Data_Exploration.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Data-Exploration.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
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        "p1sg4YGWvrf9",
        "Ir1UMfoRv4cL",
        "vAfdFwGcwLch",
        "zoEw9gbBC-PO",
        "56nxlQq8DBLC",
        "5zoYXnYCDfhv",
        "7-YuYe6SOx-A"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbUYosCxkgcy",
        "colab_type": "text"
      },
      "source": [
        "# Data Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYRPyq37kukI",
        "colab_type": "text"
      },
      "source": [
        "- Import packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFnlYbKjsdpr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import csv\n",
        "import numpy as np\n",
        "from imutils import paths\n",
        "from pathlib import Path\n",
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "#drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EyRFXmSrkVgq",
        "colab_type": "text"
      },
      "source": [
        "### Make a subset (small part of data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nus0QwNawb2x",
        "colab_type": "text"
      },
      "source": [
        "- We make a small dataset  to have insight into  original one"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znMXBWIqeh_X",
        "colab_type": "code",
        "outputId": "a24eb9ba-1bf6-42de-8866-611e34aa4f92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# X_train = pd.read_csv('X_train_sat6.csv')\n",
        "# y_train = pd.read_csv('y_train_sat6.csv')\n",
        "# !head -1000 X_train_sat6.csv > '/content/gdrive/My Drive/X_train_subset.csv'\n",
        "# !head -1000 y_train_sat6.csv > '/content/gdrive/My Drive/y_train_subset.csv'\n",
        "\n",
        "!rm -r \"/content/gdrive/ELSI-DL-Bootcamp\"\n",
        "!cd \"/content/gdrive\" && git clone --single-branch --branch 'feat#data_exploration' https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp.git\n"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'ELSI-DL-Bootcamp'...\n",
            "remote: Enumerating objects: 48, done.\u001b[K\n",
            "remote: Counting objects: 100% (48/48), done.\u001b[K\n",
            "remote: Compressing objects: 100% (39/39), done.\u001b[K\n",
            "remote: Total 48 (delta 16), reused 28 (delta 7), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (48/48), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iiP6vJXik0JY",
        "colab_type": "text"
      },
      "source": [
        "### Print current folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OsCWipWOeeet",
        "colab_type": "code",
        "outputId": "c682e952-5b27-45e2-f255-1b40875a8064",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "from subprocess import check_output\n",
        "print(check_output([\"ls\", \"./gdrive/ELSI-DL-Bootcamp\"]).decode(\"utf8\"))"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data_Exploration_and_Visualization.ipynb\n",
            "deepsat.png\n",
            "kaggle_sat.ipynb\n",
            "README.md\n",
            "sat6annotations.csv\n",
            "X_train_subset.csv\n",
            "y_train_subset.csv\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jS3rnIdRk6VG",
        "colab_type": "text"
      },
      "source": [
        "### Load dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0Oe7cVzYTkd",
        "colab_type": "code",
        "outputId": "57b4652f-c321-4b2e-a035-f4c27d51ad47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "X_train = pd.read_csv('/content/gdrive/ELSI-DL-Bootcamp/X_train_subset.csv',header=None)\n",
        "y_train = pd.read_csv('/content/gdrive/ELSI-DL-Bootcamp/y_train_subset.csv',header=None)\n",
        "class_names = list(pd.read_csv('/content/gdrive/ELSI-DL-Bootcamp/sat6annotations.csv', header=None)[0])\n",
        "\n",
        "print(\"Shape of X_train: {}\".format(X_train.shape))\n",
        "print(\"Shape of y_train: {}\".format(y_train.shape))\n",
        "print(\"Class names: {}\".format(class_names))"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of X_train: (3000, 3136)\n",
            "Shape of y_train: (3000, 6)\n",
            "Class names: ['building', 'barren_land', 'trees', 'grassland', 'road', 'water']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQ4abPcKm9l6",
        "colab_type": "text"
      },
      "source": [
        "- Original images are in shape of (999, 28, 28, 4) --> (samples, height, width, channel) , but were given in (999, 3136)\n",
        "- So we need to reshape them first to have a standard image format"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbYGrrWqlCYI",
        "colab_type": "text"
      },
      "source": [
        "### Reshape and check dimensions again"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBAEJO1BekRh",
        "colab_type": "code",
        "outputId": "c292b717-65b0-4e99-d08d-39d7867aeffb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "X_train_array = X_train.values.reshape((-1,28,28,4)).clip(0,255).astype(np.uint8)\n",
        "y_train_array = y_train.values.getfield(dtype=np.int8)\n",
        "\n",
        "print('X_train_shape:',X_train_array.shape)\n",
        "print('y_train_shape:',y_train_array.shape)"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train_shape: (3000, 28, 28, 4)\n",
            "y_train_shape: (3000, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-CgrzRhnjEd",
        "colab_type": "text"
      },
      "source": [
        "- classes are seen in shape of (999,6) where 6 is index size of encoded labels\n",
        "- therefore we should decode each label to count and visualize distribution of classes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJmMw7N0OGAm",
        "colab_type": "text"
      },
      "source": [
        "### Visualize some samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pM7o25L2OKyC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "fe8c42b0-e228-4481-b54a-3beaaf2daa8c"
      },
      "source": [
        "\n",
        "def plot_samples(channel):\n",
        "\n",
        "  fig = plt.figure(1,figsize=(8,8))\n",
        "  for i in range(16):\n",
        "      ax = fig.add_subplot(4,4,i+1)\n",
        "      img = X_train_array[i,...,:channel]\n",
        "      ax.imshow(img,cmap='gray')\n",
        "      ax.set_title(np.argmax(y_train_array[i]),fontsize=12)\n",
        "      ax.axis('off')\n",
        "\n",
        "  plt.show()\n",
        "  \n",
