{
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    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import preprocessing\n",
        "import requests\n",
        "from io import BytesIO"
      ],
      "metadata": {
        "id": "3fO2bUn6qIaU"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "BDcexMqGpWbl",
        "outputId": "6fc61f7a-2e58-4cb6-f7a5-b02ad883dedf"
      },
      "outputs": [
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.13/dist-packages/urllib3/connectionpool.py:1097: InsecureRequestWarning: Unverified HTTPS request is being made to host 'www.iam.fmph.uniba.sk'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Gender        Age  Weight  Height  Leg_Length  Arm_Length  Arm_circum  \\\n",
              "0          0  20.083333    64.7     163        34.2        36.2        29.0   \n",
              "1          0  20.083333    54.0     153        37.2        34.0        26.1   \n",
              "2          1  20.083333    61.4     165        37.7        35.0        31.4   \n",
              "3          0  20.083333    74.0     171        37.9        36.2        29.8   \n",
              "4          0  20.083333    63.6     159        38.1        34.0        29.2   \n",
              "...      ...        ...     ...     ...         ...         ...         ...   \n",
              "4974       1  79.833333    89.5     184        43.0        42.8        32.2   \n",
              "4975       0  79.916667    78.6     151        35.6        34.2        33.5   \n",
              "4976       1  79.916667    86.5     175        38.6        41.5        32.8   \n",
              "4977       0  79.916667    58.0     163        40.2        37.5        26.2   \n",
              "4978       1  79.916667    76.2     168        40.8        39.5        29.8   \n",
              "\n",
              "      Waist  \n",
              "0      89.6  \n",
              "1      85.5  \n",
              "2      70.1  \n",
              "3      91.1  \n",
              "4      74.3  \n",
              "...     ...  \n",
              "4974  112.8  \n",
              "4975  114.9  \n",
              "4976  100.2  \n",
              "4977   82.8  \n",
              "4978  103.5  \n",
              "\n",
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              "      <th>Gender</th>\n",
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              "      <th>Leg_Length</th>\n",
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              "    <tr>\n",
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              "      <td>112.8</td>\n",
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              "      <th>4975</th>\n",
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              "<p>4978 rows × 8 columns</p>\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4978,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.432332908909082,\n        \"min\": 20.083333333333332,\n        \"max\": 79.91666666666667,\n        \"num_unique_values\": 716,\n        \"samples\": [\n          30.083333333333332,\n          47.666666666666664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.29198365657108,\n        \"min\": 32.4,\n        \"max\": 218.2,\n        \"num_unique_values\": 917,\n        \"samples\": [\n          62.1,\n          63.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 140,\n        \"max\": 204,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          163,\n          156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Leg_Length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.033123682626243,\n        \"min\": 23.7,\n        \"max\": 55.5,\n        \"num_unique_values\": 240,\n        \"samples\": [\n          40.7,\n          39.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Arm_Length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7535067998546445,\n        \"min\": 29.5,\n        \"max\": 47.7,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          41.4,\n          33.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Arm_circum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.979115404741861,\n        \"min\": 19.5,\n        \"max\": 55.5,\n        \"num_unique_values\": 294,\n        \"samples\": [\n          26.5,\n          33.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Waist\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.832760265027236,\n        \"min\": 59.1,\n        \"max\": 172.0,\n        \"num_unique_values\": 749,\n        \"samples\": [\n          72.6,\n          99.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "path = \"https://www.iam.fmph.uniba.sk/ospm/Rosa/PDV/01_nhanes-weight.xls\"\n",
        "# iam.fmph.uniba.sk ma problem s bezpecnostnym certifikatom => obideme verifikaciu\n",
        "response = requests.get(path, verify=False)\n",
        "response.raise_for_status()\n",
        "data = pd.ExcelFile(BytesIO(response.content)).parse(\"Sheet1\")\n",
        "\n",
        "data[\"Age\"] = data[\"Age\"]/12\n",
        "data = data.dropna()\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Priamkova regresia\n",
        "# Weight ~ a + b * Height\n",
        "\n",
        "# Vykreslenie dat\n",
        "sns.scatterplot(x='Height', y='Weight', data=data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "iqzdNhaP4ebo",
        "outputId": "cbcdb194-5088-461b-b5d0-72326fc3872e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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5ciQsFgt+/vlnDBw4kPg7CoWCKoQ4HA6Hw+F0L7qE2Hn99dcxbNgwDBo0yOXv/vDDDxCLxQgLC+uAI+NwOJyeAx9dwemqdKrYqa+vR0lJSeO/S0tL8cMPPyA4OBhRUVEAHCGqd999Fy+99JLg9ceOHcPx48eRkZEBPz8/HDt2DM888wx++9vfIigoqMPOg8PhcLo7fHQFpyvTqTU7n3/+OTIyMgQ/nzlzJt58800AwD/+8Q88/fTTKC8vR0BAQJPf+/777/HUU0+huLgYRqMR0dHR+N3vfodnn322VWmqlub8OBwOpyei0ZmQu/0E0dF5dGwINj8whEd4OJ1CS9dvjylQ7ky42OFwOBw65yvqMf5PX1C3f/bsGMSE+XbgEXE4Dlq6fncZnx0Oh8PhdA58dAWnq8PFDofD4XCY8NEVnK4OFzscDofDYeIcXUGCj67gdAW42OFwOBwOk84aXaHRmXC+oh4nLtXg/PV6aHSmdnkfTvenS/jscDgcDqdz6ejRFbzVndOW8MgOh8PhcFpEgLccMWG+GBwVhJgw33aN6DQXOgBw+FwlFr1/kkd4OK2Gix0Oh8PheBSV9Saipw/gEDyV9VzscFoHT2NxOJwOgY8a4LQU3urOaWu42OFwOO0Or7/gtAbe6s5pa3gai8PhtCu8/qJz6MqdTLzVndPW8MgOh8NpV1pSf8HTWW1LV4+kOVvdF71/EoebnUN7trpzui9c7HA4nHaF1190LK4iaV1laGdHt7pzujdc7HA4nHbFV8G+zfi42M5pHd0pkhbgzcUNp23gNTscDqddkUvESFWriNtS1SrIJZ1zG+rKNS0seCSNwxHCH6k4HE67Uqs3YXZqNADgaElV489T1SrMTo2GRm8C4NOhx9TVa1pY8E4mDkcIj+xwOJx2xVchw/ztJzAkKgivzxyOvz00FK/PHI4hUUGYv/0EfBQdu/h29+4w3snE4QjhkR0Oh9OuhPjKMbxvELYcLBFs64zFtzvVtJDgnUwcjhAudjgcTrviaYtvT6hp4Z1MHE5TuNjhcDjtjictvj2lpoV3MnE4v8LFDofD6RA8ZfF11rQcJqSyulpNC583xuG0DC52OBxOj8LT0mru0p07yjictkZkt9vtnX0QnY1Wq0VAQAA0Gg38/f07+3A4HE4H4IyKdHZazR00OhNyt58gFlqPjg3pMi7JHM6t0tL1m0d2OBxOt4WV5mmPtFpHpZW6e0cZh9PWcLHD4XC6JR2d5unI9+sJHWWu4PVKnNbAxQ6Hw+l2tOcwTNIiC6BDh2/eakeZu0LBUwQGr1fitBYudjgcTrejvdI8tEU2PysJ312safP3o3ErHWXuCgVPERjdZao7p2PhYofD4XQ72iPNw1pk83adxpy0aKJLtLvvxyLAW46N01Pw+U/XEeangNFig1ImwTWtARkDQqmLvbtCwZMEBq9X4rgDFzscDqfb0R7GgaxF9si5Ssy6s1+bvp8r7AD2nyzHkZKmkZYxA0Kpr3FXKHiSwOD1Shx34INAORxOt6M9hmG6WmRptIdRYWOkpaR1w0zdFQqeJDB6igM2p23hYofD4XQ7nMaBzQXPrRgHulpkbwvyatP3Y9GSSAsJd4WCJwkMPtWd4w48jcXhcLolbT2Py1VRcLi/kvl+bdnJ5G6kxd3CZk8asdHeDtie0nHGaVu4gzK4gzKHDL/pcZpTVqunLrIRrjqZ3jspqK9xt5PpfEU9xv/pC+r2z54dg5gw3zY/B3de1160hwO2p3SccVpOS9dvLnbAxQ5HCL/pcWi0dpHV6EzI3XZCUF8DAOmxIdjiRieTRmfCvO0nqJEWV91R17QG1DSYoDVY4O8lRZC3HL38lS163646YsMVfARH14SPi+Bw3KSz2mx5JKlr0NoxExV1RqLQARxdXBV1xlZ/z7eSymkLIW8HAFGrDtnj8aSOM07bw8UOh9OMzrjp8UhS96VWz66v0bjYTsOdmqRbEfLd/Rr1pI4zTtvTqd1Yhw8fxtSpUxEZGQmRSISdO3c22T5r1iyIRKIm/02cOLHJ71RXV+Ohhx6Cv78/AgMD8fDDD6O+vr4Dz4LT3ejom56rBYjWRszpGvjIJczt3i62swjwliMmzBeDo4IQE+brUoS728XVE65RT+o447Q9nSp2GhoaMGjQILzyyivU35k4cSLKy8sb/9u+fXuT7Q899BAKCwvxySefYO/evTh8+DAee+yx9j50Tjemo2967i5AnK6Bj1yKVLWKuC1VrYKPvOMC7C0R8hqdCecr6nHiUg3OX69vTK9292uUt7R3bzo1jZWZmYnMzEzm7ygUCoSHhxO3FRUV4cCBA/jmm28wfPhwAMDmzZsxadIkvPjii4iMjCS+zmg0wmg0Nv5bq9W6eQac7khHt9ny8LmD7lqzFOgtw7xxsQCAoyVVjT9PVaswb1wsAr07LmLgSsh7ySWCIt3RsSGYPz6W+brucI22d0s7p3Px+Jqdzz//HGFhYQgKCsK4ceOwZs0aqFSOp6Rjx44hMDCwUegAwIQJEyAWi3H8+HHk5OQQ97l+/XqsWrWqQ46f0/Xo6JseD59373qQAG85+gV7Y964WCzKjEO9wQo/pRT1Rguig73bbREliUdXQv77S7XEVNUTY2KY7+Wj8PilpEW0tTcTx3Pw6Ct04sSJuPfeexEdHY3z589jyZIlyMzMxLFjxyCRSHD16lWEhYU1eY1UKkVwcDCuXr1K3e/ixYvx7LPPNv5bq9WiT58+7XYenK5HR970PMmwrTPwpCGT7YUNwCsHS4g+O+0BTTxunJ5CFfL5WUmY9PIR4v6+vFCFdHUIsassVa2CXNJ9zPhb223H6Rp4tNiZMWNG4/8nJycjJSUFMTEx+PzzzzF+/Hi396tQKKBQKNriEDndmI666XVW+NxT0kZdreW3tZ+bqzlWbS3mWOJx4Y33Iwn5n6saoDNZifvcWlCKHU/dify9ZwSpuNmp0dDoTQB8mMfkCdcap+fi0WKnOf3790dISAhKSkowfvx4hIeHo6KiosnvWCwWVFdXU+t8OJ5NT70pdnT43JPSRl2pZsmdz62jxVxL3o/UueXLKDLWmaworzVgSFQQ5qRGw2ixQSEV48TlWszffgJ7ctOor/Wka43Tc+lSYufKlSuoqqpCREQEAGDUqFGora3Fd999h2HDhgEADh48CJvNhpEjR3bmoXLcoKffFDsqkuRpaaP2rFlqS/Hs7ufW0WKuveZmlWsN2HKwhLiNlmr1tGuN03PpVLFTX1+PkpJf/3hKS0vxww8/IDg4GMHBwVi1ahWmT5+O8PBwnD9/HgsWLIBarcY999wDAIiPj8fEiRPx6KOP4tVXX4XZbEZubi5mzJhB7cTieCb8pthxUS1PSxvdas0S7XNra/Hs7ufW0QXo7r6fq3Sq8/9bk2r1tGuN03PpVLHz7bffIiMjo/HfzqLhmTNn4u9//ztOnjyJt956C7W1tYiMjMTdd9+N1atXN6m3efvtt5Gbm4vx48dDLBZj+vTpePnllzv8XDi3Rk+/KXZkVKuz0kY0UdIeow/W35uMRR+ccls8k461oyeNtwR3Oq5Y7+cqndraVGtXSlFyujedKnbGjh0L1hzSjz76yOU+goODsW3btrY8LE4n0JNvih0d1eqMVndXYq6tRx9crNK5LZ5px7p0cjzzHN2NmLj73bImqW+anoJDP11HmJ8CRosNSpkE17QGZAwIdfl+rHRqa1Ot3FaB4yl0qZodTvelJ98UOzqq1dGt7i0Vc61dSFmfm6t5VDTxzDrWzEu17RYxaS0anUkgdJzHufD9k9hwbzL2nywXCKExA0Ldej938VRbhZ7aCNGT6T7mCJwuTU+2au/oqJYz0tD8826vVvf2GjXA+twUUvatjSaeWce6eu8Z5Gcl3fLn1hYTw11NUr9eb6K2unfkHKuOvtZaQlmtHrnbT2D8n75Azt++xPiXvsC87SdQVqvv8GPhdBw8ssPxCHqyVXtnRLU6stW9vcQc63M7cbmWaoKXplbBV0m+9bGOVWeyQqs3ufW5tXVNlqvIlcFM9stpSaSwraMenuRKzBshei5c7HA8Bk+6KXYknRXq76hW9/YSc6zPrbhciz/eMxA22AUmeLNSo9FgtLh1rD4KWas/t/ZYYG9lkjpLXLZ3oXxbRLVuhZ7eCNGT4WKH41H0RKv27h7Vai8xF+Atx9rsJBSUVCLMX/lrIa5GjztjQvDb148ja0hvognetkfIPlztcaztscA6J6nfLOSc0CasO6GJy/aKeniSf1ZPboTo6XCxw+F4AN05qtVeYk6jM6FMY8C+U+UouGnRT1er0D/UF/cNvw1//uQc8bUd2TnVHgusq0nqZ6/WEV/HEmyV9SZ8d7EGuePUGNInsFE8fn+pBlsLSt1KfwHwqLRRT26E6OlwscPheAjuRrU8qbOEdiztIeZqdWa8fPCcILpxpKQKNgBLJ8UTxU5Hd061xwIb4C1H32BvTEmJbBK5qqgzol+wN/oGe7faALDeaMbLDwzBG0dLmzglp6pVePmBIWgwtj79tXRyvEeljTy1O4zT/nCxw+F0YTwpReDqWFyJudaKtgaThZjGARzRDjta7/jrpC3Tqe21wEYEemFSUngTUTa8bxACvOUor9UjMzkCM+/s10QIsQj0kmPTR2cFn6nz3+uyk4mvY6W/ZtawO5w6Om3U3VPGHDpc7HB6NO5GRTwhmuJJnSW3eizuiLYGyoRuJ3qT1SNSgy1ZYN29nkiiTKMzYQHhu3C+J+27MFltTPFostqI21g1Sa7ojLRRd04Zc+hwscPpsbgbFfGUaIondZbcyrG0RCgZLDbUNJigNVjg7yVFkLccQd7shTLAq/WdU+0Fa4H1lBle9ZQONSe0DjZWTdKJy7VIjw2hCq/OSht5ynXB6Ti42OH0SNyNRHhSNMWTZlzVM+o5XB0La3E+U65Fjc6MpTtPNYk6pKlVWJuTjAnxYfi0qELwunR1CPy9Oj5qwIrQ0KIwbX09uXtduFtb5K+UwVsuwZy0aEFh8ztfX8J7T9yJ5btOu5U28oQIKqd7wMUOp0fi7tOvJ0VTPGnGVX5WErzlEugoqSXWsbAW543TUwRCBwAKSqqwdMcprJiaCKPZiiPNurFmp/WjRiLaC3ciNO1xPbl7XbhbWxTiK8fWWSOw+eA5QWHzlgeHIshb5hFGjJ4KF3QdAx8XwemRuPv060k+HR09YoMVhVi+6zTypiS4dSysxTnMX0GtIykoqYLRYsOgqCC8PnM4/vbQULw+czgGRQUhd9sJaF24DLclriI0tBEN7XE9uXtdtGS0wzWtAcXlWnxdWo3iq1pc0xoAAK8cLCEWNr9yqKRx3zFhvhgcFYSYMN8WRXTc+Ty7Gnx0RcfBIzucHsmthOzdeV170NGdJa6iEEsnx7vV/eSrlCJNrWrileOkwcAuQq4zWJpEE27G30vWYU/N7kZoWnI9tfYcbuW6YNUWXapqwOIdwnTimuxkFF3VEvd3xM3olCdFUNsLT0qJ9wS42OH0SG4lZO9JPh2eNOPK3e6nBqMFs1KjYYfQIM/fm14PsrWgFP6UGVd3xYdBLhEjd/sJahqkLYWQRm9iHqeGEmVydT15ycTYf/oqwvwUMFpsqNGZ8XVpNcYOCEUE4xxu5bog1RZd0xoEQgdwRNeW7TyFjdNT8PBb3xL350506lYjXl0hNdQTBJ0n0Wqxc+nSJfTp0wciUdMBJ3a7HZcvX0ZUVFSbHRyH0164+/TriT4dnjTjyp1j0ejNmL/9BOakRQtGO1zXGvD6zOHYcqhEUA/y+szhCPSWEaNJK6clYtEHp4hPzSt2ncaKqYlYvONUm9WD+CikTEM+HwV5VhXrelqfk4zLNXrsPVkmEIExoT6w2uzMc2hLk8qaBhMznbgwM466P3einbcSQe0qtT6elBLvCbRa7ERHR6O8vBxhYWFNfl5dXY3o6GhYreywM4fjKbj79NtTfTraK6rlr5RBZ7IS01HP3zMAX52vItaDiEUibHlgCDZMT4FGb4ZWb0aAlwz+XjJH0TLlqXlghD8Wf3CySVEzcGvpA4VUjDeOlhKPUwRgTXYS9bW066lWT3aIPlpShcnJEdj82bk2PQeALhRyx6mZr2swku/77l4X7l5rXSk15Ekp8Z5Aq8WO3W4XRHUAoL6+Hkqlsk0OisPpKNx9+u0qPh1tGc5vr6hWiK8cd8WHYWCEvyAFNDQqCC989BPxdUfOVeKq1oD8vWeIdSQhvnJU1gsLWYf0CaTW+bibPjBa6IZ8zkJqFqTr6ZdaPXWfvfyVAqHj5PC5SlQ1OM67Nd+9RmfC8l2nMahPIGbdcF52fg9+CvZSEeBFjrC5e124e611pdSQp6XEuzstFjvPPvssAEAkEiEvLw/e3t6N26xWK44fP47Bgwe3+QFyOBz3aI9wfntEtQK85cibkoDFO041ESFpahXGDghlvvZKjVAQuKojcSU86gzmVotErYHd5u5qOwmWQzTrHLzlEtgBZr0SiaoGE2bcHkVMxU1LicT4uFB8Vnxd8Lo0tQoqHzle+M0ggfFjL3/3H4Dduda6UmrIE1Pi3ZkWi50TJ04AcER2Tp06Bbn81y9CLpdj0KBBeO6559r+CDkcTqtpz3B+W0e1NDoTlu48TRQtT41lCxMaBSVVWJwZT9wW6MJs0FshaXVRsI+cXJPjxEcuabWAYh2nQkp3DZmTFo2Vu063OsVlsdmpqbhVewuxPicZRsupJl1zaWoV1uckw2Kzt0udTGuvta6WGuqpKfHOoMVi59ChQwCA2bNn469//Sv8/f3b7aA4HM6t0Z7h/LbudGEd65cXqqjjBtJjQ3Dici11vw0mCzG10lclnAju5K74MFisdmJRcHSID+x2OxZ+ICwKXjUtEalqFTHtlKpWwUsmaXWkJcxPQT33ijojNfWXGqNymaYDhCkuu93OnI2lM1vx0v2Df43eKKUI8pFDKRV2vTnfq6PrZLpiaqirpMS7Oq2u2XnjjTfa4zg4HE4b0l7h/PZIjbGOdWtBKfbOS8PK3YUC0bIqKxGTXy6gvtZPKSNO/paIRNT0gTOdRpv8vWxSPLGm5cTlWswfFwsxIHBzzh0Xi28v1rRaDAR4y7GRcpzjBoQiLUaFIyVN99k7QMmM+gBArd6ElXsKBd/hiqmJCPGVY8btUcT2eZ3RirhwpSA1db6i/paEdVeoK+N0fVotdhoaGrBhwwZ89tlnqKiogM3WNMx84cKFNjs4Dqct6AqeG21Ne5jVtVdqzNWxSkQiomiRiUQYGhVINCNMU6ug0Zuw+INTgm3Oyd+k9AGrKPhoSRUgEuHEpRpBTcvIfsGI8FdiUnIEZt3UPl+hNSAiQIm1+87g9ZnDEeavQL3BCj+lFNe0Bix8/yRTDNDSHABQWt6AfafKBRGoEf2CmaM7jGYb8Ttc/2ER/jVzBF74qJjYPh9IGbyq0bPdjGkeQ4Br8XxNa2h1HRBPDXFItFrsPPLII/jiiy/wu9/9DhEREcTOLA7HU+gqnhuuaK0wcRXOV8rYhnsk2is1xjrWvCkJWLbztCCCAQCTk8ORn5WE4xeqEOavbIxEXNPoMSomBPe9+iXzWEljC36qqGcea7lGjyFRQY