3_Static And Dynamic.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNFzY6wChteS"
      },
      "source": [
        "# 1. Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yM1ectCXyO1s",
        "outputId": "d79a1fb7-20d6-409d-dd14-1453686f0504"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting kafka-python\n",
            "  Downloading kafka_python-2.0.2-py2.py3-none-any.whl (246 kB)\n",
            "\u001b[?25l\r\u001b[K     |█▎                              | 10 kB 27.2 MB/s eta 0:00:01\r\u001b[K     |██▋                             | 20 kB 23.4 MB/s eta 0:00:01\r\u001b[K     |████                            | 30 kB 11.0 MB/s eta 0:00:01\r\u001b[K     |█████▎                          | 40 kB 8.6 MB/s eta 0:00:01\r\u001b[K     |██████▋                         | 51 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████                        | 61 kB 5.5 MB/s eta 0:00:01\r\u001b[K     |█████████▎                      | 71 kB 5.5 MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 81 kB 6.2 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 92 kB 6.2 MB/s eta 0:00:01\r\u001b[K     |█████████████▎                  | 102 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████████▋                 | 112 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 122 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████▎              | 133 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████▋             | 143 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 153 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▎          | 163 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▋         | 174 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 184 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▎      | 194 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▋     | 204 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 215 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▎  | 225 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▋ | 235 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 245 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 246 kB 5.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: kafka-python\n",
            "Successfully installed kafka-python-2.0.2\n"
          ]
        }
      ],
      "source": [
        "!pip install kafka-python\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import ast\n",
        "import time\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.metrics import classification_report, average_precision_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import preprocessing\n",
        "import pickle\n",
        "from joblib import dump, load\n",
        "from statistics import mean\n",
        "from kafka import KafkaConsumer\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u2d_nGHFoepz"
      },
      "source": [
        "# **Binary Classifiers: Problem 1**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TNQEf_p6BlAh"
      },
      "source": [
        "# **Static Part**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17CerdS8hySV"
      },
      "source": [
        "# 2. Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "fRVZpOuty_N3"
      },
      "outputs": [],
      "source": [
        "## read the data\n",
        "df_data = pd.read_csv('/content/cicids_static_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "wLr14e9wzEDF",
        "outputId": "cd6cf364-086a-4624-cf79-2a06ad26f631"
      },
      "outputs": [
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              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>440</td>\n",
              "      <td>307</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>80</td>\n",
              "      <td>69</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>28985.507250</td>\n",
              "      <td>69.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>69</td>\n",
              "      <td>69</td>\n",
              "      <td>69</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>28985.507250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>251</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Destination Port   Flow Duration  ...   Idle Min   Label\n",
              "0                 53             159  ...          0  BENIGN\n",
              "1                 53           51673  ...          0  BENIGN\n",
              "2               7496              43  ...          0  ATTACK\n",
              "3              46564              61  ...          0  BENIGN\n",
              "4                 80              69  ...          0  BENIGN\n",
              "\n",
              "[5 rows x 79 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## check the 1st 5 rows\n",
        "df_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQ-jTApr_Un3",
        "outputId": "933f7502-d930-4839-fba9-5c3ac184a674"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "25191"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WSDja8oPzF81",
        "outputId": "e4788901-d54b-46e8-8ed9-a018f1de5f3c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Destination Port</th>\n",
              "      <th>Flow Duration</th>\n",
              "      <th>Total Fwd Packets</th>\n",
              "      <th>Total Backward Packets</th>\n",
              "      <th>Total Length of Fwd Packets</th>\n",
              "      <th>Total Length of Bwd Packets</th>\n",
              "      <th>Fwd Packet Length Max</th>\n",
              "      <th>Fwd Packet Length Min</th>\n",
              "      <th>Fwd Packet Length Mean</th>\n",
              "      <th>Fwd Packet Length Std</th>\n",
              "      <th>Bwd Packet Length Max</th>\n",
              "      <th>Bwd Packet Length Min</th>\n",
              "      <th>Bwd Packet Length Mean</th>\n",
              "      <th>Bwd Packet Length Std</th>\n",
              "      <th>Flow Bytes/s</th>\n",
              "      <th>Flow Packets/s</th>\n",
              "      <th>Flow IAT Mean</th>\n",
              "      <th>Flow IAT Std</th>\n",
              "      <th>Flow IAT Max</th>\n",
              "      <th>Flow IAT Min</th>\n",
              "      <th>Fwd IAT Total</th>\n",
              "      <th>Fwd IAT Mean</th>\n",
              "      <th>Fwd IAT Std</th>\n",
              "      <th>Fwd IAT Max</th>\n",
              "      <th>Fwd IAT Min</th>\n",
              "      <th>Bwd IAT Total</th>\n",
              "      <th>Bwd IAT Mean</th>\n",
              "      <th>Bwd IAT Std</th>\n",
              "      <th>Bwd IAT Max</th>\n",
              "      <th>Bwd IAT Min</th>\n",
              "      <th>Fwd PSH Flags</th>\n",
              "      <th>Bwd PSH Flags</th>\n",
              "      <th>Fwd URG Flags</th>\n",
              "      <th>Bwd URG Flags</th>\n",
              "      <th>Fwd Header Length</th>\n",
              "      <th>Bwd Header Length</th>\n",
              "      <th>Fwd Packets/s</th>\n",
              "      <th>Bwd Packets/s</th>\n",
              "      <th>Min Packet Length</th>\n",
              "      <th>Max Packet Length</th>\n",
              "      <th>Packet Length Mean</th>\n",
              "      <th>Packet Length Std</th>\n",
              "      <th>Packet Length Variance</th>\n",
              "      <th>FIN Flag Count</th>\n",
              "      <th>SYN Flag Count</th>\n",
              "      <th>RST Flag Count</th>\n",
              "      <th>PSH Flag Count</th>\n",
              "      <th>ACK Flag Count</th>\n",
              "      <th>URG Flag Count</th>\n",
              "      <th>CWE Flag Count</th>\n",
              "      <th>ECE Flag Count</th>\n",
              "      <th>Down/Up Ratio</th>\n",
              "      <th>Average Packet Size</th>\n",
              "      <th>Avg Fwd Segment Size</th>\n",
              "      <th>Avg Bwd Segment Size</th>\n",
              "      <th>Fwd Header Length.1</th>\n",
              "      <th>Fwd Avg Bytes/Bulk</th>\n",
              "      <th>Fwd Avg Packets/Bulk</th>\n",
              "      <th>Fwd Avg Bulk Rate</th>\n",
              "      <th>Bwd Avg Bytes/Bulk</th>\n",
              "      <th>Bwd Avg Packets/Bulk</th>\n",
              "      <th>Bwd Avg Bulk Rate</th>\n",
              "      <th>Subflow Fwd Packets</th>\n",
              "      <th>Subflow Fwd Bytes</th>\n",
              "      <th>Subflow Bwd Packets</th>\n",
              "      <th>Subflow Bwd Bytes</th>\n",
              "      <th>Init_Win_bytes_forward</th>\n",
              "      <th>Init_Win_bytes_backward</th>\n",
              "      <th>act_data_pkt_fwd</th>\n",
              "      <th>min_seg_size_forward</th>\n",
              "      <th>Active Mean</th>\n",
              "      <th>Active Std</th>\n",
              "      <th>Active Max</th>\n",
              "      <th>Active Min</th>\n",
              "      <th>Idle Mean</th>\n",
              "      <th>Idle Std</th>\n",
              "      <th>Idle Max</th>\n",
              "      <th>Idle Min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25191.000000</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.00000</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.00000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>2.518500e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.0</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.0</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>25191.0</td>\n",
              "      <td>25191.0</td>\n",
              "      <td>25191.0</td>\n",
              "      <td>25191.0</td>\n",
              "      <td>25191.0</td>\n",
              "      <td>25191.0</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>25191.00000</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>25191.000000</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "      <td>2.519100e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8765.751379</td>\n",
              "      <td>1.302190e+07</td>\n",
              "      <td>14.372752</td>\n",
              "      <td>16.80628</td>\n",
              "      <td>5.837956e+02</td>\n",
              "      <td>2.992545e+04</td>\n",
              "      <td>219.933746</td>\n",
              "      <td>20.452860</td>\n",
              "      <td>62.091345</td>\n",
              "      <td>72.396546</td>\n",
              "      <td>624.78143</td>\n",
              "      <td>45.731333</td>\n",
              "      <td>231.531587</td>\n",
              "      <td>227.162493</td>\n",
              "      <td>inf</td>\n",
              "      <td>inf</td>\n",
              "      <td>1.139005e+06</td>\n",
              "      <td>2.295824e+06</td>\n",
              "      <td>6.924351e+06</td>\n",
              "      <td>1.629789e+05</td>\n",
              "      <td>1.269634e+07</td>\n",
              "      <td>2.327944e+06</td>\n",
              "      <td>2.246253e+06</td>\n",
              "      <td>6.762395e+06</td>\n",
              "      <td>1.163676e+06</td>\n",
              "      <td>1.002364e+07</td>\n",
              "      <td>1.843998e+06</td>\n",
              "      <td>1.244500e+06</td>\n",
              "      <td>4.274531e+06</td>\n",
              "      <td>1.092621e+06</td>\n",
              "      <td>0.048787</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.314755e+02</td>\n",
              "      <td>3.722468e+02</td>\n",
              "      <td>5.995505e+04</td>\n",
              "      <td>6893.287051</td>\n",
              "      <td>18.223850</td>\n",
              "      <td>713.829423</td>\n",
              "      <td>139.739549</td>\n",
              "      <td>220.086914</td>\n",
              "      <td>2.957633e+05</td>\n",
              "      <td>0.026597</td>\n",
              "      <td>0.048787</td>\n",
              "      <td>0.000159</td>\n",
              "      <td>0.280378</td>\n",
              "      <td>0.302013</td>\n",
              "      <td>0.105831</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>0.000159</td>\n",
              "      <td>0.687785</td>\n",
              "      <td>157.253539</td>\n",
              "      <td>62.091345</td>\n",
              "      <td>231.531587</td>\n",
              "      <td>3.314755e+02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.372752</td>\n",
              "      <td>5.837956e+02</td>\n",
              "      <td>16.80628</td>\n",
              "      <td>2.992641e+04</td>\n",
              "      <td>7068.153348</td>\n",
              "      <td>2338.379024</td>\n",
              "      <td>10.750308</td>\n",
              "      <td>25.890358</td>\n",
              "      <td>8.175780e+04</td>\n",
              "      <td>4.553670e+04</td>\n",
              "      <td>1.631345e+05</td>\n",
              "      <td>5.662674e+04</td>\n",
              "      <td>6.095297e+06</td>\n",
              "      <td>3.628463e+05</td>\n",
              "      <td>6.366900e+06</td>\n",
              "      <td>5.795319e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>19077.629319</td>\n",
              "      <td>3.191602e+07</td>\n",
              "      <td>1220.476270</td>\n",
              "      <td>1620.27646</td>\n",
              "      <td>7.757115e+03</td>\n",
              "      <td>3.632427e+06</td>\n",
              "      <td>739.251288</td>\n",
              "      <td>60.512982</td>\n",
              "      <td>191.647090</td>\n",
              "      <td>293.360718</td>\n",
              "      <td>1481.65387</td>\n",
              "      <td>70.516212</td>\n",
              "      <td>472.544033</td>\n",
              "      <td>624.163182</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.418710e+06</td>\n",
              "      <td>7.231620e+06</td>\n",
              "      <td>2.035185e+07</td>\n",
              "      <td>2.809065e+06</td>\n",
              "      <td>3.181629e+07</td>\n",
              "      <td>9.748498e+06</td>\n",
              "      <td>7.573190e+06</td>\n",
              "      <td>2.040783e+07</td>\n",
              "      <td>9.181693e+06</td>\n",
              "      <td>2.889146e+07</td>\n",
              "      <td>9.324723e+06</td>\n",
              "      <td>5.304017e+06</td>\n",
              "      <td>1.565741e+07</td>\n",
              "      <td>8.915008e+06</td>\n",
              "      <td>0.215427</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008910</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.493288e+04</td>\n",
              "      <td>3.240846e+04</td>\n",
              "      <td>2.337848e+05</td>\n",
              "      <td>39174.193402</td>\n",
              "      <td>26.166304</td>\n",
              "      <td>1606.101337</td>\n",
              "      <td>252.870523</td>\n",
              "      <td>497.329648</td>\n",
              "      <td>1.240815e+06</td>\n",
              "      <td>0.160905</td>\n",
              "      <td>0.215427</td>\n",
              "      <td>0.012600</td>\n",
              "      <td>0.449193</td>\n",
              "      <td>0.459140</td>\n",
              "      <td>0.307628</td>\n",
              "      <td>0.008910</td>\n",
              "      <td>0.012600</td>\n",
              "      <td>0.666127</td>\n",
              "      <td>273.365361</td>\n",
              "      <td>191.647090</td>\n",
              "      <td>472.544033</td>\n",
              "      <td>2.493288e+04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1220.476270</td>\n",
              "      <td>7.757115e+03</td>\n",
              "      <td>1620.27646</td>\n",
              "      <td>3.632433e+06</td>\n",
              "      <td>14824.236344</td>\n",
              "      <td>9227.293545</td>\n",
              "      <td>1179.682409</td>\n",
              "      <td>6.436242</td>\n",
              "      <td>8.378623e+05</td>\n",
              "      <td>4.001228e+05</td>\n",
              "      <td>1.208254e+06</td>\n",
              "      <td>7.917691e+05</td>\n",
              "      <td>1.949681e+07</td>\n",
              "      <td>3.767660e+06</td>\n",
              "      <td>2.013473e+07</td>\n",
              "      <td>1.923531e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.200000e+07</td>\n",
              "      <td>-2.000000e+06</td>\n",
              "      <td>-1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-1.000000e+00</td>\n",
              "      <td>-1.200000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>53.000000</td>\n",
              "      <td>1.670000e+02</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.200000e+01</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.194258e+02</td>\n",
              "      <td>6.526833e+00</td>\n",
              "      <td>6.566667e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.370000e+02</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000e+01</td>\n",
              "      <td>2.000000e+01</td>\n",
              "      <td>3.332986e+00</td>\n",
              "      <td>0.177968</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.200000e+01</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>80.000000</td>\n",
              "      <td>3.132600e+04</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>6.400000e+01</td>\n",
              "      <td>1.280000e+02</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>83.00000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.717414e+03</td>\n",
              "      <td>1.073103e+02</td>\n",
              "      <td>1.169300e+04</td>\n",
              "      <td>1.212436e+02</td>\n",
              "      <td>3.089000e+04</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.400000e+01</td>\n",
              "      <td>4.000000e+01</td>\n",
              "      <td>6.131396e+01</td>\n",
              "      <td>20.963921</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>58.028571</td>\n",
              "      <td>27.135463</td>\n",
              "      <td>7.363333e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>73.250000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>6.400000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.400000e+01</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>1.280000e+02</td>\n",
              "      <td>235.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>443.000000</td>\n",
              "      <td>1.461366e+06</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>1.760000e+02</td>\n",
              "      <td>3.860000e+02</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>23.276598</td>\n",
              "      <td>211.00000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>15.418995</td>\n",
              "      <td>1.860465e+05</td>\n",
              "      <td>2.209945e+04</td>\n",
              "      <td>2.024310e+05</td>\n",
              "      <td>2.557764e+05</td>\n",
              "      <td>1.042964e+06</td>\n",
              "      <td>7.200000e+01</td>\n",
              "      <td>4.985900e+05</td>\n",
              "      <td>7.910771e+04</td>\n",
              "      <td>3.382411e+04</td>\n",
              "      <td>2.468160e+05</td>\n",
              "      <td>4.900000e+01</td>\n",
              "      <td>7.554750e+04</td>\n",
              "      <td>1.337233e+04</td>\n",
              "      <td>8.067318e+03</td>\n",
              "      <td>4.339350e+04</td>\n",
              "      <td>2.350000e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.040000e+02</td>\n",
              "      <td>9.200000e+01</td>\n",
              "      <td>1.149425e+04</td>\n",
              "      <td>6172.839506</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>295.000000</td>\n",
              "      <td>105.366667</td>\n",
              "      <td>128.167079</td>\n",
              "      <td>1.642680e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>135.325000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.040000e+02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.760000e+02</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>3.860000e+02</td>\n",
              "      <td>8192.000000</td>\n",
              "      <td>235.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>65464.000000</td>\n",
              "      <td>1.199998e+08</td>\n",
              "      <td>193200.000000</td>\n",
              "      <td>256740.00000</td>\n",
              "      <td>1.162781e+06</td>\n",
              "      <td>5.756816e+08</td>\n",
              "      <td>11632.000000</td>\n",
              "      <td>1983.000000</td>\n",
              "      <td>2901.750000</td>\n",
              "      <td>5795.500690</td>\n",
              "      <td>11680.00000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>3867.000000</td>\n",
