Human_Activity_Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Human Activity Recognition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7SuEAVMA69am"
   },
   "source": [
    "Belén Pulido Bravo  \n",
    "Enrique Ramírez Díaz "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this project was labelling different human activities upon the data register by 5 dimensional signals: a 2-axes accelerometer and a 3-axes gyroscope, in semi-naturalistic conditions.\n",
    "\n",
    "The set of activities recorded for labelling were:\n",
    "\n",
    "- running\n",
    "- walking\n",
    "- standing\n",
    "- sitting\n",
    "- lying\n",
    "\n",
    "The data provided for this assignment corresponded to 10 different people, 8 of them labelled and having a sample frequeency of 16 Hz, and 2 of them unlabelled. Each of the recordings lasted less than 20 minutes.\n",
    "\n",
    "The main goal of this project is to infer the activity carried out by the 2 unlabelled sequences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data available was transferred into two different pandas dataframes, one corresponding to the training data (8 labelled sequences) and which resulted into a matrix of 141426 rows and 6 columns, and another one corresponding to the testing data (2 unlabelled sequences) which resulted into a matrix of 42188 rows and 5 columns. Moreover, the first five columns of both dataframes corresponded to each of the 5 dimensional signals explained before, and the remaining column from the training dataframe which corresponds to the labelled activity that had been developed at that moment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30260,
     "status": "ok",
     "timestamp": 1589461905037,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
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    {
     "name": "stdout",
     "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&response_type=code&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\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "# Load the Drive helper and mount\n",
    "from google.colab import drive\n",
    "\n",
    "# This will prompt for authorization.\n",
    "drive.mount('/content/drive')\n",
    "\n",
    "# Change to assignment directory \n",
    "import os\n",
    "os.chdir('/content/drive/My Drive/Colab Notebooks/Lab_Exercises_DASS/LAB2/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RyWE4Y0rlAe0"
   },
   "outputs": [],
   "source": [
    "from scipy.io import loadmat,savemat\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NfPzzesaa2Sq"
   },
   "source": [
    "Load data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8Yc1GX9ktm0T"
   },
   "outputs": [],
   "source": [
    "data = loadmat('HAR_database.mat')\n",
    "train_data = data['database_training']\n",
    "test_data = data['database_test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RTvR0Exoa5p0"
   },
   "source": [
    "Create a data frame for training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JGlECVYW2prO"
   },
   "outputs": [],
   "source": [
    "d1 = {'acc_z': train_data[0][0][0], 'acc_xy': train_data[0][0][1], 'gyro_x': train_data[0][0][2], 'gyro_y': train_data[0][0][3], 'gyro_z': train_data[0][0][4], 'label': train_data[0][1][0]}\n",
    "d2 = {'acc_z': train_data[1][0][0], 'acc_xy': train_data[1][0][1], 'gyro_x': train_data[1][0][2], 'gyro_y': train_data[1][0][3], 'gyro_z': train_data[1][0][4], 'label': train_data[1][1][0]}\n",
    "d3 = {'acc_z': train_data[2][0][0], 'acc_xy': train_data[2][0][1], 'gyro_x': train_data[2][0][2], 'gyro_y': train_data[2][0][3], 'gyro_z': train_data[2][0][4], 'label': train_data[2][1][0]}\n",
    "d4 = {'acc_z': train_data[3][0][0], 'acc_xy': train_data[3][0][1], 'gyro_x': train_data[3][0][2], 'gyro_y': train_data[3][0][3], 'gyro_z': train_data[3][0][4], 'label': train_data[3][1][0]}\n",
    "d5 = {'acc_z': train_data[4][0][0], 'acc_xy': train_data[4][0][1], 'gyro_x': train_data[4][0][2], 'gyro_y': train_data[4][0][3], 'gyro_z': train_data[4][0][4], 'label': train_data[4][1][0]}\n",
    "d6 = {'acc_z': train_data[5][0][0], 'acc_xy': train_data[5][0][1], 'gyro_x': train_data[5][0][2], 'gyro_y': train_data[5][0][3], 'gyro_z': train_data[5][0][4], 'label': train_data[5][1][0]}\n",
    "d7 = {'acc_z': train_data[6][0][0], 'acc_xy': train_data[6][0][1], 'gyro_x': train_data[6][0][2], 'gyro_y': train_data[6][0][3], 'gyro_z': train_data[6][0][4], 'label': train_data[6][1][0]}\n",
    "d8 = {'acc_z': train_data[7][0][0], 'acc_xy': train_data[7][0][1], 'gyro_x': train_data[7][0][2], 'gyro_y': train_data[7][0][3], 'gyro_z': train_data[7][0][4], 'label': train_data[7][1][0]}\n",
    "\n",
    "df1 = pd.DataFrame(d1)\n",
    "df2 = pd.DataFrame(d2)\n",
    "df3 = pd.DataFrame(d3)\n",
    "df4 = pd.DataFrame(d4)\n",
    "df5 = pd.DataFrame(d5)\n",
    "df6 = pd.DataFrame(d6)\n",
    "df7 = pd.DataFrame(d7)\n",
    "df8 = pd.DataFrame(d8)\n",
    "\n",
    "frames = [df1, df2, df3, df4, df5, df6, df7, df8]\n",
    "\n",
    "df = pd.concat(frames, keys = [1,2,3,4,5,6,7,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5723,
     "status": "ok",
     "timestamp": 1589461918010,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
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     "user_tz": -120
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    "id": "Jj7X5pu7A9aV",
    "outputId": "cad1acb5-a4e1-4dc6-ae91-da4e6d39225a"
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   "outputs": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>acc_z</th>\n",
       "      <th>acc_xy</th>\n",
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       "      <th>3</th>\n",
       "      <td>0.059832</td>\n",
       "      <td>0.621057</td>\n",
       "      <td>-0.026841</td>\n",
       "      <td>0.104378</td>\n",
       "      <td>0.063853</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.044743</td>\n",
       "      <td>0.653001</td>\n",
       "      <td>-0.014540</td>\n",
       "      <td>0.103560</td>\n",
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       "      <td>-0.088788</td>\n",
       "      <td>0.071567</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>17607</th>\n",
       "      <td>0.583964</td>\n",
       "      <td>1.844136</td>\n",
       "      <td>-0.073664</td>\n",
       "      <td>-0.101740</td>\n",
       "      <td>0.081287</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>17608</th>\n",
       "      <td>0.572480</td>\n",
       "      <td>1.783769</td>\n",
       "      <td>-0.073399</td>\n",
       "      <td>-0.089936</td>\n",
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       "      <td>-0.091812</td>\n",
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       "            acc_z    acc_xy    gyro_x    gyro_y    gyro_z  label\n",
       "1 0      0.024173  0.594417 -0.022736  0.111962  0.060499      3\n",
       "  1      0.019905  0.602472 -0.012875  0.103796  0.055157      3\n",
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       "  3      0.059832  0.621057 -0.026841  0.104378  0.063853      3\n",
       "  4      0.044743  0.653001 -0.014540  0.103560  0.066084      3\n",
       "...           ...       ...       ...       ...       ...    ...\n",
       "8 17605  0.577717  1.784139 -0.081926 -0.095167  0.068769      3\n",
       "  17606  0.577288  1.860482 -0.080316 -0.088788  0.071567      3\n",
       "  17607  0.583964  1.844136 -0.073664 -0.101740  0.081287      3\n",
       "  17608  0.572480  1.783769 -0.073399 -0.089936  0.052906      3\n",
       "  17609  0.582015  1.817274 -0.088948 -0.091812  0.066497      3\n",
       "\n",
       "[141426 rows x 6 columns]"
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   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EntJF3ZybCjY"
   },
   "source": [
    "Create a data frame for tessting data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "a_YB3Rk9mBXM"
   },
   "outputs": [],
   "source": [
    "d1 = {'acc_z': test_data[0][0][0], 'acc_xy': test_data[0][0][1], 'gyro_x': test_data[0][0][2], 'gyro_y': test_data[0][0][3], 'gyro_z': test_data[0][0][4]}\n",
    "d2 = {'acc_z': test_data[1][0][0], 'acc_xy': test_data[1][0][1], 'gyro_x': test_data[1][0][2], 'gyro_y': test_data[1][0][3], 'gyro_z': test_data[1][0][4]}\n",
    "\n",
    "df1 = pd.DataFrame(d1)\n",
    "df2 = pd.DataFrame(d2)\n",
    "\n",
    "frames = [df1, df2]\n",
    "\n",
    "df_test = pd.concat(frames, keys = [1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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    "executionInfo": {
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     "status": "ok",
     "timestamp": 1589461918013,
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    "id": "bkyMiClbmY1X",
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   "outputs": [
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       "      <th>1</th>\n",
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       "      <td>-0.097420</td>\n",
       "      <td>-0.092306</td>\n",
       "      <td>0.056841</td>\n",
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       "      <th>22612</th>\n",
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       "      <td>0.435754</td>\n",
       "      <td>-0.092469</td>\n",
       "      <td>-0.096682</td>\n",
       "      <td>0.050962</td>\n",
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       "    <tr>\n",
       "      <th>22613</th>\n",
       "      <td>-0.016084</td>\n",
       "      <td>0.439480</td>\n",
       "      <td>-0.083564</td>\n",
       "      <td>-0.096922</td>\n",
       "      <td>0.043268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22614</th>\n",
       "      <td>0.003370</td>\n",
       "      <td>0.440241</td>\n",
       "      <td>-0.082508</td>\n",
       "      <td>-0.103798</td>\n",
       "      <td>0.040693</td>\n",
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       "<p>42188 rows × 5 columns</p>\n",
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      "text/plain": [
       "            acc_z    acc_xy    gyro_x    gyro_y    gyro_z\n",
       "1 0     -0.065170  0.382690 -0.213375  0.189977  0.132347\n",
       "  1      0.018816  0.564156  0.059815  0.091269  0.129378\n",
       "  2      0.028664  0.534412  0.109575  0.062156  0.158687\n",
       "  3      0.044055  0.531722  0.077823  0.070266  0.213016\n",
       "  4      0.073239  0.330309  0.042073  0.104790  0.254729\n",
       "...           ...       ...       ...       ...       ...\n",
       "2 22610  0.033105  0.432212 -0.091362 -0.101866  0.052534\n",
       "  22611  0.025125  0.416729 -0.097420 -0.092306  0.056841\n",
       "  22612 -0.004730  0.435754 -0.092469 -0.096682  0.050962\n",
       "  22613 -0.016084  0.439480 -0.083564 -0.096922  0.043268\n",
       "  22614  0.003370  0.440241 -0.082508 -0.103798  0.040693\n",
       "\n",
       "[42188 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nVeVRXtvbHbn"
   },
   "source": [
    "## Obtaining information from data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training data provided consists of 8 labelled sequences from 8 different persons who\n",
    "developed different activities. There were five different activities: Running (1), Walking (2), Standing(3), Sitting(4) and Lying(5). \n",
    "\n",
    "Each of the training sequences had a duration inferior to 20 minutes, while testing sequences are over 20 minutes. Besides, time is not equally distributed among each activity, being standing or\n",
    "sitting much more common than running for example.\n",
    "\n",
    "As the distribution of each activity seems to be similar por each person, it makes sense to take 6 people to train our model and 2 people for evaluate it, so we can have an idea of its accuracy before we apply it to the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5638,
     "status": "ok",
     "timestamp": 1589461918014,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "vZmgwlIf99gt",
    "outputId": "f4e8a5bb-1456-4c9a-b36e-a32c5a8ed448"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "#activities\n",
    "val = np.sort(df.label.unique())\n",
    "print(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5609,
     "status": "ok",
     "timestamp": 1589461918015,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "fGVjYfHd_6CY",
    "outputId": "0798eae0-b4bb-47e8-8878-b7fd55053018"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4807, 29372, 41846, 40570, 24831]\n"
     ]
    }
   ],
   "source": [
    "#rows per activity\n",
    "count = []\n",
    "\n",
    "for i in val:\n",
    "    count.append(df.label[df['label']==i].count())\n",
    "\n",
    "print(count)\n",
    "# A lot more data for activities 3 and 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 147
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5565,
     "status": "ok",
     "timestamp": 1589461918016,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "VHicTmHuSqR4",
    "outputId": "e9e1e673-b207-4d1e-8b66-5d75d4a61212"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[527, 3563, 5800, 5317, 2529],\n",
       " [657, 3666, 5943, 4571, 3574],\n",
       " [550, 3009, 4900, 5865, 3116],\n",
       " [607, 3529, 5813, 5257, 2620],\n",
       " [679, 4495, 4102, 4504, 3140],\n",
       " [585, 4390, 4411, 4645, 3396],\n",
       " [696, 3606, 5838, 4657, 3259],\n",
       " [506, 3114, 5039, 5754, 3197]]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rows per activity per person\n",
    "p_count = []\n",
    "\n",
    "for i in range(1,9):\n",
    "  p_count_i = []\n",
    "  for j in val:\n",
    "     p_count_i.append((df.loc[i]).label[df.loc[i,'label']==j].count())\n",
    "  p_count.append(p_count_i)\n",
    "\n",
    "#print(p_count)\n",
    "display(p_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 147
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5537,
     "status": "ok",
     "timestamp": 1589461918018,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "7ZxBvSnH5lr1",
    "outputId": "5d18eb89-27c6-42b6-8c07-0d6284d8c136"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.475\n",
      "19.178125\n",
      "18.166666666666668\n",
      "18.56875\n",
      "17.625\n",
      "18.153125\n",
      "18.808333333333334\n",
      "18.34375\n"
     ]
    }
   ],
   "source": [
    "# Duration (minuts) for each recording (train)\n",
    "for i in (range(1,9)):\n",
    "  print((((df.loc[i]).shape[0]))/(60*16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6844,
     "status": "ok",
