Anomaly_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Anomaly Detection",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "0O88LX8mS6tq"
      },
      "source": [
        "# Importing Libraries\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MO-FmOq2uPkd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b22f16e9-26c5-4c9e-fa3c-71dd866cdbb8"
      },
      "source": [
        "# Downloading the dataset\n",
        "!wget http://www.timeseriesclassification.com/Downloads/ECG5000.zip"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-01 11:53:54--  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’\n",
            "\n",
            "ECG5000.zip         100%[===================>]  10.12M  4.06MB/s    in 2.5s    \n",
            "\n",
            "2021-05-01 11:53:57 (4.06 MB/s) - ‘ECG5000.zip’ saved [10614407/10614407]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nb0JSJt4TfIU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30b27477-7e82-4b56-df0e-e898a754fa82"
      },
      "source": [
        "# Unzipping the datasset\n",
        "!unzip ECG5000.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  ECG5000.zip\n",
            "  inflating: ECG5000.txt             \n",
            "  inflating: ECG5000_TEST.arff       \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": "HvNPdOY5UblP"
      },
      "source": [
        "# Concatenating the train and test file into a single file named 'ecg_final.txt'\n",
        "!cat ECG5000_TRAIN.txt ECG5000_TEST.txt > ecg_final.txt"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLQ_IB5OVA_9",
        "outputId": "b371501b-70e9-4cf0-d669-bc50f54a218f"
      },
      "source": [
        "# Displaying the head of the file\n",
        "!head ecg_final.txt"
      ],
      "execution_count": 5,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uw5I0GkpVDXo",
        "outputId": "85766cfa-fd74-464a-9d0f-fb9cb96ec785"
      },
      "source": [
        "# Importing the finla file in pandas dataframe\n",
        "df = pd.read_csv('ecg_final.txt', sep = '  ', header = None)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: 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",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "OZPhrDDHVSSM",
        "outputId": "ef44455e-0418-4e15-9429-a89e4921ee50"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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",
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              "      <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",
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              "      <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",
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              "      <td>-0.495939</td>\n",
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              "      <td>-0.355336</td>\n",
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              "      <td>-0.471672</td>\n",
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              "      <td>-0.471419</td>\n",
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              "      <td>-0.701868</td>\n",
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              "      <td>-0.713831</td>\n",
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              "      <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",
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              "      <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",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 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",
              "[5 rows x 141 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fzym5dVeWnk-",
        "outputId": "dba3db57-4f77-4b69-dac9-7cbb58f29727"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 141)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoGCLjLXWswm",
        "outputId": "8ce1f2f1-0eb5-4b10-8854-d24b798d9344"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 9,
      "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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQvkVo9JW9n0"
      },
      "source": [
        "# Adding prefix to column names so that we can easily reference them\n",
        "# Original file did not contain column names so pandas creates numeric column names automatically that cannot be referenced easily \n",
        "df = df.add_prefix('c')"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0P5OZXKVXB2s",
        "outputId": "5db2291b-9e93-4dca-a86f-e3b77eba6387"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 11,
      "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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1npThR4WuNp",
        "outputId": "6072a912-1bb4-4774-bd15-a95a104bfef6"
      },
      "source": [
        "# Counting the data points of diffrent labels \n",
        "df['c0'].value_counts()"
      ],
      "execution_count": 12,
      "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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PtS2F_WpW2ht",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "d3406954-5c8c-466f-e028-406a169452c3"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 13,
      "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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlQLRGDiMFQO"
      },
      "source": [
        "# splitting into train test data\n",
        "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": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCvQlVU3MFHv"
      },
      "source": [
        "# Initializing a MinMax Scaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Fitting the train data to the scaler\n",
        "data_scaled = scaler.fit(train_data)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hI8x30UHNEqC"
      },
      "source": [
        "# Scaling dataset according to weights of train data\n",
        "train_data_scaled = data_scaled.transform(train_data)\n",
        "test_data_scaled = data_scaled.transform(test_data)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtqcxS4iNPfw",
        "outputId": "193a49f8-24e0-4c15-801d-9d1bde4f6e1e"
      },
      "source": [
        "train_data.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4000, 141)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYOUUgmTNQ8X"
      },
      "source": [
        "# Making pandas dataframe for the normal and anomaly train data points\n",
        "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": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "esn2M24NP3Em",
        "outputId": "3eeeea1b-5e75-45ed-de0f-0c7377cfb5cf"
      },
      "source": [
        "anomaly_train_data"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.54603684, 0.52609574, 0.35215565, ..., 0.32938752, 0.41559349,\n",
              "        0.4550684 ],\n",
              "       [0.39336652, 0.39486685, 0.27028019, ..., 0.37738131, 0.4863785 ,\n",
              "        0.45174016],\n",
