Anomaly_Detection_of_ECG_using_AutoEncoders.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Anomaly Detection of ECG using AutoEncoders.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMgruda3IjI+pXOT+PDWtHM",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srivatsan88/YouTubeLI/blob/master/Anomaly_Detection_of_ECG_using_AutoEncoders.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8edhGvXoqdT"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (10, 8)\n",
        "mpl.rcParams['axes.grid'] = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o97ApqxFpGmm",
        "outputId": "3bd979af-7128-415f-bd13-84a531212ac7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "!wget http://www.timeseriesclassification.com/Downloads/ECG5000.zip\n",
        "!unzip ECG5000.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-26 00:56:06--  http://www.timeseriesclassification.com/Downloads/ECG5000.zip\n",
            "Resolving www.timeseriesclassification.com (www.timeseriesclassification.com)... 109.123.71.232\n",
            "Connecting to www.timeseriesclassification.com (www.timeseriesclassification.com)|109.123.71.232|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10614407 (10M) [application/zip]\n",
            "Saving to: ‘ECG5000.zip.1’\n",
            "\n",
            "ECG5000.zip.1       100%[===================>]  10.12M  3.60MB/s    in 2.8s    \n",
            "\n",
            "2020-10-26 00:56:09 (3.60 MB/s) - ‘ECG5000.zip.1’ saved [10614407/10614407]\n",
            "\n",
            "Archive:  ECG5000.zip\n",
            "replace ECG5000.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: ECG5000.txt             \n",
            "replace ECG5000_TEST.arff? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: ECG5000_TEST.arff       \n",
            "replace ECG5000_TEST.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n",
            "  inflating: ECG5000_TEST.txt        \n",
            "  inflating: ECG5000_TRAIN.arff      \n",
            "  inflating: ECG5000_TRAIN.txt       \n",
            "  inflating: ECG5000_TEST.ts         \n",
            "  inflating: ECG5000_TRAIN.ts        \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WcrsOQIHpZdQ",
        "outputId": "f69eb456-7f3c-4be8-807b-2e0a906caec0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "!ls -alrt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 58192\n",
            "-rw-r--r-- 1 root root 10614407 Aug  9  2019 ECG5000.zip.1\n",
            "-rw-r--r-- 1 root root 10614407 Aug  9  2019 ECG5000.zip\n",
            "-rw-r--r-- 1 root root       38 Aug  9  2019 ECG5000.txt\n",
            "-rw-r--r-- 1 root root  7086857 Aug  9  2019 ECG5000_TEST.arff\n",
            "-rw-r--r-- 1 root root  1128500 Aug  9  2019 ECG5000_TRAIN.txt\n",
            "-rw-r--r-- 1 root root   790229 Aug  9  2019 ECG5000_TRAIN.arff\n",
            "-rw-r--r-- 1 root root 10156500 Aug  9  2019 ECG5000_TEST.txt\n",
            "-rw-r--r-- 1 root root  7083409 Aug  9  2019 ECG5000_TEST.ts\n",
            "-rw-r--r-- 1 root root   786781 Aug  9  2019 ECG5000_TRAIN.ts\n",
            "drwxr-xr-x 1 root root     4096 Oct 14 16:31 sample_data\n",
            "drwxr-xr-x 1 root root     4096 Oct 14 16:32 .config\n",
            "drwxr-xr-x 1 root root     4096 Oct 26 00:47 ..\n",
            "-rw-r--r-- 1 root root 11285000 Oct 26 00:50 ecg_final.txt\n",
            "drwxr-xr-x 1 root root     4096 Oct 26 00:57 .\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v7mTO_OFDyQs"
      },
      "source": [
        "!cat ECG5000_TRAIN.txt ECG5000_TEST.txt > ecg_final.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwe98Q70qCUS",
        "outputId": "8656de60-d5a7-4865-8f6a-a122305e2760",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "!head ecg_final.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   1.0000000e+00  -1.1252183e-01  -2.8272038e+00  -3.7738969e+00  -4.3497511e+00  -4.3760410e+00  -3.4749863e+00  -2.1814082e+00  -1.8182865e+00  -1.2505219e+00  -4.7749208e-01  -3.6380791e-01  -4.9195659e-01  -4.2185509e-01  -3.0920086e-01  -4.9593870e-01  -3.4211867e-01  -3.5533627e-01  -3.6791303e-01  -3.1650279e-01  -4.1237405e-01  -4.7167181e-01  -4.1345783e-01  -3.6461703e-01  -4.4929829e-01  -4.7141866e-01  -4.2477658e-01  -4.6251673e-01  -5.5247236e-01  -4.7537519e-01  -6.9420000e-01  -7.0186810e-01  -5.9381178e-01  -6.6068415e-01  -7.1383066e-01  -7.6980688e-01  -6.7228161e-01  -6.5367605e-01  -6.3940562e-01  -5.5930228e-01  -5.9167032e-01  -4.9322332e-01  -4.6305183e-01  -3.0164382e-01  -2.3273401e-01  -1.2505488e-01  -1.5394314e-01  -2.4357404e-02  -6.5608758e-02   3.4999258e-02   6.1935219e-02   7.1195420e-02   1.2392505e-01   1.0312371e-01   2.2522849e-01   1.2868305e-01   3.0248315e-01   2.5727621e-01   1.9635161e-01   1.7938297e-01   2.4472863e-01   3.4121687e-01   3.2820441e-01   4.0604169e-01   4.4660507e-01   4.2406823e-01   4.8151204e-01   4.7784380e-01   6.2408259e-01   5.7458456e-01   5.9801319e-01   5.6459190e-01   6.0797900e-01   6.2063457e-01   6.5625291e-01   6.8474806e-01   6.9427284e-01   6.6558377e-01   5.7579577e-01   6.3813479e-01   6.1491695e-01   5.6908343e-01   4.6857572e-01   4.4281777e-01   4.6827436e-01   4.3249295e-01   4.0795792e-01   4.1862256e-01   3.6253075e-01   4.1095901e-01   4.7166633e-01   3.7216676e-01   3.3787543e-01   2.2140511e-01   2.7399747e-01   2.9866408e-01   2.6356357e-01   3.4256352e-01   4.1950529e-01   5.8660736e-01   8.6062387e-01   1.1733446e+00   1.2581791e+00   1.4337887e+00   1.7005334e+00   1.9990431e+00   2.1253411e+00   1.9932907e+00   1.9322463e+00   1.7974367e+00   1.5222839e+00   1.2511679e+00   9.9873034e-01   4.8372242e-01   2.3132292e-02  -1.9491383e-01  -2.2091729e-01  -2.4373668e-01  -2.5469462e-01  -2.9113555e-01  -2.5649034e-01  -2.2787425e-01  -3.2242276e-01  -2.8928586e-01  -3.1816951e-01  -3.6365359e-01  -3.9345584e-01  -2.6641886e-01  -2.5682316e-01  -2.8869399e-01  -1.6233755e-01   1.6034772e-01   7.9216787e-01   9.3354122e-01   7.9695779e-01   5.7862066e-01   2.5773990e-01   2.2807718e-01   1.2343082e-01   9.2528624e-01   1.9313742e-01\n",
            "   1.0000000e+00  -1.1008778e+00  -3.9968398e+00  -4.2858426e+00  -4.5065789e+00  -4.0223767e+00  -3.2343676e+00  -1.5661258e+00  -9.9225766e-01  -7.5467971e-01   4.2321173e-02   1.4895093e-01   1.8352707e-01   2.9487608e-01   1.9023267e-01   2.3557500e-01   2.5348712e-01   2.2174240e-01   5.0233259e-02   1.7804214e-01   1.3956318e-01   4.6794433e-02   4.3007139e-02   1.0654432e-01   1.2654003e-02   3.9948539e-03   4.5724179e-02  -4.5999362e-02  -7.2666959e-02  -7.1077909e-02  -1.5386649e-01  -2.2725443e-01  -2.4926970e-01  -2.5348935e-01  -3.3283516e-01  -2.6432996e-01  -3.4582525e-01  -3.1078107e-01  -3.3415978e-01  -3.0617791e-01  -1.7456252e-01  -2.0616017e-01  -2.2447967e-01  -4.8683931e-02  -1.9621159e-02   9.4805186e-02   1.1678639e-01   1.8518592e-01   2.3052577e-01   1.3802707e-01   2.4442554e-01   2.7169477e-01   2.5900331e-01   2.6920166e-01   1.2706463e-01   2.0207339e-01   2.5598343e-01   1.5311526e-01   2.1021583e-01   1.9108187e-01   1.9092702e-01   2.1051006e-01   2.0710764e-01   2.0488835e-01   2.0086474e-01   3.4691963e-01   3.4800190e-01   2.5453726e-01   3.3197465e-01   3.5669030e-01   3.4992982e-01   4.3037963e-01   4.6750163e-01   4.8606309e-01   4.0318840e-01   4.7895569e-01   3.9739177e-01   4.6412003e-01   4.5179539e-01   3.6136121e-01   3.7983090e-01   3.4278528e-01   3.9910656e-01   3.8750840e-01   2.5570346e-01   2.8403408e-01   2.8499257e-01   2.5010809e-01   1.7303626e-01   1.4942257e-01   1.4168500e-01   2.2226096e-01   1.7284705e-01   1.5077932e-01   1.7656601e-01   2.8094204e-01   4.8993901e-01   6.6088884e-01   8.9319330e-01   1.0269972e+00   1.2023937e+00   1.5574096e+00   1.8084277e+00   2.1643459e+00   2.0707467e+00   1.9036136e+00   1.7644552e+00   1.5077694e+00   1.2934277e+00   8.9456215e-01   5.7801575e-01   2.4434305e-01  -2.8644345e-01  -5.1588134e-01  -7.3270694e-01  -8.3246547e-01  -8.0331806e-01  -8.3625248e-01  -7.7786456e-01  -7.7475301e-01  -7.3340394e-01  -7.2138631e-01  -8.3209517e-01  -7.1198159e-01  -7.5186730e-01  -7.5771960e-01  -8.5311967e-01  -7.6698841e-01  -6.8816064e-01  -5.1992349e-01   3.9406169e-02   5.6032676e-01   5.3835583e-01   6.5688079e-01   7.8748958e-01   7.2404623e-01   5.5578372e-01   4.7633335e-01   7.7381971e-01   1.1196209e+00  -1.4362499e+00\n",
            "   1.0000000e+00  -5.6708802e-01  -2.5934502e+00  -3.8742297e+00  -4.5840949e+00  -4.1874487e+00  -3.1514617e+00  -1.7429402e+00  -1.4906585e+00  -1.1835803e+00  -3.9422869e-01  -2.8289676e-01  -3.5692576e-01  -2.8729745e-01  -3.9948890e-01  -4.7324409e-01  -3.7904758e-01  -3.9903903e-01  -1.7859449e-01  -3.3952226e-01  -4.9844722e-01  -3.3725108e-01  -4.2547978e-01  -4.2395228e-01  -4.6317045e-01  -4.9325315e-01  -5.4974899e-01  -5.2983109e-01  -5.3093478e-01  -5.0236490e-01  -4.1736768e-01  -5.2634573e-01  -4.7100496e-01  -6.7678428e-01  -8.9861193e-01  -6.1057104e-01  -5.3016430e-01  -7.6567438e-01  -5.8193724e-01  -5.3784761e-01  -5.5638598e-01  -4.3802347e-01  -4.3624802e-01  -4.0011375e-01  -1.3498651e-01  -2.3934677e-01  -1.2024439e-01  -1.9714041e-03   2.8500048e-01   3.1564573e-01   1.0736401e-01   1.0952148e-01   1.5807843e-01   2.9189779e-01   2.6129450e-01   1.8840714e-01   1.4065136e-01   2.6336542e-01   2.8720706e-01   3.1431725e-01   1.7794238e-01   1.3829008e-01   3.2106868e-01   4.5816774e-01   2.5191615e-01   2.6135293e-01   2.7748753e-01   2.9879325e-01   4.5907535e-01   4.1914662e-01   5.2050892e-01   5.3776119e-01   6.0321071e-01   4.7563399e-01   5.2169287e-01   7.3524831e-01   7.2954106e-01   7.1852334e-01   4.9843316e-01   5.4544098e-01   6.0482668e-01   5.2129639e-01   3.9438062e-01   4.0502023e-01   5.9756305e-01   5.3113004e-01   3.7765090e-01   4.6770093e-01   4.5756933e-01   3.4111609e-01   4.0791318e-01   4.5912699e-01   4.0393661e-01   2.9819120e-01   4.6110053e-01   6.6465910e-01   5.8775722e-01   7.0132474e-01   9.1720459e-01   1.2479873e+00   1.5055679e+00   1.6401054e+00   1.8109878e+00   2.1853978e+00   2.2629849e+00   2.0529202e+00   1.8904880e+00   1.7930330e+00   1.5647844e+00   1.2346189e+00   9.0030201e-01   5.5195714e-01   2.5822163e-01  -1.2858700e-01  -9.2585287e-02  -1.6860627e-01  -4.9598880e-01  -3.9503448e-01  -3.2823831e-01  -4.4813804e-01  -2.6823009e-01  -4.5641485e-01  -3.5786694e-01  -3.1750833e-01  -4.3411223e-01  -5.4920348e-01  -3.2461549e-01  -2.6808235e-01  -2.2038421e-01  -1.1742905e-01   6.1405916e-01   1.2848254e+00   8.8607336e-01   5.3145240e-01   3.1137684e-01  -2.1918959e-02  -7.1368344e-01  -5.3219671e-01   3.2109663e-01   9.0422673e-01  -4.2179659e-01\n",
            "   1.0000000e+00   4.9047253e-01  -1.9144071e+00  -3.6163638e+00  -4.3188235e+00  -4.2680158e+00  -3.8811104e+00  -2.9932802e+00  -1.6711314e+00  -1.3338845e+00  -9.6562916e-01  -1.8331859e-01  -1.0165662e-01  -2.7387437e-01  -1.2781761e-01  -1.9598345e-01  -2.1352292e-01  -1.7647269e-01  -1.5693160e-01  -1.4917231e-01  -1.8150997e-01  -1.8007406e-01  -2.4615133e-01  -2.7425986e-01  -1.4095962e-01  -2.7744877e-01  -3.8254906e-01  -3.1193709e-01  -3.6009264e-01  -4.0596818e-01  -5.7143343e-01  -5.2410561e-01  -5.3788646e-01  -6.0677754e-01  -6.6144617e-01  -6.8337478e-01  -7.4668256e-01  -6.3566206e-01  -6.2523095e-01  -5.4009375e-01  -6.7499495e-01  -6.5394896e-01  -5.6691641e-01  -5.2404493e-01  -4.1471114e-01  -3.8778691e-01  -2.8443735e-01  -7.2744620e-02  -1.6012546e-01  -9.2159535e-02   6.7164899e-02   8.1472141e-02   5.6181748e-02   2.4692229e-02   7.4395080e-02   1.5288791e-01   9.1033246e-02   1.1015952e-01   1.1727766e-01   1.1383231e-01   1.4180793e-01   2.7149224e-01   2.4974277e-01   1.9760783e-01   2.4393891e-01   3.8653837e-01   3.0001951e-01   3.0980097e-01   3.4466985e-01   3.8970986e-01   4.4285015e-01   4.3342958e-01   5.4871006e-01   5.2503659e-01   5.2197499e-01   6.2395373e-01   5.4915930e-01   6.6918880e-01   6.2992818e-01   5.3957342e-01   5.9928297e-01   4.9785318e-01   6.3809316e-01   6.1173698e-01   4.9190824e-01   5.7394864e-01   5.1624467e-01   5.7262973e-01   4.8459064e-01   3.7438176e-01   4.6177251e-01   4.1466066e-01   3.7163879e-01   4.4516957e-01   4.8416962e-01   5.5606057e-01   6.5872426e-01   8.6115788e-01   1.0163973e+00   1.1356060e+00   1.2169730e+00   1.5530785e+00   1.7721550e+00   2.0007688e+00   1.9250032e+00   1.8984260e+00   1.7209525e+00   1.5017111e+00   1.4224917e+00   1.0232255e+00   7.7634127e-01   5.0442626e-01   5.6381865e-02  -2.3316061e-01  -4.0638829e-01  -3.2752800e-01  -4.6086800e-01  -4.0253556e-01  -3.4575164e-01  -3.5420649e-01  -4.3995884e-01  -4.2532593e-01  -4.3978873e-01  -4.5183526e-01  -3.9592642e-01  -4.4876235e-01  -3.9178907e-01  -3.7630746e-01  -4.6106907e-01  -2.5352442e-01   2.1300588e-01   4.9117320e-01   3.5081636e-01   4.9911056e-01   6.0034499e-01   8.4206940e-01   9.5207354e-01   9.9013318e-01   1.0867984e+00   1.4030110e+00  -3.8356426e-01\n",
            "   1.0000000e+00   8.0023202e-01  -8.7425189e-01  -2.3847613e+00  -3.9732924e+00  -4.3382241e+00  -3.8024222e+00  -2.5345096e+00  -1.7834233e+00  -1.5944504e+00  -7.5319899e-01  -2.9810663e-01  -4.2892797e-01  -4.9135079e-01  -3.6130424e-01  -3.3929554e-01  -3.2495154e-01  -2.9011333e-01  -3.6305106e-01  -5.2568383e-01  -5.9742301e-01  -5.7552317e-01  -5.6750306e-01  -5.0455484e-01  -6.1840616e-01  -6.8281354e-01  -7.4384873e-01  -8.1558784e-01  -8.2690193e-01  -7.8237433e-01  -9.2946196e-01  -9.9967167e-01  -1.0609695e+00  -1.0078773e+00  -1.0287349e+00  -1.1226293e+00  -1.0286499e+00  -1.0465146e+00  -1.0633724e+00  -1.1224228e+00  -9.8324201e-01  -8.5586525e-01  -7.9126600e-01  -5.8875425e-01  -5.1830558e-01  -3.9729838e-01  -3.5433285e-01  -2.0742492e-01  -8.8266017e-02  -1.6873361e-01  -6.7592085e-02  -3.2772281e-02   7.1899471e-02   8.1485179e-02   6.7225257e-02   1.3271969e-01   7.0240933e-02   1.0043175e-01   1.5041753e-01   1.0988654e-01   1.4886026e-01   2.0874762e-01   2.2823622e-01   2.7366422e-01   2.9952441e-01   3.2706231e-01   2.4231412e-01   3.8610522e-01   4.1908722e-01   4.3971785e-01   4.8895381e-01   5.4171511e-01   5.7386922e-01   6.3355476e-01   4.1167103e-01   4.4091729e-01   5.8870540e-01   5.3086502e-01   5.5896452e-01   6.0687423e-01   6.1472869e-01   5.3777156e-01   5.9929423e-01   6.4046303e-01   5.6839906e-01   5.4471048e-01   5.5795891e-01   5.3776149e-01   4.7229245e-01   4.6659561e-01   6.3441689e-01   4.2517487e-01   4.2077721e-01   4.4052297e-01   3.6102802e-01   3.5929943e-01   3.2136632e-01   4.2132549e-01   5.0512195e-01   6.7748866e-01   9.0869284e-01   9.8223031e-01   1.1553635e+00   1.3362539e+00   1.6275344e+00   1.7175939e+00   1.6964865e+00   1.7416856e+00   1.6740780e+00   1.5469278e+00   1.3317385e+00   1.1101682e+00   9.2221037e-01   5.2177665e-01   1.5485176e-01  -1.2386070e-01  -2.0299778e-01  -2.4795602e-01  -2.1912177e-01  -2.1469520e-01  -3.1921472e-01  -1.9859713e-01  -1.5161786e-01  -1.2959259e-01  -7.4938934e-02  -1.9680704e-01  -1.7479482e-01  -2.0883265e-01  -2.1075375e-01  -1.0048537e-01   1.9744621e-01   9.6660600e-01   1.1488838e+00   9.5843425e-01   1.0590254e+00   1.3716823e+00   1.2773918e+00   9.6030393e-01   9.7101956e-01   1.6143924e+00   1.4214563e+00\n",
            "   1.0000000e+00  -1.5076736e+00  -3.5745500e+00  -4.4780109e+00  -4.4082752e+00  -3.3212415e+00  -2.1051715e+00  -1.4810482e+00  -1.3013622e+00  -4.9823960e-01  -2.8692830e-01  -3.9003094e-01  -2.6384158e-01  -3.1802419e-01  -1.8591558e-01  -2.6978996e-01  -3.2224541e-01  -3.0925306e-01  -3.9110135e-01  -3.0325940e-01  -3.1929186e-01  -4.0791759e-01  -4.2157915e-01  -4.8138035e-01  -4.4751848e-01  -5.2485040e-01  -4.6911847e-01  -5.8260667e-01  -6.7903585e-01  -6.5205179e-01  -8.4685281e-01  -7.6668159e-01  -7.3514832e-01  -7.8846458e-01  -8.5127649e-01  -8.3700984e-01  -8.4350094e-01  -8.2704168e-01  -8.7806084e-01  -7.5509458e-01  -8.0992101e-01  -6.7941539e-01  -5.8341796e-01  -4.5742032e-01  -3.7778498e-01  -3.6062856e-01  -2.3172648e-01  -2.3684160e-01  -4.7710649e-02  -5.2632422e-02  -7.4354922e-02  -9.4408878e-02  -8.2684152e-02   6.7543086e-03  -8.2925194e-02  -6.1498973e-02   2.1666852e-02  -4.0822620e-02   3.8088286e-02   4.1112760e-02  -1.0716567e-01   3.4701332e-02   8.5703804e-02   1.2185632e-01   8.8891310e-02   1.8567768e-01   2.2990726e-01   1.8320411e-01   2.8684534e-01   2.7956195e-01   4.1859150e-01   3.9460935e-01   4.2764032e-01   4.4484387e-01   4.6829876e-01   5.1542459e-01   6.3629527e-01   6.3325173e-01   6.4466005e-01   6.2754545e-01   5.3004067e-01   4.9282330e-01   4.7920823e-01   4.1335998e-01   4.7562381e-01   4.3649354e-01   4.0684174e-01   4.4330030e-01   3.1768387e-01   3.6132070e-01   3.6100050e-01   2.4142577e-01   3.1811500e-01   3.2515110e-01   4.7830471e-01   4.7607975e-01   5.3206270e-01   7.9548456e-01   9.4516940e-01   1.1049370e+00   1.2906530e+00   1.4892172e+00   1.7553005e+00   1.9609181e+00   2.0424366e+00   1.9028290e+00   1.9028747e+00   1.7580745e+00   1.4257820e+00   1.2102201e+00   9.2839739e-01   6.4126402e-01   1.7552516e-01  -1.0691014e-01  -1.3204268e-01  -1.6583570e-01  -9.8570541e-02  -2.6602195e-01  -2.3729390e-01  -1.3768778e-01  -1.0067464e-01  -1.0749888e-01  -2.1036210e-01  -1.3010391e-01  -1.0763115e-01  -1.6114587e-01  -1.0131627e-01  -9.5255496e-02  -6.9172156e-02   1.5487779e-01   6.9565622e-01   1.2149308e+00   1.0890684e+00   9.8336855e-01   1.0141238e+00   9.5262870e-01   7.4932600e-01   1.0070757e+00   1.6349896e+00   1.4933655e+00  -7.8313438e-01\n",
            "   1.0000000e+00  -2.9716100e-01  -2.7666349e+00  -4.1021848e+00  -4.5896691e+00  -4.2193569e+00  -3.6504434e+00  -2.3005176e+00  -1.2939171e+00  -1.0656583e+00  -4.9052040e-01   5.0751439e-02  -7.6647389e-02  -9.4646854e-02  -1.5817964e-01  -6.8691701e-02  -1.4083211e-01  -6.9176949e-02  -1.3427557e-01  -5.9198867e-02  -1.3137923e-01  -1.8120811e-01  -2.1596475e-01  -2.1755404e-01  -2.5406003e-01  -3.3124688e-01  -3.0492329e-01  -2.7379440e-01  -3.4793484e-01  -3.8742898e-01  -4.0400320e-01  -4.2504802e-01  -5.0769997e-01  -5.8216786e-01  -5.3291699e-01  -6.3787300e-01  -6.5593804e-01  -5.4965323e-01  -6.8673176e-01  -5.8303665e-01  -6.3651154e-01  -6.2084931e-01  -4.6828900e-01  -4.2352427e-01  -3.2470491e-01  -2.6358651e-01  -1.2755785e-01   1.1174656e-02   2.4380947e-02   8.4772790e-02   5.9935601e-02   1.6447101e-01   1.6221370e-01   1.2599133e-01   1.4362743e-01   7.8258906e-02   1.7851919e-01   1.2659749e-01   1.7504434e-01   1.6945660e-01   2.9969735e-01   2.4249451e-01   2.9716945e-01   2.3840226e-01   2.4915624e-01   2.2551958e-01   2.6718444e-01   2.3662610e-01   2.5742585e-01   4.1650766e-01   4.2657605e-01   4.5903586e-01   4.1659532e-01   4.0912708e-01   3.9677707e-01   5.5726867e-01   5.3500713e-01   5.9007557e-01   5.8584409e-01   5.6813068e-01   5.5498601e-01   4.5756205e-01   4.6270393e-01   4.9946994e-01   4.5014046e-01   3.7201043e-01   4.2852068e-01   3.0460405e-01   2.4412519e-01   2.9406742e-01   2.0671765e-01   3.7245704e-01   3.9460372e-01   3.9444908e-01   4.0425396e-01   4.5989803e-01   6.7724204e-01   8.1142429e-01   8.8952757e-01   1.1303855e+00   1.2852143e+00   1.6523744e+00   1.8393372e+00   1.9485002e+00   1.9406907e+00   1.8161016e+00   1.7273154e+00   1.5512273e+00   1.2119247e+00   1.1334151e+00   7.4591889e-01   3.6093266e-01  -1.2851839e-01  -3.9421797e-01  -4.3660141e-01  -5.0647651e-01  -4.5514710e-01  -4.5786634e-01  -5.4529921e-01  -5.0609683e-01  -3.7652870e-01  -4.5780449e-01  -4.9545976e-01  -4.5869326e-01  -3.3281149e-01  -4.1033407e-01  -4.0651848e-01  -3.3723175e-01  -3.8647852e-01  -8.2580102e-02   2.2031610e-01   5.9497827e-01   5.8177878e-01   6.8440603e-01   9.1165083e-01   9.7948323e-01   1.0534584e+00   9.7478703e-01   1.1104073e+00   1.2881654e+00  -8.2338562e-01\n",
