2363 lines (2363 with data), 556.9 kB

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      "gpuType": "T4"
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      "name": "python3",
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    {
      "cell_type": "markdown",
      "source": [
        "# NAMA : Alisha Zahra Saadiya\n",
        "# NIM : 2501971742\n",
        "# KELAS : LB06"
      ],
      "metadata": {
        "id": "qzr01tiYGBh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. You are an AI Engineer in a company that works in the health sector. You are asked to create an application to help the company determine if someone has Lower Back Pain. The dataset team provided the data with the name: “dataset_spline.csv”"
      ],
      "metadata": {
        "id": "M-uoXU1dG5ZJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### a. The given dataset has several problems, preprocess the data to solve the problems from the data. Mention what problems you found from the given data, give an explanation of what approach you used and why you chose the chosen approach? Do the Exploratory Data Analysis to understand the problem."
      ],
      "metadata": {
        "id": "Bt8g4Ah8HETB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "jEb_peTYIV_t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset\n",
        "dataset = pd.read_csv('dataset_spine.csv')\n",
        "dataset.head()"
      ],
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          "base_uri": "https://localhost:8080/",
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              "   Unnamed: 0       Col1       Col2       Col3       Col4        Col5  \\\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preprocessing"
      ],
      "metadata": {
        "id": "MB_llAxUN6Hx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# menghapus kolom yang tidak berguna\n",
        "dataset = dataset.drop(dataset.columns[0], axis=1)\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "5Me9cbqiQa7Q",
        "outputId": "c8234617-8a68-4844-cfb1-618bab86ff7a"
      },
      "execution_count": null,
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              "        Col1       Col2       Col3       Col4        Col5       Col6  \\\n",
              "0  63.027817  22.552586  39.609117  40.475232   98.672917  -0.254400   \n",
              "1  39.056951  10.060991  25.015378  28.995960  114.405425   4.564259   \n",
              "2  68.832021  22.218482  50.092194  46.613539  105.985135  -3.530317   \n",
              "3  69.297008  24.652878  44.311238  44.644130  101.868495  11.211523   \n",
              "4  49.712859   9.652075  28.317406  40.060784  108.168725   7.918501   \n",
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              "       Col7     Col8     Col9     Col10      Col11    Col12 Class_att  \n",
              "0  0.744503  12.5661  14.5386  15.30468 -28.658501  43.5123  Abnormal  \n",
              "1  0.415186  12.8874  17.5323  16.78486 -25.530607  16.1102  Abnormal  \n",
              "2  0.474889  26.8343  17.4861  16.65897 -29.031888  19.2221  Abnormal  \n",
              "3  0.369345  23.5603  12.7074  11.42447 -30.470246  18.8329  Abnormal  \n",
              "4  0.543360  35.4940  15.9546   8.87237 -16.378376  24.9171  Abnormal  "
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              "      <td>98.672917</td>\n",
              "      <td>-0.254400</td>\n",
              "      <td>0.744503</td>\n",
              "      <td>12.5661</td>\n",
              "      <td>14.5386</td>\n",
              "      <td>15.30468</td>\n",
              "      <td>-28.658501</td>\n",
              "      <td>43.5123</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39.056951</td>\n",
              "      <td>10.060991</td>\n",
              "      <td>25.015378</td>\n",
              "      <td>28.995960</td>\n",
              "      <td>114.405425</td>\n",
              "      <td>4.564259</td>\n",
              "      <td>0.415186</td>\n",
              "      <td>12.8874</td>\n",
              "      <td>17.5323</td>\n",
              "      <td>16.78486</td>\n",
              "      <td>-25.530607</td>\n",
              "      <td>16.1102</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>68.832021</td>\n",
              "      <td>22.218482</td>\n",
              "      <td>50.092194</td>\n",
              "      <td>46.613539</td>\n",
              "      <td>105.985135</td>\n",
              "      <td>-3.530317</td>\n",
              "      <td>0.474889</td>\n",
              "      <td>26.8343</td>\n",
              "      <td>17.4861</td>\n",
              "      <td>16.65897</td>\n",
              "      <td>-29.031888</td>\n",
              "      <td>19.2221</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>69.297008</td>\n",
              "      <td>24.652878</td>\n",
              "      <td>44.311238</td>\n",
              "      <td>44.644130</td>\n",
              "      <td>101.868495</td>\n",
              "      <td>11.211523</td>\n",
              "      <td>0.369345</td>\n",
              "      <td>23.5603</td>\n",
              "      <td>12.7074</td>\n",
              "      <td>11.42447</td>\n",
              "      <td>-30.470246</td>\n",
              "      <td>18.8329</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49.712859</td>\n",
              "      <td>9.652075</td>\n",
              "      <td>28.317406</td>\n",
              "      <td>40.060784</td>\n",
              "      <td>108.168725</td>\n",
              "      <td>7.918501</td>\n",
              "      <td>0.543360</td>\n",
              "      <td>35.4940</td>\n",
              "      <td>15.9546</td>\n",
              "      <td>8.87237</td>\n",
              "      <td>-16.378376</td>\n",
              "      <td>24.9171</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ec60470-1e8b-4af6-a2d2-d4c5012f4626')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4ec60470-1e8b-4af6-a2d2-d4c5012f4626 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4ec60470-1e8b-4af6-a2d2-d4c5012f4626');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c48f9a5e-0ab6-482b-98f5-f03e3d827e45\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c48f9a5e-0ab6-482b-98f5-f03e3d827e45')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c48f9a5e-0ab6-482b-98f5-f03e3d827e45 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBxAKSk3JCsO",
        "outputId": "130e7fc3-84dc-4b5b-fe10-af2daabbd944"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 310 entries, 0 to 309\n",
            "Data columns (total 13 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Col1       310 non-null    float64\n",
            " 1   Col2       310 non-null    float64\n",
            " 2   Col3       310 non-null    float64\n",
            " 3   Col4       310 non-null    float64\n",
            " 4   Col5       310 non-null    float64\n",
            " 5   Col6       310 non-null    float64\n",
            " 6   Col7       310 non-null    float64\n",
            " 7   Col8       310 non-null    float64\n",
            " 8   Col9       310 non-null    float64\n",
            " 9   Col10      310 non-null    float64\n",
            " 10  Col11      310 non-null    float64\n",
            " 11  Col12      310 non-null    float64\n",
            " 12  Class_att  310 non-null    object \n",
            "dtypes: float64(12), object(1)\n",
            "memory usage: 31.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing value\n",
        "dataset.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBtKb8FMJ3b3",
        "outputId": "2014a04d-90b6-422f-97f0-82c709b2efe8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Col1         0\n",
              "Col2         0\n",
              "Col3         0\n",
              "Col4         0\n",
              "Col5         0\n",
              "Col6         0\n",
              "Col7         0\n",
              "Col8         0\n",
              "Col9         0\n",
              "Col10        0\n",
              "Col11        0\n",
              "Col12        0\n",
              "Class_att    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada setiap kolom dataset bernilai 0 yang artinya tidak ada missing value di dalam data \"dataset_spine.csv\""
      ],
      "metadata": {
        "id": "pWmdrHPULxx5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform categorical data\n",
        "dataset_replace = {'Abnormal': 0, 'Normal': 1}\n",
        "\n",
        "dataset['Class_att'] = dataset['Class_att'].replace(dataset_replace)"
      ],
      "metadata": {
        "id": "jS6OplDVOJu4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "karena pada kolom class masih berbentuk kategori maka kita perlu mengubah terlebih dulu menjadi numerik"
      ],
      "metadata": {
        "id": "SPJAzqHJQ3t9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "l8_hIcf9Qo_P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Class_att'].hist(grid = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "f9xMmkj0RT9s",
        "outputId": "4c0d6dc4-cf8e-4d77-eb64-6f8bf6cadc96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari histogram diatas dapat kita ketahui bahwa jumlah class Normal setengahnya dari class Abnormal yang artinya data sudah balance"
      ],
      "metadata": {
        "id": "n_VFiM9VRyVM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows = 6, ncols = 2, figsize =(10, 12), sharey = False)\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col1', width = 0.8, ax = axes[0, 0])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col2', width = 0.8, ax = axes[0, 1])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col3', width = 0.8, ax = axes[1, 0])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col4', width = 0.8, ax = axes[1, 1])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col5', width = 0.8, ax = axes[2, 0])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col6', width = 0.8, ax = axes[2, 1])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col7', width = 0.8, ax = axes[3, 0])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col8', width = 0.8, ax = axes[3, 1])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col9', width = 0.8, ax = axes[4, 0])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col10', width = 0.8, ax = axes[4, 1])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col11', width = 0.8, ax = axes[5, 0])\n",
        "sns.boxplot(data = dataset, x = \"Class_att\", y = 'Col12', width = 0.8, ax = axes[5, 1])\n",
        "\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vVlI9EsKTMNc",
        "outputId": "b2d9d31b-6970-45c0-aefe-be2b5ef6c108"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1200 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan boxplots diatas kita sudah tidak memiliki outlier pada data"
      ],
      "metadata": {
        "id": "lhAzy0S6V3MT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr = dataset.corr()\n",
        "sns.heatmap(corr, annot=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "DgvOz204Sl9w",
        "outputId": "cb868667-5e7d-4db6-eb9b-f91f1804eff7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan heatmap diatas kita dapat mengetahui korelasi antar variabel"
      ],
      "metadata": {
        "id": "LiW-UEhsWHES"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### b. Separate your dataset into train, test, and validation, according to the following ratio: 80% train, 10% test, and 10% validation"
      ],
      "metadata": {
        "id": "sYUbxtGicgjw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "sHB_Svncctb9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y = dataset['Class_att']\n",
        "X = dataset.drop(columns=['Class_att'])"
      ],
      "metadata": {
        "id": "arVxnsEbzO5_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_size = 0.8\n",
        "test_size = 0.1\n",
        "val_size = 0.1"
      ],
      "metadata": {
        "id": "vaEAuUVmfHgs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test_val, y_train, y_test_val = train_test_split(X, Y, test_size=test_size+val_size, random_state=40)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_test_val, y_test_val, test_size=test_size/(test_size+val_size), random_state=40)"
      ],
      "metadata": {
        "id": "s-5M5kAVffFV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Train: \", len(X_train))\n",
        "print(\"Test: \", len(X_test))\n",
        "print(\"Validation: \", len(X_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axqnDZJBgBQ_",
        "outputId": "0a6c169a-93fe-4d46-f11b-e1bdd0688a3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train:  248\n",
            "Test:  31\n",
            "Validation:  31\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### c. Create an architecture with the provision that there are 3 hidden layers, where each hidden layer uses sigmoid as an activation function. Where the number of neurons in the first hidden layer is always 512 and each subsequent layer will be N/2, where N is the number of neurons in the previous layer. In the output layer, use the activation function softmax. Display validation loss and training loss graphs"
      ],
      "metadata": {
        "id": "UrUUXa1JgX3h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Input"
      ],
      "metadata": {
        "id": "dXK1_0UKiHFG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Input(shape=(X_train.shape[1],)),\n",
        "    keras.layers.Dense(512, activation='sigmoid'),\n",
        "    keras.layers.Dense(256, activation='sigmoid'),\n",
        "    keras.layers.Dense(128, activation='sigmoid'),\n",
        "    keras.layers.Dense(2, activation='softmax'),\n",
        "])"
      ],
      "metadata": {
        "id": "j_XGblJkcwrL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "9YCaMMRnnX11"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "VyQJb3C0nK4o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R8k-PcpuyKL-",
        "outputId": "dafc913e-9c68-4f9b-cd8d-168de84280ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_51\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_182 (Dense)           (None, 512)               6656      \n",
            "                                                                 \n",
            " dense_183 (Dense)           (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_184 (Dense)           (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_185 (Dense)           (None, 2)                 258       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 171138 (668.51 KB)\n",
            "Trainable params: 171138 (668.51 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Melatih model\n",
        "history = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=50, batch_size=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-cTD4r1iPS7",
        "outputId": "5b9dd950-fa07-4559-f418-fda80a61b2c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "13/13 [==============================] - 1s 21ms/step - loss: 0.6280 - accuracy: 0.6734 - val_loss: 0.5819 - val_accuracy: 0.8065\n",
            "Epoch 2/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.5030 - accuracy: 0.7097 - val_loss: 0.3944 - val_accuracy: 0.8387\n",
            "Epoch 3/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.4130 - accuracy: 0.7621 - val_loss: 0.3215 - val_accuracy: 0.8387\n",
            "Epoch 4/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.3976 - accuracy: 0.7621 - val_loss: 0.3232 - val_accuracy: 0.8387\n",
            "Epoch 5/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.3807 - accuracy: 0.8024 - val_loss: 0.2977 - val_accuracy: 0.9032\n",
            "Epoch 6/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.3252 - accuracy: 0.8347 - val_loss: 0.3031 - val_accuracy: 0.8710\n",
            "Epoch 7/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.3522 - accuracy: 0.8266 - val_loss: 0.2851 - val_accuracy: 0.9032\n",
            "Epoch 8/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.3092 - accuracy: 0.8589 - val_loss: 0.2756 - val_accuracy: 0.9032\n",
            "Epoch 9/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.3140 - accuracy: 0.8387 - val_loss: 0.3112 - val_accuracy: 0.7742\n",
            "Epoch 10/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2913 - accuracy: 0.8508 - val_loss: 0.2650 - val_accuracy: 0.8710\n",
            "Epoch 11/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2831 - accuracy: 0.8750 - val_loss: 0.2507 - val_accuracy: 0.9355\n",
            "Epoch 12/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.3378 - accuracy: 0.8185 - val_loss: 0.2457 - val_accuracy: 0.9032\n",
            "Epoch 13/50\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.2770 - accuracy: 0.8669 - val_loss: 0.2388 - val_accuracy: 0.9355\n",
            "Epoch 14/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2752 - accuracy: 0.8508 - val_loss: 0.2534 - val_accuracy: 0.9032\n",
