EvaluationNotebook.ipynb

{
  "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": null,
      "metadata": {
        "id": "wjnjDTIOwNNT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a235c41-b239-49b7-df5a-a6396bfd791a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.metrics import roc_curve\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "nar64cYny6cl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bio_dir = '/content/drive/My Drive/Biometric Systems/Data/'"
      ],
      "metadata": {
        "id": "DCOroRd3w28O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bayes_id_dir = '/content/drive/My Drive/Biometric Systems/Data/identification_bayes.csv'\n",
        "norm_id_dir = '/content/drive/My Drive/Biometric Systems/Data/identification_norm.csv'\n",
        "bayes_vr_dir = '/content/drive/My Drive/Biometric Systems/Data/verification_bayes.csv'\n",
        "norm_vr_dir = '/content/drive/My Drive/Biometric Systems/Data/verification_norm.csv'\n",
        "bayes_all_vs_all = '/content/drive/My Drive/Biometric Systems/Data/identification_bayes_all_vs_all.csv'\n",
        "norm_all_vs_all = '/content/drive/My Drive/Biometric Systems/Data/identification_norm_all_vs_all.csv'"
      ],
      "metadata": {
        "id": "SbY4BTwNyj4Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bayes_id = pd.read_csv(bayes_id_dir)\n",
        "norm_id = pd.read_csv(norm_id_dir)\n",
        "bayes_vr = pd.read_csv(bayes_vr_dir)\n",
        "norm_vr = pd.read_csv(norm_vr_dir)\n",
        "bayes_all = pd.read_csv(bayes_all_vs_all)\n",
        "norm_all = pd.read_csv(norm_all_vs_all)"
      ],
      "metadata": {
        "id": "U910Dku8ykC4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rank_num = len(bayes_all.columns) - 1\n",
        "rank_list = [x + 1 for x in range(rank_num)]"
      ],
      "metadata": {
        "id": "yTq3_A7er3E0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_indexes(values, flag):\n",
        "  prediction_index_list = []\n",
        "  for row in values.iterrows():\n",
        "    user = row[1]['username']\n",
        "    user_score = row[1][user]\n",
        "    if flag == \"b\":\n",
        "      ordered_values = sorted(row[1][1:].tolist(), reverse = True)\n",
        "    else:\n",
        "      ordered_values = sorted(row[1][1:].tolist(), reverse = False)\n",
        "    prediction_index_list.append(ordered_values.index(user_score) + 1)\n",
        "  return prediction_index_list"
      ],
      "metadata": {
        "id": "b9nIZFy2r-zQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_cmc(indexes, rank_list):\n",
        "  cmc_list = []\n",
        "  for k in rank_list:\n",
        "    cmc_list.append(sum(1 for i in indexes if i <= k) / len(indexes))\n",
        "  return cmc_list"
      ],
      "metadata": {
        "id": "h6T4pSa-sL0p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bayes_indexes = compute_indexes(bayes_all, \"b\")\n",
        "norm_indexes = compute_indexes(norm_all, \"n\")\n",
        "\n",
        "bayes_cmc_list = compute_cmc(bayes_indexes, rank_list)\n",
        "norm_cmc_list = compute_cmc(norm_indexes, rank_list)\n",
        "\n",
        "print(bayes_cmc_list)\n",
        "print(norm_cmc_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18MvTBjCsB-M",
        "outputId": "e055f5f2-9e3b-40b7-e9b7-965d428df914"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.8604651162790697, 0.8837209302325582, 0.9069767441860465, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
            "[0.8604651162790697, 0.8837209302325582, 0.9534883720930233, 0.9767441860465116, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"BAYES\")\n",
        "plt.plot(rank_list, bayes_cmc_list, label=\"cmc\")\n",
        "plt.legend()\n",
        "plt.ylabel(\"Prob. of identification\")\n",
        "plt.xlabel(\"Rank\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "d8XivJaOkqz7",
        "outputId": "9c1b8c85-46e9-4ccf-8f56-d51e4612b2b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"L2 NORM\")\n",
        "plt.plot(rank_list, norm_cmc_list, label=\"cmc\")\n",
        "plt.legend()\n",
        "plt.ylabel(\"Prob. of identification\")\n",
        "plt.xlabel(\"Rank\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Rn6N1izrsdBb",
        "outputId": "f185e63b-584e-4d75-ccdb-1f0c90b64088"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3xU9Z3/8debcJW7BKgCAt5FRdCAt1attlZt1Var4tZd7Xbr9letWnW7Wru21W5t1d7rtj9bqWK7olJ16Ra1rJf6q9I1QS4KiKIECaBEwv0aks/vjzmxYxzCJGRyMsn7+XjMg5nvmXPmPQr55Hu+53y/igjMzMwa65J2ADMza59cIMzMLCcXCDMzy8kFwszMcnKBMDOznFwgzMwsJxcIMzPLyQXCOi1JlZI+lqP9OEkzJdVIqpb0sKR9mjjOs5K2SRqR1fYxSZWN3neZpJclbZH0tqRfSBqQtf1bkmolbZK0TtILko7P2n6KpJD0aKPjHpW0P9uy/xJmublAmH3QQOBuYBQwEtgI/GY3+2wG/m1XGyVdB3wf+BegP3BccuyZkrpnvfXBiOgDlALPAA83OlQ1cLykQVltlwKv7SafWbO5QJg1EhGPR8TDEbEhIrYAPwdO3M1uPwUulnRA4w2S+gHfBr4SEU9ERG1EVAIXkilCl+TIsBP4HTBM0uCsTTuAx4BJybFLgIuS95q1KhcIs907CViwm/esAH5FphA0dgLQE3gkuzEiNgEzgI833iHpVfwDsAZY22jzlGQbwCeAV4CVu8ln1mxd0w5g1p5JGgvcDJybx9tvA5ZIOrxReynwbtIraGwVcEzW6wslfQroC6wDzm+8X0S8IGlvSYeQKRRTgF55fSGzZnAPwmwXJB0IPA5cHRH/b3fvj4hqMqejbmm06V2gVFKuX8j2SbY3eCgiBgBDyfQMjsmxD8D9wJXAR4FHd/Eesz3iAmGWg6SRwP8At0bE/c3Y9Q4yP7Szf7DPArYD5zX6jD7AmcBTjQ8SEe8ClwPf2sUVVPcDXwZmJOMkZq3OBcI6u26SemY9ukoaBjwN/Dwiftmcg0XEOuAHwNey2taTGZv4maQzJHWTNAp4CKgi88M+17EWA09mHytr21LgZOCm5uQzaw4XCOvsZgBbsx7fAv4J2J/Mb++bGh7NOOZPgLrshoi4Hfg6cCewAfhfYDlwWkRsb+JYdwCXSxrSeENE/CUiPDhtBSMvGGRmZrm4B2FmZjm5QJiZWU4uEGZmlpMLhJmZ5dRh7qQuLS2NUaNGpR3DzKyozJ49+92IGJxrW4cpEKNGjaKioiLtGGZmRUXSsl1t8ykmMzPLyQXCzMxycoEwM7OcOswYRC61tbVUVVWxbdu2tKMURM+ePRk+fDjdunVLO4qZdUAdukBUVVXRt29fRo0ahaS047SqiGDNmjVUVVUxevTotOOYWQdUsFNMkiZLWi3plV1sl6SfSloiab6ko7O2XSrp9eRxaUszbNu2jUGDBnW44gAgiUGDBnXY3pGZpa+QYxD3Amc0sf1M4KDkcTnwCwBJewPfBI4FJgLflDSwpSE6YnFo0JG/m5mlr2CnmCLiuWTO+105F5gSmelk/yppQLIwyinAzIioAZA0k0yheaBQWa3trd64jQdfXE5tXX3aUcyK3of69+Lvjt2v1Y+b5hjEMDLz4TeoStp21f4Bki4n0/tgv/1a/z+OFcbOunq+dP9sXnprHe4Eme25cSMGdLgCscci4m7gboCysjIvbFEkfv7MEl56ax0/vXg85xy1b9pxzGwX0iwQK4ARWa+HJ20ryJxmym5/ts1SFcCUKVO48847kcTYsWMpKSmhV69ezJkzh9WrVzN58mSmTJnCrFmzOPbYY7n33nsBeOKJJ/j6179OXV0dpaWlPPXUB5YuLjqzl9Xw06de57zxw1wczNq5NAvEdOBKSVPJDEivj4hVkp4Evps1MH06cOOefti3/7CAhSs37Olh3mfMvv345tmHN/meBQsW8J3vfIcXXniB0tJSampquPbaa1m7di2zZs1i+vTpnHPOOTz//PP8+te/ZsKECcydO5dhw4bxxS9+keeee47Ro0dTU1PTqtnTsHFbLdc8OJdhA3vx7XOb/u9mZukrWIGQ9ACZnkCppCoyVyZ1A0gWgp8BnAUsAbYAn0+21Ui6FShPDnVLw4B1MXr66ae54IILKC0tBWDvvfcG4Oyzz0YSRx55JEOHDuXII48E4PDDD6eyspLly5dz0kknvXePQ8N+xeyb0xewYu1WHv7S8fTt6Zv7zNq7Ql7FdPFutgdwxS62TQYmt2ae3f2m39Z69OgBQJcuXd573vB6586dlJSUpBWtIKbPW8kjL63g6tMO4piRxV/szDoDz8VUYKeeeioPP/wwa9asAcj7VNFxxx3Hc889x9KlS5u1X3u0Yt1Wbnr0ZcbvN4CvnHpg2nHMLE9FfRVTMTj88MO56aabOPnkkykpKWH8+PF57Td48GDuvvtuzjvvPOrr6xkyZAgzZ84scNrWV1cffPXBudTXBz+5aDxdS/w7iVmxUOZMT/ErKyuLxgsGLVq0iMMOOyylRG2jvX/Hu55Zwh1PLubOC47is8cMTzuOmTUiaXZElOXa5l/nrGDmLV/Hj2a+xifH7sP5R+e819HM2jEXCCuIzdt3cs2Dcxnctwff/fSRnjfKrAh1+ALRUU6h5dKev9ut/72QyjWb+eGF4+i/ly9pNStGHbpA9OzZkzVr1rTrH6Qt1bAeRM+ePdOO8gFPvLKKqeXL+dLJB3D8AYPSjmNmLdShr2IaPnw4VVVVVFdXpx2lIBpWlGtP3l6/jRseeZkjh/Xnqx87OO04ZrYHOnSB6Natm1dba0P19cH1D89je209P540ju5dO3QH1azD879gazWTn1/KX5a8y81nj+GAwX3SjmNme8gFwlrFgpXruf2JxZw+ZiiTJozY/Q5m1u65QNge27qjjqunzmXAXt343vljfUmrWQfRoccgrG3c9vgilqzexP1fmMjevbunHcfMWol7ELZHnlr0DlNmLeOfPjyajxw0OO04ZtaKXCCsxao3budr0+Zz6If68i9nHJJ2HDNrZT7FZC0SEfzLtHls2r6TBy4/jh5dO9b6FWbmHoS10JRZy3h2cTVfP+swDh7aN+04ZlYALhDWbK+9s5F/n7GIjx4ymH84fmTaccysQFwgrFm21dZx1QNz6NujK7d/9ihf0mrWgRW0QEg6Q9JiSUsk3ZBj+0hJT0maL+lZScOztt0uaYGkRZJ+Kv8kahfueHIxr769kTsuGMvgvj12v4OZFa2CFQhJJcBdwJnAGOBiSWMave1OYEpEjAVuAW5L9j0BOBEYCxwBTABOLlRWy89zr1Vzz1+W8g/Hj+TUQ4emHcfMCqyQPYiJwJKIeDMidgBTgXMbvWcM8HTy/Jms7QH0BLoDPYBuwDsFzGq7UbN5B9c9PI8Dh/Th62e13yVOzaz1FLJADAOWZ72uStqyzQPOS55/BugraVBEzCJTMFYljycjYlEBs1oTIoJ//f181m+p5SeTxtGzmy9pNesM0h6kvh44WdIcMqeQVgB1kg4EDgOGkykqp0r6SOOdJV0uqUJSRUdd86E9eODF5cxc+A5fO+MQDt+3f9pxzKyNFLJArACyp/UcnrS9JyJWRsR5ETEeuClpW0emN/HXiNgUEZuAx4HjG39ARNwdEWURUTZ4sKd5KIQ3qjdx638v5MMHlvKPJ3ptDbPOpJAFohw4SNJoSd2BScD07DdIKpXUkOFGYHLy/C0yPYuukrqR6V34FFMb27GznmumzqVHty784MKj6NLFF5KZdSYFKxARsRO4EniSzA/3hyJigaRbJJ2TvO0UYLGk14ChwL8n7dOAN4CXyYxTzIuIPxQqq+X2o/95jZdXrOd7541laL/2t/a1mRVWQediiogZwIxGbTdnPZ9Gphg03q8O+OdCZrOmzXpjDb/88xtMmjCCM474UNpxzCwFaQ9SWzu0fkst1z40l9GDenPz2Y1vXTGzzsKzudr7RARff/Rlqjdu55Evn8Be3f1XxKyzcg/C3uf3L63gjy+v4trTD2bs8AFpxzGzFLlA2HuWrdnMN//rFSaO3pt/PumAtOOYWcpcIAyA2rp6rp46ly5dxI8uGkeJL2k16/R8gtkA+NnTS5i7fB0/u3g8wwb0SjuOmbUD7kEYFZU1/Pzp1znv6GGcfdS+accxs3bCBaKT27CtlmsenMuwgb349jmHpx3HzNoRn2Lq5L71XwtYtX4bD/3z8fTt2S3tOGbWjrgH0Yn919wVPDJnBV859UCOGTkw7Thm1s64QHRSVWu38I3HXuHo/QZw5UcPTDuOmbVDLhCdUF19cO2D84iAH180nq4l/mtgZh/kMYhO6Jd/foMXK2v44YVHsd+gvdKOY2btlH917GTmLl/Hj2a+xtlH7ctnxjdeAdbM7G92WyAknSfpdUnrJW2QtFHShrYIZ61r8/adXDN1DkP79eQ7nz4CyXdLm9mu5XOK6Xbg7Ijwim5F7pY/LGRZzRamfvE4+vfyJa1m1rR8TjG94+JQ/B5/eRUPVizny6ccwLH7D0o7jpkVgXx6EBWSHgQeA7Y3NEbEIwVLZa1q1fqt3PDIy4wd3p9rPnZw2nHMrEjkUyD6AVuA07PaAnCBKAL19cH1D89jx856fjJpPN18SauZ5Wm3BSIiPt8WQaww7vnLUp5fsobvn38ko0t7px3HzIpIPlcxDZf0qKTVyeP3kobnc3BJZ0haLGmJpBtybB8p6SlJ8yU9m31cSftJ+pOkRZIWShrVnC9msGDlem5/8lU+cfhQLiwbkXYcMysy+Zxv+A0wHdg3efwhaWuSpBLgLuBMYAxwsaQxjd52JzAlIsYCtwC3ZW2bAtwREYcBE4HVeWS1xNYddVw9dS579+7O984b60tazazZ8ikQgyPiNxGxM3ncCwzOY7+JwJKIeDMidgBTgXMbvWcM8HTy/JmG7Ukh6RoRMwEiYlNEbMnjMy3x3RmLWLJ6Ez+4YBwDe3dPO46ZFaF8CsQaSZdIKkkelwBr8thvGLA863VV0pZtHnBe8vwzQF9Jg4CDgXWSHpE0R9IdSY/kfSRdLqlCUkV1dXUekTqHpxa9w/1/XcYXPzKaDx9UmnYcMytS+RSIfwQuBN4GVgGfBVpr4Pp64GRJc4CTgRVAHZnB848k2ycA+wOXNd45Iu6OiLKIKBs8OJ9OTce3euM2vjZtPoft04/rP3FI2nHMrIjlcxXTMuCcFhx7BZA9Mjo8acs+9kqSHoSkPsD5EbFOUhUwNyLeTLY9BhwH3NOCHJ1GRPAvD89n0/adTJ00jh5dP9DpMjPL2y4LhKSvRcTtkn5G5r6H94mIq3Zz7HLgIEmjyRSGScDfNfqMUqAmIuqBG4HJWfsOkDQ4IqqBU4GKPL9Tp3XfC5X8+bVqbj33cA4a2jftOGZW5JrqQTRMr9GiH8wRsVPSlcCTQAkwOSIWSLoFqIiI6cApwG2SAngOuCLZt07S9cBTylx+Mxv4VUtydBaL397Idx9/lVMPHcIlx41MO46ZdQCK+EDn4P1vkC6IiId315a2srKyqKjonJ2MbbV1fPqu53l303aeuOYkSvv0SDuSmRUJSbMjoizXtnwGqW/Ms81ScseTi3n17Y3cccFRLg5m1mqaGoM4EzgLGCbpp1mb+gE7Cx3M8vPca9Xc85elXHbCKD56yJC045hZB9LUGMRKMuMP55AZA2iwEfhqIUNZftZvqeW6h+dx8NA+3HDmoWnHMbMOZpcFIiLmAfMk/WdE1LZhJsvTzEXvUL1xO7+85Bh6dvMlrWbWuvKZ7nuUpNvITIvRs6ExIvYvWCrLS0VlDf17dWP8iAFpRzGzDijfyfp+QWbc4aNkJtH7bSFDWX5erKyhbORAunTxRHxm1vryKRC9IuIpMpfELouIbwGfLGws2501m7bzZvVmykbtnXYUM+ug8jnFtF1SF+D15Ma3FUCfwsay3SmvXAvAxNEDU05iZh1VPj2Iq4G9gKuAY4BLgEsLGcp2r7yyhu5du3DEsP5pRzGzDiqfyfrKk6ebaL1ZXG0PVVTWMG7EAE/IZ2YFk8+SozMlDch6PVDSk4WNZU3ZvH0nr6zcwESPP5hZAeVziqk0ItY1vIiItYBv2U3RnLfWUVcflI3y+IOZFU4+BaJe0n4NLySNJMf039Z2yitr6CI4ZqQLhJkVTj5XMd0E/EXSnwGRWent8oKmsiaVV9Zw2D796NuzW9pRzKwDy2eQ+glJR5NZ0Q3gmoh4t7CxbFdq6+qZ89Y6LpowYvdvNjPbA7s8xSTp0OTPo4H9yEzetxLYL2mzFCxYuYGttXVM8AC1mRVYUz2Ia8mcSvpBjm1BZhlQa2PlS2sAmOABajMrsKYKxMzkzy9ExJttEcZ2r7yyhpGD9mJIv567f7OZ2R5o6iqmhlXjprVFENu9iKBi2VqfXjKzNtFUgVgj6U/AaEnTGz/yObikMyQtlrRE0g05to+U9JSk+ZKelTS80fZ+kqok/bx5X6tjeqN6EzWbd/j0kpm1iaZOMX0SOBq4n9zjEE2SVALcBXwcqALKJU2PiIVZb7sTmBIR90k6FbgN+Pus7bcCzzX3szuqhgn63IMws7bQ1IpyO4C/SjohIqpbcOyJwJKG8QtJU4FzgewCMYbMYDjAM8BjDRskHQMMBZ4Aylrw+R1O+dIaSvt0Z3Rp77SjmFkn0NRlrj9Onk5u4SmmYcDyrNdVSVu2ecB5yfPPAH0lDUqmF/8BcH1THyDpckkVkiqqq1tSw4pLZoGgvZG8QJCZFV5Tp5juT/68s4Cffz3wc0mXkTmVtAKoA74MzIiIqqZ+GEbE3cDdAGVlZR16+o9V67dStXYrnz9xdNpRzKyTaOoU0+zk6biI+En2NklXA3/ezbFXANm3+w5P2rI/YyVJD0JSH+D8iFgn6XjgI5K+TGZxou6SNkXEBwa6O4v3Fgjy+IOZtZF8JuvLtTjQZXnsVw4cJGm0pO7AJOB9p6YklSankyBzWe1kgIj4XETsFxGjyPQypnTm4gCZ8Yfe3Us4bJ++aUcxs05ilz0ISRcDf0dymWvWpr5Aze4OHBE7kyVKnwRKgMkRsUDSLUBFREwHTgFukxRkTjFd0eJv0sGVV9Zw9MiBdC3Jp6abme25psYgXgBWAaW8/zLXjcD8fA4eETOAGY3abs56Po3d3IgXEfcC9+bzeR3V+q21LH5nI2cesU/aUcysE2lqDGIZsAw4vu3iWC6zl9UQARNG+wY5M2s7+Sw5ep6k1yWtl7RB0kZJG9oinGWUV66laxcxfoQLhJm1nXwWDLodODsiFhU6jOVWvrSGI4b1p1f3krSjmFknks+I5zsuDunZVlvH/Kr1TBzty1vNrG3l04OokPQgmWkwtjc0RsQjBUtl75lftZ4ddfWef8nM2lw+BaIfsAU4PastABeINlBembmiuGykxx/MrG3lsyb159siiOVWXlnDQUP6MLB397SjmFknk89VTAcnaza8krweK+kbhY9mdfXB7Mq1lPn0kpmlIJ9B6l+RmQajFiAi5pOZNsMK7NW3N7Bx+04m+v4HM0tBPgVir4h4sVHbzkKEsfer8AJBZpaifArEu5IOIDMwjaTPkpmCwwrsxcoa9unfk2EDeqUdxcw6oXyuYrqCzJoLh0paASwFLiloKiMiKF9aw3H7D/ICQWaWinyuYnoT+Jik3kCXiNhY+Fi2vGYrqzduZ4JvkDOzlDQ13fe1u2gHICJ+WKBMRub0EsCEUR6gNrN0NNWDaFiZ5hBgAn9b7OdsoPGgtbWy8qU19O/VjYOHeIEgM0tHU9N9fxtA0nPA0Q2nliR9C/hjm6TrxMqX1VA2ciBdunj8wczSkc9VTEOBHVmvdyRtViDvbtrOm9WbfYOcmaUqn6uYpgAvSno0ef1pOvkKb4XWcP+Db5AzszTlcxXTv0t6HPhI0vT5iJhT2FidW3llDd27duGIYf3TjmJmndguTzFJ6pf8uTdQCdyfPJYlbbsl6QxJiyUtkXRDju0jk3me5kt6VtLwpH2cpFmSFiTbLmrBdyta5ZU1jBsxgB5dvUCQmaWnqTGI/0z+nA1UZD0aXjdJUglwF3AmMAa4WNKYRm+7E5gSEWOBW4DbkvYtwD9ExOHAGcCPJQ3I6xsVuc3bd7Jg5QYmevzBzFLW1FVMn0r+HN3CY08EliQ32iFpKnAusDDrPWOAhvstniGzKBER8VpWjpWSVgODgXUtzFI05ry1jrr6oMz3P5hZyvK5iqmlhgHLs15XJW3Z5gHnJc8/A/SVNCj7DZImAt2BNwqUs115sbKGLoJjvECQmaWskAUiH9cDJ0uaA5wMrADqGjZK2ofMuMfnI6K+8c6SLpdUIamiurq6rTIXVEVlDYft04++PbulHcXMOrmmBqlbemqpwQpgRNbr4UnbeyJiZUScFxHjgZuStnXJ5/cjc0PeTRHx11wfEBF3R0RZRJQNHjx4D+Omr7aunjlvrfP03mbWLjTVg5gGIOmpFh67HDhI0mhJ3cksMjQ9+w2SSiU1ZLgRmJy0dwceJTOAPa2Fn190Xlmxnq21dS4QZtYuNHUfRBdJXwcOzjVx3+4m64uInZKuBJ4ESoDJEbFA0i1ARURMB04BbpMUwHNkphYHuBA4CRgk6bKk7bKImJv/Vys+f1sgyOMPZpa+pgrEJDJ3TXflbxP3NUtEzABmNGq7Oev5NJKeSqP3/Bb4bUs+s5i9WFnDyEF7MaRfz7SjmJk1eZnrYuD7kuZHxONtmKlTiggqKms47TBPc2Vm7UM+VzG9IOmHDVcLSfqBJM8B0creqN7E2i21Pr1kZu1GPgViMrCRzLjAhcAG4DeFDNUZvbi0YfzBA9Rm1j7kM5vrARFxftbrb0vq0IPFaaiorKG0T3dGl/ZOO4qZGZBfD2KrpA83vJB0IrC1cJE6pxcraygbufd7S7qamaUtnx7El4ApWeMOa4FLCxep81m1fitVa7fy+RP39N5EM7PWk896EPOAoxqm/46IDQVP1cmU+/4HM2uH8ulBAC4MhVS+tIbe3UsYs0+/tKOYmb0n7cn6jMwCQUePHEjXEv/vMLP2wz+RUrZ+Sy2L39lI2Uhf3mpm7UuLCoSkD7V2kM5q9ls1RMCE0R5/MLP2paU9iHtaNUUn9uLStXTtIsaPcIEws/alRQUiIj7Z2kE6q4rKGo4Y1p9e3UvSjmJm9j55XcUk6Wjgw0AAz0fESwVN1Ulsq61jftV6LjtxVNpRzMw+YLc9CEk3A/cBg4BS4DeSvlHoYJ3B/Kr17Kir9/xLZtYu5dOD+BxwVERsA5D0PWAu8J1CBusMyitrACgb6fEHM2t/8hmDWAlkr2DTg0ZrS1vLvLi0hoOG9GFg7+5pRzEz+4Bd9iAk/YzMmMN6YIGkmcnrjwMvtk28jquuPnhp2Vo+ddS+aUcxM8upqVNMFcmfs4FHs9qfLViaTuTVtzewcftOJvr+BzNrp5pacvS+hueSugMHJy8XR0RtoYN1dOVLM+MPHqA2s/Yqn6uYTgFeB+4C/gN4TdJJ+Rxc0hmSFktaIumGHNtHSnpK0nxJz0oanrXtUkmvJ48ON714+bK17NO/J8MG9Eo7iplZTvlcxfQD4PSIWAwg6WDgAeCYpnaSVEKmqHwcqALKJU2PiIVZb7sTmBIR90k6FbgN+HtJewPfBMrIjHvMTvZd27yv1z5FBOVLazhu/0FeIMjM2q18rmLq1lAcACLiNaBbHvtNBJZExJsRsQOYCpzb6D1jgKeT589kbf8EMDMiapKiMBM4I4/PLApv1Wxh9cbtTBjt00tm1n7lUyBmS/q1pFOSx6/42wB2U4YBy7NeVyVt2eYB5yXPPwP0lTQoz32RdLmkCkkV1dXVeURqH7xAkJkVg3wKxJeAhcBVyWMh8H9a6fOvB06WNAc4mcz9FXX57hwRd0dEWUSUDR48uJUiFV750hr69ezKwUP6ph3FzGyXmhyDSMYR5kXEocAPm3nsFcCIrNfDaXSDXUSsJOlBSOoDnB8R6yStAE5ptO+zzfz8dqu8soayUXvTpYvHH8ys/WqyBxERdcBiSfu14NjlwEGSRieXyU4Cpme/QVKppIYMNwKTk+dPAqdLGihpIHB60lb03t20nTff3ezLW82s3cvnKqaBZO6kfhHY3NAYEec0tVNE7JR0JZkf7CXA5IhYIOkWoCIippPpJdwmKYDngCuSfWsk3UqmyADcEhE1zftq7VNFMv+Sb5Azs/YunwLxby09eETMAGY0ars56/k0YNou9p3M33oUHUZ55Vq6d+3CEcP6px3FzKxJTc3F1JPMAPWBwMvAPRGxs62CdVTllTWMGzGAHl29QJCZtW9NjUHcR+ZGtZeBM8ncMGd7YPP2nSxYuYGJHn8wsyLQ1CmmMRFxJICke/AMrntszlvrqKsPynz/g5kVgaZ6EO9NyOdTS63jxcoaugiO8QJBZlYEmupBHCVpQ/JcQK/ktYCIiH4FT9fBlC+t4bB9+tG3Zz4zlZiZpaup6b49itqKauvqmbN8LZMmtOSWEjOztpfPVBvWCl5ZsZ5ttfW+Qc7MioYLRBspr2xYIMjjD2ZWHFwg2kh55VpGDtqLIf16ph3FzCwvLhBtoL4+qKis8eklMysqLhBt4M13N7F2S61PL5lZUXGBaAMvLm1YIMg9CDMrHi4QbaC8sobSPt0ZXdo77ShmZnlzgWgD5ZU1lI3cG8kLBJlZ8XCBKLBV67dStXYrE0b79JKZFRcXiAJ7canvfzCz4uQCUWDPL3mX3t1LGLOPp64ys+LiAlFA5ZU1TJtdxTnj9qVrif9Tm1lx8U+tAtmwrZZrps5l+MC9uOmTY9KOY2bWbAUtEJLOkLRY0hJJN+TYvp+kZyTNkTRf0llJezdJ90l6WdIiSTcWMmch3PzYK7y9YRs/umgcfXrks/S3mVn7UrACIakEuIvMcqVjgIslNf5V+hvAQxExHpgE/EfSfgHQI1nR7hjgnyWNKlTW1vbYnBU8NnclV516kBcHMrOiVcgexERgSUS8GRE7gKnAuY3eE0DD6G1/YGVWe29JXYFewA5gA0Vgec0W/u2xVzhm5ECu+OgBaR/STkIAAAp4SURBVMcxM2uxQhaIYcDyrNdVSVu2bwGXSKoCZgBfSdqnAZuBVcBbwJ0RUdP4AyRdLqlCUkV1dXUrx2++nXX1fPXBuQTw44vGeWDazIpa2j/BLgbujYjhwFnA/ZK6kOl91AH7AqOB6yTt33jniLg7Isoiomzw4MFtmTunXzz7BhXL1nLrpw9nxN57pR3HzGyPFLJArABGZL0enrRl+wLwEEBEzAJ6AqXA3wFPRERtRKwGngfKCph1j81dvo4fP/U65xy1L58e17ijZGZWfApZIMqBgySNltSdzCD09EbveQs4DUDSYWQKRHXSfmrS3hs4Dni1gFn3yObtO7l66hw+1K8nt376CM+5ZGYdQsEKRETsBK4EngQWkblaaYGkWySdk7ztOuCLkuYBDwCXRUSQufqpj6QFZArNbyJifqGy7qlv/2EBy2u28KOLxtG/V7e045iZtYqCXqAfETPIDD5nt92c9XwhcGKO/TaRudS13Zvx8ioeqqjiyo8eyERPyGdmHUjag9RFbdX6rdz4yMscNbw/V3/soLTjmJm1KheIFqqvD659cB61dfX8eNJ4uvmSVjPrYDwHRAv96v+9yaw313D7+WO9UpyZdUj+tbcFXlmxnjv/tJgzj/gQF5QNTzuOmVlBuEA009YddVw1dQ6DevfgtvOO9CWtZtZh+RRTM33njwtZ+u5mfveFYxmwV/e045iZFYx7EM0wc+E7/O5/3+Lyj+zPCQeWph3HzKygXCDytHrDNv719/MZs08/rj394LTjmJkVnAtEHurrg+unzWfz9p389OJx9OhaknYkM7OCc4HIw32zKnnutWq+8akxHDikb9pxzMzahAvEbrz69gZue/xVTjt0CJccu1/acczM2owLRBO21dZx9QNz6dezG9//7Fhf0mpmnYovc23C9594lcXvbOTez0+gtE+PtOOYmbUp9yB24dnFq/nN85VcdsIoTjlkSNpxzMzanAtEDms2bef6h+dzyNC+3HDmoWnHMTNLhU8xNRIR/Ovv57NhWy2//aeJ9OzmS1rNrHNyD6KR3/3vW/zPotXccMahHPqhfmnHMTNLjQtEliWrN/KdPy7kpIMHc9kJo9KOY2aWKheIxPaddVz1wFz26t6VOz87li5dfEmrmXVuBS0Qks6QtFjSEkk35Ni+n6RnJM2RNF/SWVnbxkqaJWmBpJcl9Sxk1h/+6TUWrtrA988fy5B+Bf0oM7OiULBBakklwF3Ax4EqoFzS9IhYmPW2bwAPRcQvJI0BZgCjJHUFfgv8fUTMkzQIqC1U1ueXvMv/fe5NPnfsfnx8zNBCfYyZWVEpZA9iIrAkIt6MiB3AVODcRu8JoGEkuD+wMnl+OjA/IuYBRMSaiKgrRMh1W3Zw3UPz2H9wb77xyTGF+Agzs6JUyAIxDFie9boqacv2LeASSVVkeg9fSdoPBkLSk5JekvS1QoXcWR8cMaw/P500nl7dfUmrmVmDtAepLwbujYjhwFnA/ZK6kDn19WHgc8mfn5F0WuOdJV0uqUJSRXV1dYsClPbpwa8vLeOIYf1b/CXMzDqiQhaIFcCIrNfDk7ZsXwAeAoiIWUBPoJRMb+O5iHg3IraQ6V0c3fgDIuLuiCiLiLLBgwcX4CuYmXVehSwQ5cBBkkZL6g5MAqY3es9bwGkAkg4jUyCqgSeBIyXtlQxYnwwsxMzM2kzBrmKKiJ2SriTzw74EmBwRCyTdAlRExHTgOuBXkr5KZsD6sogIYK2kH5IpMgHMiIg/FiqrmZl9kDI/j4tfWVlZVFRUpB3DzKyoSJodEWW5tqU9SG1mZu2UC4SZmeXkAmFmZjm5QJiZWU4dZpBaUjWwLO0cu1AKvJt2iBZy9nQUa/ZizQ2dN/vIiMh5I1mHKRDtmaSKXV0l0N45ezqKNXux5gZnz8WnmMzMLCcXCDMzy8kFom3cnXaAPeDs6SjW7MWaG5z9AzwGYWZmObkHYWZmOblAmJlZTi4QBSRphKRnJC2UtEDS1Wlnag5JJZLmSPrvtLM0h6QBkqZJelXSIknHp50pX5K+mvxdeUXSA5J6pp1pVyRNlrRa0itZbXtLminp9eTPgWlm3JVdZL8j+TszX9KjkgakmXFXcmXP2nadpJBU2hqf5QJRWDuB6yJiDHAccIWkYlr4+mpgUdohWuAnwBMRcShwFEXyHSQNA64CyiLiCDLT5E9KN1WT7gXOaNR2A/BURBwEPJW8bo/u5YPZZwJHRMRY4DXgxrYOlad7+WB2JI0ATiezzk6rcIEooIhYFREvJc83kvlB1Xhd7nZJ0nDgk8Cv087SHJL6AycB9wBExI6IWJduqmbpCvRKFsraC1iZcp5diojngJpGzecC9yXP7wM+3aah8pQre0T8KSJ2Ji//SmYVzHZnF//dAX4EfI3MGjqtwgWijUgaBYwH/jfdJHn7MZm/bPVpB2mm0WRWJfxNcnrs15J6px0qHxGxAriTzG+Aq4D1EfGndFM129CIWJU8fxsYmmaYPfCPwONph8iXpHOBFRExrzWP6wLRBiT1AX4PXBMRG9LOszuSPgWsjojZaWdpga5k1i//RUSMBzbTfk9zvE9yvv5cMkVuX6C3pEvSTdVyyeqQRXcdvaSbyJwe/l3aWfIhaS/g68DNrX1sF4gCk9SNTHH4XUQ8knaePJ0InCOpEpgKnCrpt+lGylsVUBURDT21aWQKRjH4GLA0IqojohZ4BDgh5UzN9Y6kfQCSP1ennKdZJF0GfAr4XBTPTWIHkPmlYl7yb3Y48JKkD+3pgV0gCkiSyJwLXxQRP0w7T74i4saIGB4Ro8gMkj4dEUXxm2xEvA0sl3RI0nQasDDFSM3xFnCcpL2SvzunUSQD7FmmA5cmzy8F/ivFLM0i6Qwyp1XPiYgtaefJV0S8HBFDImJU8m+2Cjg6+bewR1wgCutE4O/J/AY+N3mclXaoTuArwO8kzQfGAd9NOU9ekl7PNOAl4GUy/z7b7fQPkh4AZgGHSKqS9AXge8DHJb1Opkf0vTQz7sousv8c6AvMTP6t/jLVkLuwi+yF+azi6UWZmVlbcg/CzMxycoEwM7OcXCDMzCwnFwgzM8vJBcLMzHJygTBrAUl1yaWQr0j6w57M/ClpU2tmM2stLhBmLbM1IsYls67WAFekHcistblAmO25WSSz9EqaKGlWMlHgCw13dEu6TNIjkp5I1kq4vfFBJJUm+36yjfOb5dQ17QBmxUxSCZkpMe5Jml4FPhIROyV9jMxd3Ocn28aRmdF3O7BY0s8iYnlynKFkpqn4RkTMbMvvYLYrLhBmLdNL0lwyPYdFZBabAegP3CfpIDIzmXbL2uepiFgPIGkhMBJYnrznKeCKiPhzG+U32y2fYjJrma0RMY7MD3nxtzGIW4FnkrGJs4HsJUO3Zz2v42+/oO0EZgOfKGhis2ZygTDbA8msn1cB1yWrwPUHViSbL8v3MGQWqDlU0r+2ekizFnKBMNtDETEHmA9cDNwO3CZpDs04hRsRdcn+p0r6ckGCmjWTZ3M1M7Oc3IMwM7OcXCDMzCwnFwgzM8vJBcLMzHJygTAzs5xcIMzMLCcXCDMzy+n/A5+7LcZGjqtdAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_1 = bayes_id['username'].tolist()\n",
        "y_2 = bayes_id['predicted_user'].tolist()\n",
        "y_3 = norm_id['username'].tolist()\n",
        "y_4 = norm_id['predicted_user'].tolist()\n",
        "y_5 = norm_vr['username'].tolist()\n",
        "y_6 = norm_vr['typer_user'].tolist()\n",
        "y_7 = bayes_vr['typer_user'].tolist()\n",
        "y_8 = bayes_vr['username'].tolist()\n",
        "\n",
        "users = set(y_1)\n",
        "users.update(y_2)\n",
        "users.update(y_3)\n",
        "users.update(y_4)\n",
        "users.update(y_5)\n",
        "users.update(y_6)\n",
        "users.update(y_7)\n",
        "users.update(y_8)\n",
        "\n",
        "user_to_num = {}\n",
        "num_to_user = {}\n"
      ],
      "metadata": {
        "id": "GYDJlXBPykHu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in enumerate(users):\n",
        "  user_to_num[i[1]] = i[0]\n",
        "  num_to_user[i[0]] = i[1]"
      ],
      "metadata": {
        "id": "m1qwFdQ6hgQG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_to_np(lista, user_to_num):\n",
        "  vettore = [user_to_num[i] for i in lista]\n",
        "  return np.array(vettore)"
      ],
      "metadata": {
        "id": "m0EN4jqqxwpI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bayes_id_y = convert_to_np(y_1, user_to_num)\n",
        "bayes_id_y_pred = convert_to_np(y_2, user_to_num)\n",
        "bayes_vr_y = convert_to_np(y_8, user_to_num)\n",
        "bayes_vr_y_pred = convert_to_np(y_7, user_to_num)\n",
        "norm_id_y = convert_to_np(y_3, user_to_num)\n",
        "norm_id_y_pred = convert_to_np(y_4, user_to_num)\n",
        "norm_vr_y = convert_to_np(y_5, user_to_num)\n",
        "norm_vr_y_pred = convert_to_np(y_6, user_to_num)"
      ],
      "metadata": {
        "id": "xd_Fd2KJhgWF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_accuracy(y, y_pred):\n",
        "  tot = 0\n",
        "  right = 0\n",
        "  for i in range(0, len(y)):\n",
        "    if y[i] == y_pred[i]:\n",
        "      right += 1\n",
        "    tot += 1\n",
        "  return right/tot"
      ],
      "metadata": {
        "id": "JViyvGKWhgcE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "compute_accuracy(bayes_id_y, bayes_id_y_pred)"
      ],
      "metadata": {
        "id": "Cx9fUnCuhgfF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41755326-a8a8-4981-91d4-5fc0a14780da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8352941176470589"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compute_accuracy(norm_id_y, norm_id_y_pred)"
      ],
      "metadata": {
        "id": "EHQZa_LeykRu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68f0e295-4dd1-4e8d-cfe9-a684fee89e76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8433734939759037"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bayes_values = bayes_vr[\"score\"].tolist()\n",
        "norm_values = norm_vr[\"score\"].tolist()\n",
        "print(bayes_values)\n",
        "print(norm_values)"
      ],
      "metadata": {
        "id": "FnA70_C57X_e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2dbffb49-6820-4dd4-81a6-4224569f7bc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7.617208753313337, 7.515210832868305, 7.644283839634487, 7.498672485351562, 7.657768794468471, 7.235296521868024, 7.512932368687221, 7.668695722307477, 7.536210196358817, -0.3415626798357282, -0.4438263348170689, -0.6159859384809222, -0.2420503752572195, 6.731988089425223, 6.771512712751116, 4.898677280970982, 7.621406010219029, 9.28665052141462, 0.6900373186383929, 4.947157723563059, 3.99606078011649, -0.5283829144069127, 0.6788805552891323, 2.6951070513044084, 4.281032289777484, 0.7435568400791713, 9.168890816824776, 9.9389157976423, 9.944129943847658, 8.181712559291295, 9.727180480957031, 10.061673845563616, 5.959642682756697, -0.4669204098837716, 2.898603711809431, 0.2351019723074776, -6.976276942661831, 0.487830707005092, 0.2217435155596052, 0.4627187592642647, 1.861746106828962, 1.874636513846261, -1.957049233572824, -0.1840590749468122, 1.8332129887172155, 6.605536324637277, 5.638493129185269, 6.350955963134766, 6.631105150495257, 6.580624171665737, 4.676131112234933, 7.516663687569754, -0.9177820682525636, -1.1474077701568604, 5.3494704110281805, -1.143424431482951, 4.840302058628628, 5.618862915039062, 