     "timestamp": 1589461919359,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "82OwEaAcmmgF",
    "outputId": "ddd66a54-3a4d-4590-9a0c-72eb859cf0bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.388541666666665\n",
      "23.557291666666668\n"
     ]
    }
   ],
   "source": [
    "# Duration (minuts) for each recording (test)\n",
    "for i in (range(1,3)):\n",
    "  print((((df_test.loc[i]).shape[0]))/(60*16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6814,
     "status": "ok",
     "timestamp": 1589461919362,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "7_Ayukoi8CHK",
    "outputId": "66382748-c7b6-4ee7-9b63-eea4422e403a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17736, 18411, 17440, 17826, 16920, 17427, 18056, 17610]\n"
     ]
    }
   ],
   "source": [
    "#rows per person (train)\n",
    "p_size = []\n",
    "for i in (range(1,9)):\n",
    "  p_size.append(((df.loc[i]).shape[0]))\n",
    "print(p_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6779,
     "status": "ok",
     "timestamp": 1589461919364,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "x7wHhzd3nCeK",
    "outputId": "f667a1cb-3691-4075-d5c6-4fe0e704db76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17736, 18411, 17440, 17826, 16920, 17427, 18056, 17610, 19573, 22615]\n"
     ]
    }
   ],
   "source": [
    "#rows per person (train and test)\n",
    "p_size_total = []\n",
    "for i in (range(1,9)):\n",
    "  p_size_total.append(((df.loc[i]).shape[0]))\n",
    "for i in range(1,3):\n",
    "   p_size_total.append(((df_test.loc[i]).shape[0]))\n",
    "print(p_size_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6751,
     "status": "ok",
     "timestamp": 1589461919366,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "o1MbQS2pLiYi",
    "outputId": "a9ba9b5a-c07c-4005-db6b-dcac29a926c7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars = ('1', '2', '3', '4', '5', '6', '7', '8','1','2')\n",
    "y_pos = np.arange(len(bars))\n",
    " \n",
    "# Create bars and choose color\n",
    "#plt.bar(y_pos, p_size,color=('m','y','g','purple','salmon','b','olive','c'))\n",
    "plt.bar(y_pos, p_size_total,color=('b','b','b','b','b','b','b','b','m','m'))\n",
    "\n",
    "# Add title and axis names\n",
    "plt.title('Distribution of the amount of data for each person (train/test)')\n",
    "plt.xlabel('Persons')\n",
    "plt.ylabel('Counts')\n",
    " \n",
    "# Create names\n",
    "plt.xticks(y_pos, bars)\n",
    " \n",
    "# Show graphic\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b360RpLmoium"
   },
   "outputs": [],
   "source": [
    "def plot_person_activities(count):\n",
    "  plt.figure()\n",
    "  bars = ('Running', 'Walking', 'Standing', 'Sitting', 'Lying')\n",
    "  y_pos = np.arange(len(bars))\n",
    "  # Create bars and choose color\n",
    "  plt.bar(y_pos, count ,color=('m','y','g','salmon','b'))\n",
    "  \n",
    "  # Add title and axis names\n",
    "  plt.title('Distribution of the amount of data for each activity')\n",
    "  plt.xlabel('Activities')\n",
    "  plt.ylabel('Counts')\n",
    "  \n",
    "  # Create names\n",
    "  plt.xticks(y_pos, bars)\n",
    "  # Show graphic\n",
    "  plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6703,
     "status": "ok",
     "timestamp": 1589461919374,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "xsu6n2I6piIK",
    "outputId": "26af5339-514e-4092-b6d5-1559989254fb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_person_activities(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 7772,
     "status": "ok",
     "timestamp": 1589461920474,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "zsxRkA2Spo1C",
    "outputId": "387261c9-c316-483f-cfef-53e514501258"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(8):\n",
    "  print('Person',i+1)\n",
    "  plot_person_activities(p_count[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "519l8xhBMHRv"
   },
   "outputs": [],
   "source": [
    "#Plot for each variable\n",
    "def plot_person(person):\n",
    "    plt.figure()\n",
    "    c = 1\n",
    "    # create a plot for each column\n",
    "    for col in list(person.columns):\n",
    "      plt.subplot(person.shape[1], 1,c)\n",
    "      plt.plot(person.loc[:,col])\n",
    "      c=c+1\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 13556,
     "status": "ok",
     "timestamp": 1589461926313,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "cMT26SMOMsnS",
    "outputId": "1df702e5-235a-4c10-9a74-1ce0956ad79e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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bRCsJ4D3gkJTyH3bpsXbZrgD2u7tuRcv5dHtGu9SbklaM2qpWoeh4tIXlMAmYD0yrF7b6ohBinxBiL3Ah8EAb1K3oRKzen8PVb23hsx3to5gUCoVz2iJaaRMgHFxa6e66FJ2btKIqAE4VVrazJAqFoj5qhrSi3fh4WzoAH25Ja1c5FApFQ5RyULQbGcXVAOgMZofXH/x8N/Pf22Y7L9cZePb7g+gMJo/I19EwmyVVeiN//XIPpVWG9hanS1FRY2TqS+t5+Is97S1Kh0EtvGdFSollLF1xMLuMqGBfooJ926R8o8lMv8dW1UlbtuM04YG+/H3NEcp1Ri4fGcfXOy1L0ScsWMG05Gh+PmxZgv6/m0/x1GWDGdYzFJ3BTKCvluhgP3aePsO05GiySqoJ9vMiOtiv2bK9ueEEf1t92HY+LTma+RN6U1hRQ0yIHxK4+f3txIb68c5NY9AIweYThYQF+NA/JgiNEGg1Aj9vLcWVNdQYzQyKDSEswKflPxigM5hIfmK17Vyr0bDoiiG8tu44V4zsQa+IgFaVf64z5KkfAYur84vUTFu6n7eG7iF+3Di+N6N6d6NKb2T+e9vr3Dt3eBy3TErgn2uPciinnJX3T27Rs9fREB05UmTMmDEyJSWlzev518/HeHnN0UbzDO8Zyp7M0jppo3qF8YfJfZgzLNbJXXWxf8EfvngAL/14xHbtxKLZaDVnlVP/x1ahN5l5e/5opIRjeeX06OZPv+gghvUMc/WrucQ1b21he1pxg/S0JXNcLuNwbhkCwYDuwU3m/WhrOo9/0z7BalqNwGT2/DPfnN/SEQXlNYx94ac6abdPTuTdTacYEBPMjw+c36ryPcGsVzZyOLccgH7RQcSF+fOHyYm8+tNRdmWU8NkdEyipNuCj1XDrf3fY7hsYG8KhnDKHZf761wuJD2+dYpRSkrjQvUOiX911Hl/tzOSTbacB+M8tYymq1KMREBfmjwCq9CbiwwPoFx3k1rqFEKn1V2VtCcpyAH7Ym9NknvqKAWDn6RKyfzjosnKwVwb2xwB9H13JvqcvItjPm3n/2oTeZHG1/PF/qQ3K+equ8xjd++wEc73RzJkqPTEhfuSX6ziQXcb5SVFoNYL8Mh01RnOjL5AjxeAIncHEir05RIf4Mi4xHF8vLQBrDuRyp1VOVxrBnt38XaqvKS4bHsf3e7KbdY+9YugR5k9WSbXL9/YI8ye3TGcrY3h8GALYnVECWL5XUnQQ6cVV6I1mqvUmiir1zZLPEXqjmYVf72uQ/u6mUwCUVLe+DmeYzBKdwYSvlwYvbeu80LWKASwz8o/nV7Dx6NlVEK5dutXhfc4UA8CUF9fbnrllO04THWLpsU/uF0luqY4pL67n1etGMHd4HPnlNfj7aAnx8wagsKKGMH9vvtnd9DM0dUAU8yf05kB2Gf9YW7cj2TsigKfnDuaVn46xx/osXPXm5jp57JWdPQNjQ1h1/5Qm628PlHIAZg6K4XBuORsfvpAD2aX0jgjk/s92MbRnKC9dPRwBGMxmss5Us/VkMXNHxBHoo+WeT3dxuJEHtz5Bvmd/7nUPXcD6w/k8v+KQLS2juJpBcd4cs1vKYtmdE0gvqmJ/dikaIfjv5jRO5Ffwy9ECXlt3zOW6rx/XiwdmJDFu0TpbWlyoH9mlOqf3JCxYwYb/m8rUlze4XI8rOOq5t7Rn/cIVQ9hxqpiEyEA+2prO5SN6kHGmiuTuwfSLbtqKaWue+f4AX6ZkNp2xER5bvo+fDuU5vZ5XVtOq8u158tv9FFfq0WoE39ZrNJfOH82r645x//QkHv9mP/nlNUzoE85905NYvPIw+7JKuWNKIlMHRHPju9uc1OCchZckczSvgowzVWw/5VqHBWDlvhzSi6rquAPtuf+z3dz/2W7b+SVDurNqfy4A0cG+5Jef/f0+uWM85/WNdFrX9IEx3Dc9yeG1CwdYXJqTlvzMDeN72awGgH9cMxwvrQa90UxcqEWBPfzlXsqqO+7YkUeVgxBiFvAqoAXelVI2uRuRJzCaJd5aQa+IAJvvdu2DF9TJ46vR0icqiD5RZ01AH63G1sN3BbOUaAScXGxpCPtGBXH7lD78fDiP2/6bQrXBhNksuXRYLL8cLWDbozMAGN8ngmuIJ6O4iv9uTuOvX+1t9nf8dPtpPt1+uk5aY4qhluYqhrd/OcEfL+jbaB6jVTl8/seJPLBsN5cM6d6sOuwJ8fNm+sAYAJ66bDBg6dF3FLy1Ggxmx89IabWBIF+vOu5ER9j7wNuS2a/+ysFGOju11uGddtbs1pPFbD15VhG88+sp3vn1VKP1tNbFVsuDn+/m651Z3P3xzmbdV6sYgDqKAWhUMbhCjzB/2/dbdMXQRvNO6BPB1pNFraqvLfGYchBCaIF/AzOBTGCHEOI7KeVBd9dVO7hsMJlZtiODx7/Zz4r7JhMV5Et0iB/H88v59Vght05KBCxRIE29oI7w1goMxsb91zqDiZxSHTqDCaNZ4qVpaJrXumfqm6IN83X84LLFqw6zeJWlB3fVqJ68/Lth7MooISk6iGCrOV9rOYT4e/HbgmntJqsn8NIIdAYzy3ac5pGv9jGhTzhv3jiaQF8vhj+zhunJ0bx3y1hb/vSiSn48kMt5fSMZ0iPU5Xqe+GY/N5+XQFSQL6eKKtl4tICJfSM4nFvO/Am9XSqjMcXQEakNWHAXY3o7XAu0zdBqIKukmmU7TvP4N/sxmCztUFyYHy9fPZzxfSI8Kk99PDYgLYSYCDwtpbzYer4QQEq52Nk9LRmQbs3gUnN7NJe+/iv7s5r/QtWvp8ZoYsDjq+uk3T45kccvHVQnraRKz4hn19JVWPvA+STFtL/rpy15YNlulu9ybyPmbob2CKW02sDp4iqP1OcuyyFhwQq3lFPLkedn2TpqnqAp+Vv6O7lrQNqTXdEegP06CZk42OehtUt2V+pbFgM/MDak2ffMHR7Xorrq4+ulJW3JnDqf+ooBsPW824oR8WFcPy6+TeuwJy7MPQPTHZnfje7ptrIm9ong5omuWQHOcGR9Gs2SxMjAVpXrKk25WprDi1cNc1tZgEcVA8CCS5IbvW5shsu6LfCk5XA1MEtKebv1fD4wXkp5j7N72jKUtf739sQcBzWXQuFJ1PN2btIZQ1mzAPtuaU9rmlNSU1MLhRDpLawvEihs4b3thZLZMyiZPYOS2TPUl7l15qUVT1oOXsBRYDoWpbADuEFKeaCN6ktxh/b0JEpmz6Bk9gxKZs/QVjJ7zHKQUhqFEPcAP2IJZX2/rRSDQqFQKFqHR+c5SClXopbuVigUig5Pxw+cbzlL21uAFqBk9gxKZs+gZPYMbSJzh154T6FQKBTtQ4deWykyMlImJCS0txjnDAaTGaNZ4u/t2XhvhULhPlJTUwullFGtLadDK4eEhAQ8sWS3ou4s7QNumsGqUCg8TyvC/+vQlcccFM3g3SYWS1MoFJ2GQUKI40KI14R1FqQQIlwIsVYIccz6t8mFpJRyUACWbRIVCkWXIB1Isn5mWdMWAOuklEnAOut5oyjloADAfpEFQzuv6aJQKFpFpbREGn0IXG5Nmwd8YD3+wC7dKUo5KAB4Y8MJ23HSY6s4WVDRSG5Fe6IzmLjjwxTu+iiVS179tb3FUXRc7Bc3jZFS1m55mQvENHVzhx6QVrSM0ioDeeU6DuWUMTgulF+OFnDbpASni7CV6RruRjXt77+4bWllResxmMzsySjhro93UlBvg5rSagOh/m27Yq89OoMJIZq/imlq+hmignxtG2q5i0M5ZWw7WcQtkxJ5dPk+2w5s6vl1jJRSCiGanMOglEMnpkxnIMBby57MUttGQe/eNIbbP2wY4ZUQEcBvx4sYk9CNoko9sSF+zBhk6TwMe3qNw/L/+uUe7praj4e/2MOjcwZy5RtnNyNafvd5jOzVjT0ZJQzoHkxuqY64MH98OsGGRJ2N9Yfzne5BDDD8mTVubQillJypMhAe6ANYNiDqHRHI5ykZfJGSwY60M4T4efH2/DGU6QxcPLg7s17ZyEWDu/PgzP62cg7nlpGSdsayr3ZRlW0HQ3c32rXW00fbTnPcbovdhAUrGBATzJG8cv5xzXDKdUauHRuPn7cWKSX/Xn+cq0fHU6U3UlypZ0xCuFvl6gDYL26aJ4SIlVLmCCFigfymbu7Qk+Dacsnuzkp2STU/Hsjlme/dvoFeq5k7PI7Xrh/pcn4pJUfyyknu3vy9NM4lXNnUxh0NbkF5DWNf+KnV5dx5fh+Wbjzp9Lo7lcMPe7O555NdbinrxKLZLdoRsqMhhDgMDMKyVNHrUsqVQoiXgCIp5RIhxAIgXEr518bK8Wg3TwgxSwhxxBpm1eRo+blKjdFEuQNXD8B5S37ukIoB4Ls92Ww6VsiERevYdfpMk/k/3naaWa/8ym/HO9sKyS3j72uO8EVKRqN59mWWklNa7SGJ6uIOxQA0qhjcyZHccrcpBoC+j65k2Y7TTWe0UmO07PneAUkAjgMngFXWtCXATCHEMWCG9bxRPKYc7PaQvgSLVrteCNFwu7M2psZowmgyU6U32jb8MZjMbLNu9O3K7kv21pbOYOJ0kfPtFaWUZJVUsz+r1GUZJ/9tPUOduHo6Or9/bxu5ZTqueGMzT367nw1H8klYsIKPtp6dl1NYUUON0cQW629u7wqQUiKlpKLGyMX/3Mi0lzewN7Okzv/K/n+UW6rj72uOUGM0cTSvnPxyHcWVenSGlu0ICHC6qIr3N51ib2YJYHGrHMsrb3F5GcVVrDmQy+s/H+fhL/fy6k/HqDGauPrNzby+7lidvJf9axMTF/9sU5iuWvY7rcpYSsndH6fy8bZ0nv/hILszSlosd1tRXKnHZJYufzd77O95ec0Rd4oFwCNf7WOz3W9vMkunCmDA46t5+Mu9jZZ3PL+Caie7UxpMZoor9bbz19cd49vdFi+QqXVK54CUsq+U8h5r1BJSyiIp5XQpZZKUcoaUsripQrrcHtLg3r1lvbUCg0ni561BZzjbKPWLDqrTqDlCCHDHzzupXwQaITCZJZtPFLW+wC5CVLBvg8HZ+vQI8yc21A+AlHRLAzqhTzhmaXn5d6SdtXDGJYaz/ZTzd+b8/lFsPOp869opSZH8eszSsPTs5k/mmfaxAJqi/rPcLcCbM1WOLVVP0is8wGP7WLub7iF+5JbpbOcJEQGk2XUag3y96BsdhMlsJtDHC40Qts6RMw4/Nwu/Fixlo/aQdoK7J3MZTJbWvX4j35RiAAjycc94f5XeREWNkT0dsBfYnrgyH6NboDcVNcY6e4ubJWgEDfzL+zIbt+7KqhtvQO0VVQd1NwCQFB1c57yjTIB0FDXXUXC097Y93ayD97XU/02D/bzw0QqKK/Rkl1a7ZBl4tfP4R4eLVpJSLsW6BO2YMWOa/YYF+XqRtmQOFTVGvLWCqhoTYQHelNcY2XCkgLnD4wA4UVBBn8hAhBBsOJLPLf/ZwQMz+nPf9H4IIajWm/D3aai1He3LW64z4O+txUuraXCfySzZcqKIwXEhdAv0wWyWSCwNk5SSN385wYurLebxV3edx+je3ZBS8kVKJvNGxtUJF/xoazqPf7O/uT9Ju7HuoQvw9dJgNEm6BfrYwi2zSqqJC/Vj+DNrKNNZXqLaQcr0okryymoYlxjO5uOFVNQYmZwUSXpRFRFBPlTVmEiIDLTVUVKl5/u9OVwypDt+3lqO5ZUzpEcoXhrh0v7JJrPEYDLbemip6WcI9vPizg9TeO+WsfSNCrK6ukBjfVmN1gUKHfXqHD0fpdUGRjy7hohAHwor9Mwa3J3Xrh/JR1vT+e/mNNY+eL7t//xlaiZSSqYlRxMR5IvOYCL5idVNfo8f/3I+/aKD0GoEG48WcKqwkvxyHTqDmScudc17606LuzHSlszh291ZJHcPITEy0GmEW1N7YLeVvA9fPIA/XdAXjWh8b/lar0ttniq9EYNREhrg7TSPPTqDiU3HCpk+MBohBMWVej7dfpq7p/btEHt/d0m3UktYfzifKUmReGk9G4ppNksO5ZZhMElGxIc1mnf1/hz+9NFOD0nWMk4ums33e7OZPTQW7yZ+y9+OF3Lju9tYcd9kBseFekjC9qWpBs8RnopW8oRyeG7eYBtcDyoAACAASURBVOZPTHBLWW0h75aF04gN9Xd7uZ6kM7qVdgBJQohEIYQPcB3wnQfrb5QLk6M9rhjA0hsdHBfapGIAiAq2+M67BTR/wpOft+PvNiUpstll1TK8nsyvXjcCjUYwb0SPJhUDwKR+kaQtmXPOKAZovCfa3vz31rEO08cmdOPPF/ZtkB5u50qZNyKOQbGWkORld05okLfWhXflqJ7uELVB/Y6Y1C+CF68axqr7p9jSpiRF8tbvRzNjYDQAr10/ks+s8n7+x4mdXjG4E4/OcxBCzAZe4ewe0i80ll/Nc2jIz4fzmNTP0qAPfvJHy4M+KAazWWKWEi+thvwyHeMWrePKUT0I8fNmcr9I24Q3sJizK/flcMXIHgghnPbAjr1wCTqDiXWH8vk8JYPNJ4rqDGaqGaiewVOWQ1OYzZLjBRVc9M+N9IkK5OeHprp8b2m1gUM5ZUzoE+E2eYoqaiiu1JMUc3YM5ZWfjrLrdAmPzRlIf7v02t/wXHhm3WU5qD2kOxnTks828scXzbYdazQCjXX5vOgQv0ZfAj9vrUs9OG+tBm+thstH9uDykZbYAb3RzNPfH+CBGf2buFvhLu6e2rfO2lf1uWRId4/IodEIQvwsVuvI+CZXfK5DqL+3WxUDQESQLxFBvnXS/uLkufT31lLdivDmc5EONyCt8Dx3Te3Lm400Pvb4eGlYdMXQNpZIYc8DM/s3qhw86Q7tHurH9/dMJikmyGN1uoMtC6fVCd9VNI1aCEfBI7OS21sERSN4azVsXTjd6fWbJvb2oDQwtGdoi+Lv25OwAB+6W+e7KFxDWQ6KBlw4IIr/3DquvcVQ2NE91I/tj05nX1YpFw6IRgiY+6/f2JdVytiut2CcogOglIOiAdeP69XeIigcEB3ix/SQs73f7++d3I7SKLo6yq2kAOC8vmcHCwfGqlVSFYpzHY8oByHE00KILCHEbutndtN3KTyJffh9fLh7N2NRKBSdD0+6lf4ppXzZg/UpFAqFooUot5JCoVAoGuBJ5XCPEGKvEOJ9IYTTGTStXZVVoVAoFK3HbcpBCPGTEGK/g8884E2gLzACyAH+7qwcKeVSKeUYKeWYqKgod4mnaAJBx13zR6FQeB63jTlIKWe4kk8I8Q7wg7vqVbiHDrwenEKhaAc8Fa0Ua3d6BdB5NiU4RxifaJlIdcN4NcdBoVB4LlrpRSHECEACacAfPVSvwkXuntqP6QNj1BwHhUIBeHjJ7uYihCgA0pvM6JhIoNCN4ngCJbNnUDJ7BiWzZ6gvc28pZasHbDu0cmgNQogUd6xp7kmUzJ5ByewZlMyeoa1kVvMcFAqFQtEApRwUCoVC0YCurByWtrcALUDJ7BmUzJ5ByewZ2kTmLjvmoFAoFIqW06H3c4iMjJQJCQntLcY5g1lKNGo2nELRqUlNTS10R7RSq5WDECIe+BCIwTKPYamU8tV6eaYC3wKnrElfSymfbarshIQEUlJSWiuiwgUOZpcx+7Vf+dcNI7l0WFx7i6NQKFqIEKKl4f91cMeYgxF4SEo5CJgA/FkIMchBvl+llCOsnyYVg8Kz7MsqAeCeT3ZhNitXo0LRiRkkhDguhHhNCIsrQAgRLoRYK4Q4Zv3rdPHTWlqtHKSUOVLKndbjcuAQ0KO15So8i/3QU3mNsf0EUSgUrSUdSLJ+ZlnTFgDrpJRJwDrreaO4NVpJCJEAjAS2Obg8UQixRwixSggx2J31KlqPMhYUii5DpbREGn0IXG5Nmwd8YD3+wC7dKe5csjsI+Ar4i5SyrN7lnVimdA8HXge+aaQctZ9DO7Ano6S9RVC0gIVf72PTsc622kPHY0dacVd0p2Zy1osTI6XMsR7nYhkjbhS3KAchhDcWxfCxlPLr+tellGVSygrr8UrAWwgR6agstZ9D8/lhbzYJC1bw7e4s7EOTL3hpPct2nHapjGUpGbZjncHkdhkVLSe/TMdDn+9BZzBxJLfclv7uryf5dPtpfv+eI0Nd4Sobjxbwu7e28N6mU01n7gJYrYomNaE7opUE8B5wSEr5Dyd5ugN5UkophBiHRSkVtbbucx290YwQlkFkgPs/283O9DPEhvnzpwv6kl5UxSNf7ePasWeX4f56Zybx4QGMTQh3Wu74RetIWzKnTtpn209zsrCSR2cPrJO+/VQxQ3qEsPFoIeMTw+kW6OOy/FJKThRU0i86yOV7zkWeX3GI7/Zk89XOzPYWpcXsySjh9+9tY/6E3vx1VnKb1rX5RCHjEsLx0rrW980prQbgSF55Ezk7HT2BLOtxnhAiVkqZY91CIb+pm90xz2ESMB/YJ4TYbU17FOgFIKV8C7gauEsIYQSqgeukmn3XKr7dncX9n+1ukP7BFksU25JVh21pF/3zF76/dzLZJToe/HyP5f4/T2J4fBgAz3x/oEE5i1ceYqFVEaw/nM+Cr/cBoBGC3NJqXrluJKnpZ7jm7S2M6d2NlPQzAJxaPBvh4lyJZTsyWPD1Pj65Yzzn9XVoSCqA7/Zke6QencFE8hOr6RcdxIJZyTy34iCD40K4aFB3pg6Iws9bixDw7PcHuW1yIgkRgbb9A/UmMweyyxjVKwwhBKcKK1l3KI9vdmfxzNwhXPXmZgDe2HCizZSD0WRm4pKfKSivoXdEABP7RHB+/yj2ZZUysU8EN72/nVmDu/PW/NGAxWIY0iPUtgvil6mZ/O2qYWg1nX6uT6C1034TFjc+wHfAzcAS699vmyqkQ8+QHjNmjDwX5zkYTWZMUuLrpXV4feLideSU6txS14CY4EZ7TI/NHsgLKw+5XN4bN45i9tDYpjMCj3+zj4+2nua5eYOZPzHB5TrONRIWrGgyT31LryVknqli8t/Wt7qcE4tm0/fRlU6vu0NWe3aePsOPB3J5+5eTLuUflxjOtORolqw6zLCeoRzLq6Da6kqdnhzNe7eMdat8nkYIoQOygVXAvVaPTQTwOZZOezpwjZSyuLFyOvQM6XOVa5duJTX9jMOXaNmO025TDNC0Kd0cxQBw98c7+fru8xjVq8kwatts7C9SM5Vy6AA8ttw9GzQ2phjcyZ6MEub9+7dm37f9VDHbT1naxb2ZpXWurTvcpLelM3Cg/hLeUsoiYHpzCnHXgPQsIcQR68SLBvGzQghfIcQy6/Vt1pDXc4YD2aWsO5Tncv5Uq4vGEY98tc8dIrUpV76xGaPJTFNWaa3xXv8FbS7rD+dTpXfv3Ax3Rq7oDCYqa4wYTGZbmpSS3721mdX7c91WT2v55Wjnig7831a3TATuMJyp1HeoYBB3DEhrgX8DM7GETu0QQnwnpTxol+0PwBkpZT8hxHXA34BrW1u3M97ZeNLW4/3iTxN55aej/HbcMv4d6u9NabXBlvfV60Ywqlc33v31JP1ignnim/3cNikRHy8Nn24/zdd3n8fWk0WcyK/k/d8s0Qxv/X40iZGBdAvw5qdD+Ty6fB/3TU8i1N+b3uEBeGkFPbsFkFNazfz3ttvqWnBJMv7eWmqMJuYO78GExevqyN03KpCZg7rbzuf+axP/u208AFqtQNuJ1j3q99iqBmkPzOjPiF5h3Pz+9gbX0osq8fXS8mVqBr8eK2TeiB48uvysIvy/i/oTHx7AwewyRvXuRkyIHxGBPkx5saEbJDzQh8uGxXLH+X3w9dLipRF4aQXVesuL5+ulpbTagElKvLWC08VVDI4Nxc9Hwz/WHEWjEby54QQAq/8yhZV7c0gvruLhiweQV6bj4S/3cseUPkxPjqZMZ+DHA3lM6BNBr/AAqvRGvLQagv28MJkkJdUGLnx5QwMZLx8Rx460M+xIS2X+hN5cOzYeo1lywztbqdKbeHBmfwRQVKl36feudT3NGtyd1QfqKpzv7pmE3mimd0QgUko0GoGUkFZUSUywH8F+XuSWuc8abYo/f7KTqf2j2JNZwvrDBfxlRhI9uwUwPD6Uo3kVRAf7cqZKzzPfH+Tlq4dzOLeMz1MymNAngtG9u+HrpSXYz4sD2fUj5t3D6OfW8umdE/DRahDCMgeoV3gAUkrOVBnIPFPFL0cLmNQvkqggX2JC/BDCMpFUbzJjMksCfLRICQ9/uYc7pvQhOTYYo8mydlnta5xRXMXMf27khSuGMCQu1GYFPXnpIKYPjKZ3RGCbfD9XafWYgxBiIvC0lPJi6/lCACnlYrs8P1rzbBFCeGGJs41qalC6JWMOBpOZJAcNk0KhUHQmTi6ajaYFg+NCiFR37AznDrdSDyDD7tx+4kWDPFJKI1AKRDgqrLWT4MxuHmDv2c0fgO4hfnXSR8SHkdw9uMXlXjnStRVGbhjfi6tG9WThJcmE+nu3uL5zlcfnDOS+af24YbwlnHdwXAg+XpbHvjZaCywWpf25O/Dq/FEvHmXmoCbnZbUrvcIDnF67e2pfpiSdjbjrHxPEjIFnv8+8EXE8MiuZO8/vw58u6MtfZiQ1WV9LFIM7cYflcDUwS0p5u/V8PjBeSnmPXZ791jyZ1vMT1jyNTu1sbbRStd6E3mQm1N+btMJK3t10kmfmDkGrsYTa9Q4PQKMRmMySf68/zq2TEgj2szTANUaT1axs3T+oWm/iq52ZDIoLcThI++ePd7Jin2Xi4qIrhtoaMUes3p/Dnz7a2Sp5PMXc4XG8dv1IAP7w3x1cMjSWq0f3BCz+fI1GcMM7W9l8wuLuqx18r9ab8NIKvF2MUQf47XghYxIs7oYVe3OYlhyNv4/jSK+mkFJiNEs+2prOsJ6hjO7tfD6Iq+gMJjRC0P9xi0X7n1vGcmFyNEUVNRzKKWdcYrhNYWWeqWLqSxt4/fqRXDI0Fr3RbLuvMVbdP4V+0UF4azX8b2s6/t5aRsSH8t2eHB6YkeTSc+xKVJQ7SFsyh+/2ZHNBUhShAS3v8LSVvK5GU9W2nS1tI0xmSXZJNfFWpXO6qIpFKw/x2vUjbc9DS3CX5eCOaKUsIN7u3H7iRf08mVa3UigemATn76PFH0sjkRAZyPOXD7VdS4w868/TagT3Ta+ryZ2FkbZEht9P6O30+g3je9mUQ7nO4DQfYGsw+8cEcTSvokXy2M9JcIWP/jC+WTNwrxzVgycvHVTHyqkfGljbI6qdpPTPa4fbrrWkUZ/U72yPbc4w18JonSGEwFsruHVSYqvKscfP2/KdVv9lCsF+3vQIs1ijEUG+TE7yrZO3Z7cAji+abTt3tZEYGBtiO55v97w9ONN163bdQxcw/e+/uJz/oZn9+feG4+gMloH2FfdN5tWfjpFdWs3+LMfjAUN6WOScO7xjLAt/37R+vPbzcQAenZ3M6N5NR9nV0tqOo1YjbIoBoFdEgG0ORkfAHcphB5AkhEjEogSuA26ol6d2AsYWLBPiflaT4CxM6hfJ3qcvYvHKw9wyKaHRvLW/WM9uAbxx42hyS3VMTmo4eay2R1U756CgvMZyP5Lo4LPusSq9kVfXHXMaH17bg5ozNJYV+3I48vwsBjy+GoCXrh7G1AHRhPh74eultUVjTUuOdvmlGRkfxsajBQyICWk6cxcguXvLvmdTc1HcRd+oINv/vKRKT15ZDYdzy7h4sCVIws9by/H8cmb8YyN9ogK5d3oS905PYvX+XIL9vBgcF8rSmywd1txSHX7eGoor9cRZleG1b2/h6bnuX3OzVuaV+3K4+2OLZX3zxN7cfF4CvcIDEEKQXVJNjdFM91A/vDQCH63G1kl58KIBbpepK+CWSXBCiNnAK4AWeF9K+YIQ4lkgRUr5nRDCD/gflhVbi7HMkG5yxsq5OgnOGWsP5nHHhylNTtRprrnryDw/8vwsm/WkN5opqdYTHezHbf/dwc+H890ykclklhzJLWdQ3LmhHFpKVkk1k5b87PT6qF5hfH33JI/IciyvnJn/3EjfqEDWPTTVI3U6o/a5tX8WdQYTRrMkyPfcncLVkdxKtYvprayX9qTdsQ74nTvqUjRNa81dqOtW8/HS2CyOt+ePts0mbS1ajVCKwQV6hPmz6v4pXPLqrwDcPz2JV9cds10f0EKLpCXUdiXd8Yy1lohAnwahvrUuPEXraZVyEEK8BFwG6IETwK1SygZrPwsh0oBywAQY3aHVzkVqgxfc7Y/76q6JXPXmFpfyems1zRosVriHgbEhdXrID8zsT8KCFfSOCOCpyxxtvNg2xFij9n7fSOCEp1j74AUUuzgPRNF8Wms5rAUWSimNQoi/AQuBR5zkvbCp6CRF44xJCKdHmD/3T286DK451I/IeenqYW4tX9E2uHuNIlcI9fdul3odER7oQ3gzVgFWNI9WKQcp5Rq7061YBpsVbUSovze/LZjW5vX8bkx805kUCkWXxp3+gduwrALoCAmsEUKkCiHubKwQtRNc+3DHFPeFbioUis5Pk8pBCPGTEGK/g888uzyPAUbgYyfFTJZSjgIuAf4shDjfWX1qJ7j2of4mPgqF4tymSbeSlHJGY9eFELcAlwLTnc1dkFJmWf/mCyGWA+OAjc2WVtFmdIToE4VC0XFolVtJCDEL+CswV0pZ5SRPoBAiuPYYuAhwz8LxCoVCoWgTWjvm8C8gGFgrhNgthHgLQAgRJ4SonfcQA2wSQuwBtgMrpJSrW1mvQqFQKNqQ1kYr9XOSng3Mth6fBIY7yqdQKBSKjomazaSogxp6UCgUoPaQVtjx6nUjGNIjtL3FUCgUHQC3LLzXVgghCoCWbhQbCXS2GdlKZs+gZPYMSmbPUF/m3lLKVs8D6NDKoTUIIVI62xpOSmbPoGT2DEpmz9BWMqsxB4VCoVA0QCkHhUKhUDSgKyuHpe0tQAtQMnsGJbNnUDJ7hjaRucuOOSgUCoWi5XToUNbIyEiZkJDQ3mIoFApFpyE1NbXQHdFKHVo5JCQkoPaQ7pi8sOIgZglPXOq5XcgUCkXTCCFaGv5fh6485qBoBiazZO3BPFx1M77z6yne23SqjaVSKBQtYJAQ4rgQ4jVhXW5ZCBEuhFgrhDhm/dutqUKUcjjHWbbjNH/5bBf/+e0Ud3yYwvS//wKA2SyRUpJeVEmZztBkObNe2ciUF39ua3EVnYAtJ4rIPFN3keaiihqe+GY/eqO5naRyzhcpGew6faa9xXAn6UCS9TPLmrYAWCelTALWWc8bpUO7lRRtzyNf7QNgf3YZACcLK8kv1zHuhXXMHBTD2oN59I4I4JeHL2y0nMO55W0uq6JzcP07WxECTi0+u9f08ysOsXxXFmMSujFvRI92kSuntJplOzK4f3pSnf1LHv5yL9A+e3K3EZVSSimE+BC4HMsOnfOAqdbrHwAbgEcaK0RZDp2MH/Zmk7BgBQezy6isMbqt3OP5FbbjjGJLr2/twTwA0oscbtXhEFesDEXXp753cvmuLAA+T8lwaz2VNUZMZtdcoX/+eCev/HTsXOrIZAK1mjhGSpljPc7FspVCoyjl0Mm455NdAMx+7VdGPLumnaVpyOp9ue0tgqIDk12ic1tZZrNk8FM/csUbv7k0VlZZYwLUysPWHTub/MGUcujEGExtNUel4dvj7OWrn36uv3htzZHcco7nW3q+RpOZGqOpnSVqHj27+butLL3JMn6xN7OUT7afrnPNZJb8bfVhiipqOFOp5+Nt6eisv5XGyUNaXKl3m2wdhJ5AlvU4TwgRC2D9m9/UzUo5dHISFqxg+a5M9meV2tI2nyjkvMXrMJjqDv7llurIL2u657b5eMNFKb9MzQRg+a5Mm9sJLC/ot7uzbOdlOve5uhQWFq88RMKCFUx58WcufmUjM/6xkZX7cuj32CoGPO58U8Vb/7OdF1cf9qCksDujxHZcpT/7LFw3Nh6AWUO6u60uvd3z/djyujsPbzxawJsbTvDEt/uZ89qvPLZ8v809+vrPxx2Wt2jlIbfJ1ho2Hi0gv7xVFlagNUrpJuBba9p3wM3W45vt0p3S5QekTxVWEhPiS4BP1/2qDyzbA8BLVw/jd2PiueGdbQAkPbaKx+cMZGSvMNYfLuBf6y0vxbVj4vnb1cOc+mr/vvZog7SHv9zL6z8f53Rx3fGHj7ae5rkfDtrOn/vhIHllOi4eHMPo3uGNyl1aZeD9305x3/QktJqub3KUVOkZ8exaXrt+JHOHx7l839sbTwKQUVxtS7v745224/1ZpQ734Vh/pID1Rwr466zkZst5LL8CjYCr3twCWAZrL3x5A6cKKwG4dFgsD100gN0ZZ+ge4k9MiC9H8yr400eptnIGPfkjA2KCOZJXzpSkSMB5r70lVNXUtZo+2prO5SN7MO3lDeSX1wCw0oGb8/s92WgE3DutH4UVZ62FL1MziQjyYeElA90mY3MxmyU3vb+dxMhA1v/f1JYWkwAcxzIQvcqatgT4XAjxByzRTNc0VYhHl88QQswCXgW0wLtSyiWN5R8zZoxszSQ4KSWJC1cysU8En945ocXltIbcUh3+PlpC/b0bXCvTGfDSCLacKGL6wCbHhwCLpdAYb/1+dJ0XtD1pKvrjijd+Y9fpEhIiAtjgIBpKZzBhlrLTK3YpJY98tZfk7iE8+8NBhseH8e2fJ7E/q5SIIB9iQ527WmqMpkatA4A+kYH8bNeQVNYYuffTXfx82OI5cDUK539b0nji2wMOr10xsodtULk1zBsRx6vXjWx1OQCPf7OPj7aebjpjM2nPqKXKGiODn/qxVXIIIVLdsYS3x9xKQggt8G/gEmAQcL0Qok2n19ZYY6q3nCziREEFOoOJt385QYG1V5GSVkxplePomq9SM8kprXZ4rRa90YzO0LjPd8LidUx9aX2D9NxSHcOeXsOgJ3/kDx+kcCinzJWv1CQdRTHYk1FchdnOSimp0iOlZNdpiwsizUE0lJSS5CdWM+jJH229VUecLKggo7iKtMJKFq86RGFFjcsT+Rrj9XXH2JFW3OpyAMprjHyeksmzVgtrT0YJJwsquPT1TUxcfHZuiJSS9zadImHBCt791WItlLvgpjtZWMncf20iNb2Y/HIdf1m226YYmoMzxQC4RTEAfLs72y3lAG2iGDxNVkk12SWWdmbxqkO8scGxy6s98JjlIISYCDwtpbzYer4QQEq52Nk9LbUc3vrlBEtWueZrDQvwJvXxmVz/zla2n7I0Bl/ffR5XvrGZsABvfnn4QooqavjtRBFPfGPxa947rR8PXTSAqS+ttzVsj85OZuag7qSmn2H5rkyWXDmMiCAfBj1p6QU8cekgnvvhIO/cNIbwQB+EgCvf2FxHlqXzRzM+MYLDuWVcu3Qrt01K5MnLzurP9KJKLnhpQ7N/j47CF3+aiL+3lktf39Tg2pOXDmJ0725sOl7ISz8eITEysI5SiA31Y3xiOBU1Jo7klfHRH8YT3y2APo+udFjXi1cP43eje9aJZ7dHSokQgtJqAy/9eJjn5g0B4D+/pfH1rkz2Z1mU9caHLySntJriSj0zBsWQUVzFv9ef4LLhsQzpEYpWCE4VVTIkLhQvjSCrpJpgPy/CAnzQG83kl+tY+PU+fj3mfHOxiX0iWHTlUPRGMxe/stGWPmdoLCv25Ti9z1WuH9eLT7efZu0D52MwScxSklOq444PU1hwSTK3TUqkpErPuEXrWl2XK3x/z2R2Z5YwLTmamGBfPtqaTmiAN1eM7ElaYSXhQT6kFVay/VQx146NZ82BPD5PyeDKUT0oqzZy+5RE9CZzkxZVa3jr96MYFBtKpd5IgI+WMH8f9CYzkUE+VBtMrDuUz9G8cgrKa7hral96hQdQpjMiBJRUGuge6oe3VpC4cCU3T+zNM9bny54vUzP5vy8sLuGHLx7ASz8esV3TagQnFs1ukezushw8qRyuBmZJKW+3ns8Hxksp73F2T0uUg9ksnTYYCoVC0Vk4Z9xKriKEuFMIkSKESCkoKGj2/ZpzYGBToVAo2hpPjvRlAfF25/YxuDaklEuxbl4xZsyYFps1Pl4ah+u49I4IYOElA6kxmrj/s9387w/jmJIUVVs37206xbjEcEqrDRRX6utM9ZdSsul4IT5aDeP7RABnB4i/vvs8RvWyrGWVW6ojJsSXD7ek89R3Fj9u2pI55JRWEx7og5Tg563lQHYpO0+X2NxVG/5vKgmRgQ7LBEtUiiOXTGdhz1MXEervTWm1gY+2ptcxow8/N4s9GSUsXL6PkwUNxxgGxobw6OxkfjlSQLnOyC2TEkjuHsyDn+9h+a4swgK8KbGOH21dOJ2dp88we2isS3LpDCb8vLW28/6Pr7I9OycWzaav1RI9+vwlmKUkv6yGXhEBLn/vpoIIfnrwAvpFB9XJe/fUvtw4oTfeWkFK2pk60UnN5b+3jmXj0UIeuWQAvl6W71lZY+SRr/byxKWDiAnxswVveILld5/HyF51133LL9MRGeTrtHNX6wa050hueR03nLu4d1o/5gyLJSk6GJ3BxKGcMsYkhNvkMJklh3PLCfT1omc3f7RCoNEIpJSYJXUi7xZ8tZdbJyUyoHtwg3ru+DCFtQfzeO36kVw6NJb7l+3m+z3uG5NpLZ50K3kBR4HpWJTCDuAGKaXTUbDWRivtzyrlwc938+y8IUywNuaeZr11YPDC5GineT7els5jy/dz8NmLCfDxolpv4kRBhcPwxF+OFnDz+9vbTF53MbxnKN/eMxmwNL56k5kQv7oRW7WhkQsvSeaPF/Stc+26pVvYevLsgPBXd010GBprNJmp1JscRoO5i+W7MqnWm7lhfK8Wl6EzmLhu6VZ2Z5QQF+rHTw9dYBuPsncfLNtxmv4xwXUaz20ni7h26dZGy/f31rLsjxPoFR6Ar5eWNzcc5zVrPL+r7ommlJi7OPL8LJuSai1t4Ub2ZLRSfaW34Ug+t/xnR4OOYXNwl1vJY5aDlNIohLgH+BFLKOv7jSkGdzCkRyhrHrigLatoksaUQi03ju/NjeN72879fbQOFQPABf1bvYdHm/DQzP70ighAZzAxb0QPvOx6T37e2jo981ouHBDNqcJTdAv0aXDt49sn2HrsWxdOp3uon8N6vbQaQv3bc2Ep0gAAFUJJREFU1jt6xcierS7Dz1vLN3+eZDuXUjI9OZqbzkuok+/asQ0V0LjExueLABx6blad86hg32bLuOuJmYx8bq1Lef94fh/b/As/bw06g2urrSZ3D8ZH677/l7vcyN/fM5l3N53kgRn93VKeq9S3hi7oH8X2x6YTHez4efckHXqb0NZaDl0V+x5e2pI5GE1m+j22isggXworamzX7p7alyO55RzKKSM6xI99WaXcNimB+PAAgny9MJokoxO62ZbpbozjL1zC4dxyvkzNJMTfm9fWHQNg2Z0TiAz2pW9UULO/R2WNkaUbT3LvtH54OWgw8st01BjNxIe77sLpqhzPr2DGP5z/n+r3dmuMJhZ+tY9rxsY322q2j7UH+Pj28dz47jan9dUYTRRX6lmxN4dbzkvguR8OcvuUPoT4eRPoq8VLq7E9s23RK1+xN4fTxVX8zTob/IUrhjSYMV2f5+YNrhO621EaZHfQ6aKVWoJSDo7JLdWhN5qp1BsZGBtS59oN72xl84kioOGL6MhvC665E+zL0hlMJD+xGi+N4HgLw+0Uzad+oz0uIZztacX0Cg9g418bX1K9uXyeksHhnHLundaPboE+tmekpY1oWyoHRxhMZm7/IIVfjtYNaokP92fFfVMI8fPGaDLzeUomSTFBjE1o2jrrLHQ6t5LCfThzsQCNLl/sLObfET27+ZN5xjI556nL6s5VrHUZXT+u5T54RfMJ9PXiD5MTbTvw1Q58LrpiqNvrumZMvMN0X23Lxgo+uWM8O055bkMdb62GuLCGM88fmNHfNvblpdW0ahypq9PhQlkVrcNdhmBYgOUFeuXaEdw6KbHONS+thoPPXswzcwe7pzKFy9jv2b3oyqHMHtqdsYktG7hsCT5eLWsyzusbyf0zktwsTePMn9C7QdqwnmEelaEzo5RDFyM51hIyd2MzekQ/3Du5QVovq58/1omVEuDjpeaUtDOJkYG8ceNot0X+uEJLlUN7MCguhLQlc7hjiqVz89Rlg2whw4qmUW6lLkatqyExMtDlexxFRj04sz83jOttm8+hUACdcvXc+6YnYTBJ5QZtJp2nG6BwiZYuiTzDblXYB2f2p29UEJOtyywrFJ25xx3s583Tcwc7DKdWOEdZDl2M2pe4Z7fmhX8O6B7ET4cse0bfN92zvmFFx+fLP02krFpt5HQuoZRDF+O6sfH0jwlqcqOd+nTgiGZFByAswIewgIaTFRVdF6UcuhhCiGYrBnBht3FFh+GC/lHkubDdq0LRGjwy5iCEeFoIkSWE2G39qJlTHYxLh7m2SJ2i/fngtnGs/sv57S2GoovjScvhn1LKlz1Yn6IZDI5zvJaTQqE4N1FuJYWNu6b2bXSGtUKhOHfwpHK4RwhxE5ACPCSldDiXXghxJ3AnQK9eKi7ZkzwyK7m9RVAoFB0Ety28J4T4Ceju4NJjwFagEMu453NArJTyNhfKLADSWyhSpLXOzoSS2TMomT2Dktkz1Je5t5Sy1Wv7e3xVViFEAvCDlLLhjtvurSfFHSsTehIls2dQMnsGJbNnaCuZPRWtZB8KcwXQ+GLrCoVCoWhXPDXm8KIQYgQWt1Ia8EcP1atQKBSKFuA2y0EIkSaE2Gedx1Bnhx4p5XxgGLABGAT8KIQY5a66nbC0jctvC5TMnkHJ7BmUzJ6hTWR254B0GjBGSulwMMc68e1eYDYwHnhVSjneLZUrFAqFwq14MpR1HvChtGijrUKIMCFErJQyx9kNkZGRMiEhwWMCKjyDlJBbpsPsho6JEBAT7Oe2paTzynQY3TzXI8BbS7dAtS5RR0YCeaU6TG54JgN9tO26DlVqamqhO6KV3KkcJLBGCCGBt6WU9U2dHkCG3XmmNa2Ocqg/z0HtId31SE0/w1VvbiYswBsfbcs9m0azpLhSz9/mj+aiwY6iqJtHRnHV/7d37kFSVWcC/323e2Z4DI9BEBEUkE1MYQoDzFokIpU1EfGxQRO13E1Fk1hlaWV3Zc3Whl1118puZRPXZDdZ4lKmkgq6umKMRipCVlRMfER0QOQRUAYEI87wdlCZgX58+8c9d+YyPd0z3ff0ne6e86vq6tunb3/nu6fPvd853/nOOVx0zzrGDksy3NLyzse6UjQ21NFy5+etyHOUh23vdXDFj17ktOF1NETY0KijM8X4xgZeWnqxRe2KQ0RGikgrsBq4TVVVRMYBK4Fp+OO+1+WbaxZg0zjMV9V9InI6sFZEdqjq74oVYozK/QDNzc1uum4NEszC/vFfzuHCPyl9z4gd7cdY9J8vWGvppzJZAP5l8Se5avZkKzLv/NUWVm9ptyLLUT6y/l/PvdeezyUzJxY+uQDfemwzz791wJJWJbMXf2x3NbAIWAMsBZ5V1e+KyFLz+VuFhFgbkFbVfeb9APAEcEGvU/YB4V3Lp5g0xxAjcCeVuC9RN0nPr762jEMgJ5mwt9uZJ2LFfeYoL2rWJY7qnUwmhHRm0P/vj4z7/gHgKpO2GFhhjleE0vNixTiYbsyo4BhYSO5chlXADeIzD+goNN7gqF2Ch2Wpu9YFJM2dnDYt/qgEN3XS4laYnghZt15VxRP8RdEbLGJ9zCoCgeseYGLoedsO9Ns9suVWmgg8IX7JJoGHVfU3InILgKoux+/iXA60AseBr1nK21FlBA3pyMbBtPDt9Rx8IxP0SGzgibiNlKoA7e7NRq2TnrXGSrkwYxD91korxkFVdwPn95G+PKwQ8A0b+Tmqm2DMIcJYNNDzELe1kmxgZBJW3UpYiYBxlJfunkNEORXWcwi77vcH0aFmxYp+B0bckt1Ae0cXD/x+T0l/6qdnnMafnXv6gM49cKyLn79cWj5hFn/qzKrefyFrrZXm//6pzW28feijyHq1d/i7q9XZ7Dl4bswhQFX5yQu7OfThSWsyi7n/CmPJ1ZkQTmayfGf19gGd/6fTxkUaAM/DSPFvrhuA/zJpq4Abge+a9yf7E+KMA/DUljbue34Xw+o8pIi2w4l0hhd3Hhpw5Vyztb2kfMJ0pjIc+egk916b01GrGmy5lUYPq+OcCSPZsPcoG/YWjMobMOMbGzh73AgrsiAYkLYmrqpp6+jiO6t3UJcQK667Yu+/Qtgac5g5aQwj65M8+Pv+F5M+kc7wzPb95TAO0/Dd92vMC3yj8KiI3IQfzXRdf0KccaAnhPH1uxYyvH7g8e23/s8GWg98OODzT6b9fFruvITGhtKKfsE966p+Q56eAelocuqTHs9987PRFSojnvT4s4c6wYD/v31xFtfMnRJZ3i0PbrDSYwR7DZYrZk3iigFuuXv7yk28tvdIpPzysK33Kq2qehj4XDFCYlmVtdIJBpCKnWWb8KSoB3V3qGSEp6InVL2bIiizqDdiNeBJcXWklrHVKAjwPHv3Qrer04q0gZHwhMzgh73mxRkHSn9o1yU8UtmBRyYERiiacah+N0XWUiutGvDHHAZbi8rAVghzgFicQ6LdbqX46mQy4ZGq4MrhjAN+d9cT/0YuhoRX3ISXVHeUTgTj4FV/3HzgZrE47luxBH+1cy2FGgWWug42G0pqaWJmMSQ9qeiw1yFwe/ZPOqskS4irrEsUF7aWyWZJehKpdVILbqUh1XMw11jl9twKtt1KCYv3QiAlzjqZqKyw1xxqckB6+W93FWWRW/Ycoa6EGpv0PD7sSrPsuZ0DOv+1t49GXpqhWpZjeKn1EK+/03cE0Y72D4ChYRyCXuKy51ojzeuYM7WJz8zIXYfq+Mk0D69/h65UpnThIS7+xERmnjnaiqze2HYr2bwXbC3pUgx1CaErlenz+TG8PslN86fHp0wf1KRx+OEzO+ks8maZNaX4eQMzJoykM5Xh3qffGvBvzot44/kDnJFExMJdT25l98H8kSSjhyUZ31j7y1hPO20kIvAfzwy8jvTFJ84YxW+WLMhJf7n1MP/61MBi6gfC1n3HWP6VudbkhQmG56yOOVi6F3p6s3bkDYQZExpJZbTP58f4xobqNw4ichb+Ak8T8Xtn96vqD3ud81n8SRdvm6THVfXbUfPOx+a7Fxb9m0QJFfarF07ny/Omlj2fMJ5XHf7rE6ksV8+ezD3XzOrze0/E2h4MlcwVsyZx6XmXEeUfW/LIJra3H+vzuyAM+9d/PZ9zzxgVIRe4+r6XOJG20wPpC+vRSjbdSt1y4quT119wNl+yENJbLmz0HNLAN1V1o1l8b4OIrFXVP/Q67wVVvdJCfv1SF3VdhgrNC6rHrZTJKvUJL/byqURKGc8Kk/Dyr88UtHjrk9HLui7hldUHbmsuQUDC4uxzHYSeA8T//CiGyJqpapuqbjTHHwDb6VkJ0GEZEaGCQ6O7SWezVpe+HsoUaiHbbI0ni4y+K5aM5Sg1sehiDZbsjjOUtdKxarZEZBowG1jfx9efFpE3RGSNiJxXQMbNItIiIi0HDx60qV5NkKiSGbfprFpd+nooU6i3aGudKvADLMo5Yc/+gLS9e6FnPMSKuJrAmnEQkUbgl8ASVe3tIN0ITFXV8/EXgvpVPjmqer+qNqtq84QJkbdBrTmqxa2UzpQWHuzIxZ/b0vd3Nh+4yYQUNamzWLSCo5V6RhycdQiwEq0kInX4huEhVX289/dhY6Gqq0XkPhEZr6qHbOQ/lPBEeO/9Lla8vMeq3LlTm/jk5J6IrXePHufZ7aVvd3ginXE9B0sUdCuZZ3nUQAfw3Ur7O+zXrYC9h48DdsccOlMZK/ruMAP+zqvUg41oJQF+CmxX1R/kOecMYL/ZZOIC/B7L4ah5D0UmNw3n1T1H+OdV26zKbZ7axGO3fqb7833P7+Lh9e9EkjmlaXhUtRwM1K0UPZ8pTSNY9+ZB63UrTNITJo5usCLrzLHD6Eplrelbn/AY32hHt1rARs/hQuArwBYR2WTS/hE4G7o3/LkGuFVE0kAncL1Wg+O8Avn+tedz15Uzrcq87ZHXOXr81DX2u05mOHPMMH79NxeVJNMTGDui9ucxxIHn5R947XYrWeilfXvxefztJR+PLKcQ9Umv5BWJe3PzghlcO/esSGHCYYbVeYyor8mpXyURuSRU9UX6CQ5W1WXAsqh5OfyHwLiRdh+6I+oTHPzg1FsslVUa6hLW83IUT6GBV5uTt0Ts161y01Rl+lYTbsTQQdLLjW/PZLNDYpJaNTAQt5KNMQeHI4wzDg6SidzVIVMZF4paKRTaEyJYodfF5zts44yDg4QnpHpNfkpn3CS2SsGT/mdIOzvusI0bfXFQ53l8eCLN4xvf7U577/2uorZMdZQPT+BEJnvK/xOw6Y/vm3OcdXDYxRkHB6ePbqCjM8Xtj75xSvpC+xufO0pg/KgGTqazOf9PwIj6hDPkDutIJUeUNjc3a0tLy2CrUfNks8q7Rzu715cJOGPMMBqS7qEz2Kj6/0++Qemxw+sZM6IuZq0clYqIbFDV5shyKtk4iMhBYG+JPx8PVNsMbKdzPDid48HpHA+9dZ6qqpHXHqpo4xAFEWmxYT3jxOkcD07neHA6x0O5dHbRSg6Hw+HIwRkHh8PhcORQy8bh/sFWoASczvHgdI4Hp3M8lEXnmh1zcDgcDkfp1HLPweFwOBwl4oyDw+FwOHKoOeMgIotE5E0RaRWRpYOsy1kisk5E/iAi20TkNpN+t4jsE5FN5nV56Df/YHR/U0QuDaXHdl0iskdEthjdWkzaOBFZKyI7zXuTSRcR+ZHRa7OIzAnJudGcv1NEbiyjvueGynKTiBwTkSWVVs4i8jMROSAiW0Np1spVROaa/63V/Dbymhp5dP53Edlh9HpCRMaa9Gki0hkq7+X96Zbv+sugs7W6ICLTRWS9SV8pIpHXDc+j88qQvnvE7JcTWzmras28gASwCzgHqAfeAGYOoj6TgDnmeBTwFjATuBv4uz7On2l0bgCmm2tJxH1dwB5gfK+0e4Cl5ngp8D1zfDmwBn9Pj3nAepM+Dtht3pvMcVNMdaAdmFpp5QwsAOYAW8tRrsCr5lwxv72sTDovBJLm+HshnaeFz+slp0/d8l1/GXS2VheAR/E3LANYDtxaDp17ff994J/iLOda6zlcALSq6m5VPQk8AiweLGVUtU1VN5rjD4DtwOQCP1kMPKKqJ1T1baAV/5oq4boWAyvM8QrgqlD6A+rzCjBWRCYBlwJrVfWIqh4F1gKLYtDzc8AuVS00s35QyllVfwcc6UOXyOVqvhutqq+o/wR4ICTLqs6q+rSqps3HV4AphWT0o1u+67eqcwGKqgumJX4x8FhcOps8rwP+t5AM2+Vca8ZhMvDH0Od3Kfwwjg0RmQbMBtabpL8y3fKfhbp4+fSP+7oUeFpENojIzSZtoqq2meN2IFiVr1J0DrieU2+iSi5nsFeuk81x7/Ry83X8FmrAdBF5XUR+KyLBHrOFdMt3/eXARl04DXg/ZBzjKOeLgP2qujOUVvZyrjXjUJGISCPwS2CJqh4D/huYAXwKaMPvMlYS81V1DnAZ8A0RWRD+0rRKKi4G2vh+vwD8wiRVejmfQqWWaz5E5A4gDTxkktqAs1V1NnA78LCIjB6ovDJff1XVhV78Bac2eGIp51ozDvuAs0Kfp5i0QUNE6vANw0Oq+jiAqu5X1YyqZoGf4HdhIb/+sV6Xqu4z7weAJ4x++023Nei+HqgknQ2XARtVdT9UfjkbbJXrPk5175RVdxH5KnAl8GXzsMG4Zg6b4w34PvuP96Nbvuu3isW6cBjfxZfslV4WTD5fBFYGaXGVc60Zh9eAj5lognp8F8OqwVLG+Ap/CmxX1R+E0ieFTrsaCCIUVgHXi0iDiEwHPoY/wBTbdYnISBEZFRzjDz5uNfkFkTE3Ak+GdL5BfOYBHab7+n/AQhFpMl34hSatnJzSwqrkcg5hpVzNd8dEZJ6pdzeEZFlFRBYBfw98QVWPh9IniEjCHJ+DX667+9Et3/Xb1tlKXTCGcB1wTbl1Nnwe2KGq3e6i2Mq5mBH1anjhR3m8hW9N7xhkXebjd982A5vM63LgQWCLSV8FTAr95g6j+5uEok3iui786Iw3zGtbkBe+r/VZYCfwDDDOpAvwY6PXFqA5JOvr+AN8rcDXylzWI/FbdWNCaRVVzviGqw1I4fuDb7JZrkAz/kNvF7AMswJCGXRuxffHB3V6uTn3S6bObAI2An/en275rr8MOlurC+YeedWUwy+AhnLobNJ/DtzS69xYytktn+FwOByOHGrNreRwOBwOCzjj4HA4HI4cnHFwOBwORw7OODgcDocjB2ccHA6Hw5GDMw4Oh8PhyMEZB4fD4XDk8P9Rocq+ZcFI/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jj+HFV/7aXnJXRVzCh75muXyG/1L7KudcYRir97RPuvHelqO8t+Von2fRUQQWm7O9jntloy2Emig6MlhHiiqGML2ZeUZhHM98coDff7i71+2//Y/1nZZ//V8102S4oAy6QjGMKThcw8Mf7Oap1Qd6vc/qPZU9N1KEBGXQFYphTEdDvr+isZuWisGAMugKxTCm40RQFz6xJnSKKAxBGXSFYljTbtGdbsn+iqYQ6qIYKMqgKxQKH+f+6ZNQq6AYAMqgKxTDGLurb4OCVFmG8EYZdIViGON06z036sDDH6iU0nBGGXSFYhgj/I4T7JodRxsCpInCCJRBH6K8vamEnDuXnlAJr7Cohn98fihEWinCjSa7q0/tt5XWB0gThRH0ux66IjyRUvLquiPc846nPtrYu5bxzfzRTMyI48sD1aza7RlWf997OwGYOzaZ6aMSeGdzKVVNDn532TQ+3l3B1XPHADA/N4VIiylo+g+GXGgpJZuL65iVnRRqVQbMnvLAne/aZgfNDhejk6IDdow2bE43VpPHP7W53AgETl0nLsLsqzVjd+lEWYN3LYcCZdCHGJuL63zGvI22qnj+WHeohnWHanzLd7/t2XflLo/hXzQ1k6evPdkQ3b44UMXMrESirV1fdkfrwr8uyI/+tYn3t5b53TYnp93ISwm6lGw8UndCu7S4CCobO5cJHpsaw6GqZt/y6KQoSmpbAfj3TfOYNy7FCPWDxhmPfkyj3RXwOjwVDTZOeWhVr9p+fufZjEqMCqg+oUSFXIYYrX5KmWYlR/HjcyaQndyzp5SV7LnYL5k5EoCyBmMMbGldK1c9t4473tjabTvRt5BuSDhY2ex3fWZ8JLoEs6ZhMWlYzZ4/fxxvzAHceufwWJsxB3hrY9cPZaOpbXacsK60rpXimpY+yWn0hnPe33q01/scqW5h6dYydhztHNqpa3Hw9qYSXl1XxMtrO0/YU9nU+/r5/r7bUEJ56EOMNpNww2ljef6zQ+x5cBERZs9r5s/Pm8jeY42c//invHHzfPJzkruVVVZv62OXWde0pbv1NHClq066ykY7UkoirSbiIy0nbN9xtJ6s5Gi/24xm0bRMdpY18OYP5pOVFE1qbASa1v8z1WhzEhthRnTxNJv6mw+JC9D3cvjJcpn1wAqevmY22ckx7Cxr4LwpGZz6yP+6lbPzaANTRsRR0+xga2k9dme7Y3Hrkk3cumSTb3l0UhQZ8ZEUFp0wS2Wv2VZSR2psBG8Ulvgemk9fM5tF00b4bf/h9jJufmUjWi89BqdbZ3tp/aALqymDfhxSSg5UNjE+PW5Acoqqm3HrknFpsf3a/59fHOZPK/ZS3+pkyQ1zeeiDXVyQl8naQ9VICfPGpZAZH0lspJloqwmzprFsexlL1h0B4Pypmdxzcd4JcidmxPX6FditSwqLajlU1cyxBhtXPruWe7+Sx+QR8VhMGg02J/UtTpJirDhcOhaTwKQJ4iMttHpv6CufXQtAfKTnUtvdQ8y2qMa/9zvndyt9nw88tJirnltLSqyVH545HotJ46InP2N0UhSrblvoe4AFCt0blJ2dndSlEe4LPRlrTRMneO+B5uZXNvap/eIn13D9/DH888sup7v0UVLb2untoz+8ubH0hHNSXt/122Tb77T4Sf/lDXLTYrCaTUgpEUKwq6z7bJ5wLeesDPpx/KegmF++uY3x6bEsmprJJTNHsv1oPbOyklh7sJrP9ld1ip+ekpPMrOxELj95NBMy4rC73BysbPbVxejPD7+rrIHfvttekvSq59cBsL20/SL74kB1tzLiIgf+07Z5UGf9cbVv3b3eztS+0mBrz6Yor7exv6KJ0yak4tYl9723g5e+LCIzPpLyXoR4cn/VXn5/2bZy3+eS2lYm3fMhS26ci8Olc+ak9H7p2hNtdsQIY94bTJrwPUTCma6M+ZIb57IgN7XH/d8sLCEjPpJD1c1kJ0ezcGIaui6xudx8ureSikY7s7KScLh1Th6ThJSSl9cWMSIhioZWJ5fOGtWl7J76H0YmRhFh1tCE8DkjAKmxEeSNjOfTvYOjwqQy6B14fs1BXy3u/RVN/LViP3/9eH+3+6w/XMP6wzU88+lBFk/P7GRg+ktDa+fReP/87ik8tXo/l5+cRWFRLbvKGjhjYhqzsxOxmjSEEGjC0yH60c5j/ObiPCZnDuwNA+D56/K54aUCHvvGSeytaOSZTw7ynVNzmDs2GYtJw+nW2VXWyOikKEyaoMXhyTRIifV47HWtTu56axsAPzlnAk+s2gfAvIf9d2D1xpj3hque8z4A77uA2AjjL3GPF2e42C4xieB76EbSG2MO8PWTRwNw2oT29pomiLaa/YZShBBcNz+nV7IToiyGeNUPL9vF/31xeMByAoUy6B0Y6MQK/ox5s91FTB+NStutu+SGuSwY77m4F070TMJ7ufei98fccSl8f2Fun47VHefmZXS6Ce66cMoJbbqKWbbxrVOyfZ/bDHqwcLsDYwSlpNexWCPQBomHPiwQ7fdnOKKyXALMgF7VBkHGx3BEl5IB9IH2mcHuoQ8l+jqyNtgYatCFEIuEEHuEEPuFEHcaKXuw0h+vXzljxrCrPDDD1HUZ3BvbpAn6WHKlVzT3cZSowhNuc7h0SutaTxiF3RvWHqzuc/2cvmBYyEUIYQL+BpwHlAAbhBDvSin714vWA1JKlu84xlmT03xx5N7Q6nCz6UitL5TR9qMEqoOrtK6VnDuXdrn9tZvmIbwdMR/vrqCh1cm4tBiPTmHuDYQ7bRk2bVw2axQXTM1k7cFq8kbE43Dr7K9o4vQJqQgBh6ta0ASkx0fy6d5KxqfHMntMEuX1Ng5XN5MaE0FJbQtPf9L76dqMoLSulfWHq1m9p4KM+EiKa1pIi4sgIcpCs91NTYuD8emxRFlMON06b24sobLRzvdOG+sbRFNQVEthUS1vFJbw1g8XYNE0fvlm92MCFCfyWkExQJdpnNNGxXdKXuiKa+eN4YFLpxmqGxgbQz8F2C+lPAgghPg3cAlgqEH/ZG8l17+4vueGg4QrjjM6HbGalUE3krc3lfL2ptIT1odzJ1cbxTWtfPsfG/q0zz8+P+x3/Yx7PzJAo+HJX781m2teWNfl9t4Yc4CjdQNL2+wKIw36KKC4w3IJMLdjAyHETcBNANnZ2fSH6F7UYjh+CHW4c8tZufzt485e33Xzx3DS6MQQaRQYXvne3G5vho6sueMsspKjsbvcTLrnwwEd95zJ6azaXcFZk9JYNC2TdQdrGJ0UxfTRiZTXt5KZEEWz3YXDrSOlpNXh5kBlM2NSohmVGEWjzUWk1URcpJmCwzW8s+koqXERA9KpLzx3XT6bjtRyoLKJ0yak8et3tpMUbSEx2kpti4O6ftQov/H0sTy3pr1I28GHFnPTy4Ws3HWsx31X/+JM3tpUys/OncDsB1ZQ283xV/zsjD7rFs6cNiG1x2yZtrd+ty4xacL39i+l5Gi9jfJ6GyePCcyAJdGfOJBfQUJcDiySUt7gXb4WmCulvNVf+/z8fFlQUGDIsRUKhWK4IIQolFLm+9tmpIdeCmR1WB7tXeeXwsLCKiFEz8PK/JMKVPVz32AzWHRVehqL0tNYlJ7tjOlqg5EeuhnYC5yDx5BvAK6SUu7odsf+HaugqydUuDFYdFV6GovS01iUnr3DMA9dSukSQtwKLAdMwIuBMOYKhUKh8I+hI0WllMuAZT02VCgUCoXhGGrQhRCHgUbADbgC+OrxbIDkBoLBoqvS01iUnsai9OwFhsXQwWfQ86WUg6HzQqFQKIYUISvOlZqaKnNyckJ1eEWYo0tJi8MdkGqJCsVgprCwsEpKmeZvm9F3iwQ+EkJI4BkpZafXj+MHFqk8dEVXfO//NrBqdwWf/uoc0uMjQ63OsKCwqIaJGXE9TrjhdOtMuPsD7vvqVK5fkBMc5RQ+ukv3Nrra4mlSytnAhcAtQohOw8SklM9KKfOllPlpaX4fMAoFAKt2eyapXrNPRe8CjcutU9lo5+tPfckPX+15pqK2ol6/fXcH20vre2itCCaGGnQpZan3fwXwNp76LgpFv7nt9S2qdGyAufGlAt8UfzuO9lyLpGO328V/+YzqPkzSrAgshhl0IUSMECKu7TNwPrDdKPmK4cPxZV3/+r/uZ41SDIyP97TX7K9pdnTb9o3CEmY9sKLTutvfUFUbwwUjPfQM4DMhxBZgPbBUSjmwqkqKYcnU3y7vtPz4yr0h0kRxPL94fcsJ63b2wqtXBAfDDLqU8qCU8iTv31Qp5e+Mkq1Q9IaPd1fQaOt75cHhTHFNywnrCotqaLK7kFKi65KCwzUUHK7pUkZ5g42aZgfl9TZW76kg586l3PiSSngIBSonTBFWlNf7nyj6vvd2cNmsUWQlRXOwqplWhxurWWNbaT1Xzsnihc8O8acVe5k3LpklN8xDC+YccYOY97YePWHd15/6ss9yZh8Xhlmx8xjl9TYyE1SGUjAxdGBRXwjn8rnVTXaWbS9nTk4SW4rryEqKZmZ2ItFWz/OvLcb78toirsjPQuKZVby2xUFcpJlP9lRyxsQ0Ii09125XdObLA9V867muJ/3oLUbM8D4c6G42rYFy7pR0nr9+TsDkD1e6K5+rDHoHpJT8p6CYX765bcCyJmfGcceiSZw9OcMAzYYGH24v4+ZXNnLRjBEs3VoW0GNdN38M919i/BRfQ41AGnSAKIuJXQ8sCugxhhvdGXQ1SXQHXvqyyBBjDrC7vJHv/l94PbBCzc2veHKcA23MwfNbKkJPq9MdahWGFUamLbZNEn0hkAd8SwiRZ5T8YPC+n3jiQDlQ2WS4TEXvUPnr3ROqt3NF4Bh0k0Q32pwcrGzmWIONm14u5PErTiIjPhKz5nk2CeEZ+OCdxg9NgNVkoqbFwYGKJuKjLLzw2SHuv2QqNqcbs6bx6b5KFuSmsOFwrZGqAnDOY59w+cmjGZkQybbSei6eMZLRSVHUtjhIifXMSyml5+bSNIHTrWPWNJxuHYdbZ095I1NGxPdqLtWOHK1r5Sf/3szL3zuFKIsJCdicblodbn777g7K6m0IATcvzOXU3FRKaluob3UyfVQC+yqamDoyHkn7IBIpJUIINEGn9W5dYnO6iYkw0+p0YzEJTEKgaQIBbC2pJyXWSoTZ6EHJPZP7q2W88r25COG9HqRHd/AsCwTSu1ICAhBC4HDraAIsJg3hay98+7ncEqdbJybCTIRZY095Iy0OF7npsSDBLSUCQXWznbTYCLaU1DN1ZDwrdh5jbGoMeSPjAc851KVESs91KoRgf2UT6XERxHj7azQNDlQ2kxBlITXWiq577oGYCDNCgCY855kOn4UQrNhZzhkT0xAINM3zXVudLnaVNXLS6EQ0Aa+uOxKU3yHnzqX8/erZaF5FW50u3DrEWE1omsDllpg0eH9rGTXNDr44UM3CiWlcOmskj3ywmxtPH8eDS3d1kvno5TMwa4JX1hZx3fwcHC6d0rpW4qMs2JxuGm0uEqIsWEyC5BgrmhCsO1TN6RPSqGtxEh9l5liDnTHJ0bh0HYBmu5uYCJPnZHp/a8+1DKV1NhwunaVbj7LxSB1PX3OyLxMoymoiwmxC994UFQ02Iiwm4iLMHKxqJjctBhCU17dy3/s7OTk7id9dNp1JmXGGn+ugzil6XC2Xk4uK+v5a/O6Wo/z4X5sM0VmhUChCwfXzx3BfP/t4gjWnaI94i3U9C55O0f7ImDc2meevy8fu0rllyUZ+uWgyJ2Ul4HJ7xLV5XuDxfnQpcbolh6qa2Hm0gdy0WB5bsZdXvjeXJruL2hYH1U12po5K4Dv/2GDQN+3MKWOTmZOTxM6jDczKTmJ2dhJ2l9vjAXpdQIHweeVt3ll1s51NR+pYODHN17a3z9/6Vie3LNnIP749B03zeNYuXVJ4uJa/ftw+8vLmhbnMG5fM/gpPaCgl1kp1k8PnPbh0idXk8a7bPMqO51jTwKxp2F1uapodpHrfOjznHg5XNRMfZSbGauamlwsHfC77wg2njeW8vAyfxywE6LpHL+8LnW+9oN2zbra7iLaa6HiqO553XUpcuo5J03C4dKqa7ERbTWR4i4i1efVNdhdRFhM7jjYwPj2W5TvKmZQZx8QMz7lt8wlscBsAACAASURBVLA1IXDrkhaHi9oWJ6MSo5BINCGQ0pNVZTVrWM2azwt065JIi6mTly/x/pfwr/VHOH9qBhnxkb5tJTWtvLO5lOvm5xAXaeamlwpodgQ+xp0YbeEf356DpcN11GR3ER9lxuWWPm96c3EtNqdOTYuDzPhI5o5L5qUvi7h05ii+OFCFJjy/zTOfHuT9H52G2SR4Yc0hrpk3hia7y3u+PL9vcW0LWUnRmE2CRpuL5BgLZfU2xqXGomngcOm4dYkE3/XdbHcRZTVh8qa8StketqtpcYCECIvGmn1VnDM5nSa7i8RoK60ON9Ed9qtstJMUY8Xh0pHSc4zYCDNSwqf7KnG5JVfPyw7IuTbSQ58P3CulvMC7fBeAlPJhf+3DMcslED3+K3++kPHpsYbLHYwEOqPieA49vNgXKlGciJSSsXcFdoKxH58zgZ+fNzGgxxhuBCvLZQMwQQgxVghhBa4E3jVQfsA59PBi0uIiDJG18udncP8lU5Ux78ApOclBO9aaO85SxrwHgnF+lDEPLmqS6A4IIdhw97k02py8vLaIrKRo7N5X6oumj+AHrxayvdR/3Yooi4kxKdH84vxJnJvnyT0fn258p8dg5rXvzwu4R9hGVnJ0UI6j6JrvnTY21CoMO9TAoj5QVt/KWxtL+eGZuUBwPJyhxqGqZh5atosVO4+x9Men8e7moyCgrM5GTmoMn+ytZEtxXY9yLpyWyVmT0rnjTU+lvxiriXduOZXXNhTzjfysgGQQDEUCGQZTIa/AoEaKKgYNlz/1BQVF/tNH5+Qk8frNC4Ks0dDGKIP+lZNG8tuv5HGoqpkoi4lpoxIMkas4kbDJclEoemJMSoxfg65qswSPU8en8MjXZrCpuI7l28uJj7IQZTHxm6/k+X0APHr5DL6ZnwXgy3JShAZl0BVhxU1njOPNjSWd1qmJooPH1XOz+d1l0wFPP8RXTxrZ4z5fmzUq0GopeokhWS5CiHuFEKVCiM3ev8VGyFUMP/yFXH93mSqyFSyy+9GZbDYFfxSwwj9Guj6PSyn/aKA8xTAkyk/J4fgeZqFXGMcNp48LtQqKAaAerYqwwl+6YU5qTAg0GR68d+tpnZZNamKQQY2RBv1WIcRWIcSLQogkfw2EEDcJIQqEEAWVlZX+migUJzBWGfSAMX10gm/A14/OHh9ibRQDpdchFyHESiDTz6a7gaeAB/AUrXsAeAz47vENjajlolAojOXZ605m05E6zpqcHmpVFAOk1wZdSnlub9oJIZ4D3u+3RophT1+KkCkGTmK0tdfGfN/vLqTF4eak+z4CCElZZEXXGJXlMqLD4mXAdiPkKoYnhfecR3ykx9d49Ya5IdZG0RGLSSMhqr2Tevt9F4RQG8XxGJXl8qgQYiaekMth4PsGyVUMQ5JjrFw6axQvfVmkOkTDlM2/OY+qJoevJK4iPFBD/xVhidOtc6iq2Vc7XKFQeAjLWi5CiEqgvzP5pgJVBqoTSAaLrkpPY1F6GovSs50xUso0fxtCZtAHghCioKsnVLgxWHRVehqL0tNYlJ69QwXAFAqFYoigDLpCoVAMEQarQX821Ar0gcGiq9LTWJSexqL07AWDMoauUCgUihMJWaHp1NRUmZOTE6rDKxSK45DSf/liRXhRWFhY1VWWS8gMek5ODioPXdEdN/yzgOpmO2//8NRQqzLkmXn/R9S1OHs9M1Srw01Vk11Nxh0ChBBdpnsP1hi6Yghjd7m5882trNx1jE1H6iiqbg61SkOaz/dXUdfiBKA3Idj1h2r4xjNfcPqjHwdaNUUfUQZdEXY8uWof/95Q7Fte+IfVoVNmGFBa1+r7/KcVe7tt2+pw881nvmR7aQMAxxpsAdVN0TeUQVeEHX/7+ECoVRg2PLX6AHe8sdW3vGTdkW7bL35yTafluQ+tCoheiv6hDLpCMYz5/Ye7Oy1XNzu6bX+o6sTwl83pNlQnRf9RBt0P20vreb3A88qv67LTKylAk93Fox/uJufOpewub+Cut7b54ry6Lnli5T5qe7gxhivrD9V0MgAOl95nGbou0XVJfYuTRpuTYw021h+q4cH3d/Lkqn1UNtqNVHnYsXLnMaqb7LQ63Oi65PEVe3l8xV62ldT7bV9a10qrw31C/P3mlwu5+vm1wVBZ4UVVWzyOV9YWcc87/su5//mKmfz0tc1d7vvSd0/huhfXA5AQZeGhy6Zz0YwRXbYfbmw8UsvX/v5Fl9u/c2oOE9Lj+NXb2wZ8rPV3n0N6XOSA5Qx1cu5capisuWOT2XuskdoWJyZN4NY9tqW3mTOK3hGW1RbD0aCX1LZw2u+N7blXF3M7RhqPnjh5TBJv/mBB0I43WAnGb6LuAWPpzqCrkEsHvvH0l4bLrPemgymCS2FRbahVUCiCjqEGXQixSAixRwixXwhxp5Gyg0FZvfEpWB9sLzNcpkJhBLoenLdzVV4keBg2UlQIYQL+BpwHlAAbhBDvSil3GnUMgGa7i5pmB1VNdr713Fr++Z1TqG1xYjULspOjEUJgc7rZXFzHmOQYUuOs1Lc4iYu0UFBUg9WksWp3BbeeNZ7aFgf7K5rYUlLP5SePNlJNH3e+tY13txwlb0Q8/91ylOzkaLKTo9lWWs/NC3OJsZpIiY0gNsJMhEXD7e2ETY62khJr5UBlMyYhyEyIJMKsYXfpNNqcJMdYcbolVpOGzeUm2mqiye6iyeZCCHjkg91sOFzLDaeNZUxqDCkxVqaMiGf5jnIe+cCT2TApI47rFoxhTHIMm4trKapu4aszR1JWZ2NMSjTJMVaaHW7iI83oEl768jBXzskm2mpC0n6j6lLi0iVRFhO69HR0SjzLbTnk/7pxXkDOb3cseHgVD142jREJUZTVtyKEID7STITZREOrE7tLJ9pqItJiwmwSmDSBlJ7vo3nHwLt1iS4lFQ124iLNuHSJw6Vz99vb+OFZ49l0pI76VgdZydHMG5dCjNWMEJ4h9BFmE063zsd7KkiIsrBi5zEmZ8ZzXl46SdFWdClxuiXVTQ4qG22MS4tlw+EaZmcnERdpxuHS0TRBUXUzSdFWspKjabK7sJo0rGbPnwCEV1fPZ9hT3sgflu/hl4smkxYXgUkTNNtdFNW0cMcbW7nrwsnk5yTz9ae67s8wkkV/XsPtF0zCbBLoUtJkd5McbSXKakKX0nOOdUlVs4Pimhb+sHwPF00fweX5o1l/qIb0uAjue28nJk34zsuL356DxaSxubiOkQmRNNicHKlpIS7SQmFRLTOzEtlT3si188dgc7r5dG8l20rr+eGZ44mPsuB06xRVt5CVFOVX55pmB80ON2NTY3C6dXQpiTCbKKlt4Sf/3sys7EROGZtMWmwE8ZEW0uIiAHDpEpdbJy7SgsUk+OvH+zl7cjoT0uOIjzLz5Kp9gOChy6aRHm98H49hMXQhxHzgXinlBd7luwCklA/7a9/fGPrSrWXcsmTjQFRVKBSKkHLulAyev75/82B0F0M3spbLKKC4w3IJ0GnKdiHETcBNANnZ2f06yIzRCTx6+QyWrDvC5uI6ACakxxJpMXH5yaOJsppwuHTueWc75+dlMD83hfpWJ0XVLby9qdQn5/YLJvHelqPsLm8E4Ir8LF4rKPZ7zEARbTUxOimKaaMSmJWViAT2HmskLtJCaW0r49Ji2Fxcx+TMeCSS8WmxONw6EWYTACYNWhyeFECz17vUNMGqXcdYvuOY7zi5aTGMTorm7Mnp/PbdHSfokRkfSbl3xN/MrER2lTVw6vhU8nOS0IQgJsJMbbODQ1XNzB+XgqYJNG8RJyGg0ebyeLmaQAioaXaSEmPFrUtue30LgO/tIth877SxzMlJ4n+7K7A5dU4Zm0x8lIVGm5O6FieJ0RZiI8w0293ERJgwaxqSdg/9UFUz0VYTcZEWqprs2JxuXG7JXz/eT3pcBBUdUiSvnpvN7OwkNA2O1tmIizRj0gRPrNzHmJRoNhyu9ek0Y3QCSdFWzJrgQGUTq3ZXMC41lhc/P8Q187JJirYigKQYK0vWHeHU8alMzIijstHOhIxYXLpESkmbPybxfJYSPtpZzvIdx7h6bjYzsxKJspo4UNHM//ZUsMV7z9x23kQe62FUqJH8+YqZJMVYces6xxrsjEiIxK1LrGYNk/C8HR1rtLO/ooknV+3jyjlZXDA1k/e2HCUzIZK/rz5AtNVEq9ONlPCXb83CrUu2ldYzOTOOpdvKyEmJobimhZLaVk4dn8qhqiaumTcGTRO8s6mU/RVNfH9hLg2tTkYmRlJWbyM7ORqTn4pkTl2y7mA1c3KSsTndmDSB2SSobXZy2+tbiLaaWDx9BCkxVjLiI7GaNUYlRuHWJUU1LYxOikITgl++uZUr5mQxLjWGaKvZ54zeeeGkgJxnIz30y4FFUsobvMvXAnOllLf6ax+OWS6B6PH/4Zm53LFosuFyB8r+ikbGpwdvAuaDlU2c/dgnQTsewPb7LiA2ImT15wYFKstl8BGsLJdSIKvD8mjvukHDngcXGSLnlrNyAfj5eRP5xfmBeRIPlGAacwCzFtyEqiU3zlXGPMRMH5XAkhvn9txQYRhGXvEbgAlCiLF4DPmVwFUGyg84EWZTJ2+i1eEmymryLducbhxunZKaVj7eU8F188cQF2nxK+v2C8LPKw8lJpMxhbaTY6wsyE3h2nmec58RH4EEoiwmYpQBDxmHHl7s65xVhA7D7gAppUsIcSuwHDABL0opTwzYDiI6GnOASIsnIyJvpIW8kfEh0mpwYtbab/Z3bz2V0tpWLpw+gu2l9UzIiMWiebJ1vvXsWrb4GWI+MyuRRy+fwcSM4L5ZDEfanJov9lfRYHOys6yRI9XN/PnKWV2GaJQxDw8MdWmklMuAZUbKVAwN0r1pXQAzRicyY3QiANNGJfjWR1vNXU6Z884tapKLYDB3bLLv84LxqQAsmtZ9+YqHLpseUJ0UvUe9oyqCghCCR78+g5OyErttd/XcbF8mhiL4HP9W2htSYq0B0ETRH5RBVwSNb87J6rHNjNEJJ6zLSvY/+EMxcCZlxLHnWKNvOSup71PKnZ+XYaRKigGgarkowoqEqBM7mdfccXYINBkeZKd0NuCzsrt/g/KHip+HD8qgK8KKEQnKGw8mqbERnZa/Nrv7Ehirf3FmALVRDBRl0BWKYUxfs7VyUmP4/dc9naBLbpzLip+dEQi1FP1ExdAVYcdF00ewdJuqUhkMrpmbTaRZ4/YO84r2xBVzsrliTv9KdygCi/LQFWHH366ezVSV5x8UhBB8I7/nzmrF4EB56AqFgjV3nNWvlEVFeGGIhy6EuFcIUSqE2Oz9W2yEXIVCERyykqNP6CBVDD6M9NAfl1L+0UB5imFMWxHQUzqMXFQoFN2jYuiKsCQpxpOP/sAl00KsiUIxeDDSoN8qhNgqhHhRCJFkoFzFMOSJK2dx31enMjEjNtSqKBSDhl5PcCGEWAlk+tl0N7AWqAIk8AAwQkr5XT8yfDMWAZOAPf3QGSDVe7zBwGDRVelpLEpPY1F6tjNGSpnmb4NhMxb5BAqRA7wvpQzYu7IQoqCrGTvCjcGiq9LTWJSexqL07B1GZbl0rK95GbDdCLkKhUKh6D1GZbk8KoSYiSfkchj4vkFyFQqFQtFLDDHoUsprjZDTB54N8vEGwmDRVelpLEpPY1F69gLDY+gKhUKhCA2GDv0XQhwGGgE34OqucyA1NVXm5OQYeXiFQqEY8hQWFlZ1leUSiFouZ0kpe0zbycnJoaCgIACHVygUwaDZ7uLiv3zGY988idnZauhJsBBCFHW1TY0UVSgU/WJLSR2Hqpp59MPdoVZF4cVogy6Bj4QQhd5BRJ0QQtwkhCgQQhRUVlYafGiFQqEY3hht0E+TUs4GLgRuEUJ0ms5ESvmslDJfSpmfluY3BKRQ+KhvdVLb7Ai1GooOSClx6/K4dSFSRnEChhp0KWWp938F8DZwipHyFcOHj/dUcNJ9HzHrgRWhVmXI87PXNvP7XoZNHlq2i9xfLaOuxcFVz60LsGaKvmKYQRdCxAgh4to+A+ejRowq+sFfVu3jO//YEGo1hg1vbyrlqdUH6CmFeXNxHc+tOQTARU9+FgzVFH3ESA89A/hMCLEFWA8slVJ+aKB8xTDA7nLz2Iq9ndb11ntUDIxb/7Wp2+3/KSj2fS6taw20Oop+YJhBl1IelFKe5P2bKqX8nVGyFcOH4+OzAE+tPhACTYYfS7d2PzF3Vw68CqGHDyptURFWCITf9S0OV5A1GZ6oTujBjTLofeCJlfuYdf9HoVZj0OLWJfUtzm7b1LT4NyiHq1r8rv9PQTH5D67E5dYHrN9w5P2tRzst96sTWrnoYYMy6H3g8ZV7qe3BICm65vcf7uak+z+i0db1OTz1kf/5XZ8e738C4zve2EpVk52ColpDdBxu3Lqk+7h5R/pS98mtS/6+ej9NdvVmFUyUQQ8QH++uIOfOpXy+fzBMshIc3t3s8QYbbX2/yXU/sfWOaMJ/qEZhHP76NwBane4T1q3YeYxHP9zDQ8t2BVotRQeUQT+OLcV1FFU3n7C+J4NyPN/5P0/a3bJt3Xc0DSfKG2wANHTjoXeFuwfv0KS1G/SPdpRz9fNr++RRKnqmq99gW2n9iW2994uKyQcXZdCP45K/fc7CP6wGPBfl7vIGVu48xgNLd/raOFzt8doj1S2c//gnrDtY7bt4NxfX+ba3OE70XoY7Gw7X4nTrvLquiOn3LvcZ3o/3VHS5z5Or9vNmYQk5dy5lwcOr2FPeiN3Vfm6/2F/F6j0V6LrkppcL+Xx/NU63Muj94ZlPDvC7pTspOFyDzenG7nKTc+dS/rv5aJf7bCvxGPUPt5fTZHehe39TXT1Ug0rI6qHn5+fLcKu2KKVk7F3Let0+wqxh72DcE6MtPPK16dz8ykbfugunZfLUNScbqudgxK1Lcn/V+3NrBK/fPJ85OclBPeZgI+fOpQGVr65/4xFCFHZVmtxQD10IsUgIsUcIsV8IcaeRsoPB6wUlfWrf0ZgD1LU4OxlzQHmJXppDkHZY1WgP+jEHE898Evj8fuWhBxcjh/6bgL/hKcyVB3xLCJFnlPxg8Oyag4bLdLh1imtaqG6ys2rXMbZ0CMcMJ5r60RE6UCIsKqLYESkldR3SQo81BP6Bt3zHsYAfQ9GOYSEXIcR84F4p5QXe5bsApJQP+2vf35BLfauTPy7fw8GqJj7fXw3ABVMzuH5BDiYhEN5sh/pWJ7cs2civL86josHGX/63v5OcmxfmEmUx8e8NRyirtzE+PZaaZgc1QezEGZEQyZ+vmMneY40cqmrh032VmDXBt07JJtpqYntpPf/dcpQHLpmGBJKiLbh1ydqDNZwxMdWT/ys8MX2TJqhosLP3WCMnj0nippcLAYixmrjvkmmkxFg5UtPCv9YfYXd5o0+Hby/IIW9EPEu3lVHRaOfqudlYzRq5abE02py0OtwkRFsA+M+GYs6fmklmQiSaEGjCk10ivP/b1gnvf5MmeHXdETYX1zEiIbLbGGwwePyKkxibGkvB4RpGJERxtK6V3eWNxEaYmDcuBYdbJyUmgpoWB6mxVppsLtLiInDrEqfbU2XQpeusP1TD3zuMXv3OqTmU1dlYNC2ThCgLCNhwqIZ541IQwjPSdVZ2In/7+ABmTfDcdfnUtTqobXYyZUQ8n+yt5N3NpZwzJYMGm5OpI+OZNy6FYw123Lrk+TUHqWt1Mm1kPCePSWLtoRrOnpTO3mONxESYGZMSTUKUhf0VTdicbv74kad0wu0XTELXJTERZu5/f2enc/Hjcybw5Kp9QTv3d104mTEpMewqa2DaqATiIs0s31FOs93FqeNTiY+y8K91R/hoZ/sD4IXr81mzr4ptpfUUdkhLPSkrkZ+eOwGbw01xbQvJMRGs2VdJweFapo2KZ1dZIxMz4lizr5L5uSlckZ/Fu1uOYtIE5+VloAnB9tJ66ludzMlJJjbSjJSSZrubpBgLFpPGsQY7v3h9C89dl0+01YRbl7ilpKbJwW2vb2k/j2ePp9nhptHm5D8FJeSmxXCgspkr52Tx7w3FpMZasZo0EqOt7Cxr8O237d7ziYu09OtcdhdyMdKgXw4sklLe4F2+FpgrpbzVX/v+GvTXNhzhl29uG5CuCoVCEWoOP3JRv/brzqAHYgq67hS5CbgJIDs7u18yLj85i60l9by67ohv3YLcFH5wZq4vF1lKOFrfyh1vbOWORZPYWFTHyl2dX/3GpcaQHh/B2oM1/fw2xvDMtSdztK6VD7eXs+5QDQtyU1g4MY3i2hYmZsTxyAe7eeiy6QDERZo5XN3Clweq+O5pYwFwuT1eo8st2V/ZxLqDNVw5J4sfvNoey//zFTMBqGqy8+DSznnBM0Yn4HRLdnm9hyvnZDE5M44oq4lIi4kIs4kmu4tIi8bTnxzgunk5pMdHIKUnPqp7/8sOn9269G1/+IPdCGBSZhxr9oU2J//n501k6sh4dh5twC0lrQ43K3YdIyHKwvfPGEeDzYXVpGE2CZpsLqKsJuKjLJg1gVnzrDdpgo1FtZ3OY2K0p80dF0wG79vK9tJ6YiPMnDI2meteXM/XZo3irU2lANy9eArgyd+eOjKee9/bQXFNK6dPSGXNvioumjGCy2aOoqLRjkTyekEJ20rrmTs2mcXTR7BsWxmnT0hj5a5jWEyCqSMTcLp1xqXGsKWknre9x5mdncjUkQlMHRnPnW91doKunpvd6R4KND84M5epI+NZtauC6aMSGJkYydqDNTTYnMzKSiQ3LZarnu9cjvfpa07mqdX7OVLT4hvQN21UPE6X5J6Lp1Beb2PH0QZOHZ/KH5fvoarJzri0GASCFqeL7aUNnDM5na/OHMl/CoqJspiZNy6Z5Bgrn+ytpMnm4tJZo0iJtVJWZyMu0ky01Ywm4IPt5Xy+v4rbL5hEcowVk+Z5+2+yu7j+xfU+Hf98xUzf263N4abZ4aKktpWvnDSSLcV1xEeZabK5iI00s7203UP/5PYzA3KeB13IJZAEosf/1Rvmcur4VFxunY/3VHLulHRfWCiQ2JxuHvlgN/dcNAWzqT2W/I2nv2DD4Vq233cBsRFBfZ5zqKqZs/64OmjH2/3AIiItpqAdbzAS6CwX6L8nqvBPsDz0DcAEIcRYoBS4ErjKQPmDklPHpwJgNmmcl5cRtONGWkzc+9WpJ6xfcuM87C496MYcYGxqTFCPp4y5YrhhZPlcF3ArsBzYBfxHSrnDKPmhIjcthh+fM4HffiWPPQ8u4sfnTOiy7e0XTOq0PDIhMtDq9RmLSQuJMW/j+2eMIys5qn154Tje/MF833K0VRlhhaK/GHpnSymXAcEdPRIAdt2/iHMeW831C3L4/sLcTtt+du4Evn/GOGI6GEW3LrG73ERbzVw9N5uZ93sq1plMqr7I8dy1eAp3eWPIHWl7Ld9eWs/Ff+l+NpznrsvnjImpVDTYeW1DMUkxVh7wZnF8cvuZvpG+itCz8/4LQq3CsCJ0rloYY9IEX9x1jt9tQohOxrytfbTVsy4x2srKny/k3D99gkVTedB9JTG661Suf3xnDlJKzp7sCV1lJUfzC+9bUUOrk3+tP8KYlOCGdYYaG399HiW1LeSmxbJy1zF+8u/NgKdT8yfnTGDyr/1PQnbpzJEcrm5h0bRMHvmgfYaptvtCERzU2faDWRuYZz0qMQqrWeOORZN6bqzoxOikaL/rRyVGcdak9C73+9l5E/nZeRMBSI21Mn1UQkD0G2pMzozrNC4hLtLMjNGJAFwyc5TPoP9y0eRu5fz5ylm+zzcvzOWtjSXKmIcA5UL6QRugQY+ymtj74IUsmjbCII2GF3sfvLDT8rcX5PD5nWf3ev+Ce87jH985xWi1hiQPXDqt0/LxzszWe8/n/R+d5lu+z09Huz++Nns0i6ZlDlxBRZ9QBr0D934lj3FBzsRQnIjVrPHRz84gb0Q8oGqdB5I5Ocm8e+upAJyfl3FCSm18pIVpHd52rl+QE0z1FH1EVVtUhC1Hqls44w8fs+zHp5M3Mj7U6gxZpJS8su4IX50x0lfmoTs+2lFOSa1nINR5j38KqFzzYBI2I0UVir6QnRKtDEUQEEJw7bwxvW5//lRPKKVtXoA/ffOkgOil6DvKoCsUin5hNWvqgRtmhCzkIoSoBIr6uXsqMFgm6xwsuio9jUXpaSxKz3bGSCnT/G0ImUEfCEKIgq5iSOHGYNFV6WksSk9jUXr2DpXlolAoFEMEZdAVCoViiNArgy6EOCyE2CaE2CyEOCHXUHh40juX6FYhxGzjVe3EswGWbySDRVelp7EoPY1F6dkLehVDF0IcBvKllH6D/UKIxcCPgMXAXOAJKeVcA/VUKBQKRQ8YlbZ4CfCS9Dwd1gohEoUQI6SUZV3tkJqaKnNycgw6vKIj5Q023Loxnd0JUZaQltsNRxxunapGO8FMJ0iJiSBSTXrdJccabLh6uOZNQpCREMlgH3dcWFhY1VWWS2/vVAl8JISQwDNSyuNfK0YBxR2WS7zrOhn046egUyNFjedAZRPnPPYJyZHmAU/wUNPsYO74VP75XVUXpSP/9/kh7n1vJ6mxEQyw7E+vqGi0c/XCcdx14YllhxVQ0WjjlN+tIjHCTFQX9fRtTjcNNhev/OR0powY3KOOhRBdpnv31qCfJqUsFUKkAyuEELullJ/2VRHvg+BZ8Az97+v+ip5xuT2n9ZGvz2Dx9IEVB/v6U18Y5ukPJdo8wf/9YiHx/Zy5vS/k/eZDdPU7dEnbNX/PxVO4Yo7/uYpX7DzGjS8VDPnruVfvcFLKUu//CuBt4HiXrRTI6rA82rtOEWScbs9wbJMBrqNJEz55inbaup2CVTRME4IhbocGRJtBN3Uz/0BbFcmhfj33aNCFEDFCiLi2z8D5wPbjmr0LXOfNdpkH1HcXP1cEjjYPZKA13dtkDHWPpj/oXosejHALgBDtx1Sc6VfOMQAADZJJREFUiEv3GOnurvk2B2eoX8+9CblkAG97y2qagSVSyg+FEDcDSCmfxjPt3GJgP9ACfCcw6ip6oi0cYDYNvAPNbNJocbgHLGeooYfAQ1f2vGt8Tkw3Uz62beup43Sw06NBl1IeBE4op+Y15G2fJXCLsaop+oPL3bO30lvMmvB5P4p22rzlYJVp15SH3i1Od89vpWZvOKYtPDNUUfloYYiUkmc/PUh1s6PP+5bWtQLGxdCP1tl4aNmuAcsCiLKY+P7CcYN+ajLpC7kEM4Y++AyRzenm6U8OBPwtr6rRDnQfQ2+7H15dV8Sn+yoHdLxJGXF8/eTRA5IRKAb3nTVEKa5p5eEPdmM1af0yzKmxEWQl+5+bsy/MGJXAZ/uqePnL/hbFbMctJQ6XzuwxSSyc6DeFdtDQ1q8WLIMuhGAw9uVtOlLHn1fuw2rWMAX4XKXEWBmb2vU1n5UURXpcBKv3DMyYO906ZpNQBl3Rexzeu/eP3zyJr540MmR6/OicCfzonAmGyNpSXMclf/t8SKTfBbtTVBPtbwWDibbY9qs3zGVOTnJIdUmPj2T93ecOWM4flu/mmU8OGqBRYFBDz8IQIzNVwoU2b3YoZBlIXwxdhVy6I9gPvmBg0jRcugzbB6wy6GGI08COzXChzfYNRsN0PLo0po+it5i0wZmHrgf5wRcMzGGe/qgMehjSmzSswUabhx6m90Gf0KUMqtc5WPPQ9SB3HgeDtgd5uKY/KoMehrRdLN312g822r5KuL6q9gVdBtfrHKx56Lqv8zi0ehiJxRTeHvqg6xTdX9HIh9vLAyJb0wRfnz2ajPhIQ+TVtTh4bUNxn4cbl9R6Ug8tQ+hOCLaHLqXktQ3FVDXZDZddWFQTVCOlCdhV1sBf/7fPcNkZ8ZF8Iz+r54b9YGh66B7P5KnVBwZU/XJ2dhILxqcapZaPQWfQ95Q38ceP9gZMvq5Lbj3bmMyO5TvKefiD3f3aN9KiMTIxyhA9wgEtyDH08gYbd761LWDy84JYsS83LZZVuyvYXd4YEPnnTMkgOcZquNxgj6gNBuNSYzBpgr9+vH9Acm5emBsagy6EyAJewlMCQALPSimfOK7NmcB/gUPeVW9JKe83VlUPF07LZN/vLgyEaCbc/QEOl3EJv3avrLV3nUNKbN9uGE2IoHa8BRrh89CDY9DtTs+5/8PlM7h01ijD5Qc6r7ojz1+fH5CY7b/XH+HX/93hG11sNL4BWEMncshZk9PZ88CiAdfCD9RDrjceugu4TUq50Vukq1AIsUJKufO4dmuklBcbr2JnNE2gBahEvcUkDL1x2oYZR1lMWAyorTKY0YJs0Nt+x4ghcO6FEL7YrZG0nZdAhcGGoocOxtRJChQ9aialLJNSbvR+bgR24Zm8YshhMri6YJss0xDKVukvvpBLkEY8DsVcfqPxjQ0I0EN2KOahhzt9etQIIXKAWcA6P5vnCyG2CCE+EEJM7WL/m4QQBUKIgsrKgQ3BDQRmTfMV+jECZy/Keg4Xgu2hG1kXfqjiGxsQIBd9KOahhzu9NuhCiFjgTeCnUsqG4zZvBMZIKU8C/gK840+GlPJZKWW+lDI/LS386nmYTQK3gS6kuxdV4IYLmvccBCv9rs1DD0SoYqjQ9pAN1G8S7IlAFL3MchFCWPAY81ellG8dv72jgZdSLhNC/F0IkSqlrDJO1cBj1gQ7jjbwzy8OGyKv8EgtoLxEaH/t/mx/Fa3O7qvvRZg1Lpk5iiirCZdb553NR2m2u/p0vOKaFmBo5fIbTdupafOkjzXYWL6jvFsDr2mCxdMySYmNADxz2H62z/9tvqW4DghuB/JwpzdZLgJ4AdglpfxTF20ygWNSSin+v72zjZHqKuP477+zsy+F3bK0DVlpy0tTjSQmFteWxraJ2lAgClYbgzGCVmPaaGJjqkExpl/8UF8Tg5H4QmwNWqTSyJemhaapsQnUZd0tVAosFFIoZSvvZpcuu/v44Z5Z7ow7MwvcmbmzfX7JzZx55r78z3PmPvecc8+5V7qdqOZ/MlGlVWB2xzV0HzlN95HTye1zRqs3OYH2lizTmxvZ2vcWW/veKr9+a5ZlH+qk7+gZHt3cd0XHzDSIWe3NV7Tte4HCbrANL78xqQdPnR0cHh/a+9Pn9vFsiXkh1zRluLa18u9ddSImU0P/GPAlYLek3mD7PnAzjL/o4gHgYUkjwBCw0upwSuDTD93J+QuXVxMsx7Tmid9C/l5jWnMj3T+4t+yzsY+eHmT5upcZCusNDUddYL9b1cXCOR2XdcymxgamN9fdVIuqoYLJXheGR2lraeSl73y86DYf/dF2Lly81C05dHGUD3a2s/Frd0y4fms2Q2uTnwPVYjJvLPoHlB4naGbrgHVJiaoV2UxDRSZYOBEt2Qwt2dIn9+BwdEHN9YHn3pjUMa3JyyZhCid7jYwZTWXOgUxD/tDe0TGjJevnTVrwDkYnVeTGRueCht/crByFXS6jY1b2gXDZhvyBAyOjRtbvU6QGLwknVVx6ml0UNHLDSP3GcvIUzg24OGrj794sRqZBeUN7R8bGvGxShAd0J1XkhnjmZtlemiDkf9WkKXwcw+gkgnNjpiFv8t3IJGr1TvXwO0ZOqshNq+598wxbeo7SE4Z+etBIntxwwhf2DrD/xHkOnxws6+fGBnFg4Dxbeo4C8J//vssMH8WSGjygO6miubGBtpb84Y2NDfKgUQFyD4z7xfZLTy+9Y17pd3/e0NbMjkOn2HHo1LjtzvnXVUagc9moVqMLu7q6rLu7uybHdtLN2aGLnBkcHv/e1pL1URQVYuDchbyJXrPaW0qORBocHuGd8/nPmH/fjNa6fwBaPSFpl5l1TfhbrQK6pHeAI1e4+fVAvcxCrRetrjNZXGeyuM5LzDGzCZ+dUrOAfjVI6i52hUob9aLVdSaL60wW1zk5vJ3kOI4zRfCA7jiOM0Wo14D+m1oLuAzqRavrTBbXmSyucxLUZR+64ziO8//Uaw3dcRzHKcADuuM4zhSh7gK6pCWS9knql7SmBse/SdKLkv4t6TVJ3wr2xyQdk9QblmWxbb4X9O6TdF+18iLpsKTdQU93sM2UtE3SgfDZEeyS9Mug5VVJC2P7WR3WPyBpdcIaPxDzWa+kc5IeSYM/JW2QNCBpT8yWmP8kfSSUT3/Y9oqeb1BE508kvR60PCNpRrDPlTQU8+v6cnqK5TlBrYmVtaR5knYG+yZJVzQjrYjOTTGNhxXeD1Frn+ZhZnWzABngIDAfaAL6gAVV1tAJLAzpNmA/sAB4DHh0gvUXBJ3NwLygP1ONvACHgesLbD8G1oT0GuDxkF4GPEv07PtFwM5gnwkcCp8dId1RwfJ9G5iTBn8C9wALgT2V8B/wSlhXYdulCepcDDSG9OMxnXPj6xXsZ0I9xfKcoNbEyhr4C9ELdgDWAw8npbPg958BP0yDT+NLvdXQbwf6zeyQmQ0DTwErqinAzI6bWU9Inwf2ArNLbLICeMrM3jWzN4B+onzUKi8rgCdC+gngMzH7kxaxA5ghqRO4D9hmZqfM7DSwDVhSIW2fBA6aWakZxFXzp5n9HThVYE7Ef+G3djPbYdFZ/WRsX1et08yeN7Pc67d2ADeW2kcZPcXynIjWElxWWYfa7yeAp69Waymd4TifB/5cah/V8mmcegvos4E3Y9+PUjqYVhRJc4HbgJ3B9M3QxN0Qa0IV01yNvBjwvKRdkr4ebLPM7HhIvw3MSoHOHCvJP0nS5k9Izn+zQ7rSegEeJKod5pgn6V+SXpJ0d7CV0lMsz0mSRFlfB5yJXcgq5dO7id6hfCBmS4VP6y2gpwZJ04G/Ao+Y2Tng18AtwIeB40RNslpzl5ktBJYC35B0T/zHUGtIxbjV0Ne5HNgcTGn0Zx5p8l8xJK0FRoCNwXQcuNnMbgO+DfxJUvtk91ehPKe+rAv4AvkVj9T4tN4C+jHgptj3G4OtqkjKEgXzjWa2BcDMTpjZqJmNAb8lahZCcc0Vz4uZHQufA8AzQdOJ0BTMNQkHaq0zsBToMbMTQXPq/BlIyn/HyO8GSVyvpC8DnwK+GIIGofviZEjvIuqLfn8ZPcXynAgJlvVJoq6uxgJ7YoR9fxbYFNOfGp/WW0D/J3BruJPdRNRE31pNAaH/7PfAXjP7eczeGVvtfiB3d3wrsFJSs6R5wK1EN0oqmhdJ0yS15dJEN8n2hGPkRlqsBv4W07lKEYuAs6FJ+BywWFJHaAovDrakyav1pM2fMRLxX/jtnKRF4T+1Kravq0bSEuC7wHIzG4zZb5CUCen5RP47VEZPsTwnpTWRsg4XrReBByqlFbgXeN3MxrtSUuXTJO6sVnMhGk2wn+gquLYGx7+LqHn0KtAblmXAH4Hdwb4V6Ixtszbo3UdsJEMl80I0AqAvLK/l9k/Uz/gCcADYDswMdgG/Clp2A12xfT1IdEOqH/hKBXw6jah2dW3MVnN/El1gjgMXifo/v5qk/4AuouB1EFhHmLmdkM5+on7m3H90fVj3c+H/0Av0AJ8up6dYnhPUmlhZh//9KyH/m4HmpHQG+x+AhwrWralP44tP/Xccx5ki1FuXi+M4jlMED+iO4zhTBA/ojuM4UwQP6I7jOFMED+iO4zhTBA/ojuM4UwQP6I7jOFOE/wFEAp7hv1sUgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot for each variable for each person\n",
    "for i in range(1,9):\n",
    "  print('Person', i)\n",
    "  plot_person(df.loc[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RiTqkbAexoIN"
   },
   "source": [
    "## Signal Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn or predict from a continuous flow of data is a difficult task and can face to some tricky\n",
    "issues. A reasonable solution to this would be grouping single observations and working with a bunch\n",
    "of instances instead of with single ones. This is what segmentation is based on.\n",
    "\n",
    "Thus, data had been split into windows. In our case, we have set 16 observations per window, which corresponded to 1 second of the signal, with an overlapping of 50% between them. For testing data, any overlapping had been introduced in order to assign the predicted label to the corresponding instance.\n",
    "\n",
    "The activity assigned to each window corresponded to the most repeated value, i.e., the mode of the labels of that window data. In the end, a three dimensional array [number of windows, number of instances, number of signal components] had been obtained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rlRgGBpbxnK1"
   },
   "outputs": [],
   "source": [
    "def windows(data,size,mode):\n",
    "\n",
    "    if mode == 'train':\n",
    "        start = 0\n",
    "        while start < len(data):\n",
    "            if(start+size < len(data)):\n",
    "              yield int(start), int(start + size)\n",
    "            \n",
    "            #elif(start+size/2 <= len(data)):\n",
    "            # yield int(start), int(len(data))\n",
    "            start += (size / 2)\n",
    "\n",
    "    if mode =='test':\n",
    "        start = 0\n",
    "        while start < len(data):\n",
    "            if(start+size < len(data)):\n",
    "              yield int(start), int(start + size)\n",
    "            \n",
    "            #elif(start+size/2 <= len(data)):\n",
    "            # yield int(start), int(len(data))\n",
    "            start += size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-xOKtwQ60sfX"
   },
   "outputs": [],
   "source": [
    "window_size = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZqwQcQFH2pD2"
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "#16 rows implies 1 second\n",
    "\n",
    "def segment_signal(data,window_size = window_size):\n",
    "\n",
    "    segments = np.empty((0,window_size,5))\n",
    "\n",
    "    labels = np.empty((0))\n",
    "\n",
    "    for (start, end) in windows(range(data.shape[0]), window_size,'train'):\n",
    "\n",
    "        #if(start+window_size<data.shape[0]):\n",
    "        acc_z = data[\"acc_z\"][start:end]\n",
    "        acc_xy = data[\"acc_xy\"][start:end]\n",
    "        gyro_x = data[\"gyro_x\"][start:end]\n",
    "        gyro_y = data[\"gyro_y\"][start:end]\n",
    "        gyro_z = data[\"gyro_z\"][start:end]\n",
    "\n",
    "        segments = np.vstack([segments,np.dstack([acc_z,acc_xy,gyro_x,gyro_y,gyro_z])])\n",
    "        labels = np.append(labels,stats.mode(data[\"label\"][start:end])[0][0])\n",
    "\n",
    "    return segments, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DdBGlTW4q5iE"
   },
   "outputs": [],
   "source": [
    "def segment_signal_test(data,window_size = window_size):\n",
    "\n",
    "    segments = np.empty((0,window_size,5))\n",
    "\n",
    "    for (start, end) in windows(range(data.shape[0]), window_size,'test'):\n",
    "\n",
    "        acc_z = data[\"acc_z\"][start:end]\n",
    "        acc_xy = data[\"acc_xy\"][start:end]\n",
    "        gyro_x = data[\"gyro_x\"][start:end]\n",
    "        gyro_y = data[\"gyro_y\"][start:end]\n",
    "        gyro_z = data[\"gyro_z\"][start:end]\n",
    "\n",
    "        segments = np.vstack([segments,np.dstack([acc_z,acc_xy,gyro_x,gyro_y,gyro_z])])\n",
    "\n",
    "    return segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tL0h9B4kbdii"
   },
   "outputs": [],
   "source": [
    "# Changing categories from 1 - 5 to  0 - 4\n",
    "\n",
    "df[\"label\"] = df[\"label\"] - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d0gFWKtj2G7t"
   },
   "outputs": [],
   "source": [
    "data, labels = segment_signal(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-0iFTfLcROcd"
   },
   "outputs": [],
   "source": [
    "test_data1 = segment_signal_test(df_test.loc[1])\n",
    "test_data2 = segment_signal_test(df_test.loc[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 56238,
     "status": "ok",
     "timestamp": 1589461969169,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "uHfrinhxZTKF",
    "outputId": "2c3df154-7f4e-48da-cbed-c3e1b02a7dac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1223, 16, 5)\n",
      "(1413, 16, 5)\n"
     ]
    }
   ],
   "source": [
    "print(test_data1.shape)\n",
    "print(test_data2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xf0p7jMTcL--"
   },
   "source": [
    "## Feature extraction\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this step was to catch the main characteristics of each segment so our classification algorithm was able to learn from representative and reliable attributes. From each window, the following features had been extracted from each original components to obtain explanatory information of the data: mean, root mean squared value, standard deviation, kurtosis, skewness and correlation between each signal component. This way, the new feature tensor obtained was a 3-dimensional array with dimensions [number of windows, number of extracted features, number of signal components]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cL_8gUpeob4w"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "#root mean squared value\n",
    "def rms(arr):\n",
    "  square = 0\n",
    "  for i in range(len(arr)):\n",
    "    square += (arr[i])**2\n",
    "  return (math.sqrt(square/len(arr)))\n",
    "48\n",
    "def extract_rmsv(data):\n",
    "  rmsv = []\n",
    "  for i in range(data.shape[0]):\n",
    "    acc_z = []\n",
    "    acc_xy = []\n",
    "    gyro_x = []\n",
    "    gyro_y = []\n",
    "    gyro_z = []\n",
    "    for j in range(data.shape[1]):\n",
    "      acc_z.append(data[i][j][0])\n",
    "      acc_xy.append(data[i][j][1])\n",
    "      gyro_x.append(data[i][j][2])\n",
    "      gyro_y.append(data[i][j][3])\n",
    "      gyro_z.append(data[i][j][4])\n",
    "    \n",
    "    rmsv_wind = [rms(acc_z),rms(acc_xy),rms(gyro_x),rms(gyro_y),rms(gyro_z)]\n",
    "    rmsv.append(rmsv_wind)\n",
    "  rmsv = np.array(rmsv)\n",
    "  return(rmsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DKdPGizKtaoY"
   },
   "outputs": [],
   "source": [
    "def extract_features(data):\n",
    "\n",
    "    mean = np.mean(data,axis=1).reshape(data.shape[0],1,5)\n",
    "\n",
    "    std = np.std(data,axis=1).reshape(data.shape[0],1,5)\n",
    "\n",
    "    kts = stats.kurtosis(data,axis=1).reshape(data.shape[0],1,5)\n",
    "\n",
    "    sk = stats.skew(data,axis=1).reshape(data.shape[0],1,5)\n",
    "\n",
    "    rmsv = extract_rmsv(data).reshape(data.shape[0],1,5)\n",
    "\n",
    "    # Bucle para sacar el array de dimensiones (n_ventanas,n_correlaciones=4,n_variables=5).\n",
    "\n",
    "    # Eliminaremos los elementos de la diagonal de la matriz de correlación puesto que son 1 por definición.\n",
    "\n",
    "    for i in range(data.shape[0]):\n",
    "\n",
    "        df = pd.DataFrame(data=data[i])\n",
    "        corr_df = df.corr(method='pearson')\n",
    "        corr_np = corr_df.to_numpy()\n",
    "        cols = []\n",
    "\n",
    "        for j in range(5):\n",
    "\n",
    "            cols.append(corr_np[ :,j])\n",
    "        \n",
    "        # Eliminamos los elementos de la diagonal.\n",
    "\n",
    "        cols[0] = np.delete(cols[0],0).reshape(4,1)\n",
    "        cols[1] = np.delete(cols[1],1).reshape(4,1)\n",
    "        cols[2] = np.delete(cols[2],2).reshape(4,1)\n",
