAKWxW1ZFUrkDAAAAUMUkdwAAAACqmLYsAAAAoDR2y6pIfhUAAACAKia5AwAAAFDFtGUBAAAApbFbVkVSuQMAAABQxSR3AAAAAKqYtiwAAACgNHbLqkh+FQAAAIAqJrkDAAAAUMW0ZQEAAAClsVtWRVK5AwAAAFDFJHcAAAAAqpi2LAAAAKA0dsuqSH4VAAAAgComuQMAAABQxbRlAQAAAKWxW1ZFUrkDAAAAUMUkdwAAAACqmLYsAAAAoDR2y6pIfhUAAACAKia5AwAAAFDFtGUBAAAApdGWVZH8KgAAAABVTHIHAAAAoIppywIAAABKU1NT7ghYBJU7AAAAAFVMcgcAAACgimnLAgAAAEpjt6yK5FcBAAAAqGKSOwAAAABVTFsWAAAAUBq7ZVUklTsAAAAAVUxyBwAAAKCKacsCAAAASmO3rIrkVwEAAACoYpI7AAAAAFVMWxYAAABQGrtlVSSVOwAAAABVTHIHAAAAoIppywIAAABKUqMtqyKp3AEAAACoYpI7AAAAAFVMWxYAAABQEm1ZlUnlDgAAAEAVk9wBAAAAqGLasgAAAIDS6MqqSCp3AAAAAKqY5A4AAABAFdOWBQAAAJTEblmVSeUOAAAAsEwZMmRItt566zRp0iQtWrRI796989JLLxXPv//++zn++OOz4YYbpnHjxll77bXz4x//OB9++GG9+9TU1Cx03H777fXmPPjgg9lyyy1TV1eXdu3aZejQoQvFc/XVV6dNmzZp1KhROnfunMcff3yJ3kdyBwAAAFimPPTQQ+nbt2/+9re/ZdSoUZkzZ066d++eGTNmJEneeuutvPXWW/n5z3+eCRMmZOjQoRk5cmSOPPLIhe510003ZdKkScWjd+/exXMTJ05Mr169sssuu2T8+PE58cQTc9RRR+Xee+8tzrnjjjvSv3///PSnP81TTz2VzTffPD169Mjbb79d8vvUFAqFwhf/OipT4+1OL3cIAFCV3n7gvHKHAABVqUndslE70WT/m8sdwmL9944+X/jad955Jy1atMhDDz2UHXfccZFzhg0blkMOOSQzZszIcst9sspNTU1N7rzzznoJnU8bMGBARowYkQkTJhTHDjjggEydOjUjR45MknTu3Dlbb711rrrqqiTJ/Pnzs9Zaa+X444/PT37yk5LiXzb+7gMAAAC+0WbNmpVp06bVO2bNmlXStQvarVZdddXPnNO0adNiYmeBvn37pnnz5tlmm21y44035tM1NOPGjUu3bt3qze/Ro0fGjRuXJJk9e3aefPLJenNqa2vTrVu34pxSSO4AAAAAVW/IkCFp1qxZvWPIkCGfe938+fNz4oknpmvXrtl0000XOefdd9/Nueeem2OOOabe+ODBg/O73/0uo0aNyj777JMf/ehHufLKK4vnJ0+enJYtW9a7pmXLlpk2bVo++uijvPvuu5k3b94i50yePLnUV7dbFgAAAFCaSt4ta+DAgenfv3+9sbq6us+9rm/fvpkwYUIefvjhRZ6fNm1aevXqlfbt22fQoEH1zp111lnFP2+xxRaZMWNGLrroovz4xz9e8hf4ElTuAAAAAFWvrq4uTZs2rXd8XnKnX79+ufvuu/PAAw9kzTXXXOj8f//73+y2225p0qRJ7rzzziy//PKfeb/OnTvn3//+d7EdrFWrVpkyZUq9OVOmTEnTpk3TuHHjNG/ePA0aNFjknFatWpXy2kkkdwAAAIBlTKFQSL9+/XLnnXdmzJgxWXfddReaM23atHTv3j0NGzbMn/70pzRq1Ohz7zt+/PisssoqxaRSly5dMnr06HpzRo0alS5duiRJGjZsmE6dOtWbM3/+/IwePbo4pxTasgAAAICSVHJb1pLo27dvbrvtttx1111p0qRJcX2bZs2apXHjxsXEzsyZM/Ob3/ymuEBzkqy++upp0KBB/vznP2fKlCnZdttt06hRo4waNSrnn39+TjnllOJzjjvuuFx11VU57bTTcsQRR2TMmDH53e9+lxEjRhTn9O/fP3369MlWW22VbbbZJpdddllmzJiRww8/vOT3kdwBAAAAlinXXHNNkmTnnXeuN37TTTflsMMOy1NPPZXHHnssSdKuXbt6cyZOnJg2bdpk+eWXz9VXX52TTjophUIh7dq1yyWXXJKjjz66OHfdddfNiBEjctJJJ+Xyyy/PmmuumRtuuCE9evQoztl///3zzjvv5Oyzz87kyZPTsWPHjBw5cqFFlj9LTeHTe3R9QzTe7vRyhwAAVentB84rdwgAUJWa1C0bq540O/CWcoewWB/+9gflDqFsVO4AAAAApflmdGV94ywbqUUAAACAbyjJHQAAAIAqpi0LAAAAKMk3ZbesbxqVOwAAAABVTHIHAAAAoIppywIAAABKoi2rMqncAQAAAKhikjsAAAAAVUxbFgAAAFASbVmVSeUOAAAAQBWT3AEAAACoYtqyAAAAgJJoy6pMKncAAAAAqpjkDgAAAEAV05YFAAAAlEZXVkVSuQMAAABQxSR3AAAAAKqYtiwAAACgJHbLqkwqdwAAAACqmOQOAAAAQBXTlgUAAACURFtWZVK5AwAAAFDFJHcAAAAAqpi2LAAAAKAk2rIqk8odAAAAgComuQMAAABQxbRlAQAAAKXRlVWRVO4AAAAAVDHJHQAAAIAqpi0LAAAAKIndsiqTyh0AAACAKia5AwAAAFDFtGUBAAAAJdGWVZlU7gAAAABUMckdAAAAgCqmLQsAAAAoibasyqRyBwAAAKCKSe4AAAAAVLGKbMuaNm1axowZkw033DAbb7xxucMBAAAAoi2rUlVE5c5+++2Xq666Kkny0UcfZauttsp+++2XzTbbLH/4wx/KHB0AAABA5aqI5M7YsWOzww47JEnuvPPOFAqFTJ06NVdccUXOO++8MkcHAAAAULkqIrnz4YcfZtVVV02SjBw5Mvvss09WWGGF9OrVKy+//HKZowMAAACSJDUVfCzDKiK5s9Zaa2XcuHGZMWNGRo4cme7duydJPvjggzRq1KjM0QEAAABUropYUPnEE0/MwQcfnJVWWilrr712dt555ySftGt16NChvMEBAAAAVLCKSO786Ec/yjbbbJN//etf+c53vpPa2k8KitZbbz1r7gAAAECFsFtWZaqI5E6SbLXVVtlss80yceLEtG3bNsstt1x69epV7rAAAAAAKlpFrLkzc+bMHHnkkVlhhRWyySab5M0330ySHH/88bngggvKHB0AAABA5aqI5M7AgQPzzDPP5MEHH6y3gHK3bt1yxx13lDEyAAAAYIGampqKPZZlFdGWNXz48Nxxxx3Zdttt6/0gm2yySV599dUyRgYAAABQ2Sqicuedd95JixYtFhqfMWPGMp99AwAAAPgsFZHc2WqrrTJixIji5wUJnRtuuCFdunQpV1gAAADAp5S79Upb1qJVRFvW+eefn549e+aFF17I3Llzc/nll+eFF17Io48+moceeqjc4QEAAABUrIqo3Nl+++3zzDPPZO7cuenQoUPuu+++tGjRIuPGjUunTp3KHR4AAABAxSp75c6cOXNy7LHH5qyzzsr1119f7nAAAACAxVm2u58qVtkrd5Zffvn84Q9/KHcYAAAAAFWp7MmdJOndu3eGDx9e7jAAAAAAqk7Z27KSZP3118/gwYPzyCOPpFOnTllxxRXrnf/xj39cpsgAAACABZb1XakqVUUkd371q19l5ZVXzpNPPpknn3yy3rmamhrJHQAAAIDFqIjkzsSJE8sdAgAAAEBVqojkDgAAAFD5tGVVpopI7hxxxBGfef7GG2/8miIBAAAAqC4Vkdz54IMP6n2eM2dOJkyYkKlTp+bb3/52maICAAAAqHwVkdy58847FxqbP39+fvjDH6Zt27ZliAgAAAD4X9qyKlNtuQNYnNra2vTv3z+XXnppuUMBAAAAqFgVm9xJkldffTVz584tdxgAAAAAFasi2rL69+9f73OhUMikSZMyYsSI9OnTp0xRAQAAAJ+mLasyVURy5+mnn673uba2Nquvvnouvvjiz91JC/hqnfKDndJ7502ywdqr56PZc/LYc2/mjF+MzMtvvrvI+cMv7pMeXTbMfj+5JX8e+4/i+M6d2uanx3TLJuu1yoyPZ+fWe57KT385KvPmzU+S1DVcLleeule22Ohb2Wid1XPPoy9lv5/8