Life_Expectancy_Prediction.ipynb

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        "# Life Expectancy Prediction using Linear Regression\n",
        "\n",
        "## Dataset\n",
        "In this dataset we predict Life expectancy of different countries. We will explore how immunization factors, mortality factors, economic factors, social factors and other health related factors affect Life expectancy of a country.\n",
        "\n",
        "There are two data files: \"LifeExpectancy_training_modified.csv\" and \"LifeExpectancy_test_modified.csv\"<br/>\n",
        "Both files have the following fields, except Life_expectancy which is not available in \"LifeExpectancy_test_modified.csv\"\n",
        "\n",
        "Features :\n",
        "- Year : from 2002 to 2015\n",
        "- Status : Developed or Developing status\n",
        "- Adult_Mortality : Adult Mortality Rates of both sexes (probability of dying between 15 and 60 years per 1000 population)\n",
        "- Alcohol : Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol)\n",
        "- percentage_expenditure : Expenditure on health as a percentage of Gross Domestic Product per capita(%)\n",
        "- BMI: Average Body Mass Index of entire population\n",
        "- Total_expenditure: General government expenditure on health as a percentage of total government expenditure (%)\n",
        "- Diphtheria: Diphtheria tetanus toxoid and pertussis (DTP3) immunization coverage among 1-year-olds (%)\n",
        "- HIV_AIDS: Deaths per 1000 live births HIV/AIDS (0-4 years)\n",
        "- GDP: Gross Domestic Product per capita (in USD)\n",
        "- Population\n",
        "- Income_composition_of_resources: Human Development Index in terms of income composition of resources (index ranging from 0 to 1)\n",
        "- Schooling: Number of years of Schooling(years)\n",
        "- Health_Index: Health index\n",
        "\n",
        "Target:\n",
        "- Life_expectancy: Life Expectancy in age\n",
        "\n",
        "\n",
        "Training dataset, \"LifeExpectancy_training_modified.csv\", contains 1064 rows and 15 columns. This is the training set containing both of the features and the target.<br/>\n",
        "Test dataset, \"LifeExpectancy_test_modified.csv\", contains 458 rows and 14 columns. This is the test set which only contains the features.<br/>\n",
        "\n",
        "Our goal is to predict Life expectancy based on the features."
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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "from sklearn.metrics import make_scorer, r2_score, mean_squared_error\n",
        "from itertools import chain, combinations"
      ]
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      "source": [
        "Load the training data \"LifeExpectancy_training_modified.csv\" in Colab and View the first 5 lines"
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          "text": [
            "Saving LifeExpectancy_training_modified.csv to LifeExpectancy_training_modified.csv\n"
          ]
        }
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        "from google.colab import files\n",
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      "source": [
        "# Load the training data\n",
        "import io\n",
        "df = pd.read_csv(io.BytesIO(uploaded['LifeExpectancy_training_modified.csv']))"
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              "   Year      Status  Adult_Mortality  Alcohol  percentage_expenditure   BMI  \\\n",
              "0  2011  Developing               93     1.57              835.062683  64.0   \n",
              "1  2014  Developing              152     6.41              789.077295  32.4   \n",
              "2  2013   Developed               68    11.82               90.585012  56.6   \n",
              "3  2014   Developed              135     0.01                1.576409  59.9   \n",
              "4  2002  Developing              192     5.93               27.638103   2.2   \n",
              "\n",
              "   Total_expenditure  Diphtheria  HIV_AIDS         GDP  Population  \\\n",
              "0               7.12          81       0.1  8734.96530   4588368.0   \n",
              "1               4.12          99       0.1  5941.84710  68416772.0   \n",
              "2              11.14          95       0.1   554.71532   8479375.0   \n",
              "3               5.57          94       0.1    12.27733   1998979.0   \n",
              "4               3.70          96       0.7   296.54617   6473164.0   \n",
              "\n",
              "   Income_composition_of_resources  Schooling  Health_Index  Life_expectancy  \n",
              "0                            0.758       13.2      0.475880             75.0  \n",
              "1                            0.737       13.6      0.203275             74.6  \n",
              "2                            0.887       15.7      0.913847             81.1  \n",
              "3                            0.797       14.7      0.275401             74.8  \n",
              "4                            0.657       11.5      0.400974             71.4  "
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              "      <th>Year</th>\n",
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              "      <th>Life_expectancy</th>\n",
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              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>Developing</td>\n",
              "      <td>93</td>\n",
              "      <td>1.57</td>\n",
              "      <td>835.062683</td>\n",
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              "      <td>0.758</td>\n",
              "      <td>13.2</td>\n",
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              "      <td>75.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>2014</td>\n",
              "      <td>Developing</td>\n",
              "      <td>152</td>\n",
              "      <td>6.41</td>\n",
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              "      <td>2013</td>\n",
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              "      <td>68</td>\n",
              "      <td>11.82</td>\n",
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              "      <td>2014</td>\n",
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              "      <td>135</td>\n",
              "      <td>0.01</td>\n",
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              "      <td>59.9</td>\n",
              "      <td>5.57</td>\n",
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              "      <th>4</th>\n",
              "      <td>2002</td>\n",
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              "\n",
              "\n",
              "<div id=\"df-f8cd2ba0-a054-4663-8b94-094d833441e8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f8cd2ba0-a054-4663-8b94-094d833441e8')\"\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-f8cd2ba0-a054-4663-8b94-094d833441e8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Show the first 5 lines\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kIRo3Da91IdF"
      },
      "source": [
        "## Data Exploration\n",
        "We can plot a histogram of the dataframe for the features except \"Status\" to understand their distributions. <br/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iemQk82B3Nam",
        "outputId": "06b0f8ae-8a57-43bd-86ff-f54e4d4b11ff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x7000 with 14 Axes>"
            ],
            "image/png": 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AAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJb6eLgAAAHivqFErbRl33+R4W8YFYA9WKgAAAABYQqgAAAAAYAmhAgAAAIAl3FMBAABwCbDj/hc77n3xljrhipUKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGCJr6cLAAAAxUWNWlnqY+6bHF/qYwKAxEoFAAAAAIsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBK+/A4ALjF8qRoAoLSxUgEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACzheyoAAAAuEt/7ApzCSgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCE76kAgFLC8+oBAJcqVioAAAAAWEKoAAAAAGAJoQIAAACAJdxTAaBM4z4FAADKPlYqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWeH2oOHnypMaMGaPo6GgFBAToyiuv1LPPPitjjLOPMUZjx45VzZo1FRAQoNjYWO3atcuDVQMAAADlh9eHiilTpmjOnDl6+eWX9f3332vKlCmaOnWqXnrpJWefqVOnaubMmZo7d642btyoKlWqKC4uTseOHfNg5QAAAED54PWPlP3iiy/UpUsXxcefekRkVFSUlixZok2bNkk6tUoxY8YM/eMf/1CXLl0kSW+88YbCw8O1YsUK9ejRw2O1AwAAAOWB169U3HjjjUpNTdXOnTslSV9//bU+++wzdezYUZK0d+9eZWZmKjY21vmZ4OBgtW7dWhs2bChxzIKCAuXm5rq8AAAAAJTM61cqRo0apdzcXDVo0EAVKlTQyZMn9c9//lO9e/eWJGVmZkqSwsPDXT4XHh7u3HampKQkTZgwwd7CAQAAgHLC61cq3nnnHS1atEiLFy/Wli1btHDhQj333HNauHDhRY85evRo5eTkOF8ZGRmlWDEAAABQvnj9SsXIkSM1atQo570RTZs21Y8//qikpCT169dPERERkqSsrCzVrFnT+bmsrCw1b968xDH9/Pzk5+dne+0AAABAeeD1KxW///67fHxcd6NChQoqKiqSJEVHRysiIkKpqanO7bm5udq4caNiYmLcWisAAABQHnn9SkXnzp31z3/+U3Xq1FHjxo21detWTZ8+XQ8++KAkyeFwaNiwYZo4caLq1aun6OhojRkzRpGRkeratatniwcAAADKAa8PFS+99JLGjBmjxx57TIcPH1ZkZKQeeeQRjR071tnnySefVH5+vgYOHKjs7Gy1bdtWq1evlr+/vwcrBwAAAMoHrw8VgYGBmjFjhmbMmHHWPg6HQ88884yeeeYZ9xUGAAAAXCK8/p4KAAAAAJ5FqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGCJr6cLADwlatTKUh9z3+T4Uh8TAACgrGOlAgAAAIAlhAoAAAAAlhAqAAAAAFjCPRWAF+D+DwAAUJaxUgEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALDE19MF4PxEjVpZ6mPumxxf6mNK3lUrAAAArCsXKxU//fST+vTpo+rVqysgIEBNmzbV5s2bnduNMRo7dqxq1qypgIAAxcbGateuXR6sGAAAACg/vD5U/Pbbb2rTpo0qVqyoVatW6bvvvtPzzz+vatWqOftMnTpVM2fO1Ny5c7Vx40ZVqVJFcXFxOnbsmAcrBwAAAMoHr7/8acqUKapdu7aSk5OdbdHR0c5/N8ZoxowZ+sc//qEuXbpIkt544w2Fh4drxYoV6tGjh9trBgAAAMoTr1+peP/999WqVSvde++9CgsLU4sWLfTqq686t+/du1eZmZmKjY11tgUHB6t169basGFDiWMWFBQoNzfX5QUAAACgZF4fKvbs2aM5c+aoXr16+uijj/S3v/1NQ4YM0cKFCyVJmZmZkqTw8HCXz4WHhzu3nSkpKUnBwcHOV+3ate3dCQAAAMCLeX2oKCoq0rXXXqtJkyapRYsWGjhwoB5++GHNnTv3osccPXq0cnJynK+MjIxSrBgAAAAoX7w+VNSsWVONGjVyaWvYsKH2798vSYqIiJAkZWVlufTJyspybjuTn5+fgoKCXF4AAAAASub1oaJNmzZKT093adu5c6fq1q0r6dRN2xEREUpNTXVuz83N1caNGxUTE+PWWgEAAIDyyOuf/vTEE0/oxhtv1KRJk3Tfffdp06ZNmjdvnubNmydJcjgcGjZsmCZOnKh69eopOjpaY8aMUWRkpLp27erZ4gEAAIBywOtDxXXXXafly5dr9OjReuaZZxQdHa0ZM2aod+/ezj5PPvmk8vPzNXDgQGVnZ6tt27ZavXq1/P39PVg5AAAAUD54faiQpDvvvFN33nnnWbc7HA4988wzeuaZZ9xYFQAAAHBpsO2eij179tg1NAAAAIAyxLZQcdVVV6l9+/Z66623dOzYMbumAQAAAOBhtoWKLVu26JprrlFiYqIiIiL0yCOPaNOmTXZNBwAAAMBDbAsVzZs314svvqiDBw/q9ddf16FDh9S2bVs1adJE06dP188//2zX1AAAAADcyPbvqfD19VW3bt20bNkyTZkyRbt379aIESNUu3Zt9e3bV4cOHbK7BAAAAAA2sj1UbN68WY899phq1qyp6dOna8SIEfrhhx+UkpKigwcPqkuXLnaXAAAAAMBGtj1Sdvr06UpOTlZ6ero6deqkN954Q506dZKPz6kcEx0drQULFigqKsquEgAAAAC4gW2hYs6cOXrwwQfVv39/1axZs8Q+YWFhmj9/vl0lAAAAAHAD20LFrl27/rJPpUqV1K9fP7tKAAAAAOAGtt1TkZycrGXLlhVrX7ZsmRYuXGjXtAAAAADczLZQkZSUpBo1ahRrDwsL06RJk+yaFgAAAICb2RYq9u/fr+jo6GLtdevW1f79++2aFgAAAICb2RYqwsLC9M033xRr//rrr1W9enW7pgUAAADgZraFip49e2rIkCFat26dTp48qZMnT2rt2rUaOnSoevToYde0AAAAANzMtqc/Pfvss9q3b586dOggX99T0xQVFalv377cUwEAAACUI7aFikqVKuntt9/Ws88+q6+//loBAQFq2rSp6tata9eUAAAAADzAtlBx2tVXX62rr77a7mkAAAAAeIhtoeLkyZNasGCBUlNTdfjwYRUVFblsX7t2rV1TAwAAAHAj20LF0KFDtWDBAsXHx6tJkyZyOBx2TQUAAADAg2wLFUuXLtU777yjTp062TUFAAAAgDLAtkfKVqpUSVdddZVdwwMAAAAoI2wLFcOHD9eLL74oY4xdUwAAAAAoA2y7/Omzzz7TunXrtGrVKjVu3FgVK1Z02f7ee+/ZNTUAAAAAN7ItVISEhOjuu++2a3gAAAAAZYRtoSI5OdmuoQEAAACUIbbdUyFJJ06c0Mcff6xXXnlFR48elSQdPHhQeXl5dk4LAAAAwI1sW6n48ccfdccdd2j//v0qKCjQbbfdpsDAQE2ZMkUFBQWaO3euXVMDAAAAcCPbViqGDh2qVq1a6bffflNAQICz/e6771Zqaqpd0wIAAABwM9tWKv773//qiy++UKVKlVzao6Ki9NNPP9k1LQAAAAA3s22loqioSCdPnizWfuDAAQUGBto1LQAAAAA3sy1U3H777ZoxY4bzvcPhUF5ensaNG6dOnTrZNS0AAAAAN7Pt8qfnn39ecXFxatSokY4dO6ZevXpp165dqlGjhpYsWWLXtAAAAADczLZQUatWLX399ddaunSpvvnmG+Xl5WnAgAHq3bu3y43bAAAAALybbaFCknx9fdWnTx87pwAAAADgYbaFijfeeOOc2/v27WvX1AAAAADcyLZQMXToUJf3hYWF+v3331WpUiVVrlyZUAEAAACUE7Y9/em3335zeeXl5Sk9PV1t27blRm0AAACgHLEtVJSkXr16mjx5crFVDAAAAADey62hQjp18/bBgwfdPS0AAAAAm9h2T8X777/v8t4Yo0OHDunll19WmzZt7JoWAAAAgJvZFiq6du3q8t7hcOiyyy7Trbfequeff96uaQEAAAC4mW2hoqioyK6hAQAAAJQhbr+nAgAAAED5YttKRWJi4nn3nT59ul1lAAAAALCZbaFi69at2rp1qwoLC1W/fn1J0s6dO1WhQgVde+21zn4Oh8OuEgAAAAC4gW2honPnzgoMDNTChQtVrVo1Sae+EC8hIUE33XSThg8fbtfUAAAAANzItnsqnn/+eSUlJTkDhSRVq1ZNEydO5OlPAAAAQDliW6jIzc3Vzz//XKz9559/1tGjR+2aFgAAAICb2RYq7r77biUkJOi9997TgQMHdODAAb377rsaMGCAunXrZte0AAAAANzMtnsq5s6dqxEjRqhXr14qLCw8NZmvrwYMGKBp06bZNS0AAAAAN7MtVFSuXFmzZ8/WtGnT9MMPP0iSrrzySlWpUsWuKQEAAAB4gO1ffnfo0CEdOnRI9erVU5UqVWSMsXtKAAAAAG5kW6j49ddf1aFDB1199dXq1KmTDh06JEkaMGAAj5MFAAAAyhHbQsUTTzyhihUrav/+/apcubKz/f7779fq1avtmhYAAACAm9l2T8WaNWv00UcfqVatWi7t9erV048//mjXtAAAAADczLaVivz8fJcVitOOHDkiPz8/u6YFAAAA4Ga2hYqbbrpJb7zxhvO9w+FQUVGRpk6dqvbt29s1LQAAAAA3s+3yp6lTp6pDhw7avHmzjh8/rieffFI7duzQkSNH9Pnnn9s1LQAAAAA3s22lokmTJtq5c6fatm2rLl26KD8/X926ddPWrVt15ZVX2jUtAAAAADezZaWisLBQd9xxh+bOnaunn37ajikAAAAAlBG2rFRUrFhR33zzjR1DAwAAAChjbLv8qU+fPpo/f75dwwMAAAAoI2y7UfvEiRN6/fXX9fHHH6tly5aqUqWKy/bp06fbNTUAAAAANyr1ULFnzx5FRUVp+/btuvbaayVJO3fudOnjcDhKe1oAAAAAHlLqoaJevXo6dOiQ1q1bJ0m6//77NXPmTIWHh5f2VAAAAADKgFK/p8IY4/J+1apVys/PL+1pAAAAAJQRtt2ofdqZIQMAAABA+VLqocLhcBS7Z4J7KAAAAIDyq9TvqTDGqH///vLz85MkHTt2TI8++mixpz+99957pT01AAAAAA8o9VDRr18/l/d9+vQp7SkAAAAAlCGlHiqSk5NLe0gAAAAAZZjtN2oDAAAAKN/KXaiYPHmyHA6Hhg0b5mw7duyYBg0apOrVq6tq1arq3r27srKyPFckAAAAUI6Uq1Dx1Vdf6ZVXXtE111zj0v7EE0/oP//5j5YtW6b169fr4MGD6tatm4eqBAAAAMqXchMq8vLy1Lt3b7366quqVq2asz0nJ0fz58/X9OnTdeutt6ply5ZKTk7WF198oS+//NKDFQMAAADlQ7kJFYMGDVJ8fLxiY2Nd2tPS0lRYWOjS3qBBA9WpU0cbNmwocayCggLl5ua6vAAAAACUrNSf/uQJS5cu1ZYtW/TVV18V25aZmalKlSopJCTEpT08PFyZmZkljpeUlKQJEybYUSoAAABQ7nj9SkVGRoaGDh2qRYsWyd/fv1TGHD16tHJycpyvjIyMUhkXAAAAKI+8PlSkpaXp8OHDuvbaa+Xr6ytfX1+tX79eM2fOlK+vr8LDw3X8+HFlZ2e7fC4rK0sREREljunn56egoCCXFwAAAICSef3lTx06dNC3337r0paQkKAGDRroqaeeUu3atVWxYkWlpqaqe/fukqT09HTt379fMTExnigZAAAAKFe8PlQEBgaqSZMmLm1VqlRR9erVne0DBgxQYmKiQkNDFRQUpMcff1wxMTG64YYbPFEyAAAAUK54fag4Hy+88IJ8fHzUvXt3FRQUKC4uTrNnz/Z0WQAAAEC5UC5DxSeffOLy3t/fX7NmzdKsWbM8UxAAAABQjnn9jdoAAAAAPItQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAs8fpQkZSUpOuuu06BgYEKCwtT165dlZ6e7tLn2LFjGjRokKpXr66qVauqe/fuysrK8lDFAAAAQPni9aFi/fr1GjRokL788kulpKSosLBQt99+u/Lz8519nnjiCf3nP//RsmXLtH79eh08eFDdunXzYNUAAABA+eHr6QKsWr16tcv7BQsWKCwsTGlpaWrXrp1ycnI0f/58LV68WLfeeqskKTk5WQ0bNtSXX36pG264wRNlAwAAAOWG169UnCknJ0eSFBoaKklKS0tTYWGhYmNjnX0aNGigOnXqaMOGDSWOUVBQoNzcXJcXAAAAgJKVq1BRVFSkYcOGqU2bNmrSpIkkKTMzU5UqVVJISIhL3/DwcGVmZpY4TlJSkoKDg52v2rVr2106AAAA4LXKVagYNGiQtm/frqVLl1oaZ/To0crJyXG+MjIySqlCAAAAoPzx+nsqThs8eLA++OADffrpp6pVq5azPSIiQsePH1d2drbLakVWVpYiIiJKHMvPz09+fn52lwwAAACUC16/UmGM0eDBg7V8+XKtXbtW0dHRLttbtmypihUrKjU11dmWnp6u/fv3KyYmxt3lAgAAAOWO169UDBo0SIsXL9a///1vBQYGOu+TCA4OVkBAgIKDgzVgwAAlJiYqNDRUQUFBevzxxxUTE8OTnwAAAIBS4PWhYs6cOZKkW265xaU9OTlZ/fv3lyS98MIL8vHxUffu3VVQUKC4uDjNnj3bzZUCAAAA5ZPXhwpjzF/28ff316xZszRr1iw3VAQAAABcWrz+ngoAAAAAnkWoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlvp4uAAAAAPA2UaNW2jLuvsnxtoxrN1YqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWXFKhYtasWYqKipK/v79at26tTZs2ebokAAAAwOtdMqHi7bffVmJiosaNG6ctW7aoWbNmiouL0+HDhz1dGgAAAODVLplQMX36dD388MNKSEhQo0aNNHfuXFWuXFmvv/66p0sDAAAAvNol8T0Vx48fV1pamkaPHu1s8/HxUWxsrDZs2FCsf0FBgQoKCpzvc3JyJEm5ubn2F3sWRQW/l/qYdu2Pt9TqLXVK3lVrafOmffeWWr2lTrt4y/5fynVK3vMz5S3nSfKeWi/lOiXP/eyfntcYc1Gfd5iL/aQXOXjwoC6//HJ98cUXiomJcbY/+eSTWr9+vTZu3OjSf/z48ZowYYK7ywQAAAA8KiMjQ7Vq1brgz10SKxUXavTo0UpMTHS+Lyoq0pEjR1S9enU5HA6315Obm6vatWsrIyNDQUFBbp8fp3AeygbOQ9nAeSgbOA9lA+ehbOA8WGOM0dGjRxUZGXlRn78kQkWNGjVUoUIFZWVlubRnZWUpIiKiWH8/Pz/5+fm5tIWEhNhZ4nkJCgriP5IygPNQNnAeygbOQ9nAeSgbOA9lA+fh4gUHB1/0Zy+JG7UrVaqkli1bKjU11dlWVFSk1NRUl8uhAAAAAFy4S2KlQpISExPVr18/tWrVStdff71mzJih/Px8JSQkeLo0AAAAwKtdMqHi/vvv188//6yxY8cqMzNTzZs31+rVqxUeHu7p0v6Sn5+fxo0bV+ySLLgX56Fs4DyUDZyHsoHzUDZwHsoGzoNnXRJPfwIAAABgn0vingoAAAAA9iFUAAAAALCEUAEAAADAEkIFAAAAAEsIFW6SlJSk6667ToGBgQoLC1PXrl2Vnp7u0ufYsWMaNGiQqlevrqpVq6p79+7FvrBv//79io+PV+XKlRUWFqaRI0fqxIkTzu3vvfeebrvtNl122WUKCgpSTEyMPvroI7fsozdw13n4s88//1y+vr5q3ry5XbvlVdx5DgoKCvT000+rbt268vPzU1RUlF5//XXb99EbuPM8LFq0SM2aNVPlypVVs2ZNPfjgg/r1119t30dvUFrnYciQIWrZsqX8/PzO+v+ab775RjfddJP8/f1Vu3ZtTZ061a7d8jruOg+ffPKJunTpopo1a6pKlSpq3ry5Fi1aZOeueRV3/vdw2u7duxUYGFgmvuTY2xEq3GT9+vUaNGiQvvzyS6WkpKiwsFC333678vPznX2eeOIJ/ec//9GyZcu0fv16HTx4UN26dXNuP3nypOLj43X8+HF98cUXWrhwoRYsWKCxY8c6+3z66ae67bbb9OGHHyotLU3t27dX586dtXXrVrfub1nlrvNwWnZ2tvr27asOHTq4Zf+8gTvPwX333afU1FTNnz9f6enpWrJkierXr++2fS3L3HUePv/8c/Xt21cDBgzQjh07tGzZMm3atEkPP/ywW/e3rCqN83Dagw8+qPvvv7/EeXJzc3X77berbt26SktL07Rp0zR+/HjNmzfPtn3zJu46D1988YWuueYavfvuu/rmm2+UkJCgvn376oMPPrBt37yJu87DaYWFherZs6duuummUt+XS5KBRxw+fNhIMuvXrzfGGJOdnW0qVqxoli1b5uzz/fffG0lmw4YNxhhjPvzwQ+Pj42MyMzOdfebMmWOCgoJMQUHBWedq1KiRmTBhgk174t3sPg/333+/+cc//mHGjRtnmjVrZv8OeSG7zsGqVatMcHCw+fXXX924N97LrvMwbdo0c8UVV7jMNXPmTHP55ZfbvUte6WLOw5+d7f81s2fPNtWqVXP5f9RTTz1l6tevX/o7UQ7YdR5K0qlTJ5OQkFAqdZc3dp+HJ5980vTp08ckJyeb4ODg0i7/ksNKhYfk5ORIkkJDQyVJaWlpKiwsVGxsrLNPgwYNVKdOHW3YsEGStGHDBjVt2tTlC/vi4uKUm5urHTt2lDhPUVGRjh496pwHruw8D8nJydqzZ4/GjRvnjl3xWnadg/fff1+tWrXS1KlTdfnll+vqq6/WiBEj9Mcff7hr17yKXechJiZGGRkZ+vDDD2WMUVZWlv71r3+pU6dO7to1r3Ix5+F8bNiwQe3atVOlSpWcbXFxcUpPT9dvv/1WStWXH3adh7PNxe/oktl5HtauXatly5Zp1qxZpVfwJe6S+UbtsqSoqEjDhg1TmzZt1KRJE0lSZmamKlWqVOyavvDwcGVmZjr7nPkN4Kffn+5zpueee055eXm67777SnkvvJ+d52HXrl0aNWqU/vvf/8rXl//MzsbOc7Bnzx599tln8vf31/Lly/XLL7/oscce06+//qrk5GSb98y72Hke2rRpo0WLFun+++/XsWPHdOLECXXu3Jlf5CW42PNwPjIzMxUdHV1sjNPbqlWrZq34csTO83Cmd955R1999ZVeeeUVKyWXS3aeh19//VX9+/fXW2+9paCgoNIs+5LGn3Y8YNCgQdq+fbs+++wzW+dZvHixJkyYoH//+98KCwuzdS5vZNd5OHnypHr16qUJEybo6quvLtWxyxs7/1soKiqSw+HQokWLFBwcLEmaPn267rnnHs2ePVsBAQGlPqe3svM8fPfddxo6dKjGjh2ruLg4HTp0SCNHjtSjjz6q+fPnl/p83sxdvxtwbu46D+vWrVNCQoJeffVVNW7c2Na5vJGd5+Hhhx9Wr1691K5du1If+1LG5U9uNnjwYH3wwQdat26datWq5WyPiIjQ8ePHlZ2d7dI/KytLERERzj5nPuHg9PvTfU5bunSpHnroIb3zzjsuy4Q4xc7zcPToUW3evFmDBw+Wr6+vfH199cwzz+jrr7+Wr6+v1q5da+/OeQm7/1uoWbOmLr/8cmegkKSGDRvKGKMDBw7YsUteye7zkJSUpDZt2mjkyJG65pprFBcXp9mzZ+v111/XoUOHbNwz72LlPJyPC/n9cSmz+zyctn79enXu3FkvvPCC+vbta7Xscsfu87B27Vo999xzzt/RAwYMUE5Ojnx9fXlCoAWECjcxxmjw4MFavny51q5dW2wZumXLlqpYsaJSU1Odbenp6dq/f79iYmIknbo2+dtvv9Xhw4edfVJSUhQUFKRGjRo525YsWaKEhAQtWbJE8fHxNu+Zd3HHeQgKCtK3336rbdu2OV+PPvqo6tevr23btql169bu2dkyyl3/LbRp00YHDx5UXl6es8/OnTvl4+Pj8kvqUuWu8/D777/Lx8f1V02FChWcNVzqSuM8nI+YmBh9+umnKiwsdLalpKSofv36XPok950H6dRjZePj4zVlyhQNHDiwVOovL9x1HjZs2ODyO/qZZ55RYGCgtm3bprvvvrvU9ueS46k7xC81f/vb30xwcLD55JNPzKFDh5yv33//3dnn0UcfNXXq1DFr1641mzdvNjExMSYmJsa5/cSJE6ZJkybm9ttvN9u2bTOrV682l112mRk9erSzz6JFi4yvr6+ZNWuWyzzZ2dlu3d+yyl3n4Uw8/en/c9c5OHr0qKlVq5a55557zI4dO8z69etNvXr1zEMPPeTW/S2r3HUekpOTja+vr5k9e7b54YcfzGeffWZatWplrr/+erfub1lVGufBGGN27dpltm7dah555BFz9dVXm61bt5qtW7c6n/aUnZ1twsPDzQMPPGC2b99uli5daipXrmxeeeUVt+5vWeWu87B27VpTuXJlM3r0aJd5eErdKe46D2fi6U+lg1DhJpJKfCUnJzv7/PHHH+axxx4z1apVM5UrVzZ33323OXTokMs4+/btMx07djQBAQGmRo0aZvjw4aawsNC5/eabby5xnn79+rlpT8s2d52HMxEq/j93noPvv//exMbGmoCAAFOrVi2TmJjo8svpUubO8zBz5kzTqFEjExAQYGrWrGl69+5tDhw44I7dLPNK6zyc7f/9e/fudfb5+uuvTdu2bY2fn5+5/PLLzeTJk920l2Wfu85Dv379Stx+8803u29nyzB3/vfwZ4SK0uEwhvVnAAAAABePeyoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAA2MoYo9jYWMXFxRXbNnv2bIWEhOjAgQMeqAwAUFoIFQAAWzkcDiUnJ2vjxo165ZVXnO179+7Vk08+qZdeekm1atUq1TkLCwtLdTwAwLkRKgAAtqtdu7ZefPFFjRgxQnv37pUxRgMGDNDtt9+uFi1aqGPHjqpatarCw8P1wAMP6JdffnF+dvXq1Wrbtq1CQkJUvXp13Xnnnfrhhx+c2/ft2yeHw6G3335bN998s/z9/bVo0SJP7CYAXLIcxhjj6SIAAJeGrl27KicnR926ddOzzz6rHTt2qHHjxnrooYfUt29f/fHHH3rqqad04sQJrV27VpL07rvvyuFw6JprrlFeXp7Gjh2rffv2adu2bfLx8dG+ffsUHR2tqKgoPf/882rRooX8/f1Vs2ZND+8tAFw6CBUAALc5fPiwGjdurCNHjujdd9/V9u3b9d///lcfffSRs8+BAwdUu3Ztpaen6+qrry42xi+//KLLLrtM3377rZo0aeIMFTNmzNDQoUPduTsAgP/D5U8AALcJCwvTI488ooYNG6pr1676+uuvtW7dOlWtWtX5atCggSQ5L3HatWuXevbsqSuuuEJBQUGKioqSJO3fv99l7FatWrl1XwAA/5+vpwsAAFxafH195et76tdPXl6eOnfurClTphTrd/rypc6dO6tu3bp69dVXFRkZqaKiIjVp0kTHjx936V+lShX7iwcAlIhQAQDwmGuvvVbvvvuuoqKinEHjz3799Velp6fr1Vdf1U033SRJ+uyzz9xdJgDgL3D5EwDAYwYNGqQjR46oZ8+e+uqrr/TDDz/oo48+UkJCgk6ePKlq1aqpevXqmjdvnnbv3q21a9cqMTHR02UDAM5AqAAAeExkZKQ+//xznTx5UrfffruaNm2qYcOGKSQkRD4+PvLx8dHSpUuVlpamJk2a6IknntC0adM8XTYA4Aw8/QkAAACAJaxUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAeCSFBUVpf79+3u6jHJv2rRpuuKKK1ShQgU1b97c1rn69++vqKioi/rs+PHj5XA4SrcgL+ZwODR+/Hjn+wULFsjhcGjfvn0eqwlA2UaoAOD1Tv+BZ/PmzSVuv+WWW9SkSRPL83z44Ycuf9DCua1Zs0ZPPvmk2rRpo+TkZE2aNOm8PnfffffJ4XDoqaeesrnCc5s0aZJWrFhxUZ91OBxyOBx66KGHStz+9NNPO/v88ssvFqosbvHixZoxY0apjlmS2bNna8GCBbbPA8A7ECoAXJLS09P16quvXtBnPvzwQ02YMMGmisqftWvXysfHR/Pnz1ffvn3VqVOnv/xMbm6u/vOf/ygqKkpLliyRMcYNlZbMSqiQJH9/f7377rs6fvx4sW1LliyRv7+/herOzo5Q8cADD+iPP/5Q3bp1nW2ECgB/RqgAcEny8/NTxYoVPV3GBcnPz/d0CRfk8OHDCggIUKVKlc77M++++65Onjyp119/XRkZGfr0009trNBed9xxh3Jzc7Vq1SqX9i+++EJ79+5VfHx8qc5n589HhQoV5O/vzyViAM6KUAHgknTmPRWFhYWaMGGC6tWrJ39/f1WvXl1t27ZVSkqKpFPX68+aNUvS/7+05c9/wMrPz9fw4cNVu3Zt+fn5qX79+nruueeK/U37H3/8oSFDhqhGjRoKDAzUXXfdpZ9++qnYNeynr/H/7rvv1KtXL1WrVk1t27aVJH3zzTfq37+/rrjiCvn7+ysiIkIPPvigfv31V5e5To+xc+dO9enTR8HBwbrssss0ZswYGWOUkZGhLl26KCgoSBEREXr++efP69idOHFCzz77rK688kr5+fkpKipKf//731VQUODs43A4lJycrPz8fOexOp+/1V60aJFuu+02tW/fXg0bNtSiRYtK7LdixQo1adJE/v7+atKkiZYvX16szyeffCKHw6FPPvnEpX3fvn1/WY/D4VB+fr4WLlzorP9C78G5/PLL1a5dOy1evLjYPjZt2vSsl+QtW7ZMLVu2VEBAgGrUqKE+ffrop59+cunTv39/Va1aVT/88IM6deqkwMBA9e7dW7fccotWrlypH3/80Vn36ftMjh8/rrFjx6ply5YKDg5WlSpVdNNNN2ndunV/uS9n3lMRFRWlHTt2aP369c55brnlFu3Zs0cOh0MvvPBCsTG++OILORwOLVmy5DyOHgBv4+vpAgCgtOTk5JR4fXphYeFffnb8+PFKSkrSQw89pOuvv165ubnavHmztmzZottuu02PPPKIDh48qJSUFL355psunzXG6K677tK6des0YMAANW/eXB999JFGjhypn376yeUPWP3799c777yjBx54QDfccIPWr19/zr+xvvfee1WvXj1NmjTJGVBSUlK0Z88eJSQkKCIiQjt27NC8efO0Y8cOffnll8X+Nvn+++9Xw4YNNXnyZK1cuVITJ05UaGioXnnlFd16662aMmWKFi1apBEjRui6665Tu3btznmsHnroIS1cuFD33HOPhg8fro0bNyopKUnff/+98w/3b775pubNm6dNmzbptddekyTdeOON5xz34MGDWrdunRYuXChJ6tmzp1544QW9/PLLLqsda9asUffu3dWoUSMlJSXp119/VUJCgmrVqnXO8S/Em2++6fxZGDhwoCTpyiuvvOBxevXqpaFDhyovL09Vq1bViRMntGzZMiUmJurYsWPF+i9YsEAJCQm67rrrlJSUpKysLL344ov6/PPPtXXrVoWEhDj7njhxQnFxcWrbtq2ee+45Va5cWREREcrJydGBAwecP3dVq1aVdOrSstdee009e/bUww8/rKNHj2r+/PmKi4vTpk2bLuhG+hkzZujxxx9X1apV9fTTT0uSwsPDdcUVV6hNmzZatGiRnnjiCZfPLFq0SIGBgerSpcsFHkUAXsEAgJdLTk42ks75aty4sctn6tata/r16+d836xZMxMfH3/OeQYNGmRK+t/mihUrjCQzceJEl/Z77rnHOBwOs3v3bmOMMWlpaUaSGTZsmEu//v37G0lm3LhxzrZx48YZSaZnz57F5vv999+LtS1ZssRIMp9++mmxMQYOHOhsO3HihKlVq5ZxOBxm8uTJzvbffvvNBAQEuByTkmzbts1IMg899JBL+4gRI4wks3btWmdbv379TJUqVc453p8999xzJiAgwOTm5hpjjNm5c6eRZJYvX+7Sr3nz5qZmzZomOzvb2bZmzRojydStW9fZtm7dOiPJrFu3zuXze/fuNZJMcnKys+30sfqzKlWq/OXxOBtJZtCgQebIkSOmUqVK5s033zTGGLNy5UrjcDjMvn37nHP+/PPPxhhjjh8/bsLCwkyTJk3MH3/84Rzrgw8+MJLM2LFjnW39+vUzksyoUaOKzR0fH+9yHE47ceKEKSgocGn77bffTHh4uHnwwQeL1f/nn8fT/43t3bvX2da4cWNz8803F5vnlVdeMZLM999/72w7fvy4qVGjxkUfTwBlH5c/ASg3Zs2apZSUlGKva6655i8/GxISoh07dmjXrl0XPO+HH36oChUqaMiQIS7tw4cPlzHGeU396tWrJUmPPfaYS7/HH3/8rGM/+uijxdoCAgKc/37s2DH98ssvuuGGGyRJW7ZsKdb/z08gqlChglq1aiVjjAYMGOBsDwkJUf369bVnz56z1iKd2ldJSkxMdGkfPny4JGnlypXn/Py5LFq0SPHx8QoMDJQk1atXTy1btnS5BOrQoUPatm2b+vXrp+DgYGf7bbfdpkaNGl303HapVq2a7rjjDuclP4sXL9aNN97ocsPzaZs3b9bhw4f12GOPudzEHR8frwYNGpR4bP/2t7+ddy0VKlRwrvgUFRXpyJEjOnHihFq1alXiz83Fuu++++Tv7+9y3j766CP98ssv6tOnT6nNA6BsIVQAKDeuv/56xcbGFntVq1btLz/7zDPPKDs7W1dffbWaNm2qkSNH6ptvvjmveX/88UdFRkY6/zB8WsOGDZ3bT//Tx8dH0dHRLv2uuuqqs459Zl9JOnLkiIYOHarw8HAFBATosssuc/bLyckp1r9OnTou74ODg+Xv768aNWoUa//tt9/OWsuf9+HMmiMiIhQSEuLc1wv1/fffa+vWrWrTpo12797tfN1yyy364IMPlJub65xfOhU4zlS/fv2LmttuvXr1UkpKivbv368VK1aoV69eJfY7vW8l7UeDBg2KHVtfX98LvuRr4cKFuuaaa5z3DV122WVauXJliT83FyskJESdO3d2uZdk0aJFuvzyy3XrrbeW2jwAyhZCBQBIateunX744Qe9/vrratKkiV577TVde+21zvsBPOXPqxKn3XfffXr11Vf16KOP6r333tOaNWucqyBFRUXF+leoUOG82iSd9yNcS/spQG+99ZYk6YknnlC9evWcr+eff17Hjh3Tu+++e8Fjnq3GkydPWqr1Qt11113y8/NTv379VFBQoPvuu69UxvXz85OPz/n/Gn/rrbfUv39/XXnllZo/f75Wr16tlJQU3XrrrSX+3FjRt29f7dmzR1988YWOHj2q999/Xz179rygegF4F27UBoD/ExoaqoSEBCUkJCgvL0/t2rXT+PHjnZcPne0PqXXr1tXHH3+so0ePuqxW/O9//3NuP/3PoqIi7d271+Vv2nfv3n3eNf72229KTU3VhAkTNHbsWGf7xVy2dTFO78OuXbucKzGSlJWVpezs7BIv6/krxhgtXrxY7du3L3ZpmCQ9++yzWrRokRISEpzjl7S/6enpLu9Pr1BlZ2e7tJ/vakppBaeAgAB17dpVb731ljp27Fhshei00/uWnp5e7G/009PTz/vYnq3uf/3rX7riiiv03nvvufQZN27ceY17vvNIpx6ne9lll2nRokVq3bq1fv/9dz3wwAMXNQ8A78BfGQCAVOxxrFWrVtVVV13l8pjUKlWqSCr+h9ROnTrp5MmTevnll13aX3jhBTkcDnXs2FGSFBcXJ+nUl4b92UsvvXTedZ5eYThzRcEd36AsyfkFdmfON336dEm6qO9e+Pzzz7Vv3z4lJCTonnvuKfa6//77tW7dOh08eFA1a9ZU8+bNtXDhQpdLdlJSUvTdd9+5jFu3bl1VqFCh2HddnHn8z6ZKlSrFzvXFGjFihMaNG6cxY8actU+rVq0UFhamuXPnuvzcrVq1St9///15H9sqVaqUeDlTST87Gzdu1IYNG853N4rNc7bj4+vrq549e+qdd97RggUL1LRp0/O6twmA92KlAgAkNWrUSLfccotatmyp0NBQbd68Wf/61780ePBgZ5+WLVtKkoYMGaK4uDhVqFBBPXr0UOfOndW+fXs9/fTT2rdvn5o1a6Y1a9bo3//+t4YNG+Z8FGnLli3VvXt3zZgxQ7/++qvzkbI7d+6UdH5/Mx4UFKR27dpp6tSpKiws1OWXX641a9Zo7969NhyV4po1a6Z+/fpp3rx5ys7O1s0336xNmzZp4cKF6tq1q9q3b3/BYy5atEgVKlQ46x+a77rrLj399NNaunSpEhMTlZSUpPj4eLVt21YPPvigjhw5opdeekmNGzdWXl6e83PBwcG699579dJLL8nhcOjKK6/UBx98oMOHD59XXS1bttTHH3+s6dOnKzIyUtHR0WrduvUF75906rg1a9bsnH0qVqyoKVOmKCEhQTfffLN69uzpfKRsVFRUsUe0nqvut99+W4mJibruuutUtWpVde7cWXfeeafee+893X333YqPj9fevXs1d+5cNWrUyOW4na+WLVtqzpw5mjhxoq666iqFhYW5rLD07dtXM2fO1Lp16zRlypQLHh+Al/Hko6cAoDScftzlV199VeL2m2+++S8fKTtx4kRz/fXXm5CQEBMQEGAaNGhg/vnPf5rjx487+5w4ccI8/vjj5rLLLjMOh8PlEaRHjx41TzzxhImMjDQVK1Y09erVM9OmTTNFRUUu8+bn55tBgwaZ0NBQU7VqVdO1a1eTnp5uJLk84vXMx43+2YEDB8zdd99tQkJCTHBwsLn33nvNwYMHz/pY2jPHONujXks6TiUpLCw0EyZMMNHR0aZixYqmdu3aZvTo0ebYsWPnNc+fHT9+3FSvXt3cdNNN5+wXHR1tWrRo4Xz/7rvvmoYNGxo/Pz/TqFEj895775l+/foVe5Tqzz//bLp3724qV65sqlWrZh555BGzffv283qk7P/+9z/Trl07ExAQYCRd0ONQ9X+PlD2Xs52ft99+27Ro0cL4+fmZ0NBQ07t3b3PgwAGXPuc6tnl5eaZXr14mJCTE5TG7RUVFZtKkSaZu3brGz8/PtGjRwnzwwQclHrczf5ZKeqRsZmamiY+PN4GBgUZSiY+Xbdy4sfHx8SlWP4Dyx2HMed6VBwCwxbZt29SiRQu99dZb6t27t6fLAUpNixYtFBoaqtTUVE+XAsBm3FMBAG70xx9/FGubMWOGfHx8/vKbrAFvsnnzZm3btk19+/b1dCkA3IB7KgDAjaZOnaq0tDS1b99evr6+WrVqlVatWqWBAweqdu3ani4P53Dy5En9/PPP5+xTtWpVVa1a1U0VlU3bt29XWlqann/+edWsWVP333+/p0sC4AaECgBwoxtvvFEpKSl69tlnlZeXpzp16mj8+PF6+umnPV0a/kJGRkaJX0b4Z+PGjdP48ePdU1AZ9a9//UvPPPOM6tevryVLlrh8OziA8ot7KgAAOA/Hjh3TZ599ds4+V1xxha644go3VQQAZQehAgAAAIAlXP50HoqKinTw4EEFBgaW2jesAgAAAGWFMUZHjx5VZGSkfHwu/FlOhIrzcPDgQW6gBAAAQLmXkZGhWrVqXfDnCBXnITAwUNKpgxwUFOThagAAAIDSlZubq9q1azv/3HuhCBXn4fQlT0FBQYQKAAAAlFsXe6k/X34HAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAkjIfKj799FN17txZkZGRcjgcWrFixVn7Pvroo3I4HJoxY4ZL+5EjR9S7d28FBQUpJCREAwYMUF5enr2FAwAAAJeIMh8q8vPz1axZM82aNeuc/ZYvX64vv/xSkZGRxbb17t1bO3bsUEpKij744AN9+umnGjhwoF0lAwAAAJeUMv89FR07dlTHjh3P2eenn37S448/ro8++kjx8fEu277//nutXr1aX331lVq1aiVJeumll9SpUyc999xzJYaQgoICFRQUON/n5uaWwp4AAAAA5VOZX6n4K0VFRXrggQc0cuRINW7cuNj2DRs2KCQkxBkoJCk2NlY+Pj7auHFjiWMmJSUpODjY+apdu7Zt9QMAAADezutDxZQpU+Tr66shQ4aUuD0zM1NhYWEubb6+vgoNDVVmZmaJnxk9erRycnKcr4yMjFKvGwAAACgvyvzlT+eSlpamF198UVu2bLnorxQviZ+fn/z8/EptPAAAAKA88+qViv/+9786fPiw6tSpI19fX/n6+urHH3/U8OHDFRUVJUmKiIjQ4cOHXT534sQJHTlyRBERER6oGgAAAChfvHql4oEHHlBsbKxLW1xcnB544AElJCRIkmJiYpSdna20tDS1bNlSkrR27VoVFRWpdevWbq8ZAAAAKG/KfKjIy8vT7t27ne/37t2rbdu2KTQ0VHXq1FH16tVd+lesWFERERGqX7++JKlhw4a644479PDDD2vu3LkqLCzU4MGD1aNHjxKf/AQAAADgwpT5y582b96sFi1aqEWLFpKkxMREtWjRQmPHjj3vMRYtWqQGDRqoQ4cO6tSpk9q2bat58+bZVTIAAABwSXEYY4yniyjrcnNzFRwcrJycHAUFBXm6HAAAAKBUWf3zbplfqQAAAABQtpX5eypwStSolaU+5r7J8X/dCQAAAPgLrFQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLfD1dAFCeRI1aacu4+ybH2zIuAABAaWClAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlZT5UfPrpp+rcubMiIyPlcDi0YsUK57bCwkI99dRTatq0qapUqaLIyEj17dtXBw8edBnjyJEj6t27t4KCghQSEqIBAwYoLy/PzXsCAAAAlE9lPlTk5+erWbNmmjVrVrFtv//+u7Zs2aIxY8Zoy5Yteu+995Senq677rrLpV/v3r21Y8cOpaSk6IMPPtCnn36qgQMHumsXAAAAgHLN19MF/JWOHTuqY8eOJW4LDg5WSkqKS9vLL7+s66+/Xvv371edOnX0/fffa/Xq1frqq6/UqlUrSdJLL72kTp066bnnnlNkZKTt+wAAAACUZ2V+peJC5eTkyOFwKCQkRJK0YcMGhYSEOAOFJMXGxsrHx0cbN24scYyCggLl5ua6vAAAAACUrFyFimPHjumpp55Sz549FRQUJEnKzMxUWFiYSz9fX1+FhoYqMzOzxHGSkpIUHBzsfNWuXdv22gEAAABvVW5CRWFhoe677z4ZYzRnzhxLY40ePVo5OTnOV0ZGRilVCQAAAJQ/Zf6eivNxOlD8+OOPWrt2rXOVQpIiIiJ0+PBhl/4nTpzQkSNHFBERUeJ4fn5+8vPzs7VmAAAAoLzw+pWK04Fi165d+vjjj1W9enWX7TExMcrOzlZaWpqzbe3atSoqKlLr1q3dXS4AAABQ7pT5lYq8vDzt3r3b+X7v3r3atm2bQkNDVbNmTd1zzz3asmWLPvjgA508edJ5n0RoaKgqVaqkhg0b6o477tDDDz+suXPnqrCwUIMHD1aPHj148hMAAABQCsp8qNi8ebPat2/vfJ+YmChJ6tevn8aPH6/3339fktS8eXOXz61bt0633HKLJGnRokUaPHiwOnToIB8fH3Xv3l0zZ850S/0AAABAeVfmQ8Utt9wiY8xZt59r22mhoaFavHhxaZYFAAAA4P94/T0VAAAAADyLUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJfTxcA4K9FjVpZ6mPumxxf6mMCAIBLEysVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAAS8p8qPj000/VuXNnRUZGyuFwaMWKFS7bjTEaO3asatasqYCAAMXGxmrXrl0ufY4cOaLevXsrKChIISEhGjBggPLy8ty4FwAAAED5VeZDRX5+vpo1a6ZZs2aVuH3q1KmaOXOm5s6dq40bN6pKlSqKi4vTsWPHnH169+6tHTt2KCUlRR988IE+/fRTDRw40F27AAAAAJRrvp4u4K907NhRHTt2LHGbMUYzZszQP/7xD3Xp0kWS9MYbbyg8PFwrVqxQjx499P3332v16tX66quv1KpVK0nSSy+9pE6dOum5555TZGSk2/YFAAAAKI/K/ErFuezdu1eZmZmKjY11tgUHB6t169basGGDJGnDhg0KCQlxBgpJio2NlY+PjzZu3FjiuAUFBcrNzXV5AQAAACiZV4eKzMxMSVJ4eLhLe3h4uHNbZmamwsLCXLb7+voqNDTU2edMSUlJCg4Odr5q165tQ/UAAABA+eDVocIuo0ePVk5OjvOVkZHh6ZIAAACAMsurQ0VERIQkKSsry6U9KyvLuS0iIkKHDx922X7ixAkdOXLE2edMfn5+CgoKcnkBAAAAKJlXh4ro6GhFREQoNTXV2Zabm6uNGzcqJiZGkhQTE6Ps7GylpaU5+6xdu1ZFRUVq3bq122sGAAAAypsy//SnvLw87d692/l+79692rZtm0JDQ1WnTh0NGzZMEydOVL169RQdHa0xY8YoMjJSXbt2lSQ1bNhQd9xxhx5++GHNnTtXhYWFGjx4sHr06MGTnwAAAIBSUOZDxebNm9W+fXvn+8TERElSv379tGDBAj355JPKz8/XwIEDlZ2drbZt22r16tXy9/d3fmbRokUaPHiwOnToIB8fH3Xv3l0zZ850+74AAAAA5ZHDGGM8XURZl5ubq+DgYOXk5Hjs/oqoUStLfcx9k+NLfcxLnR3nyS6cfwAAcJrVP+969T0VAAAAADyPUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAs8fV0AQA8I2rUylIfc9/k+FIfEwAAlH2sVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAs4cvv4BX4ojYAAICyi5UKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAltoWKPXv22DU0AAAAgDLEtlBx1VVXqX379nrrrbd07Ngxu6YBAAAA4GG2hYotW7bommuuUWJioiIiIvTII49o06ZNdk0HAAAAwENsCxXNmzfXiy++qIMHD+r111/XoUOH1LZtWzVp0kTTp0/Xzz//bNfUAAAAANzI9hu1fX191a1bNy1btkxTpkzR7t27NWLECNWuXVt9+/bVoUOH7C4BAAAAgI1sDxWbN2/WY489ppo1a2r69OkaMWKEfvjhB6WkpOjgwYPq0qWLpfFPnjypMWPGKDo6WgEBAbryyiv17LPPyhjj7GOM0dixY1WzZk0FBAQoNjZWu3btsrprAAAAACT52jXw9OnTlZycrPT0dHXq1ElvvPGGOnXqJB+fUzkmOjpaCxYsUFRUlKV5pkyZojlz5mjhwoVq3LixNm/erISEBAUHB2vIkCGSpKlTp2rmzJlauHChoqOjNWbMGMXFxem7776Tv7+/1V3FGaJGrfR0CQAAAHAj20LFnDlz9OCDD6p///6qWbNmiX3CwsI0f/58S/N88cUX6tKli+Lj4yVJUVFRWrJkifOmcGOMZsyYoX/84x/OVZE33nhD4eHhWrFihXr06GFpfgAAAOBSZ9vlT7t27dLo0aPPGigkqVKlSurXr5+leW688UalpqZq586dkqSvv/5an332mTp27ChJ2rt3rzIzMxUbG+v8THBwsFq3bq0NGzaUOGZBQYFyc3NdXgAAAABKZttKRXJysqpWrap7773XpX3ZsmX6/fffLYeJ00aNGqXc3Fw1aNBAFSpU0MmTJ/XPf/5TvXv3liRlZmZKksLDw10+Fx4e7tx2pqSkJE2YMKFU6gMAAADKO9tWKpKSklSjRo1i7WFhYZo0aVKpzfPOO+9o0aJFWrx4sbZs2aKFCxfqueee08KFCy96zNGjRysnJ8f5ysjIKLV6AQAAgPLGtpWK/fv3Kzo6ulh73bp1tX///lKbZ+TIkRo1apTz3oimTZvqxx9/VFJSkvr166eIiAhJUlZWlsulWFlZWWrevHmJY/r5+cnPz6/UagQAAADKM9tWKsLCwvTNN98Ua//6669VvXr1Upvn999/dz5R6rQKFSqoqKhI0qmnTEVERCg1NdW5PTc3Vxs3blRMTEyp1QEAAABcqmxbqejZs6eGDBmiwMBAtWvXTpK0fv16DR06tFSfuNS5c2f985//VJ06ddS4cWNt3bpV06dP14MPPihJcjgcGjZsmCZOnKh69eo5HykbGRmprl27llodAAAAwKXKtlDx7LPPat++ferQoYN8fU9NU1RUpL59+5bqPRUvvfSSxowZo8cee0yHDx9WZGSkHnnkEY0dO9bZ58knn1R+fr4GDhyo7OxstW3bVqtXr+Y7KgAAAIBS4DB//uppG+zcuVNff/21AgIC1LRpU9WtW9fO6WyRm5ur4OBg5eTkKCgoyCM12PGFcvsmx5f6mJL3fPmdHfvvLftuF7t+pgAAgL2s/nnXtpWK066++mpdffXVdk8DAAAAwENsCxUnT57UggULlJqaqsOHDztvnD5t7dq1dk0NAAAAwI1sCxVDhw7VggULFB8fryZNmsjhcNg1FQAAAAAPsi1ULF26VO+88446depk1xQAAAAAygDbvqeiUqVKuuqqq+waHgAAAEAZYVuoGD58uF588UXZ/HApAAAAAB5m2+VPn332mdatW6dVq1apcePGqlixosv29957z66pAQAAALiRbaEiJCREd999t13DAwAAACgjbAsVycnJdg0NAAAAoAyx7Z4KSTpx4oQ+/vhjvfLKKzp69Kgk6eDBg8rLy7NzWgAAAABuZNtKxY8//qg77rhD+/fvV0FBgW677TYFBgZqypQpKigo0Ny5c+2aGgAAAIAb2bZSMXToULVq1Uq//fabAgICnO133323UlNT7ZoWAAAAgJvZtlLx3//+V1988YUqVark0h4VFaWffvrJrmkBAAAAuJltKxVFRUU6efJksfYDBw4oMDDQrmkBAAAAuJltoeL222/XjBkznO8dDofy8vI0btw4derUya5pAQAAALiZbZc/Pf/884qLi1OjRo107Ngx9erVS7t27VKNGjW0ZMkSu6YFAAAA4Ga2hYpatWrp66+/1tKlS/XNN98oLy9PAwYMUO/evV1u3AYAAADg3WwLFZLk6+urPn362DkFAAAAAA+zLVS88cYb59zet29fu6YGAAAA4Ea2hYqhQ4e6vC8sLNTvv/+uSpUqqXLlyoQKAAAAoJyw7elPv/32m8srLy9P6enpatu2LTdqAwAAAOWIbaGiJPXq1dPkyZOLrWIAAAAA8F5uDRXSqZu3Dx486O5pAQAAANjEtnsq3n//fZf3xhgdOnRIL7/8stq0aWPXtAAAAADczLZQ0bVrV5f3DodDl112mW699VY9//zzdk0LAAAAwM1sCxVFRUV2DQ0AAACgDHH7PRUAAAAAyhfbVioSExPPu+/06dPtKgMAAACAzWwLFVu3btXWrVtVWFio+vXrS5J27typChUq6Nprr3X2czgcdpUAAAAAwA1sCxWdO3dWYGCgFi5cqGrVqkk69YV4CQkJuummmzR8+HC7pgYAAADgRrbdU/H8888rKSnJGSgkqVq1apo4cSJPfwIAAADKEdtCRW5urn7++edi7T///LOOHj1q17QAAAAA3My2UHH33XcrISFB7733ng4cOKADBw7o3Xff1YABA9StWze7pgUAAADgZrbdUzF37lyNGDFCvXr1UmFh4anJfH01YMAATZs2za5pAQAAALiZbaGicuXKmj17tqZNm6YffvhBknTllVeqSpUqdk0JAAAAwANs//K7Q4cO6dChQ6pXr56qVKkiY4zdUwIAAABwI9tWKn799Vfdd999WrdunRwOh3bt2qUrrrhCAwYMULVq1XgCFIByJ2rUylIfc9/k+FIfEwCA0mbbSsUTTzyhihUrav/+/apcubKz/f7779fq1avtmhYAAACAm9m2UrFmzRp99NFHqlWrlkt7vXr19OOPP9o1LQAAAAA3s22lIj8/32WF4rQjR47Iz8/PrmkBAAAAuJltoeKmm27SG2+84XzvcDhUVFSkqVOnqn379nZNCwAAAMDNbLv8aerUqerQoYM2b96s48eP68knn9SOHTt05MgRff7553ZNCwAAAMDNbFupaNKkiXbu3Km2bduqS5cuys/PV7du3bR161ZdeeWVdk0LAAAAwM1sWakoLCzUHXfcoblz5+rpp5+2YwoAAAAAZYQtKxUVK1bUN998Y8fQAAAAAMoY2y5/6tOnj+bPn2/X8AAAAADKCNtu1D5x4oRef/11ffzxx2rZsqWqVKnisn369Ol2TQ0AAADAjUo9VOzZs0dRUVHavn27rr32WknSzp07Xfo4HI7SnhYAAACAh5T65U/16tXTL7/8onXr1mndunUKCwvT0qVLne/XrVuntWvXluqcP/30k/r06aPq1asrICBATZs21ebNm53bjTEaO3asatasqYCAAMXGxmrXrl2lWgMAAABwqSr1UGGMcXm/atUq5efnl/Y0Tr/99pvatGmjihUratWqVfruu+/0/PPPq1q1as4+U6dO1cyZMzV37lxt3LhRVapUUVxcnI4dO2ZbXQAAAMClwrZ7Kk47M2SUtilTpqh27dpKTk52tkVHR7vMP2PGDP3jH/9Qly5dJElvvPGGwsPDtWLFCvXo0aPYmAUFBSooKHC+z83NtXEPAAAAAO9W6isVDoej2D0Tdt5D8f7776tVq1a69957FRYWphYtWujVV191bt+7d68yMzMVGxvrbAsODlbr1q21YcOGEsdMSkpScHCw81W7dm3b6gcAAAC8XamvVBhj1L9/f/n5+UmSjh07pkcffbTY05/ee++9Uplvz549mjNnjhITE/X3v/9dX331lYYMGaJKlSqpX79+yszMlCSFh4e7fC48PNy57UyjR49WYmKi831ubi7BAgAAADiLUg8V/fr1c3nfp0+f0p7CRVFRkVq1aqVJkyZJklq0aKHt27dr7ty5xWo5X35+fs5QBAAAAODcSj1U/PneBneoWbOmGjVq5NLWsGFDvfvuu5KkiIgISVJWVpZq1qzp7JOVlaXmzZu7rU4AAACgvLLtG7XdpU2bNkpPT3dp27lzp+rWrSvp1E3bERERSk1NdW7Pzc3Vxo0bFRMT49ZaAQAAgPLI9qc/2e2JJ57QjTfeqEmTJum+++7Tpk2bNG/ePM2bN0/SqZvEhw0bpokTJ6pevXqKjo7WmDFjFBkZqa5du3q2eAAAAKAc8PpQcd1112n58uUaPXq0nnnmGUVHR2vGjBnq3bu3s8+TTz6p/Px8DRw4UNnZ2Wrbtq1Wr14tf39/D1YOAAAAlA9eHyok6c4779Sdd9551u0Oh0PPPPOMnnnmGTdWBQAAAFwavP6eCgAAAACeRagAAAAAYAmhAgAAAIAlhAoAAAAAlpSLG7UBlF9Ro1aW+pj7JseX+pgAAFzKWKkAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACW+Hq6AMBTokat9HQJ5Q7HFACASxMrFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCk3IWKyZMny+FwaNiwYc62Y8eOadCgQapevbqqVq2q7t27Kysry3NFAgAAAOVIuQoVX331lV555RVdc801Lu1PPPGE/vOf/2jZsmVav369Dh48qG7dunmoSgAAAKB8KTehIi8vT71799arr76qatWqOdtzcnI0f/58TZ8+Xbfeeqtatmyp5ORkffHFF/ryyy9LHKugoEC5ubkuLwAAAAAl8/V0AaVl0KBBio+PV2xsrCZOnOhsT0tLU2FhoWJjY51tDRo0UJ06dbRhwwbdcMMNxcZKSkrShAkT3FK3J0WNWunpEgAAAFAOlIuViqVLl2rLli1KSkoqti0zM1OVKlVSSEiIS3t4eLgyMzNLHG/06NHKyclxvjIyMuwoGwAAACgXvH6lIiMjQ0OHDlVKSor8/f1LZUw/Pz/5+fmVylgAAABAeef1KxVpaWk6fPiwrr32Wvn6+srX11fr16/XzJkz5evrq/DwcB0/flzZ2dkun8vKylJERIRnigYAAADKEa9fqejQoYO+/fZbl7aEhAQ1aNBATz31lGrXrq2KFSsqNTVV3bt3lySlp6dr//79iomJ8UTJAAAAQLni9aEiMDBQTZo0cWmrUqWKqlev7mwfMGCAEhMTFRoaqqCgID3++OOKiYkp8SZtAAAAABfG60PF+XjhhRfk4+Oj7t27q6CgQHFxcZo9e7anywIAAADKhXIZKj755BOX9/7+/po1a5ZmzZrlmYIAAACAcszrb9QGAAAA4FmECgAAAACWECoAAAAAWEKoAAAAAGBJubxRGwDOJWrUSk+XAABAucJKBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEl9PFwAAQEmiRq20Zdx9k+NtGRcALmWsVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwxOtDRVJSkq677joFBgYqLCxMXbt2VXp6ukufY8eOadCgQapevbqqVq2q7t27Kysry0MVAwAAAOWL14eK9evXa9CgQfryyy+VkpKiwsJC3X777crPz3f2eeKJJ/Sf//xHy5Yt0/r163Xw4EF169bNg1UDAAAA5YevpwuwavXq1S7vFyxYoLCwMKWlpaldu3bKycnR/PnztXjxYt16662SpOTkZDVs2FBffvmlbrjhhmJjFhQUqKCgwPk+NzfX3p0AAAAAvJjXh4oz5eTkSJJCQ0MlSWlpaSosLFRsbKyzT4MGDVSnTh1t2LChxFCRlJSkCRMmuKdgADiHqFErS33MfZPjS31MAMClzesvf/qzoqIiDRs2TG3atFGTJk0kSZmZmapUqZJCQkJc+oaHhyszM7PEcUaPHq2cnBznKyMjw+7SAQAAAK9VrlYqBg0apO3bt+uzzz6zNI6fn5/8/PxKqSoAAACgfCs3KxWDBw/WBx98oHXr1qlWrVrO9oiICB0/flzZ2dku/bOyshQREeHmKgEAAIDyx+tDhTFGgwcP1vLly7V27VpFR0e7bG/ZsqUqVqyo1NRUZ1t6err279+vmJgYd5cLAAAAlDtef/nToEGDtHjxYv373/9WYGCg8z6J4OBgBQQEKDg4WAMGDFBiYqJCQ0MVFBSkxx9/XDExMSXepA0AAADgwnh9qJgzZ44k6ZZbbnFpT05OVv/+/SVJL7zwgnx8fNS9e3cVFBQoLi5Os2fPdnOlAAAAQPnk9aHCGPOXffz9/TVr1izNmjXLDRUBAAAAlxavv6cCAAAAgGcRKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgidd/TwUA4MJEjVpZ6mPumxxf6mMCALwHKxUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAAS/jyOwCAZXZ8oR4AwHuwUgEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIQbtQEAlxRvual83+R4T5cAAOeNlQoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACW8PQnAADKIDueUsUTpQDYhZUKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCV8+R0AAJcIO75Qzy58UR/gXVipAAAAAGAJoQIAAACAJVz+BAAAUIbYdZkal5TBTqxUAAAAALCEUAEAAADAEi5/AgAAZY4dlwDZcfmPNz1RC7ATKxUAAAAALCFUAAAAALCEy58AAAAuAd5yqZa3PKWKp3S5YqUCAAAAgCWXVKiYNWuWoqKi5O/vr9atW2vTpk2eLgkAAADwepfM5U9vv/22EhMTNXfuXLVu3VozZsxQXFyc0tPTFRYW5unyAACAzbzl8p9LHefJO10yoWL69Ol6+OGHlZCQIEmaO3euVq5cqddff12jRo1y6VtQUKCCggLn+5ycHElSbm6u+wo+Q1HB7x6bGwAAAO7hqT9vnp7XGHNRn78kQsXx48eVlpam0aNHO9t8fHwUGxurDRs2FOuflJSkCRMmFGuvXbu2rXUCAADg0hY8w7PzHz16VMHBwRf8uUsiVPzyyy86efKkwsPDXdrDw8P1v//9r1j/0aNHKzEx0fm+qKhIR44cUfXq1eVwOGyv989yc3NVu3ZtZWRkKCgoyK1zXwo4vvbjGNuL42svjq+9OL724vjaq7wdX2OMjh49qsjIyIv6/CURKi6Un5+f/Pz8XNpCQkI8U8z/CQoKKhc/sGUVx9d+HGN7cXztxfG1F8fXXhxfe5Wn43sxKxSnXRJPf6pRo4YqVKigrKwsl/asrCxFRER4qCoAAACgfLgkQkWlSpXUsmVLpaamOtuKioqUmpqqmJgYD1YGAAAAeL9L5vKnxMRE9evXT61atdL111+vGTNmKD8/3/k0qLLKz89P48aNK3Y5FkoHx9d+HGN7cXztxfG1F8fXXhxfe3F8XTnMxT43ygu9/PLLmjZtmjIzM9W8eXPNnDlTrVu39nRZAAAAgFe7pEIFAAAAgNJ3SdxTAQAAAMA+hAoAAAAAlhAqAAAAAFhCqAAAAABgCaGijJs1a5aioqLk7++v1q1ba9OmTZ4uySt8+umn6ty5syIjI+VwOLRixQqX7cYYjR07VjVr1lRAQIBiY2O1a9culz5HjhxR7969FRQUpJCQEA0YMEB5eXlu3IuyKykpSdddd50CAwMVFhamrl27Kj093aXPsWPHNGjQIFWvXl1Vq1ZV9+7di30B5f79+xUfH6/KlSsrLCxMI0eO1IkTJ9y5K2XSnDlzdM011zi/pTUmJkarVq1ybufYlp7JkyfL4XBo2LBhzjaOrzXjx4+Xw+FweTVo0MC5neNr3U8//aQ+ffqoevXqCggIUNOmTbV582bndn7HXbyoqKhiP78Oh0ODBg2SxM/vORmUWUuXLjWVKlUyr7/+utmxY4d5+OGHTUhIiMnKyvJ0aWXehx9+aJ5++mnz3nvvGUlm+fLlLtsnT55sgoODzYoVK8zXX39t7rrrLhMdHW3++OMPZ5877rjDNGvWzHz55Zfmv//9r7nqqqtMz5493bwnZVNcXJxJTk4227dvN9u2bTOdOnUyderUMXl5ec4+jz76qKldu7ZJTU01mzdvNjfccIO58cYbndtPnDhhmjRpYmJjY83WrVvNhx9+aGrUqGFGjx7tiV0qU95//32zcuVKs3PnTpOenm7+/ve/m4oVK5rt27cbYzi2pWXTpk0mKirKXHPNNWbo0KHOdo6vNePGjTONGzc2hw4dcr5+/vln53aOrzVHjhwxdevWNf379zcbN240e/bsMR999JHZvXu3sw+/4y7e4cOHXX52U1JSjCSzbt06Yww/v+dCqCjDrr/+ejNo0CDn+5MnT5rIyEiTlJTkwaq8z5mhoqioyERERJhp06Y527Kzs42fn59ZsmSJMcaY7777zkgyX331lbPPqlWrjMPhMD/99JPbavcWhw8fNpLM+vXrjTGnjmfFihXNsmXLnH2+//57I8ls2LDBGHMq+Pn4+JjMzExnnzlz5pigoCBTUFDg3h3wAtWqVTOvvfYax7aUHD161NSrV8+kpKSYm2++2RkqOL7WjRs3zjRr1qzEbRxf65566inTtm3bs27nd1zpGjp0qLnyyitNUVERP79/gcufyqjjx48rLS1NsbGxzjYfHx/FxsZqw4YNHqzM++3du1eZmZkuxzY4OFitW7d2HtsNGzYoJCRErVq1cvaJjY2Vj4+PNm7c6Paay7qcnBxJUmhoqCQpLS1NhYWFLse4QYMGqlOnjssxbtq0qcLDw5194uLilJubqx07drix+rLt5MmTWrp0qfLz8xUTE8OxLSWDBg1SfHy8y3GU+NktLbt27VJkZKSuuOIK9e7dW/v375fE8S0N77//vlq1aqV7771XYWFhatGihV599VXndn7HlZ7jx4/rrbfe0oMPPiiHw8HP718gVJRRv/zyi06ePOnyQylJ4eHhyszM9FBV5cPp43euY5uZmamwsDCX7b6+vgoNDeX4n6GoqEjDhg1TmzZt1KRJE0mnjl+lSpUUEhLi0vfMY1zSOTi97VL37bffqmrVqvLz89Ojjz6q5cuXq1GjRhzbUrB06VJt2bJFSUlJxbZxfK1r3bq1FixYoNWrV2vOnDnau3evbrrpJh09epTjWwr27NmjOXPmqF69evroo4/0t7/9TUOGDNHChQsl8TuuNK1YsULZ2dnq37+/JP7/8Fd8PV0AAO82aNAgbd++XZ999pmnSylX6tevr23btiknJ0f/+te/1K9fP61fv97TZXm9jIwMDR06VCkpKfL39/d0OeVSx44dnf9+zTXXqHXr1qpbt67eeecdBQQEeLCy8qGoqEitWrXSpEmTJEktWrTQ9u3bNXfuXPXr18/D1ZUv8+fPV8eOHRUZGenpUrwCKxVlVI0aNVShQoViTxTIyspSRESEh6oqH04fv3Md24iICB0+fNhl+4kTJ3TkyBGO/58MHjxYH3zwgdatW6datWo52yMiInT8+HFlZ2e79D/zGJd0Dk5vu9RVqlRJV111lVq2bKmkpCQ1a9ZML774IsfWorS0NB0+fFjXXnutfH195evrq/Xr12vmzJny9fVVeHg4x7eUhYSE6Oqrr9bu3bv5+S0FNWvWVKNGjVzaGjZs6LzEjN9xpePHH3/Uxx9/rIceesjZxs/vuREqyqhKlSqpZcuWSk1NdbYVFRUpNTVVMTExHqzM+0VHRysiIsLl2Obm5mrjxo3OYxsTE6Ps7GylpaU5+6xdu1ZFRUVq3bq122sua4wxGjx4sJYvX661a9cqOjraZXvLli1VsWJFl2Ocnp6u/fv3uxzjb7/91uUXW0pKioKCgor9wsSp//4LCgo4thZ16NBB3377rbZt2+Z8tWrVSr1793b+O8e3dOXl5emHH35QzZo1+fktBW3atCn2CO+dO3eqbt26kvgdV1qSk5MVFham+Ph4Zxs/v3/B03eK4+yWLl1q/Pz8zIIFC8x3331nBg4caEJCQlyeKICSHT161GzdutVs3brVSDLTp083W7duNT/++KMx5tTj9kJCQsy///1v880335guXbqU+Li9Fi1amI0bN5rPPvvM1KtXj8ft/Z+//e1vJjg42HzyyScuj977/fffnX0effRRU6dOHbN27VqzefNmExMTY2JiYpzbTz927/bbbzfbtm0zq1evNpdddtkl8di9vzJq1Cizfv16s3fvXvPNN9+YUaNGGYfDYdasWWOM4diWtj8//ckYjq9Vw4cPN5988onZu3ev+fzzz01sbKypUaOGOXz4sDGG42vVpk2bjK+vr/l/7N13eBRV4/7/e9NDSUKAJERCb6ErNYACgoQiTZQi0gVEEJGi8PVBQFEQaRYEUUxQQAUVFJASkKKIIE0FkSZNSQCBJBQJKef3h7/sxyUJJEyWhPB+XddeD3PmzDlnZsc8e++ZmX311VfNoUOHzMKFC02+fPnMggUL7HX4/zhrkpOTTYkSJcwLL7yQZh3nb8YIFbnc22+/bUqUKGE8PDxM3bp1zY8//pjTQ7ojbNiwwUhK8+rVq5cx5t9H7o0dO9YEBgYaT09P06xZM3PgwAGHNs6dO2e6detmChQoYHx8fEyfPn3MxYsXc2Bvcp/0jq0kExERYa/zzz//mKefftoUKlTI5MuXz3Ts2NFER0c7tHPs2DHTqlUr4+3tbYoUKWJGjBhhEhMTb/Pe5D59+/Y1JUuWNB4eHqZo0aKmWbNm9kBhDMc2u10fKji+1nTp0sUUK1bMeHh4mHvuucd06dLF4TcUOL7WLV++3FStWtV4enqaSpUqmblz5zqs5//jrFmzZo2RlOaYGcP5eyM2Y4zJkSkSAAAAAHkC91QAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAEAeMn78eNWsWTNL25QqVUozZ850ynhyg2PHjslms2nPnj2SpI0bN8pmsyk2NtYp/Y0dO1YDBgyw1Mbo0aP1zDPPZNOIAMD5CBUAkItt3bpVrq6uatOmzW3r02azadmyZZmuHxkZKZvNptDQ0DTrlixZIpvNplKlSlkeV3aFgQYNGig6Olq+vr6S/h2/n5+f5fFJUkxMjN588029+OKL9rKFCxcqJCREhQoV0vDhwx3qHzt2TBUqVFB8fLxD+ciRIzV//nz98ccf2TIuAHA2QgUA5GLz5s3TM888o82bN+vUqVM5PZwM5c+fX2fOnNHWrVsdyufNm6cSJUpYbj8xMdFyG6k8PDwUFBQkm82WbW2m+uCDD9SgQQOVLFlSkvT333/rySef1NSpU7V27VotWLBAK1assNd/+umnNXnyZPn4+Di0U6RIEYWHh2v27NnZPkYAcAZCBQDkUpcuXdJnn32mQYMGqU2bNoqMjExTZ/LkyQoMDFTBggXVr18/Xb161WF9kyZNNGzYMIeyDh06qHfv3un2mTqj0LFjxyzNMLi5uenxxx/Xhx9+aC/7888/tXHjRj3++ONp6s+ePVtly5aVh4eHKlasqI8//thhvc1m0+zZs9WuXTvlz59f/fv3V9OmTSVJhQoVks1ms+/D6tWr1ahRI/n5+alw4cJ6+OGHdeTIkQzH+t8Zj40bN6pPnz6Ki4uTzWaTzWbT+PHj9fLLL6tq1apptq1Zs6bGjh2bYduffvqp2rZta1/+448/5Ovrqy5duqhOnTpq2rSp9u/fL0n65JNP5O7urkceeSTdttq2batPP/00w74AIDchVABALrV48WJVqlRJFStW1BNPPKEPP/xQxhiH9ePHj9drr72mHTt2qFixYnr33Xct9fnTTz9JkiIiIhQdHW1fzoy+fftq8eLFunLliqR/Lytq2bKlAgMDHeotXbpUzz77rEaMGKG9e/dq4MCB6tOnjzZs2OBQb/z48erYsaN+/fVXTZgwQV988YUk6cCBA4qOjtabb74pSbp8+bKGDx+uHTt2aP369XJxcVHHjh2VkpJy0zE3aNBAM2fOlI+Pj6KjoxUdHa2RI0eqb9++2r9/v8P+7969W7/88ov69OmTblvnz5/Xb7/9ptq1a9vLypcvrytXrmj37t06f/68fvrpJ1WvXl0XLlzQ2LFj9c4772Q4trp16+rPP//UsWPHbrofAJDTCBUAkEvNmzdPTzzxhCSpZcuWiouL06ZNm+zrZ86cqX79+qlfv36qWLGiJk6cqMqVK1vqs2jRopIkPz8/BQUF2Zcz495771WZMmX0+eefyxijyMhI9e3bN029qVOnqnfv3nr66adVoUIFDR8+XI888oimTp3qUO/xxx9Xnz59VKZMGZUsWVL+/v6SpICAAAUFBdnviejUqZMeeeQRlStXTjVr1tSHH36oX3/9Vb/99ttNx+zh4SFfX1/ZbDYFBQUpKChIBQoUUPHixRUeHq6IiAh73YiICDVu3FhlypRJt60TJ07IGKPg4GB7WaFChTR//nz17NlTdevWVc+ePRUeHq6RI0dqyJAhOnr0qO69915VrVpVn3/+uUN7qe0cP378pvsBADmNUAEAudCBAwe0fft2devWTdK/lxd16dJF8+bNs9fZv3+/6tWr57BdWFjYbR3n9fr27auIiAht2rRJly9fVuvWrdPU2b9/vxo2bOhQ1rBhQ/tlQan++43/jRw6dEjdunVTmTJl5OPjY79k68SJE7e2E/+//v3765NPPtHVq1d17do1LVq0KN2QlOqff/6RJHl5eTmUp862HD58WOPHj9emTZv0yy+/aMCAAeratatmzpypL774Qv369dOZM2fs23l7e0uSfeYHAHIzt5weAAAgrXnz5ikpKcnhW29jjDw9PfXOO+/Yv6W/GRcXF4dLpqTsven5et27d9fzzz+v8ePHq0ePHnJzu/X/m8mfP3+m6rVt21YlS5bU+++/r+DgYKWkpKhq1aq6du3aLfed2q6np6eWLl0qDw8PJSYm6tFHH82wfpEiRSRJFy5cyHCGJyEhQU8//bQ+/vhjHT58WElJSWrcuLEkqUKFCtq2bZv9nozz589LUpZmiwAgpzBTAQC5TFJSkj766CNNmzZNe/bssb9+/vlnBQcH65NPPpEkhYaGatu2bQ7b/vjjjw7LRYsWVXR0tH05OTlZe/fuvWH/7u7uSk5OvqWx+/v7q127dtq0aVOG3+qHhoZqy5YtDmVbtmy56aVbHh4ekuQwtnPnzunAgQP63//+p2bNmik0NFQXLlzI0pg9PDzS3V83Nzf16tVLERERioiIUNeuXe2zB+kpW7asfHx8bnjZ1cSJE9WyZUvdd999Sk5OVlJSkn1dYmKiwzj27t0rd3d3ValSJUv7AwA5gZkKAMhlVqxYoQsXLqhfv35pZiQ6deqkefPm6amnntKzzz6r3r17q3bt2mrYsKEWLlyoffv2OVzz/+CDD2r48OFauXKlypYtq+nTp9/0dx5KlSql9evXq2HDhvL09FShQoWyNP7IyEi9++67Kly4cLrrR40apc6dO+vee+9V8+bNtXz5cn355Zdat27dDdstWbKkbDabVqxYodatW8vb21uFChVS4cKFNXfuXBUrVkwnTpzQ6NGjszTeUqVK6dKlS1q/fr1q1KihfPnyKV++fJKkJ5980v77G9cHoeu5uLioefPm+v7779WhQ4c063/77Td99tln2r17tySpUqVKcnFx0bx58xQUFKTff/9dderUsdf/7rvvdP/9998wyABArmEAALnKww8/bFq3bp3uum3bthlJ5ueffzbGGPPqq6+aIkWKmAIFCphevXqZ559/3tSoUcNe/9q1a2bQoEHG39/fBAQEmEmTJpn27dubXr162euULFnSzJgxw7789ddfm3Llyhk3NzdTsmTJm443IiLC+Pr6Zrh+xowZadp59913TZkyZYy7u7upUKGC+eijjxzWSzJLly5N09bLL79sgoKCjM1ms+9DVFSUCQ0NNZ6enqZ69epm48aNDtsfPXrUSDK7d+82xhizYcMGI8lcuHDB3u5TTz1lChcubCSZcePGOfR5//33mypVqtz0OBhjzDfffGPuuecek5yc7FCekpJiGjZsaJYvX+5Qvnz5clOiRAkTGBho3n//fYd1FStWNJ988kmm+gWAnGYz5rqLbQEAgKR/72MpX768nn766TS/hp1R/Xr16um5556z32R/K1atWqURI0bol19+sXRfCgDcLtxTAQBAOs6ePat33nlHMTExGf42xfVsNpvmzp3rcK/Erbh8+bIiIiIIFADuGMxUAABuqEqVKhn+VsJ7772n7t273+YR3R42m01FihTRm2++me6vggMA/g+hAgBwQ8ePH8/wMbSBgYEqWLDgbR4RACC3IVQAAAAAsIR7KgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAgBxSqlQp9e7dO6eHkee98cYbKlOmjFxdXVWzZs1saXPjxo2y2WzauHFjtrR3vfHjx8tms+nvv//OtjabNGmiJk2aZFt7APBfhAoAyAaRkZGy2WzasWNHuuubNGmiqlWrWu7nm2++0fjx4y23c7dYu3atnn/+eTVs2FARERF67bXXMrVd586dZbPZ9MILLzh5hACQN7jl9AAA4G514MABubhk7budb775RrNmzSJYZNK3334rFxcXzZs3Tx4eHpnaJj4+XsuXL1epUqX0ySefaPLkybLZbE4eKQDc2ZipAIAc4unpKXd395weRpZcvnw5p4eQJWfOnJG3t3emA4UkffHFF0pOTtaHH36okydPavPmzU4cIQDkDYQKAMgh199TkZiYqAkTJqh8+fLy8vJS4cKF1ahRI0VFRUmSevfurVmzZkmSbDab/ZXq8uXLGjFihEJCQuTp6amKFStq6tSpMsY49PvPP/9o6NChKlKkiAoWLKh27drpr7/+ks1mc5gBSb2u/7ffftPjjz+uQoUKqVGjRpKkX375Rb1791aZMmXk5eWloKAg9e3bV+fOnXPoK7WNgwcP6oknnpCvr6+KFi2qsWPHyhijkydPqn379vLx8VFQUJCmTZuWqWOXlJSkV155RWXLlpWnp6dKlSql//f//p8SEhLsdWw2myIiInT58mX7sYqMjLxp2wsXLtRDDz2kpk2bKjQ0VAsXLszUmCRp27Ztat26tQoVKqT8+fOrevXqevPNNx3qfPvtt7r//vuVP39++fn5qX379tq/f3+67cXGxqp3797y8/OTr6+v+vTpoytXrmT5WACAs3H5EwBko7i4uHRvrk1MTLzptuPHj9ekSZP05JNPqm7duoqPj9eOHTu0a9cuPfTQQxo4cKBOnTqlqKgoffzxxw7bGmPUrl07bdiwQf369VPNmjW1Zs0ajRo1Sn/99ZdmzJhhr9u7d28tXrxYPXr0UP369bVp0ya1adMmw3E99thjKl++vF577TV7QImKitIff/yhPn36KCgoSPv27dPcuXO1b98+/fjjj2kuF+rSpYtCQ0M1efJkrVy5UhMnTpS/v7/ee+89Pfjgg3r99de1cOFCjRw5UnXq1NEDDzxww2P15JNPav78+Xr00Uc1YsQIbdu2TZMmTdL+/fu1dOlSSdLHH3+suXPnavv27frggw8kSQ0aNLhhu6dOndKGDRs0f/58SVK3bt00Y8YMvfPOOzed7YiKitLDDz+sYsWK6dlnn1VQUJD279+vFStW6Nlnn5UkrVu3Tq1atVKZMmU0fvx4/fPPP3r77bfVsGFD7dq1S6VKlXJos3PnzipdurQmTZqkXbt26YMPPlBAQIBef/31LB0LAHA6AwCwLCIiwki64atKlSoO25QsWdL06tXLvlyjRg3Tpk2bG/YzePBgk96f7mXLlhlJZuLEiQ7ljz76qLHZbObw4cPGGGN27txpJJlhw4Y51Ovdu7eRZMaNG2cvGzdunJFkunXrlqa/K1eupCn75JNPjCSzefPmNG0MGDDAXpaUlGSKFy9ubDabmTx5sr38woULxtvb2+GYpGfPnj1GknnyyScdykeOHGkkmW+//dZe1qtXL5M/f/4btvdfU6dONd7e3iY+Pt4YY8zBgweNJLN06VKHehs2bDCSzIYNG+z7VLp0aVOyZElz4cIFh7opKSn2f9esWdMEBASYc+fO2ct+/vln4+LiYnr27GkvSz1uffv2dWirY8eOpnDhwrd0LBo3bmwaN26c6WMBAFnB5U8AkI1mzZqlqKioNK/q1avfdFs/Pz/t27dPhw4dynK/33zzjVxdXTV06FCH8hEjRsgYo1WrVkmSVq9eLUl6+umnHeo988wzGbb91FNPpSnz9va2//vq1av6+++/Vb9+fUnSrl270tR/8skn7f92dXVV7dq1ZYxRv3797OV+fn6qWLGi/vjjjwzHIv27r5I0fPhwh/IRI0ZIklauXHnD7W9k4cKFatOmjQoWLChJKl++vGrVqnXTS6B2796to0ePatiwYfLz83NYlzprEx0drT179qh3797y9/e3r69evboeeugh+3791/XH/v7779e5c+cUHx8vybnHAgCygsufACAb1a1bV7Vr105TXqhQoZv+5sDLL7+s9u3bq0KFCqpatapatmypHj16ZCqQHD9+XMHBwfYPw6lCQ0Pt61P/18XFRaVLl3aoV65cuQzbvr6uJJ0/f14TJkzQp59+qjNnzjisi4uLS1O/RIkSDsu+vr7y8vJSkSJF0pRff1/G9VL34foxBwUFyc/Pz76vWbV//37t3r1bPXv21OHDh+3lTZo00axZsxQfHy8fH590tz1y5Igk3fCxwanjqlixYpp1oaGhWrNmjS5fvqz8+fPby68/boUKFZIkXbhwQT4+Pk47FgCQVcxUAEAu8cADD+jIkSP68MMPVbVqVX3wwQe677777PcD5JT/zkqk6ty5s95//3099dRT+vLLL7V27Vr7LEhKSkqa+q6urpkqk5TmxvKMZPdjXhcsWCBJeu6551S+fHn7a9q0abp69aq++OKLbO0vMzJ7jHjkLYCcRqgAgFzE399fffr00SeffKKTJ0+qevXqDk9kyujDY8mSJXXq1CldvHjRofz333+3r0/935SUFB09etSh3n+/mb+ZCxcuaP369Ro9erQmTJigjh076qGHHlKZMmUy3YYVqftw/WVip0+fVmxsrH1fs8IYo0WLFqlp06ZasmRJmlf16tVveAlU2bJlJUl79+694bilf3+f5Hq///67ihQp4jBLkRnOOBYAcCsIFQCQS1x/2U+BAgVUrlw5h0eDpn7ojI2NdajbunVrJScn65133nEonzFjhmw2m1q1aiVJCg8PlyS9++67DvXefvvtTI8z9dvz678tnzlzZqbbsKJ169bp9jd9+nRJuuGTrDKyZcsWHTt2TH369NGjjz6a5tWlSxdt2LBBp06dSnf7++67T6VLl9bMmTPTvDepx6lYsWKqWbOm5s+f71Bn7969Wrt2rX2/ssIZxwIAbgX3VABALlG5cmU1adJEtWrVkr+/v3bs2KHPP/9cQ4YMsdepVauWJGno0KEKDw+Xq6urunbtqrZt26pp06Z68cUXdezYMdWoUUNr167VV199pWHDhtm/Sa9Vq5Y6deqkmTNn6ty5c/ZHyh48eFBS5i6j8fHx0QMPPKApU6YoMTFR99xzj9auXZtm9sNZatSooV69emnu3LmKjY1V48aNtX37ds2fP18dOnRQ06ZNs9zmwoUL5erqmuGH8Hbt2unFF1/Up59+muamaElycXHR7Nmz1bZtW9WsWVN9+vRRsWLF9Pvvv2vfvn1as2aNJOmNN95Qq1atFBYWpn79+tkfKevr63tLv5LujGMBALeCUAEAucTQoUP19ddfa+3atUpISFDJkiU1ceJEjRo1yl7nkUce0TPPPKNPP/1UCxYskDFGXbt2lYuLi77++mu99NJL+uyzzxQREaFSpUrpjTfesD8JKNVHH32koKAgffLJJ1q6dKmaN2+uzz77TBUrVpSXl1emxrpo0SI988wzmjVrlowxatGihVatWqXg4OBsPSYZ+eCDD1SmTBlFRkZq6dKlCgoK0pgxYzRu3Lgst5WYmKglS5aoQYMGDk9l+q+qVauqdOnSWrBgQbqhQvp3FmjDhg2aMGGCpk2bppSUFJUtW1b9+/e312nevLlWr16tcePG6aWXXpK7u7saN26s119/Pd0b4jMjO48FANwqm8nsHXEAgDxrz549uvfee7VgwQJ17949p4cDALjDcE8FANxl/vnnnzRlM2fOlIuLy01/yRoAgPRw+RMA3GWmTJminTt3qmnTpnJzc9OqVau0atUqDRgwQCEhITk9PADAHYjLnwDgLhMVFaUJEybot99+06VLl1SiRAn16NFDL774otzc+K4JAJB1hAoAAAAAlnBPBQAAAABLmOfOhJSUFJ06dUoFCxbM1DPcAQAAgDuJMUYXL15UcHCwXFyyPu9AqMiEU6dOcfMiAAAA8ryTJ0+qePHiWd6OUJEJBQsWlPTvQfbx8cnh0QAAAADZKz4+XiEhIfbPvVlFqMiE1EuefHx8CBUAAADIs271Un9u1AYAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCW5OlRMmjRJderUUcGCBRUQEKAOHTrowIEDDnWaNGkim83m8Hrqqacc6pw4cUJt2rRRvnz5FBAQoFGjRikpKel27goAAACQZ7nl9ABuZNOmTRo8eLDq1KmjpKQk/b//9//UokUL/fbbb8qfP7+9Xv/+/fXyyy/bl/Ply2f/d3Jystq0aaOgoCD98MMPio6OVs+ePeXu7q7XXnvttu4PAAAAkBfZjDEmpweRWWfPnlVAQIA2bdqkBx54QNK/MxU1a9bUzJkz091m1apVevjhh3Xq1CkFBgZKkubMmaMXXnhBZ8+elYeHR5ptEhISlJCQYF+Oj49XSEiI4uLi5OPjk/07BgAAAOSg+Ph4+fr63vLn3Vw9U3G9uLg4SZK/v79D+cKFC7VgwQIFBQWpbdu2Gjt2rH22YuvWrapWrZo9UEhSeHi4Bg0apH379unee+9N08+kSZM0YcIEJ+5J1pUavTLb2zw2uU22twkAAIC7zx0TKlJSUjRs2DA1bNhQVatWtZc//vjjKlmypIKDg/XLL7/ohRde0IEDB/Tll19KkmJiYhwChST7ckxMTLp9jRkzRsOHD7cvp85UAAAAAEjrjgkVgwcP1t69e/X99987lA8YMMD+72rVqqlYsWJq1qyZjhw5orJly95SX56envL09LQ0XgAAAOBukauf/pRqyJAhWrFihTZs2KDixYvfsG69evUkSYcPH5YkBQUF6fTp0w51UpeDgoKcMFoAAADg7pKrQ4UxRkOGDNHSpUv17bffqnTp0jfdZs+ePZKkYsWKSZLCwsL066+/6syZM/Y6UVFR8vHxUeXKlZ0ybgAAAOBukqsvfxo8eLAWLVqkr776SgULFrTfA+Hr6ytvb28dOXJEixYtUuvWrVW4cGH98ssveu655/TAAw+oevXqkqQWLVqocuXK6tGjh6ZMmaKYmBj973//0+DBg7nECQAAAMgGuXqmYvbs2YqLi1OTJk1UrFgx++uzzz6TJHl4eGjdunVq0aKFKlWqpBEjRqhTp05avny5vQ1XV1etWLFCrq6uCgsL0xNPPKGePXs6/K4FAAAAgFuXq2cqbvYTGiEhIdq0adNN2ylZsqS++eab7BoWAAAAgP/I1TMVAAAAAHI/QgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALAkV4eKSZMmqU6dOipYsKACAgLUoUMHHThwwKHO1atXNXjwYBUuXFgFChRQp06ddPr0aYc6J06cUJs2bZQvXz4FBARo1KhRSkpKup27AgAAAORZuTpUbNq0SYMHD9aPP/6oqKgoJSYmqkWLFrp8+bK9znPPPafly5dryZIl2rRpk06dOqVHHnnEvj45OVlt2rTRtWvX9MMPP2j+/PmKjIzUSy+9lBO7BAAAAOQ5NmOMyelBZNbZs2cVEBCgTZs26YEHHlBcXJyKFi2qRYsW6dFHH5Uk/f777woNDdXWrVtVv359rVq1Sg8//LBOnTqlwMBASdKcOXP0wgsv6OzZs/Lw8Lhpv/Hx8fL19VVcXJx8fHycuo8ZKTV6Zba3eWxym2xvEwAAAHceq593c/VMxfXi4uIkSf7+/pKknTt3KjExUc2bN7fXqVSpkkqUKKGtW7dKkrZu3apq1arZA4UkhYeHKz4+Xvv27Uu3n4SEBMXHxzu8AAAAAKTvjgkVKSkpGjZsmBo2bKiqVatKkmJiYuTh4SE/Pz+HuoGBgYqJibHX+W+gSF2fui49kyZNkq+vr/0VEhKSzXsDAAAA5B13TKgYPHiw9u7dq08//dTpfY0ZM0ZxcXH218mTJ53eJwAAAHCncsvpAWTGkCFDtGLFCm3evFnFixe3lwcFBenatWuKjY11mK04ffq0goKC7HW2b9/u0F7q06FS61zP09NTnp6e2bwXAAAAQN6Uq2cqjDEaMmSIli5dqm+//ValS5d2WF+rVi25u7tr/fr19rIDBw7oxIkTCgsLkySFhYXp119/1ZkzZ+x1oqKi5OPjo8qVK9+eHQEAAADysFw9UzF48GAtWrRIX331lQoWLGi/B8LX11fe3t7y9fVVv379NHz4cPn7+8vHx0fPPPOMwsLCVL9+fUlSixYtVLlyZfXo0UNTpkxRTEyM/ve//2nw4MHMRgAAAADZIFeHitmzZ0uSmjRp4lAeERGh3r17S5JmzJghFxcXderUSQkJCQoPD9e7775rr+vq6qoVK1Zo0KBBCgsLU/78+dWrVy+9/PLLt2s3AAAAgDztjvqdipzC71QAAAAgL7urfqcCAAAAQO5DqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYkutDxebNm9W2bVsFBwfLZrNp2bJlDut79+4tm83m8GrZsqVDnfPnz6t79+7y8fGRn5+f+vXrp0uXLt3GvQAAAADyLqeFij/++CNb2rl8+bJq1KihWbNmZVinZcuWio6Otr8++eQTh/Xdu3fXvn37FBUVpRUrVmjz5s0aMGBAtowPAAAAuNu5OavhcuXKqXHjxurXr58effRReXl53VI7rVq1UqtWrW5Yx9PTU0FBQemu279/v1avXq2ffvpJtWvXliS9/fbbat26taZOnarg4OA02yQkJCghIcG+HB8ff0tjBwAAAO4GTpup2LVrl6pXr67hw4crKChIAwcO1Pbt253S18aNGxUQEKCKFStq0KBBOnfunH3d1q1b5efnZw8UktS8eXO5uLho27Zt6bY3adIk+fr62l8hISFOGTcAAACQFzgtVNSsWVNvvvmmTp06pQ8//FDR0dFq1KiRqlatqunTp+vs2bPZ0k/Lli310Ucfaf369Xr99de1adMmtWrVSsnJyZKkmJgYBQQEOGzj5uYmf39/xcTEpNvmmDFjFBcXZ3+dPHkyW8YKAAAA5EVOv1Hbzc1NjzzyiJYsWaLXX39dhw8f1siRIxUSEqKePXsqOjraUvtdu3ZVu3btVK1aNXXo0EErVqzQTz/9pI0bN95ym56envLx8XF4AQAAAEif00PFjh079PTTT6tYsWKaPn26Ro4cqSNHjigqKkqnTp1S+/bts7W/MmXKqEiRIjp8+LAkKSgoSGfOnHGok5SUpPPnz2d4HwYAAACAzHPajdrTp09XRESEDhw4oNatW+ujjz5S69at5eLyb44pXbq0IiMjVapUqWzt988//9S5c+dUrFgxSVJYWJhiY2O1c+dO1apVS5L07bffKiUlRfXq1cvWvgEAAIC7kdNCxezZs9W3b1/17t3b/gH/egEBAZo3b94N27l06ZJ91kGSjh49qj179sjf31/+/v6aMGGCOnXqpKCgIB05ckTPP/+8ypUrp/DwcElSaGioWrZsqf79+2vOnDlKTEzUkCFD1LVr13Sf/AQAAAAga2zGGJPTg7iRjRs3qmnTpmnKe/XqpdmzZ6tDhw7avXu3YmNjFRwcrBYtWuiVV15RYGCgve758+c1ZMgQLV++XC4uLurUqZPeeustFShQIFNjiI+Pl6+vr+Li4nLs/opSo1dme5vHJrfJ9jYBAABw57H6eddpMxUREREqUKCAHnvsMYfyJUuW6MqVK+rVq1em2mnSpIlulHvWrFlz0zb8/f21aNGiTPUHAAAAIGucdqP2pEmTVKRIkTTlAQEBeu2115zVLQAAAIDbzGmh4sSJEypdunSa8pIlS+rEiRPO6hYAAADAbea0UBEQEKBffvklTfnPP/+swoULO6tbAAAAALeZ00JFt27dNHToUG3YsEHJyclKTk7Wt99+q2effVZdu3Z1VrcAAAAAbjOn3aj9yiuv6NixY2rWrJnc3P7tJiUlRT179uSeCgAAACAPcVqo8PDw0GeffaZXXnlFP//8s7y9vVWtWjWVLFnSWV0CAAAAyAFOCxWpKlSooAoVKji7GwAAAAA5xGmhIjk5WZGRkVq/fr3OnDmjlJQUh/Xffvuts7oGAAAAcBs5LVQ8++yzioyMVJs2bVS1alXZbDZndQUAAAAgBzktVHz66adavHixWrdu7awuAAAAAOQCTnukrIeHh8qVK+es5gEAAADkEk4LFSNGjNCbb74pY4yzugAAAACQCzjt8qfvv/9eGzZs0KpVq1SlShW5u7s7rP/yyy+d1TUAAACA28hpocLPz08dO3Z0VvMAAAAAcgmnhYqIiAhnNQ0AAAAgF3HaPRWSlJSUpHXr1um9997TxYsXJUmnTp3SpUuXnNktAAAAgNvIaTMVx48fV8uWLXXixAklJCTooYceUsGCBfX6668rISFBc+bMcVbXAAAAAG4jp81UPPvss6pdu7YuXLggb29ve3nHjh21fv16Z3ULAAAA4DZz2kzFd999px9++EEeHh4O5aVKldJff/3lrG4BAAAA3GZOm6lISUlRcnJymvI///xTBQsWdFa3AAAAAG4zp4WKFi1aaObMmfZlm82mS5cuady4cWrdurWzugUAAABwmznt8qdp06YpPDxclStX1tWrV/X444/r0KFDKlKkiD755BNndQsAAADgNnNaqChevLh+/vlnffrpp/rll1906dIl9evXT927d3e4cRsAAADAnc1poUKS3Nzc9MQTTzizCwAAAAA5zGmh4qOPPrrh+p49ezqrawAAAAC3kdNCxbPPPuuwnJiYqCtXrsjDw0P58uUjVAAAAAB5hNOe/nThwgWH16VLl3TgwAE1atSIG7UBAACAPMRpoSI95cuX1+TJk9PMYgAAAAC4c93WUCH9e/P2qVOnbne3AAAAAJzEafdUfP311w7LxhhFR0frnXfeUcOGDZ3VLQAAAIDbzGmhokOHDg7LNptNRYsW1YMPPqhp06Y5q1sAAAAAt5nTQkVKSoqzmgYAAACQi9z2eyoAAAAA5C1Om6kYPnx4putOnz7dWcMAAAAA4GROCxW7d+/W7t27lZiYqIoVK0qSDh48KFdXV9133332ejabzVlDAAAAAHAbOC1UtG3bVgULFtT8+fNVqFAhSf/+IF6fPn10//33a8SIEc7qGgAAAMBt5LR7KqZNm6ZJkybZA4UkFSpUSBMnTuTpTwAAAEAe4rRQER8fr7Nnz6YpP3v2rC5evOisbgEAAADcZk4LFR07dlSfPn305Zdf6s8//9Sff/6pL774Qv369dMjjzzirG4BAAAA3GZOu6dizpw5GjlypB5//HElJib+25mbm/r166c33njDWd0CAAAAuM2cFiry5cund999V2+88YaOHDkiSSpbtqzy58/vrC4BAAAA5ACn//hddHS0oqOjVb58eeXPn1/GGGd3CQAAAOA2clqoOHfunJo1a6YKFSqodevWio6OliT169ePx8kCAAAAeYjTQsVzzz0nd3d3nThxQvny5bOXd+nSRatXr3ZWtwAAAABuM6fdU7F27VqtWbNGxYsXdygvX768jh8/7qxuAQAAANxmTpupuHz5ssMMRarz58/L09PTWd0CAAAAuM2cFiruv/9+ffTRR/Zlm82mlJQUTZkyRU2bNnVWtwAAAABuM6dd/jRlyhQ1a9ZMO3bs0LVr1/T8889r3759On/+vLZs2eKsbgEAAADcZk6bqahataoOHjyoRo0aqX379rp8+bIeeeQR7d69W2XLlnVWtwAAAABuM6fMVCQmJqply5aaM2eOXnzxRWd0AQAAACCXcMpMhbu7u3755RdnNA0AAAAgl3Ha5U9PPPGE5s2b56zmAQAAAOQSTrtROykpSR9++KHWrVunWrVqKX/+/A7rp0+f7qyuAQAAANxG2R4q/vjjD5UqVUp79+7VfffdJ0k6ePCgQx2bzZbd3QIAAADIIdkeKsqXL6/o6Ght2LBBktSlSxe99dZbCgwMzO6uAAAAAOQC2X5PhTHGYXnVqlW6fPlydncDAAAAIJdw2o3aqa4PGQAAAADylmwPFTabLc09E9xDAQAAAORd2X5PhTFGvXv3lqenpyTp6tWreuqpp9I8/enLL7/M7q4BAAAA5IBsDxW9evVyWH7iiSeyuwsAAAAAuUi2h4qIiIjsbhIAAABALub0G7UBAAAA5G2ECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYEmuDxWbN29W27ZtFRwcLJvNpmXLljmsN8bopZdeUrFixeTt7a3mzZvr0KFDDnXOnz+v7t27y8fHR35+furXr58uXbp0G/cCAAAAyLtyfai4fPmyatSooVmzZqW7fsqUKXrrrbc0Z84cbdu2Tfnz51d4eLiuXr1qr9O9e3ft27dPUVFRWrFihTZv3qwBAwbcrl0AAAAA8jS3nB7AzbRq1UqtWrVKd50xRjNnztT//vc/tW/fXpL00UcfKTAwUMuWLVPXrl21f/9+rV69Wj/99JNq164tSXr77bfVunVrTZ06VcHBwWnaTUhIUEJCgn05Pj7eCXsGAAAA5A25fqbiRo4ePaqYmBg1b97cXubr66t69epp69atkqStW7fKz8/PHigkqXnz5nJxcdG2bdvSbXfSpEny9fW1v0JCQpy7IwAAAMAd7I4OFTExMZKkwMBAh/LAwED7upiYGAUEBDisd3Nzk7+/v73O9caMGaO4uDj76+TJk04YPQAAAJA35PrLn3KCp6enPD09c3oYAAAAwB3hjp6pCAoKkiSdPn3aofz06dP2dUFBQTpz5ozD+qSkJJ0/f95eBwAAAMCtu6NDRenSpRUUFKT169fby+Lj47Vt2zaFhYVJksLCwhQbG6udO3fa63z77bdKSUlRvXr1bvuYAQAAgLwm11/+dOnSJR0+fNi+fPToUe3Zs0f+/v4qUaKEhg0bpokTJ6p8+fIqXbq0xo4dq+DgYHXo0EGSFBoaqpYtW6p///6aM2eOEhMTNWTIEHXt2jXdJz8BAAAAyJpcHyp27Nihpk2b2peHDx8uSerVq5ciIyP1/PPP6/LlyxowYIBiY2PVqFEjrV69Wl5eXvZtFi5cqCFDhqhZs2ZycXFRp06d9NZbb932fQEAAADyIpsxxuT0IHK7+Ph4+fr6Ki4uTj4+PjkyhlKjV2Z7m8cmt8n2NgEAAHDnsfp5946+pwIAAABAziNUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALDELacHgLyn1OiV2d7mscltsr1NAAAAZA9mKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCX8TsVdzBm/JwEAAIC7DzMVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEn6nArhLOeN3So5NbpPtbQIAgNyPmQoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAltzxoWL8+PGy2WwOr0qVKtnXX716VYMHD1bhwoVVoEABderUSadPn87BEQMAAAB5yx0fKiSpSpUqio6Otr++//57+7rnnntOy5cv15IlS7Rp0yadOnVKjzzySA6OFgAAAMhb3HJ6ANnBzc1NQUFBacrj4uI0b948LVq0SA8++KAkKSIiQqGhofrxxx9Vv3792z1U3KJSo1dme5vHJrfJ9jYBAADuRnlipuLQoUMKDg5WmTJl1L17d504cUKStHPnTiUmJqp58+b2upUqVVKJEiW0devWDNtLSEhQfHy8wwsAAABA+u74mYp69eopMjJSFStWVHR0tCZMmKD7779fe/fuVUxMjDw8POTn5+ewTWBgoGJiYjJsc9KkSZowYYKTRw5knjNmagAAALLLHR8qWrVqZf939erVVa9ePZUsWVKLFy+Wt7f3LbU5ZswYDR8+3L4cHx+vkJAQy2MFAAAA8qI8cfnTf/n5+alChQo6fPiwgoKCdO3aNcXGxjrUOX36dLr3YKTy9PSUj4+PwwsAAABA+vJcqLh06ZKOHDmiYsWKqVatWnJ3d9f69evt6w8cOKATJ04oLCwsB0cJAAAA5B13/OVPI0eOVNu2bVWyZEmdOnVK48aNk6urq7p16yZfX1/169dPw4cPl7+/v3x8fPTMM88oLCyMJz8BAAAA2eSODxV//vmnunXrpnPnzqlo0aJq1KiRfvzxRxUtWlSSNGPGDLm4uKhTp05KSEhQeHi43n333RweNQAAAJB33PGh4tNPP73hei8vL82aNUuzZs26TSMCAAAA7i557p4KAAAAALcXoQIAAACAJXf85U/ArXLGD8odm9wm29sEAADI7ZipAAAAAGAJoQIAAACAJVz+BAC5GJfpAQDuBMxUAAAAALCEmQoAuRrf1AMAkPsxUwEAAADAEmYqANx1nDH7ITEDAgC4ezFTAQAAAMASQgUAAAAAS7j8CQCyibMuqwIAILdjpgIAAACAJYQKAAAAAJZw+ROQje72y1/u9v0HAOBuxUwFAAAAAEsIFQAAAAAs4fInALjLOOMyNX747+7Fj0kCkJipAAAAAGARoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCVuOT0AAABup1KjV2Z7m8cmt8n2NgHgTsJMBQAAAABLmKkAAMAiZj8A3O2YqQAAAABgCTMVAADL+KY++3FMAdxJmKkAAAAAYAmhAgAAAIAlXP4EAMBdwhmXVAGAxEwFAAAAAIsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhN+pAADkSvymAgDcOZipAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABY4pbTAwAAALheqdErs73NY5PbZHubAP7FTAUAAAAASwgVAAAAACzh8icAAHBXcMYlVc7AZVq4EzFTAQAAAMASZioAAADuAtz8DmdipgIAAACAJYQKAAAAAJbcVZc/zZo1S2+88YZiYmJUo0YNvf3226pbt25ODwsAAMDuTrmh/G7nrPfpTr2k7K6Zqfjss880fPhwjRs3Trt27VKNGjUUHh6uM2fO5PTQAAAAgDvaXTNTMX36dPXv3199+vSRJM2ZM0crV67Uhx9+qNGjRzvUTUhIUEJCgn05Li5OkhQfH3/7BnydlIQrOdY3AABAenLys1FWVB23JqeHkGk5dUxT+zXG3NL2NnOrW95Brl27pnz58unzzz9Xhw4d7OW9evVSbGysvvrqK4f648eP14QJE27zKAEAAICcdfLkSRUvXjzL290VMxV///23kpOTFRgY6FAeGBio33//PU39MWPGaPjw4fbllJQUnT9/XoULF5bNZnP6eK8XHx+vkJAQnTx5Uj4+Pre9/7sBx9j5OMbOxzF2Po6x83GMnY9j7Hx34jE2xujixYsKDg6+pe3vilCRVZ6envL09HQo8/Pzy5nB/IePj88dc2LeqTjGzscxdj6OsfNxjJ2PY+x8HGPnu9OOsa+v7y1ve1fcqF2kSBG5urrq9OnTDuWnT59WUFBQDo0KAAAAyBvuilDh4eGhWrVqaf369faylJQUrV+/XmFhYTk4MgAAAODOd9dc/jR8+HD16tVLtWvXVt26dTVz5kxdvnzZ/jSo3MzT01Pjxo1Lc0kWsg/H2Pk4xs7HMXY+jrHzcYydj2PsfHfjMb4rnv6U6p133rH/+F3NmjX11ltvqV69ejk9LAAAAOCOdleFCgAAAADZ7664pwIAAACA8xAqAAAAAFhCqAAAAABgCaECAAAAgCWEilxi1qxZKlWqlLy8vFSvXj1t3779hvWXLFmiSpUqycvLS9WqVdM333xzm0Z655k0aZLq1KmjggULKiAgQB06dNCBAwduuE1kZKRsNpvDy8vL6zaN+M4zfvz4NMerUqVKN9yGczhrSpUqleYY22w2DR48ON36nMM3t3nzZrVt21bBwcGy2WxatmyZw3pjjF566SUVK1ZM3t7eat68uQ4dOnTTdrP69zwvu9ExTkxM1AsvvKBq1aopf/78Cg4OVs+ePXXq1Kkbtnkrf2/yupudy717905zzFq2bHnTdjmX/8/NjnF6f59tNpveeOONDNvMa+cyoSIX+OyzzzR8+HCNGzdOu3btUo0aNRQeHq4zZ86kW/+HH35Qt27d1K9fP+3evVsdOnRQhw4dtHfv3ts88jvDpk2bNHjwYP3444+KiopSYmKiWrRoocuXL99wOx8fH0VHR9tfx48fv00jvjNVqVLF4Xh9//33GdblHM66n376yeH4RkVFSZIee+yxDLfhHL6xy5cvq0aNGpo1a1a666dMmaK33npLc+bM0bZt25Q/f36Fh4fr6tWrGbaZ1b/ned2NjvGVK1e0a9cujR07Vrt27dKXX36pAwcOqF27djdtNyt/b+4GNzuXJally5YOx+yTTz65YZucy45udoz/e2yjo6P14YcfymazqVOnTjdsN0+dywY5rm7dumbw4MH25eTkZBMcHGwmTZqUbv3OnTubNm3aOJTVq1fPDBw40KnjzCvOnDljJJlNmzZlWCciIsL4+vrevkHd4caNG2dq1KiR6fqcw9Y9++yzpmzZsiYlJSXd9ZzDWSPJLF261L6ckpJigoKCzBtvvGEvi42NNZ6enuaTTz7JsJ2s/j2/m1x/jNOzfft2I8kcP348wzpZ/Xtzt0nvOPfq1cu0b98+S+1wLmcsM+dy+/btzYMPPnjDOnntXGamIoddu3ZNO3fuVPPmze1lLi4uat68ubZu3ZruNlu3bnWoL0nh4eEZ1oejuLg4SZK/v/8N6126dEklS5ZUSEiI2rdvr3379t2O4d2xDh06pODgYJUpU0bdu3fXiRMnMqzLOWzNtWvXtGDBAvXt21c2my3DepzDt+7o0aOKiYlxOE99fX1Vr169DM/TW/l7DkdxcXGy2Wzy8/O7Yb2s/L3BvzZu3KiAgABVrFhRgwYN0rlz5zKsy7lszenTp7Vy5Ur169fvpnXz0rlMqMhhf//9t5KTkxUYGOhQHhgYqJiYmHS3iYmJyVJ9/J+UlBQNGzZMDRs2VNWqVTOsV7FiRX344Yf66quvtGDBAqWkpKhBgwb6888/b+No7xz16tVTZGSkVq9erdmzZ+vo0aO6//77dfHixXTrcw5bs2zZMsXGxqp3794Z1uEctib1XMzKeXorf8/xf65evaoXXnhB3bp1k4+PT4b1svr3Bv9e+vTRRx9p/fr1ev3117Vp0ya1atVKycnJ6dbnXLZm/vz5KliwoB555JEb1str57JbTg8AuJ0GDx6svXv33vSaxbCwMIWFhdmXGzRooNDQUL333nt65ZVXnD3MO06rVq3s/65evbrq1aunkiVLavHixZn6pgZZM2/ePLVq1UrBwcEZ1uEcxp0kMTFRnTt3ljFGs2fPvmFd/t5kXdeuXe3/rlatmqpXr66yZctq48aNatasWQ6OLG/68MMP1b1795s+HCOvncvMVOSwIkWKyNXVVadPn3YoP336tIKCgtLdJigoKEv18a8hQ4ZoxYoV2rBhg4oXL56lbd3d3XXvvffq8OHDThpd3uLn56cKFSpkeLw4h2/d8ePHtW7dOj355JNZ2o5zOGtSz8WsnKe38vcc/xcojh8/rqioqBvOUqTnZn9vkFaZMmVUpEiRDI8Z5/Kt++6773TgwIEs/42W7vxzmVCRwzw8PFSrVi2tX7/eXpaSkqL169c7fMv4X2FhYQ71JSkqKirD+nc7Y4yGDBmipUuX6ttvv1Xp0qWz3EZycrJ+/fVXFStWzAkjzHsuXbqkI0eOZHi8OIdvXUREhAICAtSmTZssbcc5nDWlS5dWUFCQw3kaHx+vbdu2ZXie3srf87tdaqA4dOiQ1q1bp8KFC2e5jZv9vUFaf/75p86dO5fhMeNcvnXz5s1TrVq1VKNGjSxve8efyzl9pziM+fTTT42np6eJjIw0v/32mxkwYIDx8/MzMTExxhhjevToYUaPHm2vv2XLFuPm5mamTp1q9u/fb8aNG2fc3d3Nr7/+mlO7kKsNGjTI+Pr6mo0bN5ro6Gj768qVK/Y61x/jCRMmmDVr1pgjR46YnTt3mq5duxovLy+zb9++nNiFXG/EiBFm48aN5ujRo2bLli2mefPmpkiRIubMmTPGGM7h7JKcnGxKlChhXnjhhTTrOIez7uLFi2b37t1m9+7dRpKZPn262b17t/3JQ5MnTzZ+fn7mq6++Mr/88otp3769KV26tPnnn3/sbTz44IPm7bffti/f7O/53eZGx/jatWumXbt2pnjx4mbPnj0Of58TEhLsbVx/jG/29+ZudKPjfPHiRTNy5EizdetWc/ToUbNu3Tpz3333mfLly5urV6/a2+BcvrGb/b0wxpi4uDiTL18+M3v27HTbyOvnMqEil3j77bdNiRIljIeHh6lbt6758ccf7esaN25sevXq5VB/8eLFpkKFCsbDw8NUqVLFrFy58jaP+M4hKd1XRESEvc71x3jYsGH29yMwMNC0bt3a7Nq16/YP/g7RpUsXU6xYMePh4WHuuece06VLF3P48GH7es7h7LFmzRojyRw4cCDNOs7hrNuwYUO6fxtSj2NKSooZO3asCQwMNJ6enqZZs2Zpjn3JkiXNuHHjHMpu9Pf8bnOjY3z06NEM/z5v2LDB3sb1x/hmf2/uRjc6zleuXDEtWrQwRYsWNe7u7qZkyZKmf//+acIB5/KN3ezvhTHGvPfee8bb29vExsam20ZeP5dtxhjj1KkQAAAAAHka91QAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUALhjbNy4UTabTbGxsbmqPZvNpmXLlt2wzrlz5xQQEKBjx46l23dkZKT8/PwsjQMZK1WqlGbOnJnTw7grOONYd+3aVdOmTcvWNgFkL0IFgFxl69atcnV1VZs2bXJ6KNnq1VdfVfv27VWqVKl013fp0kUHDx60L48fP141a9a8PYO7C/z0008aMGCAfTkzQRA3llEQvv5YZ4f//e9/evXVVxUXF5et7QLIPoQKALnKvHnz9Mwzz2jz5s06depUTg8nW1y5ckXz5s1Tv379Mqzj7e2tgICALLedmJhoZWi3VU6OtWjRosqXL1+O9X83ccaxrlq1qsqWLasFCxZka7sAsg+hAkCucenSJX322WcaNGiQ2rRpo8jIyJtus2XLFjVp0kT58uVToUKFFB4ergsXLkiSEhISNHToUAUEBMjLy0uNGjXSTz/9lKaNnTt3qnbt2sqXL58aNGigAwcOOKyfPXu2ypYtKw8PD1WsWFEff/xxlvbrm2++kaenp+rXr59hnf9+6xsZGakJEybo559/ls1mk81msx8Lm82m2bNnq127dsqfP79effVVSdJXX32l++67T15eXipTpowmTJigpKQkSZIxRuPHj1eJEiXk6emp4OBgDR06NMOxpM6SvPfeewoJCVG+fPnUuXPnNN8Sf/DBBwoNDZWXl5cqVaqkd999177u2LFjstls+uyzz9S4cWN5eXlp4cKF6fYXGxurgQMHKjAwUF5eXqpatapWrFgh6d/Lxrp166Z77rlH+fLlU7Vq1fTJJ584bN+kSRMNGTJEQ4YMka+vr4oUKaKxY8fKGGOv899LclJnizp27CibzWZfPnLkiNq3b6/AwEAVKFBAderU0bp16zI8TqmWL1+uOnXqyMvLS0WKFFHHjh3t6y5cuKCePXuqUKFCypcvn1q1aqVDhw7Z16e+72vWrFFoaKgKFCigli1bKjo62l5n48aNqlu3rvLnzy8/Pz81bNhQx48flyT17t1bHTp0cBjPsGHD1KRJE4fj88wzz2jYsGEqVKiQAgMD9f777+vy5cvq06ePChYsqHLlymnVqlUOfdpsNq1cuVLVq1eXl5eX6tevr71799rX9+nTR3FxcfZzdPz48WmOtSSdOHFC7du3V4ECBeTj46POnTvr9OnT9vWp59vHH3+sUqVKydfXV127dtXFixcd9qtt27b69NNPb/p+AMgZhAoAucbixYtVqVIlVaxYUU888YQ+/PBDhw+G19uzZ4+aNWumypUra+vWrfr+++/Vtm1bJScnS5Kef/55ffHFF5o/f7527dqlcuXKKTw8XOfPn3do58UXX9S0adO0Y8cOubm5qW/fvvZ1S5cu1bPPPqsRI0Zo7969GjhwoPr06aMNGzZker++++471apVK9P1u3TpohEjRqhKlSqKjo5WdHS0unTpYl8/fvx4dezYUb/++qv69u2r7777Tj179tSzzz6r3377Te+9954iIyPtgeOLL77QjBkz9N577+nQoUNatmyZqlWrdsMxHD58WIsXL9by5cu1evVq7d69W08//bR9/cKFC/XSSy/p1Vdf1f79+/Xaa69p7Nixmj9/vkM7o0eP1rPPPqv9+/crPDw8TT8pKSlq1aqVtmzZogULFui3337T5MmT5erqKkm6evWqatWqpZUrV2rv3r0aMGCAevTooe3btzu0M3/+fLm5uWn79u168803NX36dH3wwQfp7ltqsIyIiFB0dLR9+dKlS2rdurXWr1+v3bt3q2XLlmrbtq1OnDiR4XFauXKlOnbsqNatW2v37t1av3696tata1/fu3dv7dixQ19//bW2bt0qY4xat27tMGtz5coVTZ06VR9//LE2b96sEydOaOTIkZKkpKQkdejQQY0bN9Yvv/yirVu3asCAAbLZbBmOKT3z589XkSJFtH37dj3zzDMaNGiQHnvsMTVo0EC7du1SixYt1KNHD125csVhu1GjRmnatGn66aefVLRoUbVt21aJiYlq0KCBZs6cKR8fH/s5mjrm/0pJSVH79u11/vx5bdq0SVFRUfrjjz8czmfp30C3bNkyrVixQitWrNCmTZs0efJkhzp169bV9u3blZCQkKV9B3CbGADIJRo0aGBmzpxpjDEmMTHRFClSxGzYsMG+fsOGDUaSuXDhgjHGmG7dupmGDRum29alS5eMu7u7Wbhwob3s2rVrJjg42EyZMsWhvXXr1tnrrFy50kgy//zzj31M/fv3d2j7scceM61bt7YvSzJLly7NcL/at29v+vbt61B2/b5EREQYX19f+/px48aZGjVqpGlLkhk2bJhDWbNmzcxrr73mUPbxxx+bYsWKGWOMmTZtmqlQoYK5du1ahmP8r3HjxhlXV1fz559/2stWrVplXFxcTHR0tDHGmLJly5pFixY5bPfKK6+YsLAwY4wxR48eNZLs72dG1qxZY1xcXMyBAwcyNTZjjGnTpo0ZMWKEfblx48YmNDTUpKSk2MteeOEFExoaal8uWbKkmTFjhn35Zu9ZqipVqpi33347w/VhYWGme/fu6a47ePCgkWS2bNliL/v777+Nt7e3Wbx4sTHm3/ddkjl8+LC9zqxZs0xgYKAxxphz584ZSWbjxo3p9tGrVy/Tvn17h7Jnn33WNG7c2L7cuHFj06hRI/tyUlKSyZ8/v+nRo4e9LDo62kgyW7duNcb83/n56aef2uucO3fOeHt7m88++8w+9v+es6n+e6zXrl1rXF1dzYkTJ+zr9+3bZySZ7du3G2P+Pd/y5ctn4uPj7XVGjRpl6tWr59Duzz//bCSZY8eOpXssAOQsZioA5AoHDhzQ9u3b1a1bN0mSm5ubunTponnz5mW4TepMRXqOHDmixMRENWzY0F7m7u6uunXrav/+/Q51q1evbv93sWLFJElnzpyRJO3fv9+hDUlq2LBhmjZu5J9//pGXl1em699M7dq1HZZ//vlnvfzyyypQoID91b9/f0VHR+vKlSt67LHH9M8//6hMmTLq37+/li5dar80KiMlSpTQPffcY18OCwtTSkqKDhw4oMuXL+vIkSPq16+fQ58TJ07UkSNHbjjW6+3Zs0fFixdXhQoV0l2fnJysV155RdWqVZO/v78KFCigNWvWpJk9qF+/vsO392FhYTp06JB91iozLl26pJEjRyo0NFR+fn4qUKCA9u/ff8OZihudg/v375ebm5vq1atnLytcuLAqVqzocP7ky5dPZcuWtS8XK1bMfv75+/urd+/eCg8PV9u2bfXmm286XBqVWf89x11dXVW4cGGH2arAwEBJ/3fepwoLC7P/29/fP83Yb2b//v0KCQlRSEiIvaxy5cry8/NzaKdUqVIqWLCgffm/xyCVt7e3JKWZTQGQO7jl9AAAQPr3Bu2kpCQFBwfby4wx8vT01DvvvCNfX98026R+yLDK3d3d/u/UD6YpKSnZ0rYkFSlSxH6fR3bInz+/w/KlS5c0YcIEPfLII2nqenl5KSQkRAcOHNC6desUFRWlp59+Wm+88YY2bdrksO+ZdenSJUnS+++/7/CBWZL9sqWMxnq9m72Hb7zxht58803NnDlT1apVU/78+TVs2DBdu3Yty+O+mZEjRyoqKkpTp05VuXLl5O3trUcfffSGfWXHOXj9e2Cz2Rwu+4uIiNDQoUO1evVqffbZZ/rf//6nqKgo1a9fXy4uLmkuEUzvhvj0+nD2eZ8V6Y3v+rGkXrZYtGjR2zYuAJnHTAWAHJeUlKSPPvpI06ZN0549e+yvn3/+WcHBwWluzE1VvXp1rV+/Pt11qTdWb9myxV6WmJion376SZUrV8702EJDQx3akP69OTwrbdx777367bffMl1fkjw8PDL9Lft9992nAwcOqFy5cmleLi7//pn39vZW27Zt9dZbb2njxo3aunWrfv311wzbPHHihMPTt3788Ue5uLioYsWKCgwMVHBwsP744480/ZUuXTpL+1m9enX9+eefDo/T/a8tW7aoffv2euKJJ1SjRg2VKVMm3brbtm1zWP7xxx9Vvnz5NCEnlbu7e5rju2XLFvXu3VsdO3ZUtWrVFBQUZP9dkRuNP6NzMDQ0VElJSQ5jO3funA4cOJCl80f69xwaM2aMfvjhB1WtWlWLFi2S9O8H7OtnLvbs2ZOltm/kxx9/tP/7woULOnjwoEJDQyVl7hwNDQ3VyZMndfLkSXvZb7/9ptjY2Cwfg71796p48eIqUqRIlrYDcHswUwEgx61YsUIXLlxQv3790sxIdOrUSfPmzdNTTz2VZrsxY8aoWrVqevrpp/XUU0/Jw8NDGzZs0GOPPaYiRYpo0KBBGjVqlPz9/VWiRAlNmTJFV65cueGjXa83atQode7cWffee6+aN2+u5cuX68svv8zUU4FShYeHa8yYMbpw4YIKFSqUqW1KlSqlo0eP2i8PKliwoDw9PdOt+9JLL+nhhx9WiRIl9Oijj8rFxUU///yz9u7dq4kTJyoyMlLJycmqV6+e8uXLpwULFsjb21slS5bMsH8vLy/16tVLU6dOVXx8vIYOHarOnTsrKChIkjRhwgQNHTpUvr6+atmypRISErRjxw5duHBBw4cPz/Sxady4sR544AF16tRJ06dPV7ly5fT777/LZrOpZcuWKl++vD7//HP98MMPKlSokKZPn67Tp0+n+UB64sQJDR8+XAMHDtSuXbv09ttv3/DH0kqVKqX169erYcOG8vT0VKFChVS+fHl9+eWXatu2rWw2m8aOHXvTb+7HjRunZs2aqWzZsuratauSkpL0zTff6IUXXlD58uXVvn179e/fX++9954KFiyo0aNH65577lH79u0zdXyOHj2quXPnql27dgoODtaBAwd06NAh9ezZU5L04IMP6o033tBHH32ksLAwLViwQHv37tW9996bqfZv5uWXX1bhwoUVGBioF198UUWKFLE/bapUqVK6dOmS1q9frxo1aihfvnxpHiXbvHlzVatWTd27d9fMmTOVlJSkp59+Wo0bN77ppXHX++6779SiRYts2S8A2Y+ZCgA5bt68eWrevHm6lzh16tRJO3bs0C+//JJmXYUKFbR27Vr9/PPPqlu3rsLCwvTVV1/Jze3f70smT56sTp06qUePHrrvvvt0+PBhrVmzJtMf7CWpQ4cOevPNNzV16lRVqVJF7733niIiIhwe2Xkz1apV03333afFixdneptOnTqpZcuWatq0qYoWLZrhbI30b2hZsWKF1q5dqzp16qh+/fqaMWOGPTT4+fnp/fffV8OGDVW9enWtW7dOy5cvV+HChTNss1y5cnrkkUfUunVrtWjRQtWrV3d4ZOyTTz6pDz74QBEREapWrZoaN26syMjILM9USP8+napOnTrq1q2bKleurOeff97+Dfj//vc/3XfffQoPD1eTJk0UFBSU5hGqktSzZ0/9888/qlu3rgYPHqxnn332hj/ANm3aNEVFRSkkJMT+AXz69OkqVKiQGjRooLZt2yo8PFz33XffDcfepEkTLVmyRF9//bVq1qypBx980OHJVBEREapVq5YefvhhhYWFyRijb775JtOXneXLl0+///67OnXqpAoVKmjAgAEaPHiwBg4cKOnf937s2LF6/vnnVadOHV28eNEeOLLD5MmT9eyzz6pWrVqKiYnR8uXL5eHhIUlq0KCBnnrqKXXp0kVFixbVlClT0mxvs9n01VdfqVChQnrggQfUvHlzlSlTRp999lmWxnH16lUtW7ZM/fv3z5b9ApD9bOb6izEBANlu5cqVGjVqlPbu3Wu/JCm3Gj9+vJYtW5atl9E4U5MmTVSzZk2H30aANRs3blTTpk114cKFdH81+3abPXu2li5dqrVr1+b0UABkgMufAOA2aNOmjQ4dOqS//vrL4Uk4AG7O3d1db7/9dk4PA8ANECoA4DYZNmxYTg8BuCM9+eSTOT0EADfB5U8AAAAALMndF/YCAAAAyPUIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAcKpSpUqpd+/eOT2MPO+NN95QmTJl5Orqqpo1a+b0cIB0NWnSRE2aNLEvHzt2TDabTZGRkTk2JgDZg1ABINMiIyNls9m0Y8eOdNc3adJEVatWtdzPN998o/Hjx1tu526xdu1aPf/882rYsKEiIiL02muv5fSQchTnz52N9w+4M7nl9AAA5G0HDhyQi0vWvr/45ptvNGvWLD5YZNK3334rFxcXzZs3Tx4eHjk9nBzH+XPnKFmypP755x+5u7vby3j/gDsTMxUAnMrT09PhA8Od4PLlyzk9hCw5c+aMvL29c02gSElJ0dWrV3N6GLgD2Gw2eXl5ydXV1el93Wn/XQN3GkIFAKe6/p6KxMRETZgwQeXLl5eXl5cKFy6sRo0aKSoqSpLUu3dvzZo1S9K/HzhSX6kuX76sESNGKCQkRJ6enqpYsaKmTp0qY4xDv//884+GDh2qIkWKqGDBgmrXrp3++usv2Ww2h29Ax48fL5vNpt9++02PP/64ChUqpEaNGkmSfvnlF/Xu3VtlypSRl5eXgoKC1LdvX507d86hr9Q2Dh48qCeeeEK+vr4qWrSoxo4dK2OMTp48qfbt28vHx0dBQUGaNm1apo5dUlKSXnnlFZUtW1aenp4qVaqU/t//+39KSEiw17HZbIqIiNDly5ftx+pG16enXqK2c+dONWjQQN7e3ipdurTmzJmTpm5CQoLGjRuncuXKydPTUyEhIXr++ecd+k8dw5AhQ7Rw4UJVqVJFnp6eWr16tSTpr7/+Ur9+/RQcHCxPT0+VLl1agwYN0rVr1+zbx8bGatiwYfb3tFy5cnr99deVkpJir5N67f3UqVM1d+5c+zGpU6eOfvrpJ3u9m50/U6dOVYMGDVS4cGF5e3urVq1a+vzzz9Pse2bPn9R97Nu3rwIDA+Xp6akqVaroww8/zPA9uJFVq1bp/vvvV/78+VWwYEG1adNG+/bts69PnZV66aWXHLZbtGiRbDabZs+ebS/77/tSsWJFeXl5qVatWtq8eXOafjOzDxs3bpTNZtPixYv16quvqnjx4vLy8lKzZs10+PDhNG2mvk/e3t6qW7euvvvuuzR1rr+n4kbvX2r/GzduvGEbqe0UKFBAR44cUevWrVWwYEF1795d0r+hd+bMmapSpYq8vLwUGBiogQMH6sKFC2nGByDzuPwJQJbFxcXp77//TlOemJh4023Hjx+vSZMm6cknn1TdunUVHx+vHTt2aNeuXXrooYc0cOBAnTp1SlFRUfr4448dtjXGqF27dtqwYYP69eunmjVras2aNRo1apT++usvzZgxw163d+/eWrx4sXr06KH69etr06ZNatOmTYbjeuyxx1S+fHm99tpr9oASFRWlP/74Q3369FFQUJD27dunuXPnat++ffrxxx8dPqxKUpcuXRQaGqrJkydr5cqVmjhxovz9/fXee+/pwQcf1Ouvv66FCxdq5MiRqlOnjh544IEbHqsnn3xS8+fP16OPPqoRI0Zo27ZtmjRpkvbv36+lS5dKkj7++GPNnTtX27dv1wcffCBJatCgwQ3bvXDhglq3bq3OnTurW7duWrx4sQYNGiQPDw/17dtX0r8fvNq1a6fvv/9eAwYMUGhoqH799VfNmDFDBw8e1LJlyxza/Pbbb7V48WINGTJERYoUUalSpXTq1CnVrVtXsbGxGjBggCpVqqS//vpLn3/+ua5cuSIPDw9duXJFjRs31l9//aWBAweqRIkS+uGHHzRmzBhFR0dr5syZDv0sWrRIFy9e1MCBA2Wz2TRlyhQ98sgj+uOPP+Tu7n7D80eS3nzzTbVr107du3fXtWvX9Omnn+qxxx7TihUrHM6PzJ4/p0+fVv369e0f4IsWLapVq1apX79+io+P17Bhw274XvzXxx9/rF69eik8PFyvv/66rly5otmzZ6tRo0bavXu3SpUqpQcffFBPP/20Jk2apA4dOui+++5TdHS0nnnmGTVv3lxPPfWUQ5ubNm3SZ599pqFDh8rT01PvvvuuWrZsqe3bt9vvf8rqPkyePFkuLi4aOXKk4uLiNGXKFHXv3l3btm2z15k3b54GDhyoBg0aaNiwYfrjjz/Url07+fv7KyQkJMNjcLP3LyuSkpIUHh6uRo0aaerUqcqXL5+9j8jISPXp00dDhw7V0aNH9c4772j37t3asmXLHTezCuQaBgAyKSIiwki64atKlSoO25QsWdL06tXLvlyjRg3Tpk2bG/YzePBgk96fp2XLlhlJZuLEiQ7ljz76qLHZbObw4cPGGGN27txpJJlhw4Y51Ovdu7eRZMaNG2cvGzdunJFkunXrlqa/K1eupCn75JNPjCSzefPmNG0MGDDAXpaUlGSKFy9ubDabmTx5sr38woULxtvb2+GYpGfPnj1GknnyyScdykeOHGkkmW+//dZe1qtXL5M/f/4btpeqcePGRpKZNm2avSwhIcHUrFnTBAQEmGvXrhljjPn444+Ni4uL+e677xy2nzNnjpFktmzZYi+TZFxcXMy+ffsc6vbs2dO4uLiYn376Kc04UlJSjDHGvPLKKyZ//vzm4MGDDutHjx5tXF1dzYkTJ4wxxhw9etRIMoULFzbnz5+31/vqq6+MJLN8+XJ7WUbnjzFp39Nr166ZqlWrmgcffNBelpXzp1+/fqZYsWLm77//dqjbtWtX4+vrm+45lJ6LFy8aPz8/079/f4fymJgY4+vr61B++fJlU65cOVOlShVz9epV06ZNG+Pj42OOHz/usG3qf5M7duywlx0/ftx4eXmZjh07ZnkfNmzYYCSZ0NBQk5CQYK/35ptvGknm119/Ncb8e0wDAgJMzZo1HerNnTvXSDKNGze2l6W+rxEREfayjN6/1P43bNjgUJ5eG7169TKSzOjRox3qfvfdd0aSWbhwoUP56tWr0y0HkHlc/gQgy2bNmqWoqKg0r+rVq990Wz8/P+3bt0+HDh3Kcr/ffPONXF1dNXToUIfyESNGyBijVatWSZL90punn37aod4zzzyTYdvXf8MrSd7e3vZ/X716VX///bfq168vSdq1a1ea+k8++aT9366urqpdu7aMMerXr5+93M/PTxUrVtQff/yR4Vikf/dVkoYPH+5QPmLECEnSypUrb7j9jbi5uWngwIH2ZQ8PDw0cOFBnzpzRzp07JUlLlixRaGioKlWqpL///tv+evDBByVJGzZscGizcePGqly5sn05JSVFy5YtU9u2bVW7du00Y0id5VmyZInuv/9+FSpUyKGf5s2bKzk5Oc2lOl26dFGhQoXsy/fff78k3fR4pvrve3rhwgXFxcXp/vvvd3g/M3v+GGP0xRdfqG3btjLGOIw/PDxccXFx6Z4n6YmKilJsbKy6devm0I6rq6vq1avncLzz5cunyMhI7d+/Xw888IBWrlypGTNmqESJEmnaDQsLU61atezLJUqUUPv27bVmzRolJyff0j706dPH4f6d69+DHTt26MyZM3rqqacc6vXu3Vu+vr6ZOh7ZZdCgQQ7LS5Yska+vrx566CGHfa1Vq5YKFCiQ5rwGkHlc/gQgy+rWrZvuB8XUD4Y38vLLL6t9+/aqUKGCqlatqpYtW6pHjx6ZCiTHjx9XcHCwChYs6FAeGhpqX5/6vy4uLipdurRDvXLlymXY9vV1Jen8+fOaMGGCPv30U505c8ZhXVxcXJr613+o8/X1lZeXl4oUKZKm/Pr7Mq6Xug/XjzkoKEh+fn72fb0VwcHByp8/v0NZhQoVJP17fXr9+vV16NAh7d+/X0WLFk23jeuPx/XH7+zZs4qPj7/pI4YPHTqkX375JdP9XH+MUwNGZq+HX7FihSZOnKg9e/akuTclVWbPn7Nnzyo2NlZz587V3LlzMzX+jKSG7NTQdj0fHx+H5YYNG2rQoEGaNWuWwsPD7ZetXa98+fJpyipUqKArV67o7NmzcnFxyfI+3Ow9SD03r+/b3d1dZcqUSbcPZ3Bzc1Px4sUdyg4dOqS4uDgFBASku01m3y8AaREqANxWDzzwgI4cOaKvvvpKa9eu1QcffKAZM2Zozpw5Dt/0327//QY7VefOnfXDDz9o1KhRqlmzpgoUKKCUlBS1bNnS4SbiVOk9wSajp9qY624sz8j1923cLikpKapWrZqmT5+e7vrrr4tP7/hltp+HHnpIzz//fLrrU8NOKivH87vvvlO7du30wAMP6N1331WxYsXk7u6uiIgILVq06JbGLklPPPGEevXqlW6dzITl/7b18ccfKygoKM16NzfH/7tOSEiw37B85MgRXblyxX7PQFbcyj5YPadvVUb/LSQnJ6db7unpmeZx1ikpKQoICNDChQvT3SajcAvg5ggVAG47f39/9enTR3369NGlS5f0wAMPaPz48fZQkdGHh5IlS2rdunW6ePGiw2zF77//bl+f+r8pKSk6evSow7el6T2hJiMXLlzQ+vXrNWHCBIcn7dzKZVu3InUfDh06ZJ+Jkf69qTY2Nta+r7fi1KlTunz5ssNsxcGDByX9+7QuSSpbtqx+/vlnNWvW7JaCTdGiReXj46O9e/fesF7ZsmV16dIlNW/ePMt9ZCSj8X7xxRfy8vLSmjVr5OnpaS+PiIhwqJfZ86do0aIqWLCgkpOTLY+/bNmykqSAgIBMtTVu3Djt379fU6dO1QsvvKDRo0frrbfeSlMvvfP14MGDypcvn/0DdHbtQ6rUc/PQoUMOMy+JiYk6evSoatSoccPtM3r/UmdEYmNjHcqzMmtXtmxZrVu3Tg0bNrzlIAwgfdxTAeC2uv6ynwIFCqhcuXIOl6Kkfti9/sND69atlZycrHfeecehfMaMGbLZbGrVqpUkKTw8XJL07rvvOtR7++23Mz3O1G9jr//29fqnETlL69at0+0vdebgRk+yupmkpCS999579uVr167pvffeU9GiRe3X33fu3Fl//fWX3n///TTb//PPPzd95r+Li4s6dOig5cuXp/sL7KnHtXPnztq6davWrFmTpk5sbKySkpKytG9SxuePq6urbDabwzfbx44dS/Mkq8yeP66ururUqZO++OKLdMPT2bNnMz3m8PBw+fj46LXXXkv3KWr/bWvbtm2aOnWqhg0bphEjRmjUqFF65513tGnTpjTbbd261eGeiJMnT+qrr75SixYt5Orqmq37kKp27doqWrSo5syZ4/Do4MjIyDTvSXoyev9KliwpV1fXNPfZXP8+3Ujnzp2VnJysV155Jc26pKSkTI0PQPqYqQBwW1WuXFlNmjRRrVq15O/vrx07dujzzz/XkCFD7HVSP9gOHTpU4eHhcnV1VdeuXdW2bVs1bdpUL774oo4dO6YaNWpo7dq1+uqrrzRs2DD7t721atVSp06dNHPmTJ07d87+SNDUb+Mz8827j4+PHnjgAU2ZMkWJiYm65557tHbtWh09etQJRyWtGjVqqFevXpo7d65iY2PVuHFjbd++XfPnz1eHDh3UtGnTW247ODhYr7/+uo4dO6YKFSros88+0549ezR37lz74zR79OihxYsX66mnntKGDRvUsGFDJScn6/fff9fixYu1Zs2adO+r+a/XXntNa9euVePGje2PpY2OjtaSJUv0/fffy8/PT6NGjdLXX3+thx9+WL1791atWrV0+fJl/frrr/r888917NixNPek3ExG50+bNm00ffp0tWzZUo8//rjOnDmjWbNmqVy5cvrll18cts/s+TN58mRt2LBB9erVU//+/VW5cmWdP39eu3bt0rp163T+/PlMjdnHx0ezZ89Wjx49dN9996lr164qWrSoTpw4oZUrV6phw4Z65513dPXqVfXq1Uvly5fXq6++KkmaMGGCli9frj59+ujXX391mIGqWrWqwsPDHR4pm7pNdu9DKnd3d02cOFEDBw7Ugw8+qC5duujo0aOKiIjI1D0VGb1/vr6+euyxx/T222/LZrOpbNmyWrFiRZbug2jcuLEGDhyoSZMmac+ePWrRooXc3d116NAhLVmyRG+++aYeffTRLO0vgP9fDj11CsAdKPWRsuk9ItSYfx9XerNHyk6cONHUrVvX+Pn5GW9vb1OpUiXz6quv2h9lasy/j2N95plnTNGiRY3NZnN4vOTFixfNc889Z4KDg427u7spX768eeONN+yPKE11+fJlM3jwYOPv728KFChgOnToYA4cOGAkOTziNfVxsGfPnk2zP3/++afp2LGj8fPzM76+vuaxxx4zp06dyvCxtNe3kdGjXtM7TulJTEw0EyZMMKVLlzbu7u4mJCTEjBkzxly9ejVT/aQnte8dO3aYsLAw4+XlZUqWLGneeeedNHWvXbtmXn/9dVOlShXj6elpChUqZGrVqmUmTJhg4uLi7PUkmcGDB6fb3/Hjx03Pnj1N0aJFjaenpylTpowZPHiww6NGL168aMaMGWPKlStnPDw8TJEiRUyDBg3M1KlT7edF6mND33jjjTR9XP9+3Oj8mTdvnilfvrzx9PQ0lSpVMhEREfb3778ye/4YY8zp06fN4MGDTUhIiHF3dzdBQUGmWbNmZu7cuTd4J9K3YcMGEx4ebnx9fY2Xl5cpW7as6d27t/2xsM8995xxdXU127Ztc9hux44dxs3NzQwaNMjhuAwePNgsWLDAvs/33ntvmkeyZnYfUh/pumTJEodt03ukqzHGvPvuu6Z06dLG09PT1K5d22zevNk0btz4po+UvdH7d/bsWdOpUyeTL18+U6hQITNw4ECzd+/edB8pe6P/JubOnWtq1aplvL29TcGCBU21atXM888/b06dOpXhNgBuzGaMk++sAoBcYs+ePbr33nu1YMEC+6/r3m2aNGmiv//++6b3OiCtO+38sdlsGjx4cJrLBQHAGbinAkCe9M8//6QpmzlzplxcXG76S9YA5w8AZA33VADIk6ZMmaKdO3eqadOmcnNz06pVq7Rq1SoNGDAgzeNQgetl5/lz9uzZDB97Kv3744P+/v5WhwwAOYpQASBPatCggaKiovTKK6/o0qVLKlGihMaPH68XX3wxp4eGO0B2nj916tS54WNPGzdubP/NCQC4U3FPBQAATrRly5Z0L6dKVahQIfsTjwDgTkWoAAAAAGAJlz9lQkpKik6dOqWCBQve0i/LAgAAALmZMUYXL15UcHCwXFyy/iwnQkUmnDp1ihs7AQAAkOedPHlSxYsXz/J2hIpMKFiwoKR/D7KPj08OjwYAAADIXvHx8QoJCbF/7s0qQkUmpF7y5OPjQ6gAAABAnnWrl/rz43cAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsccvpASBzSo1eme1tHpvcJtvbBAAAwN2HmQoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYkqtDRXJyssaOHavSpUvL29tbZcuW1SuvvCJjjL2OMUYvvfSSihUrJm9vbzVv3lyHDh1yaOf8+fPq3r27fHx85Ofnp379+unSpUu3e3cAAACAPClXh4rXX39ds2fP1jvvvKP9+/fr9ddf15QpU/T222/b60yZMkVvvfWW5syZo23btil//vwKDw/X1atX7XW6d++uffv2KSoqSitWrNDmzZs1YMCAnNglAAAAIM+xmf9+7Z/LPPzwwwoMDNS8efPsZZ06dZK3t7cWLFggY4yCg4M1YsQIjRw5UpIUFxenwMBARUZGqmvXrtq/f78qV66sn376SbVr15YkrV69Wq1bt9aff/6p4ODgNP0mJCQoISHBvhwfH6+QkBDFxcXJx8fHyXudvlKjV2Z7m8cmt8n2NgEAAHDniY+Pl6+v7y1/3s3VMxUNGjTQ+vXrdfDgQUnSzz//rO+//16tWrWSJB09elQxMTFq3ry5fRtfX1/Vq1dPW7dulSRt3bpVfn5+9kAhSc2bN5eLi4u2bduWbr+TJk2Sr6+v/RUSEuKsXQQAAADueG45PYAbGT16tOLj41WpUiW5uroqOTlZr776qrp37y5JiomJkSQFBgY6bBcYGGhfFxMTo4CAAIf1bm5u8vf3t9e53pgxYzR8+HD7cupMBQAAAIC0cnWoWLx4sRYuXKhFixapSpUq2rNnj4YNG6bg4GD16tXLaf16enrK09PTae0DAAAAeUmuDhWjRo3S6NGj1bVrV0lStWrVdPz4cU2aNEm9evVSUFCQJOn06dMqVqyYfbvTp0+rZs2akqSgoCCdOXPGod2kpCSdP3/evj0AAACAW5er76m4cuWKXFwch+jq6qqUlBRJUunSpRUUFKT169fb18fHx2vbtm0KCwuTJIWFhSk2NlY7d+601/n222+VkpKievXq3Ya9AAAAAPK2XD1T0bZtW7366qsqUaKEqlSpot27d2v69Onq27evJMlms2nYsGGaOHGiypcvr9KlS2vs2LEKDg5Whw4dJEmhoaFq2bKl+vfvrzlz5igxMVFDhgxR165d033yEwAAAICsydWh4u2339bYsWP19NNP68yZMwoODtbAgQP10ksv2es8//zzunz5sgYMGKDY2Fg1atRIq1evlpeXl73OwoULNWTIEDVr1kwuLi7q1KmT3nrrrZzYJQAAACDPydW/U5FbWH1ub3bgdyoAAADgLHn6dyoAAAAA5H6ECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCW5PlSUKlVKNpstzWvw4MGSpKtXr2rw4MEqXLiwChQooE6dOun06dMObZw4cUJt2rRRvnz5FBAQoFGjRikpKSkndgcAAADIc3J9qPjpp58UHR1tf0VFRUmSHnvsMUnSc889p+XLl2vJkiXatGmTTp06pUceecS+fXJystq0aaNr167phx9+0Pz58xUZGamXXnopR/YHAAAAyGtsxhiT04PIimHDhmnFihU6dOiQ4uPjVbRoUS1atEiPPvqoJOn3339XaGiotm7dqvr162vVqlV6+OGHderUKQUGBkqS5syZoxdeeEFnz56Vh4dHmj4SEhKUkJBgX46Pj1dISIji4uLk4+Nze3b0OqVGr8z2No9NbpPtbQIAAODOEx8fL19f31v+vJvrZyr+69q1a1qwYIH69u0rm82mnTt3KjExUc2bN7fXqVSpkkqUKKGtW7dKkrZu3apq1arZA4UkhYeHKz4+Xvv27Uu3n0mTJsnX19f+CgkJce6OAQAAAHewOypULFu2TLGxserdu7ckKSYmRh4eHvLz83OoFxgYqJiYGHud/waK1PWp69IzZswYxcXF2V8nT57M3h0BAAAA8hC3nB5AVsybN0+tWrVScHCwU/vx9PSUp6enU/sAAAAA8oo7Zqbi+PHjWrdunZ588kl7WVBQkK5du6bY2FiHuqdPn1ZQUJC9zvVPg0pdTq0DAAAA4NbdMaEiIiJCAQEBatPm/24urlWrltzd3bV+/Xp72YEDB3TixAmFhYVJksLCwvTrr7/qzJkz9jpRUVHy8fFR5cqVb98OAAAAAHnUHXH5U0pKiiIiItSrVy+5uf3fkH19fdWvXz8NHz5c/v7+8vHx0TPPPKOwsDDVr19fktSiRQtVrlxZPXr00JQpUxQTE6P//e9/Gjx4MJc4AQAAANngjggV69at04kTJ9S3b98062bMmCEXFxd16tRJCQkJCg8P17vvvmtf7+rqqhUrVmjQoEEKCwtT/vz51atXL7388su3cxcAAACAPOuO+52KnGD1ub3Zgd+pAAAAgLPcVb9TAQAAACD3IVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsyfWh4q+//tITTzyhwoULy9vbW9WqVdOOHTvs640xeumll1SsWDF5e3urefPmOnTokEMb58+fV/fu3eXj4yM/Pz/169dPly5dut27AgAAAORJuTpUXLhwQQ0bNpS7u7tWrVql3377TdOmTVOhQoXsdaZMmaK33npLc+bM0bZt25Q/f36Fh4fr6tWr9jrdu3fXvn37FBUVpRUrVmjz5s0aMGBATuwSAAAAkOfYjDEmpweRkdGjR2vLli367rvv0l1vjFFwcLBGjBihkSNHSpLi4uIUGBioyMhIde3aVfv371flypX1008/qXbt2pKk1atXq3Xr1vrzzz8VHBycpt2EhAQlJCTYl+Pj4xUSEqK4uDj5+Pg4YU9vrtToldne5rHJbbK9TQAAANx54uPj5evre8ufd3P1TMXXX3+t2rVr67HHHlNAQIDuvfdevf/++/b1R48eVUxMjJo3b24v8/X1Vb169bR161ZJ0tatW+Xn52cPFJLUvHlzubi4aNu2ben2O2nSJPn6+tpfISEhTtpDAAAA4M6Xq0PFH3/8odmzZ6t8+fJas2aNBg0apKFDh2r+/PmSpJiYGElSYGCgw3aBgYH2dTExMQoICHBY7+bmJn9/f3ud640ZM0ZxcXH218mTJ7N71wAAAIA8wy2nB3AjKSkpql27tl577TVJ0r333qu9e/dqzpw56tWrl9P69fT0lKenp9PaBwAAAPKSXD1TUaxYMVWuXNmhLDQ0VCdOnJAkBQUFSZJOnz7tUOf06dP2dUFBQTpz5ozD+qSkJJ0/f95eBwAAAMCty9WhomHDhjpw4IBD2cGDB1WyZElJUunSpRUUFKT169fb18fHx2vbtm0KCwuTJIWFhSk2NlY7d+601/n222+VkpKievXq3Ya9AAAAAPK2XH3503PPPacGDRrotddeU+fOnbV9+3bNnTtXc+fOlSTZbDYNGzZMEydOVPny5VW6dGmNHTtWwcHB6tChg6R/ZzZatmyp/v37a86cOUpMTNSQIUPUtWvXdJ/8BAAAACBrcnWoqFOnjpYuXaoxY8bo5ZdfVunSpTVz5kx1797dXuf555/X5cuXNWDAAMXGxqpRo0ZavXq1vLy87HUWLlyoIUOGqFmzZnJxcVGnTp301ltv5cQuAQAAAHlOrv6ditzC6nN7swO/UwEAAABnydO/UwEAAAAg9yNUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALMnVoWL8+PGy2WwOr0qVKtnXX716VYMHD1bhwoVVoEABderUSadPn3Zo48SJE2rTpo3y5cungIAAjRo1SklJSbd7VwAAAIA8yy2nB3AzVapU0bp16+zLbm7/N+TnnntOK1eu1JIlS+Tr66shQ4bokUce0ZYtWyRJycnJatOmjYKCgvTDDz8oOjpaPXv2lLu7u1577bXbvi8AAABAXuS0UPHHH3+oTJkylttxc3NTUFBQmvK4uDjNmzdPixYt0oMPPihJioiIUGhoqH788UfVr19fa9eu1W+//aZ169YpMDBQNWvW1CuvvKIXXnhB48ePl4eHR7p9JiQkKCEhwb4cHx9veT8AAACAvMpplz+VK1dOTZs21YIFC3T16tVbbufQoUMKDg5WmTJl1L17d504cUKStHPnTiUmJqp58+b2upUqVVKJEiW0detWSdLWrVtVrVo1BQYG2uuEh4crPj5e+/bty7DPSZMmydfX1/4KCQm55fEDAAAAeZ3TQsWuXbtUvXp1DR8+XEFBQRo4cKC2b9+epTbq1aunyMhIrV69WrNnz9bRo0d1//336+LFi4qJiZGHh4f8/PwctgkMDFRMTIwkKSYmxiFQpK5PXZeRMWPGKC4uzv46efJklsYNAAAA3E2cFipq1qypN998U6dOndKHH36o6OhoNWrUSFWrVtX06dP1/7F353FR1fsfx98jyKKyuAGSC+67ljtptkiiUmmaqZniUpZhamqpt1zr5lba1UztVmiLWZZaN9MitzJ3c9dwya1kqRQQF0T4/v7wwfwawfUwDuDr+XjMQ+Z7vvM9n/PlgPPmLPPnn39ec4y2bduqc+fOqlevnsLDw/Xtt98qKSlJn3/+ubPKliR5enrK19fX4QEAAAAgZ06/+5O7u7s6duyohQsXatKkSTp48KCGDRumcuXKqWfPnoqLi7vusfz9/VWtWjUdPHhQQUFBunDhgpKSkhz6JCQk2K/BCAoKynY3qKznOV2nAQAAAODGOT1UbNmyRc8995zKlCmjqVOnatiwYTp06JBiYmJ04sQJtW/f/rrHSk1N1aFDh1SmTBk1bNhQhQsX1ooVK+zLY2NjdezYMYWGhkqSQkNDtWvXLiUmJtr7xMTEyNfXV7Vq1cq9jQQAAABuY067+9PUqVMVHR2t2NhYtWvXTh9++KHatWunQoUu5ZiKFStq7ty5CgkJueIYw4YN08MPP6wKFSroxIkTGjNmjNzc3NStWzf5+fmpb9++GjJkiEqUKCFfX189//zzCg0NVbNmzSRJrVu3Vq1atdSjRw9NnjxZ8fHxeuWVVxQVFSVPT09nbToAAABwW3FaqJg1a5b69OmjXr16qUyZMjn2CQgI0Pvvv3/FMX7//Xd169ZNf//9t0qXLq0WLVpow4YNKl26tCRp2rRpKlSokDp16qS0tDSFh4frnXfesb/ezc1N33zzjfr376/Q0FAVLVpUkZGRGj9+fO5uLAAAAHAbsxljjKuLyOtSUlLk5+en5ORkl120HTJiaa6PeWRiRK6PCQAAgPzH6vtdp11TER0drYULF2ZrX7hwoebNm+es1QIAAAC4xZwWKiZMmKBSpUplaw8ICNDrr7/urNUCAAAAuMWcFiqOHTumihUrZmuvUKGC/VOxAQAAAOR/TgsVAQEB2rlzZ7b2HTt2qGTJks5aLQAAAIBbzGmholu3bho4cKBWrVqljIwMZWRkaOXKlRo0aJC6du3qrNUCAAAAuMWcdkvZV199VUeOHFGrVq3k7n5pNZmZmerZsyfXVAAAAAAFiNNChYeHhz777DO9+uqr2rFjh7y9vVW3bl1VqFDBWasEAAAA4AJOCxVZqlWrpmrVqjl7NQAAAABcxGmhIiMjQ3PnztWKFSuUmJiozMxMh+UrV6501qoBAAAA3EJOCxWDBg3S3LlzFRERoTp16shmszlrVQAAAABcyGmhYsGCBfr888/Vrl07Z60CAAAAQB7gtFvKenh4qEqVKs4aHgAAAEAe4bRQMXToUP3nP/+RMcZZqwAAAACQBzjt9Ke1a9dq1apVWrZsmWrXrq3ChQs7LF+0aJGzVg0AAADgFnJaqPD399ejjz7qrOEBAAAA5BFOCxXR0dHOGhoAAABAHuK0ayok6eLFi/rhhx80Z84cnT59WpJ04sQJpaamOnO1AAAAAG4hpx2pOHr0qNq0aaNjx44pLS1NDz74oHx8fDRp0iSlpaVp9uzZzlo1AAAAgFvIaUcqBg0apEaNGunUqVPy9va2tz/66KNasWKFs1YLAAAA4BZz2pGKn376SevWrZOHh4dDe0hIiP744w9nrRYAAADALea0IxWZmZnKyMjI1v7777/Lx8fHWasFAAAAcIs5LVS0bt1ab731lv25zWZTamqqxowZo3bt2jlrtQAAAABuMaed/vTmm28qPDxctWrV0vnz5/XEE0/owIEDKlWqlD799FNnrRYAAADALea0UFG2bFnt2LFDCxYs0M6dO5Wamqq+ffuqe/fuDhduAwAAAMjfnBYqJMnd3V1PPvmkM1cBAAAAwMWcFio+/PDDqy7v2bOns1YNAAAA4BZyWqgYNGiQw/P09HSdPXtWHh4eKlKkCKECAAAAKCCcdvenU6dOOTxSU1MVGxurFi1acKE2AAAAUIA4LVTkpGrVqpo4cWK2oxgAAAAA8q9bGiqkSxdvnzhx4lavFgAAAICTOO2aiq+//trhuTFGcXFxevvtt9W8eXNnrRYAAADALea0UNGhQweH5zabTaVLl9YDDzygN99801mrBQAAAHCLOS1UZGZmOmtoAAAAAHnILb+mAgAAAEDB4rQjFUOGDLnuvlOnTnVWGQAAAACczGmhYtu2bdq2bZvS09NVvXp1SdL+/fvl5uamBg0a2PvZbDZnlQAAAADgFnBaqHj44Yfl4+OjefPmqXjx4pIufSBe7969dc8992jo0KHOWjUAAACAW8hp11S8+eabmjBhgj1QSFLx4sX12muv3fTdnyZOnCibzabBgwfb286fP6+oqCiVLFlSxYoVU6dOnZSQkODwumPHjikiIkJFihRRQECAXnzxRV28ePGmagAAAADgyGmhIiUlRX/++We29j///FOnT5++4fE2b96sOXPmqF69eg7tL7zwgv73v/9p4cKFWrNmjU6cOKGOHTval2dkZCgiIkIXLlzQunXrNG/ePM2dO1ejR4++8Y0CAAAAkI3TQsWjjz6q3r17a9GiRfr999/1+++/68svv1Tfvn0d3vRfj9TUVHXv3l3//e9/HY58JCcn6/3339fUqVP1wAMPqGHDhoqOjta6deu0YcMGSdL333+vvXv36uOPP9add96ptm3b6tVXX9XMmTN14cKFHNeXlpamlJQUhwcAAACAnDktVMyePVtt27bVE088oQoVKqhChQp64okn1KZNG73zzjs3NFZUVJQiIiIUFhbm0L5161alp6c7tNeoUUPly5fX+vXrJUnr169X3bp1FRgYaO8THh6ulJQU7dmzJ8f1TZgwQX5+fvZHuXLlbqheAAAA4HbitAu1ixQponfeeUdTpkzRoUOHJEmVK1dW0aJFb2icBQsW6JdfftHmzZuzLYuPj5eHh4f8/f0d2gMDAxUfH2/v889AkbU8a1lORo4c6XBL3JSUFIIFAAAAcAVOCxVZ4uLiFBcXp5YtW8rb21vGmOu+jezx48c1aNAgxcTEyMvLy8mV/j9PT095enresvUBAAAA+ZnTTn/6+++/1apVK1WrVk3t2rVTXFycJKlv377XfTvZrVu3KjExUQ0aNJC7u7vc3d21Zs0aTZ8+Xe7u7goMDNSFCxeUlJTk8LqEhAQFBQVJkoKCgrLdDSrreVYfAAAAADfPaaHihRdeUOHChXXs2DEVKVLE3t6lSxctX778usZo1aqVdu3ape3bt9sfjRo1Uvfu3e1fFy5cWCtWrLC/JjY2VseOHVNoaKgkKTQ0VLt27VJiYqK9T0xMjHx9fVWrVq1c2loAAADg9uW005++//57fffddypbtqxDe9WqVXX06NHrGsPHx0d16tRxaCtatKhKlixpb+/bt6+GDBmiEiVKyNfXV88//7xCQ0PVrFkzSVLr1q1Vq1Yt9ejRQ5MnT1Z8fLxeeeUVRUVFcYoTAAAAkAucFirOnDnjcIQiy8mTJ3P1zfy0adNUqFAhderUSWlpaQoPD3e4u5Sbm5u++eYb9e/fX6GhoSpatKgiIyM1fvz4XKsBAAAAuJ3ZjDHGGQO3a9dODRs21KuvviofHx/t3LlTFSpUUNeuXZWZmakvvvjCGat1ipSUFPn5+Sk5OVm+vr4uqSFkxNJcH/PIxIhcHxMAAAD5j9X3u047UjF58mS1atVKW7Zs0YULF/TSSy9pz549OnnypH7++WdnrRYAAADALea0C7Xr1Kmj/fv3q0WLFmrfvr3OnDmjjh07atu2bapcubKzVgsAAADgFnPKkYr09HS1adNGs2fP1ssvv+yMVQAAAADII5xypKJw4cLauXOnM4YGAAAAkMc47fSnJ598Uu+//76zhgcAAACQRzjtQu2LFy/qgw8+0A8//KCGDRuqaNGiDsunTp3qrFUDAAAAuIVyPVT89ttvCgkJ0e7du9WgQQNJ0v79+x362Gy23F4tAAAAABfJ9VBRtWpVxcXFadWqVZKkLl26aPr06QoMDMztVQEAAADIA3L9morLP0tv2bJlOnPmTG6vBgAAAEAe4bQLtbM46QO7AQAAAOQRuR4qbDZbtmsmuIYCAAAAKLhy/ZoKY4x69eolT09PSdL58+f17LPPZrv706JFi3J71QAAAABcINdDRWRkpMPzJ598MrdXAQAAACAPyfVQER0dndtDAgAAAMjDnH6hNgAAAICCjVABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAAS/J0qJg1a5bq1asnX19f+fr6KjQ0VMuWLbMvP3/+vKKiolSyZEkVK1ZMnTp1UkJCgsMYx44dU0REhIoUKaKAgAC9+OKLunjx4q3eFAAAAKDAytOhomzZspo4caK2bt2qLVu26IEHHlD79u21Z88eSdILL7yg//3vf1q4cKHWrFmjEydOqGPHjvbXZ2RkKCIiQhcuXNC6des0b948zZ07V6NHj3bVJgEAAAAFjs0YY1xdxI0oUaKEpkyZoscee0ylS5fW/Pnz9dhjj0mSfv31V9WsWVPr169Xs2bNtGzZMj300EM6ceKEAgMDJUmzZ8/W8OHD9eeff8rDwyPHdaSlpSktLc3+PCUlReXKlVNycrJ8fX2dv5E5CBmxNNfHPDIxItfHBAAAQP6TkpIiPz+/m36/m6ePVPxTRkaGFixYoDNnzig0NFRbt25Venq6wsLC7H1q1Kih8uXLa/369ZKk9evXq27duvZAIUnh4eFKSUmxH+3IyYQJE+Tn52d/lCtXznkbBgAAAORzeT5U7Nq1S8WKFZOnp6eeffZZLV68WLVq1VJ8fLw8PDzk7+/v0D8wMFDx8fGSpPj4eIdAkbU8a9mVjBw5UsnJyfbH8ePHc3ejAAAAgALE3dUFXEv16tW1fft2JScn64svvlBkZKTWrFnj1HV6enrK09PTqesAAAAACoo8Hyo8PDxUpUoVSVLDhg21efNm/ec//1GXLl104cIFJSUlORytSEhIUFBQkCQpKChImzZtchgv6+5QWX0AAAAAWJPnT3+6XGZmptLS0tSwYUMVLlxYK1assC+LjY3VsWPHFBoaKkkKDQ3Vrl27lJiYaO8TExMjX19f1apV65bXDgAAABREefpIxciRI9W2bVuVL19ep0+f1vz587V69Wp999138vPzU9++fTVkyBCVKFFCvr6+ev755xUaGqpmzZpJklq3bq1atWqpR48emjx5suLj4/XKK68oKiqK05sAAACAXJKnQ0ViYqJ69uypuLg4+fn5qV69evruu+/04IMPSpKmTZumQoUKqVOnTkpLS1N4eLjeeecd++vd3Nz0zTffqH///goNDVXRokUVGRmp8ePHu2qTAAAAgAIn331OhStYvW9vbuBzKgAAAOAst83nVAAAAADImwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCZ/yxlgABAABJREFUBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAAS/J0qJgwYYIaN24sHx8fBQQEqEOHDoqNjXXoc/78eUVFRalkyZIqVqyYOnXqpISEBIc+x44dU0REhIoUKaKAgAC9+OKLunjx4q3cFAAAAKDAytOhYs2aNYqKitKGDRsUExOj9PR0tW7dWmfOnLH3eeGFF/S///1PCxcu1Jo1a3TixAl17NjRvjwjI0MRERG6cOGC1q1bp3nz5mnu3LkaPXq0KzYJAAAAKHBsxhjj6iKu159//qmAgACtWbNGLVu2VHJyskqXLq358+frsccekyT9+uuvqlmzptavX69mzZpp2bJleuihh3TixAkFBgZKkmbPnq3hw4frzz//lIeHR7b1pKWlKS0tzf48JSVF5cqVU3Jysnx9fW/Nxl4mZMTSXB/zyMSIXB8TAAAA+U9KSor8/Pxu+v1unj5Scbnk5GRJUokSJSRJW7duVXp6usLCwux9atSoofLly2v9+vWSpPXr16tu3br2QCFJ4eHhSklJ0Z49e3Jcz4QJE+Tn52d/lCtXzlmbBAAAAOR7+SZUZGZmavDgwWrevLnq1KkjSYqPj5eHh4f8/f0d+gYGBio+Pt7e55+BImt51rKcjBw5UsnJyfbH8ePHc3lrAAAAgILD3dUFXK+oqCjt3r1ba9eudfq6PD095enp6fT1AAAAAAVBvjhSMWDAAH3zzTdatWqVypYta28PCgrShQsXlJSU5NA/ISFBQUFB9j6X3w0q63lWHwAAAAA3L0+HCmOMBgwYoMWLF2vlypWqWLGiw/KGDRuqcOHCWrFihb0tNjZWx44dU2hoqCQpNDRUu3btUmJior1PTEyMfH19VatWrVuzIQAAAEABlqdPf4qKitL8+fP11VdfycfHx34NhJ+fn7y9veXn56e+fftqyJAhKlGihHx9ffX8888rNDRUzZo1kyS1bt1atWrVUo8ePTR58mTFx8frlVdeUVRUFKc4AQAAALkgT4eKWbNmSZLuu+8+h/bo6Gj16tVLkjRt2jQVKlRInTp1UlpamsLDw/XOO+/Y+7q5uembb75R//79FRoaqqJFiyoyMlLjx4+/VZsBAAAAFGj56nMqXMXqfXtzA59TAQAAAGe5rT6nAgAAAEDeQ6gAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYkudDxY8//qiHH35YwcHBstlsWrJkicNyY4xGjx6tMmXKyNvbW2FhYTpw4IBDn5MnT6p79+7y9fWVv7+/+vbtq9TU1Fu4FQAAAEDB5e7qAq7lzJkzql+/vvr06aOOHTtmWz558mRNnz5d8+bNU8WKFTVq1CiFh4dr79698vLykiR1795dcXFxiomJUXp6unr37q1+/fpp/vz5t3pz8pSQEUudMu6RiRFOGRcAAAB5U54PFW3btlXbtm1zXGaM0VtvvaVXXnlF7du3lyR9+OGHCgwM1JIlS9S1a1ft27dPy5cv1+bNm9WoUSNJ0owZM9SuXTu98cYbCg4OzjZuWlqa0tLS7M9TUlKcsGUAAABAwZDnT3+6msOHDys+Pl5hYWH2Nj8/PzVt2lTr16+XJK1fv17+/v72QCFJYWFhKlSokDZu3JjjuBMmTJCfn5/9Ua5cOeduCAAAAJCP5etQER8fL0kKDAx0aA8MDLQvi4+PV0BAgMNyd3d3lShRwt7nciNHjlRycrL9cfz4cSdUDwAAABQMef70J1fw9PSUp6enq8sAAAAA8oV8faQiKChIkpSQkODQnpCQYF8WFBSkxMREh+UXL17UyZMn7X0AAAAA3Lx8HSoqVqyooKAgrVixwt6WkpKijRs3KjQ0VJIUGhqqpKQkbd261d5n5cqVyszMVNOmTW95zQAAAEBBk+dPf0pNTdXBgwftzw8fPqzt27erRIkSKl++vAYPHqzXXntNVatWtd9SNjg4WB06dJAk1axZU23atNHTTz+t2bNnKz09XQMGDFDXrl1zvPMTAAAAgBuT50PFli1bdP/999ufDxkyRJIUGRmpuXPn6qWXXtKZM2fUr18/JSUlqUWLFlq+fLn9Myok6ZNPPtGAAQPUqlUrFSpUSJ06ddL06dNv+bYAAAAABZHNGGNcXURel5KSIj8/PyUnJ8vX19clNTjrg+qcgQ+/AwAAyF+svt/N19dUAAAAAHA9QgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASd1cXgIInZMTSXB/zyMSIXB8TAAAAuYMjFQAAAAAsIVQAAAAAsIRQAQAAAMASrqlAvsB1GgAAAHkXRyoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYwt2fcNvijlIAAAC5g1AB5CJnBBUp/4QVghoAALen2ypUzJw5U1OmTFF8fLzq16+vGTNmqEmTJq4uC7gmZ4UVAACA3HDbhIrPPvtMQ4YM0ezZs9W0aVO99dZbCg8PV2xsrAICAlxdHoAruN0DlTOO1HBECQCQ22zGGOPqIm6Fpk2bqnHjxnr77bclSZmZmSpXrpyef/55jRgxwqFvWlqa0tLS7M+Tk5NVvnx5HT9+XL6+vre07ix1xnznkvUCwPXYPS4818d01u89Z9TqDM7Y/vyy7be7233fh2ukpKSoXLlySkpKkp+f3w2//rYIFRcuXFCRIkX0xRdfqEOHDvb2yMhIJSUl6auvvnLoP3bsWI0bN+4WVwkAAAC41vHjx1W2bNkbft1tcfrTX3/9pYyMDAUGBjq0BwYG6tdff83Wf+TIkRoyZIj9eWZmpk6ePKmSJUvKZrM5vd7LZSVHVx4pKUiYz9zFfOYu5jN3MZ+5i/nMXcxn7mI+rTHG6PTp0woODr6p198WoeJGeXp6ytPT06HN39/fNcX8g6+vLz8kuYj5zF3MZ+5iPnMX85m7mM/cxXzmLubz5t3MaU9ZbosPvytVqpTc3NyUkJDg0J6QkKCgoCAXVQUAAAAUDLdFqPDw8FDDhg21YsUKe1tmZqZWrFih0NBQF1YGAAAA5H+3zelPQ4YMUWRkpBo1aqQmTZrorbfe0pkzZ9S7d29Xl3ZNnp6eGjNmTLZTsnBzmM/cxXzmLuYzdzGfuYv5zF3MZ+5iPl3rtrj7U5a3337b/uF3d955p6ZPn66mTZu6uiwAAAAgX7utQgUAAACA3HdbXFMBAAAAwHkIFQAAAAAsIVQAAAAAsIRQAQAAAMASQkU+MHPmTIWEhMjLy0tNmzbVpk2bXF2Sy02YMEGNGzeWj4+PAgIC1KFDB8XGxjr0ue+++2Sz2Rwezz77rEOfY8eOKSIiQkWKFFFAQIBefPFFXbx40aHP6tWr1aBBA3l6eqpKlSqaO3euszfvlhs7dmy2uapRo4Z9+fnz5xUVFaWSJUuqWLFi6tSpU7YPk2Qu/19ISEi2+bTZbIqKipLEvnk1P/74ox5++GEFBwfLZrNpyZIlDsuNMRo9erTKlCkjb29vhYWF6cCBAw59Tp48qe7du8vX11f+/v7q27evUlNTHfrs3LlT99xzj7y8vFSuXDlNnjw5Wy0LFy5UjRo15OXlpbp16+rbb7/N9e11tqvNZ3p6uoYPH666deuqaNGiCg4OVs+ePXXixAmHMXLanydOnOjQh/m8pFevXtnmqk2bNg592D//37XmM6ffozabTVOmTLH3Yf/MQwzytAULFhgPDw/zwQcfmD179pinn37a+Pv7m4SEBFeX5lLh4eEmOjra7N6922zfvt20a9fOlC9f3qSmptr73Hvvvebpp582cXFx9kdycrJ9+cWLF02dOnVMWFiY2bZtm/n2229NqVKlzMiRI+19fvvtN1OkSBEzZMgQs3fvXjNjxgzj5uZmli9ffku319nGjBljateu7TBXf/75p335s88+a8qVK2dWrFhhtmzZYpo1a2buvvtu+3Lm0lFiYqLDXMbExBhJZtWqVcYY9s2r+fbbb83LL79sFi1aZCSZxYsXOyyfOHGi8fPzM0uWLDE7duwwjzzyiKlYsaI5d+6cvU+bNm1M/fr1zYYNG8xPP/1kqlSpYrp162ZfnpycbAIDA0337t3N7t27zaeffmq8vb3NnDlz7H1+/vln4+bmZiZPnmz27t1rXnnlFVO4cGGza9cup89BbrrafCYlJZmwsDDz2WefmV9//dWsX7/eNGnSxDRs2NBhjAoVKpjx48c77K///F3LfP6/yMhI06ZNG4e5OnnypEMf9s//d635/Oc8xsXFmQ8++MDYbDZz6NAhex/2z7yDUJHHNWnSxERFRdmfZ2RkmODgYDNhwgQXVpX3JCYmGklmzZo19rZ7773XDBo06Iqv+fbbb02hQoVMfHy8vW3WrFnG19fXpKWlGWOMeemll0zt2rUdXtelSxcTHh6euxvgYmPGjDH169fPcVlSUpIpXLiwWbhwob1t3759RpJZv369MYa5vJZBgwaZypUrm8zMTGMM++b1uvxNRmZmpgkKCjJTpkyxtyUlJRlPT0/z6aefGmOM2bt3r5FkNm/ebO+zbNkyY7PZzB9//GGMMeadd94xxYsXt8+lMcYMHz7cVK9e3f788ccfNxEREQ71NG3a1DzzzDO5uo23Uk5v2i63adMmI8kcPXrU3lahQgUzbdq0K76G+fx/kZGRpn379ld8DfvnlV3P/tm+fXvzwAMPOLSxf+YdnP6Uh124cEFbt25VWFiYva1QoUIKCwvT+vXrXVhZ3pOcnCxJKlGihEP7J598olKlSqlOnToaOXKkzp49a1+2fv161a1bV4GBgfa28PBwpaSkaM+ePfY+/5z/rD4Fcf4PHDig4OBgVapUSd27d9exY8ckSVu3blV6errDPNSoUUPly5e3zwNzeWUXLlzQxx9/rD59+shms9nb2Tdv3OHDhxUfH++w3X5+fmratKnDvujv769GjRrZ+4SFhalQoULauHGjvU/Lli3l4eFh7xMeHq7Y2FidOnXK3ud2m1/p0u9Sm80mf39/h/aJEyeqZMmSuuuuuzRlyhSHU/GYT0erV69WQECAqlevrv79++vvv/+2L2P/vHkJCQlaunSp+vbtm20Z+2fe4O7qAnBlf/31lzIyMhzeWEhSYGCgfv31VxdVlfdkZmZq8ODBat68uerUqWNvf+KJJ1ShQgUFBwdr586dGj58uGJjY7Vo0SJJUnx8fI5zm7Xsan1SUlJ07tw5eXt7O3PTbpmmTZtq7ty5ql69uuLi4jRu3Djdc8892r17t+Lj4+Xh4ZHtTUZgYOA15ylr2dX6FLS5vNySJUuUlJSkXr162dvYN29O1rbntN3/nJeAgACH5e7u7ipRooRDn4oVK2YbI2tZ8eLFrzi/WWMUROfPn9fw4cPVrVs3+fr62tsHDhyoBg0aqESJElq3bp1GjhypuLg4TZ06VRLz+U9t2rRRx44dVbFiRR06dEj/+te/1LZtW61fv15ubm7snxbMmzdPPj4+6tixo0M7+2feQahAvhcVFaXdu3dr7dq1Du39+vWzf123bl2VKVNGrVq10qFDh1S5cuVbXWae1rZtW/vX9erVU9OmTVWhQgV9/vnnBfLN6a30/vvvq23btgoODra3sW8ir0lPT9fjjz8uY4xmzZrlsGzIkCH2r+vVqycPDw8988wzmjBhgjw9PW91qXla165d7V/XrVtX9erVU+XKlbV69Wq1atXKhZXlfx988IG6d+8uLy8vh3b2z7yD05/ysFKlSsnNzS3bXXYSEhIUFBTkoqrylgEDBuibb77RqlWrVLZs2av2bdq0qSTp4MGDkqSgoKAc5zZr2dX6+Pr6Fug32/7+/qpWrZoOHjyooKAgXbhwQUlJSQ59/rkfMpc5O3r0qH744Qc99dRTV+3Hvnl9srb9ar8Tg4KClJiY6LD84sWLOnnyZK7srwXxd29WoDh69KhiYmIcjlLkpGnTprp48aKOHDkiifm8mkqVKqlUqVIOP9vsnzfup59+Umxs7DV/l0rsn65EqMjDPDw81LBhQ61YscLelpmZqRUrVig0NNSFlbmeMUYDBgzQ4sWLtXLlymyHNnOyfft2SVKZMmUkSaGhodq1a5fDL/is/1Br1apl7/PP+c/qU9DnPzU1VYcOHVKZMmXUsGFDFS5c2GEeYmNjdezYMfs8MJc5i46OVkBAgCIiIq7aj33z+lSsWFFBQUEO252SkqKNGzc67ItJSUnaunWrvc/KlSuVmZlpD2+hoaH68ccflZ6ebu8TExOj6tWrq3jx4vY+t8P8ZgWKAwcO6IcfflDJkiWv+Zrt27erUKFC9tN4mM8r+/333/X33387/Gyzf964999/Xw0bNlT9+vWv2Zf904VcfaU4rm7BggXG09PTzJ071+zdu9f069fP+Pv7O9wV5nbUv39/4+fnZ1avXu1wG7mzZ88aY4w5ePCgGT9+vNmyZYs5fPiw+eqrr0ylSpVMy5Yt7WNk3bazdevWZvv27Wb58uWmdOnSOd6288UXXzT79u0zM2fOLBC37bzc0KFDzerVq83hw4fNzz//bMLCwkypUqVMYmKiMebSLWXLly9vVq5cabZs2WJCQ0NNaGio/fXMZXYZGRmmfPnyZvjw4Q7t7JtXd/r0abNt2zazbds2I8lMnTrVbNu2zX43ookTJxp/f3/z1VdfmZ07d5r27dvneEvZu+66y2zcuNGsXbvWVK1a1eGWnUlJSSYwMND06NHD7N692yxYsMAUKVIk2y0m3d3dzRtvvGH27dtnxowZky9vMXm1+bxw4YJ55JFHTNmyZc327dsdfpdm3Sln3bp1Ztq0aWb79u3m0KFD5uOPPzalS5c2PXv2tK+D+bw0n6dPnzbDhg0z69evN4cPHzY//PCDadCggalatao5f/68fQz2z/93rZ93Yy7dErZIkSJm1qxZ2V7P/pm3ECrygRkzZpjy5csbDw8P06RJE7NhwwZXl+RyknJ8REdHG2OMOXbsmGnZsqUpUaKE8fT0NFWqVDEvvviiw2cBGGPMkSNHTNu2bY23t7cpVaqUGTp0qElPT3fos2rVKnPnnXcaDw8PU6lSJfs6CpIuXbqYMmXKGA8PD3PHHXeYLl26mIMHD9qXnzt3zjz33HOmePHipkiRIubRRx81cXFxDmMwl46+++47I8nExsY6tLNvXt2qVaty/NmOjIw0xly6reyoUaNMYGCg8fT0NK1atco2x3///bfp1q2bKVasmPH19TW9e/c2p0+fduizY8cO06JFC+Pp6WnuuOMOM3HixGy1fP7556ZatWrGw8PD1K5d2yxdutRp2+0sV5vPw4cPX/F3adZnqmzdutU0bdrU+Pn5GS8vL1OzZk3z+uuvO7xJNob5jIyMNGfPnjWtW7c2pUuXNoULFzYVKlQwTz/9dLY/ArJ//r9r/bwbY8ycOXOMt7e3SUpKyvZ69s+8xWaMMU49FAIAAACgQOOaCgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAK5DSEiI3nrrLftzm82mJUuWuKyeK2nZsqXmz59vaYxmzZrpyy+/zKWKANwOCBUAkINevXrJZrPJZrPJw8NDVapU0fjx43Xx4kVXl3ZNefXNbkETFxentm3bSpKOHDkim82m7du3u7Smr7/+WgkJCeratau9bciQISpRooTKlSunTz75xKH/woUL9fDDD2cb55VXXtGIESOUmZnp9JoBFAyECgC4gjZt2iguLk4HDhzQ0KFDNXbsWE2ZMuWmxsrIyOANWgETFBQkT0/PXB/3woULN/3a6dOnq3fv3ipU6NJ/7//73/80f/58ff/995o8ebKeeuop/fXXX5Kk5ORkvfzyy5o5c2a2cdq2bavTp09r2bJlN10LgNsLoQIArsDT01NBQUGqUKGC+vfvr7CwMH399deSpLS0NA0bNkx33HGHihYtqqZNm2r16tX2186dO1f+/v76+uuvVatWLXl6eurYsWNKS0vT8OHDVa5cOXl6eqpKlSp6//337a/bvXu32rZtq2LFiikwMFA9evSwvwmUpPvuu08DBw7USy+9pBIlSigoKEhjx461Lw8JCZEkPfroo7LZbPbnhw4dUvv27RUYGKhixYqpcePG+uGHHxy2Ny4uThEREfL29lbFihU1f/78bKf8JCUl6amnnlLp0qXl6+urBx54QDt27LjuOf3qq6/UoEEDeXl5qVKlSho3bpz96M/48eMVHBysv//+294/IiJC999/vz2Q2Ww2zZo1S23btpW3t7cqVaqkL774wmEdx48f1+OPPy5/f3+VKFFC7du315EjR+zLe/XqpQ4dOuiNN95QmTJlVLJkSUVFRSk9Pd3eJzExUQ8//LB9Li7/C39WLVlHhCpWrChJuuuuu2Sz2XTfffdJuvT9Gjx4sMPrOnTooF69etmfh4SE6NVXX1XPnj3l6+urfv36SZLWrl2re+65R97e3ipXrpwGDhyoM2fOXHFu//zzT61cudLhyMO+fft03333qVGjRurWrZt8fX11+PBhSdJLL72k/v37q3z58tnGcnNzU7t27bRgwYIrrg8A/olQAQDXydvb2/5X5AEDBmj9+vVasGCBdu7cqc6dO6tNmzY6cOCAvf/Zs2c1adIkvffee9qzZ48CAgLUs2dPffrpp5o+fbr27dunOXPmqFixYpIuvWF/4IEHdNddd2nLli1avny5EhIS9PjjjzvUMW/ePBUtWlQbN27U5MmTNX78eMXExEiSNm/eLEmKjo5WXFyc/XlqaqratWunFStWaNu2bWrTpo0efvhhHTt2zD5uz549deLECa1evVpffvml3n33XSUmJjqsu3PnzkpMTNSyZcu0detWNWjQQK1atdLJkyevOX8//fSTevbsqUGDBmnv3r2aM2eO5s6dq3//+9+SpJdfflkhISF66qmnJEkzZ87UunXrNG/ePPtf3iVp1KhR6tSpk3bs2KHu3bura9eu2rdvnyQpPT1d4eHh8vHx0U8//aSff/5ZxYoVU5s2bRyOAKxatUqHDh3SqlWrNG/ePM2dO1dz5861L+/Vq5eOHz+uVatW6YsvvtA777yTbS7+adOmTZKkH374QXFxcVq0aNE15+Of3njjDdWvX1/btm3TqFGjdOjQIbVp00adOnXSzp079dlnn2nt2rUaMGDAFcdYu3atihQpopo1a9rb6tevry1btujUqVPaunWrzp07pypVqmjt2rX65ZdfNHDgwCuO16RJE/300083tB0AbmMGAJBNZGSkad++vTHGmMzMTBMTE2M8PT3NsGHDzNGjR42bm5v5448/HF7TqlUrM3LkSGOMMdHR0UaS2b59u315bGyskWRiYmJyXOerr75qWrdu7dB2/PhxI8nExsYaY4y59957TYsWLRz6NG7c2AwfPtz+XJJZvHjxNbexdu3aZsaMGcYYY/bt22ckmc2bN9uXHzhwwEgy06ZNM8YY89NPPxlfX19z/vx5h3EqV65s5syZc831tWrVyrz++usObR999JEpU6aM/fmhQ4eMj4+PGT58uPH29jaffPKJQ39J5tlnn3Voa9q0qenfv799vOrVq5vMzEz78rS0NOPt7W2+++47Y8yl722FChXMxYsX7X06d+5sunTpYoz5/+/Tpk2b7Muz5idrLrJqyZrnw4cPG0lm27ZtDrXde++9ZtCgQQ5t7du3N5GRkfbnFSpUMB06dHDo07dvX9OvXz+Htp9++skUKlTInDt3zuRk2rRpplKlStnax4wZYypXrmzq1KljFi1aZNLS0kydOnXMli1bzIwZM0y1atXM3XffbXbv3u3wuq+++soUKlTIZGRk5Lg+APgndxdlGQDI87755hsVK1ZM6enpyszM1BNPPKGxY8dq9erVysjIULVq1Rz6p6WlqWTJkvbnHh4eqlevnv359u3b5ebmpnvvvTfH9e3YsUOrVq2yH7n4p0OHDtnX988xJalMmTJX/Su6dOlIxdixY7V06VLFxcXp4sWLOnfunP1IRWxsrNzd3dWgQQP7a6pUqaLixYs71JeamuqwjZJ07tw5HTp06Krrz3r9zz//bD8yIV261uT8+fM6e/asihQpokqVKumNN97QM888oy5duuiJJ57INk5oaGi251kXSO/YsUMHDx6Uj4+PQ5/z58871Fi7dm25ubnZn5cpU0a7du2SdOmUIXd3dzVs2NC+vEaNGvL397/mNt6sRo0aOTzfsWOHdu7c6XDalTFGmZmZOnz4sMPRiCznzp2Tl5dXtvaxY8c6nCI3btw4hYWFqXDhwnrttde0a9cuffPNN+rZs6e2bt1q7+ft7a3MzEylpaXJ29s7F7YSQEFGqACAK7j//vs1a9YseXh4KDg4WO7ul35lpqamys3NTVu3bnV4YyrJIRB4e3vLZrM5PL+a1NRUPfzww5o0aVK2ZWXKlLF/XbhwYYdlNpvtmheBDxs2TDExMXrjjTdUpUoVeXt767HHHruhi4JTU1NVpkwZh2tHslzPG+7U1FSNGzdOHTt2zLbsn2+Gf/zxR7m5uenIkSO6ePGifd6vt8aGDRvmeA1E6dKl7V/fzBzejEKFCskY49D2z2s3shQtWtTheWpqqp555pkcT0/K6RoISSpVqpROnTp11Xp+/fVXffzxx9q2bZs++OADtWzZUqVLl9bjjz+uPn366PTp0/ZAdvLkSRUtWpRAAeC6ECoA4AqKFi2qKlWqZGu/6667lJGRocTERN1zzz3XPV7dunWVmZmpNWvWKCwsLNvyBg0a6Msvv1RISMgNvZG+XOHChZWRkeHQ9vPPP6tXr1569NFHJV160/rPi5erV6+uixcvatu2bfa/0B88eNDhTWqDBg0UHx8vd3d3+wXgN6JBgwaKjY3NcU6zfPbZZ1q0aJFWr16txx9/XK+++qrGjRvn0GfDhg3q2bOnw/O77rrLvo7PPvtMAQEB8vX1veEapUtHJS5evKitW7eqcePGki4dyUlKSrriazw8PCQp27yXLl1acXFx9ucZGRnavXu37r///qvW0KBBA+3du/eqc3W5u+66S/Hx8Tp16pTDEaYsxhg988wzmjp1qooVK6aMjAx7wMn695/179692z6vAHAtXKgNADeoWrVq6t69u3r27KlFixbp8OHD2rRpkyZMmKClS5de8XUhISGKjIxUnz59tGTJEh0+fFirV6/W559/LkmKiorSyZMn1a1bN23evFmHDh3Sd999p969e2d7s3o1ISEhWrFihf0NpiRVrVpVixYt0vbt27Vjxw498cQTDn+Zr1GjhsLCwtSvXz9t2rRJ27ZtU79+/RyOtoSFhSk0NFQdOnTQ999/ryNHjmjdunV6+eWXtWXLlmvWNXr0aH344YcaN26c9uzZo3379mnBggV65ZVXJEm///67+vfvr0mTJqlFixaKjo7W66+/rg0bNjiMs3DhQn3wwQfav3+/xowZo02bNtkvYO7evbtKlSql9u3b66effrLP8cCBA/X7779f1/xVr15dbdq00TPPPKONGzdq69ateuqpp676F/uAgAB5e3vbL65PTk6WJD3wwANaunSpli5dql9//VX9+/e/ajjJMnz4cK1bt04DBgzQ9u3bdeDAAX311VdXvVD7rrvuUqlSpfTzzz/nuPy9995T6dKl7XeHat68uVauXKkNGzZo2rRpqlWrlsMRp59++kmtW7e+Zq0AIBEqAOCmREdHq2fPnho6dKiqV6+uDh06aPPmzVc8NSXLrFmz9Nhjj+m5555TjRo19PTTT9tvExocHKyff/5ZGRkZat26terWravBgwfL39/f4e5H1/Lmm28qJiZG5cqVs/+leerUqSpevLjuvvtuPfzwwwoPD3e4fkKSPvzwQwUGBqply5Z69NFH9fTTT8vHx8d+apLNZtO3336rli1bqnfv3qpWrZq6du2qo0ePKjAw8Jp1hYeH65tvvtH333+vxo0bq1mzZpo2bZoqVKggY4x69eqlJk2a2N84h4eHq3///nryySeVmppqH2fcuHFasGCB6tWrpw8//FCffvqpatWqJUkqUqSIfvzxR5UvX14dO3ZUzZo11bdvX50/f/6GjlxER0crODhY9957rzp27Kh+/fopICDgiv3d3d01ffp0zZkzR8HBwWrfvr0kqU+fPoqMjFTPnj117733qlKlStc8SiFdum5mzZo12r9/v+655x7dddddGj16tIKDg6/4Gjc3N/Xu3TvHU78SEhL073//W9OnT7e3NWnSREOHDlVERIQ+//xzRUdH25f98ccfWrdunXr37n3NWgFAkmzm8pM9AQDQpSMH5cqV0w8//KBWrVq5uhxJl4LN4sWL1aFDB1eXkifFx8erdu3a+uWXX1ShQoWbHmf48OE6deqU3n333VysDkBBxjUVAABJ0sqVK5Wamqq6desqLi5OL730kkJCQtSyZUtXl4brFBQUpPfff1/Hjh2zFCoCAgI0ZMiQXKwMQEFHqAAASLp0se6//vUv/fbbb/Lx8dHdd9+tTz75JNudkq6kdu3aOnr0aI7L5syZo+7du+dmubiC3DiKM3ToUOuFALitcPoTACBXHD16NMfbpUpSYGBgts+OAAAUHIQKAAAAAJZw9ycAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqACAfCgkJUa9evVxdRoE3ZcoUVapUSW5ubrrzzjtdXQ4A5FmECgBwsblz58pms2nLli05Lr/vvvtUp04dy+v59ttvNXbsWMvj3C6+//57vfTSS2revLmio6P1+uuvX7Fvr169ZLPZ7A93d3eVK1dOXbt21d69ex36rl692t7v448/znG85s2by2azZfu+h4SE6KGHHrK+cQCQy9xdXQAA4MbFxsaqUKEb+7vQt99+q5kzZxIsrtPKlStVqFAhvf/++/Lw8Lhmf09PT7333nuSpIsXL+rQoUOaPXu2li9frr179yo4ONihv5eXl+bPn68nn3zSof3IkSNat26dvLy8cm9jAMDJCBUAkA95enq6uoQbdubMGRUtWtTVZVy3xMREeXt7X1egkCR3d/dsAaFZs2Z66KGHtHTpUj399NMOy9q1a6evv/5af/31l0qVKmVvnz9/vgIDA1W1alWdOnXK+oYAwC3A6U8AkA9dfk1Fenq6xo0bp6pVq8rLy0slS5ZUixYtFBMTI+nS6TkzZ86UJIfTdLKcOXNGQ4cOVbly5eTp6anq1avrjTfekDHGYb3nzp3TwIEDVapUKfn4+OiRRx7RH3/8IZvN5nAEZOzYsbLZbNq7d6+eeOIJFS9eXC1atJAk7dy5U7169VKlSpXk5eWloKAg9enTR3///bfDurLG2L9/v5588kn5+fmpdOnSGjVqlIwxOn78uNq3by9fX18FBQXpzTffvK65u3jxol599VVVrlxZnp6eCgkJ0b/+9S+lpaXZ+9hsNkVHR+vMmTP2uZo7d+51jf9PQUFBki4Fjsu1b99enp6eWrhwoUP7/Pnz9fjjj8vNze2G1wcArsKRCgDII5KTk/XXX39la09PT7/ma8eOHasJEyboqaeeUpMmTZSSkqItW7bol19+0YMPPqhnnnlGJ06cUExMjD766COH1xpj9Mgjj2jVqlXq27ev7rzzTn333Xd68cUX9ccff2jatGn2vr169dLnn3+uHj16qFmzZlqzZo0iIiKuWFfnzp1VtWpVvf766/aAEhMTo99++029e/dWUFCQ9uzZo3fffVd79uzRhg0bHMKOJHXp0kU1a9bUxIkTtXTpUr322msqUaKE5syZowceeECTJk3SJ598omHDhqlx48Zq2bLlVefqqaee0rx58/TYY49p6NCh2rhxoyZMmKB9+/Zp8eLFkqSPPvpI7777rjZt2mQ/penuu+++5vch6/uXkZGh3377TcOHD1fJkiVzvA6iSJEiat++vT799FP1799fkrRjxw7t2bNH7733nnbu3HnN9QFAnmEAAC4VHR1tJF31Ubt2bYfXVKhQwURGRtqf169f30RERFx1PVFRUSanX/tLliwxksxrr73m0P7YY48Zm81mDh48aIwxZuvWrUaSGTx4sEO/Xr16GUlmzJgx9rYxY8YYSaZbt27Z1nf27NlsbZ9++qmRZH788cdsY/Tr18/edvHiRVO2bFljs9nMxIkT7e2nTp0y3t7eDnOSk+3btxtJ5qmnnnJoHzZsmJFkVq5caW+LjIw0RYsWvep4/+yb0/ftjjvuMFu3bnXou2rVKiPJLFy40HzzzTfGZrOZY8eOGWOMefHFF02lSpWMMcbce++9OX7fr/V9BgBX4PQnAMgjZs6cqZiYmGyPevXqXfO1/v7+2rNnjw4cOHDD6/3222/l5uamgQMHOrQPHTpUxhgtW7ZMkrR8+XJJ0nPPPefQ7/nnn7/i2M8++2y2Nm9vb/vX58+f119//aVmzZpJkn755Zds/Z966in7125ubmrUqJGMMerbt6+93d/fX9WrV9dvv/12xVqkS9sqSUOGDHFoHzp0qCRp6dKlV3391Xh5edm/Z999953mzJmjYsWKqV27dtq/f3+Or2ndurVKlCihBQsWyBijBQsWqFu3bjddAwC4Cqc/AUAe0aRJEzVq1Chbe/HixXM8Leqfxo8fr/bt26tatWqqU6eO2rRpox49elxXIDl69KiCg4Pl4+Pj0F6zZk378qx/CxUqpIoVKzr0q1KlyhXHvryvJJ08eVLjxo3TggULlJiY6LAsOTk5W//y5cs7PPfz85OXl5fDxc1Z7Zdfl3G5rG24vOagoCD5+/vbt/VmuLm5KSwszKGtXbt2qlq1qkaOHKkvv/wy22sKFy6szp07a/78+WrSpImOHz+uJ5544qZrAABX4UgFABQALVu21KFDh/TBBx+oTp06eu+999SgQQP79QCu8s+jElkef/xx/fe//9Wzzz6rRYsW6fvvv7cfBcnMzMzWP6cLlq90EbO57MLyK7n8ug1nKVu2rKpXr64ff/zxin2eeOIJbd++XWPHjlX9+vVVq1atW1IbAOQmQgUAFBAlSpRQ79699emnn+r48eOqV6+ewx2ZrvRGukKFCjpx4oROnz7t0P7rr7/al2f9m5mZqcOHDzv0O3jw4HXXeOrUKa1YsUIjRozQuHHj9Oijj+rBBx9UpUqVrnsMK7K24fLTxBISEpSUlGTf1tx08eJFpaamXnF5ixYtVL58ea1evZqjFADyLUIFABQAl5/2U6xYMVWpUsXhNqlZnxGRlJTk0Lddu3bKyMjQ22+/7dA+bdo02Ww2tW3bVpIUHh4uSXrnnXcc+s2YMeO668w6wnD5EYW33nrrusewol27djmub+rUqZJ01TtZ3Yz9+/crNjZW9evXv2Ifm82m6dOna8yYMerRo0eurh8AbhWuqQCAAqBWrVq677771LBhQ5UoUUJbtmzRF198oQEDBtj7NGzYUJI0cOBAhYeHy83NTV27dtXDDz+s+++/Xy+//LKOHDmi+vXr6/vvv9dXX32lwYMHq3LlyvbXd+rUSW+99Zb+/vtv+y1lsy5Cvp5Tinx9fdWyZUtNnjxZ6enpuuOOO/T9999nO/rhLPXr11dkZKTeffddJSUl6d5779WmTZs0b948dejQQffff/9Nj33x4kV9/PHHki6dxnXkyBHNnj1bmZmZGjNmzFVf2759e7Vv3/6m1w0ArkaoAIACYODAgfr666/1/fffKy0tTRUqVNBrr72mF1980d6nY8eOev7557VgwQJ9/PHHMsaoa9euKlSokL7++muNHj1an332maKjoxUSEqIpU6bY74qU5cMPP1RQUJA+/fRTLV68WGFhYfrss89UvXp1eXl5XVet8+fP1/PPP6+ZM2fKGKPWrVtr2bJlCg4OztU5uZL33ntPlSpV0ty5c7V48WIFBQVp5MiR13zjfy1paWkORxp8fX3VuHFjffTRR2rVqpXVsgEgT7OZ672qDQCAHGzfvl133XWXPv74Y3Xv3t3V5QAAXIBrKgAA1+3cuXPZ2t566y0VKlTomp9kDQAouDj9CQBw3SZPnqytW7fq/vvvl7u7u5YtW6Zly5apX79+KleunKvLAwC4CKc/AQCuW0xMjMaNG6e9e/cqNTVV5cuXV48ePfTyyy/L3Z2/UwHA7YpQAQAAAMASrqkAAAAAYAnHqq9DZmamTpw4IR8fn+u6DzsAAACQnxhjdPr0aQUHB6tQoRs/7kCouA4nTpzgAkQAAAAUeMePH1fZsmVv+HWEiuvg4+Mj6dIk+/r6urgaAAAAIHelpKSoXLly9ve9N4pQcR2yTnny9fUlVAAAAKDAutlT/blQGwAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCXuri4AAADgVggZsTTXxzwyMSLXxwTyI45UAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAA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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "###CODE TO OBTAIN AND DISPLAY HISTOGRAMS ###\n",
        "ncolumns = df.select_dtypes(include=['number']).columns.tolist()\n",
        "ncolumns = [col for col in ncolumns if col != 'Status']\n",
        "\n",
        "# Dictionary to map column index to its respective label\n",
        "labels = {\n",
        "    0: 'Year',\n",
        "    1: 'Adult_Mortality (%)',\n",
        "    2: 'Alcohol (litres per capita consumption)',\n",
        "    3: 'Percentage_expenditure (%)',\n",
        "    4: 'BMI',\n",
        "    5: 'Total_expenditure (%)',\n",
        "    6: 'Diphtheria (%)',\n",
        "    7: 'HIV_AIDS (Deaths per 1000 live births HIV/AIDS)',\n",
        "    8: 'GDP (in USD)',\n",
        "    9: 'Population (in billion)',\n",
        "    10: 'Income_composition_of_resources (Index ranging from 0 - 1)',\n",
        "    11: 'Schooling (years)',\n",
        "    12: 'Health_Index',\n",
        "    13: 'Life_expectancy (ages in year)'\n",
        "}\n",
        "\n",
        "fig, axes = plt.subplots(nrows=len(ncolumns), ncols=1, figsize=(8, 5*len(ncolumns)))\n",
        "\n",
        "# Using for loops to generate the histograms\n",
        "for i, col in enumerate(ncolumns):\n",
        "    axes[i].hist(df[col], bins=30)\n",
        "    axes[i].set_ylabel('Frequency')\n",
        "    axes[i].set_title(f'Histogram of {col}')\n",
        "    axes[i].set_xlabel(labels.get(i, ''))\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_XD0AGPM5eaD"
      },
      "source": [
        "**Inference from Histograms** <br/>\n",
        "*    **`Year`**: Data has been collected for all years from 2002 to 2014, but data collection for 2015 is low. The distribution is almost uniform (with the exception of data collection in earlier years and 2015)\n",
        "*    **`Adult_Mortality`**: It is a right-skewed distribution with two peaks. Among all the data that has been collected, the most common `adult_mortality` rate is the 0-25 and 125-150 Death in a population of 1000.\n",
        "*    **`Alcohol`**: It is also a right-skewed histogram with a long tail. This indicates that the majority of people living in the countries that are covered in the dataset are not heavy alcohol consumer. Most people consumed about 0-2.5 liters of alcohol annually.\n",
        "*    **`Percentage_expenditure`**: It is an decreasing exponential distribution. This means that for most countries in the dataset, health takes up to 0-1250% of GDP per capita. Health is very costly in most countries for many people.\n",
        "*    **`BMI`**: It is a multimodel distribution with more than 1 peaks. That means that `BMI`s vary a lot in countries across different years.\n",
        "*    **`Total_expenditure`**: It is approximatly a normal distribution with center at ~5% - 7%. The means that most countries in this dataset invest approximately 5% to 7% of their total government_expenditure on health.\n",
        "*    **`Diphtheria`**: It is a highly left-skewed distribution with a long tail. The majority of samples(93% - 100%) in ths dataset has diphtheria coverages among 1 years-old.\n",
        "*    **`HIV_AIDS`**: Both the mean and median of this histogram is at 0-3. It is an exponential distribution with a long tail. The majority of countries in this dataset has less than 3 Deaths per 1000 live births HIV/AIDS.\n",
        "*    **`GDP`**: The average values of this histogram is around the 500 value. The majority of countries in this dataset has `GDP` ~500 USD per capita. This is a high number.\n",
        "*    **`Population`**: The majority of countries in this dataset has 100000000 population. This gives us a sense that countries with large populations were targetted for data collection.\n",
        "*    **`income_composition_of_resources`**: It is a left-skewed distribution with a peaks around 0.7. That means most countries in this dataset tends to have high (>0.5) Human Development Index.\n",
        "*    **`Schooling`**: It is a normal distribution centered around 12.5 years. Most commonly countries in this dataset has a schooling curriculum of 12.5 years.\n",
        "*    **`Health_Index`**: It is a uniform distribution. Countries in this dataset have very diverse `Health_Index`.\n",
        "*    **`Life_expectancy`**: It is roughly a normal distribution but slightly left-skewed. Most people in this dateset has a `life_expectancy` ~70-80 years.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmq41MOV9Z8d"
      },
      "source": [
        "Correlation matrix to get an understanding of the correlation between life_expectancy and the other features.<br/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 823
        },
        "id": "WmuZTzx29bkO",
        "outputId": "8377913b-df71-4d9f-f7c1-2cd3c7bba113"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-9f9cd66211fe>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  corr_matrix = df.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "###CODE TO OBTAIN CORRELATION MATRIX ###\n",
        "corr_matrix = df.corr()\n",
        "\n",
        "### CODE TO CREATE HEATMAP OF CORRELATION MATRIX\n",
        "plt.figure(figsize = (8,6))\n",
        "sns.heatmap(corr_matrix, annot = True, cmap = 'coolwarm', fmt = \".2f\", linewidth = 0.5)\n",
        "plt.title('Correlation Matrix Heatmap')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8UpsTJyw97FZ"
      },
      "source": [
        "**Inference about variables from the Correlation Matrix**:<br/>\n",
        "\n",
        "\n",
        "If we have to choose one predictor for a simple linear regression model with Life_expectancy as the outcome, we will choose `Income_composition_of_resources` because it is most correlated with `Life_expectancy` according to our correlation matrix. corr(Income_composition_of_resources, Life_expectancy) = 0.756515.   \n",
        "Schooling can be the second good choice because it also has a high correlation of 0.755132. Note that in the heatmap above, the correlations of the two features each with `life_expectancy` appear to be both 0.76. This is caused by rounding.  \n",
        "Qualitatively speaking, it is better to choose `Income_composition_of_resources` as the data can be collected and interpreted easily.\n",
        "<br/><br/>\n",
        "\n",
        "**Variables not making sense** <br/>\n",
        "\n",
        "**`year`**: <br/>Data has been collected for all years from 2002 - 2014 however data collection for 2015 is very low. This is likely because the data set was potentially extracted during 2015.\n",
        "\n",
        "**`Health_Index`**: <br/>\n",
        "The feature `Health_Index` is not fully defined in the prompt. We do not know what is being calculated in this Health_Index.\n",
        "\n",
        "**Missing variables**: <br/>\n",
        "In order to calculate the life expectancy of a country, the current set of variables may not be sufficient. We believe that other variables such as Drug_usage, Mental_health, air_pollution_rate and more can be added.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYvGzY8Z-ni-"
      },
      "source": [
        "### Standardization of features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jhq-T3Ed-r8w"
      },
      "source": [
        "Feature standardization makes the values of each feature in the data have zero-mean and unit-variance. This method is widely used for normalization in many machine learning algorithms. The general method of calculation is to determine the distribution mean and standard deviation for each feature. Next we subtract the mean from each feature. Then we divide the values of each feature by its standard deviation.\n",
        "\n",
        "$x'$ = ($x$ - $\\bar{x}$)/$\\sigma$\n",
        "\n",
        "where $x$ is the original feature vector,\n",
        "$\\bar{x}$ is the mean of the feature vector and\n",
        "$\\sigma$ is its standard deviation.\n",
        "\n",
        "This is also called Z-score Normalization.\n",
        "\n",
        "Z-score Normalization on the features (except \"Year\" and \"Status\") in both training and test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
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        "id": "iD2UmlarDHY_",
        "outputId": "6a895e26-a196-4649-8717-ceaaf7a1d15e"
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        {
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
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              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
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              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
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              "  inputElement.disabled = false;\n",
              "\n",
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              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
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              "\n",
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              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
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              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
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              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
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              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
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              "  _uploadFiles,\n",
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              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving LifeExpectancy_test_modified.csv to LifeExpectancy_test_modified.csv\n"
          ]
        }
      ],
      "source": [
        "# Load the test set \"LifeExpectancy_test_modified.csv\"\n",
        "\n",
        "from google.colab import files\n",
        "uploaded_2 = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZNwygnX8mQQd"
      },
      "outputs": [],
      "source": [
        "# Read the testing set\n",
        "df_2 = pd.read_csv(io.BytesIO(uploaded_2['LifeExpectancy_test_modified.csv']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a8Hu-t4eBphh"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "###CODE TO PERFORM Z-score Normalization ###\n",
        "\n",
        "# Drop \"Year\" and \"Status\" in both training and test set and extract other columns\n",
        "features_normal_train = df.drop([\"Year\", \"Status\", \"Life_expectancy\"], axis = 1).columns\n",
        "features_normal_test = df_2.drop([\"Year\", \"Status\"], axis = 1).columns\n",
        "\n",
        "# Initialize the standard scaler, specifying that we need both mean and std\n",
        "scaler = StandardScaler(with_mean = True, with_std = True)\n",
        "\n",
        "# z-score normalization on the training and test set\n",
        "scaled_train = scaler.fit_transform(df[features_normal_train])\n",
        "scaled_test = scaler.fit_transform(df_2[features_normal_test])\n",
        "\n",
        "# Convert to dataframe for both scaled training and testing dataset\n",
        "scaled_train_df = pd.DataFrame(scaled_train, columns=features_normal_train)\n",
        "scaled_test_df = pd.DataFrame(scaled_test, columns=features_normal_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "qi144gfw4bi8",
        "outputId": "0b833fc9-b29d-4b71-ddb6-f3462bf8509e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Adult_Mortality   Alcohol  percentage_expenditure       BMI  \\\n",
              "0           -0.595190 -0.726456                0.048715  1.255572   \n",
              "1           -0.109184  0.481078                0.023969 -0.320769   \n",
              "2           -0.801125  1.830823               -0.351900  0.886429   \n",
              "3           -0.249220 -1.115662               -0.399797  1.051047   \n",
              "4            0.220312  0.361323               -0.385773 -1.827272   \n",
              "...               ...       ...                     ...       ...   \n",
              "1059        -0.059760 -1.115662               -0.398827 -1.108939   \n",
              "1060        -0.817600  1.454092                2.928709  1.155804   \n",
              "1061        -1.262419 -0.768870               -0.085509  0.836545   \n",
              "1062         0.623944 -0.659094               -0.353924 -1.118916   \n",
              "1063         0.187362 -0.843717               -0.384760  0.377610   \n",
              "\n",
              "      Total_expenditure  Diphtheria  HIV_AIDS       GDP  Population  \\\n",
              "0              0.445559   -0.171244 -0.313881  0.227204   -0.139413   \n",
              "1             -0.831846    0.692054 -0.313881 -0.000131    0.925595   \n",
              "2              2.157283    0.500210 -0.313881 -0.438595   -0.074490   \n",
              "3             -0.214434    0.452249 -0.313881 -0.482745   -0.182618   \n",
              "4             -1.010683    0.548171 -0.201351 -0.459608   -0.107964   \n",
              "...                 ...         ...       ...       ...         ...   \n",
              "1059          -2.036866   -0.123283 -0.313881 -0.474159   -0.196210   \n",
              "1060           1.028908    0.356327 -0.313881  2.455964   -0.215522   \n",
              "1061           0.045305    0.692054 -0.313881 -0.147601   -0.190580   \n",
              "1062          -0.129273   -0.746776 -0.107577 -0.445607    1.326504   \n",
              "1063           0.594590   -3.624437 -0.313881 -0.380178   -0.215091   \n",
              "\n",
              "      Income_composition_of_resources  Schooling  Health_Index  \n",
              "0                            0.671470   0.356420     -0.096784  \n",
              "1                            0.556023   0.499431     -1.055036  \n",
              "2                            1.380648   1.250240      1.442743  \n",
              "3                            0.885873   0.892712     -0.801501  \n",
              "4                            0.116223  -0.251379     -0.360090  \n",
              "...                               ...        ...           ...  \n",
              "1059                        -0.087185   0.106150     -0.523613  \n",
              "1060                         1.534578   2.894871      0.285628  \n",
              "1061                         0.385600   0.785454     -1.385942  \n",
              "1062                        -1.175690  -1.359717     -0.276187  \n",
              "1063                        -0.790865  -1.037941      1.148517  \n",
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              "      <th>Adult_Mortality</th>\n",
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              "      <td>-0.831846</td>\n",
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              "      <td>-0.000131</td>\n",
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              "      <td>0.499431</td>\n",
              "      <td>-1.055036</td>\n",
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              "      <td>-0.482745</td>\n",
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              "      <td>0.885873</td>\n",
              "      <td>0.892712</td>\n",
              "      <td>-0.801501</td>\n",
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              "      <th>4</th>\n",
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              "      <td>-1.827272</td>\n",
              "      <td>-1.010683</td>\n",
              "      <td>0.548171</td>\n",
              "      <td>-0.201351</td>\n",
              "      <td>-0.459608</td>\n",
              "      <td>-0.107964</td>\n",
              "      <td>0.116223</td>\n",
              "      <td>-0.251379</td>\n",
              "      <td>-0.360090</td>\n",
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              "      <td>-2.036866</td>\n",
              "      <td>-0.123283</td>\n",
              "      <td>-0.313881</td>\n",
              "      <td>-0.474159</td>\n",
              "      <td>-0.196210</td>\n",
              "      <td>-0.087185</td>\n",
              "      <td>0.106150</td>\n",
              "      <td>-0.523613</td>\n",
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              "      <td>1.028908</td>\n",
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              "      <td>2.455964</td>\n",
              "      <td>-0.215522</td>\n",
              "      <td>1.534578</td>\n",
              "      <td>2.894871</td>\n",
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              "      <td>-0.147601</td>\n",
              "      <td>-0.190580</td>\n",
              "      <td>0.385600</td>\n",
              "      <td>0.785454</td>\n",
              "      <td>-1.385942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1062</th>\n",
              "      <td>0.623944</td>\n",
              "      <td>-0.659094</td>\n",
              "      <td>-0.353924</td>\n",
              "      <td>-1.118916</td>\n",
              "      <td>-0.129273</td>\n",
              "      <td>-0.746776</td>\n",
              "      <td>-0.107577</td>\n",
              "      <td>-0.445607</td>\n",
              "      <td>1.326504</td>\n",
              "      <td>-1.175690</td>\n",
              "      <td>-1.359717</td>\n",
              "      <td>-0.276187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1063</th>\n",
              "      <td>0.187362</td>\n",
              "      <td>-0.843717</td>\n",
              "      <td>-0.384760</td>\n",
              "      <td>0.377610</td>\n",
              "      <td>0.594590</td>\n",
              "      <td>-3.624437</td>\n",
              "      <td>-0.313881</td>\n",
              "      <td>-0.380178</td>\n",
              "      <td>-0.215091</td>\n",
              "      <td>-0.790865</td>\n",
              "      <td>-1.037941</td>\n",
              "      <td>1.148517</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "scaled_train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "lt2vp4F44hq-",
        "outputId": "1feb56ac-ca4c-4752-b1bd-8ae7f0508ca9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Adult_Mortality   Alcohol  percentage_expenditure       BMI  \\\n",
              "0           0.764753 -1.088707               -0.387125 -0.949179   \n",
              "1           0.856804 -1.083814               -0.375553 -1.026485   \n",
              "2          -0.669720  0.222512               -0.122149  1.004070   \n",
              "3          -1.229701  0.171140               -0.374864  0.736078   \n",
              "4          -1.206688 -0.041689               -0.382428  0.633004   \n",
              "..               ...       ...                     ...       ...   \n",
              "453        -0.999572 -0.572537               -0.296791 -0.820337   \n",
              "454         2.690168 -0.540735               -0.390352 -0.866720   \n",
              "455         2.697839  0.183371               -0.358646 -0.320429   \n",
              "456         3.503291 -0.220269               -0.378653 -0.361659   \n",
              "457         4.155324 -0.078383               -0.386126 -0.418349   \n",
              "\n",
              "     Total_expenditure  Diphtheria  HIV_AIDS       GDP  Population  \\\n",
              "0             0.951465   -0.663302 -0.344694 -0.490177   -0.175739   \n",
              "1             1.156188   -0.838525 -0.344694 -0.459800   -0.178318   \n",
              "2            -0.009844    0.694674 -0.344694 -0.061370   -0.203512   \n",
              "3             0.172627    0.650868 -0.344694 -0.469038   -0.203343   \n",
              "4             0.239385    0.607063 -0.344694 -0.477809   -0.203052   \n",
              "..                 ...         ...       ...       ...         ...   \n",
              "453          -0.383686    0.169006  1.528473 -0.362118   -0.192258   \n",
              "454           0.168176   -0.093828  2.163445 -0.495116   -0.078401   \n",
              "455          -0.161161    0.256617  2.131697 -0.426439   -0.191184   \n",
              "456          -0.343631   -0.356662  2.893663 -0.464475   -0.066239   \n",
              "457           0.315043   -0.663302  4.449345 -0.452800   -0.205228   \n",
              "\n",
              "     Income_composition_of_resources  Health_Index  Schooling  \n",
              "0                          -0.976549      1.579474  -0.859554  \n",
              "1                          -1.095864      0.530838  -1.141203  \n",
              "2                           0.637039      0.845413   0.478281  \n",
              "3                           0.335912      0.094190  -0.401873  \n",
              "4                           0.273414     -0.564275  -0.437079  \n",
              "..                               ...           ...        ...  \n",
              "453                        -0.692466     -0.826244  -0.190636  \n",
              "454                        -0.834508     -1.032859  -0.366667  \n",
              "455                        -1.078819      1.695418  -0.683523  \n",