4.412764140537807, -0.9580716490745544, 7.290031433105469, 5.005673217773437, 7.488839830671038, -0.5728180408477783, 4.412782396589007, 4.879724230085101, -0.64639679590861, 5.9680427551269535, 7.463297162737165, -0.6072528759638468, 3.6729583740234375, 4.406475067138672, 4.017203903198242, 7.123382568359375, 4.841870444161551, 5.764981460571289, -1.178798755009969, 5.42791257585798, -0.3611266613006592, 7.6939817156110495, 7.360824040004185, 5.226834106445312, 7.683688027518136, 5.096827370779855, 1.021694898605347, -0.2184295952320099, -0.8644319176673889, 5.391771589006696, 5.03606071472168, -0.7440620263417562, 3.3499584197998047, 5.170622144426618, -2.0411808490753174, 3.797882556915283, 4.447046824863979, 5.919976806640625, -1.119828224182129, 0.9186808268229166, 2.3380855560302733, 1.2671287536621094, 5.257179260253906, -3.903750419616699, -2.6818957328796387, 0.4398273229598999, -18.502818298339843, 0.3666625022888183, 5.302327837262835, 2.4433393478393555, 9.0, -0.5250929355621338, -0.0921645641326904, 6.130267551967076, 4.1661480494907925, 7.86340822492327, 5.589762496948242, 2.1252389635358537, 3.056473868233817, 6.645598820277622, 7.8823089599609375, 5.424666976928711, 8.097933088030134, 5.111854008265904, 7.342649187360491, 5.1573528289794925, -2.8919098377227783, 4.247984749930246, 6.023022121853298, 2.461860111781529, 6.382269541422526, 6.203287336561415, 5.56237072414822, 6.035658942328559, 2.3032922744750977, 7.812501634870257, 8.212706974574498, 7.695134844098773, 5.349952152797154, 9.933978489467076, 9.55546133858817]\n",
            "[0.0156187883445194, 0.0174369684287479, 0.0169762319752148, 0.0152784490159579, 0.0141258644206183, 0.0383684677737099, 0.0155880876949855, 0.0114930346608161, 0.017024176461356, 0.2422594428062439, 0.248094156384468, 0.2541676759719848, 0.2324678748846054, 0.0143270428691591, 0.0108940090451921, 0.0895044888768877, 0.0384608847754342, 0.1726075857877731, 0.0952417765344892, 0.1029682040214538, 0.0988802015781402, 0.042339026927948, 0.1037317752838134, 0.113597001348223, 0.0399497548739115, 0.0788666754961013, 0.4296523729960124, 0.0276059582829475, 0.0617588659127553, 0.0242294743657112, 0.0344826221466064, 0.0143088683485984, 0.0116056457161903, 0.0593947768211364, 0.0191317304968833, 0.0057430993765592, 0.0864035827772957, 0.1066078543663024, 0.1158588051795959, 0.0368226766586303, 0.024240368977189, 0.2720367014408111, 0.167947381734848, 0.031860563904047, 0.0220872312784194, 0.0865849256515502, 0.0357461401394435, 0.016547041279929, 0.0191273689270019, 0.1008255141122, 0.0766514539718627, 0.1286876102288564, 0.2661751806735992, 0.0867065872464861, 0.1263189911842346, 0.0950217161859784, 0.1022605776786804, 0.1195281488554818, 0.2575149834156036, 0.0342792975051062, 0.0724029958248138, 0.0772978918892996, 0.1175173024336497, 0.1117369788033621, 0.092074019568307, 0.1263591249783834, 0.0910124003887176, 0.0110792922122137, 0.1231891612211863, 0.1001370787620544, 0.0827884435653686, 0.0882717669010162, 0.0382369288376399, 0.0950498666082109, 0.0553307712078094, 0.1466597914695739, 0.1059815543038504, 0.2384325265884399, 0.0101989092571394, 0.0135936002646173, 0.0676485955715179, 0.0104131283504622, 0.0876078861100333, 0.0353059271971384, 0.1182592585682869, 0.2410408854484558, 0.072384944983891, 0.1267507910728454, 0.1161220769087473, 0.0858353316783905, 0.0933191180229187, 0.1566631495952606, 0.0525495335459709, 0.0986947672707694, 0.0977974236011505, 0.1411845882733663, 0.0338654642303784, 0.1026108622550964, 0.133616828918457, 0.0955336349351065, 0.2186454981565475, 0.2817812860012054, 0.0598068485657374, 1.140023422241211, 0.0784604648749033, 0.1106941614832196, 0.0551901943981647, 9.0, 0.0604519426822662, 0.034361183643341, 0.0767304386411394, 0.1251671399388994, 0.0728102496692112, 0.0866423547267913, 0.0680522407804216, 0.014615281351975, 0.0541762624468122, 0.0560414748532431, 0.1049642324447631, 0.0759009974343436, 0.1150814890861511, 0.0192380313362394, 0.0688335716724395, 0.3073375225067138, 0.1149648938860212, 0.0759528676668803, 0.0434957402093069, 0.0639209349950154, 0.0783087346288892, 0.0903377003139919, 0.0686566630999247, 0.1186944961547851, 0.0763495905058724, 0.0487923409257616, 0.0584734763417925, 0.1108558348246983, 0.014082419020789, 0.0310851356812885]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# function to get the list of impostor attempts\n",
        "def get_impostors(df):\n",
        "    impostors_values = []\n",
        "    genuine_values = []\n",
        "    for index, row in df.iterrows():\n",
        "      if row[\"typer_user\"] != row[\"username\"]:\n",
        "        impostors_values.append(row[\"score\"])\n",
        "      else:\n",
        "        genuine_values.append(row[\"score\"])\n",
        "    return impostors_values, genuine_values\n",
        "\n",
        "bayes_impostor_values, bayes_genuine_values = get_impostors(bayes_vr)\n",
        "norm_impostor_values, norm_genuine_values = get_impostors(norm_vr)"
      ],
      "metadata": {
        "id": "lxlB2rql2Ggg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "285d3357-c7b0-47f5-dff4-746697cac521"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4.898677280970982, 0.6900373186383929, 4.947157723563059, 3.99606078011649, -0.5283829144069127, 0.6788805552891323, 2.6951070513044084, 4.281032289777484, 0.7435568400791713, 5.959642682756697, -0.4669204098837716, 2.898603711809431, 4.676131112234933, 7.516663687569754, -0.9177820682525636, -1.1474077701568604, 5.3494704110281805, -1.143424431482951, 4.840302058628628, 5.618862915039062, 4.412764140537807, -0.9580716490745544, 5.005673217773437, 7.488839830671038, -0.5728180408477783, 4.412782396589007, 4.879724230085101, -0.64639679590861, 5.9680427551269535, -0.6072528759638468, 3.6729583740234375, 4.406475067138672, 4.017203903198242, 4.841870444161551, 5.764981460571289, -1.178798755009969, 5.42791257585798, -0.3611266613006592, 5.226834106445312, 5.096827370779855, 1.021694898605347, -0.2184295952320099, -0.8644319176673889, 5.391771589006696, 5.03606071472168, -0.7440620263417562, 3.3499584197998047, 5.170622144426618, -2.0411808490753174, 3.797882556915283, 4.447046824863979, 5.919976806640625, -1.119828224182129, 0.9186808268229166, 2.3380855560302733, 1.2671287536621094, 5.257179260253906, -3.903750419616699, -2.6818957328796387, 0.4398273229598999, -18.502818298339843, 0.3666625022888183, 5.302327837262835, 2.4433393478393555, 9.0, -0.5250929355621338, 6.130267551967076, 4.1661480494907925, 7.86340822492327, 5.589762496948242, 5.424666976928711, 8.097933088030134, 5.111854008265904, 5.1573528289794925, -2.8919098377227783, 4.247984749930246, 6.023022121853298, 2.461860111781529, 6.382269541422526, 6.203287336561415, 5.56237072414822, 6.035658942328559, 2.3032922744750977, 7.812501634870257, 8.212706974574498, 7.695134844098773, 5.349952152797154]\n",
