{
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    },
    "colab": {
      "name": "Human_activity_detection.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bhaveshwadhwani/Human-Activity-Recognition/blob/master/Human_activity_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AJy5-IgcBvb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip \"drive/My Drive/Applied_AI/HAR/HumanActivityRecognition.zip\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "8e0cd10a-3491-4056-e548-60172dc398ac",
        "id": "r1zvQyIRd6V8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "baWlXsR2gGVt",
        "colab_type": "text"
      },
      "source": [
        "# HumanActivityRecognition\n",
        "\n",
        "<br>\n",
        "\n",
        "\n",
        "This project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying.\n",
        "\n",
        "This dataset is collected from 30 persons(referred as subjects in this dataset), performing different activities with a smartphone to their waists. The data is recorded with the help of sensors (accelerometer and Gyroscope) in that smartphone. This experiment was video recorded to label the data manually.\n",
        "\n",
        "## How data was recorded\n",
        "\n",
        "By using the sensors(Gyroscope and accelerometer) in a smartphone, they have captured '3-axial linear acceleration'(_tAcc-XYZ_) from accelerometer and '3-axial angular velocity' (_tGyro-XYZ_) from Gyroscope with several variations. \n",
        "\n",
        "> prefix 't' in those metrics denotes time.\n",
        "\n",
        "> suffix 'XYZ' represents 3-axial signals in X , Y, and Z directions.\n",
        "\n",
        "### Feature names\n",
        "\n",
        "1. These sensor signals are preprocessed by applying noise filters and then sampled in fixed-width windows(sliding windows) of 2.56 seconds each with 50% overlap. ie., each window has 128 readings. \n",
        "\n",
        "2. From Each window, a feature vector was obtianed by calculating variables from the time and frequency domain.\n",
        "> In our dataset, each datapoint represents a window with different readings \n",
        "3. The accelertion signal was saperated into Body and Gravity acceleration signals(___tBodyAcc-XYZ___ and ___tGravityAcc-XYZ___) using some low pass filter with corner frequecy of 0.3Hz.\n",
        "\n",
        "4. After that, the body linear acceleration and angular velocity were derived in time to obtian _jerk signals_ (___tBodyAccJerk-XYZ___ and ___tBodyGyroJerk-XYZ___). \n",
        "\n",
        "5. The magnitude of these 3-dimensional signals were calculated using the Euclidian norm. This magnitudes are represented as features with names like _tBodyAccMag_, _tGravityAccMag_, _tBodyAccJerkMag_, _tBodyGyroMag_ and _tBodyGyroJerkMag_.\n",
        "\n",
        "6. Finally, We've got frequency domain signals from some of the available signals by applying a FFT (Fast Fourier Transform). These signals obtained were labeled with ___prefix 'f'___ just like original signals with ___prefix 't'___. These signals are labeled as ___fBodyAcc-XYZ___, ___fBodyGyroMag___ etc.,.\n",
        "\n",
        "7. These are the signals that we got so far.\n",
        "\t+ tBodyAcc-XYZ\n",
        "\t+ tGravityAcc-XYZ\n",
        "\t+ tBodyAccJerk-XYZ\n",
        "\t+ tBodyGyro-XYZ\n",
        "\t+ tBodyGyroJerk-XYZ\n",
        "\t+ tBodyAccMag\n",
        "\t+ tGravityAccMag\n",
        "\t+ tBodyAccJerkMag\n",
        "\t+ tBodyGyroMag\n",
        "\t+ tBodyGyroJerkMag\n",
        "\t+ fBodyAcc-XYZ\n",
        "\t+ fBodyAccJerk-XYZ\n",
        "\t+ fBodyGyro-XYZ\n",
        "\t+ fBodyAccMag\n",
        "\t+ fBodyAccJerkMag\n",
        "\t+ fBodyGyroMag\n",
        "\t+ fBodyGyroJerkMag\n",
        "\n",
        "8. We can esitmate some set of variables from the above signals. ie., We will estimate the following properties on each and every signal that we recoreded so far.\n",
        "\n",
        "\t+ ___mean()___: Mean value\n",
        "\t+ ___std()___: Standard deviation\n",
        "\t+ ___mad()___: Median absolute deviation \n",
        "\t+ ___max()___: Largest value in array\n",
        "\t+ ___min()___: Smallest value in array\n",
        "\t+ ___sma()___: Signal magnitude area\n",
        "\t+ ___energy()___: Energy measure. Sum of the squares divided by the number of values. \n",
        "\t+ ___iqr()___: Interquartile range \n",
        "\t+ ___entropy()___: Signal entropy\n",
        "\t+ ___arCoeff()___: Autorregresion coefficients with Burg order equal to 4\n",
        "\t+ ___correlation()___: correlation coefficient between two signals\n",
        "\t+ ___maxInds()___: index of the frequency component with largest magnitude\n",
        "\t+ ___meanFreq()___: Weighted average of the frequency components to obtain a mean frequency\n",
        "\t+ ___skewness()___: skewness of the frequency domain signal \n",
        "\t+ ___kurtosis()___: kurtosis of the frequency domain signal \n",
        "\t+ ___bandsEnergy()___: Energy of a frequency interval within the 64 bins of the FFT of each window.\n",
        "\t+ ___angle()___: Angle between to vectors.\n",
        "\n",
        "9. We can obtain some other vectors by taking the average of signals in a single window sample. These are used on the angle() variable'\n",
        "`\n",
        "\t+ gravityMean\n",
        "\t+ tBodyAccMean\n",
        "\t+ tBodyAccJerkMean\n",
        "\t+ tBodyGyroMean\n",
        "\t+ tBodyGyroJerkMean\n",
        "\n",
        "\n",
        "###  Y_Labels(Encoded)\n",
        "+ In the dataset, Y_labels are represented as numbers from 1 to 6 as their identifiers.\n",
        "\n",
        "\t- WALKING as __1__\n",
        "\t- WALKING_UPSTAIRS as __2__\n",
        "\t- WALKING_DOWNSTAIRS as __3__\n",
        "\t- SITTING as __4__\n",
        "\t- STANDING as __5__\n",
        "\t- LAYING as __6__\n",
        "    \n",
        "## Train and test data were saperated\n",
        " - The readings from ___70%___ of the volunteers were taken as ___trianing data___ and remaining ___30%___ subjects recordings were taken for ___test data___\n",
        " \n",
        "## Data\n",
        "\n",
        "* All the data is present in 'UCI_HAR_dataset/' folder in present working directory.\n",
        "     - Feature names are present in 'UCI_HAR_dataset/features.txt'\n",
        "     - ___Train Data___\n",
        "         - 'UCI_HAR_dataset/train/X_train.txt'\n",
        "         - 'UCI_HAR_dataset/train/subject_train.txt'\n",
        "         - 'UCI_HAR_dataset/train/y_train.txt'\n",
        "     - ___Test Data___\n",
        "         - 'UCI_HAR_dataset/test/X_test.txt'\n",
        "         - 'UCI_HAR_dataset/test/subject_test.txt'\n",
        "         - 'UCI_HAR_dataset/test/y_test.txt'\n",
        "         \n",
        "\n",
        "## Data Size :\n",
        "> 27 MB\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KV6RqdPbgGVx",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "218yvy3ogGVz",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6J_DZHHJgGV0",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7e9oCBIgGV2",
        "colab_type": "text"
      },
      "source": [
        "# Quick overview of the dataset :\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfwjvfKGgGV4",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "* Accelerometer and Gyroscope readings are taken from 30 volunteers(referred as subjects) while performing the following 6 Activities.\n",
        "\n",
        "    1. Walking     \n",
        "    2. WalkingUpstairs \n",
        "    3. WalkingDownstairs \n",
        "    4. Standing \n",
        "    5. Sitting \n",
        "    6. Lying.\n",
        "\n",
        "\n",
        "* Readings are divided into a window of 2.56 seconds with 50% overlapping. \n",
        "\n",
        "* Accelerometer readings are divided into gravity acceleration and body acceleration readings,\n",
        "  which has x,y and z components each.\n",
        "\n",
        "* Gyroscope readings are the measure of angular velocities which has x,y and z components.\n",
        "\n",
        "* Jerk signals are calculated for BodyAcceleration readings.\n",
        "\n",
        "* Fourier Transforms are made on the above time readings to obtain frequency readings.\n",
        "\n",
        "* Now, on all the base signal readings., mean, max, mad, sma, arcoefficient, engerybands,entropy etc., are calculated for each window.\n",
        "\n",
        "* We get a feature vector of 561 features and these features are given in the dataset.\n",
        "\n",
        "* Each window of readings is a datapoint of 561 features.\n",
        "\n",
        "## Problem Framework\n",
        "\n",
        "* 30 subjects(volunteers) data is randomly split to 70%(21) test and 30%(7) train data.\n",
        "* Each datapoint corresponds one of the 6 Activities.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agQ6Jw-3gGV5",
        "colab_type": "text"
      },
      "source": [
        "## Problem Statement\n",
        "\n",
        " + Given a new datapoint we have to predict the Activity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ML3wz1dgGV7",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tHzbs7zQgGV9",
        "colab_type": "code",
        "outputId": "b5a51d76-3f03-4730-dac3-3043065097be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# get the features from the file features.txt\n",
        "features = list()\n",
        "with open('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/features.txt') as f:\n",
        "    features = [line.split()[1] for line in f.readlines()]\n",
        "print('No of Features: {}'.format(len(features)))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of Features: 561\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PPC_yqABVZb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import time\n",
        "# https://gist.github.com/greydanus/f6eee59eaf1d90fcb3b534a25362cea4\n",
        "# https://stackoverflow.com/a/14434334\n",
        "# this function is used to update the plots for each epoch and error\n",
        "def plt_dynamic(x, vy, ty, ax, colors=['b']):\n",
        "    ax.plot(x, vy, 'b', label=\"Validation Loss\")\n",
        "    ax.plot(x, ty, 'r', label=\"Train Loss\")\n",
        "    plt.legend()\n",
        "    plt.grid()\n",
        "    fig.canvas.draw()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpPYUQ0kgGWH",
        "colab_type": "text"
      },
      "source": [
        "## Obtain the  train data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAmRcPU_gGWJ",
        "colab_type": "code",
        "outputId": "5fb6c22f-c109-48b0-ced5-85665ebc719f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "# get the data from txt files to pandas dataffame\n",
        "\n",
        "X_train = pd.read_csv('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/train/X_train.txt', delim_whitespace=True, header=None, names=features)\n",
        "\n",
        "# add subject column to the dataframe\n",
        "X_train['subject'] = pd.read_csv('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/train/subject_train.txt', header=None, squeeze=True)\n",
        "\n",
        "y_train = pd.read_csv('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/train/y_train.txt', names=['Activity'], squeeze=True)\n",
        "y_train_labels = y_train.map({1: 'WALKING', 2:'WALKING_UPSTAIRS',3:'WALKING_DOWNSTAIRS',\\\n",
        "                       4:'SITTING', 5:'STANDING',6:'LAYING'})\n",
        "\n",
        "# put all columns in a single dataframe\n",
        "train = X_train\n",
        "train['Activity'] = y_train\n",
        "train['ActivityName'] = y_train_labels\n",
        "train.sample()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py:702: UserWarning: Duplicate names specified. This will raise an error in the future.\n",
            "  return _read(filepath_or_buffer, kwds)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tBodyAcc-mean()-X</th>\n",
              "      <th>tBodyAcc-mean()-Y</th>\n",
              "      <th>tBodyAcc-mean()-Z</th>\n",
              "      <th>tBodyAcc-std()-X</th>\n",
              "      <th>tBodyAcc-std()-Y</th>\n",
              "      <th>tBodyAcc-std()-Z</th>\n",
              "      <th>tBodyAcc-mad()-X</th>\n",
              "      <th>tBodyAcc-mad()-Y</th>\n",
              "      <th>tBodyAcc-mad()-Z</th>\n",
              "      <th>tBodyAcc-max()-X</th>\n",
              "      <th>tBodyAcc-max()-Y</th>\n",
              "      <th>tBodyAcc-max()-Z</th>\n",
              "      <th>tBodyAcc-min()-X</th>\n",
              "      <th>tBodyAcc-min()-Y</th>\n",
              "      <th>tBodyAcc-min()-Z</th>\n",
              "      <th>tBodyAcc-sma()</th>\n",
              "      <th>tBodyAcc-energy()-X</th>\n",
              "      <th>tBodyAcc-energy()-Y</th>\n",
              "      <th>tBodyAcc-energy()-Z</th>\n",
              "      <th>tBodyAcc-iqr()-X</th>\n",
              "      <th>tBodyAcc-iqr()-Y</th>\n",
              "      <th>tBodyAcc-iqr()-Z</th>\n",
              "      <th>tBodyAcc-entropy()-X</th>\n",
              "      <th>tBodyAcc-entropy()-Y</th>\n",
              "      <th>tBodyAcc-entropy()-Z</th>\n",
              "      <th>tBodyAcc-arCoeff()-X,1</th>\n",
              "      <th>tBodyAcc-arCoeff()-X,2</th>\n",
              "      <th>tBodyAcc-arCoeff()-X,3</th>\n",
              "      <th>tBodyAcc-arCoeff()-X,4</th>\n",
              "      <th>tBodyAcc-arCoeff()-Y,1</th>\n",
              "      <th>tBodyAcc-arCoeff()-Y,2</th>\n",
              "      <th>tBodyAcc-arCoeff()-Y,3</th>\n",
              "      <th>tBodyAcc-arCoeff()-Y,4</th>\n",
              "      <th>tBodyAcc-arCoeff()-Z,1</th>\n",
              "      <th>tBodyAcc-arCoeff()-Z,2</th>\n",
              "      <th>tBodyAcc-arCoeff()-Z,3</th>\n",
              "      <th>tBodyAcc-arCoeff()-Z,4</th>\n",
              "      <th>tBodyAcc-correlation()-X,Y</th>\n",
              "      <th>tBodyAcc-correlation()-X,Z</th>\n",
              "      <th>tBodyAcc-correlation()-Y,Z</th>\n",
              "      <th>...</th>\n",
              "      <th>fBodyBodyAccJerkMag-maxInds</th>\n",
              "      <th>fBodyBodyAccJerkMag-meanFreq()</th>\n",
              "      <th>fBodyBodyAccJerkMag-skewness()</th>\n",
              "      <th>fBodyBodyAccJerkMag-kurtosis()</th>\n",
              "      <th>fBodyBodyGyroMag-mean()</th>\n",
              "      <th>fBodyBodyGyroMag-std()</th>\n",
              "      <th>fBodyBodyGyroMag-mad()</th>\n",
              "      <th>fBodyBodyGyroMag-max()</th>\n",
              "      <th>fBodyBodyGyroMag-min()</th>\n",
              "      <th>fBodyBodyGyroMag-sma()</th>\n",
              "      <th>fBodyBodyGyroMag-energy()</th>\n",
              "      <th>fBodyBodyGyroMag-iqr()</th>\n",
              "      <th>fBodyBodyGyroMag-entropy()</th>\n",
              "      <th>fBodyBodyGyroMag-maxInds</th>\n",
              "      <th>fBodyBodyGyroMag-meanFreq()</th>\n",
              "      <th>fBodyBodyGyroMag-skewness()</th>\n",
              "      <th>fBodyBodyGyroMag-kurtosis()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-mean()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-std()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-mad()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-max()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-min()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-sma()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-energy()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-iqr()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-entropy()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-maxInds</th>\n",
              "      <th>fBodyBodyGyroJerkMag-meanFreq()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-skewness()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
              "      <th>angle(tBodyAccMean,gravity)</th>\n",
              "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
              "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
              "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
              "      <th>angle(X,gravityMean)</th>\n",
              "      <th>angle(Y,gravityMean)</th>\n",
              "      <th>angle(Z,gravityMean)</th>\n",
              "      <th>subject</th>\n",
              "      <th>Activity</th>\n",
              "      <th>ActivityName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7165</th>\n",
              "      <td>0.271353</td>\n",
              "      <td>0.027849</td>\n",
              "      <td>-0.203829</td>\n",
              "      <td>-0.392303</td>\n",
              "      <td>-0.104729</td>\n",
              "      <td>-0.000659</td>\n",
              "      <td>-0.449692</td>\n",
              "      <td>-0.123698</td>\n",
              "      <td>0.069395</td>\n",
              "      <td>-0.145411</td>\n",
              "      <td>-0.102062</td>\n",
              "      <td>-0.252405</td>\n",
              "      <td>0.406256</td>\n",
              "      <td>0.308976</td>\n",
              "      <td>0.382972</td>\n",
              "      <td>-0.136135</td>\n",
              "      <td>-0.814007</td>\n",
              "      <td>-0.840944</td>\n",
              "      <td>-0.53897</td>\n",
              "      <td>-0.642437</td>\n",
              "      <td>-0.426825</td>\n",
              "      <td>0.082406</td>\n",
              "      <td>0.231688</td>\n",
              "      <td>0.327772</td>\n",
              "      <td>-0.111448</td>\n",
              "      <td>-0.496922</td>\n",
              "      <td>0.297419</td>\n",
              "      <td>-0.123907</td>\n",
              "      <td>0.19015</td>\n",
              "      <td>-0.400091</td>\n",
              "      <td>0.17647</td>\n",
              "      <td>0.207524</td>\n",
              "      <td>-0.139335</td>\n",
              "      <td>-0.295053</td>\n",
              "      <td>0.003024</td>\n",
              "      <td>0.067414</td>\n",
              "      <td>0.158264</td>\n",
              "      <td>-0.179276</td>\n",
              "      <td>-0.352577</td>\n",
              "      <td>-0.325355</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.936508</td>\n",
              "      <td>-0.03356</td>\n",
              "      <td>0.199289</td>\n",
              "      <td>-0.064344</td>\n",
              "      <td>-0.492952</td>\n",
              "      <td>-0.109544</td>\n",
              "      <td>-0.231337</td>\n",
              "      <td>-0.000409</td>\n",
              "      <td>-0.95593</td>\n",
              "      <td>-0.492952</td>\n",
              "      <td>-0.638835</td>\n",
              "      <td>-0.533439</td>\n",
              "      <td>0.354719</td>\n",
              "      <td>-0.897436</td>\n",
              "      <td>-0.628768</td>\n",
              "      <td>0.289206</td>\n",
              "      <td>0.0169</td>\n",
              "      <td>-0.809967</td>\n",
              "      <td>-0.809567</td>\n",
              "      <td>-0.788654</td>\n",
              "      <td>-0.83963</td>\n",
              "      <td>-0.959585</td>\n",
              "      <td>-0.809967</td>\n",
              "      <td>-0.980243</td>\n",
              "      <td>-0.804277</td>\n",
              "      <td>0.078211</td>\n",
              "      <td>-0.936508</td>\n",
              "      <td>0.168635</td>\n",
              "      <td>-0.246286</td>\n",
              "      <td>-0.646544</td>\n",
              "      <td>0.104479</td>\n",
              "      <td>-0.286379</td>\n",
              "      <td>-0.526419</td>\n",
              "      <td>0.666447</td>\n",
              "      <td>-0.753446</td>\n",
              "      <td>0.267225</td>\n",
              "      <td>0.039295</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>WALKING_UPSTAIRS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 564 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      tBodyAcc-mean()-X  tBodyAcc-mean()-Y  ...  Activity      ActivityName\n",
              "7165           0.271353           0.027849  ...         2  WALKING_UPSTAIRS\n",
              "\n",
              "[1 rows x 564 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "doX7kzCugGWQ",
        "colab_type": "code",
        "outputId": "94edb3bb-0b73-4c3f-9d41-3ba3499dfdaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7352, 564)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHvgmqD_gGWW",
        "colab_type": "text"
      },
      "source": [
        "## Obtain the  test data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbBwafCvgGWY",
        "colab_type": "code",
        "outputId": "5dfc3af1-b64b-4614-eba3-5b56ef0c63d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "# get the data from txt files to pandas dataffame\n",
        "X_test = pd.read_csv('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/test/X_test.txt', delim_whitespace=True, header=None, names=features)\n",
        "\n",
        "# add subject column to the dataframe\n",
        "X_test['subject'] = pd.read_csv('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/test/subject_test.txt', header=None, squeeze=True)\n",
        "\n",
        "# get y labels from the txt file\n",
        "y_test = pd.read_csv('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/test/y_test.txt', names=['Activity'], squeeze=True)\n",
        "y_test_labels = y_test.map({1: 'WALKING', 2:'WALKING_UPSTAIRS',3:'WALKING_DOWNSTAIRS',\\\n",
        "                       4:'SITTING', 5:'STANDING',6:'LAYING'})\n",
        "\n",
        "\n",
        "# put all columns in a single dataframe\n",
        "test = X_test\n",
        "test['Activity'] = y_test\n",
        "test['ActivityName'] = y_test_labels\n",
        "test.sample()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py:702: UserWarning: Duplicate names specified. This will raise an error in the future.\n",
            "  return _read(filepath_or_buffer, kwds)\n"
          ],
          "name": "stderr"
        },
        {
          "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>tBodyAcc-mean()-X</th>\n",
              "      <th>tBodyAcc-mean()-Y</th>\n",
              "      <th>tBodyAcc-mean()-Z</th>\n",
              "      <th>tBodyAcc-std()-X</th>\n",
              "      <th>tBodyAcc-std()-Y</th>\n",
              "      <th>tBodyAcc-std()-Z</th>\n",
              "      <th>tBodyAcc-mad()-X</th>\n",
              "      <th>tBodyAcc-mad()-Y</th>\n",
              "      <th>tBodyAcc-mad()-Z</th>\n",
              "      <th>tBodyAcc-max()-X</th>\n",
              "      <th>tBodyAcc-max()-Y</th>\n",
              "      <th>tBodyAcc-max()-Z</th>\n",
              "      <th>tBodyAcc-min()-X</th>\n",
              "      <th>tBodyAcc-min()-Y</th>\n",
              "      <th>tBodyAcc-min()-Z</th>\n",
              "      <th>tBodyAcc-sma()</th>\n",
              "      <th>tBodyAcc-energy()-X</th>\n",
              "      <th>tBodyAcc-energy()-Y</th>\n",
              "      <th>tBodyAcc-energy()-Z</th>\n",
              "      <th>tBodyAcc-iqr()-X</th>\n",
              "      <th>tBodyAcc-iqr()-Y</th>\n",
              "      <th>tBodyAcc-iqr()-Z</th>\n",
              "      <th>tBodyAcc-entropy()-X</th>\n",
              "      <th>tBodyAcc-entropy()-Y</th>\n",
              "      <th>tBodyAcc-entropy()-Z</th>\n",
              "      <th>tBodyAcc-arCoeff()-X,1</th>\n",
              "      <th>tBodyAcc-arCoeff()-X,2</th>\n",
              "      <th>tBodyAcc-arCoeff()-X,3</th>\n",
              "      <th>tBodyAcc-arCoeff()-X,4</th>\n",
              "      <th>tBodyAcc-arCoeff()-Y,1</th>\n",
              "      <th>tBodyAcc-arCoeff()-Y,2</th>\n",
              "      <th>tBodyAcc-arCoeff()-Y,3</th>\n",
              "      <th>tBodyAcc-arCoeff()-Y,4</th>\n",
              "      <th>tBodyAcc-arCoeff()-Z,1</th>\n",
              "      <th>tBodyAcc-arCoeff()-Z,2</th>\n",
              "      <th>tBodyAcc-arCoeff()-Z,3</th>\n",
              "      <th>tBodyAcc-arCoeff()-Z,4</th>\n",
              "      <th>tBodyAcc-correlation()-X,Y</th>\n",
              "      <th>tBodyAcc-correlation()-X,Z</th>\n",
              "      <th>tBodyAcc-correlation()-Y,Z</th>\n",
              "      <th>...</th>\n",
              "      <th>fBodyBodyAccJerkMag-maxInds</th>\n",
              "      <th>fBodyBodyAccJerkMag-meanFreq()</th>\n",
              "      <th>fBodyBodyAccJerkMag-skewness()</th>\n",
              "      <th>fBodyBodyAccJerkMag-kurtosis()</th>\n",
              "      <th>fBodyBodyGyroMag-mean()</th>\n",
              "      <th>fBodyBodyGyroMag-std()</th>\n",
              "      <th>fBodyBodyGyroMag-mad()</th>\n",
              "      <th>fBodyBodyGyroMag-max()</th>\n",
              "      <th>fBodyBodyGyroMag-min()</th>\n",
              "      <th>fBodyBodyGyroMag-sma()</th>\n",
              "      <th>fBodyBodyGyroMag-energy()</th>\n",
              "      <th>fBodyBodyGyroMag-iqr()</th>\n",
              "      <th>fBodyBodyGyroMag-entropy()</th>\n",
              "      <th>fBodyBodyGyroMag-maxInds</th>\n",
              "      <th>fBodyBodyGyroMag-meanFreq()</th>\n",
              "      <th>fBodyBodyGyroMag-skewness()</th>\n",
              "      <th>fBodyBodyGyroMag-kurtosis()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-mean()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-std()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-mad()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-max()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-min()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-sma()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-energy()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-iqr()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-entropy()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-maxInds</th>\n",
              "      <th>fBodyBodyGyroJerkMag-meanFreq()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-skewness()</th>\n",
              "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
              "      <th>angle(tBodyAccMean,gravity)</th>\n",
              "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
              "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
              "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
              "      <th>angle(X,gravityMean)</th>\n",
              "      <th>angle(Y,gravityMean)</th>\n",
              "      <th>angle(Z,gravityMean)</th>\n",
              "      <th>subject</th>\n",
              "      <th>Activity</th>\n",
              "      <th>ActivityName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2450</th>\n",
              "      <td>0.280576</td>\n",
              "      <td>-0.009888</td>\n",
              "      <td>-0.092731</td>\n",
              "      <td>-0.978711</td>\n",
              "      <td>-0.964622</td>\n",
              "      <td>-0.889601</td>\n",
              "      <td>-0.983633</td>\n",
              "      <td>-0.964794</td>\n",
              "      <td>-0.871134</td>\n",
              "      <td>-0.912057</td>\n",
              "      <td>-0.538771</td>\n",
              "      <td>-0.760036</td>\n",
              "      <td>0.822255</td>\n",
              "      <td>0.6885</td>\n",
              "      <td>0.806211</td>\n",
              "      <td>-0.944126</td>\n",
              "      <td>-0.999681</td>\n",
              "      <td>-0.999532</td>\n",
              "      <td>-0.992755</td>\n",
              "      <td>-0.990504</td>\n",
              "      <td>-0.972553</td>\n",
              "      <td>-0.854524</td>\n",
              "      <td>-0.282707</td>\n",
              "      <td>-0.269999</td>\n",
              "      <td>-0.13638</td>\n",
              "      <td>0.224061</td>\n",
              "      <td>-0.08937</td>\n",
              "      <td>0.290032</td>\n",
              "      <td>0.192295</td>\n",
              "      <td>0.136354</td>\n",
              "      <td>-0.043291</td>\n",
              "      <td>0.065246</td>\n",
              "      <td>0.180924</td>\n",
              "      <td>-0.127875</td>\n",
              "      <td>0.09835</td>\n",
              "      <td>-0.025624</td>\n",
              "      <td>-0.165457</td>\n",
              "      <td>-0.056957</td>\n",
              "      <td>-0.090721</td>\n",
              "      <td>-0.29565</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.032366</td>\n",
              "      <td>-0.222995</td>\n",
              "      <td>-0.683098</td>\n",
              "      <td>-0.938243</td>\n",
              "      <td>-0.940839</td>\n",
              "      <td>-0.941554</td>\n",
              "      <td>-0.948435</td>\n",
              "      <td>-0.973249</td>\n",
              "      <td>-0.938243</td>\n",
              "      <td>-0.997536</td>\n",
              "      <td>-0.949271</td>\n",
              "      <td>-0.324406</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.144707</td>\n",
              "      <td>-0.259723</td>\n",
              "      <td>-0.635655</td>\n",
              "      <td>-0.947558</td>\n",
              "      <td>-0.941105</td>\n",
              "      <td>-0.940719</td>\n",
              "      <td>-0.938929</td>\n",
              "      <td>-0.979127</td>\n",
              "      <td>-0.947558</td>\n",
              "      <td>-0.998215</td>\n",
              "      <td>-0.94237</td>\n",
              "      <td>-0.404518</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.103494</td>\n",
              "      <td>0.056547</td>\n",
              "      <td>-0.292552</td>\n",
              "      <td>-0.003855</td>\n",
              "      <td>-0.007809</td>\n",
              "      <td>-0.024132</td>\n",
              "      <td>-0.358898</td>\n",
              "      <td>-0.920981</td>\n",
              "      <td>0.126866</td>\n",
              "      <td>0.067282</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>SITTING</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 564 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      tBodyAcc-mean()-X  tBodyAcc-mean()-Y  ...  Activity  ActivityName\n",
              "2450           0.280576          -0.009888  ...         4       SITTING\n",
              "\n",
              "[1 rows x 564 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j49k9jzZgGWe",
        "colab_type": "code",
        "outputId": "00fb816a-c236-4eef-9a66-9d3fa99388cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2947, 564)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g7tPDZiLgGWl",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "ytO0ZOTwgGWm",
        "colab_type": "text"
      },
      "source": [
        "# Data Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6IIjVRiegGWo",
        "colab_type": "text"
      },
      "source": [
        "## 1. Check for Duplicates"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "C-cDPdtPgGWq",
        "colab_type": "code",
        "outputId": "215ca093-d77d-404e-e22e-da4d8bed1de7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print('No of duplicates in train: {}'.format(sum(train.duplicated())))\n",
        "print('No of duplicates in test : {}'.format(sum(test.duplicated())))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of duplicates in train: 0\n",
            "No of duplicates in test : 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "misdFrJGgGW6",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qU8zipOKgGW8",
        "colab_type": "text"
      },
      "source": [
        "## 2. Checking for NaN/null values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aqw5_ojugGW9",
        "colab_type": "code",
        "outputId": "c04f28eb-1180-4383-91e9-da050f6f5c1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print('We have {} NaN/Null values in train'.format(train.isnull().values.sum()))\n",
        "print('We have {} NaN/Null values in test'.format(test.isnull().values.sum()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 0 NaN/Null values in train\n",
            "We have 0 NaN/Null values in test\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BELEmeS8gGXE",
        "colab_type": "text"
      },
      "source": [
        "   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1dZqIPfgGXG",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBtv1xoUgGXI",
        "colab_type": "text"
      },
      "source": [
        "## 3. Check for data imbalance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lm_2AaK0gGXJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_style('whitegrid')\n",
        "plt.rcParams['font.family'] = 'Dejavu Sans'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYRG6W6HgGXP",
        "colab_type": "code",
        "outputId": "2b28cddf-b353-405a-c73c-330be636ffed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        }
      },
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Data provided by each user', fontsize=20)\n",
        "sns.countplot(x='subject',hue='ActivityName', data = train)\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xw9vbG1lZWcLfd22eGxERPccEloioEcnIyMCcOXNw6dIltGnTBtOmTRPOtW3bFoBikpKR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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Spu3TuigGXW",
        "colab_type": "text"
      },
      "source": [
        "> We have got almost same number of reading from all the subjects"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "mDG1EImhgGXY",
        "colab_type": "code",
        "outputId": "b1be8cb3-be83-407b-bcb6-02d24f8bffb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "plt.title('No of Datapoints per Activity', fontsize=15)\n",
        "sns.countplot(train.ActivityName)\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2rMSwXDMzM4SFhWHBggWYMGECateujd69e2PKlCnlXqxX2ceVJyoqClFRUahduzZMTEzQ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DdX9Wo6TgGXd",
        "colab_type": "text"
      },
      "source": [
        "### Observation\n",
        "> Our data is well balanced (almost)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USJTbDAngGXf",
        "colab_type": "text"
      },
      "source": [
        "## 4. Changing feature names "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Q2fOsYtgGXh",
        "colab_type": "code",
        "outputId": "2a352911-8eb0-4717-9e23-a8e22662de5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "source": [
        "columns = train.columns\n",
        "\n",
        "# Removing '()' from column names\n",
        "columns = columns.str.replace('[()]','')\n",
        "columns = columns.str.replace('[-]', '')\n",
        "columns = columns.str.replace('[,]','')\n",
        "\n",
        "train.columns = columns\n",
        "test.columns = columns\n",
        "\n",
        "test.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['tBodyAccmeanX', 'tBodyAccmeanY', 'tBodyAccmeanZ', 'tBodyAccstdX',\n",
              "       'tBodyAccstdY', 'tBodyAccstdZ', 'tBodyAccmadX', 'tBodyAccmadY',\n",
              "       'tBodyAccmadZ', 'tBodyAccmaxX',\n",
              "       ...\n",
              "       'angletBodyAccMeangravity', 'angletBodyAccJerkMeangravityMean',\n",
              "       'angletBodyGyroMeangravityMean', 'angletBodyGyroJerkMeangravityMean',\n",
              "       'angleXgravityMean', 'angleYgravityMean', 'angleZgravityMean',\n",
              "       'subject', 'Activity', 'ActivityName'],\n",
              "      dtype='object', length=564)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ew4dzJavgGXo",
        "colab_type": "text"
      },
      "source": [
        "## 5. Save this dataframe in a csv files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGf6EzXvgGXq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train.to_csv('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/csv_files/train.csv', index=False)\n",
        "test.to_csv('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/csv_files/test.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2j5xlCXTgGXx",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGnStGSFgGX1",
        "colab_type": "text"
      },
      "source": [
        "# Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0G1a79IgGX2",
        "colab_type": "text"
      },
      "source": [
        "\"___Without domain knowledge EDA has no meaning, without EDA a problem has no soul.___\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2HC8S6dogGX6",
        "colab_type": "text"
      },
      "source": [
        "### 1. Featuring Engineering from Domain Knowledge \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9WYcoRngGX7",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "+ __Static and Dynamic Activities__\n",
        "\n",
        "    - In static activities (sit, stand, lie down) motion information will not be very useful.\n",
        "\t- In the dynamic activities (Walking, WalkingUpstairs,WalkingDownstairs) motion info will be significant.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZLF8iKRgGYB",
        "colab_type": "text"
      },
      "source": [
        "### 2. Stationary and Moving activities are completely different"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iILLCId7gGYH",
        "colab_type": "code",
        "outputId": "1d202d62-6809-45a5-e13f-b1d01a074a92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        }
      },
      "source": [
        "sns.set_palette(\"Set1\", desat=0.80)\n",
        "facetgrid = sns.FacetGrid(train, hue='ActivityName', size=6,aspect=2)\n",
        "facetgrid.map(sns.distplot,'tBodyAccMagmean', hist=False)\\\n",
        "    .add_legend()\n",
        "plt.annotate(\"Stationary Activities\", xy=(-0.956,17), xytext=(-0.9, 23), size=20,\\\n",
        "            va='center', ha='left',\\\n",
        "            arrowprops=dict(arrowstyle=\"simple\",connectionstyle=\"arc3,rad=0.1\"))\n",
        "\n",
        "plt.annotate(\"Moving Activities\", xy=(0,3), xytext=(0.2, 9), size=20,\\\n",
        "            va='center', ha='left',\\\n",
        "            arrowprops=dict(arrowstyle=\"simple\",connectionstyle=\"arc3,rad=0.1\"))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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S1LZtW/3xj3/Uxx9/rMaNG6tLly7e6+W9dkE2b96sRx99VGlpaYqJiVGDBg20Z88eLVy4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\nCCGEEEKIE9D/D1HaQvFF9RWiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1019.88x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTZqHM-5gGYU",
        "colab_type": "code",
        "outputId": "32bbe14d-3086-43bd-ade0-cd4ef64b136f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "# for plotting purposes taking datapoints of each activity to a different dataframe\n",
        "df1 = train[train['Activity']==1]\n",
        "df2 = train[train['Activity']==2]\n",
        "df3 = train[train['Activity']==3]\n",
        "df4 = train[train['Activity']==4]\n",
        "df5 = train[train['Activity']==5]\n",
        "df6 = train[train['Activity']==6]\n",
        "\n",
        "plt.figure(figsize=(14,7))\n",
        "plt.subplot(2,2,1)\n",
        "plt.title('Stationary Activities(Zoomed in)')\n",
        "sns.distplot(df4['tBodyAccMagmean'],color = 'r',hist = False, label = 'Sitting')\n",
        "sns.distplot(df5['tBodyAccMagmean'],color = 'm',hist = False,label = 'Standing')\n",
        "sns.distplot(df6['tBodyAccMagmean'],color = 'c',hist = False, label = 'Laying')\n",
        "plt.axis([-1.01, -0.5, 0, 35])\n",
        "plt.legend(loc='center')\n",
        "\n",
        "plt.subplot(2,2,2)\n",
        "plt.title('Moving Activities')\n",
        "sns.distplot(df1['tBodyAccMagmean'],color = 'red',hist = False, label = 'Walking')\n",
        "sns.distplot(df2['tBodyAccMagmean'],color = 'blue',hist = False,label = 'Walking Up')\n",
        "sns.distplot(df3['tBodyAccMagmean'],color = 'green',hist = False, label = 'Walking down')\n",