              "456                        -1.164043      0.395118  -0.789141  \n",
              "457                        -1.249268      0.470064  -0.929966  \n",
              "\n",
              "[458 rows x 12 columns]"
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adult_Mortality</th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>percentage_expenditure</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Total_expenditure</th>\n",
              "      <th>Diphtheria</th>\n",
              "      <th>HIV_AIDS</th>\n",
              "      <th>GDP</th>\n",
              "      <th>Population</th>\n",
              "      <th>Income_composition_of_resources</th>\n",
              "      <th>Health_Index</th>\n",
              "      <th>Schooling</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.764753</td>\n",
              "      <td>-1.088707</td>\n",
              "      <td>-0.387125</td>\n",
              "      <td>-0.949179</td>\n",
              "      <td>0.951465</td>\n",
              "      <td>-0.663302</td>\n",
              "      <td>-0.344694</td>\n",
              "      <td>-0.490177</td>\n",
              "      <td>-0.175739</td>\n",
              "      <td>-0.976549</td>\n",
              "      <td>1.579474</td>\n",
              "      <td>-0.859554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.856804</td>\n",
              "      <td>-1.083814</td>\n",
              "      <td>-0.375553</td>\n",
              "      <td>-1.026485</td>\n",
              "      <td>1.156188</td>\n",
              "      <td>-0.838525</td>\n",
              "      <td>-0.344694</td>\n",
              "      <td>-0.459800</td>\n",
              "      <td>-0.178318</td>\n",
              "      <td>-1.095864</td>\n",
              "      <td>0.530838</td>\n",
              "      <td>-1.141203</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.669720</td>\n",
              "      <td>0.222512</td>\n",
              "      <td>-0.122149</td>\n",
              "      <td>1.004070</td>\n",
              "      <td>-0.009844</td>\n",
              "      <td>0.694674</td>\n",
              "      <td>-0.344694</td>\n",
              "      <td>-0.061370</td>\n",
              "      <td>-0.203512</td>\n",
              "      <td>0.637039</td>\n",
              "      <td>0.845413</td>\n",
              "      <td>0.478281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.229701</td>\n",
              "      <td>0.171140</td>\n",
              "      <td>-0.374864</td>\n",
              "      <td>0.736078</td>\n",
              "      <td>0.172627</td>\n",
              "      <td>0.650868</td>\n",
              "      <td>-0.344694</td>\n",
              "      <td>-0.469038</td>\n",
              "      <td>-0.203343</td>\n",
              "      <td>0.335912</td>\n",
              "      <td>0.094190</td>\n",
              "      <td>-0.401873</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.206688</td>\n",
              "      <td>-0.041689</td>\n",
              "      <td>-0.382428</td>\n",
              "      <td>0.633004</td>\n",
              "      <td>0.239385</td>\n",
              "      <td>0.607063</td>\n",
              "      <td>-0.344694</td>\n",
              "      <td>-0.477809</td>\n",
              "      <td>-0.203052</td>\n",
              "      <td>0.273414</td>\n",
              "      <td>-0.564275</td>\n",
              "      <td>-0.437079</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>-0.999572</td>\n",
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              "      <td>-0.820337</td>\n",
              "      <td>-0.383686</td>\n",
              "      <td>0.169006</td>\n",
              "      <td>1.528473</td>\n",
              "      <td>-0.362118</td>\n",
              "      <td>-0.192258</td>\n",
              "      <td>-0.692466</td>\n",
              "      <td>-0.826244</td>\n",
              "      <td>-0.190636</td>\n",
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              "      <td>-0.866720</td>\n",
              "      <td>0.168176</td>\n",
              "      <td>-0.093828</td>\n",
              "      <td>2.163445</td>\n",
              "      <td>-0.495116</td>\n",
              "      <td>-0.078401</td>\n",
              "      <td>-0.834508</td>\n",
              "      <td>-1.032859</td>\n",
              "      <td>-0.366667</td>\n",
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              "    <tr>\n",
              "      <th>455</th>\n",
              "      <td>2.697839</td>\n",
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              "      <td>-0.161161</td>\n",
              "      <td>0.256617</td>\n",
              "      <td>2.131697</td>\n",
              "      <td>-0.426439</td>\n",
              "      <td>-0.191184</td>\n",
              "      <td>-1.078819</td>\n",
              "      <td>1.695418</td>\n",
              "      <td>-0.683523</td>\n",
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              "      <th>456</th>\n",
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              "      <td>-0.220269</td>\n",
              "      <td>-0.378653</td>\n",
              "      <td>-0.361659</td>\n",
              "      <td>-0.343631</td>\n",
              "      <td>-0.356662</td>\n",
              "      <td>2.893663</td>\n",
              "      <td>-0.464475</td>\n",
              "      <td>-0.066239</td>\n",
              "      <td>-1.164043</td>\n",
              "      <td>0.395118</td>\n",
              "      <td>-0.789141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>457</th>\n",
              "      <td>4.155324</td>\n",
              "      <td>-0.078383</td>\n",
              "      <td>-0.386126</td>\n",
              "      <td>-0.418349</td>\n",
              "      <td>0.315043</td>\n",
              "      <td>-0.663302</td>\n",
              "      <td>4.449345</td>\n",
              "      <td>-0.452800</td>\n",
              "      <td>-0.205228</td>\n",
              "      <td>-1.249268</td>\n",
              "      <td>0.470064</td>\n",
              "      <td>-0.929966</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>458 rows × 12 columns</p>\n",
              "</div>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-657a1e3e-d681-460f-b781-ed817b18ac7c');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  .colab-df-spinner {\n",
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              "  @keyframes spin {\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "scaled_test_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwfacAsAFBbs"
      },
      "source": [
        "**Advantages and Disadvantages of using Z-score Normalization?**<br/>\n",
        "\n",
        "**Advantages**:\n",
        "\n",
        "* It may **improve the interpretability of the model** since Z-score normalization would bring all variables of different units and scales to a common scale, allowing for meaningful comparison between different variables.\n",
        "* After standardization, we would **clearly know the impact of a one standard deviation increase in the feature** on our target variable.\n",
        "* In addition, Z-score normalization **facilitates convergence**, as many machine learning models assume that all features have similar variance and are centered around zero, which can be handeled by Z-score normalization. Lastly, performing normalization might make it easier for us to detect outliers.\n",
        "\n",
        "**Disadvantages**:  \n",
        "* Z-score normalization **assumes that the data follows a normal distribution**. If the data turns out to be highly skewed and does not follow a normal distribution, Z-score normalization might be a poor choice.\n",
        "* Another disadvantage might be that Z-score normalization can make it **difficult for us to interpret** the data with respect to its original scale in cases where maintaining the interpretability of the original scale of the data is crucial. This is because the transformed values no longer retain their original units and units of measurement.  \n",
        "<br/>\n",
        "\n",
        "**Is Z-score normalization needed in this dataset?**<br/>\n",
        "\n",
        "* Yes, we need to use the Z-score normalization in this dataset because almost all of the variables in our dataset take on **different units and scales**.\n",
        "* With normalization, we can make sure that **each variable contributes equally** to our analysis, irrespective of their original scales.\n",
        "* Also, using standardization in this case can make it **easier for us to interpret the coefficients** due to the fact that standardization allows for a more direct comparison of the relative importance of our variables.\n",
        "* Normalization would likely **expedite the convergence process**, as the features will be on the same scale after normalization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tr1XcvyFIcN"
      },
      "source": [
        "### One-Hot Encoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fyyGwM3JFS4Y"
      },
      "source": [
        "\"Year\" and \"Status\" can only take discrete values. We need to perform one-hot encoding on discrete values for it to be processed in the model. One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction.\n",
        "So we do one-hot encoding on \"Year\" and \"Status\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVbI2IVxFuHn",
        "outputId": "5a8d5619-3b54-4db7-9399-24fe3dcd80d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the training encoded array is: (1064, 16)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "###CODE TO PERFORM ONE-HOT CODING ON \"Year\" AND \"Status\" ###\n",
        "\n",
        "# Extract columns to be encoded\n",
        "col_to_encode_train = df[[\"Year\", \"Status\"]].values\n",
        "col_to_encode_test = df_2[[\"Year\", \"Status\"]].values\n",
        "\n",
        "# Initialize the One-Hot encoder\n",
        "encoder = OneHotEncoder()\n",
        "\n",
        "# Transform \"Year\" and \"Status\" in both training and testing datasets and convert it to dataframe\n",
        "encoded_train = encoder.fit_transform(col_to_encode_train).toarray()\n",
        "train_encoded_df = pd.DataFrame(encoded_train, columns = encoder.get_feature_names_out(input_features=[\"Year\", \"Status\"]))\n",
        "\n",
        "encoded_test = encoder.fit_transform(col_to_encode_test).toarray()\n",
        "test_encoded_df = pd.DataFrame(encoded_test, columns = encoder.get_feature_names_out(input_features=[\"Year\", \"Status\"]))\n",
        "\n",
        "# Combine the dataframe of the scaled numerical variables and the encoded categorical variables\n",
        "# Processed training dataframe\n",
        "combined_training_df = pd.concat([scaled_train_df, train_encoded_df, df['Life_expectancy']], axis = 1)\n",
        "\n",
        "# Processed testing dataframe\n",
        "combined_testing_df = pd.concat([scaled_test_df, test_encoded_df], axis = 1)\n",
        "\n",
        "\n",
        "# Print the shape of the encoded X\n",
        "print(f'Shape of the training encoded array is: {encoded_train.shape}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAoIvN2W5s6z",
        "outputId": "c167abda-933b-4d65-9031-788f079e482b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Year_2002', 'Year_2003', 'Year_2004', 'Year_2005', 'Year_2006',\n",
              "       'Year_2007', 'Year_2008', 'Year_2009', 'Year_2010', 'Year_2011',\n",
              "       'Year_2012', 'Year_2013', 'Year_2014', 'Year_2015', 'Status_Developed',\n",
              "       'Status_Developing'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "# The reason that the number of columns for the encoded array = 16 is because of the encoding process, which creates 14 new columns for \"Year\"\n",
        "# and 2 new columns for \"Status\".\n",
        "train_encoded_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IvbDqp5bJ-Xx"
      },
      "source": [
        "Other types of encodings and why did we use One-hot encoding for \"Year\" and \"Status\"?\n",
        "\n",
        "Ans-\n",
        "* There are other types of encodings, such as the label encoding, ordinal encoding, binary encodong, count encoding, frequency encoding, hashing encoding and etc.\n",
        "* We use One-hot encoding in this case for `Year` and `Status` because of the absence of any inherent order in both `Year` and `Status`. The categorical variable `Year` seems to have some sort of temporal order, but we cannot claim that the year '2006' is inherently greater than '2005', and thus the one-hot encoding still appears to be a better choice. One-hot also ensures that the model does not assume a hierarchy between categories, a condition that does not exist in this case for the variables `Years` and `Status`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fp5S0WizKI0g"
      },
      "source": [
        "## Multiple Linear Regression\n",
        "\n",
        "In the big data era, it is highly unlikely that we are interested in the effect of a single variable on another. To simultaneously account for the effects of multiple variables, we use multiple regression (which accounts for the covariances between predictors).\n",
        "\n",
        "While the algorithmic solution to multiple regression exists, it is easier to conceptualize in terms of linear algebra. The optimal $\\hat{\\beta}$ vector that minimizes the residual sum of squares is:\n",
        "\n",
        "$\\hat{\\beta} = (X^TX)^{-1}X^Ty $\n",
        "\n",
        "\n",
        "So we perform multiple linear regression on the training dataset, where the outcome is \"Life_expectancy\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3-saM1rTKjKH"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "GTfbR3b-KoeW",
        "outputId": "e30d9c15-ffbf-48fe-d68a-c56f76002587"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "### Bulding and fitting the Multiple Linear Regression model###\n",
        "\n",
        "#Loading the dataset\n",
        "X = combined_training_df[combined_training_df.columns[~combined_training_df.columns.isin(['Life_expectancy'])]]\n",
        "y = combined_training_df['Life_expectancy']\n",
        "\n",
        "#Initialize the Model\n",
        "model = LinearRegression()\n",
        "\n",
        "model.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FpQe-p3LLXgA",
        "outputId": "330d2a08-d2c7-4ff6-9839-bef877a239c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error on the training set: 12.747314971108775\n"
          ]
        }
      ],
      "source": [
        "### Evaluating the Linear Regression model by computing MSE on the training set###\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# Predict on the training set\n",
        "y_pred = model.predict(X)\n",
        "\n",
        "# Compute Mean Squared Error\n",
        "mse = mean_squared_error(y, y_pred)\n",
        "\n",
        "# Print the Mean Squared Error\n",
        "print(\"Mean Squared Error on the training set:\", mse)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O2jOEvNiO84E"
      },
      "source": [
        "Coefficients and also the corresponding variable names for the coefficients."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "461tQ8X5KA-q",
        "outputId": "19218e7b-bc4d-42ef-c866-03d5c1ed3345"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept: 69.9327\n",
            "Coefficient Name                 Value          \n",
            "Adult_Mortality                 : -2.0208        \n",
            "Alcohol                         : -0.4873        \n",
            "percentage_expenditure          : 0.6172         \n",
            "BMI                             : 0.7093         \n",
            "Total_expenditure               : 0.2577         \n",
            "Diphtheria                      : 0.5027         \n",
            "HIV_AIDS                        : -2.6842        \n",
            "GDP                             : 0.1566         \n",
            "Population                      : -0.0524        \n",
            "Income_composition_of_resources : 2.1729         \n",
            "Schooling                       : 2.6196         \n",
            "Health_Index                    : 0.2489         \n",
            "Year_2002                       : 0.5564         \n",
            "Year_2003                       : 0.8943         \n",
            "Year_2004                       : 0.5611         \n",
            "Year_2005                       : 0.5787         \n",
            "Year_2006                       : 0.2812         \n",
            "Year_2007                       : 0.2252         \n",
            "Year_2008                       : -0.0979        \n",
            "Year_2009                       : -0.4055        \n",
            "Year_2010                       : -0.5584        \n",
            "Year_2011                       : -0.7816        \n",
            "Year_2012                       : -0.7169        \n",
            "Year_2013                       : -0.8508        \n",
            "Year_2014                       : -0.8574        \n",
            "Year_2015                       : 1.1722         \n",
            "Status_Developed                : 0.3155         \n",
            "Status_Developing               : -0.3154        \n"
          ]
        }
      ],
      "source": [
        "# Print coefficient names and values\n",
        "coefficients = model.coef_\n",
        "intercept = model.intercept_\n",
        "\n",
        "print(\"Intercept:\", round(intercept,4))\n",
        "print(f\"{'Coefficient Name':<32} {'Value':<15}\")\n",
        "for feature, coef in zip(X.columns, np.ceil((coefficients * 10000))/10000):\n",
        "    print(f'{feature:<32}: {round(coef,4):<15}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q18iLjtHX25r"
      },
      "source": [
        "Problem of multicolinearity?\n",
        "\n",
        "Multicolinearity in multiple linear regression occurs when two or more independent variables in a model are highly correlated, making it difficult to determine the individual effect of each variable on the dependent variable. This can further lead to inflated standard errors and unstable coefficient estiamtes.\n",
        "\n",
        "* Yes, we suspect there could be a problem of multicolinearity between the variables `Income_composition_of_resources` and `Schooling` because they have a high positive correlation value of 0.81.\n",
        "\n",
        "* We also suspect a problem of multicolinearity between the two status variables after one hot encoding them i.e. `Status_Developed` and `Status_Developed`. This is also intuituve as if the value of one column is set, the value of the other is automatically determined as no country can be both developing and developed at the same time."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJ51d6d2YyPp"
      },
      "source": [
        "### Goodness of fit\n",
        "\n",
        "A model can always make predictions. But it is important to determine how good the model is.\n",
        "How do we know that our model captures the data well? When evaluating model fit, a good metric is $R^2$, which corresponds to the amount of variance explained by the model. The formula for $R^2$ is the following:\n",
        "\n",
        "$R^2$ = $1 - \\dfrac{RSS}{TSS}$<br/>\n",
        "where:<br/>\n",
        "$RSS = \\Sigma(y - \\hat{y})^2$<br/>\n",
        "$TSS = \\Sigma(y - \\bar{y})^2$<br/>\n",
        "\n",
        "$R^2$ is also one metric for comparing models against each other. It is intuitive to say that the model that explains more variation in the data is a better fit than one that explains less variation.\n",
        "\n",
        "Calculation of R2 score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hr0giuHXY4J5"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kFiC_mfgY7m1"
      },
      "source": [
        "$R^2$ for model with \"Schooling\" as predictor and \"Life_expectancy\" as outcome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIspp_FPaicb",