            "[0.0895044888768877, 0.1726075857877731, 0.0952417765344892, 0.1029682040214538, 0.0988802015781402, 0.042339026927948, 0.1037317752838134, 0.113597001348223, 0.0399497548739115, 0.0864035827772957, 0.1066078543663024, 0.1158588051795959, 0.1008255141122, 0.0766514539718627, 0.1286876102288564, 0.2661751806735992, 0.0867065872464861, 0.1263189911842346, 0.0950217161859784, 0.1022605776786804, 0.1195281488554818, 0.2575149834156036, 0.0724029958248138, 0.0772978918892996, 0.1175173024336497, 0.1117369788033621, 0.092074019568307, 0.1263591249783834, 0.0910124003887176, 0.1231891612211863, 0.1001370787620544, 0.0827884435653686, 0.0882717669010162, 0.0950498666082109, 0.0553307712078094, 0.1466597914695739, 0.1059815543038504, 0.2384325265884399, 0.0676485955715179, 0.0876078861100333, 0.0353059271971384, 0.1182592585682869, 0.2410408854484558, 0.072384944983891, 0.1267507910728454, 0.1161220769087473, 0.0858353316783905, 0.0933191180229187, 0.1566631495952606, 0.0525495335459709, 0.0986947672707694, 0.0977974236011505, 0.1411845882733663, 0.0338654642303784, 0.1026108622550964, 0.133616828918457, 0.0955336349351065, 0.2186454981565475, 0.2817812860012054, 0.0598068485657374, 1.140023422241211, 0.0784604648749033, 0.1106941614832196, 0.0551901943981647, 9.0, 0.0604519426822662, 0.0767304386411394, 0.1251671399388994, 0.0728102496692112, 0.0866423547267913, 0.1049642324447631, 0.0759009974343436, 0.1150814890861511, 0.0688335716724395, 0.3073375225067138, 0.1149648938860212, 0.0759528676668803, 0.0434957402093069, 0.0639209349950154, 0.0783087346288892, 0.0903377003139919, 0.0686566630999247, 0.1186944961547851, 0.0763495905058724, 0.0487923409257616, 0.0584734763417925, 0.1108558348246983]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"impostor len: \", len(bayes_impostor_values))\n",
        "print(\"genuine len: \", len(bayes_values) - len(bayes_impostor_values))"
      ],
      "metadata": {
        "id": "nM9OkdbIexiu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "820d00bb-aab1-4bf4-e83e-4e53329d9013"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "impostor len:  87\n",
            "genuine len:  52\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_norm_results(values, threshold):\n",
        "  accepted = 0\n",
        "  rejected = 0\n",
        "  for value in values:\n",
        "    if value < threshold:\n",
        "      accepted += 1\n",
        "    else:\n",
        "      rejected += 1\n",
        "  return accepted, rejected\n",
        "\n",
        "def get_bayes_results(values, threshold):\n",
        "  accepted = 0\n",
        "  rejected = 0\n",
        "  for value in values:\n",
        "    if value > threshold:\n",
        "      accepted += 1\n",
        "    else:\n",
        "      rejected += 1\n",
        "  return accepted, rejected"
      ],
      "metadata": {
        "id": "9P0CB6f4TIxM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_norm = min(norm_values)\n",
        "max_norm = max(norm_values)\n",
        "min_bayes = min(bayes_values)\n",
        "max_bayes = max(bayes_values)\n",
        "\n",
        "norm_step = (max_norm - min_norm) / len(norm_values)\n",
        "bayes_step = (max_bayes - min_bayes) / len(bayes_values)\n",
        "\n",
        "norm_thresholds = [(min_norm + (norm_step * i)) / 20 for i in range(0, len(norm_values) + 1)]\n",
        "bayes_thresholds = [min_bayes + (bayes_step * i) for i in range(0, len(bayes_values) + 1)]\n",
        "                                                             \n",
        "print(norm_thresholds)\n",
        "print(bayes_thresholds)"
      ],
      "metadata": {
        "id": "aQ0r1vn5uMIU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5db14b2f-6cb7-474b-efd3-15a68f672513"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.00028715496882796, 0.003545943700937902, 0.006804732433047844, 0.010063521165157786, 0.013322309897267728, 0.01658109862937767, 0.01983988736148761, 0.023098676093597555, 0.026357464825707493, 0.029616253557817433, 0.032875042289927374, 0.03613383102203732, 0.03939261975414726, 0.0426514084862572, 0.04591019721836715, 0.04916898595047709, 0.05242777468258704, 0.05568656341469698, 0.05894535214680692, 0.06220414087891686, 0.0654629296110268, 0.06872171834313676, 0.0719805070752467, 0.07523929580735664, 0.07849808453946658, 0.08175687327157652, 0.08501566200368646, 0.0882744507357964, 0.09153323946790634, 0.09479202820001628, 0.09805081693212622, 0.10130960566423616, 0.1045683943963461, 0.10782718312845604, 0.11108597186056599, 0.11434476059267593, 0.11760354932478587, 0.12086233805689581, 0.12412112678900575, 0.1273799155211157, 0.13063870425322563, 0.1338974929853356, 0.13715628171744554, 0.14041507044955548, 0.14367385918166542, 0.14693264791377536, 0.1501914366458853, 0.15345022537799524, 0.15670901411010518, 0.15996780284221512, 0.16322659157432506, 0.166485380306435, 0.16974416903854495, 0.17300295777065489, 0.17626174650276483, 0.17952053523487477, 0.18277932396698474, 0.18603811269909468, 0.18929690143120462, 0.19255569016331456, 0.1958144788954245, 0.19907326762753444, 0.20233205635964438, 0.2055908450917543, 0.20884963382386426, 0.21210842255597423, 0.21536721128808414, 0.2186260000201941, 0.22188478875230402, 0.225143577484414, 0.2284023662165239, 0.23166115494863387, 0.2349199436807438, 0.23817873241285376, 0.24143752114496367, 0.24469630987707364, 0.24795509860918355, 0.2512138873412935, 0.2544726760734034, 0.2577314648055134, 0.2609902535376233, 0.2642490422697333, 0.2675078310018432, 0.27076661973395316, 0.2740254084660631, 0.27728419719817304, 0.280542985930283, 0.2838017746623929, 0.28706056339450287, 0.2903193521266128, 0.29357814085872275, 0.2968369295908327, 0.30009571832294263, 0.30335450705505257, 0.3066132957871625, 0.30987208451927245, 0.3131308732513824, 0.3163896619834924, 0.3196484507156023, 0.32290723944771227, 0.32616602817982215, 0.32942481691193215, 0.33268360564404204, 0.33594239437615203, 0.3392011831082619, 0.3424599718403719, 0.3457187605724818, 0.3489775493045918, 0.3522363380367017, 0.3554951267688117, 0.35875391550092156, 0.36201270423303156, 0.3652714929651415, 0.36853028169725144, 0.3717890704293614, 0.3750478591614713, 0.37830664789358126, 0.3815654366256912, 0.38482422535780114, 0.3880830140899111, 0.391341802822021, 0.39460059155413096, 0.3978593802862409, 0.40111816901835085, 0.4043769577504608, 0.4076357464825707, 0.4108945352146806, 0.4141533239467906, 0.41741211267890055, 0.4206709014110105, 0.4239296901431205, 0.42718847887523037, 0.4304472676073403, 0.43370605633945025, 0.43696484507156025, 0.44022363380367013, 0.4434824225357801, 0.44674121126789, 0.45]\n",
            "[-18.502818298339843, -18.297318354858522, -18.091818411377204, -17.886318467895883, -17.680818524414562, -17.475318580933244, -17.269818637451923, -17.064318693970606, -16.858818750489284, -16.653318807007963, -16.447818863526646, -16.242318920045324, -16.036818976564007, -15.831319033082686, -15.625819089601364, -15.420319146120045, -15.214819202638726, -15.009319259157406, -14.803819315676087, -14.598319372194766, -14.392819428713446, -14.187319485232127, -13.981819541750806, -13.776319598269486, -13.570819654788167, -13.365319711306848, -13.159819767825528, -12.954319824344207, -12.748819880862888, -12.543319937381568, -12.337819993900247, -12.132320050418928, -11.926820106937608, -11.721320163456289, -11.51582021997497, -11.310320276493648, -11.104820333012329, -10.89932038953101, -10.693820446049688, -10.488320502568369, -10.28282055908705, -10.07732061560573, -9.87182067212441, -9.66632072864309, -9.46082078516177, -9.25532084168045, -9.04982089819913, -8.84432095471781, -8.638821011236491, -8.433321067755172, -8.227821124273852, -8.022321180792531, -7.816821237311212, -7.611321293829892, -7.405821350348571, -7.200321406867252, -6.994821463385932, -6.789321519904613, -6.5838215764232935, -6.378321632941972, -6.172821689460653, -5.9673217459793335, -5.761821802498012, -5.556321859016693, -5.350821915535374, -5.145321972054054, -4.939822028572735, -4.734322085091414, -4.528822141610094, -4.323322198128775, -4.117822254647454, -3.9123223111661343, -3.706822367684815, -3.5013224242034955, -3.295822480722176, -3.090322537240855, -2.8848225937595355, -2.679322650278216, -2.473822706796895, -2.2683227633155774, -2.062822819834256, -1.857322876352935, -1.6518229328716174, -1.4463229893902962, -1.2408230459089786, -1.0353231024276575, -0.8298231589463363, -0.6243232154650187, -0.4188232719836975, -0.21332332850237634, -0.007823385021058726, 0.19767655846026244, 0.4031765019415836, 0.6086764454229012, 0.8141763889042224, 1.01967633238554, 1.2251762758668612, 1.4306762193481823, 1.6361761628295, 1.8416761063108211, 2.0471760497921387, 2.25267599327346, 2.458175936754781, 2.6636758802360987, 2.86917582371742, 3.074675767198741, 3.2801757106800586, 3.48567565416138, 3.691175597642701, 3.8966755411240186, 4.10217548460534, 4.307675428086657, 4.5131753715679785, 4.7186753150493, 4.924175258530617, 5.1296752020119385, 5.335175145493256, 5.540675088974577, 5.7461750324558984, 5.951674975937216, 6.157174919418537, 6.362674862899858, 6.568174806381176, 6.773674749862497, 6.979174693343818, 7.184674636825136, 7.390174580306457, 7.595674523787775, 7.801174467269096, 8.006674410750417, 8.212174354231735, 8.417674297713056, 8.623174241194373, 8.828674184675695, 9.034174128157016, 9.239674071638333, 9.445174015119655, 9.650673958600976, 9.856173902082293, 10.061673845563615]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BAYES FAR/FRR RATES**"