1+QM7Ix/U6A/5x+DyxQ2pCfBhefmAo8naeEkR9tj16B/QmdpSNlOYovV6PzYfIIxpW7Sl0RKgIQi89NgRfXiCLubgIf7z0UTG1ff6l+wcTX+ctZy8j3pR6JQBM8bw2OwmLCI7N63KSEaXyYb4nTw1xmtNqsfPhhx9i3759SE1NbY/j4XDajK7kucHCHcHGCuevy0nGit2Frf5c2is1xjrWoVGBxEUbAA6dvY5n7hqA/afKm4mIEAzvFwwAyB2nJqZk9CYzMWrgqng51E9BjOzkDO6NZbsKia+Ji/AXCB3Ake5avecM1uUktVrM6sxWagTqSEkVlk5OoJotTtlMTv2xWvILSqpQb7CgF6FUUywWMeuVRCJyZ9jSyfFM8XyJ0mm3ZMcpvHT/4Fvq9OL0PFotdoKCghAcHNwex8LhtCldyXODxq0INlo4v6rBhE+LKoivudU5Tu5CO9YLlQ3U18xJi8aq3YUEEVGJjQeKqSmZvz00FH5KOZ79vx8EUYP1OcnMguhTVzTEyMfK3YWYkxZNFAssEXGkpBI6sxVLdp5ulZilmfg5qTNYqPVKNFrSkk9CKhZhdmo0AOG4j9mp0ahpEP4dHj5XiZk17GGXtPRXQUkVahpMXOxwWkWrxc7q1auxfPlyvPXWW028djg9D0+vhelKnhs0blWwkcL5LAEB0D+X9u50IR2rv5JeD8ISEayUjBgiZCaHE6MGK/cUYm1OMpY2G8OQHhuCfEZB9JGSSsxK7Ufc5kpE1OrMrS9e9pIyZ3H5KaXUeiVaistVVItW56XykWP9/qIm6T3nuI93vr6EuAj3OndZhdbu+BZxejYtEjtDhgxpUptTUlKCXr16oV+/fpDJmv6BfP/99217hByPpCvUwnQ1zw0S7TEQ0d3Ppb07XUjHyhJYLFxFU2jC5LPi61iQaXEUGjeLitjtrT0jBwGufH3kEmq6jSZmlVIxts4cgc2HzgkiV1tnjoCEohNY0+lZLfmNdV7bTjSpn3L+zednJWHh+yebHMvo2BCsmJaIqZS02YnLtW7bCtAGwHI4NFp0xWRnZ7fzYXC6El2lFqYrem4051YE29VaPX6u1sFHIYHBbINMYkVRXR2igrzc/lzaq9OFJZ43Tk/BQoLAui2ILqpdRVNY269U66nFvbRUFQCE+ysFtSupahWCveXURT1VrYKvQkqsA3r5gSFoMJLFrMlqxyuHyHOzxBDhuXsGUs9PZ7LgqQw1np84sLEzrN5ogZTRkr8uJxnr9hVhUFQgZqU2bbtfvus0XvrNIKKDss5ooXaFbS0oxf756Vi+67Tg/dbkJGPV7tPE16WpVQjy8fy/XY5n0SKxs2LFivY+Dk4XoqvUwnQHzw3W3Kiz5VrmQMR6kxWbmw2STFOrsCorCetykrFkxynq58JKUbaHg7Ir8Uxrv6aJNlcpGVdeNCSOnKvEk2NiiGInPTYEErEIk5MjmqRyrmkNaDCZsTgzDjZ7kUAILZ0UjzX7zlB9fdZlJxO/C6vNTp2NdaSkEgsyyWLHWy6Bn0KGFz8uFBzLvHGxiA/3I37WlQ1G5Ay7jThK4pG0/qjRmZC3S+jdsy4nGXfFh+ETQo3Y8L5BCPKWUcdMrJiaSHRsXpeT7LJex52WdU73hscCOa2mK9XCdHXPDdbcqHU5ydTz0OrNWLGbPIJhxa7T2JCTTCxgFcF1irIjHZRZbeIAqGKWlZJJjw1BhdZAfL90NTt9IiF0HqWqVcjNUMNfIRVYcYhEItwW6A2L1YYpzYRQhdYAqUREnDcFOASPwWJF7nZh8fL88bHUYwSAOr2FmBrrG+yNlXsKmeKqb4iP4LO+qjVQR0lMTo7A1oILxPEUS3acwvp7k2G02ATf0abpKWgwWanXWpTKh+jY7Eq0XKpqEBhDtrRlndN9casbi+StIxKJoFQqoVarMWvWLMyePbtNDpDjeXS1Wpiu7LnBmhu1bOdpasqw3kRvTS4oqcKlGnKqZv29ydh/slzga3P4XCVW7DqNFVMTsXiHcFxCezkoA+zBnJGBXtTIACuqZ7HakKZWCaIG+dlsV+YGk5VYiDv7zW+wd14axsWFNTmWpN4BUErF+OO7pxEX4Y+wmxbqXzQGhNcYmIXGZbUGomOzj4sp5MG+ckFqLE2twpSUCDz/3knia46WVKHBRC78tdmBE5dqiQIqPMCLOYHdYLZRI3Ouxkz08hc6NrO4pjUQHbB5yzqn1WJn+fLlWLt2LTIzM3H77bcDAL7++mscOHAAc+fORWlpKZ588klYLBY8+uijbX7AnM6nO9TCdBXcTRm6iq7VUtp6w/wURAM/ABgY4Y/FH5xs9YBJV/i4MKXzkpPnSm2cngI7hMZ0N4svVlSPNudpeN8gakTo+2YC4mZqdCaBf9Ho2BDkZyXhy/NVxHb/N2eNwMsPDCGmh15+YAjC/IWiJVWtwpTkCKq3TbpahRMXa4gL/i8u2r1p9TU6o4V6nGkxIcx91hnMxMjcrY6ZIFHTYGKKfN6y3nNptdgpKCjAmjVr8MQTTzT5+WuvvYaPP/4Y77//PlJSUvDyyy9zsdNN6Q61MF0Fd1OGrqJvtJoVVuEuq8PpVmq1ZBK2KZ1ULCY+/X/+03VqFOpm8UU7JlrUYE12EpbsENaKrMlOwm//dZzaOUUbw5C36zRyx8VgYC9/wbgIkQh4o4CcHhLBYYpI2may2KjeNnlTE5H9ylHiObuCdt0E+8jxp09/Ih7LU2PVzH3SIr3tkQ531ZLOW9Z7Lq0WOx999BE2btwo+Pn48ePxxz/+EQAwadIkLFq0yOW+Dh8+jBdeeAHfffcdysvLsWPHjsbOL7PZjGXLlmH//v24cOECAgICMGHCBGzYsAGRkZGN++jXrx8uXrzYZL/r169v0ftz3Ker18J4Im3ZJh7kIxekaZyw6lJYhbuuOpwajPR0E0D3ZapqMDFN6SrrjcT3C/NT4LtLNa1u22ah0ZmQv+cMBkcFYXazVNWmA8V47XfDsHZ/kbDde9YIHC8lRxSKyrXIz0rEsmbpyDS1CvlZSThxqZb4uoKSKsxJ60/cdvBsBU7/oiGm1Mpq9NQIzYnLtUhXq4hpp1S1ii6CrTZqxOSYi8n0tEhve6TDXbWk85b1nkurv/ng4GDs2bMHzzzzTJOf79mzp9FZuaGhAX5+fi731dDQgEGDBmHOnDm49957m2zT6XT4/vvvkZeXh0GDBqGmpgZ/+MMfMG3aNHz77bdNfjc/P79JFKkl7825dbpyLcyt0tZFurSi4PX3JruVMuzlr2zsuGoeoVibk4RNB4qJIqG6weRWh5O3XAI/Lxl13pYYwKGfriPMTwGjxYYanRlfl1Zj7IBQ+Cql+P3WrzEnLVqwcM/ffgL/e/wO4ntabHZmCojWts2ios6IT4sr8GmxMOWUO06N9fuLiNENsUiEYX2DiPvcOD1FIHQAZ7E43XkZAAxmsmj5x+EL2DMvDat2Fwq8baakRFDPb2tBKXY+lYpVe4XdWLNTo28M9RQW8WoZwzy3FpRi59xUrNoj3OfcDHrUpz3S4SyR39KWdU83S+W4R6vFTl5eHp588kkcOnSosWbnm2++wf79+/Hqq68CAD755BOMGTPG5b4yMzORmZlJ3BYQEIBPPvmkyc+2bNmC22+/HZcuXUJUVFTjz/38/BAeHt7iczAajTAaf31a1Gq1LX4th9PWhoqs1uuVuwuxOjsJSylpFVabeJTKB5vuGwSN3twYfQvwkiEy0AsLJsYRO7z+37DbkKoOIaYo+wR5IV0dQqzpyZucgOU7T1PreRZPisfek2WCxTA6xAd9g70xLCqI3NKtDkFlHTmyExGgxMYDZJdkAFiXQ27bZi1ctFomwIVR4blKzLqzH3FbmL+CMceKbnAIAEoZ3XDQYiMX/lbUGahpwWFRQfiwsJwYEZq//QT25KYRj4MVZdGZrCir0RP3OefNb7AnN43sAt0O6XCWyG9Jy3pXMEvluEerxc6jjz6KhIQEbNmyBR988AEAYODAgfjiiy9w5513AkBjOqut0Wg0EIlECAwMbPLzDRs2YPXq1YiKisKDDz6IZ555BlIp/dTWr1+PVatWtcsxcjyPtnxSaw9DRVYR8idFFXg4LZqaVlmUGU/tjhKBXLy7/t5kaofX0hsdXqRF9Hq9EU9lxMAGu0C0pNwWgMU7yAM7D5+rxOOEwlHnv9dmJ2Fuhpq437nj1LimNRAX/Jv30ZyjJVUwmK3IbeXMKR+5hPhzwJHGY3VO0WgwsOdY0UiPDUGEP3nw6Oszh0MiEhGjq6y04HP3DMTmg+eIxdLOaArp78VLJmamRb9lFG6zam/aIx3ubst6S/62AfCoTxfFrQRmampqh089NxgMWLhwIR544AH4+/86a2X+/PkYOnQogoOD8eWXX2Lx4sUoLy/Hn/70J+q+Fi9ejGeffbbx31qtFn369GnX4+d0Dm39pNYehoquCjU1zYoqndYPseF+zO6o7CGRmHVnPyy4ySn3mtaAKzU6t3xtyjUGPPzWt8R006UaHfscKBETR7uzFXPe+oa439xt3+PdJ0bh/769LIhC5QzuDW+5hFqfcqVG3+qFy1cupUZFgrxlzLRZn2ChM/Xo2BAEeLNrT3oTHK1Hx4ZgdXYSVlM8ccQiEdZmJxH3x5pV9c/D57FyaiJMrfS9yc9KwvKpicjfUyiImKzKok9SB1zX3ribDmc9xLS2ZR1w/bd9VWvAmn1FXSLqw1NxQlokdrRabaPAcJXyuVmItBVmsxn3338/7HY7/v73vzfZdrNoSUlJgVwux+OPP47169dDoVAQ96dQKKjbON2H9ojCtEcHCatQ01suQbTKB/8+9rNggV0xJRGvfXGB+LrD5yqxmODOm6ZWYeW0JKZIoJ1D/Q3rf9IT/Oszh1PPAWAXPuuMVup+c8epsZwShVq5h13vQoO1cK3LScYfbhj2NY+K9PJXIm+X8FicnVMv3T+YGKUwWISePk7SboyTIHkF1RnM+JRiOHjkXCWMFht1UVuVlYRFhFlVG6enIIISTQGAP777I9HXZ+2+M8hMDsfs1GgszIxDvcEKX6UEFVojfrhcS23Xby8rivZIN7n623Ylnj1FTPBUHJkWiZ2goCCUl5cjLCwMgYGBRFNBu90OkUgEq9W9kC0Np9C5ePEiDh486FJMjRw5EhaLBT///DMGDqTPh+F0f9ojCuPrwszNldkbCVahZt6UBOTvJT/dr97LXuzLavVEkbBqN/t1tOnW3owUj6PLh1zP42qoo58X/TNzVSfDGt/Aek/awrVkxylsuDcZ88bFYtGNRd05O8rC6EgqKKlCvcGCXoSIWABArSNZn5MMi81OXJyWT02kilJvuQRikYhaEO4qPUSKply4Xo8Zt0cRI1ezU6MRFeyFNXuLiBGhO/qrmLU3np5KBlx3h9HwpBE5XWVuYWfQojvzwYMHGzutDh061K4HdDNOoXPu3DkcOnQIKpXK5Wt++OEHiMVihIWFdcARcjyZ9ojCyCVipieMnDZumgGrUHNoVCDR6RgAjpRUYdaN2ozWcKSkEk+OJYuExunWhEV0TXYS9dyLy7T44z0DiXU3z93tqBMhka4OgUQkcmuyOQAoZGJiCig/KwmTXj7S6v0dPleJOqMFrxwsEUz3djWigdV6T6sjUUqFn7XzOFYxIldz0qKxcje9INyZpgMAOwA0ez4lHafFZqeOhACAlVMTmQKK5mTdFVLJAPuhw5V49pQROV1lbmFn0CKxc3NnVUu6rFpKfX09Skp+/UMuLS3FDz/8gODgYEREROC+++7D999/j71798JqteLq1asAHO3vcrkcx44dw/Hjx5GRkQE/Pz8cO3YMzzzzDH77298iKIjcBsrpObSHj0etnu0JQ2vddQXtSfxCZUOr9wW4vjnLpWSRsC4nGev2FxFTGZsOFGPppHis31/UZJFNV6uwaFIcZvzjK8y4PUpQJ/LwW9/g7UfugNFsFbxudlo/SMT0adusyeaA4zumpWRYTsguoz4Eo8Inx8ZQX+Mtl8DfS47cbScEIsm5qJPqSFguwkfOVeKpG+/ZvCA6LSaEafBIS9M1uk6/d1JwnIsnxTMLvq02O7W+hmWdsOiDUx6fSgbYDx2uxLOnjMjpSnMLOxq3CpSPHDmC1157DRcuXMC7776L3r174z//+Q+io6ORlkZuXSTx7bffIiMjo/HfzvqbmTNnYuXKldi9ezcAYPDgwU1ed+jQIYwdOxYKhQLvvPMOVq5cCaPRiOjoaDzzzDNN6ng4PZf28PHwVcjwwD+PUz1haK27LYG0kPgrTczX3BbkRZ7xlMWe8eSrlBJFQnWDCTlD6dOtfeQSzB2nxoLGug0pGoxmWO12VNab6KMUGowYFBWEWc0+s+3HL+HF3wxCgLeceDy1erPLSBptAd44PQWf3+Tto5RJcE1rwJ39VchkLFwikYjY/aU3W6m1N3mT45G38zRRJC18/yS2UAqiWYuTt1yCUD8lccbV2AGh1NcBQBXhCd+V6/Q8F+7C9Ubydlbq5GKVDt9dpBs/VjU4ru/WpLjaczYf7aEDoItnTxqR09XmFnYkrRY777//Pn73u9/hoYcewvfff9/oV6PRaLBu3Trs37+/xfsaO3Ys7HY7dTtrGwAMHToUX331VYvfj9OzaA8fjxBfOYb3JXvCtMdNL8RXznSnLSzTENvSr9cZMTQqkG6udkMgNP8MrtUZqamMrEGR8JKJsflQiSCqtWxyAibEhSIuMkCwqJ0p0yAy0As/Nlu009UqrL1pcjvpeH6uanA7kma22rD/ZFmzaFIIUmNUSI1R4RNC+7XT8ZfU7p2Z1AuzUqNhJxxL0m0BWLzjNPE4jjAiLUsnxxNfAzhSVasok+ufGst2s6bVQbFmnyll7BQsrWarst5EFTT1JvpMrS0POgQgq+6IRHvP5qOJ564wIofPLaTTarGzZs0avPrqq/j973+Pd955p/HnqampWLNmTZseHIdzq7S1j0dnzAWbm6GGzU7woMlQ43hpFVF4vfP1Jbz3xJ1YtrN15mo2m52ayoiL8Kea+L382U9YNiURS3cKjQrzs5JQWadHZnJEk8jONa0BV7UGBHjJqJ+bu5G0a1oDlu44JahpOVJSiaU7TmP9vckwEtqvV2cnCZyAned4VWPE/O0niMdyxcWAzV9q9cTU4MkrGuridGd/FTVS9iVrRIM6BDaKFmKN/LADzCial4wsduqNZqqgyR4ciRW7Ke3zACYlR7Q6xRXgLWfOL2sv4dEVRuTwuYV0Wi12zp49i9GjRwt+HhAQgNra2rY4Jg6nTWnrsRYdedOrrDdhzptkD5o5b36DLQ8Opb6u3mBqtbmazsROZdCEUEyYH/J2niKPRNhdiKkpESjTGBrfWyQSoUxjwKdF17BiSiK1YyfEV47UmKaNCc5u0NQYFfVJtUZnIs5/AhyCR2e2YMP0FGj0Zmj1ZgR4yeDvJUOD2YyDlHbvAC+Z2633YX5KRAY0/dwjA5To5ad0eNjsOi1YnFjt+qyxD09lqEGrk2ft89DZCswbR267z82IhdVuJ35HQV5ybProLFHQ1BusDPdoeoE9q5hWozMhfy95ftnqvWca06LtQVcYkdMVRFln0GqxEx4ejpKSEvTr16/JzwsKCtC/P3loHYfT3eiom55Gb6IusAAglQhtIJx4yWWtNlcL8KKfk85It5Vw1SK+YOJA7PqxjNjSrLdYmB07y6ckYBFhtMW6m1JgzanT00Wbo21bjOff+1HgQbQmOxkhvnJU1gtrpeRSeifeNa2BmW6UiIB9p8oJIkINq91G7GSqZ9TQ6ExWlGsMROPAh9/6BtseJc8Tq6gzUgeB/ni5FtmDemNKckSTfVZoDQjxk0MuFmP/6auC+Waj+gdTh5mWa9kRL1akiVZMW1lvwpfnqxAX8asNiVMAHz1f1aM7jpx0BVHW0bg1LuIPf/gDtm7d6ng6KyvDsWPH8NxzzyEvL689jpHD6bF4y9l/oqE+cmKtxNlyrVv5eVbOXymXUMclWGzs+roKrZHa0rzmRmSDlOZZses0xsX3IkaMlt0YbWGw2ARCwVtB9wRi1cIs23kKL9yXgu8u1QrOsaLOSK0fui3QG/lZicjbeZqYWqGlxgBgdVYSnnv3R6HB4b3JTDNCi81GFZgms1UgzFLVKsSE+uC5iXGwNUtHpqpV+MP4Adh0oAjRYX4Iu0kg/6IxQHG5FpGB3tT5Zo+P6Y8/fyK0F3BlxcCKNPkopMRIks5kxpYHh2BrQamgBmzLgy0bAMsdhnseLRY7paWliI6OxqJFi2Cz2TB+/HjodDqMHj0aCoUCzz33HObNm9eex8rhdHlae5MVi0XUaMK4uFAo5RJitw4r6sGClfPv5SvH1lkjsPngOWFtxqBIpiszjaMlVTBabUwzOynBxBQAvr1Yg2qdSTBRPE2twuosuifQKEYtTEFJFZZNTsCJS8JjyRoUiQf+SW6vL7qqxb+PlRJTK+W1empq7GhJFfQmK7Fu5WqtHnlTErF6r3BEw/KpibhWayDuEwDqTVZi1GfWG99g68zhxG16kwX7Tl8DcE2wv925qcyhqwsnxhHFzonLtcyIV4WWPOQ1XR1C9CBy+j1tOnBWIAId0SoR1uSQx2g46UoOw1yUtR0tFjsxMTHo27cvMjIykJGRgaKiItTV1aG+vh4JCQnw9fVtz+PkcDqctr7RuHOTlYpF1GjC0xMGUEcpOKMeAL2tl3Z+rPbbVw6WEBe8VXvPIG9KAtEA0ZWvDexgmtk9dzfZCX1OWjT1/L/9uQbzx8VCDAi8faRieuoPAMo0TSd4OyM7Gw8U4493DySe4755aVj/YTFxvENCBNv1vY7S0u2jlGHjgSKigNrwYRGenjCA+Lr02BB8zxjMKZOKcfJyrWCURIaLdnaWBw9Fj+JsuRbrb7hHC4rBs5JwVasnRqCeylCj3mQhikCHDxK9HstopqfGupLDcFcSZV2BFoudgwcP4vPPP8fnn3+O7du3w2QyoX///hg3bhzGjRuHsWPHolevXu15rBwPozs/dbT1jcbdmyxrqGOD0UK96X97sQY1OjPydpGnfosALGCcHynnf76intq2fORcJRZNjCMuXCumJmDalqPUz6Z5K/fNHC2pwqJM8krKqhPa+FEx3n9ilKADrEJrcDmYM4zgbXPzyASSGSMrosVK1QCAjFF3dbD4OjUq9Pw9cYL6m/QWfN6BXuRZXA0U0QWw67UAwGC2kYeZZiXBDiAzOQIzb6QoFVIxKuqM0JktmP0mebDsw299g/97/A5iilbjwhhPa6A7WXcVh+GuJMq6Ci0WO2PHjsXYsWMBOCaQf/nll43i56233oLZbEZcXBwKCwvb61g5HkR3eeog3RQBtPmNxt2bLGuoI8tYbk5aNPJ2CluvnecwdVAktUaG1s3iyp31Uo2OKMr2nSrHsKggolBKVauYiyzALjamMeP2KCzbKRynAADr702mFummqVWoN5pbPTLhSq2eWs906hcNs01cZ7QQF3VXn0uD0UJs59eZrMgYGIroUF9iLZdSJibXCOUk4674MKL/kBejXmtrQSkCvehO1qRxGADwv8fuoBbfe8slUMqkRNF5TyL7odpLLqF693QVh+GuIsq6Em45KCuVSowbNw5paWnIyMjAhx9+iNdeew3FxcVtfXwcD+RWnzo8JSJEE2xLJ8e3+Y3mVm6ytLRSuZZes8GKenx7sQbLpiRg1w+/CAo8Z6dFo6qBfH6u3FnlEjF14dqVm4pvS6sR5q/81c1Yo4c6zBcBXuz9qghF06NjQ9CbMUqCdf6r956htm3PSo2GwUROgxwtqYLtJqPTm2dO+cgk2DpzBDYfEtYz/WF8LMbHhwF2uyAK83B6NHoHeePE4QuC101JiaCeH+AwTVxCMDIcFxeKFVMTsYTQwbb2xjgQ2hBUmv9QqK+cen5bZ45o9EoiRgMpf0vuFJEfLanCqSt08ZiqVkEEUO9NeVMSqO8JeI7DcFcRZV2JVokdk8mEr776CocOHcLnn3+O48ePo0+fPhg9ejS2bNnSpnOzOJ7LrTx1eEpEiCXYZrowiHPnRuNqWrqvktx5wnIXvlZnpBbhspiTFo3VewqpBZ7Lp5IXhFsZlCgVi7D/VLlgsV+Tk4xALxnT9TXcX0kUezU6k1vnrzNZUVrVQIxCzd9+Ai/+ZhDxdd5yCaRictHsuuwkvHLoHMU8T4RH06OJ4zJ+vFKL149cIL7uxKVaagQqXR2Co+fJ550QGYDVewqJdUer955B8m0BxKjP1oJSGMzkNniD2co8vzU5ScTrl7VoKyRipKtDiBE/lqHimn1F2DMvDct3nRaI1XnjYvFJkbDAGnD8bcslwplwTjzJYZiPfWh7Wix2xo0bh+PHjyM6OhpjxozB448/jm3btiEigv30wel+uPvU4Ul5aJZgc4U7NxrWtPRxcaGQURZRlgiUiUVYTunW6c0QjkxPnJJKWCljWlidWutykrFqDzmFnTclAcsJKaUjJVVYuuMUXrhvUIsccUmjJB5OiyYWIUcEsIWzrFlLtOimCltajQ1r0rjGQK+fOlJSiQWZA6lmhKQuJoAdgVo2JR4P/es4Mf01sl8wBvcJJHa3PZwWjT7B3li5u1Cw7eUHhkBrMGHl7sImAiQ9NgSLMuOY51dvtDjShq0Yh1GrM2N2Wj8AwoiXmFFErjNZodWbMCUlsqkfUJ0RYX4KvPbFBeprNXpTl3AY5mMf2p4Wi50jR44gIiKisRh5zJgxUKlUrl/I6Xa4+9ThSXlolmBjtcu6e6Op0dE9Wp6eMADLGPU1NBHorZBi0+7TmJ0ajYU3BnP6KaW4pjXgTBk91O+KOoMF17QGwdN9L38lIgO9iE/+vfyVWJWVBKNFuIgMiQokdjABjs4pjcGMFz86y3TEBYRdZX5KGTQ6S5PBpH5KKeqNZvjI6eIyXa1CZIASVwmOxm/OGo6L1eTIHivaoHUxRLPBRYEvCZ3JirJaPTECpdE5Fm2SoJmSHIHV+84wRzTQapKWTooXRFqOnKtEboaL8zNYMOvOflgwcWCT67CwTIsJ8WGIi/AXiDIfpQRPbfseG6enNBksW6E1wGhhf17+SjkmJYU3ifYN7xuEqgYTs1jcRyFz6TDsCWl2Pvah7Wmx2KmtrcWRI0fw+eefY+PGjXjggQcwYMAAjBkzplH8hIayWxc5noc7f9juPnV4Uh6aJdi2FpRi//x0ooW/80bD+txI27zkUsz/19fEzpPKevpoA5YIbDBa8JvhfbC1oLRJRMRRC9Ifa7KSkEc4B1atCwD4KaX4rOgaet2or6k3WnDiYg3S1CGQSsRY8N5JwZP/xhsRKNIicvZaHfP96vQWfFpUgU8JhbEAqEM019+bDJlUhM0HhYNJ54+LxcKJcQJvmFS1Cs9PjINWbyI6Gs/LiMXI6GBiV5lMIqYW6foq2bfSAC8ZcZ/h/kpm4W9EoBInjwrbxGeM6IO/vn+SKFqsjPlmrBENR0uqYLRY8frM4QjzVzQRLc0jYc3x95bhrwebprnS1CqsmJaIoVGByNt5WlAfdt/Q2/DKg0PwMuH7Wz0tiWmo6KuUUl2CW3Jvor3WU9LsAB/70Na0WOz4+Phg4sSJmDhxIgCgrq4OBQUFOHToEDZt2oSHHnoIsbGxOH2aPPmX43m4+4ft7lOHJ+WhQ3zluCs+DAMJT5xny7UI8pZRIxi0z23j9BTYIezkGh0bglVZibijf7Bbc5VoItBqs+ONZkIH+LX2Zm1OEp7KUOP5m562640W+MokzKiH3WYnCoHoEB9crtYRn/wXvn8SW25EoJp//66+V1ahKgCHrwoh9anVm7HlENn3BwAWZ8ZjTmo0FjWLGkhEIvyV4hcEOByNSdEUH4WEOvAya3Akc3FWSsWY3GwMwzWtAd5yCV6fORxbDpUI9vn6zOEI8pYTF7zKehNV0FQ1CEdd3AxtRIO3XAI/pRx/+vgnQVppdXYS0xzwxMUaot/RiYs1+LjwapN6Jeff2Zp9Z3BPgtAd+2hJFSrqDNQJ87NSo9FgtFAfODZNT8HP1Tr4KCRNrvvoYG+XjROekmZ3wsc+tB1udWMBDvETHByM4OBgBAUFQSqVoqioqC2PjdOO3OoftjtPHZ2Vh6bdFPOmJGBps46V9Bvuww0mK1G0rMtJxso9hcTP7fOfrmP/yXKBGDh8rhLLdxXi+XsGwmixCW7eYf4K5vE70jXCc7A16+65mSMllWgwWbG52dO2cyzA2qwkLNtFGG1wo/aG5ZRLfL9zlbimNZLb571kTCHgTZmm7QozI4JxtKQKIgBbm5kVpqpVWJIZz3ydntIOnTUokmp++OePz2JNdjJxyvzanGT8Uq1D/1Af+CikjcLLRyEBYMffKILNWfjrrCG6uftLazBTI0KUkqtGWDVJqwg1SUdKqrBmbxHWZCdhWbNJ8ulqFfKzEjH55QLiPtVhvgj27Ut1xw6meB5V68x47t0fqdPu//Pw7Vixu1Dw97lpegpMVpvgune6irNozzS7J6TGejotFjs2mw3ffvstPv/8cxw6dAhHjx5FQ0MDevfujYyMDLzyyivIyMhoz2PltCFt8Yfd2qeO9sxD024mtCjM+nuTsXQH+cZecL6KKloW7ziFQX0CiSmXMD8F03TvqbExxIhBcXkdUwQqZeTiZeeEahpltXrKIvoTpg2KdGu0AQuNwUys9YkM9MLanGQsJRQgr73RjUWLshWXa6ldXq5GU9QbLeRJ3C78a3Qmsu+NxWajfgb7T1/D03cNIE6ZV0rFuCaVYPNnzSMmN2wO3Cj8XTYlnhplmhDfCxPiwvBpsfAaTVerUN1gIp5fWgy9JunT4go8nzkQSyYnwA6gTu/4OwOAqnoj9bvwkknxxtGfqOJ52WRy559CKmYOwDWabcQHjp+rdQKhAziiTEt2nMJL9w+mDsa91TQ77R5UXqvH5z9dFwxQHTsgFBFdyJOsq9NisRMYGIiGhgaEh4cjIyMDf/7znzF27FjExMS05/Fx2onOqp9pjzw0TdCwojDX601UYeJKtMy6sx9xm8lKt6kHAKVM3KRLSiQSoXegF0arQ5CqDqF2OTV/gnWeAy3K4gpn3cbz7wuLhl2NNmAJDH9GrY+XRIylhIVSKRE3RtkWE3xh1mQn475XvyS+n4+L9JdUIiIu6qxJ8d5yCVS+CvzQzMwuXa1C9uDezPlfWr0FA3r5CxZTjc4kEDqAQ8yUadg2Bw0GC9H8USYWU6NMIhRjxdRE6C1WQVTrkfT+6Kvywc5mHktpLTi/Or0Ff/70J0HEZBnDu8aVOzYtCuVqptbXP1cTX+ejkFDfr6CkCjUNJqrYuZU0O1XQxIbgYo2eOkDVWy7hEZ4OosVi54UXXkBGRgYGDCDPY+F0LTqzfqYt89CsdBwrCmNgLNq0mgZX20N96ekob7kEgd5yQcRodGwIxgwIpYrAqgYTvjxfRVy05TJ2xxHL94YmzFyNNvCj+AVNiAttYrjnxA7gmlYPH6Uca/edERRS546LBcQiLN1xivg0nrfrNJZkxuHZd08K9i0Viag+LemxIZBLhENSU9Uq3JMQTu0Q6hvsjXX7zjSJejm3bfiwCHPSoqnRBpqXUkWdEd9dqiV+h64MFf29ZejdrGusd4ASRis9ylRQUoWqehMxinhNa8C/CL4+BSVVWLWnkHl+JovwPQtc+AG1xAWaxNlyLRZnxsFmLxKIhLkZahwvJZ97vYEd7WN1zbmbZtfoTLhYrSMKmlH9g4mRJue/12W7N7CX03paLHYef/zx9jwOTgfTXXwcWOk4VhSGVRTrasGnLVDecnrh77LJ8USvmeY1UiQvmVceHIryZhGAyAAlJCIgN0MNQFjEuSorCVM2k+soALowYz5Rq0OI55h64+n+GsXR2Ucpw5bPfiIKiH8evoDnJw6kp3LOVWLp5Hi889jIxnoXZ3u5wWyh+rSsmpaINZT26799fg55kxOwZKcwkjR9aG+E+CrwrwKho/Hs1Gj4yCVEMZCqVlGvG63BTE055QzuTb1mUm8UNu8lFIv3D/UlvpcTkQj48VJts+hUCCYmhmMBIaIHOD7rJ8bEEM8vXR2CLy+Qv6PVe89g77w0rNh1WvA9+Huxlxh/LynRHXvp5AQ89u9v8eL9gyCViBujgWarDXPf/h4rpyUS9+erZEf7/Bldc+6m2Wt1ZqqgKas1MCNbDSa2GOS0HW4XKHO6Nt3Fx8FVOo4VpaEtMte0BmYI3U8pJS749QYL1Usn+bYAorU/wK6RCvKSQ2eyErujBvcJhEwiInb5AMCQqEDqIuotJy8KxWUax5RxwmiD2Wn9cL3OSIwYWO122OwgHqc61BePjVVj04FiooBw5VFTZ7DgwvWGxvSY3mzFNY0eo2JUmP/Oj8ga0lvgTFyrM1Frj2LC/LBsJzmStHxXITKTwpk1JqTvfnZqNKp1JgQT6jaCvOX40yfkupXrdQbqNbPgnjis2UsWbE+NVTM/swAvGSYlh2NW6k3DN7UG6M3syIdMIiIOF82bmoDsV+jDRS02OyYJhq4aoZDSXZLT1SGQiUXErsfqej02PzgU+c0MFdPUKvxr5ghUaMjCusFoYRbDB/mw72vupNkbTMLaMCe1evb9yVXdGaft4GKnB9MdfBxcpeMCKVGYs1frGgt8BYtzmC/W5yRjyY5TxBqaDR+Sp5Bb7XbM336C2EFyxc0RFDbYqe3Va/cXYX1OMs5dq2+yTQSgVmeiLqKzU6NRb7QQF+0FmfF48J9fYcbtUQIBkbvtBN594o6m7+V0HraLqMc5OTkCHzYTQTcfl+t5RVKiiOoX4oOX7h+E+1/7SvCaoVGB1P0xHaQZ0UDH+9uxNjsJerMN2hvRBi+ZGPO3n8Cm+wYRC8mXTKZ3f/l7yzHnrS+J10xNgwlfXqgmpr+++bma3Qp+qQaLCeJ651Op1M8FcIgk0qR4u91OXZid40dI0bkJ8WF4buJA4AB5LphETB5KujY7CYspqc3VewuxJjuJeP2azHZqV9y6nGRqvU6Tz6CVafaGW5h47yqNyWk7uNjp4XR1HwdX6bi+Km9imPyO/ipc1eiJURG73XGTognBJZMTiFPIHxjRB8P7BhEX0m2PjGSeB61GSmeyUhfKoyVV0BosSOwd0CTUH+qngN5kpQqv+dtP4G8PDSUKtg9PlyM+3J94DuPjQqGQSonFu6RoiJNe/kpqmupoSRVzlEa6WkX0cLnZE4c4JJTR5eJuTVaIrxxeMimWNosKpalVePmBoSgq1xCLia/W0ge2iuGIwJE+7zdnjaCmvx5J648pKZFYsVs4Hyp/WiImU1KYYjE9opmqVsFqsxMjkOty6D47LGfpT4sq8EgaeS7Y9TojXj9SSux6vFyjJ0ZnAIfgaTBZidfvE29/h33z0ohdcS0ROu5Ae6ACXBdah/mxbSc4bQcXOxyPorV+FK7ScRGU8QYavQlPvu2wqr/ZLRYA5m77Hm8/MhLhN26ON/ubAGCOTKDNeOoT5OVWjRTrqREA9GYr/kLokFmdlYRhUWThlaZWQSkj157cFR+GNTlJxDbxlVMTsWpPIbH2Rn8LBd/VDfRRGnlTE6npE6fjL+m7UErF1Hb2IIq3ixPa0/jG6SkCoQM4Ft9lO09hXU4y/nv8kkCYTEuJpHY52e126nyvqGBv5FEmfwNAflYiccFnjUwo19DTZrNTo3GZEoFcs68I++anIW+n0JtJ7iJ6IZWIBQI5Ta3C1JQIPP+esPAccJ3+qW+W+nRGGFNjVAi+cc9oD3FDuj+F+Smogqa4XEuNEnelcoHuABc7HI/BXUdnlvig7XPFtERsui8FrxcITedeuG8Qdbr1hukpEAFYQGl133igmOhfs/FAMda5uOmRPGoCXLkPy4SttgUlVViz7wzysxOxbKfwyX9WajSCfYSTxkfHhiA/KwkRgV7EJ+M6gxkzRpIN4qYPobctuwrl+yhk+P3Wb4hRqNLKBurC7S2XQC6R4Ln/+5E4vmL5lAQsEZhGhuD/DbuNGUmqqDMS3y/cX8nsgGogROGO3ki75E12tNY3RyGTwEcuEaSOrmkNMDE6ro6WVMFothMFa2ZiOPE1gGP6/DxGxG/zA0OIr9OZrLhUpSNe22aXlgsS4uvKGBEvl+kfb5mg086ZqmovAcFyTt9IeeBafePvqauXC3QHuNjheAS34ujsjs/O16XV2NesVRT4tcZka8EFaudUZnIEs9WdFtJ/9u6B1Jve5aoGHCmpJHrUsNIOAIg1HVsLSmGw2IhP/vO3n8C2R0ZSBSIAKKViyCRiyCQiyKRiKKVi1NrsVG+X5bsLsWpqIi7W6ATHUl1voj75OjuOaKmctxnpv8dG9ydOIXeOr5iUFE70tlm19wxzbpbBRK5nctU5U0eJRhwpqcLSyQlYl5PU+P0qZRJc1eghE4vwz8MXEBcZ0PjZi0QilGkMULvphSWX0tOC17QGDKOkWtNjQ5h2BX7NOpmc0ZTjpdWYEBeGuEhhFO1MmeZGaFT4OlanFrsrUIUTF2spEbbTLRrr0NoIMuv+tPDG/YklaLp6uUB3gIsdTrvQ2kGZVQ3uOTq767MT5qeg1gSwakwOn6vETEoBK6u4FQC0ejP6h/oKzqNCa8CVWj2xCLd/qC9WTk3EymYjHJx1G1KJmOgl8/IDQ1CvN1OFl5dcQiwM3Tg9BQCIBmmD+5C7u5znvnRSPMy2pk/5vQOUGNI3ECOjg7F0J2HUQHYyTFZ6F5ufUkpd9MYOCMNfPj1HPR7ad/FpUQWeuSuWKASr6014atv3xMiHK+8pP0bths5kaTIuwk8phbdcDLPNhgfv6IvXC4TRsnsZ0TIA8CeM4UhTq+AlFTOL79dkk9OUq7OSkL+3kPhe4+JCEegtJ15rj6fHIGtwJJY1G/aZplZh5bQkGMwW4uuyB0dSRdmZMg1WZyUhj3jNJFHHU7TE/d2dCHJLHOdjwoR/2xzPgYsdTpvDupnQUkArpiWy3VspT7Hu+uyw6kjcLWB1tY22WDYYLcyBlqunJREHWp4p12BrgdAgrrGmY1oS8f1Gx4bg5BVyMe2xC1WIDPAiGqTFhrG9XTR6M3mAaKgvgrylxFRNhVaP3gFe2H78IlF8vFFwAflZicRaEVfpE9Z3oTPaqENZaWMKXA37pDkzh/jKEeQjF6QUHQ7RSdhaQI6WrdhViLzJ8cSuKudMMVJ66E+f/IQ/TBiAKc2K7yu0BvTyU+L5d3/A2ntTYLHaUac3w89LBqlEhOp6Ax4c2Zc4v+3pCQOwYhe5fmhycgT+eeQ81aiQ1sp/pVpPFbkPjuyLWp0Rc8epseCm677BaIZGR69JAtju7+5GkDvLcZ7TdnCxw2lTXN1MaCmglbvZ7q00oeCuzw6rJsBVvQBrO62VNE2tgi/F0ExnZndcGa3WRu+cRp8ZrQHjBobhhY9+or7OYrMRn/xXZSXiUpUOu5qNDEi/YarXfFF27m/uWLa4sBAGczr/vXRSPLHLJ1WtwpqsJCyfmigYypqmVmH9jVQkaVF3NS2d9T15yyVEPxnWUNa5//0e//z9cCylDPv82yFylGnLg0OJn2lBSRV+qTVQI4xHSiqxdEo88TtcPjURFXUG4t9L7jg1Nh0owr3D+jQW3zuvvb9++hNWTEvEt6XVCHOmTE2O62lUdDAe++/3mHF7lEB0VtabqNFOViSU9cDBsmqYv/0E3n18FF74+CeBEFo5lf1gxIrAuTsTsDMd5zltAxc7nDbF1c2ElQJ6kuLeyupWctdnp6LOSO2OYm0bHRtCLWBlGQ7OSo2GzmghpvB0RvpTqrdcAplEQoyYDO8bjBBfOWbcHkWs2aluMBNFgt5oxb+OXBAsskdKqlDOcHz98kIVczzDMYrDrkN4kYcgHS2pgs5sxd8OlWB2ajQWNj7FS1ChNaJGb8ZnxdfxGcEgkOU+nB4bQnV0Tr3RjTYrNboxauCnlOKa1oBzV+uoabMBvXwhFQFLJ8fDDlFjVEQEQGIHpqT0xqA+QYK6nEBvGfUz1bjoOqqsMxI/lwf/+RXemD2C+JrhUUEY3CeQOPF96aR4aPVmoitzdIgPlkyKx7P/96Ngn6/PHE49RqPFRp3AvrWglPrAceJyLYZRarXSY0Pw4xVhXc7Rkirk7yl0zFL7QFjw7cr93d0ITXdxnO/JcLHDaVNuxdFYIRMTO4RYLZru+uxkDAjF2AGhTWpTlDKJI2IyIBRjBoRS29md/99824ppifh/rx0jPhkvev8k/vf4KGKH19LJ8dTPZE5aNFZR2o9X7zuDrbNGYCPBmfjlB4ZA5SvDkD6BTVrrh/QJhFgioj6Js1p+txaUYsdTdyK/matvqlqFFVMTMW0LfTwFK+2gM1px6Ox1RIf6Ni6WerPEUaSqDqG+DiI7Vk1LxMrdhYLIR35WIirrjEThmZsRC5lEJBADaTfOY1jfYGL0ZvHEOFzRGLD54DnBjK/542MREaDEPw6fF0w2vz1aRY1EKGWuhpmK8VVpteBzGXRbACq0ZNEd4C3Dix+fJV4zMokYmxkp07VZSVifk9QY9VHKJLim0SM8gN7G7S2TUP2AXn5gCKQicnpva0Ep9s5Lw/JdwhQlyyvIWfBNinityU5i1s24G6HpLo7zPRkudjhtiqubCSu1EOglb3WLZkt8dmj7LKvVt3owp/NYaEM7my9ozs6T397RFyt3kWdjZZdp3DJsc84yIi1cSqkYy6cm4s2jpYJUzersZIT4ylFZbxLsk/X96ExWlNcaiLU1FpuNKWikYvqkcX8vKXWxnJISgRBfucAP6ZrWADGAtfuLiNGrtfuKsGJKArFupXegUlDwDTjSSvl7CjFtUCRxnya7HVuaCR3AsfhOSo7E/lPnyN1fjAGbdrudWQfUO0BJGATqhfuH3Ya/fEpOYcolYmokiTTM08mJS7WwAdh/qlxwzdwRo6JG9ZRyMTYfJAsoERxpNRLD+gbh25+riZ91Zb2ReT1p9OSoZf7eM3jpN4MAgNgg0ZIIDa25wtU9obUdXpyOhYsdTpvi6mZCSwE5bzTutGi6ugmR9tlYW0Rwb2UN5nRC27Z11ghsPnhOsGgvyYyndg4t21mIXXNTkb/nTFO/GHUIZC7qh2hpkPjIACzbcYqw+Domib9wXwpmv/mt4HWuHF9/qtDi7oReAsdmhYQxAyk2BKWVDcTjTFeHQC4RY1uzAmVnGsRLKsY7j40SOAU7u4d+uKJBXGRA48+d4vLL8w7fG5K79DWtgTo360hJFZ4YG4PBfYNgsdobR0JEqbxhMNuoEbEwfwWzbuWpsTEAIEjz+Coc6TQ7hEW6s26M9SCmnEJ98PzdA1FZbxKIfNb8K5ZJ5Zy0aMEwT+dnsmbvGarZZKC3nOk/tGQSeZ7YyqmJ+H//cERCnTi/PzFDHAOARCyiPgRc1Rrw/aVaQTfh2AGhiAj0Yj4c6UxWYgOFs1OL9nfvrkcYp+PgYofTpriKtDj/v61Dwa0VSe4WKrriFcoT7lVK/QjgiJiU1eoxK7UfFmQObNJx5W6xtKv5T0smxRP9ea5UN2BNVhK+PF8pSGWkxYTAChDHJazLTsbqbEfXlCCSlJUEi91GTI9EBnqhRmfC70f1w+tHLhANAFfsJrsWr91XhH/NHIEXPiKn8YwWC/RmG3wUUtjsgNVmh85kgVxC/0xDfOXoHehNPMc/jB9AfZ2rDj6SRUCaWoXswb0xdxs59ek0+aOmnLKTsGF6CjR6M7R6MwK8ZPD3ksHAEDu+Cvotn3XNfFZ8HY+m6ynmgK7nvpGigWW1emyYnkKM6N2TEM4U3QUEUf3r+1mI3YTRIT7wlkuoD0cABGlmwHWn1q14hHE6Di52OG7jbrjXXTdRd8PEJGfiBqMwhdPkvVwUjpJwdKzQb8KsIs7wAKWgFiZNrcKGnGRmxIRmAudq8W0wCr1P0m50RlXUGwmpjBDcEaPCasIU7oKSKhw9X4lvSqvw/MQ4LL4pkmK22vDq5yV4bEwM9p0qF9S6zE6Lho9Cij9/8hMxBaQ10CdKx0X646WPiqliYH1OMjbtEIqWNdlJ1DQeayTEwkz3O/hI6aOCkiqs2l2IGbdHUUUGjaMlVTCYbVhKaGdfl5OMCfFhRH8pnclMvZ5coaGMaGgyS4WAr1KKk5drBbPkpg/pjWWUdvY/f3oWa7KSsGzX6SYiIj02BPlZiVSfHYD8WTv/vS47ufHBqPm943xFvVsPQO314MRpWzpV7Bw+fBgvvPACvvvuO5SXl2PHjh3Izs5u3G6327FixQr885//RG1tLVJTU/H3v/8dsbGxjb9TXV2NefPmYc+ePRCLxZg+fTr++te/wteX7QnCuTVchW3dSQHdyvvRhNClqgbB9GTHgkevWwEcwqS1sIqzT5dpqCmurbNG4OQVDXExvFKrx+y0fgCEU6NZJnCu5j/5KKTE9/vmYg12//ALUXgs31WIQX0CiSmg2wK9MXRsMPL3FhLTTc2LUB37rIIdDlFSdLUOr88cLqjLqWu2wN4MKxJxtKQKl2v0VJfdLQ8OxYx/CKels0ZC2O30IZrXtAZmJILWqXakpBJPjiV3IbpyNP6llnx+S3eexpLMOOibWRqkqlUI8JZRI3C9g+jpFm+5BNEqH/z72M+C6/fuhF4Mt+MQeMskxAecijoj9bM+WHwdizKtmJQU0egFpZCKUVFnhNlqw5AossFlujoEXzK6Alku2O52anEPnq5Bp4qdhoYGDBo0CHPmzMG9994r2L5p0ya8/PLLeOuttxAdHY28vDzcc889OHPmDJRKR9HeQw89hPLycnzyyScwm82YPXs2HnvsMWzbtq2jT6fH0NFhW43OhOW7ThNN8NbtO4NFmfFYvOOUcFzEvckCoQP8Orhxy4NDMOMfxwXvl6pWQeKiZoAkrljF2XY78MrBc8QnTrFIhPRYFXGxr79pennzqdFltTqqCVzvQC9m4SstEhEd4uOWZ0qvQCU2fFhErLu5qjVQ91lQUoV6kxVvP3IHUSgtm5JAfB3gOnpFi84V3OjmIfnssBbDQ2crmM7E+dMSkUfoLHLVqSaXigVioSURDBpHzlWiMt3UxIjSeT2ZzHb86VBxk7Z7Zzt7cbmWKlrypiQIvh/n5/CXT39C/rREQZQmVa3C3Aw1pGIR8QHnAqWOy8mVGj1xntg7j46kmhEumxKPnL99Sd2ns+iZFO1lTS8HHI7Vrf27B7gHj6fQqWInMzMTmZmZxG12ux1/+ctfsGzZMmRlZQEA/v3vf6NXr17YuXMnZsyYgaKiIhw4cADffPMNhg93+EBs3rwZkyZNwosvvojIyMgOO5eeREeHbasaTJhxexQxt//8PXFYv/8MUQjV6c3swsnJ5MLJ2anRTLFDizKtvzeZWpx9e79gaoHydxdrkD8tsfHp32kcWKE1YFSMo22Z9OQ/NGoEFr1/0jGEsNmi1mCyMAtfjRZyXQfND8eJieJabLXb8SBlSGhaDKN9HEC90SKY3A44vqNTjIJpV4sTK7VUZzATHZ1ZC9NrX1xAVkokpiRHNuvwMqKXnxIbDpA7w67XGZidRX5KKeZmxGDBxIGN32G90QyZSIRhUUGtTmF6yyUIDxB2nKXeiLJ98VMlPjx9TfC6Z+6KxdyxMbDZ7YLXDe4TiNV7z1DnsNXoTMS6nDlvfYMPnrqTKBKaz9pqjp1yKRacr0Lp9XqimHPlrhzoLaNGe9fmJGNKcjj2nroqeN1d8WGQS8jDgVl/99yDx3Pw2Jqd0tJSXL16FRMmTGj8WUBAAEaOHIljx45hxowZOHbsGAIDAxuFDgBMmDABYrEYx48fR05ODnHfRqMRRuOvXUFarbb9TqQb0tFhWwtjAKUYZ/H8xIFEr5nswezZQlo9uXDyf19fwou/GUS8QQOgRrVW7i6kTjaXMxbex8f0xzWtgWgc2C/EB397aCjuf02YcqmqN2LLg0OxuVnEyNnpsuj9k9TC19d+N4x4LP4uFqAIf3KqQ2QH9Tt6aiy59diJLyGl5qRMa8C8cWrADkGnmspXzkwdsVJAvgoZ0dH54LNjqBGxoVGBAIDh0cJOLaPNckNACEWEVCxiHqdIJMIrB0sENVJ/mBCLtTc6oATzoRhRnzlp0cgntNY7R1DQ2uCTIgMw561viY7GV2r0TC+dygYTcZ/ecgmkYrJIWJ2VhHFxocS0aLpaRf3+3vn6Et574k5iEfnanGSMjwslmlCmqVXwkkmw4P2T5PTfjlPYMD0FWoNF8Pe7cloiFn1wqtV/99yDx3PwWLFz9apDXffq1avJz3v16tW47erVqwgLC2uyXSqVIjg4uPF3SKxfvx6rVq1q4yPuOXR02NZGGEPg5EhJJZ4wkL1mWP4mgGO0A6lwctP0FDSYrMToTX5WEn6ubCCmnBa+fxImq41Yn3BVa6AWKI8b2AsbDhTRZ2NlJRFTLsm9A7B2n7Bg+GhJFfL3nmEWvtJ8bxRSCXPKeoCXlNhV1TyCdDPHLlQxF3vKSCkAwNDbghDoLUdmcjhmpd5Uu6E1QCkRUyMRizPj8KePzxL3maZWwWa3E6MUF6t1yJuSiNV7hUaFK6clAiIRVu4izbhKxoAwX9ydFC7Yp0wiYh7npg+LiDVSEAH5WYlNir79vGSwWG0wWeh1K6NY3kwllZiV2o+4zWixUeeC7c5NFUyJB379zhdOjCPu02mKSfKXWrbrNBZOHIgJ8b0ErtMjooORteUocZ8zbo9CHqWIfNnO03j+noEwEFK7s1KjmQXvBSVVqDdYiH+/rGj2J0UVWDwpnnvweDgeK3bak8WLF+PZZ59t/LdWq0WfPn068Yh+pSv8Ufgqpcx6ENoMKHfRMeooAHpthtN0j3TzTlOrEOxDNjEE6C2oebtO499zbhfUJ6SpVXj7kTugM5Inm1c3mLB15ghsPiQsUM5MFA5KdHK0pAp6s5WYcrHY7Mz6Gtr4jfTYEGJNT5paBY3eRK2HmJ0ajRqdSdipFRuC2F5+xOMAHE65u3PTBF45qWoVnhqrZroI9wpUYhUhSgEAbz8yEo/+mxyJ+P3Wr/Hu46NgsAhraJZPTYRYBOIk7mkpkcjd9j1evH8QljTrKjOabVjxIdmMcNnOU/jH74fhy/NNt0UGKJFyWyDuf+0Y8TgrtEZ8eaGamh5qnjZs1IUiOx5J64/JyRECoSBzUW9Gg5UWZJkRHi2pgo1SPnVnDF14fXexBnKpBPsJXXqJvQNwZ38VPi0WdpQxxdy5SsxOjSZGbOdvP4E3KSM2nGgNFsRFtL6+SKsn/90D3IPHU/BYsRMeHg4AuHbtGiIiIhp/fu3aNQwePLjxdyoqmv4xWCwWVFdXN76ehEKhgEJBH/jXWXSVP4oGI7sepMHIFicsSIWDAV5ssedqqCdpUV+Xk4xe/o4i99a0oB45V4lrdQZiIe6mA0VYNTWRnP4SAa8cIhco17v4vHRGKzHlwppXBAAyqZgYEVoxNREnr9Q2mbnkjE55ySSYv/1r6nDGf/1+uDAKca4Sc8eqqU7HC98/ifJaPXEBevitb/D+k3dS26GtjKieRm+mRiIA4HxlA3Gu1KdFV3GspIr4XWw6UIS/zBiMdfuLBAvw0ikJzKhAg9FKTEXGhPpSj/O13w2jpoe2PDgEMrEYaw6cEXyHa7KT4ae04PUCoVBYMY09KLOXv5IYnesdqMS4uFAkRAYIhBerKw4AzDYb1uUkCYSXguFpNCctGqv3FJK79A4UY8XUROgtwo4yCWX8hBODmX5NuIo60+qI3I1mcw8ez8FjxU50dDTCw8Px2WefNYobrVaL48eP48knnwQAjBo1CrW1tfjuu+8wbJijBuHgwYOw2WwYOXJkZx26W3SlPwqN3sycVrztEfc+e1rh4PocegGgy9oMpRQv3T/4VwGllCLIR94odEjiqs5A9+Dxlkug8lHgh2ZRgXS1Cg+n94fFZkfuthOCERRLJsdTozCuOr9oN2DahPXGY5VJiBGhmgYDEiIDkEfonlkxJRGj+geTI0KMtt7vL1dTnY7feWwUPj5TTl2A6gxmzM2IgQ3CNE8dw+/IlbeNykeG6oZfXy+6ERdJjgjEpgPkUQtxkQFY30zoAI4F+JcatnneVa1wiKqrmqWIACU1PSQGMG1QJAZFBTV+h07xsXrfGYyPCyMe56o9Z6iDMtPVKkjFIqLn0fB+wVg8KR4rdxcK/JempEQI9nUz/l5SgduOCIDKR04VXizrAMdEeLJA1jFMEwF6hGp0bAiUhIcfJ46aHvI1FeIrx13xYRgY4S8QgmfLtdQiZO7B4zl0qtipr69HScmvF3tpaSl++OEHBAcHIyoqCk8//TTWrFmD2NjYxtbzyMjIRi+e+Ph4TJw4EY8++iheffVVmM1m5ObmYsaMGV2uE6sr/VH4K2XMJ2p3anauaQ3UNvGVewqxOptsVZ+flYi1e4uI+0xXh0AiEqGXv7JR3NwMTVytzkqi3qCdxZ+kRQYiESYlhRNHUMxkLJQFJZVMnxIviucPq77GOTWaFBFal5OElz8jOz2v2luI9TnJMFqEn/XKaYmYSmmhjgv3Fwgdx7lVYeXu05iTFk18HeC4Xqb//UuiePZmOP6yRluMiwtFkLcCL378k/D7zU5CnyAvZA3pLVi4RvQlCz0nLGNIiUhETEf9cLmGepw2O73W6UhJFRZkxmHnj2WCqM/s1GjqgM0j5yqxdFI8sdPwuXvisPmzn5p0jTmPc+OBYoyPCyN+hycu1QqihE4mxIXCZrOjefOUHUBFvRErpyZgwftC4eUKq81O/C7W5SRRP0/W8N+N01NgMFupNVnLpyZSO8ACvOUOAbnjlEAIrstJpt6buQeP55RmdKrY+fbbb5GRkdH4b2cdzcyZM/Hmm29iwYIFaGhowGOPPYba2lqkpaXhwIEDjR47APD2228jNzcX48ePbzQVfPnllzv8XG6VrvRH0ZJheq2lpsFEvel/Vnwdz91tIbb1Gi1WPJweTQx3P5WhhlwiIv6xGSw2qrhavrsQyybHE4WCq3oBmg8Ni38cvoCdc1Oxek+hIF0xO60ftWZJozfh4bRoiAFht860RPzmtWPExTcy0IsaZXIYr1mRn5UEo8XWWBSrkIphstKHfYb60c34CkqqsDiTPNk9Ta2CUiqmiue7E3phUlIvTB/WR5Ae+7iwHPlZDoM8krfNUsr3m7fzNLbOHoFVzSIY6WoVsgZFUoXu6TINte7qzVkjoJBLiHVACybGYUTfIGKBstFFlKJCKzTec/77ubsHUl+nNViIURGj2YKcYX2IaTOWgFq99wz2zUsj+ggtn5qIX2r1xBReboYaw/oFEYVXmD+7lCDcX0l8XUyoL1ZnJWL5rsJWD//V6ExYuuMUMb358qc/YW1OMvFYNDqTwK0a+LUomhZ57+kePJ5UmtGpYmfs2LGw06Q0HHbk+fn5yM/Pp/5OcHBwtzAQ7Ep/FK7mX7mj2rUuagJq9RbiYjh2QCgeprTLPnzD42NZs7RSemwIlk6Opz9Rn6vEwolxxBoEV8WfNKO7E5fpT8bDooLwadHVZkZvjtlYC947iS0PDiXuU+XjcKGd1CxVVaE1oFZnos4dGh0byjyHBqMFa/YJIyLrcpKpKQCdi7qjBpOFWD+UNzURJlp1KwC9wYwFE+OpbcYbPiR725TV6onHCTgWqOtaIzE6l7/3DB4b3Z/oiWS30+uupqREYl+zeUzObS8eOIth/YKI4oM1mJPF0ZIqLMoUUwubfRRk4TUlOQJ/PUju4APoAkpnsqKy3kgUCWarHVsOkSOFAJA/LYl47sXldcz0tM0OTG42tf6a1uFZZLezx87QXNoDvOVYMjmh1feulkTenb9380NVezwYdhU8rTTDY2t2ehpd7Y/C1fyr1uLK38VXSU7lXK83MlNqv9ToBWmlI+cqXdZf6IwWYpeIq+JPWh3J1oJS7HwqFav2Co3e8rMSUVrVgNcLSgXbNkxPQaC3jPiEq5RL8H/fXMK9zaIeAODnJcOLnwiN+o6WVGFJJnsUho9cQjRs+/MnZ7EuJxlHS4RDQv1d1A/5KWVEUabRmeDvLaNe+70CvaizqpbtOI1BUeS6j4QIf+bx1DI6+J67eyBR7IxgGEOG+Smo4srZ7v3wW98KtuWOU7vtFWQwWYmC5vWZw+ElkxAFRvPo0s04BBRdzHsrpPjLZ0JPpyWT6A8OR0uqYLCQj3N0bAjVn2bdDSHbL9S3SQq6TGPAoeIKvPibQW6NnQHcu3e5irzX6k1YuadQEMHYOD3llh4MPSUF5A6eVprBxY6H0B7RkvaGdbNp7R9pkI+cWThYoTUSXgXmBOtbwWKz04s/JycQbezTY0NQ3UAubh7WNwg/XK4hpxYsVoHQARwLhQjAhpxkLJscDztEja3QgB0Wuw3zJwykzqM6camWeCwmq41a6zMuLhRKuQRvHi0VRGGWT0uE1W4ntp6nqUOY359cIsZiQlowXR2CVVmJ1Gtfb6a3PLM8Y9ydFg842qxJUT1WIbmr0RU05+mtBaXYNTcV+XvPtHpchMVGHngpFomwNjuJKALHDGBH9er05AhdqloFpbSpCHZGH3VGdnRKZ7QSr/tZb3yDnXPvRGZyBGY2m38lFYuwdHICPv/pV3NAkUiE3oFe+O3tUQjwlhObC0i1eSRaK5RcRd6NZhsxgrHwRgTDnQdDT0oBuYOnlWZwseNBtHW0pLMor9Xj85+uI8xPAaPFhhqdGV+XVmPsgFBEUIZ29vJXNj7lNa8JWJuTjBcOFBPfq6LOSE0PsZ6MWcWtrK6jI+cqsWhiHDHS8tRYNcL9FcRtuRlq+HtJsYtQbJqZRPfZcc6OWrOvSCBo1uUkY8kuSt0RwylXozNTvXSemTAQy3cKTeCOlFTh29Ia7D9VRmw933CgGKuzkog1HSunJcFoIS+iR0oqYbLa4CWTIDNJuOhpXUyfZ6YNGd8vK2Lip5QSo3qLKHVHgEM8sYqXIwO8iCmn4nItviypxPKpCbBY7Y01UlKJCLA7RDIt6nP0PP0a1ZutRMHmasBtsI+ceP3OGxcLpUSEa1oDgF9HmlzTGpgDRAGHQzYt8nq5Wk/sGrsrPgwrpiZi/8lyQQp67IBQanPBupxkRKl8mMfjDixvMdb9whnBcEbm7YCrIfEAPC8F5A6eVprBxY6H4W5o1lPQ6Ey4WK3D3mb1C6lqFfqH+MBms2MRYWjnxukpjtkz42Kb1AQ0GK1QSMRYOjmBaON+Z38VhvUNEnRIpd0o0p28mfxkvLWgFHvnpQmmcbvqOgKASzU6qmfMa78bRkwBzd32Pf4z53ZiDUKDi3ol2nTrKzX0uhRW1OPbG4ssLc1BK14O81dQt+07dRUzhkcRazpm/OMYdTwFANQbLHjxQDHiIgMai1ZFIhF+qdVjcJ9A6usAepvxmTINVk5LxHJCe/1z9wzE5s/I6aj02BCcuFRDjOpNuqKhCiiHaeRwbD5UQkwrBXvLBKkcp/OyBMAiQk3SuuxkLJ+SgHxC4frKqexrVGuw4MPmETh1CEb1VzEdsuVSMaY0u0YrtAbIxIDpRlSvuQhMU4dQfZLS1SG4qmWnjEkMjgrE4g9OEoX15z9dx35CfVRBSRWW7DiFl+4f3OIIT0theYu5Gj6qNZjw9c/V1Ic/Ep6WAnIHTyvN4GKH06bU6syCWU3Ar74hk5IjiE8rjhtYOfGGOTo2hBoK/rmqAbnbTuCVh4YKHG8BUC31h0QFQgQQF2eTxcocJiiXiKlzgML9HY6/zReETfelQG+2Er1IArzde8Kh1Z244kyZBssmJyBv12nBwjyGUbzsKlVTZ7Lgqbe/J25jfZ6+SikevKMvthaUCrqj7h92G3MhDaVE0h4c2Rdl1XQjw+2P3kHs4FsxNQHTKGMKVu87g73z0rBid6Eg5XR7dDCWEbq/nGmlaSkRVOfl3HFq8sK98xSWToojTig3mC3Mz1QpExP9eaoajMjNUDce283nnpsRi19q9AjzVwpqwL67VIOvL1QTW9b/8ulPWJWViBW7TgtE2aqsRLxEGduRHhuC4qta4uiViAAFXviI7IUU5kcX3QUlVahpMEEpFbdprQvLW6y8lj7o1VsugZ9CRnz4iw7xgbdcQjwuT0sBuYOnlWZwscNpUxpM9NkzR0qqMCuV7LcS5qfAd5dqqN0llfUmxIQJ7dj9GkzIz0rC6V80jSH7BpMVVzV6pMWomDf25n2ATtM5GcN4LF0dgtNlGuJxyiQiugcPRFg6Ob7JQuIsupaKxXS/HEbKxVXqpHegF1UIXNfqsXBiHKTNBKJELKLu05sx1sF5PDRCfeXERe297y5DIRHjjYJSanfU4klxsO0vEpzH3HFqnC3XEiNpH3x3GWkDwqjjMvafLicKofPXG6gLl85kRXWDCZlJ4Zh1U7rN2SHEGt1BsyQoKKnCwkzyXKmCkipAJCYWNu+fn8aM0HjJyN1YWYMiUVarJ0YYzRYbwgnjOVLVKuRPS0JsmD/+VXCB2LJeXqsnzvB64j/f4e+/HYY6QlQ2PysJZpsN+buFkasV0+heV65Et9Zgxtr9Rcxal9bWFLK8xVhF5nlTEgTT54Ff70frsskePZ6WAnIXTyrN4GKH06a4aqWl3agsNjtzqnKDkfwk46uQQikTEz0+hkQFope/khiW7+WvgAjA1maTuh2phSQsn5pITo1lJaKizoiXDwq9VlZNTcR/jl2kCjar3YYTl2sbt+nNEpy4XIsgbxm1hiY/i56KY/m+vD5zOPQmM1HQvPvNJcxJ749lOwkDLbOS8PrM4dhCSMdMSgrHhPgwfFoknFeUHhvSWM/RnMykXlDKpMTPenW2Y8GjCYVPiyrw25F9icIkd9v3eO+JUY3zqJx1JBVaAxZMjIcEII5FGNlfhSmbC4gLqasRHFabDWUaQ2OaRCQSoUxjQKyLVCRrgW4wWKnXTJ3BTKy9EYnsWDUtSTBk0ykUTl6uIS6w+XsKMTw6GGarXdDlVFFnxHdHqomvW7mnkFhb5vz3sskJWNNsKG2qWoUFmXGw2snDcXUmK9bsLCRGoDZ8WEStOXNVfO6nlDFrXWhDflmFv6yUzNlyLdZTusqGRAUSa5IAp6cV+brxtBTQreAppRlc7HCYsJ6ASNtYwwQBxwgD0o29d6AS6z+kT1Vel002+9KbrFSPjzX7irAhJxnRoT7wUUgboyk+CglkYhHVVDBvVyGmpkQQ/Vu+v1SDnSd+Ib7fhgPF+NfMEXjho2LinCOlTEp82r53SG/84Z0fsHJaoqDTxQ47hkYFEqNMEf5Kqu+LWCTCuuwkHCmpbBLxuqbR4/HRMVi+p5A436uywYi/UT7P9fuLsTYnydF5cnPRqDoEK6cm4JcaA3Fhvj1aRW0fz9t5GquzkojfrRNj82GYN4zvZqf2w1WNgSh0+4X4INxfSRyLcEd/FfW9WIXNE+LC4CWXIjKgaT1IZIASfl7sWylrgQ4k1PM4Rb6/l4xYJzO0byBqGozEcSDVDUbU6k3ESNrC909iVmo0MVr0+szh1NZ6VnTqaEkVbIQZZs5uwjXZv36/Nxfoao1mzBjZl2pw6COXEMXONa2B+h05TSpJHD5XiYo6o6DzzbmNVfjLSsnkZyVRjQx/qqgnHosTWhTR01JA3QEudjhUWK2PIgALCNvW35tMXyziwxDmryDe2HOG9Ka2Sh8tqaK27rLSZkdLqqAxWNBgtMKnceyACA1GK7QGC72498aNnWRx//pM4SBMJ3ER/njpI/qco0nJwrqNoyVVWLG7EP/43TCsIaRqVk5LpEaZUm4LpNrwHzlXCa3BQhQCd/RX4bd39MXrBcJF5p7EXvR0TEkldGYrMpPDMSu1X5NImUwihq9STKxJIrVIOykoqYKBEfXwlksQrfLBv4/9LIykTUvEit30FMHSSfHE1BjLIftMmQZrspLw5Xmhl9Cd/VUo05LF1ZCoIKZfTgUl6pWuVuHUlVqi8Nx+/CKenziQWCdz6ooWu374hXgNp6od8+RI3UpvP3IHKjTkY3GFxWanRqCqdGTLBef3m7v9hOBesWRSPN44SrZcAIC8yQnENKw61Bf50xKpnX/CwRW/otGb3S78dZWSIUUwXD38sebbeVIKqDvAxQ6HiKvWx0xKofHK3YVYnBkHm124cP/x7oFYt4/s3rqS0SoNgDpJ3VXaTG+2CtInqWoVnh4/gPk6WtqBlY5gDTZk1SsdOVcJzVihaHOkHc7gjv7BxELq6/Vk7yEntMGUX12oxr5TZLdfmp+Rk19q9ESRMCEuFM/ePRB7CUJgRLSKacRYZzBT60/yJsdj9V6yoNGbrUyha7GRF70j5yqxJJPskD2wly8ACL2E1CqMignBP49cIB7LCx8VI39aIpYRur9yM9SICFAK6sCcabyfq3QCSwLnYFmpWEx8OLgnsReef49+7leaDdF0CpNNB4qwYmoi3nlsZGO0008pRb3RjEDGIuotlyA6hCw6X35gCOyMMhqt3kK8VywGfS7Y0ZIq2AFiCjrMX4Hfvf41Vk5LJHb+vTn7dqooc9V676rwt7UpmTA/BVMEh/mxR2Z4SgqoO8DFDoeIq9bHmZSQ9idFFXiIUmNRrjEwowa0VmmAXpDn6snJZCEbr+VmsIscaek2VpGuq8JJ5naK98aRc5XIzYhB/U3Gbc5C6mAXN0ERZTBlRICSGtUSUWYjucI5LZz0Wa/awxay/kp6zdKgPkFEM0LAUevCgtUhI5dJCFEoEUL9lAKvJ8AhVvN2ncagPoE4WHwdzTlYfB3P3kWeRzX7zW+w46k7kTtO3cxWwQKz1YbXj1ygDpYldXG1RJSKRSKiSJqdGg2d2YrNB5umKtNuRINohfl5k+OxmlJo6+xqpKGkTBKvdzVixGghFvSXXG/A5Ro9MRUHADqThXjuW2eNgK+cveS1deFvgLccG3k6yiPgYodDxFXrI2vh1pnJXQt/e4g848kV6eoQ+FLGSbh6cjpGMfvSmazUG/uEuFBqum1SUjgmJ/VCdJifQEQEuWghZ9Vt+HvJiMLkna8vIcRXiS0HhYWoy6YIw/w3bw/xlRPPYWpKBEJ85Zhxe5Tg/USwMzvRaJ1hzKjWuUo8MSaGuD1VrYJCKqa29bKuQ6mELcykFMfjx8f0x3WtgRiFSrktwGV6k0a9kT62RNssnSq6kU4N9Aazi+sP42Op78fCSqmhARzFxETfplo91Usm6bYAqugsKKlCbgb5OFPV9PooX8ZEewDwUUhx36vHBD93VURusZLPXSwS4cXfDOrwwl+ejvIMuNjhEHHV+qikRD62FpRSF3VXXRTh/kpiaiEm1BdGRiHf2uwkLN1ximi8NuuNr4mFmkqJmHpj/8OEAdQoxZ8++QlLJsULPGrS1Cr8ZthtTOFF61ZKVasgBoiFr1tnjcD6/WeIjsY1DSZqRGRVVpKgO8b5ey8cOEstpM4aFIk5aeTPJW9qArJfIXvQuIpqScQiYv3F7NRoVOtMuLNZ0bAzwuRHmYkGAAUllczP+2JVA/F1d8X3wlrK93uV8h05YZ0nTVwBjoX9L58Kh6v+wUU6lVYnc7pMQ/cfYoh85/uT9qk1WPDM/34gis5fatifi1QiIna+3d5fhc+KrhLfr4JRaJweG0I1GmYVKKfHst3P6w0WbJye0sThXSmT4JrWgIwBoe0mQHg6qvPhYodDJMRXjrviwzAwwl9wkzpbrkVEgJIaKr5crSPus6LOyJxyLBIB/W/qnPJTSuEtF8NksaHBTA55a3Qm5O8906R91XmDrtUZ8K+ZI8izo7KT8PvXv0bWkN6CG3tVg4lZhLyiWV0G4Hi6Xb3HUa8Ee5FAeM0bFwulTOin41zwRSIQC1/7h/ji9phgPDCyr0CwfXepBicu1RJTJ/VGCzHdAgBxkfRC6vw9ZzA8mjyh+8PT5RgWFURcYF2lE3Um8nyk+dtP4L0nRmHZlHgcazb+oHeAEr5yGTKTeiGGEEk7d1WL/BsuyYL266mJMFksxM/bbqfXirgi2JscgSsu16L0OrnzJlWtgtkqTKc6PHZctFErpOjdTAT3DlDitiBvTEqOwMrdQk+cFVMTMeMfx6gPIzqThSis+4c4xiyQolPvPTGKeZy+Cimx861/qC/GDAhD/t4zBAPLkBvfX6Ggu2/5lARY7Xbi9xcR4IVV0xKxcnehIDW0YloiplKsGgBHTY6XXCIwMB0dG+Jybhina8PFDodIgLcceVMcAy+bRzDW5iRjE6VNXCwSYcO9yQJRMzo2BBkDQpEaoyLOv1qdlQiLzY5XDpYQfEMSqR0WlfUmfFpUQfR+uTuhl0DoAE1bnr+9VNP4c2c0gfWEzkrXfFp8Hc/cPYDYDtxgtMBuFxMX/HeOX8T4+F7Ez9NfKcXCifGC1u00tQrLpyZiUlJEk6d4kUiEyAAlLJTuNVfnwJrQ7S2XYN/8NOTtFHbB3BbkxYw0XNXoqWksX4UUZbV6YlopJswXC+6JwzJCJG1NdjL+8ulZotBdt/8MnpkwkPh51zFqRU5crqWex4S4MIQHeBHHPqzOToKNsjjPTo1GdT25W8luB9McUCmTED+XeRmxuFprIJ7fNa0eG6anEFu6X3lwKAK8ZBRh7YM12Um4UNkgEEk+cgnzOGUSEbFr7LXD5/Hc3QOJ17aXVIK8KfGYROjugwgoqyaf39xt32P/vDRsmJ4Cjd7cmBoK8JLBYGa7n/sopI7Gi2bfb1eaOcVxDy52OEQ0OhOW7iRHMJbtdBRqkjhyrhIGM9lEDABW7zjTrLPI4ScjFokEBmnAjUnju89gXU4S0dennmI2CDgWftoT/PeXahGl8sY/CY6w9w7p7bZ761WNkditlKpWIW9KAn6gLJS06db3DetD9ajZ+GERlk9NJPqwjOpP74BydQ40hkQFAnbyiI2LVTrMTusHwC4Qq8/fMxAGk5UyIDUWNpsd/zhygdg99HNlAzZ/JvQSKripYJgm3J6eMIC4bSzjCX5rQSlV0C2fmoAlNG+mnaexaloSVczGRQYQ3+/Q2QrMG+eodxEImnGx+O4i2eQPAFZOTSSe3+TkNMHgWOfrpiRHYGsBuaMMANbnJOP9768IrtFxA0OpKdOH06IBkUhwbaerVZidFg2bnfygEhfpj9V7z9yYifarSeMvGgM+3V+EZZMT8P1l4cPIxMReEIlFeP69H4lF1qyaHLlE3OVnTnHcg4sdDhFWN5arQs06g5k42uHC9XrkDL2N2AqeNzmBmjr67lKNo3aB4NWRn0W3la9nuNrOSYsmpqOcvjd5kx1Rrea4StfQOFpSBYvVThQKzqd+Utqhl78SJy7VErdJxSIso3QOLd9diLzJ8cSiUpa3B+AY+EmLUFTWG6mGdPNuFBk3j7LMedMxi4o0osBgtkJvseJ3d/RDuabpwMjIACUSe/szvYRY16Ge8oRfer0BE+LDEEdI0RaXaSECsHRyPOwQNY4+EMGOBpOVWrxcUOLwgro5PeSMsuUMicTUzeRap9e+uICslEhMSY5s1mJtRLifAnPe/Ib4Oqf3FKmdHaCL/DB/JfXv7GiJY7gsScw9NdZGLSI/9YuGOu7DOSaFxPCoIAzuE0iMQD2cFg0RgB8FAioE63KSqIagK/cUYk1OMpYR3Iw3Tk9BRZ2BOWLlVmZOtXYEBadj4WKHQ+RWurFo7ZsWm51qIqZ1IUya12UAjiex5btOY9W0RFys1gluXv6UDi7AdffQoolxxAU/MlDJ7ICidSsBgFZvJgqFA0+nY8uDQ4iDMDMGhFLHaCzKjGM63i6YOJB4Dn5KKbPA8/BP14nzpha+fxKv/W4YtWB2WFQQeRaVOgQfn7mKuHD/m9qIHd/N8+/9iLcfGUkd+TG8bzDTo4d1Hfp7yYjnP6RfIAZFBWLVnkJBBGP51ERUN5jw509+EkwMn++iM6reSJo07jBxvDNGRUy1Do0KhBUOgdm8xbqywcRMyeiMVqyYlgiL1d4oyqQSEaooKTPAdVSPNlz2ywtVGNaX/P1ue2Qk/vwJ5TokpAOdBHjL8OLHZ5kmnMJIbyUu1+ipovOz4utYMNFC7X7Sm63MsTT+bj7MsAxYaSMoOB0LFzs9AHeeOFx1Y9EiHKz2TZKlvBMfBb3rhiVMDp+rxNLJ8dj5wy+Chev/MbqjXHG5hjwxu+R6AzWcnzc1EQ/+8ytqYWiQj5y4+HrLJNj21UVizYO3XII/Nevicb63K6+VWp2ZeA5z3vwG7zw2ijhXaeHEgTCYbfjrp8LF/m8PDYNSSja5ezgtGlkpkTh2oUrQkTOqvwqlVTr8q1n6JFWtwobpKZBJxPgnJY216UAxHh/TH2arnfiZ0q7D9NgQ+MolxIiJRCTCij3kFO2qPYVYOimeuMguoUQonPgqpMQ0bN6u01iXnYzxcWGCz2Z4dDCu1uiJM9MmJ0cw389PKcWuH8oaX1dvsuL7SzWYmBhOfY2rjkja9q0FpdjxVCrW7D0jKCaWu9hnHeVBRi4RU+8HLBNOmiBzotFbMDCcfI/zUUipD1wiAC/dP5i5b+L7uTBg5XVAngEXO90cd584XA2i66vyJhYhs4yydJShd4CjKJgWMXHFLxS32O8v12LF1ESs3C2sv+gdxH7aCvWTEwXWzema5iLi06Kr2PLAUOpgTplERIyYGCxW6oygSUlCU7mWEugto4pEs8VKLKQWi0QCoQPceEIXAb8Zfhvxs37v28t49u44YkfOiOhg/Oern6kLzNLJ8XiQcv4Pp0Wjb7A3lu8uFGzbOmsEgrxlxJZndZgvzDYrRsUEQ2+2QXtjEGpMqA8MZvr4CqczM0mwkgqQbz4eWm3KiUu1sNntBFfmEPQO8kZUsBdOHBbWcs0Ydhu96FsdAm85fbI5zSvJVbs3LTLpiDDZBaNCrmkN8HLhSuytEBY3p6pV0JnpUSuAHoVyJdhYEd16A328TEFJFeoNFvTyZ+5egCsDVl4H5BlwsdONuZUnDleD6CICvfDCbwahpsEErcECfy8pgrzlTSYpC/bpRf+D/7ToGrVQM5yxTwAI9SMbAN47pDde+KiYONBTqzdTF4Q0tYoazj5xuZYazn9hejJ9MCdEyJsSjzePlgqiKUsnJ1CfNpvXsTQ/lnS1iliD4WyxJpGqVsEOEAup981LY5rcLZ0Uj/99c1nwWT9/Txw27D9DrNtYtdvRzp4QGUCMzoggop4/a6aYWCTCoolxRBHx+Jj+EIskxC62pyewvW3MVjvxespM6kWN6s1OjUZlHTl9REvDOkSMnXh+BSVVWH+gGPlZ5Nb6/OxEbPyQXIS8/sMirM1JxlJC1+OdMQ6BBbuwkDx/WiLW7isinkO6OgQysZg48f2OaAlblMkkxAgj6+EHoIsaVsdcmlqFIB/6fcZVet6dmp322Cen7eFipxtzq08cLOdPdyJGrGjRiUu1GDMglJh28FNK6a6+sSE4dUVDLTSmdesMv9GuTDLPm5UaDZOF/NRZXObwdjl2oUpgfph8WyCepxXTllTCbLUT0xz2G+dPiiawxjdsLSjFzqdSsWqv0Gtldmo0FFKyr8+8cbH4pOgacZ+uZo1p9GZiZGfLZ+cQFxmATwnePt9dqkHelASs2iuMzrz8wBDBd9D882HNFHtyjJkoIialROC1L84TRcSTY9l1KzT34R8va3CwuILccfX1JcRFkEMC7s5M23fqKmYMj8Ks1GgsaFbUfqVKj32nyd/hweLrWJhJruf5pVaHt74sJbbr680WPJweDb3FKrhmnspQw2yzEf15ZBIRnsqIgQ124uskIhHx/HPHqd0anlpUpsGanCSimFuXk8x84HKVnndnXER77JPT9nCx041pyROHq3oekvNnSyJGAIj7pUWLlk1OwKUqncBPxw47dCYLVZjkZyVS27ZZ3To6sxXPvfsjMR01f/sJvDn7dkHUJF2twu9G9YX1RkqiebomNsyP+F5OaJOh9SYLtWjy7oRe1KfYYVGB+PFyDXHx3X78IlZOTSQOUozwV+K9b68QxRWrdgoAfJVS6swlhUSMIQS36uKrWuowTwB4xkWkhVVUW2+yEB2y5WIxNUJ17EIVMxJBcx9es68Ie3LTsHz3acH5r85KwjpKVMQVzNErFitO/aIR1PMk9ya3sjup01vw52a1XqlqFZZNTsADlJTh5OQIPPzWt8S/iYff+gbvP3kn8bof1CeQ+boPnryTeIzFZRrq0OAVUxNQVmMgOqpHBHjBbLPipfsH/xpZVkoR5PNrZJl2X3OVnnc1LoK031vdJ6dj4GKnG+PqicNLLiG2c7uq53EVMbqqNWDNviLqfknRIqPFhteOCJ/EAeB/j91BbXutqmd3rLDy/joTfZaRQiomPv1WaI34F2Vwo6uIgd1Otv4P9Jbjn0cuEOt5thZcwKqsROQRpmnPTotGtMoHu34UGu7lZyXhz5+cRWSwT6OHCQD8ojFg00fF2PzgUOK4iPuH3casSxFBGIVx/ntddjKOnnd8546F2YoKrQHj43rhhY9+In4mR0uqsGwy+zZES2d4yyVQh/oKPhund1GIrxyVhM4kR7Htncjfe0YoBqbEI+dvXxLfT2ey4qqWXLj+S7WeGhVxlYZVUobLessliFaRJ41PS4mkzjfbWlBKHYBbXqun/i1p9Gbm34RWbyZe97M0Bubr6o0WYov80ikJ+M2rxzDj9ijBsfxcpYNYBKK4mp0WDa3OAnWoPzGK4yrqzErPsyLdtP1unJ7i9j45HQcXO90YV08c31+qdauex1XE6EqN3uV+m+/7fEU9NZXhTYg2ONM7tIXCCa1bp6LOyAyhK6TkqdGZieF4/v2TxH1+yYgYpKlViAz0wg+XmpoYpqtVmDooAk9PGEgca5E3JRE6o4kohBa8dxIbp6dgwcQ4LJGIUXejCNdstaG8Ro8PfignHmfuODW2fPYTMR218UAxlk6KF8yOcrr20tJfJy7VwmK3EecZuapZEIHuIpyuVqGijtx1ljclgTq6I2/naWycnkJs9deZrCinuA+X1xqY4jmwWd2Z8zqUy0SY/SY5ulFRZ2DWh3lTCnzzpiRQI2KbDhTh33Nux9r9RcTC7ZNXaijCWkYVJsP7BlHPGwD0LgqKafgopNh03yCB23GFVo/KehPxWHbnpmLjAaFL+5GSKtgArJ5GNhkFgLX7zmDWnf2wYOLAJn8v6/adwdqcZLcGc7Ki2Qtv3Nf4sE/PhoudbkyAtxxrspOI4xlWMdI/rup5XEWMaLD2yxJQYojwxqwR2HzwnEAo3Du4N2NKtwrBPnJiOmpwVCBiQn2IhZq5GWp8RqnNuFRDnvsFAO98fQnvPjGK6L6bn5WELZ/9RGwvl4vFWEabt7W3EGtzkrH+gDAlsem+lMZOJUEaYEoi9cl/RN9gqpmbY06XnWgAGOwrw2tfXCCeu2OSuJE42mDV1ESmV45IDMzLIBen546LRZgf2eRwSJ9ALP6AXCNVUFKFxZnkVvF0dQi+bSZkneSOU1MFa6paBR+FMI2XplYhe3BvgWhpHD8iEuPR9P6YnBwh6BqLDPRCgFJKPL+U3gHU84uLDKAOqxWLRFg+NQErCR1s9w7pTZ01VqszMR8ACss0xGM5cbmW+jpnNFAiFt0QwiKIRI5/e8npyw8pMnXzOZptNmJUOm9qPOaPH0B9cKhuMDU+bLVGiLSk/pFkpHqrcKPCtoOLnW6Mc0gmqRvpYqWO+QTLehpnRYxY7aus/bIElNVux5aD54gh9Jc+OYs12clYtpMwbysnGY+99S0WZsbdVODpGE9RoTHiybe/Izr+zn7zG/ztwaHEuVkKKT2SNOP2KKylfN4vfXwWj42OEaSO0tUqTEmht5cXlFThF4KrratOpdX7zuCNWSOw8YDw/SYnR1AnogPAoolxTbpuAKBMY2CmDDMGhhGfxI+WOLxr8qYkEBfuVLUKPlIJ6iVmzBunboxe+SqlaDCaIRUDJ3/RNIlsOb9DjQu/lQYTOX2yJicJGz4sIkY+Sivq8cd7BhKLbRfcE4c1lFlrGz4sok6Sn5oSCbEY+NeRCwJhnTc1EZerda0W1q5MMa9rjeSi/V2FyM9KJM4au3/Ybejlr4TNTig0HquG2UJuyX/n60t49/FRxK6x2WnREIuBz4quNdbe1BstOHGxBqNiVFTh5Sp6/EstOXosgog6D2/13kKsyU5i7pdGZ3RccaPCtoWLnW4Ma0jm6zOHM1/L6iBgFRrnZyVh0stHqK/195LhmtYgaFkP8ZVTLfzlUnqxaXSoL9bsLSQKjDV7z+Cl+wdjwwFySoYWzveWSxARqMSJAmEai1UwfGd/FbYcLCF2JOWOU+PFj4qJgu2XGnp7OUA3UXPdqRRDfD+WuePRkipIJWTjQNa5i0TsjqqlkxOos7EMNitUvgrk7Wy+WIZgdXYiQnyVWEJoIV82OYH4fk78lDJiR5IMwB/vjsOK3eThon///BzZD8lsIX63ABAXQZ8kv2bfGWQmhRM78fL3FGLFtETqCA4arpyQNZTF90hJJco0BqIYuFKjxyP/Zhcak66LVx4cColIRPRt8pVLoZRKyINHQ+lDXl0ZKtJsFcxW+rVdUFIFvdm9uXAd3XHFjQrbHi52ujFag5k6B6b4qpY+H6hc67KDgJb3BoDUGBUGEvb78/V6SMUi4lNeujoEizLjiQtQzmD6YE7nEy5tEZo3Lpa4AD01Vk09tzlp0Vizlxz5+OunPzW2UTe/ebOcZFlP4q5gmaixFj2aSKI52jqpN1qozsvbHrmDeO4GE3sRqTNYqLOxbFYZ8vaSfWjydhViGiHyVVBSBS+ZhF0LI5Pg3e+vCByG7x92G1btptT67DqN1dMS8eWNjqybC637MYaruoq00LoCC0qqYKJ8h6z0kKsZbaG+Cuo2WkSs1kWB8i+1lAijSIRJSeFE36b02BDi98eKTBaUVOGHSzVumR9qXUT7nBGY1qaHOrrjihsVtj1c7HRjArxk9LlKE+MwMSkcy3cJ5wOtnPZrqJd1U6DlvZdPSRA8/fcOUOL+YbehXKMnPuUN7hOEdfuFAsM53G9OWjTxJuzqCdditRFD799erKbeTEfdiNCQ+Kz4OmalRhPFgC/DuZV1nCyTtHQ1Oy3IEkK0bazjBBxjD2gzrj4sLCeeu58Xe59+XlJiauxgUQWemziQaWT49IRY4ndYbzIzvZJ0ZgsxEjGZMHPJyXcXa2AhhA3sAKrqjHh8TH/iHCiTlX0dsrbTxOeZMg1WTUvECkJ6KIIxo82V8KZdF66ciWkcOVeJ2anR1DEpNKHHikyu3ldEnT6/JjsJ0/9O7pjzU8qYgz79lDK30kOuTFbbWnhwo8K2h4udbgxrDsymj85iakoEsSNn3f4zWDklETqTFZ//dB1hfgoYLTbU6Mz4urQaYweEIoJyU9DoTPhFYyAKmuhQX1yq0hGPp1pnYi54T4yJIS7ArCdcb7kEwT5y6iynySnhqKwzwUchbUxX1BvNkEnEzBumXCImHsv0Ib2pooV1nFsLSqk39vysRKzdT3G1jQ1Bdb2JuMgUl2tx9pqW6EEjFtG7n1LVKohFINa65GcnNrYKO3HWMnnJ2