              "       [0.66165586, 0.75136705, 0.70959038, ..., 0.15203245, 0.2072104 ,\n",
              "        0.30963706],\n",
              "       ...,\n",
              "       [0.58122047, 0.57240472, 0.39287094, ..., 0.32309346, 0.41186439,\n",
              "        0.40845571],\n",
              "       [0.70698484, 0.7982501 , 0.77487296, ..., 0.23053824, 0.31421167,\n",
              "        0.37774737],\n",
              "       [0.69314707, 0.79831145, 0.82004413, ..., 0.68561341, 0.61110713,\n",
              "        0.53512758]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITVZR-beOYvI"
      },
      "source": [
        "# Making pandas dataframe for the normal and anomaly test data points\n",
        "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": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "S2ligR14O2Yk",
        "outputId": "da6da3d0-72ca-4220-de6e-5035c3d1e5e8"
      },
      "source": [
        "# plotting the first three normal data points\n",
        "plt.plot(normal_train_data[0])\n",
        "plt.plot(normal_train_data[1])\n",
        "plt.plot(normal_train_data[2])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffa545eea50>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zlmHSmXhifw+9vigP7+mavJCjkoKgGmJbLJkur7V4yCOEyT45O08RCv86vJeKZJKN0laQRNYld6nRBF3jHU3LiJpq1pBbBSVqdWNTf4A0OhxJAz1TWJDJdJLB8CDOuBrglMb3/bYVj31tP6NcQ5LAtYGGRBjZMMyh7sXjPwY43OOnodiGxahXD7hPQtHSbC/w7mA31fZqhBA8eVBNhHvx+ADRxBnNyGzFOFOqBbtYLPSdp4Yo1IWw5k0W9Df73qQlOsifjPhZKqk/i/lq76AJusY7mubOnQDUr/4jAFJphZMDQa5bUoAxaWVAxCdd0xfuQyAojUQJybkT+37njusyeKbLBaBiA7UhD5IuwSn3mdm/CxchBEd6fKytzFMPKAq4m3klN5+mwKuAYCjWQ7W9mkPdPto8Yd6/tpxwIs0rp4YmLmYrxq4o6CR5UQi6oghebfFQrA8jWQsmvf6rU7/CaXTw7nCEZRlvzGlf2yXepYom6BrvaJq7XqU8lSZ37UcB6BgOE08p3L7OhUgXEpQlAsGJQa7eoCrENTEfI6aKiQvaSsa+PtNCB3BtpCqlpuq1+Tpn74PMMV3eCL5IktUVGUH3daKkovxjuIl/2Ps3WOydJJQwNY4anjzYi0kv8/e3raQo18TTh85I/bQWIwP5etuiEPQjvX684Ti5aT/kTOzzPhAeYFfPLj7Q8EEMkkwlAZRkLi1eTdA1NC4tsQAtkT4azEVgVv3CJ/rV/taryh3kWtSKx/6etyZc1hNS/eqrEh4iuTUT1xx1uehMkJM/+T3L11GdqSbtC8+Pn/ViONyjdkhcU+lQD7hPctxoZCgVIqWkMJSp4/Vc1iqePtzHzStKyLMYed8VZbx8aohgbNzw5MzPyKnPWRQtdHeeGsImxZBFCiwTLfQnWp5ACMGdS+8GWwl5KQ9Kooj2QMe87FUTdI13LMkjv6JDr6PBdV322Im+AAadRH2xjcpytb/56Z79E67rCfWglwysSntI59VNXNSaEXR7+ZTZEOTkUZZXi06AJ754etgd6fZh0ss0lqiFU7hP8pI1B72k4ysbvoIiqxW1j78ZwxdJcsd69cnltrXlJFIKLx4fG4KRFXTJsCgs9N2nh7m2LPN/Oc69llSSPNH8BJtcm6jIrQB7ObmJIZREEb2hKYLBlwBN0DUuT4aa4K3/OOcpbU2/JiVJNFRtzh5r6g9QX5yLUS+zYbl6vLVvYi56b7CXAkM+BklgKK6f8BpGC5jsU7tbMuhdG3GlFELzmK98oRzu8bGy3J7tYSIGm3gp185VZVdz38r7yJcbEYqB5w/H+OTmWrY2qsHDdZV5lDnM7Dg5TtDNeaAz4hTSoshy6fVFWW7PPGGMs9CPeY7hjrr5YMMH1QO5ZeTEhlAShYRTAUZiI5d8r5qga1yebP8aPP8AdL111lNaMqlljfljeeRN/QGWl6lW6LW1NThSEidiE9vd9oZ6KRBqNkNu+bLJCxctg5IzRwaMw7WB6mQcSddPLLnwx9Gl0grHegOsGQ2IAi3Dx+nSSWyr2oYsyfzVxm+z0fQAv//iDfzNH6xAJ6sWrSRJbG0o4rUWD6m02oUSSQJrEc50mpH4pRe9C0EIwXA4TpkxrB4Y50MfDKs3qVp7ptDI7sIQ7keJqzezjkAH/rifvf17L9l+NUHXuPzwdUHLdvXrN/5t6nOSUVrSIfTIVDvUykZPKM5QMM6KMtWfbtTrqI1VsMck8Hmas5f2hHoojKVJC4mCmimE+76n4JZvnH1/FRupTibB6KXfN78jy6ZDy1CIaDLNmoo8UNKw/WvsiHQhATdV3QTArcvr+fmH7x5zyYxja2MRwViKwz3j0jRtxRQkE4STYWKp+Z3ycy4iiTSxpEKxLlMzMM5Cd0fVXPoiSyaV0V6GnAhiSqi/P+3+dr6y6yt8avuniCQvTc2BJugalx8HfqH+u/YjcOpZcDdPPsfXRbPRSJ25CL2kp9Ud4qevq5b4qKADFBm3kZIkXjjyUwBCiRD+uJ+a8BCndA0YLHmT1zZa1IZcZ6Nwqdo3XU5z0rPw/egHulQreq0rFx67D974AS8VVrCuaC2FOedvqrq5vhBZgl3NnrGD1mKK46rITWcg93wxOqzCKanB8vE+dE/Ug0E2jPV+z1XTVGtlGRk9/3X4v9jTvwdFKAxGBrkUaIKucXmRTsHBX0DDzXDz10Fvht1TWOnedlqMBhoddfznrja2fWcX/7GzlYZiG6vHuRYKSm+mNpHid32vA6q7BWBVtJ9W+1UXt0ejhQpUwW/yTN38az74wsMH+O9XJ+/n7XYvxbkmqsOH4eQztG76As3pEDfXvHta6zosBtZU5vFq87gGrLYilobVzJlTIwt3JJ8npNYh5EshkGQwO8Zei3oozClEGg1+Z+oOGsxBcqQS+sJ9lFrVuoShyBm5+HOEJugalxfNz0OwHzZ8HKyFJNZ+mONNv4bQxG7Ofk8Tg3o9DcVX8MrJIZaW5LLrqzew/cvXYzPps+fVFDtYEXBwOB2gK9BFT6YVQFUqia9sMxdLtVl9dF8oueiptMILxwf43eHJgdq97V6urHUitbwAOiPP5uUjSzK31t467fWvbyziSI8PXyTT48RWQl3AjV7Wc9J77slQ88lwSN2vLe1X01BlXfY1d8Q98QklI+g1Bj/GdAUOk4N/2vxPgCboGhoXx8FfQm4ZNNyCN+blj1Od3FNWRPPJ30w4rcWj9iOvLVjN4R4fm+oLqS6wTlqutsCKErgCSQj+8+1/5eCQ2kI1L6nHVHORFjpQbitHJxZOLnrPSJRkWtDUH5gQqO0ZidDnj3F1rROaX0BUb+K5rpe4puyaablbRtnaWIQi4PXTGbeLtRiDkqLBXssp78K10IfDqoVuSQcmFRV5Yp6JP4NctXePSzdCbvhOHv+Dx1lZoLaT0FwuGhoXSjwIrTtg5QfpDPfy4Wc/zOGA6kI43Tsx26UloPrLRbKceEphQ/UURUBAdaGVQ4kN3BYK87ueV3jwxINYFcGx1DLqSqa+Zjro7eWUp5QFk4ve6g4BkFIEx/v82eN729W0wuvy/eBp5nDVWnpDvby39r0XtP6aijwcOYYxt4tNDSQutboWtIXuyVjopoRvUlGRJ+KhKGdcbxejBcx5lMkjBCJGymxlWAwWcg25moWuoXFW0inoOwRntp9teRHSCVj+h3z5pa/THxzhv9/1UyQBnd6JgdGWmAc7Otr71Ufo9dVTBDeBMruZTl0Vf+kTPJtw8tUld/J/PcO8pqyhrsh28Z8ht4zaZHzB5KK3ucPZrw92jWWjvN3hxW7WU+dVYwjPSjFMOhPbqrZd0Po6WeLKmvyxtTMtEpaZChmODeOOLMwBZ8OhBFajDl1sZGJRUTrJSHxk8lOK3UWR4mEkPFYZW2wp1gRdQ2NKevbR+eMtPPrI+0ge+dXE15p+B9ZiTtnyaA7uJerehHfYRbkuh47YkHojAFAUmkWUBmMeB7p9lDvM2Y6BZyLLEtUFVh7N/wxVgye576Xv8YfhCIeN63Baz5HJcj5yy6hKJklKQwuiL3qbJ4TTaqTcYc6W+QPsafdyZY0TueV5UkXLeHHgTW6ovAGb8cJvZqtcDlrdISKJVLaidqlOdXMtVCt9OBynwGaCyPAEQR9tWVBoOUPQHS7yU25C8RSJlJp3rwm6xuWBENB7YExIZ4jv+JN85akP8YfmIN8odLL9jX+CTBtWkjFofhGWvY+fHPs5QjGSGLmWR9/uptpaRqdOAneTuq1AH6cNehqtFRzsHGH9Wdwto9QUWHk0fh189nWovIrThkaMRfXnvOa85JaqPV3kJD2BgZmtNQu0usPUFVpZW5XHoW41TdETitPmDrOp0gCdu2mvvRZvzMsNlTdc1HusKnegCLV4K+tyUVQJWqiZLt5wggKrQRX0nIkpi8BElwuA3YU9oYr3aAC42FKs+dA1Fi69vuj0rMrd/wY/vhGOPzkr7/u/x37JizYLn1jxR+TrrbwqQnDgQfXFtlcgGaa39jpe7Hie5MhVrHGV82qLm6LcBjoNBkS3OuS3b2A/YVmm3FpPnz/G+qpzC3ptoZVObwTFWQ+ffJF7xT/NzN0CYC+nKtOk68jg/KcutrlD1BVZWVuZR7c3ynAoztsZ//n1pmZQUnRmbmJ1jrpzLXVWVrnUlL9jvYFs+X9u1I/LtnD96J5QgjKLAun4xKKijItoksvF4cKcHMFEAu84QR+ODpNW5r4qWBN0jWkjhOC7L55i07de5unD5wnmHfuNWn4P0Pn6rLx/W2CIvJRgle1jbKm6idetuaR2/bNa3n/gF2By8ItQCyCR9m3hX+5cDcBgyEVIlhnueROA5oEDAKRRJw2dLSA6Sk2hlURKoc8fJRhL4g4lZi7ouaVUpVQ/64l5zkX3R5N4Mp9pTaY97u7WYb7/UgsFViPVqQ4AOvSqXJxzZug5KLGbKLQZOdbrz5T/F0PYzXLn8gWb6TIcilNpylSyji8qiqkW+mQfutqUrEwazhYllVhKSIv0JeksqQm6xrRIphX+7FeH+MnLx1gi9XK0+xw9OAZPwJOfJVp2FXtZRaR196zsYUSEcKR0/L9XTrO14nr8ksLhlB9+9m449SzKsvfxYtcOzMk1rCuvobEkly0NRRxuV0fFdQ4cAsamFHWF6jEbZFaU28/6ngDVBRYAOjyRbPBwSdHkFMcLwlZCaUpBVqR5z0Vvy2S41BVauaLCgSzBA08coWUoyA/uWYfecxLyqumM9FOUU4TVcHGfXZIkVrkcHO3N+OhtRRAaZKlzKZ2BzktWHj9dFEXgDScoN00u+/dEVEEvyDlj4IVDbcpWJnmzgdFiixovuBR+dE3QNabFm3v38KUTH+KE+RPsMH2V8vYnzn5y6w5Ix/m67XN8yZWgPdIB0Zk3YfJKSSwpI4e6fejjS9FLenZd/UfwkV/DJ16g6Zo/xh1143XXs6letZzu3liBe0QV7M5wn9oDPdyHKw2HelKsduVlOwiejdpCVcDah8PZ9L4ZW+g6A7K1CGdST09ovgVdvUnVFanj5RpLcgkn0jzwnmVsbihUO1cWL6cz0EmVvWpG77Wq3EHLUEjNdbeVQGiIZc5lCATNI1O0aJhHArEkKUVQop/cmMsddZNvyscgGyZelOmyWc4wI6Mul0wA+FL40RedoG/v3M5ntn8GRSizst5vT/+WL778Rf7+zb/n6danZ2XNy5F46+vUyIOkrvsSXn0Jy327zn6yp4V0jpPf9B4gaIzxqsWM0jXDjnNC4NEJ8nUOinNN/PS1ATaUbuDVQKta5l91DTsH30JCJhlaypYGVdC3NBQhpfLRIdNh0MPr36U5OUKDzsKJPv95A6IAJblmzAaZXafcPHukH50sUeW0zOzzAHJuKVVJiaF4x4zXGo8Qgq8+fpi32qb3iN/mCaGXpeyTyCc21fLprXV8aksdpJPgackKeo29ZkZ7W+Wyk1YEpwaCYC2CsDvrk+8MLIyq2VFGc9ALdRlBH2+hRz2TM1wgK+hl0jAj41wuoFnoU+KP+9ndt5uB8OxkBjx+6nHe7H+TFzpe4GtvfI1gIjgr615upLydpJHRb/trOopvYn36CLFwYOqTh0/TI1egs6lZJUeNJgaPn+MGMA3C/m5COpk8YxGf3lrH7tZhGmxX0epvzU5Y39W9i3xdAza9IzsqzZFj4IoKJ3qliE5bPm17/p02WVCvKyGZFuf1n4OauthQnMtLTYPsODnEloZCjPpZ+NOxl7MsnSKSdhNNzV