        "outputId": "1a07822c-69f5-4825-a5dc-3781844532e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 Score for 'Schooling' as predictor: 0.5702\n"
          ]
        }
      ],
      "source": [
        "# Print R2 score\n",
        "\n",
        "x1 = combined_training_df[['Schooling']]\n",
        "y1 = combined_training_df['Life_expectancy']\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(x1, y1)\n",
        "\n",
        "y_pred1 = model.predict(x1)\n",
        "\n",
        "r2 = r2_score(y1, y_pred1)\n",
        "print(f\"R2 Score for 'Schooling' as predictor: {r2:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fGTnWcobCc_"
      },
      "source": [
        "$R^2$ for model with \"Schooling\", \"Adult_Mortality\" as predictor and \"Life_expectancy\" as outcome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XqBXqRZlbjy7",
        "outputId": "67a0c5ef-a8f5-42ef-c3e0-d02ee5a2572d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 Score for 'Schooling' and 'Adult_Mortality' as predictor: 0.7248\n"
          ]
        }
      ],
      "source": [
        "# Print R2 score\n",
        "\n",
        "x2 = combined_training_df[['Schooling','Adult_Mortality']]\n",
        "y2 = combined_training_df['Life_expectancy']\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(x2, y2)\n",
        "\n",
        "y_pred2 = model.predict(x2)\n",
        "\n",
        "r2 = r2_score(y2, y_pred2)\n",
        "print(f\"R2 Score for 'Schooling' and 'Adult_Mortality' as predictor: {r2:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dE1PDtR0bVdX"
      },
      "source": [
        "$R^2$ for model with \"Schooling\",\"Adult_Mortality\" and \"Population\" as predictor and \"Life_expectancy\" as outcome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5WxDGrKqbyR2",
        "outputId": "2ca9e8fa-fd24-44dc-e052-a4f8ea0f5799"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 Score for 'Schooling','Adult_Mortality' and 'Population' as predictor: 0.7249\n"
          ]
        }
      ],
      "source": [
        "# Print R2 score\n",
        "\n",
        "x3 = combined_training_df[['Schooling','Adult_Mortality','Population']]\n",
        "y3 = combined_training_df['Life_expectancy']\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(x3, y3)\n",
        "\n",
        "y_pred3 = model.predict(x3)\n",
        "\n",
        "r2 = r2_score(y3, y_pred3)\n",
        "print(f\"R2 Score for 'Schooling','Adult_Mortality' and 'Population' as predictor: {r2:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vYdvN1CTcMuN"
      },
      "source": [
        "You can see $R^2$ is always going up as we keep adding features.\n",
        "\n",
        "This is one drawback of only using $R^2$ to evaluate your model. Adding predictors seems to always improve the predictive ability of your model, though it may not be true.\n",
        "\n",
        "That is to say, we are not necessarily interested in making a perfect prediciton of our training data. If we were, we would always use all of the predictors available. Rather, we would like to make a perfect prediction of our test data. In this case, adding all the predictors may not be a good idea due to the trade-off between bias and variance. Thus, we are interested in the most predictive features, in the hopes that we can create a simpler model that performs well in the future.\n",
        "\n",
        "This is why we consider another metric, Adjusted R2.\n",
        "The adjusted R-squared increases only if the new term improves the model more than would be expected by chance.\n",
        "\n",
        "\n",
        "$AdjustedR^2$ = $1 - \\dfrac{(1-R^2)(n-1)}{(n-p-1)}$<br/>\n",
        "where:<br/>\n",
        "n = number of samples<br/>\n",
        "p = number of features\n",
        "\n",
        "Code for calculation of adjusted R2 score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CCqEwoDNcOgb"
      },
      "source": [
        "Adjusted $R^2$ for model with \"Schooling\" as predictor and \"Life_expectancy\" as outcome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mCgTtpskch0n",
        "outputId": "a909bd93-b354-4c89-ed1c-85945d5f39ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adjusted R2 Score for 'Schooling' as predictor: 0.5698\n"
          ]
        }
      ],
      "source": [
        "# Print Adjusted R2 score\n",
        "X1 = combined_training_df[['Schooling']]\n",
        "Y1 = combined_training_df[['Life_expectancy']]\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(X1,Y1)\n",
        "\n",
        "n = len(X1)\n",
        "k = X1.shape[1]\n",
        "\n",
        "y_pred1 = model.predict(X1)\n",
        "\n",
        "r2 = model.score(X1, Y1)\n",
        "\n",
        "adjusted_r2 = 1 - (1 - r2) * (n - 1) / (n - k - 1)\n",
        "\n",
        "print(f\"Adjusted R2 Score for 'Schooling' as predictor: {adjusted_r2:.4f}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKLKf_ODc842"
      },
      "source": [
        "Adjusted $R^2$ for model with \"Schooling\", \"Adult_Mortality\" as predictor and \"Life_expectancy\" as outcome."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6j0eaIxdFoD",
        "outputId": "9eda222c-a2ce-4744-a422-24ead8a1cd20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adjusted R2 Score for 'Schooling' and 'Adult_Mortality' as predictor: 0.7243\n"
          ]
        }
      ],
      "source": [
        "# Print Adjusted R2 score\n",
        "X2 = combined_training_df[['Schooling','Adult_Mortality']]\n",
        "Y2 = combined_training_df[['Life_expectancy']]\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(X2,Y2)\n",
        "\n",
        "n = len(X2)\n",
        "k = X2.shape[1]\n",
        "\n",
        "y_pred1 = model.predict(X2)\n",
        "\n",
        "r2 = model.score(X2, Y2)\n",
        "\n",
        "adjusted_r2 = 1 - (1 - r2) * (n - 1) / (n - k - 1)\n",
        "\n",
        "print(f\"Adjusted R2 Score for 'Schooling' and 'Adult_Mortality' as predictor: {adjusted_r2:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3oT1NbDdRcK"
      },
      "source": [
        "Adjusted $R^2$ for model with \"Schooling\",\"Adult_Mortality\" and \"Population\" as predictor and \"Life_expectancy\" as outcome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KLOFJL2LdVEz",
        "outputId": "ca3e51ca-e1f6-43a1-ed8d-a3263d8b1010"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adjusted R2 Score for 'Schooling', 'Adult_Mortality' and 'Population' as predictor: 0.7241\n"
          ]
        }
      ],
      "source": [
        "### WRITE CODE ###\n",
        "\n",
        "# Print Adjusted R2 score\n",
        "X3 = combined_training_df[['Schooling','Adult_Mortality','Population']]\n",
        "Y3 = combined_training_df[['Life_expectancy']]\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(X3,Y3)\n",
        "\n",
        "n = len(X3)\n",
        "k = X3.shape[1]\n",
        "\n",
        "y_pred1 = model.predict(X3)\n",
        "\n",
        "r2 = model.score(X3, Y3)\n",
        "\n",
        "adjusted_r2 = 1 - (1 - r2) * (n - 1) / (n - k - 1)\n",
        "\n",
        "print(f\"Adjusted R2 Score for 'Schooling', 'Adult_Mortality' and 'Population' as predictor: {adjusted_r2:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXZsajCMrh8l"
      },
      "source": [
        "**R2 scores**\n",
        "*   'Schooling' as predictor: 0.5702\n",
        "*   'Schooling' and 'Adult_Mortality' as predictor: 0.7248\n",
        "*   'Schooling', 'Adult_Mortality' and 'Population' as predictor: 0.7249\n",
        "\n",
        "**Adjusted R2 scores**\n",
        "\n",
        "*   'Schooling' as predictor: 0.5698\n",
        "*   'Schooling' and 'Adult_Mortality' as predictor: 0.7243\n",
        "*   'Schooling', 'Adult_Mortality' and 'Population' as predictor: 0.7241\n",
        "\n",
        "**Insights**\n",
        "*   **Overfitting scenario**: With increase in number of features R2 and adjusted R2 has increased. Which is expected. However, the increase in R2 is minimal when the feature of 'Population' is added. Further, the model is penalized when this feature is added as the adjusted R2 goes down. Therefore we can omit this feature from the analysis\n",
        "*   **Can we increase the Adjusted R2 further?** We tried adding the most correlated feature 'Income_composition_of_resources' to improve adjusted R2 however this led to a minor but notable increase (0.03 units) in value. This feature can be added but does not add a lot of value. Similar calculations can be done for all combination of features to identify the best possible combination.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDHU67n8d4x7"
      },
      "source": [
        "### K-fold Cross-Validation\n",
        "\n",
        "However, adjusted $R^2$ is not enough to help us ahieve the best model, a more robust method is k-fold cross-validation.\n",
        "\n",
        "* Randomly split dataset into K equal-sized subsets, or folds\n",
        "* Treat each fold as validation set (train on all but K'th fold and test on K'th fold only)\n",
        "\n",
        "* The overall error is then the mean error over all K models.\n",
        "* Most common are 5- or 10-fold cross-validation\n",
        "\n",
        "So we implement 5-fold cross-validation by yourselves to find the best model in terms of Mean Square Error(MSE)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature selection and Removing Multi Colinearity\n",
        "\n",
        "\n",
        "# Assuming 'df' is our DataFrame with features and target variable\n",
        "df = combined_training_df\n",
        "\n",
        "columns_to_drop = ['Life_expectancy', 'Year_2002', 'Year_2003', 'Year_2004', 'Year_2005', 'Year_2006',\n",
        "                   'Year_2007', 'Year_2008', 'Year_2009', 'Year_2010', 'Year_2011',\n",
        "                   'Year_2012', 'Year_2013', 'Year_2014', 'Year_2015', 'Status_Developed', 'Health_Index', 'Population']\n",
        "\n",
        "# Define the features and target variable\n",
        "X = df.drop(columns=columns_to_drop, axis=1)\n",
        "y = df['Life_expectancy']"
      ],
      "metadata": {
        "id": "tZqBdlabeXI5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are removing the columns `Health_Index`, `Population`, and all the `Year` variables because they have negligible correlation with the dependent variable `Life_Expectancy`.  \n",
        "We are doing this before running the k-fold Cross validation to avoid very high processing time when we run the k-fold Cross validation and look for the optimal combination of variables to get the minimum mean squared error and maximum R2 value."
      ],
      "metadata": {
        "id": "XAQBebdyem0Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Code to perform K-Cross validation on the features which have some significant value of correlation with the dependent variable.\n",
        "\n",
        "# Function to calculate R-squared and error\n",
        "def calculate_metrics(model, X, y):\n",
        "    y_pred = model.predict(X)\n",
        "    r2 = r2_score(y, y_pred)\n",
        "    mse = mean_squared_error(y, y_pred)\n",
        "    return r2, mse\n",
        "\n",
        "# Function to perform k-fold cross-validation\n",
        "def cross_validate(model, X, y, folds=5):\n",
        "    kf = KFold(n_splits=folds, shuffle=True, random_state=42)\n",
        "    r2_scores = []\n",
        "    mse_scores = []\n",
        "\n",
        "    for train_idx, val_idx in kf.split(X):\n",
        "        X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]\n",
        "        y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]\n",
        "\n",
        "        model.fit(X_train, y_train)\n",
        "\n",
        "        r2, mse = calculate_metrics(model, X_val, y_val)\n",
        "        r2_scores.append(r2)\n",
        "        mse_scores.append(mse)\n",
        "\n",
        "    return r2_scores, mse_scores\n",
        "\n",
        "# Function to get all possible feature combinations\n",
        "def get_all_feature_combinations(X):\n",
        "    all_features = X.columns\n",
        "    all_combinations = chain(*[combinations(all_features, i) for i in range(1, len(all_features) + 1)])\n",
        "    return list(all_combinations)\n",
        "\n",
        "# Create a linear regression model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Get all feature combinations\n",
        "all_feature_combinations = get_all_feature_combinations(X)\n",
        "\n",
        "# Perform cross-validation for each feature combination\n",
        "results = []\n",
        "for features in all_feature_combinations:\n",
        "    selected_features = list(features)\n",
        "    X_subset = X[selected_features]\n",
        "\n",
        "    r2_scores, mse_scores = cross_validate(model, X_subset, y)\n",
        "\n",
        "    avg_r2 = sum(r2_scores) / len(r2_scores)\n",
        "    avg_mse = sum(mse_scores) / len(mse_scores)\n",
        "\n",
        "    results.append({\n",
        "        'Features': selected_features,\n",
        "        'Average_R2': avg_r2,\n",
        "        'Average_MSE': avg_mse\n",
        "    })\n",
        "\n",
        "# Store all the feature combinations along with their corresponding R2 and MSE values in a dataframe\n",
        "result_df = pd.DataFrame(results)"
      ],
      "metadata": {
        "id": "czIiMtEroXoj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we print the list of features which give the lowest Mean Square error and Maximum R2 value from all the various combinations of features possible."
      ],
      "metadata": {
        "id": "CPTy4_XDf0o5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfrarPLhN1EB",
        "outputId": "31888e8d-1f61-4489-c592-0d11ff899ac6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features with Highest R2 values and Lowest Mean Square Error MSE\n",
            "['Adult_Mortality', 'Alcohol', 'percentage_expenditure', 'BMI', 'Total_expenditure', 'Diphtheria', 'HIV_AIDS', 'Income_composition_of_resources', 'Schooling', 'Status_Developing']\n",
            "\n",
            "Features with Highest R2 values and Lowest Mean Square Error MSE\n",
            "0.8271\n",
            "\n",
            "Features with Highest R2 values and Lowest Mean Square Error MSE\n",
            "13.4054\n"
          ]
        }
      ],
      "source": [
        "### Show features and their corresponding R2 values and Mean Squared Error MSE for the model\n",
        "\n",
        "#Sorting the features\n",
        "result_df_sorted = result_df.sort_values(by='Average_R2', ascending=False)\n",
        "\n",
        "#Print the feature combinations for the model with the Highest R2 and Lowest Mean Square Error\n",
        "print(\"Features with Highest R2 values and Lowest Mean Square Error MSE\")\n",
        "print(result_df_sorted.Features.iloc[0])\n",
        "\n",
        "#Print the Average R2 value for this model\n",
        "print(\"\\nFeatures with Highest R2 values and Lowest Mean Square Error MSE\")\n",
        "print(round(result_df_sorted.Average_R2.iloc[0],4))\n",
        "\n",
        "#Print the Average MSE value for this model\n",
        "print(\"\\nFeatures with Highest R2 values and Lowest Mean Square Error MSE\")\n",
        "print(round(result_df_sorted.Average_MSE.iloc[0],4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-WBDncGtiY2"
      },
      "source": [
        "### Test your model\n",
        "Now, we apply our best model to predict the target values from the test feature set \"LifeExpectancy_test_modified.csv\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tYJ-KOqzuLj2"
      },
      "outputs": [],
      "source": [
        "\n",
        "columns_to_drop_training = ['Health_Index', 'Year_2002', 'Year_2003',\n",
        "                   'Year_2004', 'Year_2005', 'Year_2006', 'Year_2007', 'Year_2008', 'Year_2009',\n",
        "                   'Year_2010', 'Year_2011', 'Year_2012', 'Year_2013', 'Year_2014', 'Year_2015',\n",
        "                   'Status_Developed', 'GDP', 'Population']\n",
        "columns_to_drop_test= ['Health_Index', 'Year_2002', 'Year_2003',\n",
        "                   'Year_2004', 'Year_2005', 'Year_2006', 'Year_2007', 'Year_2008', 'Year_2009',\n",
        "                   'Year_2010', 'Year_2011', 'Year_2012', 'Year_2013', 'Year_2014',\n",
        "                   'Status_Developed', 'GDP', 'Population']\n",
        "\n",
        "# We select the same set of features we got from running various K-fold cross validations.\n",
        "best_features = ['Adult_Mortality', 'Alcohol', 'percentage_expenditure', 'BMI', 'Total_expenditure',\n",
        "                 'Diphtheria', 'HIV_AIDS', 'Income_composition_of_resources', 'Schooling', 'Status_Developing']\n",
        "\n",
        "\n",
        "# Selecting the best set of features of the training data\n",
        "X_train = combined_training_df.drop(columns=columns_to_drop_training, axis=1)\n",
        "X_train = X_train[best_features]\n",
        "y_train = combined_training_df['Life_expectancy']\n",
        "\n",
        "# Fitting the model now with the best set of features on the training data\n",
        "final_model = LinearRegression()\n",
        "\n",
        "final_model.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Computinf Mean Squared Error of the final model on the training data\n",
        "\n",
        "# Predict on the training set\n",
        "y_pred = final_model.predict(X_train)\n",
        "\n",
        "mse = mean_squared_error(y_train, y_pred)\n",
        "print(mse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6c00HPLk2cx",
        "outputId": "ba6815f4-f344-48c7-febe-f2cdf42056df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13.140460157488937\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, we get a resonably low mean squared error in the new model on our training data."
      ],
      "metadata": {
        "id": "stfsCgxzlGlQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Fitting the final model on Test data\n",
        "\n",
        "# Preparing the test data\n",
        "X_test = combined_testing_df.drop(columns=columns_to_drop_test, axis=1)\n",
        "X_test = X_test[best_features]\n",
        "\n",
        "# Make predictions on the test data\n",
        "y_pred_test = final_model.predict(X_test)"
      ],
      "metadata": {
        "id": "eCD77VqulBA_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YAUlAuc09Uk9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "4b6919a8-3ab7-4bb8-efa6-e44db1b2e87e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  7.,   6.,  18.,  49.,  75., 101., 122.,  56.,  21.,   3.]),\n",
              " array([43.31836077, 48.0605281 , 52.80269543, 57.54486277, 62.2870301 ,\n",
              "        67.02919743, 71.77136476, 76.51353209, 81.25569942, 85.99786675,\n",
              "        90.74003408]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Prediction on test set as y_pred.\n",
        "plt.hist(y_pred_test, label='Data Points')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "df_pred = pd.DataFrame({'Predicted_Values': y_pred_test})\n",
        "\n",
        "df_combined = pd.concat([combined_testing_df, df_pred], axis=1)\n",
        "\n",
        "#Get output of the predicted values\n",
        "df_combined.to_excel('output.xlsx', index=False)"
      ],
      "metadata": {
        "id": "KPgFCuqJhxhz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights**\n",
        "\n",
        "* The histogram of the predicted `life_expectancy` is a normal distribution similar to that of the training set.\n",
        "* The average of the distribution is 72 - 76 range, this makes intuitive sense as the range of values are similar to life expectancy of humans - 72.6 years\n",
        "https://ourworldindata.org/life-expectancy"
      ],
      "metadata": {
        "id": "6z8nqVYrbxe8"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
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