        "plot_samples(channel=3)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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acvpl/Y0vDNNjU5xCmz1OJ5FzBeqXJ7w2TS5eGKZHRnkwhLDPT3MeNkpl7A+P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\n3BzF+0RRFENjzC/Cf/2+tfaGMeY5Y8zNh9EmhUJB+Mt+5uqbruJ7Gtb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pTwS3\nMoM/hnH8r8yhA+3OqI5njDEeuM9EvT7LM+BO48DcZkJyKZ3QXm7VuRKXC2dwBhF98ly48Xg45ni+\nc4WrLIECvnkBAAAgAElEQVSobyI4vQOuRjlcEMqEW1AOY2A9bk8fjGh/DeH8isZ87dSaoJy2sMLy\nCufgM1ffdBUKhUKhqAj60FUoFAqFoiJUSi/n4FbiiSDEMTBNruX0g+9AcAFwExJx6k0R0W8ILt9t\nTAuCKCQ1ojPqKXdHSCdEU0RAqzQD/vuEBUcWlEIIn5sAJZIIxRZ0BbJCQN4CNYOBl3PRMSfHMeWZ\n8jr7YWNvl5SbsphTMMNdCmrQnT/B8s48Q9TN/kVSjDpxjitSnTpLn2u3OJW7sEKyP8mI3AU6p7hr\nkQFXnSQBBa3uIiu2+/ZrkCcGDlSPLJg30kTQZAHVn8UDloeuFgmsuSTllHG0hepIvBlFMV31ImOM\nee6naZzzAaeQd1+h9TseC3efBVivKSg1HeH08niBNu29f8uVidY2YW8PofNf5xt9CNJuQZuUg7pN\nPj6dCdGHe7Gg/erkjnLvKuUVlu9Rr055M6d43gTUtcYJuEzd5N+1uED9mpkVLlQyQvohA1dymvL1\nmoJpKgw4rZ5DVxNY554IVO+4NOaOoK8zUPcLQaZvb5crrSEaDeLw0wl3GWo10QWJ96UGAVZSCFKR\nZryOWpPcggYjXsfV179api+/+IUyPX/8GVZu5gSp0jUXT7O8Ye+9+/YX0DddhUKhUCgqgj50FQqF\nQqGoCPrQVSgUCoWiIlRq0y1S+DphsMrBLuqHQpoRpeQykPiS9gWwvbVE5J8lMIY2QPox9bgdFKXS\n8EK5tdxelKNdznBbj4HIE24OtTiiHEiouS73JfBSqmME1/StGDe027qijVM2F5ne1kaZzoW9KB6S\njXf2WJflPfahHyvTm3WypS6v8KgizTrZmVZvvM7ytjdpTGog3cn0RI0x6G2FwdD7PS7hOH/6dJkO\nu9x+HIIr0xDs2FbY/1D+0/H4XNTBvWgEMpO12iwrd/8G2ZVmFs6xvHabS99NA/4G7Y3RgO+N2IGI\nVkIaz27TXjzxMUovneR7+fqblHd/htcRtGnNDO6RnX4y5OXCgPaoD3bEgZD12wcJRyOCtJsulY33\naR6dnMs0wnUSM7woxiOGOYb9Gn5I1AFnzH7C92+0M103sAzuMbhWuICNST7V87ntPYO1HEBUHXHE\nmAxsptYRd0rgnS7PqVwogsKnGMQepTaFHPBoSONa5Py8jAzV70A7PGGrjsfkapRFfP/u3L1bpvf7\ndCcjFC5OXbiL8+pX/4DlZX108/t58yDom65CoVAoFBVBH7oKhUKhUFSESullDFBcOJwKcsBNyGac\nw3BAJccFGjoQ0Ws8oC0SoZxSD6irGFgoGgkq2yOaBWLYm1S4Frl1+lu6dqRwS92xRIO4Iqi8BWom\nE0pTCXDDSFumQv0KlbGsK/riHBzx4v1gsENUay5chs584ONlWroS+HC9f8cjGiedrLNyE5do6Tzj\nijlZBFQWRN8pCjkZ9N1+QDTZ4jHuMmR6NFZHH+EuAnffJsp36+6LZfrpH/hrrFwOEaB8MRdpDNFT\nYIHECXctWjlDSlmFcEky8Z6ZNq58E36Hb/KxXLwAKkXzfE5nTlNeG6wEt/4Nr99/jMp9+Kc4vXf9\n39K4XAQzUipMLwWsJw9cl5iZwRjT6IIKknB/ikZQP3TZLgiTQQIuhLsiItAs9WV+iSo5/kFOswa3\nqL2bF/mezLlF5dCRWqBdRcD1+iyZNqKY09we2q3AvJeIoPAemlFyMXbgTjQa0v6t1Tq8XI3GywUz\nTC6UoApmwhLfBe+PDVCy6o34nsnBzNCY4+YaF6IfQaAi0145w7+rSWbBTmOZ5TVOP2YOgr7pKhQK\nhUJREfShq1AoFApFRaiUXjYoei0UqfwQnv9CairHd31DlNdE1IEqV+NM/J5o0+dmQZw/8zkltb1L\nFAYTx86EmhTcCvQEbeMC5TKBG7OjCe+XB+1wAk6hOXB3Og+AuvM45TcZ0ecmggLP3OkGyF4++gj9\nUeOUWgZBJRpdTsFs37xYprszFHR+f+0aK7dy6tky7YkAFg5ezbb03VZM++4WKV41QxJk75v7rJxX\no/rWr77C8vpwS7vRerxM1xoLrJxTUJ+ThFNjnQ5RXn2wR+ys81vUJ5/6gTJdJHxN+N5059MYY3Zv\nUx+Wj/D9dfIk0Wphk893foP2zZ3XaV1vCFq3NSGa0XuZ17EDHgYJ3EZtnuCTOgu3WhMI6hEUfI/u\n3qE1MxzxOnIfbt2C2csZiJvrsOy8Dl+D/mP0fbUdavv9r8jA6TSPnsP3aOuI1M47XKRgtnM9fkN5\nOCE1L8eIsyOnvvoujQkqUBljDF6IdsTVZgf2ZaM+V6aTlKtEuRCoJUtprKKY3wKvBwGUEyYr8AQZ\nww1lT8jyZT7dZnbA3GSMMcef/v4y/cbrXynTzQ4P5OC1qR1OwPdIv8+DlDwI+qarUCgUCkVF0Ieu\nQqFQKBQVQR+6CoVCoVBUhEptul4DgrYLb5YUArp7wt3CBRsO0PVmHPLr6y643eTCBhuCvdcDlxWT\ni0gWM/Q7JAb1GKleglJHhQxYDs3HyD8yeHMG1/kzYYP2IJpSDhFSImHbzMH2W4i+OFa2+XDRnSe3\nmwGoLBnD3YSkrbYGgeVvv/a7ZfrcD/0H/AvA5tSqywjiZI/xwXa93+c20jAkt4jRmNwHFk8+wsqZ\nRZqb25deZVknL3yCmgT2qLDBg2wPNum7HZeP/d4mRV2yMDZuzOdzAlFKioS7SeUB/75pIG+SHa1+\nkn9fY5n2TSFUhfbXaL1uzVDfd45zu/Ttl+BOhjgDEnADQzc8u8ftnj1c1xlVsi8iCU0gsljeEmeK\nT3X48+CSuMX34cwiNWRugR+XxTEan627tC62t3jHQrCrtkQEK7msDxsFnJ3D/X2W59fIVpuJ+zF4\niOXFe99fQZtpJs63FOyznkcdLYRr0QDVn0CFTkZJS9mZzm3Q47j3wCzpppnDPYm9tRssL2yQXTgA\nP7L9e2+yct2558r0aMhd/lq1hjkI+qarUCgUCkVF0IeuQqFQKBQVoVJ62cmIspjE/Hk/AQojzznl\nloCEVARuMaG4vt7wqY4g4K4KbZ8oKow53xO/O3xQfBoiXSuuwzfrQBG+K7A8UNmo350JNSlQu8lE\nnyeGqBq8lZ4LwfQJUOqxdMMqpqtIZYGiC2qcboyGZAe4f+XLLO/ZH/xPyrRz/vkyvTC7wsoFENB6\nvMddfLaBKmu2ztJ3XeJUEMQ7N3Mrp8p0lnAqPgOh+yPHzrO8HaChhlu36TMbr7FyPrgjHDn9JM/D\nvozouxxhLtjdIJWv1rxQ7hFBvacBH1SWFh7hdOFyl9yJhomgcpdpv10At7bJChecX09pQvox36PJ\nKQhgDi55G1/nriO9m+B25EMbW3wNzp+gzTezxPfCBAI07INiVCGG2IJQUyGCqPRfp+8eQb8yEXQj\nNqBUNssPi9lUmKYOGR6otbmBCGoAw58l8qwAd0mkkDNebjykfdhoca7cRSU6OLYC0Y7ZkNZIFJGr\nnaSrI1C9k7Sx54AbD/gN5gVfpynMkyfcfWYa5Bq0fIb6sr9+kZWbnCWFqtDnay5wJU3/buibrkKh\nUCgUFUEfugqFQqFQVAR96CoUCoVCUREqtem6IHuYCJtQBqR/IqQO0SDQmqW0b0UgbXC7cYSNFO0S\n4xHx+mnCy8Ug5WeZHUIEmQdJNSv6YiCCUgo2BV/YoGO4Am9FgHs3BUk8n9rriPY6EFA5FFHri2C6\nEnMOuARk6ZDl1TtkEzn9+MdYXrNBNpzgDMkq+sIdoTUDNr4hX6pnL3ygTN94m2yrr/7xV1i5CGx0\np569VKY7R7j9+MyjFN0nFvbejTf+vExj4KYnfuQ/ZOXaHXIXyEVf0HUDI1mFLT5nNbCLDfvcZajT\n5LKTUwF0fetNbq/6xi1aT2nC5yMD22R3jvruiYhhxV0awCeW+T4//2Nkw77zJu2HP/0Cl9RMwJ4X\ng9RgPeNtCiFMWH+L9yW5Ru3qNKjT/iK3I45naP9u8WsFxm2CqxG0qXGct8MB26Qw95qrf87n/7Dh\ngh16Mha25oj+9sTFlFoIazmDSGkOH8dGk+QdHUfYT2OIcOTRdxUiopoHUrzYDmkbx0hIzSa/78Dd\nidDFSciVgruSFfd03IDG6kN/5efK9O4qdy1qQpQkKy4BNGtcRvRB0DddhUKhUCgqgj50FQqFQqGo\nCJXSywXQxlnKaZUspNf+hnhD70JwYQ8oDMGmGg/rzzh1td+jwgm43eQOp41dF5WgiN5w2vyaew3a\nZARlNIipzpSpufA2efCbR0ZdQoq6APcJ4wjVKWBVUiHzJSOaHDaCJinyzDS5epFXo/HZj7ki1e42\n0TXxLilZLSwfZ+XGu2BK8AQVBD8XgyZ9V3ely8rt3AeXhjly46l3+Xz2+qRWVQ95X46eJgUa8F4z\nKytnWbmFoxQU+9a111neBHjuEKg74/MtOIF9sb16h+XNPHnSTBspRPrZWheB3/eI5m01+Do89zzN\nVfs0qYVde5mvyRjMLQtPCXe9PlGXd6/R/PQLTmlm4DqyeB7qOycUo4BuTva5+ab7UXJD8kAlav0i\n/67JOszbPN9PzS6VbY0pr/tJvrbWXqb2br/GTQaFP123vhT8ghLh7sNeucTZlIObE/OxdN5bkaqQ\n5i0wC4Yhmib4XBQQHQqjiblCrivDc1A8uvC8RyveeMzH2wvp4VLkvL39fYoQ1OrQHLbmuPtfPIYz\nJeQ0t+vyuX8Q9E1XoVAoFIqKoA9dhUKhUCgqQqX0sgW6xw+FKDi8lc8LlZ5OHUW1IVD9RNyAzogK\nKkTAg2GCgQeoXJpxOqAOiiI+UH+xUGcfQxNdcYs6DOA2XgFUdiFutALVJgRWTJASZz2G9k6GXOEH\nLoQbv+CVRAeLo7wvDAeknpRnnG7s+ifKdK09y/KyPlF7E7j5nRTiZjaMQS5E0ic7FFg+CIiGevaH\nedCEF37/n5XpBVCkWjyzzMqNdm5RewWFtj6iW8+nTxLVvLX+FisXjYmWbncXWV5QI5NDnMCtUXHL\n3oN12z7yOMvbWBXXZ6eAE+eoPeMNEYzdo7YtnuM0rLcHe+XLlJ4TKmyNx2md8DvJxrzxGu2Vt/+M\nbsOHKyJ4ySy1awVu6J9Y4XaprSF9bnWVr8+t29TGyYT6PKoJWxEo59W3eF8WumD+OEX1tyzfo/eA\nNo/EGeCY6QYlmUR04zdJed8cVJoSlqg0p/Xqe9SfPBMqYnC+WSvMZ6ACmMCal8cS3uyH492II9w0\nm6QYZa1Q9wNTGipXOUIhqoBzygpvknaLzqmooEAGeHvbGGNqbTKfGHE733cOfqTqm65CoVAoFBVB\nH7oKhUKhUFQEfegqFAqFQlERqrXpghtMrcZtpAXYcQthU4vBroLRMBxxzd2AfWyciivlCfD8YHLJ\nha02mtDnah2qv+4J9waQ7nFSbsOJclC8gogXwuvFOBCCaDzhtpIxuDWlOdVvDW8vKns5TWkznq7a\nTR2CuDdmuFoSBos2Uj0mp34HLrnPxOmElWt5oHYT8uDQMdhS+n2y70aDXVauAe4gk+17ZXrMm2Tq\nPtn2o70dlrdy/NkyPXv0XJm2lrd3+fSFMr1x/ybLc8B+FNTBbSHmhqtbV98u0+v37rG8ekc0egqo\nw5aaOcbXU/copaWddXubyoYwj/NHhIvJiNbky6/wOsY9cO/oULk5EZFsvEDjl/ap3F3upWVWr1Bn\nxjURPqhBdfY3qB35QNjoGpTnjHh714ZwT2QP3Av3+bmUgVuQ2+F9Ge1MN3JUAZdFnDq/v5LDmZiL\nvRcGdOYkMSpqiXMbIpk5wjBcb9GeRZc/jAJkjDEFuDIVYDP2PK4mFYIbItqIjeERiTKIJFRvcjt/\nAu5KrogQlEV0j8BCfYMev0vR7pJrYG55G5PawVGj9E1XoVAoFIqKoA9dhUKhUCgqQqX0MopQWXFx\nHOm3MBfXvD1wBfIxaILkax8sem2MMX6baIDQgvqVuL+eWLhSDlRKnArXohZ+t1B6AdeXHGjuzBPU\nzBBcgUTQhBjidvvAkCShEAsHNRcrlHuKfLpqN1