7XxXZPmMf39/Dr/T3TOr91KEyV05J9Urn7ykr+6r3L1R4lw62gJAk46/DGvBftPx9lZbkaGD3a61cHXYSGKMspQZbk7CSohcKoDzyfqRtzFZqnEHSDGSyFVOlHskFRp9mJXtJrgj4VtQ6193C7v/08Z06PcDLMZtdmfnDjD0iLNPsG9s3KupcbhmA3Pl0h6AzEa2/GJCVxH3lxynPT7mZeC+VhsKpzFQ+bcki0z8yP3t53GIC8nHLuvrISs0Gmr1/9Xfj5sZ8zEB6gydvE8NASti0vnuBG2VxfQDhcQLuzmoc2fRKjpCOn4PMArKuauqDoTP7jo+v59Wev5fDXbuHnH7/4kv8J5JayKhkGSXBy+Csg0xQAACAASURBVPTsrAkcyBTvHO72nedMlTaPmuEyJUNqi4Qui9oWd6aCXpGfQ57FoFaj2kpApDEkgpRYSrJ9chYKw5nGXHYlCEhqZk4Gd9Q91jb3TBwuKnVefBHVhy5LMoVmJ0NNT6nVzHPIohX0Nv/sDGENJUNYDVbWFK3BrDOzZ2DPrKy7aFEUaHkJHroLnv9LABIpBUe8n4hFfZx0LLuegMhBnPr95OujPnQRN2+YTSikuKX6FgJ6EJEm9fH9ImkfUNPaSvLqsJsNvHdVGS8fUfjIsvt4rPkxHnjtAQAivqX88eaJaXWb6gtREoV0Bbv5Xe9O3rfkNva5C6kpsFBoM03r/SvyLWysceKwGM5/8nTJLWNlZqrNq+1HZ23Zg5l2t6fdoYmzPKcgrQg6hiNnjwm4T4Ik05FRipm6XCRJYlW5Q01ddGQGbPu6qMityHayXCh4Mha6Ne1Xuyzq1KZtQohsp8UpsVdMyHIBKJYMDAa7YeDYnO550Ql6vikfh8kxaxZ6JBnBarBi1BnZULKBt/rOPuHmHcHj98NDd6hl9IceAiHoGA7jktyIPNU6qy3J5zVlNc6+neoNYDwZS7PdkcRutPPRFR8F4LRJou/kxd8se0bU/29XsTpQ4q6NlQTjKRoNH2JrxVb2D+5HShVwZcUyrqhwTLh2fVU+crqItEgSTUX5yPKPcKBr5Lz553NObpmai67oODDQNGvLHujyYTXqEIKxjJKz8GqLm0RKYW3lWZ5Uhk6AcwmdkT5kSVZnZ86QhhIbre4QIi8TYPV1UmGrWLAWujk1sY+LP+4npaTOLuiOTPl/ZEzQS4TEkE4Hs+ham4pFJ+iSJFFrr50VQRdCEE6FsejVYNA1ZdfQ6m+9ZGW6C450CuXU7xms/QDKzf8AMT+Ehmjt81LKCOaiGgByjDoOmq/GlvBA/6EJSyjuZtKAxzrAZtdmVhSsQCfpOG4y0nXo5Yve2lCkH0kI6ivU3PKra51UOS38en8f397ybepsa4l6ruUzW5dMutZs0LHMqR6/qvQqckQlnlBiWgHROSW3DD1gSefRPltPnPEUpwYC3LFBFd4jPecW9If3dFFoM/Ku5SVTnzDUBMXL6PR3Um4tx6ibQbuDDGUOM5FEmmBOZu7qSAcumwt3dHZjCTNlOJQgz2JAjnon+M+zk4rOrBIdxe7CKsIMejwoilqAV5yIM6TXqRXNc8iiE3SAury6WRH0aCqKIpRsX4qry64GYE//O9Tt4utEVpL8S3MxX3ktY3m7T9LffRpZEuSXj5W79xdvRUGC5hcmLDHSdYKDxhziRLih8gZMOhON+Y0cMlmy1vvFMJzwkp8WuJyqJSnLEndtqGB36zDfeKaN3qb7qDbcyg2NxVNef2PdGtKxEu6u/zj7u9QKyOkEROeUXHX4QaWchy/VPSs9XY70+FAE3LSsmCqn5Zx+9H5/lB1Ng9y1sXLqIG8yBt42KF5BR6Bjxv7zUUrsZgAGYwY1FXCkM2v5L6TpRcPhOAVWI4SHJ2W4wBRFRaNkXEm58UFOZ9Jci6NBwrJMOD69uMbFsigFvdZey3BsGH/83NbH+Yik1IIBq14NCC11LiXPlMdb/e9Mt0uoV33sL6q5gkGj+scb628imClNNxTUZM8tKXVxVCxBtL5yxhon2GVSrZnRG+TKwpU0mQyYIxfvIx1RQuSldZgNYwMG7thQgUkv89uDfawsd/Avd65GzgwnPpNtS6uJtP8ZT+7OYecpNzaTfsr5l5eUzECEFUYrQj9Ci8dzngvOz2g3w3WV+aypzDunoD+6txsB3HvlWXLLPc0gFETRMjoDnbMm6KON0Pr9McivVl0uGUFfSH50TyhBgdUE/i5wVI4dP5+gj+aiS8Mc6BwBRaE4qD71D85xV8nFKeizlOkSSqh3T6tRFXRZkrmq9Cre6n9rQXTAu9T4utSAzTVXXc0Dd91AQFjoOHmA1HAnPlnmX3pezFbz1RfbeD29Avr2T4jcG3ytNJmtFOcU4zSrwr6qYBVhGcLKxbuyvFICuzIxgFmel8PuB27i8N/ewoOfuIp15/CJryx38Be3LuWlpkGeOtTH2sq87GT6eSPHCbKB9UbVjbG9ZeaB0QOdIywpsqpj3yoc9PljDAUnP+an0gq/erubrQ1FVBWcJad+SL3BDzvKiKQisybopRkLfSAQg/waGOnEZVNFcDQFdSHgDSeoyEmorse8sZvecFTN759UJTpKplq03uRnf+cIBPtwJtT/A59moU9mtgQ9nFILBqzjfgzXlF/DUGSI077ZSyNbLCQGT+EWdqpdLlZX5tNnqCLadxxzuIddFgv/2/ZbnmhRK0SXFFl5Q1mFpKSgU01JFOkUBYleenJ0NDgbsuuuLFSntrj1fsSZQdRp4tEp5DK5RL/AZpp2Tvjnb6jnyc9v4sqafD643nVR+5hVZBkKG1kX6ADg7d4TM1pOCMHBbh/rqvJ5o/cN6kpUo+RI9+Qn2b3tXgYCMe69qnLSa1kGj4LOSIdOvfHNNMNllGK7emMe9Mcgrxr83RQY88jR5yyowOhwKE6dMVOcNU7QvXEvelmPzXCWzKDcMkBirSPM/q4RGD6NJVMIGU2Gp75mlliUgl5uK8cgG2gPzFDQExlBf/HvIKVGtK+vuB6Al7suPoC3WDGMnKZduKjIVx+JjWXLqUh1U46bjhzVd/3IyUdQhMKSYhv7lUZSshHa1KKhjraTyCQZ1EVpzG/Mrrskbwl6ZNpMEgG/94L3FQr2E5Fl7IazWEQXwBUVDh7/7HV8cP3MszVmhdV3U9FzAFnoOe2bWZOuQ90+vOEEVSVBPvvSZ3l94OfoZInDPZOtwv2dI0gSXFd/jjFyvQehdDX9mSBgua18RvsbxWzQ4bQa6Q9kXC7pBFJoAJfNtWCKixIphZFIkkppVNDHbny+mA+nyTk2HPpMdAbILaXB7KfNHSbcd4qcTHB0rvvVLEpB18t6qu3VM7fQR10u3jZ47buA2khnddFqdnTtmPE+Fxt5kU6GTFXoM0U5lQ1rKZL8rJI66MtRrZHuYDev975OgdWIxWKlLecKaFcFve3kIToNBlIoEwTdIBso1uXRZdDj7btw0ersPwJAvrlsph9x4bHmHnSSDpcwM5LsJJW+uCeYwUCMzz90gFK7GY+i1gfs7nyepaU29rRNvoke6vZRX2TDbh6XV+/vVd0LAEoa+g6Ca0O2B8lok6nZoMRuHrPQIRsYXSg+9MGA6iKpkDNxjbwxd9NIbIR883kC6nYXZZmbwXDXCSyy6laLpjRBn5JaRy0d/o4ZrRH2q70jrKY8eO07WZ/htqptNHmbFlTEfc4JD5Or+Inax4pyDKVqzneD3MuAUc8VhVdQnFPMw00PI0kS19QVsCO2DAaPQcjNcPsRmo2qQIwXdIBySzldBj3BgQu/Cbf1q66Ioovsw72gyS2FhpvZGPUhmbtoHrzwQH8kkeKPH9yHP5rk/310BS92v4A9naZXiXF1bYh9nd4JRS6jrpkJueeKAj+9BZ75svq9+xQkw+DawFBkiFxDLhbDzPvXjFLmMI/50GFCLvpCiF8NZAS9JD0IBsuELBdv3Eue+TwVxvnVOAKnMcoKyaFmcvJqAOY8LXNRC3p3sJvkDKoPw94WAGy3fhtMufC7LwFk5yW+k9wuKXemH3XhOCEuWpr9sp8U1fZq7lp6F2/0vUGbv43NDYU8H1HPSb3yTd47/L8cyilEL+uptddOWL8mv55uvZ6Iu+OC99bjzRQVFS2/4GsXBes+yrVhH5Iuzo62Axd8+ZMHezna6+cH96zjdGQXYSXO1zyqVe5QnkUR8PLJsYB0lzeCN5yYGETuPwSBHjUNNRWH3v3q8Yygz6Z1DqqFPuCPZVL8pKyFHk1F8cYu3C032/T7VUF3JPpV//k498qoy+WcrLoDKTzIxwtOYAl2YHGqxkgkpeWhT0mto5a0SM+oQ1s404faUncjbPkKdL8F3jaq7dXU59W/o9wuwx3HAch1jRNNRyUYLCSBwXQUl83FHQ13APBK1ytsqS/iqKgjobeh3/9TekQhR8vXUueow6CbWCLfWLqCmCzjC1y4y2Uw3IckBHWZoqLLjoZ3c6WkWr9v9l94V8qm/gC5Zj3blhXxyMlHWJHWcUvJRurTcNS3l1K7mRePjzWzy6Y2ju9j05Lpy5MIQsfrqqCbHeCsmxNBL7WbGQ4niKNX0zczFjqwIPzoA37VkrZE+iakLMI0XS6Nt4Kjkg8rz1KUGsDgVA2laDo+J/sdZdEKenGO+gs2Er/4PtuhQC+ygBxzPntM16gHM4UyN1XdxIGhAwvCWrgURPtOEBcGSqrGslPULIwGBvR6FAQum4siSxEum4vjw8epKrBQWWDjFct7OGXfxH3ibxlK909ytwBUO1QfpDd24WlpnvgwzrTAVXSOAN5iRm+ksG4bNQmFtuCh859/Bs0DIZaV5vJW/1u0+du41zuEVLOVLfkrOSCi3LYkymstHrUHOar/3GLUTczDb34BSq8AfQ40P68Kevk6kGUGI4OzLuhlDjV1cSgQz6QudmRTF3uD8+9H7/fHsBp1yIHuCRkuSSVJMBk8v8tF1sHGT1AdPoxeUuignBwBEWUBCLokSbdKknRKkqTTkiQ9cJZz7pYk6YQkScclSXp4drc5mdES5PgM7niRiBurpEOSJH50WKFFcZFsUgNK76p6F4pQeKnzpVnZ74Jn+DRtopS6kol9UChcSo9eLeYZLf5YWbCSE8OqX3tzfSFf9t3JJxJ/zvIlhQxFBlmav5QzGc1h9qUvvHhmQPFSnNRjMeov+NpFQ2Ej10bDhKUWEqnE+c/PIITg1GCQxpJcfnz0xxQb7Lw3FIbqTWy94j5SkkQNTxJNpnm9Rf3ZH+waYXWFYywPPzQEfQdg+fuh7gZoegYGj4NrA2klzXB0ePZdLo5xueh51Woueq4LnaTL/m7NJ/2+GEscAik6MkHQfTH16ea8LheA9fchMjq12+cgB4mocvEu4ulwXkGXJEkH/BB4D7ACuFeSpBVnnNMA/CWwSQixEvjSHOx1Ama9+gsRT12koIc9hFIRLHozybTC2x1edijr0HXvhliAZc5l1OfV81TrU7O464WLNdhGj64CnS4+MbWqZhO9NjUgNGpBrSpcRW+oF2/My5aGQsKJNL2+KEur1KyhqSz0UkspOgEjugtrH5qOBenUp8hVztI343KhaBlXx2IgJ9nRPv0WzoOBOP5oEqujm32D+/iYuRKjzgSuDaypu5lcZI7792I3ybxwfIBYMs2J/gBrK8e5DFq2A/ADaYTfl9RAsA9EGlwbGI4Nkxbp7JCG2WLUQh8YrRYN9pODzNaKrfyu7Xczio3NBv2BGCstmQD1+Bz0zBP7eV0uANZCpFWqi/J33RZykIkq079ZXwzTsdCvAk4LIdqEEAngUeD9Z5zzKeCHQogRACHEnHe3mrGFPnCEiCxjM+ZypMdHJJFmR3o9spKE1peRJInb62/niPsIbb7ZaZy0YEnFcSb68Ftr+fgLH2fb49v49t5vq0NE1t9P77WfQS/ps3/UKwvUQqETwye4dkkho4aew6FagI3OyYKuk3UUCgseXQolOf1f6p62F4jJMjbTivOfvJgpWsrGWBwEbG9/Y9qXnRpUb5DHQk+Sb8rnDncfVFwJBjMG2cAtztVsNyh8rKaLx/f38NGf7CGZFmf4z18gYC/jZx3P8qtY19jxTEAUZjdlEcb6uQxkUxcF+Lq5q/EuvDEvL3fPb0LCgD9Kgynjbh1voWcqPacl6AC3/CPPr/ou+4bAjExESc32VicwHUF3AeMdnz2ZY+NpBBolSXpDkqS3JEm6daqFJEn6tCRJ+yRJ2ud2z6yngUmnVptdvKAfJSxJWM1O3mxV80W7rasIy7lZP/r76t6HTtLx29bfzmivCx3hbUOHQsq5hNO+09iNdh49+Sif2f4ZBNAb6qPUWopOVl0vKwpUcT3mOYYjx8CG6nyWuQRPtP6CWkctBeapC4CK9U66DXpGhrqmfH0qjrWqLq/i0htm9BkXPPm12NFRGM/hyPD+7OGRcIK7//NNtYR8CpoHgsimPo6OvMVHG+/EMnAUajZlX7/nqq8QlWXyzc/wZ+9q5NRAEJ0sjQl6Igytr/B21VoUoXDS34ZSvk7tR5JbOpaDbp1dQbeb9eQYdJnUxUyOt6+D68qvo8xaxuPNj8/q+52Ntzu8/PuOlgnHkmmFoWCcGt3kKtGRTP/6fNM0Bd1aSO3muwHQKzJRkZ75ps/BbAVF9UADcANwL/BjSZImRQ2EEP8thNgohNhYVDSzR+hRQU+kL/IRZuAoIaMZq8nBm23DLC+zc9WSYl5jnRrxV9IU5hSyxbWFZ1qfITXHd9b5JNCj+iyTxRWklBT3r7yfv7rmr2jzt3HSe5LeUC+u3LF7uM1oo8Zew/FhNTPm+/dcgaPmEULJEP96/b+etYKuzFJxwcVFTUPHkAWsq75yBp9wEaDTIxUsYV1CZih+Kuv2euJAD3s7vPzfJ49OWXR0ajCIw6lma30wIYNQYMlN2deXlaxlvd7BY5FW/nRzCa/+xY089SebKM5VLWR2fhPiAXY71IBzOBmmc9tfwh0/AcbmYM62y0WSpLFcdFtm7dAQOlnHHQ13sKd/D12B6d/4L5