            "   1.0000000e+00   4.4676853e-01  -1.5073974e+00  -3.1874679e+00  -4.5074621e+00  -4.6042007e+00  -3.6361150e+00  -2.3116038e+00  -1.5977275e+00  -1.3624498e+00  -6.6921633e-01  -1.7339277e-01  -2.0511622e-01  -2.3078667e-01  -1.7712137e-01  -2.3850955e-01  -2.0507877e-01  -2.3054363e-01  -2.0915931e-01  -2.9293956e-01  -2.5506852e-01  -2.5398990e-01  -3.5197992e-01  -4.2773929e-01  -4.6594770e-01  -4.6028596e-01  -4.7506210e-01  -4.4974621e-01  -5.6228003e-01  -5.3278100e-01  -6.3579716e-01  -6.3658771e-01  -7.4954800e-01  -8.0678463e-01  -6.9629187e-01  -8.5528234e-01  -8.9625530e-01  -9.0567196e-01  -9.0845622e-01  -8.3008356e-01  -8.3883747e-01  -6.9212508e-01  -6.4576927e-01  -4.8887225e-01  -4.0645909e-01  -3.5637924e-01  -2.2657834e-01  -2.0574481e-01  -7.1802463e-02  -6.6372326e-02   7.5019777e-03   3.0631739e-02   2.0969858e-02   3.3968155e-02   1.6488977e-01   8.3023926e-02   4.5058956e-02  -4.7666788e-03   1.2381566e-01   1.0804433e-01   7.3935543e-02   1.1256906e-01   5.5672868e-02   1.2375141e-01   2.2032513e-01   2.3411098e-01   3.2279232e-01   2.1513818e-01   3.3896743e-01   3.3478514e-01   3.2808378e-01   5.1228301e-01   4.9876823e-01   6.0277392e-01   6.5327531e-01   6.8093211e-01   7.2841436e-01   6.8048301e-01   6.4050410e-01   6.2050170e-01   6.5800821e-01   7.1363176e-01   6.2167160e-01   4.8816176e-01   4.5528345e-01   4.2458401e-01   5.0446979e-01   4.2813301e-01   4.5083786e-01   4.4836001e-01   3.8991267e-01   3.9158529e-01   3.4956959e-01   4.1467343e-01   4.7231448e-01   5.6453121e-01   7.3509325e-01   7.9975056e-01   1.1259942e+00   1.1641296e+00   1.2818475e+00   1.5527349e+00   1.6916357e+00   1.8828098e+00   1.7603130e+00   1.8258701e+00   1.6193333e+00   1.3949016e+00   1.1327886e+00   9.2835160e-01   5.9884412e-01   2.2924343e-01  -1.4470770e-01  -2.7006863e-01  -3.2319297e-01  -3.4959834e-01  -2.9816321e-01  -2.5184329e-01  -2.9194076e-01  -4.1334591e-01  -3.0745492e-01  -3.3082525e-01  -4.3238918e-01  -2.8993487e-01  -3.2493274e-01  -3.0928759e-01  -2.5256740e-01  -2.8258614e-01  -1.6934497e-01   2.5872903e-01   8.9662717e-01   1.0882033e+00   1.0013058e+00   1.2920592e+00   1.3786665e+00   1.0147645e+00   8.2079301e-01   1.0343880e+00   1.2584334e+00   9.6121472e-01  -9.9947589e-01\n",
            "   1.0000000e+00   8.7630577e-02  -1.7534903e+00  -3.3044731e+00  -4.7046566e+00  -4.6864151e+00  -3.6118167e+00  -2.2672676e+00  -1.5708930e+00  -1.4177903e+00  -5.0078809e-01  -2.2517739e-01  -3.0883362e-01  -2.1473324e-01  -2.1450921e-01  -2.2312107e-01  -2.6233599e-01  -2.1181590e-01  -2.9909881e-01  -2.5995633e-01  -3.1610955e-01  -2.5866848e-01  -2.8714668e-01  -3.6685535e-01  -3.0598032e-01  -4.1670806e-01  -4.0230307e-01  -3.8155410e-01  -5.1989677e-01  -5.2095179e-01  -5.9691915e-01  -6.5070030e-01  -6.7963616e-01  -7.5152681e-01  -7.2522391e-01  -8.3070065e-01  -8.0731727e-01  -8.4838836e-01  -7.4455386e-01  -6.9997298e-01  -7.5435259e-01  -6.2191821e-01  -5.3394237e-01  -3.9659590e-01  -2.2089032e-01  -1.7931625e-01  -9.4803401e-03  -2.4018025e-02   4.1459691e-02   9.4492517e-02   8.3212059e-02   9.9031316e-02   4.5914253e-02   1.0951946e-01   1.6816200e-01   2.6346944e-01   1.3610056e-01   1.5883825e-01   8.9803130e-02   1.4660419e-01   1.9676887e-01   2.6898346e-01   2.3726297e-01   1.8635578e-01   2.4141723e-01   2.7842589e-01   3.8315578e-01   3.8957693e-01   3.9354878e-01   5.7738569e-01   5.8774864e-01   5.8188293e-01   5.4491690e-01   6.5788513e-01   6.0177743e-01   6.4273411e-01   6.9906231e-01   6.8011030e-01   6.9863975e-01   7.1617522e-01   6.6862611e-01   6.7182660e-01   5.7504464e-01   5.2921250e-01   5.4333826e-01   6.0328119e-01   5.5471386e-01   5.6362046e-01   4.8903431e-01   4.4695034e-01   4.5643246e-01   3.9325248e-01   4.4143912e-01   4.4738863e-01   2.7405506e-01   3.6460866e-01   4.0121863e-01   4.4788969e-01   5.4780929e-01   6.4209010e-01   7.8832807e-01   7.4980141e-01   8.2085218e-01   9.6416033e-01   1.0618841e+00   1.3408602e+00   1.1378081e+00   8.6401264e-01   6.8398010e-01   6.2842387e-01   5.0113433e-01   4.9129218e-01   4.6193736e-01   3.2981245e-01   1.2041510e-01  -1.4196808e-01  -1.9517724e-01  -1.0265211e-01  -7.6308425e-02  -1.1651106e-01  -1.9168563e-01  -2.3429165e-01  -1.6112658e-01  -1.4536342e-01  -2.1218169e-01  -1.3568405e-01  -1.3344050e-01  -1.7007668e-01  -3.5913592e-02   3.3224263e-01   1.0632505e+00   1.5757540e+00   1.7090456e+00   2.1429259e+00   2.3935280e+00   1.9255500e+00   1.0276237e+00   5.7345293e-01   1.9297149e-01  -6.4868286e-01  -2.4410678e+00\n",
            "   1.0000000e+00  -8.3228111e-01  -1.7003675e+00  -2.2573013e+00  -2.8536712e+00  -2.8533008e+00  -2.7014866e+00  -2.2857261e+00  -1.5555120e+00  -1.2666223e+00  -1.0859570e+00  -6.9464057e-01  -5.8851692e-01  -6.6246942e-01  -7.6800441e-01  -7.7255050e-01  -6.2105407e-01  -6.1418665e-01  -6.3012434e-01  -6.6957728e-01  -6.8415362e-01  -6.9294243e-01  -6.1130156e-01  -6.4700697e-01  -6.2136212e-01  -6.4107130e-01  -6.5907019e-01  -6.2097711e-01  -6.7492061e-01  -6.6416383e-01  -7.3694270e-01  -7.6923705e-01  -6.9965708e-01  -8.0030165e-01  -7.2742332e-01  -7.5043487e-01  -7.8906542e-01  -7.3295704e-01  -7.6548658e-01  -6.9708867e-01  -7.6728083e-01  -7.1641729e-01  -6.4584327e-01  -5.7317813e-01  -5.6025580e-01  -6.1643357e-01  -6.1431553e-01  -5.5193907e-01  -4.0993954e-01  -4.6597041e-01  -4.2836411e-01  -4.2283947e-01  -3.7138890e-01  -3.9472419e-01  -4.2201833e-01  -3.5485451e-01  -4.0841286e-01  -4.2534175e-01  -3.8702144e-01  -4.0999646e-01  -4.2726131e-01  -3.8690279e-01  -3.7227239e-01  -3.8801449e-01  -3.2093223e-01  -3.6169826e-01  -3.0421592e-01  -2.2082560e-01  -2.6522743e-01  -2.5316306e-01  -1.7678608e-01  -1.8033685e-01  -1.3543422e-01  -1.0218701e-01  -6.9772168e-02  -1.2025854e-02   1.9392818e-02   2.3502179e-03   2.5684537e-02   1.0619972e-01   6.0628283e-02   1.4100528e-01   9.8627186e-02   1.0026031e-01   1.2496920e-01   1.3151283e-01   1.5842003e-01   1.0196096e-01   1.6902623e-01   1.9830596e-01   1.6513906e-01   1.9785921e-01   1.1946397e-01   2.4410863e-01   3.6000277e-01   4.6532602e-01   5.0420990e-01   5.5611785e-01   6.8718873e-01   8.2299787e-01   9.2351214e-01   1.0769364e+00   1.1586477e+00   1.1558893e+00   1.1376925e+00   1.0217586e+00   9.1858098e-01   8.3113319e-01   6.8050602e-01   5.4029633e-01   4.6392539e-01   1.3576907e-01   4.0805901e-02   5.5551077e-02   1.8917608e-02   1.4897340e-01   3.9533782e-01   7.7973296e-01   9.4139313e-01   1.0007652e+00   9.8593040e-01   9.7372193e-01   1.0344785e+00   1.0622647e+00   1.0609783e+00   1.1161974e+00   1.2243507e+00   1.4212402e+00   1.6446545e+00   2.0071813e+00   2.1185986e+00   2.0283038e+00   2.1105038e+00   2.2036678e+00   2.2275444e+00   2.1717334e+00   2.0459377e+00   2.1263716e+00   2.1268518e+00   1.6792986e+00   9.6581366e-01\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ret6bTxmoxfj",
        "outputId": "5f154642-8adb-40d8-f44b-f7253a0f5472",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "df=pd.read_csv('ecg_final.txt', sep='  ', header=None)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvX3zzjLo8KR",
        "outputId": "9d9c41db-11c5-416c-d699-1b242af4bf86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>...</th>\n",
              "      <th>101</th>\n",
              "      <th>102</th>\n",
              "      <th>103</th>\n",
              "      <th>104</th>\n",
              "      <th>105</th>\n",
              "      <th>106</th>\n",
              "      <th>107</th>\n",
              "      <th>108</th>\n",
              "      <th>109</th>\n",
              "      <th>110</th>\n",
              "      <th>111</th>\n",
              "      <th>112</th>\n",
              "      <th>113</th>\n",
              "      <th>114</th>\n",
              "      <th>115</th>\n",
              "      <th>116</th>\n",
              "      <th>117</th>\n",
              "      <th>118</th>\n",
              "      <th>119</th>\n",
              "      <th>120</th>\n",
              "      <th>121</th>\n",
              "      <th>122</th>\n",
              "      <th>123</th>\n",
              "      <th>124</th>\n",
              "      <th>125</th>\n",
              "      <th>126</th>\n",
              "      <th>127</th>\n",
              "      <th>128</th>\n",
              "      <th>129</th>\n",
              "      <th>130</th>\n",
              "      <th>131</th>\n",
              "      <th>132</th>\n",
              "      <th>133</th>\n",
              "      <th>134</th>\n",
              "      <th>135</th>\n",
              "      <th>136</th>\n",
              "      <th>137</th>\n",
              "      <th>138</th>\n",
              "      <th>139</th>\n",
              "      <th>140</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.112522</td>\n",
              "      <td>-2.827204</td>\n",
              "      <td>-3.773897</td>\n",
              "      <td>-4.349751</td>\n",
              "      <td>-4.376041</td>\n",
              "      <td>-3.474986</td>\n",
              "      <td>-2.181408</td>\n",
              "      <td>-1.818287</td>\n",
              "      <td>-1.250522</td>\n",
              "      <td>-0.477492</td>\n",
              "      <td>-0.363808</td>\n",
              "      <td>-0.491957</td>\n",
              "      <td>-0.421855</td>\n",
              "      <td>-0.309201</td>\n",
              "      <td>-0.495939</td>\n",
              "      <td>-0.342119</td>\n",
              "      <td>-0.355336</td>\n",
              "      <td>-0.367913</td>\n",
              "      <td>-0.316503</td>\n",
              "      <td>-0.412374</td>\n",
              "      <td>-0.471672</td>\n",
              "      <td>-0.413458</td>\n",
              "      <td>-0.364617</td>\n",
              "      <td>-0.449298</td>\n",
              "      <td>-0.471419</td>\n",
              "      <td>-0.424777</td>\n",
              "      <td>-0.462517</td>\n",
              "      <td>-0.552472</td>\n",
              "      <td>-0.475375</td>\n",
              "      <td>-0.694200</td>\n",
              "      <td>-0.701868</td>\n",
              "      <td>-0.593812</td>\n",
              "      <td>-0.660684</td>\n",
              "      <td>-0.713831</td>\n",
              "      <td>-0.769807</td>\n",
              "      <td>-0.672282</td>\n",
              "      <td>-0.653676</td>\n",
              "      <td>-0.639406</td>\n",
              "      <td>-0.559302</td>\n",
              "      <td>...</td>\n",
              "      <td>1.173345</td>\n",
              "      <td>1.258179</td>\n",
              "      <td>1.433789</td>\n",
              "      <td>1.700533</td>\n",
              "      <td>1.999043</td>\n",
              "      <td>2.125341</td>\n",
              "      <td>1.993291</td>\n",
              "      <td>1.932246</td>\n",
              "      <td>1.797437</td>\n",
              "      <td>1.522284</td>\n",
              "      <td>1.251168</td>\n",
              "      <td>0.998730</td>\n",
              "      <td>0.483722</td>\n",
              "      <td>0.023132</td>\n",
              "      <td>-0.194914</td>\n",
              "      <td>-0.220917</td>\n",
              "      <td>-0.243737</td>\n",
              "      <td>-0.254695</td>\n",
              "      <td>-0.291136</td>\n",
              "      <td>-0.256490</td>\n",
              "      <td>-0.227874</td>\n",
              "      <td>-0.322423</td>\n",
              "      <td>-0.289286</td>\n",
              "      <td>-0.318170</td>\n",
              "      <td>-0.363654</td>\n",
              "      <td>-0.393456</td>\n",
              "      <td>-0.266419</td>\n",
              "      <td>-0.256823</td>\n",
              "      <td>-0.288694</td>\n",
              "      <td>-0.162338</td>\n",
              "      <td>0.160348</td>\n",
              "      <td>0.792168</td>\n",
              "      <td>0.933541</td>\n",
              "      <td>0.796958</td>\n",
              "      <td>0.578621</td>\n",
              "      <td>0.257740</td>\n",
              "      <td>0.228077</td>\n",
              "      <td>0.123431</td>\n",
              "      <td>0.925286</td>\n",
              "      <td>0.193137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.100878</td>\n",
              "      <td>-3.996840</td>\n",
              "      <td>-4.285843</td>\n",
              "      <td>-4.506579</td>\n",
              "      <td>-4.022377</td>\n",
              "      <td>-3.234368</td>\n",
              "      <td>-1.566126</td>\n",
              "      <td>-0.992258</td>\n",
              "      <td>-0.754680</td>\n",
              "      <td>0.042321</td>\n",
              "      <td>0.148951</td>\n",
              "      <td>0.183527</td>\n",
              "      <td>0.294876</td>\n",
              "      <td>0.190233</td>\n",
              "      <td>0.235575</td>\n",
              "      <td>0.253487</td>\n",
              "      <td>0.221742</td>\n",
              "      <td>0.050233</td>\n",
              "      <td>0.178042</td>\n",
              "      <td>0.139563</td>\n",
              "      <td>0.046794</td>\n",
              "      <td>0.043007</td>\n",
              "      <td>0.106544</td>\n",
              "      <td>0.012654</td>\n",
              "      <td>0.003995</td>\n",
              "      <td>0.045724</td>\n",
              "      <td>-0.045999</td>\n",
              "      <td>-0.072667</td>\n",
              "      <td>-0.071078</td>\n",
              "      <td>-0.153866</td>\n",
              "      <td>-0.227254</td>\n",
              "      <td>-0.249270</td>\n",
              "      <td>-0.253489</td>\n",
              "      <td>-0.332835</td>\n",
              "      <td>-0.264330</td>\n",
              "      <td>-0.345825</td>\n",
              "      <td>-0.310781</td>\n",
              "      <td>-0.334160</td>\n",
              "      <td>-0.306178</td>\n",
              "      <td>...</td>\n",
              "      <td>1.557410</td>\n",
              "      <td>1.808428</td>\n",
              "      <td>2.164346</td>\n",
              "      <td>2.070747</td>\n",
              "      <td>1.903614</td>\n",
              "      <td>1.764455</td>\n",
              "      <td>1.507769</td>\n",
              "      <td>1.293428</td>\n",
              "      <td>0.894562</td>\n",
              "      <td>0.578016</td>\n",
              "      <td>0.244343</td>\n",
              "      <td>-0.286443</td>\n",
              "      <td>-0.515881</td>\n",
              "      <td>-0.732707</td>\n",
              "      <td>-0.832465</td>\n",
              "      <td>-0.803318</td>\n",
              "      <td>-0.836252</td>\n",
              "      <td>-0.777865</td>\n",
              "      <td>-0.774753</td>\n",
              "      <td>-0.733404</td>\n",
              "      <td>-0.721386</td>\n",
              "      <td>-0.832095</td>\n",
              "      <td>-0.711982</td>\n",
              "      <td>-0.751867</td>\n",
              "      <td>-0.757720</td>\n",
              "      <td>-0.853120</td>\n",
              "      <td>-0.766988</td>\n",
              "      <td>-0.688161</td>\n",
              "      <td>-0.519923</td>\n",
              "      <td>0.039406</td>\n",
              "      <td>0.560327</td>\n",
              "      <td>0.538356</td>\n",
              "      <td>0.656881</td>\n",
              "      <td>0.787490</td>\n",
              "      <td>0.724046</td>\n",
              "      <td>0.555784</td>\n",
              "      <td>0.476333</td>\n",
              "      <td>0.773820</td>\n",
              "      <td>1.119621</td>\n",
              "      <td>-1.436250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.567088</td>\n",
              "      <td>-2.593450</td>\n",
              "      <td>-3.874230</td>\n",
              "      <td>-4.584095</td>\n",
              "      <td>-4.187449</td>\n",
              "      <td>-3.151462</td>\n",
              "      <td>-1.742940</td>\n",
              "      <td>-1.490658</td>\n",
              "      <td>-1.183580</td>\n",
              "      <td>-0.394229</td>\n",
              "      <td>-0.282897</td>\n",
              "      <td>-0.356926</td>\n",
              "      <td>-0.287297</td>\n",
              "      <td>-0.399489</td>\n",
              "      <td>-0.473244</td>\n",
              "      <td>-0.379048</td>\n",
              "      <td>-0.399039</td>\n",
              "      <td>-0.178594</td>\n",
              "      <td>-0.339522</td>\n",
              "      <td>-0.498447</td>\n",
              "      <td>-0.337251</td>\n",
              "      <td>-0.425480</td>\n",
              "      <td>-0.423952</td>\n",
              "      <td>-0.463170</td>\n",
              "      <td>-0.493253</td>\n",
              "      <td>-0.549749</td>\n",
              "      <td>-0.529831</td>\n",
              "      <td>-0.530935</td>\n",
              "      <td>-0.502365</td>\n",
              "      <td>-0.417368</td>\n",
              "      <td>-0.526346</td>\n",
              "      <td>-0.471005</td>\n",
              "      <td>-0.676784</td>\n",
              "      <td>-0.898612</td>\n",
              "      <td>-0.610571</td>\n",
              "      <td>-0.530164</td>\n",
              "      <td>-0.765674</td>\n",
              "      <td>-0.581937</td>\n",
              "      <td>-0.537848</td>\n",
              "      <td>...</td>\n",
              "      <td>1.640105</td>\n",
              "      <td>1.810988</td>\n",
              "      <td>2.185398</td>\n",
              "      <td>2.262985</td>\n",
              "      <td>2.052920</td>\n",
              "      <td>1.890488</td>\n",
              "      <td>1.793033</td>\n",
              "      <td>1.564784</td>\n",
              "      <td>1.234619</td>\n",
              "      <td>0.900302</td>\n",
              "      <td>0.551957</td>\n",
              "      <td>0.258222</td>\n",
              "      <td>-0.128587</td>\n",
              "      <td>-0.092585</td>\n",
              "      <td>-0.168606</td>\n",
              "      <td>-0.495989</td>\n",
              "      <td>-0.395034</td>\n",
              "      <td>-0.328238</td>\n",
              "      <td>-0.448138</td>\n",
              "      <td>-0.268230</td>\n",
              "      <td>-0.456415</td>\n",