      ],
      "metadata": {
        "id": "5DSOxrVkiSaI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bayes_far_rates = []\n",
        "bayes_frr_rates = []\n",
        "for threshold in bayes_thresholds:\n",
        "\n",
        "  false_positive, true_negative = get_bayes_results(bayes_impostor_values, threshold)\n",
        "  bayes_far_rates.append(false_positive / (false_positive + true_negative))\n",
        "\n",
        "  true_positive, false_negative = get_bayes_results(bayes_genuine_values, threshold)\n",
        "  bayes_frr_rates.append(false_negative / (false_negative + true_positive))\n",
        "  \n",
        "print(bayes_far_rates)\n",
        "print(bayes_frr_rates)"
      ],
      "metadata": {
        "id": "0rdPTxqycV8I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40a6c832-2641-4608-8983-ced3f4ec1286"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9885057471264368, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9655172413793104, 0.9540229885057471, 0.9540229885057471, 0.9540229885057471, 0.9540229885057471, 0.9425287356321839, 0.9425287356321839, 0.9425287356321839, 0.9425287356321839, 0.896551724137931, 0.8620689655172413, 0.8390804597701149, 0.7816091954022989, 0.7586206896551724, 0.7586206896551724, 0.7586206896551724, 0.7471264367816092, 0.735632183908046, 0.7011494252873564, 0.6896551724137931, 0.6781609195402298, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.632183908045977, 0.6206896551724138, 0.6091954022988506, 0.5977011494252874, 0.5977011494252874, 0.5862068965517241, 0.5747126436781609, 0.5632183908045977, 0.5402298850574713, 0.5057471264367817, 0.45977011494252873, 0.4482758620689655, 0.40229885057471265, 0.3448275862068966, 0.28735632183908044, 0.22988505747126436, 0.19540229885057472, 0.1724137931034483, 0.11494252873563218, 0.10344827586206896, 0.09195402298850575, 0.09195402298850575, 0.09195402298850575, 0.09195402298850575, 0.09195402298850575, 0.06896551724137931, 0.05747126436781609, 0.034482758620689655, 0.022988505747126436, 0.011494252873563218, 0.011494252873563218, 0.011494252873563218, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
            "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.019230769230769232, 0.038461538461538464, 0.038461538461538464, 0.038461538461538464, 0.038461538461538464, 0.038461538461538464, 0.038461538461538464, 0.038461538461538464, 0.07692307692307693, 0.11538461538461539, 0.15384615384615385, 0.15384615384615385, 0.19230769230769232, 0.23076923076923078, 0.23076923076923078, 0.23076923076923078, 0.23076923076923078, 0.23076923076923078, 0.23076923076923078, 0.25, 0.28846153846153844, 0.3076923076923077, 0.3076923076923077, 0.3076923076923077, 0.3076923076923077, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.3269230769230769, 0.34615384615384615, 0.34615384615384615, 0.34615384615384615, 0.36538461538461536, 0.36538461538461536, 0.4807692307692308, 0.4807692307692308, 0.5, 0.5769230769230769, 0.6730769230769231, 0.8076923076923077, 0.8269230769230769, 0.8461538461538461, 0.8461538461538461, 0.8461538461538461, 0.8461538461538461, 0.8461538461538461, 0.8653846153846154, 0.8846153846153846, 0.9038461538461539, 0.9230769230769231, 0.9807692307692307]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NORM FAR/FRR RATES**"
      ],
      "metadata": {
        "id": "3YpJGPUCibZL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "norm_far_rates = []\n",
        "norm_frr_rates = []\n",
        "for threshold in norm_thresholds:\n",
        "\n",
        "  false_positive, true_negative = get_norm_results(norm_impostor_values, threshold)\n",
        "  norm_far_rates.append(false_positive / (false_positive + true_negative))\n",
        "\n",
        "  true_positive, false_negative = get_norm_results(norm_genuine_values, threshold)\n",
        "  norm_frr_rates.append(false_negative / (false_negative + true_positive))\n",
        "  \n",
        "print(norm_far_rates)\n",
        "print(norm_frr_rates)"
      ],
      "metadata": {
        "id": "Ehkq2O9XiaSC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71f8a8a9-8da0-46b1-bd51-7dd87fd1a2b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.022988505747126436, 0.022988505747126436, 0.04597701149425287, 0.05747126436781609, 0.06896551724137931, 0.06896551724137931, 0.10344827586206896, 0.11494252873563218, 0.13793103448275862, 0.14942528735632185, 0.1724137931034483, 0.1839080459770115, 0.21839080459770116, 0.3103448275862069, 0.3103448275862069, 0.3218390804597701, 0.39080459770114945, 0.42528735632183906, 0.4482758620689655, 0.5057471264367817, 0.5517241379310345, 0.5977011494252874, 0.632183908045977, 0.6551724137931034, 0.6781609195402298, 0.735632183908046, 0.7701149425287356, 0.7816091954022989, 0.8275862068965517, 0.8390804597701149, 0.8505747126436781, 0.8505747126436781, 0.8505747126436781, 0.8620689655172413, 0.8735632183908046, 0.8735632183908046, 0.8735632183908046, 0.8850574712643678, 0.8850574712643678, 0.8850574712643678, 0.8850574712643678, 0.8850574712643678, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.896551724137931, 0.9080459770114943, 0.9080459770114943, 0.9080459770114943, 0.9080459770114943, 0.9080459770114943, 0.9080459770114943, 0.9310344827586207, 0.9310344827586207, 0.9310344827586207, 0.9310344827586207, 0.9310344827586207, 0.9425287356321839, 0.9425287356321839, 0.9425287356321839, 0.9540229885057471, 0.9540229885057471, 0.9540229885057471, 0.9540229885057471, 0.9540229885057471, 0.9655172413793104, 0.9655172413793104, 0.9655172413793104, 0.9655172413793104, 0.9655172413793104, 0.9655172413793104, 0.9655172413793104, 0.9655172413793104, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736, 0.9770114942528736]\n",
            "[1.0, 1.0, 0.9803921568627451, 0.9803921568627451, 0.8627450980392157, 0.6666666666666666, 0.5490196078431373, 0.5294117647058824, 0.49019607843137253, 0.47058823529411764, 0.43137254901960786, 0.35294117647058826, 0.27450980392156865, 0.27450980392156865, 0.27450980392156865, 0.27450980392156865, 0.27450980392156865, 0.2549019607843137, 0.23529411764705882, 0.19607843137254902, 0.19607843137254902, 0.17647058823529413, 0.17647058823529413, 0.17647058823529413, 0.17647058823529413, 0.1568627450980392, 0.1568627450980392, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.13725490196078433, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.11764705882352941, 0.09803921568627451, 0.09803921568627451, 0.09803921568627451, 0.0784313725490196, 0.0784313725490196, 0.058823529411764705, 0.0392156862745098, 0.0392156862745098, 0.0392156862745098, 0.0392156862745098, 0.0392156862745098, 0.0392156862745098, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0196078431372549, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"NORM\")\n",
