43.5-autoencoder_anomaly_conv1d.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "YfIk2es3hJEd"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras import layers, losses\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from tensorflow.keras.models import Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ErGrTnWHoUYl"
      },
      "source": [
        "## Third example: Anomaly detection\n",
        "\n",
        "## Overview\n",
        "\n",
        "\n",
        "In this example, you will train an autoencoder to detect anomalies on the [ECG5000 dataset](http://www.timeseriesclassification.com/description.php?Dataset=ECG5000). This dataset contains 5,000 [Electrocardiograms](https://en.wikipedia.org/wiki/Electrocardiography), each with 140 data points. You will use a simplified version of the dataset, where each example has been labeled either `0` (corresponding to an abnormal rhythm), or `1` (corresponding to a normal rhythm). You are interested in identifying the abnormal rhythms.\n",
        "\n",
        "Note: This is a labeled dataset, so you could phrase this as a supervised learning problem. The goal of this example is to illustrate anomaly detection concepts you can apply to larger datasets, where you do not have labels available (for example, if you had many thousands of normal rhythms, and only a small number of abnormal rhythms).\n",
        "\n",
        "How will you detect anomalies using an autoencoder? Recall that an autoencoder is trained to minimize reconstruction error. You will train an autoencoder on the normal rhythms only, then use it to reconstruct all the data. Our hypothesis is that the abnormal rhythms will have higher reconstruction error. You will then classify a rhythm as an anomaly if the reconstruction error surpasses a fixed threshold."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5estNaur_Mh"
      },
      "source": [
        "### Load ECG data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y35nsXLPsDNX"
      },
      "source": [
        "The dataset you will use is based on one from [timeseriesclassification.com](http://www.timeseriesclassification.com/description.php?Dataset=ECG5000).\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://storage.googleapis.com/download.tensorflow.org/data/ecg.csv"
      ],
      "metadata": {
        "id": "w5KPKIsmpzWG",
        "outputId": "766ffe72-4f88-444b-86b8-b9b70b5c2bb1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-10-13 10:43:26--  http://storage.googleapis.com/download.tensorflow.org/data/ecg.csv\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.79.207, 108.177.119.207, 108.177.126.207, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.79.207|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7877383 (7.5M) [text/csv]\n",
            "Saving to: ‘ecg.csv’\n",
            "\n",
            "ecg.csv             100%[===================>]   7.51M  11.2MB/s    in 0.7s    \n",
            "\n",
            "2023-10-13 10:43:27 (11.2 MB/s) - ‘ecg.csv’ saved [7877383/7877383]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!head ecg.csv"
      ],
      "metadata": {
        "id": "Gitjvp5rop62",
        "outputId": "787fbb34-ca38-494a-ba95-dc6615c87fcb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "KmKRDJWgsFYa",
        "outputId": "051bcc90-3545-49b5-c5b4-6dc092daaba2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "outputs": [
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          "data": {
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              "[5 rows x 141 columns]"
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2de2ebe-de1a-4f7f-bb24-8ed091c5ff99')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f2de2ebe-de1a-4f7f-bb24-8ed091c5ff99 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f2de2ebe-de1a-4f7f-bb24-8ed091c5ff99');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fe2747b5-8e0d-4883-ad32-a3ab2b8f86da\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fe2747b5-8e0d-4883-ad32-a3ab2b8f86da')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fe2747b5-8e0d-4883-ad32-a3ab2b8f86da button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Download the dataset\n",
        "dataframe = pd.read_csv('http://storage.googleapis.com/download.tensorflow.org/data/ecg.csv', header=None)\n",
        "raw_data = dataframe.values\n",
        "dataframe.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "UmuCPVYKsKKx"
      },
      "outputs": [],
      "source": [
        "# The last element contains the labels\n",
        "labels = raw_data[:, -1]\n",
        "\n",
        "# The other data points are the electrocadriogram data\n",
        "data = raw_data[:, 0:-1]\n",
        "\n",
        "train_data, test_data, train_labels, test_labels = train_test_split(\n",
        "    data, labels, test_size=0.2, random_state=21\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "byK2vP7hsMbz"
      },
      "source": [
        "Normalize the data to `[0,1]`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "tgMZVWRKsPx6"
      },
      "outputs": [],
      "source": [
        "min_val = tf.reduce_min(train_data)\n",
        "max_val = tf.reduce_max(train_data)\n",
        "\n",
        "train_data = (train_data - min_val) / (max_val - min_val)\n",
        "test_data = (test_data - min_val) / (max_val - min_val)\n",
        "\n",
        "train_data = tf.cast(train_data, tf.float32)\n",
        "test_data = tf.cast(test_data, tf.float32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdSYr2IPsTiz"
      },
      "source": [
        "You will train the autoencoder using only the normal rhythms, which are labeled in this dataset as `1`. Separate the normal rhythms from the abnormal rhythms."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "VvK4NRe8sVhE"
      },
      "outputs": [],
      "source": [
        "train_labels = train_labels.astype(bool)\n",
        "test_labels = test_labels.astype(bool)\n",
        "\n",
        "normal_train_data = train_data[train_labels]\n",
        "normal_test_data = test_data[test_labels]\n",
        "\n",
        "anomalous_train_data = train_data[~train_labels]\n",
        "anomalous_test_data = test_data[~test_labels]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVcTBDo-CqFS"
      },
      "source": [
        "Plot a normal ECG."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ZTlMIrpmseYe",
        "outputId": "83e64825-b0f5-4cf6-d527-2f81b6f6d0b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.grid()\n",
        "plt.plot(np.arange(140), normal_train_data[0])\n",
        "plt.title(\"A Normal ECG\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpI9by2ZA0NN"
      },
      "source": [
        "Plot an anomalous ECG."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "zrpXREF2siBr",
        "outputId": "27a27ea6-dc94-4fe8-e37b-f1893776be41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.grid()\n",
        "plt.plot(np.arange(140), anomalous_train_data[0])\n",
        "plt.title(\"An Anomalous ECG\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0DS6QKZJslZz"
      },
      "source": [
        "### Build the model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "59UwrlyNtwWa"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "bf6owZQDsp9y"
      },
      "outputs": [],
      "source": [
        "class AnomalyDetector(Model):\n",
        "  def __init__(self):\n",
        "    super(AnomalyDetector, self).__init__()\n",
        "    self.encoder = tf.keras.Sequential([\n",
        "      keras.Input(shape=(140,1)),\n",
        "      layers.Conv1D(10, 10, activation=\"relu\"),\n",
        "      layers.Conv1D(20, 10, activation=\"relu\"),\n",
        "      layers.Conv1D(30, 10, activation=\"relu\")])\n",
        "\n",
        "    self.decoder = tf.keras.Sequential([\n",
        "      layers.Conv1DTranspose(30, 10, activation=\"relu\"),\n",
        "      layers.Conv1DTranspose(20, 10, activation=\"relu\"),\n",
        "      layers.Conv1DTranspose(1, 10, activation=\"sigmoid\")])\n",
        "\n",
        "  def call(self, x):\n",
        "    encoded = self.encoder(x)\n",
        "    decoded = self.decoder(encoded)\n",
        "    return decoded\n",
        "\n",
        "autoencoder = AnomalyDetector()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "gwRpBBbg463S"
      },
      "outputs": [],
      "source": [
        "autoencoder.compile(optimizer='adam', loss='mae')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuTy60STBEy4"
      },
      "source": [
        "Notice that the autoencoder is trained using only the normal ECGs, but is evaluated using the full test set."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_train_data_r = normal_train_data[..., tf.newaxis]\n",
        "test_data_r = test_data[...,tf.newaxis]"
      ],
      "metadata": {
        "id": "I0IN94wfucFC"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "V6NFSs-jsty2",
        "outputId": "dac354d2-90cb-476d-c2f1-bd7d0b752fc7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "5/5 [==============================] - 11s 975ms/step - loss: 0.0547 - val_loss: 0.0531\n",
