[VGU]_HDDA_Research_on_gaming_habits_of_VGU_students.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patheffects as PathEffects\n",
        "import seaborn as sns\n",
        "\n",
        "import scipy.cluster.hierarchy as shc\n",
        "\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import Normalizer\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/MinLee0210/-VGU-HDDA-Survey-responses.git"
      ],
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        "outputId": "63c7dc8c-bfb7-47ac-e04b-47d3aa9e6d82"
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          "text": [
            "Cloning into '-VGU-HDDA-Survey-responses'...\n",
            "remote: Enumerating objects: 14, done.\u001b[K\n",
            "remote: Counting objects: 100% (14/14), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 14 (delta 4), reused 14 (delta 4), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (14/14), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_path = '/content/-VGU-HDDA-Survey-responses/'\n",
        "df = pd.read_csv(df_path + 'survey_response.csv')\n",
        "df.head()"
      ],
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              "            Timestamp What is your gender?  \\\n",
              "0  12/9/2022 22:24:35                 Male   \n",
              "1  12/9/2022 22:29:04                 Male   \n",
              "2  12/9/2022 22:30:59                 Male   \n",
              "3  12/9/2022 22:31:40               Female   \n",
              "4  12/9/2022 22:48:42               Female   \n",
              "\n",
              "   Which year are you studying at university?  \\\n",
              "0                                           4   \n",
              "1                                           3   \n",
              "2                                           4   \n",
              "3                                           1   \n",
              "4                                           4   \n",
              "\n",
              "  How long do you usually play game in a day?  \\\n",
              "0                                  30 minutes   \n",
              "1                                      1 hour   \n",
              "2                                     3 hours   \n",
              "3                          30 minutes or less   \n",
              "4                          30 minutes or less   \n",
              "\n",
              "  What is the main reason you play games?  \\\n",
              "0                Connect with your friend   \n",
              "1                               Kill time   \n",
              "2                               Kill time   \n",
              "3                               Kill time   \n",
              "4                               Kill time   \n",
              "\n",
              "             What kind of games do you usually play?  \\\n",
              "0  Strategy    (LOL, Plant vs Zombie, Tower Defen...   \n",
              "1  RPG    (Elden Ring, God of War, Monster Hunter...   \n",
              "2  Survival horror    (Dead by daylight, The fore...   \n",
              "3  Simulation    (the Sims, Jurassic World , Fall...   \n",
              "4   Puzzle    (2048, Brainout, Escape Simulator,...)   \n",
              "\n",
              "  What device do you use to play game?  \\\n",
              "0                          PC / Laptop   \n",
              "1                              Console   \n",
              "2                          PC / Laptop   \n",
              "3                          PC / Laptop   \n",
              "4                         Mobile phone   \n",
              "\n",
              "  Do you prefer playing games alone or with friends?  \n",
              "0                                  Play with friends  \n",
              "1                                  Play with friends  \n",
              "2                                         Play alone  \n",
              "3                                         Play alone  \n",
              "4                                         Play alone  "
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              "      <th>Timestamp</th>\n",
              "      <th>What is your gender?</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12/9/2022 22:24:35</td>\n",
              "      <td>Male</td>\n",
              "      <td>4</td>\n",
              "      <td>30 minutes</td>\n",
              "      <td>Connect with your friend</td>\n",
              "      <td>Strategy    (LOL, Plant vs Zombie, Tower Defen...</td>\n",
              "      <td>PC / Laptop</td>\n",
              "      <td>Play with friends</td>\n",
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              "      <td>1 hour</td>\n",
              "      <td>Kill time</td>\n",
              "      <td>RPG    (Elden Ring, God of War, Monster Hunter...</td>\n",
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              "      <td>12/9/2022 22:30:59</td>\n",
              "      <td>Male</td>\n",
              "      <td>4</td>\n",
              "      <td>3 hours</td>\n",
              "      <td>Kill time</td>\n",
              "      <td>Survival horror    (Dead by daylight, The fore...</td>\n",
              "      <td>PC / Laptop</td>\n",
              "      <td>Play alone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12/9/2022 22:31:40</td>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>30 minutes or less</td>\n",
              "      <td>Kill time</td>\n",
              "      <td>Simulation    (the Sims, Jurassic World , Fall...</td>\n",
              "      <td>PC / Laptop</td>\n",
              "      <td>Play alone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12/9/2022 22:48:42</td>\n",
              "      <td>Female</td>\n",
              "      <td>4</td>\n",
              "      <td>30 minutes or less</td>\n",
              "      <td>Kill time</td>\n",
              "      <td>Puzzle    (2048, Brainout, Escape Simulator,...)</td>\n",
              "      <td>Mobile phone</td>\n",
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        "df = df.drop(['Timestamp'], axis=1)\n",
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              "  What is your gender?  Which year are you studying at university?  \\\n",
              "0                 Male                                           4   \n",
              "1                 Male                                           3   \n",
              "2                 Male                                           4   \n",
              "3               Female                                           1   \n",
              "4               Female                                           4   \n",
              "\n",
              "  How long do you usually play game in a day?  \\\n",
              "0                                  30 minutes   \n",
              "1                                      1 hour   \n",
              "2                                     3 hours   \n",
              "3                          30 minutes or less   \n",
              "4                          30 minutes or less   \n",
              "\n",
              "  What is the main reason you play games?  \\\n",
              "0                Connect with your friend   \n",
              "1                               Kill time   \n",
              "2                               Kill time   \n",
              "3                               Kill time   \n",
              "4                               Kill time   \n",
              "\n",
              "             What kind of games do you usually play?  \\\n",
              "0  Strategy    (LOL, Plant vs Zombie, Tower Defen...   \n",
              "1  RPG    (Elden Ring, God of War, Monster Hunter...   \n",
              "2  Survival horror    (Dead by daylight, The fore...   \n",
              "3  Simulation    (the Sims, Jurassic World , Fall...   \n",
              "4   Puzzle    (2048, Brainout, Escape Simulator,...)   \n",
              "\n",
              "  What device do you use to play game?  \\\n",
              "0                          PC / Laptop   \n",
              "1                              Console   \n",
              "2                          PC / Laptop   \n",
              "3                          PC / Laptop   \n",
              "4                         Mobile phone   \n",
              "\n",
              "  Do you prefer playing games alone or with friends?  \n",
              "0                                  Play with friends  \n",
              "1                                  Play with friends  \n",
              "2                                         Play alone  \n",
              "3                                         Play alone  \n",
              "4                                         Play alone  "
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features ={\n",
        "    \"What is your gender?\": \"Gender\", \n",
        "    \"Which year are you studying at university?\": \"Year of Studying\", \n",
        "    \"How long do you usually play game in a day?\": \"Operating time per day\", \n",
        "    \"What is the main reason you play games?\": \"Reason\",\n",
        "    \"What kind of games do you usually play?\": \"Category\",\n",
        "    \"What device do you use to play game?\": \"Device\",\n",
        "    \"Do you prefer playing games alone or with friends?\": \"Alone/Friend\"\n",
        "}\n",
        "df = df.rename(columns = features)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FdmAGsmQ_jRG",
        "outputId": "24e6bf7a-a686-4d9e-cac6-987242cae67d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Year of Studying Operating time per day                    Reason  \\\n",
              "0    Male                 4             30 minutes  Connect with your friend   \n",
              "1    Male                 3                 1 hour                 Kill time   \n",
              "2    Male                 4                3 hours                 Kill time   \n",
              "3  Female                 1     30 minutes or less                 Kill time   \n",
              "4  Female                 4     30 minutes or less                 Kill time   \n",
              "\n",
              "                                            Category        Device  \\\n",
              "0  Strategy    (LOL, Plant vs Zombie, Tower Defen...   PC / Laptop   \n",
              "1  RPG    (Elden Ring, God of War, Monster Hunter...       Console   \n",
              "2  Survival horror    (Dead by daylight, The fore...   PC / Laptop   \n",
              "3  Simulation    (the Sims, Jurassic World , Fall...   PC / Laptop   \n",
              "4   Puzzle    (2048, Brainout, Escape Simulator,...)  Mobile phone   \n",
              "\n",
              "        Alone/Friend  \n",
              "0  Play with friends  \n",
              "1  Play with friends  \n",
              "2         Play alone  \n",
              "3         Play alone  \n",
              "4         Play alone  "
            ],
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "mapping_gender = {\n",
        "    'Male': 0, \n",
        "    'Female': 1, \n",
        "    'Other': 2\n",
        "}\n",
        "\n",
        "df['Gender'] = df['Gender'].map(mapping_gender)\n",
        "\n",
        "operating_time = np.unique(df['Operating time per day'])\n",
        "mapping_operating_time = {categories: label for label, categories in enumerate(operating_time)}\n",
        "print(mapping_operating_time)\n",
        "df['Operating time per day'] = df['Operating time per day'].map(mapping_operating_time)\n",
        "\n",
        "reasons = np.unique(df['Reason'])\n",
        "mapping_reasons = {categories: label for label, categories in enumerate(reasons)}\n",
        "print(reasons)\n",
        "df['Reason'] = df['Reason'].map(mapping_reasons)\n",
        "\n",
        "category = np.unique(df['Category'])\n",
        "mapping_category = {categories: label for label, categories in enumerate(category)}\n",
        "print(category)\n",
        "df['Category'] = df['Category'].map(mapping_category)\n",
        "\n",
        "device = np.unique(df['Device'])\n",
        "mapping_device = {categories: label for label, categories in enumerate(device)}\n",
        "print(device)\n",
        "df['Device'] = df['Device'].map(mapping_device)\n",
        "\n",
        "al_fr = np.unique(df['Alone/Friend'])\n",
        "mapping_al_fr = {categories: label for label, categories in enumerate(al_fr)}\n",
        "print(al_fr)\n",
        "df['Alone/Friend'] = df['Alone/Friend'].map(mapping_al_fr)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "lSoPac9JBgbD",
        "outputId": "bd14c089-9185-4131-a4a8-bb52611cf489"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'1 hour': 0, '2 hours': 1, '3 hours': 2, '30 minutes': 3, '30 minutes or less': 4, 'Over 3 hours': 5}\n",
            "['Connect with your friend' 'Kill time' 'Plowing the top race']\n",
            "['Action    (PUBG, Free fire, Cuphead, Shadow fight, Honkai Impact 3rd, ...)'\n",
            " 'Adventure    (Minecraft, Genshin Impact, Mini World, Roblox, ...)'\n",
            " 'Other...' 'Puzzle    (2048, Brainout, Escape Simulator,...)'\n",
            " \"RPG    (Elden Ring, God of War, Monster Hunter, Assassin's Creed,...)\"\n",
            " 'Simulation    (the Sims, Jurassic World , Fallout Shelter,...)'\n",
            " 'Sport    (Forza Horizon, FIFA, NBA,...)'\n",
            " 'Strategy    (LOL, Plant vs Zombie, Tower Defense, Warcraft, Civilization,...)'\n",
            " 'Survival horror    (Dead by daylight, The forest, Outlast, Left 4 dead,...)']\n",
            "['Console' 'Mobile phone' 'PC / Laptop' 'VR devices']\n",
            "['Play alone' 'Play with friends']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Year of Studying  Operating time per day  Reason  Category  Device  \\\n",
              "0       0                 4                       3       0         7       2   \n",
              "1       0                 3                       0       1         4       0   \n",
              "2       0                 4                       2       1         8       2   \n",
              "3       1                 1                       4       1         5       2   \n",
              "4       1                 4                       4       1         3       1   \n",
              "\n",
              "   Alone/Friend  \n",
              "0             1  \n",
              "1             1  \n",
              "2             0  \n",
              "3             0  \n",
              "4             0  "
            ],
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-26894b85-5401-49fe-ac8c-4b6be034e800');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
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    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yU1IQmhIEGSO",
        "outputId": "5d012c89-d4c1-45a1-a07e-89ca93fed861"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(117, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "h63eYInASHdK",
        "outputId": "2242c14d-d370-4744-d13f-c02981cfa01f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Gender  Year of Studying  Operating time per day      Reason  \\\n",
              "count  117.000000        117.000000              117.000000  117.000000   \n",
              "mean     0.615385          2.264957                2.555556    0.948718   \n",
              "std      0.797044          1.255286                1.913854    0.786153   \n",
              "min      0.000000          1.000000                0.000000    0.000000   \n",
              "25%      0.000000          1.000000                1.000000    0.000000   \n",
              "50%      0.000000          2.000000                2.000000    1.000000   \n",
              "75%      1.000000          4.000000                4.000000    2.000000   \n",
              "max      2.000000          4.000000                5.000000    2.000000   \n",
              "\n",
              "         Category      Device  Alone/Friend  \n",
              "count  117.000000  117.000000    117.000000  \n",
              "mean     3.794872    1.410256      0.418803  \n",
              "std      2.917068    0.696715      0.495485  \n",
              "min      0.000000    0.000000      0.000000  \n",
              "25%      1.000000    1.000000      0.000000  \n",
              "50%      4.000000    1.000000      0.000000  \n",
              "75%      7.000000    2.000000      1.000000  \n",
              "max      8.000000    3.000000      1.000000  "
            ],
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              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Year of Studying</th>\n",
              "      <th>Operating time per day</th>\n",
              "      <th>Reason</th>\n",
              "      <th>Category</th>\n",
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              "  </thead>\n",
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              "      <th>count</th>\n",
              "      <td>117.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>117.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.615385</td>\n",
              "      <td>2.264957</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>0.948718</td>\n",
              "      <td>3.794872</td>\n",
              "      <td>1.410256</td>\n",
              "      <td>0.418803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.797044</td>\n",
              "      <td>1.255286</td>\n",
              "      <td>1.913854</td>\n",
              "      <td>0.786153</td>\n",
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              "      <td>0.696715</td>\n",
              "      <td>0.495485</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1> TRAINING MODEL WITH NORMALIZATION </h1>"
      ],
      "metadata": {
        "id": "5EKL0n24lRjT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def _plot_kmean_scatter(X, labels):\n",
        "\n",
        "    num_classes = len(np.unique(labels))\n",
        "    palette = np.array(sns.color_palette(\"hls\", num_classes))\n",
        "\n",
        "    fig = plt.figure(figsize=(12, 8))\n",
        "    ax = plt.subplot()\n",
        "    sc = ax.scatter(X[:,0], X[:,1], lw=0, s=150, c=palette[labels.astype(np.int)])\n",
        "\n",
        "    txts = []\n",
        "\n",
        "    for i in range(num_classes):\n",
        "        xtext, ytext = np.median(X[labels == i, :], axis=0)\n",
        "        txt = ax.text(xtext, ytext, str(i), fontsize=24)\n",
        "        txt.set_path_effects([\n",
        "            PathEffects.Stroke(linewidth=5, foreground=\"w\"),\n",
        "            PathEffects.Normal()])\n",
        "        txts.append(txt)\n",
        "    plt.title('t-sne visualization')\n",
        "    plt.grid()"
      ],
      "metadata": {
        "id": "SQjZ2WRTk6OS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()\n",
        "X = np.array(df.iloc[: 117, :]).astype(np.float32)\n",
        "X = Normalizer().fit_transform(X)\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "voCvFibpJj7y",
        "outputId": "fdf1aad0-e674-4d6c-cd87-56e40bb34921"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(117, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 7))\n",
        "plt.title(\"Dendograms\")\n",
        "dend = shc.dendrogram(shc.linkage(X, method='ward'))\n",
        "plt.axhline(28, linestyle='--')\n",
        "plt.xlabel('sample indice')\n",
        "plt.ylabel('dissimilarity metric cluster')"
      ],
      "metadata": {
        "id": "bjtpFU51FkQD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "186ad804-fc68-4b94-bdc2-d4740e5f747a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'dissimilarity metric cluster')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 7))\n",
        "plt.title(\"Dendograms\")\n",
        "dend = shc.dendrogram(shc.linkage(X, method='average'))\n",
        "plt.xlabel('sample indice')\n",
        "plt.ylabel('dissimilarity metric cluster')"
      ],
      "metadata": {
        "id": "Hbonzs9Kuxrb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "e51e3bc7-2365-4485-83e3-8ff1c3bebeb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'dissimilarity metric cluster')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 7))\n",
        "plt.title(\"Dendograms\")\n",
        "dend = shc.dendrogram(shc.linkage(X, method='single'))\n",
        "plt.xlabel('sample indice')\n",
        "plt.ylabel('dissimilarity metric cluster')"
      ],
      "metadata": {