        "plt.legend(loc='center right')\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HRkYSHR0NaF0Ojh07RuPGjenUqRN7Hj1j5Ovri7Ozc9FFGR7+99+SoAshhBAF\nkl2d3qhRoyzrODs74+vrC8CePXvo3LnzE8+pi7LP6MEDCAoyfIJuYQEzZsCBA/DYzR8hhBC5y7WL\ne3h4ON7e3qSlpaGUws3NjT59+tCkSROmTp3Kl19+SYsWLRgxYkTRRZmRoFepAv7+RbcfIYQQohzL\nqU7/6quvaNWqFS4uLrzwwgtMnz6dvn378swzz7B06dKSDlsUAfOrV7U/2rQxfOFvvgmff64l6seO\nFX4KNyGEqEByTdCbN2+On5/fE5/Xr1+/6KdWy5CRoDs7w759kJYGxsbFs28hhBCinMipTn/vvff0\nf5uZmWU7P7ooX8yuXNH+MHQLOkDlytrUuK+9Btu2QVE24gghRDmTr2fQS0zGM+j9+kFcHFy/XrLx\nCCGEEEKUYeaXL0OtWtrz4kVh7Fho3Ro+/FA/0K8QQojclY0EPTwcrK2hc2ftvTyHLoQQQghRYGZX\nrhRN63kGY2P44gu4eVPr7i6EECJPyk6CXqsWtGwJlSpJgi6EEEIIUVCpqZhdv160CTpA375a9/b5\n86X3oxBC5FHZStBNTeH552WgOCGEEEKIgrpyBaPk5KJP0AG+/BLMzGDSJFCq6PcnhBBlXNlI0CMi\noGZN7e927aQFXQghhBCioM6d016LI0GvUwc++QR+/RU2by76/QkhRBlXNhL0jBZ0AAcHCAuD0NCS\njUkIIYQQoiy6eBFlYgLNmxfP/t58Ezp1gqlTITKyePYphBBlVOlP0NPTtRb0jAS9XTvtVbq5CyGE\nEELkX0AAyc8+q43rUxyMjeGbb7Tk/MMPi2efQghRRpX+BP3BA0hNfTJBl27uQgghhBD5FxBAUuPG\nxbvPtm3h/ffhu+/g6NHi3bcQQpQhpT9BDw/XXjOeQX/mGWjYUBJ0IYQQQoj8Sk6G69dJLu4EHWDu\nXGjQACZMkLnRhRAiB6U/QY+I0F4zWtBBa0WXLu5CCCGEEPlz7RqkpZHUqFHx79vSElauhMuX4bPP\nin//QghRBpT+BD2jBT1zgu7goM2nGRNTMjEJIYQQQpRFly4BlEyCDjBwILzwAixcCHfulEwMQghR\nipXNBL1dO20uzfPnSyYmIYQQQoiyKCAAdDqSGzYssl3Ex0NUlNabPluff64NAjx7dpHFIIQQZVXZ\nSdCrV//7MxkoTgghhBAi/wIC4LnnUBYWBi86KAg8PLSe7NWqQd262phw6emPrfjcc/Duu7BhgzS2\nCCHEY0p/gh4Rof0rn3kqkHr1tIRdEnQhhBBCiLy7dAmef97gxf76K7RsCQcOgLc3LFmiTbP+xhta\nr/YnWtNnzYKqVeGjjwweixA8PRtVAAAgAElEQVRClGWlP0EPD8/avR1Ap5OB4oQQQggh8iMtDa5c\ngRYtDFrsjRvw4ovw7LNw4QJ88glMnQpHjsDy5bBnD7z1lvZ0op6NDXzwAezaJddzQgiRSdlM0EEb\nKO7iRUhJKf6YhBBCCCHKmlu3tOnNDJigx8fD0KHa39u3Q+ax53Q6eOcd7VHztWvhyy8f2/itt6BK\nFVi0yGDxCCFEWVc2EvSMOdAza9dOq2SuXCn+mIQQQgghypqAAO3VgAn6okXaY+SbN0NOU6v/4x/g\n6QkzZ2r3CPSqVtWS9K1b4epVg8UkhBBlWelP0CMism9BzxgoTrpFCSGEEELkLiMJbtbMIMUFB8MX\nX8BLL4GbW87r6XTw9ddgbAxTpjy2cMoUMDPTChJCCFHKE/TUVLh/P/sEvVkzMDeXgeKEEEIIIfLi\n6lVtkN1q1QxS3Jw52pOGCxfmvm69elpX9+3bYffuTAtsbWHUKPjXv+DhQ4PEJYQQZVnpTtDv39dG\nFMkuQTcxgdatJUEXQgghhMiLq1fB3t4gRV25AuvWac+YZ37u/GmmToWmTbVR3rNMvTZpkvYw+7/+\nZZDYhBCiLCvdCXrGHOjZPYMO2kBx/v6PDQsqhBBCCCGeYMAEfdkybQZcb++8b2NqqrWinz8PP/+c\naUH79uDkBKtWyTWdEKLCyzVBDwkJwcvLi4EDBzJo0CA2bNgAwPLly+nRoweenp54enpy+PBhw0cX\nEaG9ZteCDtpz6FFREBRk+H0LIYQQ5UxOdXpmJ06coH379vr6/euvvy6BSIXBxcbC3bsGSdAfPoQN\nG2DkyJwv0XLy8svQpAnMm/dYLj5xIvz5J/z2W6HjE0KIsswktxWMjY3x9vamZcuWxMbGMnz4cLp1\n6wbAq6++ymuvvVZ00WW0oD8tQQetFf3ZZ4suDiGEEKIcyKlOb9KkSZb1nJyc+Oabb0ooSlEkrl/X\nXg2QoK9bB3Fx8O67+d/WxARmzYJx42DnTvDweLRg5EitD/zatdC9e6FjFEKIsirXFvRatWrRsmVL\nAKysrGjUqBFhYWFFHhiQe4LeqpX2evly8cQjhBBClGElWqeLkpUxgnshE/T0dG1E9m7dtJ7pBTF6\nNDz33GPTn1tawvDh8NNPkJBQqBiFEKIsy7UFPbM7d+4QEBBA27ZtOXPmDJs2bcLPz49WrVrh7e3N\nM88889Ttk5KSCMiYgzMHiYmJ+nVqXrpEdWNjLoeG/p2sP8a+cmUe/vknYbmUK7LKfJ5F0ZHzXDzk\nPBcPOc/lS+Y6/XFnz55l8ODB1KpViw8//JCmTZs+tay81O9lQXn+jVf/7TdqAZdTU1EBAQU+1uPH\nKxMY2IA337xLQEB0geN55RUbFi6szebNt3Bw0BLyyt2702D9eu588w0x/fsXuOzHlefv9XFyrOWT\nHGsFo/IoNjZWDR06VO3Zs0cppVRERIRKTU1VaWlpasmSJcrb2zvXMi5dupS/dSZMUMrW9ukbNG2q\n1Isv5lquyCov34UoPDnPxUPOc/GQ81w8iuM8P16nZxYTE6NiY2OVUkodOnRI9e3bN9fyystvo7wc\nR7a8vJSqX1//tqDHOm6cUtbWSsXFFS6c2FilqlVTasiQTB+mpiplZ/fYh4VXrr/Xx8ixlk9yrOVD\nXo8tT6O4p6SkMHnyZDw8POjXrx8ANWrUwNjYGCMjI0aMGMGFCxcMf/cgPDz30Ufs7CAkxPD7FkII\nIcqh7Or0zKysrLC0tASgV69epKamEhkZWdxhCkMzwAjuCQmwbZvWE71y5cKFY2kJb72lzYt+5cqj\nD42NtWfRd+3SBgEWQogKKNcEXSnFrFmzaNSoEePGjdN/Hp6py/mvv/6aa/e3ApEEXQghhDCYnOr0\nzCIiIlCPhtc+f/486enp2NjYFGeYoihcvapNQl4IO3ZATIz2DLkhvPuuNvXaF19k+nDUKEhOBh8f\nw+xECCHKmFyfQT99+jTbt2/H3t4eT09PAN5//3127tzJ5UeDs9WtW5d58+YZPrrwcG1ezKeRBF0I\nIYTIk5zq9ODgYABefvll9uzZw48//oixsTHm5uYsWbIEnU5XkmGLwrp/X2uRLmQL+qZNUKcO9O5t\nmLBq1YJXX4X167Vp12rXBhwdtRHk/PygKGcKEkKIUirXBN3JyYkr+r5Hf+vVq1eRBJRFXlvQ4+K0\nW7rW1kUfkxBCCFFG5VSnZzZ69GhGG6qJVJQOBhjBPSpK63n+3ntaT3RDmTYN1qyB5cthwQJAp4Mh\nQ2DVKm3udisrw+1MCCHKgDw9g14ikpIgOjpvCTpIK7oQQgghRHYMkKDv2gWpqfDCCwaK6ZGmTbPm\n44D2QVIS7Nlj2J0JIUQZUHoT9IgI7VUSdCGEEEKIgrt6FUxMtK7jBeTrq11ydexouLAyzJihtdCv\nXfvog27doHp1rZu7EEJUMKU3Qc8YhK5mzaevJwm6EEIIIUTOrl6FRo2gUqUCbZ6QAL/8Ap6eYFQE\nV46dO0P37rBkCaSkoN1M8PCAnTsffSCEEBVH6U/Q89qC/miAGyGEEEIIkUkhp1jbv18b7mfoUAPG\n9Jjp0+H2bdi69dEHQ4bAgwdw9GjR7VQIIUqh0pug57WLu40NmJlJC7oQQgghxOPS0+HatUIl6L6+\nUKWK4UZvz467OzRvDp9/DkoBLi5ai/8vvxTdToUQohQqvQl6XlvQdTptXg5J0IUQQgghsrp7V+uj\nXsAEPT1dm/984EBtzvKiYmQEH3wAZ89qPduxstL6vctAcUKICqZ0J+impnmbOk3mQhdCCCGEeFLG\nCO5NmxZo89OntU6N7u4GjCkHY8Zo9xFmzHj06LmbG5w/L48xCiEqlNKdoNeqpbWQPyb2fCzpSel/\nfyAJuhBCCCHEkwo5xdquXdqlWP/+BowpB5UqwWefweXL8N13/L3TvXuLfudCCFFKlN4EPSIi2+7t\nKfdTON3+NFffuvr3h5KgCyGEEEI86epVqFwZ6tQp0Oa7d0OHDlCjhoHjysHgwdCzJ8ydC5H12miP\nMcpz6EKICqT0JugZLeiPiTkVg0pVhH4fysPjD7UP7ey0kT4TEoo5SCGEEEKIUuzqVa17ewHmR7t3\nD/74AwYMKIK4cqDTwVdfafOivzlRh+rXX2tBT0srviCEEKIEle4EPZs50GNOxQBgWtuUa29fQ6Wp\nv6daCw0tzgiFEEIIIUq3QkyxtnevNqJ6cSboAO3awT//Cdu2wcbKE7Vs/cyZ4g1CCCFKSOlO0LNp\nQY8+GY2FvQVNvmxC7JlYgr8N/jtBl27uQgghhBCalBS4ebPAA8Tt3q11bXdyMnBceTB9utbV/e2N\nHTlGFzh8uPiDEEKIElA6E/S4OK27eg5d3K07WFPzxZpUda7KzZk3STa31RZKgi6EEEIIoblxQ+sa\n3qxZvjdVCvbtg759wdi4CGLLhbExbNkCdeoa4Wa0lxO+MpK7EKJiKJ0Jeg5zoCeFJJF8NxlrJ2t0\nOh1NlzclLSaNm2sfHYYk6EIIIYQQmitXtNcCJOgBARAWBi4uBo4pH+zs4MABqGmVQO9jC1j8WRqp\nqSUXT14lpSZx++FtLoZf5Peg3wmICCBdpee+oRBCACYlHUC2MhL0x55Bz3j+3NpJmxvd8nlL6k2p\nR9AXQdjpWlBFEnQhhBBCCE1Ggl6AZ9APHtRe+/QxYDwFUK8e/O/TY0yapJj+4RC+XQuvvqqN9t68\necm07mcnKTWJPYF72HJxCz9f+Zm4lLgsy61NrXGq40Sf5/owof0EbK1sSyhSIURpV7oT9Mda0GNO\nxYARWDtY6z9rMKcBYZvDuBYxDce7x3hy1nQhhBBCiAroyhWtscPGJt+bHjgADRpAw4ZFEFc+2Q3t\njO+k2viM9uXLW0OYORNmzgQLC2jbFhwdoXdvrTt+1arFH59vgC+T/juJsLgwqltUZ3Sb0XSo0wFr\nM2usTa0Jjwvnj7t/cOLuCeYemsuCowvwauPFtK7TaF6jefEHLIQo1Upngh4Rob1mk6BbPm+JseXf\nt0tNrE1ovLgxAa8kE3HuAk8+tS5EyVFKcfPmTRITE0s6lDLJ3NycevXqUalSpZIORQghyp6rVwvU\nvT09HQ4dAk9PbdqzEmdri65FC4bfX8Pwo0O4eRP+9z9tYPczZ+CHH2DlSq01fdQomD0bmjQp+rDu\nx99n8i+T2XxhMw61Hfje83v6NupLJeMn66yx7cYCcOXeFZYeX8qGcxtYd3Yd3t29mdNrDqbGpkUf\nsCgXUlJSuHPnTrm9tkxJSSEgIKCkwyiUwl6/ls4EPZsu7kopYk7GUH1Q9SdWr/ViLa6MPk/0naqS\noItSJS0tDWtra5577jl0peIqp+xQSnH//n3u3LlDw9LQhCOEEGXNlSvg4ZHvzc6fh8jIku/enkWf\nPrBxI6Sm0rChCQ0bgpeXtig1FU6cgK1bYc0a2LQJFi7URoIvqqr3YvhF+v3Qj4j4CP7R+x981P2j\nbBPzxzWr0YzV7qv5Z59/MuPXGSw4uoAdV3ewYcgG2tVuVzTBinLlzp075fraMiEhAQsLi5IOo8AM\ncf1aegeJs7SEypX1HyUFJZESkYJ1B+snVtcZ67Cq8YCYh/I8jyhdlFJUr169XP4DWtR0Oh3Vq1cv\nt3eIhRCiSD14oF1PFaAF/cAB7bVUJejdu0NsrHb34DEmJtCtG3z5pTZw/bBh8OGH8NJLUBRVyOV7\nl3HZqI2e98frfzCn15w8JeeZ1bSsyTrPdex4eQfhceF0/LYjG89tNHywotxJTEyUa8tSzBDXr6U3\nQX+8e/vJrAPEPc66QRKxKQ1QySlFHp4Q+SH/gBacnDshhCigQg4Q17SpNkBbqdGtm/b6229PXa12\nbfj3v+Gzz7QWdS8vbaY5Q7l2/xrOG5zRoePA2AM42DkUqjx3e3cuTrpIzwY9Ges3lvlH5qOUMlC0\noryS66PSrbDfT9lJ0E/FoDPRYdnGMttNrJobk0ZlEo7fKY4IhShTVq1axaBBg/Dw8MDT05Nz584x\na9Ysrl+/DsDq1av160ZHR7Np0yb9+7CwMCZPnlzsMQshhCiEq1e113y2oKemwuHD4OxcBDEVxrPP\nancMcknQQevWPn06fPEFbNsGixYZpoflneg7OG90JjU9lf1j9htsgLfqlauza9QuRrcZzeyDs5mw\nYwKp6WVgPjlRIS1cuJD169fr37/22mvMmjVL/37RokWsW7cux+0dHLSbWidOnODNN998YvmhQ4dY\ns2aN4QIug3JN0ENCQvDy8mLgwIEMGjSIDRs2APDgwQPGjRtHv379GDduHA8fPjRcVBER2Q8Q18YS\nY/Ps59OwdrTS1jsaarg4hCgH/P39OXToEL6+vuzYsYN169ZRu3ZtFixYQJNHo+h88803+vWjo6P5\n8ccf9e9tbW1ZtmxZsccthDC8nOr0zJRSzJ8/n759++Lh4cGff/5ZApGKQrtyRRs1rVGjfG125gzE\nxJTCBB20VvRjx/K8+vvvw9SpsGlTNbZsKdyu09LTGOUziqiEKPZ57aNlrZaFK/AxpsambByykZnd\nZ/Kd/3eM2z5O5k4XpZKjoyP+/v4ApKenExUVpW/wAe26MyMJL4jevXszYcKEQsdZluU6SJyxsTHe\n3t60bNmS2NhYhg8fTrdu3fDx8aFLly5MmDCBNWvWsGbNGqZPn26YqMLDtTkzHlFKEXMqhpov1sxx\nk8oda6HjIbGnk5En0YX4W0REBDY2NpiaaiPEVqtWDQAvLy9mzJjBnj17SExMxNPTkyZNmpCens7t\n27fx9PSka9eujBo1iokTJ7Jz5058fHw4cOAACQkJBAUF4erqyowZMwDYunUr3333HdbW1jRv3hxT\nU1PmzJlTYscthHhSTnV6k0xDXh85coRbt26xd+9ezp07x8cff8zWrVtLMGpRIFeuaMm5af5GB894\n/rx3b8OHVGhdu2r914OCoH79PG3y2Wdw4EA8kyZVpkcPqFu3YLteeHQhR/46wnrP9bSt3bZgheRC\np9OxwGUBlStV5v8O/h+WlSxZNWiVdGcWpYqDgwOffPIJANeuXaNp06ZERETw8OFDLCwsCAwMpEmT\nJowdO5bo6GhSU1N57733cHV1zbHM8+fPM2fOHJYtW8axY8e4evUqc+bMwdvbGysrKy5evEhERATT\np0/Hzc2N9PR05s2bx/Hjx7Gzs8PExIThw4fj5uZWXKehSOWaoNeqVYtaj1qzraysaNSoEWFhYezf\nv58ffvgBgCFDhuDl5WWYBF2pJ7q4JwQmkPogNcfnzwGM6tfBijPEXGpQ+BiEKAobN8L33xu2zPHj\nYcyYp67SrVs3VqxYQf/+/enSpQsDBw6kY8eO+uUffPABmzZtYvv27YA2Oui1a9eyvM8sICAAPz8/\nTE1NcXNzw8vLCyMjI1atWoWPjw+WlpaMHTuW5s1lblchSpuc6vTMCfr+/fsZMmQIOp2Odu3aER0d\nTXh4uH47UUZcuVKg588PHIBWrZ7oyFg6ZH4OfeTIPG1iYgKffBLMCy80Yfx4+OWX/I/sfizoGP84\n/A9ebvUyY9o+vc41hJk9ZhKbHMui3xZhWcmSxf0WS5IuslcC15a2trYYGxsTHByMv78/7dq1Iyws\njLNnz2JlZYW9vT3m5uasWLECKysrIiMjeemll3Bxccn2d3zmzBnmz5/PypUrqVOnDsce6yUTHh7O\n5s2buXHjBpMmTcLNzY29e/dy9+5ddu3axf379xk4cCDDhw837HkoQfmaZu3OnTsEBATQtm1b7t+/\nr6+sa9asyf3793PdPikpKdd57VIiIiA1lTCliHy0buIubRS8e9Xv8TAgh670yclYc5XQm425dOmS\n/EOWi8TExDI/x2BZoJQiISEBAOPkZIzTDdtdLS05mbRH5efEyMiITZs2cebMGU6ePMmUKVOYPHky\naWlpJCUlkZCQkCXOxMRE0tPTs32fnJxMhw4dMDExIT09nYYNG3Lz5k2ioqJwcHDAzMyM1NRUXFxc\n+Ouvv/RlFEZe5sOU33PxkPNcvmSu0zMLCwujdu3a+ve1a9cmLCxMEvSyJD0drl2Dvn3ztVlysja/\n+OuvF1FchdW2rTbDTz4SdIDnnkvh00/h3XfBxwfycx3/IPEBr/z0CvWfqV9srdk6nY6FLguJTY5l\nyfElVK9cnZk9Zhb5foXIKwcHB/z9/fH392fcuHGEhYVx5swZrK2tcXR0RCnFkiVLOHnyJEZGRoSF\nhXHv3j1q1szaGzowMJA5c+awdu1abG2z7wPt6uqKkZERTZo04d69ewCcPn0aNzc3jIyMqFmzJp06\ndSryYy5OeU7Q4+LimDx5MjNnzsTKyirLMp1Ol6d/sMzMzGjRosVT1wm8dQsA29atsX207vXvrhNt\nFk0rj1YYVcr5sflgqxDSY01paN4Qi0Zld/684hAQEJDrdyEK7/z583/P5fj66wa/6sl+RIbs9ezZ\nk549e9KyZUv8/PwwNjbGzMwMCwsLdDqdPk5zc3OMjIyyfW9qakrlypX1yypVqqQvx8TEJMvnmd8X\nRqVKlXL9rcrvuXjIeS4exXET5Gl1ekHk5QZ8WVBebkKZ3L1L08REQqpU4UEOx5PdsZ46ZUFCwnM0\nbRpEQEBscYSab8+2bo3x/v3czMf3lJiYSK9eAdjbN2TKFCMaN76BmVneRkr/5+l/EhQdxCbnTQTf\nDCaY4IKGnm8Tn5vIzbCbzDowC/N4cwY8OyDXbcrLbzgvKuqxpqSk/N0AMmKE9p+h5dLA0rp1a06e\nPMnly5epV68eVatW5bvvvsPS0hJPT09++uknIiIi2LRpE5UqVWLAgAE8fPgQKysrfaNQUlIS1atX\nJzk5mbNnz9KzZ099+ampqSQkJJCamvooHC2ejG1TU1NJTk7Wf56WlpblfWmQlwamnOQpQU9JSWHy\n5Ml4eHjQr18/AKpXr67v8hYeHq5/rrWwjDNa4jPdYYk5FYNVO6unJucA1rVj4DrEnI6RBF2IR27c\nuIGRkRHPPfccoF3816lTh2vXrunXMTExISUlhUqVKmFpaUlcXFy+9tG6dWsWLlzIw4cPsbS0ZO/e\nvdgXoGulEKLoZVenZ2Zra0to6N8DroaGhubYspEhLzfgy4JycxMqKAgAu969scvheLI71q1bte7f\no0fXx8amyKMsGGdn+OwzWjz3HOTxJnDGsa5cCa6u8MsvzfH2zn27C2EX+PeNfzPJaRIju+e9xd6Q\nttlvw3WjKzNPzqTL813oUr/LU9cvN7/hPKioxxoQEGCQBpDC6NSpEz/88AP169fHysoKKysr4uLi\nuHHjBgsXLmTHjh3UqlWLKlWqcPz4cUJCQjA3N8/SKGRmZkbVqlVZsGAB48aNo2rVqvqW8IxGHhMT\nE0xNTfXHm7Ftx44d8fPz46WXXiIyMpLTp0/j6elZ4ucls+wamPKasOc6irtSilmzZtGoUSPGjRun\n/9zZ2Rk/Pz8A/Pz8cHFxyU/MOTKJjNT+eNSVTqUpYk7HUKVDlVy3tXxOodOlEnumdN71FaIkxMfH\n4+3tzcCBA/Hw8CAwMJB33nknyzovvvgigwcPZtq0adjY2ODo6Ii7uzuffvppnvZha2vLm2++yYgR\nI3j55ZepW7cu1tY5jxkhhCgZOdXpmWXU70opzp49i7W1tXRvL2sy5kDP5xRrBw6AgwOlNzkH6NBB\nm9j87Nl8b+riAp6esHAhZFxu5kQpxXu/vEdV86rM6zOvgMEWnrmJOX4j/ahXpR6eWzy5EXWjxGIR\nIoO9vT1RUVFZHpGyt7fHysqKatWq4eHhwcWLF/Hw8GD79u00espsEjVq1OCbb75h3rx5nDt3Lk/7\n79+/P7a2tgwcOJDp06fz/PPPl6/rTpWLkydPKnt7e+Xu7q4GDx6sBg8erA4dOqQiIyPVmDFjVN++\nfdXYsWNVVFRUbkWpS5cu5bpO8Jw5SoFSd+8qpZSK/TNWHeSgClkfkuu2auxYdbLSOnW239nc163g\n8vJdiMI7d+5cSYdQbGJjY5VSSqWkpKg333xT7d271yDl5uW3Kr/n4iHnuXgU5XnOqU7fvHmz2rx5\ns1JKqfT0dPXxxx8rFxcX5e7urs6fP1+iMRen8nIc6p13lLK2Vio9PcdVHj/W+HilTE2V+uCDog6u\nkIKCtOvEr77K8yaZj/XCBW3z//u/p2/z06WfFB+jvj7xdUEjNajLEZeVzSIb1XJFSxWdGJ3jeuXm\nN5wHFfVYy/txx8fH52m9jOvOyMhI5eLiosLDw4syrHzL7nvK63eXaxd3JycnrmTciX1MdvOnFpZJ\nVJT2R40agNa9HXjqCO56dnZYpQZw73QTlFIyUJwQxejrr7/m2LFjJCUl0b1796dOpyGEKBlPq9Mz\n6HQ65s6dW0wRiSJx5YrWep6P66Bjx7RB4vr0KcK4DKFuXahdG06eLNDmrVppj+x+9ZU2R3p2T2gm\npCQwbe80WtdqzZtObxYyYMNoVqMZ/xnxH9z+5YaXrxc+L/lgpMu1I6wQ5dbEiROJjo4mJSWFt956\n64kB6MqyfI3iXhyM79+HqlX183bGnIzByNKIys0r576xnR3Waj+h9weQFJSE+bPmRRytECLDhx9+\nWNIhCCGEAC1B79EjX5scOADGxvnerPjpdFo39wIm6ABz5sC2bfDFF7BgwZPLvzrxFbce3GL/mP2Y\nGJWeS2XXRq580e8LpuyZwseHPi7RrvdClLSM6b7Lo1J368344UOoXl3/PuZUDNaO1uiM83AX2M4O\na7SBr2LOxBRViEIIIYQQpVN8PNy+ne/nzw8ehI4doUw8xtmhg3YT4mEOU+/molUrbaq1FSsg5rHL\nxZikGD4/9jkDmw7EuaGzAYI1rMmdJjOu3Tj+eeSfbLu0raTDEUIUgdKZoD8anUQpRey5WKwc8zgF\njJ0dlgSCkSL2tAwUJ4QQQogK5vp17TUfM2nExMAff5SB7u0ZOnTQXk+fLnAR06dr+f3332f9fNWp\nVUQmRDKn55xCBFh0dDodqwatonO9zoz1G8u50LwNqiWEKDtKX4IeHa1/ICjlXgrpCelYNMzjkPl2\ndhiTjGWdFGlBF0IIIUTFU4AR3P/3P21gdOfS12CcPScn7bUQ3dw7doTu3eHLL7VjB4hLjmPxscX0\nb9yfTvU6GSDQomFmYobPiz7YmNvgucWTiLiIkg5JCGFApS5BN3r4UJ+gJ91NAsCsnlneNrazA8Cq\n1gNiTseglCqSGIUQQgghSqWMBL1p0zxvcuCANvRP165FFJOh1agBDRsWKkEHeP99uHULfH2196tP\nrSYiPoK5vUr/IIl21nb4vuRLaGwoI7aOICUtpaRDEkIYSKlL0I0zJejJd5MBMK1rmreNK1eGKlWw\ntgohJSyF5JDkogpTiDJl1apVDBo0CA8PDzw9PTl37hzr168nISHBYPtwdnYm8tHEsiNHjjRYuUII\nIfLhyhWoXx8sLfO8yYED0KULWOSxw2KpUMiB4gAGD4bGjWHJEohPiefzY5/j2siVLvW7GCjIotWh\nbgfWDl7L4b8OM+WXKSUdjqggFi5cyPr16/XvX3vtNWbNmqV/v2jRItatW/fUMhwcHAA4ceIEb775\n5EwJ+/fvZ82aNQaJ18vLiwsXLujf37lzB3d3d4OUXVRKV4Kenp6li7u+Bb1uHlvQQRsozkh7/kq6\nuQsB/v7+HDp0CF9fX3bs2MG6deuoXbs2GzduNGiCntmWLVuKpFwhhBC5uHIlX8+fR0WBv38Z6t6e\noUMHbTC88PACF2FsDFOmwO+/w8yfviUsLqzUPnuek1FtRjG963RWnlrJmtOGSWiEeBpHR0f8/f0B\nSE9PJyoqiusZY1+gXXdmJOAF5eLiwoQJEwpVRllWuhL06Gh06elZE3QdmNbOYws6aAPFJf4JOmSg\nOCGAiIgIbGxsMH00dWG1atXYs2cP4eHhjB07Fi8vLwDmzp3LsGHDGDRoEMuWLdNv7+zszLJlyxg6\ndCgeHh4EBgYCEBUVxaf8KkwAACAASURBVPjx4xk0aBCzZs3K8khJ5jujXl5eTJ48GTc3N6ZNm6Zf\n7/Dhw7i5uTFs2DDmz5+f7R1UIYQQ+aAUXLoEzz+f500OH9Y2KzMDxGXIGCiukK3or74Kz9ik8u2F\nJfRs0JMeDUr7PHNP+sTlE9yauPH2rrc5FXGqpMMR5ZyDgwNnz54F4Nq1azRt2hRLS0sePnxIcnIy\ngYGBPP/888TFxTF27Fj99eOvv/761HLPnz/PkCFDCAoKwsfHh3nztGkEvb29mT9/PiNHjsTFxYVf\nfvkF0G4OfPzxx7i5uTFu3DjeeOMN/bK88vHxYdKkSXh5edGvXz++/vrrApwRwys9kzsCPOoemzlB\nN7U1xahSPu4j2NlhcuIElZtVlhZ0UaqEbgwl5PsQg5ZpN96O2mNqP3Wdbt26sWLFCvr370+XLl0Y\nOHAgY8aMYf369WzYsIFqj/5/mzp1KlWrViUtLY1XX32Vy5cv07x5cwBsbGzw9fVl06ZNfP/99yxY\nsIAVK1bg6OjIO++8w6FDh9i2LfvpXi5dusR///tfatWqxcsvv8zp06dp3bo1c+bM4V//+hf169fn\n/fffN+h5EUKICun2bYiLg5Yt87zJwYNa1/ZOpXdMtOw5Ompzop88CYMGFbgYKyvoPcmH7aa3GdVw\nWe4blELGRsb8OPxHOn3XiSnHptCzTU8aVG1Q0mGJYrBx45MzERTW+PEwZkzOy21tbTE2NiY4OBh/\nf3/atWtHWFgYZ8+excrKCnt7e0xNTTEyMmLFihVYWVkRGRnJSy+9hIuLCzrdk1Nnnzlzhvnz57Ny\n5UpsbGy4ePFiluXh4eFs3ryZGzduMGnSJNzc3Ni7dy93795l165d3L9/n4EDBzJ8+PB8H++FCxfY\nsWMHFhYWvPDCC/Tq1YvWrVvnuxxDKl0t6I8n6HeS8j5AXAY7OwgJwaq9FbFnpAVdCEtLS/2dyGrV\nqjF16lR8fHyeWG/37t0MHTqUIUOGcO3aNX1LOUC/fv0AaNWqFXfv3gXg5MmTeHp6AtC7d2+eeeaZ\nbPffpk0bateujZGREc2bN+fu3bvcuHGD+vXrU79+fQAGFeLiSgghxCN//qm95iNBP3BAG83cNB+d\nFUsFa2to0aLQLegAQfWWQmRjLvqW7udSn6aqeVV+HvkzyenJDPn3EOKS40o6JFGOOTg44O/vr+/O\n7uDgwJkzZ/D398fR0RHQpstesmQJHh4ejBs3jrCwMO7du/dEWYGBgcyZM4dVq1ZRp06dbPfn6uqK\nkZERTZo00Zdx+vRp3NzcMDIyombNmnTKx13GzDcJunbtio2NDebm5vTt25fThZi+0VBKdQt68t1k\nzBub568MOztISMC6RaX/Z+++w6OovgaOfzebSnogBQiG3nuo+lIEKQIhiCIgXYoIAgrSLDRRRARF\nUAGRIj+pShVBBBSVlhACCIlKFwIkISGkbtrO+8eQSEnPZks4n+fhCdm9O3Nmd7M7Z+695xL1bSpp\nUWnYelnat44ojXwG++Tb211StFotLVu2pGXLltSsWZPt27c/cP+1a9dYtWoV3333Ha6urkybNo3U\n1NTs+21sbACwsrIiM2s9mgKyve+sT6vVFvrxQgghCqiQCXpUFJw9CwMGlGBMJal5c/jxR3WMfg69\ncgVx7PoxTkYdo1nGZ6xZp2XuHHBxMXCcRlKrXC0WtFrAmN/H8PLOl9n4/MYceytF6TF4cN693SUl\nax76P//8Q40aNfDx8WHVqlU4OTnRu3dvAHbt2kVsbCxbt27FxsaGDh06PHBumcXT05PU1FTCw8Px\n9vbOcX+2xbiC6O7uTnx8fPbvd+/exc3NLfv3h/9GzOFvxjx70N3dAXWIe6EKxEH2UmvOldTiVzLM\nXTzuLl26xJUrV7J/Dw8Pp0KFCjg6OpKUpF5hT0pKwsHBAWdnZ27fvs1vv/2W73abN2/Orl27AHU+\n+d27dwscU5UqVbh27RrXr18H4McffyzEEQkhhMhRWJh6HnTvPCo/v/6q/rS4AnFZmjeH6Gh1aH8R\nfXLsE1ztXPl44DASEuC+4tQWqV35dnz4zIdsPreZeX/MM3U4opRq2rQpv/zyC66urmi1Wtzc3EhI\nSODUqVPZdYgSEhIoW7YsNjY2HDt2LHsE5sNcXFxYsWIFCxcu5Pjx44WKYd++fej1em7fvk1QUFCO\n7Vq0aMHOnTuzayBt27btgd72w4cPExcXh06nY//+/dkjAEzJvHrQ79xRf3p4kJmSScadjCIn6E7u\nMYCGxJOJlO1a1rBxCmFBkpOTmTt3LvHx8Wi1Wvz8/JgzZw67d+9mxIgReHl5sW7dOurWrcuzzz6L\nj49PgT6cxo4dy6RJk+jevTtNmjTJdVhSTuzt7Zk5cyYjRoygTJky1K9fvziHKIQQAtQe9EIUiDt4\nUB0pbgbno0Vzf6E4v8LPuf737r98H/Y9b7R6g3atnWjdGpYsgddeAyvz6sIqlMlPTuZ05GneOfgO\nDbwaEFArwNQhiVKmZs2a3Llz54HlymrWrElSUlJ2baOAgABeffVVAgICqF+/PlWrVs11e+XKlWP5\n8uWMHDmSmTNnFiiGLl26cPToUbp160b58uWpW7cuzs7Oj7R78cUXuXTpEj179kSj0VC/fn0mTZqU\nfX/Dhg0ZN24ckZGR9OzZ0+TzzwFQjCgsLCzvBnPnKgooik6nJJ1PUn7hF+XmmpuF3Ym6jW+/VY5V\nP6b82fvPogdciuX7WgiDOH36tKlDMFuJiYmKoiiKXq9XZs6cqaxevTrHdgV5r8r72TjkeTYOS3ye\nLTHmnFj0cWRmKoqjo6KMH1+g5mFhYUrNmorSo0cJx1WSdDpFsbFRlClT8myW2+v65k9vKtrZWuVq\n3FVFURRl40b1FHLXLoNHajRZx5qclqz4L/dXnD9wVs5FnTNxVCXDov9eC+n+Yy3tx52cnFzgtlnn\nkrGxsUrHjh2VqKioQu3r+++/V2bPnl2oxxRUTq9TQV8787o+GBuL3sEB7OxIi0gDwLZiIecc3OtB\nl0JxQpi3LVu2EBgYSPfu3UlISKBv376mDkkIISxXISu437plzT//WODyavezs4OGDeFE4ZcWS0lP\nYWXoSnrX6c0Trk8A0Ls3VKwIixcbOlDjc7BxYHu/7ZSxKUPPDT2JTYk1dUhCGNzo0aMJDAxkwIAB\njBkzBk9PT1OHZBDmNcQ9NpZMV1esUCu4A4Wv4u7qCvb2cPMmzk2did4UTXpsOjYeNoaPVwhRZEOH\nDmXo0KGmDkMIIUqHsDD1ZwET9KCgMoAFzz/P4u8PmzcXulDc5nObidPFMab5mOzbbGxg7Fh46y11\ntkAhiuGbJV8XX7b23Ur7Ne3p910/fhzwI9ZW5nXqL0RxrFu3rliP7927d3ZRO3Nidj3omfeWakqN\nuJegF3YOukbz31JrTZ0AKRQnhBBCiFIuq4J7AeegHz/uiIeH2gFt0Zo1g7g4uHSpUA9bFrKMWmVr\n0c6v3QO3jxyp9vN8ZplLoj/iyUpP8mX3L/n50s9M+XmKqcMRQhSA+SXo98rep0akonXWYu1chCt9\n9xJ056ZqoQAZ5i6EEEKIUu3cuUJVcD9+vAzt21t2MTRA7UGHQg1zP33rNMeuH+MV/1ceWVKpXDl1\n2bl16/5bXMjSDW86nHEtxvHJsU/49sy3pg5HCJEP8/pYfqgHvdC951kqVIAbN7DxsMG+sj0JIdKD\nLoQQQohS7MwZKGD14cuX4cYNW8sf3g5Qvz7Y2kJISIEfsjxkOXZaO4Y0HpLj/RMmQEoKrFxpqCBN\nb2HnhbR5og2jfhjFuahzpg5HCJEHs03Q0yLSCl8gLsu9HnQAp6ZSKE4IIYQQpVh6utqD3qhRgZrv\n36/+LBUJuq2temGigAl6QmoC686so2/9vng4eOTYpkEDtXje0qWQkWHIYE3HRmvDphc24WzrzPOb\nnychVTqvhDBX5pOgK8qjPeiFLRCXpXx5iI+H5GSc/Z1JuZBCxt1S8gkrRCE1adKk2NvYsGED27dv\nN0A0QgghDO6vvyAtDRo3LlDzffvAxyed2rVLOC5jadZMTdAVJd+mG85uIDEtkdH+o/NsN2ECXLsG\npemrr7xzeTa+sJHzsecZsWsESgGeLyEe9sEHH7BmzZrs34cPH87bb7+d/fuHH37I6tWr89xG1rnp\n8ePHeeWVVx65/8CBA6xYscIwAd/n+vXrD6zdbq7MJ0FPSYG0NPQuLiiZCqk3ijHE/f6l1rIKxYXK\nlUIhiqp///706tXL1GEIIYTIyenT6s8C9KBnZKg96E8+mVSYoufmzd8f7t6FixfzbKYoCstOLKOh\nd0Na+bbKs22PHlClSulYcu1+7Su35/0O77P53GaWnVhm6nCEBWratCmhoaEA6PV67ty5w4ULF7Lv\nDw0NLXbnUMeOHRk1alSxtmHJ8k3Qp0+fTuvWrR+42rBkyRLatGlDYGAggYGBHDp0qPiR3KvEkenq\nSlpUGmQWoYJ7Fj8/9eelS1IoTogcHDx4kD59+tCrVy+GDh3K7du30ev1dO7cmdh7f4t6vZ5OnToR\nGxvLkiVL+PrrrwEYNGgQCxYs4IUXXqBLly6cuFeYJyUlhQkTJtCtWzfGjh1Lnz59+PPPP012jEKI\nnOX0vX6/48eP4+/vn/0dv3TpUiNHKArt9Gl1TfBatfJteuKEWvT8qadK0XlRVqG4fIa5n7hxgtBb\noYz2H/1IcbiHabUwbhz88UehprdbhClPTaFLtS5M2jeJv2//bepwhIVp0qQJp06dAuD8+fPUqFED\nR0dH7t69S1paGhcvXqRu3bokJSUxZMgQnnvuOQICAtifNbcmF2fOnKFXr15cu3aNrVu3MmfOHACm\nTZvG3Llz6devHx07dmTv3r2Aep46a9YsunbtyrBhwxg5cmT2ffc7e/YsPXv2pGfPnnz77X9FElNT\nU5k+fToBAQH06tWLY8eOATBq1Cj++usvAHr16pX9Hbh48WI2b97M8ePHGTRoEOPHj6dr165MmjTJ\n4KNR8i2R3rt3bwYOHMjUqVMfuH3o0KEMHz7ccJHcl6AXeYm1LPXrqz/PnsW2UyfsfO1kqTVhct/c\nusWqe7URDOXl8uUZ7ONT6Mf5+/uzefNmNBoNW7ZsYeXKlUybNo2ePXuyc+dOhg4dypEjR6hduzYe\nHo/O0cvMzOS7777j0KFDLF26lDVr1rB+/XpcXV358ccf+eeff6THXQgzldv3+v2aNWvG8uXLjRiV\nKJbTp9VFu63zX/lm3z51RdrWrZONEJiR3F8orm/fXJstO7EMRxtHBjQcUKDNvvwyzJoFH30EmzYZ\nKFYzYKWxYlXgKhp82YCB2wZy5OUj2GhtTB2WKIJvTn/DqtBVBt3my01eZnCjwbne7+3tjVar5caN\nG4SGhtK4cWMiIyM5deoUTk5O1KxZE1tbW6ysrPj8889xcnIiNjaWvn370rFjxxwvjp08eZK5c+fy\nxRdf4O7uztmzZx+4PyoqivXr13Pp0iVeffVVunbtyr59+4iIiODHH38kJiaGbt268fzzzz+y7enT\npzNjxgyaN2/O/Pnzs2/PStZ37drFxYsXGT58OD/99BPNmjUjJCSEihUrotVqs0cLnDhxgtmzZxMd\nHU1YWBi7d+/Gy8uL/v37ExISQrNmzYr0fOck3x705s2b43pvXniJur8HPSINoOhF4jw9wcsL7r24\nTk2dSAwpRVeKhSimW7duMXz4cAICAli5ciXnz58H4Pnnn2fHjh0AfP/99/Tu3TvHx3fq1AmAevXq\nERERAUBISAjdunUDoGbNmtQqQE+OEML4jPa9Lozn9OkCF4j76Sd1yrabW2YJB2VEtrbqgu55LLUW\np4tjw9kNvNTgJVzsXAq0WVdXePVV+O47uG8Eb6lQwbkCK3qs4MSNE7z323umDkdYmCZNmhAaGpo9\nnL1JkyacPHmS0NBQmjZtCqhTShYtWkRAQADDhg0jMjKS27dvP7KtixcvMmPGDL788ksqVKiQ4/6e\neeYZrKysqF69evY2QkJC6Nq1K1ZWVnh6etKyZctHHhcfH09CQgLNmzcHIDAwMPu+kJAQevbsCUC1\natWoUKECly9fxt/fn+DgYE6ePEn79u1JSkoiJSWFiIgIqlatCkDDhg3x8fHBysqK2rVrZ58LG0oR\nFhlXffvtt2zfvp369eszbdq0An3Zp6amEh4enuN9zmfO4AukODhwO+QqAP8m/4s2XFuk+J6oUgWr\n4GCuhIejq6QjeVcy506cw8rRfKbdm5JOp8v1tRCGoygKKSkpAPRxdaVPCZwUZ22/IDFkmT17NoMG\nDaJ9+/YEBwezbNkyUlJScHNzw93dnUOHDnH69Gnee+89UlJSSE9PJz09nZSUFDIzM7O3mZaW9sDt\naWlp2fvS6/WkpqbmG19+0tPT832vyvvZOOR5fnycOnWKnj174uXlxdSpU6lRo0ae7fP6frcklvge\n10ZHUzMqils+PtzJJ/b4eCuOH6/JyJExFnmsefGpWhWXPXv4JyyMhyfX63Q6FuxbQEpGCp09Ohfq\nuLt1s2bRomq8/fZdZs26ZeiwDa4wr2tdTV16Ve7F+7+/Tx3rOjQuV7Aig+aitL2H83L/sWaddwH0\nqdmHPjX7GHx/+Z27NWjQgODgYP766y98fX1xc3Nj5cqVODo6EhgYSEpKCjt27CA6Oppvv/0WGxsb\nnn32We7evYuTk1P2eWRqaiply5YlLS2NU6dO0bZtWxRFIS0tjYyMDFJSUsi4t5RCVkxZj83IyHjg\nvPPh89Csx9x/Hpyamoper8/zvLVGjRr8+eeflC9fnlatWmUfQ+3atbNj1mq1j8Tz8HNWkPPX3BQp\nQe/fvz9jxoxBo9GwePFiPvzwQ+bNm5fv4+zs7KhTp07Odx4+DIDW0xP3THcSrROp91Q9NNqiVTBJ\na9WKDjVrMtLVle5dPTn7+Vl8U31xbSa9BgDh4eG5vxbCYM6cOYODg4NJY9BoNI/EkJycTKVKlXBw\ncGDPnj1otdrsNn379uXtt98mMDAQJye1yKKNjQ02NjY4ODig1Wqxs7PDwcGBlJQUrKyscHBwoHnz\n5hw4cIC2bdty4cIFLly4kN2uOGxsbPJ9r8r72TjkeTYOU59w1qtXj4MHD+Lo6MihQ4cYO3Ys+/bt\ny/MxeX6/WxCLfI9fVTs1fDp3xief2Ldtg8xMeOmlctjbR1veseblmWdg82bq2NpC9eoP3BUWFsb2\n69tpXqE5Lzz1QqE2W6cODBsGa9e68+mn7tl1iM1VYd/Da6uupcGXDZj751xCXwnFVlvE0asmYJF/\nr0V0/7GGh4eb/NyyZcuWrFu3jkqVKuHk5ISTkxNJSUlcunSJDz74AAcHB1JTU/Hy8sLFxYVjx45x\n8+ZN7O3tcXBwyD43tbOzw83Njffff59hw4bh5uZGw4YNsbW1xdraGgcHB6ytrbG1tc0+5qzHtmjR\ngu3bt9O3b19iY2MJCQkhMDDwgefGwcEBFxcXzp07R7Nmzdi3b1/2eWvLli356aefaNeuHZcvXyYy\nMpI6depga2tLhQoV2L9/PxMmTCApKYn58+fz8ssvZ8d8/3nzw/Flyen8taDf70XqTi5XrhxarRYr\nKyvDFYK6fw769VRsy9sWOTkH2NKqFYfr1mX6xYvYNHYEICFE5qGLx09KSgpt27bN/rd69Wpee+01\nJkyYQO/evXFzc3ugfYcOHUhOTs51eHtuXnrpJe7cuUO3bt349NNPqV69Os7OzoY8FCGEETg5OeHo\nqH5vtmvXjoyMjOzikcIMFaKC+08/gbMztMq7gLllypr/mUNFt5O3TxIWHcboZnkvrZabyZPV6vcL\nFhQnQPPkYufC590+Jyw6jIVHFpo6HGEhatasyZ07d2h03+dOzZo1cXJyyq5dFBAQwNmzZwkICGDH\njh3Zw8NzUq5cOZYvX86cOXMKnFd26dIFb29vunXrxuTJk6lbt26O553z5s1jzpw5BAYGPlDM7aWX\nXkJRFAICAnjjjTeYN28etrbqBSp/f3/Kli2Lvb09/v7+3Lp1y6BzzPOlFMC1a9eU7t27Z/8eGRmZ\n/f/Vq1crr7/+ekE2o4SFheV+59SpimJrq4SdO6eEdgxVQlqFFGibuWnx66+K+44dCr/8onx27Zry\nh/cfStiQPPb/mMnztRAGc/r0aVOHUGhnzpxR+vfvX+jHZWRkKDqdTlEURbl69ary9NNPK6mpqcWO\npyDvVXk/G4c8z8ZhjOf54e/1+0VFRSl6vV5RFPUzrF27dtm/56a0vDcs8jj69FEUP798m+n1ilK5\nsqIEBqq/W+Sx5iU1VVFsbRXlzTcfuavHqh6K6zxXJTE1scibHzJEUeztFeXmzWLEaARFfV2f3/S8\nYj/XXrkQc8HAEZWcUvcezsP9x1rajzs5ObnAbRMT1b/p2NhYpWPHjkpUVFRJhVVoOb1OBX3t8h3i\nPnHiRIKCgrhz5w5t27Zl3LhxBAUFZZefr1ixYnYZ/GKJjQUPD9BoSItIo0y9MkXe1LG7dwlSFJau\nXs2mYcOY/++/bG/hJEutCZGPFStWsGHDBhYUoZsgJSWFwYMHk5GRgaIozJw5M/tKpBDCfOT0vZ41\nx69///789NNPbNiwAa1Wi729PYsWLcp3SSphQsHB0KJFvs0uXIArV9Te4FIpq1DcQz3ot5Nv89P1\nnxjdbDSOto5F3vw778D//qdWdF+0qLjBmp/FXRez7+I+xv44lj0D9sjfvLAIo0ePJj4+nvT0dMaM\nGYOnp6epQzKIfBP0RTl8CvXpY/hiBMTGgrs7AKkRqbh3di/yphZHROCi1TLk3Dlq//EHz3Trxo/d\nnGj7WhKZKZloHYpWeE6I0m7UqFGMGjWqSI91cnJi69atBo5ICGFoOX2v32/gwIEMHDjQSNGIYomO\nVrPuMWPybZpVRqBLl5INyaT8/WHjRtDrwUqdxbnm1BrS9em84v9KsTZdvToMHAhffgmTJkHFioYI\n2HxUdKnI+x3eZ/ze8Ww6t4l+9fuZOiQh8rVu3TpTh1AizKek+Z074OGBPklPZkImdr5FWwM9IjWV\n76KjGV6+PE41atBh3z6ecnFhec140qwg6UySgQMXQgghhDCB4GD1ZwF60Pftg6pVoVq1Eo7JlJo1\ng7t34eJFAPSKnuUhy2larin1vOoVe/MzZqhF9mbNKvamzNKY5mPwL+/P5J8nk5JevBVYhBBFZz4J\n+r0h7vpIPQB2FYuWoH8ZEUGmovBaxYpQvz6a8HBmVKrEDasM9naVQnHC+JT7ClKIwpHnTggh8hAc\nrC4pdm/d4dykp8PBg9C5s5HiMhV/f/XnvWHuv1z+hQuxF+hbra9BNl+1qjpYYdUqCAszyCbNitZK\ny8LOC7kef51Pj31q6nBEHuT8yLwV9/UxuwQ981YmULQEXZeZyfKbNwkoW5aqDg5Qvz6kpdHp9m1a\nOTuzfhDEnIo3dORC5Eqj0RATEyMfpEWgKAoxMTHY29ubOhQhhDBPwcHqOmD5rJjx+++QmAhduxop\nLlOpV0+di34vQV8WsoyyDmXp7Gu4KxPvvANOTjB9usE2aVbaVW5HYK1A5v0xj6ikKFOHI3Jgb28v\n55ZmzBDnr0VaB71EZPWgR6k96LYVC19cakNUFLfT05ng66veUL8+AJpz55jx9NN0S/iTzdo4Ghos\naCHyptVqSUhIIDo62tShWCR7e3t8s/6ehRBC/EdRICgIunfPt+nOnWBnpy4VXqrZ2qrLzYWEcDPh\nJtv/2s7rLV/HTlu0UZk5KVcOpk2Dt96C/ftL53M6/5n51PuiHnMOzWFpt6WmDkc8xNfXl+vXr5fa\nc8v09HRsbGxMHUaxFPf81TwS9LQ09dJuMYa4K4rC4uvXqe/oyNNZ6zrXrq0WCTl7lq7PP0/Du7as\napvKuykZ2DmYx6GL0k2j0VClShVThyGEEKK0+fdftUhc8+Z5NlMUNUHv2BEci17E3HL4+8P69aw6\n+TUZ+gxG+Y8iIyrDoLt44w1YuRLGjVOXoS9tC5bUKleLV/xfYdmJZYxrMY5a5WqZOiRxHxsbm1J9\nbhkeHk6dOnVMHYZJmccQ9zt31J8eHmRGZWLtbl3oSuu/373L6aQkxles+N/SEA4OatnNs2fRaDRM\nxpub5eHr0GsGPgAhhBBCCCPKKhCXT4IeFgaXL0NAgBFiMgf+/mQmxLMi6Es6VulIjbI1DL4Le3tY\nvBj++guWLDH45s3CzPYzKWNThukHSulYfiHMmHkk6LGx6s97PehFqeC++Pp1PKytGeDt/eAd9evD\n2bMAPN+0AjX+gY8SIsjQ64sbtRBCCCGEaRw+rGaKjRrl2WzXLvVnjx5GiMkc+PvzYw34N/kGo5uN\nLrHd9Oihzi6YOVNd6a608XL04s0n32TbX9sIvRlq6nCEeKyYXYKeGZVZ6OHtV3U6tt++zcjy5Smj\nfajnvV49OH8edDocKjkw4icbrtplsD5KCl8IIYQQwkL99hu0apXv+Opdu9Qi749NOY969fistQZf\nvTOBtQJLdFeff64W0R81Sp1KUNpMaDkBN3s3Zh+abepQhHismF2Crr+lL3SBuM8jItAAYypWfPTO\n+vVBr1fHIQEBrh5Uvwxzr16VXnQhhBBCWJ74eDh1Ctq2zbNZZCQcPfoYDW8Hwu5eYH8VhTFXymGj\nLdlCU35+MH8+/PwzrF5dorsyCVd7Vya2msiOv3dw8uZJU4cjxGPDPBL0e3PQ9U5u6GP0hepBT8rM\n5KubN3nO05Mncipnf6+Se9Ywd/d27gxaDedTUthUSqsfCiGEEKIUO3pU7Xxo0ybPZtu3qz27vXsb\nKS4zsDRoKXaKlpF7o9XnqISNHg3t2sHEiRARUeK7M7rxLcfjbu8uvehCGJF5JOj3etDT0p1BKVwF\n9/9FRhKXkcGEnHrPAWrUABub7ATdtZ0r//cH1NbZ8t6VK2SWxjFJQgghhCi9fvsNtFp1iHsevv9e\nrZXboIGR4jKxCtDT/AAAIABJREFUOF0ca0+v5SXHVpSLSoQLF0p8n1ZWakX3tDQ1WS9tp5Wu9q5M\nbD2RnX/vlF50IYzEfBJ0jYbUBDUxL2iROEVR+Oz6dZo6OfGUq2vOjWxs1OXW7iXoDpUdcKhkx8hf\n7fk7JYUtMhddCCGEEJbk99/V5cScnHJtEhsLv/wCzz+vzpN+HKwKXUVyejLjWo5TbwgKMsp+q1eH\n99+HH36A9euNskujGtdiHO727sz6dZapQxHisWA+Cbq7O2k304GC96AfuHOHsORkxvv6/re0Wk7u\nq+QO4NbOjeZrkqlXpgzvXb2KvrRd7hRCCCFE6aTTqYlnPsPbd+6EjAw1QX8cZOozWRq0lDZPtKHJ\nUy+AszMcOWK0/Y8fD61bq2uj37hhtN0ahau9K2+0eoNd/+zibNTZ/B8ghCgW80nQPTxIjUgFKHCR\nuMUREXjZ2NDPyyvvhvXrw9WralEV1AQ9MzKDyTY+hCUn873MRRdCCCGEJQgKgtTUfBP077+HJ56A\nZs2MFJeJ7fpnF5fjLjO+5Xh1+H+LFupcfSPRamHNGvX6yYgRpW+o+5jmYyhjU4aPj3xs6lCEKPXM\nK0G/ngq2YFM2/6qbF5KT2R0TwysVKmBnlc9hZBWKCwsD1HnoAE8ft6J2mTLMkV50IYQQQliCffvU\nbLBdu1ybxMWpzXr3fjyGtyuKwrw/5lHFrQq9avdSb3zySThzBhITjRZHzZpqVfc9e+Drr422W6Mo\nW6Ysw5sMZ/2f67kef93U4QhRqplXgh6RitZLm/dw9XuWRkSg1Wh4tUKF/LeflaCfOweAQzUHbCvY\nkngonnf9/DiblMS227eLcwRCCCGEECVvzx51LLWbW65Ntm5Vi5b172/EuEzolyu/EBQRxJSnpmBt\nZa3e2Lq1WsU9ONiosYwdCx06wBtvwOXLRt11iZvYeiJ6Rc/iY4tNHYoQpZrZJehWXvmHFJ+Rwapb\nt3jR05PydgWYr165MpQpkz0PXaPR4NbOjbhDcbzo6UlNBwfmXLkivehCCCGEMF+RkXDyJDz7bJ7N\nNmyAatWgeXMjxWVi8/6Yh4+TD0MbD/3vxqwK90Yc5g5qVfdVq9SRC8OGGWWlN6Op7FaZPvX6sDxk\nOXd1d00djhClllkl6GkRaVj55B/S2lu3SMjMZIKvb8G2b2UF9eo9Uigu7WYaaRd1vOPnx5mkJHZK\nL7oQQgghzNVPP6k/u3bNtcmtW3DwoNp7/jgMbw+OCGb/pf1MbDURe2v7/+5wd4c6dYxaKC6Lnx8s\nXgyHDsHSpUbffYma/ORkEtISWBGywtShCFFqmT5B1+shLg7FzR3dNR1ab23ezRWFJRERtHJxoYWL\nS8H381Al96x56HGH4ujv5UV1BwfmXL2KIr3oQgghhDBHe/eCtzc0bpxrky1b1FOrx2V4+7w/5uFm\n78boZqMfvbN1azh2zCQV24YOVQc6vP02XC9FU7ablm9Khyod+PT4p6Rlppk6HCFKJdMn6HfvgqKQ\nZuOFkqqgrZh3gr4nNpbzKSmMr1ixcPupX1+9rHyvl7xMrTLYeNtw99BdrK2sePuJJwhNTOSHmJii\nHokQQgghRMnIzFR70Lt0UUcG5mL9emjYEOrWNWJsJhIWHca2v7YxrsU4nO2cH23w5JMQE4PtpUtG\nj02jgc8/V1+2CROMvvsSNeXJKdxIuMH6P0vhou9CmAHTJ+ixsQDoMsoB5Jugf3b9OhVsbXnB07Nw\n+3moUJxGo8GtrToPXVEUBnh7U8XenveuXi3cdoUQQgghStqRI+o5Ux7zz//+W+0wHjTIiHGZ0Mxf\nZ+Jo46gurZaTe5Xuyxi5UFyWKlVgxgy1aN+uXSYJoUR0rtaZht4N+fjIx+iVUjTJXggzkW+CPn36\ndFq3bk2PHj2yb4uLi2PYsGF07tyZYcOGcfduMQpF3EvQU1PVaqTaCrkn6OFJSey7c4dXK1TAJr+l\n1R6WlaA/NA899Voquis6bKysmOjrS3BCAqeNuCSHEEIIYUw5fa/fT1EU5s6dS6dOnQgICODcvQvb\nwsQ2bQJ7e+jePdcm33yjdq4PGGDEuEzk6LWjfBf2HZOfnEy5MuVyblStGvj6mixBB5g0SS2D9Npr\nkJRksjAMSqPR8GbrNzkXfY495/eYOhwhSp18s9zevXuzcuXKB25bsWIFrVu3Zt++fbRu3ZoVK4pR\nKCKrBz3BUQ2oQu4hLYmIwE6j4ZWCLK32sPLl1YIh989Db/vfPHSAfl5eWGs0rLt1q/DbF0IIISxA\nTt/r9/vtt9+4cuUK+/bt47333mPWrFnGC07kLCNDnVzeowc45zCUG3Uo9TffqPXjypc3cnxGpigK\nb/78Jj5OPkx6clLuDTUaaN8ex6Agk8xDB7CxgeXL4d9/oTT9KfWr349KLpVYcGSBqUMRotTJN0Fv\n3rw5rq6uD9x24MABevXqBUCvXr3Yv39/0SPIStDv2GJd1horx5xDupOeztpbt3jJ2xtPW9vC70ej\neaRQnGM9R6w9rLl7SB0BUM7Wlu4eHnwbFUVGaVoXQwghhLgnp+/1+2V9x2s0Gho3bkx8fDxRUVFG\njFA84tAhiIqCfv1ybXLwoFqMbOhQ44VlKtv+2saRa0eY3X42TrZOeTdu3x7rmBj46y/jBJeDp56C\nkSPhk0/gzz9NFoZB2WhteL3V6xy6eoigiCBThyNEqWJdlAfFxMTg5eUFgKenJzEFLKyWmppKeHj4\nA7e5h4XhA8Rc0YG3Fp1O90gbgE3JySTr9XRPS8vx/oLw9vXFdccOzp88ieLgAIC2iZbo/dEo4eqV\n1Q4ZGexIS2P1mTP8X0HWWLdQuT3PwrDkeTYOeZ6NQ57nx0NkZCQ+Pj7Zv/v4+BAZGZn9vZ+TnL7f\nLZG5vsd9li3DpUwZzletipJLfIsXV8DFxYkaNc4THp5/b7G5Hmt+0vXpTNw7kaouVWlt3zrfY7Dx\n9aU6cHPjRuLyuMBR0oYNs2LTpuqMGZPCihXXSmw/xnxd2zq2xdnGmXf3vsunT35qlH3ez1Lfw0Uh\nx/p4KVKCfj+NRoOmgAtt2tnZUadOnQdvvNcbro2zx7muI1p77aNtgCvh4XjpdPRu0KDA+3vE6NGw\nYQO1Q0Nh+HAArvW4xsUDF6lSpgr2fvZU1euZdeQIh2xtGZlDHKVFeHh4js+zMCx5no1DnmfjkOfZ\nOCzxxCTH73cLZJbvcZ0ODhyA556jdpMmOTaJjYX9+2HECGjcuHaBNmuWx1oAnx3/jKuJV9nVfxcN\najbI/wG1a5Pu40P58HDKm/h4Z86ESZOciIiowzPPlMw+jP26jokaw4IjC7D1tqWaRzWj7Rcs9z1c\nFHKspUNBv9+LVMW9bNmy2cPdoqKi8PDwKMpmVLGxKE7O6P5Nxd7PPtdmwQkJNHd2LnpyDtCmDTRo\nAEuXZs9FKtu9LADRW6MBsLOyoq+XF1tv3yYhI6Po+xJCCCEskLe3N7fuq8Vy69YtvL29TRjRY27z\nZrhzJ8+x6+vWQWqqOoy6NLt85zJvHXiLztU6071G7sXyHqDRkNSiBfz6q7pAvAmNHQuVK8OUKSYP\nxWAmtJyAVqNl0dFFpg5FiFKjSAl6hw4d2L59OwDbt2+nY8eORY/gzh3S3fzQJ+uxr5xzgp6QkUF4\ncjItXFyKvh9Q56GPHQunTsHRowCUqVEGpyZORG38b37dYG9vUvR6vo+OLt7+hBBCCAuT9R2vKAqn\nTp3C2dk5z+HtogQpCixZArVrQy7nWooCK1ZAixbQqJGR4zMivaLn5Z0vo7XS8lXAV4XqsElu3Rqi\no+HkyRKMMH92dvD++xAaChs2mDQUgynvXJ6BDQey+tRqbiffNnU4QpQK+SboEydOpF+/fly+fJm2\nbduyZcsWRo0axeHDh+ncuTNHjhxh1KhRRY8gNhZdmSoAufaghyQkoADNc6lcWigDBoCrK3z+efZN\nXv28SAhKIOVyCgCtXFyo7uDAusjI4u9PCCGEMCM5fa9v2LCBDfcyhnbt2lGpUiU6derEu+++y8yZ\nM00c8WMsKAhOnFDX6MolIT16FMLCoDinYpbg86DP+fXKr3zS5ROecH2iUI9NbNNGff527y6h6Aqu\nXz9o2hTefludvVAavPnkm6RkpPBF8BemDkWIUiHfOeiLFuU8ZGXt2rWGiSA2Fp1tY4Bce9CDExIA\nAyXoTk7qMLEvvoCFC8HHB88XPbk09RJRm6Lwm+aHRqNhkLc3s65c4V+djifscx96L4QQQliS3L7X\ns2g0GknKzcWSJeqyaoMH59pk+XL11KZvXyPGZWTnY84zdf9UutXoxrDGwwr9+EwPD3WIwe7d6kRw\nE7KyggUL1AERn3+urpNu6ep61qV7je4sDVrK5Ccn42DjYOqQhLBoRRriblCxseg06oKddn45V00P\nSkigir095YqyvFpOxoyB9HT46isAHCo74NLa5YFh7gO9vVGAb6UXXQghhBDG9s8/sHGjWtQ2lw6K\nqCi1yZAhapJeGiWlJdH/+/7YWdsVemj7A7p3h+BgMIPzug4d4NlnYe5ctbxAaTD5yclEJ0ez9rSB\nOvCEeIyZRYKeqvdE66rFxs0mxybB8fGG6T3PUrMmdO6sXnZOTwfAq68XSaeTSPorCYCqDg78n6sr\n6yIjUZT8lysRQgghhDCYd94Be3uYNi3XJl99BWlp6gj40ihDn0Hf7/oSeiuUdc+to4JzhaJvrEcP\n9eeePYYJrpjmzYO4OMhnQIvFaOvXluYVmrPw6EIy9ZmmDkcIi2baBF1R1B50nVuuw9uj0tK4mppq\n2AQd1G+ziAjYsQMAzz6eoIHoTf8Vhhvs7U14cjIh94bYCyGEEEKUuBMnYMsWdfxzLhX009Phyy+h\nUye1hlxpoygKY3ePZff53Xze7XN61OxRvA02bgwVKpjFPHRQC/q9+CJ8+incLgW11TQaDZOfnMyF\n2Avs+HuHqcMRwqKZNkFPSoL0dHRJTrkWiMuaf17sCu4P69YN/Pyyi8XZVbDDrZ0bURujsnvM+3h6\nYqfRSLE4IYQQQhiHXq8m5uXK5TlBeft2tZ9h3DgjxmYkiqLw3m/vseLkCqY9NY3RzUYXf6MajdqL\nvmePev5pBmbNguRk+OgjU0diGM/VeY4qblX4+MjHpg5FCItm2gQ9NhYF0MXZ514gLj4eK6CpoSdX\nabXqXPRff4WzZwHw7OtJ8l/JJJ1RP7jdbGzoWa4c66OiSC8tC1YKIYQQwnx9+SX89ht8+CHk0Tmx\nZIm6pna3bsYLzRh0GTpe3vkyM3+dycCGA3m/4/uG23j//mpyvnOn4bZZDHXqqIsLLV0Kt26ZOpri\ns7ayZmLriRy9fpTD/x42dThCWCyTJ+gZOJGps8qzB71OmTI4WedbcL7wXn5ZXZTyC3VZCM/nPUEL\nUZseXBP9dno6e2NjDb9/IYQQQogsly7BlCnQtat6jpKL06fh999h7Fi1v6G0uB5/nbar27Lm1Bpm\ntJ3B2l5rsdIY8FS1bVvw9YX16w23zWKaMUOtIzBvnqkjMYxhjYfh4eDBgiMLTB2KEBbLtAn6nTvo\n8AFyXmJNURSCEhIMP7w9S7ly6tXUb76Bu3ex9bTF/Rn3B4a5d/HwwNPGRoa5CyGEEKLk6HTqOYm1\nNaxYkeu656D2njs45JnDW5SktCQWHF5A42WNCb8dzra+25j99GzDJuegrnHWvz/s3Ws2E7+rV4dh\nw2DZMrh2zdTRFJ+jrSNjm49l5987+fv236YORwiLZPIe9OwEPYce9Ks6HbfT0w1fIO5+Y8eqw53u\nrevu1c8L3WUdCcHq3HcbKyv6e3mx8/Zt4u5VfBdCCCGEMBhFgdGjISgI1qyBSpVybRoTA99+CwMH\ngoeH8UI0NEVROB9zno8Of0TVz6oyZf8UmlVoRtCIIHrV7lVyOx4wADIy1CJ8ZuKdd9S3wPsGHM1v\nSq+1eA07azsWHS0lJeqFMDIzSNDV6qQ59aBnFYgr0QS9WTN48km1QkdSEuV6lUNjq3lgTfTBPj6k\nKgpboqPz2JAQQgghRBHMn692FMyaBc89l2fTlSvVznZzX1pNr+i5nXybi7EXOXnzJD9f/Jk1p9bw\n/m/vM3T7UPw+9aPm0ppM3T+VRt6NOPzyYfYO3EsdzzolG1jDhlC/PqxapWbFZsDPD0aOhK+/Vmc5\nWDovRy+GNBrC2tNriUyUEahCFFYJTOwuhHs96FaOVlh7PBpKUEICthoNDQ1dIO5h8+dDmzbw8cfY\nzJyJR1cPojZHUe3jamisNDR1cqJOmTJ8ExnJyArFWINTCCGEEOJ+ixfD9Onq0Ot3382zaVqa2vyZ\nZ9Q801xk6jMJighi5987OXnrJJfvXObq3aukZabl2N7L0Ys2T7ThrTZv0bFKR2qUrWG8YDUatUjw\nmDFw5Ag89ZTx9p2Ht99Wrxm89x6sXm3qaIpvYuuJrAhZweLji/mg4wemDkcIi2LyBD3Vqjz2le3R\n5DDXKjg+nsZOTthalXBH///9H/Tpoybqw4fjPcibmJ0xRG2IwnuANxqNhsHe3ky/fJlLKSlUdXAo\n2XiEEEIIUfotWwavvw69e6s96Pmc72zYADdvmk8CF5kYyQe/f8CGsxuITo5Gq9HS2KcxjX0a06t2\nLyo6V8TN3g03ezfcHdyp6FyRii4VsbfOuTCw0QwerGbEixaZTYJeoQK8+qp6AWbaNKhVy9QRFU/N\nsjV5oe4LLA1ayuQnJ+Pu4G7qkISwGCZP0HVWdXIc3p6pKIQkJjLE29s4scyfry678dZbeK5Zi1MT\nJy69fYlyz5dDa69lgLc3b12+zP8iI5lRubJxYhJCCCFE6bR6tZqRde+uZt42Nnk2VxT4+GN1dHbn\nzkaKMRfJ6cksOrqI+Yfnk5Kewgt1XyCwViBdq3e1jETM0VGd8z9/vjqmvGpVU0cEqIn58uUwe7ZZ\nFZovsnfavsOWsC0sPr6YWe1nmTocISyG6eegK545Foj7KzmZxMzMkqvg/rAqVeCNN2DdOjQhJ6j6\nUVVSr6Zy44sbAFSyt+dpNze+uXUru8K7EEIIIUShrVoFw4dDp07w3Xdga5vvQ/btg7Nn4c038yzw\nXuJO3TpF3c/r8u4v79KpaifOjTnHxhc20r9Bf8tIzrOMHauOWPj4Y1NHks3LC8aPh40b1dfa0jX0\nbkiv2r1YfHwx8anxpg5HCIth0gQ9IyqJjEzHnAvExat/yCVaIO5h06ern45vvIFHR3fcu7hzde5V\n0u+o1dsH+/hwUafjWLx8yAghhBCiCD75RE3OO3eG7dvBvmDDvT/+WB0G3b9/CceXhy3ntvDUqqfI\nVDI5NPQQW/tupVY5Cx2LXbEijBqlLml37pypo8k2eTI4O8PMmaaOxDDeafMOcbo4lgYtNXUoQlgM\nkyboukj1EnBOPejBCQk4a7XUKlPGeAG5uKhrXBw+DFu2UG1+NTLiMvj3w38B6F2uHGWsrPhG1kQX\nQgghRGEoCsyYARMnwgsvqNPqCniOc+oU7N+v9q4WoLPd4BRFYeYvM3nxuxdp7NOYEyNP0NavrfED\nMbTZs9VseOJEs6no7uGhDujcuhVOnjR1NMXnX8Gf7jW6s+joIhLTEk0djhAWwbQJ+h11vlVOPehB\nCQk0c3bGytjjuIYNg0aNYOpUnGpZ4z3Im+uLr6P7V4eztTXPlSvHpqgoUvV648YlhBBCCMuk18OE\nCWqJ7uHD1THMhci0Fy4EJyd45ZUSjDEXiqIwbf805vw2h2GNh3Fw8EG8nYxUH6iklSundlXv26de\nMDETb7wB7u7w1lumjsQw3m37LjEpMXwR/IWpQxHCIpg2QY9Xrxw/3IOeqtdzOjHRuMPbs2i1alXP\nK1fg00+p8l4VAC6/exlQh7nfychgd0yM8WMTQgghhGXJyFAv/i9ZApMmwVdfqecaBXTtmprPjxgB\nbm4lGGcu3vvtPT468hFjmo3h655fY2dtZ/wgStKYMeqadcOHw/Xrpo4GAFdXtcj8Tz/B3r2mjqb4\nWvq2pGv1rsw/PF/mogtRAKZL0FNT0aW5Y2WdiY3Xg5VLzyQmkq4opknQATp0gMBAeP997G3j8B3v\nS+S6SBJPJ9LR3Z3ytrZ8c+uWaWITQgghhGVITYUXX4RvvoG5c2HBgkJXeFu4UP35+uslEF9++z6y\nkJm/zmRIoyEs6bYkxyVxLZ6tLWzerL5W/fpBerqpIwJg3DioXl29ppORYepoiu/9Du8TmxLLwiML\nTR2KEGbPdAn6nTvo8MGubMYjH/hBCQkAxqvgnpMFC9QP64kTeWL6E1i7WXNx8kWsgAHe3vwYG8vt\ntDTTxSeEEEII85WSAs89B9u2wWefqV2ihUxwo6LUGmYDB4KfXwnFmYv/nfkfb/78Jn3q9mFlz5VY\naUy78E+JqlVLHdlw+DAMGWIWGbGtrXoqGhamvgcsXdPyTelTtw+Lji0iOina1OEIYdZM92kbG0sq\n3tj7PHpXcHw8XjY2VLIz4TCqGjXUL9MNG7D5Yy+VZ1fmzs93OP/aeQZ5eZGuKGyKlg8YIYQQQjwk\nKQl69lTHJ69YoXaHFsHixaDTwdSpBo4vH0euHWH4zuG0r9ye//X+H9ZW1sYNwBT69YMPP1TXpB80\nCMygEyYwENq3V2sLxsWZOprie+/p90hJT+GD3z8wdShCmDWTJug6fLD3ffRDPzghgebOzqYfSjV9\nOjRoAK++SsVBTlSaXIkbX9zAfspNGjk6yjB3IYQQQjwoIQG6dYODB2HNGhg5skibuXsXli6F55+H\n2rUNG2JersZdpdfGXjzh+gTf9fkOW60JysabytSpMH++Oum/fXuTz0nXaNRV+WJj1fqClq5WuVoM\nbTyUL058wb93/zV1OEKYrWIl6B06dCAgIIDAwEB69+5dqMdmRsSQjhv2fg4P3J6k1xOenGza4e1Z\nbG3h66/h5k00U6ZQdX5VKk2txI1lN+j6uxVBCQn8nZxs6iiFEEKIAvvtt9/o0qULnTp1YkUOY2e3\nbt1Kq1atCAwMJDAwkC1btpggSgul00FAgDpU+ttvYfDgIm/qiy8gPt64lbwTUhPosaEHaZlp7Oq/\ni7Jlyhpv5+ZiyhTYtAn+/BMaN4bVq9Uq/CbSuPF/NQbPnzdZGAYzs91MNGiY+WspWehdiBJQ7B70\ntWvXsmPHDrZu3Vqox+mOqlXR7ZuUf+D2cxkZKGC6AnEPa948u+qq5pdfqDqvKk+89QQt5iZgpYdv\nbkovuhBCCMuQmZnJnDlzWLlyJbt37+aHH37gwoULj7Tr1q0bO3bsYMeOHfTp08cEkVqgzEwYMAAO\nHVKLwvXrV+RNJSerPafPPgtNmhgwxjxk6jN5aetLhEeHs6XPFmqXM2K3vbl58UUIDlbnpr/8MrRp\noy5GbyJz54KdHYwfbzbLtRdZJddKjGsxjrWn1hJyI8TU4Qhhlkw2xF0XfA0A+7oeD9z+573qmWaT\noAPMnq3OSR8xAk1yMlXmVqHJWD+aBcPq8OukxZtHxU8hhBAiL2fOnMHPz49KlSpha2tL9+7dOXDg\ngKnDKh0mT4atW9XM+qWXirWpr7+G6Gjj9p5P2z+NH/75gc+e/YxO1ToZb8fmqnZt+P13tQf9/Hnw\n91fXsr971+ihlC+vJul796oF5y3dO23fwdPRkwl7J6BY+hUHIUpAsat+DB8+HI1GQ9++fenbt2+e\nbVNTUwkPDwfAMUxdR/xa+jVuhN/IbnNap8NXqyX64kXMqQSbw7vvUnnwYGLGjCFq2jSU/goBP9jx\nrlMq7w07Qu92Ltg9Y2f6efMFpNPpsl8LUXLkeTYOeZ6NQ55nyxcZGYmPz3/VWb29vTlz5swj7fbt\n20dwcDBVqlRh+vTplC9f/pE297v/+92SFfU97rJzJxU/+YTYl14isksXKMZzkZam4YMPquHvn07Z\nsleLs6k83X+s31/6no9PfMxL1V/iaaenS8Vreb9ifXa1bInVzp14fvYZ7kuWkLl+PZFTpxLfrVuh\nq/IXR8eOUK9eZcaOtcHP7yKurjkPu7eUz+nX6rzGjBMzWLRvEd2e6FakbVjKsRqCHOtjRimGW7du\nKYqiKLdv31YCAgKUoKCgPNuHhYWp/4mJUS4wSvlVe0DRZ+ofaFPh0CHlxbNnixNWyRkzRlE0GkU5\nckRRFEVJychQ/H89rlgd+EV5I+AX5UyPM0ry5WTTxlhA2a+FKFHyPBuHPM/GIc+zcZTk87xnzx7l\nrbfeyv5927ZtyuzZsx9oExsbq6SmpiqKoigbNmxQBg0alO92S8t7o0jHcfq0otjbK0q7doqSllbs\nGJYuVRRQlJ9+Kvam8pR1rL9c/kWxmWOjdF7XWUnPTC/ZnZqIwd6fwcGK4u+vvkAdOyrK+fOG2W4B\nhYQoipWVogwdmnsbS/lbzMjMUJosa6L4LvJVktKSirQNSzlWQ5BjLR0KemzFGuLu7e0NQNmyZenU\nqVOOV+FzFBqqVnD31qCx+u/qY1RaGjf0evMa3n6/Dz+ESpVg+HBITsZeq+XQ//nzbFkPPpkIH/nG\nEFQ3iH8/+hd9hukKigghhBA58fb25tZ9K5BERkZmf5dncXd3x9ZWrdzdp08fzp07Z9QYLUpGhlrB\ny9VVHXtsY1OszSUnq0OZ27aFTkYYZX426iy9Nvaimkc1Nr2w6fFYTq04mjWD48fh88/hxAm1gtua\nNUabGN60KUybpu7yhx+MsssSo7XSsrjrYq7HX2f+H/NNHY4QZqXICXpycjKJiYnZ/z98+DA1atQo\n2INPnkSHN3bVnR64OTghAcA8KrjnxNkZVq6Ev/9W1zdNTsZRq2V7g/qMKl+e//WFhQtt+fvtS5xs\neZLE04mmjlgIIYTI1qBBA65cucK1a9dIS0tj9+7ddOjQ4YE2UVFR2f8/ePAg1apVM3aYlmPRIjh5\nUl0Pzcur2Jv78ku4dUtdUqukR0/fTL5J1/91pYxNGfYO2IubvVvJ7rC00GphzBg4e1YtJDxsGPTv\nb7SFymcTivPCAAAgAElEQVTMgIYN1dX7YmKMsssS08avDX3r9WX+4fn8fftvU4cjhNkocoIeExPD\nSy+9RM+ePenTpw/t2rWjbdu2BXqsPiiUJE01HBs/+GUQHB+PFdDUySnnB5qDTp1g7Vp1fdN7Sbq1\nlRXLatZkbpUq7KqTytzdjsTG6AhpFsLldy+jT5XedCGEEKZnbW3NjBkzGDFiBN26dePZZ5+lRo0a\nLF68OLtY3Lp16+jevTs9e/bkm2++Yd68eSaO2kydPw8zZ0KvXupi5cWUkKAO1OvcWe1BL0lxujhe\n+e0V4lPj2TNgD35ufiW7w9LI1xf274d58+D776FRI3V5vRJmZ6cuEhAToxaYt/Qaa592/RQHGwdG\n7BqBXpHzZSGgGEXiKlWqxM6dO4v02PhjcegVO9yefihBT0igqlaLk7WZD7EaOFD9RBwyBAIDYedO\nNA4OvO3nh6+dHSP+/ps3Nzrw2doyXJ17lejvo6m1qhaurVxNHbkQQojHXLt27WjXrt0Dt02YMCH7\n/5MmTWLSpEnGDsuy6PVqF6adnTrc2QDd3YsXw+3bau95SYrTxfHst89yJfEKewfspZFPo5LdYWmm\n1apjzjt0UCv3t22rdnG//TaU4Llso0awYAG8/jp89plaXN5S+Tj58EmXTxi2YxjLTixjTPMxpg5J\nCJMz/jJr8fHEXS8LGgW3tv8l6IqiEJSQQINizt8ymkGD1J70AwfUnvSUFACG+Piwu0EDLqenMnhw\nAjZ7apCZmEno/4VyZc4VmZsuhBBCWLqVK9X1zj/+GCpUKPbm7txRNxUYCC1aGCC+XMSmxPLMN88Q\nciOERa0W0bFqx5Lb2eOkRQsIDYUBA2DWLHj6afj33xLd5fjx6unn5MnqtHhLNqTREDpV7cTU/VP5\n927JPm9CWALjJ+ihocTRGMcqCjYe/yXjV3U6bqenW06CDmqSvmbNI0l6Zw8Pfm/cmAxFoYfTZVKO\n1sS7vzdXZl7hVPtT6K7qTBu3EEIIIYomIkLNip5+Wi0aawALF6rLa8+ZY5DN5Sg6KZqn1z7N2aiz\nbOu7jWd8nym5nT2OnJ3Vsefr1sGpU+pE8WXL1NEWJUCjUZdo9/WF556DGzfyf4y50mg0rAhYgaIo\nvPLDK7I2unjsGT1B1weFEk993DqWfeD2rAJx9S0pQQcYPFj9hDxwQL30rVOT78bOzhxt2pTytrY8\ne+EsZxaWpc7/6pB0JongRsFEbow0ceBCCCGEKBRFgVdfhfR0+Oorgwxtj4iATz6Bvn3VnK4khEWH\n8dSqpzgfc56d/XfSvWb3ktmRUKdBnjoF/v7qe6V1a/j11xLZlYcH7NgB8fHQu3d2P5FFquxWmQ+f\n+ZC9F/by2fHPTB2OECZl9AQ9fv919Njh1u3BIWFBCQnYajTUNPf55zkZMgRWrYKff4YXXoC0NAD8\n7O35o0kTWrq40C8sjG/bpuEf6o9jHUfC+4cTPiic9Jh0EwcvhBBCiALZvBl27VInihuouv1bb6mr\ntZVULb5t4dtoubIl8anx/DzoZzpX61wyOxL/qVZNLSD3zTfqFZinn1bnqe/YAZmZBt1VgwZqp31Q\nkDoN3sCbN6qxzcfSs1ZPJv88maCIIFOHI4TJGD1BjzupAA/OPwe1gntjJydsS3pdkZIydKg6lGn3\nbnW5jYwMADxsbNjXsCF9PD2ZdPEib+kjaPBbI/xm+hG1MYqgukFEbYqS4TxCCCGEOYuJgXHj1LWw\nDVSVKzhYzeHeeAOqVDHIJrOlZabx1oG36L25N3U963Ji1AmeeuIpw+5E5E6jUadCnj+vzmG4cEGt\n+F+tGsyfr1YENJDnnlOLDG7fDu+952Oxld01Gg2rA1dT3rk8fb/rS5zOOEvXCWFujJug6/XE3a6I\nk0/iA/PPMxWFkMREmjs7GzUcg3vlFfj0U9i6VR36fu8ypr1Wy8a6dXnD15fFERH0+zscr3efwD/E\nH/sn7AnrF8bZwLPorsvcdCGEEMIsTZyoVnP7+muDVOjW69U838tL7UU3pOCIYJqtaMa8P+YxvMlw\nDg09hK+Lr2F3IgrGwUF971y6pC7HVrWqWvnd11ft3AkONshuxo1TN7t5szsTJlju8mseDh5semET\n1+Ov8/KOl6UDSzyWjJqgK0nJxFMPtxZ2D9z+V3IyiZmZtHBxMWY4JWPCBHUh0w0bYMSI7OIgVhoN\ni6pX59Pq1dl2+zZtQ0O5W8uGJkebUO3jatzZf4fgesFc+/QamToLHp8khBBClDZ796pd3dOmGWyi\n+Ndfw9Gj8NFHYKjTnzhdHJP3TabV162ITYllZ7+drOy5Entre8PsQBSdtbU6UfzgQfjzT3UR8+++\nUyvAt2ihTp0oZjL6wQcwZEgMS5aoVd5LqD5diWvl24r5z8xn21/beOfgO6YORwijM2qCro/TqfPP\nA/0euD04Ph7A8nvQs0ydqi6zsWYNjB37wAfuBF9fdjVowD8pKTQPCeFkciKVJlWi+Z/NcWnpwsU3\nLnK82nGuL70uiboQQghhagkJ6gi52rXhHcMkC1FR6qlCu3bqgLviSklPYcHhBVRdXJWPj37M8CbD\nOTfmHAG1Aoq/cWF49evDF1+opdeXLIHYWHU1oDZt4Pffi7xZjQamTIli0iRYulStV5eaasC4jeiN\nVm8wsulIPvjjA5YGLTV1OEIYlVET9IyETECPa+CDE62CExJw1mqpVaaMMcMpWTNmqN++y5apVTuu\nXMm+q3vZshxt0gRbjYa2p07xXVQUDtUcaLSvEY0ONsKhmgMXxl3gePXjRHwRIYm6EEIIYSpvvw3X\nrqlrn9vZ5d++AF5/HRIT4csvi1cIPkOfwVchX1F9SXWm7J9CK99WhL4SyoqAFbjauxokVlGCXFzg\ntdcgPFw9X7x0Cdq2hR494OLFIm1So4EFC9Rp7hs2QJcuEGmBCwdpNBq+6P4FPWv1ZPye8Xwf9r2p\nQxLCaIyaoGfqrHByjsamrO0DtwclJNDM2RkrSy0QlxONRi3JOmuWOie9Zk11uY3r1wGo7+REkL8/\nTZyc6BMWxtwrV1AUBfen3Wl8qDGN9jfCvrI958ee54j3EcKHhBOzOwZ9moWOVxJCCCEszZEjalfk\n2LHwlGEKrG3ZoiZO774LdeoUbRt6Rc/mc5up+3ldRv0wCj9XP34d8is/DviRxj6NDRKnMCIbG3WU\nxoUL6jTJ335Te9k/+CB7ZaDCUHvSYf16tbp7kybwxx8lEHcJs7ayZsPzG2hdqTUDtg5g9z+7TR2S\nEEZh3ARdb4tbnQcLoaXq9ZwuDQXicqLRwMyZ6lXQ4cPVCWfVq6uXzm/9f3t3Hh9FkTd+/NM9VyaT\nyQ0JEIgEEBACARFBQHcTDiEJCIrorgq6rseKrngg7P5YEVldEFdlfVbledRdvA8EFiKigKAoICJr\nEMJ9hxyQcyaTOXqmfn80GQ0gTEjIgfXmVa+Z6Z7ufLt66OrqrqoupLXZzOrevbklIYEZBw9yS14e\nbr8fRVGIyYihz5d96L26N62ub0XJf0rYlrWNrxO+ZuftOyl+rxhPfgtttyRJkiRJzZ3LpfcTTkrS\nK0oNoKBAv1Z/xRUwfXrdlxdCsHLvSq743yuY8OEEzAYzS29ayld3fMU1l1zTIDFKTSg8XG99mZcH\nmZl664161K5vvhk2bYKICPjVr/TB5FvamGvhpnCW3byMnq17MubdMby29bWmDkmSLrhGfsyaSnR6\nXK0puU4nPiEuzgp6jaQkvR3b7t3w29/qV+NTUuD3vyfsm29Y2LUrf+3YkbeLi/nVf/9L4ckOQ4qi\nEJMeQ7fXunFV0VWk5qQSNyaO44uPs+OmHWxI2sDGjhvZccsO8l/Op2pnlRztUpIkSZIawvTpsGsX\nvP46NMA5iqbpT92qqtLHm6vrQPCb8zeTvjCda9+6lhJXCf++7t98f8/3jO46GuViaoEoQbt2+gBy\ny5bpfSGGDIHf/17vq15Hqan6QPFjxsAjj0B2tt71vSWJtcaydtJahqYM5Xf/+R2zv5gtz3eli1oj\nV9AFUTdfVmvKNw4HwMUxgvu5XHKJfhc9L0+/rPnOO3DVVSiXXcaf3n2XRW3bsq2qiv7ffUeu01lr\nUdWsEjcqju7/6s6g44Pou7kvnZ/vjL2fnfLV5ey5dw+bu29mQ/sN5E3Mo/CNQjzH5B12SZIkSaqz\nVatg/nx9KOyMjAZZ5YwZsHq1fr2+W7fQlztYfpDfLPoN/f+vP9uLtzP/2vnsmryL23rfhkE1NEhs\nUjOVlQU7dsCjj+oXirp1gzfeqPNt8Kgovb7/wgv6IPI9esC//92y7qZHmCNYdvMybut9GzM+n8H4\nD8ZT5ilr6rAk6YJo1Aq6qngxpZ4yQFxlJa1NJto30MArLUKXLnpFvaBAf23VCqZNY9xll/Hl//0f\ngbIyrtq0if+8/bZ+9XTzZjhxIri4alKJ7BdJ0h+T6PFBDwYeG8iVe6/k0gWXEjUoipKcEnbetpMN\n7TawOW0zB2YcoHJzJSLQgo7EkiRJktQUCgr04a+7ddPHkmkAH3ygdy2+6y790dehKHeX89hnj9Ht\nxW4s3rmYPw/5M3sf2Mv9V96PxfgLOmf6pbPZ9GfxffcddOqkD/ufkaG37qgDRdGvN33/vX5XfdIk\nvRX94cMXJuwLwWQw8a8x/2LO0Dn8Z9d/GL1yNMt3L2/qsCSpwTVqBd1o0k4brnSzw8EVdvsvs3mW\n3a73b/vyS/1AO3Uqfbds4ZvJk+m+Zw/XJSbyzFtvIfr31yvxvXvDww/rz2OtqgquRlEUrJ2stP19\nW3q814NBxYO4/LvLSZmTgjHSyKGnDvFd/+/Y0G4Du36/i8KFhWgHNFlhlyRJkqSf8vngxhv1R6t9\n+KHeJ7ie1q7V6/tXXaXfwTxnCH4fL37zIp3nd2bu13OZ0HMCuyfvZnb6bCItv4DWhtKZ9eoFX32l\nj/a+dav++fHHwe0+97I/0aWL/pucPx/WrdMHKvzb385rLLomoSgKUwdN5du7viXeEk/2O9nc+MGN\n5B3Pa+rQJKnBNGoF3WCrXQl3aBp5Ltcvo3n7uVx6qT4IzfbttN2/n3W33soNdjtT77mHWz/9lPdf\neYUv+/Rhz0cf4Rg3DhEbC7/+tX5U3batVjslRVWw97HTYWoH+nzRh0HFg+j2RjeihkRR/H4xOyfu\npCSzhK/ivuL74d+z/8/7KXi1gNKVpVTtqEKr1JowIyRJkiSpCQhB4tNP6wNyLVigtwOupy1b9L6/\nnTrpDeLCws725wWL8xbT86We3L/ifnol9GLLXVv493X/pn1U+3rHIl0EVFUf7X3nThg/HmbN0m+H\nf/ZZnVdz//16j8sRI/ThFnr10nt2tBS9Enrx3tD3mHnNTFbsXUHPl3py2+LbZEVduijUcYiS+jFE\n1X682haHAwEX9wBx5yncZuPdfv3odvAgsw8d4q1LL9Ur8SfbxoVrGm3KykgsLCTxo49IfO89EhMS\nSLz0UtqkppIWF0e7k90GTHEmEm9JJPGWRIRf4NrpYtfiXdgO26jcWMHhv5XBKU9vM0QasKZYCesU\nhrWTFWuKFWsXK7ZUG+ZWZiRJkiTpovLUU8S8+67+fKrf/rbeq/vyS70LcUyM3vAtNvbnv/v1ka95\n9LNH+frI13SP786ym5eR2SXzl9m6UDq3hAR48039nPAPf4Dhw/Wxjf7+9zqtpkMH/UnAK1boFfZh\nw/RB5ObOrds4CU3FbDDz+K8e577+9zH3q7m8+M2LvJH7BgOSBnB72u3c2ONGosOimzpMSaqzRq2g\nK/baTcU2nxwgTlbQz0xVFGZ17MiUpCSOejwUer21U9u2FHbowI6KCtYEApTVXJrfvRuAPocPk719\nO9m7d9P3xAlUgwHF4cBWUUGv0lKMTif4fAQw4rEl44ntiieyEx5LEh4SqBZtqNoWScmyEoT3xzv0\n5jZmInpHYOttIyI1AmsXK9bOVkyxpqbIJkmSJEmqn/nz4f/9Pyqys4lqgH7n770Ht98Oycn6zc2k\npDN/b0/JHqavns6ivEUkRiSyIGsBt/e5HaPaqKdnUks1dCjk5uqtKZ9+Gj7+mJgHHoC//KVOjwkY\nORJ++AGef15vzNmzpz5ewsyZ0Lr1hQu/ocSHxzN32FweueoR3vj+DV7772vcvfxu7vv4Pga1H8TI\nziMZ0XkEqa1Tm+XAiv6AH6fXidfvRaCfb5tUE3aLXR4LfqEad6+ba995/cbhoGNYGPFmeUf2bGJM\nJmJMJlLP8T1PdTVFX35J/jff8IXVyrJ27Zg9YgSzRo6kjcNB5u7dZB86xNCyMjw+HzGXXALR0aiK\ngvXYMazHjkH+t5C/VH8Gh6ZBRATihrF4ht2MK7EfVTvcOL934vzeSdnqMoTvx4q7McaItZMVS7IF\nU6wJY4wRY7QRY4wRQ4RBvxOgAoreh0gEBAF3oFb66fpqGGwGzIlmTAkmzIlmzAlmTPEmeWdBkiRJ\nqh8h9FrIrFkwdizHHn+cKPX8e/+53Xpz4eefh0GD9LuTZ6rgHK44zJz1c1jw3QIsBgtP/OoJHh74\nMDaz7fy3RfplCgvTf8O/+Q3cey+JTz6p//CefVZvv16H1UybBr/7HTzxhN7VfeFC/Qb9Qw9BYuKF\n24SG0trWmoevepiHBj7E5mObWZy3mBV7VzBt9TSmrZ5GhDmC/u36M6DdAHon9ia1dSpd4rpcsEqw\nR/NwzHGMfEc+RyuPkl+pvx516O/zHfmUVpfi9Dp/dh3hpnBiwmKIM8Vx6Q+XkhyVTPf47vRo3YPL\nWl0mx6W4SDVuBf3UAeIqK7lS9j9vMBarlQ7Dh9Nh+HAGAo8BJ7xeVpSWsqykhPejo/m/yy8nTFXp\nbzRyc3IyWXFxJJ2pU5zfr48e8s47KB9+SNjbbxAWF0fsDTfATWPgpWsIGMOo3lNN9b6Taa+eXNtd\naOUavjIfwnNhBqJTrSrWztYzJkuSBUWVlXdJkiTpLMrK9NrI4sX668svw54957269ev1R1Xv3Kk3\nF372WTCd0rAs73gec76aw1vb3gLgzj53MvNXM0mISKjPlkiS3g1y1SqO/uMfJM2fD9deq6c5c/QO\n5iFq1QpefFH/Dc+apf+O//EP/bf9yCN6s/jmTlEU+rfrT/92/Xl66NPkV+az9uBaNhzdwIajG5jz\n1Rz8wg/ozeQ7RnekU2wnUqJTaGtvS2JEIgkRCURZorCZbUSYIzAoBgQCIQQevweHx4HT66S0upQC\nZwGFzkIKnAUUOPT3xxzHOO46flpsNpONpMgk2kW245rka4gPjyfSEondbCfMGBaM36N5cHgdODwO\nTlSfYFfBLn4o/oHlu5fj1n4cGLB9ZHt6tO5Bz1Y96dm6J6kJqXSP747VZG2czJYuiCZrN1Hs9XLI\n42GybN5+QcWbzdyamMitiYl4AwG+rKhg2YkTfFRYyL179nDvnj2kRUSQHh1NnMmE3WDAbjAQYTBg\nT0vDfvnl2OfMwf7VV9gXLcL+zjuYX3kFLBbUIUMIHz4cdfhwuLY7gUAAze/HezJVBQI4qn1UOn04\nqzX8FRWEHTmK5cAhLIeOEHb4ELbyEqxeF1avC1u0DavLSVhBAWFeD2FeL6qAgAjDJ6LxiRh8ROMT\nsXi8rXFvb4tjZ3uO+xMhYEIBvCaojvAj2vnwtw+gJZvwdbTgSw7H0y4Md7wZp1HFAVQGAmhC0Nlq\n5bLwcLqHh9PRasUg78xLkiRdvISARYv024IFBTBvnv7+PI/9ubn6YNpLluhN2j/5pPaNy2pfNR/l\nfcT/fve/rDu0DqvRyn1X3MdDAx+iQ1QLqO1ILYei4Bg2TB9I7n/+B558Un8C0Jgxeu160KCQf+dd\nu8Jbb+k35//2N3jpJX2VI0fqlfVRo06/ANVctYtsx297/Zbf9tLHlnBrbnae2Mm2om38UPwD+8r2\nsa9sH+sPr6fSU3lef8OoGkmwJdDG3oYOUR3o364/SZFJemXc3i74PtISeV4tQPPy8ujevTv+gJ+D\n5QfZfnw724u366/Ht/P5gc/x+D0AqIpKl9gupCak0rNVT7rFd9MvQMSkEGs9y2AYUrNRrwr6F198\nwV//+lcCgQDjx4/nrrvuCnnZmv7ncgT3xmNWVTJiYsiIieEunw8lOZllJSUsLynhxfx8vOIsd7vt\ndn0wkkmTMAlBhM+HLxDAZTIRKCuDr78OLYj2EdC+B1D/0XF/nuFkqhEAnKA5oVCfYvYKwl1+VCEo\njfmxhDH7BB2LBB2LFVJKjXQ8YaLjcSPtT6iYAgqGCAOGKAPGKCPGSL0Jv6mVKZjMrfXm9wa7AdVU\n92aSQgjKysrwHziAevAgyqFDqFVVqF4vqqah+HyoPh+q349qtaLabCg2G4GICKrtdqqjo3G3bUt1\nYiJuu53qQIDqQAD3Ka/eQIBoo5F4kymYWplMhBmaX98sSZIa1rnKbq/Xy9SpU9m+fTvR0dE899xz\nJP1cJ+qWxO/Xh1KfN09/XFXPnvqj1Pr3r/OqnE59VQsW6I+siozU7zZOmQIREeDwOFi5byVLdy1l\n+e7llLvLSYlJ4a/pf+Wuy+8iPjy+4bdPkmpYLPpFp0mT9PEV5s+HpUv1Ed8nTtRHgA/xVniXLvDq\nq3q39gUL4PXX4brroE0bmDBBf5b61Vef1ou1WQszhpGWmEZaYtpp81w+F0XOIoqqiqj0VFLlrcLp\ndeIXfhQUFEXBbDBjN9uxW+xEWaJoY29DfHg8qnLhH45lUA10iu1Ep9hOjO46OjjdH/Czt3Qv24q3\nkVuUy7bibWwt2MqiHYuC/dpBbzLfKrwV8eHxtVKsNRabyYbVZCXcFE64KRyr8SfvzzA9zBgmu5te\nIOddQff7/cyaNYvXX3+dhIQEbrjhBtLT0+ncuXNIy2+urEQF+kZEnG8IUj0oikJ3m43uNhtTTx6k\nPYEADk3D4ffj8Ptxnnz96bSaz06/H5OqYquqwrZvH7a9e7GdOEHE8ePYior05HJhc7uxxcZi69UL\nQ79+eK64Anf37rhVNVhh/Gnl0R0IUO33B98HAIFecRU173/mM4BJVbGrKnZNw15Vhb3SgeVIJaZ9\nTkxH/BiPgKlUoFYq+KsVNJdCuaJwMEFwoB0caK9yMNlMbrKZT/oqgN4EyqgJ2hdodMwPEFeiYnWq\nWI8phO9SsVVBuAtsVT+mcBdE+AKYjBqbIvLxRkBpDJREK5yIghJ7gBK7oDQS/XMUlESrlEQb0Ewn\nD3ZxcXqqq8pKPZ2H8IBCrN9AjM9AnGYgNqCnuIBKlF8h3GTAZjZgMxuxhRmJCDNii7BgDzcRYTNi\nt5mICDNiqkcfzouW2w1VVeDx1E4+n/6sZbtdP8uPiIAmvFASEAJPIIACDXfBRtP0uzbyAlCTC6Xs\n/uCDD4iMjOSzzz4jJyeHefPm8fzzzzdh1PVQVgYbNujDVC9eDPn50L49/POf+m3AEAfSqqzUB9Ha\nuBFWr4Y1a/T/0snJ8NTTfoZPOMgh9/fM/PprNhzdwLfHvsXr9xJrjWV019FM7D2RX13yq0Y5gZek\noNhY/Rb4o4/CO+/oNexHHtFTt256zfqaa/Q76x06nPXuenIy/PWvev/0jz/WK+0vvaSPtxARoY8A\nP3gwXH459OmjF2ctUbgpnI4xHekY07GpQ6kTg2qga3xXusZ35YbLbghOr/JWsa9sH/vL9rOvdB/H\nHMc4UX2C41XHOeE6we6S3ZxwncDhdZzX3z1XJd5mthFlidJTWO3X6LDoWtMiLZHyGHnSeVfQc3Nz\nSU5Opn17/dmcmZmZrF69+qwV9D3V1czcvh27wcAXFRV0Dw8nog6jTEoXlkVVsZjN1Pm6fu/ep0/z\n+6GkRH9t06YhwmscHo9+AnfkCFX5+ewsKSHP5WIHsCM8nLwOcXzXw05leDgBw7kOIirWahU1AFW2\nU3/nBpSAILbcR3yZn7hyP52PBIhzGIiqNGLwmND8KppP/3ZABaHorwDCooBFAbOeVFVg8QjMrgAm\np57MVWByK5irDZi9YPaCxaO/GjVwRkBF1I+pPBoqogQVURqVkRoF0bDz5LzqcE4XAFwn0ymMPkGY\nB8LcgjCvwOoNYPUIrJrA6hOEawHC/BDmU7B6FcI0hTCvilAUNFXFZ1DRVPCpCpoq8BtBMwr8RoFm\nBP/J936jQKgCX8CLYfNBhBLQEwGE4sePQBAgoOjhCgWM/gBmfwCz5sesBTD7/Jj8Acy+muTHpAnM\n3gAmTaBqQEAB1QiqCVQTQjGCakQRBoQf/TqOXyD8oPhBERqq5kX1uVE1N/jcqH4PqvCjCk1PJy8/\nKULBazTiCjNTZbVQFWalKjwcly2cKpuVaqsFV5gJl8VItUWl2qzgNYIhAMYAGASYAmAICEwBgVEE\nMAX8mPwaxoCGGYFJFfhNKl6zitdkwGM04FGVk0nFoyjBpP3kBM2i+Ylxe4h2u4mu9hDpdBG3ch3R\nrmqiXR5iqqqJrnIR7XQTVVWNqvkRgQABzQteD/jcCJ8XEdAQBgViohDxMYjYWGgVixITi8UeiSXC\njiUiEnNUJJbIKCz2KCyqilmAJQAWAaoQCH8A3B40txu3x43H66Xa7cXt9+NRBB6DcjKB26DgMSpg\nMmI0GTCZTBiMRownk8Fk0t/XTFdVDIEA+Hw43G4cHg8OrxdndTUOl+vHaT6fftEScKgqzpPLhfn9\nhGl+woTAIhQsKFiEqqeAgsUbwOINYPYGMHoDqL4A+ASKJlB8J5MmUAN+rrnvqnMcW85fKGX3mjVr\nmDx5MgAjRoxg1qxZCCGa9k5JIKCXJ5r246vLBQ6Hnior9ebq+fl6OngQtm+H/fsRQhAIs+EfMQr/\n3JvRRmbhFSq+0gBezY3D6afC4afSEeCHHV5Wri+msMjP0eIqDhc62H3IQVF5JZgdYHEQ16GYLg8e\nIzIpn0r1AE+U7OZPC/WmpRaDhX5t+/HglQ+SeWkmV7W/So7CLDU9mw3uvFNPe/fqg8itWwfvvqtX\n2sfp6PgAABgySURBVEG/UNyli96XvWNH/VFurVvrr9HR+vzwcIzh4Yz+dTijR1lxeQys+Vxh+XK9\na8fixfqqFAU6d4aUFL1in5ysP8kgJkZP0dH6NWmzWb/ZbzbryWQ6754m0s+wmW30SuhFr4Szj0Pg\n8/uo1qqp9lXj8rlw+VxUa/r7UKedOr9mALwKTwUV7opgE/yzsZvt2Iw24tfG167UW/QKfM0FgFOT\nzWwLXhgwGUyYVNNpr0bVGHzfHEfz/6nzLjWKiopI/MmQjgkJCeTm5p51GU0Ivnc6qfT7qdQ0Hj55\ngiBdhAyGlvFsjlNZLHqJkpKCDbj8ZKpFCERlJa7jx6k8cYKKsjIqFYVKi4VKk4kKo5FKo5FKReGw\nrxRbVBQJQKIQJAKJBgNtYmKIb90aY0TEWUujgBZAK9HwFHjwFniDSSvX8Lv8BJwB/C4//io/iqKg\nWlXUMBW1nRp8b7Aa9GlGP6qrArWqFNVxHLWsGIPfhSo8qMKNWu1BrapGzXejWo0QZkVYrAhzONVh\n4VRYrFQFVJx+A05hwClUXBhwBhSqAoIqFKqFglNVqTaouAwq1UYDVSYVt8lAtUXFZTFQZlNxW8y4\nwxSqwxQ8YQpe8+l5oAQERi2A0S8wanorBqMGBr9+gcGgqRj8CqpfQRFmDFWgCFADp7/WvFcEeAzg\nNIHvZNIsP74/NYkmuJAbVg3WarB4BFa3wOoOYHX6iT/uIdztI9ztw+zz41cNaAYDPuPJV4MBzWDE\nZzSiGU1oBn1bKw2gGfV8M3vB5IMIH8T4fvxc8/rT90IBh92AMyIchz0chx0OREBuAjjsUHUhGj95\nKuF4JRw/ctosg6bvd58JAjXlqgL83Dg4AvCdTA3BYMBgDidcA6um76Nwl/4qFCgzg/csqS6/pR0N\nFPKZhFJ2FxUV0ebkhVWj0YjdbqesrIzYsz3Iu6Ft2qR35K6q0ivkZ+uCdSq7nWXRt3LTsffwKGb8\nPd+B0b8HdTHsWgR7AqGtJ+5kOqVHVgmANQ7UtiRHJ3Nt52vpFt+NHq16kJaYhsVoCT1WSWpsnTvD\n1Kl68vv1QRQ2bdIf0btrF2zdqg+o4Dv3wTMcyAKyVBUMBorNCWwZ+hjfDpjM99/r18m2bIETJ0IP\nT1V/TFFRem+ULl3Od2OlUJkMekX2Qo4K79E8wcp6haeCcnd58P1PXw8WHUQJU6jwVFDkLGJ3yW4q\n3BU4vI5aA+Q1hJpuC6qiBt/XvA5IGsDnEz9v0L8Xqka9rJtiMLA4/Ce34txu8vLyTvvemaZJDU/m\ncwOIioKoKOyAHWh36vzwM916hjKvl7KjR0P/OxbgkpPpFOrJfz8VOPnvzCtqczLVnQLBbZUk6cLw\neM59l6G58Xg8DVumREbqTdPPU2fgWw6c/NQX2NIQUZ2dA/Y79l/4v9OAfknnAXJbf0ZYmN7M/Zpr\nGuRvXwJcQh433HCub4ZG0+BsmyP3a8tkO/mvndIOwtBTjeSmiup0DZ3noZbv511BT0hIoLCwMPi5\nqKiIhISzPyYkLe30wRgkSZIkSWocoZTdCQkJFBQUkJiYiKZpOBwOYmJizrpeWb5LkiRJUsM47wac\nqampHDx4kCNHjuD1esnJySE9Pb0hY5MkSZIkqQGFUnanp6ez+GRn0pUrVzJgwAA5Uq8kSZIkNRJF\niLp07Kpt3bp1PPXUU/j9fq6//nruvffehoxNkiRJkqQGdqay+4UXXqBnz55kZGTg8Xh49NFHycvL\nIyoqiueeey44qJwkSZIkSRdWvSrokiRJkiRJkiRJkiQ1DPmwOUmSJEmSJEmSJElqBmQFXZIkSZIk\nSZIkSZKagSavoK9YsYLMzEy6devGtm3bfvZ7X3zxBSNGjGDYsGEsWLCgESO8OJSXl3P77bczfPhw\nbr/9dioqKs74vWeeeYasrCyysrL4+OOPGznKli/UfJ47dy6ZmZmMHDmS2bNnI3ua1E0o+bxx40bG\njBkTTKmpqaxataoJom25Qv09Hzt2jDvuuIORI0cyatQojtblEYJSyPncvXv34O/5nnvuaeQoLx71\nze8jR44wfvx4hg0bxoMPPojX622s0OsslG3Ny8tjwoQJZGZmkp2dXavsnzZtGunp6cF8aG6PeTrX\nuaHX6+XBBx9k2LBhjB8/vtax6ZVXXmHYsGGMGDGCL7/8sjHDPi/n2tbXX3+dUaNGkZ2dzcSJE8nP\nzw/Oa4nHjnNt70cffcSAAQOC2/XBBx8E5y1evJjhw4czfPjw4ICXzdm5tvWpp54KbueIESPo169f\ncF5L2rfTp09n4MCBZGVlnXG+EILZs2czbNgwsrOz2b59e3BeS9un9Saa2N69e8W+ffvELbfcInJz\nc8/4HU3TREZGhjh8+LDweDwiOztb7Nmzp5EjbdnmzJkjXnnlFSGEEK+88oqYO3fuad/5/PPPxaRJ\nk4TP5xNVVVVi3LhxwuFwNHaoLVoo+bxlyxYxYcIEoWma0DRN3HjjjWLjxo2NHWqLFko+/1RZWZm4\n4oorhMvlaozwLhqh5vMtt9wi1q9fL4QQwul0ynyuo1DzOS0trTHDumjVN78feOABsXz5ciGEEDNm\nzBBvvfXWhQm0AYSyrfv37xcHDhwQQghRWFgoBg0aJCoqKoQQQjz22GNixYoVjRZvXYRybvjmm2+K\nGTNmCCGEWL58ufjjH/8ohBBiz549Ijs7W3g8HnH48GGRkZEhNE1r9G0IVSjbumHDhuCx96233gpu\nqxAt79gRyvYuWrRIPPHEE6ctW1ZWJtLT00VZWZkoLy8X6enpory8vLFCr7O61nEWLlwopk2bFvzc\nkvbtN998I3744QeRmZl5xvlr164Vv/vd70QgEBBbt24VN9xwgxCi5e3ThtDkd9A7depESkrKWb+T\nm5tLcnIy7du3x2w2k5mZyerVqxspwovD6tWrue666wC47rrrzngnce/evfTr1w+j0Uh4eDhdu3bl\niy++aOxQW7RQ8llRFLxeLz6fL/gaHx/f2KG2aKHk80+tXLmSIUOGYLVaGyO8i0aoxw1N0xg0aBAA\nNptN5nMd1fX3LNVPffJbCMHGjRsZMWIEAGPHjm3W5yOhbGvHjh255JJLAEhISCA2NpbS0tLGDPO8\nhHJuuGbNGsaOHQvAiBEj2LBhA0IIVq9eTWZmJmazmfbt25OcnExubm5TbEZIQtnWAQMGBI+9aWlp\nFBYWNkWoDaI+5/3r169n0KBBREdHExUVxaBBg5p1C4m6bmtOTs7P3oFu7q644gqioqJ+dn7N8UpR\nFNLS0qisrKS4uLjF7dOG0OQV9FAUFRWRmJgY/JyQkEBRUVETRtTylJSU0Lp1awBatWpFSUnJad/p\n1q0bX375JdXV1ZSWlrJp06YWfYBvCqHkc58+fbjyyisZPHgwgwcPZsiQIXTq1KmxQ23RQsnnn2rJ\nBVpTCiWfDx48SGRkJJMnT+a6665jzpw5+P3+xg61RQv19+zxeBg3bhw33nijrMTXQ33yu6ysjMjI\nSIxGIwCJiYnN+nykrsfK3NxcfD4fHTp0CE577rnnyM7O5qmnnmpWzflDOTcsKiqiTZs2ABiNRux2\nO2VlZS3uvLKu8X744YdcffXVwc8t7dgR6vZ++umnZGdn88ADD1BQUFCnZZuLusSbn5/P0aNHGTBg\nQHBaS9u3Z3NqXtQcX1vaPm0Ixsb4I5MmTeLEiROnTX/wwQcZOnRoY4Twi3C2fP4pRVFQFOW07w0e\nPJht27Zx0003ERsbS1paGqraIq7hNKr65vOhQ4fYt28f69atA+COO+7g22+/rdWnSKp/PtcoLi5m\n9+7dDB48uMFjvBjUN581TePbb79lyZIltGnThilTpvDRRx8xfvz4CxZzS9QQv+fPP/+chIQEjhw5\nwsSJE7n00ktrVaSkH12o/I6IiLgg8dZHQx4rH330UebMmRMs+x966CFatWqFz+djxowZLFiwgMmT\nJzfsBkgNaunSpfzwww+8+eabwWkX47Hj17/+NVlZWZjNZt59910ee+wxFi5c2NRhXVA5OTmMGDEC\ng8EQnHYx7lupkSro//rXv+q1fEJCQq07uUVFRSQkJNQzqovP2fI5Li6O4uJiWrduTXFxMbGxsWf8\n3r333su9994LwMMPP0zHjh0vRKgtWn3z+bPPPqN3797YbDYAhgwZwtatW2UF/RQN8XsGfSDKYcOG\nYTKZLkCULV998zkxMZHu3bvTvn17ADIyMvj+++8vVLgtVkP8nmvKvfbt29O/f3927NghT8R+xoXK\n7xEjRlBZWYmmaRiNRgoLC5v8fKQhttXpdHL33XczZcoU0tLSgtNr7r6bzWbGjRvHa6+91qCx10co\n54YJCQkUFBSQmJiIpmk4HA5iYmJa3HllqPF+/fXXvPzyy7z55puYzeZay0PLOXaEsr0xMTHB9+PH\nj+eZZ54JLvvNN9/UWrZ///4XOOLzV5ff4scff8xf/vKX05aHlrNvz+bUvKg5vra0fdoQWsTt0dTU\nVA4ePMiRI0fwer3k5OSQnp7e1GG1KOnp6SxZsgSAJUuWkJGRcdp3/H4/ZWVlAOzcuZNdu3YF+5VK\noQkln9u2bcvmzZvRNA2fz8fmzZtlE/c6CiWfa+Tk5JCZmdlYoV1UQsnn1NRUKisrg31WN23aROfO\nnRs1zpYulHyuqKgINi8uLS3lu+++k/l8nuqT34qicOWVV7Jy5UpAH1m4OZ+PhLKtXq+X++67jzFj\nxnDttdfWmldcXAzofe9XrVpFly5dLnzQIQrl3DA9PT044vPKlSsZMGAAiqKQnp5OTk4OXq+XI0eO\ncPDgQXr16tUUmxGSULZ1x44d/OUvf+Gll14iLi4uOL0lHjtC2d6a3yboYw3UnEcNHjyY9evXU1FR\nQUVFBevXr2/WLehCrePs27ePyspK+vTpE5zWEvft2dQcr4QQ/Pe//8Vut9O6desWt08bRNOOUSfE\np59+KoYMGSJ69OghBg4cKO644w4hhD6S6J133hn83tq1a8Xw4cNFRkaG+Oc//9lU4bZYpaWl4rbb\nbhPDhg0TEydOFGVlZUIIIXJzc8Wf/vQnIYQQbrdbjBw5UowcOVKMHz9e7NixoylDbpFCyWdN08SM\nGTPEtddeK0aOHCmeeuqppgy5RQoln4UQ4siRI2Lw4MHC7/c3VagtWqj5vH79epGVlSWysrLEY489\nJjweT1OF3CKFks9btmwRWVlZIjs7W2RlZYn333+/KUNu0eqb34cPHxbXX3+9GDp0qLj//vub9e89\nlG1dsmSJuOyyy8To0aODqab8v/XWW0VWVpbIzMwUDz/8sHA6nU22LWdypnPD559/XqxatUoIoZ/X\n3H///WLo0KHi+uuvF4cPHw4u+89//lNkZGSI4cOHi7Vr1zZJ/HVxrm2dOHGiGDhwYHAf3n333UKI\nlnvsONf2zps3T4waNUpkZ2eLW265Rezduze47AcffCCGDh0qhg4dKj788MMmib8uzrWtQggxf/58\n8cwzz9RarqXt2ylTpohBgwaJyy67TAwZMkS8//774u233xZvv/22EEKIQCAgZs6cKTIyMkRWVlat\np3u1tH1aX4oQ8gHMkiRJkiRJkiRJktTUWkQTd0mSJEmSJEmSJEm62MkKuiRJkiRJkiRJkiQ1A7KC\nLkmSJEmSJEmSJEnNgKygS5IkSZIkSZIkSVIzICvokiRJkiRJkiRJktQMyAq6JNVDZWUlb731FgBH\njx6lV69ejBkzhtGjR3PTTTexf//+Oq3v1ltvZdu2bef83qpVq+jatSv79u07r7iPHj1K165dee65\n54LTSktL6dGjB7NmzTqvdUqSJEnSxUSW8ZIkNQVZQZekeqisrOSdd94Jfu7QoQNLly7lP//5D9dd\ndx2vvPLKBfm7y5cv5/LLLycnJ+e815GUlMS6deuCnz/55BM6d+7cEOFJkiRJUosny3hJkpqCsakD\nkKSW7Nlnn+Xw4cOMGTOG5OTkWvOcTieRkZEAeDweZs6cyQ8//IDBYGDatGkMGDAAt9vN9OnT2blz\nJykpKbjdbgA+/PBDdu3axZ///GcA3n//ffbu3cuf/vQnqqqq2LJlCwsXLuSee+7hgQceCP7NBQsW\nsGzZMhRF4eqrr+aRRx7h0KFDPP7445SWlmIwGHjhhRdQVRWr1UqnTp3Ytm0bqamprFixgpEjR1Jc\nXAzAmjVreOmll/D5fERHRzNv3jzi4+MpLS3l4Ycfpri4mLS0NL7++msWLVqEy+XizjvvJC0tja1b\nt9KzZ0+uv/565s+fT2lpKfPmzaNXr164XC6efPJJ9uzZg6ZpTJ48maFDh3L06FGmTp1KdXU1ADNm\nzKBv375s2rSJF198kZiYGHbv3k2PHj2YN28eiqJc8P0rSZIk/XLJMl6W8ZLUJIQkSeftyJEjIjMz\nM/g+NTVVjB49WmRkZIhBgwaJ/Px8IYQQr776qpg2bZoQQoi9e/eKa665RrjdbvHaa68Fp+fl5Ynu\n3buL3Nxc4XQ6RUZGhvB6vUIIISZMmCB27twphBBi6dKlYvr06cHp27ZtE0IIsXbtWjFhwgThcrmE\nEEKUlZUJIYS44YYbxKeffiqEEMLtdguXyxWMe9WqVeJvf/ubOHbsmLjtttvEokWLxBNPPCGEEKK8\nvFwEAgEhhBDvv/++ePrpp4UQQjzxxBPi5ZdfFkIIsW7dOnHppZeKkpISceTIEdG9e3exc+dO4ff7\nxdixY8W0adNEIBAQn332mbj33nuFEEI8++yzYsmSJUIIISoqKsTw4cNFVVWVcLlcwu12CyGEOHDg\ngBg7dqwQQoiNGzeKvn37ioKCAuH3+8WNN94oNm/e3GD7UJIkSZLORJbxsoyXpKYg76BLUgOqaf4G\n8PHHHzNjxgxeffVVtmzZwi233AJAp06daNu2LQcOHGDz5s3ceuutAHTr1o2uXbsCYLPZGDBgAGvX\nriUlJQWfzxecl5OTw2233QbAqFGjyMnJoWfPnmzYsIFx48ZhtVoBiI6Oxul0UlRUxLBhwwCwWCy1\n4h0yZAgvvPACcXFxjBo1qta8wsJCpkyZwvHjx/F6vSQlJQGwZcsWXnzxRQCuvvpqoqKigsskJSUF\n4+zcuTMDBw5EURS6du1Kfn4+AOvXr2fNmjW89tprgH7noaCggNatWzNr1ix27tyJqqocPHgwuN5e\nvXqRmJgYzKf8/Hz69et3HntIkiRJks6PLONlGS9JjUFW0CXpAklPT2f69Onnvfz48eN5+eWXSUlJ\nYdy4cQCUl5ezceNGdu/ejaIo+P1+FEVh6tSp5/U3zGYzPXr04PXXXycnJ4c1a9YE582ePZtJkyaR\nkZERbIIWyvpqqKoa/FwTa4358+eTkpJSa9l//OMfxMfHs3TpUgKBAL169Trjeg0GQ611SZIkSVJj\nk2W8LOMl6UKRg8RJUj3YbDaqqqrOOG/Lli106NABgH79+rFs2TIADhw4QEFBASkpKVxxxRUsX74c\ngN27d7Nr167g8r1796awsJDly5eTlZUFwMqVKxkzZgyff/45a9asYd26dSQlJfHtt99y1VVX8dFH\nHwX7d5WXlxMREUFiYiKrVq0CwOv1BufXuOOOO3jkkUeIjo6uNd3hcJCQkADAkiVLgtP79u3LihUr\nAP1KeUVFRZ3ybPDgwbz55psIIQDYsWNH8O+1atUKVVVZunSpLKAlSZKkJiXLeFnGS1JTkBV0SaqH\nmJgY+vbtS1ZWFnPnzg0OJjN69Gj+/ve/M3v2bAB+85vfIIQgOzubKVOm8PTTT2M2m7n55ptxuVyM\nHDmS+fPn06NHj1rrHzlyJH379g02MVu+fDlDhw6t9Z3hw4ezfPlyrr76atLT07n++usZM2ZMsHnZ\n3LlzWbhwIdnZ2dx0002cOHGi1vJdunRh7Nixp23b5MmT+eMf/8i4ceNqFeyTJ0/mq6++Iisri08+\n+YRWrVoRERERcp794Q9/QNM0Ro8eTWZmJi+88EIwjxYvXszo0aPZv38/4eHhIa9TkiRJkhqaLONl\nGS9JTUERNZe4JElqdu6++24mTZrEwIEDmzqUIK/Xi6qqGI1Gtm7dysyZM4N98iRJkiRJCo0s4yVJ\nOhPZB12SmqHKykrGjx9P165dm1XBDXDs2DEefPBBAoEAJpOJJ598sqlDkiRJkqQWQ5bxkiSdjbyD\nLkmSJEmSJEmSJEnNgOyDLkmSJEmSJEmSJEnNgKygS5IkSZIkSZIkSVIzICvokiRJkiRJkiRJktQM\nyAq6JEmSJEmSJEmSJDUDsoIuSZIkSZIkSZIkSc2ArKBLkiRJkiRJkiRJUjPw/wGr+d1zG3AM4QAA\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJd_xc3bgGYb",
        "colab_type": "text"
      },
      "source": [
        "### 3. Magnitude of an acceleration can saperate it well"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "fMprQcdPgGYc",
        "colab_type": "code",
        "outputId": "b4b0a2dd-a06a-4497-b36c-0e6f9aeabc6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        }
      },
      "source": [
        "plt.figure(figsize=(7,7))\n",
        "sns.boxplot(x='ActivityName', y='tBodyAccMagmean',data=train, showfliers=False, saturation=1)\n",
        "plt.ylabel('Acceleration Magnitude mean')\n",
        "plt.axhline(y=-0.7, xmin=0.1, xmax=0.9,dashes=(5,5), c='g')\n",
        "plt.axhline(y=-0.05, xmin=0.4, dashes=(5,5), c='m')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WFspqtSoiIqLUOhEREVq2bJkkafXq1br99ttlsViqvW0AAC7H6UixadOmevvtt12/YS8v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\nREQS/gF8zOWucB3r9wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YOtm3hzggGYg",
        "colab_type": "text"
      },
      "source": [
        "__ Observations__:\n",
        "- If tAccMean is < -0.8 then the Activities are either Standing or Sitting or Laying.\n",
        "- If tAccMean is > -0.6 then the Activities are either Walking or WalkingDownstairs or WalkingUpstairs.\n",
        "- If tAccMean > 0.0 then the Activity is WalkingDownstairs.\n",
        "- We can classify 75% the Acitivity labels with some errors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pscv0DrcgGYi",
        "colab_type": "text"
      },
      "source": [
        "### 4. Position of GravityAccelerationComponants also matters "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Roa09hogGYl",
        "colab_type": "code",
        "outputId": "4d953011-aa19-4e17-d94b-a7d641cb8eca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "sns.boxplot(x='ActivityName', y='angleXgravityMean', data=train)\n",
        "plt.axhline(y=0.08, xmin=0.1, xmax=0.9,c='m',dashes=(5,3))\n",
        "plt.title('Angle between X-axis and Gravity_mean', fontsize=15)\n",
        "plt.xticks(rotation = 40)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CT08P69evBwDo6+tj9uzZmDBhAgBgzpw50NfXb3MshBBCZIfFMAzTUiE/Pz+c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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DM1nQ20egGYq",
        "colab_type": "text"
      },
      "source": [
        "__ Observations__:\n",
        "* If angleX,gravityMean > 0 then Activity is Laying.\n",
        "* We can classify all datapoints belonging to Laying activity with just a single if else statement."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxZevBYSgGYr",
        "colab_type": "code",
        "outputId": "72b725ce-0214-4988-c0f2-b9d7d975b242",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "sns.boxplot(x='ActivityName', y='angleYgravityMean', data = train, showfliers=False)\n",
        "plt.title('Angle between Y-axis and Gravity_mean', fontsize=15)\n",
        "plt.xticks(rotation = 40)\n",
        "plt.axhline(y=-0.22, xmin=0.1, xmax=0.8, dashes=(5,3), c='m')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xBby9vUWvuVwuAIj6+9/eHx48eFBvHUuWLEHnzp2Rl5eH3377rd596G3Dhw/H8OHDUVhY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\nl/KS+QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svUHpX1egGYw",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_PMPaIIgGYx",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ovJHeo8hgGYy",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_uCyilyBgGY0",
        "colab_type": "text"
      },
      "source": [
        "# Apply t-sne on the data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vjL2DywLgGY2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIx9y8shgGY7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# performs t-sne with different perplexity values and their repective plots..\n",
        "\n",
        "def perform_tsne(X_data, y_data, perplexities, n_iter=1000, img_name_prefix='t-sne'):\n",
        "        \n",
        "    for index,perplexity in enumerate(perplexities):\n",
        "        # perform t-sne\n",
        "        print('\\nperforming tsne with perplexity {} and with {} iterations at max'.format(perplexity, n_iter))\n",
        "        X_reduced = TSNE(verbose=2, perplexity=perplexity).fit_transform(X_data)\n",
        "        print('Done..')\n",
        "        \n",
        "        # prepare the data for seaborn         \n",
        "        print('Creating plot for this t-sne visualization..')\n",
        "        df = pd.DataFrame({'x':X_reduced[:,0], 'y':X_reduced[:,1] ,'label':y_data})\n",
        "        \n",
        "        # draw the plot in appropriate place in the grid\n",
        "        sns.lmplot(data=df, x='x', y='y', hue='label', fit_reg=False, size=8,\\\n",
        "                   palette=\"Set1\",markers=['^','v','s','o', '1','2'])\n",
        "        plt.title(\"perplexity : {} and max_iter : {}\".format(perplexity, n_iter))\n",
        "        img_name = img_name_prefix + '_perp_{}_iter_{}.png'.format(perplexity, n_iter)\n",
        "        print('saving this plot as image in present working directory...')\n",
        "        plt.savefig(img_name)\n",
        "        plt.show()\n",
        "        print('Done')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "P7I5EVylgGZD",
        "colab_type": "code",
        "outputId": "96725a1f-192a-418d-95c2-4be78ba1d5a0",
        "colab": {}
      },
      "source": [
        "X_pre_tsne = train.drop(['subject', 'Activity','ActivityName'], axis=1)\n",
        "y_pre_tsne = train['ActivityName']\n",
        "perform_tsne(X_data = X_pre_tsne,y_data=y_pre_tsne, perplexities =[2,5,10,20,50])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "performing tsne with perplexity 2 and with 1000 iterations at max\n",
            "[t-SNE] Computing 7 nearest neighbors...\n",
            "[t-SNE] Indexed 7352 samples in 0.426s...\n",
            "[t-SNE] Computed neighbors for 7352 samples in 72.001s...\n",
            "[t-SNE] Computed conditional probabilities for sample 1000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 2000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 3000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 4000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 5000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 6000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 7000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 7352 / 7352\n",
            "[t-SNE] Mean sigma: 0.635855\n",
            "[t-SNE] Computed conditional probabilities in 0.071s\n",
            "[t-SNE] Iteration 50: error = 124.8017578, gradient norm = 0.0253939 (50 iterations in 16.625s)\n",
            "[t-SNE] Iteration 100: error = 107.2019501, gradient norm = 0.0284782 (50 iterations in 9.735s)\n",
            "[t-SNE] Iteration 150: error = 100.9872894, gradient norm = 0.0185151 (50 iterations in 5.346s)\n",
            "[t-SNE] Iteration 200: error = 97.6054382, gradient norm = 0.0142084 (50 iterations in 7.013s)\n",
            "[t-SNE] Iteration 250: error = 95.3084183, gradient norm = 0.0132592 (50 iterations in 5.703s)\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 95.308418\n",
            "[t-SNE] Iteration 300: error = 4.1209540, gradient norm = 0.0015668 (50 iterations in 7.156s)\n",
            "[t-SNE] Iteration 350: error = 3.2113254, gradient norm = 0.0009953 (50 iterations in 8.022s)\n",
            "[t-SNE] Iteration 400: error = 2.7819963, gradient norm = 0.0007203 (50 iterations in 9.419s)\n",
            "[t-SNE] Iteration 450: error = 2.5178111, gradient norm = 0.0005655 (50 iterations in 9.370s)\n",
            "[t-SNE] Iteration 500: error = 2.3341548, gradient norm = 0.0004804 (50 iterations in 7.681s)\n",
            "[t-SNE] Iteration 550: error = 2.1961622, gradient norm = 0.0004183 (50 iterations in 7.097s)\n",
            "[t-SNE] Iteration 600: error = 2.0867445, gradient norm = 0.0003664 (50 iterations in 9.274s)\n",
            "[t-SNE] Iteration 650: error = 1.9967778, gradient norm = 0.0003279 (50 iterations in 7.697s)\n",
            "[t-SNE] Iteration 700: error = 1.9210005, gradient norm = 0.0002984 (50 iterations in 8.174s)\n",
            "[t-SNE] Iteration 750: error = 1.8558111, gradient norm = 0.0002776 (50 iterations in 9.747s)\n",
            "[t-SNE] Iteration 800: error = 1.7989457, gradient norm = 0.0002569 (50 iterations in 8.687s)\n",
            "[t-SNE] Iteration 850: error = 1.7490212, gradient norm = 0.0002394 (50 iterations in 8.407s)\n",
            "[t-SNE] Iteration 900: error = 1.7043383, gradient norm = 0.0002224 (50 iterations in 8.351s)\n",
            "[t-SNE] Iteration 950: error = 1.6641431, gradient norm = 0.0002098 (50 iterations in 7.841s)\n",
            "[t-SNE] Iteration 1000: error = 1.6279151, gradient norm = 0.0001989 (50 iterations in 5.623s)\n",
            "[t-SNE] Error after 1000 iterations: 1.627915\n",
            "Done..\n",
            "Creating plot for this t-sne visualization..\n",
            "saving this plot as image in present working directory...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 731.875x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Done\n",
            "\n",
            "performing tsne with perplexity 5 and with 1000 iterations at max\n",
            "[t-SNE] Computing 16 nearest neighbors...\n",
            "[t-SNE] Indexed 7352 samples in 0.263s...\n",
            "[t-SNE] Computed neighbors for 7352 samples in 48.983s...\n",
            "[t-SNE] Computed conditional probabilities for sample 1000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 2000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 3000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 4000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 5000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 6000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 7000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 7352 / 7352\n",
            "[t-SNE] Mean sigma: 0.961265\n",
            "[t-SNE] Computed conditional probabilities in 0.122s\n",
            "[t-SNE] Iteration 50: error = 114.1862640, gradient norm = 0.0184120 (50 iterations in 55.655s)\n",
            "[t-SNE] Iteration 100: error = 97.6535568, gradient norm = 0.0174309 (50 iterations in 12.580s)\n",
            "[t-SNE] Iteration 150: error = 93.1900101, gradient norm = 0.0101048 (50 iterations in 9.180s)\n",
            "[t-SNE] Iteration 200: error = 91.2315445, gradient norm = 0.0074560 (50 iterations in 10.340s)\n",
            "[t-SNE] Iteration 250: error = 90.0714417, gradient norm = 0.0057667 (50 iterations in 9.458s)\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 90.071442\n",
            "[t-SNE] Iteration 300: error = 3.5796804, gradient norm = 0.0014691 (50 iterations in 8.718s)\n",
            "[t-SNE] Iteration 350: error = 2.8173938, gradient norm = 0.0007508 (50 iterations in 10.180s)\n",
            "[t-SNE] Iteration 400: error = 2.4344938, gradient norm = 0.0005251 (50 iterations in 10.506s)\n",
            "[t-SNE] Iteration 450: error = 2.2156141, gradient norm = 0.0004069 (50 iterations in 10.072s)\n",
            "[t-SNE] Iteration 500: error = 2.0703306, gradient norm = 0.0003340 (50 iterations in 10.511s)\n",
            "[t-SNE] Iteration 550: error = 1.9646366, gradient norm = 0.0002816 (50 iterations in 9.792s)\n",
            "[t-SNE] Iteration 600: error = 1.8835558, gradient norm = 0.0002471 (50 iterations in 9.098s)\n",
            "[t-SNE] Iteration 650: error = 1.8184001, gradient norm = 0.0002184 (50 iterations in 8.656s)\n",
            "[t-SNE] Iteration 700: error = 1.7647167, gradient norm = 0.0001961 (50 iterations in 9.063s)\n",
            "[t-SNE] Iteration 750: error = 1.7193680, gradient norm = 0.0001796 (50 iterations in 9.754s)\n",
            "[t-SNE] Iteration 800: error = 1.6803776, gradient norm = 0.0001655 (50 iterations in 9.540s)\n",
            "[t-SNE] Iteration 850: error = 1.6465144, gradient norm = 0.0001538 (50 iterations in 9.953s)\n",
            "[t-SNE] Iteration 900: error = 1.6166563, gradient norm = 0.0001421 (50 iterations in 10.270s)\n",
            "[t-SNE] Iteration 950: error = 1.5901035, gradient norm = 0.0001335 (50 iterations in 6.609s)\n",
            "[t-SNE] Iteration 1000: error = 1.5664237, gradient norm = 0.0001257 (50 iterations in 8.553s)\n",
            "[t-SNE] Error after 1000 iterations: 1.566424\n",
            "Done..\n",
            "Creating plot for this t-sne visualization..\n",
            "saving this plot as image in present working directory...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 731.875x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Done\n",
            "\n",
            "performing tsne with perplexity 10 and with 1000 iterations at max\n",
            "[t-SNE] Computing 31 nearest neighbors...\n",
            "[t-SNE] Indexed 7352 samples in 0.410s...\n",
            "[t-SNE] Computed neighbors for 7352 samples in 64.801s...\n",
            "[t-SNE] Computed conditional probabilities for sample 1000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 2000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 3000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 4000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 5000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 6000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 7000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 7352 / 7352\n",
            "[t-SNE] Mean sigma: 1.133828\n",
            "[t-SNE] Computed conditional probabilities in 0.214s\n",
            "[t-SNE] Iteration 50: error = 106.0169220, gradient norm = 0.0194293 (50 iterations in 24.550s)\n",
            "[t-SNE] Iteration 100: error = 90.3036194, gradient norm = 0.0097653 (50 iterations in 11.936s)\n",
            "[t-SNE] Iteration 150: error = 87.3132935, gradient norm = 0.0053059 (50 iterations in 11.246s)\n",
            "[t-SNE] Iteration 200: error = 86.1169128, gradient norm = 0.0035844 (50 iterations in 11.864s)\n",
            "[t-SNE] Iteration 250: error = 85.4133606, gradient norm = 0.0029100 (50 iterations in 11.944s)\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 85.413361\n",
            "[t-SNE] Iteration 300: error = 3.1394315, gradient norm = 0.0013976 (50 iterations in 11.742s)\n",
            "[t-SNE] Iteration 350: error = 2.4929206, gradient norm = 0.0006466 (50 iterations in 11.627s)\n",
            "[t-SNE] Iteration 400: error = 2.1733041, gradient norm = 0.0004230 (50 iterations in 11.846s)\n",
            "[t-SNE] Iteration 450: error = 1.9884514, gradient norm = 0.0003124 (50 iterations in 11.405s)\n",
            "[t-SNE] Iteration 500: error = 1.8702440, gradient norm = 0.0002514 (50 iterations in 11.320s)\n",
            "[t-SNE] Iteration 550: error = 1.7870129, gradient norm = 0.0002107 (50 iterations in 12.009s)\n",
            "[t-SNE] Iteration 600: error = 1.7246909, gradient norm = 0.0001824 (50 iterations in 10.632s)\n",
            "[t-SNE] Iteration 650: error = 1.6758548, gradient norm = 0.0001590 (50 iterations in 11.270s)\n",
            "[t-SNE] Iteration 700: error = 1.6361949, gradient norm = 0.0001451 (50 iterations in 12.072s)\n",
            "[t-SNE] Iteration 750: error = 1.6034756, gradient norm = 0.0001305 (50 iterations in 11.607s)\n",
            "[t-SNE] Iteration 800: error = 1.5761518, gradient norm = 0.0001188 (50 iterations in 9.409s)\n",
            "[t-SNE] Iteration 850: error = 1.5527289, gradient norm = 0.0001113 (50 iterations in 8.309s)\n",
            "[t-SNE] Iteration 900: error = 1.5328671, gradient norm = 0.0001021 (50 iterations in 9.433s)\n",
            "[t-SNE] Iteration 950: error = 1.5152045, gradient norm = 0.0000974 (50 iterations in 11.488s)\n",
            "[t-SNE] Iteration 1000: error = 1.4999681, gradient norm = 0.0000933 (50 iterations in 10.593s)\n",
            "[t-SNE] Error after 1000 iterations: 1.499968\n",
            "Done..\n",
            "Creating plot for this t-sne visualization..\n",
            "saving this plot as image in present working directory...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 731.875x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Done\n",
            "\n",
            "performing tsne with perplexity 20 and with 1000 iterations at max\n",
            "[t-SNE] Computing 61 nearest neighbors...\n",
            "[t-SNE] Indexed 7352 samples in 0.425s...\n",
            "[t-SNE] Computed neighbors for 7352 samples in 61.792s...\n",
            "[t-SNE] Computed conditional probabilities for sample 1000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 2000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 3000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 4000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 5000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 6000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 7000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 7352 / 7352\n",
            "[t-SNE] Mean sigma: 1.274335\n",
            "[t-SNE] Computed conditional probabilities in 0.355s\n",
            "[t-SNE] Iteration 50: error = 97.5202179, gradient norm = 0.0223863 (50 iterations in 21.168s)\n",
            "[t-SNE] Iteration 100: error = 83.9500732, gradient norm = 0.0059110 (50 iterations in 17.306s)\n",
            "[t-SNE] Iteration 150: error = 81.8804779, gradient norm = 0.0035797 (50 iterations in 14.258s)\n",
            "[t-SNE] Iteration 200: error = 81.1615143, gradient norm = 0.0022536 (50 iterations in 14.130s)\n",
            "[t-SNE] Iteration 250: error = 80.7704086, gradient norm = 0.0018108 (50 iterations in 15.340s)\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 80.770409\n",
            "[t-SNE] Iteration 300: error = 2.6957574, gradient norm = 0.0012993 (50 iterations in 13.605s)\n",
            "[t-SNE] Iteration 350: error = 2.1637220, gradient norm = 0.0005765 (50 iterations in 13.248s)\n",
            "[t-SNE] Iteration 400: error = 1.9143614, gradient norm = 0.0003474 (50 iterations in 14.774s)\n",
            "[t-SNE] Iteration 450: error = 1.7684202, gradient norm = 0.0002458 (50 iterations in 15.502s)\n",
            "[t-SNE] Iteration 500: error = 1.6744757, gradient norm = 0.0001923 (50 iterations in 14.808s)\n",
            "[t-SNE] Iteration 550: error = 1.6101606, gradient norm = 0.0001575 (50 iterations in 14.043s)\n",
            "[t-SNE] Iteration 600: error = 1.5641028, gradient norm = 0.0001344 (50 iterations in 15.769s)\n",
            "[t-SNE] Iteration 650: error = 1.5291905, gradient norm = 0.0001182 (50 iterations in 15.834s)\n",
            "[t-SNE] Iteration 700: error = 1.5024391, gradient norm = 0.0001055 (50 iterations in 15.398s)\n",
            "[t-SNE] Iteration 750: error = 1.4809053, gradient norm = 0.0000965 (50 iterations in 14.594s)\n",
            "[t-SNE] Iteration 800: error = 1.4631859, gradient norm = 0.0000884 (50 iterations in 15.025s)\n",
            "[t-SNE] Iteration 850: error = 1.4486470, gradient norm = 0.0000832 (50 iterations in 14.060s)\n",
            "[t-SNE] Iteration 900: error = 1.4367288, gradient norm = 0.0000804 (50 iterations in 12.389s)\n",
            "[t-SNE] Iteration 950: error = 1.4270191, gradient norm = 0.0000761 (50 iterations in 10.392s)\n",
            "[t-SNE] Iteration 1000: error = 1.4189968, gradient norm = 0.0000787 (50 iterations in 12.355s)\n",
            "[t-SNE] Error after 1000 iterations: 1.418997\n",
            "Done..\n",
            "Creating plot for this t-sne visualization..\n",
            "saving this plot as image in present working directory...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 731.875x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Done\n",
            "\n",
            "performing tsne with perplexity 50 and with 1000 iterations at max\n",
            "[t-SNE] Computing 151 nearest neighbors...\n",
            "[t-SNE] Indexed 7352 samples in 0.376s...\n",
            "[t-SNE] Computed neighbors for 7352 samples in 73.164s...\n",
            "[t-SNE] Computed conditional probabilities for sample 1000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 2000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 3000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 4000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 5000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 6000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 7000 / 7352\n",
            "[t-SNE] Computed conditional probabilities for sample 7352 / 7352\n",
            "[t-SNE] Mean sigma: 1.437672\n",
            "[t-SNE] Computed conditional probabilities in 0.844s\n",
            "[t-SNE] Iteration 50: error = 86.1525574, gradient norm = 0.0242986 (50 iterations in 36.249s)\n",
            "[t-SNE] Iteration 100: error = 75.9874649, gradient norm = 0.0061005 (50 iterations in 30.453s)\n",
            "[t-SNE] Iteration 150: error = 74.7072296, gradient norm = 0.0024708 (50 iterations in 28.461s)\n",
            "[t-SNE] Iteration 200: error = 74.2736282, gradient norm = 0.0018644 (50 iterations in 27.735s)\n",
            "[t-SNE] Iteration 250: error = 74.0722427, gradient norm = 0.0014078 (50 iterations in 26.835s)\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 74.072243\n",
            "[t-SNE] Iteration 300: error = 2.1539080, gradient norm = 0.0011796 (50 iterations in 25.445s)\n",
            "[t-SNE] Iteration 350: error = 1.7567128, gradient norm = 0.0004845 (50 iterations in 21.282s)\n",
            "[t-SNE] Iteration 400: error = 1.5888531, gradient norm = 0.0002798 (50 iterations in 21.015s)\n",
            "[t-SNE] Iteration 450: error = 1.4956820, gradient norm = 0.0001894 (50 iterations in 23.332s)\n",
            "[t-SNE] Iteration 500: error = 1.4359720, gradient norm = 0.0001420 (50 iterations in 23.083s)\n",
            "[t-SNE] Iteration 550: error = 1.3947564, gradient norm = 0.0001117 (50 iterations in 19.626s)\n",
            "[t-SNE] Iteration 600: error = 1.3653858, gradient norm = 0.0000949 (50 iterations in 22.752s)\n",
            "[t-SNE] Iteration 650: error = 1.3441534, gradient norm = 0.0000814 (50 iterations in 23.972s)\n",
            "[t-SNE] Iteration 700: error = 1.3284039, gradient norm = 0.0000742 (50 iterations in 20.636s)\n",
            "[t-SNE] Iteration 750: error = 1.3171139, gradient norm = 0.0000700 (50 iterations in 20.407s)\n",
            "[t-SNE] Iteration 800: error = 1.3085558, gradient norm = 0.0000657 (50 iterations in 24.951s)\n",
            "[t-SNE] Iteration 850: error = 1.3017821, gradient norm = 0.0000603 (50 iterations in 24.719s)\n",
            "[t-SNE] Iteration 900: error = 1.2962619, gradient norm = 0.0000586 (50 iterations in 24.500s)\n",
            "[t-SNE] Iteration 950: error = 1.2914882, gradient norm = 0.0000573 (50 iterations in 24.132s)\n",
            "[t-SNE] Iteration 1000: error = 1.2874244, gradient norm = 0.0000546 (50 iterations in 22.840s)\n",
            "[t-SNE] Error after 1000 iterations: 1.287424\n",
            "Done..\n",
            "Creating plot for this t-sne visualization..\n",
            "saving this plot as image in present working directory...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 731.875x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07LU9FrFgGZK",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpKc-aeGsgzq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANDQGCWgsgzy",
        "colab_type": "text"
      },
      "source": [
        "## Obtain the train and test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "j7waRBSjsgz0",
        "colab_type": "code",
        "outputId": "4c6cb0e6-e65b-4cc7-f5c3-7899fefe955c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "train = pd.read_csv('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/csv_files/train.csv')\n",
        "test = pd.read_csv('drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/csv_files/test.csv')\n",
        "print(train.shape, test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(7352, 564) (2947, 564)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "UVU1VHTesgz7",
        "colab_type": "code",
        "outputId": "784b715b-a7ad-4fdb-f0ce-91a76fc254ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "train.head(3)"
      ],
      "execution_count": 0,
      "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>tBodyAccmeanX</th>\n",
              "      <th>tBodyAccmeanY</th>\n",
              "      <th>tBodyAccmeanZ</th>\n",
              "      <th>tBodyAccstdX</th>\n",
              "      <th>tBodyAccstdY</th>\n",
              "      <th>tBodyAccstdZ</th>\n",
              "      <th>tBodyAccmadX</th>\n",
              "      <th>tBodyAccmadY</th>\n",
              "      <th>tBodyAccmadZ</th>\n",
              "      <th>tBodyAccmaxX</th>\n",
              "      <th>tBodyAccmaxY</th>\n",
              "      <th>tBodyAccmaxZ</th>\n",
              "      <th>tBodyAccminX</th>\n",
              "      <th>tBodyAccminY</th>\n",
              "      <th>tBodyAccminZ</th>\n",
              "      <th>tBodyAccsma</th>\n",
              "      <th>tBodyAccenergyX</th>\n",
              "      <th>tBodyAccenergyY</th>\n",
              "      <th>tBodyAccenergyZ</th>\n",
              "      <th>tBodyAcciqrX</th>\n",
              "      <th>tBodyAcciqrY</th>\n",
              "      <th>tBodyAcciqrZ</th>\n",
              "      <th>tBodyAccentropyX</th>\n",
              "      <th>tBodyAccentropyY</th>\n",
              "      <th>tBodyAccentropyZ</th>\n",
              "      <th>tBodyAccarCoeffX1</th>\n",
              "      <th>tBodyAccarCoeffX2</th>\n",
              "      <th>tBodyAccarCoeffX3</th>\n",
              "      <th>tBodyAccarCoeffX4</th>\n",
              "      <th>tBodyAccarCoeffY1</th>\n",
              "      <th>tBodyAccarCoeffY2</th>\n",
              "      <th>tBodyAccarCoeffY3</th>\n",
              "      <th>tBodyAccarCoeffY4</th>\n",
              "      <th>tBodyAccarCoeffZ1</th>\n",
              "      <th>tBodyAccarCoeffZ2</th>\n",
              "      <th>tBodyAccarCoeffZ3</th>\n",
              "      <th>tBodyAccarCoeffZ4</th>\n",
              "      <th>tBodyAcccorrelationXY</th>\n",
              "      <th>tBodyAcccorrelationXZ</th>\n",
              "      <th>tBodyAcccorrelationYZ</th>\n",
              "      <th>...</th>\n",
              "      <th>fBodyBodyAccJerkMagmaxInds</th>\n",
              "      <th>fBodyBodyAccJerkMagmeanFreq</th>\n",
              "      <th>fBodyBodyAccJerkMagskewness</th>\n",
              "      <th>fBodyBodyAccJerkMagkurtosis</th>\n",
              "      <th>fBodyBodyGyroMagmean</th>\n",
              "      <th>fBodyBodyGyroMagstd</th>\n",
              "      <th>fBodyBodyGyroMagmad</th>\n",
              "      <th>fBodyBodyGyroMagmax</th>\n",
              "      <th>fBodyBodyGyroMagmin</th>\n",
              "      <th>fBodyBodyGyroMagsma</th>\n",
              "      <th>fBodyBodyGyroMagenergy</th>\n",
              "      <th>fBodyBodyGyroMagiqr</th>\n",
              "      <th>fBodyBodyGyroMagentropy</th>\n",
              "      <th>fBodyBodyGyroMagmaxInds</th>\n",
              "      <th>fBodyBodyGyroMagmeanFreq</th>\n",
              "      <th>fBodyBodyGyroMagskewness</th>\n",
              "      <th>fBodyBodyGyroMagkurtosis</th>\n",
              "      <th>fBodyBodyGyroJerkMagmean</th>\n",
              "      <th>fBodyBodyGyroJerkMagstd</th>\n",
              "      <th>fBodyBodyGyroJerkMagmad</th>\n",
              "      <th>fBodyBodyGyroJerkMagmax</th>\n",
              "      <th>fBodyBodyGyroJerkMagmin</th>\n",
              "      <th>fBodyBodyGyroJerkMagsma</th>\n",
              "      <th>fBodyBodyGyroJerkMagenergy</th>\n",
              "      <th>fBodyBodyGyroJerkMagiqr</th>\n",
              "      <th>fBodyBodyGyroJerkMagentropy</th>\n",
              "      <th>fBodyBodyGyroJerkMagmaxInds</th>\n",
              "      <th>fBodyBodyGyroJerkMagmeanFreq</th>\n",
              "      <th>fBodyBodyGyroJerkMagskewness</th>\n",
              "      <th>fBodyBodyGyroJerkMagkurtosis</th>\n",
              "      <th>angletBodyAccMeangravity</th>\n",
              "      <th>angletBodyAccJerkMeangravityMean</th>\n",
              "      <th>angletBodyGyroMeangravityMean</th>\n",
              "      <th>angletBodyGyroJerkMeangravityMean</th>\n",
              "      <th>angleXgravityMean</th>\n",
              "      <th>angleYgravityMean</th>\n",
              "      <th>angleZgravityMean</th>\n",
              "      <th>subject</th>\n",
              "      <th>Activity</th>\n",
              "      <th>ActivityName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.288585</td>\n",
              "      <td>-0.020294</td>\n",
              "      <td>-0.132905</td>\n",
              "      <td>-0.995279</td>\n",
              "      <td>-0.983111</td>\n",
              "      <td>-0.913526</td>\n",
              "      <td>-0.995112</td>\n",
              "      <td>-0.983185</td>\n",
              "      <td>-0.923527</td>\n",
              "      <td>-0.934724</td>\n",
              "      <td>-0.567378</td>\n",
              "      <td>-0.744413</td>\n",
              "      <td>0.852947</td>\n",
              "      <td>0.685845</td>\n",
              "      <td>0.814263</td>\n",
              "      <td>-0.965523</td>\n",
              "      <td>-0.999945</td>\n",
              "      <td>-0.999863</td>\n",
              "      <td>-0.994612</td>\n",
              "      <td>-0.994231</td>\n",
              "      <td>-0.987614</td>\n",
              "      <td>-0.943220</td>\n",
              "      <td>-0.407747</td>\n",
              "      <td>-0.679338</td>\n",
              "      <td>-0.602122</td>\n",
              "      <td>0.929294</td>\n",
              "      <td>-0.853011</td>\n",
              "      <td>0.359910</td>\n",
              "      <td>-0.058526</td>\n",
              "      <td>0.256892</td>\n",
              "      <td>-0.224848</td>\n",
              "      <td>0.264106</td>\n",
              "      <td>-0.095246</td>\n",
              "      <td>0.278851</td>\n",
              "      <td>-0.465085</td>\n",
              "      <td>0.491936</td>\n",
              "      <td>-0.190884</td>\n",
              "      <td>0.376314</td>\n",
              "      <td>0.435129</td>\n",
              "      <td>0.660790</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.936508</td>\n",
              "      <td>0.346989</td>\n",
              "      <td>-0.516080</td>\n",
              "      <td>-0.802760</td>\n",
              "      <td>-0.980135</td>\n",
              "      <td>-0.961309</td>\n",
              "      <td>-0.973653</td>\n",
              "      <td>-0.952264</td>\n",
              "      <td>-0.989498</td>\n",
              "      <td>-0.980135</td>\n",
              "      <td>-0.999240</td>\n",
              "      <td>-0.992656</td>\n",
              "      <td>-0.701291</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.128989</td>\n",
              "      <td>0.586156</td>\n",
              "      <td>0.374605</td>\n",
              "      <td>-0.991990</td>\n",
              "      <td>-0.990697</td>\n",
              "      <td>-0.989941</td>\n",
              "      <td>-0.992448</td>\n",
              "      <td>-0.991048</td>\n",
              "      <td>-0.991990</td>\n",
              "      <td>-0.999937</td>\n",
              "      <td>-0.990458</td>\n",
              "      <td>-0.871306</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.074323</td>\n",
              "      <td>-0.298676</td>\n",
              "      <td>-0.710304</td>\n",
              "      <td>-0.112754</td>\n",
              "      <td>0.030400</td>\n",
              "      <td>-0.464761</td>\n",
              "      <td>-0.018446</td>\n",
              "      <td>-0.841247</td>\n",
              "      <td>0.179941</td>\n",
              "      <td>-0.058627</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>STANDING</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.278419</td>\n",
              "      <td>-0.016411</td>\n",
              "      <td>-0.123520</td>\n",
              "      <td>-0.998245</td>\n",
              "      <td>-0.975300</td>\n",
              "      <td>-0.960322</td>\n",
              "      <td>-0.998807</td>\n",
              "      <td>-0.974914</td>\n",
              "      <td>-0.957686</td>\n",
              "      <td>-0.943068</td>\n",
              "      <td>-0.557851</td>\n",
              "      <td>-0.818409</td>\n",
              "      <td>0.849308</td>\n",
              "      <td>0.685845</td>\n",
              "      <td>0.822637</td>\n",
              "      <td>-0.981930</td>\n",
              "      <td>-0.999991</td>\n",
              "      <td>-0.999788</td>\n",
              "      <td>-0.998405</td>\n",
              "      <td>-0.999150</td>\n",
              "      <td>-0.977866</td>\n",
              "      <td>-0.948225</td>\n",
              "      <td>-0.714892</td>\n",
              "      <td>-0.500930</td>\n",
              "      <td>-0.570979</td>\n",
              "      <td>0.611627</td>\n",
              "      <td>-0.329549</td>\n",
              "      <td>0.284213</td>\n",
              "      <td>0.284595</td>\n",
              "      <td>0.115705</td>\n",
              "      <td>-0.090963</td>\n",
              "      <td>0.294310</td>\n",
              "      <td>-0.281211</td>\n",
              "      <td>0.085988</td>\n",
              "      <td>-0.022153</td>\n",
              "      <td>-0.016657</td>\n",
              "      <td>-0.220643</td>\n",
              "      <td>-0.013429</td>\n",
              "      <td>-0.072692</td>\n",