              "      <td>-0.357867</td>\n",
              "      <td>-0.317508</td>\n",
              "      <td>-0.434112</td>\n",
              "      <td>-0.549203</td>\n",
              "      <td>-0.324615</td>\n",
              "      <td>-0.268082</td>\n",
              "      <td>-0.220384</td>\n",
              "      <td>-0.117429</td>\n",
              "      <td>0.614059</td>\n",
              "      <td>1.284825</td>\n",
              "      <td>0.886073</td>\n",
              "      <td>0.531452</td>\n",
              "      <td>0.311377</td>\n",
              "      <td>-0.021919</td>\n",
              "      <td>-0.713683</td>\n",
              "      <td>-0.532197</td>\n",
              "      <td>0.321097</td>\n",
              "      <td>0.904227</td>\n",
              "      <td>-0.421797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.490473</td>\n",
              "      <td>-1.914407</td>\n",
              "      <td>-3.616364</td>\n",
              "      <td>-4.318823</td>\n",
              "      <td>-4.268016</td>\n",
              "      <td>-3.881110</td>\n",
              "      <td>-2.993280</td>\n",
              "      <td>-1.671131</td>\n",
              "      <td>-1.333884</td>\n",
              "      <td>-0.965629</td>\n",
              "      <td>-0.183319</td>\n",
              "      <td>-0.101657</td>\n",
              "      <td>-0.273874</td>\n",
              "      <td>-0.127818</td>\n",
              "      <td>-0.195983</td>\n",
              "      <td>-0.213523</td>\n",
              "      <td>-0.176473</td>\n",
              "      <td>-0.156932</td>\n",
              "      <td>-0.149172</td>\n",
              "      <td>-0.181510</td>\n",
              "      <td>-0.180074</td>\n",
              "      <td>-0.246151</td>\n",
              "      <td>-0.274260</td>\n",
              "      <td>-0.140960</td>\n",
              "      <td>-0.277449</td>\n",
              "      <td>-0.382549</td>\n",
              "      <td>-0.311937</td>\n",
              "      <td>-0.360093</td>\n",
              "      <td>-0.405968</td>\n",
              "      <td>-0.571433</td>\n",
              "      <td>-0.524106</td>\n",
              "      <td>-0.537886</td>\n",
              "      <td>-0.606778</td>\n",
              "      <td>-0.661446</td>\n",
              "      <td>-0.683375</td>\n",
              "      <td>-0.746683</td>\n",
              "      <td>-0.635662</td>\n",
              "      <td>-0.625231</td>\n",
              "      <td>-0.540094</td>\n",
              "      <td>...</td>\n",
              "      <td>1.553079</td>\n",
              "      <td>1.772155</td>\n",
              "      <td>2.000769</td>\n",
              "      <td>1.925003</td>\n",
              "      <td>1.898426</td>\n",
              "      <td>1.720953</td>\n",
              "      <td>1.501711</td>\n",
              "      <td>1.422492</td>\n",
              "      <td>1.023225</td>\n",
              "      <td>0.776341</td>\n",
              "      <td>0.504426</td>\n",
              "      <td>0.056382</td>\n",
              "      <td>-0.233161</td>\n",
              "      <td>-0.406388</td>\n",
              "      <td>-0.327528</td>\n",
              "      <td>-0.460868</td>\n",
              "      <td>-0.402536</td>\n",
              "      <td>-0.345752</td>\n",
              "      <td>-0.354206</td>\n",
              "      <td>-0.439959</td>\n",
              "      <td>-0.425326</td>\n",
              "      <td>-0.439789</td>\n",
              "      <td>-0.451835</td>\n",
              "      <td>-0.395926</td>\n",
              "      <td>-0.448762</td>\n",
              "      <td>-0.391789</td>\n",
              "      <td>-0.376307</td>\n",
              "      <td>-0.461069</td>\n",
              "      <td>-0.253524</td>\n",
              "      <td>0.213006</td>\n",
              "      <td>0.491173</td>\n",
              "      <td>0.350816</td>\n",
              "      <td>0.499111</td>\n",
              "      <td>0.600345</td>\n",
              "      <td>0.842069</td>\n",
              "      <td>0.952074</td>\n",
              "      <td>0.990133</td>\n",
              "      <td>1.086798</td>\n",
              "      <td>1.403011</td>\n",
              "      <td>-0.383564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.800232</td>\n",
              "      <td>-0.874252</td>\n",
              "      <td>-2.384761</td>\n",
              "      <td>-3.973292</td>\n",
              "      <td>-4.338224</td>\n",
              "      <td>-3.802422</td>\n",
              "      <td>-2.534510</td>\n",
              "      <td>-1.783423</td>\n",
              "      <td>-1.594450</td>\n",
              "      <td>-0.753199</td>\n",
              "      <td>-0.298107</td>\n",
              "      <td>-0.428928</td>\n",
              "      <td>-0.491351</td>\n",
              "      <td>-0.361304</td>\n",
              "      <td>-0.339296</td>\n",
              "      <td>-0.324952</td>\n",
              "      <td>-0.290113</td>\n",
              "      <td>-0.363051</td>\n",
              "      <td>-0.525684</td>\n",
              "      <td>-0.597423</td>\n",
              "      <td>-0.575523</td>\n",
              "      <td>-0.567503</td>\n",
              "      <td>-0.504555</td>\n",
              "      <td>-0.618406</td>\n",
              "      <td>-0.682814</td>\n",
              "      <td>-0.743849</td>\n",
              "      <td>-0.815588</td>\n",
              "      <td>-0.826902</td>\n",
              "      <td>-0.782374</td>\n",
              "      <td>-0.929462</td>\n",
              "      <td>-0.999672</td>\n",
              "      <td>-1.060969</td>\n",
              "      <td>-1.007877</td>\n",
              "      <td>-1.028735</td>\n",
              "      <td>-1.122629</td>\n",
              "      <td>-1.028650</td>\n",
              "      <td>-1.046515</td>\n",
              "      <td>-1.063372</td>\n",
              "      <td>-1.122423</td>\n",
              "      <td>...</td>\n",
              "      <td>0.982230</td>\n",
              "      <td>1.155363</td>\n",
              "      <td>1.336254</td>\n",
              "      <td>1.627534</td>\n",
              "      <td>1.717594</td>\n",
              "      <td>1.696487</td>\n",
              "      <td>1.741686</td>\n",
              "      <td>1.674078</td>\n",
              "      <td>1.546928</td>\n",
              "      <td>1.331738</td>\n",
              "      <td>1.110168</td>\n",
              "      <td>0.922210</td>\n",
              "      <td>0.521777</td>\n",
              "      <td>0.154852</td>\n",
              "      <td>-0.123861</td>\n",
              "      <td>-0.202998</td>\n",
              "      <td>-0.247956</td>\n",
              "      <td>-0.219122</td>\n",
              "      <td>-0.214695</td>\n",
              "      <td>-0.319215</td>\n",
              "      <td>-0.198597</td>\n",
              "      <td>-0.151618</td>\n",
              "      <td>-0.129593</td>\n",
              "      <td>-0.074939</td>\n",
              "      <td>-0.196807</td>\n",
              "      <td>-0.174795</td>\n",
              "      <td>-0.208833</td>\n",
              "      <td>-0.210754</td>\n",
              "      <td>-0.100485</td>\n",
              "      <td>0.197446</td>\n",
              "      <td>0.966606</td>\n",
              "      <td>1.148884</td>\n",
              "      <td>0.958434</td>\n",
              "      <td>1.059025</td>\n",
              "      <td>1.371682</td>\n",
              "      <td>1.277392</td>\n",
              "      <td>0.960304</td>\n",
              "      <td>0.971020</td>\n",
              "      <td>1.614392</td>\n",
              "      <td>1.421456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>4.0</td>\n",
              "      <td>-1.122969</td>\n",
              "      <td>-2.252925</td>\n",
              "      <td>-2.867628</td>\n",
              "      <td>-3.358605</td>\n",
              "      <td>-3.167849</td>\n",
              "      <td>-2.638360</td>\n",
              "      <td>-1.664162</td>\n",
              "      <td>-0.935655</td>\n",
              "      <td>-0.866953</td>\n",
              "      <td>-0.645363</td>\n",
              "      <td>-0.093342</td>\n",
              "      <td>0.099746</td>\n",
              "      <td>0.121585</td>\n",
              "      <td>0.125760</td>\n",
              "      <td>0.071543</td>\n",
              "      <td>0.050503</td>\n",
              "      <td>0.021200</td>\n",
              "      <td>0.026780</td>\n",
              "      <td>0.158262</td>\n",
              "      <td>0.099955</td>\n",
              "      <td>-0.009558</td>\n",
              "      <td>0.078789</td>\n",
              "      <td>0.086691</td>\n",
              "      <td>0.046382</td>\n",
              "      <td>0.068272</td>\n",
              "      <td>0.102911</td>\n",
              "      <td>0.106083</td>\n",
              "      <td>0.057099</td>\n",
              "      <td>0.028555</td>\n",
              "      <td>0.046705</td>\n",
              "      <td>0.046524</td>\n",
              "      <td>0.095233</td>\n",
              "      <td>0.059824</td>\n",
              "      <td>-0.021798</td>\n",
              "      <td>-0.022737</td>\n",
              "      <td>-0.083263</td>\n",
              "      <td>-0.125486</td>\n",
              "      <td>-0.082084</td>\n",
              "      <td>-0.023000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.643776</td>\n",
              "      <td>0.597168</td>\n",
              "      <td>0.625141</td>\n",
              "      <td>0.648181</td>\n",
              "      <td>0.594234</td>\n",
              "      <td>0.630589</td>\n",
              "      <td>0.654510</td>\n",
              "      <td>0.602251</td>\n",
              "      <td>0.656946</td>\n",
              "      <td>0.603251</td>\n",
              "      <td>0.599274</td>\n",
              "      <td>0.561487</td>\n",
              "      <td>0.547185</td>\n",
              "      <td>0.537524</td>\n",
              "      <td>0.603885</td>\n",
              "      <td>0.541677</td>\n",
              "      <td>0.580715</td>\n",
              "      <td>0.689346</td>\n",
              "      <td>0.746181</td>\n",
              "      <td>0.867390</td>\n",
              "      <td>0.783529</td>\n",
              "      <td>0.698720</td>\n",
              "      <td>0.798727</td>\n",
              "      <td>0.875711</td>\n",
              "      <td>0.985118</td>\n",
              "      <td>0.799260</td>\n",
              "      <td>0.577579</td>\n",
              "      <td>0.394182</td>\n",
              "      <td>0.458721</td>\n",
              "      <td>0.411458</td>\n",
              "      <td>0.205543</td>\n",
              "      <td>-0.472419</td>\n",
              "      <td>-1.310147</td>\n",
              "      <td>-2.029521</td>\n",
              "      <td>-3.221294</td>\n",
              "      <td>-4.176790</td>\n",
              "      <td>-4.009720</td>\n",
              "      <td>-2.874136</td>\n",
              "      <td>-2.008369</td>\n",
              "      <td>-1.808334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.547705</td>\n",
              "      <td>-1.889545</td>\n",
              "      <td>-2.839779</td>\n",
              "      <td>-3.457912</td>\n",
              "      <td>-3.929149</td>\n",
              "      <td>-3.966026</td>\n",
              "      <td>-3.492560</td>\n",
              "      <td>-2.695269</td>\n",
              "      <td>-1.849691</td>\n",
              "      <td>-1.374321</td>\n",
              "      <td>-1.288459</td>\n",
              "      <td>-1.117578</td>\n",
              "      <td>-0.536271</td>\n",
              "      <td>-0.251050</td>\n",
              "      <td>-0.215496</td>\n",
              "      <td>-0.072500</td>\n",
              "      <td>-0.170556</td>\n",
              "      <td>-0.182279</td>\n",
              "      <td>-0.271306</td>\n",
              "      <td>-0.242693</td>\n",
              "      <td>-0.225397</td>\n",
              "      <td>-0.187956</td>\n",
              "      <td>-0.215918</td>\n",
              "      <td>-0.173543</td>\n",
              "      <td>-0.233401</td>\n",
              "      <td>-0.190231</td>\n",
              "      <td>-0.160668</td>\n",
              "      <td>-0.241456</td>\n",
              "      <td>-0.204146</td>\n",
              "      <td>-0.228155</td>\n",
              "      <td>-0.084758</td>\n",
              "      <td>-0.142782</td>\n",
              "      <td>-0.153214</td>\n",
              "      <td>-0.314872</td>\n",
              "      <td>-0.335928</td>\n",
              "      <td>-0.266875</td>\n",
              "      <td>-0.313519</td>\n",
              "      <td>-0.304439</td>\n",
              "      <td>-0.294795</td>\n",
              "      <td>...</td>\n",
              "      <td>0.427870</td>\n",
              "      <td>0.471064</td>\n",
              "      <td>0.532288</td>\n",
              "      <td>0.541352</td>\n",
              "      <td>0.588983</td>\n",
              "      <td>0.753851</td>\n",
              "      <td>0.650792</td>\n",
              "      <td>0.720441</td>\n",
              "      <td>0.752308</td>\n",
              "      <td>0.813847</td>\n",
              "      <td>0.690120</td>\n",
              "      <td>0.685244</td>\n",
              "      <td>0.632990</td>\n",
              "      <td>0.688632</td>\n",
              "      <td>0.676820</td>\n",
              "      <td>0.639733</td>\n",
              "      <td>0.708132</td>\n",
              "      <td>0.675063</td>\n",
              "      <td>0.646640</td>\n",
              "      <td>0.635341</td>\n",
              "      <td>0.670459</td>\n",
              "      <td>0.732434</td>\n",
              "      <td>0.866642</td>\n",
              "      <td>0.845284</td>\n",
              "      <td>1.045767</td>\n",
              "      <td>1.429693</td>\n",
              "      <td>1.783791</td>\n",
              "      <td>1.697750</td>\n",
              "      <td>1.492639</td>\n",
              "      <td>1.378843</td>\n",
              "      <td>1.218185</td>\n",
              "      <td>1.258419</td>\n",
              "      <td>1.907529</td>\n",
              "      <td>2.280888</td>\n",
              "      <td>1.895242</td>\n",
              "      <td>1.437702</td>\n",
              "      <td>1.193433</td>\n",
              "      <td>1.261335</td>\n",
              "      <td>1.150449</td>\n",
              "      <td>0.804932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.351779</td>\n",
              "      <td>-2.209006</td>\n",
              "      <td>-2.520225</td>\n",
              "      <td>-3.061475</td>\n",
              "      <td>-3.065141</td>\n",
              "      <td>-3.030739</td>\n",
              "      <td>-2.622720</td>\n",
              "      <td>-2.044092</td>\n",
              "      <td>-1.295874</td>\n",
              "      <td>-0.733839</td>\n",
              "      <td>-0.562769</td>\n",
              "      <td>-0.461068</td>\n",
              "      <td>-0.310064</td>\n",
              "      <td>-0.004490</td>\n",
              "      <td>0.184161</td>\n",
              "      <td>0.252896</td>\n",
              "      <td>0.302077</td>\n",
              "      <td>0.237223</td>\n",
              "      <td>0.304171</td>\n",
              "      <td>0.276120</td>\n",
              "      <td>0.330877</td>\n",
              "      <td>0.356674</td>\n",
              "      <td>0.323301</td>\n",
              "      <td>0.413409</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.328516</td>\n",
              "      <td>0.358460</td>\n",
              "      <td>0.353359</td>\n",
              "      <td>0.451590</td>\n",
              "      <td>0.419947</td>\n",
              "      <td>0.330580</td>\n",
              "      <td>0.317684</td>\n",
              "      <td>0.270813</td>\n",
              "      <td>0.258002</td>\n",
              "      <td>0.317464</td>\n",
              "      <td>0.358579</td>\n",
              "      <td>0.295808</td>\n",
              "      <td>0.276734</td>\n",
              "      <td>0.230999</td>\n",
              "      <td>...</td>\n",
              "      <td>0.716374</td>\n",
              "      <td>0.802243</td>\n",
              "      <td>0.803918</td>\n",
              "      <td>0.826138</td>\n",
              "      <td>0.774642</td>\n",
              "      <td>0.637822</td>\n",
              "      <td>0.747995</td>\n",
              "      <td>0.762681</td>\n",
              "      <td>0.807652</td>\n",
              "      <td>0.816406</td>\n",
              "      <td>0.818481</td>\n",
              "      <td>0.900709</td>\n",
              "      <td>0.876553</td>\n",
              "      <td>0.861227</td>\n",
              "      <td>0.764280</td>\n",
              "      <td>0.628381</td>\n",
              "      <td>0.485624</td>\n",
              "      <td>0.462129</td>\n",
              "      <td>0.442520</td>\n",
              "      <td>0.402201</td>\n",
              "      <td>0.380095</td>\n",
              "      <td>0.289096</td>\n",
              "      <td>0.214385</td>\n",
              "      <td>0.106227</td>\n",
              "      <td>-0.009595</td>\n",
              "      <td>-0.025391</td>\n",
              "      <td>0.035677</td>\n",
              "      <td>-0.030661</td>\n",
              "      <td>-0.203568</td>\n",
              "      <td>-0.445023</td>\n",
              "      <td>-0.896575</td>\n",
              "      <td>-1.512234</td>\n",
              "      <td>-2.076075</td>\n",
              "      <td>-2.586042</td>\n",
              "      <td>-3.322799</td>\n",
              "      <td>-3.627311</td>\n",
              "      <td>-3.437038</td>\n",
              "      <td>-2.260023</td>\n",
              "      <td>-1.577823</td>\n",
              "      <td>-0.684531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.124432</td>\n",
              "      <td>-1.905039</td>\n",
              "      <td>-2.192707</td>\n",
              "      <td>-2.904320</td>\n",
              "      <td>-2.900722</td>\n",
              "      <td>-2.761252</td>\n",
              "      <td>-2.569705</td>\n",
              "      <td>-2.043893</td>\n",
              "      <td>-1.490538</td>\n",
              "      <td>-0.938473</td>\n",
              "      <td>-0.485372</td>\n",
              "      <td>-0.374298</td>\n",
              "      <td>-0.389414</td>\n",
              "      <td>-0.146131</td>\n",
              "      <td>0.212746</td>\n",
              "      <td>0.429797</td>\n",
              "      <td>0.499480</td>\n",
              "      <td>0.536705</td>\n",