pt69t9tsnZz3o92ywTqrZ4VGy+fNyVPzq+GP58o7HvnqvgAA+IoM/dX1ueryS3LgwT/IyQNOT5LMmjUrl/38Z7lv5F8ye/acbLtd1/zkzLOz2mrNkyR/vuvOnHPW6Yu8330PPJxVV1stg84cmLv/NHyh8+u1bZvf3Xn3V/Y+AJRPRSR3HnjggXKHACzGDlusm2v/8Lc8+Y9/Z7kGtTnnuO65+7LDs8VBl2Xmx3PqzT1+/64pFBa+R4d2rTL84j752c0P5sjBw9J69Wa58rS90qC2NgOvuidJ0qC2Jh/NmptfDHs0vXfedJGxzPh4dq79w7g898rkzPhodrbbvE2uOq13Znw8Ozfe9fel/u4A8FV5fsJz+eOwO7L+BhvWG7/kwiF5+K9jc8HPL8tKTZrkwvPPzakn/Tg3/vq2JMl3evRMl67b17vmnDNPz6zZs7LqaqslSU4ZcHr6nfh/lfHz5s7LQd/vnV2/s9tX/FYAlEtFrLkzePDgjBkzZqHxGTNmZPDgwWWICFhgr/5D85u/PJV/THw7z70yOcec94es3WqVbLHRt+rN22z9NXLCgdvnuPP/sNA99t11s0x4dXKG3DQmr/3n/Tw8fmLOuHpkjt1n26y0QsMkycyP5+SEn9+Vm/70RKa8999FxvLMPyfld6OezT8mvp03J0/N7feOz/2PvZyum7dZ6u8NAF+VmTNn5KyBp+aMQYPTpGnT4vj0//43d935x5x0yoBs3XnbbNx+k/z03PPz7Pin89wz45MkjRo1SvPmqxePBrUN8vfHH8te39uneJ+VmjSpN+cfL0zItGnT8t3e3/u6XxX4Jqqp4GMZVhHJnUGDBqVnz5655JJL6o1Pnz4955xzTpmiAhal6Yp1SZIPpn1UHGtct3yGDto/J178p0x5f/pC19Q1bJCPZ9VfHP2jWXPSuG75bLHhtxaaX6rNN1gjnTusnb8+PfEL3wMAvm4/+3/npusOO6XzttvVG//HC89n7tw56bxtl+JYm3XXS6s11sizz45f5L1G/PmuNGrcKLt+p8din3fXH/+QbbbtkjVaf/F/5gJQ2SqiLStJfv3rX6dv37557rnn8stf/jINGzYs6bpZs2Zl1qxZ9cYK8+emprZiXg2+MWpqanLRiXvk0WdezwuvTSmOX3hCr/ztuTdy91//scjrRj32cvrt1zX7fWez/H70c2m1apOcfsS3kyRrNG+yxHG8MnxAmq+8YpZrUJvzfjU6Q//8xBd7IQD4mt17z4i8+I8X8uvfDlvo3Hvvvpvll1++XjVPkqy6WvO89+6i17q7684/ZLeevdKoUaNFnn/n7bfz6CN/zXkXXPTlgwegYlVE5U6S7LLLLnnsscfy2GOPZeedd87bb79d0nVDhgxJs2bN6h1z/zPuK44Wlk2XnfzdbLJeyxx69u3FsV7bb5SdO62XUy8fsdjrRj/+Sk6/+p5ccWrvfPjg4Dx7R//cO+6lJMn8+YtYpOdz7PrD69L1iKtz/EXD02//T5JGAFDpJk+elIt/NiTnXXBR6urqvvT9nn3m6Ux87dXstfe+i51z95+GZ6UmTbLzt3f90s8DSD75D76VeizLKqK8ZcGP0LZt2/ztb3/Lfvvtl06dOuXaa6/93GsHDhy40FbqLbqf95XECcuyS/vvmd27bphuP7o+/3lnWnF8505ts963Vs3ke8+qN/+3/+/gPPLM6+nR74YkyRW3P5Irbn8kazRvkg+mfZR11lgl5/5wt0x86/0ljuWNSR8kSZ5/bUparNokZxyxa3436tkv8XYA8NV78YXn8/777+WQ/f9vfZx58+bl6SefyO9uvy1XXnN95syZk/9Om1aveuf9997Nas2bL3S/4X/8fTbYaONs3H6TRT6vUCjkT8P/kN33+G6WX760qngAqlNFJHcKn9pep2nTpvnLX/6SE088Mb179/7ca+vq6hb6Lx9asmDpurT/nvnuTu3Tve8NxcTKAj+/5aHc9D9tUU/+5oScdsWIjHj4xYXuNendTxZL3u87m+dfk6fm6Zfe+lKx1dbUpK6h/5sHoPJt3blLbv/DXfXGBp99RtZZd930OfyotGq1RpZbbvk8/tjfsut3uidJXp84MZMnTcpmm3Wsd93MmTNy/70j0/eE+v+R89OefOLv+debb9ZbbBmAb6aK+Deim266Kc2aNSt+rq2tzRVXXJEtttgiY8eOLWNkwGWnfDf7f2fzfH/AbzJ95qy0XHWlJMmH0z/Ox7PnZsr70xe5iPK/pkytlwg66aAdct/f/pn5hUL22mmTnPKDHXPIWb+t15a1UZsWabh8g6zSdIU0WaEum62/RpLk2ZcnJUmO3Xvb/GvK1Lz0xjtJku07tsmJB22fXwzTiglA5VtxxRXTbv0N6o01atw4KzdbuTi+1/f2zqU/vyDNmjXLiiutlIuGnJfNNu+YDpt3rHfdfSPvybx587J7rz0X+7y77vx9Nu2w2ULPBPgylvX2p0pVEWvu9OnTZ5F9x4cffnhuuummMkQELHDs3ttm5SaNM+oXR+f1u08vHvt2W7J1brp32SD3X3NMHrmxb3put2G+P+A3+fPY+gswD7+4Tx67+fjsscPG2anTenns5uPz2M3HF8/X1tZk8A975LGbj88jN/bNsftsmzN/cW8GX3//UnlXACi3/qcNzA477pzT+p+Qow/7QVZr3jwXXnrFQvP+dOcfssuu31lo8eUFpv/3vxlz/yhVOwCLMWTIkGy99dZp0qRJWrRokd69e+ell16qN+fjjz9O3759s9pqq2WllVbKPvvskylTptSb8+abb6ZXr15ZYYUV0qJFi5x66qmZO7f+TsEPPvhgttxyy9TV1aVdu3YZOnToQvFcffXVadOmTRo1apTOnTvn8ccfX6L3qSl8uifqa3TFFVfkmGOOSaNGjXLFFQv/A2uBmpqaHH/88Ys9vyiNtzv9y4YHAMuktx+wbh0AfBFN6iqiduIr1/bke8odwmK9enHPkufutttuOeCAA7L11ltn7ty5Of300zNhwoS88MILWXHFFZMkP/zhDzNixIgMHTo0zZo1S79+/VJbW5tHHnkkySfrpnXs2DGtWrXKRRddlEmTJuXQQw/N0UcfnfPPPz9JMnHixGy66aY57rjjctRRR2X06NE58cQTM2LEiPTo0SNJcscdd+TQQw/Ntddem86dO+eyyy7LsGHD8tJLL6VFixYlvU/Zkjvrrrtunnjiiay22mpZd911FzuvpqYmr7322hLdW3IHAL4YyR0A+GKWleROu1MqN7nz/P/7dmbNmlVvbFHr9C7KO++8kxYtWuShhx7KjjvumA8//DCrr756brvttuy77ye7Er744ovZeOONM27cuGy77ba55557sscee+Stt95Ky5YtkyTXXnttBgwYkHfeeScNGzbMgAEDMmLEiEyYMKH4rAMOOCBTp07NyJEjkySdO3fO1ltvnauuuipJMn/+/Ky11lo5/vjj85Of/KSkdy/b330TJ07MaqutVvzz4o4lTewAAAAAy54hQ4akWbNm9Y4hQ4aUdO2HH36YJFl11VWTJE8++WTmzJmTbt26FedstNFGWXvttTNu3Cdrfo4bNy4dOnQoJnaSpEePHpk2bVqef/754pxP32PBnAX3mD17dp588sl6c2pra9OtW7finFJUxILKC8yePTsTJ05M27Zts9xyFRUaAAAAUMEGDhyY/v3r7yJYStXO/Pnzc+KJJ6Zr167ZdNNNkySTJ09Ow4YNs/LKK9eb27Jly0yePLk459OJnQXnF5z7rDnTpk3LRx99lA8++CDz5s1b5JwXX1x49+HFqYgMysyZM3P88cfn5ptvTpL885//zHrrrZfjjz8+3/rWt0ouQwIAAAC+OpW8W1apLVj/q2/fvpkwYUIefvjhryCqr0dFNAUOHDgwzzzzTB588ME0atSoON6tW7fccccdZYwMAAAA+Kbq169f7r777jzwwANZc801i+OtWrXK7NmzM3Xq1Hrzp0yZklatWhXn/O/uWQs+f96cpk2bpnHjxmnevHkaNGiwyDkL7lGKikjuDB8+PFdddVW23377elnATTbZJK+++moZIwMAAAC+aQqFQvr165c777wzY8aMWWijp06dOmX55ZfP6NGji2MvvfRS3nzzzXTp0iVJ0qVLlzz33HN5++23