MITBIH_BeatClassification_Shayan Like.ipynb

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  "metadata": {
    "colab": {
      "name": "MITBIH_BeatClassification_ShayanLike.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/M-torki/ECG-Classification/blob/main/MITBIH_BeatClassification_Shayan%20Like.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbySwp4_VbdE",
        "outputId": "f895ee3b-269b-49d6-f780-b1ba46ae8972"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "paaKTpplKWTf",
        "outputId": "e86cfe5c-bde1-4a7d-fe0a-b1d7706e904b"
      },
      "source": [
        "cd /gdrive/MyDrive/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8y_Vk76KzZT",
        "outputId": "495ef1ef-90d0-4c6d-8ec6-3ec121bed1c7"
      },
      "source": [
        "cd mit-bih-arrhythmia-database-1.0.0/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/MyDrive/mit-bih-arrhythmia-database-1.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNuw0CtZLJO-"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import scipy\n",
        "import shutil\n",
        "from IPython.display import display \n",
        "import keras\n",
        "from keras import optimizers, losses, activations, models\n",
        "from keras.layers import Dense, BatchNormalization, Input, Activation, Dropout, Convolution1D, Conv1D, MaxPool1D , MaxPooling1D, GlobalMaxPool1D, GlobalAveragePooling1D, concatenate, MaxPool2D\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D , Lambda, GaussianNoise\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.optimizers import Adam , Adadelta , Adagrad , SGD , RMSprop\n",
        "from keras import regularizers\n",
        "import keras.backend as K\n",
        "from random import choice,shuffle\n",
        "from sklearn.metrics import classification_report\n",
        "import glob"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJxcPiyxj8RM",
        "outputId": "6421cdd8-95e1-4f1e-fd43-1fdd12665c4f"
      },
      "source": [
        "ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100.atr    108.at_  117.at_  200.xws  210.dat  221.atr  234.atr\n",
            "100.dat    108.atr  117.atr  201.atr  210.hea  221.dat  234.dat\n",
            "100.hea    108.dat  117.dat  201.dat  210.xws  221.hea  234.hea\n",
            "100.xws    108.hea  117.hea  201.hea  212.atr  221.xws  234.xws\n",
            "101.atr    108.xws  117.xws  201.xws  212.dat  222.at_  ANNOTATORS\n",
            "101.dat    109.atr  118.atr  202.atr  212.hea  222.atr  a_train.txt\n",
            "101.hea    109.dat  118.dat  202.dat  212.xws  222.dat  A_train.txt\n",
            "101.xws    109.hea  118.hea  202.hea  213.atr  222.hea  best_model.h5\n",
            "102-0.atr  109.xws  118.xws  202.xws  213.dat  222.xws  best_modelozal.h5\n",
            "102.atr    111.atr  119.at_  203.at_  213.hea  223.atr  F_train.txt\n",
            "102.dat    111.dat  119.atr  203.at-  213.xws  223.dat  J_train.txt\n",
            "102.hea    111.hea  119.dat  203.atr  214.at_  223.hea  L_train.txt\n",
            "102.xws    111.xws  119.hea  203.dat  214.atr  223.xws  MIT_BIH_Record_100.csv\n",
            "103.atr    112.atr  119.xws  203.hea  214.dat  228.atr  MITBIHtest1.csv\n",
            "103.dat    112.dat  121.atr  203.xws  214.hea  228.dat  MITBIHtest2.csv\n",
            "103.hea    112.hea  121.dat  205.atr  214.xws  228.hea  MITBIHtest.csv\n",
            "103.xws    112.xws  121.hea  205.dat  215.at_  228.xws  MITBIHtrain1.csv\n",
            "104.atr    113.atr  121.xws  205.hea  215.atr  230.atr  MITBIHtrain2.csv\n",
            "104.dat    113.dat  122.atr  205.xws  215.dat  230.dat  MITBIHtrain.csv\n",
            "104.hea    113.hea  122.dat  207.atr  215.hea  230.hea  \u001b[0m\u001b[01;34mmitdbdir\u001b[0m/\n",
            "104.xws    113.xws  122.hea  207.dat  215.xws  230.xws  N_train.txt\n",
            "105.atr    114.atr  122.xws  207.hea  217.atr  231.atr  RECORDS\n",
            "105.dat    114.dat  123.atr  207.xws  217.dat  231.dat  R_train.txt\n",
            "105.hea    114.hea  123.dat  208.atr  217.hea  231.hea  sairandata.csv\n",
            "105.xws    114.xws  123.hea  208.dat  217.xws  231.xws  SHA256SUMS.txt\n",
            "106.atr    115.atr  123.xws  208.hea  219.atr  232.atr  slash_train.txt\n",
            "106.dat    115.dat  124.atr  208.xws  219.dat  232.dat  testdata.csv\n",
            "106.hea    115.hea  124.dat  209.at_  219.hea  232.hea  traindata.csv\n",
            "106.xws    115.xws  124.hea  209.atr  219.xws  232.xws  V_train.txt\n",
            "107.atr    116.atr  124.xws  209.dat  220.atr  233.atr  \u001b[01;34mx_mitdb\u001b[0m/\n",
            "107.dat    116.dat  200.atr  209.hea  220.dat  233.dat\n",
            "107.hea    116.hea  200.dat  209.xws  220.hea  233.hea\n",
            "107.xws    116.xws  200.hea  210.atr  220.xws  233.xws\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "2j4jsBO2j_2n",
        "outputId": "3b24b8b4-c0de-45c9-ef98-04e86939480d"
      },
      "source": [
        "mitbih_train = pd.read_csv('MITBIHtrain.csv', index_col=0)\n",
        "mitbih_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.716367</td>\n",
              "      <td>0.605889</td>\n",
              "      <td>0.334334</td>\n",
              "      <td>0.062152</td>\n",
              "      <td>0.001482</td>\n",
              "      <td>0.074463</td>\n",
              "      <td>0.101657</td>\n",
              "      <td>0.168074</td>\n",
              "      <td>0.192648</td>\n",
              "      <td>0.191673</td>\n",
              "      <td>0.180997</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.936186</td>\n",
              "      <td>0.851839</td>\n",
              "      <td>0.522560</td>\n",
              "      <td>0.177218</td>\n",
              "      <td>0.101580</td>\n",
              "      <td>0.226688</td>\n",
              "      <td>0.307603</td>\n",
              "      <td>0.312247</td>\n",
              "      <td>0.302577</td>\n",
              "      <td>0.307154</td>\n",
              "      <td>0.307559</td>\n",
              "      <td>0.323197</td>\n",
              "      <td>0.311100</td>\n",
              "      <td>0.303209</td>\n",
              "      <td>0.318524</td>\n",
              "      <td>0.317493</td>\n",
              "      <td>0.294123</td>\n",
              "      <td>0.284727</td>\n",
              "      <td>0.287552</td>\n",
              "      <td>0.302002</td>\n",
              "      <td>0.290240</td>\n",
              "      <td>0.270584</td>\n",
              "      <td>0.269801</td>\n",
              "      <td>0.272320</td>\n",
              "      <td>0.252298</td>\n",
              "      <td>0.247972</td>\n",
              "      <td>0.239045</td>\n",
              "      <td>0.234538</td>\n",
              "      <td>0.237144</td>\n",
              "      <td>0.223231</td>\n",
              "      <td>0.212339</td>\n",
              "      <td>0.234465</td>\n",
              "      <td>0.244725</td>\n",
              "      <td>0.244923</td>\n",
              "      <td>0.259700</td>\n",
              "      <td>0.296385</td>\n",
              "      <td>0.327229</td>\n",
              "      <td>0.360961</td>\n",
              "      <td>0.398028</td>\n",
              "      <td>0.440913</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89756</th>\n",
              "      <td>0.170161</td>\n",
              "      <td>0.175384</td>\n",
              "      <td>0.161912</td>\n",
              "      <td>0.159929</td>\n",
              "      <td>0.167725</td>\n",
              "      <td>0.169953</td>\n",
              "      <td>0.170139</td>\n",
              "      <td>0.173534</td>\n",
              "      <td>0.183595</td>\n",
              "      <td>0.178868</td>\n",
              "      <td>0.170358</td>\n",
              "      <td>0.163676</td>\n",
              "      <td>0.183728</td>\n",
              "      <td>0.176299</td>\n",
              "      <td>0.174333</td>\n",
              "      <td>0.173748</td>\n",
              "      <td>0.192752</td>\n",
              "      <td>0.210310</td>\n",
              "      <td>0.221938</td>\n",
              "      <td>0.224181</td>\n",
              "      <td>0.252555</td>\n",
              "      <td>0.266563</td>\n",
              "      <td>0.270775</td>\n",
              "      <td>0.262857</td>\n",
              "      <td>0.239972</td>\n",
              "      <td>0.216164</td>\n",
              "      <td>0.201028</td>\n",
              "      <td>0.174862</td>\n",
              "      <td>0.165812</td>\n",
              "      <td>0.170751</td>\n",
              "      <td>0.170872</td>\n",
              "      <td>0.162807</td>\n",
              "      <td>0.150584</td>\n",
              "      <td>0.173192</td>\n",
              "      <td>0.332145</td>\n",
              "      <td>0.660377</td>\n",
              "      <td>0.984904</td>\n",
              "      <td>0.950598</td>\n",
              "      <td>0.515910</td>\n",
              "      <td>0.162880</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89757</th>\n",
              "      <td>0.310288</td>\n",
              "      <td>0.313636</td>\n",
              "      <td>0.320426</td>\n",
              "      <td>0.319495</td>\n",
              "      <td>0.310250</td>\n",
              "      <td>0.319191</td>\n",
              "      <td>0.325578</td>\n",
              "      <td>0.325349</td>\n",
              "      <td>0.319269</td>\n",