C66PjIJejebCN47QImswZGobWDfuK02EL/D0bGh1Dbq+dtP4I1ZI4jpqHpGZOvxMf1RWWckXr+5GWrcndCLKHYi/F2MH2FEWmgDS393Rz9Y7XZieqiqzsh0c66mDANlRUVuRXTTRle8/MAQWGyUnBPoDwE6kxUXK3XU9PQf7x5IrAHzkonx+szh2HKohDiyxVcucTs95E4Xl7two8K2h4udbgxrDoxzKvauH8uIHTlWux1l1TrsPVkmFC0hPvCWS4h/5LU6MzYfJI9MAICFE+OIx+Oq2FQuFRMX4N5BXtSFe9nkeKzdd4Yo6N799jIWTozHKwdLCFb8SXj1oWG4Utu0QDQyQIlXHhyKQB8Z0ezMbLNRnWRZ09KHRAVCZEeTFnI/LxksVhse/893eO33w2A02wQDGNdmJcEGYOePwiGoa7OTYbHbsZzgQbMmKwkPp9EXSqvNjtmp0VjYzLVXZ3RMkyYtJPcOicTq7CTk7Twl+DzX5CRBozcS52b1D/V1GWnykgsHRh4tqcKCe4SRiJsdpwO8ZXi52bWYpnaM56DBKrQGgPxpSURh4qsUzn+6ebuPXEIUpUVlGtQbyANbK+uN2FpwgfgQ8PYjI5lC7/8eH0XsQlw1LRFr9p0hnvuZMg1WZycRi/3zs+ndmwDddRqg/80D7GiSozGBHGF97p6BxNl8cqkYfztUQk23rctOuqX0UGu7uNyFGxW2PVzsdGNchUIrKB0bALA6K4kpWtZlJxP/6OtNdIFF+zngOoRuttqIT3mXKnXUJ9xBfQIQHqDE1oJSQUfSgolxgoUZcITWl+8qxJLMOIHJYapahfnj1FBIJESzs1H9VVjw3kmsnJYoiCaUMo7z4bRo2ACc/kXTKKAaTFZc1eixbHIC7HZgUrMBjBVaAyAClu04JfhcC0qqsGzXaWrkbs2+M7gnMZy4wG47fhHP3jWQOIV7SnI4dSE5XlqDry9U4vmJcVjcTLD9+ZOzeGJMTBMB5fxc3vyyFAvuiWMKBQlExAhVvcHMfIqvN5iJn43Vaner0PpoSRUMZivxc7tQ2cD8fhUyiWDUQu8AJX4z9DaIAfxAaGefkhKB598jezodu1BFndGWrg7Bp0XXiKaYv1Tr8eDIvjA2a+1OVavw4Mi+MNkcHVc3C90GowWw2zEsKogc9Ymlu04fLamC3Q5q9JEamSzT4nSZhpoa05usxNl89QYLM0JcZ2TXq3lKeqij02Y9AS52ujHu+uEcLXFMfmbd9BsoQ/x0RrbxGK11mRlCjw3Bl+eriDf2ITOHU59wLVbgjYJSstuxi5ti9RjhQnm0pAqTkyOw5WAJcZ/LdxfilYeGCiIDTt+bB//1FdEttviqFr4UY7lQfznW7TtDLMDe9shIYnGu8xxYkbs+QV7YSdj2cFo0fBUS/Nhs8U1XqzB9aG98d6mW+H5hfgrs+KEcOwhGht5yCf4wfgC2NkupOr1PxAByMxwF480/t6fHx0IplxBfu+7GsEty8auIHlEQ2akROFeF1g0mC/E6fH3mcMyjXIfFV7XwlpE7kqJDfVFWQ07XlNXSu/S2FpRid24aVuwWOms/d89AbD54jtiF2fveZHx25ipV6D4zYSAKSqoaBYbO5NjWP8QHuePUAIS+VCumJmLaFnrUx2y1Eb2JVmUlQSoSESOTa7KTcbGqAf84coGYGvNWSPDcuz8K6m7mj4+lHgdAv/84uZX0UFt74nRk2qwn4PFip1+/frh48aLg50899RReeeUVjB07Fl988UWTbY8//jheffXVjjpEj+VW/HBciRbawEhXKQklxaemqExD7XJi3UxP/aLBnTGqJj9rdLVltKzTOpWc0NJqvfyVLmuLiL43ewvxx7sGYvEOYZ3Bh39IQ63OTG7PDfFFcp9AothxdQ6syF3e5ARy6qTOiDcKSqkRr8dG98dfPhXWrLAKsJ1Tv2neJ6uzk6DyVRDneIX7K7GIEr26XKOnfxcllXge5FoRg9lGneUkEdMHrwJ0n6UKrYEaadk/P03gSA00TfM8+64wgsNqPdeZrDBbyFGm8xV1eCQtWvDAkqpWISbUB0l3DcCpK78aBIpEIkQGKDEsKggyqYhYWzW4TyAuV+uI9UNmi5UpIkgGgQUlVTh+oQofNrvmndvydp1GZlI48TMTAcgdF0usu3lybAz1OABH00Z7pIfayxOno9JmPQGPFzvffPMNrNZf/5BOnz6Nu+66C7/5zW8af/boo48iPz+/8d/e3t4deoyeCisUuirLUWxKCxO77K6hiBqlVMyM0EjEImLNwwMj+6LOYCSaxxlMFurNVC4VYVFmHFbsFnaVjYkNpR6/q7QZbbtLfxOGJ84SiteMXCLBlkPsxZBUFOtu98zREuekdWFB6aSkCKzcc4Z6XfxhfCxR7LCOhdWWXVBSBaPFhk0HipEQGSCY41VnpEcYXdV50a6Z6/VGaou1VCxitjwHe8uItWMj+6twZ0wIlhLqXVhT3VmpXUe0U0UUdKlqFRQyclFwZmI4Zr7xNTZOTxGkU//4fz/ijdkjiGnY3HGx8JKKsf9UuSB6c0eMCn9890fMuD1K0FGX3Js17iOksY2/Oa4eHFjt+nPS+hO36UxWph2Bv5eszdND3BOna+DxYic0tOmCtWHDBsTExGDMmDGNP/P29kZ4eHhHH1qXIDLQi5jbrjOaqcWmr88cDrEIzM4MiUhEDNvKxCJquHv5lAQ88E9yKmfR+yfxv8dG4U+fCgtq1+Yk494hkZicEimo2+gdqMSK3WTH1IWZ9AWY5WHCinq5K5IAoMFoIUYvDBZ2ylAkItc8nPpF49Y5OI+FJCzrjWZseXAIsc5py4NDYKe4uVVoDdTrxRV1egsOFl/HQUL0KjUmhPo6V9+Fl4xcFBzA6Iz7x+EL2J2bilW7C4ljRKw2O7GmRW+y4s+fnCWmo1jdXwBdlG0tKMXeeWlYvkvYpbdyWiLkYhH1etp0Xwq2NkvhpqtV+NtDQ7FqdyExDQuRCFNThG35R0qqsHznaSy8ZyAu3mSA6Yygnrumxbxx5HEfeVMTkP3KUeL5uXpwYG03UOZ0ScViph1Bg9GC/qG+bZoe4p44XQOPFzs3YzKZ8N///hfPPvtsk66Lt99+G//9738RHh6OqVOnIi8vjxndMRqNMBp/HS6o1Wrb9bg7E1p4dVVWIr1rASKszkrE7LR+IImW2Wn9YIedOJtmzbREhPsrMKlZuLtCa4ANdurgv9xxaqykGb3tPI012UlYvEPoopuflYQTlDoSu53+xHmmTIP8aYlEi/slk+Lx0sdnifusqDNSn7Zdted6ySSCxIodrlOGRrOw5sFZLD1tUCRxIno+Y/YZ4Eg3/nStzrH/G8Z517QG9A9RYfNn5NEcgAirshKJ0anwAC/kZ5E/z95B7DA+adDrr8dJ3+ZKsAZ6y4i1IiyLgGF9g/BR4VXMSo0WjBiZu+17/Ofh25vUtOjNDgGVGqPCp8XXienGsQPoEUaAPsx2WN8gnLpSSxRQGz4sxsKJA5HYOwDSm4bAhvopIBWL8JdPfnKrVo0WTfnuUi3ys5Ow80eh6ed9Q29zCK+UyKbCq84IwE4Vc7fy4ED7zEQiMLvU/vvwSABtmx7injhdgy4ldnbu3Ina2lrMmjWr8WcPPvgg+vbti8jISJw8eRILFy7E2bNn8cEHH1D3s379eqxataoDjrhzYYVXDWYbs97BaLVh+/FLxI6O7ccvYVx8L+J+j1+sQUyoNwZHBcIOUWNXTmSgEiKG+GAZ+R0+V4nLhPlQBSWOWT9zM2IQF+4viPp8daES88fFQgwIFuAnxqghBjB3nLrJotZgNEMmFWFxZhyMZqvgdbdHB6GXvwI2CJ8cnYWhJJyDFEkusx/+IZ34GiekmgenKF2cGUes2zCabRgSFUj8rMfFhUIhERNrhEb1V1GLkI+UVMJktRHf7/n3fsT/Hh9FrOlQSIS2AU6cTsc0X6PrdUbqa4vKNFiTnYRlOwjt7tlJWLWHMiNp3xmsykokisQVN2ZV/avggmDbpvtSoJA29QsCHF1VKl851V25oKSSKcqUUnJq96mxahjNVmw5+CPp68Bz9wwkdvANjQpyu1aNFk2ZkxaN5TvprtNbHhiCSUnhTaIlzinqtHOvqDMyawortAbisbAmxat85EwXaNrrbgXuidM16FJi5/XXX0dmZiYiIyMbf/bYY481/n9ycjIiIiIwfvx4nD9/HjEx5GK1xYsX49lnn238t1arRZ8+fdrvwFtBW1b0s8KrGh37pqfVW5CflYTPf/r1SVUkEqF3oBd+M/Q2ZFLmX63YXYgP56fjywtVjWaE9UYLKuqMGN4vEMunJGL13kJBWJ7l+Ao4np5odSRLJ8fj+xtDO2+29x8X3wsykViwAFdoDQj3U6Bca8DmgyWCRSY3Q42oQC+iEFKIxfjvVxeJC/4/vijBs3cNIBaGzs1Q47uL1cRzMFls7E40Ss3DkZJKLBI5amJuFnpD+gTCDju1FfrpCQOIBcNHb3SU0dyqAUfKiRaZe+WznzArrb8g0lCrM2D51ESs3iNMDeVNTYTFbqO2kI+JVVEHveZNTcR/jpY2aXf395LBbLXBYLYS02IAcLD4Op6ZQE7jAXaB0HF+Nl5SMZ69eyBxsnt0qC/VXfm/X13Eu0+MwnLB7C+H59FvXz+OrCG9ifOo3n3iDup1r6EUtceE+hLPG3AdTfGm+AENjSIXXwOOiBBr6vfG6SmCh6702BBkDAhFaowKS3YQfH2yEvFzpY4oAmelRiPIR1hoPDo2BFKJUDje/FpXBejuwD1xugZdRuxcvHgRn376KTNiAwAjRzrClCUlJVSxo1AooFDQHU07i7au6GeFV30Y6QEA+P/tnXl4U2X6/u8sTZp0b9KVLhRSaJu2yCZCWxREKBRaKt8ZxWXYdFwojAuKSFnKIu7juOuMgPMb0RlHKas6gIAUEFHBQilLAWUrLS3d2+zn90dIJT3v+wYC3d/PdXFdkOQk55yE8z7nee7nfnxVcggANheWOi3Cw2O1GBITSN2u0WTF5UYj8TmlTIbFm8lDOy0u7PYjAlT41/e/EdtQ6wzki/4tkf7I21xEvMtdkZ2ETYcuEBc1wG4e17LFPE2nwew7Y/HMmDi8sOmISNOyKFOPez8ka5Kmr96Pfz9yG178SjwVe2xiCLVkmJsRj+x3ydb4ANBkshLbsldkJ+HTfeSgrMHoXikDALwoJafbYzXQ+vRAbj7BxHBiEqobjcTSZk2jCcE+SmpJVeUhw4Jx8USdTGWdAZkDIoit/k+M6gOttwIvTUoWZfzmflEIo8WG1btPi8/3+ASqfiou3A8rGF1VuePiicHO06P7YvnGI8Qs6dKNR5DVvwd1IK1SLqc6E7ecTu7YF9bcN5bFQ6pOg6gANf5OaPdO09G1UwBQazBRb9QksM/Amjqsp1OJy2oT8NLXR4nXg4o6I2au+Zlajvrn9FuJupvKBhPTWbo1gh3uidM56DTBzqpVqxAcHIyMjAzm6w4ePAgACAtjT83taLSGop+VXq2oMzIFyD6eHli47jD6Rfk3m9n9Pk6imHn3bzDbcL66CcE+9oBSIpHgfHUT6ox0ISpAT3enxWpx6FwNdZGZPy6e+NzlRjN1UQ/2VVI9anaXVKLRbCFPDAfwYnYSMVtU22SmapIAeiv4L2dr8O3RcuJiWNVgYrb1tjSHA36fJzY3PR55G4tEC1cqQ/TLIlWngZdSTrzbDvFV4XlKi/iC/EMYlxSGeaRBkTotlmTp8dOZamJGQS6VIHedeEiofVsNcZDk7pJK/GWkDZ88dBuWtLAySNVp8MlDt6G2yeh0vh2fd6GaXDoB2F1lu6/8NkiuxckRfpj35SHqsNrJQ6KIj09PjSEOLHW0XzsW9ZbsPVVJ/b909EItnh7Tl+gx9MyYOLz2v6NEI0qaMB2wB2X+KgVRw/dCdhIWbygiev6kxWoxqGcAzNbf39uhxZRJJcxylJ/Kg6q7WbG5mBjk//uHM3j1D/2ox3EjcE+cjk+nCHZsNhtWrVqFKVOmQC7/fZdPnjyJNWvWYNy4cdBoNCgsLMSTTz6J4cOHIzk5uR33+PppDUU/K71a1WjGzBE64kVv5kgdGk0W3HtrFHGI6LSUGHgrZGT31lgtfDzlIrfYcD9PNDCEuA6TtMXryfOhaGLb3SWVsFIuxKzWZFedILR93V1i93chaW8+fXgI8z1pLNtUjE2zUpG77rDoXP9xYARVs8Jyrt129BKeGt2XWKoxWunfA2APBGnlA4PZilkjdU7v2WA0o8FkpRscltAHPu4qqUCj2UofWDs2jtjq7up9PRVyUaAD/O7ts2xiInVIKA1Xv5lGo5WoWXLVjSWhJBtYOjZW+zXLcPDZsXG4j9IRWdtoRvbASOL3MCo+BKPig4lBS25GvEiYDtivW/PWHkK/SH/idj/9VoUFGQnEgPwuxuelxWqbb6Ra4qdWIC8rEc99Uej0nm2RZeGeOB2bThHsbN26FWfOnMH06dOdHlcoFNi6dSveeOMNNDQ0IDIyEpMmTUJubm477an7tIai309tT+NfPczT00OGsloD9OG+yH53DzFNPH31fnz5+DDqEFHAPtl8xd1JovdN6aVBaS15EOiC8QHUfW00WdFotBBT2pX17OwGrZvJnYngDjxk9HQ3TeTJ8vhgtYI3mqyobjIhNyO+WdTtq/IAIMBqs2L5xCTsOVmB4KtmcZXVNGFYby0mvU8vcTUaLSi7IvK8uuNqSEwgfT91WmwrLifeGTvKB+J5Yq6da1mBQpPJSv2tldeSS6Ku3lcikeAAJVu0suA0Ggj+PbtLKplialbLOmDvcCMFwetmpjC3C/dXEb17XOnYaO3XjSYrrIINGS3a0stqDbBYbdTs452PD8P7/ztG+T9/FMsnJhH1aEkRfsTjBthl0empMVhKCEh3l1TiZRzFiuwkmCy26y4N8SwLh0SnCHZGjx5NTKNGRkaK3JM7K62l6KfpbmKDvZlpYkFgG6HZBGBz4QVRyj6lt0Y0fNGxzaFzbF+Yb4+VE/dnrJ7toaSiiCpZPjTldUb6vui0qDdYqAslLVCSSSRUjw9WdkqtkCFArcCek5VOnTVlNU0Y1kuDsjoDdYjmg0OjiToRtUIGjbcSb2w7IdqXW2MCMT2VvJ8OXxRacOmllBPLP66cs1nBpZdSTg1MZLS0h4v3bTRaqNmiNyf3Rz1lRlKj2UL9Dn085czfjFImJR6Dp4eUKZpVXNmupSZJRWmvduBPCb7SdFpYLAIu1BhEBoChfgZqMKeQiQeuOthdYh8hQwqCL7kZkLoqCxotNreDFp5l4bSkUwQ73YHWUPQ364Ba6HK+uzLWgEWDi4F5lxtMRPOx89XiFnEHi9YXYdMVk7SWQdLiCXrc8yHZ0dnTgz1R2ltJFnH+5c5YjNGHYNmmYtHC1TvIC8+O6QsIYlHwjLQYRASoiHN5Vk4djJ/PkLuqjFYbnvz3QWK2TBCAAVH+xEUmb0ICymqNRFfb/lEBeJPie2ODXa9ECnYWZMRjEaXjKm99EQbFBBIXrq8Ol2JglD/VtVciITsv/9+ACIaLrgaXGygDH0trIZWAGpiwyif29mTyQqv1VlKDbgDIy9QTt6s3WKk+LdNX78dnfx6KvPXi32/OSB1AOTd39AliimbPVDZi6ur9on1ZkZ1I9XRK1WkQEaAiaoRyRuqw9WgZMZBYPXUwNZijjYBx0GC0kAeWMkp/AD0oc0WD0QK/EJ9WCVpu9hwrTseHBzsdhNZQ9LN0QHtOVVIFyo67WBa0uzWzVaB6pqwsOI0LNU0tDNtkKK81wmS14pX/64fSGufhh+F+npBJ6IMic0bEUrtSpBIJbutFXtSnrtqPd+8bQBQF/3KuGh/torQfe8jw9Og+WLapWLSoTexnt0QgLTJDegZSF5kB0QFYTHG1vdxgYgqpBYBYAukfHUAUBDved2pKDGZ8/KPoObVChvzHU4gzyqalxKC6wUQ8L698cxRLshKxiOD2u3CCHkqZFPkHyQMfJYLAKJkexQvZSUTPo7xMPS7VGYj6IotNLNy++n2baA68MrYw1irYiLociQT48dfLxGN4bqwUs1fSje7emtyf+FlLNxVTv4upKTEwWMkaIa2PEoWUkqlNEKjBXCNluK8DlYfMaXK94//utqNl1IzX8FgtojVqYpt4hAuzydbyp2mtOVacjg0PdjoQN7vWzNIBrSw4jfyZw/Djr1XNpRNPDxku1jQhzE8FDxk99Z6m06K4tAYfTRkkausN9vXAO/cNIAYt79w3AFpvBYwWATKpFFKJFXKp1O76KpPA04NsdNc3xAcGM1mDYDBbcbnBRDxGh16AtKgDQKOZvKh9NGUQMVsCAHFhvnhhE7n9OG/jEeRN0OO3qkZRoGe10RcZo4Vu8OjKBK7RZCWWQOpciGJpDIwOwC9nq4kB4mc/nEFcmC9xu5ggb7z+zdEWi6FdEP3GlmNIjQ2iumMvydK7DExIQWmT2YJpq8kDPatd+EhV1JMzTWcuNzBLVQoZuWwW5K3E45/8THxPCYDbejnbNTi6job1CmRquU5XNlD1Ux9Pu5W4XU2DEbkZCTBaxIFnjwAVNZjLGamjHnuqTgOpVEL8vzQ+KRTLJiZiPsEvZ9nERPQIUBOvawDa3J+Gz7HqvvBgp4NxM2vNrnRAgIRYOpmWGoOaJrpfxdIsPUw2QeRQ67hT91ZaiUHLE3fGwlMux5KN4rEPyyYm4Z97f6XeGc9caffcaKlBWLapGJ/9+TbqEbKEse6k1/tH+mNlwWniovbZD2ewJEuP9b84ZzDSdBpMGtADADnrM/wGBpZ6K+UY+zexwePXT6Qxt7OXQJwze2k6LeaNjUOD0UI8hgUT9NQ5R4OiAnBLpL/I88eREZJTtDeObiwWLUuqjkChqsFMXbhdjWgI8/MkjpL448AIRAV6EcubOSN1uFDVhBBfT6cgHwB+q2zEi5OSiaW4MfoQPD8uAYvWi0ctLJuYiP97fy91P+VSCTXL1GCyEHU5yzYV44P7BxKzMKybmCMXapCXSXaWnpoSA5lUHJwMj9Vi3rh4LFpP9s9auvEIXv1DP+p1ra39afgcq+4LD3a6MCwd0ILxCVi6gVw6EQAsm5iI+//xA/Gu2SoIWEKx4nd4qpCClvFJYXhzm1hHUVBSidz8Q5iWEoOvDpeJ9lUiSDCQ4uCaptNCCrqINUBNE3FqoPFWYEV2oqjLKSJQRS3FCaDrS/4xZTCWbzxCHqS4rgiLJyTg2S8OifbFi2Fhz5r/dLW4VaRzkrNHNHh6yDBzZG88O7avUwu5XCZBPaWF2mqjzznyU3vgVWonDzBndF/qMbJmg6kVMgSqFUQtzMRbwt0e0VDYInvlOG9LNhRhzpi+xOOXSQGVwgPvEkwMl2YlYtmmI1Rh/lctgn/7PlZiwboizE2PwzP/LRTvp05zZb6UmLRYLQ6eraYGQvVmCx7718+ix//76FDqTcx9Q6JxqoKeSVrz0BBihqai3oStxeVEXRUAZgBBG1R8dQB3M+FzrLovPNjpwrB0QAOi/DHvS/HCC9gvwiaLgAFR5G6JsfpQtzxVgn09qeUa+5TyOOJzFfVGPD6iN9ET6PEROjSayReoVJ0GPfxVRE3H4iw9bDZg86FS0R38bb01+Oe0wTheXu/0fuF+nogN8sLzhBlBu0sqIcUx9IvyJ5rH7TpRgXlj44n7olLQBdhHLtRgaZYe8/PFd9vTUmJwucFI8YsJZU5/bjRZiKMyFmQkEMclAMDm2anU/XTVyfPcWHpA6qMSGxU6cHi4EMuGG45gQUY8UZt0+FwN5lAE6I5RBOt+uUD0kCqrNREzJuV1RvxE0eUsWl+EfpH+RMPMEMbvfteJCuRmkH8XOSNjEeKrJGbgFk/QY9uxi9SOwSBvsg+Np4cMs1eSb2Ic+iFaAOXjSTbyO1XRQHy9A1YA0db6GT7HqvvCg50uDk0H5OoCVWswUxdLlucNQC8duTJlqzeQ39cGYMbHZG3GjI/34z+PDKWa4DVZLET/GplEgoUbxIvorpJKLN9UjKfu6kMsxd3aM5A6ZX1XSQWmpvSkH5/RQrxrNlttVAH2w2m9UWcwU++2//voMOLiW9tkYU5/XjV1MHE7q00s9nZwtqqJmhVw1clT20TWEKXoNFDKpJiRGkMc2OrKw2X+uHhiR9LzGfGYsvIHLM7Ui6aXmyw26vwrwJ6Fomm5qAaHDD8ZV7/7WgP5N+rrIYdBEDA2KbTZxdzh1i2RCLgrPhS5+WIjyo+mDKKO9Wg0WehZ0lgtVf/m0NAQu5hclINpAUR76Gf4HKvuCw92ugGkuzFfT/JFzYFaIcOfKHeArjxVPCn+IK70J76U9xUEgdkhc7aqkRgM5K0vwv+bMQTzN4g1QswZSGG+1BlIeRvYgzJZC5uPp5ycKUsMYQqwAYG4XapOAw85OWPiym6fNiOokrLYAXb9yCw3O3n8VB7EgHRail0f5q2UE0tHrBIXYA/KSTO3LDYbXvtjP5Rcyc5dbaoY7q9yKwvlKmihPX8tuquWJbBUnQbLs5Pw6tdHiaXdFdmJ2EwojdkzjBIsmBBP1NdE+qvsbfKEOWw5I3SIDFARt3t5UjIaTFZiFuaF7CTcFR+MLYQyFiuAaA/9DJ9j1X3hwU4X4Xp9I7w95UxNh0MLQVoss2/pwdzWm3JXWV5rYPqGqBUy4l16DxfpbIVMSr0Tn59Pntd0vqpJ9HoHLLMzVpkOoLvspug01EVPJZfjX98fR3y4n6h0sq24DIsn6InaokExgTh3uYlYyth3mj4fKS1WiwKC5QAAWGz0GUgHzlZjYDQ5K7Di7iTqd5um02JrcRk1O0XyXnKw5iH2CA6bzV4ebSkYPl5WD62Xgpid6xPiw3zPOkoWypWgnfZ8eZ2Rem4cuivSb3T+WrqOjVUS3lVSAZsN1EGZjSayJqvRZIWBYuQHQDT7CrAHJc+vPYQVdyfBeJ1ux+2ln+EOy90THux0AdypezcY6U6xU1NiIIWEmu61CDYsGK/H0o1FRE8VmyAQ7+LD/VVYOjEJufmEFtXsJDz4j33I6t9D1GJcVmugDy1lpN5DfT2pd/AsXN3B00i74k9Ey2BUNZiIIzYsgg2Th0QTRc8zUmNgtgkEbZEWPQLUiAhQ4R8FYvPDh1J7YVxiGBauKxLrPTL1uOcDsoHjofN0QfTR0lrMGxsHm0A2aZyTHgcbQbw7Z0xfvPXtCaKANVWngYHRev/Dr5eZQVuovxJ7SpyzN+W1BqTqtHiOMJh0d0klZo5gf7+B3gpi0B0RINZ/XX2cpOdTdBoMiQlEiK8SNpBNBUtryYE3S8fmck6XyUrM6JbVGamarBSd3dzTgQDAof13lYUxmK/f7bg99TPcYbn7wYOdTo67de+aJjOe+6IQL01Kbh7q6PDKmftFIVZPHYRlExPxPME7Q7ABL39TTGw1ffGrYjwzJo54F//YJz/jrcn98Wx6HJ6XSVHXZIaPygMWqw2VdQacrWoiZg2eGdMHMykC5ZkjdAj1URIXJ9pIAOBKlxMlgHJ1B9+D4ly7KFOPez8kD1mc/ekBfPHYMPGIjSujO2j6mkPna7Cq4DShw6sCgEDtfJMAWJGdJNJ72LuqbHj7vgF469sToiBp9shYpCeGYtnGYqffVFqsFs+Njce9H+4lHt/UVfuxcsog4vf+4XcnMWtkLHGu0tSUGOagTIkEmHlHb5FxpOO7N5ptxOzNIIa2as9JetYrVaeBQirFM+lxmHflN+qr8oDZaoNgE9jmljaBWIo0WW3IWUPXT7193wDq8dN0bK5KY7QMo42hydpdUgmLTSBOL3c1+6zOYEbvYO/rCiC4fobTlvBgp5Pjbt3bT+WBFyclE31RXpyUDF9PBZZsPEIMaAQA3x69ROw8AYA5o+OYWpFLdUYE+yphE+wX30t1RoT50UtVcaG+mE4RKE9fvR/v3k92Qma5QK8sOE0dXdHDX+xB8/vzWqg8ZKIyQHmtAXKpBP0i/Kj6GgkgDlpOVOD5cfHUkuFHUwbhg52nqF03tJJaQUllc0dRyxLPsYt1WHfgPLH1+u+7TmHR+ASMTQzF1GHOotjaJhN1iCQAKORS9Lhq2r1EIkEPP0/c3b8H/vgBOUhyBIE0EsP9mN/93/80iBjsLWFoqz774Qz+++gwYoZx4QQ9ZBLgJUKGKi8zEWYLOaAxmK0orzMSu7gGCEB/Smdjmk6Ln89UUY+fpmMrrzUwXYtpgYIrbVVVgwn9Iv2bv3vH78JsZWeS3MnCcP0Mpy3hwU4np6aJLTSuoTjweinlWE2x6JfA7rND88643YVhG22uVlqsFjFaLzy/ViwYfiE7iVoiAMAU29Ke+8OACKq2aGivQFgEgShuhSBgRloMSCLOOWP6YumGImJ7eYpOgxXZSTBaxIvo8uwkPPCPfcT9l1zZlnTsMomEOdCSpbGRSiWiYDZVZx/2eS+lbDYtJQZVjWZiB9QnDP2MWiGDr0pBHFh6Wy8N8/vzUsio3xPA/u5pv2+WtureW6OwbCPZBO/lr4pxZ3wIVZw+MDoAFptANPL7/JGhxP0cnxxG7WKbM6Yv3tpG7vBK1WngS9DWpeo0GNZbi7TYIDy/9tB1BQp+KgVznIuPijxnbnRCiFvBlQOappDrZzhtBQ92OhGkC4Zawf4K1RTDunqDhbq4FJRUMtvLvZXsz/RRycllngl6FJ4jm7m99NVRPDMmDoD4jjrYl+wZ4sDTgzz1vMlipWqLFk3QY/7aQ2RBbawW2beEE7NFjSYLMdAB7AtZg8mKF7KT0GCyNpfpvBQy1DUZUEkZhilAoLZeRwSqsGi92MDR8e+56WRNBwCYLOL5UAUllTCabYxZVEBuRgLx/faeopd/FoxPwOL1h4kmlQvXFVH9cFJ1GjRZrFT9mKvv3lU5h8SwXhqsLDiNuHC/5sccrsx7Tl3G5CHRxO12XRmgez8haGUFJlIAn+07Qy7x7TyJ3PEJMFisou2WZiXir1uOkZ2JNx3Bkkw9xiaFYcrVGTiKCaEDrbcCK6cOJpYwV04djOILtcTfxRtbj2NJlh6L1hVddxbGlaaQ62c4bQEPdjoJtAvGfIIhmYMUnQZSik2/q4wQK9hRyqXMMo/aQ4rnCb4hNY0GJIb74bP9Z4kZhbpGsp/MtuJypkg1zFds/Z+i0+D22CA89slPeGlSssg2v8FkpXezXDEA/OLAedF7pvbWMs9bg9FCbiOemIT3HxiID747KXrPPw6MgJdCXBorqzXAbGVrLGyU6kJarBZ7T5G3k0jAfE+BkixaWXAa63NSiVO/k3v4UU0qd5VUYP74eGJH2bDeWlyqN1I1S9uKy5ldXrS5UgBdW+Uhcz9bpiQ4U6fqNFicmYhzVY1OejSH1qei3oj7bovCqgLnz3OMZjHbyPPNzDYbvjxYSt2X6SkxxHM+PFbL9Kh5p4WZJPD74NyB0QHEbb49egnPpl+/CJnPouJ0FHiw0wlgXTDm2QRqinxaSgwEkC/crjJC3ko5VTxoNFupXh3zM+JhtglY1mJYpn1B0Iv28+p/zx8Xh/6R/k4ak/6R/tjwy3ksydRj4foikWg2L1OP1745SswWqZVSVNSbiMML//MIfZ4WAKqRH+18OqBlU/acrMC2Fu3Xjv388bcqrP35HHFBXzllEPvzrFZiB9CiCXrc+yG546q60cwsZTRQdB2NJissNvLU7zqGGBywZxJJHWVDe2vh4ymnlqpYU9gdXV4k0mK1EGwCpqbEiEwFVQo53iCMLbmWbJlcKiEGNNNW/YC//2kQlrYIdFN0GizJTMSKzcXETOGafWfw/Lh4nLrU0DyQt9FkP6esMSgAUENpzf7uREWzX1LLTHBFvYl4kwKwjREBoM5gQVzo9WVh+CwqTkeBBzutxPX63rBgXTBsoKfIP9t3BnPHxhH3RSqVYGRcEBLC/UQL3pELNZBJJVTxoNpDBoWHlKh38VLIiC2/BSWVWLy+CMsnJhGP49jFOqgVHli9+6jY3n9iImyCDc+PixNli6yCDdkDI4l36Xf374GxiSHoHewjOkYfF6U4b085DlIGRbLa4PdQsilhfiqqTiYzORy5+WSXYHZoBcilUkxPiWnuqHMs6BablTqYcmxiCDOzQStTpug0sNmE69bzACBOdt9VUoHc/ENYwdBrsaawf7jzJOZnxMNotona65dNTMT5qkaU1RqaP/9aTQVp2bKRcUHwVXkQB2UuytTjYo2BGEAJsGvA3t5eIjrf88fF47VvjiIm2KdZBySRSHChxp4FZRHso6QOpBUg9sW5lq4qV6aY1wufRcXpKPBgpxW4kXkvpMCk3ki/IHh6SKkX0pwRsZBLJcSL3oIJ8ZibHo8lG4tEC/qC8XrIpRKqeLCq0YTlG4uREO6H4KuEmudrDKgzWakLSUFJJXXC9Sv/l4wF+WINza6SSizML8IL2YlYvOmQOJM0PoGqP3lh0xHkZiSIApAefp5QK+QYGRdE7ChL0WngKZcRtRIvfXUUz6T3Bb6GaIHNzUhA9rvkqeCsQZlLN9I7h+wt8nRDukAvDxy9WAvAeUHvHeRFFaDXGSzUcyYBsCQrkeoVdKmOXP5k6XlYJbWCkko0mK1UzdKSTD3OVTXBclUE4ujyGhwdAKPFimfS+2KeLN7JyuDQ+WpovJTEtvTeQd7EfXFgstqIx//EqD5YRJnTBdAzQkazjTrupLLOxAzWWb9Rf7UHwv2cB2aG+3li9bTBWEwwafzuiuaIBa1lPVWnQYAbN2t8FhWno8CDnZvMjdSoaUHSkqxE6nRn1oV0xsf78d9HhxH3parBTJ1AvnRjEV774y0AyOZb56ub8P2py0ggCDwb3LyTC/FVMR1hqxrNTmWAZnEvwzckKdIfF2sNxAUvJsgbz47pC2OLspMjSKw3mqlllSdH9yH617C0snKZhLqfrM6hlQWnqWWcuelxKKccX78If0Z3k7jU5qCgpBJNJis1UxgX7kvdz3UzU7B4g3g/F03QI/PtAuJ2AFDbZIYEEmJ5TAJA6SEldnnljIyFQiZD+hs7Re+5IScVL35NHvnx+B066r4A9u+YdPyXG0zU3+jukkpYrAIx09JgslLLdHc+PgzvU4LgResO45n0OOJv9KHUXs2DbFuel1tjAlF8sY64LyyTxuGxWkQFqIiapBeyk9yaRM69dDgdBR7s3GTcrVGzgqSF6w4jL1OP3y43ii5edQYLszWXFlzIpVJmN1Ztk5l6cWsyW6llkLv796AGZgDgrSTfybEMANUKGbw85U4eLoA9Q+Mhl1I/b0TfYJFfCvD7nfiSzESqZ4rFYiMuFnKpBHnri6hdXIsnJOBMVZP4e2qyMHUyNAZGBeCrolLi4iuVAH+jiE0vXinfkDBQsmsOGkxWouB70QQ9Jr5Dzlw1mqy43GAkDzq1WJmCd1+VB/5GCLyB32dAkbq8AAmWZetF2wB2EfaBM9XE73A/Y8FP0WnQsnjoCORddX5ZbQIx0+Knpl9mXQXB0+pNxHP6y7lqrCw4RTwveeuPYOXUwXjp66NEZ+3MfuFYvJ7cVRXmr8Jrf7wFVQ0m1Bos8PWUI8BL4VagA1ybl87NLPlzODR4sHOTcbdG7SpImp8Rj/yD50UlpwnJYczPk8skxAu+hSZMuEI1xb8EADReCry+5TjlbpTdZuyjFHuqpOo01CnNADBzRG9U1hlE+hUBwOUGE2aO6I1XvjlO3JalzWg0W4gmcLtLKvDw8N7EBf+5sXHMydfzM+Kx7pcL4u+pXxhTJxPqpySWThw+LFuPiv2OxuhDqAu6RCKhBldqyqBWBzZBIC+wZ6owMDqA6jwc7qdyWuwlEgnC/Tzho5TjzrggxBP0YcUXaqAkzIZy4GoGVJ3BSjx+k8WKt+/rj5WEDqiH0nphQnI4Fq4Xa28eSu0FL6XYayZVp8EkV4G8p5yYZbsl0h+j4oOJnlW0afAOBIE8BHbNQ0Pov8OSCjxq6E0N8ldMTGJ2VYX4erod3JAI91fhlT/0+z2AUskRoLYHUDdS8udwrgce7Nxk3K1RuwqSzlU1EUtOhedqmFoJhUxKXLTHJoYyP4/mzwMAJgu9dORoMyYFNMuzk/DBdyeJ3SxqDxlVpDpGH4pLdUbiQpIzQocx+lBisNPEyCYAgMFsI4qQX8hOEhkfAvbForyW7WNynvI9VTWYmTqZJ0bFEoXGH+85jafH9MGd8cGitm2LxUYNoNL1IVhF8FNJ02kwLjGUuvimxWrRw8+T6IQ8JEaDflEBWLKB4FuUqYdVEPBVy46rWC1Se2uwcLweufmHRPuybGISqhqN1MDM5MK5t9FkIf6+/zgwAn/dcpyaEVqSpScGdJfqDFhJmB1VUFKJheuLkJsRTxRop+g0MFvF5cHdJZVYvrnYbjZptoo0SRovdgYjgjKFXOEiy0QzW9xdUgmj1dam3ja0gOaF7CQs3lDE29I5bQIPdm4y7taoXQVJNBatL8Lm2WlYsO6wqC17eXYSFq8nCyqVcinTn0cltwc7pBSzq8Cs3mAhpsIbjGbcERdCtOJ/MTsJeZmJImM6+5gFCd7eTi7XAPZyVMsOqTSdFn5q9jn1VsoxLSVG5MHTaLZSS3zu0uhCuJ2bkYC/bnUu5Tj8eQSQB4Gm6LR4lZJhO3axDvkHzpMX+6+OIjcjnrj45ozQQQIQP29Yby1e/Io8E63wbDXyD5wnjsPYfeqyaCaYY19y1x3Gkkw9NWgbow9hnlcLwYdod0klzlU1MTNCJis50B2fHIZnv6D4BZ2owLPpfani7cv1ZPH27pJK1F0ZvPtsi9/akdIa5vUi1NeTmIU5X00eHuqAVXKjOZy3BkzbjLWH0C/Snxh087Z0zs2GBzs3GXfnvbCCpLRYunlao8mKqkajvQ28hZOqwWyhzq8ymK3MoYZyqYR6R/Z8RjzzHKgVMmIq/HgZPbux/7fL2HakzCn4cAwmNVjogcLukkoYLFaMayEYLq81QCljmx8q5VKiB8/njwylHhtrgCjL5M6VTuZ8NTkjtOdUJSVQqCBm+xxEa7yYpok2AURBcKPJinqz88Ls+B7KapuoM9H+++hQ/EQpqYWxSlEn7IEH7Xdx2EXmktbqzyrDAvbyEWkg7W+VjcztLtWRdUks8TYANBqt+HjPr6JrwiuTkjEoOpA4cHfZxMTm60XL60Z1o5lqHXH0Qi3TbLEtO6BY5XnXvj68LZ1z8+DBTivgzrwXVpC0JCsR497cRd22yWQjOql+xDCk23a0HEN7aYgCXZkU8FTI8PTnvxDvyMaeYbdDe1EMC22CQNWY9NJ6wXdgJHEw6RN39qEeB2BfSEgaoa/+kko1P8wZac9gkPZFzdAPrSw4jY2zUoleK3lZeox/i9x15M5YA8DupUILFFgLusXKduipbSLPv0rRafDCxCTiTK2lExOh9bYb07XEZhOo2ZkJyWFMrUttk4UatC3dVIxNs1OxIF+c8WN1ebk632qlDBlvirdd48IvKNxfJdIBOc4N6f0c+KrkRN2Kp1yKJWuPtMgw2kuYL399FMuzk4jXDX+1B54fl4BF6w+L9yUrEW99e4L42z5WWtumHVCussBsXx/els65efBgp5VwpyZOC5IAYFB0ADXrQ7u7ZfHBzlOY2K8HTl5qcHpcIpEgKkCNeqOFeke2dOMRrJ+ZgkWENuNZI2PhTykfNZks1AXx/wZE4JX/kUsyz49jC2pVCvLC9r8jZUjppcHMkTonF90GoxlSCWARbPjDwAg0mqyovaIf+sPACAg2gVri6x/lTxXwGi1W9I/yJ25XXmd0a+wBazFgLeisgA0AVBRN1u6SSpTWNBGdnpdtPIKXJiUTs2EB3gr87VuyK/GyjcVUHyGArQ9rNFlRUWcklhtNjC4vVgYuRaeB2kOGFXcnIdhH+bsOqtaAqEA1dShpqk6DeoOZuC8Wqw3DemsQF+YrzrSU1kKtkGNOi5uH4bFaLM1KxKSBEfioQBzkT0uJQWUDvZSTR5iZ5tAWvTAxEc+tPUTUo7VlachVed6f4uvD29I5Nxse7HQwaEESLeuzKFOPCZRswoGz1dQSwKDoAPirPTAuMdQpuBoUHQA/tQI//3aZ2Spd1WgiZoWkVxzuSVoff5UCr1ICmguEMo6DHcfYs7Fo2/33x3PI6tdDZFaYduVOXAqJaBio/bkkPHFnLNHkLmdkLGQSCVEUe8/ACGppcGhMIEJ8lbARnnM3I8T6fuVSCVOTRUOtkCHAS0E8vmkpMaLWagck7YyDXSUVeOyO3sRgJ0WnoQZeDrw9PbDzREXz77DJLMOBs9UI8Q2hBiZHL9Rg3rg42DYXiwPyEfbvcHNhqVMwNDxWizv6BOGF7CTi72JZdhImvbeHmNnqE+yNv/9pEJ7PFwcYy7OT8OJXxcQs6Z5TldhUeIGqR1s8gdxa72rsw1mKUD43/3CbCn9daRijNWqiANvVcFEO53rhwU4ngZb1cczAIaWs1x04j/83Ywhy88V6gKUMPQAAeCnlzFbpRpON2Lb9+pbj+O+jw0SCaYfWh7Ygskoy72w/iXUzU7BkwxGRCHnWyFjsPUW+6C/O1IuOHbAHMAvyD2NcUhhRNLtg3WEszdKLNC3ltQYEeClwuqKRmNkx2WyQSkAMAisbTchZQx52aZ/DRc4IldUaqAHNkQs1yMvUYxFhZpgEwKyR9tEApOzb9mNiUSgATE+NwbKNR6iL78LxCXghO7F5jpOnhwwXa5rQSJmn5YAUfDn0YQqphJlN8VLIiMHXxFvCMSO1F3Fa+n23RUPhIcX4Ft9Fea0BXkoZlmwsEgUL352owNwvCvHi3UmYOTK2RTbQAsEmUDNJoxNDkZtPHpOSm38Y/SL9idsF+yipYvjdJZWwUoaSuioP0f4/tbXw15WGMcyNkj+H4w482GkH3DXRomV9VhJajFN0Gnz4p0FYtrGI2D2zZEMRVtydDKVcStwXhVzKbJWelkIuS+SM1BHHPnx3ogLTqulGd6wMRqPJivNVTegX5e8kQj5wthpTV/2Af/yJrE0K9fWkLiQs1+JdJypwvtpA1bTMTY8jHns6o3NI5SGjmj8OuvL9AOJFO8xPhQXjE5BHKBk+cFs0bIKAsYmhTuL0sloDLtWZEBGowvik8BaLvRGhvkocOltD3M9hvTTUcpN9zhPNtVfD1OX4qjyoBo5NVisWjNdj6UZxS/uSrEQs2SAu1+wusZvnDewZQAw8c9YcwLv3DcD5GoNopEmM1gtbKcL9707Y3brfalGScwSJj9zeC3/dIva36R/pTz1vu05UYEZKDMXviq2top5PF+Uh1v+nthb+utIwtmUbPKf7woOdNqY1TLTeobjoVjWYsOfUZcQRxjr8cq4GDUYLnv68iLgvJoJniIOCkkpMT+1FfI510fdV0X9urJJMWqwWP7a4s78ahVyKFdmJIh8aV9kGlh6G5VNitpLdlb87cQmDogOJ26k8xGaKDkprmkQT0R2L9iff/4ox+lDic5V1JqwsOE18z5FxQVg0PkE0pV2AAAkkeHh4DJpadLml6DSQSdmTts9XNZFdezewzSRLa5qImcBlm4rx5WPD8PI35Jb2sloDNTDZVVKBqSk9iRoiAAjz98Qvu6udvX2ulKpYXKwlD/QEgPnj4onBDgu1QoYwf08cKBBnpzKTw5lBIm1Wlbvdm0D7CH95QMNpb3iw04Zcy9wsANeV9WHV7uuMdEHwyqmDsZAyLPC5Lwox30V7uSDQ70hpWh9vpZyqIzlyoQZLsvRYmF8kKlUtHJ+ALMqYArVChkAvBdEXZkmWnrmQsO5+Wc8JlLEAscHeCPZWwiYAXkpZc9u2l1IOi2DD1JQYYskl3F+FXILpXuoV0z2rIGDdL+Kum/HJYXjmi0LiPiaE+yE3X/z9AvbFcGA0OSPS4MKIkYbDPZpkJrlsYhL+732y1gWwO2HTWtoTwujt3Czsw1yleJXQAVXdRN4PBxIJ2XV8ZcFpCABRYxIRQL9RYZUGl24swoLxCcRuSpZIl1UeeiE7CXkbiojbceEvp7vCg502xNVIiIu19jvd68n6sGr3YX6e1NlQdQYLtaX5uxMVmMfOriPYlzzeINRPSQ2w7ugTRC3X3DckGtUNJuKATasgYGCUP3F/F4xPwCJC0LarpAIL19OzDWk6DS43mIiL2tHSWhw6Ty7zqBUy+HspiG7OfxkZC0ECvPPtCZGL8OIJeuStL8LiTH2zS7LDvyY3/zA+nn4rckbqnLp8GowWWAQbpq3cj8WZelEHEMsTxlVZZeowckYkZ6SOmWFjZQzqDBZip9LmwxcQH+ZLnUXFMrlz1ULegzK40hEkkjqg8rL01GniaVeCAZJG6M3J/WE0W6kdk7RMC6s0uKukErnjE9wS6bLKQ3lZiTBars/ri8PpyvBgpw1xJSosrTagX6R/s/7CsfguWncYr/6hH/Eixardmyz0UhStTOOg3mhhmJbVYFtxOTEzUFZrpGp9nhsrxeyVZJHu7E8P4N+P3EbUyYyKD0ZeViIWrhN7rfSP8ifeFQPsbMPy7CSYrDbivLHFmYkoPFdNfM8FGQl4cXMxsTX718oGvNUi0HHsx4qvivHWfQPwyjdi9+i37huA1745iphgH6eOI8e5zurfgxiYsHyUWCU61vMrC05j06xUUdYvTafBkiw900vGWynHH97fK3pcrZBhQ04qFq8Xv+eCCXrYGLoVtomjBofOVhPLXz+crsSGX8RmjPbBukVYkqmHySL+PS3NSsSSjWSNEAAsvyLsv56OSVcBW5OJHEDdiI7PHa8vDqcrw4OdNoQVmKgVMoT7q3Cg4BSx5Zfmt8Gq3bOCK1cXYB9POeamx2PJxiKigdqfPvoBWf17ND/u0AJJJfQpzoJg96mhtSDTKmNbi8vx4G09iVmDOheDFBuMFuIsLpsgYBHFp2TR+sNYkplIzFzdEumHIF8ldbTB4g1HiNkiD5kEr31DzrJJcQzPpPclTqmelhIDuYSso2EFAjSthwOav0n/KH+YrDaiu7LJYqN2jaXoNGCpfSQSwcnvyMdTjnqjGTWNRoT6iuc/OThaWounx/SFDYLou1hwZQo7qUz50ZRBTNfmstqW3j12I796g5nqOm7Xa9EDM1qAQSvfOfDx9GgVTQvXyXA4v9Ohg53FixcjLy/P6bG+ffvi6NGjAACDwYCnn34an332GYxGI8aMGYN3330XISHseTrtBSswWTA+gXlHSfPbYNXuWYJnliDYYbpGGoZZUFKJhfmH7Z1em46IFuc0nZb6mduPlWPWCEo79IhY7DpxiaqVMFisOHS+RuS1kpFEn/quVsgQqFZgQYuMUJpOg7ysRBw4U03cbndJJRpMFrJxIGO0QWWdiVrCWzRejw92niJ+nqsp1XPT44jbHS2tpXrJhPl6Mtu5ewSoqDOebDaBmGH75ok0ahlyWkoMKDEZ8iboUVZrFM03cwxytdkELJuYSByZMD8jHn94fy/uvTVK9F2crmig6rFcZbYgAdGte+H4BKbOy9VcKVqA4c68PA6Hc/Po0MEOAOj1emzdurX533L577v85JNPYtOmTfj888/h5+eHnJwc3H333di9myxmbW9YgQmrHMPy2wDod5TVTWamIDgvU08cezAtJYY5DHPXlU4v0uI88w4ddT8/2HkKWf3Cid4noX5KJIT54R+EzNY79w1AZKAK/2/vr6Kp2ZP696AeY25GvKj05dj/ReuLmK6+tDbxMfoQauYq0FtB1Ugt3cj+PFb3l0ImFbkvp+k0mDcuHucvNxFbus0MQfTUlBg0mszUGU/33xZF3Be5VILP9p2hbrdwQgKxZDioZwBy15EH0gLA0sxEvLDxCLEc9eOvVYgP9SGetzUPDYHWW4GXJiUj2FfppINy1VVmtZEHiLpye3ank8ndeXkcDufm0eGDHblcjtDQUNHjNTU1+Oijj7BmzRqMHDkSALBq1SrEx8fj+++/x2233dbWu3pN0AKTk5fqmdvR7jQd0O4oacZyfx7eGwfPVhMXrn//cAaP3dGb+Xk007I9pyqZrs0BagVGxoc4TURP7OEHCYB/FJwiLkDjk8KwsuAUMWj5rbIRD6X2QkZSmMjoLjnCn5ihAFwPIZRTFsuqBnpp0Mxo12f5+gDssmK90YJ+UQFOZaUDZ6tRXmvAU58fFC32AFDTaMbsT+n6qI+n3UoU4c4aGYuffrtM3I/TlY2YOzYOSzYcaRF0arFwQgIqG4zIGRnbQmRtRaOZPci1yWLF1uJy4vRrtUKGdTNTsP/Xy6LvN9RPic/+fJuoHOkYoskSWu+ljFhhuT3fSBaGa2g4nPalwwc7J06cQHh4ODw9PTF06FCsWLECUVFR+Omnn2A2mzFq1Kjm18bFxSEqKgp79+5lBjtGoxFGo7H537W1ta16DC0hBSZ+KvZFz5UGg2ZUGB2oxvjkFsZydUb0DFSjZ6DaKd0vkUjQw1+FB26NctmCTFucVxacxoZZqchbX0S9i/UDRBPRT5TVURfEYMbUbMDuHUMyuusV5M08BhppsVqcrmggPucho2cMXAWkrM9jdTl5KeXExddDJsHb9w0gGuAtzUoEAGqGQiaVEAPd6av34+37BhC3kUskuFDdRJwwf6G6CcG+SvTSqlBRb4LVKkDtIUdUgBrHy10E8kb2ebMJdCPDxevFGaOCkkos3XgEy7OTkLv20HWNWAHsvk2tMcKAa2g4nPajQwc7Q4YMwerVq9G3b1+UlpYiLy8PaWlpOHz4MC5evAiFQgF/f3+nbUJCQnDx4kXm+65YsUKkBWpvvD3lTI2Ftyf9q3JlVEibf3Whukk0HygtVovb+wS5bVrWaLJCChAnPLcMcK6GNS6Cpb+wCoJIewHYMymPMkpqgN1hmVQemjVSh2BvJfG5Hv5irYsDjZeS+Xk9AlTE91ySpcfSjUeI27CEv4Jgb3MnZcMWMdruU3Ua1BrM1EDIkxLIeipkosDq6vd8ITvp930D4NhxmhjagQ/DbNLuUVNEzOqV1xqopdZtRy/h2XQLdcQKKzANUCt4FobD6WJ06GBn7NixzX9PTk7GkCFDEB0djf/85z9QqdxzGwaAefPm4amnnmr+d21tLSIjI29oX2+UBqOFqbGgCSOvxaiQdEdZ02jC3P8Wijp5dl2ZD/T25P5umZbdFR8MD5mU6G/C8gtiTb5mlXgkjO6vvacqGW3LWggAxhHmXzWZrDhb1YSpKTFXzUeyd399XVRKFVlLJWAO35QAxC4nqQA8cFs0jC2sAhz6KVq0kxzhjze2kt18d52owLPpfYki5KkpMfBlBM8BXuJAd3isFgFqD6ardqPZivn54ploK+5OYpaUAtT0wJrlUcMKkAGgpsmCvqHuCYbdzcK4OwqGw+G0Lh062GmJv78/+vTpg5KSEtx1110wmUyorq52yu6UlZURNT5Xo1QqoVSy78LbmpomM577ohAvTUpuNp1ztMPO/aKQOv/JlVEhbehfeZ2ROTW5vM6I2BAf6h3ukqxEjIy/hGAf5e8jGmoNuD1Wi7lfHnIZfLXEW0HPbJXXGkUZEQcsJ+eVBaex9vFhWNLCvTZFp8Ez6X1hslgQE+QFL6X8KrdjGeRSCfadvoxXvjkuek+1QoZNs1KJImsBArNbyWKzETVEabFaZCaHEctKn+77DbkZCcTjo2VgHJyraiK+5+xPD2DV1MHEbewjI8iZubOX6SaGgF0nRPreF68vworsJDxPKCm9NCkZIb6ebnnUuLJPoAV0rSUYbo1RMBwO5+bQqYKd+vp6nDx5Eg8++CAGDhwIDw8PbNu2DZMmTQIAHDt2DGfOnMHQoUPbeU+vHz+VB16clExsh31xUjJ8KaUAV0aFtKF/ru+K7c/T7nAFQFQCGx6rxdBeGvz0WxXxPVnBl8FqpWa2Bvb0R5i/vQTWsgQU7EsPWhtNVpRWG4gL/venKpDcwx9vEdqhZ42IxZAY8oyrQdEBUMqliAnybh4J4e0pg5dSDk+5DGv2/Ub8vDX7fsMTo/oQ33PXiQrMHxePdb9cEHkaLZygh1mwEbucAl2IZRUyKTUrQhrdkaLT4KHUXtTMXF6mHlpvBe69NYpoD6BWkrNzW4rLMW9cPLM05I5HDctnKFWnQYAX2334ekutLK41w3qz4ZkkDufa6NDBzpw5czBhwgRER0fjwoULWLRoEWQyGSZPngw/Pz/MmDEDTz31FAIDA+Hr64tZs2Zh6NChHbYTi4WXUs6cMv7aH28hbudq+jGtVdaLUTYC2GWl5gt7ifjCvmDdYWbrbp3BTLxANxgs1O6hshojBIFcAoIgYTrsXqpznrTuMD9M0wVhWQt/GuCqduisRKpINcxfBS+lXCTErWww4eG0Xnh7e4m4y2lELLYfE3cbOag1mImmiW9sOYZ7b40iPieTSqhls7RYrf38EEjRaSCRgBiUXaozYGXBKaLz8PLNxfjn9FuxfHOx6Pg+mjIIlXX0wKS2yYxeQd7Mhfh6PWqOldZiWXYi5hP8eV7ITmIGLjc7C+NuhvVG4JkkDufa6dDBzrlz5zB58mRUVlYiKCgIqamp+P777xEUZJ9a/Ne//hVSqRSTJk1yMhXs6JAW+3qDhamHqDdYEEKYicgSErNaZdUKGVNfwgp2WBd2Vy3dKoUMOZ8eEF2g52fEU71tRuekEP1rAODOuCDkZZH9gmak9UJPjRr5pCGaSWHsdmizlXnnT1qYS8rrYTDbiL43Ib5KqqkgYA9K/7ZN3FU1LSUGEkGCA2erRIaKfp5yatnsmTF9YTBZidmbWSNjYRMEYuv5GH0Inv2C7PcUF+aLFZQAUSqR4LZe5GyY4/hcQctS0EpOS7ISEeavwmt/vMXJyiDAi52haY0sjLsZVndpr0wSh9NZ6dDBzmeffcZ83tPTE++88w7eeeedNtqjG4d2Nzb7zljmdrSLpbv6AykkyBlh71YSu9rGQsow/3d1YacxPFaLn89UEy/QY89UU3U5MoYIedvRS5gzxorpKTGiAZtHSmvw0S6xd09BSSXOVzcx97XJZL1ukbWXUo4HV/6A6akxTovthRoDag1m5qgFCORMy3NfFGLNQ7cRA5Ps/j3w6Cc/E92Fp6/ej/88MpQcePko8fI3R4mfV15rFO2fA1fDRR+7nezNdC3+NK6yFKwSWIiv53WVn1ojC+NuhtVd2iOTxOF0Zjp0sNPVYN2NPUpZKBywLpbuGJZVNZmoWQiD2YrqJhOi4EXc1tWFPYIyiTqPMUhy6cYjyH88BXktRmak6DSoZ1j0qxUyeMhkRK3TovF6vP3tSea+0jBZbNd916xSyDAwKoAYEKT21jDFyzbYiNvljNRhKWWMyOL1RXj6rr6Yt1aciUnTaaGQSkQhqwSARbBh0sBI6nwvGq5GMCg93POncbej0F1aIwvjbobVXdo6k8ThdHZ4sNOGsO7GWM7D13KxvN7FwFvpgcl/30fMQizbVIwNOanUbV1d2AvP1RCt/3+raKT6mzSarDhd2UDMNqiVbB+WPIKx3O4rxnI0/RBrNlharBZ7KA67rLtmf5UHckbqAAgEIbUn/r7pCLXj6uHh5GB3KKP1eteJCjyXHkceJZERj9e3HEdGv3CRu/Khc7X46lApcV+OltZRv1tXfjn+Kvf8ado6S9EaWZi2HgnR1pkkDqezw4OdNoR1N7ay4DQ2zkrFYobz8M1E663AoGhyFsJVcOVKRzHuzV3USdQsvClOwak6DbUtnRkMlFRgakpP4nMrC04jf2YKlm444myqqNMiNyMB2e/S56uxSopRgWqMSw539u6pM0Ilk2LeuAQsXi/WDy3OTITRbCHqa2S06ZpXMFisxM/zkAHj+oUTM14zUmOQm5GABeuc92V4rBYP3BqFFJ2W+N1Ga9TMANFdf5q2zlK0VhamLUdCtHUmicPp7PBgpw1xdTcmk0gwNikMU4b1dFq4WoMbvROlXdh/raRPoma1CqfpNAj0UhAXfLlUguUTk7DnZAWCr5qPVFbT5NJrhcbA6AD8cqYK/aL8nUYfHDhbjcsNRqbDLuuuOcxfhZFxwU7C5sQefgjx9cTF6ibi7Ci1XIo3tpwkZloE0H2EAHs2heSQXVZnpHb3AcDiCXrmwkx6DgBmjtDBJgii72jmCLZbNYu2zlK0ZhamrUZC8OGiHM71wYOdNoR1N7ZgfAJy8w8TA4HhsdpW6a64ljtRlo8H6cLuzfBFWVlwmqrLmZYaAy8PGXGOV6iPJ0rrjNh8qLRFuUaL5yL9mccY7KskBlAzUmMQo/HCul8OiTIt9wyMcPuu2ZXQVqWQiVrW/dQKzM9IwI7jl5q3ccwpiw5Uu+X2W1prYHabWQWBuTCTnjtZXo/pq/cT7QGmr96PDTmpbv1G2yNL0RUGc3aFY+Bw2goe7LQhrLuxAVH+mPclueX3WnQL7pqLsRY8d3w8tN4KaqljYHQAthRfJGYwnv1vIf7zCNkM0mwT8MbW44T5VxUYy9Le6LTYVlxO/LzHP/kZax66ldq27M5d840IbWkmjXf0CXJrX2jjRa71eRK1BjPVHgBwv9zUXlmKrjCYsyscA4fTFvBgp42h3Y2dokzZdsBaSFrDXOxGfDxYpY59pyupXUcvbDqCuHA/BPvYXZElEgnOVzeh0WylZimWbSrGxlmpWLTusEikuygzAZlv76aWpLyUHtS2ZXfumt0V2rJMGudeOdfXuy9eCvZ/bVfPk2jNchPPUnA4nNaEBzvtAOluzNeTXv4B6AtJa5mLubtwV9SbmKUO2oyv1N4a3BLpT2yHvqNPEHU/G01WVDeaiO7KBrMVA6MDqIJaR1BF43rvmt0V2l7Lue4dzHYfbomU4a5sn3/FFj6TaO1yE89ScDic1oIHOx0EdxeS1mrbrTWYoVbIMD01hjgHibZwuyp10LxYArwUeGv7EaKg9vmx7NEWngo5LtQYRC30r285jn8/MhR5bdTh5m7mozW6keRSCdPXx51gh4tiORxOZ4UHOx0EdxeS1mrb9VN54M3J/YmZljcn96cOJvVmeOIA9oCAVK4oraELak1WGzNLYbKQDfkAu4leR28Hbo3ykMZLgRWbi4l6pX//cAav/qHfdb8nwMtNHA6nc8KDnQ6EOwtJa+ko3B1MqpBJmYGJQiYld/pcqqfuS02jmZmlMJjIYtvhsVpovNzzfnEHdwPW1igP+akVWDA+Ac+vFXebvZCd1CnaqzkcDudmwYOdDsb1LiStpaNwdzBpdZOJGZjUNJkAwhgKPxV9P388U4WjpbXULMXiTL1bYwpaA3cC1tYoD9U0mrBk4xGik/XSjUfw6h/68YCFw+F0G3iw08nxUyvw0qRk7Dh+CcE+yt8N92oNGNEnyO0Fzd3y2NVjKFoGJrM/PUAdQ8EK2o6V1mLxBL0oS+EIBsI6WGnFnczHzS4PVdSbsLW4HFuLy6nP82CHw+F0F3iw0wWgebTczuhicoW75TF3x1C4GkHhKqDpCqWVjj7sksPhcDorPNjp5LA8Wm6k9dzd8tiNlGSuNbshABCN8+Y4wQdFcjgczu/wYKeT01qt520RtNA+l/S61jBO7MrwQZEcDofzOzzY6eS0ZrmiNYIWV5DGXgBoFePErgz3xOFwOJzf4cFOJ6e1yxVtqYWhZW/mZ8S3Svaqq8M9cTgcDscOD3Y6OV2lXMEaezGlqom5LRfb0ukKwm0Oh8O5UaTtvQOcG8NRrhgeq3V6vLOVK1jaI1dwsS2Hw+FwWPDMThegK5QrWNqjA2erkRarJQZDnSl7xeFwOJz2gQc77QBJhHujgUlnL1ewtEcrC05j8+w0LFx3mIttORwOh3Pd8GCnjeEt1GRY2qNB0QEIUJMHiPJAh8PhcDiu4JqdNoQlwn3ui0LUNJraac/an2vRHvmpFegd7I1bogLQO9ibBzocDofDuSZ4ZqcNaS0DwK5CV9AecTgcDqfjwYOdNoTPK3JNZ9cecTgcDqfjwctYbQifV8ThcDgcTtvDg502xCHCJcFbqDkcDofDaR14sNOGdBUDQA6Hw+FwOhNcs9PGcBEuh8PhcDhtCw922gEuwuVwOBwOp+3gZSwOh8PhcDhdGh7scDgcDofD6dLwYIfD4XA4HE6Xhgc7HA6Hw+FwujQdOthZsWIFBg8eDB8fHwQHB2PixIk4duyY02vuuOMOSCQSpz+PPvpoO+0xh8PhcDicjkaHDnZ27tyJmTNn4vvvv8eWLVtgNpsxevRoNDQ0OL3u4YcfRmlpafOfl19+uZ32mMPhcDgcTkejQ7eef/31107/Xr16NYKDg/HTTz9h+PDhzY+r1WqEhoZe8/sajUYYjcbmf9fW1t74znI4HA6Hw+mQdOjMTktqamoAAIGBgU6Pf/LJJ9BqtUhMTMS8efPQ2NjIfJ8VK1bAz8+v+U9kZGSr7TOHw+FwOJz2RSIIgtDeO3Et2Gw2ZGZmorq6GgUFBc2Pf/jhh4iOjkZ4eDgKCwsxd+5c3Hrrrfjyyy+p70XK7ERGRqKmpga+vr6tehwcDofD4XBuDrW1tfDz83O5fnfoMtbVzJw5E4cPH3YKdADgz3/+c/Pfk5KSEBYWhjvvvBMnT55E7969ie+lVCqhVCpbdX85HA6Hw+F0DDpFsJOTk4ONGzfiu+++Q0REBPO1Q4YMAQCUlJRQg52WOJJbXLvD4XA4HE7nwbFuuypSdehgRxAEzJo1C2vXrsWOHTsQExPjcpuDBw8CAMLCwq75c+rq6gCAa3c4HA6Hw+mE1NXVwc/Pj/p8h9bsPP7441izZg3WrVuHvn37Nj/u5+cHlUqFkydPYs2aNRg3bhw0Gg0KCwvx5JNPIiIiAjt37rzmz7HZbLhw4QJ8fHwgkUhu2v47tEBnz57lWqAW8HNDhp8XOvzckOHnhQw/L3S60rkRBAF1dXUIDw+HVErvuerQwQ4t8Fi1ahWmTp2Ks2fP4oEHHsDhw4fR0NCAyMhIZGdnIzc3t0N8gdcqnOqO8HNDhp8XOvzckOHnhQw/L3S647np8GUsFpGRkdeVweFwOBwOh9P96FQ+OxwOh8PhcDjXCw92WhGlUolFixbxNncC/NyQ4eeFDj83ZPh5IcPPC53ueG46tGaHw+FwOBwO50bhmR0Oh8PhcDhdGh7scDgcDofD6dLwYIfD4XA4HE6Xhgc7HA6Hw+FwujQ82HGD7777DhMmTEB4eDgkEgny8/Opr3300UchkUjwxhtvOD1++fJl3H///fD19YW/vz9mzJiB+vr61t3xVsbVeZk6dSokEonTn/T0dKfXdMfzAgDFxcXIzMyEn58fvLy8MHjwYJw5c6b5eYPBgJkzZ0Kj0cDb2xuTJk1CWVlZGx5F6+Dq3LT8vTj+vPLKK82v6Y6/mfr6euTk5CAiIgIqlQoJCQl4//33nV7TXX8zZWVlmDp1KsLDw6FWq5Geno4TJ044vaYrnpsVK1Zg8ODB8PHxQXBwMCZOnIhjx445veZajvvMmTPIyMiAWq1GcHAwnnnmGVgslrY8lFaBBztu0NDQgH79+uGdd95hvm7t2rX4/vvvER4eLnru/vvvR1FREbZs2dI85PTqCe6dkWs5L+np6SgtLW3+8+mnnzo93x3Py8mTJ5Gamoq4uDjs2LEDhYWFWLBgATw9PZtf8+STT2LDhg34/PPPsXPnTly4cAF33313Wx1Cq+Hq3Fz9WyktLcXKlSshkUgwadKk5td0x9/MU089ha+//hr/+te/UFxcjCeeeAI5OTlYv35982u6429GEARMnDgRp06dwrp163DgwAFER0dj1KhRaGhoaH5dVzw3O3fuxMyZM/H9999jy5YtMJvNGD169HUdt9VqRUZGBkwmE/bs2YOPP/4Yq1evxsKFC9vjkG4uAueGACCsXbtW9Pi5c+eEHj16CIcPHxaio6OFv/71r83PHTlyRAAg7N+/v/mxr776SpBIJML58+fbYK9bH9J5mTJlipCVlUXdpruel3vuuUd44IEHqNtUV1cLHh4ewueff978WHFxsQBA2Lt3b2vtaptD+790NVlZWcLIkSOb/91dfzN6vV5YsmSJ02MDBgwQ5s+fLwhC9/3NHDt2TAAgHD58uPkxq9UqBAUFCX//+98FQeg+56a8vFwAIOzcuVMQhGs77s2bNwtSqVS4ePFi82vee+89wdfXVzAajW17ADcZntlpBWw2Gx588EE888wz0Ov1ouf37t0Lf39/DBo0qPmxUaNGQSqVYt++fW25q23Ojh07EBwcjL59++Kxxx5DZWVl83Pd8bzYbDZs2rQJffr0wZgxYxAcHIwhQ4Y4peZ/+uknmM1mjBo1qvmxuLg4REVFYe/eve2w1+1DWVkZNm3ahBkzZjQ/1h1/MwAwbNgwrF+/HufPn4cgCNi+fTuOHz+O0aNHA+i+vxmj0QgATllRqVQKpVKJgoICAN3n3NTU1AAAAgMDAVzbce/duxdJSUkICQlpfs2YMWNQW1uLoqKiNtz7mw8PdlqBl156CXK5HLNnzyY+f/HiRQQHBzs9JpfLERgYiIsXL7bFLrYL6enp+Oc//4lt27bhpZdews6dOzF27FhYrVYA3fO8lJeXo76+Hi+++CLS09Pxv//9D9nZ2bj77rub575dvHgRCoUC/v7+TtuGhIR02fNC4uOPP4aPj49T2r07/mYA4K233kJCQgIiIiKgUCiQnp6Od955B8OHDwfQfX8zjsV73rx5qKqqgslkwksvvYRz586htLQUQPc4NzabDU888QRSUlKQmJgI4NqO++LFi06BjuN5x3OdmQ49CLQz8tNPP+Fvf/sbfv75Z+rU9u7Kvffe2/z3pKQkJCcno3fv3tixYwfuvPPOdtyz9sNmswEAsrKy8OSTTwIAbrnlFuzZswfvv/8+br/99vbcvQ7FypUrcf/99zvdtXdX3nrrLXz//fdYv349oqOj8d1332HmzJkIDw93unPvbnh4eODLL7/EjBkzEBgYCJlMhlGjRmHs2LEuB0t3JWbOnInDhw83Z7M4PLNz09m1axfKy8sRFRUFuVwOuVyO3377DU8//TR69uwJAAgNDUV5ebnTdhaLBZcvX0ZoaGg77HX70KtXL2i1WpSUlADonudFq9VCLpcjISHB6fH4+PjmbqzQ0FCYTCZUV1c7vaasrKzLnpeW7Nq1C8eOHcNDDz3k9Hh3/M00NTXh+eefx+uvv44JEyYgOTkZOTk5uOeee/Dqq68C6N6/mYEDB+LgwYOorq5GaWkpvv76a1RWVqJXr14Auv65ycnJwcaNG7F9+3ZEREQ0P34txx0aGirqznL8u7OfGx7s3GQefPBBFBYW4uDBg81/wsPD8cwzz+Cbb74BAAwdOhTV1dX46aefmrf79ttvYbPZMGTIkPba9Tbn3LlzqKysRFhYGIDueV4UCgUGDx4sahE9fvw4oqOjAdgv3h4eHti2bVvz88eOHcOZM2cwdOjQNt3f9uKjjz7CwIED0a9fP6fHu+Nvxmw2w2w2Qyp1vnzLZLLmTCH/zQB+fn4ICgrCiRMn8OOPPyIrKwtA1z03giAgJycHa9euxbfffouYmBin56/luIcOHYpDhw453UBs2bIFvr6+ohuyTkc7C6Q7JXV1dcKBAweEAwcOCACE119/XThw4IDw22+/EV/fshtLEAQhPT1d6N+/v7Bv3z6hoKBAiI2NFSZPntwGe996sM5LXV2dMGfOHGHv3r3C6dOnha1btwoDBgwQYmNjBYPB0Pwe3e28CIIgfPnll4KHh4fw4YcfCidOnBDeeustQSaTCbt27Wp+j0cffVSIiooSvv32W+HHH38Uhg4dKgwdOrS9DummcS3/l2pqagS1Wi289957xPfojr+Z22+/XdDr9cL27duFU6dOCatWrRI8PT2Fd999t/k9uutv5j//+Y+wfft24eTJk0J+fr4QHR0t3H333U7v0RXPzWOPPSb4+fkJO3bsEEpLS5v/NDY2Nr/G1XFbLBYhMTFRGD16tHDw4EHh66+/FoKCgoR58+a1xyHdVHiw4wbbt28XAIj+TJkyhfh6UrBTWVkpTJ48WfD29hZ8fX2FadOmCXV1da2/860I67w0NjYKo0ePFoKCggQPDw8hOjpaePjhh51aHAWh+50XBx999JGg0+kET09PoV+/fkJ+fr7TezQ1NQmPP/64EBAQIKjVaiE7O1soLS1t4yO5+VzLufnggw8ElUolVFdXE9+jO/5mSktLhalTpwrh4eGCp6en0LdvX+G1114TbDZb83t019/M3/72NyEiIkLw8PAQoqKihNzcXFHbdFc8N6RzAkBYtWpV82uu5bh//fVXYezYsYJKpRK0Wq3w9NNPC2azuY2P5uYjEYRupNricDgcDofT7eCaHQ6Hw+FwOF0aHuxwOBwOh8Pp0vBgh8PhcDgcTpeGBzscDofD4XC6NDzY4XA4HA6H06XhwQ6Hw+FwOJwuDQ92OBwOh8PhdGl4sMPhcDgcDqdLw4MdDofT5ejZsyfeeOONa379r7/+ColEgoMHD7baPnE4nPaDBzscDqfDMHXqVEycOFH0+I4dOyCRSEQTm2ns378ff/7zn2/qvq1evRr+/v439T05HE7bIG/vHeBwOJybTVBQUHvvAofD6UDwzA6Hw+l0FBQUIC0tDSqVCpGRkZg9ezYaGhqan29Zxjp69ChSU1Ph6emJhIQEbN26FRKJBPn5+U7ve+rUKYwYMQJqtRr9+vXD3r17AdgzS9OmTUNNTQ0kEgkkEgkWL17cBkfK4XBuBjzY4XA4nYqTJ08iPT0dkyZNQmFhIf7973+joKAAOTk5xNdbrVZMnDgRarUa+/btw4cffoj58+cTXzt//nzMmTMHBw8eRJ8+fTB58mRYLBYMGzYMb7zxBnx9fVFaWorS0lLMmTOnNQ+Tw+HcRHgZi8PhdCg2btwIb29vp8esVmvz31esWIH7778fTzzxBAAgNjYWb775Jm6//Xa899578PT0dNp2y5YtOHnyJHbs2IHQ0FAAwPLly3HXXXeJPnvOnDnIyMgAAOTl5UGv16OkpARxcXHw8/ODRCJpfg8Oh9N54MEOh8PpUIwYMQLvvfee02P79u3DAw88AAD45ZdfUFhYiE8++aT5eUEQYLPZcPr0acTHxztte+zYMURGRjoFKbfeeivxs5OTk5v/HhYWBgAoLy9HXFzcjR0Uh8NpV3iww+FwOhReXl7Q6XROj507d6757/X19XjkkUcwe/Zs0bZRUVE39NkeHh7Nf5dIJAAAm812Q+/J4XDaHx7scDicTsWAAQNw5MgRUUBEo2/fvjh79izKysoQEhICwN6afr0oFAqnchqHw+k8cIEyh8PpVMydOxd79uxBTk4ODh48iBMnTmDdunVUgfJdd92F3r17Y8qUKSgsLMTu3buRm5sL4PfszbXQs2dP1NfXY9u2baioqEBjY+NNOR4Oh9P68GCHw+F0KpKTk7Fz504cP34caWlp6N+/PxYuXIjw8HDi62UyGfLz81FfX4/BgwfjoYceau7GailmZjFs2DA8+uijuOeeexAUFISXX375phwPh8NpfSSCIAjtvRMcDofTluzevRupqakoKSlB796923t3OBxOK8ODHQ6H0+VZu3YtvL29ERsbi5KSEvzlL39BQEAACgoK2nvXOBxOG8AFyhwOp8tTV1eHuXPn4syZM9BqtRg1ahRee+219t4tDofTRvDMDofD4XA4nC4NFyhzOBwOh8Pp0vBgh8PhcDgcTpeGBzscDofD4XC6NDzY4XA4HA6H06XhwQ6Hw+FwOJwuDQ92OBwOh8PhdGl4sMPhcDgcDqdLw4MdDofD4XA4XZr/D449jaaRp0x1AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(data[\"Height\"]).reshape((-1, 1)) # reshape... LinearRegression vyzaduje maticu X-ov\n",
        "Y = data[\"Weight\"]\n",
        "\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OL9q2_oV3Z6Z",
        "outputId": "76d5ca0c-cb6f-45b8-8146-f291c21e6469"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[163],\n",
              "       [153],\n",
              "       [165],\n",
              "       ...,\n",
              "       [175],\n",
              "       [163],\n",
              "       [168]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lm = LinearRegression()\n",
        "lm.fit(X, Y)\n",
        "# intercept si prida automaticky\n",
        "\n",
        "# Parametre\n",
        "lm.intercept_, lm.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xS8q7Bg3ByH",
        "outputId": "d539a436-d960-4c58-a1fb-89789100125c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(np.float64(-68.83176566805159), array([0.89644869]))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='Height', y='Weight', data=data)\n",
        "\n",
        "# dokreslenie regresnej priamky\n",
        "a = lm.intercept_\n",
        "b = lm.coef_\n",
        "x_line = [min(data['Height']), max(data['Height'])]\n",
        "y_line = [a + b * xi for xi in x_line]\n",
        "\n",
        "plt.plot(x_line, y_line, color='red', label='Regresna priamka')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "LSdfYtkH5-Dw",
        "outputId": "6d4b6d79-d479-4cb8-adb8-07e169541332"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Weight vs. vsetky ostatne\n",
        "\n",
        "# vysvetlujuce bez Gender\n",
        "X = data.copy(deep=True)\n",
        "X.drop(labels=\"Gender\",axis=1,inplace=True)\n",
        "X.drop(labels=\"Weight\",axis=1,inplace=True)\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "O2BT3u8Kqht5",
        "outputId": "ed680175-f3fa-408a-cf86-77f6b811a175"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Age  Height  Leg_Length  Arm_Length  Arm_circum  Waist\n",
              "0     20.083333     163        34.2        36.2        29.0   89.6\n",
              "1     20.083333     153        37.2        34.0        26.1   85.5\n",
              "2     20.083333     165        37.7        35.0        31.4   70.1\n",
              "3     20.083333     171        37.9        36.2        29.8   91.1\n",
              "4     20.083333     159        38.1        34.0        29.2   74.3\n",
              "...         ...     ...         ...         ...         ...    ...\n",
              "4974  79.833333     184        43.0        42.8        32.2  112.8\n",
              "4975  79.916667     151        35.6        34.2        33.5  114.9\n",
              "4976  79.916667     175        38.6        41.5        32.8  100.2\n",
              "4977  79.916667     163        40.2        37.5        26.2   82.8\n",
              "4978  79.916667     168        40.8        39.5        29.8  103.5\n",
              "\n",
              "[4978 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-10bbb6d5-eba0-4750-8f90-0674871e90a2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Leg_Length</th>\n",
              "      <th>Arm_Length</th>\n",
              "      <th>Arm_circum</th>\n",
              "      <th>Waist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20.083333</td>\n",
              "      <td>163</td>\n",
              "      <td>34.2</td>\n",
              "      <td>36.2</td>\n",
              "      <td>29.0</td>\n",
              "      <td>89.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.083333</td>\n",
              "      <td>153</td>\n",
              "      <td>37.2</td>\n",
              "      <td>34.0</td>\n",
              "      <td>26.1</td>\n",
              "      <td>85.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20.083333</td>\n",
              "      <td>165</td>\n",
              "      <td>37.7</td>\n",
              "      <td>35.0</td>\n",
              "      <td>31.4</td>\n",
              "      <td>70.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.083333</td>\n",
              "      <td>171</td>\n",
              "      <td>37.9</td>\n",
              "      <td>36.2</td>\n",
              "      <td>29.8</td>\n",
              "      <td>91.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.083333</td>\n",
              "      <td>159</td>\n",
              "      <td>38.1</td>\n",
              "      <td>34.0</td>\n",
              "      <td>29.2</td>\n",
              "      <td>74.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4974</th>\n",
              "      <td>79.833333</td>\n",
              "      <td>184</td>\n",
              "      <td>43.0</td>\n",
              "      <td>42.8</td>\n",
              "      <td>32.2</td>\n",
              "      <td>112.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4975</th>\n",
              "      <td>79.916667</td>\n",
              "      <td>151</td>\n",
              "      <td>35.6</td>\n",
              "      <td>34.2</td>\n",
              "      <td>33.5</td>\n",
              "      <td>114.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4976</th>\n",
              "      <td>79.916667</td>\n",
              "      <td>175</td>\n",
              "      <td>38.6</td>\n",
              "      <td>41.5</td>\n",
              "      <td>32.8</td>\n",
              "      <td>100.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4977</th>\n",
              "      <td>79.916667</td>\n",
              "      <td>163</td>\n",
              "      <td>40.2</td>\n",
              "      <td>37.5</td>\n",