              "      <td>0.558162</td>\n",
              "      <td>0.543721</td>\n",
              "      <td>0.550670</td>\n",
              "      <td>0.560516</td>\n",
              "      <td>0.537170</td>\n",
              "      <td>0.537986</td>\n",
              "      <td>0.549989</td>\n",
              "      <td>0.519434</td>\n",
              "      <td>0.508304</td>\n",
              "      <td>0.540967</td>\n",
              "      <td>0.570611</td>\n",
              "      <td>0.587472</td>\n",
              "      <td>0.556779</td>\n",
              "      <td>0.519021</td>\n",
              "      <td>0.507215</td>\n",
              "      <td>0.464078</td>\n",
              "      <td>0.450806</td>\n",
              "      <td>0.475988</td>\n",
              "      <td>0.485856</td>\n",
              "      <td>0.466050</td>\n",
              "      <td>0.392451</td>\n",
              "      <td>...</td>\n",
              "      <td>0.762903</td>\n",
              "      <td>0.757111</td>\n",
              "      <td>0.704867</td>\n",
              "      <td>0.726405</td>\n",
              "      <td>0.720765</td>\n",
              "      <td>0.769695</td>\n",
              "      <td>0.809712</td>\n",
              "      <td>0.703485</td>\n",
              "      <td>0.610350</td>\n",
              "      <td>0.683131</td>\n",
              "      <td>0.746369</td>\n",
              "      <td>0.605945</td>\n",
              "      <td>0.437233</td>\n",
              "      <td>0.395451</td>\n",
              "      <td>0.396267</td>\n",
              "      <td>0.396361</td>\n",
              "      <td>0.354488</td>\n",
              "      <td>0.279633</td>\n",
              "      <td>0.227358</td>\n",
              "      <td>0.123992</td>\n",
              "      <td>-0.031222</td>\n",
              "      <td>-0.155341</td>\n",
              "      <td>-0.219141</td>\n",
              "      <td>-0.239644</td>\n",
              "      <td>-0.333443</td>\n",
              "      <td>-0.557080</td>\n",
              "      <td>-0.811259</td>\n",
              "      <td>-1.072168</td>\n",
              "      <td>-1.497530</td>\n",
              "      <td>-2.047796</td>\n",
              "      <td>-2.495989</td>\n",
              "      <td>-2.821782</td>\n",
              "      <td>-3.268355</td>\n",
              "      <td>-3.634981</td>\n",
              "      <td>-3.168765</td>\n",
              "      <td>-2.245878</td>\n",
              "      <td>-1.262259</td>\n",
              "      <td>-0.443307</td>\n",
              "      <td>-0.559769</td>\n",
              "      <td>0.108568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.728813</td>\n",
              "      <td>0.192597</td>\n",
              "      <td>-0.733884</td>\n",
              "      <td>-1.779456</td>\n",
              "      <td>-2.345908</td>\n",
              "      <td>-2.977565</td>\n",
              "      <td>-3.380053</td>\n",
              "      <td>-3.417164</td>\n",
              "      <td>-3.030925</td>\n",
              "      <td>-2.313867</td>\n",
              "      <td>-1.671269</td>\n",
              "      <td>-1.255303</td>\n",
              "      <td>-1.146982</td>\n",
              "      <td>-1.049088</td>\n",
              "      <td>-0.513524</td>\n",
              "      <td>-0.100892</td>\n",
              "      <td>-0.087888</td>\n",
              "      <td>-0.179308</td>\n",
              "      <td>-0.226885</td>\n",
              "      <td>-0.156335</td>\n",
              "      <td>-0.141478</td>\n",
              "      <td>-0.024932</td>\n",
              "      <td>-0.089615</td>\n",
              "      <td>-0.165480</td>\n",
              "      <td>-0.043243</td>\n",
              "      <td>-0.132201</td>\n",
              "      <td>-0.193009</td>\n",
              "      <td>-0.177407</td>\n",
              "      <td>-0.276008</td>\n",
              "      <td>-0.202072</td>\n",
              "      <td>-0.110621</td>\n",
              "      <td>-0.176676</td>\n",
              "      <td>-0.177095</td>\n",
              "      <td>-0.277154</td>\n",
              "      <td>-0.054093</td>\n",
              "      <td>-0.188261</td>\n",
              "      <td>-0.283490</td>\n",
              "      <td>-0.250398</td>\n",
              "      <td>-0.280567</td>\n",
              "      <td>...</td>\n",
              "      <td>0.343283</td>\n",
              "      <td>0.351328</td>\n",
              "      <td>0.350081</td>\n",
              "      <td>0.487850</td>\n",
              "      <td>0.514130</td>\n",
              "      <td>0.655713</td>\n",
              "      <td>0.659875</td>\n",
              "      <td>0.574770</td>\n",
              "      <td>0.675136</td>\n",
              "      <td>0.624451</td>\n",
              "      <td>0.697013</td>\n",
              "      <td>0.749857</td>\n",
              "      <td>0.829295</td>\n",
              "      <td>0.871171</td>\n",
              "      <td>0.863445</td>\n",
              "      <td>0.916639</td>\n",
              "      <td>0.841814</td>\n",
              "      <td>0.961598</td>\n",
              "      <td>0.877710</td>\n",
              "      <td>0.831334</td>\n",
              "      <td>0.774570</td>\n",
              "      <td>0.894903</td>\n",
              "      <td>1.087536</td>\n",
              "      <td>1.021885</td>\n",
              "      <td>1.208764</td>\n",
              "      <td>1.623936</td>\n",
              "      <td>1.851147</td>\n",
              "      <td>1.818792</td>\n",
              "      <td>1.672569</td>\n",
              "      <td>1.389086</td>\n",
              "      <td>1.219499</td>\n",
              "      <td>1.267275</td>\n",
              "      <td>1.678989</td>\n",
              "      <td>2.483389</td>\n",
              "      <td>2.569073</td>\n",
              "      <td>2.122891</td>\n",
              "      <td>1.753963</td>\n",
              "      <td>1.538975</td>\n",
              "      <td>1.713781</td>\n",
              "      <td>1.309382</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 141 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      0         1         2         3    ...       137       138       139       140\n",
              "0     1.0 -0.112522 -2.827204 -3.773897  ...  0.228077  0.123431  0.925286  0.193137\n",
              "1     1.0 -1.100878 -3.996840 -4.285843  ...  0.476333  0.773820  1.119621 -1.436250\n",
              "2     1.0 -0.567088 -2.593450 -3.874230  ... -0.532197  0.321097  0.904227 -0.421797\n",
              "3     1.0  0.490473 -1.914407 -3.616364  ...  0.990133  1.086798  1.403011 -0.383564\n",
              "4     1.0  0.800232 -0.874252 -2.384761  ...  0.960304  0.971020  1.614392  1.421456\n",
              "...   ...       ...       ...       ...  ...       ...       ...       ...       ...\n",
              "4995  4.0 -1.122969 -2.252925 -2.867628  ... -4.009720 -2.874136 -2.008369 -1.808334\n",
              "4996  2.0 -0.547705 -1.889545 -2.839779  ...  1.193433  1.261335  1.150449  0.804932\n",
              "4997  2.0 -1.351779 -2.209006 -2.520225  ... -3.437038 -2.260023 -1.577823 -0.684531\n",
              "4998  2.0 -1.124432 -1.905039 -2.192707  ... -1.262259 -0.443307 -0.559769  0.108568\n",
              "4999  2.0  0.728813  0.192597 -0.733884  ...  1.753963  1.538975  1.713781  1.309382\n",
              "\n",
              "[5000 rows x 141 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXiWstff2nGZ",
        "outputId": "67273769-9a3f-45d0-b4f7-c91128c36b24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
              "            ...\n",
              "            131, 132, 133, 134, 135, 136, 137, 138, 139, 140],\n",
              "           dtype='int64', length=141)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJpNGiV2E7P3"
      },
      "source": [
        "df = df.add_prefix('c')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3lmOvxVFLUn",
        "outputId": "bf220ea4-028d-4486-e1b1-756d0b085985",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['c0', 'c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9',\n",
              "       ...\n",
              "       'c131', 'c132', 'c133', 'c134', 'c135', 'c136', 'c137', 'c138', 'c139',\n",
              "       'c140'],\n",
              "      dtype='object', length=141)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tIB1Si7xnGMX",
        "outputId": "66c35088-f439-470e-de45-5a7111242b7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "df['c0'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    2919\n",
              "2.0    1767\n",
              "4.0     194\n",
              "3.0      96\n",
              "5.0      24\n",
              "Name: c0, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qC-89H_TTx_S",
        "outputId": "49b7afb6-9e25-4119-869e-faad88af8157",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>c0</th>\n",
              "      <th>c1</th>\n",
              "      <th>c2</th>\n",
              "      <th>c3</th>\n",
              "      <th>c4</th>\n",
              "      <th>c5</th>\n",
              "      <th>c6</th>\n",
              "      <th>c7</th>\n",
              "      <th>c8</th>\n",
              "      <th>c9</th>\n",
              "      <th>c10</th>\n",
              "      <th>c11</th>\n",
              "      <th>c12</th>\n",
              "      <th>c13</th>\n",
              "      <th>c14</th>\n",
              "      <th>c15</th>\n",
              "      <th>c16</th>\n",
              "      <th>c17</th>\n",
              "      <th>c18</th>\n",
              "      <th>c19</th>\n",
              "      <th>c20</th>\n",
              "      <th>c21</th>\n",
              "      <th>c22</th>\n",
              "      <th>c23</th>\n",
              "      <th>c24</th>\n",
              "      <th>c25</th>\n",
              "      <th>c26</th>\n",
              "      <th>c27</th>\n",
              "      <th>c28</th>\n",
              "      <th>c29</th>\n",
              "      <th>c30</th>\n",
              "      <th>c31</th>\n",
              "      <th>c32</th>\n",
              "      <th>c33</th>\n",
              "      <th>c34</th>\n",
              "      <th>c35</th>\n",
              "      <th>c36</th>\n",
              "      <th>c37</th>\n",
              "      <th>c38</th>\n",
              "      <th>c39</th>\n",
              "      <th>...</th>\n",
              "      <th>c101</th>\n",
              "      <th>c102</th>\n",
              "      <th>c103</th>\n",
              "      <th>c104</th>\n",
              "      <th>c105</th>\n",
              "      <th>c106</th>\n",
              "      <th>c107</th>\n",
              "      <th>c108</th>\n",
              "      <th>c109</th>\n",
              "      <th>c110</th>\n",
              "      <th>c111</th>\n",
              "      <th>c112</th>\n",
              "      <th>c113</th>\n",
              "      <th>c114</th>\n",
              "      <th>c115</th>\n",
              "      <th>c116</th>\n",
              "      <th>c117</th>\n",
              "      <th>c118</th>\n",
              "      <th>c119</th>\n",
              "      <th>c120</th>\n",
              "      <th>c121</th>\n",
              "      <th>c122</th>\n",
              "      <th>c123</th>\n",
              "      <th>c124</th>\n",
              "      <th>c125</th>\n",
              "      <th>c126</th>\n",
              "      <th>c127</th>\n",
              "      <th>c128</th>\n",
              "      <th>c129</th>\n",
              "      <th>c130</th>\n",
              "      <th>c131</th>\n",
              "      <th>c132</th>\n",
              "      <th>c133</th>\n",
              "      <th>c134</th>\n",
              "      <th>c135</th>\n",
              "      <th>c136</th>\n",
              "      <th>c137</th>\n",
              "      <th>c138</th>\n",
              "      <th>c139</th>\n",
              "      <th>c140</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.527400</td>\n",
              "      <td>-0.262476</td>\n",
              "      <td>-1.649511</td>\n",
              "      <td>-2.492211</td>\n",
              "      <td>-3.119443</td>\n",
              "      <td>-3.167438</td>\n",
              "      <td>-2.866308</td>\n",
              "      <td>-2.273126</td>\n",
              "      <td>-1.798127</td>\n",
              "      <td>-1.410124</td>\n",
              "      <td>-0.936082</td>\n",
              "      <td>-0.586326</td>\n",
              "      <td>-0.434430</td>\n",
              "      <td>-0.337621</td>\n",
              "      <td>-0.258235</td>\n",
              "      <td>-0.184443</td>\n",
              "      <td>-0.120015</td>\n",
              "      <td>-0.070396</td>\n",
              "      <td>-0.041849</td>\n",
              "      <td>-0.037049</td>\n",
              "      <td>-0.041243</td>\n",
              "      <td>-0.048908</td>\n",
              "      <td>-0.060176</td>\n",
              "      <td>-0.070725</td>\n",
              "      <td>-0.085491</td>\n",
              "      <td>-0.101144</td>\n",
              "      <td>-0.116070</td>\n",
              "      <td>-0.134461</td>\n",
              "      <td>-0.155811</td>\n",
              "      <td>-0.177542</td>\n",
              "      <td>-0.200685</td>\n",
              "      <td>-0.224244</td>\n",
              "      <td>-0.246686</td>\n",
              "      <td>-0.271476</td>\n",
              "      <td>-0.290321</td>\n",
              "      <td>-0.304461</td>\n",
              "      <td>-0.315111</td>\n",
              "      <td>-0.316851</td>\n",
              "      <td>-0.313070</td>\n",
              "      <td>-0.303238</td>\n",
              "      <td>...</td>\n",
              "      <td>0.946527</td>\n",
              "      <td>1.051337</td>\n",
              "      <td>1.138071</td>\n",
              "      <td>1.187551</td>\n",
              "      <td>1.201909</td>\n",
              "      <td>1.190866</td>\n",
              "      <td>1.154605</td>\n",
              "      <td>1.086347</td>\n",
              "      <td>0.986863</td>\n",
              "      <td>0.864419</td>\n",
              "      <td>0.720300</td>\n",
              "      <td>0.570000</td>\n",
              "      <td>0.428955</td>\n",
              "      <td>0.319734</td>\n",
              "      <td>0.242586</td>\n",
              "      <td>0.181059</td>\n",
              "      <td>0.134708</td>\n",
              "      <td>0.109781</td>\n",
              "      <td>0.093630</td>\n",
              "      <td>0.081028</td>\n",
              "      <td>0.065780</td>\n",
              "      <td>0.047326</td>\n",
              "      <td>0.025409</td>\n",
              "      <td>0.004531</td>\n",
              "      <td>-0.009790</td>\n",
              "      <td>-0.019087</td>\n",
              "      <td>-0.026208</td>\n",
              "      <td>-0.029561</td>\n",
              "      <td>0.000637</td>\n",
              "      <td>0.086588</td>\n",
              "      <td>0.146319</td>\n",
              "      <td>0.070973</td>\n",
              "      <td>-0.062914</td>\n",
              "      <td>-0.276206</td>\n",
              "      <td>-0.507667</td>\n",
              "      <td>-0.761043</td>\n",
              "      <td>-0.834741</td>\n",
              "      <td>-0.642263</td>\n",
              "      <td>-0.487242</td>\n",
              "      <td>-0.706008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.760372</td>\n",
              "      <td>1.152369</td>\n",
              "      <td>1.445493</td>\n",
              "      <td>1.386409</td>\n",
              "      <td>1.302802</td>\n",
              "      <td>1.104382</td>\n",
              "      <td>0.906133</td>\n",
              "      <td>0.731627</td>\n",
              "      <td>0.623100</td>\n",
              "      <td>0.637149</td>\n",
              "      <td>0.657945</td>\n",
              "      <td>0.592049</td>\n",
              "      <td>0.493606</td>\n",
              "      <td>0.426206</td>\n",
              "      <td>0.393553</td>\n",
              "      <td>0.382488</td>\n",
              "      <td>0.386315</td>\n",
              "      <td>0.400830</td>\n",
              "      <td>0.415267</td>\n",
              "      <td>0.423797</td>\n",
              "      <td>0.429734</td>\n",
              "      <td>0.434976</td>\n",
              "      <td>0.441768</td>\n",
              "      <td>0.447727</td>\n",
              "      <td>0.454600</td>\n",
              "      <td>0.461603</td>\n",
              "      <td>0.472726</td>\n",
              "      <td>0.483574</td>\n",
              "      <td>0.492048</td>\n",
              "      <td>0.503468</td>\n",
              "      <td>0.516169</td>\n",
              "      <td>0.530030</td>\n",
              "      <td>0.543061</td>\n",
              "      <td>0.552285</td>\n",
              "      <td>0.560649</td>\n",
              "      <td>0.564906</td>\n",
              "      <td>0.566459</td>\n",
              "      <td>0.560789</td>\n",
              "      <td>0.548964</td>\n",
              "      <td>0.536229</td>\n",
              "      <td>...</td>\n",
              "      <td>0.520027</td>\n",
              "      <td>0.574633</td>\n",
              "      <td>0.614855</td>\n",
              "      <td>0.623050</td>\n",
              "      <td>0.608319</td>\n",
              "      <td>0.582721</td>\n",
              "      <td>0.553923</td>\n",
              "      <td>0.531272</td>\n",
              "      <td>0.507226</td>\n",
              "      <td>0.485600</td>\n",
              "      <td>0.483479</td>\n",
              "      <td>0.511334</td>\n",
              "      <td>0.546391</td>\n",
              "      <td>0.565580</td>\n",
              "      <td>0.571998</td>\n",
              "      <td>0.573798</td>\n",
              "      <td>0.584480</td>\n",
              "      <td>0.589680</td>\n",
              "      <td>0.591613</td>\n",
              "      <td>0.597117</td>\n",
              "      <td>0.603953</td>\n",
              "      <td>0.610288</td>\n",
              "      <td>0.619812</td>\n",
              "      <td>0.633101</td>\n",
              "      <td>0.649239</td>\n",
              "      <td>0.667657</td>\n",
              "      <td>0.680713</td>\n",
              "      <td>0.693871</td>\n",
              "      <td>0.751844</td>\n",
              "      <td>0.920052</td>\n",
              "      <td>1.171299</td>\n",
              "      <td>1.404842</td>\n",
              "      <td>1.646585</td>\n",
              "      <td>1.895134</td>\n",
              "      <td>2.059758</td>\n",
              "      <td>2.086218</td>\n",
              "      <td>2.056220</td>\n",
              "      <td>1.859296</td>\n",