              "      <td>0.331440</td>\n",
              "      <td>0.344319</td>\n",
              "      <td>0.339334</td>\n",
              "      <td>0.337858</td>\n",
              "      <td>0.336588</td>\n",
              "      <td>0.339555</td>\n",
              "      <td>0.343966</td>\n",
              "      <td>0.337829</td>\n",
              "      <td>0.334111</td>\n",
              "      <td>0.325639</td>\n",
              "      <td>0.329698</td>\n",
              "      <td>0.338193</td>\n",
              "      <td>0.337987</td>\n",
              "      <td>0.340513</td>\n",
              "      <td>0.336636</td>\n",
              "      <td>0.324691</td>\n",
              "      <td>0.333071</td>\n",
              "      <td>0.344326</td>\n",
              "      <td>0.347733</td>\n",
              "      <td>0.343796</td>\n",
              "      <td>0.336907</td>\n",
              "      <td>0.347922</td>\n",
              "      <td>0.346259</td>\n",
              "      <td>0.336474</td>\n",
              "      <td>0.336307</td>\n",
              "      <td>0.351594</td>\n",
              "      <td>0.353497</td>\n",
              "      <td>0.345496</td>\n",
              "      <td>0.351017</td>\n",
              "      <td>0.366250</td>\n",
              "      <td>0.362621</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89758</th>\n",
              "      <td>0.640392</td>\n",
              "      <td>0.652751</td>\n",
              "      <td>0.641387</td>\n",
              "      <td>0.648786</td>\n",
              "      <td>0.668659</td>\n",
              "      <td>0.673252</td>\n",
              "      <td>0.676784</td>\n",
              "      <td>0.662583</td>\n",
              "      <td>0.648405</td>\n",
              "      <td>0.656537</td>\n",
              "      <td>0.666554</td>\n",
              "      <td>0.661058</td>\n",
              "      <td>0.652796</td>\n",
              "      <td>0.666258</td>\n",
              "      <td>0.670545</td>\n",
              "      <td>0.662712</td>\n",
              "      <td>0.654865</td>\n",
              "      <td>0.656233</td>\n",
              "      <td>0.676326</td>\n",
              "      <td>0.670319</td>\n",
              "      <td>0.654956</td>\n",
              "      <td>0.662942</td>\n",
              "      <td>0.672954</td>\n",
              "      <td>0.662617</td>\n",
              "      <td>0.658481</td>\n",
              "      <td>0.668790</td>\n",
              "      <td>0.655069</td>\n",
              "      <td>0.661458</td>\n",
              "      <td>0.662173</td>\n",
              "      <td>0.659336</td>\n",
              "      <td>0.663143</td>\n",
              "      <td>0.681641</td>\n",
              "      <td>0.700340</td>\n",
              "      <td>0.681638</td>\n",
              "      <td>0.684597</td>\n",
              "      <td>0.702147</td>\n",
              "      <td>0.702619</td>\n",
              "      <td>0.690812</td>\n",
              "      <td>0.672112</td>\n",
              "      <td>0.664354</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>89761 rows × 187 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              0         1         2         3  ...  183  184  185  186\n",
              "0      0.818086  0.720088  0.469979  0.180799  ...  0.0  0.0  0.0    0\n",
              "1      0.765217  0.644483  0.439419  0.185536  ...  0.0  0.0  0.0    0\n",
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              "4      0.936186  0.851839  0.522560  0.177218  ...  0.0  0.0  0.0    0\n",
              "...         ...       ...       ...       ...  ...  ...  ...  ...  ...\n",
              "89756  0.170161  0.175384  0.161912  0.159929  ...  0.0  0.0  0.0    4\n",
              "89757  0.310288  0.313636  0.320426  0.319495  ...  0.0  0.0  0.0    4\n",
              "89758  0.640392  0.652751  0.641387  0.648786  ...  0.0  0.0  0.0    4\n",
              "89759  0.509921  0.513104  0.518813  0.499047  ...  0.0  0.0  0.0    4\n",
              "89760  0.168525  0.187534  0.191523  0.186246  ...  0.0  0.0  0.0    4\n",
              "\n",
              "[89761 rows x 187 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "QncRT8hDe5bL",
        "outputId": "160630fe-c646-4d9f-d1f8-f905a8f736c1"
      },
      "source": [
        "from sklearn.utils import shuffle\n",
        "mitbih_train = shuffle(mitbih_train)\n",
        "mitbih_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
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              "    <tr>\n",
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              "<p>89761 rows × 187 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              0         1         2         3  ...  183  184  185  186\n",
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              "...         ...       ...       ...       ...  ...  ...  ...  ...  ...\n",
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              "\n",
              "[89761 rows x 187 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "vLQp9aFSErvP",
        "outputId": "54147aeb-f1bc-4a89-c4cf-8b00a8d8eb99"
      },
      "source": [
        "mitbih_test = pd.read_csv('MITBIHtest.csv', index_col=0)\n",
        "mitbih_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0.143460</td>\n",
              "      <td>0.144616</td>\n",
              "      <td>0.147074</td>\n",
              "      <td>0.156461</td>\n",
              "      <td>0.140906</td>\n",
              "      <td>0.147026</td>\n",
              "      <td>0.143310</td>\n",
              "      <td>0.139978</td>\n",
              "      <td>0.136986</td>\n",
              "      <td>0.144952</td>\n",
              "      <td>0.144537</td>\n",
              "      <td>0.148532</td>\n",
              "      <td>0.148671</td>\n",
              "      <td>0.145647</td>\n",
              "      <td>0.154023</td>\n",
              "      <td>0.142038</td>\n",
              "      <td>0.152283</td>\n",
              "      <td>0.135725</td>\n",
              "      <td>0.145436</td>\n",
              "      <td>0.130999</td>\n",
              "      <td>0.136494</td>\n",
              "      <td>0.123359</td>\n",
              "      <td>0.129645</td>\n",
              "      <td>0.111348</td>\n",
              "      <td>0.119866</td>\n",
              "      <td>0.103650</td>\n",
              "      <td>0.112705</td>\n",
              "      <td>0.100051</td>\n",
              "      <td>0.114408</td>\n",
              "      <td>0.118852</td>\n",
              "      <td>0.150043</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.626616</td>\n",
              "      <td>0.078349</td>\n",
              "      <td>0.143361</td>\n",
              "      <td>0.144182</td>\n",
              "      <td>0.149653</td>\n",
              "      <td>0.143237</td>\n",
              "      <td>0.147057</td>\n",
              "      <td>0.139515</td>\n",
              "      <td>0.138309</td>\n",
              "      <td>0.137285</td>\n",
              "      <td>0.130809</td>\n",
              "      <td>0.134419</td>\n",
              "      <td>0.134541</td>\n",
              "      <td>0.131312</td>\n",
              "      <td>0.136321</td>\n",
              "      <td>0.140675</td>\n",
              "      <td>0.146366</td>\n",
              "      <td>0.141496</td>\n",
              "      <td>0.143835</td>\n",
              "      <td>0.127826</td>\n",
              "      <td>0.137391</td>\n",
              "      <td>0.128488</td>\n",
              "      <td>0.144688</td>\n",
              "      <td>0.132729</td>\n",
              "      <td>0.147991</td>\n",
              "      <td>0.142868</td>\n",
              "      <td>0.152672</td>\n",
              "      <td>0.137744</td>\n",
              "      <td>0.141330</td>\n",
              "      <td>0.127886</td>\n",
              "      <td>0.137960</td>\n",
              "      <td>0.123181</td>\n",
              "      <td>0.132702</td>\n",
              "      <td>0.127907</td>\n",
              "      <td>0.139360</td>\n",
              "      <td>0.152153</td>\n",
              "      <td>0.181011</td>\n",
              "      <td>0.185007</td>\n",
              "      <td>0.196108</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.724917</td>\n",
              "      <td>0.925841</td>\n",
              "      <td>0.340448</td>\n",
              "      <td>0.063178</td>\n",
              "      <td>0.160610</td>\n",
              "      <td>0.134079</td>\n",
              "      <td>0.156437</td>\n",
              "      <td>0.134946</td>\n",
              "      <td>0.151477</td>\n",
              "      <td>0.135368</td>\n",
              "      <td>0.145631</td>\n",
              "      <td>0.136021</td>\n",
              "      <td>0.152237</td>\n",
              "      <td>0.145192</td>\n",
              "      <td>0.148037</td>\n",
              "      <td>0.141005</td>\n",
              "      <td>0.150003</td>\n",
              "      <td>0.152689</td>\n",
              "      <td>0.154716</td>\n",
              "      <td>0.150691</td>\n",
              "      <td>0.155950</td>\n",
              "      <td>0.153019</td>\n",
              "      <td>0.155187</td>\n",
              "      <td>0.156573</td>\n",
              "      <td>0.154304</td>\n",
              "      <td>0.156126</td>\n",
              "      <td>0.159617</td>\n",
