From 0c28c1117836ed1c85471244eec80bda72f7ae3b Mon Sep 17 00:00:00 2001
From: Nifacy <mai.grishin.alexey@gmail.com>
Date: Fri, 28 Nov 2025 11:38:47 +0300
Subject: [PATCH 1/2] add jupyter notebook with solution

---
 stud/grishin/lab-2/main.ipynb | 3212 +++++++++++++++++++++++++++++++++
 1 file changed, 3212 insertions(+)
 create mode 100644 stud/grishin/lab-2/main.ipynb

diff --git a/stud/grishin/lab-2/main.ipynb b/stud/grishin/lab-2/main.ipynb
new file mode 100644
index 0000000..5f71295
--- /dev/null
+++ b/stud/grishin/lab-2/main.ipynb
@@ -0,0 +1,3212 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "ux4iEF0x9wUa"
+      },
+      "outputs": [],
+      "source": [
+        "import itertools\n",
+        "import dataclasses\n",
+        "import random\n",
+        "import tqdm.notebook\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import sklearn.cluster\n",
+        "import sklearn.mixture\n",
+        "import sklearn.model_selection\n",
+        "import sklearn.metrics\n",
+        "import sklearn.model_selection"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "hY2xjP5NJkPO"
+      },
+      "outputs": [],
+      "source": [
+        "RANDOM_SEED = 42\n",
+        "\n",
+        "random.seed(RANDOM_SEED)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wO5UpQlYJaYw"
+      },
+      "source": [
+        "### Загрузка датасета\n",
+        "\n",
+        "Для начала загрузим датасет. В этот раз воспользуемся библиотекой `pandas` и\n",
+        "представим датасет в виде `pandas.DataFrame`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "I8-zJSwPAJJd",
+        "outputId": "96e5f76d-4739-4716-98c9-55fe78beb789"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.1252039875904805,\n        \"min\": -10.70273630672936,\n        \"max\": 11.07473499056978,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          1.698938497981321,\n          8.749480611116606,\n          -4.792276091132777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8309153164832717,\n        \"min\": -2.366946290079579,\n        \"max\": 10.90000648008181,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          1.31272614069482,\n          7.558598942712011,\n          2.936054191148225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "dataset"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-db680b62-bca8-4837-9068-390a4e05be14\" 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",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>-6.383976</td>\n",
+              "      <td>0.831166</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>-6.676808</td>\n",
+              "      <td>-2.087329</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.380398</td>\n",
+              "      <td>1.726267</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>-5.974045</td>\n",
+              "      <td>3.571401</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>-7.555926</td>\n",
+              "      <td>3.254611</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db680b62-bca8-4837-9068-390a4e05be14')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-db680b62-bca8-4837-9068-390a4e05be14 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-db680b62-bca8-4837-9068-390a4e05be14');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c77afebc-3f64-466d-9c9c-a06b834c1cc2\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c77afebc-3f64-466d-9c9c-a06b834c1cc2')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c77afebc-3f64-466d-9c9c-a06b834c1cc2 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "          0         1\n",
+              "0 -6.383976  0.831166\n",
+              "1 -6.676808 -2.087329\n",
+              "2  0.380398  1.726267\n",
+              "3 -5.974045  3.571401\n",
+              "4 -7.555926  3.254611"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset = pd.read_excel('datasets/lab2-04.xlsx')\n",
+        "dataset.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XxaKw2BWJe2G"
+      },
+      "source": [
+        "### Визуализация\n",
+        "\n",
+        "Визуализируем датасет в виде диаграммы рассеяния. Аналогично прошлой лабораторной работе, воспользуемся для этого библиотекой `matplotlib`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "c18BDD4tAdtB",
+        "outputId": "60714ab5-2c7c-4f4f-f0a9-31ca51c92a40"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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vvffw1FNP4dFHH0WXLl0wa9Yslxq05513Hv71r3/hiSeewG9/+1vYbDbce++9yMzM9JgtvvPOO9G9e3c8//zzmDdvHqKjozFw4ECXuq4TJkzAhg0bMHfuXPzxj3+E3W5Hv379XN7nvLw8/Pe//8XcuXPx1ltvobKyEt27d8cFF1zgkkoye/ZsVFRUYP369R65vb6aNm0aLrnkEtWfV1ZW4tZbb8VNN92EGTNmmPrcROQbi6T3kp6IyMmhQ4eQm5uLDRs2uLSlNXo/f7vyyitRUVGBoqKioI0h0J5++mnMnTsXJ0+e1B1kExGFMubIEhEREVFYYiBLRD7p2rUrZsyYoVj/1cj9iIiIRDFHloh8kpGR4bG5y5f7ERERiWKOLBERERGFJaYWEBEREVFYYiBLRERERGGp0+XI2u12HDt2DElJSabXIiQiIiIi30mShLq6OvTs2RNWq/q8a6cLZI8dO4bs7OxgD4OIiIiINBw5cgTnnHOO6s87XSCblJQEoOONSU5ODvJoxNntdhw5cgTZ2dler0wocvEcIIDnAfEcoA6Rfh7U1tYiOzvbEbep6XSBrJxOkJycHHaBbFJSEpKTkyPyhCVtPAcI4HlAPAeoQ2c5D7TSQCP3lRMRERFRRGMgS0RERERhiYEsEREREYUlBrJEREREFJYYyBIRERFRWGIgS0RERERhiYEsEREREYUlBrJEREREFJYYyBIRERFRWGIgS0RERERhiYEsEREREYUlBrJEREREFJYYyBIRERFRWOoS7AEQERERkat2u4StJVU4UdeE7klxGJmbjiirJdjDCjkMZImIiIhCyJqiMsxduRdlNU2O27JS4jBnSh4m5mcFcWShh6kFRERERCFiTVEZ7l1W6BLEAkB5TRPuXVaINUVlQRpZaGIgS0RERBQC2u0S5q7cC0nhZ/Jtc1fuRbtd6R6dEwNZIiIiohCwtaTKYybWmQSgrKYJW0uqAjeoEMdAloiIiCgEnKhTD2KN3K8zYCBLREREFAK6J8WZer/OgIEsERERUQgYmZuOrJQ4qBXZsqCjesHI3PRADiukMZAlIiIiCgFRVgvmTMkDAI9gVv73nCl5rCfrhIEsERERUYiYmJ+FV24ZDluKa/qALSUOr9wynHVk3bAhAhEREVEImZifhfF5Nnb2EsBAloiIiCjERFktGN2vW7CHEfKYWkBEREREYYmBLBERERGFJQayRERERBSWGMgSERERUVhiIEtEREREYYmBLBERERGFJZbfIiIiIjJJu11i/dcAYiBLREREZII1RWWYu3IvymqaHLdlpcRhzpQ8duTyE6YWEBEREfloTVEZ7l1W6BLEAkB5TRPuXVaINUVlQRpZZGMgS0REROSDdruEuSv3QlL4mXzb3JV70W5Xugf5goEsERERkQ+2llR5zMQ6kwCU1TRha0lV4AbVSTCQJSIiIvLBiTr1INbI/UgcN3sRERFRUETKDv/uSXGm3o/EMZAlIiKigIukHf4jc9ORlRKH8pomxTxZCwBbSkegTuZiagEREREFVKTt8I+yWjBnSh6AjqDVmfzvOVPywnK2OdQxkCUiIqKAidQd/hPzs/DKLcNhS3FNH7ClxOGVW4aH3SxzuGBqAREREQWMnh3+o/t1C9zATDAxPwvj82weeb8AUFBcGfa5wKEopGZkv/rqK0yZMgU9e/aExWLBhx9+6PJzSZLw1FNPISsrC/Hx8Rg3bhwOHDgQnMESERGRbpG+wz/KasHoft0wbVgvjO7XDWv3luPSBesxfclmPPDuTkxfshmXLlgfdukToSqkAtn6+noMHToUixcvVvz5Cy+8gL/85S949dVXsWXLFiQmJmLChAloagrPk52IiKiz6Uw7/CMtFzgUhVRqwbXXXotrr71W8WeSJGHhwoV44oknMG3aNADAP/7xD/To0QMffvghbrrppkAOlYiIiAzoLDv8tXKBLejIBR6fZ2OagQ9CKpD1pqSkBOXl5Rg3bpzjtpSUFFx88cUoKChQDWSbm5vR3Nzs+HdtbS0AwG63w263+3fQJpLHG05jJnPxHCCA5wGF/zlgAfDU5IG4f/kOAHAJ9ORw7qnJA2G327GluAonTzchs2scRijklbbbJWwr8X6fYNlSXIkTtY2I8jKcE7WN2FJcgVEGcoHD/TzQIvq6wiaQLS8vBwD06NHD5fYePXo4fqZk/vz5mDt3rsftR44cQVJSkrmD9CNJklBdXQ2LxQKLJTT+SCmweA4QwPOAIuMcGJQE/HlKNlbuKkNNY6vj9pT4aEwZmgV7XQXuX/Kt4s8G90wBAOw5VqP6ePk+wXSy/BRGZmpXXjhZfhSl0fW6jx8J54E3dXV1QvcLm0DWqMceewwPPfSQ49+1tbXIzs5GdnY2kpOTgzgyfex2OyRJQnZ2NqzWkEptpgDhOUAAzwOKnHMgJwe4ZmS+x4zqun3luH/5jjMztWcDNAvasK70CF6+OQMA8MDKI17vM2FwcMtdHWtNxNaTP2re735bL+TkGJuRjYTzQI28gq4lbAJZm80GADh+/Diyss6enMePH8ewYcNUHxcbG4vY2FiP261Wa9j94uUxh9u4yTw8BwjgeUCRcw5YrcAlAzId/263S3hm1X60ScozjBYAz6zaD0mSNO9zzeCeQU0zuLhfBronx2vmAl/cLwNWg+OMlPNAiehrCptXnpubC5vNhs8//9xxW21tLbZs2YLRo0cHcWRERERkBtEas+W1zZr32VpSZdq42u0SCoor8dHOoygorhRq1sBuX4ERUjOyp0+fxsGDBx3/Likpwc6dO5Geno6cnBw8+OCDePbZZzFgwADk5ubiySefRM+ePXH99dcHb9BERERkCjNrx5p1rDVFZZi7cq9LgJ2VEoc5U/I0u3XJ3b7cH28TfDxpC6lA9r///S+uuuoqx7/l3NZbb70Vb731Fn73u9+hvr4ev/rVr3Dq1ClceumlWLNmDeLiwr/WHBERUWdnZu1YM44l14F1n3+V68CKtJ5V6/bFmVhzhFQge+WVV0KS1KfrLRYLnnnmGTzzzDMBHBURERGZrd0ueQR3ojVmJUnC8dpmv9ahNbMOrNzti8wXUoEsERERRT5vy/VzpuTh3mWFsEC5xqycd6p0H5z5900jsn0eo2i+7taSKgapQRQ2m72IiIgo/Gm1bQWAV24ZDluKa2qALSXOsZQv556630f20roDuHTBep9awIrm2JqZ10v6cUaWiIiIAkJ0uX7jI2M180rl3NNF6w/ipXXfexxPTx6rEtEcWzPzekk/BrJEREQUEHqX60WW7N/dVqp6LJE8VqVc3SirRThf19dcXPINA1kiIiIKCLOX633NY9UqrSWSr8vqA8HFHFkiIiIKCLOX630JjLVyddcUlanm4jrn61JwcUaWiIiIAsLs5fpDFfVC93MPjPWU1mId2NDGGVkiIiIKCDPbtrbbJSzdVKJ5vyyFwFhPSoI87tH9umHasF4Y3a8bg9gQwkCWiIiIAsas5fpF6w/iVGOb5v1uGpHjEXiytFbkYGoBERERBZSvy/Wis7EA0CcjweM2ltaKHAxkiYiIKOB8adu6taQKpxpbhe5bUdeMdrvkEiTLubre0gsAoLq+2dD43KmV+CLfMZAlIiKisKJnyX/eJ/vwxsYSR0ktoCOIfnJSHu5bXqj52An5WT4FnVolvsg3zJElIiKisKJ3yd+5pJYsLTFG83HOG76MECnxRb5hIEtERERhRU4NEJ0nlctszV25F+32jn/5e8OXVokv9/GQMQxkiYiIKKx4K+Olxr2klr83fOkt8UXGMJAlIiIiv2q3SygorsRHO4+ioLjSlFlItTJeWuQZVq1ZXQuUa9DqfR6z7kfKuNmLiIiI/Mafm52cy3htOngSizYUaz5GnmGVZ3XvXVYIC+CSAqC3OYO35zHrfqSMM7JERETkF4HY7CSX8Zo9/jzdM6xmNWdQ4u8ZX+rAGVkiIiIyndZmJws6NjuNz7OZUlPV6Ayrr80ZzB4P6cMZWSIiIjJdMDY7GZ1hlWd1pw3rhdH9upkWXPpzxpc6cEaWiIiITBeszU7+mmGNlPHoEQ4dyRjIEhERkemCudnJl/a3ZnIPBCcP6ekSCIZyoBguHckYyBIREZHp5M1O5TVNinmyFnQssYfjZqeWNjveLjiEw1UN6J2egF+M7oOYLq7ZmlqBYCgHivImPfffm7xJL5TSIhjIEhERkekidbPT/NV7seTrEjiXwn1u9T7cdVkuHruuo0mDViD4/y7LxZKvSzyOHQqBYqA36fmKm72IiIjIEK1GB+G22Unr9cxfvRevfeUaxAKAXQJe+6oE81fv1QwEJUAxiJV/DgS3dW24dSTjjCwRERHpJro0Hi6bnbReT0ubXTUAlS35ugSX9s/0GghqcQ4Ug5HnG24dyRjIEhERkS56cygDtfnK6OYpkddztLrRYybWnV0C3i/80fgLcBKsQDHcOpIxkCUiIiJhoZpDaXTzlOjrGTeoh9A4fqio1zdwFRldY005jl7htkmPObJEREQkLBRzKH1phSv6eqAY1nna9WMNUhOihe7rzW/+uROrdx/zmrPrD/ImPQAe7XVDcZMeZ2SJiIhIWKBzKLXSBXydIRYdp90ueVRfUGKBZwBoRHltM+5bvsPlNrUZZrPr0cqb9NxnuG0hUh7MGQNZIiIiEuZLDqVIwOV8n0MVDVixtRTlterpAnpmiJXydEVfzztbjwjdTwJQ3dCKG4afg3+ZlC8rc87ZvSavI9Xhsz1leGbVftPr0YbLJj0GskRERCTMaA6lSA6r0n3cuW8o83WG+MLeaUhPjEZVfavQcUSNGZCBjQdPory22bRjOs8wXz2wO/Ycq8EDK4+gTXINLs2qRxsqHdK8YY4sERERCTOSQymSw6p2H3futVZ9mSFeU1SGK17cYHoQCwC25Dg8PXWw6ceVZ5i3/FCJlbvKVFMqgODWow0UBrJERESki1qjg7TEaCy++QKXWUCtHFagI+B6+uM9gtupXNMF5Blib6wWoLq+xeU20cBZLws6ZppH5qZjfJ7NlI1fSjb/UIWaRvUAPNQaF/gLA1kiIiLSbWJ+Fp6clIf0xBjHbVX1rZj3yT6XKgGiOaxGluBP1DUhymrB1KHel8/tEjBz+dnqBd6CazPIM9JbS6pwqsH82d4OYqMPlcYF/sJAloiIiHRbU1SGmcsLUeU20+le8sqfgVRG11hsOliB97aJbaqSl9q1gmujuiXGuOSl+uO1yzO+o/qK5a6GSuMCf+FmLyIiItJFT8krfwRSFgCpCdH4zT93Cs/kOi+1+yPATIqLwuPXDUJKfAza7RKirBbTX7tzDvLFfbthWXw0LGhTvW8oNS7wFwayREREpIuekleiVQ4kScLx2mahBXO5xJURHWW9zOm+5ayuqR2/+b9dAM5WYxifZ/P62oGO/F3R/VjOdVztdjumDM3CutIjHvVtQ7Fxgb8wtYCIiIh00VPySrTKgbzD399hV0bXWKzYWmr48SJxoZxesXZvueZrFw1i0xOj8eXDV7lspBvcMwUv33yBx6Y7W0qcodJb7XYp4J3EfMUZWSIiItJFb8krtU5R6YkxmDct3xFwKXaTSo7FiD5pWLm73OdxZ6XEARIMbSxLjY/G7WP64N4r+2P74WqU1zRi3if7PHKEAdf0io2PjFXtknVdvg1vbjok9PxV9a3Yfrjao67rhMFZuGZwT58bF4jU+Q1FDGSJiIhIFyNNEcbn2bCvrA5vfP0D6lvaAQCV9S2Y98leWK0dP0+Jj8HvJpyHqvoWpCXEoLqhBanx0Xju0/2mjHvq0CxU1BtrUFDT2IqF6w7gPFsSJuZnoaC4UjGIlTmnV6h1ydpaUiUcyALqM+G+Ni6QS5G5/y7NaqzgTwxkiYiISBc5XeDeZYVC+Zlrisrw6PvfKpaiKq9pwj3LCpGaEO3ycz25o6I+3lWGy8/tbuix8lAe/+BbjB3YQ3dHMTnYlKsmrNp9DBmJsbAlx7m04PXGHxvn9GzcC8V8WwayREREpJtauoBNoe3sPcsKVY8jB1DuQa4/0jPLapoACZobsLypqm/FqPmf49bRvYXu7xx8Ki3fizRM8GcFAj0b90KxXS0DWSIiIjJEbclcnrmTZ/tCSUV9s+pssqiq+ha8tO4AUhOiUdPQKpReobZ8X3MmgE+MiXKkXLgfB/BfBQK9M8uhhlULiIiIyDB5yXzasF4Y3a+bS7Dlr8YDvuieFKfaYlcPi8p/O/9bDj5F2vQmxXXBg1f3R2q86wytWgWCdruEH07WY+Uu3yoM6N24F2o4I0tERER+EWqzeLbkWMcMqTybvLm4EjOXF+JUo766tBI60iFmjzsX724r9ZpeIRLQl9c24+K+Gbj/6nM1KxCsKSrDvJV7kBPbiK0nLWiXLIYrDBjZuBdKGMgSERGRXwRiFk9PesD0kTkuQWGU1YIxAzLw/M/OV1z2F9EnIwEbHxnrNfgU3cxVXtukWYFATlGwWiTkZDo91mCFAb0b90INUwuIiIjIL+TZPn+GQHqCzz4ZiQA8C/+Pz7Nh8c3DhZoduOueFOc1vQIAqk6LlfzSup9IisLclXt1pxmopVoYbawQSJyRJSIiIr/wNtsnS4yNQn1zu+GNV3p0T4pTLfx/04gcQ5USqgXq0qYnxggdy/l+cpku97qz/qowoLVxL1QxkCUiIiK/USvTlRofjVsv6Y2Rud2wft9xfLDzKKrq/VNHVs7zrK5vxn3Ld3j8vKymCS+t+97Qsed9sg8T8rO8BnzdkwU3VJ25n1qwfV2+Teg4RnOTfW2sEAwMZImIiMgrpdlBPTN1SrN91We6ev3584OO+6UnxuD6YT0xPs+GC3unYcsPlfjl37b6PFMrAXj8ukF4/MMiH4/kSWgGVPQFSN67bIl2AQvVCgP+wECWiIiIVKnNDurdIe8827emqAwzl3sGa9X1LVi66RBG5qYjposVXaKspqUbPPb+tzjd3GbS0VyV1zR6/bloW9wTdU144bPvvObAWi2ApPKmhHqFAX/gZi8iIiJSJM8Ouudlyjvk1xSV6T6mng1LZpbv8lcQCwAVp1tcNo+5b7YSnSGtqm/RLNNll862jnUWDhUG/CGsAtn29nY8+eSTyM3NRXx8PPr164d58+ZBUrs0ISIiIkP8tUNez4alcFkiX7ThIKYv2YwH3t2J6Us249IF612CfLl6gxoLOma507vGCj3fHWP6oEdy+FUY8IewSi1YsGABXnnlFfz973/H4MGD8d///he33347UlJS8Otf/zrYwyMiIooY/tohLzrLunRTCX4xqjdsyXHCdViDpcatmYJ7TdcoqwVTh2bhta9KVI8xZ0oeUuLFqhuMz7PhsWsH4usd+3FTl2R0T04IiwoD/hBWgew333yDadOmYdKkSQCAPn36YMWKFdi6dWuQR0ZERBRZRANOvcv/orOs/9l7HP/ZexwJMVG6jh8K5KX/uSv3YnyeDWv3luN1L0Hsry7PxcT8LLTbJeEuWxZI6JuZiJycXrBaw2qB3VRhFchecskleP311/H999/j3HPPxa5du7Bx40b86U9/Un1Mc3MzmpvPJlnX1tYCAOx2O+x2u9/HbBZ5vOE0ZjIXzwECeB5Q4M6BzMQYRFm00wYyE2NUx9Jul7CtpAonTzchs2scRuSm46LeqeiVEovjtcrBmrvm1jZEWYDEmCjUt7TrfBXBdaK2EQUHT2Leyj2wqryXFgCrdh3Db685D1FWC56aPBD3nykRptRl66nJA2GBFPGfBaKvK6wC2UcffRS1tbUYOHAgoqKi0N7ejueeew4zZsxQfcz8+fMxd+5cj9uPHDmCpKQkfw7XVJIkobq6GhaLBRZL51s6IJ4D1IHnAQXqHLB1kTA+pwtqG1tVZweT46PRPeo0vth+AnVNrUiKi0afjARYLRbsOVaDlbvKXJbdU+KjMWVoFh6/IhPvbC7teD2C40mJBy7MycD67076/NoCae+BH5AT2+jSTtZTI77esR99MxMxKAn485Rs1fduUFIrSktLI/6zoK6uTuh+YRXI/vOf/8Q777yD5cuXY/Dgwdi5cycefPBB9OzZE7feeqviYx577DE89NBDjn/X1tYiOzsb2dnZSE5ODtTQfWa32yFJErKzszv1EkJnxnOAAJ4HFNhzYNqlMV5nB++8NBu3vlfsksNqS47D5CFZeHPjkTOPsTg9rg3rSo/g5ZsvwC/GZ+DZVft05L+2Y1D/NHxfX4NTDa3adw8Rtp4x2HpSO9C8qUsycnJ6AQBycoBrRuZ7zGY758BG+meBvIKuxSKF0Zb/7OxsPProo5g5c6bjtmeffRbLli3D/v37hY5RW1uLlJQU1NTUhF0gW1paipycnIg8YUkbzwECeB5Q4M8BtTqyU4dm4fWvSjxmVLVazcp5nhsfGQsAuH/5dqwuOm72sMPOirtG6do053weSLD4rbWsr80wjBKN18JqRrahocHjjzYqKipi80OIiIiCTakr14W903DFixu8luZS41ztYGRuOjaXVPlh1K4SY6NQ3yyWXysH4qkJ0QGZ+fW1icFne8rwzKr9PjesUGJWMwx/CqtAdsqUKXjuueeQk5ODwYMHY8eOHfjTn/6EO+64I9hDIyIiiljOXbkAoKC4UrNwv5YTdR3BbFW9/4NF0SAW6GiTO21YT1w9qAcgdXTlqqhrxrxP9pk+Ll+bGOw5VoMHVh5Bm+T6WPfyX0Z4a5Xr67HNFFbrUi+//DJuuOEG3HfffRg0aBB++9vf4u6778a8efOCPTQiIqJOw4yOW92T4kzt3KXEgo6ZVRFXnJuB9MRoVNa34G+bDmHGG1vw23/tQmwXKzKSxBoV6OVLE4N2u4SVu8pMb1ghH9sfzTD8IaxmZJOSkrBw4UIsXLgw2EMhIiLqtHzpuOW8lL41AGkFt1+Si5fWfa95vy+/r/C4TZ59fHDcuaaNZ9ZV/TGgR1ef8023lVSdqWqg/HijDSsA/zXD8IewmpElIiKi4JNbruoNwdyX0rVat/rCagEW33wBZo3tb2iswNnZx3e3lcKWHGvoGO7G9M/AtGG9MLpfN582TZ087Z+GFXoe4+8ZdREMZImIiCJMu11CQXElPtp5FAXFlaYvAUdZLZgzJQ+A53yg5cz/7r481yNIdV9Kdz6O2ewSkJYY63WsIuTZx+kjcwwfQ9YtMcbwpi53mV3FLgCMzJ6LPsaXmXmzhFVqAREREXkXqJ3mE/Oz8Motwz2ey+b0XL+bOEizdNPE/CzcOaYP3tx0yLSxyeQZQ7WxWi0dAa+IPhmJeOWW4Xj64z0or23WfoCCacN6mla6akRuOlLio2FBm+LPfamGIM+Ui7TKDTYGskRERBEi0DvNlUpzOQer7tUO1IzLs/klkHWeMXQfq95KBBV1zeieHIeWduOz2+PzbIYf6y7KasGUoVlYV3pE9T5GqyHIs9j3Liv0qAvsa6UFszGQJSIiigBaO80t6NhpPj7PZmoAIhqsejMyNx225DgdXb60pSZEw26X0G6XFAPrj3Ye1XU8X8tvZbnNYJrRaGBwzxTceWkXLNl4yGVm2WoB7ros16eLFpEZ91DAQJaIiCgChNNOc3dr95ajqU281quIUw2tmPHmFtW0ikDmd1rgOoNpVvrHnmM1eHPjEdjd6shKEvD6VyW4ICfN64y5Fq0Z91DAQJaIiCgChNNOc2erdx/Dfct3qP48tosFzW3Gl/PV0iou7J2mK0fWKPcA1az0D606shYAj77/LZ7+eK/LTLfegNmMGXd/YiBLREQUAUJhp7ne5fLVu8swa4V6EAsACTFd0NxmvPuXWlrF9sPVfglipw3tifxeKchIioUt2fU9MDP9Q6SObEeLXdf3LtQ6c/mKgSwREVEECPZOc73L5WuKynDf8kLN41Y3tCI9MQbV9S2Kr0uEUlqFv2amxw7qjslDejoC+q0lVY5g1sz0D9E6skrP4a986WBgHVkiIqIIoFXbFfDfTnN5udw9SJNn/9YUlbncLs9Mirp+WE9Txrlub7nj+SvqjJXQ0nKooh6XLliP6Us244F3d2L6ks24dMF6rCkqczy/FpEgW7SOrBLngDnccUaWiIgoQgRjp7mR5XKtmUl34/NsOFLdgLV7T/g01jc3HYLVasG/C4+iqr7Fp2MpSUuIxkvrDnjcXl7ThHuWac8+y0TSP7TqyIoItXxpIxjIEhERRZBA7zQ3slyuJ4DKSolDZV2zz0GsbMnXJaYcR4la6oNoSoSe9A/nOrLutV5FhUJnLl8xtYCIiCjCyDvNpw3rhdH9uvk1D9JItQQ9AdSTkwbhqZVFuscVaDcMP+fM5irjJOhL/xjcMwUv33wBbO6tgJNjkZoQrdpO1wLPurbhijOyREREpEikCoGRaglaG9OAjqL+i6ZfgLTEWFTViwWIMV2saGmzC93XTFkpcRjTvxv+VfijT8e5Y0wf3ekfEwZn4ZrBPT1+T2v3lodFZy5fMZAlIiIiD6JVCIxUS/DWAlW2aPpwXDckS1cHroevOQ/PrfatA1fX2CicbtbXnGHOlDykxMf49LyA8Ra2SrVew6Uzl68YyBIREZELPUX7tYJSCcDEwT3w1qYSpCfGwJYSj5G56aqBlnuwLDrj2y0xBrde0gd/21TidaZXyy9H98FfvygWvr88i9pul2BLjkV5rbFqCLbkWNglCR/tPKo6++08Q56ZGANbF++vMhw6c/mKgSwRERE5GKlCoBaUyp2zln5z2OU4zsGqVqA1MjcdtuQ4l+5USuZNy0dMF6vmTK833RJjMKZ/hq5AVp5FjbJacNOIbCz8/KDOZ+3Q1GbHjDe2OP6t1BHM+f2NskgYn9MF0y6NwbXn91I9bqh35vIVN3sRERGRg54qBM4m5mdh4yNjseKuUbhjTB8A6u1fy5zqy2ptTFu7txxNbd6X+u++PBfXDemYFU2Jj8EdY/ogMTZK87W6mzctHzUNLaqbpNylxkc7UibWFJXhLbeAXYT8XO4bxZxr8KrV6a1tbMX9y3d41OntTDgjS0RERA5GqhDIoqwWjMxNx0P/3Kn5eAna3aXUUhxkXWOj8MLPhuC6IT0Vc3r1uPvyXFitwL3LdgjP5N4+pg+irBbNcXqTkhCtWO1Anv1++uM9ACyqM+RA5HTpMoIzskRERORgpAqBMz3NDrx1l/KW4iDrGtsFE/KzVGcsRaQnRmPRTRfg8nO749F/fyscjKYlRGPW2AFC41SSlRKH2eMGeC3ZJQEor232mlYRSV26jOCMLBERETkYqULgTG+3KLX7iwTE5bXN2FxcaSiQnHVVf4zpn4Hq+mbM+2SfriDYAmD+T89HlNWCguJK3QF0emIMnpyUh1a7eaXCIqFLlxGckSUiIiIHuQoBAI9cUZEapHq7RandXzQwK/ihwtBMbGNLG2oaWzBz+Q5dj09NiHap2lBe06j7uavqWzBzeSEOVTTofqyaSOjSZQQDWSIiInIhVyHw6BiVEucSxCmRZ3RFsjW9dZcSD8yM5YW+uekQHn1fPJVAtnj6cJdKAvM+MVa3VgLw7rZS2JJjvXbgkrt0qYmkLl1GMLWAiIiIPBitQepcV9YbC7zP7F7YOw3pidFeu3qlJkQjKc54KKOnpaycUjHqTCkrXzZ4ycpqmjB73AAsXHdAtQPXtGE98dpXJarHkNvaAkBBcWXE1otVw0CWiIiIFBmtQapWV1am1CHMmVyBQKs17amGVsz/dL/u8enlnlJhdIOXkpz0BNUOXE9OGqQ545uaEA27Hbh0wXrNLmyRiIEsERERmc55Rre8tglVp5tdOnsZLbkVDO5tXfVUZtBSWFqNp6fmK85+izzPqYZW3Lfcc/ZbqQtbJGIgS0RERH6hd0bXzJlOM6TGR2PxzcMxyq1Rg5kVAt7eXIp1+04ozp768jxqXdgiDTd7ERERUUgwc6bTDLePycWYARkeQaDZFQKcu3iZ+TydocYsA1kiIiIKina7hILiSny08ygKiiu9Fv4Phj4ZCYq3y5UZzOLcoavdqa+vngoQ3kRyjVmmFhAREVHAKbWUTU9ULzMVDGozonJlhns0KjPo4Tx7KqdjOFeAUKtqICKSa8xyRpaIiIgCSq2lrFaVgkCyWjpKgKmZmJ+F2ePONf153WdP1Wr69kiOw4yR2bAle5+xjfQas5yRJSIiooAR3dClNAMpqfzMH+wSsP1wtdfNarPG9seKrYdRXtts2vMqzZ4q1fS9qHcqjv54BE9MzsR97+xUfU8aW9uxdm95xFYu4IwsERERBYzohq60xBiXf9tS4vDqLcPxqsLspDMzN+d7y9ltt0vYXFyJC3JSvR4jISZK6Lm0OnTJFSCmDeuF0U5VFCYM7pixTVHp/lXT0Kq4kSxScEaWiIiIAkZ049FNI7Ixpn8GKk43e3Sqcp6dzEiMBSxw3O/C3mnYfrga/9lThqXfHPZprFWnlWda1xSV4dH3v/XaGSw1IRq3X5KLWWP7Y+3ectXmEIBnwwW9xufZ8PTHexR/FulluBjIEhERUcCIbjz66xfF+GDHUcyZkuexvK9Vn3ZkbjpmLt/u0zgBoLK+GR/tPOoSSK8pKtPc5DV73Lm498p+2H64Gqt2H0P3pDh8+fBV2H64Gmv3luPDncdQVd/iuL97wwW9OppOqKc3KG0kixQMZImIiChg5JJS5TVNmnmuRrtTbS2pMmXj2F+/+MHx37bkODw1OQ/PrFKe+XT21jclWL7lEI7XOQWrybF4eupgPDVlMH4/Kc+ji5cvM6Wis9yRWIaLObJEREQUMHJJKUC7hJRafVUt/gjYymubcN/yQqGNXdUNrS5BbMfjm3HPmVxVtXxXo0RnuSOxDBcDWSIiIgootZJSSox0pwrlgO3R97/VFZSL0GqcoLWRLJwxkCUiIqKAm5ifhY2PjMWsq/oJ3X/TwQpHBzCtQFC089a4Qd1N7dAl4lRDKzYXV5p6TG+z3M4byQC4dFIzO6AOBubIEhERUVBEWS0Y0z8TizYUa9530YaDjv/O0tgc5dwRy1uo9vm+E1h883CkJcbgRF0Tvth/Ah/sPKb3Zej2TXEFxgzIMPWY8iy3e3UEeSMZAFy6YL3Lz7Tex3DAGVkiIiIKGq1lcSXyJjC5Nmq7XfKYaZyYn4XFN1+gWVd23id7MTI3HdOG9UKvtHjjL0SHtwoO+aWuqzzLveKuUfjzTcOw4q5R2PjIWABQ7KTm/j6GI87IEhERUdA4z56Kduxyro1qt3cEo0ozjWmJsfC2eu5elmp03wyh2WE1XWO74HRzm+b96pvbDVVjEOFemsxbJ7VIqDHLGVkiIiIKKj2bv2RyEHrfcvWZxnV7y4WOtengSbTbJYzITUfXWP1zfOf3SsbvrxuEbb8fh1SVDltK9FZjMEKrk5qRzXShhDOyREREFHQT87NcOnYdOF5neHZUnmn8YOdRofsv2lCMZVtKAUBoRtXdt0dr8e3RWvxtUwn+56Jz8NpXJUJjDESTgkivMcsZWSIiIgoJzvVVx/TP9OlYEoCq+lakJ8YI5d+eamj12nJWRHlNE17/qgR3X56LlHixmVl/B5CRXmOWgSwRERGFHCObwJRcP6wnAO3mC2aQkwQ+3lWGRTddIPQYfweQkV5jloEsERERhRyR2qgixufZdOff+kJOGbBGWUIigBStMRuOG70ABrJEREQUotQ2gdlS4vDXmy8QDhTPNl/o7/cxyypON4dMAOntffRH5YRA4mYvIiIiClnum8C6J3UEp1FWC6xWi2LZLqVAsaP5QoZLYwV/OnC8DmP6Z2LxzcM9yoPZgtCIwNv7GM4YyBIREVFIc6+NKtPqZuUeKMr5ouU1TUL1an2xaEMxFm0oRlZKHJ6cNAhpibFBDyDV3sdwxkCWiIiIwpaemUYjzRd8VV7ThJnLd+CVW4Zj2rBeAXjGzoU5skRERBTWnMt2je7Xzetsp1q+aFpCtF8qG8jBciCaH3RGYTcje/ToUTzyyCP49NNP0dDQgP79+2Pp0qW46KKLgj00IiIiCgNKs7h2u4QZb27xy/MFqvlBZxRWgWx1dTXGjBmDq666Cp9++ikyMzNx4MABpKWlBXtoREREFEbc80U/EuwC5gut5gftdiniNmP5W1gFsgsWLEB2djaWLl3quC03NzeIIyIiIqJIEIjOVt6eY01RmcemtawgVDcIN2EVyH788ceYMGECfv7zn+PLL79Er169cN999+Guu+5SfUxzczOam5sd/66trQUA2O122O12v4/ZLPJ4w2nMZC6eAwTwPCCeA0a12yVsK6nCydNNyOwahxFus51Vp5sQZfFPDqsFQI/kOFzUO1Xx9/bZnjLcv3wHJABRThOwJ2sbMeud7Xj55gswYbBrMBvp54Ho6wqrQPaHH37AK6+8goceegiPP/44tm3bhl//+teIiYnBrbfeqviY+fPnY+7cuR63HzlyBElJSf4esmkkSUJ1dTUsFgssFi4zdEY8BwjgeUA8B4zYc6wGK3eVoaax1XFbSnw0pgzNwuCeKbBLElZ+8x1GZpofyMq/oRmjMnH0xyMeP7dLEj7a+B1GqDy3BcBHG7/FeV1bYHX6fUf6eVBXVyd0P4skSWGzhS4mJgYXXXQRvvnmG8dtv/71r7Ft2zYUFBQoPkZpRjY7OxvV1dVITk72+5jNYrfbUVpaipycHFitLDYRLrRmAPTgOUAAzwPiOaCX82ynM/mT+OWbL0BKXAx+8TftjV5pidE4Vd+qWLbLAiA1IRoxXaw4Xns27rAlx+GJyYM8ZlRlm4srhZ777TsuxiinnN5IPw9qa2uRlpaGmpoar/FaWM3IZmVlIS8vz+W2QYMG4d///rfqY2JjYxEbG+txu9VqDbtfvDzmcBt3uDAryV4+ztq95fhw5zFU1bc4fuZrvhPPAQJ4HlB4ngPB2MjUbpfwzKr9aJOUn8cC4JlV+/G7CeehXeU+zqYNPQd/23QIgHInsWd/MkR396yT9S1Cz32yvsXj9x2O54Eo0dcUVoHsmDFj8N1337nc9v3336N3795BGhFFCrOS7JWO46y8pgn3LisM+97WRER6BGsj09aSKtXPY+BsWSznCQdvxuXZMCI3XbOTmJ4SW6KbzAKxGS0chVUgO3v2bFxyySX4wx/+gBtvvBFbt27F66+/jtdffz3YQ6MwtqaoDPcuK/RYKtIbdKodx5mEjiv3uSv3YnyejWVViCjimfUZa4RWuStZetdYr61rLegIVuXZVb2zrt5otc11fm7yFFZz0SNGjMAHH3yAFStWID8/H/PmzcPChQsxY8aMYA+NwlS7XcLclXsVPzykM/97/INv0dLmffekt+MoHVcujE1EFMm0PmMB/3a8Ep3FtCV3zKgC8OjuJf97zpQ8R7Cqp5OYFrltruhzk6uwCmQBYPLkyfj222/R1NSEffv2eS29RaRFa9kJAKrqWzFq/udYU1Tm03Hcic4UEBGFK9GlfX9d2MuznWohoAUdKQ4jc9NVW9faUuL8ng4WzOcOd2GVWkBkNtFgsqq+xesSmJGglPlORBTpRD8b/XVhL8923rusEBYob9Bynu1Ual0bqO5awXzucMZAljo1vcGkWm6rnuMw34mIOotQ2Mgkz3ZqbdCSubeuDaRgPne4YiBLnZJcBqa8tgnpidGoqm/VfIzzEpj7B41Wsr6M+U5E1JmEykYmM2Y7g1E+jLQxkKVOR6tElhalJTBvy1fO1GYAiIgikd6lfX+PxehsZ7DKh5G2sNvsReQLuQyM0SAWUF8CU0vWT0+Mxp1j+mDFXaOw8ZGx/NAjok4l3DcyqX1vyOXDvG0EJv/jjCx1GiIlsqwWQK0KjMgSGJP1iYg8hetno1b5MNYFDz4GstRpiJTIkoNYX5bAmKxPROQpHD8b9ZQPC7fXFimYWkCdhmh5l2vzeyAlPtrltnBZAiMiIvMEu3wYaeOMLHUaouVdPi067vjv1Pho3D4mF7PG9ueyERFRJxMK5cPIO87IUqeh1eFFyanGVry07nt8VlTut3EREVFo0tMZjIKDgSx1Gt76WWuZtaIQq3cfM39QRETkVbtdQkFxJT7aeRQFxZVoV9uR6wfevjdYFzw0MLWAOhW1Di9a7BJw3/IdeNVqYZ4sEVGAhEL9Vr2dwSiwGMhS2DLaZcW9DMyB46exaMNBoedkmRUiosCQ67e6z7/K9VsDuQE3XMuHdQYMZCks+XqV7lwGpqC4UjiQZZkVIiL/C8X6reFYPqwzYI4shR21LitlNU24Z1khVu/W12VFTuYXxTIrRET+pad+K3VuDGQprIh05zKyMeumEdnC92WZFSIi/2L9VhLFQJbCimh3rvuW7xDqf72mqAyXLliPl9YdEHr+9MRolNc2BXznLBFRZ8L6rSSKObIUVvRcfWvlT6ltJPCmqr4Vs9/bCSDwO2eJiDoLOeWrvKZJ8TPago6qAazfSpyRpbCi5+rbW/6USIqCFnnnrMjMLxERiWP9VhLFQJbCilkbs0RSFABg5pX9kJ4Yo/gzOQieu3Kv4TSDYBb6JiIKZXL9VpvbZ74tJS6gpbcotDG1gMKKfJV+z7JCofsfOH4aBcWVHvX+RFMUfqioR1V9i+rPnXfO6i3LEgqFvomIQhnrt5IWzshS2JmYn4W/3jwcIp9jizYcxPQlmzHm+fUuKQCiKQqfFpUL3U/vzlm1EmJMVyAiciXXb502rBdG9+vGIJZcMJClsHTdkCwsmn6B8P3LaztqzMoB4sjcdKQmRJs2Hj25u1qFvgHf0hWIiIg6CwayFNK85ZBeN6QnXr1luK6c2cfe/xbtdglr95bjVEOrz+OzoCMdQM/OWRb6JiIiMgdzZClkieSQOudPbTxwEou/KPZ6zOqGVnxzsAJzV+71eXxGd86y0DcREZE5OCNLQac066onh1TOn7JYxILJ9wt/FKpYoMXozlmzCn2z4gEREXV2nJGloFKadbUlx6Gprd1rDumj//4WSXHRGNXXOfFfLJCrb2n3acypCdFYPH04RrltOmi3S0I7a80o9M2KB0RERJyRpSBSnXWtbdLMXz3V2IoZb2zBpQvOViMY3TdD6HlH9EkzNmD5uRtaYbVaXIJUudXt9CWb8cC7OzF9yWaXsTnzVugb6AjHvaUrfLaHFQ+IiIgABrIUJGZ01gI6NkXJwduoft00KxGkJkTj1ktykZUSpxhEinLOXzVSSksu9J2iMF5vr8EuSXh21T5WPCAiIgIDWQoS0c5aIiR0VCMAgOd/er7X+z7/0/MR08XqdUZUhJy/6msprRqFmeeahlbVAPhQRQPKa1nxgIiICGAgS0Fi9o786oZWLFp/ABPzs/DqLcNhS451+bktORavOm3MUmt9KDJTa7UAF/buSE8wWkrLaABc1yRWMowVD4iIqDPgZi8KCj0NBEQt3XQIs8YOEG5pOD7PhqTYaBT8UAGgo/IBJGDGm1u8Po9dArYfrsboft0Ml9LSEwA7t75NihNr4uCP95eIiCjUMJCloBDZuZ+aEI3YLlaU1zYLHfNUY6sj8JNLcqlR2vX/78IfcW2+Tei55MDUaCktowFwn4wE2JLjcKym2XDFAyIiokjB1AIKCm879+V/z//p+dj06NV4586LkRATJXRckQBRbXNWWU0T/rbpkNDzyIGpHJCrpSOodf4yGgBbLRY8MXmQ49juzwXob9BAREQUrhjIUtCo5ak6NxqIslowZkAG7r68n9AxRZoIPP3xHsPVEuTA9MLeaSgorsSq3cdw04gcx8/c7wsoB5ZaATAApMZHwy5JHnmyEwZrv29ERESdAVMLKKhE81lnje2Ppd+UqNaXFV1SX7T+oHCqgtJzAMDUoVm44sUNLjO6csks5/HZvDQokGek711WCAuUWznItXLlRgfX5PVw/Ez0fSMiIopkDGQp6LTyWeX7PP/T83HPskKPn4kuqa8pKsNL6743PE5bShymDs3C61+VeASeNQ2tkADMHjcAfTIShQJLeUbaPVfXnVyP9q8zhmFQ0tnbRd43IiKiSMbUAgpZ7XYJBcWV+GjnURQUV2J8ng2v3jIcWQaW1OVyV0Y9OWkQvnz4Kny8q0y1ZJYFwLvbjmDykJ6ODWdaJuZnYeMjY/HOnRcjNV65IoH8fM+u2ge7xEYHREREMs7IUkhSqiogL7FvfGSs7iV1XxswVDe04i+fHzBUMktLlNUCq9WCU43qNWIldLTuPVTRgD69vR+v3S4x5YCIiDoFBrIUcuSqAu5zj/ISu5ENTb42CFi04aBfn0v0MVoNEbxdAHATGBERRRqmFlBI8bXlq5pANggw8lyij/HWEEGtrJh8AaDU8paIiCicMZClkGK05asWkXJXvlKrGStCpB6tLTkOfTISFH/urwsAIiKiUMZAlkKK0Y5XWrw1YDCT0WYEIg0inpg8CFaL8rH9dQFAREQUyhjIUkgx2vFK5l7pwHkGUq0BQ3qi+nK9Hr+6PFcoD1VtjFoNIiYMVj+2vy4AiIiIQhk3e1FIkZfYy2uaFJfJvTU+ENnopNRIoLy2CbPf2+nTuC0APt5Vht9NHKRZy9bbGL01OrDb7arH9fUCgIiIKBzpmpE9cuSIv8ZBBEBsiV1p+V7PRie5kcC0Yb0wul832JJ9D+5Elu5Fx+g+PpFUBZEcW6P5u0RERKFKVyA7cOBAPPXUU2hoaPDXeIg0l9jdl+993egkB4FmUFu69/dmLKMXAEREROFMVyC7du1afPbZZxgwYADeeustPw2J6GzHqxV3jcKfbxqGFXeNwsZHxirmoPq60ck5CPSV2tJ9IDZj6b0AICIiCne6cmQvueQSbNmyBf/4xz/w+9//Hi+//DIWLlyIyy67zF/jo05MXmLXomejk7+6XnnL3dU7Rl94y7ElIiKKNIY2e/3yl7/EDTfcgOeffx7XXnstJk6ciBdffBG5ublmj49Ik+gGpkMVDbh0wXqPjVZPTsrDvE/2Gn5+kaX7QG7GEr0AICIiCnc+ld+65ppr8P/+3//DBx98gLy8PPzud7/D6dOnzRobkRCRjU6pCdFYuO57xY1W9y333IClh8jSPTdjERERmU/XjOyrr76Kbdu2Ydu2bdi3bx+sVivy8/Nxzz33YOjQoXj33XeRl5eH999/HxdddJG/xkzk4aYR2Xhp3QGP2y04u5nK20YrI+4Y0wfj82xCS/dyHu69ywpdxiSPEeBmLCIiIr10zcg+99xzqKmpwS9/+Uts2LABp06dwvbt27F48WL86le/wvr163HPPffgtttu89NwXT3//POwWCx48MEHA/J8FHrWFJXh0gXrFYNYoGO2dPa4ATjV0Gr6c79feBR2STwU5mYsIiIic+makRWpI3vnnXfiySefNDwgUdu2bcNrr72GIUOG+P25KLjUNmjJdVnVQsnZ487FrLH98fHOo34Z16nGVsx4Y4tH0wVvuBmLiIjIPKZ39urevTvWr19v9mFdnD59GjNmzMCSJUvw7LPP+vW5SD8zKwOodcKSN2h5mw9duqkEkiRhydc/GHpuUWVnGhqIzqpGWS0YmZvueI+2llTpfo/8VX2BiIgonJgeyFosFlxxxRVmH9bFzJkzMWnSJIwbN04zkG1ubkZzc7Pj37W1tQAAu93uteVnqJHHG+pj/mxPGZ5dtQ/ltWcDT1tyHJ6YPAgTButbOv9sTxnuX74DEoAopxjtZG0j7l+xHXC73V1dUwteXv+95v3MMm/lHlw9sLvXgLLdLuGvGw7iHwWHcKrxbLqDLTkOj183CGkJMTh5ugmZXeMwwi04lX//a4qO4rlPvjPlPabwEy6fBeQ/PAcIiPzzQPR1mR7I+tu7776LwsJCbNu2Tej+8+fPx9y5cz1uP3LkCJKSkswent9IkoTq6mpYLBZYLKE587bnWA3e2VyKnFggJ/Ps7RY04u21hbDX5WBwzxShY9klCR9t/A4jMn3ZjhVojfjH2v/i6kE9FH+651gN3i88ioaWdpzbFUBX18cu+7zQ5f6JsVG4IDsVg7JS0CcjARYAe0qO4b3dVciJtfj8HlN4CofPAvIvngMERP55UFdXJ3S/sApkjxw5ggceeABr165FXJxYvc3HHnsMDz30kOPftbW1yM7ORnZ2NpKTk/01VNPZ7XZIkoTs7GxYrT5VTfOLdruEGSsOorxW+Y/JAuDwlyfxxcP5Qkvgm4srsba0DZ4NV0Pb1pMn0fOcczxmRj/bU4YHVh45kwoh+prs2PBjFYAq2JLj8Ni152H193XYetKCdsnzGM7vMQBsK6lSnd1tt0tef06hK9Q/C8j/eA4QEPnngbyCriWsAtnt27fjxIkTGD58uOO29vZ2fPXVV1i0aBGam5sRFRXl8pjY2FjExsZ6HMtqtYbdL14ecyiOe0tJJY7WNMNbkHa0phn/PXxKqFj/yfoWxWDN39ITo1FVf3bJPzUhGgB0VT14ZtV+JMfFoqK+Gd2T4nBh7zQ8s2o/2nx4PcdqmvHAe7swMlNCu6QcyAId7/Ffv/gB724r9cgrljekqeUdi25Yo+AL5c8CCgyeAwRE9nkg+prCKpC9+uqr8e2337rcdvvtt2PgwIF45JFHPIJYChyzW7Ca0eHKiEnnZ6FPt0Skd42FLbljE9Xzn+7Dkq9LhI9RVtOEGW9ucfzbPTg2Qk+CxUvrvve4rfzMhrRfXZ6L178q8Theuc4Na0RERKEgrALZpKQk5Ofnu9yWmJiIbt26edxOgWV2C9aRuemwJce5bGgKhLc3lwI4O0PZbpfw5kbxIFaJr0GsGSR0zJUv+doziHX++dyVezE+z8Y0AyIiCguRNxdNQWF2C9YoqwXTR+aYNj69ymqacM+yQtzyxmbYw2m/mRcS4PW1SOh43VtLqgI1JNLQbpdQUFyJj3YeRUFxJdoj5WQkIjJJWM3IKvniiy+CPQSCf1qw9slIMHOIhmw9VB3sIQScaPoH+ZdaLvNTkwdiUPgUXCEi8ivOyHYSgZjZMasFqzzWA8dPmz7GG4b3gi05OPm34SJY+cl0lty1zjmIBTpyme9fvgN7jtUEaWRERKEl7GdkSVsgd6n72oJVaaxmuuzcTCy4YSi2llTh06Iy/KPgsF+eJxRZAFgs6ukFFnRcdIimf5B/tNslzF2p3LVOvm3lrjJcMzIfEbhRmYhIF34MRjhvMzv3LivEmqIy058zymrB6H7dMG1YL4zu100oiG23S/jzugO4R2GsZuqeFOcY37VhtDu/a6xv15zyb+Cuy3I7AlqVn+tN/yDzbS2p8vo3IAGoaWzFNuYyExExkI1kIjM7c1fuDfoGkjVFZRjz/OeKZaPMorTZTN6gFsq6xnbBg1cPQOGT42FLVt9Mp0VO73jsujxT0j/If0RzlE+eZi4zERFTCyKYyMyOvEtdpEmBP8gzxv4Mpb3NNt540Tn48+cH/fjsnqxelvfdnW5uw3v/PYKBWUl4YvIgvL22UHcw++SkQbhtTK7jtfua/kH+JZqjnNk1tC/CiIgCgTOyEczsJgVm8zZjbCal2cY1RWW4dMH6gAexQEcQ+9NhPYXvL6eBAMCMUTnooXOzWkZSrEeQaiT9gwJDpJRdSnw0RjCXmYiIgWwkM7tJgTtfKyFozRib4clJg7DxkbEeQaxS3nAg2XXcVzrzv2dX7cOgrGR88fBVWHHXKMy6qp/Q41mFILzIpewA9VzmKUOzePFBRASmFkQ0eWanvKZJcdbTl13qZlRC8OdMsPzanJfUAaClzY7HP/jW77PAWj41sMmuvLYJhyoa0Kd3x2zqyNx0/LvwqF9+vxRccik7978xm6OOrHa3uHa7xPQRIop4DGQjmD+aFADqea3yErjopiF/zRSqvbbVu8vwyPu7UdfU5pfn1aO5zVgovbesBlee+W8zf78MekKPWi6zBRJKS0u9PjaQJfeIiIKJgWyE8zazY+RLTasSggUdlRDG59k0AyGtGWNR7punbClxuGlEDprb7CgorsTI3HS8sGYfXvuqxIdnCQ3bD1Wj3S456oea8ftdU1SGpz/ei/Jap8cnx+HpqQx6gk3OZXZm10jh0XuhyYsYIgpnDGQ7ATN3qZtZCcF5RtEXi6ZfgLTEWJyoa8Khinqs2FrqUsorNSEapxq0l2LDQVObHVt+qMSl53Z33ObL73dNURnuUXj/y2ubcM+yQrzKklxhRe+FJmduiSjccbNXJ2HWLnWzKyFMzM/C4psvgNEJoLSEaEzIz8Loft0Q28WKhesOoLy22eU+/ghiE2KiMGWIDXFdAj9ztfkHz0L4RptQPPr+t17v89j73wa9zjCJ03OhGYxmKUREZmMgS7r4oxJCWmKscF1Vd9UNrXhrUwk+2HE0oJu4GlrasXJ3Oe65QqxygLlcX6XR6hGbf6jUDPKrG1qx+YdKwyOlwBK9gCyvbQqLZilERFqYWkC6+KMSgq/VC+Z9ss+nx/ti6TeHYEuOw/Fa3/J89RjV92zKhi9LwwXFYgFqQXElxvTPMDZYCijRC8iq080h3yyFiEgEZ2RJF5Eal3orIYRzndOaxjakJ0QHtJxXTWMLAPV6uPLS8OrdxzRmakVHzVm5cCHSTCErJQ7piTFCxwtWsxQiIlEMZEk3eae8LcU1AFXqoCVC68s31O0trwvo8/1h9X60tNm9Lg1LAGat2IHpSzbjgXd3YvqSzRjx3Dqs3n0273F0X7FZVtH7UfCJXmjaUuKFjhfOF5lE1DkwtYAMMbMSglnVCzqL8tomvF1wSLMzmfsEbFV9C+5bXojJRVn4800XYFS/bpoVHVITojGKS8thRaQkW7td8luzFCKiQGIgS4Yp1biU6a1NKVcvmLVih+GNX2rkZgGRVIbrcFWD4ceu2l2Grw+cxIKfDcHzPz1fsfyW7Pmfns+aomGm3S4hJT4Gv5twHqrqW5DeNRa2ZNe/QX81SyEiCjQGsmQ6oxuQfKle4I08EyXPIP+joASfFh03/4kCqHd6gk+Pr2lswz3LCvHXmy/A7HED8LeNJahx6nhmS47F01MHs5ZogJjVlMDb35778cxulkJEFAwMZMlUvrSvNXNjidUCvPizIeiZluASFIzu1w3V9c1hHcj2SI7FL0b3wRsbS3zuiuY+A54aH43bx+Ri1tj+nI0LELOaEhj52zMzRYiIKBi42YtMo9VVCPBem9LMjSV2CY4gdmtJlWPnfkubPajluszwPxflIKaL1bGpxxfuv4qaxlYsXPc91u4t9/nYpM2spgS+/O2Z1SyFiCgYOCNLpjHavlZeVi2vbUJ6YjSq6s3JY123txwP/XOny5jMPH6w9MnoSCuYmJ+F/3dZHyz5+pBpx1ZqY0r+obedrDdmto4mIgonDGTJNEba1yotq5rlzU2HPG4L9yAWADK7dsxct9sl/Gv7j6Yfn0FPYJgZfJrdOtqsnF0iIn9jIEum0du+Vi2nj9SlxEdjxJmSSIvWH0R1Q5vGI4zTCnoY7PjGzODTzNbRZuXsEhEFAgNZMo2e9rXellVJ3dDsFERZLVhTVIaX1n3v1+fyFvQw2PGdmcGnWa2jfdmsSUQUDNzsRabR075Wa1mVlO06UuPo6uULbxOnchtTtaDHrA1KnZ1oO1nRpgQ3jchWDWIB7bqwLW12PP5BkeHNmkREwcBAlkyl1b52fJ4NBcWV+JTBjiE1ja14Z7N2Vy93cvhyx5g+WHHXKCyaPhwWaF9wuPO1MgWdpefCz5s1RWW4dMF6vLTugOLPRVpHrykqw6j561BV36J6H+ecXW/a7RIKiisdlUJ4LhCRPzG1gHQRyYtUq025dm85Ll2w3ueZ2ClDbNh26BTKazvnjG5pdaPux8i74D8tKsfvJ3UER69Y9RfD5+54c/nalEArz3z2uHM1awLrzVX3lrPrLeWE9WqJyB8YyJIwPXmR7u1rzdzYNS7PhoU3DcfWkipsOliBRRsOmnDU8NHQbGyDl3uQKV9wbP6hEgXFlQAkjO6bgVFeAlCzd8eT8aYEWnnmFgDvbivFrLH9VS9AjeSqq+XsesuvvWdZoUeLaOZUE5EZGMiSEF82gZi9sat7UpwjUB6Zm45/F/6oq8NVtBVotZs0mCDY8P0J9EiKwYm6FkPvqXOQuXZvucvFyaINxV4DDDM3KNFZ7hd+IrYJzo4vWn8Q724rVbwATYmPEV4h8bZhTCTlxDmIBbiBjIjMwRxZ0uRrXqRZG7uUNr845xmKCucgFgCq61tR29Ru+MLAvfyZnk1bZm9QIuNOnhb7m3pp3feqv2O9HdzUcnaN/I0zp5qIzMBAljTpyYtUIrrMnBAdpfoz980vzhtKUuJj8JfpF6gGV5GosbXd0OO6xkbhwt5phi9OzNqgRGK8bZySG2MYIR/lw53HhO6fnhDtdebUaCqJ6AYyIiI1TC0gTb7mRYouM//q8r64uG83rN1bjg93HnPZQe28+UUpVzeui5U1aQWcbm7H5S+sx/SRvQ1v2vJ1gxKJUctJf2ryQAxKAqob1CsMiJAAr1UKnP35pgtw2bmZqj/3NZWEOdVEZBQDWdLka17kyNx02JLjNKsMvLutFPdfPQCj+3XD7yflKW5OUcvVbWoL83yBACqvbRZupqAWYBjdoERivOWk3798BxZOOgfzv64I2HiqNIJmrYYMWphTTURGMZAlTb52DYqyWjB9ZI5m8FRe2+yYAVTa/NJul/D0x3s48xpA3gIMIxuUSJtI2seHu46hvNYOzwQP/9AKNOWUk3uXFcICCP+NinYcU8IWyUQEMJCNaGZ90Hv7khLNi+yTkSD0XN6WGBetP4jy2mah45BvfAkwyDciOen1ze0QCWJT46NR09jq9QJUkiQcr232qbUtoJ5ykpYQjeqGVsOfHYDnZ1l1fTPmfbKPLZKJiIFspNJT81WEr3mRvqYnrCkqE14OjyTx0VY0+rnMgi8BBpnPzHzR28f0wcJ1B7z+jneUVuO1r0pUj6HnPPDWDMWXpg/uj1XCcl5EnRMD2QjkS81Xb3zJi/QlPUFeau2M/B3Ezh43AO9uO8JNWyFE9KIvLTEGlae9z7bOGjsA59mS8PTHe11y1OXfMQC87iWI/dXlubrPA6WUE5HPDqUVpLV7y4Ubqcjd6+au3IvxeTZehBF1EgxkI4xWfp2vH/RG8yJ9SU8wqw5tOLIASIiJQn2LsXJb3o4rBzqzxg4IWK4h8xq1iVz0pcRHY+7VgzFrxU7BvyfXI0mSBLsdmPeJ90YlH+8qw+8mDjLld+Tts0Np1tWWHIumNruunHi2SCbqfBjIRhg9NV8D/UFvND2hM5fmkQDUt7Tjgav7453NhwD4VnIJUA50AnEumJ3u4i5SgmSRi74pQ7Nw7flZWGyx4omPiryWqlNcnaltxn3LCzXHEojPCm9jNKozf2YQdTYMZCOMrzVf/c1IeoLZpXn07KoOFe124H9G5KBw70Fdj0uIiUJyXLTisnIgUwf8le7ifHx/BsmB5u2ir6OObCs+21OGeZ/sdwli0xOj8eSkQZiYn2Vaa+jymkYfj6DO7PbVMpbzIuo8GMhGGF83VQWC3vQEX2pUTh6She2Hq12CgdQzu6jDyaINBxFlkTBSvSa9orsv74dZY/sHdabS3+ku/g6SzaRn1ljtos8CCZ9u/hYPrDyCNsn1sdX1rZi5fAdesVqQEh9jSkrOvE/2IT4myi/vodlpQ6y2QdT5MJCNML7WfA0EvUvARmtUpsZHY/qIHPzpxmHYfrgaJ+qakNE1Fr/5504zXkbIS02Ixqyx/YNe79Wf6S7+DpLNZGTWWOl319pmx8pdZZqv+XcTzjNl3NX1LX67IDBzZYjVNog6J2uwB0DmkoM+wLPKZCh80K8pKsOlC9Zj+pLNeODdnZi+ZDMuXbAea4rKvD5OXmq1pYjPJJ9qbMWMN7fgihc3oKaxBdOG9YLVYuk0tWif/+n5Af89t9slFBRX4qOdR1FQXIl2u+TXdBc9QXIwybPG7mMtr2nCPcsK8czKPY73S8u2kirUNKqvKMivWbT9rBZ5RHNX7hUanx5mrgzZUuJCavadiAKDM7IRyNear/7i6xLwxPws2O0Q2qSidvzmEG5la7EAkglxQrByQ9VmHG8akS30eCNBTajnhANinbr+tukQ/rbpENITo/GTYb0wLs+mulJx8rTYa0nvGovU+Gic8hL0yhI1KmOYsUlUaSVGXkFSuxixAEhJiEZclyiXPO+slDg8OSkPaYkxKK9pRFV9C9K7xiIlPgbtdokzskSdCAPZCOVLzVc9RNMEzFgCbrdLmPeJ/nqyzsd/4WdDdD8+UCQJSIyNOtO1SZ+k2Cj87MJzMGFwVlB263u7SHlp3QGkJkSjpsF7zVMj6S7hkBOuJw+0qr4Vb246hDc3HVK9IMnsKvZabMlxuH1MrlAjkV+O7o1XvvxB835GLwi8pVVMHZrltSHD8z89X/WzbE1RGV747LuI2eRHRPoxkI1g/s6N1JPzp2cJeGRuuuKXli8bQ+Tj7y+vNfT4QDESxAJAYmw0RvXtFpRcWJGLFJnZXcTCISfcaPCntlIxIjcdKfHRsKBN8XHOr3lkbjoWf3EQLSorEfKM53v//VFoTEYuCLxd5NyzzPvqinNDBvdzO5w2+RGR/zBHlgzxlvN377JCj5xX0S/ztXvLVXNozVgePlLtv1JCwXS8Vvl9DwSRi5RTDa14cNy5HjnOvuY1hnpOOGB8NlgtNzXKasGUoR3vl9KrkgA8OamjicELa/apBrHyfU81tGrm01rQcZGq94JAJK3C23N+vKtMMS9X5Lj+yOklotDDGVnSzUiagOiX+d82HfK4TQ6OHxw3wPCYZb3TE3w+RtfYKJw2OHPqL2bs0DfaUED0AqNPRgI2PjLW9HSXUM0Jl/lSPk4tN3VwzxS8fHMGnlm1X/EiYt4n+2C3S1jytfqSvV5aFwRK548ZqyhKebmiKzybiythtVpMOd8ipeEGUaRhIEu6GSmnJPJlbrUAShMocpC2YmspbMlxOF6rPyAAOmaUfjG6D97YWGIoqJCFWhAr82VDji8NBfTkqfor3SVQOeFGGC0f52zTwQrH67qodyoAYMLgLEiSVXHzY3lNE2a9u9OXYTukJ0bjDz853+t5oHb+XJdv8/n55Qsl50DywPE6ocfOXF7ostnNaP5spDXcIIokTC0g3YzsFBdZAva2Ciiho2Xl9JE5hgPQOVPyENPFijlT8sKus5ce6/aW67q/3jQRd/JFilrIaHRZWi85SJ42rBdG9+sWEkGszEj5OGeLNhx0pNpc+eIG7DlWg5Y2O5746FvF+5t5fj85ebBmEKt2/rypsMKiV/ekOI+yfYs2FAs91r1ig+g57czXvw8i8i8GsqSb6AxcRV2zS46a2pe5LSUOd47pI3TMnG4JSE2IFh6rbPa4AY62nd+V1yEhJkr3McLFm5sOCX+56sk1VKoRC4RHnmoomJifhY2PjMWKu0YJn+9Kjtc2YdnmUlz8h3Woqvd/hzpbsvrfu8j5Y7Uo5/JqkS+AquubFQNJI0TyZ53P800HKvD0x8zFJQplTC0g3URz/uZ9sg9vbCxxWX5TWwLeWlIlNHtTdboZp3S2l01LiMa9V/bHmqIyPPr+t7ofH45Ec2VF00QWrT+Id7eVqi6thnqeaqiQZ41H9+uGEbnpHu+XCPlv7nRzG4yFiOLSE6NxYe801Z+L5MDKMZ5SxQpJ4b/lfwPAk5PyMO8T5UDSKG8pOEopBEaPRUSBEVaB7Pz58/H+++9j//79iI+PxyWXXIIFCxbgvPPMacVIYvTk/CmVwlHKkxQto5SeGKN7vNUNrRj5h3WdIoCViX65iqaJKNUidf/dhnKeaihyfr/W7i3HhzuPmdaNyyxV9a244sUNqhcjoufP1QMzsbesTvEiB4DqBVBKfIzuQF+0CYT72NXKeYkIZsMNos4urALZL7/8EjNnzsSIESPQ1taGxx9/HNdccw327t2LxMTEYA+vU1GbgXMnupveW3DsvDydEq8/kAXQqYJYmciXqy+NApR+t/6uXRxpnGdofz8pz2Uzk2geqL95q8sqev58vv8kFt10AbolxSpe5KhdAH2086jQ8Wdd1R8DenRF96Q42CUJM97YovkY57F7S5EQEcyGG0SdXVgFsmvWrHH591tvvYXu3btj+/btuPzyyxUf09zcjObmZse/a2s7CuLb7XbY7aHbrtSdPN5QGvM1eT1w9cDu+Mc3JfjDp/u93vdEbSO2FFdglJcg55q8HvjrjGF4dtU+l3aUtuQ4PDF5EK7J64F2u4SeKTE4Xtusepxwpla5AQCiLBKskBAlOMGZmRiD1rZ2bCupwsnTTcjsGocRbjOkF/VORa+UWMOVIACx3y1pswC4OLdjGX9zcQxe+eKg4v30ngdmmbdyD64e2N3w+TP7n4VY+D/DMGVIzzO3SLCfOdmdX7vzzzITYxBl0T4zL+mb7jj/2u2S1zFZAPRI7qj+IH+ebimuxInaRt3vqdKxAiEUvw8o8CL9PBB9XWEVyLqrqakBAKSnq++Gnj9/PubOnetx+5EjR5CUlOS3sZlNkiRUV1fDYrHAYgnOUq1dknCoogF1Ta1IiotGTrcElFY2oL66BiMztb9sTpYfRWl0vdf7DEoC3r6pn8vz9MlIgNXSitLSUgDAncOSsW7fCVNeUzixWiTkdgVgkbxWeLAASI6PRtXxo7h/3XbUOC2zpsRHY8rQLAzumeK47fErMvHO5o73Vi2HUYvI75bE2bpIGJ/TBbWNnm19Rc8D8zXiH2v/i6sH9XC59fErMrHszPmjZdnnO4D6Spfzzxtv7wNw9ly3dTmN0tKz55+3cxoAZozKxNEfjzhuP1l+SugzzP25lY4VCKHwfUDBF+nnQV2dWJm9sA1k7XY7HnzwQYwZMwb5+fmq93vsscfw0EMPOf5dW1uL7OxsZGdnIzk5ORBDNYXdbockScjOzobVGvhiE5/tKfOYKXWdPdT+I7rf1gs5OWKzdn16q/8sozoKW786KXScQPjJBb3w2Z5yNLT4t75slAWAJGFbhQXtkvL7Ld9656XZePCTEkgutwIWtGFd6RG8fHMGJgzuWCbOyQGsSRmKM+E3XpSNv6w/oDk2Pb9bEjPt0hjcv3wHANdgTOQ88JetJ0+i5znnOM4dAOh1joRPfmjBZ3uOCx2j9MuT+OLhfOHcabX3QX70yzefjz69XVMevJ3TT0we5DJ+ADjWmoitJ8Xa9GodKxCC/X1AoSHSzwN5BV1L2AayM2fORFFRETZu3Oj1frGxsYiNjfW43Wq1ht0vXh5zoMe9pqgM972z0yMoahecwJA3al3cLwNWEzb+dE9OCPgXuDf/Kjx25r/8PyY7gHZJPYDJSolz7PRu8xLsPrNqP64Z3NMRTFx7fi9cM7gntpZUobymEVX1LUjvGovuXWORmfSj12VaM3+3dNa15/fCohlWxTx0rfPAn5zPHddd/mJjOVrTjH8UHMZtY3KFglm190GrIYHzOa21+fDifhnonhzvdbNpj+RY/PHGYag43RwSGxmD9X1AoSWSzwPR1xSWgeysWbOwatUqfPXVVzjnnHOCPZyI5usmCH/UEfWl5WckS0+MxpcPX4Xth6t1d14DOjYe1TS24IXPvnN5fGpCtGNjl9omPFYm8A+5ssHmHyox851Cod34/iafOzWNLYZ3+SuV5vPGaEUM0c2HIptNn546GGP6Z2gei4gCK6xCeEmSMGvWLHzwwQdYv349cnNzgz2kiOdLr3SgY7ZOabezL5wL8PsqJb4Lbhh+Dm6/pDcSoz3/HIymHSUGoeFCVX0rth+uNtR5DVDvYFRzpuJDilsjCn/8bslTlNWCMf0z8PzPzg/2UBz+s6fMpwtcQL0zlrfGG/7s3OatYQvPc6LQFVYzsjNnzsTy5cvx0UcfISkpCeXlHa04U1JSEB8fH+TRRSaj9RF/Obo3rs3P8tvym2j5L2+6xkYhProL/lV4NjcuPTEGF+emoV9mEkb364YvvjuOJV8f0n3s+pZ2pCdGB6TzkjN5tkqEaPkheTY2rosV7/y/i0NmabWzGZ9nww3De+GDHfpyOf1h6TeHfT6GUvk2pYYEWikEQMf5a0b9YtZCJgo/YRXIvvLKKwCAK6+80uX2pUuX4rbbbgv8gCKM0peB0fqI1+ZnmVpPVGlszsuu971T6LI7X8Tp5nacbnbdoFVd34I1Rcfxyi29MKpvN/z2/3YZHvNPhvUypde8HvJ7I9JcYmTu2WofIh2+ymubYbVYMG1YL9PHTd45B3iBLrvlT85pLmqpCt7q2ALK3bhEgl81rIVMFF7CKpCVJGZE+oval8GTk/J05aMqBUn+Gpv8RTWmfwYW/Ox83LusEIB4ySglzrNESbHRPqVVJMfHIDUhOiDNGOTNKHZJwqrdx3DTiBwsXPe9cF6r0XQE8j9fOk6Fi/KaRrzw2XdeVwSUmqqovTdawS8RRY6wypEl/1DLjSyvacLM5YWYOrTji0BrIkjP5h+1PDh3q3cfwz0qY3POr1PLbzNCniUq+KHCp+MsXPd9wDqKSQCa2uyY8cYWPPDuTry07nukJEQL57UaSUfwRvT3Sx3U3i9fN1sGk8UCTB1iE7pvxelm4Q2KMq10GKAj+OW5RxTZwmpGlszXbpfw6Pvfep0J+XhXGRbffAHmfbLP5cvGvQuVTXA5T3QpcPXuMsxasUPxGEqzNM75beW1TZi3ao+POaq+reEG+uvTPWiuaegoIj973AD0yUj0mu9nJB1BjdlLvZHGPU2mur7Z429Lfr9S4mN8WhUIJkkCPt5d7vU+FnRsIhRtx3uirsnx/m06WGGoOgcRRRYGshFG76aHResPeJ01lL8M0hJjsfGRsS7HvrB3mmOXvPtzqY1DdClwTVEZ7lte6PW1Kn1ROee3xUdbfUo3GN2vG/5d+GP4BhLoCBTe3XYEGx8Z6/U88FZ+SD6WyEw7l3q9Uwrylcjv1x1j+gRmYEEgn2d6Vi0OVTTg0gXrdf1NMh2GKLIxkI0gemfC2u0SlgpuRjpR16S4CUJppkM933YQ5n2yTzMPbuzAHpi7cq/QuOSxKVGrbGBLjkVTm90xY6mkW2IMRvRJx9ShWXjtqxLhsYQaPbNS8vv16PvfegQXqW4pCkpEKh8o5Tl2FnpyXeX364OdR/08quDpcebvUCSQlWduF677XvdFqdENq0QUHhjIRggjM2FbS6qEC6yLfhl4G8d9y5XTBGRy0PV2wSFdMy7exqZWTmft3nLV2UcAqKxvweUvbEBVQ4vwOHyVEBOFnilxOHiyXvvOOumZlapRCCxqGlo1Z1RFKh901qVeI7muEjpqA6cnxqC6viUs82TVzB53LiTJjoWfHxS6v+T2/yL8sfGUiEIPN3tFAKObHkSDm9SEaKEvA5FxiDhc1SB83yyBLyqlQuoim8PKa5vQ0mYXHouvGlva/RLEAmIXIr5unon0yge+bGDzpbHIsOwUQ48LZW99UyIcxKbGR2P2uAG6UhDYdY6o8+CMbAQwOhMmOst6+yViPdF97QIm652eIHxfX76oJuZnYezAHhg1f13AGxco8ceMm55ZKV9nVM2ufBBKfN3A5kvwvn7/SYzP6461e08YPkaoqdYRlC6eMRwVp5t1HV904ykRhT8GshHA6EyY1k51oGM2dtbY/o5/K23iAjqCoE/dWk3qJQddvxjdB29sLPE6LqsFWDT9AtXcX28b3px/fqK2OSSCWKOsFuDOS/vgjTPdx7zVjNV6X3ydUTWz8kEo0Zu2Y2ZjEdm6EA1i0xKiccOFvfCv7Ud1BaeibMmxGNW3m0vZLW9mXdUPY/pnshsXUSfCQDYCGJ0J09qpDgDP//R8xxeC0qyUvAlIb71Ub4X6Y7pYNce1aPpwXDdEf5cf0V3j4cIuAWMH2nBh73TPTW0ar9t9RtHXGVVv51O4LvXq3cDmbaOjnsYiSs8VTO7tllPjo3H7mD4Y0D1Js7qIL6aPzEGU1SJ8kTR7/HlhdX4Rke8YyEYAX2bC1Hb2uwc5arNSRgv+p7h1vHJfChQdlzOtmbNfXZ6L178qCXpQYLYTdU2YNqyXao940RlFM2ZUVStFhOlSr550C28tVmcu3+E4/9QuzkKR/Dv/8uGrPErtAcCFz6716/P3yUgEEJkXSURkDgayEcDoh7y8BNrcZsf/3jAUsHR02FGqCWtmdyELgPjoKCy+czgq6j2fT6ZWcUDpy0pk5mzJ15EXxAJnZ0iVyqPpnVE0I1jQ83sLdaLpFuW1TXhhzX6BxiLDMe+T8FgRcF8lcT+3Nh2sMHwhGx0FtLZr38959j/SLpKIyBwMZCOE3g95b0vN7l9YZm3iksmzWEDHF9WJuo4ZLaVgRyk4UyIycyZFWBQrMkMqOqP41qYSZCTFontSnGKwpTdYEP29hTrRdIsqwRaraYkx2PjIWGz+oRIz3ykULn/njQUdNVlvvPAc/EWwQ5aStIRolzxXrd95QXGl4ecSCWKVKpJE0kUSEZmDgWwEEf2Q17t5xV/lkmYud/0i96WNabiWdDLKebYM6AgqlH7nou/LvE/2Of5bzulMS4zt9MGCaLpFemKM0PHkxiJj+mfg+Z+d71PnOWdPTx2MiflZsFqtWPj5Ad2P75YYg8evG4RTDS1IT4yBLSVe4HfuvytDC4CbRuRg1e5jHudfpFwkEZE5GMhGGK0PeSPdl/xVLsl9NsqXNqbBLukU6LxHebYMgEfLTucLAiPvi5zT+cotwzFtWC/TxhyORNMtUuLFAlmRpXI9kuO64IUbhjj+Xu6/egDeKjike8m/sr4Fv/m/XQDOnj9aFy4X53bDIh9mgNWkJURDAvDSuu8dt/lykUtEkY0NEToZ0aXmzcWVjuLvdkmCLTkW/p6PEym6r0aeOQvGnGF8tBVdY6MC9nzpiTH48uGrAAD3Liv0+H3KFwRrisoMvS++/B4ikVrzDFtKnMdGObX32QL1pfKNj4zFirtG4c83DcM7d16Md/7fxbhlVI7Q2OZMdg3uoqwWPP/T8/W8PA/y+bN69zHVBhBrisrw8L92+fQ8Sm4Y3gvVDa0egbjzOU1E5Iwzsp2M6FKz+7J/6plZEn/PPBptYxpltWDq0Cy89lWJ/wanorE1cN2/AKCqvgXbDlUJz6xrlTJT0pnbySrRStvxZaOc0iqK1WLBss2lmuPqmebZPGRifhZe9WGmVx77rBU74HwdY0uOxfSROahtbMWbmw7pPq6I1UXlqmNSWi0iIuKMbCcjutTsvuxfc2aGJOVM3VijUuPFHq8353VNURleD0IQGywFxZXCZaFE2vGq6Wy5x94otTp25m3mdvHNFyAlPka4va08w+uNLVl9o5/zTO8V52YKvDpP7kMsr23GS+sO+C2IBYCGFvVdYM7nNBGRjDOynYxINy8l8oxIfHQUJl1swztbjhh6/l+O7o2/rNfusa4nt9Ps8mDhQezVyoGo+4xiRV2zywYvNcHOPQ437u9zRmIsth2qwuMfFOna2Og8w6u2yeyJyYO8zkxGWS2oaWzBl9+f9PFVhRZeXBGRM87IdjLyFyQA3fmk8ozIyboWw8//9ubDsCXrzyX0xuzyYKHOlhyH0X0zhO7rHIg6zyjeNibXUE4naZPf59guVsxcUYiFnx9Q3djoLedTnuF1n5m1JcdhxqgcTBisHAS32yUUFFfigx1H8fgH3/r+gkIML66IyBlnZCNYS5sdbxccwuGqBvROT8AvRvdBTBer4wvy6Y/3oLy22XH/lPguqGls0zxuQozxjU3VDa342fDu+HfhUdM69Pg6Q9M1NgqnmwUKW4aIx68bhFH9uiErJc5rAO8tENVqTywBuC7fplrfl7xTK3Enk1c4nv54D5LiohUbkQDKubkX9U7F0R+VV0RCtQVzakI0Zl7ZH8+t1l4F8MaWHMuLKyJywUA2Qs1fvRdLvi5xyXN7bvU+3HVZLh67Lu/MLa7BidUiFqzkZSXjw53HDI/t34VHkRgTheguVq9takX5OkPzws+GYN4n+3SnWwRLWkKM0Oa2qUOzvAagauWfrJaO/Mg3Nx3Cm5sOsfSRTqKpLhI68k5nvLHFcZvSe+2+GcxuV95cuHp3Ge5bXujL0P3mVEMr8rKSNS++tEwfmcOLKiJywUA2As1fvVcxwLFLwGtfleCHinqs23vC44u2WqD2ZHpiNI5UN/g8xvqWdqClHbPHnYs+GQm6iu7LrXXlGaoLe6cZyvvtkRSDudPyzxSSt+je2e8rCzpmqkTed2ffFFfiotxu+HiX91JEH+8qw+8mes+jdJ7xW7u3HH/bdMhzk48P9X07I19SXYy+16t3H8OsFTsMPWegfL7/OOZMycM9y4wH230yEk0cERFFAubIRpiWNjuWfO199/5ahSDWnVroU1XfircFygKJendbKa4984W96kzdSm87utcUleHSBesxfclmPPDuTkxfshlXvLgBU4dmeR23u66xXfD1I1c7ggW1HefdBDs26SUHzHYDfXNf+fIgRs1fpxksie7wjrJaMDI3HZ96KX0EsK6sKF9SXYy81x0zsTs8LkBCzd/OVDuYPW6A4WPIqy9yHrBoFQgiilyckY0wbxd4zqgZkZYYg6p645u6RJXVNGHU/HWoqve+o7vdLmHR+oMu3X6cj/H6VyX41eW5+HhXmdBs2OnmNrxdcAgZSbGO2WClfMQLe6fhihc3mJ52kJIQjVMNrUI5yUqc3y9vRIMq0UYZvtaVdZ9Nj8T8W19TXfS81+EwEyuT68B++fBVWLH1CMprxQN+uRXwyNx0xTxgpr8QdV4MZCPM4Srfl/0B4MlJg2BLiUd5TSPmfbLPr0Gte1Dmvry6pqjMY2OaOwnAP//7I7Y8Pg5/+fwAFm3QLvHlXH7K+YtQDh7koOvafBv+tumQaWkHD1w9AO9tOwJAX0qBEaJBlWjA68tsY2cJQIyWuHOn9F632yVsKa7EyfJT+OhAM176XPs8DxVygL79cDWentqx0VC+3Rv5MufJSYOwaP0BvLTugMd9ApH+0hkuwojCEQPZCNM73bPTjxG2lHiM7tcNf173fUBmZp05L6/a7R1dxkQCguqGVrzyxUGM6Z8hFMg6Uwqe3YMus2Zk3958SHhG1SjnGSwRogGv0dlGtV38kZh/q1URQpT7e7169zE88VERahpaMDJTwtaTFugvoue7a/Nt2FJSafgcPlHXhGnDeiluNEw700HQfRPo1KFZeGbVPtVZXH93/uosF2FE4YiBbIT5xeg+eG71PsPpBe5LeEqzH4rPO6o3Pvn2mKkBWllNE574qEhXILB00yHce2V/3TNizl+EdruEmct3+G3Tl7+DWKDj9WhVLZC12yXYJQmp8dEe9U5legNj9+OLttONlBkutYoQqQnRuG10H7y77QiO1yqfn0rvtfMGzig/vUWJsVGoFyhDNzHfhilDeuKJj4pcLnJFg3Y5QFdr+wt0pLqU1zSiqr4FpdUNQq2n/dVWuTNdhBGFIwayESamixV3XZbr9YN/fF53rNt7AoB6HVegI7gQdd35WRjTv5vwcqEovbPBpxpbsf1wtaEZMfmLUG/wHKpe/6oEF+SkqX7JynnHSzeVqAawgPH6vrJA5d+GGrVALcpqwcCsJMXzU+m9Xr27TCiQ85VIEAsAhyoasHDd94q1h71RCtDdS4vJahpb8MJn3xmq/mBm56/OeBFGFG5YtSACPXZdHu6+PBfun6tWC3D35blY8ssRqj3hX7llOMbn2fDWphLhLxG58L7azv9Af7yfqGtSHYuIQMyYpifG+Py+WACP37G7pz/eg00HKzx2d68pKsOFz67FS+u+9xrEAmfPC6OzToHIvw1Vzt3URvfr5gh21M5P9/e63S7hiY+KAj5uJRZ0NCRYsbVUd9UTPRdD8gyo0RJmZnb+0nMRRkTBwRnZCPXYdXn4zTUDFTt7AeqzRWv3luPSBet1fYk4L2ErHfdYdQN+86/dfnmdStSWLivqml02ePmLt1lgeVbqyUl5mLnctxxKCYC36l1qBfe1GinIUhOisXj6cIxyCsCM8Hf+rRlENvKIbvYRvZ+3GVvZ1pKqgOeoK5FHNH1kjlC6kXvVE9FmJ6LNJNTGaDT9RU1nvggjChcMZCNYTBcr7rysr+rP3Zf1tNpqqnFfwnY/bkGxzgMaJH+RXdg7DQXFlThR14SMrrGOSHGgLRm25DivuYmJsV1wutlYSSzZr6/ujz8r7CZ3npWamJ+FV6yeOZRZZ4LclPhozFxe6HW2NDEmqqOxhA5lNU3Cy9SnGlphtVp8XjLV2sXvjwBED5GNPKKbffRuClJbWpeFSoAkB6LNbcpdxdzJVU/07vD3pZkEYDz9RU04XIQRdXYMZAmAbzMhgPc8sZG56Ug9UzfVX+RnnTo0C1e8uEH1yzAxNko1mJIAn4NYqwWYedUADMpK9gho3GelvM3IFRRXai756w1ijTAjkPK2i9/X/FtfiWzkASC02ccfm4KCGSDNuqo/BvTo6nJe/lmhjrMSueqJXuU1jbofA3SkPDw9dbDpm65C/SKMiBjI0hm+zIRobdZZu7fcr0EsAKTER+PSARl4/asSr8G42oaWHsmxaGqz+zxOuwRsP1ztCFI3F1ei4IcKAB0zb6P6ur4/ajNyogFkanw0ahpb/bY5zaxASm0Xv+iSsz+IbOR5+uM9ACyam33GDuzhl01BciBl9G9zdG46Cgzmb47pn+GxYqOVVuBrYGckjWL2uHMxa2x/v1wIhfJFGBF1YCBLAMyZeVMr4K6n+oFeCTFRaGhpx6nGVqzaXWboGA9ePQAjctNdckl9Ib8Pa/eWuwRuizYcFK49KRpA3j6mDxYKlkjTK8vkmSaRnNBAEtnI460Jh3yfspomzFu1xy+VGZwDKT0XK11jrXjhZ0PRapcMBbLJcV1wtLoBb35dg/TEGHRPjjsT1GvzJbBL7xorfF+LBVg8/QJcN6SnoecSFYoXYUR0FgNZAmDOzJvSMXzNedPSYMLy+l/WH8Cto3ubMJoO3ZPifF5mFl3SnDV2AM6zJeHRf3+rmYqghwX+mWnSygkNJDPzT9/eXOq351QLpLy59ZJcXDekJwqKK3U/HwDUNrXhtwY2aD447lyfAjtbsvjnkCQB1gBdBIXaRRgRncXyW37UbpdQUFzpUfooFMmBk9GP5fTEaJTXNnm8Tl+CBaslMKW77BKw9JvDPh/Hgo5ZzAt7p3ldZgY6lpm9nQ/yTJx8XPfnAc4GmhPzs7B4xnBD41WSlhDdKYq8ByP/1OhzTszPwsZHxuL6YWKzj5IEFBRXorym0ZRSb6L6ZPjWWVD+HBIhp2sE6nNVrZQaEQUXZ2T9JNxaGjovYRpRVd+K2e/tBOD6Og9VNBge03XnZxlOFzDCaoFPHdGAjuBy++FqXcvMauWa1GbieiTH4ckprhtbRvXt5rUzl7u7L8/Fx7vKXLtOxUfj9jF9MGvsgE7xJS0y690jORaARbXShSi13FHRUl1Ax9/ozy/KxspdRzWf791tR/DXL8TKhRipfqHGOVDX89pkelIpIrWRBhHpw0DWD4wuKxv54DfTxPwsLL55OGatKDQc0AFnX+evLs/F6wY6EqXGdwEsFtUgVk/Apoee1+y+8cM5X+6jndqBBtAxW611weOypFnbgKS2Wlx2wUBEd4lyOVaU1YLbx/QRqvE5e9wAPDDuXPxu4qBOvVQqspHn6amDAUB3lzhnapuCVu8+dqbN69lzWetid1TfbkhNiAbgfVOUyKaprJQ43HhRNv78ue851u6Bui8X8vIFnGi6TKiUKCOi4GAgazKjLQ3NmME1IxBOS4wRCujuu7If3t12RPELU36dS772XkFAzalG9RJYNww/Bz2SY7FYcLZJry5WQKRU5m2X9ME1g22K77Xo8rFaq0/3Cx55SdNuT0Npabvq73TW2AFY+s0hr5UXss7k1QKhla8aLKIbefTmqDpT2hQ0f/VexXq+ZRoXu1FWC569Ph//WGts5SQ9MRpPTh4MW3Icqutb8PgH3xo6jhI5UDejDNnE/CwkxUZjxpvaGzBZw5Woc2MgazIjfeVFPvivyevh9XnNSmUQnd2wWrzP+mh1nTLqX4U/mn9QJ4L13nHNYJtqECi6ZK3W6tNouaYoqwXP//R8xXOJpYLUiWzkMdol7slJg3DbmFy3mdgyr00pJHj/3U8YnAV7XQ4Of3kSx2q8V1VwV1XfCltyHGoaWzBzuf7mJ2p+dXkuJuZnGb6QVzKqXzevf0dAR/c5u11Cu13ieU3USXGzl8n0tjTU+uAHtDc0qPUmlwPhNUXaeabyxrQDx08LjT8w27BCU2pCtNeyVCIbtaaPzEF5rfk93OUZRvcNM7aUuE6xgcsokY08zve5bUyu5ubI1PhoDMxKdrmt3S7hiY+KNMej9bsf3DMFXz58FWaPG6B5LHflNY0+NT9R8vGuMseKkOiFvBZvf0eyUw2tmPHmFly6YL3Q5xwRRR4GsibT29JQ9IN/m8oHv1mB8KUL1mP6ks1YtMGztaozeWd+Z16Svv2SXM3ZHzmgtKkElH0yEoWey2i5po2PjMWKu0bhzzcNw4q7RmHjI2MZxJpIKMhqbMWMN1yDrK0lVcJF/0V+9+9uOyJ0LGdV9S2ml8STg1O9F/Ja1P6O3Om5aCeiyMLUApPpbWko+oF+8nQTeqZ53m4klcGZWlqDEufl6VF9tZf9IlFqQjRmje0vdF+tFrQijOb/Mf/V/0TruzqnCDWL5q5A+3e/TWeNZvmzR0/TAT3kc1yEnvPa0SXvh0rMfKdQcQOYL93TiCi8cUbWZHrqfwJAhuCXSrdE5fvpmQFxr2vb0mbXtcTovDwtMiOlR3pitAlH8b+fXdALW0uqhGtXqi1Za9XtlWe+2cM9tMmz3+/ceTFS45XPYeeVkQyVv2N36Yne01eAjotbPSR0fPboaToAALFdxP7C5Qs1f5zXUVYLrBaL1yoGRtNxiCi8cUbWD3S1NPRxOlM0ED5UUY9LF6x3GU96YrRL6R81s67qhzH9MxU3wBjdzX3VwEz86rJ+OFHXhB9OnsYbX/+g6/EiUhOiEdclyiUX1ZdasQDw5qZDeHPTIZ9rArOHe+SIslpgtYoFWbB0BHJafy/PTsvX/N1ndtUXkN4xpo9jQ5ae1ZTmNu/3cl5l8ud5bXbaAhFFBgayfiLa0rCiXmzXcWV9M86Jcb1tTVGZZv9zCzoCOqX6oiJBLAAM6JGkukwtv863NpUI7eKWbdh/Ev9zUTb2HqvxuoPbF6caWvHOncNhtVp07TQXoaeUkBr2cI8cosFTxelmzYL/d1/e0V5Wy4gzs5+iF5FXD+yBguJKnKhrwk0jcrBw3fdCjxPhHJz667z2R9oCEYU/BrJ+JJKnKPqh2zH7Uu/4t0huqxwyt7aL5+UpURujc93aai+1S9XG9uj733qteWqGivpmTBvWCwCEGxWIMCsnjz3cI4OeIGt0v26KgV63xBjMm5aP64a4Bnru9aEv6p0KQLwLlgVASkI0fvN/u1xWJzoaK0Dxb1B0tSYxNgp//PlQl+C03S4hJT4Gv5twHqrqW5DeNRa2ZN/Pa737D4ioc2AgG2QjBWZVslLiMCI3HUd/7AhkvVUqcJaaEI3Wdgmnm9UbDGixWoBh2amOmRw50Fq7t9xwgXigIxD0dxALuAYYZs/UmNUikxuzwp/eIEv0AkapPnSvlFg8fkUmcnLkbnwX4OF/70Z9s2ebWXl5v+NvzfXvraahFRKAB68egNZ2O46eakTP1HiM6Z+BE3XNjpbT3kRbrRifZ/M6XjkNx9eLM6bjEJESBrJBFmW1YOrQLK/L61OHZrl8OG8urhQKIPXOkiqxS8CYBetdSgalJkQHJAj1hdx0wC5J+GjnUXRPisOFvdP8UmmBOXlkJMjSuoBZvbsM9y337OB1vLYJ72wuhTUpAxaLFfM+2acYxAIdfwNNbXbFv1d5jH9Zf8Alb/yDHUdx04hs1XE5O9XY6riQM6Ojlxam4xCROwayQdZul/DxLu+1Dz/eVYbfXnMeAOCzPWV47H3vebFmc697GepBLNDxJd3UZseMN862uMxKicPUoVl4/asSj2DDF8zJI8DcIGv17mOYtWKH4s/k8/aJD4tQVd+meh7PHncuJMmOhZ97rw3tvvmxvKYJL607gISYKDS0KAfILvevbTK1o5cWpuMQkTMGskGmVQcWONsQofpkDR5YeQRtEj+wvUmJ74KaxjaPgLu8pgmvf1WCX12ei493lflcFJ45eeTOjCBrTVEZ7luuHMTK5HQBSaXQlQXAW9+UGFqVkQNPSbDHdNXpZp/rWevFdBwikjGQDTLRZenjtY34bFdZp2o+YMSk823YfvgUaho984LlL+iPdh7DizcMxZaSSmw6WIkdR07pfp5Qy8lz3xDEGargUQuyRH5H8symryT4llokAWhsFdskmp4Yw9JYRBQ0YRnILl68GC+++CLKy8sxdOhQvPzyyxg5cmSwh2WI6LJ0VX0LahpbYU77gchjsQC/uiwXV57XA598u1n1fhKA8tpm/OJvWx23pcR3QZtdUs0zVHu+uy7LDYmcPG8bbEJhfCT+OxJZoQk1tpR44fsyDYeIzBZ2nb3ee+89PPTQQ5gzZw4KCwsxdOhQTJgwASdOnAj20AwR7YQj2lYyNT4af715uNdjAh2BWKT42fBe+G7etXjsujxDMz61jW1oaG7HDcN7CT/GLgGvf1US9N7u8gYb9+CHvedDh57fUajNWGp13JO7dLFTHREFS9gFsn/6059w11134fbbb0deXh5effVVJCQk4G9/+1uwh2aIaEvbHoIzGYtnDMd1Q7I0jymY/hYWfjr8HMR06TiVjcz4yG/FpuJK2JJjdc15z125V7hdrZna7RI2HazAo//+VnWDDRC88VEHrU1QgOvvSM/5m5oQ7bf1GTnwfHZavtfgVE6t0dua271dNs9RIjIqrFILWlpasH37djz22GOO26xWK8aNG4eCggLFxzQ3N6O5+Wz3rNraWgCA3W6H3e5bowCzXJPXA3+dMQzPrtrnUrDclhyHJyYPwjV5PdDa1o7U+C7oYlGuCdtRbioOI/ukwW63ez3mxME2vFVwyM+vKnAKDlZgdN+OmZ6LeqeiV0osjtfqL7F1orYRvx47AC+v7+iCJvL4E7WN2FJcgVEB2Hgin7Nrio7iuU++c/xeo7xEM4EcH3naUlyJE7WNwr8jkfM32irh5hHZiErphl+v2AVAudxXYmwXQzWk5cc/NXkgJgy2aX42yZ+jIp9jdrsdn+0pU73PhMFMhREhfxaEyncYBUeknweiryusAtmKigq0t7ejR48eLrf36NED+/fvV3zM/PnzMXfuXI/bjxw5gqSkJL+M04hBScDbN/XDoYoG1DW1IikuGn0yEmC1tKK0tBSSJOG6c5NQ11QFu6T8xTVjVCaO/nhE85iHKhqwNzNyZkCS20+htLTU8e/Hr8jEO5s7/q33VfaJa8Sfp2Rj5a6yMznJ2k6WH0VpdL32HX0kSRL2lBzDe7urkBNrQU6m2OMCNT7ydLL8FEYK/K05/460zt+bR56DnvHtSE9qUzxXU+KjMWVoFiQ78M7WUoUjeCc/flBSx2eP1meTM6377jlWg3c2lyInFi7nrwWNeHttIex1ORjcM0X3mDsbSZJQXV0Ni8UCSyTliZEukX4e1NXVCd0vrAJZIx577DE89NBDjn/X1tYiOzsb2dnZSE5ODuLIlPXprXy73W6HJEm4ObO3y2wcoD2b4X7M7GwJT64rE5q17BrbBT+7oBd6pcWjqqEVr35ZrOPVBMbMyX2Rk5MBoGPJ8seWBKR1b8HXByt0beACgPttvTCqXzdcMzIf//imBH/4VPkCyf0xOTn+n/FsbWvHC2v2Y+tJC9p1lGAL1PjI07HWRGw9+aPm/Zx/Rzk5gDUpQ3XWcvygHigtLUV2djb2nz6O3adO4Hjt2ZnX7klWDKqPQ5+MBHTr0YpPi8q9PneP5Fi88LOhqKxvRmbXji6CShUv1D6blCjdt90uYcaKgyivVS8ZdvjLk/ji4XxW3NAgfx9kZ2fDag27DEEySaSfB/IKupawCmQzMjIQFRWF48ePu9x+/Phx2Gw2xcfExsYiNtZzo5TVag27X7zVasXE/F6YkH+OoVJLzuV/bhyRg5fWHdB8TE1TO/5W0DHrYkuORVJ8jKO1ZShITYjG6P6ZsFotWFNUhkff/9atfqzYF6JcE/bifhmwWi2wWoHbLu2HNzYd1mw7Kj/G37YfrsSpxja0S2KBbKDHR54u7peB7snxus+ha8/vhWsG91T8O7fb7bBarVi77zjue2fnmeOefWxZbStecmqCkBjTBfUqjQ0sAJ6ako9Lz+0OwL9l3LaUVOJoTTO8/U0erWnGfw+fYo1YAfJ3WLh9j5G5Ivk8EH1NYRXIxsTE4MILL8Tnn3+O66+/HkDHFcnnn3+OWbNmBXdwAWSkGLhS+Z/E2ChdM5bHa5tDJoCVPf/T8xF1Joi9Z5lnO08R3lqIhlJv95OnxXe0h1qd287Kl3PI29+5XZLw7Kp9Qn+PchDb1S1n1r38l54ybkYCXtaaJSJ/CKtAFgAeeugh3HrrrbjoooswcuRILFy4EPX19bj99tuDPbSQpdYDXe+yu/z46CgLWtuDH9LefXlHHdd2u4SnPzbetjc9MQbP/SRfseZqoHu7ewsQMruK72hn7/nQ4Y9z6FBFw5m0A/GLlKS4LnjtlgtRUd/scW6pfUbIJcJeuWW4oYDXmWhFhkMVDcKviYgo7ALZ//mf/8HJkyfx1FNPoby8HMOGDcOaNWs8NoBRB2/lf4wKhSAWAD7eVYbfTRyErSVVKK9t1n6AiicmDVKdcSqvaURVfQsevuY8VDe0ID0xBraUeL90ztIKEIb3TkNibBcA6hcgqQnRWDx9OEb168aZ2BBiRutaZ3VN+rt2ldU0wWq1YNow13rJWiXCLOgoETY+z4a1e8uFA153I3PTYUuO1fxbfXdbKWaN7c/zl4iEhF0gCwCzZs3qVKkEvgjVTkFx0VY0CbbAVCP3bvd1KdK9M5FSQCmTA0t/BLHeAoRfXZ6LVbuOISe2DUqzcPItz//0fIwZkGHq2MgccqqAfJG0avcxwwFtUpz3RgVqlP5WtD4jJHT8rW3+oVI44FVLlZg+Ujs3X/67Zp4sEYkIy0CWxIVivllaQjT+8j8X4BdLt2rfWYM8u2WUe7chtYBSViYw86SXSNH8174qQZRFUi25xVSC8GBWO+E+GQmwJcfhWI2+vHWlvxXRz4iC4kqhgNdbENonI1HouULxc4uIQlPkbXMjF6JBXnpijJ9HcpYE4OJ+3ZCV4nvf9YzEWMeSpRHu3YZE0zDM7Jjl66x5emI0vnz4KgaxIc7MdsJWiwVPTB6k6/m7JcYotogVvxAUO9+9BaGiz+XLxSkRdS4MZCOcaA/0zY9djRV3jcJL/zMMibFRfh3TqYZWbD9cjalDfQ+8fvN/u7B2bzmenjpY92NnjzvXJfgTDSidZ57M4OvsU1V9x/tJoUtvq1oREwZ3bCKzJYsFffOmKddnFf2MGN1XLGXFWxA6MjcdqQnqaRHycykF3EREShjIRjjRHugxXawY3a8buifF6q5mYER5bRM+3iU+A6XmeG3HbBYAvHrLcK9fks6sFmBA964ut+kNKM1a/jRj9olLsaFNNA9V78XRxPwsbHp0LGaPG+D1fndfnovrhihfOHr7jJDHNiw7FXZJgi05VjPg9RaErt1b7lbn2fO5WDKOiPRgINsJyOV/bG5L+baUOI+yOjPfMVaLVa+q082mbEJzns0an2fD9ifG4507L8Z1+d6rWNglYOZy1+VcvQGlWcufWjNigRwL+abdLqGguBIf7TyKguJKxwyrP2uoRlkteGDcuXj1luEe6TrpidH4680X4LHr8rweQ+0zQvZpUTl+8betqGtqc2zsciZSt1ielfYmLSEa4/OUm9sQESnhZq9OQqv8j9YmJ7PInYzMzMl132QyZkAGxgzIwOrdxzBrxQ54W6113mUtB5RaAbb8Gsxa/vRWNF+L2WMh47xt5DKaG6pUV9ji5edfPnwVth+uNlTiS/6MWLT+gGplAbm5QkpCtMvMqshmQ5HUneqGVlYsICJdGMh2ImqdgvxRa1aNBODx6wahvKbR9GO7z2alJcZ6DWLdA2DngFLrvTB7+VOtaH5WShymDs3C61+VGJoFo8DQKp+2+OaO2VKtVrXuFTSUzoenJg/EoCTgsz1leGbVfsXA2b1WrB5LN5Vo3ic+OgqL7xyu2FxBDTt7EZE/MJANQ2b3Qze71qwFHYX5Y7tYFYuf/3rFDr8Eze6zWUa+ONUCSpmRUkmivM2aX5CThnkr9wA4ewHAkluhQaShwLxP9uLJSYMwc/kOoVa13gLj+5fvwG/GdMMfN1WiTbJ4/NyX8nCL1h/EqcY2zfupNVfwhhULiMgfGMiGGW+zMEYDGrNnQCQAz12fj++P12Hh5wcVf24mCzrKh5XXNKKguNIR/Bn94nQOKOXOXuldY2FL9v2iQYvarPnE/CxcPbA7vt6xHzd1SUb35AS/j4XEiG7kSkuMFWpVK1LhYOOBCkgKWdUijQnUtNslodlYmd7PDTl1R8+sNBGRFgayYWTPsRo8sPKI6bMwZs+AjB2YiWdW7TvTC97/JACV9S2Y/c9dAM4G9uPzbIa/ONUCymCKslrQNzMROTm9YLVyn2ao0DPzP21YL81WtSKBsZ6UGVFbS6pwqlG89a3ezw1vueBMkyEio/htGCba7RJW7ioztQ6lzIxd887W7z8ZsCBWiRzYr91bLlR6jF+c5Au9M//yRdK0Yb0cudnOzFoh8Wc5OaO1XkUrqBARieKMbJjYVlKFmsZWKFd6ND4LA/i2a96d1eJ9tigQnJdXNz4yVmg5V4TZuckUGcxeMjdrhcSf5eR8uQDUqqBCRKQHA9kwcfK02GyJnCeq9wtCa5OTKDOC2NnjzsWSr3/A6WbtTSdqnAN7M744vZVW4ixS52b2krlIYOztUEZzTbWeFwAsFmDx9At8PudDMXWHiMITUwvCRGZXwTaUn+zD9CWb8cC7OzF9yWZcumC9cA/3iflZ2PjIWMy6qr+hMV6b73sh86yUOMwa2x+FT45HYozvp6e8XKq1nOuNvIPcPcCXUxhE31+KXGYumYt047t0QAYsXn5uZMZUq8MXAPzlpguQlhjr0fCBiChYOCMbJqobWoTuV1Xvej+9G8GirBaM6Z+BRRs8qw1o6ZeZqPsx7p6clOeYOf3V5f1UC7OL8nWZVqS0kpEd4hR5zFwyV1shsTnqyLaiT+8cjwomvpZkk5/30fe/9WglmxAThSc/KnK5nasSRBRsDGTDQLtdwh9W70NOrP7HGgm2RJYYlYzum4F/bT9qaKNXYkwUrhrYHc+s2uvyeIsFkHyY9BmWnWr8wRAvrcRuRASYu2SuFhhbIKG0tBQTBmfhmsE9/ZJr6h7EAkBDSzsaznT2kvlaMYWIyFdMLQgDW0uqfKoC4BxsiRBZYnRmQcfMzKh+3TB9ZI7Qc/z+uoG4Lr8HEmOjAHS0vly1u8zjdfoSxALA8i2HHf/dbpdQUFypa1lUb1MFI89BpEYrJcaXlBkl8gqEKF8rphAR+YozsmEgGOV4RDd/uefk9clIEDp+eU0TPi067ve2uIerGgAY36ylp7QSN4SRWYJVIcNIlz+uShBRMDGQDQPBKsfjvrR5qKIeK7aWurSddc/JE32OD3Ye9XsQCwC90xO8tvvUWhYVLa1UXd+Mmcs9W+9y6ZX0CuYFkS8XzWZ3CCQiEsFANgyMzE2HLTkOFjQq/tyCjlxStZU9X1o/ykuX8gxRTrdEVJ1uRnpiDGwp8R4zRSKBX3piDCrrxTav+cJqAW6+uDfG/vELw5u1REorPTkpD/M+4YYw8p3IRdc1eT389vy+XDSb3SGQiEgEc2TDQJTVgicmDwKgXm7nrstyTS/HI1tTVIZLF6zH9CWbMfu9nZj3yT688Nl3qGlscTmmHOxel29TDWIBYNqwnobGodddl+Vi55FTwpu11GiVVkpLjPH5OYi0KmQA/s9FNdLlT86RN3KhTETkK87IhokJg7Ngr8vB4S9P4miN8tL+BTlpqh2sxufZDDVKEF2WV1oOde/yJY8lJT4Gf9t0yOhbocliAX51WS4euy4PH+08KvQYrWVRb6WVzHoO6txEK2RsK6lCz2j/jEFvlz+2eiaiYGMgG0YG90zBFw/n47+HTykGpGrB1tq95bh0wXqhnDvnTSYZibF4+mPtJXO7HZi53DPYlSsO3DGmD8bn2RxjbbdLhsp7ibh+WE+8cMNQxHTpWGwQXe6sqGvGRzuPeg3y1Uor6dkQRqRG9ELn5Okm9Ezz3zjUNnqmJnREz86luXytW0tE5CsGsmFGq06l+8/1bHRSmlX1Rp4heuKjIq/B7qdF5fj9pLMzNnpnffT4nxE5jiAWEKuJa7V0dEST6d1YI7ohjEuv5I3ohU5Hl796v45F7aIYgM/VFIJVkYGIIhNzZCOYnpw7tTasIty7ibk/j1J+qFreqVFqeXoiNXHdUw71tp4VaSnKpVfSopWfKp/jIwJ0QaRUo9bXurXO+fZG2mgTEbljIBvBRHPuNv9QqRrwmkVp2XRifhY2PjIWK+4ahV+O7i18LL3Bot6g2cjGGq0NYVx6JS2RfkGkdrGs98KRiMgZUwtCiNlLbqI5dwXFlYZmYi0A0hKjUVXv2c7SndqyqXMqxD8KDivex9nscefi3W2luvvLy0uli9YfxEvrvtd8HiNF3r1tCCMSoZaf6nyO2+32II7QGK3VIZaoIyKjGMiGCH8UQRffXKR/Llb+qvnZ8F5Y8vUhr/cVKc0jksualRKHWWP7Y9bY/oaDxXe3lQrdT6a30oBWDjORlki8IBJdHWJ3MCLSi6kFIcBfS26iOXej+2boPrYtJQ6Lbx6OVbvLNe/7+HWDsLWkCh/tPIqC4krF5XqRXNbG1nas3VtuOE/PSPtNVhqgYPA1FzXUiF4QskQdEenFGdkgE11yu3pgd93HFulKNWdKHkb166a5675Hciz+eOMwVJxudswQiQaGcz7e47IhTG2mWV5WffT9b11K/MhqGlpxz7JCzB43AH0yEnXPVOn5kmSlASLzsEQdEfkLZ2SDTE8RdCNENiGJbDJ5eupgjOmf4TJDJBoYulc18DbTPD7PhrguyqelHGS/tO6AoR3Per8kw3ljDVEoEV0d4oUjEenFQDbI9BRBN8q5OsCfbxqGFXeNwsZHxrrMiBrZdW909sRbVYCtJVUor232fJAKPekXou03s1hpgMhUkV6RgYiCh6kFQRaoIugim5D0bjIR2aClRm1zh94cOT07nkUaMcweNwCzxg7gFyqRyUQqMhAR6cVANshEu0KNyE3H0R/9280H0Lfr3owOXe6Bq5FZXj07ntW+TH2tEEFE2iKxIgMRBRcD2SAT3ZDljw96M+rWqgWG6Qbry1bXN8Nq8ey2JWLd3nKhIJxfpkTBwxJ1RGQmBrIhIBhF0M2sW6sUGF7YOw1XvLhBc6bZeXPHmqIyzFy+w3CHsTc3HUJyfAz6ZCRoBqf8MiUiIgp/DGRDRCBnCeW6te4Bo7xxyshGJ6XAUM9Ms7cyZHo4d+1iugAREVFkY9WCEBKIIuhadWsB5WoCRuiphGCkWYEW9nAnIiKKbJyR7WTMahUpml8rOtPsj44+7OFOREQU2RjIdjJmtIrUm18rko/qr44+7OFOREQUuZha0Mn42ipSzq91n9X1dRlfrlbgL+zhTkREFHkYyHYyI3PTkZoQrfpzb60i/ZVfK1crMCEtVxV7uBMREUUephZ0Mmv3luNUg3p9VwnqdWvNyq91JlKtwALggasHIDczEd2T4lBd34J5n+wV2hymVOaLiIiIIgMD2U5EDhq9SUuIxvg8m+LPzMivdSdSrUACcHHfbi7B8YT8sxvIDlXU46V1B3Q3lDCjIQQREREFDwPZTkQkaKxuaFWdUfU1v1aJ0eDYfQPZebYkXT3czWwIQURERMHBQLYT8XVGdWRuOrJS4nR169JiVnCsp6GEPxpCEBERUeBxs1cn4mvQGGW1YM6UPABnl+1lWsv4auTgWO0R3jafKY1Pq6FEIBtCEBERkX8xkA1D7XYJBcWV+GjnURQUVwoHXWYEjXq6dYnwR3DsjeiGtc0/VJryfEREROQ/TC0IM5/tKcMzq/Ybyu2Ug8Z7lxXq3hjlTM8yvgg5ONaT42qUaHrFzHcK8fzPzmeKARERUQhjIBtG9hyrwQMrj6BNcg0Y9eR2mhU0inTr0sPs4FiNaHrFqcZW5ssSERGFOAayYaLdLmHlrjLV3E4LOnI7x+fZAj6jahat4NiMcllaG9bcib6nREREFHgMZMPEtpIq1DS2wjOTtIPeZgRmz6j6m1nlspzTK7QYafBAREREgRM2m70OHTqEO++8E7m5uYiPj0e/fv0wZ84ctLS0BHtoAXHytPnNCMKFXC7LfZOWnFKxpqhM1/Hk9IrUePVWvc4i8T0lIiKKBGEzI7t//37Y7Xa89tpr6N+/P4qKinDXXXehvr4e//u//xvs4fldZlfzmxGEA61yWXpSKpxNzM9CUmw0Zry5RfO+kfaeEhERRYqwCWQnTpyIiRMnOv7dt29ffPfdd3jllVc6RSA7IjcdKfHRsKBN8edGmhGEA9FyWUaW/0fkpiM9MQZV9cqz+pH6nhIREUWKsAlkldTU1CA93XuQ0dzcjObmZse/a2trAQB2ux12u92v4zOTBRImD7Fhw5EjAJRLZz01eSAskGCPoGL+J2obEGXRfj0nahtgt6cJH/ezPWV4dtU+1DQ0I0phIjcU31P5nA2n85bMx/OAeA4QEPnngejrCttA9uDBg3j55Zc1Z2Pnz5+PuXPnetx+5MgRJCUl+Wt4ppMkCVlx7fjjpGys2l1+ZuNXh5T4aEwZmoVBSa0oLS0N4ijNl9RWj5GZ2kFkUlstSkvbhY6551gN3tlcipxYICdT+T6h+J5KkoTq6mpYLBZYLKyi0FnxPCCeAwRE/nlQV1cndD+LJElBnWp69NFHsWDBAq/32bdvHwYOHOj499GjR3HFFVfgyiuvxBtvvOH1sUozstnZ2aiurkZycrJvgw8gu92O0tJS5OTkQIIF20qqcPJ0EzK7xmGEH0pntdslvz+H6DiufHEDjtcql8uyAOiRHIcvHr5KaHzy8cpr1dMV0hKjsfF3VyOmS2jthXQ+B6zW0BobBQ7PA+I5QEDknwe1tbVIS0tDTU2N13gt6DOyv/nNb3Dbbbd5vU/fvn0d/33s2DFcddVVuOSSS/D6669rHj82NhaxsbEet1ut1rD7xctjtlqtuGSAylSiCcwqdaVEby1YqxV4cspgR7kspZSKJ6cMRnSXKKHn31JSiaM1zVArYwYAFafbsONIjU8lt8yoeavE+RygzovnAfEcICCyzwPR1xT0QDYzMxOZmWJB2dGjR3HVVVfhwgsvxNKlSyPyFxdscqkr99lPPd3DvB3bSIBsZgtb0VJavpTc8ueFABEREZ0V9EBW1NGjR3HllVeid+/e+N///V+cPHnS8TObzRbEkUUOf5W6AnwPkM3qRiZaSstoyS1/XggQERGRq7AJZNeuXYuDBw/i4MGDOOecc1x+FuQ034jhr1JXZgXIZnQj02pR60vJLX9eCBAREZGnsFmbv+222yBJkuL/yBz+WnbXEyD7m9yiFvDMkpX/PWdKnqFAM5ReJxERUWcQNoEs+Z+/lt1FA99NB0/io51HUVBciXY/1m2Vc25tKa6vw5YS59PSfyDyb4mIiOissEktIP/z17K7aOC7aEOx47/9vTnKrJxbZ/7OvyUiIiJXnJElB38tu8sBsp5HyZuj1hSV6XouPeSc22nDemF0v24+561qvU4LOgJ0trwlIiIyBwNZcuGPZXdvAbIaeUZ47sq9fk0zMJM/82+JiIjIE1MLyIM/lt3VasF6Y7RKQjCZWfOWiIiIvGMgS4rMKHXlzj1APnD8NBZtOKj5uHDbHOWPCwEiIiLyxECWAso5QC4orhQKZMNxc5Q/LgSIiIjIFXNkKWi4OYqIiIh8wUCWgoabo4iIiMgXDGQpqPzVnICIiIgiH3NkKei4OYqIiIiMYCBLIYGbo4iIiEgvphYQERERUVhiIEtEREREYYmBLBERERGFJQayRERERBSWGMgSERERUVhiIEtEREREYYmBLBERERGFJQayRERERBSWGMgSERERUVhiIEtEREREYYmBLBERERGFJQayRERERBSWGMgSERERUVjqEuwBBJokSQCA2traII9EH7vdjrq6OtTW1sJq5fVHZ8RzgACeB8RzgDpE+nkgx2ly3Kam0wWydXV1AIDs7Owgj4SIiIiIvKmrq0NKSorqzy2SVqgbYex2O44dO4akpCRYLJZgD0dYbW0tsrOzceTIESQnJwd7OBQEPAcI4HlAPAeoQ6SfB5Ikoa6uDj179vQ649zpZmStVivOOeecYA/DsOTk5Ig8YUkczwECeB4QzwHqEMnngbeZWFnkJVUQERERUafAQJaIiIiIwhID2TARGxuLOXPmIDY2NthDoSDhOUAAzwPiOUAdeB506HSbvYiIiIgoMnBGloiIiIjCEgNZIiIiIgpLDGSJiIiIKCwxkCUiIiKisMRANgw899xzuOSSS5CQkIDU1FTF+5SWlmLSpElISEhA9+7d8fDDD6OtrS2wA6WA6tOnDywWi8v/nn/++WAPi/xo8eLF6NOnD+Li4nDxxRdj69atwR4SBdDTTz/t8Tc/cODAYA+L/Oirr77ClClT0LNnT1gsFnz44YcuP5ckCU899RSysrIQHx+PcePG4cCBA8EZbJAwkA0DLS0t+PnPf457771X8eft7e2YNGkSWlpa8M033+Dvf/873nrrLTz11FMBHikF2jPPPIOysjLH/+6///5gD4n85L333sNDDz2EOXPmoLCwEEOHDsWECRNw4sSJYA+NAmjw4MEuf/MbN24M9pDIj+rr6zF06FAsXrxY8ecvvPAC/vKXv+DVV1/Fli1bkJiYiAkTJqCpqSnAIw0iicLG0qVLpZSUFI/bV69eLVmtVqm8vNxx2yuvvCIlJydLzc3NARwhBVLv3r2ll156KdjDoAAZOXKkNHPmTMe/29vbpZ49e0rz588P4qgokObMmSMNHTo02MOgIAEgffDBB45/2+12yWazSS+++KLjtlOnTkmxsbHSihUrgjDC4OCMbAQoKCjA+eefjx49ejhumzBhAmpra7Fnz54gjoz87fnnn0e3bt1wwQUX4MUXX2Q6SYRqaWnB9u3bMW7cOMdtVqsV48aNQ0FBQRBHRoF24MAB9OzZE3379sWMGTNQWloa7CFRkJSUlKC8vNzlcyElJQUXX3xxp/pc6BLsAZDvysvLXYJYAI5/l5eXB2NIFAC//vWvMXz4cKSnp+Obb77BY489hrKyMvzpT38K9tDIZBUVFWhvb1f8O9+/f3+QRkWBdvHFF+Ott97Ceeedh7KyMsydOxeXXXYZioqKkJSUFOzhUYDJ3+9Knwud6bufM7JB8uijj3ok7bv/j19QnY+e8+Khhx7ClVdeiSFDhuCee+7BH//4R7z88stobm4O8qsgIn+49tpr8fOf/xxDhgzBhAkTsHr1apw6dQr//Oc/gz00oqDhjGyQ/OY3v8Ftt93m9T59+/YVOpbNZvPYvXz8+HHHzyh8+HJeXHzxxWhra8OhQ4dw3nnn+WF0FCwZGRmIiopy/F3Ljh8/zr/xTiw1NRXnnnsuDh48GOyhUBDIf/vHjx9HVlaW4/bjx49j2LBhQRpV4DGQDZLMzExkZmaacqzRo0fjueeew4kTJ9C9e3cAwNq1a5GcnIy8vDxTnoMCw5fzYufOnbBarY5zgCJHTEwMLrzwQnz++ee4/vrrAQB2ux2ff/45Zs2aFdzBUdCcPn0axcXF+MUvfhHsoVAQ5Obmwmaz4fPPP3cErrW1tdiyZYtqlaNIxEA2DJSWlqKqqgqlpaVob2/Hzp07AQD9+/dH165dcc011yAvLw+/+MUv8ML/b+f+XVLf4ziOv6SL5lBRBFaCUdDQEiFh1FBGU0hbIEIgzi39miJxqYYzBS61RFtD/4C0ODRoQ9ISFAQ5VVMQFAQh7ztcCC73wuEc8Pvhw3k+wOWLw2vz+f3w9fvjh15eXrS7u6u1tTVFIhG349EWtVpNV1dXWlhYUFdXl2q1mjY2NrS6uqre3l7X89AGm5ubyufzmpqaUiqV0uHhoT4+PlQoFFxPQ0C2t7e1vLys4eFhPT09qVQqqaOjQ7lczvU0tMn7+/u/TtwfHx91c3Ojvr4+JRIJra+va29vT2NjYxoZGVGxWNTQ0ND3De8fwfVrE/Bz+XzeJP3nU61Wv7/TbDZtaWnJotGo9ff329bWln19fbkbjba6vr626elp6+npsc7OThsfH7eDgwP7/Px0PQ1tVC6XLZFIWDgctlQqZfV63fUkBCibzdrg4KCFw2GLx+OWzWbt4eHB9Sy0UbVa/d/f/3w+b2b/vIKrWCxaLBazSCRii4uLdn9/73Z0wEJmZq4iGgAAAPhdvLUAAAAAXiJkAQAA4CVCFgAAAF4iZAEAAOAlQhYAAABeImQBAADgJUIWAAAAXiJkAQAA4CVCFgAAAF4iZAEAAOAlQhYAAABeImQBwENnZ2eKRqN6fn7+vlYoFDQxMaG3tzeHywAgOCEzM9cjAAC/xsw0OTmpubk5lctllUolnZycqF6vKx6Pu54HAIH4y/UAAMCvC4VC2t/f18rKigYGBlQul3V5eUnEAvijcCILAB5LJpO6vb3VxcWF5ufnXc8BgEDxjCwAeKpSqeju7k6tVkuxWMz1HAAIHCeyAOChRqOhdDqt4+NjnZ6eqru7W+fn565nAUCgeEYWADzTbDaVyWS0s7OjXC6n0dFRzczMqNFoKJlMup4HAIHhRBYAPPL6+qrZ2Vml02kdHR19X89kMmq1WqpUKg7XAUCwCFkAAAB4iT97AQAAwEuELAAAALxEyAIAAMBLhCwAAAC8RMgCAADAS4QsAAAAvETIAgAAwEuELAAAALxEyAIAAMBLhCwAAAC8RMgCAADAS38DRhQfOt/t+28AAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.scatter(dataset[0], dataset[1])\n",
+        "plt.title('Диаграмма рассеяния')\n",
+        "plt.xlabel('$x$')\n",
+        "plt.ylabel('$y$')\n",
+        "plt.grid(True, alpha=0.4)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sUEqMjwBE92s"
+      },
+      "source": [
+        "### Подготовка датасета\n",
+        "\n",
+        "После того, как мы визуализировали датасет, с которым будем далее работать,\n",
+        "попробуем его проанализировать и извлечь полезную для дальнейшего обучения\n",
+        "информацию."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mwALaoqYFJmT"
+      },
+      "source": [
+        "#### Количество кластеров\n",
+        "\n",
+        "На основе оценки визуализации можно сделать вывод о том, что датасет\n",
+        "представляет из себя 3 кластера. При этом кластеры имеют эллипсоидную форму."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rS6HGL5AFWbR"
+      },
+      "source": [
+        "#### Разметка данных\n",
+        "\n",
+        "Так как ранее мы сделали вывод о том, что кластеры в датасете имеют\n",
+        "эллипсоидную форму, то попробуем на основе разметить данные. Размеченные\n",
+        "данные нам помогут при дальнейшей оценке обученных моделей кластеризации.\n",
+        "\n",
+        "Для этого опишем кластер в виде отдельного класса `Cluster`, который\n",
+        "будет содержать конфигурацию и логику по определению, принадлежат ли\n",
+        "переданные входные точки кластеры или нет."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "E0rZvZAQFy2s"
+      },
+      "outputs": [],
+      "source": [
+        "@dataclasses.dataclass\n",
+        "class Cluster:\n",
+        "  center: tuple[float, float]\n",
+        "  size: tuple[float, float]\n",
+        "\n",
+        "  def contains(self, points):\n",
+        "    cx, cy = self.center\n",
+        "    w, h = self.size\n",
+        "\n",
+        "    x = (points[:, 0] - cx) / w\n",
+        "    y = (points[:, 1] - cy) / h\n",
+        "\n",
+        "    return x ** 2 + y ** 2 <= 1.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fVynEnj9JGVX"
+      },
+      "source": [
+        "Далее, опишем функцию, которая создаст `numpy`, элементы которого\n",
+        "будут соответствовать уникальному номеру кластера. На вход такая функция\n",
+        "будет принимать набор кластеров и входные точки."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "EhgbnLfrGnVt"
+      },
+      "outputs": [],
+      "source": [
+        "def split_on_clusters(clusters, points):\n",
+        "    cluster_ids = np.array([list(range(1, len(clusters) + 1))])\n",
+        "    contains_matrix = np.array([cluster.contains(points) for cluster in clusters])\n",
+        "    return (cluster_ids @ contains_matrix)[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LMIlmhq7JQ8Z"
+      },
+      "source": [
+        "И в конце опишем вспомогательную функцию, которая будет визуализировать\n",
+        "кластеры. На вход такая функция будет принимать массив двумерных точек\n",
+        "и массив с индексами кластеров. Для каждого из кластеров функция будет генерировать уникальный цвет."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "EI4Ogay1HK3P"
+      },
+      "outputs": [],
+      "source": [
+        "def _get_color():\n",
+        "  return tuple(random.random() for _ in range(3))\n",
+        "\n",
+        "def draw_clusters(points, cluster_ids):\n",
+        "  plt.figure(figsize=(8, 6))\n",
+        "  plt.title('Визуализация кластеров')\n",
+        "  plt.xlabel('$x$')\n",
+        "  plt.ylabel('$y$')\n",
+        "  for cluster_id in set(cluster_ids):\n",
+        "    cluster_points = points[cluster_ids == cluster_id]\n",
+        "    plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label=f'cluster-{cluster_id}', color=_get_color())\n",
+        "  plt.legend()\n",
+        "  plt.grid(visible=True, alpha=0.4)\n",
+        "  plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RhLC-l8AJ0wL"
+      },
+      "source": [
+        "Теперь, опишем 3 кластера через параметры эллипса и кластеризуем наш датасет.\n",
+        "В результате кластеризации добавим к датасету новый признак `cluster`. После\n",
+        "кластеризации визуализируем полученный датасет."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "FImYCPiHH5UP",
+        "outputId": "576cbccb-1d40-412f-9323-7ea1af01dc6e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "clusters = [\n",
+        "  Cluster(center=(8.0, 8.0), size=(3.5, 4)),\n",
+        "  Cluster(center=(-6.5, 1.0), size=(4.5, 5)),\n",
+        "  Cluster(center=(1.5, 1), size=(3.5, 4)),\n",
+        "]\n",
+        "\n",
+        "points = dataset[[0, 1]].to_numpy()\n",
+        "cluster_ids = split_on_clusters(clusters, points)\n",
+        "draw_clusters(points, cluster_ids)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "x3jLRk9DKFmU",