              "      <td>6692.644993</td>\n",
              "      <td>inf</td>\n",
              "      <td>inf</td>\n",
              "      <td>9.630000e+07</td>\n",
              "      <td>8.190000e+07</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>9.630000e+07</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>8.270000e+07</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>8.260000e+07</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.939424e+06</td>\n",
              "      <td>5.134800e+06</td>\n",
              "      <td>3.000000e+06</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1306.000000</td>\n",
              "      <td>11680.000000</td>\n",
              "      <td>2161.280000</td>\n",
              "      <td>4380.487987</td>\n",
              "      <td>1.920000e+07</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2251.333333</td>\n",
              "      <td>2901.750000</td>\n",
              "      <td>3867.000000</td>\n",
              "      <td>3.939424e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>193200.000000</td>\n",
              "      <td>1.162781e+06</td>\n",
              "      <td>256740.00000</td>\n",
              "      <td>5.756816e+08</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>187113.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>1.016597e+08</td>\n",
              "      <td>2.529324e+07</td>\n",
              "      <td>1.016597e+08</td>\n",
              "      <td>1.016597e+08</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>6.270000e+07</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>1.200000e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Destination Port   Flow Duration  ...      Idle Max      Idle Min\n",
              "count       25191.000000    2.519100e+04  ...  2.519100e+04  2.519100e+04\n",
              "mean         8765.751379    1.302190e+07  ...  6.366900e+06  5.795319e+06\n",
              "std         19077.629319    3.191602e+07  ...  2.013473e+07  1.923531e+07\n",
              "min             0.000000   -1.000000e+00  ...  0.000000e+00  0.000000e+00\n",
              "25%            53.000000    1.670000e+02  ...  0.000000e+00  0.000000e+00\n",
              "50%            80.000000    3.132600e+04  ...  0.000000e+00  0.000000e+00\n",
              "75%           443.000000    1.461366e+06  ...  0.000000e+00  0.000000e+00\n",
              "max         65464.000000    1.199998e+08  ...  1.200000e+08  1.200000e+08\n",
              "\n",
              "[8 rows x 78 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## some stats about the data\n",
        "df_data.describe()\n",
        "## From this we can drop the columns with only zeros: FWD"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2M6ndtfIzPGz",
        "outputId": "f23465f1-64dc-4ea9-fbc5-4284caa42924"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index([' Destination Port', ' Flow Duration', ' Total Fwd Packets',\n",
              "       ' Total Backward Packets', 'Total Length of Fwd Packets',\n",
              "       ' Total Length of Bwd Packets', ' Fwd Packet Length Max',\n",
              "       ' Fwd Packet Length Min', ' Fwd Packet Length Mean',\n",
              "       ' Fwd Packet Length Std', 'Bwd Packet Length Max',\n",
              "       ' Bwd Packet Length Min', ' Bwd Packet Length Mean',\n",
              "       ' Bwd Packet Length Std', 'Flow Bytes/s', ' Flow Packets/s',\n",
              "       ' Flow IAT Mean', ' Flow IAT Std', ' Flow IAT Max', ' Flow IAT Min',\n",
              "       'Fwd IAT Total', ' Fwd IAT Mean', ' Fwd IAT Std', ' Fwd IAT Max',\n",
              "       ' Fwd IAT Min', 'Bwd IAT Total', ' Bwd IAT Mean', ' Bwd IAT Std',\n",
              "       ' Bwd IAT Max', ' Bwd IAT Min', 'Fwd PSH Flags', ' Bwd PSH Flags',\n",
              "       ' Fwd URG Flags', ' Bwd URG Flags', ' Fwd Header Length',\n",
              "       ' Bwd Header Length', 'Fwd Packets/s', ' Bwd Packets/s',\n",
              "       ' Min Packet Length', ' Max Packet Length', ' Packet Length Mean',\n",
              "       ' Packet Length Std', ' Packet Length Variance', 'FIN Flag Count',\n",
              "       ' SYN Flag Count', ' RST Flag Count', ' PSH Flag Count',\n",
              "       ' ACK Flag Count', ' URG Flag Count', ' CWE Flag Count',\n",
              "       ' ECE Flag Count', ' Down/Up Ratio', ' Average Packet Size',\n",
              "       ' Avg Fwd Segment Size', ' Avg Bwd Segment Size',\n",
              "       ' Fwd Header Length.1', 'Fwd Avg Bytes/Bulk', ' Fwd Avg Packets/Bulk',\n",
              "       ' Fwd Avg Bulk Rate', ' Bwd Avg Bytes/Bulk', ' Bwd Avg Packets/Bulk',\n",
              "       'Bwd Avg Bulk Rate', 'Subflow Fwd Packets', ' Subflow Fwd Bytes',\n",
              "       ' Subflow Bwd Packets', ' Subflow Bwd Bytes', 'Init_Win_bytes_forward',\n",
              "       ' Init_Win_bytes_backward', ' act_data_pkt_fwd',\n",
              "       ' min_seg_size_forward', 'Active Mean', ' Active Std', ' Active Max',\n",
              "       ' Active Min', 'Idle Mean', ' Idle Std', ' Idle Max', ' Idle Min',\n",
              "       'Label'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## get names of columns of data\n",
        "df_data.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m9c0cGlgh5KR"
      },
      "source": [
        "# 3. Data Cleaning (Remove NaNs and Infinities)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fT6SNy_w1cUW",
        "outputId": "d89ac402-f1d5-49b4-b006-e923077de628"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Flow Bytes/s'], dtype='object')"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## get the columns with null values greater than zero\n",
        "df_data.columns[df_data.isna().sum() >0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yDXjp7y_2tmv",
        "outputId": "2a85a18f-f4c6-45c8-afcf-71462fa7d99b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## check if this column has really missing values\n",
        "df_data['Flow Bytes/s'].isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rT-cvklph_vz"
      },
      "source": [
        "Here there was a problem in dropping the infinity values; even though I'd drop them they persist and keep being there: some reasons:\n",
        "1.  There might be no nulls but 'np.inf' values\n",
        "2.   Check from the describe if there are any inf values for different columns (as these won't appear in conditions of nulls)\n",
        "3.   Drop by condition better than the index as this might be tricky; condition gets it at once with inplace = True\n",
        "4.   I'm not sure if this affected but make a copy for the dataframe to drop on"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "EBgAvN-b8I4Z"
      },
      "outputs": [],
      "source": [
        "## Drop nulls ==> this drops the nulls in flow/bytes\n",
        "## Since the dataframe is of a quite large number we can easily drop the values ((we don't need to know the column or anything; the above trials was for us))\n",
        "## But we need to remove it from the whole dataframe\n",
        "df_data_copy = df_data.copy()\n",
        "df_data_na = df_data_copy.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W1YhJIxFAIlW",
        "outputId": "650ec60e-b65d-4190-840f-a4339e8b04e3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "25185"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## length should be less by 6\n",
        "len(df_data_na)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "okF_yemXD16E"
      },
      "outputs": [],
      "source": [
        "# Here we drop the infinity values in Flow Bytes\n",
        "df_data_na.drop(df_data_na[df_data_na['Flow Bytes/s'] == np.inf].index, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfCUxuM_EXlN",
        "outputId": "a8ab70b5-f366-4189-af9e-eec2fc79dfb2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[] 0\n"
          ]
        }
      ],
      "source": [
        "## Recheck\n",
        "idx = df_data_na.index[df_data_na['Flow Bytes/s'] == np.inf]\n",
        "idx = list(idx)\n",
        "print(idx, df_data_na['Flow Bytes/s'].isna().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gy_uPJDuAUKt",
        "outputId": "ae35f79f-c18e-4137-f65b-033fe6685720"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    2.517900e+04\n",
              "mean     1.661845e+06\n",
              "std      2.785234e+07\n",
              "min     -1.200000e+07\n",
              "25%      1.194084e+02\n",
              "50%      4.713444e+03\n",
              "75%      1.860465e+05\n",
              "max      2.071000e+09\n",
              "Name: Flow Bytes/s, dtype: float64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## we can see the range of values (-1.2x10^-7 to 1.86*10^5) if we want to replace the NaN by mean value: the value was fixed\n",
        "df_data_na['Flow Bytes/s'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eHEMaxKkAa99",
        "outputId": "fc587788-efa6-4c79-80a5-f94322a50b0b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Int64Index([], dtype='int64')"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "### Now we need to remove the infinity values in Flow Packets ==> we can see there are no inf values\n",
        "idx2 = df_data_na.index[df_data_na[' Flow Packets/s'] == np.inf]\n",
        "idx2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fIIj_NU659f2",
        "outputId": "0d992a4a-9629-413f-a1d0-5e7e8fd8d86c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    2.517900e+04\n",
              "mean     6.680076e+04\n",
              "std      2.420045e+05\n",
              "min     -2.000000e+06\n",
              "25%      6.514248e+00\n",
              "50%      1.070754e+02\n",
              "75%      2.209945e+04\n",
              "max      3.000000e+06\n",
              "Name:  Flow Packets/s, dtype: float64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data_na[' Flow Packets/s'].describe() ## And its describe is cleaned now"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkg_2ula3Ew3",
        "outputId": "f3db878d-2a6d-4739-9931-fe4c144acf10"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "25179"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## check the number of rows in dataframe\n",
        "len(df_data_na)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioaE1V1miHJE"
      },
      "source": [
        "# 4. Split the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Zh2YIahbzaQF"
      },
      "outputs": [],
      "source": [
        "## get the data\n",
        "df_x = df_data_na.iloc[:, :-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "9mGSH4tAzkzV"
      },
      "outputs": [],
      "source": [
        "## get the label of the data\n",
        "df_y = df_data_na.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "-STrGnZp475L"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df_x, df_y, test_size=0.3, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ztnfthfdiK6O"
      },
      "source": [
        "# 5. Classifier A (XGBoost)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zJfX9xBA5ze3"
      },
      "source": [
        "**Evaluation during training by cross-val**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "LlOT5mxe8kAi"
      },
      "outputs": [],
      "source": [
        "## Encode (y_true) to avoid mixing the classes by label encoder due to \n",
        "y_true_enc= []\n",
        "for i in range(len(y_train)):\n",
        "\n",
        "    if y_train.iloc[i] == \"BENIGN\":\n",
        "       y_true_enc.append(0)\n",
        "    else: \n",
        "       y_true_enc.append(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_JdXafd9gH2",
        "outputId": "5c0279e4-8624-43d9-eef6-a047278360c1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "17625"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(y_true_enc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "E2sjjDa-6jy7"
      },
      "outputs": [],
      "source": [
        "## This needs the y-true to be encoded\n",
        "### we make this on the train data then test by test data\n",
        "\n",
        "def cross_val_scores_print(clf, X_train, y_train, y_enc, cv):\n",
        "    ### Macro f1 scores\n",
        "    f1_scores = cross_val_score(clf, X_train, y_train, cv=cv, scoring='f1_macro', error_score=\"raise\")\n",
        "    print(\"Macro F1 Scores of each iteration from the 10 iterations: {0}\".format(f1_scores))\n",
        "    print(\"Average Macro F1 Score of the 10 iterations: {0}\".format(mean(f1_scores)))\n",
        "    print(50*'-')\n",
        "    ### Precision ==> We have to make it: macro/micro/weighted in case of multi class, elsewise it throws nans\n",
        "    p_scores = cross_val_score(clf, X_train, y_enc, cv=cv, scoring='precision_macro', error_score=\"raise\")\n",
        "    print(\"Precision Scores of each iteration from the 10 iterations: {0}\".format(p_scores))\n",
        "    print(\"Average Precision Score of the 10 iterations: {0}\".format(mean(p_scores)))\n",
        "    print(50*'-')\n",
        "    ### Recall\n",
        "    r_scores = cross_val_score(clf, X_train, y_enc, cv=cv, scoring='recall_macro', error_score=\"raise\")\n",
        "    print(\"Recall Scores of each iteration from the 10 iterations: {0}\".format(r_scores))\n",
        "    print(\"Average Recall of the 10 iterations: {0}\".format(mean(r_scores)))\n",
        "    print(50*'-')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "aZKH4hbwz2j8"
      },
      "outputs": [],
      "source": [
        "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,\n",
        "    max_depth=1, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j8IfKjphGnTk",
        "outputId": "d1dbe90d-7b95-4cfd-a0ae-49d8185b1f84"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Macro F1 Scores of each iteration from the 10 iterations: [0.96555016 0.95293934 0.95523146 0.97368264 0.97196312 0.97007202\n",
            " 0.9661982  0.97325288 0.97855876 0.97422846]\n",
            "Average Macro F1 Score of the 10 iterations: 0.9681677039730142\n",
            "--------------------------------------------------\n",
            "Precision Scores of each iteration from the 10 iterations: [0.96929449 0.96011559 0.96938553 0.98140668 0.98100375 0.97281913\n",
            " 0.97805904 0.97122928 0.96549355 0.96549355]\n",
            "Average Precision Score of the 10 iterations: 0.9714300607705366\n",
            "--------------------------------------------------\n",
            "Recall Scores of each iteration from the 10 iterations: [0.96188102 0.94066504 0.94454784 0.96626591 0.96334193 0.94780151\n",
            " 0.9425473  0.95631097 0.95568283 0.95568283]\n",
            "Average Recall of the 10 iterations: 0.9534727159192634\n",
            "--------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "cross_val_scores_print(clf, X_train, y_train, y_true_enc, cv=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-Y-ddMr0Mlu"
      },
      "source": [
        "**Evaluation on test data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "8xCGM74g5wig"
      },
      "outputs": [],
      "source": [
        "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,\n",
        "    max_depth=1, random_state=0).fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "8XWC72RopQUK"
      },
      "outputs": [],
      "source": [
        "y_pred = clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "1_yJv_p3qFIj"
      },
      "outputs": [],
      "source": [
        "## Requires encoding of the input\n",
        "## Manual Encoding is maintained here: as for automatic encoding; the \"attack\" class is considered as negative class due to the Alphabet\n",
        "def pr(y_test, y_pred):\n",
        "    y_pred_enc_f = []\n",
        "    y_test_enc_f = []\n",
        "    for i in range(len(y_pred)):\n",
        "        if y_pred[i] == \"BENIGN\":\n",
        "          y_pred_enc_f.append(0)\n",
        "        else: \n",
        "          y_pred_enc_f.append(1)\n",
        "\n",
        "        if y_test.iloc[i] == \"BENIGN\":\n",
        "          y_test_enc_f.append(0)\n",
        "        else: \n",
        "          y_test_enc_f.append(1)\n",
        "\n",
        "    avg_pr = average_precision_score(y_test_enc_f, y_pred_enc_f)\n",
        "    return avg_pr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cx7f8VMtqx2E",
        "outputId": "c5987e17-4311-45cd-f288-b9c90bcb5e91"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average precision Recall score: 0.9052908242099844\n"
          ]
        }
      ],
      "source": [
        "average_precision = pr(y_test, y_pred)\n",
        "print(\"Average precision Recall score: {0}\".format(average_precision))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMtJoPeYpL-b",
        "outputId": "400f22fd-442f-403e-8f9b-c18b920229fd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      ATTACK       0.96      0.93      0.95       741\n",
            "      BENIGN       0.99      1.00      0.99      6813\n",
            "\n",
            "    accuracy                           0.99      7554\n",
            "   macro avg       0.98      0.96      0.97      7554\n",
            "weighted avg       0.99      0.99      0.99      7554\n",
            "\n"
          ]
        }
      ],
      "source": [
        "## Classification Report\n",
        "c_report = classification_report(y_test, y_pred)\n",
        "print(c_report)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cm8iYD9i_ugj"
      },
      "source": [
        "The macro average of f1 score is equal to 97% and the recall avg is of 96%  \\\n",
        "These metrics are quite high but we shall check the performance on the other classifier to decide which to use in the adaptive model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XSjSiypjjXiE"
      },
      "source": [
        "# 6. Classifier B (Decision Tree)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7HOeh24AnQz"
      },
      "source": [
        "**Evaluation during training by cross-val**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "9DZm3s-eAtPZ"
      },
      "outputs": [],
      "source": [
        "clf2 = DecisionTreeClassifier(random_state=0, class_weight = 'balanced')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JonLle9AtVI",
        "outputId": "d36d128e-5203-455c-c372-84dd4f7d5b07"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Macro F1 Scores of each iteration from the 10 iterations: [0.98691098 0.98067253 0.99023485 0.98372475 0.98561616 0.98863648\n",
            " 0.98538976 0.98857662 0.99018264 0.99028527]\n",
            "Average Macro F1 Score of the 10 iterations: 0.9870230031872043\n",
            "--------------------------------------------------\n",
            "Precision Scores of each iteration from the 10 iterations: [0.98950403 0.97575284 0.99023485 0.98628362 0.97948352 0.98736129\n",
            " 0.98412305 0.98986877 0.99279291 0.98774316]\n",
            "Average Precision Score of the 10 iterations: 0.9863148041781026\n",
            "--------------------------------------------------\n",
            "Recall Scores of each iteration from the 10 iterations: [0.9843525  0.98572431 0.99023485 0.98119986 0.99195355 0.98992019\n",
            " 0.98666494 0.98729308 0.98760715 0.99286137]\n",
            "Average Recall of the 10 iterations: 0.9877811804553231\n",
            "--------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "cross_val_scores_print(clf2, X_train, y_train, y_true_enc, cv=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "Ds4Ye2s7HjhS"
      },
      "outputs": [],
      "source": [
        "clf2 = DecisionTreeClassifier(random_state=0, class_weight= 'balanced').fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "WvjJr7N-kJk8"
      },
      "outputs": [],
      "source": [
        "y_pred2 = clf2.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLF9cHwVkJmW",
        "outputId": "1bdb485a-4733-497b-ed11-1815327e788a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      ATTACK       0.99      0.98      0.99       741\n",
            "      BENIGN       1.00      1.00      1.00      6813\n",
            "\n",
            "    accuracy                           1.00      7554\n",
            "   macro avg       0.99      0.99      0.99      7554\n",
            "weighted avg       1.00      1.00      1.00      7554\n",
            "\n"
          ]
        }
      ],
      "source": [
        "c_report2 = classification_report(y_test, y_pred2)\n",