ZH3mjmf7fv4ei4NsNDwThCQBlu0JvV2acZLsjlkqGxxEa5w4ycloksAEHvBcbn7VRkjo2nB3haCJEUQrQDzagCP2eMCnosfZF5nf1HiBjMmPUW9nWMcG1dAasrHDwTWwMRD3SrLXRvr78dd9TN7r6ZjVBbyHg7VWE2lKp/1KXWUt5V9S50ko7tndvpCfVk/eejrCxcyQmPeiP4n5M/4IT3CF/f9PUp/eejVDkbiMoyvQPHp7exdIr2xBD5CSMry6YRhFrsFDZycyKIkFLs7lMbxP3q7W7yLAZODgR5ZO9kgTs1ECTPHsKit1Dw9s+gbC1UXj3hnHsbP0SPXsfre75PvtXIKlcm8N1/BN78Eay/jzeDbdn/4yYpDdXXAaqg6yV9ttHabJLNRbdlrP+w+tT+gYYPoJN0/Lrl17P+nmdyXeeP2Gn6Ms++sjN7bDRlsSA1MDkHPeNycZjOSB44B5Ikcf3SYkRSIsrsDLc/G9MR9LeBBkmSaiVJMgL3AE+fcc5vUa1zJEkqRHXBzKnjeUYWuhAw0k5IkoknDMRTCtcuKWBNZR4vK+tI60xwXHWzbK3YitPs5NfNc//LdSH0+aLZ8uSZkhg8RZ9wYnGo1kOJtYR8cz5Xll7JM23P4I15Jwn6qoJVDEWHeKjpIX516lfcv+J+bq2Z0tOWpbF8jbr3kebpbWzgCKcNOvSJYirzZ69KccFStIybQn2ItJGf7f89B7p8tAyFeODWZVxT5+Q725vxjZuCk1YELUNBjCY/5YZcpOFWuO5PJwgQwLb1n6FIgUdaf5vtWYSShme+BBYnPdd+lu5gN/cuuxejbJyQZTIUGaLQUogszX6Gc6kjI+hGm5ouGRrz1292bea5tudQxNwJYCKlcEXsAFYpzu2n/xq3VxXr0aKi3FjfBHcLqBa6w+RAL19YxtWWhkKktI4Ic1sFe97/JSFECvgC8ALQBDwmhDguSdLXJUm6LXPaC8CwJEkngFeArwohhudq06D2B4GL9KEnQpBOECaNLyQjS3BVrZOV5XZikpk2x7XQ9DQoCgadgQ82fJBdPbvUIOEC4U8ePsD9P9t7UWXSD+/pYtO3XiYcV91IJl8rvbpKQin1v2z08fqWmlvoD/cDTGmhA3xr77dYXbiaL2744nnfd1WZ6nsfik6vcCTQvpM+gx6dbtlZ+5xfVhQtxSQUyqnliPct/v3lZixGHX+wppy/u20lgWiS/9g15q7q9kaIJRXSOi9lEZ9aALPi9knLGnRG7iq/ntd1Sbp+dY8qnA/freaav/ubvDmiCvgW1xaWOpdywjsm6HORgz5KqcPMYCCGIlDdGuGxuNp7at/DYGSQg0MH5+S9ATq6Olkqd9OZdw3LpC4GHlOT8wYygm4KTS4q8sV90/efj6PAakQWeqJz/Gs8rduuEOI5IUSjEGKJEOIbmWNfE0I8nflaCCG+LIRYIYS4Qgjx6FxuGtTHGJPOdHEWemQYAYSVJJ4ALC+z48gxYDGqQw9ekq6FYH/W7XJX410IIS5ZoOZ8xFNpjvX6OTkQZHfrhd032z1hvvHMEfT+dvZ2eEEICmOdBK01DEQG0Mtjj9fbqrahk9RA6JmCvsy5DFmSyTXk8s/X/3P2Bnsuymxl6AR4U9MLiDd3qU2/ShxrL+QjLl4ybRc+XL4EyeDj1fbj3Lwqhz995VNs73+QG5ZbeHxfD/GU+iR1ckDNcAknBygLeuCaz2cHGZ/JXVv/Dj0yjwwfgO+tgvZX4Q++D6vv4s2+NymxlFDrqGVFwQqahpuylvFQZGjW/eejlDnMpBSBJxQHW1HWQge4sfJGzDozv2+fYhD5LOFr2pl5s7/k9/a7uGLgSRKDp+j3x6gyBpGjw1BQP+GaaVWJToHFqEdSDKqgz2GvmkVbKQqq2+WifOiRYWKShIIgEjNQ5Rx7nF9d4eCX3uUInQlOqG6Xcls511dczxPNT8x7fiyoftNkWv2l+J83OrLHI4lzB27TiuCrjx/mc7qn2G78KkdOnCQy0ouNCBQ1MhgepMRSkn28dpqdbCzdCDBpOHCOPocvb/gy37vxe5PE/mzoZT3OtIERaXoNqE5l0kVXF1/mKYujFDYAErfqVXei3nYSd86DHHIf4sdHfswh/pyA/g2eP6Y+KTb1B0CKE0pHKEsLWPfRsy+dU8jNte/mqfxCIkUNdH/oQb4aPsGf7PgTXu99nWvLr0WSJJY7lxNKhrKDJuai7H+UyszfXac3AtbiCRa6xWDhhsobeLHjRZJzVIyj63qDsDBRvvxaCrZ8EoDjb75Avz/K9ZYO9aSKicF4b8x7URZ6jlGXEXQJcQGFYxfKohf0i7PQRwhn/IyRmI4C29jg2zWVefRG9USqb4QTT2Wn2t+99G6GY8MLor/L6ODfD6xzsePkIC2DQb746EE2/uNLDJ3Dr/7Qnk4Odnr4hHkXRimNaNlOb4vamjbXtYLByOAka+zjKz/OLdW3TJmKeP/K+7Nj5qZLruTAq1MgOHjuE5U0zUoYc0rP2vJzDGG4nDDkQF4VpSPdNOY3kufaxVHv2zxw5QM8dftTrCtZS07Zk/xk7yv0jET42RvtrKlVb+zljmowTx4AMp4PL/swQSXBf268g48d/g6v976OO+KmzlHHBxs+CIylpDYNNxFOhgknw3Mm6HWF6qSwdk94koUOqttlJD4yZzN+S7xv02RYicFoYuP6q/GTi+fka/T7Y1xpaAXZAGVrJlzji/su0kLXgWJAkSTiMxydeS4WtaAbdcaL86FHhgnL6kcPxwwU2sZGm22pV1OU9lq2qm6XHrVZ0ibXJsqt5Tzb9uzMNz5DjvX6cVh0/J9bl6KTJG7/4Rs8daiPSCLNW+1n7z3zmwO9fKyoBUtsEAWZZcE36Wk5DICrfjUD4QFKrBMFfZNrE9+54Ttnb+Z/gViNpXh0OtJ9h899YrCfFoMea8LO0tJzC9VlRdEy8DSzxbWFaCrCJtcm7l56N7WOWr63+ZvYDcW0yf/B/Q/uAAH3X6O6WMrLNpx36TVFa1juXM7/HP8fkukkP7/15zz2h4/x6B88yrridQDU59VjkA2cGD4xJ33Qx+PKy0EvS3R4wqqFHvGowdoMm12byTXmzo3bJeyhItlBf776c5N1MoGiddRFjnKs18/K9CkoWw0Gc/YSIQS+2AwEXag6E43N3Ri6RS3oJp1pBoKeESjFSME4Qa8qsNBYYuOXnqUgydCqVqzJksyaojW0+FqmWvGScrjHS07lf/P9w3/HbWvLSaYF3//QWixGHfs6phb0QCzJkR4fH9a/DLZSvA13slk+irftABFMlFTUMBQZotRaOqd7z82txa3XEew8T7DL102/Xo+cdOLKy5nTPS0oilVBf3/1LWxxbeEfrvsH9WbauRv7D9bzw/z1SLoovbpH+ccPrCI5orYJKKvddt6lJUnic2s+R0N+Az99909Z6pzcc8egM9CY38hb/W9lx8HNlQ9dr5OpcloyFnqxmkMfHSugMuqMbC7fzN6BC+9AeT7CLa8CkKi4LnusZOX1LJH7yUuPUBU7OcndEkwGSYnURbtclLTqCYjMYcO/d66gZ4JHQjFTaDVOePldy0vY2ZUgVbJGDR5lqM2rpS/UN+dN6s9FLJmmPfkMYbmFV9t+zz++t4bX/s+N3L7OxfqqfPZ1TF1RuLfNS4kYps63G9Z9lPx178cmxbg5/ToD+kp8CT9JJTlnf7yjlDoqSUoSI33n7vud9nXi1cmYjSXvjAyXUZZsg3SCWncrP3rXjyiyFKm/g7+8AxJB1va+zTLbVqyONv5wTRn9Q0fRC0FR3fkFHeDGqhv5zW2/oSH/7GUidzbeSZO3iQdeVfvwzZWFDlBTaFUFfbR45wy3S0VuBe6Im7QyuwU5oZOvEBEmnA1XZY8Za64F4F7dyxiU+CRBz1aJXoSFbtTJCFRrPxoPXOy2z8s7V9Az/kahmCZY6ADblpeQVgTtuRug522Iq02nljiWIBAznpQ0E15oOYi+8CUK0gpBSdD31P2UWPXgbuYu+zFODgQIxCYHkd5o9XCv4VU1wr7+j9DV30gSA3YpQshel328LrXMrYVek6euHxxpOud5I94WFEki1zo7k+YXDdWbwJyndjwEGOmAh+5W+52svx/6DvGRVVeTECHa/e30BToplQzIhtl7irmz8U6+f+P3SYs0EhJFOXPXGK2mwErncARhzbRGDk8U9FJrKWmRxhO98C6d50LuP8T/3955x8dVnvn++55zps9opJFk2Spucjeu2GCDDcH0EpN2KWEDCcnCbiDJEu5uRIN0zAAAIABJREFUYHPDZrM1yWZT7oaUze6SDSyQQBK4hBKKCabGFWMbZMtdsq1ep8+c9/5xzoy6JdmSpZHe7+fjjz1zzpx55vjMb57zvE9516xkXmm3taHSFUjN4DNOq1nZSAq6EAIN6/8oqmLo/XMmaYudbqsXtDRdFPl7eujLK/Ip8jt5IboAzBQcfRuA2fYItANtZzbM90z4t13/iJZ28f0668LfeXIL/GA5/HA11+/9MrOozQ4w6M5bB5q43FOFKFtp9Z92+qgrtC5YrWheNse+dwx9pJlTaA0aDsfrsj+U/dHQYk0pKs4bmcHEOYNuwPyrYd9zkE7Cm/9mXYN/8gQs/gjINMuT1kLojmOvcSIVptQ98n3iL51+KY9f9zg/2PCDER0915tZRV6iyTSN0u4U0tkzpTVzx5hxOEYKV+Q4J/UplAa7YuQ4vYhpyyiQbVY7gl5FRWci6ACaZi0CRxLD6zg6rPcYtSOfBU57UTTaQsRpX6T9eOi6Jrhk/hR+XlOC1BzZIcgz8magC33Mui9KKTkZr2Z6eBZL4wlCjgA7Z55nzTtcezcAK/UDfeLojZ1xqk62UZmuhtKV2ecDS64BoHzesi4PfZRj6NOD1vEbDB3qB+57fbLNit9W5I3uD8y4ZMF11rDmvU/Bjodh2Y3WqLby80DozKzfT4GrgJ1HXuG4oTO1oHJUzJgZnMmHKj40KsfOvoed6XI4Zv3d20PPOBiZ63NXTSur/+Elvv70Huo7TrNSOpXAn2wi4SvjN9W/4Y7f39FVoJdpm1C+uk/FbWaYzunE0AF0W9CjStD754w8dHv12oGHPHffYozLFpXQENNpL1qRFXSH7qAiUMHBtrERdCt2L5nriCOAZVOWs1NLwZ1/gMu/AQ4fF/lr2NJL0N860MRscQJnOmJNobEJnncLLL+F/MWXUxeuG7WeHd3J3L436BqcGDjTpdFuCjU7NLo/MOOSyg1WKfwz90AqChd80Xre5YfSFYgjb7JsyjK2Nu+lQdcptat2c5FZtqBXdxhWmmCvGHomBFgXtgT9uy/uozOW4hdvH+Gib23i+d0nhv+mHcfRkIhgOb8//HveOvFWdgG4h6D34nQac3XHMPyA8tAHxGW4iJ3O0NVIE2GHCxCEfP5+U/IunFOErgnecy6zmhhFrP/MyvzKMRP0DvtCmCnaIX8Gy6eu4kj7EetC03QoXc4K/RA7j7WS7NaZ780DjZzntGevdhN0vCH4yIPgKciWeI9Gz47ueB1eNOmi1vDAyff630lKWpNWnHF+0SQLuQA4vTDnUoi3w/xrewzrZsYFULuN5UY+tTKBFILSXkVfuURp0IPT0DjcFOlT/g9WEyyX7uJk+CS7a9vYVNXAXZdU8vKXL2bB1Dzuefxd9hzvGZOub+1gS/XAQh9rspqcOQunZ/vWbKnbYm2svATmXQ2Leo98sKYVuXU3HuP01isMwwrzRrsPkBlhclvQT8dDlxIiTUR0B7p098hB747fZbCsPMjvOucB0hqcixVHP9p+dEwqRluiVsx5WqIBSs5hebFVEr+z3p5DWbqCadH9pJIJ9hzvWknffqSVS/JqweGFonm8dfwtPv38p3t8hpPhk6Mebsng0Qs4rPkHFvRIE82aiZHWmVV4et5QzrP4o4CAdb2Gf81cB2aSFdt/mX1qmn/a2bVtBNE0wYyQl4OZ4qJegt4ZT1HoLqYuUseDr1YTcBl8au1MZhb5+Omt57LOdYDUv19J+nvL4KeXkPq/51HwvZnM/MX5NLT2v0bTVGs5ZOnCvGwYZctJW9DdQfjkYxCa1eM1bfE2Xjr6EhV5p1/k5nBYHRqjyfBpH2MwclrQnZqT+HCHrsbbwUzRqesg3RT6+hd0sLz0X9eVIA13tq/L7PzZpGWaox2j36u5N3Vha7GzONoAU89hcdFiDM1gZ0OXoOtmgvmihgP1XRdzbWuUBeYBmLoUdIMn9z/JtrptPe40+qsSHS3yHCFqdBeybndX97/utB6lUddxmB4C7sF7xExIzvk4/MUuqDiv5/MV5wOCxW0ncdh3U6W+3L6LmVnk6you6hVy+frTezla72RT9X6e232SWy+YQdDjgKYDTPn9F/j31F9TYtbxcnsFJ5MetncW8LK5kmLRxnvbNvf7fuF6a8G93W9dexWBCrac3DJgoztTmty/+X7qInV8fe3XT/tzOp1WZl1kFNOec1rQT8tDj1jNrMJCINPOAT10gLWVhcSlQYd/NtRbaXaVQWsB6kDr2c90aQhbXrdfpqFkMS7dxaLCRWyr22ZdjGXWgudS7QDHW62Lpj2WJBJPMC26D0pXkDJTvHX8LYCsoEsprT4uo5zhkqHQXUyLDiKd6N9LbztGo67jlEPvOT3hEKJPlgUAnnwoXYErNIdFhecAo7+QPdrMKvJxpNlOXezloe+ubcOnF5ISLeR7HHz23BD8+g74t1VWR9T193LiU6/zb6H7WHPsLm5s/yLGtd8GoPmD1/p7O5Itx2iSARrNYxjC4Kb5N1EXqaOms/8uoD9772dsrt3MfavvY2nx0tP+nA5XHoaUo1rHktuCbsfQh9VCNmLdYoUxSaWdfVIWu7NyeoEV39OmQ8MHgLXyLxBjkrrYFLEF3TShxPoyX1x+MbsadvE3b/4NibwycOez2nmE43aT/hOtMSrFcRxmDEpXsLtxN+0J6ziZH6X6SD0JM8E039m5dZ/qm0LMsH+Ia7b03aHVFnRjgg+GPl1ufBhue4YrZl7JqpJV2fm6ucrMQh+JlEmnEbIE3f4+J9MmBxs7mRMqRxjtvPOV9YR+9znY/Wurs+SXdsGlD7BiThm//fyF/N+bV/DgJ1dy2fnLaXZMJa9hW79TnvSOWupFMftbP2BOwRzWla0DYOvJvsO5E+kED+1+iA0VG7hh/g1n9DkNlw+PKZWHPhAu3YVEDm88nO2hd5hpzJSrR2Ou3rgdOqtmFLA1WgLttRBrw2N4KPWXcqj10JmaP2yao5YQe4QLCqwY3+eWfI47lt7Bb6p/w5++eAfJactZrh3keKu1WHy8LcpSYYdWSlewuXYzmtAo9hRnPfTMwlCmMdNoUxEsAS1Jg2dq/4Jue+gB9yRMWRwKwTLIm8ati2/lv676r7G25ozJZLrUm3mQToDd6+RIU4RkWjIjWEpKpmh7/h4r4+zD34cr/wECXdeHpgk+vKyUq5dYTkl02nksl1Vs72cWqzd6kjbXFPY272Vx4WJmBWcRcoe64ujdeKP2DTqSHXxi3ifOuJ+Rx+XEIyXR052DPARyXtBhmEMubEFvT8ftoqKBQy5gxdFfb7M9xXrLS58dnE11W/XwDT5DWqNWloszfy7YzcU0ofGFFV/g/vPuZ3v9dnYVT2dG+jCNLfb0ldYYS7SDmA4fFM7hjdo3WFq0lCVFS6hutT7D7qbd6ELvt7fHaDCrwPrS7fHPh2N9v0SdTYfo1DWr7F0x4Vk4zcr+OBiza0Ps4qLq+g5AstywskLq9jwJ6+6BFbcMeszQwvVMEa1s37Wz77ZUPSf8hbTF21hUuAghBKtKVrGlrm8c/YUjLxB0BVlTuuYMPqGF16njNpWg9+CpnbV84kdvkjZl9lbzdAS9MxXvt6ioN2srC9kn7bSwBiuOPrdgLofaDp3+gOrTpN2urPT10x/8mllWkdB2lxMdk2DbB0gpOdEWZYl2CDFtKU3xFvY07WFd2Toq8yuz2Tp7mvZQmV952ulYw2WaPUNyl3MatB2Fjp6ToBrarAXn0sDo9RBRjB/yvU5mFnrZ02pXbdrFRfvqOvmq8QjL3/5HAOqW3wAbHhjSMT2zLwSgY1/PhdFYRwt+IhzLs671xYVWDv/qqas5GT5JbWfXuORYKsamo5u4bPplQxrgMqhNTh2XFIRHUTdyTtA7Yim2HmmhoSOOW7cugGELumYQTkesPi6+U8cfl5YFaTZKSGjurIe+uHAxKTPF/paz23mxM96OISW+UN/+JvnufCqDlWyLWz0vpqVqaY+lON4aY5ZWhyialx10nRH0tExzuP0wexv3Zi/ss0GmuGhXpty7V9il2a4KnBFUIZfJwtLyfLY22gV+9sJo3fEj3Gb8npK5lrNyctYFVLcd5NJfXcoF/3MBFzx6Aa8cfaX/AxYvJG74KWt/18qgsTlx1PrO1npMDM3INilbPdUqJOoednm99nUiqQhXzrxyRD6j16HjMiEySgM7IAcFvazA+mWtaYmctofe6Csklg5jJgoHDbkYusa8qUGO6tOzpeqZWPOeJmt6/cG2g6x/bD1VzVXD/TjDIpZowW+aGP6+wyYAVpasZGdLFWmgTDRyvDVKY2srIdohv4LNNZsJuUMsLFxIZb6VrfN67eu0xFvOqqAXeazeI1VJB+jOnoIebaFVWotGmb4vionP0vIg73fYHnr7ceu5mkcxMCm45AGcmpO6cB0Pv/8wHYkONs7ZiM/h4z/e+4/+D6hpUH4+q/R9fPv3Xd/L5hPW2let3sHc/LlZDZkdnE3IHWJrXdfC6POHnyfkDmXF/kzxOg0cpqYEvTtzYnv5kv4ktS3hbAx9WKGPaDMf+K10ODNeSmgQDx2smaN7kqVIO9OlzF9GnjMvu5j4ytFXaI238sLhF4b5aYZHItmGz5RW75Z+OLfkXMKpMHsCJVlBl63WKLFkoIzNtZu5uPxiNKExM28mmtB4+sDTAJxTdM6o2t6dPGceGg4aEi0kpyyBmm7ZBfUf0KRbc0znFeZuwYxieCyryKeZAJ15lfCHb5E6/BZXx56lKnQJomgOJb4S9rXu43cHf8c1s67hvvPu49OLP82uxl3sbtzd7zFds9cyT9Tw+q792XYYkYbDAJxMNTM7f3Z2XyEE55acm81HDyfDvFbzGpfPuBxD639O63DxOHUcUhCVw0jiGCY5J+gl7e9yj+NJ6hqbTtNDb6bKZXkCPlGB0xj8FCyaFmB3shTRWQeRZoQQLCpclBX0t09Y3Rg31/YtZHjl6Cs89sHIzMxOpjvwmSZ4+u+3cm6JNX1lRzBEmWjgeGsUw+5R8Y7spDPZyaXTrb7ZbsNNub+c6tbqHreeZwMhBIWeItDbqXYsgNrtVmdBgPq9NOo6SCj29f/DpZh4LC7NQwiNx+Z8Bxwe9J9fS56IULvoTwGr6+IbtW8QS8e4acFNAFxfeT1ew8ujHzza/0FnbwDgJt82/u6ZvZimJN1yjA6hUxdrYEagZ+hy9dTVnAifoLazlhcOv0A0FeW62deN2Gf0OnUcpk6UvqmUI0XOCbrTa3nXzc1Npx1Dr9LBRRFF3vwhvWTBtDz2Sbvk1y4wWly4mP2t+2lPtLOjbgd+h58Pmj+gPtKz0u2hPQ/x3W3fHZEG/Skzgl+aA3roU31TKfWVstPtpEJrZM/xdqaYVjzy5fb9eA1vj9X6jIcyr2DeWc9lLvVPweeN8HxbudWAqs4KX1H/PicNJzqBEfOMFOMfr9NgXkmAzY0++JNfkzL8vJZeQtF8a+hEpuhtWfEyFoQWAOB3+tlYuZHnDj2XbZzVg7KVMGUxf+bfzK6aNr7w2A5k6zH2OK01nBl5vQS9xAqtbK3bym+rf8vMvJksK17W57Cni8epoytB74XLSnFqbW0+7Rh6FQkc6bJB4+cZFkwNsM/smemyqHARKTPFL6t+ScJM8LklnwOsvNUMUkr2tewjkopkUwTPhCRRvKcIuYAVR98uI0wVTWw/3ESZaCQldDbVbWFd2bpsmAq6ql7PKTx74ZYMxZ5ifJ4IT9TZcfJMHL3+fY7pPtza0H5sFROHpeVBdtW0Iqcs5Oerf82fJe9hzhSrQ2Gm6+KN82/s8ZqbF95M0kzyq6pf9T2gELDqMxS07eUfz0/y4t46vLGTVHut/kC9Bb0yv5ICVwFP7nuSHfU7+Micj4zYLF2wfrQMqRNjGIWQwyQHBd3qh9DR1jz8PHTTJBpt4bAZxYxPO2WVaHcCbgdGQRlRzZfNdMksjD6892EMzeDmBTczxTulR9iltrOWsN2I592GQYYiD4EUCatK1Dtwi9uVJStpNuOcMKC1oZZS0ciOvKk0xZrYMH1Dj30zC6OLx6D96qzgLNrSJzju0Ag7Cy1BlxJZv5fjmpOAY3Tb+CrGH0vL82mJJKlpifJes05BfgF+l3WXdmHZhawvW88VM6/o8ZrZwdmsL1vPL97/BZ2JfppxLb0BHF4+qb/MK3evYqGzgZZ86y5/el7P1gpCCFZNXcXOhp3oQmdj5cYR/Xxep45mGiQEwyuGHAY5KOh2C8qOVpyaJchDXhSNt1FtaJhIwu0lQ/bQARZOC1ItpsPJXUDXwmhTrIllxcvwOrysL1vPW8ffImmvYle1WKvrAtHVEfEMSIokHqmDMbDdGW97n9NBuWigXDSyKZiHoRlcVH5Rj33XTFvD+VPPz5Y+n01unH8jutAom/EOO+VcS9A76xDRZlp1KHKr+PlkY1m5dVd2xy+28fzuk8wt8We3rZ66mgcve7DHHWaGu5bfRVu8jYfffxiAtJnOtprGHYRzPgbvPUH5oxvISzXTWFhCyB0i4Az0OdaqklWA9QMy0oVtHoeOMK189tHq55Kzgu5MR4gmLPOH3BP99e9R5bJOaLhzCpcuHHqe88Jpebwdn408vhNSCYQQ2VS/tdOsON+6snV0Jjuz4r2vZR8CwdrStV0dEU+TtClJaGlc4tQFDpmUwBZdp0w0UiYa+aNDcm7JuX0u4GJvMT+78mejOgR4IEp8JVw/53o6HW/yQrwMmg/C4deRQFRPWO0BFJOK+VMDVBb7SKVNPraynL+8cmiVy4uLFvOhig/x33v+m6rmKm597laufPLK7Mg4Vn0WkhFwuOH2FzhiaEwP9NP4DEvIHZqjT2hnJPA6laD3xRb0gIjQ1GEtLgzJQ9/8HXjje3xQtgxhupkbms5Fc4c+i3HhtDy2m3MQ6Xi2Q2Am7JJZaFwzbQ2GZvDqsVcB2Ne8j+l501k7bS3HOo6d0aDb1miMpCZxi1NXc+a7LS+nWdeoEPVMFc00kKYicPp9nEeL28+5HROTP+TbhR/bf06LpiE1k7mFZWNrnOKs4zQ0Xr73Q7z45Yv5p48tYXHp0Ltt3r38bjqSHfyv//e/qG6tpiPRwWNVdnZZ2Ur487fgzs0w/XyOth/tEz/PMCNvBm/c/Eafu9mRwOs0QFqCHhmlIRe5J+hO6zbMT5RGW9AHjaEffh1e/gYsuYEtjiDJ2FT+dH3lsBY8Fk3LY7tpp/bV/BGAj8z5CJ9e/OlsmMPv9LN22lpeOvISUkqqWqqYVzCP5VOsQRRnEkev67Aac7mNUw/sdWgOgq4gDQ4PK7X9gEmLGadwHIYwKgIVXDnjKpoK9tMpDDj0GvvteY1zCvr3oBSK/pgfms/H536cxYWLefy6x7mo/CIeff/RLk+4ZBE43ESSERqiDQMKOjBqLTDcDg1MK0w8WlOLck/QbQ/dR4z6NisVMGEO4qEffBWEjnndv3K4oxqXWc7G5cOrQiwv8BB2TaHVUQLHLEGfGZzJvavuRdf07H6Xz7ic4+HjbK3byrGOY8wvmM/CwoUYmnFmgt5pNdvyOPyD7GkNsW1x+VitVdGqa0ig0DP+BB3gqllXILQk73itL9gOhxX+KQsoD10xPL5+wdd59LpHmRmcye3n3E5LvIWnqp/qsU9mME3vBdGzgRACDSvVerQGReeeoOsOMDyEjDgnWq3Fx0Fj6DVboWQRzx08ginirJu+FJehn/o1vdA0wcoZBWxNVyJtQe+PDdM3YAiDH+78IWB5DplBFO/Wdwl60kzys/d+xonOoQ25zQy38LkGvw0NuUO0OJwERYQm+8dmPHro0PXFel2zhPw9rFaq5f7cnZOpGHtWTlnJ0uKl/HzPz3tklBxpt2brnspDH000O2QaifVt6zsixx+Vo442rgAlrgTH22KDTy0yTajdTrr0XL75kpVSePOKc0/rbf90/SzejFci2mugvX8hDrqCnDftPLbVbQOsoh2A5cXL2d24m5Nhq7Pgw3sf5vvbv88//fGfhvTeTZ3WBeB3D57OV+gppEW3/mszZfTj1UMvtwccb5eWkB/SnXj0PLyOU4eWFIpTIYTgM4s/Q01nDZuObco+nxH0gRZFRxvdFvRovH2QPU+PnBX0IkecmpYoTt156hh6036It7GpYzonIlZrzHm9BsAOlXVziugosuLh6aMDe+mXz7gcgIAzkJ0CdMP8G9A1na+89hVqOmr40bs/IuAIsOnYpmyTr1PRaXvyeQM05upOgauAFiyvpMlr5e2HhvBDMBZ4DA/5ziIOOVxUBy/kuGFQ4lU9XBRnziUVl1DmL+PhvQ9nnzvSfoQpnilj5jAYuuW4RBJK0LtwBcjX49S2RnHprlMLut346Tvv51E5LYrH8GRT+4aLEIIrL7uSuDQ4uHPTgPttmL4BTWjMK5iXXXidkTeDr635Gtvrt3PLs7cgpeShqx8iz5nHgzsfHPS9w3ZLgYLA4PMjQ54Qrek4KaDZroobrx46wIy86SSd7Xwqdi9JRyezgmpBVHHm6JrOzQtuZnv9dt5vsiq8j7YfHZP4edYm3VoDUzH07rgC5IkoHbEUDm0QQa/dStLw80FqKlOLwlQEKs6onHfD4nIOGHNIHHpnwH1C7hB3Lr2zTy7rhys/zMbKjTTHmrlz2Z3MK5jHZ875DK/VvMauhl2nfN9Y1BrMUZA3uPcacoeQSFo1jSa3H4fmIODoW0QxXphTMAvD1cSJtgias4WZ+Sp+rhgZPjr3o3gMD4+8/wg76ndQ1VI1ZvFzAI8e5J3Dx7hh2ugU8+WsoPuw0pE0HIN66HWBRUg0GmO1Z/yfqWmCWMlKKlP7ica63tc0e/Zn+Pzyz3P1rKv7vP5ra77Gdy7+Drctvg2ATy74JCF3iC+/+mX2tewb8H1jcTuGPkRBB6u4qMnhotBTOKI9KUaaikAF6J1ozkYQabUgqhgx8px5bKzcyO8O/Y7PPP8ZCt2F2e/eWKA5vXilREuNzhi6nBV0Z9rK49RwDLwomohA3R4OuxfhdQpqwzUjUmCjla3ALZLUVO0A4NCBKvb87Uqq9uwY9LVuw80VM6/IjrTyOrz85PKfIKXk1udu5c3aN/t9XTLVBoAvMLjYZQS9ecnHaPIVjNsMlwyZW2DdZ02TKfWrwRaKkeOWhbegC50N0zfwyw//klnB01tDGwmE04qhkxxidfswyVlB11NWIx5dnMJDP/EuyDR7tXkU5EVImakRWd0OzbMqQ9sOWgujdVt+yxJxkPAbPxn2sRIpk//zeDNLtK9R6ivlz1/+c/5z938ipWTr4WbWf+sVTrbFSKY7cZsmhm/w/hJdgv5RmmVq3C6IZsj8nxg+qyNlmV/loCtGjlnBWbx242t85+Lv9Nu/5WyiO+yipbEs/RdCXCWEqBJCVAsh7jvFfh8XQkghxKqRM7EfXAF0u7OaIU6R5VJrpQ5uT88mELA83JFYECmdtYh26UU7sd0y57g14KLy5LNdgxqGyMNvH2HrkRZ+uzXMjPhXuGz6ZXx323f5q9f+ij+++Dj/3vlFfr/lPVIyjFdKqx/FIGQFPdZMU6xpXC+IAtm7JqffGg+mPHTFSON1eMdF2FF32YI+Vh66EEIHfghcDSwCbhZC9Bk7L4QIAF8CBl4tHClcATQzgZMk4lQx9M6TYHg4GPXi8lgN8Eci5GIYBgcdc8hv3QNSMr1jB3Uyn6DZRqLqxSEfpzWS4Psv72f93CK+eOlcfrO9kbz227l7+d08f/h5jI5