        "id": "Y8cJzt2FBAtZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "c8807bf0-41ba-442d-8f0f-2e95a502c6df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'dissimilarity metric cluster')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 7))\n",
        "plt.title(\"Dendograms\")\n",
        "dend = shc.dendrogram(shc.linkage(X, method='complete'))\n",
        "plt.xlabel('sample indice')\n",
        "plt.ylabel('dissimilarity metric cluster')"
      ],
      "metadata": {
        "id": "hathA_e8n_WG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "308171a1-8fa2-4005-fed3-e2b7a84e3ced"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'dissimilarity metric cluster')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='ward')\n",
        "labels = cluster.fit_predict(X)\n",
        "\n",
        "\n",
        "_plot_kmean_scatter(X[:, [2, 4]], labels)"
      ],
      "metadata": {
        "id": "3pi48olBKspQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "ad0b8d13-85ab-4e30-ce2b-90d28785604f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-f01e928d6b9c>:8: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sc = ax.scatter(X[:,0], X[:,1], lw=0, s=150, c=palette[labels.astype(np.int)])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='average')\n",
        "labels = cluster.fit_predict(X)\n",
        "\n",
        "\n",
        "_plot_kmean_scatter(X[:, [2, 4]], labels)"
      ],
      "metadata": {
        "id": "R7k7jXHjLDkj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "925d557a-ef43-4547-8324-61dc7f090fb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-f01e928d6b9c>:8: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sc = ax.scatter(X[:,0], X[:,1], lw=0, s=150, c=palette[labels.astype(np.int)])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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Ctekb7Ji00oozr7nQfMiLcFDCUmjA5FVWFE02J7s8TWrc36tq7etzb7sxbZ09jhURERFpn2bT36QVVuz/U5fi8ZNXKNsYIlMYzboLK0FQ/AXc6pauC3h4BoOIiGi8NNsa4elQtmLEAHfH2L2ZRPFksii8SvHCeM3+0yUiIkoYzf42rd+r7sr6BpXjiWKpckEOhIrzMzVLcuNXDBERUYbQbGtEyKfu1HHAy1PNlBxn3nDhwJ+7IBWexDDm6FB7FXdCJCIiGi/NBuEsq7pTzWaV44li4ZUfNuPc28rPRggdsPyOEtW7ABIREdHFNPvbdMISdbtvTbicu3VRYu35XYeqEJxbbMCaO8sxgW0RREREMaHZGeFoGxNcNL4liOJp2XGqhuhix17sUTx27v/Jx7wbCyAyfBtwIiKiWNLsjHD7cV9cxxONx57ftSOiYmGT+r0ehmAiIqIY02wQjoTV7SwXUbeMK9G4HH3eoWq8u53L+xEREcWaZoOwtcSobnypZrtEKMUc394DyUVKiIiIkk6zQXjSCit0CrOt0AOTV3JnOUqMIypngwEgJ58f1IiIiGJNs79ds/MMKJ+Tg8Z9vVHHls3ORk6BZn8UNE4+ZxgnXnKg7ZgP4ZCEpciAKattKJ1xaRdX+l3q+3Dm3VhwSa9FREREo9N0+nO3KeurdLeFIKWEELwYiYY68NcuHPx7NyLBoT3np3a6UDQ5C6v+pRyWQnX/jHR6dceZzgBMvIIbaBAREcWaZlsjWg73wtGoLAi7WoJoPuiNc0WUbvY92YkDf+66KAQP6Djlx5b7G+BzqpvhLZpiVjV+wcc4G0xERBQPmg3CDe9Gb4kYOl75xgakfc6mAA4+3R11nLsthPf+1qXquS//dKHisTojMPtaBmEiIqJ40GwQDvnVXZYf9PEyfiXCIQmfM4zwKLOkWlG3zQEo/BZP7XKpOn5yi02oWpijaOyiTxQpfl4iIiJSR7M9wmabPq7jM03zoV7UbXGg/l0PZBgQAqiYn4MZG+yoXKC97akb9ys/oxDsjaC9zoeKecrCLQCs/UYFtn2nEc3vjd6SM+/GfMzclKf4OYmIiEgdzQbhicuteO9v0U9tD5i0nBcjjWb3o+04+sLQJb+kBBr39aJxXy+mrLbiittLNLXzmdozCmrHA8DVd1ei/YQXe37Xia7TfkQiEnqjQOW8HCy+uVj1RXhERESkjmZ/0+ZVmlB2WTZaDkW/CK5khhn5NVkJqCr9HHq6+6IQPNzJnS6Y7Xos/Jh2TuOb7Qb0dim/CM5sv7QzCsVTs7H5/qpLeiwRERGNj2Z7hAFg+R0lUQNKllWHq75QmqCK0ksoEMHBp5VdCHb0BQcCHu3sUz3pqlzFY3NLDCiepm4lCCIiIko+TQdhnyuMUJSLmEJ+Cd8lbHCQCc687kKwV9kVY+GAxKlXXHGuKHEmr7LBYFbW6jFjoz3qGtShQARtdV40HeyFozEQixKJFKt/142XftCEl37QhNOvOpNdDhFRytBsawQAvPlwO0L+sYNcOCDxxi/acP0PJiSoqvRx7k23qvFd5/1xqiTxsnL1WPnlMuz872ZEQqOPq15swYzNo1/Q5u0J4dAz3Tj1igsBz/sfyoomZ2HG5jxMuoq96RQ/ux9rw7EXnZCD5gMa9vbitZ+1YeIVFqz8cnnyiiMiSgGanRFuP+5F11llwaynPoDWo9xQYzilPz+tqlpowfq7K1E05eL+cVOuDnM+mI9VXyuDbpSLBF1tQTz/rQYcfd4xJAQDfZtxvPaTVrzzSHtcaifa9mAjjj4/NARfIIGzb3jw9J3nEl4XEVEq0eyM8Pnd6jbIOP+OG6Uzs+NUTfrx9oTgc6pbCcFeboxTNclTNisb1/xHNTrP+NB61IdIUMJSbMCExRboTaN/jpQRiZcfaoanY4zpZADHXnQgr8qEaevtsS6dMtjxl3oU7ZbpaAji7UfasPSWkgRURUSUejQbhINedSEuoHK81nWc9Kl+zISlyi8wSzeFtWYU1iq/IK5xXy96GpT1Ah95rgdT19mi9hlT6ujtCuH4dgdaj3oRDkrkFBgwZbUNlfNzUmIZwT2PdSoee3yrk0GYiDKWZoOwyaJuOausXG6oMVi92i2nBZBbor0Z4Ut1YqfyC5KczUG0HvWhbBbPSKSDfU924tAz3cNaDvw4/44H9goj1txZDluFKVnlAQBCPuU7P8oI0HHah6JJXPmEiDKPZnuEJ16pbnZy4hXanc28FL4edStp2CuNo/bKZqL24+pm1N1twThVQrHU2xXCwb8PD8HvczQFseXbjXB3JO/vs7te/dmcpv0qP/gSEWmEZmeEC2qyUDzNrCiQFE7OQtFkzoYM1qsyCJdM589vwJ7HO+BzqPv56fT8EJHqnC0BeBX8vXq7wzjwly4svyM565Mbs9W/rRuyU29OpLc7hBMvOdF5yodIGLCVGzF1rQ35E7j5ERHFjmaDMNC3ocYL9zbA7xq9/5cbalws0BtGj8ql0Gqv5DJgQF9v9ZHnetQ9SABFU/nLPdUd3+YEFHYcnH3DjcWfLEpKy1Vukfq39UkrUuffr4xI7P19J46+2AM56HNH04G+i0sr5+dgxZdKVbe/ERGNJPWmAWLIVmHC5geqUDpz5NnKkhlmbLqvCvbK5PbzpZpTr7jGXDt3OGu5EWWzc+JXUBo5tnXs7ahHUj4nG7YyHoOprum9XsVjwwGJ1mPJW5Ixt2TsMGwtN2LyKisu/3QR1t1VDnNu6syJvPXrdhx5bmgIHqxxfy+2PdiEYJTNkqKJhCS6zvnRfsIHTxJbWYgouVLn3S9ObOUmbLy3Cj31fpx724OAJ4wOmwFrH6rmKbZR1O9R1y9YtdASp0rST73KZft0BoEFNxXGqRqKpZBfXfAKB5RfsBZr6+4uw9Nfbbjo9vwJJiz/51IU1KTme1/rMS9OvBT9QtPO037UbXHgshvyVb+G3x3G0ed7cOJlJ7zd76ftstnZmLk5D9WL+X5GlEk0H4QH5FVnIa+6781/5049Q/AY1C49Zy3lahEAIKVUPUu16BOFKJrC/up0kJNvgJqPOdl5yXt7tZeZseprpXjlh61Dbl/xlTLkDToD1tvbixMnTqCtrQ2RSATl5eW47LLLoNMl52RhnYozKsd3ODD7ujxVy9X1doWw9duNcDZfPAPcctiLlsNezPmnfCz4KD+cEmUKTbdG0KUx5ao7LLJUjtcqIQRMFnU/i8oFbClJF2r6aHNLDCidkdwPODVLrbjp1xNRMKkv+Gbl6i6E4EcffRRz5syB1WrF/PnzsWHDBmzatAnz5s1DeXk57rjjDrS2to719HHRuE95+4m7LYSeRmVrdQN9H1Rf+kHziCF4sIN/78ZJFcsfElF6U/RbWwixSQhRJ4Q4KYS4a4T7JwghXhZC7BNCvCeEuCb2pVKiTFymfCk5g1mgagFPJQ5Q87MrqM1ib3Aaqb3Kqnh1j+kb7CmxsYY514APfGcCbn5yCj7y60kXbn/zzTdx6NAhGI1GzJ07Fxs2bMCKFStQWlqKtrY2PPzww1i6dCna2toSWm9I5RkVNeslN7/nRdcZZRcBH362W1UdRJS+ogZhIYQewE8BbAYwC8DHhBCzhg37FoA/SSkXAPgogJ/FulBKnNrlVsUzm5NWWGFMwaWXkmX6RjugMP9Mv5rbKqcTo1kHa6kRhqyx/4InLLFg1jV5Carq0vzTP/0Tdu3aBYfDgQMHDmDLli3YtWsXGhsb8fjjj8NkMuHcuXP47//+74TWZbarWwnCbFM+/sTLymd5HY3BpF7sSESJoyTBLAFwUkp5WkoZAPAkgBuGjZEAbP1/tgNoil2JlGiGLB1WfLEUuii/Y/ImmLDwY+ylGyy/OgsLFfQX1iy1YMrq1FmyipQxmAU23leJ8rnZF33gyc7TY/5HCrDyq2UpMRs8lk2bNmHFihXIyhp6rYRer8cnP/lJ3HrrrQCARx55JKF11S5X/m+iaEqWqusTnM3K2ygAwNXClSSIMoGSqzkqAdQP+roBwNJhY+4DsFUI8SUAFgDrY1IdJU3lAgvWfbMCux/pQE/D0F8gQg/ULMnFstuKYcrhWp7DXXZDPrKsOuz/c9eQq9KBviA1/Wo7Fny0MOXDUjz11PtxbKsDzQe9CAciyMk3YNIKKyattMGUk9pnGAprzbj67ko4WwJoPeJDOBiBpciIynk50Bm08Xe6ePFiPPzww+jtVd6zGwvTrrbh6As9o+7cN9iMjepm3dX+exOpfRgSUYwIKcfusRJC3Ahgk5Tytv6vPwVgqZTyi4PG/Ev/c/2XEOIKAL8BcJmUQ9/OhBC3A7gdAEpLSxc9+eSTMf1mxhLySwQ8YcgwEDZ6kWPOhcGsjV9a8RbySQS9EciIhM4gYMrVR50tTgchXwQBTwQy0hfuTRY9fEEPcnNjt912wBO5sOyW3tj3sxMZfth5OkOj7ryn0wtYSw0wmFMzhbjd7pgeH8mi1+sxd+5c2O0jt+fcdttt+M1vfoMVK1Zg165dAIDDhw+jvb097rX5XWG4O0Jjbl5ituphKVa3KoenIwSfU/mOj3nVJuiNyv+xauXYoPjg8ZF8a9as2SulXDz8diXvJI0Aqgd9XdV/22CfBbAJAKSUbwohzACKAAy50kJK+UsAvwSAxYsXy9WrVyut/5J1n/Pj9Z+3oevs+xdJWDacQtPWauTXmHDlHaUorOVSapmk/YQPbzzcBkfDxadK864/gwULruQmK3Gy+7F2nH9+7CWyvFkCG+6pTMltz3fu3IlEvG8li9frxa9+9Ss8+uij0Ov1uP/++y/cN3v27ITVce4dN/b/sROOxqHtCWa7HjM323HZDfkQI3yiPP2GE2//ugPB3sigx+iw8itlyKrV49n/V3/RY0ZSOisb6z5TqapmrR8bND48PlKXkiC8G8BUIUQt+gLwRwF8fNiY8wDWAXhECDETgBlA/KcOoug+78eL9zcOeVMccv+5ALY+0IAN91QxDGeI9hM+bPuPRoT8I083BX0RvHhfAzbfXwVbBcNwLLnbgjj2QvR1YkN+iX1/7MTVd6sLIqReR0cHNmzYAABwOp2or69HIBDAwoUL8dBDD2HNmjVJqatmSS5qluSi5XAvOk75IcMS1nITqhdboB+l/eTPnz9zUSsSAPgcEWx9oAkFtSZMvDIXZ99wj/naOj0w/8MFMfk+iCj1RQ3CUsqQEOKLALYA0AP4rZTysBDiAQB7pJTPAPhXAL8SQnwNfSe0bpHRei4S4I1ftI0aggcEvRJv/KIV131/QoKqomSRUuK1n7aOGoIH+F0RvPmrNmy8typBlWWGuu0OKH1XaD7ohbMlwOXl4iwUCmHfvn1DbtPpdKisrERRUVGSqnpf2ewcRdu3//VLZ0cMwYN1nQlAbxKovtwy6g6QeqPAii+XonRm9iXVS0TpR1GTlZTyeQDPD7vtnkF/PgJgeWxLG5+Okz50nla2ZmT3uQDa6rwomc43Py1rfs+r+Erw1qM+9NT7L+xGSOPXdsynfLDsG88gHF9FRUU4ceIEgL62iD179uDxxx/Hs88+i+eeew5/+MMfcNNNNyW5yrE5WnzwtIcUjW2v8+NTf6hC03u9qNvqRMuhXoSDEtn5BkxeacW09XZYCjNmw1Uigoa3WD73ztinvy4a/7abQVjjzu9WeUy842EQjqFISN1JIrXjST2DwYApU6Zc+HrOnDm45ZZbcMstt+Cxxx7DHXfcgRtuuOGiZdZSyY7vtqgb//1mrL+rApXzuBEQEWl4i+WAR90ORYEoLRSU/vxulceER/kV5hSdpUjd526140k9vzuMrnN+dJz0wdG/XbEQAvfeey8AoLu7Gy+//HIyS4xK6WzwgLY6bpRBRO/T7G+arFx1GT/LooH1wGhMPCaSa8oqG86/M3Jv5nA5BQaUz43eG0qXJuSP4NAz3XjvqW5g0OfD5Z8vweRVNtTW1sJgMCAUCqG5uTl5hY7g8HNdOPjXnkuevJD8fEtEg2g2COdVq+sttFcp36GI0tOEpbk4vl35NqsTlnHNx1iqXJADe6XxoiWxRjJzsx26DN5wJN4MWTrM/3Ahpl9tR9dZPwK9EVgKDRfaw44cOYJQqG+mtby8PJmlDvHUV3sGl8IAACAASURBVM/C1aJuBng4pdvHE1Fm0Ow7Qk+9uu0xlfxypvRWflk27BXKPvCUzc5GHtcSjimhE1hzZzlyCsaeaZ+0wopZH1C3axgp5/V60dPTAwDIzjOgcr4FtVdaL4TgcDiMb33rWwCAyspKrF27Nmm1DgiHJP76xfGHYAC44nPFMaiIiLRCszPCaveVdzSpG0/pRwiBq75Yii0PNCLkG/1CLLNdjys+V5LAyjKHrdyEzd+uxnt/68KZ11xDlrKzVRgxY1Mepl9tG3GzBBqfQG8Yphw92tvbMXfuXHz4wx/G2rVrMXXqVFitVvT09OCNN97AE088gT179gAA7rzzTphMyf1AGOiNYPt3G+HpGH8IFnqgaiHP9BDR+zQbhHu71L1pRluDkrShcJIZG++pxBu/bEP32Ys//BjNOmy4vwrWMrbKxIul0IArPleCRZ8oQvtxL8KBvuWriqem3k5yWuFuC6L9hA+1y60AAIfDgV//+tf49a9/PeJ4g8GAe+65B1/5ylcSWeaIXv3fFnScULYUZjTXPcS1wYloKM0GYXOeugudzHbNdomkBSllwmYBCyeZcd33JqD9uBfnd3sQ9EaQlavHxCtyceBMA0NwgphydKiczyWsEmHP7zpQMLFvCbTq6mocOHAAL774Inbv3o3Tp0/D7XbDYrFg2rRp2LhxIzZu3IiKiookVw10nPKhcV/vuJ/HYBa49nuVsJfxwxYRDaXZIGyvMKEByt9A7dxON+Hc7UEc3+7AqV0ueHvC0BsFyudkY/rVdlTMy4l7MC6elo3iacPWjj4T15ckSrjerhDq93rQccqPaevtMNv0mDt3LubOnRv1se72IHKLk/fB8PgO5Re3DiGAvCojsvMMuPKfS2HJ0+yvOiIaJ82+O9QsycXhZ3qUj1/KvrFEOvumC6/9rA2R4Ps9ouGARMPeXjTs7cWEJRas+FIZ9Eb2ihKNR+N+D2QY6O0M4el/PYdp6+2oXmRBQW0WdPqL/30FfRG0HPLi/G43yufkJDUI95y/tJaI3BIDrv9BTYyrISIt0mwQLppiRuGkLEXbLOfXmLirXAI1H+rFqz9pHXM9z/PvePDGw61Y8cWyxBVGpEFB7/sfNv2uCA4+1Y2DT3XDkCVgKzfBUmiAPksg7JfwOkLoOuNHJAyYbXosuy09LxpdeitXhiAiZTTdGHvl/y2BMWfsb9GYLXDlHaUJqogAYP+fuxQtan/mNTd6GriaB9F4mG0jXy8R8kt0nfWjfq8HZ99w97VPnOwLwULft7lGss/I5E1Qv7WzwSzYe05Eimk6COfXZGHTvZXInzhy/2/+BBM23FOFwlr1b7Z0abrP+9Fe51M8vm6rI47VECVGyB+BsykAV0sQkdDoS/fFQ9WiHBiylAfaLJsO675RgcoFyQ+T09fbVI0XeuCDP6yOUzVEpEWabY0YkGXTw1ZqRM/5AOSgHTmFAKxlxlFnSyg+Ok6p6/nrPK08NBOlmp7GAI4+34Mzr7surF1ttusxZbUVMzbmIacg/m/Bphw9Jq2wKtpV0ZSrw40/mQi9KTXmSAonmVG5IEfRyhGWYgOu+241TLl8Tyci5VLj3S5OertCeOHfG3Dubc+QEAwAUvb1ob7w7w1wd3BXuUSREXWzYcP/3ojSRcO7Hjz3zXqc2OEcsoGLzxHGoad78I9v1qPrXGzWx41m4ceLRj0zNsCQJbD26+UpE4IHrPxyGUpnjr3s2eU3F+FD/zuRIZiIVEutd7wYe/3nrVF3I+rtCuH1n7UlqCKyqVyjl2v6UjrqqffjlR+1IBwY/YOfzxHGju81we+O/2Y+phwdNt5TickrrdCN0PdbPNWMDfdUpuRFw8ZsHdb/WyWuvKMEhZPeb2PTGQUmrbDimgerMPMabslNRJdGs60RjsYAmg96FY1tPeJFd70f+dXsFY630lnZsJUb4WxWNgs/bZ26HkGiVHDkuZ4xQ/AAb3cYp15xYta1+XGvyZSjx/IvlGLRJ4tw9i03vN0hGMw6VM7LubDZRqrSGwSmrLZhymob/O4wwgGJrFxdys1eD+dzhVG/2wO/OwyjWYfKhTnILeKHe6JUotkgfOZ1l7rxr7mQ/7HU/mWgBUIIzL4+H28+HH0WvmhKFspm5ySgKqLYCXojOPOGW/H449sTE4QHmG16zNhgT9jrxVpWGrQ/+N1h7HmsA2ffdCM8aK108QhQucCCyz9dBGsJAzFRKtBsEPY51Z1u9Drif3qS+kxdY4OrJYBDT4++4Ym9yoQ1d5YnsCqi2HB3BBXNBg9wNgcRiUjodNw8JtkiIYlzb7txfLsD3ef7lm7MqzZh2no7apblQm+I/nfkc4Wx5f5GOEZY+lFGgIa9HnSc9GHTfZWwlXNHU6Jk02wQNmSpO2VmNKf2KTatWfixIhRPy8bRF3rQcuj9FpacAgOmrbNhxmY7TDmpP/NDNJzqOCsu4TEUc57OEHZ8rwk99UMDbNsxH9qO+XDwqS6su6si6k577/y2fcQQPJjPEcau/2nFB77Hpd6Ikk2zQbhyQQ6OPKd8i+XKBTwFn2jViyyoXmSBpzMEb08IepOAvdLEmTFKa7klRhhzdAj2KlvyJH+CCYLHfFIFvRFsf7ARjqbRr11wNAax/TtNuObBqlE/pPd2hXDuHWVtMV1n/Wir86bkBYpEmUSz06BqTzmpXc2AYsdSaEDRZDPyq7MYgintGbJ0mLzSqnj89KvTt1833Ukp0XSwF1vubxgzBA9wNgdx4qXR12M++6Zb0a6ZA06/pu5aFiKKPc0G4dOvRl88fuh45Re3EBGNZeY1eTBZor+9WsuMqL1KeWhOF71dIZx9y43Tr7rQcsQLKRO7m54SvV0hPP9vDdj+YBO6zirfyv34Nueo34/XMfZyncP5eG0KUdJptjUi2vrBF4/nphpEFBvWEiPW3VWBlx5qgt81couEtcyI9XdXaOr6hJ7GAA78qRPn93iGzIxaS42YsdmOGRvtECL5Z3387jC2PNAIV4v6931XaxDd5wM4/44bZ153w+8Kw5ClQ/baELJ96nYAUnstCxHFnmaDsH6ERePHHJ/i61ESUXopnmrG9T+YgBM7nDi+w4nerr4P5/YqE6avt2HyKhuM2dp53+k45cO2B5tG7I12tQax+5EOdJ3x48o7SpIeho/8o+eSQvCA5+6uHxL0A54IhDOMuq3qzkRWzue1KUTJptkgXDorG0dfcKgaT0QUS9l5Bsz9UAHmfqgAIX8EQmjzQ3c4EMHLP2iOeoHgqVdcKJyUhRkbk7cTXDgkceJldYF1ODV9wKMx2/WYsDR3/E9EROOivXfkflWLLMgpVJbzs/P1mHC5Jc4VEVEmM2Sl/k5ol+rMG254e5Slw2MvOpLaM9xz3p/03lwhgKWfLVa0LjERxZdmZ4R1OoHFnyrCrv9pAaK85y76RBF0er4hEREpFQ6H8dZvOnD2NbfqDUTaj/uStmxYSEWt42HM1iHovXiG3GTR4YrPlaBmCWeDiVKBZoMwAExclotwoARv/qodkeDFb346A7D0MyWYpMGrtomI4qVxnwc7HmqOOskwGk+nuouZYykn/9J+7Qld385wSpXNzsakFVacfcMFnysCY7YO1YssqL0qFwaNnhkgSkeaDsIAMHmlDZXzLTjxshPn3nIj4AlDn6XDgo8UYMpaG7Ltmv8REBHFTOcZH3Z8v3lcz6E3Je8MnLXUiOLpZrTX+ZQ9QAA1SywwWfRjriE8nIxI1CzNRQ37gIlSWkakQLNNjzk35GPODfkAgJ07z2LO6oIkV0VElH5e/s/xhWCdUaBkWnIvTp65OQ/tdS1Rx+kMwDXfrkJBrRmndjlVBeHcEm7SRJQOeH6GiIgUCbjD6O0c34VmNUstMNtG3qI4USYuy8XMa8be0U+nB1Z+pQwFtWYAQM3SXEWbpAyYssY2rhqJKDEYhImISJG6HcqXpBxJllWHeTemxtm4y28uxrLbimEtu3jmtnSmGVd/qxITLn+/rcGQpVO8HXb5nGwU1GTFrFYiip+MaI0gIqLx87sufTbYUmTAmq+Xw1ZmimFF4zNtvR1T19nQesQLZ0sQOr1A0eQs5FWPHGLnfbgAjqYAzr/jGfU58yaYsOJLZfEqmYhijEGYiCjNyYhE0CdhMAvodPG7EK14qhmAulnh8rnZmLLahpoludCl4Lq5QgiUzc5B2ezoY3V6gVVfLUPdVgfqtjrgaHp/dzqdQWDOB/Mx+7p8mHJ4spUoXTAIExGlqdZjXtRtceD8bjciIUDogeqFFkzfYEf5nNhv31uz1Aqha1W8jFhOkR5X310Z8zpkRKLpQC96GgMQAiiYmIWy2YnZrljoBGZsysOMTXnoPOOH3xmGwSxwpLkBC1YXJqQGIoodBmEiojS09/cdOPxsz5DbZBg4v9uD87s9mLbehqWfLYYQsZ2FrVqYg/o9vYrGXvHZkpi+NgDUbXPg0NPd8HQMXYvYXmHE3BsLUHtl4taFL6x9v4XiyPgW0yCiJOH5GyKiNHP4H90XheDhjm93Yv+fumL+2mvurIDZHn3Vh+rFOahcENut69/9Qwfe/k37RSEYABxNQbz641YcfWHsnwsR0WAMwkREaSQciODQ092Kxh59vgeB3vEtdzaSmx6uRdHUkS8oEzpgxiY71txZEdPXbDzgwaGno4fcPY91oOusP6avTUTaxdYIIqI0cvYtN/wuZU26Ib/EqV0uzNyUF/M6rvl2NcLhMHY/0omus37odAI1yyyYuTk/5q8FAMdeVHaRnpTAsa0OXHl77NsyiEh7GISJiNJI1xl1s53dcZwd1ev1WBaHPuDhfK4wmvYr60sGgLOvu3DF52LfH01E2sPWCCIiDZMy2RWMn98VVvV9hPwSIZ8GvnEiijsGYSKiNGKvVLchhdrxqchgUjmzKwC92scQUUZiECYiSiO1y60wZisLeToDMGW1Lc4VxZ+lyIi8auWBvnxONnR6BmEiio5BmIgojRizdZixUdnFb1PX2mG2RV/qLB1Mv9qufOwG5WOJKLMxCBMRpZn5NxVg0oqxN46YcLkFl99clKCK4m/KGhtKppujjqu+3ILqhbFdv5iItItBmIgozQidwFX/XIqVXylF6cyh4bBoahaWf6EEq75WBp1BO+0BeqPAursqULVolJArgMkrrVj55TIInXa+byKKLy6fRkSUpiZeYcXEK6zwOkIIeCIw5eiQnafdt3Vjtg5rv16O7no/jm93wtEYgBBAwcQsTFtnh7XMmOwSiSjNaPcdk4goQ2TbDcjOoLbY/OosLL21ONllEJEGsDWCiIiIiDISZ4SJiIjSXFudF6deccHTGYLeKFA6KxtTVllhsmhj1RCieGEQJiIiSlOejiBe+VELOk4O3Uq7fo8H+//Yifk3FWDWtflJqo4o9TEIExERpSFvTwhbHmiEuy004v0hv8SexzsRDgFzbmAYJhoJe4SJiIjS0P4/d40agoeM+2MnPB3BBFRElH4YhImIiNJMoDeCM6+5FI2VEeD4DmecKyJKTwzCREREaab1iBchv1Q8vuFdTxyrIUpfDMJERERpJuiLqBof8ikPzUSZhEGYiIgozZjt6pZFM9u4jBrRSBiEiYiI0kzZrGxk5ysPt7XLc+NYDVH6YhAmIiJKMzq9wLT1yvbVNmbrMGmlLc4VEaUnBmEiIqI0dNkN+Sifkz3mGJ0BWPHlUphy+OueaCT8l0FERJSG9AaBtd+owMxr7DBmi4vuL5yUhfV3V6JqgSUJ1RGlB+4sR0RElKb0RoHLby7G/JsKcf5tNzwdIehMAmWzslE02Zzs8ohSHoMwERFRmjOadZi8in3ARGqxNYKIiIiIMhKDMBERERFlJAZhIiIiIspI7BEmIiKijOFsCeD4Nica93sQ8kuYbXrULrdiymorTBbuwJdpGISJiIgoI+z7YycO/r0bkO/f5ukIofO0Hwf+0okVXy7jcnMZhq0RREREpHnv/a0LB58aGoIHC3oldv5XM1qPehNbGCUVgzARERFpms8ZxntPdUcdFwkBe3/fkYCKKFUwCBMREZGmnXzZiUhwlKngYTpO+tF5xhfniihVMAgTERGRprUeU9fu0HqEQThTMAgTERGRpoUVzgZfGB9SN57Sl6IgLITYJISoE0KcFELcNcqYm4QQR4QQh4UQT8S2TCIiIqJLk1ukbpEsteMpfUX9mxZC6AH8FMDVABoA7BZCPCOlPDJozFQA3wSwXErZLYQoiVfBRERElJlkRKLxQC9aj3gRCUlYigyovcqKbPvYcWbyahtO7nQpeg1Trg7Vl3MJtUyh5CPPEgAnpZSnAUAI8SSAGwAcGTTmcwB+KqXsBgApZVusCyUiIqLMdf4dN/b8rgPuttCQ29/9Qydql1ux5NZiGM0jn+gunZGNwslZ6Dzlj/o609fbYTCxczRTCCnH7oMRQtwIYJOU8rb+rz8FYKmU8ouDxvwdwHEAywHoAdwnpXxxhOe6HcDtAFBaWrroySefjNX3oYrb7UZubm5SXptSG48NGguPDxoNj434CrgjcLcHMVZkMZp1sJUbATHy/ZGQhLM5OGa/sMmig7XUOM5qL8bjI/nWrFmzV0q5ePjtsWqCMQCYCmA1gCoAu4QQc6SUPYMHSSl/CeCXALB48WK5evXqGL28Ojt37kSyXptSG48NGguPDxoNj43YCvRGcGqXEydfcsLRHEAkqOxx1R/Mx4KPFI56v9cRwnt/7cbpV10IeiMXbs8tNmDaBjtmXZsHnW6UJD0OPD5Sl5Ig3AigetDXVf23DdYA4G0pZRDAGSHEcfQF490xqZKIiIgyQucZP176fhO8PWHVjz3xkhPzPlQAnWHkMJttN2DpZ4qx8OOFaDvmRcgnYbbrUTLdDBGHAEypT0kTzG4AU4UQtUIIE4CPAnhm2Ji/o282GEKIIgDTAJyOYZ1ERESkce72IHZ899JCMAD4HGFFWyQbzTpUzregZlkuSmdmMwRnsKhBWEoZAvBFAFsAHAXwJynlYSHEA0KI6/uHbQHQKYQ4AuBlAF+XUnbGq2giIiLSnsP/6IHPeWkheIDfE4k+iKifoh5hKeXzAJ4fdts9g/4sAfxL/39EREREqgR9EZzepWyJs7EYszm7S8pxfRAiIiJKOmdTYMgFbJfClKtD2czsGFVEmYBbpxAREVHSyRh0NExZZYN+hDWAA70ReDqDEELAWmIYcQxlJgZhIiIiigtncwAndzrhbgtBpxconm7GpBXWETe+sBQZIHSXHojzJpgw90MFQ27rPu/Hked6cPZNN8KBvvWDTRYdpqy2YeZmOyxFsV8zmNILgzARERHFVMATxus/b0P9Xg8waP+K06+58O4THZh3YwFmXZs/5DHZeQZULbKgfrdH3YsJoGpBDpZ/oRSmnPcD9vndbuz6cSsiwzbQCHgiOPJcD07tcmL93RUorDWr/v5IOxiEiYiIKGaCvgi2PdiEztMjb2cc9ErsebwTIZ+8aAZ39gfy0LDXE3VWWG8UqFqYA1u5CZNXWWErNw25v/ucf8QQPJjfFcGO7zXj+v+cALNVr+ybI81hkwwRERHFzOFnu0cNwYPt/0sXHE2BIbeVTM/GsttKIMZIJ6ZcHTbeW4lVXyvHgo8WXhSCAeDIcz1jhuABPkcYJ3c6o44j7WIQJiIiopiIhCVOvKQwWErg+DbHRTdPXWvDhnsqUb3YMiQQG8wC09bbcO2D1SiaMno7Q6A3grNvuhXXfGIHg3AmY2sEERERxUTHSR+83co3xDi/24PLP1180e2lM7JROiMb3p4QXK1B6PQC9koTjNnR5+88HUGEFcwGD3C1BBEJS+j0XH84EzEIExERUUyoXQc42vjsPAOy89RFFaE2z4pLeAxpBlsjiIiIKCayVF50pna8ErmlRpgsyuNNQU0WhI5JOFMxCBMREVFMFE7KQm6J8hnciVfkxrwGg0mHySutisdPu9oW8xoofTAIExERUUwIITB9g13ZWD0wbV18QujMa/KQZY0eceyVRky6SnloJu1hECYiIqKYmbk5DxXzcsYeJIBlny2J285uucVGrLurAmbb6K0X9koj1n+zAoYsRqFMxovliIiIKGZ0eoE1Xy/H3t914OTLToT8Q1dwyC0xYOHHizBxWezbIgYrmmzG9f85ASdfduL4DgfcbSFAAPk1Jky/2o5JV1kZgolBmIiIiGJLbxBYcksx5n+4AGded8PdHoTOIFA8zYzKeTkJuzjNbNPjshvycdkN+YiEJKADdLwwjgZhECYiIqK4MFn0inuG401nYACmizEIk2Z4HSGE/RJZNj2MZp7uIiJKBQF3CD1NQehNAvkTTNDp+P5MqYNBmNJaOCRxepcTdduc6DrTt7e90APViyyYuTkPpTOzk1whEVFmOrfbhX1/6IKzKXjhNqEHKubmYOlni5EbpwvliNRgEKa0FfRG8NJDTWg96htyuwwD59/x4Pw7Hsz/SAHmfrAgSRUSEWWmtx9pR92Ljotul2GgcV8v/v6Vc9hwbyVKpnGygpKL5ycobb36k9aLQvBw+//YhVO7nAmqiIiI6rb1jBiCB4uEga0PNCHkU7clM1GsMQhTWuo+50fDXo+isQef6oaUMvpAIiIatwN/6VY0LhKSePfJzjhXQzQ2BmFKS8d3KJ/ldTYH0XLYG8dqiIgIANpP+uBzhBWPP/2qK47VEEXHIExpqafer3J8IE6VEBHRgJYj6iYdAr1sjaDkYhCmzMDlI4mIiGgYBmFKS/k1WerGT1A3noiI1KuYq24VCJOFMYSSi0cgpaVp65XvVGSvNKJsFpfoISKKt8KJZmTn6RWPn7LaFsdqiKJjEKa0lFdlQs2yXEVj536I6wgTESXKgo8VKhqnNwnMv4nvz5RcDMKUtpZ/vgQV83JGHyCAxZ8qQu2V1sQVRUSU4aassuGyf8obc4zOKLDpgUoYTIwhlFzcWY7SliFLh7XfKMe5t9yo2+pAW13f5hp6o0DNFbmYsdGOosnmJFdJRJR5Fn60CGWzsrH3953oPvf+qj06o0D1YguW3FKEbDsjCCUfj0JKazq9QO1yK2qXWxH0RRAOSJhydNAZuEwEEVEyVcy1oGKuBaFQBJ72EAwmHSyFjB2UWnhEkmYYzToYOQFMRJRSDAYd7OWmZJdBNCI25xARERFRRuKMMBERkcY4GgOo2+ZAy2EvwkGJnAIDpqyyYuIVudDzAjWiCxiEiYiINEJGJN55tAN1WxxDbne1BNF6xIt3n+zEmjvLeSExUT9+LCQiItKI3Y9dHIIH83aHsf07TehpDIw6hiiTMAgTERFpgKMpgGNjhOABAU8EB/7UmYCKiFIfgzAREZEGHN/mAKSysef3eNDbHYpvQURpgEGYiIhIA1qOehWPlWFc2ISIKJMxCBMREWlAJKhwOrhfOBCJUyVE6YNBmIiISANyCtQtBGUpMsapEqL0wSBMRESkAZNX2RSPzS0xoHQml1AjYhAmIiLSgJplucgpVDYrPHNzHoQQca6IKPUxCBMREWmA3iCw9hvlyLKO/at90korZmyyJ6gqotTGIExERKQRBTVZ2PztKtQuz4Vu2OSwtcyIJbcUYfnnSzgbTNSPWywTERFpiK3MhBVfKsPlnw6j7ZgX4aBETqEBJdPNDMBEwzAIExERaZDZpseEJbnJLoMopbE1goiIiIgyEoMwEREREWUkBmEiIqIYCIfD8DrCyS6DiFRgjzAREdElCgTCeO3Hregp9OP3vzhz4XazTY/FNxdi0lXKN7kgosTjjDAREdElcLUF8Mdbz6BhTy+kHHqfzxnGaz9pw47vNSanOCJShEGYiIjoEjz9L+cho3RCNO734q3ftCWmICJSjUGYiIhIpX1/7EQkpGzsiR3O+BZDRJeMQZiIiEiloy/2KB4rI8CxbcrHE1HiMAgTERGpFPLK6IMGOf+2J06VENF4cNUIIiIiAEFfBGdec+H4Did6zvshAeRVmzBtnR2TVlhhNF/63JGMqAvORJQYDMJERJTRwiGJzpM+vPrTVnjahzb+dp8N4O3ftOPws91Y/80K2MpNAAC9EQgHlb9GyczsWJZMRDHCIExERBnJ2RTA0S0OnN7lRDBKq4O7LYTt32nCtd+tRlauHhOvtOLUKy5lLySABR8ujEHFRBRr7BEmIqKM07DXg2fvqkfdFkfUEDzA3R7C8f4VIJbdXgQIZa9VOY+zwUSpikGYiIgyStdZP175UQvCAfV9u8e3OSAjEnq9Huu+WR51vLXMiHV3VV5KmUSUAAzCRESUUQ49041w8NIuXvN0hOBz9u2iUTnXguv/cwKsZcaLxukMwNR1NnzwRzXjqpWI4os9wkRElDF8rjDOv+Me13PIyPt/zqsy4YM/qsHOl89g3h3F8HvCKJqSjdLpbIcgSgcMwkRElDFczQHFO8KNxGTRIcumv/gOAUxZbb/0JyaipGBrBBERkUKTV1qhNyi8So6IUh6DMBERZQxruQk646UFWUOWwIyNeTGuiIiSiUGYiIgyhtmqR81Si+rHGbIEVn2tbMQL44gofbFHmIiIMspl1+Xj/DseRcun6YxA7ZVWzP5AHvKqsxJQHRElEoMwERFllPyaLKz+Whle+VELQv6Rw7Cl0IAlnylC6YxsmCwjXBxHFCORkMS5d9w4vt2Jnno/hBDIrzFh2no7qhdboNOzJz2eGISJiCjjVC6w4LrvT8CxrT049YoLAU/fmmjWMiOmX23DlDU2mHIYgCm+3O1B7PheExyNwSG3Nx/0ovmgFwUTs7D2/5UjJ59xLV74kyUiooxkLTPi8puLsegTRfC7w9DpBUwWHYTgDBzFX8ATxrYHm+BqCY46puusHzu+24TN366CIYuXdcUDf6pERJTRdHqBbLsBWbl6hmCKGb87jLqtDuz9fQe83WE0vdcLKd9vxTm+3TlmCB7QfT6AU7tc8Sw1o3FGmIiIiChGwoEI9vyue5jFGgAAIABJREFUEyd3Oi9ckGnZEML27zTBWmbE4k8WoWphDo7vcCh+zrptDky/mhu2xANnhImIiIhiIByS2PFQM+q2OkZclcTVEsTO/2pG3TYH3G3KtzjsOR9A0BeJPpBUUxSEhRCbhBB1QoiTQoi7xhj3ISGEFEIsjl2JRERERKnvyLPdaDnkHXOMlMCexztUP3ckFH25P1IvahAWQugB/BTAZgCzAHxMCDFrhHFWAF8B8HasiyQiIiJKZZGIRN12p7KxIUCnYlESk0UHUw5P4seDkp/qEgAnpZSnpZQBAE8CuGGEcd8G8H0AvhjWR0RERJTy2ut86O1U3u5gMCsPtlNW2yB0vJAzHpT8LVQCqB/0dUP/bRcIIRYCqJZSPhfD2oiIiIjSgt8dVjVepxeKZoX1JsEL5eJo3KtGCCF0AP4bwC0Kxt4O4HYAKC0txc6dO8f78pfE7XYn7bUptfHYoLHw+KDR8NigoC8Cy4aRl0PT2/2wbDg15DaDSSAv3wB3WxBylPZfIQBrqRF7j9UDx2JdMQHKgnAjgOpBX1f13zbACuAyADv7118sA/CMEOJ6KeWewU8kpfwlgF8CwOLFi+Xq1asvvfJx2LlzJ5L12pTaeGzQWHh80Gh4bFA4KPGXL5yB33Xx6g6WDafg2Tp5yG2zr8/DomuL0FbnxaFnutH4bu+FQCz0QPUiCy67IR9Fk82JKD9jKQnCuwFMFULUoi8AfxTAxwfulFI6ABQNfC2E2AngzuEhmIiIiCjRImGJhr0eOJqDEAIorM1C+ZycmL+O3igwZbUNh5/tiTpWCGD6+r52h5Lp2Vj79Wx4OkNwNAUgANirTNxWOUGi/pSllCEhxBcBbAGgB/BbKeVhIcQDAPZIKZ+Jd5FEREREah15rgeH/9ENb/fQ/l1bhRHzbyrExGW5MX29OR/MR+P+XvTUB8Yct+CjhcgtMQ65zVJogKWQ4TfRFP3EpZTPA3h+2G33jDJ29fjLIiIiIrp0b/+2HXVbR969zdkUxK4ftcD3mWLM2BC7C9FMOXps+PdKvP6zVjTu773ofmO2DvNvKsDMzXkxe83/396dx8lV1Xkf/5yq6urqfc2+EUIChCQQCEQJQocsBAcJKq4wyiL6gIyjIIo4w6AMgyLuD46Do48OOgZ3USQJZAGBsCRAErIAScjS2XurXms/zx+90J10V99Kaumu+r5fr7xe1ff++t5fuk/X/dW5554jJ0cfPURERCSr7HmpdcAiuLeX/99RRk8voHy8N2nn9pW6WXDnWJr2h9ixppm2o2FaStzM/NQIJl9UQl4C06ZJ6um3ISIiIlll+/LBi2DoXOXNScF8IsrHeZlzbTWXfGEMxSM8TFtYpiJ4CNJvRERERLJGe0OEw1vjL3Pc29vPtaQwGxnqVAiLiIhI1gg0J7awRagtRjQywES+kvVUCIuIiEjW8PgSW4rY5QG3R8sX5yoVwiIiIpI1SkblUTo2b/DALuNmF6UwGxnqVAiLiIhI1jDGMG2h8ynRTl+UvOnTZPjR9GkiIiKSElEbYU/7azSHj+AyHsb4TmdE/qSUn/f0haXsWdfK0bcCceMmX1TM2FnJX2VOhg8VwiIiIpJU1lo2ND3GxqbltEUb++wb7ZvKhVUfY3zB9JSd3+11seArY3n2oUPUbjh+YQvjgmkLyzj/k9Upy0GGBxXCIiIikjTWWp488iO2t/y93/2HAm/xp/338d4xX+DUojkpy8Nb6OLSO8bStC/Im6uaaT4YxriganI+UxeUaTljAVQIi4iISBJtaV49YBHcLUaU5Yd+yHWn/JBCd2lK8ymfkM8F141I6Tlk+NLDciIiIpI0G/0rHMVFbJCtzWtSnI1IfCqERUREJCmOBvdQH9rrOH6wnmORVFMhLCIiIknRFmkcPOgk4kWSTYWwiIiIJEWeKz+l8SLJllMPy0XbWom1t0MslulUREREss6o/Cn4XMUEYq2O4icVnpPijETiy4lCuHX9S/hXraBj2xYAgjWL2f+Nr1