i3NGjRqVpk2bpn379sU5n77HgjkL7tGwYcN06tSp3pz58+dn9OjRxTmlqIi2rAWrUv+vGTNmVHTJFwAAACxLvin/it63b9/cdtttueuuu9KkSZPiGjnNmjVL48aN06xZsxx55JHp379/Vl111TRt2jTHH398unTpkm233TZJ0r1797Rv3z4/+MEPcuGFF2by5Mk588wz07dv32J72HHHHZerrroqp512Wo444oiMGTMmv/vd7zJixIhiLP3790+fPn2y1VZbZZtttslll12WGTNm5PDDDy/5fSoiubPVVltlxIgROf7445P8Xw/fDTfcsESZKgAAAIDPc8011yRJdt5553rjN910Uw477LAkyaWXXpra2trss88+mTVrVnr06JFf/OIXxbkNGjTI3XffnR/+8Ifp0qVLVlxxxfTp0yeDBw8uzll33XUzYsSInHTSSbn88suz5ppr5oYbbkiPHj2Kc/bff/+88847OfvsszN58uR07NgxI0eOXGiR5c9SUygUCl/ge1iqHn744fTs2TOHHHJIhg4dmmOPPTYvvPBCHn300Tz00EPp1KnTEt2v8Xanf0WRAsA329sPnFfuEACgKjWpq4hVT75yG5w2stwhLNY/L9yt3CGUTUX83bf99ttn/PjxmTt3bjp06JD77rsvLVq0yLhx45Y4sQMAAAB8NWpqair2WJZVRFtWkrRt2zbXX399ucMAAAAAqCoVUbnz1FNP5bnnnit+vuuuu9K7d++cfvrpmT17dhkjAwAAAKhsFZHcOfbYY/PPf/4zSfLaa69l//33zworrJBhw4bltNNOK3N0AAAAQPLJblmVeizLKiK5889//jMdO3ZMkgwbNiw77bRTbrvttgwdOjR/+MMfyhscAAAAQAWriOROoVDI/PnzkyT3339/dt999yTJWmutlXfffbecoQEAAABUtIpYUHmrrbbKeeedl27duuWhhx4q7jc/ceLEJdrXHQAAAPjq1NYu4/1PFaoiKncuu+yyPPXUU+nXr1/OOOOMtGvXLkny+9//Ptttt12ZowMAAACoXBVRubPZZpvV2y1rgYsuuigNGjQoQ0QAAAAA1aEikjsLPPnkk/nHP/6RJGnfvn223HLLMkcEAAAALLCs70pVqSoiufP2229n//33z0MPPZSVV145STJ16tTssssuuf3227P66quXN0AAAACAClURa+4cf/zxmT59ep5//vm8//77ef/99zNhwoRMmzYtP/7xj8sdHgAAAEDFqojKnZEjR+b+++/PxhtvXBxr3759rr766nTv3r2MkQEAAAAL1OjLqkgVUbkzf/78LL/88guNL7/88pk/f34ZIgIAAACoDhWR3Pn2t7+dE044IW+99VZx7D//+U9OOumk7LrrrmWMDAAAAKCyVURy56qrrsq0adPSpk2btG3bNm3btk2bNm0ybdq0XHnlleUODwAAAMgnu2VV6rEsq4g1d9Zaa6089dRTGT16dHEr9I033jjdunUrc2QAAAAAla0ikjtJMmbMmIwZMyZvv/125s+fn6effjq33XZbkuTGG28sc3QAAAAAlakikjvnnHNOBg8enK222iprrLGG1bcBAACgAvn39cpUEcmda6+9NkOHDs0PfvCDcocCAAAAUFUqYkHl2bNnZ7vttit3GAAAAABVpyKSO0cddVRxfR0AAACgMtXU1FTssSyriLasjz/+ONddd13uv//+bLbZZll++eXrnb/kkkvKFBkAAABAZauI5M6zzz6bjh07JkkmTJhQ79yynn0DAAAA+CwVkdx54IEHyh0CAAAA8DnUX1SmilhzBwAAAIAvRnIHAAAAoIpVRFsWAAAAUPmsi1uZVO4AAAAAVDHJHQAAAIAqpi0LAAAAKImurMqkcgcAAACgiknuAAAAAFQxbVkAAABASeyWVZlU7gAAAABUMckdAAAAgCqmLQsAAAAoia6syqRyBwAAAKCKSe4AAAAAVDFtWQAAAEBJ7JZVmVTuAAAAAFQxyR0AAACAKqYtCwAAACiJrqzKpHIHAAAAoIpJ7gAAAABUMW1ZAAAAQEnsllWZVO4AAAAAVDHJHQAAAIAqpi0LAAAAKImurMqkcgcAAACgiknuAAAAAFQxbVkAAABASeyWVZlU7gAAAABUMckdAAAAgCqmLQsAAAAoia6syqRyBwAAAKCKSe4AAAAAVDFtWQAAAEBJ7JZVmVTuAAAAAFQxyR0AAACAKqYtCwAAACiJrqzKpHIHAAAAoIpJ7gAAAABUMW1ZAAAAQEnsllWZVO4AAAAAVDHJHQAAAIAqpi0LAAAAKIm2rMqkcgcAAACgiknuAAAAAFQxbVkAAABASXRlVSaVOwAAAABVTHIHAAAAoIppywIAAABKYresyqRyBwAAAKCKSe4AAAAAVDHJHQAAAKAkNTWVeyyJsWPHZs8990zr1q1TU1OT4cOH/8971izyuOiii4pz2rRps9D5Cy64oN59nn322eywww5p1KhR1lprrVx44YULxTJs2LBstNFGadSoUTp06JC//OUvS/YykdwBAAAAljEzZszI5ptvnquvvnqR5ydNmlTvuPHGG1NTU5N99tmn3rzBgwfXm3f88ccXz02bNi3du3fPOuuskyeffDIXXXRRBg0alOuuu64459FHH82BBx6YI488Mk8//XR69+6d3r17Z8KECUv0PhZUBgAAAJYpPXv2TM+ePRd7vlWrVvU+33XXXdlll12y3nrr1Rtv0qTJQnMXuPXWWzN79uzceOONadiwYTbZZJOMHz8+l1xySY455pgkyeWXX57ddtstp556apLk3HPPzahRo3LVVVfl2muvLfl9VO4AAAAAJVlcu1IlHLNmzcq0adPqHbNmzfrS7zxlypSMGDEiRx555ELnLrjggqy22mrZYostctFFF2Xu3LnFc+PGjcuOO+6Yhg0bFsd69OiRl156KR988EFxTrdu3erds0ePHhk3btwSxSi5AwAAAFS9IUOGpFmzZvWOIUOGfOn73nzzzWnSpEn23nvveuM//vGPc/vtt+eBBx7Isccem/PPPz+nnXZa8fzkyZPTsmXLetcs+Dx58uTPnLPgfKm0ZQEAAABVb+DAgenfv3+9sbq6ui993xtvvDEHH3xwGjVqVG/808/abLPN0rBhwxx77LEZMmTIUnnukpDcAQAAAEqypLtSfZ3q6uqWelLlr3/9a1566aXccccdnzu3c+fOmTt3bl5//fVsuOGGadWqVaZMmVJvzoLPC9bpWdycxa3jszjasgAAAAAW4Ve/+lU6deqUzTff/HPnjh8/PrW1tWnRokWSpEuXLhk7dmzmzJlTnDNq1KhsuOGGWWWVVYpzRo8eXe8+o0aNSpcuXZYoTskdAAAAYJkyffr0jB8/PuPHj0+STJw4MePHj8+bb75ZnDNt2rQMGzYsRx111ELXjxs3LpdddlmeeeaZvPbaa7n11ltz0kkn5ZBDDikmbg466KA0bNgwRx55ZJ5//vnccccdufzyy+u1c51wwgkZOXJkLr744rz44osZNGhQnnjiifTr12+J3kdbFgAAAFCS2kruy1oCTzzxRHbZZZfi5wUJlz59+mTo0KFJkttvvz2FQiEHHnjgQtfX1dXl9ttvz6BBgzJr1qysu+66Oemkk+olbpo1a5b77rsvffv2TadOndK8efOcffbZxW3Qk2S77bbLbbfdljPPPDOnn3561l9//QwfPjybbrrpEr1PTaFQKCzRFVWg8XanlzsEAKhKbz9wXrlDAICq1KRu2WiM+c5Vfyt3CIs1qt+25Q6hbJaNv/sAAAAAvqG0ZQEAAAAl+YZ0ZX3jqNwBAAAAqGKSOwAAAABVTFsWAAAAUJIafVkVSeUOAAAAQBWT3AEAAACoYpI7AAAAAFXMmjsAAABASWotuVORVO4AAAAAVDHJHQAAAIAqpi0LAAAAKImt0CuTyh0AAACAKia5AwAAAFDFtGUBAAAAJdGVVZlU7gAAAABUMckdAAAAgCqmLQsAAAAoSU30ZVUilTsAAAAAVUxyBwAAAKCKacsCAAAASlKrK6siqdwBAAAAqGKSOwAAAABVTFsWAAAAUJKaGn1ZlUjlDgAAAEAVk9wBAAAAqGLasgAAAICS6MqqTCp3AAAAAKqY5A4AAABAFdOWBQAAAJSkVl9WRVK5AwAAAFDFJHcAAAAAqpi2LAAAAKAkurIqk8odAAAAgComuQMAAABQxbRlAQAAACWp0ZdVkVTuAAAAAFQxyR0AAACAKqYtCwAAACiJrqzKpHIHAAAAoIpJ7gAAAABUMW1ZAAAAQElq9WVVJJU7AAAAAFVMcgcAAACgimnLAgAAAEqiKasyqdwBAAAAqGKSOwAAAABVTFsWAAAAUJIau2VVJJU7AAAAAFVMcgcAAACgimnLAgAAAEpSqyurIqncAQAAAKhikjsAAAAAVUxbFgAAAFASu2VVJpU7AAAAAFVMcgcAAACgimnLAgAAAEqiK6syqdwBAAAAqGKSOwAAAABVTFsWAAAAUBK7ZVUmlTsAAAAAVUxyBwAAAKCKacsCAAAASlKrK6siqdwBAAAAqGKSOwAAAABVTFsWAAAAUBK7ZVUmlTsAAAAAVUxyBwAAAKCKacsCAAAASqIpqzKp3AEAAACoYpI7AAAAAFVMWxYAAABQklq7ZVUklTsAAAAAVUxyBwAAAKCKacsCAAAASqIrqzKp3AEAAACWKWPHjs2ee+6Z1q1bp6amJsOHD693/rDDDktNTU29Y7fddqs35/3338/BBx+cpk2bZuWVV86RRx6Z6dOn15vz7LPPZocddkijRo2y1lpr5cILL1wolmHDhmWjjTZKo0aN0qFDh/zlL39Z4veR3AEAAACWKTNmzMjmm2+eq6++erFzdtttt0yaNKl4/Pa3v613/uCDD87zzz+fUaNG5e67787YsWNzzDHHFM9PmzYt3bt3zzrrrJMnn3wyF110UQYNGpTrrruuOOfRRx/NgQcemCOPPDJPP/10evfund69e2fChAlL9D41hUKhsERXVIHG251e7hAAoCq9/cB55Q4BAKpSk7plo3bimGHPlzuExbru+5t8oetqampy5513pnfv3sWxww47LFOnTl2oomeBf/zjH2nfvn3+/ve/Z6uttkqSjBw5Mrvvvnv+/e9/p3Xr1rnmmmtyxhlnZPLkyWnYsGGS5Cc/+UmGDx+eF198MUmy//77Z8aMGbn77ruL9952223TsWPHXHvttSW/w7Lxdx8AAADwjTZr1qxMmzat3jFr1qwvfL8HH3wwLVq0yIYbbpgf/vCHee+994rnxo0bl5VXXrmY2EmSbt26pba2No899lhxzo477lhM7CRJjx498tJLL+WDDz4ozunWrVu95/bo0SPjxo1boli/UHLnr3/9aw455JB06dIl//nPf5Ikt9xySx5++OEvcjsAAACAL2XIkCFp1qxZvWPIkCFf6F677bZbfv3rX2f06NH52c9+loceeig9e/bMvHnzkiSTJ09OixYt6l2z3HLLZdVVV83kyZOLc1q2bFlvzoLPnzdnwflSLfFuWX/4wx/ygx/8IAcffHCefvrpYhbsww8/zPnnn/+FFv4BAAAAKl8l75Y1cODA9O/fv95YXV3dF7rXAQccUPxzhw4dstlmm6Vt27Z58MEHs+uuu36pOL8KS1y5c9555+Xaa6/N9ddfn+WXX7443rVr1zz11FNLNTgAAACAUtTV1aVp06b1ji+a3Plf6623Xpo3b55XXnklSdKqVau8/fbb9ebMnTs377//flq1alWcM2XKlHpzFnz+vDkLzpdqiZM7L730UnbccceFxps1a5apU6cu6e0AAAAAKtq///3vvPfee1ljjTWSJF26dMnUqVPz5JNPFueMGTMm8+fPT+fOnYtzxo4dmzlz5hTnjBo1KhtuuGFWWWWV4pzRo0fXe9aoUaPSpUuXJYpviZM7rVq1KmaqPu3hhx/Oeuutt6S3AwAAAKpEbU1NxR5LYvr06Rk/fnzGjx+fJJk4cWLGjx+fN998M9OnT8+pp56av/3tb3n99dczevTo7LXXXmnXrl169OiRJNl4442z22675eijj87jjz+eRx55JP369csBBxyQ1q1bJ0kOOuigNGzYMEceeWSef/753HHHHbn88svrtY6dcMIJGTlyZC6++OK8+OKLGTRoUJ544on069dvyX6XJZqd5Oijj84JJ5yQxx57LDU1NXnrrbdy66235pRTTskPf/jDJb0dAAAAwNfqiSeeyBZbbJEtttgiSdK/f/9sscUWOfvss9OgQYM8++yz+e53v5sNNtggRx55ZDp16pS//vWv9dq8br311my00UbZdddds/vuu2f77bfPddddVzzfrFmz3HfffZk4cWI6deqUk08+OWeffXaOOeaY4pztttsut912W6677rpsvvnm+f3vf5/hw4dn0003XaL3qSkUCoUluaBQKOT888/PkCFDMnPmzCSf9LWdcsopOffcc5fo4V+VxtudXu4QAKAqvf3AeeUOAQCqUpO6L7QZddX54R9eKHcIi3XNPu3LHULZLPFuWTU1NTnjjDNy6qmn5pVXXsn06dPTvn37rLTSSl9FfAAAAECFqOTdspZlS5zcWaBhw4Zp337ZzYoBAAAAVIIlTu7ssssuqfmMVN2YMWO+VEAAAAAAlG6JkzsdO3as93nOnDkZP358JkyYkD59+iytuAAAAIAK81nFHpTPEid3Lr300kWODxo0KNOnT//SAQEAAABQuqW2nPchhxySG2+8cWndDgAAAIASfOEFlf/XuHHj0qhRo6V1uy/lg7HnlzsEAKhKq2zdr9whAEBV+ujpq8odwtdi2djwvfoscXJn7733rve5UChk0qRJeeKJJ3LWWWcttcAAAAAA+HxLnNxp1qxZvc+1tbXZcMMNM3jw4HTv3n2pBQYAAADA51ui5M68efNy+OGHp0OHDllllVW+qpgAAACACmS3rMq0RO1yDRo0SPfu3TN16tSvKBwAAAAAlsQSr4W06aab5rXXXvsqYgEAAABgCS1xcue8887LKaeckrvvvjuTJk3KtGnT6h0AAADAN1NtTeUey7KS19wZPHhwTj755Oy+++5Jku9+97v1eu0KhUJqamoyb968pR8lAAAAAItUcnLnnHPOyXHHHZcHHnjgq4wHAAAAgCVQcnKnUCgkSXbaaaevLBgAAACgci3r7U+VaonW3LHlGQAAAEBlKblyJ0k22GCDz03wvP/++18qIAAAAABKt0TJnXPOOSfNmjX7qmIBAAAAKpiOnsq0RMmdAw44IC1atPiqYgEAAABgCZW85o7sHAAAAEDlWeLdsgAAAIBlk92yKlPJyZ358+d/lXEAAAAA8AUs0VboAAAAAFSWJVpQGQAAAFh2WY63MqncAQAAAKhikjsAAAAAVUxbFgAAAFCSWn1ZFUnlDgAAAEAVk9wBAAAAqGLasgAAAICSqBCpTH4XAAAAgComuQMAAABQxbRlAQAAACWxWVZlUrkDAAAAUMUkdwAAAACqmLYsAAAAoCS1+rIqksodAAAAgComuQMAAABQxbRlAQAAACXRlVWZVO4AAAAAVDHJHQAAAIAqpi0LAAAAKEmttqyKpHIHAAAAoIpJ7gAAAABUMW1ZAAAAQElqbZdVkVTuAAAAAFQxyR0AAACAKqYtCwAAACiJrqzKpHIHAAAAoIpJ7gAAAABUMW1ZAAAAQElqtWVVJJU7AAAAAFVMcgcAAACgimnLAgAAAEpSE31ZlUjlDgAAAEAVk9wBAAAAqGLasgAAAICS2C2rMqncAQAAAKhikjsAAAAAVUxbFgAAAFASbVmVSeUOAAAAQBWT3AEAAACoYtqyAAAAgJLU1OjLqkQqdwAAAACqmOQOAAAAQBXTlgUAAACUxG5ZlUnlDgAAAEAVk9wBAAAAqGLasgAAAICS2CyrMqncAQAAAKhikjsAAAAAVUxbFgAAAFCSWn1ZFUnlDgAAAEAVk9wBAAAAqGLasgAAAICS1OrKqkgqdwAAAIBlytixY7PnnnumdevWqampyfDhw4vn5syZkwEDBqRDhw5ZccUV07p16xx66KF566236t2jTZs2qampqXdccMEF9eY8++yz2WGHHdKoUaOstdZaufDCCxeKZdiwYdloo43SqFGjdOjQIX/5y1+W+H0kdwAAAIBlyowZM7L55pvn6quvXujczJkz89RTT+Wss87KU089lT/+8Y956aWX8t3vfnehuYMHD86kSZOKx/HHH188N23atHTv3j3rrLNOnnzyyVx00UUZNGhQrrvuuuKcRx99NAceeGCOPPLIPP300+ndu3d69+6dCRMmLNH7aMsCAAAASvJN2SyrZ8+e6dmz5yLPNWvWLKNGjao3dtVVV2WbbbbJm2++mbXXXrs43qRJk7Rq1WqR97n11lsze/bs3HjjjWnYsGE22WSTjB8/PpdcckmOOeaYJMnll1+e3XbbLaeeemqS5Nxzz82oUaNy1VVX5dprry35fVTuAAAAAFVv1qxZmTZtWr1j1qxZS+XeH374YWpqarLyyivXG7/ggguy2mqrZYsttshFF12UuXPnFs+NGzcuO+64Yxo2bFgc69GjR1566aV88MEHxTndunWrd88ePXpk3LhxSxSf5A4AAABQ9YYMGZJmzZrVO4YMGfKl7/vxxx9nwIABOfDAA9O0adPi+I9//OPcfvvteeCBB3Lsscfm/PPPz2mnnVY8P3ny5LRs2bLevRZ8njx58mfOWXC+VNqyAAAAgJLUpnL7sgYOHJj+/fvXG6urq/tS95wzZ07222+/FAqFXHPNNfXOffpZm222WRo2bJhjjz02Q4YM+dLPXVKSOwAAAEDVq6urW6pJlQWJnTfeeCNjxoypV7WzKJ07d87cuXPz+uuvZ8MNN0yrVq0yZcqUenMWfF6wTs/i5ixuHZ/F0ZYFAAAA8CkLEjsvv/xy7r///qy22mqfe8348eNTW1ubFi1aJEm6dOmSsWPHZs6cOcU5o0aNyoYbbphVVlmlOGf06NH17jNq1Kh06dJlieJVuQMAAACU5JuyW9b06dPzyiuvFD9PnDgx48ePz6qrrpo11lgj++67b5566qncfffdmTdvXnENnFVXXTUNGzbMuHHj8thjj2WXXXZJkyZNMm7cuJx00kk55JBDiombgw46KOecc06OPPLIDBgwIBMmTMjll1+eSy+9tPjcE044ITvttFMuvvji9OrVK7fffnueeOKJetull6KmUCgUlsL3UlE+nvv5cwCAha2ydb9yhwAAVemjp68qdwhfi188+nq5Q1isH23XpuS5Dz74YHbZZZeFxvv06ZNBgwZl3XXXXeR1DzzwQHbeeec89dRT+dGPfpQXX3wxs2bNyrrrrpsf/OAH6d+/f73WsGeffTZ9+/bN3//+9zRv3jzHH398BgwYUO+ew4YNy5lnnpnXX38966+/fi688MLsvvvuJb9LIrkDAHyK5A4AfDGSO+W3JMmdbxptWQAAAEBJar8hbVnfNBZUBgAAAKhikjsAAAAAVUxbFgAAAFCS2m/KdlnfMCp3AAAAAKqY5A4AAABAFdOWBQAAAJREV1ZlUrkDAAAAUMUkdwAAAACqmLYsAAAAoCR2y6pMKncAAAAAqpjkDgAAAEAV05YFAAAAlERXVmVSuQMAAABQxSR3AAAAAKqYtiwAAACgJCpEKpPfBQAAAKCKSe4AAAAAVDFtWQAAAEBJamyXVZFU7gAAAABUMckdAAAAgCqmLQsAAAAoiaasyqRyBwAAAKCKSe4AAAAAVDFtWQAAAEBJau2WVZFU7gAAAABUMckdAAAAgCqmLQsAAAAoiaasyqRyBwAAAKCKSe4AAAAAVDFtWQAAAEBJbJZVmVTuAAAAAFQxyR0AAACAKqYtCwAAAChJjb6siqRyBwAAAKCKSe4AAAAAVDFtWQAAAEBJVIhUJr8LAAAAQBWT3AEAAACoYtqyAAAAgJLYLasyqdwBAAAAqGKSOwAAAABVTFsWAAAAUBJNWZVJ5Q4AAABAFZPcAQAAAKhi2rIAAACAktgtqzKp3AEAAACoYpI7AAAAAFVMWxYAAABQEhUilcnvAgAAAFDFJHcAAAAAqpi2LAAAAKAkdsuqTCp3AAAAAKqY5A4AAABAFdOWBQAAAJREU1ZlUrkDAAAAUMUkdwAAAACqmLYsAAAAoCQ2y6pMKncAAAAAqpjkDgAAAEAV05YFAAAAlKTWflkVSeUOAAAAQBWT3AEAAACoYtqyAAAAgJLYLasyqdwBAAAAqGKSOwAAAABVTFsWAAAAUJIau2VVJJU7AAAAAFVMcgcAAACgimnLAgAAAEpit6zKpHIHAAAAoIpJ7gAAAABUMW1ZAAAAQElq7ZZVkVTuAAAAAFQxyR0AAACAKia5AwAAAJSkpqZyjyUxduzY7LnnnmndunVqamoyfPjweucLhULOPvvsrLHGGmncuHG6deuWl19+ud6c999/PwcffHCaNm2alVdeOUceeWSmT59eb86zzz6bHXbYIY0aNcpaa62VCy+8cKFYhg0blo022iiNGjVKhw4d8pe//GXJXiaSOwAAAMAyZsaMGdl8881z9dVXL/L8hRdemCuuuCLXXnttHnvssay44orp0aNHPv744+Kcgw8+OM8//3xGjRqVu+++O2PHjs0xxxxTPD9t2rR0794966yzTp588slcdNFFGTRoUK677rrinEcffTQHHnhgjjzyyDz99NPp3bt3evfunQkTJizR+9QUCoXCEn4HFe/jueWOAACq0ypb9yt3CABQlT56+qpyh/C1uPeFd8odwmL1aL/6F7qupqYmd955Z3r37p3kk6qd1q1b5+STT84pp5ySJPnwww/TsmXLDB06NAcccED+8Y9/pH379vn73/+erbbaKkkycuTI7L777vn3v/+d1q1b55prrskZZ5yRyZMnp2HDhkmSn/zkJxk+fHhefPHFJMn++++fGTNm5O677y7Gs+2226Zjx4659tprS34HlTsAAABAScrdevVZx6xZszJt2rR6x6xZs5b4HSdOnJjJkyenW7duxbFmzZqlc+fOGTduXJJk3LhxWXnllYuJnSTp1q1bamtr89hjjxXn7LjjjsXETpL06NEjL730Uj744IPinE8/Z8GcBc8pleQOAAAAUPWGDBmSZs2a1TuGDBmyxPeZPHlykqRly5b1xlu2bFk8N3ny5LRo0aLe+eWWWy6rrrpqvTmLusenn7G4OQvOl2q5JZoNAAAAUIEGDhyY/v371xurq6srUzRfL8kdAAAAoCQ1WcJtqb5GdXV1SyWZ06pVqyTJlClTssYaaxTHp0yZko4dOxbnvP322/Wumzt3bt5///3i9a1atcqUKVPqzVnw+fPmLDhfKm1ZAAAAAP+/ddddN61atcro0aOLY9OmTctjjz2WLl26JEm6dOmSqVOn5sknnyzOGTNmTObPn5/OnTsX54wdOzZz5swpzhk1alQ23HDDrLLKKsU5n37OgjkLnlMqyR0AAABgmTJ9+vSMHz8+48ePT/LJIsrjx4/Pm2++mZqampx44ok577zz8qc//SnPPfdcDj300LRu3bq4o9bGG2+c3XbbLUcffXQef/zxPPLII+nXr18OOOCAtG7dOkly0EEHpWHDhjnyyCPz/PPP54477sjll19er3XshBNOyMiRI3PxxRfnxRdfzKBBg/LEE0+kX78l28HUVugAQJGt0AHgi1lWtkIf/eK75Q5hsXbdqHnJcx988MHssssuC4336dMnQ4cOTaFQyE9/+tNcd911mTp1arbffvv84he/yAYbbFCc+/7776dfv37585//nNra2uyzzz654oorstJKKxXnPPvss+nbt2/+/ve/p3nz5jn++OMzYMCAes8cNmxYzjzzzLz++utZf/31c+GFF2b33XdfonevyOTO1KlTs/LKK3/h6yV3AOCLkdwBgC9Gcqf8liS5801T9rasn/3sZ7njjjuKn/fbb7+sttpq+da3vpVnnnmmjJEBAAAAVL6yJ3euvfbarLXWWkk+WTRo1KhRueeee9KzZ8+ceuqpZY4OAAAAWKCmgv9alpV9K/TJkycXkzt333139ttvv3Tv3j1t2rQprjANAAAAwKKVvXJnlVVWyb/+9a8kyciRI9OtW7ckSaFQyLx588oZGgAAAEDFK3vlzt57752DDjoo66+/ft5777307NkzSfL000+nXbt2ZY4OAAAAWKBm2e5+qlhlT+5ceumladOmTf71r3/lwgsvLG4ZNmnSpPzoRz8qc3QAAAAAla0it0L/smyFDgBfjK3QAeCLWVa2Qn/gpffKHcJi7bLhauUOoWzKvuZOktxyyy3Zfvvt07p167zxxhtJkssuuyx33XVXmSMDAAAAFij3jlh2y1q0sid3rrnmmvTv3z89e/bM1KlTi4sor7zyyrnsssvKGxwAAABAhSt7cufKK6/M9ddfnzPOOCMNGjQojm+11VZ57rnnyhgZAAAAQOUr+4LKEydOzBZbbLHQeF1dXWbMmFGGiAAAAIBFqV22u58qVtkrd9Zdd92MHz9+ofGRI0dm4403/voDAgAAAKgiZa/c6d+/f/r27ZuPP/44hUIhjz/+eH77299myJAhueGGG8odHgAAAEBFK3ty56ijjkrjxo1z5plnZubMmTnooIPSunXrXH755TnggAPKHR4AAADw/1vWd6WqVGVN7sydOze33XZbevTokYMPPjgzZ87M9OnT06JFi3KGBQAAAFA1yrrmznLLLZfjjjsuH3/8cZJkhRVWkNgBAAAAWAJlX1B5m222ydNPP13uMAAAAIDPUVNTuceyrOxr7vzoRz/KySefnH//+9/p1KlTVlxxxXrnN9tsszJFBgAAAFD5agqFQqGcAdTWLlw8VFNTk0KhkJqamsybN2+J7/nx3KURGQAse1bZul+5QwCAqvTR01eVO4SvxcMvf1DuEBZr+/VXKXcIZVP2yp2JEyeWOwQAAACgBMt491PFKntyZ5111il3CMAS+N3tt+V3d/w2b/3nP0mStu3Wz7E//FG232GnevMKhUL6Hnd0Hnn4r7n0iqvz7V27Fc899rdxufrKy/PyP19K48YrZM+9euf4E07Kcsv93/+X9MjDf801V1+ZV195OXV1ddmy09Y5+bQB+da31vx6XhQAvqRTjuie3t/ePBu0aZmPZs3JY8+8ljMuvysvv/F2cc6915+QHbdav9511//+4fz4/92eJDlkz865fvAPFnn/tb/9k7zzwfRcd84h+cF3t13o/AuvTkqnff9fkqS2tiZnHrd7Dtx967RcrWkmvfNhbvnzY7ng+pFL63UBKKOyJ3d+/etff+b5Qw899GuKBChFi5atcsJJp2TtddZJoVDIn+8anhP69c0df7gz7dr93/84/c2vb07NIlY1e+nFF9P3uKNz1DHH5bzzf5a3356S8wb/NPPnz8/Jpw5Ikvz73//Kicf/KD/oc3iG/OznmT79v7noZ0PS/4Tjc8fv7/za3hUAvowdtmyXa+8YmyeffyPLLdcg5/TbM3df0y9b7H1eZn48uzjvV394JOdec3fx88yP5xT//Pv7nsqoR1+od9/rzvlBGtUtn3c+mJ4kOeWi3+esK+4qnl+uQYM8dsfA/HHU/21acvJh38nR++6Qo8++5ZOkzyZr55eDDsm06R/lF799aKm/OwBfr7Ind0444YR6n+fMmZOZM2emYcOGWWGFFSR3oMLsvMu3630+/oST8rvbf5tnnxlfTO68+I9/5Nc335jf3vGH7Lrz9vXm3zvyL9lggw1z3I8+Wddj7XXWyYn9T81pJ5+Y437UNyuuuFL+8fzzmT9/fvr9+MTiulyHHnZETjz+R5kzZ06WX375r+FNAeDL2avfL+p9Puanv8m/xlyQLdqvlUeeerU4/tHHszPlvf8u8h4fz5qTj2f9X7Kn+SorZedtNshx59xaHJs2/eNMm/5x8fOeO2+WVZo2zi1/Glcc23bz9XL3Q89m5MPPJ0nenPR+9tttq2y1iSp6YMnULuvbUlWosm+F/sEHH9Q7pk+fnpdeeinbb799fvvb35Y7POAzzJs3L/f8ZUQ++mhmNt98iyTJRx99lIGnnZzTzzw7zVdffaFrZs+enYZ1dfXGGjVqlFmzZuWF5z/5H5wbb7JJampqMvzOP2TevHn573//mxF/viudu2wnsQNA1Wq6UqMkyQcfzqw3vv/uW+VfYy7IE8NOz+Djv5vGjRb/z7qD99gmMz+enTvvH7/YOX16d8mYx17Km5P+b9HTvz3zWnbZZsO0W7tFkqTDBt9Kl47r5b5HXljcbQCoImWv3FmU9ddfPxdccEEOOeSQvPjii585d9asWZk1a1a9sUKDutT9z788AkvPy/98KT846IDMnj0rK6ywQi694uq0bdcuSXLRz4Zk8y22yC7f7rbIa7frun1uveXm3DPi7nTfrWfefffd/PKaq5Mk777zTpJkzTXXyrXX35hTTz4x553z08ybNy+bd9wiV11z3dfzggCwlNXU1OSiU/bNo0+/mhdenVQcv+OeJ/LmpPcz6Z0P02H91jnvhL2ywTotcsApNyzyPn16d8kd9zxRr5rn09ZYvVl6dG2fw04fWm/85zeNStOVGuWZO8/MvHmFNGhQk59efXduv+eJpfaOAJRP2St3Fme55ZbLW2+99bnzhgwZkmbNmtU7LvrZkK8hQlh2tWmzbn73h+H5zW9/l+/vf2DOOn1AXn3llTw4ZnT+/tjfctqA0xd77XZdt89JJ5+W8wb/NFtv0SHf7dWjuBhzzf/fgvXuO+/knJ+ele9+t3duveP3ufHm32T55ZfPKSf9OIVC4Wt5RwBYmi4buF82abdGDv3JTfXGb/zjI7l/3D/y/Ctv5fZ7nsiRZ92SvXbtmHXXbL7QPTpvtm42Xm+N3Dx83ELnFjh4z86Z+t+P8qcHnq03vm/3LXNAz61z2Ok3p8tBP8tRZ9+SE3+waw7es/PSeUFgmVFTwceyrOyVO3/605/qfS4UCpk0aVKuuuqqdO3a9XOvHzhwYPr371//Hg1U7cBXafmGDbP2/7/TXftNNs3zE57Lrb/5dRrV1eVf/3oz23fZut78k088Plt22iq/GnpLkuTQww7PD/oclnfeeTtNmzbLW//5T6647OKsueYnO2Hd/ttb02SllXLSKacV73H+BRel+6475blnn8lmm3f8el4UAJaCSwd8P7vvsGm6HXlZ/vP21M+c+/fnXk+StF1r9Uz897v1zh32vS4Z/+K/8vQ//rXY6/vstW1+O+LxzJk7r974+Sf2zs9vGpVh9z6ZJHn+lbey9hqr5tTDv5Nb//zYkr8UABWl7Mmd3r171/tcU1OT1VdfPd/+9rdz8cUXf+71dXULt2B9PHdpRgh8nvnz52fO7Nn5Ud/j8719v1/v3L6998wpAwZmp513qTdeU1OTFi1aJknu+cvdadVqjWzcfpMkyccff1ys4lmgtkFt8VkAUC0uHfD9fPfbm6f70Zfnjbfe+9z5m2/4yX/omPzuh/XGV2zcMPt8Z8ucfeWfFnVZkmSHTuun3dotMnQRlT2NGzXM/EL9f4bOm18oblwAQHUre3LHv6hBdbn80ouz/Q47ptUaa2TmjBn5y4i788TfH8811/0qzVdffZGLKK+xRuusueZaxc9Db7whXbffITW1tRk96r7ceMP1ueiSy9KgQYMkyQ477pTf/Hporv3FVenZa4/MnDEjV1x2SVq3/lY22rj91/auAPBlXDZwv+zfc6t8/6TrMn3Gx2m5WpMkyYfTP87Hs+Zk3TWbZ/+eW+Xeh5/Pe1NnpMMG38qFJ++dvz75cia8XH95gn17dMpyDWrz2xF/X+zzDuvdJY8/O7Hemj4L/GXscxlwZI/8a9IHeeHVSem40Zr58SG75NfD/7Z0Xxr45lvW+58qVNmTO2PHjs1GG22UFi1a1BufM2dOxo0blx133LFMkQGL8v777+XMgQPyzjtvZ6UmTbLBBhvmmut+lS7bfX4b5QIP/3Vsbrju2syePTsbbLhRLr/q6uK6O0nSedsuGXLhxRl64w0ZeuOv0qhxo2y+ecf84pfXp1GjRl/FawHAUnfsfp/879hRN5xYb/zos2/Jb/78WObMmZtvd94w/Q7aJSs2bph/T/kgw0ePzwU33LvQvQ7r3SV3jXkmH07/aJHParpSo/TetWNOuej3izzf/2fD8tMf7ZHLT98/q6+yUia982F+9ftHcv5193y5lwSgItQUyrw6aW1tbVq2bJk777wz2267bXF8ypQpad26debNm/cZVy+atiwA+GJW2bpfuUMAgKr00dNXlTuEr8XfXp1a7hAWa9u2K5c7hLKpiCbbAw44ILvuumuGDh1ab9yuOAAAAFA5air4r2VZ2ZM7NTU1GThwYG655Zb069cv/fv3LyZ1amqW7R8HAAAA4POUPbmzIJGz9957569//Wt+//vfp2fPnpk6dWp5AwMAAACoAmVP7nzaFltskccffzxTp07NrrvuWu5wAAAAgE+pqancY1lW9uROnz590rhx4+LnVq1a5aGHHsquu+6atddeu4yRAQAAAFS+su+W9VWwWxYAfDF2ywKAL2ZZ2S3r8dc+LHcIi7XNes3KHULZLFeOhz777LPZdNNNU1tbm2efffYz52622WZfU1QAAADAZ1nGu58qVlmSOx07