vsUA7RvvOp0jJGB5zaBdk0BVEExpN0SaaY83jPuTidXgp9hRjihhTPFP67ainUEwEDJedLjmGHvp5QLWU8qCUMgE8Blzfz35/B3wTGJ2fnu7Yt01+omjyVDH0MDh9NHTEEY5GXLprxDoLNgXPoSJxkGjtbgplC094b6RJBuh457/7f0HNNnj6i1ZXQZsfvFxNRyzJV69dyD2XzeVTa2bw0FuHuTC4kXUJjR+HfOxwFzGv7U0SxPEwtOpWTWjku/I53H6YlJka9x46dN05Ke9cMZEx3H7eNWeTdOSNyvGHIuhlwLFuj2vs57IIIVYCFVLK353qQEKIO4QQW4UQWxsaGoZtbBa7n4tfRE/toSfCSKePlkiSpNZARaACTYzMskGqZDkOUjS/auWQV55/LU+nLyD/6EsQ7VXWa6bhqbtg+8/hh2vg1X/mrf31/Pdbh7lxdQULpuYhhOAvr5pPoUen87HP8Y0TxxCmh2+UFXOethtTS+Lh1K1zuxNyh7JTksa7hw5dcXTVw0UxkdF8hVyf+HsildeOzvHP9ABCCA34V+DewfaVUv5USrlKSrmquPgMmse7ujx0pIP4QClAiTApuzthxKwb0XLfwGxrmaD4wJM0yDyWLz+P7QVXo8skvPrPILulMe78H2t03dXfggXXwqv/xOsPf4OZRT6+ctWC7G55LoOHy59iTex1fhy/iQsKP0+1GWanV0NqCVxi6HM/Q+4QR9utRkShAYZKjycyHrpaEFVMZLxO6y47khy7iUW1QPfAc7n9XIYAcA7wqhDiMLAGeHpUF0azPdGjIA3i5kCC3klC8wImrckTI1ohNn32ApqlH6eM8662mJKgm6K5q/kf83J458eWR55OWqmTm/4BylbBeXdQfdEP+INYzRfE4zzysWLyvd1E+u0HWXD0f3jCeT3/kb6Wu9Z8hIAjwAt+P2FN4NGH3tYz5A6RllY3ylzw0DNrGyoHXTGRyQp64syHxvfHUAR9CzBXCDFLCOEEbgKezmyUUrZJKYuklDOllDOBt4GNUsqto2IxZAU9pMeR0jhlDD2meRBGOymZHNEhD2UFXvYwB4C6gpUIITh/dhF/nfg0tcv/AnY+At+qhAfXQMcJuOLv2HKkhY//+C3+WfscDqeLklf/qsuTbzoAL/0tLLiOhbd9nweuW8SCkgIumX4Jr/p8tGo6+e4MyqN5AAAQh0lEQVShp1x1T1XMhRj6suJllPpKR3TKukIx3vA4LEGPjpWgSylTwN3AC8D7wC+llHuEEN8QQozsFNWhYg+KzjdiSNMgno4jZT+TtBNhIritCkRGtgeyEIITPitcIqdfAMD5s0Lomsa/xD8KNz4CS28gHZrDofmf5W925nHLz94h5HPyk89vRL/y7+HwZnjxAasj5DP3gOGGa/+VxWUF3L7OKn64YsYVdAqTDl2jyF8wZPsygq4LnXxX/oh97tFiqm8qL3ziBWbnzx5rUxSKUcPrtIZej5aHbgxlJynls8CzvZ57YIB9P3TmZg2C7aEX6HGk6ceUJikzhUPvtWiY6CTsciEMqxHOSM/OPFDxCf5xt2DNfCu6VOBzctclc/jBy/vZsGAl5154Kbf+5x+pru/E46jh0gVT+IePLiHkc0LoNji+E978ARz6g1UEdc2/QKDnLM21pWvxGR7CqSj+YSwYFrgLsn+P1EKwQqE4MzzZkMvoxNCHJOjjDpfVsSyoxUinrYEP8XS8r6DHO2k3XHhcVkgmzzmyqUIzZs/n67uv546KLs/5ixvmsHl/A3/9m/fwuww6Yyl++qlz+dD8KTiNbsIqBFz3XSicA7//P1C6Elbd3uc9nLqTiysu4dlDz+Kb0if9f0AycfNciJ8rFJOFTAx9zEIu4xKHF4RGQIuRNq3fpH5TFxNhWtNOvB6renOkBf2GVeW8fO/FFPm7CmEMXeN7Ny7HNCXJtOSxO9dwxeKpPcU8gxBwwd1wx6twy69A6z/P/IoZVwDgc/iGbFsms2W8l/0rFJOJ0V4UzU0PXYhsC10zrYNO34XRdBLScVqSDtz+OKbuxqkPPe1vKBi6RkWob6P8GYU+nvnievwug+LAEKoeS5efcvNF5Rdx59I7WV+2fsi2FbjGfx90hWKykQ25JJWg98SVRyAZJZUyQIdYuleBaiIMQFPCgdMRRz/LTXlmFQ3dmx4Mh+7g7hV3D+s1GQ9dhVwUivFDyOvkqbsu7NcRHAlyWNAD+JJRUmkrlNHHQ7cFvT5uoBvteMe4y9rZJuAIcM2sa1hfPnSvXqFQjC6GrrGsYvSyznJb0DuiJFMDxNBtQW9OOkGLjXj8fLwjhOCbF31zrM1QKBRnkdxcFAVw+vHKCImk9RH6CrrVXjeMi7SIjHkfZIVCoRhtclfQXQE8MkoyZS0yDOShh/GQlGEl6AqFYsKT0yEXtxkmYeoITiHo0k3cVIKuUCgmPjnsoefhMsPEktbQh76LopmQi5NIsnPSxdAVCsXkI4cFPYArHcFMDxJDFxomphJ0hUIx4clpQQfwmiZA357odsgFtxVjVyEXhUIx0clhQbf6ufhsQe9IdvTcbgu65rZCMnku5aErFIqJTU4vigL4SSN1L03Rpp7bE50khQOnxyqxVR66QqGY6OSwoFsedx4RhDOf5lhzz+2JMDHhweW0QjFK0BUKxUQndwXdafVK8YoYOAr6FfQIbpxOu3XuKE3ZVigUivFC7gs6cYQRpDnW2HN7opMIbgwjDqaKoSsUiolP7i6KOjKCHsNr5NMSa+m5PRGmw3SjGVFgeL3EFQqFIhfJXUHPhlzieLQ8WmItmNLs2p4I02G6EHoMn8OHoeXuzYhCoVAMhRwWdKufsJcYbi1ISqboSHSlLprxTjqlC0RUFRUpFIpJQe4KuqMrhu4UlmA3xbpSF2W8kzBu1WlRoVBMGnJX0HUDqbvwiTgOLEFvjnbLdEl0EpEuUqrTokKhmCTkrqADOH14iaFLS7C7py6KRJgwbhJSeegKhWJykPuCLuII02oDkM10MdNo6RgRu3WuiqErFIrJQE4LunD68IsYpK14etZDzw63cBNNqda5CoVicpDTgo7Th19LkEgJgq5g16JoVtBdRNMqhq5QKCYHuS3oDi9+ESeWTBNyh/p46G3C+nhK0BUKxWQgtwXd6cdLf4JuDbdo161e6CrkolAoJgM5LuhevCJGdAAPPeZQHrpCoZg85Lig+/AQJ5Y0CblDXVkutqCnnUrQFQrF5CG3Bd3hwyOjxJJpCt2FtMZbSZkpsFsAmC57WpEKuSgUiklAbgu604dbxoglUhS4CwBojbdmPXTTYe2mBF2hUEwGclzQvWiYpJNxQu4QgDWKLiPoTqv7ogq5KBSKyUCOC7pVIaqlwllBb441Z7NcpMNEExpeh3fMTFQoFIqzRW43Cc8IdSJMyNNd0MMkMUBPEjACaCK3f7cUCoViKOS20tlDLvRUhEJ3IdAl6BHpAj1KwKHCLQqFYnKQ44JuhVz0VJSAM4AhDFpiLch4J514kKoXukKhmETkuKBbIReHGUVKQb47n+ZYM6lYJ2HpIk1EZbgoFIpJw5AEXQhxlRCiSghRLYS4r5/tXxZC7BVC7BJCvCyEmDHypvaDHXLxESOeSpPvyqc13ooZbqQdH0kZIc+lBF2hUEwOBhV0IYQO/BC4GlgE3CyEWNRrtx3AKinlUuAJ4FsjbWi/dBtDF02kCbqCtMXbEO21HJeFxM1OFXJRKBSThqF46OcB1VLKg1LKBPAYcH33HaSUm6SUEfvh20D5yJo5ALaH7hUxYimToDNIa7wVo/MEx2URMTOsFkUVCsWkYSiCXgYc6/a4xn5uID4LPNffBiHEHUKIrUKIrQ0NDUO3ciDsGHqm42K+O5/2WAuameAoBSTNuAq5KBSKScOILooKIf4EWAV8u7/tUsqfSilXSSlXFRcXn/kbZkMuMSvk4gzSmmhDAjXC8sxVyEWhUEwWhlJYVAtUdHtcbj/XAyHEZcBXgYullPGRMW8QDCem5sAr4sRTVgw9YaaICcFxzRJ7JegKhWKyMBQPfQswVwgxSwjhBG4Cnu6+gxBiBfATYKOUsn7kzRwY0/DiJUYsaRJ0BQFo0zTqhAdQjbkUCsXkYVBBl1KmgLuBF4D3gV9KKfcIIb4hhNho7/ZtwA/8SgixUwjx9ACHG3FMhxefHXLJd+UD0Gh4iNn3HkrQFQrFZGFIvVyklM8Cz/Z67oFu/75shO0aMtLhwyPixFJpSmwPvdZViDudBFTIRaFQTB5yu1IUwGl56N1DLseNIG6XFcZXgq5QKCYLOS/owunHK+JEEymCTlvQdS9ORwJQIReFQjF5yHlBNzx+vMRo6IgT1JwAHJdOPO4EDs2BS3eNsYUKhUJxdsjtfuiA5vSRpyc52hzBHWnGbZrUmjoed4KAFkAIMdYmKhQKxVkh5z10nD78Is6xlii01RA0TZoAw4ipcItCoZhU5L6gO3x4iHGsOWIJetokrEmErgRdoVBMLnJf0J0+XDJKfUeMZMtR8k2TuJ7GVMMtFArFJGMCCLoXXaZxkiLScAQfBlKPEjfDStAVCsWkIucXRTNj6DzESbccxS3daHqUcCqpBF2hUEwqct9Dd1gtdH3E0NtrENKH0MN0JDpUDF2hUEwqcl/Q7SEXixy1BCNHaE8VgjBJmSnloSsUiknFhBH0T7k2A7ArNi+7SQm6QqGYTEwYQb8w9Ud2i7nUJboGZ6hpRQqFYjIxYQTdIMWTiTXItDe7Kc+hBF2hUEwecj/LxR5DJxH8Lr0GmU5kN6mQi0KhmExMGA+9uXg19RT08NCVoCsUislE7gu6rwh8UwgvuQ2AAncwu0nF0BUKxWQi9wXd4YH/vY/gqhsBqAjl4TUsLz3gUB66QqGYPOR+DB1ACIJeB3lug4oCDzFnPhKJQ3eMtWUKhUJx1pgYgm7znRuWUxHy8MCWICmZGmtzFAqF4qwyoQT98kUlAARdQRLdsl0UCoViMjChBD3DrYtupTPZOdZmKBQKxVllQgr6+vL1Y22CQqFQnHVyP8tFoVAoFIASdIVCoZgwKEFXKBSKCYISdIVCoZggKEFXKBSKCYISdIVCoZggKEFXKBSKCYISdIVCoZggCCnl2LyxEA3AkdN8eRHQOILmjDbK3tEll+zNJVtB2TvanI69M6SUxf1tGDNBPxOEEFullKvG2o6houwdXXLJ3lyyFZS9o81I26tCLgqFQjFBUIKuUCgUE4RcFfSfjrUBw0TZO7rkkr25ZCsoe0ebEbU3J2PoCoVCoehLrnroCoVCoeiFEnSFQqGYIOScoAshrhJCVAkhqoUQ9421Pb0RQlQIITYJIfYKIfYIIb5kPx8SQrwohNhv/10w1rZmEELoQogdQohn7MezhBDv2Of4cSGEc6xtzCCEyBdCPCGE+EAI8b4QYu04P7f32NfBbiHEo0II93g6v0KI/xRC1Ashdnd7rt/zKSx+YNu9SwixcpzY+237etglhPiNECK/27b7bXurhBBXjgd7u227VwghhRBF9uMzPr85JehCCB34IXA1sAi4WQixaGyt6kMKuFdKuQhYA9xl23gf8LKUci7wsv14vPAl4P1uj78JfFdKOQdoAT47Jlb1z/eB56WUC4BlWHaPy3MrhCgDvgisklKeA+jATYyv8/sQcFWv5wY6n1cDc+0/dwA/Oks2duch+tr7InCOlHIpsA+4H8D+3t0ELLZf86CtIWeTh+hrL0KICuAK4Gi3p8/8/Eopc+YPsBZ4odvj+4H7x9quQWx+CrgcqAKm2c9NA6rG2jbblnKsL+0G4BlAYFWuGf2d8zG2NQgcwl7M7/b8eD23ZcAxIIQ17vEZ4Mrxdn6BmcDuwc4n8BPg5v72G0t7e237KPCI/e8e+gC8AKwdD/YCT2A5JIeBopE6vznlodP1BclQYz83LhFCzARWAO8AJVLKE/amk0DJGJnVm+8BfwWY9uNCoFVKmbIfj6dzPAtoAP7LDhH9TAjhY5yeWyllLfAvWF7YCaAN2Mb4Pb8ZBjqfufD9ux14zv73uLRXCHE9UCulfLfXpjO2N9cEPWcQQviBJ4G/kFK2d98mrZ/fMc8XFUJcB9RLKbeNtS1DxABWAj+SUq4AwvQKr4yXcwtgx56vx/ohKgV89HP7PZ4ZT+dzMIQQX8UKeT4y1rYMhBDCC/w18MBoHD/XBL0WqOj2uNx+blwhhHBgifkjUspf20/XCSGm2dunAfVjZV83LgQ2CiEOA49hhV2+D+QLIQx7n/F0jmuAGinlO/bjJ7AEfjyeW4DLgENSygYpZRL4NdY5H6/nN8NA53Pcfv+EEJ8GrgNusX+EYHzaW4n1A/+u/b0rB7YLIaYyAvbmmqBvAebaWQJOrAWPp8fYph4IIQTwH8D7Usp/7bbpaeA2+9+3YcXWxxQp5f1SynIp5Uysc/mKlPIWYBPwCXu3cWErgJTyJHBMCDHffupSYC/j8NzaHAXWCCG89nWRsXdcnt9uDHQ+nwZutbMx1gBt3UIzY4YQ4iqssOFGKWWk26angZuEEC4hxCysxcY/joWNGaSU70kpp0gpZ9rfuxpgpX1tn/n5PdsLBCOwwHAN1kr2AeCrY21PP/atw7pF3QXstP9cgxWbfhnYD7wEhMba1l52fwh4xv73bKwLvxr4FeAaa/u62bkc2Gqf398CBeP53AJ/C3wA7AZ+AbjG0/kFHsWK7ydtcfnsQOcTa8H8h/Z37z2s7J3xYG81Vuw58337cbf9v2rbWwVcPR7s7bX9MF2Lomd8flXpv0KhUEwQci3kolAoFIoBUIKuUCgUEwQl6AqFQjFBUIKuUCgUEwQl6AqFQjFBUIKuUCgUEwQl6AqFQjFB+P8V+YVKxlZTEwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "MTzbDn_XPVJP",
        "outputId": "760b543d-dcd2-4e67-b6b9-3541e29d5f69"
      },
      "source": [