        "plt.plot(norm_thresholds, norm_far_rates, label='FAR')\n",
        "plt.plot(norm_thresholds, norm_frr_rates, label='FRR')\n",
        "plt.legend()\n",
        "plt.xlabel(\"Treshold\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "60Q636BHiQSB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "3245c7ef-f2fa-45d4-c79e-501f8456d799"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"BAYES\")\n",
        "plt.plot(bayes_thresholds, bayes_far_rates, label='FAR')\n",
        "plt.plot(bayes_thresholds, bayes_frr_rates, label='FRR')\n",
        "plt.legend()\n",
        "plt.xlabel(\"Treshold\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "-uQmLa_YcWBm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "fcd09e52-85a0-4c81-ef1b-4103347e3bd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ROC curve Norm\n",
        "norm_frr_prob = [(1 - x) for x in norm_frr_rates]\n",
        "plt.plot(norm_far_rates, norm_frr_prob)\n",
        "plt.plot([0.0, 1.0])\n",
        "plt.xlabel(\"FAR\")\n",
        "plt.ylabel(\"1-FRR\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "BAsGQn_tcWDu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "fed3d7dd-d233-47d5-a731-ad26dcc714a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd3QVBfrG8e9L6CX0XgwgomADI3bFgmKDVVcF14Ki/LagIq6rrl3XXta1rC6uWFdR10LoYmFRFCQoCoJopIZeAwIJKe/vj7nrxlASIHMnN/f5nJNz7r0z5D5DezJ3Zt4xd0dERJJXlagDiIhItFQEIiJJTkUgIpLkVAQiIklORSAikuSqRh1gdzVp0sTT0tKijiEiklBmzJixxt2b7mhZwhVBWloamZmZUccQEUkoZrZoZ8v00ZCISJJTEYiIJDkVgYhIklMRiIgkORWBiEiSC60IzGy4ma0ys9k7WW5m9oSZZZnZN2bWPawsIiKyc2HuEbwI9N7F8tOBTrGvQcAzIWYREZGdCK0I3H0ysG4Xq/QFXvbAVKCBmbUMK4+ISEW2Oa+Ax96fx8wlG7ZfuG0LTLwdNiwO5b2jPEbQGlhS7Hl27LXtmNkgM8s0s8zVq1fHJZyISDxt2VbIEx9lMWtpzi8XLJgMzxwFU/4GP7wfynsnxMFidx/m7ununt606Q6vkBYRqVxycyDjGnjpbLAqMGAMHH5lKG8V5YiJpUDbYs/bxF4TEUlu342FMUPhp5VwzLXQ82aoViu0t4uyCDKAwWY2AjgCyHH35RHmEREJlbvz8IR5rNiYu92yvPwiGpPDcTP/BCvGQ7Ou0O81aB3+CZWhFYGZvQ70BJqYWTZwB1ANwN2fBcYCZwBZwBbg8rCyiIhUBGs3b+Pvk36kQe1q1K1R7L9fd3oVTuajms9Tb1UenHhrsCdQtXpccoVWBO7ev5TlDvwhrPcXEamoru+1H5cclRY8ycmG0UPhhwnQ5nDo8xQ02z+ueRJuDLWISKVQVAQzXoCJd4AXQu8HoMcgqJIS9ygqAhGRclRY5Ax9cybLc7Y/DpBfWARAvc2L4KXfw6Ip0KEnnP03aJgW15zFqQhERMrRhi3bGDlzGWmNa9Oifs1fLKuV4jzQ/CP6fP4KVK0ZfAzU7WIwiyhtQEUgIhKCK45tz6X/PQ4AsGIWjBwMOTNh/7PgjEcgtWIMU1ARiIiEqSAPJj8Mn/4VajWE81+ELr+KfC+gOBWBiEhYlnwR7AWsmQeH9IfT7oPajaJOtR0VgYhIOatFLulzH4IJr0H9NvCbt6HTKVHH2ikVgYhIOaq26D+8X/1G2i5eDYdfBafcATXqRR1rlxJi6JyISIW3dT2M/AOpb53PNqoyvscLcOYjFb4EQEUgIrL35o6Cp4+Ama+ztcc1nLHtflY1TJybLuqjIRGRPfXTKhh7A8x5D1ocBBe9yZbUA8ib/EHUyXaLikBEZHe5w9cjYPxNkL8FTrotGBKXUg1+yos63W5TEYiI7I4NS2D0EMj6gLyWh7Pu5EcpaNQJcvKBfDZsyY864W5TEYiIlEVREWQ+Dx/cibtzb9HlPL/gZPyf2QR32v2l6imJcwhWRSAiUpo1P0DG1bD4c+h4EhtOeoh/Pvk953RrzdEdG2+3erWUKpzatXkEQfeMikBEZGcK8+GzJ2HSA8GtIn/1DBzSn6LN24Dv6dauAeenty3121R0KgIRkR1Z/jW5b/+emmtms7Zdbxb0uIv8Wk1h/jo25ibecYBdURGIiBSXnwuTH8I/fZxNRXUZkj+E8d/3gO9/BH78xap1qleO/0Irx1aIiJSHxVODIXFrf2Dbgf05JfNEzjn6QF7v2mK7VaulGIe2bRBByPKnIhARydsEH94NXzwH9dvCxe+wueVx5GROpH2TOhy1gwPClYmKQBLahi3bmLN8I0vXb406iiSo5qumcNisO6m9dQVZaRcxa/9rKdxQm80rl0YdLW5UBJIQioqchWs3M3f5JuYu38ic5RuZu3zjDu8LK1IW9fmJ26q9yvEpk8kqasWN+bcz47vO8F3WL9ZrXLd6RAnjR0UgFc7mvAK+W7GRObH/9Ocu38h3yzexNb8QgJQqRsemdejRvhFdWqZyQMtU9mlcmyoV6I5PUrHVyhpNw0m3UGXrOnLSr6XG4UN4vGrN7darllJlu/sOV0YqAomMu7MsJ5e5yzb+4qf8Reu24B6sk1qzKge0TOXCw9vSpWUqXVqlsm+zutSslhJteElMm1bA2D8G00JbHgKXvEP9lgdTP+pcEVMRSOhGzlzK/NWbf36+MTc/9pP+JnK2/u987H0a1+aAFqmc270NB7RM5YCW9WjdoBamn/Rlb7nDzNdgws3B6aGn3AlHXQ0p+i8QVAQSB9e9MZMi/9/zmtWqsH+LVM44qCVdWtajS6tUOrdIpW4N/XWUEKxfBKOuhfkfQ7ujoc+T0GTfqFNVKPqXJ6Ercrj25E5c12u/qKNIMikqDE4H/fBuMIMzHoH0gVAlcYbBxYuKQEQqn9XzgiFxS6bBvqfAWY9Dg8SfCRQWFUGCWvNTHsM/XcCqTYl3EwyR0BTmw5TH4T8PQfU6cM4/4OALgz0C2SkVQYLZuq2Q4VMW8MykH9maX0iL1Ip/alvbRrUqzaX4UoEt+wpGXg0rZ0HXc+D0h6Bus6hTJQQVQYIoKnLe/Wopj7w/j+U5ufTq0pwbe+/Pvs3qRh1NJFr5W4Mx0Z89CXWawoX/ggPOijpVQgm1CMysN/A3IAX4p7s/UGJ5O+AloEFsnZvcfWyYmRLRlKw13DtmLnOWb+TgNvV5/MJDOaJD5Z59IlImC6cExwLW/QjdLoFT/wK1tPe5u0IrAjNLAZ4GehHcx226mWW4+5xiq90KvOnuz5hZF2AskBZWpkQw+fvVDJs8n4KiIgA25xUya2kOrRvU4m/9DuXsg1tRpYo+75Qkl7sRPrwLpv8TGuwDl46EDj2jTpWwwtwj6AFkuft8ADMbAfQFiheBA6mxx/WBZSHmqfBmLFrHVS9n0qRuDVo3rAVA7eop3Hz6/lx2dJquphUB+GEijBoCG5fCkb+Hk24NDgzLHguzCFoDS4o9zwaOKLHOncD7ZnY1UAc4ZUffyMwGAYMA2rVrV+5BK4IfVm7iihczadWgFm/99iia1K0RdSSRimXLOhh/M3wzApruDwMnQtvDo05VKUR9ZUV/4EV3bwOcAbxiZttlcvdh7p7u7ulNmzaNe8iwLc/ZyqXDv6B61Sq8fEUPlYBIce4w+x146nCY/W844Ub4v8kqgXIU5h7BUqD4FRxtYq8VNxDoDeDun5tZTaAJsCrEXBVKzpZ8Lhv+BZtyC3jj/46kbaPaUUcSqTg2Locx18O8MdCqG/QZCS0OjDpVpRNmEUwHOplZe4IC6AdcVGKdxcDJwItmdgBQE1gdYqYKJTe/kCtfns7CNVt48YrD6doq2WcgisS4w1evwIRboTAPet0THA/QkLhQhPa76u4FZjYYmEBwauhwd//WzO4GMt09A7geeM7MriM4cDzA3X3n37XyKCgs4urXvyJz0Xqe6t+dozs2iTqSSMWwbgGMugYWTIZ9joU+T0DjjlGnqtRCrdfYNQFjS7x2e7HHc4BjwsxQEbk7t42czcQ5K7mrT1fOPLhl1JFEoldUCNP+AR/dA5YCZ/0Vug/QkLg40H5WBP76wQ+8/sUS/nBiRy47Oi3qOCLRWzUXRg6GpZnQ6bSgBOq3jjpV0lARxNmrUxfxxIc/cEF6G/54aueo44hEq2AbfPpXmPww1KgH5/4TDvq1hsTFmYogjsbPXs5tI2dz8v7NuO+cg3TnLUluS2cEQ+JWfQsH/hpOfxDq6FhZFFQEcTJt/lquGTGTbm0b8NRF3amaos89JUlt2wKT7oPPn4a6LaD/COh8etSpkpqKIES5+YV89N0q3p6RzaTvV9O+SR2GDzicWtU1KkKS1IJPgjOC1s2HwwZAr7uhpk6bjpqKoJy5O18u3sA7X2Yz6utlbMwtoHlqDa48rj0Dj21Pg9rVo44oEn+5OTDxDpjxAjRsD5eNgvbHR51KYlQE5SR7/Rbe/XIp73y1lAVrNlOzWhV6d23Bud3bcMy+TUjRxFBJVvPGw+jr4KcVcNRgOPEWqK4r6CsSFQGQX1jEN9kbKCjc/WvZFq3bwjtfZjN1/joAjmjfiN/17MjpB7agXs1q5R1VJHFsXgPjbgzmAzXrAhe+Cm0OizqV7ICKABgxfQm3vTd7j399WuPaDO21H+d0a61ZQSLuMPttGPen4L4BPf8Mx14HVfWxaEWlIgA25xUA8MKAw6lRdffO5kmtVY2urVJ1KqgIQM5SGDMUvh8PrQ+DPk9B8y5Rp5JSqAiKObJDY53RI7Iniorgy5dg4u1QmA+n3QdH/Baq6N9TIlARiMjeWfsjjLoWFn4CaccFQ+IadYg6lewGFYGI7JnCApj2DHx0L6RUg7OfgO6XajxEAlIRiMjuW/ltMCRu2ZfQ+Qw481FIbRV1KtlDKgIRKbuCPPjk0eCrZgP49XDoeq72AhKcikBEyiY7M9gLWD0XDr4QTrsf6jSOOpWUAxWBiOzats3BcYCpfw8+/rnoTdjvtKhTSTlSEYjIzs3/TzAkbv1CSB8Ip9wJNVMjDiXlTUUgItvbugEm3gZfvgyNOsKAMZB2bNSpJCQqAhH5pe/GwOihsHkVHHMt9LwZqtWKOpWESEUgIoGfVgfzgb59B5ofCP1fh9bdo04lcaAiEEl27vDNmzD+xuDA8Im3wrFDgovEJCmoCESSWU52cK+AH96HNocHQ+Ka7R91KokzFQFQ5Lt/HwKRhFZUBDOGw8Q7wQuh9wPQY5CGxCUpFQEw6bvVtG5Qa7dHUIskpDVZkHE1LP4MOvSEs/8GDdMiDiVRSvoimLlkA18sXMdtZ3Whim4nKZVZYQF8/hRMuh+q1oC+T8Ohv9F4CFERPPfJfOrVrMqFh7eNOopIeFbMgpF/gOVfw/5nBUPi6rWIOpVUEEldBEvWbWHcrOVcdXwH6tZI6t8KqawK8mDyw/DpX6FWQzj/JejSV3sB8gtJ/b/f8CkLqGLGgKPToo4iUv4WTwuOBayZB4f0D+4aVrtR1KmkAkraIsjZks8b05fQ55BWtKyvqyalEsn7CT66B6b9A+q3gd+8DZ1OiTqVVGChniZjZr3NbJ6ZZZnZTTtZ5wIzm2Nm35rZa2HmKe61LxazZVshVx6nW+pJJfLjR/DMUTDtWehxFfz+c5WAlCq0PQIzSwGeBnoB2cB0M8tw9znF1ukE3Awc4+7rzaxZWHmK21ZQxIufLeDYfZvQpZUmKUolsHU9TLgVZr4KjTvB5eNhn6OiTiUJIsyPhnoAWe4+H8DMRgB9gTnF1rkKeNrd1wO4+6oQ8/xs1NfLWLkxjwfPOzgebycSrrmjYMz1sHkNHDsUTrgRqtWMOpUkkDCLoDWwpNjzbOCIEuvsB2BmU4AU4E53H1/yG5nZIGAQQLt27fYqlLvz3Cfz6dy8Hifs13SvvpdIpDathHE3wJyR0OKg4IYxrQ6NOpUkoKgPFlcFOgE9gTbAZDM7yN03FF/J3YcBwwDS09P3ah7Ep1lr+G7FJh769cGYTqGTROQOX78O42+G/K1w8u1w9DUaEid7LMwiWAoUv0qrTey14rKBae6eDywws+8JimF6WKGGTZ5P03o16Htoq7DeQiQ8GxbDqCHw44fQ9kjo8yQ03S/qVJLgwjxraDrQyczam1l1oB+QUWKd9wj2BjCzJgQfFc0PK9Dc5Rv55Ic1DDg6jRpVNVxLEkhREUwbBk8fCYunwukPw+XjVAJSLkLbI3D3AjMbDEwg+Px/uLt/a2Z3A5nunhFbdqqZzQEKgRvcfW1Ymf75yQJqVUvhN0fs3XEGkbha8wOMHAxLpkLHk+Hsx6GB/g5L+Qn1GIG7jwXGlnjt9mKPHRga+wrVyo25ZHy9lIt6tKNB7ephv53I3ivMh8+egEkPBreK/NUzwRXCOrYl5Szqg8VxM27WcvILncs0TkISwfKvgyFxK2YFs4FOfxjqNY86lVRSSVMEeQVFALSor/OrpQLLz4X/PABTnoDajeGCV6BLn6hTSSWXNEUgUuEt+hwyBsPaLDj0YjjtL8HEUJGQ7XERmFkdd99cnmFEklLeJvjgLpj+XHAQ+JJ3oeNJUaeSJFJqEZhZa6Al8I27b4vNAxoCDAB0Mr7I3sj6ILguICcbjvgtnHQb1KgbdSpJMru8jsDMhgAzgSeBqWZ2JTAXqAUcFn48kUpqyzp497fw6nnBGUFXTIDTH1QJSCRK2yMYBHR293Vm1g74nmBS6Izwo4lUQu7BbKCxfwwmhh73Rzj+Bg2Jk0iVVgS57r4OwN0Xm9k8lYDIHtq0IpgS+t1oaHkIXPwOtNQEXIleaUXQxsyeKPa8ZfHn7n5NOLFEKhF3mPkvmPDn4B7Cp9wFRw2GFJ20JxVDaX8TbyjxXHsDIrtj/UIYdS3MnwTtjg6GxDXZN+pUIr+wyyJw95d2tix2zEBEdqSoEL54Dj68C6wKnPkoHHYFVAn17rAie6Qsp48eRXCTmcnuvsrMDgZuAo7jl2OmRQRg9bxgSFz2F7BvLzjrr9BA/1Sk4tplEZjZw8BZBKeQ3mhmE4ArgfuBK8KPJ5JACvPh08dh8kNQvQ6cMwwOvkBD4qTCK22P4Eygm7vnmllDgltPHujuC0NPJpJIln