urV8v0oqB/169+o0w3O0ssb7C1VaZboP7UaPJy\nMVBBkz1OG0cx9e3ulRfLdPv4B1i5Rz/4I2V69c3fL9NPPf9LrJzv0ncNhVtHc5natbhMNPrtyy+y\ncgNQv6m3OBW8D8pbNqT1sbO1zspt371epm9d40peH/6pv2mmjWhI4xq0OHU2iWntrf8h318BCL+3\nHqNya5whN/3L6P7H6cjuCpgk+uAyt8GKmdE+tWuU0rzVjvD6htCmwBNufQXtZ+cUfZcbctoyABH8\n+gpfF/kE2rtKdGyacheTxZPULu8EyzLrXxcuSoeMJCbXFyNoXQvUaKM1z/IypGHB3GeFC461QF8L\n9ycPgtU74E5kxfmOz4IMziyb8/ZmQD070u8IzXjQ3kQEefDANxXbbgw7to0LgRJm5k+zcuOI1Osc\nEdjEKw4+c/VNV6FQKBSKiqAPXYVCoVAoKoI+dBUKhUKhqAjV2nQhAriMuBOCXbSw3DbpgItJkYJt\nwBdRLTK0R/G8NKY6HeDrayIoRAJXxROwYU2Ea1F/RAaA2OO2DAclylDuMpGRPKiNroh4YUFejQUH\nEXaOBMajEDHrc2+607sPUb39cI7lwa19s7j8GMsLHLLjpmNwmfB4dJ+9fbrCL83mq9deLdMnQJox\n7HDJyQgCVS/94N8q08szM6xcATKTkbvG8l77s98u0088/+ky7Te51GMK9vuJcF3q96kvyQDdG4Tb\nGwQJj4TNs17n4zMN7FyhdWiFy9nOdWpPLCT/ZmAovG1YkyKaWGeO+n50hq/lGkzJ5TWqY/8an/wB\nRJtpHKH5jUJhs/QhIpkn7juAe0sIbjXRFq+jP4YoOlv83oK/BHc3lmgdj27xcr1VmvvRFm9HXBwc\n9Pz9oIBzxPW4IdSFcyqORizP8ehgxAhoUjrRwp2SoMb7DUGGTKNBdxz6vU1eDOyxDrgJYdB6Y4zJ\nUI5RyECmcOB0wX2x19tm5fwA9lfM1yZKzaK7pRNyl9AczcTCtjxJDnYB0zddhUKhUCgqgj50FQqF\nQqGoCBbpB4VCoVAoFNODvukqFAqFQlER9KGrUCgUCkVF0IeuQqFQKBQVQR+6CoVCoVBUBH3oHgBr\n7WettS9aayNr7T952O1RHA6stY9aayfW2v/zYbdF8f5grf3it+Zy8K1/lx52mxTfPqy1obX2c9ba\nW9bavrX2FWvtTzzsdh0W9KF7MO4bY37FGPOPH3ZDFIeKf2iMeeFhN0JxaPhsURStb/177ODiiu9g\neMaYO8aYTxhjusaYXzDG/La19vRDbNOhoVJFqu9GFEXxO8YYY639sDHm+ENujuIQYK39j4wxe8aY\nrxpjHnnIzVEoFICiKIbGmF+E//p9a+0NY8xzxpibD6NNhwl901V8T8Fa2zHG/PfGmL/zsNuiOFT8\nqrV2y1r7FWvtJx92YxSHB2vtsjHmvDHmzYfdlsOAPnQV32v4ZWPM54qiuPuwG6I4NPy8MeasMeaY\nMeY3jDG/Z60993CbpDgM2HeC/v6WMeY3i6J4+2G35zCgD13F9wystR80xvyoMeZ/ethtURweiqL4\nelEU/aIooqIoftMY8xVjzKcP+pziOxvWWscY80+NMbEx5rMPuTmHBrXpKr6X8EljzGljzG37TpSS\nljHGtdY+URTFhx5iuxSHi8K8K/6L4rsJ9p0N+jljzLIx5tNFUSQHfOS7BvqmewCstZ61tmaMcc07\nB3TNWqs/Vr478RvGmHPGmA9+698/MsZ83hjz4w+zUYpvH9baGWvtj//FvrTW/qwx5uPGmD942G1T\nvC/8mjHmcWPMTxZFMX7YjTlM6EP3YPyCMWZsjPnvjDF/41vpX3ioLVJ8WyiKYlQUxdpf/DPGDIwx\nk6IoNg/6rOI7Fr55x6Vv0xizZYz5b4wxP10UxeWH2irFtw1r7SljzM+Zd34Yr4H/9c8+5KYdCjTK\nkEKhUCgUFUHfdBUKhUKhqAj60FUoFAqFoiLoQ1ehUCgUioqgD12FQqFQKCpCpa4v/+pzv0a3tuQF\nLvZ3xrI8j1zuXNcv07Vmk5Vz3ABq4G5dfo3yNjfWy/TJkx9g5a5e/gbVX6f60zRl5YJaWKZHY36j\nvdPpwueoL4PhNisXx1GZPnaSa7Rv7a6W6aMnzpbp4XiflcvSuEwXGW+j59H0/pW/+rcO3W/x1379\nH5aT1mm7LG92hsYnrIcsLwWPq8uXemW6YXZZORfGP0ojljcZ0+9FXDq55b8j84Q+57uUV+/WWbko\nouFJYpZl6nWaQ89tQBt4wSynMQgbbZZn4fdtEk2o7pDXMbdIdbgen7LN9UGZ/tv/6X89FT/Uk8/8\nWDmakz2eV5+jcYh3+TjPL9J4rjxVK9N3b/F9vn2H5sMKz8vFWaqj3qV97ls+V7PnKT0Y0FqatTkr\n11qhvDdf5GvQXKXkT/0P1N4U5tcYY+5epLlaG/ZZ3v6btO9XzlP97nU+NrNPUTv21/k+cf1Rmf5n\nv/4PD31Of+lX/tdyAl798v/F27V0rEw7fsDyuouny3Se0bj2dvkZtrN6pUy7Ln+cdFq0B7KC1rnn\n8fFZOLJSpk8+8UyZ3l67x8rVZmbL9Gifn4N//of/qkzPtajc05/8q6zceEDzORqMWN6966+Vaceh\nNo7GA1au1eqU6Sznz6rFlRNl+n/5n/+3B86nvukqFAqFQlER9KGrUCgUCkVFqJRenkzo1T7w3/ur\nfd9nf0dAw4Y1yrMu/81gfaJuBj1Og6QDokisQ+U21q+xciHQxp0u0Qi5YMORVkgErWscoBwcyqs3\nOE126sxTVL/lX3AkJEo5SYmH8wNOkznwuVjwor1dTsEcOgoaf5+zZsaFvzPx0y6PiXUZAwHTlWvC\np3HMYv4FeQ5j7tD4WKH+Z8EcYUNaA77PqaU0onKxmGsPqCbPpToKw9ep16ZywdNdlucsEW2ZvEw0\nuntlnZWzXTCReHxdFc70lQ29Nn2ns8f7N+nT+g0WBI1f0Nzd+TrVYec45eu1qA/5kPcnhekvJvTd\nwSleR1yntZDfgjX4NKdIdy7S3098go/l2lmYxyv0XTOP8z73gW2+8zW+v+aX6O/FDljOjvO1Oj9D\nf3fn+OJqxvz7DhtXXvnjMl0TJo8koj2QD/lZ0e/R3+c/9BnKcFu83DbFDbHCZDgaEi3rwgEhLECm\nt7tTptEcV2t2WLlandbfxvVLLM9GYH6ap4V0481vsHJPf+pnynQy4WbBvY1bZXp/B/ZlKuxN8K56\n+uyTvB2hMGM8APqmq1AoFApFRdCHrkKhUCgUFUEfugqFQqFQVIRKbbrowuJ43JaRgj0yzbkNx3pk\nD8jAuDqC69/vlKO/s1RqSpP9KAUbrCfsZLMLdH19PCR3lkL4NyQRtbHT4rYHZndFe6CwVff7pLO/\n3+ea+7MLy2U6z6mOmdkjrNz6Ptkhkoj32TXC0HrIKAzY7lxuT7NFLosT0GgKLj2usGEaS3PjiPoc\ncCvLYHzygtdhwUaUFu9tty3ARvyucQOjdA7zaXK+dhZDsCG2xLrKyS3lxTEZCq+u8vY2Tg/LtBfz\n7ZnH3G1qGhhtUB8iYaPrwhwcXeG2q70JjctgQHulNWTFjFPAuIj5noDdfukRmG/hipLcoDYun6T/\nj+/UWLn+LtzjeInfp1i4QHXu4N2Qq7zPGazx/oSvwdt3qL2r4Lpk7vNzaXaTyi0v8bW1mU1X+77R\nprsFmTCmTkZ0t6C7dJLl9bfvl+n1Gy+W6fGAu/U1m3BXIef1jwZbZdqH83hufoF/14Du37z6lT8q\n00dWeJsWwjNl+tj5CyyvSGieXnnhi2V6cfGjrFw+ou/KC3EnCKYmGoErY3uGlfPheXT9yiss7/xT\nz5uDoG+6CoVCoVBUBH3oKhQKhUJRESqlly3QG0mSvGe5WKg/WaAZM8hLYk73bO0QJdJo8uvxcwtz\nZTp30O2I17G3R5RIGBBdFXhc/SoGuZ7eHnf7cBwa1jpQz7nhV89jcAXyPE7P7m1TnRZcU7KI15FD\nHd0up23G/Sm7DIXoBiCoUEtj5zqcUktALcxF9yr73svRZuJvMAvYgjKtoHyRQSoKasdQ8Msu+Dg5\nltcxHlIlNoR0wvuVzRAN1a/Ns7wkofm997u0xja7fP0tQxsDqdqWTd9lKE/A7Umw7H6TKOU44m0J\nwLxgA8obCneLYkxznFlOB0cp7bG9e+Bmc5Kvi6hB6+ftG/QZb4fvoRNnqC9L57hpZ+1tWjO7HtH2\nR5/lY751D9zRVviAdPepnyH6vs0KM8AulEt4X556ho/BYaPRIXWm4+eeY3kTMFe8+ZX/m+V5DVrL\nPThX5V6OHRrzVofTsOMRuQLFsA6GA3EuwRZIxuDK5/N3wmREimBewMfxyBminj/SIlMCqmkZY8zl\nl4m+Pvfsj7A8HxZ8vU7zkgl/0RhcjYqMr+/rb3zNHAR901UoFAqFoiLoQ1ehUCgUioqgD12FQqFQ\nKCpCpTbdwhA3jlEcZJ6MljMBDj0IiWuvBdyGc2TpVJnOhctQFoHdD658D/o7rJwfkszZGGQrE+Fa\n5ML9cpQaNIZLmU1GJIWW5MJWDWMgHWwcS3VOQOJsMllj5VCOciJkzZx8ujZAZt0R0TYM2FndnNuB\nQDHQhGAjGkbc1aLVJXeEwgo7WQ7uYbCMC+EG4MAyKMCWnAkXJNeQvdJaOZ9UiR/CXBjuNrOS0efO\ntLiLSpbReHxph2xa3bZcE1RnmgqZRDnGU4ALX+k2+Dg0j9DfrXkejWcCbXXaMDcTsc/BjjjT5evi\n9OkC8ii9t8nnauMuzAd816c+zu2jH/wg7eVU2Aevtcgl5OVL1Kb+TSHH+iSdMSfuiLkC6cf1Bs3N\ntRf4uXR/RHbKe7u8L0+ema5bX5zQ+Ax2+NnRPkYuOZMR9+3y4Tz2fRpXW+N3W9pNshnLQ6wGZ2nQ\npkWv/8EAACAASURBVD2aFtwOurB0tEzXZ+guji/uNKRjamMYcJlVA/cIzjxB8rrXX32ZFdtapchF\nRx7hbprP/th/VqZf/pN/Wab3V2+xctEExsoKO//SkjkI+qarUCgUCkVF0IeuQqFQKBQVoVp6GSi9\nWLgMYZB4pDOMMcYCjVfk7+121G4SnbHT5wGQt+4RlXDqzKNluhZw16IJUJzNJlEp9SZv06hHtLFg\njU0YAC2KwctzTp+GoGDUBwUUY4xJIWg70teOcC0qYlBcijn96Jjp0sse9M0VAcStiy49fJllQPPm\nGbhkCJWyAijqTLhaYNkaRloSZoAMlGowVJTjyjUG5o2crysHohNZcGuywgyysEC0k+uJgOfgojSY\no/qPnuXtRdGyNOHzKYNZTQPRgOiymRk+5kuPUHrZ47Ta7pDW6/6A1sX2JqcSw4j28sJpPgfLj4HL\nEMQNv/MKNy2MQRnqiXka18ceZcVMuwbKc3N831y+i32jObi/Keb+Cu3lcchNBj6suwIsO7HYh/E+\ntaMpXMRGdw6OSvN+0N9eLdOnn/gwy8Nz5fmf/tss7/qrXyrTa/dulumaMNvtDmmdu0JxLwGVwQZE\nCJr0+d7IodxrX/vTMv19H//3WLlaneYii/m6Go2pzjq4eo5H3D3p7FMfKNPW5/N5+Zt/UqbRrWko\ngti7YBYMG5xuL2qC9n4A9E1XoVAoFIqKoA9dhUKhUCgqQqX0cgSUgOOK28Ae/R3HPMA40s0+iJPj\njed36icaqtHitPEpUEvJc+q2LygRD4LOI32d9ThlVMAN5TiS6lrUxhjKyT6PR9TeZMLrT0AIHQMB\nBDU+ZSncivXedQV6ur+pLIjXO/L3GwStKBxOBaUp0LUw72HA6XfHEiWYhJxiLEAcv4Brk3nOb3Cj\nChoGUJC3rQukS12eh0ULuHlZC/l87r5MFFdwmdOZm3Cr+qM/TPO52OBrOIQbm0OXj2nkTl+Rqg30\n52ybj0MKccN7j/PPrd0nqm5wH27lR7yOOpghjnUEtQ79296jcch6fP2cPEF74NYO0bNf/FO+D09/\niMZ5+0X+XW+9RvVv3KH2PvcYPw+e+Qh9V49Pqdl6BcZqjkwLm0LRrP9FqsMteP33b0x3TtOY9tSw\nz4MVzM9B8BShDoa3nlmgGtFcC2s0F8pNSNuPwIujEAFttrc3yvSJR56gT4vvwlvDieHrqlaj9be3\nR0ENFlZOsXIu3Kjeu3ud5d27SgvcCYnKDuAz7wDOG3HDOggOVhjTN12FQqFQKCqCPnQVCoVCoagI\n+tBVKBQKhaIiVGrTdSAYdZ5zOw3GqXaNtPdSOsfA6aIcXu32fN41D+xFYyiXDYTNqUFcPtoUHGFf\nC+sQ3UREx0EbY+BTJbmwQWP0Cs8TrgNgi8zBNaHf48oxdVDoepdNRfoyHTIcVCFyhLuSh3MjovG4\n4DK0Bv18go9BOoLIPyIaFCqEoYePFUOQoSsQ2G1D8XvThUpyoTRl3QLywJ4jjE6vpXQXoTHi7S3A\n/j