        "print(c_report2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Training on the whole data and saving the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "clf2 = DecisionTreeClassifier(random_state=0, class_weight= 'balanced').fit(df_x, df_y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YeMAh4J4BDEl"
      },
      "source": [
        "The decision tree had much more improvement, whether on the cross-val score or the test scores so we will choose to continue with it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "od9GWicFUHDM",
        "outputId": "1e036e71-f362-499d-c168-27852543ef99"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['model_binary.bin']"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dump(clf2, 'model_binary.bin', compress=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xYMZ6A3BWFT"
      },
      "source": [
        "# **Dynamic Part (Adapt)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDRqDx_2DVHN"
      },
      "source": [
        "# 7. Load the model and Kafka"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "fygH1BZsBpBk"
      },
      "outputs": [],
      "source": [
        "model = load('/content/model_binary.bin')\n",
        "### Initialize the adaptation model same as the static one\n",
        "model_adapt = load('/content/model_binary.bin')\n",
        "\n",
        "# instantiate the KafkaConsumer Class using the arguments mentioned.\n",
        "# do not change any arguments other than the first positional argument.\n",
        "consumer = KafkaConsumer(\n",
        "    'task1',   # change this to \"task2\" for the IOT Botnet Detection  ---- important ----\n",
        "    bootstrap_servers=\"34.130.121.39:9092\",\n",
        "    sasl_plain_username=\"student\",\n",
        "    sasl_plain_password=\"uottawa\",\n",
        "    security_protocol=\"SASL_PLAINTEXT\",\n",
        "    sasl_mechanism=\"PLAIN\",\n",
        "    auto_offset_reset='earliest',\n",
        "    enable_auto_commit=False\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkPLe58VDyQN"
      },
      "source": [
        "# 8. Initializations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "hKSTpmS_BpCw"
      },
      "outputs": [],
      "source": [
        "## Fix the naming of columns\n",
        "df_data_na.columns = df_data_na.columns.str.replace(' ','')\n",
        "df_conc = df_data_na.copy()\n",
        "\n",
        "# Data Stream flowing in.\n",
        "# i = 0\n",
        "dict_str_problem = []\n",
        "f1_attack_static = []\n",
        "f1_macro_static = []\n",
        "avg_pr_static = []\n",
        "precision_static = []\n",
        "recall_static = []\n",
        "\n",
        "#### We initialize them by zero since there's no adaptation model at the first iteration to predict by\n",
        "f1_attack_dynamic = []\n",
        "f1_macro_dynamic = []\n",
        "avg_pr_dynamic = []\n",
        "precision_dynamic = []\n",
        "recall_dynamic = []"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KyUVZqr5D-qI"
      },
      "source": [
        "# 9. Adaptation Part (Loop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "4-kc1LL_BpGK"
      },
      "outputs": [],
      "source": [
        "for j in range(100):\n",
        "    i = 0\n",
        "    y_pred = []\n",
        "    df_total = pd.DataFrame()\n",
        "    \n",
        "    for message in consumer:\n",
        "        # print(f\"Consuming the {i+1}th data packet!\")\n",
        "        data_packet = message.value\n",
        "\n",
        "        ##############################################\n",
        "        ##### 1st: Read Data ######\n",
        "        ## to read in a dataframe: https://stackoverflow.com/questions/17839973/constructing-pandas-dataframe-from-values-in-variables-gives-valueerror-if-usi\n",
        "        \n",
        "        dict_str = data_packet.decode(\"UTF-8\")\n",
        "        ## To evade the problem of failed packets => we just skip it and get another packet\n",
        "        try:\n",
        "          my_data = ast.literal_eval(dict_str)\n",
        "        except:\n",
        "          dict_str_problem.append(dict_str)\n",
        "          ## This adds the failed packets to a pickle file in order to be investigated afterwards (it happens in the Attack and Benign pkts)\n",
        "          with open('failed stream.pickle', 'wb') as handle:\n",
        "                pickle.dump(dict_str_problem, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "          continue\n",
        "\n",
        "        ## you may change this to a list or dictionary instead of complete dataframe, but the dataframe is to maintain the same names\n",
        "        ### This saves only one row each time\n",
        "        df = pd.DataFrame(my_data, index = [0])\n",
        "        df.columns = df.columns.str.replace(' ','')\n",
        "\n",
        "        ####### 2nd: Predicting using the static model over each point from the stream #######\n",
        "        x = df.iloc[:, :-1]\n",
        "        y = df.iloc[:, -1]\n",
        "        \n",
        "        ## Predict\n",
        "        pred = model.predict(x)\n",
        "        ## pred is numpy array while the y_test is a series\n",
        "        y_pred.append(pred)\n",
        "        \n",
        "        ####### Append the whole new stream in dataframe #######\n",
        "        df_total = df_total.append(df, ignore_index = True)\n",
        "\n",
        "        ###################################################################\n",
        "        ######### This process continues untill we reach 1000 packets then start adapting #############\n",
        "\n",
        "        ######## 2nd: Adapt ############\n",
        "        if i == 999: \n",
        "            #### True y ####\n",
        "            y_test = df_total.iloc[:, -1]\n",
        "\n",
        "            ###### Static Model Metrics #### \n",
        "            ### Note: we've already predicted each point using the static model\n",
        "            report = classification_report(y_test, y_pred, output_dict=True)\n",
        "            f1_attack_static.append(report['ATTACK']['f1-score'])\n",
        "            f1_macro_static.append(report['macro avg']['f1-score'])\n",
        "            avg_pr_static.append(pr(y_test, y_pred))\n",
        "            precision_static.append(report['macro avg']['precision'])\n",
        "            recall_static.append(report['macro avg']['recall'])\n",
        "\n",
        "            # print('f1 score for static model of attack for iteration {0} is: {1}: '.format(j, report['ATTACK']['f1-score']))\n",
        "\n",
        "            #### Previous dynamic Model Metrics; at first it's exactly like the static #####\n",
        "\n",
        "            y_pred_d = model_adapt.predict(df_total.iloc[:, :-1])\n",
        "            report_d = classification_report(y_test, y_pred_d, output_dict=True)\n",
        "            f1_attack_dynamic.append(report_d['ATTACK']['f1-score'])\n",
        "            f1_macro_dynamic.append(report_d['macro avg']['f1-score'])\n",
        "            avg_pr_dynamic.append(pr(y_test, y_pred_d))\n",
        "            precision_dynamic.append(report_d['macro avg']['precision'])\n",
        "            recall_dynamic.append(report_d['macro avg']['recall'])\n",
        "\n",
        "            ## Start from 1000 to the rest of the dataframe: It can be (1000) instead of length\n",
        "            df_reduce = df_conc.iloc[len(df_total): , :]\n",
        "   \n",
        "            ### Concatenate the new stream with the resuced dataframe\n",
        "            df_conc = pd.concat([df_reduce, df_total])\n",
        "            ### Check the new concatenated dataframe has the same shape each time\n",
        "            # print(df_conc.shape)\n",
        "            # print(\"finished iteration {0}\".format(j))\n",
        "\n",
        "            #### Retrain the model ####\n",
        "            X_train = df_conc.iloc[:, :-1]\n",
        "            y_train = df_conc.iloc[:, -1]\n",
        "\n",
        "            ### Note that \"fit\" always start from scratch; not like transfer learning so it's the same as initializing the model from begining #### \n",
        "            model_adapt = model_adapt.fit(X_train, y_train)\n",
        "            break\n",
        "        \n",
        "        i += 1     \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_X4pgVHsEIDL"
      },
      "source": [
        "**Important note:**\n",
        "\n",
        "Some packets fail during reading in 'ast.literal_eval'; these packets are distributed between Attack and benign, and here we're saving them for furrther investigation\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4udAXa_AEhx9"
      },
      "source": [
        "# 10. Plot Performance metrics of the adaptation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "_eUzXtFcBpJB",
        "outputId": "f06ba073-0147-4611-8c52-7ccd6b8c9a08"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Attack F1 Scores')"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "## F1 Score of Attack (Positive)\n",
        "plt.plot(f1_attack_static, label = 'Static')\n",
        "plt.plot(f1_attack_dynamic, label ='Dynamic')\n",
        "plt.xlabel(\" # of Iterations\")\n",
        "plt.legend()\n",
        "plt.ylabel(\"Attack F1 Scores\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "hOhawLlwEsKK",
        "outputId": "f5625fd1-ca1f-4df9-922b-9ea3091ce28b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Average Precision Recall')"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "### Average Precision Recall\n",
        "plt.plot(avg_pr_static, label = 'Static')\n",
        "plt.plot(avg_pr_dynamic, label ='Dynamic')\n",
        "plt.xlabel(\" # of Iterations\")\n",
        "plt.legend()\n",
        "plt.ylabel(\"Average Precision Recall\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pu19b3ZXEsw-"
      },
      "source": [
        "# 11. Average of Performance Metrics over Iterations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AG4mdNlGEsLb",
        "outputId": "425cfbff-86e1-46c2-fb9e-beb2d9c213fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average of the Precision for Static = 0.9876\n",
            "Average of the Precision for Dynamic = 0.9901\n",
            "--------------------------------------------------\n",
            "Average of the Recall for Static = 0.9856\n",
            "Average of the Recall for Dynamic = 0.9896\n",
            "--------------------------------------------------\n",
            "Average of the Macro-F1-Score for Static = 0.9865\n",
            "Average of the Macro-F1-Score for Dynamic = 0.9898\n",
            "--------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "## precision\n",
        "print(\"Average of the Precision for Static =\", round(mean(precision_static), 4))\n",
        "print(\"Average of the Precision for Dynamic =\", round(mean(precision_dynamic), 4))\n",
        "print(50*'-')\n",
        "\n",
        "## Recall\n",
        "print(\"Average of the Recall for Static =\", round(mean(recall_static), 4))\n",
        "print(\"Average of the Recall for Dynamic =\", round(mean(recall_dynamic), 4))\n",
        "print(50*'-')\n",
        "\n",
        "## F1 Score\n",
        "print(\"Average of the Macro-F1-Score for Static =\", round(mean(f1_macro_static), 4))\n",
        "print(\"Average of the Macro-F1-Score for Dynamic =\", round(mean(f1_macro_dynamic), 4))\n",
        "print(50*'-')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IPfS-V_QE9kq"
      },
      "source": [
        "# 12. **Additional**: Save the performance metrics you want in pickle files for further investigation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "Q-JnmUjDEsPA"
      },
      "outputs": [],
      "source": [
        "## Not Mandatory so it's commented\n",
        "\n",
        "# with open('F1_attack_static.pickle', 'wb') as handle:\n",
        "#       pickle.dump(f1_attack_static, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "# with open('F1_attack_dynamic.pickle', 'wb') as handle:\n",
        "#       pickle.dump(f1_attack_dynamic, handle, protocol=pickle.HIGHEST_PROTOCOL)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-pqVWJZS-HW"
      },
      "source": [
        "# **Multi-Classifiers: Problem 2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OJqRseZpj_e"
      },
      "source": [
        "# 2. Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MKTLQ_IGS878",
        "outputId": "254214c9-4023-4f1d-c648-6e821c2d0f16"
      },
      "outputs": [
        {
          "data": {
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MI_dir_L5_weight</th>\n",
              "      <th>MI_dir_L5_mean</th>\n",
              "      <th>MI_dir_L5_variance</th>\n",
              "      <th>MI_dir_L3_weight</th>\n",
              "      <th>MI_dir_L3_mean</th>\n",
              "      <th>MI_dir_L3_variance</th>\n",
              "      <th>MI_dir_L1_weight</th>\n",
              "      <th>MI_dir_L1_mean</th>\n",
              "      <th>MI_dir_L1_variance</th>\n",
              "      <th>MI_dir_L0.1_weight</th>\n",
              "      <th>MI_dir_L0.1_mean</th>\n",
              "      <th>MI_dir_L0.1_variance</th>\n",
              "      <th>MI_dir_L0.01_weight</th>\n",
              "      <th>MI_dir_L0.01_mean</th>\n",
              "      <th>MI_dir_L0.01_variance</th>\n",
              "      <th>H_L5_weight</th>\n",
              "      <th>H_L5_mean</th>\n",
              "      <th>H_L5_variance</th>\n",
              "      <th>H_L3_weight</th>\n",
              "      <th>H_L3_mean</th>\n",
              "      <th>H_L3_variance</th>\n",
              "      <th>H_L1_weight</th>\n",
              "      <th>H_L1_mean</th>\n",
              "      <th>H_L1_variance</th>\n",
              "      <th>H_L0.1_weight</th>\n",
              "      <th>H_L0.1_mean</th>\n",
              "      <th>H_L0.1_variance</th>\n",
              "      <th>H_L0.01_weight</th>\n",
              "      <th>H_L0.01_mean</th>\n",
              "      <th>H_L0.01_variance</th>\n",
              "      <th>HH_L5_weight</th>\n",
              "      <th>HH_L5_mean</th>\n",
              "      <th>HH_L5_std</th>\n",
              "      <th>HH_L5_magnitude</th>\n",
              "      <th>HH_L5_radius</th>\n",
              "      <th>HH_L5_covariance</th>\n",
              "      <th>HH_L5_pcc</th>\n",
              "      <th>HH_L3_weight</th>\n",
              "      <th>HH_L3_mean</th>\n",
              "      <th>HH_L3_std</th>\n",
              "      <th>...</th>\n",
              "      <th>HH_jit_L0.01_weight</th>\n",
              "      <th>HH_jit_L0.01_mean</th>\n",
              "      <th>HH_jit_L0.01_variance</th>\n",
              "      <th>HpHp_L5_weight</th>\n",
              "      <th>HpHp_L5_mean</th>\n",
              "      <th>HpHp_L5_std</th>\n",
              "      <th>HpHp_L5_magnitude</th>\n",
              "      <th>HpHp_L5_radius</th>\n",
              "      <th>HpHp_L5_covariance</th>\n",
              "      <th>HpHp_L5_pcc</th>\n",
              "      <th>HpHp_L3_weight</th>\n",
              "      <th>HpHp_L3_mean</th>\n",
              "      <th>HpHp_L3_std</th>\n",
              "      <th>HpHp_L3_magnitude</th>\n",
              "      <th>HpHp_L3_radius</th>\n",
              "      <th>HpHp_L3_covariance</th>\n",
              "      <th>HpHp_L3_pcc</th>\n",
              "      <th>HpHp_L1_weight</th>\n",
              "      <th>HpHp_L1_mean</th>\n",
              "      <th>HpHp_L1_std</th>\n",
              "      <th>HpHp_L1_magnitude</th>\n",
              "      <th>HpHp_L1_radius</th>\n",
              "      <th>HpHp_L1_covariance</th>\n",
              "      <th>HpHp_L1_pcc</th>\n",
              "      <th>HpHp_L0.1_weight</th>\n",
              "      <th>HpHp_L0.1_mean</th>\n",
              "      <th>HpHp_L0.1_std</th>\n",
              "      <th>HpHp_L0.1_magnitude</th>\n",
              "      <th>HpHp_L0.1_radius</th>\n",
              "      <th>HpHp_L0.1_covariance</th>\n",
              "      <th>HpHp_L0.1_pcc</th>\n",
              "      <th>HpHp_L0.01_weight</th>\n",
              "      <th>HpHp_L0.01_mean</th>\n",
              "      <th>HpHp_L0.01_std</th>\n",
              "      <th>HpHp_L0.01_magnitude</th>\n",
              "      <th>HpHp_L0.01_radius</th>\n",
              "      <th>HpHp_L0.01_covariance</th>\n",
              "      <th>HpHp_L0.01_pcc</th>\n",
              "      <th>Source</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19.570634</td>\n",
              "      <td>152.679121</td>\n",
              "      <td>39991.937640</td>\n",
              "      <td>20.937891</td>\n",
              "      <td>150.455024</td>\n",
              "      <td>38960.674060</td>\n",
              "      <td>23.168006</td>\n",
              "      <td>145.454142</td>\n",
              "      <td>36954.215040</td>\n",
              "      <td>33.115077</td>\n",
              "      <td>123.515434</td>\n",
              "      <td>27655.538890</td>\n",
              "      <td>62.619034</td>\n",
              "      <td>102.471926</td>\n",
              "      <td>15409.868150</td>\n",
              "      <td>19.570634</td>\n",
              "      <td>152.679121</td>\n",
              "      <td>39991.937640</td>\n",
              "      <td>20.937891</td>\n",
              "      <td>150.455024</td>\n",
              "      <td>38960.674060</td>\n",
              "      <td>23.168006</td>\n",
              "      <td>145.454142</td>\n",
              "      <td>36954.215040</td>\n",
              "      <td>33.115077</td>\n",
              "      <td>123.515434</td>\n",
              "      <td>27655.538890</td>\n",
              "      <td>62.619034</td>\n",
              "      <td>102.471926</td>\n",
              "      <td>15409.868150</td>\n",
              "      <td>18.885473</td>\n",
              "      <td>156.041505</td>\n",
              "      <td>202.780449</td>\n",
              "      <td>349.823034</td>\n",
              "      <td>1.602610e+05</td>\n",
              "      <td>-2.956340e+04</td>\n",
              "      <td>-0.370432</td>\n",
              "      <td>20.044037</td>\n",
              "      <td>154.488822</td>\n",
              "      <td>200.790831</td>\n",
              "      <td>...</td>\n",
              "      <td>30.034811</td>\n",
              "      <td>5.307252</td>\n",
              "      <td>1.298518e+02</td>\n",
              "      <td>4.880738</td>\n",
              "      <td>181.620841</td>\n",
              "      <td>227.548005</td>\n",
              "      <td>291.835506</td>\n",
              "      <td>59579.844560</td>\n",
              "      <td>17447.876</td>\n",
              "      <td>0.446623</td>\n",
              "      <td>4.927844</td>\n",
              "      <td>181.698241</td>\n",
              "      <td>227.581665</td>\n",
              "      <td>291.280119</td>\n",
              "      <td>59578.905890</td>\n",
              "      <td>17793.81865</td>\n",
              "      <td>0.455634</td>\n",
              "      <td>4.975746</td>\n",
              "      <td>181.767997</td>\n",
              "      <td>227.609424</td>\n",
              "      <td>290.719232</td>\n",
              "      <td>5.957503e+04</td>\n",
              "      <td>1.814181e+04</td>\n",
              "      <td>0.464725</td>\n",
              "      <td>4.997565</td>\n",
              "      <td>181.796886</td>\n",
              "      <td>227.619989</td>\n",
              "      <td>290.465036</td>\n",
              "      <td>5.957232e+04</td>\n",
              "      <td>1.829905e+04</td>\n",
              "      <td>0.468843</td>\n",
              "      <td>4.999756</td>\n",
              "      <td>181.799689</td>\n",
              "      <td>2.276210e+02</td>\n",
              "      <td>290.439555</td>\n",
              "      <td>5.957202e+04</td>\n",
              "      <td>1.831480e+04</td>\n",
              "      <td>4.692557e-01</td>\n",
              "      <td>Provision PT 838 Security Camera</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49.893840</td>\n",
              "      <td>169.297843</td>\n",
              "      <td>54532.338720</td>\n",
              "      <td>62.957476</td>\n",
              "      <td>169.735104</td>\n",
              "      <td>54283.995040</td>\n",
              "      <td>82.340884</td>\n",
              "      <td>168.919514</td>\n",
              "      <td>53449.819730</td>\n",
              "      <td>98.214224</td>\n",
              "      <td>164.300338</td>\n",
              "      <td>51038.672810</td>\n",