O2YDFFc+ZijNYYFxEROVkel5czSy/h1abHHcXPLFuU4oxE4svqQtjGYhz56Y9pee6Z4/Z1bN9Kx/atFL/7IkbddAvGpVEiIiIiJ2tOxVJ2tW3AHz4UN25m2WJG5k9OU1Yi/cvq6q/hD7/ptwjurXXds9T/9tdpykhERCS7FbhL+cC4f2VU/pR+9xtczC7/B2qqr0tvYiL9yNoe4VhHO01PLXcU61+1goorrsJdVJTirERERLJfiaeKj0y4j9qOrWxtXkNz+Agu42GMbxozyhZS4qnKdIoiQBYXwi3rnsUGAo5ibShEy/PPUL7o8hRnJSIikjvGF0xnfMH0TKchMqCsHRoRqq1NMH5fijIRERERkaEoawthEp0IQjNHiIiIiOSUrC2EvRMmJRSfn2C8iIiIiAxvWVsIl7x7Hq6CAkexxuej5MKLUpyRiIiIiAwlWVsIu/J9lC+5wlFs+eL34iooTHFGIiIiIjKUZG0hDFBx5Qcou3Rx3JjSmgVUvv9DacpIRERERIaKrJ0+DcAYw4hP3EDRuXPwr1pJ28ZXIBYDDIXnnNe5xPLMszOdpoiIiIhkQFYXwt0KZ8yicMYsbDhMrKOD2g0bGHvd9ZlOS0REREQyKCcK4W4mLw93Xp6mShMRERGR7B4jLCIiIiIyEBXCIiIiIpKTVAiLiIiISE5SISwiIiIiOUmFsIiIiIjkJBXCIiIiIpKTVAiLiIiISE5SISwiIiIiOSmnFtToZo5ZUMNay/Lly1m3bh3BYJDTTz+dj3/84/h8vgxlKCIiIiKplpOF8MSJE3ter1u3jttuu40XXnihT8zdd9/N1772NW688cZ0pyciIiIiaZDThfCmTZtYtGgRbW1tTJw4kTvvvJPS0lJ++tOfsmbNGj71qU8RCoW4+eabM5yxiIiIiCRbTo4RdrvdWGv53Oc+R1tbG3PnzuX111/n5ptv5pprrmHVqlXccccdAHzpS1+irq4uwxmLiIiISLLlZCEMsGvXLp5++mkAHnzwQUpKSnr2GWO49957qaqqorW1ld/85jeZSlNEREREUiQnCuG2115h779+iR03fJxoaysAzz33HACVlZXMmzfvuO/Jz8/n8ssv7xMrIiIiItkj68cIH/jON2jf9BoAeWPH4S4uBt4pbmfOnHncLBLdZsyY0SdWRERERLJHVvcIH/rxD3uKYADv6DE9rzds2ADA9OnTB/z+s846C4A9e/ZonLCIiIhIlsnaQjgWCND64vN9trl8BT2vm5qaABgxYsSAx+i9z+/3JzlDEREREcmkrC2E637zK7C2zzaT5+153d7eDoDX27nNxqLHHaN7X+94EREREckOWVsIB95647htNhzqeV1Q0Nk7HAp1bjMu93Hx3ft6x4uIiIhIdsjaQtjGYsdtiwU6el6Xl5cDxB3723tfd7yIiIiIZIesnTUir3ok4f21fbaFDx3qeX3eeefxyiuvsGXLlgGP0b1v4sSJVFdXpyZREcBaS/vrm2he+xSh2n1gDPkTJlE6fyGF02dkOj0REZGslLWFcPWHr2Hvxlf6bAsd3E+0rRV3UTHz5s3jJz/5Ca+//jrW2n6nUNu8eTNAv/MMiyRLxN/Ewe9/i+CunX22hw8dpPXlF/CdfiZj/un2nqn/REREJDmydmiEd9w43OUVfTdaS2DHm8A7xW19fT3PP//8sd9OMBhk+fLlfWJFki0WCHDgwf84rgjuLfDGNg585xvYcDiNmYmIiGS/rC2EbSwG/fTydmzfBsCUKVO4+OKLAbjjjjto7VpxDjpvU//bv/0bdXV1FBcX8+EPfzg9SUvOaX56NaF9eweNC+7aQcsLWthFREQkmbK2EG57dQPRxobjtrc8/wzRSARjDD/4wQ8oLCxk3bp1zJw5k4cffphly5axePFivvnNbwJw//33x51rWOREWWvxr3nScbx/9coUZiMiIpJ7snaMcMf2rf1uj/r9vP36Zk47ZzZnn302K1eu5Atf+AIvv/wyn/nMZ3rixowZwz333MNNN92UrpQlx0T9TYQPHXQcH3x7F7FgAFe+L4VZiYiI5I6sLYRtZODxlLs2vsYYl6Fo1jnMmzePF198kccff5wXXniBYDDItGnTuPbaazV3sKTUiYz5teEwqBAWERFJiqwthD1VA093Zq3l4A8epOrqj1K+4DJMXh5XXHEFV1xxRRozlFznLi3FePLifmjrzVVQgKuwKMVZiYiI5I6sHSNceuF7wBXnvxeJUL/sl+z58ufxr34SGz1+iWWRExV4eyfNT6+m+enVBHa+1W+MK99H8flzHR+z5ML3YOK1aREREUlI9vYIV1ZRPGcurS+tixsXaainfctmyi5dlKbMJJu1vbqBhsd+T/DtXX22eyeeQuX73n9c4Vu26PLO2SCsjX9gt5uyBZclO10REZGcltXdSyOvv4n8U06NG+OdeAojb/hM3BgRJ/yrn+TgDx48rggGCO3dzaGHvkvTisf7bPedOoURn7yx36n+erhcjLrxZrxjxyU7ZRERkZyWtT3CAK6CQsbdeTcNj/2e5mfWEmtteWdfUTGl76mhcukHcemhODlJwT27OfrIzwbt2a1b9kt8U6bhO21qz7aymoV4Kqtp/OsfCbz5Rp/4gjPPouLKD1B45lkpyVtERCSXZXUhDODy+aj+8DVUXvUhOrZvJdbezt5GP6d890e4vN5MpydZoump5YMPbwCwlqannmB0r0IYoGjWORTNOofQ/lpCB2oB8E6YiHf02FSkKyIiIuRAIdzN5fVSNOucztdr16oIlqSxkQitL8Yfi95b6/qXBpwP2DtuPN5x45OZnoiIiAwgq8cIi6RDrL0dGwo6/4ZIhGhLy+BxIiIiklIqhEVOkjmBuwsn8j0iIiKSXCqERU6Sy+cj/9TTHMd7J0zCU1qWwoxERETEiZwZIyySCGstm1o7WNPYzJFQGGNgWqGPRZVljM0/vje3bMFijuza4ejYmrM6e4WPHMK/+ik63twO0Sie6hGUvqeGwlnnaDEUEZEhSIWwyDHqQmEe3HuI3YG+4353dgRZXu+npqKUG8eOwNNr7t+SuRfS8ve1dGzfGvfY+VOmUjLv4pTkLZljYzHqfvUL/KtX9pk9JLjnbdo2vIR37HjGfP6L5I0cncEsRUTkWOqiEOklBty7+8BxRXA3C6xpbObHtYf7bDceD2P++Q4Kz5494LELzprJ2Nvu1IwlWejo//wM/6oVA06hFzpQy/5v3EukqTHNmYmISDzqERbppSkS4XAoPGjcs/5WFlZ2cEbRO4uxuAoKGPuFLxN4eyfNa54iuG8vYPGOm0BZzcI+i2hI9gjueZvmtU8NGhdpqKfxL39kxD/ekIasRETECRXCIl3CMUtLJOY4fmWDv08h3M03eQq+yVOSmZoMYf5VTzqObXn+71R96OO4fMfPIS0iIumnQliky+5AkCgOVofrsrm1PYXZSLq1RiL86lA9G1s7iNgYBS4XNRWlLK0uxxXnQbf2rZsdnyPW0UFg11sUTp+ZjJRFROQkORojbIxZYox5wxizwxhzZz/7bzPGbDXGbDLGrDLGTEp+qiKpFYo57w0GCDlZUlmGhUcO1nHT9t2saWqhIRKhORrjcDjCo0ca+MS2XbzoH3gBFBsKJXSuRONFRCR1Bi2EjTFu4CHgcmA68DFjzPRjwl4F5lhrZwG/Ax5IdqIiqVaRl9gNkkqPbqhkg0cO1vF4fdOA9wIiFr677zCvNLf1u99TUZHQ+TwVlQlmKCIiqeKkR/gCYIe1dpe1NgQsA5b2DrDWrrHWdt8nfgEYn9w0RVJvbL4Xn3E+kcrF5SUpzEbSoTUS4fH6JkexPzpmppBuJRc6nw7PO34i+ZMmO44XEZHUcnLVHwfs6/V1bde2gdwIPHEySYlkSpnH7Sgu3xjmV5amOBtJtV8eqncc2xqL0dHP8JmSiy7BVVDo6Bhli5Y4Pp+IiKSesYOMczTGXA0ssdZ+quvrfwTmWmtv7Sf2WuBW4BJr7XETsRpjPg18GmDUqFHnLVu27OT/ByegtbWV4uLijJxbhrbW1laC+T780eiAMQYY5c2jUCuFDXt7AyEiCTwgWREKUVF6/J2AWKCD8OHDYAceZ+4uLsFTPeKE8pShT9cViUftI/Pmz5+/wVo759jtTgY57gcm9Pp6fNe2PowxC4GvMkARDGCtfRh4GGDOnDm2pqbGwemTb+3atWTq3DK0rV27litqaniywc9f65qOm1P4rKICPjyyktP7mTZNhp+btu2iJer8IcmlB94e8L0jsGsn9X94lI4tm/ssrOGpqqb8svdStuhyTK/VCCW76Loi8ah9DF1OCuGXganGmMl0FsAfBT7eO8AYMxv4Lzp7jo8kPUuRNFtUWcbCilK2tHVwOBTGbQxTC3yM82lVuGzic7kSKoTdcQpZ36lTGPfFuwgdOkjgrTew0Sh51dUUTJ+J0d0DEZEhadBC2FobMcbcCqwA3MDPrLVbjDFfB9Zbax8DvgUUA7/t6vHYa629MoV5i6ScMYYZxYXMyHQikjKXVJTwuyPOlz2ucDCG3Dt6DN7RY04mLRERSRNH8z9Za/8G/O2YbXf3er0wyXmJiKTcVdUV/PFIIwOPCH/HyDwPeRraICKSVTQRqojkLI/LxS3jR/HDAaZG64kzhq9OHsu2g7vTkpeISCq1RhrY3vJ3WiL1uPEwvnA6kwvPxSQwhWi2UCEsIjltXnkJXgM/3n+Utn6mRxuR5+Grp4xllNfLtgzkJyKSLMFYO2uP/JS3Wl8g1ute2Gv+v1Hiqeai6muZWvyuDGaYfiqERSTnnV9WwvllJbzS3MaTjX4C0RhlHjcfHFnJBF9+ptMTETlp4ViAP+2/j8PBnf3ub4nU8cSh7xMZGeLMUucLBQ13KoRFRLqcW1rEuaVFmU5DRCTpXmr4w4BF8Dssq4/+hElF51Dozo1Fo3JvMIiIiIhIDonYMFua1ziKjdowWx3GZgP1CIuIiAxxgWgrW5ufZnvL32mLNpJnvEwsnMWsssVU50/KdHoyxO1v30og1uI4fkfri8ypWJrCjIYOFcIiIiJD2J62jfzt0PcI246ebR3A682reL15FTPLFlNTfV1OPvEvzgRjbQnFB6KtKcpk6NFfjYiIyBB1sONN/nrowT5F8LE2+1fy97pfpjErGW68rsKE4vPdicUPZyqERUREhqjnG5YRteFB4zb6n6A5fDQNGclwNL5gOj5XseP4KUUXpDCboUWFsIiIyBDUENrP/o6tjmItltebn0pxRjJceVxeziy9xFGsCzczSi9NcUZDhwphERGRIehAx/YE499IUSaSDS6o/CDV3sEfrKwZcQOFnvI0ZDQ0qBAWEREZgqI2klB8zEYHD5Kcle8q5APj/pVTi87HYI7bX+guZ/GozzKjbEEGsssczRohIiIyBJXljUwoviSvOkWZSLbwuYu5Yszt+MOH2db8DK2RetzGw7iC6UwpvgC3yb2yMPf+xyIiIsPAxMKzKXJX0hZtcBR/Vun8FGck2aIsbxTvqvpQptMYEjQ0QkREZAhyGRezyy93FFvtnciEgpkpzkgk+6gQFhERGaJml1/BGSUXx40p9YzgijFfxJjjx32KSHwaGiEiIjJEGWNYNPJmxvqm8Zp/OQ2h2p59XlchZ5ZczPkVV+XUU/4iyaRCWEREZAgzxjCjbCEzyhZyJLCLtmgTHuNltO808ly+TKcnMqypEBYRERkmRvpOzXQKIllFY4RFREREJCepEBYRERGRnKRCWERERERykgphEREREclJKoRFREREJCepEBYRERGRnKRCWERERERykgphEREREclJKoRFREREJCepEBYRERGRnKRCWERERERykgphEREREclJKoRFREREJCepEBYRERGRnKRCWERERERykgphEREREclJKoRFREREJCepEBYRERGRnKRCWERERERykgphEREREclJKoRFREREJCepEBYRERGRnKRCWERERERykgphEREREclJKoRFREREJCepEBYRERGRnKRCWERERERykgphEREREclJKoRFREREJCepEBYRERGRnKRCWERERERykgphEREREclJKoRFREREJCepEBYRERGRnKRCWERERERykgphEREREclJKoRFREREJCepEBYRERGRnKRCWERERERykgphEREREclJnkwnICLp4ff7WblyJc888wxvvvkmbW1tVFdXs3DhQpYuXcqECRMynaKIyJAVi8XYsWMHGzZsYO/evQDU1NQwd+7cDGcmJ0OFsEgOePDBB/mXf/kXgsHgcfv+/Oc/88///M985Stf4d5778UYk4EMRUSGpqamJj7wgQ+wYcMGmpub++x74IEHVAgPcyqERXLAtm3bCAaDnHfeeVx11VXMnj2b8vJytmzZwsMPP8yGDRu47777GDlyJJ/73Ocyna6IyJARCARYs2YNAG63mxkzZrB9+/Z+OxZk+FEhLJIDLrroIj796U8f13Mxb948brjhBpYsWcKqVat48MEHufnmm8nLy8tQpiIiQ0tRUREPPfQQ5513HrNmzaKgoIAJEyZQW1ub6dQkCfSwnEgOuP766we8fefxeLjrrrsA2LdvH6+88ko6UxMRGdJKSkq45ZZbmDt3LgUFBZlOR5JMhbCIMHny5J7X7e3tGcxEREQkfVQIiwivvvpqz+upU6dmMBMREZH0USEskuOi0Sj3338/APPnz2f8+PEZzkhERCQ9VAiL5Lh77rmH9evXk5eXx7e//e1MpyMiIpI2KoRFctgjjzzCv//7vwPwve99j9mzZ2c4IxERkfRRISySo/74xz9y/fXXA3D33Xdzyy23ZDgjERGR9MqpQri5rY2te2uJxWKZTkWGqfZojPZoFGvtcfvqQxGOhkI97as9Gh0wNlmi1tIaiRLqp01HYpaWSJRI7Pjz/+Uvf+EjH/kI0WiU22+/nXvuuSdlOSZLUzjC4VCISA7+/XZEo+xs76AhFDrhY8Rru4mK2DCBaCtRGznpY2WTYCxGayRKLM7P2FpLMNpGKNaR1HNHYiEC0VZiNprU4w4nMRsjEG0lHBs6C13YrpwsqbsOJCJqI3REW4jYcNrP7Q8d4XDHTkLRE38fS4WcWFDjB+te5qWCEiJuNxjD4mCYa17bzvntzXz+wgsynZ4McS2RKKsbm3mqwc/RcOeFv9LjYUFlKbOLC/n90QZea2mn9+XHA3SXCN2xCytLKfMk50/u7Y4gK+qbeN7fSqjrontGoY/FlWUUuV081dDMhpY2YkC+MXz91PFMKsgH4IknnuDqq68mHA5z66238q1vfWvILqvsj0T4+YE61re0Ee5VXEz25XPt6CrOKi7MYHap9/sj9fylrolArw8zLmBWcQG3TRiN1+2O+/2t0ShrGpp5ssHPka62W+Fxc2lFKYsqyyjPS6w9vt22gU3+lexp3wRYXLiZUnw+s8ouY1zBmYn+97JCJGZ53t/CgWCYT27dBYDPZbiovITLKsuY4Ov8u2sJ17G5+Um2NK+lI+oHoCJvLDPLFnJmaQ35rsTbcszG2Nn2Epv8K9nfsRUAt8ljavG7ObvsMkb5piTpfzm0NYT2s9G/gu3NfydsOz9gjMqfwsyyRUwrmYfHpH+BoKbQQTb6V7Ct5RlCsXZGBxfz630rmFm6iDNK3oPH5U1rPgc63mCTfwU7Wl8iRgQwTCycxayyRUwuPC9l14D2qJ/HD36bg4G3oNeHgXxXMe+u+gizyhal5LyJMKnsrYpnzpw5dv369Sk/zw3rN9Pu6zsB9uLanawc3/kGURAM8PCs07WSlgCwdu1aampqer7eFwhy/+4DNEROvpelzO3mzlPGMLnAd1LHWV7fxC8O1jnqX8gzhtsnjuackiIAVqxYwdKlSwkGg9x00038+Mc/xuUamjeGdncE+Ndd+/sUwMf64IgKPjSqKm05Hds+UumOt/ayLzhwz4kH+OG0iVR4+7+gHgiG+I/dB6gL999rW+J28aVJY5laOHh7jNkYTx75EW+0PDtgzJyKpVxY9bFBj5VN2qMxHthzgO3tgT7XlW5u4KZxI5mSv4/HDz04YC9wed5orhr7VUrzRjg+dyQW4olD3+ft9g0DRBguqrqGcyuucHzM4Wh7y7M8dfg/idH/e/QY3zSuHPNl8t1FST1v98pyDzzwAHfccUeffbvbXuXxQ98h2qvXdfTOxRyashKAkfmnsnTsnRS4S5Oa00BeqP8tLzX+fsD904ovZPGoz+Iy8T9YJ+pI4G0erb0rbm/4hIJZvH/cXUk970CMMRustXOO3e7oCmiMWWKMecMYs8MYc2c/+/ONMY927X/RGHPKyad88j774qvHFcHH6sj38U+vbklTRjKctESiSSuCAfzRKN/YfZDGAQoTJ17yt/Jzh0UwwKfHjewpgletWsVVV11FMBjkuuuuG9JFcCgW4+5BimCA3x9t5LmmljRllT73794ftwiGzjsOn39rX7/72qLRuEUwQEs0xjf3HKAuNPgt0mfrfhm3CAZY3/hnXmt6YtBjZZPv7TvI9vbAgPujwC8ObOGxgw/EHQrRFD7Enw/cTyTm/Jbx6qM/iVMEA1ierR/89zac7Wt/nScP/2jAIhjgYOBNHj/0nTRmBeMKzmRE/uQB9x8J7uIvB7+V0mFz3Tb5V8YtggHebH2eZ+r+J+nnfrT2q4MOCdnXsYk1R3+W9HMnYtCroDHGDTwEXA5MBz5mjJl+TNiNQKO19jTgu8A3k51oog7UNVBfWOwotqmgiH2Hj6Y4IxluVjc2J60I7uaPRlnZ4D/h7//d0QbHsdV5Ht5TXgLA008/zfve9z4CgQDXXnst//3f/z1ki2CARw839Az5GMyvDtWnOJv029jqbPxo0FpW9dOe1ja2xC2Cu7VGYzxRH789tkeb2eRf6Sif9Y1/yplxw2+0d7DJwe9prPt6W0IkAAAPv0lEQVRZonbwMauN4QO80fqco3M3hQ6x3WGB+2LD79NScGXCS41/wDL4MwO1HVvY37EtDRl1ynP5OLc8fk/8ocBb7Gl/LaV5xGyUlxv+6Cj2df9TtEWaknbu1Ud+4uh303nuVUk774lwciW8ANhhrd1lrQ0By4Clx8QsBX7R9fp3wAKT4UGH339zJzhNwRi+v2tPahOSYae/AiMZ1jQ2Ez2BC9Mb7R3sDTjvMarsNR75vvvuo6Oj86L9u9/9jtLSUoqKivr998gjjyScW7KtbWx2HNsQibA3MHQejjlZPzuQ2IfyRw8f/0HgqQTa7tNNzYT7eaCy29bmNV1jCgfXHvWzs/Vlx+cezp5qGLyNuglQ7d7k+Jiv+59yFtf8FDi8L9QUPkhtR/bd9WwI7e8ZF+3EZv+TJ3W+JUuW9HmfrK2tBeCuu+7qs/1Pf/oT0DncZdCcmp39vk/UrrYNtEUbHcXGiLK1eU3Szr295e+OYy1RtvrXJu3ciXJSCI8Det9/q+3a1m+MtTYC+IH0Ddzrx9H8+EMijlXnPblxm5Jd2qPRnoeLkq0pEsV/Aj3NezqS86RtIBCgvb19wH+RSOZ79NoTnBlic0t7ijJJv21tic0m0Brt+7MKxWIcdDDcoff318dp63XBxDoJ6kK50amwx8GHL5+px22c/y6OOvxZO43rlo2/k7rg3oTiE/2ZHevY981ukUikz/Zo1Pl7e6J/W4lK9PhHg7uTdu6ITex6tbMtcx+gB31YzhhzNbDEWvuprq//EZhrrb21V8zrXTG1XV/v7IqpO+ZYnwY+DTBq1Kjzli1blsz/Sx+72wPEXP33CJeFgvi9+X22uWKWUxw8NCLZrbW1leLiYmLA7hT2Mk7Kz8ed4D2T5mjU0e3uboX5+Xx0fg0AoV7Tug3G4/Hg8XhoamritddSe+tuILsS/NlXeTyUeZL7oEd/uttHKtUGQ46HhQAYOmfR6GaBtxP8+U3I95I3wB205sgRAtFWx8cqcpdT5KlM6PzD0f5giGCv31N/1xU3YQrMEcfHNJi4Y0u7NYUPJjT9WrGnikJ3meP44SAYa8MfPuw43mO8VHoTWz7e5/Pxrne9C3D+HpqXl4fb7ebAkb08++o7Pb55wTLC+X3v1LiNhyrvxIRySkRbtJG2iLMeYYB8VxFleaOScu4jwV0JxSfz3AOZP39+vw/LOZk7Zz8wodfX47u29RdTa4zxAGXAcffrrLUPAw9D56wRqXz6+ovr1lNbUt7vvv6e7h3T4ue6d5+XsnxkeOieFcBay2ff2ENDCnpHS9wuPnHGZDwJjh7a0trO/+4+kND3TGhqYV55Cd4BZhaIp7y8PG0zJBzrF1t30hHndv2x/uPU8Zyahg+y6Zg14se1h1mbwAOAxS4X108/tc+2P7yxu2eqv8EUuFz81xmn4B1gzPhLDb/nhQZnY4QBFo/6LGeUvMdx/HD1/X2HWOd/5wNC/7NGdDAnfxlu4+x3Ue2dRM3E6weNe/roz9nocNw2wJVj7uSUonMcxw8HdcG9/O8+58O4phRdQM2Ya0/4fIm8h0ZthBfDj3Boyuaebb1njeg2qfBsasZ+4oRzGsybLc+z/PCjjuMvqPgg76qqScq5H9rx30QdDqkCuKT6Os4uT865E+VkaMTLwFRjzGRjjBf4KPDYMTGPAZ/sen01sNpmeHT+P02eCE5TsJabJ45JbUIyrBhjWFCZmqltaipKEy6CAc4qLmRsfmLT/D1Ue5jv7j3In4428Oejjfz5aCN/PdrY7wIcQ8lFXQ/5OVHucaelCE6Xm8ZUJxT//hHHf+BfWOm89++S8pIBi2CA6aXzceGst93nKuG0ormOzz2cLawY/P0hSgH1sRmOjzmjbIHDuIWOj1nqGcmkwlmO44eL6vyJjPZNdRw/M4Gf2cnY4l/Dsn13sa9j86CxM0pTO4fulOILKHB4J8Dg4qzS+Uk792nFzt8HDC7OLl+StHMnatBCuGvM763ACmAb8Btr7RZjzNeNMVd2hf0UqDLG7ABuA46bYi3dJo0eSWnA2bjB4kAH08aNTXFGMtwsqCyldJAFCxJV5HJxWQJFyrGuqq5IKD4GvNjcxrLDDfz6cD2/PlxPYyQat/AZCq4ZVe14tZ+rRyb2Mxnq3G43pzuca9pj4B9GHD8MYX5FKeUOhooUuAxLqvq/c9at2FPJmaWXOMpndvk/pH2hgEw5q7iQ0x18ADsYuQgXg3+ALfFUc0bJxY7OXeUdz5Si8x3FzqlYijFD++/9RJ1f8X46BwfFNyr/NCYUzEx9QkBp3ggaQv1Pa9hbtXcSk4vOTWkubuPhvEFmr+h2ZsnFlOQl9iE8noUjbsHJ7wbg9OJ5STvviXD012Gt/Zu1dpq1doq19r6ubXdbax/reh2w1n7IWnuatfYCa21ig0NS5AezzsAbjj9gOy8c4v/OOj1NGclwUu7x8OVTxlDiTs5FpMjl4ouTxlDtPfHFWy6uKOXDI098/OWFZcVcMzqjz7E64nO7+OrksYP2Qy6pLGNhZfxCbjj62pTxVA9SyLqAB6b0P76w1OPmy5PGUhbng1yBy3D7xDGMdnCXoWbEDUwuij90bGbpIs6vvGrQY2WT2yeO6TM++1gGuHr0DN475vN4zMAfEIo9VSwd+xW8Lud3NhaN+izjfPFX8zu/4v2Oe5mHo8lF51Iz4npMnIKr2juJ9435YtpWz5xQOIMFIz8T9y5KRd44rhz7ZVxp+IBybsX7OLssfm/rKYWzqRl5Y1LP63a7uWrMVweNG5k/hcWjP5vUcycq61eWA/j3515ia3EZsa6LwuLanTw5djLTWxq584Jztaqc9OhvDGhDOMKK+ibWNLbQ3PVEcLHbxSXlpcwsLuDPRxvZ3h7oM5mRC3pmUOyOXVJVxoiTKIJ729rWwfL6JjY0t/VMJT/J52VRZRkFLherG5vZ0mv2gTMLfVxWVc7c0qIhu5xyfw6HQvzsQB2bW9v7zEg5xpvHR0dVMrfM+RCKZEjnynIAP91/hLVNzYR7NS4DTCnI585JYykepFhuDEdY2eBndWNzz0wlRS4XF1eUsKSqnFEJtMeYjfFm6/Ns9q/kYODNnmw6l2ldzKmDFMrZKhiLsaaxmbr1L/HXcZ1jtT0GLigt5vKq8p6V+xpDB3jNv7zPMsBF7kpmlC1gZtkiCk9glbGojbCt+Rk2N6/seeLf4GJy0XmcXbaYCYXp6QXNtEOBHWxsWs6Othd7VnPrXr56eumlCX3ASJYjgbfZ6H+CN1vXEbVhRu9cTMcZm5hZupCzyi49oSW1T8butlfZ6F/B3vaNPYtcjM6fysyyRZxeclHKivKm0CH+euhBGkK1fbbnmXzOKb+Cd1d9KCXn7c9AK8vlRCHcbeveWg40+4kcOMCSxZlf31qGnniFTsRamsIRLFDh8eDpNStJKBZjT0eQCJ0zQnjdZsDYZGqPxmiJRsk3hvK8voMJWiJR2qIxitwuStIwo0IqRWIxdgdChG2McfleSj1OB04kV7oL4W61HSF2dQSozHMzoyTxpWKj1tLY1R7LPR7yTrI9tkebCcXa8bmK8blTO4vGcLF27VpmzbuIsLWUud34BriTFImFaI82YXBR5KlMWgHSHmkibIP43CVpL7KGinAsQHvUj9vkUTxEZi7pzmnDc68z/5JLM94REYy20RFrwesqPKEPXycqGo1SG9hMINrKaN80yrwj03bubgMVwpm5mmTI9Injmc541tZpFTlJnMeYAYc1eF0uphb1nbv6ZIZAOFXodlE4wAW3xOMe9gVwN4/LxWlZ9EBcosYXeBlfcOJjb91x2u6JKHSXpvUiOlxU5g1+SfW4vJS6kl8EFHqyb4hQovJcPsoy0PsbT3dOLrZlvAgGyHcXke9O/MP0yXK73UwaojOXZOcIehERERGRQagQFhEREZGcpEJYRERERHKSCmERERERyUkqhEVEREQkJ6kQFhEREZGcpEJYRERERHKSCmERERERyUkqhEVEREQkJ6kQFhEREZGcpEJYRERERHKSCmERERERyUnGWpuZExtzFNiTkZNDNVCXoXPL0Ka2IfGofchA1DYkHrWPzJtkrR1x7MaMFcKZZIxZb62dk+k8ZOhR25B41D5kIGobEo/ax9CloREiIiIikpNUCIuIiIhITsrVQvjhTCcgQ5bahsSj9iEDUduQeNQ+hqicHCMsIiIiIpKrPcIiIiIikuOythA2xiwxxrxhjNlhjLmzn/35xphHu/a/aIw5Jf1ZSqY4aB+3GWO2GmM2GWNWGWMmZSJPSb/B2kavuA8aY6wxRk+C5xAn7cMY8+Gu948txpj/TXeOkjkOri0TjTFrjDGvdl1f3puJPOUdWTk0whjjBt4EFgG1wMvAx6y1W3vF3ALMstb+H2PMR4H3W2s/kpGEJa0cto/5wIvW2nZjzM1AjdpH9nPSNrriSoDHAS9wq7V2fbpzlfRz+N4xFfgNcKm1ttEYM9JaeyQjCUtaOWwfDwOvWmv/0xgzHfibtfaUTOQrnbK1R/gCYIe1dpe1NgQsA5YeE7MU+EXX698BC4wxJo05SuYM2j6stWuste1dX74AjE9zjpIZTt47AO4FvgkE0pmcZJyT9nET8JC1thFARXBOcdI+LFDa9boMOJDG/KQf2VoIjwP29fq6tmtbvzHW2gjgB6rSkp1kmpP20duNwBMpzUiGikHbhjHmXGCCtfbxdCYmQ4KT945pwDRjzHPGmBeMMUvSlp1kmpP2cQ9wrTGmFvgb8E/pSU0G4sl0AiJDmTHmWmAOcEmmc5HMM8a4gO8A12U4FRm6PMBUoIbOO0nPGGNmWmubMpqVDBUfA35urf22MebdwCPGmBnW2limE8tV2dojvB+Y0Ovr8V3b+o0xxnjovEVRn5bsJNOctA+MMQuBrwJXWmuDacpNMmuwtlECzADWGmN2A+8CHtMDcznDyXtHLfCYtTZsrX2bzjGjU9OUn2SWk/ZxI51jyLHWrgN8QHVaspN+ZWsh/DIw1Rgz2RjjBT4KPHZMzGPAJ7teXw2sttn45KD0Z9D2YYyZDfwXnUWwxvjljrhtw1rrt9ZWW2tP6XrA5QU624gelssNTq4tf6KzNxhjTDWdQyV2pTNJyRgn7WMvsADAGHMmnYXw0bRmKX1kZSHcNeb3VmAFsA34jbV2izHm68aYK7vCfgpUGWN2ALcBA06TJNnFYfv4FlAM/NYY85ox5tg3M8lCDtuG5CiH7WMFUG+M2QqsAe6w1upuYw5w2D5uB24yxmwEfg1cp064zMrK6dNERERERAaTlT3CIiIiIiKDUSEsIiIiIjlJhbCIiIiI5CQVwiIiIiKSk1QIi4iIiEhOUiEsIiIiIjlJhbCIiIiI5CQVwiIiIiKSk/4/4IjiZOT2wEsAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='single')\n",
        "labels = cluster.fit_predict(X)\n",
        "\n",
        "_plot_kmean_scatter(X[:, [2, 4]], labels)"
      ],
      "metadata": {
        "id": "yGW8ePjeLP60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "95855d84-6f06-4879-872f-da10027edd97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-f01e928d6b9c>:8: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sc = ax.scatter(X[:,0], X[:,1], lw=0, s=150, c=palette[labels.astype(np.int)])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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7/O8rqo0kg9jrX6dxIiIiIv3T7Rhhl6lAZX2hRkmyz3TvpZjkXoQ9/nU4HtqBuIzBZSrEZPdilNrGZDqeLh1u26Jq7uv9gY8x1atsDmciIiLqnW4b4YnuC/BR07PK6z2cPq0rs8HWORMEaS+SCKirTwY1SkJERJQ7dDs0wh9vUFcfU1dPlEpWo1NdvUFdPREREfWk20b4UHCTpvVEqTTSMQsGFW/QjHXN1TANERFRbtDt0IhoMqyyPqRREqL+7fN/gI+a/oYk4orqLQYHJrjP1zgVERGR/um2EbYb3ZrWE6XCa3W/xP7gx4rrBQy4pORrqlcBJCIiop50OzRijFPdW8ejnedqlISod+81PKmqCfaYinFl+V0Yw2ERREREKaHbO8IDLUzQs/4Eyu3jNUpD1NPWljcU156b/y+YX3AdRI4vA05ERJRKuv1XtS68T1X9CZX1RIPx3qknFY8JBtof5mQTTERElFq6/Zc1KROq6hNQV080GFtaX1NV74ud0igJERFR7tJtI+w1l6irN5VqlISoux0tqyGRzHQMIiKinKfbRnii+wLF87IaYMQkzyKNExG126zybjAAuEz5GiQhIiLKbbp9WM5hysNwxzQcbts8YG2lfQpcpoI0pKJs1JbwYWfr26gN70FSxuE2FWGypxoV9olndb5Qwq/6mLkF153VtYiIiKhvum2EAaA1Vq+ozhevh5QSQgiNE1G2+bjpeWxsfhEJGeu2fZd/DUqtY3B5+XfgNhWqOqdBGNXVw4Tx7gWqjiEiIqKB6XZoxPG2nWiO1SiqbY2dwLHQdo0TUbb5oHEFPm76W48m+LSTkQN4/viP0JbwqTpvmW2sqvrzCm5UVU9ERETK6LYRPtT2ibr6oLp60rfmaC02Nr80YJ0vXo8NTS+oOveiwi8qrjXCjHMKrlB1fiIiIlJGt41wLBlRWR/WKIm+JGQcoYQP8T7ukurF9tZVAKSi2t3+tapePx5LMUY6zlFUu7DoZsXnJSIiInV0O0bYYfSoqrerrM81x9p2YFvrmzgU/ARJJCAgMMIxE9O9l2Kkc1am46Xc4bYtimujyTbUhvdihGOG4mOurrgbL9Y8gKOhbX3WzMu/DjPzlio+JxEREamj20Z4vHsh1jcrf8t6gnuhhmmy29pTf8aW1te7bZOQONy2GYfbNmOyuxoXl9yuq5XP1L6jEFdZDwDXVN6LutCnWNf4JOojByFlEkZhxgjHDFxQfIvqh/CIiIhIHd02wgWWSgyzT8Wx0I4BaytsE1FkHZGGVNlnY/NLPZrgM+3yr4Hd6MXCIv081OUweRFMNCmvN3rP6jrl9nG4vupHZ3UsERERDY5+buH1YknJ12AfoEGxGdy4tPQbaUqUXeLJKDY2DfzAGABsbX0dkURQ40TpM8F1vuJaj6kEZbbxGqYhIiIiLei6EQ4l/AM+xBSXkbNa4CAX7PW/j6hsU1Qbl1Hs9r+rcaL0mexZDLOwKaqd7r1swDmo48koakN7cbRtO5qiyqb1I0qVg4FP8Ertw3i59mHs8b2X6ThEREOGbodGAMBbp/6AuOx/7GZcRrG6/ve4efjDaUqVPT4NfKiqviFyVKMk6WczurC07E68Wvd/kUS8z7rRzjmYmbesz/1t8RZsbH4Zu/3vIpL85x3zUusYzMxbhglu5XeeidRaW/8EtvregESyc9vhtk1YWf8oxjsXYGn5nRlMR0SUebq9I1wX2odTkcOKahujx1AT2q1toCyk9PunV6Oc5+CayntRau25AIbN4MK5+Z/B5WXfhqGPhwRbY/V45vh/YEvra92aYKB9MY43T/4G7556XIvoRPh7zU+xxfdatyb4nyT2BT/Ak0fuSnsuIqKhRLd3hA8EN6irD6xHpX2SRmmyTzDeglBS3Ypp+eZyjdJkTpV9Mj477CeoDx9CTXg3EjIGj6kYo51zYDJY+jxOyiReqXsI/nhDv+ff2voGCi1VmOpdkurolMN2tLytaLXMpthxvFv/OBaX3Kp9KCKiIUi3jXA0GdK0Xu9OhverPmaMa54GSYaGEtsolNhGKa4/3LYZTdHjimo/aXkVUzwXDzjOmIaOQLwJO1pX43io/Zcjl6kAkz3VGOmYOSSmEXyv8QnFtdt8K9kIE1HO0m0jbDM6Vda7NEqSndQvOS3gMZdokiUb7fStUVzbEqtDTXg3quyTtQtEKfNB4wpsan6525CDkxHgQHA98s0VuLL8LuRbKjKYEIhJ5SsdSiRRHz6IEttoDRMREQ1Nmb91oZFxrvNU1i/QKEl2CiZaVNUXmCv7HCubi06E96mq98XqNUpCqRSMN2Fj84t9jLsFmmO1eKHmx/DH+h8So6WG8DHVxxwJKl9JkYhIT3R7R7jYOgLltvGoU9CQlFrHoNQ2Jg2psofaRrjcNkGjJNnnvYa/oC3RquoYgzBqlIZSpSV6AkEFf6/BRDM+bnoOS0q/loZUPVlNdtXHmAzqj9FaMN6MHb63cTJ8ABIJ5JnLMcVzEYqswzMdjYh0RLeNMNC+oMbfjv8Q4WTf8wTbDG5cwgU1uokk2tCociq0CW51d+D16kR4Pza3vKryKIEy6zhN8lDqbPetAiAV1e4LfIDziz6fkSFXblOR6mMmuS/QIMnZkTKJdY1PYWvLG0gi0bn9CLZia+sbGOGYiaWld8CqcvgbEVFvdP1edr6lAjdU3Y9KW++zQVTYJuK6qvtQYKlMc7Khbbf/3X7nzj1TnrkcVY4pGibKHttaV6o+Zrh9GvIsZRqkoVQ62rZNcW1cRlET2qNhmv55TMrH61sMDthMQ+cZibdP/RGbW17t1gR3daRtC/5e+9MBF0saSELGcSpyBHXhTzM6lIWIMkvXd4QBIM9SjmurfojGyDHsD36MSCIIv8mDm4Y9xLfY+nAwuFFV/UjnORolyT4HVU7bZxQmzC+8QaM0lEqxZARmFfUJGdUsy0CuKbsXTxz/lqLapSXf1DiNcrWhPdjpe3vAuvrIQWxtfRNz8pervkY4EcCWltew0/cOgonmzu1V9imYmbcMo51zVJ+TiLKXru8Id1VoHYZ5BddhUfEX4TTmswnuh9qp5PLMpRolyS5SSkRV3qVaWHgzymw9F+ygocdlyldV7zDlaZRkYHm2Miwr/faAdQsLbsZI19D5RVbNOyo7Wt+ClL0/tNiXQLwJzx7/PtY3v9CtCQaA46Gd+Efdz/FB4wpV5ySi7JYzjTApZzOoe5tUbb1eCSFgM6gbtzjSMUujNJRqE1WMo/WYSlBpm6hhmoGNc8/D7SP/iBJLz2nR8s3luGXYI5hdcFUGkvXtcNtmxbW+eD2aojWK66WUeKXuYbTE6vqt29j8InapmP6QiLKboqERQoilAH4NwAjgj1LKB8/YPxzAnwHkddTcI6V8LcVZKU3GuubjaEjZeEizsGGkk83caeNc87Hdt1pRbbF1FMcGZ5EJ7vOxRzyjqHa699IhsbCGzeTC54Y/kOkYiqkd9xtVMV/y0dA2nIocUlS7qfkVTPZUq8pCRNlpwJ/UQggjgN8CWAZgMoAbhRBnzvz/HwCelVLOAvA5AI+mOiilzwT3QlgV3tmc6L4AliE49VKmTPdeBkDZCnHTvZdoG4ZSymywwWsuhVlY+60b45yLWXmXpymVvtiNXlX1DqNHce1O3zuKa5tjNajN4MOORJQ+Sm5ZzAWwX0p5UEoZBbACwJlPKEgAp38ieQHUpi4ipZvZYMVlpd+EAf3PbVtoGY7zCm9MU6rsUGgdhvMKPzdg3VjnPEx2V2sfiFLKLGy4tvI+DLdPx5m/8DiMeVhQ8FksK/vWkLgbnI0muBcqri21joVXxfMJLdH+h0T0qI+dUFVPRNlJydCISgBdlyo6DmDeGTX3AVgphLgDgBPAkpSko4wZ6ZyF5RXfw7sNj6MperzbPgOMGOOai4uKb4PV6MhQwqFrTv5y2AxufNz0tx4P5JiFDdO8l+C8ws/ldLPUGDmGba0rcSy0HbFkFC5TPia6L8BEzyJYDUP7NVViG4VrKu9FS/QEasK7EE/G4DYXYYRjBoxC9xPxaGqa5xJsaXm9z5X7upqRd5mqc6v9703wERqinCCk7H+CeCHEdQCWSilv6/j8CwDmSSm/2aXmOx3n+oUQYgGAPwGYKs94pFcIcTuA2wGgtLR09ooV6Xs6NyYjiCSCkEggGTLD4bLBLGxpu342i8kwoskQpEzCIEywGV0D3i3OBrFkGJFkEBJJCBhhMzoRDsbgcqXu4b9IMoh4MgIJwCTMsBpdEAqHTuhVIN7Y58p7BmGE11QKs2Fo/rcZCARS+vqgnsIJP3zxBvS3eInd6IbbVKzqvP54A0IJn+L6QsswGIXyCfP42qD+8PWReRdeeOEmKWWP+RGV3L6oATCsy+dVHdu6+gqApQAgpfxQCGEDUASgvmuRlPIxAI8BwJw5c2R1dbXS/GetIXIEq+p/h1ORw53byg5ciiPDVqLIMgJLSr+GEusozXPQ0HEi/ClW1/+hx51uABh26GpMP28WF1nRyNpTT+Bga//P0dYLK66t/MGQXPZ8zZo1SMfPrVy3P7AeHzY+g+ZY939q7EYvZnqXYU7+cgjR8xfKfb4P8HbDHxFNtv3zGIMXy8r+DTbjKDx97P8oun6lfTIurvyyqsx8bVB/+PoYupQ0whsAjBNCjEJ7A/w5ADedUXMUwMUAHhdCTAJgA3AqlUHPRkPkKJ6r+VG3H4rd9keP4Pnj9+Paqh+wGc4RdeFP8feanyAuI73ujyXDeO74fbi+6kfIt1SkN5zO+WL12Nr6+oB1cRnBh43P4JrKe9OQioaisa65GOuai2NtO1EfOYCkTCDPUo7Rzjl9Dj/506Gv9xiKBAChZCteqL0fxZZRGO86D/sCH/R7bQOMmF9wfUq+DiIa+gYcBCWljAP4JoA3AexG++wQO4UQ9wshru4o+98AviqE2ArgrwBulQONuUiD1fW/77MJPi0mQ1h98vdpSkSZJKXEqpO/7bMJPi2c9OPt+v9KU6rcsb11NWQ/b3d3dTS0HS1RPqyU64Y5pmB2/tU4t+AzGOea32cT/D+H7+i1Ce7qVPQQArFGjHae22eNUZixrOxbqLRPGlRuIsoeip7s6JgT+LUztv2gy8e7ACh/3DcNToT3oz5yUFFtQ/QIakN7UWGfoHEqyqSjoW2KnwSvCe9GY+QYCq3DBi4mRWrDaqajkqgN7+E8yzSglvAJ+OPK3oCsjezFHVV/xdG2bdjmW4njbTsQlzE4TfmY5F6Eqd4lcJsKNU5MREOJbh9xPhBYr6p+f+BjNsI6dyCwQVX9/uB6NsIplJBxTespN7104meq6l+u+08sr7gHI5wzNEpERNlEt41wJBlUVT/QEArKfpFkQF19Qt1riPrnNhUpfpfmdD1pqyVahz3+dQglfDAZLBjpmIVhjqmZjqWKT+Hd4NNqQ3s1SkJE2Ui3jbDVoG6aEqtR2UpqlL1sKl8TNr4mUmqyZzEOBJW9U+MyFWC4Y7rGiXJXMN6M1fV/wJG2reg6TdnmlleRb67EhSVfQZX9zAVEh4ZPml7F+pbnz/rmhUQixYmIKJvpdsbwIou6t7QLzFUaJaGhYozrzHVg+jfWNV+jJLlppGMW8s3KpqWb6V0GQw4vOKKltngL/nb8hzjStgW9zdXbHKvBizUP4Fjb9vSHG8CfD38L65r+Mqh38JQuH09EuUG3/9I0xo4NXNTFmfNVkv4Ms09FvlnZlGhV9imcSzjFhDDgqvK74DQW9Fs30X0BZuVdmaZUueeNk/8Pvnh9vzVJxPHmyd8MmXHaCRnH/xz+Jlrjg59J5OLir6YgERHphW4b4WaV68o3RWs1SkJDhRACl5V+c8AVBe1GLy4q4T+WWsizlOOGYT/GVM/FMAtrt3355gpUF38Zl5R8o9fFEmhwWqJ1eOrod3E8tFNRfVuiFfsDH2ucamCRZBueP/4j+OMNgz6XAUaMdJ2TglREpBe6HSMciDepqh9oDkrShxLbaFxb+QOsrn8MDdHDPfabDTZcX/Uj5Jk5bZdW3KZCXFTyVSwsuhl1oX1IyCicpnyU2cZlOppu+WL1eK7mvj6Xtu7L/sBHmODO7MyYb574fzgR+TQl57qp6qGUnIeI9EO3jbDTmCXFvdwAACAASURBVKeq3mHwapSElJBSpu0uYIltNG4a/iDqQvtwMLgBkWQINqML410LsOP4ITbBaWI1ODDSOTPTMXLCew1Pqm6CASCscvadVDsZPoDDbZsHfR6zsOHGygeRZ+N/20TUnW4b4XxLBaDieQoup5t+vtgpbG9djT3+tQgmWmASZgyzT8M07yUY4ZiheWNcbh+Pcvv4M7Ye0vSaROkWiDfhUHDTWR2b6QfLdvjeOssjBQrMVXCa8nBJ6b/CZVJ3Y4SIcoduG+ExrrnY1PKy4vqxKmcUoMHZ5/8Qq+ofRULGOrfFZRSH2jbhUNsmjHHOxWVld8AkzBlMSZT9Dge3IHnGlGEGGFFoHY5S62iUWEejyDqic8x2OOnHHv/72Ol7C2NdczMRuVND5OhZHec1leDzIx5OcRoi0iPdNsJltrEosY5WNIF/kWUEV5VLo2NtO7Dy5G96/OPc1YHgerx18g+4rOybaUxGpD8xGer82CjMqC76Eia4z4fJYOnzmEr7ZBiEMWunEKwu/lKmIxBRltDtrBEAsKTkf8FicPRbYxZ2LCn9WpoSEQB81PS3fpvg0/YG1qExejwNiYj0y270dH48r+BaTPFe1K0Jbm1txTvvvIPnnnsOzz33HE6dal+pbW7+v2T8HZki63DVx5iFDSM49pyIFNJ1I1xkHYHrKn+IIsvI3vdbhuPaqh+gxDoqvcFyWEPkKOrCypc43d66UsM0ROkRS0bQHK1FS+xE2ufmHeWY3Tnsocrevnzyrl27cOONN2L8+PHIy8vDRRddhOuvvx7XX389du/eDQBwDoFxtdM8S1TVCxjxhWG/1CgNEemRbodGnGY3epBnLkVj9Cgkkp3bBQS85rJud0tIeycjB9TVhwce2kI0VDVFa7Cl5TXs9b+PmAwDaJ+nerK7GjPyLoPL1P/iIqlgNTow0X0BtvtWwyjaf+Tv27cPK1as6KzJz89Hc/PQm0KyxDYaIx2zFM0c4TYV43PDfga7Ud1S6kSU23TdCAfiTfjb8R/0OhG7hMSB4HqcjOzH9ZX3w20uykDC3CNlcuCirvVQV080VBwKfoLXT/wKcRnttj2UaMWmlpew278GyyvuRbF1hOZZziu6CXXh/UjK9iFJVVVV+P73v4/Zs2dj9uzZEEKgqmpoLjO/tOxOvFz7EGrDu/usWVR0C2bmXZ7GVESkF7puhFed/N2AqxEF4k1YWf8orq38QZpS5Ta1c/RyTl/KRo2RY702wV21JVrxcu2DuHn4w7BpfBfTanDg2sof4ES4fWGKOXPmYM6cOZ37a2uH7sqaFoMdn6n8d+z1r8O21pWdD0AbhRnjXPMxw7sUpbYxGU5JRNlKt41wU7QGx0LbFdXWhHahMXIMhdZhGqeiSvtk5JnL0RJTtgT2VM/FGiciSr3NLa/22wSfFkw0Y5fvXZyTf4XmmaxGB0Y4ZyAh451DJJIyAYMwan7twTIKEyZ7qjHZU41wIoC4jMJmcPU788VQEEr4cTC4AeFEAGaDDaMc5/DdR6IhRreN8D7/+6rq9/jXYaH1Ro3S0GlCCMzOvxpv1f9hwNpS61hUOaakIRVR6kSTIewLfKC4fodvdVoa4dNON8EAsqIJPpPWd89TIZwI4L2GJ7Av8GG3udLfxeMY6ZyFRUVfhNdcksGERHSabhvhtoRPVX3oLJYfpbMzxXMhWqInsKnlpT5rCixVuLL8rjSmIkoNf6xB0d3g01pidUjKJAxC15P4ZIWEjGN/4GPsaF2Nhmj7Yh6FlmGY5l2Csa753X6J6Eso4cfzNT9CUy9TP0okcSi4CSfD+3Fd5X3Is5Sn/GsgInV02wibDVaV9TaNklBvFhbdiHL7eGxteR3HQjs6t7tMBZjquRgzvMtgNfY/BzSRPghou5g4KeGPN+Ll2gfRGD3WbXtteA9qw3uwvunvWF5xDzzm4n7Ps+bUf/faBHfVlmjF6yd/jRuHPTjo3EQ0OLpthEc6ZmFzy6uq6im9RjtnY7RzNvzxRrTFW2ASFuRbKnlnjLKax1wCi8GBaLJNUX2RZTgEX/MZFU2G8GLNT9Ec6/uhweZYDV6sfQCfrfppn7+kB+JNOBBYr+iapyKHURvay1VNiTJMtz991b7lxNkJMsdtKkSpbQwKrcPYBFPWMxusmORepLh+mvcSDdNQf6SUONq2Hc8d/1G/TfBpLbE67PS93ef+TwMfKlo187S9/nWKa4lIG7rtOvb43lNXH1BXT0TUl5l5l8NqcA5Yl2cuw0T3+WlIlF6BeBM+DXyEPf73cDy0C1LKTEfqIRBvwjPH/x0v1v4UDdHDio/b7lvV59fTFlf3rEkbn00hyjjdDo0IDDB/8Jn8MXX1RER98ZpLcHXFPXil9iGEk/5ea/LMZVheca+unk9oitbgo8ZncTC4sdudUa+5FDO9yzDdexmEyPyI6HAigBdq7kdL7ITqY1tjJ9EQPYoDgfXYF3gfoYQfZoMVxfGL4E6GVZ1L7bMsRJR6um2EjcKsrn6Iz0dJRNml3DYOnx/+MHb43sIO31sIxJsAtM+IMs2zBJM8i2Ex2DOcMnVOhg/g77U/7XVsdGvsJN5teBz1kUNYUvK1jDfDn7T846ya4NNWHLsXskujH0kG4Ur4cMi3UtV5RjhmnnUGIkoN3TbClfbJ2NL6uuL6KvtkDdMQUS5ymPIwt+BazC24FrFkBAJiyC8CcTbiySheqXt4wAcEd/vfRYl1NGbkXZamZD0lZBy7fO8M6hxSxTjgvjiMXox1zRv0eYhocHQ7RniUczZcpkJFtU5jPsY4z9U4ERHlMrPBqssmGAD2BT5AW6JFUe3W1jcyOma4MXI042NzBQSqi7+iaF5iItKWbv8rNAgDLij6Al4/8WsA/f/QPb/o5qxcYYmI6GzNmTMH0Wj7wh+x2D9XP7v11lvhcv1z9bZVq1ahtLS0x/GJRALvNPwJ+wLrVC8gUhfel7Fpw9RkHQyLwY5oMtRju9XgxEUlX8VY19y05CCi/um2EQaAca75iJdE8fap/+q2zOVpBphwYcmXMUGHT20TEfVnx44diEQiPbYfOnSo2+fxeLxnTXAzXql7CAPdZOhLIN54VselgtOUf1bHCRggkVRcX2WfgonuC7DP/wFCST8sBjtGO2djgut83b4zQJSNdN0IA8AkzyKMcM7ELt87+DTwESKJIEwGKxYUfBZTPBfBYfJmOiIRUdpt2rRJ0RCFkpKSbp/Xhw/hlbr/HNS1jSJzjaDXXIpy2wTUhfcqqhcQGOOcC6vR2e8cwmdKyiTGuuZxHDDREKf7RhgAHEYP5uQvx5z85QCANYfX4NyC6syGIiLKoClTppzVcf+o+/mgrmsUZpTbxw/qHIM1M28Z6k4M3AgbYMINVT9GiW0UdvvWqmqEveaSgYuIKON0+7AcERGlVigRQCAxuGENY53z4DB6UpTo7IxzzcdM7+X91hhgxLKyf0OJbRQAYKxrnqJFUk6b7LlwUBmJKD3YCBMRkSI7W98a1PE2gxvzCq5LUZrBWVR8Cy4svg1ec1mPfRW2SfhM5X9gjOufswmZDVbFy2EPs09DsXVEyrISkXZyYmgEERENXjjR+yp5SrhNRbiy/LvIs/RsPDNlmncJpnouxvHQLrTGTsAgjCi1jkGhdViv9fMLrkdztBYHguv7PGehZTiWlt2hVWQiSjE2wkREWU7KJKIyDLOwwSC0e6Ov1DYOUDkF73D7dEz2VGOMa+6QnDdXCIFhjikYhoHHTBuEEZeXfQvbWldiW+tKNMdqu+wz4dz8z+Cc/KtgNTi0jExEKTT0fioREZEitaE92Nr6Jg4ENiCJOAwwYpTzHEz3Xophjmkpv9449zy8cVL5NGIuYxGuqbw35TmkTOJI21Y0RWsACJRYR6LKcXYP/6klhAEz8pZiRt5S1EcOIZTwwSJs2Hu8DgsKq9OSgYhSh40wEVEWer/hKWxqeaXbtiQSOBDcgAPBDZjmWYLq4q9ACJHS645ynIODbRsV1V5Y8pWUXhsAtreuwsbml+CPN3Tbnm+uwLyC6zDefV7Kr9mXEuuozo/3oi5t1yWi1OHDckREWeaT5n/0aILPtN23Gh81PZvya19ZcRccxoHnXx/tmINRzlkpvfb7DX/FO6f+1KMJBoDmWC3eOPkItrS8ntJrEpG+sREmIsoi8WQUG5tfUlS7peU1RBJtKc9w26g/oNw6rtd9AgZM9y7FlRV3pfSaR4Jbsall4K/7vYYncCpyOKXXJiL94tAIIqIs8mngI4STymZviMkI9vjXYkbe0pTnuH7Yj5FIJLC28XGcihyGQRgw1jkfM/OXpfxaALC19Q1FdRIS21pX4uKS2zXJQUT6wkaYiCiLnIocUll/WJsgAIxGoybjgM8USvhxpG2L4vq9/vdxUfFXUz4+moj0h0MjiIh0TEJmOsKghRN+VV9HXEYQk2ENExGRXrARJiLKIgWWSk3rhyKTsKg8QpzFMUSUi9gIExFlkfHuhTALu6JaA0yY5KnWNlAauM1FKLT0vtpbb4bbp8EgjBomIiK9YCNMRJRFLAY7ZuRdpqh2qvciOIwejROlxzTvJSpqL9UwCRHpCRthIqIss6DgBkx0X9BvzRjnubig6JY0JdLeZM+FqLBNGLButPNcjHaek4ZERKQHbISJiLKMEAZcWvqvWFr6b6iwTeq2r8w6DpeUfAOXl30bRqGfiYFMwoyrK+7BKOfsPioEJrkXYWnZnRCC/7QRkTL6+SlJRJRjxrsXYLx7AdrirYgkg7AYHHCa8jIdSzMWgx1XlX8XjZFj2O5bjeZoDQCBYutITPVejDxzWaYjElGWYSNMRJTlHCYvHBh42WO9KLQOQ3XxlzIdg4h0gO8fEREREVFO4h1hIiKiLFcX2otd/ncRiDfCKMyotE/GZPdiWI3OTEcjGtLYCBMREWUpf6wBr534FU5G9nfbfjC4ER82PoP5BTfgnPwrMpSOaOhjI0xERJSF2uIteL7mfvji9b3uj8sI1jX+BUnEMSd/eZrTEWUHjhEmIiLKQh81/a3PJrirDxufgT/WkIZERNmHjTAREVGWiSTbsNe/TlGtRBI7fG9pnIgoO7ERJiIiyjI1oV2IyYji+kPBTzRMQ5S92AgTERFlmVgyrK5eqqsnyhVshImIiLKM3ahuARW70aNREqLsxkaYiIgoy1TZJ8NpzFdcP8G1UMM0RNmLjTAREVGWMQgjpnqXKKq1GOyY6FmkcSKi7MRGmIiIKAvNyV+OYfZp/dYYYMLS0jthNTjSlIoou7ARJiIiykJGYcJVFXdjpvdymIW9x/4S62hcU3kvRjpnZSAdUXbgynJERERZyiTMWFR8C+YX3oADgY/hjzfAKCyosk9GqW1MpuMRDXlshImIiLKcxWDDJM/iTMcgyjocGkFEREREOYmNMBERERHlJDbCRERERJSTOEaYiIiIckZL9AS2+1bhcHAL4jICu9GDCe6FmOyuhtXozHQ8SjM2wkRERJQTPmx8BhuaXwQgO7f54w2ojxzER43PYVnZnZxuLsdwaAQRERHp3vqmF7Ch+e/o2gR3FZMh/KPuF6gJ7U5vMMooNsJERESka6GEr6MJ7l8ScaxreCoNiWioYCNMREREurbT9w4SMqao9mRkP+rDhzROREMFG2EiIiLStdrQHlX1NeFdGiWhoYaNMBEREema0rvB/6yPa5SEhhpFjbAQYqkQYq8QYr8Q4p4+am4QQuwSQuwUQjyd2phEREREZ8dtKtK0nrLXgNOnCSGMAH4L4BIAxwFsEEK8LKXc1aVmHIDvAVgopWwWQpRoFZiIiIhyk5RJHGnbiprQLiRkHC5TESa6z4fD5O33uMmeauzyr1F0DZvBhTHOc1OQlrKBknmE5wLYL6U8CABCiBUAlgPoOoDmqwB+K6VsBgApZX2qgxIREVHuOhBYj/canoQv3r3F+KDxr5jgXojq4i/BbLD1emyFfSJKrWNwMnJgwOtM9S6ByWBJSWYa+oSUvc+n11kgxHUAlkopb+v4/AsA5kkpv9ml5kUA+wAsBGAEcJ+U8o1eznU7gNsBoLS0dPaKFStS9XWoEggE4HK5MnJtGtr42qD+8PVBfeFrQ1vhZAC+2Cn0NQcwAJgNNuSbywGIXvcnZRzNsbp+xwtbDU54zaWDTNsTXx+Zd+GFF26SUs45c3uqVpYzARgHoBpAFYC1QohpUsqWrkVSyscAPAYAc+bMkdXV1Sm6vDpr1qxBpq5NQxtfG9Qfvj6oL3xtpFYk2YY9vrXY6XsbzdE6JKDsYbey/M9gQeFn+9zfFm/F+ubnscf/HqLJUOd2j6kY07yXYlbeFTCI1M8jwNfH0KWkEa4BMKzL51Ud27o6DuBjKWUMwCEhxD60N8YbUpKSiIiIckJ95BBerv1PtCVaBi4+w07f25hbcC2Movf2xmHyorr4yziv8CbUhvYgJsNwGL2osE2A0KABpqFPyd/6BgDjhBCjhBAWAJ8D8PIZNS+i/W4whBBFAMYDOJjCnERERKRzvtgpvFT7s7NqggGgLdGKWgVLJFsMNox0zsQ413xU2iexCc5hA/7NSynjAL4J4E0AuwE8K6XcKYS4XwhxdUfZmwAahRC7ALwD4LtSykatQhMREZH+fNLyD4QSvkGdI5wMpigN5QJFY4SllK8BeO2MbT/o8rEE8J2OP0RERESqxJJh7PGvHfR5LMKegjSUK/heABEREWVcc7S22wNsZ8NmcKHSPilFiSgXpGrWCCIiIqKzlkRy0OeY5Fnc6xzAkWQbArFGQAh4TSWcJ5g6sREmIiIiTbRE67DTtwa+eD0MwogK2wRMcF8ASy8LX3hMRRAwQJ5lQ1xoGY65Bdd229YQOYrNLa/i08CHiMsogPa5gid7qjHTuwxuM5dSznVshImIiCilIokgVtX/DgeDm9B1EYy9/nVY1/A05hVch3Pyr+h2jMOUh1HO2TgYVDvzqsBIxyxcWvoNWA2Ozq0HAhvwxslHeiygEUkGsbnlVez2rcU1FfeixDZK7ZdHOsJGmIiIiFImlgzj77U/RX2k91lUYzKEdY1/QVyGe9zBPSfvShwKbhrwrrBRmDHKcQ7yLOWY7F6MPEt5t/0NkSO9NsFdhZN+vFz3IG4e/nPYjW6FXx3pDRth0q39+/fj1VdfxYcffogDBw4gFouhoqICl19+OT7zmc+gsrIy0xGJiHRnU/MrfTbBXX3U9BzGuRYg31LRua3CPgEXldyGt+v/2GczbDO4cHXFPSizje3z3JtbXu23CT6tLdGKXb41mJ1/1YC1pE9shEmXbr75Zjz99NM9tm/duhWvv/46vvOd7+AXv/gF7rjjjgykIyLSp6RMYKfvbYXVEttbV2FR8Re7bZ3iuQj55gp80vIPHAp+0tkQm4UNE93n45z8q+A1l/Z51kiyDfsCHyrOvMP3FhvhHMZGmHRp//79MJlMuOqqq3DFFVdgypQpsNvt+Oijj/DII49g165duPPOOzF69GhcccUVA5+QiIgGdDK8H8FEs+L6A8ENPRphAKiwT0SFfSKC8Rb4YichhBEFlkpYDAPPEeyPNSi6G3xaa+wEkjIBgzAqPob0g40w6dKtt96KF154ocfwhxkzZuDmm2/GnDlzsHfvXjz00ENshImIUiSich7ggeYNdpry4DTlqTqngFBV336E2mNIL7igBunS17/+9T7HALtcLtx5550AgLVr18Lv96czGhGRbql96MymwUNqXnMprAan4vpi6wgIwXYoV/FvnnJSVVVV58eRSCSDSYiI9KPEOhoeU4ni+vGuBSnPYDJYMMm9SHH9NM8lKc9A2YONMOWkDRva56ksLi5GQUFBhtMQEemDEALTvZcqqjXAiKmeizXJMTPvctgMA99tzjdXYoL7fE0yUHZgI0w559SpU3j00UcBALfccgsMBv5nQESUKjPzlmG4Y8YAVQIXlnxFs5XdPOZiLK+4B3ajp8+afHMlrqn4HswGqyYZKDvwYTnKKYlEArfccguamppQVVWFe++9N9ORiIh0xSCMuKr8u1jX8CR2+t5BXHYffuYxlWBh0U0Y55qvaY5S2xh8fvjPsdP3Dna0vgVfvB6AQJFlBKZ7L8EE9/lsgomNMOUOKSXuvPNOvPHGG7BarVixYgWHRRARacAoTFhcfCvmF1yPvYH34YudglGYUG4bjxGOGWl7OM1u9GBO/nLMyV+OhIxDwAADH4yjLtgIU06QUuLee+/Fo48+CpPJhGeffRYLFy7MdCwiIl2zGp2KxwxrzSjY8lBPfFWQbrTG44gkJTxGI2zG7r/x33fffXjwwQdhMBjw1FNP4eqrr85QSiKi3BKOB9Acq4VJWFBoGc7nMmhIYSNMWS2elFjb4sOqJh8OhdvHoZWaTfjxmCp4TO0v75/85Ce4//77IYTAn//8Z9xwww2ZjExElBMO+Dfgg6a/ojlW27nNACOGOabjwuKvwKPRg3JEarARpqwVSiTx0JFa7G4Ld24rMBlx76jKzib4Zz/7Gb7//e8DAP74xz/i85//fEayEhHlkndPPY6trW/02J5EAkfaNuOJI/+Gayt/iHL7+AykI/onvj9BWes3x092a4LNQuB7IytQajEDAB5++OHOWSF+//vf48tf/nJGchIR5ZLtLat6bYK7SiKBF2rvRzQZ7reOSGtshCkrHQlHsMkf7LZtusuBYbb2qXB+/etf4+677wYA/PKXv8Rtt92GRCLR6x8pZdrzExHp1cfNzymqS8g4PmxcoXEaov6xEaas9FaTr8e2PJOx8+MHH3yw8+Nvf/vbMJlMff7ZuHFjWjITEendifB+tCVaFdfv8b+nYRqigXGMMGWlY+FIv/tPN7lKCCFSEYmIKOcdD+1SVR9JtmmUhEgZNsKkGyejsc6Pjx07lsEkRERElA04NIKy0gh7z2Ux97SFsCsYykAaIiICgBH26arqrQanRkmIlOEdYcpKS/K9eKOx+zi0uAQePFyLGW4HKq2Wzu0eowGXF+WnOyIRUc4pto2E05iHYKJFUf1kT7W2gYgGwDvClJWqbBbM97h6bI9KiQ2+IF481dz5J8/M3/eIiNJlQeGNiuqMwoIFBVzgiDKLjTBlra9XlWCGy9HnfgHgC2VFOM/rTl8oIqIcN9mzGLPzrum3xijMuKHyfpgMln7riLTGW2WUtawGA+4eUY6PWgNY2dSKvR2La5iFwAKvC5cVeDHGYctwSiKi3LOw6HMYZp+MdY1PoSF6pHO7UZgx2jkHi4tuhcPkzWBConZshCmrGYXAwjw3Fua5EU4mEU1KOIwGmDglGhFRRg13TsdNzumIJ+Pwx0/BZLDAbSrMdCyibtgIk27YDAbYONiHiGhIMRlMyLeUZzoGUa/YNhARERFRTuIdYSIiIp1pitZge+sqHA/tRFzG4DIVYLJ7Mca5FvABNaIu2AgTERHphJRJvNvwZ2xrfbPb9tbYCdSEduGDxhW4svwulNrGZCgh0dDCoRFEREQ6sbbhiR5NcFfBRDNerH0ATdGaNKYiGrrYCBMREelAc7QWW/tpgk+LJIP4qPHZNCQiGvrYCBMREenA9tZVAKSi2oPBjQjGm7UNRJQF2AgTERHpwPHQbsW1SSRQG96rYRqi7MBGmIiISAcSMqaqPp6MapSEKHuwESYiItIBl6lAVb3bXKRREqLswUaYiIhIByZ7Fiuu9ZhKUGmbpGEaouzARpiIiEgHxrrmw2UqVFQ7M28ZhBAaJyIa+tgIExER6YBRmHBV+d2wGdz91k10L8IM79I0pSIa2tgIExER6USxdQRuqPoxJrgWwnDG4rF55jIsLroVl5R8nXeDiTpwiWUiIiIdybOU4bKyO3BB4ouoC+1BXMbgNhWi3DaBDTDRGdgIExER6ZDD6MEY19xMxyAa0jg0goiIiIhyEhthIiIiIspJbISJiIhSIJFIoC3RmukYRKQCxwgTERGdpWgiijdPPoJQpBC/PfT7zu12oweLCm/BBM/5GUxHRAPhHWEiIqKz0Bqtx2OHvoRDbRsByG77Qgkf3qz/DV6qeTAz4YhIETbCREREZ+HJo99BEol+a46EtuCd+j+lKRERqcVGmIiISKUPGp9BAnFFtTt8b2mchojOFhthIiIilba1vKG4ViKJbS2rNExDRGeLjTAREZFKURlSVX8g+LFGSYhoMDhrBBEREYBYMoy9/nXY7nsLjZGjAIACyzBM816Mie4LYDbYzvrcSZlMVUwiSiE2wkRElNMSMo6T4f148+Rv4Y+f6ravIXoY75z6EzY1v4JrKr6HPEs5AMAIMxKIKb5GhX1SSjMTUWqwESYiopzUHK3F1tY3sce3dsChDr54PV6sfQCfG/Yz2IwujHefh93+dxVeSWBB4fWDD0xEKccxwkRElHMOBTfhr8fuwbbWNxWP9/XFT3XOAHFR0e0AhKLjRthnnG1MItIYG2EiIsoppyKH8dqJXyEuo6qP3d66ClImYTQacU359was95rLsLzynrOJSURpwEaYiIhyyqbml5GQysf3duWPNyCU8AEAhjun4+bhP4fXXNajzggTpnouxhdH/GpQWYlIWxwjTEREOSOU8GN/YP2gzpHEP2eAKLRU4YsjfoU1h97B1OKvIZwIotQ+FpX2CYONSkRpwEaYiIhyRku0DkmFK8L1xmpwwm709LJHYLK3+qzPS0SZwaERRERECk1yL4JR8B4SkV6wESYiopyRZymHUZjP6lizsGJ63mUpTkREmcRGmIiIcobd6MZY5zzVx5mEFcvKvo28Xh6MI6Lsxfd3iIgop8zOvwoHgusVTZ9mhBnj3efhnLwrUWgdloZ0RJRObISJiCinFFlH4PKyb3fMJRzptcZtKsTioi+j0j4RVqMzzQkplyRkHAcC67HdtxqNkWMQQqDIMgLTvEsw2jkHBmHMdERdYyNMREQ5Z6RzFm4a/p/Y1rISu/3vIpIMAgDyzGWY6rkEUzwXwmp0Lywx+gAAIABJREFUZDgl6Z0vdgov1T6I5lhNt+3HQttxLLQdxdaRuLr8/8Bpys9QQv1jI0xERDkpz1yGRcW34PyimxFOBGAQRlgNTgihbOlkosGIJIJ4sfanaImd6LPmVOQwXqr9Ga6v+jHMBmsa0+UOPixHREQ5zSCMcJi8sBldbIIpZcKJALa1rsT7DU8hmGjG0bZtkFJ27t/uW91vE3xaQ/Qo9vjXahk1p/GOMBEREVGKxJNRvNf4JHb71nQ+kFkWvxQv1j6APHMZzi/6PEY5zsGO1rcUn3Nb6ypM816iVeScxjvCRERERCmQkHG8UvcQtreu7HVWkpbYCbxa9wtsa10FX7xe8Xkbo0cRTYZTGZU6KGqEhRBLhRB7hRD7hRD39FN3rRBCCiHmpC4iERER0dD3SfMrOBba0W+NhMR7DX9Rfe6kPPulwalvAzbCQggjgN8CWAZgMoAbhRCTe6lzA/g3AB+nOiQRERHRUJaUSWz3rVZWizgMUD4tmtXghNXAWUy0oOSO8FwA+6WUB6WUUQArACzvpe7HAP4TAO/dExERUU6pC+9FIN6ouN5ssCmuneyphhAczaoFJd/VSgDHunx+vGNbJyHEOQCGSSlfTWE2IiIioqwQTgRU1RuEUdFdYZOw8EE5DQ161gjR/ivK/wVwq4La2wHcDgClpaVYs2bNYC9/VgKBQMauTUMbXxvUH74+qC98bVAsGUZZ7NJe95kjXpQd6L7PJCxwmvLhi9VDQvZ6nICA11yKLcf2ANiT6sgEZY1wDYCuC6xXdWw7zQ1gKoA1HfMvlgF4WQhxtZRyY9cTSSkfA/AYAPz/9u47vq3ybhv4dUuyJEu25BnHdpzlDDKcAQkrlDgkNtCmCTOlwFNSVl8KbV9oaWl5gY5PW2h5umj7UCi0dDwNtFCaEjIIiaGQHcheJHGGEzvekrXX/f7hETu25SNbyzrX9y/5nJ91fk5uS5eP7nOfOXPmyPLy8sF3PgRVVVVI1LEpuXFsUDgcH9Qfjg0KSD9erv4yPKG2XvtGHqtEXem6HtsuyVqCeXmfQa37MHa0rMQJ10ddgVgDLcaZL8Gc7KUoMJbGpX+1UhKEtwOYKIQYh/YAfBuA2zt3SiltAPI6vxZCVAH4xoUhmIiIiCjeQjKIaudOtPhrISAwwjAOJaayqB9HJ9IwzVKOna3/HrBWQKDMuggAUJg+GZ9NfxRtgSa0+M5CAMjRj+JtleNkwCAspQwIIR4CsBaAFsDLUsr9QojvA9ghpVwZ6yaJiIiIIvVx6yp81PIWnMGWHtuz04pwee4yTMy4PKrHm5N9I064dqHJdzps3RW5t8GSNqLHtkxdLjJ1uVHthwamaI6wlPJtAG9fsO3JfmrLh94WERER0eBVNbyMPbZ1fe5r8Z/F6rpfwJ1/N2ZY+57XOxgGrQk3FT+Bded+i5OuXb326zXpuDxnGWZlXR+1Y9LQ8BbLRERElFKOOrb1G4K7e6/hDyhOn4pc/aioHTtda8HSosfQ7DuDA/aNsPsbENBmYkH+vbgo86qIlk2j2OOidERERJRSdtvWKKqTkNirIDAPRo6+GFfl3YlPFz4Miy4fZdZFDMFJiEGYiIiIUoYj0Iwz7gOK6w+3fRjDbijZMQgTERFRynAH7RHVe0NOBGUgRt1QsmMQJiIiopSRJiKbfqCBDlrBS6bUikGYiIiIUoY1rQDZaUWK68eaZ8ewG0p2DMJERESUMoQQmN5xswolZlgrYtgNJTt+FkBEREQxEZQBnHTtgt1fD43QodA4GfmGMTE/bpllET5xbEad55OwdZMzrsJo04yY90PJi0GYiIiIokpKiZ2tK7G7dU2vu7qNNE7Elbmfx6j0qTE7vk6jx9Kib2Nd3W9Q7drZa7+ABtOtizA/766Y9UDDA4MwERERRY2UEu/U/xaH2v7T5/46zyd488wP8enChzHePCdmfRg0Jny26FE0eU9jn/1dtPhroYEGIwzjMM26kLczJgAMwkRERBRF++0b+g3BnUIIYk3dc1g+9jmYtJaY9pNrKMH8/OUxPQYNX7xYjoiIiKJmt22torqA9OKAfWOMuyEKj0GYiIiIoqLBexJNvlOK6wc6c0wUawzCREREFBXOQMvARUOoJ4o2BmEiIiKKijSNIab1RNGmqovlgk4HQi4XEAoluhUiIqKUU2AohVGTAU/Ioah+jGlWjDsiCk8VQdixYxts766F++B+AIC3vBJnnv4+rAsrYZ5zGYQQCe6QiIho+NNp9JhimY+PW1cpqi/jXd0owVI6CMtQCPUvPY+2D9/vtc996ADchw4g44qrUHDflyE0nCVCREQ0VHOyl+K4cyds/rqwdWXWSowwjItTV0R9S+n01/zGa32G4O4cmz9A09//FqeOiIiIUlu61oKbip9AgaG0z/0CGszO+gzK85bHtzGiPqTsGeGQ24XW9WsU1dreXYvsxTdAazbHuCsiIqLUl6nLxedKfoga9wEcsG+E3V8PjdCh0DgJ062LeFc3ShopG4TbNn8A6fEoqpU+H9o2vY+siutj3BUREZF6jEqfilHpUxPdBlG/UnZqhK+mJsL60zHqhIiIiIiSUcoGYUS6EARXjiAiIiJSlZQNwvqSMRHVGyKsJyIiIqLhLWWDcOYV86BJT1dUK4xGZF55VYw7IiIiIqJkkrJBWGMwIuu6xYpqsyo/DU26KcYdEREREVEySdkgDADZS26C9ZrKsDWW8oXIufHWOHVERERERMkiZZdPAwAhBPK/cDfMF8+B7d11cO7+CAiFAAiYZl3SfovlspmJbpOIiIiIEiClg3An0/QZME2fAen3I+R2o2bnThQt/2Ki2yIiIiKiBFJFEO4k0tKgTUvjUmlERERElNpzhImIiIiI+sMgTERERESqxCBMRERERKrEIExEREREqsQgTERERESqxCBMRERERKrEIExEREREqsQgTERERESqpKobanQSF9xQQ0qJNWvWYPPmzfB6vZg8eTJuv/12GI3GBHVIRERERLGmyiA8evTorsebN2/GI488gi1btvSoefLJJ/G9730P99xzT7zbIyIiIqI4UHUQ3rNnDyoqKuB0OjF69Gg89thjsFgseOmll7Bx40bce++98Pl8eOCBBxLcMRERERFFmyrnCGu1Wkgp8dWvfhVOpxOXXXYZ9u3bhwceeAB33HEH3n33XTz66KMAgG9+85tobGxMcMdEREREFG2qDMIAcPz4cbz33nsAgGeffRaZmZld+4QQ+MEPfoDc3Fw4HA689tpriWqTiIiIiGJEFUHYuesjnHrimzh69+0IOhwAgA8//BAAkJOTg3nz5vX6HoPBgOuvv75HLRERERGljpSfI3z2Z0/DtWcXACCtqBjajAwA58NtWVlZr1UkOk2fPr1HLRERERGljpQ+I1z3/HNdIRgA9CMLux7v3LkTADB16tR+v3/atGkAgJMnT3KeMBEREVGKSdkgHPJ44Ni6qcc2jTG963FraysAID8/v9/n6L7PZrNFuUMiIiIiSqSUDcKNr/0VkLLHNpGm73rscrkAAHp9+zYZCvZ6js593euJiIiIKDWkbBD2fHK41zbp93U9Tk9vPzvs87VvExptr/rOfd3riYiIiCg1pGwQlqFQr20hj7vrcVZWFgCEnfvbfV9nPRERERGlhpRdNSItbwT8Z2p6bPPX1XU9vuSSS/DRRx9h//79/T5H577Ro0cjLy8vNo0SAZBSwrVvD+xV6+GrOQ0IAUPJGFgWLIJp6vREt0dERJSSUjYI5y27A6d2f9Rjm6/2DIJOB7TmDMybNw8vvvgi9u3bBylln0uo7d27FwD6XGeYKFoCtlbU/vKn8B4/1mO7v64Wju1bYJw8BYVf+XrX0n9EREQUHSk7NUJfXAxtVnbPjVLCc/QIgPPhtqmpCZs2bbrw2+H1erFmzZoetUTRFvJ4cPbZH/UKwd15Dh/E2Z89Den3x7EzIiKi1JeyQViGQkAfZ3ndhw4CAEpLS3H11VcDAB599FE4Ou44B7R/TP3UU0+hsbERGRkZWLZsWXyaJtWxv7cBvtOnBqzzHj+Kti28sQsREVE0pWwQdn68E8GW5l7b2za9j2AgACEEfvWrX8FkMmHz5s0oKyvDCy+8gBUrVqCyshLPPPMMAODHP/5x2LWGiQZLSgnbxncU19s2rIthN0REROqTsnOE3YcO9Lk9aLOhet9eTJg1GzNnzsS6devw8MMPY/v27fjSl77UVVdYWIjvfve7uO++++LVMqlM0NYKf12t4npv9XGEvB5oDMYYdkWx4Ha7sWvXLhw8eBC1tbXw+/3Iy8tDeXk5pk2b1u9t3omIKLZSNgjLQP/zKY/v3oVCjYB5xizMmzcPW7duxapVq7BlyxZ4vV5MmjQJd955J9cOppgazJxf6fcDDMLDyiuvvIL777+/x7rk3Y0dOxa/+MUvsHTp0jh3RkREKRuEdbn9L3cmpUTtr55F7i23IWvhtRBpaVi8eDEWL14cxw5J7bQWC4QuLewfbd1p0tOhMZlj3BVFW2NjIwKBAGbPno1Zs2ahuLgYgUAAx44dw8qVK3HixAnceOONWL16Na699tpEt0tEpCopG4QtV34KzW+8BvRxYw0AQCCAphV/gW3damQvvgGW+ddAaHvfXY5oMDzVx+A7dRIAoB9VAmPpxF41GoMRGXMvQ9vmDxQ9Z+aVn4LQpOy0/pR18803Y/ny5cjNze21r7a2FgsWLMDhw4fx1FNPobKyktMkiIjiKGWDsC4nFxlzLoNj2+awdYHmJrj274X1moo4dUapzPnxTjSvfB3e6uM9tutHj0XOZ29ExtzLemy3VlzfvhqElOGfWKuFdSHPFg5HY8eO7XdfYWEhvv3tb2P58uXYunUrGhsbeXEuEVEcpfTppRFfvA+GsePD1uhHj8WIu78UtoZICduGd1D7q2d7hWAA8J06gbrf/Byta1f12G4cX4r8u+7pc6m/LhoNCu55APqi4mi3TEmgpKSk67Fer09gJ0RE6pOyZ4QBQJNuQvFjT6J55euwv1+FkKPt/D5zBiyfKkfO0puh4UVxNETekyfQ8OeXBzyz27jiLzCWToJxwvmpEtbyRdDl5KHlrX/Cc+Rwj/r0KdOQveQmmKZMi0nflHhvvvkmAKCsrAxWqzXB3RARqUtKB2EA0BiNyFt2B3JuuBXuQwcQcrlwqsWGsT//LTQ8+0JR0rp+zcDTGwBASrSuX42RE3rOGTbPmAXzjFnwnamB72wNAEBfMhr6kUWxaJcSpHPlCJfLherqavzhD3/Ar3/9a5hMJjz33HMJ7o6ISH1SPgh30uj1MM+Y1f64qoohmKJGBgJwbA0/F707x45t/a4HrC8eBX3xqGi2R0kkJycHTqezx7Zbb70VTzzxBMrKyhLUFRGReqX0HGGieAi5XJA+r/JvCAQQbGsbuI5STm5uLnJzc5GRkdG1be3atXjrrbf6XWeYiIhih0GYaIjEID5dGMz30PB38uRJNDY2oq2tDdXV1fjJT36CQCCA73znO1i+fDmkkuk1REQUNQzCREOkMRphGD9Bcb2+ZAx0Fl4UpXZjx47Fo48+it///vcAgL/97W/Yt29fgrsiIlIX1cwRJoqElBKn3Huw374Rdn89BAQKjZNQZq1Atr73BWzWhZWoP35U0XNzzerU1Vbnx+H1NtQfciMUBDLydZhQbkHxLBOEpu8l8pYtW4avfe1raGhowKpVqzhXmIgojhiEiS7Q5m/EW3XPosF7osf2c95j2GVbg6mWcizIvwdacf7XJ/OyK9H2nyq4Dx0I+9yG0onInHd1LNqmBAqFJLa/0ojD62xAx+yGiQstmHFTNgCB6k0OFFxkhDkvrdf3arVaZGdno6GhodeFdEREFFucGkHUjUQIb5z9Qa8Q3L3igH0j1p97vsdWodOh8GuPwjRzdr/PnT6tDEWPPMYVS1LQtpcbcHjt+RCcO96AK+4bAXNuGsy5Ooy/KhPmvLQ+5wBXV1fj6NH2TxMmTux9K24iIoodnhEm6sYVaIXNf27AusOOD1DmXoSi9Iu6tmnS01H08LfgqT4G+8b18J4+BUBCX1wCa/miHjfRoNTRVO3FkfX2HtsKppy/Sc+6detQUVEBIQTEBXcQdDqd+MpXvoJQKITc3FzceuutcemZiIjaMQgTdQhIP9wh5cua7bGt6xGEOxnHlcI4rjSarVESO/yOrdc2Tdr5wLt48WKMGjUKlZWVmDJlCqxWK1wuF7Zt24bVq1ejvr4eAPDUU08hnXe5JCKKKwZhog6N3hMIyaDi+lOuvTHshuLNE3Dgw6a/4qR7N4KhAPSadEy1lOOSrKXQaPqfRVa319V7Y7cZEPn5+aiursbvfve7Pr+/oKAATz/9NJYvXz7En4CIiCKlKAgLIa4D8EsAWgC/l1I+fcH+RwDcCyAAoAHA3VLKk1HulSimAqHIbmgQlLwBQqp4v/HP2NX6NronWHfIjs3Nr2Jr8+u4ruArmJB5WZ/fG/D1nvdrO3N+bJw6dQofffQRtmzZgqNHj6K1tRXp6ekYM2YMKisrMXv27LBBm4iIYmfAICyE0AL4DYAKADUAtgshVkopu18e/zGAOVJKlxDiAQA/AfC5WDRMFCtmXXaE9Tkx6oTiqT0Er+p3fwgBvH3u5/is5pt97jdl6+Cx9fwk4fROJ05sbsPYKzKh1Woxd+5czJ07N6p9ExHR0Ck5DXEpgKNSyuNSSh+AFQCWdi+QUm6UUnZ+PrgFwKjotkkUe9n6IqRpjIrrp2RyGbThzhNwhA3B3a0799s+t4+/OrP3Rgm8/8tzePv/nca+f7Wg/ogbfk9oKK0SEVEMKJkaUQzgdLevawD0/Rlhu3sArB5KU0SJYtIqu+ObThgwzbIgxt1QrH3Q9BfFtd6QA76Qu9f2CfMzsfsfzfC7egfdxqNeNB71dn0978ERKP2UZXDNEhFR1ImB7m0vhLgFwHVSyns7vv4vAJdJKR/qo/ZOAA8BmC+l9Pax/34A9wNAQUHBJStWrBj6TzAIDocDGRkZCTk2JTeHwwEYvXAFe68E0ElAwJpWAL3GFMfOKBYafacQkgHF9QZfNqyZvafQ+N0htJ3zQ4Y56WvM1MKcz+uTUxXfVygcjo/EW7BgwU4p5ZwLtyt5VT4DoKTb16M6tvUghFgE4HH0E4IBQEr5AoAXAGDOnDmyvLxcweGjr6qqCok6NiW39rGxGHtt7+Cj1rd6rSk8Kn0aLs9ZhqL0yQnqkKLpheP3wRPBknmjq5f2+9rReMyDj19tQu1ed49VI8x5Okz9dBYuut7aax1hSh18X6FwOD6Sl5IgvB3ARCHEOLQH4NsA3N69QAgxG8Dv0H7muD7qXRLFWZm1AtMti1Dj3g+b/xw0QouRxonI0RcnujWKIr3GGFEQ1ghtv/vySo2o+E4x7LU+1B/2IBSUyMhLQ2FZOoSGAZiIKBkNGISllAEhxEMA1qJ9+bSXpZT7hRDfB7BDSrkSwE8BZAD4e8cZj1NSyiUx7Jso5oQQKDFNRwmmJ7oVipEpmfOxteUfiuvN2oFXFrEU6mEp5G20iYiGA0UT1qSUbwN4+4JtT3Z7vCjKfRERxdzc7BuwveWfCGHgG6lYdCOgFWlx6IqIiOKFq7gTkWppNDpUFHx5wDqt0OHGosfj0BERUew5As3Y0fIvbGx4Ge83/AnHnTsgw13tm8J4CTMRqdrkzHnQQY/1Dc/DG3L22m/R5eOGosdh1RcAOBj/BomIosQbcqGq/iV84tjS45OwXba3kanLw1V5d2JixuUJ7DD+GISJSPVKM+eiNHMuqp0fYa/tHfhCHpi0VlyWczNyDSUDPwERUZLzhzx488wPcc57rM/9bYFGrK77JQIjfJhiUc8NoxiEiYg6jDNfjHHmixPdBhFR1G1rfqPfEHyexIaGFzHGPAsmrTpu/sM5wkREREQpLCD92G/fqKg2KP04oLA2FfCMMBERUZLzBB04YH8Ph9r+A2ewBWlCj9GmGZhhrUSeYUyi26Mkd8Z1IKI10486tmJO9tIYdpQ8GISJiIiS2Ennbrxd9wv4pbtrmxvAPvu72Gd/F2XWSpTnLYcQ/JCX+tbXhcDheIKOGHWSfPhbQ0RElKRq3UfwVt2zPULwhfba1uE/jX+JY1c03Og1pojqDdrI6oczBmEiIqIktal5BYLSP2Ddbttq2P0NceiIhqNR6VNh1GQori81XxrDbpILgzAREVESavadwRn3AUW1EhL77Otj3BENVzqNHlMs8xXVaqDFdMs1Me4oeTAIExERJaGz7kMR1h+OUSeUCi7NuRl5+oEvrCzPvxsmXVYcOkoODMJERERJKCgDEdWHZHDgIlItg8aEm4qfwHjzXAiIXvtN2ixUFjyI6daFCegucbhqBBERURKypo2IqD4zLS9GnVCqMGozsLjw67D5z+Gg/X04Ak3QCh2K06eiNONSaIX6YqH6fmIiIqJhYLRpJszaHDiDzYrqp1kWxLgjShXWtAJcnntrottICpwaQURElIQ0QoPZWdcrqs3Tj0ZJelmMOyJKPQzCRERESWp21mJclHl12BqLLh+LC78BIXrP+ySi8Dg1goiIKEkJIVAx4gEUGSdhl20Nmn01Xfv0GhOmZF6Nudk3qOoqf6JoYhAmIiJKYkIITLcuwnTrItR7jsMZbIVO6DHSOAFpGmOi2yMa1hiEiYiIhokRxvGJboEopXCOMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqZKqgrDT2YjTZ7YgFAokuhUaprwhF7xBF6SUvfa1BZpg9zUgFAq11wb7r42WkAzCE3QgEPL12heUAbiDbQjK1BjvzkArbL5zqvz99QXdOOc+BoevedDPEW7sRiog/fAEHSkztqLFH/LCE3QgJEP91kgp4Q064Qu5o3rsQMjXcexgVJ93OAnJEDxBB/whb6Jb6SI7epKI3ftAJDrfFwLSH/dj23z1OOc+Bl+w9/tVIukS3UA8rNr+OKozjyGkBSCAkf5K/PrQbRjrGIvFc55OdHuU5NzBNuy3b8A+23rYAw0AgAxdDqZZFmKsaTa2Nb+Ok65dCOH8G5AGOoQQ6KqdblmI6ZZFMOmsUemp3luNPa1rccSxCQHZ/qJSZLwIM6yV0GvM2Gdfj2rnTkiEIKDBWPNszLBWYoxpZlSOHy+ugA3vNf4Rx507EOz2wp1vGIdP5d6JUaZpCewu9rY1vY6drf+GX3q6tgloMDp9Bq4f+Qj0Wn3Y7/cEHdhv34i9tndgD9QDAMzabEyzXIMyawXMuqyI+ql27sQe2zqcdO0BIKGBFqUZczHDei2K06dE/POlgqAM4EjbJrT4z+J/jt8FAEgTRlyUeRVmWK9FrqEEANDmb8Re+zvYb6+CO2gDAGSnFaHMughTLOUwaEwRHzskQzjm3IY9tnU44z4AANCKNEzMuAIzrdeiwFgapZ8yuTX7zmC3bS0O2f8Dv2z/A6PAUIoyawUmZc6DTqTFvadWXy1229biYNv78IVcGOmtxN9Or0WZpQIXZX4KOk34391oO+s+jD22tTjq2Nbx3iQw2jQDM6wVGGe6BEKImBzXFbRhVe1/o9bzCdDtjwGDJgNX5H4OM6wVMTluJEQsz1aFM2fOHLljx46YH+f5fbfDZ+z51/nIY5WoK10HAEjzCtw76U9IS4v/Lwoln6qqKpSXl3d93eQ9jTfP/hjO4ODPxHVK11qxtPAxjDCOG9Lz7G5dg/cbXxnUGYYyyyKU598Tsxe9aGrwnMBrZ57oEYAvdGn2zbg899a49XTh+Iilv556FE2+0/3u10CH5aOfQ4Y+u8/9Lb6zePPsj9AWaOxzv1GTiSVF38RI48QBewnJEN6p/y0Ot33Qb82c7KW4