0Pw9H2ub0IbeOxCK20O5q+TbfZoHa7AR+jdEx5Oz1uf8+baOciu3oq5j7yqM6NDT5Gu9DdjXWy\n4fsNbs8/BpGe1kGRqjvPx2dzn86YK5u8jkmP9sbJx6i+ZWFjDyLqSzIWympbVEcMrlbJG7wddfAi\nmT/G609uTVdlDO+2DLbvsrwspbFzA+760pmliGV7O2RzjWLhPuOSLbXemmN5nkvjg0HhXXE3IYQz\n7MS5x+Eze6ycC3t59ugKyyvgvtD+DkXxai0cZeUmcHdm894tkUdzffTo2TK9m2+wcjHYyTNxNyQU\nd4QeBH3TVSgUCoWiIuhDV6FQKBSKilApvRzHoF7i8a/OQRTfE3lpQTRFloAQt1Awcj10I+HY26PA\nBhZU8K343VHAVXQX6pfqVzW4Gi6p5yiC9qLwv6C8UXVKtjgDVyO8lh8EnPqMgVYJAz4e9frB6ijv\nB0VIdEwmggRgm7OM97sOwdjjOo1BNuT0nQsi5ulQzFNK8wHMrSlEOzwYV1SQsgGnNlmgaiHIboE6\nL3Iaf9kvA3TwUAS3RnrcJmi3EJQxrH1XuKl1c+kTdvjIuuQXky7y+di+BW5grwg3uTb1wzVUh+vy\nchmw0pHlPjhzMJw1CB7R6/E5fT2idh1/kuZ0v8HHZ/N1+u7+Pl8/T5+nshc+AQFQbnGVordv0Txe\nu8gp9Z1VWE9gJegX3OWxHlAduXjNSY6ZqQLdZxZPPsby0J3Ib3ZY3vqNK2XaAu3q17grpgHT2kSo\n6rn2wecWmmuMMcb3aR1s371ZpouCm8e2togef6LJ5ykAc18Ke2rQ44pU9Q4FJHBqPGhHWKf1Mtgn\najsRgVgMuAkFAT8Dbl960RwEfdNVKBQKhaIi6ENXoVAoFIqKoA9dhUKhUCgqQqU23Xqd7AG54OuZ\npKPlvwUsuGm44IKTpNxe5EFAYUe48TQa9N0onYj2XWOMScCVKRlBBBwRHD0aky1yboZflW93wI0H\n2r61s83KjSdk+wlq3FbrumBPg6/OhF3PA7tzKiKAYOSNaaBIyBZjC26fc2JqZ9bkbk7FHs1NDJFO\nfLkaITKP4/N+5+iKAkZdWwgbonlwkHAv4WOF9vZCfMYBO7FToMuQsAuD/d5m3FaLy4wNVcbnPYc5\nzEPR56a8qXD46J6hdkcjblPuj8FFps9dcLwRjZH1yFbWFOuiNkd9asV8ws89TXUMtqn+3g4rZoZN\n2hBrqxDsnsdoN2ZCtriFNp/TOKY6rv1LmgNPBJWvLVP9aY+fWcES/e3UwRaf8n51myRB2xRyptn2\ndN36mg261yHdEmOQTM0mvB3Hzj9XplcvvVKmxRHD7LapCIMVYGQhiEbU6nK7cAzneAfOvZmV06yc\nBfvpnWuXWN5JkI8MIXpQa4m7DA13qB0LR7lE5L6zDu2lMysXzxkXfFiHA+5CFdYOjhqlb7oKhUKh\nUFQEfegqFAqFQlERKqWXUeLJNSJgOaQDoeqBCiBIPbfbXEUlAvWYTKgxZaA4hMHRU0EzJhmoSQF1\n64iIPRggfr/Ho3egGhaqUNUbPAIF0pF5yqk8Fr0D2mREVAscj0L0ecqCVBhsQ4pOmcIFWjEXgeXB\npcIHVR97XgQQ34Q6Ck61MrUwyMsE5evBeGUFNFKEZLJQrhCRYHJUhgL6Os+F+kxB1KF0Dcldop0c\nVMkKhesNKNwUCXc7SsTf04ATgZvcFd62HDwsEtHBFMwJdUsU3sop7pZhQYHr1Ic59dw8CnRtTOmZ\nGb6Qz/wojUMGLj79hlBFA1o6Fopmuxv0uZnTVN+RD/C1ugeuagtLfPwnAY1P7w64CTX42KBn48Ya\nXzO796a8SaEp0YifU1def6FM+w7fwD/01/9umbYenVtXX/ojVq4JbjdeyM9jdMNzfGrIQNgLGjOz\nZfrssz9Spm+9+qes3OKRk2V6OOSuQCOgedtLy2W6u7DEym3duF2me6tcoWt/l8YHaXNp1BmPQbnq\n1Ad4/ZvXzUHQN12FQqFQKCqCPnQVCoVCoagIldLLSI/5nrxVSi/xSSxubQL16nuoNsJpCgucoxVi\n9D7QnTnejha3XdOcKEK85ezm8vYsQQp4TyLoJyixxDGnv9gNZdHeBJSmHCgXCXWUZoNuRhbill2R\nTlnBCKhcGfAgc6n9gbjBmmQ0/kg+OiLQQAa30eUY2xRUfoAadgXtmQE35AClLFefTZGvFjQ30PsW\nzCKuCEiQsRv4fJ4KoO8cuMqcSzMLrM2aoLlbk+kHPBjdprnaz3j/xltEobo5H0G8Rd/2iLo9fYT3\nwQeVq/njfJzXduhz9/rU160x73fwAtWxfITGMsz4ek8mVK65yOtowA3jzhmieHOhLlcDVTA34ETj\n9joEgNihcl4igl0ART3e5RR15k/3vccDU91NoJONMcaFd67uIpfG2rvzapmePXKmTJ975lOs3I23\nvlqm6zVx6x+GKwaFtlZnlpV75gc+U6bvXqHvHY35WC03SDUrAorXGGOsT9994026bX3z9ddYOQMB\n6W9ffYtlBUCV16Bcvc6DkjSbdE75LU5fH63JgPfvhr7pKhQKhUJREfShq1AoFApFRdCHrkKhUCgU\nFaFSm26tTlfPM+Eig7ZV6e7j+/S5grl28CowqL3jSpckyIN0GvF2BB4GhQd7jrApFmA/St5lOwU3\nFbDBBiG3WWKEiiTh7Ujfwz4dBtztyELUdhkxyfWm/ZsKbKnCpSeAthQisjzaanch4sjxgtuENkAF\nzKZ8TbgQmcTAXPDITcYUoExmIYKUcUSUKxyqVCiigbtPDv2ULlq4naQilQttdOB+Qd3h6zQDI/RY\nqLaN7fRtuu05cr3Y4l4ZJh1S/+bafL22Pw7ugK9TXiT2eQrL9/WbvP7RdSo7yWnfSLWzCQSnj5cg\n7y6f0xYIDs16vI4Y2nX5Dv1/7b5YP1s0bzfXxXwMaK4msH/TVeGOBgp1MxfEGXBxuipjHVDLWzl9\nXuTSeG2u84DuG2s3yvT5YxfK9M3dVVbOwr2G0YAHnZ/p0He7oIa1s73Oyt289FKZzhKy1d65/iYr\nF4OR+Mz5p1je3ga5/9SaC/S9wo0J78fkwjXV8zFyHCj9CaUtPCxCj+/JVPoKPgD6pqtQKBQKRUXQ\nh65CoVAoFBWhUnq5AGpuGdRFjDFmDILYoxGnKVyg4FIIJt9scbWb8YioIU/QywnSkxDk2HOFKk6L\nrnyjmHWRc5qs1oCgycJVB2nXCAJAF8LtyPHQJYZPRRhgsHT67kywUTGIuufCZSKWwZcPGcis5ELR\nJkZ3HxFUIgPadBacr/YNH2MfFLtGjqDsWDwIUPYS6lcGKGsL7jiOoG4xMIIk/AoDYwxUeSaoJAzK\n4Nb4uiqgXUi3ZyK4h2+oXRNBqW9F01ekSndhb8R8D7kwZkPL5zv9GlFzFtyJijqv45RL5RpDvubX\n9ykIelKjdrRC4U4HdHOyQ+PnGbGXa/TdQYOP5aJLcxqtUv23xPIZQGAT4/Hg9BM4ftIeuoTxOjwI\n0hKI+W6enK7JIM6o/fPdBZbngpltV1C+gU9rdLi7UaadXAQUARNLIM6AyZjOT6RrMfCNMcbcvPTN\nMh0CxWvF46m3Sm1cc/nZf+cauQl95Ed/hr435OWuv/b1Mr1y9BGWt7tF1Dm6CXrSpAdU+cbaVZbX\naHB3qAdB33QVCoVCoagI+tBVKBQKhaIiVEovO/CMjydcUcQBakJeFsOLpj4EEwh8Th1kCdygfJfi\nFSECtSdX3AiNgK7N4CasFRpGqJIVj8WNR6BMHRaQQN5ypjrjiNfhC0rjQfUZw6lKV6jppAmv87BR\nwPf5MgYydNUTeeMx0Ks1uAlY8LnYhVvEniuCOUC/C1wglpdzoM5aQPOON5KNMaaYAJ3kidvzHaIt\nB8Aw2oSv4QD4dhnQI8th3bpwK94IFSUcN8FTBvnBsTrfL4o29PUup1OtQ+OXxnx9pjAU9RrQ7F1+\nxHgwpxtrnC7v3Sfq8uhTQHMvc8I/eYnyPvQo1X+Uh041V9Zp/Y+F98EX3qI2jrepHWGLt7e5AGaS\neT7+zh61110G1TweNtt0YS9/6Dl+m/biznRvL2Mc8aZQghrvkqLf8Uf4beAXPv9bZfr6m6QSJePF\nOmhmEB4MKSgQonUI1cuMMabVxHjk6IEilOwmNJ9XLr7M8up1GtfE0uc84SSzdutymW42OzwTAjSg\nl0IsAo0EDtUfiZjlga/xdBUKhUKh+I6BPnQVCoVCoagI+tBVKBQKhaIiVGvTZW4G3JaBalLtVpfl\nYXD6jc17ZXpulkd4wBpFgBSTvYe6VC4Lwu+QrU26Qh54PHpEp0P2gFy4E6ESlAN2uXcraJn3zEM3\nIcyT7ggF2CxlHb7L7UeHDWZCdqXCGNlFC6G6NEnAZgp2Vle0P4f1IhWeDMxhkdEydqyIagPBy2ug\nAJYJN6ZoDOtDuCcFYFryBvS9sXDzsuimZuVkg30LXVtEtB4cqyLjtqR60DPTxu2LwzLtWP793iL0\nV6zDwCV7W3NCfVgS+2sGgtNv13je7BGykbYHoEbWFGfFh2kOtufobEi3RJSqbWrHm3eEQlhAeV1Q\n1zryiIisNEPzc/8l0WmYjlqL7LahuI7ROkqf21nlbVysTfe9Z2udzrBTj/O9EcMYpDF3BVo4cbZM\nT/pkpF4+zYO247WRu9dfZHkhROqJwa1S3ldBl0t0Ky3EHQ8X1OWaQmkqhTNluLVWpuvdRVbu0Wd+\nqExfeuPrLA8V5vCbHbHP8aANhH16PBIybg+AvukqFAqFQlER9KGrUCgUCkVFqJRe9oA6S0QQYvff\nEajdAUUXB65ry0AD6DIzjrm7A7raoFpVJgLLB+CStLxMiumj4YCVQ/rX96RwNv1tgQbNhJqLA+5K\njgiIPhlT+1GkWwY1cOx7u0bJsocOD5aPCFaAf+eZaHMElHsLXH8mnDIqLFJNfOzyIQTICKkO5xHu\nRmaBrkrfojHN+4IOnwNFIY/TX06B/QR3sIL3ywMWNBcuZu9lIpAzlBXE10lzwTh5sBvZYWI8pC/1\nu0LhqU3UqBvJ+aY1Hx4Bcfuc7w1nSBRe5EjfQBqN1fvgMiQCvR89QZ/b+hoFvv9mj58H0YCo8jPn\n+VH3yDPUt7u74K4oXNOaY8rz67zPPsyeA+WSWT5xTXD1OvJBPqZba++9fw8DXkjjc+/m2yxvcX65\nTG+D2c4YY1xwbwwbM2W6M3eClasvkp/W6j0eFH4CLnXoNpgmUikP5hrOlDTl44iBBzxB+bbnwdQI\ntPn25nVWbuMaBVHwhIuT8en8QTUsTwRHSVIyu/g+n888k+qE74a+6SoUCoVCURH0oatQKBQKRUXQ\nh65CoVAoFBXhIcpAcpsrxluXQezRxWRmBgMjcz49iqhOtAcaY5jYHl5ff5esGdgKsoxsD45wewnA\nj8QRfjwJREJq1cn9qVGfNxxky+gPdlhOmlGkJQ8kLYtC2nTp70zYuJN32U4OF4UDV/1dIZMJLjlO\nJOyx4GLlNEFmL+V2fgsuAqlwy7Jgy241aQyeEeX629SOL3yVyg1m+HedDiBqVIe7yuTgklRA5COT\n8+0zicHRwOV12IJsa6wKsU4xUIsnJCJnrZQRPXy44MISSFv8HvSvIey9sNRWGjTfgxW+LvpjuHex\nxm12gz2qs4AoQGad36e4+kVy13M9KpcEfO69Bs1PuMDbkY2obI/itZvdTSnLSXbhaCAkIpfAzfE8\n3FPI+FnxobPUr0eP8nH7ZjBdqdalFbqXMtjdZHnrE+rbnSuv8w+CTbcF9tI05W5rXk55Fz78MZZ3\n+/IbZXrzNskv1htcftGHyEIxuJS5Isi8A+cBe2AYY1pzZJ9utCGIveVzNpqjc7W3vcHzBtQ3B12X\nYu66xJ4RqTgrcrXpKhQKhULxHQN96CoUCoVCURGqpZcheDzSs8YYk8F1cCui0qBbj2MTKMfrd4Gb\n84SCVApRgTAKUCboSLyWXqsTJdhs8fbitfckeu+ISTm4uuQicDpGtXB9PhVIuYyGRJu3OpyaaTaJ\nFu3vcTUUb8qzy8Y7FRFBXKTbOI0YgwvYXEafS8VyREEaR1zbN8DqpItUx+UjXM3s3lUau1d3iE5b\n8DgtlBgaR18EVx86GH0E5laYQfIMlWrqLI8pVKVIKQr3JKCQXZ9ToomZPr08M0eUXmOG03t2i8Y5\nz/l8D8Y0npt36XOteV5uuE996NT5Bm48Rnmr9yHKyxqvw1uA86BGc+Dvc5ezWkjjN7jO67iU0hxv\n9antRcLnrXWG5s2zwuVvjeoY3aX6Fxq8XB+m/pXbvM9/9LvUz1/9j82hI4GwVfWFZZY3Bno1Fe4z\ndZDVQpWlrbXLvFydygUiytnxc4+V6bMf+MEy/caXP8/KpWCOw31uxQGP0X4WZlZYXgImyAlEsKvV\neGSlWpOo57XbV1heq0URmQZ9opojoaoWOrRGagF3UYxjqXD4buibrkKhUCgUFUEfugqFQqFQVIRK\n6WW82eUIOjUHJsEmguoI6HXeBXUaGah+AgHo5Ws+Ur5INbtC2USqAJXtK3h92f/f3rn2xnVVYXif\n29w8npn4kjpOHMe5NElJCW1BVBWtQCCKqFTxJ/hngPgBiC8VKr2BaKHQ0iQtaZzEdh3fYns89zk3\nPhTNetdSqiDVPp/e59O2z/aZfTtne9baa70qGX36jXXLZTF3xok+TeyB/dQzpsMKJPRGyw+aYpxz\nrgsn7nLTjiAy9vdjxsvAhGwyanlg285j3Y4MhObDgXQuq5v2w0ntzOl7hCAasNIUE9dKok2Mbx2K\nSbkHZsTWsqrmcjz5mhkxBAentMEc7nwzvmAOs6caXQXmGkxyXmTTTkk9a9Y6+j9MV9+W0nU4tbmt\n1+Tigqzfs6/rZ+/D38t8z90Ak29dj0O0K3M6d1Pf/ygWs/SjezLmA9dT9YI2ZJeDMann+rNuvCYu\npoUr+rm59QkI19+Ra6NpVc2lgdyj4bS53c3L3E3BC2xqUUdE3N+RfsZfaVfUYEv37biZX7o8Kd/7\n5C/q2t6mZGuqVbRZfQQukLkzK5PyytUXVL3OoZioDw/39IfDyeEb134q7ai8o6r1EzlRjJENkflO\nGAYyrrs7m/qz9uRktgcnoAcd7XI7OJDMWwtLV9W1Czd/OSmPD7cn5Qd3dHsPHoMQjolcqVa1W/NJ\n8JsuIYQQUhDcdAkhhJCC4KZLCCGEFESxIUPg5yuVTYgJ+BCsGk8C1wLw7QVG2QZDTGwokAc+3QBU\ngeKR8TmVxG+A6kG5ESXP4ZoN5ECVIaciRczRcziWXw20L2DgS5/HifgoxkPtA8ohc4xVHLJH7o8b\n9JPnvvE3QjQN+nCdcy4YyPgE0MakrNvrtTFmSM+TPw0i5zXxNbZT7TffWZSfWz+W8a6f0pmAvAhC\nu0wYj5dCO8DBnholoQgygvl6eTs/hsxh4Av2jFB3CM3yYt3GweDkQ4YOP4ZxSPXrobksneq19Xpt\n1iVrVP+e/D68oJ+b9lD6tPeu9pEGdXguQb0lM5nVSuBjP31O5mrxeT3ow1h+vv2uUffpyD1R8Kxq\n4+wS8NWW9XeU8qxcG4Ovv72p+zxdlfU/c1av42df+IZDJMdEH0JfFs6tqGujjvhgs0yvrWfmzk/K\nPrzEOgc6i5MPKkbb61rRZ+W570/K6yBwPzDvsBhCgVA9CMXtv24HhJwGJtQO5m17QxbgeKA/q9GQ\nMza9tu7L9sOPJuXlqy9PyvXHi6re3vaDSTk12cfS+OkZxvhNlxBCCCkIbrqEEEJIQRRqXkZLWren\nE2dnmJHKhIcEcFQcE/zb8B4Mmcky/TU/+wZR4mqzpeqlKJwM2YGGRqAhSyFzkDEzovjyoCdmLKuZ\nnIzF9PFo90t1rXlGTBoZ9Cs3ncaMM6EJoSqVTFakYyYAG3IWajNLBO3sl/QyC0dyjN+HhDGxDRFA\nAXpjoo49sQkO3pcsXanfUfUWwM1QWpZ1VMn12KBod5br9mKrMggn8kwGnkxFiui58EEcIQ9ADMJ4\nAJKKmM2GB3oNb+3r8IeTIPXEbJw4vda2d6QP/qo2k47BvNcfSqei+7qD+13IVmVs8NdBGKAhiYPc\n7W3z7IHIQQBZoqpGxL55UeanYkKzjkDYIAJBjtd+YsRLGtLeh//Q92iAO6R8Wfr/8EPtajncg/le\n0fd4vHayIvZTTQhZHJnE/dDV0VCb8Mfgduu3JRynUtfvy4Wzl+RaYEytEJqZ7EqoTqs1r+p18Z2G\nRfOuC0N4wHw9bqOBuDd8eF/WKtoM7eB90DXhRDM9EZ3x4N2W9A5VvQgz1Bnn4lTp6d9j+U2XEEII\nKQhuuoQQQkhBcNMlhBBCCqJQn+4I1B/Qh/u/30xKufHpYihMBnZ+q15TmZIcbqWKzuc2AiWgEPxP\n2CbnnEsgPMcDh1sWm7AXONqem7SBKG4elUWFot8/UNU6R48n5Yf3/66uXaz+aFKuz85MynFux0b+\nb4oi61M5WYFsdMBkxoeJeuupUfQJYd6GEaTMTPRyHINCk8tt3+Qeq6n4xgOjUOVBKNM0Cl+bEIks\ngzAm48v0AlQWQv+6Cd8ai88pL+tUgHhOoQShbp5Zw7GT9RiYcau3TjYEzDnnGlPgAxubNJDXpd3N\nlu7fqIHzKH1a39TPVyTZ9dxLb2qf7muvyOd9+Y7cb6Op19b+QP5uD8KTygM9lgvPyvqZvaHHbvNQ\n2p+15f4PzBpsgLLScKB9tdcvSHn2HKyfQI/brffk3fHwbf0e8U4/XfT82+DjWQgT9jjoih+029X+\nzamGHLYoNWcn5emGVvHq7UtKxMik9sWzLkFV3seDrQ1VbzSSdnjwrq9UdFhaAKkqO/s6DWQIftYE\n0seG5p2IUZStlvZPY5xjB9JFlkv6HrUpGYPIpBFevPKsexr8pksIIYQUBDddQgghpCAKNS+nYLp1\nRrXHqaQ/NmRIvt77YPK1WaIOQcR9qq7VZnIwVeYQEmLVg1AxSIf46DahSdkzmaCcL3+4DxlcQhNi\nMlUTM8ULP/yVuoZWi1SZkLVZD0OXbAaX0eBkFUx8UBbybV4uiEfwTAiOB6pDCZh8a0aoPvZlveQm\nw5gPpqssgDlL9Rh4KbQDVE9sOIIPJillG3fOBZ6YedMATKKpFrDGMIbQ3h9M1EGA6lK6XgamZ9+E\nuSzUG+6k+d6P5Vk7fGDC7jIQNv9Sz0fUlLp10EqPO8ZUPy3m5rSnn6nPV+Wz//UpqC319D2qZQjB\nCWW+u4faHL+xJeun0dYhYkefS3tnrkv5dEk/y7vbcv+GfvTczkjacf8D+ey9kX7OO9D+TluPW7Nx\nsi6D9oGYYVtgJnbOuTCS+XzmjF5bTTAjn7v+g0l5f2tN1Xu0ekc+62hfXTv67AP5rEDGfzTWIWCY\nhSoFZS3PxNNdvvHKpDzo6zCer+5+KNd64MY07/cKqACVayakEj4vymVNROa93e7KZ9cr+h0w60cu\n2QAAB7FJREFUbOuQxSfBb7qEEEJIQXDTJYQQQgqiUPNyuSTmjCSxpisxBaXmZLMfiknPA7OlTf9e\nhpNqvjlQ7IE5cjwWU1A8NqcH8XhbjiZkI8QOtueSOSHnQ6adWg1EE3Jj+nSYZN+cagRzZzyC7Cix\nEXoHs2Vo/oWKpk/WHJnD/2yROVUdg4k2yIy5DQ7ILoIIRGaSx+Ay8EzncshElIIggRIncM75mKQe\nzVWJyXAF669qMus0TkHyfVhYR/t6BeaQBc0PtOkqwfGBORyafqVwYjgwydTLZW2WOwnufSbrqdTT\nprOFMzIO/im9DvvrUt7pQnavVD/ncSL9/eKeNuWurkGGs4pkSFq4oudqeCRzf7Qv9xsY/fDOI2lH\npakzLl2Ck9LDLSkf7uj1MzMjn11/Ub8u2wdwinpN2jTq6Dmdm5d6wZKew/LH1r11vFTLEvkwMKbP\nQU8ED5YuvaKudQ7kWrMh75FuV79Tzq9cm5TvdrTJN4Pn3ofseKVUPzdRVez247Gsl8qUntAklff2\n/NIVdW199WOpl4ObYaDHO4YMVc2K7kutIu1IRvB3oT5lP9MSM3080ut7ONan9Z8Ev+kSQgghBcFN\nlxBCCCkIbrqEEEJIQRTq0x2D/zQMjAB4CbP+aP8LitqXod54qO3pIfpdM+MjhTCk0QCzU2l7PbqC\nMcNKuaz9a/2ehOOkJkwlhc/GyJHQZGxJwPVgw4kSODqPzuuaOeYeQ6Yse/94rMfxuAmcdMBLdIiW\nX5Xx8vp6nrp1+bsKZC8aOuPzhmxSLtF+oBTCf9DPHwTaR4YhVeoQgHH6ezC9yVivnTaEvaB4VW78\n2M6HPjvdDjUzufiVUisyAwtmkOk1t92dcifNpRWZm+quXmutF6W/a2u6fzGEZvTBZ320qp+vRk2e\nvZHJ8HSwLcpj167IWuhN63HeW4VBa0l7SyW93uOhfPa+CQUaQyaw0UBmZ+euvkdlRvzazcfax330\nWPriwbmLmy/rwwnpnIzjB+/pdVxqPt0H+G2IQSGoY9Rynn/555PymYvfUdfe/8NvJuX1hxIWND23\nrO/vSZY9VCZyzrmZ+bOTcqkm/tPN9h1VrxyK77YGITizC1o83htJBr+dtT11LR1I3y5dlb7srq+q\nejde/cWkHEZ6Pj9663eT8uJ58VXPnL2o6nW25QBDGpv5LJswwifAb7qEEEJIQXDTJYQQQgqiUPOy\nNaEiYzDplSMTOwKWwHJJjnXHRoSgVBFzXH+gzUT9viTVrkJWEt+EAilzMIjCj0fGVAvZsDDDkHPO\n+ZBZKQHzpjW/VKvYT5OFC37OcwwZ0iZYzNBlzdzW3HzsRBDKZbI4eZD0Pe4ZEYKhtKt0To7mD45s\n1ikID/Ntpikx43g5ZKAx5mUMFfOUWIaq5nx8FEzCepX1K5M+5742m0eQOSzxdPqiEITrAwgPC0wW\nLszCEzq9XpKjkxawcC7bl2fo/FXzbMzKoN19W5vnuxCe42LpU2jCQ1oNMZHXl43A/YH8fHddxnlk\nggPb8AxEOzK/qXm/jB9LvakZvX56qbwDkp60NxjrNl1ckj7PXzbm07MyVuFXMqelad2OXQhrWr6k\nr3W3T1bE/tY//zopo2DA1225Pin7ZZ38PwehhBSejcS8c2//+yP4G92XDMLyDvdEGMEZ1+LyJTEH\nr61+Oil32zuq3sqrb0zKm/duq2sXLt+QH8CV0FpcUvVap8VkvX7/lrq2vysm66lZMVcHj3dVvb19\nUe24cvUlde3Rxn33NPhNlxBCCCkIbrqEEEJIQXDTJYQQQgqiUJ9uAnn9po1iyngoPhfrIz06Evv6\nCFI4lira95tAWEkQGv8CpGAMwI+bGzFz/C9kPJTPyowaTA5OQe3lcM4H32EYlKGs25SCUlEY6qnI\nQTEoB79YraaPpI+hXmbCpNLYtuyYAZWhzLTfQYhWUtX+8GQTQqoui18sGRlfKvgGI1/7o3xfQraG\nY5m1PDX/R6LPPoSUnCYsDX35vgldymPwH2dyNiAqab9tAD7uVLv/XOhD3RKmhDT5SkvgPzZqNfmh\nTXx6/OxtyJzeNipH9/8s67e9o0NdEnge5s7IvF1bmlb14kTq7eV6kKIahI+dkvCh1CjzJF3we1dg\nTIwKUNKSeYxHRkUHFKxqF6Vfja4O03rpDfn59Jx+39x+S9rYOCefNf+cXqvrf5LP6m3pPmdWoeyY\niSCMLR/rcdz4QkJ3Nh7eVdeufvdnk3J99sykHI90KskQlki9oVWMepAWcgRpFcsgAu+cc/2hXGtA\nysmppvYzDyA1ZX9/XV1D/3Hr1PlJeXdrQ9X72x9/Oym//Oav1bXVBfF/b/7ns0l5+fWbql5zRsZj\nc/2euhaUjHLRE+A3XUIIIaQguOkSQgghBeFZMW9CCCGEnAz8pksIIYQUBDddQgghpCC46RJCCCEF\nwU2XEEIIKQhuuoQQQkhBcNMlhBBCCoKbLiGEEFIQ3HQJIYSQguCmSwghhBQEN11CCCGkILjpEkII\nIQXBTZcQQggpCG66hBBCSEFw0yWEEEIKgpsuIYQQUhDcdAkhhJCC4KZLCCGEFAQ3XUIIIaQguOkS\nQgghBcFNlxBCCCkIbrqEEEJIQXDTJYQQQgqCmy4hhBBSEP8Fw+Uvqa4U4sMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d57nb61hTjDv",
        "colab_type": "text"
      },
      "source": [
        "- Channel 4 contains Near-infraded information(pixels)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_4KSkh2SvwI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "526e0abf-6707-4dc0-c1bd-dcdcccc56c65"
      },
      "source": [
        "plot_samples(channel=4)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Okbh0BSXAQkx34Rx9vgPxs/8R3jfvek6TaG3XflOAW9Af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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWv7xv-UlWum",
        "colab_type": "text"
      },
      "source": [
        "### Decoding classes "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhHhWKJeyvVP",
        "colab_type": "text"
      },