            "Epoch 15/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2647 - accuracy: 0.8750 - val_loss: 0.2771 - val_accuracy: 0.8387\n",
            "Epoch 16/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2629 - accuracy: 0.8871 - val_loss: 0.3192 - val_accuracy: 0.8065\n",
            "Epoch 17/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2803 - accuracy: 0.8589 - val_loss: 0.4643 - val_accuracy: 0.7742\n",
            "Epoch 18/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2832 - accuracy: 0.8710 - val_loss: 0.3791 - val_accuracy: 0.7419\n",
            "Epoch 19/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2745 - accuracy: 0.8790 - val_loss: 0.3632 - val_accuracy: 0.8065\n",
            "Epoch 20/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2727 - accuracy: 0.8790 - val_loss: 0.2346 - val_accuracy: 0.8710\n",
            "Epoch 21/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2517 - accuracy: 0.8871 - val_loss: 0.2285 - val_accuracy: 0.8710\n",
            "Epoch 22/50\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.2296 - accuracy: 0.9032 - val_loss: 0.2888 - val_accuracy: 0.8387\n",
            "Epoch 23/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2359 - accuracy: 0.8911 - val_loss: 0.2572 - val_accuracy: 0.8710\n",
            "Epoch 24/50\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.2242 - accuracy: 0.8911 - val_loss: 0.2729 - val_accuracy: 0.8387\n",
            "Epoch 25/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2239 - accuracy: 0.8992 - val_loss: 0.2287 - val_accuracy: 0.9355\n",
            "Epoch 26/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2446 - accuracy: 0.8750 - val_loss: 0.2783 - val_accuracy: 0.8387\n",
            "Epoch 27/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2223 - accuracy: 0.8831 - val_loss: 0.5252 - val_accuracy: 0.7419\n",
            "Epoch 28/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.3388 - accuracy: 0.8306 - val_loss: 0.4193 - val_accuracy: 0.7419\n",
            "Epoch 29/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2451 - accuracy: 0.8992 - val_loss: 0.2352 - val_accuracy: 0.8710\n",
            "Epoch 30/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2133 - accuracy: 0.9113 - val_loss: 0.2314 - val_accuracy: 0.8710\n",
            "Epoch 31/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2124 - accuracy: 0.8992 - val_loss: 0.2846 - val_accuracy: 0.8387\n",
            "Epoch 32/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2495 - accuracy: 0.9032 - val_loss: 0.3400 - val_accuracy: 0.8387\n",
            "Epoch 33/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2495 - accuracy: 0.8710 - val_loss: 0.2624 - val_accuracy: 0.8710\n",
            "Epoch 34/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2082 - accuracy: 0.9032 - val_loss: 0.2346 - val_accuracy: 0.8710\n",
            "Epoch 35/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1933 - accuracy: 0.9194 - val_loss: 0.2596 - val_accuracy: 0.8387\n",
            "Epoch 36/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1891 - accuracy: 0.9315 - val_loss: 0.2795 - val_accuracy: 0.8710\n",
            "Epoch 37/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2009 - accuracy: 0.9113 - val_loss: 0.2558 - val_accuracy: 0.8710\n",
            "Epoch 38/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1942 - accuracy: 0.9234 - val_loss: 0.2727 - val_accuracy: 0.8387\n",
            "Epoch 39/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2150 - accuracy: 0.8871 - val_loss: 0.2591 - val_accuracy: 0.8710\n",
            "Epoch 40/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2582 - accuracy: 0.8952 - val_loss: 0.5555 - val_accuracy: 0.7742\n",
            "Epoch 41/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2361 - accuracy: 0.8952 - val_loss: 0.3263 - val_accuracy: 0.8387\n",
            "Epoch 42/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1885 - accuracy: 0.9113 - val_loss: 0.2428 - val_accuracy: 0.8710\n",
            "Epoch 43/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1891 - accuracy: 0.9113 - val_loss: 0.2395 - val_accuracy: 0.9032\n",
            "Epoch 44/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2077 - accuracy: 0.8871 - val_loss: 0.2278 - val_accuracy: 0.9032\n",
            "Epoch 45/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1929 - accuracy: 0.9113 - val_loss: 0.2642 - val_accuracy: 0.8387\n",
            "Epoch 46/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1752 - accuracy: 0.9435 - val_loss: 0.2529 - val_accuracy: 0.8710\n",
            "Epoch 47/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1725 - accuracy: 0.9435 - val_loss: 0.2529 - val_accuracy: 0.8710\n",
            "Epoch 48/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1782 - accuracy: 0.9315 - val_loss: 0.3127 - val_accuracy: 0.8387\n",
            "Epoch 49/50\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1740 - accuracy: 0.9153 - val_loss: 0.3030 - val_accuracy: 0.8387\n",
            "Epoch 50/50\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1877 - accuracy: 0.9153 - val_loss: 0.2309 - val_accuracy: 0.9032\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Validation Loss', 'Training Loss'], loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "nSINXY3TiWEG",
        "outputId": "82f77c48-e22f-40c8-fd33-c910ca610735"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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rADNnzuT6669n3LhxPPXUU+zeXes/ve2223j88ccZPXo0Dz30kFsGbleRyI2nUBSITYOC/VCcA4kZ/l6RIAiCV4kKN7Ll0fF+O7crXHfdddx666288sorvP3223Tr1o1TTz0VgH//+9/85z//4cUXX6R///5ER0dzxx13UFVV5bH1rlixgiuuuIJHHnmE8ePHEx8fz0cffcRzzz3nsXM4cvzoAkVRsFqtXjkXaJVel19+Od9//z0//PADDz30EB999BEXXngh119/PePHj+f7779n4cKFzJo1i+eee45bb73Va+uRyI0nibWFEsV3IwhCCKAoCmZTmF9urnayveSSSzAYDPzvf//j3Xff5dprr7UfY9myZZx//vlceeWVDBw4kK5du7Jjxw6nj92nTx+ysrI4fLj2vX/lypV19lm+fDmdO3fm/vvvZ+jQofTo0YP9+/fX2cdkMjU7dqBPnz5s2LCB0tJS+33Lli3DYDDQq1cvp9fsCvrry8rKst+3ZcsWCgoK6Nu3r/2+nj17cuedd7Jw4UIuuugi3n77bftj6enp/P3vf+eLL77grrvuYs6cOV5Zq46IG0+ii5uSnKb3EwRBEHxKTEwMU6ZM4b777uPw4cNcc8019sd69OjBTz/9xPLly9m6dSs33XRTnUqg5hg3bhw9e/bk6quvZsOGDfz222/cf//9dfbp0aMHmZmZfPTRR+zevZuXXnqJL7/8ss4+GRkZ7N27l/Xr15Obm0tlZWW9c11xxRVERkZy9dVXs3nzZpYsWcKtt97KVVddZffbuIvFYmH9+vV1blu3bmXcuHH079+fK664gnXr1rF69WqmTp3KqaeeytChQykvL2fGjBksXbqU/fv3s2zZMtasWUOfPn0AuOOOO1iwYAF79+5l3bp1LFmyxP6YtxBx40liJHIjCIIQqFx33XUcO3aM8ePH1/HH/Otf/+LEE09k/PjxnHbaaaSlpXHBBRc4fVyDwcCXX35JeXk5w4cP5/rrr+eJJ56os895553HnXfeyYwZMxg0aBDLly/ngQceqLPPxRdfzIQJExg7diwpKSkNlqObzWYWLFhAfn4+w4YNY/LkyZxxxhnMnj3btR9GA5SUlDB48OA6t0mTJqEoCl9//TWJiYmccsopjBs3jq5du/Lxxx8DYDQaycvLY+rUqfTs2ZNLLrmEiRMn8sgjjwCaaJo+fTp9+vRhwoQJ9OzZk1dffbXF620KRVVV1atnCDCKioqIj4+nsLCQuLg4zx78t+dh8SMw8HK48DXPHlsQBMHPVFRUsHfvXrp06UJkpFSECp6nqb8xV67fErnxJOK5EQRBEAS/I+LGk9jFjZSCC4IgCIK/EHHjSXTPjfS5EQRBEAS/IeLGk+iRm/JjWpdiQRAEQRB8jogbT1KnS7GUgwuCIAiCPxBx40kUBWJtfQbEdyMIgiAIfkHEjafRp4OL70YQBEEQ/IKIG08TI5EbQRAEQfAnfhc3r7zyChkZGURGRjJixAhWr17d5P4FBQVMnz6ddu3aERERQc+ePZk/f76PVusEeuRGxI0gCIIg+AW/ipuPP/6YmTNn8tBDD7Fu3ToGDhzI+PHjGx3LXlVVxZlnnsm+ffv47LPP2L59O3PmzKFDhw4+XnkTiOdGEAQh6MnIyODFF190ev+lS5eiKAoFBQVeW5NQi1/FzfPPP88NN9zAtGnT6Nu3L6+//jpms5m5c+c2uP/cuXPJz8/nq6++YvTo0WRkZHDqqacycOBAH6+8CcRzIwiCEDAoitLk7eGHH3bruGvWrOHGG290ev9Ro0Zx+PBh4uPj3Tqfs4iI0vCbuKmqqmLt2rWMGzeudjEGA+PGjWPFihUNPuebb75h5MiRTJ8+nbZt23LCCSfw5JNPNjsi3qeI50YQBCFgOHz4sP324osvEhcXV+e+u+++276vqqrU1NQ4ddyUlBTMZrPT6zCZTKSlpaEoisuvQXAdv4mb3NxcLBZLvRHtbdu2JTu7YWGwZ88ePvvsMywWC/Pnz+eBBx7gueee4/HHH2/0PJWVlRQVFdW5eRW750bmSwmCIPibtLQ0+y0+Ph5FUezfb9u2jdjYWH744QeGDBlCREQEv//+O7t37+b888+nbdu2xMTEMGzYMBYtWlTnuMenpRRF4a233uLCCy/EbDbTo0cPvvnmG/vjx0dU5s2bR0JCAgsWLKBPnz7ExMQwYcIEDh+uvXbU1NRw2223kZCQQFJSEvfeey9XX321SxPLj+fYsWNMnTqVxMREzGYzEydOZOfOnfbH9+/fz6RJk0hMTCQ6Opp+/frZfa3Hjh3jiiuuICUlhaioKHr06MHbb7/t9lq8id8Nxa5gtVpJTU3lzTffZMiQIUyZMoX777+f119/vdHnzJo1i/j4ePstPT3du4t07FJcU+ndcwmCIPgTVYWqUv/cVNVjL+Of//wnTz31FFu3bmXAgAGUlJRw9tlns3jxYv78808mTJjApEmTyMzMbPI4jzzyCJdccgkbN27k7LPP5oorriA/P7/R/cvKynj22Wd57733+PXXX8nMzKwTSXr66af54IMPePvtt1m2bBlFRUV89dVXLXqt11xzDX/88QfffPMNK1asQFVVzj77bKqrqwGYPn06lZWV/Prrr2zatImnn36amJgYAB544AG2bNnCDz/8wNatW3nttddITk5u0Xq8RZi/TpycnIzRaCQnp24n35ycHNLS0hp8Trt27QgPD8doNNrv69OnD9nZ2VRVVWEymeo957777mPmzJn274uKirwrcPQuxZZKLTWV2Nl75xIEQfAn1WXwZHv/nPv/DoEp2iOHevTRRznzzDPt37dp06aOl/Oxxx7jyy+/5JtvvmHGjBmNHueaa67hsssuA+DJJ5/kpZdeYvXq1UyYMKHB/aurq3n99dfp1q0bADNmzODRRx+1P/7yyy9z3333ceGFFwIwe/bsFlUH79y5k2+++YZly5YxatQoAD744APS09P56quv+Nvf/kZmZiYXX3wx/fv3B6Br167252dmZjJ48GCGDh0KaNGrQMVvkRuTycSQIUNYvHix/T6r1crixYsZOXJkg88ZPXo0u3btwmq12u/bsWMH7dq1a1DYAERERBAXF1fn5lUcuxTLCAZBEISAR79Y65SUlHD33XfTp08fEhISiImJYevWrc1GbgYMGGDfjo6OJi4urtHqXwCz2WwXNqB9gNf3LywsJCcnh+HDh9sfNxqNDBkyxKXX5sjWrVsJCwtjxIgR9vuSkpLo1asXW7duBeC2227j8ccfZ/To0Tz00ENs3LjRvu/NN9/MRx99xKBBg/jHP/7B8uXL3V6Lt/Fb5AZg5syZXH311QwdOpThw4fz4osvUlpayrRp0wCYOnUqHTp0YNasWYD2g509eza33347t956Kzt37uTJJ5/ktttu8+fLqE9MGhRkiu9GEITgJtysRVD8dW4PER1dNwJ0991389NPP/Hss8/SvXt3oqKimDx5MlVVVU0vKTy8zveKotT5MO7M/qoH023ucP311zN+/Hi+//57Fi5cyKxZs3juuee49dZbmThxIvv372f+/Pn89NNPnHHGGUyfPp1nn33Wr2tuCL96bqZMmcKzzz7Lgw8+yKBBg1i/fj0//vij3WScmZlZx1yVnp7OggULWLNmDQMGDOC2227j9ttv55///Ke/XkLD6L6bYoncCIIQxCiKlhryx82LVUfLli3jmmuu4cILL6R///6kpaWxb98+r52vIeLj42nbti1r1qyx32exWFi3bp3bx+zTpw81NTWsWrXKfl9eXh7bt2+nb9++9vvS09P5+9//zhdffMFdd93FnDlz7I+lpKRw9dVX8/777/Piiy/y5ptvur0eb+LXyA1oOcbGcphLly6td9/IkSNZuXKll1fVQuziRiI3giAIrY0ePXrwxRdfMGnSJBRF4YEHHmgyAuMtbr31VmbNmkX37t3p3bs3L7/8MseOHXOqnHzTpk3Exsbav1cUhYEDB3L++edzww038MYbbxAbG8s///lPOnTowPnnnw/AHXfcwcSJE+nZsyfHjh1jyZIl9OnTB4AHH3yQIUOG0K9fPyorK/nuu+/sjwUafhc3QYkubsRzIwiC0Op4/vnnufbaaxk1ahTJycnce++93m8j0gD33nsv2dnZTJ06FaPRyI033sj48ePrFNU0ximnnFLne6PRSE1NDW+//Ta333475557LlVVVZxyyinMnz/fniKzWCxMnz6dAwcOEBcXx4QJE3jhhRcAzSt73333sW/fPqKiohgzZgwfffSR51+4B1BUfyf4fExRURHx8fEUFhZ6z1z85wfw9S3Q7XS46kvvnEMQBMHHVFRUsHfvXrp06UJkZKS/lxNyWK1W+vTpwyWXXMJjjz3m7+V4hab+xly5fkvkxhuI50YQBEFoIfv372fhwoWceuqpVFZWMnv2bPbu3cvll1/u76UFPK2qiV+rQboUC4IgCC3EYDAwb948hg0bxujRo9m0aROLFi0KWJ9LICGRG29g71Kcr3UpDovw73oEQRCEVkd6ejrLli3z9zJaJRK58RBWq0pheTVHiipsXYptTQXFVCwIgiAIPkXEjYdYuTePgY8s5PK3Vmn9F2J0341MBxcEIbgIsToUwYd46m9LxI2HiI/SyugKy7XhY7WmYhE3giAEB3q5cFlZmZ9XIgQrehdoZ8rdm0I8Nx6ivrixzZcScSMIQpBgNBpJSEiwzz8ym81ONZQTBGewWq0cPXoUs9lMWFjL5ImIGw+hi5uqGisV1RYi9YqpEhE3giAED2lpWlS6qYGQguAuBoOBTp06tVg0i7jxEDERYRgNChabsTgyRiI3giAEH4qi0K5dO1JTU6murvb3coQgw2QyYTC03DEj4sZDKIpCXGQYx8qqKSyvpq29142IG0EQgg+j0dhiX4QgeAsxFHuQOr4bMRQLgiAIgl8QceNB7OKmzEHciOdGEARBEHyKiBsPElcncmNLS5XlQU2VH1clCIIgCKGFiBsPUictJV2KBUEQBMEviLjxIHXEjXQpFgRBEAS/IOLGgzTeyE+mgwuCIAiCrxBx40F0cVN0/AgGSUsJgiAIgs8QceNB6kVu7GkpidwIgiAIgq8QceNBGh+eKZEbQRAEQfAVIm48SOPiRiI3giAIguArRNx4kLjGxI14bgRBEATBZ4i48SB65KbALm70+VISuREEQRAEXyHixoPEmzVxU1VjpaLaUmsoli7FgiAIguAzRNx4kBhTGAZF2y4srwZzGzBogkdSU4IgCL6nssbCfxbtZNOBQn8vRfAhIm48iMGg1PXdKIr4bgRBEPzIL9uP8sKiHTyzYJu/lyL4EBE3HkYqpgRBEAKHgjLtvTivRKwBoYSIGw9jFzdleiM/fQSDzJcSBEHwNWVVNQCUVNb4eSWCLxFx42HqR270iikRN4IgCL6mvNoKiLgJNUTceJj6vW4kciMIguAvyvXITYWIm1BCxI2HaTRyUyLiRhAEwdeUVVkAqLJYqayx+Hk1gq8QceNhGjcUi7gRBEHwNWXVtYJGojehg4gbD6OLm6J6k8FF3AiCIPiaiioHcSO+m5BBxI2HaTQtVZYrXYoFQRB8TJmDuCmWyE3IIOLGw9QTN45dikuP+GlVgiAIoUmdtJREbkIGETcepp64cexSLKkpQRAEn6JXS4F4bkIJETcepp64AWnkJwiC4Ccc01KlVSJuQgURNx6mQXEjIxgEQRD8Qnm1eG5CERE3HiberImbyhorFfo/laSlBEEQ/EK5VEuFJCJuPEyMKQyDom0XHd/rRhr5CYIg+BTHtJR4bkIHETcexmBQGhjBIPOlBEEQ/IFEbkITETdeoJ7vxt7IL8dPKxIEQQg9aixWqixW+/fiuQkdRNx4gcZHMIihWBAEwVc4mokBSiqrG9lTCDZE3HiBRsVNWS5Y5J9LEATBFzimpEDSUqGEiBsvUM9zE+XQpbhEUlOCIAi+oOx4cSNpqZBBxI0XqBe5MRgcGvmJuBEEQfAFx4ubYonchAwibryANPIThNbFsdIqDhWU+3sZgoep57mRyE3IIOLGCzQpbqTXjSAEHBe/tpxxz/9S939WaPXonpuocCMgnptQQsSNF9DFTVGDkRsRN4IQSFitKntySymrsrDnaIm/lyN4kDLbLKnUuAjb9xYsVtWfSxJ8hIgbLyBpKUFoPTgOUzxUUOHHlQieRk9LpcRE2O+T6E1oIOLGCzQ8GVwa+QlCIFJaWevLEN9NcKGnpeKjwjGFaZc7ETehgYgbL9Bw5EZGMAhCIOJ4sTso4iao0KulokxGYiPCACgVcRMSiLjxArq4KShzFDe2UnAxFAtCQFEq4iZo0dNSZpORaJu4kREMoYGIGy+gN/GrrLFSoZci6pGb0qPSpVgQAghHcSNpqeBCNxSbTWHE2MSNpKVCAxE3XiA2IgxF0baL6nQp1v65KDnin4UJglCPEhE3QUt5lTY0MzLcSEykTdxI5CYkEHHjBQwGhbjIhroUSzm4IAQajtVSx8qq7Z/2hdZPebUeuan13MjwzNBAxI2XaNhULL4bQQg0HKulQMrBgwndUGw21UZuxHMTGoi48RJNV0xJrxtBCBSOr56R1FTw4FgtJZ6b0ELEjZdoupGf9LoRhEDheHEjFVPBQ3kDkRvx3IQGIm68RNON/CRyIwiBQkm9tJSIm2BBLwWPCnf03Ii4CQVE3HiJuCYjN+K5EYRAQY/cRJu04YoSuQkeatNStaXgxSJuQgIRN15CJoMLQuugxFYd1aNtLCCRm2CivKq2WirGVsEqaanQICDEzSuvvEJGRgaRkZGMGDGC1atXN7rvvHnzUBSlzi0yMtKHq3WOpj03Im4EIVDQIzc928YAUi0VTNgjN+FiKA41/C5uPv74Y2bOnMlDDz3EunXrGDhwIOPHj+fIkcYb3cXFxXH48GH7bf/+/T5csXPo4qaoIc9Naa50KRaEAKFW3GiRm8OF5Vitqj+XJHgIu+fGZCRWDMUhhd/FzfPPP88NN9zAtGnT6Nu3L6+//jpms5m5c+c2+hxFUUhLS7Pf2rZt68MVO0eDkRtzEhgjABWKDvpnYYIg1EE3FHdNicagQLVFJbek0s+rEjxBnWopidyEFH4VN1VVVaxdu5Zx48bZ7zMYDIwbN44VK1Y0+rySkhI6d+5Meno6559/Pn/99Vej+1ZWVlJUVFTn5gsaFDcGAyRmaNv5e32yDkEQmkaP3MRHhZMWp6W4D4jvptVTVWOlxhaBM4eHOTTxk6h5KOBXcZObm4vFYqkXeWnbti3Z2Q37Unr16sXcuXP5+uuvef/997FarYwaNYoDBw40uP+sWbOIj4+339LT0z3+OhqiQXED0KaL9jV/j0/WIQhC09irpSLCaJ8QBYipOBjQozZQt4lfaZUFVZW0Y7Dj97SUq4wcOZKpU6cyaNAgTj31VL744gtSUlJ44403Gtz/vvvuo7Cw0H7LysryyTobFzddta/HJHIjCIFAib0UXMRNMKH7bcIMCqYwg13cWKwqFdVWfy5N8AFh/jx5cnIyRqORnJy6HXtzcnJIS0tz6hjh4eEMHjyYXbt2Nfh4REQEERERLV6rq+jipqLaSmWNhYgwrYcGiXrkRsSNIPibGouVyhrtQhcdEUaHRF3cSMVUa0cfgBoVrr33mk1GFAVUFYorq4my9TUSghO/Rm5MJhNDhgxh8eLF9vusViuLFy9m5MiRTh3DYrGwadMm2rVr561lukVsZBiKom3Xid7okRsRN4LgdxyHZkZHGO2RG2nk1/pxnCsFWiGK3VQsFVNBj9/TUjNnzmTOnDm88847bN26lZtvvpnS0lKmTZsGwNSpU7nvvvvs+z/66KMsXLiQPXv2sG7dOq688kr279/P9ddf76+X0CAGg2Jv912nHFz33Bzbq32EEATBb+gN/MKNChFhRjokaIZiSUu1fvS0lNkhQiMjGEIHv6alAKZMmcLRo0d58MEHyc7OZtCgQfz44492k3FmZiYGQ60GO3bsGDfccAPZ2dkkJiYyZMgQli9fTt++ff31Ehol3hxOUUVN3chNfDooBqgug5Kc2sZ+giD4HEczMSCemyDCcfSCTkxkGBRK5CYU8Lu4AZgxYwYzZsxo8LGlS5fW+f6FF17ghRde8MGqWk58VDhZlNcVN2EmTeAU7NdSUyJuBMFvlDqYiaFW3Bwrq6asqgazKSDeIgU3KLd3J679cCzzpUIHv6elghkpBxeEwEb33OgXvbjIcHvqQqI3rZvyan2ulGPkRuZLhQoibryIXdyUSTm4IAQi9jLwiFpfhl4xdVAqplo1xxuKQTw3oYSIGy8SH2UCoLD8uH+kRIncCEIgcLznBsR3Eyw4jl7QkREMoYOIGy/SbCM/KQcXBL9SaquWiqkjbqRiKhhoUNzYRzCIuAl2RNx4EfHcCEJgU9JE5EZ63bRuymyl4JHhDUVuZL5UsCPixos0Km704ZkVBVCW79M1CYJQi56WcozcdNDFzTERN62ZhiI3sZHSxC9UEHHjRXRxU3S8uDFFQ4ytBFxMxYLgN/RqKUdDsd1zUyjipjWjj1+oUy0lnpuQQcSNF2k0cgMOqSkRN4LgL5pKS2UXVmCxShfx1oq9WipcPDehiIgbL9K0uJFycEHwNw2lpdrGRmA0KFRbVHJLKv21NKGFVFTXLwWPlshNyCDixovo4qagvKr+gzIdXBD8TslxHYoBwowG0uK0iikxFbdeyhry3NjETamIm6BHxI0X0cVNRbWVyhpL3QclLSUIfqe0gSZ+IOXgwUBTaSmJ3AQ/Im68SGxkGIqibUs5uCAEHrWG4rozpKSRX+untlqqvqFYPDfBj4gbL2IwKPYwaL2KKd1zU5INVaU+XpkgCNCwoRgcet1IOXirpbwBz01shBZNr6yxUlVj9cu6BN8g4sbLxJsbMRVHJUJkgrZ9bJ9P1yQIgkZDHYrBsZGfzJdqrTSUlnJMP4rvJrgRceNlnKqYEt+NIPiFskbSUh0lLdXqKbf3uakVNGFGg13siO8muBFx42Wc63UjvhtB8DVVNVaqLFpqIsbUiOdGGvm1SlRVtY9fcBQ3IL1uQgURN17GLm7KGhA3ejm49LoRBJ/jmJZorFqqoKxa0hetkMoaK6qt/2LUceImVnrdhAQibrxMbeSmgX8kSUsJgt/QL24RYQbCjHXfCmMjw+1ziA5L9KbVoVdKQV3PDTiWg8vwzGBGxI2XiZO0lOAGBwvK2X20xN/LCGoaMxPrdBBTcatFT0mZjPWFq5SDhwYibryMU4biwiyoaaCLsRCSqKrKxa8u59yXfm/470bwCKWNlIHrSDl460U3Ex+fkgIZnhkqiLjxMk2Km5i2EG4G1aoJHEEAiitryC6qoLzawq4jxf5eTtBS0killI50KW69NDR6QceelpLITVAj4sbL6OKmXhM/AEWRGVNCPfJKaqN4e3PL/LiS4KZ2aGb9CyBAhwQzIOKmNaJ7bhqK3IihODQQceNlmozcgPhuhHrkOUyi3psrvhtv0Vh3Yh09ciPDM1sfuufmeDMx1P6+xXMT3Ii48TLNipvEDO2rlIMLNnIdIjf7JHLjNZrz3HSQXjetlnJn0lISuQlqRNx4meYjN3o5uERuBI280trIzZ5cmTvmLexpKVPThuLswgosVtVn6xJajn30QgO/Wz0tJf2LghsRN15GFzfl1ZaGB7W1Ec+NUJe8OpGbUlRVLqzeoDlDcWpsBEaDQrVFJdchVSgEPvrQTHMDaSmJ3IQGIm68TGxkuH27yXLwY/vAKlNqhbqem/JqCzlFcmH1BrVpqYYNxWFGA2lxmu/mgJSDtyqaLgXX3pPFcxPciLjxMkaDYu902qC4iesIhjCwVELxIR+vTghEckvr9jzaK6kpr9Cc5wYcfDdiKm5VlDVRLSV9bkIDETc+oEnfjTEMEjpr25KaEqgbuQERN96iuWopkF43rRW7obiBtFSs9LkJCUTc+IAme92AlIMLddA9N73axgKwL0/EjTfQP9031ucGHKaDi7hpVTTZxE8iNyGBiBsf4HTFlJSDC0CeLS01JCMRgD1HRdx4A3vkppFqKXAYwSDzpVoVuqG4oWopR0OxVargghYRNz6g+V43ErkRNGosVo6VaeJmaGdN3EjkxjvUdigWz02wYe9QHF7/Euf4+9aHpwrBh4gbH+B8l2KJ3IQ6x8qqUVVtMseJnTRxk5lXJn1WvIAzhuL20sivVVJmEy3mBiI3EWEGwo0KIKmpYEbEjQ9wvpHfXpCeJiGN3sAv0WwivY0Zk9FAlcUqkQMv4IqhuKCsWpq+tSKaqpZSFKXWdyOm4qBFxI0PiGtO3CR0BhSoKoayPN8tTAg4dDNxUrQJo0Ghc5I2vFEqpjyLqqqU2g3FjYub2Mhw4mweDRGYrYeK6sYNxVDruykWwRq0iLjxAc1GbsIjIa6Dti2+m5BG74SbFGMCICM5GhBx42kqa6z2VF9jTfx0ak3FIm5aC/bITQOl4FDbyE8iN8GLiBsf0Ky4AfHdCIBD5CYmAoCuIm68gqPXoqlqKXA0FUvFVGuhqbQU1M6XEs9N8CLixgc02+cGasWNlIOHNLrnJjlaIjfeRPfPmE1GDAalyX2l103rwz5bqhHhqkfrJHITvIi48QFORW6kHFygfuSmi03cSDm4Z3HGTKwj4qb1Ud5EEz+AGNvMP/HcBC8ibnyALm4KypqK3DhUTAkhS65d3GiRG13cZOWXNTxVXnCL0srmzcQ6esWUeG5aB1ar6tDErzHPjfZ7lwq44EXEjQ9IMGviprza0vgFSkYwCNSmpZKitchNamwEZpMRqwpZx8r8ubSgormJ4I50TBRDcWuiosZi327MUBwbKZ6bYEfEjQ+ItYVAwYkuxWW5UFHkg1UJgYielkq2RW4URSEjyea7kTEMHsOZ0Qs6eloqu7BCmim2AnQzMTRVLWUrBRfPTdAi4sYHGA2K/ZNCo+ImMg7Mydq2mIpDljx7KXiE/b4uKeK78TTOjF7QSY2NxGhQqLGqHC2ubHZ/wb/ofpvIcEOjZnEZnhn8iLjxEVIOLjRHeZXF3lhO99wAdLFFbvZIxZTH0C9qZifEjdGgkBYnvpvWQnOVUuAwPLOiifdjoVUj4sZHOFcOrpuKxXcTiuh+G5PRYO/DAQ4VUyJuPEaZvTtx854bkAGarYnmGviB9LkJBUTc+AiXysElLRWS5DlUSilKbThdet14nlIXPDdQWzEl4ibw0YdmNlYpBQ7jF8RzE7SIuPERzqWlpBw8lLFXSjmkpKC2S/Hhwgq7n0BoGa70uQEZwdCaaK7HDYjnJhQQceMjxHMjNIe9x010RJ37E6NN9r8fMRV7BlcMxQAdEiUt1Vqw97hpKi0lpeBBj4gbH+FSWqroIFTLHJtQI++4Bn6OiO/Gs5TYmvi5HrmR/8tAp8ypyE3t4ExVlfL+YETEjY+Ic0bcRCeDKRZQoWC/bxYmBAx6GXhyTES9x3RxIxVTnsGVJn4ghuLWRHkzQzOh1nNTY1WplM7fQYmIGx/hVORGUaBNhrYtqamQI69UT0tJ5MbblFa5lpZqF68ZigvLqyWVEeDUVks1/rs1hxvRPftiKg5ORNz4CKfEDUg5eAiT20ADPx2pmPIsrhqKYyPDibN92j8s0ZuApryqduJ7YxgMCjEm8d0EMyJufIRTfW5AysFDmKY8N11lOrhHcdVQDLW+mwMibgKa2iZ+TaccdWFbIpGboETEjY+QyI3QHHopeEoTkZvckiqKpKtqiyl10VAMkBKr/V7ybSJUCEzK7OMXmhY39l43lfL/FIyIuPERzosbKQcPRVRVbTJyExMRZr+4iu+mZaiqavfcOGsoBhf+hwW/4kyfG6iN2ulCVwgu3BI3WVlZHDhwwP796tWrueOOO3jzzTc9trBgQ39jLKuyUG1pwp2vR24KMsEi4dJQoai8hhrbxOk2DRiKoXbGlPhuWkZZlQW9+teVtFSCWfsfLhBxE9A4UwoOjr1u5PcZjLglbi6//HKWLFkCQHZ2NmeeeSarV6/m/vvv59FHH/XoAoMFvRQcmvnkF9sejBFgrYaiA43vJwQVubaUVGxkGBFhDb8pdxFTsUfQ/TYGpelGb8fjtG9O8Cv2Jn7NjNaIEc9NUOOWuNm8eTPDhw8H4JNPPuGEE05g+fLlfPDBB8ybN8+T6wsajAbFPqytSXFjMEBSN2173Xs+WJkQCOgpqYZ63OhIxZRnKHGYK+U4w6s5JC3VOnA1LVUs1VJBiVviprq6mogI7U140aJFnHfeeQD07t2bw4cPe251QYZTjfwATr5T+/rbs/DXl15elRAI6A38GupxoyO9bjyDO2ZigIQo7XdTUCaG4kCmrNo2ONNJQ7FEboITt8RNv379eP311/ntt9/46aefmDBhAgCHDh0iKSnJowsMJpz+5DfgEhg5Q9v+6hbI3uTllQn+Jre0cTOxjmNaSlrGu48euTG7YCYGFz6cCH6lzIkOxYA9ki59boITt8TN008/zRtvvMFpp53GZZddxsCBAwH45ptv7OkqoT4u5ezHPQLdTofqMvjwcijN9fLqBH+S10QDP53OSWYUBYoqasgvleiBu5S52J1YR9JSrYMKZ9NSErkJalz777Zx2mmnkZubS1FREYmJifb7b7zxRsxms8cWF2y49OZoDIPJc2HO6VrPm0+uhqlfgTG82acKrQ+756aJtFRkuJH28VEcLChnX15pk0JIaBxHz40r6NVSIm4CmzInm/jpwzPFcxOcuBW5KS8vp7Ky0i5s9u/fz4svvsj27dtJTU316AKDCbu4KXPyzTEqES79UBumuf93+PGfXlyd4E/0Bn7NCRb7AM2j4rtxF3c9N44fTiQtGLjUpqWaqZaSyE1Q45a4Of/883n33XcBKCgoYMSIETz33HNccMEFvPbaay4f75VXXiEjI4PIyEhGjBjB6tWrnXreRx99hKIoXHDBBS6f0x/Eu/PJL7U3XDwHUGDNW/DH295ZnOBXcpto4OdIRrIWGZUxDO5TO3rBNc+NLm6qLaq93FgILCxWlSrblO/mDMXiuQlu3BI369atY8yYMQB89tlntG3blv379/Puu+/y0ksvuXSsjz/+mJkzZ/LQQw+xbt06Bg4cyPjx4zly5EiTz9u3bx933323fR2tAbdz9r0mwun/0rbn3w37l3t4ZYK/qa2Wai5yEwNIOXhLcHVopo7ZZCTcqJWOFzgbfRV8iu6nAhc8NyJughK3xE1ZWRmxsbEALFy4kIsuugiDwcBJJ53E/v37XTrW888/zw033MC0adPo27cvr7/+Omazmblz5zb6HIvFwhVXXMEjjzxC165d3XkJfqFF1RZj7oJ+F4K1Bj6+CgqyPLw6wZ/klep9bpqO3HSxRW725pZ5fU3BijtDMwEURRFTcYCjR9QUBSLCmr682fvcSFoqKHFL3HTv3p2vvvqKrKwsFixYwFlnnQXAkSNHiIuLc/o4VVVVrF27lnHjxtUuyGBg3LhxrFixotHnPfroo6SmpnLdddc1e47KykqKiorq3PxFi94YFQXOfwXS+kNZLnx0OVTJBS4YqLZY7ZGA5j03WuRmn5SDu03tXCnX6ymkHDywsTfwCzc226DR3qFYxi8EJW6JmwcffJC7776bjIwMhg8fzsiRIwEtijN48GCnj5Obm4vFYqFt27Z17m/bti3Z2dkNPuf333/nv//9L3PmzHHqHLNmzSI+Pt5+S09Pd3p9nqbFn/pM0ZrB2JwM2Rvh6+kgF7hWzzFb1MagQEJU09VwHROjMBoUyqst5BRV+mJ5QUeJm4ZiqP39SFoqMHG2xw3UipuKamvT8/6EVolb4mby5MlkZmbyxx9/sGDBAvv9Z5xxBi+88ILHFnc8xcXFXHXVVcyZM4fk5GSnnnPfffdRWFhov2Vl+S+d45HZNAnpMOU9MITBX1/AitkeWp3gL3QzcZvoCAyGpj9thhsNdGqjpab25JZ4fW3BiLuGYpD5UoGOK+LGUdyWiu8m6HCrzw1AWloaaWlp9ungHTt2dLmBX3JyMkajkZycnDr35+TkkJaWVm//3bt3s2/fPiZNmmS/z2rVFHdYWBjbt2+nW7dudZ4TERFhHxXhb/Q3xhZPFe48CiY8pZmLFz2sfd9hSMsXKPgFvQy8Ob+NTkaSmb25pezLLWNUt+b3F+rirqEYpJFfoFOh97gJb/53awozEBFmoLLGSkllDQlm5/7/hNaBW5Ebq9XKo48+Snx8PJ07d6Zz584kJCTw2GOP2cWGM5hMJoYMGcLixYvrHHvx4sX2VJcjvXv3ZtOmTaxfv95+O++88xg7dizr16/3a8rJGfQ3xrIqS8vDoMOuh77nawbjT6dBRaEHVij4gzwny8B1agdoSuTGHUpbIG70C2BBuXSIDkRcidwAxErFVNDiVuTm/vvv57///S9PPfUUo0ePBjQvzMMPP0xFRQVPPPGE08eaOXMmV199NUOHDmX48OG8+OKLlJaWMm3aNACmTp1Khw4dmDVrFpGRkZxwwgl1np+QkABQ7/5AJD4qnHCjQrVFJaeogo6JLejmrCgw6SU4+CcU7Idvb4fJb2v3C62KXCfLwHW62sWNGMrdwd1qKRBDcaCjl4I3VwauExMRRm5JlTTyC0LcEjfvvPMOb731ln0aOMCAAQPo0KEDt9xyi0viZsqUKRw9epQHH3yQ7OxsBg0axI8//mg3GWdmZmIwuBVgCjiMBoUuydHsyClhZ05Jy8QNQFSCNqLh7Qna9PCuY2HI1R5Zq+A78pwYmumIRG5aht1Q7OL4BXBILYuhOCDRq6Waa+Cno/e6kREMwYdb4iY/P5/evXvXu793797k5+e7fLwZM2YwY8aMBh9bunRpk8+dN2+ey+fzJz3bxrIjp4TtOcWM7e2BURXpw+D0B2DRQ/DDvZA+HFL7tPy4gs/QG/glOzkrSh/BkJlfhsWqYmzGhCzUpSWRmwSJ3AQ0ep8bZ9NS9nJwidwEHW6FRAYOHMjs2fWrdGbPns2AAQNavKhgpldbrfnhjpxizx101G3aBPGacs1/U13uuWMLXsfuuWliaKYj7eOjMIUZqLaoHDwmv2tXsFhrRydES7VU0FHm5ERwHX14pnhugg+3IjfPPPMM55xzDosWLbIbf1esWEFWVhbz58/36AKDjZ5pXhA3BgNc+Aa8NhqOboUf74NJL3ru+IJXybWnpZyL3BgMChlJZnbklLA3r5ROSS1Mb4YQju353aqWMnuo4lHwCvYmfk6mHGNleGbQ4lbk5tRTT2XHjh1ceOGFFBQUUFBQwEUXXcRff/3Fe++95+k1BhV65GZnTgkWqwcb8MWkwkVvAgqsfVvz4AitAvtcKSc9NwAZSTbfzVHx3biCPhHcaFCabc/fEJKWCmz0yE2ks56bCPHcBCtu97lp3759PePwhg0b+O9//8ubb77Z4oUFK+ltzPbeCpn5ZXb/hEfoNhZOvhN+fx6+uQ3aD4bEDM8dX/AKeloq2clqKYAuKdrfzb48qZhyBXuPG1Pz7fkbwjEtZbWqzTZdFHxLebWL1VISuQlagqMMqRVhNCj0aKvNB9qe7cHUlM7Y/4OOw6GyCD67FizyCTOQKauqsXtAXIncdLFFbvbIdHCXaImZGGpLwa2qfNoPRMpd9tzIfKlgRcSNH+jpDVOxjjEcJv8XIuPh4Fr4+THPn0PwGHrUJjLc4PQbMtRWTO0TceMSLWngB1q6IzJce9sUU3HgIU38BB0RN37AKxVTjiR0gvNs1WzL/gO7Fje9v+A3HBv4uZIm0cXNgWNlVNXI0D9nacnoBR0ZwRC46FFQVyM3xZKWCjpc+g+/6KKLmny8oKCgJWsJGbxSMXU8fc+DodfCH3O14Zrdz/DeuQS3sfttXEhJAaTERhBtMlJaZSEzv4zuqTHeWF7QUVrVsrQUQEKUiZyiSmnkF4CUudjELzpCIjfBikuRm/j4+CZvnTt3ZurUqd5aa9Cgp6X2HC317qfuIdoICw78AS7M/BJ8hz4009kycB1FUehmEzQfr8n0+LqCFXt3Yjd63OhI5CZwsXcodrYUXJr4BS0ufXx5++23vbWOkKJ9fCQxEWGUVNawN7eUXrZIjsdJ7Qvh0Zq5+Og2aNvXO+cR3CbXxQZ+jtx8ajdu/mAdc37by6D0RM4Z0M7Tyws6Wuq5AZkvFci4nJYSz03QIp4bP6AoCj31iilvpqaMYdBxiLadtcp75xHcpnYiuGuRG4CJ/dtx0yldAbjnsw3s9ObfUpDQ0mopgAR7Iz+ZDB5o6E0anZ4tJWmpoEXEjZ/QozVevyB1HK59PbDGu+cR3EJPS7nqudG5Z3wvRnVLoqzKwk3vraWoQqIJTSGG4uDG1Wopx8iNqnqwqargd0Tc+Andd+OVXjeOpI/QvkrkJiCpjdy4J27CjAZevmww7eMj2ZNbyl2fbMDqyc7XQYYnIjcyXypwcbXPTaxttpSq1gojITgQceMnvNrrxpGOQ7WvebugNM+75xJcxrEU3F2SYiJ47cohmIwGftqSw2u/7PbU8oIOffxCtAs9hY7HnpaSaqmAotpipcYm7M3hzonXyHADRluXaUlNBRcibvyELm7255fZP214BXMbSO6pbUtqKuDIbWHkRmdgegKPnt8PgGcXbufXHUdbvLZgRNJSwYtj5MXZtJSiKNLrJkgRceMnkmNMtIk2oaqw64iXhx/afTervXsewSWsVpV8u+fG/ciNzqXDO3HZ8HRUFW776E+y8mXu1PF4Ii0l1VKBif4hMcygYHJhKKqYioMTETd+wmcVUwDpNnGTJeImkCgor0a3xySaWxa50Xn4vH4MTE+goKyav7+/lopq8RE4Ulql97lpSRM/SUsFIq5WSunEyvDMoETEjR/RxzB4vWJKFzcH14JF/oEDhTyb3yY+KtylT5pNERFm5LUrTiQp2sRfh4r4vy83SRWIA57ocyOG4sBE73HjbEpKR4ZnBicibvxID71iytviJrkXRMRDdRnkbPbuuQSn8ZTf5njaJ0Tx8uWDMSjwxbqDvL9yv0eP35qpFTct71BcXFlDjUU6fwcKrlZK6ejl4OK5CS5E3PgRvdfNDm+XgxsMtVVTkpoKGOw9blpQKdUYo7olc9/EPgA8+t0WNh8s9Pg5WiN2Q7GT7fkbQhc3AEVyQQwYylwcvaAjnpvgRMSNH+mZqombQ4UV3m++pve7EVNxwNDSHjfNcf2YLpzeO5Vqi8p3Gw975RytiRqLlUrbLLeWGIrDjAb788VUHDjUDs107bImnpvgRMSNH4k3h5MWFwn40HcjzfwCBt1z4y1xoygKwzLaAHCkqMIr52hN6D1uoGWeG6iN3hSUyQiGQKHCPlfKtd+tHsWTyE1wIeLGz/TUU1M5Xi4H7zAEUKAgE4qzvXsuwSlyS/WhmZ5PS+mkxmrHPmoTUqFMia2axmQ0tNjALb1uAg9XRy/o2D03Im6CChE3fqaXXg7ubd9NZBy01Zq8ie8mMNAjN+7OlXKG1DhN3BwpEnHjCTOxjoibwEMvBXfZUBwhaalgRMSNn+nhqzEMAB2HaV/FdxMQtGQiuLOkxmppzyPFkpbyRHdiHRE3gYe71VKxkZKWCkZE3PiZXr4UN/YhmiJuAoE8e1rKi5EbW1rqWFk1VTWhXbbsie7EOvp8qUJp5BcwlNk8N5EuNvGLsQ3PFHETXIi48TM9bGmp3JIqe5rCa+im4kProUbSFP7GPjTTi5GbBHM44UZtMGCo+2480cBPRyI3gUdL+9xIWiq4EHHjZ8ymMDq1MQM+MBW36QrmJLBUwuGNrj134yew7l3vrCsEqayx2JuGedNzoygKKTG67ya0U1MllS0fvaCjz5cqEHETMNSKG+lzI4i4CQh6+io1pSjuDdE8sg2+uAG+uRXydntnbSFGvi0lFWZQiIsMb2bvlpFiazdwtFgiNwAxHjAU29NSIm4CBj0t5fZsKRE3QYWImwDAZwM0wb1+N8tfrt3eudCz6wlRdDNxm2gTBoPi1XPpvpsjIS5uPNGdWEfSUoFHuT44U6qlBETcBAQ+G8MAdSeEOzNQsegQbPy49nsRNx7BF34bHRE3Gl7x3IihOGAor26Z56bKYqWyxtLM3kJrQcRNANDTYYCm1yc4tz8RFCMUH4bCA83vv/JVsFZrfh2Afb9DpZe9QSGAHrnxpt9GRy8HPxri5eB6kzePVEtFab83idwEDrXjF1wTN46RPIneBA8ibgKArinRGA0KxRU1ZHvb9GkyQ1p/bbs53015AfwxT9sePwsSM8BSBXt/9eICQwN9aKY3y8B1UmKlkR9In5tgx11DsdGgEG2L9ojvJngQcRMARIQZ6ZIcDfigYgqc73ez9m2oKoaUPtDjLO0GkpryAL5o4KcjaSkNTxqK422G4vJqi6QyAgR3xy+AwwgGidwEDSJuAgTdVOxz301j1FTCyte07dG3gcFQV9x4O30W5OR6eSK4I/oIhlCvltI/lbv6yb4hYiPCUGw+cIneBAbupqVAysGDERE3AYKj78br6OImeyNUlze8z4aPoCQH4jrACZO1+zJOhrAoKDoIR7Z4f51BjJ6WSvbi0Ewd3XOTW1KJ1Rq6otSThmKDQwl/kYibgKDCTUMxQIztdymem+BBxE2A4NMxDPHpEJMG1ho49Gf9x61WWP6Stn3SLRBmiy6ER0GXU7RtSU21CLuhONb7kZvkGBOKAjVWlfyyKq+fL1AprfScoRhqe90U+LFi6psNh3jrtz1+O3+goKqq24MzoTZVKZGb4EHETYDQ01YOvjOnxPufrhWl6X432+dD3i6IiIchV9d9rMeZ2tedP3l3jUGOPmojyQeRmzCjwW5cDmVTcUlLp4LvWACf3wCV2gcQf5uKK6ot3P3JBh7/fiu7jvjgQ1EAU1ljRX/bdMtzYxO8xSJuggYRNwFC5zZmTGEGyqstHDjWSKrIkzTmu1FVWPaitj3sOoiIrfu47rvJXAnlx7y6xBajqvD+xfDWOKgJnIiFqqrklvrOcwOQrI9gCOFy8NKqFg7O/OVp2PQJbJsP1Iobf0VuNh4opMqiDUPdnh3a7Rn0Silw13MjaalgQ8RNgBBmNNAtxZedih0qphzNwZkr4cAaMEbAiL/Xf15iZ0jpDaoFdi/x/jpbwrG9sGuR9nqyN/l7NXZKKmvsE7p9EbkBSJURDC333BQe1L4e2wf4P3KzLrP2w8WuIyEubmx+G5PRQJjR9cta7QgG8U8FCyJuAoheesWUL8RNu4FgNEFZLuQ75Oz1qM2gyyC2bcPPbS2pqUyHlNuhdf5bx3Hofptok9GtELo7hHo5eGWNhWqLJuLdEjeWas1gDwEjbtbudxA3R0Nb3LSkDBxqo3m6L0to/Yi4CSB03812X5SDh0VAu0Ha9oE12tcjW2HHj4ACI29t/Ll6amrXT5r5OFBx9BM1ZJz2E/YGfj7ocaOji5tQjdw4XrSi3bkAluQAtgjnsb2Af8WNqqr8KZEbO7UN/NwUN9LnJugQcRNA+LRiCuqbivUBmX3OheTuTTzvJDDFQulROBw4oqEeASpufNnjRqc2chOanhs9JRUZ7l7agqLDtdu2yI0/J4Nn5pfZ/44A9hwtwRLCZf5lbg7N1KntcyNpqWBBxE0Aofe62X20hGqLDyIidnGzRvMTbPxE+370HU0/L8wE3cZq24Gamiov0CJROke3QVWp35bjiL07sY/8NgAptl43oVotVVLZQjNx0cHa7eLDUF3u18iN7rcZ2DEeU5iByhorB31RiBCg6J4bd8zE4Oi5kchNsCDiJoDokBCF2WSk2qKyP88HF+KONnFz5C/49RltQGbnk6Hj0OafG+ijGA7+AaiQ2AVi24NqhcMb/b0qoLYM3BdDM3X0LsWh6rlpsZm4+HDd7wsyHaqlfF+Jp/tthma0oattdMuuo6FbDt7itJQeuZG0VNAg4iaAMBgUeuidin1R2hnXDuI7aRf+tfO0+0bf7txzdVPxwXVQctQry2sRupk4fQS0H6xtB4ipOM/HZeBQ13Pj9cnzAYi9x427oxeKDtX9/tg+4v04GXzd/gIAhnROpFuqVoiw0xdz6QKUWkOxe79f6XMTfIi4CTD0iimflINDbWoKILVvrWhpjtg0reIKVSu3DjR0v02nEdDBJm4OBoa4yfVhAz8dfQRDebUlJEPvLe5O3KC40dNSvv15llTWsC27CIATOyXSwyZuQtlUXKaPXnAzLaUbiiVyEzyIuAkwdN+NTwZoQl1xM+o27NMAnSFQU1OWGji4VtuuE7kJDFNxnh8MxVEmI7G2C3sopqb0Bn5udyfW01JxHbSvx/bZJ4MXllf5NBq2MasAq6qlsdPiI+mui5sQLgcvb6GhWJ8TVlBeHdLz14IJETcBRq+05iumLFaVgwXlbM8ubvmbapdTQTFAQmfoP9m15+riZvdiTVAECkf+gqoSiIiDlD7Q/kTt/vzdmtHYz9iHZvqwFBwgRffdhKCpuMWeG91Q3HmU9vXYPhJskZtqi2o3tPoC3W8zuFMCQK24OVISkilHgPIqrQDDXXHTLj6SMINCVY2V7KLQrCgMNjwzQU7wGHrkZl9eKZsPFnK4sIL9eaVk5ZexP7+MzLwyDhwrt7ddf+yCE7jqpM7unzC1N1y/SBukaQx37bkdhkBUGyjPhwOra9/4/Y0+UqLjMDAYwNxGE28F++Hweuh6mj9X55fIDUBKTAR7jpaGZDl4aUuqpVS1thS800jY9Ckc24fZZCTMoFBjVSksr8bsrp/HRfRKqSGdEwHokhyNQdF6tBwtrrR3ow4lyqptQzPdTEuFGQ10SjKz52gpe3NLaZ8Q5cnlCX5AIjcBRmpsBPFR4VhVOPfl37nh3T94/PutvLNiP0u3H2VPbilVFisGW/bo5cU7qWjpp8YOQyC+g+vPMxih+zhtO5BSU5krta/6iAmADrbojZ99NxaHydy+9NxAaI9gKKnUq2ncECDlx8Bi+5l1Gql9PbYPBd/Pl7JaVdZlFgCa3wYgIsxIpzZmIHR9Ny2tlgLsVWd7cgOjZYTQMkTcBBiKojChXxqg9V44oUMc5/Rvx82ndeOpi/rzv+tH8Pu9Y9ny6ATax0dypLiST/7I8t+C7b6bAOp3o0duOjmImwDx3Rwrq0JVNWtTotnFSFkLCeUuxbWRGzcufnpKypwMSd0BBarLoPSog+/GN+JmT24JheXVRIYb6Ns+zn5/qPtuWlotBdDVNttvT4j+DIMNSUsFIE9PHsCDk/o26w/4+2ndePDrv3h96W4uHdYJU5gftGr3MwAFcjZD4QGI7+j7NThSdAgKMzUfUYchtffrvhs/i5vsQi0llGg2udcptwWE8nypFnlu9JRUXDutgWV8RyjMgvy9Pm/kp5eAD+iYQLjD30+31BgWbT0SupEbexM/9/+nutgiN3slchMUSOQmQHHmTfiSoemkxkZwqLCCz9cd8MGqGsDcRvO2QGBEb/QS8LYnQERs7f3tBgKKdlHyY1+e33bmAjCgY7zPz13byC/0PDclLRI3tsiNXimVmKF9dSwH91FaSjcT6ykpne4pod3rpjYt5f7ndRE3wYWIm1ZMZLiRG0/pCsCrS3f5ZmRDQ/QMoNSUnpJy9NsARMZBcg9t24/RmyXbjgBwRu9Un587NYRHMOil4G4ZivUy8Nh22tdEm4HfoWLKZ5Gb48zEOnrzz9BNS7WsFBxqPTdZ+WVU1QTwQGDBKUTctHKuGNGZ5BgTWfnlfL3+UPNP8Aa672bPUqhp4sKpqpqw+GMuVJV5Zy0NmYl1/Oy7KSir4o/9+QCM9YO4SQnhtJRuKHYvcmP7v4prr31tKHLjA3FTWFbNTlvaSS8D1+mWol2YjxZX+qVjsr/xhKE4JTaCmIgwrCpk5kv0prUj4qaVE2Uycv0YLXrzypJd/pkMnDZAKyWvLoX9y+o/Xn4MVr0Jr4+BN0+D7+6EBfd5fh1VZZBtmx/VqSFxo/tu/FMx9cuOo1hV6J0WS8dEs8/Pr3tuCsurW15h18qo9dy4Yyg+Xtx00b46iJuCcu/Pl/ozS4vaZCSZ6/VIio0MJ81WDReKvhu7odjNUnDQijn01NSeo6EnbvYcLWHu73sprggOcSziJgi48qTOJJjD2Ztbyncb/RC9UZTasQ16aspqhT2/wOfXw7O94Id7IGcTqlHr7aKuexeObPPsOg79CdYaLX0Qn17/ccfIjR+anS3eqqWkTvdD1Aa0smXddK6PgAgVWtTnpl5aykHcmPX5Ut5vYrmuEb+Njl4xtTsExY3dUNyCyA2Etu/m4W+38Oh3W7jglWXsOtL6h7CKuAkCYiLCuG609oY7++dd/mkfrqemts+HX/8NLw+Gd8/TGp5ZKqFtf5j4DI/1+pIfLcNQVCssetiza8hySEk1NEYirT8oRijJqT8ryMvUWKws3e5fcaMoCikxoZma8oqhuPgQiSbNm+GLyeD2/jadmxY3oei78YShGEJX3FitKn/axPPuo6WcP3sZ32887OdVtQwRN0HC1aMziI0MY+eREn78K9v3C+h6GhjC4Ng++Plx7WtEHAy9Fm5cCn//jYoTr+eTv0p5pmYKFgyw4wfY97vn1tCYmVjHZIbUPtq2j3036zILKKqoIcEczuBGPnn7gtQQHMGgqqr7kZuqMqgo1LbjbJEbcxswaQbeNGsOAEVe9rlYrCp/ZjYduekWwgM0yzzguQHomhKaaam9eaUUV9YQGW5gZNckSqssTP/fOp6cv5UafxWqtBARN0FCXGQ400ZlAPDyz7t8P2MmMg76XqBtdx4NF74Bd22Hc1/Q0kGKwpJtRyiprGGP2p4PLWdo+y78l5bCaimqWlsG3pi4AYfUlG99N4u3aRfBsb1SMRpcGE7qYWob+YVOOXhFtRU9mOly5EZPSZliNLEOtg6MGQAk12iPe9vEuyOnmNIqC9Emo33+3PHYy8GDIKXgClar6rG0VNdkWyO/EIvcbMgqAOCE9vG8d91wbjpV83G++eservzvqlaZxhZxE0Rce3IXok1Gth4uYpHN3+FTLnwd7t0H0+bDwEu1SIkDjtVcL1ZfRE1YtBZB+euLlp87d6dmXA6LgnYDGt/PTxVTP/vZb6MTihVTekoK3Jg9pKekYtvVTXXaysETKrTHC7wsbvT+NoM6JTQqjnu01S7MB46Vh5RhvNKhbLslhmKAjGTtPSu3pJKiIDHWOsPGA1p0ckDHBMKMBu6b2IfXrjiRaJORlXvyOfel3+1tCFoLIm6CiASziatGZgDw8s87fR+9MYZDVMMh86KKan62eU5O7JRALvH8mnK59uDiR5suIXcGPWrT4cSmB4B2cOhU7KOfT2ZeGTuPlGA0KJzSM8Un52yMUOx1o/dAiTYZMbgaNXPsTuyILXITU6Y1zywqr/aq183e36aJlGZStIkEcziqCrtDyHej/36h5eImNjLc/gFgXwhFb9bbIjcD02ubi07s346vZ4ymW0o02UUVTHljBe+t3N9qJs8HhLh55ZVXyMjIIDIykhEjRrB69epG9/3iiy8YOnQoCQkJREdHM2jQIN577z0frjawuX5MF6LCjWw8UMgvO5rvxFteZeHbDYfYkePdUPaCzdlU1VjpnhrDDbbS9edLztRKyAv2w5q3WnaCrCb62ziS2g+MJi3Kc2xfy87pJD/bUlJDOyfaS4f9hT0t1QrDzO7iUTOxjk3cRBRnAmBVoaTKexVT9kqpRszEoBnG9dRUKPludL9NZLjBdfHaAF1DrBy8qsbKlsNFAAzsmFDnse6psXw942QmnpBGtUXlga82c/enG1tFZNDv4ubjjz9m5syZPPTQQ6xbt46BAwcyfvx4jhxpOK3Spk0b7r//flasWMHGjRuZNm0a06ZNY8GCBT5eeWCSHBPBFSM6AfDS4sajN9mFFTz94zZGPrWYWz/8k8mvLbfPPfIG32zQUlLnDWzPqG7JGBTYfLSGgpH/0Hb45RlNcLhLc2ZinTCTNpoBfOa7+Xm7JjLP6OPflBSE5giGUlsDP4+UgevYysGNhfuJsJXXe2sEQ15JJfvytKaXg9ObNqOHYjm47rdpaaWUjt1UHCKRmx05xVTVWImPCqdzUv3+WzERYbx6xYn839m9MSjw+boDzPl1jx9W6hp+FzfPP/88N9xwA9OmTaNv3768/vrrmM1m5s6d2+D+p512GhdeeCF9+vShW7du3H777QwYMIDff/dg1U0r58ZTumIKM7Aus4Dlu/PqPLYhq4DbPvyTk5/+mdeW7qagrBqjQaGoooZ7PtvglZDj0eJKlu3SZiqdN7A98eZw+ts+IfxkOgNS+kBFAfz2vHsnKMuH3B3advrw5vf3oe+mtLKGlbbfwem923r9fM0Rimmplg3NPK6Bn45Dl+KEKO243jIV6yXgPVJj7FPIGyMUy8HLPdDAz5FQKwfXU1IDOsajNNRCAy0qeOMp3fi/s7Vq0zX7A99/41dxU1VVxdq1axk3bpz9PoPBwLhx41ixYkWzz1dVlcWLF7N9+3ZOOeWUBveprKykqKiozi3YSY2L5LJhWhO7lxbvpMZiZf6mw1z82nLOf2UZ32w4RI1VZUSXNrxx1RB+vH0MEWEGftuZy3sr93t8PfM3HcaqwsD0BDJsbxxjuicD8NuuY3Dmo9qOq96AgkzXT6BHbZJ7amW6zaH7bg56X9z8viuXKouVzklme4t8f6KnpXJLKv3TzdoPlHiyO7FOQidAgeoyOkdqF0FviZvGhmU2RCiWg9u7E7ewUkqni61iam9uaPwMNx4oAOqnpBpCb2OxIzvwK/L8Km5yc3OxWCy0bVv3E23btm3Jzm68V0thYSExMTGYTCbOOeccXn75Zc4888wG9501axbx8fH2W3p6A51rg5CbTu1GuFFh1d58Rj/9M7d8sI61+48RblS46MQOfHfryXx800jG90ujR9tY/jmxNwBPzt/KHg9/6vt6veZbOG9g7QViTA9N3CzblYu12zjIGKM1+/v5cddPYC8BdyJqA7WRm8PrPVOG3gR6ldTYXqmNfiryJW2iTSiK5hHJKw2N6I09cuNO2qKxtFSYCeI7AtAjXIvMFXgpLdXYsMyG0D03e3NLW21/Elcpr9Z+vy3tcaOjp6X2Hi1tNebZllBbKRXfzJ7Q01aRl11U4bU0rKfwe1rKHWJjY1m/fj1r1qzhiSeeYObMmSxdurTBfe+77z4KCwvtt6ysLN8u1k+0T4hi8hBNyOUUVdIm2sRtp3dn2b2n8/wlgzihQ90/5KtHZjC6exIV1Vbu/GSDx94Ys/LLWJdZgKLApAG1F4jBnRIxm4zklVaxNacYznpMe2Djx3B4g4sn0cXNSc7tn9wLws1QVQJ5O107lwtYraq9QiwQ/DYAYUYDSdF6r5vQEDduG4otNVo3a6hvKAZ7airDqHmqvBG5qbZY7Z+sT+yc0Oz+HRKiiAo3Um1R2Z/vpeG0AYYn5ko5kp5oxmhQKK2yBH3LhLKqGnsxyaD0hGb3j40Mp0NCFADbvVyE0lL8Km6Sk5MxGo3k5OTUuT8nJ4e0tLRGn2cwGOjevTuDBg3irrvuYvLkycyaNavBfSMiIoiLi6tzCxX+Mb4X14zK4KmL+rP8n6cz86xepNqG6x2PwaDw78kDiY0MY0NWAa8u3e2RNehG4pFdk+qc2xRm4KSuSQD8vjNXi6b0/5v24MIHnC/TtlTDwbXadnNmYh1jmDbsE7zqu/nrUBFHiyuJNhkZ3sWJdJmPSA2xXjel7k4EL8kB1ap13o5uoITf1uumI5qA9Ya42Xq4iIpqzeypN5hrCoNBoVuqFnkIldSUp7oT65jCDKQnahfwYK+Y2nywCKsKaXGRjV4bjkdvIrk9O7AtHn4VNyaTiSFDhrB48WL7fVarlcWLFzNy5Einj2O1WqmsDI03aldIjDbx8Hn9uHR4JyKd+FTTPiGKR8/vB2henU22cGVL+NYmbs4f1L7eYyfbfDe/28zGnP6AVqa99xfYtbje/g2SvRFqKrT+OkndnV+Y3XfjvYopvSvxmB4pRIR55o3XE+gVU0dDxFRcWqWPXnC1gZ/NbxPbDgwNvFXaIjftrFoK3RuTwXW/zeBOCU6XOYdaOXiFh7oTO2KfDh7kvpsNDfS3aY6ebW3iRiI3TTNz5kzmzJnDO++8w9atW7n55pspLS1l2rRpAEydOpX77rvPvv+sWbP46aef2LNnD1u3buW5557jvffe48orr/TXSwgqLhjUgbP7p1FjVbnzk/Ut6mewPbuYbdnFhBsVJvRrV+9x3Xezem++dp7EzjD8Ru3Bnx4AqxPnznQYudDQBagx2js08/MSP2+zdSUOkJSUTm3kJjTKwd1OSxU7iJuGsJWDp1RrvhxvzJfSK6Waat53PKFWDl6blvJMKThAV927FOSRmw22lOcAJ8zEOr1tkZsd2YH99+W5vwY3mTJlCkePHuXBBx8kOzubQYMG8eOPP9pNxpmZmRgcLlqlpaXccsstHDhwgKioKHr37s3777/PlClT/PUSggpFUXj8gv6s2XeMXUdKeObH7Tw4qa9bx/pmg2YkPq1XaoMlrN1TY2gbF0FOUSVr9uUzpkcKnHI3/Pk+HNkCy1+C0Xc0POFbx1UzsY5uKs7eqKW2mupq7AZHiirsRr3Tevm3K/Hx2MvBQyYt5ebQzMa6E+vYIjcJldrfuTfSUs407zueUCsH93RaCkKnHFx/j3KmUkpHj9xsyy5CVdWAKJRoCL9HbgBmzJjB/v37qaysZNWqVYwYUeudWLp0KfPmzbN///jjj7Nz507Ky8vJz89n+fLlImw8TJtoE09f3B+Aucv2snx3rsvHUFW1TuO+hlAUhZO7axf+33fazhGVCKfaGvstehg++BsUHmjsJK6biXXadIWIeC2ldWSra891giU2I/HAjvF2MREo2OdLhUpayt3ITWPdiXVs4sZceYQIqjxeLZVdWMHBgnIMitZGwVkcIzehUO1TXuXZaimo7VIczOImv7SKTJvpvL8TlVI63VKj7b3RcgL4PSQgxI0QeJzeuy2XDdc6Hd/9yQaXh8j9mVVAVn45ZpORcX0ab153Sk9bv5udDgJqxM1wxkNgjIBdP8ErJ8Efc+uXbRdmaaW6hrDaSIyzGAzQfqC27YXUlD0lFQCN+47HmyMYZs3fym0f/kl1AJUhu5+WaqQMXMecBKYYFFQ6KLkej9zoJeC90+Jcijp1ToomzFbtc9iLXccDBb1DsTO+QmfR01KZ+WUB9bfsSfQqvK7J0S6NhYkIM9ojW4HsuxFxIzTKv87pQ6c2Zg4VVvDwN3+59NxvbBPAz+rbtkmj32ibqXjL4SJy9YutwQBjZsLff4eOw6GqGL67E949D/Id2n7rzfvSBtSbQO4Udt+NZ03FlTUWu1gLlBJwR7w1gqGgrIo3ft3DNxsOsWhLTvNP8BG14xfcNBQf38BPR1Hs0ZtOSo7nxY09JZXg0vPCjQZ7G/1QMBV7Iy3VNi6CqHAjNVaVrCAtqd+Q5Xx/m+Pp1TbwK6ZE3AiNEh0RxvOXDMSgwBfrDvLj5sNOPa/GYuW7jdq+5w9qJKRvIzkmgj7ttPJ8fUSDnZSecO2PMOEprS/Nvt/g1VGw4hXNbJxpG5bZycWUlI6XxjCs2pNPWZWFtnER9GsfeK0HHEcweDJtsdGhuu7DNYHTT8rtJn7NiRtwEDdHPN7UbK0LzfuOp0eqdvHZGQLiptwL4kZRlKD33dg7E7uQ8tSxV0wFsKlYxI3QJEMz2nDTqd0A+MdnG1m6veGBpo6s2JNHbkklieZwTrZVRDWFXjX1+84GvD0GI5x0M9y8XOtiXFMOC/4P5o6HXYu0fVw1E+vo5eA5f0G156IYekoqULoSH4/uuamssVJU4blJ1vqbJcBvO48GzCdevRTcpbSUqtampZwUN8WVNR5rfllRbeGvg9qnYmfGLhxP9xAaw1A7fsGz9TFdUoJX3KiqygZ7Z+IEl5+v97rZIWkpoTVz57ienNgpgaKKGqbNW8NzC7c3OZdIT0md3b8d4cbm/8Qc+900Gklo0wWu/hbOfRFMsXBgDRzbqz3mqplYJz5d801YazSB4wFUVbX3tzm9txdSUqoKx/bDho/h2zvgq1sg17Uuy5HhRuIitQvBUQ+mpvQ3S4OiLfPTPwIjeuPWVPDyY5rZHBr33EAdcQN4TCxuPlhIlcVKcoyJTm1cT7mGUjl47VRwz/aS6maL3OwOwnLwQ4UV5JZUEmZQ3IouO4qbQJ1RJ+JGaBZTmIH/3XASV4zohKrCyz/v4qr/rmqwfX9FtYUfN2tNzRqrkjqe4V3aYAozcLiwgt1Nla8qCgydBtNXQo+ztPuSejReqtsciuJx383uoyVk5ZdjCjPY/UQtwmqBwxth1Zvw6TR4vi/8ZwB8eSOsfRvWfwCvj4HVc5zv6ox3Kqb0yM2VJ2mdez/544Df5xupqupe5EZPSZmTICyi8f1svW46Gzw7gmHNPi0lNbRzG7eif6FUDu7pqeA6tZGb4PsZbrQ17+uVFuuWEbtTGzOR4QYqa6z2iqtAQ8SN4BSR4UaeuLA//7l0EGaTkeW78zjnpd9YtSevzn5Ltx+luLKGdvGRDMtwbuRAZLiRYRla6P23hlJTxxPfES7/BKZ+A1d86vJrqYOHfTeLbYMyR3ZNcr06x5G/voT3LoKnOsMbY+CHe+CvL7TGcoYw6DgMRt0KXU/TUnXz74b3L67tzdIMuu/GUxVTOUUV5BRVYlBg5pk9STSHk11UwS87jnrk+O5SVmWxaz6XIjfO+G3AHrlJV44AqsfEzR/78gEYmuF6Sgpqhz/ml1aRX+r5zsmBRJlNvHqyQzE4TgcPvshNS1JSAEaDYvd1BaqpWMSN4BLnD+rANzNG0yM1hiPFlVw2ZyWvLt2F1Raa1Bv3TRrY3ul28UD9fjfNoSjQ9VQtXdUSPDyGYfE2DwzK3L0EPr0Gdi/WKsUi4qD7ODj9X3DN9/DPLLh+EZz1OFz5JUx8BsIitf1fPQk2f9HsKewVUx6K3Oht3HukxpJgNnHRidrE7A9X+zc1pZuJDQpEhrvwdmfvTtyMuElIBxTMVJBEEQVlLRcSVqvKH7ZKKWc/IByP2RRmH3DoS99NQVkVZ//nN/7+3lqf9djxhqEYahv55RRV2v+OggX9/3WQC2MXjifQTcUibgSX6Z4ay9czRnPR4A5YVXjmx+3c8O4fZOWX2SMXzqakdHRT8co9eb7tK6FHbnK3Q0XLPoEUlFXZZwGN7eWmuCnNgy//rm2fMFkrh793H1z5OZxyD2ScXLfs3WCAETfBTb9Cu0FQUQCfTYPPb4DygkZP4+kRDBsP1C0rvWy4NpF+yfYj5BT5r9eK7oGJjghzLb1T5ISZGLSUla3JXyfliEciN7uOllBYXk1UuJG+Lai284epeNb8bWw5XMSPf2Wz4C/ftAMo85LnJj4qnOQYExBc0RurVWXTwZZFbgB6pWl/X4FqKhZxI7iF2RTGc5cMZNZF/TGFGVi87QhnvfArlTVWuqZEu2xS69sujqRoE6VVFv60zdPxCbFp2qdz1QrPdIGXBmtdkX/4p+Zj2f2zZuB1Ys7Vgr+ysVhVeraNId0NEyiqCt/MgJJsSO4F570Maf21irHmSOmlRXNO+QcoBtj0Cbw2CvYsbXB3T49gsM+osZWVdk+NZVhGIhar6ldj8Wbbm3hGUrRrT7R3J3ZCpDukpjwxX2r1Xi0lNbhTglOG/MboYRM3O4/45uKzak8eHzv8rpsrPPAUeuTGk038dGoHaAaPuNmTW0JJZQ2R4Qb734g79ErT3uO3SVpKCDYUReGy4Z344uZRdE4y26sWzh/YwWUTpMGgMEqvmtrpY5/GSX+HsCitaip/D+xcCKte03ws712oGXifSIPXT64tPz+OyhoLLy3eBWBPybjMH/+F7fO1yeiT/+t6Y0JjOJx+P1y7UBsvUXQQ3j0ffrhXa3hYmmc3HXsyLaWqtZ8EBzo0BLt0mNbh+uM/suxpS1+z0uYJG9ktybUnNted2BGHiilPjGCo9du4l5LS8WXkprLGwv99uQnQorbxUeHsPFLCV38e9Op5LVaVyhot0mv2cCk4OMyYCqKKKb15X/8O8YS1QDzrjfz25ZW1aMCytxBxI7SYEzrE8+2tJ3P+oPZkJJm5ZJh7F/cxNnHz2/HN/LzN6Nvh/w7BnVts5eYvwMgZ0OtsSO6piQ1LFWRv0qI6q96oV5n00eosDhaUkxobwdUjM1xfw5FtsOB+bXvcw1rExl3Sh2nprKHXad+veh3+eyb8uys83RnePI1R6+/lzrDPGJD/Ixz4A8ry3T5dVn45BWXVmIwGeqfVRuzO7t+O2MgwsvLLWebGfLIjRRUt9m2ssImbk7q6KBScNRQDtMkAPJeW0iulhntI3PiiHPyNX/aw+2gpyTERPHb+Cfzd1hvrhUU7qKrxXpq53OGi6um0FDiaigPTV+IOG92YBN4QbeMiiI8Kx2JV2ROA4k/EjeAR4iLD+c+lg1l6z1jaxUe5dQy94d+GrAKPd3ttFoMB4jtAl1Ng6LUw/gm47EOYsQbuz4bbN8CgK7X01Q//gO/v0qaJo1VrvPyzFrW59YwerldtVFfA59drfVW6naHN1moppmg493m44jOtokof/lhRCIf+JGXft9we9gX3VbwAb52hpeR++bdbp9JTUn3axWIKq31LiTIZuXCwdt6PXDQWP/bdFoY/uZhP1zYyNNUJDhWUsz+vDIPiRhTEFXFjLwdv+QiGQwXlHCwox2hQGNQpoUXH0sXNocIKrxpi9xwtYfYS7e//wUl9iTeHc82oDFJjIzhwrJwPV2e6ddznf9rBKc8sYcXuvEb30SulFAUiwjx/OdOrzoIpLbX+OH+cuyiKUjuGISfwUlMiboSAoX1CFF1TorGqsGKPj6M3TWEwaqmH82fDmY8CipZC+mAylB/j7WX7yC2ppFMbM1OGprt+/MWPQM4mMCfDBa9pQstT9DgTpn4NM7fA/x2Gm1fAlPcpP+1BPqwZy0prH9QYW+pl6ZNadMpFmvokqKemFm7JJs/JsvPP1x7gv79rDRq/Xu9+WmPVXu2i2L9DPHGRzg8GpKpMM2aDy56bghaKG71Kqm8714ZlNkSC2WQ3xDbZP6oFqKrK/V9upqrGyik9U5g0QPtbijIZufWMHoDWF0sXIc7yw6bDvLR4J5n5Zdzw7h9279TxVFTZUlLhRq90A+/qkJYKhgnrVTVWth7ShMggN8YuHE9Pm6k4ECumRNwIAYU9NeVsSbgvURQthXXpBxAeDXuWYpkzjvm//A5o/V1Mrn563LkIVr6qbV/wKsR6cYq4yQxt+0KfSUSeOpOHuYlLqx7gwLXroO8FWlRq/j0uNQMEx54Z9T8J9m0fx8CO8VRbVD5f13wUZvPBQrt3A2DN3mNuRx30T/wndXXTbxMerZXhN4dN3KRxjLKyln3Cb2l/m+PpluJd383n6w6yYk8ekeEGnrjghDoCY8rQdDq1MZNbUsnby/Y5fcys/DL+8flGANpEmyiprOHquasbrFgqq/ZOjxudTklmFAWKK2vILWn9/YK2ZRdRZbGSYA53q/P18eim4kCsmBJxIwQUY3rY+t342nfjCr3P0QZ6xnXEmL+LD9T/45KkfS6Xv1NyFL6ypaCG3wg9x3t+rY2gKErd6eBnPa4NJ81cAZucb4xosar2T9WNDeC7dLgWvfloTVaTn37zS6u46b21VNZYGdsrhfQ2UVRZrE2mJZpi5R5NKLgsbuwpqXaaoG0OcxI1YdEYFJWo0pYZaHW/jbv9bY7Hm6bi/NIqnvh+CwC3n9GzXoWgKczAzDN7AvDGL7udSjVXW6zc+uGfFFfUcGKnBBbPPJV+7ePIK63iyrdWkV1Yt61A7Vwp74ibiDAjHRO1NHswlIM7Nu/zRKSrdjq4iBtBaJKTuiURZlDYn1cWMIMXG6TdAHIv+4ENajcSlFKeKnsQw5/vOv98VYWvb4HSI5Da15bu8i2O08FJSIcxd2kPLHwAKp17s9p9tISyKgtmk9EeJTieSQPbYzYZ2XO01F7mfDwWq8ptH/7JwYJyOieZeXHKYE7rqfUKcqfL8cGCcjLzyzAaFNejIK74bQAUheo4TcDFlrsvbooqqu1ltUPdmATeEN4UN0/O38qxsmp6p8Vy/ZiGm2lOGtieXm1jKaqo4fVfdzd7zGcXbmd9VgFxkWG8dNlgEqNNvHPtcLokR3OwoJypc1fVaZRob+AX7vlKKZ2uNlPxniAYZaGPXRjYQr+Nji5uDhaUU1zhY59kM4i4EQKKmIgwBtuMlAGZmnLgP6uKuKTyAX6LOBWDWgPf3qZVPFmc+CdfPUcrOTdGwMVvQbh7JuyWkBKjR25sXphRt2ol5CXZ8MszTh1D73R6Qod4jI10pI6JCLNHtT5a07Cx+N8LtvP7rlyiwo28cdUQ4s3hnNpTi+It3XHEZb/DSlu054QO8cS64rcB57sTO6AmaPO02lQdcu1cDqzdfwxVhc5JZlLjIt0+jiN6i3xPi5vlu3P5bO0BFAWeuLB/o/14jAaFu8f3AuDtZXubbBq5dPsR3vhlDwDPTB5Ax0QtEpQcE8G71w6nbVwEO3JKuHbeGruHx9uRG3AoBw+KyE0B0PJKKZ14czhptr/VHTmBJf5E3AgBhz6K4d0V+ygJ0LbnmXllfLg6k0pMhP1tLpx2n/bAitnwRDt4eSj871JN7PzxNuz9TYsIqKo2gXzhv7T9z3oM2vbzy2vQ01L2AahhETDhaW175atwdEezx9A7Ezf3SVBPTc3fdLheeuKHTYd5/RftU/3TkwfYy8lHdkvCZDSQlV/OvjzXongr3S0BB9cjN4DRNgYkzZpNZY17PT/sfpvOnklJQW3kZn9+mcdKsiuqLfzry80AXDGiE0OaiTKN65PKiZ0SqKi2MttWVXg8OUUV3PXJBgCuOqkzE06o218ovY2Zd68dQXxUOOsyC7j5/XVU1VjtpeCeHprpiL8qpoorqrn70w0tMtU7UlpZYxe5norcAPRMC8zUlIgbIeCYMiyd5BgT27KLmf7BOt+OY3CSFxbtoMaqMqZHMiO7J8Np/4TJcyEqEazVkLcTdvygiZ3v7oB3zoXn+8CTHeDtiWCp1CabD7/Rb6+hwREMPc+CnhO1hoY/NG8udrZnxsCO8fROi6WyxsqXf9Yai3fmFHP3p9pF7YYxXer4lqIjwhjWRbtwLt1+xNmXBdT2txnpqt8G3BI34SldgZb1uqn123gmJQVaL5KYiDAsVpV9eZ65OL+6dDd7cktJiY3gHxN6N7u/oijcM17b78PVmfXSzRaryh0frSevtIo+7eK4/5w+DR6nV1osc68ZSmS4gV92HOXuTzfYzebe6HGj46/IzUers/hs7QFu/2g9s3/e2eJqrc0HC7Gq0C4+0mORQYBebQNzDIOIGyHgSIuP5L9XDyMq3MgvO47y4NebA6oMc1t2EV/ZPk39Y7zDm/sJF8M9e+DOv7Ty63Oeg5Nu0URMm66gGKG6VOs1E50K57/qnGHVSzQ6gmHCk1q6bM9S2Ppto8+vqrGy9bD2hjawGXGjd7OGWmNxUUU1N763ltIqCyO7JnFvAxdKPTXliu8mK7+MA8fKbX4bN6IgxU7OlXLAYIvcdHJzBENljcWe4hvWxXORG0VR7NEbfe5ZS9h1pITXlmrRl4cn9XO6xH5ktyTG9Eim2qLywk91I4KvLNnFij15mE1GZl8+uMkxCkM6t+G1K4cQZlD4ZsMheyTIm2mprjYv2f68Ump8+EHru421Kc5nF+7g4W/+alGn79qUlOeiNhC4YxhE3AgBycD0BF66bDAGRZss/erS5s2IvuLZBTtQVTi7fxr9j3+jMBggvqPWOG/Y9TBhFlzxKdz2p9YMcMYfcPmncOMSiEnxy/p1UhobwdCmK4y+Tdte8H9a35cG0MtKE83hpLdp3jN0waAORIQZ2JZdzJ9ZBcz8eAN7c0tpHx/J7MsHN9gK/lSbqXjlnjynW7yvspmW+3eId69XjB65cWb0go6tkV+6coRCNyaDbz5YRGWNlTbRJntvFU8x8YQ0AOYt29eiDwmqqvJ/X26i2qJyeu9Uzu6f5tLz77F5b75cf9D+KX/13nxeXKSJncfOP6FRU7ojY3ul8twlAwHNyArejdy0i4skIsxAtUW1n8/b7M8rZcOBQgwK3G7rF/TOiv3c+tGfbqc99Uqpxqoa3cWxYiqQPoSKuBECljP7tuWhSZof5d8Ltnss99wS1u4/xqKtORgUmHlmL9eeHGaC5B5a6ifezflTHqSeodiRk2dCfDoUZsHvLzT4fP3Nsr+TZaXx5nDO6a8JhpvfX8uirTmYwgy8ftUQkmxrOZ6ebWNIi4ukotraaKXV8eil4y7PkwKw1ECJbZq1C5EbEtKxohCjVFB6zLUUGjj6bRI93ozu0uGdiDYZ2Z5T3CKT/rcbD7N6bz5R4UYeOa+fy+sc0DGBiSekoarw7ILtHCut4vaP/sSqwkWDO3DxEOf/J84f1IGHJ/W1f9/sXKmD65yuADweg0Hx+QDN7zZq0cPR3ZO588yevHTZYMKNCt9vPMy0t9e4VZm0wV4pleDBlWq+LkWBY2XVAdULSMSNENBcPSqD60/WPhXf8+lGVu1xr+eJJ1BVlX8v2AbA5CEd7eH+1opuKM4vraw/vdlk1kZQACz7jzZQ9DjcKSvVjcU5tmjR4+ef0KRfR1EUTuvlWmqq1kzshrgpydGaGRrCINqFyFpYBPlGrQGlJa/+z6o5PN3fxpH4qHAuGaZ1zn7L1vnZVSxWlf/YIiw3n9bNvan3wF1n9cSgwMItOUydu5rDhRV0TY7msQtOcPlY14zuwl1n9iTcqDRtat69BOaM1ebCuRlZ8PUAzW83aNHDSQM0gX3ewPbMvWYY0SYjy3fncembK2sLAZwgr6SSA8e0qFO9aHMLiTIZyUjSfj6BZCoWcSMEPP93dh8mnpBGlcXKje+t9cmU44b4bWcuK/fkYzIauH1cT7+swZMkRUdgUMCq0vBohD7naek1SyX8+H/1Ht7o0BDMWYZlJNpF4RUjOtkvuk1hLwl3wlSclV/GwYJywgyKe71idL9NTJo2dsMF8k3ahUgp2OfS86xWlbX7PduZ+HiuHd0FgwK/7jjq1gXou42H2H20lPiocK4ZneH2OrqnxnLxiVqEZtPBQkxhBl6+fDDRbo6auPWMHmx+ZDyTmmqgueUr7WvmCvjrC7fOU1sx5f33np05xWzLLibcqDC+X23qb0yPFD688SSSok38daiIya8vZ7+TJvGNtkabXVOiXRtF4iQ9babi7QFkKhZxIwQ8BoPCC1MGcWKnBArLq7nm7dUufWrxBFrUZjsAV57UmQ4Jvu9L42mMBoXkplJTigITn9GiGDt+gB0L7Q+VVdWw84huJnb+k6CiKLx6xYk8dsEJ9pRjc4zqnozRoLD7aGmzjR31KqkBHePdu2C6USllf2qkNiQ0vMi1QZG7j5ZwrKyayHAD/dp79lO1TnobMxNs3pu3fnMtsmSxqry0eCcA15/cpcUXx9vH9cBk81f965w+LX7NEWFNiFBVhZ0/1X7/08PaoFoXqZ0O7v3Izbe2lNSpPVOIN9f9WQ/omMBnN4+iY2IU+/PKuPi1FY3O3XLEWykpHd13s0MiN4LgGpHhRuZMHUrnJDMHjpVz/TtrXB7G1xK+23iYTQcLMZuM3DK2m8/O623qjGBoiJRecJJtRMSP90KNJoI2HyzCqkJanOtlpT3bxnLVSZ2dnsMVHxXOibbGjr/ubDo11aKUFNQdveAi5dFaFMpc6toEdD0lNSg9wfXZZC5w/RitXP3r9YeabKZ3PI5Rm6tbELXR6Zho5s2pQ3jywv5cdVLnFh+vSY5sgaKDEBYFcR2gMLN2lpsL+Cotpaoq39lSUucOaFhgd0mO5oubR9GnXRy5JZVc+uZK/vv7Xj5anWm/fXjcbfFWLerpyf42jtgrpgIocuO9ntWC4GGSYiKYN204F726jA0HCrntw/W8cdWQRjvjeoqth4v4p22Q3/VjutqjHcGAVg5eVL9iypFT/gEbP9V8N8tfhlPuduhv4503y+M5tWcKa/Yd45ftR7liRMMXRFVVWWWbJ+WWmRjc6k6sU+XmCAbdTOwNv40jJ3ZKZEjnRNbuP8Z7K/Zz11nNG+ItVpWXbeXW13kgaqNzWq9UjxynWXbaoo1dxsAJk+HLG+G352HwlRDj/Br0CrZDhRWUV1m8Vnr+16Ei9uSWEhFmYFzfxofopsZF8vFNJ3HDO3+wam8+j323xanje7pSSqeXbTr4zpxirFYVg5ffk51BIjdCq6JLcjRzpg7FFGZg0dYcbvlgLeuzCrxWgnikqILr5q2x92KZMba7V87jL5qsmNKJjNM6KQP89hwUHW5yErg30C+Gy3fnNdppNyu/3O63aa5rbqO0IC1ljddEV2Kla+Jmjd1v411xA9jN+e+v3G+fy9QU3286zK4jJcRFhrXIa+M39JRUj7Og/9+g/WCoKoYlT7p0mMRoE4m2FJE3U1Pf2nrbnNEntdk2BnGR4bxz7XBuOa0b4/qkOtza1rmd2Ve73XJaNwZ5Sdx0TorGZDRQVmXxWbl8c0jkRmh1DM1owwuXDGLGh+tY8FcOC/7KoW+7OC4b0YkLBrV3fZZQI5RV1XDdO39wqLCCrinRvH7lEK+mDfxBvREMjdH/b7D6TTiwBn5+nI0HJgOem1HTHH3bxZEcYyK3pIq1+481GJnRU1ID0xOaLw1ujCLXG/jpGJM04ZBoydXSd2HNR/iyCyvIyi/HoGBPvbnEwgdg23cOVUDqcdu2zehkuPQDzurXjvQ2UWTll/P5ugNc2URaqI7XZkxXrxhRvUp5AWSu1La7j9N6UI1/UusQvu4drTt4275NHsKRLsnRHMssYG9uKX3bx3l8uVpKSvv7m9RISup4IsONTnWJ9jbhRgPdUmPYeriIbdnFblfTeZLgeqcWQoZzBrTji5tHccGg9pjCDGw5XMQDX21mxJOL+efnG9nQwmiO3hJ+08FC2kSbePuaYfXMfcFAgyMYGkJRYPwsANT1HxCT/xfgu8iNwaBwSo+mS8JbNHJBp9j9yE1kQltK1QgMqFDgnO/mD1vUpk+7ONdFeUEmLH9JSxce22u77YOC/bZbpuYxKcyEQ+vgzw8wGhSuHa2JsLm/722y422rj9rsWQqqBZJ7gq2DNJ1HaVWAqrV2vpuT1JqKvVMx9WdWAQcLyok2GRnb20dpOw8SaGMYRNwIrZbBnRJ58dLBrLrvDP51Th+6pURTVmXhozVZnP/KMs556XfeX7nfrYZXT/2wlYVbtCZzc6YOoXOSZ7vGBgopjY1gaIj0YXDCZBRU/hX2AZ3bRJFgNnl5hbWc2kS/G1VVW24mVlX3uhPbiDebyFRtPolj+5x6zh8t6W+z5Wvta8dhcO1C7XbdT3DdIu12/WK4/ufaoa47fgDgkqHpxEWGsSe3lMXbGi6vt1hVXm7NURuom5Jy5MxHwBAOuxfDzkVOH87bAzT13jZn9m3b5AiKQKV2DIOIG0HwCInRJq4f05VFM0/lk5tGcuHgDvZozr++2syop37mjV92O92+//2V+5nzm9bs7Nm/DWSIB6c0BxqpjY1gaIxxD1FjiGCkcQtXJmz24srqM6ZHCoqiGbxziupGmjLzyzhcWNFwQ7fCA1DuxFyl8mNQYzuuG+ImwWwiU9U+cavHnGuWt2ZfC/rbbLb1bBl4KXQaod3Sh2siNH0YdBwKHYfAkGu0/Q6uheJsoiPCuNxmym6sLHz+psPsbM1RG6sVduni5sy6j7XpCiNu0rYX3q91pXYC3VS8xwsVUxaryve2EvAme/YEMLqpOFDKwUXcCEGDoigM79KGF6YMYtV9Z/DAuX3pmhJNcUUNs37YxhnP/cLX6w82GYr/ZcdRHvpGS7ncdWbPOlOqgxE9LXW0uNK5NF5CJxbEXQzAJcfehBrftVtvE22ye3x+PS56o49cGJSeULeSJXszvDwE5pwO1c0YHfWojTkJwl2fmhwfFc5eVRNFln3Lm92/uKKarYe1YYNDXRXQx/ZpqSbFoKVZmiI2DdqfqG3vWADA1aM6E2ZQWLU3n00H6vZJcfTaXHdyK43aZG/Uuk2bYqDTyPqPn3IPRLWBo9tg3TynDtlFj9wcLfF4AcPqvfkcKa4kLjKMMT38O3POXXraet3sPlrSqOnfl4i4EYKSxGgT153chZ/uPJV/Tx5AWlwkBwvKuf2j9Zz/yjL7xdCRbdlFTP9gHRarysUndmTG6cFVGdUQell7lcVKoZPTrJ8rPYejajzx5VmwZo43l1ePxqaEN5iSslrh+7u0aEz+nub7m+jdid0oAweINhn5QT0JAOP276Cs6VlY6zILsKqQ3iaKtHgXxdRfX2lfM052rqS519na1x0/AtAuPsoeIXjr97rRm1YftYHalFSXUxs2dkclwFhb1+0lT0JF843wMpKiURQoqqjhrk828OWfB1zqF9QUepXUxBPatdqihQ4JUcREhFFjVX3S7LA5WudPURCcxGhQ+NvQdJbcfRr3jO9FtMnIpoOFXDZnJdfNW8MuW5fdI8UVXDfvD0oqaxjRpQ2zLurv8QGGgUhkuJH4KO2TuTNdn48UVbCnWOG5mku0O355utmLuCfRxc1vO3OpsWifDjW/jbaGOuJm40eQtRKw/R5/ewFKmmgC2IIycNAihwcie7HZmoFiqYKNnzS5v72/jTtpz7++1L72u8i5/XtN0L7uXmKPYF1nKwv/buNhDtnKd63HRW30v41Wh97f5viUlCNDrtHMxmV5WouDZogMN9q9UV/8eZA7P97A8CcWM+HFX3n8uy0s2X7Ercai1RYrP2xq3Skp0P7+A2kMg4gbISSIMhmZPrY7v/xjLFed1BmjQWHxtiOMf/E3/u/LTdzwzh8cLCina3I0b1wVfCXfTVFbMdW8uNH722xIOgfanqB94l36lFfX58ig9ATio8IpLK+2r2VfXhnZRRWYjAZO7GTzrpQf08qkAcY9XNvfZGkT/U1a0J1YJz4qnI8sY7Vv1r3T5KDGWr+Ni+ImbzccXg+KEfpMcu45bU+AuI5QUw57fgHghA7xjOyahMWqMm/5PgDmbw6CqE1ZPhz8Q9tuStwYw+Gsx7Xtla85ZQJ//7oRfHD9CP5+ajdO6BCHomgG2rd+38u0t9cw6JGfuPTNFbz5q/Mev+W78zhWVk1StImTurZuf1+vtMAZwxA67+CCgJaGeeyCE1hwxymc2bctFqvK/1ZlsuFAIYnmcOZeM8ynFUCBQLMjGBzQOxOfkN6mdmr4mrfg6A5vLa8ORoPCmB7a9G09NaWnpOr4bX5+AspyIaU3jJwOZ9nWunYeHNna8MHtZeAd3F5fvDmcbyyjsBgjtdb/B9c2uF9VjZX1tnk/w1w1E+uDILucovWvcQZFgV4Tte3t8+1333CKFr35cFUmRRXV/GeRFrW59uQurTdqs/tnrdQ7tR/Ed2x63x5n2YbDVsGih5s9tCnMwOjuyfxzYm++u3UMa/91JrMvH8ylw9LpkBBFlcXKyj35PDl/G9fOW0NJZfORHL1K6uz+7Qgztu5Lsj5jKhAqplr3T1IQ3KR7agxzpg7l4xtPYnAnLRrw5tShZCQHZ8l3U6Tq5eBOVEzZOxOnJ2gXhZ4TtV4iPz3gxRXW5Xjfjd1vozf2O7Qe/vivtn32s9on9IzR0PtcW3+TRtbagjJwnfiocIqIJqudrfx47bwG9/vrUCEV1VYSzeH2KelOY09JXeja8/TU1I4Fmh8JOK1nKt1SoimurOGmd9ey80gJsZFhTLP1wmmVOJOS0lEUm/BVtJ+r3vTPSdpEmzh3QHueungAv987liV3n8bDk/oSbTKyfHceV7y1imOljZvuK2ssLNicDbTulJROTz1yI2kpQfAvI7om8eUto/nzgTO9PtsnUHE2LaWqqj1yYx/Ad9ZjtqnhP2p+juY4tg++uRX+e5ZWyeQGurjZeKCA/NIquzn8pK5tak3EqlXrqtxlTO0Tz3xUW+uun2DX4voHtncnbpm4AVifbKtg2vwFVNZ/o9f72wzp3MY1b1fuLsjepL0OZ1NSOhljtOqhkmwtrYXWHPG6k7WBmnoTxOtac9TGaoFdtt41x/e3aYy0E+DEq7TtxoSvEyiKQpfkaK4Z3YUPbzyJRHM4G7IKmPLminqtC3R+2X6U4soa0uIiGeruyJAAQo/cZOaX+XSwcUOIuBEECIhBb/4ixSZuVuzOY9eRxruvZuWXU1BWjclooLetYRfJPWDY9dr2wn9pF5eGKMiEb27TyrLXvQtZq2DeOXDgD5fXmxoXSZ92cagqvLN8H0eKK2v9Nn++p/ktTLFw5mN1n5jUTWu5D9pF7Pi1eiAtlWhLad6xMooDxo5QXcrhZf+rVzq82j4s08ULmh616XoamF0U42ER0O10bXv7D/a7LzqxA0nR2rpbfdTm0J+aQTgiTuv54yxj79fK6g+sro3gtYABHRP45KaRpMVFsiOnhMmvL2d/Xv0Kom9tvW3OHdAuKN6DkmIi7BWYO3K808nZWUTcCEKIM6BjAooCWw4XceYLv3Drh382GFbeYIva9GkXW9dwfeq9EJkAOZvhz/frPqnwAHx3J7x0omawtdZoF9iOw6CiAN45D/b+6vKa9eiN3oRucKcEIqsLa30TY+9rOAJzyj3aWo/8VXet1eW1jf5akJa6dHi6bTihwjsVpwCQs/QNTn56CQ99vZnfdh6lssZir5Ry2UzsbkpKR/fd7KgVN5HhRm48RYveTB/bvfVGbaA2JdVtrJaOdJbYNGg3UNu2Ga5bSo+2sXz695F0TjKTlV/O5NdXsN3Bi1JWVcOiLTkAnBsEKSmdQGnmJ+JGEEKc4V3a8O2Mkzmzb1tUVTM4nvXCr9zywVp7kzmoNRPXG5ZpbqMJHICfH9fSMEWH4Pu74aXB8MdcsFZrPUeuXQBXfQlXfaV9X10K70+G7T+6tObTbKMYSm2TrU/qmgSLH4HyfM1IOvymhp9obgOn/qPuWqH203p4NES6Py+rd1ocX00fzer/O4M+E26khjAGGXYTW7idd1bs56r/rmbwoz9xrKyaiDADJ3RwYQDj0e2aKDOEQ+9z3Ftgj7O0CEX2pjrzr248pSu/3jOWm2wip9XS2MgFZ+hyqvZ1r2fEDUB6GzOf/n0kvdNiOVpcySVvrGBdpiaif952hPJqC+ltomrTvEGA3szP3+XgIm4EQeCEDvHMmTqU7287mYknpAEwf1M2E//zGze99webDxbWmokbeiMedj206QalR7RozH8GaQ3+LFWa1+Oa+XD1N9BJa3JHRAxc/gn0OgcslfDxFbD5c6fXe2KnRGIiaid/j4s7AGvf0b4551kwNjEVfNgNWgv+0iOw7D/afY5l4B7ob5QaF8lFYwYT1lcTIW+esIVLh6WTEhtBmU2QDemcSESYCzOE9MZ93cZClJv+jOhk6GhL1+yoFZSKotApydy6ezuVHNG6NoM2BdxVutrEzZ5fmizhd5XU2Eg+vnEkJ3ZKoLC8mivfWsXvO3PtVVKTBrRv3T/34+htMxVvl8iNIAiBQr/28bx25RB+vGMM5wxoh6LAgr9yOPfl3+2plIHpCfWfGGbSzMWgXWAsldBpFFz9LVzznVatdDzhkXDJO9D/Ei1d9dl1jVYXHY8pzMAoW3VUZBj0+/MRQIWBl2mTn5sizATjHtG2l8/WUmf27sTup6Qa5MSpAHTK+oanzuvJqvvO4Kvpo3ng3L48eWF/1471l22WlLON+xrDnppyLVoW8Ogm8XYDtTSTq6SfBEaT5r3K2+XRpcWbw3n/+hGM6ZFMWZWFa+et4Wfb0NJgqJJypGfbWGIjwzCbXBDuXkDEjSAI9eidFscrl5/IwjtO4fxB7TEoYFW1EQPdUhopXe51Noz4O3Q/E6Z+DdPma71YmsIYDhe+AUOvBVT49nZNcDjBGX20sQP/SF6JIXs9RMRrFVHO0GeSJr5qymHxY1B0ULu/BWbiBuk6FuLTNX/R1m8xGBQGpSdw3cldXGs7cGSrNgfJaKoVJ+6iP3/vrw1WcrVa7CXgbqSkAExmSB+hbe9Z6pElOWI2hfHW1UM5u38aVRYr1RaV7qkx9khHsDCwYwIbHzqLN6cO9es6RNwIgtAoPdrG8p9LB/PTzFO5YUwXnv3bQIyNVXUoCkx8Gq78TKvmcTbUbjDAOc/D6Nu17xfeD0tmNZsamDwknefP6cDV5e9qd5z+L+fmLOlr1ZsQbvzIPlCyJWXgDWIwwmBbmfG6d9w/jm4k7naGNhepJST3hMQuWsrQmfL91oClBnbbIjfdnehv0xhe8N04EhFm5OXLTuSy4ekAXDa8U1ClpECrPA2E1yTiRhCEZumWEsP95/RlYn8PX/x1FEVLFZ1u6zPyy1Pw432QswVyd0L+Xlv6KEdrr19RhNFSwUV5czBWFkBaf1v0xwU6nAgDpmjbmSu0r56O3AAMvgJQYN9v2ugEV1HVlldJOaIotYM0HUrCWzUH1mijQKISoWMLIga672bvb423NWghRoPCrIsGsPr+M7i2tY64aAU04boTBEHwIYoCp9wNEbHwwz9g1WvazRnOeb5pE3FjnPEgbPlamxwOnvfcgDYCoPs4rXngn+9ps65cIecvyN0BxoiWp6R0ek2Ala/AzgXaRdzgX39Ei9llq5LqdkbLXkv7E7UeSRUFkL1Rm0nmJfTO4IJ3kMiNIAiBxYib4MI3tYomc7JWmh1u1kqgG2L4ja41bHMkvqM2e0rHzYngzTLkau3rnx+Apdq15+pRmx5nQqQLpeNN0Wmk9nMty3OrkWLA0VK/jY4xrNb87qF+N4J/kMiNIAiBx8Ap2u14VFWLNFirbSJBbVFfGgBOvhM2fgIVRVoXY2/QcwJEp2jl5zsWQJ9znXuep1NSOsZwzZuy+TNtkGanEZ47tq8pOqT17UGB7me0/HhdT9Mqyfb+Aiff0fLjCX5BIjeCILQeFEX7dB0epUUxWipsQEuD3fQrzFjjmeM1hDEcBl2uba971/nnZW+C/N0QFgk9x3t2TcFSEq7PkuowxPkp6U2hm4r3r4Ca5ofJCoGJiBtBEARzG4ht691znGhLTe36CQoPOvccvbdNjzM1EeZJup+hDeA8ug3y93j22L7ElSngzpDaB6JTtTYBB9Z45piCzxFxIwiC4AuSukHnk7WJ5es/aH7/OimpFjbua4ioRM17Ay6PvwgYaqpg91Jt21PiRlFq+zOJ76bVIuJGEATBV+jG4nXvgdXa9L6H18OxfRAW5fmUlE4DgzRbFVkroapYM56382Blk30Uw1LPHVPwKSJuBEEQfEWfSZqvpzATtnyl9WZprFmhHrXpOR5MLnQzdgVd3OxfDuUF3jmHN9Enu3cfpzWD9BS67+bgWs1oLrQ6pFpKEATBV4RHaY0DV78Jn03T7lOMmuCJSqx722PrHuzJKqnjadMVkntB7nbNmNt/svfO5Wn2L4eNH2vbw2/w7LETO0NihhY5279c6wsktCokciMIguBLTroFUnprvXsAVAuU52tVUQf/0AzHmz6B0qNgiml575bmaI1VU5Ya+P5ubfvEqS3rStwYXh7FIHgXidwIgiD4kjZdYPoqbbu6QuuGW36sgVuB5v0wmb27nl4TYdmLWtWRpVorWw901syBI39BZAKc8bB3ztH1VG0emJiKWyUibgRBEPxFeCSEp0Fsmv/W0HEYmJO0bsWZK6HLGP+txRmKc2DJk9r2uIcgOsk759EjN0f+gpKjEJPi2eMHw9iLAEbSUoIgCKGMwQg9bNVYS5/SyqsDmZ8ehMoibe6T3jvIG0QnQ9sTtG1Pp6bW/w+e6QLLZ3v2uIIdETeCIAihzsl3aAMj9/8O8+9qvILL3+xbBhs/AhQ45znvRz684bvJ3wPf36VVyi28H/76ynPHFuwEhLh55ZVXyMjIIDIykhEjRrB69epG950zZw5jxowhMTGRxMRExo0b1+T+giAIQjOk9ILJc0ExaOMhVr7q7xXVx1IN820m4iFXa+MWvI29342HxI3VCl9Nh+oyiLCN+vjy73BwnWeOL9jxu7j5+OOPmTlzJg899BDr1q1j4MCBjB8/niNHjjS4/9KlS7nssstYsmQJK1asID09nbPOOouDB51sZy4IgiDUp+dZcNYT2vaC+7UBn4HE6jlwZItWJn/GQ745Z+dR2oiKgv1aWXhLWf0GZC6H8Gi4cYk2vLSmHD68zPmRHIJT+F3cPP/889xwww1MmzaNvn378vrrr2M2m5k7d26D+3/wwQfccsstDBo0iN69e/PWW29htVpZvHixj1cuCIIQZJx0s83HosJn10LOFn+vSKM428FE/LA2C8wXRMTWRohaGr3J2w2LHtG2z3pUG8cxeS6k9IGSbPjwUqgqbdk5BDt+FTdVVVWsXbuWcePG2e8zGAyMGzeOFStWOHWMsrIyqquradPGR3/sgiAIwYpi87JkjIGqEvjfFK1SyJOU5cO8c+GFE2DNf7V0U3MsfEAbs9BhCAye6tn1NEfX07SvLfHdWC3w1S1alKbLqTDkWu3+yDi4/CNtfET2RvjixubHcghO4Vdxk5ubi8VioW3butN427ZtS3Z2tlPHuPfee2nfvn0dgeRIZWUlRUVFdW6CIAhCIxjD4ZJ3te7FhZnw8RVQU+mZYxdnw7xzYN9vUJgF38+EV0bA5i8av6jv+11raogCZz/r2TELzmA3Ff/qvtF65WvaHCxTDJw/u+5rSMyASz8Aowm2fQc/P9riJQsBkJZqCU899RQfffQRX375JZGRkQ3uM2vWLOLj4+239PR0H69SEAShlWFuA5d/oples1bBt7e3vILq2H6YO0HzzcSkwen/0iIW+bu1URRzxsLun+s+x1Jd24l46DTocGLL1uAOHYdp3aRLj2prd5XcnfDzY9r2WY9DQqf6+3Q6Cc6zlYX//oJWKi60CL+Km+TkZIxGIzk5OXXuz8nJIS2t6aZWzz77LE899RQLFy5kwIABje533333UVhYaL9lZWV5ZO2CIAhBTXIPuOQdbfbVhg+1i667HN2hCZtjeyGhM1z7I5xyD9y+Hk77Py2icXg9vHchvHOeNrASYNUbcHQrRLWB0x/wxKtynTATdBqpbbvqu7Fa4KuboaYCuo6FIdc0vu/AKTDGJuS+uU2baSW4jV/FjclkYsiQIXXMwLo5eOTIkY0+75lnnuGxxx7jxx9/ZOjQpmeKREREEBcXV+cmCIIgOEG3sXD2M9r24kdg67euH+PQenh7AhQf0mZqXfujNoICNMPuaffC7Ru0mVtGk+ZtmXM6fHwlLJ2l7XfmI74zETdEVzf73ayYDQfWQESclo5SlKb3H3s/9D0frNXw0RWQv9e99Qr+T0vNnDmTOXPm8M4777B161ZuvvlmSktLmTZNm5g7depU7rvvPvv+Tz/9NA888ABz584lIyOD7OxssrOzKSkp8ddLEARBCF6GXQ/Db9S2v7gRdv6kRSScYf8KeGeSNtqh3SC4Zj7Eta+/X3QyTJgFM/6AgZcBiiakqkqgw1AYdKWnXo176L6bfcu0oZ3OcHQ7/GwrrR//BMR3bP45BgNc8Lr2syrP1wzdFYVuLTnU8bu4mTJlCs8++ywPPvgggwYNYv369fz44492k3FmZiaHDx+27//aa69RVVXF5MmTadeunf327LPP+uslCIIgBDfjZ0G307Xmcx9Mhuf7wvx/aLOoGjMC71qkpZkqi6DzaLj62+bnQCV2hgtfh5uXQ69zNLPtpP/43kR8PGkDtP46VcVwyImGe5YaLR1lqYTu42DwVc6fy2SGyz6C2PaQu10TlIHaMTqAUVQ1tH5qRUVFxMfHU1hYKCkqQRAEZ6ko1Eqy//oKKh2iCbHtod8F0O9CLcpiMMCWr+Gz67T0Svczteorb0839zYfXwVbv4Gx/4JT72l639+e19J4EfFwywqI7+D6+Q6th/+epQmkc57TImghjivXbxE3giAIgvPUVMKepfDXl7Dtey0yoxPXETJO1kq3VasmeC58UzPltnbWvKXNhMoYA9d8p0VTaiqgsth2K4LKEig+DF9PB0sVnP8qDL7C/XOueBUW3AdhUXDTr5DS03OvpxUi4qYJRNwIgiB4iJpKrXz7ry9h23wtbaNz4lQ490XvD7f0Fbm7YPYQQIHIeE3QqE14j3qMh8s/bt5E3BRWK7x/EexZAu0GwnWLAl8oqiqseAVOuBji2nn00CJumkDEjSAIgheoroDdi2Hrd5DcHU6e2bILe6ChqvDaaDjy13EPKFrVl34zxWhl9Gc+BjEpLT9v0WF4bSSUH4OT79TGTwQyv78Aix7WSv5vWQGmaI8dWsRNE4i4EQRBENyiogjydoJJFzMx2hBMbxuet3wDn1wFKFpKLONk757PXda9B9/M0LbPegJGzfDo4V25fvu9WkoQBEEQWgWRcdp8q5SeWsolItY3lVx9z4PBVwIqfHETlBd4/5yusm0+fHubtj36do8LG1cRcSMIgiAIgc6EpyGxCxQd0IzNgcT+5doIDdUKg66AcY/4e0UibgRBEAQh4ImIgYvmaOMwNn8GGz/194o0sjfD/y7VKsd6ToBJLwWE10rEjSAIgiC0BtKHwan/0La/nwkFmf5dz7F98P7FWt+j9JNg8ttgDPPvmmyIuBEEQRCE1sKYu6HjcK2vzhc3OT8KozlKc7X0UnW5c/uXHIX3LoKSbEjtC5d/FFCNGkXcCIIgCEJrwRgGF72plZxnLodlL7bseHm74ds74IV+8PZE+Hd3rbv0tu+18v6GqCzWxnDk74b4TnDlF9p4igBCSsEFQRAEobXx5wfw9S1gCIPrF0H7wa49P2s1LPuPJmKwyYCI+LqjNSLioNfZWqfpbqdrDQRrKuGDv2kT0s1JcO1Cra+RD5A+N00g4kYQBEFo9agqfHq1NsfLaIK0/tpsr45DtXL1Nl3rG3utVtg+H5a/BFmrau/vOQFG3QadR8HBtbD5C63rdPGh2n0i46H3uVCWDzt+0CJHV38LHU70zetFxE2TiLgRBEEQgoKyfHj3PMjeVP+xqDaayOk4VBM9hVmwYjbk7dIeN5pgwCUw8lZI7V3/+VYrHFitiZy/vtK8NTqGcLjiU+g21isvqzFE3DSBiBtBEAQhaFBVOLYXDqyFg3/AgT8ge6M2uLMhIuJh2LUw4u8Qm+bcOawWyFwJf32hRXxOvRf6TPLca3ASETdNIOJGEARBCGpqKrX+M7rYOfiH1h9n6DRtoGlErL9X6BauXL8DoyBdEARBEATPEBYBHYdotxE3+Xs1fkFKwQVBEARBCCpE3AiCIAiCEFSIuBEEQRAEIagQcSMIgiAIQlAh4kYQBEEQhKBCxI0gCIIgCEGFiBtBEARBEIIKETeCIAiCIAQVIm4EQRAEQQgqRNwIgiAIghBUiLgRBEEQBCGoEHEjCIIgCEJQIeJGEARBEISgQsSNIAiCIAhBRZi/F+BrVFUFoKioyM8rEQRBEATBWfTrtn4db4qQEzfFxcUApKen+3klgiAIgiC4SnFxMfHx8U3uo6jOSKAgwmq1cujQIWJjY1EUxaPHLioqIj09naysLOLi4jx6bKE+8vP2LfLz9i3y8/Yt8vP2Le78vFVVpbi4mPbt22MwNO2qCbnIjcFgoGPHjl49R1xcnPxz+BD5efsW+Xn7Fvl5+xb5efsWV3/ezUVsdMRQLAiCIAhCUCHiRhAEQRCEoELEjQeJiIjgoYceIiIiwt9LCQnk5+1b5OftW+Tn7Vvk5+1bvP3zDjlDsSAIgiAIwY1EbgRBEARBCCpE3AiCIAiCEFSIuBEEQRAEIagQcSMIgiAIQlAh4sZDvPLKK2RkZBAZGcmIESNYvXq1v5cUNPz6669MmjSJ9u3boygKX331VZ3HVVXlwQcfpF27dkRFRTFu3Dh27tzpn8W2cmbNmsWwYcOIjY0lNTWVCy64gO3bt9fZp6KigunTp5OUlERMTAwXX3wxOTk5flpx6+a1115jwIAB9kZmI0eO5IcffrA/Lj9r7/LUU0+hKAp33HGH/T75mXuOhx9+GEVR6tx69+5tf9ybP2sRNx7g448/ZubMmTz00EOsW7eOgQMHMn78eI4cOeLvpQUFpaWlDBw4kFdeeaXBx5955hleeuklXn/9dVatWkV0dDTjx4+noqLCxytt/fzyyy9Mnz6dlStX8tNPP1FdXc3/t3d3IVG0bRzA/2vrbmo9umXtroVlVJsVClnKYhGllNZBn2SwxEYHomlY1EFQoh1EQVBUB1L0dRJJBpYVfZjVQtKHmatWJhmSQW4moal94l7vgTzDO289L++bu05O/x8MzNz3vXrNnzm4mJlllyxZgr6+PmXNtm3bcPnyZZSVlcHj8eDt27dYvXq1hlUPXxMnTsT+/ftRW1uLx48fY/HixVixYgWePXsGgFkHU01NDY4dO4aEhATVODMPrFmzZqG9vV3Z7t27p8wFNWuhQUtOTpa8vDzluL+/X2JiYmTfvn0aVqVPAKS8vFw59vv9YrPZ5MCBA8pYV1eXmM1mOXfunAYV6ktHR4cAEI/HIyID2YaGhkpZWZmypqmpSQDI/fv3tSpTVywWi5w4cYJZB1FPT49MmzZNKisrZeHChVJQUCAivL4DraioSBITE386F+yseedmkL59+4ba2lqkp6crYyEhIUhPT8f9+/c1rOzP0NraCp/Pp8o/MjISKSkpzD8Auru7AQBjxowBANTW1uL79++qvGfMmIHY2FjmPUj9/f0oLS1FX18fnE4nsw6ivLw8LF++XJUtwOs7GF6+fImYmBhMmTIFLpcLbW1tAIKf9R/3w5mB1tnZif7+flitVtW41WrFixcvNKrqz+Hz+QDgp/n/PUe/xu/3Y+vWrUhNTcXs2bMBDORtMpkQFRWlWsu8f11jYyOcTie+fPmCUaNGoby8HDNnzoTX62XWQVBaWoonT56gpqbmhzle34GVkpKCM2fOwOFwoL29HXv27MGCBQvw9OnToGfN5oaIfiovLw9Pnz5VPSOnwHM4HPB6veju7saFCxfgdrvh8Xi0LkuX3rx5g4KCAlRWVmLkyJFal6N7mZmZyn5CQgJSUlIwadIknD9/HmFhYUH933wsNUjR0dEYMWLED294v3v3DjabTaOq/hx/Z8z8Ays/Px9XrlzBnTt3MHHiRGXcZrPh27dv6OrqUq1n3r/OZDJh6tSpSEpKwr59+5CYmIjDhw8z6yCora1FR0cH5syZA6PRCKPRCI/HgyNHjsBoNMJqtTLzIIqKisL06dPR0tIS9Oubzc0gmUwmJCUloaqqShnz+/2oqqqC0+nUsLI/Q1xcHGw2myr/jx8/4uHDh8z/F4gI8vPzUV5ejtu3byMuLk41n5SUhNDQUFXezc3NaGtrY94B4vf78fXrV2YdBGlpaWhsbITX61W2uXPnwuVyKfvMPHh6e3vx6tUr2O324F/fg34lmaS0tFTMZrOcOXNGnj9/LtnZ2RIVFSU+n0/r0nShp6dH6urqpK6uTgDIwYMHpa6uTl6/fi0iIvv375eoqCi5dOmSNDQ0yIoVKyQuLk4+f/6sceXDT25urkRGRsrdu3elvb1d2T59+qSsycnJkdjYWLl9+7Y8fvxYnE6nOJ1ODasevnbu3Ckej0daW1uloaFBdu7cKQaDQW7evCkizHoo/Pu3pUSYeSBt375d7t69K62trVJdXS3p6ekSHR0tHR0dIhLcrNncBMjRo0clNjZWTCaTJCcny4MHD7QuSTfu3LkjAH7Y3G63iAx8HbywsFCsVquYzWZJS0uT5uZmbYsepn6WMwA5ffq0subz58+yefNmsVgsEh4eLqtWrZL29nbtih7GNm3aJJMmTRKTySTjxo2TtLQ0pbERYdZD4T+bG2YeOFlZWWK328VkMsmECRMkKytLWlpalPlgZm0QERn8/R8iIiKi3wPfuSEiIiJdYXNDREREusLmhoiIiHSFzQ0RERHpCpsbIiIi0hU2N0RERKQrbG6IiIhIV9jcENEfz2Aw4OLFi1qXQUQBwuaGiDS1ceNGGAyGH7aMjAytSyOiYcqodQFERBkZGTh9+rRqzGw2a1QNEQ13vHNDRJozm82w2WyqzWKxABh4ZFRSUoLMzEyEhYVhypQpuHDhgurzjY2NWLx4McLCwjB27FhkZ2ejt7dXtebUqVOYNWsWzGYz7HY78vPzVfOdnZ1YtWoVwsPDMW3aNFRUVAT3pIkoaNjcENFvr7CwEGvWrEF9fT1cLhfWr1+PpqYmAEBfXx+WLl0Ki8WCmpoalJWV4datW6rmpaSkBHl5ecjOzkZjYyMqKiowdepU1f/Ys2cP1q1bh4aGBixbtgwulwsfPnwY0vMkogAJyM9vEhH9IrfbLSNGjJCIiAjVtnfvXhEZ+KXynJwc1WdSUlIkNzdXRESOHz8uFotFent7lfmrV69KSEiI+Hw+ERGJiYmRXbt2/WMNAGT37t3KcW9vrwCQa9euBew8iWjo8J0bItLcokWLUFJSohobM2aMsu90OlVzTqcTXq8XANDU1ITExEREREQo86mpqfD7/WhubobBYMDbt2+Rlpb2X2tISEhQ9iMiIvDXX3+ho6PjV0+JiDTE5oaINBcREfHDY6JACQsL+5/WhYaGqo4NBgP8fn8wSiKiIOM7N0T023vw4MEPx/Hx8QCA+Ph41NfXo6+vT5mvrq5GSEgIHA4HRo8ejcmTJ6OqqmpIayYi7fDODRFp7uvXr/D5fKoxo9GI6OhoAEBZWRnmzp2L+fPn4+zZs3j06BFOnjwJAHC5XCgqKoLb7UZxcTHev3+PLVu2YMOGDbBarQCA4uJi5OTkYPz48cjMzERPTw+qq6uxZcuWoT1RIhoSbG6ISHPXr1+H3W5XjTkcDrx48QLAwDeZSktLsXnzZtjtdpw7dw4zZ84EAISHh+PGjRsoKCjAvHnzEB4ejjVr1uDgwYPK33K73fjy5QsOHTqEHTt2IDo6GmvXrh26EySiIWUQEdG6CCKif2IwGFBeXo6VK1dqXQoRDRN854aIiIh0hc0NERER6QrfuSGi3xqfnBPR/4t3boiIiEhX2NwQERGRrrC5ISIiIl1hc0NERES6wuaGiIiIdIXNDREREekKmxsiIiLSFTY3REREpCtsboiIiEhX/gWZ+5kNUtIFqQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Karena train loss nya menurun maka model sudah bagus"
      ],
      "metadata": {
        "id": "Vae7Ab_ljF94"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### d. After knowing the result of (1c), modify the architecture in (1c) to get your optimal accuracy value (you can add or subtract the architecture, or change the hyperparameters, or use tuning on the hyperparameters). Explain your reasoning for using your chosen approach."
      ],
      "metadata": {
        "id": "APB6pYeM_JuS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ann = tf.keras.models.Sequential()\n",
        "\n",
        "#ann.add(tf.keras.layers.Dense(256, activation='relu'))\n",
        "\n",
        "ann.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "\n",
        "ann.add(tf.keras.layers.Dense(64, activation='relu'))\n",
        "\n",
        "ann.add(tf.keras.layers.Dense(2, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "YAWh24IJ_T_V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.compile(optimizer = 'adam', loss='sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
        "history1 = ann.fit(X_train, y_train, validation_data=(X_val, y_val), batch_size = 30, epochs = 32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVBBu-5DAiyp",
        "outputId": "fd5e0df2-f020-4030-d3fd-4cc9bc9e18d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/32\n",
            "9/9 [==============================] - 2s 25ms/step - loss: 0.4318 - accuracy: 0.8347 - val_loss: 0.4618 - val_accuracy: 0.8710\n",
            "Epoch 2/32\n",
            "9/9 [==============================] - 0s 9ms/step - loss: 0.2713 - accuracy: 0.8831 - val_loss: 0.2071 - val_accuracy: 0.8710\n",
            "Epoch 3/32\n",
            "9/9 [==============================] - 0s 8ms/step - loss: 0.2683 - accuracy: 0.8548 - val_loss: 0.2637 - val_accuracy: 0.9032\n",
            "Epoch 4/32\n",
            "9/9 [==============================] - 0s 9ms/step - loss: 0.2362 - accuracy: 0.8911 - val_loss: 0.2323 - val_accuracy: 0.8710\n",
            "Epoch 5/32\n",
            "9/9 [==============================] - 0s 9ms/step - loss: 0.2148 - accuracy: 0.8992 - val_loss: 0.2419 - val_accuracy: 0.8710\n",
            "Epoch 6/32\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.1850 - accuracy: 0.9073 - val_loss: 0.2082 - val_accuracy: 0.8710\n",
            "Epoch 7/32\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.1804 - accuracy: 0.9315 - val_loss: 0.2263 - val_accuracy: 0.8710\n",
            "Epoch 8/32\n",
            "9/9 [==============================] - 0s 8ms/step - loss: 0.1778 - accuracy: 0.9274 - val_loss: 0.2326 - val_accuracy: 0.8387\n",
            "Epoch 9/32\n",
            "9/9 [==============================] - 0s 10ms/step - loss: 0.1906 - accuracy: 0.9113 - val_loss: 0.2242 - val_accuracy: 0.8710\n",
            "Epoch 10/32\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.1710 - accuracy: 0.9234 - val_loss: 0.2254 - val_accuracy: 0.8710\n",
            "Epoch 11/32\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.1840 - accuracy: 0.9194 - val_loss: 0.2705 - val_accuracy: 0.8387\n",
            "Epoch 12/32\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.1665 - accuracy: 0.9395 - val_loss: 0.2428 - val_accuracy: 0.8387\n",
            "Epoch 13/32\n",
            "9/9 [==============================] - 0s 8ms/step - loss: 0.1797 - accuracy: 0.9153 - val_loss: 0.2316 - val_accuracy: 0.9032\n",
            "Epoch 14/32\n",
            "9/9 [==============================] - 0s 9ms/step - loss: 0.1845 - accuracy: 0.9153 - val_loss: 0.2225 - val_accuracy: 0.8710\n",
            "Epoch 15/32\n",
            "9/9 [==============================] - 0s 9ms/step - loss: 0.1703 - accuracy: 0.9315 - val_loss: 0.2114 - val_accuracy: 0.9355\n",
            "Epoch 16/32\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.2148 - accuracy: 0.8831 - val_loss: 0.2056 - val_accuracy: 0.8710\n",
            "Epoch 17/32\n",
            "9/9 [==============================] - 0s 8ms/step - loss: 0.2145 - accuracy: 0.9032 - val_loss: 0.2493 - val_accuracy: 0.8387\n",
            "Epoch 18/32\n",
            "9/9 [==============================] - 0s 8ms/step - loss: 0.1876 - accuracy: 0.9113 - val_loss: 0.2174 - val_accuracy: 0.9032\n",
            "Epoch 19/32\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.1664 - accuracy: 0.9234 - val_loss: 0.2406 - val_accuracy: 0.8387\n",
            "Epoch 20/32\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.1577 - accuracy: 0.9395 - val_loss: 0.2478 - val_accuracy: 0.8710\n",
            "Epoch 21/32\n",
            "9/9 [==============================] - 0s 8ms/step - loss: 0.1571 - accuracy: 0.9274 - val_loss: 0.2310 - val_accuracy: 0.8710\n",
            "Epoch 22/32\n",
            "9/9 [==============================] - 0s 9ms/step - loss: 0.1600 - accuracy: 0.9234 - val_loss: 0.2395 - val_accuracy: 0.8710\n",
            "Epoch 23/32\n",
            "9/9 [==============================] - 0s 8ms/step - loss: 0.2112 - accuracy: 0.8952 - val_loss: 0.2587 - val_accuracy: 0.8710\n",
            "Epoch 24/32\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.1792 - accuracy: 0.8992 - val_loss: 0.2384 - val_accuracy: 0.8065\n",
            "Epoch 25/32\n",
            "9/9 [==============================] - 0s 9ms/step - loss: 0.1561 - accuracy: 0.9355 - val_loss: 0.2816 - val_accuracy: 0.8387\n",
            "Epoch 26/32\n",
            "9/9 [==============================] - 0s 9ms/step - loss: 0.1865 - accuracy: 0.9113 - val_loss: 0.3573 - val_accuracy: 0.8387\n",
            "Epoch 27/32\n",
            "9/9 [==============================] - 0s 9ms/step - loss: 0.3071 - accuracy: 0.8629 - val_loss: 0.2859 - val_accuracy: 0.8710\n",
            "Epoch 28/32\n",
            "9/9 [==============================] - 0s 8ms/step - loss: 0.2926 - accuracy: 0.8710 - val_loss: 0.3736 - val_accuracy: 0.8387\n",
            "Epoch 29/32\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.1807 - accuracy: 0.9032 - val_loss: 0.3014 - val_accuracy: 0.8710\n",
            "Epoch 30/32\n",
            "9/9 [==============================] - 0s 9ms/step - loss: 0.1889 - accuracy: 0.9194 - val_loss: 0.2904 - val_accuracy: 0.8065\n",
            "Epoch 31/32\n",
            "9/9 [==============================] - 0s 8ms/step - loss: 0.2302 - accuracy: 0.8911 - val_loss: 0.2617 - val_accuracy: 0.8387\n",
            "Epoch 32/32\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.1607 - accuracy: 0.9395 - val_loss: 0.2306 - val_accuracy: 0.8710\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history1.history['val_loss'])\n",
        "plt.plot(history1.history['loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Validation Loss', 'Training Loss'], loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UbQg1LG5Au9D",
        "outputId": "1a97a1d5-01ef-4c80-af13-4fe99ad13e8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://www.section.io/engineering-education/build-ann-with-keras/"
      ],
      "metadata": {
        "id": "6eO2vdC3BjF9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ann2 = keras.Sequential()\n",
        "ann2.add(Flatten())\n",
        "ann2.add(Dense(256, activation = \"relu\"))\n",
        "ann2.add(Dense(128, activation = \"relu\"))\n",
        "ann2.add(Dense(64, activation = \"relu\"))\n",
        "ann2.add(Dense(32, activation = \"relu\"))\n",
        "ann2.add(Dense(2, activation = \"softmax\"))"
      ],
      "metadata": {
        "id": "XkHJUAr_C-pJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann2.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "g_eF2mWTniKt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history2 = ann2.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=30, batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5KGBHPmzq-IE",
        "outputId": "a196e267-0e25-4bea-ae4f-88b50a32b935"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2556 - accuracy: 0.8750 - val_loss: 0.3540 - val_accuracy: 0.8387\n",
            "Epoch 2/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2386 - accuracy: 0.9073 - val_loss: 0.3438 - val_accuracy: 0.8387\n",
            "Epoch 3/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2160 - accuracy: 0.9153 - val_loss: 0.2540 - val_accuracy: 0.9355\n",
            "Epoch 4/30\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.2383 - accuracy: 0.8871 - val_loss: 0.3508 - val_accuracy: 0.8387\n",
            "Epoch 5/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2098 - accuracy: 0.9073 - val_loss: 0.2969 - val_accuracy: 0.8710\n",
            "Epoch 6/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2030 - accuracy: 0.8992 - val_loss: 0.3966 - val_accuracy: 0.8387\n",
            "Epoch 7/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2461 - accuracy: 0.8710 - val_loss: 0.2977 - val_accuracy: 0.8387\n",
            "Epoch 8/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2092 - accuracy: 0.9194 - val_loss: 0.2742 - val_accuracy: 0.8387\n",
            "Epoch 9/30\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.2109 - accuracy: 0.9073 - val_loss: 0.2822 - val_accuracy: 0.8387\n",
            "Epoch 10/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.1981 - accuracy: 0.9234 - val_loss: 0.3382 - val_accuracy: 0.8387\n",
            "Epoch 11/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.1976 - accuracy: 0.9113 - val_loss: 0.2795 - val_accuracy: 0.9032\n",
            "Epoch 12/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2027 - accuracy: 0.9073 - val_loss: 0.2839 - val_accuracy: 0.9032\n",
            "Epoch 13/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2295 - accuracy: 0.8952 - val_loss: 0.3021 - val_accuracy: 0.8387\n",
            "Epoch 14/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2088 - accuracy: 0.9032 - val_loss: 0.4845 - val_accuracy: 0.7419\n",
            "Epoch 15/30\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.2491 - accuracy: 0.8710 - val_loss: 0.4238 - val_accuracy: 0.8387\n",
            "Epoch 16/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2522 - accuracy: 0.9032 - val_loss: 0.2645 - val_accuracy: 0.9032\n",
            "Epoch 17/30\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.2010 - accuracy: 0.9073 - val_loss: 0.3042 - val_accuracy: 0.8387\n",
            "Epoch 18/30\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.1880 - accuracy: 0.9113 - val_loss: 0.4040 - val_accuracy: 0.8065\n",
            "Epoch 19/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2103 - accuracy: 0.8911 - val_loss: 0.2723 - val_accuracy: 0.9032\n",
            "Epoch 20/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.1789 - accuracy: 0.9355 - val_loss: 0.3185 - val_accuracy: 0.8387\n",
            "Epoch 21/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.1748 - accuracy: 0.9274 - val_loss: 0.3516 - val_accuracy: 0.8387\n",
            "Epoch 22/30\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.1702 - accuracy: 0.9355 - val_loss: 0.3651 - val_accuracy: 0.8710\n",
            "Epoch 23/30\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.1703 - accuracy: 0.9194 - val_loss: 0.3038 - val_accuracy: 0.9032\n",
            "Epoch 24/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.1643 - accuracy: 0.9274 - val_loss: 0.3537 - val_accuracy: 0.8387\n",
            "Epoch 25/30\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.1641 - accuracy: 0.9315 - val_loss: 0.3985 - val_accuracy: 0.8065\n",
            "Epoch 26/30\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.1626 - accuracy: 0.9435 - val_loss: 0.3342 - val_accuracy: 0.8387\n",
            "Epoch 27/30\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.1737 - accuracy: 0.9315 - val_loss: 0.3708 - val_accuracy: 0.8387\n",