MvfzAz5XKvGGXPj32Z/grOdQj/eVThpocc2I+5Cpy8equmf7PQuclTYSNxQ9DktavuJjB0I+rK77JapdO/upELgq9w5cnL1Y8XMOR4faPsD6c//T40REd4XGSVhS+C0YtOa49XTUsRVrz/26x2tX9/ExwjAeS4seQ7rWEpd+tjT9HdtaXu93/6SMK1FZ8CA0QhvV49Z7qvFqzXfCvleVpM/AjcXfiepx+yOE2CmlnHPhdkVTI4QQ1wkhDgshjgohHutjv0EI8WrH/q1CiLFDb3noXtr1X71C8IX8Bok/HvhinDqi4cQdbItaCG5/Phv+Vfs0nIGWQT/HUcc2vNf4x0F/zLbXvh5bm/8x6OPHSyDkw2tnngwbggFgW8vrONz2YZy6ip83z/w4bAgGgBAC+NPp/9vnPm/QGTYEA4An1IaVZ59Bm7//mk4fNP4lbAgGgB0t/8Ku1tUDPlcqWV37C5z1HOp3fwhBrK9/Hitrnwk7FaLVX4d/nf1xn1Oc+rOh4cUwIRgAJD5oGvj/bTg77dqHd879tt8QDAC1niNYVfezuPVU6z6CNXW/CvvaVe89jn/X/jQNjxDJAAAPnUlEQVSm0+Y67bGtCxuCAeCIYxPeb/xT1I/9as3jA75XnXbvwcaGl6N+7EgMGISFEFoAvwFwPYCpAD4vhJh6Qdk9AFqklBMA/BzAM9FuNFJNLcfgNCubA+M2BdDQeDDGHdFws9++IWohuJM7aMMe27qBC/uxLQoh9uPWVVGfnxhtm5pfRVAqCwUfNv01xt3E3yn3bkV1AenFPtu7vbYfaKsKG4I7eUIO7LKFD6+uoF3xmN3R8qZq5g3Xug/jlHuPololY7nFfxaHHcr+qGv11eGQwoC7tfn1uASuRNjW8gYkwp/sAoAa936cccfnPX57yz/DBvNOdZ5PcNK1K6a9hGQQ25v/qah2n209nIHWqB17Q/2Liv5v2o/d+zUsnpScEb4UwFEp5XEppQ/ACgBLL6hZCuCVjsf/ALBQJPiz19XHngaUdiCANSefjWk/NPzE6pdzv33joC5oqXUfRqPv1JCP75ceHGr7z5CfJ5YO2qsU1zoCzWj0Dv3fJVlEenZkc9Orvbbtta1X/P0H7e+FvXDmgH1j13z3gbiCNhxzbFd87OFsr135v7FS+xT+v+2zrwcUfirU6q9FjXv/ELpKTs2+M13zopXYa3snht20s/sbIgq3sRhD3R137oQzqOwTyBCCOGDfGLVjR/IeIxHEAVtV1I4dKSVBuBhA98/oajq29VkjpQwAsAHIjUaDg9VmdEZY74pRJzQceYOurouLos0VbIWr42KZSDT4Tkath0Zv9J4rFryhyH4fT7n2xqiT+Dsb4ZkrT8jR4+tAyIdWf21E3+8INPW7P9Kx0hjFcZrMYvE71KDwOZXWdUrF/5NI//iN9N9sMJp8pyKathbr1+FIn7/BeyJqxw4o/ESv0zFn4v6AHvBiOSHELQCuk1Le2/H1fwG4TEr5ULeafR01NR1fH+uoabzgue4HcD8AFBQUXLJixYpo/iw9NHiqIUXfP1ua1wq/oWcQEVIgf4gXMdHw53A4kJGRAYlQVF8ULpRnGAMNIrswwR20K/q4W4l0rQWZuryoPFcs1HuPR1SfocuFSRudFTnC6RwfsdTsq4nwTURghOH8a5eERIO3OqJj5upLoO3nynp7oB6eoKPPfX0xa7Ng1uVEdPzhqNl3BgF5fpmuvt5XIiUgkG8Y+H2o1V8b0fSmeP1+xJM35ITNf05xvU7okaMfFcOOAF/IhVZ/XZ/7+hofWqFDrn50zPpxBlsiuibFoDHDmlYQlWNH+hoezWP3Z8GCBX1eLKdk+bQzAEq6fT2qY1tfNTVCCB0AK4BepxiklC8AeAFoXzUilldf/2nnn9Fq7fuscF9X91rt6Si/mBfNqV3nqgBSSvzh5INwBKI7Rxhov1r/5nFfgFZEtnphjWs/3jj7v1Hp4eq8L2BWVnlUnisWnj/2CnxS+Rv950b9CAXG8THsqF08Vo1Yf+55HGirUlxv0GRg2fier11/PPFG11J/A9Fr0nHT2N/1u5zTtubXsaVZ+bz2yoIHcVHmpxTXD1er636JTxybu77u630lUnn6MSgfPfD70HsNf8TuCK41WFL4GMaaZw2ltaTT6D2F/z39Z8X1peZLUV54Zww7ar/o8U8nH0Zf01b6Gh9jTDNRXvSFmPVzpG0T1pzrPXWqP+2r8JRH5di/Ofp7BBVOqQKA+XnLMTNB70lKpkZsBzBRCDFOCKEHcBuAlRfUrARwV8fjWwBskAmenX/d6IeVTqECJFBR9NDAdaQaQghMsyyMyXNPtZRHHIIBYJRpGrLTioZ8fJ3Q46LMq4f8PLE0OfMqxbUmbVZcQnC8LMi7L6L6uVk39to23bpI8fdPyZwfdk3TqZYFij+9MGoyMcF8meJjD2fTLcr/jRU/p1XZa04k/78W3QiMMc0YbEtJK88wWtHSf53KIvg3G6ystJEoSZ+uuH66JbZr6JZmXIp0hZ8ECGgwzbIgaseekKH8dUBAg5lZ10Xt2JEaMAh3zPl9CMBaAAcBvCal3C+E+L4QYklH2UsAcoUQRwE8AqDXEmvxNiJ/OoxuZS/eBo8GRSMviXFHNNyUWRZGfZ1Hg8aMmdZrB/39c7JvGHIPZdYKGLWx/Xh/qObl3QGNwvv9XJ59S4y7iS+tVosi42RltdDh4pzP9No+zbIAJu3AN8tIE+kDvgFl6HIwxTJfUT+zsz4T9xsFJEqJaRoKFf4/KRnLmbo8xX+g5upHodQ8V1HtnOylECI1byI7N/tGKLkqvsAwASXpZbFvCB3/3gp6ytOPwTjzxTHtRSt0uCRL2TrSUzKvRmZa9KbLLcr/MpSuWDA5Y17UjjsYin47pJRvSyknSSlLpZQ/7Nj2pJRyZcdjj5TyVinlBCnlpVLKyCaHxMgXJ78I7QBT7bR+4K5Jv49PQzSsmHRZWFL4LRg1mVF5PoPGjMWF3xjSi80Uy9W4PGfZoL9/UsaVmJd7x6C/P170GiNuKHp8wDORM63XYXpW7M/0xNsto76HDG34cSKgwe2jf9LnvnStBUuKvhX2bFCaSMfiwq8jK23kgP2U59+NcebwJwvKLBWYmzP0P9SGk8WFXx9gTq/A1Xl34dOFD0Mn+v8DIUOXi6VF34ZeY1R87IqCB1FsDH83v7nZNyo+yzwcjTNfjPL8L4YNnnn6Mfhs4TfidhOhEtN0LBzxpbCvXdlpxVhS9C1o4vAHysXZn8VMa/g/dseaZqN8xD1RPa5Wq8UNhY8PWDfCUIrKkQ9G9diRSvk7ywHAP7Z/FbWWesiOcTnyWCXqxq1DkS0fS2f9jHeVoy59zQF1BJqxu3UtDrRthDtoBwAYNRmYYpmPkvQy7Gj5F2o9h3pcLSyg6VpDsbN2pvW6iO4cFU6N+wB2t65BtXNn15qVefoxKLNWQK9Jx377hh5LJhUbp2BG1rWYYL5sWNxVrpPNdw4bG1/GadfeHmtSZqUV4sqc2zAhM74fw8fzznIAsKH+JRy0VyGI7subCRQYSrGk6DGkD3Bm3xlowR7bOuy3b+haqcSgMWNK5tWYmXVdRBenhGQIRxybsNe2DrWeI129tN+mtRLjBwjKqcof8uKAfSOObWtEzfi3ALSfAZ6QcSlmZl2Pwo6P71t8Z7HLtqbHbYDN2hxMty5EmbUCpkF8+hSUARy0v4+99nVdF/cKaDDOfAlmWitRYorPWdBEq/Mcxe7WNTjq3Np1I4vO21dPtVwT0R8Y0VLvqcZu22occWxGUPox8lgl3BftQZllEaZZrxnULbWH4oTzY+y2rcUp1+6u96qRhokos1ZgcuZVMQvlrb46vFX3LJp9NT22pwkDZmUtxhVxvDNof3eWU0UQ7nT6zBY024+jvsaKioreHycShQs6QRmAK9AKCQmzLrvHPN9AyIcG70mEEECeYQx0Qt9vbTR5Qy54gm3QCQPMup4fhbuDbfCGnDBozEjXRuesdqKEQgHU+04gEPIjR18Mky4+tya9ULyDcKcmXw3qPcdh1uZgtFn5HMROIRmEM9ACCQmTLgu6flaIUMoVtMMXcsGoyUj6aTbxUlVVhTlXzUBQ+pGutfYbvgIhH1zBVghoYNblRC2AuAKt8EsvjNrMuIesZOEPeeAK2qAVachIkpVLOnva+eE+LJh/TcJPRHiDTrhDbdBrTIP642uwgsEgajx74Qk6MNI4CVb9iLgdu1N/QTg2785JqqT4cpQUX46qc1WJboWGIa3Q9TutQafRozC954Ub0Zxv1R+DxtTvm166NnPYB+BOGo0OI40TEt1GwuTqRyF3CEs/aYQ2quPRpLXE9U10uFASvnQaPSya6IcAk27gOeGpLk1jhDUBZ3/D6exJg4MJD8EAYNCaYdCa435crVaLMUm6cklqzqAnIiIiIhoAgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpkpBSJubAQjQAOJmQgwN5ABoTdGxKbhwbFA7HB/WHY4PC4fhIvDFSyvwLNyYsCCeSEGKHlHJOovug5MOxQeFwfFB/ODYoHI6P5MWpEURERESkSgzCRERERKRKag3CLyS6AUpaHBsUDscH9Ydjg8Lh+EhSqpwjTERERESk1jPCRERERKRyKRuEhRDXCSEOCyGOCiEe62O/QQjxasf+rUKIsfHvkhJFwfh4RAhxQAixRwjxrhBiTCL6pPgbaGx0q7tZCCGFELwSXEWUjA8hxLKO14/9Qoj/jXePlDgK3ltGCyE2CiE+7nh/+XQi+qTzUnJqhBBCC+AIgAoANQC2A/i8lPJAt5ovA5ghpfw/QojbANwopfxcQhqmuFI4PhYA2CqldAkhHgBQzvGR+pSMjY66TACrAOgBPCSl3BHvXin+FL52TATwGoBrpJQtQogRUsr6hDRMcaVwfLwA4GMp5f8IIaYCeFtKOTYR/VK7VD0jfCmAo1LK41JKH4AVAJZeULMUwCsdj/8BYKEQQsSxR0qcAceHlHKjlNLV8eUWAKPi3CMlhpLXDgD4AYBnAHji2RwlnJLxcR+A30gpWwCAIVhVlIwPCcDS8dgK4Gwc+6M+pGoQLgZwutvXNR3b+qyRUgYA2ADkxqU7SjQl46O7ewCsjmlHlCwGHBtCiIsBlEgpV8WzMUoKSl47JgGYJIT4UAixRQhxXdy6o0RTMj6+C+BOIUQNgLcBfCU+rVF/dIlugCiZCSHuBDAHwPxE90KJJ4TQAPgZgOUJboWSlw7ARADlaP8k6X0hRJmUsjWhXVGy+DyAP0op/1sIcQWAPwshpkspQ4luTK1S9YzwGQAl3b4e1bGtzxohhA7tH1E0xaU7SjQl4wNCiEUAHgewRErpjVNvlFgDjY1MANMBVAkhTgC4HMBKXjCnGkpeO2oArJRS+qWU1WifMzoxTv1RYikZH/egfQ45pJSbARgB5MWlO+pTqgbh7QAmCiHGCSH0AG4DsPKCmpUA7up4fAuADTIVrxykvgw4PoQQswH8Du0hmHP81CPs2JBS2qSUeVLKsR0XuGxB+xjhxXLqoOS95U20nw2GECIP7VMljsezSUoYJePjFICFACCEmIL2INwQ1y6ph5QMwh1zfh8CsBbAQQCvSSn3CyG+L4RY0lH2EoBcIcRRAI8A6HeZJEotCsfHTwFkAPi7EGKXEOLCFzNKQQrHBqmUwvGxFkCTEOIAgI0AHpVS8tNGFVA4Pr4O4D4hxG4AfwOwnCfhEisll08jIiIiIhpISp4RJiIiIiIaCIMwEREREakSgzARERERqRKDMBERERGpEoMwEREREakSgzARERERqRKDMBERERGpEoMwEREREanS/wfom66DXcrVZgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='complete')\n",
        "labels = cluster.fit_predict(X)\n",
        "\n",
        "_plot_kmean_scatter(X[:, [2, 4]], labels)"
      ],
      "metadata": {
        "id": "VlFYHHCcL4YB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "2c28c76f-b8be-4a76-fb89-12dfd809453e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-f01e928d6b9c>:8: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sc = ax.scatter(X[:,0], X[:,1], lw=0, s=150, c=palette[labels.astype(np.int)])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1>EVALUATING MODEL USING PCA</h1>"
      ],
      "metadata": {
        "id": "kZrizwMclc_4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.decomposition import PCA \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "Y = np.array(df.iloc[:117])\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(Y)\n",
        "scale_my_data = scaler.transform(Y)\n",
        "\n",
        "for my_data in scale_my_data:\n",
        "    print(my_data)"
      ],
      "metadata": {
        "id": "ZL3J90kBbKFe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b3d878b-19f4-468f-fbbf-8853198bb6f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.7754048   1.38813357  0.23322372 -1.2119753   1.10347586  0.85010446\n",
            "  1.17803018]\n",
            "[-0.7754048   0.58807629 -1.34103641  0.06551218  0.07062246 -2.0328585\n",
            "  1.17803018]\n",
            "[-0.7754048   1.38813357 -0.29152966  0.06551218  1.44776033  0.85010446\n",
            " -0.84887469]\n",
            "[ 0.484628   -1.01203827  0.7579771   0.06551218  0.41490692  0.85010446\n",
            " -0.84887469]\n",
            "[ 0.484628    1.38813357  0.7579771   0.06551218 -0.27366201 -0.59137702\n",
            " -0.84887469]\n",
            "[-0.7754048   0.58807629 -0.29152966 -1.2119753  -1.30651542  0.85010446\n",
            "  1.17803018]\n",
            "[-0.7754048  -1.01203827  0.7579771   0.06551218 -1.30651542  0.85010446\n",
            " -0.84887469]\n",
            "[-0.7754048  -0.21198099 -1.34103641  1.34299965 -0.96223095 -2.0328585\n",
            " -0.84887469]\n",
            "[ 0.484628    1.38813357  1.28273048 -1.2119753   1.44776033 -0.59137702\n",
            "  1.17803018]\n",
            "[ 0.484628   -0.21198099 -0.29152966  1.34299965  1.10347586  0.85010446\n",
            " -0.84887469]\n",
            "[ 1.74466081  1.38813357 -0.81628303  0.06551218 -0.27366201 -0.59137702\n",
            " -0.84887469]\n",
            "[ 0.484628   -1.01203827  0.7579771   0.06551218  0.41490692  2.29158594\n",
            " -0.84887469]\n",
            "[ 1.74466081 -0.21198099 -1.34103641  1.34299965  0.07062246 -0.59137702\n",
            "  1.17803018]\n",
            "[-0.7754048  -1.01203827  1.28273048  1.34299965  0.75919139 -2.0328585\n",
            "  1.17803018]\n",
            "[-0.7754048   1.38813357  1.28273048  1.34299965  1.10347586  0.85010446\n",
            " -0.84887469]\n",
            "[-0.7754048   0.58807629 -0.81628303  1.34299965  0.41490692  0.85010446\n",
            "  1.17803018]\n",
            "[ 1.74466081 -1.01203827 -0.29152966  1.34299965 -0.96223095  2.29158594\n",
            " -0.84887469]\n",
            "[-0.7754048  -0.21198099 -1.34103641  1.34299965  0.41490692 -2.0328585\n",
            " -0.84887469]\n",
            "[-0.7754048  -0.21198099 -1.34103641  1.34299965  0.75919139 -2.0328585\n",
            "  1.17803018]\n",
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            "  1.17803018]\n",
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            "  1.17803018]\n",
            "[-0.7754048  -1.01203827  1.28273048 -1.2119753   1.10347586 -0.59137702\n",
            " -0.84887469]\n",
            "[-0.7754048   1.38813357 -0.29152966  0.06551218  1.10347586 -0.59137702\n",
            "  1.17803018]\n",
            "[-0.7754048   1.38813357 -0.81628303 -1.2119753  -1.30651542  0.85010446\n",
            "  1.17803018]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cov_mat = np.cov(df.T)\n",
        "\n",
        "eigen_vals, eigen_vects = np.linalg.eigh(cov_mat)\n",
        "print('Eigen values:\\n')\n",
        "print(eigen_vals)\n",
        "print('Eigen vectors:\\n')\n",
        "print(eigen_vects)\n",
        "\n",
        "propor_ith = [(i/np.sum(eigen_vals)) for i in (sorted(eigen_vals, reverse=True))]\n",
        "propor_k = np.cumsum(propor_ith)\n",
        "\n",
        "plt.bar(range(1, 8), propor_ith, label='The kth Principal Component')\n",
        "plt.step(range(1, 8), propor_k, label='The first k Principal Components')\n",
        "plt.xlabel('Components')\n",
        "plt.ylabel('Proportion of variation')\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "2jQUCtNOu2cz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "f46bddaa-5c28-4b90-be85-fba8f079dfda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Eigen values:\n",
            "\n",
            "[0.15488115 0.44497605 0.53776252 0.64582281 1.57724899 3.4176698\n",
            " 8.95373418]\n",
            "Eigen vectors:\n",
            "\n",
            "[[ 0.00675102 -0.31910472 -0.7050468  -0.61038348 -0.15102826  0.02884625\n",
            "  -0.06946926]\n",
            " [-0.03744693  0.04825858 -0.22478019  0.00465185  0.95687722  0.17337927\n",
            "   0.00683226]\n",
            " [ 0.05193809  0.04341456 -0.02742164 -0.10389369  0.1636014  -0.94632221\n",
            "   0.24815442]\n",
            " [ 0.34272356  0.10009267 -0.61529077  0.68445373 -0.13352183 -0.03613501\n",
            "   0.07955228]\n",
            " [-0.00736565 -0.01226678  0.01145341 -0.09226045 -0.04915463  0.25253225\n",
            "   0.96175017]\n",
            " [ 0.07554821 -0.93920781  0.17015256  0.26549971  0.0975541  -0.0483602\n",
            "   0.02972648]\n",
            " [ 0.93414847  0.04096516  0.20967863 -0.26294006  0.07106316  0.07851628\n",
            "  -0.03702853]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_pca = PCA(n_components=2)\n",
        "new_scale_my_data = scale_my_data[np.isfinite(scale_my_data).all(1)]\n",
        "principalComponents_mine = my_pca.fit_transform(new_scale_my_data)\n",
        "print(principalComponents_mine.shape)"
      ],
      "metadata": {
        "id": "D9BE9lAvbY1_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33c0155d-3d30-4362-aa96-55e24c3632c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(117, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dend = shc.dendrogram(shc.linkage(principalComponents_mine, method='ward'))\n",
        "plt.axhline(10, linestyle='--')\n",
        "plt.xlabel('sample indice')\n",
        "plt.ylabel('dissimilarity metric cluster')"
      ],
      "metadata": {
        "id": "_re26e7Xbbun",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "27693837-1d03-4665-8945-891301e0d6a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'dissimilarity metric cluster')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='ward')\n",
        "labels = cluster.fit_predict(principalComponents_mine)\n",
        "\n",
        "_plot_kmean_scatter(principalComponents_mine, labels)"
      ],
      "metadata": {
        "id": "yCrj6RNJbegw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "eea1f625-cdd8-48c8-a481-8f04dfb20501"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-f01e928d6b9c>:8: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sc = ax.scatter(X[:,0], X[:,1], lw=0, s=150, c=palette[labels.astype(np.int)])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "principalComponents_mine"
      ],
      "metadata": {
        "id": "ZEDaclBRDFku",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1bc2258-fdc8-4bf9-ca9a-6684516dc493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.46437696,  2.47373246],\n",
              "       [ 1.46495346,  0.29078745],\n",
              "       [-1.12060906,  1.72692806],\n",
              "       [-1.17724944, -0.68743984],\n",
              "       [-0.21070868,  0.11104568],\n",
              "       [ 1.53394311,  1.33151847],\n",
              "       [-0.83164462, -0.48097496],\n",
              "       [ 0.08499096, -1.30605442],\n",
              "       [ 0.54695651,  1.39053841],\n",
              "       [-1.50442156, -0.30303354],\n",
              "       [ 0.80944383, -0.5780747 ],\n",
              "       [-1.50476878, -0.14000385],\n",
              "       [ 1.08064663, -1.24550047],\n",
              "       [-0.80355417, -0.75787722],\n",
              "       [-2.28240888,  1.40174305],\n",
              "       [-0.17677645,  1.21220483],\n",
              "       [-0.78187346, -1.44648578],\n",
              "       [-0.46655627, -0.95092887],\n",
              "       [ 0.44473394, -0.31463006],\n",
              "       [-1.33023185, -0.43975017],\n",
              "       [ 1.63971483,  0.60554754],\n",
              "       [ 1.03731231, -0.90785934],\n",
              "       [-0.26245442,  0.48091891],\n",
              "       [-1.41686726, -0.22716834],\n",
              "       [ 2.20798615,  1.27899438],\n",
              "       [-0.13391784,  1.90038267],\n",
              "       [ 0.41577645, -0.25672997],\n",
              "       [ 0.06546886,  0.60267916],\n",
              "       [ 1.30467451, -1.05319002],\n",
              "       [-0.71056202,  0.02685314],\n",
              "       [-1.62543715,  0.48367626],\n",
              "       [-1.96933337, -0.16048982],\n",
              "       [ 1.41157924, -0.52682   ],\n",
              "       [ 0.740974  , -1.23677386],\n",
              "       [-0.67440624,  0.30956187],\n",
              "       [-2.02229336,  0.15341094],\n",
              "       [-2.50776987, -0.21052264],\n",
              "       [ 0.29189586,  2.1727479 ],\n",
              "       [-2.05696777,  1.38882686],\n",
              "       [-1.48755035,  0.39489487],\n",
              "       [-1.60863295, -0.11293323],\n",
              "       [ 2.18856756, -0.89486174],\n",
              "       [-2.64565667, -0.12174125],\n",
              "       [-0.95359426, -1.20780522],\n",
              "       [ 1.91371284, -0.42885193],\n",
              "       [ 2.75783788, -1.55814974],\n",
              "       [-1.33144611, -0.49572264],\n",
              "       [-2.64565667, -0.12174125],\n",
              "       [-0.12439204,  2.58834623],\n",
              "       [-1.35292969, -1.29064696],\n",
              "       [-1.84825077,  0.34733828],\n",
              "       [ 1.5171389 ,  1.92812795],\n",
              "       [ 1.48141045, -0.14279235],\n",
              "       [-2.02229336,  0.15341094],\n",
              "       [-0.74232988, -0.77336609],\n",
              "       [ 2.41400866, -0.90777793],\n",
              "       [ 2.53239677, -1.54523356],\n",
              "       [-1.35292969, -1.29064696],\n",
              "       [-0.34582089, -0.28007108],\n",
              "       [-0.21070868,  0.11104568],\n",
              "       [ 0.13312053, -0.53932613],\n",
              "       [-0.45052662,  0.4037029 ],\n",
              "       [ 0.10506131, -0.30590345],\n",
              "       [-0.33179128, -0.39678242],\n",
              "       [-0.45287388, -0.90461052],\n",
              "       [-0.34975304,  0.97608054],\n",
              "       [ 2.30695567, -1.53231737],\n",
              "       [-0.78167449, -1.70768488],\n",
              "       [-1.0044211 , -0.54948497],\n",
              "       [-0.23012727, -2.06281043],\n",
              "       [ 2.55181536,  0.62862256],\n",
              "       [ 0.91525917,  2.44790009],\n",
              "       [-0.34975304,  0.97608054],\n",
              "       [ 0.01349476,  2.49956484],\n",
              "       [ 0.54678095, -0.8056703 ],\n",
              "       [ 2.55181536,  0.62862256],\n",
              "       [-2.28240888,  1.40174305],\n",
              "       [-0.07047461,  1.33057772],\n",
              "       [ 0.29237206, -1.19716597],\n",
              "       [-0.53722904, -0.67825316],\n",
              "       [-0.17838154, -2.43268366],\n",
              "       [ 1.84473835, -0.24448992],\n",
              "       [-0.34688689, -0.23787413],\n",
              "       [ 2.18856756, -0.89486174],\n",
              "       [-0.78167449, -1.70768488],\n",
              "       [-0.78167449, -1.70768488],\n",
              "       [-0.2237393 , -1.10897782],\n",
              "       [-1.59647966,  0.42577616],\n",
              "       [ 1.5171389 ,  1.92812795],\n",
              "       [ 2.43073276,  0.12079446],\n",
              "       [ 0.72107921, -0.04071612],\n",
              "       [ 0.51733696,  2.15983171],\n",
              "       [-2.64565667, -0.12174125],\n",
              "       [ 0.49463912,  1.30893492],\n",
              "       [-0.43606968, -1.50122   ],\n",
              "       [ 0.61310734, -0.95370259],\n",
              "       [ 0.95693655, -1.6040744 ],\n",
              "       [-1.05708572, -0.46805877],\n",
              "       [-1.84825077,  0.34733828],\n",
              "       [-1.57695757, -1.48295741],\n",
              "       [ 2.30965016, -0.38703364],\n",
              "       [ 2.18856756, -0.89486174],\n",
              "       [ 1.18229754,  0.00819128],\n",
              "       [ 0.68981807,  2.46081628],\n",
              "       [ 2.36231478, -0.46845983],\n",
              "       [-0.91778569, -0.7620668 ],\n",
              "       [ 1.74258   ,  1.91521176],\n",
              "       [-1.38064447,  1.35007829],\n",
              "       [-0.0485637 ,  0.33729047],\n",
              "       [ 1.38425024,  0.71856077],\n",
              "       [-2.28240888,  1.40174305],\n",
              "       [-0.22621852, -0.86155424],\n",
              "       [ 2.32596003, -0.48996946],\n",
              "       [ 0.97259464, -0.53330507],\n",
              "       [-1.0714107 , -0.11887287],\n",
              "       [ 0.3939741 ,  1.63822809],\n",
              "       [ 1.88046681,  1.82643038]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='single')\n",
        "labels = cluster.fit_predict(principalComponents_mine)\n",
        "\n",
        "_plot_kmean_scatter(principalComponents_mine, labels)"
      ],
      "metadata": {
        "id": "EUzNlP-RB-MY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "7aab1750-54da-41cd-ba18-bed8e4303176"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-f01e928d6b9c>:8: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sc = ax.scatter(X[:,0], X[:,1], lw=0, s=150, c=palette[labels.astype(np.int)])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='complete')\n",
        "labels = cluster.fit_predict(principalComponents_mine)\n",
        "\n",
        "_plot_kmean_scatter(principalComponents_mine, labels)"
      ],
      "metadata": {