            "Epoch 2/20\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0522 - val_loss: 0.0499\n",
            "Epoch 3/20\n",
            "5/5 [==============================] - 0s 21ms/step - loss: 0.0513 - val_loss: 0.0497\n",
            "Epoch 4/20\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.0501 - val_loss: 0.0478\n",
            "Epoch 5/20\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0484 - val_loss: 0.0459\n",
            "Epoch 6/20\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0455 - val_loss: 0.0428\n",
            "Epoch 7/20\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0418 - val_loss: 0.0452\n",
            "Epoch 8/20\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0396 - val_loss: 0.0364\n",
            "Epoch 9/20\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.0364 - val_loss: 0.0370\n",
            "Epoch 10/20\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0341 - val_loss: 0.0336\n",
            "Epoch 11/20\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0309 - val_loss: 0.0312\n",
            "Epoch 12/20\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0289 - val_loss: 0.0373\n",
            "Epoch 13/20\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0297 - val_loss: 0.0343\n",
            "Epoch 14/20\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0279 - val_loss: 0.0305\n",
            "Epoch 15/20\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0258 - val_loss: 0.0273\n",
            "Epoch 16/20\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.0241 - val_loss: 0.0278\n",
            "Epoch 17/20\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0230 - val_loss: 0.0274\n",
            "Epoch 18/20\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0219 - val_loss: 0.0251\n",
            "Epoch 19/20\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0200 - val_loss: 0.0288\n",
            "Epoch 20/20\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0205 - val_loss: 0.0285\n"
          ]
        }
      ],
      "source": [
        "history = autoencoder.fit(normal_train_data_r, normal_train_data_r,\n",
        "          epochs=20,\n",
        "          batch_size=512,\n",
        "          validation_data=(test_data_r, test_data_r),\n",
        "          shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "OEexphFwwTQS",
        "outputId": "9aaf26c4-78d9-48dc-a73f-d19e92c0aa4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d296efe1b70>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n",
        "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ceI5lKv1BT-A"
      },
      "source": [
        "You will soon classify an ECG as anomalous if the reconstruction error is greater than one standard deviation from the normal training examples. First, let's plot a normal ECG from the training set, the reconstruction after it's encoded and decoded by the autoencoder, and the reconstruction error."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_test_data_r = normal_test_data[..., tf.newaxis]"
      ],
      "metadata": {
        "id": "TYgRcLRSvgdx"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "hmsk4DuktxJ2",
        "outputId": "212088fd-bce3-4ba2-8fd8-51d03aec99b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "encoded_data = autoencoder.encoder(normal_test_data_r).numpy()\n",
        "decoded_data = autoencoder.decoder(encoded_data).numpy()\n",
        "\n",
        "plt.plot(normal_test_data[0], 'b')\n",
        "plt.plot(decoded_data[0], 'r')\n",
        "plt.fill_between(np.arange(140), np.squeeze(decoded_data[0]), normal_test_data[0], color='lightcoral')\n",
        "plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocA_q9ufB_aF"
      },
      "source": [
        "Create a similar plot, this time for an anomalous test example."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "anomalous_test_data_r = anomalous_test_data[...,tf.newaxis]"
      ],
      "metadata": {
        "id": "UykXmkmrvxJY"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "vNFTuPhLwTBn",
        "outputId": "f83daf1d-3e23-4444-88fa-19fe78d1da01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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AsmXL6NWrF++//7511suaNWsICQkpd41eb85aWLYLpj7KBgiWD3qTyTzS/N577yUmJoY1a9bwyy+/sGDBAl577TX+9a9/1fhcZVlaNsquOF1cXFzuHHs9T21cHBQpimJ9ZiGEcKTNm83TkFu65fH6dWvYmxLEa38M4eOVHsw7aqBth9pPUlBVSEoyr9IcUknA4u1SiJe+kOwiF04cyKNnEw5Ymm7bjyMpirlbpjG2Oo5fAfOH+lNPPcUzzzxDt27d0Ov1JCYm0qFDh3JbaGgoYG4l+OOPPyp88AN4eXkRHBxcYQzJn3/+Sbdu3WyqV2hoKPfffz8rV67k0UcfZenSpQDWmT9GY80ZFFu1agVASkqKdd/FuVOqex7L/Wq6V9euXdmyZUu5wOjPP//E09OT1q1b11hPIYRwtJ9+Mn8d2eE4Y7sdZM7I3xgafpwSk5YFT9i2snJmJuTnmz93AisZw6IoEFraynLiUNNOmXF5BiyXsFtvvRWtVst7773HY489xiOPPMLy5cs5duwYu3fv5u2332b58uUAzJw5k+zsbCZMmMDOnTs5cuQIH3/8MfHx8QA8/vjjvPzyy6xYsYL4+HiefPJJ9uzZw0MPPVTr+jz88MP8/PPPnDhxgt27d7N+/Xq6du0KQNu2bVEUhdWrV3P27Flyq2nVcnV15corr+Tf//43Bw8eZOPGjTzzzDPlzqnpecLCwti7dy/x8fGkp6dXGtg88MADnDp1in/9618cOnSIVatWMW/ePGJjY62tPEII0ZgsAUt0hyPWfY8P/R2AD79vxemTlf/RVhlLd1AL13xcnSrPtWIdeHusaafnr9Nv6EWLFhEWFoaLiwtRUVFs37692vMzMzOZMWMGQUFB6PV6OnXqxI8//livMi9XOp2OmTNn8sorrzB79mzmzJnDggUL6Nq1K6NHj2bNmjWEh4cD0LJlS3777Tdyc3MZNmwYffr0YenSpdbujgcffJDY2FgeffRRevbsydq1a/n+++/LzRCqidFoZMaMGdb7d+rUiXfffReAkJAQ5s+fz5NPPklAQAAzZ86stqxly5ZRUlJCnz59ePjhh3nhhRfKHa/peaZNm0bnzp3p27cvrVq1qnQGUkhICD/++CPbt2+nV69e3H///dxzzz0VgiMhhGgMp0+bV1ZWUBnZ/qh1/+Cwk1wZmkhRiY5Xn0qvpoTyqhtwa3EhF0vd6txQFLVs23gtrFixgkmTJrFkyRKioqJYuHAhX331FfHx8fj7+1c432AwMGjQIPz9/XnqqacICQnh5MmT+Pj40KtXrzqVebHs7Gy8vb3JysrCy8ur3LHCwkJOnDhBeHg4Li4utjyqELUm/86EEPbw/vtw773QJySJuGnvlzv265H23PLpnbg5FXMiUYN/YM25U/73P5g2Da7ueIQvJ35W6TmLtlzJ0z/HcFO/E3yzPdwuz1Fb1X1+X8zmFpbXX3+dadOmMXXqVLp168aSJUtwc3Nj2bJllZ6/bNkyMjIy+O677xg0aBBhYWEMGzbMGqzUpUwhhBCiObJ2B3U8VuHYyA7H6B18mvxiJ157qnazFq0tLN7VtbBkAnAyrWn/sWVTwGIwGNi1axfR0dEXCtBoiI6OLpf9tKzvv/+eAQMGMGPGDAICAujRowcvvfSSdXBkXcosKioiOzu73CaEEEJcyoqLYd06c6fHqDLjVywUBR4ZvAmAj1d6YjTW3EFindLsWXPAYsnF0lTZFLCkp6djNBoJCAgotz8gIIDU1NRKrzl+/Dhff/01RqORH3/8kTlz5vDaa69ZxyfUpcwFCxbg7e1t3SyzYoQQQohL1ZYt5unMLVzz6R2cXOk5MZ2O4OlcREqWB9vW1jxjqDZjWMrnYmm6DQAOnxZhMpnw9/fnv//9L3369GH8+PE8/fTTLFmypM5lzp49m6ysLOt26tQpO9ZYCCGEaHjW6cwdj6HVVN56otcZGdXR3Pry7ad5NZZ5IS1/1YGIj6s5FwvAiQM1l9lYbApY/Pz80Gq1pKWllduflpZWZTr0oKAgOnXqhFZ7YXBQ165dSU1NxWAw1KlMvV6Pl5dXuU0IIYS4lFnHr7Sv2B1U1rVdDgHw/W+eNZZpThpXfQsLXOgWasq5WGwKWJydnenTpw9xcXHWfSaTibi4OOt6NhcbNGgQR48eLZcl9PDhwwQFBeHs7FynMoUQQojmJDsbLKuiXNW+4oDbsqI7HsVJY+RwWgv+2ZpZ5Xl5eZCZaU4aV+uA5WjTzcVic5dQbGwsS5cuZfny5Rw8eJDp06eTl5fH1KlTAZg0aRKzZ8+2nj99+nQyMjJ46KGHOHz4MGvWrOGll15ixowZtS5TCCGEaM5OnDB/bemWRyuP/GrP9XYpYmi4+YLvPsqq8jzLgFsP5yK8XAzVlmlNHney7tnYHc3mtYTGjx/P2bNnmTt3LqmpqURGRrJ27VrroNnExMRyGUNDQ0P5+eefeeSRR4iIiCAkJISHHnqIJ554otZlCiGEEM2ZZcF6S+BQk2u7HCLuWAdW/ezK01WcU5sBtxbWqc1JTXeJwTrVbObMmVVmLd2wYUOFfQMGDGDr1q11LlMIIYRozqwBi2/VLSZlXdMlntg117HjuD+nj+UT0r7iwroXVmmuuIbQxS6FXCxNN5RqAKasLNT86pve7Elxc0PjbfvS4EIIIZo3a8DinVmr8wM9c+nX+hQ7kkL59v10Zr7UpsI5tUkaZ2HNxZLhhWoyoTTBtdUu24DFlJVFzjvvQEnli0E5hE6H58yZtQ5apkyZYl3IsKyYmBjWrl1r79oJIYRoJLZ2CQFc2yWeHUmhrFqjY+ZLFY9bAxaP2gcs6Xnu5KZk4hniU+t6NJSmF0I1EDU/v2GDFYCSEptbdEaPHk1KSkq57fPPP6/03MpWJzYYqh9oVZW6XieEEMJ2dQtYzNObN/4TQO75ir+zbRnD4uNa1ORzsVy2AculQq/XExgYWG7z9fUFQFEUFi9ezA033IC7uzsvvvgizz77LJGRkfzvf/8rtxBfYmIiY8eOxcPDAy8vL2677bZyuW+quk4IIYTjJSSY86XYErB09DtHqHcmxUYtf6w6W+H4hTEstcte29RzsUjAcol79tlnufHGG9m3bx933303AEePHuWbb75h5cqV7NmzB5PJxNixY8nIyGDjxo2sW7eO48ePM378+HJlXXydEEIIx8vMvJAvJbSWY1gsBoUlALD+l6IKxyxJ44JqMegWmn4ulst2DMulYvXq1Xh4eJTb99RTT/HUU08BcMcdd1TIV2MwGPjoo49o1aoVAOvWrWPfvn2cOHHCuu7SRx99RPfu3dmxYwf9+vWr9DohhBCOd/Kk+WtLtzw89BW79qszOOwkX/wdye/bys8SMhggLc0cBIXUsoWlQ0vz2kQH4ptmaNA0ayWsRowYweLFi8vta9GihfX7vn37Vrimbdu25YKOgwcPEhoaWm6RyG7duuHj48PBgwetAcvF1wkhhHC8uoxfsRhc2sKyK6EVuRlFeLTQA5CSYj7urC2hpVvtxk5aFlzcedDd5no0BAlYmjh3d3c6dOhQ7fHa7KvtvYQQQjQsW3OwlNXWJ5PW3pkkZfmw6fsURk9pDZQfcKvUMnmtJWDZn+RHYXYRLl56m+vjSDKG5TLQtWtXTp06VW5V6wMHDpCZmUm3bt0asWZCCCFszcFSlqJcaGVZ/8uFmUKWgKW241cA2vpm4uuaT7FJy54N52yui6NJwNLEFRUVkZqaWm5LT0+3qYzo6Gh69uzJxIkT2b17N9u3b2fSpEkMGzas0i4lIYQQDac+XUJgHscC8Pv2C+NYLAFLiA0Bi6KU6Rb6o6BOdXEkCViauLVr1xIUFFRuGzx4sE1lKIrCqlWr8PX1ZejQoURHR9OuXTtWrFjhoFoLIYSorXoHLG3NBVjGsQDs2GE+FuRZuwG3FleUBiw7djW98EBRVVVt7ErUV3Z2Nt7e3mRlZeHl5VXuWGFhISdOnKiQW+RSyHQrLh1V/TsTQoia+PqqZGYqbHngXbr6V8ynUhNVhZ5vPExStjc/fZCEGtCaa64BRVH5+e5l9A9NqnVZqw925s4VE+gedJb9yY6fhFHd5/fFLttBtxpvbzxnzpS1hIQQQjSa+uRgsVAUcz6WFXt78e1KldW7VEDh/qhtNgUrAFeEmFtYDqa2JPdcAR4tXetUJ0e4bAMWMActSAAhhBCikdQnB0tZg0sDlv/+YE5f0aFlOnOuirO5nCDPHAI8ckjL9WRXXAbDbgupc53srel1UgkhhBCXifqOX7EYUjpTCECjmHh33CrcnG0f8lB24O2OTRdS9L/55oVxMY1FAhYhhBCikdQnB0tZbX3N+VgAZg7canNXUFmWbqGdu7XmrzshNlblyitV4uMbb9jrZd0lJIQQQjSm+uRgKUtRYNHY79l8sg2PDN5Ur7IsLSy7D3tSUgL/939gMinc2nMvHQPbAo0zlOKyCViawWQo0YTJvy8hRF3Yq0sIYFi7Ewxrd6Le5VgCliNnW/LS/CJ279bj7VLAizG/gHpvvcuvq2bfJeTk5ARAfgPOBhKXH8u/L8u/NyGEqA17Biz24ueeb52xNO8Fc3r++dG/4u+R14i1ugxaWLRaLT4+Ppw5cwYANzc3lNourCBEDVRVJT8/nzNnzuDj44NWq23sKgkhLiEJCeYpyE0pYAG4IuQ0p7J8AIgKTWTSFbsbt0JcBgELQGBgIIA1aBHC3nx8fKz/zoQQojbskYPFUXoHp7DqQHd0GiOvX7cGTRPoj7ksAhZFUQgKCsLf35/i4rrPcxeiMk5OTtKyIoSwmb1ysDjCTT3288XfEUzpu5vuAU3jj/3LImCx0Gq18sEihBCiSTh82Py1bRPrDgJo45PF1hmLG7sa5TSBRh4hhBDi8mNJxNYrOLVxK3KJkIBFCCGEaASWgKV38Gm7lKc9dgx9XBwUFtZ88iXosuoSEkIIIZoCkwl2lS5SeIU9AhaTCbdvv0WTm4tTfDx5d9yB6uNT/3KbEGlhEUIIIRpYfDzk5Ci46orp0upsvcvTJiWhyc01f3/mDB5Ll6JNqnt6/qZIAhYhhBCigVnHrwSloNPWP1O204EDABR37IipfXs0eXm4f/hhswpaJGARQgghGtjOneavvVsn178wVcXp4EEATDffjOavvzBFRaGUlOC8a1f9y28iJGARQgghGpilheWKoPqPX9GkpKDJykJ1ckIzYQJ4eqKZOxcArSXZSzNQp4Bl0aJFhIWF4eLiQlRUFNu3b6/y3A8//BBFUcptLi4u5c6ZMmVKhXNGjx5dl6oJIYQQTVpxMezZY+4GuiKk/i0sltaVkk6d0HXvbt45aBCqoqDNyEDJzq73PZoCmwOWFStWEBsby7x589i9eze9evUiJiam2rT3Xl5epKSkWLeTlUR8o0ePLnfO559/bmvVhBBCiCZv/34oLFTw0hcS7ptRv8JU1Tp+xThqFIqudPKvtzdERACgS0ys3z2aCJsDltdff51p06YxdepUunXrxpIlS3Bzc2PZsmVVXqMoCoGBgdYtICCgwjl6vb7cOb6+vrZWTQghhGjyrPlXQpLrvUaP5uxZtOfOoWq1aG+/vdwxZfhwoPl0C9n0qgwGA7t27SI6OvpCARoN0dHRbNmypcrrcnNzadu2LaGhoYwdO5Z//vmnwjkbNmzA39+fzp07M336dM6dO1dleUVFRWRnZ5fbhBBCiEuBdfxKsB27g9q3RxcZWf7gkCHAZdrCkp6ejtForNBCEhAQQGpq5amFO3fuzLJly1i1ahWffPIJJpOJgQMHklRmqtXo0aP56KOPiIuL4+WXX2bjxo2MGTMGo9FYaZkLFizA29vbuoWGhtryGEIIIUSjsWeGW+vsoOHDUZydyx8sDVi0aWko+fn1vldjc3im2wEDBjBgwADrzwMHDqRr16689957PP/88wBMmDDBerxnz55ERETQvn17NmzYwMiRIyuUOXv2bGJjY60/Z2dnS9AihBCiySsogP37SzPc1nPArZKZiTY1FVVRUO64o+IJ/v6onTqhHD6MNjGRki5d6nW/xmZTC4ufnx9arZa0tLRy+9PS0ggMDKxVGU5OTvTu3ZujR49WeU67du3w8/Or8hy9Xo+Xl1e5TQghhGjq9uwBo1HB3z2XEK/6DWdwOnIEAGNoKE79+1d6jmUcS3PoFrIpYHF2dqZPnz7ExcVZ95lMJuLi4sq1olTHaDSyb98+goKCqjwnKSmJc+fOVXuOEEIIcan580/z194hyShK/crSHT4MgLFfPxRX18pPsnQLNYOBtzaPT46NjWXp0qUsX76cgwcPMn36dPLy8pg6dSoAkyZNYvbs2dbzn3vuOX755ReOHz/O7t27ufPOOzl58iT33nsvYB6Q+/jjj7N161YSEhKIi4tj7NixdOjQgZiYGDs9phBCCNG4kpLgxRfN+VdGtD9ev8KKi9GdOAGAcv31VZ83dCgA2uRkKCqq3z0bmc1jWMaPH8/Zs2eZO3cuqampREZGsnbtWutA3MTERDRl5mmdP3+eadOmkZqaiq+vL3369GHz5s1069YNAK1Wy969e1m+fDmZmZkEBwdz9dVX8/zzz6PX6+30mEIIIUTjMZlg8mTIzDSvznxP3x31Kk+XkIBSUoLJywttdYlW27RBDQ1FOXUKXVISJe3b1+u+jUlRVbX+qy41suzsbLy9vcnKypLxLEIIIZqcN96A2FhwczLw+/+9Rwe/+iWMc1mzBv2OHRiuvBKnzZtRqulfUu+6C+WTTygcOpSiq66q1309H3oIjY9Pvcooy5bPb1lLSAghhHCgffvgySfNbQMvjf6l3sEKqmodcKtGR1cbrAAopd1CTgcOQElJ/e7diCRgEUIIIRyksBAmTgSDQSGm02EmX1H/1ZM16eloMjNRtVo0N99c8wW33orq64s2PR3nrVvrff/GIgGLEEII4SDPPGNuYWnlnss7N6yq98wguDA7qCQsDF3peNBq+fjAa68B4LJxI0pWVv0r0QgkYBFCCCEc4LffrHECb4/9gVYe9sk2a+kOMg0YUDG7bRWUyZNRo6JQiotxXbvWLvVoaBKwCCGEEHZ2/jxMnmwetzK1705Gdzpsn4ILC9FaksCNG1f76zQalKVLUTUanA4eRFca9FxKJGARQggh7CgvD+6+G5KSFNq3OMcLV/9it7J1CQkoJhPGli1xsnXGT8+eMHMmAC5r18IlNklYAhYhhBCiGmlp5qRvtREXZ44LvvsOtBoT/71pJe7OxXari7a0IsYOHdD4+tp8vfL886h6Pdpz59Ckp9utXg1BAhYhhBDiIlu3wmOPQUQEBAZCaCi0CzNy990qq1ZVbJwoLoZp0yA6Gk6cgNZeWXx5x2f0aV2/BQ4vpk02l6f26lW3Ary8YOBAAGum3EuFBCxCCCFEGatXw4AB5gGz+/aBgopWMXHipJYPPlAYNw7e/U9uuWtefx3+9z9QFJVp/bezZca7jOxwzL4VU1V0pQGLUsv1+yqjjBgBgDYhwR61ajA2p+YXQgghmqvMTPi//1MBhegOR7kjcg/Dwo/jrDOyNbENK/d35/O/I3lqrhM33mEiOERDUhI8/7z5mjev/4FJV/zlkLppMjJQCgvN+VcGD657QaUBi+7kSXNTkT3mWjcACViEEEJcNr7/HpKT4d57QVfJJ+CsWZCcrNCuxTk+um0Fbs4XMsOO6niUq9ofI/5sK3Ynh/DQ5HN89WtLHn0U8vIUrgxN5K7ejglWALSnTwNgDApC26ZN3Qvq3x/V1RVNXh6aM2cwla4F2NRJl5AQQojLwvbtcOONKtOnQ8zwQi4ec/rbb7B0qfn7t2/4vlywYqHVqLxx/Wo0iomv41ryxCNFfPklaBQTr177o0MbKyzjV0zt2tU6/0qlnJ1h0CDAPOvoUiEBixBCiGavuNjcqmIymSOK3/504YqeRWzerHLkCOxesp3EGx+iB/u4p98OBoUlli+gzCjbXkGp3B+1DYBXFuoBuKffTnoGpjn0GawDbnv3rndZlnEsErAIIYQQTch//mMeQNvCNZ8fJn9IuxbnOJWqZ/ygJLZ1upMrpkcxJfst1itX8fwVK6zXKVlZuC9disd770FBgXX/UyPWE+JlTnHv55bH0yPWO/YBjEa0KSnmOpXO8qmXsgNvTab6l9cAJGARQgjRLGRlwV9/wTffwNtvw6ZN5sWJjxyB+fNLV0se8zNDwk/y27SlvBswh3g6cyefAnBO2wo/NR3/L5eh5OaiSU/HY9kydKdPo01NxeW336z38tAX8+64VXRomc7CG1bj41ro0GfTpKejFBejOjujsUfA0rcvqpsbmoICNGfO1L+8BiCDboUQQlySkpLMCdq2bIEtW1ROnKg4gKSFjxEvHw1FRQoj2h9jfM+9oKoEbPmJ6Wm/A1DSpg3FDz6I7003oQ4Zgvb0adw//hglJwdNfj4mLy802dk479xJca9eGFu3BmBYuxPs/NeiBnlW64Db4GC0gYH1L9DJCYYMgZ9/RnfiBAZ7lOlg0sIihBDikrN3L/TsqfKvf8Fnn2ENVlq559Kv9SliOh3Gx7WAjEwtCQkKrk7FvHHtahTVhMuPP+LyuzlYKRwzBmX7dlwffRRNeDjK+vWo3t5o09LQ5OdjDAqieMUK1AkTUFQV19WrwWhs8Oe1Drjt2BGlsulNdXCpjWORFhYhhBCXlKNH4eqrVTIzFbq2OsPY7geICj1F7+DT+LgWWc8rMSrsSGrNxhPtuDI0kTDXFFy//gHnAwdQFYWiW2/FeelSNF5eFwrv2BF++QU1JoaSVq1QP/wQ/cCBcMUVqD/+iDY1Feft2zHUI3FbXVhaWNQ+fexXaNl8LCYTaJp2G4YELEIIIS4ZSUkQHa2SlqbQIyCV1VM+LBeklKXTqgxoe4oBbU+hO3oU13dXocnJQdVoKLr7bpzfeguNq2uF65T+/VHT09Hm56Px9DTv9PeHV1+F//s/XNavp7hbN1Rvb0c+6gUlJWjTzDOQlPokjLvYFVegenig5OaiTUnBGBJiv7IdoGmHU0IIIUSp5GQYNQpOnjSvgrzyrk+qDFasiopw+eEH3D/5BE1ODsYWLSh68UX0ixZVGqxYKFrthWDFsu/ee1H79UMxGNBv3myPR6oVbWoqismEyc0Nbd++9itYpzMvfgToDh+2X7kOIgGLEEKIJi8+HgYOVDl0CEK8svh20sf4e+RVe432xAk8Fy9Gv2sXAEWDB2Navx6XJ5+sW+I1jQblxRcBcN69GyU/3/Yy6sAyfsUYEoKmVSu7lq2MHQuAU3y8Xct1BOkSEkII0XTl5rJ/xT/8+5FUrso5RyfXRCZ334nP/iJQFFS9HlOLFphatkR1d0eTnY3m/Hl08fHot28HwOTjg+HRR9E/9hiKi0v96hMdjdqzJ8q+fThv307R8OH1f8YaaFNTAVA7dUKx9ziT665D1WjMrTiZmag+PvYt344kYBFCCNHoiopg924wJSXjvWkNHjvW4xa/G7+Mw/RA5RPLiQXAltqXa+jfH+Xdd3Gx12BVRUF56im4/XZzwDJwoDnVvQNpzp41f9O9u/0L9/MzL0395584xcdjiIqy/z3sRAIWIYQQjSrr7wSWjfyEoee+pQ+7KxxPJogsfSvCWxeibeENer15VovJhJKTg5Kaiub8eRSTCVWnw+Tjg6lVK4zjxuH81FNoPDzsW+FbbkGdNQvNqVM4//WXYz/kVfVCwBIR4ZBbKOPGwZ9/opOARQghhLhIURF89hnqBx/g/ccfPFK624TCfqfebHcbSo5/OK7hfnQd1YaBd3TAKbjq5GamrCyMCQko/v5oWrRAq9fj5Ki663QoTz4JM2ag37IFQ9++oNU65FZKbi6awkJUQHPFFQ65B2PHwuOPm/OxFBRANYORG5MELEIIIRpOfj7873+or7yCcvo0CuYgZQMjCBnamvBnp9Cjf38i3N1tKlbj7Y2mVy/H1LkyU6eizpmDJiMDp3/+odhBrR+a0iWlTb6+aEJDHXIPOnZE7dgR5cgRnI4epbhnT8fcp55klpAQQgjHy86Gl19GDQuDhx5COX2aIjcfnlZepA2J7Br7FJ3WLcV5xAg0NgYrjcLVFR58EADnnTsddhttaXeQKSAAxYHvRbnpJgB0TXi2kAQsQgghHCczE5591hyoPPkkytmzlHj7sL7XdLpp43lJfYreXXN55L2edZtq3IiUe+9F1WjQJSZaW0LszTJ+RW3bFkWpuFaS3VimNx85Yl4xsgmSgEUIIYT9FRXBG2+gtm8P8+ejnD9PcYtWLG8/h8DCk1z197scz/EnzDeD/y02oAvwb+wa2y4kBGJiAHD+6y+H3EJbGgipnTs7pHyr/v1R/fxQioqa7NpCErAIIYSwH5MJPv8cunaF2FiUjAyMrVqxa9gDtDPEM+XYc5wr8qKtz3nm37iVLatS8B/aNMdM1IYybRoATn//7ZBFES0tLIqjx5VotXDjjQDo169vlAUea1KngGXRokWEhYXh4uJCVFQU20uT81Tmww8/RFGUcpvLRYl7VFVl7ty5BAUF4erqSnR0NEeOHKlL1YQQQjQGVYUffoDeveGOO+DECUweHuRcN5bZnVfQb+M7JOX60qFlOiuf2sThfQXMXXklgUO6O7arw9Guuw61ZUs0ubno7P25VVCAJjcXAMWeix5WQZk7F9XDA93p0w269EBt2RywrFixgtjYWObNm8fu3bvp1asXMTExnDlzpsprvLy8SElJsW4nT54sd/yVV17hrbfeYsmSJWzbtg13d3diYmIoLCy0/YmEEEI0rPXrYeBAuOEG2LsXVa+n8Kqr+On617hy+3u8umkEKgp39d3D9p/OcuOLg3FuHdzYtbYPJyeYMgUwp+u3J0t3kMnTE214uF3LrlTr1vDmmwDoN2xAU83nemOwOWB5/fXXmTZtGlOnTqVbt24sWbIENzc3li1bVuU1iqIQGBho3QICAqzHVFVl4cKFPPPMM4wdO5aIiAg++ugjkpOT+e677+r0UEIIIRrA9u3m1Qivugq2bkV1cqJwyBAOTp3D+JS3ue7z+zhwJgBf13yW37+eD38Nx7df18autd0p994LgO7oUZScHLuVa+kOMvr7ozTQytDK1Kmoo0ahGI24fvttk+oasilgMRgM7Nq1i+jS1R0BNBoN0dHRbNlSda7k3Nxc2rZtS2hoKGPHjuWff/6xHjtx4gSpqanlyvT29iYqKqrKMouKisjOzi63CSGEaACqCr/9Zh5sGhUFv/6KqtVSFBVFzoMPsrvH7Qz96GG+P9gNrcbEfQN3ceDHo9z17nA0DfSh2+C6dEGNikIxmXDes8duxVqmNKtt2jRct5mioHz4IaqnJ7qUFFzi4sz/zZsAmwKW9PR0jEZjuRYSgICAAFJLF2e6WOfOnVm2bBmrVq3ik08+wWQyMXDgQJKSkgCs19lS5oIFC/D29rZuoY5KpiOEEMLMYIAvvjAHKSNHwi+/oGo0GCIjyZk5k8IxY9iX157rP5zMuXx3IgJT2PruFpbEdSdweMSlPU6lFpT77gPAeccO8wwpO7BOle7UyS7l1VpwMCxaBIB+82Zcv/++SbS0OHyW0IABA5g0aRKRkZEMGzaMlStX0qpVK9577706lzl79myysrKs26lTp+xYYyGEEFbJyTB/vjmPyu23w44dGLVOZEQO5twDD5N13U3kuvvz1+kgblg+mYwCN3oHn2bdl+n0/b9B9V8d+VJx++2orVujyc7GZcMGuxRpbWFxxKKHNVDuugvTa6+hKgrOf/2F2+ef2y0QqyubUvP7+fmh1WpJS0srtz8tLY3AwKrXeCjLycmJ3r17c/ToUQDrdWlpaQQFBZUrMzIystIy9Ho9er3elqoLIYSorcJCWLUKli9H/flnFJMJBTiv82NhyUwWG6dzdo8/7Kl46RXBp/npiwz8h1y6U5XrxNUV5b334Nprcd62DUOvXphq+blYKYMBJTMTAE0DzBCqjCY2FrVNG9SJE3E6ehSPDz+EyZPBx6dx6mPLyc7OzvTp04e4uDjrPpPJRFxcHAMGDKhVGUajkX379lmDk/DwcAIDA8uVmZ2dzbZt22pdphBCiHo6fRqWLoUbb0Rt1QomTICffkIxmSgJDWXXkPsJKDnN88pcFHc3tIqpQhHDwo9fnsGKxTXXoF5/PYrJhOuaNeacNHWkOXfOvM6SqysaRyeNq4Zyyy2wfj2qlxcUF4ObW6PVxebFD2NjY5k8eTJ9+/alf//+LFy4kLy8PKZOnQrApEmTCAkJYcGCBQA899xzXHnllXTo0IHMzExeffVVTp48yb2lo6oVReHhhx/mhRdeoGPHjoSHhzNnzhyCg4MZN26c/Z5UCCGEmdEI8fGwbRv88Qf8/jscO2Y9rAAmLy8MkZEUR0RQ4BPAhMX3U4wz9w3YxeJfumAqySMrrQDVYMDJRYuzs4Lexw+NV7vGe64mQFm0CPXXX9GdOoXTnj0U13GFZesaQv7+aH197VlFmykDB6Lu2oWSl+e4BRhrweaAZfz48Zw9e5a5c+eSmppKZGQka9eutQ6aTUxMRKO50HBz/vx5pk2bRmpqKr6+vvTp04fNmzfTrVs36zmzZs0iLy+P++67j8zMTAYPHszatWsrJJgTQghRDZMJcnLg/Pny25kzkJJiHo9y+DDq7t0oeXnlLlUBY0gIJZ06Udyxo7k7o/R3+du/D+ToOT/83XN54WVnNO7uaICW3p4N/4xNXWgoPPccPP44LuvWURIejlqHgMMypdnUujU6TeMnpVc6dKCxh00rqtpE5ivVQ3Z2Nt7e3mRlZeHl5dXY1RFCiPozmczdNMeOwdmzFYOQ8+fNCwuW+VnNzESpZTeE6uSEMSiIkjZtMLZpQ0loqHkF4oskZPhw5bsPUFjixPv3bWTqkqHNfsZPvZWUoPbti/L33xj9/cm95x6wcdyl2xdf4HToEEWTJ6P/8EPH1LMJsOXz2+YWFiGEEHZSVASHDsHevXD4sDlASU6GU6dQjx9HsTHbtyWMULVaVFdXVBcX81d3d0yenqgeHpi8vTEGB2Py87O2oFRatRIt3/3TjTc2DaawxIlh7Y4z6fluEqzUhk6Hsno1aq9eaM+cwe3bb8m/7bZq33c5JhPa0tmvDl9D6BIiAYsQQjSUpCTYtAn+/BM2bULdvx+lpKTSUxVA1Wgw+figeniYAw9LEGLZygYlZfbh5FT3KmZ58eHOPny4qw/p+e4AeLsU8OYzqej8L+/xKTZp3Rp++AF12DCcDh1Cv3EjRSNG1OpSbUICmrw884DbG25wcEUvHRKwCCGEoyQmwpo15uBk0yaUxMRyhxVAdXHBGBCA0d8f1dPT3BLi6YmxRQtUb2/zKroNYPPJNizZGsWaQ10wquaWgGDPbO4ZdoD7HnQlZOSVDVKP5kQZOBB1yRK4915cNm6kpFMnjCEhNV7nvH8/ACURETi1b+/oal4yJGARQgh7On4cvvkGvv7avNZOKQVQFQVjYCDGtm0pCQ3FGBJiDkoauZvlf9v78tiP11p/HhyWwP3jkrh5Rmv07aOkG6gelHvuQf3uO5TVq9EdOVJzwFJSgu7AAQDUG29EaQIDbpsKCViEEKK+Dh+Gr79G/fprlL/+su5WwTygtX17a4Bi6+DL+kjLcSfuWAeCPHMY3u54pXHRxuNhPPHTGADG997P4zPy6H1rVzReYQ1Wz+ZOufZaWL0aXWIiNeWK1R07hqawEJOHB9oJExqkfpcKCViEEMJWqgoHDpiDlG++Qdm3DyjTihIeTnHXrhR36YLq2bBTf/MNOpbt7MsPB7uy/VQoaulQ3KvaH+WVMT/RwS/Deu6JDF+mfHUrRlXDbb3288naAHT+rRq0vpeFoUMBzANpS0pAV/VHr1Npd1Bx7944t2nTINW7VEjAIoQQtaGq5tk8X39t3g4dAi4Mji1p147ibt0o6dwZ1d29Uaq4Nr4jj/94DaeyfKz7IgJTiE9vxW/HOjBw8XQmX7Gbdi0zaOGaz8JNgzlf4MYVwadZ+rGLBCuO0rUrqq8vyvnzaFNSMFaVfM1gwCk+3vz9rbdKV9xFJGARQojqZGfDp5/Cf/8Le/ZYd6taLSUdOphbUjp3rjSHiT2pKry9eSDxZ/0I8swhwDMXL30hhSVO5Bmc2JQQxo/xXQBo7Z3JI9cdZNxED8KGhHPslMq/pp7j520tWbqjf7lyAz1y+PrdZLx69nNo/S9rimJuZVm1Cu3Jk1UGLLojR1AMBkw+PuhuvbWBK9n0ScAihBCV+esvWLwY9dNPUfLzgdIgpVMnc5DSqRPYKRv36oOd2XiiHU8M24ife36l53yzvwdz142qthydxsiMobt49j9eeF9xpfUv9I5d4actLfn+ixx+/OgM6elwLtsJg0Hh1YcTCLthsF2eQ1RNGTYMVq1Cl5iIoYpzLLODivv1w7k0e7y4QAIWIYSwKCw0d/csWgRbtwLmLh+jnx+Gvn0pjohAtfPib+uOdGDSl7dhUjWsP9aOb+78hLa+WeXOSc9z44mfRgMwLiIe/0ANqWd1ZOdqcHMuxs3FhK9nMdNuz6bv5CtQKhnYqygw9nZPxt5+8Ziaxlsb5rJSOo5Fl5hozmJ80ewfJS8P3eHD5h/Gj5fuoEpIwCKEEAkJsGQJ6vvvo6SnA+ZxKcXdumHo2xdj27YOmXq8LzWAqV/dgknV4Kwt4eg5P2Lev4ev7/yEHoFnrOc9+dNozuW70z0glU+/dMatc7jd6yIcrFcvVA8PlNxcNGfOmNdqKsN5504UoxFjUBA6Wfi3UhKwCCEuXwcOwEsvoX7+OYrJdGGV4r59MfTu7dAZPinZHkz47HZyDXqGhJ/g/eVO3DjFl3+Oe3LNB1P518DN3NTjHw6n+/H1/p5oFBNLZh3GrfNQh9VJOJBOB4MGwc8/ozt5EkPZgKW4GOdt28zf3nwzLi1bNlIlmzYJWIQQl59//oF581BXrkRRzRN/i9u3x9CvHyUdOzo8u2yewYkJn9/O6WxvOvmd5avleQQM6cEfO+H6kbn8+ZcHL66/ihfXX4WTxgjAv4btZNAD/WsoWTRlyrBhFwKWqCjrfqe9e9Hk52Py9kb3yCONWMOmTQIWIcTlIzHRHKh89JG1RaW4a1cKhw7FFBTUIFUwmhSmfXMTf6cE09Itj28XHiVgyAAAfH0hbosHH7+VzopPilm/P4Bik5b2Lc7x3Ju+KHYa5CsayZAhAGhPnjRP+1IUMJnQb94MQPHVV+McLt19VZGARQjR/J0/b+76efttlKKiC4HK8OGYGng2xpxfRvFjfBf0uhK+fGo7Xe8YXu64Xg/3Pu7HvY9DenIRv36eSlS3LLwiIhq0nsIB+vVDdXZGk5eH5tw5TH5+6A4fRnvuHKpej+bxx2WwbTUkYBFCNF+FhfDOO6gvvYRy/jwKUNK2LYWjRmFs3doht/xw5xWs/Kc7L49ZS1f/s+WOvb+jL+9uNbemvHf374x4Ymi1H1B+wXomPNrWIfUUjUCvhyuvhN9/x+Xnnynp1Amn0tw+hkGDcO7Tp3Hr18RJwFKdjAy44QZITTWvFSKLUAlxaTCZ4NNPUZ95BiUx0Tw12d+fwuho8xgVB/0Ve/ycL4//eA3FJi03LJ/ED5OX08U/HVWFJduieObnqwF45po/uev1KJRqUrSL5kkZMwZ+/x2nI0dwOnIEMM9I45FHZKHDGsj/LdXx8kLdsgXFZMJ05Aiazp0bu0ZCiJqsWwezZsGePeZZP56eFI4cSXFEhMP/6JgfN5JikxZFUTmb58ENyyfzxR2f887mAaz8pwcAU/r9xbwPO6NppPT9opE9+ijGli0xrVuHcuAAmqQkDP37o7/66sauWZOnqKqqNnYl6is7Oxtvb2+ysrLw8vKya9lq69Yop09TsmIFuttus2vZl7WMDPjuO/jtNwgJgb59zVvbttKSJeomORkeesic+A1Q9XqKhgyhKCoKnJwcfvttia2JWXYPGsXE2ld38dh/u7P38IUkczqNkZdu3MQji7uja+Xn8PqIS4NqMoHJdNm2ttny+X15viFbhITA6dOox441dk2ah+3b4dlnUdetQykpqXBY1enA3x8CA1ECAyEwEAICzF9btTIfa9XKvPn5NcgHkWjijEZzCv2nnkLJyUHVaDD060fR0KEOW4SwoFjHz4c7EeiZQ1ToKQCe+cX8F/KdffcSPaMncZNduGpgPvuOuBHkmc3Hs3Zy1azBKM7ODqmTuDQpGo38kVZLErDUQA0JMS/OnpDQyDVpBtauRb3pJpSCAvOYgoAAijt3RsnLQ5uSgjY11RzEJCebt1pQvb3NgUtAAPj7o/j7XwhqygY3lv3yi6F52b0b/u//YOdO84DakBAKrr++QhZReykq0fLR7it47fchpOaak8pFBiUzoG0iO5JCcXMyMH+uEcXFBT8X2LjNja/fSWLMgAxCRo6QGSBC1IMELDWxzCQ4dapx63Gp++or1IkTUYqLKe7QgcLRozH5XdQsbjSa01bn5qLk5V34vvRnTV4eSn6++eeCAnPCr6wsyMqCWrSAqc7O0KYNSng4hIVd2Dp1gogIkL98Lx05OTB3Lupbb6GYTKh6PYXR0Rj69HFYUPpTfCdm/TiGU1k+AAR5ZnO+wJU9KcHsSQkG4MGrdtN2zIVVj319Ydqc1oBjZiQJcTmRgKUmISHmrykpjVuPS9kHH6Deey+KyYShRw8Kxo0zp6m+mFaL6u2N0du75jJNJnPQYgliygY0eXnlf7bsMxjg6FHzdhHV2RmuuALlyishKso89dBB68eIevr1V/O/p5MnUQBDjx4UxsQ4LI1+dqEzs9eO5tM9vQEI9Mhh1vV/c9+cYPJ82rD4hWSWfOKNrz6PWS/4oDg4S64QlysZdFsD4yefoL3rLowhIWiTkuxa9mVhzRrUG25AMZko6tOHwmuvbZxuGaMRJTsbTWamecvKuvB9WhqagoIKl6h+fnDlleYg5sorYcAAsPNKvcIGWVnw+OOwdCkAJh8fCq67jpIOHex+q4x8V/anBbAvNZAlW6M4leWDoqjMHLKDF970xrNXp3LdO6qqYsrMQuvrY/e6CNGcyaBbeyrtElLOn0c1GuWvJ1v8/TfqhAnmlpUrrqDwuusar8VCq0X19cXo64vx4mOqiiYjA21Sknk7fdo8niY9HVavNm+A6uICo0ah3HgjXHuteUyMaBhr18K0aVD6R0NR//4UjhxpTsRlRwXFOu78Yjxxx8oHQW19zvPf2L+JfiwKjatrhesURZFgRQgHk4ClJqVdQpr8fEwpKSgOyo7Z7KSkoF53HUpuLiXh4RRce23T7V5RFEwtW2Jq2ZLiXr3M+4qL0aamWoMYXVISmqws+OEH8waoEREoMTEQEwPDhlXezSXqJzMTYmPhgw8AMPr6UjB2LMawMIfc7vEfr7EGK2G+GXQPSqdv1xz+NcuNlv2HO+SeQojakd+wNfHyQtXrUYqKMMXHo5GApWZFRTB2LEpSEsaWLcm77TaHr35rd05OGENDMYaGmn9WVTRpaTgdOoRTfDzalBSUvXth71549VXUVq3gzjtRJk2CyMhGrXqzsWYN6n33oSQnoyoKhqgoCq+6ymGDoz/a1ZtP/uqNRjHx3az1XPt0fxSPjjKzR4gmQgKWWjB5e6M9cwb18GEYObKxq9P0vfIK7NiBydWV/IkToZIm9EuOomAKDKQoMJCi4cNRcnPRHT9u3g4fRnP2LLzxBrzxBmqfPiiPPgq33CJ5YuoiIwMefhg+/tg8/b1FCwrGjcPYpo3dbrEpoS0vrR9O94AzXNflEG5OBh778RoAnrlmC9c9O0hWRhaiiZFBtzUwpqVhGjIEpyNHKH78cZxeecWu5Tc7R46g9uyJUlRE/i23UNyjR2PXyPFKStAdPYrz3r3o4uNRjOZRMmpAAMycifJ//2fOBSOqp6qwYgXqI4+gpKaaW1UGDKBw+HC7tqqsOdSZu7+6hSJjxb/XxnSJZ1WcN07BjsnjIoQoz5bPb8miVQsmyzTbxMTGrUhTp6owfTpKURHF7dtT3L17Y9eoYeh0lHTpQv5tt5HzyCMUDh+OycMDJS0NZc4c1NatUe++G/bta+yaNl3//GNuvbz9dpTUVIx+fuTdfTeFV19t12Dl8z0RTFpxG0VGHVd3OsLk6AT83PMAaN/iHB/+1yDBihBNVJ0ClkWLFhEWFoaLiwtRUVFs3769Vtd98cUXKIrCuHHjyu2fMmUKiqKU20aPHl2XqjmEaglYTp9u3Io0dZ99BnFxqDqdefryZdj3r3p4UDR8ODkPP0z+jTdSEhSEYjCgfPABRESgDh0Kq1aZ08kLyM6GRx9FjYyE9evN/3auuorc//u/C+OH6imzQM+qf7oy/duxTP/uRoyqhtuv2Muqn/R8uC6MlEw3tv10hs0/nsN/SE+73FMIYX82j2FZsWIFsbGxLFmyhKioKBYuXEhMTAzx8fH4VzPNMyEhgccee4whQ4ZUenz06NF8UDoTAEBv5+mK9WFpYVHS0hq5Jk3Y+fOosbEoQNGwYZhatGjsGjUunY7iXr0ojohAe+oUzlu34nToEMoff8Aff6C2bYvy0ENw991Qm0R5zY2qwqefoj7+OEpqKgpQ3KULBTExqL6+drmFoUTDv76/ga/39cSoXvjbbPqgnbz5ZWtrS4pOp9B/tD8g09SFaMpsbmF5/fXXmTZtGlOnTqVbt24sWbIENzc3li1bVuU1RqORiRMnMn/+fNq1a1fpOXq9nsDAQOvma6dfWvZgDVjOnaMZDPlxjBdeQDlzBmOrVhQNGNDYtWk6FAVjmzYU3HYbOQ8+SNGgQZhcXVFOnoTYWNTgYNR//QuOH2/smjaczZthyBC46y5z90+LFuRNnEj+hAl2C1aMJoX7v72RFXt7YVQ1dPI7y4zhf7H25e28/X176fYR4hJkU8BiMBjYtWsX0dHRFwrQaIiOjmbLli1VXvfcc8/h7+/PPffcU+U5GzZswN/fn86dOzN9+nTOnTtnS9UcyhKwaLKyUPPzG7k2TdCpU6iLFgFQGBMj+UiqoPr4UDhqFDmPPELBdddhbNXKvGzAO++gduyIOn48/P13Y1fTceLj4aabYNAg+PNPVCcnCqOjyX3gAUo6drTbbVTVnE9l5T89cNIa+Sb2Vw4eceKd9b2JmdUfbYum88eQEKL2bPpkSU9Px2g0EhAQUG5/QEAAhw4dqvSaTZs28f7777Nnz54qyx09ejQ33XQT4eHhHDt2jKeeeooxY8awZcsWtJXk7ygqKqKoqMj6c3Z2ti2PYTPV0xNVUVCMRkxHj4IluZgwmz8fpaiIkrAwStq3b+zaNH3Ozhj69sXQpw+648dx3rwZp2PH4Msv4csvUQcPRrn3XvO0aHf3xq5t/e3fDy+9hLpihXmhQkWhuHdvCocPR7XzrL4So8Lzv41k2c6+KIrK/+7ZwI0LhqLIwpZCXPIc+qdwTk4Od911F0uXLsXv4pV5y5gwYYL1+549exIREUH79u3ZsGEDIyvJe7JgwQLmz5/vkDpXSqtF9fREyc5GjY+XgKWsQ4dQP/gABcyp0i/DgbZ1piiUtG9PSfv2aFJS0G/ahNOBAyibNsGmTagPPGCeNfPgg+bVpC8lqmru+vnPf+C77wDM41Q6d6Zw5EhMdl7WIKfImY9392bx1iutqykvHL+BuxYOlGBFiGbCpoDFz88PrVZL2kWDT9PS0ggMrNgnfOzYMRISErj++uut+0wmk/nGOh3x8fG0r+Qv8nbt2uHn58fRo0crDVhmz55NbGys9efs7GxC7TSjoComb2802dmox4459D6XnDlzUEwmirt0sdusjsuRKSiIgltvpTAzE+e//8bp77/RZmTA++/D+++jDhmC8sgjcN11TTsZXVERfP01LFwIO3cCoCoKJV27UjhkCKagILve7nSWJ+9ti+LDXX3ILjInemvplsfcsTuY+V5flOaQtFAIAdgYsDg7O9OnTx/i4uKsU5NNJhNxcXHMnDmzwvldunRh30W5J5555hlycnJ48803qwwykpKSOHfuHEFV/HLT6/UNPovI5O0Np05BQkKD3rdJ27kTvv4aVVHMKdNFvak+PhQNG0bR0KFoT57Eefv28rOLWraESZNQpk6FHj2aRouWqsKff8Inn6B++SXK+fPm3Votxb16UXTllXZvUTmR4cuCDcNYub8HJSZzt3HHlunMvP4wUx4NwLP7MEmpL0QzY3OXUGxsLJMnT6Zv377079+fhQsXkpeXx9SpUwGYNGkSISEhLFiwABcXF3pclOnUx8cHwLo/NzeX+fPnc/PNNxMYGMixY8eYNWsWHTp0ICYmpp6PZz/W5HGnTjVuRZqK4mKYMcP8bUSE3T+QLnuKgjEsjIKwMHOry86dOP/1F5pz5y4sARAWhnLddTBmDAwfDm5uDVc/gwE2boRVq1C//x6l9P8LBTB5emLo1w9Dnz6oDhiDE3e0PXd/fTNZhebWk0FhCTxyRwo3zGyHU9BAu99PCNE02BywjB8/nrNnzzJ37lxSU1OJjIxk7dq11oG4iYmJaDS1n3yk1WrZu3cvy5cvJzMzk+DgYK6++mqef/75JpWLxZo8LiWlcSvSVLz4ImzfjuriIq0rDqb6+FAUHU3RiBHmJQD++gvdkSMoCQnwzjvwzjuoej0MG4ZyzTXm4KVrV/suEpiba57B9PvvsHEj6p9/ouTmAuYgRXV2prhbNwwREeaVlG34HVBbqgrvbBnAvHXRmFQN/Vuf4rVZSQyc3B2NV5jd7yeEaFpkLaEaGNPSyF2yBF18PO6ff44xJARtUpJd73HJ2brVPJPFaLx81gtqaoqK0J04ge7IEZyOHkWTlVXusKrTQZcuKN26QUgIBAdDYKA5SZ2HB3h6mjfL98XFcP68eTtzxrwMxalTcOwY7NmDeuQIykW/Kkzu7pR06UJxp06UtGvn8LE1j66+hvd39gPgrr5/8+5yDzy6yaw0IS5ltnx+S8KMWrImjzt/HtVoRKlkuvVlITcX7rwTxWjE0LOnBCuNRa+npEsXSrp0oVBV0Zw9i+7oUfN2+jRKUZF5OvH+/Xa5nQKYPDwwhoZS0rYtJWFh5m5AB7SkVOaXwx14f2c/NIqJf9/0B4+81xNdy8s8m7IQlxkJWGrJmjwuPx9TSgpK69aNXCM7SE+HI0egSxeoTYbRoiK47z44dgyTlxcF11zj+DqKmikKJn9/DP7+GAYOBFVFycpCm5aG5tw5NLm5KDk5aPLyoKgIpajIvL6RwWD+ubTlRHV2RnVxQXVzw+Tjg8nbG5OXF6aAAIyBgageHo3yeIXFWmb9NAaAGUN38dgnUSguLo1SFyFE45GApbZcXFCdnVEMBkzx8Wgu9YDl7FnUfv3MKeIBNSgIrrwS5c03obLZW8ePw223wa5dqIpC/o03gkwZbZoUBdXHh5LSAe7VUlUoKTG3lDTRVsOFfw4m4XwLgj2zmfeKhwQrQlymGqY9tzlQFGsri3r4cCNXpp6Ki+G221BOnkQt/ZBSUlJQvv0W4403mj/EyvrmG9TevWHXLkyuruTffjvG8PBGqLiwO0Uxjz1posHKiQxf3vhjMAAL7tpNi35dGrlGQojGIi0sNlAt00bPnGncitTX44/Dhg2ozs7k3nsvJi8vtMnJuH/2GdpduzAtXYrmvvvM537/PdxyCwpQEhpK/i23XJgxJYQDqSo88dNoiow6hrc7zh3P9pDcKkJcxqSFxQaqZZq1g9cucqiPP4Y33wQg/6abzAMnXVwwtmtH0WDzX7I88QRqVhb88w/qxIkAGHr3Jm/KlEYJVkqMCgfPtOJAWisOnmnFiQzfCo1AonkxmhQeXXMtvxzphJPWyMJnUtG1qnp5DyFE8yctLDa45AOWb75BnTbNvO7PsGGUdCnfvF40aJA5Jfz58xjvvhvt33+j5OZSEhZGwXXXcSyzFQ//cB0nMlowuc8u7u67k5buBZzJdeeLv3vx8+GO3Nd/O2O7H7S5agXFOjafbEv7lucI88207ldVuOebm1l1oHu58x8a9CfzR/1al7cgmrjCYi3TVt7EDwe7oSgqr926kYiJgxu7WkKIRiYBiw3U0kRcamnCrEvKO++gPvggiqqy3m0MoX2H0YpC6+HkbE9+iu9Ml64K12x+Ae3KlYB5dlTuLbeyZMdAnvt1JAUl5lwbL66/itf+GELf1klsTWxjTY++83Rr2rX8Hz0D0yrWAUg478PelEBMqrlxL6fImXVHOvLr0Q7kFzvj65rPr/e+T/uWGQB88lckqw50R6OYaOmWj9GkIaPAjUVbruSOyD10bpVu02s4neXJO1sGMrDtSa7vWvkK46JxFBs1bD/VmhfXj2DzyTCctSW8P20Dd7w+SBYwFEJIwGKT0hYWpSEDlq1b4bnn4JFHYNSoWl+Wk2POB4aqwtNPw4IFKMC7TOdf+W/T7sPzrLzrE9r4ZPFTfCemfzuOzEJX4Fq+5gA3s5I83BiR+ysJi7twNs88pXVYu+NMGFfI0u8C2H28JZsSzINv+7U+hcZFz7aj/kz58lbW3/dfvFwMAOxJDuSrfRGsO9KBw+mtqqyzs7aE8wVujP/sdtbd8z6Zha48WTqd9dnrN/P0p5EoWoWxNxXxw1o9s9fG8M2dn9ZqOR2jSeF/O/rxQtxV5Bj0/Hdbf1ZP+ZABbWWphYakqvB3SiCf/x3JsXMtcHUqxtWpmNwiPX+cCCfHYP5/zEtfyBePbWL0vGEoTXmxRyFEg5FMtzWwZLoF0G/ciMv69RQPG4bThg12vU+Vbr8dvvgCVVHg2WdR5sypdsE7oxGmT4elS2FwRDbLNPfScc9XADzNC/wQMJXMEi9OnfMgyDObMZ0Ps2xnXwC6tjqDv28hJRk5zMhYwPumKcQRDYC7k4EXbt7CzDe6oAsMQFXh9x+z2Pp9KiOvzOGKmzpyvsSb3t0NnEpz5qbu+3kh5hfm/zqSFXt7WeunVUxEBKXgpjev2q3RmIjqmM5NN0HolW0ZOMqTU2l6BoedoKhEx46kUAaFJRD3uzP60GDAnHy1W1cThmINn074gmu7xFf7Ck9k+HLvNzex67R5Krqvaz7nC9wI8szm9/97j1Ye+XX8jyNs8cXfEby9eQD/pFVc2d2ipVseIzslMGt6JlfcOwClgRLTCSEahy2f3xKw1KBswOK8ZQuuP/9Mcb9+OG3fbtf7VKlbNzh4YUyIOmYMyuefm1OsX8RggDvvhK++gjac5DvG0Zs9GHDi/3iPg6Ej+f7TLAzhPbh6SD4HEy4sTDd90E7+878WuHVpZ75