              "      <td>0.157090</td>\n",
              "      <td>0.157087</td>\n",
              "      <td>0.159690</td>\n",
              "      <td>0.153042</td>\n",
              "      <td>0.160368</td>\n",
              "      <td>0.155025</td>\n",
              "      <td>0.164455</td>\n",
              "      <td>0.148839</td>\n",
              "      <td>0.164279</td>\n",
              "      <td>0.166476</td>\n",
              "      <td>0.195927</td>\n",
              "      <td>0.193884</td>\n",
              "      <td>0.213307</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.808576</td>\n",
              "      <td>0.788044</td>\n",
              "      <td>0.190653</td>\n",
              "      <td>0.098258</td>\n",
              "      <td>0.149267</td>\n",
              "      <td>0.143545</td>\n",
              "      <td>0.132782</td>\n",
              "      <td>0.144616</td>\n",
              "      <td>0.139957</td>\n",
              "      <td>0.152961</td>\n",
              "      <td>0.145420</td>\n",
              "      <td>0.153656</td>\n",
              "      <td>0.133656</td>\n",
              "      <td>0.148766</td>\n",
              "      <td>0.132011</td>\n",
              "      <td>0.143753</td>\n",
              "      <td>0.134510</td>\n",
              "      <td>0.141149</td>\n",
              "      <td>0.132400</td>\n",
              "      <td>0.145588</td>\n",
              "      <td>0.144996</td>\n",
              "      <td>0.148380</td>\n",
              "      <td>0.128242</td>\n",
              "      <td>0.142866</td>\n",
              "      <td>0.141165</td>\n",
              "      <td>0.145535</td>\n",
              "      <td>0.140712</td>\n",
              "      <td>0.140404</td>\n",
              "      <td>0.142225</td>\n",
              "      <td>0.129839</td>\n",
              "      <td>0.123652</td>\n",
              "      <td>0.115944</td>\n",
              "      <td>0.109504</td>\n",
              "      <td>0.114472</td>\n",
              "      <td>0.126316</td>\n",
              "      <td>0.114458</td>\n",
              "      <td>0.133728</td>\n",
              "      <td>0.135339</td>\n",
              "      <td>0.165036</td>\n",
              "      <td>0.168561</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22433</th>\n",
              "      <td>0.683369</td>\n",
              "      <td>0.411722</td>\n",
              "      <td>0.449075</td>\n",
              "      <td>0.385808</td>\n",
              "      <td>0.404331</td>\n",
              "      <td>0.369036</td>\n",
              "      <td>0.357278</td>\n",
              "      <td>0.313978</td>\n",
              "      <td>0.288052</td>\n",
              "      <td>0.226511</td>\n",
              "      <td>0.152880</td>\n",
              "      <td>0.077764</td>\n",
              "      <td>0.049958</td>\n",
              "      <td>0.033013</td>\n",
              "      <td>0.041782</td>\n",
              "      <td>0.047675</td>\n",
              "      <td>0.090148</td>\n",
              "      <td>0.141058</td>\n",
              "      <td>0.213274</td>\n",
              "      <td>0.255239</td>\n",
              "      <td>0.318193</td>\n",
              "      <td>0.315649</td>\n",
              "      <td>0.337365</td>\n",
              "      <td>0.336104</td>\n",
              "      <td>0.352339</td>\n",
              "      <td>0.347233</td>\n",
              "      <td>0.382970</td>\n",
              "      <td>0.379104</td>\n",
              "      <td>0.396842</td>\n",
              "      <td>0.394083</td>\n",
              "      <td>0.403831</td>\n",
              "      <td>0.398338</td>\n",
              "      <td>0.414159</td>\n",
              "      <td>0.409609</td>\n",
              "      <td>0.415770</td>\n",
              "      <td>0.409602</td>\n",
              "      <td>0.413180</td>\n",
              "      <td>0.414005</td>\n",
              "      <td>0.416421</td>\n",
              "      <td>0.419894</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22434</th>\n",
              "      <td>0.746623</td>\n",
              "      <td>0.439526</td>\n",
              "      <td>0.394551</td>\n",
              "      <td>0.371400</td>\n",
              "      <td>0.360849</td>\n",
              "      <td>0.335792</td>\n",
              "      <td>0.306598</td>\n",
              "      <td>0.266873</td>\n",
              "      <td>0.227089</td>\n",
              "      <td>0.166971</td>\n",
              "      <td>0.082925</td>\n",
              "      <td>0.030109</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>22438 rows × 187 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              0         1         2         3  ...  183  184  185  186\n",
              "0      0.898901  0.365866  0.119414  0.191118  ...  0.0  0.0  0.0    0\n",
              "1      0.916503  0.917102  0.439387  0.128241  ...  0.0  0.0  0.0    0\n",
              "2      1.000000  0.626616  0.078349  0.143361  ...  0.0  0.0  0.0    0\n",
              "3      0.724917  0.925841  0.340448  0.063178  ...  0.0  0.0  0.0    0\n",
              "4      0.808576  0.788044  0.190653  0.098258  ...  0.0  0.0  0.0    0\n",
              "...         ...       ...       ...       ...  ...  ...  ...  ...  ...\n",
              "22433  0.683369  0.411722  0.449075  0.385808  ...  0.0  0.0  0.0    4\n",
              "22434  0.746623  0.439526  0.394551  0.371400  ...  0.0  0.0  0.0    4\n",
              "22435  1.000000  0.920349  0.761597  0.461385  ...  0.0  0.0  0.0    4\n",
              "22436  0.711396  0.470275  0.387301  0.390259  ...  0.0  0.0  0.0    4\n",
              "22437  0.652674  0.422224  0.463661  0.391712  ...  0.0  0.0  0.0    4\n",
              "\n",
              "[22438 rows x 187 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "GkfFDqImfT6C",
        "outputId": "b81e28c0-6d68-498c-9faa-0a58f812ee76"
      },
      "source": [
        "mitbih_test = shuffle(mitbih_test)\n",
        "mitbih_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.334341</td>\n",
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              "    <tr>\n",
              "      <th>13688</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>18825</th>\n",
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              "      <td>5.442451e-01</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
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              "      <td>1.105581e-01</td>\n",
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              "    <tr>\n",
              "      <th>14529</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>15614</th>\n",
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              "      <td>0.697172</td>\n",
              "      <td>0.695058</td>\n",
              "      <td>0.640451</td>\n",
              "      <td>0.607653</td>\n",
              "      <td>0.547121</td>\n",
              "      <td>0.528291</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3772</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.597719</td>\n",
              "      <td>0.393628</td>\n",
              "      <td>2.861451e-01</td>\n",
              "      <td>0.202043</td>\n",
              "      <td>0.170536</td>\n",
              "      <td>0.123925</td>\n",
              "      <td>0.122211</td>\n",
              "      <td>0.112331</td>\n",
              "      <td>0.112121</td>\n",
              "      <td>0.104784</td>\n",
              "      <td>0.108817</td>\n",
              "      <td>0.103089</td>\n",
              "      <td>0.106805</td>\n",
              "      <td>0.108293</td>\n",
              "      <td>0.102579</td>\n",
              "      <td>0.105999</td>\n",
              "      <td>0.106547</td>\n",
              "      <td>0.108808</td>\n",
              "      <td>0.105506</td>\n",
              "      <td>0.110732</td>\n",
              "      <td>0.103577</td>\n",
              "      <td>0.114420</td>\n",
              "      <td>0.106407</td>\n",
              "      <td>0.121013</td>\n",
              "      <td>0.118302</td>\n",
              "      <td>0.134526</td>\n",
              "      <td>0.133758</td>\n",
              "      <td>0.153493</td>\n",
              "      <td>0.156460</td>\n",
              "      <td>0.175335</td>\n",
              "      <td>0.188434</td>\n",
              "      <td>0.208531</td>\n",
              "      <td>0.218016</td>\n",
              "      <td>0.243218</td>\n",
              "      <td>0.250481</td>\n",
              "      <td>0.272379</td>\n",
              "      <td>0.277366</td>\n",
              "      <td>0.286490</td>\n",
              "      <td>0.288424</td>\n",
              "      <td>...</td>\n",
              "      <td>0.106864</td>\n",
              "      <td>0.115694</td>\n",
              "      <td>0.134414</td>\n",
              "      <td>0.144781</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21833</th>\n",
              "      <td>0.959794</td>\n",
              "      <td>0.720607</td>\n",
              "      <td>0.761364</td>\n",
              "      <td>7.444010e-01</td>\n",
              "      <td>0.765233</td>\n",
              "      <td>0.761182</td>\n",
              "      <td>0.778389</td>\n",
              "      <td>0.766069</td>\n",
              "      <td>0.730940</td>\n",
              "      <td>0.687575</td>\n",