        "id": "n2aDUx0Wnvw-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "7bdcca08-7bbe-46d4-c821-6ef056458108"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-f01e928d6b9c>:8: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sc = ax.scatter(X[:,0], X[:,1], lw=0, s=150, c=palette[labels.astype(np.int)])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='average')\n",
        "labels = cluster.fit_predict(principalComponents_mine)\n",
        "\n",
        "_plot_kmean_scatter(principalComponents_mine, labels)"
      ],
      "metadata": {
        "id": "mm3tjvEJnx1p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "0964bc91-87e1-49ae-f15a-fed31c7b33b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-f01e928d6b9c>:8: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sc = ax.scatter(X[:,0], X[:,1], lw=0, s=150, c=palette[labels.astype(np.int)])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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jszGP44+04N+HftmtAD4qqPiwvO7P+Nj9dsxjDIROxx+FRNmIX/lERFnqE+9KRGSbqtg9vg0IRFpiGmd94/PwROr7jVvV8ASCUV9MYxARacUimIgoS+3zf6g6VkEEBwJbNY8RjPqwy7dOVWxUhvGJ5x3NYxARxYJFMBFRlgoprRrjg5rHOBjYhogMqY7f69+oeQwioliwCCYiylJWvUtjvFPzGNoLbW3xRESxYhFMRJSlxjhmq4416awYZp+ieQythbMlhkKbiCgWLIKJiLLUaOcpsOpzVMWOc54Ok86ieYwq20kw6+yq47UU5kREA8EimIgoSxmEEeeU/C/0wthnXLF5BE4puDymMYw6M8Y51a3/a9bZMdY5J6ZxiIi0YhFMRJTFhtjG46LyO1BiHtntmF4YcaJzHi6quCOmu8BHnVJwWY/n70wHA84q+T6MOnPM4xARacEd44iIslyZdQy+Wvkb1AX34kBgKyIyBLshD6MdszRPnuuJUWfBhRU/w7v1T2Gnby2iMnzc8ULTUMwtvAZDbOMHPBYRkVosgomICABQbBmBYsuIhJzbpLNiYckNmF14JXZ718EXaYZBZ0KVdQLKrGMTMiYRUV9YBBMRUdLY9C6clHt2qtM4Tn19PZ5++ulj/962bduxvz/88MMwGNp/VNrtdlx//fVJz4+IEoNFMBERZbXq6mr86Ec/6vHYrbfeeuzv5eXlLIKJMsiAi2AhRCWAfwAoASABPCal/P1Az0tERJQMRUVFvRbBnblcA++PJqL0EY87wREAN0spPxRCOAFsEkIsl1J+EodzExERJVR5eTnuv//+VKdBREk24CXSpJQ1UsoPO/7uBbADQMVAz0tERERElChx7QkWQgwDMBnA+/E8LxEREaWH6tad+Mj9FiIyCIehANPzLoLNwFYRGnyElDI+JxLCAWAVgN9IKV/u4fj1AK4HgJKSkqnPP/98XMal+PL5fHA4HKlOgwaA13Bw4/Ub/DL1GoaUVngi9VBkpNsxg86MPGMZRBz34AopAURlBEIIGIUVepG8ufyZeg2zRdfrN3/+/E1Symld4+JSBAshjACWAnhTSvlAf/HTpk2TGzduHPC4FH8rV67EvHnzUp0GDQCvYepEZQQNbQcQVoKwGXKRb9LeGcbrN/hl4jXc5V2PN470PefdJKz4xtBHYDHEXjwqUsHG5sXY5l6GQNTd6YjAUNskzC64EkXmoTGfX61MvIbZpOv1E0L0WATHY3UIAeBvAHaoKYCJiDJNW9SPD1uWYrvnHQSiLcdeLzaPwEk5Z2Oca24KsyMaGEWJYNmRP/QbF5KtWFx9F66ouie2caSC12sfxGf+D3o4KnEgsBXVrbvwlfKfoMw6JqYxiDqLx3OLUwFcDWCBEGJLx59z43BeIqK0F4i04MXDv8AHzYuPK4ABoK5tL5bX/Qkr6h5DvFrPiJLtveaXoCCqKrY+tB+BiLv/wB582LKklwL4C2HZiqW19yOihGIag6izeKwOsUZKKaSUk6SUJ3f8eS0eyRERpbtXax9EU+hQnzHbPW9jc8urScqIKL4+9b6rKf79ppc0j6FIBdvcy1TFtkbd2OVbp3kMoq7i18FORJRlalp3oia4U1Xs5pbXoEh1d9OI0kko2qop3hdp0jzG4dbt8EUaVcd/6l2teQyirlgEExHFaLvnHdWx/mgT9ge2JDAbosTQCb2meKPOrHkMX6RZU7xfYzxRT5K33ggRUYZxh49ojK9NUCZEiVNmGYt9gU2q4ye4ztA8htbC2aAz9Xm8JVyLQ4FPEJUhOAz5GGqfDIMwas6LMhuLYCKiGAmh7WGaDtruqBGlg7mF12DfQXVFsEXnxBDbeM1jlFvHQS+MiMqwqvgq66QeX69vO4C1jc/iYGAbgC8mo1r1Lkx0LcKM/Is039mmzMV2CCKiGJVaRic0nigd5JhKMMo+U1XsGcXXxzSGTe9SPYaAwMSchd1er2ndhZcO/QIHA1vRuQAGgNaoBxua/42lNb9jbz4dwyKYiChGE10LVe+QVWIeiRLLyARnRJQY55b9sM8iVUCHM4u/h5GO6TGPcUrBZbDqc/qNm5r3ZbiMxce9FlFCeLX2foRlsM/37g9sxsbm/8acI2UWFsFERDFyGgsxKefMfuMEdDil4KtJyIgocc4t+yGuqXoIw2xTYNbZYRQW2PW5mJJ7Ab4z4kmc4JozoPO7jMW4uOLnyDWW9nhcBz1m5F2E2QWXdzu227e+yw5zvfvIvZx3gwkAe4KJiAbktMJrEJZt+KSXlSL0wohFxd9Fla3nHkaiwSTXVIovld+asPPnmypwddUD2BfYjB2eVfBFGqEXRgyxjseEnDPgMOT3+D4tS6b5o834PPAxhtpPilfaNEixCCYiGgCd0GFh8f9gvGsBPnIvw37/FkRkG2z6XJzgPK3PH9xE1J0QOoywT8UI+1TV7/F32a2x/3gusUYsgomI4qLMMhplnPhGlBIG0feSaQONp8zEIpgojchoFP5NH6B196eQ0SiMhUVwzp4LQ25uqlMjIkpbVbZJqGvbqypWBwOGWLUv40aZh0UwUZrwrH0XjS8+h2jL8Y/pGv/9ApynzEHR1d+Czqx9JyYiokw30bUQm5pfgYTSb+woxwzYDP2vQkGZj6tDEKUB94plqHv8T90KYABANArvmlWovv9uyLC6heSJiLKJ01iIGfkX9Rtn1edwpRY6hkUwUYqFGxtQ/8yT/cYFd32K5teXJjwfIqLBaGb+JZiVf2mvOzPmGktxUcUdyDGWJDkzSldshyBKMc87bwFK/4/wAMCz8i3knf9lCB1/fyUi6mpG/sUY7zoD2z0r8HnrdkSVMOyGPIxznY7htsmatzqnzMYimCjFfBvfVx0baWpE8LPdsI4em8CMiIgGL7shFzPyL8YMXJzqVCjN8VciohSL+n2a4hW/P0GZEBERZQ/eCSZKMb3NDsXrVR2vs9sTmA2ROpHmJnjWrEKkoR5Cb4BlzFg4ps2EMPDHChENDvxuRZRi9qkz0PLaK6pi9Xn5sIwYleCMiHqnBIOoe+qv8G1YD0Sjx153v70MDa5/oOCSy+GaOz+FGRIRqcN2CKIUy5m/EBBCXey8MyD0Pc98Jko0JRRC9e9+C9/6NccVwEdFPW7U/f1RtCx/PQXZERFpwyKYKMWMRcUovPzqfuPMI0cj95wLkpARUc9aXl+C4J5d/cY1PPsPhOvrkpAREVHsWAQTpYHcs85F0Tevh97p6n5Qp4Nj1qmo+PHt0Jm43z2lhoxG4V65QmWwVB9LRJQi7AkmShM5py+A69S58G14D617dgKRCAyFxXDNmQtDfkGq06MsF9y7B9HmJtXx/o3vo/DSKxKYERHRwLAIJkojwmCAc/YcOGfPSXUqRMfRujRfNMCl/IgovbEdgoiI+qV1aT69jUv5EVF6YxFMRET9sowcDUN+vup4x/SZCcyGiGjgWAQTEVG/hE4H17yF6oK1xBIRpQiLYCIiUiX37PNhGT2237jCK66BsbAoCRkREcWORTAREamiM5lQfstP4Dx1LtDDpi36nFwUX3sDchednYLsiIi04eoQRESkms5sQcm3v4uCS6+Ad+27CNfXQxgMsIweA8fUGRAG/lghosGB362IiEgzQ24e8s77cqrTICKKGdshiIiIiCjrsAgmIiIioqzDIpiIiIiIsg6LYCIiIiLKOiyCiYiIiCjrcHWIFItGo2h46m/wrl8NhMPHXtfn5aPwa9+Ac+qMFGZHRJRYUlEQ9XoACeidToge1h8mIkoEFsEpFPX5sP+H34UMh7ofa27CkUcegHfSZJT/6LYBjxVpaYZn5Qp41r6LaHMzhNkE24kT4VqwCLZx4wd8fiIiLSIeNzzvvAXPqhWINDUBAPROF5ynzUPOGWfCWFCY4gyJKNOxCE6h/bd8r8cCuLPAts2oe/JxFH/j2zGP4/twI4785WHI0BdjyUgYvg/eg++D9+CYcQpKrr+Ri9wTUVK0Hfoc1b/7LaItzce9HvV60PLaK/CsXIGyH94Kq4otmomIYsWe4BRxv70cMhhUFetZ9U7M47Tu+hS1f3zwuAK4K9+G9ah78vGYxyAiUivq8/ZYAHemBPyoefBehOvrkpgZEWUb3vpLkcbF/1IfLBU0vb4E+edcoHmcppf/BUSj/cZ516xC3rlf0nz+TCXDYXg3rIdn1QqEa2sBnQ6W4SPgWnAmbBMmQQiR6hSJBiXPqrf7LICPUgJ+tLz1BoquuCYJWRFRNmIRnCKKz6cp3r9pg+YiOHT4EFo//UR1vPud5UDFME1jZKJQTTWqH7gHkS53ofybN8G/eRMsY8eh7H9vgd5uT1GGNBgpra0IbP8Iit8HncMB2/hJ0FksqU4r6dwrV6iO9a5ZhcJLroAwGhOYEcWqoe0gDrd+gqgMw2EoxAj7VBh0plSnRaQai+DBIqpofktw7x5t8Z/tyfoiONLSgsP33oVoc1OvMcGdO1Dz+/tQcdsdnMlO/Yr6fWj697/gWbcaMth67HWd1Qrn7LnIv/ir0NtsKcwweZS2YLdfLvuM9/sRaW6Esbg0gVmRVjWtO7Gm8VnUBHce97pF58TEnIWYmX8JdILfGyn9sQhOEWE0QYbaVMebhw7TPIZUNBbOWuMzUMubr/ZZAB8V3PUp/B9uhGP6zCRkRYNV1OvB4bvvRKj6ULdjSmsr3CveROvOHaj4v59D73CkIMPkiAYC8K5dhdadO1KdCg3QwcA2LKm5D1EZ7nYsqHjxQfNiNLQdxHllN0MnOO2I0hv/h6aIc85cTfEFV39L8xjG4pKExmcaGQ7Ds3ql6nj328sSlwxlhCN/e7THAriz0KGDqHvisSRllHxNryzG/pu+g4ZnnoJ/4wZN79XZ7TDkFSQoM9IqpLTitdqHeiyAO9sX2ITNLUuTlBVR7FgEp0jxNdcCKidXmUeMgj6Gx+7WE06EsUT9Y0TX6Qs0j5FJQjXVUHxe1fHB3bsSmA0NduG6WgS2fqgq1v/hBwg31Cc4o+RrePE5NL38gqanXp0555zOfuA0ssPzLkJKQFXsNvcySMmni50FIi3Y59+Ez3wbUN92INXpENgOkVJlN/8ENb/7bZ8xepcLlT+/K6bzCyGQe84FqFex/Jl56HBYT5wArFoV01iZQEYjmuOllFwpgnrkWbsakFJdsJTwrluN/C9dlNikkqjt8wNoefW/Mb9fZ7Mjd+HZccyIBmqnb43qWG+kAYdbd2CIjZsxNYYOYUPTv/GZbwMUfLFaU7F5BKbkno8xztkpzC678U5wCtknTMKQO/8fDIVF3Q/qdLBNmYbhDw/sMWnOvDOQc9a5fcYYS0pR9oNbsr6YM+QXADr1XxKGwqKs/5xR7yJNjRrj++9FH0zcK5bH/F6dzY6ym34MY1FxHDOigQpE3Nrio9riM1FN6068eOgO7PatP64ABoC6tr1448jDWN/4QoqyI94JTjFL1VAM+90jiLa2wv3Ocig+P8zDR8AZxwlXRVdcA+vIMWhZ/jqCu7+Yzat35cB1+gLknnVeRk/KUcuQkwv7SZPh37xJVbxr7vwEZ0SDmc6kbamoTHvs79+i7uuoM73TBefc+chZsIjbJqcho86c0PhM0xYNYEnN7xBSWvuM+6B5MYrMwzDKwYnWycYiOE3orVbkJ3CzCseMWXDMmIVwXS0izc0QJhPMlUO5VXIXuWedB/+WD/t9jK2z2lgEU5+sJ06Ae4X6yZO28RMSmE3yKcG+f/B3VXLjTXBMnQGh4WkMJddQ28loDH2uKtYozCi3jktwRulth3cVgoq6eSabW15lEZwC/G6TZYzFpbCOHQfL8JEsgHtgPeFEFF719T4nLQqLBaX/ezMMOblJzIzSkYxGEfX7IHvYldE+eRoM+fmqzmMoLIJt0uR4p5dSeodTU7yppIwFcJqbmLMQgLoWsLHOOTDrsmP969584lE/x6YmuAvNoeoEZkM9YRVE1EXuwrNhKi1D82tL0PrJx18cMBjgmDYDeedfCPOQytQlSCkX2LEd7hXL4N+8sX1bcp0O9slTkXPGmbCdOBEAIHQ6FF7xddT+6aG+nywIgcIrv55xBaBjxiloee0VVbGm8iEwVw1NcEY0UDnGEszIuxAbml/uMwA0JFsAACAASURBVM5pKMTM/EuSlFX68ka0rfjijTQgz1SeoGyoJyyCiXpgm3ASbBNOQriuFqHaWgi9HqbKKhhcOalOjVKs/tmn4F72+vEvKgr8mz6Af9MHcC1YhKKrvwUhBBzTZ6Lk+htR98TjPS4TJsxmFF97AxxTpiUp++TJmb8QLW++2v5LQn+xZ5yZhIwoHmYVXAad0GND02Io6L6iTqFpGM4v+xHshrwUZJde9EJbn7/WeBo4FsFEfTAWl3LLVjqmeel/uxfAXXjeXg6904WCCy8FADhPmQPbpJPhXbMKvk0boAQC0NlscEybCeeceRm7ZbKxqBhFV38L9U/9tc874fYp0+GavzCJmdFAzci/GBNyFmG7+20cDu5AVIbhNBRgnHMeKrkk2jHl1hOwx/eeqlijsKDIPCyxCVE3LIKJiFRQ2trQrPLxfsubryLv7POhs1oBAHq7A7lnnYfcs85LZIppJ2feGdDb7Gh86TmE644cd0xYrMiZvxAFl1yeca0g2cCmd2F6/lcwHV9JdSppa5Jrkeoi+ATnHJh01gRnRF2xCCYiUsH3/jooAb+qWBkMwrtuNR/zo31lGvv0mQh8tBXBz3YD0QiMRSVwzJwNncWS6vSIEmaIbTxG2Kdjr/+DPuNs+hxMy+MvE6nAIpiISIW2g9q2OW07uD8xiQxCQgjYJ50M+6STU50KUVKdXfJ9vHnkD/jMv6HH405DIb5UdhucRq6LnQosgomI1FC7BfKx+MSkQUSDh0FnwnllP0JNcDc+ci9HTXAnFBmFy1iM8a75GOWYBQMnxKUMi2AiIhVM5RXa4iu0xRNR5iqzjEaZZXSq06AuOBuBiEgF5+w5EGZ128AKoxHOU09PcEZERDQQLIKJiFTQWW3IWXi2qljX/IXQOxwJzoiIiAaCRTARkUoFF38VzlPm9BnjmD4LhV/9WpIyIiKiWLEnmIhIJaHTofj6G2GfPA0tK95EcOeOY8csY8YiZ8GZcMycDSFECrMkIiI1WAQTEWkghIBjxiw4ZsxC1ONBNOCHzmbjltpERIMMi2AiohjpXS7oXa5Up0FERDFgTzARERERZR0WwURERESUdVgEExEREVHWYRFMRERERFmHE+OIKCsoioKW15YguPtTQALm4SOQd8GF0Bn4bZCIKBvF5bu/EOLvAM4HUCelnBCPcxIRxcuRJx+Hd/VKIBo99lpg22Y0L1kMx4xTUHrD91OXHBERpUS82iGeBKBuP1EioiSqfuAeeFeuOK4APkZR4HtvLQ795hfJT4yIiFIqLkWwlPJdAE3xOBcRUbx4Vq9EYNuWfuOCu3ei+dVXkpARERGlC06MI6KM1fTKy6pjW5a9lsBMiIgo3QgpZXxOJMQwAEt76wkWQlwP4HoAKCkpmfr888/HZVyKL5/PB4fDkeo0aAB4DTtIibYD+zS9xVRZBaFP7UQ5Xr/Bj9dw8OM1HNy6Xr/58+dvklJO6xqXtO/2UsrHADwGANOmTZPz5s1L1tCkwcqVK8FrM7jxGrYL1dbg4FOPanpP+W13wDZufIIyUofXb/DjNRz8eA0HN7XXj+0QRJSRdE6n5vfoHdrfQ0REg1NcimAhxHMA1gMYK4Q4JIS4Nh7nJSKKlcHugM5mVx0vTCaYK6sSmBEREaWTuLRDSCmviMd5iIjiyXXaPLS8+aqqWMf0WQnOhoiI0gnbIYgoY+VfegX0OTn9xulsdhRd/a0kZEREROmCRTARZSydwYChdz8IQ3FJrzH6vHxU3fMAdBZLEjMjIqJUS+1aQERECaaz2TDs3t8jsGM7Gl98FuG6OgAShoJCFFx4GewnT0l1ikRElAIsgokoK9jGjYft579JdRpERJQm2A5BRERERFmHRTARERERZR0WwURERESUdVgEExEREVHWYRFMRERERFmHRTARERERZR0ukUZERJRlpJSoCYXRpihwGQwoMLIcoOzD//VERERZIqQoWN7kxvImD2pD4WOvj7NZcFZBLmblOFKYHVFysQgmIiLKAoFoFHfvr8bu1rZux3YEgtgRqMWZ/hx8q7woBdkRJR97gomIiLLAHw4d6bEA7mxZkxuvNjQnKSOi1GIRTERElOEOBtvwoTegKnZpQwuiUiY4I6LUYxFMRESU4d5u8qiObY5EscnrT2A2ROmBRTAREVGGqw6FNMXXtIX7DyIa5DgxjoiIKMPpIDTGUzYJKgqqgyHoBVBhNsGgy47/ASyCiYiIMtxwqxlbfOp6ggFghNWcwGwoXWz3BfDP2gbsD37xpEAHYLzdim+VF6HMbEpdckmQHaU+EdEgJxUFUb8PSlsw1anQIHRGnkv1D/wKsxHjHba45+CJRLHVG8Bmrx/VbdraMyj+Ftc14df7q48rgAFAAfCRvxU37z6ILRneG847wUREaSxcV4uWFcvgXbMKir/9B5J56DC45i+Cc/Zp0Jky+04NxUehyYiF+TlY1uTuM04AuLS4IK5jH24LYXFdE973+BHutOrEWJsFFxTmYpqLG3Qk24ceP16oa+ozRgFw74Ea/GHMMOSbMrNc5J1gIqI05dv0AQ7e/mO433ztWAEMAG0H9qP+ycdx+De/QMTTd1FDdNTXywoxJ8fZ63EdgG+VF8V117hdgVbc8dkhrHH7jiuAAWBnIIjfHazFf+u5LnGy/bO2QVWcoiF2MGIRTESUhoJ796D2z7+HDPc+S7/twD7UPHQvpKIkMTMarPRC4HuVJbh9WDlmuOwwivbJcg69Dmfn5+C+0VVYlJ8Tt/H80SjuO1CDQD//P5870ojNGf7YPZ00hiKoCalf/WNjBl+bzLy/TUQ0yDUv+Q8QifQb17b3MwS2bYb95KlJyIoywUSHDRM7en6jUkIvtK0codaqZi+8UXW/oC1taMFkpz0hedDxdgZaNcWHpYSiKNBl4IoRmfcRERENcpGmRvi3fqg63v3OWwnMhjJZogpgAFjZrH6Dju3+VtRpuDtJFA8sgomI0kzboc8BDS0ObQcPJDAbotjU9dHK05N6FsFJcYLNqineKERG3gUGWAQTEaUh2X/IceEa44mSwKDxLrNBl7i70vSFfJMB5Saj6vjprsxtU2ERTESUZkxlFYCGAsJUMSSB2RDFZqyGO45WncBQMzfoSJZrygpVxekAXFOqLnYwYhFMRJRmjEXFsI2fpDo+Z94ZCcyGKDaL8l2qY0/LdcGiZ0mSLCc77biqpO/1oHUAfjK0DLnGzF1DIXM/Mso4SigE3/o1CGzfBqUtBENeHpyzT4N1zAmpTo0yiAyH4du8EeEjtRB6PSwjR8M6dlzS88g7/8sIfPJRv73BpiGVsE+ZnqSsiNQ7yWHDSQ4btvazXXOOQY8vFeYmKSs66oKiPIyxWfCP2gbsbW071oSlQ/u1+0Z5IUoyfDMeFsE0KHjWrELDc/84bsMAAPCsXAHz0OEovfEHMBaXpii7LzQ3N+Ohhx7C+vXrEQqFMGbMGNx8880YO3ZsqlOjfkgp0bxkMdzL30DUe/ysdlPFEBRccjnsk6clLR/rCSei+NobUPf3R4FotMcYY2k5yn54G4Ren7S8iNQSQuCHlaV46PNabOmlEM43GPB/w8pQqKFHleJnrN2K34ysRERRUBMKwyAESoyGjJ0I1xWLYEp7nnffaS8EetF2YB8O/fZXGHLHr2EsSF3v0qOPPorbbrsNbvcXO3itWrUKf/vb33Dttdfij3/8I4xGfqNPR1JK1D3+J3jXre7xeOjwIdQ8fD+Kr70BrjmnJy0v16lzYR5SiZblb8D3/rpjG2cYioqRM38hXPMWQm+zJS0fIq0seh3+b1g5tvsCWN7kwa5AEFFIlJiMWJDnwuwcB0xZUnClM4NOh0pL9vVkswimtBb1+1H/9JP9x7U0o/GFZ1D63R8kPqkeLFmyBDfccAMA4Nxzz8Vdd90Fl8uFv/71r7jvvvvw+OOPw2az4aGHHkpJftQ375pVvRbAx0iJuiceg3XsOBiLipOTGADz0OEoue47KLr6m4i63RAGA/S5eRAsHGgQGe+wYbyDv7BReuF3UUpr3jWrIENtqmJ9mzYg0tKS4Iy6i0ajuPnmmwEAV155JZYuXYrJkydj5MiRuPvuu/H3v/8dAPDII49g165dSc+P+ud+6w11gdEo3CtXJDaZXujMFhiLS2DIL2ABTEQUB/xOSmlNy65ZiEbR+PILkBo2GYiHrVu3Yvfu3QCAX/ziFxBdlrb62te+hpEjR0JRFPznP/9Jam7Uv1D1YbQd2K863vfe2sQlQ0REScN2CEprSlDbHufed99BtKUFZd//EUSS+m/Xrm0viqqqqjBmzJhux3U6HRYsWIDPPvvsWCzFh4xEEPhoCyItLRAmE2wnToAhL1/TObpOgus3vlPPNxERDV4sgimt6Z3q15k8KrBtM+qe+itKrvvOca+3fX4QnnffRrjuCIROD8uo0XDNnR/TGJ2tW7cOADB+/PheY44eW7duHaSU3e4WkzYyEkHTKy/D885bxxexej3sk6ei4JIrYCotU3UuYbZoGltYtMUTEVF6YhFMac05czYCWzdrfp933WrkX3gpjAWFiPp8OPKXRxD4eOtxMf7NG9G0+CXknnM+8i+6LObC9OOPPwYAnHBC7+sVjxvXvs5sQ0MDamtrUVamrkCj7mQkguqH7kXrx9u6H4xG4d+4Aa2f7kDFbXfAXFnV7/nMVUOhz8tHtLlJ1fj2kyZrTZmIiNIQe4IprTmmz4LelaP9jYoCz6q3oQSDqL7vrm4F8FEyEkbzksVoeO4fMefo9XoBALm5vS/2npPzxcfg8/liHouApv+81HMB3Ini86L2kftV9YcLnU7Tjms5CxapjiUiovTFIpjSmjAaUXrjTRAx7FoTqj6EljeWqpr05F72OoL798WQIRAKhQAABkPvD1Y6rw/c1qZutQvqTgmFVK/OEK47goDKiZW5Z50HU9WwfuNcCxbBMnK0qnMSEVF6YxFMA6YoChr/+zL2/e//YM83Lm//c+3XUP3APQjV1gz4/Nax41Bx2x3Qa5zwBAi4V72tOtrz9jKN529nt9sBAMFgsNeY1tYvJvg5HI6YxiEgsHUzFJ9Xdbxn7buq4nQWCypuvR3W8RN7DtDrkXvOBSj62jdVj90XpS2IUPVhhKoPQ+n4JYqIiJKLPcE0IBGPGwdvvwWKt0thEo0gsG0LDm7bgoIrrkbeWecNaBzLyNEouuobqP3DA6rfo3c6Vfd5AoB/i4bl2DopKirCnj17cPjw4V5jjh4TQqCgoCCmcQiItDRri29WH693OFHx49vRdvAAPKveRriuFtDpYRk5Cq7TF8CQ03u7i1qh6sNoWfYavOvXQHY8EdDZ7HCeehpyzzw3qZtwEBFlOxbBNCCf/+zW7gVwF43P/ROG/AI4p88a0Fj2yVOhz81DVE0hpNfDeuJEeN55S/X5lT7u5PZl5syZWL9+PbZv395rzNFj48ePh9PpjGkcgua2GF0MbTTmqqEoujo+d3w783+0FbWPPNBt8xcl4Id7+RvwrluN8h/+X9zHJSKinrEdgmLWsuJNRD3q1kxteDb2iWdHCb0e+V+6UFVszvxFMJWVazq/3hXbUmmnnnoqgPZVIsLhcI8xW7duPS420wX3fYYjf/0z9n73Wuz55hXY+71vo+4ff0Pboc8HdF7buPGAhlU8em1vSLJQbQ1q/9C9AO5M8ftR/dC9QDSaxMyIiLIXi2CKWctrS1THRpubENSwK1dvchacibwvX9xnjGPWqSi88hqYh1TCNKT/JbKOcs6aHVNOp556KoQQ8Pv9+O9//9vteH19Pd54o31b3tNOOy2mMQaThuefxqFf3Q7vmlVQAn5ASig+LzxvL8fnd9yKZg3/b7oyFpfANvFkdcEGA3LmLoh5rHhyL3/9WPtDXxSfV/PmHUSJUtMWwpKGZrxwpBGvNrSgIdTzL/lEgxXbIShmEQ39tgDg37QBlqHDBjxuwYWXwjZhEtwrlsG38X0gEgGEgG3iSchZsAi2k6YcW/M3Z+GZqH/yr/2fVK+Ha35sS1+VlZXhiiuuwLPPPoubb74ZJ554Ik488UQAQEtLC775zW+ira0NlZWVuPjivgv4wa7plcVoeWNp7wFSovFfz0BntyPn9NgK1ILLrkRw96dQWvveTbDgK5fGfHc/npRQCN51q1XHRzVM/CNKhNq2EP5eU4+PfK2QnV5/prYBU5x2fKu8CPlGlg80+PF/MSWNjETidi7r6LGwjh4LGf0ulGArdGYLRA9LlLlOPwOtn2yHb8P63k8mBIq/8W0YCwpjzuf+++/HmjVrcPDgQUyePBlXXXUVXC4XXnzxRVRXV8NgMODJJ5+EJYN3G1NaA2h+rfud8J40LX4RrjmnQ+j1mscxD6lE+a0/Q83D9/c88VGvR8GFlyHv/C9rPnciRFua+y3YO5ORCJTWVuis1gRmRdSz6rYQfrn3MDw9tOUoADZ6/di/tw2/GjEEBSyEaZDj/2CKmc5ibX/crZI5AeurCr0eenvvS44JIVByw/dhLCuH+603ofiP36jCWF6BgkuugGPKtAHlUVpaivfffx+//e1v8eijj+KJJ544duycc87BXXfdhSlTpgxojHTnWbsaUuXkwmhLM/wffgBHjJMlLcNHYth9D8O3cQO861Yj6m6BMJlgmzAJrtPPgKGPjUuSLpadCHXcVptS44+HjvRYAHfWEI7gscN1+MkwbfMuiNINi2CKmeOUOfCseFNVrDCb4Zw6PcEZ9TK2ToeCCy9F3nlfhn/jBoTrjwB6PSwjR7dPtIqT0tJSPPzww7jtttuwefNmtLW1YcyYMZg4MT0mZyVa277PNMUH930WcxEMAMJggHPW7Jh7uZPFkJcPvStH9SRSYTRBZ87cJwaUvnYHgvisVd1mPtt8AdS0hVBm1r4CC1G6YBFMMSu89Ap43lkOqNiaNifGftt40plMcM6ek/BxKioqUFFRkfBx0o2aLYqPE9UYP0gJgwGuufPRvPQ/quL1ztT3MVN2WudW348uAaxz+3BxsdZNjIjSB1eHoJjpLBaU3XRrv497rSdOROHlX0tSVpQqptIyTfHGktIEZZJ+chaeBb0rp984Q1Ex9NxRkFLEG9G2PJ/WeKJ0wyKYBsQ+6WQM+cVvYKoa1u2Yzm5H3pcvRsWttyc/MUo655zTAZ26bynCbIbzlOxYMxkADLl5KL/lp9Dn5vUeU1SM8lt+qvpzSBRvFr22/3ta44nSDdshaMAsw0ag6s57EPF6ENjyIZS2IMxVw2EdMzbVqVESGQsK4Zx1qqrlwHLmL4TOaktCVunDXDUUVXfdB8/qd+BZuQLhI7UAAFPFELjmL4Tr1NPbV4TY8WmKM6VsNdVpx1tN6tepnuq0JzAbosRjEUxxY3C64DptXqrToBQq+vp1iDQ1ovXTT3qNsU+dgYJLr0xiVulD73Ag75wLkHfOBe1LBgoR0zJxRIlwksOGYqMBdeH+l7McbjFjtI0TOGlw47MMIoobndmM8lt+isKrvg5jl22rTVXDUPTN61F6400s/NA+YY6fB0onOiFwfUUxDP2s0GfWCVxXUZScpIgSiHeCs1jz66/As+odKIEAhNEI20mTgcoRqU6LBjlhMCB30TnIXXQOQtWHoQQC0DkcmifOEVHyTXDYcNvQcvz50BE09TDxrdhowPcrSzHSyrvANPixCM5C3k0foO7Pv++2g5vn7eVom3cWGv71LAovy87H1RRfpvLsWyqOaLCb6LDhkbHDsNHjxwceH1oVBXa9HrNyHDjZYYMulg1giNIQi+As49/yIY48cn8fERItr70CQKLwsquSlRYREaURvRCYmePAzBwu2TfYtIQjCEkJl0EPC1eb6ROL4Cxz5PE/qYpreX0p8s+/EDpbds3gJ+qNEonA/dabCB+pgc5ihWvuPJjKeKebiFKvTVHwTrMHy5vcONwWBgAYBDDT5cA5BbkYxUmMPWIRnEX8H22B4vepC5YSDS8+i+KvX5fYpIjSnBKJoPbh+xH4eOtxuyO2vL4EhsIiFH/7u7CNHZfCDIkom3kiUdy9vxr7gsdveR2RwFq3D+vcPnyzrAhnFvS/YU+24X3yLOJesVxTfGDLhwnKhGhwUCIRHLjl+whs29zj9uCRhnpU33Mn/PxaIaIUuf9gTbcCuDMJ4Imaemzy+JOX1CDBIjiLyGCrpnglHE5QJkSDQ80D/w/Rlua+g6REzSP3Q4n0v7YqEVE8bfcFsDMQ7DdOAlhc35T4hAYZFsFZRO/S9ihEZ7UmKBOi9KcEg2jd8bG64GgUTYtfSmxCRERdvNWsfoe/Pa1t2N/a+x3jbMQiOIvkXXChpnjnnLkJyoQo/TW/9gogpep475qViUuGiKgHnwdDmuIPtWmLz3QsgrOIubIKhoJCdcF6PfIuuCixCRGlsfCRGk3xSqu2diMiooHiis0Dw9Uhskz5j2/Hwdt/DET77l8svvY70HF9QVJJaWuD9721CGzdDKUtCL3TBeesU2GbdDLEIP1/JExmbW8YpB8nEQ1eQy0mfK7h7u5QiymB2Qw+LIKzjKm0DFW/uQ/V996FSFNj9wCdHiU33gTn9FnJT44GJe/6Naj/5xNQAsfPPPa9txbG4hKU3ngTzEOHpyi72Dlnnwbv6pWq482VVYlLhrLWwWAb3JEoLDodhlvNMHC3NupkYX4O1rjVLX061mZBpUXjL/cZjkVwFjKVlmHYA39E8MB+NL/yMqIeN3QWC1zzF+KQx88CmFTzrl+DI4/9sdfe2XDdERy+59eouP1XMA+pTHJ2A2MbNx46pxOK16sqvvCKqxOcEWULKSVWNHuwrNGNg53u8uUa9Jif58IFhXmw6fnkgYAT7FZMsFvxsb/vdiwB4OLi/OQkNYjwqyiLWYYOQ9n3f4Qht/8K5Tf/BI4p01OdEg0iSlsb6v/5RL+Tx5TWABqefSpJWcVX0dXfUhVnGTUGlhGjEpwNZQNFSjxy6Aj+Wl1/XAEMAC2RKBbXN+OXew/BE4mmKENKNz+sKsXYPnaE0wP4n4piTHJwB9iuWAQTUUx8763t1gLRm9Yd2xGqqU5wRvHnnHEKCr9xHdDHI2jzyNEo/+kvk5cUZbSX65uxrp/H2wfbQnj489okZUTpzq7X445hFbihohgjrV+0O1h0AgvzXLhnVCXm5blSmGH6iks7hBDibAC/R/svHH+VUt4Tj/MSUfryb9uiPlhKBLZthqmsPHEJJUjuvIVwTJuFxheehn/TBihtIQidDqYhlSi47ErYxo1PdYrUIRqWOLzFj9bmKAxmgbKJNtjyB0/XX0hR8GZji6rYj/2t2NfahuFW9ngSYNAJzMtzYV6eC8GogpCUsOt10LOHvE8D/u4ghNAD+COARQAOAfhACPGKlPKTgZ6biNKX0tb/LkXHxQe1xacTg8OBkmtvAK69IdWpUA+iYYmtLzVh99tutHm/2N5a6IHKqXZMubIArtL0nxW/0eOHN9p9e+7evNPswXBrUQIzosHIoteh9+YI6iwe7RAzAOyRUu6VUoYAPA/gy3E4LxGlMYNT2+M1fY62HQuJ1IiGJVbcU42P/9t8XAEMADIKHNzgx+t3HELz5+m/U9aRkLat6rXGE9HxhNSwI1KPJxDiEgBnSymv6/j31QBmSim/1yXuegDXA0BJScnU559/fkDjUmL4fD44HI5Up0EDkKxrqLS2qt9QQgiYK4dyLV0V+DWoTaApgtaW/ieJ6Y0CuZXJuRsc6zV0R6JojPS9hntndp0OJSaj5nGof/w6HNy6Xr/58+dvklJO6xqXtGYpKeVjAB4DgGnTpsl58+Yla2jSYOXKleC1GdySdQ2llDj4kx8hXNt/IeycczpKFixIeE6ZgF+D6kXaFLz03f0I+dW1EEz7cRmGTLUnOKvYr+Gn/lb8ct9h1fFfLc7HPC57lRD8Ohzc1F6/eNyWOQyg8wKgQzpeI6IMJoRA6Y03QWfvu6gwVVah8MqvJykryiaHPvSrLoAB4LPV6tZ8TpUT7FZUmdXdrTYIYEE+Z/wTDUQ87gR/AGC0EGI42ovfywFcGYfzxlU0GkXU64EpNy/VqdAARX1eeN5didZdnwKRCAyFhXDNmQfLqNGpTi2hQrU1iDQ1QphMMFcNg86U+ok+5sqhGHL7nah/9im0bv/ouDWDhdEIx6xTUXjFNdDbuD4lxV+gSdtauYFm9a0GqXJ5aQHuO1CD/hoVzyvIQ45h8Kx8QZSOBvwVJKWMCCG+B+BNtC+R9ncp5fYBZxYntY//Cb731wGd+qx0NhtyL7gQ+edckMLMKBZNr7yM5iWLIcPHTwjxrFwBy6gxKL3xJhjyMuvxoHfDeriXvYbgnt3HXtPZHXDNOR25514AQ05uCrMDTOUVqLjlpwgdqUVg22YowSD0rhw4ps6Anj11lEAGs7blnwym9F8uaorTjhuHlOAvh48g0kslfHZBDi4vyazvc0SpEJdfI6WUrwF4LR7nipdoayv233QDZFv3GcFKIICmF56Bb+27qLrrvhRkR7Fo/PcLaF6yuNfjwT27cPieOzHkZ3dC32nlgqjPB+/61QjVVEMIHczDR8Ax45S0uJPan4bnn0bLG0u7va74fWh581X4Nr6Piv/7OYxFxSnI7nimklKYFp2T6jQoi5RNsLXvB6tyfnfZpMHxRGJOrhPj7FasaHJjrdsHdyQCi06HyU47zsx3Ybg1uQtguSMRfOJvRUiRyDUYMMFh5fqzlBEy9lnKgVu+32MB3Fno0Oc4fO9dqLj1Z0nKimIVqq1B89L/9BsXPlKLpiWLUXTl1yEjETS88Aw8q1ZAho7ffrThuX8i/0sXIves8xKV8oB5Vq/ssQDuLNLYgJoH70XlXfdCcOUFyjLOUiPKJ9lQvTXQb6zeKDBq3uDpoS0wGnBZSQEuKylIWQ51oTCeP9KIDR7fcXel8w16LMrPwZeK8lgM06CWkUWwf+tmKP6+t5081TnmHwAAIABJREFUqvWTjxGNRqHX6xOc1eAgI2HUP/MUfBvWI+rzQmexwH7yVOSccRYsI0amLC/3O8uP6zfti3fNu8i/8DLU/fVP8G/6oMcYxe9Dw3P/RNTnQ8HFX41nqnHT/PoSVXGh6kMIbN0M++SpCc6IKP1MvaoA9buCCLf2PUFu8uX5sDj5fV6tw8EQ7tx3GO5o977rpkgUL9Q1YU9rG35UVcpCmAatjLx1VPePv2mKb3jisQRlMri0vPUGQocOwb38dUTdLUA0CsXvh3ftuzh05+2of/YpDHRd6VgFtm5WHasE/Gj+70u9FsCdNS9ZjOC+zwaSWkK07t6JcLX6RVY8776dwGyI0ldelRmLflYOW37PBa7O0F4on3geJ0WrpUiJ+w7W9FgAd7bJ68fLdU1Jyooo/jKyCI661e29flRw984EZTJ4eNevQcPTT6Kv5jr3stfR9J+XkpZTZ0o/rS1d+VQUwEe5VyzTmk7CheuOJDSeKJMUjrTgooeH4bT/LUHFZBsKRphRfIIFk7+aj4v/OAzjL2ABrMVmbwC1Kneje6vJg4iSmpsjRAOVke0QaidJHAtX8QUsw2FACIgMXJJGKgoa//2CqtiW15cgd9E5SZ/1r3e6EG1Wf8chUl+nOta38X2UXPedWNJKGKG1PUefef8vibTQGQSGz3Zi+GxnqlMZ9Fa1eFTHuqNRbPb5Md3FlWBo8MnIn5x6mw1Rr/ovYlPFkB5fj3o8cK9aAc/KFYg0NrTHDqlEzoJFcJ46FzpzcmfoJkpg2xZEGupVxcpQCJ41q5B3dnInlDlnnYrGg/tVxerz8jUVzDIYhIxGtReeCWQZMQoQQnUfdKavkUxEydMY1raecpPGeKJ0kZHtEPkXXqopvuTb3e8CBvfuwYHbb0bTv184VgAD7StK1P/j7/j8Fz9FWGXhmO609sS27d2ToEx655o7D8JkVhd76lxN5xZmM4Rej+DePah78nEcvu83qH7w/6F56X8R9aj/ZSqejMUlsE2YpDo+Z/6iBGZDRNnEpHGim1FkZClBWSAj7wTnLFiE+v/f3n2Hx1mdaQO/z/Q+o1FvlmXZlhsu2MYFA+42EDDFJBASUtjlWzaQZBMgm4VkEzZlCYGEhNTdZEmAQIDgUIMtXDDGprjjLndbsrqm95n3+0MYJKvMO9Joiub+XVeuK9YcaR78WjP3nPec5zzzZyAcf02TurQcSmPP2zjhtlY0PvLfA3aYCDc1ovGnP0bl93+U/TPCcTY/XEiKJTY+GZQmM4r/5W40/epnA9ZrmHYx7Dd8Gr4PdyN46qSsn22YOgNnf/Q9BI4c6vF1355daP/788i76lrYr78JIsU7oO3Xfxr+Qwd6HQxyIfO8BdBWjkpRVdnL1RjC4TonGvb4EAlI0NuUqF5gxtgrzNAYM+cuAFG6TTEZcNAXkDVWAJhs0g9vQTJ0hiM45PMjHJOQr1ZholEPBbtWUBwjMgQDwKgHH8Lp++8BYv23zVEYTaj68SO9vu5Y+7qsFmvhpka4t22BdeHSIdWaburiksTGFyU2PllMF89C2Te/jfbn/oLgyeM9HlMYjLAsWor8Gz4NoVDAsng5WmV2/QgeP9pjtr+HSASdL78IKRRCwc2fG+p/QkJ0Y2pQ+rV7cO7xn0MK+PscY7pkLopu/5eU1pWNdj7Thn0vO3rsF/B1RNB+PIg9z7fj8q+WoHyGMX0FZjlPaxjNB/2IhiQY8lUom2qAQskAkq0W51nwYmsHojJWY00zGVCsUQ9/Uf1oCIbwXHM7tru86D49UqRWYWW+DVfmW1M+gUHZY8SGYE1pGap/9hs0PPIjhE6f6vmgQgHD9ItR9tV7en1fLBSC+53Nsp/HueHNrA/Bpkvmou0vf0bMH7/hPISA5YrFw19UPwyTpsDwvR8hcKwe/iOHIIUjUBcUwjhzNhTaT5ZLWC5bCO/O7fDt2Tngz9OOqUHwePzlII43XoX5siugLa8c8n9DIgxTpmH0w4/BtXkjXFveQqSzA0KthmHiFFiXLIe+dmJK68lGu59vx76X+u8YE/ZL2PjIOSx7oBzFE9I/o5VNHGeC2PlsOxp2+nosX9fnKVG73Iopq/KgUDCAZJs8tQrXF9rxQpz2Z3qFwC0l6TvM45g/gB+daIS3j8mulnAEf25qw8lAEHeWFzEIU59GbAgGAKXVilEPPgQAcG3bgoijE9rKKhgHWGsZbm1GzOeV/RyhM6cgxWJZfVqXQquDdclyWSeyGWfOhibBmePhoKsZB11N/5vBhEKB0ru/gba//BmuzRshRXouKVDoDbBddQ0ca+Wf9u1aX4fC27486JoHS2m2IO/qVci7elXKnzvb+R0R7Pt7Z9xxsQiw8y/tuPLBvjfJUm9tRwOo+2Fjn4dU+Duj2P3XDnQcD+LyfythEM5Cq4vskCQJa1o70df9VKtKiW+OKkGVTt5ejWQLxWL46alzfQbg7jY73Bij12Jlvi1FlckTDAaxbds27Nq1C+fOnYNGo8HkyZNx1VVXwWq1pru8nDGiQ3B3lnkL5A1MtN2hJMnewZ/J7Dd8GuGWpgHH6MaOR/HtmdVKbCBCpULhbV+G/brVcL3zFsLnzgEKBbTVY2CeeymCJ4+jQ2ZrOADw7t2FwmGsl5KvfoMLcpewtx4JoONUEPaq9LypZ5NouGv2PN4pbac/8OLAKw5MWcU+vdnopuJ8LMqz4M1OF/a6fQhJEmwqJS63mTHXaoImjZM/25wedEbk/XL/o92JFfbMWBbhcDhw9913Y82aNfB6e0+4qdVq3HrrrXjsscdgsWTPMd/ZKmdCsFzq/HwIjQZSKCRvfHFJRrXWGiyhUKD4zq9B/dqr0FaN7rGpTFVYBOuiZbAuXQGFRpO+Igcp4uxEpLUV4dauAyUUOi0inR2IBeRt/DgvFkxsPKVf86G+11L3O/6gP2tCcPNBP5r2+xEJxWDMV2H0fHPKjgU+uc0Nf6e8AHJonROTrrFxNjhLFWjUuLk4HzcXp2/ZQ182O