              "      <td>0.579382</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.841270</td>\n",
              "      <td>0.532061</td>\n",
              "      <td>-0.624871</td>\n",
              "      <td>-0.900160</td>\n",
              "      <td>-0.988296</td>\n",
              "      <td>-0.983322</td>\n",
              "      <td>-0.982659</td>\n",
              "      <td>-0.986321</td>\n",
              "      <td>-0.991829</td>\n",
              "      <td>-0.988296</td>\n",
              "      <td>-0.999811</td>\n",
              "      <td>-0.993979</td>\n",
              "      <td>-0.720683</td>\n",
              "      <td>-0.948718</td>\n",
              "      <td>-0.271958</td>\n",
              "      <td>-0.336310</td>\n",
              "      <td>-0.720015</td>\n",
              "      <td>-0.995854</td>\n",
              "      <td>-0.996399</td>\n",
              "      <td>-0.995442</td>\n",
              "      <td>-0.996866</td>\n",
              "      <td>-0.994440</td>\n",
              "      <td>-0.995854</td>\n",
              "      <td>-0.999981</td>\n",
              "      <td>-0.994544</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.158075</td>\n",
              "      <td>-0.595051</td>\n",
              "      <td>-0.861499</td>\n",
              "      <td>0.053477</td>\n",
              "      <td>-0.007435</td>\n",
              "      <td>-0.732626</td>\n",
              "      <td>0.703511</td>\n",
              "      <td>-0.844788</td>\n",
              "      <td>0.180289</td>\n",
              "      <td>-0.054317</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>STANDING</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.279653</td>\n",
              "      <td>-0.019467</td>\n",
              "      <td>-0.113462</td>\n",
              "      <td>-0.995380</td>\n",
              "      <td>-0.967187</td>\n",
              "      <td>-0.978944</td>\n",
              "      <td>-0.996520</td>\n",
              "      <td>-0.963668</td>\n",
              "      <td>-0.977469</td>\n",
              "      <td>-0.938692</td>\n",
              "      <td>-0.557851</td>\n",
              "      <td>-0.818409</td>\n",
              "      <td>0.843609</td>\n",
              "      <td>0.682401</td>\n",
              "      <td>0.839344</td>\n",
              "      <td>-0.983478</td>\n",
              "      <td>-0.999969</td>\n",
              "      <td>-0.999660</td>\n",
              "      <td>-0.999470</td>\n",
              "      <td>-0.997130</td>\n",
              "      <td>-0.964810</td>\n",
              "      <td>-0.974675</td>\n",
              "      <td>-0.592235</td>\n",
              "      <td>-0.485821</td>\n",
              "      <td>-0.570979</td>\n",
              "      <td>0.273025</td>\n",
              "      <td>-0.086309</td>\n",
              "      <td>0.337202</td>\n",
              "      <td>-0.164739</td>\n",
              "      <td>0.017150</td>\n",
              "      <td>-0.074507</td>\n",
              "      <td>0.342256</td>\n",
              "      <td>-0.332564</td>\n",
              "      <td>0.239281</td>\n",
              "      <td>-0.136204</td>\n",
              "      <td>0.173863</td>\n",
              "      <td>-0.299493</td>\n",
              "      <td>-0.124698</td>\n",
              "      <td>-0.181105</td>\n",
              "      <td>0.608900</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.904762</td>\n",
              "      <td>0.660795</td>\n",
              "      <td>-0.724697</td>\n",
              "      <td>-0.928539</td>\n",
              "      <td>-0.989255</td>\n",
              "      <td>-0.986028</td>\n",
              "      <td>-0.984274</td>\n",
              "      <td>-0.990979</td>\n",
              "      <td>-0.995703</td>\n",
              "      <td>-0.989255</td>\n",
              "      <td>-0.999854</td>\n",
              "      <td>-0.993238</td>\n",
              "      <td>-0.736521</td>\n",
              "      <td>-0.794872</td>\n",
              "      <td>-0.212728</td>\n",
              "      <td>-0.535352</td>\n",
              "      <td>-0.871914</td>\n",
              "      <td>-0.995031</td>\n",
              "      <td>-0.995127</td>\n",
              "      <td>-0.994640</td>\n",
              "      <td>-0.996060</td>\n",
              "      <td>-0.995866</td>\n",
              "      <td>-0.995031</td>\n",
              "      <td>-0.999973</td>\n",
              "      <td>-0.993755</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>0.414503</td>\n",
              "      <td>-0.390748</td>\n",
              "      <td>-0.760104</td>\n",
              "      <td>-0.118559</td>\n",
              "      <td>0.177899</td>\n",
              "      <td>0.100699</td>\n",
              "      <td>0.808529</td>\n",
              "      <td>-0.848933</td>\n",
              "      <td>0.180637</td>\n",
              "      <td>-0.049118</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>STANDING</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 564 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   tBodyAccmeanX  tBodyAccmeanY  tBodyAccmeanZ  ...  subject  Activity  ActivityName\n",
              "0       0.288585      -0.020294      -0.132905  ...        1         5      STANDING\n",
              "1       0.278419      -0.016411      -0.123520  ...        1         5      STANDING\n",
              "2       0.279653      -0.019467      -0.113462  ...        1         5      STANDING\n",
              "\n",
              "[3 rows x 564 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8QomrpCsg0C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get X_train and y_train from csv files\n",
        "X_train = train.drop(['subject', 'Activity', 'ActivityName'], axis=1)\n",
        "y_train = train.ActivityName"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Uwwynupsg0H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get X_test and y_test from test csv file\n",
        "X_test = test.drop(['subject', 'Activity', 'ActivityName'], axis=1)\n",
        "y_test = test.ActivityName"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pjsTW2Qsg0M",
        "colab_type": "code",
        "outputId": "7404b66f-1e97-462d-cf87-c3fb51497572",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print('X_train and y_train : ({},{})'.format(X_train.shape, y_train.shape))\n",
        "print('X_test  and y_test  : ({},{})'.format(X_test.shape, y_test.shape))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train and y_train : ((7352, 561),(7352,))\n",
            "X_test  and y_test  : ((2947, 561),(2947,))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kr1gocbfsg0R",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U6vb-9Xcsg0T",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iIXJ0VRJsg0V",
        "colab_type": "text"
      },
      "source": [
        "# Let's model with our data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVepbh02sg0W",
        "colab_type": "text"
      },
      "source": [
        "### Labels that are useful in plotting confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cp3dqQFgsg0Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels=['LAYING', 'SITTING','STANDING','WALKING','WALKING_DOWNSTAIRS','WALKING_UPSTAIRS']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ulGcJN3sg0d",
        "colab_type": "text"
      },
      "source": [
        "### Function to plot the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9THJaWAasg0f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "plt.rcParams[\"font.family\"] = 'DejaVu Sans'\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=90)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5454FGBQsg0l",
        "colab_type": "text"
      },
      "source": [
        "### Generic function to run any model specified"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pF6cP86zsg0n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from datetime import datetime\n",
        "def perform_model(model, X_train, y_train, X_test, y_test, class_labels, cm_normalize=True, \\\n",
        "                 print_cm=True, cm_cmap=plt.cm.Greens):\n",
        "    \n",
        "    \n",
        "    # to store results at various phases\n",
        "    results = dict()\n",
        "    \n",
        "    # time at which model starts training \n",
        "    train_start_time = datetime.now()\n",
        "    print('training the model..')\n",
        "    model.fit(X_train, y_train)\n",
        "    print('Done \\n \\n')\n",
        "    train_end_time = datetime.now()\n",
        "    results['training_time'] =  train_end_time - train_start_time\n",
        "    print('training_time(HH:MM:SS.ms) - {}\\n\\n'.format(results['training_time']))\n",
        "    \n",
        "    \n",
        "    # predict test data\n",
        "    print('Predicting test data')\n",
        "    test_start_time = datetime.now()\n",
        "    y_pred = model.predict(X_test)\n",
        "    test_end_time = datetime.now()\n",
        "    print('Done \\n \\n')\n",
        "    results['testing_time'] = test_end_time - test_start_time\n",
        "    print('testing time(HH:MM:SS:ms) - {}\\n\\n'.format(results['testing_time']))\n",
        "    results['predicted'] = y_pred\n",
        "   \n",
        "\n",
        "    # calculate overall accuracty of the model\n",
        "    accuracy = metrics.accuracy_score(y_true=y_test, y_pred=y_pred)\n",
        "    # store accuracy in results\n",
        "    results['accuracy'] = accuracy\n",
        "    print('---------------------')\n",
        "    print('|      Accuracy      |')\n",
        "    print('---------------------')\n",
        "    print('\\n    {}\\n\\n'.format(accuracy))\n",
        "    \n",
        "    \n",
        "    # confusion matrix\n",
        "    cm = metrics.confusion_matrix(y_test, y_pred)\n",
        "    results['confusion_matrix'] = cm\n",
        "    if print_cm: \n",
        "        print('--------------------')\n",
        "        print('| Confusion Matrix |')\n",
        "        print('--------------------')\n",
        "        print('\\n {}'.format(cm))\n",
        "        \n",
        "    # plot confusin matrix\n",
        "    plt.figure(figsize=(8,8))\n",
        "    plt.grid(b=False)\n",
        "    plot_confusion_matrix(cm, classes=class_labels, normalize=True, title='Normalized confusion matrix', cmap = cm_cmap)\n",
        "    plt.show()\n",
        "    \n",
        "    # get classification report\n",
        "    print('-------------------------')\n",
        "    print('| Classifiction Report |')\n",
        "    print('-------------------------')\n",
        "    classification_report = metrics.classification_report(y_test, y_pred)\n",
        "    # store report in results\n",
        "    results['classification_report'] = classification_report\n",
        "    print(classification_report)\n",
        "    \n",
        "    # add the trained  model to the results\n",
        "    results['model'] = model\n",
        "    \n",
        "    return results\n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22aLqwUlsg0s",
        "colab_type": "text"
      },
      "source": [
        "### Method to print the gridsearch Attributes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRHIbA8Rsg0u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def print_grid_search_attributes(model):\n",
        "    # Estimator that gave highest score among all the estimators formed in GridSearch\n",
        "    print('--------------------------')\n",
        "    print('|      Best Estimator     |')\n",
        "    print('--------------------------')\n",
        "    print('\\n\\t{}\\n'.format(model.best_estimator_))\n",
        "\n",
        "\n",
        "    # parameters that gave best results while performing grid search\n",
        "    print('--------------------------')\n",
        "    print('|     Best parameters     |')\n",
        "    print('--------------------------')\n",
        "    print('\\tParameters of best estimator : \\n\\n\\t{}\\n'.format(model.best_params_))\n",
        "\n",
        "\n",
        "    #  number of cross validation splits\n",
        "    print('---------------------------------')\n",
        "    print('|   No of CrossValidation sets   |')\n",
        "    print('--------------------------------')\n",
        "    print('\\n\\tTotal numbre of cross validation sets: {}\\n'.format(model.n_splits_))\n",
        "\n",
        "\n",
        "    # Average cross validated score of the best estimator, from the Grid Search \n",
        "    print('--------------------------')\n",
        "    print('|        Best Score       |')\n",
        "    print('--------------------------')\n",
        "    print('\\n\\tAverage Cross Validate scores of best estimator : \\n\\n\\t{}\\n'.format(model.best_score_))\n",
        "\n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_KxAedhIsg0z",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-8XBB3Qsg00",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZOcPdLwsg02",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Umy8W4uOsg03",
        "colab_type": "text"
      },
      "source": [
        "# 1. Logistic Regression with Grid Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAqWlO-Zsg04",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn import metrics\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "mfXs69f8sg09",
        "colab_type": "code",
        "outputId": "86c13dfb-3020-4d80-f3ff-d1f4985a682d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "# start Grid search\n",
        "parameters = {'C':[0.01, 0.1, 1, 10, 20, 30], 'penalty':['l2','l1']}\n",
        "log_reg = linear_model.LogisticRegression()\n",
        "log_reg_grid = GridSearchCV(log_reg, param_grid=parameters, cv=3, verbose=1, n_jobs=-1)\n",
        "log_reg_grid_results =  perform_model(log_reg_grid, X_train, y_train, X_test, y_test, class_labels=labels)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training the model..\n",
            "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:  1.8min finished\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done \n",
            " \n",
            "\n",
            "training_time(HH:MM:SS.ms) - 0:02:07.698399\n",
            "\n",
            "\n",
            "Predicting test data\n",
            "Done \n",
            " \n",
            "\n",
            "testing time(HH:MM:SS:ms) - 0:00:00.009011\n",
            "\n",
            "\n",
            "---------------------\n",
            "|      Accuracy      |\n",
            "---------------------\n",
            "\n",
            "    0.9626739056667798\n",
            "\n",
            "\n",
            "--------------------\n",
            "| Confusion Matrix |\n",
            "--------------------\n",
            "\n",
            " [[537   0   0   0   0   0]\n",
            " [  1 428  58   0   0   4]\n",
            " [  0  12 519   1   0   0]\n",
            " [  0   0   0 495   1   0]\n",
            " [  0   0   0   3 409   8]\n",
            " [  0   0   0  22   0 449]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hERERkUJPi4JFRERskRYFi4iIiBQsqtCIiIjYoqJVoFGFRkRERAo/VWgkT23fu5Nzly7k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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-------------------------\n",
            "| Classifiction Report |\n",
            "-------------------------\n",
            "                    precision    recall  f1-score   support\n",
            "\n",
            "            LAYING       1.00      1.00      1.00       537\n",
            "           SITTING       0.97      0.87      0.92       491\n",
            "          STANDING       0.90      0.98      0.94       532\n",
            "           WALKING       0.95      1.00      0.97       496\n",
            "WALKING_DOWNSTAIRS       1.00      0.97      0.99       420\n",
            "  WALKING_UPSTAIRS       0.97      0.95      0.96       471\n",
            "\n",
            "          accuracy                           0.96      2947\n",
            "         macro avg       0.97      0.96      0.96      2947\n",
            "      weighted avg       0.96      0.96      0.96      2947\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSzIZgaFsg1C",
        "colab_type": "code",
        "outputId": "6b57c162-da2b-4ea1-c20b-f3b6a1324ca8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        }
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.grid(b=False)\n",
        "plot_confusion_matrix(log_reg_grid_results['confusion_matrix'], classes=labels, cmap=plt.cm.Greens, )\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GRETECqlyIyIiIpJLqHIjIiJihSy4cKPKjYiIiFgWVW5ERESskObciIiIiOQS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\nVFjcEBERkaj8H77niGsYPckWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3m2_QJxzsg1H",
        "colab_type": "code",
        "outputId": "6460a59e-da35-458e-d385-18527cff1cce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "# observe the attributes of the model \n",
        "print_grid_search_attributes(log_reg_grid_results['model'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------------------------\n",
            "|      Best Estimator     |\n",
            "--------------------------\n",
            "\n",
            "\tLogisticRegression(C=30, class_weight=None, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
            "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
            "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
            "                   warm_start=False)\n",
            "\n",
            "--------------------------\n",
            "|     Best parameters     |\n",
            "--------------------------\n",
            "\tParameters of best estimator : \n",
            "\n",
            "\t{'C': 30, 'penalty': 'l2'}\n",
            "\n",
            "---------------------------------\n",
            "|   No of CrossValidation sets   |\n",
            "--------------------------------\n",
            "\n",
            "\tTotal numbre of cross validation sets: 3\n",
            "\n",
            "--------------------------\n",
            "|        Best Score       |\n",
            "--------------------------\n",
            "\n",
            "\tAverage Cross Validate scores of best estimator : \n",
            "\n",
            "\t0.9461371055495104\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWh4hH2usg1M",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3jWUcyCsg1O",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pV-S_8zjsg1P",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "pF09bKCXsg1S",
        "colab_type": "text"
      },
      "source": [
        "#  2. Linear SVC with GridSearch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "m3PCVs1_sg1d",
        "colab_type": "code",
        "outputId": "6e6836dc-aa63-45ec-8ce3-d0f9742859e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "parameters = {'C':[0.125, 0.5, 1, 2, 8, 16]}\n",
        "lr_svc = LinearSVC(tol=0.00005)\n",
        "lr_svc_grid = GridSearchCV(lr_svc, param_grid=parameters, n_jobs=-1, verbose=1)\n",
        "lr_svc_grid_results = perform_model(lr_svc_grid, X_train, y_train, X_test, y_test, class_labels=labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training the model..\n",
            "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n",
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:   32.8s finished\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done \n",
            " \n",
            "\n",
            "training_time(HH:MM:SS.ms) - 0:00:40.865910\n",
            "\n",
            "\n",
            "Predicting test data\n",
            "Done \n",
            " \n",
            "\n",
            "testing time(HH:MM:SS:ms) - 0:00:00.010694\n",
            "\n",
            "\n",
            "---------------------\n",
            "|      Accuracy      |\n",
            "---------------------\n",
            "\n",
            "    0.9667458432304038\n",
            "\n",
            "\n",
            "--------------------\n",
            "| Confusion Matrix |\n",
            "--------------------\n",
            "\n",
            " [[537   0   0   0   0   0]\n",
            " [  2 430  56   0   0   3]\n",
            " [  0  11 520   1   0   0]\n",
            " [  0   0   0 496   0   0]\n",
            " [  0   0   0   2 412   6]\n",
            " [  0   0   0  17   0 454]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vX1asWEGJEiWoVq0a/fr1Y+zYsQAsXLiQ69evM2zYMABWr17NggULSE1NxdHR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\nIYQQQug9KWiEEEIIofekoBFCCCGE3vs/CzSRZaXtITwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-------------------------\n",
            "| Classifiction Report |\n",
            "-------------------------\n",
            "                    precision    recall  f1-score   support\n",
            "\n",
            "            LAYING       1.00      1.00      1.00       537\n",
            "           SITTING       0.98      0.88      0.92       491\n",
            "          STANDING       0.90      0.98      0.94       532\n",
            "           WALKING       0.96      1.00      0.98       496\n",
            "WALKING_DOWNSTAIRS       1.00      0.98      0.99       420\n",
            "  WALKING_UPSTAIRS       0.98      0.96      0.97       471\n",
            "\n",
            "          accuracy                           0.97      2947\n",
            "         macro avg       0.97      0.97      0.97      2947\n",
            "      weighted avg       0.97      0.97      0.97      2947\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDlMuxG6sg1m",
        "colab_type": "code",
        "outputId": "e67331d8-34e2-495b-df42-74410addbb2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "source": [
        "print_grid_search_attributes(lr_svc_grid_results['model'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------------------------\n",
            "|      Best Estimator     |\n",
            "--------------------------\n",
            "\n",
            "\tLinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
            "          intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
            "          multi_class='ovr', penalty='l2', random_state=None, tol=5e-05,\n",
            "          verbose=0)\n",
            "\n",
            "--------------------------\n",
            "|     Best parameters     |\n",
            "--------------------------\n",
            "\tParameters of best estimator : \n",
            "\n",
            "\t{'C': 1}\n",
            "\n",
            "---------------------------------\n",
            "|   No of CrossValidation sets   |\n",
            "--------------------------------\n",
            "\n",
            "\tTotal numbre of cross validation sets: 3\n",
            "\n",
            "--------------------------\n",
            "|        Best Score       |\n",
            "--------------------------\n",
            "\n",
            "\tAverage Cross Validate scores of best estimator : \n",
            "\n",
            "\t0.9457290533188248\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_Mwc_p1sg1q",
        "colab_type": "text"
      },
      "source": [
        "# 3.  Kernel SVM with GridSearch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "bqxImpvAsg1s",
        "colab_type": "code",
        "outputId": "cf5a30a5-f3fa-4621-aa33-61b0e57d4379",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "parameters = {'C':[2,8,16],\\\n",
        "              'gamma': [ 0.0078125, 0.125, 2]}\n",
        "rbf_svm = SVC(kernel='rbf')\n",
        "rbf_svm_grid = GridSearchCV(rbf_svm,param_grid=parameters, n_jobs=-1)\n",
        "rbf_svm_grid_results = perform_model(rbf_svm_grid, X_train, y_train, X_test, y_test, class_labels=labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training the model..\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done \n",
            " \n",
            "\n",
            "training_time(HH:MM:SS.ms) - 0:03:05.890991\n",
            "\n",
            "\n",
            "Predicting test data\n",
            "Done \n",
            " \n",
            "\n",
            "testing time(HH:MM:SS:ms) - 0:00:02.659645\n",
            "\n",
            "\n",
            "---------------------\n",
            "|      Accuracy      |\n",
            "---------------------\n",
            "\n",
            "    0.9626739056667798\n",
            "\n",
            "\n",
            "--------------------\n",
            "| Confusion Matrix |\n",
            "--------------------\n",
            "\n",
            " [[537   0   0   0   0   0]\n",
            " [  0 441  48   0   0   2]\n",
            " [  0  12 520   0   0   0]\n",
            " [  0   0   0 489   2   5]\n",
            " [  0   0   0   4 397  19]\n",
            " [  0   0   0  17   1 453]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vqlhs/3ejCo2IiIjke5oULCIi4og0KVhERETEtqhCIyIi4ojsq0CjCo2IiIjkf6rQyEO1\nfutGTp47nXvHfKxmxarWDkFEHMjNGzezXWr/MPyTG27aIg1o5KE6ee40PT4eZu0wLOra6sO5dxIR\neUiOHLr/56Y5Mg1oREREHIzBYFCFRkRERPI/OxvPaFKwiIiI5H+q0IiIiDggJzsr0ahCIyIiIvme\nKjQiIiIOyN4mBatCIyIiIvmeKjQiIiIOxoAqNCIiIiI2RxUaERERR2OwvwqNBjQiIiIOyM7GMzrl\nJCIiIvmfKjQiIiIOx/6e5aQKjYiIiOR7qtCIiIg4IFVoRERERGyMKjQiIiIOxgAYUIVGRERExKao\nQiMiIuKANIdGJI/MGzad1KW/sHfO+hz7zHxjPL8t/IE9n63Dv0JV0/oezUM5vPB7Di/8nh7NQ/Mi\n3L9l7eq1VPOpgW8lP6ZNmZ6tPT09ne5de+BbyY9G9QM4lnjM1DZt8jR8K/lRzacG69asy8uwH4hy\nVI7K0QYZsm6sZ6mXNWhAIzZr4dpwWr3VPcf21nUDqehdloqvNKTvh28ya+B7ALgXduOdsCHUGxBM\n3f5teSdsCG6FiuZV2PfNaDQyeOBQlkdHEbf3Z8KXhHNg/wGzPgvnf467uxv7Du1lwOD+jB71NgAH\n9h8gfGkEu+NjWRGzjEEDhmA0Gq2Rxj0pxyzKUTmK5WlAIzbr+707SLt0Psf2dvVb8MX6CAB2HNiN\nW6EilPAoTsvaAaz7+XvOXTrP+csXWPfz97Sq0ySPor5/u3bGUr58OcqWK4urqyudOocSvSLarE/0\nimi6hXUDoGNIBzZv3ExmZibRK6Lp1DmUggUL8nTZpylfvhy7dsZaIYt7U45ZlKNytEVOBoPFXlbJ\nxyrvKvIQeD9RguOnkk3LJ86cxPuJEngXK8Hx03esL1bCGiHeU3JyMqVKlzIte5fyJin5ZI59XFxc\nKFK0CGfPniUp+WS2bZOTk7E1yjF7H+WoHMUyNKDJJ/z9/XNsa9euHUOGDDEtL126lMGDB5uWL1++\nTLNmzTh+/DgjR45k9erVAISFhdGxY0dTv7179xIWFmZajo+PJywsjBYtWtChQwf69u3LoUOHHmZa\nIiJiBYb/PfrAUi9r0IAmnzty5AgZGRnExsZy9epVADp16kRKSgo//fQTADNnziQkJITSpUtn2z4t\nLY0tW7ZkW3/mzBkGDx7MkCFDWLt2LVFRUfTt25fjx49bNqEHkHQmhdLFvUzLpZ4oSdKZFJLOplD6\nyTvWn02xRoj35OXlxYnjJ0zLSSeS8PYqmWOfW7ducfHCRYoVK4a3V8ls23p5eWFrlGP2PspROYpl\naECTz0VHR/PCCy/QsGFDNmzYAGRdijdu3DgmTZrE3r172b59O717977r9r1792b27NnZ1i9evJj2\n7dtTs2ZN07ratWvTrFkzyyTyN6zYtpYezbKuYKpXpSYXrlwiJe0Ua2K30KJWY9wKFcWtUFFa1GrM\nmtjsgzZrq12nFgkJR0g8msiNGzcIXxpBUHCQWZ+g4CC+XPQlAJHfRhHQNACDwUBQcBDhSyNIT08n\n8WgiCQlHqFO3tjXSuCflmEU5KkdbZG8VGt2HJp9btWoVCxYs4Pfff2fx4sUEBwcDULlyZRo2bMgr\nr7zCp59+iqur6123r1GjBuvWrWP79u08/vjjpvUJCQm0b98+T3LIyVdvfUyTavV5oqgHx7/axTtf\nvE8Bl6yv7GfRi1m1cyNt6gWS8PkPXE2/Ts/pQwE4d+k8E76cya6PYwAY/+WHnLvH5GJrcXFxYcbM\n9wlu0w6j0cjLr/TAx9eH8e9MoGbtmrQNDuKVXi/T6+U++Fbyw93dnUVffQ6Aj68PIaEh+PvVwsXF\nhQ8/+gBnZ2crZ5SdclSOylHyiiEzMzPT2kFI7vz9/YmLizNbt3fvXt59912++eYbjEYjTZs2ZcWK\nFbi5uQFw/PhxXnvtNVatWmXaZuTIkTRp0oRWrVoRFhbGiBEjuHz5MrNnz2b48OFMnTqVRYsW0b9/\nf9q3b2+qyHTq1InLly/ToEEDxowZk2Oci5YvocfHwyzwCdiOa6sPWzsEEXEgRw4dxdfH96Hu8+sN\nUQzYMu2h7vN233edR5UqVXJs37p1K++++y4ZGRl06tSJvn37mrUnJyfz5ptvcunSJYxGI8OHDycg\nIOCe76lTTvlYTEwMR48eJTAwkObNm3P58mXWrl1rajcYDDg55X6I69evT3p6Onv27DGtq1ChAvv3\n7zcth4eHM2jQIC5fvvxwkxAREYdiNBoZP348c+fOJSYmhujoaBISEsz6zJo1i9atW7Ns2TJmzJjB\nf/7zn1z3qwFNPpWRkcF3333HihUr2LhxIxs3buTTTz8lOjo6943v4vXXX2fu3Lmm5W7duhEVFcXu\n3btN665fv/6P4xYREdtgrTk08fHxlClThtKlS+Pq6kpQUJBpDujtsf35B/SlS5coXrx4rvloDk0+\nce3aNRo3bmxa7ty5M56ennh6eprW1alThyNHjnDq1Kn7Ovi3CwgIwMPDw7T85JNPMmPGDKZPn05q\nairFihXDzc2Nfv36/fNkRETE6iw5eTctLc3stiBdunShS5cuAKSmplKixF/3BvP09CQ+Pt5s+/79\n+9O7d28WL17MtWvXWLBgQa7vqQFNPnHw4MFs6/r372+27OzszI8//mhaLlWqVLaKzeTJk00/L1q0\nyKwtMjLSbLlGjRosXrz4b8csIiKOycPDI9vvlAcRExNDhw4d6NWrF3FxcYwYMYLo6Oh7TqPQKScR\nEREHY8B6p5w8PT1JSfnr3mCpqalmZxsAIiIiaN26NZB1UUx6ejrnzp275341oBEREZE84+fnR2Ji\nIsePH+fGjRvExMQQGBho1qdkyZJs27YNyLqBbHp6utm0iLvRKScRERGHY8Ub4Lm4MHbsWPr06YPR\naCQkJISKFSsyc+ZMqlatyvPPP8/IkSMZM2YMCxcuxGAwMHny5Fzj1YBGRERE8lRAQEC2+8oMGjTI\n9HOFChX45ptvHmifGtCIiIg4GgNYqUBjMZpDIyIiIvmeKjQiIiIOyFpzaCxFAxoREREHZG8DGp1y\nEhERkXxPFRoREREHYwCcVKERERERsS2q0IiIiDggOyvQqEIjIiIi+Z8qNCIiIo7GoKucRERERGyO\nKjQiIiIOx4AB+6rQaEAjIiLiYAzolJOIiIiIzVGFRkRExAGpQiMiIiJiY1ShERERcUB2VqBRhUZE\nRETyP1Vo5KGqWbEq11YftnYYFvXo67WsHYLFpcxcb+0QLK6oq7u1Q5CHJDMz09oh5EuaQyMiIiJi\nY1ShERERcTR2+OgDDWhEREQcjAGD3Q1odMpJRERE8j1VaERERByQnRVoVKERERGR/E8VGhEREQek\nOTQiIiIiNkYVGhEREQekCo2IiIiIjVGFRkRExNHoxnoiIiKS3xnQZdsiIiIiNkcVGhEREQdkb6ec\nVKERERGRfE8VGhEREYejh1OKiIiI2BxVaERERByQKjQiIiIiNkYVGhEREQdjMNjffWg0oBEREXFA\nOuUkkkfWrl5LNZ8a+FbyY9qU6dna09PT6d61B76V/GhUP4BjicdMbdMmT8O3kh/VfGqwbs26vAz7\ngbT0bcjB8av4beJq3mzVJ1v7Ux5erB8ynz1jl7Fp2Od4u3ma2nrUb8fhCas5PGE1Peq3y8uwH8jG\ntZtoUL0xz1ZtwP9N/zhb+7YfttO8fiu8C5dhZVS0WduSxeHU92tIfb+GLFkcnlchPzBH+K46RI5r\n1lHd15+qlasxfer72drT09MJe6kHVStXo/FzTcxznDKdqpWrUd3Xn3Vr1+dl2PI/GtCITTIajQwe\nOJTl0VHE7f2Z8CXhHNh/wKzPwvmf4+7uxr5DexkwuD+jR70NwIH9BwhfGsHu+FhWxCxj0IAhGI1G\na6RxT04GJz556W1af9QXn3eC6VoniColy5v1md7p33yxfTnVx7dnfPSnvNdxKADujxXlnbb9qPde\nF+q+15l32vbD7bEi1kjjnoxGI6OGjOGrZYvYunsTUeHLOXTgsFkf79LezJzzAR26tDdbfy7tHO9P\nmsGqLSv5bms070+awflz5/My/PviCN9VR8lxyMChLFsZye74WMK/uXuObm5u/HowngGD+jHmrb9y\njFgSwc97drE8OorBNppjNn+ed7LEywo0oBGbtGtnLOXLl6NsubK4urrSqXMo0SvM/3qPXhFNt7Bu\nAHQM6cDmjZvJzMwkekU0nTqHUrBgQZ4u+zTly5dj185YK2Rxb3XLViPh1B8cPXOCm8abfLNrFe2q\nB5r18SlZgY0HdwCw6dAOU3tL3wasO/AT565e4PzVi6w78BOtfBvmeQ65iYv9hbLln6ZM2TK4urrS\nPrQda6LXmvV5qkxpfPx8cHIy/+9o8/otBAQ2wt3DHTd3NwICG7Fp3eY8jP7+OMJ31RFyjL0jx9Au\noUSvjDHrE7Myhu7/y7HD7TmujCG0i3mOsTaYo73TgEZsUnJyMqVKlzIte5fyJin5ZI59XFxcKFK0\nCGfPniUp+WS2bZOTk/Mm8Afg7Vac42kppuUT51Pxdvc067Pn+EE6+jcHoIN/c4o8WgiPx93wdvM0\n3/ZcqtnpKFtxMvkkXt4lTcslvUtw8o7jmPO2KXiV8rpt25KcTE65xxbW4QjfVUfJ0bvUbXF6e5Oc\nlJy9j1mORTl79izJScmUum1bL2/bzPFOBoPBYi9r0KRgGzZr1iyio6NxcnLCycmJ8ePHM336dEaM\nGMH48eO5ceMGFy5c4Pr163h6Zv0yO336NE8++WS29Z988gk9evQgIiICDw8PKlWqRM+ePRk5ciQA\n8+bN4+rVqwwYMACA5cuXM3fuXDIyMnB2dsbPz48333yTIkVs77SGPRseMZWPu77NK8+1Z+tvsZw4\nl4IxIx+UskVE8pgGNDYqLi6OzZs3ExUVhaurK2lpady8edPUHh6eNUEyMjKSX3/9lbFjx5ptn9P6\nP7m6urJ27Vr69u2Lh4eHWdvWrVv5/PPPmTt3Lp6enhiNRqKiojhz5kyeDWi8vLw4cfyEaTnpRBLe\nXiXv2qdUKW9u3brFxQsXKVasGN5eJbNt6+Xlha1JOn+K0h4lTMul3DxJOpdq1ufkhdOEzB4IwOMF\nHyOkZgsuXLtE0vlUmlSq+9e27p5sPrQzbwJ/ACW9SpKc9Ndf8ieTUih5x3HMedsS/LR1223bnuS5\nxvUfeoz/lCN8Vx0lx6QTt8WZlISXt1f2PmY5XqBYsWJ4eXtx4rZtk5NsM8c72dlFTjrlZKtOnz6N\nu7s7rq6uAHh4eJiqLQ+Di4sLXbp04fPPP8/WNnv2bEaMGGF6P2dnZ0JDQylXrtxDe//c1K5Ti4SE\nIyQeTeTGjRuEL40gKDjIrE9QcBBfLvoSgMhvowhoGoDBYCAoOIjwpRGkp6eTeDSRhIQj1KlbO89i\nv1+7EvdSsXgZni7mTQHnArxYpw0r9mwy61OskJupfDuq9avM/zESgDX7fqSFTwPcHiuC22NFaOHT\ngDX7fszzHHJTo1Z1fk84yrHEP7hx4wbLIpbTIqj5fW3bpFkAmzds5fy585w/d57NG7bSpFmAhSN+\ncI7wXXWEHGvdkWPEkgiC2rYx69OmbRsW/y/HqNtzbNuGiCXmOda2wRztnSo0NqpBgwZ88skntGzZ\nkvr169OmTRvq1q2b+4YPoFu3brzwwgv06WN+uXBCQgK+vr4P9b0elIuLCzNmvk9wm3YYjUZefqUH\nPr4+jH9nAjVr16RtcBCv9HqZXi/3wbeSH+7u7iz6Kmtw5uPrQ0hoCP5+tXBxceHDjz7A2dnZqvnc\njTHDSP+vJ7Jm8FycnZyY/2Mk+08m8J8XBhB77FdW7tlEk2fq8l6HoWSSydbDsfT7ejwA565eYELM\nLHa9tRSA8dGfcu7qBWumc1cuLi5M+mACXV/ohtGYQdceXajsU4kp46dRo2Z1WrZtQVzsL/R6sQ/n\nz19g3ap1TJv4AVt/3oi7hztDRg6iVaOsX5xDRw3G3cPdyhll5wjfVUfJ8YOZ7/NCUHuMRiM9XgnL\nynHcBGrW+ivH3q/0oWrlari7u/PFlwuBrBw7dupIzWq1sz4rG83RjBXnuliKITMzM9PaQcjdGY1G\nYmNj2bFjB0uWLGHYsGFERUUxYsQI/Pz8gAc75RQYGGiaQ+Pv709cXBwzZ87ExcWFRx55xDSHpm7d\numzYsIHChQtz6NAhRowYwZVf/eFUAAAgAElEQVQrVxg6dCht2pj/xXKnffv3Ub5S2Yf/YdiQR1+v\nZe0QLC5lpv3fR6Ooq+0NjuTvsfdfY78fTsTX5+H+kfnt99FMOjT/oe7zdosbvEuVKlUstv+70Skn\nG+bs7Ey9evUYOHAgb7/9NmvXrs19owf08ssv8+2333Lt2jXTugoVKrBv3z4AKlWqxPLly2ncuDHX\nr19/6O8vIiJ5z4D9XeWkAY2N+v3330lMTDQtHzhwwCKTzNzc3GjVqhURERGmda+99hpTp04lJeWv\nS2Q1mBERsS/2NqDRHBobdfXqVSZOnMjFixdxdnamTJkyjB8/nkGDBj309+rVqxdffvmlaTkgIIC0\ntDReffVVjEYjRYoUoWLFijRsaHs3bhMREQHNoZGHTHNo7IPm0Eh+Yu+/xiwxhyby+2gmJyx8qPu8\n3efPTtAcGhEREZEHpVNOIiIiDsjeLttWhUZERETyPVVoREREHI1BFRoRERERm6MKjYiIiIMxYH+P\nPtCARkRExAHZ24BGp5xEREQk31OFRkRExAHZWYFGFRoRERHJ/1ShERERcUCaQyMiIiJiY1ShERER\ncTS6sZ6IiIiI7VGFRkRExMEYsL8KjQY0IiIiDsjOxjM65SQiIiL5nyo0IiIiDsf+nuWkCo2IiIjk\ne6rQiIiIOCJVaERERERsiyo0IiIijsYOb6ynAY3IA7r8yQ5rh2BxhdpXtXYIFndtxUFrhyAPib39\nYpa/RwMaERERB2MAnOxsHKgBjYiIiAOyt8qWJgWLiIhIvqcKjYiIiANyUoVGRERExLaoQiMiIuJw\n9OgDEREREZujAY2IiIiDMZA1ALDUKzdbt26lZcuWNG/enDlz5ty1z6pVq2jTpg1BQUEMGzYs133q\nlJOIiIjkGaPRyPjx41mwYAGenp6EhoYSGBhIhQoVTH0SExOZM2cOX3/9NUWLFuXs2bO57lcVGhER\nEUdjyLrKyVKve4mPj6dMmTKULl0aV1dXgoKC2LBhg1mfpUuX0q1bN4oWLQpAsWLFck1JFRoREREH\nZK1JwampqZQoUcK07OnpSXx8vFmfxMREAF588UUyMjLo378/jRs3vud+NaARERGRhyotLY2OHTua\nlrt06UKXLl3ue3uj0cixY8dYtGgRKSkpdO/enZUrV1KkSJEct9GARkRExMFkPcvJchUaDw8PIiMj\n79rm6elJSkqKaTk1NRVPT89sfapXr06BAgUoXbo0Tz/9NImJiVSrVi3H99QcGhEREckzfn5+JCYm\ncvz4cW7cuEFMTAyBgYFmfZo1a8bOnTuBrGpPYmIipUuXvud+VaERERFxQNaaQ+Pi4sLYsWPp06cP\nRqORkJAQKlasyMyZM6latSrPP/88jRo14scff6RNmzY4OzszYsQI3N3d773fPIpfREREBICAgAAC\nAgLM1g0aNMj0s8FgYNSoUYwaNeq+96kBjYiIiAOytzkn9paPiIiIOCBVaERERByMgdxvgJffaEAj\nIiLiaAzWmxRsKTrlJCIiIvmeBjRis9auXks1nxr4VvJj2pTp2drT09Pp3rUHvpX8aFQ/gGOJx0xt\n0yZPw7eSH9V8arBuzbq8DPuBrFuzHn/fWlSrUoP3p36QrT09PZ0eL71CtSo1aNIg0JTjxvUbaViv\nMXX969OwXmM2b9qS16Hft5a1Ajj42SZ+++9W3uz0Rrb2p570Zv27X7Pn4zVsem8J3sX+uiX6lJ5v\n8eun69k/ewMzX/tPXob9QBzhu6oc7SPH21nrWU4Wy8cq7yqSC6PRyOCBQ1keHUXc3p8JXxLOgf0H\nzPosnP857u5u7Du0lwGD+zN61NsAHNh/gPClEeyOj2VFzDIGDRiC0Wi0Rhr3ZDQaGTpoGJErI4jd\ns5PwJd9yYP9Bsz6fL/gCN3c34g/8Qr+Bb/D2W+8AWQ9qC49aws64bXw2bzav9nzNGinkysnJiU9e\nn0jrd17G5/Xn6dr4BaqUrmjWZ3qfMXyx8Vuq92/J+K9n8t4rIwGoX6UWDXxqU61/C6q+0Zw6FasR\n4PesNdK4J0f5rirH/J+jvdOARmzSrp2xlC9fjrLlyuLq6kqnzqFEr4g26xO9IppuYd0A6BjSgc0b\nN5OZmUn0img6dQ6lYMGCPF32acqXL8eunbFWyOLeYnf9TLnbcgzt3JGYlTFmfWJWrqJb2EsAdAhp\nz+ZNW8jMzKS6f3VKepUEwMe3CtevXSM9PT3Pc8hN3WdqkJCcyNGUP7h56ybfbF1Ju2dbmPXxKV2R\njXt+BGBT/E+0e7Y5AJmZmTziWhBXlwIULOBKAZcCpJ4/k+c55MYRvqvKMUt+z/F2Bgu/rEEDGrFJ\nycnJlCpdyrTsXcqbpOSTOfZxcXGhSNEinD17lqTkk9m2TU5OzpvAH0ByUjKlSnmblr29vUm+M8ek\nk6Y+Li4uFC1ahLNn08z6LItcTnX/6hQsWNDyQT8g72IlOH7mr8/+xJmTeBczf2bLnqP76fhcawA6\nPNeKIo8VxqOwG9sP7mZT/E+cXBTLyUWxrNm9hYPHE/I0/vvhEN9V5ZitT37M0d5pQJNHZs2aRVBQ\nEMHBwbRr146wsDDatWtH8+bNqVWrFu3ataNdu3bs3r0byHp2ha+vL19//bXZfgIDAxkwYIBpefXq\n1YwcmVWij4yM5Nlnn6V9+/a0aNGC3r17m/YHMHLkSFavXg1AWFiY2ZNQ9+7dS1hYmGk5Pj6esLAw\nWrRoQYcOHejbty+HDh16+B+M/CP79x1g7Oh3+OiTD60dyt82fN67BPjVY/dHqwio+iwnzpzEmJFB\n+ZJlqFK6AqVerod3j7oEVnuOhr51rR2uiN2wtzk0umw7D8TFxbF582aioqJwdXUlLS2Nmzdv4unp\nyY4dO5g/fz6fffaZ2TarV6+mevXqxMTE0LVrV7O2ffv2kZCQQIUKFbK9V5s2bRg7diwA27dvZ8CA\nAXzxxReUL18+W9+0tDS2bNmS7fbTZ86cYfDgwUyfPp2aNWsCEBsby/Hjx6lUqdI/+izul5eXFyeO\nnzAtJ51Iwvt/p1ju7FOqlDe3bt3i4oWLFCtWDG+vktm29fLyypO4H4SXtxcnTiSZlpOSkvC6M0fv\nkpw4kYT3/3K8cOEixYp5ZPU/kcRLnboxZ/5nlCtfLk9jv19JZ1Mo/cRfn32pJ0qSdDbVrM/JtFRC\n3s2aA/T4I48R0qA1F65c5NWWXdl+MI4r168C8N3Pm6lfuSY/7NuZdwncB4f4ripHsz75NUd7pwpN\nHjh9+jTu7u64uroCWY9Vv/NR6XeKiYlh5MiRpKammj1mHaBnz57MmjUr1/d99tln6dy5M0uWLLlr\ne+/evZk9e3a29YsXL6Z9+/amwQxA7dq1adasWa7v+bDUrlOLhIQjJB5N5MaNG4QvjSAoOMisT1Bw\nEF8u+hKAyG+jCGgagMFgICg4iPClEaSnp5N4NJGEhCPUqVs7z2K/X7Vq1+TIbTlGLI2kTds2Zn3a\ntG3Dl4u+AiDq22UENGmMwWDg/PnzhLTrzH/eHUf952xvouyfdh3eQ0XvsjztWZoCLgV4sXEwK3aY\nXwFSrIi76X4Yozr3Y/66rO/rH6eTCfB7FmcnZ1ycXQio+iwHbPCUkyN8V5Vjlvye453srUKjAU0e\naNCgASdPnqRly5aMGzfO9Ej0nJw8eZLTp09TrVo1WrduzapVq8zaW7duzf79+zl27FgOe/iLr68v\nv//++13batSoQYECBdi+fbvZ+oSEBHx8fHLdtyW5uLgwY+b7BLdpR42qNQkJDcHH14fx70wg+n8T\nZ1/p9TJnz6bhW8mPj2b8HxMnjQfAx9eHkNAQ/P1q8UJQez786AOcnZ2tmc5dubi48P6H02kf1JFa\n1erQMbQ9Pr5VmDDuXWJWZh3zl3uGkXY2jWpVavDxzE8Y/+44AD779L/8fuR3Jr87lfq1G1K/dkNO\nnTptxWzuzphhpP+st1kzYREHZm9k6Q/R7P/jMP/pPpTgelmTf5v41efQZ5s5NGcznm5P8O43HwMQ\n8WMMR04eY++na9nz8Rr2HN1P9M711kznrhzlu6oc83+O9s6QmZmZae0gHIHRaCQ2NpYdO3awZMkS\nhg0bRseOHe96ymnevHlcvHiRIUOGcPDgQd566y0iIyOBrDk0ERERbNy4kd27d9O4cWM2b97M5MmT\niYyM5NdffzWdcgJYt24dS5YsYe7cuYwcOZImTZrQqlUrwsLCGDFiBJcvX2b27NkMHz6cqVOnsmjR\nIvr370/79u1NFZlOnTpx+fJlGjRowJgxY+6Z5779+yhfqawFPkHbYcy4Ze0QLK5Q+6rWDsHirq04\nmHsnERtw5NBRfH18H+o+V21fw1cXVjzUfd5u9FP9qVKlisX2fzeq0OQRZ2dn6tWrx8CBA3n77bdZ\nu3Ztjn1jYmKIjIwkMDCQN954g8OHD5OYmGjWp127dsTGxmY7HXWn/fv333X+zJ/q169Peno6e/bs\nMa2rUKEC+/fvNy2Hh4czaNAgLl++nEuWIiKSLxh0ykn+ht9//91sQHLgwIEcJ4wdPXqUK1eu8P33\n37Nx40Y2btxI3759iY42vx9CgQIFePnll1m4cGGO77tz506WLl1K586d7xnf66+/zty5c03L3bp1\nIyoqyuwKqevXr99zHyIiItakq5zywNWrV5k4cSIXL17E2dmZMmXKMH78+Lv2jYmJoXnz5mbrWrRo\nwZAhQ+jfv7/Z+k6dOmWbHLxq1Sp+/vlnrl+/TqlSpfjoo4/uWaEBCAgIwMPDw7T85JNPMmPGDKZP\nn05qairFihXDzc2Nfv36PUjaIiJiw+zr0ZT3mEOT2+mFQoUKWSQgyd80h8Y+aA6NiO2wyByaHWtY\ncmHlQ93n7UaW7pfnc2hyrNAEBQVhMBi4fbzz57LBYGDz5s15EZ+IiIg8ZAaw2lwXS8lxQLNli+0+\nvVdERETkdvc1KTgmJsZ0A7aUlBR+/fVXiwYlIiIiluVwVzmNHz+eHTt2sHz5cgAeeeQR3nnnHYsH\nJiIiInK/ch3QxMXFMX78eNOTfN3c3Lh586bFAxMRERHLMRgMFntZQ66Xbbu4uJCRkWEK8Ny5czg5\n6fY1IiIi+ZUB650aspRcBzTdunVjwIABpKWl8dFHH/Hdd99lux+KiIiIiDXlOqBp3749vr6+/PTT\nTwDMnDmTZ555xuKBiYiIiOXYV33mPu8UbDQacXFxwWAwkJGRYemYRERERB5IrpNhZs2axbBhwzh1\n6hSpqakMHz7c7MnQIiIiks/Y4cMpc63QLFu2jGXLlvHoo48C8K9//Yv27dvz2muvWTw4ERERkfuR\n64CmePHiGI1G07LRaKR48eIWDUpEREQsx6EefTBp0iQMBgNFixYlKCiIhg0bYjAY+PHHH/Hz88vL\nGEVERETuKccBTcWKFQGoUKECAQEBpvXVq1e3fFQiIiJiUda6AZ6l5Dig6dSpU17GISIiInnI3m6R\nm+scmj/++IMZM2aQkJDAjRs3TOvXrFlj0cBERERE7leuA7SRI0fSsWNHAP773//SqlUrWrdubfHA\nRERExHLs7VlOuQ5orl+/TqNGjQB46qmnGDJkCFu3brV4YCIiIiL3K9dTTq6urmRkZFC6dGm+/vpr\nPD09uXLlSl7EJiIiIhbgkA+nHDVqFFevXmXMmDHMmDGDS5cuMWnSpLyITUREROS+5Dqg+fMy7UKF\nCjFt2jSLByQiIiIWZnCgG+v169fvnhN7Pv74Y4sEJCIiIvKgchzQdO/ePS/jEMk3nJ3u6yH1+dq1\nFQetHYLFPRpUydoh5Imr0fZ/LDPJtHYIFmaZ/Bzmxnr169fPyzhEREQkjxgAJ+xrQGNvNwoUERER\nB2T/tXMRERHJxt5OOd13heb2xx6IiIiI2JJcBzTx8fEEBwfTokULAA4ePMiECRMsHpiIiIhYStaN\n9Sz1soZcBzQTJ05k9uzZuLm5AVC5cmV27Nhh8cBERERE7leuc2gyMjLw9vY2W+fkpLnEIiIi+ZWB\nrMcf2JNcBzQlS5YkPj4eg8GA0Whk0aJFPP3003kQmoiIiMj9ybXUMm7cOBYsWEBycjLPPfcce/bs\nYdy4cXkQmoiIiFiKwWCw2Msacq3QFCtWjBkzZuRFLCIiIpIXHOlZTn8aM2bMXUdbutJJREREbEWu\nA5rnnnvO9HN6ejrr1q2jZMmSFg1KRERELMeAAYOdPSwg1wFNmzZtzJbbtWvHSy+9ZLGARERERB7U\nAz/64MSJE5w5c8YSsYiIiEgecbg5NHXq1DHNocnIyKBo0aIMGzbM4oGJiIiI3K97DmgyMzNZvnw5\nnp6eQNYN9eztYVYiIiKOyN5+n99zRpDBYKBv3744Ozvj7Oxsd8mLiIiIfch1inPlypXZv39/XsQi\nIiIiecRgwX/WkOMpp1u3buHi4sKBAwcIDQ2ldOnSPPbYY2RmZmIwGIiKisrLOEVEROQhMWC9p2Jb\nSo4Dmk6dOhEVFcWsWbPyMh4RERGRB5bjgCYzMxOAp556Ks+CERERkTxgsL+nbec4hyYtLY0FCxbk\n+BKxtLWr11LNpwa+lfyYNmV6tvb09HS6d+2BbyU/GtUP4FjiMVPbtMnT8K3kRzWfGqxbsy4vw34g\nytE+cmxZqwkH527mt/nf82bnN7K1P1Xcm/Xvfc2eWWvZNHUp3k+UMLVN7jWKvbPXs3f2ejo3Ds7L\nsB/I2jXrqO7rT9XK1Zg+9f1s7enp6YS91IOqlavR+Lkm5sdxynSqVq5GdV9/1q1dn5dhP5C1a9ZR\nw9cfv8rVc8yxx0sv41e5OgHPNTXlePbsWVo3a0NxtxIMHajbmlhLjgOajIwMrly5kuNLxJKMRiOD\nBw5leXQUcXt/JnxJOAf2HzDrs3D+57i7u7Hv0F4GDO7P6FFvA3Bg/wHCl0awOz6WFTHLGDRgCEaj\n0Rpp3JNyzJLfc3RycuKTfhNpPaYHPn0D6dqkHVWeqmjWZ/qrY/hiw7dUf70F47/8kPd6jgSgTd1A\nalaoSo03WlJvUDDDQ1+j8GOFrJHGPRmNRoYMHMqylZHsjo8l/Ju7H0c3Nzd+PRjPgEH9GPPWX8cx\nYkkEP+/ZxfLoKAbb6HE0Go0MHTiMqJWR/By/i/BvIjiw/6BZn8/nf4Gbmxt7D+6h/6B+vP3WWAAe\neeQR3h43hklT3rVG6H+bkwX/WSefHDz55JP0798/x5eIJe3aGUv58uUoW64srq6udOocSvSKaLM+\n0Sui6RbWDYCOIR3YvHEzmZmZRK+IplPnUAoWLMjTZZ+mfPly7NoZa4Us7k05ZsnvOdatVIOEk4kc\nTfmDm7du8s2WFbSr38Ksj89TFdn4y48AbNrzE+2ebWFav/XXnRgzjFxNv0b80QO0qtUkjzPIXewd\nxzG0SyjRK2PM+sSsjKH7/45jh9uP48oYQruYH8dYGzyOsTtjKWeWYwjRK+/4rq6MoVtY1qN/OoS0\nN+X4+OOP81zD5yj4SEFrhC7/k+OA5s85NCLWkJycTKnSpUzL3qW8SUo+mWMfFxcXihQtwtmzZ0lK\nPplt2+Tk5LwJ/AEox+x98mOO3sVKcPz0X3GdOHMS72IlzPrs+f0AHRu0BqBDg1YUebwwHoXd2PP7\nAVrVCuDRgo9QrIg7TavVp/STXnka//1ITk7Gu9Rtx8Lbm+Sk5Ox9zI5jUc6ePUtyUjKlbtvWy9s2\nj2Ny8klKlfI2LXt7e3MyKbfvalaO+ZGBrHvNWeplDTkOaBYuXJiHYTimSZMmmX3OvXv3ZvTo0abl\nyZMnm+YrLVy4ED8/Py5dumRq37FjB6+99lq2/YaFhbF3714Ajh8/TosWLfj+++/N+kdGRlK5cmUO\nHvyrpNq2bVtOnDgBwJUrV3jnnXdo1qwZHTp0oGPHjixduvThJS/iQIb/dyIB1Z5l98ffEeD3LCdO\nn8SYkcG63VtZtWsTP32wjK9Hfsy2A7sxZtje6RiR/CDHAY2bm1texuGQatasSVxcHJA1Z+ncuXMk\nJCSY2uPi4vD39wcgJiYGPz8/1q5de9/7T0lJoU+fPrz55ps0atQoW3uJEiWYPXv2XbcdM2YMRYsW\nZe3atURFRTF37lzOnz//IOn9I15eXpw4fsK0nHQiCW+vkjn2uXXrFhcvXKRYsWJ4e5XMtq2Xl+39\n1ascs/fJjzkmnU0xq6qUeqIkSWdTzPqcTEslZEJfavZvzeiFUwG4cOUiAJO++T/8+7WixVvdMBgM\nHE76Pe+Cv09eXl4knbjtWCQl4eXtlb2P2XG8QLFixfDy9jL9oQSQnGSbx9HLqyQnTiSZlpOSkijp\nndt3NSvH/Mly1Rmbq9CI5fn7+/PLL78A8Ntvv1GxYkUef/xxLly4wI0bNzhy5Ag+Pj788ccfXL16\nlcGDBxMTE5PLXrOcPn2aXr16MWTIEJ5//vm79mnSpAkJCQn8/rv5f6B//PEH8fHxDB48GCenrK+I\nh4cHffv2/QfZPpjadWqRkHCExKOJ3Lhxg/ClEQQFB5n1CQoO4stFXwIQ+W0UAU0DMBgMBAUHEb40\ngvT0dBKPJpKQcIQ6dWvnWez3Szlmye857jq0h4peT/O0Z2kKuBTgxYAXWLHd/IqsYkXcTf/Jj+rS\nn/lrlwBZE4o9Cmf98ehXtjLVylZh7c9b8zaB+1DrjuMYsSSCoLZtzPq0aduGxf87jlG3H8e2bYhY\nYn4ca9vgcaxVpxZHzHL8lqC2d3xX27bhy0VfARD17TJTjmIbcn3atliOp6cnzs7OJCcnExcXR40a\nNUhNTeWXX36hUKFCPPPMM7i6uhITE0ObNm2oXbs2R48e5cyZMzzxxBP33PfIkSMZNGgQrVq1yrGP\nk5MTffr04bPPPmPKlCmm9b/99huVK1c2DWaswcXFhRkz3ye4TTuMRiMvv9IDH18fxr8zgZq1a9I2\nOIhXer1Mr5f74FvJD3d3dxZ99TkAPr4+hISG4O9XCxcXFz786AOcnZ2tlktOlKN95GjMMNL/07dZ\n8+5inJ2cmb92CfuPHeY/YcOI/S2eldvX0aRafd7rOZLMzEy2/rqDfp+MAaCAcwG+n/4tABevXqb7\n1IE2ecrJxcWFD2a+zwtB7TEajfR4JSzrOI6bQM1afx3H3q/0oWrlari7u/PFlwuBrOPYsVNHalar\nnfV9sNHj6OLiwvszp9MuqD1GY8b/cqzChHETqVnLn6DgIF7u1YM+r7yKX+XquLu78/mXf93CpEoF\nXy5dvMSNGzdYuSKaFauWU8WnshUzyp2Tnd2HxpCp2b9WNWzYMAIDA9m6dSs9e/YkNTWV3bt3U7hw\nYc6fP8/w4cNp27YtH3/8MU8//TTvvfcepUuXpnv37uzYsYP58+fz2Wefme0zLCwMDw8PUlNTWbBg\nAY8++iiAWf/IyEh+/fVX3nrrLYKCgpg7dy6vv/46s2fP5tChQ0RGRvLJJ58AMGvWLFavXs3Zs2f5\n4Ycf7pnPvv37KF+prGU+LJGH6NGgStYOIU9cjT6Ye6d8LhP7/jV29HAivj5VH+o+N/28iZ+ddz3U\nfd4uqGAwVapUsdj+70annKzsz3k0hw8fpmLFilSvXp1ffvnFNH/m0KFDJCYm0qtXLwIDA4mJiSE6\nOjrX/fbp04eqVasyaNAgbt26lWM/FxcXevXqxX//+1/TugoVKnDw4EEyMjIAeP3111m+fLnuPyQi\nIjZLAxorq1mzJps2baJo0aI4Ozvj5ubGpUuX+OWXX/D39ycmJoYBAwawceNGNm7cyA8//MCpU6dI\nSkrKdd+jR4+mUKFCjB49+p6X4Xfo0IFt27aRlpYGQJkyZahatSoffvih6QZY6enpupRfRMROGAAn\ng8FiL2vQgMbKnnnmGc6dO0f16tXN1hUqVAgPDw9iYmJo1qyZ2TbNmzc3TQ7etm0bjRs3Nr3+vGoK\nsu4xMHnyZE6fPs3UqVNzjMHV1ZWwsDCz+ym8++67nD9/nubNm9OxY0d69uzJv//974eVtoiIyEOl\nOTTyUGkOjeQXmkNjPzSH5sFt/nkTcS4/P9R93q6Va5Dm0IiIiIg8KF22LSIi4mgMBpwM9lXTsK9s\nRERExCGpQiMiIuJg/nw4pT3RgEZERMQBGezsTsE65SQiIiL5nio0IiIiDshaN8CzFFVoREREJN9T\nhUZERMThGDSHRkREROSf2Lp1Ky1btqR58+bMmTMnx35r1qyhUqVK7N27N9d9qkIjIiLiYP58OKU1\nGI1Gxo8fz4IFC/D09CQ0NJTAwEAqVKhg1u/y5ct88cUXZs86vBdVaERERCTPxMfHU6ZMGUqXLo2r\nqytBQUFs2LAhW7+ZM2fy6quvUrBgwfvarwY0IiIiDshgcLLYKy0tjY4dO5peS5YsMb1vamoqJUqU\nMC17enqSmppqFtu+fftISUmhSZMm952PTjmJiIg4IEtOCvbw8CAyMvJvbZuRkcHkyZN57733Hmg7\nVWhEREQkz3h6epKSkmJaTk1NxdPT07R85coVDh8+TI8ePQgMDOSXX37h9ddfz3VisCo0IiIiDsZg\nMFhtUrCfnx+JiYkcP34cT09PYmJieP/9903thQsXZseOHablsLAwRowYgZ+f3z33qwGNiIiI5BkX\nFxfGjh1Lnz59MBqNhISEULFiRWbOnEnVqlV5/vnn/95+H3KcIiIikg9Y9GnbmfduDggIICAgwGzd\noEGD7tp30aJF9/WWmkMjIiIi+Z4qNCIiIg7ISY8+EBEREbEtqtCIiIg4IGvOobEEDWhEREQcjAED\nBoN9naTRgEZEHNKV6APWDiFPPNatmrVDsLjzi3ZZOwSLyrRGuSMf0oBGRETEAWlSsIiIiIiNUYVG\nRETEAVl0UrAVqEIjIiIi+Z4qNCIiIg7IoDk0IiIiIrZFFRoREREHYzDY3xwaDWhEREQcjkGXbYuI\niIjYGlVoREREHJC9PfrAvrIRERERh6QKjYiIiAPSZdsiIiIiNkYVGhEREQdjwP4u21aFRkRERPI9\nVWhEREQcjsHu5tBoQDCZKCYAACAASURBVCMiIuKAdMpJRERExMaoQiMiIuKA9OgDERERERujAY3Y\nrLWr11LNpwa+lfyYNmV6tvb09HS6d+2BbyU/GtUP4FjiMVPbtMnT8K3kRzWfGqxbsy4vw34gytFO\nclyzjhq+/vhVrs70qe9na09PT6fHSy/jV7k6Ac81NeV49uxZWjdrQ3G3EgwdOCyvw34gLas35uCM\ndfw2cyNvtnstW/tTT3ixfswi9kyNYdPYL/H2KGFqu/X1YeKmrCRuykqW//uzvAz7gaxfs4HaVevi\nX6U2M6Z9mK09PT2dnt1641+lNs83bM6xxD8AOJb4ByWKetOwTgAN6wQwpJ9tH0v467JtS72sQQMa\nsUlGo5HBA4eyPDqKuL0/E74knAP7D5j1WTj/c9zd3dh3aC8DBvdn9Ki3ATiw/wDhSyPYHR/Liphl\nDBowBKPRaI007kk5ZrGHHIcOHEbUykh+jt9F+DcRHNh/0KzP5/O/wM3Njb0H99B/UD/efmssAI88\n8ghvjxvDpCnvWiP0++ZkcOKTXuNo/V4vfIa2pGuDYKp4VzDrMz1sFF9sjaL6iCDGf/sx73Udbmq7\nduM6/m8G4/9mMO2mZR8M2QKj0cjwQSOIWLGUHXt+ImJJJAcPmB/HRQsW4+bmRtyBWN4Y+DrjRv/H\n1Fa23NP8sGsLP+zawoxPsg9qxfI0oBGbtGtnLOXLl+P/27vzuKjq/Y/jrwHCBVPAFBDNHUVBFpdy\nS6+GK4iAmmaUIrncbi6Z+5qJiXt1UzLNrdUdYTB3s7qYGqCCGyLIooIKGkuCAr8/+DmJLEoOHGf4\nPH1wH5xlDu/vnC585nu+33MaN2mMsbExg4cMInhPcKF9gvcEM9x7OACeXh4cPXyU/Px8gvcEM3jI\nIKpUqUKjxo1o2rQJJ0+cUqAVpZM2FtD1Np46cYomj7Rx0BteBAc91sYgNcO93wTAw2ugpo0mJiZ0\n6tKJKlWrKBH9qXVo5sDl5KvEpiRwP/c+P/wvGPf2rxfap5V1Mw5HhQJwJCoU93avF3eo59YfJ8No\n0rQxjZo0wtjYGK8hHoQE7S20T0jQXoZ5DwXA3XMAPx85Rn5+vhJxtUCFCoNy+1KCFDTiuXTt2jXq\nN6ivWbaub03Stesl7mNkZETNWjW5ffs2SdeuF3nttWvXKiZ4GUgbi+6jm228Tv361ppla2trric9\nqY21uH37doXmfBbW5hYk3P67TYm3b2BtZlFon9NXL+DZoTcAHh16UbP6i5jXMAWg6gtVOLloN6EL\nt+PezqXigpfB9WvXsW7w93msZ12vyHm8fu061vXrAf9/HmvWJPV2KlBw2alrh+70e92N//0aWnHB\nhYbMchJCCPHMPvzmE/7rM58R3Tw5dv4kibevk5tXcImw4XuvcS0tmcZ1G3B4zjecTbjIleR4hRNr\nj6WVBZGXT2Ne25yIsAiGD/YmNPw3atasqXS0kqnkPjRPbdGiRWzcuFGzPGrUKGbNmqVZXrx4MRs2\nbABg48aN2Nvbk56ertn++++/M2ZM0Wut3t7enD17FoCEhAR69erFL7/8Umj/nTt30rJlSy5c+Pv6\np6urK4mJiQBkZmYyb948Xn/9dTw8PPD09GTr1q0ltiUxMZE2bdowcOBA+vbty6BBg9i5c2ehfQ4e\nPIibmxt9+/bFzc2NgwcPAnDhwgXc3d01+wUHB9OmTRvu378PwMWLF3Fzc9O0zdPTU7Pv2bNn8fb2\nBuCvv/5i8uTJuLm54erqyrBhw0hKSsLd3R13d3c6d+5M165dNcs5OTmaXC1atCAmJqZQe1xdXTXv\nc9u2bXF3d6dPnz74+/tr9rt16xZjxoxhwIAB9OvXj3fffbfE90jb6tWrR2JComY5KTEJ63pWJe7z\n4MED/rz7J7Vr18a6nlWR19arV69igpeBtLHoPrrZRisSE5M0y0lJSVhZP6mNd6ldu3aF5nwWSanJ\nNKj9d5vq17YkKS250D7X01LwWv5vnKcPYNYPBWNI7mYV/E6/9v/7xqYkcPTc7zg1alVByZ+eVT0r\nkhL+Po/Xkq4VOY9W9axISizoJXzw4AF//vkn5rXNqVKlCua1zQFwdHakUZPGxETHICpWuRU0zs7O\nhIeHA5CXl0daWhqXL1/WbA8PD8fJyQkAtVqNvb09+/fvf+rj37hxA19fX6ZNm0bXrl2LbLe0tCQg\nIKDY186ePZtatWqxf/9+du3axbp167hz506pP+/ll19m9+7d7N27l5UrV7Jp0yZ27NgBFBQt/v7+\nrF69mr1797J69Wr8/f25cOECNjY2XL9+nYyMDE27mzZtyvnz54u8DwCpqan8/PPPRX7+5s2beeml\nlwgKCiI4OBg/Pz/q1KlDYGAggYGBDB06lBEjRmiWjY2NgYICqm3btqjV6hLb1q5dOwIDA9m9ezdH\njhzhjz/+AOCzzz6jU6dO7Nmzh5CQECZPrriR++3at+Xy5RjiYuPIyclh29bt9HfrX2if/m79+XbL\ntwDs3LGLbv/qhkqlor9bf7Zt3U52djZxsXFcvhxD+w7tKiz705I2FtD1NrZt35aYR9q4/ccd9Hd9\nrI2u/fh2y3cA7NqxW9NGXXEy5gzNLRvRqE59XjB8gaGdXNlz6lChfWq/aKZp04yB4/j6yHYATE1q\nYmxkrNmnc4u2nEu8zPPGuZ0TMZevEBd7lZycHHZs3UVf176F9unr2ofvt/wAQODOPbzWvSsqlYpb\nN29pBqzHXYnjyuUYGjVuVMEtKDtVOf5TQrkVNE5OTkRERAAQHR1N8+bNMTEx4e7du+Tk5BATE0Or\nVq2Ij48nKyuLiRMnlvpH91E3b97Ex8eHSZMm0bNnz2L36d69O5cvX+bKlSuF1sfHx3PmzBkmTpyI\ngUFB883NzRk9evRTt61BgwZMnz6dLVu2ALB+/XrGjBlDgwYNNNtHjx7N+vXrMTAwwM7OjjNnzgAQ\nFRXFm2++SVhYGFBQ0Dg7O2uOPWrUqGILsZs3b2Jh8fc16yZNmmiKlpJkZmbyxx9/4Ofn91TvbdWq\nVbG1tSU5ueDTVEpKCpaWf0+9bNmy5ROPoS1GRkas/HQ5bv3ccbRzxmuQF61at2LBvI8JDipoywif\nd7h9O5XWLez5bOXnLFy0AIBWrVvhNcgLJ/u2DOg/kFWfrcDQ0LDCsj8taaP+tHH5p8tw7z8QZ/t2\neA32pFVrWz6evxD1/7fxHZ+3SU1Nxb6lA5+v+i8L/P6eHWPbrDUzpszkm83f0rxRiyIzpJ4HuXm5\n/Ofrj9g3cyPnV+xja2gI5xKj+WjwRNzaFvwO7t7qFS6uPMjFlQexMK2N367VANhaN+PUJ7uJWBLM\nkbnfsjgwgPNJz19BY2RkxNJV/ni5DqZDm454DHLHtlVL/D76RDM42HvkW6SmpuJk244vPl3N/IUF\ns9V++/V/dG7blS7tu/H2sJGs+Hw5ZuZmSjbniVSAgUpVbl9KKLcxNBYWFhgaGnLt2jXCw8NxdHQk\nOTmZiIgIatSogY2NDcbGxqjVavr160e7du2IjY3l1q1bvPTSS6Uee/r06UyYMIE+ffqUuI+BgQG+\nvr58+eWXhS6jREdH07JlS00x80+1bt1aUyxdvnyZUaNGFdpub2/Pd98VfCJzdnYmLCwMR0dHVCoV\nr7zyCsuXL2fEiBGEh4fz3nvvaV7n6OjIgQMHOH78OCYmJpr1Xl5e+Pj4sG/fPl599VU8PDxo1KhR\nqRkPHTpE165dady4MWZmZkRGRmJnZ1fi/nfv3uXq1au0b98egOHDhzNp0iS++eYbOnXqhKenZ6Gi\nqrz16deHPv0Kn+O5H83RfF+1alW++/GbYl87beZUps2cWq75tEHaqCdt7NubPn17F1o3Z/5szfdV\nq1blmx+2FPva85ejyjWbtuyNOMreiKOF1s3b9ve9Wnb8/hM7fv+pyOtCL4XRZkq/8o6nFb36utCr\nb+FBy7PmzdB8X7VqVTZ9v6HI69w9BuDuMaDc84nSlessJycnJ8LDwzWXVZycnAgLCyvUK6FWq+nf\nvz8GBgb06tWLn34q+n+Ix3Xs2JGgoCD++uuvUvdzdXUlIiKChISEEvdZs2YN7u7udOnSpUxtK8tU\nvYfvw5kzZ7C3t+fll18mPj6e1NRUsrKyePnllwvtP27cONasWVNona2tLQcPHmTUqFHcvXuXQYMG\nFRoXU5yH7y1Av379SuylOXXqFAMGDOC1116jS5cu1KlTB4CuXbty8OBBhgwZwpUrV/Dw8CA1NfWp\n2y2EEOL5JZecyuDhOJpLly7RvHlzHBwciIiI0BQ4Fy9eJC4uDh8fH3r06IFarSY4OPiJx/X19cXO\nzo4JEybw4MGDEvczMjLCx8eHr776SrOuWbNmXLhwgby8PKCgeAgMDCQzM7NMbTt37hxNmzYFoGnT\npkRGRhbaHhkZSbNmBTeecnBwIDIyUtNLAwU9WGq1WrP8qI4dO5Kdnc3p06cLrTcxMaFXr17Mnz+f\nAQMGFDvW5qE7d+5w/PhxZs+eTY8ePVi/fj179+4tthBr164de/bsITg4mO3bt2vG9wCYmpri5ubG\n0qVLsbe35+TJk0/5DgkhhBAVp9wLmiNHjlCrVi0MDQ0xNTUlPT2diIgInJycUKvVvP/++xw+fJjD\nhw/z66+/kpKSQlJS0hOPPWvWLGrUqMGsWbNK7S3x8PAgNDRU07PQsGFD7OzsWLVqlWYQV3Z2dpl6\nXBITE1myZAlvvfUWUDDuZe3atZpZVImJiXz55Zf4+PgAUKNGDSwtLdm5c6dmALCTkxObNm0qNH7m\nUePGjWPdunWa5T/++IO7d+8CkJOTw+XLl0ud8bFv3z7c3d05cuQIhw8f5ueff6Z+/fqcOlXyjcke\njv15WACGhoZqesEyMjKIj4/HysqqxNcLIYTQFeX32AO9fPSBjY0NaWlpODg4FFpXo0YNzM3NUavV\nvP564btJuri4aC6NhIaG8tprr2m+Hs6agoL584sXL+bmzZssWbKkxAzGxsZ4e3sXuomVn58fd+7c\nwcXFBU9PT0aOHMmUKVNKbUt8fLxm2vbEiRPx9vbGy8sLKLgc9OGHHzJu3Dj69OnDuHHjmDJlCra2\ntprXOzs7k5OToykIHB0dSUhIKDTD6VHdunXD3Nxcs5yQkMBbb72Fm5sbHh4e2NnZ0bt372JfCwWz\nmx5/b3v16vXEHrChQ4dy8uRJEhMTiYqKwsvLCzc3N4YOHcrgwYNp06ZNqa8XQgghlKDK1937Novn\nUNS5KJq2aKx0DCGeKC8/T+kIFcJkuMOTd9Jxd7bo96XwhMtJ2LWy1+oxj58O5Y75Ta0e81ENM5oX\n+lBfEeTRB0IIIYTQefLog0dcvHiRqVMLTxE1NjZm27ZtCiUSQgghyocu3dzxaUhB84gWLVoQGBio\ndAwhhBCiXKkAA4WmV5cXueQkhBBCCJ0nPTRCCCFEpaPc9OryIj00QgghhNB50kMjhBBCVEJKPaKg\nvEgPjRBCCCF0nvTQCCGEEJWNSv+mbUsPjRBCCCF0nvTQCCGEEJWMClDpWZ+GFDRCCCFEpaPCQC45\nCSGEEEI8X6SHRgghhKiEZNq2EEIIIcRzRnpohBBCiEpIpm0LIYQQQjxnpIdGCCGEqGQKpm1LD40Q\nQgghxHNFemiEEEKISkjfxtBIQSOEEEJUOioM9OwijX61RgghhBCVkvTQCCEqpQf5D5SOUCH++u6s\n0hHKXTWvVkpHKFebR/lj18pe68fVt0tO0kMjhBBCCJ0nPTRCCCFEJSPTtoUQQgghnkPSQyOEEEJU\nNioZQyOEEEII8dyRHhohhBCi0lHp3RgaKWiEEEKISkjfChq55CSEEEIInSc9NEIIIURlJIOChRBC\nCCGeL9JDI4QQQlQyqkf+V19ID40QQgghdJ700AghhBCVkNxYTwghhBDiOSM9NEIIIUSlIzfWE0II\nIYQe0LeCRi45CSGEEELnSQ+NEEIIUQnJoGAhhBBCiOeM9NAIIYQQlYwKGUMjRIXZ/9N+2rRypHUL\ne5b6LyuyPTs7m7eGvU3rFvZ07diNq3FXNduWLl5K6xb2tGnlyIF9ByoydplIG/WjjQf3HaRt6/Y4\n2jqzYsnKItuzs7MZ8aYPjrbO9Oj8Olfj4gttT4hPoJ5ZfT5b8XlFRS6zynAeezt148IXh4hec5Rp\nnuOKbH+5jjUHF3zL6VV7ObLwB6xrW2q2NXipHvvmb+bc5weJ+vwADevWr8joOufYsWP07t0bFxcX\n1q5dW2T7hg0b6NevH25ubrzzzjskJSU98ZhS0IjnUm5uLhPHf0Bg8C7Cz/7Bth+3cf7c+UL7bPx6\nE2ZmpkRdPMv7E//DrBlzADh/7jzbtm4n7Mwp9qh3M+H9SeTm5irRjFJJGwvoQxsnT5jC9qBtnDh9\nnB0/7uDCuQuF9tm8YQumZrWIOB/Gv8ePY97M+YW2z5wym9d7v16BqcumMpxHAwMDvhizgL4LRtDq\nfReGdR2Abf1mhfZZNmImm4/sxGFiXxb8+CmfeE/VbNs8cQVLd62l1fuv02GKOyl3blV0E8pIVa7/\nSpObm8uCBQtYt24darWa4OBgLl++XGgfW1tbduzYQVBQEL1792bp0qVPbJEUNOK5dPLEKZo2bULj\nJo0xNjZm8JBBBO8JLrRP8J5ghnsPB8DTy4Ojh4+Sn59P8J5gBg8ZRJUqVWjUuBFNmzbh5IlTCrSi\ndNLGArrexj9O/kGTpk1o3KQRxsbGeA7xRB0UUmifkKC9vOk9DICBXu78fORn8vPzAQgOVNOw8cvY\ntmpZ4dmfVmU4jx2aO3L5+lVikxO4/+A+P/wahPsrvQrt06pBcw6f/R8AR86G4t7BBQDb+s0wMjDk\n4OlfAci8l8VfOfcqtgE65MyZMzRs2JAGDRpgbGxM//79OXToUKF9Xn31VapVqwaAo6MjN27ceOJx\npaARz6Vr165Rv8HfXbbW9a1Juna9xH2MjIyoWasmt2/fJuna9SKvvXbtWsUELwNpY9F9dLKNSdex\nrm+tWba2rsf1x9p4PemaZp+HbUy9nUpGRgarln3K9NnTKjRzWVWG82htbkHCrb9zJd6+jrW5RaF9\nTsedx/PVPgB4vNqbmtVfxPxFU2ysm3An8092TAsgbIWaJe/MwMDg+f/zqlKpyu2rNMnJyVha/n25\nzsLCguTk5BL33759O6+99toT2/P8v+NCCKGnPvnYn3+PH0eNGjWUjiKewocb/OjW+hXCVqjp1vpV\nEm9dJzcvDyMDQ7q2as+HG/1o/+EAmli+zIgeg5SO+0TleckpNTUVT09PzdePP/74jzIGBgYSGRmJ\nr6/vE/fViYJm0aJFbNy4UbM8atQoZs2apVlevHgxGzZsAGDjxo3Y29uTnp6u2f77778zZsyYIsf1\n9vbm7NmzACQkJNCrVy9++eWXQvvv3LmTli1bcuHC39fEXV1dSUxMBCAzM5N58+bx+uuv4+Hhgaen\nJ1u3bi2xLcVlmT59Oj/99JMmU+/evRkwYABDhw7lypUrABw5coSBAwcyYMAA+vXrxw8//MCaNWtw\nd3fH3d0dW1tbzfebN2/WHNvd3Z1JkyY91c/z8vLi/Pm/r4tv374dNzc33NzccHV15eDBgyW2S9vq\n1atHYkKiZjkpMQnrelYl7vPgwQP+vPsntWvXxrqeVZHX1qtXr2KCl4G0seg+OtlGayuSEv8esJiU\ndA2rx9poZV1Ps8/DNprXNuePE6eYN3Me9s3bsObzNSz3X8Ha1UUHSCqtMpzHpNRkGrz0d676ta1I\nSi3ca3A9LQUv/7E4f9CfWd8WjOm4m/knibdvEBF7ntjkBHLzctn9+36cm9hVaP7njbm5OTt37tR8\nvfHGG5ptFhYWhS4hJScnY2FhUeQY//vf/wgICGDNmjUYGxs/8WfqREHj7OxMeHg4AHl5eaSlpRUa\nQBQeHo6TkxMAarUae3t79u/f/9THv3HjBr6+vkybNo2uXbsW2W5paUlAQECxr509eza1atVi//79\n7Nq1i3Xr1nHnzp2yNK+IZcuWsWfPHjw8PFiyZAn3799nzpw5BAQEsGfPHnbv3k2HDh0YN24cgYGB\nBAYGUrVqVc33b7/9NgAxMTHk5eVx6tQpsrKynvjz3nzzTZYsWaJ5TwICAvjuu+8ICgrixx9/pEWL\nFs/UrrJo174tly/HEBcbR05ODtu2bqe/W/9C+/R368+3W74FYOeOXXT7VzdUKhX93fqzbet2srOz\niYuN4/LlGNp3aFdh2Z+WtLGArrfRuZ0zMZdjiIu9Sk5ODju37qSfa99C+/Rz7cN3W74HYPeOQF7r\n/hoqlYqfjuzlbPQZzkafYdz745g87QNG/3u0Es0oVWU4jyejT9PcqhGN6tbnBaMXGNrFjT0nCs/I\nqv2imeZyygyvf/P1oYIPrycvn8bUpCYv1TQHoId9J84lRFdsA8pKpdwlJ3t7e+Li4khISCAnJwe1\nWk2PHj0K7XPu3Dnmzp3LmjVrqF279lM1SSfuQ+Pk5MQnn3wCQHR0NM2bN+fmzZvcvXuXatWqERMT\nQ6tWrYiPjycrK4t58+YREBCAl5fXE4998+ZNpk2bxqRJk+jZs2ex+3Tv3p1Tp05x5coVmjRpolkf\nHx/PmTNnWL58ueZ6qbm5OaNHa+cXUrt27di0aROZmZnk5uZiamoKgLGxcaEcJQkODmbAgAFcuXKF\nQ4cO4ebmVur+jo6OrF+/HoDbt29jYmJC9erVATAxMcHExOQZW/T0jIyMWPnpctz6uZObm8s7I96m\nVetWLJj3Mc7tnHF1688In3fweceX1i3sMTMzY8t3mwBo1boVXoO8cLJvi5GREas+W4GhoWGFZX9a\n0kb9aeOyVUvw7O9Fbl4ub70zHNvWtvjNX4RTW0f6ufXDe6Q3o0eMxdHWGTMzM77+Zr3SscukMpzH\n3Lxc/vPVXPbN24yhoSFfH9zKuYRoPho2iVOXzxJ08iDd7V7lE++p5Ofnc+zcCd77ci5Q8EH7w41+\nHFrwLSqVij9iIvnqwA8Kt+j5ZWRkxNy5c/H19SU3NxcvLy+aN2/Op59+ip2dHT179mTJkiVkZWUx\nYcIEAKysrErsWHhIlf9wqP1zrkePHnzzzTccO3aM/Px8kpOTcXJyokaNGixfvpzvvvuONWvWkJeX\nx7hx4+jZsyfbtm3jpZde4vfff+frr7/myy+/LHRMb29vLl68yIQJExg+fLhm/aP779y5k8jISNq0\naUNoaCj+/v64uroSEBDAxYsX2blzJ1988cVTt6O4LNOnT6d79+706dMHb29vpk6dir29PevWrSMy\nMpJVq1Yxa9YsDh8+TMeOHenevTuurq6FBp05OTlperEe6t27Nxs2bODKlSt88803mv8YSvp5Gzdu\nJDU1lQ8++IDc3FxGjx5NTEwMHTt2xMXFpUgFXZyoc1E0bdH4qd8PIZSSk5ejdIQKYWzw5K56XVfN\nq5XSEcrV5lH+eLu98eQdyyA88g+q1i/HPo3rxtja2pbf8YuhE5ec4O8/2A8vLzk5OREWFkZ4eDjO\nzs5AweWm/v37Y2BgQK9evTTjRErTsWNHgoKC+Ouvv0rdz9XVlYiICBISEkrc5+GYli5dupS4T0ld\ncY+u//DDD3F3dycsLIxp0wpmP/j5+bFx40batGnD119/zcyZM0vNe/bsWczMzKhXrx4dO3bk3Llz\nJV4K+/DDD+nRowcBAQGaws7Q0JB169bx2Wef0ahRIz755BM+//z5vemXEEKIyk1nCpqH42guXbpE\n8+bNcXBwICIiQlPgXLx4kbi4OHx8fOjRo4fmZj1P4uvri52dHRMmTODBgwcl7mdkZISPjw9fffWV\nZl2zZs24cOECeXl5AJoxLZmZmSUex9TUlLt37xZad+fOHczMzDTLy5YtIzAwkNWrV2Nl9ffAuxYt\nWjBixAi+/vpr9u3bV2q71Go1sbGx9OjRAxcXFzIyMkocV7Rs2TIOHTqEh4cHH3/8sWa9SqWiTZs2\njBkzhhUrVpRpXJIQQojnWXnOcVLmkQo6VdAcOXKEWrVqYWhoiKmpKenp6URERODk5IRareb999/n\n8OHDHD58mF9//ZWUlJSnul3yrFmzqFGjBrNmzaK0K3AeHh6EhoaSmpoKQMOGDbGzs2PVqlWaO19m\nZ2eXeoxGjRqRkpJCTEwMAElJSVy8eLHUrrnMzEx+//13zfKFCxewtrYucf+8vDz27t3Lnj17NO/H\n6tWrSy3wVCoVEyZMICIigpiYGJKTk4mKiir0M5/HmQlCCCEE6MigYAAbGxvS0tJwdXUttC4zMxNz\nc3PUanWR50G4uLigVqtxcHAgNDS00I15Pv30U833KpWKxYsXM3bsWJYsWUL37t2LzWBsbIy3tzd+\nfn6adX5+fixZsgQXFxdMTU2pWrUqU6ZMKbEdxsbGLF26lBkzZpCdnY2RkRELFy7kxRdfLPE1+fn5\nrFu3jrlz51K1alWqVaumGSRdnFOnTmFhYVFoGlz79u2JiYkhJSWlxNdVrVoVHx8f1q9fz3vvvYe/\nvz8pKSlUqVIFc3NzPvrooxJfK4QQQrc8aTbSs1BicK7ODAoWukEGBQtdIYOC9YcMCi678Mgwqjd4\nQavHfFTeNaMKHxSsMz00QgghhNAepca6lBcpaMrJxYsXmTp1aqF1xsbGbNu2TaFEQgghxN+koBFP\npUWLFgQGBiodQwghhKgUpKARQgghKhkV5TsoWAk6M21bCCGEEKIk0kMjhBBCVDqq///SH9JDI4QQ\nQgidJz00QgghRCVUvmNoKv4Wd9JDI4QQQgidJz00QgghRCVUvvehqfgeGilohBBCiEpI326sJ5ec\nhBBCCKHzpIdGCCGEqITkxnpCCCGEEM8Z6aERQgghKhnV///TJ9JDI4QQQgidJz00QgghRCUkPTRC\nCCGEEM8Z6aERQgghKhuV/s1ykoJGCCGEqITkkpMQQgghxHNGemiEEEKISkguOQlRivs594m9dFXp\nGEKISuTcJ3uVKssnzQAAIABJREFUjlCusrOzlY6gE6SgEVrl6OiodAQhhBBPIDfWE0IIIYR4DkkP\njRBCCFEpSQ+NEEIIIcRzRXpohBBCiEpIv/pnpKARQgghKiV9m7Ytl5yEEEIIofOkh0YIIYSolKSH\nRgghhCgkLS2NAwcOEBkZqXQUUUlJD43QKdHR0cTHx9OzZ08AFi1aRHp6OgBvvfUWrVu3VjLeM8vI\nyODWrVs0atQIgL1792ruEtqlSxdeeuklBdNph76fQ4Bt27Zx9+5dfH19AejatSuZmZnk5+czdepU\nhg0bpnDCZzdmzBgmT56MjY0NKSkpeHp6YmdnR3x8PEOGDGHEiBFKR3xmhw8fpkWLFlhbWwPw3//+\nl/3791OvXj1mzZpFgwYNFE74bPSrf0Z6aISOWb58OWZmZprlX3/9le7du/PKK6/wxRdfKJhMO/z9\n/QkLC9Msr1ixgrNnz3Ly5Ek+++wzBZNpj76fQ4AffvgBLy8vzXLt2rUJCwvj+PHjqNVqBZNpT2Ji\nIjY2NgDs3LmTTp06ERAQwNatW9mxY4fC6bRj5cqVmJubA3DkyBGCgoJYtGgRPXv2ZP78+cqGE0VI\nQSN0SkpKCs7OzprlGjVq0Lt3bwYOHEhaWpqCybTj7NmzeHh4aJZNTEyYM2cOfn5+REdHK5hMe/T9\nHALk5+cXKtr69OkDQJUqVbh3755SsbTKyOjvDv7Q0FC6desGFJxPAwP9+NOiUqmoVq0aAPv378fL\nyws7OzsGDx5Mamqqwum0QVWOXxVPLjkJnZKZmVloeevWrZrv9eEXTG5ubqGplEuWLNF8//CyjK7T\n93MIRc/V2LFjAcjLy9Obos3KyootW7ZgaWnJuXPn6Nq1KwD37t3jwYMHCqfTjvz8fDIzM6lWrRrH\njx/nzTff1GyTB0Y+f/SjjBaVRt26dTl9+nSR9REREdStW1eBRNqlUqm4efOmZvlhl35ycrLe3DNC\n388hQOfOnVm5cmWR9Z9++imdO3dWIJH2Pew13LlzJytXrqRmzZpAwXn09PRUOJ12vPPOOwwcOBAv\nLy+aNGmCvb09AOfOnaNOnToKp3tWKlSq8vtSpEX5+fn5ivxkIf6BM2fOMHHiRDw9PWnVqhUAUVFR\n7Nq1i1WrVtGmTRuFEz6bwMBANm/ezPTp07G1tQUKfnn6+/vj7e3NwIEDFU747PT9HAJkZWUxe/Zs\nzp49S8uWLQG4cOECdnZ2LFy4EBMTE4UTlq9r165Rr149pWNoRXJyMrdv36Zly5aaS2kpKSnk5uZi\nZWWlcLp/7kzUGSyblt8kg9uxdzW/wyqKFDRC59y6dYtvv/2Wy5cvA9CsWTOGDx+uFzOAAI4dO8aX\nX36paV/z5s159913NWMU9IG+n8OHEhISNGOfmjVrxssvv6xwIu0KDw8nOTmZ9u3bU7t2bS5cuMBX\nX33FqVOn+Pnnn5WOV25iY2NZv349CxcuVDrKPyYFjRBCiCe6du1aqdv1offC39+fo0ePYmtry9Wr\nV+nSpQvbt29n9OjRDB06lCpVqigd8ZlduHCBJUuWkJKSQs+ePRk+fDgff/wxp0+fxsfHR6enpp+N\nOoNl0/K7bHYr9k6FFzQyKFjoFG9v7xKvz6pUKjZt2lTBibTrv//9b4nbVCoV7733XgWmKR/6fg6h\n4B4txUlLS+P27ducP3++ghNp388//8zu3bupUqUKd+/epXv37gQFBVG/fn2lo2nNnDlzGDZsGI6O\njvzyyy8MHDiQgQMHsmzZMr0o2PSNFDRCp0ybNq3IutOnT7Nu3TrN/SJ0WfXq1Yusy8rKYseOHdy5\nc0cvChp9P4cAQUFBhZYTExP56quvCA0NLbHY0TVVqlTR/FGvVasWDRs21KtiBiAnJ0czwLlJkyZs\n3ryZqVOnKpxKe1R6dms9KWiETrGzs9N8f+LECVavXk12djbz58/XizEmPj4+mu8zMjLYvHkzO3fu\npF+/foW26TJ9P4ePiouLIyAgQHOJYvbs2bzwwgtKx9KKhIQEzXR0KCjaHl0OCAhQIpZWZWdnc+7c\nOR6OzDA2Ni60rA93tdYnMoZG6JxffvmFNWvWYGxszNixY3n11VeVjqRVd+7cYcOGDQQFBeHh4cHb\nb79NrVq1lI6lVfp+Di9dukRAQADR0dH4+vri6uqKoaGh0rG06sSJE6Vu79ChQwUlKT/e3t4lblOp\nVGzevLkC02jX2agzWDUtv9sk3IxNk0HBQpTGy8uLtLQ0Ro0ahaOjY5Htuv6Jyd/fnwMHDjBkyBCG\nDx+ul9N79f0cAtja2mJlZUW3bt2KLWRmz56tQCoh/iYFjRAK0+dPTAAtW7bE2NgYQ0PDQgNn8/Pz\nUalUhZ7zpKv0/RxCwbONSru52KOPt9BVbm5upW5/fByRLtq/f3+p23v16lVBSbTvbNQZ6jWzKLfj\np1xJlYJGCCHE8y8pKanU7Q+fUK3LZsyYUer2Tz75pIKSaJ8+FjQyKFjoFH3+xAQF42dKY2pqWkFJ\nyo++n0Og0ODY4ujDgNmSCpZTp06hVquZN29eBSfSvtIKllu3blVgEvE0pKAROuXIkSOlbtf1P4ae\nnp6oVCqK6zhVqVQcOnRIgVTape/nENCbGWlP69y5cwQFBbFv3z6sra314hwW588//2Tfvn0EBwcT\nExPDr7/+qnSkZyLTtoVQ0Hvvvad397p41JYtW/Siq740utxN/7Tu379f4kMoly5dqhczgGJjY1Gr\n1QQHB2NmZka/fv3Iz89ny5YtSkfTqnv37nHo0CGCgoI4f/48mZmZfPHFF7Rv317paOIx8rRtoVNG\njhzJ2rVrefDggdJRysV//vMfpSNUiCtXrrB48WJGjx7N6NGj8ff3JzY2VulYWrNgwQKOHj1aaF1e\nXh7Tp0/nwoULyoTSsr59+3L8+HG+/PJLvv/+e7y9vTUPb9QXkydPpnfv3vz22294e3tz+PBhatas\nySuvvKIHbVWV81fF0/UzIiqZXbt2cevWLTw9PTl16pTScbSuMozRDw8P5+2336Z69eoMGTKEIUOG\nUK1aNby9vYmIiFA6nlasW7eOxYsXc+DAAaDgU/64ceO4f/++XoyfgYLHdNSpU4e3336b2bNnExoa\nqnf//V6+fJmaNWvStGlTmjZtWmT2oXi+yCwnoZMiIyMZMWIElpaWhX7B6PpU0Y4dO9K/f/8St+vD\n/Ut8fX159913eeWVVwqtP3HiBGvXrmXdunUKJdOuGzduMGrUKN566y327NmDvb09M2fOVDqW1mVl\nZXHo0CHUajXHjx/H3d0dFxcXunTponQ0rYiJiUGtVhMSEoKZmRmxsbEEBwfr/JPhz0adpX4zy3I7\n/o0rt2TathBPEhoayqJFi+jSpQtvvvlmoa5fXR9/8q9//Yvx48eXuF0f7l/Su3dv9u3bV+ZtuiQq\nKgqAlJQUpk+fTqdOnfD19dVs14ebBz548AAjo8LDMO/evctPP/1ESEiIXjxk9HGRkZGo1Wr27t2L\npaUlP/zwg9KR/jEpaIRQ2KRJk7hx4wbz58+nRYsWSsfROg8PD3bt2qV0jHLl6enJzp07i92mL+2v\nDDcP1JdzVZpvvvmGt956q8j6/Px8Tp06pdMDgyPPnaV+M6tyO/71mJtyHxohStOpUycGDx5c7LZb\nt27pfDfwzZs3lY5Q7q5fv87ChQuLrM/Pzyc5OVmBRNpX2kwffRknVBk+C+/YsaPYgkalUul0MaOv\npKAROuXxYkbf7guh6wXZ05g6dWqJ2x59Ere+mjhxYpEZULooNTWVDRs2lLh95MiRFZhG/DP6NcBZ\nChqhc/T5vhCVYQaFPowDehb60rORl5dHZmam0jHK1cWLF3F2di6yXl+eraZvv22koBE6ZfLkyZw6\ndYrOnTvj7e3Nq6++iouLS5EZM7rqxo0bxV6OeUgfZjmV9nwclUrFokWLKjBNxdOXorVOnTp6f98k\nGxsbdu/erXQM8ZSkoBE6Rd/vC1G1alW9mAFTmu7duxdZd/36dTZt2kRubm7FByoHpT3L6UnP69IV\n+tLTVLnpz+9OkIJG6JjAwEDNfSFGjBiBmZkZmZmZejEgGAoePqnvl2R69+6t+T4hIYGAgABOnTrF\nu+++y6BBgxRMpj2lPctJX57ztHr1au7fv88LL7wAFNz9+dixY9SrV09vnuXUp08fpSOIMpA7BQud\n07RpU8aPH89PP/3ErFmz8PDwYNCgQQwdOlTpaM/s4R8HfRcTE8OHH37I2LFjadu2LWq1mjfffBNj\nY2Olo2lFhw4div1q0KABZ86cUTqeVkyZMoWkpCQArl69ytChQ0lISODbb79l+fLlCqfTDnNzc+Li\n4oCCHqkZM2bg7OyMm5ub5l5DukylUpXblxKkh0boNDs7O+zs7JgyZQqrV69WOs4zmzt3bqm/KPXh\nctT48eOJiorCx8eHmTNnYmBgQEZGhma7qampgum0LzU1lb1796JWq0lJScHFxUXpSFrx559/0qhR\nI6DgkST9+/dnzpw55OTk4OXlxeTJk5UNqAWbN2/W9JgGBwdz8eJFDh06xPnz5/Hz8+O7775TOKF4\nlBQ0Qi8YGBiwfft2nR+k6O/vj0ql0oxPePyTjj7ckC0yMhKA9evX8/XXXwMUau+hQ4cUy6YtGRkZ\nHDhwgODgYGJjY+nVqxeJiYkcO3ZM6Wjl4vjx45o7IRsbG+vNuDZDQ0NNr+nRo0dxd3fHzMyMTp06\nsXTpUoXTicdJQSP0hj4MUpwyZQqWlpbUrVsXKPjku2/fPurXr6/zxdpDhw8fVjpCuevUqRNt2rRh\n4sSJtG3bFpVKpXlQpb5o0aIF/v7+WFhYEB8fT+fOnYGCnht9YWBgQEpKCrVq1SI0NLTQYO979+4p\nmEwUR8bQCL2hD58K582bpxlHcvLkSZYvX46Hhwc1atRg7ty5CqcrP/Hx8XzxxRelPphTl3zwwQfk\n5OTw0Ucf8eWXXxIfH690JK1buHAhZmZmJCYm8vXXX1OtWjWgYCaivgx8Hj9+PF5eXvTo0YMePXrQ\nvHlzoOBBqg0aNFA43bNSles/RVokz3ISusTJyanYwiU/P5/s7GzOnTunQCrtGTBgAHv27AHgo48+\nwtzcnPfffx8Ad3d3AgMDlYynVcnJyezdu5egoCAuXbrEmDFjcHFx0atndCUkJKBWq1Gr1cTFxfH+\n++/j4uJC48aNlY4mntKDBw/IzMykVq1amnVZWVnk5+djYmKiYLJnE3kukobNy68oS7x8TZ7lJERp\nwsPDlY5QrvLy8jRPMQ4NDeXjjz/WbNOXe7T8+OOPBAcHk5KSQp8+ffDz8+Pf//633lxSA9i4cSPO\nzs60atWKsWPHMnbsWC5duoRarWb06NF6cfnJ29u7xF5RlUqlF0/bjouLY8mSJcTHx2NjY8O0adOw\nsLCgevXqSkcTxZCCRojnSP/+/XnrrbcwMzOjatWqtGvXDiiYFlujRg2F02nHxx9/jKOjI8uWLcPe\n3h7Qj8uFj0pOTmbRokVcuXIFGxsbnJ2dcXJyYuTIkUyaNEnpeFoxbdq0IutOnz7NunXrMDc3VyCR\n9s2cOZOBAwfSrl07Dh8+zMcff8x///tfpWNpjX79v04uOQnx3ImIiODmzZt07txZ80kwNjaWrKws\nvZi2nZaWxk8//YRarebmzZv07duXXbt28fPPPysdTetycnKIjIwkPDyciIgIwsPDqVmzJiEhIUpH\n06oTJ06wevVqsrOzGTt2LN26dVM6klY8fpnXw8ODXbt2KZhIeyLPRdKoHC85JcglJyGEo6NjkXX6\nNObCzMyMYcOGMWzYMG7cuEFISAi1a9emb9++uLi48MEHHygdUWuys7PJyMggPT2d9PR06tatq1dj\nhH755RfWrFmDsbExY8eO5dVXX1U6klY9HJf38HP/vXv3Ci3r8gcMFfrXMyo9NEKIChUREVFs0RYb\nG4tardaLsTRz5swhOjoaExMTHBwccHBwwNHRsdDAUl3n5eVFWloao0aNKvZ86vIf+4e8vb1L3KZS\nqXT6vlBR5yJpZPNyuR0/PjqpwntopKARQlQofeq2L8moUaNIS0vDxsYGJycnHB0dsbGx0atPxPr8\nx74yKChoGpbb8eOjE+WSkxBC6Lr169eTn59PdHQ04eHhbNiwgUuXLmFqaoqjoyPjx49XOuIz27Jl\ni9IRyt3+/fsLLatUKszMzGjZsqXeDNLXJ1LQCCEqVEJCQqE7rj4uICCgAtOUH5VKhY2NDTVr1uTF\nF1+kRo0aHD16lDNnzuhFQTNgwACcnZ01M7h0/0ZzRR05cqTIujt37nDx4kX8/Pzo2LGjAqm0R3/6\nCwvIJSchRIXq1asXCxcuLHF7hw4dKjBN+di8eTPh4eGEh4djZGSEk5MTTk5OODs7Y2Njg4GB7t+k\n/dKlS5o2hoeHk5WVpWmjk5MTDg4OSkcsN0lJSUycOJFt27YpHeUfizoXSWObRuV2/KvRCXLJSQih\n36pXr64XRUtpkpKS6NOnDzNmzNA8l0vf2NjYYGNjwxtvvAEUPFU8JCSETZs24e/vz/nz5xVOWH6s\nra158OCB0jHEY6SgEUJUqJo1a3Lz5k3q1KkDwO7du9m3bx/W1tb85z//wdTUVOGEz27GjBlKRyh3\nubm5nDt3jvDwcMLCwoiPj8fCwoLBgwcXO+tJn1y5ckXzzDXdpdKrQeogBY0QooKlp6fzwgsvAAUP\n4Fy2bBlz5szh/PnzzJ07l88++0zhhOJpODs707RpU4YPH87kyZP1cgxNcWO97t69y82bN1m6dKkC\niURppKARQlSovLw8TS9MSEgIb7zxBr1796Z37964u7srnE48LT8/PyIiIti2bRs7d+7E3t4eR0dH\nnJycsLCwUDqeVjz+1HCVSoWpqSkNGzbUgx4aZR07dgw/Pz/y8vIYPHgwo0ePLrQ9JyeHqVOnEhUV\nhampKStXrqR+/fqlHlMKGiFEhcrNzdX7B3BWBq6urri6ugLw119/cebMGcLDw1mxYgX3798vdoaQ\nrnnasV5vvPEGP/74Yzmn0R+5ubksWLCADRs2YGFhwaBBg+jRowfNmjXT7LNt2zZq1qzJgQMHUKvV\nLFu2jFWrVpV6XClohBAVqjI8gLOyyMrK4vTp05pxNGfPnsXKygpnZ2elo1Wo7OxspSP8IyqFJm6f\nOXOGhg0bai5T9u/fn0OHDhUqaA4fPqy5a3jv3r1ZsGAB+fn5pY77kYJGCFGhxo0bR8eOHTUP4Hz4\nCyovL485c+YonE48rYEDB3L9+nXs7OxwcnLCx8cHBwcHTExMlI5W4XRxcK3xC8bEXrpabse/efMm\ns2bN0iy/8cYbmhlxycnJWFpaarZZWFhw5syZQq9PTk7GysoKACMjI1588UXS0tJKfZK7FDRCiAqn\n7w/grAwWL15MixYtdPKPuYDmzZuX6/FtbW157bXXyvVnPE4KGiGEEGXWsmVLLl26xPr164mOjgYK\n/kiOHDmSli1bKpyuYsn9acvGwsKCGzduaJaTk5OLDCS3sLDg+vXrWFpa8uDBA9LT0zEzMyv1uLp/\nu0ohhBAV7uDBg/znP/+hffv2LFq0iEWLFtG+fXvGjx/PwYMHlY5XoZYsWaJ0BJ1ib29PXFwcCQkJ\n5OTkoFar6dGjR6F9evTooXmI7b59+3j11Vef2Bsojz4QQghRZgMGDGD16tVFptImJiby73//mz17\n9iiUTHu2bdvG3bt38fX1BaBr165kZmaSn5/P1KlTGTZsmMIJddfPP//MokWLyM3NxcvLi3HjxvHp\np59iZ2dHz549yc7OZsqUKZw/f55atWqxcuXKJ97rSAoaIYQQZda/f3/UanWx2/r160dISEgFJ9I+\nLy8v1q1bp7nUMXDgQHbv3k12djajRo3im2++UTiheJRcchJCCFFmhoaGXLt2rcj6pKQkDA0NFUik\nffn5+YXGbfTp0weAKlWqcO/ePaViiRLIoGAhhBBlNn78eEaOHMmYMWNo3bo1AJGRkaxdu5YpU6Yo\nnE470tPTCy0/fBRCXl4eaWlpSkQSpZBLTkIIIf6RCxcu8PXXX3P58mUAmjZtyqhRo/RmltP8+fOp\nVasWkyZNKrR+5cqVpKWlsWDBAoWSieJIQSOEEEIUIysri9mzZ3P27FlNkXbhwgXs7OxYuHBhpbyJ\n4PNMChohhBD/yK5du9iyZQtXrlwBoEmTJrz99tsMHDhQ4WTalZCQoLnXTrNmzXj55ZcVTiSKI2No\nhBBClNmuXbvYtGkT06dPp3Xr1uTn5xMVFcXSpUsB9KKoeTjo2dDQsNBltIfr69Wrp0guUTzpoRFC\nCFFmQ4YMYcWKFcXeh+aDDz5g69atCiXTHjc3t2LXp6Wlcfv2bc6fP1/BiURppIdGCCFEmWVkZBQp\nZgDq169PRkaGAom0LygoqNByYmIiX331FaGhoYwZM0ahVKIkUtAIIYQos6pVq/6jbbooLi6OgIAA\nTp8+jY+PD7Nnz+aFF15QOpZ4jFxyEkIIUWYODg4lDo5NSEggIiKighNp36VLlwgICCA6OhpfX19c\nXV315qaB+kgKGiGEEGWWlJRU6nZra+sKSlJ+bG1tsbKyolu3bsUWMrNnz1YglSiJXHISQghRZk9b\nsLzxxhv8+OOP5ZymfPj5+T3xCc/i+SEFjRBCiHKTnZ2tdIR/zNPTU+kIogykoBFCCFFudLmH4+Gz\nm0oSEBBQQUnE05CCRgghhCiGj4+P0hFEGUhBI4QQotzo8ryT+/fv07lz52K3LV26lA4dOlRwIlEa\nA6UDCCGE0F9LlixROsI/tmDBAo4ePVpoXV5eHtOnT+fChQvKhBIlkoJGCCFEmW3bto1169Zplrt2\n7YqzszNOTk58//33mvU2NjZKxNOKdevWsXjxYg4cOADAvXv3GDduHPfv35fxM88hKWiEEEKU2Q8/\n/ICXl5dmuXbt2oSFhXH8+HHUarWCybSnQYMGbNy4kVWrVvH9998zcuRIGjZsyPLly+VOwc8hKWiE\nEEKUWX5+PmZmZprlPn36AFClShXu3bunVCytioqK4vbt23z44YesWrUKS0tL3N3diYqKIioqSul4\n4jFyp2AhhBBl5uLiorkU86i8vDxcXFw4dOiQAqm0y9vbu8RtKpWKzZs3V2Aa8SQyy0kIIUSZde7c\nmZUrVzJp0qRC6z/99NMSZwbpmi1btpS4TR+eVaVvpIdGCCFEmWVlZTF79mzOnj1Ly5YtAbhw4QJ2\ndnYsXLgQExMThROWr+7duxeZASWUJT00Qgghyqx69eqsWLGChIQEoqOjAWjWrFmJT+DWN9IX8PyR\ngkYIIUSZXbt2DQBDQ0NND82j6+vVq6dIroqiy4900FdS0AghhCizMWPGFLs+LS2N27dvc/78+QpO\npH2lPcvpzp07FZhEPA0ZQyOEEOKZJSYm8tVXXxEaGoq3t3epM4R0xYkTJ0rdLo8+eL5IQSOEEOIf\ni4uLIyAggNOnT+Pj48PAgQP1/qZz169fR61W4+vrq3QU8Qi55CSEEKLMLl26REBAANHR0fj6+uLn\n54ehoaHSscpNamoqe/fuRa1Wk5KSgouLi9KRxGOkh0YIIUSZ2draYmVlRbdu3YotZGbPnq1AKu3K\nyMjgwIEDBAcHExsbS69evQgJCeHYsWNKRxPFkB4aIYQQZebn56f3M306depEmzZtmDhxIm3btkWl\nUhV7d2TxfJAeGiGEEKIYGzduJCQkhL/++ov+/fvTr18/Ro4cqRePddBHUtAIIYQos9KmNAMEBARU\nUJLyl5CQgFqtRq1WExcXx/vvv4+LiwuNGzdWOpp4hBQ0QgghyqwyTGneuHEjzs7OtGrVCiOjghEa\nly5dQq1WExISIpefnjNS0AghhCiz3377rcSHUC5dupQpU6ZUcCLt8/f3Jzw8nCtXrmBjY4OzszNO\nTk44OTlhamqqdDzxGClohBBClFnv3r2ZMWMG3bt316zLy8tj5syZ3Lx5k/Xr1ysXTstycnKIjIwk\nPDyciIgIwsPDqVmzJiEhIUpHE4+QWU5CCCHKbN26dbz77rvcv38fFxcX7t27x4QJE6hRo4ZejZ8B\nyM7OJiMjg/T0dNLT06lbty4tWrRQOpZ4jPTQCCGE+Edu3LjBqFGjeOutt9izZw/29vbMnDlT6Vha\nM2fOHKKjozExMcHBwQEHBwccHR2pVauW0tFEMQyUDiCEEEL3REVFcfv2bT788ENWrVqFpaUl7u7u\nREVFERUVpXQ8rbh27Ro5OTnUqVMHCwsLLC0tqVmzptKxRAmkh0YIIUSZlfbwSZVKxebNmyswTfnJ\nz88nOjqa8PBwwsPDuXTpEqampjg6OjJ+/Hil44lHSEEjhBBCqyIiInB0dFQ6hlbduHGDsLAwwsLC\nOHr0KHfu3OHUqVNKxxKPkIJGCCGEVnXv3p2jR48qHeOZbd68WdMzY2RkpJmy7ezsjI2NDQYGMmrj\neSKznIQQQmiVvnxOTkpKok+fPsyYMYO6desqHUc8gRQ0QgghtEpfHlo5Y8YMpSOIMpCCRgghRJmV\n9iynO3fuVGASIQrIGBohhBBlVhme5SR0ixQ0QgghtOb69euo1Wp8fX2VjiIqGbnkJIQQ4pmkpqay\nd+9e1Go1KSkpuLi4KB1JVEJS0AghhCizjIwMDhw4QHBwMLGxsfTq1YvExESOHTumdDRRSUlBI4QQ\nosw6depEmzZtmDhxIm3btkWlUnHgwAGlY4lKTO4KJIQQosw++OADcnJy+Oijj/jyyy+Jj49XOpKo\n5GRQsBBCiH8sISEBtVqNWq0mLi6O999/HxcXFxo3bqx0NFHJSEEjhBCizDZu3IizszOtWrXCyKhg\n9MKlS5dQq9WEhITI5SdR4aSgEUIIUWb+/v6Eh4dz5coVbGxscHZ21jzryNTUVOl4ohKSgkYIIcQ/\nlpOTQ2RkJOHh4URERBAeHk7NmjUJCQlROpqoZGSWkxBCiH8sOzubjIwM0tPTSU9Pp27durRo0ULp\nWKISkh6JCO0cAAAFyUlEQVQaIYQQZTZnzhyio6MxMTHBwcEBBwcHHB0dqVWrltLRRCUl07aFEEKU\n2bVr18jJyaFOnTpYWFhgaWlJzZo1lY4lKjHpoRFCCPGP5OfnEx0dTXh4OOHh4Vy6dAlTU1McHR0Z\nP3680vFEJSMFjRBCiGdy48YNwsLCCAsL4+jRo9y5c4dTp04pHUtUMlLQCCGEKLPNmzdremaMjIw0\nU7adnZ2xsbHBwEBGNIiKJbOchBBClFlSUhJ9+vRhxowZ1K1bV+k4QkgPjRBCCCF0n/QJCiGEEELn\nSUEjhBBCCJ0nBY0QokLY2tri7u6Oq6sr48eP56+//vrHx/r9998ZM2YMAIcOHWLt2rUl7vvnn3/y\n7bfflvlnfP7556xfv/6p1z9q+vTp/PTTT0/9sxITE3F1dS1zRiHE36SgEUJUiKpVqxIYGEhwcDAv\nvPACP/zwQ6Ht+fn55OXllfm4PXv2ZPTo0SVu//PPP/n+++/LfFwhhG6RWU5CiArXrl07Ll68SGJi\nIqNGjcLBwYGoqCjWrl1LbGwsn3/+OTk5OTRo0IBPPvkEExMTjh07xqJFi6hWrRpt27bVHGvnzp1E\nRkYyd+5cbt26xbx580hISABg/vz5bNmyhfj4eNzd3enUqRPTpk1j3bp17N27l5ycHFxcXDQ3gVuz\nZg27d+/G3NwcKysrWrduXWo7tm7dyo8//sj9+/dp2LAhS5YsoVq1agD873//Y+3atWRmZjJ9+nT+\n9a9/kZuby7Jlyzhx4gQ5OTkMHz6coUOHltO7LETlIgWNEKJCPXjwgGPHjtG1a1cArl69ir+/P46O\njqSmprJmzRo2bNhA9erVWbt2LRs2bODdd99lzpw5bNq0iYYNGzJx4sRij71w4ULat2/PF198QW5u\nLllZWUyePJno6GgCAwMB+PXXX7l69Srbt28nPz+fcePGcfLkSapVq0ZISAi7d+8mNzcXDw+PJxY0\nLi4uDBkyBICVK1eyfft2vL29gYJpzdu3byc+Pp63336bTp06sXv3bl588UV27NhBTk4OQ4cOpXPn\nzqhUKm29vUJUWlLQCCEqxL1793B3dwcKemgGDRpESkoK9erVw9HREYDTp09z+fJlhg0bBsD9+/dx\ndHTkypUr1K9fn0aNGgEwYMAAtm7dWuRnHD9+nCVLlgBgaGjIiy++yN27dwvt89tvv/Hbb78xcOBA\nALKysoiLiyMzM5PXX39d08PSo0ePJ7YpOjqaVatWkZ6eTmZmJl26dNFs69u3LwYGBjRq1IgGDRpw\n5coVfvvtNy5evMi+ffsASE9P5+rVq5p2CSH+OSlohBAV4uEYmsdVr15d831+fj6dO3dmxYoVhfY5\nf/681nLk5+czevToIpd6Nm7cWOZjTZ8+ndWrV9OyZUt27tzJiRMnNNse73VRqVTk5+cze/ZsTe/U\nQ4mJiWX+2UKIwmRQsBDiueHo6EhYWBhXr14FCnpPYmNjadKkCUlJScTHxwOgVquLfX3Hjh357rvv\nAMjNzSU9PR0TExMyMzM1+3Tp0oUdO3Zo1iUnJ3P79m3at2/PwYMHuXfvHhkZGRw5cuSJeTMzM6lT\npw73798nKCio0LaffvqJvLw84uPjSUhIoHHjxnTp0oXvv/+e+/fvAxAbG0tWVlYZ3yUhRHGkh0YI\n8dwwNzfnk08+4YMPPiAnJweAiRMn0rhxYxYsWMDo0aM1g4IfLVIemjVrFnPmzGHHjh0YGBgwf/58\nzfOFXF1d6dq1K9OmTSMmJkbTQ1O9enWWLl1K69at6devH+7u7pibm2Nvb//EvBMmTGDw4MGYm5vj\n4OBQKJOVlRWDBg0iMzOTjz76iCpVqjB48GCSkpLw9PQkPz8fMzMzVq9eraV3T4jKTR59IIQQQgid\nJ5echBBCCKHzpKARQgghhM6TgkYIIYQQOk8KGiGEEELoPClohBBCCKHzpKARQgghhM6TgkYIIYQQ\nOk8KGiGEEELovP8DFa7GhT8zIncAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-------------------------\n",
            "| Classifiction Report |\n",
            "-------------------------\n",
            "                    precision    recall  f1-score   support\n",
            "\n",
            "            LAYING       1.00      1.00      1.00       537\n",
            "           SITTING       0.97      0.90      0.93       491\n",
            "          STANDING       0.92      0.98      0.95       532\n",
            "           WALKING       0.96      0.99      0.97       496\n",
            "WALKING_DOWNSTAIRS       0.99      0.95      0.97       420\n",
            "  WALKING_UPSTAIRS       0.95      0.96      0.95       471\n",
            "\n",
            "          accuracy                           0.96      2947\n",
            "         macro avg       0.96      0.96      0.96      2947\n",
            "      weighted avg       0.96      0.96      0.96      2947\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxwuhehDsg1x",
        "colab_type": "code",
        "outputId": "2d133631-e027-4e9c-8bfe-915702e8caeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "source": [
        "print_grid_search_attributes(rbf_svm_grid_results['model'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------------------------\n",
            "|      Best Estimator     |\n",
            "--------------------------\n",
            "\n",
            "\tSVC(C=16, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma=0.0078125, kernel='rbf',\n",
            "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "    tol=0.001, verbose=False)\n",
            "\n",
            "--------------------------\n",
            "|     Best parameters     |\n",
            "--------------------------\n",
            "\tParameters of best estimator : \n",
            "\n",
            "\t{'C': 16, 'gamma': 0.0078125}\n",
            "\n",
            "---------------------------------\n",
            "|   No of CrossValidation sets   |\n",
            "--------------------------------\n",
            "\n",
            "\tTotal numbre of cross validation sets: 3\n",
            "\n",
            "--------------------------\n",
            "|        Best Score       |\n",
            "--------------------------\n",
            "\n",
            "\tAverage Cross Validate scores of best estimator : \n",
            "\n",
            "\t0.9440968443960827\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "Iy8XTcU_sg13",
        "colab_type": "text"
      },
      "source": [
        "# 4. Decision Trees with GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "t-Xi_NaUsg14",
        "colab_type": "code",
        "outputId": "d38a11b7-1846-4968-8f19-54b4f12b292e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "parameters = {'max_depth':np.arange(3,10,2)}\n",
        "dt = DecisionTreeClassifier()\n",
        "dt_grid = GridSearchCV(dt,param_grid=parameters, n_jobs=-1)\n",
        "dt_grid_results = perform_model(dt_grid, X_train, y_train, X_test, y_test, class_labels=labels)\n",
        "print_grid_search_attributes(dt_grid_results['model'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training the model..\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done \n",
            " \n",
            "\n",
            "training_time(HH:MM:SS.ms) - 0:00:08.058557\n",
            "\n",
            "\n",
            "Predicting test data\n",
            "Done \n",
            " \n",
            "\n",
            "testing time(HH:MM:SS:ms) - 0:00:00.007041\n",
            "\n",
            "\n",
            "---------------------\n",
            "|      Accuracy      |\n",
            "---------------------\n",
            "\n",
            "    0.8646080760095012\n",
            "\n",
            "\n",
            "--------------------\n",
            "| Confusion Matrix |\n",
            "--------------------\n",
            "\n",
            " [[537   0   0   0   0   0]\n",
            " [  0 386 105   0   0   0]\n",
            " [  0  93 439   0   0   0]\n",
            " [  0   0   0 470  18   8]\n",
            " [  0   0   0  12 347  61]\n",
            " [  0   0   0  73  29 369]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iIo3HScFERES6iJOCiYiIiNQLOzRERES6SLsaNOzQEBERkeZjh4aq1aETR5D54G7FAzWY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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-------------------------\n",
            "| Classifiction Report |\n",
            "-------------------------\n",
            "                    precision    recall  f1-score   support\n",
            "\n",
            "            LAYING       1.00      1.00      1.00       537\n",
            "           SITTING       0.81      0.79      0.80       491\n",
            "          STANDING       0.81      0.83      0.82       532\n",
            "           WALKING       0.85      0.95      0.89       496\n",
            "WALKING_DOWNSTAIRS       0.88      0.83      0.85       420\n",
            "  WALKING_UPSTAIRS       0.84      0.78      0.81       471\n",
            "\n",
            "          accuracy                           0.86      2947\n",
            "         macro avg       0.86      0.86      0.86      2947\n",
            "      weighted avg       0.86      0.86      0.86      2947\n",
            "\n",
            "--------------------------\n",
            "|      Best Estimator     |\n",
            "--------------------------\n",
            "\n",
            "\tDecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=7,\n",
            "                       max_features=None, max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, presort=False,\n",
            "                       random_state=None, splitter='best')\n",
            "\n",
            "--------------------------\n",
            "|     Best parameters     |\n",
            "--------------------------\n",
            "\tParameters of best estimator : \n",
            "\n",
            "\t{'max_depth': 7}\n",
            "\n",
            "---------------------------------\n",
            "|   No of CrossValidation sets   |\n",
            "--------------------------------\n",
            "\n",
            "\tTotal numbre of cross validation sets: 3\n",
            "\n",
            "--------------------------\n",
            "|        Best Score       |\n",
            "--------------------------\n",
            "\n",
            "\tAverage Cross Validate scores of best estimator : \n",
            "\n",
            "\t0.8490206746463548\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zw6HrTRJsg18",
        "colab_type": "text"
      },
      "source": [
        "# 5. Random Forest Classifier with GridSearch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "KHlcmPr6sg1-",
        "colab_type": "code",
        "outputId": "67fd6a1a-3154-43c6-a655-c61fab2e7a34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "params = {'n_estimators': np.arange(10,201,20), 'max_depth':np.arange(3,15,2)}\n",
        "rfc = RandomForestClassifier()\n",
        "rfc_grid = GridSearchCV(rfc, param_grid=params, n_jobs=-1)\n",
        "rfc_grid_results = perform_model(rfc_grid, X_train, y_train, X_test, y_test, class_labels=labels)\n",
        "print_grid_search_attributes(rfc_grid_results['model'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training the model..\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done \n",
            " \n",
            "\n",
            "training_time(HH:MM:SS.ms) - 0:04:56.534617\n",
            "\n",
            "\n",
            "Predicting test data\n",
            "Done \n",
            " \n",
            "\n",
            "testing time(HH:MM:SS:ms) - 0:00:00.023134\n",
            "\n",
            "\n",
            "---------------------\n",
            "|      Accuracy      |\n",
            "---------------------\n",
            "\n",
            "    0.9087207329487614\n",
            "\n",
            "\n",
            "--------------------\n",
            "| Confusion Matrix |\n",
            "--------------------\n",
            "\n",
            " [[537   0   0   0   0   0]\n",
            " [  0 419  72   0   0   0]\n",
            " [  0  54 478   0   0   0]\n",
            " [  0   0   0 483  11   2]\n",
            " [  0   0   0  36 337  47]\n",
            " [  0   0   0  41   6 424]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1AY23tzcnkk6YlpNPJOPjXfqufcqU8eHWrVtcuniJ4sWL4+NdOtu23t7eVon7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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-------------------------\n",
            "| Classifiction Report |\n",
            "-------------------------\n",
            "                    precision    recall  f1-score   support\n",
            "\n",
            "            LAYING       1.00      1.00      1.00       537\n",
            "           SITTING       0.89      0.85      0.87       491\n",
            "          STANDING       0.87      0.90      0.88       532\n",
            "           WALKING       0.86      0.97      0.91       496\n",
            "WALKING_DOWNSTAIRS       0.95      0.80      0.87       420\n",
            "  WALKING_UPSTAIRS       0.90      0.90      0.90       471\n",
            "\n",
            "          accuracy                           0.91      2947\n",
            "         macro avg       0.91      0.90      0.91      2947\n",
            "      weighted avg       0.91      0.91      0.91      2947\n",
            "\n",
            "--------------------------\n",
            "|      Best Estimator     |\n",
            "--------------------------\n",
            "\n",
            "\tRandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
            "                       max_depth=7, max_features='auto', max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=50,\n",
            "                       n_jobs=None, oob_score=False, random_state=None,\n",
            "                       verbose=0, warm_start=False)\n",
            "\n",
            "--------------------------\n",
            "|     Best parameters     |\n",
            "--------------------------\n",
            "\tParameters of best estimator : \n",
            "\n",
            "\t{'max_depth': 7, 'n_estimators': 50}\n",
            "\n",
            "---------------------------------\n",
            "|   No of CrossValidation sets   |\n",
            "--------------------------------\n",
            "\n",
            "\tTotal numbre of cross validation sets: 3\n",
            "\n",
            "--------------------------\n",
            "|        Best Score       |\n",
            "--------------------------\n",
            "\n",
            "\tAverage Cross Validate scores of best estimator : \n",
            "\n",
            "\t0.9173014145810664\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uowKcXisg2C",
        "colab_type": "text"
      },
      "source": [
        "# 6.  Gradient Boosted Decision Trees With GridSearch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "rlLq22tLsg2D",
        "colab_type": "code",
        "outputId": "af898c5b-4a11-456b-a251-eed41ad7e067",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "param_grid = {'max_depth': np.arange(5,8,1), \\\n",
        "             'n_estimators':np.arange(130,170,10)}\n",
        "gbdt = GradientBoostingClassifier()\n",
        "gbdt_grid = GridSearchCV(gbdt, param_grid=param_grid, n_jobs=-1)\n",
        "gbdt_grid_results = perform_model(gbdt_grid, X_train, y_train, X_test, y_test, class_labels=labels)\n",
        "print_grid_search_attributes(gbdt_grid_results['model'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training the model..\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done \n",
            " \n",
            "\n",
            "training_time(HH:MM:SS.ms) - 0:43:35.410602\n",
            "\n",
            "\n",
            "Predicting test data\n",
            "Done \n",
            " \n",
            "\n",
            "testing time(HH:MM:SS:ms) - 0:00:00.064529\n",
            "\n",
            "\n",
            "---------------------\n",
            "|      Accuracy      |\n",
            "---------------------\n",
            "\n",
            "    0.9236511706820495\n",
            "\n",
            "\n",
            "--------------------\n",
            "| Confusion Matrix |\n",
            "--------------------\n",
            "\n",
            " [[537   0   0   0   0   0]\n",
            " [  0 397  93   0   0   1]\n",
            " [  0  38 494   0   0   0]\n",
            " [  0   0   0 483   6   7]\n",
            " [  0   0   0   9 371  40]\n",
            " [  0   1   0  25   5 440]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4f/58gQ1ovLy8OHXylHn79KnTeHtVuGufihW9uX37Npd/u0yZMmXw9qqQ5Vgv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\nIXTe/wHzc19wl0whggAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-------------------------\n",
            "| Classifiction Report |\n",
            "-------------------------\n",
            "                    precision    recall  f1-score   support\n",
            "\n",
            "            LAYING       1.00      1.00      1.00       537\n",
            "           SITTING       0.91      0.81      0.86       491\n",
            "          STANDING       0.84      0.93      0.88       532\n",
            "           WALKING       0.93      0.97      0.95       496\n",
            "WALKING_DOWNSTAIRS       0.97      0.88      0.93       420\n",
            "  WALKING_UPSTAIRS       0.90      0.93      0.92       471\n",
            "\n",
            "          accuracy                           0.92      2947\n",
            "         macro avg       0.93      0.92      0.92      2947\n",
            "      weighted avg       0.93      0.92      0.92      2947\n",
            "\n",
            "--------------------------\n",
            "|      Best Estimator     |\n",
            "--------------------------\n",
            "\n",
            "\tGradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
            "                           learning_rate=0.1, loss='deviance', max_depth=5,\n",
            "                           max_features=None, max_leaf_nodes=None,\n",
            "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                           min_samples_leaf=1, min_samples_split=2,\n",
            "                           min_weight_fraction_leaf=0.0, n_estimators=130,\n",
            "                           n_iter_no_change=None, presort='auto',\n",
            "                           random_state=None, subsample=1.0, tol=0.0001,\n",
            "                           validation_fraction=0.1, verbose=0,\n",
            "                           warm_start=False)\n",
            "\n",
            "--------------------------\n",
            "|     Best parameters     |\n",
            "--------------------------\n",
            "\tParameters of best estimator : \n",
            "\n",
            "\t{'max_depth': 5, 'n_estimators': 130}\n",
            "\n",
            "---------------------------------\n",
            "|   No of CrossValidation sets   |\n",
            "--------------------------------\n",
            "\n",
            "\tTotal numbre of cross validation sets: 3\n",
            "\n",
            "--------------------------\n",
            "|        Best Score       |\n",
            "--------------------------\n",
            "\n",
            "\tAverage Cross Validate scores of best estimator : \n",
            "\n",
            "\t0.904651795429815\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0PxmEcACsg2H",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# 7. Comparing all models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "twFmQXNAsg2J",
        "colab_type": "code",
        "outputId": "74869603-29de-4188-b8ee-edd92aca3fbb",
        "colab": {}
      },
      "source": [
        "print('\\n                     Accuracy     Error')\n",
        "print('                     ----------   --------')\n",
        "print('Logistic Regression : {:.04}%       {:.04}%'.format(log_reg_grid_results['accuracy'] * 100,\\\n",
        "                                                  100-(log_reg_grid_results['accuracy'] * 100)))\n",
        "\n",
        "print('Linear SVC          : {:.04}%       {:.04}% '.format(lr_svc_grid_results['accuracy'] * 100,\\\n",
        "                                                        100-(lr_svc_grid_results['accuracy'] * 100)))\n",
        "\n",
        "print('rbf SVM classifier  : {:.04}%      {:.04}% '.format(rbf_svm_grid_results['accuracy'] * 100,\\\n",
        "                                                          100-(rbf_svm_grid_results['accuracy'] * 100)))\n",
        "\n",
        "print('DecisionTree        : {:.04}%      {:.04}% '.format(dt_grid_results['accuracy'] * 100,\\\n",
        "                                                        100-(dt_grid_results['accuracy'] * 100)))\n",
        "\n",
        "print('Random Forest       : {:.04}%      {:.04}% '.format(rfc_grid_results['accuracy'] * 100,\\\n",
        "                                                           100-(rfc_grid_results['accuracy'] * 100)))\n",
        "print('GradientBoosting DT : {:.04}%      {:.04}% '.format(rfc_grid_results['accuracy'] * 100,\\\n",
        "                                                        100-(rfc_grid_results['accuracy'] * 100)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "                     Accuracy     Error\n",
            "                     ----------   --------\n",
            "Logistic Regression : 96.27%       3.733%\n",
            "Linear SVC          : 96.61%       3.393% \n",
            "rbf SVM classifier  : 96.27%      3.733% \n",
            "DecisionTree        : 86.43%      13.57% \n",
            "Random Forest       : 91.31%      8.687% \n",
            "GradientBoosting DT : 91.31%      8.687% \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8YQKMdTsg2P",
        "colab_type": "text"
      },
      "source": [
        "> We can choose ___Logistic regression___ or ___Linear SVC___ or ___rbf SVM___."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t_z9uVq3sg2R",
        "colab_type": "text"
      },
      "source": [
        "# Conclusion :"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1gVksmHcsg2T",
        "colab_type": "text"
      },
      "source": [