            "Epoch 28/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.1770 - accuracy: 0.9234 - val_loss: 0.5513 - val_accuracy: 0.8065\n",
            "Epoch 29/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2261 - accuracy: 0.8831 - val_loss: 0.5153 - val_accuracy: 0.7742\n",
            "Epoch 30/30\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2494 - accuracy: 0.8468 - val_loss: 0.3158 - val_accuracy: 0.9032\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history2.history['val_loss'])\n",
        "plt.plot(history2.history['loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Validation Loss', 'Training Loss'], loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Yo4t-fuWrKd9",
        "outputId": "f375100a-3ee8-43ec-b58e-36b2d331ea17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://cdn-edunex.itb.ac.id/44851-Intelligent-System/165183-Neural-Network/1680768062141_09-Neural-Network.pdf"
      ],
      "metadata": {
        "id": "vjxqoeYbNRx3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ann3 = keras.Sequential([\n",
        "    keras.layers.Input(shape=(X_train.shape[1],)),\n",
        "    keras.layers.Dense(256, activation='relu'),\n",
        "    keras.layers.Dense(128, activation='relu'),\n",
        "    keras.layers.Dense(2, activation='softmax'),\n",
        "])"
      ],
      "metadata": {
        "id": "33BOlzSJt1Pe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann3.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "Ny6jJRQQLoI3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Melatih model\n",
        "history3 = ann3.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=50, batch_size=25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBmL7d1OLuY_",
        "outputId": "fd7c5c44-e212-45f3-c25e-f2d33f866789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "10/10 [==============================] - 1s 60ms/step - loss: 0.1943 - accuracy: 0.9234 - val_loss: 0.3276 - val_accuracy: 0.8710\n",
            "Epoch 2/50\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2060 - accuracy: 0.8952 - val_loss: 0.2740 - val_accuracy: 0.8710\n",
            "Epoch 3/50\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 0.1806 - accuracy: 0.9153 - val_loss: 0.3814 - val_accuracy: 0.8387\n",
            "Epoch 4/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2162 - accuracy: 0.8952 - val_loss: 0.4613 - val_accuracy: 0.7742\n",
            "Epoch 5/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2766 - accuracy: 0.8629 - val_loss: 0.7081 - val_accuracy: 0.7742\n",
            "Epoch 6/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2460 - accuracy: 0.8911 - val_loss: 0.4560 - val_accuracy: 0.8387\n",
            "Epoch 7/50\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.1905 - accuracy: 0.8952 - val_loss: 0.2898 - val_accuracy: 0.8387\n",
            "Epoch 8/50\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.1967 - accuracy: 0.9113 - val_loss: 0.2923 - val_accuracy: 0.8710\n",
            "Epoch 9/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2466 - accuracy: 0.8790 - val_loss: 0.3048 - val_accuracy: 0.9355\n",
            "Epoch 10/50\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2805 - accuracy: 0.8508 - val_loss: 0.3072 - val_accuracy: 0.8710\n",
            "Epoch 11/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2757 - accuracy: 0.8629 - val_loss: 0.2761 - val_accuracy: 0.8710\n",
            "Epoch 12/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2543 - accuracy: 0.8911 - val_loss: 0.3557 - val_accuracy: 0.8710\n",
            "Epoch 13/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1793 - accuracy: 0.9234 - val_loss: 0.2967 - val_accuracy: 0.8710\n",
            "Epoch 14/50\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2229 - accuracy: 0.9032 - val_loss: 0.2944 - val_accuracy: 0.9032\n",
            "Epoch 15/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2147 - accuracy: 0.9032 - val_loss: 0.3366 - val_accuracy: 0.8710\n",
            "Epoch 16/50\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.1598 - accuracy: 0.9315 - val_loss: 0.3904 - val_accuracy: 0.8387\n",
            "Epoch 17/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1564 - accuracy: 0.9476 - val_loss: 0.4247 - val_accuracy: 0.8387\n",
            "Epoch 18/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1593 - accuracy: 0.9194 - val_loss: 0.4375 - val_accuracy: 0.8387\n",
            "Epoch 19/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1537 - accuracy: 0.9315 - val_loss: 0.3988 - val_accuracy: 0.8387\n",
            "Epoch 20/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1511 - accuracy: 0.9395 - val_loss: 0.4245 - val_accuracy: 0.8387\n",
            "Epoch 21/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1584 - accuracy: 0.9234 - val_loss: 0.3446 - val_accuracy: 0.8710\n",
            "Epoch 22/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1656 - accuracy: 0.9153 - val_loss: 0.3719 - val_accuracy: 0.8387\n",
            "Epoch 23/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1628 - accuracy: 0.9315 - val_loss: 0.4114 - val_accuracy: 0.8387\n",
            "Epoch 24/50\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2282 - accuracy: 0.8831 - val_loss: 0.5240 - val_accuracy: 0.8065\n",
            "Epoch 25/50\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2052 - accuracy: 0.8952 - val_loss: 0.6458 - val_accuracy: 0.7742\n",
            "Epoch 26/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2547 - accuracy: 0.8669 - val_loss: 0.6303 - val_accuracy: 0.8065\n",
            "Epoch 27/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1936 - accuracy: 0.8952 - val_loss: 0.3334 - val_accuracy: 0.8710\n",
            "Epoch 28/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1623 - accuracy: 0.9153 - val_loss: 0.3395 - val_accuracy: 0.8710\n",
            "Epoch 29/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1710 - accuracy: 0.9194 - val_loss: 0.3319 - val_accuracy: 0.8710\n",
            "Epoch 30/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1643 - accuracy: 0.9113 - val_loss: 0.3127 - val_accuracy: 0.9032\n",
            "Epoch 31/50\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2058 - accuracy: 0.9113 - val_loss: 0.3126 - val_accuracy: 0.8710\n",
            "Epoch 32/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2184 - accuracy: 0.8871 - val_loss: 0.3879 - val_accuracy: 0.8387\n",
            "Epoch 33/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1914 - accuracy: 0.9032 - val_loss: 0.3376 - val_accuracy: 0.8710\n",
            "Epoch 34/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.3162 - accuracy: 0.8387 - val_loss: 0.4129 - val_accuracy: 0.9032\n",
            "Epoch 35/50\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 0.4522 - accuracy: 0.8347 - val_loss: 0.3582 - val_accuracy: 0.8387\n",
            "Epoch 36/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2389 - accuracy: 0.8911 - val_loss: 0.3369 - val_accuracy: 0.8710\n",
            "Epoch 37/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1716 - accuracy: 0.9194 - val_loss: 0.3539 - val_accuracy: 0.8710\n",
            "Epoch 38/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1412 - accuracy: 0.9435 - val_loss: 0.5616 - val_accuracy: 0.8387\n",
            "Epoch 39/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1389 - accuracy: 0.9435 - val_loss: 0.4120 - val_accuracy: 0.8387\n",
            "Epoch 40/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1301 - accuracy: 0.9476 - val_loss: 0.5421 - val_accuracy: 0.8065\n",
            "Epoch 41/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1472 - accuracy: 0.9395 - val_loss: 0.5189 - val_accuracy: 0.8387\n",
            "Epoch 42/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1222 - accuracy: 0.9516 - val_loss: 0.4513 - val_accuracy: 0.8387\n",
            "Epoch 43/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1153 - accuracy: 0.9597 - val_loss: 0.3754 - val_accuracy: 0.8710\n",
            "Epoch 44/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1218 - accuracy: 0.9677 - val_loss: 0.3804 - val_accuracy: 0.8387\n",
            "Epoch 45/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1156 - accuracy: 0.9476 - val_loss: 0.5310 - val_accuracy: 0.8065\n",
            "Epoch 46/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1167 - accuracy: 0.9516 - val_loss: 0.4635 - val_accuracy: 0.8387\n",
            "Epoch 47/50\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 0.1304 - accuracy: 0.9355 - val_loss: 0.4877 - val_accuracy: 0.8387\n",
            "Epoch 48/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1168 - accuracy: 0.9556 - val_loss: 0.3546 - val_accuracy: 0.8065\n",
            "Epoch 49/50\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1005 - accuracy: 0.9718 - val_loss: 0.4435 - val_accuracy: 0.8387\n",
            "Epoch 50/50\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1477 - accuracy: 0.9355 - val_loss: 0.4974 - val_accuracy: 0.8387\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history3.history['val_loss'])\n",
        "plt.plot(history3.history['loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Validation Loss', 'Training Loss'], loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "oFinlmJvMDbU",
        "outputId": "afee5637-5cd6-448d-9003-2a780a3b639e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://www.tensorflow.org/guide/keras/sequential_model"
      ],
      "metadata": {
        "id": "eNsqCteNRYSZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ann4 = Sequential([Dense(256, activation=\"relu\", kernel_initializer='normal',\n",
        "                             input_shape=X_train.shape[1:]),\n",
        "                       Dense(128, kernel_initializer='normal', activation='relu'),\n",
        "                       Dense(64, kernel_initializer='normal', activation='relu'),\n",
        "                       Dense(32, kernel_initializer='normal', activation='relu'),\n",
        "                       Dense(2, kernel_initializer='normal', activation='sigmoid')])"
      ],
      "metadata": {
        "id": "A1BmdeiSSb0m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann4.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "6Z4rVaWxS9AJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Melatih model\n",
        "history4 = ann4.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=35, batch_size=25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xczJDVHSTMer",
        "outputId": "2844d805-41a8-4a59-902a-bfef42cd44d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/35\n",
            "10/10 [==============================] - 0s 30ms/step - loss: 0.2679 - accuracy: 0.8871 - val_loss: 0.2896 - val_accuracy: 0.8710\n",
            "Epoch 2/35\n",
            "10/10 [==============================] - 0s 16ms/step - loss: 0.2576 - accuracy: 0.8790 - val_loss: 0.2672 - val_accuracy: 0.8710\n",
            "Epoch 3/35\n",
            "10/10 [==============================] - 0s 16ms/step - loss: 0.2494 - accuracy: 0.8871 - val_loss: 0.2834 - val_accuracy: 0.8387\n",
            "Epoch 4/35\n",
            "10/10 [==============================] - 0s 19ms/step - loss: 0.2599 - accuracy: 0.8710 - val_loss: 0.4042 - val_accuracy: 0.7742\n",
            "Epoch 5/35\n",
            "10/10 [==============================] - 0s 17ms/step - loss: 0.2462 - accuracy: 0.8710 - val_loss: 0.2375 - val_accuracy: 0.8710\n",
            "Epoch 6/35\n",
            "10/10 [==============================] - 0s 21ms/step - loss: 0.2476 - accuracy: 0.8831 - val_loss: 0.2971 - val_accuracy: 0.8387\n",
            "Epoch 7/35\n",
            "10/10 [==============================] - 0s 14ms/step - loss: 0.2600 - accuracy: 0.8750 - val_loss: 0.4299 - val_accuracy: 0.7742\n",
            "Epoch 8/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.2581 - accuracy: 0.8629 - val_loss: 0.2351 - val_accuracy: 0.9355\n",
            "Epoch 9/35\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 0.2353 - accuracy: 0.8911 - val_loss: 0.2995 - val_accuracy: 0.8387\n",
            "Epoch 10/35\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 0.2283 - accuracy: 0.8831 - val_loss: 0.3633 - val_accuracy: 0.8387\n",
            "Epoch 11/35\n",
            "10/10 [==============================] - 0s 15ms/step - loss: 0.2399 - accuracy: 0.8871 - val_loss: 0.2753 - val_accuracy: 0.8387\n",
            "Epoch 12/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.2185 - accuracy: 0.8992 - val_loss: 0.3005 - val_accuracy: 0.8710\n",
            "Epoch 13/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.2507 - accuracy: 0.8750 - val_loss: 0.3541 - val_accuracy: 0.8387\n",
            "Epoch 14/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.2408 - accuracy: 0.8831 - val_loss: 0.2997 - val_accuracy: 0.8387\n",
            "Epoch 15/35\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 0.2085 - accuracy: 0.9073 - val_loss: 0.3224 - val_accuracy: 0.8710\n",
            "Epoch 16/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.2059 - accuracy: 0.9032 - val_loss: 0.2941 - val_accuracy: 0.8710\n",
            "Epoch 17/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.1982 - accuracy: 0.9113 - val_loss: 0.2436 - val_accuracy: 0.9032\n",
            "Epoch 18/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.2419 - accuracy: 0.8831 - val_loss: 0.3411 - val_accuracy: 0.8387\n",
            "Epoch 19/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.2065 - accuracy: 0.9073 - val_loss: 0.4254 - val_accuracy: 0.8387\n",
            "Epoch 20/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.1976 - accuracy: 0.9032 - val_loss: 0.3900 - val_accuracy: 0.8387\n",
            "Epoch 21/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.1935 - accuracy: 0.9073 - val_loss: 0.3715 - val_accuracy: 0.8387\n",
            "Epoch 22/35\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.1821 - accuracy: 0.9032 - val_loss: 0.3364 - val_accuracy: 0.8387\n",
            "Epoch 23/35\n",
            "10/10 [==============================] - 0s 33ms/step - loss: 0.1786 - accuracy: 0.9032 - val_loss: 0.2429 - val_accuracy: 0.9355\n",
            "Epoch 24/35\n",
            "10/10 [==============================] - 0s 27ms/step - loss: 0.2254 - accuracy: 0.8952 - val_loss: 0.2813 - val_accuracy: 0.9032\n",
            "Epoch 25/35\n",
            "10/10 [==============================] - 0s 18ms/step - loss: 0.1842 - accuracy: 0.9194 - val_loss: 0.3239 - val_accuracy: 0.8710\n",
            "Epoch 26/35\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1541 - accuracy: 0.9556 - val_loss: 0.4785 - val_accuracy: 0.8387\n",
            "Epoch 27/35\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.1393 - accuracy: 0.9476 - val_loss: 0.2774 - val_accuracy: 0.9032\n",
            "Epoch 28/35\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.1826 - accuracy: 0.9315 - val_loss: 0.4211 - val_accuracy: 0.8387\n",
            "Epoch 29/35\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1847 - accuracy: 0.9153 - val_loss: 0.3720 - val_accuracy: 0.8387\n",
            "Epoch 30/35\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 0.1600 - accuracy: 0.9355 - val_loss: 0.5710 - val_accuracy: 0.8387\n",
            "Epoch 31/35\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.1775 - accuracy: 0.9315 - val_loss: 0.4336 - val_accuracy: 0.8387\n",
            "Epoch 32/35\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.1616 - accuracy: 0.9234 - val_loss: 0.6008 - val_accuracy: 0.8387\n",
            "Epoch 33/35\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1320 - accuracy: 0.9556 - val_loss: 0.3730 - val_accuracy: 0.8387\n",
            "Epoch 34/35\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.1409 - accuracy: 0.9476 - val_loss: 0.6568 - val_accuracy: 0.8065\n",
            "Epoch 35/35\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.1580 - accuracy: 0.9274 - val_loss: 0.4851 - val_accuracy: 0.8387\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history4.history['val_loss'])\n",
        "plt.plot(history4.history['loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Validation Loss', 'Training Loss'], loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "kr2O5JF8T7_C",
        "outputId": "e3dae7ba-96af-4b9b-b20f-1678f7c74900"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://medium.com/@danang.pratama/pinn-untuk-menyelesaikan-persamaan-differensial-dengan-python-part-1-pdb-d7b58104cd87"
      ],
      "metadata": {
        "id": "3kFAfQ0TUuD7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = Input(shape=(12,))\n",
        "x = Dense(256, activation='relu')(inputs)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dense(64, activation='relu')(x)\n",
        "predictions = Dense(2, activation='softmax')(x)"
      ],
      "metadata": {