              "      <td>0.609772</td>\n",
              "      <td>0.553007</td>\n",
              "      <td>0.578008</td>\n",
              "      <td>0.567303</td>\n",
              "      <td>0.541216</td>\n",
              "      <td>0.539955</td>\n",
              "      <td>0.559845</td>\n",
              "      <td>0.543768</td>\n",
              "      <td>0.530646</td>\n",
              "      <td>0.527173</td>\n",
              "      <td>0.535789</td>\n",
              "      <td>0.540670</td>\n",
              "      <td>0.540858</td>\n",
              "      <td>0.529091</td>\n",
              "      <td>0.543936</td>\n",
              "      <td>0.542938</td>\n",
              "      <td>0.554312</td>\n",
              "      <td>0.545307</td>\n",
              "      <td>0.554122</td>\n",
              "      <td>0.545100</td>\n",
              "      <td>0.555396</td>\n",
              "      <td>0.552722</td>\n",
              "      <td>0.564164</td>\n",
              "      <td>0.558988</td>\n",
              "      <td>0.570030</td>\n",
              "      <td>0.577125</td>\n",
              "      <td>0.591392</td>\n",
              "      <td>0.590395</td>\n",
              "      <td>0.611444</td>\n",
              "      <td>0.607579</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.726398</td>\n",
              "      <td>0.158171</td>\n",
              "      <td>7.629761e-02</td>\n",
              "      <td>0.124730</td>\n",
              "      <td>0.132485</td>\n",
              "      <td>0.127368</td>\n",
              "      <td>0.130953</td>\n",
              "      <td>0.113177</td>\n",
              "      <td>0.123349</td>\n",
              "      <td>0.121486</td>\n",
              "      <td>0.132063</td>\n",
              "      <td>0.133651</td>\n",
              "      <td>0.127822</td>\n",
              "      <td>0.125521</td>\n",
              "      <td>0.125539</td>\n",
              "      <td>0.126619</td>\n",
              "      <td>0.119793</td>\n",
              "      <td>0.130866</td>\n",
              "      <td>0.120502</td>\n",
              "      <td>0.132042</td>\n",
              "      <td>0.126032</td>\n",
              "      <td>0.146098</td>\n",
              "      <td>0.131541</td>\n",
              "      <td>0.136033</td>\n",
              "      <td>0.127669</td>\n",
              "      <td>0.136956</td>\n",
              "      <td>0.120697</td>\n",
              "      <td>0.132998</td>\n",
              "      <td>0.115937</td>\n",
              "      <td>0.116202</td>\n",
              "      <td>0.107341</td>\n",
              "      <td>0.119259</td>\n",
              "      <td>0.110779</td>\n",
              "      <td>0.116541</td>\n",
              "      <td>0.117109</td>\n",
              "      <td>0.133440</td>\n",
              "      <td>0.158928</td>\n",
              "      <td>0.181445</td>\n",
              "      <td>0.202773</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6926</th>\n",
              "      <td>0.997111</td>\n",
              "      <td>0.815794</td>\n",
              "      <td>0.645863</td>\n",
              "      <td>4.486077e-01</td>\n",
              "      <td>0.314852</td>\n",
              "      <td>0.183443</td>\n",
              "      <td>0.107861</td>\n",
              "      <td>0.075489</td>\n",
              "      <td>0.061472</td>\n",
              "      <td>0.049735</td>\n",
              "      <td>0.057193</td>\n",
              "      <td>0.059733</td>\n",
              "      <td>0.066742</td>\n",
              "      <td>0.073392</td>\n",
              "      <td>0.061774</td>\n",
              "      <td>0.060207</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.069628</td>\n",
              "      <td>0.058068</td>\n",
              "      <td>0.063346</td>\n",
              "      <td>0.056460</td>\n",
              "      <td>0.066687</td>\n",
              "      <td>0.065595</td>\n",
              "      <td>0.068991</td>\n",
              "      <td>0.065972</td>\n",
              "      <td>0.095990</td>\n",
              "      <td>0.101777</td>\n",
              "      <td>0.130586</td>\n",
              "      <td>0.149462</td>\n",
              "      <td>0.174079</td>\n",
              "      <td>0.201431</td>\n",
              "      <td>0.231542</td>\n",
              "      <td>0.224922</td>\n",
              "      <td>0.242591</td>\n",
              "      <td>0.241627</td>\n",
              "      <td>0.239374</td>\n",
              "      <td>0.224956</td>\n",
              "      <td>0.208293</td>\n",
              "      <td>0.199330</td>\n",
              "      <td>0.192590</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22438 rows × 187 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              0         1         2             3  ...  183  184  185  186\n",
              "17307  0.902063  0.651560  0.230743 -1.110223e-16  ...  0.0  0.0  0.0    0\n",
              "13688  0.955911  0.847215  0.675994  5.084110e-01  ...  0.0  0.0  0.0    0\n",
              "18825  0.714300  0.696257  0.637524  5.442451e-01  ...  0.0  0.0  0.0    2\n",
              "130    0.912151  0.585726  0.084008  1.105581e-01  ...  0.0  0.0  0.0    0\n",
              "14529  0.867236  0.775690  0.638796  5.717995e-01  ...  0.0  0.0  0.0    0\n",
              "...         ...       ...       ...           ...  ...  ...  ...  ...  ...\n",
              "15614  0.941660  0.817285  0.652893  5.066677e-01  ...  0.0  0.0  0.0    0\n",
              "3772   1.000000  0.597719  0.393628  2.861451e-01  ...  0.0  0.0  0.0    0\n",
              "21833  0.959794  0.720607  0.761364  7.444010e-01  ...  0.0  0.0  0.0    4\n",
              "751    1.000000  0.726398  0.158171  7.629761e-02  ...  0.0  0.0  0.0    0\n",
              "6926   0.997111  0.815794  0.645863  4.486077e-01  ...  0.0  0.0  0.0    0\n",
              "\n",
              "[22438 rows x 187 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wMtlYevH-IN"
      },
      "source": [
        "##balancing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIKuVtqVfd_Z"
      },
      "source": [
        "from sklearn.utils import resample\n",
        "df_1=mitbih_train[mitbih_train['186']==1]\n",
        "df_2=mitbih_train[mitbih_train['186']==2]\n",
        "df_3=mitbih_train[mitbih_train['186']==3]\n",
        "df_4=mitbih_train[mitbih_train['186']==4]\n",
        "df_0=(mitbih_train[mitbih_train['186']==0]).sample(n=10000,random_state=42)\n",
        "\n",
        "df_1_upsample=resample(df_1,replace=True,n_samples=10000,random_state=123)\n",
        "df_2_upsample=resample(df_2,replace=True,n_samples=10000,random_state=124)\n",
        "df_3_upsample=resample(df_3,replace=True,n_samples=10000,random_state=125)\n",
        "df_4_upsample=resample(df_4,replace=True,n_samples=10000,random_state=126)\n",
        "\n",
        "mitbih_train=pd.concat([df_0,df_1_upsample,df_2_upsample,df_3_upsample,df_4_upsample])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hCUDuhC_c1GF",
        "outputId": "ea9a57d9-ef37-4dbe-d774-c9323fa82235"
      },
      "source": [
        "mitbih_test.iloc[:,-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17307    0\n",
              "13688    0\n",
              "18825    2\n",
              "130      0\n",
              "14529    0\n",
              "        ..\n",
              "15614    0\n",
              "3772     0\n",
              "21833    4\n",
              "751      0\n",
              "6926     0\n",
              "Name: 186, Length: 22438, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWnZi8F6EvBq",
        "outputId": "396e16c3-da1a-45b3-ff01-232b34925702"
      },
      "source": [
        "print(mitbih_test.shape , mitbih_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(22438, 187) (50000, 187)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "mTAuQhNzNHiL",
        "outputId": "595ef12e-a811-4726-e7a8-017e162c02f0"
      },
      "source": [
        "plt.plot(mitbih_test.iloc[1,:-1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f922dde5a90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dX7bYnYwM/Qv//hydPXl++LFTDtiNtyqVTqlTVCSOEhPQx2RTGAOdCcjQl2/excMrW7n6nFkcc+i4ka7O64Tb56rJRSReEhPQjTE05pLxoOg7Hl1LfTbNpTG5qqVURjfnEomlxAR0cD8uqvXLFtvae3hg6WY+dOoUmurjeQuDjJpcRGIpUQF93JhMzT+G7pZH1tDTV+Dv3zJzpKsypFTKkDLqFBWJm0QF9AkNOXZ29ox0NSr2t9Xb+MkjL/PhU6dy7GHxazuPyqRTesCFSMwkKqA312fZ2dE70tWoyM6OHq6+ZylHt4zlhg+cMNLVGVY2ZZShi8RMsgJ6Q65mA/otj6xhe3s3N17y5v5LMOMsk06pU1QkZhIW0LPs7Ki9Jpe29h5uf3Qt75szmROOaBrp6pQlmzbqFBWJmUQF9AkNWdp78vT01Vag+cmfX6azN8/V75410lUpW1pNLiKxk6iA3uTvd1JLHaOL17Zx619f4YOnTGFWzDtCozIpdYqKxE2iAvqEBnfddq20o7fu6ebTdz3NtAn1fO2i+HeERmXTRm3oIjGTqIDeXO8z9P0I6NYWg9SB/JFSX77AZ+9+hl2dvdz8d6cxvsaeg5pJp9TkIhIz8b+cYh80+wx9R4Udozc9tJob/7CKU2c009GT57mNu/jUO4/mf733OB5YuonphzQwd+YhFAqWzt48jXWVr77v/f4lHluzne9efDKzjxhfcTkjJZNSp6hI3CQyoO+qIEO/f8kGvrNoJfOOPIS93X3k0inOOf5Qfvznl1n4/Kusb+sgl0nx7Q+fxJ2PrWXV1r3cevlczjhq4j7P697F67n54Ze5dN40PhKDR8pVIpM2ujmXSMwkLKC7Jpd9zdCXbdrFF3/xPG87ZhK3ffx0chnXElUoWL7+6+X86tnN3DD/BO56Yj3/896lNOTStIyr4/LbnuSik4+gqT7Lf3vn0bSMq3vD+VhruW/JBq79xfO889iWmms3j8qkUsrQRWImUQG9MZcmmzbs7Cw/Q+/pK/CP9z/LhMYcP/zYKf3BHNw9S74+/0S++v4TSKUMF8yZzM0Pv8wlp09j4tg6rr7nGf78Uis7Onr43fIt3DD/RDp788ydOYFDx40ZMJ9lm3bx7UUreeSlVs46aiI/vuw06jLxus/5vlCnqEj8JCqgG2Noqs/t04+Lbn54NS9u2cOtl88d8jFvKX93wYlj6/jKhbP7h//sijMAeHbDTq64YzGfuH0x4K62+cqFs9nb3ceLW/bw4qu7eXr9TsbWZfjq+2dz+Vkz+x8SUat0HbpI/CQqoIMLpuVe5dKbL/Czx9Zx7uzDOGf2YRXP8+RpzTx49TtYtnkXYzJpvv7r5XzuvmcB164/c2Ij157/Ji6dNz22t8TdV9l0ir19tX1nS5GkSVxAb96HgP7XVdvY3t7DxVXomGwZV8e7jjsUgAc+/VaefKWNo1oamdJcjzG1nY0PJqMMXSR2EhjQc2xo6yhr2geWbqK5IcvZPhBXy5hsmncc21LVMuMmk1anqEjcJOqHReBuoburjE7Rvd19LFq+hQvmTB7QESrlyaTUKSoSN4mLZBMac2Vdtrhg6Wa6egt84M1TDkKtkieTTuk6dJGYSVxAb6rP0tVboKt36IdFd/fluemh1Zw8tYnTZ044iLVLjqx+KSoSO4kL6BMahr+fy72LN7BpZyfXvOe4RHZYHgyZtDpFReImcQE9/Px/qFvo5guWmx5azbyZh/D2WZMOZtUSRU0uIvFTVkA3xpxnjFlpjFltjLl2kPGfM8asMMY8Z4z5ozFmRvWrWp5mf513W/vgAf2l1/bw2u5uPnr6NGXn+yGTMvTpfugisTJsQDfGpIGbgPOB2cClxpjZJZM9A8y11p4E/Bz4drUrWq7JzfUAbN7ZNej4p9fvAOC0GWo73x+ZlG6fKxI35WTo84DV1to11toe4B5gfnQCa+1D1tpw8ffjwIjdQnBKcz0pA+u3tw86/ql1O5jYmGPGxIaDXLNk0TNFReKnnIA+BdgQeb/RDxvKFcCD+1Op/ZHLpJjcVM/6IX5c9Mz6nZwyfYKaW/aTbp8rEj9V7RQ1xlwGzAW+M8T4q4wxS4wxS1pbW6s56wFmTGxg3SABva29h1e2tau5pQrSqRT5gh3whCcRGVnlBPRNwLTI+6l+2ADGmHOALwEXWWu7ByvIWnuLtXautXZuS8uB+2n8jIkNg/78/+l1rv381OnNB2zeo0XW3y1SWbpIfJQT0BcDs4wxRxpjcsAlwILoBMaYU4Cf4IL51upXc99MO6SBbXt72FvyTNCn1+8gkzKcNFUBfX9l0m7XUceoSHwMG9CttX3AZ4BFwAvAfdba5caY640xF/nJvgOMBe43xiw1xiwYoriDYsYhjQCs317M0q21/H7Fa7x5WjP1udp9sERcZNMuQ+/VpYsisVHW3RattQuBhSXDrou8PqfK9dov4QqW9W0d/Q9gXrphJ6u27uWfPzRnJKuWGOEBHXll6CKxkbhfioJrcgFY31a8dPH+pzYyJpviwpMmj1S1EiU0uShDF4mPRAb0pvoszQ1Z1vkml67ePL9+djPnnziZcWOS8cSgkdbfKaoMXSQ2EveAi2DGIQ2s297BU+vauP7XK9jT1cdHT582/AelLOoUFYmfxAb0aYc08JvnXuWvq7cxaWwdN17yZs48auJIVysx1CkqEj+JDeh/d8YMxtZlmDvzEN5zwmGMV1NLVfV3iuo6dJHYSGxAP+voiZx1tDLyAyWT8p2iup+LSGwkslNUDrzQ5KI2dJH4UECXivR3iqoNXSQ2FNClIhldtigSOwroUpGMbs4lEjsK6FKR/l+KqlNUJDYU0KUi6hQViR8FdKlIur/JRRm6SFwooEtFsv1XuShDF4kLBXSpiK5yEYkfBXSpSFadoiKxo4AuFUnrskWR2FFAl4pk0groInGjgC4VyabC/dDV5CISFwroUpGMrkMXiR0FdKlIVs8UFYkdBXSpSFqXLYrEjgK6VEQ35xKJHwV0qYgxhkzKqFNUJEYU0KVimbRRhi4SIwroUrFMKqVfiorEiAK6VCyTNuSVoYvEhgK6VMxl6AroInGhgC4Vy6bVKSoSJwroUrF0Sp2iInGigC4Vy6bVKSoSJwroUrFMSp2iInFSVkA3xpxnjFlpjFltjLl2kPF1xph7/fgnjDEzq11RiZ9MWp2iInEybEA3xqSBm4DzgdnApcaY2SWTXQHssNYeA3wf+Fa1Kyrxk00bPSRaJEbKydDnAauttWustT3APcD8kmnmA3f41z8H3m2MMdWrpsRROmV0cy6RGMmUMc0UYEPk/UbgjKGmsdb2GWN2AROBbdWopMRTNpXiqXU7OPd7fx7pqojUlM++exbvP/mIqpdbTkCvGmPMVcBVANOnTz+Ys5YD4PK3zGDh86+OdDVEak5TffaAlFtOQN8ETIu8n+qHDTbNRmNMBmgCtpcWZK29BbgFYO7cuTpXr3EXnnQEF55U/SxDRCpTThv6YmCWMeZIY0wOuARYUDLNAuDv/euPAH+y1ipgi4gcRMNm6L5N/DPAIiAN3GatXW6MuR5YYq1dAPwb8DNjzGqgDRf0RUTkICqrDd1auxBYWDLsusjrLuDi6lZNRET2hX4pKiKSEAroIiIJoYAuIpIQCugiIgmhgC4ikhBmpC4XN8a0Ausq/Pgk3G0Fov8ZZthw4/dn2IEsW8sSz7K1LPEsu5aWpdJbo8yw1rYMOsZaW3N/uOvfB/wfblgln4lD2VqWeJatZYln2bW0LMPFuUr+1OQiIpIQCugiIglxUO+2WEW3DPF/uGGVfCYOZWtZ4lm2liWeZdfaslTNiHWKiohIdanJRUQkIWquycUYcx5wL9AI5IE1wAxgB7ATOA7oAF4Fjgb6gBwQHolnI/8t7g6SUdZ/xjBw/YTPRR+tV/DvB3vcXp8ve7hH8dlBphluXjbyP0yXp7gsBT+uB6jDHbi7/ety6zDY8KGmeyMFoNfPu5LPD2e4Mku30WDrFtz6S5VMF9ZxWLd6rKJUonSfy+O+E+v8sGlAPbAW+Km19vpKZ1RTGXrkgdVXAe/HragPA0cB7bg7QhpgBfA48ClcUH8N2ItbiR24558W/OsP4Vbwk8DLwG5gGW7ddPm/l4DzgKdxB429uGtIO3FB85O+jE5f3m7/ub2+6k/jDjzr/Os88Btgj3/9W/96N/Cc//8DXBDe7Jdzj1/2PLDF/8/75e7w9Sj4ZX0R96jAHO7AYn192v3fFj9soV8nf/XL1Yl7WMkWP/89vg67fdnbgH/AHSxv9WV24x5mshS3Q3b79f9ZYLkflvKffxkYBxwK/M3X4T7/GQvcFVnmNl+fn/hlsxQfmnKu/7/NDy/4OvdGlrUTeNYPI/LZXcBW/5nbI8PCQfCuSJn/xa+/nf49kbJX+3H45Q3rv8+/7vL/Q927/HzCl7vPT/+qf5339bLAH/w01q8ngPsjZa3yw17wZewC/kIxUIRlfsWPz/vPtuP2i1CHXj9+k1/Gdl9PCzzqp8lT3P6/xO1rfX46cN+zcNAOy4EvD9x3IMyv3a+76LCeSF06cdt/daTe4W+V/0wn8H/9+PD92uPrtQ1opZisvRCZR1jf23D7Vt7PK9Q1D3zNf74XlyCG/WCXHx+2325gpR/XRtFG/9k9keXbiotJvf51m5/HKl/ed3Df0weBz+9PMIcaC+gUH1h9L25j7QTmW2u34Hbec3ErOQ28A3efdvz7EIALwJ9wK7kLeAa3wadQ3GjH4DZACrcBeq21/4ELCk3AE7gNnsLtKOtxGzmF28lexG2k8GSnX+MOEpv9fNPA3bizjC7gRNwXu86P7wYOAbK4DW98vd7i6z+e4hlEO+55rlk/bAtwGPBnP58OPzz6BQjZ5nL/+l/9vHf4+VrctbNpX+56ilnu3UAzLtCM8eMfxT1DNu+Xuxm4E2jxw/Hr5gvW2r3W2lb/WYPbkdN++d6OO6BN9Nt2HTDLf7YDGOvX95G+jk2+jJRfB4XI+7Bv5/2wtf79br+uC748cPtFCrcPvYviF/29vtzxFINkD+4Lu5niGdFXKGb36ch2yfqywvC9FLO0lJ+mx4+zfj59fl0Ey/24JyPLEp77F4LzNlyACePC9m6L/De4/XcTA8/u+nw5af866/+PpxjQeiJl9/ppQ0Cvj6zXcOAmspzRs9w63P6ZKRkfDixhfodRPECG+ayN1Dnsj53+/Rjc9zhHMejm/TqxuG0QAu8aP88wHNwBuReX6PX58eP8vFPAr/z/jC+3Efc9AWiILF/YT8IZfsEvc1i2V3Hrt9fXbSouUezDBfv9dyAubj9Qf7inId3qX8/Efdl+iMvQQ8a9F/eFa8cFmmdwWWvIbnYBb6KY8c3004asOwS+cKTv839/A66jmDn04XaIAi4gR1+HbGB7ZD55il8OG/kL2UrINsOObCP/bWS+3RSzbovLhDv9sF7cjrsl8pnwehfFTMWWlB8ysDAulFU6XcjEQl3C+HCmEYZ1U8z08pGyChTPIpZS/MJaXGDvBT7jhz+H28l7fBltfrqdg6wbG5lXeN03xDTRaTtKpg3ZeCjn34dYB2E7hXFtg5Qftms3A5c9Wka+pLww3xAUwhlDAfhnBmb7YR5hHe6OLEsoL2SReymeoUXLCPMbaj2F4WEZdg4yLvo/X/L5fOSzoU7bBpnfcPMvXd+l8wnr7I3Kiu6bQ81nDwO/f+Fzt/vXrX58L8Xv1WB1Ka1DR8m07bgzrl0UD+BtuLP3E0b7D4uywH/gnqh0BO5I/hLuyNgCfBX3M9v7cIFkLK6JI6zsILwu4JoTwmnSXtzK3g58CbfyQzNDDrdxfkbxi/V7/9krKe4cBdwXbgnF4P20f53x417Cbdwdfvxzvr7/5OuV92WHTDBk/9+I1DvUOUPxFPsvkeX7BW6n3YsLliEr6MMdHFf6+YSmljV+Oe/znzcUM8Houhvj6xWGZSne1mEXxSztJb8OcsDJuG0W9sHjcZnfA778Y3FB3/hlucNPl/F1Cwe2V/zw0DwWvji7cAEoHHjv89N14NY9uOwyZGGhH2I8xSzzAxSDX8jmQuYVMkeLO/sqZQcZZiL/wwEkZJlZ3H4WhoWsPZhEMbv7vB/3QmPtwhgAAATQSURBVOSzm/3rpX45srgznRDoon1CHSWfeYpic0loOgxZeQ9u/8zjzkwtbh8NzT3hQGQoNkGFfTxktaFZpQeX+RpfBgw8mIYkqp3iGVFInkKzV2jyacftY+F7HPajMK8QVEMZYZ/NRKaLBmBw2zicPez006SA+X6aVyju582+Lv8Wmc8jFJMeKCY/Y3DfuQ2471wvcAHwMPCPuP1xAS6peYD9MdJZ9z5m6GcBiyIZ+hZckPgD7rRxLcWj7F5cm9hDuB3lT7hgtBEXgPv852f6DdOG+5L24Xb0EIj7cG2/y/yGe8VvhEUUA/WLfnhoO92D+1KEdrg+4PuRMm7CfSnbKTbZ3ILb8V7ydd4dWZY3ykz25S+aEf5v/9eHO3gsBb7r37+Ga7LZQzGLCGVsi5QV1lf4MrZTzHRCEIhmRN24A10Y9lncGVRou90FfNzXcQ9wDW7n76IYmMI6tn76zRSzplX+f96v0/A6elbTjdtPQvD4Fu6L1YtrI34Kt4/0+G0a+ijCtsz71yED7wVOZ2AmnqeYiYbMOZpFhukKFNvNLa4vo51iH08Bt68UfH3D9nstsixhfq3+9UKKwfmuknEFBu7bYV+4nmKgDGdXD/jx3RSbMUJ/Ti+vP9Osxl/0LKnLb4dwttgamXfYH6J1COu/K1Lv3ZFx4cAR/tpL5mtx+0wY9hjFg2vY334UGR/a9DdHhn23pLzQdxFe76W4H+3F7V9rKSaOIcmaNFoy9P4HVuMyjEm4gPARa+1U4DTcUXsprkPsIlzGVYdbeWlc+1erLy8sf9b/bcQF55/gVvAu/9cAHO6nXQacRPF0F+B3uHa/NC4o/NKXsZZiBheaDyzwbl+H0Nb2EPBRigFrMvAFP48/UMxawpcsD3zQ1+1F3I77Gm7H68Ad6d/l10XIrrdRbELYgWu7Cxncm/xnLvfLsxfXcflpP4/FFL8Qr1DMSNooBth6imck9biMY6Nf7vAlW4nrxA7r/WL/meW4zOjnuGYtcFcynY7LXn6LO/iGtu4Q0Ooo9ju04w7OKb9+jsB1roVMcKn/3+m3ywT/+hK//K8C78M14R2O25Zf9PXfiDtFDl/Ecbj9BT+vjzAwaPfi9pkuiu3sXX4eIYPcQTHYEpkGXPYbMsrQX/Jzimcm4azscYpZf+j7OIPi2dq5uO12iH/f5tdTq38fmspe9ssT2tHDWV7ojwjb+xn/mR0Us/sHKR5owvrow/UrgNs3O/zrAm6f3BT5fAjOIeClffm9uG0U+qlC+Z24M9U2imep3ZF6hnp3MjAbD31AIaA/F3kdziyaKFoZWQ+hb6AlUt4/+eX6UeQzoW8jbLtwQA/Lvgu3PXcDK621R+ISnK247fs9vz62U6Ga+2GRMeYC4B7cFybsgOEIuN0Pz+B22jG4QBs61Kpx2Vk4fa1UgYGd0cOVF8aHU7mwzF24nSN6uWIq8pkw7QZcoAudc7lB5hEtY7g6ROuaZ/hLX8Np8tiS4QtxB5VwNVEdbsc+rGSeFhfMD/VlZSP1CDtvOYlJtDxKlmOwYaXLUGvJj8THUPtcFy45AXcw2QZ83Fr7KBWquYAuIiKDU9YhIpIQCugiIgmhgC4ikhAK6CIiCaGALiKSEAroIiIJoYAuIpIQCugiIgnx/wGqmVmOVwBQqgAAAABJRU5ErkJggg==\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": 283
        },
        "id": "Y2GG9ypAgOke",
        "outputId": "0cb4b7ec-b16d-42fc-bca8-2f8da0b93c6e"
      },
      "source": [
        "plt.plot(mitbih_train.iloc[1,:-1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f922d69bc10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "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": "nnBWka8-JN2i",
        "outputId": "ea0d10e1-da34-4149-fa92-ca07eab8fb83"
      },
      "source": [
        "mitbih_test.iloc[:,-1].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    18054\n",
              "4     2230\n",
              "2     1442\n",
              "1      552\n",
              "3      160\n",
              "Name: 186, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0iL_sXPlHNR",
        "outputId": "c94cab2b-3ab0-4189-a251-9048d146b738"
      },
      "source": [
        "mitbih_train.iloc[:,-1].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    10000\n",
              "3    10000\n",
              "2    10000\n",
              "1    10000\n",
              "0    10000\n",
              "Name: 186, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "he6Lk0NMImIK"
      },
      "source": [
        "mitbih_test = mitbih_test.sample(frac=1).reset_index(drop=True)\n",
        "mitbih_train = mitbih_train.sample(frac=1).reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KdrCzI6edlB8"
      },
      "source": [
        "x_train = mitbih_train.iloc[: , :-1]\n",
        "x_test = mitbih_test.iloc[: , :-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRechECxMebd"
      },
      "source": [
        "y_train = mitbih_train.iloc[: , -1]\n",
        "y_test =  mitbih_test.iloc[: , -1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-14TeRlNmmP",
        "outputId": "5316a7e1-30fb-49b0-ca8a-f69966557cda"
      },
      "source": [
        "print(x_train.shape , x_test.shape)\n",
        "print(y_train.shape , y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000, 186) (22438, 186)\n",
            "(50000,) (22438,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwDg62jt7y95",
        "outputId": "704dee81-41d6-4879-ac7c-984e5446579e"
      },
      "source": [
        "from keras.utils import to_categorical\n",
        "\n",
        "y_train = to_categorical(y_train, num_classes=5, dtype='float32')\n",
        "y_test = to_categorical(y_test, num_classes=5, dtype='float32')\n",
        "\n",
        "print(y_train.shape , y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000, 5) (22438, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aB7QUMC8U7cS",
        "outputId": "00327403-fc93-4815-c247-49dcff474d35"
      },
      "source": [
        "x_train = np.expand_dims(x_train , -1)\n",
        "x_test = np.expand_dims(x_test , -1)\n",
        "\n",
        "print(x_test.shape , x_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(22438, 186, 1) (50000, 186, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBnzaX9YZYLv",
        "outputId": "2857cabd-48a3-4c30-8b33-588118a3efa7"
      },
      "source": [