9yyxzaHwjjkC2CiMf3Lndra2vDUU08BACZMmICFCxdi9OjR8Pl8eOWVV7Br1y488cQTOH78ODZt2pQRwX0kYwi+gEJvgGnOfLjf3iRrvGXhkuEtKIWEEFCYzKj8/n8jdK4RUY8bCp0emorKrPxFjPl9aPrt473WBfsP7odj7esJtSADAJWZt6iyTSzB9qWxcObf1Tn3oQ8f/LkNjjM9P6jveKod1ZeZMfu2Aqh1wztDd/xt+QHE1x7Bmfe9qJrLwxQoebK1l7EQAqtXr8Z9992HWbNm9Xhv/d73voeHHnoI3/72t7F582bU1dVh+fLlaax25MvehazDyLbiagh1/N2uSqsNlssWpaCi1NOUlkE/rhbaylHZGYBDITQ++lD/G+MkCb4P90Ak0N7ONPfSJFVHqaJMdNN6hr8int3hxfr/buwVgIGuJQpHN7jw5g8bEQkNvExhqHwdiQWKzb9sQv2G9PTcppEp4V7GGXInoqamBs8//zxmz57d671VCIH77rsPNTU1AIAnnngiDRXmlgx/yU8PbUUlSv716wMGYaXFirJvfCvlxweTPO63NyFQfzjuOEnmEgehVsNyxcj8wDOSqbSJvcQF3anvgS1XyBfD2483xV3j3FofwN6/yT8xcTAS/XuVosC237fg+Bb5M8hEA5mUQG9ilQBqDdnRz1+hUGD27NkAgNbWkXEgVyZjCO6HccZMVH7vx7BcsbjHSWUKkxm2q65B5fd/DG1VdRorpIE4N9bJHquIt/lAqUTxHV+ByppZu4spvkTDWtiXucshjm92IeyXV1/9Bheiw7i0o2TK4NZWbn+yDbFI5v4dU/ZYbpe/PO0SiwlWVfas/jxx4gQAoLy8PM2VjHzZ868iDTTlFSj60h0ouOU2RDraAYWAOr9Q1lIJSp9IRztCZ8/IHh9zuZD3qevg3Phmr0NStFWjkX/TLTBMmZbsMikF1PrEQrDGkLnzAie3xT/A57ygO4ZzH/pQcfHwHABSu9SKA684Em6ME3BGcep9D6rnm4elLsodZVoNVtitWNvhHHCcWanATUX2FFU1dNu3b8d7770HALjuuuvSXM3IxxAsg0Kng6aMn8iyRSwYTPh78q6+FnnX3gDvzu0It7VAKFXQj6uFbmz/vYgp842abcThdQO/SfYYf0nmnhoX9CS2VGM4l3aYitSYfG0e9r0UvwfzhZr2+RmCKSm+UFoAAWBth7PP7qb5ahXuHVWKUm12dDXyeDz40pe+BACYN28err32WgBAMBaDNovPIshkDME04ijNFkAI2f2bhUYLodNDCAHz3PnDXB2lUulFBljL1HA2huOOza/RIn9M5q4bTLTjQ6Kz4Im6+JZ8CAXw4d87E+qvPpzLNCi3KITAF8sKsSLfiroOJw54/QhJEgrUKlxhs2COxQRVhmyIiycajeK2227Dvn37YLPZ8PTTT0OhUCAck3BP/WmYlEoss1tweZ4FqizcrJ6pGIJpxFGaTDBMnQ7fnl2yxpvnXZqVHTBInvl3FqPuBw2IBPsPX0IAo+dl9ibXiplGtB2Td5dDpRUomWIY5oqAGZ/JhxSTBjyW+kIGO992KLlKtRrcVpq9RxnFYjHccccdWLNmDQwGA1599VVUV3ftOXqmuQ2t4QhawxH8vrEV7zg9uLeqFDrODCcF/xZpRLItWylvoBCwLlkxvMVQWhWO02HZA+XQ2/o/SEKSuvrs7n818dv7qTJusQUKmfmxeoE5ZeubJ6ywQcg9o0MAY6/gUgii8yRJwl133YU//vGP0Gq1ePnll3HppV3tOLc53Xi9vedyrv1eP357tiUdpY5IDME0IhmmTEPep+JvKii49QvQjqpKQUWUThqjQtYa2R1Pt6P5YGKnzKWK3qbCrM8XxB1nKlJhxmdSd7qXwa5C1Rx5s+jl0w2wlGXH+kwaOfZ7fHjsTBO+WX8K36w/hZ+dPocPPb50lwVJkvC1r30Nv/nNb6BWq7FmzRosWdJ1ANcutxePn23u8/vec3nQGJR3qi0NjPelaMTKX30zVAWF6HztJURae35y1pRVwH7dapgumZvQzww1NsC5fh0872/76EQ9HYzTZ8K6ZAU30WWww+uccfvrAgAk4OA/HCiemP7jVfsyYYUNCpXAzr+0I+TtfSBG8UQdLru7BDpLao9yn/tPRXA3hdF+vP/lGtZyNS69sziFVVGu80SjePR0Ew54e36wbQiG8Z7Li/EGHe4ZVQqLKrW/L0BXAP7GN76BX/7yl1CpVHj++edx5ZVXAgAiMQmvtzkwVq9DQzAET7Tn77oEYH2HC58vjf+hmAbGEEwjmnXhElguXwTf/g8RbmwABKCtqoa+dmLCP8ux7nW0PfNkjw13Mb8f7m1b4N62BdalK1Fw6xe4vjgDJXLM75kdXoS8UWiMqX9jlGP8EivGLDDjxFYPmvb5EA1LMNhVqLnCgvxqbfwfMAw0BgWWf7cce17owNFNLoQ8n7xpq/UKjLncjOk32aE1ZebfKY084ZiEh042ot7f/wezI74AfnyyEd8bU57S7guSJOG+++7Dz3/+cyiVSjz77LNYtWrVx4+rFAL3V3d1pIpJEl5s7cQLLT0PwDnLmeCkYAimEU8oFDBeNA24aPC9fl3vbEbbX/484Bjnm29AYTAg/4ZPD/p5BhL1euHe8hZ8H+5BLBSA0mKDef5lME6/GIKbJPoV9sf6nDXtjxQF/M7MDcFA1yEg4xZZMG5RnINeUkitU2DW5wow49N2nPvQj6A3Co1BiZIp+oQ7WxAN1dsO94AB+LwTgSA2drqwMj91hyF95zvfwU9/+lMoFAo8+eSTuPHGG/sdqxACq4vs2OJwoyn0SZeb4/4AHj19DrPMRsyzmjPmWOhswxBMFIcUi6HjxedkjXW88Spsy69K+nHazg3r0Pbs05BCPV/Uvdvfg6qwCKV3fQPaqtFJeS7fgX1wb3kLkY52CLUa+olTYLl8IZSm7NzQpFQn/uYwmO+hLkqNAhUzM7ffMuWGujiHaHT3ZkfqQvDWrVvxwx/+EABgs9nwhz/8AX/4wx/6HFtRUYEnnngCAFCt0/YIwe5oDO+7vHjf5cWTTW34f+XFmGXh712iGIKJ4vDt2YVIe5ussVIoBNeWt5C38uqkPb9zQx1a//zHfh+PtLag4aH/QsX934emvGLQzxNuaca5Xz6K0JlTPb7u+3APOtY8B/t1q5F39ap+vjtzKVQCRRN0aDkUkDXeXKKGsYAvjUTZyheN4URA/qFJZ4MhOCIR2FJwtHIg8MnrUEdHB9avX9/v2PHjx3/8/wdaZeeOxvDo6XP45qhSzGQQTghf6YniCBw/mtD4YILjBxL1+dD27FNxx8V8XrT99WmUfeNbg3qeSEc7zv74+4h2dvT5uBQOo/35ZyBFo7Bfe8OgniOdapdbZYfg8UstXNdNlMXCkvzlTx9/Tyw1h7jMmDEDGzZskDVWr/9kg64nOvDO3hiAPzS2YLp5NJR8/ZKNIZgongRfUKVY4i/A/XFv2dRrCUR/fB/uRril75Y68bS/+Fy/Abi7jr+/APOll0Odn127kqvmmFA/2YWm/QO3P8sbpcH4pdYUVUWUOVyRKHzRGExKBUxp6JaQTEalEjqFQEBmsFULkbIOEXl5eVi0aFFC39MeDuOwN/6H+I5IFDtcXlxizeyDfzIJQzBRHKqikoTGa0oSGz8Q39498gdLEnz79gCKxPqwRj0eeN7fJm9wLAbXpvXIv/EzCT1HuimUAovuKcXmXzShYVff/UELxmmx6J7SrN3E5euMIOyPQWtSprxFWq4KeqI4vtmNzjNdO/XzKjWoyaLDQGKShK1OD9Z1OHHE1xWyBIApRj1W5Fsxy5KdYUolBC61mrG+0yVr/FyrKaXdIQYSlSTU+wI4P5VyJhDEa20OBCV5gX6nmyE4EQzBRHEozYm9qeknTknac8dkzgKf5z9yGFL1OEiSJPuWvv/QAUgh+e12fHt3Z10IBrpadS35Vhla6wM4UudEx8kgJADWMg3GL7WgNAXHDCdbLCrh+NtuHF7n/KRHrwBKJusxYYUVo2bzzXA4xGISdj3TjkNrnYiGeoaTXX9th21VBFJMgsjgHfsRScJjZ5rwgcvb4+sSgA+9fnzo9WNJngX/VFaYlcuDVuZbsanThXjtwcVHYzPFug4n/nRO3h6Uvsid/aYuDMFEcfj27EpofODEMRgmX5SU51ZZE9ux7Hn3HYR0Rpz+1tdhXboC1mVXxn0DiwXlrZX9ZHxiwTzTFI7ToXCcLt1lDFk0LGHTI+fQsPuCmW0JaNrnR9M+P8YvtWDO7dkZYjLZ1t+09Nt7OhKU4HdE8c5vW7DgXzP3cJA/n2vrFYAvtL7TBbtahRuL7CmqKnkqdVr8c3kRftfQgv5ioQBwe1khavSZ83pwQkZbt4HYsnwpS6plxvw/UQaLejwJjY8lOH4g5vkLBvV94ZZmtP3lz2j+3eOQ4txGU1kTmwVRJjiehse7f2jpHYAvcORNF/a91JmiirJLJBSDrzOCkC+xNfynt3tkHb5yfLMbZ3YMHDLTxRGJYH2nvBZir7c5EEziPodUWphnwbdHl2GioXfIHW/Q4b6qUiy1Z9br2VDncS+1Zc9ynEzAmWCiOJSGxFrOKBIcPxDDtIuhKizqdeyzXJ5334F29JgBW7bpJ06B0paHqENeWDLPu3RQtVDyeNvCOL5Z3il4B15zYNJVNig1nPMAgOaDfhxa68SZDzwfH6VdVKtD7XIrquaZoIizhOHwWvn9Zw+vc6IyA3smb+p0ISozbXljMWxzerAwL3MOZknEVJMBU00GnA2EcDoYBCSgQqfBKF16TleMp0yjHvT31ui1GNdH4Kf+8VWRKA7jzNkJjTfNuiRpzy0UCpTe/c0hBWtn3T8G7FghlEpYFy2V9bMURhPM8wY3O03JU7/RJbtpSdAdw6n3M3NGMtV2P9+Otd9vwKl3PwnAANByOIC3f9mMTT89h2i4/3QY9sdwbt/AHUa6a9zrQziQebOoZwOJHbk7Eo7ordBpMN9qxnybOWMDMNA1e60cxOolq0qJuysyd/lNpmIIJorDcNE0qAqLZI3VT5wMTVl5Up9fO6oKFQ88CMPUGQN3TO9HpL0N/kP7BxyTd/UqGOIcKy00GpR85etQaDnTkG7OhnD8Qd3Hn83+EDNUR9Y7sfdvA9/tOLvTh23/0/9dl7A/ltj9agkIJ7jcIjUSex3hivLUyVOrsNAmf9ZdAJhpNuC/xlSgRJtYZyBiCCaKSygUKL7jKxCagV9gFCYzCr9w+7DUoCkrR9k3voWqnzyGwttuhzLBDXOR9vYBHxcqFUq/di9sV13T56yzbvwElH/rOzBMSl7nC0qhHE8xUkzCh2vkLfc58bYb7pa+P2SoDYqEPocK0fU9maZKn1hYqsrgmdOR6IulhZhhHrhbTZVWgy+U5OMX46twb1UZioawjCKXcU0wkQz6cbUou/d+tPzhdwg3NfZ6XFtdg+I7vgJNSdmw1qEuLIJ18TK43t6IqNMh+/uEOv4LpFCpUPDpW2FftRreXdsR6WiHUKuhnzAZ2spRQymbksw+WoNT7yYyPrdDTMNuH7xtEVljJQmoX+/Exbf0PhBGrVOgbLqh317TFyqbbsjIvtMLbRY819yBsIzes2alAnOytF9wtlIrBO4dVYq3HG7UtTtxvNsR0LUGHZbbrZhvNbHrSxIwBBPJpB9Xi6r/fhS+Ax/Cu3MHYn4flGYzTJfMh25MTWprqZ2I4Inj8gYrFNCPnyD7Zyu0WpjncvNbJhu7yII9L3QgJiPX6fOUqJyVeZuzUqnzdGJtpzpP9798pHaZVXYInrA8szoPnGdWKbEi34pX2+J/kL6uMA/qDO53PFIphMCiPAsW5VnQFgrDH4vBqFTCrmZsSyb+bRIlyDDpIhgmJacP8GBZFy+DY+3rXdNWcRinz4TKnp+CqihV9FYVapdbcfD1+J0Kpt5gh2IwO22oTxUXGzF+qQVH3hz4NLLxSy0on5G5Hz4+W5wPVySKzY7+u4xcU2DD1QV5KayK+lLApQ7DhiGYKAupi0pgXX4lnGtfH3CcQm+A/cZPp6iq5Aq4o/C0hCEUApZSdUbeVk6nmZ8rQNAdG7Bn7dQb81C7LDNnI1MprzKx5SC2yoHXzM65vRB6mwoHXnN0bZbrRm1QwGBXYc6qwoTrTCWFEPjXimLMs5qwrsOJ3W4fJABKAcw2m7A834pJRn26yyQaVgzBRGkQC4Xg3f4+wm0tECoVdONqoR9Xm9DPKPjM5wBJgrPujT5nhJUWK0q/fi+05ZXJKjsl2o4FcOBVB06//0kbK7VeYMxlFkz6lA3mIs6KAIBCIbDgK8WovtSEw3VONOz0QZIAhVpg9BwTaldYR8TJeMlQNsMAY4FK1rpgIYDxSwb+4CCEwLTVdkz6lA0n3nHDcaZr+UTeKA1GzzfjnXdPZ816zRlmI2aYjQjHJARiMeiVCqiypHaioWIIJkohKRpFx5rn4dxQh5ivZ+9WTWUVCj7zWRimDNyq7DyhUKDws1+AdfEyODfUwX9gH6RwGAqdHkW3/wtMc+ZDEaejRaY5sdWNd37V3KOHKwCE/RIOr3PixFY3lv57GQrGMtydVz7diPLpRsSiEiLBGNQ6BQTXcPagUAhcdF0e3v3f1rhjqxeYYS6W90FLrVPEDczZQq0QUCt45C7lFoZgohSRYjE0/ern8O78oM/HQ2dOofHRh1By51dhmj1X9s/VlJSh8LNf+PjPJzZtguWyhUMtN+Xajwf6DMDdhTwxrH+oEaserYLOzDfs7hRKAY2Bfyf9Gb/UCl9HBHtf7L9VWvl0A+b9c2YvYyCi5OEiO6IUcb75Rr8B+GOxGJp//ytEXPKPZh0pDrzmGDAAnxd0x3B048Cbkoj6Mv3T+Vj+3XJUzTFCdPu8UFirw4K7irHovlIeL02UQzgTTJQCkiTBuX6dvLHhMFybN8L+qeuGuarMEfREceo9j+zx9RtcmHItd61T4kom6VEySY9IMIagJwa1TkBj5Aw6US7iR16iFAieOIZwc5Ps8Z5tW4axmszjbYvI6nl7nrs5jFgskfNriXpSaRUw5qsYgIlyGGeCiVIgmuDyhqgr+bf7w/6udlodp4KABFgrNKi53Aytqe8QEAnFcGKLB/UbnHCdC0MogPwxOoxfZkHFxUYokrj5SiT4cVwIJHR8LRER0YUYgolSQGgT62YgdMnrfiBJEvb+rRP7X+1EJNBz9nTXs+2oXWbFxbfm9wi1joYQ1v+4sVdLqcY9PjTu8aFwnA5Tb7LjzPseeFojUKiAogl6jF1oGdSGNXOJGhqjAiFvLP5gAPljtFnTgoqIiDITQzBRCuhqxkFhMiPm6f9gg+6M0y5O2nO/98dWHKnre2Y5GpJw4DUHfJ0RXHZ3MYQQ8HVGUPeDBvg7+9+l1lofwPofNfb42tkdPux5rgNTrsvDtBvtCdWo0ihQc4VZ1gloAHgABBERDRlDMFEKKDQaWBZcAccbr8oab128LCnP2/ihr98A3N3JrR5UzjKier4ZB193DBiABxINS9jzfAeiIQkX35LYUc2TrrLhxBYPAq6BnzuvSoPR80yDqi+ZJEnCyZMncfz4cXR2dkKj0WD8+PGora3lLHUCIsEYPK0RQJJgLOTJgESUOgzBRCmSd8118O7ZifC5xgHH2a5eBU1ZeVKe8/A6+WuRD69zYtRsE45uGvp65H0vdaLmcjOs5fIP6zAWqLHk22XY8FAj/I6+g3BelQZL/r0s7W2sbrnlFrzxxhtwOBy9Hquursbtt9+Oe++9F5osO6wkldzNYRx43YHjm90fHz2s0glUX2rGpKttsJbx746IhhdDMFGKKI0mlP/7d9H068cQOHyw1+NCpUbeNdfBvurGpDxfLCLh7A5v/IEfaTkUQOtRP4Jueety4zlc58QlX0wD5XZXAAAgAElEQVTs4IH8ai1WPTIKR99yo369E66mMAS6NuTVLreiaq4JSnX6Z1nffPNNOBwOVFZWYsKECbBarWhtbcXu3btx4sQJPPDAA9i1axdeeOGFdJeakZoP+bHhJ+cQ9vX8txYJSKhf78KJLW4s/EYpyqYZ0lQhEeUChmCiFFJZbaj49n8icOIYXJs3ItLeBqFUQjeuFpbLF0FpMiftucKBGKQE82zIk5wADABnd3oTDsEAoDEqMekqGyZdZUtaLcn2i1/8ApdffjnKy3vO2LvdbnznO9/BY489hr/97W/YsmULFixYkKYqM5OvI4KND/cOwN1FghI2/ewcPvXflbCUcEaYiIYHQzBRGuiqa6CrrhnW51DrFBBKQEpgea+1TJPw9/QnEhy5fXxvueWWPr9uNpvx8MMP48UXX8SZM2fw9NNPMwRf4HCdU1YXkEhAwqE3Er+bQEQkF3cgEI1QCpVA5Uyj7PHFE3WwlmsS+p6B6Kwj4xCCtqMBbP1dC/7xn2ex98UOBL1RxKL9B3y1Wo1JkyYBAEKhUKrKzAqSJKF+g/w158c2uxENj9wPU0SUXpwJJhrBapdbcfp9eeuCa1d0LT+YdLUNZ7Z7E15KcaHqS5O3tCMdgp4oNj/WhHMf+j/+WuvhAHY/14GiWh2W3d/3Bj2fz4cdO3YAAGbMmJGyerNByBtDwCn/NkPYF4PfEYGpUD2MVRFRruJMMNEIVjrFgAkr4/fUHbPAjKo5XTPARbV6zP2nwiGdyKbUCIxbZBn8D0izSCiG9T9u7BGAu5v+mfw+A/ChQ4dwyy23oK2tDbW1tbj99tuHu9SskujJgF3fk/6NkERD0RkK4a0OJ97qcMIZSsJaM0oazgQTjXCXfLEQOqsS+19x9NqMpNIK1K6w4uKb83v0th232ApziQb7X+lE424fpI/uSCtUgM6qgq+950ly3QklsOCuYugsw78cIuiJonGvD5FADFqLEmVTDVAloX1a/XoX2o4F+33cVtG1Weupp57Cgw8+iFgshpaWFrjdbuj1enzpS1/Cww8/DL1eP+RaRhKNQQlziRruprCs8fo8JfS27F5W4+uMIBqSoLMq2QM5x2zpdOFPTW1wR7u/7rbColTg9tJCzLEl926ZIxxBIBaDWaWEUZndvzepwhBMlAOmXm/HxCttOLnVjY6TXetUreVqjLnMDI2h7xfLkkl6lEzSw9sWhrs5DKEQsFVqoNYrsPu5Dhx5s/cGp7zRGsz8bAHKpg5vaytfZwS7nmnHyW2eHmtGNUYFxi6yYPpNdqi0gw8cR+rk9Vd2OByor6/v8TWVSgWNRoNIpP8PCrls/BILdjzdLmvsuMUWKJRZOBMsAYfWOnCkzgXH2a7fN4USGHWJCRNWWlFUyw9HI90fGlpQ19n3+ndXNIafnW3GNYEAbi0Z2sbPiCRhc6cLdR0unAh0fXAXAKabDVhpt2KaOTl7PEYqhmCiHKHWKTBuceLHDRsL1DAW9FyTefEt+Zh6Qx5Of+CFtzUMoRIortWhcPzwv7l72sJY+70GeNt6h8yQN4YDrzrQejiApfeXDWrmzdMWhrNx4JlKX0cEOosSX/rSl7B69WpIkoT29nZs2LABjz/+OH73u99hzZo1ePfdd1FdXZ1wDSPZuMUWHF7n7DolbgD6PGVWHo8d9EThPBfG6Zfbenw9FgVObvPg5LsezPpcASZdnbktAGlotjnc/Qbg7l5pc2Ky0YDpgwyqwVgMD586h33ensu2JAC73D7scvtwTYENt5YUDOrn5wLemyGiQVFpFRizwIyLrrdjyjV5KQnAALD5saY+A3B3rfUBfPDntgHH9Ccqo7XbobVdM8VGoxElJSUoLS3FlClT8NWvfhUffPABqqur0dLSggceeGBQNYxkGqMSS+8vg6mo/zkYg12Jpf9RBr0t++ZpNj3ahEhggF2lErD9yTaces+TuqIopf7vnPzXnv9paBn08/zmbEuvAHyhV9ocWNve+2RL6sIQTERZo7U+gLb6/tfqdnfibTeCnsQ3oeisyribAo9udKHuhw3Y/2on6jc6IUmfBGer1Yq77roLAPDSSy9xWUQfLCUaXPPQKFzyxYKP11cDgKVUjVmfz8c1PxmFvEotXE0h7Hu5EzufacO+lzvhasrslnPNh/xoPjBwKDnvwzWdw1wNpYMnGoUrKv91pz0SRSiB8ec1BkN4zyXvg9RLrQ7EJLYa7Ev2fcwmopx1ZL28tboAEA1LOLnVg9rlid1S15qUKJ9hwNmdvh5fVyiBaTflo2SSHo4zQRyuc2LHU+0Yv8yCcYt6puaamq6DULxeL/x+P8zm7G4XNxzUegUmrLRhwkobYjEJkPDx+l93Sxhv/7IZjXt9Xfd2P7LzmXaUTTVgzu2FMBdlXtu0+jfl90DuOBlE29EACsbqhrEiSrXDcWZm+3I6GMJYQ2J30tZ3uCA31nZEItjp9mGWheuDL8QQTERZIeyP4XSCt5B9nYObhZ2w0tYrBM+6rQATPuqlXDheh3FLrPC0hqE1995YuH37dgBdJ8iZTKZB1ZBLFN3aoLmbw3jjP8/C7+hjdkwCGvf48MZ3z2Ll9ytgLs6sIHx+E1wi4xmCRxYFEt/IqRxEP8qGYGL/1hqCIcwCQ/CFuByCiLLCwdcdCPsTu6Wn1Ayus0DZVAOm3pjX42uVs7rCbENDw8dLHEyF6l6b706cOIFf//rXAIAvf/nLPVrPUXzv/Lq57wDcjd8RxTu/aU5RRQngpc55k42Jf6gZpUn8wxz/qSUHZ4KJKOPFYhKOrJd/q/m80imDb9U2/aZ8mArV2PdSJ1znwlDru8LuX//6Vzz22GNYvXo15s6di4qKCqhUKpw5cwYbNmzAk08+CZfLBaPRiK997WuDfv5c1HEqiJbDAVljWw4F0HkqiLwq7TBXJV9elQYdJ+StWe8anzm1U3JolEoUqJRoi8hb51uiUUM5iJ6+o3Ra7PL44g/8yGidJv6gHMQQTEQZr/NUCL6OxJY22Ku1KBw3tFvNYxdaUHOFGc0H/B/PKgshcPr0aTz66KP9fl9NTQ2efPJJtkdL0Mmt7oTGn9jqyaggWbvUimOb5P03FNRokV+dObVT8txZUYz/Otkoa+xdFUWDeo4ldgtebuuUtS64SK3CVNPw9m7PVgzBRJTxIsEBWk71Y/ZtyemNKYRAyeRP3kC++tWvYvbs2di4cSP27t2Ls2fPIhwOw2azYc6cOVixYgXmzZsHtTqz1qtmg4A7sesccGfWEbQFY3Uon2FAvIZUQgBTV9tTUhOl3mSTAbcW2/F0c8eA424vLUh4Q9x5RRo1rrCZsckR/0PXjUV2Lsvqx5BCsBDiJgDfAzARwCWSJG1PRlFERN3prYndLtSaFSieODx9i5VKJRYsWIAFCxYMy8/PZWpdYm/UGn3mbWu5/Ksl+Mff9/f7uFAAc/+pEBUzuElpJLum0I6JRj1+dbYF50I9D98p16pxV0UxqvVDu1P1T2VF8MZi+MDl7fNxAeDm4nxckWcZ0vOMZEOdCd4H4AYAv0tCLUREfbKUapBfo0X7MXnrLcct5ot+NqqcacTB1+W3wauYmXlBUq1XwFKqxpSvFOPwOida67vWOKu0AtULzJiw3DqsSzhiEQmn3vfg7E4vwn4JOosSo+ebUHYRb4en2liDHj8bXwUA6Ah1dXOwa5K3NlelEPi3yhJsd3tR1+7EPq8fEgC1EJhnNWGF3YoaA7uPDGRIIViSpIMAOM1ORMNuwgor3vl1/NOVFEpg/JLsO26XgJLJBlgrNHDKaDVmq9CgZFJqTikcjDGXmTHmMjMiwRgiIQkag+LjPsjD5ewOL7b9T0uv7hpHN7pgLVfj8q+VIG8U1yGnQzLDb3cKIXCJxYRLLCaEYxKCsRj0SsWg2q7lIiEl4RQRIcQmAPcMtBxCCHEHgDsAoLi4eOazzz475Oel5PN4POxrmuVG8jX0tkUQcPW/DlQIwFiohtaUebfJ5RrJ10+OSFCC61wI0gDLg4Wi6+6ASpuZb/TpuIZhXwzu5jAGektXKAUsZWoo1Zn595ZJcv33MNtdeP0WLVq0Q5KkWReOizsTLIR4E0BJHw/dL0nSS3ILkiTp9wB+DwCzZs2SFi5cKPdbKYU2bdoEXpvsNpKvoSRJ2P+KAwdfd/Sa7cobpcGMm/NRcXHm3SJPxEi+fnK1HPZjy+PN8LT27ghiKlRhwV3FKKrN3FngVF/DWETCC185iYAz/kZB42Q9lnynPAVVZTf+HmY3udcvbgiWJGlpMgoiIhoqIQSmXJuHSVfZcHanF67mMBQKgYKx2mENRSFvFG3Hg4iFJRgLVcir5C3l4VRUq8f1j1WhYZcPp973IOSLQWNQoOoSE8pnGCAUnMns7tT7HlkBGACa9vvhaAjBVs6+sURskUZEWUehEhh1yfDfqvS0hLF3TSdObnUjEvzkPnP+GC0mXmXDmAXmYa8hVwmFQMVMY0Zufss0Z3f03R1goPEMwURDPDZZCHG9EOIsgHkAXhNCrE1OWURE6dV5KojXHziLoxtdPQIwALQfD2LL483Y/lRbmqoj+kQ4kFh/5bA/8b7bRCPRULtDrAGwJkm1EBFlhGhYwvqfnBtwEx4AHHjVgbxRGtRczpZslD5ac2J9tHUJjicaqbJ3CzUR0TA5uc0NX7u8Y5oPvBbvfDCi4VU9T/6yHKEEquay6wERwBBMRNTL0U3xjyI9r/NUCO3HA8NYDdHASqfqYSmTd0z3qNkmGOzcDkQEMAQTEfXiaQnHH9R9fB+tvIhSRQiBy79aArVh4Ld0c4kac75cmKKqiDIfQzAR0QUUqsRacCU6PlOFvFGc/sCD41vcaPzQh1h06IcpUWrYR2ux8vvlKJ7Y+5hcoQSq5hix8vvl0Fm4HpjoPN4TIRqhYhEJZ3Z40Xk6CADIG6VF5UzjiAlsw6l4gg7uJnmzwQoVUFCT3X2DfR0R7H6+o1crOH2eErXLrJhybR7/3WSBvEotVvxnBRxngjiz04eIPwatRYnRc7kEgqgv/K0gGoEOveHAhy91wt/Zs7uBPk+JKdfmYeKVtjRVlh1ql1tlrwsedYkJelv2vpR6WsJY+2ADvG29l3T4O6PY/VwHWg4HsOjeUigZhLOCrVILGw90IYqLyyGIRhhfRwTvP9HWKwADXaHmgz+1YcfT7G87kPwxOoyeH38HvdqgwNQb7SmoaPhserSpzwDcXeMeH3b/tT1FFSWH3xFBy2E/WusDCPnYF5eIesve6Qsi6qVpvw9+R/zjU/e/4kDZNANKpxhSUFV2uvTOYgDAya2ePh/XWZRYdG9pVp+81XTAj46TQVlj6ze4MG21HSptZs+dtB7xY/8rDpzZ6YX00a+CSitQfakZk6+1wVKSvuslxSQ07PKh5UgAsagEc5Ea1ZeaoDFynS5ROjAEE40gh9Y6AZkrHQ6vdTIED0Cp7tpxP/HKAA6vdaL5kB/RsARjgQpjF1ow5jIz1LrMDoTxHHvLJXtsyBvD6Q+8GX1U9LHNLmz9XcvH4fe8SFBC/QYXTr3nwZJvlaJwvD7ltZ3Y6sauZ9p7dRLZ8XQbxi6yYObnCrjchCjFGIKJRohIMIYzO7wwLJE3/swOL8KBWNYHueFWOE6HwnG9d9yPBF6ZB4IMdnwqtR0N9BmAuwt5Y9jw8DmseqQqpV0Sjqx34t3/bQX6aLYRCUo49IYT7qYwFt1bCoWSQZgoVfjuRzRChLyxAQPAhaRY1/dQ7lKqEwtciY5PpQOvOWT9+w+6Yzi6Uf4M+FB52sJ47499B+DuGnb7cOgNZ2qKIiIAnAkmiivgimD7U2049a4X0VDXO5lSI1A5y4jZXyiA3poZv0aqQczoqvWZG2po+JVM0qNhl0/++MmpX0YgR9ATxen3+1673Zf6DS5MWZU3jBV94kidS/aH08N1Tky8ygoh+HtJlAqZ8e5NlKGaDvpQ94PGXm9i0ZCEk1s9OPWuB0vuK0XZdGN6CuxGY1CgeKIOcqNA8UQdNAZuyMkmHaeCOPKmE52nQwCAvEoNxi+zwl41uHZYYxdZsPu5DkTD8Q/FKByvG/TzDDdPawSxBO6CuFvCkGIShGL4w+apd+WHc3dTGB0ngsgfMzKX3xBlGoZgon542sJ9BuDupBiw/ifncO0jo2AtTX+XgNrlVuw4KG/s+GXW4S2GkiYciGHL4804s93b4+uthwM48qYLFTMNuOyuEqj1id0N0JqUmLbajp3PDNz+TKEWmHlrfsJ1p4oiwc9yQoGUBGCga5Y6ofFuLlEiShWuCSbqx/v/1yrrNqYUA97/Y+vwFyRD1RwTtMb4v9aVs4wYPTd+H1xKv1hEwsaHz/UKwN2d3eHDhofPIRpJ/JjjKavyMP3Tdoh+/tlojAosvrcURbWZuRQCACwlamhM8t/OCmpSN9Oa6AcTtYFvy0Spwt82on407Ja/VrJpvx+xSPpncIRCwFSsxrjFlj5DjVAAYxdbcPnXS1I2E0ZDc2KrG037/XHHNR/w48QWeafcXWjqDXZc/4sqXHRdHvJGa2AuUaOwVoc5Xy7Ejb8ajbKpmd1KT6lRYOwVFtnja1N4F6RylvylUga7EvljMnPJCdFIxOUQRH1wN4cS7rTQeTaE/NGZsZZv3h1FmHqjHfUbnHCc6Vo/aqvUYNxiK4z5/LXPJofXyu8YcLjOibEL5YfB7kwFasy4OR8zbs7cZQ8DmXS1Dce3uBFwDvyLmz9Gi6oU3gWpXWaV3fVh3BIrW6QRpRDfDYn6EBtEO9SYjM1FqWTMV2H6TdkZaKhLyBtF2zF5J7oBQPuxIIKeKLSm3NvwaLCrsOw/yrD+oUb4OvoOwvljtFj8rdKUtnqzlmsw7SY79jzfMeC4/BotJn9K5kk3RJQUDMFEfTAWqwCBuL09u7OW8zYmJVcklPgHq+ggvmekyKvSYtUjVTj2thv1bzrhOheGUAL5Y3SoXWbBqNkmKNJwKtu0G+1Q6wT2vNCJsP+CZVMCGDXLiPl3FmX8kdREIw1DMFEfVCoF8sdo0S5zFi5vlAYabmihJNMaFVCohey7DAoVEtogNhKp9QpMWG7FhOWZ1f1k0tV5GLfEihNb3Gg5HEAsKsFcpMbYhRaYS9TpLo8oJzEEE/Vj9m0FeOM/G2SNnfk5Ljug5FNqFKiaY8SJLfJ6zVbNMUGlye0QnMnUOgXGL7Vi/NLMCuhEuYqvlkT9KKrV4+LPxg+3U1fnoWxq+g/LoJFp4kpb19KceAQwYSXXlBIRycUQTDSAKdfmYdF9pbCU9r5daS5W4YpvFmP6as4C0/ApGKvDLBl3GmZ+Nh+F4zKjOwkRUTbgcgiiOCovNqLyYiO87RE0H/QBElA0QQdTYfpPiEuVcCCGE1vcaNzjQzgYg96mwphLzSidqocQbOk03CZdnQeDXYW9L3Z+3PLuPFuFBhfdkIfq+eY0VUdElJ0YgolkMuarMGbB4HqwZrNjb7nw/p/aEPb13NV+fLMb1nI1rvh6CWyV7Iwx3EbPM2P0PDOaD/nhOB2CBCCvUoPiiZl7khsRUSZjCCaifh3d5MLW37b0+7izIYy1DzbgygcrYCnNnZnxdCqeoEfxBAZfIqKhYgimrBaNSDj9ngdHN7ngbYtAoRYonqBH7XIrbBUMZUMR9sfwwZ9a444LumPY/lQbFt9bloKqiIiIkoMhmLJW5+kgNvzkHLxtPY93c5wO4fA6J8YttmDO7YU8hnSQjm12IeyX15+2YacPntYwTIXp6XfqaQujaZ8fkZAEg12J8mnGlJ4KRkRE2YchmLKSuyWMuh80IuDq+3hUAKjf4IIEYP4dRakrbARp2OWTPVaSgIbdPtQuS23/U0dDCDv/0o6GXV5I3ZYs6yxKjFtiwbQb7Wk5IYyIiDIfW6RRVtr7QseAAfi8oxtc6Dgl79Q36ikSjMUf1H18ILHxQ9V+IoA3vnsWZ3f0DMAAEHBF8eGaTmx4+Bxikdw9RpiIiPrHmWDKOkFPFCe3yTtBCwAOr3Ni3j9zNjhRWrMyofE6a2LjhyIWkbDpkSaEvAMH78Y9Pux9sQPTP81ezkSUHQ56/ajrcGKvx4dgTEKeSonLbGYssVthVzO2JRP/NinrtB4JIBqWP7vXtN8/jNWMXNULzDj9vlfWWJVWoHJW6k7NO/2Bp9da8P4cedOFi26wQ8llEUSUwaKShN82tOBth7vH11vCEfyttRMvtzlwV0Ux5lhNQ34uVySKDZ1ObHa40RGOQCMUmGLSY5ndionG3Ok+wxBMWSeRAAyAt8MHqXKWEcYClaywOeYyMzSG1M0EH3/bHX/QRwKuKBp2ezFq1tDfOIiIhsv/Nbb2CsDdhSUJvzjbhP9QlmGyyTDo59nt9uLnZ5oQiH3y3hhAFFudHmx1enCp1YQ7K4qhGuJBSDFJwh6PD+s6nDjuCyIGCSUaNRbbrbjUaoJGkf4VuemvgChBxvzEPrsZ7PysNxgKhcAV/1YCtX7gF8L8MVrMvLUgRVV18TvirwfvMb4zsfFERKnUFAxjfacr7rioBPy1pWPQz1PvC+CR0z0D8IXecXrwvw3994eXwxuN4r9ONOChU+ewy+2DMxqFOxpDvT+I3zW04Bv1p3E2EIr/g4YZQzBlnYKxOlgT6AFccwWPkx2sghodVnyvAsWTet8eU2oExi62YPl3yqHWp/alJNH2ZyoNl0IQUeZ6s9MJufcsj/gCOOUf3Ibv55rbEZbiP9MmhxsNgwypUUnCT06dw0FfoN8xbeEIfniyAR1hecvahgunyCgrTbzSinf/J/5BDjqrEmMWMAQPhb1KixXfLYejIYTG3T5EgjHorEpUzTFBa0rdEojuSqca0HK4/xfY7oQCKJky+FuHRH0JeqJoqw8gGpFgLFAhv1qX7pIoi9UPEBj7HO8PoEqf2HH1jcEQ9nnl75FZ1+HEl8oKE3oOANju8uKwjP+ezkgUr7U58PnS1N5J7I4hmLLS+CVWtB8Pon59/7eP1AYFFt1TCpWWNzySwVauga08M07hG7fYgg9f7EBMxiqHipnGhJfQEPXH3RzG3r914OQ2T4/9CXmjNJh4lQ1jF1rSWB1lq5iM2dnuogmOB4CjvoDs2WagK2gPRl2HU/bYtxwufKbYnrb1wXxnoKw175+LYB+txcHXHXCdC3/8daEERs0yYtpN+Tw6eYQy5KkwbbUdu/468No4jVGBi29mezRKjo5TQdT9oAFBd+/WfJ2nQ9j62xZ0nAwCo1NfG2W3Yo0G9QkscSjRJH46Z6I7IwYTtAHImgU+zxONoSEYRnWCs9rJwhBMWa12mRXjl1rQcjgAb1sESpVA4XgdN8PlgIuutwMA9rzQ94ywsUCFRfeUwpohs9eU3aJhCRseOtdnAO7u0BtOFN+a2oNjKPsttluwxSmv602hWoWLBtEdoijBHsNFgwjaQOLhebBhOxmYFCjrCSFQPCF3+hrSJy663o6xiyyo3+BC414foiEJ+jwVai43o3KWEQolN8RRcpzc5oavQ94mnoAzvZt9KPtMMupRo9fimIzZ4KsLbFAMon3ZJKMeJRo1mkLh+IMBLM4b3NKeogSeQwGgQJO+KMoQTERZTW9TYeoNdky9wZ7uUmgEO7pJfm/qSEhC29EACsZysxzJ981RpfjBiQY0DhAgl9utWJlvG9TPF0LgUwU2/G9j/E3lo7QaTBtkL+JFeRY809wua+wMsxE2VfqiKHcMERERxeFtkzezdZ6nNbHxRHa1Cg/WVOD6wjxYVT0774zVa3F3RTG+PIhuDd0ttVtxZb51wDGFahXuqSod1Gwz0BWCjcr48VKga1Y7nTgTTEREFIciwWO3FQn2siYCAJNSic8U5+PGIjtOBYIIxSTYVEqUapO3t+ELpYUYp9fh9XYHjnZbfmFSKnCFzYJrC22wDmF21qJS4p5RpfjJqUb4+zmUQwD4YmkBJqX5iGaGYCKS7fQOD/wdUdirNSgcy3XYlDuKJ+jhapQ3uysEUMilEDQEKiFQox++f0PzbWbMt5nRGAyhIxyBWiFQrdMmrVXZRKMePxhTib+3deJdp+fjAzoEgClGPa4pzMPUIRz9nCwMwUQ0IL8zivUPNaLjeM8NG0IJVM014fK7S9JUGVHq1K6won5D/GNtAUBjVEJv49srZb4yrQZlSZxl7q5cp8FXKorx+ZICnAkEEUVXa7fBdp0YDlwTTET9cjWF8MKdJ3oFYACQosDJdzx47o4TaaiMKLXsVVqMuTz+6ZMaowL6vPScpEiUiSwqJSabDJhqMmRUAAYYgoloAC/fexpSnJanAVcUr/776dQURJRG8+8owpjL+g/COqsSS79dBiXXAxNlBd6vIaI+HapzICZzg3vHyRBCoSg0Gs6A0cilUAks+EoxJl5pxeF1TjQfDCAajsGYr0bNQjPGLDB3HdN+Nt2VEpEcDMFE1Kc9zw98JPGFtv26BVd8vXSYqiHKHPljdJj/L9z4RpTtuByCiPoU8iZ29GvHqdAwVUJERJR8DMFElBSD7KtORESUFgzBRNQnnSWx9b2FE3h7mIiIsgdDMBH1afZtBQmNn3t7YuOJiIjSiSGYiPo0ep4Zar28NQ4lU/RQKtkZgoiIsgdDMBH168ZfjoYyTm9za4UGyx8oT01BREREScIQTET90piUuPmJalTNNUJcMNGr1gtMv9mOVT8dlZ7iiIiIhoB9goloQEql8uP+v6FQFP62KEzFSi5/ICKirMYQTESyaTRKaMoYfomIKPtxOQQRERER5RyGYCIiIiLKOQzBRERERJRzGIKJiIiIKOcwBBMRERFRzmEIJiIiIqKcwxBMRERERDmHIZiIiIiIcg5DMBERERHlHIZgIiIiIso5DMFERERElHOGFIKFEA8LIQ4JIfYKIdYIIWzJKoyIiIiIaLgMdSa4DsAUSZKmAjgC4NtDL4mIiIiIaHgNKQRLkrROkqTIR398F0DF0EsiIsos7trEG84AAAfsSURBVJYw2o8H4G4Op7sUIiJKElUSf9aXAfw1iT+PiChtYjEJxza5cbjOiY4TwY+/bh+tRe1yC2oWWqBQiDRWSEREQyEkSRp4gBBvAijp46H7JUl66aMx9wOYBeAGqZ8fKIS4A8AdAFBcXDzz2WefHUrdNEw8Hg9MJlO6y6Ah4DVMDndzGCFvrN/HNUYFzMXqpD8vr1/24zXMfryG2e3C67do0aIdkiTNunBc3BAcjxDiiwD+H4AlkiT55HzPrFmzpO3btw/peWl4bNq0CQsXLkx3GTQEvIZD9/4TrTj0hjPuuNrlVsz5cmFSn5vXL/vxGmY/XsPsduH1E0L0GYKH2h1iJYD7AFwrNwATEWWyoCeK+vUuWWPrN7oQcEWHuSIiIhoOQ+0O8TgAM4A6IcRuIcRvk1ATEVHaHNvsRjQs7w5ZLCzh2GZ5gZmIiDLLkDbGSZI0NlmFEBFlAldjKLHx59gxgogoG/HEOCKibkSCHR8EG0QQEWUlhmAiom7yx2gTHK8bpkqIiGg4MQQTEXUzer4JGpO8l0aNUYHqS9lGiYgoGzEEExF1o9IoMPX6PFljp1yXB5WWL6NERNmIr95ERBeYdHUeLooThKessmHKNfLCMhERZZ5kHptMRDRizPhMPipnGXF4nROn3vMgEpCg0gpUzTGhdrkVBWO5FpiIKJsxBBMR9aOgRoeCO3W49M5ixKISFEq2giAiGim4HIKISAYGYCKikYUhmIiIiIhyDkMwEREREeUchmAiIiIiyjkMwURERESUcxiCiYiIiCjnMAQTERERUc5hCCYiIiKinMMQTEREREQ5hyGYiIiIiHIOQzARERER5RyGYCIiIiLKOQzBRERERJRzGIKJiIiIKOcwBBMRERFRzmEIJiIapEgohqAnilhUSncpRESUIFW6CyAiyiaxmIRT73pwpM6J5oMBAIBSIzB6vgkTVliRX61Lc4VERCQHQzARkUyRUAybHmlC4x5fj69HQxKObXLj+FtuzLqtABOvtKWpQiIikovLIYiIZNr6m5ZeAbg7SQI++FMbTr7rSWFVREQ0GAzBREQyOBpCOLlNXrjd+7eOYa6GiIiGiiGYiEiGI3VO2WMdZ0JoPuQfxmqIiGioGIKJiGToPBVMbPzJxMYTEVFqMQQTEckgJdgFjU3TiIgyG0MwEZEMtgrNsI4nIqLUYggmIpJh/DKr7LGWUjVKJuuHsRoiIhoqhmAiIhnsVVqUzzDIGnvRdXkQQgxzRURENBQMwUREMl12dwkKawc+EW76TXbUXGFJUUVERDRYPDGOiEgmjUGB5Q+U49hbLhyuc6LzVAgAIJRA5UwjJqywomSyvNliIiJKL4ZgIqIEKNUC45daMX6pFX5HBJGABK1FCY2BN9aIiLIJQzAR0SDpbXwJJSLKVpy6ICIiIqKcwxBMRERERDmHIZiIiIiIcg5DMBERERHlHIZgIiIiIso5DMFERERElHMYgomIiIgo5zAEExEREVHOYQgmIiIiopzDEExEREREOYchmIiIiIhyDkMwEREREeUcIUlS6p9UiFYAp1L+xCRHAYC2dBdBQ8JrmN14/bIfr2H24zXMbhdevypJkgovHJSWEEyZSwixXZKkWemugwaP1zC78fplP17D7MdrmN3kXj8uhyAiIiKinMMQTEREREQ5hyGYLvT7dBdAQ8ZrmN14/bIfr2H24zXMbrKuH9cEExEREVHO4UwwEREREeUchmDqRQjxsBDikBBirxBijRDClu6aKDFCiJuEEPuFEDEhBHc4ZwkhxEohxGEhxFEhxL+nux5KjBDij0KIFiHEvnTXQokTQlQKITYKIQ589Pr5tXTXRIkRQuiEEO8LIfZ8dA2/P9B4hmDqSx2AKZIkTQVwBMC301wPJW4fgBsAbE53ISSPEEIJ4FcArgQwCcAtQohJ6a2KEvQEgJXpLoIGLQLgm5IkTQIwF8BX+DuYdYIAFkuSNA3AdAArhRBz+xvMEEy9SJK0TpKkyEd/fBdARTrrocRJknRQkqTD6a6DEnIJgKOSJB2XJCkE4FkAq9JcEyVAkqTNADrSXQcNjiRJ5yRJ2vnR/3cDOAigPL1VUSKkLp6P/qj+6H/9bn5jCKZ4vgzgH+kugigHlAM40+3PZ8E3YKK0EEKMBjADwHvprYQSJYRQCiF2A2gBUCdJUr/XUJW6siiTCCHeBFDSx0P3S5L00kdj7kfX7aGnU1kbySPnGhIRUWKEECYAfwPwdUmSXOmuhxIjSVIUwPSP9jOtEUJMkSSpz3X6DME5SpKkpQM9LoT4IoBPAVgisY9eRop3DSnrNACo7Pbnio++RkQpIoRQoysAPy1J0ovprocGT5IkhxBiI7rW6fcZgrkcgnoRQqwEcB+AayVJ8qW7HqIc8QGAcUKIaiGEBsDNAF5Oc01EOUMIIQD8AcBBSZIeTXc9lDghROH5jlZCCD2AZQAO9TeeIZj68jgAM4A6IcRuIcRv010QJUYIcb0Q4iyAeQBeE0KsTXdNNLCPNqPeBWAtujbkPCdJ0v70VkWJEEI8A2AbgFohxFkhxO3prokScimAzwNY/NF7324hxFXpLooSUgpgoxBiL7omFuokSXq1v8E8MY6IiIiIcg5ngomIiIgo5zAEExEREVHOYQgmIiIiopzDEExEREREOYchmIiIiIhyDkMwEREREeUchmAiIiIiyjkMwURERESUc/4/IPke8HndPpkAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "DrVoPr9ZO7UQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "d1cfdc41-7c00-4400-e934-c7cb3f2ed663"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Year of Studying  Operating time per day  Reason  Category  \\\n",