              "      <td>124.460107</td>\n",
              "      <td>146.697496</td>\n",
              "      <td>42083.223280</td>\n",
              "      <td>49.893840</td>\n",
              "      <td>169.297843</td>\n",
              "      <td>54532.338720</td>\n",
              "      <td>62.957476</td>\n",
              "      <td>169.735104</td>\n",
              "      <td>54283.995040</td>\n",
              "      <td>82.340884</td>\n",
              "      <td>168.919514</td>\n",
              "      <td>53449.819730</td>\n",
              "      <td>98.214224</td>\n",
              "      <td>164.300338</td>\n",
              "      <td>51038.672810</td>\n",
              "      <td>124.460107</td>\n",
              "      <td>146.697496</td>\n",
              "      <td>42083.223280</td>\n",
              "      <td>49.886530</td>\n",
              "      <td>169.313860</td>\n",
              "      <td>233.534964</td>\n",
              "      <td>295.273638</td>\n",
              "      <td>9.040787e+04</td>\n",
              "      <td>-2.457803e+03</td>\n",
              "      <td>-0.039193</td>\n",
              "      <td>62.898830</td>\n",
              "      <td>169.837418</td>\n",
              "      <td>233.073744</td>\n",
              "      <td>...</td>\n",
              "      <td>97.549007</td>\n",
              "      <td>1.686998</td>\n",
              "      <td>6.319929e+01</td>\n",
              "      <td>1.980432</td>\n",
              "      <td>69.960478</td>\n",
              "      <td>3.999805</td>\n",
              "      <td>101.835497</td>\n",
              "      <td>15.998438</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.988213</td>\n",
              "      <td>69.976286</td>\n",
              "      <td>3.999930</td>\n",
              "      <td>101.846358</td>\n",
              "      <td>15.999438</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.996055</td>\n",
              "      <td>69.992095</td>\n",
              "      <td>3.999992</td>\n",
              "      <td>101.857221</td>\n",
              "      <td>1.599994e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.999605</td>\n",
              "      <td>69.999210</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>101.862109</td>\n",
              "      <td>1.600000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.999960</td>\n",
              "      <td>69.999921</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>101.862598</td>\n",
              "      <td>1.600000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>Provision PT 737E Security Camera</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.996527</td>\n",
              "      <td>449.011775</td>\n",
              "      <td>409.365474</td>\n",
              "      <td>2.002395</td>\n",
              "      <td>448.141152</td>\n",
              "      <td>739.076602</td>\n",
              "      <td>2.166552</td>\n",
              "      <td>419.128740</td>\n",
              "      <td>11039.485060</td>\n",
              "      <td>4.621621</td>\n",
              "      <td>278.780846</td>\n",
              "      <td>34237.687910</td>\n",
              "      <td>29.401508</td>\n",
              "      <td>286.241737</td>\n",
              "      <td>32040.668620</td>\n",
              "      <td>1.996527</td>\n",
              "      <td>449.011775</td>\n",
              "      <td>409.365474</td>\n",
              "      <td>2.002395</td>\n",
              "      <td>448.141152</td>\n",
              "      <td>739.076602</td>\n",
              "      <td>2.166552</td>\n",
              "      <td>419.128740</td>\n",
              "      <td>11039.485060</td>\n",
              "      <td>4.621621</td>\n",
              "      <td>278.780846</td>\n",
              "      <td>34237.687910</td>\n",
              "      <td>29.401508</td>\n",
              "      <td>286.241737</td>\n",
              "      <td>32040.668620</td>\n",
              "      <td>1.996402</td>\n",
              "      <td>449.036043</td>\n",
              "      <td>19.999968</td>\n",
              "      <td>449.036043</td>\n",
              "      <td>3.999987e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.997840</td>\n",
              "      <td>449.021626</td>\n",
              "      <td>19.999988</td>\n",
              "      <td>...</td>\n",
              "      <td>14.978833</td>\n",
              "      <td>10.667405</td>\n",
              "      <td>2.061570e+02</td>\n",
              "      <td>1.996402</td>\n",
              "      <td>449.036043</td>\n",
              "      <td>19.999968</td>\n",
              "      <td>449.036043</td>\n",
              "      <td>399.998701</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.997840</td>\n",
              "      <td>449.021626</td>\n",
              "      <td>19.999988</td>\n",
              "      <td>449.021626</td>\n",
              "      <td>399.999532</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.999279</td>\n",
              "      <td>449.007209</td>\n",
              "      <td>19.999999</td>\n",
              "      <td>449.007209</td>\n",
              "      <td>3.999999e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.428558</td>\n",
              "      <td>450.295009</td>\n",
              "      <td>20.081460</td>\n",
              "      <td>450.295009</td>\n",
              "      <td>4.032651e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.978833</td>\n",
              "      <td>455.354366</td>\n",
              "      <td>1.959295e+01</td>\n",
              "      <td>455.354366</td>\n",
              "      <td>3.838837e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>Samsung SNH 1011 N Webcam</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>60.000018</td>\n",
              "      <td>0.004849</td>\n",
              "      <td>1.000065</td>\n",
              "      <td>60.017569</td>\n",
              "      <td>4.743299</td>\n",
              "      <td>1.063813</td>\n",
              "      <td>76.195918</td>\n",
              "      <td>4110.590208</td>\n",
              "      <td>2.936177</td>\n",
              "      <td>218.185372</td>\n",
              "      <td>17685.800370</td>\n",
              "      <td>24.603310</td>\n",
              "      <td>201.078998</td>\n",
              "      <td>16329.248820</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>60.000018</td>\n",
              "      <td>0.004849</td>\n",
              "      <td>1.000065</td>\n",
              "      <td>60.017569</td>\n",
              "      <td>4.743299</td>\n",
              "      <td>1.063813</td>\n",
              "      <td>76.195918</td>\n",
              "      <td>4110.590208</td>\n",
              "      <td>2.936177</td>\n",
              "      <td>218.185372</td>\n",
              "      <td>17685.800370</td>\n",
              "      <td>24.603310</td>\n",
              "      <td>201.078998</td>\n",
              "      <td>16329.248820</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>84.852814</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.740000e-178</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>12.477802</td>\n",
              "      <td>12.558425</td>\n",
              "      <td>1.474595e+02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>84.852814</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>84.852814</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>84.852814</td>\n",
              "      <td>4.550000e-13</td>\n",
              "      <td>5.280000e-89</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.215485</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>84.852814</td>\n",
              "      <td>9.090000e-13</td>\n",
              "      <td>-4.150000e-39</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.290294</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>9.540000e-07</td>\n",
              "      <td>84.852814</td>\n",
              "      <td>1.290000e-12</td>\n",
              "      <td>8.690000e-30</td>\n",
              "      <td>9.560000e-18</td>\n",
              "      <td>SimpleHome XCS7 1002 WHT Security Camera</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100.707918</td>\n",
              "      <td>226.708372</td>\n",
              "      <td>54562.182272</td>\n",
              "      <td>155.736482</td>\n",
              "      <td>252.269792</td>\n",
              "      <td>58012.457557</td>\n",
              "      <td>446.854937</td>\n",
              "      <td>301.030305</td>\n",
              "      <td>60959.734097</td>\n",
              "      <td>4257.526218</td>\n",
              "      <td>335.363145</td>\n",
              "      <td>60179.750153</td>\n",
              "      <td>24034.070444</td>\n",
              "      <td>341.480197</td>\n",
              "      <td>59796.846865</td>\n",
              "      <td>100.707918</td>\n",
              "      <td>226.708372</td>\n",
              "      <td>54562.182272</td>\n",
              "      <td>155.736482</td>\n",
              "      <td>252.269792</td>\n",
              "      <td>58012.457557</td>\n",
              "      <td>446.854937</td>\n",
              "      <td>301.030305</td>\n",
              "      <td>60959.734097</td>\n",
              "      <td>4257.526218</td>\n",
              "      <td>335.363145</td>\n",
              "      <td>60179.750153</td>\n",
              "      <td>24034.070444</td>\n",
              "      <td>341.480197</td>\n",
              "      <td>59796.846865</td>\n",
              "      <td>33.985501</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>1.164153e-10</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.613446</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000346</td>\n",
              "      <td>...</td>\n",
              "      <td>13697.367283</td>\n",
              "      <td>46217.470655</td>\n",
              "      <td>6.967790e+13</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>554.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>Provision PT 838 Security Camera</td>\n",
              "      <td>mirai_udp_attack</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 117 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   MI_dir_L5_weight  ...             Class\n",
              "0         19.570634  ...            BENIGN\n",
              "1         49.893840  ...            BENIGN\n",
              "2          1.996527  ...            BENIGN\n",
              "3          1.000000  ...            BENIGN\n",
              "4        100.707918  ...  mirai_udp_attack\n",
              "\n",
              "[5 rows x 117 columns]"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## read the data\n",
        "df_data = pd.read_csv('iot_static_data.csv')\n",
        "\n",
        "## check the 1st 5 rows\n",
        "df_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cO6qeI63p7Zy",
        "outputId": "4942c88a-bd09-4c4b-ebd1-b183b97edc0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index([], dtype='object')"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## get the columns with null values greater than zero\n",
        "df_data.columns[df_data.isna().sum() >0]\n",
        "\n",
        "### No missing: The data is clean but the data has categorical features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUqJO4caqPLC"
      },
      "source": [
        "# 3. Data Cleaning \n",
        "There's a categorical features, we can encode it. However, after reading about it, it seems to be not that important and it will need further work in the streaming case. Beside, there were moments were the stream would bring some values in this feature that wasn't in the training set.\n",
        "Moreover, it had low influence on the performance of the model over different classes. This feature is the \"***Source***\" feature."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbqNQR6-p7bb",
        "outputId": "dbee6ce7-df48-479e-a0c0-c96f9eb7a027"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "117\n"
          ]
        }
      ],
      "source": [
        "df_x = df_data.loc[: , df_data.columns != 'Class' ]\n",
        "print(len(df_data.columns))\n",
        "\n",
        "df_y = df_data.loc[: , df_data.columns == 'Class']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MRhuhrzPp7ec",
        "outputId": "dac78358-e9e3-4b50-938a-a452e8e63f6f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['Source'], dtype='object')\n"
          ]
        }
      ],
      "source": [
        "categ = df_x.dtypes == 'object'\n",
        "idx = np.where(categ)[0]\n",
        "col = df_x.iloc[:, idx]\n",
        "col_names_categ = col.columns\n",
        "print(col_names_categ)\n",
        "\n",
        "### Drop the categorical columns\n",
        "df_x.drop(columns = col_names_categ, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5HMF0d6p7jr",
        "outputId": "8ff07c5f-8b10-42ca-dc31-e0f06b275104"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "115"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df_x.columns) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4c_UvFFZq8Uq"
      },
      "source": [
        "# 4. Split the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "nzcQPHEpq0Jg"
      },
      "outputs": [],
      "source": [
        "X_train_m, X_test_m, y_train_m, y_test_m = train_test_split(\n",
        "    df_x, df_y, stratify = df_y, test_size=0.3, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N03VJ3LGrEh9"
      },
      "source": [
        "# 5. Classifier A (XGBoost)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3sd44NYJra3"
      },
      "source": [
        "**Evaluation during training by cross-val**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "OFOIgRoWKwE1"
      },
      "outputs": [],
      "source": [
        "clf_m = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,\n",
        "    max_depth=1, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "15cw9F5hJtf5"
      },
      "outputs": [],
      "source": [
        "### Encoding here can be done easily\n",
        "le = preprocessing.LabelEncoder()\n",
        "y_true_enc_m = le.fit_transform(y_train_m)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3g13INMEUocb",
        "outputId": "ade95dcc-433c-4505-82be-1e39e4c52718"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Macro F1 Scores of each iteration from the 10 iterations: [0.0888162  0.86260432 0.87704347 0.94980422 0.95311771 0.78087605\n",
            " 0.08876163 0.08773446 0.86756427 0.85605394]\n",
            "Average Macro F1 Score of the 10 iterations: 0.6412376263318424\n",
            "--------------------------------------------------\n",
            "Precision Scores of each iteration from the 10 iterations: [0.08531133 0.87011192 0.86026936 0.93956106 0.97215285 0.76866627\n",
            " 0.08671329 0.17503392 0.88082916 0.84103896]\n",
            "Average Precision Score of the 10 iterations: 0.6479688123558395\n",
            "--------------------------------------------------\n",
            "Recall Scores of each iteration from the 10 iterations: [0.12587413 0.86477689 0.90903263 0.97728105 0.95099068 0.84970492\n",
            " 0.09090909 0.09108392 0.8593956  0.88796929]\n",
            "Average Recall of the 10 iterations: 0.6607018202824655\n",
            "--------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "cross_val_scores_print(clf_m, X_train_m, y_train_m, y_true_enc_m, cv=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjjhFSzZK07T"
      },
      "source": [
        "**Evaluation during test**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "scHIYhYLq0K5"
      },
      "outputs": [],
      "source": [
        "clf_m = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,\n",
        "    max_depth=1, random_state=0).fit(X_train_m, y_train_m)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWM7qhjKq0OP",
        "outputId": "84f747ab-51a6-4f66-b431-e59b495d8bf0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                       precision    recall  f1-score   support\n",
            "\n",
            "               BENIGN       1.00      1.00      1.00      6686\n",
            "  gafgyt_combo_attack       0.00      0.00      0.00         6\n",
            "   gafgyt_junk_attack       0.96      0.96      0.96       113\n",
            "   gafgyt_scan_attack       0.97      0.93      0.95        60\n",
            "    gafgyt_tcp_attack       1.00      0.97      0.98        64\n",
            "    gafgyt_udp_attack       0.99      1.00      0.99       134\n",
            "     mirai_ack_attack       0.88      1.00      0.94        29\n",
            "    mirai_scan_attack       1.00      1.00      1.00        26\n",
            "     mirai_syn_attack       1.00      0.97      0.98        30\n",
            "     mirai_udp_attack       1.00      1.00      1.00       532\n",
            "mirai_udpplain_attack       0.60      1.00      0.75         3\n",
            "\n",
            "             accuracy                           1.00      7683\n",
            "            macro avg       0.85      0.89      0.87      7683\n",
            "         weighted avg       1.00      1.00      1.00      7683\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_m = clf_m.predict(X_test_m)\n",
        "c_report_m = classification_report(y_test_m, y_pred_m)\n",
        "print(c_report_m)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRykw-VvtIIo"
      },
      "source": [
        "# 6. Classifier B (Decision Tree)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7eqliXyK9xs"
      },
      "source": [
        "**Evaluation during training by cross-val**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "NbiqFAykLboF"
      },
      "outputs": [],
      "source": [
        "clf2_m = DecisionTreeClassifier(class_weight = 'balanced', random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJ7YdkidK9GK",
        "outputId": "20f8be1b-8225-4ca1-defa-41e61b899d77"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Macro F1 Scores of each iteration from the 10 iterations: [0.98880299 0.81156806 0.90872581 0.89803201 0.90572391 0.96346628\n",
            " 0.81444972 0.95668596 1.         0.89305057]\n",
            "Average Macro F1 Score of the 10 iterations: 0.9140505299128483\n",
            "--------------------------------------------------\n",
            "Precision Scores of each iteration from the 10 iterations: [0.99431818 0.81133272 0.90836364 0.89416486 0.9025974  0.99391234\n",
            " 0.81408754 0.9492     1.         0.89436027]\n",
            "Average Precision Score of the 10 iterations: 0.9162336953955136\n",
            "--------------------------------------------------\n",
            "Recall Scores of each iteration from the 10 iterations: [0.98484848 0.81212121 0.90909091 0.90291375 0.90909091 0.94848485\n",
            " 0.81481481 0.98201058 1.         0.89393939]\n",
            "Average Recall of the 10 iterations: 0.9157314907314907\n",
            "--------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "cross_val_scores_print(clf2_m, X_train_m, y_train_m, y_true_enc_m, cv=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbO-JzcpLgXJ"
      },
      "source": [
        "**Evaluation During test**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "O-3CIdvBtOXh"
      },
      "outputs": [],
      "source": [
        "clf2_m = DecisionTreeClassifier(class_weight = 'balanced', random_state=0).fit(X_train_m, y_train_m)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "64a7rpdBtOXi"
      },
      "outputs": [],
      "source": [
        "y_pred2_m = clf2_m.predict(X_test_m)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgIPlAzBtOXi",
        "outputId": "a96c0c9a-8a00-4c4b-dd17-bbff5119da10"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                       precision    recall  f1-score   support\n",
            "\n",
            "               BENIGN       1.00      1.00      1.00      6686\n",
            "  gafgyt_combo_attack       0.67      0.67      0.67         6\n",
            "   gafgyt_junk_attack       0.98      0.98      0.98       113\n",
            "   gafgyt_scan_attack       1.00      1.00      1.00        60\n",
            "    gafgyt_tcp_attack       1.00      0.97      0.98        64\n",
            "    gafgyt_udp_attack       0.99      1.00      0.99       134\n",
            "     mirai_ack_attack       0.97      1.00      0.98        29\n",
            "    mirai_scan_attack       1.00      1.00      1.00        26\n",
            "     mirai_syn_attack       1.00      1.00      1.00        30\n",