              "      <td>26.2</td>\n",
              "      <td>82.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4978</th>\n",
              "      <td>79.916667</td>\n",
              "      <td>168</td>\n",
              "      <td>40.8</td>\n",
              "      <td>39.5</td>\n",
              "      <td>29.8</td>\n",
              "      <td>103.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4978 rows × 6 columns</p>\n",
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              "\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",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 4978,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.432332908909082,\n        \"min\": 20.083333333333332,\n        \"max\": 79.91666666666667,\n        \"num_unique_values\": 716,\n        \"samples\": [\n          30.083333333333332,\n          47.666666666666664,\n          23.333333333333332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 140,\n        \"max\": 204,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          163,\n          156,\n          185\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Leg_Length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.033123682626243,\n        \"min\": 23.7,\n        \"max\": 55.5,\n        \"num_unique_values\": 240,\n        \"samples\": [\n          40.7,\n          39.4,\n          35.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Arm_Length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7535067998546445,\n        \"min\": 29.5,\n        \"max\": 47.7,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          41.4,\n          33.3,\n          38.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Arm_circum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.979115404741861,\n        \"min\": 19.5,\n        \"max\": 55.5,\n        \"num_unique_values\": 294,\n        \"samples\": [\n          26.5,\n          33.4,\n          27.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Waist\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.832760265027236,\n        \"min\": 59.1,\n        \"max\": 172.0,\n        \"num_unique_values\": 749,\n        \"samples\": [\n          72.6,\n          99.4,\n          71.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lm = LinearRegression()\n",
        "lm.fit(X, Y)\n",
        "\n",
        "# vypisanie parametrov\n",
        "names = np.append(\"intercept\", np.array(list(X.columns)))\n",
        "pd.Series(np.append(lm.intercept_, lm.coef_), index=names)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "vvVWH4bFqHFi",
        "outputId": "df3742ef-2347-4050-dd27-a01955c2bc51"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "intercept    -117.836408\n",
              "Age            -0.097700\n",
              "Height          0.468451\n",
              "Leg_Length      0.183715\n",
              "Arm_Length     -0.109532\n",
              "Arm_circum      1.536749\n",
              "Waist           0.724694\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "\n",
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              "      <th>0</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>intercept</th>\n",
              "      <td>-117.836408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.097700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Height</th>\n",
              "      <td>0.468451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Leg_Length</th>\n",
              "      <td>0.183715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arm_Length</th>\n",
              "      <td>-0.109532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arm_circum</th>\n",
              "      <td>1.536749</td>\n",
              "    </tr>\n",
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              "      <td>0.724694</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Novi \"nahodni\" ludia\n",
        "# ... mozete vyskusat svoje udaje\n",
        "X_new = pd.DataFrame({\n",
        "    \"Age\": random.choices(data[\"Age\"], k=5),\n",
        "    \"Height\":random.choices(data[\"Height\"], k=5),\n",
        "    \"Leg_Length\":random.choices(data[\"Leg_Length\"], k=5),\n",
        "    \"Arm_Length\":random.choices(data[\"Arm_Length\"], k=5),\n",
        "    \"Arm_circum\":random.choices(data[\"Arm_circum\"], k=5),\n",
        "    \"Waist\":random.choices(data[\"Waist\"], k=5)})\n",
        "\n",
        "X_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OTBYh88ZFZCB",
        "outputId": "e6ad369d-4a0f-4f20-f12d-ea2c4dabb9ce"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Age  Height  Leg_Length  Arm_Length  Arm_circum  Waist\n",
              "0  71.666667     163        38.1        42.0        34.0  113.3\n",
              "1  46.000000     171        32.9        37.0        41.0   80.6\n",
              "2  34.583333     172        46.5        39.8        34.6  102.9\n",
              "3  48.666667     163        36.5        38.5        46.9  110.7\n",
              "4  37.750000     188        40.7        39.0        27.6   83.3"
            ],
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.666667</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_5da17800-09fb-41bc-9054-a27f0c010b17\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_new')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5da17800-09fb-41bc-9054-a27f0c010b17 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_new');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_new",
              "summary": "{\n  \"name\": \"X_new\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.571113928286715,\n        \"min\": 34.583333333333336,\n        \"max\": 71.66666666666667,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          46.0,\n          37.75,\n          34.583333333333336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 163,\n        \"max\": 188,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          171,\n          188,\n          163\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Leg_Length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.084092839435567,\n        \"min\": 32.9,\n        \"max\": 46.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          32.9,\n          40.7,\n          46.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Arm_Length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.840652058374966,\n        \"min\": 37.0,\n        \"max\": 42.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          37.0,\n          39.0,\n          39.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Arm_circum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.364916835918787,\n        \"min\": 27.6,\n        \"max\": 46.9,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          41.0,\n          27.6,\n          34.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Waist\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.314307036232494,\n        \"min\": 80.6,\n        \"max\": 113.3,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          80.6,\n          83.3,\n          102.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predikcie\n",
        "lm.predict(X_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwBu-uvKHoh_",
        "outputId": "48cc9842-4cfc-4957-bb6b-4e41a323b428"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 88.27588696,  81.18320331,  91.28438349, 108.55225295,\n",
              "        72.53104974])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardizacia dat\n",
        "scaler = preprocessing.StandardScaler().fit(X)\n",
        "X_scaled = scaler.transform(X)\n",
        "\n",
        "X_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iDdFeIJhMKt8",
        "outputId": "d6345973-d1ee-4ef3-fe39-64a0eea3f8ce"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.74246598, -0.45830186, -1.02369265, -0.46467205, -0.83299215,\n",
              "        -0.5829174 ],\n",
              "       [-1.74246598, -1.45503682, -0.27977761, -1.26373345, -1.41548344,\n",
              "        -0.84190015],\n",
              "       [-1.74246598, -0.25895487, -0.15579177, -0.90052373, -0.3509304 ,\n",
              "        -1.81466465],\n",
              "       ...,\n",
              "       [ 1.89909511,  0.7377801 ,  0.06738274,  1.46033951, -0.06972771,\n",
              "         0.08664777],\n",
              "       [ 1.89909511, -0.45830186,  0.46413743,  0.00750059, -1.39539753,\n",
              "        -1.01244977],\n",
              "       [ 1.89909511,  0.04006562,  0.61292044,  0.73392005, -0.6723049 ,\n",
              "         0.29509731]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# kontrola 1\n",
        "X_scaled.mean(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3dcNRIoMLUP",
        "outputId": "a19c9052-2a90-4eba-86ea-83cc1b46e255"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.82702833e-16, -7.93615430e-16,  1.37027125e-15, -1.89554189e-15,\n",
              "       -4.73885473e-16, -1.14189271e-16])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# kontrola 2\n",
        "X_scaled.std(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWXvX0ReMLg4",
        "outputId": "6600bfab-d886-4601-b486-11368c41f223"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., 1., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Regresia na standardizovanych datach\n",
        "lm_scaled = LinearRegression()\n",
        "lm_scaled.fit(X_scaled, Y)\n",
        "\n",
        "# vypisanie parametrov\n",
        "names = np.append(\"intercept\", np.array(list(X.columns)))\n",
        "pd.Series(np.append(lm_scaled.intercept_, lm_scaled.coef_), index=names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "2gZETOTmML10",
        "outputId": "e5f644e0-2f7b-4319-9ac8-c2f53c625280"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "intercept     81.411270\n",
              "Age           -1.605271\n",
              "Height         4.699859\n",
              "Leg_Length     0.740872\n",
              "Arm_Length    -0.301567\n",
              "Arm_circum     7.650882\n",
              "Waist         11.472757\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>intercept</th>\n",
              "      <td>81.411270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-1.605271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Height</th>\n",
              "      <td>4.699859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Leg_Length</th>\n",
              "      <td>0.740872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arm_Length</th>\n",
              "      <td>-0.301567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arm_circum</th>\n",
              "      <td>7.650882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Waist</th>\n",
              "      <td>11.472757</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predikcie z noveho modelu\n",
        "lm_scaled.predict(X_new)\n",
        "# nonsens... preco?"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6CKuDOQkMlpo",
        "outputId": "ed5156de-5e96-4903-fea0-4f9f656c311d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.13/dist-packages/sklearn/utils/validation.py:2732: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2307.99867458, 2062.85179667, 2301.98681082, 2413.65719956,\n",
              "       2089.62316634])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ta ista transformacia na X_new\n",
        "X_new_scaled = scaler.transform(X_new)"
      ],
      "metadata": {
        "id": "hl423hwoMmBd"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X_new_scaled uz nebude \"standardizovane\"\n",
        "X_new_scaled.mean(axis=0), X_new_scaled.std(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xuJM4L89NToV",
        "outputId": "18b80f87-b679-401f-923b-f3d221580788"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.05963872,  0.37895551,  0.15169311,  0.64674972,  0.73772572,\n",
              "        -0.04221194]),\n",
              " array([0.79319898, 0.91047252, 1.12761424, 0.5979628 , 1.32313535,\n",
              "        0.86522555]))"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Opravene predikcie\n",
        "lm_scaled.predict(X_new_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PA5PXE2yOR3o",
        "outputId": "3c460880-7d8b-46a1-dae0-85a002a10f02"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 88.27588696,  81.18320331,  91.28438349, 108.55225295,\n",
              "        72.53104974])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predikcie z povodneho modelu\n",
        "lm.predict(X_new)\n",
        "# to iste!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDMO2rc-ORnY",
        "outputId": "4b87e9b0-3a0a-4f66-9ecf-cf720cd0c2e1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 88.27588696,  81.18320331,  91.28438349, 108.55225295,\n",
              "        72.53104974])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jcRIFH8BOizW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}