              "0         0                 4                       3       0         7   \n",
              "1         0                 3                       0       1         4   \n",
              "2         0                 4                       2       1         8   \n",
              "3         1                 1                       4       1         5   \n",
              "4         1                 4                       4       1         3   \n",
              "..      ...               ...                     ...     ...       ...   \n",
              "112       2                 2                       1       0         0   \n",
              "113       2                 4                       4       2         0   \n",
              "114       0                 1                       5       0         7   \n",
              "115       0                 4                       2       1         7   \n",
              "116       0                 4                       1       0         0   \n",
              "\n",
              "     Device  Alone/Friend  \n",
              "0         2             1  \n",
              "1         0             1  \n",
              "2         2             0  \n",
              "3         2             0  \n",
              "4         1             0  \n",
              "..      ...           ...  \n",
              "112       2             1  \n",
              "113       1             1  \n",
              "114       1             0  \n",
              "115       1             1  \n",
              "116       2             1  \n",
              "\n",
              "[117 rows x 7 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Year of Studying</th>\n",
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              "      <th>3</th>\n",
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              "      <td>4</td>\n",
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              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>112</th>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>0</td>\n",
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              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>117 rows × 7 columns</p>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8a1cd451-ab4d-440d-8e10-c2790d0e9486 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8a1cd451-ab4d-440d-8e10-c2790d0e9486');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1>EDA</h1>"
      ],
      "metadata": {
        "id": "RfrQgT-XplyQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TLrf8D0Js0qd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# male_af = df[df['Gender'] == 0][df['Alone/Friend'] == 0]\n",
        "# male_af.shape[0]\n",
        "\n",
        "\"\"\"\n",
        "GENDER X ALONE/FRIEND\n",
        "    GENDER: 0/1: Male/Female\n",
        "    ALONE/FRIEND: 0/1: Alone/Friend\n",
        "\"\"\"\n",
        "features = ['Male', 'Female', 'Others']\n",
        "gender_af = np.zeros((len(np.unique(df['Gender'])), len(np.unique(df['Alone/Friend']))))\n",
        "for row in range(len(gender_af)):\n",
        "    for col in range(len(gender_af[0])):\n",
        "        gender_af[row, col] = df[df['Gender'] == row][df['Alone/Friend'] == col].shape[0]\n",
        "\n",
        "print(gender_af)\n",
        "\n",
        "width = 0.2\n",
        "\n",
        "fig1 = plt.figure(1)\n",
        "plt.bar(np.arange(len(features)) , gender_af[0, 0], width=width, label='Male')\n",
        "plt.bar(np.arange(len(features)) + width, gender_af[1, 0], width=width, label='Female')\n",
        "plt.bar(np.arange(len(features)) + 2*width, gender_af[2, 0], width=width, label='Others')\n",
        "plt.ylabel('Number of each people want to play with friends/alone')\n",
        "plt.xlabel('Gender')\n",
        "plt.legend()\n",
        "\n",
        "fig2 = plt.figure(2)\n",
        "plt.bar(np.arange(len(features)) , gender_af[0, 1], width=width, label='Male')\n",
        "plt.bar(np.arange(len(features)) + width, gender_af[1, 1], width=width, label='Female')\n",
        "plt.bar(np.arange(len(features)) + 2*width, gender_af[2, 1], width=width, label='Others')\n",
        "plt.ylabel('Number of people want to play with friends/alone')\n",
        "plt.xlabel('Gender')\n",
        "plt.legend()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "G2-Ks00nptH_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        },
        "outputId": "bb4de94d-e29f-4900-c472-9f440cf15337"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-29-a7488222bd1d>:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  gender_af[row, col] = df[df['Gender'] == row][df['Alone/Friend'] == col].shape[0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[42. 26.]\n",
            " [14. 12.]\n",
            " [12. 11.]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Z = np.array(df.iloc[: ,1 : 3]).astype(np.float32)"
      ],
      "metadata": {
        "id": "Moim4Q_NCJFc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Z"
      ],
      "metadata": {
        "id": "fIMnOIP0CXxS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68288fd8-d8ff-4384-8cc9-64d2dab276ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4., 3.],\n",
              "       [3., 0.],\n",
              "       [4., 2.],\n",
              "       [1., 4.],\n",
              "       [4., 4.],\n",
              "       [3., 2.],\n",
              "       [1., 4.],\n",
              "       [2., 0.],\n",
              "       [4., 5.],\n",
              "       [2., 2.],\n",
              "       [4., 1.],\n",
              "       [1., 4.],\n",
              "       [2., 0.],\n",
              "       [1., 5.],\n",
              "       [4., 5.],\n",
              "       [3., 1.],\n",
              "       [1., 2.],\n",
              "       [2., 0.],\n",
              "       [2., 0.],\n",
              "       [1., 1.],\n",
              "       [2., 0.],\n",
              "       [1., 0.],\n",
              "       [4., 4.],\n",
              "       [2., 1.],\n",
              "       [4., 1.],\n",
              "       [4., 1.],\n",
              "       [3., 0.],\n",
              "       [2., 0.],\n",
              "       [2., 0.],\n",
              "       [2., 4.],\n",
              "       [2., 5.],\n",
              "       [1., 2.],\n",
              "       [1., 2.],\n",
              "       [2., 0.],\n",
              "       [3., 1.],\n",
              "       [1., 5.],\n",
              "       [1., 5.],\n",
              "       [4., 1.],\n",
              "       [4., 4.],\n",
              "       [2., 5.],\n",
              "       [1., 5.],\n",
              "       [1., 1.],\n",
              "       [1., 5.],\n",
              "       [1., 5.],\n",
              "       [2., 0.],\n",
              "       [1., 0.],\n",
              "       [1., 4.],\n",
              "       [1., 5.],\n",
              "       [4., 5.],\n",
              "       [1., 5.],\n",
              "       [2., 2.],\n",
              "       [4., 2.],\n",
              "       [1., 2.],\n",
              "       [1., 5.],\n",
              "       [2., 0.],\n",
              "       [1., 0.],\n",
              "       [1., 1.],\n",
              "       [1., 5.],\n",
              "       [2., 2.],\n",
              "       [4., 4.],\n",
              "       [4., 4.],\n",
              "       [2., 5.],\n",
              "       [2., 0.],\n",
              "       [3., 4.],\n",
              "       [2., 4.],\n",
              "       [1., 5.],\n",
              "       [1., 2.],\n",
              "       [1., 4.],\n",
              "       [2., 4.],\n",
              "       [1., 4.],\n",
              "       [4., 1.],\n",
              "       [4., 1.],\n",
              "       [1., 5.],\n",
              "       [4., 5.],\n",
              "       [4., 4.],\n",
              "       [4., 1.],\n",
              "       [4., 5.],\n",
              "       [4., 5.],\n",
              "       [1., 0.],\n",
              "       [2., 2.],\n",
              "       [1., 4.],\n",
              "       [1., 1.],\n",
              "       [3., 1.],\n",
              "       [1., 1.],\n",
              "       [1., 4.],\n",
              "       [1., 4.],\n",
              "       [1., 0.],\n",
              "       [1., 5.],\n",
              "       [4., 2.],\n",
              "       [3., 1.],\n",
              "       [2., 1.],\n",
              "       [4., 0.],\n",
              "       [1., 5.],\n",
              "       [4., 4.],\n",
              "       [1., 4.],\n",
              "       [1., 4.],\n",
              "       [1., 4.],\n",
              "       [1., 5.],\n",
              "       [2., 2.],\n",
              "       [1., 5.],\n",
              "       [2., 1.],\n",
              "       [1., 1.],\n",
              "       [4., 4.],\n",
              "       [4., 2.],\n",
              "       [3., 0.],\n",
              "       [1., 4.],\n",
              "       [4., 1.],\n",
              "       [4., 1.],\n",
              "       [2., 1.],\n",
              "       [4., 0.],\n",
              "       [4., 5.],\n",
              "       [2., 0.],\n",
              "       [2., 1.],\n",
              "       [4., 4.],\n",
              "       [1., 5.],\n",
              "       [4., 2.],\n",
              "       [4., 1.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "sns.distplot(Z)"
      ],
      "metadata": {
        "id": "dJIplKCbPm8H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "8aa80c57-1050-41df-8062-09befe543aaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f58bfaba670>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F = np.array(df.iloc[: ,3 : 7]).astype(np.float32)"
      ],
      "metadata": {
        "id": "uH2-qun4t24J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(F)"
      ],
      "metadata": {
        "id": "f4H_K-DauQnx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "358585ef-4000-4574-af61-c9a27b332a27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f58bfa17c70>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\n",
        "for i in range(1, 118):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
        "    kmeans.fit(df)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "plt.plot(range(1, 118), wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1LlQSMvo9CYc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        },
        "outputId": "9330da14-80d5-42c6-dcb6-07448961b9cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (103). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (104). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (105). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (106). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (107). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (108). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (109). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (110). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (111). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (112). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (113). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (114). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (115). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (116). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n",
            "<ipython-input-35-6b9dd46970a3>:5: ConvergenceWarning: Number of distinct clusters (102) found smaller than n_clusters (117). Possibly due to duplicate points in X.\n",
            "  kmeans.fit(df)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\n",
        "for i in range(1, 10):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
        "    kmeans.fit(df)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "plt.plot(range(1, 10), wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "RkxfJp4cWqW1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "8e2f6c7a-b3ce-4d6f-8279-b0f054eeb40f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wcss"
      ],
      "metadata": {
        "id": "v-OAW8mzAW-S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a265f87-26cf-4364-af50-84392b4624d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1824.9230769230771,\n",
              " 981.990029325513,\n",
              " 761.8686217008799,\n",
              " 596.0567145862551,\n",
              " 520.7107215365281,\n",
              " 475.5372346189281,\n",
              " 414.8234071484071,\n",
              " 367.5449125264141,\n",
              " 338.17656926406926]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yellowbrick.cluster import KElbowVisualizer\n",
        "model = KMeans()\n",
        "# k is range of number of clusters.\n",
        "visualizer = KElbowVisualizer(model, k=(2,30), timings= True)\n",
        "visualizer.fit(df)        # Fit data to visualizer\n",
        "visualizer.show()        # Finalize and render figure"
      ],
      "metadata": {
        "id": "hWuUqRUs9yQr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "399984ff-bd75-4202-c8ac-1268e2a1a260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f58bc463be0>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.inertia_"
      ],
      "metadata": {
        "id": "5IO_QBlM-Z37",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ef03b33-8fbd-4768-eccb-ee3da21ef7c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "338.17656926406926"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AgglomerativeX = AgglomerativeClustering(n_clusters=4, affinity = 'euclidean', linkage = 'ward')\n",
        "y_AgglomerativeX = AgglomerativeX.fit_predict(df)\n",
        "df['Cluster'] = y_AgglomerativeX"
      ],
      "metadata": {
        "id": "Z5r9FZXVfDGf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "dPFL511UZy8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "x5Q7mx5hEy95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "047bd6be-156e-4a45-ca7c-a81d31fea7be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Year of Studying  Operating time per day  Reason  Category  \\\n",
              "0         0                 4                       3       0         7   \n",
              "1         0                 3                       0       1         4   \n",
              "2         0                 4                       2       1         8   \n",
              "3         1                 1                       4       1         5   \n",
              "4         1                 4                       4       1         3   \n",
              "..      ...               ...                     ...     ...       ...   \n",
              "112       2                 2                       1       0         0   \n",
              "113       2                 4                       4       2         0   \n",
              "114       0                 1                       5       0         7   \n",
              "115       0                 4                       2       1         7   \n",
              "116       0                 4                       1       0         0   \n",
              "\n",
              "     Device  Alone/Friend  Cluster  \n",
              "0         2             1        0  \n",
              "1         0             1        0  \n",
              "2         2             0        0  \n",
              "3         2             0        1  \n",
              "4         1             0        1  \n",
              "..      ...           ...      ...  \n",
              "112       2             1        2  \n",
              "113       1             1        1  \n",
              "114       1             0        3  \n",
              "115       1             1        0  \n",
              "116       2             1        2  \n",
              "\n",
              "[117 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70ec0305-faed-432e-a87a-75878943cf16\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "      <th>1</th>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
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              "      <td>1</td>\n",
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              "      <td>3</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>117 rows × 8 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70ec0305-faed-432e-a87a-75878943cf16')\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-70ec0305-faed-432e-a87a-75878943cf16');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_df = df.groupby(['Cluster'])\n",
        "result_df.head(100)"
      ],
      "metadata": {
        "id": "9HdwomuvOEgV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "79e6ff4c-f7a9-4ccb-a0a4-a0f05530ed77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Year of Studying  Operating time per day  Reason  Category  \\\n",
              "0         0                 4                       3       0         7   \n",
              "1         0                 3                       0       1         4   \n",
              "2         0                 4                       2       1         8   \n",
              "3         1                 1                       4       1         5   \n",
              "4         1                 4                       4       1         3   \n",
              "..      ...               ...                     ...     ...       ...   \n",
              "112       2                 2                       1       0         0   \n",
              "113       2                 4                       4       2         0   \n",
              "114       0                 1                       5       0         7   \n",
              "115       0                 4                       2       1         7   \n",
              "116       0                 4                       1       0         0   \n",
              "\n",
              "     Device  Alone/Friend  Cluster  \n",
              "0         2             1        0  \n",
              "1         0             1        0  \n",
              "2         2             0        0  \n",
              "3         2             0        1  \n",
              "4         1             0        1  \n",
              "..      ...           ...      ...  \n",
              "112       2             1        2  \n",
              "113       1             1        1  \n",
              "114       1             0        3  \n",
              "115       1             1        0  \n",
              "116       2             1        2  \n",
              "\n",
              "[117 rows x 8 columns]"
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85d7e4b7-2730-42a1-acc4-42415d5200a0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85d7e4b7-2730-42a1-acc4-42415d5200a0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.values\n",
        "_plot_kmean_scatter(result_df, y_AgglomerativeX)"
      ],
      "metadata": {
        "id": "92-q85KaZ6a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "4e6a309e-b2c4-4e6f-cf58-8b9a7020ccc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-46-e6aa650b99a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# df = df.values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0m_plot_kmean_scatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_AgglomerativeX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m    909\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 911\u001b[0;31m         raise AttributeError(\n\u001b[0m\u001b[1;32m    912\u001b[0m             \u001b[0;34mf\"'{type(self).__name__}' object has no attribute '{attr}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m         )\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrameGroupBy' object has no attribute 'iloc'"
          ]
        }
      ]
    }
  ]
}