+        "outputId": "880b361d-9aa8-46b0-a6b9-feab9a37c456"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.1252039875904805,\n        \"min\": -10.70273630672936,\n        \"max\": 11.07473499056978,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          1.698938497981321,\n          8.749480611116606,\n          -4.792276091132777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8309153164832717,\n        \"min\": -2.366946290079579,\n        \"max\": 10.90000648008181,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          1.31272614069482,\n          7.558598942712011,\n          2.936054191148225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "dataset"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1fa3bc94-83c9-44d3-b015-eaf8ad66158e\" 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",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>cluster</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>-6.383976</td>\n",
+              "      <td>0.831166</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>-6.676808</td>\n",
+              "      <td>-2.087329</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.380398</td>\n",
+              "      <td>1.726267</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>-5.974045</td>\n",
+              "      <td>3.571401</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>-7.555926</td>\n",
+              "      <td>3.254611</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1fa3bc94-83c9-44d3-b015-eaf8ad66158e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1fa3bc94-83c9-44d3-b015-eaf8ad66158e 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-1fa3bc94-83c9-44d3-b015-eaf8ad66158e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d2ab51f1-8f63-4d52-9e71-e3607b9db8ab\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d2ab51f1-8f63-4d52-9e71-e3607b9db8ab')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d2ab51f1-8f63-4d52-9e71-e3607b9db8ab button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "          0         1  cluster\n",
+              "0 -6.383976  0.831166        2\n",
+              "1 -6.676808 -2.087329        2\n",
+              "2  0.380398  1.726267        3\n",
+              "3 -5.974045  3.571401        2\n",
+              "4 -7.555926  3.254611        2"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset['cluster'] = cluster_ids\n",
+        "dataset.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s2webrLeKQlY"
+      },
+      "source": [
+        "### Обучение моделей"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-sGYdukzK93P"
+      },
+      "source": [
+        "Теперь, перейдем к обучению моделей для решения задачи\n",
+        "кластеризации."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w3Zh8VXLLCY7"
+      },
+      "source": [
+        "#### Определение метрик\n",
+        "\n",
+        "После обучения каждой из рассматриваемых далее моделей\n",
+        "у нас возникнет необходимость в оценке их качестве. Для\n",
+        "этой цели нам необходимо выбрать метрики, на основе которых\n",
+        "мы будем непосредственно оценивать качество.\n",
+        "\n",
+        "При обучении модели использовались следующие метрики:\n",
+        "\n",
+        "- **Коэффициент силуэта**. Эта метрика показывает, на сколько\n",
+        "  \"хорошо\" сгруппированы кластеры. Эта метрика определяется\n",
+        "  за счет среднего расстояния до всех точек одного кластера и\n",
+        "  среднего расстояние до точек другого кластера. Задача модели состоит в том, чтобы минимизировать внутрикластерное расстояние и максимизировать межкластерное.\n",
+        "\n",
+        "- **Однородность**. Данная метрика подходит для нашего случая,\n",
+        "  так как мы провели предварительно разметку датасета. Она позволяет определить, не пытается ли модель соотнести в один\n",
+        "  кластер точки разных кластеров.\n",
+        "\n",
+        "- **Полнота**. Данная метрика позволяет определить, не\n",
+        "  \"разбрасывает\" ли модель точки одногго кластера по разным.\n",
+        "\n",
+        "- **V-мера**. Аналог **P-value** из задачи классификации.\n",
+        "  Является средним между однородностью и полнотой. Позволяет\n",
+        "  рассмотреть результаты обучения более полно.\n",
+        "\n",
+        "Напишем для определения метрик вспомогательную функцию."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "SkmLD7GvMvDa"
+      },
+      "outputs": [],
+      "source": [
+        "_METRICS = [\n",
+        "    {\n",
+        "        'key': 'silhouette_score',\n",
+        "        'formatted_key': 'Коэффициент силуэта',\n",
+        "        'metric': lambda X, y, y_pred: sklearn.metrics.silhouette_score(X, y_pred),\n",
+        "    },\n",
+        "    {\n",
+        "        'key': 'homogeneity_score',\n",
+        "        'formatted_key': 'Однородность',\n",
+        "        'metric': lambda X, y, y_pred: sklearn.metrics.homogeneity_score(y, y_pred),\n",
+        "    },\n",
+        "    {\n",
+        "        'key': 'completeness_score',\n",
+        "        'formatted_key': 'Полнота',\n",
+        "        'metric': lambda X, y, y_pred: sklearn.metrics.completeness_score(y, y_pred),\n",
+        "    },\n",
+        "    {\n",
+        "        'key': 'v_measure_score',\n",
+        "        'formatted_key': 'V-мера',\n",
+        "        'metric': lambda X, y, y_pred: sklearn.metrics.v_measure_score(y, y_pred),\n",
+        "    },\n",
+        "]\n",
+        "\n",
+        "\n",
+        "def calculate_metrics(X, y, y_pred, *, formatted=False):\n",
+        "  if formatted:\n",
+        "    return pd.DataFrame([\n",
+        "      {'Метрика': metric['formatted_key'], 'Значение': metric['metric'](X, y, y_pred)}\n",
+        "      for metric in _METRICS\n",
+        "    ])\n",
+        "  else:\n",
+        "    return {\n",
+        "      metric['key']: metric['metric'](X, y, y_pred)\n",
+        "      for metric in _METRICS\n",
+        "    }"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_VxynP656x5u"
+      },
+      "source": [
+        "#### Разделение на тестовую и обучающую\n",
+        "\n",
+        "Для обучения и тестированния модели нам необходимо два разных\n",
+        "датасета. Для этого разделим датасет на обучающую и тестовую выборки. Разделение будет происходит в стандартном соотношении $80 \\%$ на $20 \\%$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "qb10J2iV6z6B"
+      },
+      "outputs": [],
+      "source": [
+        "ds_train, ds_test = sklearn.model_selection.train_test_split(dataset, test_size=0.2, random_state=RANDOM_SEED)\n",
+        "\n",
+        "X_train, y_train = ds_train[[0, 1]].to_numpy(), ds_train['cluster'].to_numpy()\n",
+        "X_test, y_test = ds_test[[0, 1]].to_numpy(), ds_test['cluster'].to_numpy()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6ppoo9yTdV9K"
+      },
+      "source": [
+        "#### DBSCAN\n",
+        "\n",
+        "Первой моделью, которую мы обучим, является DBSCAN. Эта модель не требует\n",
+        "знаний о количестве кластеров и подходит для кластеров произвольной формы.\n",
+        "Иными словами, она является общей моделью для решения задачи кластеризации.\n",
+        "\n",
+        "Попробуем для начала установить значения гиперпараметров модели вручную\n",
+        "и проанализируем обученную таким образом модель."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "nHME3M0ldomk",
+        "outputId": "e5c9d662-9459-43f9-ccce-1d366a858794"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"calculate_metrics(X, y, model\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"\\u041c\\u0435\\u0442\\u0440\\u0438\\u043a\\u0430\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"\\u041e\\u0434\\u043d\\u043e\\u0440\\u043e\\u0434\\u043d\\u043e\\u0441\\u0442\\u044c\",\n          \"V-\\u043c\\u0435\\u0440\\u0430\",\n          \"\\u041a\\u043e\\u044d\\u0444\\u0444\\u0438\\u0446\\u0438\\u0435\\u043d\\u0442 \\u0441\\u0438\\u043b\\u0443\\u044d\\u0442\\u0430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u0417\\u043d\\u0430\\u0447\\u0435\\u043d\\u0438\\u0435\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28927260382703746,\n        \"min\": 0.3286243079475014,\n        \"max\": 0.9654475659376226,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9654475659376226,\n          0.8933384654295248,\n          0.3286243079475014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f68e64cd-5446-419b-b212-d41b0ee1520a\" 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",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Метрика</th>\n",
+              "      <th>Значение</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Коэффициент силуэта</td>\n",
+              "      <td>0.328624</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Однородность</td>\n",
+              "      <td>0.965448</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Полнота</td>\n",
+              "      <td>0.831252</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>V-мера</td>\n",
+              "      <td>0.893338</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f68e64cd-5446-419b-b212-d41b0ee1520a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f68e64cd-5446-419b-b212-d41b0ee1520a 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-f68e64cd-5446-419b-b212-d41b0ee1520a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f2aff7be-d21b-4c97-a99b-1bcbc29de877\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f2aff7be-d21b-4c97-a99b-1bcbc29de877')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f2aff7be-d21b-4c97-a99b-1bcbc29de877 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "               Метрика  Значение\n",
+              "0  Коэффициент силуэта  0.328624\n",
+              "1         Однородность  0.965448\n",
+              "2              Полнота  0.831252\n",
+              "3               V-мера  0.893338"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X, y = dataset[[0, 1]].to_numpy(), dataset['cluster'].to_numpy()\n",
+        "model = sklearn.cluster.DBSCAN(eps=0.5, min_samples=3)\n",
+        "model.fit(X)\n",
+        "\n",
+        "calculate_metrics(X, y, model.labels_, formatted=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Bv8WcTV-eN-C"
+      },
+      "source": [
+        "Как видим, обученная модель далека от идеале. Значение коэффициента\n",
+        "силуэта равно `0.3`, что ближе к середине диапазона возможных значений\n",
+        "$[-1, 1]$. Провизуализируем кластеры, на которые модель разбила датасет."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "lGiWOrktee00",
+        "outputId": "7874fcc3-54d7-4abd-c771-f57865c420f1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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66ScsWrQIf/zxh7LsmDYjRozA+vXrMWzYMEyaNAn5+fn44IMPEB0djT/++EM5Ljo6Gm+88QZWrFiBfv36YcyYMXBycsLJkycREhJi0BelAQMG4F//+hdWr16Nc+fO4bHHHoNUKsW1a9ewZ88ebNq0SfkZA+SB+wsvvICTJ08iMDAQ//vf/3Dnzh2VVePXX38du3btwvDhwzFz5kzIZDJs27YNmZmZ+Oqrr9RWWX/88Ufk5OQoUwt27NiBJ598UrkRkBBiImuWTCCE2CZN5becnZ2FHj16CFu2bFEpg6QoH+Xi4iLk5uaqHKdpOaulS5cKHTp0EFxcXARPT0+hR48ewqZNm4T6+nq1OdTW1go+Pj6Cl5eXUF1drXZ/Tk6OMHr0aMHb21vw8vISxo0bJ+Tl5QkAlGW6du3aJXTp0kXYtGmTWukmbeW3AAj79u1TGTtlyhSV8ltffvml0Lt3b8HT01NwcXERoqOjhTlz5qiUtOJ5Xli1apXQpk0bwcnJSYiLixO+/fZbtWMpXj99/xQGDBggABA2bNigMsem5a6OHTsmsCwr/N///Z/GcYaU32rskUceESIiIoSKigrhP//5j9C/f3/hwIEDauM0ld/65JNPhPbt2wtOTk5Cx44dhaSkJI3jBEEQ/ve//wlxcXGCk5OT4OPjIwwYMED48ccf1cbpKr+l8NFHHwm9evUSXFxcBA8PD6Fr167Ca6+9JuTl5SnHtGnTRhgxYoTwww8/CN26dVPOcc+ePWrHu3HjhjB27FjB29tbcHZ2Fh588EHh22+/1TgvxT8HBwehTZs2wsyZM4WSkhKtcyWEGIYRBGoxQgixPQ0NDQgJCcHIkSPxySefWHs6VpOWloZBgwZRNygLi4yMRJcuXfDtt99aeyqEEANQjiwhxCalpKSgoKAAiYmJ1p4KIYQQG0U5soQQm5Keno4//vgDK1asQFxcHAYMGGDtKVmVTCbD0KFDrT0NQgixSbQiSwixKVu2bMG///1vBAQE4NNPP7X2dKyuW7duapvACCGEyFGOLCGEEEIIsUu0IksIIYQQQuwSBbKEEEIIIcQutbrNXjzPIy8vDx4eHiptCAkhhBBCiG0QBAEVFRUICQnR2c651QWyeXl5CA8Pt/Y0CCGEEEKIHjdv3kRYWJjW+1tdIOvh4QFA/sKIaVdpK3iex82bNxEeHq7zmwlpuegzQAD6HBD6DBC5lv45KC8vR3h4uDJu06bVBbKKdAJPT0+7C2Q9PDzg6enZIj+wRD/6DBCAPgeEPgNErrV8DvSlgbbcZ04IIYQQQlo0CmQJIYQQQohdokCWEEIIIYTYpVaXIysGx3Gor6+39jRU8DwPjuNQU1PTonNhzEEqlYJlWWtPgxBCCCEWRoFsE5WVlcjJyYGtde4VBAEcxyErK4vq3+rBMAzCwsLg7u5u7akQQgghxIIokG2E4zjk5OTA1dUV/v7+NhUwCoKA+vp6SKVSm5qXrREEAQUFBcjJyUH79u1pZZYQQghpwSiQbaS+vh6CIMDf3x8uLi7Wno4KQRAgkUjg6OhIgawe/v7+yMrKQn19PQWyhBBCSAtGyZYaUKBo3+j9I4QQQloHCmQJIYQQQohdokCWEEIIIYTYJQpkWzhFlYNz585ZeyqEEEIIIWZFgSwxSHJyMry9vS16jj179qBjx45wdnZG165d8d1331n0fIQQQgixTxTIWgDH8Th6JgufH7yIo2eywHG8tadkcziOA8+rvy4nTpzAxIkT8cILL+Ds2bMYNWoURo0ahYsXL1phloQQQgixZRTImllKWgY6jHkPQ1/+FFOX7MXQlz9FhzHvISUtw6Ln5Xke69atQ3R0NJycnBAREYGVK1eqjdO0opqSkqKy0//8+fMYNGgQPDw84OnpiV69euHUqVNIS0vDc889h7KyMjAMA4ZhsHTpUgBAbW0t5s6di9DQULi5uSE+Ph5paWlq592/fz86deoEJycnZGdnq81v06ZNGDZsGObNm4fY2FisWLECPXv2xObNm83yOhFCCCH2QOA5cJdOoOH4PnCXTkDgOWtPySZRHVkzSknLwKQFe9C0J1hefjkmLdiDnavHYdTAWIuce8GCBdi6dSs2bNiAvn374tatW7hy5YpRx5o8eTLi4uKwZcsWsCyLc+fOQSqVIiEhARs3bsTixYtx9epVAFB2z5oxYwYuX76M3bt3IyQkBPv27cOwYcNw4cIFtG/fHgBQVVWFtWvX4uOPP4avry8CAgLUzv3rr79i9uzZKrcNHToUKSkpRj0XQgghxN40pKeiLnkRhOJbytsYWTAcp66AQ/wIK87M9lAgayYcx2Puhh/UglgAEAAwAOZt+AEj+3UAy5p3IbyiogKbNm3C5s2bMWXKFABAu3bt0LdvX2RlZRl8vOzsbMybNw8dO3YEAGUgCgBeXl5gGAZBQUEq45OSkpCdnY2QkBAAwNy5c3HgwAEkJSVh1apVAOQNJz788EN0795d67lv376NwMBAldsCAwNx+/Ztg58HIYQQYm8a0lNRu/5FoElEIRTflt8+eysFs41QaoGZHD+fjdz8cq33CwBy8stx/Lz65XRTZWRkoLa2FoMHDzbL8WbPno1p06ZhyJAhWLNmDW7cuKFz/IULF8BxHGJiYuDu7q78d+TIEZXHOjo6olu3bgDkwW/jsYpglxBCCGmtBJ5DXfIiNA1i790LAKjbtpjSDBqhFVkzuVVYadZxhjCkna5EIoEgqP4HUl9fr/Lz0qVLMWnSJKSmpuL777/HkiVLsHv3bowePVrjMSsrK8GyLE6fPq3WElaReqCYpyIXNyQkRKUkmEwmAwAEBQXhzp07Kse4c+eOygowIYQQ0hLxGekq6QTqBAhFeeAz0sHEPtRs87JltCJrJsF+7voHGTDOEO3bt4eLiwsOHTqkd6y/vz8qKipw9+5d5W2aaszGxMRg1qxZOHjwIMaMGYOkpCQA8lVVjlP9JhgXFweO45Cfn4/o6GiVf9oCUAcHB5VxikD24YcfVnseP/74Ix5++GG9z40QQgixZ0LpHf2DDBjXGlAgayZ9ukcgNMATjJb7GQBhAZ7o0z3C7Od2dnbG/Pnz8dprr+HTTz/FjRs38Ntvv+GTTz5RGxsfHw9XV1csXLgQN27cwM6dO5GcnKy8v7q6GjNmzEBaWhr+/vtvHD9+HCdPnkRsrHyTWmRkJCorK3Ho0CEUFhaiqqoKMTExmDx5MhITE7F3715kZmbi999/x+rVq5GammrQc3nllVdw4MABvPvuu7hy5QqWLl2KU6dOYcaMGSa9RoQQQoitY7wD9Q8yYFxrQIGsmbCsBO/MGgoAasGs4ue3Zw01+0YvhUWLFmHOnDlYvHgxYmNjMWHCBOTn56uNk8lk2L59O7777jt07doVu3btUpbQkj8PFkVFRUhMTERMTAzGjx+P4cOHY9myZQCAhIQETJ8+HRMmTIC/vz/WrVsHAEhKSkJiYiLmzJmDDh06YNSoUTh58iQiIgwL3BMSErBz50589NFH6N69O7788kukpKSgS5cuxr84hBBCiB2QxMaDkQVDPZJQYMD4hkASG9+c07JpjNA0YbKFKy8vh5eXF8rKyuDp6alyX01NDTIzMxEVFQVnZ2ejjp+SloG5G35Q2fgVFuCJt2cNNan0liAIqKurg6Ojo0rNV6LOHO+jLeJ5HtnZ2YiIiIBEQt9BWyv6HBD6DLRs96sWAKqbvuR/+53uVS1o6Z8DXfFaY7TZy8xGDYzFyH4dcPx8Nm4VViLYzx19ukdYbCWWEEIIIS2HQ/wIYPZW9TqyvsFwnLKcSm81QYGsBbCsBP17Rlp7GoQQQgixQw7xI8D2HiavYlB6B4x3oDztQMLqf3ArQ4EsIYQQQoiNYSQs2M4J1p6GzaPr3YQQQgghxC5RIEsIIYQQQuwSBbKEEEIIIcQuUSBLCCGEEELsEgWyhBBCCCHELlEgSwghhBBC7BIFsi1cVlYWGIbBuXPnrD0VQgghpMUTeA7cpRNoOL4P3KUTEHjO2lNq0SiQJQZJTk6Gt7e3xY5/6dIlPP3004iMjATDMNi4caPFzkUIIYSYU0N6Kqpf7o2a5U+j9r2XULP8aVS/3BsN6anWnlqLRYGsBXAcjyOn7uDzA1k4cuoOOI639pRsDsdx4Hn116Wqqgpt27bFmjVrEBQUZIWZEUIIIYZrSE9F7foXVdrKAoBQfBu161+kYNZCKJA1s5SfbyJm5H4Mnf4zprz5K4ZO/xkxI/cj5eebFj0vz/NYt24doqOj4eTkhIiICKxcuVJtnKYV1ZSUFDAMo/z5/PnzGDRoEDw8PODp6YlevXrh1KlTSEtLw3PPPYeysjIwDAOGYbB06VIAQG1tLebOnYvQ0FC4ubkhPj4eaWlpaufdv38/OnXqBCcnJ2RnZ6vNr3fv3nj77bfxzDPPwMnJySyvDSGEEGJJAs+hLnkRAEHTvQCAum2LKc3AAqhFrRml/HwTE1/7Re1jnJdfjYmv/YJd6/pi1CPhFjn3ggULsHXrVmzYsAF9+/bFrVu3cOXKFaOONXnyZMTFxWHLli1gWRbnzp2DVCpFQkICNm7ciMWLF+Pq1asAAHd3dwDAjBkzcPnyZezevRshISHYt28fhg0bhgsXLqB9+/YA5Kuta9euxccffwxfX18EBASY58kTQgghVsRnpKutxKoSIBTlgc9Ip7azZkaBrJlwHI8575zW+l2MATD33TMYOSAULGvehfCKigps2rQJmzdvxpQpUwAA7dq1Q9++fZGVlWXw8bKzszFv3jx07NgRAJSBKAB4eXmBYRiVy/7Z2dlISkpCdnY2QkJCAABz587FgQMHkJSUhFWrVgEA6uvr8eGHH6J79+7GPlVCCCHE5gild8w6johHqQVm8svZAuTmV2u9XwCQc6cKv5wtMPu5MzIyUFtbi8GDB5vleLNnz8a0adMwZMgQrFmzBjdu3NA5/sKFC+A4DjExMXB3d1f+O3LkiMpjHR0d0a1bNwDy4LfxWEWwSwghpPUQOAEVaRUo3lWMirQKCJym5SDbx3gHmnUcEY9WZM3kdqH2INaYcYZwcXERPVYikUAQVH9R1NfXq/y8dOlSTJo0Campqfj++++xZMkS7N69G6NHj9Z4zMrKSrAsi9OnT4NlWZX7FKkHinkqcnFDQkJUSoLJZDLRz4EQQoj9K91bgpxZOajPuf83SBomRdiGMHiP8bHizAwniY0HIwuGUHwbmvNkGTC+wZDExjf31Fo8WpE1kyA/ccGk2HGGaN++PVxcXHDo0CG9Y/39/VFRUYG7d+8qb9NUYzYmJgazZs3CwYMHMWbMGCQlJQGQr6pynGqyelxcHDiOQ35+PqKjo1X+aas84ODgoDKOAllCCGk9SveWIHN8pkoQCwD1ufXIHJ+J0r0lVpqZcRgJC8epKxQ/Nb0XAOA4ZTkYCQtiXhTImknfOH+EBriofXwVGABhga7oG+dv9nM7Oztj/vz5eO211/Dpp5/ixo0b+O233/DJJ5+ojY2Pj4erqysWLlyIGzduYOfOnUhOTlbeX11djRkzZiAtLQ1///03jh8/jpMnTyI2NhYAEBkZicrKShw6dAiFhYWoqqpCTEwMJk+ejMTEROzduxeZmZn4/fffsXr1aqSmGlZupK6uDufOncO5c+dQV1eH3NxcnDt3DtevXzfpNSKEEGIbBE5AzqwcXRv8kTM7x+7SDBziR8Bp9lYwMtUFHMY3GE6zt8IhfoSVZtayUSBrJiwrwbtzewHQ9l0MeGdOT7Nv9FJYtGgR5syZg8WLFyM2NhYTJkxAfn6+2jiZTIbt27fju+++Q9euXbFr1y5lCS3582BRVFSExMRExMTEYPz48Rg+fDiWLVsGAEhISMD06dMxYcIE+Pv7Y926dQCApKQkJCYmYs6cOejQoQNGjRqFkydPIiIiwqDnkZeXh7i4OMTFxeHWrVt45513EBcXh2nTphn/4hBCCLEZlccq1VZiVQhA/c16VB6rbL5JmYlD/Ai4fHASzou/gtPMD+G8+Cu4bP4dbO9h1O3LQhihacKkFR09ehRvv/02Tp8+jVu3bmHfvn0YNWqU8n5BELBkyRJs3boVpaWl6NOnD7Zs2aKyq16f8vJyeHl5oaysDJ6enir31dTUIDMzE1FRUXB2djbqOaT8fBNz3jmtsvErLNAV78zpaVLpLUEQUFdXB0dHR5War0SdOd5HW8TzPLKzsxEREQGJhL6Dtlb0OSD2/hko3lWMv5/N0juuzfZIyCbaf9pZQ3oq6pIXqZTnYmTBcJy6wqRVWnv/HOijK15rzKY2e929exfdu3fH888/jzFjxqjdv27dOrz33nvYtm0boqKisGjRIgwdOhSXL1+2mYBl1CPhGDkgFL+cLcDtwmoE+bmgb5y/xVZiCSGEEHsiDZaadZwtU3T7appHoej2BUo5MJlNBbLDhw/H8OHDNd4nCAI2btyIN998E0899RQA4NNPP0VgYCBSUlLwzDPPNOdUdWJZCQY8QCU2CCGEkKbc+7lDGiZFfW69tg3+kIZJ4d7PXcOd9kN/ty8GddsWg+09jDaBmcCmAlldMjMzcfv2bQwZMkR5m5eXF+Lj4/Hrr79qDWRra2tRW1ur/Lm8vByAfEme53mVsTzPQxAE5T9bZctzswWK90/Te2zPFM+nJT0nYjj6HBC7/wwwQMiGEGRNypL/LKjeBwAh60PA8zwqj1ai/nY9pEFSuPd1B8OqptYJnIDKX3SPsRbu8m/gSvMBXUFqyR00XP4NbKeHDT6+3X8O9BD7vOwmkL19+zYAIDBQdaUzMDBQeZ8mq1evVm5UauzmzZvw8PBQuY3jOHAch/r6epvMN2loaLD2FOxCfX09OI7DrVu31Ora2jNBEFBSUgKGYShPuhWjzwFpEZ+BnoDzLheUflECruT+xifWh4X3eB/cxh1cSbyiet9H8vtc41wBAFVnq9Qf32SMNfF38lEfpT9Ald7Jh8Q92+Djt4jPgQ4VFRWixtlNIGusBQsWYPbs2cqfy8vLER4ejvDwcI2bvbKysiCVSuHo6NjcUxXFVudlS3ieB8uyCA4OtpncaXNQXDEIDw+3yS9apHnQ54C0mM9ABCA8ob6iWvZNKbImZcFZaPL7mwFq9lUjaKd8Qat4UpHOMd6jrNtUgavMRU3mr3rHOQe+CtbACj9AC/ocaKG4gq6P3QSyisL6d+7cQXBwsPL2O3fuoEePHlof5+TkBCcnJ7XbJRKJ2hsvkUiU32xs7dtN43QCW5ubrVG8f5reY3uneE4t7XkRw9DngLSYz4AE8BrkpfxR4ATkzcoDU6/l7xwD5M3KgyAIusfMzoPPUzKrphkwnR4C6x2gt9uXQ6eHwBj5PraYz4EGYp+T3TzzqKgoBAUFqXSvKi8vR3p6Oh5+2PDcEkIIIYTYFlE1ZnPq0ZCrI9XOAnVoBU5ARVoFincVoyKtQlSzBur21TxsakW2srJSpYNTZmYmzp07B5lMhoiICLz66qt466230L59e2X5rZCQEJVas4QQQgixT/W3dASxVjpW6d4S5MzKUQmwpWFShG0Ig/cY3ekLDvEjgNlb1evI+gbDccpyKr1lBjYVyJ46dQqDBg1S/qzIbZ0yZQqSk5Px2muv4e7du/jnP/+J0tJS9O3bFwcOHGhReZCEEEJIa2XO2rHmOFbp3hJkjs9Uywyoz61H5vhMRH0BUcEs23sY+Ix0CKV3wHgHQhIbTyuxZmJTgezAgQN1lpZiGAbLly/H8uXLm3FW9i0rKwtRUVE4e/aszlxiQgghpDkJnCBPJbhVD2mwvG6sqBqzoVIIgoCGvAaL1qEVOAE5s3J0lYFFzuwceD3lrTcXl5GwYDsnmDQfopnd5MjaE57jkX0mFxkHryH7TC54ruXUeEtOToa3t7fFjr9161b069cPPj4+8PHxwZAhQ/D7779b7HyEEEKaX+neElxqexHXB1/D389m4frga7jU9iLKvi5F2IYw+SDNaaUI2xCG8I3hmscAgAD4vuBr8hxF5euaOReXGM6mVmRbgj/T/sLPG35BZf5d5W3uAW54ZFZfxAxsa8WZ2RaO45SVBRpLS0vDxIkTkZCQAGdnZ6xduxaPPfYYLl26hNDQUCvNlhBCiLnov1wfhagvojTnpa6/n5ca9QXUxijcXnobRR8Xicpj1UZsjq0583qJ4WhF1oz+TPsL+xf8oBLEAkBl/l3sX/AD/kz7y2Ln5nke69atQ3R0NJycnBAREYGVK1eqjdO0opqSkqJS0uv8+fMYNGgQPDw84OnpiV69euHUqVNIS0vDc889h7KyMmWJq6VLlwKQd1CbO3cuQkND4ebmhvj4eKSlpamdd//+/ejUqROcnJyQna1eAHrHjh146aWX0KNHD3Ts2BEff/wxeJ5XqVZBCCHEPum9XI/7l+s7/9UF0Yfao832SEQfao/ON7qoBKXeY3zQ+a8uCFoarOFg9wPj0r0lRs1VbI6tOfN6ieFoRdZMeI7Hzxt+0Tnm8IbjiO4XCQlr/u8PCxYswNatW7Fhwwb07dsXt27dwpUrV4w61uTJkxEXF4ctW7aAZVmcO3cOUqkUCQkJ2LhxIxYvXoyrV68CANzd5TlIM2bMwOXLl7F7926EhIRg3759GDZsGC5cuID27dsDAKqqqrB27Vp8/PHH8PX1RUBAgN65VFVVob6+HjKZzKjnQgghxHYYcrneY6AHPAZ6aB97T9HHhVqPJSaPVVOuLsMy4vJ1zZCLS0xDgayZ5Jy/pbYS21RFfiVyzt9CRE/zXiKvqKjApk2bsHnzZkyZMgUA0K5dO/Tt2xdZWVkGHy87Oxvz5s1Dx44dAUAZiAKAl5cXGIZRNqhQjE9KSkJ2djZCQkIAAHPnzsWBAweQlJSEVatWAZC3jv3www/RvXt30XOZP38+QkJCMGTIEIOfByGEENti7sv1hgbGTekrrRW2IUyeBsFANZhV5OuuD7Nq0wVCqQVmc7ewyqzjDJGRkYHa2loMHjzYLMebPXs2pk2bhiFDhmDNmjW4ceOGzvEXLlwAx3GIiYmBu7u78t+RI0dUHuvo6Ihu3boBkAe/jccqgt3G1qxZg927d2Pfvn1UYo0QQloAc1+uNyUwVuTqNg2EG6ckeI/xQdQXUZCGqs5HGiZF1BdRRuffEvOhFVkzcfNzNes4Q7i4uIgeK5FI1Eqc1der/ke8dOlSTJo0Campqfj++++xZMkS7N69G6NHj9Z4zMrKSrAsi9OnT4NlVeviKVIPFPNU5OKGhITg3Llzyvuapg688847WLNmDX766Sdl8EsIIcS+mftyfe31GlHjmgbGhpTW8h7jA6+nvDWmHxDro0DWTMK6B8M9wE1neoFHgDvCumtOSjdF+/bt4eLigkOHDmHatGk6x/r7+6OiogJ3796Fm5sbAKgElAoxMTGIiYnBrFmzMHHiRCQlJWH06NFwdHQEx3EqY+Pi4sBxHPLz89GvXz9Rc3ZwcEB0dLTG+9atW4eVK1fihx9+wAMPPCDqeIQQQmwfwzJmu1wvcALy38vXO05TYGxoSgLDMqLydUnzo9QCM5GwEjwyq6/OMYNm9bHIRi9nZ2fMnz8fr732Gj799FPcuHEDv/32Gz755BO1sfHx8XB1dcXChQtx48YN7Ny5E8nJycr7q6urMWPGDKSlpeHvv//G8ePHcfLkScTGxgIAIiMjUVlZiUOHDqGwsBBVVVWIiYnB5MmTkZiYiL179yIzMxO///47Vq9ejdTUVIOey9q1a7Fo0SL873//Q2RkJG7fvo3bt2+jspLq9BFCSEtgrsv1t1fdBl+sv0677zQ/tcCYSmu1HBTImlHMwLZ4cvVQuAe4qdzuEeCOJ1cPtWgd2UWLFmHOnDlYvHgxYmNjMWHCBOTnq39Tlclk2L59O7777jt07doVu3btUpbQAgCWZVFUVITExETExMRg/PjxGD58OJYtWwYASEhIwPTp0zFhwgT4+/tj3bp1AICkpCQkJiZizpw56NChA0aNGoWTJ08iIiLCoOexZcsW1NXVYezYsQgODlb+e+edd4x/cQghhNgUReksXeW1dBE4AQUiVmMBwCnaSe02Kq3VcjCCrp6wLVB5eTm8vLxQVlYGT09PlftqamqQmZmJqKgokzYX8RyPnPO3cLewCm5+rgjrHmzySqwgCKirq4Ojo6NKzVeizlzvo63heR7Z2dmIiIhQayRBWg/6HBD6DAAVaRW4PviaqLEh74Yi4D8BKquyAifgUtuLutMLAETujoTPONPLP2or8WWKlv450BWvNUY5shYgYSVmL7FFCCGEEDlDLvnnzclFwYZ8lS5fDMsg9N0wZE3I1PnY3Hm58B7jY1LQqa/EFzFNywvhCSGEENKiGXrJX1OXLwc//Wt5ig1fxhJT4ouYhgJZQgghhNgVRRkviF0obdT+VuDkP1h6w5fYdryK+RDjUCBLCCGEELuiKOMl/0HkgwTVFVZLb/gypMQXMR4FsoQQQgixKIETUJFWgeJdxahIqzDLKqS2Ml76KFZY9a7qMoA0XHxzBm3nMdc4ohlt9iKEEEKIxVhys1PjrlsVh8pxZ9UdvY9RrLCaszmDrvOYaxzRjFZkCSGEEGIRzbHZSdF1K3hpiMErrOZqzqCJpVd8iRytyBJCCCHE7PRudmLkm528nvI2uaYqYPwKa+NVXXPWebX0ii+RoxVZQgghhJidNTY7GbvCqljVlU2UwWOgh9mCS0uu+BI5WpFt4bKyshAVFYWzZ8+iR48e1p4OIYSQVsJam50stcLaUuZjCIHnwGekQyi9A8Y7EJLYeDAS1trTUkGBrAVYohWdrUhOTsarr76K0tJSixx/7969WLVqFa5fv476+nq0b98ec+bMwT/+8Q+LnI8QQohlWHOzk2KF1dqaxgM+433UWuXaarzQkJ6KuuRFEIpvKW9jZMFwnLoCDvEjrDgzVRTImhm1ohOH4zgwDKPWH1omk+GNN95Ax44d4ejoiG+//RbPPfccAgICMHToUCvNlhBCiKEUm53qc+s158ky8r+P9rjZia/jUfBhAepu1MKxnRP8X/KHxFH175m+eMCW44WG9FTUrn8RTd84ofi2/PbZW20mmKUcWTOyZis6nuexbt06REdHw8nJCREREVi5cqXauOTkZHh7e6vclpKSAoa5/w3w/PnzGDRoEDw8PODp6YlevXrh1KlTSEtLw3PPPYeysjIwDAOGYbB06VIAQG1tLebOnYvQ0FC4ubkhPj4eaWlpaufdv38/OnXqBCcnJ2RnZ6vNb+DAgRg9ejRiY2PRrl07vPLKK+jWrRt++eUXs7xOhBBCmofOpgV2vNkpd34OzrufQ96cXBR+WIi8Obk4734OufNzlGP0xQM583KQOc42W9cKPIe65EXQ1ZKsbttiCDzXrPPShgJZM7F2K7oFCxZgzZo1WLRoES5fvoydO3ciMDDQqGNNnjwZYWFhOHnyJE6fPo3XX38dUqkUCQkJ2LhxIzw9PXHr1i3cunULc+fOBQDMmDEDv/76K3bv3o0//vgD48aNw7Bhw3Dt2jXlcauqqrB27Vp8/PHHuHTpEgICAnTOQxAEHDp0CFevXkX//v2Nei6EEEIsR1+jA3vb7KTv+eTOz0H+O/lA0xiOA/LfyUfu/Bz98YAAFKzP1zIB+f9Ys3Utn5Gukk6gToBQlAc+I73Z5qQLpRaYiSG7M82dt1NRUYFNmzZh8+bNmDJlCgCgXbt26Nu3L7Kysgw+XnZ2NubNm4eOHTsCANq3b6+8z8vLCwzDICgoSGV8UlISsrOzERISAgCYO3cuDhw4gKSkJKxatQoAUF9fjw8//BDdu3fXef6ysjKEhoaitrYWLMviww8/xKOPPmrw8yCEEGI5Yi+N28tmJ33Ph6/jkb9BSwB6T/6GfHg86qE7HtDHgvGCqNOX6m8qYcg4S6NA1kys2YouIyMDtbW1GDx4sFmON3v2bEybNg2fffYZhgwZgnHjxqFdu3Zax1+4cAEcxyEmJkbl9traWvj6+ip/dnR0RLdu3QDIg99OnTop71u4cCEWLlwIAPDw8MC5c+dQWVmJQ4cOYfbs2Wjbti0GDhxoludHCCHENIpL501XHRWXxqO+gEow21ybr4zdPCXm+dRm16mvxDbFAUWfFhv/BBqf20qtaxlvcVdzxY6zNApkzcSauzNdXFxEj5VIJBAE1f9S6+tV/2NZunQpJk2ahNTUVHz//fdYsmQJdu/ejdGjR2s8ZmVlJViWxenTp8GyqmU53N3vJ/G7uLgoc3FDQkJw7tw55X0ymUxljtHR0QCAHj16ICMjA6tXr6ZAlhBCbEBzNzoQy9jNU6KfzxNeouZR+2eNgTPXzCHQOiGaJDYejCwYQvFtaNulx/gGQxIb39xT04hyZM3Emq3o2rdvDxcXFxw6dEjvWH9/f1RUVODu3bvK2xoHlAoxMTGYNWsWDh48iDFjxiApKQmAfFWV41S/ksbFxYHjOOTn5yM6OlrlX+MUhMYcHBxUxjUOZJvieR61tbV6nxshhBDLs0ajA31M2Wwt9vmIzVitPlkN1tf0Wqt/T81CyZ5inTm7lsBIWDhOXaH4qem9AADHKcttpp4srciaiTVb0Tk7O2P+/Pl47bXX4OjoiD59+qCgoACXLl1SSzeIj4+Hq6srFi5ciJkzZyI9PR3JycnK+6urqzFv3jyMHTsWUVFRyMnJwcmTJ/H0008DACIjI5WX/Lt37w5XV1fExMRg8uTJSExMxLvvvou4uDgUFBTg0KFD6NatG0aMEF+iY/Xq1XjggQfQrl071NbW4rvvvsNnn32GLVu2mOW1IoQQYprmTqXTly5g6gqx2HkKgiBf/uP1DDTTn/mG3AZkPZOlcpu2FWZz16N1iB8BzN6qXkfWNxiOU5bbTOktgAJZs5LvzoTmSxvrLVsXbtGiRXBwcMDixYuRl5eH4OBgTJ8+XW2cTCbD9u3bMW/ePGzduhWDBw/G0qVL8c9//hMAwLIsioqKkJiYiDt37sDPzw9jxozBsmXLAAAJCQmYPn06JkyYgKKiIixZsgRLly5FUlIS3nrrLcyZMwe5ubnw8/PDQw89hCeeeMKg53H37l289NJLyMnJgYuLCzp27Ijt27djwoQJpr9IhBBCTGZKKp2YgKvxmNrrtSjaWiivRas4bpNgztTN1mKfT/F/i0SNgwBwRRx8pshQss08+bIKjXN2PUfJUx1KU0qQNyvP7PVoHeJHgO09zOY7ezFC04TJFq68vBxeXl4oKyuDp6enyn01NTXIzMxEVFQUnJ2djT6HJTp1CIKAuro6ODo6qtR8JerM9T7aGp7nkZ2djYiICLVGEqT1oM8BsfZnQOAEXGp7UW+jg843uqj87ROTw6ppjKbjA1CW7yreVYy/n83SO+822yMhm6iexsbX8bgYfgFcoXnrorb5tA1yF+SiIbfBrMdVvL6x1zrh6rdXUTOxGkw9ozYGgE2WOBNLV7zWGP0WtADF7kzZRBk8BnrYXIkRQgghxFjGNDoQk8OqbYyaJrVWTVkhLt1bgsvtL5k9iAUAaagjwjeGm/24ihXmiqMVKP2ixGr1620FpRYQQgghxCDaUulYPxbh74errAKKymGdlSPPQRUbczVKF1C2wtUVALNAQ6Hqyqi2klsma9J6l/VlwRWZP1CuPFoJrkTHca1cj7a50IosIYQQQgzmPcYHoe+GgfW/vybGFXDInZurUiVAVA5rTr1Rl+Drb9WDYRn4PKPn8jkHZD1zv3qBzuDaDBQr0pXHKi0SxAIARGaGWqsebXOhQJYQQgghBivdW4KsZzLBFagGoE1LXlkykHIIdED5z+Uo+p+4jViKS+16g2tj5+PvoJKXapHnrijn2V/cKqsl6tfbEgpkCSGEEGIQvekCMDyH1SCM/JL931OzcOPR6+CKRax6NrrUbokAU+IlQcjbIWBlDsq8VLM/90Y5yB4DPMD6sFapX29LKJAlhBBCiEEMKXklqmFQmBQOoQ7ia7DeK3FlbDpC7XXzdN9qjC/jkT01G9cHX8OlthdRurdE/3MHAAOqWUnDpMoVX4Zl4D3+XkqFyE13LREFsoQQQggxiCFNEURVOdgQdn+Hv4XjLodABxRuLTT+ACICT0V6RdnXpXqfO0Sm0LL+LDr92VllI51rnCsid0ZCGqq68ts44DWEwHPgLp1Aw/F94C6dgMBbKL/XjKhqASGEEEIMYmjJK21VDhz8HBC2+X6VA41jwhzgnuCO0i9KTZ93uBQQYNRKLitj4T/TH4Hzg3D3xF3U5dYhd06uWo4wAJWOYp1vdEHUF1Eaa+h6j/FGwaYCUefnCjjcPXFXrQKB9ygf+DwlM7l+fUN6qnonL1kwHKeusKlOXk1RIEsIIYQQgyhLXulpitA4P9PrKW9UXahGwYZ88BXyPq8NBQ3InZMDRiK/n5U5IHhVCBoKGuDg64CGogawvizy5uWZZd4+E3zQkG9cgwKuhMPtZbfh0tkF3mN8UJFWoTmIVWiUXuE9xgdeT3mrBZuVxypFB7KA9pVwRf16YzWkp6J2/Yto+mYKxbflt8/earPBLAWyLVxWVhaioqJw9uxZ9OjRw9rTIYQQ0gIo0gUyx2fKL5E3jn805GeW7i1B9vRsjaWo6nPrkTkuU73eKgvRl93FKvm8BB5DtXeJ0unec8x+KRueT3gZlF4B3A82FVUTSr4ogUOAA6ShUpUWvLpYYuOcwHOoS14EXYV+67YtBtt7mM21pwUoR9YiBJ5D4aXfkXs8FYWXfreLHBOxkpOT4e3t3Szn2r17NxiGwahRo5rlfIQQQsSTpwtE6c3PLN1bgsxxmdrrqd6Ln9Tut8Cfzvqb8hVkvRuwdOAKOFyMuCh6w1jj4LN0bwkutb2I64Ov4e9ns3Djsevga3j9B7FgBQI+I10lnUCdAKEoD3xGutnPbQ60Imtmt34/iIvJq1FTfFt5m7MsCF2mLkDwg49ZcWa2heM4MAyjtU94VlYW5s6di379+jXzzAghhIil7ZK5YiVWWabLhjTkN2hfTRaJK2jA7aW35avIxZyo9AptncQUpcMk7hLwlRqCWgtXIBBK75h1XHOjFVkzuvX7QZxa/6pKEAsANcV3cGr9q7j1+0GLnZvneaxbtw7R0dFwcnJCREQEVq5cqTZO04pqSkoKGOb+fxznz5/HoEGD4OHhAU9PT/Tq1QunTp1CWloannvuOZSVlYFhGDAMg6VLlwIAamtrMXfuXISGhsLNzQ3x8fFIS0tTO+/+/fvRqVMnODk5ITs7W+Nz4TgOkydPxrJly9C2bVuTXxtCCCGWo7hkLpsog8dAD5Vgy1KNB0whDZZqXU02CKPl/zf6WRF8iqm7K/GWIHBRIFiZ6uV7bRUIBE5AzZ81KP68GBVpFcratQY/De9As45rbrQiayYCz+Fi8mroyjG5uG01gh4YbJEckwULFmDr1q3YsGED+vbti1u3buHKlStGHWvy5MmIi4vDli1bwLIszp07B6lUioSEBGzcuBGLFy/G1atXAQDu7vJvmjNmzMDly5exe/duhISEYN++fRg2bBguXLiA9u3bAwCqqqqwdu1afPzxx/D19UVAQIDG8y9fvhwBAQF44YUXcOzYMaOeAyGEEOuztfaoDmEOyhVSxWpyRVqFvEOZmKYKjd2rZRu0NBhFHxeqVSQIWx+mDD7FBPQNOQ3wGOiJ4EUheisQlO4twc25N1EaU4qqw3fBNDDyc24IM7jkliQ2HowsGELxbWhbWmZ8gyGJjTfouM2FAlkzKco4rbYSq0pATdFtFGWchl/nB8167oqKCmzatAmbN2/GlClTAADt2rVD3759kZWVZfDxsrOzMW/ePHTs2BEAlIEoAHh5eYFhGAQFBamMT0pKQnZ2NkJCQgAAc+fOxYEDB5CUlIRVq1YBAOrr6/Hhhx+ie/fuWs/9yy+/4JNPPsG5c+cMnjchhBDb0iztUQ1ID/Cb5qcSFDIsA8/Bnoj4vwiNl/3FcIp2Que/uugMPutz60Qdqz63Tm8FAkWKgsAKQEzjx8pr10Z9AYOCWUbCwnHqintVCzTv3HOcstwmN3oBlFpgNrWl4spniB1niIyMDNTW1mLw4MFmOd7s2bMxbdo0DBkyBGvWrMGNGzd0jr9w4QI4jkNMTAzc3d2V/44cOaLyWEdHR3Tr1g2APPhtPHbVqlWoqKjAP/7xD2zduhV+fn5meS6EEEKsR1RnK1MZEHw6RTvLH8IJqEirQPEu+WV5r6e8Ebk7yqAuWwrSYKnO9AoAqNdVpsuAcYa0BjaEQ/wIOM3eCkYWpHI74xsMJxsuvQXQiqzZOHn7m3WcIVxcXESPlUgkEATVD3h9verljqVLl2LSpElITU3F999/jyVLlmD37t0YPXq0xmNWVlaCZVmcPn0aLKv6W0CReqCYpyIXNyQkRGXVVSaT4caNG8jKysLIkSOVt/O8PPHdwcEBV69eRbt27UQ/V0IIIdals0zXPRIPibyurJEbrwwhDZaidG+JxuYEvtP8jKqU0FCgP33CwV9cuNV4nKJMV9O6s2JbAxtaV9YhfgTY3sPkVQxK74DxDpSnHdjoSqwCBbJm4hvbC86yINQU34G2HBNn30D4xvYy+7nbt28PFxcXHDp0CNOmTdM51t/fHxUVFbh79y7c3NwAQONl/JiYGMTExGDWrFmYOHEikpKSMHr0aDg6OoLjVP9Lj4uLA8dxyM/PF11lwMHBAdHR0Sq3ubq64sKFCyq3vfnmm8rUifDwcFHHJoQQYju0dfViZSz8ZvjBvZ8Hyr8tQ/GOYnCFFqoje6+CQENBPbKeyVK7uz6nHreX6ipBpV3uvFx4j/HRWVHA0E5o2oJt76e9RR3H2NxkRsKC7Zxg1GOthQJZM2EkLLpMXYBT61+FthyTLlMWWOSbjbOzM+bPn4/XXnsNjo6O6NOnDwoKCnDp0iW1dIP4+Hi4urpi4cKFmDlzJtLT05GcnKy8v7q6GvPmzcPYsWMRFRWFnJwcnDx5Ek8//TQAIDIyEpWVlTh06BC6d+8OV1dXxMTEYPLkyUhMTMS7776LuLg4FBQU4NChQ+jWrRtGjBB3ScLZ2RldunRRuU1RYaHp7YQQQpqPptVBQ0pBaSrT1VAo7+p1Z/n9sk6svwNkk3zg9aQ33BLcUHGkAn8Nv2H6Sq0AhKwLxc2Xb5p4IHWiVkDFzl/QXqarPrdedBewZslNthGUI2tGwQ8+hgdmb4SzTLVEhbNvIB6YvdGidWQXLVqEOXPmYPHixYiNjcWECROQn5+vNk4mk2H79u347rvv0LVrV+zatUtZQgsAWJZFUVEREhMTERMTg/Hjx2P48OFYtmwZACAhIQHTp0/HhAkT4O/vj3Xr1gEAkpKSkJiYiDlz5qBDhw4YNWoUTp48iYiICIs9Z0IIIZbXtIj/9cHXcKntRZTuLTHoOI3zSLniBmQ9k6l2mZwrbEDBewXgihsgcZRAIpWYLd3g5r80dxYzhzo9m7nEtsWtv12vNwcWLLTnHFuwcYKtYoSmCZMtXHl5Oby8vFBWVgZPT9U2dTU1NcjMzERUVBScnZ2NPofAcyjKOI3a0gI4efvDN7aXySuxgiCgrq4Ojo6OKjVfiTpzvY+2hud5ZGdnIyIiQmsjCdLy0eeANOdnQNvqoCKQ0lTfVB+BE3Cp7UXtuZ730gA63+iCki9K8PezWQbPu7mFvB0C155uWlesK9IqcH3wNf3HeTcUeXNyRZ1TkAq4O/Au3A67gWlgTHpPbJGueK0xu/otyHEcFi1ahKioKLi4uKBdu3ZYsWKF2uYla2MkLPw6P4jQPiPg1/lBm0+UJoQQQpqy1A55QzYs2csl8jur7uhcsVZWb9Dm3kqqVOSmMP9X/CEN0d0auLWwq0B27dq12LJlCzZv3oyMjAysXbsW69atw/vvv2/tqRFCCCEtiiEBpyHEbkTKfz8ffD1vWvetZsKVqKYsKGq6KoJZhmXg84zuADNsfRikoY6izuf1pDc6Xe0M/9kBiEhug+hD7dH5RpdWF8QCdrbZ68SJE3jqqaeUm4ciIyOxa9cu/P7771aeGSGEENKyiA04Dd0hL3aVtTylDOUpZZC429Wam5y8oSdyZufA6ylvlH1divx31fetKATMCYD3GB8InABpmBT1ufXaCiBBGiZPXRAYAc4xzpBFyFp1mpFdBbIJCQn46KOP8OeffyImJgbnz5/HL7/8gvXr12t9TG1tLWpra5U/l5eXA5DnGClqlCrwPA9BEJT/bJUtz80WKN4/Te+xPVM8n5b0nIjh6HNAmuszwAaxEBz0/71hg1itcxE4AZW/VKL+dj2kQVK493WHax9XOLRxQH2elmCtCa6GAxwAiZsE/F37+tzX3apD2eEy3Jx7U96JSxMGKP6yGEFvBYNhGYRsCEHWpCz5feoFkBCyPgQCI7T43wVin5ddBbKvv/46ysvL0bFjR7AsC47jsHLlSkyePFnrY1avXq3ccd/YzZs34eGhWiqD4zhwHIf6+nqb/HbT0CBu12NrV19fD47jcOvWLbUGDfZMEASUlJSAYRja8NeK0eeANNdnQGgjoGZMDbhSTuvqIOvNoji8GLd+ugWulAPrzcIp2gmMhEHV2SqUflGictmd/YiF93gfsO86oPSj0nsnEjcfibcEbgnuqPiu3OTn1pyyLmWhIqZcpZ2sJtcOX4NzjDPQE3De5aL+2vnIX7vyuAqUZ1e0+N8FFRUVosbZVSD7xRdfYMeOHdi5cyc6d+6Mc+fO4dVXX0VISAimTJmi8TELFizA7NmzlT+Xl5cjPDwc4eHhGqsWZGVlQSqVwtFRXJ5Kc7PVedkSnufBsiyCg4NbXNUCQRAQHh5uk1+0SPOgzwFpzs+A1zhPnauD/q/6o/SxUvml8HvqQ+vgPd4bNRur4Sw0+R3MADX7qhG5MxJBLwYid26uymP18e/iB+Y8wBVbpoyWJXgGeYA/rH++flP8IIuQyX+IAIQn1FezG1dCaOm/CxRX0PWxq0B23rx5eP311/HMM88AALp27Yq///4bq1ev1hrIOjk5wcnJSe12iUSi9sZLJBLlNxtb+3bTOJ3A1uZmaxTvn6b32N4pnlNLe17EMPQ5IM31GZCN8YVkp0S9y1S4FD4TfJD/Tj4gAEyjwqYN2Q0ofLtQ5TYVDJA3Ow+db3SBz1MyZE78C2VflYmaT/6S/HuHsJ+/g2WflYmar1Owk+r7KQG8BnnpfIziM8AIjEkNK3QReM4qbWvFfrbtKpCtqqpSe2Isqz03hxBCCCGm0dSVyy3BDZfbX9JduF+bRtUO3Pu5o/KIYVUPjMF4MBAqROYw3GvOyfqyFmug0PR8ig1cxihNKUHerDy1drZhG8JMrmLQkJ6KuuRFEIrvt+9lZMFwnLoCDvHiunZaml0FsiNHjsTKlSsRERGBzp074+zZs1i/fj2ef/55a0+NEEIIabEUXbkUKtIqdJfmEqH+ljyY5QotHyyKDmIBOPg5wGeSDzyf8AIEeVeu+jv1ohsVGOTeomnY+jCjVlCrzlaheFIRmHrVxyrKf0V9AaOD2Yb0VNSufxFNv5kIxbflt8/eahPBrF1dl3r//fcxduxYvPTSS4iNjcXcuXPxr3/9CytWrLD21GxWVlYWGIbBuXPnrD0VQgghLYShJbc0kQZLzXIcnRiAlYm7DO4xzAOsH4uGggYUbCrAjUevI/v5vyFxYiANtEwtW1OaGAicgNIvSszesAKQpxPUJS+CroPXbVsMgbd+rrJdBbIeHh7YuHEj/v77b1RXV+PGjRt46623bG4DlMBz4C6dQMPxfeAunbCJN9pckpOT4e3t3aznPHr0KEaOHImQkBAwDIOUlJRmPT8hhBBVJnXcutfFyr2fe7N07vKfGSBqXMWBCrXVYcXKZu31Wi2PMlzgG0Fosz3S5CYGlb9UqjViUGFkwwoA8pzYRukEmg4uFOWBz0g3+NjmZlepBfbAHvJJbAHHccoNWfrcvXsX3bt3x/PPP48xY8Y0w+wIIYToomi5qrVwvzZNLqUrj2NimoJGLBC5IxLeY3xQ9HGh4XMFlI0Nij4uhEOoAxryGgw/RhMej3iopGkYq/62ZRpWAIBQeses4yzJrlZkbZ0in6TptxhFPklDeqrFzs3zPNatW4fo6Gg4OTkhIiICK1euVBunaUU1JSVFpRLC+fPnMWjQIHh4eMDT0xO9evXCqVOnkJaWhueeew5lZWXKygBLly4FIG88MXfuXISGhsLNzQ3x8fFIS0tTO+/+/fvRqVMnODk5ITs7W9RzGz58ON566y2MHj3a4NeFEEJaI0tfGWRYBmEbwu790PRO+b+AuQGQhqmuuDa9lK5yHHPjAAd/qe65iiEA9Tn18HvRz/hj3OPg72D0pq6mpEHiVrONWfVmvAPNOs6SaEXWTPTnkzCo27YYbO9hFilbsWDBAmzduhUbNmxA3759cevWLVy5csWoY02ePBlxcXHYsmULWJbFuXPnIJVKkZCQgI0bN2Lx4sW4evUqAMDdXf4f5IwZM3D58mXs3r0bISEh2LdvH4YNG4YLFy6gffv2AORVJ9auXYuPP/4Yvr6+CAgQd7mHEEKIeM11ZdB7jA+ivoB6aa4wKcLWy3fMh6wK1VsWynuMD/xfqUTBpgKzzU1BsRqpba5gAYiM8Z2inRH1RRRuvnoTDbnGNSjymeRjtrJY7n3dwX7Eag+sTaiGIImNByMLhlB8G9q6YTC+wZDExht8bHOjQNZMDMknYTsnmPXcFRUV2LRpEzZv3qysp9uuXTv07dsXWVlZBh8vOzsb8+bNQ8eOHQFAGYgCgJeXFxiGQVBQkMr4pKQkZGdnIyQkBAAwd+5cHDhwAElJSVi1ahUAecetDz/8EN27dzf2qRJCCNGhuXeaayrN1ThYbVrtQBuvJ70tEsg2Xo1sOldDKxHU36mHNEQKodb43AKvJ72NfmxTDMvAe7wPavZVax1jbDUERsLCceqKe5+le/XI7t8LAHCcsrxZ6snqQ6kFZmLNfJKMjAzU1tZi8ODBZjne7NmzMW3aNAwZMgRr1qzBjRs3dI6/cOECOI5DTEwM3N3dlf+OHDmi8lhHR0d069YNgDz4bTx21apVOHbsmMptO3bsMMvzIYSQ1sBaO80VwapsogweAz2MCpzc+7lDGmrejV+sjIXACSq79hvP1dBKBHlzcvH3xCyjy4UpNrgpCJyAirQKFO8qRkVahVHVBVzjXOH/qr98ZbkxFgiYE2BSHVmH+BFwmr0VjCxI5XbGNxhONlJ6C6AVWbOxZj6Ji4uL6LESiUSlSxggXyltbOnSpZg0aRJSU1Px/fffY8mSJdi9e7fWHNXKykqwLIvTp0+DZVX/a1KkHijmqcjFDQkJUSkJJpPJ4OLionJbYKD1c28IIcReWPPKoKnKvi4FX2Pe5kZcMYcbj13X2hygOSomKDGqq6Ole0s0p2QY2MSg6mwVajZWg+GafHnggfx38+EW76ZzxVwfh/gRYHsPs0pnL7EokDUTa+aTtG/fHi4uLjh06BCmTZumc6y/vz8qKipw9+5duLm5AYDGGrMxMTGIiYnBrFmzMHHiRCQlJWH06NFwdHQEx6l+G42LiwPHccjPz0e/fv1EzdnBwQHR0dFqt2u6jRBCiH72tNO8sZI9xch6Jkvr/YwzA6HG+Mv52poDuCW4GZQjayxp+P2cYUAexGaOz1QLFQxtYqCoI+ssOGu4EwADZE/PhuTVHHnFBsV8DAyYGQlrc198GqNA1kysmU/i7OyM+fPn47XXXoOjoyP69OmDgoICXLp0SS3dID4+Hq6urli4cCFmzpyJ9PR0JCcnK++vrq7GvHnzMHbsWERFRSEnJwcnT57E008/DQCIjIxEZWUlDh06hO7du8PV1RUxMTGYPHkyEhMT8e677yIuLg4FBQU4dOgQunXrhhEjTLv8UFlZievXryt/zszMxLlz5yCTyRAREWHSsQkhpKWwhZ3mAicYtPpX8mUJsiZn6Twm425aIKsI6nJm58DrKW/lfO6euGuRINZ7kg9ce7pAGiCFNNRR5TUQOAE5s3J07QtXm6c2YurIckUcOGiujWtK1y9bQjmyZmTNfJJFixZhzpw5WLx4MWJjYzFhwgTk5+erjZPJZNi+fTu+++47dO3aFbt27VKW0AIAlmVRVFSExMRExMTEYPz48Rg+fDiWLVsGAEhISMD06dMxYcIE+Pv7Y926dQCApKQkJCYmYs6cOejQoQNGjRqFkydPmiXQPHXqFOLi4hAXFwdAnsMbFxeHxYsXm3xsQghpKRRXBnVtY2d8Qyy207x0bwkutb2I64Ov4e9ns3B98DVcansRpXtLtI7PmpCpN5jkC3mw/g4mlb3S1BzAUl3FvB73QsDMQEhDHZVteBX5r5XHKnXXzDWgiYHYOrKazgEY3/XL1jBC04TJFq68vBxeXl4oKyuDp6enyn01NTXIzMxEVFQUnJ01LNWLJPCc2fNJBEFAXV0dHB0dVWq+EnXmeh9tDc/zyM7ORkREhKhGEqRlos8B0fUZuF+1ANB0ZdBSiyraLpcrgs+mbVgFTsClthdFN0Lwf8UfBe8VmNyMwP8Vf4StD4fACch/P9+gqgViBS0NQtHHRRrzXyt/EVdmrM32SMgmynSOKTtchj/WnofbYTcwDcbFBdGH2pulOYMl6IrXGqPUAguw9XwSQgghLZND/Ahg9lb1OrK+wXCcstwiQawxl8v1rkw24fWkN2oza1G+v9ykuRZsKgBYoPizEnAFxtWC1YX1ZXF76W212+tz65E5LlP0ccRsRNNbR1YES61KNycKZAkhhJAWpLl3mhtyuVyx+mdIACUNl6I+v97kIFahYL3569XqJXYl2YAmBip1ZJtuzRGpWSs3WAhdlyKEEEJaGMWVQYc+o8F2TrBouSSxQWnjcYYEUKFvhyLnPzcNnldz85kiA1dk4u4xwbAmBq5xrojcGalWg9chzAGsr56uX+HGdf2yNbQiSwghhBCNxOz5EBuUNh7n3s8d0jCpvCyUtpVEFojcEQkHf6n4JgROAGrFDTUnaZgUHoM9ULKt2KTj+L/ib3AlAe9RPvB5SqZWLaLs61J53rLmQkpGd/2yNRTIatDK9r+1OPT+EUKI6RrSU9VzbWXBcJy6QiXXVm9QquFyOcMyCNsQpjnQuidyZxR8xvqgeJf44DDkrRDkzcsTPV4TiYcEfIVhzRnCNoSBlZkeUhnbwlZTK2DvMT6I+gKaGy+sN6zxgi2jQLYRqVQKhmFQUFAAf39/m6oOIAgC6uvrwfO8Tc3L1giCgIKCAjAMA6nU/nN/CCHEGu5XP1CNMIXi2/LbG1U/0BuUCoDXKC/kv58PB38HON6rrao10GrSQEDsiq+DvwP8ZwSgYFOB7pVePfxm+CN/tfimEYpVVIET4BDqgIZc4zaROYQ5QOAFFO8q1lqDt3GdXjaIhdBG95P0HuNjUmcve0CBbCMsyyIsLAw5OTnIysqy9nRUCIIAjuPAsiwFsnowDIOwsDC1drmEEEL0E3gOdcmLoKsMQd22xWB7D1OmGWgLShWdswrfL1Q5SuPuUvoCLfd+7pCGSlW6U2kStjkcEkeJ3pVeXRz8HeDxiIdBgaxiFZVhGfg+74s7K4zrnCZUC7jx6P3mP007cDVtays4CKgZUwOvcZ6QjfHVelxNq7UtCQWyTbi7u6N9+/aor7etkhQ8z+PWrVsIDg6m2pF6SKVSCmIJIcRIfEa6SjqBOgFCUR74jHSVUpONg9Ky/aXyUldaUlvrc1S7S+kKtMq+LgVfo/tSf8DcAPiMla+KsjIH+M/0R9H/igxPEdgcDq6oQXQQzMpYZcpE6d4SFH5QqOcRGkgA8FDbKNa4AxcAjXV6uVIOWZOyINkpaTGpAoaiQFYDlmVtLhDieR4sy8LZ2ZkCWUIIIRYjlIpbUdQ0jmEZuPdzx99TskQcQH87Vq2NFu6ReEoQ8VEEfMbJ1FYsDRUwNwCMBMgcnyV6Jdd/pj8YltE7T11YbxZcsYaI/14N3puzboIRGO0L5BDf1rYlooiIEEIIIUqMd6BJ4wxpdqCrHavORgv3SDzlK5GKQNKYIJb1Z9FmVyQ8hnoi+1/ZooNR1pdF0MJgUfPURBouRdDSIM1BrIIANOQ06E6rMKCtbUtEK7KEEEIIUZLExoORBUMovg1tZQgY32BIYuM1Pt7QblHaxosJiBtyGlCRVmFUIBn4RhA8HvFAQ0E9cufmGhYEM0DEfyPAsAwq0ioMDqBZfweEvhMGod58VXZaQpcuY9CKLCGEEEKUGAkLx6krFD81vRcA4DhludYmC4Z2i9I2XmxgVmlEIAkAfCUHrrgBWROzDHo8K2MR9UWUMie1LrfO4HNzBQ3IeiYTtdfNV/S2JXTpMgYFsoQQQghR4RA/Ak6zt4KRBanczvgGw6lR6S1NFHVltXaVakRXdynRgZmRlXwKNhUge7r4VAKFyN1RKpUEcmfnGHV+CEDRx4VwCHXQ2YHLIcwBrEzHvp0W1KXLGJRaQAghhBA1DvEjwPYeprezV1MqdWV1DtTdXcotwQ2sH6uzqxcrYyHxNH6Dk0EtZe81dlBUWDBlg5dCfU49gpYG4fay21o7cMmekSH/nXztB7nX1haAPM2hhdaL1YYCWUIIIYRoxEhYlRJbYmmtK3tP06YHTSkqEOhrTcsVc7g1X1epMDNp0tbV2A1emji2dULUF1EaO3CFvh2K3Lm5Oh/PylgIPHCp7UX1Dl4bWk4HL20okCWEEEKI2ak0O8itQ31Bg0pnL2NLbllD07auhlRm0Ofur3cRvilcY2MIMefhijlkTVBf/W5ch7YlB7MUyBJCCCHEIgztKmXOlU5zYGUsIndHwWOgh0rgbc4KAUVbClH+TZnG1VOTznOvDm1LrzFLm70IIYQQYhPMudJpDv4zA+A52FMtCDR3hQDF6mnp3hLznqcV1JilQJYQQgghViFwAirSKlC8q1i+UcmIUlaW5BTtpPF2ZWUGc2nUoUvg7i9HG1IBQpeWXGOWUgsIIYQQ0uw0tZRl/WyrPby2FVFlZYZxeiozGKLR6qkiHUOlAoSWqgZitOQas7QiSwghhJBmpa2lrL4qBc2KlZcA08Z7jA+Clgab/bRNV0/lFSCiIA1VDUaloVL4vugrv11HUCsNa9k1ZimQJYQQQkizEb2hS3NTMc33WQIH3D1xV+eQoIVB8oYGZqRp9dR7jA86/9UF0Yfao832SEQfao9OVzrDtZcbQt8JlQ/S8prw1TzKvi416xxtCQWyhBBCCGk2Yjd0sX6qAaI0TIqoPVGI2qO+Oqn6QFNneJ+unF1Ffq/bQ9pXbQFA4i4y1NLToUtRAUI2UaZSRcF7lHzFVlv3L66Y07iRrKWgHFlCCCGENBuxG498p/nC4xEPNNxpUOtU1bjmqkOAvMWrYpxbghvunriL0pQSFL5faNpcCxo03l66twTZ07N1dgZjZSz8ZwYgaGEQyr4u1docAoBawwVDeT3lDebVm5rvbOFluCiQJYQQQkizEbvxKH/1HZR8VoywDWFqtWj11ad17+eOzAl/mTRPAGgoqEfxrmKVQLp0b4neTV5BS4MROD8Qd0/cRckXJZAGS9HpWmfcPXEXZftLUbyzBFyjILlpwwVDVR6rREOu5qAbgMaNZC0FBbKEEEIIaTaKklL1ufV682SN7U5VeazSLBvH8tfkK/+/NFSK0PVhyJmtZeWzkYL381HwUT64vPtzcAh1QPjGcIStD0fo22FqXbxMWSkVu8rdEstwUY4sIYQQQpqNoqSU/Ac9g7XUV9XHEgFbfW49siZk6l75vIcr4lSCWABoyG1A5jh5rqq2fFdjiV3lbolluCiQJYQQQkiz0lZSSiMjulPZcsCWPT3boKBcDL2NE/RsJLNnFMgSQgghpNkpSkoFLgwUNb7i5/sdwPQFgmI7b3mO9DRvhy4RuCIOFWkVZj2mzlXuRhvJAKh0UjN3QG0NlCNLCCGEEKtgWAYegz1xZ9UdvWPvrLyt/P/SMCnCNmjfHKXSEUtHrFb+bTkid0fBwc8B9bfqUfZ9GUp3WL5MVcXhCngO9jTrMeWr3FCrjqDYSAYAl9peVL9Px+toD2hFlhBCCCFWo/eyuAaKTWCK2qiKmq6NVxq9x/ggclek3rqyuXNz4N7PHbKJMji1cTT+iRigYHOBReq6amqc0PlGFwDQ2Emt6etoj2hFlhBCCCFWo7J6ykB/xy9ApTaqwAO5czSsQm4Ig4O/FNBVvKBJWSr3gR6iVoe1kXhKwJfz+qdfwRtVjUGMpqXJdHZSawE1ZmlFlhBCCCFWZdDmL4V7QWjWBO0rjWX7S0UdquJQOQROgHs/d0g8DQ+NXB5wQcg7IeiS1xWsr/jWYoZWYzCG3k5qRmymsyW0IksIIYQQq/Me46PSsavmcrXxq6P3VhqLdxaLGn5n1R0U/p+8C5iYFdWmqk9Vo/pUNQo2FsD3OV/kv5Ov/0HN1KSgpdeYpRVZQgghhNgElfqqpm6GEgCugAPr7yAq/5Yr4nS2nBWjPrce+e/mI2BuAFiZuJVZSweQLb3GLAWyhBBCCLE5xmwC00Q26V4OanOkf97LEij5vARtdkSKeoilA8iWXmOWAllCCCGE2BwxtVHF8HrS2/D8W1PcSxlgHBibCCDF1pi1x41eAAWyhBBCCLFR2jaBScOkiNwdKTpQVDZfeCPI8pO+p+FOg80EkLpex6gvouy6jixt9iKEEEKIzWq6CUwaLA9OGZYBwzKay3ZpCBQZloHHIx4qjRUsqeZyNTwGeyJyd5Tm8mDrm7cRga7X0Z5RIEsIIYQQm9a0NqqCvm5WTQNFRb5ofW69uHq1Jriz6g7urLoDaZgUoe+EwsFfavUAUtvraM8okCWEEEKI3TJkpdGo5gsmqs+tR9bELER9EQXZRJnlT9jKUI4sIYQQQuyaStmugR46Vzu15YuyvqxloqJ7wXJzND9ojewukM3NzcWzzz4LX19fuLi4oGvXrjh16pS1p0UIIYQQO6HY/BV9qD3abI9E9KH2iNwVBRjeC0EcO++eZcvsKrWgpKQEffr0waBBg/D999/D398f165dg4+P/e62I4QQQkjza5ovWrxLXBcwU+hrfiDwHPiMdAild8B4B0ISGw9GIr7lbWtkV4Hs2rVrER4ejqSkJOVtUVFRVpwRIYQQQlqC5uhspescDempqEteBKH4lvI2RhYMx6kr4BA/wuJzs1d2Fcju378fQ4cOxbhx43DkyBGEhobipZdewosvvqj1MbW1taitrVX+XF5eDgDgeR48b6lrCOanmK89zZmYF30GCECfA0KfAWMJnIDKXypRf7se0iAp3PuqbgirK6yD4GChHFYGkIZK4drHVeP71vD7d6jd9G8AAtB4Bba0ANUbp8PplS1wePBxlce09M+B2OdlV4HsX3/9hS1btmD27NlYuHAhTp48iZkzZ8LR0RFTpkzR+JjVq1dj2bJlarffvHkTHh72U4JCEASUlJSAYRgwjH3XfCPGoc8AAehzQOgzYIyqs1Uo/aIEXAmnvI39iIX3eB+4xrlC4AXc+ioP3CBOx1GMdO8t8v2nH27m3lS7WxB41B9KgRD1kNYDMD9/DWlgJzCMpNHjWvbnoKKiQtQ4RhAEu9lC5+joiAceeAAnTpxQ3jZz5kycPHkSv/76q8bHaFqRDQ8PR0lJCTw9PS0+Z3PheR7Z2dmIiIiARGJ3e/RaLYHnwF/5HUJpPhjvAEg6Pmh0vhN9BghAnwNCnwFDlaaUIGtSlnqprXuxX+TOSLA+Drgx7LreY7G+LLhiTnPZLgZgfVgwzgwa8hqUN0vDpAh9OxTeozTv5+Eu/4qalRP0ntv5jc/BdnpY+XNL/xyUl5fDx8cHZWVlOuM1u1qRDQ4ORqdOnVRui42NxVdffaX1MU5OTnByclK7XSKR2N0br5izvc3bXnAcj+Pns3GrsBLBfu7o0z0CLGv4a61I1m84dQANv+wFyouU95ma70SfAQLQ54DY52fAGhuZBE5A3qw8MPVaViwZIG92HoJXhoBp0L+q6fuMLwreK7h3cNXjAECbD9oY3D2LL8uHhNe/EsyU5au93/b4ORBL7HOyq0C2T58+uHr1qsptf/75J9q0aWOlGZGWIiUtA3M3/IDc/HLlbaEBnnhn1lCMGhgr+jiakvUbE4pvo3b9i8DsrZS8TwhpNay1kanyWKVKxy8198piNRQ0aB/TiNeT3nDv6663k5gh3bMY70Czjmtt7CqEnzVrFn777TesWrUK169fx86dO/HRRx/h5ZdftvbUiB1LScvApAV7VIJYAMjLL8ekBXuQkpYh6jgN6amoXf+i1iBWTv4Vvm7bYggivoETQoi90/a7UfHFviE91WLn1lfuSkHq7wBpmFS5sqqGAaTh8tVVTTVoO9/ootYOVyxJbDwYWTB0nZzxDYEkNt6o47d0dhXI9u7dG/v27cOuXbvQpUsXrFixAhs3bsTkyZOtPTVipziOx9wNP2hMdxLu/fvP2lTU1en+ti7wHOqSF0Fcv0MBQlEe+Ix0wydMCCF2RPfvRst/sRdbUksa6oiwDWHyH5rGk/d+DlsfpkwRMKSTmD6MhIXj1BWqJ2tycscpy6merBZ2FcgCwBNPPIELFy6gpqYGGRkZOktvEaLP8fPZaiuxTRWWVqHdUxt1rszyGel6VmLVCaV3DBpPCCH2Rv/vRst+sXfv527QSqum1rXSMCmivogyesVVDIf4EXCavRWMLEh1er7BcKJUNJ3sKkeWEHO7VSiuXWBhaRUmLdiDnavHacyZNSYopXwnQkhLJ/Z3o6W+2DMsg7ANYcgcnykPZjVs0Gq80uo9xsfgzVrm4hA/AmzvYdTZy0AUyJJWLdjP3aDx8zb8gJH9OqhVMzAsKGXA+AZTvhMhpMWzhY1M8pVW6N2gpZxLk9a1zYmRsGA7J1jl3PaKAlnSKilKbeXml8PP2xWFpVV6HyMAyMkvx/Hz2ejfM1LlPkWyvlB8G7rzZCnfiRDSeuj/3dg8X+zNsdIqcIJVVmqJbhTIklZHU6ktQ2hKR1Ak69eufxHq168ajfMNhuOU5ZTvRAhpFXT/bmzeL/amrLSW7i3RvKK7QX1FlzQvu9vsRYgptJXaMoS2dARtyfrw9IXD4y/CefFXcNn8OwWxhJBWxd43MpXuLUHm+Ey1erT1ufXIHJ+J0r0lVpoZAWhFlrQiukptKUgYBryWrs0M5E0S+nSP0Pp4StYnhBB19vq7UeAE5MzK0V49jAFyZufA6ylvSjOwEgpkSashptSWIojVsrkVb88aqrdtLSXrE0KIOnv83Si2M1jlsUqrbRBr7Si1gLQaYkttjRoUC29PF5XbQgM8tZbeIoQQ0jKJ7QwmdhwxP1qRJa2G2FJbKYfvNz7w8XTBjAkPYv6UfnpXYgkhhLQsojuDiRxHzI/+MpNWo0/3CIQGeGpt8KJJSXk1Vmw9gq+PXLHYvAghhNgmQzqDEeugQJa0GiwrwTuzhgLQ/jtJm8RFX2Hvz5fNPylCCCE6CTwH7tIJNBzfB+7SCQg812znVnQGk//Q9E75/zTuDEaaHwWypFUZNTAWO1ePQ0iAp0GP43gBk9/4EilpGfoHE0IIMYuG9FRUv9wbNcufRu17L6Fm+dOofrk3GtJTm20O8s5gUZCGqqYPSMOkiPoiiurIWhnlyBK7pejOdauwEsF+7ujTPUJUHuuogbEY2a+D8rFXsgqwJumYqHNqa1FLCCHEvBrSU+81UlCtfSUU35bf3ow1aM3RGYxYBgWyxC5p6s4VGuCJd2YNFVVZgGUlyjazR89kiQ5ktbWoJYQQYj4Cz6EueRF0FXCt27YYbO9hzVaL1pTOYMRyaFmJ2B1t3bly88sxccEeg3NZFZvAxBJbxosQQohx+Ix0CMW3dIwQIBTlgc9Ib7Y5EdtEgSyxK2K6cxmzMev5p+JEjxVbxosQQohxhNI7Zh1HWi4KZIldEdOdy5CNWSlpGegw5j2s2HpE1Pn9vF2Rm1+Oo2eywHG8qMcQQggxDOMdaNZxpOWiQJbYFUMu68/b8IPOYFNbioIuhaVVeH5ZCoa+/Ck6jHmPqhgQQogFSGLjwciCoauAK+MbAklsfHNOi9ggCmSJXTHksr5iY5YmYlIU9MnLL8ekBXsomCWEEDNjJCwcp65Q/NT0XgCA45TlzbbRi9guCmSJXTHXxiwxKQoA8NqUvvDzdtV4nyII1rfyqwvH8Th6JgufH7xI6QqEENKIQ/wIOM3eCkYWpHI74xsMp2YsvUVsG5XfInZF0Z1r4oI9osZfySrA0TNZajVmxaYoXMsuQmFpldb7BRhfksvUEmKEENLSOcSPANt7mLyKQekdMN6B8rQDWokl99CKLLE7owbGYsfKsZBI9BeiXpN0TJ7POnqTSgqA2BSFfYfFpQ0YWpJLW34upSsQQogqRsKC7ZwAhz6jwXZOoCCWqKBAltilMY90wmcrnhY9PregAhMbBYh9ukdA5uVitvkYkrurKz/XHOkKhBBCSGtBgSyxabpySMc80gm7Vo8zKGd2xppvwXE8vjl2FcVl1SbPjwEQFuCJPt0jRD9GX35u43QFQgghhGhHObLEZonJIR01MBYj+3XA8fPZOPT7X1i37Redxywqq0baqUzM3fCDyfNTJDa8PWuoSv6tPmLTEKiDGCGEEKIbrcgSq9O06mpIDinLStC/Z6SonFkA2HHgD4Nqx2oTGuCJnavHGbwxS2wagr5xVPGAEEJIa0crssSqNK66+nugurZBZw7py6u/hZebE/r3jFSuhgoii8JWVtWZNGeZpwu2r3wa/eMiVVZiOY7H8fPZuFVYiWA/d7VKCQqKEmJ5+eUanyMDeZCsK12BKh4QQgghtCJLrEjbqmtuQQWKy3XnrxaXV+PxmdtVumsN6NVG1HkNyWfVdm5WIlEJUhWtboe+/CmmLtmrs/OXooQYoLlnjQDd6Qr7j1yhigeEEEIIKJAlVmKOzloAkNsoeOsfFwmZp+5KBL6eLvj32N4IDfDU2vhQjMb5q8aU0ho1MBY7V4+Dj4b56qqmwPMC5r93kCoeEEIIIaBAlliJ2M5aYgiQVyMAgA8WPKFz7OYFT8DR0UHniqgYivxVU0tplWhYeS4pq9YaAN/IKUZufoXWeVHFA0IIIa0JBbLEKsy9I7+orBprtx3DqIGx2LV6HEL8PVTuD/X3wK5GG7MUK6IhTUp3hQZ4gtET3bISBg91CQNgfCktYwPgsspa3ZO7hyoeEEIIaQ1osxexCkMaCIj1wee/Y/6UfioluXRtvBrZrwO83J1w5PTfYBigf1wbgAEe/892nefheAG/XcxB/56RRpfSMiQAbtz61svdSdT5LPH6EkIIIbaGAlliFWJ27su8XODk6IC8Au2X0hsrLq9WBn6KklzaaNr1/1nqeYweJG7HvyIwNbaUlrEBcLswGUIDPHDztvEVDwghhJCWglILiFXo2rmv+Hnz60/gz32v4Lv3n4Wbi1TUccUEiFqrJeSXY/Pn6aLOowhMFQG5tmwEbZ2/jA2AJRIGa2c+pjx203MBhjdoIIQQQuwV/bUjVqMrT1XRaIBlJRj0QFvMfjZB1DHFNBGYs/6A0dUSFIHpQ13CcPRMFr48dBnPPxWnvK/pWEBzYKkvAAbk9Wo5jlfLk31yQEe9rxshhBDSGlBqAbEqsfms86f0wwef/661vqzYS+prtx0Tnaqg6RwAMO7Rzug0brPKiq7M0wVggOKy+/MLDfDE21oaFChWpCct2AMG0BhYK2rlKhodPNm/g/I+sa8bIYQQ0pJRIEusTl8+q2LMBwuewMQFe9TuE3tJPSUtAyu2HjF6nqEBnhj3aGds3PGrWuBZUl4NAcCiFwegXZivqMBSsSLdNFe3KUU92p2rxqJHlKvydjGvGyGEENKS0fINsVkcx+PomSx8fvAijp7Jwsh+HbBr9TiEGnFJXVHuyljrXnkMl/bMwBc/XtJaMosBkPT1WYwd3Emlda4uowbG4uremfju/Wc1NkdQHBsAXn/vIHje1BYShBBCSMtBK7LEJmmqKqC4xH5170yDL6mb2oChuLwaq5OPGVUySx+WlYCVSDQ2R1A9dgVu5BQjMlJ3K16O4ynlgBBCSKtAgSyxOYqqAk3XHpWX2I3Y0GRqg4A1Sccsei6xj9HXEEHXFwDaBEYIIaSloWUaYlNMbfmqTXM2CDDmXGIfo6shgrayYoovAJpa3hJCCCH2jAJZYlOMbfmqj5hyV6bSVjNWDHH1aD3QLkym8X5LfQEghBBCbBkFssSmGNvxSh9dDRjMydhmBGIaRKyZ+RgkEs2zt9QXAEIIIcSWUSBLbIqxHa8UmlY6aLwCqa0Bg5+3a9PDGOXVyQ+LykPVNkd9DSKeHNBR6zEt9QWAEEIIsWW02YvYFMUl9rz8co2XyXU1PhCz0UlTI4Hc/HI8vyzFpHkzAPb8eAkr/j1Yby1bXXPU1eiA57WnBZj6BYAQQgixRwatyN68edNS8yAEgLhL7Jou3xuy0UnRSGDCY13Qv2ekWl1aY4i5dC92jk3nJyZVQVyOrXH5u4QQQoitMiiQ7dixIxYvXoyqqipLzYcQvZfYm16+N3WjkyIINAdtl+4tvRnL2C8AhBBCiD0z6K/ajz/+iB9++AHt27dHcnKyhaZEyP2OVz98kIjkZWPwwweJuLJ3psYcVFM3OjUOAk2l7dJ9c2zGMvQLACGEEGLvDMqRTUhIQHp6Oj799FO88cYbeP/997Fx40b069fPUvMjrZjiErs+hmx0slTXK125u4bO0RS6cmwJIYSQlsaozV6JiYkYO3Ys1qxZg+HDh2PYsGF4++23ERUVZe75EaKX2A1MN3KK0GHMe2obrda98hhe23TQ6POLuXTfnJuxxH4BIIQQQuydScs0jz32GKZNm4Z9+/ahU6dOeO2111BZSeV9SPMSs9HJ19MFb209onGj1eQ3vtR52V8fMZfuaTMWIYQQYn4GBbL//e9/8cILL6Bbt27w8vLC4MGDcezYMUyfPh2bNm3CqVOn0KlTJ5w6dcpS8yVEo+efitNarksAIDDQudHKGDMmxOvM3W2MNmMRQggh5mfQX82VK1eirKwMiYmJOHz4MEpLS3H69Gl88MEH+Oc//4mff/4Z06dPx9SpUy00XVVr1qwBwzB49dVXm+V8xPakpGWgw5j3sGLrEY33hwZ4YtGLA1BcVm32c+/8/g+DqgzQZixCCCHEvAzKkRVTR/aFF17AokWLjJ6QWCdPnsT//d//oVu3bhY/F7EubRu0FHVZta2qLnpxAOZP6YcvfrxkkXkVl1fj8Znb1Zou6EKbsQghhBDzMXtnr4CAAPz888/mPqyKyspKTJ48GVu3bsVbb71l0XMRwwk8Bz4jHULpHTDegZDExoORsEYdS1snLMUGLV2pAR98/jt4XsCmXb8adW6xcu81NBC7qsqyEvTpHqEMZo+fzzY4mLVU9QVCCCHEnpg9kGUYBgMGDDD3YVW8/PLLGDFiBIYMGaI3kK2trUVtba3y5/JyeUDE87zOlp+2RjFfW59zw+/foe7TpRBKbitvY3yC4Ji4FA4PPm7QsfYfuYJ/vPkVBACs5H5m6Z3CCiQu+gpocntTZZU1WJN0TO84c3l90w8Y0ae9zoCS43i889kv+HDPSZSU1yhvDw3wwOoZj8LX2wW3C+8iyM8NCd1Ug1PF+78/LQPz3/8RufkVKo9fO/MxPDmgo2WeHLEZ9vK7gFgOfQYI0PI/B2Kfl9kDWUvbvXs3zpw5g5MnT4oav3r1aixbtkzt9ps3b8LDw8Pc07MYQRBQUlIChmHAMJYPyozBZ15A/Y+fAd5R8n9KDLD/U0irBUiiuoo7Fi/gy+9/xYCuPpaZrIV8/MXPGN4nRuN95/68hZ3fX0BVdR26t3EB4KJy///2HFb52d3VEQ90DkX39kFoFyYDwwB/XMnC/77NQPtAB7QPVH1tPv78Z6C+DD1igs36nIhtsYffBcSy6DNAgJb/OaioqNA/CHYWyN68eROvvPIKfvzxRzg7O4t6zIIFCzB79mzlz+Xl5QgPD0d4eDg8Pc3TlrQ58DwPQRAQHh4OicT2LiELPIfqdWNVVmJVMWC+zITLphOi0gyOnc1CyvE8806yGaRdSEdwSJjayuj+I1fwj2U/GVwl4dv0OwDurdi+PAT7jmTiyIUScLz6kRgA1++k48IXMwAAJ/7I1rq6y3G8zvuJ7bL13wXE8ugzQICW/zlQXEHXx64C2dOnTyM/Px89e/ZU3sZxHI4ePYrNmzejtrYWLKsaJDk5OcHJyUntWBKJxO7eeMWcbXHeXMZvYIpytdZJBQAU5gBXT0LSOUHv8W4XVWkM1izNz9sVhaVVyp99PV0gMDCo6sG8jQfh5eGM/OIqBPu546EuYZi78SAaTHg+N2+XI3HJPgzs6gOOF7S+Nn/fLsfbnx3H/74+q5ZXrNiQpi3vWOyGNWJ9tvy7gDQP+gwQoGV/DsQ+J7sKZAcPHowLFy6o3Pbcc8+hY8eOmD9/vloQS5qPUHrHrOPM0eHKGGMe6YTocBn8vF2VLWff/PAQNu4Uv2EsJ78cj/9nu/LnpsGxMQwJgTWVIsu7tyHt1ckPY+OOX9WOl2fghjVCCCHEFthVIOvh4YEuXbqo3Obm5gZfX1+120nzYrwDzTquT/cIhPp7ILdAXI6MuXy0V97MQ7FCyXE83tv9m0nHNDWINQcB8tSD93b9prUxBANg3oYfMLJfB0ozIIQQYhforxUxC0lsPBhZMNT7VikwYHxDIImNF3U8lpXg+VE99Q+0kNz8ckxcsAcjXtkO3gopDpYgADrTNQTIV5OPn89utjkR3QSeA3fpBBqO7wN36QQEnrP2lAghxKbY1YqsJmlpadaeAgHASFg4Tl2B2vUv4n5jWOW9AADHKcsNqifbLszXrHM0xi/nWl9Qd6uw0tpTIAAa0lNRl7wIQvEt5W2MLBgOU5YDQXQFihBCAFqRbTWaY2XHIX4EnGZvBSMLUrmd8Q2G0+ytcIgfIeo4HMfj6JksXMkqMPsc/zGiO0L97afsmjVYKz+Z3NeQnora9S+qBLEAIBTfRu2mf4PPvKDlkYQQ0rrY/Yos0U/byo7j1BWig0uxHOJHgO09zOjOXpp21JvT4AfbYcuCkTh+Phv7Dmfgv1+Kq0fcEjAAJBJGa3oBAyg3uBHrEXgOdcmLoHmLn/y2hl/3Q+g3DGiBO5UJIcQQ9FuwhdO5srP+RTSkp5r9nIyEBds5AQ59RoPtnCAqiOU4Hqv+dwQTF+yxWBALyFcbWVaC/j0jMXqQ/ezO93RzNOnxiszlmRMfAgP1TGbFz2/PGkobvayMz0hX++9VlQChshT8ld+bbU6EEGKr6C9WCyZmZadu22KrbyBJSctAzOhNGstGmQsDIKzJamOf7hEIDbDtphgebk5444X+uPndXIQGGJ8SERrgiZ2rx2HVjEexc/U4hDR53or7qfSW9YkvZZdv4ZkQQojto9SCFkzUyk5RHviMdLAimhRYQkpaBiYt2GNwxytD6FptnDqyB1Z+ctSCZ1cnkTCiKyFU3K1F8jfn0CU6EGtnPoaPP/9Zd9MJDda98hheGveg8rmPGhiLkf064Pj5bNwqrESwnzv6dKfOXrZCfCm7AAvPhBBCbB/95WrBzN2kwNw4jsfcDT9YNIgFNK82pqRloMOY95o9iAUAnhcwcXhX0eMVzQoAYNqYXggxcGU2QOauFqQq0ismPNYF/XtGUhBrQ0SVsnP3hqTjg805LUIIsUn016sFM3eTgqYU1QU+P3gRR89kgeN4gx5//Hy2RfNhAflq5JW9M9WC2EkWzsXVRzCgNq1w79/r7x1Et+ggXPxiBn74IBHzp/YT9XiqQmBfFKXs7v3U9F4AgMPDTxpUyo4QQloqSi1owRQrO0LxbWjOk2XA+AaLblLQmKbqAopuWGLzLC1Zr1SxA7/xJXUAqKtrwH/Wplp8FViflMMZBj8mJ78CN3KKERnZBv17RqJP9whs/+488vLLtby7VIXAXjnEjwBmb1WvNuIbDKfEZZCIqCPLczxyzt/C3cIquPm5Iqx7MCS08k4IaWEokG3BLNGkANCe16q4BC5205ClVgq15cTu/fkyXlr9Dcoqay1yXkPU1Bm3we78tdsY3Ff+/1lWgndmDcWkBXu0vLviqxBwHE85szZGWyk7AQyQrbtRx59pf+HnDb+gMv+u8jb3ADc8MqsvYga2tfTUCSGk2dBfqhbOXE0KFHTltSpum7fhB1FpBoqqAYZuXmqKlageITTAE2++OAC1dZwy5WHh5h8x+Y0vbSKINcVvf9xUeW1HDYw1uQpBSloGOozehKEvf4qpS/Zi6MufosPoTUhJM3zVmJiXMaXs/kz7C/sX/KASxAJAZf5d7F/wA/5M+0vldp7jkX0mFxkHryH7TC54A1OECCHEmmhFthUwtUlBY/ryWgUAOfnlOH4+G/17Ruo8VuMVRVN8uuJp+Hm74lZhJW7kFOGTlDMqpbxkni4oLq826Ry2oqa2AcfOZuGRB6OVt5lShSAlLQMTNbz+uQUVmLhgD3ZRSS67wnM8ft7wi84xhzccR3S/SEhYCa3cEkLsHq3IthLGrOxoIjavVey4UQNjsX3lWEgkxq3Lyrxc8NSAjujfMxJOjize2noEeQUVKmMsEcS6uUgxdkgnuDg1/4abX86pX1Y2pgoBx/F4ec23OsfMWPOtwZv4iPXknL+lthLbVEV+JXLO3zJ45ZYQQmwRBbLEIGLzWg3Jf/XzdhVdV7Wp4rJqfLjnd+w68IdFNnFpC6/vVtfjy58uY/azfcx8Rv2EJk/S2OoRR89kobhMd5BfVFaNo2eyjJwpaW53C6tEjavMvytq5ZbSDAghto5SC4hBFHmt5twpb2r1gtc2HTTp8broC4y3fPE7Qv09kFdQ0WyVEPr1vP/amlI94ujZv0Wd7+jZvzGoN11mtgdufq6ixlWVVoteuY3oGWqOqRFCiEXQiiwxiCKvFdBW4VL8TnkFe65zWlxRAz9v12Yt51VaLt+wpq0erqJ6xN6fL+tcqW26squN2HHE+sK6B8M9wE3nGI8Ad7h6u4g6ntgVXkIIsRYKZInBzLFTvjFzVS+wlvPXmrcz2sLNP6KurkFn9QgBQOKir1QqEUQ+sR57f76sHDegVxtR5xM7jlifhJXgkVl9dY4ZNKuP3mBXQewKLyGEWAulFhCjmLJTvilzVS9oLXLyK/B/e0/p7UzGNck7LiytwuQ3vsTYIZ2QvHQM+sdF6q3o4Ovpgv5xkeaYNmkmMQPb4snVQ9WqEXgEuGPQrD6IGdgWPMfDPcBNZ3qBR4A7wroHN8eUCSHEaBTIEqMpdsprYmiBfUX1gn8s+srojV/aKJoFtKQyXH/llhj92C9/uoxD6X/hw4Uj8cGCJzSW31LYvOAJaoxgZ3iOh7OnE/r/+yFUlVbD1dsF7gFuKp29FCu3+xf8oPU4g2b1oU5ghBCbR4EsMTtjNyCZUr1Al9AAT7w9a6hyBXnLlyeNahFrS9qG+pj0+JKKGkxcsAc7Vo7FohcH4P3d6SitqFHeH+rvgXdmD6Mass3EXO1kddWFbXo8MSu3hBBi6yiQJWZlSvtaU6sXNCZhGHz05lMID/JUWQ3u3zMShaVVdh3Ihvq7419jHsCmXb9prR4hVuKir1RSEHw8XTBjwoOYP6UfrcQ2E3M1JVDUhW1KURf2ydVD1Y4XM7AtovtFmiWIJoQQa6DfVsRsTG1fa87qBbwgKIPY4+ezlTv36+oaLFquqzlMfTIOjo4OyuoRpmiaR1taXo23th7BN8eumnxsop+5mhKI7eilqS6shJUgomcoYh9rj4ieoRTEEkLsCq3IErMxtn2tIp82N78cft6uKCw1T8mfb45exfPLUlTmZM7jW0vbUF8A8rzimRMfwqZdv5nt2ALkOcXzNvyAkf060KqsBRnaTlYXQzp6UV1YQkhLQoEsMRtj2tdqyqc1l82fp6vdZu9BLAAE+clLJ3Ecj+3fnTf78bV94SDmZc7gU2y9V7HjzJWzSwghlkaBLDEbQ9vXasunJdp5e7ogoZu8s9fabcdQpKfFrCn0fTEReA58RjqE0jtgvAMhiY0HI2EtNp+WxpzBp9h6r2LGmStnlxBCmgMFssRsDGlfqyuflmjXKzYELCtBSloGVmw9YtFz6fpi0pCeirrkRRCKbylvY2TBcJy6Ag7xIyw6r5bCnMGnoqOXqXVhjdkwRggh1kTXiojZGNK+Vl8+LdHsdEaesquXKViJ9j5qDICwe184NGlIT0Xt+hdVglgAEIpvo3b9i2hITzVpbq2F2HayYpsSdHuqk8779dWFbahrwI9rdX850rZhjBBCrIUCWWJW+trXjuzXAUfPZGGfHZe/sqbS8mpsTTlt8JcARdg6Y0I8fvggEZ+ueBoM9H/haErgOdQlLwJ01Kao27YYAs8ZNL/WSGw7WX25qX+m/YWPxmzHia0nNd7vEeCudyX1z7S/8N+nPkN1aY3WMcD9nF1deI5H9plcZBy8huwzuRT4EkIsilILiEHE5EVqa1/7zbGr6DDmPZNXYscO6YRfz99EbkGFScexV5lGdPVSVCNIOZyBNf95FCwrwc7V4zQ2rnhbR+MKPiNdbSW26ZmEojzwGelgOycYPM/WxtSmBNpSARQSXnwAD03ppTMY1neMpnTl7OrKr6V6tYQQS6BAlohmSF5k0/a15tzY9US/jkheOgbHz2fj8KlMrEk6Zoaj2o+7NfVGPa5pNQLFF46jZ7Jw9OzfEARgQK826B8Xqf0YpXfEnUvkOGJ8UwIx5bsufH0FD03ppbUKgZhjNKUtZ1dffq2zpxNqymuVt9MGMkKIOVAgS0RR5EU2vaSsyIvE7K1aN/mYe2NXsJ+7MlDu0z0Cn6WeN6jDlVQqQX29/V7u/OHENQT7ueN2YaVRr2njagTfHLuqsiq7NvmYznbCjHegqHOIHUfkFE0JDJHzh7jyXb9tO40/vs7QuErq7Omk9xiNacvZFRMQNw5iAdpARggxD7quQ/QyNS/SXBu7NG1CarzBTCx7DmIBoLC0GmWVtUZ/MWha/qzpe6NoJ5ySpp7HLImNByMLhnp2rQIDxjcEkth4I2dHxKoqFFd67cTWU1o7h10/mmXQObXl7IqpiasNbSAjhJiCAlmilyF5kZqIbZTg5iLVel/TTUgcx+PomSx8fvAiZJ4u+HTFGDDaN+K3OFVa0gv0vQQero54qEuY0e2EGQkLx6krtJxN/rPjlOVUT9ZMdG2ccvVzMfn4GT/8KWqcs7eTzpVTsTVxNRGzgYwQQrSh1AKil6l5kWIbJbw66WH07xmJb45exe4fLqh04Wq8CUlTNzBnRwcIVJRW7yptRVUdYse+jxdG9TSqnTAAeQrJ7K3q+dK+wXCcspzqyJqJto1Tg15NgFOUA2pKTW+Goa9KgcKIpUMQFa+5HBsgviauNqYEwoSQ1o0CWaKXqXmRfbpHINTfQ2+VgeT9Z7Hguf7o3zMSa/7zqFrVA0UjAE2bxmrqGkTNkQB5BRWimyloW013iB8Btvcw6uxlIbo2Tn3z5o/ot+gBnP2/5ithV1NWq/N+MQ0ZdDE1ECaEtF6UWkD0MjUvkmUleH5UT73nyS2owPHz2crH9O8ZiQmPdUH/npHKdII56w9QN7BmpGs1nZGwYDsnwKHPaLCdEyiINRMxG6cyDl4zOmg0hr5AU0xNXG0MafrQGNWrJYQAtCLboomp+SqGIi9SXrWAgeoFbHF5ke3CfEWdS1c+7dptx5DXSmvHNrfG7YRJ8xKzcaquSlwJNidPJ9SWa19N9QhwAy8IuFug/dK+2EBTW01cZy9n1JRpT2EQ0/Shafmw6tIaHN50XGMlBqqAQEjrQoFsC2VIzVcxTM2LFJsnq21cSlqG6MvhLYmrs1Trxi5z0fzVRHt3L2JZ5swX7TWhm9aOXwAwaFZf5F28g1M7zukYoz/QVNBWE/f6sSyTmj40fawmVM6LkNaJAtkWyJSar7qYkhfZp3sEQgM8tdZ71bUCqNhh3xpZOohd9OIA/O/rswZ19yKWJTZf1MXbGdXF2lc6PQLc8dCUnvBrK8PP639BZUHjININg+6lAugKYh+Y3MPgoFBTTVwxTR80NW24fizLoK5jgLycV3S/SOoaRkgrQYFsC6O/5iuDum2LwfYeZnSagTGtRxX1Xict2GPwCqC56tDaIwaAm6sjKqvqzH7c0ABPzJ/SD/On9NO4sc4SOI5vtnPZKzEbp5w9ndB/dk98++YhrWMar6QKTX4f8IIAgedxeNMJnXO5+uN19P93vFmCQl1NHzSturr5u4Kr01ybWhdFOS9DG0wQQuwTBbItjCE1X40JSE0xamAsdq4ep1Y6S98KoNg6tC2RAKCyqg5vvNAfW/dqv0RsCE1fHJqW2LIETWXTdHURM5S5csKtTbFxStdKZIch0YgZFI2RKyX46e1jKmW0Gl+y11b94G5BFb5540e9c2mOoFDXHI1F5bwIaT0okG1hTK35ammjBsZiZL8OBq3Kic2vFavpirA94HgBzz0Zh1Pnrhj0OHcXR3i5O6mUPrNG6oC2smmKLmI7V48zaT7mzgm3Nm0bpzwC3DHw1YfhFOWAa0f+wuFNJ1SCWGdvZwx8JQExA9uKqn4ghiWrI5hrjk1ROS9CWg8KZFsYU2u+NgdFaS2x9OXX6jJ2SCf8+keOyiqgzMsFRWWmF5NvTmuSjoGVMBjY1cegx8169uFmTR3QRF8XMQbyLmIj+3Uwal6Wygm3BE15oNou22vLKwUDnPzpDI4tOwk0qThVU1qDb944CGb1UDh7OpklCD286TgcnB0ssoHKlNa22hhbzosQYp8okG1hFDVfheLb0LzuyIDxDdZa87U5GJonqSu/VheZpwuef6onPlk0Cr9dzMGtwkoE+Lhi2oqvzfI8bJ2vpwvmT+ln8BcHc9OX46yri5g+ls4JNydtnbp0lYzSlFfaUN+AKz9d03muwxuOo9+/zfPfeHVpjcWqAVgiBcCQKguEEPtH/7W3MIqar/d+anovAP01Xy0pJS0DHca8h6Evf4qpS/Zi6MufosOY95CSprtLkSK/NiTAU/S5isur8fh/tqPTuM0oLq/GhMe6gGUlraYW7eYFTzT7RiqO43H0TBY+P3gRR89kgeN40TnOxuRCG5ITbk2KPNCmq4+KklE/bzwuuqh/zh+3UFuue/NfRX4lqszQwraxwxuOm73pgDlTADwC3Kn0FiGtEK3ItkCm1ny1FFPzJEcNjAXPC5j8xpcGnbfx8WuN2AXdXBgGEMyQvBtmpfJZ2jZzPf9UnKjHG5MLbes54YC4PNAzn/+BM5//AWdvZ3QaGoPo/pFa0w6qCsUFqK7eLnD2dEKNjoYIClJXKer1NFkwdeOXprQKcRUanOHgxDYpH+aOga88DBdvF1Tm30VVabXy+fIcTyuyhLQiFMi2UKbUfDWE2DQBc+RJchyP1zYdNHiOjY//34UjDX58cxEEwMPVERVGlNrydHPEs4/3wFMDO1qlpJWuLykrth6BzMsFJWXVBtcQ1scecsINyQOtKa1RBrXa0g5c/VxEHcs9wA09J3TFia2n9I7t8XQXnPzsrN5xxqYC6Eqr6Phoe521bB9bMEBrDdo/0/7C0S2/UYcvQloxCmRbMGNrvoplSDklQ/Ik+3SP0Bgcm1JPVnH8izfyjXp8czEmiAUAD1cn9OvZxiq5sGK+pDD37jR3FzF7yAk3NvjT1qkqrFswnDwdAehvKxvWPRjp286Aq9OeEuDs6YyL3+hO7VEwJhVAW3ktxfPTpXFDhqYrwfqOS2kGhLQOdP2FGEWxAtc0sFRcxm+a8yo2//Gbo1e15tCao55s1q1Sk49hi/IKKjS+7s1BzJeUovJqvPniALUc59AAT5NKb9l6Tjhgeh5o09xUCStBxyHtdT5m4CsJkLASHN2SrjOIBYCa8hqVEl7aGFMNwNTyWld/vK4xL1fMcS2R00sIsT20IksMZkyagNj8x82fq2/KUQTHb744wPhJ39M21LDyVZoYe/nfksxRxsrYrltiv2C0C/PF1b0zzV4KzFZzwhXE5IHqoik3NTDGDyPfehSHN57QeNzDm45D4AWc3nXe6Hk3pa8agKYcWFPLa2nLyxVz3Ir8SmSfzYNEwogqd6ZPS2m4QUhLQ4EsMZgx5ZTE1IJlJQw4Xv1eRZD2v5QzCPX3QF5BhVENDcICPPGvMQ9g067fjKpJq2BrQayCKWWsTOm6JfZLSrCfu8VKgTVXTrgxxHTq0if7VI4yGAvpKs/3bT+gLSBAY4euyvy7+HaR/s5dYrh4O+PR+QN0XqbXlgMbM6idyedXpGY0DpSLMotFPfbbNw6qbHYzNn+2pTXcIKQlodQCYjBjyikpasEC2i4AQ2MQqyAAyC2owPOjehodgL49aygcHR3wzqyhdtfZyxDfHL1q0HhD00SaUnxJafq+KjCQf4kwZjOXIRQ54Q59RoPtnGATQayColOXe4CbUY//LekMUpf8hC9e3o+Px+/EnT8L0VDXgB/fPmbmmaob9EofvUGsttJiZz7/w+Tzu/m54s+0v/DRmO344uX9SF3yE35LPiPqsU0rNijyZ/9M+0v0+RUNN5qWeVM03GhITxV9LEKI+VEgSwwmdgUuv7gSXKMcNW21YEMDPDFjgrjNOFEhPpB5itu13diiFwdg1MBYcByPy3/lw81FavAx7MXmz9NF58rqSxMB5OkKHMdrrBELiPuSYuxmrpYkZmBb/HPvsxj/wZPoOaGb0cepzL+L83sv4b9PfooaEbmtptIVfIvJVWUk2r7i6OcR4K5syGDODmC68mcFngN36QQaju9Dw4VjehpuAHXbFkPgbbesHyEtHaUWEIOJbRn72qaD2LTrN5XL06MGxmJkvw5qeZLHz2drzI9tqrC0CsXlhhV69/Vywdxn+yAlLQMvr/kWxXbWntYYYnNlxaaJrN12DP/7+qzW1APFlxRN6QnWqGlrqxSduiJ6hiKsR7Da5XhD1N3VXffVHFy8nRHSRXvpMjG5qoKOKy36DHzlYRzedNzox2ujLfdWUwqBbvcbbliyQgwhRDu7CmRXr16NvXv34sqVK3BxcUFCQgLWrl2LDh06WHtqrYohLWM1NTvQlCepLzhW1Br18zb80mxRWTWiRm4wOAC2Z2JzZcWmiazYekTttqbvrbYvKa19JVabmIFtlfVRrx/NQsYPf4qqHtCcqktr8PG4nVrzSsWWFovq0wYF1wpVgl6PAHcMmtUHANQCesV9zp5OBgf6YptANJ27IoVAfBPs+6zZcIOQ1s6uAtkjR47g5ZdfRu/evdHQ0ICFCxfisccew+XLl+HmZlzuGTGOthW4psTuptcVHDe+PG1MWgGAVhXEKogJUo3ppqWg6b211GaulqrxCu3A/zyssplJbB6opemqyyq2tFjm8b/xxIohcJW5aqwgoK3hQcbBa6KO/9BzPeEbKYObnysETsCemd/ofUzjuQs8pyOFQD9rNtwgpLWzq0D2wIEDKj8nJycjICAAp0+fRv/+/TU+pra2FrW197+dl5fLgy6e58Hz9lNjUDFfW5rzk/07YESf9vjvVyex4P2fdI69VViBX85loV9cpM7j7Vw1FvPfO4jc/Arl7WEBHlgz8zE82b8DOI5HeKAH8gpMrylri7RVblDcJ2Hk/ytGkK8r6usbcOKPbNwuvIsgPzckdFNdIX24axgigjyRl29cJQhA3HtLRGCAsB7yOq3ZZ52BT7UEshL52Obe4XB40y9o2ydCpXxVSNdAuAeJKy2WuuwQRiwdjA5D7lcyUP4+a/TcG9/n4uss6nmG9QpBRJw8TYDneL1z8ghwR0jXQOX5ucu/gSvNBwzeIMiAkQUBHXo36+9mW/x7QJpfS/8ciH1edhXINlVWVgYAkMlkWsesXr0ay5YtU7v95s2b8PDwsNjczE0QBJSUlIBhGDCM8ZsnTMHzAm7kFKOsshZe7k6ICvFBZl4JqiuKMLCr/vqsBXduIztb91+lHlGu+P7dp1TO0y5MBomEQXZ2NgBg1rjOSD32p1mekz1hJAxiQl0hMIzevENvTxcUF9zBc28cRmmj1WhvTxeMHdIJPWLuBw1r/x2Pj/eeNmluYt5bIp7gI8C/jzdqyzWUepMALqGO8mC2Wf9+CTjyxXG069NG5da4f3fC+b2XRB3h6J4TKOPKEBjjJ+6Mul6He5w9nSD4NCh/P4iZU/cxscjJzVH+zN/JR33Uw6LmdJ/897D00X9AkpNr4GNNYwt/D4j1tfTPQUVFhf5BsONAlud5vPrqq+jTpw+6dOmiddyCBQswe/Zs5c/l5eUIDw9HeHg4PD09tT7O1vA8D0EQEB4eDomk+QOG/UeuqK2USiQMeAM2csx7MQgREeJKMEVGttF6n+xqBdIu6N8Y1lwmDe+Kr9Ou4G61ZTffsBIGjCDg6MVSrau2il9lMyfGIHHFT2qrrAxK8PXxPHz21tN4ckBHAJC/J1IvjSvhU0b2wMpP9Jd4MuS9JeI8NJzDN29qqAUrASAA5RermjmQBc5euIywkFB5Ddt7wkLDcPtIIa6lZYo6xrk7GXjhi4miGxNofR3uGfBWH7Rp8vsiIiIC3lJvHH7vhFru7cCZD6vMHwC4ylzUZP4qaj4KjCwYjv9YAocHHzfoceZg7b8HxDa09M+B4gq6PnYbyL788su4ePEifvlFd+kXJycnODk5qd0ukUjs7o1XzLm5552SloFJC79UC4p01X1tTLFRq2+PSLPMPdjfQ/S5m8NnqabXyhSLF+Svu7bnHxbgibWvPIbXNh1Eg45gd97GgxjZv6MyzWDUoE4Y2b/jvSoGFSgsvQs/b1cE+bkjyNddaxMKc7+35L4Og6LBrJRormwgQB7EWuGKYtrGX9G+f1tIWInGRgj6VNyuxLmvLqHnuK6iglltr4NiQ5i2GrcdBkWjff+2GnNvm2I6PQTWOwBC8W1ozpNlAFkQnF7eBJQV2kTDDWv9PSC2pSV/DsQ+J7sMZGfMmIFvv/0WR48eRVhYmLWn06LpqjMqhiXqiIot/9Xa+Hm74tKeGfjtYo7BndcA+Ya74vJqLNpySOXxMk8X5cYubZvwqDKBZSgqG9w8k4f9bx5ErYjd+JamKF1VU15rdLeytE0ncGrXedFdthpXeDCk3axiM50+jISF49QV96oWaP6kO01dAYcu/fQeixDSvOzqr48gCJgxYwb27duHn3/+GVFRUdaeUounr86oPqEBniqlt8yhcQF+U/l4OOMfI7rj5fEPamySYGzakburo4kzM1xhaRV+u5hjVOc1QHuHr5J7ObY+XqoVIyzx3hJ1ElaCNr3DMHTBQGtPRen6kUy9jRD00dZli+d4ZJ/JRcbBa8g+k6tsXKAISmMfa4+InqGiUxPEcogfAafZW+WbtxphfIPhNHsrtaIlxEbZ1Yrsyy+/jJ07d+Lrr7+Gh4cHbt++DQDw8vKCi4txZZmIbmKDoqamj+2N0YNiLVZHVGz5L108XB3h4izFZ6nnlbf5ebuib1wbdIz0Q/+4Nvjh1+vYtOs3g49dWVUHP29XFJaKq7NpLor6rWI0HqevwxcDwMXRAd+99yzyS6qoRqwVRPeLROcRHXDpe8NaEFvCmS8umO1YhzccR3S/SK2pCu4BbnpXbnmON3i1VhOH+BFgew8Dn5EOofSOTaQQEEJ0s6tAdsuWLQCAgQMHqtyelJSEqVOnNv+EWhiO49WK2RtbZ3T0oFiz1hPVNDdFAf6jZ7Iw+Y0vUVJhWDH5iqo6VFSp7oYuKq3C14czMGH1OPTvGYkX39pv9JyfGdpVVLcyc1K8NmKaS/Tpfn9zlpgOX7kFFWBZCSY8pn1zJbEMlQCvhX130JeqoKuOLQCjg19tGAlLXboIsSN2FcgKAmVEWkpKWobG9qLrXnnMoHxUTUGSpeamaI86qHdbfLhwJCYt2APA2JLmUD5WUeTfy93JpLQKH09nyLxcmqUlLgMgxN8DHMfjy0OX8fxTcXhr6xHRea3GpiMQy/sz7S+jc1HtRWX+XRzdovvKR+OVWwVtr42+4JcQ0nK0sO/2xBjaciPz8svx7BtfYvyjnQHcD4K0MWTzD8fxOHomC58fvIijZ7LAcZq3X+/9+TImapnbpAV7kJKWAeB+qkFIgOkl1RSboY6c/tvoYzAA3tp6pFmCWEA+5+q6Bjw+czumLtmLFVuPwMfTRXReqzHpCDrnw3PgLp1Aw/F94C6dgMBzoh7XWmnLC+U53uRcVKthgA6PtdM/DsDd4iq9lQ8UK7cKYl6bwxuOK19LQkjLZFcrssT8OI7Hy2u+1ZkbuefHS9i+cixe23RQJaBs2oUqNMATb99bJdVF3wqrwt6fL+Mfi77SeAxN7VEVqQaKy+SvbTpoUo6qKfWlrXHtoGnQXFJeDQHAohcHoF2Yr868VmPSEbRpSE9FXfIiCMX3gw5GFgzHqStowwzU8zmrS2tweNNxjZfGnT2dDCptZVME4OrBG3qHOXs6I32buHa8dwurlK9f9qkc0cGvmMoFhBD7RIFsCyPwnEEbFdZuO6Zz1VCxOunn7Yqre2eq5Kk+1CVMuUu+aZCkKaeVZSXK1d+mwZJihVWxWpiSloHJb3yp+7lCvYwUy0qU/9/FWWpSukH/uDb4LPW8SekF1qQI9pO+Posre2fqXCVXVIKYtGCPWjqC4lhiVtob0lPvlTBSPYJQfFt+eyvf/S227qri0njPCd2aaWbWU1MuPre9JKcUH43ZblBwf7eweTdcEkKaFwWyLYihK2Ecx2Pz57+LOvatwkqVIFFB04YuXfm2r206qHP1d96GH/B4QnvM3SA+J1Bb3qa2ygah/h6ormtASVm11gDX38cVfbpHYPyjnbFhh2Edf2yJtpqxmiher5dXf4victUvNzIv/VVBBJ5DXfIiaP7aIH+H67YtBtt7WKvcBW5MrmvGDy23FbO7vxsaahtQI7I2rrOnM05sPWXwedz8XA1+DCHEflAg20IYsxJ2/Hy2skaoPmJzI3WtuIpdYf2/vacMWgXVNbfG6QaNV4e/OXZV6+ojABSUVKHT2PdR1Ew5rgDg5uKI8EBPXMkqNPuxDdmkpekzUVJWrbJirgmfka7yJUqdAKEoD3xGeqvbFW5srmt1aQ1cvJ1RXWpYRQ5bl/DiA/j/9u49PIry7B/4d3dDTiSbcyAkhHASAggYTgICATkpRSlKeRUtWEv7tlAUPFS0yIsUUVFBwFZLq+ir9YcoxaK+AnIQBMRKBAUDCjQNhIQQICSBHMju/P6IE/YwO/PM7G42u/l+rsvrMruzM88mQ3Lv89zPfUt2YN/fdASmJv3rKrGpMcjok6b7dUQUPLjZKwRoz4QBdW886bbhRjS4SbRGCeVGatUiFXWy6KLwsRkCeZvyTPLUsb0wPCerMZ9Wa3NY0blK1NTVC4/FW1eq6/wSxAJiH0REfn6PLN/scWOeVH5WaCyixzU3njZkiTh9qNhwrmvbnm0Mva45y3v3sHAQG2mNwJCZA1BzSX9Xs5Fzh/q8cQIRNS+ckQ0BRmfCRGdZZ00dKFT43tsuYLJO6QnCx3rTHnVSbjZuHdIVnW9f0eSNC5T4Y4OYnk1aIrVk1dIUTPFiAZfocc2Jt7VKvcnT/Pee/6DzsCyc2F1g+BzNTc0l8RnmiX8ciysX9a2MxKbGYOTcoSy9RdQCMJANAUZnwrR2qgNAkjUKv59+rb+40iYuoCEI+seOfEPjl8lB168n98dL73yhOi6L2YQ3F9+huMztaaOZ0vMlZRXNIog1ymI2YfbUQVj5Y/cxtZqxWt8Xb2vJmrMHwZSYBulCCZTDchNMSWkwZw8Suk5zobdWqVKXKW/zNE98XuDV6/0lMi4SPW/thiMfH9MVnIqKSWmN9jntnMpuqblxRg4yB2QY7uxFRMGHgWwIMDoTprVTHQBWz/9JY7CjtIkrMS4KkOC2OUhzLPAcdIWHh2mO683Fd2DyqB5uj2uV9lJ6PpjZ7BJuvek63Ni7veL7Xqbyvl1LnnlbS9ZktiB8xuIfc7WVf8Lh058Kqo1eorVKtVqsjnxgKGJSWxsvpRXgXjCR8ZGoccjTjbRGIGdqbyRlxWPTE1v9dt3ek3rAbDEjo0+a5vcvNjUGQ345gAEsUQvDQDYEeDMT5mlnf4ZLTVhPm7iMFvxPsEY5Bb+uNWhFx+VIq7TXg9MGY8Xb+wIdE/hccVkVpo7tpbipTU/JM1/Ukg0bNAGYt8a9ekZSGsKnPxV0pbdEcltFWqxuemIL+k/ri6/ePuinkfpPbGoM7l9/F84cPus0ywwAf5qw1q/XTsiIAwCYLWaMmnuTatUH5sMStUwMZEOA0Zkweam5ts6GNQtuAySg9OIVxZqwnjYBGRovgOjIVnhryR0oveB+PZmnigNKObFaG5VMAFa+80XIBbHAtRlSpfJoIt8Xx6YSnmbC9XRtCxs0AZYB43XVM26uRHNbRVqsHtt6HBOXjMGOl/YGVZODkXOHIiw8zK2pwH/+ddrQBiwAMIeZYa/X3iznmJJxXW4n3LZ0nNuMN/NhiVo2BrIhQu9MmNpSs2sw5KtNXDJ50xDQEIQVl1Vhz6FCxSBVKThTIrJRybELWSgQmSEV3cD1p/VfIjUxBmnJMYpd3ES7tjWOzWwJiRJbormtV8qrhWZuo+Kj8KsN9+BU3hn88w9bUCtYQ1VLTEpr9JrYHV+8dsDwOSLjIp3yXLUCxFNfFxm+lkgQq1Q667rcTugyLMstB5kzsUQtFwPZECI6Eya61CzTU4NUj2lPvO9Us1SpTa0of42xuXKcIQWAXXkFirPWot+XR1/a0vj/cvOK5PhozZnwUCeamxkdr90wAmiY4TVbzOgwIAPj5ufqbpDgyah5DdUTTCaTvtqsP4pKiETu7CGorqhBdHwUYlJbaweIfv5ceP3t3XFs2wm3YNVsMbPlLBE1YiAbYrRmwvQuNQPim4D0ci287ymQFuGvMYoymQCpCSd85RlSAOg2eaXHTVxGvi9nSitwzxPv4e9Lp2Dq2F4+G3MwEs3NjLRGCJ1PZKlcj/CYVhj/xKjGWdPB9/XD1+u/Fe6WJau+WIP/W7wdwLWyYlqznBl92wHIMzRuNZFxkYAkOXXx0lPqjIhalpY3xdLCiS417/q6ALvyCrBuy2HYbHa0S4ltnAX0F5Gi+57IG5X8PUYl0ZFhsLYOb7LrpSRE4cj62QCAu+evd/t5yh8INu7MN/R98ebnEIrkgDMmtbXT47GpMY2lt+SZWzWelsp/teEe/Ozl2zBh0WjcuWoipqyciD539BQa281zhzkFd2aLGWPn54q9MQ/ksmLHtp/w2ADi+50n8X9Ltnt1HSU9J3RDzaUat0BcHtP3O0/6/JpEFNw4I9vCiC41uy77J8ZFNc7Y+nPiUavovicWixk/G9MTy9/e57exeXKlpum6fwHAuYvV2HOoUHhmXauUmRKjP4dQpZWb6c2ueqWlcpPFhEPvH9EcV2xb9xl3X8z0AsCmP2xxumFap0Sjz6SeqKmoRd66bwyfV82x7SdUn3csdUZEBHBGtsURXWp2Xfa/+GOZrQSrWC6gJ4mCr9eb87pxZz5WBCCIDZRdX/9HuAuXSDteT1pa7rEaOeDMHtsVmTnpbsGU2sztxCVjEWmNEG5va3SG13Es8kxv1uD2Au9OgcunnsvnrmDvmn/5LYgFgPpq9Q+FcqkzIiIZZ2RbGJFuXkrkWb7oyFa4Y3QPrNlgbHf0r+/oj6Wv79Y8Tk9up6/LgwUD0XxcORB1LWVWeqHKaYOXJ4HOPQ42rjO3UYlRKDp4Blue/cypQoFWzqfIDG/unMGqM5Nmixk1FbUo2HfK+Btqhrxp90tEoYczsi2MXCsUgO58UnmWr8SLWbo1G75Cukq+rQkNTQ/USkq58nV5sOYuIzUWI/p1EDrWMRCVS5lNHdsLv50yUDV31sjPgRrIM7eWcAs+/MMW7PvbAbcyWyI5n2ozvH0m90TXEcpBsN1mR2FeEb775HtsefYz799QM+Ntu18iCi2ckQ1hdXX1eHXDVzhZdBGd0hPw68n9ER4e1rjU/NCLn+DMucrG4xNiI3GxUrtfeuuoVobHVHapGvfc2htvffyNV0X3HXm7/B0bHY7KK3VenaMpPT17DIbfkIX0VKtqAK8WiGq1J5YATBqZ7bG+L6n7fudJodJa21/8HBGtw3HlYrViTVSl3Nx217fB6aLTHq/rbW6sP0RaIzBwRg52rfQu/ScmpbXHdAoiapkYyIaox1dvxUvvfAG7QxOAx1ZtxQN33YinZ48B4D4jazGLzdH27tIW/2/zYcNje+vjbxATHY7wMItqm1pR3i5/v/LEbXj0pS260y0CJSk+Smhz25QxPVUDUE9tgC1mE2x2CavX7cfqdfu9qu/bEtltdmxf/rnQsVXnLmP9nE2NXyulHLhuBrPblfNrj20/jk1PbDU4av+qqahFatdkzZq8WnpP6sGNXkTkhIFsCHp89VbFAMdul7D87X34ofA8Ptr9vVvQVnap2u01rpLjo1FQXO71GKt+nAFdMHMEOmck6Sq6L7fWlYv139grw1Deb7vkGLzw0C2YlJsNs9mke2e/t0wAkuKihL7vjj47UIDB12fi3a3qu9rXbz2Cxb+5WTOYlXNnN+06htXr9rt1QPOmvm9LdPpQseFgTU45kMt6iTq2/QQ+XPCpoWs2lZOf/0cz71dLQkacD0dERKGAH21DTF1dPV56R73n+4cKQawrT3OzZeVX8JcN+jsHefL6B1/jp7ndAQDvbfsOu/IKVGuXbtyZj26TV2LcrDcxY+EGjJv1JnpMWY2fjempOm5X1tbhyH9/TmNg5mlnf0qCf/Lx5IDZZqCLwnNv7EHn21do5gXLVQu0WCxmDO2TiX/syFd8nnVl9fHFZqQdy/doVjaQNczEboHUzFswy9UOhswcYPgccn6snAcsWgWCiEIXZ2RDzKsbvnJKJzAqKT4aZeX+3x18urQCnW9f4XQtpaVsm82OZ9/YjcVr3DevFJVWYMXb+/DgtMF4d+sRoY1fFZfr8OqGr5CaGNM4G+y6s1+e7e0xZbXP0w4SrVE4X1GNixXaOclKRH82ovnDoo0yvK0rK9ltmi2Ug50vNiPJZaa0WrEGw0ysox3L9+D+9Xfhm43foeqcvllrudyYUh4wO38RtVwMZEPMyaKLPjnPcw+MQ3pqLIpKK/HoS5v9GtS6ntt1KXvjzny3jWmuJABvfngQJ/85F0vX7sYzAiW+HMtPOQbPcqAmpzD8dGQ2Vq/b77O0gyfuH461//zaB2fSJpo/LBrwerOxrn7/R6hbuwDShWt1QE2JaQifsRhhgyYYPm9zI9eA9XbDldLMrt1mR+HBIhSfLUXR9nPYt8Z3qyNNobK0CmcOn8WoefpTDHIfGIIv3sjD3jX/cnvOaEqGHi3hQxhRMGIgG2I6pSf45DzpqbEYnpOFp1/7rElmZh05LmXb7RLueeI9oQDy/KVqPP/WHozs31EokHWkFDy7boLy1Yzsq+9/5ffvqQkNwblo+SzRgNfoxrr6/R+h9sWZcP0uShdKGh6ftyZkglmRGrAiXGd2j20/ga3LdqGmogbW66NR8W1g6ql2HdkRp74uRk25sdWEy2VXkD22q2L3sci4SECSnFrUxqbGoNuYLtixYo/mLK6/On+1lA9hRMGIgWyI+fXk/nhs1VbD6QWOAdDGnfmKS/lKfjW5PzZs/86nAdrp0go8sOxjXQHky+u+xMP3DNW9+cuxraue4NmIpvhgIEG7aoHMZrPDZrMjwRrl1tFNpjcwdhqL3Ya6tQug/FGg4Ttf98aTsAwYHzIzXJ7axEZaI3DDlOvx7T/zVYMy165dO1fvw1dvH2z4wk87G1pFt8LVK1c1j7sutzO6j+6CT5ftRrVjMCu4ZCEH6J7a/gLXNsxdKa/GpaKKa+9dg2hKhh4t6UMYUTBiIBtiwsPD8MBdN6qWZfrJsOvw0e7vAXiu4woADy8Xn1G64+YeGDmgI+6ev97tvN7QG/RdqKjGF4dPq9ZI9UTOA9UbPDdXK97eh4G9MjxWGpDzjlev+9JjAAsYr+8rs+fvd5rJcidBOn8G9vz9sPQcovv8zZWnQM1sMSOlS5LqjO3IuUMbZxWPbT8uHMh5QySIBYCLp8uxVymlQeAfjWuA7lpaTFZTUYtdf/7CUHqGLzt/tcQPYUTBhlULQtDTs8dg7rTBMLvUhbWYTZg7bTDWP/dfijv001Ot+PvSKZg4rBv+tP5L4W5ZcuF9Tzv/9XYQ81ZxWZXHsYhoihnT5Phor78vJmjX/n34xU+w418nsW7LYaeKEBt35iPz1heweM1nqkEscO2+MFp6Syo/69PjgokcqGWP7YrMnPTG4FSta5djnqfdZseny/SlyfhTTEprfLNRubqFCMcA3RO5mYTRHGNfdv7S8yGMiAKDM7Ih6unZY/A/vxqp2NkLgOIO/aF9MrFp9zF0m7xSV8tXxyVspfOeKqnALxdv9MfbVCTncbqOpfRCldMGL38xmQBPVbXkJfpnHxiLe554z6sNZBLgVvPV9fmic5W4dc5bjY+lp1o1GynIEq1ReGvJHRh+Q5ZXnb1M8W18epw/2G12xZlTvcfoOU5txlZ2+lCx8/J9gPWe1ENxs5WrqPhIp3HHpsZg5Nyhmhux9DSTUOI64+utlvwhjChYMJANYeHhYfjdf93o8XmLxexUSmnjznzcPX+97sDKdQnb9by78gp0ntEYOUi8sVcGduUVoLisCqkJ0Y1Twr26pCI9JRZnzlUqvkcTgNjW4ai47F272sd/MRxL/rZL8fwAGruXKXXVyvgxyE2wRmLaE++rzpbGRIWjqlrfWItKK4SCWKAhTcNiNnvdntacPQimxDRIF0qgHLabYEpKgzl7kFfXMUqknJNoySe9paE8La3LfLlM7g05ELXV2YSOH/nAUMSkttYM5l1500wCEJvx1SMYPoQRtXQMZAlAQ77kw8s3G54dfGT5Zkwc1k0x6BnaJxOJ1iindrS+JgeJU8b0RI8pqz3OKMdGh3sMYiXA6yDWYjbh0Z/fhF5d2rgFqa4teD3NilssZuzKK9Bc8tcbxBrhTbktmclsQfiMxT9umHGdg274yYVPfyogOYbyMrYrx3JOADSPuS63k9C59JaG8uUyuV433peDpKxEp0B072ti5b5iUlsb2nBlNIiNSWmNUfN8X0e2uX8IIyIGsvQjrYL4arSK5W/afcyvQSwAJFijMGpgR6x4e59qMF55RTn4a5cSi+q6elzQ2S7Wlc0u4YvDpxuD1F1fF+CzA/+ByQQMv6GD2/fHdfZaJhpAJlijUF5R7bfNaUbLbbkKGzQBmLfGvYRRUhrCpz8VkF3fIsvY21/UXubesXwPOg3J1DyXkdJQ3takbZ/TDqfyzhh6bWb/DKdg9PudJ4XSCrxZ3r9Srv/f35CZ/XHj9H4+L7kFNO8PYUTUgIEsAfDNzJvSOeSZXn9pHdUKl6uv4kJFNd779DtD53ji/uEY2ifTKZfUG/L3YdPuY06zss+8vluxa5kS0QBy9tSB+KNgiTS9MgyW2/IkbNAEWAaMbzZF5UWWsUW6T1WWVmHHyr2a5zJSGspoTdpWrVth/OMjYa+3Gwpkw2Na4dKZCnz1fRmi46PQOika214U23TmzfJ+dHyU+MEmYOIfx6LbqM6GriWqOX4II6JrGMgSAN/MvCmdw5uZXhGXq8VKBql55vXd+O87jfd/d5WWHOMx39i18YInQ/tkqtbClfOBfz99GHp0SsVvl36omYqghwnGy22pntdsaTYltnyZf3ro/SN+u6anmrRqcu68Ht1GdUZhXpHu6wFAXdVVbF6yU/frhszs79XyvmsVB1USYNKo2uErze1DGBFdw/JbfmSz2bErr8Ct9FFzJAdORv8sJMdHo6i0wu19ejPTazGbmqR0l80u4eV3v/T6PCY0zGLe2CvDY76xY9cytfvBYjHj+R/r+bp+D1zruk7Kzcbbf7zD0HiVJMVFeVVuK1gEIv/U6DWvy+2EX224B9njuwq+QkJhXhGqSi8jKj7S0DWNSMiI9+r1ciqFqB3L98DeRL9X5Q9hYUN/CkvPIQxiiZoJBrJ+snFnPrpNXolxs97EjIUbMG7Wm+g2eSU27jReg9GfHAMnI8rKr+AXiza6vc8Tp88bPudPR2U3aWMCrZqsahyDyy8On1adhXbMKQY8f+DxVAs3PTXWLdAcnpOFBKv4suzcaYPdzptojcKCmSPwn48eCvkgFhALmmJSWiMmRccsoQql3FG7zY7CvCLkb/kBhXlFqkGZ2WJGr1u7C13rmw/y8e6sf+LjRds0y3e1ivbdwpxjoK7nvcnkVApRcroGEbVcTC3wA6PLyjabXXEHe1OZlJuNt5bciXsXvG+4xS1w7X0+OG0wVgiWenKUaI0ETCaPOa/+qoCgVpPVldkEOB7uWJFg3ZbDQucoLqvCxp35itUN5Dxap8oG5yqRGFWH3MF90KqV8z9di8WM2VMHCrUUXjBzBB7/xQgs/s3NAb3fAk0k/3TUvIagSm+OqhLX3NFj209g67JdqHEINNVKdQENm7ci4yI0ryVSezY2NQa9JnbDvr8dEBi9NsdAXW8ZMkdyKsWWpTtRU1Gred3mUqKMiALDJEmeSreHpoqKCsTFxeHSpUuwWvV3fdJis9lVGwrIuY1HN8xxChq0Ahq73Y7CwkJkZmbCbFYONnwRCO/KK8C4WW9qHvfo9Jvw2gd5HrtgmQCYzSZdwaGIeyf0QVpyLJ57w3jRdDVhFjPqBWaOZv1sIG4b0V3xey36PVwwcwT+uOYztw888uyu6wcerXvAZrMjc8ILqpUXMhTuvZZOKehyLeCvdIwopWYAO1fvU207q1aq69iO4/hs3R5UfHsF0LmqHhUf2Vjjtbq8Gluf3SUULIrQKkPmepyWgq9O473fbdI87mcv32ao1FcwE/l7QKEv1O8D0XiNM7I+prW5SalUlcgM7m3Du6leVysQFiWa02oymVRbuWp1nTLqfz865PNzOhIJYgHgthHdFctmAWIbtdqlxOK1jXkqHdzVa/MqsVjMePmxnyjeS655tXSNSIct12MuX7iCnS/t1Tx37gNDkDPlepeZ2OOqQSygXqqr64hOKL9ajq9LvkPVWX2BdXV5DWJSW6Omohabntiq67Vq+k/ri+tyOwmVNBMtQ5Z5QzvN0mOR1gjY7RLsNrtfym8RUfPHf/k+JhoIysepNSIQ3RgkB8KuAbQcCIvk5cp5mkcLzgmN39Q0m4WbpURrlGpZKpGNWr+YlIOic5Uez+GaRytKzqtNd8urtbaIDVxGyR22ssd2RWZOumJQ5HhMzpTrNfNrI60RSOmc5PSY3WbHp8u0y1hp5X62uS4Zv1x/N4bM1F9to6r0sldtYJUc23q8sTWvaBkyLSL5sjUVtXjvd5vwl8lv4fudJ3WNmYhCAwNZHxMtYyUfJzqDu/cb5YDGV4GwvDHtmdfV/8jKO/OH39BB9bhQNmvqQM1ZTc8btRoCys4ZSR5e6cxI1YdJudk4tmEONr/8c6xdNBmbX/45jm6YwyDWh0SDrPVznIOs04eKhfJXAbHcz28+0F87+Up5tVdtYJXIwalovqrocXK+rNaHBrl7GoNZopaHqQU+Jlr/U57REw1USsouo0NSrNvjRlIZHHlKa1DiuDw9PCdL9X2GqiRrFH4/fZjQsVotaEUYre/rqWMY+Y5ofVfHFrW2Opvw+bVKdZ3+Rnv201Vsaoy+pgM6yGkZIvSUIZPTOk7lncE//7AFtSo5vUa6pxFRcOO/dh/TU/8TAFITxH6hpyQo//HRk8rgWuaprq7e42yuEsflabX3aURyfOB6yutx1y29sedQoXBNYDmgnDq2F4bnZDX+3LXq9soz377srEW+J9d3vXPVRERa1asJ7Fi+B1GJYkFkVHykZpvXK2X6K3eMnDtUX9MBAJZwsXqpcm6x1vmNtLA1W8wwWUyqQSzAclxELRFnZP1AXlZW2ny1zHXzlWgU6OE40UD4xOnzbtUUkuOjVTdsyX4/YxhGDejoVgXB0/sUMX5IF8ydNgTFZVX4obAMK/6uv0yXliRrFCIjwpxyUS1eVlJYvW4/Vq/bb2gjnSP5g8Dd89d76ODOjVnBwmwxw2w2ae7+ryytgkmC5gYmABj9yHDNWcXoZH0zqzlTezduyBIZg0xkFlkOTkVKmhltYevrtAUiCg38K+knonmKpRfEfumeu+A++7JxZz5+ufgD1deZ0NCpafGaz9yCTZEgFgCyO6Y4zSY6kt/ncw+MFTqX7JO9x3GhohqHvi/Gkr/t8kmrWVfnK6qx5snbG38Gzz0w1meVFPRspPNEK4+WOa3BQzR4unKxWjO3tv+0vug2qrPmuTJ66+uC1emmDijMK8KxbSfQ+3bf3luOwamnvNbY1Bjh0ltK/JG2QETBjzOyfiSSpyiaA9k22fmPgkhuqzyzV3dVPC9PiacxOtat1dugwARg9tIPcd4PjQ0clV64gqljewGAcKMCEUZLZLlSy6Ol4KEnyMrMSVfMrY1KiMToh4eh26guTq+RqwHIOajtrm8DQKyhgyzSGolPntqOqnOXHR6LAExAzSX3meSo+EihTWmtolvhlgWjnIJTu82OSGsEhv/mRlwpr0Z0fBRiUlu7lTTTS05bUJtJNpK2QETBjYFsgMm5kmpL8xmpVgzpnYmiotMA1CsVOEqIi8LVqzZUXqkzPD6L2YQB2e2wK6/AKdDatPuYoZQCmQT4PYgFnINwoxunPNHaSCeKG7OCn94gS6R2LeChQ1bb1rjhNz2QmZmJ63I7YeKSsfjk6R24etnzqkZNhXtQKqdCDL6/H2z1EipLKmFtG4PMfhm4fP4KPl60TfN9W8LM6DIsS328P3b08nYDlj/TFogoeDGQDTCLxYyfjemJ5SqtXKeM6ek0Q7fr6wKhAFKtw5Mom13CdZNXOqUh+KtFrC/JTQdsNjvWbTmMtOQY3Ngrwy+VFoyUyKLQYiTIkuvSenJs+3HFpgVVpZdxaMMRxLeKh8lkxo6X9ngMYmNSWqO+tl41f/eL1/MgOaTcHPm/79H79h4ej3dUU1GL04eKkZmT7rGjl2PVBqNpBTJPlSKUuqcRUcvAQDbAbDY73t16RPWY9VuPYNGvRwIA/vnZUfz2mY+aYmiNXHNpm3sQCzTMllbX1ePWOW81PpaeasXPxvTEirf3uW2w8oavZ3opOPkyyDq2/QQ+XPCp6jFbn9uNGpXl/yEz+0OyA/v+9pXqeSSXvPGq0svYu+ZfaBXVClcFcterSi/7tKOXFtHZbCJqGRjIBphWHVjgWkOEi+fP4t5Fn6LeD61fQ0libCQuVNa4zUifKa3Airf34cFpg/Hu1iOG0yJkrjWBiXwRZH2/8yQ2PbFF8zildAFHee8eRs0lseYLSiRJ7PfMlfJqXR291GahRWnNZhNRy8FANsBEl6XPlFbhox3ftajmA0bccXMPfPHNKaDS/Q+4vEHr3S2H8coTE/H5wVPY8dVJfHm4SPd1mluJLMluw/n8A6gtP4eI+BQkZfeDySxW/5N8y1OQ5bppSynAFZnZFOVNEAsA9TX1QsdFx0exNBYRBUxQBrIvv/wyli1bhpKSEvTp0werVq3CwIEDAz0sQ0SXpcsuXUZ5ECzpB4rZBDxw92CMH9IV72/z3LZTAlB0rhITH/x742MJ1kjU19t1bYozm02Yc9eNzaJEVvGXW3B47VLUXChpfCwysS16zZiPtIH6yqKRf6htgnJMORCZ2Wxu9JQAY2ksIvK1wE8l6bRu3TrMmzcPCxcuRF5eHvr06YNx48ahtLQ00EMzRLTDU3Kc2B+ARGsU3l5yp+o5AcBs8kU/rubhnlt74+LOx/H07DGGNl6VV9Sg6kod7p3QR/g1NruEFW/v86qOrC8Uf7kFX734oFMQCwA1F87iqxcfRPGX2kvU5F/yJijXAFXeBPX9zpONjzW3GcvI+EjV5+VKDP7q6EVEpCXoAtkXX3wRM2fOxH333YcePXrglVdeQXR0NF577bVAD80Q0Za27VJjhc731h/vwORRPTTPaRfMfwsGd4/vjfDwhsUFIxuv5O/Ejn/9G+1SYnW13H1k+WbhdrW+JNltOHd4Hw69+iSUt601PHb4jaWQ7N7VESbjRDdB2X+8h/TMWEbGqbfE9VZsagzGPDJc9Ri5EoNctUHkWKDh+1KYV4T8LT+gMK+o8f0TEekVVKkFdXV1OHDgAObPn9/4mNlsxujRo7Fvn3L5qtraWtTWXis9U1HRsMHHbrfDbm8evzxvG94Nf3/6Tvx+5RYUlV5rp5qRGotn5ozFbcO74erVeiTERSHMXK54joaNR7G4qW8m7Ha76jlvG9Edf1r/Lz+/q6az++sCjOiXBQAYfH0GMttacaa0Unc+cXFZJZ64fxie/ttuAGJVDYrLKvH5wQIMuyFL59X0k+/Zoi+3IP9/l6Hm4tmGJ1RyYasvnsO5775Cco8Bfh8fuSs8WISqssuqUwaVZVUoPFiEzBvS0e76Nohpq16P1hRmQu/buyM+IgEfLvBc6zU8uhXqrhjvmJf74GB0HdERE58egx0r97pVYsidMxhdhmc1/h7tMjxL6NgfPjvpdkxMamuMnDMEXUewfJYI+XdBc/kbRoER6veB6PsKqkC2rKwMNpsNbdq0cXq8TZs2OHr0qOJrli5dikWLFrk9furUKcTGis1yNoW+HaPxfy/cjhOnL+BSVS3iYiLQOSMRZrMJhYWFkCQJd4zoiEuVNbB7qFrwy8n9GpsmqJ3zxOkL+O5oQlO9Nb9LiLyKwsLCxq+f/c0g/HXDAUPn6to2HP+7cDTe+/Q74Zzkc2dLUFjo/8UNSZJw5vh3OL/rfZjiM4D4DKHXnTlbiisxhdoHks8Vny2F9XrtWdbis8VAYcPM+Q2/6YFDGzyX5Os9KRutUiyISGyFYQsH4OjWH1BbeS2/O9Iajm6juwJ2Ow5t1J/6EmmNQLfRXRDRMQyFhYWI6BiGcS8Mw8XTl1BbVYeImHAkZMTB9OPvJkdax579vgyHNhyBuQ1gbeP4fZHw2bo9KL9ajjbXJesec0sjSRIuXrwIk8kEUwiliZE+oX4fVFZWah+EIAtkjZg/fz7mzZvX+HVFRQXat2+P9u3bw2q1qrwyMLKyOig+brfbIUkSfhHZBr9ftVV55nZEd6Fztm/fHrOX7xaatbS2boVpt/RBZlo8zpdfxvP/67lxQ6DMu78zMjMbSmDZbHacPFePuKQ22P7lCVTqnJF6ZGZbDLshCxNGDsAr7/8L81ep1/KUXyNf359s9VeR//+WAwV5gI50gXZtfovkJhgfKThvwZ5vtT9Upc1MQ2ZmQ6WDzMxMxLeK9ziz2XlYFgoLC9G+fXvUFdhw5Yc6XD53Lbe2PllCfXcJCenxaJPSFj9sP+l2PUcxKa0x7vFcVF+sQXRyFDJ6K5cL65Al+J49HGu32fHJ3F2oKvWcB3zwbD7uf/cu1oTVIP89aN++Pcxmfq9aqlC/D+QVdC1BFcgmJyfDYrHg7NmzTo+fPXsWbdu2VXxNREQEIiLcc8nMZnPQ/eDNZjNuy83GxBHZ2HOo0KllrEgJKJvN3vi66RNvwOI1n2m+5mJlHVa/25CG0C4lFnGxkbh4qbrZlAFLskZhRE4nmM1mbNyZj1lLPzTUsEGuCXtT36zGe+O3UwbhpXf2e+wE5voafyv7/iDqqy7BZLfBJBTImhCZ1AYpPfrDFGT3eqjI7JuOmGTt1rWZfdOd7qFuI7ug6/BOiuW67HY7zGYzTuwuwKbH3Tt/XSmtxr4115ogaDU2GPXgTeg4sOGDjkiJMKNOHyxGVYlGrdmSKpz59ixrxAqQf08F298x8q1Qvg9E31NQBbLh4eHo168ftm3bhkmTJgFo+ESybds2zJ49O7CDa0IWixnDc7J0vWbjznw8vHyzUxOA2NbhqLwsXnKq+Jz+vFN/Wz3/J7BYGoLYu+avN3QOTzVh5Y14d89f79YJLBB1ZGvLz+k4umGEvabPZz3ZADLSutbxtZ4COskuYcfKvUJjkINY15xZ145joiXCAGMBL2vNEpE/BFUgCwDz5s3D9OnT0b9/fwwcOBArVqzA5cuXcd999wV6aM3Wxp35uHv+ercgVE8QC1wL5MLDzKirD3xy+dxpgzEpNxs2mx0PvfiJ4fMkJ0Rj5aMTFGvCTsrNxt+XTnH7EJCeasWyueN8XkdWrbFBRHyK8Hkik9qg13TWkW0OfNm6Vnbx9CXd9WYjYsJx27PjUX2h2i34lEuEuZJLhN22dJyhgNeRaEWGi6fLdbwrImrpgi6QnTp1Ks6dO4cnn3wSJSUl6Nu3Lz755BO3DWDUwGaz4+Hlm306k9ocglgAWL/1CBb/5mbsOVSIM+fEksKVPDvHPSCV0zCKSitRVn4ZT/16JM5XVCM5vjXSU2OF0zn00GpskHhdH1gio6GWVNAqJh79HliO5J4DOBPbjPiida2j2ip9H0IBoLL0MsxmE7LHdnV6XLREWJdhWTi+u0A44HWV0ScNrVOinfJ5lXz7QT5unN6PebJEJCToAlkAmD17dotKJfBGQzAmljDdlKIiwlBdK9YC05PTpRWNOb/eSHep0auUhnHtWCue90M6gdzYwLXol9zYoPPE+3B67yeweaxU0JBK0OdXi5By/Y0+HRv5hpwqIC/LH9t2wnBAGxETbmgMSsv2It3EKkurcCrvjHDA6ylVos+knti7Rr30X2XpZZw+VMw8WSISEpSBLInzNsjzh6S4KKx96qeY+MDftQ/WIG94Myoj1Yqhfa7t6PeUhiErKq3A3fPX4+9Lp/gsrUCy23B47VKoNTY4sek1SGaLx5JbTCUIDkaX5V0lZMQhJrW15uYpV0rL+6I5qae+LhIKeNWC0ISMOKFrMU+WiERx7SbEiQZ5yfFN2ANdAob3zUJ6qvflz1ITozG0TybapRirCey4WUtPGoYvO3qdzz/g1mJWj1bWBNz80mYGsc2cnla1WkxmE0bOGaLr+lEJkYotYoW7iQnmJ6kFoaLX0tPhjIhaNgayIW5on0ykp1o9tl01oWFW8sQHD2Lzyz/HawsnIba1sWVLUecrqvHF4dP42ZieXp9r5lMfYNPuY3hh3njdr10wc4TTrKpoGoaEa2kNvqCvGoG7qxUXceHYQZ+MhfxDb6taEV1HNGwii0lpLXT86IeHKS75Z/RJQ0yq+jliU2OQ0U9sqV8tCM3ok4ZIq3pr3djUGMWAm4hICQPZECeXkALgFsw6lpAKDw/D8JwstE2K0V3NwIii0gq8u9Vz9yJRZ85V4u4fy269s3QKEq1RQq8zm03onuVcBUBvGoav0jb0VCPwxNtgmPxLNA/19KFiXee9LrcTfvWPezBkpnoL4v7T+qLbqC6Kz8klwtSk9UyBZLOjdYr6TKlWEHp8dwFqKmo9Pg94LkdGRKSEvy1aALmEVDuXpfz0VKtTrufGnfmY9of3m2RMZeVXfLIJTV7tfGT5Zkwc1g2FHz+Ej1fdg0kj1fNX7XYJ9zzxHjbuvNbCU2+urTe5uY6SsvshMrEt3D9qiPNFMEzes9vsKMwrQv6WH1CYV9Q4w+rPGqpmixlDftG/YXbWZWY1Kj4SE5eMRe7swarnkEuEeZqZ/X7Hv/H+gx+h7rJ6pzy1IFRkVjoyLhJdhmWpHkNE5IibvVqISbnZmDism8eOYFqbnHxF7oaVHC+2HCrCcal/eE4WRvbvhJH9O2HD9u9w74L3Ybd7fldyAGyxmBvTMLQCbPk9OG4S84bJbEGvGfN/rFrg2npB89WITGqDpOx+PhkLGae2kctobqhS4wHHzzuuz/9y/d04c/isoRJfcomwL97I81hZ4OqPDRUirRFOM6siNXFFZqVrLtWwYgER6cJAtgXx1BHMH7VmPZEALP3daBSVGq/76onrUn9yfLRqEOsaADt28tL6Xvi6o1fawLHoP2+Fex3ZpLZIH3IrTmx6HZ6SQ9i9K/C0GgpMXDKmocqARqtax2V5T4HxyAeHIKJjGH747CR2rNirGDi71orV48C6Q5rHhEWG4c4lYxWbK3jCzl5E5A8MZIOQXKxfaWbVCF/XmjUBSIyLQkR4mGKjgp8/uQGSH6Jm16V+0RxWx+M8dfKSZfipoxfQEMy27X+zYmevhK598O0bz6LG4XiW3GoeRJbMd760DyMfGIpNT2zxeIzjsrxaYLzpD1vR+5fd8M1fjwF29+e1GhOo+eKNA6it0M6Rr/LQXEENKxYQkT8wkA0y//zsKB5escWtXerzXgRXvq41KwFY+egEfHeyFEv+tsv9eR8HsSY0tJktKq3ErryCxsBeNIfV9TjHNAy5s1dyfHRjOoGvmyE4MpktSO450H2MA8ciNWck8r/cgzj7FEQlpDq1r6XAEd3IFRUfKdSqViQw/s+/Tqk+r9aYwBO7zY68dd8KH6935lSujqBnVpqISAsD2SBy8Pti3LvoU9S7LJef8bJIv682LcluGdoVj67YjCIv2sbqIQE4d/EKfrHoHwCuBfYTh3VDeqoVZ0orFFMF1HJdPaVhBJLJbEFMuyykZ2bCbOY+zeZCz5J59tiumq1qRQJjSaNKl1ZjAiWnDxVrVhRwpHfmVK6OoDTTLGPFAiLSi78xgoTNZsd7n36n0vvJeJF+rVqzev3fnh+aLIhVIgf2m3YfEyo95s8ZVgp9epfM5Va12WO7IjMn3S1w81WOqN7z6Dk+NrW1oZlTT9URYlNjDKdDEFHLxhnZILH3m0KUV1R7fN5145Iejpuc9O6ZdzuX2QSbygarpiChIVB9ZPlmHN0wRzHnNd1Arquvc5MpNPh6ydxXOaJ6z6Pn+JFzbzI8cypXR1CblSYiEsVANkiUlIn1VJfzRPUGW1qbnET5IohdMHMEVvx9n1eNGRwDe63SYyI27sxXDIa9yU2m0ODrJXORwNikcSojuaYi14UJmPjHsV7PnMqz0kRE3uJH4CDRNlms7uqjL23GuFlvYsbCDRg36010m7zSqei/mkm52Ti2YQ4eu2+YoTH+VKMJgYiMVCt+P30YTn/8MGKiW3l9Pnkjm5zzOnVsr8ZSW6LkGruuAb6cwiD6/aXQ5cslc5FOWx0GtFd93kiuqch1f/LUaETFR7o1fCAiChTOyAaJ8+We0woclZU757np3QhmsZgxsn9HPPP6bt1jvK5Dsu7XuHr2gbGNM6dzpw3B4jWfeXU+bzeyqdXYdUxhkJsqUMvlyyVzOTBWqnCQ++BgRHQMQ4esTLc6siKNCUSuu+WZnai55Lzxq1VUGD59frfT43LdWua2ElGgMJANAjabHfNXb0XXNvp/XEaCLXnzl6fd/p6M6NcBb3100NBGr5iocIwf2sWt2oHJ5F25rgHZ7Yy/GNo1dr3JTabQ48slc0+BMUxAYWEhuo7ohK7DO/kl19Q1iAWAq9X1uFpd7/SYt3VriYi8xSmkICDXMzXKMdgSIW/+Atx3+ysxoSElYPgNWfjFpByhazzzuzGYNDIbsdHhAICq6jq89+l3bkGwtzVn//pBXuP/22x27MorwLoth7Err0CowoPepgpGrkHkiVaFA63n9RKpYatkx/I9TDMgooDgjGwQ8FXDAj3nEd385VrGqnNGktD5T5dW4IMd+X5vi3uy6CIA45u19DRV4IYw8hW7zR6QXf0iNWyVGKlbS0TkCwxkg4CvGhboPY/rbv8Tp8/jbxvznNrOupaxEr3G/9v8rd+DWADolJ7QuFnL9Xoi+cNaaRZyU4Wy8iu454n3DF2DyNH3O0+65cY2VS6qNzVsfVX/lohIDwayQaAhmIr1+LwJgFmlfqtaByst8m5/uYZqx3YJKCu/guT41khPjXUrYyUS+CUnROPcRf//0bOYTfjl7Tm4/r/+ZHizllqNXXk2+tkHxuLRl7ZwQxh57fudJxXLeDnmonYZnuW363tTw9ZX9W+JiPTgX9UgYLGY8eycsQA8d6iac9eNMKk8700Hq40789Ft8kqMm/UmfrFoIx59aQsW/HkbLlRUO51TDnZ/OjLbYxALAFPHXm9oHHrNuetG/Cv/jPBmLU/kNIt2qVanx9NTrfj70ilIjo/2+hpEIvmp/s5FlWvJ6mWkbi0RkS9wRjZI3DaiO3D1En44ux+FJcodqgb2yvDYwWrisG6GGiWILssr5YeazSbYHWaJ5bEkWqOwet1+498MDWYT8MDdg/H07DFYt+Ww0Gu08ofVmir46hrUsonkp1aWVuH0N8WAWCq6biLNHZQYqVtLROQLDGSDSN/r0nD43dnY9+1pxYDUU7C1afcxdJu8UmgTkmMb1tTEaDz84ieaS+Z2u6SYHyr9GMTOnjoIE4d3axyrzWY3VN5LxH+N64VXH78N4eENt7Zozm7phSqs23JYNciX0yxc6dkQRuSJaI7plbJqRCeF+20cnmrYRlojAZPkVJrL27q1RETeYiAbZDwFU56e17PRSWlWVY28ZP7Aso9Vg92NO/LxzO/GNAaHanmn3rrvtpzGIBYQq4lrMZvw6EtbGr/WW2lAdEOYkRxlajlEc0yjk6MA2Pw6Fo81bAGvqylIdhvs+fshlZ+FKb4NzNmDYDJb/PE2iKgF4FpQCNPqSgU0zKjabHaPbVhFuHYTc72OUn6op7xTo+Ratq7BokhNXNdNcnpbz6pdwxc5ytQyiOSnxqbGIKN30+SiKtWo9bZubf3+j1A9awBqnroDtSt/i5qn7kD1rAGo3/+Rn94FEYU6/mUNYaJdqXblFXgMeH1FKT90Um42jm2Yg80v/xz/fecA4XPpDRb1Bs2uQb4IrQ1hLL1FWuT8VDXBnItav/8j1L44E9KFYqfHpQslqH1xJoNZIjIkOH8jhijJboPtyF7U7/kHbEf2QrJ7t3wourlo19f/MTQTawKQHC+2HOopP1ROhfjpSLFAb8HMEYaCRTloXjBzhNB1jFQacAzM1y6ajM0v/xxHN8xhEEvC5PxU15nZ2NSYoG4DK9ltqFu7AMqJRA2P1b3xpNe/84io5WGObDNRv/8j1K1d4DRbYUpMQ/iMxQgbNMHQOUU3FxlpAyvPgk67pTdeeucL1WOVlvxdieSyZqRa8fvpw/D76cMUqweIeO2Dr4WOk+mtNKCVw0ykxVN+arDOxAJoyIl1mYl1JkE6fwb2/P2w9BzSZOMiouAXvL8ZQ4i/ltzk4NBTbqicVzqiXwfd505PteKtJXfivW3faR679HejsedQIdZtOYxdeQWKy/UiuaxXaq5i0+5jjcHi1LG9MDwnSziI1Uq1UMJKAxQI3uaiNjdS+VmfHkdEJOOMbIBpL7mZUPfGk4joN1b3uUW6Ui2bOw7Db8jS3HXfLiUWaxbcjtKLVxpnQUUDw7kvfOK0IcxTVQA5z3TWMx/iwqVqt/NcrKjGXfPXY8HMEeickaR7NlbP7CorDRD5jim+jU+PIyKSBffH/BAgvOR29EtD5xfZhCSy6/75eeMxckAnp1lQ0cDQtaqBWlWAicO6ITJc+fOVHGQvXvMZZizcgHGz3kS3ySuFqwvonV1lpQEi3zBnD4IpMQ2e11tMMCW1gzl7UFMOi4hCAP9KB5j4klup4WuIbEIysuve6LK7WlWAPYcKceZcpfC59JTK0kq1kGWw0gCRT5nMFoTPWCx/5fosACB8+lOsJ0tEujG1IMDEl9xSvbqOyCYktTasSkQ2aHniWBXAcVx6N1c5dhibOKyb6gyqSCOGBTNH4PfTh3EmlsjHwgZNAOatcd/UmpSG8OlPGd7USkQtGwPZAJOX3KQLJVAOrUwwJaXB3H0gcLrI7+PRs+veFx26XANXI7O8noJiJfLMs2sHs4xUK5bp6OZFRPqFDZoAy4Dx7OxFRD7DQDbA5CW32hdnAh62ZPlryc1msxsuYyXzFBgmx0erdvySuQauZeVXYDabYLfrD4s37TomFITrnXkmIt8xmS0ssUVEPsNAthkQWXKz28U6TInauDPfLfj0VE1Ai1JgeGOvDPSYslq1EoJrVYCNO/NxzxPvGe4wtnrdfiRYI4UqGrDeKxERUfBjINtMNOWS28ad+bh7/nq3gFHeOGVko5NSYChS+ksONG02u0/a5C5e81nj/xsNzImIiCg4cC21GZGX3MKG/hSWnkP8lk7gKWBUqyZghJ5KCEaaFWjRU9GAiIiIgg9nZFsYrYBRdOOUaH6taD6q3moFIvRUNCAiIqLgw0C2hRENGNWO05tfK5KP6q9WsHoqGhAREVFw4RRVCyMaMHo6Ts6vdZ3V9XYZX65W4C/+mPElIiKiwGIg28IM7ZOJRGuUx+dNaKip6lhNQOav/Fq5WoGRklui/DXjS0RERIHD1IIWZtPuY7hQUe3xeQnO1QQc+Sq/1pFItQKzCZj/i+HompmMtOQYlJVfwaMvbRHaHKZU5ouIiIhCAwPZFkQOGtUkxUVh4rBuis/5Ir/WlUi1ArsEDM/JcgqObx/RvXED2YnT57F4zWdCZb4c2Wx2fP71OZSUVaNtchRuuiGFG8KIiIiCCAPZFkQkaDx/qdrjjKq3+bVKjAbHrhvIenRKVdyA5qnt7Mbtp/DQ8wdQVFrtcHwUXni4HyaNai88fiIiIgocBrItiLczqkP7ZCI91aqrW5cWXwXHetrObtx+Cnc9+rlCQ4hq3PXo53jnuZsYzBIREQUBrqO2IN4GjRaLGc/PHQfg2rK9TGsZ3xM5OPZUr0Bt85nS+IbnZGHq2F4YnpPlMZ3goecPqG5Ye/iFPJ80hCAiIiL/YiAbhCS7DbYje1G/5x+wHdkLyW4Tep0vgkY93bpE+CM4VvP51+ec0glcSQBOn72CXQdKfXI9IiIi8h+mFgSZ+i8/Rv0bT0K6UNz4mCkxDeEzFiNs0ATV18pB493z1+veGOVIzzK+CDk41pPjalRJmecg1tHdj32OP/9hEFMMiIiImjEGskHE/u9vUfuX38Jsr3d6XLpQgtoXZwLz1mgGs74KGkW6denh6+DYk7bJnmvoOrpYcZX5skRERM0cA9kgIdltqN/7AeAxu9OEujeehGXAeJjMFtVzNVXQqJdWcGyz2b0e8003pCA9NQpnSqtVa9fKHn4hDxNHpAf8e0NERETuGMgGCfvRLyFdvqRyhATp/BnY8/fD0nOI5vl8PaPqbxt35ivOIj+vM/XAYjHjhYf74a5HP9c8Vs6X/fzrcxjRv42RYRMREZEfBc00U0FBAe6//3507NgRUVFR6Ny5MxYuXIi6urpAD61JSOVim4+k8rN+HknT27gzH3fPX+9WA/dMaQXunr8eG3fm6zrfpFHt8c5zNyHBGi50vGheLRERETWtoJmRPXr0KOx2O1599VV06dIFhw8fxsyZM3H58mU8//zzgR6e35niUwWPC62ZQ7UWtg0JFcAjyzdj4rBuupb/J41qD2tMK9z62x2ax4rm1RIREVHTCppAdvz48Rg/fnzj1506dcKxY8fw5z//uUUEsubuA2FqHQf3IlUyE0xJaTBnD2rKYfmdVjcyCcDp0gqP3cjU3NQ3GcnxESgrr1V83gQgvU00brohRdd5iYiIqGkETSCr5NKlS0hMTFQ9pra2FrW11wKVioqGoMhut8NuD56i9xJMMA++DfbvPm185JqG4Dbi54sgwQQpiN6XluJzlbCYPQXvzsfp+Xn+c8cpPLr8a1ysqIXSRK58xefn9YXJhGZxr8j3bHMYCwUO7wPiPUBA6N8Hou8raAPZ48ePY9WqVZqzsUuXLsWiRYvcHj916hRiY2P9NTyfkyQJl+LSgftXwr7vn04bv0wx8QgbfBvMbXsBhYUBHKXvJUbVIff6BKHjCgXf+8GjF/HX939A1zSga5pyhYcEawTuHJOJvp0l4fP6myRJuHjxIkwmE0wm7eCeQhPvA+I9QEDo3weVlZVCx5kkSRKpQuQ3jz32GJ599lnVY/Lz89G9e/fGr4uKijBixAjk5ubir3/9q+prlWZk27dvj4sXL8Jqtaq8snmx2+0oLCxEZmYmTJAaqhiUl8IUn9qQdqBRcksvyW7z+zVE2Gx29PrZapwprVTMkzUBSE+NxbfvzhbKkbXZ7Oj50w9Vu3slJ0Tg2Ac/QXh48/qc53gPmM1Bs0+TfIz3AfEeICD074OKigokJCTg0qVLqvFawP9SP/TQQ5gxY4bqMZ06dWr8/zNnzmDkyJEYMmQI/vKXv2iePyIiAhEREW6Pm83moPvBy2M2m82w9Brqt+vU7/8IdWsXGOoepkWy22DP3w+p/CxM8W1gzh6kGiCbzWY8+0BDNzJAuRvZMw+MQ6tWYrfy7rxzKCxRr0Jw9nwt9h++6FXJLb3vU5TjPUAtF+8D4j1AQGjfB6LvKeCBbEpKClJSxDbTFBUVYeTIkejXrx9ef/31kPzBBVr9/o8auoS5zH/q6R6mdm4jAbIvW9iKltLypuSWPz8IEBER0TUBD2RFFRUVITc3Fx06dMDzzz+Pc+fONT7Xtm3bAI4sdEh2G+rWLoAvuoe58jZA9lU3MtFSWkZLbvnzgwARERE5C5pAduvWrTh+/DiOHz+OjIwMp+cCnOYbMuz5+51mEd3p6x7W+CofBci+6Eam1aLWm5Jb/vwgQERERO6CZm1+xowZkCRJ8T/yDdGuYHq7h+kJkP1NblELuFfkbSy59VCO7pleoHm9TyIiopYgaAJZ8j/RrmB6u4eJBr62w7tRv+cfsB3ZC8lu03UNPeQWte1SndMH0ttE453nbsKkUe0NnddfHwSIiIhIWdCkFpD/mbMHwZSYBulCCZSXx411DxMNfK9uWHHtNX7eHDVpVHtMHJGOz78+h5KyarRNjsJNN6QYmomV+euDABERESnjjCw1MpktCJ+xWP7K9VkAQPj0p3Tnd8oBsuf2uu7kzVH1+z/SdS09LBYzRvRvg6njszCifxuvglhA5H2aYEpqF3JthImIiAKFgSw5CRs0ARHz1sCU6FwJwpSUhgiDO+7VA2RPGmaE69540q9pBr7krw8CREREpIypBeQmbNAEWAaM92lB/7BBE4B5a9zqq6ozViUhkDy9T1NSGsKnP8XSW0RERD7EQJYUmcwWnwePrgGy/fT3TnmxngTb5ih/fBAgIiIidwxkqUk5Bsi2I3uFAtlg3Bzljw8CRERE5Iw5shQw3BxFRERE3mAgSwHDzVFERETkDQayFFD+qJJARERELQNzZCnguDmKiIiIjGAgS80CN0cRERGRXkwtICIiIqKgxECWiIiIiIISA1kiIiIiCkoMZImIiIgoKDGQJSIiIqKgxECWiIiIiIISA1kiIiIiCkoMZImIiIgoKDGQJSIiIqKgxECWiIiIiIISA1kiIiIiCkoMZImIiIgoKDGQJSIiIqKgFBboATQ1SZIAABUVFQEeiT52ux2VlZWoqKiA2czPHy0R7wECeB8Q7wFqEOr3gRynyXGbJy0ukK2srAQAtG/fPsAjISIiIiI1lZWViIuL8/i8SdIKdUOM3W7HmTNnEBsbC5PJFOjhCKuoqED79u1x6tQpWK3WQA+HAoD3AAG8D4j3ADUI9ftAkiRUVlaiXbt2qjPOLW5G1mw2IyMjI9DDMMxqtYbkDUvieA8QwPuAeA9Qg1C+D9RmYmWhl1RBRERERC0CA1kiIiIiCkoMZINEREQEFi5ciIiIiEAPhQKE9wABvA+I9wA14H3QoMVt9iIiIiKi0MAZWSIiIiIKSgxkiYiIiCgoMZAlIiIioqDEQJaIiIiIghID2SCwZMkSDBkyBNHR0YiPj1c8prCwEBMmTEB0dDRSU1PxyCOPoL6+vmkHSk0qKysLJpPJ6b9nnnkm0MMiP3r55ZeRlZWFyMhIDBo0CF9++WWgh0RN6H/+53/c/s1379490MMiP9q1axcmTpyIdu3awWQyYePGjU7PS5KEJ598EmlpaYiKisLo0aPxww8/BGawAcJANgjU1dVhypQp+M1vfqP4vM1mw4QJE1BXV4e9e/fijTfewNq1a/Hkk0828UipqT311FMoLi5u/O93v/tdoIdEfrJu3TrMmzcPCxcuRF5eHvr06YNx48ahtLQ00EOjJtSzZ0+nf/Off/55oIdEfnT58mX06dMHL7/8suLzzz33HFauXIlXXnkF+/fvR+vWrTFu3DjU1NQ08UgDSKKg8frrr0txcXFuj3/88ceS2WyWSkpKGh/785//LFmtVqm2trYJR0hNqUOHDtLy5csDPQxqIgMHDpRmzZrV+LXNZpPatWsnLV26NICjoqa0cOFCqU+fPoEeBgUIAOkf//hH49d2u11q27attGzZssbHysvLpYiICOmdd94JwAgDgzOyIWDfvn24/vrr0aZNm8bHxo0bh4qKChw5ciSAIyN/e+aZZ5CUlIQbbrgBy5YtYzpJiKqrq8OBAwcwevToxsfMZjNGjx6Nffv2BXBk1NR++OEHtGvXDp06dcK0adNQWFgY6CFRgPz73/9GSUmJ0++FuLg4DBo0qEX9XggL9ADIeyUlJU5BLIDGr0tKSgIxJGoCc+bMQU5ODhITE7F3717Mnz8fxcXFePHFFwM9NPKxsrIy2Gw2xX/nR48eDdCoqKkNGjQIa9euRbdu3VBcXIxFixZh2LBhOHz4MGJjYwM9PGpi8t93pd8LLelvP2dkA+Sxxx5zS9p3/Y9/oFoePffFvHnzkJubi969e+O///u/8cILL2DVqlWora0N8LsgIn+45ZZbMGXKFPTu3Rvjxo3Dxx9/jPLycrz77ruBHhpRwHBGNkAeeughzJgxQ/WYTp06CZ2rbdu2bruXz5492/gcBQ9v7otBgwahvr4eBQUF6Natmx9GR4GSnJwMi8XS+O9advbsWf4bb8Hi4+Nx3XXX4fjx44EeCgWA/G//7NmzSEtLa3z87Nmz6Nu3b4BG1fQYyAZISkoKUlJSfHKuwYMHY8mSJSgtLUVqaioAYOvWrbBarejRo4dPrkFNw5v74uDBgzCbzY33AIWO8PBw9OvXD9u2bcOkSZMAAHa7Hdu2bcPs2bMDOzgKmKqqKpw4cQL33ntvoIdCAdCxY0e0bdsW27ZtawxcKyoqsH//fo9VjkIRA9kgUFhYiAsXLqCwsBA2mw0HDx4EAHTp0gUxMTEYO3YsevTogXvvvRfPPfccSkpK8Ic//AGzZs1CREREYAdPfrFv3z7s378fI0eORGxsLPbt24e5c+finnvuQUJCQqCHR34wb948TJ8+Hf3798fAgQOxYsUKXL58Gffdd1+gh0ZN5OGHH8bEiRPRoUMHnDlzBgsXLoTFYsFdd90V6KGRn1RVVTnNuP/73//GwYMHkZiYiMzMTDz44IP44x//iK5du6Jjx45YsGAB2rVr1/iBt0UIdNkE0jZ9+nQJgNt/O3bsaDymoKBAuuWWW6SoqCgpOTlZeuihh6SrV68GbtDkVwcOHJAGDRokxcXFSZGRkVJ2drb09NNPSzU1NYEeGvnRqlWrpMzMTCk8PFwaOHCg9MUXXwR6SNSEpk6dKqWlpUnh4eFSenq6NHXqVOn48eOBHhb50Y4dOxT//k+fPl2SpIYSXAsWLJDatGkjRURESDfffLN07NixwA66iZkkSZICFUQTERERERnFqgVEREREFJQYyBIRERFRUGIgS0RERERBiYEsEREREQUlBrJEREREFJQYyBIRERFRUGIgS0RERERBiYEsEREREQUlBrJEREREFJQYyBIRERFRUGIgS0RERERBiYEsEVEQeueddxAVFYXi4uLGx+677z707t0bly5dCuDIiIiajkmSJCnQgyAiIn0kSULfvn0xfPhwrFq1CgsXLsRrr72GL774Aunp6YEeHhFRkwgL9ACIiEg/k8mEJUuW4M4770Tbtm2xatUq7N69m0EsEbUonJElIgpiOTk5OHLkCLZs2YIRI0YEejhERE2KObJEREHqk08+wdGjR2Gz2dCmTZtAD4eIqMlxRpaIKAjl5eUhNzcXr776KtauXQur1Yr169cHelhERE2KObJEREGmoKAAEyZMwOOPP4677roLnTp1wuDBg5GXl4ecnJxAD4+IqMlwRpaIKIhcuHABQ4YMQW5uLl555ZXGxydMmACbzYZPPvkkgKMjImpaDGSJiIiIKChxsxcRERERBSUGskREREQUlBjIEhEREVFQYiBLREREREGJgSwRERERBSUGskREREQUlBjIEhEREVFQYiBLREREREGJgSwRERERBSUGskREREQUlBjIEhEREVFQ+v9cgMktc1uobwAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "draw_clusters(X, model.labels_)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "joWNYFnyemSD"
+      },
+      "source": [
+        "Как видим, модель оказалось очень чувствительной к выбросам и не отнесла их\n",
+        "ни к каким кластерам. Основные кластеры модель правильно распознала, необходим\n",