    "        cols[3] = np.delete(cols[3],3).reshape(4,1)\n",
    "        cols[4] = np.delete(cols[4],4).reshape(4,1)\n",
    "\n",
    "        # Obtenemos la matriz de correlación sin los elementos de la diagonal.\n",
    "\n",
    "        corr_window = np.hstack((cols[0],cols[1],cols[2],cols[3],cols[4]))\n",
    "\n",
    "        if i ==0:\n",
    "\n",
    "            corr = corr_window.reshape(1,4,5)\n",
    "\n",
    "        else:\n",
    "\n",
    "            corr_window = corr_window.reshape(1,4,5)\n",
    "\n",
    "            # Apilamos las matrices de correlación para cada ventana en una nueva dimensión.\n",
    "\n",
    "            corr = np.vstack((corr,corr_window))\n",
    "        \n",
    "    # Obtenemos la matriz de features, de dimensión (n_ventanas,n_features,n_variables) apilando media, std, skewness, kurtosis y correlación en la primera dimensión (de las ventanas).\n",
    " \n",
    "    features = np.concatenate((mean,rmsv,std,kts,sk,corr),axis=1)\n",
    "\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lufWcJxso9DQ"
   },
   "outputs": [],
   "source": [
    "data = extract_features(data)\n",
    "\n",
    "n = int(data.shape[0]*0.75) #split index\n",
    "\n",
    "test_data1 = extract_features(test_data1)\n",
    "\n",
    "test_data2 = extract_features(test_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 78373,
     "status": "ok",
     "timestamp": 1589461991403,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "FeU3kPbWZppE",
    "outputId": "e99324eb-4813-4446-d8a5-060c00a47780"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17677, 9, 5)\n",
      "(1223, 9, 5)\n",
      "(1413, 9, 5)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(test_data1.shape)\n",
    "print(test_data2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u2OmRkpB_7Ds"
   },
   "source": [
    "## CNN model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final part of the project is the classification or recognition stage. In particular, the\n",
    "classification algorithm we have implemented is a CNN, for which we used Pytorch. Specifically, the neural network is composed by a convolutional layer, a maxpool stage and three dense layers, each one followed by a non-linear activation function. The network took as inputs the windows grouped in batches. Batch size has been set to 32. Regarding convolutional layer, it was fixed to receive an input with 1 channel, using a convolving kernel of size (2,2) and giving as output an 8 channel array. Then, maxpooling carried out a max over on each (2,2) square. Following, three dense layers separated by non-linear functions performed linear operations. First, two activation functions were rectified functions and the last one was a logsoftmax, which received a final 5 dimensional array for each window and computed the probabilities for each window to belong to each class. This way, we started with a [batch_size, 1, n_features, n_components] array and we get a [batch_size, nlabels] matrix. The model was trained with data corresponding to 6 people and validated with the remaining\n",
    "two. Also, during this training stage, an analysis of the cross validation loss during the epochs\n",
    "was accomplished in order to control overfitting and got the optimal number of training epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VZMmh3I8_8qb"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch import optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OV8Py1nk51b2"
   },
   "outputs": [],
   "source": [
    "data = torch.from_numpy(data).float()\n",
    "labels = torch.from_numpy(labels).float()\n",
    "test_data1 = torch.from_numpy(test_data1).float()\n",
    "test_data2 = torch.from_numpy(test_data2).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HM3eKDdq7lFy"
   },
   "outputs": [],
   "source": [
    "train_data = data[0:n]\n",
    "train_labels = labels[0:n]\n",
    "\n",
    "valid_data = data[n:]\n",
    "valid_labels = labels[n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 81312,
     "status": "ok",
     "timestamp": 1589461994414,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "Ou0ImGrsj_2C",
    "outputId": "d2849ebe-525d-49af-a940-bc8b90a8bbd8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1223, 9, 5])"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UKNSI65EQN_s"
   },
   "outputs": [],
   "source": [
    "def batch(data,targets,size):\n",
    "\n",
    "    start = 0\n",
    "\n",
    "    while start < len(data):\n",
    "\n",
    "        if(start+size < len(data)):\n",
    "\n",
    "          yield data[start:(start+size)], targets[start:(start+size)]\n",
    "\n",
    "        start += size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8rDaK-cpkqb0"
   },
   "outputs": [],
   "source": [
    "def batch_test(data,size):\n",
    "\n",
    "    start = 0\n",
    "\n",
    "    while start < len(data):\n",
    "\n",
    "        if(start+size <= len(data)):\n",
    "\n",
    "          yield data[start:(start+size)]\n",
    "        \n",
    "        #else:\n",
    "\n",
    "          #yield data[start: ]\n",
    "\n",
    "        start += size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p0Reu24-QOHW"
   },
   "outputs": [],
   "source": [
    "class cnn(nn.Module):\n",
    "    def __init__(self,nlabels=5):\n",
    "        super().__init__()\n",
    "\n",
    "        self.nlabels = nlabels\n",
    "\n",
    "        # convolutional layer\n",
    "        self.conv = nn.Conv2d(in_channels=1, out_channels=8, \n",
    "                               kernel_size=2, stride=1, padding=0)\n",
    "        \n",
    "        # Max pool layer\n",
    "        self.pool = nn.MaxPool2d(2,2)\n",
    "\n",
    "        # Spatial dimension of the Tensor at the output of the 2nd CNN\n",
    "        self.final_dim1 = int((9-1)/2)\n",
    "\n",
    "        self.final_dim2 = int((5-1)/2)\n",
    "        \n",
    "        # Linear layers\n",
    "        \n",
    "        self.output1 = nn.Linear(8*self.final_dim1*self.final_dim2,32)\n",
    "\n",
    "        self.output2 = nn.Linear(32,12)\n",
    "\n",
    "        self.output3 = nn.Linear(12,self.nlabels)\n",
    "    \n",
    "        self.relu = nn.ReLU()\n",
    "        \n",
    "        self.logsoftmax = nn.LogSoftmax(dim=1) \n",
    "        \n",
    "\n",
    "        \n",
    "    def forward(self, x):\n",
    "\n",
    "        x = self.conv(x) \n",
    "        x = self.relu(x) \n",
    "\n",
    "        x = self.pool(x)\n",
    "\n",
    "        # Flatten the tensor into a vector\n",
    "        x = x.view(-1, 8 * self.final_dim1 * self.final_dim2)\n",
    "        # Pass the tensor through the Dense Layers\n",
    "        \n",
    "\n",
    "        x = self.output1(x)\n",
    "        x = self.relu(x)\n",
    "\n",
    "        x = self.output2(x)\n",
    "        x = self.relu(x)\n",
    "        \n",
    "        x = self.output3(x)\n",
    "        x = self.logsoftmax(x)\n",
    "        \n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ORRbVgceQOJ9"
   },
   "outputs": [],
   "source": [
    "class cnn_extended(cnn):\n",
    "    \n",
    "    def __init__(self,nlabels=5,epochs=200,lr=0.001,batch_size=32):\n",
    "        \n",
    "        super().__init__(nlabels)\n",
    "\n",
    "        self.epochs = epochs\n",
    "\n",
    "        self.lr = lr\n",
    "\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "        self.optim = optim.Adam(self.parameters(), self.lr)\n",
    "\n",
    "        self.criterion = nn.NLLLoss()\n",
    "\n",
    "        self.loss_during_training = []\n",
    "\n",
    "        self.valid_loss_during_training =[]\n",
    "        \n",
    "    def trainloop(self,train_data,train_labels,valid_data,valid_labels):\n",
    "        \n",
    "        for e in range(int(self.epochs)):\n",
    "\n",
    "            running_loss = 0.\n",
    "\n",
    "            counter = 0\n",
    "\n",
    "            for data,targets in batch(train_data,train_labels,self.batch_size):\n",
    "\n",
    "                self.optim.zero_grad()\n",
    "\n",
    "                data = data.view(self.batch_size,1,9,5)\n",
    "\n",
    "                out = self.forward(data)\n",
    "\n",
    "                loss = self.criterion(out,targets.long())\n",
    "\n",
    "                running_loss += loss.item()\n",
    "\n",
    "                loss.backward()\n",
    "\n",
    "                self.optim.step()\n",
    "\n",
    "                counter += 1\n",
    "\n",
    "            self.loss_during_training.append(running_loss/counter)\n",
    "\n",
    "            with torch.no_grad():\n",
    "\n",
    "                running_loss = 0.\n",
    "\n",
    "                counter = 0\n",
    "\n",
    "                for data, targets in batch(valid_data,valid_labels,self.batch_size):\n",
    "\n",
    "                    data = data.view(self.batch_size,1,9,5)\n",
    "\n",
    "                    out = self.forward(data)\n",
    "\n",
    "                    loss = self.criterion(out,targets.long())\n",
    "\n",
    "                    running_loss += loss.item()\n",
    "\n",
    "                    counter += 1\n",
    "\n",
    "                self.valid_loss_during_training.append(running_loss/counter)\n",
    "\n",
    "                print(\"Epoch %d. Training loss: %f, Validation loss: %f\" \n",
    "                      %(e,self.loss_during_training[-1],self.valid_loss_during_training[-1]))\n",
    "                \n",
    "    def final_trainloop(self,train_data,train_labels):\n",
    "        \n",
    "        for e in range(int(self.epochs)):\n",
    "\n",
    "            running_loss = 0.\n",
    "\n",
    "            counter = 0\n",
    "\n",
    "            for data,targets in batch(train_data,train_labels,self.batch_size):\n",
    "\n",
    "                self.optim.zero_grad()\n",
    "\n",
    "                data = data.view(self.batch_size,1,9,5)\n",
    "\n",
    "                out = self.forward(data)\n",
    "\n",
    "                loss = self.criterion(out,targets.long())\n",
    "\n",
    "                running_loss += loss.item()\n",
    "\n",
    "                loss.backward()\n",
    "\n",
    "                self.optim.step()\n",
    "\n",
    "                counter += 1\n",
    "\n",
    "            self.loss_during_training.append(running_loss/counter)\n",
    "\n",
    "            print(\"Epoch %d. Training loss: %f\" \n",
    "                      %(e,self.loss_during_training[-1]))\n",
    "\n",
    "    def eval_performance(self,dataset,labels):\n",
    "        \n",
    "        loss = 0\n",
    "\n",
    "        accuracy = 0\n",
    "\n",
    "        counter = 0\n",
    "\n",
    "        with torch.no_grad():\n",
    "        \n",
    "            for data, targets in batch(dataset,labels,self.batch_size):\n",
    "\n",
    "                data = data.view(self.batch_size,1,9,5)\n",
    "\n",
    "                probs = self.forward(data)\n",
    "\n",
    "                top_p, top_class = probs.topk(1,dim=1)\n",
    "\n",
    "                equals = (top_class == targets.view(data.shape[0],1))\n",
    "\n",
    "                accuracy += torch.mean(equals.type(torch.FloatTensor))\n",
    "\n",
    "                counter += 1\n",
    "\n",
    "            return accuracy/counter\n",
    "\n",
    "    def predict(self,dataset):\n",
    "\n",
    "        counter = 0\n",
    "\n",
    "        with torch.no_grad():\n",
    "        \n",
    "            for data in batch_test(dataset,1):\n",
    "\n",
    "                data = data.view(data.shape[0],1,9,5)\n",
    "\n",
    "                probs = self.forward(data)\n",
    "\n",
    "                top_p, top_class = probs.topk(1,dim=1)\n",
    "\n",
    "                # top_class = torch.squeeze(top_class)\n",
    "\n",
    "                if counter == 0:\n",
    "\n",
    "                    predictions = top_class\n",
    "                \n",
    "                else:\n",
    "\n",
    "                    predictions = torch.cat((predictions,top_class),0)\n",
    "\n",
    "                counter += 1\n",
    "\n",
    "            return predictions\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 255736,
     "status": "ok",
     "timestamp": 1589462168962,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "-kEd4N_oQOMt",
    "outputId": "055427d5-2617-44c8-e934-3dd99bf5cea6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0. Training loss: 1.136004, Validation loss: 0.734839\n",
      "Epoch 1. Training loss: 0.681293, Validation loss: 0.396129\n",
      "Epoch 2. Training loss: 0.463484, Validation loss: 0.270262\n",
      "Epoch 3. Training loss: 0.363594, Validation loss: 0.197135\n",
      "Epoch 4. Training loss: 0.303765, Validation loss: 0.192894\n",
      "Epoch 5. Training loss: 0.258849, Validation loss: 0.166492\n",
      "Epoch 6. Training loss: 0.229986, Validation loss: 0.165563\n",
      "Epoch 7. Training loss: 0.221825, Validation loss: 0.168120\n",
      "Epoch 8. Training loss: 0.214074, Validation loss: 0.170090\n",
      "Epoch 9. Training loss: 0.208538, Validation loss: 0.172623\n",
      "Epoch 10. Training loss: 0.203403, Validation loss: 0.174366\n",
      "Epoch 11. Training loss: 0.201479, Validation loss: 0.180329\n",
      "Epoch 12. Training loss: 0.196238, Validation loss: 0.185248\n",
      "Epoch 13. Training loss: 0.195079, Validation loss: 0.191502\n",
      "Epoch 14. Training loss: 0.191694, Validation loss: 0.193886\n",
      "Epoch 15. Training loss: 0.188813, Validation loss: 0.190397\n",
      "Epoch 16. Training loss: 0.188518, Validation loss: 0.190252\n",
      "Epoch 17. Training loss: 0.184886, Validation loss: 0.190243\n",
      "Epoch 18. Training loss: 0.182758, Validation loss: 0.182865\n",
      "Epoch 19. Training loss: 0.192715, Validation loss: 0.168959\n",
      "Epoch 20. Training loss: 0.179735, Validation loss: 0.176097\n",
      "Epoch 21. Training loss: 0.176598, Validation loss: 0.172936\n",
      "Epoch 22. Training loss: 0.173912, Validation loss: 0.159303\n",
      "Epoch 23. Training loss: 0.171622, Validation loss: 0.152785\n",
      "Epoch 24. Training loss: 0.180079, Validation loss: 0.139791\n",
      "Epoch 25. Training loss: 0.171402, Validation loss: 0.151943\n",
      "Epoch 26. Training loss: 0.168598, Validation loss: 0.145956\n",
      "Epoch 27. Training loss: 0.166442, Validation loss: 0.134606\n",
      "Epoch 28. Training loss: 0.163690, Validation loss: 0.127228\n",
      "Epoch 29. Training loss: 0.166506, Validation loss: 0.129188\n",