        "In the real world, domain-knowledge, EDA and feature-engineering matter most."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tnOhwQ3v7F5u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing Libraries"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VfD8ndU7F53",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJhEuD0P7F5_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Activities are the class labels\n",
        "# It is a 6 class classification\n",
        "ACTIVITIES = {\n",
        "    0: 'WALKING',\n",
        "    1: 'WALKING_UPSTAIRS',\n",
        "    2: 'WALKING_DOWNSTAIRS',\n",
        "    3: 'SITTING',\n",
        "    4: 'STANDING',\n",
        "    5: 'LAYING',\n",
        "}\n",
        "\n",
        "# Utility function to print the confusion matrix\n",
        "def confusion_matrix(Y_true, Y_pred):\n",
        "    Y_true = pd.Series([ACTIVITIES[y] for y in np.argmax(Y_true, axis=1)])\n",
        "    Y_pred = pd.Series([ACTIVITIES[y] for y in np.argmax(Y_pred, axis=1)])\n",
        "\n",
        "    return pd.crosstab(Y_true, Y_pred, rownames=['True'], colnames=['Pred'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6u1XKH7W7F6H",
        "colab_type": "text"
      },
      "source": [
        "### Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0iqgXdf7F6J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data directory\n",
        "DATADIR = '/content/drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWKzeOQr7F6O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Raw data signals\n",
        "# Signals are from Accelerometer and Gyroscope\n",
        "# The signals are in x,y,z directions\n",
        "# Sensor signals are filtered to have only body acceleration\n",
        "# excluding the acceleration due to gravity\n",
        "# Triaxial acceleration from the accelerometer is total acceleration\n",
        "SIGNALS = [\n",
        "    \"body_acc_x\",\n",
        "    \"body_acc_y\",\n",
        "    \"body_acc_z\",\n",
        "    \"body_gyro_x\",\n",
        "    \"body_gyro_y\",\n",
        "    \"body_gyro_z\",\n",
        "    \"total_acc_x\",\n",
        "    \"total_acc_y\",\n",
        "    \"total_acc_z\"\n",
        "]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoCQZzaO7F6U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Utility function to read the data from csv file\n",
        "def _read_csv(filename):\n",
        "    return pd.read_csv(filename, delim_whitespace=True, header=None)\n",
        "\n",
        "# Utility function to load the load\n",
        "def load_signals(subset):\n",
        "    signals_data = []\n",
        "\n",
        "    for signal in SIGNALS:\n",
        "        filename = f'drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/{subset}/Inertial Signals/{signal}_{subset}.txt'\n",
        "        signals_data.append(\n",
        "            _read_csv(filename).as_matrix()\n",
        "        ) \n",
        "\n",
        "    # Transpose is used to change the dimensionality of the output,\n",
        "    # aggregating the signals by combination of sample/timestep.\n",
        "    # Resultant shape is (7352 train/2947 test samples, 128 timesteps, 9 signals)\n",
        "    return np.transpose(signals_data, (1, 2, 0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5Mig-JX7F6Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def load_y(subset):\n",
        "    \"\"\"\n",
        "    The objective that we are trying to predict is a integer, from 1 to 6,\n",
        "    that represents a human activity. We return a binary representation of \n",
        "    every sample objective as a 6 bits vector using One Hot Encoding\n",
        "    (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html)\n",
        "    \"\"\"\n",
        "    filename = f'drive/My Drive/Applied_AI/HAR/UCI_HAR_Dataset/{subset}/y_{subset}.txt'\n",
        "    y = _read_csv(filename)[0]\n",
        "\n",
        "    return pd.get_dummies(y).as_matrix()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8UAM4ts7F6e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_data():\n",
        "    \"\"\"\n",
        "    Obtain the dataset from multiple files.\n",
        "    Returns: X_train, X_test, y_train, y_test\n",
        "    \"\"\"\n",
        "    X_train, X_test = load_signals('train'), load_signals('test')\n",
        "    y_train, y_test = load_y('train'), load_y('test')\n",
        "\n",
        "    return X_train, X_test, y_train, y_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zuduMUH7F6n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing tensorflow\n",
        "np.random.seed(42)\n",
        "import tensorflow as tf\n",
        "tf.set_random_seed(42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DgiGP-vh7F6w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Configuring a session\n",
        "session_conf = tf.ConfigProto(\n",
        "    intra_op_parallelism_threads=1,\n",
        "    inter_op_parallelism_threads=1\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bm1DoeTO7F62",
        "colab_type": "code",
        "outputId": "0cc11990-c844-4d51-d38b-e5350afd47ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Import Keras\n",
        "from keras import backend as K\n",
        "sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)\n",
        "K.set_session(sess)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFmH-Yaz7F67",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing libraries\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM\n",
        "from keras.layers.core import Dense, Dropout"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pbSMPRK7F6_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initializing parameters\n",
        "n_hidden = 32\n",
        "epochs = 30\n",
        "batch_size = 16\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZqAAW42U7F7E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Utility function to count the number of classes\n",
        "def _count_classes(y):\n",
        "    return len(set([tuple(category) for category in y]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JiXBRQ2p7F7J",
        "colab_type": "code",
        "outputId": "8d113019-b8f3-442d-8361-f8f29ba5e092",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "source": [
        "# Loading the train and test data\n",
        "X_train, X_test, Y_train, Y_test = load_data()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  if sys.path[0] == '':\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPdMHwFH7F7N",
        "colab_type": "code",
        "outputId": "4834fd74-574e-4ad6-e281-b394df1550ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "timesteps = len(X_train[0])\n",
        "input_dim = len(X_train[0][0])\n",
        "n_classes = _count_classes(Y_train)\n",
        "\n",
        "print(timesteps)\n",
        "print(input_dim)\n",
        "print(len(X_train))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "128\n",
            "9\n",
            "7352\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1Tk3AUz7F7U",
        "colab_type": "text"
      },
      "source": [
        "- Defining the Architecture of LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdJViXPT7F7W",
        "colab_type": "code",
        "outputId": "0da0e2e2-6eaa-4850-b880-33b3b19cf2b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "source": [
        "# Initiliazing the sequential model\n",
        "model = Sequential()\n",
        "# Configuring the parameters\n",
        "model.add(LSTM(64,return_sequences=True, input_shape=(timesteps, input_dim)))\n",
        "# Adding a dropout layer\n",
        "model.add(Dropout(0.7))\n",
        "model.add(LSTM(32))\n",
        "# Adding a dropout layer\n",
        "model.add(Dropout(0.6))\n",
        "# Adding a dense output layer with sigmoid activation\n",
        "model.add(Dense(n_classes, activation='relu',kernel_initializer='he_normal'))\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_20 (LSTM)               (None, 128, 64)           18944     \n",
            "_________________________________________________________________\n",
            "dropout_18 (Dropout)         (None, 128, 64)           0         \n",
            "_________________________________________________________________\n",
            "lstm_21 (LSTM)               (None, 32)                12416     \n",
            "_________________________________________________________________\n",
            "dropout_19 (Dropout)         (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 6)                 198       \n",
            "=================================================================\n",
            "Total params: 31,558\n",
            "Trainable params: 31,558\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6u6sxZR07F7c",
        "colab_type": "code",
        "outputId": "63e876c9-9d28-40bf-91ff-f7ee63d9032b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "# Compiling the model\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0814 08:03:48.703269 140336617305984 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8C95vo7i7F7h",
        "colab_type": "code",
        "outputId": "6febf767-dbee-4688-a7e4-441e25c74144",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Training the model\n",
        "model.fit(X_train,\n",
        "          Y_train,\n",
        "          batch_size=batch_size,\n",
        "          validation_data=(X_test, Y_test),\n",
        "          epochs=epochs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0814 08:04:01.099930 140336617305984 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/30\n",
            "7352/7352 [==============================] - 190s 26ms/step - loss: 1.9318 - acc: 0.3855 - val_loss: 1.4064 - val_acc: 0.4944\n",
            "Epoch 2/30\n",
            "7352/7352 [==============================] - 185s 25ms/step - loss: 1.3454 - acc: 0.4816 - val_loss: 1.2470 - val_acc: 0.5741\n",
            "Epoch 3/30\n",
            "7352/7352 [==============================] - 184s 25ms/step - loss: 1.2108 - acc: 0.5000 - val_loss: 1.2850 - val_acc: 0.5721\n",
            "Epoch 4/30\n",
            "7352/7352 [==============================] - 188s 26ms/step - loss: 1.1307 - acc: 0.5227 - val_loss: 1.2625 - val_acc: 0.4913\n",
            "Epoch 5/30\n",
            "7352/7352 [==============================] - 185s 25ms/step - loss: 1.0214 - acc: 0.5181 - val_loss: 1.2549 - val_acc: 0.5456\n",
            "Epoch 6/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 1.0139 - acc: 0.5479 - val_loss: 1.1065 - val_acc: 0.6186\n",
            "Epoch 7/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.9358 - acc: 0.5688 - val_loss: 1.1780 - val_acc: 0.6373\n",
            "Epoch 8/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 1.4578 - acc: 0.5155 - val_loss: 0.8273 - val_acc: 0.6369\n",
            "Epoch 9/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.8244 - acc: 0.5853 - val_loss: 0.8393 - val_acc: 0.6352\n",
            "Epoch 10/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.8558 - acc: 0.6032 - val_loss: 0.8215 - val_acc: 0.6396\n",
            "Epoch 11/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.8245 - acc: 0.6122 - val_loss: 1.2644 - val_acc: 0.5300\n",
            "Epoch 12/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 1.0716 - acc: 0.5302 - val_loss: 1.5114 - val_acc: 0.4968\n",
            "Epoch 13/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.9078 - acc: 0.5428 - val_loss: 1.1656 - val_acc: 0.5222\n",
            "Epoch 14/30\n",
            "7352/7352 [==============================] - 182s 25ms/step - loss: 0.9788 - acc: 0.5462 - val_loss: 1.0110 - val_acc: 0.5243\n",
            "Epoch 15/30\n",
            "7352/7352 [==============================] - 186s 25ms/step - loss: 0.8637 - acc: 0.5571 - val_loss: 0.9567 - val_acc: 0.5134\n",
            "Epoch 16/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.8623 - acc: 0.5506 - val_loss: 0.8834 - val_acc: 0.5799\n",
            "Epoch 17/30\n",
            "7352/7352 [==============================] - 182s 25ms/step - loss: 0.8392 - acc: 0.5734 - val_loss: 0.8735 - val_acc: 0.5243\n",
            "Epoch 18/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.8479 - acc: 0.5548 - val_loss: 0.9208 - val_acc: 0.5819\n",
            "Epoch 19/30\n",
            "7352/7352 [==============================] - 182s 25ms/step - loss: 0.7998 - acc: 0.6084 - val_loss: 0.7995 - val_acc: 0.6067\n",
            "Epoch 20/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.8889 - acc: 0.6330 - val_loss: 1.0306 - val_acc: 0.6101\n",
            "Epoch 21/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.9530 - acc: 0.6430 - val_loss: 0.8687 - val_acc: 0.6166\n",
            "Epoch 22/30\n",
            "7352/7352 [==============================] - 182s 25ms/step - loss: 0.7223 - acc: 0.6499 - val_loss: 0.7625 - val_acc: 0.6237\n",
            "Epoch 23/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.6952 - acc: 0.6581 - val_loss: 0.7844 - val_acc: 0.6026\n",
            "Epoch 24/30\n",
            "7352/7352 [==============================] - 184s 25ms/step - loss: 0.6875 - acc: 0.6601 - val_loss: 1.2797 - val_acc: 0.5236\n",
            "Epoch 25/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 2.3553 - acc: 0.5518 - val_loss: 2.0921 - val_acc: 0.5758\n",
            "Epoch 26/30\n",
            "7352/7352 [==============================] - 184s 25ms/step - loss: 0.8806 - acc: 0.6575 - val_loss: 1.1252 - val_acc: 0.6115\n",
            "Epoch 27/30\n",
            "7352/7352 [==============================] - 182s 25ms/step - loss: 0.6667 - acc: 0.6696 - val_loss: 0.8390 - val_acc: 0.6043\n",
            "Epoch 28/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 1.1439 - acc: 0.6427 - val_loss: 0.8186 - val_acc: 0.6664\n",
            "Epoch 29/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.7800 - acc: 0.6547 - val_loss: 0.7032 - val_acc: 0.6963\n",
            "Epoch 30/30\n",
            "7352/7352 [==============================] - 185s 25ms/step - loss: 0.7069 - acc: 0.7103 - val_loss: 0.5875 - val_acc: 0.7693\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa1ed1c0f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "7d6a9536-6333-463a-aa74-74fc2176b70e",
        "id": "3dw1cDAJvFzC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        }
      },
      "source": [
        "# Confusion Matrix\n",
        "print(confusion_matrix(Y_test, model.predict(X_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pred                LAYING  SITTING  ...  WALKING_DOWNSTAIRS  WALKING_UPSTAIRS\n",
            "True                                 ...                                      \n",
            "LAYING                 510        0  ...                   0                27\n",
            "SITTING                  0      405  ...                   0                11\n",
            "STANDING                 0      100  ...                   0                 6\n",
            "WALKING                  0        0  ...                  40                 2\n",
            "WALKING_DOWNSTAIRS       0        0  ...                 402                 9\n",
            "WALKING_UPSTAIRS         0        0  ...                   9               450\n",
            "\n",
            "[6 rows x 6 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "5caf21cc-ec8e-4e8c-e80a-dd109110016c",
        "id": "Eg1-wVy8vFzG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "score = model.evaluate(X_test, Y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2947/2947 [==============================] - 6s 2ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "9eb7a168-995f-45a4-f21c-b0da7c69bdd8",
        "id": "CcbuFYQCvFzK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4080225666194856, 0.8971835765184933]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCl6FnXpPjOm",
        "colab_type": "code",
        "outputId": "1299a2e7-d9e5-454d-87a7-edc33ee6a20d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "# Initiliazing the sequential model\n",
        "model = Sequential()\n",
        "# Configuring the parameters\n",
        "model.add(LSTM(64,return_sequences=True, input_shape=(timesteps, input_dim)))\n",
        "# Adding a dropout layer\n",
        "model.add(LSTM(32))\n",
        "# Adding a dropout layer\n",
        "model.add(Dropout(0.6))\n",
        "# Adding a dense output layer with sigmoid activation\n",
        "model.add(Dense(n_classes, activation='relu',kernel_initializer='he_normal'))\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_1 (LSTM)                (None, 128, 64)           18944     \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 32)                12416     \n",
            "_________________________________________________________________\n",
            "dropout_36 (Dropout)         (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_27 (Dense)             (None, 6)                 198       \n",
            "=================================================================\n",
            "Total params: 31,558\n",
            "Trainable params: 31,558\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sAFesly5P43K",
        "colab": {}
      },
      "source": [
        "# Compiling the model\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "4fc04b89-19c1-489c-e8b9-efd756a1f1f2",
        "id": "pPycgaw3P43Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Training the model\n",
        "model.fit(X_train,\n",
        "          Y_train,\n",
        "          batch_size=batch_size,\n",
        "          validation_data=(X_test, Y_test),\n",
        "          epochs=epochs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/30\n",
            "7352/7352 [==============================] - 180s 24ms/step - loss: 0.4943 - acc: 0.8406 - val_loss: 0.3551 - val_acc: 0.8364\n",
            "Epoch 2/30\n",
            "7352/7352 [==============================] - 176s 24ms/step - loss: 0.3577 - acc: 0.8427 - val_loss: 0.3363 - val_acc: 0.8431\n",
            "Epoch 3/30\n",
            "7352/7352 [==============================] - 178s 24ms/step - loss: 0.3383 - acc: 0.8444 - val_loss: 0.3363 - val_acc: 0.8357\n",
            "Epoch 4/30\n",
            "7352/7352 [==============================] - 175s 24ms/step - loss: 0.3071 - acc: 0.8468 - val_loss: 0.2375 - val_acc: 0.8642\n",
            "Epoch 5/30\n",
            "7352/7352 [==============================] - 177s 24ms/step - loss: 0.2397 - acc: 0.8590 - val_loss: 0.2697 - val_acc: 0.8610\n",
            "Epoch 6/30\n",
            "7352/7352 [==============================] - 177s 24ms/step - loss: 0.2621 - acc: 0.8618 - val_loss: 0.2275 - val_acc: 0.8693\n",
            "Epoch 7/30\n",
            "7352/7352 [==============================] - 180s 24ms/step - loss: 0.2386 - acc: 0.8675 - val_loss: 0.2763 - val_acc: 0.8725\n",
            "Epoch 8/30\n",
            "7352/7352 [==============================] - 175s 24ms/step - loss: 0.2397 - acc: 0.8828 - val_loss: 0.2267 - val_acc: 0.8936\n",
            "Epoch 9/30\n",
            "7352/7352 [==============================] - 176s 24ms/step - loss: 0.2206 - acc: 0.8883 - val_loss: 0.3420 - val_acc: 0.8773\n",
            "Epoch 10/30\n",
            "7352/7352 [==============================] - 177s 24ms/step - loss: 0.1818 - acc: 0.8880 - val_loss: 0.4045 - val_acc: 0.8670\n",
            "Epoch 11/30\n",
            "7352/7352 [==============================] - 178s 24ms/step - loss: 0.2947 - acc: 0.8561 - val_loss: 0.2507 - val_acc: 0.8625\n",
            "Epoch 12/30\n",
            "7352/7352 [==============================] - 174s 24ms/step - loss: 0.2215 - acc: 0.8664 - val_loss: 0.2415 - val_acc: 0.8690\n",
            "Epoch 13/30\n",
            "7352/7352 [==============================] - 179s 24ms/step - loss: 0.2737 - acc: 0.8629 - val_loss: 0.2280 - val_acc: 0.8574\n",
            "Epoch 14/30\n",
            "7352/7352 [==============================] - 176s 24ms/step - loss: 0.3198 - acc: 0.8573 - val_loss: 0.2822 - val_acc: 0.8564\n",
            "Epoch 15/30\n",
            "7352/7352 [==============================] - 178s 24ms/step - loss: 0.5596 - acc: 0.8264 - val_loss: 0.3518 - val_acc: 0.8332\n",
            "Epoch 16/30\n",
            "7352/7352 [==============================] - 177s 24ms/step - loss: 0.3421 - acc: 0.8355 - val_loss: 0.3127 - val_acc: 0.8345\n",
            "Epoch 17/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.2650 - acc: 0.8427 - val_loss: 0.2641 - val_acc: 0.8414\n",
            "Epoch 18/30\n",
            "7352/7352 [==============================] - 184s 25ms/step - loss: 0.2356 - acc: 0.8580 - val_loss: 0.5635 - val_acc: 0.8315\n",
            "Epoch 19/30\n",
            "7352/7352 [==============================] - 182s 25ms/step - loss: 0.3286 - acc: 0.8502 - val_loss: 0.2159 - val_acc: 0.8630\n",
            "Epoch 20/30\n",
            "7352/7352 [==============================] - 180s 25ms/step - loss: 0.2270 - acc: 0.8588 - val_loss: 0.2794 - val_acc: 0.8786\n",
            "Epoch 21/30\n",
            "7352/7352 [==============================] - 181s 25ms/step - loss: 0.2117 - acc: 0.8726 - val_loss: 0.2202 - val_acc: 0.8914\n",
            "Epoch 22/30\n",
            "7352/7352 [==============================] - 180s 24ms/step - loss: 0.2324 - acc: 0.8717 - val_loss: 0.2098 - val_acc: 0.8664\n",
            "Epoch 23/30\n",
            "7352/7352 [==============================] - 180s 24ms/step - loss: 0.2368 - acc: 0.8809 - val_loss: 0.2543 - val_acc: 0.8907\n",
            "Epoch 24/30\n",
            "7352/7352 [==============================] - 179s 24ms/step - loss: 0.4671 - acc: 0.8730 - val_loss: 0.2831 - val_acc: 0.8537\n",
            "Epoch 25/30\n",
            "7352/7352 [==============================] - 180s 25ms/step - loss: 0.2117 - acc: 0.8715 - val_loss: 0.2318 - val_acc: 0.9118\n",
            "Epoch 26/30\n",
            "7352/7352 [==============================] - 182s 25ms/step - loss: 0.1947 - acc: 0.8767 - val_loss: 0.1863 - val_acc: 0.9035\n",
            "Epoch 27/30\n",
            "7352/7352 [==============================] - 183s 25ms/step - loss: 0.1722 - acc: 0.8918 - val_loss: 0.2024 - val_acc: 0.9125\n",
            "Epoch 28/30\n",
            "7352/7352 [==============================] - 179s 24ms/step - loss: 0.2336 - acc: 0.8926 - val_loss: 0.1891 - val_acc: 0.9023\n",
            "Epoch 29/30\n",
            "7352/7352 [==============================] - 179s 24ms/step - loss: 0.2082 - acc: 0.8918 - val_loss: 0.2011 - val_acc: 0.9069\n",
            "Epoch 30/30\n",
            "7352/7352 [==============================] - 180s 25ms/step - loss: 0.2575 - acc: 0.8773 - val_loss: 0.2742 - val_acc: 0.8418\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f92099e1048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "b60d8d8d-284b-4ff6-e9ad-0bd60796d721",
        "id": "YcB6S06qPyCv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        }
      },
      "source": [
        "# Confusion Matrix\n",
        "print(confusion_matrix(Y_test, model.predict(X_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pred                LAYING  SITTING  ...  WALKING_DOWNSTAIRS  WALKING_UPSTAIRS\n",
            "True                                 ...                                      \n",
            "LAYING                 510        0  ...                   0                26\n",
            "SITTING                  0      214  ...                   1                 0\n",
            "STANDING                 0       23  ...                   0                 0\n",
            "WALKING                  0       69  ...                   8                 0\n",
            "WALKING_DOWNSTAIRS       0        9  ...                 360                 0\n",
            "WALKING_UPSTAIRS         0       20  ...                  17                 4\n",
            "\n",
            "[6 rows x 6 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "6b9aa58a-d901-4cfa-8170-54ab93441dab",
        "id": "NX2VgA5XPyDB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "score = model.evaluate(X_test, Y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2947/2947 [==============================] - 12s 4ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "8fd0a92f-d66c-41fa-ba50-9a103f0f8c8e",
        "id": "krZHVwUpPyDF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.27423708396197416, 0.8418165167226684]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Mu-2GF3B71U",
        "colab_type": "text"
      },
      "source": [
        "### Expermenting with different architecture "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "17fc4000-94cb-4302-cd8f-50fc1073b426",
        "id": "18qEeWskB6Ww",
        "colab": {}
      },
      "source": [
        "# Initiliazing the sequential model\n",
        "model = Sequential()\n",
        "# Configuring the parameters\n",
        "model.add(LSTM(n_hidden, input_shape=(timesteps, input_dim)))\n",
        "\n",
        "# Adding a dropout layer\n",
        "model.add(Dropout(0.5))\n",
        "# Adding a dense output layer with sigmoid activation\n",
        "model.add(Dense(n_classes, activation='sigmoid'))\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_3 (LSTM)                (None, 32)                5376      \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 6)                 198       \n",
            "=================================================================\n",
            "Total params: 5,574\n",
            "Trainable params: 5,574\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "e4KXI7x8B6W8",
        "colab": {}
      },
      "source": [
        "# Compiling the model\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "40cb0cac-2a38-4eb1-db8f-3e086e40fcdf",
        "id": "WGoIR5abB6XA",
        "colab": {}
      },
      "source": [
        "# Training the model\n",
        "model.fit(X_train,\n",
        "          Y_train,\n",
        "          batch_size=batch_size,\n",
        "          validation_data=(X_test, Y_test),\n",
        "          epochs=epochs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/30\n",
            "7352/7352 [==============================] - 92s 13ms/step - loss: 1.3018 - acc: 0.4395 - val_loss: 1.1254 - val_acc: 0.4662\n",
            "Epoch 2/30\n",
            "7352/7352 [==============================] - 94s 13ms/step - loss: 0.9666 - acc: 0.5880 - val_loss: 0.9491 - val_acc: 0.5714\n",
            "Epoch 3/30\n",
            "7352/7352 [==============================] - 97s 13ms/step - loss: 0.7812 - acc: 0.6408 - val_loss: 0.8286 - val_acc: 0.5850\n",
            "Epoch 4/30\n",
            "7352/7352 [==============================] - 95s 13ms/step - loss: 0.6941 - acc: 0.6574 - val_loss: 0.7297 - val_acc: 0.6128\n",
            "Epoch 5/30\n",
            "7352/7352 [==============================] - 92s 13ms/step - loss: 0.6336 - acc: 0.6912 - val_loss: 0.7359 - val_acc: 0.6787\n",
            "Epoch 6/30\n",
            "7352/7352 [==============================] - 94s 13ms/step - loss: 0.5859 - acc: 0.7134 - val_loss: 0.7015 - val_acc: 0.6939\n",
            "Epoch 7/30\n",
            "7352/7352 [==============================] - 95s 13ms/step - loss: 0.5692 - acc: 0.7477 - val_loss: 0.5995 - val_acc: 0.7387\n",
            "Epoch 8/30\n",
            "7352/7352 [==============================] - 96s 13ms/step - loss: 0.4899 - acc: 0.7809 - val_loss: 0.5762 - val_acc: 0.7387\n",
            "Epoch 9/30\n",
            "7352/7352 [==============================] - 90s 12ms/step - loss: 0.4482 - acc: 0.7886 - val_loss: 0.7413 - val_acc: 0.7126\n",
            "Epoch 10/30\n",
            "7352/7352 [==============================] - 90s 12ms/step - loss: 0.4132 - acc: 0.8077 - val_loss: 0.5048 - val_acc: 0.7513\n",
            "Epoch 11/30\n",
            "7352/7352 [==============================] - 89s 12ms/step - loss: 0.3985 - acc: 0.8274 - val_loss: 0.5234 - val_acc: 0.7452\n",
            "Epoch 12/30\n",
            "7352/7352 [==============================] - 91s 12ms/step - loss: 0.3378 - acc: 0.8638 - val_loss: 0.4114 - val_acc: 0.8833\n",
            "Epoch 13/30\n",
            "7352/7352 [==============================] - 91s 12ms/step - loss: 0.2947 - acc: 0.9051 - val_loss: 0.4386 - val_acc: 0.8731\n",
            "Epoch 14/30\n",
            "7352/7352 [==============================] - 90s 12ms/step - loss: 0.2448 - acc: 0.9291 - val_loss: 0.3768 - val_acc: 0.8921\n",
            "Epoch 15/30\n",
            "7352/7352 [==============================] - 91s 12ms/step - loss: 0.2157 - acc: 0.9331 - val_loss: 0.4441 - val_acc: 0.8931\n",
            "Epoch 16/30\n",
            "7352/7352 [==============================] - 90s 12ms/step - loss: 0.2053 - acc: 0.9366 - val_loss: 0.4162 - val_acc: 0.8968\n",
            "Epoch 17/30\n",
            "7352/7352 [==============================] - 89s 12ms/step - loss: 0.2028 - acc: 0.9404 - val_loss: 0.4538 - val_acc: 0.8962\n",
            "Epoch 18/30\n",
            "7352/7352 [==============================] - 93s 13ms/step - loss: 0.1911 - acc: 0.9419 - val_loss: 0.3964 - val_acc: 0.8999\n",
            "Epoch 19/30\n",
            "7352/7352 [==============================] - 96s 13ms/step - loss: 0.1912 - acc: 0.9407 - val_loss: 0.3165 - val_acc: 0.9030\n",
            "Epoch 20/30\n",
            "7352/7352 [==============================] - 96s 13ms/step - loss: 0.1732 - acc: 0.9446 - val_loss: 0.4546 - val_acc: 0.8904\n",
            "Epoch 21/30\n",
            "7352/7352 [==============================] - 94s 13ms/step - loss: 0.1782 - acc: 0.9444 - val_loss: 0.3346 - val_acc: 0.9063\n",
            "Epoch 22/30\n",
            "7352/7352 [==============================] - 95s 13ms/step - loss: 0.1812 - acc: 0.9418 - val_loss: 0.8164 - val_acc: 0.8582\n",
            "Epoch 23/30\n",
            "7352/7352 [==============================] - 95s 13ms/step - loss: 0.1824 - acc: 0.9426 - val_loss: 0.4240 - val_acc: 0.9036\n",
            "Epoch 24/30\n",
            "7352/7352 [==============================] - 94s 13ms/step - loss: 0.1726 - acc: 0.9429 - val_loss: 0.4067 - val_acc: 0.9148\n",
            "Epoch 25/30\n",
            "7352/7352 [==============================] - 96s 13ms/step - loss: 0.1737 - acc: 0.9411 - val_loss: 0.3396 - val_acc: 0.9074\n",
            "Epoch 26/30\n",
            "7352/7352 [==============================] - 96s 13ms/step - loss: 0.1650 - acc: 0.9461 - val_loss: 0.3806 - val_acc: 0.9019\n",
            "Epoch 27/30\n",
            "7352/7352 [==============================] - 89s 12ms/step - loss: 0.1925 - acc: 0.9415 - val_loss: 0.6464 - val_acc: 0.8850\n",
            "Epoch 28/30\n",
            "7352/7352 [==============================] - 91s 12ms/step - loss: 0.1965 - acc: 0.9425 - val_loss: 0.3363 - val_acc: 0.9203\n",
            "Epoch 29/30\n",
            "7352/7352 [==============================] - 92s 12ms/step - loss: 0.1889 - acc: 0.9431 - val_loss: 0.3737 - val_acc: 0.9158\n",
            "Epoch 30/30\n",
            "7352/7352 [==============================] - 95s 13ms/step - loss: 0.1945 - acc: 0.9414 - val_loss: 0.3088 - val_acc: 0.9097\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x29b5ee36a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "809e42a6-b0f7-4f3e-c59c-1e9fcf16a6ab",
        "id": "olc9TLErB6XF",
        "colab": {}
      },
      "source": [
        "# Confusion Matrix\n",
        "print(confusion_matrix(Y_test, model.predict(X_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pred                LAYING  SITTING  STANDING  WALKING  WALKING_DOWNSTAIRS  \\\n",
            "True                                                                         \n",
            "LAYING                 512        0        25        0                   0   \n",
            "SITTING                  3      410        75        0                   0   \n",
            "STANDING                 0       87       445        0                   0   \n",
            "WALKING                  0        0         0      481                   2   \n",
            "WALKING_DOWNSTAIRS       0        0         0        0                 382   \n",
            "WALKING_UPSTAIRS         0        0         0        2                  18   \n",
            "\n",
            "Pred                WALKING_UPSTAIRS  \n",
            "True                                  \n",
            "LAYING                             0  \n",
            "SITTING                            3  \n",
            "STANDING                           0  \n",
            "WALKING                           13  \n",
            "WALKING_DOWNSTAIRS                38  \n",
            "WALKING_UPSTAIRS                 451  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "11ca601c-1697-4e2c-eba0-9d8d3fb8be74",
        "id": "lePJSqgtB6XJ",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(X_test, Y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2947/2947 [==============================] - 4s 2ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "601774cd-4aed-4637-a8d4-851f657d29fe",
        "id": "vTmcWzksB6XS",
        "colab": {}
      },
      "source": [
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3087582236972612, 0.9097387173396675]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rheb2bR17F8C",
        "colab_type": "text"
      },
      "source": [
        "- With a simple 2 layer architecture we got 90.09% accuracy and a loss of 0.30\n",
        "- We can further imporve the performace with Hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8HZ7UZounnS",
        "colab_type": "code",
        "outputId": "3721f9bb-2448-4429-95ef-0536e4dfc871",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "\n",
        "from prettytable import PrettyTable \n",
        "\n",
        "x = PrettyTable()\n",
        "x.field_names = [\"Model no.\", \"Architecture\",  \"Test Loss(Cross-Entropy)\", \"Test Accuracy\"]\n",
        "x.add_row([1,\"1 LSTM Layers - RMSProp, Sigmoid \", 0.302, 0.909 ])\n",
        "x.add_row([2,\"2 LSTM Layers - Adam, Relu\", 0.274, 0.84 ])\n",
        "x.add_row([3,\"2 LSTM Layers - Adam, Relu, Weights-He Normal\", 0.408, 0.8972 ])\n",
        "\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----------+-----------------------------------------------+--------------------------+---------------+\n",
            "| Model no. |                  Architecture                 | Test Loss(Cross-Entropy) | Test Accuracy |\n",
            "+-----------+-----------------------------------------------+--------------------------+---------------+\n",
            "|     1     |       1 LSTM Layers - RMSProp, Sigmoid        |          0.302           |     0.909     |\n",
            "|     2     |           2 LSTM Layers - Adam, Relu          |          0.274           |      0.84     |\n",
            "|     3     | 2 LSTM Layers - Adam, Relu, Weights-He Normal |          0.408           |     0.8972    |\n",
            "+-----------+-----------------------------------------------+--------------------------+---------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwS-ZKojux2w",
        "colab_type": "text"
      },
      "source": [
        "## Conclusion:\n",
        "\n",
        "1. LSTM Layers are taking a lot of time to train.\n",
        "2. We got good accuracies but not as good as (expert features+Classiic ML)Models\n",
        "3. We will go for other DL models for trying to get better results  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRyMPlYRQdPG",
        "colab_type": "text"
      },
      "source": [
        "### LSTM are not giving the results as we are getting with expert features and for that we will work with CNN models . As read in research papers and blogs found out that CNN works typically well as compared with other models .\n",
        "References:\n",
        "1. https://machinelearningmastery.com/deep-learning-models-for-human-activity-recognition/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4XCqAhMB3co",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers.embeddings import Embedding\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras import backend as K\n",
        "from keras.preprocessing import sequence\n",
        "from keras.initializers import RandomNormal\n",
        "from keras.initializers import glorot_uniform,glorot_normal \n",
        "from keras.layers.normalization import BatchNormalization\n",
        "import sqlite3\n",
        "import pandas as pd\n",
        "import re\n",
        "import nltk\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0k0Hw0Dwb0QV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Reshaping the input for CNN models"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzWYJriXCUe6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_shape = (timesteps, input_dim, 1)\n",
        "X_train = X_train.reshape(X_train.shape[0], timesteps, input_dim, 1)\n",
        "X_test = X_test.reshape(X_test.shape[0], timesteps, input_dim, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mk6PTExVb7mo",
        "colab_type": "text"
      },
      "source": [
        "### We will try with three Conv layers :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-f5O7pasOFqS",
        "colab_type": "code",
        "outputId": "bb786b3e-dd52-4357-fe42-c3c3833d3853",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(64, kernel_size=(5, 5),activation='relu', padding='same', input_shape=input_shape))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(n_classes, activation='softmax'))\n",
        "\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "model.summary()\n",
        "history = model.fit(X_train, Y_train, batch_size=256, epochs=30, verbose=1, validation_data=(X_test, Y_test))\n",
        "score = model.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])\n",
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "ax.set_xlabel('epoch') ; ax.set_ylabel('Crossentropy Loss')\n",
        "\n",
        "x = list(range(1,epochs+1))\n",
        "vy = history.history['val_loss']\n",
        "ty = history.history['loss']\n",
        "plt_dynamic(x, vy, ty, ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_59 (Conv2D)           (None, 128, 9, 64)        1664      \n",
            "_________________________________________________________________\n",
            "conv2d_60 (Conv2D)           (None, 128, 9, 32)        18464     \n",
            "_________________________________________________________________\n",
            "conv2d_61 (Conv2D)           (None, 128, 9, 32)        9248      \n",
            "_________________________________________________________________\n",
            "flatten_13 (Flatten)         (None, 36864)             0         \n",
            "_________________________________________________________________\n",
            "dense_25 (Dense)             (None, 100)               3686500   \n",
            "_________________________________________________________________\n",
            "batch_normalization_28 (Batc (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dropout_35 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_26 (Dense)             (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 3,716,882\n",
            "Trainable params: 3,716,682\n",
            "Non-trainable params: 200\n",
            "_________________________________________________________________\n",
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/30\n",
            "7352/7352 [==============================] - 5s 693us/step - loss: 0.1642 - acc: 0.9313 - val_loss: 0.1904 - val_acc: 0.9250\n",
            "Epoch 2/30\n",
            "7352/7352 [==============================] - 1s 143us/step - loss: 0.0622 - acc: 0.9774 - val_loss: 0.1402 - val_acc: 0.9591\n",
            "Epoch 3/30\n",
            "7352/7352 [==============================] - 1s 142us/step - loss: 0.0481 - acc: 0.9815 - val_loss: 0.1578 - val_acc: 0.9538\n",
            "Epoch 4/30\n",
            "7352/7352 [==============================] - 1s 142us/step - loss: 0.0423 - acc: 0.9835 - val_loss: 0.0846 - val_acc: 0.9677\n",
            "Epoch 5/30\n",
            "7352/7352 [==============================] - 1s 143us/step - loss: 0.0395 - acc: 0.9837 - val_loss: 0.1183 - val_acc: 0.9557\n",
            "Epoch 6/30\n",
            "7352/7352 [==============================] - 1s 142us/step - loss: 0.0354 - acc: 0.9851 - val_loss: 0.0859 - val_acc: 0.9687\n",
            "Epoch 7/30\n",
            "7352/7352 [==============================] - 1s 142us/step - loss: 0.0353 - acc: 0.9860 - val_loss: 0.0998 - val_acc: 0.9621\n",
            "Epoch 8/30\n",
            "7352/7352 [==============================] - 1s 142us/step - loss: 0.0334 - acc: 0.9869 - val_loss: 0.0866 - val_acc: 0.9661\n",
            "Epoch 9/30\n",
            "7352/7352 [==============================] - 1s 143us/step - loss: 0.0310 - acc: 0.9878 - val_loss: 0.1239 - val_acc: 0.9545\n",
            "Epoch 10/30\n",
            "7352/7352 [==============================] - 1s 143us/step - loss: 0.0302 - acc: 0.9886 - val_loss: 0.0906 - val_acc: 0.9696\n",
            "Epoch 11/30\n",
            "7352/7352 [==============================] - 1s 143us/step - loss: 0.0263 - acc: 0.9891 - val_loss: 0.0784 - val_acc: 0.9747\n",
            "Epoch 12/30\n",
            "7352/7352 [==============================] - 1s 142us/step - loss: 0.0240 - acc: 0.9906 - val_loss: 0.0735 - val_acc: 0.9751\n",
            "Epoch 13/30\n",
            "7352/7352 [==============================] - 1s 143us/step - loss: 0.0250 - acc: 0.9901 - val_loss: 0.0782 - val_acc: 0.9695\n",
            "Epoch 14/30\n",
            "7352/7352 [==============================] - 1s 144us/step - loss: 0.0181 - acc: 0.9936 - val_loss: 0.0730 - val_acc: 0.9747\n",
            "Epoch 15/30\n",
            "7352/7352 [==============================] - 1s 143us/step - loss: 0.0195 - acc: 0.9925 - val_loss: 0.0891 - val_acc: 0.9713\n",
            "Epoch 16/30\n",
            "7352/7352 [==============================] - 1s 143us/step - loss: 0.0168 - acc: 0.9942 - val_loss: 0.1130 - val_acc: 0.9573\n",
            "Epoch 17/30\n",
            "7352/7352 [==============================] - 1s 143us/step - loss: 0.0149 - acc: 0.9948 - val_loss: 0.0812 - val_acc: 0.9744\n",
            "Epoch 18/30\n",
            "7352/7352 [==============================] - 1s 143us/step - loss: 0.0147 - acc: 0.9952 - val_loss: 0.0979 - val_acc: 0.9732\n",
            "Epoch 19/30\n",
            "7352/7352 [==============================] - 1s 144us/step - loss: 0.0152 - acc: 0.9942 - val_loss: 0.1003 - val_acc: 0.9745\n",
            "Epoch 20/30\n",