              "      <td>1.830987</td>\n",
              "      <td>1.414465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-6.729499</td>\n",
              "      <td>-7.090374</td>\n",
              "      <td>-5.132459</td>\n",
              "      <td>-5.363241</td>\n",
              "      <td>-5.375715</td>\n",
              "      <td>-5.330194</td>\n",
              "      <td>-4.782240</td>\n",
              "      <td>-4.311288</td>\n",
              "      <td>-4.071361</td>\n",
              "      <td>-4.142476</td>\n",
              "      <td>-4.040526</td>\n",
              "      <td>-3.724538</td>\n",
              "      <td>-3.264576</td>\n",
              "      <td>-3.222345</td>\n",
              "      <td>-3.263484</td>\n",
              "      <td>-3.219212</td>\n",
              "      <td>-3.202187</td>\n",
              "      <td>-3.147504</td>\n",
              "      <td>-3.459917</td>\n",
              "      <td>-4.102102</td>\n",
              "      <td>-4.311241</td>\n",
              "      <td>-4.099888</td>\n",
              "      <td>-3.614973</td>\n",
              "      <td>-3.822298</td>\n",
              "      <td>-3.795567</td>\n",
              "      <td>-3.690817</td>\n",
              "      <td>-3.542485</td>\n",
              "      <td>-3.631665</td>\n",
              "      <td>-3.587112</td>\n",
              "      <td>-3.452215</td>\n",
              "      <td>-4.074752</td>\n",
              "      <td>-4.137454</td>\n",
              "      <td>-4.059662</td>\n",
              "      <td>-3.626646</td>\n",
              "      <td>-2.829471</td>\n",
              "      <td>-3.585866</td>\n",
              "      <td>-4.003401</td>\n",
              "      <td>-3.900724</td>\n",
              "      <td>-3.325329</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.662170</td>\n",
              "      <td>-2.634950</td>\n",
              "      <td>-2.634768</td>\n",
              "      <td>-2.591663</td>\n",
              "      <td>-2.446476</td>\n",
              "      <td>-2.234175</td>\n",
              "      <td>-2.431403</td>\n",
              "      <td>-2.681604</td>\n",
              "      <td>-2.864706</td>\n",
              "      <td>-3.415656</td>\n",
              "      <td>-3.497624</td>\n",
              "      <td>-3.091282</td>\n",
              "      <td>-2.608510</td>\n",
              "      <td>-2.383204</td>\n",
              "      <td>-2.678883</td>\n",
              "      <td>-3.305661</td>\n",
              "      <td>-3.179590</td>\n",
              "      <td>-3.311559</td>\n",
              "      <td>-3.501555</td>\n",
              "      <td>-4.007212</td>\n",
              "      <td>-4.199563</td>\n",
              "      <td>-3.335678</td>\n",
              "      <td>-3.127603</td>\n",
              "      <td>-3.042532</td>\n",
              "      <td>-3.116177</td>\n",
              "      <td>-3.972320</td>\n",
              "      <td>-4.754057</td>\n",
              "      <td>-4.290016</td>\n",
              "      <td>-3.802469</td>\n",
              "      <td>-4.014642</td>\n",
              "      <td>-3.971715</td>\n",
              "      <td>-4.167040</td>\n",
              "      <td>-4.557576</td>\n",
              "      <td>-4.530488</td>\n",
              "      <td>-4.563270</td>\n",
              "      <td>-5.150100</td>\n",
              "      <td>-4.880460</td>\n",
              "      <td>-5.496107</td>\n",
              "      <td>-5.880890</td>\n",
              "      <td>-6.092227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.004511</td>\n",
              "      <td>-2.701576</td>\n",
              "      <td>-3.668096</td>\n",
              "      <td>-4.227247</td>\n",
              "      <td>-4.007470</td>\n",
              "      <td>-3.480479</td>\n",
              "      <td>-2.779941</td>\n",
              "      <td>-2.165851</td>\n",
              "      <td>-1.774124</td>\n",
              "      <td>-1.362068</td>\n",
              "      <td>-0.935730</td>\n",
              "      <td>-0.704797</td>\n",
              "      <td>-0.557938</td>\n",
              "      <td>-0.457267</td>\n",
              "      <td>-0.392099</td>\n",
              "      <td>-0.346567</td>\n",
              "      <td>-0.329879</td>\n",
              "      <td>-0.332123</td>\n",
              "      <td>-0.342879</td>\n",
              "      <td>-0.359587</td>\n",
              "      <td>-0.373523</td>\n",
              "      <td>-0.392615</td>\n",
              "      <td>-0.415157</td>\n",
              "      <td>-0.441247</td>\n",
              "      <td>-0.470702</td>\n",
              "      <td>-0.501586</td>\n",
              "      <td>-0.538227</td>\n",
              "      <td>-0.570103</td>\n",
              "      <td>-0.616792</td>\n",
              "      <td>-0.650610</td>\n",
              "      <td>-0.695386</td>\n",
              "      <td>-0.735151</td>\n",
              "      <td>-0.770997</td>\n",
              "      <td>-0.799982</td>\n",
              "      <td>-0.812191</td>\n",
              "      <td>-0.827238</td>\n",
              "      <td>-0.815797</td>\n",
              "      <td>-0.795940</td>\n",
              "      <td>-0.762395</td>\n",
              "      <td>...</td>\n",
              "      <td>0.598936</td>\n",
              "      <td>0.637738</td>\n",
              "      <td>0.665225</td>\n",
              "      <td>0.688164</td>\n",
              "      <td>0.700096</td>\n",
              "      <td>0.713628</td>\n",
              "      <td>0.725200</td>\n",
              "      <td>0.719349</td>\n",
              "      <td>0.687158</td>\n",
              "      <td>0.628526</td>\n",
              "      <td>0.495111</td>\n",
              "      <td>0.255371</td>\n",
              "      <td>0.016633</td>\n",
              "      <td>-0.128735</td>\n",
              "      <td>-0.190527</td>\n",
              "      <td>-0.222440</td>\n",
              "      <td>-0.254884</td>\n",
              "      <td>-0.269984</td>\n",
              "      <td>-0.280098</td>\n",
              "      <td>-0.281648</td>\n",
              "      <td>-0.276871</td>\n",
              "      <td>-0.275941</td>\n",
              "      <td>-0.283400</td>\n",
              "      <td>-0.294317</td>\n",
              "      <td>-0.297383</td>\n",
              "      <td>-0.306833</td>\n",
              "      <td>-0.316086</td>\n",
              "      <td>-0.319952</td>\n",
              "      <td>-0.335665</td>\n",
              "      <td>-0.403909</td>\n",
              "      <td>-0.655753</td>\n",
              "      <td>-1.089300</td>\n",
              "      <td>-1.588885</td>\n",
              "      <td>-2.147449</td>\n",
              "      <td>-2.731550</td>\n",
              "      <td>-3.030509</td>\n",
              "      <td>-2.868331</td>\n",
              "      <td>-2.153513</td>\n",
              "      <td>-1.657771</td>\n",
              "      <td>-1.596692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.297541</td>\n",
              "      <td>-1.661892</td>\n",
              "      <td>-2.585677</td>\n",
              "      <td>-3.387934</td>\n",
              "      <td>-3.468718</td>\n",
              "      <td>-2.947061</td>\n",
              "      <td>-2.285578</td>\n",
              "      <td>-1.750157</td>\n",
              "      <td>-1.422570</td>\n",
              "      <td>-0.910487</td>\n",
              "      <td>-0.503252</td>\n",
              "      <td>-0.411444</td>\n",
              "      <td>-0.352522</td>\n",
              "      <td>-0.294927</td>\n",
              "      <td>-0.238326</td>\n",
              "      <td>-0.186184</td>\n",
              "      <td>-0.144882</td>\n",
              "      <td>-0.125969</td>\n",
              "      <td>-0.131354</td>\n",
              "      <td>-0.138634</td>\n",
              "      <td>-0.147518</td>\n",
              "      <td>-0.160052</td>\n",
              "      <td>-0.175798</td>\n",
              "      <td>-0.193161</td>\n",
              "      <td>-0.211104</td>\n",
              "      <td>-0.228285</td>\n",
              "      <td>-0.243069</td>\n",
              "      <td>-0.269368</td>\n",
              "      <td>-0.276044</td>\n",
              "      <td>-0.299238</td>\n",
              "      <td>-0.308305</td>\n",
              "      <td>-0.315026</td>\n",
              "      <td>-0.329079</td>\n",
              "      <td>-0.343601</td>\n",
              "      <td>-0.356198</td>\n",
              "      <td>-0.363587</td>\n",
              "      <td>-0.361086</td>\n",
              "      <td>-0.359822</td>\n",
              "      <td>-0.343371</td>\n",
              "      <td>...</td>\n",
              "      <td>0.839880</td>\n",
              "      <td>0.922165</td>\n",
              "      <td>1.030927</td>\n",
              "      <td>1.148533</td>\n",
              "      <td>1.245474</td>\n",
              "      <td>1.278506</td>\n",
              "      <td>1.188410</td>\n",
              "      <td>1.047127</td>\n",
              "      <td>0.924997</td>\n",
              "      <td>0.820237</td>\n",
              "      <td>0.718438</td>\n",
              "      <td>0.626942</td>\n",
              "      <td>0.507614</td>\n",
              "      <td>0.367181</td>\n",
              "      <td>0.254663</td>\n",
              "      <td>0.112411</td>\n",
              "      <td>0.021386</td>\n",
              "      <td>-0.008728</td>\n",
              "      <td>-0.017624</td>\n",
              "      <td>-0.024967</td>\n",
              "      <td>-0.036221</td>\n",
              "      <td>-0.054501</td>\n",
              "      <td>-0.070819</td>\n",
              "      <td>-0.087635</td>\n",
              "      <td>-0.098353</td>\n",
              "      <td>-0.103667</td>\n",
              "      <td>-0.104560</td>\n",
              "      <td>-0.074941</td>\n",
              "      <td>0.015636</td>\n",
              "      <td>0.188872</td>\n",
              "      <td>0.473642</td>\n",
              "      <td>0.658318</td>\n",
              "      <td>0.717808</td>\n",
              "      <td>0.604747</td>\n",
              "      <td>0.334857</td>\n",
              "      <td>-0.030767</td>\n",
              "      <td>-0.195151</td>\n",
              "      <td>-0.194423</td>\n",
              "      <td>-0.160262</td>\n",
              "      <td>-0.565588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.500061</td>\n",
              "      <td>-0.677290</td>\n",
              "      <td>-1.513964</td>\n",
              "      <td>-2.235369</td>\n",
              "      <td>-2.530967</td>\n",
              "      <td>-2.398813</td>\n",
              "      <td>-1.823494</td>\n",
              "      <td>-1.484923</td>\n",
              "      <td>-1.063708</td>\n",
              "      <td>-0.476660</td>\n",
              "      <td>-0.245829</td>\n",
              "      <td>-0.203943</td>\n",
              "      <td>-0.173206</td>\n",
              "      <td>-0.114979</td>\n",
              "      <td>-0.022799</td>\n",
              "      <td>0.079113</td>\n",
              "      <td>0.182544</td>\n",
              "      <td>0.254382</td>\n",
              "      <td>0.294451</td>\n",
              "      <td>0.307700</td>\n",
              "      <td>0.312671</td>\n",
              "      <td>0.309714</td>\n",
              "      <td>0.310192</td>\n",
              "      <td>0.310867</td>\n",
              "      <td>0.307251</td>\n",
              "      <td>0.310081</td>\n",
              "      <td>0.308852</td>\n",
              "      <td>0.303877</td>\n",
              "      <td>0.295029</td>\n",
              "      <td>0.291460</td>\n",
              "      <td>0.286058</td>\n",
              "      <td>0.277438</td>\n",
              "      <td>0.268212</td>\n",
              "      <td>0.267853</td>\n",
              "      <td>0.256795</td>\n",
              "      <td>0.238882</td>\n",
              "      <td>0.233686</td>\n",
              "      <td>0.222895</td>\n",
              "      <td>0.215748</td>\n",
              "      <td>...</td>\n",
              "      <td>1.312210</td>\n",
              "      <td>1.521455</td>\n",
              "      <td>1.701035</td>\n",
              "      <td>1.765994</td>\n",
              "      <td>1.752310</td>\n",
              "      <td>1.686186</td>\n",
              "      <td>1.586685</td>\n",
              "      <td>1.424607</td>\n",
              "      <td>1.240844</td>\n",
              "      <td>1.051336</td>\n",
              "      <td>0.929503</td>\n",
              "      <td>0.861611</td>\n",
              "      <td>0.807057</td>\n",
              "      <td>0.773711</td>\n",
              "      <td>0.713567</td>\n",
              "      <td>0.627149</td>\n",
              "      <td>0.572461</td>\n",
              "      <td>0.550587</td>\n",
              "      <td>0.520526</td>\n",
              "      <td>0.486922</td>\n",
              "      <td>0.438161</td>\n",
              "      <td>0.385089</td>\n",
              "      <td>0.317233</td>\n",
              "      <td>0.251191</td>\n",
              "      <td>0.200961</td>\n",
              "      <td>0.180407</td>\n",
              "      <td>0.177057</td>\n",
              "      <td>0.215272</td>\n",
              "      <td>0.346149</td>\n",
              "      <td>0.715405</td>\n",
              "      <td>1.062134</td>\n",
              "      <td>1.169420</td>\n",
              "      <td>1.238687</td>\n",
              "      <td>1.264471</td>\n",
              "      <td>1.234433</td>\n",
              "      <td>1.034584</td>\n",
              "      <td>0.945237</td>\n",
              "      <td>0.951681</td>\n",
              "      <td>1.002692</td>\n",
              "      <td>0.272012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.966414</td>\n",
              "      <td>3.479689</td>\n",
              "      <td>2.660597</td>\n",
              "      <td>1.899798</td>\n",
              "      <td>2.147015</td>\n",
              "      <td>1.614375</td>\n",
              "      <td>1.868728</td>\n",
              "      <td>1.804251</td>\n",
              "      <td>1.683730</td>\n",
              "      <td>1.898627</td>\n",
              "      <td>2.033007</td>\n",
              "      <td>2.159861</td>\n",
              "      <td>2.390867</td>\n",
              "      <td>2.433236</td>\n",
              "      <td>2.160356</td>\n",
              "      <td>2.125758</td>\n",
              "      <td>2.566762</td>\n",
              "      <td>2.099727</td>\n",
              "      <td>2.117594</td>\n",
              "      <td>2.076279</td>\n",
              "      <td>2.053132</td>\n",
              "      <td>2.091794</td>\n",
              "      <td>2.003258</td>\n",
              "      <td>1.950623</td>\n",
              "      <td>1.958402</td>\n",
              "      <td>1.966838</td>\n",
              "      <td>1.941571</td>\n",
              "      <td>1.964678</td>\n",
              "      <td>1.866897</td>\n",
              "      <td>1.870258</td>\n",
              "      <td>1.862162</td>\n",
              "      <td>2.063265</td>\n",
              "      <td>1.941110</td>\n",
              "      <td>1.931091</td>\n",
              "      <td>1.890201</td>\n",
              "      <td>1.834472</td>\n",
              "      <td>1.721037</td>\n",
              "      <td>1.619301</td>\n",
              "      <td>1.583824</td>\n",
              "      <td>...</td>\n",
              "      <td>2.942679</td>\n",
              "      <td>2.836767</td>\n",
              "      <td>2.820616</td>\n",
              "      <td>2.940154</td>\n",
              "      <td>2.685726</td>\n",
              "      <td>2.959324</td>\n",
              "      <td>2.895572</td>\n",
              "      <td>2.604823</td>\n",
              "      <td>2.478775</td>\n",
              "      <td>2.513328</td>\n",
              "      <td>2.599892</td>\n",
              "      <td>2.937152</td>\n",
              "      <td>2.950522</td>\n",
              "      <td>2.830852</td>\n",
              "      <td>2.861781</td>\n",
              "      <td>2.828775</td>\n",
              "      <td>2.702857</td>\n",
              "      <td>2.538437</td>\n",
              "      <td>2.680203</td>\n",
              "      <td>2.764017</td>\n",
              "      <td>2.839710</td>\n",
              "      <td>2.714548</td>\n",
              "      <td>2.869542</td>\n",
              "      <td>3.052377</td>\n",
              "      <td>3.141808</td>\n",
              "      <td>3.215135</td>\n",
              "      <td>3.198790</td>\n",
              "      <td>3.086137</td>\n",
              "      <td>3.157157</td>\n",
              "      <td>3.037078</td>\n",
              "      <td>2.960969</td>\n",
              "      <td>3.007606</td>\n",
              "      <td>2.854680</td>\n",
              "      <td>2.912394</td>\n",
              "      <td>2.937685</td>\n",
              "      <td>3.351437</td>\n",
              "      <td>3.432518</td>\n",
              "      <td>3.579448</td>\n",
              "      <td>4.425976</td>\n",
              "      <td>7.402103</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 141 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                c0           c1  ...         c139         c140\n",
              "count  5000.000000  5000.000000  ...  5000.000000  5000.000000\n",
              "mean      1.527400    -0.262476  ...    -0.487242    -0.706008\n",
              "std       0.760372     1.152369  ...     1.830987     1.414465\n",
              "min       1.000000    -6.729499  ...    -5.880890    -6.092227\n",
              "25%       1.000000    -1.004511  ...    -1.657771    -1.596692\n",
              "50%       1.000000    -0.297541  ...    -0.160262    -0.565588\n",
              "75%       2.000000     0.500061  ...     1.002692     0.272012\n",
              "max       5.000000     4.966414  ...     4.425976     7.402103\n",
              "\n",
              "[8 rows x 141 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCE6DvClE2zg"
      },
      "source": [
        "train_data, test_data, train_labels, test_labels = train_test_split(df.values, df.values[:,0:1], test_size=0.2, random_state=111)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_pX0PmWT_hL"
      },
      "source": [
        "scaler = MinMaxScaler()\n",
        "data_scaled = scaler.fit(train_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlS6sk50UEVF"
      },
      "source": [
        "train_data_scaled = data_scaled.transform(train_data)\n",
        "test_data_scaled = data_scaled.transform(test_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwpDFKmIHL79",