+        "тюнинг модели."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BZ1d8vsHfA1w"
+      },
+      "source": [
+        "##### Улучшение бейзлайна\n",
+        "\n",
+        "На основе прошлых выводов у нас возникла необходимость в улучшении бейзлайна\n",
+        "с целью получения более точной модели. Попробуем воспользоваться методом\n",
+        "подбора гиперпараметров. В качестве подбираемых гиперпараметров возьмем\n",
+        "`eps`, `min_samples` и `algorithm`.\n",
+        "\n",
+        "При подборе гиперпараметров нам необходимо понять, какая конфигурация\n",
+        "для модели окажется лучшей. Оценку итогового качестве конфигурации\n",
+        "будет проводить на основе значений метрики коэффициента силуэта."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 332,
+          "referenced_widgets": [
+            "0305c3c7c8fb43e29d4f824517045517",
+            "44c1bb8481694276901054e871cadd38",
+            "1b99544a2b754464b2604bb444255f3d",
+            "69136efaa7a44fcebb3e695b8bb63064",
+            "24a321c87593420fa675a13514700825",
+            "b241c68c332e4f0d8cf3648f8b49c3c3",
+            "d016ef4c32d24a418e6a236323f51beb",
+            "a18c57576abc42329f168b2c2672de90",
+            "a1ef54bd2cb8470baa5773d8cf162572",
+            "784b17ca93244be38c825eb8502c7a08",
+            "bbf363f6d5b64ce8bd7a5d48e59fa500"
+          ]
+        },
+        "id": "rdOForJLfXB-",
+        "outputId": "c8ee4b47-b937-430b-a299-c5dbbbfc0a0d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0305c3c7c8fb43e29d4f824517045517",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/160 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"result[result['score'] == result['score']\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"eps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.7666666666666666,\n        \"max\": 0.7666666666666666,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.7666666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_samples\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"algorithm\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"brute\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.18687833744435e-16,\n        \"min\": 0.6976112834706539,\n        \"max\": 0.6976112834706539,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.6976112834706539\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1f815edc-a3fd-4ec1-9805-6459f79207ef\" 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",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>eps</th>\n",
+              "      <th>min_samples</th>\n",
+              "      <th>algorithm</th>\n",
+              "      <th>score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>96</th>\n",
+              "      <td>0.766667</td>\n",
+              "      <td>3</td>\n",
+              "      <td>ball_tree</td>\n",
+              "      <td>0.697611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
+              "      <td>0.766667</td>\n",
+              "      <td>3</td>\n",
+              "      <td>brute</td>\n",
+              "      <td>0.697611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>98</th>\n",
+              "      <td>0.766667</td>\n",
+              "      <td>3</td>\n",
+              "      <td>kd_tree</td>\n",
+              "      <td>0.697611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>99</th>\n",
+              "      <td>0.766667</td>\n",
+              "      <td>3</td>\n",
+              "      <td>auto</td>\n",
+              "      <td>0.697611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>100</th>\n",
+              "      <td>0.766667</td>\n",
+              "      <td>4</td>\n",
+              "      <td>ball_tree</td>\n",
+              "      <td>0.697611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>101</th>\n",
+              "      <td>0.766667</td>\n",
+              "      <td>4</td>\n",
+              "      <td>brute</td>\n",
+              "      <td>0.697611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>102</th>\n",
+              "      <td>0.766667</td>\n",
+              "      <td>4</td>\n",
+              "      <td>kd_tree</td>\n",
+              "      <td>0.697611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>103</th>\n",
+              "      <td>0.766667</td>\n",
+              "      <td>4</td>\n",
+              "      <td>auto</td>\n",
+              "      <td>0.697611</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f815edc-a3fd-4ec1-9805-6459f79207ef')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1f815edc-a3fd-4ec1-9805-6459f79207ef 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-1f815edc-a3fd-4ec1-9805-6459f79207ef');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-95c479ff-fa0f-45cf-b229-6afb1d87e1d4\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-95c479ff-fa0f-45cf-b229-6afb1d87e1d4')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-95c479ff-fa0f-45cf-b229-6afb1d87e1d4 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "          eps  min_samples  algorithm     score\n",
+              "96   0.766667            3  ball_tree  0.697611\n",
+              "97   0.766667            3      brute  0.697611\n",
+              "98   0.766667            3    kd_tree  0.697611\n",
+              "99   0.766667            3       auto  0.697611\n",
+              "100  0.766667            4  ball_tree  0.697611\n",
+              "101  0.766667            4      brute  0.697611\n",
+              "102  0.766667            4    kd_tree  0.697611\n",
+              "103  0.766667            4       auto  0.697611"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "eps_range = np.linspace(0.1, 1.1, 10)\n",
+        "samples_range = range(3, 7, 1)\n",
+        "algorithm_range = {'auto', 'ball_tree', 'kd_tree', 'brute'}\n",
+        "hyper_params_range = tuple(\n",
+        "  itertools.product(\n",
+        "    eps_range,\n",
+        "    samples_range,\n",
+        "    algorithm_range,\n",
+        "  )\n",
+        ")\n",
+        "\n",
+        "def learn_model(eps, min_samples, algorithm, X, y):\n",
+        "  model = sklearn.cluster.DBSCAN(eps=eps, min_samples=min_samples, algorithm=algorithm)\n",
+        "  model.fit(X)\n",
+        "  metrics = calculate_metrics(X, y, model.labels_)\n",
+        "  return metrics['silhouette_score']\n",
+        "\n",
+        "result = pd.DataFrame([\n",
+        "  {\n",
+        "    'eps': eps,\n",
+        "    'min_samples': min_samples,\n",
+        "    'algorithm': algorithm,\n",
+        "    'score': learn_model(eps, min_samples, algorithm, X, y)\n",
+        "  }\n",
+        "  for eps, min_samples, algorithm in tqdm.notebook.tqdm(hyper_params_range)\n",
+        "])\n",
+        "\n",
+        "# Выводим конфигурации с наилучшим результатом\n",
+        "result[result['score'] == result['score'].max()]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NvyPShHxhByi"
+      },
+      "source": [
+        "Как видим, одной из самых лучших конфигураций оказалась конфигурация\n",
+        "`eps=0.77`, `min_samples=3`, `algorithm=ball_tree`. Стоит отметить использование\n",
+        "алгоритма `ball_tree`, так как он основан на представлении множества\n",
+        "соседних точек в виде шарообразного множества, что отлично накладывается\n",
+        "на текущий датасет.\n",
+        "\n",
+        "Увеличение значения для параметра `eps` связано с тем, что DBSCAN чувствителен\n",
+        "к масштабу значений. В прошлый раз мы взяли слишком маленькое значений `eps`,\n",
+        "из-за чего как раз и возникла проблема того, что DBSCAN брал небольшие\n",
+        "объединения точек в отдельные кластеры."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "G5zUnJCkhxzG",
+        "outputId": "1758e020-e7f3-4c4c-93c5-0534dc6973c5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"calculate_metrics(X, y, model\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"\\u041c\\u0435\\u0442\\u0440\\u0438\\u043a\\u0430\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"\\u041e\\u0434\\u043d\\u043e\\u0440\\u043e\\u0434\\u043d\\u043e\\u0441\\u0442\\u044c\",\n          \"V-\\u043c\\u0435\\u0440\\u0430\",\n          \"\\u041a\\u043e\\u044d\\u0444\\u0444\\u0438\\u0446\\u0438\\u0435\\u043d\\u0442 \\u0441\\u0438\\u043b\\u0443\\u044d\\u0442\\u0430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u0417\\u043d\\u0430\\u0447\\u0435\\u043d\\u0438\\u0435\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13834069228570361,\n        \"min\": 0.6976112834706539,\n        \"max\": 0.9917880573525903,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9917880573525903,\n          0.9722699860242505,\n          0.6976112834706539\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fdc9f785-bf4a-4d95-b639-a9ff1e859bc8\" 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",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Метрика</th>\n",
+              "      <th>Значение</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Коэффициент силуэта</td>\n",
+              "      <td>0.697611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Однородность</td>\n",
+              "      <td>0.991788</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Полнота</td>\n",
+              "      <td>0.953505</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>V-мера</td>\n",
+              "      <td>0.972270</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdc9f785-bf4a-4d95-b639-a9ff1e859bc8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fdc9f785-bf4a-4d95-b639-a9ff1e859bc8 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-fdc9f785-bf4a-4d95-b639-a9ff1e859bc8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-7f1381c6-71bb-40f5-8e23-787880cd1d35\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7f1381c6-71bb-40f5-8e23-787880cd1d35')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-7f1381c6-71bb-40f5-8e23-787880cd1d35 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "               Метрика  Значение\n",
+              "0  Коэффициент силуэта  0.697611\n",
+              "1         Однородность  0.991788\n",
+              "2              Полнота  0.953505\n",
+              "3               V-мера  0.972270"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model = sklearn.cluster.DBSCAN(eps=0.77, min_samples=3, algorithm='ball_tree')\n",
+        "model.fit(X)\n",
+        "calculate_metrics(X, y, model.labels_, formatted=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "23dxfdGfh6ma",
+        "outputId": "5d067dc4-7827-4a22-c44b-5f9ad7fb7ac0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "draw_clusters(X, model.labels_)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nTyNVUzxh9-h"
+      },
+      "source": [
+        "Как видим, модель стала справляться с поставленной задачей кластеризации\n",
+        "значительно лучше, однако результат все еще далек от идеала. Попробуем использовать другие модели для решения задачи кластеризации."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PLji0oJp7jdv"
+      },
+      "source": [
+        "#### KMeans\n",
+        "\n",
+        "Попробуем теперь взять модели, имеющие больше ограничений в отличие от DBSCAN\n",
+        "и предназначенные для более узкого спектра задач. Одной из такких моделей является `KMeans`, основанная на алгоритме K-средних.\n",
+        "\n",
+        "Задача модели `KMeans` состоит в минимизации суммы квадратов расстояний\n",
+        "до центроидов. Текущий датасет отлично подходит для данной модели, так как\n",
+        "она особенно подходит в случаях, когда:\n",
+        "- Кластеры имеют выпуклую форму (в том числе и эллипсоидную)\n",
+        "- Количество кластеров известно\n",
+        "- Кластеры имеют одинаковую плотность, в чем мы можем убедиться по отсутствию\n",
+        "  \"дыр\" в визуализации кластеров датасета."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "KbCII2-u7kfn",
+        "outputId": "4e07f6ea-802b-4fb5-94e5-55596e9f5e5d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"calculate_metrics(X_test, y_test, model\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"\\u041c\\u0435\\u0442\\u0440\\u0438\\u043a\\u0430\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"\\u041e\\u0434\\u043d\\u043e\\u0440\\u043e\\u0434\\u043d\\u043e\\u0441\\u0442\\u044c\",\n          \"V-\\u043c\\u0435\\u0440\\u0430\",\n          \"\\u041a\\u043e\\u044d\\u0444\\u0444\\u0438\\u0446\\u0438\\u0435\\u043d\\u0442 \\u0441\\u0438\\u043b\\u0443\\u044d\\u0442\\u0430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u0417\\u043d\\u0430\\u0447\\u0435\\u043d\\u0438\\u0435\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14567302700016055,\n        \"min\": 0.7086539459996789,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.7086539459996789\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e641624d-d234-4237-a2b9-0e64747d4ea5\" 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",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Метрика</th>\n",
+              "      <th>Значение</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Коэффициент силуэта</td>\n",
+              "      <td>0.708654</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Однородность</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Полнота</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>V-мера</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e641624d-d234-4237-a2b9-0e64747d4ea5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e641624d-d234-4237-a2b9-0e64747d4ea5 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-e641624d-d234-4237-a2b9-0e64747d4ea5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f615ffe5-6712-4bf8-9c85-b572486fd383\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f615ffe5-6712-4bf8-9c85-b572486fd383')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f615ffe5-6712-4bf8-9c85-b572486fd383 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "               Метрика  Значение\n",
+              "0  Коэффициент силуэта  0.708654\n",
+              "1         Однородность  1.000000\n",
+              "2              Полнота  1.000000\n",
+              "3               V-мера  1.000000"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model = sklearn.cluster.KMeans(n_clusters=3, random_state=RANDOM_SEED, n_init=\"auto\")\n",
+        "model.fit(X_train)\n",
+        "calculate_metrics(X_test, y_test, model.predict(X_test), formatted=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Wk11Rm9Kjl9C"
+      },
+      "source": [
+        "Как видим, модель показала \"идеальные\" результаты! V-мера, однородность и\n",
+        "полнота показываются пороговые значения `1`, что говорит нам о том, что\n",
+        "модель идеально распознает кластеры, в которых  находятся точки.\n",
+        "\n",
+        "Проверим это визуально, провизуализировав то, как модель разбивает весь\n",
+        "датасет на кластеры."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "voEiW2wyj23S",
+        "outputId": "c73c0abf-2d35-4223-9ed3-381a8050704f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "draw_clusters(X, model.predict(X))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T7qIBZtc-gjv"
+      },
+      "source": [
+        "#### GMM\n",
+        "\n",
+        "В конце рассмотрим еще одну модель для решения задачи кластеризации - GMM.\n",
+        "Главная особенность GMM состоит в том, что она построена на основе\n",
+        "вероятностных распределений, в отличие от `KMeans`, который основан\n",
+        "на геометрических методах.\n",
+        "\n",
+        "GMM отлично подходит для случаев, когда кластеры имеют эллипсоидную форму,\n",
+        "что как раз подходит для текущего датасета.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "QELBLCO1-i0f",
+        "outputId": "df9e7706-e404-400e-87f4-99dcafbf46c6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"calculate_metrics(X_test, y_test, model\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"\\u041c\\u0435\\u0442\\u0440\\u0438\\u043a\\u0430\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"\\u041e\\u0434\\u043d\\u043e\\u0440\\u043e\\u0434\\u043d\\u043e\\u0441\\u0442\\u044c\",\n          \"V-\\u043c\\u0435\\u0440\\u0430\",\n          \"\\u041a\\u043e\\u044d\\u0444\\u0444\\u0438\\u0446\\u0438\\u0435\\u043d\\u0442 \\u0441\\u0438\\u043b\\u0443\\u044d\\u0442\\u0430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u0417\\u043d\\u0430\\u0447\\u0435\\u043d\\u0438\\u0435\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14567302700016055,\n        \"min\": 0.7086539459996789,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.7086539459996789\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a5ee1bfb-164f-4358-970a-9e06ccf4f3e9\" 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",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Метрика</th>\n",
+              "      <th>Значение</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Коэффициент силуэта</td>\n",
+              "      <td>0.708654</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Однородность</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Полнота</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>V-мера</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5ee1bfb-164f-4358-970a-9e06ccf4f3e9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a5ee1bfb-164f-4358-970a-9e06ccf4f3e9 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-a5ee1bfb-164f-4358-970a-9e06ccf4f3e9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-1e8fe4de-2b72-4b25-87e8-f994a4b9a28b\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1e8fe4de-2b72-4b25-87e8-f994a4b9a28b')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1e8fe4de-2b72-4b25-87e8-f994a4b9a28b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "               Метрика  Значение\n",
+              "0  Коэффициент силуэта  0.708654\n",
+              "1         Однородность  1.000000\n",
+              "2              Полнота  1.000000\n",
+              "3               V-мера  1.000000"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model = sklearn.mixture.GaussianMixture(\n",
+        "    n_components=3,\n",
+        "    covariance_type='spherical',\n",
+        "    random_state=RANDOM_SEED,\n",
+        ")\n",
+        "model.fit(X_train)\n",
+        "calculate_metrics(X_test, y_test, model.predict(X_test), formatted=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JbnL-ljOksBI"
+      },
+      "source": [
+        "Как видим, результаты GMM ничем не отличаются от KMeans для текущего датасета.\n",
+        "Модель также показывает \"идеальные\" результаты! Убедимся в этом, провизуализировав то, как модель кластеризует весь датасет."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "K31g1cmxk2Zv",
+        "outputId": "bcec2bbd-c44e-4e82-d725-28457598a2b3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "draw_clusters(X, model.predict(X))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iZDadZQVk8jC"
+      },
+      "source": [
+        "### Итог\n",
+        "\n",
+        "Итого, мы можем сделать вывод о том, что модели `KMeans` и `GMM` идеально\n",
+        "подошли для решения задачи кластеризации  для текущего датасета. Использование\n",
+        "модели `DBSCAN` не дало столь положительных результатов, так как модель\n",
+        "имеет более общее назанчение нежели другие модели и справляется с текущим\n",
+        "датасетом хуже.\n",
+        "\n",
+        "| Модель                       | Коэффициент силуэта | Однородность | Полнота | V-мера |\n",
+        "|------------------------------|---------------------|--------------|---------|--------|\n",
+        "| DBSCAN (бейзлайн)            | 0.32                | 0.96         | 0.83    | 0.89   |\n",
+        "| DBSCAN (улучшенный бейзлайн) | 0.69                | 0.99         | 0.95    | 0.97   |\n",
+        "| KMeans                       | 0.71                | 1.00         | 1.00    | 1.00   |\n",
+        "| GMM                          | 0.71                | 1.00         | 1.00    | 1.00   |\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
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From 3e4f5010168707c4c13ce2b29afe35b4543e73cc Mon Sep 17 00:00:00 2001
From: Nifacy <mai.grishin.alexey@gmail.com>
Date: Fri, 28 Nov 2025 11:39:03 +0300
Subject: [PATCH 2/2] add dataset file

---
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 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 stud/grishin/lab-2/datasets/lab2-04.xlsx

diff --git a/stud/grishin/lab-2/datasets/lab2-04.xlsx b/stud/grishin/lab-2/datasets/lab2-04.xlsx
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