dszkyZPTokWLdOzYMTU1NfW2PV/wuaamJvPmzStHiAAAAABVoSzJnYkTJ2b11Vcv/hkAAACAL6YsyZ111llnkX8GAAAAKpi+rIpU9t2ykuSWW25J165d07p167zxxhtJkssuuyx33XVXmSMDAAAAqGxlT+5cc8016d+/f3bfffdMnTq1uMbOyiuvnMsuu6y8wQEAAABUuLInd6688spcf/31OeOMM9KgQYPi+FZbbZXnnnuujJEBAAAAn1ZTwX8ty8qe3Jk4cWK22GKLhcbr6uoyY8aMMkQEAAAAUD3KntxZd911M378+IXGR44cmY033vjrDwgAAACgipRlt6xP69+/f/r27ZuPP/44hUIhjz/+eH77299myJAhueGGG8odHgAAAPD/q1m2u58qVtmTO0cddVQaN26cM888MzNnzsxBBx2Ub33rW7n88stzwAEHlDs8AAAAgIpW9uTORx99lO9973s5+OCDM3PmzEyYMCGPPPJI1lxzzXKHBgAAAFDxyr7mzl577ZVf//rXSZLZs2fnu9/9bi655JL07t0711xzTZmjAwAAABaoqeBjWVb25M5TTz2VHXbYIUny+9//Pi1btswbb7yRX//617niiivKHB0AAABAZSt7cmfmzJlp0qRJkuS+++7L3nvvndra2my77bZ54403yhwdAAAAQGUre3KnXbt2GT58eP71r3/l3nvvTffu3ZMkb7/9dpo2bVrm6AAAAICicvde6ctapLInd84+++yccsopadOmTTp37pwuXbok+aSKZ4sttihzdAAAAACVrey7Ze27777ZfvvtM2nSpGy++ebF8V133TXf+973yhgZAAAAQOUre3InSVq1apVWrVrVG9tmm23KFA0AAACwKDXLev9ThSp7WxYAAAAAX5zkDgAAAEAVq4i2LAAAAKDy1ejKqkgqdwAAAACqmOQOAAAAQBXTlgUAAACURFdWZVK5AwAAAFDFJHcAAAAAqpi2LAAAAKA0+rIqksodAAAAgComuQMAAABQxbRlAQAAACWp0ZdVkVTuAAAAAFQxyR0AAACAKqYtCwAAAChJja6siqRyBwAAAKCKSe4AAAAAVDFtWQAAAEBJdGVVJpU7AAAAAFVMcgcAAACgiknuAAAAAKWpqeBjCbRp0yY1NTULHX379k2S7LzzzgudO+644+rd480330yvXr2ywgorpEWLFjn11FMzd+7cenMefPDBbLnllqmrq0u7du0ydOjQJQu0RNbcAQAAAJYpf//73zNv3rzi5wkTJuQ73/lOvv/97xfHjj766AwePLj4eYUVVij+ed68eenVq1datWqVRx99NJMmTcqhhx6a5ZdfPueff36SZOLEienVq1eOO+643HrrrRk9enSOOuqorLHGGunRo8dSfR/JHQAAAKDqzZo1K7Nmzao3VldXl7q6uoXmrr766vU+X3DBBWnbtm122mmn4tgKK6yQVq1aLfJZ9913X1544YXcf//9admyZTp27Jhzzz03AwYMyKBBg9KwYcNce+21WXfddXPxxRcnSTbeeOM8/PDDufTSS5d6ckdbFgAAAFCSmgr+a8iQIWnWrFm9Y8iQIZ/7TrNnz85vfvObHHHEEamp+b/+rltvvTXNmzfPpptumoEDB2bmzJnFc+PGjUuHDh3SsmXL4liPHj0ybdq0PP/888U53bp1q/esHj16ZNy4cV/2Z1iIyh0AAACg6g0cODD9+/evN7aoqp3/NXz48EydOjWHHXZYceyggw7KOuusk9atW+fZZ5/NgAED8tJLL+WPf/xjkmTy5Mn1EjtJip8nT578mXOmTZuWjz76KI0bN17id1wcyR0AAACg6i2uBevz/OpXv0rPnj3TunXr4tgxxxxT/HOHDh2yxhprZNddd82rr76atm3bLpV4lyZtWQAAAEBJamoq9/gi3njjjdx///056qijPnNe586dkySvvPJKkqRVq1aZMmVKvTkLPi9Yp2dxc5o2bbpUq3YSyR0AAABgGXXTTTelRYsW6dWr12fOGz9+fJJkjTXWSJJ06dIlzz33XN5+++3inFGjRqVp06Zp3759cc7o0aPr3WfUqFHp0qXLUnyDT0juAAAAAMuc+fPn56abbkqfPn2y3HL/t2rNq6++mnPPPTdPPvlkXn/99fzpT3/KoYcemh133DGbbbZZkqR79+5p3759fvCDH+SZZ57JvffemzPPPDN9+/YttoYdd9xxee2113LaaaflxRdfzC9+8Yv87ne/y0knnbTU30VyBwAAAChJTQUfS+r+++/Pm2++mSOOOKLeeMOGDXP//fene/fu2WijjXLyySdnn332yZ///OfinAYNGuTuu+9OgwYN0qVLlxxyyCE59NBDM3jw4OKcddddNyNGjMioUaOy+eab5+KLL84NN9yw1LdBT5KaQqFQWOp3LbOP55Y7AgCoTqts3a/cIQBAVfro6avKHcLX4h9vzSh3CIu1cesVyx1C2ajcAQAAAKhitkIHAAAASvMFd6Xiq6VyBwAAAKCKSe4AAAAAVDFtWQAAAEBJavRlVSSVOwAAAABVTHIHAAAAoIppywIAAABKUqMrqyKp3AEAAACoYpI7AAAAAFVMWxYAAABQEl1ZlUnlDgAAAEAVk9wBAAAAqGLasgAAAIDS6MuqSCp3AAAAAKqY5A4AAABAFdOWBQAAAJSkRl9WRVK5AwAAAFDFJHcAAAAAqpi2LAAAAKAkNbqyKpLKHQAAAIAqJrkDAAAAUMW0ZQEAAAAl0ZVVmVTuAAAAAFQxyR0AAACAKqYtCwAAACiNvqyKpHIHAAAAoIpJ7gAAAABUMW1ZAAAAQElq9GVVJJU7AAAAAFVMcgcAAACgimnLAgAAAEpSoyurIqncAQAAAKhikjsAAAAAVUxbFgAAAFASXVmVSeUOAAAAQBWT3AEAAACoYtqyAAAAgNLoy6pIKncAAAAAqpjkDgAAAEAV05YFAAAAlKRGX1ZFUrkDAAAAUMUkdwAAAACqmLYsAAAAoCQ1urIqksodAAAAgComuQMAAABQxSR3AAAAAKqYNXcAAACAklhypzKp3AEAAACoYpI7AAAAAFVMWxYAAABQEluhVyaVOwAAAABVTHIHAAAAoIppywIAAABKpC+rEqncAQAAAKhikjsAAAAAVUxbFgAAAFASu2VVJpU7AAAAAFVMcgcAAACgimnLAgAAAEqiK6syqdwBAAAAqGKSOwAAAABVTFsWAAAAUBK7ZVUmlTsAAAAAVUxyBwAAAKCKacsCAAAASlJjv6yKpHIHAAAAoIpJ7gAAAABUMW1ZAAAAQGl0ZVUklTsAAAAAVUxyBwAAAKCKacsCAAAASqIrqzKp3AEAAACoYpI7AAAAAFVMWxYAAABQkhp9WRVJ5Q4AAACwTBk0aFBqamrqHRtttFHx/Mcff5y+fftmtdVWy0orrZR99tknU6ZMqXePN998M7169coKK6yQFi1a5NRTT83cuXPrzXnwwQez5ZZbpq6uLu3atcvQoUO/kveR3AEAAACWOZtsskkmTZpUPB5++OHiuZNOOil//vOfM2zYsDz00EN56623svfeexfPz5s3L7169crs2bPz6KOP5uabb87QoUNz9tlnF+dMnDgxvXr1yi677JLx48fnxBNPzFFHHZV77713qb9LTaFQKCz1u5bZx3M/fw4AsLBVtu5X7hAAoCp99PRV5Q7ha/HOfyv3X7hXb1L6yjODBg3K8OHDM378+IXOffjhh1l99dVz2223Zd99902SvPjii9l4440zbty4bLvttrnnnnuyxx575K233krLli2TJNdee20GDBiQd955Jw0bNsyAAQMyYsSITJgwoXjvAw44IFOnTs3IkSO/3Mv+D5U7AAAAQNWbNWtWpk2bVu+YNWvWYue//PLLad26ddZbb70cfPDBefPNN5MkTz75ZObMmZNu3boV52600UZZe+21M27cuCTJuHHj0qFDh2JiJ0l69OiRadOm5fnnny/O+fQ9FsxZcI+lSXIHAAAAqHpDhgxJs2bN6h1DhgxZ5NzOnTtn6NChGTlyZK655ppMnDgxO+ywQ/773/9m8uTJadiwYVZeeeV617Rs2TKTJ09OkkyePLleYmfB+QXnPmvOtGnT8tFHHy2NVy6yWxYAAABQmgreLWvgwIHp379/vbG6urpFzu3Zs2fxz5tttlk6d+6cddZZJ7/73e/SuHHjrzTOr4LKHQAAAKDq1dXVpWnTpvWOxSV3/tfKK6+cDTbYIK+88kpatWqV2bNnZ+rUqfXmTJkyJa1atUqStGrVaqHdsxZ8/rw5TZs2XeoJpP+vvXsPquK+/z/+OoBcPHA4Em+YIBqMCh3qxMskjFHEIpxO0yBIZYxViZCbFxQab5OqqBNpbTUYHTStLdjWWK1Ga8FUCQ1EkolVosZGQhVl1BmS8I1VB6OgsL8/Mjm/nKjhkKicDc/HDH+c/ex+9r37z44vP+9dwh0AAAAAANCpNTY2qra2VqGhoRo2bJi6dOmisrIy53hNTY3Onj2rmJgYSVJMTIyOHz+uTz/91LlPaWmpbDaboqKinPt8dY4v9/lyjjuJcAcAAAAAALjF4sF/7fHCCy+ooqJCdXV1evfdd5WcnCxvb29NmjRJwcHBysjIUE5Ojt566y1VVVXpqaeeUkxMjB599FFJUkJCgqKiojRlyhQdO3ZM+/bt0y9/+UvNnDnTuVroueee0+nTpzV//nx99NFHKigo0Pbt25Wdnd3OatvGO3cAAAAAAECncv78eU2aNEmfffaZevTooccee0zvvfeeevToIUl6+eWX5eXlpQkTJqipqUmJiYkqKChwHu/t7a3i4mI9//zziomJkdVq1bRp07R8+XLnPv3791dJSYmys7O1du1aPfDAA9q0aZMSExPv+PVYDMMw7visHezajY6uAAAAc+o2YlZHlwAAgCldPbK+o0u4J/6v0XP/wd09sPOuX+m8Vw4AAAAAANrF4sFfy+rMeOcOAAAAAACAiRHuAAAAAAAAmBhtWQAAAAAAwC2Wdn+XCvcCK3cAAAAAAABMjHAHAAAAAADAxGjLAgAAAAAAbuFrWZ6JlTsAAAAAAAAmRrgDAAAAAABgYoQ7AAAAAAAAJka4AwAAAAAAYGKEOwAAAAAAACbG17IAAAAAAIBb+FqWZ2LlDgAAAAAAgIkR7gAAAAAAAJgYbVkAAAAAAMAtFtGX5YlYuQMAAAAAAGBihDsAAAAAAAAmRlsWAAAAAABwC1/L8kys3AEAAAAAADAxwh0AAAAAAAAToy0LAAAAAAC4ha4sz8TKHQAAAAAAABMj3AEAAAAAADAx2rIAAAAAAIB76MvySKzcAQAAAAAAMDHCHQAAAAAAABOjLQsAAAAAALjFQl+WR2LlDgAAAAAAgIkR7gAAAAAAAJgYbVkAAAAAAMAtFrqyPBIrdwAAAAAAAEyMcAcAAAAAAMDEaMsCAAAAAABuoSvLM7FyBwAAAAAAwMQIdwAAAAAAAEyMtiwAAAAAAOAe+rI8Eit3AAAAAAAATIxwBwAAAAAAwMRoywIAAAAAAG6x0JflkVi5AwAAAAAAYGKEOwAAAAAAACZGWxYAAAAAAHCLha4sj8TKHQAAAAAAABMj3AEAAAAAADAxi2EYRkcXAaDzaGpqUl5enhYtWiQ/P7+OLgcAANPgGQoAuB3CHQD31OXLlxUcHKxLly7JZrN1dDkAAJgGz1AAwO3QlgUAAAAAAGBihDsAAAAAAAAmRrgDAAAAAABgYoQ7AO4pPz8/LV26lBdBAgDQTjxDAQC3wwuVAQAAAAAATIyVOwAAAAAAACZGuAMAAAAAAGB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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1K3NQQkjn2jG",
        "outputId": "2018b490-403b-4554-ed48-cb5ef9655458"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(233062,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "import numpy as np\n",
        "\n",
        "# Assuming y_pred_train_prob and y_pred_test_prob are the predicted probabilities for each class\n",
        "# Extract the probabilities for each class\n",
        "y_pred_train_prob = model(X_train_tensor).numpy()\n",
        "y_pred_test_prob = model(X_test_tensor).numpy()\n",
        "\n",
        "# Convert probabilities to class indices\n",
        "y_pred_train = np.argmax(y_pred_train_prob, axis=1)\n",
        "y_pred_test = np.argmax(y_pred_test_prob, axis=1)\n",
        "\n",
        "# Calculate ROC AUC score for each class\n",
        "roc_auc_train = []\n",
        "roc_auc_test = []\n",
        "\n",
        "for i in range(num_classes):  # Replace num_classes with the actual number of classes\n",
        "    roc_auc_train.append(roc_auc_score(y_train == i, y_pred_train_prob[:, i]))\n",
        "    roc_auc_test.append(roc_auc_score(y_test == i, y_pred_test_prob[:, i]))\n",
        "\n",
        "# Print or use roc_auc_train and roc_auc_test as needed\n",
        "print(\"ROC AUC Train:\", np.mean(roc_auc_train))\n",
        "print(\"ROC AUC Test:\", np.mean(roc_auc_test))\n",
        "\n",
        "# Plot ROC curves\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "for i in range(num_classes):\n",
        "    fpr_train, tpr_train, _ = roc_curve(y_train == i, y_pred_train_prob[:, i])\n",
        "    roc_auc_train_class = auc(fpr_train, tpr_train)\n",
        "    plt.plot(fpr_train, tpr_train, label=f'Train ROC Curve (Class {i + 1}, AUC = {roc_auc_train_class:.2f})')\n",
        "\n",
        "    fpr_test, tpr_test, _ = roc_curve(y_test == i, y_pred_test_prob[:, i])\n",
        "    roc_auc_test_class = auc(fpr_test, tpr_test)\n",
        "    plt.plot(fpr_test, tpr_test, label=f'Test ROC Curve (Class {i + 1}, AUC = {roc_auc_test_class:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve for each class')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "XYIzPwGWn0-t",
        "outputId": "35ae82f0-3615-4d8c-c716-5a986a1d9559"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC AUC Train: 0.9403517530706061\n",
            "ROC AUC Test: 0.9385001325657004\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}