        "print(y_test.shape , y_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(22438, 5) (50000, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KktatnjPIB54"
      },
      "source": [
        "##network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDPmFsRY5Bih"
      },
      "source": [
        "m = Sequential()\n",
        "\n",
        "m.add(GaussianNoise(0.1))\n",
        "\n",
        "m.add(Conv1D(16,\n",
        "                input_shape=[186, 1],\n",
        "                kernel_size=80,\n",
        "                strides=2,\n",
        "                padding='same',\n",
        "                kernel_initializer='glorot_uniform',\n",
        "                kernel_regularizer=regularizers.l2(l=0.0001)))\n",
        "m.add(BatchNormalization())\n",
        "m.add(Activation('relu'))\n",
        "m.add(MaxPooling1D(pool_size=2, strides=None))\n",
        "\n",
        "m.add(Conv1D(32,\n",
        "                kernel_size=3,\n",
        "                strides=1,\n",
        "                padding='same',\n",
        "                kernel_initializer='glorot_uniform',\n",
        "                kernel_regularizer=regularizers.l2(l=0.0001)))\n",
        "m.add(BatchNormalization())\n",
        "m.add(Activation('relu'))\n",
        "m.add(MaxPooling1D(pool_size=2, strides=None))\n",
        "\n",
        "m.add(Conv1D(64,\n",
        "                kernel_size=3,\n",
        "                strides=1,\n",
        "                padding='same',\n",
        "                kernel_initializer='glorot_uniform',\n",
        "                kernel_regularizer=regularizers.l2(l=0.0001)))\n",
        "m.add(BatchNormalization())\n",
        "m.add(Activation('relu'))\n",
        "m.add(MaxPooling1D(pool_size=2, strides=None))\n",
        "\n",
        "m.add(Conv1D(96,\n",
        "                kernel_size=3,\n",
        "                strides=1,\n",
        "                padding='same',\n",
        "                kernel_initializer='glorot_uniform',\n",
        "                kernel_regularizer=regularizers.l2(l=0.0001)))\n",
        "m.add(BatchNormalization())\n",
        "m.add(Activation('relu'))\n",
        "m.add(MaxPooling1D(pool_size=2, strides=None))\n",
        "\n",
        "m.add(Conv1D(96*2,\n",
        "                kernel_size=3,\n",
        "                strides=1,\n",
        "                padding='same',\n",
        "                kernel_initializer='glorot_uniform',\n",
        "                kernel_regularizer=regularizers.l2(l=0.0001)))\n",
        "m.add(BatchNormalization())\n",
        "m.add(Activation('relu'))\n",
        "m.add(MaxPooling1D(pool_size=2, strides=None))\n",
        "\n",
        "m.add(Lambda(lambda x: K.mean(x, axis=1)))  # Same as GAP for 1D Conv Layer\n",
        "\n",
        "m.add(Flatten())\n",
        "m.add(Dense(32, activation='relu'))\n",
        "m.add(Dropout(0.5))\n",
        "m.add(Dense(16, activation='relu'))\n",
        "\n",
        "m.add(Dropout(0.5))\n",
        "m.add(Dense(5, activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6w7bq1-oMapb"
      },
      "source": [
        "m.build(input_shape=(None, 186, 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fie9PHhLMhNF",
        "outputId": "3a1adab6-41a9-4bb4-fe36-73d72ab63965"
      },
      "source": [
        "m.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "gaussian_noise (GaussianNois (None, 186, 1)            0         \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 93, 16)            1296      \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 93, 16)            64        \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 93, 16)            0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d (MaxPooling1D) (None, 46, 16)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 46, 32)            1568      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 46, 32)            128       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 46, 32)            0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d_1 (MaxPooling1 (None, 23, 32)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_2 (Conv1D)            (None, 23, 64)            6208      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 23, 64)            256       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 23, 64)            0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d_2 (MaxPooling1 (None, 11, 64)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_3 (Conv1D)            (None, 11, 96)            18528     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 11, 96)            384       \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 11, 96)            0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d_3 (MaxPooling1 (None, 5, 96)             0         \n",
            "_________________________________________________________________\n",
            "conv1d_4 (Conv1D)            (None, 5, 192)            55488     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 5, 192)            768       \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 5, 192)            0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d_4 (MaxPooling1 (None, 2, 192)            0         \n",
            "_________________________________________________________________\n",
            "lambda (Lambda)              (None, 192)               0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 192)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 32)                6176      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 16)                528       \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 5)                 85        \n",
            "=================================================================\n",
            "Total params: 91,477\n",
            "Trainable params: 90,677\n",
            "Non-trainable params: 800\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9VCdu9JMjba"
      },
      "source": [
        "m.compile(optimizer=Adam(learning_rate=1e-3),\n",
        "            loss='categorical_crossentropy',\n",
        "            metrics=['accuracy'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7knLahViE8v5"
      },
      "source": [
        "##confusion matrix plotter"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "88WRNGnOMqNF"
      },
      "source": [
        "def conf_plotter(y_true, y_pred, classes,\n",
        "                normalize=0,\n",
        "                title=None,\n",
        "                cmap=plt.cm.pink_r,\n",
        "                mode = 'train',\n",
        "                save = 0,\n",
        "                name = 'conf.jpg'):\n",
        "  # if not title:\n",
        "  #     if mode == 'train':\n",
        "  #         title = 'Normalized confusion matrix on train dataset'\n",
        "  #     if mode == 'test':\n",
        "  #         title = 'Normalized confusion matrix on test dataset'\n",
        "  #     if mode == 'prob':\n",
        "  #         title = 'Normalized confusion matrix on Non-Overlapping test dataset'\n",
        "          \n",
        "\n",
        "  from sklearn.metrics import confusion_matrix\n",
        "  \n",
        "  cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "  if normalize:\n",
        "      cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "\n",
        "  fig, ax = plt.subplots(figsize=(4,4))\n",
        "  im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "  ax.figure.colorbar(im, ax=ax ,fraction=0.046, pad=0.04)\n",
        "  ax.set(xticks=np.arange(cm.shape[0]),\n",
        "        yticks=np.arange(cm.shape[1]),\n",
        "        xticklabels=classes, yticklabels=classes,\n",
        "        title=title,\n",
        "        ylabel= 'True label',\n",
        "        xlabel='Predicted label')\n",
        "  \n",
        "  for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] ):\n",
        "                  \n",
        "      item.set_fontsize(8)\n",
        "  \n",
        "  for item in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
        "      \n",
        "      item.set_fontsize(8)\n",
        "\n",
        "      \n",
        "              \n",
        "  plt.gca().invert_yaxis()\n",
        "  plt.setp(ax.get_xticklabels(), rotation=0, ha=\"right\",\n",
        "          rotation_mode=\"anchor\")\n",
        "\n",
        "  fmt = '.2f' if normalize else 'd'\n",
        "  thresh = cm.max() / 2.\n",
        "  for i in range(cm.shape[0]):\n",
        "      for j in range(cm.shape[1]):\n",
        "          ax.text(j, i, format(cm[i, j], fmt),\n",
        "                  ha=\"center\", va=\"center\",\n",
        "                  color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "  fig.tight_layout()\n",
        "  if save == 1:\n",
        "      plt.savefig(name)\n",
        "  return ax"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQS8yWJrMnTt",
        "outputId": "4632b383-e967-4ed5-e941-995ea95aea54"
      },
      "source": [
        "batch_size = 128\n",
        "\n",
        "m.fit(x=x_train,\n",
        "        y=y_train,\n",
        "        batch_size=batch_size,\n",
        "        epochs=20,\n",
        "        verbose=1,\n",
        "        #validation_split = 0.2,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "391/391 [==============================] - 23s 54ms/step - loss: 1.4100 - accuracy: 0.4360\n",
            "Epoch 2/20\n",