      "Epoch 30. Training loss: 0.165272, Validation loss: 0.139387\n",
      "Epoch 31. Training loss: 0.165333, Validation loss: 0.143458\n",
      "Epoch 32. Training loss: 0.161814, Validation loss: 0.123703\n",
      "Epoch 33. Training loss: 0.165772, Validation loss: 0.137258\n",
      "Epoch 34. Training loss: 0.181856, Validation loss: 0.113705\n",
      "Epoch 35. Training loss: 0.164573, Validation loss: 0.145550\n",
      "Epoch 36. Training loss: 0.157218, Validation loss: 0.123425\n",
      "Epoch 37. Training loss: 0.152237, Validation loss: 0.121941\n",
      "Epoch 38. Training loss: 0.154097, Validation loss: 0.128109\n",
      "Epoch 39. Training loss: 0.150745, Validation loss: 0.119274\n",
      "Epoch 40. Training loss: 0.149741, Validation loss: 0.127627\n",
      "Epoch 41. Training loss: 0.146965, Validation loss: 0.122680\n",
      "Epoch 42. Training loss: 0.143172, Validation loss: 0.122547\n",
      "Epoch 43. Training loss: 0.143008, Validation loss: 0.131244\n",
      "Epoch 44. Training loss: 0.141158, Validation loss: 0.126596\n",
      "Epoch 45. Training loss: 0.139626, Validation loss: 0.134247\n",
      "Epoch 46. Training loss: 0.138797, Validation loss: 0.130000\n",
      "Epoch 47. Training loss: 0.139293, Validation loss: 0.134641\n",
      "Epoch 48. Training loss: 0.135856, Validation loss: 0.132250\n",
      "Epoch 49. Training loss: 0.135496, Validation loss: 0.134572\n",
      "Epoch 50. Training loss: 0.134920, Validation loss: 0.134234\n",
      "Epoch 51. Training loss: 0.132983, Validation loss: 0.135827\n",
      "Epoch 52. Training loss: 0.130533, Validation loss: 0.129550\n",
      "Epoch 53. Training loss: 0.132317, Validation loss: 0.136425\n",
      "Epoch 54. Training loss: 0.128357, Validation loss: 0.130983\n",
      "Epoch 55. Training loss: 0.128879, Validation loss: 0.143586\n",
      "Epoch 56. Training loss: 0.129482, Validation loss: 0.126512\n",
      "Epoch 57. Training loss: 0.123929, Validation loss: 0.129119\n",
      "Epoch 58. Training loss: 0.124019, Validation loss: 0.141604\n",
      "Epoch 59. Training loss: 0.122967, Validation loss: 0.125023\n",
      "Epoch 60. Training loss: 0.122299, Validation loss: 0.149157\n",
      "Epoch 61. Training loss: 0.123740, Validation loss: 0.124718\n",
      "Epoch 62. Training loss: 0.125447, Validation loss: 0.129913\n",
      "Epoch 63. Training loss: 0.119302, Validation loss: 0.122943\n",
      "Epoch 64. Training loss: 0.121852, Validation loss: 0.118546\n",
      "Epoch 65. Training loss: 0.117152, Validation loss: 0.114463\n",
      "Epoch 66. Training loss: 0.118295, Validation loss: 0.113010\n",
      "Epoch 67. Training loss: 0.114445, Validation loss: 0.115159\n",
      "Epoch 68. Training loss: 0.114769, Validation loss: 0.114630\n",
      "Epoch 69. Training loss: 0.113284, Validation loss: 0.103613\n",
      "Epoch 70. Training loss: 0.112785, Validation loss: 0.100456\n",
      "Epoch 71. Training loss: 0.111743, Validation loss: 0.098817\n",
      "Epoch 72. Training loss: 0.110467, Validation loss: 0.095804\n",
      "Epoch 73. Training loss: 0.105777, Validation loss: 0.090711\n",
      "Epoch 74. Training loss: 0.106595, Validation loss: 0.092308\n",
      "Epoch 75. Training loss: 0.103354, Validation loss: 0.091719\n",
      "Epoch 76. Training loss: 0.103780, Validation loss: 0.099384\n",
      "Epoch 77. Training loss: 0.099806, Validation loss: 0.094480\n",
      "Epoch 78. Training loss: 0.104014, Validation loss: 0.095086\n",
      "Epoch 79. Training loss: 0.099712, Validation loss: 0.093045\n",
      "Epoch 80. Training loss: 0.097428, Validation loss: 0.096602\n",
      "Epoch 81. Training loss: 0.094927, Validation loss: 0.110183\n",
      "Epoch 82. Training loss: 0.093870, Validation loss: 0.104061\n",
      "Epoch 83. Training loss: 0.092408, Validation loss: 0.132982\n",
      "Epoch 84. Training loss: 0.097358, Validation loss: 0.094303\n",
      "Epoch 85. Training loss: 0.091618, Validation loss: 0.114726\n",
      "Epoch 86. Training loss: 0.089424, Validation loss: 0.169461\n",
      "Epoch 87. Training loss: 0.098348, Validation loss: 0.095194\n",
      "Epoch 88. Training loss: 0.091065, Validation loss: 0.117573\n",
      "Epoch 89. Training loss: 0.089084, Validation loss: 0.182122\n",
      "Epoch 90. Training loss: 0.097202, Validation loss: 0.092354\n",
      "Epoch 91. Training loss: 0.087648, Validation loss: 0.172658\n",
      "Epoch 92. Training loss: 0.095516, Validation loss: 0.092354\n",
      "Epoch 93. Training loss: 0.083599, Validation loss: 0.161318\n",
      "Epoch 94. Training loss: 0.086359, Validation loss: 0.102685\n",
      "Epoch 95. Training loss: 0.081200, Validation loss: 0.143464\n",
      "Epoch 96. Training loss: 0.083374, Validation loss: 0.151660\n",
      "Epoch 97. Training loss: 0.082615, Validation loss: 0.107189\n",
      "Epoch 98. Training loss: 0.078377, Validation loss: 0.128529\n",
      "Epoch 99. Training loss: 0.079354, Validation loss: 0.151978\n",
      "Epoch 100. Training loss: 0.215181, Validation loss: 0.174773\n",
      "Epoch 101. Training loss: 0.112048, Validation loss: 0.138497\n",
      "Epoch 102. Training loss: 0.105532, Validation loss: 0.149850\n",
      "Epoch 103. Training loss: 0.099048, Validation loss: 0.146542\n",
      "Epoch 104. Training loss: 0.090133, Validation loss: 0.139564\n",
      "Epoch 105. Training loss: 0.083450, Validation loss: 0.140924\n",
      "Epoch 106. Training loss: 0.081172, Validation loss: 0.135210\n",
      "Epoch 107. Training loss: 0.078366, Validation loss: 0.127960\n",
      "Epoch 108. Training loss: 0.077311, Validation loss: 0.126673\n",
      "Epoch 109. Training loss: 0.075548, Validation loss: 0.142934\n",
      "Epoch 110. Training loss: 0.076627, Validation loss: 0.195720\n",
      "Epoch 111. Training loss: 0.077407, Validation loss: 0.118999\n",
      "Epoch 112. Training loss: 0.070769, Validation loss: 0.111315\n",
      "Epoch 113. Training loss: 0.073119, Validation loss: 0.199612\n",
      "Epoch 114. Training loss: 0.087504, Validation loss: 0.103858\n",
      "Epoch 115. Training loss: 0.077154, Validation loss: 0.189239\n",
      "Epoch 116. Training loss: 0.073457, Validation loss: 0.105292\n",
      "Epoch 117. Training loss: 0.067086, Validation loss: 0.096714\n",
      "Epoch 118. Training loss: 0.066214, Validation loss: 0.104513\n",
      "Epoch 119. Training loss: 0.069574, Validation loss: 0.102647\n",
      "Epoch 120. Training loss: 0.065445, Validation loss: 0.100651\n",
      "Epoch 121. Training loss: 0.067584, Validation loss: 0.133705\n",
      "Epoch 122. Training loss: 0.066508, Validation loss: 0.191178\n",
      "Epoch 123. Training loss: 0.068457, Validation loss: 0.123881\n",
      "Epoch 124. Training loss: 0.064597, Validation loss: 0.185246\n",
      "Epoch 125. Training loss: 0.069293, Validation loss: 0.111601\n",
      "Epoch 126. Training loss: 0.066598, Validation loss: 0.168690\n",
      "Epoch 127. Training loss: 0.066800, Validation loss: 0.111098\n",
      "Epoch 128. Training loss: 0.064538, Validation loss: 0.128637\n",
      "Epoch 129. Training loss: 0.060047, Validation loss: 0.127363\n",
      "Epoch 130. Training loss: 0.059864, Validation loss: 0.143160\n",
      "Epoch 131. Training loss: 0.059913, Validation loss: 0.223155\n",
      "Epoch 132. Training loss: 0.066463, Validation loss: 0.250896\n",
      "Epoch 133. Training loss: 0.297758, Validation loss: 0.214907\n",
      "Epoch 134. Training loss: 0.111914, Validation loss: 0.131337\n",
      "Epoch 135. Training loss: 0.090763, Validation loss: 0.102481\n",
      "Epoch 136. Training loss: 0.076881, Validation loss: 0.093838\n",
      "Epoch 137. Training loss: 0.067371, Validation loss: 0.103122\n",
      "Epoch 138. Training loss: 0.062239, Validation loss: 0.101911\n",
      "Epoch 139. Training loss: 0.064576, Validation loss: 0.229253\n",
      "Epoch 140. Training loss: 0.064895, Validation loss: 0.240967\n",
      "Epoch 141. Training loss: 0.063526, Validation loss: 0.268367\n",
      "Epoch 142. Training loss: 0.064764, Validation loss: 0.244130\n",
      "Epoch 143. Training loss: 0.062360, Validation loss: 0.216807\n",
      "Epoch 144. Training loss: 0.059717, Validation loss: 0.122773\n",
      "Epoch 145. Training loss: 0.056108, Validation loss: 0.110620\n",
      "Epoch 146. Training loss: 0.057595, Validation loss: 0.132157\n",
      "Epoch 147. Training loss: 0.058157, Validation loss: 0.110160\n",
      "Epoch 148. Training loss: 0.058103, Validation loss: 0.235581\n",
      "Epoch 149. Training loss: 0.055034, Validation loss: 0.227741\n",
      "Epoch 150. Training loss: 0.053686, Validation loss: 0.158723\n",
      "Epoch 151. Training loss: 0.049855, Validation loss: 0.123979\n",
      "Epoch 152. Training loss: 0.050296, Validation loss: 0.127204\n",
      "Epoch 153. Training loss: 0.053146, Validation loss: 0.133966\n",
      "Epoch 154. Training loss: 0.048476, Validation loss: 0.124490\n",
      "Epoch 155. Training loss: 0.049364, Validation loss: 0.134937\n",
      "Epoch 156. Training loss: 0.051253, Validation loss: 0.139439\n",
      "Epoch 157. Training loss: 0.089141, Validation loss: 0.128580\n",
      "Epoch 158. Training loss: 0.053854, Validation loss: 0.117606\n",
      "Epoch 159. Training loss: 0.045604, Validation loss: 0.116669\n",
      "Epoch 160. Training loss: 0.046954, Validation loss: 0.119814\n",
      "Epoch 161. Training loss: 0.044894, Validation loss: 0.117388\n",
      "Epoch 162. Training loss: 0.049313, Validation loss: 0.233263\n",
      "Epoch 163. Training loss: 0.045018, Validation loss: 0.129561\n",
      "Epoch 164. Training loss: 0.044381, Validation loss: 0.131683\n",
      "Epoch 165. Training loss: 0.045670, Validation loss: 0.154160\n",
      "Epoch 166. Training loss: 0.043477, Validation loss: 0.133605\n",
      "Epoch 167. Training loss: 0.044356, Validation loss: 0.135380\n",
      "Epoch 168. Training loss: 0.045067, Validation loss: 0.195678\n",
      "Epoch 169. Training loss: 0.042737, Validation loss: 0.186300\n",
      "Epoch 170. Training loss: 0.044065, Validation loss: 0.152629\n",
      "Epoch 171. Training loss: 0.040845, Validation loss: 0.165733\n",
      "Epoch 172. Training loss: 0.041166, Validation loss: 0.134776\n",
      "Epoch 173. Training loss: 0.041688, Validation loss: 0.169667\n",
      "Epoch 174. Training loss: 0.043497, Validation loss: 0.195160\n",
      "Epoch 175. Training loss: 0.042184, Validation loss: 0.159078\n",
      "Epoch 176. Training loss: 0.038683, Validation loss: 0.162975\n",
      "Epoch 177. Training loss: 0.052201, Validation loss: 0.143405\n",
      "Epoch 178. Training loss: 0.043894, Validation loss: 0.170616\n",
      "Epoch 179. Training loss: 0.039924, Validation loss: 0.180712\n",
      "Epoch 180. Training loss: 0.068275, Validation loss: 0.120446\n",
      "Epoch 181. Training loss: 0.040980, Validation loss: 0.132152\n",
      "Epoch 182. Training loss: 0.037450, Validation loss: 0.132310\n",
      "Epoch 183. Training loss: 0.038961, Validation loss: 0.146637\n",
      "Epoch 184. Training loss: 0.035807, Validation loss: 0.137068\n",
      "Epoch 185. Training loss: 0.041692, Validation loss: 0.139501\n",
      "Epoch 186. Training loss: 0.040251, Validation loss: 0.137067\n",
      "Epoch 187. Training loss: 0.036392, Validation loss: 0.142456\n",
      "Epoch 188. Training loss: 0.040188, Validation loss: 0.147262\n",
      "Epoch 189. Training loss: 0.038032, Validation loss: 0.163354\n",
      "Epoch 190. Training loss: 0.045942, Validation loss: 0.139696\n",
      "Epoch 191. Training loss: 0.036602, Validation loss: 0.144369\n",
      "Epoch 192. Training loss: 0.035258, Validation loss: 0.159837\n",
      "Epoch 193. Training loss: 0.039411, Validation loss: 0.147097\n",
      "Epoch 194. Training loss: 0.037497, Validation loss: 0.148664\n",
      "Epoch 195. Training loss: 0.033807, Validation loss: 0.146104\n",
      "Epoch 196. Training loss: 0.036211, Validation loss: 0.182279\n",
      "Epoch 197. Training loss: 0.032753, Validation loss: 0.163005\n",
      "Epoch 198. Training loss: 0.034237, Validation loss: 0.188578\n",
      "Epoch 199. Training loss: 0.033735, Validation loss: 0.177719\n"
     ]
    }
   ],
   "source": [
    "my_CNN = cnn_extended()\n",
    "\n",
    "my_CNN.trainloop(train_data,train_labels,valid_data,valid_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 255701,
     "status": "ok",
     "timestamp": 1589462168967,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "nBHXAUZ9S9Tr",
    "outputId": "23847e65-a662-4265-aac6-f18c822f7202"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy 0.986489\n",
      "Validation accuracy 0.967391\n"
     ]
    }
   ],
   "source": [
    "train_performance = my_CNN.eval_performance(train_data,train_labels)\n",
    "\n",
    "valid_performance = my_CNN.eval_performance(valid_data,valid_labels)\n",
    "\n",
    "print(\"Train accuracy %f\" %(train_performance))\n",
    "print(\"Validation accuracy %f\" %(valid_performance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 256432,
     "status": "ok",
     "timestamp": 1589462169711,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "wpXgAOexUZ1q",
    "outputId": "37feb58a-0136-4b42-b74f-3e9c231d0b56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7262cfb278>"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(my_CNN.loss_during_training,'-b',label='Training Cross Entropy Loss')\n",
    "plt.plot(my_CNN.valid_loss_during_training,'-g',label='Validation Cross Entropy Loss')\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, first epochs rapidly make the cross entropy losses decrease. Around 30 epochs, they are almost stabilized, but some small decreases and increases are shown. The key point here is that from 80 epochs, validation loss slowly increases again, while training loss keeps decreasing. This phenomenon is called overfitting: at that point, the neural network stops learning and starts memorizing the training instances, which affects its generalization ability, and the accuracy on the validation test gets worse. Therefore, network is trained again with the number of epochs that minimizes validations loss and considering the joint of training and validation sets, avoiding the overfitting part (early stopping).\n",
    "\n",
    "Thus, 98.65% and 96.73% accuracies have been obtained for training and validation set respectively in the first case. On the other hand, the model with the optimal number of epochs yields a 95.1% of success rate on the train data and a 98.8% on the validation instances. It’s clear that the second case is a superior model and is the one we will use in the remaining tasks.\n",