            "7352/7352 [==============================] - 1s 144us/step - loss: 0.0118 - acc: 0.9959 - val_loss: 0.1071 - val_acc: 0.9729\n",
            "Epoch 21/30\n",
            "7352/7352 [==============================] - 1s 144us/step - loss: 0.0142 - acc: 0.9946 - val_loss: 0.1345 - val_acc: 0.9700\n",
            "Epoch 22/30\n",
            "7352/7352 [==============================] - 1s 147us/step - loss: 0.0192 - acc: 0.9933 - val_loss: 0.1227 - val_acc: 0.9653\n",
            "Epoch 23/30\n",
            "7352/7352 [==============================] - 1s 146us/step - loss: 0.0169 - acc: 0.9942 - val_loss: 0.0790 - val_acc: 0.9766\n",
            "Epoch 24/30\n",
            "7352/7352 [==============================] - 1s 146us/step - loss: 0.0124 - acc: 0.9959 - val_loss: 0.0938 - val_acc: 0.9756\n",
            "Epoch 25/30\n",
            "7352/7352 [==============================] - 1s 147us/step - loss: 0.0127 - acc: 0.9952 - val_loss: 0.0909 - val_acc: 0.9717\n",
            "Epoch 26/30\n",
            "7352/7352 [==============================] - 1s 146us/step - loss: 0.0107 - acc: 0.9959 - val_loss: 0.0839 - val_acc: 0.9721\n",
            "Epoch 27/30\n",
            "7352/7352 [==============================] - 1s 148us/step - loss: 0.0097 - acc: 0.9966 - val_loss: 0.1044 - val_acc: 0.9736\n",
            "Epoch 28/30\n",
            "7352/7352 [==============================] - 1s 149us/step - loss: 0.0121 - acc: 0.9956 - val_loss: 0.1200 - val_acc: 0.9745\n",
            "Epoch 29/30\n",
            "7352/7352 [==============================] - 1s 146us/step - loss: 0.0092 - acc: 0.9971 - val_loss: 0.1321 - val_acc: 0.9734\n",
            "Epoch 30/30\n",
            "7352/7352 [==============================] - 1s 149us/step - loss: 0.0094 - acc: 0.9961 - val_loss: 0.1106 - val_acc: 0.9741\n",
            "Test loss: 0.11055568987172415\n",
            "Test accuracy: 0.974097960271146\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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URSsSH35oU+5TTz3SlpwM555riiTeFF88sH9/rCUoGBMnWhm6M8+MTn9BP8nc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\nWSUOJGZmVslfhVN4pvvDlXgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JO_P3jBrcLI-",
        "colab_type": "text"
      },
      "source": [
        "## Observations:\n",
        "\n",
        "1. We are getting good results as compared to model with LSTM layers\n",
        "2. Model is overfitting . We can try with use of Dropout Layers .\n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlotk3YaOarx",
        "colab_type": "text"
      },
      "source": [
        "## Model is overfitting . We will use more dropout layers for overcoming this"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NDsVukPM5HW",
        "colab_type": "code",
        "outputId": "52b08288-e0a1-47c1-cd4d-c87429dd9490",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(128, kernel_size=(5, 5),activation='relu', padding='same', input_shape=input_shape))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(n_classes, activation='softmax'))\n",
        "\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "model.summary()\n",
        "history = model.fit(X_train, Y_train, batch_size=256, epochs=30, verbose=1, validation_data=(X_test, Y_test))\n",
        "score = model.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])\n",
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "ax.set_xlabel('epoch') ; ax.set_ylabel('Crossentropy Loss')\n",
        "\n",
        "x = list(range(1,epochs+1))\n",
        "vy = history.history['val_loss']\n",
        "ty = history.history['loss']\n",
        "plt_dynamic(x, vy, ty, ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_56 (Conv2D)           (None, 128, 9, 128)       3328      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_30 (MaxPooling (None, 64, 4, 128)        0         \n",
            "_________________________________________________________________\n",
            "dropout_33 (Dropout)         (None, 64, 4, 128)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_57 (Conv2D)           (None, 64, 4, 64)         73792     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_31 (MaxPooling (None, 32, 2, 64)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_58 (Conv2D)           (None, 32, 2, 64)         36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_32 (MaxPooling (None, 16, 1, 64)         0         \n",
            "_________________________________________________________________\n",
            "flatten_12 (Flatten)         (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 100)               102500    \n",
            "_________________________________________________________________\n",
            "batch_normalization_27 (Batc (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dropout_34 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_24 (Dense)             (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 217,554\n",
            "Trainable params: 217,354\n",
            "Non-trainable params: 200\n",
            "_________________________________________________________________\n",
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/30\n",
            "7352/7352 [==============================] - 5s 690us/step - loss: 0.2510 - acc: 0.8868 - val_loss: 0.2693 - val_acc: 0.8858\n",
            "Epoch 2/30\n",
            "7352/7352 [==============================] - 1s 128us/step - loss: 0.1361 - acc: 0.9439 - val_loss: 0.1823 - val_acc: 0.9364\n",
            "Epoch 3/30\n",
            "7352/7352 [==============================] - 1s 128us/step - loss: 0.0849 - acc: 0.9694 - val_loss: 0.2915 - val_acc: 0.8919\n",
            "Epoch 4/30\n",
            "7352/7352 [==============================] - 1s 129us/step - loss: 0.0618 - acc: 0.9784 - val_loss: 0.1479 - val_acc: 0.9392\n",
            "Epoch 5/30\n",
            "7352/7352 [==============================] - 1s 129us/step - loss: 0.0520 - acc: 0.9808 - val_loss: 0.1286 - val_acc: 0.9464\n",
            "Epoch 6/30\n",
            "7352/7352 [==============================] - 1s 127us/step - loss: 0.0466 - acc: 0.9824 - val_loss: 0.2496 - val_acc: 0.9152\n",
            "Epoch 7/30\n",
            "7352/7352 [==============================] - 1s 126us/step - loss: 0.0449 - acc: 0.9832 - val_loss: 0.0910 - val_acc: 0.9679\n",
            "Epoch 8/30\n",
            "7352/7352 [==============================] - 1s 128us/step - loss: 0.0434 - acc: 0.9835 - val_loss: 0.0681 - val_acc: 0.9706\n",
            "Epoch 9/30\n",
            "7352/7352 [==============================] - 1s 129us/step - loss: 0.0426 - acc: 0.9831 - val_loss: 0.0964 - val_acc: 0.9597\n",
            "Epoch 10/30\n",
            "7352/7352 [==============================] - 1s 128us/step - loss: 0.0407 - acc: 0.9832 - val_loss: 0.0815 - val_acc: 0.9693\n",
            "Epoch 11/30\n",
            "7352/7352 [==============================] - 1s 127us/step - loss: 0.0396 - acc: 0.9842 - val_loss: 0.1159 - val_acc: 0.9608\n",
            "Epoch 12/30\n",
            "7352/7352 [==============================] - 1s 129us/step - loss: 0.0374 - acc: 0.9849 - val_loss: 0.1251 - val_acc: 0.9479\n",
            "Epoch 13/30\n",
            "7352/7352 [==============================] - 1s 129us/step - loss: 0.0372 - acc: 0.9840 - val_loss: 0.1108 - val_acc: 0.9529\n",
            "Epoch 14/30\n",
            "7352/7352 [==============================] - 1s 127us/step - loss: 0.0383 - acc: 0.9841 - val_loss: 0.0737 - val_acc: 0.9725\n",
            "Epoch 15/30\n",
            "7352/7352 [==============================] - 1s 129us/step - loss: 0.0361 - acc: 0.9850 - val_loss: 0.1353 - val_acc: 0.9495\n",
            "Epoch 16/30\n",
            "7352/7352 [==============================] - 1s 129us/step - loss: 0.0332 - acc: 0.9870 - val_loss: 0.0821 - val_acc: 0.9627\n",
            "Epoch 17/30\n",
            "7352/7352 [==============================] - 1s 127us/step - loss: 0.0328 - acc: 0.9863 - val_loss: 0.0908 - val_acc: 0.9641\n",
            "Epoch 18/30\n",
            "7352/7352 [==============================] - 1s 130us/step - loss: 0.0313 - acc: 0.9872 - val_loss: 0.0666 - val_acc: 0.9764\n",
            "Epoch 19/30\n",
            "7352/7352 [==============================] - 1s 129us/step - loss: 0.0336 - acc: 0.9856 - val_loss: 0.0698 - val_acc: 0.9746\n",
            "Epoch 20/30\n",
            "7352/7352 [==============================] - 1s 129us/step - loss: 0.0343 - acc: 0.9865 - val_loss: 0.0699 - val_acc: 0.9754\n",
            "Epoch 21/30\n",
            "7352/7352 [==============================] - 1s 128us/step - loss: 0.0291 - acc: 0.9884 - val_loss: 0.0609 - val_acc: 0.9783\n",
            "Epoch 22/30\n",
            "7352/7352 [==============================] - 1s 126us/step - loss: 0.0289 - acc: 0.9886 - val_loss: 0.0630 - val_acc: 0.9738\n",
            "Epoch 23/30\n",
            "7352/7352 [==============================] - 1s 129us/step - loss: 0.0291 - acc: 0.9881 - val_loss: 0.0857 - val_acc: 0.9647\n",
            "Epoch 24/30\n",
            "7352/7352 [==============================] - 1s 128us/step - loss: 0.0282 - acc: 0.9885 - val_loss: 0.1368 - val_acc: 0.9478\n",
            "Epoch 25/30\n",
            "7352/7352 [==============================] - 1s 127us/step - loss: 0.0260 - acc: 0.9893 - val_loss: 0.1554 - val_acc: 0.9466\n",
            "Epoch 26/30\n",
            "7352/7352 [==============================] - 1s 126us/step - loss: 0.0268 - acc: 0.9899 - val_loss: 0.0720 - val_acc: 0.9695\n",
            "Epoch 27/30\n",
            "7352/7352 [==============================] - 1s 130us/step - loss: 0.0260 - acc: 0.9892 - val_loss: 0.0866 - val_acc: 0.9643\n",
            "Epoch 28/30\n",
            "7352/7352 [==============================] - 1s 126us/step - loss: 0.0225 - acc: 0.9909 - val_loss: 0.1325 - val_acc: 0.9512\n",
            "Epoch 29/30\n",
            "7352/7352 [==============================] - 1s 130us/step - loss: 0.0235 - acc: 0.9905 - val_loss: 0.0696 - val_acc: 0.9748\n",
            "Epoch 30/30\n",
            "7352/7352 [==============================] - 1s 128us/step - loss: 0.0245 - acc: 0.9903 - val_loss: 0.0716 - val_acc: 0.9777\n",
            "Test loss: 0.07162463552604258\n",
            "Test accuracy: 0.9776609062865341\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IBgLQtCl89BF1Fn5Fjx7RDXJv26ZjHVVZFjXFO+/An/5Uc+czDCP9SYqkPBEZAwwAhoSt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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6uNeEIYcinz",
        "colab_type": "text"
      },
      "source": [
        "### Observations:\n",
        "1. We reduced overfitting but still we have much gap between train and test data scores and loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vx6qYpMjMD0m",
        "colab_type": "code",
        "outputId": "fc6fe05c-368a-4385-8b63-f9e078a07242",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(128, kernel_size=(5, 5),activation='relu', padding='same', input_shape=input_shape))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "\n",
        "model.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "\n",
        "model.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(n_classes, activation='softmax'))\n",
        "\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "model.summary()\n",
        "history = model.fit(X_train, Y_train, batch_size=256, epochs=30, verbose=1, validation_data=(X_test, Y_test))\n",
        "score = model.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])\n",
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "ax.set_xlabel('epoch') ; ax.set_ylabel('Crossentropy Loss')\n",
        "\n",
        "x = list(range(1,epochs+1))\n",
        "vy = history.history['val_loss']\n",
        "ty = history.history['loss']\n",
        "plt_dynamic(x, vy, ty, ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_53 (Conv2D)           (None, 128, 9, 128)       3328      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_27 (MaxPooling (None, 64, 4, 128)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_54 (Conv2D)           (None, 64, 4, 64)         204864    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_28 (MaxPooling (None, 32, 2, 64)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_55 (Conv2D)           (None, 32, 2, 64)         102464    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_29 (MaxPooling (None, 16, 1, 64)         0         \n",
            "_________________________________________________________________\n",
            "flatten_11 (Flatten)         (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_21 (Dense)             (None, 100)               102500    \n",
            "_________________________________________________________________\n",
            "batch_normalization_26 (Batc (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dropout_32 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 414,162\n",
            "Trainable params: 413,962\n",
            "Non-trainable params: 200\n",
            "_________________________________________________________________\n",
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/30\n",
            "7352/7352 [==============================] - 5s 741us/step - loss: 0.2105 - acc: 0.9085 - val_loss: 0.2021 - val_acc: 0.9260\n",
            "Epoch 2/30\n",
            "7352/7352 [==============================] - 1s 168us/step - loss: 0.0906 - acc: 0.9695 - val_loss: 0.1243 - val_acc: 0.9588\n",
            "Epoch 3/30\n",
            "7352/7352 [==============================] - 1s 169us/step - loss: 0.0591 - acc: 0.9808 - val_loss: 0.1208 - val_acc: 0.9572\n",
            "Epoch 4/30\n",
            "7352/7352 [==============================] - 1s 169us/step - loss: 0.0491 - acc: 0.9833 - val_loss: 0.0726 - val_acc: 0.9714\n",
            "Epoch 5/30\n",
            "7352/7352 [==============================] - 1s 167us/step - loss: 0.0437 - acc: 0.9836 - val_loss: 0.1191 - val_acc: 0.9561\n",
            "Epoch 6/30\n",
            "7352/7352 [==============================] - 1s 171us/step - loss: 0.0439 - acc: 0.9837 - val_loss: 0.1105 - val_acc: 0.9587\n",
            "Epoch 7/30\n",
            "7352/7352 [==============================] - 1s 170us/step - loss: 0.0390 - acc: 0.9853 - val_loss: 0.0793 - val_acc: 0.9733\n",
            "Epoch 8/30\n",
            "7352/7352 [==============================] - 1s 172us/step - loss: 0.0419 - acc: 0.9839 - val_loss: 0.0938 - val_acc: 0.9663\n",
            "Epoch 9/30\n",
            "7352/7352 [==============================] - 1s 170us/step - loss: 0.0382 - acc: 0.9852 - val_loss: 0.0773 - val_acc: 0.9729\n",
            "Epoch 10/30\n",
            "7352/7352 [==============================] - 1s 171us/step - loss: 0.0389 - acc: 0.9851 - val_loss: 0.0960 - val_acc: 0.9661\n",
            "Epoch 11/30\n",
            "7352/7352 [==============================] - 1s 168us/step - loss: 0.0371 - acc: 0.9860 - val_loss: 0.0699 - val_acc: 0.9731\n",
            "Epoch 12/30\n",
            "7352/7352 [==============================] - 1s 170us/step - loss: 0.0348 - acc: 0.9865 - val_loss: 0.0917 - val_acc: 0.9602\n",
            "Epoch 13/30\n",
            "7352/7352 [==============================] - 1s 171us/step - loss: 0.0321 - acc: 0.9873 - val_loss: 0.0665 - val_acc: 0.9744\n",
            "Epoch 14/30\n",
            "7352/7352 [==============================] - 1s 169us/step - loss: 0.0350 - acc: 0.9855 - val_loss: 0.0772 - val_acc: 0.9696\n",
            "Epoch 15/30\n",
            "7352/7352 [==============================] - 1s 171us/step - loss: 0.0301 - acc: 0.9877 - val_loss: 0.0584 - val_acc: 0.9774\n",
            "Epoch 16/30\n",
            "7352/7352 [==============================] - 1s 171us/step - loss: 0.0310 - acc: 0.9878 - val_loss: 0.0648 - val_acc: 0.9807\n",
            "Epoch 17/30\n",
            "7352/7352 [==============================] - 1s 169us/step - loss: 0.0286 - acc: 0.9888 - val_loss: 0.0633 - val_acc: 0.9764\n",
            "Epoch 18/30\n",
            "7352/7352 [==============================] - 1s 170us/step - loss: 0.0288 - acc: 0.9887 - val_loss: 0.2431 - val_acc: 0.9388\n",
            "Epoch 19/30\n",
            "7352/7352 [==============================] - 1s 169us/step - loss: 0.0287 - acc: 0.9881 - val_loss: 0.0577 - val_acc: 0.9775\n",
            "Epoch 20/30\n",
            "7352/7352 [==============================] - 1s 171us/step - loss: 0.0251 - acc: 0.9895 - val_loss: 0.1332 - val_acc: 0.9599\n",
            "Epoch 21/30\n",
            "7352/7352 [==============================] - 1s 172us/step - loss: 0.0247 - acc: 0.9904 - val_loss: 0.0680 - val_acc: 0.9714\n",
            "Epoch 22/30\n",
            "7352/7352 [==============================] - 1s 172us/step - loss: 0.0248 - acc: 0.9899 - val_loss: 0.0665 - val_acc: 0.9783\n",
            "Epoch 23/30\n",
            "7352/7352 [==============================] - 1s 170us/step - loss: 0.0224 - acc: 0.9910 - val_loss: 0.0700 - val_acc: 0.9771\n",
            "Epoch 24/30\n",
            "7352/7352 [==============================] - 1s 170us/step - loss: 0.0239 - acc: 0.9907 - val_loss: 0.1886 - val_acc: 0.9485\n",
            "Epoch 25/30\n",
            "7352/7352 [==============================] - 1s 171us/step - loss: 0.0246 - acc: 0.9901 - val_loss: 0.0733 - val_acc: 0.9762\n",
            "Epoch 26/30\n",
            "7352/7352 [==============================] - 1s 171us/step - loss: 0.0200 - acc: 0.9924 - val_loss: 0.0544 - val_acc: 0.9806\n",
            "Epoch 27/30\n",
            "7352/7352 [==============================] - 1s 168us/step - loss: 0.0223 - acc: 0.9904 - val_loss: 0.0696 - val_acc: 0.9802\n",
            "Epoch 28/30\n",
            "7352/7352 [==============================] - 1s 168us/step - loss: 0.0194 - acc: 0.9921 - val_loss: 0.0743 - val_acc: 0.9765\n",
            "Epoch 29/30\n",
            "7352/7352 [==============================] - 1s 171us/step - loss: 0.0200 - acc: 0.9916 - val_loss: 0.0762 - val_acc: 0.9796\n",
            "Epoch 30/30\n",
            "7352/7352 [==============================] - 1s 171us/step - loss: 0.0185 - acc: 0.9923 - val_loss: 0.0598 - val_acc: 0.9821\n",
            "Test loss: 0.05977197116488045\n",
            "Test accuracy: 0.982072170686026\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XPzWLYcCXzrm1zrndwAxgTPAOzrn5zjlPWf4f4GWMnAi85Zzb4pzbCrwFnOTHIFu3hh0t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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0w1og99lDDJh",
        "colab_type": "code",
        "outputId": "447e5ad5-f512-4514-e12c-92f43f434c24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_1 = Sequential()\n",
        "model_1.add(Conv2D(128, kernel_size=(5, 5),activation='relu', padding='same', input_shape=input_shape))\n",
        "model_1.add(Conv2D(128, (5, 5), activation='relu', padding='same'))\n",
        "model_1.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_1.add(Dropout(0.6))\n",
        "\n",
        "model_1.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model_1.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model_1.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_1.add(BatchNormalization())\n",
        "model_1.add(Dropout(0.7))\n",
        "\n",
        "model_1.add(Flatten())\n",
        "model_1.add(Dense(100, activation='relu'))\n",
        "model_1.add(BatchNormalization())\n",
        "model_1.add(Dropout(0.5))\n",
        "model_1.add(Dense(n_classes, activation='softmax'))\n",
        "\n",
        "model_1.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "model_1.summary()\n",
        "history_1 = model_1.fit(X_train, Y_train, batch_size=256, epochs=30, verbose=1, validation_data=(X_test, Y_test))\n",
        "score_1 = model_1.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Test loss:', score_1[0])\n",
        "print('Test accuracy:', score_1[1])\n",
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "ax.set_xlabel('epoch') ; ax.set_ylabel('Crossentropy Loss')\n",
        "\n",
        "x = list(range(1,epochs+1))\n",
        "vy = history_1.history['val_loss']\n",
        "ty = history_1.history['loss']\n",
        "plt_dynamic(x, vy, ty, ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0815 11:35:15.672336 140275984717696 nn_ops.py:4224] Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
            "W0815 11:35:15.778605 140275984717696 nn_ops.py:4224] Large dropout rate: 0.7 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_7 (Conv2D)            (None, 128, 9, 128)       3328      \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 128, 9, 128)       409728    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 64, 4, 128)        0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 64, 4, 128)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 64, 4, 64)         204864    \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 64, 4, 64)         102464    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 32, 2, 64)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 32, 2, 64)         256       \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 32, 2, 64)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 100)               409700    \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 1,131,346\n",
            "Trainable params: 1,131,018\n",
            "Non-trainable params: 328\n",
            "_________________________________________________________________\n",
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/30\n",
            "7352/7352 [==============================] - 9s 1ms/step - loss: 0.3429 - acc: 0.8551 - val_loss: 0.5128 - val_acc: 0.8299\n",
            "Epoch 2/30\n",
            "7352/7352 [==============================] - 4s 479us/step - loss: 0.1988 - acc: 0.9137 - val_loss: 0.2536 - val_acc: 0.9002\n",
            "Epoch 3/30\n",
            "7352/7352 [==============================] - 4s 483us/step - loss: 0.1391 - acc: 0.9409 - val_loss: 0.5219 - val_acc: 0.8365\n",
            "Epoch 4/30\n",
            "7352/7352 [==============================] - 4s 484us/step - loss: 0.0910 - acc: 0.9637 - val_loss: 1.0533 - val_acc: 0.7864\n",
            "Epoch 5/30\n",
            "7352/7352 [==============================] - 4s 484us/step - loss: 0.0639 - acc: 0.9745 - val_loss: 0.5508 - val_acc: 0.8123\n",
            "Epoch 6/30\n",
            "7352/7352 [==============================] - 4s 485us/step - loss: 0.0530 - acc: 0.9803 - val_loss: 0.6761 - val_acc: 0.8101\n",
            "Epoch 7/30\n",
            "7352/7352 [==============================] - 4s 485us/step - loss: 0.0468 - acc: 0.9821 - val_loss: 0.4613 - val_acc: 0.8294\n",
            "Epoch 8/30\n",
            "7352/7352 [==============================] - 4s 485us/step - loss: 0.0440 - acc: 0.9820 - val_loss: 0.2806 - val_acc: 0.8862\n",
            "Epoch 9/30\n",
            "7352/7352 [==============================] - 4s 482us/step - loss: 0.0445 - acc: 0.9822 - val_loss: 0.2801 - val_acc: 0.8947\n",
            "Epoch 10/30\n",
            "7352/7352 [==============================] - 4s 482us/step - loss: 0.0434 - acc: 0.9823 - val_loss: 0.1439 - val_acc: 0.9459\n",
            "Epoch 11/30\n",
            "7352/7352 [==============================] - 4s 485us/step - loss: 0.0406 - acc: 0.9837 - val_loss: 0.0835 - val_acc: 0.9700\n",
            "Epoch 12/30\n",
            "7352/7352 [==============================] - 4s 487us/step - loss: 0.0379 - acc: 0.9844 - val_loss: 0.1138 - val_acc: 0.9587\n",
            "Epoch 13/30\n",
            "7352/7352 [==============================] - 4s 486us/step - loss: 0.0369 - acc: 0.9850 - val_loss: 0.1021 - val_acc: 0.9615\n",
            "Epoch 14/30\n",
            "7352/7352 [==============================] - 4s 490us/step - loss: 0.0403 - acc: 0.9828 - val_loss: 0.0698 - val_acc: 0.9734\n",
            "Epoch 15/30\n",
            "7352/7352 [==============================] - 4s 488us/step - loss: 0.0346 - acc: 0.9857 - val_loss: 0.0787 - val_acc: 0.9734\n",
            "Epoch 16/30\n",
            "7352/7352 [==============================] - 4s 489us/step - loss: 0.0316 - acc: 0.9871 - val_loss: 0.1203 - val_acc: 0.9563\n",
            "Epoch 17/30\n",
            "7352/7352 [==============================] - 4s 490us/step - loss: 0.0303 - acc: 0.9875 - val_loss: 0.0951 - val_acc: 0.9649\n",
            "Epoch 18/30\n",
            "7352/7352 [==============================] - 4s 492us/step - loss: 0.0334 - acc: 0.9867 - val_loss: 0.0972 - val_acc: 0.9645\n",
            "Epoch 19/30\n",
            "7352/7352 [==============================] - 4s 492us/step - loss: 0.0269 - acc: 0.9888 - val_loss: 0.1251 - val_acc: 0.9470\n",
            "Epoch 20/30\n",
            "7352/7352 [==============================] - 4s 492us/step - loss: 0.0278 - acc: 0.9892 - val_loss: 0.0846 - val_acc: 0.9692\n",
            "Epoch 21/30\n",
            "7352/7352 [==============================] - 4s 491us/step - loss: 0.0273 - acc: 0.9890 - val_loss: 0.0752 - val_acc: 0.9759\n",
            "Epoch 22/30\n",
            "7352/7352 [==============================] - 4s 491us/step - loss: 0.0262 - acc: 0.9893 - val_loss: 0.0805 - val_acc: 0.9678\n",
            "Epoch 23/30\n",
            "7352/7352 [==============================] - 4s 494us/step - loss: 0.0250 - acc: 0.9900 - val_loss: 0.1074 - val_acc: 0.9615\n",
            "Epoch 24/30\n",
            "7352/7352 [==============================] - 4s 494us/step - loss: 0.0191 - acc: 0.9919 - val_loss: 0.0691 - val_acc: 0.9761\n",
            "Epoch 25/30\n",
            "7352/7352 [==============================] - 4s 497us/step - loss: 0.0194 - acc: 0.9927 - val_loss: 0.1342 - val_acc: 0.9674\n",
            "Epoch 26/30\n",
            "7352/7352 [==============================] - 4s 497us/step - loss: 0.0235 - acc: 0.9911 - val_loss: 0.1011 - val_acc: 0.9654\n",
            "Epoch 27/30\n",
            "7352/7352 [==============================] - 4s 493us/step - loss: 0.0202 - acc: 0.9922 - val_loss: 0.0650 - val_acc: 0.9792\n",
            "Epoch 28/30\n",
            "7352/7352 [==============================] - 4s 494us/step - loss: 0.0164 - acc: 0.9940 - val_loss: 0.0705 - val_acc: 0.9790\n",
            "Epoch 29/30\n",
            "7352/7352 [==============================] - 4s 499us/step - loss: 0.0156 - acc: 0.9938 - val_loss: 0.0663 - val_acc: 0.9768\n",
            "Epoch 30/30\n",
            "7352/7352 [==============================] - 4s 497us/step - loss: 0.0144 - acc: 0.9939 - val_loss: 0.0696 - val_acc: 0.9796\n",
            "Test loss: 0.06964582370217265\n",
            "Test accuracy: 0.9796403167123992\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WqepqVd0DzAQm1DhGgfaB55lAiY/piRovJQWwsQrGmPjj53KcPYHQZtQiIKfGMbcD74nI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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D46z_jjodB2w",
        "colab_type": "text"
      },
      "source": [
        "### Observations:\n",
        "1. We have used dropout layers to reduce overfitting .\n",
        "2. We have used BN,Pool layers to increase performance ."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g2k96KDRBGAg",
        "colab_type": "code",
        "outputId": "3c2e36a1-d659-4a8a-a4a9-6ece9cabbc6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_2 = Sequential()\n",
        "model_2.add(Conv2D(64, kernel_size=(5, 5),activation='relu', padding='same', input_shape=input_shape))\n",
        "model_2.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model_2.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_2.add(Dropout(0.5))\n",
        "\n",
        "model_2.add(Conv2D(32, (5, 5), activation='relu', padding='same'))\n",
        "model_2.add(Conv2D(32, (5, 5), activation='relu', padding='same'))\n",
        "model_2.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_2.add(BatchNormalization())\n",
        "model_2.add(Dropout(0.5))\n",
        "\n",
        "model_2.add(Flatten())\n",
        "model_2.add(Dense(100, activation='relu'))\n",
        "model_2.add(BatchNormalization())\n",
        "model_2.add(Dropout(0.5))\n",
        "model_2.add(Dense(n_classes, activation='softmax'))\n",
        "\n",
        "model_2.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "model_2.summary()\n",
        "history_2 = model_2.fit(X_train, Y_train, batch_size=256, epochs=30, verbose=1, validation_data=(X_test, Y_test))\n",
        "score_2 = model_2.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Test loss:', score_2[0])\n",
        "print('Test accuracy:', score_2[1])\n",
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "ax.set_xlabel('epoch') ; ax.set_ylabel('Crossentropy Loss')\n",
        "\n",
        "x = list(range(1,epochs+1))\n",
        "vy = history_2.history['val_loss']\n",
        "ty = history_2.history['loss']\n",
        "plt_dynamic(x, vy, ty, ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0815 11:28:44.837903 140275984717696 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0815 11:28:44.848655 140275984717696 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0815 11:28:44.893887 140275984717696 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W0815 11:28:44.896788 140275984717696 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "W0815 11:28:44.905226 140275984717696 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "W0815 11:28:44.953710 140275984717696 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "W0815 11:28:46.645333 140275984717696 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
            "\n",
            "W0815 11:28:46.834158 140275984717696 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "W0815 11:28:46.855683 140275984717696 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 128, 9, 64)        1664      \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 128, 9, 64)        102464    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 64, 4, 64)         0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64, 4, 64)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 64, 4, 32)         51232     \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 64, 4, 32)         25632     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 32, 2, 32)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 2, 32)         128       \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 32, 2, 32)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               204900    \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 387,026\n",
            "Trainable params: 386,762\n",
            "Non-trainable params: 264\n",
            "_________________________________________________________________\n",
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/30\n",
            "7352/7352 [==============================] - 9s 1ms/step - loss: 0.3185 - acc: 0.8711 - val_loss: 0.4499 - val_acc: 0.8771\n",
            "Epoch 2/30\n",
            "7352/7352 [==============================] - 2s 234us/step - loss: 0.1709 - acc: 0.9287 - val_loss: 0.3076 - val_acc: 0.9088\n",
            "Epoch 3/30\n",
            "7352/7352 [==============================] - 2s 236us/step - loss: 0.1008 - acc: 0.9624 - val_loss: 0.9853 - val_acc: 0.8225\n",
            "Epoch 4/30\n",
            "7352/7352 [==============================] - 2s 237us/step - loss: 0.0694 - acc: 0.9741 - val_loss: 1.0780 - val_acc: 0.7912\n",
            "Epoch 5/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0555 - acc: 0.9800 - val_loss: 0.6017 - val_acc: 0.8350\n",
            "Epoch 6/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0527 - acc: 0.9793 - val_loss: 0.5365 - val_acc: 0.8552\n",
            "Epoch 7/30\n",
            "7352/7352 [==============================] - 2s 236us/step - loss: 0.0492 - acc: 0.9813 - val_loss: 0.1479 - val_acc: 0.9427\n",
            "Epoch 8/30\n",
            "7352/7352 [==============================] - 2s 232us/step - loss: 0.0440 - acc: 0.9821 - val_loss: 0.3598 - val_acc: 0.8753\n",
            "Epoch 9/30\n",
            "7352/7352 [==============================] - 2s 232us/step - loss: 0.0407 - acc: 0.9838 - val_loss: 0.1931 - val_acc: 0.9256\n",
            "Epoch 10/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0400 - acc: 0.9837 - val_loss: 0.0826 - val_acc: 0.9694\n",
            "Epoch 11/30\n",
            "7352/7352 [==============================] - 2s 233us/step - loss: 0.0407 - acc: 0.9829 - val_loss: 0.1354 - val_acc: 0.9472\n",
            "Epoch 12/30\n",
            "7352/7352 [==============================] - 2s 233us/step - loss: 0.0379 - acc: 0.9844 - val_loss: 0.0795 - val_acc: 0.9731\n",
            "Epoch 13/30\n",
            "7352/7352 [==============================] - 2s 233us/step - loss: 0.0371 - acc: 0.9846 - val_loss: 0.0873 - val_acc: 0.9726\n",
            "Epoch 14/30\n",
            "7352/7352 [==============================] - 2s 231us/step - loss: 0.0347 - acc: 0.9852 - val_loss: 0.1365 - val_acc: 0.9484\n",
            "Epoch 15/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0322 - acc: 0.9858 - val_loss: 0.2183 - val_acc: 0.9259\n",
            "Epoch 16/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0321 - acc: 0.9866 - val_loss: 0.1121 - val_acc: 0.9665\n",
            "Epoch 17/30\n",
            "7352/7352 [==============================] - 2s 234us/step - loss: 0.0314 - acc: 0.9868 - val_loss: 0.1976 - val_acc: 0.9423\n",
            "Epoch 18/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0317 - acc: 0.9863 - val_loss: 0.2139 - val_acc: 0.9555\n",
            "Epoch 19/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0269 - acc: 0.9894 - val_loss: 0.1263 - val_acc: 0.9513\n",
            "Epoch 20/30\n",
            "7352/7352 [==============================] - 2s 234us/step - loss: 0.0265 - acc: 0.9883 - val_loss: 0.1657 - val_acc: 0.9403\n",
            "Epoch 21/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0246 - acc: 0.9901 - val_loss: 0.1205 - val_acc: 0.9540\n",
            "Epoch 22/30\n",
            "7352/7352 [==============================] - 2s 236us/step - loss: 0.0243 - acc: 0.9899 - val_loss: 0.1014 - val_acc: 0.9715\n",
            "Epoch 23/30\n",
            "7352/7352 [==============================] - 2s 234us/step - loss: 0.0221 - acc: 0.9913 - val_loss: 0.0866 - val_acc: 0.9771\n",
            "Epoch 24/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0242 - acc: 0.9906 - val_loss: 0.1821 - val_acc: 0.9460\n",
            "Epoch 25/30\n",
            "7352/7352 [==============================] - 2s 237us/step - loss: 0.0221 - acc: 0.9915 - val_loss: 0.0829 - val_acc: 0.9756\n",
            "Epoch 26/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0196 - acc: 0.9921 - val_loss: 0.0664 - val_acc: 0.9799\n",
            "Epoch 27/30\n",
            "7352/7352 [==============================] - 2s 235us/step - loss: 0.0171 - acc: 0.9936 - val_loss: 0.0641 - val_acc: 0.9811\n",
            "Epoch 28/30\n",
            "7352/7352 [==============================] - 2s 236us/step - loss: 0.0163 - acc: 0.9938 - val_loss: 0.0963 - val_acc: 0.9757\n",
            "Epoch 29/30\n",
            "7352/7352 [==============================] - 2s 237us/step - loss: 0.0219 - acc: 0.9909 - val_loss: 0.1343 - val_acc: 0.9769\n",
            "Epoch 30/30\n",
            "7352/7352 [==============================] - 2s 236us/step - loss: 0.0166 - acc: 0.9931 - val_loss: 0.0986 - val_acc: 0.9768\n",
            "Test loss: 0.09862902752178446\n",
            "Test accuracy: 0.9767560301937004\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WBDdOYT3QMeB5B98+v2ZAH9yqbmuBQ4EZkWhsBrjwQmjUqOoG53gZzex3wAGQ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\nSlhQMMYYU8KCgjHGmBL/D9vXDkQYiUoIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gyqwgpaOBXoB",
        "colab_type": "code",
        "outputId": "59021668-4dfd-4951-8668-9fdf47c6e1ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_2 = Sequential()\n",
        "model_2.add(Conv2D(128, kernel_size=(5, 5),activation='relu', padding='same', input_shape=input_shape))\n",
        "model_2.add(Conv2D(128, (5, 5), activation='relu', padding='same'))\n",
        "model_2.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_2.add(Dropout(0.8))\n",
        "\n",
        "model_2.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model_2.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model_2.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_2.add(BatchNormalization())\n",
        "model_2.add(Dropout(0.7))\n",
        "\n",
        "model_2.add(Flatten())\n",
        "model_2.add(Dense(100, activation='relu'))\n",
        "model_2.add(BatchNormalization())\n",
        "model_2.add(Dropout(0.5))\n",
        "model_2.add(Dense(n_classes, activation='softmax'))\n",
        "\n",
        "model_2.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "model_2.summary()\n",
        "history_2 = model_2.fit(X_train, Y_train, batch_size=256, epochs=25, verbose=1, validation_data=(X_test, Y_test))\n",
        "score_2 = model_2.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Test loss:', score_2[0])\n",
        "print('Test accuracy:', score_2[1])\n",
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "ax.set_xlabel('epoch') ; ax.set_ylabel('Crossentropy Loss')\n",
        "\n",
        "x = list(range(1,25+1))\n",
        "vy = history_2.history['val_loss']\n",
        "ty = history_2.history['loss']\n",
        "plt_dynamic(x, vy, ty, ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_19 (Conv2D)           (None, 128, 9, 128)       3328      \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 128, 9, 128)       409728    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 64, 4, 128)        0         \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 64, 4, 128)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 64, 4, 64)         204864    \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 64, 4, 64)         102464    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 32, 2, 64)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 32, 2, 64)         256       \n",
            "_________________________________________________________________\n",
            "dropout_14 (Dropout)         (None, 32, 2, 64)         0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 100)               409700    \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 1,131,346\n",
            "Trainable params: 1,131,018\n",
            "Non-trainable params: 328\n",
            "_________________________________________________________________\n",
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/25\n",
            "7352/7352 [==============================] - 5s 677us/step - loss: 0.3749 - acc: 0.8484 - val_loss: 0.3813 - val_acc: 0.8604\n",
            "Epoch 2/25\n",
            "7352/7352 [==============================] - 4s 479us/step - loss: 0.2264 - acc: 0.9009 - val_loss: 0.3193 - val_acc: 0.8823\n",
            "Epoch 3/25\n",
            "7352/7352 [==============================] - 4s 480us/step - loss: 0.1582 - acc: 0.9309 - val_loss: 0.6222 - val_acc: 0.8375\n",
            "Epoch 4/25\n",
            "7352/7352 [==============================] - 4s 483us/step - loss: 0.1084 - acc: 0.9571 - val_loss: 0.8395 - val_acc: 0.8080\n",
            "Epoch 5/25\n",
            "7352/7352 [==============================] - 4s 486us/step - loss: 0.0739 - acc: 0.9716 - val_loss: 0.8819 - val_acc: 0.8068\n",
            "Epoch 6/25\n",
            "7352/7352 [==============================] - 4s 484us/step - loss: 0.0610 - acc: 0.9772 - val_loss: 0.9143 - val_acc: 0.8053\n",
            "Epoch 7/25\n",
            "7352/7352 [==============================] - 4s 486us/step - loss: 0.0511 - acc: 0.9814 - val_loss: 0.9736 - val_acc: 0.7628\n",
            "Epoch 8/25\n",
            "7352/7352 [==============================] - 4s 485us/step - loss: 0.0467 - acc: 0.9820 - val_loss: 0.4975 - val_acc: 0.8478\n",
            "Epoch 9/25\n",
            "7352/7352 [==============================] - 4s 483us/step - loss: 0.0446 - acc: 0.9827 - val_loss: 0.7331 - val_acc: 0.8238\n",
            "Epoch 10/25\n",
            "7352/7352 [==============================] - 4s 485us/step - loss: 0.0468 - acc: 0.9810 - val_loss: 0.3990 - val_acc: 0.8557\n",
            "Epoch 11/25\n",
            "7352/7352 [==============================] - 4s 485us/step - loss: 0.0458 - acc: 0.9814 - val_loss: 0.1171 - val_acc: 0.9598\n",
            "Epoch 12/25\n",
            "7352/7352 [==============================] - 4s 484us/step - loss: 0.0413 - acc: 0.9830 - val_loss: 0.1394 - val_acc: 0.9442\n",
            "Epoch 13/25\n",
            "7352/7352 [==============================] - 4s 484us/step - loss: 0.0419 - acc: 0.9826 - val_loss: 0.0897 - val_acc: 0.9683\n",
            "Epoch 14/25\n",
            "7352/7352 [==============================] - 4s 485us/step - loss: 0.0388 - acc: 0.9840 - val_loss: 0.2559 - val_acc: 0.8984\n",
            "Epoch 15/25\n",
            "7352/7352 [==============================] - 4s 485us/step - loss: 0.0380 - acc: 0.9839 - val_loss: 0.1334 - val_acc: 0.9494\n",
            "Epoch 16/25\n",
            "7352/7352 [==============================] - 4s 489us/step - loss: 0.0363 - acc: 0.9849 - val_loss: 0.1139 - val_acc: 0.9568\n",
            "Epoch 17/25\n",
            "7352/7352 [==============================] - 4s 491us/step - loss: 0.0367 - acc: 0.9851 - val_loss: 0.1103 - val_acc: 0.9613\n",
            "Epoch 18/25\n",
            "7352/7352 [==============================] - 4s 492us/step - loss: 0.0333 - acc: 0.9858 - val_loss: 0.1316 - val_acc: 0.9573\n",
            "Epoch 19/25\n",
            "7352/7352 [==============================] - 4s 492us/step - loss: 0.0337 - acc: 0.9864 - val_loss: 0.1197 - val_acc: 0.9679\n",
            "Epoch 20/25\n",
            "7352/7352 [==============================] - 4s 492us/step - loss: 0.0336 - acc: 0.9860 - val_loss: 0.1722 - val_acc: 0.9369\n",
            "Epoch 21/25\n",
            "7352/7352 [==============================] - 4s 491us/step - loss: 0.0313 - acc: 0.9869 - val_loss: 0.0907 - val_acc: 0.9697\n",
            "Epoch 22/25\n",
            "7352/7352 [==============================] - 4s 491us/step - loss: 0.0296 - acc: 0.9871 - val_loss: 0.0883 - val_acc: 0.9739\n",
            "Epoch 23/25\n",
            "7352/7352 [==============================] - 4s 492us/step - loss: 0.0267 - acc: 0.9895 - val_loss: 0.1185 - val_acc: 0.9637\n",
            "Epoch 24/25\n",
            "7352/7352 [==============================] - 4s 493us/step - loss: 0.0256 - acc: 0.9906 - val_loss: 0.1783 - val_acc: 0.9264\n",