        "# plotting the first three anomaly data points\n",
        "plt.plot(anomaly_train_data[0])\n",
        "plt.plot(anomaly_train_data[1])\n",
        "plt.plot(anomaly_train_data[2])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffa54135e50>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "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": "8lvdVfHGPtKB"
      },
      "source": [
        "class Autoencoder(Model):\n",
        "  def __init__(self):\n",
        "    super(Autoencoder, self).__init__()\n",
        "    self.encoder = Sequential([\n",
        "                                Dense(64, activation='relu'),\n",
        "                                Dense(32, activation='relu'),\n",
        "                                Dense(16, activation='relu'),\n",
        "                                Dense(8, activation='relu')\n",
        "    ])\n",
        "\n",
        "    self.decoder = Sequential([\n",
        "                               Dense(16, activation='relu'),\n",
        "                               Dense(32, activation='relu'),\n",
        "                               Dense(64, activation='relu'),\n",
        "                               Dense(140, activation='sigmoid')\n",
        "    ])\n",
        "\n",
        "  def call(self,x):\n",
        "    encoded = self.encoder(x)\n",
        "    decoded = self.decoder(encoded)\n",
        "    return decoded"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EaejgzKQfrGC"
      },
      "source": [
        "# Instantiating the Autoencoder\n",
        "model = Autoencoder()\n",
        "\n",
        "# creating an early_stopping\n",
        "early_stopping = EarlyStopping(monitor='val_loss',\n",
        "                               patience = 2,\n",
        "                               mode = 'min')\n",
        "\n",
        "# Compiling the model\n",
        "model.compile(optimizer = 'adam',\n",
        "              loss = 'mae')"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5iLAZNQNg3m1",
        "outputId": "2e0ef3e6-a0db-4e7f-9ffc-ae9f82596e2d"
      },
      "source": [
        "# Training the model\n",
        "history = model.fit(normal_train_data,normal_train_data,\n",
        "                    epochs = 50,\n",
        "                    batch_size = 120,\n",
        "                    validation_data = (train_data_scaled[:,1:], train_data_scaled[:,1:]),\n",
        "                    shuffle = True,\n",
        "                    callbacks = [early_stopping])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "20/20 [==============================] - 2s 31ms/step - loss: 0.1336 - val_loss: 0.1109\n",
            "Epoch 2/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0903 - val_loss: 0.0839\n",
            "Epoch 3/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0573 - val_loss: 0.0789\n",
            "Epoch 4/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0502 - val_loss: 0.0758\n",
            "Epoch 5/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0475 - val_loss: 0.0748\n",
            "Epoch 6/50\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0467 - val_loss: 0.0740\n",
            "Epoch 7/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0460 - val_loss: 0.0723\n",
            "Epoch 8/50\n",
            "20/20 [==============================] - 0s 8ms/step - loss: 0.0431 - val_loss: 0.0690\n",
            "Epoch 9/50\n",
            "20/20 [==============================] - 0s 8ms/step - loss: 0.0412 - val_loss: 0.0662\n",
            "Epoch 10/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0385 - val_loss: 0.0650\n",
            "Epoch 11/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0372 - val_loss: 0.0643\n",
            "Epoch 12/50\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0373 - val_loss: 0.0642\n",
            "Epoch 13/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0368 - val_loss: 0.0630\n",
            "Epoch 14/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0368 - val_loss: 0.0625\n",
            "Epoch 15/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0364 - val_loss: 0.0623\n",
            "Epoch 16/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0365 - val_loss: 0.0622\n",
            "Epoch 17/50\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0358 - val_loss: 0.0614\n",
            "Epoch 18/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0353 - val_loss: 0.0608\n",
            "Epoch 19/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0351 - val_loss: 0.0606\n",
            "Epoch 20/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0349 - val_loss: 0.0602\n",
            "Epoch 21/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0343 - val_loss: 0.0599\n",
            "Epoch 22/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0332 - val_loss: 0.0593\n",
            "Epoch 23/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0321 - val_loss: 0.0580\n",
            "Epoch 24/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0312 - val_loss: 0.0577\n",
            "Epoch 25/50\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0293 - val_loss: 0.0568\n",
            "Epoch 26/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0292 - val_loss: 0.0561\n",
            "Epoch 27/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0288 - val_loss: 0.0553\n",
            "Epoch 28/50\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0289 - val_loss: 0.0553\n",
            "Epoch 29/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0290 - val_loss: 0.0547\n",
            "Epoch 30/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0284 - val_loss: 0.0541\n",
            "Epoch 31/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0282 - val_loss: 0.0534\n",
            "Epoch 32/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0280 - val_loss: 0.0529\n",
            "Epoch 33/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0274 - val_loss: 0.0521\n",
            "Epoch 34/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0274 - val_loss: 0.0515\n",
            "Epoch 35/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0273 - val_loss: 0.0517\n",
            "Epoch 36/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0276 - val_loss: 0.0511\n",
            "Epoch 37/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0266 - val_loss: 0.0501\n",
            "Epoch 38/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0265 - val_loss: 0.0497\n",
            "Epoch 39/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0263 - val_loss: 0.0497\n",
            "Epoch 40/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0268 - val_loss: 0.0494\n",
            "Epoch 41/50\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0263 - val_loss: 0.0500\n",
            "Epoch 42/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0262 - val_loss: 0.0490\n",