    "\n",
    "Talking about the obtained success rates,the quality of the results is very high and predictions are extremely accurate. It seems that a convolutional neural network is a right approach to this problem. Thus, a satisfactory human recognition system had been developed. However, we must mention that we were not predicting the activity of each observation itself, but the mode of the activities of 16 observations. Therefore, accuracy would be lower due to the observations in the window that do not have the mode as label. Nevertheless, when analysing the data, we saw that the same activity is performed along a large number of observations, which make sense since normally, a person performs the same activity for at least several seconds. Consequently, a small number of observations are misclassified due to this reason."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 256425,
     "status": "ok",
     "timestamp": 1589462169716,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "qVhT2UHrk2Db",
    "outputId": "822a7aae-14ca-42cc-efa2-da34e529c1e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optim_epochs = my_CNN.valid_loss_during_training.index(min(my_CNN.valid_loss_during_training)) + 1\n",
    "\n",
    "optim_epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qoEPpSmSc5I3"
   },
   "source": [
    "Early stopping at  epochs. After that, it starts overfitting.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 81554,
     "status": "ok",
     "timestamp": 1589462260039,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "bG7-J6iaUZ3n",
    "outputId": "15527005-1d70-4110-db88-cb1285568c7b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0. Training loss: 0.929641\n",
      "Epoch 1. Training loss: 0.318316\n",
      "Epoch 2. Training loss: 0.259498\n",
      "Epoch 3. Training loss: 0.243211\n",
      "Epoch 4. Training loss: 0.240031\n",
      "Epoch 5. Training loss: 0.238071\n",
      "Epoch 6. Training loss: 0.230717\n",
      "Epoch 7. Training loss: 0.226214\n",
      "Epoch 8. Training loss: 0.221790\n",
      "Epoch 9. Training loss: 0.215040\n",
      "Epoch 10. Training loss: 0.213339\n",
      "Epoch 11. Training loss: 0.202966\n",
      "Epoch 12. Training loss: 0.183492\n",
      "Epoch 13. Training loss: 0.180033\n",
      "Epoch 14. Training loss: 0.179251\n",
      "Epoch 15. Training loss: 0.172015\n",
      "Epoch 16. Training loss: 0.170182\n",
      "Epoch 17. Training loss: 0.168392\n",
      "Epoch 18. Training loss: 0.163660\n",
      "Epoch 19. Training loss: 0.160073\n",
      "Epoch 20. Training loss: 0.157330\n",
      "Epoch 21. Training loss: 0.152456\n",
      "Epoch 22. Training loss: 0.149167\n",
      "Epoch 23. Training loss: 0.146841\n",
      "Epoch 24. Training loss: 0.145118\n",
      "Epoch 25. Training loss: 0.142380\n",
      "Epoch 26. Training loss: 0.138850\n",
      "Epoch 27. Training loss: 0.136137\n",
      "Epoch 28. Training loss: 0.133602\n",
      "Epoch 29. Training loss: 0.131634\n",
      "Epoch 30. Training loss: 0.128430\n",
      "Epoch 31. Training loss: 0.126686\n",
      "Epoch 32. Training loss: 0.124172\n",
      "Epoch 33. Training loss: 0.120330\n",
      "Epoch 34. Training loss: 0.120758\n",
      "Epoch 35. Training loss: 0.118804\n",
      "Epoch 36. Training loss: 0.113998\n",
      "Epoch 37. Training loss: 0.113869\n",
      "Epoch 38. Training loss: 0.111703\n",
      "Epoch 39. Training loss: 0.112142\n",
      "Epoch 40. Training loss: 0.108019\n",
      "Epoch 41. Training loss: 0.111811\n",
      "Epoch 42. Training loss: 0.108940\n",
      "Epoch 43. Training loss: 0.108668\n",
      "Epoch 44. Training loss: 0.107032\n",
      "Epoch 45. Training loss: 0.112536\n",
      "Epoch 46. Training loss: 0.104998\n",
      "Epoch 47. Training loss: 0.105860\n",
      "Epoch 48. Training loss: 0.105668\n",
      "Epoch 49. Training loss: 0.102792\n",
      "Epoch 50. Training loss: 0.103171\n",
      "Epoch 51. Training loss: 0.099981\n",
      "Epoch 52. Training loss: 0.100438\n",
      "Epoch 53. Training loss: 0.099408\n",
      "Epoch 54. Training loss: 0.103914\n",
      "Epoch 55. Training loss: 0.097503\n",
      "Epoch 56. Training loss: 0.104694\n",
      "Epoch 57. Training loss: 0.100095\n",
      "Epoch 58. Training loss: 0.096519\n",
      "Epoch 59. Training loss: 0.093944\n",
      "Epoch 60. Training loss: 0.091240\n",
      "Epoch 61. Training loss: 0.089943\n",
      "Epoch 62. Training loss: 0.088121\n",
      "Epoch 63. Training loss: 0.086522\n",
      "Epoch 64. Training loss: 0.086999\n",
      "Epoch 65. Training loss: 0.084656\n",
      "Epoch 66. Training loss: 0.083680\n",
      "Epoch 67. Training loss: 0.082501\n",
      "Epoch 68. Training loss: 0.080766\n",
      "Epoch 69. Training loss: 0.080512\n",
      "Epoch 70. Training loss: 0.079009\n",
      "Epoch 71. Training loss: 0.078136\n",
      "Epoch 72. Training loss: 0.076068\n",
      "Epoch 73. Training loss: 0.078148\n"
     ]
    }
   ],
   "source": [
    "my_CNN = cnn_extended(epochs=optim_epochs)\n",
    "\n",
    "my_CNN.final_trainloop(data,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1361,
     "status": "ok",
     "timestamp": 1589462261424,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "K268ai4NUZ7s",
    "outputId": "563d7955-ec46-43e9-b75f-163ba56431f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy 0.951464\n",
      "Validation accuracy 0.988225\n"
     ]
    }
   ],
   "source": [
    "train_performance = my_CNN.eval_performance(train_data,train_labels)\n",
    "\n",
    "valid_performance = my_CNN.eval_performance(valid_data,valid_labels)\n",
    "\n",
    "print(\"Train accuracy %f\" %(train_performance))\n",
    "print(\"Validation accuracy %f\" %(valid_performance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2340,
     "status": "ok",
     "timestamp": 1589462262419,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "WdpOTjX3UZ9t",
    "outputId": "bf1c2629-0ad9-4f69-933a-02b01e84954e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1223])\n",
      "torch.Size([1413])\n"
     ]
    }
   ],
   "source": [
    "predictions1 = my_CNN.predict(test_data1)\n",
    "predictions1 = torch.squeeze(predictions1)\n",
    "print(predictions1.shape)\n",
    "\n",
    "predictions2 = my_CNN.predict(test_data2)\n",
    "predictions2 = torch.squeeze(predictions2)\n",
    "print(predictions2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2328,
     "status": "ok",
     "timestamp": 1589462262421,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "sv86ozw2u4V-",
    "outputId": "3394e950-368e-4a2a-dc10-7fe023ad39f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4]\n",
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(predictions1))\n",
    "print(np.unique(predictions2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0NWalxDXvU-x"
   },
   "source": [
    "Let's remember that every label in predictions makes references to each window. This means that every target describes 48 observations, so we have to repeat every component 48 times to get a category per observation in the test set. Moreover, a few test observations have been lost in the windowing, but we can attach these few ones to the last label without any significant mistake. The following code do this for both test people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "df-xpyFeKIlK"
   },
   "outputs": [],
   "source": [
    "def get_final_pred(predictions,fin,ref,window_size = window_size):\n",
    "\n",
    "    final_predictions = np.empty(ref,dtype=int)\n",
    "\n",
    "    for i in range(fin):\n",
    "\n",
    "        final_predictions[window_size*i:window_size*(i+1)] = predictions[i]\n",
    "\n",
    "    final_predictions[window_size*(i+1): ] = predictions[-1]\n",
    "\n",
    "    return final_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eA6_7L8FKGs3"
   },
   "outputs": [],
   "source": [
    "\n",
    "final_predictions_1 = get_final_pred(predictions1,predictions1.shape[0],df_test.loc[1].shape[0])\n",
    "final_predictions_2 = get_final_pred(predictions2,predictions2.shape[0],df_test.loc[2].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fj3yzM57sjQB"
   },
   "outputs": [],
   "source": [
    "#Original categories were from 1 to 5\n",
    "final_predictions_1 = final_predictions_1+1\n",
    "final_predictions_2 = final_predictions_2+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1582,
     "status": "ok",
     "timestamp": 1589466728356,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "aHHmNjDFkZ7A",
    "outputId": "cc478b18-e1bd-4cb2-d7b4-7a05099a2438"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test1=df_test.loc[1]\n",
    "df_test1['label']=final_predictions_1\n",
    "df_test1\n",
    "plot_person(df_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted labels for the first test person showed a similar distribution to the ones from the training set. We observed that all activities were present and that each signal component presented\n",
    "peaks of activity as before. On the other hand, the second person presented a different labels\n",
    "distribution, where running was not between the predicted activities. Standing was the main activity\n",
    "for this person and walking, sitting and lying were in a second plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2904,
     "status": "ok",
     "timestamp": 1589462263059,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "YGXQiPjOZEd1",
    "outputId": "89c28eab-fdf4-491a-95ac-1739a17ca7ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Activity Person 1')"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(final_predictions_1)\n",
    "plt.title(\"Activity Person 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 879,
     "status": "ok",
     "timestamp": 1589464966826,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "EZ9PtCOGUOg7",
    "outputId": "55f3849a-5d9b-408c-cc57-6f79876691a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1104, 2576, 6288, 6693, 2912]\n"
     ]
    }
   ],
   "source": [
    "count_t1 = []\n",
    "\n",
    "for i in val:\n",
    "    count_t1.append(sum(final_predictions_1==i))\n",
    "\n",
    "print(count_t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 982,
     "status": "ok",
     "timestamp": 1589464975857,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "LHBAhyr7UoAM",
    "outputId": "d1b8755a-aa7e-4cc6-f997-ed913b0fca07"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_person_activities(count_t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1452,
     "status": "ok",
     "timestamp": 1589466777047,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "bZ9HJFo6mgCb",
    "outputId": "e101a6bb-306b-4f1b-be0d-8f533707fc3b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test2=df_test.loc[2]\n",
    "df_test2['label']=final_predictions_2\n",
    "df_test2\n",
    "plot_person(df_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 963,
     "status": "ok",
     "timestamp": 1589464991021,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "vZCTiOQmZqMj",
    "outputId": "5fe05f5f-b22e-49f6-d14d-54db5b39fdd6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Activity Person 2')"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(final_predictions_2)\n",
    "plt.title(\"Activity Person 2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1032,
     "status": "ok",
     "timestamp": 1589465031923,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "dc1qsGaxVLw7",
    "outputId": "7c7670fe-aa12-4bb5-8264-e0b0a378e454"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1680, 13895, 2704, 4336]\n"
     ]
    }
   ],
   "source": [
    "count_t2 = []\n",
    "\n",
    "for i in val:\n",
    "    count_t2.append(sum(final_predictions_2==i))\n",
    "\n",
    "print(count_t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 971,
     "status": "ok",
     "timestamp": 1589465035289,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "hu784PEtVQLL",
    "outputId": "440f4d0f-8c3a-4a8d-a647-2eaabd842016"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_person_activities(count_t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WSxRi4wgRkhb"
   },
   "outputs": [],
   "source": [
    "final_predictions_1 = final_predictions_1.reshape(1,final_predictions_1.shape[0])\n",
    "final_predictions_2 = final_predictions_2.reshape(1,final_predictions_2.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1049,
     "status": "ok",
     "timestamp": 1589465052858,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "nEurwrw1NaJ-",
    "outputId": "1e911c74-6483-4900-fc6f-880a9a335d26"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19573\n",
      "(1, 19573)\n"
     ]
    }
   ],
   "source": [
    "print(df_test.loc[1].shape[0])\n",
    "print(final_predictions_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1011,
     "status": "ok",
     "timestamp": 1589465055821,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "EYc71d4TQ6zd",
    "outputId": "f26f858c-4af8-4505-f74a-9bf03350fee7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22615\n",
      "(1, 22615)\n"
     ]
    }
   ],
   "source": [
    "print(df_test.loc[2].shape[0])\n",
    "print(final_predictions_2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GFow7GlIRHDf"
   },
   "source": [
    "Above, we see that we got a prediction for each test observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1307,
     "status": "ok",
     "timestamp": 1589465059356,
     "user": {
      "displayName": "BELEN PULIDO BRAVO",
      "photoUrl": "",
      "userId": "09868762370980769562"
     },
     "user_tz": -120
    },
    "id": "1azYx6yfNrfy",
    "outputId": "fe50366f-d5be-4632-e859-3c96ce920d76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n",
      "[2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(final_predictions_1))\n",
    "print(np.unique(final_predictions_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QLAqzT3bRKVm"
   },
   "outputs": [],
   "source": [
    "results = {'labels_test_1': final_predictions_1, 'labels_test_2': final_predictions_2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "45nICkbzSeCy"
   },
   "source": [
    "Let's generate the mat file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qErNTu5KSXPj"
   },
   "outputs": [],
   "source": [
    "savemat(\"results\",results)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Lab_Exercise2_100415296_100421721.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}