            "     mirai_udp_attack       1.00      1.00      1.00       532\n",
            "mirai_udpplain_attack       1.00      1.00      1.00         3\n",
            "\n",
            "             accuracy                           1.00      7683\n",
            "            macro avg       0.96      0.97      0.96      7683\n",
            "         weighted avg       1.00      1.00      1.00      7683\n",
            "\n"
          ]
        }
      ],
      "source": [
        "c_report2_m = classification_report(y_test_m, y_pred2_m)\n",
        "print(c_report2_m)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Training on the whole data and saving the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "clf2_m = DecisionTreeClassifier(class_weight = 'balanced', random_state=0).fit(df_x, df_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YJNyw7FfTpLk",
        "outputId": "c8a8c17d-8de5-4194-a667-9ebb5e8002b0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['model_multi.bin']"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dump(clf2_m, 'model_multi.bin', compress=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJnn8878tc9b"
      },
      "source": [
        "# **Dynamic Part (Adapt)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9Bu6chZL2Bz"
      },
      "source": [
        "# 7. Load the model and Kafka"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "HINzN_KhL2B1"
      },
      "outputs": [],
      "source": [
        "model_multi = load('/content/model_multi.bin')\n",
        "model_multi_adapt = load('/content/model_multi.bin')\n",
        "\n",
        "# instantiate the KafkaConsumer Class using the arguments mentioned.\n",
        "# do not change any arguments other than the first positional argument.\n",
        "consumer = KafkaConsumer(\n",
        "    'task2',   # change this to \"task2\" for the IOT Botnet Detection  ---- important ----\n",
        "    bootstrap_servers=\"34.130.121.39:9092\",\n",
        "    sasl_plain_username=\"student\",\n",
        "    sasl_plain_password=\"uottawa\",\n",
        "    security_protocol=\"SASL_PLAINTEXT\",\n",
        "    sasl_mechanism=\"PLAIN\",\n",
        "    auto_offset_reset='earliest',\n",
        "    enable_auto_commit=False\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NC0pOIU0MBa1"
      },
      "source": [
        "# 8. Initialization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "dQD1aV91MDo1"
      },
      "outputs": [],
      "source": [
        "df_data.drop(columns = ['Source'], inplace = True)\n",
        "df_data.columns = df_data.columns.str.replace(' ','')\n",
        "df_conc_multi = df_data.copy()\n",
        "\n",
        "#### s: static, d: dynamic => subscripts are used in the multiclass problem\n",
        "#### while the naming is full in case of binary classification\n",
        "dict_str_problem = []\n",
        "f1_attack_mirai_s = []\n",
        "f1_attack_gafygt_s = []\n",
        "f1_macro_s = []\n",
        "precision_s = []\n",
        "recall_s = []\n",
        "\n",
        "### For Adaptive Model\n",
        "f1_attack_mirai_d = []\n",
        "f1_attack_gafygt_d = []\n",
        "f1_macro_d = []\n",
        "precision_d = []\n",
        "recall_d = []"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_N7czfqLMkMc"
      },
      "source": [
        "# 9. Adaptation Part (Loop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8UxhdhHXMwIO",
        "outputId": "9b1cb0db-0e35-44a6-8f24-7c98d870029f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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      ],
      "source": [
        "for j in range(100):\n",
        "    i = 0\n",
        "    y_pred = []\n",
        "    df_total = pd.DataFrame()\n",
        "    \n",
        "    for message in consumer:\n",
        "        # print(f\"Consuming the {i+1}th data packet!\")\n",
        "        data_packet = message.value\n",
        "\n",
        "        ##############################################\n",
        "        ##### 1st: Read Data ######\n",
        "        ## to read in a dataframe: https://stackoverflow.com/questions/17839973/constructing-pandas-dataframe-from-values-in-variables-gives-valueerror-if-usi\n",
        "        \n",
        "        dict_str = data_packet.decode(\"UTF-8\")\n",
        "        ## To evade the problem of failed packets => we just skip it and get another packet\n",
        "        try:\n",
        "          my_data = ast.literal_eval(dict_str)\n",
        "        except:\n",
        "          dict_str_problem.append(dict_str)\n",
        "          ## This adds the failed packets to a pickle file in order to be investigated afterwards (it happens in the Attack and Benign pkts)\n",
        "          with open('failed stream.pickle', 'wb') as handle:\n",
        "                pickle.dump(dict_str_problem, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "          continue\n",
        "\n",
        "        ## you may change this to a list or dictionary instead of complete dataframe, but the dataframe is to maintain the same names\n",
        "        ### This saves only one row each time\n",
        "        df = pd.DataFrame(my_data, index = [0])\n",
        "        ### Drop the Source column ####\n",
        "        df.drop(columns = ['Source'], inplace = True)\n",
        "        df.columns = df.columns.str.replace(' ','')\n",
        "\n",
        "        ####### 2nd: Predicting using the static model over each point from the stream #######\n",
        "        x = df.iloc[:, :-1]\n",
        "        y = df.iloc[:, -1]\n",
        "        \n",
        "        ## Predict\n",
        "        pred = model_multi.predict(x)\n",
        "        ## pred is numpy array while the y_test is a series\n",
        "        y_pred.append(pred)\n",
        "        \n",
        "        ####### Append the whole new stream in dataframe #######\n",
        "        df_total = df_total.append(df, ignore_index = True)\n",
        "\n",
        "        ###################################################################\n",
        "        ######### This process continues untill we reach 1000 packets then start adapting #############\n",
        "\n",
        "        ######## 2nd: Adapt ############\n",
        "        if i == 999: \n",
        "            #### True y ####\n",
        "            y_test = df_total.iloc[:, -1]\n",
        "\n",
        "            ###### Static Model Metrics #### \n",
        "            ### Note: we've already predicted each point using the static model\n",
        "            report = classification_report(y_test, y_pred, output_dict=True)\n",
        "            try:\n",
        "              f1_attack_mirai_s.append(report['mirai_udpplain_attack']['f1-score'])\n",
        "            except: \n",
        "              ##### If the class didn't appear in the stream put it with 0\n",
        "              f1_attack_mirai_s.append(0)\n",
        "            try:\n",
        "              f1_attack_gafygt_s.append(report['gafgyt_combo_attack']['f1-score'])\n",
        "            except:\n",
        "              f1_attack_gafygt_s.append(0)\n",
        "\n",
        "            f1_macro_s.append(report['macro avg']['f1-score'])\n",
        "            precision_s.append(report['macro avg']['precision'])\n",
        "            recall_s.append(report['macro avg']['recall'])\n",
        "\n",
        "            # print('f1 score for static model of attack for iteration {0} is: {1}: '.format(j, report['ATTACK']['f1-score']))\n",
        "\n",
        "            #### Previous dynamic Model Metrics; at first it's exactly like the static #####\n",
        "\n",
        "            y_pred_d = model_multi_adapt.predict(df_total.iloc[:, :-1])\n",
        "            report_d = classification_report(y_test, y_pred_d, output_dict=True)\n",
        "            try:\n",
        "              f1_attack_mirai_d.append(report_d['mirai_udpplain_attack']['f1-score'])\n",
        "            except: \n",
        "              ##### If the class didn't appear in the stream put it with 0\n",
        "              f1_attack_mirai_d.append(0)\n",
        "            try:\n",
        "              f1_attack_gafygt_d.append(report_d['gafgyt_combo_attack']['f1-score'])\n",
        "            except:\n",
        "              f1_attack_gafygt_d.append(0)\n",
        "\n",
        "            f1_macro_d.append(report_d['macro avg']['f1-score'])\n",
        "            precision_d.append(report_d['macro avg']['precision'])\n",
        "            recall_d.append(report_d['macro avg']['recall'])\n",
        "\n",
        "            ## Start from 1000 to the rest of the dataframe: It can be (1000) instead of length\n",
        "            df_reduce = df_conc_multi.iloc[len(df_total): , :]\n",
        "   \n",
        "            ### Concatenate the new stream with the resuced dataframe\n",
        "            df_conc_multi = pd.concat([df_reduce, df_total])\n",
        "            ### Check the new concatenated dataframe has the same shape each time\n",
        "            print(df_conc_multi.shape)\n",
        "            print(\"finished iteration {0}\".format(j))\n",
        "\n",
        "            #### Retrain the model ####\n",
        "            X_train = df_conc_multi.iloc[:, :-1]\n",
        "            y_train = df_conc_multi.iloc[:, -1]\n",
        "\n",
        "            ### Note that \"fit\" always start from scratch; not like transfer learning so it's the same as initializing the model from begining #### \n",
        "            model_multi_adapt = model_multi_adapt.fit(X_train, y_train)\n",
        "            break\n",
        "        \n",
        "        i += 1     "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U2jfB1quM-i5"
      },
      "source": [
        "# 10. Plot Performance metrics of the adaptation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "5DDBsTXOM-i6",
        "outputId": "8b40d1a9-fe78-4ceb-9e99-540a7e4645dc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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o3uQykyMctlbim296Ir75pifi5CPWJl/HZSExVsdKgRwX56bH370YM7Eljzn9kzjs7deYfz988GbkXL2XYrSk/g+MtTFjlDBuKr9TvxnJcjwMMQ7aAw2S4WdWt/XB5x8LADjglZ8HELZXYynxWw8/FE8/ZiPmj36SOfctD/8TDFB9VsbLS6qt6rk3gcfLwTZtDDEGW+N7WXDtcyjFCGPOcMspf1/0Tz0j2i7Y/RwJGbw3YyFx9CH74clHrsegxj7o5+73TzwUB62bx1hWn92RlHjMEQfiJcfvj+EDH2Wu54G//xEAwJdO+zV8801PxDmvLljbgzZ/CiPOnXszGsvgObNYru1fHXIeBe2+OaZgMANCbexrxvi4xX1mCUmTuxUr7Zh8DcDT1N9PU69nB6keWMrLtyRDSAayHAgYnCj0d9VAFEK9FjVtSFH2IdRWPnReCzF2+21hcSywMMyRsUSG9ImMpSycHavNjCP9ku3aTuuAMNds2tDnJNV1k2qCHwUmbSkZWUaY42WAm/sj1KpB/18HgizaVRjyUvJ1lMxoNYdJPVam7EDufVgRW0KUB++lVNefA8+RYIbssGu7HmvSGnMZZL2dkqJ4nv23VRv5uHDWs/l15vuh4+YZgNEiMCzDEpQ1nHuXMcgCOTE4YhNYSuTEzjkbmx2DFJDIkGW50z9tF+x7JGT43ghm5BlhIJeQ1zy72kZnkMgzILSm0LYGSzuA+Q3lBzrkp+2P7leWQyJz7k1hFwLnzKKzjc4gQTW2T3o2T8rq9WsEF/diUswiK+fDKARrm4no80R0PBFpAdvHAWwiom8A2KRezw5c3ngNwWqQUx6enGPQ32X9UCbcNBZho6IfynzOaTv0IGksjiQWhjlyqh9cPoyzY/WDYgOURe1D2Qkycg2mADaTtHJM1IM0GldXFIIZOVHhNCT0x6waEiYYYoEhL5nXwwbj5hxHsjMRNaF0xvZ9x2RV2JLY5Cy1M1wda40TaQR6wrR/m0PW2iliAQr0T4/bTBbjkoZrIJiCtkVKIM8IGO8BBpb2IsuD49jYqS4rdz1+I46Jed9zJuqeQ5YCAhnIOCZFv0TAnsbujZSMTNuHWsekaCtHYU/CTg6QEwot4YLlmOgFsjeXgHIIZFUHKjSGZHfHhLjeydXXWJ9T6eCl2xqSAjwFxmQW4tdXBt6+XH22B8ALpn3MKPQgVwOCuaCqJHPhvbaY4H0v1wz6htVMLWOSDZzXomYVtjQSWBhkyCCTJlYNIcYYeG1mLEFBh0kga3NNUsD1hnUiSPd66QdoHGFM8owwx0tJjp0MrF5jIJaYsxyTgVxOuo7MDMlpzk/ZMdeJ3ZexKmxJZHJGA2PS6p7q30nXGTbsQe3iJ8xy6rZyUTAmGKyB8Fbldn8zoiBjotkNW+BrVtItQpBld0v7GRJUCjFGDs+Z4AbnXa3Ss3zg9k+3YU/4HHZMDGMi6h0TthiTLKOok5NnBCzeBWywwod6geyx78iygmXg5n5OxJiwqLV9PkssjX1tYWtYQCJv/l4D9u0Ca8YBKE6zvPCYmDGRgUFfPX7EQ5WiOL7xoKV62/OkLSyOBOY7hHJMm7ZjAhl2yiYY9DUdWDHGRD9Ao1GYMSlWRMthp8z/fptQDgsMrVDOQC4mXUfddJvVtX/OPWYMypEFNBz6+Zehib4lC2Z+p1eqeg2k/qhb/BCHFxN61ZsJ5TAPFyC9Vbnd3xhjYvdDQ4dwQufeCG0/I+NXBhjegjGpaVMKCCoZE92/EGMiJMIshwRyIgy4TSgn7JgIxb5gKZ0xkeSFciIsEU3AmBShnPg9k974Y2/hlwSWU2FM9m3HxNOYOFRVZ42JuzqvvWkyoq9gURzfxBwTQjljFcqBDFK3MYRCOYXnXD0GyRmEcmJ6lmnAuxfSM0g2tPGdk0tJ169NKCdjiTlpMyZpoZyQpqAJnYxFj86gLA9P/IY1COs7OpAJZYxfj70azVn5o/BEpY9vMyYxx0TWMCZOP0w/db+6h3JijnXonEUDY8IyzJiEFmUywkQUuhD17NZpYEwoR9aEcjRj4mtMfMaklBpUQjmxc2aJvCOrTQ2Oic/YmfmoTSiHxV4pfl1ZeBoTPdamoTGRKUYjqjHxGRPvYQ1qTAQWhjqU08Yx0RNZ+Zu4xmQG4lcpADDQgdpuQjlJu/H+cUBjounVIS8lMSblqrW5HwTpaEwGMk38Kls4Pwa+ULrHbBEJ5egwRojmnpb4tWQP4mOJGkI52Xix0LJlWRHKCWUYRRgTX7OhkbQoi6Bk/MK/DYWzGzU7JpTjil85IN5sEr9qRy6mgRGqzYzrQzlG/Lpg1biqY0y8eyMlgiwRTRTKqdcn+iyxsa8ttETEEkx9KKceEcZkGlk56YxJRGOS5RUPmv0Bq7/OXDgmg0KIF6KW490uBhV5oZzg5DwL8au/Ophm0xHRcEhjIpiRIV3/0Vb8OpBlKCdfAcbk3iB+XQ2IZaaEJj0NKduF5zQc+4TS+aSaZ4ciz6yefEksGmdDUkMop8KYDJx+mLYnYe285IHqx4FQTpNmRzEmur8yYheKz+rFrwPFfMYcf2mHcoiC/RLMGICLwp4ds3Ki4UCWnW10BlHr5Eo7ogBrjmtD/7EAr8asnFUFjzExEwEzQFnaclgjxpjUOTd1WTk1jIlPI45EIZJcGOYdGJOqSDfjcDiIpFCpxVN0TiLO1lTA7iRdMiYh4wsMaYwc9QIwjTbi14xlUUtC1SEYiKWk69jG+Sk75q4Ge8wYUcekRmPSUfxqGF0TytEr1jobE16oOIyJcjYkwixxGcrZAwxCoRxvoTSJY2LKLTSFcsprLmX9c1iGcnKnXyHNXiEqDRzXMCa6RkvMMSl+nEEgyyjYLyEZa7EHAKdrTAKhnOmLX+sTJ/R1KRkTne3YJpTTMybN8LJynDzt1oyJ6+WWcdaujElWYUz0g+SvkBbVRLswzNrXMYmIX2OMCTDlMIG/Opgi/MnB0MShUA6zCbekME5t65gAKAw7gFwWdHDTdSzHY+MhSjSIB3tMF0SRUE7N5Dy5+FVNDIFJutK/RvHronE26kM5KEI5w2oox2c36rRwjfCTB/x+BxYyTenCOhMk04yJcCfUpFCOLNJ/m55dbfdr65gwYy2rysy1GpN4Vk6d+HVAM6pj4jF2ZVZTm1DOdNKF923HxM/KsVeoE9cxSQhRtMzKMfSw95vFkXZMdCinBWMiq8xOdIBy/WqmE2bJmPjiVx3KiYhf51XmTApj0k78qs5NOyYizTFxGLxEsJ/B1WOmsFNmbRjHpBLmSHdofcTEr3WaKIpQ+0IyiAAaLxpno1iVh5wYYABRTEBBxsQ9x1AabjIaQpEhwWqUPTA/KqqNZnnm9K+V+JULXYhmTGIaGN2mDuXEnJx1vLN40Sorp7w3deJX59xaoCkrx7dH3EFLVIhfe8akHp7GxKHnJ83KiTgRleN3yMrxH/ilUdGGEb+2SheutplHBihxvdHohBlqTEptjqseDzkEUrKpNZIkTG1Tx0S3N3Ydk6br6GsKUkBm/PWOyYpACyorjkk4lNspPKd/G6kjUWenYmJIzQBgVDImEllQryIkl5WLHcbE1WxoTCJ+7ZIuLCMsh92mk5XjiV9T2BfDmKhnN6qBSRK/IpExEeZ9iTypn9pGd1k8Zg0ZnRXxK7dnxohlIZOYEPu2Y+JrTMxEgClk5aQwJiKRMYnHRAFgj2JM5gc5spYxRh0TdRmTMP1LM2VMpiyqRZU98LNzbAhmU2tk2uJX057aq6dcdTWFctSx2ohfZ+E89oiCKCwALVfl7ljqImg2v42ID+tsDEUWKpoBwGh3yZhQFlxI2Wyiw5goBsIfazGnLAWUyJhQK8akSFHNlSPFXrqwthO6oGFYFwInKycufnUZk5j4da3cVbwIMiaeTY7VMQn0c2LHJKWOiWFMNJPfZgHf1zFphp+VY1NVk2blpEy4LbNyYkXb/FBOG1W2ZnacrJyI+NWvQDsVzJAxKdkD17MPaUyELDUmNGWNSeYxJpkxbmmMSavU0l78uqLIzOQcdkz8VMoyBbz9sXzNkUwK5YSzcnTBMHihnJj4dQ5VxsTXbGhMFMoxoYgmxsQKa0iuz1NQjAn54lfP9tUVNNSOXGbShWOOk2ZMGsSvUoVyglk5VcaEA5VfQ3aBGq5fHfKGxAnhj78OIueMRS9+bURtHZPJsnL0zapL5avPysmSGZNFK5RDLbNyyjbL38RDOcpDnipjUmVspgWz+vM0JiGHQDJjTqX0tqkxkuI0lBqTRUBK5Oo4Tdexkx6hZ0xWFhTWWRh74N2HqTAm0mNMGuqYhDQwpsjXqKxN4k9+dp/NlgoWY+JviqfRRXtg/bhoM+JYywAbExOCapAUYOTIVSjH6P+8rKY6TVcRyikr5caeL2P3NWMScXLWyF3A3H7OPm1NWTn2XBJLOacJFo+NdUz80HJKgkelf31WTjO8rByHnp80KydprxzZijHhALsBeIxJy1COadMTv4ZDOd1pwngHZseYlHof11AGQzmSMdAak6Tdhd3/6+BoTNRursBsxK+VrLAeMwXlOpTjhzPCk3OX8JxGrPJmE2NSHNcdqEVdDijGRGlMIuFryWGNidHX+IwJTx7KiVUT5S6hHC7Er/pe6f75DHRok0QNyYycJDI5Uj9pqGPCqiR9xMlZI3e5YRygPiuHcpcl4ghLNKH4tS4jUXr2qEtaeJ+VkwI/K8dezUyalSO9wRU7fmgSjGTlxFZIrmPSMpQTaDMmgtJGrtN25vEOqOPPwDHxVl+aofAnbR1bHqpUwGmLX017o0XHMWm6jl3Er/BYoh6zRSxl1oTUpih+9SfOlJIEWUQXVhZNKxkTPyXV/u4wyJi4Jd41WLiLtFboxJg0OO8qlJPrUI7Q4Xa3rdLxqzYhJGPB2u+qiTHRm/jFQjkLcpcbxgFqGRMmL5TTIH7tGsqpr/xaHhuw5rgWtpsgy/OcAPu2Y6IdAP1yKpVfXSq9trx5o8bEy8oJsBtAsU8OACwMsqjYLd6F6oOeR3YoNuLXaa7GV4AxYU/g6xsVbTt0ddaZiV/He0zKMNB8HbvUMemzclYWWcuU2YlCOXq+99I16yYTvcColI3XoRyHManJytF7PdkaEzPR+6zQJKGc+hV/aCHVxJiQLFbpee5mEfkOlB8qsyEkY4FsxyTmOJV2P6ewkyOZsSBSGBNh3rdDOXUh3kkSFJoyOv0tMrpoiXrGJAXaAVBwqNLOjIlbc6Rb5VfLYbL7UcOYDDLCIM9aMya+FkZvpR4Wv3YXVkUxw6wcPwQmzUopPFnMqsBajDFpuo6dxK/srQZ7zBRmvxhvTEVDOZMwJpV04fRQTogxybx04UJjEq5jEmJMKMKYYJKsHF/o7/fFmvjtc2neKyc3jhSMGN7tZ92E7wiAEX++tJOmdxeOOjliZw1jUs3KYSoXynV2wbDaHZzCVPGr9BiTduLXXmPSDI8xcS58Fn5Io/C83CRvMsqYyJItsZibWN2CpZHAmmFxHoR2ZeN9lbuORYcGaGYYhyk6ESuQleNnSFRWeOoBH4oWdUzaiF8RZkyarmMb50ejDLfNwNHrUYGZnP0VfmRcd7mn5rd+KCdB/FqGcqohJbMxn0kXzoNOThHK0RqTtWXb3qZ4Gklh7AiMwD5iD8xCytirhOdQpaga8atnF7Sd8DU8NvxQTlQDYzmLWVT8ighjEs/KkZbTWDeG9IKyLWPCUiIjrs1IrIpfO4Ry+jomCfAYEyeVrzVj4nq5nBLKacmYhFJ7gSIrZ147Ji2pPPZWIPp3ocmZZrEan2FWDnwjZ2U12Q+1/jtvUWCtzQSTxxiTxlCOOlYHxqRPF14ZUEwAGllNdhI0R34bYg8q/UM4k062YEwEM4ZyqZhQ8qF5P2tgizo90y0LrKXosIpqo3HHxEz4daEcBuZTGBPLcaoTv87LOsbEcziVxsSwSTXnXC4e2137Oruv4Vce7hLKyeCSAV2xbzsmerM8BZcxmUxjUirT6+qYyOJzf/DaDpPVj1IrUQ3lLAyLWxUTu8W74Bo3Ow/fx95W+dWnNW1h2pK1kZ9+0AYtGJN2oRx1rApjUn/OdYYyigmo3B7tEUuZja0m6yaVJpS/VU177FlKsgkAACAASURBVEGwf9FQDgKMSVj8KnXG2mANQFS27dUF0ZikjklbxqR01urbZMpBWQbJVGVMDCNdfD/kTBSVoZvFr9qe6sqvsVDO/HhnC41JphwT/5zjGpO2CxNhZRNFv2PmRzj9bFv5lbPeMakHi5I+g2c0Jt4rp7h7jYwJUHVeKoyJO9iq4leBBSuUUzSR1ndfQGf23wgxJpjBanymWTluf3WGRA5par8A5cQ/aJOV06qOSSQrp+E6dgrlTCI87NEaRmPiLwQaQjldNuiuMCYp6cJa/Or3w9QxsRgTysPiV82YDBec98sS7xHh7yQak6bKrwnsQdkhUbBBAIS1S2+pMUlgTCRjDktlP5ocJ4ho5VfJjPlajYl1/fR2KWSH9Ms++ehaYM2uWBtDRfDf4T5nfVZOAioaE/X/RJVf9eBJWDXEvuMwJlnFM604JiNpGJP2oRzXuImaAbq3MSbV1O3y3HSKNWCFclTmQT7lOiYmlDNebMWYtHF+DBpWnD2mCzM5+ymzevaYovi1sleJmQSbNSaVTDTJyMAOYxIK5TAzmIGBXDJpxRqUuaGRsnHVr06Miec0eKjYqwTn3S7qZVe39R0oP1Rhw6nlgvjzxcZZVKGcmJMz3gUsbHQ/CDEmOh0dFmNi7EL1+Npudw7l1O0uHBG/tvGys15jkoBIVs5kdUy0QSoHaO3x7f/ttgIakxh1uzgSWBhojYnrYDTBj7NyzQDNOnrjtZhhVk5F/CptxmSyUE4n8etoT7usnEnEr73GZEVQ1jFxnzcz9vw6JjWr3SZU6pgk6NjKicoL5TBjPhPFczew0oUr/dVsYogxaRC/dnim24tfi/frnkM7RdVlTFwHyi+5bsOpfouaZ5fL/mWR3YUhxw2MiaW7U++xJUyuFb82ZDXFUC5I47/Tqc/muLH5qwYE0YdyGuExJs5qZkpZOY11TOz/TVvhrJx6xsQVv6aWjfc1Jini16muxjm8spxJ245jYodyiv9zscd83oRue+W4jEnTdSxXcI2HMJgkXbBHe0QZk6ZQTifGBM5vOWHxExNDSslYoKKKqWFMAlk52mkfysUKY1KKSSNhrE7iV22/YoyEZ6+Sxa8WY+LfmyTxaxpjIq0Qfp6F+zUvlQ1IycrRjm+q+LUjY8IdxK/R+asGfSgnBbE6Jp0YEy8rJ2XVwO5vyrZijElYUFtoTHQox3UwmlBdgcRDOZnRr+wdGpM68etiQPyaz0j8mndlTDqIX2fiPPaIIsYaxJjA6YhfvVBOisYkUMdkrXZMjMYkqzg5xmkPMCaGLZpiKEc7Uv7mhxr6fZ89qGdMyvCBvUtvVfxaE8qpMCYRx8nqX1HHpPqdtax2FvYZE8Cdd1h6jMkKiF/rNCaxUE4bjQlLd3+gjti3HRNZDljAm2zaaky88tCTMSaWw5TAmCyNRCVdOLVsPHsCOv3AhViD2TAm+hpMP/RAlZVROJSjH7RciV8HNL1Qji5YB6DKmMwwlDMTBqpHBRTZYReR1P6J6pj46Zqafq9jTIz4tRrKWUPu/jd1jMlALDrF1QBUN8VTmIr4NbADeNGkZiTUeSWJX2UwlAPPntYtBMZ2LRfUTPya0UERygk5Oet4d/GHz5gA7rwjRcmiWIxJ3Rjqmi7MXcSvOmzVMyZTxjQZE4++TEnlS8vKKXc5jmtMZFVjkjjRGzrYW1mFYo2lkZvipDdBamETKo6UFcpZskI5+r5nYgkiG6qv1vcndYLRBetENjSMCedz6rOGUE4XoWTPmKwoskgdE2MHIhP9RHVM9IqV3Wc32L/IQkVKxjw8xiQgfnWE4UM3lJN5m+IZsDvhtwF5dtRHmY7bPEmbfrJwxa8RG11XYE0zJvrZbdTASBEVv66VqYxJOQ/YTmPdoijmiDZBzxdJoRxTYC3C+Ncgs2UKE2Dfdkz8rBz7hrdmTETw/8kZk0ElJlrVmIhKVk5quIW9B13WrMJiRm4iTBKPbkBFbxHLyjGhnEWIQVHZsnHn30SnQbcjBmsNY8LDdUV3GtOFVbd7xmTVIotkpphVZGBXX2DSUE7xOqUkQSvGJKsyJoZNFEsVxiTzNsUrf9R9DJYC+8gE6elq2tQxARDMyqnUMYloTIZyyTy7UQ2MtdjMs7D4dS2UY+Jn5QABxsTWmHgOVE3l17abeOr5oi4jseJUd7A1fYG1FHiMiawwJh3Er96KtW41U5+VY4lf/QfJa9OpY2IcjLS+VzQmeoAG65jofsxAY9KlsEMTPA2PU8fE0phIw5gsQgwKw9PkmKTuLiyNY7KuZEyMcas/57oVXAy9xmRlkeVKuxASsKPqNKRMpDHEKr8mhXL8kvQSWEAolOMxJpbT7jMmerfeWOXXWrY4AlMrKaYx8WxgSijHzsqRdijHs9n1dUzUfkFz62r7ZztleaQk/XodypnfL9BZa97xGJPMY0zCoZxurLaeL1I0JsZspSy+/f5BGm3SJNi3HZMIYyIYTv2QtLa8DJAmOpO5nDiDWTmW+NXzTP2qrHYdE5iBmeg8VMSvcUpPvzeTrJwVZEyGGTtZOfq+Z+Mlw5g0p/K6/0e/F2JMtHFrKknfSfzqnXOPmSKL6Cxii46ULJIYqnuVNIdy8kj4VUouHZNBXGNS6q9CjMnA6YfBNEI50b1oIqGcOvGrpWsIh3Ka25LMGMil8tmNPPi20xML5azj3RgN1oVFoJmXNZTZoRzNmOg+BX6uExRaLh719+syEiv2KCZFqEGGVSx+JaLNRHQREV1IRCd4n/0SEX2diC4goq8S0QNm0QcAgBfvmqiOiW+ImlYN9vt1WTkhxiShjkly5VcvC6dOBNXVG6/FCmhM/AyJwjGxQjnqvpMTyqnvT6r4VbcjBmuB0e6CNTGOSf0xOjEmmJ2jtxpxT9uSPAunzFJkXNeFC5oQ26sktH2ERgbXdmgIZiyYUI6VleM99/WMSazAmh6D7RmTrIHxqzC8Cc67w5hYmr3KoswLldkQkjGUy+bZjdpA675ngVAOM2M/7MZoEGBLgEBWjiV+1Rv01YpftR1vd+2TKr964488xy4Fq1b8SkQHADgNwBMAvAjAGd5XXgPgH5j5CQD+EcAfT7sPBh5j4lBknTUm7pba0ZtmD+w6jYmT3hauW+DWMWkZbjHGTa3sa0I5s2FMIuGsKaAipFPXZM5jTLSdysaLkCqUMy3xq868kYN1qiR9umNSZyhjyO5FjMlqsCUU2S8mxhpMJn5V/wvXMUmr/FrdxG/eY0xghQvKY5bC8GhWTuK5p6AM5TQ4Jup74wTnPXM0JjnIZFC6Nnos4vfG7Bc0t975bax/4HAoR0jGfrQb4+H6SGfzsm05LhmTLFBgrUb82jbcrr9fl5Go7aS+5tyhBtVqDuU8BsA3mHmZmW8AsB8RzVuf/xDA/urvAwDcMoM+FKhk5RT/S56AMfFy+KPxX/vBq8vK8cVQqHq1S1r8auk02jImFY0JMdjTfZTe+Cw0JjNwTMxD6j5AQ+Kg+DUTixDDxFBOW/Hr3DoVylk0xi2VMelSx+ReIn69x21JbgqsBcKx9v8KUxG/tkgXziOLCcEqlJMNAO1g1NUxCTAmsU38GtniGmTcYA8ioRzmgo0Iwa9jwp7znqJXESqUQ03Prum/2sTPa0ooxmQ8rGFM7Gtg1bMyG7TWsERdF4/2fBGbO/zjUof7nE+pjslg4haqOAjAHdbrOwEcCODn6vX5AM4jopcDmEdhfBwQ0YtQrJCwadOm7j3xGZOJ6pi44YLG1UwyY+Klj6G6Etmj65iwPbhaZuUEBrSUErkV6jIpxNNkNzrEKVOhjRd51y8ayhkvQg6nLH5Vx5aW+JXW36/oTuomfh3Er/eSUM49bksowhpQjDGZqPKrO+b0BFtHv8fFr4wFdve/qRO/ZuNqHRMAGHM2G8akKZRjtDNWXyVjkFPlN3YmCMNawOnr5zk5sU38hmIRNHdgbf9sWx1iTKQE5jGCDFzLojOe+NViziuhnFrGpK1jUtoiIcbG6bRRYYk73Od8FWfl3I5yFQMAG9V7Gv8fgLcy8yMAvAPAO/0GmPksZj6FmU857LDDuvfEZ0zsFQm5DERSW0AL8asI/w3AKfwWKLBmG6KxkBhLLkI51mBMHpjm4axSvv7kXBq5KToRkc3OpgGToeI5KAOPMZHMBcUoR5CKMUnWfzRm5SjHZLgWkCNgeWfrUE4bxiTzWaJ9G/e4LclNyqynMYkwV1MRv3olwes1JmFqXzJjDstuNVfKKk6Orb/yGRMATvqtaSYlIzHW3ybGz7NX9uQcc+CJpdmfRcLaD0g9I/7meDHxay6XQHNrIJmaHRMZLkkvmJFTjc6CPPFrTVYOc9U2dHVM7O/HFmW+ro46LCpXcyjnOwBOIqIhET0IwE5mq9YvQAC2q79vQbECmg3k2M3KsT1mO9aX1Ja3Um0yGg5jEqgaGWJMAm0ujovjLgwypx0hRkndtrfptl8DgBi7bZS0cFrbSTBl9qfvmFRoYS1+JeFm5VgZCjqV1z93H6mOiRi77WLPnc10sIKO5Y7bOCY6jXGa92j14h63JTqUUxm/DYxJm3tqfivcMccmgy4+jgZG1O7amLFQGhN7/xvKy/Hj9TekMQGgKqlOLyunaWL193Sxn7/Ys2gzJtIOlXiMiay5N0IyBmq/oOA5a1hawyyQlSMEYwB3Qex21hO/ZnHxK1B1onJtxxPtf9mv8vsx2zf2xl+MFaxDDmlYxkkw9VAOM99BRB8A8DUADOB0IjoewFOY+d0A/hLA3xPRGMAQwB9Ouw8G0s3KcWK41DKUE5kEozRrUygnoDEpVfhlm3rlv+CFcpIL7HgsjKzxnI0BnIX4dZZZOd4DNCCu1DHxHZNG8WuixkSzS6Vjcgdobo3+sP63HfQI5Up932dMVoMtKSu/utdb34doKGcCxkRWGJPwvWYpkZE+ntsPyYx5dhmTQmBZZUwIsnBMEhmTqWhMGphmnz3QfQ23WTLQbCUT+P2M3RtmhmRVZG64ED5n810vlOPZB6HYWc4iU2tEY8JZqTGRnjM2tHycnCVA7RkTe76IZSRWKlG3vM96e45pMCaz0JiAmT8K4KPe25erz34I4KRZHLfaERFhTOB6rinwQjjN6cJ2KCcgfnWycrwJ1lohOY6Jow/pFsrhmgGadR30dfCv2xRRcaTUOQ6oWvlVOyZSsRnNdUwSQzlqZSVV+AaLd4GGa8Kx+cgx2ugRjCN879CY3OO2JI+UZS9Xk56YtMs2A/q33pgLhXZtCDE2BrwifpUqlGOzIJRX2pJcLV3vtEPVcdxlJW1+q4/fECoJsQcxX7xwBAKhHKPRcZ0c/97oQ+QOY9IUylGVXwNZOQPIQnQc7KynKbSYc8MSWf3zbUNnjYktA4jYPt9xiznf8WNI5MDqrWOyamA7APAmm4kZkzahnDTGRD95tvHQIYmFobvPRWqBHfbatClKf4DmTUajC1aQMYHNmPihHFPTIa1cfGodE8O8aMYEDAzW1K66TL+47F8qGmP0PaYKE8rxnPhG8WsHQqvUp6g3GsSvNuPpP8uCgXn2WBDKKuyLHeaMMiaVMJayKV0Yk0THpGR4mzUmmWVPmXLTPz+rxJT69zJ89D0bJDAmtq0JbeInmZFDgKIak0hWTpZXRLr+30A8C6sJ0pEBRBwTb/yZMZ6oJdLtrlaNyeoBC8dzdTxm/X6qBfFW5Y2hnCbxa01WTiiUs8YXvyYKVP205joR1EzqmExQjKkJur/GAGnxq7+7sMWYlJUd09iMxjomuq6NZkwAYLhQv+rS/erAmNAsMqd6REFZBhEQQxKHx3WXTCv/t5VQTsTGOOFIbzwUm9K5jAlnucPG6n7WOSYCVWa57YRlw9ihWB2TOvFr5Fkki6GQdnVbz0bH2tLXu9DZrClSjpPEr+E6Js0aEzsrR81D2aCS1gxUp6fO4ldrvoix7X6Bv9i2CzHI3jFJhJcu7GyaprUnqQbeX6lOK124oY7J0tgK5Vh9TS2wU6YcKjrTcW68UM4sC6zNYIWf+ZOD+j8nNqJhAO6+IXPtNthr8v/M5GA7JooxaQzldMjg8J2xHrOHQFadSCNGO2Vvlxgqv00I5ZjfiqqDNFdhTAKhHGlt9jdoqzFpPwbbMiYhByLYpi5UZvfXz0iMsC9mh2WxaBYVTRoYsKr8GnBMckhTO6ba2fAmfnbGlH0rfQfXFEhrnZVjj5Xwbyt1TCLhyhhKxmRyhci+7Zh4Jekr4lcg/Qb7WokAu1E5tvk78GBTKmNS/D0/dA1EsvPgPegOYyIjoZxprsZnqDHRqz+fVh9AYsmrY6JDOTSvNSYNbEZyKGfstAugMG5UY9ysftn/p6DijPWYOUKTcxYx2vak0lYA6zMm9qaUwe9bY9jXwAhmDD3GBJbA0j5myZhUNSYy4JTRNEI5DXYzC7AHdeJXMlk5ZRG5Mq25GiKRlcmf1X5B9WFYeyuCkPi1COXUaExiJemtexML5dg2a5J04Zi+zmynYNKF2zEmejz2jEkTKoyJNRHoi5fMmHhMicdEBI8d+lu3ZRiTUjtiwi4x8St3GJhenJqdVZYVo1aK6lZttzj+TBgTz8jpe5JTTShn2LAXhkJ6KEc47QJIZkzKUE7t1xzc28SvqwGhexnNykmouxFDydIlZuXYE4znaMtExsQJ5QTEr8E6JhOEcvIGxk+/n4fYg2i6cCl+dTYq9Gxf7N4U5fuVALhRY1I+f3mgFJaQjAGJuGMSYUzIujdOKMfus32/Wz7/rmMSYUwq4teGxbd/jD6UkwjbAYB34SdlTBqMRnJJet+DRkT8OuhYYM2ng9keoHbcUVZ+MxXMMiunki5cXr9YHRPNbCQXP+vImKSJX7szJn0oZ+UgWkzOfqpnG1RCOQ06Nkf86mflBBmTqvhVSljC8EAoh7Kq/TKMSZdQjnLkmzQmIZajJpRDmZUu7OsAI2m49t/2TswhlkjDLt4WF7/KevGrbRNt8WtD7RbHMZkBY+LbIz22e/HrtOExJq74dTLGZOI6JiGNSUCFvzgSyAgY5uQYiORNnLw2XWckotSephOxAoyJvwtmDunWMVGrQgYhU8Z3WunCpoDdcC2Kel8o6eCG61gRmyVgJtsG9KhFSAypjXVsUzyg2an14YdyfPbABzuhnCpjMpRLgKp0XDSYVxhewfZmfyHGJK84wdMQv0aTDqTnTCSEcnJIJyvHD+0G03A9vYkdzkpnTELiVxTi17Yakyw39zl2zrLGEW2CozGJzB2+PWqbFq5Z6F5j0oQoYwKLMUl8uDyHxB/0wWOH/tbHNNtdlx40+ewGgMWxwMIwBxF1YkyMcTNq+LAIyg/rTAV2OzPZKycifvWycsaCsUAj0HBNfLdYD+l1TNQ9y7PSsCvj1nQd6zbriqExRt9j6pColhZoqmMCTM6YNO2V42jEgozJkqMboSwsfl0wYYwwYzJNjYkJ5TQwEnmAPYgzJqWdZ1gakzrGxMt8WbAEwEGWSMNyKvKMqu1Kri/L7tetsjfxa2CJRE0WVhNsexfLSKyKX9vdZz2fUD65W7FvOya+xsRhTKaTlTN7xkQW+hKvr+kaE4/ZcQRUYcZkamECrrkGU0AWEb/qUA5b93stFbR2WclzOqEcswNsPigN+3BtcKXpw2gKOhRY65IR0aMbQqGczJv8zHcTCoJFj2NWrG4DMcakbgUtJArGxM60CTgmbhijypg4m+IpNC7KatCokfIZXsfRi7TJpSMQ1pgEJvwoY7Km9tm1NwzNiMzvTbtcFFiLsgYVxqSYh6il+HWiUE6EMfFT3dsyY3o8Zj1j0gA/K8cvsAZ01phQUyinU1aOMkReHZOFQVbtaypj4q1AYiIoUUMLd4bdx1loTLxJ2r8nS+NypbSGRsBwDfJM7xY7rVBO0U6eWY7JYCG40vRhb9YV29LdR3nOPWOyUggLQMPP/2Ti14j4kDjIvjkpwl7/pGQMPMYkKn6lZfBgAaDqzr1OJVXdTCSMlYJJ0oXTQjnlnjP+PRLWz/2wjuOY1D271gJVMyZ+H/O6UE4lK6c+lOP8PUEoBxG773zF2DzVpZZaIt1u1mtMGlDJyrE+QlvGxGVK/Ak/eOzQ37qtUFZOYCWyNBJTZUzsB07GKkfuNYyJa4D867c0KmPLa2gEDBbSQzns/h+D0ZjkuRXKSaz82kEo2WflrDxCYkifrTPfnar41V44VB1pe+UbFL9Kd2O+eChnOciWAHA3xdPtTCMrZ6qhnDBj4tuDevFrWZY/xBL5/QNLw5hIzxkdQLRgTFRWjiN+Lb/u/D2trJyIXaqEclqKX818kveOST08jYnjfVKAhahDpY5JQ/ytQ1aOnmBtZ2dxLDGvHZMumTO6tkegME+0CuzeypjAvSdaAGuKSNmMSVMox1u9xr+nDGleZUxS65gA6avrPBJC6DE7SGrBmNiP6ITiV8SeT91+Q+XXgfTShbPcYWMBK4wR0JcAcDfFU2hkiyOwSxI01THJFUuUVMfE1nTYdUxqxa/l7zVrBMAsKpocpxhjIo3GJIUxEQhm5UTO2XE+28YJE/SJ1Tom6vqlakxkH8pJQ6SOCaDixkAHxiTOblSOHfobUCGm9KychWG1r23Fr0U3RDRtTNb1tytWiDHx0xf1+1oAawqsDRZAg/CmbD5aZ+Xkg9aMSV3p6Rh6jcnKo8iwcm9QLKSWUkI9Bn/vJPseh+h3rhG/shwj53HjJn72sxFCMJTTcsIybVmDvHHiV9+vq4KqURQ0CzEmXignKn51dTYhlqjsX8lYao2n4+RIpTGJZuVYY8mrY5IHmJ3Z1DGpTxfWx89aOqCl3q5nTOrhZ+VYN5kpMKrq4O8AHGA3KscO/Q0ohymQlaPaNOwGtMakqodpG8oB1MCOtFG3+uqMGWfl5N7KyF8pLZpQDrBGrQpzHcppqDWfLn5VGpM8L1ad2RDIcnAgk8NHJ8akd0xWHIysIoaMxd8nCuX46eP+s+t/v0YMOZBLxR8WE0KWjsH8jFUYI8qY5FWnrCNjklSHw2OJYim+pn+KhSlDOVklW0/biWgox0uZDrFEGjZjEhK/CmbkJGqycsKMCWW5YYlmLX6NzR0V8WtLLZFmTPp04SbYabnwBuaUNCbTzMoJrZCKrJxAXxP7bbcpxDgaa5R7ZVaOrzFx/9eMiZRFujAGCyaUM63Kr/occ82Y6DopVJ3MfHRJLe3FrysPSXk1ZRbh579uy/omxMSvgJcqqtu3NWJe/4baMfFL0gdCTwtYBrVhTNDNMXFsTGT82u9LMW509AwLY9lTf0INhnK8Z2+BliHzeSDLakM5fh0Tv19SooEx8XaT18kZOlvQD1/Zi2k5Qbi9gX0LXWez7UKyxqRnTNLgMyYORTZhVg6aGBObLQgxJmVM1A8PAeXqYtEWv3apY+KLXe1wkIgM1mlNejPWmPjsgZm0oZ06FcoxcfQFZIZ9agjlsLd6jUBnRmR5XmRAKAMfov8rx4ikL9bBsGm9Y7JikAExZDnWqmJSjc7i14DGhAOMCdcsfgYcZkxsNlb3d03WVmOinPEJGJOUUI4Q40ZW0a82GgrlhEIkvjOxAJWZhDBLVOkfS+iZxXckumhMiAbBc3bCOhMsHl3GpDqWQk5bWwfU1HTqs3IaEKljAqAUv07KmERS+boxJlXqdnEcrmOSni5styk85ya8b87UwgQrxZhE0ob1DsOmVsPAThduYDMSxa9aq1IwJmtMeiZTzapLISV+7vTJch67pGr26AYZuJdlKKeq2TC/68yYFK9D9sDpl6M5cPsxlNXaJHrydsoEMGMNRlGNCVtiUo0ylNNuDIqUxY9nr5p0WNJzTGCFcurEr77Ds4BlcF4uKuKOU9mJjKqsqpQqK6dl5ddMCVakF76KhnLaLkwaFrWh8FFsjMeg2+0Zkyb4WTmSi9LusEI5bRiTbOg4EcuKdQkZDTP4sqHHnjCc/HUvr123KR3GxNXDjDBoFcop2ywcE/06VHRnmeOrhdaQ9jWY/kQ6oOJ62ZStoIGlMSlDOfOKMckHaY6J4GKsNDoMwgrlDBdMQauiWmizjkWPxxTGRI+zZc77UM4KggPbC2RQYy9Q4t3YmJa3yIw5K5RjntWg+LV4b5mrBcGGEcYEcO2VEb9GGBNpFyzT7ehzbzkG2Rm/ccakPOeCPTDXs5YxUc815U6a6zKXLJGQKJ83P5SDkcWY1GtMxiiOVbbr1zGpCeXEsnKovDcycs7Sut9dQjl1Y0lfj2L8Fe+VYzw1lKOycmLn3gL7tmNSYUyAofZMYxqTSz8OfPYV1aakwG6Zl1s7WwM06JjowZfPeSGNakzUZOM4bRbvLY0E5gcuYzLCIL1sPEvTphRjsHUMW2ehB+u4hdMDAK8+61J89tIbI8e2r8F0J1Jp9dfWlggammM5oRwaAYM1JeXbyJgUY6Wp2ywFJFOxidjCRmBhQ/F+Yrrw2YN34OTs+0mMiR5nxTn3jMlKQdqTiUKmnqvqpniMDw3ejWdmF3USvw7zzBG/GnsQoN/18zvGIEljEnJMZBNjEspIUjakayjHfmZ9kH3Oij0wNjtwPY09tuyp0UbYtk6xL7otn9kqnDOb7Yz0D8rGAMhQFcjrOibRlNlYVk5e2n0hrXnK6oZj81qHcsrrGspI1NdjmGfmfPR9Ts7K0dGEKYhfJ29hNSPImGQAhMWYeBf91quBm6+sNCUV0zAQY+QoDNOIBgCWwpX0zKTssQV6QEWycvw2lwVjzqv82mZgEgvTps7K0a9DjMmI2g36H960Aw8+eF34Q31t/WswBQgxRgbVXytDQtAQc7yMPCOMRBmOGWJc9APAmKubslXa15NEYyinGEsZAPz664BH3VUcM0H8KphxHF2PB9PNSZOYXpGMaNCpuFWPbuAAtZ+heI5C4teH0VZ8PzuifShHTZy2+LW0BwGNibIRIxpU7NiAxwChWBSYTpcLFHNMyRjS2P2efYwIYzKiGGsOvAAAIABJREFUAeZ5udX5aUdqRHHH2jlnKVybHcrKqYRycqeekW37hNWWz5gU9qG4BiGWSKOwMQOAw4yJVJVf0xmTsiS9Pp/SGRNB8euI2i0egUKTYux+gMrTw8e2efo+pzqgvfg1FV5WTjkwAcERxkSOg8JIkqIIoWgnAhIjFBNdO8ZEhzfCWTm6TW08hJRG/e0wJsmhnLKfLAVgv7ZXTtrIYVhZIdVBSI5Pqhy5BlOAvuYjDM3kkEFCZMU9Knb+LGPLOURpmFOqsrK3eo1BipJ9W3cwcNCRAOoFdOanqubBACKJUBLWPeoZk5VDaFO3TD3/oSyXHBI5ZAfGBM6Ys59dGZhMzEQVGA+kV8XW6jUzjImlMVGaCEQEizGNSejcm2DbmJhjbdtV6bEHqeJXex8j20Y79t9mIvQ10GXta4TrxBIiK9oM7gbMOpQzDF+ESlaO0phkfiinyhJ1tdFAsYAy1zWkMeEAY2LGeJqtMdtz9KGcBgTqmMzpGKPeF8K/SWIEyFGwrWXbMQmEXdzvR9gCw5gEsnIgK+GhsWAMM7evYxokT/R+yIlk+dpJIbPZmBaT3khIw0xUYDtnU55IZSCsQZCQVFzvocWYlFuRKyOFZsZEymKsNIpfpcW+2e/XpRwqCFE4JQNvZRTtk0XldtnZtUc3hFiDTD2roRLvQ4wxTLynNoQac7bGpAy7xrNyQuHXTDsm1gRJRmBpPffMGFrPRuUYNefeNpQTemZ9uOGXsWuzg+nCXvggy6yFituWtNryxa+2fQidc9m/QscG2GXzy8+FEMiJ4zqLmjom+nxEpJ96DHQK5dp2P5SVo45jj79YuDKGMpTTMyb18DQmUjKGgxTGpHrTiSVGXBqAjGXhICCcyle0QYVj5BQaq2dMTJva6EhG7u2E3EYHQij7KYUoNCbmddkvtpyeNoO+YExiCnsrnDV1xqTsb2ZRt1KJjfPMynIIMSZNTgMXY6UVY2KhzriVXyo+zyM0dfVQyjBRPEbfY/rg0O7C6rkKhXIKxkQkp4Dbvx0OrFCO9+z6kDULlcy3MyhTUt1QDpCTdJgVB9ameHbbYxqYgmCpkMK2MTUaE+ucXZsdcEy0xkQ5XUS2xqRsq9BulG35oRzbPtRpTDJLY5IHQjksvdBSpYHYXjkJ55xw/aIIzC02jPh1UIZy9BhPdUB1aLFnTJoQYEwMRRbbK0eOC9bEQxFmKWO5tggqmpWT5YVjVMuYlB50xtU2x0JikGvGpDh2G8ZE6y4A9dDI8hi2CMqEjmjYatCPhMQoZoBnypgU7dn9zVCe63xW9A2w6WplpGpU9xrJGhMpyrFkv19j3MqDFONs6MW8o1+37lHbVM0e3SFD4Qz1/FcqqUrGAOOCBZtQ/Er+s+tBTzDFMxBhTDKbMQmLX4cYO99zjhHRmOh+yVaOiW1j4oyJadsTvwYLrOlMEO1U2AXW7H42iF+HECb8Us+YiNpQTukoxR09p4q4Zkx0gTUv5OSEcqz73dqeSnssBUI5IfGrun7J4lfus3LS4GflOBqTQG0QIKwxYQaBnTTdzPZAQ6EcfezMU/SbrBx16b06Jv4KaSwZA0djQioVNV1jMrZWSsQCQpcGsvo9EWMSC+XMNCunZA9MurBmTAAMs1L74q+IZGIoxxYiRsHhUE6dgM7+LQDklDaJufeoZ0xWCqGJKlfPVaWOiWJMUsNz/m/tTLCQPXD6ZT0DtuaAmS3GpJwkQhlpOr21lcYENhNRX6jQhglFBK6bads+Z1noLbT9CznvRnBp1zGxGFTb9rn232ZMPNYowBKZ/ulwMaz6KLLKakQZqFgdE6vGjOOMOenCEzCmLKBLwjU5JvrvPMIKxg+hrnsvfm0Auw+cHWMcs8tCGIQ0Jjp/3HIIHMYkVEW0C2OC6gppLBmD3GJ3sjwp40OD2F01gAUk5RBMwb1yCm+8BWMiGaOY0zHjrBzAZQ8ylI7JfF5cO6C47wMus3JSQzlzKXVMIqEcBASTla/IkjFJcUzcc+4dk5UCR8SvofsghMRQMSZtfXGjMQkwJsGN1zjMmEhWmirA1ZhkVWeiyEhpqTFxbEq6Y8I2y1kjfjU2UIVfMiIlZq9+3xxfT4aZJX617GkhfkVQr6KzctIYk4DGxHEe1NyRVPm1TM4oi9+Njb5N901DT/x1jFMMpJjdWEaidvqK8adOQV2/5KwcqUM5vWNSDymiWTnRyq8hjYmVDUNO2KUas3WPHWJMQhoTuw6H2+ZYSFNsR1N/dapxH7roGKAGjhRgygoBqHXu2hESXRiTeyArp6SxLY2J45gwxsJmTKShq5N2/k2sYxLXmDSLX01xNsikUE7pPPbi15VEkDFRz5VvtPUz1YUxMaEcS3wo6nQBZqJyQ7s6dCkpB7TIH+VK1he/Fmxi3DHxx5ptU7owJnU2hnQ6rvq+YEaeEXIKC9F1mzqUQ9YOynY/i3pD8VBODmmJX2s0JiyMjcl1HRPHeVDXgyKTc7Tya/Wcdd9M2x1ttPoxJLKo7XNCOd74S9YSyT6Uk4ZKHRNU04VTNCY6/55LytSm9EKpfAVbkyUwJuVAcdssQzm5nZWT5WlhAgVi4bRJLIIDVBs+2UJjwlw4JePGrJzZMSaShs5up0xlKGcsyxjwAGMzFlJCOanpwsx14teG68iaMRknhnLKc6ZeY7JiKDKsqpOzDDFXalwOqKP41dIWkGMPAhoTtsaDvfkdMwYkipR1C2Uox2VMbP1VBSHxq9WvYEZiBNqm2c+sj4xdGyglI8sIWRYWv2oWJrMZE1sM7zAm1vWt1DERbvXYWB0Ta/GT1YhfY6GxpqwcVinSeVZlibrYaHNYthakdeJXL5Rj7kWCY8KGMZmxY0JE69X/AyJ6OhFtTGmUiDYT0UVEdCERnRD4/E1EdD4RXUBET+zW9QRUKr+WaudxrPJrUGNSMia2xkSLoEKpfCVj4m0AVpOVk1k58kZxLxlDOyunC2OSWeEhlmDKK5OzoVmzdJpQh0rGjVk502dMpNVfhzFRq555KvsnpIq5m1BOs0ZHK/gbHYYJsnJI5/0nMyZqxZTtfaGcvdmWhO7lgIrnqkJzS4sx6VLHxMrGsIWWQUfaPAPuClqo+jjsORtmVW6HcjRjEplMOIuwRbpfrRgTy8bUhXIsGyiYkROQEwWvpzRZMDpdODf3xLZ9Rruhr69dx0RdAxqUoZyY42Q7izlXQznmerTVmOSuriajKkvE1vVrvW0IS1VdJxzG1tdjOPDEr1lNgkflENNzTJpaOAfAEwH8BYD7AHgtgN+q+wERHQDgNAC/AuD+AD4B4CTr898CsJGZn9y924nws3Ks2J3kmjomLADmkgY1GhOvZkbmshvBY7fQmBBEpc2xsAqsqW2yJTdrJDSI3X5qxkT4jIlefWVDZDKtoqNmSpoZkzlAtKsS2QTDHmTl6sv28AcZY2yycuCsCiV5zqLfbSunX7/OMgp/mWU8K6fBedCOyQDjpH1VzIozi684VzH2WlvClCHjkkWVopA7y2xoNvEktXjQuqGu4ldbW2CLuUNFsViKQivmLVSEYgiZXPMe2sRPNjImoVBOaafahHLMij8bRjehtEMlLEv2IMuoXvyal/bUaM6s68fS0274jAmVjEmt+JUlxoYxUefjrDsbHJNIVo4dZtOhHJ8lSrl+UWjGJJKRGKxj4sxx6Y7JSohf9VV5ADO/EkB4QwUXjwHwDWZeZuYbAOxHRPPW578LYIGIvkxEn0hdObWGv1ke4MQYx6aOSXjF47Am6jsja48Hm84MpfJ1ycrx25SSCyGbrzGh6qZdMWR2KEfpZ5gySPIZExX2oPRBr5mScWxlOMM6JtIO5ViGiDVjkkvTL5aiYBi0MW1gTOwqiPbrEEiWanf3g7zxOpaOSVqVUD05BUMIqx97rS3xGRM7jGi/Lt4s72nrUI4XarBX58FN/FRGmN8/GWNMrCJe5TFRqzEJ1zGxKf42oRw7FNnMSGghaCl+jRdY0+dGlvg1g3DaEpIxyKppuH44K8QSmXO3nLIgYyLbMCZlcoapYyKL8JXW1YTCRJ1COUr8GgtjO6EcroZyUhkTM69OiKZWBkT0lwCu068T2jwIwB3W6zsBHGi9PgyAZOYnAfgOgD/3GyCiFxHRuUR07k033ZRwyAD8zfLgpQsjwphoVbWtM9EaEyeUM0XGhCXAXGlTT6z6YdLUX9tQjm4TQoBYFo6JNzlLyxtPHfSGMWnMypldHRPphXJYpwtTqX3x476yoY6JLQSzX4fA3L2OiS4bPqC0OiZ7OWOy99oS714axyTAGph7inErxkQGxpzLHoQXPxJZZawJVUulGsoJi19t/ZUPDjjYeRNbHIG74q/RmFjnbItfg3vl+Kt0L5Rj91MyI88JRIE6JmSxRjWhHLt/po6J3S9tV7LI1BrZK0f3n1X4KqMqS8SOjW7LmJQh/HAoxxW/spTIia0xnnC8WIZiBzS18rsALgXwTiJaA+CdCW3eDmB/6/VG9Z79+bnq73MBPMJvgJnPYuZTmPmUww47LOGQARgHIJKVw6RSOgMaE/t/wNGY2IwJ19FcUqZn5QAAS7dNOTYTvs+YyJSMDwVy2lShHMoroRx9vtzGMdEak6Q6JlN2TFR7bDkmue2Y5KXDZIy6Vt03pAtLjzGpdRqkLNrzkCJ+1axXqh6BrXu0FxZY22ttSYWREOV9KF5b90KUuqE2GhOfpZPSfXZDq1w2jonH6HCMMdEZKq74NecyI6WCiPi11vZFoK+T/cz6sNtmUbIH8VCOmwlCWR5cqEhZPGO50m74dUwGVlZOYx0T1SYFxK9oxZhYGhNrg0WhGZPMZ0zK69d2E09iAa7LyvEYEy12Le9FGmMSqunUBU2t3IliVfI/AYwApAgFvgPgJCIaEtGDAOxk5iXr8wsAnKj+PhHAta16nArfAUAxwcwNrBhjYDtzw5TYjonWmHC5eshQhg2CwjQWKivHmwRDWTnqfTsUAYcxcbNyksqdK9htSi1+rcnK4Tw9TNAYyplhVo4xcrkvfi12CJ2zGBMSbm0Bifo6MCbeOqjWEvChNTvVD7LG65gZ2r/dXjlt7tEqwl5rSwqNic2YlPeheG0xJrJdNV/Tpj/mFINaPruB8aonAm8ilVpT5WtMAoxJIX4dRydSzsLpwqZfrRgT5dDl9RoTY1dV5VctBA0nPyp9niV+dRwTq5+CLb2KHcrR+wVZWTnR/lnOTiiUY+5TUh0TW2OSmfORzjlX2RjO2qcLF1k51aSHst/l+JOyHNOhMR5FzBZ2QBOd+o8ALgLwZGZ+GxH9OYAv1/aN+Q4i+gCAr6GIK59ORMcDeAozvxvAxwF8mIi+isJAvWTCcwjDdwDgMSaSXe/V/50dyrGzcoofO6vz5jomDVk56v0MwvHw9cRqCqwZjUlCuXMFu00W0h2gHHBM2mTlGPHrymfluOxByZjoWHmRLqwearOKUdfW2vl3x44d2L59O8Zjq0y3ZHzgGYdi4xrC4+57KH629fqo+HXNI5+P3eLZ2LJli/P+wmNOxVgsVt638dyTNmGLPAuHYgEY3YYtW+6sPeflwQZsecpZmB/sh1vHL8DtNW1PA4PBAAcffDA2bNgwjeb2XltilTkH3JU/4NLc+tkZkEgSNJs2fV2TZJc9iDEmFGZMchJRxsTOIpRqI8m4xiSvOMGNti+ClBW/w/CycNiDFPErkRvKKa/fGFIOywnfcSaK9OrUyq+czZm/9e8BYPv27Tj6/odgy0FnAbczcFfg+VxzAvD4DwBbtgBP/CgwuB+wZQvEeIS7n3IWxgv3wfOPLvrxvKMOxgFrdxsbMnf/E7DloLMwn+8Hlsu1tsXH3K/+EQCJ7XIZa+c2VH47HAlj855wvwNwww1bQcrWbBk/H2L7ndh+567aYyz80uPw0/sfhyzQr7a2pMkxOZiZzyCi56jXkdQEF8z8UQAf9d6+XH22hFkZEKcTVcbEdkzGMsKYyBBjUvxtHBPtRKjVeZwxCWlM1IC3NSZAwZhAmjadUI6XldOGMclZWv0clxoTqjImkqlYLSQzJuz8X4GcnWNSsgdzxTlKWWRIaMYkY9yt+kWexoQtFuvmm2/G4Ycfjvn5UlM5FhLL63fg4PXz2L5zCQ85dEPpHHrYecscBuPdWDjs6KT3bSz8/BY8iOewg9eADzwSG9dEJgeFPTvvwpodwM7BgVgY34lBTdvTwNLSErZt2zYtx2SvtSX+ClqMC7LHPFcxxqRNKMfTmEhVVj50jLJjhfDaX6hI6TIAGqbwlbMVRUPdjcw9d6090P1qVWDNrMLnkNcxEuacS/Ygy8LMpe5/boVydEEw2/ZJISDkAHlWsCb2vRkHxK9RjYnFwmQeY3L77bfjiCOOwH2WMuA+DwGGAX33rluBHfPAoUcD63cB+x8OrD0Qo+UlDLePsGe/B+MXS0MQAbuXBQ5aP4f77le0s+uOX2DN7jnsHu6PfLyINS2e/103EwiMgVzC8twBWH/wA5zPdy6OQNt34eD187hzzwgPue9a5Bv2YOfgQKwf347RwUdiOFevV995201YWFobtEttbUljVg4RPQAAiOgQFKuSvQMhxsSv/GdVXS1/pzUmtvi1zMrRbWdces4xmrVeY2Jl5aj3bW+chSwZk8zLymklfhVlm0pjUoqgyjZMfDBLKAymoHcVjmtMtPh1BqEcw5jMFfn2Xtx/SLJkcjyNiR/KsZ0SACZ9RGeLN5RYQ3iObZ53SbVMCUdxf0hps/qE8K/LhNh7bYmn6Sr1TYHJ2TAmsqX4tfjftk9k2YPg8yPDWTl6b6io+FUGHJOoxsRji4z2oFzspIKt6xbVmFh2lbUuJIvXMSlDOcqOmnOUjj21K6pmvvhVOyZGYxJfnNlsUWivnEwbDYo9oYQiY9Q9FyJtBUqbQMUb1smy+YTa2Av72Pr4Hhybx/4bwZ/UHKOKtrakiTF5PYBPAjgOwNkoag/sHfDTclE8/GUdEwZC2RnayASciWWLMckhy70VJs3KUe9ndpuyrAZa7i5sa0zSDILbpiiMHQ2KapbS7ZdElpTmqjFqzMqx4q1TZkz0qo/yosiVEOPi7qgVkhPKMTutljHkFEcpafpngCNfazYebL6X9uAba9HGUqwW7LW2xGcRpTX2APf517qh1Gq+GtqJseuY2DYmWF3aEr86GhiuZ0zsUA4lFASzz12IMXLA6lcLjYk6FtVopHx7JWShe4iJX/W1N4yJVSrfvn7QVWR16rHVVCEWtq5XVqMxYdsxKb7j9st6RoMg93vGgdGrIOWaEBWPuddy16eewGBjkWo0c+qbXDmPlCNz5/75iDomRJQBeB0zP35Kx1pZNGhMRFRjoh7UULowW4yJI36NZeVk7bNyrIdyJHQop6oxyRJ3CPPbzDRjQnlFY6JXX6mhHNEUytHirtB1nhBm1aqM3MgTJBbpwsV5kNnx09orp4YVYs9m1PkAFGNMoiumYjX36le/GpdedhnWZGMcdtj98do3vh082oOTTz45+rutW3+Cay75Gp74jOeCALzrXe/C05/+dGzatCnewVWAvd6WeCyi9IWBgbBvTmnVfDVCoZyhJX7lwELEbIdQEb+G97/RjglbTo6xARGNCXlOj3/ubTQm2gbUibd9e1WEcrI4Y8JuVo4WkUoxLjKTLPGwST32xa9+kTmPJar2r1j8EMvq5oJJfglb8zw5/xXnxKDgsqhkTDS0LbnyyiuRZRke9KAH4c/+7M+wY8cO15YwlE0qfrt161ZcccUVeNazngUAeM+7/xqbfuUJOOSxj9KdUP1KMIL2MaaEqGPCzFKp4dcxc73qZTUipDGxShLLWFZOSGPii19ZsxspGhNvEvQdpqxkTHJ22zR7FkySleP009KYoFoqXyIDsuaKpRracaqt/KpZo6mnC+vV1xwGJLFoVn7VOiZkdvwsNSb110/9jtzX7RH+3XnnnYfxeIzP//s5eABuxk9v34NzLvw+7rj15lrHZNtPtuGcL30NT3zG8wAAb37zmzv2a2Wx19sSj0UsGROtXyhtRcaaMWlXkt6IXwflwimDNAwgQsyEFr9mVfFrwZi4uhHNKrDDmLjPhg8OMCYA6m1fBJr1oTweyslRtYEmdTbImCiha+4y0EKMsUBlW3ap9ywgfi2E89XMnlD/TMhHysDmgi0ZE/0uhd4n+GQMK99Ccx/allx44YUACp3LOeecgxtvvNGzJVZDzNi6dSvOOecc45i84U/fiK237QLpSJN3HmkjORbWbo+mUM6xAK4noisBSADMzE+dypFnjQBjIiUbvUaUMTHpwpECa0VDxQAdJGpMRtUMH2MIdP+Us6PbLBgTdxVltslOKHeu4bdpNCb+5KyMnL9CqkPSXjl6v6ApFwQzqz51bkJn1ZhQjsR4WTkY7MbRY1lNiyOBrbftwvJYYuv2Xdi1JHDr3YsAgLlBWI6157Y9yOQY87TDeX/37Ys4aj0jFFldt24drrnmGlxzzRbc/yH74aADNuID738fdu/aifPPPx+f/OQnccYZZ+C73/0u7rrrLrzqVa/CH/zBH+D9f/sBXHbpJbj62c/D/3vrH+Fv3/I3eMUrXoGTTjoJ73vf+/CpT30Ka9euxebNm/HSl740+VquEPZaW1IN5ajxpJ9/a8nspIBPKn6FBGV5IUoPLn6kFX6VTluOZkL3zTgmVhaRtnMRjQllmeeYqN8Oqk5ZEwzrM5ir7jGk+whh2atxURRNORPh3YV1KEdl5ahzNFl2g1KzZzMm9r3RJfz1NaizgbZuBSwCmwsyFseMrTfvNHoXB3t2AXePAOwAto+A0W5gPoMUEtltIyzu2oVbRyPM5RkWRxLr5nPsv3aIhWGOEGOibcmPf/xjHHPMMTjwwAPx3ve+F3fffbdjS779zQtw19078coXPQ8v3fwyvPe978XFF1+MJzzhCXjPe96D9/zN+/DU//ECPP0pT8RZHzkTX/ni57BuwPi9F74Qf/g/Hp8cOp55KEfheVM6zsojwpg45Y2DjIlw/7fa0hoTlmPkxImMSUNWjs2YeOxGHWOSqgPxWZhMVQD0i4yZUE5W7jXRBM1IRA3wDBkTlu7KbbRcOBCsDNHAEriVWTl6r5xwaGnrbbtwyt98o2OPbqy884Xfvx82Paj6zZNPPhmbN2/GW/78z/HzG7fhNS9/MV79x6fjzltvxlvf+lYAwNve9jasW7cOS0tL2LRpE172spfhj//oVfjXT34cf/O3H8D6xZuBz/w3AODKK6/E5z73OVx44YUYDAatdnxdQey9tiTzQyXu2HM28eTSMelUx8RoTHT6e1EMkQPjlWRRNCtUxyS301/1aVjVRU13E+pu2E4ET8CY2LqwusqqRheiGJMssNOugawWWAOAsbIHrj1VdUw8J0eX8HeycpIYE4GcvI0+mbH1zjFO+dRFDRfjVu9///0S577ucTjmfhtQ8hhaoVrakte85jXYunUrTj/9dLzhDW/AjTfe6NiSbMfLsGck8dgnPA2//8IX4g1veAPOOussfOQjH1H9Lv67+sc/xPn/9QV846tfwZq7rsFdw/sAo1uR5HLwCjEmzLyNiJ4B4BgAVzPzF6Zy1JVARGOiyxtL5vBKPliS3s3KkWJUCMCMN95mrxwvK8djTMhmTNRxh5W9ctrUMbEoSiV+BWXF5Oz1S8er0xkTdV3qsnJmpDExanx1vcajou4WmTom0lw/8sWvCF+/Iw5ah3Nf9zgsjSS23bYLh2xYwC92LOLwg9ZhfhhhTLb/FMQCC/c5wnl/9x2/wIPX7Yn2/9RTT8VvnvJU7Hf3DXjcb78Sr3jt653PzzzzTHz+859Hnue45ZZbcMsttxjbQF4J/B/96Ec46aSTMBgU55dPYROtaWNvtiX+CloLLsmwBuXYzh3GJP0Y4TomxfMeq9bJqopzJZ3ZhHLc9M484JhkJswZycrxFiomlFPHFkegSxIgG9RP/NnAsES6WmtU/GoYE6Ux0Y6JtgeWPXXFry5jYheZ81mi8lhuSYKCMckr/Tpi/wHOPe3XESxLv3g3sONnwIFHArdfB2x8IDC/DiwZtP1q7Fk4BLeO5rEwLBiTNcMcRxy0Th2vbMae/k899VSceuqpRldy2mmnOYc888wz8bl//jSyfIBbb7sdt26/rdovhS1X/RiPfPSvIh8U13EwGAKjVMJkBcSvAEBEf4dis62LADyHiJ7GzK+e0rFni2BWTuF9FznxiDAmNSXpWcUvR8tKmV6TypealWMxJgNy2Q0ZYUzapPTmig4ec1aGctR+O5VQjkoXblvHpJ4xyWbEmLhxfqEMEQxjwlHGJKbRWRjmOOZ+G7A4EiACHnjAWqyZy3H0IfspKrWKXZgHscTa+7n5+TsHd2N+OeyY3HTTTVi/fj3AwH7r12HdurW4+eabMaSiv3fccQc+9rGP4YorrsBoNMJDH/pQMDOGc0OMxwIm7qusxXHHHYczzzwTQgjkeV6kSsb26riHsDfbEp9F9BkTR7OhxlXekTFx65gUz66IVOuElAiJX3W6sK8byQIakzJjLTy+7U3xikO6z10wIzECvekgZXlNKMc9Z8mKMaGwnfEdk5IxWXb7acSv1Tom5UaGpfg11L8y889iTCqbCzIWBoRjDt0AUOAZ3COBuSFw3/WFM3jgOmBhgxK8DrFr7Rrst1zYnd1LY6ybH1i2RzESVDIm2pZs2LAB++23H9avX4+f//znJpSlbcl3zvtnLIoMJ/z6k8BSYm5uzikqqXHMscfhzA9+EGKsMo7MuaWO5ZXRmGxiZq2g+Qci+vpUjroSiNQxcfZK8FfyzAjXMXE1JjxW3viwGmMuf6OzcrIIY+KWpJdijAwADcpBr8Wlw0rl13TnIVN0sF51ZYrFkJ4oV++7QW0KrCmmZFRX+XXWjIm6B+Nl5ZhkZbqwZnLCGpN4f9oJ0jn8LNY8nzfeeCNe//rXYzweIRsv4mlPeQIbQ4X0AAAgAElEQVSe+fwX4DWv2Iwrr7wS73//+/Gwhz0MJ510Eo499lgcdNBBAIDjHnYsrtv2U7xw88vxv097sWnvuOOOw7Of/Wz82q/9GtatW4eXvvSlq1FjsvfaEo9FNOLXYZUxybibxiQkfs29Z7fSLS7Fr07/NGPi6UbygMbElEeoqWNiT9L+ubepY5Ky+PHtlaljEhO/qnMxlV+Vc2EYk6G70Cv0Kl4ZefY0OZFwtl+SACyq2UKm3VgNAc+oqNdEpdC1cFJsQazVvNe6tiVZlmE8HuOZz3wmXvjCF+IFL3iBY0ue9JwX4uijjsKBB+wPgLFp0yNw3XXX4bnPfS7e/va3m/YfdtzD8BtPfRoe/4TfwPo54Pde9BL8wbNPSqNMpljCoMkxyYnocEXDHpHw/dWD0F45EsgzlHsl+Cv52N+exkRXfsw1TRiqKeIwJs1ZOWK8VDgmWQ5h0ZjA/8/em0brmlRlgk9EvN855+YoiIUkg5RS2AgyiCJoKqYKDa1SWpVaWKCFgOPqQhxam15YXZbVy9l2UQ5lLQZdhaDl1NV2i4oDkA2aomUhIAioKJCiOdwEcrjnfG9E9I+IHbFjR8Q7nIl7L2evlXnud873vdP3vhE7nv08z24gJiua+A2qXIFQKadCDfx6xIQM1pZxTI6Z/EqyzFTKiSskQkzg0vElv5YCMZk6nuWqnLB2aWcmvdzkCU94At74xjfilg/8La5Tt+MAAz58zSfhTW/Kdelf+qVfqj5395234qZfeznuufLBuOLu9+HlL39Zmmxe+MIX4oUvfOHEOX3U49IdS8QzkQiXDQIo3WtmNfk1/EwcE05+VR3ExBPHRChnkgdKeYlbiYksc8pQ8txt+dyt4ZgQj011EAkgIrwqj1fWY5L86p3F6HW6magfECGohhFprW/zVay10PCslNMmv6YElBIT57olpvmoExgPVJN7yavl5NfwBxpLZMix5MIt78A4nIOxF+D0Dq68+mrcdFPm0/3Ez7wUHzh/DwDg2c//Zvy7734B9s6/C/dc+WDg7vctxkvaY+H6mBscvgPALyulrkVowvWtx7LX04gOYlKQX+VKnqMkLR8T4pgQYjIj5VvUXTgen6OJVefOv4RIbJKPSU4qliQPztqU7FDzJvIx8XIV5lxAUVYkJtlgzYcsX2b4hSrnpDgmQfdiU025lgtrUcoJxnp949E8ZCyRyk0RvqY/ma2VVhqsLbOkvdjikh1LJIoo772Cs4GjkV95Ly9CDyw6z48LHBNpCBYs6ceGj0kuG6dzE32kZNSJSXnuazgmhJgobQJXwzkoVnIku/s0Xnmbyu91yaTcZjpHXSYm/DtKfBUlkgnR5HOqlBOuCTParJoL+mgZMiMXbi6M8ngT/dXarI1Dzf0tSbPcLltMJURHl68X7+NoMUd+/UMAn3VsezvN6PTKKcivFWJSGyXxbeVSDksiulK+daocb2mbQ0oiksGacH5dyjGxVB7SBjb2h8nkV0EAjX03eg9lc/vSpMiIJ+ZEVTli1Uock5gsDtrX5NdUyjEw/kJ/22tKOZ69kcci2/jMsV8Uno6ltK++FOKSHktmUIPCx4SQvMOWckxZylET5FfFEJOCY+JL11gexDVL26B/m85UoGUp52iIiVM6yWitHTGQ9BaAcw4GAIxJvbwomZgq5ViWmCjdQ0wmkhxhyy+TMQqbVEWZN1c1F/TBYXX2mZaDDBhi0q0G+YhIHLYlheoiGrTlbEEvj+90x5pJhpxS6udVTP1UiJ8/ncM6hhAJACeS6oSYCFVOgZJMqHIEutGS8q1V5bikKsnoRlXKIQRioVw4Z/gcMXGB/CpLOeRjMtHASgbnljTdX8l35QQ4JnRNs29BiWIFm/qImFSW9MtUTcse/94DO20bH+rIlJj4hUmGLweWS8iW/pIeS3QbMdFNxCT+Db6UEc+EREysQA+aib1vo5w2dstVjfKM3JbyAi0QwZviAZn4m859pfNrKOVk23geabxSQ0F+bUl8+TYLxCSOB5bGaEq4nO36mKSxnpdyWqocdnw0pvWaC3ZjcqLPIwE5v5aPOM9Y1j37RXuM1rhBeYjc0xp01vtu4rM25qj7D/AxdYo/H3Asez2NEAkAZdtF2+u1HBNPls4lutEbNJIiRapyWHkpISbjNm4zQ7dbMVgVPiaLSjl5Qk7kVwTyq5eOtLT6WsUxyXdrMzE5YcRk9DoNvpQskionyIWJ/Er3QkZMJsmv8efiZ7ID2y5/RP3iB5/v79JJSwBcymNJhwBqiPzKExPON1sxaadeOUP4bseRkIxc1qgOK5ZlK9t4InMONWIiFT6Zf9WXCwPIzfvEua9acMSyS7aNt+LP5EkSnKmV9DFp3fDOBjSYzoeOlxYqekgokXVol3JkqbejGsqut3lMq63yp0q7yH9rkWQ5p621CRom1CGrOcySvrHp8i+ilLN04XRc3UXnEhOllPrU+I9PXfD+iycEx4SjD0kudkiOSSrlxAeoLeVj/Aph/V5I85IqJ6MwRHYj8qZW7LNxm0ucXwl61LTq8oFj0rK1V6z+u7SUs+WJSUuZc4KqnDzI0UAUrp8eCDHJcmFdWdL3e2GENwjy6wQy0euV068xx02mzy5f//j0uRX9Ky6euGTHEokiJuL1hlCDfG8bhpKsQROcWISMtFBRE4sfFxYTlW28CxyXHmJSIDwLEBMgT8pOnHtTkdgL4oOocpvpuOm1YqjxjPMrqQnT+RBiQuOByYhTkh5LxMTWiAlHidK+qJSj85hWkV8XowYCoqDfeJ9GFKVkG1BGfl397PP99TgmvHxNY+DysUax/x815gaHbwXwCqXUuwC8DMALZt5/8YTgmDiGmHR9TAqOSW0jL8mvWg+JqNrcf8/HpIGYuG10KtRD4Hq4YEm/MSrfHNEXZKltvGM10fCgu8gxIR8TnjC5TKRbipiwQalpssYRE+BYlTmJ4S+vHyEmyqVSk/YWVpn81M3JhePPZeTXfkx1F37vX78Xn/rIx+KGG78Bn/OlX4OvfeaNeOc73zmzRR/O4Rjqvs961rMO/dlDxqU7lnRKOSkxYeMGl5mqI5RytiNDDyS6SduPpZzKNj5a0rc4JsQ1SzHDMUnPFyUmVpz7wi7n4b1x8UPJg0BMModjSAupzAvplEyqUo4cT5nCx3kYhcwxrK5BlgsDGSVKx0f+NXoAKQ0lYjI7LVcTPf9EhmgTttHhvtKn3vve9+I+97kPbrjhBjzxiU/EM57xjOY4otgnm2CML/9SJSadKMeR0yO//hmAzzm2vZ1mTCEmPR+TAiXhSQolJnGCTYnJhJRvimNSICblNpXRqZRDZN38WQfoncW28Y7VREOys4XpICbwNnAvVpBfefmmN2gk1IheH9NC2SfEhJJFQpwiYqJcRkz8CK/yrS59H3ox80zS1torJKUmP+8BPPrTH4Xf/8WfAAD8P2//EL7qq74Kb37zm7G72+qwg6ricxTA5Od//nQpHpfyWCJRRC8nZ/Z8