PcTH5Z3M5fTyf1IQiOrQpJGhQ5xoz/G7ZojJ0iEqJUYOztgSD0dyId2PPA9w8Jperb/TCLzKsylwae/eqDL6yhJwC81/VXvpCtn/0N51viyp33pOzjLz8qpYw3wy2PvAuLk7GSss7kNaKGz++i7RcT7z0hcy/aTtTnu3IwGt8OXjMhWHhx1l51ydoNZf8/wZN2tbEUEYvuxsAvbaEa7sdJnqYAYOiJz8PMBkZcqWB/tcG4hQSJLOBhLhMSMBiR2UDFqfdu3H7/ntKundHZ6eU59UqKED18EAxmSi86ir0GzeiGI2oLVrA/Pko991nXeCuoABuuQV+/BEGaf5kne4aXA3ZZGhacoPpO/TtA/lmRQk+fcwDbTMy4Karc/jnkIY3p25jwkv90Hh62qXar79awqOzzIFKVGgi/4lNZOB9V6Cp5WrCP642cv0NCiZVw9K713PP0mEV/tLOzYXO7YtJPuPE1R0P09Y3E42iEuSZw6iOR+jmf4bdp4O55dOJnC9wo3tAKqsWJdDuxv4oGg0HD0K/K0rIK9RxR+RfDGybiIezAV/XfK4IScZTb6iyfjuTQjCpCv1Dy68ppapwMtOH1l5Z6LSX/P9WdnXfNzfy5b4Irusez/+WlOAf1Um6eoQQErDYU7mAZf9+3L7+mpJ27dAdO2bX+1Rqxw7o3x+Tmxs5jz+O9vRpXFeuRJthHpCqtmmD8uqrcOut3DFR4fPPYZQ2jh+Va9GVFGEMDKT4rbfI7jualj5GtL4+5YpXVSjJzEbn7WHXpndVhWVv5+CecpBbHmqLLjDA5jJ+/7WI0xsOcNuj4RXqbfHxxyqTJlX+l3iodybnC1zJNejp1/oUP3xynoBhEeXO+fwzE3dMrPjcGsVEz8BUBrZN5MGBfxLkdWHM0s6kEEYvm4qqKmyd8S4d/c5Zj725aSDzfh2Fv3su47r/w60999G39elajbFpzs7ludLt9ViKjDr+eGcbg2dE1XyREOKyIAGLHZUNWHRHjuD+6acYg4LQJifb9T6V+t//YNo0itu1I3/SpNIKGXHevRv9xo1oSgf/GocMI3LrEoKKE/lRez06o4Hidu1gxQqc+vZ1fD0biarCsndyObYpCZNRxWiE/QmebNgfQGHpbKah4Sf4bkUhvv26VlrGh+8V8P37aeTlqeQV6jh13pPE8z7W4x1aprP27g/wc88nu9CZIUvu52Smed2lm3vs4/1bzLOpzua60futB8ktHTRqMbXPTt64fo0Dnv7S8fbmAcz55Wp6BSWza78L2hayaKEQwkxWa3YQax6WoiJUVXV8P/vffwNgCiwzSFGrxdCvH4aICPRbtqDftAntHxvZRQQqijlY6dgRVq7EqZmvpKwocM+/POBf5fPJ5GUVs25FMif3ZXLPDDc8ulQerABM+T9XpvxfmPVnVVVJOpDJxtVZPPUfP46m+3Hbp3fw/eTlPLrmWk5m+hLgkUNariff7O/JI4M30SPwDP/5Yyi5Bj2RQcnMe7KAL1bq+fL3ED7Y1Zf7orbT1f+so15Dk6aq8OHOPgDcM/ok2hYDGrlGQohLlQzBt4ElD4tSVGReMM7R9uwBwBhQSZeKXk/R8OHkPvAAh1v2x5li9Bgo7toV5fvvm32wUh13byfG3RfMQ293w6NLmE3XKopCaHcf7nyiLT//7kZLLwO7k0MY/t/7+GpfBFqNiU+f3MktN5r/+y/YMJyE8z4s22FuyXp+2jHGPdieLza0ZtwN5oHAlrVwLgf/pPnzysahJGebx0P9fiKMYxkt8XQu4s4HarEiuBBCVEECFhtYWliUoiLUoiIH30xF3bsXAGNg1dNATS1aMMPnY65lNeu63o/yww/ousgCcfbQtavC6p+dcNOXcPScOS387JgtXPXYQJ57UYdGo7LmUFemfXMTxSYtw9sd55qHLwSKT88xN2B+vb8HJzKa/4f1F39HMHLpvby0fgRXLnqAZTv6WKfM39bnAD69OzZyDYUQlzIJWGxhCVhKSlDzHZy7IyEBJTsbVavF5Ff1GipGk8L2pDb8yLV4zbofXfv2jq3XZebKKxW+Wang6WIgptNhnlncFkWvp2tXmDjB3IKyIykUgBceSkHjeyEw6dMHYqJLMKkaFm4aVKFsVTUn1ftyb0/O5bk2zAPVUWGxls/29OJAWsXEf8VGDU/8NJr7v72RwhInWrrlkV3kQuya66xLKtw3tbjGKfFCCFEdGcNiA7VsevBz58Df33E3s4xfadUKqvlFvz8tgOwiF7z0hUSOaOm4+lzGRl+jJfWMirPBD13LCwNG5z2n47MVKkajwk09/2HAPZEVrn1mno6ff4XP/o5k1rCNBHrmsikhjO8PdGXt4U6czjbn02nhms9zV69jYuQeFAWKSrRsP9UaH9fCKpc5aCjpeW7c8fkEtpcGZjGd4okdvAl3ZwOrDnTj23+6W1ugnhi1hXlL27JkeTFzXnQnz+BE35Ak+t7WuTEfQQjRDEjAYgudDlWrNedCOX/esfeyjF+ppjsIYMvJNgD0b5OEc+twx9bpMubmqQPKz25p3x6enWPky/9m8NK8QjTu7hWuGzwYhg4s5vfNTty1YjzJ2V6k5l7Id+PmZMDPPZ/ETB9mrhrLp39F4u1SyO8nwskvNgfII9od48nhG4hqk1ShfHvIKtRzvsCVIM8c9LryCfgOn23JbZ/dQcL5Frg5GSgoceLnw535+XD5AMRLX8h79/3JbQuuROPuziNzYdxEI8teOsmEq1LReMtUZiFE/UjAYiNVr0fJz0ctzYXiMKUtLJUOuC1jS6I5YBkYkSVN7o3gmXk6np7jh6KpurXt6XlO/B4Du5NDAPPSADf0PMIN1xgYdWtL9O3a8sZz53nuNQ+2JLa1XufvnktGgSvrj7dn/fH2RHc4wn9v+pYWbgXlyj+T646PSwHOOpNNdTeaFBZvjeKF366yTgP3c8vD3yMXD2cD7s4GdicHk1XoSluf83z3yn5c+vfllacz+WStP1rFxMiOx7lpVCY33OFNy34jyuXzCW+v5fn32wJtq6iBEELUngQstnJ2hvx8yMx06G3Uv/9GofoWFlWFraUBy5Ahl3l2skZUU9K9UaPgiYfzOflnIjePyuSaSQG4duxZ7ronXvJl/L1G3n7mFB7FZ7n2WoUrRgeTmOnEC4+f5eMfW/Hr0Y7c+ukdfDfpYzz1BlQV3tk8gLnrRuHnnsfEyD1M7rOb8BY1t/4dSW/JzFU3sO2U+d+PTmOkxKQlPd+d9PzyLUX9W5/im/+dpXXMEACWrXblrfMGTGmpeLZvJxlrhRANQhLH1aBs4jgAj8WL0aalUfzOOzjNmGHXe1llZYGPDwDZs2ahVpHS/vg5X654+0GctSWc23cKj67SJdRc7d1dzIjhKhk5zgwJO8GKOz5jflw0722r2NVydcfDzB6+gd4hKdZ9mQUurD/Wjm2nQtmR1Jq/U4IoMWnxdC7ipdu2ct9LHTif68ypowWcPW0gN7OEnBwVvcbA2IneNk8PF0KI2pDEcQ5kTR7nyBaW0unMJi+vKoMVgM2l3QdXhCTj3j7EcfURjS7iCid++lVl5LBi/kgIJ/LNhziT5wHA8zf8QZfoMP67zIlf/w7glyOd+OVIJ67tcpCYjkdYE9+Z3452oNhUvsswusNRFi3IoONNQ1A0GgKAgKpz7AkhRKOSgMVG1plCWVmOu4ll/EotB9wO7JqO4tzGcfURTUL//grfr9YwZoyRM3keOGmN/HfqBia9OQCNmxu3/AsO7yvgucez+PwXf9Yc6sqaQxcikC6tzjCkSyoD+hUzcIQLHQeHoPHp0IhPJIQQtScBi40sLSxqTo7jbmLjgNvBgy75Xj1RSyNGalm1ysgbs0/z4A2HGTNnaLkxJJ16uvLJWlee+quQ5x7P5MghE6N6JTN+goZe14Wj8Y2opnQhhGi6JGCxlaWFxZEBy759AJiqCVjO5blyPKMlCiqDr/ZwXF1EkxMzRkvMmBCg6m7Abr1d+OLXwNI1r4IbrnJCCOEgkunWRtb0/KUrJTvkHinmwZKm0oG3lTmWYU4SF+KdRcuuQQ6ri7i0OXyBTiGEaCASsNjIOujWgQELpUnpVBeXKk+xrE0T1iITxdOzyvOEEEKI5kACFhtZB93m5TnmBiUlKKXdTapr1evLnDhvzrraLjBP/ooWQgjR7EnAYiNrC4ujApYy06Vr08LSrk2JY+ohhBBCNCF1ClgWLVpEWFgYLi4uREVFsX379lpd98UXX6AoCuPGjSu3X1VV5s6dS1BQEK6urkRHR3PkyJG6VM3xLGNYCgpqOLGOLN1Bzs7VLnp48rw5YGnfXmYICSGEaP5sDlhWrFhBbGws8+bNY/fu3fTq1YuYmBjOnDlT7XUJCQk89thjDBkypMKxV155hbfeeoslS5awbds23N3diYmJobCw0NbqOZy1S8hRdavF+BWAE5aApZNM9BJCCNH82RywvP7660ybNo2pU6fSrVs3lixZgpubG8uWLavyGqPRyMSJE5k/fz7t2rUrd0xVVRYuXMgzzzzD2LFjiYiI4KOPPiI5OZnvvvvO5gdyNLWhWliqGb+SZ3AirXTF3w49qj5PCCGEaC5sClgMBgO7du0iOjr6QgEaDdHR0WzZsqXK65577jn8/f255557Khw7ceIEqamp5cr09vYmKiqqyjKLiorIzs4utzUUa8BSVIRqNNr/BrUIWBJKW1d8XApoEeZj/zoIIYQQTYxNAUt6ejpGo5GAixKaBQQEkJqaWuk1mzZt4v3332fp0qWVHrdcZ0uZCxYswNvb27qFhoba8hj1U9olpBgMqEVF9i+/FgGLZcBteIsMNNXkahFCCCGaC4fOEsrJyeGuu+5i6dKl+Pn52a3c2bNnk5WVZd1OnTplt7JrYm1hMRgcM46lFmNYLC0s4f45KNUMzBVCCCGaC5tGbPr5+aHVaklLSyu3Py0tjcBKFuo7duwYCQkJXH/99dZ9JpPJfGOdjvj4eOt1aWlpBAVdyNialpZGZGRkpfXQ6/XoLdOLG5ha5r5qRga0aGHfG9QqYCnNwdLaAS08QgghRBNkUwuLs7Mzffr0IS4uzrrPZDIRFxfHgAEDKpzfpUsX9u3bx549e6zbDTfcwIgRI9izZw+hoaGEh4cTGBhYrszs7Gy2bdtWaZmNTqdDLU3UpmZk2L98G7qE2oXLlGYhhBCXB5vnxMbGxjJ58mT69u1L//79WbhwIXl5eUydOhWASZMmERISwoIFC3BxcaFHjx7lrvcpHXNRdv/DDz/MCy+8QMeOHQkPD2fOnDkEBwdXyNfSJCiKORdLYSFqaXBhV7VJy2+Z0txRuoOEEEJcHmwOWMaPH8/Zs2eZO3cuqampREZGsnbtWuug2cTERDQa24bGzJo1i7y8PO677z4yMzMZPHgwa9euxaWGXCSNRXV2RikstAYXdlVDC0uJUSEx0weADl2d7X9/IYQQogmqU9axmTNnMnPmzEqPbdiwodprP/zwwwr7FEXhueee47nnnqtLdRqcdRxLmTT6dlNDC0tStjclJi16bQmhXb3sf38hhBCiCZK1hOrAGrBkZdm/7BpaWCwp+dv6nkfX0tfu9xdCCCGaIglY6sCSnl91RMK6GlpYrDlYWmahNNEuMyGEEMLeJGCpC0e1sBiNKKVBUFUtLCdKpzSHBTtoaQAhhBCiCZKApQ6syeNycuxbcJkxMTW1sLRr64BlAYQQQogmSgKWOrCu2Jyba9+CLd1Bzs5QRQZby5TmDh0U+95bCCGEaMIkYKkD66BbRwUsVbSuqCokZJi7hNp3cbLvvYUQQogmTAKWOnB4wFLF+JWMfFdyDOZ7t+/hbt97CyGEEE2YBCx1YekSys+3b7k1BCyWAbfBntm4BcmUZiGEEJcPCVjqwDrotoEDloNnWgEQ3jIDxdPTvvcWQgghmjAJWOrA2iVUYOepxTWMYfn9RDgAA7tloCgy6FYIIcTlQwKWOrDMElIKC+1bcDUBi6rChuPtABg1UlZpFkIIcXmRgKUuLF1CRUWoJpP9yq0mYDlwxp+zeR64ORkYeE0L+91TCCGEuARIwFIH1haWoiIwGOxXcDVjWCytKwPDEnHrEGq/ewohhBCXAAlY6sA6hsVgQLVnt1A1LSwbj5vHr4zom4miq9Mi20IIIcQlSwKWOrDOElJVVHuuJ1RFC4uhRMOfCWEAjIqRYEUIIcTlRwKWunC6kGVWPXfObsWqVbSw7DzdmrxiZ1q55xI5KtBu9xNCCCEuFRKw1IVGYx3HYgky7KKKFpb1x8zjV4Z1SEQXFGC/+wkhhBCXCAlYqpGXB9Mf92TqVzdjNJXPe2Idx1JmheV6MRpRSruXLm5h2Vg64PaqAXmSf0UIIcRlSQKWauh0sPRjN779pwc5Rfpyx6wrNturhaXMWJiyLSxZhXp2nQ4BYNR1lSeUE0IIIZo7CViqodeDq6s5SVtmQflgwe4tLJbuICcn0Gqtu/9MaItR1dC+xTnaDZLpzEIIIS5PErDUwMe7NGApvKh1ozRgUbOz7XOjjAxzeReNX/l6X08AhnU5jcbHxz73EkIIIS4xErDUwLeFecxI1kUBizV5nL0ClkpmCP1xoi0r/+mBoqjcfXuefe4jhBBCXIIkYKmBj485YMksLN/yYe0SsnfAUtrCUmzU8PiP1wAwtf8eBt4TaZ/7CCGEEJcgCVhq4Otr/lqhhcUSsOTm2udGFwUsS7ZFceisPy3d8njx31qUStL1CyGEEJcLCVhqYBk2klXVoNucHPvcqEzAkpztycsbhgHw3C07CRjW0z73EEIIIS5RErDUoMoWFstYEweMYZn/60hyDXr6tT7FtJc6Se4VIYQQlz0JWGpgbWG5OGAp7aKx96DbYmdXvj/QDYCXH0rEKSTIPuULIYQQlzAJWGpgCVgqDLq1tLDYeQzL8YJgCkqc8HfPZcjE9vYpWwghhLjEScBSgyq7hCwtLPn59rlRacDy9/lwAK7qnIA2UNYNEkIIIUAClhpdaGGpfAyLkp+PajTW/0alAcvW9E4ARA8ukLErQgghRKk6BSyLFi0iLCwMFxcXoqKi2L59e5Xnrly5kr59++Lj44O7uzuRkZF8/PHH5c6ZMmUKiqKU20aPHl2XqtldjS0shYWohYX1vo9l1efd583dQFePdat3mUIIIURzobP1ghUrVhAbG8uSJUuIiopi4cKFxMTEEB8fj7+/f4XzW7RowdNPP02XLl1wdnZm9erVTJ06FX9/f2JiYqznjR49mg8++MD6s16vr1BWY6hyWrMlYCkpwZSRAe7u9btRacByHl96BqYQ0r9t/coTQgghmhGbW1hef/11pk2bxtSpU+nWrRtLlizBzc2NZcuWVXr+8OHDufHGG+natSvt27fnoYceIiIigk2bNpU7T6/XExgYaN18LU0bjayqLiGcnVFLu2zUtLT63cRoRCldrfk8vozsmYLGw6N+ZQohhBDNiE0Bi8FgYNeuXURHR18oQKMhOjqaLVu21Hi9qqrExcURHx/P0KFDyx3bsGED/v7+dO7cmenTp3Pu3LkqyykqKiI7O7vc5iiWuKmwxInC4gurKKPRWMexqGfO1O8mpcEKmAOWUdFq/coTQgghmhmbApb09HSMRiMBAeVnrwQEBJCamlrldVlZWXh4eODs7My1117L22+/zahRo6zHR48ezUcffURcXBwvv/wyGzduZMyYMRirGMy6YMECvL29rVtoaKgtj2ETLy9QFHMAUdU4Fs6erd9NSoOzbDzR6RSGXt+ifuUJIYQQzYzNY1jqwtPTkz179pCbm0tcXByxsbG0a9eO4cOHAzBhwgTruT179iQiIoL27duzYcMGRo4cWaG82bNnExsba/05OzvbYUGLRgPeXpCZZQ5YAjwvrJpszcVS34AlPR2Ac7RkUFgC7h3D61eeEEII0czYFLD4+fmh1WpJu2jMRlpaGoGBgVVep9Fo6NChAwCRkZEcPHiQBQsWWAOWi7Vr1w4/Pz+OHj1aacCi1+sbdFCuj8+FgKUsSwuLWk33Va2UXp+OHyP7ZaLoGiSOFEIIIS4ZNnUJOTs706dPH+Li4qz7TCYTcXFxDBgwoNblmEwmioqKqjyelJTEuXPnCApqGmnpfUt7aKrMdls6w6euilMutLDEXOtUr7KEEEKI5sjmP+VjY2OZPHkyffv2pX///ixcuJC8vDymTp0KwKRJkwgJCWHBggWAebxJ3759ad++PUVFRfz44498/PHHLF68GIDc3Fzmz5/PzTffTGBgIMeOHWPWrFl06NCh3LTnxuTjY54NVGUulszMepWfvO8cbYEcrQ/RI5tGkCaEEEI0JTYHLOPHj+fs2bPMnTuX1NRUIiMjWbt2rXUgbmJiIhrNhYabvLw8HnjgAZKSknB1daVLly588sknjB8/HgCtVsvevXtZvnw5mZmZBAcHc/XVV/P88883uVwsmVXkYqGeAcuZA+m0BZw8ndH6t6pXWUIIIURzVKfBEjNnzmTmzJmVHtuwYUO5n1944QVeeOGFKstydXXl559/rks1GkyV2W4tXUL1DFiyT5jHsHj4alE0slqCEEIIcTH5dKyFKldstnQJ5eTUq/ySVPMYllatTPUqRwghhGiuJGCphRpbWPLyqKuzZ8El39zC0rqTSw1nCyGEEJcnCVhqwbqeUFEVg27rEbBs2wZ+mFtYPHq2r3M5QgghRHMmAUst1DToVikoQC0pqVPZ27ZBS8wtLJq2suChEEIIURkJWGqhpi4hpaAAtaCgTmVv26paAxaCg+tcRyGEEKI5k4ClFmpsYTGZ6pTt1mSCQ9uzcMLcOqM0kUR5QgghRFMjAUstVNXCgrMzqmJOKleXFZsPHwanbHOgozo5o1huJIQQQohyJGCpBeug20IXVLXMAUW5sJ7QResr1UbZ8SuqmysaN7d61lQIIYRoniRgqQVLwGJSNeQUOZc7Zs12W4cVm8vOEFLd3MBFpjULIYQQlZGApRZcXcHZ2dy0UmUulvR0m8st28Ji8vBAKe1eEkIIIUR5ErDUgqKAj7c5YKkq2y02DrotKIC9e1VrCwuenvWupxBCCNFcScBSS74tql+xmfPnbSpv924oKVFo65RsLsfS7ySEEEKICiRgqSUfnyoCljougHjkiPlre9fT5m9khpAQQghRJQlYaskST1SVi8XWFpbTpXFKgKZ0OnTLlvWpnhBCCNGsScBSS2WnNpdlbWHJzrapvKQk89eWaunYFwlYhBBCiCpJwFJL1my3VQy6VXJybCrP0sLibSptmfH3r0/1hBBCiGZNApZaqnI9IUuXkI0rNlsCFveSLACUgIB61U8IIYRoznSNXYFLRU1dQorNAYt5mrS+qLRlRtYREkIIIaokLSy1ZB10W1WXUEEBanFxrcoyGCAtTcGDXDQmo/l6CViEEEKIKknAUksXWlj05fZbW1gKClALCmpVVkqK+WuwJtVchk6HIoNuhRBCiCpJwFJL1oClimnNiqqi1jI9v2X8Sie3U+Yy3NxQZOFDIYQQokoSsNRSVYNucXJC1WqB2q/YbJnS3MHV/I3q5oYiCx8KIYQQVZKApZaqGnSLoli7hdQzZ2pVlqWFpa3e3DekurujaOQ/hRBCCFEV+ZSsJUsLS65BT7Gx/GuzTm22MWBprSsNWLy87FJHIYQQormSgKWWvL0vfJ9dxcDb2q7YbAlYAjVnzdeXLVwIIYQQFUjAUks6HXh4mHOnVDW1ubYBi2UMix+lg3RbtLBLHYUQQojmSgIWG/j6mAOWKrPd1nIBREvSOB9ThnmHBCxCCCFEtSRgsYGPrwJUsmKzpUuoFgGLqlZMyy8LHwohhBDVk4DFBn5+5oDlXH75nCnWFpasrBrLSE8Hg8FcjquhdIVnWfhQCCGEqJYELDYIDDR/Tc31LLffloDF0rrSyj0XTUE+IAsfCiGEEDWpU8CyaNEiwsLCcHFxISoqiu3bt1d57sqVK+nbty8+Pj64u7sTGRnJxx9/XO4cVVWZO3cuQUFBuLq6Eh0dzZEjR+pSNYeyLPdzJtej3H5rev7c3BrLsAQsQR7ZKPnmgMUaCQkhhBCiUjYHLCtWrCA2NpZ58+axe/duevXqRUxMDGeqyEHSokULnn76abZs2cLevXuZOnUqU6dO5eeff7ae88orr/DWW2+xZMkStm3bhru7OzExMRQWFtb9yRzAElekXRywWFpYbAhYwr3OoBhl4UMhhBCiNmwOWF5//XWmTZvG1KlT6datG0uWLMHNzY1ly5ZVev7w4cO58cYb6dq1K+3bt+ehhx4iIiKCTZs2AebWlYULF/LMM88wduxYIiIi+Oijj0hOTua7776r18PZW5UBS+k6QJq8PFRVrbYMy5TmTq6l6whptSh+fvatqBBCCNHM2BSwGAwGdu3aRXR09IUCNBqio6PZsmVLjderqkpcXBzx8fEMHToUgBMnTpCamlquTG9vb6Kioqoss6ioiOzs7HJbQ6iyS8iyAGJeHhgM1ZZhTcvvbP5GdXNDcXe3b0WFEEKIZsamgCU9PR2j0UjARYNEAwICSE1NrfK6rKwsPDw8cHZ25tprr+Xtt99m1KhRANbrbClzwYIFeHt7W7fQ0FBbHqPOrINucypvYVEMBtTMzGrLsAQsoU6p1msVV9dqrhBCCCFEg8wS8vT0ZM+ePezYsYMXX3yR2NhYNmzYUOfyZs+eTVZWlnU7deqU/SpbDUvAcr7AjaISrXW/6uKCqpinKpssEUkVLIeDNeaVnWXhQyGEEKJmOltO9vPzQ6vVkpaWVm5/WloagdXMdNFoNHTo0AGAyMhIDh48yIIFCxg+fLj1urS0NILKDD5NS0sjMjKy0vL0ej16vb7SY47UogU4OakUFyuczXOntXdpV5RGg+rqipKfj5qcDFdcUWUZSUkqoNDKZG5hMVlWVRRCCCFElWz6097Z2Zk+ffoQFxdn3WcymYiLi2PAgAG1LsdkMlFUVARAeHg4gYGB5crMzs5m27ZtNpXZEDQaCPA3D6pNq6JbiGq6xvLzITPT3BLjayxdd0gG3AohhBA1sqmFBSA2NpbJkyfTt29f+vfvz8KFC8nLy2Pq1KkATJo0iZCQEBYsWACYx5v07duX9u3bU1RUxI8//sjHH3/M4sWLAVAUhYcffpgXXniBjh07Eh4ezpw5cwgODmbcuHH2e1I7CQpWSDpd9UwhLmp9Ksuakt/JgEtBpvkHSRonhBBC1MjmgGX8+PGcPXuWuXPnkpqaSmRkJGvXrrUOmk1MTERTZkxGXl4eDzzwAElJSbi6utKlSxc++eQTxo8fbz1n1qxZ5OXlcd9995GZmcngwYNZu3YtLi4uFe7f2AIDzS0kFWYKWQKWs2ervNYypTnYKxuNJWeLJI0TQgghamRzwAIwc+ZMZs6cWemxiwfTvvDCC7zwwgvVlqcoCs899xzPPfdcXarToGpMz3/uXJXXWrPceuVcyIobHGzvKgohhBDNjkxPsVFVuVhMlhaWjIwqr600YGmgKdlCCCHEpUwCFhtV2cJSi4AlOdn8tbX7OTQFBQAobdvavY5CCCFEcyMBi40sAcuZ3PLZaa3J46pZsdkyHjdcdyEtvyYkxP6VFEIIIZoZCVhsZOkSqmpas5KTU+W1lvUhW2tK0/J7eKB4elZ5vhBCCCHMJGCxUdkFEMuuc2gNWKpZANESsARhbmoxeXigWLqShBBCCFElCVhsZAlYDEYdWYUXpl2XWwCxNCnexdLSzIFMS5M5clG9vVFKU/oLIYQQomoSsNjIxQV8vE1A+UUQyy2AWMk4lpKSCzOefYrTzde0bOng2gohhBDNgwQsdWBJHlc2221NCyCeOweqqqCg4m44b97ZqpXjKyuEEEI0AxKw1EFQcCXZbksXQATMCyBexDJDqKVbPtrc0oG5/v4OracQQgjRXEjAUgc15mKpZAFEy4DbVu55F5LGlVmdWgghhBBVk4ClDi7kYqn9AojWgMUj78I6QpLlVgghhKgVCVjqwJqLxYYFEC+0sORaW1gUCViEEEKIWpGApQ7K5mIpy7oAYnp6hWssjS5t9CkoRiMgafmFEEKI2pKApQ6q6hKqbgFESwtLmC7JfK6LCxo/P4fVUQghhGhOJGCpA0uXUGoV6fk5f77CNZaAJVRjnkGkenigeHhUOE8IIYQQFUnAUgeWFpbzBW4UlWit+6tbANESsASoKeZzvbxQnJwcW1EhhBCimZCApQ5atAAnJ3Oa/bN5F1Ztrm4BREtafj+TeUCu6uvr6GoKIYQQzYYELHWg0UCAvzkASassPX9ubrkFEFX1QguLT4mk5RdCCCFsJQFLHQUGVZKe3xKw5OeXWwAxLw8KCsznexSVDsiVLLdCCCFErUnAUkdB1oDlQrZb64rNBgOmMgNvLVOa3ZwMOOdnWwpomIoKIYQQzYAELHVkTc+fU/kCiGqZBRAt3UF+ZdPyBwc3SD2FEEKI5kACljoKCTF/TcnxurCz7AKIZdYTsgQs/mUCFqV16wappxBCCNEcSMBSR2Fh5q+JmT7l9le2AKIlYAlyy0RTUGD+QbLcCiGEELUmAUsdWeKNxEzvcvsrWwDR8m07/SnzORoNGmlhEUIIIWpNApY6srSwnMr0wWS6sN8asFiaVcp8G65LNJ/j4YHiVaYrSQghhBDVkoCljlq3Bq1WpdikJbXsTCFLwHLunHWfJWAJ0Ziz3Jo8PFAs5wkhhBCiRhKw1JFOB61DzMnhTpXpFjJZVmwuswCipUsoUDWPa1G9vVFKZxMJIYQQomYSsNRDWLg56Cg78LayBRCt05pN5shFstwKIYQQtpGApR7CwqoOWJTMTOu+M2fMLTEt85PMOyxJXIQQQghRKxKw1ENlU5vLricEUFJyYTiLT9ox8zd9+zZQDYUQQojmoU4By6JFiwgLC8PFxYWoqCi2b99e5blLly5lyJAh+Pr64uvrS3R0dIXzp0yZgqIo5bbRo0fXpWoNyhqwZPlY9128AGJ6OqiqQmtO4ZR9HlVRUEaNavjKCiGEEJcwmwOWFStWEBsby7x589i9eze9evUiJiaGM2Wm8Za1YcMGbr/9dtavX8+WLVsIDQ3l6quv5nSZ1PUAo0ePJiUlxbp9/vnndXuiBnQhF4uPdd/FCyBaXkuMfj0ApoAAtF27NmQ1hRBCiEuezQHL66+/zrRp05g6dSrdunVjyZIluLm5sWzZskrP//TTT3nggQeIjIykS5cu/O9//8NkMhEXF1fuPL1eT2BgoHXz9fWt2xM1IEsLS1KmtzUXi8kSsBgMmJKTrQHLMM0fABg7dkRjmUkkhBBCiFqxKWAxGAzs2rWL6OjoCwVoNERHR7Nly5ZalZGfn09xcTEtWrQot3/Dhg34+/vTuXNnpk+fzrkyeUwuVlRURHZ2drmtMbRuDRqNSpFRx5m80kUQXV0xtmoFgPrFF9Ypzf2M28z7+vdvjKoKIYQQlzSbApb09HSMRiMBAQHl9gcEBJBaZu2c6jzxxBMEBweXC3pGjx7NRx99RFxcHC+//DIbN25kzJgxGI3GSstYsGAB3t7e1i00NNSWx7AbJ6cLuVjKdgsVd+9u/uabbzhzBtzIo6PhAADKyJENXU0hhBDiktegs4T+/e9/88UXX/Dtt9/i4uJi3T9hwgRuuOEGevbsybhx41i9ejU7duxgw4YNlZYze/ZssrKyrNupU6ca6AkqqiwXS3G3bgBo//6brBPn6M92tBgxeXmhlRYWIYQQwmY2BSx+fn5otVrSyizsB5CWlkZgDblF/vOf//Dvf/+bX375hYiIiGrPbdeuHX5+fhw9erTS43q9Hi8vr3JbY6ksF4vJ3x9jq1YoRiOt/1zBIP4EwBgWhuairjAhhBBC1MymgMXZ2Zk+ffqUGzBrGUA7YMCAKq975ZVXeP7551m7di19a5GDJCkpiXPnzhEUFGRL9RpFZblY4EK3UMTRb6wBiykyUlLyCyGEEHVgc5dQbGwsS5cuZfny5Rw8eJDp06eTl5fH1KlTAZg0aRKzZ8+2nv/yyy8zZ84cli1bRlhYGKmpqaSmppJbmlgtNzeXxx9/nK1bt5KQkEBcXBxjx46lQ4cOxMTE2OkxHaeyqc1wIWDpnf2HNWBRhg5tyKoJIYQQzYbO1gvGjx/P2bNnmTt3LqmpqURGRrJ27VrrQNzExEQ0mgtx0OLFizEYDNxyyy3lypk3bx7PPvssWq2WvXv3snz5cjIzMwkODubqq6/m+eefR6/X1/PxHM/SwnIqy7vcflOrVhS3CsDpbBpOFGN0ckZz1VUNX0EhhBCiGVBUVVUbuxL1lZ2djbe3N1lZWQ0+nuX4cWjfHlx0xaQ8/RJle3ySVu6j+95vACgJD0d7+DCKzuYYUQghhGiWbPn8lrWE6smSi6WwxIkzue7ljn2rudn6val7dwlWhBBCiDqSgKWenJ0hJLhiLhaAb9KGsQPzIGN1xIiGrpoQQgjRbEjAYgeV5WLJLHBhb2og4/iOpOnP4HT33Y1UOyGEEOLSJwGLHVSWi+XPk20xqRrcWuoJXvAYGh+fyi8WQgghRI0kYLGDyqY2/3EiDIAhnZLReHtXvEgIIYQQtSYBix1YpjYfy2hp3fdHgnnniMGGhq+QEEII0cxIwGIHffqYv/5+Ipw1hzpzLs+Vf9LMSxWMuMatEWsmhBBCNA8SsNhBZCTE/svckvLAd2P5bE8kAF1bnSG4T+OsJC2EEEI0JxKw2Mm/X3MmqnchWYWuzFl3NQBDuqSg8fRs5JoJIYQQlz4JWOzEyQlWfOuCr2exdd+IIcXVXCGEEEKI2pKAxY7atoUPP9YC4KQxMuJaj0aukRBCCNE8SK54O7thrIZVXxXCoX3494lo7OoIIYQQzYIELA5wwy0uQL/GroYQQgjRbEiXkBBCCCGaPAlYhBBCCNHkScAihBBCiCZPAhYhhBBCNHkSsAghhBCiyZOARQghhBBNngQsQgghhGjyJGARQgghRJMnAYsQQgghmjwJWIQQQgjR5EnAIoQQQogmTwIWIYQQQjR5ErAIIYQQoslrFqs1q6oKQHZ2diPXRAghhBC1ZfnctnyOV6dZBCw5OTkAhIaGNnJNhBBCCGGrnJwcvL29qz1HUWsT1jRxJpOJ5ORkPD09URTFrmVnZ2cTGhrKqVOn8PLysmvZlwp5B/IOQN4ByDu43J8f5B2Afd+Bqqrk5OQQHByMRlP9KJVm0cKi0Who3bq1Q+/h5eV12f7jtJB3IO8A5B2AvIPL/flB3gHY7x3U1LJiIYNuhRBCCNHkScAihBBCiCZPApYa6PV65s2bh16vb+yqNBp5B/IOQN4ByDu43J8f5B1A472DZjHoVgghhBDNm7SwCCGEEKLJk4BFCCGEEE2eBCxCCCGEaPIkYBFCCCFEkycBSw0WLVpEWFgYLi4uREVFsX379saukkMsWLCAfv364enpib+/P+PGjSM+Pr7cOYWFhcyYMYOWLVvi4eHBzTffTFpaWiPV2PH+/e9/oygKDz/8sHXf5fAOTp8+zZ133knLli1xdXWlZ8+e7Ny503pcVVXmzp1LUFAQrq6uREdHc+TIkUassX0ZjUbmzJlDeHg4rq6utG/fnueff77cWifN7R38/vvvXH/99QQHB6MoCt99912547V53oyMDCZOnIiXlxc+Pj7cc8895ObmNuBT1E9176C4uJgnnniCnj174u7uTnBwMJMmTSI5OblcGc35HVzs/vvvR1EUFi5cWG6/I9+BBCzVWLFiBbGxscybN4/du3fTq1cvYmJiOHPmTGNXze42btzIjBkz2Lp1K+vWraO4uJirr76avLw86zmPPPIIP/zwA1999RUbN24kOTmZm266qRFr7Tg7duzgvffeIyIiotz+5v4Ozp8/z6BBg3BycuKnn37iwIEDvPbaa/j6+lrPeeWVV3jrrbdYsmQJ27Ztw93dnZiYGAoLCxux5vbz8ssvs3jxYt555x0OHjzIyy+/zCuvvMLbb79tPae5vYO8vDx69erFokWLKj1em+edOHEi//zzD+vWrWP16tX8/vvv3HfffQ31CPVW3TvIz89n9+7dzJkzh927d7Ny5Uri4+O54YYbyp3XnN9BWd9++y1bt24lODi4wjGHvgNVVKl///7qjBkzrD8bjUY1ODhYXbBgQSPWqmGcOXNGBdSNGzeqqqqqmZmZqpOTk/rVV19Zzzl48KAKqFu2bGmsajpETk6O2rFjR3XdunXqsGHD1IceekhV1cvjHTzxxBPq4MGDqzxuMpnUwMBA9dVXX7Xuy8zMVPV6vfr55583RBUd7tprr1XvvvvucvtuuukmdeLEiaqqNv93AKjffvut9efaPO+BAwdUQN2xY4f1nJ9++klVFEU9ffp0g9XdXi5+B5XZvn27CqgnT55UVfXyeQdJSUlqSEiIun//frVt27bqG2+8YT3m6HcgLSxVMBgM7Nq1i+joaOs+jUZDdHQ0W7ZsacSaNYysrCwAWrRoAcCuXbsoLi4u9z66dOlCmzZtmt37mDFjBtdee225Z4XL4x18//339O3bl1tvvRV/f3969+7N0qVLrcdPnDhBampquXfg7e1NVFRUs3kHAwcOJC4ujsOHDwPw999/s2nTJsaMGQNcHu+grNo875YtW/Dx8aFv377Wc6Kjo9FoNGzbtq3B69wQsrKyUBQFHx8f4PJ4ByaTibvuuovHH3+c7t27Vzju6HfQLBY/dIT09HSMRiMBAQHl9gcEBHDo0KFGqlXDMJlMPPzwwwwaNIgePXoAkJqairOzs/V/TouAgABSU1MboZaO8cUXX7B792527NhR4djl8A6OHz/O4sWLiY2N5amnnmLHjh08+OCDODs7M3nyZOtzVvb/RXN5B08++STZ2dl06dIFrVaL0WjkxRdfZOLEiQCXxTsoqzbPm5qair+/f7njOp2OFi1aNMt3UlhYyBNPPMHtt99uXfzvcngHL7/8MjqdjgcffLDS445+BxKwiApmzJjB/v372bRpU2NXpUGdOnWKhx56iHXr1uHi4tLY1WkUJpOJvn378tJLLwHQu3dv9u/fz5IlS5g8eXIj165hfPnll3z66ad89tlndO/enT179vDwww8THBx82bwDUbXi4mJuu+02VFVl8eLFjV2dBrNr1y7efPNNdu/ejaIojVIH6RKqgp+fH1qttsIMkLS0NAIDAxupVo43c+ZMVq9ezfr162ndurV1f2BgIAaDgczMzHLnN6f3sWvXLs6cOcMVV1yBTqdDp9OxceNG3nrrLXQ6HQEBAc3+HQQFBdGtW7dy+7p27UpiYiKA9Tmb8/8Xjz/+OE8++SQTJkygZ8+e3HXXXTzyyCMsWLAAuDzeQVm1ed7AwMAKkxFKSkrIyMhoVu/EEqycPHmSdevWWVtXoPm/gz/++IMzZ87Qpk0b6+/HkydP8uijjxIWFgY4/h1IwFIFZ2dn+vTpQ1xcnHWfyWQiLi6OAQMGNGLNHENVVWbOnMm3337Lb7/9Rnh4eLnjffr0wcnJqdz7iI+PJzExsdm8j5EjR7Jv3z727Nlj3fr27cvEiROt3zf3dzBo0KAK09kPHz5M27ZtAQgPDycwMLDcO8jOzmbbtm3N5h3k5+ej0ZT/1ajVajGZTMDl8Q7Kqs3zDhgwgMzMTHbt2mU957fffsNkMhEVFdXgdXYES7By5MgRfv31V1q2bFnueHN/B3fddRd79+4t9/sxODiYxx9/nJ9//hlogHdQ72G7zdgXX3yh6vV69cMPP1QPHDig3nfffaqPj4+ampra2FWzu+nTp6ve3t7qhg0b1JSUFOuWn59vPef+++9X27Rpo/7222/qzp071QEDBqgDBgxoxFo7XtlZQqra/N/B9u3bVZ1Op7744ovqkSNH1E8//VR1c3NTP/nkE+s5//73v1UfHx911apV6t69e9WxY8eq4eHhakFBQSPW3H4mT56shoSEqKtXr1ZPnDihrly5UvXz81NnzZplPae5vYOcnBz1r7/+Uv/66y8VUF9//XX1r7/+ss6A+f927lA3YSiM4jgztCGEBIVpKkhIEDUYXgCDIkFWESyGByA8BQ7DQ/AANQhwtVVIFAYEAcGZI4N0jC1j/cL+v6Sqzc29J+ntEW0fWW+73Vaj0dBqtdJisVCtVlMYhlkt6dvuZXA6ndTpdOR5nuI4vtojj8fjZYxXziDN7VdC0nMzoLB8YTKZyPd95fN5NZtNLZfLrKf0FLlcLvWYzWaXaw6HgwaDgcrlsgqFgrrdrjabTXaT/gO3heU/ZDCfzxUEgRzHUb1e13Q6vTp/Pp81Ho9VqVTkOI5arZaSJMlotr9vt9tpOBzK9325rqtqtarRaHT1YHq1DKIoSr3/e72epMfWu91uFYahisWiSqWS+v2+9vt9Bqv5mXsZrNfrT/fIKIouY7xyBmnSCsszM3iTPvy+EQAAwCDeYQEAAOZRWAAAgHkUFgAAYB6FBQAAmEdhAQAA5lFYAACAeRQWAABgHoUFAACYR2EBAADmUVgAAIB5FBYAAGAehQUAAJj3Dgyb5CkKHYwvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "encoded_data = autoencoder.encoder(anomalous_test_data_r).numpy()\n",