0V7AWsnA1dz4XTH4K6uhWqJIaynD6aC+Du683sB5WASDH5W2HS/fDZk1CnGfR7DfY/M+pUIrultCLoYGbF7yrWvvhzd9dYREleC6dAxtWw7kfofin0ugdqNYg6lchuK60I+pZ4/mhYQUQSRu5G+OBOyHweGuwDl46EDj0jDiWy50orggXuvjguSUQSwffvw+ghsHEZHPkHOOmW4MCwSAIr7aTm9/77wMzeDjmLSMW1eS28fRW8dj7UqAcDJ0Lv+1QCUimUtkdQ/Ly3DmEGEamQ3OHbd2DsnyB3A5xwIxx3PVStEXUykXJTWhH4Th6LVH4bl8OYoTBvLLTqBn0zoHnXqFOJlLvSiuAQM9tIsGdQK/aY2HN399RQ04lEwR2+fBnevw0K8+DUv8ARv9OQOKm0Sps1lBKvICIVwroFMOoaWDAZ9jkW+jwBjTtGnUokVLv9I46ZDXL3YWGEEYlMUSFMexY+vAeqVIWzHoful2lInCSFPdnX/S2gIpDKY+UcyBgMS2dAp9OCIXH1W0edSiRu9qQINEFLKoeCbfDpYzD5EaiZCuc9DweepyFxknT2pAjOBjCzy939hXLOIxIfS2cEQ+JWzYGDzofeD0CdJlGnEonEbheBu2fHHt4FqAgksWzbAh/fC1P/DnVbQP8R0Pn0qFOJRKq0+xF8s7NFQPPyjyMSogWTIeMaWL8ADrscet0FNetHnUokcqXtETQHTgPWl3jdgM9CSSRS3nJzYOLtMONFaNgeLhsF7Y+POpVIhVFaEYwG6rr7zJILzGxSKIlEytO8cTD6OvhpJRx9NfT8M1SvHXUqkQpllydJu/tAd/90J8suKu2bm1lvM5tnZllmdtMu1jvPzNzM0kuPLFIGm9fAvwfC6/2gViO48oPgCmGVgMh2Qrtm3sxSgKeBXkA2MN3MMtx9Ton16gHXAtPCyiJJxB1m/RvG/QnyNgV7AMdeB1WrR51MpMIK87LJHkCWu893923ACLa/0Q3APcCDQG6IWSQZ5CwN9gDeuRIadYDffgI9b1QJiJQizCJoTXCz+//Kjr32MzPrDrR19zG7+kZmNsjMMs0sc/Xq1eWfVBJbURFkDoenj4D5/4HT7oOB70OzA6JOJpIQIhunaGZVgMeAAaWtG5ttNAwgPT1d47Dlf9b+GJwSuujT4Eygs5+ARu2jTiWSUMIsgqVA22LP28Re+696wIHAJAsu6W8BZJhZH3fPDDGXVAaFBcFFYR/fCyk1oM+T0O0SjYcQ2QNhFsF0oJOZtScogH7Az2cauXsO8PM1/bHTUf+oEpBSrZgdDIlb9hV0PhPOfBRSW0adSiRhhVYE7l5gZoOBCUAKMNzdvzWzu4FMd88I672lkirIg08eDb5qNoBfvwBdz9FegMheCvUYgbuPBcaWeO32nazbM8wskuCWTA/2AlZ/BwdfGAyJq90o6lQilYLuvScV27bN8NFfYOozkNoKLnoL9js16lQilYqKQCqu+ZOCM4I2LIL0gXDKncF9A0SkXKkIpOLZugHevxW+egUadYQBYyHtmKhTiVRaKgKpWL4bA6OHwubVcMwQ6HkTVKsVdSqRSk1FIBXDT6uC+UDfvgvND4KLRkCrblGnEkkKKgKJljt88waMvyk4MHzSrcGeQEq1qJOJJA0VgURnw5LgXgFZE6FND+j7FDTtHHUqkaSjIpD4KyqCzOfhgzvBi6D3g9DjKqiSEnUykaSkIpD4WpMFGVfD4s+gw4lw9uPQMC3qVCJJTUUg8VFYAJ8/CR/fD9VqQt+/w6EXaTyESAWgIpDwrZgFI/8Ay7+G/c8KhsTVaxF1KhGJURFIePJzYfLDMOXx4L7BF7wMXXZ0kzoRiZKKQMKxeFowJG7N93DIRXDavRoSJ1JBqQikfOX9BB/eDV8Mg/pt4OK3Yd9Tok4lIrugIpDyk/UhjBoCOUuC00FPvh1q1Is6lYiUQkUge2/rephwC8z8FzTuBJePg32OijqViJSRikD2zpwMGPtH2LwGjh0KJ9wYnB4qIglDRSB7ZtPKoADmZkCLg+A3b0HLQ6JOJSJ7QEUgu8cdZr4GE/4M+VuD4wBHX6MhcSIJTEUgZbd+EYweAj9+BG2PhD5PQtP9ok4lIntJRSClKyqC6c/BB3cFIyHOeCS4dWSVKlEnE5FyoCKQXVv9fTAkbslU6HhyMCSuQbuoU4lIOVIRyI4V5sOUv8F/HoRqteFXz8Ih/TQkTqQSUhHI9pbNDMZDrJgVzAY64xGo2yzqVCISEhWB/E/+1mAPYMoTUKcJXPAKdOkTdSoRCZmKQAKLPg/2AtZmQbeL4dS/QK2GUacSkThQESS7vE3B2UDTnwsOAl/yHnQ8MepUIhJHKoJk9sPEYEjcxqVwxO/gpFuhRt2oU4lInKkIktGWdTD+ZvhmBDTpDAPfh7Y9ok4lIhFRESQTd5jzHoy9IZgYevwNwVfVGlEnE5EIhXppqJn1NrN5ZpZlZjftYPlQM5tjZt+Y2Ydmtk+YeZLaphXwxsXw1gBIbQ2DJgUfBakERJJeaHsEZpYCPA30ArKB6WaW4e5ziq32FZDu7lvM7HfAQ8CFYWVKSu7w1avB/QIK86DX3XDkHyBFO4MiEgjzf4MeQJa7zwcwsxFAX+DnInD3j4utPxW4OMQ8yWf9Qhh1LcyfBPscA2c/AU32jTqViFQwYRZBa2BJsefZwBG7WH8gMG5HC8xsEDAIoF07zbkpVVFhcM/gD+8GS4EzH4PDLteQOBHZoQrx+YCZXQykAyfsaLm7DwOGAaSnp3scoyWeVd8FF4ZlT4d9ewVD4uq3iTqViFRgYRbBUqBtsedtYq/9gpmdAtwCnODueSHmqdwKtsGUx2Hyw1C9Lpz7HBx0vobEiUipwiyC6UAnM2tPUAD9gIuKr2Bm3YB/AL3dfVWIWSq3pV8Go6JXzoYDz4PeD0LdplGnEpEEEVoRuHuBmQ0GJgApwHB3/9bM7gYy3T0DeBioC7xlwU+ui91dU87KKn8rfHwffP4U1G0O/V6H/c+IOpWIJJhQjxG4+1hgbInXbi/2+JQw379SW/hpsBewbj50vyw4LbRWg6hTiUgCqhAHi2U35G6ED+6AzOHQMA0uzYAOOzzGLiJSJiqCRPL9BBh9HWxaDkcNhhP/DNXrRJ1KRBKciiARbF4L42+CWW9C0/3hgpehTXrUqUSkklARVGTuMPttGPen4COhE26C44ZqPpCIlCsVQUW1cRmMuR7mjYVW3aHvU9C8a9SpRKQSUhFUNO7w5Uvw/m1QmB/cMvLI30OVlKiTiUglpSKoSNbNh4xrYOEnkHYcnP03aNwx6lQiUsmpCCqCokKY+gx89BdIqQZnPR5cG6AhcSISByqCqK2cEwyJWzoD9usdTAqt3zrqVCKSRFQEUSnYBp8+BpMfgZqpcN7zwZwgDYkTkThTEUQhe0awF7BqTjAhtPeDUKdx1KlEJEmpCOJp2xb4+F6Y+neo2wL6vwGde0edSkSSnIogXhZMDobErV8Y3C2s111Qs37UqUREVAShy80Jrgn48iVo2B4uGw3tj4s6lYjIz1QEYZo3LhgS99NKOPpq6PlnqF476lQiIr+gIgjD5jXBfKDZb0OzrtDvX9D6sKhTiYjskIqgPLnDrLdg3I2QtwlOvAWOGQJVq0edTERkp1QE5SUnG0YPhR8mQOv0YEhcswOiTiUiUioVwd4qKoIZL8DEO8AL4bT74Yj/05A4EUkYKoK9sfbHYEjcok+h/QnBkLhG7aNOJSKyW1QEe6KwAKY+DR/fByk1oM+T0O0SjYcQkYSkIthdK2YH4yGWfQWdz4QzH4XUllGnEhHZYyqCsirICwbEffoY1GoI578IXX6lvQARSXgqgrJY8gWMHAxr5sHB/aD3/VC7UdSpRETKhYpgV7Zthg/vgWnPQmpr+M2/oVOvqFOJiJQrFcHO/PgxjLoGNiyGw6+Ek+8I7hsgIlLJqAhK2roB3r8FvnoVGnWEAWMh7ZioU4mIhEZFUNzc0TDmeti8Go69Dk64EarVijqViEioVAQAP62CsTfAnPeg+UFw0Qho1S3qVCIicZHcReAOX4+A8TdB/hY46TY45lpIqRZ1MhGRuEneItiwBEYPgawPoE2PYEhc085RpxIRibsqYX5zM+ttZvPMLMvMbtrB8hpm9kZs+TQzSwszDwBeBF88B38/EhZ9Dqc/BFeMVwmISNIKbY/AzFKAp4FeQDYw3cwy3H1OsdUGAuvdfV8z6wc8CFwYVqYOtowar5wN2VOhw4nBkLiG+4T1diIiCSHMPYIeQJa7z3f3bcAIoG+JdfoCL8Ue/xs42SycmQ2dl7/HuOo3U2XNXOj7d7jkXZWAiAjhFkFrYEmx59mx13a4jrsXADlA45LfyMwGmVmmmWWuXr16j8LUbmkFMpsAAAXSSURBVNmZb+sdRd7/TYVuv9GMIBGRmIQ4WOzuw4BhAOnp6b4n36PHCWfBCWeVay4RkcogzD2CpUDbYs/bxF7b4TpmVhWoD6wNMZOIiJQQZhFMBzqZWXszqw70AzJKrJMBXBZ7/GvgI3ffo5/4RURkz4T20ZC7F5jZYGACkAIMd/dvzexuINPdM4DngVfMLAtYR1AWIiISR6EeI3D3scDYEq/dXuxxLnB+mBlERGTXQr2gTEREKj4VgYhIklMRiIgkORWBiEiSs0Q7W9PMVgOL9vCXNwHWlGOcRKBtTg7a5uSwN9u8j7s33dGChCuCvWFmme6eHnWOeNI2Jwdtc3IIa5v10ZCISJJTEYiIJLlkK4JhUQeIgLY5OWibk0Mo25xUxwhERGR7ybZHICIiJagIRESSXKUsAjPrbWbzzCzLzG7awfIaZvZGbPk0M0uLf8ryVYZtHmpmc8zsGzP70MwS/j6dpW1zsfXOMzM3s4Q/1bAs22xmF8T+rL81s9finbG8leHvdjsz+9jMvor9/T4jipzlxcyGm9kqM5u9k+VmZk/Efj++MbPue/2m7l6pvghGXv8IdACqA18DXUqs83vg2djjfsAbUeeOwzafCNSOPf5dMmxzbL16wGRgKpAede44/Dl3Ar4CGsaeN4s6dxy2eRjwu9jjLsDCqHPv5TYfD3QHZu9k+RnAOMCAI4Fpe/uelXGPoAeQ5e7z3X0bMALoW2KdvsBLscf/Bk42S+ibGJe6ze7+sbtviT2dSnDHuERWlj9ngHuAB4HceIYLSVm2+SrgaXdfD+Duq+KcsbyVZZsdSI09rg8si2O+cufukwnuz7IzfYGXPTAVaGBmLffmPStjEbQGlhR7nh17bYfruHsBkAM0jku6cJRlm4sbSPATRSIrdZtju8xt3X1MPIOFqCx/zvsB+5nZFDObama945YuHGXZ5juBi80sm+D+J1fHJ1pkdvffe6kS4ub1Un7M7GIgHTgh6ixhMrMqwGPAgIijxFtVgo+HehLs9U02s4PcfUOkqcLVH3jR3R81s6MI7np4oLsXRR0sUVTGPYKlQNtiz9vEXtvhOmZWlWB3cm1c0oWjLNuMmZ0C3AL0cfe8OGULS2nbXA84EJhkZgsJPkvNSPADxmX5c84GMtw9390XAN8TFEOiKss2DwTeBHD3z4GaBMPZKqsy/XvfHZWxCKYDncysvZlVJzgYnFFinQzgstjjXwMfeewoTIIqdZvNrBvwD4ISSPTPjaGUbXb3HHdv4u5p7p5GcFykj7tnRhO3XJTl7/Z7BHsDmFkTgo+K5sczZDkryzYvBk4GMLMDCIpgdVxTxlcGcGns7KEjgRx3X74337DSfTTk7gVmNhiYQHDGwXB3/9bM7gYy3T0DeJ5g9zGL4KBMv+gS770ybvPDQF3grdhx8cXu3iey0HupjNtcqZRxmycAp5rZHKAQuMHdE3Zvt4zbfD3wnJldR3DgeEAi/2BnZq8TlHmT2HGPO4BqAO7+LMFxkDOALGALcPlev2cC/36JiEg5qIwfDYmIyG5QEYiIJDkVgYhIklMRiIgkORWBiEiSUxGIlJGZFZrZzGJfabHXh5hZrpnVL7ZuTzPLia33nZk9ElVukdKoCETKbqu7H1rsa2Hs9f4EFz6dW2L9T9z9UKAbcJaZHRPHrCJlpiIQ2Qtm1pHgQr1bCQphO+6+FZjJXg4GEwmLikCk7GoV+1jo3dhr/QhGI38CdDaz5iV/kZk1JJj3Mzl+UUXKTkUgUnbFPxo6J/Zaf2BEbNLl28D5xdY/zsy+JhgINsHdV8Q5r0iZqAhE9pCZHUTwk/7E2ITTfvzy46FP3P0QoCsw0MwOjX9KkdKpCET2XH/gzv9OOHX3VkCrkveDjo2DfgC4MYqQIqVREYjsuX7AuyVee5cdT7N9Fjj+v6ecilQkmj4qIpLktEcgIpLkVAQiIklORSAikuRUBCIiSU5FICKS5FQEIiJJTkUgIpLk/h+8+nm8jQPoZAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ROC curve bayes\n",
        "bayes_frr_prob = [(1 - x) for x in bayes_frr_rates]\n",
        "plt.plot(bayes_far_rates, bayes_frr_prob)\n",
        "plt.plot([0.0, 1.0])\n",
        "plt.xlabel(\"FAR\")\n",
        "plt.ylabel(\"1-FRR\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "y6io9ut_cWGD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "77b1498e-2154-49b3-a783-0ac70fe770d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "HTER"
      ],
      "metadata": {
        "id": "qUYg-62fbEJ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hter_bayes = [((bayes_far_rates[i] +  bayes_frr_rates[i]) / 2) for i in range(len(bayes_frr_rates))]\n",
        "plt.title(\"BAYES\")\n",
        "plt.ylabel(\"HTER\")\n",
        "plt.plot(hter_bayes)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "_1tY80P7cEk7",
        "outputId": "d1cc0910-5d89-45ff-8bdc-189c6a5cd9dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hter_norm = [((norm_far_rates[i] +  norm_frr_rates[i]) / 2) for i in range(len(norm_frr_rates))]\n",
        "plt.title(\"NORM\")\n",
        "plt.ylabel(\"HTER\")\n",
        "plt.plot(hter_norm)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "afZevhNZa_fy",
        "outputId": "673f0fbb-b6ad-43e3-c21b-af10bcb9df39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}