      "source": [
        "- Encoding Example:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PtsnE5Wvysr4",
        "colab_type": "code",
        "outputId": "58c4b132-9958-4128-f018-a9c0f2677876",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "import numpy as np\n",
        "from keras.utils import to_categorical\n",
        "data = np.array([1, 5, 3, 8])\n",
        "data_encoded = to_categorical(data)\n",
        "print(data)\n",
        "print(data_encoded)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 5 3 8]\n",
            "[[0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lX5kIgy__TE2",
        "colab_type": "text"
      },
      "source": [
        "- original labels given:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yChEi6xyBlS",
        "colab_type": "code",
        "outputId": "7570f843-7ec6-49ef-c485-f250a6ba5c4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "y_train.head(10)"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   0  1  2  3  4  5\n",
              "0  0  0  1  0  0  0\n",
              "1  0  1  0  0  0  0\n",
              "2  0  0  0  0  0  1\n",
              "3  0  0  0  0  0  1\n",
              "4  0  0  0  0  0  1\n",
              "5  1  0  0  0  0  0\n",
              "6  1  0  0  0  0  0\n",
              "7  0  0  0  0  0  1\n",
              "8  0  1  0  0  0  0\n",
              "9  0  0  1  0  0  0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TvqGaVbGolXO",
        "colab_type": "text"
      },
      "source": [
        "- In order to decode values, we will use numpy's argmax function, which returns us the index of maximum value for each row"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJk_G_O-ekZf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5042f170-8cdf-44ee-849b-d626eda2691b"
      },
      "source": [
        "class_ = []\n",
        "\n",
        "# we use y_train as dataframe\n",
        "\n",
        "for inx,cols in y_train.iterrows():\n",
        "  \n",
        "    #print(cols.to_list())\n",
        "    class_.append(np.argmax(cols.to_list()))\n",
        "    \n",
        "# make DataFrame \n",
        "y_train = pd.DataFrame(class_,columns=['class_name'])\n",
        "\n",
        "print('y_train shape:', y_train.shape)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "y_train shape: (3000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fUQuhaHpEqf",
        "colab_type": "text"
      },
      "source": [
        "### Check number  of samples for each class \n",
        "- classes : (0,1,2,3,4,5)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCTku31OeXiM",
        "colab_type": "code",
        "outputId": "0c42470d-a090-430e-ea71-3c4e3e7abda2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "y_train['class_name'].value_counts()"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5    1111\n",
              "1     659\n",
              "2     526\n",
              "3     495\n",
              "0     147\n",
              "4      62\n",
              "Name: class_name, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_mMwgOPphH2",
        "colab_type": "text"
      },
      "source": [
        "- Alternativly we can use 'Counter' function as well"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K51S3McPeG_z",
        "colab_type": "code",
        "outputId": "db1cc792-c9f3-480d-cefe-672ac6ecd75e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from collections import Counter\n",
        "\n",
        "Counter(class_).most_common()"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(5, 1111), (1, 659), (2, 526), (3, 495), (0, 147), (4, 62)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRmRQqSlpq8F",
        "colab_type": "text"
      },
      "source": [
        "### Visualize class distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLK9GVw3ekqz",
        "colab_type": "code",
        "outputId": "0a28a58b-90a8-40ce-c0d5-b761c282b02a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "y_train.class_name.value_counts().plot(kind='bar',title='Number of classes')\n",
        "# plt.tight_layout()\n",
        "# plt.xticks(range(0,6), y_train.index, fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEFCAYAAADqujDUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAE4hJREFUeJzt3X+wZGV95/H3R0YwgDIgsyzMjFwq\njBLyYwUngIubTcQggpWhatXSGJmQgalsYcQ1ZpmYqmXX6C5mt5bVMkuF8EPcjb9CzMIKFUNAtNRA\nGJASZXS5wcFh5MdVfouoyHf/6Gd2m+sdZub2ne6Zed6vqq4+53mec8739IXz6XNOd0+qCklSf543\n6QIkSZNhAEhSpwwASeqUASBJnTIAJKlTBoAkdcoA0B4lyUeSvG9C206Sy5M8nOQfdnDZjUles7Nq\nk+ZiAGinage2B5PsN9R2VpIbJ1jWzvIq4NeBZVV13KSLkbbFANA47AWcO+kidlSSvXZwkcOBjVX1\n/Z1Rj7TQDACNw38G3p1k8eyOJFNJKsmiobYbk5zVpn87yZeSXJjkkSR3J/nnrX1TO7tYPWu1Bye5\nLsnjST6f5PChdR/V+h5K8s0kbxrq+0iSi5Jcm+T7wK/NUe9hSa5uy08nObu1rwEuAV6Z5Ikk/2Gu\nFyLJ2Uk2tNruTHLsHGOOS/L3bX/vS/LhJHu3vrTX4sEkjyW5I8kvtL5T2zofT7I5ybuH1vn6JLe3\ndX45yS8N9Z3Xxj/eXpOT5qpde6Cq8uFjpz2AjcBrgE8D72ttZwE3tukpoIBFQ8vcCJzVpn8beBo4\nk8GZxPuAbwN/CuwDnAw8Duzfxn+kzf9K6/8g8MXWtx+wqa1rEXAM8F3g6KFlHwVOZPDm6AVz7M8X\ngP8OvAB4OTADvHqo1i8+x2vxRmAz8MtAgCOBw4dfpzb9CuCEVuMUsAF4Z+t7LXArsLit4+eAQ1vf\nfcC/aNMHAse26WOAB4Hj22u4um1vH+Bl7TU5bOjv8bOT/u/Gx3gengFoXP4d8HtJlsxj2W9V1eVV\n9RPgk8By4L1V9cOq+lvgRwwOpltcU1VfqKofAn/E4F35cuD1DC7RXF5VT1fVV4C/YnBg3uKqqvpS\nVT1TVU8NF9HWcSJwXlU9VVW3M3jXf8Z27sdZwJ9U1S01MF1V98weVFW3VtVNrcaNwJ8B/7J1/xh4\nIXAUkKraUFX3DfUdneRFVfVwVd3W2tcCf1ZVN1fVT6rqCuCHDELmJwyC4Ogkz6+qjVX1j9u5P9rN\nGQAai6r6GvAZYN08Fn9gaPoHbX2z2/Yfmt80tN0ngIeAwxhcoz++XQZ5JMkjwFuBfzrXsnM4DHio\nqh4farsHWLqd+7Ec2ObBNclLk3wmyf1JHgP+I3Bw258bgA8zOAN6MMnFSV7UFv1XwKnAPe3S1ytb\n++HA78/a7+UM3vVPA+8E/n1b3yeSHLad+6PdnAGgcTofOJtnHzC33DDdd6ht+IA8H8u3TCTZHzgI\n+A6Dg/vnq2rx0GP/qvrXQ8s+18/jfgc4KMkLh9pewuCyzvbYBPzsdoy7CPgGsKKqXgS8h8HlnkGB\nVR+qqlcARwMvBf6gtd9SVauAfwL8L+BTQ9t9/6z93reqPt6W+1hVvYpBUBTwge3cH+3mDACNTXu3\n+UngHUNtMwwOoL+VZK8kv8P2HSSfy6lJXtVunP4xcFNVbWJwBvLSJG9L8vz2+OUkP7ed9W8Cvgz8\npyQvaDdS1wD/czvruoTBzfBXtJu5Rw7foB7yQuAx4IkkRwH/L6BavccneT6D8HwKeCbJ3knemuSA\nqvpxW/6ZttifA7/blkuS/ZKcluSFSV6W5NVJ9mnr+sHQctrDGQAat/cyuBk77GwG72K/B/w8g4Ps\nKD7G4GzjIQY3VH8LoF26ORl4M4N38/czeLe7zw6s+y0MbpR+B/hr4Pyq+rvtWbCq/hJ4f6vvcQbv\n0g+aY+i7gd9sY/6cQWhu8aLW9jCDy0/fY/ApK4C3ARvbZaPfZXB5i6paz+A1/nBbbprBDWsY7PsF\nDG6G38/g7OEPt2d/tPtLlf8gjCT1yDMASeqUASBJnTIAJKlTBoAkdcoAkKROLdr2kMk5+OCDa2pq\natJlSNJu5dZbb/1uVW3zZ1d26QCYmppi/fr1ky5DknYrSX7qN6bm4iUgSeqUASBJnTIAJKlTBoAk\ndcoAkKROGQCS1CkDQJI6ZQBIUqd26S+CzdfUumvGur2NF5w21u1J0kLwDECSOmUASFKnDABJ6pQB\nIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS\n1KltBkCSy5I8mORrQ20HJbkuyV3t+cDWniQfSjKd5KtJjh1aZnUbf1eS1TtndyRJ22t7zgA+Apwy\nq20dcH1VrQCub/MArwNWtMda4CIYBAZwPnA8cBxw/pbQkCRNxjYDoKq+ADw0q3kVcEWbvgI4faj9\nozVwE7A4yaHAa4HrquqhqnoYuI6fDhVJ0hjN9x7AIVV1X5u+HzikTS8FNg2Nu7e1ba1dkjQhI98E\nrqoCagFqASDJ2iTrk6yfmZlZqNVKkmaZbwA80C7t0J4fbO2bgeVD45a1tq21/5SquriqVlbVyiVL\nlsyzPEnStsw3AK4GtnySZzVw1VD7Ge3TQCcAj7ZLRZ8FTk5yYLv5e3JrkyRNyKJtDUjyceBXgYOT\n3Mvg0zwXAJ9Ksga4B3hTG34tcCowDTwJnAlQVQ8l+WPgljbuvVU1+8ayJGmMthkAVfWWrXSdNMfY\nAs7ZynouAy7boeokSTuN3wSWpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKRO\nGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQB\nIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASerUSAGQ5N8k+XqSryX5eJIXJDkiyc1J\nppN8Msnebew+bX669U8txA5IkuZn3gGQZCnwDmBlVf0CsBfwZuADwIVVdSTwMLCmLbIGeLi1X9jG\nSZImZNRLQIuAn0myCNgXuA94NXBl678COL1Nr2rztP6TkmTE7UuS5mneAVBVm4H/AnybwYH/UeBW\n4JGqeroNuxdY2qaXApvask+38S+evd4ka5OsT7J+ZmZmvuVJkrZhlEtABzJ4V38EcBiwH3DKqAVV\n1cVVtbKqVi5ZsmTU1UmStmKUS0CvAb5VVTNV9WPg08CJwOJ2SQhgGbC5TW8GlgO0/gOA742wfUnS\nCEYJgG8DJyTZt13LPwm4E/gc8IY2ZjVwVZu+us3T+m+oqhph+5KkEYxyD+BmBjdzbwPuaOu6GDgP\neFeSaQbX+C9ti1wKvLi1vwtYN0LdkqQRLdr2kK2rqvOB82c13w0cN8fYp4A3jrI9SdLC8ZvAktQp\nA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIA\nJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ1aNOkCtOOm1l0z1u1tvOC0sW5P0nh4\nBiBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6NVIAJFmc5Mok30iyIckrkxyU5Lokd7XnA9vYJPlQ\nkukkX01y7MLsgiRpPkY9A/gg8DdVdRTwz4ANwDrg+qpaAVzf5gFeB6xoj7XARSNuW5I0gnkHQJID\ngF8BLgWoqh9V1SPAKuCKNuwK4PQ2vQr4aA3cBCxOcui8K5ckjWSUM4AjgBng8iRfSXJJkv2AQ6rq\nvjbmfuCQNr0U2DS0/L2tTZI0AaMEwCLgWOCiqjoG+D7//3IPAFVVQO3ISpOsTbI+yfqZmZkRypMk\nPZdRAuBe4N6qurnNX8kgEB7YcmmnPT/Y+jcDy4eWX9banqWqLq6qlVW1csmSJSOUJ0l6LvMOgKq6\nH9iU5GWt6STgTuBqYHVrWw1c1aavBs5onwY6AXh06FKRJGnMRv010N8D/iLJ3sDdwJkMQuVTSdYA\n9wBvamOvBU4FpoEn21hJ0oSMFABVdTuwco6uk+YYW8A5o2xPkrRw/CawJHXKAJCkThkAktQpA0CS\nOmUASFKnDABJ6pQBIEmdMgAkqVOjfhNYWnBT664Z6/Y2XnDaWLcn7So8A5CkThkAktQpA0CSOmUA\nSFKnDABJ6pSfApLGzE85aVfhGYAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaA\nJHXKAJCkThkAktQpA0CSOmUASFKnRg6AJHsl+UqSz7T5I5LcnGQ6ySeT7N3a92nz061/atRtS5Lm\nbyHOAM4FNgzNfwC4sKqOBB4G1rT2NcDDrf3CNk6SNCEjBUCSZcBpwCVtPsCrgSvbkCuA09v0qjZP\n6z+pjZckTcCoZwD/Dfi3wDNt/sXAI1X1dJu/F1jappcCmwBa/6NtvCRpAuYdAEleDzxYVbcuYD0k\nWZtkfZL1MzMzC7lqSdKQUc4ATgR+I8lG4BMMLv18EFicZMs/NbkM2NymNwPLAVr/AcD3Zq+0qi6u\nqpVVtXLJkiUjlCdJei7zDoCq+sOqWlZVU8CbgRuq6q3A54A3tGGrgava9NVtntZ/Q1XVfLcvSRrN\nzvgewHnAu5JMM7jGf2lrvxR4cWt/F7BuJ2xbkrSdFm17yLZV1Y3AjW36buC4OcY8BbxxIbYnSRqd\n3wSWpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcM\nAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQ\npE4ZAJLUKQNAkjplAEhSp+YdAEmWJ/lckjuTfD3Jua39oCTXJbmrPR/Y2pPkQ0mmk3w1ybELtROS\npB03yhnA08DvV9XRwAnAOUmOBtYB11fVCuD6Ng/wOmBFe6wFLhph25KkEc07AKrqvqq6rU0/DmwA\nlgKrgCvasCuA09v0KuCjNXATsDjJofOuXJI0kgW5B5BkCjgGuBk4pKrua133A4e06aXApqHF7m1t\nkqQJGDkAkuwP/BXwzqp6bLivqgqoHVzf2iTrk6yfmZkZtTxJ0laMFABJns/g4P8XVfXp1vzAlks7\n7fnB1r4ZWD60+LLW9ixVdXFVrayqlUuWLBmlPEnScxjlU0ABLgU2VNV/Heq6GljdplcDVw21n9E+\nDXQC8OjQpSJJ0pgtGmHZE4G3AXckub21vQe4APhUkjXAPcCbWt+1wKnANPAkcOYI25YkjWjeAVBV\nXwSyle6T5hhfwDnz3Z4kaWH5TWBJ6pQBIEmdMgAkqVMGgCR1ygCQpE6N8jFQSXqWqXXXjHV7Gy84\nbazb29N4BiBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXK\nAJCkThkAktQpA0CSOmUASFKn/PcAJGk77Wn/3oFnAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlT\nBoAkdWrsAZDklCTfTDKdZN24ty9JGhhrACTZC/hT4HXA0cBbkhw9zhokSQPjPgM4Dpiuqrur6kfA\nJ4BVY65BkgSkqsa3seQNwClVdVabfxtwfFW9fWjMWmBtm30Z8M2xFQgHA98d4/bGzf3bve3J+7cn\n7xuMf/8Or6ol2xq0y/0WUFVdDFw8iW0nWV9VKyex7XFw/3Zve/L+7cn7Brvu/o37EtBmYPnQ/LLW\nJkkas3EHwC3AiiRHJNkbeDNw9ZhrkCQx5ktAVfV0krcDnwX2Ai6rqq+Ps4ZtmMilpzFy/3Zve/L+\n7cn7Brvo/o31JrAkadfhN4ElqVMGgCR1ygCQpE7tct8DmJQkr2LwTeWvVdXfTroebVuSo4ClwM1V\n9cRQ+ylV9TeTq2x0SY4DqqpuaT+Xcgrwjaq6dsKlLYj2t1vF4O8Hg4+DX11VGyZX1c6T5KNVdcak\n65it25vASf6hqo5r02cD5wB/DZwM/O+qumCS9e1MSc6sqssnXccokryDwd9sA/By4Nyquqr13VZV\nx06yvlEkOZ/B72UtAq4Djgc+B/w68Nmqev8EyxtZkvOAtzD4KZh7W/MyBh8L/8Tu/v9ektkfbQ/w\na8ANAFX1G2Mvait6DoCvVNUxbfoW4NSqmkmyH3BTVf3iZCvceZJ8u6peMuk6RpHkDuCVVfVEking\nSuB/VNUHh/+2u6O2by8H9gHuB5ZV1WNJfobB2c4vTbTAESX5P8DPV9WPZ7XvDXy9qlZMprKFkeQ2\n4E7gEqAYBMDHGQQcVfX5yVX3bD1fAnpekgMZ3AdJVc0AVNX3kzw92dJGl+SrW+sCDhlnLTvJ87Zc\n9qmqjUl+FbgyyeEM9nF39nRV/QR4Msk/VtVjAFX1gyTPTLi2hfAMcBhwz6z2Q1vf7m4lcC7wR8Af\nVNXtSX6wKx34t+g5AA4AbmVwsKgkh1bVfUn2Z/c/gMDgIP9a4OFZ7QG+PP5yFtwDSV5eVbcDtDOB\n1wOXAbv72duPkuxbVU8Cr9jSmOQA9owD5DuB65PcBWxqbS8BjgTevtWldhNV9QxwYZK/bM8PsIse\na7u9BLQ1SfYFDqmqb026llEkuRS4vKq+OEffx6rqNydQ1oJJsozBO+X75+g7saq+NIGyFkSSfarq\nh3O0HwwcWlV3TKCsBZXkeQw+dDF8E/iWduazR0lyGnBiVb1n0rXMZgBIUqf8HoAkdcoAkKROGQCS\n1CkDQJI6ZQBIUqf+L9VO+rDLEzcsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onBkBBFeqFGc",
        "colab_type": "text"
      },
      "source": [
        "- As it is seen on the graph, Class '5' has more and '4' has less samples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6gMYPvRjd2t",
        "colab_type": "text"
      },
      "source": [
        "## Undersampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PtlXvQq6ApT3",
        "colab_type": "text"
      },
      "source": [
        "- To balance our dataset, we can reduce major classes to minor ones in number"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynlaIiRbf7t9",
        "colab_type": "code",
        "outputId": "ac573ebb-5a17-4c85-922e-16cc16608401",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "grouped = y_train.groupby('class_name')\n",
        "y_train_under_sampled = grouped.apply(lambda x: x.sample(grouped.size().min()).reset_index(drop=True))\n",
        "Counter(y_train_under_sampled['class_name'].values).most_common()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 24), (1, 24), (2, 24), (3, 24), (4, 24), (5, 24)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uz0AnKEpeGTC",
        "colab_type": "code",
        "outputId": "bf90a885-b895-4e3e-db3b-58b8a10e528d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "y_train_under_sampled.class_name.value_counts().plot(kind='bar',title='Number of classes')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tmG6heXNdRVWtAFbM9XamkmRlVS2bxLa3hZbH1/LYwPHt6LbX8c3mkMudwOKB9/v3bZKk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\nkhphoEtSI/4Hro8CDB+d//AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F8eHUvzAjt_i",
        "colab_type": "text"
      },
      "source": [
        "## Over-sampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKpCdDpfu3RO",
        "colab_type": "text"
      },
      "source": [
        "- Oversampling refers augmentation when handling with images\n",
        "- Following example can be considered as for numerical data over-sampling, since we can fill new samples by mean and variance "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qnWvSlKvfdM",