        "decoded_data = autoencoder.decoder(encoded_data).numpy()\n",
        "\n",
        "plt.plot(anomalous_test_data[0], 'b')\n",
        "plt.plot(decoded_data[0], 'r')\n",
        "plt.fill_between(np.arange(140), np.squeeze(decoded_data[0]), anomalous_test_data[0], color='lightcoral')\n",
        "plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocimg3MBswdS"
      },
      "source": [
        "### Detect anomalies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xnh8wmkDsypN"
      },
      "source": [
        "Detect anomalies by calculating whether the reconstruction loss is greater than a fixed threshold. In this tutorial, you will calculate the mean average error for normal examples from the training set, then classify future examples as anomalous if the reconstruction error is higher than one standard deviation from the training set.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TeuT8uTA5Y_w"
      },
      "source": [
        "Plot the reconstruction error on normal ECGs from the training set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "N7FltOnHu4-l",
        "outputId": "c7278a62-5cc2-4f50-fc99-941fed7834cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "74/74 [==============================] - 0s 3ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "reconstructions = autoencoder.predict(normal_train_data_r)\n",
        "reconstructions = np.squeeze(reconstructions)\n",
        "train_loss = tf.keras.losses.mae(reconstructions, normal_train_data)\n",
        "\n",
        "plt.hist(train_loss[None,:], bins=50)\n",
        "plt.xlabel(\"Train loss\")\n",
        "plt.ylabel(\"No of examples\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mh-3ChEF5hog"
      },
      "source": [
        "Choose a threshold value that is one standard deviations above the mean."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "82hkl0Chs3P_",
        "outputId": "c60fb462-3231-41e4-ea5a-5862d69aa104",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Threshold:  0.02711183\n"
          ]
        }
      ],
      "source": [
        "threshold = np.mean(train_loss) + np.std(train_loss)\n",
        "print(\"Threshold: \", threshold)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uEGlA1Be50Nj"
      },
      "source": [
        "Note: There are other strategies you could use to select a threshold value above which test examples should be classified as anomalous, the correct approach will depend on your dataset. You can learn more with the links at the end of this tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpLSDAeb51D_"
      },
      "source": [
        "If you examine the reconstruction error for the anomalous examples in the test set, you'll notice most have greater reconstruction error than the threshold. By varing the threshold, you can adjust the [precision](https://developers.google.com/machine-learning/glossary#precision) and [recall](https://developers.google.com/machine-learning/glossary#recall) of your classifier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "sKVwjQK955Wy",
        "outputId": "4a4328d8-8db0-4ba5-9574-db4702d21b09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 0s 18ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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y8nKPlit5wq/T6ZQkRUVFubUvWbJEDRs2VPv27ZWRkaEzZ85U2kdmZqYcDodrSUhI8LoOAABgDVf0HJnqUF5erscee0zdunVT+/btXe333XefmjVrpvj4eO3atUuTJk3Svn379P7771fYT0ZGhtLT012vS0pKCDMAAASpKwoy27Zt02effaZjx46pvLzcbd3s2bOvqJC0tDTt3r1bX375pVv7ww8/7Po6KSlJcXFx6tmzp/Lz89WyZctL+rHb7bLb7VdUAwAAsBavg8yzzz6rKVOmqHXr1oqJiZHNZnOt++nX3hg7dqw+/PBDff7552rSpEmV23bp0kWStH///gqDDAAAuHZ4HWTmzZun119/XaNGjbrqnRtjNG7cOK1cuVIbNmxQixYtLvuevLw8SVJcXNxV7x8AAFib10EmJCRE3bp188nO09LStHTpUv31r39VeHi4ioqKJEkOh0N169ZVfn6+li5dqrvuukvR0dHatWuXJkyYoO7duys5OdknNQAAAOvy+q6lCRMm6OWXX/bJzhcsWCCn06kePXooLi7OtSxfvlySFBYWpvXr16tXr15q06aNJk6cqKFDh2r16tU+2T8AALA2r2dkHn/8cfXr108tW7ZUu3btVLt2bbf1ld1NVBFjTJXrExISlJ2d7W2JAADgGuF1kBk/frw+++wz3XnnnYqOjr7iC3wBAACultdBZvHixXrvvffUr1+/6qgHwM80n/yRv0sAgIDl9TUyUVFR3PYMAAACgtdBZvr06Zo2bVqVHxMAAABQE7w+tfTiiy8qPz9fMTExat68+SUX++7YscNnxQEAAFTF6yAzaNCgaigDAADAe14HmWnTplVHHQAAAF7z+hoZAACAQOH1jMzFixc1Z84cvfPOOzp8+LDOnTvntv7EiRM+Kw4AAKAqXgeZGTNm6NVXX9XEiRM1ZcoU/f73v9fBgwe1atUqTZ06tTpqBBBAeK4NgEDi9amlJUuWaOHChZo4caJCQ0M1fPhwvfrqq5o6dao2b95cHTUCAABUyOsgU1RUpKSkJElSgwYN5HQ6JUm/+tWv9NFH/E8NAADUHK+DTJMmTVRYWChJatmypT799FNJ0rZt22S3231bHQAAQBW8DjKDBw9WVlaWJGncuHF66qmn1KpVK40YMUIPPfSQzwsEAACojNcX+86cOdP19bBhw9SsWTNt2rRJrVq1Uv/+/X1aHAAAQFW8DjI/17VrV3Xt2lWSZIyRzWa76qIAAAA84fWppVGjRqm0tPSS9oMHD6p79+4+KQoAAMATXgeZv//970pOTlZOTo6rbfHixerQoYMaNmzo0+IAAACq4vWppa1bt+rJJ59Ujx49NHHiRO3fv1+ffPKJZs+erTFjxlRHjQAAABXyOsjUrl1bzz//vOrVq6dnnnlGoaGhys7OVkpKSnXUBwAAUCmvTy2dP39eEydO1HPPPaeMjAylpKRoyJAh+vjjj6ujPgAAgEp5PSNzyy236MyZM9qwYYO6du0qY4xmzZqlIUOG6KGHHtL8+fOro04AAIBLeD0jc8sttygvL891y7XNZtOkSZOUk5Ojzz//3OcFAgAAVMbrGZnXXnutwvaOHTsqNzf3qgsCAADwlNczMpL0l7/8Rd26dVN8fLwOHTokSZo7d67WrFnj0+IAAACq4nWQWbBggdLT03XXXXfp5MmTunjxoiQpMjJSc+fO9XV9AAAAlbIZY4w3b2jXrp2effZZDRo0SOHh4fr73/+u66+/Xrt371aPHj30/fffV1etV6SkpEQOh0NOp1MRERH+LgfXkOaTP/J3CdeEgzP7+bsEANXA07/fXs/IHDhwQB07dryk3W63V/jRBQAAANXF6yDTokUL5eXlXdK+Zs0atW3b1hc1AQAAeMTru5bS09OVlpams2fPyhijrVu36u2331ZmZqZeffXV6qgRAACgQl7PyPzXf/2XnnvuOU2ZMkVnzpzRfffdpwULFmjevHm69957veorMzNTt956q8LDw9W4cWMNGjRI+/btc9vm7NmzSktLU3R0tBo0aKChQ4equLjY27IBAEAQuqLbr++//359++23On36tIqKinTkyBGNHj3a636ys7OVlpamzZs3a926dTp//rx69erldq3NhAkTtHr1aq1YsULZ2dk6evSohgwZciVlAwCAIOP1XUvV6bvvvlPjxo2VnZ2t7t27y+l0qlGjRlq6dKnuvvtuSdLevXvVtm1b5eTkuJ4uXBXuWoK/cNdSzeCuJSA4VdtdS9XJ6XRKkqKioiRJubm5On/+vFJTU13btGnTRk2bNlVOTk6FfZSVlamkpMRtAQAAwSlggkx5ebkee+wxdevWTe3bt5ckFRUVKSwsTJGRkW7bxsTEqKioqMJ+MjMz5XA4XEtCQkJ1lw4AAPwkYIJMWlqadu/erWXLll1VPxkZGXI6na6loKDARxUCAIBA41GQiYqKcj2x96GHHtKpU6d8WsTYsWP14Ycf6rPPPlOTJk1c7bGxsTp37pxOnjzptn1xcbFiY2Mr7MtutysiIsJtAQAAwcmjIHPu3DnXtSaLFy/W2bNnfbJzY4zGjh2rlStX6m9/+5tatGjhtr5Tp06qXbu2srKyXG379u3T4cOHlZKS4pMaAACAdXn0QLyUlBQNGjRInTp1kjFG48ePV926dSvc9vXXX/d452lpaVq6dKn++te/Kjw83HXdi8PhUN26deVwODR69Gilp6crKipKERERGjdunFJSUjy6YwkAAAQ3j4LMW2+9pTlz5ig/P182m01Op9MnszILFiyQJPXo0cOt/Y033tCoUaMkSXPmzFFISIiGDh2qsrIy9e7dW/Pnz7/qfQMAAOvz+jkyLVq00Pbt2xUdHV1dNfkUz5GBv/AcmZrBc2SA4OTp32+vP2vpwIEDV1UYAACAr1zR7dfZ2dnq37+/EhMTlZiYqAEDBuiLL77wdW0AAABV8jrIvPXWW0pNTVW9evU0fvx414W/PXv21NKlS6ujRgAAgAp5fY1M27Zt9fDDD2vChAlu7bNnz9bChQv1zTff+LTAq8U1MvAXrpGpGVwjAwSnavuspX/84x/q37//Je0DBgzg+hkAAFCjvA4yCQkJbg+o+9H69ev5XCMAAFCjvL5raeLEiRo/frzy8vJ02223SZI2btyoRYsWad68eT4vEAAAoDJeB5lHH31UsbGxeuGFF/TOO+9I+vd1M8uXL9fAgQN9XiAAAEBlvA4ykjR48GANHjzY17UAAAB45YqeIwMAABAIrmhGBgAChSe3uXOLNhC8mJEBAACWRZABAACWdVVBxhgjLx8MDAAA4DNXFGTefPNNJSUlqW7duqpbt66Sk5P1l7/8xde1AQAAVMnri31nz56tp556SmPHjlW3bt0kSV9++aUeeeQRff/995d8BhMAAEB18TrIvPTSS1qwYIFGjBjhahswYIBuvPFGTZ8+nSADAABqjNenlgoLC10fTfBTt912mwoLC31SFAAAgCe8DjKJiYmujyb4qeXLl6tVq1Y+KQoAAMATXp9amjFjhoYNG6bPP//cdY3Mxo0blZWVVWHAAQAAqC5ez8gMHTpUW7ZsUcOGDbVq1SqtWrVKDRs21NatW/n8JQAAUKOu6CMKOnXqpLfeesvXtQAAAHiFJ/sCAADL8nhGJiQkRDabrcptbDabLly4cNVFAQAAeMLjILNy5cpK1+Xk5OjFF19UeXm5T4oCAADwhMdBZuDAgZe07du3T5MnT9bq1at1//336+mnn/ZpcQAAAFW5omtkjh49qjFjxigpKUkXLlxQXl6eFi9erGbNmvm6PgAAgEp5FWScTqcmTZqkxMREff3118rKytLq1avVvn376qoPAACgUh6fWpo1a5aee+45xcbG6u23367wVBMAAEBNshljjCcbhoSEqG7dukpNTVWtWrUq3e7999/3WXG+UFJSIofDIafTqYiICH+Xg2tI88kf+bsE/P8Ozuzn7xIAeMnTv98ez8iMGDHisrdfAwAA1CSPg8yiRYt8vvPPP/9czz//vHJzc1VYWKiVK1dq0KBBrvWjRo3S4sWL3d7Tu3dvrVmzxue1AAAA6/Hrk31LS0vVoUMHvfzyy5Vu06dPHxUWFrqWt99+uwYrBAAAgeyKPmvJV/r27au+fftWuY3dbldsbGwNVQQAAKwk4D9racOGDWrcuLFat26tRx99VMePH69y+7KyMpWUlLgtAAAgOAV0kOnTp4/efPNNZWVl6bnnnlN2drb69u2rixcvVvqezMxMORwO15KQkFCDFQMAgJrk11NLl3Pvvfe6vk5KSlJycrJatmypDRs2qGfPnhW+JyMjQ+np6a7XJSUlhBkAAIJUQM/I/Nz111+vhg0bav/+/ZVuY7fbFRER4bYAAIDgZKkgc+TIER0/flxxcXH+LgUAAAQAv55aOn36tNvsyoEDB5SXl6eoqChFRUVpxowZGjp0qGJjY5Wfn68nnnhCiYmJ6t27tx+rBgAAgcKvQWb79u268847Xa9/vLZl5MiRWrBggXbt2qXFixfr5MmTio+PV69evfTMM8/Ibrf7q2QAABBA/BpkevTooao+6mnt2rU1WA0AALAaS10jAwAA8FMBffs1ANQUTz6tnE/RBgIPMzIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyeCAe8DM8GA0ArIMZGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFmh/i4AAKyi+eSPLrvNwZn9aqASAD9iRgYAAFgWQQYAAFgWQQYAAFiWX4PM559/rv79+ys+Pl42m02rVq1yW2+M0dSpUxUXF6e6desqNTVV3377rX+KBQAAAcevQaa0tFQdOnTQyy+/XOH6WbNm6cUXX9Sf/vQnbdmyRfXr11fv3r119uzZGq4UAAAEIr/etdS3b1/17du3wnXGGM2dO1dTpkzRwIEDJUlvvvmmYmJitGrVKt177701WSoAAAhAAXuNzIEDB1RUVKTU1FRXm8PhUJcuXZSTk1Pp+8rKylRSUuK2AACA4BSwz5EpKiqSJMXExLi1x8TEuNZVJDMzUzNmzKjW2hCYPHnGhxX3havH8QKCV8DOyFypjIwMOZ1O11JQUODvkgAAQDUJ2CATGxsrSSouLnZrLy4udq2riN1uV0REhNsCAACCU8AGmRYtWig2NlZZWVmutpKSEm3ZskUpKSl+rAwAAAQKv14jc/r0ae3fv9/1+sCBA8rLy1NUVJSaNm2qxx57TH/4wx/UqlUrtWjRQk899ZTi4+M1aNAg/xUNAAAChl+DzPbt23XnnXe6Xqenp0uSRo4cqUWLFumJJ55QaWmpHn74YZ08eVK333671qxZozp16virZAAAEEBsxhjj7yKqU0lJiRwOh5xOJ9fLBDnuTEEg4NOvAd/w9O93wF4jAwAAcDkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFl+/awlAEDFPPnIDT4OAWBGBgAAWBhBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWFaovwsAPNF88kf+LgG4pnnyM3hwZr8aqARwx4wMAACwLIIMAACwLIIMAACwLIIMAACwrIAOMtOnT5fNZnNb2rRp4++yAABAgAj4u5ZuvPFGrV+/3vU6NDTgSwYAADUk4FNBaGioYmNj/V0GAAAIQAF9akmSvv32W8XHx+v666/X/fffr8OHD1e5fVlZmUpKStwWAAAQnAJ6RqZLly5atGiRWrdurcLCQs2YMUN33HGHdu/erfDw8Arfk5mZqRkzZtRwpbgaPOwO1xr+zQO+E9AzMn379tWvf/1rJScnq3fv3vr444918uRJvfPOO5W+JyMjQ06n07UUFBTUYMUAAKAmBfSMzM9FRkbqhhtu0P79+yvdxm63y26312BVAADAXwJ6RubnTp8+rfz8fMXFxfm7FAAAEAACOsg8/vjjys7O1sGDB7Vp0yYNHjxYtWrV0vDhw/1dGgAACAABfWrpyJEjGj58uI4fP65GjRrp9ttv1+bNm9WoUSN/lwYAAAJAQAeZZcuW+bsEAAAQwAL61BIAAEBVAnpGBtXDk2dYHJzZrwYqAYJPTT4jxoo/y1asGYGNGRkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZPBAPAAAL4aGC7piRAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlsVzZK6Cr+7l96QfT/uqSZ7WDSCw+epnOdB+J/C8lWsDMzIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyeI6MhdTkMxoC7XkQAK6MFX+WA+13na+eB2bFZ9ZYYVzMyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMuyRJB5+eWX1bx5c9WpU0ddunTR1q1b/V0SAAAIAAEfZJYvX6709HRNmzZNO3bsUIcOHdS7d28dO3bM36UBAAA/C/ggM3v2bI0ZM0YPPvig2rVrpz/96U+qV6+eXn/9dX+XBgAA/CygnyNz7tw55ebmKiMjw9UWEhKi1NRU5eTkVPiesrIylZWVuV47nU5JUklJic/rKy87c9ltPNmvJ/0AAPzDV7/HffV3KFj3VVm/xpgqtwvoIPP999/r4sWLiomJcWuPiYnR3r17K3xPZmamZsyYcUl7QkJCtdR4OY65ftktAMBHfPV7vCb/HgTTvk6dOiWHw1Hp+oAOMlciIyND6enprtfl5eU6ceKEoqOjZbPZvO6vpKRECQkJKigoUEREhC9LDViMmTEHq2ttzNfaeCXGHExjNsbo1KlTio+Pr3K7gA4yDRs2VK1atVRcXOzWXlxcrNjY2ArfY7fbZbfb3doiIyOvupaIiIig+gfiCcZ8bWDMwe9aG6/EmINFVTMxPwroi33DwsLUqVMnZWVludrKy8uVlZWllJQUP1YGAAACQUDPyEhSenq6Ro4cqVtuuUWdO3fW3LlzVVpaqgcffNDfpQEAAD8L+CAzbNgwfffdd5o6daqKiop00003ac2aNZdcAFxd7Ha7pk2bdsnpqmDGmK8NjDn4XWvjlRjztchmLndfEwAAQIAK6GtkAAAAqkKQAQAAlkWQAQAAlkWQAQAAlhX0Qebll19W8+bNVadOHXXp0kVbt26tcvsVK1aoTZs2qlOnjpKSkvTxxx+71p0/f16TJk1SUlKS6tevr/j4eI0YMUJHjx516+PEiRO6//77FRERocjISI0ePVqnT5+ulvFVxB9jbt68uWw2m9syc+bMahlfRXw5ZkmaPn262rRpo/r16+u6665TamqqtmzZ4rZNMB1nybMxB9tx/qlHHnlENptNc+fOdWsPtuP8U5WN2Z/H2dfjHTVq1CVj6dOnj9s2wXaMPRmzv3+WfcoEsWXLlpmwsDDz+uuvm6+//tqMGTPGREZGmuLi4gq337hxo6lVq5aZNWuW2bNnj5kyZYqpXbu2+eqrr4wxxpw8edKkpqaa5cuXm71795qcnBzTuXNn06lTJ7d++vTpYzp06GA2b95svvjiC5OYmGiGDx9e7eM1xn9jbtasmXn66adNYWGhazl9+nS1j9cY34/ZGGOWLFli1q1bZ/Lz883u3bvN6NGjTUREhDl27Jhrm2A6zsZ4NuZgO84/ev/9902HDh1MfHy8mTNnjtu6YDvOP6pqzP46ztUx3pEjR5o+ffq4jeXEiRNu/QTbMfZkzP78Wfa1oA4ynTt3Nmlpaa7XFy9eNPHx8SYzM7PC7e+55x7Tr18/t7YuXbqY3/zmN5XuY+vWrUaSOXTokDHGmD179hhJZtu2ba5tPvnkE2Oz2cw///nPqxmOR/wxZmP+/UPx81+GNaUmxux0Oo0ks379emPMtXGcfz5mY4LzOB85csT8v//3/8zu3bsvGV+wHueqxmyM/45zdYx35MiRZuDAgZXuMxiP8eXGbIx/f5Z9LWhPLZ07d065ublKTU11tYWEhCg1NVU5OTkVvicnJ8dte0nq3bt3pdtLktPplM1mc32eU05OjiIjI3XLLbe4tklNTVVISMgl0/S+5q8x/2jmzJmKjo5Wx44d9fzzz+vChQtXPhgP1cSYz507p1deeUUOh0MdOnRw9RHMx7miMf8omI5zeXm5HnjgAf3ud7/TjTfeWGEfwXacLzfmH9X0ca7Of9cbNmxQ48aN1bp1az366KM6fvy4Wx/Bdoylqsf8I3/8LFeHgH+y75X6/vvvdfHixUueABwTE6O9e/dW+J6ioqIKty8qKqpw+7Nnz2rSpEkaPny464O6ioqK1LhxY7ftQkNDFRUVVWk/vuKvMUvS+PHjdfPNNysqKkqbNm1SRkaGCgsLNXv27KscVdWqc8wffvih7r33Xp05c0ZxcXFat26dGjZs6OojGI9zVWOWgu84P/fccwoNDdX48eMr7SPYjvPlxiz55zhX13j79OmjIUOGqEWLFsrPz9eTTz6pvn37KicnR7Vq1QrKY3y5MUv++1muDkEbZKrb+fPndc8998gYowULFvi7nBpR1ZjT09NdXycnJyssLEy/+c1vlJmZadnHZt95553Ky8vT999/r4ULF+qee+7Rli1bLvmlF0wuN+ZgOs65ubmaN2+eduzYIZvN5u9yaoSnYw6m43zvvfe6vk5KSlJycrJatmypDRs2qGfPnn6srPp4MuZgOsZBe2qpYcOGqlWrloqLi93ai4uLFRsbW+F7YmNjPdr+xz/ohw4d0rp169xmJmJjY3Xs2DG37S9cuKATJ05Uul9f8deYK9KlSxdduHBBBw8e9H4gXqjOMdevX1+JiYnq2rWrXnvtNYWGhuq1115z9RGMx7mqMVfEysf5iy++0LFjx9S0aVOFhoYqNDRUhw4d0sSJE9W8eXNXH8F0nD0Zc0Vq4jhX57/rn7r++uvVsGFD7d+/39VHMB3jivx8zBWpqZ/l6hC0QSYsLEydOnVSVlaWq628vFxZWVlKSUmp8D0pKSlu20vSunXr3Lb/8Q/6t99+q/Xr1ys6OvqSPk6ePKnc3FxX29/+9jeVl5erS5cuvhhapfw15ork5eUpJCSk2mcvqmvMFSkvL1dZWZmrj2A7zhX56ZgrYuXj/MADD2jXrl3Ky8tzLfHx8frd736ntWvXuvoIpuPsyZgrUhPHuab+XR85ckTHjx9XXFycq49gOsYV+fmYK1JTP8vVwt9XG1enZcuWGbvdbhYtWmT27NljHn74YRMZGWmKioqMMcY88MADZvLkya7tN27caEJDQ83//u//mm+++cZMmzbN7ba2c+fOmQEDBpgmTZqYvLw8t9vWysrKXP306dPHdOzY0WzZssV8+eWXplWrVjV6K19Nj3nTpk1mzpw5Ji8vz+Tn55u33nrLNGrUyIwYMcKSYz59+rTJyMgwOTk55uDBg2b79u3mwQcfNHa73ezevdvVTzAdZ0/GHGzHuSIV3ckRTMe5Ij8fsz+Ps6/He+rUKfP444+bnJwcc+DAAbN+/Xpz8803m1atWpmzZ8+6+gmmY+zJmP39s+xrQR1kjDHmpZdeMk2bNjVhYWGmc+fOZvPmza51//Ef/2FGjhzptv0777xjbrjhBhMWFmZuvPFG89FHH7nWHThwwEiqcPnss89c2x0/ftwMHz7cNGjQwERERJgHH3zQnDp1qrqH6lLTY87NzTVdunQxDofD1KlTx7Rt29Y8++yzbr8oqpsvx/zDDz+YwYMHm/j4eBMWFmbi4uLMgAEDzNatW936CKbj7MmYg+04V6SiIBNMx7kiPx+zv4+zL8d75swZ06tXL9OoUSNTu3Zt06xZMzNmzBhXSPhRMB1jT8bs72PsazZjjPHPXBAAAMDVCdprZAAAQPAjyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAIGgcPHpTNZlNeXp6/SwFQQwgyAHzGZrNVuUyfPv2q+l61apXPagUQHEL9XQCA4FFYWOj6evny5Zo6dar27dvnamvQoIE/ygIQxJiRAeAzsbGxrsXhcMhms7m1LVu2TG3btlWdOnXUpk0bzZ8/3/Xec+fOaezYsYqLi1OdOnXUrFkzZWZmSpKaN28uSRo8eLBsNpvrtSeys7PVuXNn2e12xcXFafLkybpw4YJr/bvvvqukpCTVrVtX0dHRSk1NVWlpqSRpw4YN6ty5s+rXr6/IyEh169ZNhw4duvpvFACfYUYGQI1YsmSJpk6dqj/+8Y/q2LGjdu7cqTFjxqh+/foaOXKkXnzxRX3wwQd655131LRpUxUUFKigoECStG3bNjVu3FhvvPGG+vTpo1q1anm0z3/+85+66667NGrUKL355pvau3evxowZozp16mj69OkqLCzU8OHDNWvWLA0ePFinTp3SF198IWOMLly4oEGDBmnMmDF6++23de7cOW3dulU2m606v00AvESQAVAjpk2bphdeeEFDhgyRJLVo0UJ79uzRn//8Z40cOVKHDx9Wq1atdPvtt8tms6lZs2au9zZq1EiSFBkZqdjYWI/3OX/+fCUkJOiPf/yjbDab2rRpo6NHj2rSpEmaOnWqCgsLdeHCBQ0ZMsS1v6SkJEnSiRMn5HQ69atf/UotW7aUJLVt29Yn3wsAvsOpJQDVrrS0VPn5+Ro9erQaNGjgWv7whz8oPz9fkjRq1Cjl5eWpdevWGj9+vD799NOr3u8333yjlJQUt1mUbt266fTp0zpy5Ig6dOignj17KikpSb/+9a+1cOFC/etf/5IkRUVFadSoUerdu7f69++vefPmuV0DBCAwEGQAVLvTp09LkhYuXKi8vDzXsnv3bm3evFmSdPPNN+vAgQN65pln9MMPP+iee+7R3XffXa111apVS+vWrdMnn3yidu3a6aWXXlLr1q114MABSdIbb7yhnJwc3XbbbVq+fLluuOEGV70AAgNBBkC1i4mJUXx8vP7xj38oMTHRbWnRooVru4iICA0bNkwLFy7U8uXL9d577+nEiROSpNq1a+vixYte7bdt27bKycmRMcbVtnHjRoWHh6tJkyaS/n1bd7du3TRjxgzt3LlTYWFhWrlypWv7jh07KiMjQ5s2bVL79u21dOnSq/lWAPAxrpEBUCNmzJih8ePHy+FwqE+fPiorK9P27dv1r3/9S+np6Zo9e7bi4uLUsWNHhYSEaMWKFYqNjVVkZKSkf9+5lJWVpW7duslut+u666677D5/+9vfau7cuRo3bpzGjh2rffv2adq0aUpPT1dISIi2bNmirKws9erVS40bN9aWLVv03XffqW3btjpw4IBeeeUVDRgwQPHx8dq3b5++/fZbjRgxopq/UwC8YgCgGrzxxhvG4XC4tS1ZssTcdNNNJiwszFx33XWme/fu5v333zfGGPPKK6+Ym266ydSvX99ERESYnj17mh07drje+8EHH5jExEQTGhpqmjVrVuE+Dxw4YCSZnTt3uto2bNhgbr31VhMWFmZiY2PNpEmTzPnz540xxuzZs8f07t3bNGrUyNjtdnPDDTeYl156yRhjTFFRkRk0aJCJi4szYWFhplmzZmbq1Knm4sWLvvsmAbhqNmN+MucKAABgIVwjAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALOv/A3nyUZMToMHFAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "reconstructions = autoencoder.predict(anomalous_test_data_r)\n",
        "reconstructions = np.squeeze(reconstructions)\n",
        "test_loss = tf.keras.losses.mae(reconstructions, anomalous_test_data)\n",
        "\n",
        "plt.hist(test_loss[None, :], bins=50)\n",
        "plt.xlabel(\"Test loss\")\n",
        "plt.ylabel(\"No of examples\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFVk_XGE6AX2"
      },
      "source": [
        "Classify an ECG as an anomaly if the reconstruction error is greater than the threshold."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "mkgJZfhh6CHr"
      },
      "outputs": [],
      "source": [
        "def predict(model, data, threshold):\n",
        "  reconstructions = model(data)\n",
        "  data = np.squeeze(data)\n",
        "  reconstructions = np.squeeze(reconstructions)\n",
        "  loss = tf.keras.losses.mae(reconstructions, data)\n",
        "  return tf.math.less(loss, threshold)\n",
        "\n",
        "def print_stats(predictions, labels):\n",
        "  print(\"Accuracy = {}\".format(accuracy_score(labels, predictions)))\n",
        "  print(\"Precision = {}\".format(precision_score(labels, predictions)))\n",
        "  print(\"Recall = {}\".format(recall_score(labels, predictions)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "sOcfXfXq6FBd",
        "outputId": "7a638deb-7439-495a-dcad-7c35c20bcbbe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy = 0.941\n",
            "Precision = 0.9940828402366864\n",
            "Recall = 0.9\n"
          ]
        }
      ],
      "source": [
        "preds = predict(autoencoder, test_data, threshold)\n",
        "print_stats(preds, test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = predict(autoencoder, test_data, threshold+0.001)\n",
        "print_stats(preds, test_labels)"
      ],
      "metadata": {
        "id": "eQXpKMxAw8_X",
        "outputId": "672ea813-4533-45b4-978b-fff2b8b8f46f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy = 0.944\n",
            "Precision = 0.9615384615384616\n",
            "Recall = 0.9375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HrJRef8Ln945"
      },
      "source": [
        "## Next steps\n",
        "\n",
        "To learn more about anomaly detection with autoencoders, check out this excellent [interactive example](https://anomagram.fastforwardlabs.com/#/) built with TensorFlow.js by Victor Dibia. For a real-world use case, you can learn how [Airbus Detects Anomalies in ISS Telemetry Data](https://blog.tensorflow.org/2020/04/how-airbus-detects-anomalies-iss-telemetry-data-tfx.html) using TensorFlow. To learn more about the basics, consider reading this [blog post](https://blog.keras.io/building-autoencoders-in-keras.html) by François Chollet. For more details, check out chapter 14 from [Deep Learning](https://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "autoencoder.ipynb",
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}