        "outputId": "85683942-4ead-42ff-eca3-31364d7bd53c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "train_data_scaled"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.50002524, 0.42299659, ..., 0.61680162, 0.58388556,\n",
              "        0.3649298 ],\n",
              "       [0.        , 0.48602565, 0.31200637, ..., 0.60438613, 0.6676527 ,\n",
              "        0.40538671],\n",
              "       [0.        , 0.6760041 , 0.6161329 , ..., 0.73484988, 0.64562905,\n",
              "        0.43438114],\n",
              "       ...,\n",
              "       [0.25      , 0.70698484, 0.7982501 , ..., 0.23053824, 0.31421167,\n",
              "        0.37774737],\n",
              "       [0.        , 0.46498254, 0.25827775, ..., 0.6340938 , 0.66453824,\n",
              "        0.47717869],\n",
              "       [0.25      , 0.69314707, 0.79831145, ..., 0.68561341, 0.61110713,\n",
              "        0.53512758]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKV9TTWsEgJV"
      },
      "source": [
        "normal_train_data= pd.DataFrame(train_data_scaled).add_prefix('c').query('c0 == 0').values[:,1:]\n",
        "anomaly_train_data = pd.DataFrame(train_data_scaled).add_prefix('c').query('c0 > 0').values[:,1:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYimLgBNFzUj"
      },
      "source": [
        "normal_test_data= pd.DataFrame(test_data_scaled).add_prefix('c').query('c0 == 0').values[:,1:]\n",
        "anomaly_test_data = pd.DataFrame(test_data_scaled).add_prefix('c').query('c0 > 0').values[:,1:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HesvBlQ4fNm7",
        "outputId": "d742d4d5-c32f-48b1-a807-8ee410265166",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(normal_train_data.shape)\n",
        "print(normal_test_data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2356, 140)\n",
            "(563, 140)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6I5lZBafq3z",
        "outputId": "c939f0ec-d645-4015-db90-d3c5015430bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(anomaly_train_data.shape)\n",
        "print(anomaly_test_data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1644, 140)\n",
            "(437, 140)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5pBJpf7xTUJ",
        "outputId": "657c5ad8-6e51-41c9-d217-537a609dfa44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.plot(normal_train_data[0])\n",
        "plt.plot(normal_train_data[1])\n",
        "plt.plot(normal_train_data[2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f38d4a6fe80>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGwy1DNZyTKK",
        "outputId": "c0dd66c5-aba7-418c-e3db-60ae6912be5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.plot(anomaly_train_data[0])\n",
        "plt.plot(anomaly_train_data[1])\n",
        "plt.plot(anomaly_train_data[2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f38d330f3c8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydd3xb1d3/30dbsi157+3svQchJIxAgACFUkoHPA+FQkuBtvTXPrSlpbS0PG2hLZSWQtuHQikj7J2wSSCQPZ3lvYcsW7ItW9Y6vz+OJNuxHY84hAS9X6+8ZN177tWRIn3u937XEVJKokSJEiXKyY/mRE8gSpQoUaKMD1FBjxIlSpRThKigR4kSJcopQlTQo0SJEuUUISroUaJEiXKKoDtRL5ycnCzz8/NP1MtHiRIlyknJ9u3bW6SUKYPtO2GCnp+fz7Zt207Uy0eJEiXKSYkQomqofVGXS5QoUaKcIkQFPUqUKFFOEaKCHiVKlCinCFFBjxIlSpRThKigR4kSJcopQlTQo0SJEuUUISroUaJEiXKKEBX0KFGOF437oOy9Ez2LKJ8jooIeZQDRHvnjxAe/hae+Cl2tJ3omUT4nRAU9SgQpJVe+eiW/3frbEz2VU4OuVvB1wfZHTvRMonxOiAp6lAh7WvZQ7CjmmUPP0OZpO9HTOfnxONXj5ofB7z2xc4nyuSAq6FEivFL2CnqNHm/Qy3Mlz/Xbd6j1EGevPZsKV8UJmt1JSLcT4nOhsxH2PXuiZxPlc8CIBF0IsVoIcUgIUSqEuG2Q/blCiPeEEDuFEHuEEBeM/1SjHE+8AS9vVLzBqrxVLMlYwlMHn8IX9EX272zeSXN3M48WP3oCZ3mS4XHClDWQOh02PQDR2ESU48ywgi6E0AJ/Ac4HpgFfEUJMO2LY7cBaKeVc4Ergr+M90SjHlw21G2j3trM8YzWXTbiSpq4m3q1+N7K/uqMagFfLX426Y0ZCwAfeTjAnwIJroLkYWstP9KyinOKMxEJfBJRKKcullF7gKeCSI8ZIwBr62wbUj98Uo4w3vqCP2o7aftteLnuZFHMKv30hwG+ehQxLFmsPrY3sr2mvId4YT0+gh2cOP/NpT/nkozvkPzfFQ8EK9XfVphM3nyifC0Yi6FlATZ/ntaFtffkF8HUhRC3wOnDzYCcSQlwvhNgmhNhmt9vHMN0o48FjxY+x5oU1HHAcAJRYb6zdyIqsc6lp7aGm1YOjpYi99n0EZRBQFvqCtAWclnmacscEfEd7iSjhgKg5HpIngiUZqj46sXOKcsozXkHRrwD/klJmAxcA/xZCDDi3lPJhKeUCKeWClJRBF9yI8inwavmrBGSA32z+DUEZ5K7Nd2HUGZlsWgPAnRdPx9uVSnegi/rOegLBADUdNeRYc7hq2lXYu+28X/v+iX0Tn3X6WuhCQN5pyMqPeLO4MZrnH+W4MRJBrwNy+jzPDm3ry7XAWgAp5ceACUgejwlGGV9K20opdZYyN3Uuu+y7+NGGH7GpfhM3z72ZimYteq3gykU5nJ47U413ltLc1Ywv6CMnLofF6YvRCA2HWg+d4HfyGaevhQ6QtwzhquYX/17PlopooVGU48NIBH0rMFEIUSCEMKCCni8fMaYaOBtACDEVJehRn8pnkHWV69AIDfeuuJeZyTNZX7meqYlT+fLkL7Or2sm0DCtGnZZJiRMA2N9yiJoO5XHLjctFr9WTGZNJVfuQq2BFgf4WOrBXNwOARZqDHG7qOFGzinKKM6ygSyn9wE3AeuAAKpulWAjxSyHExaFhPwC+KYTYDTwJ/LeM3ld+5pBSsq5yHQvTFpJiSeFnS37GlMQp/OK0XyDQsrfOxZwcJUD5iYkEvfHssx+KZLikmrIobe4gXp9JZVTQe7Efhn3Pw0f3Q0ej2hay0H/wSiWPfFTBTe9000EMy/SHKG3uPIGTjXIqM6JFoqWUr6OCnX23/bzP3/uBZeM7tSjjzcHWg1S1V/Hf0/8bgKlJU3nmIpWxcrCxnS5vgNkhQc+wmQn2pFPmKmVCRzZ6jYFz79mFLyAwpumITa5ESokQ4kS9nc8GDbvhoTN6n/t7YMUPIxb6KyXdPHd4P0JAoGgxpzUc5EV7VNCjHB+ilaKfI9ZVrkMndBRalvKHNw8RDPbeRO2uUQIUttCz4s0EetJp7Kqm3FlOnDYNX0Bw92UzwZeCN9hNS3fLCXkfnyka9qjHrz8P5kRoD4WXPE48GFk2KYMNPzyTV246nfipK8kK1NHaWDP0+aJEOQaigv454v2a91mQvoD/fa2a+98t5e0DTZF9u2pcWE068ntKwFlDmtWE9KYTJMCWxi0YSSXGoOXKhTkkGDIBon50AEcJaA1QuBKsWdDRoLZ3O2knhqRYI7lJFmZk2SBzHgBJXWW4uqNpn1HGn6ign6TYO9w4OrtGPL62o5ZyVzkZ+rlsqWhFpxE8vKG3cnFXjZPZOfFo1n4N1l6NQSuwalVyU7e/G39PIgUpMQghyLSo7cMJupSSvfa9+IP+MbzDkwRHGSQWgkYLcenQrmrqpKeNNhlDUqyhd2x8LgCZoiXqR49yXIgK+knKmrXf5KJnvjHi8R/WfQjAR3tSyE+y8D+rp7Ctqo3tVW3sq3NxuKmDhZkm5TKo3wEVH5AVkwtSfUU6O23kJ8UAUBCfDVI7rKA/tv8xvvr6V3mn+p0xvstPmZ5O2PUEbHsEdj+t/OHD0VICSSojCGtGJCgacLfhlDEkxxh7x1ozkQiyhIOyqKBHOQ6MKCga5bNFTVs7bs0BhMbPx3XbWJq1YNhjNtZtJMGQQVmDmb98dQorJ6fwwHul3PXafsqaO0m3mvjKpABsDh9wL1m2X1Ddk0pA10iny8yl1tchOJucxBgCziQqXJVDvt4HNR9w77Z7AShzlo3Duz7OBHzw9Neg/P0+27ww76qjHONX/Vkmn6+ex2WC2w4BH4EuJ64jLXStHuIyyHG1cDAaGI1yHDhpLXQpJbuad30uq+6e2v0RQqPcGH/a9uCw4z1+D1satuDvmMzUDBsXzEwnxqjj60ty2VntxGrW89T1S0jxhVLupl8GFRtYoC3D50kDYJmvnrMrfg9VH5EVbyboTabMWTno61W1V/GjDT9iatJUUi2pn31fu5Tw6veVmK/5I9x6QIlz6VtHP85VDUFffwsdqax0T1jQjf0OEfE5FBqclERz0aMcB05aQd/WtI2r3riKbU3bTvRUPnXer1JmtL9tOfudWyhuKea18te465O76AkMdBNsbdyKJ+ChqamQLy/IjqQafnN5Id9cXsBT1y8hJ9ECbZXqgFV3gjmBs1ufxNuZT4w2nsl+l9rXfJDsBAvSm0yDu5ZAMDDg9Z49/CzeoJf7zryPIlsR1e3Vx+VzGDe2/gN2/hvO+BEs+AZYM2HC2VD2vrLCh6KlVD0mT1SPcSpYTEcD2h4nLmJIijH0P8aWTZZooTRqoUc5Dpy0gl7XqdLD9tj3nOCZfLr0+ANUuYuJ1WQx03I5Qpq5+o2ruW3jbTx96GleKn1pwDEb6zaixYjwFHLR7MzI9niLgZ9eOI3sBIva0FYBRivYcmDqRWS4duJrW8xi/b0UaJrVGPsBshOUhe6XPhq7Gvu9lpSSt6reYknGEtJj0sm15lLVXvXp30lJCd4RBI19Htjwe8hfDmf+pHf7hHOgxwV1RzEYHCFBTwoJujVDPTqr0fm7cMkYko+w0LFlk+i3U9fmpts78GIYJcqxcNIKejgHuthRfIJn8unySbkdTJXMTJrDGRNy6G46m9y4fC7N+hGiJ4+Hd/9zQFbJJw2bkd2FrJyUNcAF0I+2SkjIV82kUqZg9LaRQCebyzqYoA0L+iEybCbwqeZqVa7+7pQDrQeo66xjVd4qAPKseXT4Omjr+ZR7qH/4R7hnIlR/cvRxu5+AziZY8SP1vsMUrgShhdK3hz7WUaJK+y2J6nnYQm9WXSxdxJA4wELPQSt9JEkXZVErPco4c9IKuqPbAcB+x/4TPJNPlxeLdyC0HlYXncayCcn42k5nZez/svaDZLqaz6Cpu551Fesi47t8XVS6KujuzOLy+aGuxz4P1O8cePLWCiXoAMmTACgS9TS2e8gXIUu8+QA6jSDZqM5V1dFf0N+qegut0HJmzpmAEnTg03W7SAk7HlULTPznS1C3Y/BxAT98dB9kzVcWel/M8ZC98OiC3lKi3C3hC4ElUeWkhwTdq7di0B3xE7OplM9s0RIV9Cjjzkkr6GELva6zDleP6wTP5tPj47qtACzOms/MLBtxRh1/fPswZr2W6xesIeBJ40/b/hbpY17iLEEiMQVzOHNKqjrJh3+Ev58N7j6VnsEgOKsgsUA9Dwn6ZG09VtzYZLuyQLtbwW0n25aGRhopaSuJnCLsblmYvpAEUwLQK+iV7ZXj9yG4HfDJg0O7VOp3qruNlT9Wwvz4ZdA1SIfD/S+qcad/v791HmbCOepcnUP0mXOU9QZEQZ0jLh2alZHRHhuIpItGiFeCnikc1LZ1H/19RokySk5qQddpVNbl58Xt4vUHccrDxGiSyIzJRKfVsLgwCYC7L5vFraumkBpcTZOniver1eo44Ta3y3JnYdRplfW67zmQgYjwANBRr9L0wha6LQd0ZmaZmsgVoYrSKaGlYu0HyUmwoPFM5oOaD/pdPKraqyLuFoDM2Ey0Qjt+FrqjDP55Dqy7Tfm+B2Pfc6DRw6Lr4aL7oLsNGnYNHLf9X0qQJ184+HkmnK0ey94duK+nU31mfQUd1EUvFFwuid/Pre/fitvn7t1vywag0NBGU7tn6PcZJcoYOKkFfX7afODz43ZxuHvQmivJi50RyVT5/qqJ/O6Ls1g9Ix2dVsOd53wFGdSydp9yFexqKkYGzMzPKlQnad6vfL8AzQd7Tx7OcAkLukYDyROYqG0kPyzoky+IHJcdb6azbRrN3c2RwPQbFW8gEJyVexagArh/fruc9PFqt2s/DP9cpRpfFayAjx9QAt+XYBCKX1RibEnsDVi2VQ0c17Bb+co1Q/wMMuaoIHHtloH7HEdkuACNLg/BuFDqIuDReej2d/N6RZ++diYbGK1MMLTR6IoKepTx5aQVdEe3g0JbIdmx2Z8bQa9ztqPRt5MXVxjZNj3TxhULe9cfWVaYgezJo7htOwB77QcIeDKYlhla8nX/SyA0oLeAvY+gt1aox4SC3m3Jk8kL1vT6z3OXgtEWynSx4OuYgk6j582qN+n0dvL0oac5O/dsks1qbZN3DzRz/7ulmGRapAXvMbH/RehywLVvwWUPK3/1+p/2H1O7BdprVS49qBREjb73ghXGWQU97ZA+a+jX02ggZXL/C1+YSIaLstDbPT7OvOd99nfGRIb0COUSevbws/2PteWQo3VELfQo485JKeg9gR46fB0km5OZljTtcyPo5W2q8VN2XNqQY3RaDSnaGTj9VbR6Wql1lxH0ZDItIyToxS9C3jJIn9lf0NsqVVZHyCUAQMpkEn1NTNVUK8vTYIHUKWA/RHaCGYImptoW8HbV2zx16Ck6vB1cN+u6yOHri9WFQBtMHZ/Uxe42MMRB8gTlq17xIzj8Bmx+SLmSfB5ltetMvdWbGq3qoXKkoDeG0l3TZx79NVOm9P+cIsfvVReKxCIA9tW56PYFONQVC0AQ8NBBoimR/Y79/b+jtmzSpZ3GqKBHGWdOSkEPZ7iEBb2usw5neMmvU5hKpxLIvIQMqNkKddsHHTczaT4IydqDz+CXXqyaXOItoeyLlkMw7RIlVM0HlBCCykGPz1Hl6WGSJyGQnGvajyYkXOHjsuPNABRYltLgbuCh3Q+xLHMZ05OmA+ALBHnnoEp19HYn0u3vxt59jItYdbWCJaH3+eJvQ9FZ8MaP4D+Xq77kB16B024Bk7V3XEL+IIK+V13AUqcd/TVTp0JXS/8AMqjPPn0G6E2AEnSAve0qp79BmJAEuHLylRi1Rp47/FzvsfE5JPibsXf0EAh+/iqdoxw/TkpBD2e4JJuTmZ6sBOTzEBit71C+7AkJmfDSd+DZb/QKch9W5s9HBg08fuAJNT5+stqx/yVAwNSLlVCFMlaA3hz0voQyXfS+jt7sl5Qp0N1Kuq4DISA2MBud0OEJeLhuZq91/km5gw6PnziTjvZ2G3Ds7Xbruhp5MNbUm9WkM8DXnoPVv4XKD8Hrhq8/B2cd4YZJyFculr407FHulJAgD0nKFPUYSkUEIBhQ2S99eujsrWsHoLhDWehVWvVYGF/IqrxVvFz2Mn/d9VdleNiyMfvbMctuWjpH0ABslHh8Ad4sbsTZ5R33c0f5bDMiQRdCrBZCHBJClAohbhtk/x+FELtC/w4LIY6ruRwW9CRzEtOTphOrj+W2jbfxVtUwvTdOcpq7lMWbZbAoS7utctAc6zk5SQS6CnB5W5FBLXPSlTBz6A3IWQRxaUrMoFeo+uagh0kqUv728N+gXC6Aoe0wuYkWqpolZ+aeyZKMJZEgNSh3i1mv5dK5WTQ64oBjF/QXvE38Ve/hCy99gfdr3lcbNRpY8i347m64aYtKNUSlUEYKrBLylLumu8/XsnHv8O4WUBc+6O92sR9SOe5Zve+3uM5FQoyGRtQCITVadQeTYk7hu/O+y5LMJTy4+0HOf/58akzKz54pHOMaGA0EJX946zBL736H6/+9nfveKRn+oCinFMMKuhBCC/wFOB+YBnxFCNHvPlVK+X0p5Rwp5Rzgz8Dzx2OyYSIWuimZOEMcj1/wOJmxmdz6/q08EbJKT0UcHjtIHbaWPpkd+54dMK4gKQZdj8q+CHrTmJGZBB1NKnVvYiilMKWPULnqlLWeWNj/RDpjb5A0vC85dCFoOczcnHh2VLdxzxn38NCqhyKZN8Gg5M3iJlZMSmFKupUejxW9Rn/MgdH6QBfx6Eg0JXLzuzezqW5T7864dDD0BiT/ue+fnPPMObR52novVGEr3d2iUg6PFhCNnDdDBYL7WujhdgDZykLv8Pgod7ShyfsVLVb1Hut0qiI32ZxMekw6fz7rz6xds5ZufzcvdKr/vyzRMq5+9HcONHH/OyXMzU1gZpaN9w9F12n/vDESC30RUCqlLJdSeoGngEuOMv4rqIWijxthH3qiWZVcF8UX8fgFj5NnzWNzw+ajHXpS0+FvRY8NEfad552uFic+okGWRiMoipsLQNCTwdSMOCgL9SSfeK56jEtXKXT2g7DlYWWJTxvkvzXkdokIelwGaI3QVsW8vASaO3poaO9BI3q/SrtqnTR39HDejDTykyyABpshhUZ348Dzj4J6/BTqYnniwifIis3i/p33Dxpo7fJ18ci+R3B4HDyw84FeQQ/70UcaEAVVLJR6RGC0dpv67EJxheL6djSmBryyg5j4ZtpkLM0aFYsIZ/yAWsN1SeYSXm3eRhBloY9npsu64kZsZj0PXTWfL87LoqLFTZXDPfyBxxGvP8jZ977PzDvWs/L37/GzF/fR2XMKL3hyghmJoGcBfRdBrA1tG4AQIg8oAAapxAAhxPVCiG1CiG12+9ith5buFhKMCeg1vQE8vUZPVmzWsQfePsN0B1qxaBKVmyVpIiz8BnQ2QtVHsPlh+NeaiFthUdZ0fO0z0bjnkZcUAyVvQWxar1UqhLLS67bD9kdg6kUDXS6gXA5C02upazQqeOqsYm6OClDuqOrfp2V9cSM6jeCsyWnkJyur2SKSjk3Qg0HqNZJMgw2j1sgNs26g2FHMuzUDv2rPlzxPu7edRemLeLbkWQ4RWu4tnIveuFc9jkTQYWAAuW6HcreE8tf31bnQmtRKReaYNp4LLGe/NokYfQwWvaXfqS4qvIiG7ma2m0xkaMYvF90XCLJ5fzn/jv0z+rYyVkxWVcEfHD6xv4d1xY2U2d2cOSWVyelxPL65ivPv28D2qk+5t8/nhPEOil4JPCulHLSNnJTyYSnlAinlgpSUlDG/SEt3C0nmpAHbU8wpET/zqYaUEp9wYdUnqlv+rPkw6XzQx8Az/w1v/BAqN8KepwGYnZ2Ip+5rTLLNRysDqtpxwjn9S9xTp6jiGo8Llt48+Asv/Q587VkwxvZui88DZzVTMuIw6TXsrO71TUup3C1Li5KwWfSkW00YdRpEIP6YBN3f7aBZqyXTpCzei4ouIt+azwM7H4hUqgL4gj4e2/8Y81Ln8YeVf8BmsHH37geQ5oReC71hj6qEDTfVGo6+AWSvG5qL+/nP99W5iI1TAeuA1s5d/quoNib1s87DnJlzJmadmdfik8jTu2hqH5+g6ObyVmZ7dzKrYwO8eCOV7s3kpPhPuNvl8U+qyE208Kcvz+Ghqxaw9oalBIPwnf8M0V8nyjExEkGvA3L6PM8ObRuMKznO7hYAh8cx6I8l1ZJKS3dLvx/4qUJHjx+0LlJ1FtUdMGu+ygufdokK+J19B2TOVeXsUjIrW2WWTM2wqguAx9nrPw8T9qPnLIachYO/cExybwl8mPhcaKtCr9UwK0v50cOUNHdS0eLm3OnpgHL/5CfF4PPaaO5qHvP6ok2tJQSEIDNG5eDrNDpunHMjpc5SXit/LTJuXcU6GtwNXDvzWmxGG9+e8222N23nYEJWf5fLSK1z6J/pUr8LZPCIDBcXhhhVI9DpbwHhR2voGPQ7atFbWJW3ijdNOlJ0znFzuawrbmCBTq0Ra2/Yxi3vfxdb5jt8XObA4zsxbXoPN3XgrdzC0/pfoPnwXpCShfmJ/NdpeTS2e6JZOMeBkQj6VmCiEKJACGFAifbLRw4SQkwBEoCPx3eKA2npbhn0x5JiSSEgA7R6BmnEdJJT3daG0PaQH3YfZIcsxAt+Bzdvh+W3wvxrVGl/zRayE8xce3oBVyzIVu4WoYXCM/ufNFP52TntltFNJiFPWaw9HczNi6e43hURjfX7lBV+7rTe4qf8ZAsdnTEEZCAS0B4t9U4lVpmxvYVP5+Wfx7Skafxp+5/o8nXR7m3n/p33MyF+AqdnnQ7A4ozFAJTGhiz0mq3QchjyTx/5i/fNdCl/T/0dCoh29vgpb3HhoY5USyqSIBp9K0I7uKADrClcQ4eQVJtaxyUoGg5Cn2GphOxFbMmdB0BLYCvdPi9bK0/A70FKmp//H54z3EF612F491eqXiAYpCBZ3e1VtJxY//6pyLCCLqX0AzcB64EDwFopZbEQ4pdCiIv7DL0SeEoe55UMpJQ4uoew0M3Kb2jvOvX86KUOZQHm+5yq5D1thtphjOsNWM74oqqk3P4IQgh+tmYac3MTlAjlLFKdB/uSuxhu2g5T14xuMvGqgyLOaubmJOALSIrrVR72+v2NzM2NJ83am9+dnxxDq1P50sfqdqkPNffKtPW2JtAIDT9Z/BOau5t5aM9D/Gbzb7B32bnztDsjQdqcuBx0Gh1lRiM4q+G9X4MlCeb918hfPDZN9T1/82eqIVjmPHXnAhxqbEcYmgni59w8FXDOz+giINpJMQ/uVpyfNh8BNOrcNI2DD31njZPWDjcF3hLIXsjm3NkAuAOdGK0VJ8Tt4qnZyelN/2GH7RzEDw7StfhbfL/yee557lIKQnGVymMI2Eopee+QKs6K0suIfOhSytellJOklEVSyl+Htv1cSvlynzG/kFIOyFEfb9w+N56AhyTTID50i/oBnYqB0SpXSNDdDcpdoBtkoQpjLMz6EhS/oNwwoAJ59kOq0dRgJE8YfPvRSAgJelsV83LVRWJndRu1bV3sq2vnvJC7JUxBUgzeHuUCanA3jP71gPrQClUZ8UX9ts9Omc0lRZfwr+J/8Vr5a9ww6wZmpfSmI+o1evLi8igXAbX+Z/l7sOy7/WMCIaSUuHpcAzNnhFAXy8w5cPGf4b9eiewqt7vRhAKi5+WfB8AXFgn8dA9poRu0BlK0Fpo1fjw9HtzHmPWxrbKVKaIabbAHshewpXU/y0QMFgkZGYdOSGC0vkR1t/Sd9n06NVq+TQNvx1h4tKuc7p5iNAIq7GMX9PcONXPNI1s5/74NvHfo1IybjYWTrlK0b1HRkaRalIV+KgZGa9uVZZvdWtYvIDeAOV8DvwdKQ2mKHY2qCCapaOhjRkvEQq8i1WoiK97MPzZWcO2/VH52RNCbD8DDKzmt4V/o/arQZswWelczKX4/hkH62Hxv/veI0ccwK3kW35z1zQH7C+MLKfepOwgsSbDwun77ewI9PH3waS587jJOf+p05v97AZe+9EUqXBW9g9b8Aa59E+Zd3e9iUOXoQmeux6KzMCtlFlaDleLW3QBDCjpAtjGBOp2OVNqO2e1SbndzurkSgNqEbOo66zgjbSEr3G66tTsobXZR2zaC5fjGke7GQwSkID1/Cje9exN77Hu5Y9p1WAMB/rr5l2QnWCgfo8vFFwhy12sHyEuykBxr5JpHtvLE5rHVOJQ2d/LK7voxHftZ5KQV9MF+LGGRPxVdLs2h95Ta09HrbhmMtBkqzTCcNx1uldunzesxY0lS2TVO9SO69vQC0mwm4i16rlmWr26p2xvg8cvBfojcnffyjv52TMI0Zgu9oaeNTH9A5X8fQbI5mRcufoF/nPePSI/8vhTFF1HjcdCj0ajFLPoUIAH8bMPvuGvzXVQ63PQ0n4vbsZBS52GeLX5n2HlVONyYYxqZnDgZjdCQG5fLrmZlnQ7lcgHIismgTq8jTRx7X/Tylk6WGiogNo0tbpVhvHjaVzjP3UWXdKO1fPpuF21rGbWkgqmT7U3buWXeLVy+8Ltci40P3dUkJ9WM2Yf+n0+qKLe7uf3Cabz4nWVMz7Ty9Laa4Q8chEc+quC7T+3E1eUb0/GfNU4+QfcMLeh6jZ5EUyLN3aeehd7isaMJaokLyqM3lNKbVMFLuLLxiDav44IQyu0Syuv+xukFvPSdZTx9w1LuuGg69HSopd88TvjGOuR/vUIyLpICYuwWuq+DTKlR3RMHIS0mDbPOHHn+yu56rnz4Y/yBIIW2QoIEqbr6OVh6U7/jgjLI29VvQtcMbp/zdz658W4ePP9OpN/Clvrhu3hWOjoI6uuYkqgyYXKtuXT5lTU82F1kmCxrLk1aLcmaYy8uKre7mRY8DFkL+KRxM8nmZAqzl7LMNgmzFNiSDwzpdunw+DjvjxtY9r/vcuH9G3l5nKxVS2clDbosdtlVEdyK7BUAfGXOt0j1+6Kqrn4AACAASURBVOnSPUlli3vUHThdXT7+9E4JpxUlcc7UVEx6LWdNSWVfnYsOz+hFuandQ1DCh6VjC9Z/1jjpBL1vp8XBSLWkjq+F7vPAP8+FvQNL7D9N2r0OrEEDAnr7sAxF6pQ+gl4GOnPvAsbjRXzuwIZXYT78IzTtgy89ChmzEQVn0KJJItk/NkEPyiANwW4yhWH4wSEe+7iST8pb+ahM9c0HKJfeAUvN7WjajQ8nc5PO4KuL80iMMXDWlDSMMpOazvKjvoaUkmpXNUHRw9RElQmTa82N7A/HdAYjO2EiUgji9I00usYe2HN1+fC7W0nx1iCz5rOlYQuL0hchhMA0+UIWd3Vhii1hU2kLXv/AdN6nt9bgbz7InGwDNcZ7eGD7w2OeSwQpSempwWXJZ2vjVhJNiRSEgtnmmVdwSbefBlmL2+cZdVDzme01OLt8/OSCqZFWE0sKkwgEJdsqR1+sFK4D+ODwqWEEnnSCnhuXy0WFF2EzDrz1huNQXLTrcajZDLufGr9zjoGuQBspQQm23P6tYQcjZapqh+vzqIWMkyYMvSrPWAkVFw3o9tjVqqpWp10CE8+JbO7UJZDsC4zJ5WLvsuNHkqGNYUd1G//+uJL73ykZ8jbZ3tHDtlAl4ku76si35aMRGspcZQPGPl38OlJquHRS/xz9DHMBbllLMDh0TUOr20u3UBbtpATVIiE3Tgm6TuiIN8YPeWxWorooW8wOqlvHHhwsa+lkjka9r9cMEofHwZKMJWrn5POZ2+OhQzbTFXCxrap/+qLP3UbGe7fytvGHxGjuQJrKafLvHvNcwsj2Osx48NoK2NK4hYXpCyPii97M5KwlBAWYjHWj8qNLKfnP1mJyit4hP6X3Tm1ebgIGrYZPyh2jnmv47uiDw/Zj79f/GeCkE/Tl2cv5zfLf9Osd0pdUS+r4CXrABx/ep/6u2qSenyC8OMkM9EDaMP27QeVNy6DKt3aUjm9ANExCnlrxp/sIq2jzQ+DtgDN+2G+zx5BIhrcHZ4+Tbv/oFkeudyvRNPnMXPbXTfzspWL+8NZhrnts66BFM28faEJKmJsbz/p9jQQDOrJjsyl39re4pZR81PA+wa4izpqc12/f1ORJoOlhW10FQ1HpcKMxqLvB8GLYYQs90Zw45HcUINuqavXiYjt4dXfDmNwFoNwtU0Q1T8XF8pNDqkI2nG1D+kzm6dRFxRhbyQd9/eiOMvwPLOG8wAc8nDGVN3zNmDHh19hxdR/b97y9TsVvXMnKuFqUvqjf/kmFofRO0x4qhxH0/Q1O3jmg7uq2VbbSaPg3TsNbvFT2UmSM2aBlTk78qAXdHwjS0tlDps1EU3sPh5o6RnX8Z5GTTtCHI9WSSqunFV9wHMR37zPgqoa5Xwefe9BWtZ8GXn8AqXWR4+3oLXI5GuExjXtUMc14+s/D9Ml0ieBxwScPwpQ1avGHPnhNyeSFFkserdulvlMJut4fS5xRx6bbzuKBr85lW1Ubtzy5E3+gvxW9bl8juYkWfnjuZNzeAG8faFKZLq7+gl7qLKUj0EiGfiE2s77fvtNyVZ/998qHtlgrW7rQGOwkGJOJNajMl7w49bkcLSAa3q+TgKmLjh4/T28dW1Cv3N6JjC3n18mJrMhewUOrHurtHyME0077fxiCkiUJm3jnYLOyQgM+5HPX4fd08i3rL/ib2cNZfh1Xd3Sg0bvYV39sLktnjYo91Mcqd8aC9AX99udOughjUJJsLjlqYDQYlNz2wrf48aYLue+j17n3k/+gizuAUWvipdKX+o1dUpjI3joX7aO4MDrcXoISvjhfFattOMF9b8aDU07QUywpSFTxUSAYoN3bPrYTBQOw8Q8q53vVrwABFR+MyxzDt3ZdvsFTyXY17+Kerffg8XsIBiUv7SlHaLyk+X3Dr7ADKiiq0av+5zIwvhkuYeJDvmJnn3SxbY9Aj2uAdQ5ATCqF/mMTdOm1kZ1oITPezJpZmdyxZhpv7m/iX5sqI2PbPT42lbWwekY6iwuTSLMaeWlXHYW2QirbK/u1Hnit9E2kFJwdWtS6LyvzVS77zsZBlp8LUeVwozG2UBifH9kWb4rHarAeNWURQKvRkqkx0Cy7WFKYyP99WIEvMPqWFeV2N+6YFnQS7l15LyZd/wU7DPP+ixkaMz2aQ3js5eytc8H7/4uo38EPe64jYYYevwxw8xm/ptCjLNRtdaWjnkdfvI2H6ZJGqmUtSaYkCqwF/fZrzfEUCQNBY8tRXS4fHLbj1u+nW+flHyW3sc/zCEnaaXxv3nc50HqAQ62HImOXFCYRlMqKHylhd8vs7Hgmp8Wd8EZm48EpJ+h9q0X/tudvXPTCRWPr7VKzWaX8LfueauKUPhMqNhzz/A40tDP/rrd5bHMxK55ewf077u+3PyiD3LHpDh7d/yiXv/ANzrnvZX720S8AyPf5R2ah6wzKKg/noh8PC71PcVGEAy+rHPnMgUVMmtgUMkMuq1ELekcdiYEADm+sWss0xH8vKxgghu8dbMYXkJw3PQ2tRnDx7EzeP2Qnw5yHP+inpqPXEn694k2C3Tmsnjpp4Nsz29DJeKo6hg6Mlre40RlbKLT1F6wbZt3AFyd+cdj3laWLo0728M3lhdS7PLy+d/TxhfKWTlr13eRqLRi0gwSNhWDexIs5aNDxuPFOrE9eDBvv5YOY1WyzLMce3EqhrZAJEy8gL121DCi2D4w1ROho7B83eeFbavWsPmjbyiiXaRxw7uzvP+/DpNgc6vV+7PamIV/qwQ27aTZIvul0ca7biwiYuWvy1VxYX4JOo+vndpmXp/zoH5eN3O0SDoimWU2smJzC1oo2urwnd2vfU07Qw5kFDe4Gnj38LK2e1t4ly0ZD5UeA6G1MVbhCibz32Ao0NpbYaXV7ufOtN/AEPPx9799ZV7Eusv/l0nWUu8rRuRdS6S7GnvBzDNZ93GKdyRker2qbOxJSp0DYV308fOgmm8pHD/cW72hSrXgnnT/ocGNCOmn+AILRZ7pUOEvI8fmp6TaSFW/ut++605UYvrGvkUBQ8sTmalLijJHWvmdOScUflPg86ntxuO0wAOWuchq6y9F2z2VW1uAB9lRjPu2BmiGbW1W0NSE1XeRb8/ttv3r61ZyZe+agx/Qly5xMnVZwZkEMRSkxPP7J6FZ0CgQl9pYWavSSopAhMxhzc1fgF4LNCQXY3T6cBRfybccVXLHYxk77DlblqYBwfu5yAOrbhrgr2f4o3DsZPvidel67HXY/GboT7BX5OHclxaZMmruaWZC2YNBTTUpfgEOnJbX9w0HXVS2ud1HWpBYwWZJ1Ove0NLOprYNlr32NhA//xJn6FF4rfy3iWjXptczJjWdjycjTD8MFXWlWI3Ny4vEGglS2fLoFWOPNKSfo4WrRF0pfiBQhjalZV9VHkDYdzKFFiQtWQMCrRP0YKK5vJ81qZGJOG1JqyDRN5mcf/Yxdzbt4/JMKfvb+Hwj0pDJZdx03TL6TOamz+Od5/+CbPQJNUtHwa2CGCbtmLMm972G8mXoxHHhV+c5L3lTbJq8edKglPgM9YNPEjCrTRUpJmauCCV4fTf6YfhY6wFlTUilIjuGfG8v541uH2VzRyg9WTUKjUVbhhFTl2+7pSiPRlMjLZapbxfqK9SAF85JWoNMO/jOYlDgRYWhmT+3A74+UkurQknr5tvwRv5++ZMVk0qbV0u2q5JypaeyucdHjH3lnxNq2LjJlNTU63YC7hL7MTpmNQLBn1kV8yXM759d/A6mPITWthKAMRgQ9Jv8MkvwB/D2hlFe3A/Y8o9oNb38UXvmu6hW08R6VPfXer9W4Lgd0hhIR/D0k+hqpjFPfucmJg6fYTsxXF7wcw66I77rbG+Dq/9vCZX/9iJuf2Elq7CE0UjJ9zjcQK28jztOIOO0mmPVlLqnZR6unlbcqe5edPG96OgcbOyht7hzR59fc7kEjICnWGOk91NQxfguOnAhOOUFPMCagFVo+rPswsi2cuz5iAj6o2QJ5p/Vuy10KGt3I3S6dzYNa88X17czMiicvowULWVQUX0GMzsZVb1zFr3feDIYmbpr7LZ66/jRuXvoF/n3Bv1mYvlB1URyJuyVMuOXr8XC3hJl3tboL2PssHF4H1uwhq1itySoPPgFLxCc+EhweB05fBxN9XpwMFHSNRvCNZfnsrnXxwHulfHlBDlcu6pMLHmvEatJR2dLDlZOvZEPtBspd5bxe/gb+rnwW5Q4thIuzpiM0fj4oH2ixtnX58KDuNI70EY+UbJtyW9Xa9zE3V1mI++pGHvMpt7tJM5QTFILClKHbAduMNiYkTKDJu5/cRAsNLg9XLMjmw8Z3ybfmR1IuyZxLrj+A1DapTJe374Dnr4OHlsMrt0DRmXDjJtCb4ckr1SpYE0Lpnk37AJCt5WgJ0hyrArPh7J8jmZSsDA6rpYrfrjtIMCi5/90SNhy2o9dq0Gs12BKbKPL5iMmaDyt+BD+uhXPvgvN+wzK/jikYuWvzXVSHGretmZWBEIy4OKqp3UNKnBGtRpBmVb2RmsdxBakTwSkn6FqNNlKhF26h6vCMUtAb9qislr6CboxVfvT6YTJddj8N/zwP7pkIr93ab1e3N0C5vZOpGXHsc+zjnMIFpFiScZd/F7/jbPTmRgpthdww/7L+5/R2qUWcU6eP/D2ELfSxNN8aKZlzIW0mbPs/KHsPJp03oHAnTHyKEvTkgHFUa4uWOlWAboLXR5uMIzvBMmDMF+dnkxRjYGaWjTsv6f8ZCSGYkBpLaXMnV0y+AoPGwK8+/hWVHRX422czJ2foXPG5GeqiuNd+aMC+SocbjdGOVujJjB1b0VY4F72urZR5ucqi3Vk98uKYMnsnMUb1WRZlLj7q2JXZK9ncuJmpU3Zg0Go4d46GbY3bWJW3qtfHrTOSobXi1ndRVtugljec9gW4/BG48F68lz/OX3d5cZ72U5UOG5MCa/6ojm1WmS2dterRadEQZ4gbMhc/0ZRIstaCXd+Npmkvv1t/iL9vKOdL87N5+oalvPG906nGwaygvvcOM9yQLiYZ3cr/4Y815Wh6Ovne85fQ9dr3SXPtYUl+Iq/srh9RTnlTe0/EMk+JM0a2ncyccoIOvYHRa2dcC4zBQq/6SD3mntZ/e3weuGqHPq5xL7xwvcrNTpsJh9dDn8KUg43tBCWkJ3XS7m