            "Epoch 25/25\n",
            "7352/7352 [==============================] - 4s 494us/step - loss: 0.0278 - acc: 0.9889 - val_loss: 0.1811 - val_acc: 0.9507\n",
            "Test loss: 0.18112290080562157\n",
            "Test accuracy: 0.9507408769890378\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xY1vuwphICWeV1GuBTcCHwHuB7V2P44pa1snsj8xMaz4yJhLCGZJ6PXCcqm7z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\nWLTIXQWuqmUtwtG8Odx4o2s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luSPyx4oiCnwgIvmBZcUTTSdV3Ri4/1+gk5/B+ORaEVkc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\nZrlhuQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGd7TWLHF_1l",
        "colab_type": "code",
        "outputId": "e7f4d75c-8bbb-4b76-fdef-b7f1daaf1329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_3 = Sequential()\n",
        "model_3.add(Conv2D(64, kernel_size=(5, 5),activation='relu', padding='same', input_shape=input_shape))\n",
        "model_3.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(Dropout(0.5))\n",
        "\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.5))\n",
        "\n",
        "\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.5))\n",
        "\n",
        "model_3.add(Flatten())\n",
        "model_3.add(Dense(100, activation='relu'))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.5))\n",
        "model_3.add(Dense(n_classes, activation='softmax'))\n",
        "\n",
        "model_3.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "model_3.summary()\n",
        "history_3 = model_3.fit(X_train, Y_train, batch_size=256, epochs=25, verbose=1, validation_data=(X_test, Y_test))\n",
        "score_3 = model_3.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Test loss:', score_3[0])\n",
        "print('Test accuracy:', score_3[1])\n",
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "ax.set_xlabel('epoch') ; ax.set_ylabel('Crossentropy Loss')\n",
        "\n",
        "x = list(range(1,25+1))\n",
        "vy = history_3.history['val_loss']\n",
        "ty = history_3.history['loss']\n",
        "plt_dynamic(x, vy, ty, ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_23 (Conv2D)           (None, 128, 9, 64)        1664      \n",
            "_________________________________________________________________\n",
            "conv2d_24 (Conv2D)           (None, 128, 9, 64)        102464    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 64, 4, 64)         0         \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 64, 4, 64)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_25 (Conv2D)           (None, 64, 4, 32)         18464     \n",
            "_________________________________________________________________\n",
            "conv2d_26 (Conv2D)           (None, 64, 4, 32)         9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling (None, 32, 2, 32)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 32, 2, 32)         128       \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 32, 2, 32)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_27 (Conv2D)           (None, 32, 2, 32)         9248      \n",
            "_________________________________________________________________\n",
            "conv2d_28 (Conv2D)           (None, 32, 2, 32)         9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 16, 1, 32)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_12 (Batc (None, 16, 1, 32)         128       \n",
            "_________________________________________________________________\n",
            "dropout_18 (Dropout)         (None, 16, 1, 32)         0         \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 100)               51300     \n",
            "_________________________________________________________________\n",
            "batch_normalization_13 (Batc (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dropout_19 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 202,898\n",
            "Trainable params: 202,570\n",
            "Non-trainable params: 328\n",
            "_________________________________________________________________\n",
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/25\n",
            "7352/7352 [==============================] - 5s 670us/step - loss: 0.4313 - acc: 0.8343 - val_loss: 0.2170 - val_acc: 0.8891\n",
            "Epoch 2/25\n",
            "7352/7352 [==============================] - 2s 221us/step - loss: 0.2883 - acc: 0.8726 - val_loss: 0.2458 - val_acc: 0.8788\n",
            "Epoch 3/25\n",
            "7352/7352 [==============================] - 2s 219us/step - loss: 0.2217 - acc: 0.8995 - val_loss: 0.2570 - val_acc: 0.8915\n",
            "Epoch 4/25\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.1754 - acc: 0.9226 - val_loss: 0.3630 - val_acc: 0.8606\n",
            "Epoch 5/25\n",
            "7352/7352 [==============================] - 2s 220us/step - loss: 0.1227 - acc: 0.9494 - val_loss: 0.8001 - val_acc: 0.7912\n",
            "Epoch 6/25\n",
            "7352/7352 [==============================] - 2s 220us/step - loss: 0.0874 - acc: 0.9667 - val_loss: 1.1919 - val_acc: 0.7399\n",
            "Epoch 7/25\n",
            "7352/7352 [==============================] - 2s 218us/step - loss: 0.0689 - acc: 0.9742 - val_loss: 1.1142 - val_acc: 0.7621\n",
            "Epoch 8/25\n",
            "7352/7352 [==============================] - 2s 220us/step - loss: 0.0576 - acc: 0.9792 - val_loss: 0.9188 - val_acc: 0.7496\n",
            "Epoch 9/25\n",
            "7352/7352 [==============================] - 2s 221us/step - loss: 0.0537 - acc: 0.9786 - val_loss: 1.3420 - val_acc: 0.7564\n",
            "Epoch 10/25\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0577 - acc: 0.9779 - val_loss: 0.7701 - val_acc: 0.7924\n",
            "Epoch 11/25\n",
            "7352/7352 [==============================] - 2s 223us/step - loss: 0.0489 - acc: 0.9812 - val_loss: 0.5626 - val_acc: 0.8375\n",
            "Epoch 12/25\n",
            "7352/7352 [==============================] - 2s 221us/step - loss: 0.0470 - acc: 0.9818 - val_loss: 0.3007 - val_acc: 0.8755\n",
            "Epoch 13/25\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0435 - acc: 0.9833 - val_loss: 0.4282 - val_acc: 0.8436\n",
            "Epoch 14/25\n",
            "7352/7352 [==============================] - 2s 230us/step - loss: 0.0423 - acc: 0.9832 - val_loss: 0.1828 - val_acc: 0.9276\n",
            "Epoch 15/25\n",
            "7352/7352 [==============================] - 2s 226us/step - loss: 0.0423 - acc: 0.9834 - val_loss: 0.1363 - val_acc: 0.9462\n",
            "Epoch 16/25\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.0396 - acc: 0.9842 - val_loss: 0.1497 - val_acc: 0.9408\n",
            "Epoch 17/25\n",
            "7352/7352 [==============================] - 2s 225us/step - loss: 0.0402 - acc: 0.9835 - val_loss: 0.1335 - val_acc: 0.9526\n",
            "Epoch 18/25\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0417 - acc: 0.9832 - val_loss: 0.1512 - val_acc: 0.9496\n",
            "Epoch 19/25\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.0386 - acc: 0.9845 - val_loss: 0.0948 - val_acc: 0.9634\n",
            "Epoch 20/25\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.0408 - acc: 0.9838 - val_loss: 0.1007 - val_acc: 0.9668\n",
            "Epoch 21/25\n",
            "7352/7352 [==============================] - 2s 221us/step - loss: 0.0405 - acc: 0.9835 - val_loss: 0.0661 - val_acc: 0.9740\n",
            "Epoch 22/25\n",
            "7352/7352 [==============================] - 2s 223us/step - loss: 0.0385 - acc: 0.9839 - val_loss: 0.0624 - val_acc: 0.9783\n",
            "Epoch 23/25\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0356 - acc: 0.9857 - val_loss: 0.0908 - val_acc: 0.9645\n",
            "Epoch 24/25\n",
            "7352/7352 [==============================] - 2s 221us/step - loss: 0.0358 - acc: 0.9854 - val_loss: 0.0936 - val_acc: 0.9627\n",
            "Epoch 25/25\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0338 - acc: 0.9852 - val_loss: 0.1191 - val_acc: 0.9579\n",
            "Test loss: 0.11909454988765167\n",
            "Test accuracy: 0.9578667606059923\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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0aYypfbwEhR+AbZU5uYicIiKrRGSNiEwq5ZjzRWS5iCwTkZcq8z6xLDvbmo4q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\nQQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JATvMNMYH7Wo",
        "colab_type": "code",
        "outputId": "1cf23dc4-f5f1-45cf-e945-a1e7903ffa90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_3 = Sequential()\n",
        "model_3.add(Conv2D(64, kernel_size=(5, 5),activation='relu', padding='same', input_shape=input_shape))\n",
        "model_3.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(Dropout(0.7))\n",
        "\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.6))\n",
        "\n",
        "\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.6))\n",
        "\n",
        "model_3.add(Flatten())\n",
        "model_3.add(Dense(100, activation='relu'))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.5))\n",
        "model_3.add(Dense(n_classes, activation='softmax'))\n",
        "\n",
        "model_3.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "model_3.summary()\n",
        "history_3 = model_3.fit(X_train, Y_train, batch_size=256, epochs=50, verbose=1, validation_data=(X_test, Y_test))\n",
        "score_3 = model_3.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Test loss:', score_3[0])\n",
        "print('Test accuracy:', score_3[1])\n",
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "ax.set_xlabel('epoch') ; ax.set_ylabel('Crossentropy Loss')\n",
        "\n",
        "x = list(range(1,50+1))\n",
        "vy = history_3.history['val_loss']\n",
        "ty = history_3.history['loss']\n",
        "plt_dynamic(x, vy, ty, ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_35 (Conv2D)           (None, 128, 9, 64)        1664      \n",
            "_________________________________________________________________\n",
            "conv2d_36 (Conv2D)           (None, 128, 9, 64)        102464    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling (None, 64, 4, 64)         0         \n",
            "_________________________________________________________________\n",
            "dropout_24 (Dropout)         (None, 64, 4, 64)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_37 (Conv2D)           (None, 64, 4, 32)         18464     \n",
            "_________________________________________________________________\n",
            "conv2d_38 (Conv2D)           (None, 64, 4, 32)         9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling (None, 32, 2, 32)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_17 (Batc (None, 32, 2, 32)         128       \n",
            "_________________________________________________________________\n",
            "dropout_25 (Dropout)         (None, 32, 2, 32)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_39 (Conv2D)           (None, 32, 2, 32)         9248      \n",
            "_________________________________________________________________\n",
            "conv2d_40 (Conv2D)           (None, 32, 2, 32)         9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_20 (MaxPooling (None, 16, 1, 32)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_18 (Batc (None, 16, 1, 32)         128       \n",
            "_________________________________________________________________\n",
            "dropout_26 (Dropout)         (None, 16, 1, 32)         0         \n",
            "_________________________________________________________________\n",
            "flatten_8 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 100)               51300     \n",
            "_________________________________________________________________\n",
            "batch_normalization_19 (Batc (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dropout_27 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 202,898\n",
            "Trainable params: 202,570\n",
            "Non-trainable params: 328\n",
            "_________________________________________________________________\n",
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/50\n",
            "7352/7352 [==============================] - 4s 535us/step - loss: 0.4545 - acc: 0.8228 - val_loss: 0.8408 - val_acc: 0.7844\n",
            "Epoch 2/50\n",
            "7352/7352 [==============================] - 2s 223us/step - loss: 0.3092 - acc: 0.8599 - val_loss: 0.7267 - val_acc: 0.8009\n",
            "Epoch 3/50\n",
            "7352/7352 [==============================] - 2s 226us/step - loss: 0.2625 - acc: 0.8777 - val_loss: 1.1622 - val_acc: 0.7798\n",
            "Epoch 4/50\n",
            "7352/7352 [==============================] - 2s 226us/step - loss: 0.2296 - acc: 0.8894 - val_loss: 1.0814 - val_acc: 0.7836\n",
            "Epoch 5/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.2087 - acc: 0.9019 - val_loss: 1.1423 - val_acc: 0.7640\n",
            "Epoch 6/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.1780 - acc: 0.9174 - val_loss: 1.2035 - val_acc: 0.7603\n",
            "Epoch 7/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.1541 - acc: 0.9302 - val_loss: 1.3670 - val_acc: 0.7446\n",
            "Epoch 8/50\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.1251 - acc: 0.9478 - val_loss: 1.2715 - val_acc: 0.7548\n",
            "Epoch 9/50\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0999 - acc: 0.9622 - val_loss: 2.3074 - val_acc: 0.7275\n",
            "Epoch 10/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0807 - acc: 0.9705 - val_loss: 1.8830 - val_acc: 0.7289\n",
            "Epoch 11/50\n",
            "7352/7352 [==============================] - 2s 226us/step - loss: 0.0680 - acc: 0.9757 - val_loss: 1.4760 - val_acc: 0.7401\n",
            "Epoch 12/50\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0581 - acc: 0.9796 - val_loss: 0.9046 - val_acc: 0.7976\n",
            "Epoch 13/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0534 - acc: 0.9801 - val_loss: 1.0729 - val_acc: 0.7729\n",
            "Epoch 14/50\n",
            "7352/7352 [==============================] - 2s 225us/step - loss: 0.0535 - acc: 0.9799 - val_loss: 0.5502 - val_acc: 0.8221\n",
            "Epoch 15/50\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0516 - acc: 0.9806 - val_loss: 0.3413 - val_acc: 0.8711\n",
            "Epoch 16/50\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0465 - acc: 0.9816 - val_loss: 0.4095 - val_acc: 0.8551\n",
            "Epoch 17/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0454 - acc: 0.9827 - val_loss: 0.4661 - val_acc: 0.8445\n",
            "Epoch 18/50\n",
            "7352/7352 [==============================] - 2s 226us/step - loss: 0.0444 - acc: 0.9828 - val_loss: 0.3243 - val_acc: 0.8752\n",
            "Epoch 19/50\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0451 - acc: 0.9824 - val_loss: 0.1003 - val_acc: 0.9592\n",
            "Epoch 20/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0406 - acc: 0.9848 - val_loss: 0.1675 - val_acc: 0.9260\n",
            "Epoch 21/50\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0418 - acc: 0.9839 - val_loss: 0.0827 - val_acc: 0.9679\n",
            "Epoch 22/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0400 - acc: 0.9835 - val_loss: 0.2373 - val_acc: 0.9104\n",
            "Epoch 23/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0393 - acc: 0.9852 - val_loss: 0.1226 - val_acc: 0.9450\n",
            "Epoch 24/50\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0423 - acc: 0.9830 - val_loss: 0.0725 - val_acc: 0.9744\n",
            "Epoch 25/50\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0392 - acc: 0.9841 - val_loss: 0.0815 - val_acc: 0.9715\n",
            "Epoch 26/50\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0373 - acc: 0.9852 - val_loss: 0.2075 - val_acc: 0.9151\n",
            "Epoch 27/50\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0381 - acc: 0.9845 - val_loss: 0.0792 - val_acc: 0.9712\n",
            "Epoch 28/50\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0372 - acc: 0.9849 - val_loss: 0.0728 - val_acc: 0.9760\n",
            "Epoch 29/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0357 - acc: 0.9851 - val_loss: 0.1645 - val_acc: 0.9356\n",
            "Epoch 30/50\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0334 - acc: 0.9866 - val_loss: 0.0987 - val_acc: 0.9619\n",
            "Epoch 31/50\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0332 - acc: 0.9866 - val_loss: 0.1215 - val_acc: 0.9602\n",
            "Epoch 32/50\n",
            "7352/7352 [==============================] - 2s 226us/step - loss: 0.0352 - acc: 0.9857 - val_loss: 0.0641 - val_acc: 0.9768\n",
            "Epoch 33/50\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0317 - acc: 0.9872 - val_loss: 0.0909 - val_acc: 0.9750\n",
            "Epoch 34/50\n",
            "7352/7352 [==============================] - 2s 225us/step - loss: 0.0319 - acc: 0.9870 - val_loss: 0.0866 - val_acc: 0.9624\n",
            "Epoch 35/50\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0340 - acc: 0.9855 - val_loss: 0.0872 - val_acc: 0.9684\n",
            "Epoch 36/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0291 - acc: 0.9882 - val_loss: 0.1273 - val_acc: 0.9493\n",
            "Epoch 37/50\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0314 - acc: 0.9870 - val_loss: 0.0707 - val_acc: 0.9774\n",
            "Epoch 38/50\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0329 - acc: 0.9869 - val_loss: 0.0943 - val_acc: 0.9721\n",
            "Epoch 39/50\n",
            "7352/7352 [==============================] - 2s 230us/step - loss: 0.0279 - acc: 0.9886 - val_loss: 0.0694 - val_acc: 0.9791\n",
            "Epoch 40/50\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0261 - acc: 0.9890 - val_loss: 0.1090 - val_acc: 0.9636\n",
            "Epoch 41/50\n",
            "7352/7352 [==============================] - 2s 230us/step - loss: 0.0262 - acc: 0.9887 - val_loss: 0.0864 - val_acc: 0.9694\n",
            "Epoch 42/50\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0271 - acc: 0.9893 - val_loss: 0.0658 - val_acc: 0.9815\n",
            "Epoch 43/50\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0264 - acc: 0.9895 - val_loss: 0.1208 - val_acc: 0.9588\n",
            "Epoch 44/50\n",
            "7352/7352 [==============================] - 2s 231us/step - loss: 0.0219 - acc: 0.9912 - val_loss: 0.1126 - val_acc: 0.9662\n",
            "Epoch 45/50\n",
            "7352/7352 [==============================] - 2s 231us/step - loss: 0.0235 - acc: 0.9901 - val_loss: 0.1213 - val_acc: 0.9597\n",
            "Epoch 46/50\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0240 - acc: 0.9909 - val_loss: 0.1073 - val_acc: 0.9697\n",
            "Epoch 47/50\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0249 - acc: 0.9899 - val_loss: 0.0824 - val_acc: 0.9721\n",
            "Epoch 48/50\n",
            "7352/7352 [==============================] - 2s 231us/step - loss: 0.0227 - acc: 0.9909 - val_loss: 0.0779 - val_acc: 0.9756\n",
            "Epoch 49/50\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0217 - acc: 0.9914 - val_loss: 0.0751 - val_acc: 0.9762\n",
            "Epoch 50/50\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0218 - acc: 0.9919 - val_loss: 0.1061 - val_acc: 0.9683\n",
            "Test loss: 0.10611134655401126\n",
            "Test accuracy: 0.9682728295250185\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Veq2CMSY+eEkKG0Tkn8AFwCwRqevxdTXOp5/CrbeW/Nzy5dCwIbRuXb0x1WRt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\nFLCkYIwxpoAlBWOMMQX+PwmZYrKcs4vGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkNWOvIxIxs1",
        "colab_type": "code",
        "outputId": "6a9fc750-8f80-4d7b-8d2f-eda93ab75b70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_3 = Sequential()\n",
        "model_3.add(Conv2D(64, kernel_size=(5, 5),activation='relu', padding='same', input_shape=input_shape))\n",
        "model_3.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(Dropout(0.7))\n",
        "\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(BatchNormalization())\n",
        "\n",
        "\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(BatchNormalization())\n",
        "\n",
        "model_3.add(Flatten())\n",
        "model_3.add(Dense(100, activation='relu'))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.5))\n",
        "model_3.add(Dense(n_classes, activation='softmax'))\n",
        "\n",
        "model_3.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "model_3.summary()\n",
        "history_3 = model_3.fit(X_train, Y_train, batch_size=256, epochs=30, verbose=1, validation_data=(X_test, Y_test))\n",
        "score_3 = model_3.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Test loss:', score_3[0])\n",
        "print('Test accuracy:', score_3[1])\n",
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "ax.set_xlabel('epoch') ; ax.set_ylabel('Crossentropy Loss')\n",
        "\n",
        "x = list(range(1,30+1))\n",
        "vy = history_3.history['val_loss']\n",
        "ty = history_3.history['loss']\n",
        "plt_dynamic(x, vy, ty, ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_41 (Conv2D)           (None, 128, 9, 64)        1664      \n",
            "_________________________________________________________________\n",
            "conv2d_42 (Conv2D)           (None, 128, 9, 64)        102464    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_21 (MaxPooling (None, 64, 4, 64)         0         \n",
            "_________________________________________________________________\n",
            "dropout_28 (Dropout)         (None, 64, 4, 64)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_43 (Conv2D)           (None, 64, 4, 32)         18464     \n",
            "_________________________________________________________________\n",
            "conv2d_44 (Conv2D)           (None, 64, 4, 32)         9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_22 (MaxPooling (None, 32, 2, 32)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_20 (Batc (None, 32, 2, 32)         128       \n",
            "_________________________________________________________________\n",
            "conv2d_45 (Conv2D)           (None, 32, 2, 32)         9248      \n",
            "_________________________________________________________________\n",
            "conv2d_46 (Conv2D)           (None, 32, 2, 32)         9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_23 (MaxPooling (None, 16, 1, 32)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_21 (Batc (None, 16, 1, 32)         128       \n",
            "_________________________________________________________________\n",
            "flatten_9 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 100)               51300     \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dropout_29 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 202,898\n",
            "Trainable params: 202,570\n",
            "Non-trainable params: 328\n",
            "_________________________________________________________________\n",
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/30\n",
            "7352/7352 [==============================] - 4s 561us/step - loss: 1.2326 - acc: 0.5694 - val_loss: 2.6819 - val_acc: 0.4432\n",
            "Epoch 2/30\n",
            "7352/7352 [==============================] - 2s 218us/step - loss: 0.7567 - acc: 0.7053 - val_loss: 1.7428 - val_acc: 0.5517\n",
            "Epoch 3/30\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.5066 - acc: 0.7975 - val_loss: 2.5908 - val_acc: 0.5405\n",
            "Epoch 4/30\n",
            "7352/7352 [==============================] - 2s 221us/step - loss: 0.3381 - acc: 0.8769 - val_loss: 2.5298 - val_acc: 0.5131\n",
            "Epoch 5/30\n",
            "7352/7352 [==============================] - 2s 219us/step - loss: 0.2138 - acc: 0.9221 - val_loss: 2.6172 - val_acc: 0.4856\n",
            "Epoch 6/30\n",
            "7352/7352 [==============================] - 2s 219us/step - loss: 0.1758 - acc: 0.9338 - val_loss: 2.9302 - val_acc: 0.4537\n",
            "Epoch 7/30\n",
            "7352/7352 [==============================] - 2s 221us/step - loss: 0.1603 - acc: 0.9410 - val_loss: 2.7660 - val_acc: 0.4364\n",
            "Epoch 8/30\n",
            "7352/7352 [==============================] - 2s 218us/step - loss: 0.1416 - acc: 0.9442 - val_loss: 2.0992 - val_acc: 0.4798\n",
            "Epoch 9/30\n",
            "7352/7352 [==============================] - 2s 221us/step - loss: 0.1326 - acc: 0.9493 - val_loss: 2.2566 - val_acc: 0.4988\n",
            "Epoch 10/30\n",
            "7352/7352 [==============================] - 2s 220us/step - loss: 0.1322 - acc: 0.9479 - val_loss: 1.4126 - val_acc: 0.5789\n",
            "Epoch 11/30\n",
            "7352/7352 [==============================] - 2s 218us/step - loss: 0.1246 - acc: 0.9497 - val_loss: 2.0539 - val_acc: 0.5097\n",
            "Epoch 12/30\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.1245 - acc: 0.9490 - val_loss: 0.6392 - val_acc: 0.7832\n",
            "Epoch 13/30\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.1229 - acc: 0.9502 - val_loss: 0.7884 - val_acc: 0.7689\n",
            "Epoch 14/30\n",
            "7352/7352 [==============================] - 2s 221us/step - loss: 0.1210 - acc: 0.9489 - val_loss: 0.5540 - val_acc: 0.8124\n",
            "Epoch 15/30\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.1076 - acc: 0.9544 - val_loss: 0.5636 - val_acc: 0.8069\n",
            "Epoch 16/30\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.1113 - acc: 0.9547 - val_loss: 0.8160 - val_acc: 0.7781\n",
            "Epoch 17/30\n",
            "7352/7352 [==============================] - 2s 219us/step - loss: 0.1064 - acc: 0.9546 - val_loss: 1.1757 - val_acc: 0.6488\n",
            "Epoch 18/30\n",
            "7352/7352 [==============================] - 2s 220us/step - loss: 0.1047 - acc: 0.9558 - val_loss: 0.4074 - val_acc: 0.8341\n",
            "Epoch 19/30\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.1096 - acc: 0.9547 - val_loss: 0.4937 - val_acc: 0.8439\n",
            "Epoch 20/30\n",
            "7352/7352 [==============================] - 2s 223us/step - loss: 0.1093 - acc: 0.9536 - val_loss: 0.3611 - val_acc: 0.8839\n",
            "Epoch 21/30\n",
            "7352/7352 [==============================] - 2s 225us/step - loss: 0.0978 - acc: 0.9582 - val_loss: 0.5909 - val_acc: 0.8449\n",
            "Epoch 22/30\n",
            "7352/7352 [==============================] - 2s 226us/step - loss: 0.0931 - acc: 0.9581 - val_loss: 0.5909 - val_acc: 0.8280\n",
            "Epoch 23/30\n",
            "7352/7352 [==============================] - 2s 225us/step - loss: 0.0939 - acc: 0.9606 - val_loss: 0.4361 - val_acc: 0.8761\n",
            "Epoch 24/30\n",
            "7352/7352 [==============================] - 2s 225us/step - loss: 0.0943 - acc: 0.9595 - val_loss: 0.2940 - val_acc: 0.9321\n",
            "Epoch 25/30\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.0900 - acc: 0.9608 - val_loss: 0.2705 - val_acc: 0.9243\n",
            "Epoch 26/30\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0849 - acc: 0.9655 - val_loss: 0.3081 - val_acc: 0.9148\n",
            "Epoch 27/30\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0756 - acc: 0.9671 - val_loss: 0.8576 - val_acc: 0.7316\n",
            "Epoch 28/30\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.0848 - acc: 0.9612 - val_loss: 0.4719 - val_acc: 0.8571\n",
            "Epoch 29/30\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.0698 - acc: 0.9712 - val_loss: 0.4234 - val_acc: 0.8741\n",
            "Epoch 30/30\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.0757 - acc: 0.9682 - val_loss: 0.6220 - val_acc: 0.8096\n",
            "Test loss: 0.6220432024602066\n",
            "Test accuracy: 0.8096369189208024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GpRQOPdTW0VIKhYXm1bRypeUo6toVrrsOunWDM86A99+3ILTy7NoFt99u5z3+OFxxhUUt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\ndAvgtJcdtiUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "grS1y9ZAJ9NY",
        "colab_type": "code",
        "outputId": "f7cb786c-0213-484e-bb21-b03c7fb3a62b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_3 = Sequential()\n",
        "model_3.add(Conv2D(64, kernel_size=(5, 5),activation='relu', padding='same', input_shape=input_shape))\n",
        "model_3.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(Dropout(0.7))\n",
        "\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(BatchNormalization())\n",
        "\n",
        "\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2, 2), strides = 2))\n",
        "model_3.add(BatchNormalization())\n",
        "\n",
        "model_3.add(Flatten())\n",
        "model_3.add(Dense(100, activation='relu'))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.5))\n",
        "model_3.add(Dense(n_classes, activation='softmax'))\n",
        "\n",
        "model_3.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "model_3.summary()\n",
        "history_3 = model_3.fit(X_train, Y_train, batch_size=256, epochs=40, verbose=1, validation_data=(X_test, Y_test))\n",
        "score_3 = model_3.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Test loss:', score_3[0])\n",
        "print('Test accuracy:', score_3[1])\n",
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "ax.set_xlabel('epoch') ; ax.set_ylabel('Crossentropy Loss')\n",
        "\n",
        "x = list(range(1,40+1))\n",
        "vy = history_3.history['val_loss']\n",
        "ty = history_3.history['loss']\n",
        "plt_dynamic(x, vy, ty, ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_47 (Conv2D)           (None, 128, 9, 64)        1664      \n",
            "_________________________________________________________________\n",
            "conv2d_48 (Conv2D)           (None, 128, 9, 64)        102464    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_24 (MaxPooling (None, 64, 4, 64)         0         \n",
            "_________________________________________________________________\n",
            "dropout_30 (Dropout)         (None, 64, 4, 64)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_49 (Conv2D)           (None, 64, 4, 32)         18464     \n",
            "_________________________________________________________________\n",
            "conv2d_50 (Conv2D)           (None, 64, 4, 32)         9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_25 (MaxPooling (None, 32, 2, 32)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_23 (Batc (None, 32, 2, 32)         128       \n",
            "_________________________________________________________________\n",
            "conv2d_51 (Conv2D)           (None, 32, 2, 32)         9248      \n",
            "_________________________________________________________________\n",
            "conv2d_52 (Conv2D)           (None, 32, 2, 32)         9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_26 (MaxPooling (None, 16, 1, 32)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_24 (Batc (None, 16, 1, 32)         128       \n",
            "_________________________________________________________________\n",
            "flatten_10 (Flatten)         (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 100)               51300     \n",
            "_________________________________________________________________\n",
            "batch_normalization_25 (Batc (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dropout_31 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 202,898\n",
            "Trainable params: 202,570\n",
            "Non-trainable params: 328\n",
            "_________________________________________________________________\n",
            "Train on 7352 samples, validate on 2947 samples\n",
            "Epoch 1/40\n",
            "7352/7352 [==============================] - 4s 591us/step - loss: 0.3590 - acc: 0.8546 - val_loss: 0.5514 - val_acc: 0.8386\n",
            "Epoch 2/40\n",
            "7352/7352 [==============================] - 2s 216us/step - loss: 0.2096 - acc: 0.9087 - val_loss: 0.5065 - val_acc: 0.8540\n",
            "Epoch 3/40\n",
            "7352/7352 [==============================] - 2s 217us/step - loss: 0.1441 - acc: 0.9400 - val_loss: 0.8521 - val_acc: 0.8135\n",
            "Epoch 4/40\n",
            "7352/7352 [==============================] - 2s 216us/step - loss: 0.1014 - acc: 0.9605 - val_loss: 0.6996 - val_acc: 0.8216\n",
            "Epoch 5/40\n",
            "7352/7352 [==============================] - 2s 218us/step - loss: 0.0755 - acc: 0.9711 - val_loss: 0.9969 - val_acc: 0.7702\n",
            "Epoch 6/40\n",
            "7352/7352 [==============================] - 2s 217us/step - loss: 0.0621 - acc: 0.9762 - val_loss: 0.7991 - val_acc: 0.7913\n",
            "Epoch 7/40\n",
            "7352/7352 [==============================] - 2s 217us/step - loss: 0.0531 - acc: 0.9803 - val_loss: 1.0233 - val_acc: 0.7824\n",
            "Epoch 8/40\n",
            "7352/7352 [==============================] - 2s 217us/step - loss: 0.0469 - acc: 0.9817 - val_loss: 1.2507 - val_acc: 0.7649\n",
            "Epoch 9/40\n",
            "7352/7352 [==============================] - 2s 217us/step - loss: 0.0462 - acc: 0.9807 - val_loss: 0.7020 - val_acc: 0.8020\n",
            "Epoch 10/40\n",
            "7352/7352 [==============================] - 2s 217us/step - loss: 0.0423 - acc: 0.9830 - val_loss: 0.4424 - val_acc: 0.8388\n",
            "Epoch 11/40\n",
            "7352/7352 [==============================] - 2s 217us/step - loss: 0.0427 - acc: 0.9818 - val_loss: 0.2136 - val_acc: 0.9297\n",
            "Epoch 12/40\n",
            "7352/7352 [==============================] - 2s 218us/step - loss: 0.0422 - acc: 0.9824 - val_loss: 0.2479 - val_acc: 0.9196\n",
            "Epoch 13/40\n",
            "7352/7352 [==============================] - 2s 220us/step - loss: 0.0409 - acc: 0.9838 - val_loss: 0.2350 - val_acc: 0.9207\n",
            "Epoch 14/40\n",
            "7352/7352 [==============================] - 2s 225us/step - loss: 0.0404 - acc: 0.9832 - val_loss: 0.2079 - val_acc: 0.9355\n",
            "Epoch 15/40\n",
            "7352/7352 [==============================] - 2s 223us/step - loss: 0.0391 - acc: 0.9829 - val_loss: 0.1687 - val_acc: 0.9420\n",
            "Epoch 16/40\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0386 - acc: 0.9840 - val_loss: 0.1331 - val_acc: 0.9529\n",
            "Epoch 17/40\n",
            "7352/7352 [==============================] - 2s 221us/step - loss: 0.0390 - acc: 0.9833 - val_loss: 0.0694 - val_acc: 0.9709\n",
            "Epoch 18/40\n",
            "7352/7352 [==============================] - 2s 225us/step - loss: 0.0383 - acc: 0.9839 - val_loss: 0.1949 - val_acc: 0.9323\n",
            "Epoch 19/40\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.0373 - acc: 0.9843 - val_loss: 0.1478 - val_acc: 0.9434\n",
            "Epoch 20/40\n",
            "7352/7352 [==============================] - 2s 225us/step - loss: 0.0363 - acc: 0.9847 - val_loss: 0.1226 - val_acc: 0.9606\n",
            "Epoch 21/40\n",
            "7352/7352 [==============================] - 2s 226us/step - loss: 0.0351 - acc: 0.9852 - val_loss: 0.2458 - val_acc: 0.9078\n",
            "Epoch 22/40\n",
            "7352/7352 [==============================] - 2s 223us/step - loss: 0.0353 - acc: 0.9845 - val_loss: 0.0817 - val_acc: 0.9674\n",
            "Epoch 23/40\n",
            "7352/7352 [==============================] - 2s 224us/step - loss: 0.0319 - acc: 0.9861 - val_loss: 0.1391 - val_acc: 0.9431\n",
            "Epoch 24/40\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.0329 - acc: 0.9859 - val_loss: 0.1560 - val_acc: 0.9397\n",
            "Epoch 25/40\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0317 - acc: 0.9864 - val_loss: 0.0882 - val_acc: 0.9767\n",
            "Epoch 26/40\n",
            "7352/7352 [==============================] - 2s 225us/step - loss: 0.0314 - acc: 0.9869 - val_loss: 0.2127 - val_acc: 0.9261\n",
            "Epoch 27/40\n",
            "7352/7352 [==============================] - 2s 222us/step - loss: 0.0330 - acc: 0.9860 - val_loss: 0.0728 - val_acc: 0.9782\n",
            "Epoch 28/40\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0298 - acc: 0.9871 - val_loss: 0.1228 - val_acc: 0.9588\n",
            "Epoch 29/40\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0289 - acc: 0.9878 - val_loss: 0.1185 - val_acc: 0.9591\n",
            "Epoch 30/40\n",
            "7352/7352 [==============================] - 2s 226us/step - loss: 0.0300 - acc: 0.9871 - val_loss: 0.1453 - val_acc: 0.9491\n",
            "Epoch 31/40\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0265 - acc: 0.9886 - val_loss: 0.0843 - val_acc: 0.9742\n",
            "Epoch 32/40\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0269 - acc: 0.9881 - val_loss: 0.0820 - val_acc: 0.9722\n",
            "Epoch 33/40\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0245 - acc: 0.9897 - val_loss: 0.2095 - val_acc: 0.9211\n",
            "Epoch 34/40\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0255 - acc: 0.9890 - val_loss: 0.0792 - val_acc: 0.9766\n",
            "Epoch 35/40\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0240 - acc: 0.9899 - val_loss: 0.0913 - val_acc: 0.9753\n",
            "Epoch 36/40\n",
            "7352/7352 [==============================] - 2s 226us/step - loss: 0.0240 - acc: 0.9896 - val_loss: 0.0795 - val_acc: 0.9780\n",
            "Epoch 37/40\n",
            "7352/7352 [==============================] - 2s 229us/step - loss: 0.0216 - acc: 0.9910 - val_loss: 0.1278 - val_acc: 0.9633\n",
            "Epoch 38/40\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0209 - acc: 0.9916 - val_loss: 0.0718 - val_acc: 0.9784\n",
            "Epoch 39/40\n",
            "7352/7352 [==============================] - 2s 227us/step - loss: 0.0207 - acc: 0.9915 - val_loss: 0.0999 - val_acc: 0.9691\n",
            "Epoch 40/40\n",
            "7352/7352 [==============================] - 2s 228us/step - loss: 0.0218 - acc: 0.9905 - val_loss: 0.0839 - val_acc: 0.9772\n",
            "Test loss: 0.08391317656654564\n",
            "Test accuracy: 0.9772084692109428\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C7dwJ+/aldknhhBPcinLLl8OPf5zoaIwxycZLUtgEzBeRd4BD/p2q+kDcokoQ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\nHAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "45NKCQxIpVDd",
        "colab_type": "text"
      },
      "source": [
        "### We have tried models with various config's with different Number of Conv Layer with and without BN , Dropout(tried differnt rates) , Max Pool , also experimented with different filter size , strides"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJ7ghJw1r30y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from prettytable import PrettyTable \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sk9TaOuejxOu",
        "colab_type": "code",
        "outputId": "7414521d-b0ea-4f0c-87d4-d6803bba3621",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "\n",
        "from prettytable import PrettyTable \n",
        "\n",
        "x = PrettyTable()\n",
        "x.field_names = [\"Model no.\", \"Architecture\",  \"Test Loss(Cross-Entropy)\", \"Test Accuracy\"]\n",
        "x.add_row([1,\"3 Conv Layers Without Dropout,BN\", 0.1105, 0.9740 ])\n",
        "x.add_row([2,\"3 Conv Layers with dropout,BN\", 0.0716, 0.977 ])\n",
        "x.add_row([3,\"3 Conv Layers with Dropout,BN, Maxpool \", 0.0597, 0.982 ])\n",
        "x.add_row([4,\"4 Conv Layers with Dropout,BN, Maxpool\", 0.0696, 0.9796 ])\n",
        "x.add_row([5,\"4 Conv Layers with Dropout,BN, Maxpool\", 0.0986, 0.976 ])\n",
        "x.add_row([6,\"4 Conv Layers without Dropout,BN, Maxpool \", 0.180, 0.951 ])\n",
        "x.add_row([7,\"6 Conv Layers with Dropout,BN, Maxpool\", 0.119, 0.9578 ])\n",
        "x.add_row([8,\"6 Conv Layers with Dropout,BN, Maxpool\", 0.1061, 0.968 ])\n",
        "x.add_row([9,\"6 Conv Layers without Dropout,BN, Maxpool\", 0.622, 0.809 ])\n",
        "x.add_row([10,\"6 Conv Layers with Dropout,BN, Maxpool\", 0.0839, 0.9772 ])\n",
        "\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----------+--------------------------------------------+--------------------------+---------------+\n",
            "| Model no. |                Architecture                | Test Loss(Cross-Entropy) | Test Accuracy |\n",
            "+-----------+--------------------------------------------+--------------------------+---------------+\n",
            "|     1     |      3 Conv Layers Without Dropout,BN      |          0.1105          |     0.974     |\n",
            "|     2     |       3 Conv Layers with dropout,BN        |          0.0716          |     0.977     |\n",
            "|     3     |  3 Conv Layers with Dropout,BN, Maxpool    |          0.0597          |     0.982     |\n",
            "|     4     |   4 Conv Layers with Dropout,BN, Maxpool   |          0.0696          |     0.9796    |\n",
            "|     5     |   4 Conv Layers with Dropout,BN, Maxpool   |          0.0986          |     0.976     |\n",
            "|     6     | 4 Conv Layers without Dropout,BN, Maxpool  |           0.18           |     0.951     |\n",
            "|     7     |   6 Conv Layers with Dropout,BN, Maxpool   |          0.119           |     0.9578    |\n",
            "|     8     |   6 Conv Layers with Dropout,BN, Maxpool   |          0.1061          |     0.968     |\n",
            "|     9     | 6 Conv Layers without Dropout,BN, Maxpool  |          0.622           |     0.809     |\n",
            "|     10    |   6 Conv Layers with Dropout,BN, Maxpool   |          0.0839          |     0.9772    |\n",
            "+-----------+--------------------------------------------+--------------------------+---------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7m3sXXJYikgb",
        "colab_type": "text"
      },
      "source": [
        "## Conclusion :\n",
        "\n",
        "1. We could achieve the scores above that of (expert eng. features + traditional ML algos.) Models.\n",
        "2. Using Dropout , BN , Max Pool layers we could achieve better perfomance over train and test data.\n",
        "3. We have highest accuracy and lowest loss for Model 3,\"3 Conv Layers with Dropout,BN, Maxpool \", 0.0597, 0.982 "
      ]
    }
  ]
}