            "391/391 [==============================] - 21s 54ms/step - loss: 0.8750 - accuracy: 0.6988\n",
            "Epoch 3/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.7517 - accuracy: 0.7540\n",
            "Epoch 4/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.6808 - accuracy: 0.7775\n",
            "Epoch 5/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.6577 - accuracy: 0.7908\n",
            "Epoch 6/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.6227 - accuracy: 0.8049\n",
            "Epoch 7/20\n",
            "391/391 [==============================] - 21s 54ms/step - loss: 0.5864 - accuracy: 0.8207\n",
            "Epoch 8/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.5815 - accuracy: 0.8229\n",
            "Epoch 9/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.5705 - accuracy: 0.8275\n",
            "Epoch 10/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.5587 - accuracy: 0.8303\n",
            "Epoch 11/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.5365 - accuracy: 0.8369\n",
            "Epoch 12/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.5284 - accuracy: 0.8427\n",
            "Epoch 13/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.5317 - accuracy: 0.8402\n",
            "Epoch 14/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.4980 - accuracy: 0.8547\n",
            "Epoch 15/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.5008 - accuracy: 0.8522\n",
            "Epoch 16/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.5038 - accuracy: 0.8521\n",
            "Epoch 17/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.4958 - accuracy: 0.8530\n",
            "Epoch 18/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.4879 - accuracy: 0.8556\n",
            "Epoch 19/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.4905 - accuracy: 0.8588\n",
            "Epoch 20/20\n",
            "391/391 [==============================] - 21s 53ms/step - loss: 0.4778 - accuracy: 0.8595\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f922ddbe6d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "pg9mwnSYfG44",
        "outputId": "5a60b16c-a61b-4dc5-ce20-7191dd776f88"
      },
      "source": [
        "pred = m.predict(x_train)\n",
        "pr = [np.argmax(pred[i]) for i in range(len(y_train))]\n",
        "Y = [np.argmax(y_train[i]) for i in range(len(y_train))]\n",
        "\n",
        "conf_plotter(Y , pr , ['N', 'S', 'V', 'F', 'Q' ] , normalize=1, cmap = plt.cm.Reds ,mode='train', save=0 , name='RawData_Deep_physionet.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f92270fbed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zXnDRe3nLM4+z4e3/AnD8UA5R3Trh5++Pn8VCVLdOHPl2r1vz+fT+E9/9FqlSXQdjsVh4Y9qrJPS/DetZK3ePuIOWzaOZ8NJk4mNjSOqTyOiRdzLyvoeIahNPcFAg8975T9Hrm7aM4eSpUxQWniZtcTrL01JoER3t1nwzp79Gr36DsFqt3DPyTlq2aM6EF18mPi6WpD69GT1qBCPufYDIVrEEBwWRPOdtAFq2aM7tgwbQou31WCwW3pwxDX9/f7dme2P6VBL627LdPWK4PdsrxMfFFGUbee+DRLWOIzgoiHnvzi7KdtvA/rSM72D7N854za3ZAM5arXw05kkeWb4AP39/Nrw9l/zde+jz12c5lLGNnZ8s5druXUiaPBFjDPvWfcFHj4wDYHvKQpr16MqzOzdijGH3shV8s3iZW/P5+v7z1etgxJOPuxCR7kBPY8xzztrGx8WYLetWeSxLRYmfe/8g3E0fW3LpZv58yHkjL2nX5UYytm0vt3y0uqKGWXSN830csStzawUfvHbRPHoEY4xZA6zx5DaU+qOzDfrtm4cwlaqLpJQqhQ/fKqAFRqkqQIdrUEp5hAB+PnpFmxYYpSo7EcRHn7ymBUapKsBHe0haYJSq7ATw1yMYpZSn6ElepZRn6NfUSilP8tUjGB/9cksp5Srb19QVH65BRBJEZK+I7BOR8WW0uV1EdovILhH5wNk69QhGqcpOKj4cg4j4A28CNwM5wBYRWWSM2V2sTRTwDNDJGPOjiFztbL16BKNUped8qAYXulDtgX3GmAPGmEIgGehXos19wJvGmB8BjDFOxyXVAqNUVeDv53wqXxhwuNh8jv13xV0LXCsiX4jIRhFJcLZS7SIpVdmJyyd564tIRrH5WcaYWRexJQsQBXQHwoF1ItLKGPNTeS9QSlV2rl1od7Sc8WBygYbF5sPtvysuB9hkjDkNHBSR77AVnC1lbdB3CowBzp71doqy+fiAU69fdY23I5Rp5o/uHeHf7U4XeDtB2Ywr/59wy4UwW4AoEWmKrbAMBe4o0WYhMAx4R0TqY+syHShvpb5TYJRSl0QExPk5lnIZY86IyBhgOeAPvG2M2SUik4AMY8wi+7JbRGQ3YAWeNMaUO1p/mQVGRO4pJ8zbl/KPUEp5hjvupjbGpAPpJX43odjPBnjcPrmkvCMY9z7bUinlOT56JW+ZBcYYMwdARCzAYOAq4C0g9vJEU0q5RKTCXSRPcSXVXCAQGGaMOQNM9mwkpdRF8xPnkzdiudDmKmPMP4HfPR1GKXUJhPKfei9u+ZbpkrjyLdL3IjIEuEJEBgDufeq5UqrCvPVoWGdciXUPtvMv27BdfHOfRxMppS6O/RyMs8kbnB7BGGN+F5EtwI/YbobSrpJSvsZHv0VyWtZE5N/ASKAmMFJEZns8lVLq4vjoSV5XzsE0McbcfG5GRFZ4MI9S6iK540peTynvSt4e9h9/EJFngO1AG2w3PCmlfIbvDspb3hFMF/t/9wLVgevt8wc9mkgpddF8dUze8q7k/evlDKKUukSC186xOOPKSd47ReRzETkmIvtLDFhz2S1bsZLoth2IimnHlBl/v2B5QUEBQ++6l6iYdnTo0Yus7EMAfLZqDfFdb6J1x67Ed72JVWvXeybfpytoFhNPZKtYpkx7vdR8Q0beTWSrWK7vdhNZ2dlFyya/NoPIVrE0i4ln+Wcr3Z6tyc09uGvHJu7ZuYV24/58wfLaDcMZvGQBIzat47ZladQKCy1aNjDtIx7OO0D/VKfjPF+yZStWEd2uM1FxHZny+hsXLC8oKGDoPQ8QFdeRDj17k3XosMPyQ4dzqB1+DdPeeMuD+ToRFdehnHz3ExXXgQ49E8k6ZPvb27x1G7FdbiK2y03EdO7BgsXpF7y2onz1a2pXtvoo0A3YCTQDvvFoonJYrVbGjBtPekoyuzZ/QXLqAnbv2evQZvZ77xMYGEjmji2MffhBxr8wCYD69YJZ9OH7fL1hHe/+cyYjH3jYI/keefwJli5IYffWTcz7OIXd3+5xzDdnLkGBgezbuZ3HxjzM089PBGD3t3tITkllV8ZGli1M4eHHxmG1uu9+U/Hzo8frU1nQ/3bejbuB6NsGEhzdzKFNt8mT2P3Bh8y9visbJ0+j81+fL1q25fWZLLv3IbflKclqtTLmyb+Q/vH77Nq4luTUhRe+t3PnEVi3LpnbNjD2ofsZP/Elh+XjnptIYs8eeIIt3zOkf/wBuzauK/1vb+4HBNYNJHPbRsY+9EBRvuuaR7Nl9XK2r1/J0pR5PPjYk5w5c8Z94Vy5itdLXShXCswvxhgrcBoIBWKcvUBE1olIYLH5v4lIt0uPabN56zYiI5oQ0bQJ1atXZ8jA/qQtWerQZlH6UkbdMQSAwf37snLteowxxLZpTWhIAwBaNo/mt99+p6DAvQMNbc7YSmRERFG+oYMHkVbi0yptcTqjhg+z5RvQj5Vr1mKMIW1xOkMHDyIgIICmTZoQGRHB5oytbsvWID6On/Yf5ERWNmdPn2ZPygKuuTXRoU1wdDMOrVkHwOG16x2WH16zjsJTP7stT0mbt263vbdNGtvf236kpS93aLNo6TJGDbsdgMH9bi16bwEWLllKk0aNaFGiaLo3X9Ni+fqXkm95iXyfY4zhyiuvxGKxnY34veB3j5wvET9xOnmDKwVmiojUACYBM4EL+yUXWgzcWmy+E/D5xcdzlJuXT3jY+XGIw8NCyc13vHMhN/8IDe1tLBYLdevU4djx4w5tUtM+Ia5NawICAioa6YJ8DcOd5CvWpijfsePk5pfy2jz33ZVRKzSEU7nnR0D8OTeP2qEhDm2O7vyGqH62ty2y360E1KlNjeAgt2UoT27+Ecf3NjSE3Pwjjm3yjtDQ3m0r/t7+/PMvTP37m7zw9DgP5ssnvFiX0ZavlPfWIV/tor+9TRnbuK5jV1p3upG3ZkwtKjhuYXs4dUUH/faIMrcqIn4i4gd8BhQCXwD9gTkurHc+9kceiEgc8JX9KMjrdn27h/EvvMg//zbN21F8ztq/vEB4l07cuWE14Z1v4FRuHsaN3TRPmfjqNMY+dD+1atX0dpQyXR8fxzcb1rF55TKmvP4Pfv/dvRfEu+GxJR5RXhldiW2k3NKU29E1xuwTkTD7kc8AYEFp7UTkfuB+gEYNw52GDQsNIafYp3BObh5hIY6fwmEhDTicm0t4WChnzpzhxMmT1AsOLmo/cPgo5vxrJtdENHW6vYsVFhrC4Rwn+extwsPCzuerF0xYSCmvLXGEURE/5+VTu9gRQq2wUE6VOEL6Jf8InwwbBUC1mjWJ6t+XghMn3ZahPGEhDRzf27x8wuxd2qI2oQ04nJt3wXu7OWMbqWmLefqFF/npxEn8/PyoERDAmPvLHJTxEvKFkJObVyJfKe+tQ75TRX975zRvdi21atbkm2/3EB/r9GyDi7x3pa4zZR7BGGNuNMb0KG1ycd2fAT3tU6lX/xpjZhlj4o0x8VfVq+d0he3iYsncf5CDWdkUFhby4fyFJPV2fDRL394JzPngQwBSFn5Cj66dERF++ukEt95+B5MnPk+nDteXtvoKa9c2jsz9+zmYlUVhYSHJKakk9XE8z5HUJ5E578+z5VuQRo9uXRERkvokkpySSkFBAQezssjcv5/28W3dlu3I1u0ERkZQp3Ej/KpVI3rwAA6UOH9Vo15w0cnA9k+OZdd777tt+860i4uxvbfZh+zvbRpJib0c2vRN6MWceR8BkJK2uOi9Xbc0jYNfb+Hg11v480P38czjj7q1uJzPd4CD2cX+9hJvKZHvlhL5OiEiHMzOLjqpm33oMHsy99GkUcMLtlEhPnqS15ODfs/Hdr7mkDHGLWdTLRYLb0ybTMLA27Faz3L3ncNo2TyaCS9PIT42hqTeCYweMZyR9z9MVEw7goOCmPe27bEvM//9H/YdOMiLU6fx4lRb92j5go+5+qqr3BGtKN/M6a/Rq98grFYr94y8k5YtmjPhxZeJj4slqU9vRo8awYh7HyCyVSzBQUEkz7ENb9yyRXNuHzSAFm2vx2Kx8OaMafj7u+9JBsZqZfXjTzNo0ceIvz/fvPcBx77dyw3Pj+fIth0cWLKMhl060XnS82AMOV9sYNXYp4pef/tniwm+NorqtWpyX+ZOPn3oUbJXrHZbPovFwhtTXyFh0DCsVit3Dx9Ky+bNmPDKVOJj2pDUuxejRwxj5IP/R1RcR4KDApk3+59u2/7F57P/7b3yKvExMfZ8dzDywTFExXWw5/sXAJ9v2Myrf3+DapZq+Pn58ea0KdR34QPVZbaHU7tvfW4k587Ce2TlIvuA54wxyc7axsfGmC1rffc2J7FU83aEcs2oH+HtCGV6LG+XtyM44ZvdC4B2N95Cxvavyg3Y9k9BZtMd3Z2uq9rfFm4t57lIHuH0CEZE6gIPAUHAX4BEY8xiV1ZujImsWDyllEt89FYBV46r/gvsADravwka69lISqmL4sNDZrpSYK4wxiwDzl166JulUqk/LAF/f+eTF7hykvc7EXkaCBaRxwBf71Ar9cdTWbtIxpiHsRWV97ENmfmox1MppVxXmbtIItIVOAlsAk7Y55VSPqNyd5FutP9XgFbYru5d57FESqmL56NdJFeeKuAw8JSILPRcHKXURTvXRfJBrlwHU/ya6xCgvufiKKUuSWUtMMC522kNtsGmXBmuQSl1mQiCeOkcizPlFhix3eM9zBiTUF47pZQX+XAXqbzxYGYb241KmSIyREQiRSRCRHz3phel/qjc8DW1iCSIyF4R2Sci48tpN0hEjIg4va+pvCOYcwOm1AJ62adz3HsvvFKqAqTCX0OLiD/wJnAztmefbRGRRcaY3SXa1Qb+jO2yFafKKzBxIrKOC28N8Nzt10qpi+eeLlJ7bBfSHgAQkWRso1LuLtHuReBV4ElXVlpegdlujLmxnOVKKV/hWoGpX+KxQ7OMMbPsP4cBxZ8Dk8P5hy3aNyFxQENjzBIRqXCBubzOWuG3U95OUabXmrhvdDlPePKYPnDzUnlyTKQKE1fuRxZXB5w6eqnjwdjH554B3HUxryuvwPS7lCBKqcvMPSPa5QLFx/EMt//unNrAdcAa+wDiDYBFIpJkjCnzYYzlPTr28oz2rJSqIJePYMqzBYgSkabYCstQ4I5zC40xJyh2ka2IrAGeKK+4gGvjwSilfF0Fv6Y2xpwBxgDLgW+Bj4wxu0RkkogkXWos3zkHo5S6NG4a9NsYkw6kl/jdhDLadndlnVpglKr03NJF8ggtMEpVBT56q4AWGKWqAi0wSimPkIrfKuApWmCUqgpcuiDv8tMCo1RVoF0kpZRHaBdJKeVR2kVSSnmMdpGUUh4heqGdUsqT/HzzHIxvlr1yLFu1luhONxHV4UamvPHWBcsLCgoYev//EdXhRjokDiDrUA4A76cuJPamPkWTf8g17Pim5GBdFdfk5psY/dUm7v0mg/ZP/PmC5XUahXN7+gLu2ryeIcsXUSssFICrW1/H8DXLuXvrl9y1eT3NBg9we7Zln66gWUw8ka1imTLt9QuWFxQUMGTk3US2iuX6bjeRlZ1dtGzyazOIbBVLs5h4ln+20u3ZKkW+z1YQHduOqNZxTJleer6hI+8hqnUcHbr3JCv7EADHjh2nR2Jfav8pnDGPuzRO08URAT8XJi/wWIERkToiskRE1ojIRlcGCHbGarUy5pkXSP/gHXatW07ygk/YvTfToc3sDz4iMLAOmRtXM/aBexj/0qsADB/Un+0rl7B95RLemzmdpo0aEnNdi4pGciB+ftz8t6mk9Ludt2M70vy2QdSLbubQpvvkF9n1/oe8274LG155ja6Tngfg9K+/sWT0Q7zT9gY+7ncbPaa+TEDdOm7LZrVaeeTxJ1i6IIXdWzcx7+MUdn+7x6HN7DlzCQoMZN/O7Tw25mGefn4iALu/3UNySiq7MjaybGEKDz82DqvVWspWqna+MY8/Sfr8j9mVsZHkj1NLzRcYWJfMr7cx9pGHGG/PV6NGAJOe/wuvvTzJrZkciJ/zyQs8udWRwHz7XZedgb0VXeHm7V8R2bQxEY0bUb16dYb0v5W05Z85tFm0fAWjbh8EwOBbE1n5+ZcXjFg2b8EnDOl/a0XjXOYwpT0AABSySURBVCCkXVt+3H+QE1nZnD19mj0fzyfy1kSHNvWim3Fo7XoADq1dT+StvQH4cd9+ftp/AIBf8o/w6w9HuaK++55xtzljK5EREUQ0bUL16tUZOngQaYsdbpwlbXE6o4YPA2DwgH6sXLMWYwxpi9MZOngQAQEBNG3ShMiICDZnbHVbtsqYb8jggaQtccy3aMnSUvPVrFmTzjd0pEaNGm7N5MANTxXwBE8WmF+BjiJS3xhzxhhT4fEwc/OPEB4aUjQfHhJCbv7/SrT5Hw3tbSwWC3Vr1+bY8R8d2nyUtoRh/ftWNM4FaoWGcCrn/CBgp3LzqBUW4tDm+53fENXPVtyi+t1KQJ3a1AgOcmjTID4O/+rV+emA+4bBzM3Lp2F4WNF8eFgoufn5ZbaxWCzUrVOHY8eOk5tfymvzHF/7R8gX7mQbuXl5jvnq2vJ53LnrYJxNXuDJAjMXOASsFpEVItKgZAMRuV9EMkQk44fjl+GNADZt28GVV9TguubNnDf2gDXPTKBhlxsYuWENDbt04lRuHqbY4XzNBn+iz+y3WPrAGPDlsWKVb/mjHcEYY04bYyYZY1oBs4GxpbSZZYyJN8bEXxUc7HSdYSENyCn2qZGTn09YyJ9KtPkTh+1tzpw5w4lTp6hX7AgheeEnDB3g/qMXgJ/z8qld7FOudlgoP+c6fsr9kn+EtKGjeK9jd9a/8BIABSdso5NWr12bQfOTWT/xZfI3lzsS4UULCw3hcLGjq5zcPMJCQspsc+bMGU6cPEm9esGEhZTy2lDH1/4R8uU42UZYaKhjvhO2fJfFH+0cjIg0FpFq9tnv3bGtdjGtyTyQxcHswxQWFvLhwsUk3dLToU3fW25izkepAKQsXkqPTh2xD1LM2bNn+XhROkM90D0CyM/YRlBkBHUbN8KvWjWibxvIviXLHNpcUS+46NPk+ifHsnPO+wD4VatG/w/fY9cHH/LdgkVuz9aubRyZ+/dzMCuLwsJCklNSSerjeH4oqU8ic96fB0DKgjR6dOuKiJDUJ5HklFQKCgo4mJVF5v79tI9371MWKk++bNvfXsp8kno75uvbO6HUfB7nw10kT14HEwN8JCK/AaeBuyu6QovFwhuvTCRh2Cis1rPcPew2WkZfy4RXXyc+phVJvXoy+o4hjBzzOFEdbiQ4sC7z/vWPotev27CZhqEhRDRuVNEopTJWKysee4rBn6Tg5+/Pzjnvc+zbPXR6/hmObNvO/iXLaNi1M10nPY8xhpzPN7BirO1ry+hB/QnvfANXBAdz3Z22E4VL73+E77/+xi3ZLBYLM6e/Rq9+g7Bardwz8k5atmjOhBdfJj4ulqQ+vRk9agQj7n2AyFaxBAcFkTznbQBatmjO7YMG0KLt9VgsFt6cMQ1/N//BVoZ8b0yfSkJ/W767Rwy353uF+LiYonwj732QqNZxBAcFMe/d2UWvb9qiNSdPnaKw8DRpi9NZnpZKi+bR7gvoo1fyiq88Eya+TSuz5VP3f3K7yzR9LlKV5Sv/HyhNuy43krFte7nVIz6ysdk0/Wmn67L0f2TrpT4X6VLplbxKVXaC1y6kc0YLjFJVgY/eKqAFRqlKT292VEp5iuCzJ3m1wChV6Yl2kZRSHqRdJKWUR2gXSSnlOaJj8iqlPEjPwSilPOLciHY+SAuMUlWBj3aRfDOVUuoi2L+mdjY5W4tIgojsFZF9IjK+lOWPi8huEflaRFaKSGNn69QCo1QVICJOJyev9wfeBBKBFsAwESk5aPV2IN4Y0xpIAaY6y6UFRqnKTnDHgFPtgX3GmAPGmEIgGehXvIExZrUx5lf77EYg3NlKtcAoVemJOwpMGHC42HyO/XdlGQ0sdbZSnznJe/Sbb5kd2c7bMcr0RL77n6HkTubsWW9HKFvBL95OUL5qAd5OUDZXx6pxbYCt+iJSfCzWWcaYWRcbSUTuBOKBbs7a+kyBUUpdItev5D1azoBTuUDDYvPh9t85bkqkJ/As0M0YU+Bsg9pFUqrSc0sXaQsQJSJNRaQ6MBRwGGJSRGKBfwFJxpjvXUmmRzBKVQUVvJLXGHNGRMYAywF/4G1jzC4RmQRkGGMWAa8BtYCP7d9KHTLGJJW3Xi0wSlV2brqS1xiTDqSX+N2EYj/3vOBFTmiBUaoq8NErebXAKFUV6HANSinP0BHtlFKecu5KXh+kBUapSk+fKqCU8qDL8gzsS6AFRqnKTtBzMEopT9ExeZVSnqRdJKWUR4i4ejf1Zeebx1XlCO/Zg8HbNnLbjs20fvzRC5bXDA+j95KF9P98FQM3rCX8FtvVzWKx0PVfMxm4cR2DM76kzbg/eyTfshWriG7Xmai4jkx5/Y0LlhcUFDD0ngeIiutIh569yTp02GH5ocM51A6/hmlvvOX+bJ+tIDq2PVFt2jJl+t9KzzbqHqLatKXDjT3Jyj4EwLFjx+nRO4naDRoyZtxTbs9VlG/laqKv70ZUu85M+fubpecb/RBR7TrT4Za+Rfsu69BhrgyPJLZ7L2K79+LBcc94Jt+KVUS37UhUTHumzPhH6fnuuo+omPZ06JFQtP8+W7WG+K49ad2xG/Fde7Jq7Xr3h6v4zY4eUakKjPj5ccP0V1k+cAip7TpxzeCBBDa71qFN7FPjODg/jYWde7DqrvvoNMM2ql/EgH74Vw9gfoeuLOhyE9F3j6JWo4albeaSWa1Wxjz5F9I/fp9dG9eSnLqQ3Xv2OrSZPXcegXXrkrltA2Mfup/xE19yWD7uuYkk9uzh1lxF2cY9Rfr8j9i1ZQPJKans3rPHMdt7/yUwMJDMr7Yy9pGHGD9hIgA1agQw6bm/8NrLk9yeyyHf08+R/uF77PpiFcnz09i99zvHfO8n2/Jt+ZyxD97L+L++UrTsmiaN2b5mOdvXLOef0yd7Jt+4p0lPmceuzZ+TnDr/wvf2vfcJDKxL5o7NjH34Aca/8CIA9evVY9GH/+XrDWt5959vMPKBR9yeDxHnkxd4pMCISHcROSUigfb5d0UksqLrvSo+jpMHDnIqK5uzp09zIHUBjW9NdGhjjKFanVoAVK9bh1+PHDn/+5pXIv7+WK6owdnTpzl96lRFIznYvHU7kRFNiGjSmOrVqzNkYD/S0pc7tFm0dBmjht0OwOB+t7Jy7XqMfVChhUuW0qRRI1pEN3NrLoDNGVuJjGhKRNMmtmyDBpK22HFAskVL0hl1x1Bbtv79WLlmHcYYatasSecbOlAjwHMDM23etoPIpsX23YAk0pZ+6phv6aeMGjrYli+pDyvXf1G07zxt89Ztjvtv4ADSlixzzJe+jFF3DLHl69+36L2NbdOK0JAGALRsHs1vv/1OQYHToVQukrgwXX6ePII5DNzrzhVeGRLCL7l5RfO/5OZxZUiIQ5ttr0wlcshtDNvzNb1SkvnyCdvh8sGFizj9y6/csW8XQ3fv4Ot/vEnBjz+5Mx65+UcIDzs/ymB4aAi5+Ucc2+QdoWFYKAAWi4W6depw7Phxfv75F6b+/U1eeHqcWzOdz5bvmC0slNz8/BLZ8mkYHnY+W906HDt23CN5Lsx3hPDQ0PP5Stt3+SX3XW2OHf8RgIOHDhN3YwLd+w5m/YZN7s+XV+K9DQu5cP/lH6FhWLH9V6c2x4477r/UtMXEtWlFgFuLtf1CO2eTF3jyJG8a0FdEXvfgNi5wzW0DyXw/mZ1v/D+ubh9P93//P1Lbd+bq+DiM1coHUdcREBjIrZ8uJm/1Wk5lZV/OeGWa+Oo0xj50P7Vq1fR2lEon5E9Xk71jE/WCg9i642sGjLyXb75YSZ3atb0dzcGub/cw/oVJLF/wkQfW7pvfInmyrFmBT4CBZTUQkftFJENEMk65cKj7a34+NcPOf8rVDAvl1xKfIs1GDufA/IUAfL85A/+AAGrUq8c1tw0iZ8VKzJkz/H70KP/buIn6sTGX9i8rQ1hIA3Jyz48ymJOXT5j90LioTWgDDtuPws6cOcOJkyepFxzM5oxtPP3CizRt3Y6/v/VvJs/4BzNnve3GbCGO2XLzCCtx9BcWGsLhnNzz2U6cpF69YLdlKD9fA3Lyzh+dlrrvQkruu1PUCw4iICCAesFBALSNac01TRrz3b4D7s0XWuK9zc2/cP+FNOBwbrH9d/IU9YKD7e3zGDj8Lub8aybXRDR1a7aiITP/KOdgivkPcF9ZC40xs4wx8caY+Nou7IAftm6nzjUR1GrcCL9q1YgYNIDsEv3gnw/nENq9KwCBzaLwr1GD348e5eecHEK7dQHAcuWVXN0unhPfZVbgn3ahdnExZO4/yMHsQxQWFvLh/DSSEns5tOmb0Is582yfYClpi+nRtTMiwrqlaRz8egsHv97Cnx+6j2cef5Qx99/jvmxt48jcf4CDWdm2bKnzSeqT4JitdyJzPki2ZVuYRo9uXS7bJejtYtuQeSDr/L5bsIikhJsd8yXczJzkFFu+RUvo0aUTIsIPR49htVoBOJCVTeaBg0Q0aeTefHGxjvtv/gKSepd4b3v3Ys4HH9ryLfyk6L396acT3Hr7HUye+BydOlzv1lxFfLTAePQ6GGPMTyKyF7jJLeuzWvnyifEkLvwY8fPju7kf8NOevcQ9O56j23dwKH0Zm/4ygS4zX+e6Rx4EY1j34BgAds96m65v/YNBmz8HEb777zyO73LvkwIsFgtvTH2FhEHDsFqt3D18KC2bN2PCK1OJj2lDUu9ejB4xjJEP/h9RcR0JDgpk3ux/ujVDudmmTSWh/2CsZ63cPWI4LZs3Z8JLrxAfG0tSn0RGj7yTkfc9SFSbtgQHBTHvnf8Uvb5pyzacPHWKwsLTpC1ewvK0VFpER7s335QXSbjtTlu+O4bQMroZEyZPIz6mNUmJtzB6+FBGPjyWqHadCQ4MZN6/bV9lr9uwiRemTKdaNQt+4sdb0yYTHBTktmxF+aZNIWHgENt7e+cdtGwezYSXpxAfG0NS7wRGjxjOyPsfISqmvW3/vf0vAGb+ezb7DmTx4tTpvDh1OgDLF3zE1Vdd5aZ03isgzognzsKLSHegpzHmORGJAvYC1xpj9pX1mib+FvPclYFuz+Iuow9/4+0I5bP48KM39LEll6xdt5vJ2L6j3OoR36aV2bJ8gdN1+YVEbS3nqQIe4ZEjGGPMGmCN/edMKtn1NkpVPr55BKO3CihVFejNjkopj/DiSVxntMAoVRVogVFKeYpoF0kp5TF6BKOU8gzv3czojBYYpaoCPYJRSnmEPhdJKeVRegSjlPIY36wvWmCUqvz0sSVKKU85Nx6MD/LNsqeUukgVH5NXRBJEZK+I7BOR8aUsDxCRD+3LN4lIE2fr1AKjVFVQwQGnRMQfeBNIBFoAw0SkRYlmo4EfjTGRwOvAq85iaYFRqtITdzwXqT2wzxhzwBhTCCQD/Uq06QfMsf+cAtwkToY89JlzMNlnrUfv+/mYu0bgrg8cddO6ALgvKMR5I9e5PZ+bab5L5+5sjZ012Lp9x3KpFVTfhXXVEJGMYvOzjDGz7D+HYXsSyDk5QMnxPYvaGGPOiMgJoB7l/Ht9psAYY9w1fiAiknG5R+66GJqvYnw5nzeyGWMSnLfyDu0iKaUAcoHijzoNt/+u1DYiYgHqAsfKW6kWGKUUwBYgSkSaikh1YCiwqESbRcAo+8+DgVXGyaDePtNFcrNZzpt4learGF/O58vZymQ/pzIGWA74A28bY3aJyCQgwxizCJgNzBWRfcBxbEWoXB55qoBSSoF2kZRSHlTpC4yIdBeRUyISaJ9/V0QivZ3rHBGpIyJLRGSNiGwUEZ/59kNE1p3bb/b5v4lIN29mKo39Pc6278M1IlLX25kARKSXiKy3Z5phv1hNFVPpC4zdYeBeb4cow0hgvjGmO9AZ20PofMVi4NZi852Az72UxZm5xpju9umEt8OISH3gWSDB/t7+ADzg1VA+qKoUmDSgr49+gvwKdBSR+saYM8aYU94OVMx87Fdrikgc8JUxxurdSJVGH2xF79xjK18H+nsxj0+qKgXGCnwCDPR2kFLMBQ4Bq0VkhYg08Hagc+yP8g0TkRrAAMD580e9Z4S9K/KOt4PYhQB552aMMb8D1b0XxzdVlQID8B/gPm+HKMkYc9oYM8kY0wrb13xjvZ2phM+AnvZphZezlOdcF+lubwexywdCz83Yi7Qe/ZVQZQqMMeYnbOc32ns7S3Ei0lhEqtlnv8f39vl84AngkDGmwNthKpGlwEgRqWmffwzfPgL0iqp2od0/gEe8HaKEGOAjEfkNOA34yicwAMaYr0QkHPint7NUJsaY70VkMrDM/k3cbuAOL8fyOXqhnVIVJCKNgY+AvsaY772dx5dogVFKeYyvnQ9QSlUhWmCUUh6jBUYp5TFaYJRSHqMFxoeVuMkvzX4x18W8fqJ9HTEiMrqcbURczPpK/G6NfXSzstr3dGG9d4mIr95LpipAC4zvm2u/me5LbKOIASDi+qP8jDE7jDGzy1jcHXCpwCh1sbTAVB47gHD7cBQzsV3gdYWIzBORVfYHYlUTkWARWS0iS7Ff1Ww/SnnJ/vND9mEjVotIM+AuYLqITHd1faWxP7RrjYhkiMjIYotG2+/Bmm1vd8E2PLO7lC/QAlN5dAW+s//8hTHmFmxDVCwyxvQA1mA7wrkX+I8xJhHb0IdFRORq4DagkzHmRiATeBcYZ4wZd7HrK2Gd/UirA47DFnxjjOkJFIpIhzK2oaooLTC+b4SIrAYCsQ1LAbDV/t/mwFgRWYNtMOarsXV3ttuXbyuxrqbAtnNDMhhjzpZYfrHrK66tiKwAVmJ7MuA55167A4gsYxuqiqpq9yJVRXONMc+dm7E/SO9cYdgLrDTGpNqXVQMeB9pguzcmFtsgzuccAGJFxM8Yc9Z+Huc0549MLnZ9xT2F7egkl/NHWthfm27/71xsD+oquY3hF7E/VCWiRzCV2yxggIisFJFVQBy2YSsesJ8zcbg72hjzA5AKfGk/KorC1k15VkQmXOz6SliA7QjrP8BPxX7fXERWAlcYYzaUsQ1VRem9SEopj9EjGKWUx2iBUUp5jBYYpZTHaIFRSnmMFhillMdogVFKeYwWGKWUx2iBUUp5zP8Hf1ObOlEDBzAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvMBDPtFOtUY"
      },
      "source": [
        "##classification report"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "JhjoZGszwAa6",
        "outputId": "77358979-792c-4a20-af0e-0c11de5295df"
      },
      "source": [
        "pred = m.predict(x_test)\n",
        "pr = [np.argmax(pred[i]) for i in range(len(y_test))]\n",
        "Y = [np.argmax(y_test[i]) for i in range(len(y_test))]\n",
        "\n",
        "conf_plotter(Y , pr , ['N', 'S', 'V', 'F', 'Q' ] , normalize=1, cmap = plt.cm.BuGn ,mode='test', save=0 , name='RawData_Deep_physionet.png')\n",
        "classification_report(Y, pr, labels = [0, 1, 2, 3, 4], target_names=['N', 'S', 'V', 'F', 'Q' ],\n",
        "                      output_dict = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'F': {'f1-score': 0.16262295081967212,\n",
              "  'precision': 0.09084249084249084,\n",
              "  'recall': 0.775,\n",
              "  'support': 160},\n",
              " 'N': {'f1-score': 0.8825465857267977,\n",
              "  'precision': 0.9703792256093511,\n",
              "  'recall': 0.8092943392046084,\n",
              "  'support': 18054},\n",
              " 'Q': {'f1-score': 0.5590994371482176,\n",
              "  'precision': 0.4806451612903226,\n",
              "  'recall': 0.6681614349775785,\n",
              "  'support': 2230},\n",
              " 'S': {'f1-score': 0.13096559378468367,\n",
              "  'precision': 0.16905444126074498,\n",
              "  'recall': 0.1068840579710145,\n",
              "  'support': 552},\n",
              " 'V': {'f1-score': 0.5527562983287603,\n",
              "  'precision': 0.43163225555122714,\n",
              "  'recall': 0.768377253814147,\n",
              "  'support': 1442},\n",
              " 'accuracy': 0.7751136464925573,\n",
              " 'macro avg': {'f1-score': 0.4575981731616263,\n",
              "  'precision': 0.42851071491082726,\n",
              "  'recall': 0.6255434171934697,\n",
              "  'support': 22438},\n",
              " 'weighted avg': {'f1-score': 0.8055831654177064,\n",
              "  'precision': 0.8610986634924789,\n",
              "  'recall': 0.7751136464925573,\n",
              "  'support': 22438}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uEJ1WS2LFGTs"
      },
      "source": [
        "##saving the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IS8qXwWv1fHC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "505c6b44-2034-473a-f1c2-d6f3568e054b"
      },
      "source": [
        "'''\n",
        "# serialize model to JSON\n",
        "model_json = model.to_json()\n",
        "with open(\"shayanmodel.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "model.save_weights(\"shayanmodel.h5\")\n",
        "print(\"Saved model to disk\")\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\n# serialize model to JSON\\nmodel_json = model.to_json()\\nwith open(\"shayanmodel.json\", \"w\") as json_file:\\n    json_file.write(model_json)\\n# serialize weights to HDF5\\nmodel.save_weights(\"shayanmodel.h5\")\\nprint(\"Saved model to disk\")\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HRrPrGKFCUB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}