1nfvps/vq1ebjcOjLkF3jtC+t57PzH0B5Z3r53LSAjPbhHRGKBugAUrBz5MaNFCGWlN+1TF8BJg7tbAPQxCXjRkeoVNLgb8PhHZgmVtilBL/L6BrXQASwGHW98bznPfGspJv3A1gBFKbGU2d0kmZO4sPBCtjVtQ6DB3zmDmdmD+88BVWEqxaAVo1UON8JgJzMme+D/1wjJClnVde3VkSZnOwYR9Fb34AtBlDZ3ojW3oJGQlzgwsNuXG+fcyOr81Wxq+xcrzniFmzd8HaPWyEVFF/Ubl2MrxKWDru2PqP/TpTfBjMtg4XX84YPD3Lf711x/OAW54DpY8ye1HGFsOjQVA9BVuhGP1OPS95AXlzdoQDTMxORplBhN3BH7In/7oAyrWc9PLlB3oVXtVXQQYLZliIvlouvJXvhtfqfLoQw/d1a9ivznKn4X/B0VLe4R3ek0tXtIi9WDtwujTktijGFc13g9EZySgj49eTqLMxYzL20eOqEbvYVetUm5Ko7s7GfLVoI+1NV/2yOgM8E31rE962tqEYimvZHd4Z7hQYOyqmYkz2B2Tjyv3nI6z33rNHLikwZWwPZ0wru/hpwlUDQwvW5INFp1ezzrSyM/ZizM+pJaNFpvgYIzhh4nBC4RT3qoVWzfbJOjUeosJUFrIikYxKuPH5AvHiY1zjSomIPyo9s7enB1+/j6tK8DYGUqRYnpWE2Dnw/ArDNj0aTS6h14R1Fhd6M1tDAhoXBE72MwEqw5WIKSKrdqDTwvL4EdVc5+Y2rbulj067cH7Qi4t86Fx9hJttAPSFc8Ep1Gx93L72Z1/mo2N37ImsI1vHLpK5GS/DCTstTKVbGV/1AVxyEjYm+tk0dL7sUQv43dna/zduGPevvop02PuFxM1RvYEpxCq7+RPNvg7pbIayVNp8xoZL5vC4t0pdxx0TQSYlSmzp4mdSc8cyhXklYP5/2a077+Gt+ZdzOvx5h4YfZF5DS9y3xdGS/vHmpRtV6aO3q4wrMWHlgA/h5S44xRC/2zyO1LbufhVQ+jERoSTYmjs9CDQaje1N/dEsaWo1rTdg1yvp5O2LMWpn0BRzCGWzYrYfYe7u3YV1zfjs2sp7brIGadmaJQb+9JaXGRL/IANt0P7mblOzyKtXPCMCeoDoaLvzVswLZTF0+2R2XeVLWPLKOj1FnKBF0cQTTExycd1eIbinBgtLS5k0kJk7h98e10NZ7H7Oyh3S1h0s35+HQNA9oMlDS70BgcFMWPzX8OIDQa5mli+LCzEuntZl5uPI3tHuqdvZW026va8Aclz+2ohf0vQ6u6W/D4AhxqbKdB56XQMPz7ACXqvz3jt7x7xbv8ctkvIwkEfckLrWrVqPXTPPHLSKCmtYsbX34QnXU3VoMNk62YX766l62Vrfz4+T3s9WernvvOamydZXysm0lLd1OkyGooFqQvoEcG+HtKBk8Wvcklc3rXnt9Ts5HYYJDCrCXDvq9rZ1zLkowl3O0+SElMAj+2vsWrexqO6nbp8QdodXsp9JVCex0cXk+a1URzNCj62SRcdp1kThqdhd68X2Vt5A2yRJkttPyZaxDrct9zquR9wTU8u72WuoCNQ8FsGnb2piTur3cxPdPKPsc+piZOHbTVaz/aG2DTn2H6pUOv+flZ4Mwfwzl3DDvMY0yioFsVr4xE0KWUlDnLKMJIh4gjO3Gg/3wkFKUoQS+zq+yHZWkX0dqWypycod0tkWNtE9AYWjjU3D/T5XBrFYjggJTF0XLuxEup02rYv+W+iB+9r9tlb61KuW0r3Qprr4L1twOqnsEabKVar6UwNmfgiYdAIzRHLXrKDQUry3UGzns3nTm/fIuVf/0Lbea1TLLO5xen3UFQ00l9zz6+9LePeXJLDU9VW1UG2NZ/AlCbNgWJ7NeobDBWZK/g4qKLeTBWz+bmberOGPX/vs2+i+k9XjTps4d9T1qNlruX302MPpbbs3KY2/UhhvZKalqHbjERbgqW5A3d+ex+kjSr8fPhchFCrBZCHBJClAohBl2VSAhxhRBivxCiWAjxxPhOc+wkmkdpodduUY+5gwSZIoLe348upaTr438gU6YSzFrEk1uqWZSfSG3iYtLadtDc6sQfCHKwsYPJ6RYOOg4yM3kEixRv/Ye6Izj75yOf/2cYvymZzICTJFPSiAS9qauJTl8nE/1B7NJK1iD+85GQk2jBoNVQFkpn212jRHL2UQKiYWanTUGIIFtrewOjwaCkzq3cMGNNWQxz1rxvoZPwZvGTTE2LwajTsLO61+2yp85FgkXPrZpQc7iS9dBpZ0+tiyzjYfxCUJQ05Zjm0Bezzkyq3kppznyuWJVCWtFzWHIeo8CWz0Or7+GM7DOI0cewcHoVv7pkOhecfoANZvX7ktsfoUVa0WSqz3W4i50Qgp8u/ilFtiL+JyWZph2PALCxbiPlPQ4u7Pb2Lq84DMnmZG6Zdwv7fU42Wyxcp32dnTVDB5iVa0US212nstdK3iTf3I29o2fQvPiThWEFXQihBf4CnA9MA74ihJh2xJiJwI+BZVLK6cD3jsNcx0SSabQW+gGVaxs/yO2iLWQJHSHoxbu3YGnZw4uac9hY5qDS0cVXF+cy4/RLMAkfjz+zlndLKvAbD1AtnsQb9DIj5SiLVIBKndz5b5UWNsIv9WcdGZNCIu1kx+aOSNBL2tTiHAUdrVQGUgcNiI4ErUZQkBwTyU/eXevEoNUwJX2YrpXA4mz1Vd/X0ivodc5ugvpaQAzwQY8WmymeRbYi3tT2oC95jVnZtoiFHgxKiutc3FzYxArtHj6IOQ+Cfti7lj21LjJj1Z1iUfpRVrAaA3lJk3nLXcITtd/HHtzCjbNv5PlL1pJsTsaoNXJmzplU92wmEPc+Gx2P0pa4m6DQITwuNgWnExunLpjDWegAFr2FP5z5R3q0On7evBHp7ebve/5OBlrWWPJAO8xdbB/WFK4h2ZzMI5mFXK7dwJ7qoX/3ze0eEulA53erVb6CfhZ2vENQgqPz5PWjj8RCXwSUSinLpZRe4CngkiPGfBP4i5SyDUBK+ZlpLpxkTsLR7Rh5a8zmA6rKcjBfrSVR9RY/QtDtlSog9PfqNG5c91Pi4+tYPSOdtJln8bjVytO6B/nB5i9gyX2EHa3rWJi+kCXpw/gGD6+DziZYcM3I5n0SoI1LxSj8JOtTRiTokZTF1hoqZdqgKYsjZUJqbMTlsqvGybRMKwbd8F//ovgCkFoq23vL4ctb3OhiD1EQNwWrYfiLwnCcO+1r1Or1HNz4WxbkWtlX58LV5aO8xY3b6+dix9/pMKTw7bav4kufBzv/w56aNlIslQAUZC095jn05fpZ13PN9Gv4/Rm/540vvsG353wbvbY3eLw6fzUd3g7u3X4vCcYEhL6D3UZ197oxOJOg1k6iKZE4Q9yIXq/AVsAPCi9jk0nPT9+8nl32XVzjcKDPXz6qeRu0Br4+9et8EnBSaZQ0VJUMObap3UOuCMnU5PMhYzaTG18N7Tu1BT0L6Os0rg1t68skYJIQ4iMhxCdCiEHz14QQ1wshtgkhttntn04jnCRTEr6gjw7fCFtjHq2AR4jeTJc+dNrV83OX56KxfYwx60kCeDjQWcs9ifFMQjJR+xVyvbey4csf8n/n/R/xpmFu97c9Atas3sKNUwBjvFprNCkYh8PjoNN79Iq+UmcpqaYkEnzdVMm0AWX/o6EoNZbq1i7W7WtkR1UbC/NHVj2r1+oxk05LT+8FaF9DPRpTLWdkj05whuKsvHPQouHNnjqu0ryFLyB5dW89e+ucTBNVJLftpnPhLXQF9WxPOB+ai7nFeTctmlrytTFYjCMTzpGyJGMJty64ldUFq0mPSR+wf2nmUpLNycxJmcO9K+8FYL0mEYBq2yIaumqGLCgaiiuW/oSl3gCvOHaRJDVc6tPC8h+Meu5fmvwlLFojj9iseJpLhmx41tTRQ4E2JOgJ+TDry1id+8mk5aT2o49XUFQHTARWAl8B/i6EGKBYUsqHpZQLpJQLUlKO3l50vEg0qS/aiPzonXaVwXK0joaDCLrPWU8ADZMnq9vDrmALv9/6e+7YdAcJOgsP1FXy/Kov8No3ryHOGDPYWfvTVgll78Lcq0Z1y/lZJyYxAwCbT2X0VHUc3UovdZZGepRUyvQxu1wAilJiCEq48T/bmZIRx81nj7wDZYoxj25RRzDkW93StAkhJOcWrBjzfPqSYEpgYcYi3oxPJn3HvSxJ7uH5HXXsrW1nsV7dpaTPv4gFeQn8vwMTCGgMrNRtZrPFwsrJl4/LHEaDQWvghYtf4JHVjzA7ZTZaoWedLo0HAxeTlTeR6vbqyEIfI0Xo9PwyYxVZPj83OeyYzrpd3RGPEqvByqX55/NWjIV0WcuhxsENuaZ2D1NMoUB3fF6ksjpTtJzU5f8jEfQ6oG8YPTu0rS+1wMtSSp+UsgI4jBL4E064anREgh6qdouUzQ+GLWuAoOu6mujUJXGg7RB6jZ6vTvkqz5U8x4HWA/x00W3Y0MGu/4x80rueDBXtXDXyY04CrElK0ONDyQdVrqEFPSiDlDvLmaBRbpYGbQaJQ6V2joCJqcqKLUyJ5dFrFh01//xI8q1FCH0bpQ7VG6i8czuaYBwzkoeJg4yCc/PPpRofJZogd5mfYHtVG2/ub2SlpRJiUhEJ+dzzpdk4Ahb+4L+cnxqX40dydt45w577eBBviken0WHQGpgUP5kmUxe/9V3J9Gwj9m77mILF6XOv5o3aei6PKYT5Y3c1zslahl8IrIZadtY4Bx3T3N5Dka4FYtPAYAGrKmDK0LSd8i6XrcBEIUSBEMIAXAm8fMSYF1HWOUKIZJQL5ugLMn5KJJmUoI+oQVd4Hc6jWug50NkIfvWf7ujsweZ30GNJZX/rfiYlTOL787/P1MSpXFx0MedMulQ1rNr91MhXPCpZD9mLerNqThEsIQs9PhR0OpqFXtdRhyfgYUJA4keHLj5nTDnoYaZmxPHLS6bzxHWLSYo1jurYmamqRP/j6gMEggGc7CNNP+uoKxKNlrNyzkIjNKyftIwJ9rdIE23UtnUzQx6GnEVIoKp7G989L4W/eNewMT6eJFPSyLKljjPz0mejt9QBAVITlRtttBY6AJnzEGf9DC59eMjFwEfChHjV7kJvbmL3EIJe7+xWPvSEfLUhTn03i4ztJ3U/l2G/kVJKP3ATsB44AKyVUhYLIX4phLg4NGw94BBC7AfeA34opRz9An/HgYiFPpJMl+b9YE6E2KFbkUZEtl3lr5Y0d5Iq2hDWdA44DjA1aSomnYmn1jzFXcvuUmPnXgVdLaoVwHB02qF+J0w4MZbX8URYkgkiMLjbyIjJOGpgtMSpAloTut00iFQKUofPGT/qawvB1UvzSbWOsFtlH5bkqP4we5sPsq1xD2jdTIs/eu+U0ZJkTmJh2kLeCrqQwA1ph0iknaSeWnxZ87jz4zu56d2beM3+C74w3waWg6zMWTnmtgPjyczkmUjhJc7agkuq/7fR+tABdVd6xv8b2TKLRyHPmocO8Jg6BhV0KSV1zm7SAo29gm6ygc5MnqE90lb3ZGREJoaU8nUp5SQpZZGU8tehbT+XUr4c+ltKKW+VUk6TUs6UUp7YFZX7kGBMQCM0A10uXa0QOKKZvf2gCogezRI8Ihe9pLmTNNGGMz6Jdm97ZPV3jdD0WpRFZ6t+FyNxu5SFKksnnnqCjlZHh4hD220nz5oX6ZI3GGVOlVVS6GykLJBKUeoIYg/HiZmpBcignq2Odfzv5t8iQMkS8wAAIABJREFUpWB59iCVxMfIufnnUuluoCS5gDXGnczXluIDbnBs4rmS51hTuIbqjmoq9L/HLz2cnXv2uM9hLMxKUas7XXVWF3/b81fmps5lYsKJ87jqtXrydVYadD1U2F0D1mtt6fQS9Huxept6BV0IsGaQpT31XS4nNVqNlnhj/EAL/aEzVOVdOJ1RylDK4jAtao/IRa9ocJAoOqmyKP9uWND7T0IHs7+sLPTOYTI6S95SXexGUCF3MuLWJ2LqcZBnzaOyvXLIdNISZwmZMZlYHBVUBNMoTI79lGfai1ajJY4JtAZKqOtoxte6nJkZY+uweDTOylVulzcziki1f8L9C1vZaolhq+swP170Y+5efje3zL2Fclc5Fp2FxRnje5cwVrJjs0kwJvDE4X/g9rm5Y+kd4+qOGgtFMRmU6bWk42BbZf8CozpnN1nCzv9v772jG7uve9/PBkBUggTYyWGbXtRGo1F1U2zLKpYl6zlOpCjX9rNiX3vFy3q5ecmVk1yv+/RenuMkz/fGjpJYyVPiFsklTcmTLcuyvWxVayyNymiKODPkcEgOO1hQCeL3/jgHINiGGFaAsz9rcQ3xOweHew6JLzb2bxfBzAg6QLCJOkY2d8hlM5DNRc+RmbbK948/YQ01BqufQ3J8aUG3N0+ygj7YZ3mZR5nCKc7FPZP9v2nN93zt24tfOzNt95h+Lzg2568m6ammPD1Ka7CVidQEkeTCMc6TkZPsCLbimJqky9SzvW7jBB3gn+56hNbx/8nAm7/L1OBttFUvPyd+MWp8NVxVfxVPZSYgM4Xv9W/xUvUWXOLigzs+CMDHL/04d+24i3v33rvwyLkNQES4rPYyps009116X65H0UayI7yLHpeLbWW986YY9YzGZ3LQ8wW9opFwepjhaIpUehljK4uAzakac5hXLZqwR9KVBeCpz8OZF2c2RGuXEPQyn+VB2/1cosOWsB+dirAttG3xrne1u6D5anjlm4t3a+x9xWq9uwnj51nS/nqqzSi1Xit0tVAcfSozxemx02y3m051mnq21W5cyAWgKVTOd//zu3j/5Y3cuLsOj2ttYtc3td3EqVgfHcFamE7yC6+Hy2ovw19mvYGICA++7UE+e+Cza/Lzl8ttW2/jusbr+MTln9hoUwDYUXclRoR9tb387K3ZNS89kdjCgh5spHxqEDAMlmi16MUh6HM99ITtFb77jyDYAI+8zxp2C4VNBapshvEeRqMp3HHrj+VorG/hcEs++++14vQ9iwzJeOspEMeFtcktMRzBeuokgg+rNXHneOe8c7rHu5nKTLHTWKmFk/7WC0ozXCv8bhcP/cYBHvnY2jVKu6ntJgThqeY9TIpwJD1uTawqct6/7f387fv+Fo/zwjKI1oodTVbb32DgHB0Dk7M6WPZGElbKotNj7W1lqWjCmUkRZqJki4suDkH3Vs9OW8x66OF2+K0fwU0PWt9vu7GwYobKZoh05zZEB50OhlKRpQX90v/Fah1w+JsLHz/xA9hy1bIKKkqFslAjXplCYmU4xbngxujMlKIU0zjw1q6sX0opUeOr4UD9AX7omOLlcCPTGK5tKI5YeSnREmzFbWDcYTlcz+SFXc6OxtnpHoJw2+zQpp262CCjnBkuzWHRF4eg+6qJp+PEpuxfUtz20H0hy0N/2/3wiafhI/9W2AVrdmGGO/jWz9+kXkY55bYqGHeElxj35q2EfXfA6/9ktdvte3Xm2GgX9B2GPbdf4P+utAhUWXsQsaE+moPNC3roHZEOHOKgfWKYPmpory+s3/dm4aa2m+iI9fLovhtxO9xcUbc5N8jXEqfDyTaHlzPTY9QFPbPCLj2ROO30zW96Z++PbfVM8GzH7Lh7qXBRCPq88v9syMW7vNzm3orLETPN4LHn+JWmaSKBqlk/57wcvA9Sk/C9j1uZNi/8jbV+9N+tf/fdsfhzNwHBWit2HhvppTXYuqiH3hJswTV8mtPTdbl+5hcL2bDLMz3PsL9uf9GEMUqN7d5aOpjinTuqeKZjKNcW99zoBA3ps1C7e/YTglb45W11KX721mDhDf2KiJIT9B8f6+dT3/hlrq9GIWRnjA7a8e6ch75Ug6xF+MqJKjJG+OI1MfaWR4n4rY57YU8BDZ9ar4UHuuDTz0Hb2+CZL8FUwhL0+ss2TavcxXBXWh9rp8b6rFz0iTPzXjgdkQ62V26H0S66zcUn6HX+Oq6suxKgJOLnxcqOinbOuZy8q2mMSGyKN3qsnPRwsgeXSc9PgLDj6VdUxugfT3Ki//zN44qRkhP0iUSaHxw5x88v4CNRfcDagOuP9VsL2Ri6b3mCfjTipKesjZaJV2HiHBG3lYGw2ITzeXiC1hzGGx+wWuT+/P+B7hc3vXcO5Oa0mol+2iraiKfjDMRmcvOnpqc4M36G7RWtlCVHOGtqNjzDZSN4X/v7AKvzobI8tjddA0Dt1HM4BJ4+NkBPJM5OsXsxzfXQXW4I1LLNY83+/dmJ9ekIu5qUnKDfcmkDNeVuvvF8Z8HPqffbgh7NCnoEHGXWYONlcHY0Rk/FFdD9Eoz3EinzUF5WPqtndEG0v8NKZfzZnwIG9l4Egu4NMUUZZbGBXHl4furi2cmzTJtptrqsZloDzjqaKpffZbFU+bVdv8ZX3v0VrqjV+PlyaW+1hpYP9v6cq9ur+P7rffSMxtkpdm/Bml3znxRsxJ8cYGdd+bx0x1Kg5ATd43Jy99WtPH1sgO6Rwnaiy93lBMoCMx56PGLFz5fR7CmaTDM0mWKi7qA1QzQ5zqjTVbh3no8IvON/t76v3jnfY9iMiDBZVo03NTQj6HlNurIx9daM/adZ2YLDUYTDsdeYMmcZN7bcuKKGZBc7zcEWnAidIyf4wL4Qbw1M8rMTg+x09DBd0QKeBUJ5FU0w3sc7dtby4ukR4qnp9Td8BZScoAP8xrWtOET45ouFTY4Hy0ufFXJZZrile9R6E5HWmSkxETHLE3SAXTfD3g/A9b+9rDeYUiTpraEyPULYU4vb4Z7VRvfMhC3oKauww1PTvhEmKpuAMmcZW7w1dDnh/YFjAHz3l2fZ7TiLY7F6k2AjTPTyzl01pNIZXjxdFD0GC6YkBb0p5OOmvfV8+6VuElOFvYPW++s5Fz1nPUhElr0hmp0kXtO8A4JWmlOE9NITiBZDBH79m5tq1NxSpP111MoYQxNTtFa0zvLQu8a7CLqDVEwMMWWcVNYVPtFeUebSXr2bTo+X8JkfcVVbmGQqxTbpQ+oWmXlQ0QSxYa5rLcflEF7qLKDtdhFRkoIO8KGrmonEpni9Z6yg8+sD9TMx9GzIZRmcscM8rdUBaLU2rCLTicIyXBQAHMEG6mSUc+MJ2iraZsXQuye6aQ22khru4pypYkvVxZXhoqwubZVbOVNWRubED7jtklpaZAA3U4sPsbGLi7yJAZpCPs6MxBc+r0gpWUGvKbcaE00m0kucaVHvr2cwPshUZmplIZeRGOUeF2F/mdVzpSzA6NTk8j30ixB3uJEqmaR/dJy2ija6J7pJZ6zfY9d4F63BVtIjXfRQQ8sKBkMrSntFO3EyDCRH+UDVmZkNUVvQ3xh6g28fy2uYZws64320VPkK3qcrFkpW0AMea9ZmNFWgoAfqMRiruGhFIZcYzWGftVm1/zdI3v8KsXR8+TH0ixC/XS06MdTLtsptpDNpTo+dZmp6ir5oH60VrTgnztJjalY0R1RRchvv5VXUPfd/8Z/32B537S6MMTz4/IN84RdfyDkUhO3BHIPHaAn7OTuqgr4uZAU9liwsht7gt4oGzkXPWSGXRTz0VDrDofPEzc6MxGitsr1GESL2PqYKeuH4wnb5/0gvV9VfBcCh/kP0TPaQMRlay7fgjQ9w1tTQFFJBV5ZPTtD3fxh6X+bg2W9aMw08QV4eeJmjI0eZNtMMxuwUxeodUNkKJ56kpcrP0GSKWIFOYzFQkKCLyC0iclxEOkTkgQWOf0xEBkXksP31W6tv6mwCbqt96WSycA8doH+sy+pLvkgM/S+ePsGv/s3zPHdyfuGSMYbu0TxBh1w/77BXY+iFInZxUXqsjy3lW2gMNPLSuZdmMlwcPhxkmPA04C3b+BFrSulS76/H5/JxOhCy6jxSE7n04G+8+Y3ceb1Ra6QkIrD7Vjj1E9qsUgjOjpZOHH1JQRcRJ/AQcCuwD7hHRBYa+vdtY8x+++vvVtnOefjdtodeaMglW1yU7R2yQMhlYCLBI890AvDQTzrmHR+cTJKYytCygKCrh34B2CXWZqIfEeHqhqs5dO5QbnO0ddpqBTAd3FxDspX1R0RmNt7f/yXrb6/lWronuvnxmR/z3lZr9kDvZO/Mk3bfCukEexNWm+tSiqMX4qFfA3QYY04ZY1LAY8Cda2vW0rhdDtxOB9ECE/8r3BX4XD76J+yy3wVCLn/54w6mpjPce20rz3YM88qZ2aOrsimL+R76aNI6R7NcLoBALQAuu7fOwfqDjCZH+Un3TwiWBQlHrXvqrFrG5HhFmUNO0Mtr4f5X4Z2/x6PHHsUpTn7nqt8B5gh629vAU0HTuZ8Cm0/QtwDdeY/P2mtz+ZCIvCYi3xORBZOHReSTInJIRA4NDq68rNbvcRItMOQiIrOLi+aEXLpHYjz6izP82tUt/MFtewn5y+Z56dlfbEvVTFw3Yndu1CyXC8DlJuYK4UsOkpiazjWgeuncS7RUtJCJWH9u/tplTI5XlDm0VbTRM9nD1PQUlHlBhGd7nuWGLTfQWtFKlbeKvmjfzBNcbth5E97TP8RfJnRvppBLgfw70G6MuRx4CvjaQicZYx42xhw0xhysra1d8Q8NuF1EC9wUBbtaNG7HxucI8LdetEIxn333TgIeF//rDVv50dEBjp0bz52TzUFvDs/30Cs9y8trv1hJ+2upYYzO4Wgujg7QGmwlPtTJoKmkqVo/9Sgrp72inWkzzdlJ69N5cjpJ53hnbiBNU6CJnsme2U/afRsSHeS9we5N56H3APked7O9lsMYM2yMyQ7h+zvgqtUx7/wEPM4L2oGuD9TTbwvw3JBL92iMlrCfhkprJuhHrm/D6RAePzzzUax7JEZ9hWfWRt1YcoxgWZAyx8aPSCslrOKiCB0Dk7k4OkBrRSvpkTN2yqLmoCsrp72iHYDOsU7AGkCeMRl2ha3mXE3lTbM9dLBqTMTBr7iPbDoP/SVgp4hsFRE3cDfweP4JItKY9/AO4Ojqmbg4frer4CwXsIuLpiaZhnkhl4HxBHUVM4MEwgE3122r4gdHzuXWuoZj8wpdRhOjGm5ZBr6qJhpkhI4Bq+f0wXprBmRrsBXn+FnOag66skpsD23HKU6ODB8B4MToCYDZgj7ZR8ZkZp7kC0Gwka2Ofs6OxFY+7KL/CHyhFYZPruw6S7CkoBtj0sBngCexhPo7xpgjIvKgiGT7vX5WRI6IyKvAZ4GPrZXB+Vge+oWFXNJkGHY6YU6IpH88SX2Fd9bazZc0cGowSsfABN0jMQ51jXD11tlTiSLJiG6ILgNn81U0ygjjZ633/htbbuTG5hu5rvFafPE+ek0NjSHvEldRlKXxl/nZXbWbVwZeASxB9zq9tAStwENTeROpTGr2IHmAyhbqM4NMJNOMxadWZkT/m5AcswbBryGuQk4yxjwBPDFn7fN5338O+NzqmrY0AbeL4cnC41u5XHR/JXV5w2GNMfSPJ6iv8HIqcoovvvRFEukEU9MGT72Xv/zFAJWZK3GI8JHrZ2/UjSZGqfHVrM5/6GJi963w/d+n4dxPgTsJe8N85T1fgegwZJJMehrwuDQHXVkdDtQd4HsnvsfU9BQnRk+wI7QDp8P6+2oKWIVuvdFeav15e3uhFipHngesDLeQ3718A+J2sWLXs3Ddp5Z/nSUo2UpRsKpFCy39h5lc9HPe2Q2fxuNpkukMdUEPf3/k7/ll/y9xOpy4nOAJv8zTw1/in87+39x+eQONc4YtRJIRLSpaDqFW+v072R9/PjfrEYBJK8Rl7OIjRVkNDtQfIDGd4M2RNzkxcoJdVTPDLRrLrYhx3+ScOHplC77YORxkcm2zl03M9v67noM1nFVa0oLudzsLLv0Hch+xTntmf5Tvn0gAEApkeLLzSW7fdjuP3PwIX7/163yy7Vsk+m9DAkdpaH1x3jUjyYgWFS2Toab3cBXH6O3Ny4qdsF5UZZVNG2SVshnJzmh9qvMpRpOjufg5zHjo8zJdQi2ISVPH6Mp7umQFPTYEQ2+t7FrnoaQF/UI99HJ3OW3GydGy2f/t/nFL0DsTzxFPx7lr5125Y7dduoWpkXcQnL6Sb3c8zKuDr+aOJdIJ4um4eujLxLH3VpxiGH9tJpqXHrME3Ve9UKmDoiyPGl8NbRVt/EvHvwDMEvRydzkV7or5mS6VlgO42xvJFRUum9jIzMjLrmdXdq3zUNqC7naRmMqQns4sfbLN3jQcldlvAv3jVsblC4PfZ3vldi6vuTx3bFttOX/0/n18+b1foM5fx39/7r/ndsO17H9lNO25nnMmjO/0D3NrYwOWt17XpEVFyupyZd2VjKesupKdoZ2zjjWVN82uFoWcoF9aPparQVk2sWGovxTK662wyxpR2oLusTY1YgVOLQLYm0zSY1KMJWcGY/SPJ3C4+zkeeYO7dt41b47jb71jGwdbt3D/gfvpiHTwVJe1U62CvjIqAx6ecVzNluHnIG29qU4OdhMxAbY36kazsrocqDsAQJ2/bl6qcVNgAUEPWYK+yxNZBUEfAX81tN2A6Xp2zeLoJS3ouQZdFxBH3xuz8p6Pjsykyg+MJwhUH8YlLj6w/QOLPvfm9pvZVrmNvz7810xnphlNWEVKKujLp7fySjyZeC4/d2qsjwETYlttYIMtUzYbB+otQc8Pt2RpKm+iN9o7O9/cHQBfFa3OYc6OxmZv3l8ocUvQk1uuQ8Z7+MGzv1j+tc5DSQt61kMvuLgonWRv3Bb04RlB7x9P4io/wVX1V1HlrVrs2TgdTj59xac5OXaSJ04/kZtRqjH05VNWux0AM3IKAGe0nzFXTe7NWlFWi9ZgK5dWX8rbt7x93rHGQCPxdDz3qTtHqIV6M8jUtKE3ssw4ujFWyMUf5rjnMsuWiVeWd60lKOlXTeACW+gSjxDKZGgqC84S9N7JftL+Hq5v+vCSl3hf+/v46mtf5Q+e+YPc2vneBJTzE96yG96C8b63qNwL/tQQKd/lSz9RUS4QEeHR2x9d8Fiz3ar57MTZ2Q5aZQuhvuOA1cspv3V2wUzFIJ0AfzXPT9TxXPp27t554MKvUwAlLeh+20MvuEGXnRK3N9AyK+TSl3od/HB90/VLXsIhDv7kHX/CT7t/amXNVLSph74CLt3RzthP/AyfOUb5dIbw9AgSbFz6iYqyiuQmG010cVntZTMHKlvwnvwxYOgajvG2Hcu4eMwuKvJX8+rRcV6vuI9P7bhmxTYvREkLenl2rmihIRf7Y/3emkt4uuO7TKYmCZQFiDrexC9B9lQtMgl8DrurdrO7aveybFZms7epkmPSgGvoFD29PbRKGl+V5qAr60tzsBlB6B7vnn0g1IJjKkadM0bXSHR5F8/moPuqeLV7jCtb127PraRj6Nk4a8G56FlB32J54sdHjzMSTSH+E2wNXIlDSvp2lCROhxALtOKPdtPTfRqAynodbKGsLx6nh4ZAA10TXbMP2KmLV1ZO0DW0zEwXW9BHCdITibO/RQV9QXJpi4U26Bo5BcFG9tVbVWOvDLzCS71HcLgmubx6bT4CKUvjrt1GQ2aAtzqOAVCvOejKBtBa0cqZ7IjKLHbq4qWBMbqWm7oYt7Lh3hyzWmyvpaCXdMglsJyQS9V2anw1XFJ9CV9++cs0B6ygWCHxc2VtqGvbS1nnNJMnXwCBQLXOElXWn9ZgKz/s+uHsxUrr0+JOT4Qz/VGMMfPqVJbE9tAPDzlwOoRLmtZuGE5Je+j+sgvcFB0+CVVbAXjk5kf48K4P0x19i+lkHbtrVEQ2ivo2a+/iiozloRNs2EBrlIuVtoo2xpJjs4oO8VdBmZ9mxzDR1DTD0dSFXzg2DAi/OJdhd30Qn3vtuoiWtKC7nA48LkdhaYvJCYgOQNU2wOqR/N+u/2/cUfvHJHrumTXcQllfHNXW7+RKRwdxZxDKdLCFsv60Bi1vvGs8L44uApUt1GWsWcRdw8sIu8RGML4QL5+dYP8abohCiQs6WJkuBRUW2RuiVG+ftSyprVQ6W7X39kYSbGLa4cYvSZLelc+aVZTlkE1dPDMxJ44ebqcybnViPLOcTJfYMFPuEBOJNPubVdDPi7/QqUVZQbc99CwLTSpS1hmHAxOyXkyesKYsKhvDluAWBJm/MVq1Dfd4FyJmmR76MFGnFTff21ixCpYuTkGCLiK3iMhxEekQkQfOc96HRMSIyMHVM/H8BNyuwjZFs7P85gj64ESS2qCGWzYalx128VVp21xlY/A4PTQGGmeHXACqtiFTUS4JJpYn6PERJm1Br69cW61ZUtBFxAk8BNwK7APuEZF9C5wXBO4H5k+BWEMK7ok+chrKG6yGO3kMTSapKVdB33DszWrdEFU2kgVTF+2/zQPBCF3Dywm5jDBGEIdAdWCDBR24BugwxpwyxqSAx4A7Fzjv/wS+CCRW0b4l8budhWW5jJyaFz8HGJ5MUR1YwaxAZXUI24JeroKubBytwdb5MXT7U/0VgRFO9E+SuIB23dnGXMOmnOpyD07HBaY8XiCFCPoWIL8e9qy9lkNEDgAtxpj/73wXEpFPisghETk0ODh4wcbOIjMNiTECbtfiWS5TM21ZGZlJWcwSS6WJT01ToyGXjUc9dKUIaK1oZTw1TiSR13WxsgXEydUVESaTaX56fKDwC9qNuQbSAerWQWdWvCkqIg7gS8DvLnWuMeZhY8xBY8zB2tplZjO8/j3423fDF5rhT1rZSs/iHvpzX4G/PAgv/A1M9kPVbA99eNLKKVUPvQhouwH2/yZsfddGW6JcxGQzXTrHO2cWXW4ItdBs+qgp9/Bvh3sXfvJC2I25elP+ohH0HqAl73GzvZYlCFwK/FREOoHrgMfXbGNUxIqD77oZgC3m3OIx9IE3wWTgB//VejxnQ3Ro0pqSozH0IsAThA8+BIHqjbZEuYjZU7WHMkcZf/ziHzOSGJk5ULUNx2gnH7iikaePDTCemCrsgnaVaHfSR11w7bPpChH0l4CdIrJVRNzA3cDj2YPGmDFjTI0xpt0Y0w68ANxhjDm0JhZf+iH46L/Dr/wRAJUSXXxi0chpaH8HXGb3Oa+/ZNbhnIderh66oijQEGjgy+/+MqfHTvPxH3ycofiQdaBqG4yc4s79W0ilM/zgjXOFXTAr6HHvuhQvLinoxpg08BngSeAo8B1jzBEReVBE7lhrAxfFZyXoVzJJajpDKj1nULQxlqDX7oG7HobPvgI1swfDDkctD71aPXRFUWzevuXt/NV7/oqu8S6+/ubXrcXwVkhEuKI6Q1u1n8cLDbvYjbmGTHBdQi4FNecyxjwBPDFn7fOLnHvjys0qAHvIa7mx0ohiqTRuV56nHRuB5Jj1zupwzAu3AAxpDF1RlAW4pvEadlXtmplsZuuHjJzmA5c38Vc/7WA8MUWFt+z8F8q2zjVBaosk5FKcOF3gDlKemQAgOrdadNTqrT03syWfockk5R4X3jIt+1cUZTZ7qvZwfOS4NTg66xCOnmZnfTkZYw2XX5K4lS0zjr84Qi5FjS+ELyvoc6tFFyn1z2d4MqXxc0VRFmRP1R5Gk6MMxAYg3A4IjJzKJVFkP+Gfl9QkaYeHNK6iyXIpXrwhfOnzCbpAaPFhCcNRrRJVFGVhsiMpj40cgzIvVGyBkVM5J3C4QEGfcljdQ9dDa0pb0H0h3OlxYIGpRSOnrV9A2eJxK60SVRRlMXaFdwG2oIMVvh05lSvfzyZVnJdUlLjDT6WvbF1Cu6Uv6FOWoM9roTty6rzxc7A+MmmGi6IoCxEoC9AabOX46HFrof5S6HmZ8MhhRAoMuSQnieFZl3ALlLqge0O47Oki88r/R06dN36eyRhGoklqNIauKMoi7KnaM+Ohv+v3oXILru9+hJ2+SYYnC/HQJ5kw65ODDqUu6L4QzpQl6JOJPEFPjENs6LyCHolPkTGasqgoyuLsqdpD90Q3E6kJaxzdPY9BapK/kD9nZKKALJdUlPFpz7pUiULJC3oYSScIu6c5OZjX1rKAlMXsu6uGXBRFWYzdVbsBODF6wlqo2ws3PsDe6ROYiaWLi0xqkkjarSGXgrCLi66qc/Bm7/jMegEpi4M5QVcPXVGUhZmV6ZIlZM0eTU+OLvl8k7RCLus1RKe0Bd0u/7+iJsObfeNkMsZazwp6+HweurWhoWmLiqIsRq2vlipv1WxB94UByMRHFnnWDCY5QdR4qFunMZelLei2h74vnGEymebMiD0eavgkBOrAU77oU4e106KiKEsgIuyr3scbQ2/MLNqCXpYam99Dau7zp2JE8WnIpSBsD317ubUh+mbfOBx+FF59FNrfxmO/OMO7//ynTGc99zyGoykcAiHfEr0YFEW5qNlfu5+OSAfjKTusawt6SKKMRM+TuphO4chMETVeFfSCsG/sFl8Sl0NwvPJ1+NdPWS1z73yIn3cMcWooyumhyXlPHZpMURXw4FjjkVCKopQ2V9RdAcDrg69bC1lBZzI3U2FBUpbuxPBoDL0g7JBLWWqcHbUB3t75FWh7O/zGd8Ad4Pg5qy3AGz3j8546PKk56IqiLM1lNZfhEAeHBw9bC2V+Mg43IZlk+Hweui3oCfFR7imose2KKXFBr7T+jY9ydb2xOi/uuQ3KvCTT05weslIZX+8Zm/fU4ag25lIUZWkCZQF2hXdxeMAWdBEy3hCVLFFclLQE3bjLEVmfSEBpC7rDCZ5KiEe4psJqUznmt5pxdQxM5mLnbywk6JPJXE8GRVGU83FF7RW8PvQ60xmrZ5QbRZQOAAAR6klEQVT4w4Qkev4GXSm7NuY8yRmrTWkLOlgbo4kIez3WJO5jKWv49Il+K9xy3bYq3uzNS2kEpqYznBtPrNtGhaIopc0VtVcQnYrSEekAwOGvIixRhs7XoMsOuTiKTdBF5BYROS4iHSLywALHPyUir4vIYRF5RkT2rb6pi+ALQTxCc6aPtHHwykQFAMfOTeB2Orj98iYmkmm6simNwGtnx0hMZTjQFl43MxVFKV321+4H4NXBVwEQX5hq51IeuiXoZb5gbmk8Nc5t/3wbT5x6YrFnrYglBV1EnMBDwK3APuCeBQT7H40xlxlj9gN/Cnxp1S1dDK/loXvHTtHnaODnp6zwyvFzE2yrDbC/xdo4zQ+7vHDKGgt13TadMK8oytI0B5up8lblBB1fNuRyPg/dCrnkC/pIfITuiW6mzSKD7VdIIR76NUCHMeaUMSYFPAbcmX+CMSY/jSQAzE/8Xit8IWsQ6/BJpkJbee7kML2ROMfPTbCnIciu+iBup4M3emcE/fmTw+xpCFKljbkURSkAEWF/7X5e6HuB1HQKfGEqzMT5s1ySVtjX46/ILY0mrXYBVd6qNbGzEEHfAnTnPT5rr81CRH5bRE5ieeifXehCIvJJETkkIocGBweXY+98fGFL0EdOUtO2D2Pga8930jeWYHdDBW6Xg90NwZyHnkxPc6hrRL1zRVEuiF/f/esMxAb42pGvgS+E1yQYn4guen4maR3zBCpzayMJq11A2Ls24d5V2xQ1xjxkjNkO/FfgjxY552FjzEFjzMHa2trV+cHeEEQHYSpGRdNurm4P8w/PdgKwp8H6qHPplgre6BnHGMOr3Vb8/PrtKuiKohTODVtu4Ka2m3j4tYfpdVp55enosDVEegFS8XEyRvAHZjZFRxMb76H3AC15j5vttcV4DPjgSoy6IOzyfwCqd/ChA80k7f4Ku3OCXslYfIpfdo3y/MlhROC6rSroiqJcGL938PcQEf506HkAfNMTROeOv7SZio0Tw0OlfyabLivoIU9oweeslEIE/SVgp4hsFRE3cDfweP4JIrIz7+H7gbdWz8Ql8M4W9Nsub8TjchD0umistDqc3XJJA81hH/d97RCPv9rDJU0VVPq1h4uiKBdGY3kj/2nff+LpyFGGHQ5C5ykumopPEMVLZV6/qJHECD6XD69rbbovLinoxpg08BngSeAo8B1jzBEReVBE7rBP+4yIHBGRw8B/AT66JtYuRNZDd1lTuSu8Zdx7bRvv29eQq86qLvfw6Ceuw+92cnIwyvUaP1cUZZnsrdoLwIDLSUgW7+cynZgkaryE/DPJF6PJ0TULtwAU1GDAGPME8MSctc/nfX//KttVOHajHKq2gcN6f/r8B+anwbdU+fnHT1zH5//tDX71qpZ5xxVFUQqhxlcDwKDTEvS+sYVH0WWS8z300cQoYc/a1b+sT8eYtSQbcjnPdKIsW2sCfOO+a9fYIEVRNjO1fiuhY8jppJIo3SPxhU9MRYnhpXaOoGffENaCzVH6D1C9Y2PtUBTloiDnobvKaCiL0T0aW/A8R8oKuczy0JOja5ayCJtB0ION0HQAdrxnoy1RFOUiwOP0UOGuYNDjo8mTpHtkEUFPx4iLD2+ZJbPGGEYTRRBDL2pcHvjkTzbaCkVRLiJqfbUMRyepL4txdnThkIsrHWPK6c8lZ8TTcZLTSfXQFUVRiokafw2DTidVjig9o/FZ3VyzuDMx0i5/7nGuSnQNN0VV0BVFUS6QWl8tQw6oYJLUdIb+iTmZLsbgycQxZYHc0lpXiYIKuqIoygVT66tlkDS+aasB17xMl6kYDsys4RbZxlwaclEURSkiqn3VpDAk01aj2Xkbo3brXHHPCPpaN+YCFXRFUZQLptZn5aKPZGK4SM9PXbSHWzi9M73QNeSiKIpShGSLiwadTrYH0/NCLtMJKxTj9M0WdLfDjT9vo3S1UUFXFEW5QPLL/3dXTM/z0GOT1vyFWdOKEiOEveFcGuNaoIKuKIpygWRDLkMuJ9vKU/TMyUWPTViCnj+tKJKMrGm4BVTQFUVRLphAWQCfw82g00mbL0nfWJyp6UzueCJqbZZ6y2emFY0m1rbsHzZDpaiiKMo6IyLUeKsYco7yLm+cjIHeSJzD3RG21gTwxixBD5TPHj/XUrG2nV5V0BVFUZZBrb+OQWcndS4r3HL/Y4c53B1hR105/6PdFvSKmQE8o8m1bZ0LGnJRFEVZFjWBBoacTqodVs754e4IN2yvpmNgktM9/QAEg5aHnppOEZ2KrnnIRQVdURRlGdT6axl0uQiaSe69tpUv33Mlj3zsasL+Ms72DzJlnFSWW6X/61FUBAUKuojcIiLHRaRDRB5Y4Ph/EZE3ReQ1EXlaRNpW31RFUZTiocZXQ9QhxGND/PFdl3HHFU14y5z82tUt+EkQxYvXbUW1c0VFng3OchERJ/AQcCuwD7hHRObOeHsFOGiMuRz4HvCnq22ooihKMZFNXRyOD81a/81r2whIgoT4cmvF5KFfA3QYY04ZY1LAY8Cd+ScYY35ijMlm1r8ANK+umYqiKMVFVtAHkpFZ6y1VfvYEUyRdM31czkycAaA5uLbSWIigbwG68x6ftdcW4z7g+ysxSlEUpdjZFrLmGB+1G3TlMIZL5TRbdl+dW+oc68Tv8ufeBNaKVd0UFZHfBA4Cf7bI8U+KyCEROTQ4OLiaP1pRFGVdaQg00OTw8rKkZh+Y6EOi/bhaDuaWOsc7aa9sX9OyfyhM0HuA/Gz4ZnttFiLyXuAPgTuMMcmFLmSMedgYc9AYc7C2dm3fqRRFUdaaA94GXi5zYKbTM4s9L1v/Nl2ZW+oc66S9on3N7SlE0F8CdorIVhFxA3cDj+efICJXAl/FEvOB1TdTURSl+DgQbGPY5eTM0Jszi72vgDih4TIAEukEfdG+4hB0Y0wa+AzwJHAU+I4x5oiIPCgid9in/RlQDnxXRA6LyOOLXE5RFGXTcCC8G4CX+16YWex9Ger2gttqk3tm4gwGQ3tl+5rbU1DpvzHmCeCJOWufz/v+vatsl6IoStGzLbST0PQ0Lw8c5i6gP3oOX98rVOy+PXdO51gnQHF46IqiKMrCiL+K/Ykkr4we4/TYae761w/y536ZHT8f7wSgrWLt6y1V0BVFUZaLr4qrEkm6EoN8+kefZiId5ZzLCVsO5E7pHOuk3l+Pv2ztJhVlUUFXFEVZLr4wB5JWUl9/rJ9mp5+I0wV1l+RO6RxfnwwXUEFXFEVZPt5K9iZTHPTU8eAND3IgLUTKPOByA2CMsVIW12FDFFTQFUVRlo/TRZmnkr+vvJoPbLudcHSESF7t0HBimImpCfXQFUVRSgJfCOKjMNlPKBUnToZ42hp6kctwUQ9dURSlBPCFIT4CA0cJZaYBGEtaQ6KzGS7qoSuKopQCvrDloQ8eI2wPio7YHRh7J3txipPGQOO6mKKCriiKshKygj5wlJDLmlCUHWgxEBug2luN0+FcF1NU0BVFUVZCnoceClstdbMe+lB8iFr/+jUiVEFXFEVZCTkP/Rih6l1AnoceH1BBVxRFKRl8YTAZSI5RWWd1WMxuig7GBqnz1a2bKSroiqIoK8E3MyfUVb+PCncFo8lRUtMpIskINf6adTNFBV1RFGUl5Ak6tXsJeUJEEhGG7OHR6qEriqKUCllB91dDeS0hb4jR5CgDMWvWj8bQFUVRSoWsoNfuBSDsCTOWHMt56Gs9GDofFXRFUZSVkBX0uj0AVHoq1UNXFEUpSQI1sPWdsOf9gOWhZ2PoTnFS5a1aN1MKEnQRuUVEjotIh4g8sMDxd4rIyyKSFpFfXX0zFUVRihSHEz7677D93QCEvCES0wnOTJyhxleDQ9bPb17yJ4mIE3gIuBXYB9wjIvvmnHYG+Bjwj6ttoKIoSikR9lghmLdG31rX+DkU5qFfA3QYY04ZY1LAY8Cd+ScYYzqNMa8BmTWwUVEUpWQIeUIAdI13rWv8HAoT9C1Ad97js/baBSMinxSRQyJyaHBwcDmXUBRFKWpCXkvQp800df71y0GHdd4UNcY8bIw5aIw5WFu7vu9ciqIo60E25AJQ41u/KlEoTNB7gJa8x832mqIoijKHSk9l7vti9NBfAnaKyFYRcQN3A4+vrVmKoiilSb6gF92mqDEmDXwGeBI4CnzHGHNERB4UkTsARORqETkLfBj4qogcWUujFUVRihWXw0WFuwJYfw/dVchJxpgngCfmrH0+7/uXsEIxiqIoFz1hb5jx1HhRxtAVRVGUC6DSU4lLXIS94aVPXkVU0BVFUVaZsCdMta96XatEocCQi6IoilI49+69l8H4+tfaqKAriqKsMtc3Xb8hP1dDLoqiKJsEFXRFUZRNggq6oijKJkEFXVEUZZOggq4oirJJUEFXFEXZJKigK4qibBJU0BVFUTYJYozZmB8sMgh0LfPpNcDQKpqz1qi9a0sp2VtKtoLau9Ysx942Y8yCfXk3TNBXgogcMsYc3Gg7CkXtXVtKyd5SshXU3rVmte3VkIuiKMomQQVdURRlk1Cqgv7wRhtwgai9a0sp2VtKtoLau9asqr0lGUNXFEVR5lOqHrqiKIoyBxV0RVGUTULJCbqI3CIix0WkQ0Qe2Gh75iIiLSLyExF5U0SOiMj99nqViDwlIm/Z/67vsMHzICJOEXlFRP7DfrxVRF607/G3RcS90TZmEZGQiHxPRI6JyFERub7I7+3v2H8Hb4jIoyLiLab7KyKPiMiAiLyRt7bg/RSLL9t2vyYiB4rE3j+z/x5eE5F/EZFQ3rHP2fYeF5Gbi8HevGO/KyJGRGrsxyu+vyUl6CLiBB4CbgX2AfeIyL6NtWoeaeB3jTH7gOuA37ZtfAB42hizE3jaflws3A8czXv8ReB/GGN2AKPAfRti1cL8BfADY8we4Aosu4vy3orIFuCzwEFjzKWAE7ib4rq//wDcMmdtsft5K7DT/vok8NfrZGM+/8B8e58CLjXGXA6cAD4HYL/u7gYusZ/zV7aGrCf/wHx7EZEW4H3Ambzlld9fY0zJfAHXA0/mPf4c8LmNtmsJm/8NuAk4DjTaa43A8Y22zbalGetF+27gPwDBqlxzLXTPN9jWSuA09mZ+3nqx3tstQDdQhTXu8T+Am4vt/gLtwBtL3U/gq8A9C523kfbOOXYX8C37+1n6ADwJXF8M9gLfw3JIOoGa1bq/JeWhM/MCyXLWXitKRKQduBJ4Eag3xvTZh84B9Rtk1lz+J/D7QMZ+XA1EjDFp+3Ex3eOtwCDw93aI6O9EJECR3ltjTA/w51heWB8wBvyS4r2/WRa7n6Xw+vs48H37+6K0V0TuBHqMMa/OObRie0tN0EsGESkH/gn434wx4/nHjPX2u+H5oiJyOzBgjPnlRttSIC7gAPDXxpgrgShzwivFcm8B7NjznVhvRE1AgAU+fhczxXQ/l0JE/hAr5PmtjbZlMUTED/wB8Pm1uH6pCXoP0JL3uNleKypEpAxLzL9ljPlne7lfRBrt443AwEbZl8fbgDtEpBN4DCvs8hdASERc9jnFdI/PAmeNMS/aj7+HJfDFeG8B3gucNsYMGmOmgH/GuufFen+zLHY/i/b1JyIfA24H7rXfhKA47d2O9Qb/qv26awZeFpEGVsHeUhP0l4CddpaAG2vD4/ENtmkWIiLA/wscNcZ8Ke/Q48BH7e8/ihVb31CMMZ8zxjQbY9qx7uWPjTH3Aj8BftU+rShsBTDGnAO6RWS3vfQe4E2K8N7anAGuExG//XeRtbco728ei93Px4GP2NkY1wFjeaGZDUNEbsEKG95hjInlHXocuFtEPCKyFWuz8RcbYWMWY8zrxpg6Y0y7/bo7Cxyw/7ZXfn/Xe4NgFTYYbsPayT4J/OFG27OAfW/H+oj6GnDY/roNKzb9NPAW8COgaqNtnWP3jcB/2N9vw/rD7wC+C3g22r48O/cDh+z7+69AuJjvLfB/AMeAN4BvAJ5iur/Ao1jx/SlbXO5b7H5ibZg/ZL/2XsfK3ikGezuwYs/Z19vf5J3/h7a9x4Fbi8HeOcc7mdkUXfH91dJ/RVGUTUKphVwURVGURVBBVxRF2SSooCuKomwSVNAVRVE2CSroiqIomwQVdEVRlE2CCrqiKMom4f8HGpFGLPiyL+UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BT8AWH77GPqA"
      },
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(64, activation='relu')) \n",
        "model.add(tf.keras.layers.Dense(32, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(16, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(8, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(16, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(32, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(64, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(140, activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyNt_jgu9udm"
      },
      "source": [
        "class AutoEncoder(Model):\n",
        "  def __init__(self):\n",
        "    super(AutoEncoder, self).__init__()\n",
        "    self.encoder = tf.keras.Sequential([\n",
        "      tf.keras.layers.Dense(64, activation=\"relu\"),\n",
        "      tf.keras.layers.Dense(32, activation=\"relu\"),\n",
        "      tf.keras.layers.Dense(16, activation=\"relu\"),\n",
        "      tf.keras.layers.Dense(8, activation=\"relu\")])\n",
        "    \n",
        "    self.decoder = tf.keras.Sequential([\n",
        "      tf.keras.layers.Dense(16, activation=\"relu\"),\n",
        "      tf.keras.layers.Dense(32, activation=\"relu\"),\n",
        "      tf.keras.layers.Dense(64, activation=\"relu\"),\n",
        "      tf.keras.layers.Dense(140, activation=\"sigmoid\")])\n",
        "    \n",
        "  def call(self, x):\n",
        "    encoded = self.encoder(x)\n",
        "    decoded = self.decoder(encoded)\n",
        "    return decoded"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Z2Dz2AwepJy"
      },
      "source": [
        "model= AutoEncoder()\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
        "                                                    patience=2,\n",
        "                                                    mode='min')\n",
        "\n",
        "model.compile(optimizer='adam', loss='mae')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aoUEESNAjs2h",
        "outputId": "acbec483-dde9-4a0c-8b89-ef6707e88e0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        }
      },
      "source": [
        "history = model.fit(normal_train_data, normal_train_data, \n",
        "          epochs=50, \n",
        "          batch_size=128,\n",
        "          validation_data=(train_data_scaled[:,1:], train_data_scaled[:,1:]),\n",
        "          shuffle=True,\n",
        "          callbacks=[early_stopping])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "19/19 [==============================] - 0s 13ms/step - loss: 0.1251 - val_loss: 0.1109\n",
            "Epoch 2/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0756 - val_loss: 0.0810\n",
            "Epoch 3/50\n",
            "19/19 [==============================] - 0s 6ms/step - loss: 0.0519 - val_loss: 0.0765\n",
            "Epoch 4/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0481 - val_loss: 0.0749\n",
            "Epoch 5/50\n",
            "19/19 [==============================] - 0s 6ms/step - loss: 0.0474 - val_loss: 0.0737\n",
            "Epoch 6/50\n",
            "19/19 [==============================] - 0s 6ms/step - loss: 0.0465 - val_loss: 0.0726\n",
            "Epoch 7/50\n",
            "19/19 [==============================] - 0s 6ms/step - loss: 0.0449 - val_loss: 0.0696\n",
            "Epoch 8/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0421 - val_loss: 0.0665\n",
            "Epoch 9/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0396 - val_loss: 0.0643\n",
            "Epoch 10/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0382 - val_loss: 0.0627\n",
            "Epoch 11/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0376 - val_loss: 0.0623\n",
            "Epoch 12/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0371 - val_loss: 0.0619\n",
            "Epoch 13/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0367 - val_loss: 0.0614\n",
            "Epoch 14/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0364 - val_loss: 0.0614\n",
            "Epoch 15/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0361 - val_loss: 0.0610\n",
            "Epoch 16/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0360 - val_loss: 0.0608\n",
            "Epoch 17/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0359 - val_loss: 0.0605\n",
            "Epoch 18/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0358 - val_loss: 0.0605\n",
            "Epoch 19/50\n",
            "19/19 [==============================] - 0s 6ms/step - loss: 0.0357 - val_loss: 0.0605\n",
            "Epoch 20/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0356 - val_loss: 0.0605\n",
            "Epoch 21/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0356 - val_loss: 0.0604\n",
            "Epoch 22/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0356 - val_loss: 0.0599\n",
            "Epoch 23/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0354 - val_loss: 0.0601\n",
            "Epoch 24/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0354 - val_loss: 0.0597\n",
            "Epoch 25/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0353 - val_loss: 0.0602\n",
            "Epoch 26/50\n",
            "19/19 [==============================] - 0s 5ms/step - loss: 0.0352 - val_loss: 0.0598\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ladnnGGlP_p"
      },
      "source": [
        "encoder_out = model.encoder(normal_test_data).numpy()\n",
        "decoder_out = model.decoder(encoder_out).numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmGNYqc-B7-w",
        "outputId": "ec699d4c-f7f7-49a6-9dd1-bdfa305fa467",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "encoder_out.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(563, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QX9_AIeLB98s",
        "outputId": "d3733f4c-d4b3-46a3-a48f-7ce3540fb1e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "decoder_out.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(563, 140)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gyxn45SlCD5m",
        "outputId": "45bf3447-7d4f-41a3-9ac0-9298865855cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.plot(normal_test_data[0],'b')\n",
        "plt.plot(decoder_out[0],'r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f38d3278908>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QTrghNvvCbRU"
      },
      "source": [
        "encoder_out_a = model.encoder(anomaly_test_data).numpy()\n",
        "decoder_out_a = model.decoder(encoder_out_a).numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a80shfIjJ8eO",
        "outputId": "ad60fd8d-15e2-47b4-ea5d-7b90935baa96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.plot(anomaly_test_data[0],'b')\n",
        "plt.plot(decoder_out_a[0],'r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f38c92a67f0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3NCj7SAKC9-",
        "outputId": "3b1616b9-8e69-457a-e56e-96c1262d2044",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        }
      },
      "source": [
        "reconstructions = model.predict(normal_test_data)\n",
        "train_loss = tf.keras.losses.mae(reconstructions, normal_test_data)\n",
        "\n",
        "plt.hist(train_loss, bins=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 2., 10., 17., 33., 33., 38., 49., 28., 43., 33., 40., 20., 18.,\n",
              "        22., 23., 22., 14., 10.,  9.,  8.,  6.,  9.,  9., 11.,  3.,  4.,\n",
              "         8.,  6.,  3.,  7.,  6.,  1.,  1.,  2.,  0.,  1.,  1.,  0.,  2.,\n",
              "         1.,  1.,  0.,  3.,  1.,  1.,  0.,  3.,  0.,  0.,  1.]),\n",
              " array([0.0120889 , 0.01388571, 0.01568252, 0.01747933, 0.01927614,\n",
              "        0.02107295, 0.02286976, 0.02466657, 0.02646338, 0.02826019,\n",
              "        0.03005701, 0.03185382, 0.03365063, 0.03544744, 0.03724425,\n",
              "        0.03904106, 0.04083787, 0.04263468, 0.04443149, 0.0462283 ,\n",
              "        0.04802511, 0.04982192, 0.05161873, 0.05341554, 0.05521235,\n",
              "        0.05700916, 0.05880597, 0.06060278, 0.06239959, 0.0641964 ,\n",
              "        0.06599321, 0.06779002, 0.06958683, 0.07138364, 0.07318045,\n",
              "        0.07497726, 0.07677407, 0.07857088, 0.08036769, 0.0821645 ,\n",
              "        0.08396131, 0.08575812, 0.08755493, 0.08935174, 0.09114856,\n",
              "        0.09294537, 0.09474218, 0.09653899, 0.0983358 , 0.10013261,\n",
              "        0.10192942]),\n",
              " <a list of 50 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5wl8BCdOXtr",
        "outputId": "85fa3dfb-e6c0-4470-af09-7ed0e7625393",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.mean(train_loss) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.034049201540143965"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTKYBuMmOa0L",
        "outputId": "cd48608f-ef8e-49b2-aaef-ed9655bfa18f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.std(train_loss)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.015575275935371249"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUvStai_PI-6"
      },
      "source": [
        "threshold = np.mean(train_loss) + 2*np.std(train_loss)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ti0FXOy6PRDL",
        "outputId": "a40eaa8d-450e-41c1-8e37-681d92251f8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "threshold"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06519975341088646"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jCBLwpGNb3E",
        "outputId": "9e5901f6-72e3-41c3-c109-10a8cb91d559",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        }
      },
      "source": [
        "reconstructions_a = model.predict(anomaly_test_data)\n",
        "train_loss_a = tf.keras.losses.mae(reconstructions_a, anomaly_test_data)\n",
        "\n",
        "plt.hist(train_loss_a, bins=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 1.,  0.,  1.,  0.,  0.,  3.,  2.,  1.,  0.,  4.,  8., 16., 32.,\n",
              "        29., 44., 59., 45., 44., 55., 26., 32., 14.,  3.,  3.,  0.,  0.,\n",
              "         1.,  1.,  3.,  2.,  0.,  1.,  0.,  1.,  1.,  1.,  0.,  0.,  1.,\n",
              "         2.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
              " array([0.04654098, 0.04947389, 0.0524068 , 0.05533971, 0.05827262,\n",
              "        0.06120553, 0.06413844, 0.06707135, 0.07000425, 0.07293716,\n",
              "        0.07587007, 0.07880298, 0.08173589, 0.0846688 , 0.08760171,\n",
              "        0.09053462, 0.09346753, 0.09640044, 0.09933335, 0.10226626,\n",
              "        0.10519917, 0.10813208, 0.11106499, 0.1139979 , 0.11693081,\n",
              "        0.11986372, 0.12279663, 0.12572954, 0.12866245, 0.13159536,\n",
              "        0.13452827, 0.13746118, 0.14039409, 0.143327  , 0.14625991,\n",
              "        0.14919282, 0.15212573, 0.15505864, 0.15799155, 0.16092446,\n",
              "        0.16385737, 0.16679027, 0.16972318, 0.17265609, 0.175589  ,\n",
              "        0.17852191, 0.18145482, 0.18438773, 0.18732064, 0.19025355,\n",
              "        0.19318646]),\n",
              " <a list of 50 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYC7DCva7-V8",
        "outputId": "9ee045e8-f85e-463b-b7bd-c29074e92183",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.hist(train_loss, bins=50, label='normal')\n",
        "plt.hist(train_loss_a, bins=50, label='anomaly')\n",
        "plt.axvline(threshold, color='r', linewidth=3, linestyle='dashed', label='{:0.3f}'.format(threshold))\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VF_R5SmNrOj",
        "outputId": "f64293aa-690a-4174-e601-b8bcf7f38d4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.mean(train_loss_a) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09570660657358443"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UrkKNqLrOQiU",
        "outputId": "07ddf291-899d-46b5-94fd-946013c8a237",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.std(train_loss_a) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.013657688591124185"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2BG6vMvRfUs",
        "outputId": "4da61e33-1536-4d04-8cb0-e0e550039b34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "tf.math.less(train_loss, threshold)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(563,), dtype=bool, numpy=\n",
              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True, False, False,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True, False,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True, False,  True,  True,  True, False,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True, False,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True, False,  True,\n",
              "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "       False,  True,  True,  True,  True,  True,  True, False,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True, False,  True,  True,  True,\n",
              "        True,  True,  True, False, False,  True, False,  True,  True,\n",
              "       False, False,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "       False,  True,  True,  True,  True,  True,  True, False,  True,\n",
              "        True,  True,  True,  True, False,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True, False,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True, False,  True,\n",
              "        True,  True,  True,  True,  True, False,  True,  True,  True,\n",
              "        True,  True,  True, False,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True])>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uLFse0tOhVZ"
      },
      "source": [
        "preds = tf.math.less(train_loss, threshold)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbKh_VjjPATN",
        "outputId": "b5daa96b-2acc-4fdd-abea-5b42be7a63d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.math.count_nonzero(preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=int64, numpy=534>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXvOeXRzZZMS",
        "outputId": "4ed72d94-4a26-4a28-ccb7-891e79911342",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "preds.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([563])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnzeBK8IPa9j"
      },
      "source": [
        "preds_a = tf.math.greater(train_loss_a, threshold)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMIBKpjEZIum",
        "outputId": "a2314fe1-7856-48ac-ff6a-26d144f587d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.math.count_nonzero(preds_a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=int64, numpy=431>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RkV-9XX8ZUgM",
        "outputId": "f390392a-6432-43a4-92a7-420682a51e24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "preds_a.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([437])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxZSNINNZpXj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}