m6LNQ1/5JYNKOTkxiZ9VBg4ml1z4ccVSTlVq8pSYLEBMZBlD7kOXfJAslY6T+Jr2BwyVBWpEyTc4cSMjv/YWPzwxyaXdOEbHJMe5PJ5KXog0YOQokRnydcmlpqw0rEm5CxGTNP7U5FfGbxcllIiYiIHl8Y9/PH7nd34HAPAHf/AHnXEkLGo4UisOqDiatN8ZVY4cR06UY6KUeqFS6j7x3zcopd6plPpzpdTTjmWvpxESMYn3QeljIsosLeMhIN1EB6KUE+q/nTLFlCqnhZgQ9JhUOQ6jdVkqTMcXfUyWTKy8JuriuerI9PfCLZKXcnQDxmwFT0yaJmsR4cmIyTGWc3yoLStx/cJAojCAdRf2Fo4lJpjxMVmjylHdkrJgt9d7KT72mU94Aj790z8dX/qlX4o//dM/BQD8zd/8DZ7ylKcAAB72sIfhe7733+ML//nz8LXPeS4A4LbbbsUXfdEX4Qu+4AvwuZ/7uXjXu94FAHjOc56D5z3veXj605+O66+/Hq961avw1Kc+FY9//ONxyy23pO0BwMHBAZ773Ofi8z7v83DDDTfgLW95S/9kDxGXxVgiERMyUdvUktmClL4CMaHHbYcSky09u0NY/DTHmNApvLKNj4iJbiAmFfkV83JhIKMZmfi76TcX7EVEeDSb+HnQa1rwwbtgiqaxopRTjgdJkSj5Kvzxr8iv1HqkPL7UHZnx5urjmlstTJNfEa0DQilHnixCd2GoLhr7pCc9qTmOPP2Z3wAAePSTvhgv/r4fxpOf/GQ885nPBADcdttt+Gdf9nQ858YvwRff8GS896/eA3iP57zwf8c3fdM34+nP/p/xxU/7n2bHkW9+4XfjKV/+rGMZR3rL16/03p+P//5xAF8G4HMBvPhIezvNEKqcJvl1imPSQExGTzdsTiK6pZxJxIRddlXKhYkPomi1wCW4jLOxpNyS/BY4xwQdVQ6tvkx70GgF55W0EROXrwEd/zFFQkwSeZhqyoExb5RLvXyMH0OZikLpNuK0vQD8/duhb/1z7N3xTuh/iD9v/XPg79/e/E/d8W7o299T//62vwDG/W5iUv3aAw9+8IPx/Oc/Hy972csAAK94xSvwvOc9D0DolvqV/+yf4nd/5eU4f/483vbO9+Daa67Ba17zGrzuda/Di1/8YvzAD/xA2txjHvMYvOY1r8GjHvUo3Hzzzfjt3/5tfM3XfA1+8Rd/sdjtS1/6Utz//vfHTTfdhN///d/Hox71qMXfwcK45McSiSJSZ+thp5bMko8JgEORXzeR/EqW4EqT51CrlBPJrw3n1ynEhD+H84hJiW4k1CCOfc2EqRNh8WPYNgX5tRivAkpUmKI11STR5oDOR5eLx8Svk3yVGfIrUI+BSZFksiqnSnI8AHvQHS9w6zuAO/4K+Id3hp+3viP/7Y6/hL7tXdi94x0wt74Dm9vegeHWd4RxCYR0qBZoUkRrHPm6r/4KAArjaPGVX/4leP3rX4877rgDb3vb23Dttdfi1b/8X/Fzv/L/4rtf9L/h5T/542l8evSjH43XvPIn8IhHPGJ2HPlH97sfXvt/vepYxpFeKWcfAJRSnwjAeu/fHV8vxyY/2iFVOS3y6wqOiYXOevkGsare/zpVDjgKE1dIo/UYGojJUtt4bghEvgDBTTL215AcEyHlGzY7rc2m4IjJ2EJYKo7JMSImgvwKm7+TAG17OB9IhdpbeDbwdp1f7/hL4Kc/B1cBeHj81cPrdxXRKbrgCgC48eVYXnf1eN/73ocv+7Ivw80334x77rkHv/7rv44XvehFAIBhGPDoRz0SOLgdD37wg3H7+Q/h486fx7d/59fjgx/8IA4ODnD11VenrT3ucY8DADzoQQ/CAx/4wPRvuZJ529vehq/4iq9Ir0+gAeAlP5ZIQzCaQIdNKZl1EalIsQoxEeTXWFZOC5WmKsclHxOeOAW5cNvHhNBYCu0pwW+vUSX5NVvA6/7Y1wnvyZIgjjFOIhKUmGSUiJQ0tfqFPhQl03Q+pGC0YoyeMGtTwsdE6zYHxnHyKyEmlfOrh//Q+4Ff/trF14ViE//7FPmHb34TcP9Hsl9Mo7GtceT1v/wzgAKGweAxj3oEAOAhD3kIbr/9dtx55534lm/4Jtzydx+E9iPM7hWgcYvGkQde9wD8408Jo2FvHHn6k5+YAKGjjiPdxEQp9SwAnw3g/wYAFVqaXnGkvZ1mCI4J3TyUMTedX3mGLBCTUASJ0ilphjbpY7JMleNtWcpBREwGfXjEhB4k/nBmubBQ5USOSU/K14rR8sRkimNy/IgJ0oBaSri1zogJHZfxY0CDKFS7eyju+ynAN78JH7mwxd996AI++ROuxF/dejcecO0ert5rQ937t/4lrDmHK+57XfH7e+66E1dsTB8xYf8HgD/94zfjrW99Kz7zMz8TN954I77lW74Fn//5n1/Xidmg9Opf+EU87nGPw4te9CL8xm/8Bn7sx34snyJbUvF/y+N51KMehde97nWpZHQCDQAv/bFEGoJRB+Gd2L033tfWy8RkBcdE+piMjG8hFzcxCsSkIr+OTRRE8lV0RBN733gqa8TP5DFlqKTHs+FCIkW28XWpJJeeCSVKPiY96/eOj4kcT7nCJyHmacdtjknFgXG51JQQE4Wa/Hrtg0My0YpxHzj/18CV/wi4+x/CmBMTou2t78F2uAq3bK/Cx1+1gwvbwJN70H0pVYn8FaGc4fFHf/RHzXFkb3cHFxIFNn/Oe49XvvKV+PRHPxY/9NPfij+/+fX4oR/5UWawRtfWz44jN73pZnzRkz83XNIjjiO9xOQ5AL4NwK0Afjj+7h8D+JlD7+m0Q3BMEhGSw4I9xEQPgmNiYaGyfTLPxtXxqHI826aPScRoXemm6iIKs7iUkwc3HwckagzmBU9FUb26I+VrBSVOo/NFklKc64mpciLCQ63cxxIxGVJi4sKqUOXEokt+3ewB938k7D0HuGDugb7/tbhgPwR73yuAKzrokQXc5mrgEx5S/v7c7cBdf9sHTDzwlre+HTfc+A24sL+Pqz/hgXj1q1+N3d1dfN3XfR0e9KAHpRqx+FiKL/rCL8TXPe/5eMMb3oBHPvKR1XuXxPOf/3x84zd+I66//nrs7Ozgx37sx/DYxz72UNvqxHNwiY8lEkWkyXhnp5TMhoTAwakB2o/rVDlxoKeFyMjQTt8ZYxQcnNrUtvE+HEeLNyL5KsoJ/pXch1ioJKn0lIdTb1u+LL9KqbET9gYqCgD0JGJiC8TEiFKOMplI63022HQtxIQSk045my/0iJ/YIr9i2APu/2nti7C9EOowVz8A2L0qvM+Qg7CH21yDCwfXwl27h/Egun9vKAGWSp7w+k/+5E9www034MKFC7jf/e7XGUf69+JTn/pUfNW/+GrcdNMb8BmP+fRiX4qUyTPA7/Of/3w872u/Gl/8jGdi76prjzyONO9I7/3fA/hfxe/+EsBfHnpPpx0Tqpx0Y/Y4JsO5JmKy2cQHfSxh1mbfg9WqHDJYG8IKaQoxWaicySzyIZHJqJU6tPDy8KHvRk/K14rROuxtDO7aH2cQE67KOZ6gejUhJmDQN7RO12d0HhojvLjmaoGqaZkqp/tpAJJVn+OTHvpQvPftb8a1+l4o7/DBvU/BJ973mvAZ73H99dcXycZ73vMe3HXr+wAo/Mef/insnn8XLnzcw5oks5/92Z9N/37xizOV48Ybb8SNN96YtgcAOzs7eMUrXnGUE52My2EskSgiJSabnVIyGxKCEc7sQo/joUo5yZl0S9LZqVJO5IVpWcoJPiZtxKRMJgwEmijPXRBBE2/NTCgSe0FjTBIkdBAThhI5xxCT1pAnEBOVSrs0HmT7hfC6keR0EBPZB4jza5KPiSC/Kj9jRVZJ/Rty4ckgT9gwTjz0oQ/F+fPnm+/k44i95S2AUnjLH/4+4IJi6aUvfWl67+++8WZ86N4RD7nvFXj3P3wEHlv87I9/L9z9Hw3/wT/Dd3/nt+HK+9wfQH8c+Zn/8/vhlcaVn/iwBecxHZeMydHqmPAxMSquUCpVTrxBN3uCY+JgvcZmEDBhRDdaUr5pVQ677A3EhMouo3MYDHsvcUwWyl15TXS+lBNh1g6M2YrReextqCbeSs4Yz4aO/5iC6tUE3RKKZYYhcUzCcXkYbwu58Bzi1OCldqNnsDYv6aFiji5ev/a1r8UznvGMIqGoPnVpNvG7dEOgiMnHJN17sZQTERNrwgq3OS50oiLns0TbK9Nc/KjY8kEagqWSUov8KnxMlLNwHeIr7R9gSZnN595tLtgLR+ViSnYk+TUvpAglyuRXNMmvyll4Rn41YjygRVmSOUfpMS8LyV5aSpwzBU/KeK+cCslZsqJp+piE3/u4idp4keQ6+WUvqnEkaZBV83Npn9JUUjWOsRvHNyBdOq3H14ZU5cgViUNICuINHN4Ub9DhnCjxBPLrMGwAi5yNmwGjMh2OCesTswQxYaqcUWVVzpE4JmmVECRzVMpRzVJOWH2ZDiO9FaNz2B1M/Pcpc0wiijXEFZIi3g+pcpBLOUaQXzFjUEdmQ0q1+lWsi25a4mNSo+JAEXfylKc8JfE92ltjK6azzORUQqKI1A5h0Lpo4ukiUuFiYnIYjkny2WDogZ1BTPIK38FoHUi4ag1iMjENCD4IoUPKTCgSO0GlHBpjvEBMuLOqZaWcSfKrL8mv9F0pttAbodNCi+wipnxMcjLWRnQ4YtIs5ayxpC+cX/nfmpkJeGYy9fzX40j2QGhKjb18GUs5EVfv2x7Iwzsd51exT3XuWPZ6GiFUOZL8OqnK2eyVZmtRlbPZlNl47pXTsWOPniPFaqenyknJDnFMXFDlFByTjJgsK+Vw8qtJiIkiuTBkYsIRk/kBxxaIyRKOyTGTXzliEicBo8tS12g9NERiUvlSNI6dLvvsg+ab75mzjU/Dl1rzCIrB7IQt6RcPRoeIS2ksqVBE5p3BJbPWh4TAxQZ3azgmGdENY9Roa/RAho68MKmcCZ2JbZNj4oXvkpYeP3IfifwaE5OIjJopRWIvvAt9vhYhJgElCuRXLCa/JjWIy4tHXnLKiDk7x4pj0j6+rEga0tyx2sckDRUdxATMkl5sTVG6sMImnu92ysWV77M8vNaR9PbRf8/asWRtKee/rnz/Ry9EAkCVBq2Rs9wux2SvqA87G27+HeKYCPJr0958McckfvEF9BjKLKMNK6Dis41uor3ILPIhNe0j8it02aFYxUGjJ+VrxdZ67G0IMZnplUOvjyuEXFjxUo420JH8ejA6DChLOdzSf29vD3feeWfx4HDvxmh51D2M7qO+wDY+PMjTzopF+LCqOmzT8zXhvcedd96Jvb29k9rFJTOWSEMwaocAlAgEGZv5VMpZY0kfkhKlYvdzhpj0umGr+CxXtvHORfJru5QjVTkF/0qeuykXKqkkYiYUiZ1I5NeuKqdUES4hv6pYHqIwDcSEu922yK95ESs4JpWPCSO/xs70vLngMAwYRzfjfjqVVOQEQLUc1ng5BktSherDmBixih5/noze2jWliai3f5ixpJkqK6X+U2ePj1h0aEo9B8A3IJzOv/be/7fGe74XwLO890dnyrRCJABNH5NBJA2EkmzOFTCscyMsS0xUfJ8xQ1LQdPc/p8oBipKS0rRNh9F5bHgpx8WBaDH5tSSqKTdCx1bqkI3sfGDx96R8rSDya/j36apyQjdkkyYN5TLZjSMm+6PDBmPpY8KcMq+77jrccsstuPXWW9PfP3TvFrfftY+du6/C+269C/detYvbz7XlwvbW9+Fg58M4d8c9xe8P7r0bO3e9H+P5TdMP5mB0sOf/FruDgh4v4I4diw/fcc3kKd97/u9g7AWYOz3M+fdj/yqP3XNXzV2pQ8fe3h6uu+66+TdOxOUwllQoIlul80kvkV+HmJj45aUcInkCYYxyY16d00KlOq5Uyilt4z2NY12DtfwcBvPBCVWOQEyQUI1DICaOUNnI1ZPk0mSwllEi53n5vYeY5PE0LVQc4+jAZLSjQX5NTT6Fj4kk53omlW4hJve73/3wnrf+EXYNsPlwJwFwFrj9/cDeR4ALHwI+ckVKQA5uex+s3sEHxvMY79jDvaPFha2FuzMo6y/c/rcANNQVH8buXR+AvXMntwaYi1vfjwt7B/DjPgZ7DzZ3ld/5rR/Zx/7osH/7Lt5/+z042NnH1dvboO6+EvbW9+Ng526cu/0jk7s4uO1vYfUOzn2onjvWjiW9O/JLATwTdfozq/+J9tMvAPBEAA8E8J8BXC/ec3/Me1cdLdxYJACVj4n3IesVXBIAwLBb/N7ZkJjs7lDZgHcCNu2VEe1fm0rhA6nvVqYo5VASMVrRKydukwyfnLXZYKx1CQrExGSSVyzlGF+unEa1162vtqIgvzalfGNEeE5IlaN0XiFRKWcYAK0TGnQwMpSIPsuang3DgIc8pJT6/tyb3osfee378dZ/+xn45z//W/jO//FT8a8e89Dmcdz+qi/Gux72XDzm2f+2+P073/y7ePhrn40PfO2b8MBPrm/1t33gQ7jwC/8KD3vw/XDlB/4//OhDfhLf8dxnT57zzT/xA7jPnW/FfZ//K7jff3k23vqFP4uHP+YrJj9zEcQlP5ZUKKIL7RAARJ5F+P0Yya8+cUyW3+9jRAaA2GROlHZbiYmOCColTuNI7qz9jsE+ji18G36K/ErSWdo2yYWZ0m9pkCVBFzEZ2TmzbRPK0URMfAcxcXnxGCwd4nGrhsGaQEz6iA7vlRPGdaMzWnzNNdfgtt97CR5+9T4e+sLfal+Ee88Dv/BE4NHPBP7sF4DvuS0lRH/+S1+Hj1z5UHzLB/8lfupZn4H/dst5vOkv78RvfGt4VN76/d+I/d2PxxVPeh4e/tpn44PP/WN84kP+SXs/xXFb6Fd9Fv748T+E7d+9Ddd98PfxSf/mbcV7XvGrb8W7//5uvOSrH44v+bnfww8+9E/x5X/349j93ttw+6u+GO9ujHEy/uK/fA3uvvZ/wGP+9c/NHtNc9O7I1wB4t/f+7/gvlVK/uWCbTwBwk/f+AMBfK6WuVkrteu/32Xu+B8D3A/jVwxz0onBtxKRw/qvQjG34ndkpOCbOWjivsTcYjF43EJOeIiWzt1P4NmKSShFmSIqZ0flkuJQ+q01h+DSVmIBl+B46rQxUlNSWHJOyXr0MMfHYS+TXVinHhYfupDgmyJ1KNVshQWWDtf3RYqMsPK+3zyBOvHlib0CkIJWTjFynbl9H5z02GEPZEAthf08S6eU8oIsgLvmxRKKIvD8L5y+Q86uP36leU8rx4p6zHD1oIyYariC/Jg4M/Wz5mAiOifHT5Nd0r9Gzy1Q5veaCvVBRLqw7iElGNbJlAgBc85H34OMOOpb00sdEjAfUqViWcvi2tBtDiZTQ1ySRlsfHDNZUJr8ejGxbsJPyayl24GOHl1wYqR6K3+pxl9EAACAASURBVLdUSs2FtSM0kJBy3biXuCtueD02y5VToTpj4WGiyTHx3j9PDiTx99+zYJsfD4ALq+8EcF96oZT6JwCuit1Gm6GUerZS6jeVUr9JzYJWhy8vkpXkV+KYSPKrHsJ/bFChmvLehuDLUorWLeWQKgc+1xRFwhRO2KTSUeqN4UOvlxIxCUkNN3yaCl4T9coUiQnnWQAZFu4R01oxOo/dTWn5Xx7AyXFMqLZMZDfttnA+muAxOXVATFrk1/6xuOgQCaAaHGSQyknG3OARCIouTWKLPC9i8kiD75qW8x+tuBzGksoQrCjl5DGEOCZuiLzelT4mhiEmlq3OfQeZUL7kmGQOzDRiwrcViOHzHJMiKYvPWbe5YC+iJUHyhemUcrTRRYn8MW/5d/jsW36u2cSPuHHptdZwXqWxztD1K8ivopTjt0UvrR5iUsjENSvl8CTHzyQmWiQmDD33bC5poUQJHVpRbgdKhVflX0XvYX2E6FyLcuWC73kOfVsTzcREKfUd7N+ft3KbdwD4OPb62vg7in8L4PumNuC9f6X3/mne+6cdusYtSialgRHaiIndhhW+HkqOCZVyNqFfjuIwq+qsGhK/QufXQAcx0QViQslOQEyEXFgbZvg0l5iUzP6cmAwFzwJAZPhz6eGSxMRhY3S/JTnn2fBrcBwR0QO+Qkq9jCqOiS0G6TkfGOpCCiC3L+hEMqwTkVbZnXMOiIkNfCYsQ0xUVCIRGuNb3jEXWVwOY0llCCZUOZUlPSEmKxJHfs8ZrZgL6gRiEseSxNmgJHiKYyLI+kFK3+cpJIv3eB97n4m/veaCvZCLH4n4cQ6HZ4iJGe/BjrvQ9jHxlnkBhbAMHSZkx7MJv/YxcbAMNUqE3w6io4hj4msfk9nJOSEm22oeCCgRla9QWefrmIj2ODC9SPNE5N+1hBPEcaKFsGdI1DrE5His0Xpb+RL27+9duc2bAVyvlNoopR4C4C4BvX4ygJ+MUO4DlFIvWbn9ZeHLyagu5SAmIIJjog2kJb2LA9HeJurrUxJh+qWcniLFlccFIAwuzBuFmswFVY4gv/ImWDOoBk1cgbdSIiaQPibxplqDmFh2M7cN1k5QlZPq1TkxSbJBplraj4iJTEwmSzkFYtI2dqLQgr9CYWauo3UIx7WilEOrQzOT9FxkccmPJToSDLnza0l+DfdS8g8xGzio1XLhXMrhiwqNquEmHReVcqi/Dkl6JzoG8waWqengJGJS9sqBOPemIrETVC5OtvGSXMrs7gv0wO1j8Psd51dXdg5HmZhIubXR9WJDu1EgJu0ybEZMDH1JgQ/Ek4fOeJDfwCwnxPfj2UJZq4Z1fpJbryvl2pTkmsi/ayMmRAymc02lnIVcIu3dJPq2Jo7dYM17f14p9VMAXo/ApP9WpdRjATzFe//D3vsn0XuVUu/x3r/guI8BQJ4UY9SlnHgDSY6J3gTU5ODufE42lnKGWMphScSkXLiFFki5cNgQsu6e5IFBKre7Ye+Nn+05E8ooa6Ia2nOOSZk5Kx8g3bxaWCIXdthojU3sl1MfgESNjm+FT+hBQkw8R0w0S0wsrpCeDp1VA0WhkKh8CsqYK+X0riPB/gkxWbK6jqvD5G65YuK7FONiGUuq75IpQRxzfs6Oqxs4NayatEkWC8R7biw9PZqqHARnZWkbTzyQto9JjfB4tQAxYUlZnrDE+DkTSUU0i0hEt1vis9gDDH47QX4VyAPyWJfJr5y7IRrvCWVSnwOTib+EmMhyi5Yu0zK4b5UYNwqUSNfNBjVssTBdjpjQddXdsS8Y2eUig7dluXIJl6jHtztM9K7gY5VSv43ApOf/9t77p85t1Hv/cgAvF7/+7433nYxUGKgSAJoTk4+J82ha0ieOSSkXDoiJjiz8mqja3H8TMWnAXQwxMVQe8g5b63HVboNjspCgymuiXmkY1tRPiRtUR05Oj/jVCus8jJlCTJj7Lb8GxxBp9RCfJBO/IwBF0nUwOlwjOCay6ZkM6/IDqgld60RvhZTr1P1SzgALtVnOMckrpvbAeZHGJT+WVN+ly26jnGeRjc0CeXNVKScaiQGIK2WaoMKz2+IFaO/Cc1xxYPqlHI6YhMlouvRQoRsMMekZv/UikDeHjCZ2OCZh4tdQPnL53AEGd9Ap5bhClQMExMSkRZkukIhWp2Ipme5xYAqDtcgxkXwVg0apvjhg5lsl3sfVVyYmUHx6kojJUvJr6kGkhu7YR4hdLuXk8XQpl0i3Ft2HjN4d+bhj2fpHM1yf/Go4+bXLMcm/D6sExUo5TJXTgVkLVQ4wi5hoRqilGzSUSmpVTq/7pQxeEw3y4MwxqRCTVMoR9fSJ2Nrgs7IxegYxORmOSejtE2zBjd9mdj4j9u5HgzXFB+lVpZyOGiCGQZgcZGQIfAIxUTa0P8AyPgKR38yllZhc8mNJhUgIHxO6r50DNrBQETFZo8qR5Ndkz07E9aYqJxJX5TNrp0o5efIjnpPXnc7ZQIVuSO7BulIOISbTqhwar1JZwwXEpE1+bSAmSsP4bRgXtC64MC3yq9xGlwNTJE4RMTGylDNNJo47iKWcMqHyTAShG+RXWgSlRHTh85/Ir1Hm3Br7CLFL5FfvcgK6kEt0nKqcZmLivf+bY9n6RzO8Lb54Tn5NX7iU8hYck5r8urfRoT+LE+TXWVUOco2uZbCmTKG793G1tbWuJL/Gz2rVhkKrQ2A1Ua8MhtTnonaPJTZ57ta7lGMSyK9tH5OT45hwNr6FxuCzvI2rboLza+mCyZueKekpAyTpHDBdyvHOZcM6eXwz15FgdLXCvjzLLeOAcQkkJpfDWFKVHtjkLEsjm3ivObWsgzVFQOkY+XWbywYc5eChYxM/LVHOKbkwG684wtOLCt3g5FdlFnEPKBLHJHEkpIEZnXPpdqvtPozbbyMmEYUpzhEmjQeGjpOXSLQCf6S1H+FMvlY9DkwqB0XlH5yF2UCUctqOu+VBmyb5lSeNzVJORLV75NxepISP5MKN+9I6YGMyYnIYLtEsv2ZFHA+F9mIMyTHh5FfKmKc4JkIuXJJftxg9H5g6xM+eKqdCTLRITDhiwlU5UR6YmorNISZ8cNMwaLujAhwxacOYraDEaWN015XxpBATbqzkEM4tlXLYQL4/OgxqFIkJDbbtQZUjJtLzoHgfXf+Gl4yeU+VEjokyG4xqgF7Cek/dZMvmcWdxsjGJmLBJz7rgTXMYxKTwMVG5lKNSWaNNfkWLF+b7cmG+LUJ4pibSCt04Evk1Ngol9FJ+liZlgZgoe4DBLeeYWDEeVDLcSi5cbmOKA2PJkoBafKhsSQ+gtiZoRUJMGhwTRn5NDuV0XJCWDmvlwkNXkega5NdcrlzGJaLS4nHE5ZuYiASgIL9SjbFCTNock+RjMpjE+HZs1bCOY9JGTHSFwjhspcGai+SnhTVG2XSKEBMdCbT8BiVG9VwJggdX5WxPWZVD6AEQBsjB9zkmA1xyVwRQ+T5Uh72Q/GpZ7VaGWVLKiSUmB+bKOxWsnr62s+tZHD4kp0e50A4BiKUcVhoJZOj1HJOilKNViR50eAE6lhErXlhKmKd9TGw83kmOiUQ3jsAx0RVHojwnx1SENK5qOGg/wviDCR+TchqT4wFHiWjCL3ghgmPS48CAEX+Tj4lIHoy3UHOSWWWaHBOeNKYEqlL89C39e8F7EE2VcmhuDB8aE8F76fescXyqnMs3MREJAC/lDHRjaoF22G14mJscE4O9HQPrNbQ/wMi+tDbHxOYkh16HjVW1RehQytn6uE1tImIi5MJRAq16UKMIunGHYRNLOdkNEXooSzkRgqbeC0s8MkbnMRiNwfT7WECzxORYVTm5lDPGc7PUM0PngWh/tBggEZNpHxiOVHV7dIA/8PVjlN042+dM5Fc97ITV9YJJjDP+Lev/cRYnGxX65R0sJcXMx4h4Q2rYwC/8Tik4YmK0ShLkYdhEBLDFMXGAGUIbBjCSY2o90Sa/El+FEJ4lpRxKvoKPCTv3NaWciPAMw07clijlxNfDsBOfYYediPKaLvm1rcoZ/BYjlbyVgaLr2fAx0b7khUxxYPIYMwC+RX6dTvTCZ3VXlUPf86BVNa5SmWgt+dVz0YMY9ymc9xji/TdoFQnelHx3Ft/ytNAujR8mLt/EpIeYkI+JR4djEks5ViAmXmNv0BExGacRE+8B+LKMsQAxKUoRcBitxyA5JnoFlOdypuy1CRM0wgpQenlobwuG/xK58OhcIL9q3TZYkwTgY/YxASvlDBgL8qvyocxEiIliiAkNxD3ERE4Ss6WcBnyZCaq95AfxuIbF0lK+OuQr9bM42ajIkKKMiFQayeU5p4cmYbUXsolfkvoT56yDmBQGazSWTXBMCh+TiJhwNLE+d4FucH5NDy3uRDJYo8lLTqzJb0PHBMphlyUmrXWN9q5OTFQcD5h9AC/lVIgJRjgmme5yYBhaRHOHTHLmODvpsy1VDufVkK2FJL+yUtiSMRrI8wSN+z25cDKV1EqUK5f5mJiTtqS/LELIclN3Yf6FNzkmhJiwL92VlvTacQVIQ8pHAwQnv86qcraMVBaa0G2tS1ls+mwhF54v5RAXBionJk0fkwjDrVF8jDbLhW2zV44o5RzjCp/XhVNiImBWo1VS5fDBIrex7/M/NOOY9Es5jAcgj2/mOtrYiVabTWF+NxV8dWhXwuhncfiQHA4lyhmJTEqEZj0EWexRfEy4AkS3kQny0JEW6olIPaPKScqwBYhJQje4WmNlEz8i6/Y4UpLDoWAzYmL75FdpvyDHA6+yDTvxKCRiwsfkKQ5MUcoRPibO+WUEUB3Jry3kHCKBqpxfcz+ztaUcGvenSjlAVIX59QmonjHrWxOXb2IifUw8d35llvSSY2I2YaUhOCac/Gr8tmCmVysjel2gBVOqnBZvxYWBIzne+ER+XSwXKzJ8jY2fQEziA96DMVsxOh8M1kzHkl6azB27KievWjd+zNbUkay10Rr7o4WJq9gUnRURRYGYqL7zq7cs0RMxx5x31sIoDz2E1fUixASuSMbOSjmnE1UjuyIpzsqUxBsaIvl1TWIinF9zQ7toitUq5QiL8nx8fY4J2CSdTP5a74uRJmma3Fy2gF+NmKBU0klvjMLuPp7zrorcO3fQJ7/KkkgcD1rJI/EopjgmPQ4MJ4QmxITxQJLB3tzk3EVMDOOYUIKa/66jR4qRCN5MpB5EkVtIisTiPb5siVB8zwv5bL32HIeJyzcxqVQ5KLp3tn1MxsyJqDgmWS5s/Fbc9D3ExOSseA4x8TUKs7W57pcSm6Jt+Dxiwh/0TSrlRPIru0FpkDMrpGhkmd/lYZwgYqIEYrIBs5UmxCSWcjbCx4Tk1t1SjkPpY9JZFKZGa42Hce46Umt6IkqahT4mOCvlnHrUklmJGmTy6wAb+ttoA4115Fc+MYChB3OlHKmkW4qY0PFiwqmUmuLxXjn0nAW/pTWISR77mv1X+EIqokS8lBPOsRxnSEJbbCaOdXKhB4Th2Cgh8RVKmm6TzOL4AlrEEVVqzKlmOSZtVQ5kKUciJgkh6xxfJyT5FUClSAy8urgfBXCrf9fjUYo4K+UsCYmYyO6dCTFhF9yNTY4J3ZAJMYGrHqBq3/FvizkmHCak15Fcmj4Xt6lXkF85YjIoF/85VMoUYvivqV9Sk8HBaIytUk6FmBwj+bVADwwG5VDWlB0GrUOvHGWhWL1d9Vj3dNjOZ+fXKR8TDpGK0DPXMf1+hX0570WxtLHWWRw9JIpYSNUVs6R3OBpiEtcgWqlAfmeTYMv51ciJKjnTTnNMtER4JjgmgEA3nBMT/jrEBGwx0SrlcA6HQU5MtB+h4Sr0slvKUY6pSvI5G1X7mBhflrMkSpQPsMExYYiJ86E0Nkt+JfltAzHRRSlHJlBCHr6wiWduTVKP+xTWl4sx3hxxeSnnDDGZD4mY+DzZpNqdFqvODseEIEaSCwMoHs5qNVMgJpJj0lblFNvUkfzqGMfE520ulQvD28BFiNvMu6tbpeebfiXHRGsMWmFsdheOg4ZEjY4hCvRA/CTEZNAZMeFw9RxHZzH5NbHd+3Lh7nUcs9dE4CMss6SfGtjP4mRCkiF5GZGbjHEJuNfLvlMKjugaraDh03ig2MTKg55ZaQimJxCTwscklR6mJ1KeBFOPqnDu7YSpF5yo2pS7C4RXw2GHoU47qL1MpAcJ0BgPlIZRTIZbkV/L/jYSJaLwrlyQhu7CeW1rI8dkHjFh2yh+n8vr3IWVFkaZ/LqwlB/DMRfhXjsTidgZuOJ7XtTE7ywxWRBSlcMQk7QK7nJMavKrg8LOoDNTma3Oa1VO/BJXICblNsNAFOS42YmPtrm07XXFIo+hmYyZblpDg9yKxMTG4xt6lvSOfQe0SjimqNADoOKYDEal7sJ8Vag7D2c6bE5EnCC/JrJhw2Bt7jo6tqpdurqmJl4AcRvOEpPTiIoMWZUzYmnEOQzKQZkdeD2g5bDZi+KeU6pAZUNZqN4WISa1My3dW41Jgq3KE8KzBDHh/JpDIibkwwGEpKGJmKTFhS4QEyAkJm4BYuLFGM19YJLBpuiVI1GOFgdGyfHUSfIromfSAsQEqI47oESlehTIHDdZylnaxNMViElbkSjnR+pcDSz/nmkOOY64fBMTocqR5B47yTEZ6lKOMuFzKLPxacRkuSoHKJMd7aNcuIGY6B7UWF2Dsmabd6eZMoUQk5Aw6RXoxjYiOkPPYI3DlYK3c9Qo0APuUMj2RYjJAJuvGVgpZwFiIk2OeCRDqAnya/ec6f7S0UBrIWLi+Tkfoy/MWfQjy/NjKdS75uTsWNdxr4ZkaLgkSvJrmBisGA948HYIkgOj/Rj8NpSCDM9Kz0vIr4AoG7qclPUcaXtBlgRhm43E2ueuzdRkdFcdpD/vNhGT2tTLiXGBu1wng824Hd9BjVqlUs6vSYiJIL8a2Hn3U23Kn7R9NpeQJT3AFKV+/eIxbIA4JryUU37WCZTYwBfk17nv2TsH02nPcZi4fBMTkQCEsgNLTGzP+bW2pIcLio+NYYlJYeY1xTEhVc20Kidsk5dybCjltDgmS6E83nGXIyZmCP1ykG9QYlQvtTt3zsN7BIO1HvlVIiYrLLrnokQPeoiJjgZrpVfDXHfmQjo3ZbDGjItaMXrd3UdSBJF9+YLVtWarwzPy6+lFnggiYdmNbDWZB+3sPbIJiMlhfUxUCaVLaT/AbcbrpnjKjbA9Qiub/Jz3MMoV/KvmsTF0Q3IP1pjI8QZ3oZRTk0stGwONKOXsqrHKxam8wUMiJlA6JyYCMaF+QU3EZIqcGxcGHFHlpbzJUKb8mU5GhyQJmfwKICtKUylnpfMr87PKxo81YqLZYswoh1IuPP09JzLtWWIyEyIBcL6EqpqIScEx2QK0Uo51zECakvXLhpRvkmMygZjwbcIVyVSpyjlEKYfr9PWQbNST9TWD4ZbYnW+Zk+LQkwtLxOTY5cIEp4vrp3VCTIKPyVggJnPXj6NrWngeFO9jK5Hm3yeuo2c8gKV8hKJ8deZjcmohUcRCqs4QEz8SYhLKwWZNEz9flg9DKadED4r3s8Sk5sCwlb0Mhr7k3j7TE6nlSbAvuQfrEJMysZaSVTnxG1WWcnZRu79KDxKAodmc/Mq4G0ar4L7gfWy8uAwxKciv5GMiyK8afpaz00NM1BxigtK3ZnliUtpEAC3ya7kYK0s589+znbBOOExcvolJg2OSSzmhvpqbyxF7iVnSAwzlCAPRxmhYxCZHU+TXSVVOXRPNHJN8wxLHZNPimCxlZQtoNO8u16ULVQ4rKc3d9ER2DaUc3TZYcznZqRRQR4wCPeBERNqXdxiMwsHWYkdZKJNbu+dVQ6eUUyAm6CImfgYxaa666BhSKWez2IxLwbJ75QwxOa2QKGJhg86IgZ6+04iYDCvkwrwNQlXKaSAmqQyph4oDo922i5gQGgswQ7gZjokkv3q22FiVmLDFj0NWM6VjExN/IL9yjslYPYtc6ZO2U5VySsSE5gHrPJxrN95rcWC46y13frUSMZlV5dQoNpBRIiCrh4A8bPLrN/rl5HfPehDlBqailNMgv/oCMZlB0M8Sk4XRUOUU5FdS5QAlMZX3t6GBhiMmEiZsSflWIybxZuGW6ulBEseoVtjGO7a64YgJy5xpcpaDxmxiEh/GwQSOSZP8ypQzlQLqiDFJfiUfE62xHfNqIX12tsFeqZDo5X+LEJPOOXtGUFzKMeHfkYepBvazOLngKCInXHJfECr1EE9tVSmHIyYKlSVBDzHRvLSbODB5QVIF65XiFpYeJMdkclE2EWRJAMSJv4E003ilSC6sOGJSk19bTquT5FeVZbGWEJOG+61tPbs+N2/MqhzpY2KbZPjyoNuICVcPaY2cQMVzHlS5eFyKmGa5sEklfMmvK+fH2POHc4lmvmfewfg44vJNTIQsV/aiSD4mAHvotpljAuQaaJxgh4L8ymDWimOyUpWTJps8idOgttE1YrK4xshWILy5kjKMne1yKYcGjeZDKWK0VMoJTfyacuGKY3KMpRyGHniBnGTnVwU7UuNCpsqZkVsvL+Vkw7pWTNnGF6trtQz215Lwe4y+MGcxHTxZ56gBN+/yY1ZaeTVgA9tVdMkoDK50qcrh5M30/iQB1ZUhmPYjXG+CaPiYTPXKSefucxmrMIJcrcqZmFh9nYztCFVOi/wqyaaFzBVI108rQCmVrrNz2RRNer60jk/1EBPuYwI3X8rpqXJ0v5TjhGfSFBorg95nzJAVnZVcGOm6pFJOwSWa3ped8HQ6TFy+iYlETFiekrJcLZOGsURMEtktZI9KqSwTVDwb73FMFqpyBImTE9QyxyQjJott4+WDFMOYsr+GZFQ3YUwRNECEbp0dg7UT5JiUg1yeJNK+aNAgpcTAfQqmDdZCKSf8e4r8miaqzkA0iTxZyTFZSH7lA/tZKefUokxM2jA35w0Rx6Sn6JJRoHRRrpnQA10vfhKfRA0N2/jcsr4KNvnZOJHqmYm0V8o5DGJSLMIaclyOepJcmPa3qxqJCWpVTtEugG2LK+3o/ANqNKY2Fa1zzhtu+Jgw23hrbVBKzZFfJzgmhY8JJVDeVxyONc9/um/NwKwSBGIiya8rOSZT7TkOE5dvYiI5Ji3yq0RM7FhyTLh2X6AaUtpb7RvAYlVOSggiCsNqt5uGKmexbbwka8XgpRxnXWZUG/ZAz5JfqZQT1EqLfEyOETHh6IFvrJDgLTZGw41BbqgbiEnXY4RLy4+AmEwOHo4ZrOkBZgEfga8Ovapr9GdxcsFRxHJyZjC34JhslO0mtTJck/y6QJVjTGUINoWYcIQ3+G6MUMO8KieNXywpW8sx4ZblrjEeFHJcxVQ5e9cA6JdylDCsbI0HGq64vkCYjG1COWrEpPIxYY6/pMrhtvFpPFiMmNTzQA8xkYnJmiaeGTExeYxfRH5d/j2fkV+XhlTlCHKPXYKYUD8TdkMWjo9Ac9BYzTERE6y8QcMfsypnqW18WIHkZIdCm+yF4tyYb6pImFtSyrG2NAJqO7+eEmJSfSdMypeSB26w1oYzKQqzoQWISW8gmqwDi9V1y9lTBpdbnhmsnW5wFJE7mHJVHucNwQwhuViMmAjyq8pQumy4CWRuWAHtE/l1gmPCEV7rbERK5xCTzGcqib/zED8P3uStmbQzZ1WlDTQ8dtUB/G5ITFqlnFZ/lor8KhGTRCr1iWezTJWTlVKZY5LR4zSezE3OPY5Jh/waSjmlm+8a52e6L4tSjpOlHOlj4lYhY24GPV4bx7OVizEmEJO0CpaqnC7HRGT6yIhJS8q3WpUjSJy8ptx0fl3MMXGdUg5HTCybvCekfCJILrwxCpuW86vUtR+zIViJHgiOTkJMVCqRlByTmMhMlXIYYtL3MSHyazu/nxw83HoFB18drm05fxZHC44i1uTXSDplSivoDYaGiqS7fTE+BR+T/uJHtkPgE+kkYqJ12laSNy9ATDjH5CjkV76YkIgft7unSXoHI9TOlfBQHcSkXujx4wPiGK08DPL1BTL5NSQmAjFpPV8FYhLuB24b7xpE+2Z0VDl83Ne69DFJflNHKOVoMzBFZ01+1aos5YChQ/OqnMx5Oo74mEJMeMYcEBOpyulwTJi6pIIJjxUxyegG3aCplMM4Jocjv5aJCVf2JE07S7rmygRJW58Qk05yxvvXHCtiUpfXJMdkYIZFKBKTaefXgvyq+6UcP6vKmTjnlGgOy8mvBRS+jnh4FkeLqpSj8zMrDdZoDBngFufi1tVyTb74kYiJLCNyMmSY4BcYrKVy0E77vTFC2/us+OHI5JpeOeTDkbbZmvgFSrSLLTDswZsd7KixUshxFCZtRixUSIZbdG9GmBN6BGCOElEU5FemygGIr1IT7Zuh8/Urth9RIr4oAgT5lS0e15dysuhBIiZWICbSkn4WMVlaxloYl29iIlQ5tuh/AjjPkosZjokkfIWfE4nJalWO4K20SjkcMVloG69czvATQhJbqXNlSmb4Z9lyJeUTQRb0G6OD+qVCTBhqFDZ+vByTAj0Q30lETIxRGFROAChk0zMZa31MTK+UM4ESFa3pzQYDLPwM7F+QB9W6+v5ZHC04isj5TVAMgWAoGMyAYRX5te5VUpBflS9QzCQNbqhcjB+LpnRFsLGFjnduhe+Qk2DFuAe95oK9KBGThjeGsxVKtIst1LAHb/aCwdqiUo5ATGLfnTb5FRhULfFtjoGihAdnO8nD4TkmBq7gegBhoeREuX1VE89Uci7H/eItruTVcR+TJRwTJxCdo8blm5i0fEzEjZnqhbMcE66VJ2lqSdKq9g2sUOXUE2tCTBo+Jktt47nBGu2T5M6cZyEZ1Uuy8RIx0am0w/fNz+1EOCYV+bVETDZaZcSEJQ9V0zMR1pcKiRZ9hn++51swTX7lq2sTFBwzsD83wWupGs7i5IKjiEXjOD7Bco6BionJGvIrM4A0DQnjUgAAIABJREFUTK4JYYYI5BWv5ouJVMqxcJ0JQvHEZHEpJz+7xSKtpUicCG5J0EJMuN09oUR7agsMOwExwdghv3YQk8Xk17qUw1Gi1vGF8cwV5ZbUK+mwzq8J2Yl/TklPSXYG1nHMvLOwtCClMV6MfYU4RCP4qbBF5RyXKHFg5jxcFsblm5iIBECSXwGGUMz4mPCGcbJ80JLyHVaVw9GNGjEpORtLbOM5g75IOlBOztysKZ3bLGISBohN9DGxcvY+BcREqpkgERNeyuGICZXCeqUchq7xhl/1+zJE2vz7FHO+UHBssFmwujaFEukMMTnN4CtoXU3OpMrJPiaEgq0jv8aP65JjkkjpPDFpkF/pmZ1ETJQJ9xEyYjLXK8ez+1h5npQ1FIm9bbCmgwBxOBrkUoEShcRkD37YbTfxayQm2RE6JwABMQkvax+T2mSOo0Rps9LxVyAm3i7lmPQRE91ATKzzzJqAXb+ldUKXexD1FIlNH5NV5NezUs6yaDq/hn/TF5/qhRwxMZucPTOOiRLJw2JVjlLpJg4fdFWmLIm1nJ2dLOkFZ2MJlCfJZEAbMXHVTT+fRCQfE6OwMSrJh1NUiMnxkjWLAamjytkY1eSYKGFIJYNPElPkV+4P0IopxCQ1xdK5r8rcOFMgJmcck1ONwsujQkxISpsbMyozYFghF7YeBfmVG1zphlsnd/OUx6dhJ8ivNcdkFjHhpRxvp8e+3vmJFbVXDQ4H33Z83zl1AJgdwOxgR22r5J33+KLwYqFCSAQvlQGxlJN65Ujya41IcP8ayTFxjnN25hCTckxO21eSCxOPpfAxYWTnFYiJS/dSW5E4SX5dwCXKfLszxGQ6pCqHGRjpCjFhKx5tqvKL4nwVARO2pHwFYkKfKTgm4rI3UJhB9TkmdOzziYkDJ9Tyc+bKnrz6Wn7TZ+fXUMqpOSaE8JQs9uOKEj0QhGSVB402YhI/1/GaL2H1KfIryfDaD+OkbTyXluplfAROHmzW6M/ixIKjiLwdQkAN4vPDJJ1KryzltMivQr3RQkwSZ0CoctBBTIrxyi7jmHhl0qJCe8ExWZmYpOahLfKr46Wc8L4rsB8Rk72gymHXUxpD5uOtxwPdIL/aSH41cFUy4RtoZ9EckVQ5jPzqF17PHmJCKBGtRXWDv5K+75U+JlkE0RZOTJFfl3CJnFCJHTVOJDFRSj1HKfUmpdQblVKfIf72XUqpm+Pf/oNS8eofdwhZLofnE/zWQkx4KYc4JrDpQZck2FnEJL5nrSon7Nc1VTnAMq8RyXIHMkqUbnBnWaaf3zOnysm9coLB2raryjk5jklGD0TNljgmZrqUM+38Ou9jkqDzqVJO54FWbsSIiKiZDQa4RRwTLr/+WCjlXBRjCUoUseA3cc6GZ7whswko2BHIr2k8MFTKyferVEFwQzDj+4hJQX5NZM1pVY4kv/LnbGlikidWKrO0SiWOjU/h5546AIaAmMhSjpOWBOwcgXI8bZFfnfesw3JtsFYl/ryMRYgJL+WQcmmx86uYfuP4u6Myfw+ISY9oGLrK+dnXpRyOvlGyx3l1HIlawiWSidNR49gTE6XUfQC8AMAXAHg2gJeIt/ya9/6zvfefC+D+AL7wuI8BQI2Y+DIjBADvWxyTWi6sRV2V/2xJ+QpVDtBATOSDVGb4dPPxh6lCTBbYxpeICTNPA0NMbEZMSo7J9IBDFvRdg7UT5piYRmJSdhfuIybp4empcrzwMek6v7pye/LvU6UcbhsefUzm+qrwJl4fCz4mF81YgvK71HxsUbnhZvYxCYjJZiX5tTS48tWiwjPERBKva1VO+57kaGziOc0oKUqOyRFLOQVHaorDEX5egQvAsAdExIQ/iz23UTkeUPmqxd1wvt3IkKNE5fFxVDbwZoCV5NceYhIXv4MO2yw8UgR/ZU0TT89kzi1zTrqmnFdnwHy+FnCJkqfTRazKeQKAm7z3B977vwZwtVJql/7ovX83e+8+sKI3+JqY8TEB8iQNbwPC4p3gmGTyayKLNrJxKeUrVDnAoRETA5cN1gQCscQ2nj9IdSkn7tONkC2rl5QJKBHZGIVNq5RzGqocwfuR13swKnXr5ByTOR+YpeRXWiH3EJNJ23hnc2t6HRGTidV1bnOf75U1rpuXaFwcYwlKFFFxiSonv9NgbzYRMVnn/FqUchRDD4gfVpRyBGLCEtXQP6bDG6Fn3NpS3jwR3ESuXKQ1FmW98xNS2mb/FV4miu87h4OYmOxiR40oh9mODXrDa6rlYxKs3l1I+AVq1Ca/ugqdNcidhRP5dViKmBjx63C8G4mYNJr4rWri6bI6MyW5bFyyDcSEI3ZLEtBs4nbxJiYfD+A8e30ngPvKNymlngzgAQDe0Pjbs5VSv6mU+s1bbrnlcEfRQEy6pRxnRc0/fo5uNNbJtpL2NqR8Ncck3kTeg7vIphAkTlrdazgMybOkocqZmZh4TbRKTNgNKqWHyxCT0mCtkgufMGLC0QOZ2NH1HrTCBgxejzGXmCxFTKhpWreUMyHp034Ly6D6QU13opWrw48RVc7FMZagfCbKklqGubk3jTIbbFAbgvVC9vIqSjkJMeHkV5oIqJSTV9DGj1VjOwrFkhxv62ejFXyhosRxLVblNO/fPmJC7zuH/UB+HXaDj0kTMWmgHUAaFwglkuRXx0zRJMrR45hIYi2hT6UqZ8ZgrafKia93dF3KkYjEYUs5LUWiE4gJcZwIyV/CJZKIzlHjJBKTOwB8HHt9bfxdCqXUowH8AIBn+oarlPf+ld77p3nvn3bdddcd7iikKqdATOJbfPy754lJWy7cU4C0pHxdjoks8VCIiVWnbLyFmHBVzvTNwqV9MjHhPAvZd6Mp5RNBiQnJhb1HObGeoCqnRg8Ey50Qk2isFH7HuwuXTc+q7TOitJlQ5Xlnk2Fd8++THBMLW5RypsmvdWLyMaHKuTjGEpSTM2+HAK0LxMRCA0pBRcTkcORXFAZXqQzL4HcnVqhczmwYJ04GT0x4I8mpKJIyvuA7RClnqlzM7e7pfXuR/KqGveBj4nhiUo5bKTrjgmkhEWN8hoY6MZGJPzeXo23yUk6Skx9WlWOolBOvdSrl1ByONU08PetBpBuKRImY6Eh+VYdATC5m8uvNAK5XSm2UUg8BcJf3fp/+qJR6GICXIwwkt53A/kNIH5NJxMSVD6nkmAgiEMCSiIaUr6vKkSUeCvEgke1wQEz6qpzZG9PXg1v2MWGqHImYNKR8MkbroFS4iQnVKVCTE1TltNCDtA/66W0XMQFKC+9q+6KU05ULs5VIK6Zs45UfM5pl5hUcTq4OGbfhMo6LYyxBjZh49mzToK38mJJNZQw2ymaC5kxIxIQ3UUs8Es4xEe0QuCGY8Ra+KxeujRVnExOBmHAkYmkpRy5+liImITHZAYaa/CpRmLyhdml8Eyd8wxAT3+g+DrTL2drXyPkAjpiE+aKHoMrjk/NAOk6hyOTk10x2Xl4aV4VcmNC3/L2l5vJUBFAxMV6RgF705Ffv/XkAPwXg9QBeDeCFSqnHKqX+l/iWH0dYBf2cUup1SqkvOe5jAFCpcgpviuTWxzkm8Us2A+OYsBUSKWXkTdWQ8vURE5GwUMgHKa52DC/lSI7JAtt4bgSlGBse4JLZsc7GF5ZyyJWWUB07iZgsf5DmoiK9dZxfB8MQkwbrvpcohVJO/NhUKYc98K2YKrcoN2aOCSk4Ji65XB1+LCAmF81YghJF5O0QuC8I5w2RaVkxLkxtv2VwJdADxyaTNFEx8qtiiEk/MaHxiqHEKzkmhyK/CjnpHCJB7zNwCTFZS36VaDFxN2hItY5fx4bzawvRqVCY6AnDlTOLVTnlcdP3PBCyU5BfS7LzGvK797Ya97kisU1+Xccl4h2MjyOOZysivPcvR1jJ8Pjv8W9fehL7rA9CcEwcsDNI8itDIyxHTDQAlXX+sMVKFcjISUvK11XlyISFQvR8oZvPwE8iJvPkV8fKQyzTBruBvK1aVi9pEDfaXBqjY9xyZc4Jckxa6EHYF2fME2JSq3KAacSkkJZPkV/dNGIy5aCrmSonKTgWkF/5inNNn5JLNS6KsQQlilj0Z2GTs3ZbhpiEiY6UGnPRauJXeRCxUk7imDDCepKvY+yiILyr+OJSDi9jCe7BUsREWhLMIiZ85R05Jjuq9DGR41YK4TlFfx8kd8N5YKTGezVPZZL8SuOqJ8QkIzhmxrBuyscEaCVQvnJWXdXEs2GwxhWJTfIrs6RfwiWSzrRHjZMo5VwcIVU5rVJOocphHBMgrCLoZmDS1GKiBZsoKsREAWSrEJ1I5xATmeEPKjPJJQKxxDaeq3LyAFbeoM66ilHdeihlWJf5L0Nc6p0eYiImaYlmaRPJrwgyXKjqmk+Rx2RfpRNBTPyYOsCqiJgsK+Xke+VjgPx60URZyrHFOFAiJmVi4pcmJk3yq3xWGWFRtEPgZM0BtqvKyYnJyCz050oP2flTe5ues6YisRNJWs/vX9SIBMR4FU5oD2pTIyay427ajlw80oSPupSTyK9CldNCdLRwQwUyusFt4+c5Jm3EhBKoTYP8Clm6W0N+Z35WLeI/kV+NIL+u4pjMCAHWxuWbmFSISe1jUvTKkasHPaTfmWZi0pfyVZLgWcREPkjhGIidHY6xRGGW2MYXskZepgG7QX2WC+sJmFXG1mY0h36OXIJQISbzCM/SkOiBRLHou9loD6NcJpnybUwcj7xXpizpkztn6+8TqxrNOCbQA3Zm+AiVAkHNmx6dxfEFX6EWpV3Gs9B+m1CwlEyMyxIT56SPSUOV00BMUkdwXsppWRLESERaO6JoJDkRkmOSnrOWIrF7fjUvTMrdWxwTAMCwC212sSMN1sS4lY63s3hM/iAcMSETzYr82kZ0JNphYvdyx5KHWQKooAGkX8frOaDhYyIXj2tUOS6rM1uJCV1Tui6plNO4x3tBZNqLWZVzcUQDMal6JXDEJPVyyN4SIWGJxDaRfU9J+SoTtUqVIy67hAfJAdBIBCKjMEvkYiViUpJfdcIJLcv0c0lptpTjXEJKUmIyi5gclyqnzTGBqP9uNEKJpJGYLC3lTPmYcBleczsT11F7xkcw83yEPBHF70h/TMiFL5rgz0RVyonfA+cNUf+ZNYgJR3R5jb+FmMh2CGQI5jvdcinS/cMXY2s4Juy4morETrhqxd9AJHyNEgEAht2EmLgGYlKZekkTzISYlMiA9dy4bFNtQyI6BemZVDmplJM5JnOGdbMcEyLpFnLhhkBhBWJS+1fViYn0MVmjvvIzZpNr4/JNTESzPFnDBQCbnF9dvXrQJmTTtFJIX5K86Wsp32ERk+pB0oKzwVVGC+RinKiWZYWUUGnYKJmtBo2F5NeEmBhCTFoIz0mqcuLxdspsg3KhRCKvN5aXco5Cfp0qtyjmzrlkdW3l6nCBt8BZHF/IyZmjdVmVw3yDUilnGfm1tDMofUx0Q5Uj2yEQYuJ8lAt3Vq6aIyaHUOVoNq42FYm9bQjEBI3EWolVegqzCww72FGlL0y6HnIy7CDQEjFxDDGpyK8djkmNmMTExPvFCNScKkeSX4Pip5z415DflUB2R6+bpZzKxySJMeYRk1xaPEtMpsPZ4ovnvSioJFlyTKiUIzgmAkKTk2FLytdHTHock5L8ShPVLv92fLnNJclD6WPCatHI5++9zYxqzbPxefJr4pjE5GPkpYgT5JhI9IA/QGlfCPLAgJjUA8UUedg5FPdKt8IyyzHpf0faZY4JYn17anVdrQ4X2ESfxfGFJIA2SzlumxGTleTXJT4mxWSSJqqSYxJ6v/QRk6zwsazD9XxionlSJibnwyEmdWLNSzkSMcGwhz1I8mu7lCOPj8YJ2Ri1dGuViUkL0WFzSkJMoo9JgZgc1vk1ckwU8XGYrFl2k14wRueTyQovoB77Kh+TiJjwe3yWS+TOSjnLouVjojulnML5VXBMEmIiFCBJ7UIPOp+UM3M9vffYEZP5G5M71hZdSGkb8QaVfTeWcEwCYhL5Kq1Szgmqcir0oIuYhNWjk9cbwFR3ZivulR5i4v08+bVnG6+9hdPlICxbkRfbcuXq0GsTmkuexakEXwiYDsytfLb+TivwpYiJ9DFRHJmoFz+yHQI9s6Fk3eeYpPvHBcSEDOEmg5NfOarRUiR2IpdM8lhX+4Tw8hU7/mEXMDuBY8KeRd/zzpDlFl1O+IWPSSK/znNMpH9N2FYu5axHTMrjlogJQBy3nJQmcukK8nvRFRl1Gbsmv4p7fAGXqDq+I8blm5g0nV/Dv9NEyn1MKo7JEBMWiZiUN31LylcjJstUOT49SOHnrhIIxCEQE7lN3tyLJmfJqF6GmGTzN+qAXJRyThAxqdEDkZjEnzvKdTkmk6Ucca901TJHUOVopsrRCfY/6G6rIvqdlXJONeiZ8M7BKN9BTMbU1ZfIlG7iO+XR9DERUv8ikU7PQCynxOObR0xYEuy3GBc4RgQ+E+PXSL7dguRLOoM2EZOC8FsjJhX5VXij5JNsLx6Hlo9JkvhKVU4tkS17JMVFGRms+azKqQw0ZcwgJgMb941SEY0pyc6rmni2EBM29tGaOhnSkiqHvucFXKKzxGRpSFWOR0V+ddzHpIWYNDgmSV8v+QHS+fVQHJNyhbThb3MlCrPENp63Z28hJpYQEy+MgRrELxktlVNRymmqco5nIpXogfQtoIGBEBPbsOd2neMhqJjXW/nvxYEcQZVj4RP5tXEPyeOSq0P9seFjcrFEQiSEOo7D3AViQuTXTgdrGdPk15h8sMTEOwvL2iHQQsV6HzkmncSEtb0v2iJMRVHKsdWEv6yUIxCTBseE292XiElo4rdRtrieXbdRkTzQOFFxNzxT5Swhv3LeIl2D+B7nANgR40IEqjhO2r44TjoFnvRwsvPiJp6+5JhYoUicI78u4RJdCk38PvrRaJbnGuTXjJi4CY5JWcuVPIaWlO/QqhwBPe4ogUBwxETN28aX0GjNMXGKEJMIca646bfWJ6RkYxqlnBNFTGr0AKjRrI1yGFSP/No+HoKKjSz7Nco5wep54kFUfdt4w31M4iQ2RX6tzKTOEJNTDUJMpNso7z2jfUZMUilnoVx4mvwan12uyhGKMDo+56Iqp7NyTSaKzkYV0ZLEhPuYuOrc/SLyKyE87P6V5FK+kOIoSDRYAwA/po4EVWO7fLgSMSmRCL7Y8LZtMsdRIoqimWvcB0dMwFoSTEYHMZE+JkAk3zP+ijmEKkc520TK0+s5H5MFXCLvbJ5PjyEu08SkXNUAkvxKNcYZxIRxTLR4GJUYNI4TMSFUZopjsqiUA1sx04vEJE7OLbOmuZveOlcjJqekypErpZ753aAdBthMMuXb6PiYpNWDuFda5RzvD+9jwr+bNAi7BeRXhhKdISanGHFyllL1KjERHJMliIlrrFh5ySSR0sXixxWJSXhmrfMY4LpeGiViMjaJ4TL4QqUs5TQUid1zpPs3E/1lYl2qCNlzNeyF/wBgzKWx7ipdfDf0d2rip5SCUoIXUiEmNSKpOXeQjtMTYuID+XghAhUPUBx2mewAIUl1vjZvW9WSYraUI3xMgLJcuYRLNKdQXBmXZ2LSqPU54eYJIFAHqSTS4pjYrMqhLyfV+CaIaaFPTwsxmVblVGQt3UdMllgSN0s5ggTFk7IpmFXG1vmElGSOyemocir0QEoMdR6IBrRRjZ5BUZLOiXvFtQiwrnzgq300BjeKEjEJ9e0pxESy8s8M1k43aHKujO50XphwxMSs8DGRvUrCitVXCxW+yvVVYhIRHe8xYKxLEzES18yNh0NMOPegNfZ1QjYdhG77hNDEX6lyonJN2Qvp17k8JFQ5FQ+wJL8CzAagZ8vf8TGp0O1osGZjYrJocu5xTBKyI8mvvuZwTKCxMpRYQFWqHIGYkKwaYuE3xSWaa8+xNi7PxKSRADR9TJzPK/mOJb2E0DJy0pfyBcSEq3L0IsREojA7U4jJElUOq4kmNGRClcNhwrkygbVZldM0WDtBVU6FHkjyq6JSTlg9NjkmaPvArEFM1IwqZ4o5r31utGYWrK5bKNFZKef0IiESwnWY8yzCBEAGa1ECvgBN6LWdJ/QgeZXw+1VOBITouNjxtkd+ZYjJ0tKD16VcGOLcC0VibxvCkkA1EYlsd2+0SEwiYqIaiMks+bXJ3YjGiR0vl1Y5W/rXAKKU48Zlk3NHlaOFrBnIpZwkwkh91Vb4mPiZUo64/4aELJVz3iSXaG4sXBmXZ2LSSAC4HI/LxdJKvmNJPwoFSFW/XOxjskKVkx6kCVXOgnILz/ClYy0Qb1BfJyYtKZ+MLe+Vc+o+JiV6UHV+1vn6DRjbcmFlmqWw3AK89Lxp8nZ9W4qcYgLVMH7M5NcFq2vp/IizxOR0I6KIXpZyqE+UtcV3msaRJcRQiZikUk5c/DRsxGU7BM6B0cqj1+E2lZ7dGL10lpUe6F7j3IOmIrETlSWBrp8NbndfNvELBmsAoBzjmHTIr/L4aJzgJRJCTHwnMekiJgntiN9NUco5GsekpcrhpZyC7DyBxsoouiKj5tfJUk5SmZnSvmKSS+RsINUeU1yeiUkjAeDkVy4XqxATgkD1ALjcR8YMZdIgyy41YsJuusQxcfk1D4HCJMSEk7uFKmc5YhJlbR3yKyEmTt70C1Q5086vJ6fKaSlUwi7K2u2AgJi0OCa+o2qynVJO08vE2eJ6VvuYKIlp1pp+kX257MNxRn491aBnoiK/KiKmjoU3zZrEpEZMUBiZJVK6cH7lJcqkGup0y6VI948NY14LTaxCB/RASqWb/LpOSB5byyCQ291nR1sVxuSEmOTERKLZ+RzFGN1Y6KWu4b7NMeEoUXl8fcRE+oV0o6PKMa1Sjgo+Jk2EbIWPSbEgFWNxVcpR5Ty1hEskS4tHjcszMZlDTDg8r+PKmR76xPcIHJMtPegEQaafdNPXUr5559eOKkdCj/poPialwVou06TDjNuo6oMLOCaj9TBTBmteJGHHyjER6AHtQyQqRhH5tYGYnEopp588GCYXNsmMa16Vc1bK+ShFRBElv4lW9jYmJhIx8ROEZoqM0sWfETEh9CBNvL4cY5qqHDFeyUgIr7MFWXcyIp8hSaUl92AVYtJH/Hjpmd43qg2gFOOYtEo54hw6lg5FKYfIr13EpD6+VjNX3ivnqBwT1SC/GoaYFNtewzGbIb9KnQKZxmVeZaMqUO3jLDGZj4YslxsYFYRGUme4MdRlSYMeOSZpdT6QDLB90/vjUOUI6HGjjsgxYQ8Sd1xM26DJuXXTzxmsOVeTX5sck+NX5Uj0QK5eM8ckyIXbiEk7Ucrk1/DaTJFfmYqgGRO28YZxTMiMq+dEC+RkzLCB3Zypck4tCDFJna0bMDc3zUsr8CUck26vEkJQaWIofYIk+VV7B+eoW26bY2JYKUe5tsdPFVqgRckYcDliIpsOqpYqp4ESjSoan0XERFuGmAhjSAo6vqycigsAgZhYD8B1vEd0g2My5WPiV5RyOqoc00qgNBL5tfi+Fywe0+6aiMkE+ZU4Jmu4RGeIyYJoISY98ivnmPCsmTgmI5G2ypucvrS2lM+VN91CVY4SK6TNFGKylGOiywe9vEFN4pjI1dfcajwgJlTuoN/1j/ckVDlVWS1lnnnlMTBZbrGNDuIkERN9BMRkyjbeYKzJrxOICXnNtJrHncUpRJwIpIcOh7lDsilKOQsQkyb5VfFnN44xPo8xVTuEyInwCTHpdBdOiZSD8ss4JkRUzSoYmTAtMJHzIoloISaoz3lU8Tyij4niiYlvq3LkONAiv6ZSjtu2k4mG6qWlyoG3yTZe+YUqpy5iEhMTyYVxHlUn8xXPf80xkYiJJL9GjokokU9xic5KOUuipcrpkV85x4TXGaMlvRNZufTOaEn5asQkToKLfUzqzPmwiEldypGqHFfDcA0pn4zRZYM1pRQ2RtWIiUSNjkuVI9ADSUjOPiY+9sppc0xa169yQZxITOY4JpOlHMYxSXbYkw9+fc5zHT/P4hgjooiyHQKfnDUsvGIcNWAV+dUI8mtGDxgvJH1IkF81kV87TqYxUpLjxhLhmQgvEBPJPZhC+vLhEqEyT8qtUolMJrYJMYmJieOIidgmhSC/0jkX/iCR/Kp6KEfn+KqxxrlkGx+6+K5BTMr30uLRdMivhWfSiiaeEjHxKM05a/KrQEyWIGOC83TUuDwTk0YC4ISzIiAQEzuWE2m0pB/jCoRKOZVXRkfKt87HRD5IjVKOFyjMAtt4XsoxLY4JTc7ipm9J+WSMzGANCBN41SunuAbznJilIdEDaQTEEZMNWN2fb6ODOK3xMZErkSoaBDoKw5LXpLqYQkyEwdKZKud0I5dyynYIaTVpXVDlpIlLhRLBCsSEI7qlkZmG80osflyj/OrSPdTr8poWKM4uVuUorWNiUvJE1iAmsukgVJ1Yc05cQkx0SEhyKSdzTDKhto08yPFBkl/thJKmKWduqHLgbbKNXzw5dxCTnEDVzq8QLtNrmngqlHOHE312euRXWR2Y5BK56fYca+PyTEw6iIlW5URa+Zhw+FNwTFI2K2/6hpTv8Kqcch/DjI/JHA/EtHxMKgJto5TTkPLJGG02WAOAjdawslfOCSEmLfSA/0zMfuUCYtIgAroOx2Q1+XVWLjyFmNDqet7HRLpcKm1KOflZnGxo8gkpSzmF/NYzxAQIDfIW3PO9tvPF+BWl/RTK2dLcj3xMxo6TKZ0GQ3i578pkxCRCSqWbisTuSZbIs2qpXny9kLIMgXJQRWICSYKnw+2Mp3zC1wnlGDG2Fi6LERObkgclUY1ezPTKqbkwRyvl6IaPyWQphxATsxYxOUtMpqPDMSlW+EpFHxMqs7Q5JgSNmjh5SF5DU8p3WFWOgM42x+hj0iK/OpWPS9ar5xETcT2NwtZKhEdcg2PzMSnRA0nSous4wEfEpE4ePKYRE24Pzn8vDmTS+XXKNn71BZSTAAAYh0lEQVTAiMIzB4BagJiYaqV+TITis5gMlRCJOkEEwvcwYCz4TBb6cD4mifwqJhPuYyKSYlqoZMSkV8rJiYleyDFBXKjYxK+hMaWhSOyEtCSYK+UQSjTqWMpRClvswLhalSPJr5IbQeNEa8JXHVM0Qonyvlzwh5Foh7ep3KKWqpwk6kLH1PIxSd2FBUI2gcZW5yKQXa9KRaIkX2vBMVmEjC1NyhbG5ZmYNFU5mfxKf6p8TIxMTBjHZGgjJk0p3yFVORViMsExWSLpLTJ8hKaFfOAMk3NDlbOgTDC67PwKBJM1W3FMygz/+BATSX5tc00SYtJR5bQ5JnGTifxa/p6HrN3Wb5gyWHNVYjKNmJSrQ96j5SxOPghFJMSEYG5u8R7cfHNCYDFA+fnvp+Y1lWXYsK3SrRNyohK9fHqJSS7lRN+VBYgJoRtZKl1O+Iua+IkVf4sj1TpnS4kJgK3alKqcTmKSk4ey5FSQShP51bb7BYlSk/SvKRATzRGTBYlJl2NSc2ESsbYpF16emPC5o2ewlhGT8ntexCU6Q0wWxIyPCYDcK6HgmIjExG5TXTUlJHKV3pTySVWOnuSYpC80rfyjQdgkYjJ/Y0o42EGXqzCVfUz4TdWS8skYrUsGa0Cwpd9K59cTRkxo9afFA1RwTFQmmRbbOAby6xzHZMprZGCqHGgdXB1nelEAdWJ8lpicUkQUUZr7cYv3AeXiwS7kmEjyqxZN/IAaMZH9T8gQjBATkqDLMKz0vBgxiZO0EzL9piKxe5Lzi5/WeFUlJg3EpHJ+FUljKsMX5FcyRdu2lTSinF0lJlyVw5QzSxGo4mc67oASGWmd733dMHRFE0/e3gCIAgj22YzYhddU8qLEcwliEkqLC859YVyeiUnT+RUwxfcaM+aEmGw7HBNyfqWHsZwEW1K+NmLSV+XQSiJnqGGimkRMFtjGD6pegRQ+JoQaVKqcZXLhgV3QwShY6fxacUyOifwq0QNSBwmPGYPQxK9Nfm0jTrmUE15PlXJmEZOJ62hgi/ttVMPkJFatDlPzuLPE5FQifpdJqi54Fs6OoTEjS4JHNUwmmxQJpaN7jtrO8/FLCcTES45JOD4fORh6xsckJSYdWTEPSrBTZ2UqY7UUiZ2QPDbVIL9yJRIQERNVJiaylDP6RhkmHR/JmqmUI5vjodthWXJgUsmUOS+HPzhWylk4OXcQEyCccykX7pi3rTBYU95W5FdVICYhKVFJFVaKCxZxifwZ+XU+KIMXiEmT/KpNeL9rICZumzLlnlw4S/k4x2RscEzGfFwdxEQmEfwGDbwVaajUv1Hy6qZcgVTkVzJYE74rZo786lqICU9M5DXQx4eYiO9ENptKiIm3kWTaGCw6iR1JnnMpp+FqS5sQD3z9hinExBbunBZ6untnguhLlIh8ds7iZINQxIqELOTCYEmwg5mUgFNQjylOfpWISUBf8raUVNRo4piU5FwZmnHiJCmyF1R2sWPJMWkqEnshUVltoFXgTgCo7O6BMF6NDDEZ1U5RyoEbm+WDhKCqcvE4sDFNKwXrXH0d0wGWiVNSZ0p+iBthVLBK6G6rPsD4s36vg65KOWNUD3l5/Q5JfvVKyoVLhSXNOxkxWcAl6nwXh43LNDEpERPvfZ/8SmiGbXBM7JhKOUPqlRNWGBzdqKR8PUWKtGmPkRETPqjpmvwqOCZTN2YFPQKhyVIjc1ZCirakfjk6n3rkAMBgdG2wdmKqHFleK5PF5JhJcuHGqrDPMWmTX3uqnKmBfbqUY+HZ/TZigPLTiAlfHdJqcCqZOYtjDIEapEGbEBM3homv+E7NIo5JcnoX5FeOHkglRUW8jivoRH4d8oQuY/QBfQny5gWqnJiYZH4NJWXLVTmVDwez8uc/wcoyTmk4kZhIxKRNXC2TxrSobHI32qZokgMjHX/jC5DBmnNreuVMIya6QX6tykQNjk4vZIkscQtpnw7Fol2nxITmvHkuURgLzxKT6RAJAM0ppiC/xkyU+B9NxGRkK1XKvsUkCES/AkF+bfErpE17DHpg5WrByFKOEsnDRI3RJgdIgZjwGzROzrJ+qfS83flofYWYlORXybNZtnpcEi3pbHhdckzgLQbVto2f9TERcuFmKQfzPiZDI/nxPhi/lYiJKQ20qgOTPKAopzxLTE4nIoqYyhlCLgwb0TmOmCgDtcaSPiXDKBUgiM+u4HBJlLN0Z+0nHMRXWTqRErpBfXjkWLgoORbqF7p/CYmQdvdALOUYXsrZgfFcLtwmXFZqvY4Md0pJIxcVrnF8pLYkIYVezTGpj93JUk5SD8ly+7C4lKOFQtLTYjyG9WLRjtLHxCwpG3t3ZrA2GyIBsAKeB5hxTVLMtDgmuZQzxJqtSUZrJbohGfNNRYpMWOjtXsVtilLOBGLi1bTBTpqwxHE2JcdOrL4WqXJcxTHZSoO1ljLpGKJCDzqqHDh7ZMRkypJ+FgrvIE/WOuyo8rjmVtdVjZ41YzuLU4jE4eiQX50NSagpVTmLeuXINgh0z5gyMeHwu3LlvZcI6wsQE1L4GD82+VdVENFaLHa4i+xsCB5bKg/EcSqTihmiC4mY1BwT21gYSDUJEFAi6WNiY/ml2S9Id1Q5ph7TSEgxyznjn+M/WThVl3ICYuKqxeNiubAoZ0viv3OlMCRZ0ldcogny66WAmCilnqOUepNS6o1Kqc8Qf9tTSv28Uuqm+HPv2A8gkV9jphdXJHyFn77wKAuuEZOQTHhJfjXkZzKRmHhbb4vIrw3olCYcvk0ryWHys7OlnNLrI+zHlAmVGkIpx9sCzlR6mIUJrfPierYM1gQC5R3Q8gNZG84Wbo1JZkdKBNqvtxiUSJAoOogTmQ3RudFP11Xl9AcibYZ2YiJdXIHoKTPdvZOfM8Hol7uPyUd9LKF9mQHGuzQ4D4YWKuGnc2MkNAvEZEkpR3jnpBq/LscDbrAWJqq6tJvMBzvkVyCXhTTaijUZ1HfHbvfjtul5I2PAJaUcV96/tM14/+bSM0/sDBw5vwIY9Q4GlpjIZ4Iij9Xl2MfHtEHnZKKFcig9CA5MqUgCkOYOzUo5ixITYRPAw8J0DdZkuX1VKadQY5bEf+s8jOGISbwf4z0+LPmel0qlF8aCdHldKKXuA+AFAJ4I4IEA/jOA69lbngPgnd77Zyml/k18/R+Pss9xe5AgQQDQF+7GDoALFsDW4t4DYtKXicmBDVmo2+6HdtraYLuN/AUMGMYDjAcXAACbhJiUWSQQVyDjBVy49+7w3vEA2FyZtjVAQ9st7MEFDNpgf1t+wRdsjZg4aOxgBLYX4i/qxEl7m/Yp4+Ceu5rbLIigSkO5LWAPmohJb9sAoMZ97PqDdHxXqC3swb3pM8N2H1ppHMRz1V6F7+Sej7QThRXhx/2ytpwIoaKUM+73ERNtoP1YneN2/17s4gDa7QNbQNt97OIAB/v3VO/VEhWSEVdd8nP7Fy5gF6j5CNsL3Wvux/1ixUTnun/vXZPf05rY3T2XDbAugvhojCX9g4n9Yrb3xpdlGdHu31tzTNQAbfdnv5/9C/dgFwcwbh/YamgXn3mJdo55W9ptq2fWwMId3ANgOjGxysBvL2BYiJgQinFwz0fCa6FIdAf9+zbFuF+WIuM29++9C5udXVy45y5cxX5P5+xYKceqDQab9+XHgw75NZ6TGKM3bLzaVVv4g3th3H7byyV+9p67PwwzbLAfx9OiRKbDd3JObWG398L4LUZ9xfR1CBspf7IIx7nNx+m38FsLZQ8KREItGKPT7hpou/bb9Fl7cC/OIe9ziPdfRkxi2figHgPTPtz2WBGTY09MADwBwE3e+wMAf62Uulopteu9Jzr1kwH8UPz3rwP4LhxxMHnzq78PT/qrl1S/f9IPvxHncU16vbcx7N8a/+H33oPP2tyNz3/fKwEAr7WPx9d/z28CAL7GvBfft/l7PP6PvxOj1xg24UHfuyJsb+fcVWlb+2oXT3zXjwA/+CPpd784fgG++0/Ctl5g3o9v3/wF9G99F+7wV+Ez4j4o7oMP4092FYa9vM0DtYvP+euXAP8HO69H/4v872EPD/IfBH7wuuY1oaWj2T2XfndBn4PayQ+ONXv4zA//DnAP8I7Np6Xfm51zGJTD0Nk2ALwZAH4v/gfg1QDwDwDent/zFvfJ+KfxXJ+q/xz/aQfY++EHd7e5ND4bwHl1NehMduk7uSJeP6VDWe7XvhEPAPC3O+eqbTizh0ds315dvycD+Is9AD8eXn8Cvf6l+jgeAeAPzafVf4ihd87BKA8j9kHfjd69Mv1uX53Dkz7wMuAHX9bc1hMB3Ir7pNfDbjj7B7/y+ub7DxN3fft7cdU195l/4+nFqY8lvdA753BOHeAxb/hGAMAmPlc7e+F7eMwbvh5QgGbP11bv4Ym3/Srwg786ue3rEe+xnwivr42/37Bnd6t28aQPvAL4wVcAAD4TwJ9e8Tnp72pzDvfFh3HfP/q2ULbYZKRBxj528MR3/ygA4LbN582dejqnR772X4bj2gv3rdYaB97g/2/vToOkqM84jn9/e8we3CBXOJVrYXcBEUUUFS0tt9AoBMV4FiaxrLwwWlZpUppUjAnRGH0TKZGIFEkkEcsrJQoaE4WNRiIsSzwgioWCCh64AqvCwu6TF91z7EUWdnan2Xk+VVMz/+6ent/09jzz7+6d7pOrb4Pq2w47j+nADiU/B7nhPI97sBxIqVcpy+9AThENsWT9rsvrHtSr8PM0HdhJ/2avFa8DBUWp9TTGqVvuggV3AbAcYFcwblPRqc3mkZsfLPtu940AYGg4PP65CxpF8MLtPAuwPhj0eu+KFt5904Dh5z6/eSfmoGJM37YQFgQrw+KUcW/FyhOPc2JFFOpgq/U/1VBgR15yZ2JDbgFT9lYmnntdeGNBML4sHjO+jscKqDcx5bUb4bUbW3yNqUB1C8vxaHVEx6QfUJPS/hLoC+xsYXx8XCOSrgKuAigvL286upkhp86lasCoRsMOxnrz6/7TEu1YXg6njeqXaC+8YgrvfVqL1d5H1Z7NAOT3LmVRt2AVzD00jg2fTEXUU9x3KCX5Qc99wJCRbJ3zHKPKpyff4JzlbN+5tdHr9+97IouKBgCQVzeKqs/OBIxvioewqE8ZTW2ofYopU85KtA/NW86Bgx9RkJfSqx4yNfGw9OKbqVpXctj/28iNFVJ6yvmJds4Vj1I2YGiiPWzePVS9WRnkHXVict7nXE51rJiG1OtSNJEjMX5wTwrygl7yZ/sOsKPm60bT1PYYzaKewd8lp76MDZ+MOewvT45Ej0GjEl/TI0tOYuuc5xg9YlwwQIJrV8G+j9m1r46y8ec2e/6YS+6gamPLX+rdC/IYO7BHov3OJ/uoPdDyLvkxJzafd1zp2d+lOr+oxeWYk19I6ekXJ9uXPkzVB2+2Oi+AnoPHJMrw6Ekz2LRnCfUH0rO3BKC8sA1be52r02tJa8rPu4aNRT2xhkMU9hrIhN5BLenZux9vn/8o+/d8gnLyKJsxO/GcXvMWUbV1fZvm3y2Wx7hByXVu+556Jky9MPk+Ll3abP0YVJKsQWWzrmdjnyFYwyGK+w6hJNZ6x2TPnOXs2Pkukhg/9f9/kU44/SKqGxbTcHA/+cW9KBsxNsiUk8O2bz/OV59vb9N77DNsfOJxybQKNtUtpj7cIw1hvZqWzJN7xaOU9k9+8abWq7heQ0qavc7gEeOCelCarP/7LllBsX1KLLd5vUpdjnGlZ11CdU4uDYeSP0/OLehG+aSUmnHlY1DzPjv37GfnnmBP2siJMw+7DAAYMB6uXwt9RjQbVT/vEfbXfZTYiN79VR0f7A4+4/1Hn5SYruzcq9lY2KNN/98jidJpyXVp+GX3UvXGmkbTHNe9gOF9g8+/mfH+1wUcPzD4rigs7s6WWY/xdc3Hh32dQePS1zGRpeOYf+oMpQqgwsxuCtvVwLT4Vo6kR4G7zaxa0mTgx2Z2eWvzq6iosNWrV7c22jnXySQ9b2Zt2DRs9+t4LXGuC2utlnTEAeV1wAxJ+ZKGA7Upu14B1gCzwsezwrZzzjXltcS5LJT2jomZ1QAPEBSJvwA3SZos6ZZwkmVAuaRKoDxsO+dcI15LnMtOHfE/JpjZUmBpk8HV4bhvgFZ3tzrnXJzXEueyT3R+G+icc865rOcdE+ecc85FhndMnHPOORcZ3jFxzjnnXGSk/Twm6SbpXeC9Nkw6mOSJl6LMc6aX50yvtuQcZWZjOiNMOnktyRjPmV5dKWeLtSTyHZO2krS6M0761F6eM708Z3odKzk70rGyDDxnennO9GpPTj+U45xzzrnI6Eodk0cyHaCNPGd6ec70OlZydqRjZRl4zvTynOl11Dm7zKEc55xzzh37utIeE+ecc84d47xj4pxzzrnI6BIdE0nzJb0q6RVJUzKdJ07SiWGmtZL+IekESYWSlkuqDO8LM50zTtJYSQclzYhqTkknSXpB0kuS7lHg/jDnSkl9I5BRkhZK+pek1yVdHqWckp6X9Jmkn6bkbZZNUt+wXRmOV6YydxavJenhtSRtGbOylhzzHRNJfYAfATOBq4DfZTRQYzuBCjM7E7gX+AUwH9hiZmcA/w3bUfEzkpeOn0/EckqKAXcDc83sbDO7FTgfKA5zPgbcmsmMoVKg1MymA+cAvyJaOb8P3JLSbi3brcCKcHi3cLouy2tJWnktSY+srCXHfMcEOAWoNLM6M9sG9JBUkOlQAGa2y8z2hc0DwCHgLGBlOOyZsJ1xkqYBu4APw0FRzDkdqAX+HG41nkE0c34M1EnKB3oAXxChnGb2YZNBrWWLTOZO4rUkDbyWpFVW1pKu0DHpB9SktL8EMr4LLpWkbgQ93d/SOG+Ust5OsAURF8Wc3wImAVcCVwMPAcfROGefzERrpAZ4F3gHqCb42zddnlHIGddatr5hOz48CutAR/Jakh5eS9InK2tJXodE61xfAL1T2r3CYZEQ9nRXAL8xs7clpeaNRFZJFwDrzWx3yqG/yOUkyPCqme0F9kr6HMilcc6a1p7cic4DhgCjCTJVAi8QvZxxTf/W8Ww1YftLorMOdCSvJe3ktSTtsrKWdIU9JuuAGZLyJQ0Has3sQKZDAUjKITjJzNNm9nQ4eA0wK3w8i+Rx2EyaDMyUtJrgg3AvsJno5VwHjJWUJ6kHMAB4gujlFFBjZvXAPiAGvEj0csa1tk5GcV3tSF5L2s9rSXplZS3pEidYk/Q94AeAATea2foMRwJA0iXAMiCe5w2CfwJaCgwlOAZ7rZntz0jAFkhaBiwBNhDBnJKuBq4H8gl2F/8VuB+YCOwFrjGz3ZlLCJJygYcJtnIKgD8BC4lITkkPAaeF2d4EvtNSNkn9gD8CPYH/ADeYWUMmMncWryXp47Wk/bK1lnSJjolzzjnnuoaucCjHOeecc12Ed0ycc845FxneMXHOOedcZHjHxDnnnHOR4R2TLCQpJukJSQWSnjyC5xWFZ0l8Kfw5ZXz4TElLwsezU8e1I2NvSdektOdLOq+983XOpY/XEtcRvGOSnSYS/GRrMrDpCJ43GdgRXltieyvTzAbaXEzCn8O1pDeQKCZmtszM/tbmpM65zuC1xKVdVzjzqzsCkn4PTCX4jfnFwB5Jh8xsQZPpriM4nwPAYuAPwCJgoKSVZnZhC/OeAFQAkyRtNbNLJd0AzCNY1x42syWS5gMXEJw/YG14hsgLCa4F8ZyZ/Ry4GThJ0ssEp98+GdhqZo80zWZmS8N5zgYagLHAD82sUtK9wOnAfuBBM1vRnuXnnAt4LfFa0mHMzG9ZdgMeB4rD+8IWxvcn2PqJhbdN4bCZwJIWpk8MJzgJ1Izw8XjgWYKzF+YCrxJcS2E+sIrkeXS6hfcKpxkOjAReTHmNOwiu+NpatvkEZ8WE4IQ/j4eP3wLywsc5mV72fvNbV7p5LfFbR9z8UE4WkTRH0hqCKzuuCu9Xh1snqU4A3rDgKqt1BGeZPP4oXrIMmAC8BPyd4Kx/w8Jxr1n4CQfmSloLvBy+9jBad7hsG8L77QRFC+AnwNLwLJTjj+I9OOea8FritaQj+aGcLGJmT0l6D7iM4GJgl5nZ7S1Mug2YKCkWtsvDYaVteJk6kuvVZmAjMNfMTFK+mR2UNBmoT3nOL4ESgsu5v0KwtZM6n7Zkm0BwGvE4Kdiv+6KZPSNpBnAnMLcN78E5dxheS7yWdCTvmGSfacC/U+6bMbNPJT0A/DMctNDMPku5WujhrATulLTZzK6X9CKwRlI98I2ki1p4zpMERWQLUBsO2xVO/wTwwFFmywNWheMKCYqJcy49vJa4DuHXynHOOedcZPj/mDjnnHMuMrxj4pxzzrnI8I6Jc8455yLDOybOOeeciwzvmDjnnHMuMrxj4pxzzrnI8I6Jc8455yLjfxGaopCThtjnAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 650x260 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "## F1 Score of positive Classes\n",
        "fig = plt.figure(figsize=(10, 4), dpi=65)\n",
        "ax1 = fig.add_subplot(1, 2, 1)\n",
        "ax2 = fig.add_subplot(1, 2, 2)\n",
        "\n",
        "ax1.plot(f1_attack_gafygt_s, label = 'Static')\n",
        "ax1.plot(f1_attack_gafygt_d, label = 'Dynamic')\n",
        "ax1.set_xlabel(' # of Iterations')\n",
        "ax1.set_ylabel('F1 Scores')\n",
        "ax1.set_title('Gafygt Attack')\n",
        "ax1.legend()\n",
        "\n",
        "ax2.plot(f1_attack_mirai_s, label = 'Static')\n",
        "ax2.plot(f1_attack_mirai_d, label = 'Dynamic')\n",
        "ax2.set_xlabel(' # of Iterations')\n",
        "ax2.set_ylabel('F1 Scores')\n",
        "ax2.set_title('Mirai Attack')\n",
        "ax2.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "M09crmI1M-i7",
        "outputId": "da01cec3-c061-4851-930b-d1c73fa29480"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 650x260 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "### Precision and Recall as there's no avg precision recall for multi class\n",
        "fig = plt.figure(figsize=(10, 4), dpi=65)\n",
        "ax1 = fig.add_subplot(1, 2, 1)\n",
        "ax2 = fig.add_subplot(1, 2, 2)\n",
        "\n",
        "ax1.plot(precision_s, label = 'Static')\n",
        "ax1.plot(precision_d, label = 'Dynamic')\n",
        "ax1.set_xlabel(' # of Iterations')\n",
        "ax1.set_ylabel('Precision')\n",
        "ax1.set_title('Precision')\n",
        "ax1.legend()\n",
        "\n",
        "ax2.plot(recall_s, label = 'Static')\n",
        "ax2.plot(recall_d, label = 'Dynamic')\n",
        "ax2.set_xlabel(' # of Iterations')\n",
        "ax2.set_ylabel('Recall')\n",
        "ax2.set_title('Recall')\n",
        "ax2.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dj7ewoIgM-i9"
      },
      "source": [
        "# 11. Average of Performance Metrics over Iterations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1gJikJsqM-i9",
        "outputId": "38bb14e7-f45c-4be0-ea31-8da77af3e732"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average of the Precision for Static = 0.9665\n",
            "Average of the Precision for Dynamic = 0.9659\n",
            "--------------------------------------------------\n",
            "Average of the Recall for Static = 0.9674\n",
            "Average of the Recall for Dynamic = 0.9645\n",
            "--------------------------------------------------\n",
            "Average of the Macro-F1-Score for Static = 0.966\n",
            "Average of the Macro-F1-Score for Dynamic = 0.9638\n",
            "--------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "## precision\n",
        "print(\"Average of the Precision for Static =\", round(mean(precision_s), 4))\n",
        "print(\"Average of the Precision for Dynamic =\", round(mean(precision_d), 4))\n",
        "print(50*'-')\n",
        "\n",
        "## Recall\n",
        "print(\"Average of the Recall for Static =\", round(mean(recall_s), 4))\n",
        "print(\"Average of the Recall for Dynamic =\", round(mean(recall_d), 4))\n",
        "print(50*'-')\n",
        "\n",
        "## F1 Score\n",
        "print(\"Average of the Macro-F1-Score for Static =\", round(mean(f1_macro_s), 4))\n",
        "print(\"Average of the Macro-F1-Score for Dynamic =\", round(mean(f1_macro_d), 4))\n",
        "print(50*'-')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_tTAwivKM2Zu"
      },
      "source": [
        "# 12. End\n",
        "Notes, observations and Insights can be found in the report"
      ]
    }
  ],
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    "colab": {
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      "name": "Ass2_AI_in_CS.ipynb",
      "provenance": []
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      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
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