            "Epoch 43/50\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0261 - val_loss: 0.0491\n",
            "Epoch 44/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0259 - val_loss: 0.0486\n",
            "Epoch 45/50\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.0257 - val_loss: 0.0494\n",
            "Epoch 46/50\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0255 - val_loss: 0.0489\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJaXZqEpg8GE"
      },
      "source": [
        "# predictions for normal test data points\n",
        "encoder_out = model.encoder(normal_test_data).numpy()\n",
        "decoder_out = model.decoder(encoder_out).numpy()"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLNcwREih5BC",
        "outputId": "83f2ed6f-92c2-4824-806e-2de7151dc934"
      },
      "source": [
        "encoder_out.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(563, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V6m0WV80h6rW",
        "outputId": "4f2cc0dd-e582-428e-e33a-3a114bf5eec2"
      },
      "source": [
        "decoder_out.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(563, 140)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "lyGlYWPah7_h",
        "outputId": "876c8dd2-1a50-4150-d8da-20dc9c62df3a"
      },
      "source": [
        "# plotting normal test data point and its predictiction by the autoencoder\n",
        "plt.plot(normal_test_data[0], 'b')\n",
        "plt.plot(decoder_out[0], 'r')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffa51e2f950>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "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": "m7ZTj6saiJPr"
      },
      "source": [
        "# predictions for anomaly test data points\n",
        "encoder_out_a = model.encoder(anomaly_test_data).numpy()\n",
        "decoder_out_a = model.decoder(encoder_out_a).numpy()"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "P2UI_yvOi33V",
        "outputId": "31551fd0-5eaa-42b1-c74e-67982c0b541b"
      },
      "source": [
        "# plotting anomaly test data point and its predictiction by the autoencoder\n",
        "plt.plot(anomaly_test_data[0], 'b')\n",
        "plt.plot(decoder_out_a[0], 'r')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffa4c4ee950>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "WKWvXpCFjAKu",
        "outputId": "a0c48128-28de-44bb-8fe9-ecc107616a09"
      },
      "source": [
        "# reconstruction loss for normal test data\n",
        "reconstructions = model.predict(normal_test_data)\n",
        "train_loss = tf.keras.losses.mae(reconstructions, normal_test_data)\n",
        "\n",
        "# Plotting histogram for recontruction loss for normal test data\n",
        "plt.hist(train_loss, bins = 10)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([148., 251.,  98.,  33.,  20.,   4.,   5.,   2.,   0.,   2.]),\n",
              " array([0.00997054, 0.01834588, 0.02672122, 0.03509656, 0.0434719 ,\n",
              "        0.05184724, 0.06022257, 0.06859791, 0.07697325, 0.08534859,\n",
              "        0.09372393]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdmXLSBaja5j",
        "outputId": "4550b752-0670-4a3c-a4fc-c9908f2ff246"
      },
      "source": [
        "np.mean(train_loss)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.024762785864752123"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vtW3CBUjjiIU",
        "outputId": "e766c857-2dc6-492d-dd45-275cb5962f1b"
      },
      "source": [
        "np.std(train_loss)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.010359763380848144"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "-r38UR6Qjw8M",
        "outputId": "ffd1278a-b299-402a-f573-65a9b7f74bc0"
      },
      "source": [
        "# reconstruction loss for anomaly test data\n",
        "reconstructions_a = model.predict(anomaly_test_data)\n",
        "train_loss_a = tf.keras.losses.mae(reconstructions_a, anomaly_test_data)\n",
        "\n",
        "# Plotting histogram for recontruction loss for anomaly test data\n",
        "plt.hist(train_loss_a, bins = 10)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  4.,  29., 202., 170.,  20.,   5.,   3.,   2.,   1.,   1.]),\n",
              " array([0.03149332, 0.04845921, 0.06542511, 0.08239101, 0.09935691,\n",
              "        0.11632281, 0.13328871, 0.1502546 , 0.1672205 , 0.1841864 ,\n",
              "        0.2011523 ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_6QYRT2kpJQ",
        "outputId": "fa0ef513-0b2e-4928-8eb0-169b68bd0d2e"
      },
      "source": [
        "np.mean(train_loss_a)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0827475774879134"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XQikvTQuky7-",
        "outputId": "3f139441-04ca-4166-bf53-09388142f1c2"
      },
      "source": [
        "np.std(train_loss_a)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.016099476704093455"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dy7SKgIojlJA"
      },
      "source": [
        "# setting threshold\n",
        "threshold = np.mean(train_loss) + 2*np.std(train_loss)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yU2D1OA1ju3-",
        "outputId": "7f41e25e-0a15-435c-8141-e742c133a8b1"
      },
      "source": [
        "threshold"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04548231262644841"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "CA9baHE3j-NT",
        "outputId": "4caee125-6bbe-449f-c519-c5ffdf8c862a"
      },
      "source": [
        "# Plotting the normal and anomaly losses with the threshold\n",
        "plt.hist(train_loss, bins = 10, label = 'Normal')\n",
        "plt.hist(train_loss_a, bins = 10, 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": 40,
      "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": "YhkCxKbTk1eh"
      },
      "source": [
        "# Number of correct predictions for Normal test data  \n",
        "preds = tf.math.less(train_loss, threshold)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6B9m-mfnk-HH",
        "outputId": "fc3cdba3-9d8a-4aae-8b78-3e73d98c74b1"
      },
      "source": [
        "tf.math.count_nonzero(preds)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=int64, numpy=535>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-zCwgsYlI0v"
      },
      "source": [
        "# Number of correct predictions for Anomaly test data \n",
        "preds_a = tf.math.greater(train_loss_a, threshold)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DH0nTPxrlSTi",
        "outputId": "4f4af546-41b4-4bc8-8647-c98ad30c9f06"
      },
      "source": [
        "tf.math.count_nonzero(preds_a)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=int64, numpy=433>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3lKPX8tlaiW",
        "outputId": "1d5da799-7a6d-4a97-f312-3a6f1d68e7ef"
      },
      "source": [
        "preds_a.shape"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([437])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    }
  ]
}