        "colab_type": "text"
      },
      "source": [
        "### Let's save first counts for each class "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjAqIeS0jwTN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_5,class_1,class_3,class_2,class_0,class_4 = y_train.class_name.value_counts()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1sg4YGWvrf9",
        "colab_type": "text"
      },
      "source": [
        "### Then let's seperate our dataset into every class to apply over-sampling seperately "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DrqO_PPqjwku",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train_0 = y_train.class_name[y_train.class_name == 0]\n",
        "y_train_1 = y_train.class_name[y_train.class_name == 1]\n",
        "y_train_2 = y_train.class_name[y_train.class_name == 2]\n",
        "y_train_3 = y_train.class_name[y_train.class_name == 3]\n",
        "y_train_5 = y_train.class_name[y_train.class_name == 5]\n",
        "y_train_4 = y_train.class_name[y_train.class_name == 4]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ir1UMfoRv4cL",
        "colab_type": "text"
      },
      "source": [
        "### Applying over-sampling for the Class '0' and Class '4' by referring Class '2'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIsQCR6lu19k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train_0 = y_train_0.sample(class_2,replace=True)\n",
        "y_train_4 = y_train_4.sample(class_2,replace=True)\n",
        "\n",
        "y_train_over_sampled = pd.concat([y_train_0,y_train_1,y_train_2,y_train_3,y_train_4,y_train_5],axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAfdFwGcwLch",
        "colab_type": "text"
      },
      "source": [
        "### Visualize again"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1J4VLNLrwVX",
        "colab_type": "code",
        "outputId": "a588ca10-2a7f-45ca-ddc0-73af2c9a3466",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "\n",
        "y_train_over_sampled.value_counts().plot(kind='bar',title='Number of classes')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e6waSrE2yLsm66enpeZYhSZrNvIK+qu6vqm1V9QTwAf5xemYzsHxo1WWtbaZtXFxVq6tq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\ncwa9JHXOoJekzv0/AyOpsHcMo0MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "857_VZ20t2FK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YHO6jYQ5j6A",
        "colab_type": "text"
      },
      "source": [
        "## Image Augmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_5814tyB8XY",
        "colab_type": "text"
      },
      "source": [
        "- As for images, we use typical image processing approaches such as flipping, rotating, padding etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOeaXh1pDloT",
        "colab_type": "code",
        "outputId": "fb22d0b5-1feb-4323-8594-034153ec23a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "chosen_idx = np.random.choice(100, replace=False, size=1)\n",
        "\n",
        "image = X_train_array[chosen_idx,...,:3]\n",
        "image = np.squeeze(image,axis=0)\n",
        "\n",
        "print('image_shape:' , image.shape)"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_shape: (28, 28, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoEw9gbBC-PO",
        "colab_type": "text"
      },
      "source": [
        "### Horizontal Flip"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izXpId9n5l_F",
        "colab_type": "code",
        "outputId": "5b922409-b386-4f17-f161-a1ab85f7b61f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "def horizontal_flip(image):\n",
        "  \n",
        "    image_hor_flip = image[:, ::-1, :]\n",
        "    \n",
        "    plt.subplot(121)\n",
        "    plt.imshow(image)\n",
        "        \n",
        "    plt.subplot(122)\n",
        "    plt.imshow(image_hor_flip)\n",
        "    plt.show()\n",
        "  \n",
        "horizontal_flip(image)"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vn3Wwzk5652k1hT2hG4ZhBAQz6IZhGAHBDLphGEZAMINuGIYREO5p0EXkGyKyICJn7yob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\n6IZhGAHh/wGcUHQ2YYAKtgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56nxlQq8DBLC",
        "colab_type": "text"
      },
      "source": [
        "### Vertical Flip"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6FNeSJxDC8_",
        "colab_type": "code",
        "outputId": "3e0bc087-0663-4bc4-99e7-958e1758f01a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "def vertical_flip(image, rate=0.5):\n",
        "  \n",
        "    if np.random.rand() < rate:\n",
        "      \n",
        "      image_ver_flip = image[::-1, :, :]  \n",
        "  \n",
        "      plt.subplot(121)\n",
        "      plt.imshow(image)\n",
        "\n",
        "      plt.subplot(122)\n",
        "      plt.imshow(image_ver_flip)\n",
        "      plt.show()\n",
        "    \n",
        "vertical_flip(image)   "
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9/znnHAD6t46IPC8ip0TkVKvJ/3Q2jO1kK2y70TDbNrafDTl0EYnhlsF/yzn3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\nhmEYIcEcumEYRkgwh24YhhES/j/JGDWmznXREAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwv6OfCXDxbW",
        "colab_type": "text"
      },
      "source": [
        "### Random Rotate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w7xgYhueD1ZE",
        "colab_type": "code",
        "outputId": "3ba9887b-404c-4926-af13-73d888f60d34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "from scipy.ndimage.interpolation import rotate\n",
        "from imutils import resize\n",
        "\n",
        "\n",
        "def random_rotation(image, angle_range=(0, 180)):\n",
        "  \n",
        "    height, width, _ = image.shape\n",
        "    angle = np.random.randint(*angle_range)\n",
        "    image_rotate = rotate(image, angle)\n",
        "    image_rotate = resize(image_rotate, width,height)\n",
        "    \n",
        "    plt.subplot(121)\n",
        "    plt.imshow(image)\n",
        "\n",
        "    plt.subplot(122)\n",
        "    plt.imshow(image_rotate)\n",
        "    plt.show()\n",
        "\n",
        "random_rotation(image, angle_range=(0, 180))"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ndCGEiAky6EIIERNk0IUQIibIoAshRExoqlO0WDBceL3R6ZFZ5ifb7x72KeeJ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\nCBET/j9GPSmHSs7uhwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4fIZmEXJF1F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}