        "id": "YFLjTREFUyto"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Model"
      ],
      "metadata": {
        "id": "dbkBeokVWBsD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann5 = Model(inputs=[inputs], outputs=[predictions])\n",
        "ann5.compile(optimizer='sgd',\n",
        "loss='sparse_categorical_crossentropy',\n",
        "metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "6zH6LScBVqAr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Melatih model\n",
        "history5 = ann5.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=25, batch_size=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uldi96UlW9H8",
        "outputId": "d2224b6d-d281-4cc9-daea-06e24f4b4cb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "13/13 [==============================] - 0s 10ms/step - loss: 0.1092 - accuracy: 0.9677 - val_loss: 0.5889 - val_accuracy: 0.9032\n",
            "Epoch 2/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1152 - accuracy: 0.9476 - val_loss: 0.6160 - val_accuracy: 0.8065\n",
            "Epoch 3/25\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1182 - accuracy: 0.9516 - val_loss: 0.6257 - val_accuracy: 0.8387\n",
            "Epoch 4/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2012 - accuracy: 0.9113 - val_loss: 1.0377 - val_accuracy: 0.7742\n",
            "Epoch 5/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1154 - accuracy: 0.9758 - val_loss: 0.5807 - val_accuracy: 0.7742\n",
            "Epoch 6/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2341 - accuracy: 0.9073 - val_loss: 0.5167 - val_accuracy: 0.8387\n",
            "Epoch 7/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1369 - accuracy: 0.9556 - val_loss: 0.6630 - val_accuracy: 0.8065\n",
            "Epoch 8/25\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1142 - accuracy: 0.9516 - val_loss: 0.5647 - val_accuracy: 0.8710\n",
            "Epoch 9/25\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0775 - accuracy: 0.9798 - val_loss: 0.5993 - val_accuracy: 0.8065\n",
            "Epoch 10/25\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0977 - accuracy: 0.9516 - val_loss: 0.5115 - val_accuracy: 0.8387\n",
            "Epoch 11/25\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0881 - accuracy: 0.9597 - val_loss: 0.3601 - val_accuracy: 0.9032\n",
            "Epoch 12/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1786 - accuracy: 0.9153 - val_loss: 0.8208 - val_accuracy: 0.8065\n",
            "Epoch 13/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1008 - accuracy: 0.9556 - val_loss: 0.7017 - val_accuracy: 0.8065\n",
            "Epoch 14/25\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0669 - accuracy: 0.9758 - val_loss: 0.6389 - val_accuracy: 0.7742\n",
            "Epoch 15/25\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1106 - accuracy: 0.9597 - val_loss: 0.5574 - val_accuracy: 0.8065\n",
            "Epoch 16/25\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.4559 - accuracy: 0.8629 - val_loss: 0.4170 - val_accuracy: 0.8710\n",
            "Epoch 17/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1580 - accuracy: 0.9274 - val_loss: 0.8124 - val_accuracy: 0.8065\n",
            "Epoch 18/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1226 - accuracy: 0.9476 - val_loss: 3.2705 - val_accuracy: 0.5484\n",
            "Epoch 19/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.3429 - accuracy: 0.8669 - val_loss: 0.5959 - val_accuracy: 0.7742\n",
            "Epoch 20/25\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.1437 - accuracy: 0.9355 - val_loss: 1.2085 - val_accuracy: 0.7097\n",
            "Epoch 21/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1790 - accuracy: 0.9194 - val_loss: 0.5133 - val_accuracy: 0.7742\n",
            "Epoch 22/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.2477 - accuracy: 0.9032 - val_loss: 0.5473 - val_accuracy: 0.8710\n",
            "Epoch 23/25\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1262 - accuracy: 0.9435 - val_loss: 0.6531 - val_accuracy: 0.8065\n",
            "Epoch 24/25\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0902 - accuracy: 0.9637 - val_loss: 0.6595 - val_accuracy: 0.7742\n",
            "Epoch 25/25\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.0852 - accuracy: 0.9758 - val_loss: 0.7101 - val_accuracy: 0.8387\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history5.history['val_loss'])\n",
        "plt.plot(history5.history['loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Validation Loss', 'Training Loss'], loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "yPqtYB9IXgQ7",
        "outputId": "c5c7095c-12ed-4bcf-92c1-0c908762275f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Yvnw5unfvDjs7O8yfPx/FxcUGK294eDimTZuGpUuXYtSoUXBycsL69evxySefGOwcFd25LIMgCNBojNfHasmSJXj00UexdetW/PPPP1i8eDHWr1+PBx54ALNmzcKoUaOwdetW7Ny5E8uWLcMnn3yCefPmGa081WENDxERNYggCFBZKGFtKV2sLBSwtbIwyUXfGXknT54MhUKBdevW4aeffsJTTz2lO8bhw4cxYcIEPPbYYwgODka7du1w5cqVeh+7S5cuiI+PR2Jiom7b0aNHK+1z5MgRtG3bFm+88Qb69OmDjh07IjY2ttI+VlZWdS6p0KVLF5w9exZ5eXm6bYcPH4ZCoUBgYGC9y6wP7fOLj4/Xbbt06RIyMzPRtWtX3bZOnTrhhRdewM6dO/Hggw9izZo1uvv8/Pzw9NNP448//sCLL76Ib7/91ihlrQ0DDxERNZi6wnB0tZkOTQcAe3t7TJkyBQsXLkRiYiKeeOIJ3X0dO3bErl27cOTIEURGRuI///lPpRFIdRk5ciQ6deqEGTNm4OzZszh48CDeeOONSvt07NgRcXFxWL9+PaKjo/HFF19g06ZNlfbx9/dHTEwMIiIikJqaiqKioirnmjZtGqytrTFjxgxcuHABe/fuxbx58/D444/r+u80lFqtRkRERKVLZGQkRo4cie7du2PatGk4ffo0jh8/junTp2Po0KHo06cPCgoKMHfuXOzbtw+xsbE4fPgwTpw4gS5dugAA5s+fjx07diAmJganT5/G3r17dfeZEgMPERE1WGkTCTyA1KyVkZGBUaNGVepv8+abb+Kuu+7CqFGjMGzYMHh7e2PixIn1Pq5CocCmTZtQUFCAkJAQzJo1C++++26lfe6//3688MILmDt3Lnr27IkjR47grbfeqrTPpEmTMHr0aAwfPhweHh7VDo23tbXFjh07kJ6ejr59++Khhx7CiBEjsGLFCv1ejGrk5uaiV69elS7jx4+HIAj4888/4eLigiFDhmDkyJFo164dNmzYAABQKpVIS0vD9OnT0alTJ0yePBljxozB0qVLAUhBKiwsDF26dMHo0aPRqVMnfP31140ur74EURTN+x1qYNnZ2XByckJWVhYcHR3lLg4RUZNRWFiImJgYBAQEwNraGgCQnleMmxn5AABbKwt08DT9cGNqPqp7j2k19vubNTxERNRg6godYc29hodaNgYeIiJqsEpNWi2rwYCaGAYeIiJqsDs7LbewXhLUhDDwEBFRg1UMPKIognmHzBUDDxERNVjpHf122I+HzBUDDxERNdidAYf9eMhcMfAQEVGDVQk8rOEhM8XAQ0REDaYNOBYKRaXbROaGgYeIiBpEoxGhKWvCsrKQvk7u7NNDZC4YeIiIqEG04UYQBFgppYU4W0oNj7+/P5YvX17v/fft2wdBEJCZmWm0MlHtGHiIiKhBtOFGKQhQKswz8AiCUOtlyZIlDTruiRMnMGfOnHrvP2DAACQmJsLJyalB56svBquaWchdACIiapq0y0pYKCoEHjMbpZWYmKi7vmHDBixatAhRUVG6bfb25Wt/iaIItVoNC4u6vxo9PDz0KoeVlRW8vb31egwZFmt4iIioQXQ1PArzreHx9vbWXZycnCAIgu725cuX4eDggH/++Qe9e/eGSqXCoUOHEB0djQkTJsDLywv29vbo27cvdu/eXem4dzZpCYKA7777Dg888ABsbW3RsWNH/PXXX7r776x5Wbt2LZydnbFjxw506dIF9vb2GD16dKWAVlpaiueeew7Ozs5wc3PDq6++ihkzZui1kvudMjIyMH36dLi4uMDW1hZjxozB1atXdffHxsZi/PjxcHFxgZ2dHYKCgrBt2zbdY6dNmwYPDw/Y2NigY8eOWLNmTYPLYmoMPERE1CClag2EknxYqAugLM2HUJIPTVEeUGyCiwFrkl577TW8//77iIyMRI8ePZCbm4uxY8diz549OHPmDEaPHo3x48cjLi6u1uMsXboUkydPxrlz5zB27FhMmzYN6enpNe6fn5+Pjz/+GP/9739x4MABxMXF4aWXXtLd/8EHH+Dnn3/GmjVrcPjwYWRnZ2Pz5s2Neq5PPPEETp48ib/++gvh4eEQRRFjx45FSUkJACAsLAxFRUU4cOAAzp8/jw8++EBXC/bWW2/h0qVL+OeffxAZGYmVK1fC3d29UeUxJTZpERFRg6iL89D9xy66266mPPnrCYCVnUEO9fbbb+Oee+7R3XZ1dUVwcLDu9jvvvINNmzbhr7/+wty5c2s8zhNPPIGpU6cCAN577z188cUXOH78OEaPHl3t/iUlJVi1ahXat28PAJg7dy7efvtt3f1ffvklFi5ciAceeAAAsGLFCl1tS0NcvXoVf/31Fw4fPowBAwYAAH7++Wf4+flh8+bNePjhhxEXF4dJkyahe/fuAIB27drpHh8XF4devXqhT58+AKRarqaENTxERNQgGjNrvmoo7Re4Vm5uLl566SV06dIFzs7OsLe3R2RkZJ01PD169NBdt7Ozg6OjI1JSUmrc39bWVhd2AMDHx0e3f1ZWFpKTkxESEqK7X6lUonfv3no9t4oiIyNhYWGBfv366ba5ubkhMDAQkZGRAIDnnnsO//d//4eBAwdi8eLFOHfunG7fZ555BuvXr0fPnj3xyiuv4MiRIw0uixxkreFZuXIlVq5ciRs3bgAAgoKCsGjRIowZM6bGx2zcuBFvvfUWbty4gY4dO+KDDz7A2LFjTVRiIiLSKlHY4PyMSHg5qeBgZYFrt/NgoVCgi4+D8U9uaWuwQ9nZVa4peumll7Br1y58/PHH6NChA2xsbPDQQw+huLi49iJZWla6LQgCNGUdu+u7v9yrzc+aNQujRo3C1q1bsXPnTixbtgyffPIJ5s2bhzFjxiA2Nhbbtm3Drl27MGLECISFheHjjz+Wtcz1JWsNT+vWrfH+++/j1KlTOHnyJO6++25MmDABFy9erHb/I0eOYOrUqZg5cybOnDmDiRMnYuLEibhw4YKJS05ERGoREC1toVTZQ2ntANHSFqUWNhAtbaXmJmNeBMFoz+vw4cN44okn8MADD6B79+7w9vbW/TA3FScnJ3h5eeHEiRO6bWq1GqdPn27wMbt06YLS0lIcO3ZMty0tLQ1RUVHo2rWrbpufnx+efvpp/PHHH3jxxRfx7bff6u7z8PDAjBkz8L///Q/Lly/H6tWrG1weU5O1hmf8+PGVbr/77rtYuXIljh49iqCgoCr7f/755xg9ejRefvllAFK76q5du7BixQqsWrXKJGUmIiKJblmJCvPwiKIIjQgojZdHjK5jx474448/MH78eAiCgLfeeqvWmhpjmTdvHpYtW4YOHTqgc+fO+PLLL5GRkQGhHmHv/PnzcHAor2kTBAHBwcGYMGECZs+ejW+++QYODg547bXX0KpVK0yYMAEAMH/+fIwZMwadOnVCRkYG9u7diy5dpH5aixYtQu/evREUFISioiJs2bJFd19TYDadltVqNTZu3Ii8vDyEhoZWu094eDgWLFhQaduoUaNq7bVeVFSEoqIi3e3s7GyDlJeIqKUr1Q1LV0AhAAIEiBCh1oi6ANQUffrpp3jqqacwYMAAuLu749VXX5Xlu+PVV19FUlISpk+fDqVSiTlz5mDUqFFQKpV1PnbIkCGVbiuVSpSWlmLNmjV4/vnncd9996G4uBhDhgzBtm3bdM1rarUaYWFhuHnzJhwdHTF69Gh89tlnAKS5hBYuXIgbN27AxsYGgwcPxvr16w3/xI1EEGVuMDx//jxCQ0NRWFgIe3t7rFu3rsY+OVZWVvjxxx91veAB4Ouvv8bSpUuRnJxc7WOWLFmCpUuXVtmelZUFR0dHwzwJIqIWoLCwEDExMQgICIC1tTUuJWSjVKNBRy8H2Fgqq9wmw9JoNOjSpQsmT56Md955R+7iGMWd77GKsrOz4eTk1ODvb9lHaQUGBiIiIgLHjh3DM888gxkzZuDSpUsGO/7ChQuRlZWlu8THxxvs2ERELZUoipWatACY7eSDTVVsbCy+/fZbXLlyBefPn8czzzyDmJgYPProo3IXrUmSvUnLysoKHTp0AAD07t0bJ06cwOeff45vvvmmyr7e3t5VanKSk5Nrna5bpVJBpVIZttBERC2cRhQhonym5Yp/GXgMQ6FQYO3atXjppZcgiiK6deuG3bt3N6l+M+ZE9sBzJ41GU6nPTUWhoaHYs2cP5s+fr9u2a9euGvv8EBGRcWhDjUIQoGDgMQo/Pz8cPnxY7mI0G7IGnoULF2LMmDFo06YNcnJysG7dOuzbtw87duwAAEyfPh2tWrXCsmXLAADPP/88hg4dik8++QTjxo3D+vXrcfLkySY1LI6IqDko1VSu3QGkVdMBBh4yT7IGnpSUFEyfPh2JiYlwcnJCjx49sGPHDt0U33FxcVAoyrsZDRgwAOvWrcObb76J119/HR07dsTmzZvRrVs3uZ4CEVGLU7H/TqXAwxoeaiRjjqOSNfB8//33td6/b9++KtsefvhhPPzww0YqERER1UQ7dDk/Px9KG2mOF4vqAo/MswVT06Wdzbo+Q+/1ZXZ9eIiIyDwplUo4OzsjJSUFNo4lEEsEiEoNCgsLAQCa0mKIpcUoKhRRWNh05+EheWg0Gty+fRu2trawsDB8PGHgISKietOOio1JSEZeUSnyVBYozrQCAOQVlSIjvwTZlgoUZXB0LOlPoVCgTZs29ZpNWl8MPEREVG+CIMDHxwffnUzDtrOJeKx/G8wMCgAAHLxyG0v2XkQXH0eseLSzzCWlpsjKyqpS311DYuAhIiK9peWVIDFXDSsra92MuPZ2triVo4ZKVVxlllwiuck+0zIRETU9GfklAABnW0vdNu31rIISWcpEVBsGHiIi0ltmWahxsbXSbXOyKQ88Mi/TSFQFAw8REektM18aPlyxhkcbeEo1IvKL1bKUi6gmDDxERKS3jDxt4Cmv4bG1Uurm5WGzFpkbBh4iItKLWiMiu7AUAOBSoYZHEIRKzVpE5oSBh4iI9FIxzGgDzp23GXjI3DDwEBGRXjLK+u84WFvAQln5a8SprMYnM5+Bh8wLAw8REekls5oh6VraGp5s1vCQmWHgISIivWhHaFUckq7FJi0yVww8RESkl/JJBxl4qOlg4CEiIr3o5uCxqblJi4GHzA0DDxER6UXbh8ellj48DDxkbhh4iIhILxn5VScd1HJk4CEzxcBDRER60a6jVd0oLW0zVyYDD5kZBh4iItJLfUZpcVg6mRsGHiIi0ktGXi3z8NiySYvMEwMPERHpJaugfsPSRVE0abmIasPAQ0REesnQNWnVPEpLrRGRV6w2abmIasPAQ0RE9VZUqkZ+WZCprobHxlIJS6UAgM1aZF4YeIiIqN6yyubgUQiAg8qiyv2CIJQ3a3EBUTIjDDxERFRvFZeVUCiEavdx0g1NLzZZuYjqwsBDRET1Vj7pYNX+O1ocmk7miIGHiIjqTbusRHXraGlxeQkyRww8RERUb7VNOqjFwEPmiIGHiIjqrWIfnpow8JA5YuAhIqJ603ZErk8fHgYeMicMPEREVG+ZZctKVDfpoFb5iumlJikTUX0w8BARUb2Vj9KquUlLe5+2vw+ROWDgISKiesssqHnhUC0OSydzxMBDRET1xlFa1FQx8BARUb3p5uFhp2VqYhh4iIioXkRRrBB46q7hyS4shSiKJikbUV0YeIiIqF7yi9UoVmsA1D5KSxt41BoRuUUcqUXmgYGHiIjqRdth2UqpgI2lssb9rC0VsFJKXy9s1iJzwcBDRET1kpFXPumgIFS/UjoACIIAp7IaIG0TGJHcGHiIiKhetOGlthFaWhyaTuaGgYeIiOpFu6yEUy39d7Q4UovMjayBZ9myZejbty8cHBzg6emJiRMnIioqqtbHrF27FoIgVLpYW1ubqMRERC1XRn7dy0poMfCQuZE18Ozfvx9hYWE4evQodu3ahZKSEtx7773Iy8ur9XGOjo5ITEzUXWJjY01UYiKiliszr+5JB7UYeMjcWMh58u3bt1e6vXbtWnh6euLUqVMYMmRIjY8TBAHe3t7GLh4REVWgHaXFJi1qisyqD09WVhYAwNXVtdb9cnNz0bZtW/j5+WHChAm4ePGiKYpHRNSiZdRjWQktRwYeMjNmE3g0Gg3mz5+PgQMHolu3bjXuFxgYiB9++AF//vkn/ve//0Gj0WDAgAG4efNmtfsXFRUhOzu70oWIiPSXqUcfHueywJPJwENmQtYmrYrCwsJw4cIFHDp0qNb9QkNDERoaqrs9YMAAdOnSBd988w3eeeedKvsvW7YMS5cuNXh5iYhaGu3CoU42HJZOTY9Z1PDMnTsXW7Zswd69e9G6dWu9HmtpaYlevXrh2rVr1d6/cOFCZGVl6S7x8fGGKDIRUYujTw0P+/CQuZG1hkcURcybNw+bNm3Cvn37EBAQoPcx1Go1zp8/j7Fjx1Z7v0qlgkqlamxRiYhaPF0fHrt61PDYMvCQeZE18ISFhWHdunX4888/4eDggKSkJACAk5MTbGxsAADTp09Hq1atsGzZMgDA22+/jf79+6NDhw7IzMzERx99hNjYWMyaNUu250FE1NxpNKIuvGj759SGNTxkbmQNPCtXrgQADBs2rNL2NWvW4IknngAAxMXFQaEob3nLyMjA7NmzkZSUBBcXF/Tu3RtHjhxB165dTVVsIqIWJ6ewFBpRuu6s59ISGo0IhaLmtbeITEH2Jq267Nu3r9Ltzz77DJ999pmRSkRERNXRNmfZWSlhZVF3909t4NGIQG5xKRyt664VIjIms+i0TERE5k07vLw+tTsAYG2phKosGGVxxXQyAww8RERUJ20Nj3M9RmhpsR8PmRMGHiIiqlOmHrMsazHwkDlh4CEiojpp5+CpzzpaWgw8ZE4YeIiIqE4Zekw6qMXAQ+aEgYeIiOrEJi1q6hh4iIioTromrXpMOqilbf7K5CgtMgMMPEREVKcM1vBQE8fAQ0REddItHGqnfx8erphO5oCBh4iI6pRZINXwONmwhoeaJgYeIiKqU2YeR2lR08bAQ0REtSpRa5BTVAqg/ktLAAw8ZF4YeIiIqFbawCIIeo7SYuAhM8LAQ0REtdLOweNobQmlQqj347TD0rMLS6DRiEYpG1F9MfAQEVGttLMs67NwKFBewyOKQE5hqcHLRaQPBh4iIqpVpi7w1L//DgCoLJSwtpS+ZtisRXJj4CEiolqVTzqoXw0PwH48ZD4YeIiIqFbaPjzOenRY1mLgIXPBwENERLVqaJMWwMBD5oOBh4iIaqXttKzPOlpaDDxkLhh4iIioVromrQb14ZFCknZpCiK5MPAQEVGtMhs4LB1gDQ+ZDwYeIiKqVfkorYY3aXHFdJIbAw8REdWqcTU8FgBYw0PyY+AhIqJaafvfNKiGx5ZNWmQeGHiIiKhGhSVqFJZoALAPDzVtDDxERFQjbXOWhUKAvcpC78cz8JC5YOAhIqIaZVQYki4I9V8pXUs3LD2fgYfkxcBDREQ1Kg88+vffAcpreHIKS6HWiAYrF5G+GHiIiKhGWdoRWg1YRwsoDzwAkFPIWh6SDwMPERHVKKMR62gBgJWFAjaWSgDsx0PyYuAhIqIalU862LAaHoAdl8k8MPAQEVGNtCGlIUPStRh4yBww8BARUY0y8hrXaRng5INkHhh4iIioRto+PA2ZZVlLW8PDoekkJwYeIiKqUVZB+Tw8DcUmLTIHDDxERFSjjEYsHKrFFdPJHDDwEBFRjTK1Ew/aNL5JizU8JCcGHiIiqpYoirp+Ny52bNKipo2Bh4iIqpVbVIrSsuUgDNFpmYGH5MTAQ0RE1dLW7qgsFLAumy25ITgsncwBAw8REVUr0wBD0gEOSyfzIGvgWbZsGfr27QsHBwd4enpi4sSJiIqKqvNxGzduROfOnWFtbY3u3btj27ZtJigtEVHLUr5SesP77wAcpUXmQdbAs3//foSFheHo0aPYtWsXSkpKcO+99yIvL6/Gxxw5cgRTp07FzJkzcebMGUycOBETJ07EhQsXTFhyIqLmz9CBJ6eoFOqyPkFEpmYh58m3b99e6fbatWvh6emJU6dOYciQIdU+5vPPP8fo0aPx8ssvAwDeeecd7Nq1CytWrMCqVauMXmYiopZC2+fGUE1agFTL42LXuOMRNYRZ9eHJysoCALi6uta4T3h4OEaOHFlp26hRoxAeHl7t/kVFRcjOzq50ISKiumXkaScdbFxAsVQqYGsldXpmx2WSi9kEHo1Gg/nz52PgwIHo1q1bjfslJSXBy8ur0jYvLy8kJSVVu/+yZcvg5OSku/j5+Rm03EREzZWhmrQADk0n+ZlN4AkLC8OFCxewfv16gx534cKFyMrK0l3i4+MNenwiouaqvEmLgYeaPln78GjNnTsXW7ZswYEDB9C6deta9/X29kZycnKlbcnJyfD29q52f5VKBZVKZbCyEhG1FOU1PI3vc6Mbms7AQzKRtYZHFEXMnTsXmzZtwr///ouAgIA6HxMaGoo9e/ZU2rZr1y6EhoYaq5hERC2SbuFQG9bwUNMnaw1PWFgY1q1bhz///BMODg66fjhOTk6wsbEBAEyfPh2tWrXCsmXLAADPP/88hg4dik8++QTjxo3D+vXrcfLkSaxevVq250FE1BxlldXwGGJUFefiIbnJWsOzcuVKZGVlYdiwYfDx8dFdNmzYoNsnLi4OiYmJutsDBgzAunXrsHr1agQHB+O3337D5s2ba+3oTERE+svIZx8eaj5kreERxbonoNq3b1+VbQ8//DAefvhhI5SIiIgAQK0RkV0ohRMnG8PV8GRxeQmSidmM0iIiIvORXVAC7W9SgwxL5wKiJDMGHiIiqkI7QstBZQFLZeO/KtikRXJj4CEioiq0/XecDFC7A3BYOsmPgYeIiKrIKigboWWAOXgAjtIi+THwEBFRFeXraBm2hodNWiQXBh4iIqrCkLMsA+WBJ7eoFKVqjUGOSaQPBh4iIqrCkOtoAYBjhdmaswtLDXJMIn0w8BARURWGruGxVCpgZ6UEwGYtkgcDDxERVWHIdbS0tOGJgYfkwMBDRERVaGdEdrEzXODRNmtlltUeEZkSAw8REVVh6CYtAHCykVYzYg0PyYGBh4iIqsg0QpMW5+IhOTHwEBFRFdpmJ0NNPAhwLh6SFwMPERFVUlyqQV6xGoDhJh4EGHhIXgw8RERUibZ2RyEAjtYMPNQ8MPAQEVEl2gU+nWwsoVAIBjuuE4elk4waFHji4+Nx8+ZN3e3jx49j/vz5WL16tcEKRkRE8sjIM/wILaDCiun5DDxkeg0KPI8++ij27t0LAEhKSsI999yD48eP44033sDbb79t0AISEZFpaWt4DNl/B2CTFsmrQYHnwoULCAkJAQD8+uuv6NatG44cOYKff/4Za9euNWT5iIjIxIwxQgvgsHSSV4MCT0lJCVQqFQBg9+7duP/++wEAnTt3RmJiouFKR0REJmeMZSUA1vCQvBoUeIKCgrBq1SocPHgQu3btwujRowEACQkJcHNzM2gBiYjItHSTDhqphievWI0StcagxyaqS4MCzwcffIBvvvkGw4YNw9SpUxEcHAwA+Ouvv3RNXURE1DSVN2kZtobH0dpCd53NWmRqFnXvUtWwYcOQmpqK7OxsuLi46LbPmTMHtra2BiscERGZXvk6WoYNPBZKBRxUFsgpKkVmQQnc7FUGPT5RbRpUw1NQUICioiJd2ImNjcXy5csRFRUFT09PgxaQiIhMy1hNWkD5iunsx0Om1qDAM2HCBPz0008AgMzMTPTr1w+ffPIJJk6ciJUrVxq0gEREZFrawGPoUVoAOy6TfBoUeE6fPo3BgwcDAH777Td4eXkhNjYWP/30E7744guDFpCIiEzLWE1aAIemk3waFHjy8/Ph4OAAANi5cycefPBBKBQK9O/fH7GxsQYtIBERmY4oikabeBBgDQ/Jp0GBp0OHDti8eTPi4+OxY8cO3HvvvQCAlJQUODo6GrSARERkOgUlahSXSkPGjdqkxeUlyMQaFHgWLVqEl156Cf7+/ggJCUFoaCgAqbanV69eBi0gERGZjnbSQUulAFsrpcGPr601Yg0PmVqDhqU/9NBDGDRoEBITE3Vz8ADAiBEj8MADDxiscEREZFqZ+eULhwqC4VZK19KO0spk4CETa1DgAQBvb294e3vrVk1v3bo1Jx0kImriykdoGb7/DsA+PCSfBjVpaTQavP3223ByckLbtm3Rtm1bODs745133oFGw+nCiYiaKt0ILRvD998BGHhIPg2q4XnjjTfw/fff4/3338fAgQMBAIcOHcKSJUtQWFiId99916CFJCIi0yifdNC4NTwclk6m1qDA8+OPP+K7777TrZIOAD169ECrVq3w7LPPMvAQETVR5etosYaHmpcGNWmlp6ejc+fOVbZ37twZ6enpjS4UERHJI8NENTwMPGRqDQo8wcHBWLFiRZXtK1asQI8ePRpdKCIikocx19GSjisFnvzi8vl+iEyhQU1aH374IcaNG4fdu3fr5uAJDw9HfHw8tm3bZtACEhGR6ZQ3aRmnhsfBuvy4WQUl8HDgiulkGg2q4Rk6dCiuXLmCBx54AJmZmcjMzMSDDz6Iixcv4r///a+hy0hERCZizHW0AECpEOBgLf3WZrMWmVKD5+Hx9fWt0jn57Nmz+P7777F69epGF4yIiEyvfB0t4zRpAVI/npzCUgYeMqkG1fAQEVHzZOxh6QCHppM8GHiIiAgAoNGIRh+WDnCkFslD1sBz4MABjB8/Hr6+vhAEAZs3b651/3379kEQhCqXpKQk0xSYiKgZyykqhUaUrmtDiTEw8JAc9OrD8+CDD9Z6f2Zmpl4nz8vLQ3BwMJ566qk6j11RVFQUHB0ddbc9PT31Oi8REVWlrd2xsVTC2tLwK6VrccV0koNegcfJyanO+6dPn17v440ZMwZjxozRpwgApIDj7Oys9+OIiKhmGUZeOFRLt2J6PgMPmY5egWfNmjXGKodeevbsiaKiInTr1g1LlizRredFREQNl6kbkm68/jsAm7RIHg0eli4HHx8frFq1Cn369EFRURG+++47DBs2DMeOHcNdd91V7WOKiopQVFSku52dnW2q4hIRNSmmGKEFMPCQPJpU4AkMDERgYKDu9oABAxAdHY3PPvusxgkPly1bhqVLl5qqiERETVaGCUZoARyWTvJo8sPSQ0JCcO3atRrvX7hwIbKysnSX+Ph4E5aOiKjpYA0PNWdNqoanOhEREfDx8anxfpVKBZWKa7UQEdUl08jLSmgx8JAcZA08ubm5lWpnYmJiEBERAVdXV7Rp0wYLFy7ErVu38NNPPwEAli9fjoCAAAQFBaGwsBDfffcd/v33X+zcuVOup0BE1GyUj9IybpOWs410fAYeMiVZA8/JkycxfPhw3e0FCxYAAGbMmIG1a9ciMTERcXFxuvuLi4vx4osv4tatW7C1tUWPHj2we/fuSscgIqKGMcU6WkB5DU9BiRpFpWqoLIw35w+RlqyBZ9iwYRBFscb7165dW+n2K6+8gldeecXIpSIiapl0TVpGnGUZABysLSAIgChKtTyeDgw8ZHxNvtMyEREZhm6Ulp1xA49CIcBBJf3e5kgtMhUGHiIiAlBxlJZxm7QAwInLS5CJMfAQERFK1RrkFJYCMH6TFsCRWmR6DDxERKTrsAwYd6X0O8/BwEOmwsBDRES65ixHawtYKI3/1aAbms4FRMlEGHiIiMhkC4dq6VZMZw0PmQgDDxERVZh00PjNWQCbtMj0GHiIiMjkNTwMPGRqDDxERGSyhUO1uGI6mRoDDxERIbOgbNJB1vBQM8XAQ0REuj48pqrhcebEg2RiDDxERGSydbS0WMNDpsbAQ0REuj48LnambdLK5Dw8ZCIMPEREpGvSMsUsy0D5PDxFpRoUlqhNck5q2Rh4iIhI16Rlqk7LDioLCIJ0nSO1yBQYeIiIqLxJy0SBR6EQ4GjNfjxkOgw8REQtXGGJGgVlzUpOJhqlBbDjMpkWAw8RUQunrd1RKgQ4WluY7Lwcmk6mxMBDRNTCaScddLaxhKDtWGMCrOEhU2LgISJq4TLyykZombA5C6iwYjqHppMJMPAQEbVwph6hpcUaHjIlBh4iohYus0A7Qsu0NTwMPGRKDDxERC1cRlkNj5ONPDU8nIeHTIGBh4iohSufg6f51PDkFpVi1o8n8VP4DYMfm5omBh4iohZO14fHROtoaTkbMfBsOZuA3ZHJ+GhHFNQa0eDHp6aHgYeIqIUz9TpaWsas4dkblQIAyCksxcWELIMfn5oeBh4iohZOrlFaumHpBg48RaVqHLqaqrt9JDrNoMenpomBh4iohWtufXhOxGQgr7h8BXYGHgIYeIiIWjxdk5apA0/Z+YpLNSgsUdexd/39e1lqzgr2cwYAnIhJR3GpxmDHp6aJgYeIqAUTRVG2Ji17KwsoylayMGQtj7b/zn+GtIObnRUKStQ4ezPTYMenpomBh4ioBcsrVqO0bBSTqQOPQiHo+vEYKvDEpOYhJjUPlkoBgzu6I7S9GwDgyDU2a7V0DDxERC1YRp5Uu2NloYC1pem/Egw9NF3bnNXX3xUO1pYY0N4dAHAkOrW2h1ELwMBDRNSCVeywbMqV0rV0HZcNtIDovrLmrOGBngCAAWU1PGfiMlFQbLh+QtT0MPAQEbVgmQXy9N/RMuTQ9LyiUhy7ng4AGN5ZCjxt3Wzh62SNYrUGp2IzGn0OaroYeIiIWjC5Jh3UMuTQ9EPXUlGs1qCNqy3ae9gBAARBQCibtQgMPERELZpcI7S0DBl49pb137m7s2el5jltsxbn42nZGHiIiFowXR8eO3lreBq7Yrooirrh6NrmLC3tSK1zNzORXciV2VsqBh4iohYso6yGx8mmadfwXEzIRnJ2EWwslegX4FrpPl9nGwS420EjSpMQUsvEwENE1ILJtayElrOtYQKPdnTWwA7usLZUVrk/lM1aLR4DDxFRC6btw+MsU+AxVA3PvxX671QntB0DT0vHwENE1IJpR2k5yz0svSx4NUR6XjHOxGcCAIYFelS7T/+ywBOZmI30vIafi5ouBh6qt2X/RGLiV4d1M7MSUdNnPqO0Sht8jP1XUiCKQGdvB/g621S7j4eDCoFeDgCAo9dZy9MSyRp4Dhw4gPHjx8PX1xeCIGDz5s11Pmbfvn246667oFKp0KFDB6xdu9bo5SQgNi0Pqw9cR0R8JjacjJe7OERkINoJ/+Ru0souKIEoig06xr+XbwOouTlLq7wfD+fjaYlkDTx5eXkIDg7GV199Va/9Y2JiMG7cOAwfPhwRERGYP38+Zs2ahR07dhi5pLT2yA1oP4s2nIhv8AcTEZkPtUbU9Z2RO/AUqzUoLNHo/fhStQb7o2rvv6PF+XhaNgs5Tz5mzBiMGTOm3vuvWrUKAQEB+OSTTwAAXbp0waFDh/DZZ59h1KhRxipmi5dTWIKNJ28CABSCtBrxsZh0XZs4ETVNUq2KdN1ZpmHp9ioLKBWCLnzZWFUdYVWb03GZyC4shbOtJXq1cal1337t3KAQgOu385CUVQhvJ+vGFJ2amCbVhyc8PBwjR46stG3UqFEIDw+v8TFFRUXIzs6udCH9bDx5E7lFpWjvYYfJffwASLU8RNS0aZuz7KyUsLKQ5+tAEIRGjdTSTjY4tJMHlIraFz91srFEt1ZOAIDw62zWammaVOBJSkqCl5dXpW1eXl7Izs5GQUFBtY9ZtmwZnJycdBc/Pz9TFLXZUGtE/Bh+AwDw5MAAPBLSBgCw7XyiwVY3JiJ5ZOiGpMtTu6Pl1IiRWnvrGI5+J10/nmts1mppmlTgaYiFCxciKytLd4mPZ82EPvZeTkFsWj4crS3w4F2tENzaCZ29HVBUqsHmiFtyF4+IGkE3QkumZSW0HBtYw3MrswCXk3KgEIAhHasfjn6nAbqFRNPYF7GFaVKBx9vbG8nJyZW2JScnw9HRETY21Q9FVKlUcHR0rHSh+vvhcAwAYGpIG9haWUAQBDzSV6ol++V4HD8wiJow7SzLcvXf0Wpok5a2dqdXGxe42NXvOfT1d4GFQsCtzALEp1ffMkDNU5MKPKGhodizZ0+lbbt27UJoaKhMJWreLidl40h0GpQKAdMH+Ou2T+zVClYWClxOysG5m1nyFZCIGqV80kF5a3gaG3jq25wFALZWFujVxhkAh6e3NLIGntzcXERERCAiIgKANOw8IiICcXFxAKTmqOnTp+v2f/rpp3H9+nW88soruHz5Mr7++mv8+uuveOGFF+QofrO39vANAMCoIC+0qjCZl7OtFcZ28wYArGfnZaImS+5JB7WcbKQBw/qsmF5YosbhssAyPLD+gQcAQis0a1HLIWvgOXnyJHr16oVevXoBABYsWIBevXph0aJFAIDExERd+AGAgIAAbN26Fbt27UJwcDA++eQTfPfddxySbgTpecXYdEbqo/PkwIAq90/pK3Ve/iviFvKKGj5DKhHJJ9NMani0TWr61PCEX09DYYkGPk7W6OLjoNf5Ks7Hw2b5lkPWeXiGDRtW65utulmUhw0bhjNnzhixVARI/XOKSjXo1soRfdpWnduifztX+LvZ4kZaPraeS8Tkvhz9RtTUmNsoLX0Cz76y5qxhgZ4QhNqHo9+pVxtnqCwUSM0twrWUXHT00i8wUdPUpPrwkGmUqDX4qWwo+lMDA6r9MBEEQVfLs/5EXJX7icj8aWt4XMykD09mPQOPKIr4t56zK1dHZaFEX39XAGzWakkYeKiKbecTkZxdBHd7Fcb18Klxv0m9W0GpEHA6LhNXknNMWEIiMoTMAm0NT9Malh59Oxfx6QWwUip0zVP64rpaLQ8DD1Wxpqyz8mP920BlUfM0754O1hhR9utq/XF2XiZqajLytH14mlaT1r9lzVn92rnCTtWwnhnaoHT0ejrUGvbjaQkYeKiSM3EZiIjPhJVSgWn92ta5/9SymZf/OHMTRaVqYxePiAxIGzDkH6VVvmJ6ffzbgOHod+reygn2KgtkFZQgMpFLDrUEDDxUibZ2Z3ywLzwcVHXuP6STB7wdrZGZX4KdF5Pr3J+IzENxqQa5ZSMsnW1k7sNjW17DU9eoqezCEpy8kQGgcYHHQqlAvwBtPx42a7UEDDykk5RViG3nEwEATw70r9djlAoBk/u0BsDOy0RNibb/jiCU96GRizZwlahFFJTUXlN88EoqSjUi2nnYoa2bXaPOq+3HE86Oyy0CAw/p/PfoDZRqRIQEuOpWFK6Ph/v4QRCAw9fSEJeWb8QSEpGhaBf/dbKxrHOVcWOztVLCoqwMdfXj0a6Ofreekw1WR7uu1vGYdJSoNY0+Hpk3Bh4CIM1auu6YVEPzVD1rd7T8XG0xqIP0wfHrSXZeJmoKdMtKyFy7A0jTXJSvmF5z4NFoROxrxHD0O3X2doCLrSXyitVcJqcFYOAhAMDmM7eQkV+CVs42uKert96Pf6RsTp6Np+JRyl9KRGbPXCYd1KrPSK3zt7KQmlsMe5UF+pTNo9MYCoWA/u20zVrsx9PcMfAQRFHUdVaeMaBtg6q37+nqBVc7KyRnF2Ff1G0Dl5CIDC3LTCYd1KrPXDza0VmDOrjDysIwX18Vl5mg5o2BhxAenYao5BzYWikxpU+bBh3DykKBSXe1AsAFRUl/ablFXNPIxJpiDc9eAzZnaWkXEj0Zm4HCOjpMU9PGwEP44XAMAGDSXa11w0MbYkrZelp7o1KQnF1okLJR8/dT+A30/r/dmP7DcdzKLJC7OC1GhpksHKpV11w8KTmFun42wzp7GOy87T3s4OmgQnGpBqfjMgx2XDI/DDwtXGxaHvaUVRM/oWdn5Tt18HRAX38XqDUifjt10wClo+ZOoxHxzf7rAICDV1Mx6rMD+OV4HGt7TOBmhjSiUu5JB7WcbWuv4dE2lXdv5QRPB2uDnVcQBF2zFoenN28MPC3c2iM3IIrAsEAPtPewb/TxKi4oquF07VSHw9GpuJVZAEdrC/Ru64LcolIs/OM8pv9wXPeFTIaXkl2omyh0YNkIS7nV1aSlHZ013IDNWVra4ensx9O8MfC0YDmFJdh4UqqJeXJggEGOOba7NxxUFohPL0D4dX54UO20/b0m9mqFX/8TijfHdYHKQqGr7fn5WCxre4zgx/AbKFZr0LutC3q3dZG7OABQ67D0ErUGB69Io6gM2X9HSzsB4dn4TN3s09T8MPC0YBtP3kRuUSnae9hhSEfD/MqztbLAhF6+ANh5mWqXnleMXWW1DFP6+kGpEDBrcDtsnz8Efdq6IK9YjTc2XcBj3x9DfDprewwlr6gU/zsqzbk1e3A7mUtTrrZRWidupCOnqBRudlboocekqPXl52oLP1cblGpEnLiRbvDjk3lg4Gmh1BoRP4bfACDV7giC4WZa1c7Js+NCEtLzig12XGpeNp25hWK1Bt1aOSLIt/xLLMDdDhv+E4pF93WFtaUCh6+lYfTyA/jf0Vg2kxrAxpPxyCoogb+bLe7p6iV3cXRqa9LaW9bPcGigBxRGmhV6QDvpRx/78TRfDDwt1L+XUxCblg9Haws8WDac3FC6tXJCkK8jitUabDpzy6DHpuZBFEX8WlYDqO33VZFSIeCpQQH45/kh6Osv1fa8uZm1PY1Vqtbg+7JRmTMHt5N9SYmKahulZYjV0esyoIN2Ph5OQNhcMfC0UGvKPvSmhrSBrZWFwY//SIj0JbbhBEfcUFUR8ZmISs6BykKB+4N9a9wvwN0OG+aEYvF4qbbnSHQaRi0/gP+G32BtTwPsuJiM+PQCuNha4qG7WstdnEpqquGJS8tH9O08KBUCBnc03HD0O4WWzbh8MSEbmfmsmW6OGHhaoMtJ2TgSnQaFADwe2tYo57g/2BfWlgpcSc7F6bhMo5yDmi7tmmvjuvvovuhqolAIeHJgALY/PwQhAa7IL1bjrT8v4tHvjnKxWj2IoojVB6IBAI+H+sPGSilziSqrOCy94o+kfy9L/bz6tHWp873SGJ6O1ujgaQ9RBI5eZz+e5oiBpwVac+gGAGB0N2+0drE1yjmcbCwxtrsPAKmWh0grr6gUf0UkACifrLI+/N3tsH52fywZ3xU2lkocvZ6OUcsP4McjrO2pjxM3MnD2ZhasLBSYbqQfOo2hDTOlGhH5xeUzHu8tm3/HmM1ZWuXz8bBZqzli4Glh0vOKsTlC6ldjqKHoNZla1qz199lE5BTWPF08tSxbzyUir1iNAHc7hATotwCkQiHgiYEB2D5/MPoFuKKgRI3Ff13E1G+PIjYtz0glbh5WH5AmeJx0V2u426tkLk1VNpZKWCqlPkWZZc1a+cWluuktTBl4OB9P88TA08L8cjwORaXSyJg+Rp5/o09bF7TzsENBiRp/n0006rmo6dhQ1pw1uY9fg0cHtnWzwy+z++PtCUGwsVTiWEw6Ri8/iLWHY1jbU43o27nYHSk1Dc0abNwfOg0lCEJ5P56yuXiOXEtDcakGrV1s0MGz8ROj1qVfgBsEAbiakouUHC6Po6/CEjX2X7mNpX9fxHcHr8tdnCoYeFqQErUGP5UNRX/KwEPRqyMIAh4pa7JgsxYBwLWUHJyKzYBSIWBS78aNDlQoBEwP9ceO+UPQv51U27Pk70uY/E04tpxLQEExF4LU+u6gNEhhZBcvg8yobix3zsXzr3Z25UBPo39eAYCLnRW6+jgC4PD0+hBFEddv5+KHQzGY8cNxBC/diRk/HMeawzd0/fTMieGH55DZ2nY+EcnZRXC3V2FcDx+TnPPBu1rjox1ROHszC5cSstHV19Ek5yXztKFsKPrdnT0Nth5SGzdbrJvVHz8fi8Wyfy7jZGwGTsZmwM5KiVFB3hjf0xeDOrjDUtkyf9+l5hbh99PSjOpzhpjPRIPVqThSSxRF3fw7pmjO0hrQ3g0XE7IRHp2GCT0NO2VHc5BXVIoj0WnYfyUF+6/cRnx65QV/vR2tMSzQA0M7eUAURZME1fpi4GlB1hy+AQB4rH8bqCxMM0LD3V6Fe7p6Ydv5JGw4EYelE7qZ5LxkfopLNfj9tNR/bEqf+ndWrg+FQsDjof4Y3tkTvxyPw58RCbiZUYA/ztzCH2duwdXOCmO7e2NCz1bo3cbFaJPXmaOfwmNRXKpBsJ8z+vqbxzISNak4F8/lpBwkZhXC2lKhW/rBFAa0d8e3B2PYj6eMKIq4kpyLfVFSwDlxIx0l6vJmY0ulgJAAVwzt5IGhnTzRycverEJORQw8LcTpuAxExGfCSqnAtH6mHaExpW8bbDufhE1nbmHh2C6wtjSv4bBkGnsik5GeVwxPBxWGBRpnPpXWLrZ4eVRnvHRvIE7HZeKviFvYci4RaXnF+N/ROPzvaBxaOdtgfLAv7g/2RRcfB7P9cDaEgmI1/lvWjD1ncDuzf67OFWp49pY1Zw1o727Sz4y+Aa5QKgTEpecjPj0ffq7GGclqzrIKSnD4Wir2R93G/iu3kZRduT+Tn6sNhnXyxNBOHght7wY7VdOIEk2jlNRo2tqd8cG+8HAw7QiNwR3c0crZBrcyC/DPhUQ80Mu8JjzTir6di/+Gx2JEF0+jTnDWUmnXVnu4T2tYGLl5SRAE3cKYb93XFUei0/BnRAJ2XEzCrcwCrNofjVX7o9HR0x4Tevri/uBWaOPW/L7Yfjt9Exn5JfBztcGoIPNZRqImFZu0jsVINSzGWB29NvYqCwS3dsLpuEyEX09rVoGnVK1BYakGBcVqFJaoUVSqRmGJBoUlauQXq3HuZib2X7mN03GZUFfo/K+ykGrZhnbywLBAT/i72Zp9eK4OA08LkJRViH/OS6Oknhzob/LzKxQCJvfxw2e7r2D98XizDDx7L6fguV/OIKeoFGuP3MDgju5YOKYL+xwZSEJmAQ5cleZTmWzg5qy6WCgVGNLJA0M6eeDdkm7YezkFf0Yk4N/LKbiakouPd17BxzuvoKefMyb09MW4Hj4G618kJ7VGxPdlI2VmDgwwesg0BG3giU3Px6nYDACm7b+jNaC9uxR4otNM/n6tL41GxKYztxB+PQ0FJWoUlZSHl8KyIFNQXDnUlOoxgrG9hx2GdvLEsEAPhAS4NouaeQaeFuC/R2+gVCMixN8V3Yyw0nB9PNynNT7fcwXHYtJx/XYu2pnJSBFRFPHNgev4YPtliKL0nzwuPR8Hr6bi0LWDeLBXa7x4byf4OtvIXdQmbePJmxBFafr+tm52spXD2lKJMd19MKa7D7IKSrDjYhL+ikjAkehURMRnIiI+E+9suYQB7d1xf09fjO7mDUdr483ua0y7LiXjRlo+nGws8bCZfmnfSTtKa09kMjQiEOjlgFYy/N8Lbe+GFXuv4Uh0qtl1vAWAM3EZWPzXRZy7mdXgY6gsFLC2VMLaUgEbSyWsLZXwc7Ut64vj0axqtrQYeJq5whI11h2ThoQ/NchftnL4OttgaCcP7I26jQ0n47FwTBfZyqJVWKLGq7+fw59ls/5ODWmDpfcHISmrEB/uuIwt5xLx++mb2HIuAU8NCsAzw9o32S8/OWk0om6Iqj4zKxubk40lJvfxw+Q+fkjJKcTWc4n4MyIBEfGZOHQtFYeupWLxnxcxpa8fZg4KaHJfAN+W1e481r9Nk+ljoa3h0c60PKyzPE3Lvdu6wEqpQHJ2Ea6n5pnNUP7bOUX4YPtl/HZKGnXnoLLA46Ft4emgKgsvUoBRWSphbSFdt7ZU6gKN9rbKQmF2Ic4Umsb/AgIg1UYUlmiQU1SC3MJS5BaVIrewFDlFpcgrkm7nVNieW1SKmxn5yMgvQStnG9zT1VvW8k/p2wZ7o27j91M38dK9gbIOE07MKsB//nsK525mQakQsGR8VzzWvy0EQUAbN1usePQuzBqcife2ReJ4TDpW7ovG+uNxeG5ER0zr1xZWFubfPGAuDken4lZmARytLTC6m7zvwZp4OljjyYEBeHJgAGLT8vD32QRsOnML0bfzsPbIDfwUfgNju/tgzpB26NHaWe7i1ulUbAZOxWbASqnAjFB/uYtTb3eulXV3oOmbswCpJvCuts44ej0dR6LTZA880hxqsVi+6wpyikoBAA/1bo1XR3c2eZ/MpoyBxwyIooik7ELE3M5DdGoert/ORWxaPrIKKgSbsou6gbPIPjUoAEqZh+KO6OIJd3sVUnOLsCcyRbYvv1OxGfjPf08hNbcILraW+Hpa72qHvfb0c8aGOf2xJzIF72+/jGspuVj69yWsPXIDr4zqjLHdvVvkryR9aTsrT+zVqkn0A2jrZoe5d3dE2PAOOHQtFasPXMfBq6nYci4RW84lol+AK/4ztB2GdfI02+Ht2lluJ/byhadj0+mPVDHwOFpboLeRZ4OvzYD27jh6PR3h0al4vL98a48duZaKJX9fxJXkXABAj9ZOWHJ/EO5qY95TDJgjBh4Tyi0qRcztPFxPzcX123m4XhZuYlLzKi2WVxdBAOytLGBvbQF7VflfB+1tlSXsVcqy7ZbwcFDJ0vHvTpZKBR7q3Rqr9kdj/Yk4WQLPryfj8eamCyhWa9DZ2wHfTu9Ta1OFIAgY2dULwwI98OvJm/h01xXEpuUjbN1p9PRzxutju+i9HlRLkp5XjF0XpSUNzKk5qz4EQcDgjh4Y3NEDlxKy8d3B6/jrbAKOxaTjWEw6OnjaY87gdpjQy9dk81rVR2xaHrZfTAIAzBps3hMN3snZ1kp3fUgnD1k7Wg9o74ZPd0kzLms0osnD7a3MAry79RK2nZf+LV3trPDKqEBM7uNntkHb3DHwGJhaI+JmRj6u385D9O1cXE/N04Wc5OyiGh+nVAho42qLdu52aOdhB393O7jZWUnh5Y5AY2OpbLJv+Cl9/bBqfzT2X7mNhMwCk3UGLlVr8O62SN3w/FFBXvh0cs96922wUCrwaL82mNDTF98evI7VB64jIj4Tk78Jxz1dvfDq6M4GW+snNbcIF25l4WJCNi4lZONCQhYslQo8McAfD/dpbVZfrnXZdOYWitUadG/lhCBfeTrMG0JXX0d8OqUnXh4diDWHb2DdsThcS8nFK7+fw0c7o/DEAH881q8tnGzl7+P1/aEYiCIwPNADnbwc5C6OXirW8Mj9I61Ha2fYWimRkS9NgmiqEZuFJWp8e+A6vtp3DYUlGigE4PH+bbHgnkCzeH81ZYIoii1qpb3s7Gw4OTkhKysLjo6GewOfvJGO1/44j7i0fBSrNTXu525vhQB3O7Rzt0c7Dzu085D++rnYtph+IVO+CcexmHS8MLITnh/Z0ejny8wvxtx1Z3DoWioAYP7Ijnju7o6NCo0pOYVYvvsqNpyIh1ojQqmQ1g17fmTHeg9pFkURtzILcOFWNi4lZOFCQjYuJmTVGoy9Ha3x9NB2eCSkjdk3D4miiNHLDyIqOQfvTOwma7OAoWUXlmD98Tj8cOiGblI2WyslJveRt4NzRl4xQt/fg8ISDdbN7ocB7d1lKUdDFZao0ef/dqNYrUH4a3fDTeZV3Wf8cBz7r9zGm+O6GL22TBRF7I5MwTtbLiEuPR8AEBLgiqX3B6GLD6fHABr//c3AYyCXErIx9ouDAKThfgFlNTWVwo27PRM6gM1nbmH+hgg4WFtgWr+2mNjLF529jfMf+kpyDmb/dBKxafmwsVTi08nBGNPdcOuIXUvJwfv/ROlWora1UuI/Q9pj9pAA2FqV1x6pNSJiUnNxMSEbFxOydTU42kUSKxIEIMDdDkG+TgjydUSQryOupeRi1f5oXRjycFDhP0Pa4dF+bSqdx5ycicvAA18fgbWlAsdeH1mlQ2pzUFyqwdbzCfhm/3VcTsoBACgEYGx3H/xnSHt0b23aWq0v91zFJ7uuoFsrR/w9d1CT7GMmzb8jondb+ZuKv9kfjWX/XMaIzp74/om+RjvP9du5eHvLJeyLkuaq8nJU4fWxXXB/sG+T/Dc0FgYePRkr8BSWqHEsJh3t3O3QytmmyTY5mUJhiRoTvzqs+4IAgM7eDpjYqxXuD/Y1WDPX7kvJeH79GeQVq9HaxQbfTu9jtF9Kx66n4b1/LuNsfCYAKZBM798WKTlFuJiQhcjEHBSUVO2nZakU0NHTAd1aOeoCThcfx2qb2gpL1Nh46iZW7YvGrUxpwT5XOyvMHtwOj4e2hb2ZDT1e+Mc5/HI8Hg/2aoVPp/SUuzhGJYpipQ7OWv3bueI/Q9pjaCcPo38mFJaoMeiDf5GaW4zPH+nJhS8N4PzNLIxfcQj2KgtELLrH4H2K8opK8eW/1/D9oesoUYuwVAqYNbgd5g7v0GSmEjAlBh49GSvwkH4KS9TYezkFmyNuYe/l27pmQEEA+gW4YmLPVhjT3adBtQKiKOLrfdH4eGcURFE63srHesPVzqruBzeCKIrYej4RH26P0lVJV2RrpUQXH0ddrU2QrxM6etnr3SenuFSDP07fxNf7onXncba1xFMDAzBjgL9Z1KTkFZUi5N3dyCtWY8Oc/ujXznSLP8qtYgdn7cy2HT3t8eK9nTC6m+FqF++0/ngcXvvjPFo522Dfy8Na7OrwhqTWiOj19k5kF5Zi07MD0MtAI6NEUcRfZxPw3rZIXa3tsEAPLLqvq9lMymqOGHj0xMBjfrLyS7DtQiI2nbmF4zHpuu1WSgXu7uyJib1aYXhnj3oFg4JiNV7+7Sy2nJOW0ni8f1ssGt/VpB/+RaXSZI/h0WkIcLdD17JwE+BuZ9CpAUrVGvwZkYAVe68hJjUPAOBgbYEnB/jjqUEBlUa8mNqvJ+Lxyu/nEOBuh39fHNoiq+UTMguw9ojUwTm3bO6UJwf64/WxXQz+ftRoRNzz2X5E384zSX+TlmTOTyex81IyXh4ViLDhHRp8nBK1BtG3c3HxVjY2nIzXfda1cbXFovu6YkQXzxb5/0QfDDx6YuAxb7cyC/BnxC1sPnNLN+8EIM3JMa6HDyb0bIUQf9dqmwduZRZgzk8ncTEhGxYKAUsnBJl8ZXg5qDUitpxLwIp/r+FqivSa2VkpMX2AP2YNCpCl4+eklUdwKjYDr47ujGeGtTf5+c1JdmEJvtp7Dd/sl+bGCfF3xYppvQy6XteeyGTM/PEkHKwtEL5whNk1bzZlaw/HYMnflzCogzv+N6tfvR6TV1SKy0nZupGWFxOyEZWcg+LS8gEt1pYKzB3eAbMGtzP7AQjmolkEnq+++gofffQRkpKSEBwcjC+//BIhISHV7rt27Vo8+eSTlbapVCoUFhZWu/+dGHiaBlEUEZmYgz8jbuHPiATdSBgA8HWyxv09W1Xq7HziRjqe/u8ppOUVw9XOCiun3dWimlEA6Vf+9otJ+GLPVV3/KBtLJR7r3wazh7Qz2YKY11JyMPLTA1AqBIQvvLtZLMRpCDsvJuHFX88ip6gUng4qfD3tLvTxN0zHXO3Ix/8MaYeFY+VftqU5uZKcg3s/OwCVhQLnltxbpaY5NbeobDBCFi6VBZyYtDxU981qr7JAVx9H9GjthCcHBciyTlhT1uQDz4YNGzB9+nSsWrUK/fr1w/Lly7Fx40ZERUXB07PqPAxr167F888/j6ioKN02QRDg5eVVr/Mx8DQ9ao2IYzFp+PNMAradT9RNrQ5InZ37Bbhi3fE4lKhFdPFxxLfTe6O1S9Na98iQNBoRuyOT8eW/13D+lrS4oMpCgakhbfD00PbwdjJuAHl36yV8ezAG93T1wrfT+xj1XE3N9du5ePp/p3AlORcWCgFvjuuCGQP8G9WUcTY+ExO+OgwLhYCDrw6HjxO/RA1JFEX0fXc3UnOLsXxKT1hZKHTh5mJCNlJyqp9GwtNBhSBfR12TdpCvI/xcbDmgpRGafODp168f+vbtixUrVgAANBoN/Pz8MG/ePLz22mtV9l+7di3mz5+PzMzMBp2Pgadpq6mzMwCM6+6Djx7uYbbDtE1NFEXsi7qNL/69ijNxmQCkflFPDvTHy6MCjTKLbXGpBv2X7UF6XjG+n9EHI7rU74dIS5JXVIpXfz+n62c2sacvlj3YAzZWDWvWmLvuNLacS2wRo+HkMu+XM/j7bEK19wkCEOAm9dXThpuuPo5c48oIGvv9Les3Q3FxMU6dOoWFCxfqtikUCowcORLh4eE1Pi43Nxdt27aFRqPBXXfdhffeew9BQUHV7ltUVISiovIEnp2dbbgnQCZnbanEmO4+GNPdR9fZeefFJPRv54Y5Q9qx018FgiBgeGdPDAv0wOFrafhiz1Ucv5GObw5cx6XEbHw17S6Dr/6+JzIZ6XnF8HRQYWgneVa6Nnd2Kgt8ObUXerVxwXvbIrE5IgGXk3Kw6rHe8He30+tY8en52HZeCk7sqGw8E4J98ffZBFgpFQj0dqhQc+OIzt7VTyNB5kfWf6XU1FSo1eoqzVFeXl64fPlytY8JDAzEDz/8gB49eiArKwsff/wxBgwYgIsXL6J169ZV9l+2bBmWLl1qlPKTvJxsLTE1pA2mhrSRuyhmTRAEDOrojkEd3fHP+UQs+PUsDl5NxaSvj+D7GX3Rxs1wzX/ahUIf7tNa1nWQzJ0gCJg5KADdfB0Rtu4MLiflYPyKQ1g+padetWI/HI6BRgQGd3Q32dIHLdHIrl44u+he2KqUHO7fhDW5f7nQ0FBMnz4dPXv2xNChQ/HHH3/Aw8MD33zzTbX7L1y4EFlZWbpLfHy8iUtMZD7GdPfBxqdD4eWowtWUXEz8+jBO3kiv+4H1kJBZgANXpZliJ/dpWguFyqVfOzdsfW4Q7mrjjJzCUsz88SQ+3XUFGk3dPQ2y8kuwoSxgzmbtjtE52Voy7DRxsv7rubu7Q6lUIjk5udL25ORkeHvXbyVtS0tL9OrVC9euXav2fpVKBUdHx0oXopasWysn/Bk2CN1aOSI9rxiPfnsMm87cbPRxN568CVEEQtu5oa2bfk0zLZmXozXWzwnF9FBpCoUv9lzFUz+eQGZ+ca2P+/l4LPKL1ejs7YDBHZvWmllEcpA18FhZWaF3797Ys2ePbptGo8GePXsQGhpar2Oo1WqcP38ePj7Gm8GUqLnxdrLGr/8JxeggbxSrNXhhw1l8sjOqXjUL1dFoRPx6UqpteCSEtTv6srJQ4O0J3fDp5GBYWyqwL+o2xq84hIsJWdXuX1SqxtrDNwBItTvsu0ZUN9nr5xYsWIBvv/0WP/74IyIjI/HMM88gLy9PN9fO9OnTK3Vqfvvtt7Fz505cv34dp0+fxmOPPYbY2FjMmjVLrqdA1CTZWlng62l36SYG/PLfa5i3/gwKq1nzqy6Ho1NxK7MAjtYWGBVUv9pZqurBu1rjj2cGoo2rLeLTC/Dg10fw+6kKtW/JF4Gv+uH8n18gJacIXo4qjA/2la/ARE2I7F3Lp0yZgtu3b2PRokVISkpCz549sX37dl1H5ri4OCgU5bksIyMDs2fPRlJSElxcXNC7d28cOXIEXbt2lespEDVZCoWAV0d3Rjt3O7y+6Ty2nkvEzYwCfDu9t14TBmo7Kz/QqxVnjW2krr7SSufzN5zB3qjbeHHjWUTEZ+Kt+7rCavtC4PZldL/9HloLH+GxgYNhZSH771aiJkH2eXhMjfPwEFXv6PU0PP2/U8jML4GvkzW+m9G3XiN/0vOK0f+9PShWa7D1uUEI8nUyQWmbP41GxBf/XsXne65CFIHHvWPxTmZ5bfcesQ/6vLbdLBaLJTKFxn5/86cBEQEA+rdzw6ZnB6Kdux0Ssgrx8Koj2BOZXOfjNp25hWK1Bt1bOTHsGJBCIWD+yE74YUZfOForMTH9BwDAcYs+KBGVGCGchFP8vzKXkqjpYOAhIp0AdztsenYgBrR3Q16xGrN+OonvDl5HTRXBoiji17LmrMl92VnZGIZ39sTu+4rQW3EVBaIVwnKfxFrNGOnOf14BSuq3jiBRS8fAQ0SVONla4senQjA1xA+iCPzf1ki8vukCSios46EVEZ+JqOQcWFsqcD87zxqHRgPPkx8BAMLdHsRtuOBmj3mAgw+QcQM4/Lm85SNqIhh4iKgKS6UC7z3QHW+O6wJBAH45Hocn1hxHVn5Jpf20Q9HHdvdhXxJjifwLSDoPWDlg+Mz3cPCV4Vg0qR8w6l3p/kOfAukx8paRqAlg4CGiagmCgFmD2+Hbx/vA1kqJw9fS8MDKw7iRmgdAWgTzrwhpQcUpnFnZODRqYO970vXQZyHYucHP1RZKhQAEPQgEDAFKC4HtC2s/DhEx8BBR7UZ29cJvTw+Aj5M1rt/Ow8SvD+PY9TRsPZeIvGI1AtztEBLgKncxm6dzvwKpUYC1MxAaVvk+QQDGfgwoLIAr/wBR/8hSRKKmgoGHiOrU1dcRf4YNRHBrJ2Tml+Cx749h+e4rAKR1szjTrxGUFgP7lknXB80HrKsZAecRWB6E/nkVKCkwWfGImhoGHiKqF8+yNZ/GdvdGiVpEQlYhlAoBk3q3krtozVPE/4DMWMDOEwiZU/N+Q14BHHylfQ8tN1nxiJoaBh4iqjcbKyVWTL0L8+7uAAAY38NHrxmZqZ5KCoH90sgsDH4RsKplMVaVPTC6rJ/Poc+A9OvGL58xaTTShcjAGHiISC8KhYAX7w1E+MK78eFDwXIXp3k6+T2QkwA4tgb6PFn3/l0nAu2GAeoi4J/XjF0648m9DawaBKwcAGQnyF0aamYYeIioQXycbLiOkzEU5QIHP5WuD30FsFDV/RhBAMZ8BCgsgas7mmYH5tJi4NfpQMpF4HYk8L9JQEGm3KWiZoSfVkRE5uTYKiA/FXBtB/R8tP6P8+gEDJgrXf/nlabXgXn7a0DcEcDKAbD3AlIuAesf5UzSZDAMPERE5qIgEzjyhXR92OuAUs/JHIe8DDi2AjLjpP48TcXJH6RmPAjApO+Ax34HVI5A7GHgj9nSfEREjcTAQ0RkLo58CRRmAR5dgG4P6v94KztglLYD83IgLdqgxTOK2CPAtpel6yPeAgJHA97dgUd+BpRW0kzT/7wK1LCeG1F9MfAQEZmD3NvA0ZXS9bvfABTKhh2n6wSg3fCyDsxmHhQy44ENjwOaUmnm6EELyu8LGAI88A0AATjxLXDwE9mKSc0DAw8RkTk4vBwoyQN8ewGd72v4cQQBGFvWgfnaLiBqm8GKaFDF+VIfnfxUqUZnwgqp7BV1exAY84F0/d93gNP/NX05qdlg4CEiklt2AnD8W+n63W9W/eLXl3tHYMA86fo/r0nhwpyIIvDXXCDpHGDrDjzyS81zDfX7DzDoBen6388DUdtNV05qVhh4iIjkduAjqQmqTSjQfoRhjjnkJWken6w4aUV1c3J4OXDhd2kdsMk/Ac51LD47YjEQ/CggqoGNTwDxJ0xRSmpmGHiIiOSUHgOc/km6fvdbja/d0bKyA0aXrcV1+HPz6cB8ZSewe6l0fcyHgP/Auh8jCMD9XwAd7gFKC4B1DwO3rxi3nMYiihxqLxMGHiIiOe3/QOq02/7u+n3566PLeKnGSF0sjYSSuwNz6lXg95kARKD3k0DfmfV/rNISmPwj4HsXUJAhTUyYnWi0ohpcXioQ/pU0i/R7vsCuxUBpkdylalEYeKhpy0kCtr8u/UJWl8pdGiL93I4Czm2Qrt/9puGPr+3ArLQCovcAl7cY/hz1VZAJ/PIIUJQtNd2N+VD/Y1jZAdM2Aq7tpaa6nx+ShvGbK3UpcGUHsOEx4JNAYMfr0oSKolpq1vtmKJAQIXcpWwwGHmqaRBGI+AX4KgQ4+hXw1zzg6/5A5N/y/4olqq+97wGiRhqV1aq3cc7h1h4Y8Jx0fftCoDjPOOepjUYN/D4LSLsm9Sua/F/Awqphx7JzBx7/Q5qNOfkCsH6a+dWUpF4Ddi8BPgsC1k2WPpc0pVLt1H2fAQ+tAew8pCU0vhsB7HsfUJfIXepmTxDFlvXtkJ2dDScnJ2RlZcHR0VHu4lBDZCcAf8+X1gwCAK9uQE4ikJ8m3fbrB9zzNtCmv2xFJKpT4lngmyEABOCZw4BXkPHOVZwv/TjIipdWXx+xyHjnqs6uxVKNhoUN8NR2wLdn44+ZeBZYMw4ozpEWT33oh4bPXWQIRbnApc3Amf8BceHl223dgB6PAL2mVf43zksDti6QHgMAPsHAxFWAV1dTlrpJaez3NwMP1V9xnvTBae8hz/lFEYj4WWrCKsqSqumHvQYMeB4oyZc6ZoZ/JXVqBIDAccDIxYBHoDzlJarNz5Ol0N7tIeCh741/vsgtwIZp0vw8zx4F3DsY/5wAcP63sn47ACZ9D3R/yHDHvr5f6sujKQFC5kjNZIbq9F0fogjEHwfO/ARc2CTNowQAgkLqYN3rMaDT6Nprsy78Dmx9UeqXpLQChr8u1cjJGd7MFAOPnhh4GiA7ETj6NXByjfRrqtNoYPBLgF9f05Uh66Y0B8e13dLtVr2BCV8Dnp2rlnX/+9IEZaJa+uDp9Zi0LpGjj+nKS1Sb+OPA9/cAghKYe0JqdjI2UQR+fliajLD93cBjfxg/HCScAX4YDZQWSnPpjFxi+HNc+B347Snp+ojFwOAFte9vCDlJwNn1Um1O2tXy7a7tpc+b4Kn6fd7kJEmfb1fK5hhqHQJMXGm6UNpEMPDoiYFHD7evAEc+B85ukH5B3SlgiFQ9HjDUeB+coih1SN7xhhS2lCrpF1DoXEBpUXvZ9ywt76RpYQOEPgsMfB6wdjJOWYnq68fxQMwBoNfj0gzDppIWLfV1UxdL8990nWC8c+WmAKuHAdm3gI73AlPXG6/W4uhKabV1QPoh1Gua4c+hLpE6IJ/5H3B1p/SDCgAs7YCgB6Sg06Z/wz8LdTXYC6WO3RY2UkAMmQMo2N0WYODRGwNPPcQflxYejNpavq1NKDBwPuDaTmo6Orde6oQHAK36SJOcdRpt2OCTGQf89Rxwfa90u3Vf6cPMo1P9jxF3FNi1CIg/Jt22cZVWlO47E7BQGa6s1DBFucDxb4DovUDbAUCfmYCDl9ylMq7r+4Gf7pealp47DTi3Me35/30XOPChtKr63BM1z3DcGKXF0nOMCwfcOgKz9xj/h8auRdJnk6AEHt0AdLynccfLTgQSI6S+QgkRwM3j5f0EAamvYK/HgaCJgMqhceeqKDNemoX6+j7ptv9gYMJXgEtbw52jiWLg0RMDTw00GulXy+HPgbgj5dsDx0m1Im36Vd4/Mw44/EXZcPCyERJe3aRq66AHGvdLThSBU2uAnW8BxbmAhbU0IVv/Zxp2XFGU1hPavQRILZuszLkNcPcioNsk/nqSQ3GetJTCkS8qf4koLKV/k/5PS2tKNTeiKDVl3Twh/XIf+5Hpy1CcD3zdT/o/bKxmpr/nS/+HVU5S2HHvaPhz3EmjATY/I/0Ys7QFZmwBWtdj5JsoSrVQCRFSuNGGnNzkqvvaeQI9pwI9H9Pvh5e+NBrg5PdSiCvJB6zsgVHvAXdNN20fJTPDwKMnBp47lBYDF36TwsvtSGmbwhIIniJ1Bq7rP3VOsjQs/MT3UjgBpHbsQS8APaboP/Q044Y0xDzmgHTbr7/068YQbdnqUiDif8DeZUBukrTNuwdwz1KpTwMZX3G+9EF+aLm0aCQg1Rr2egyI+kcKAlp+/aWQ2/m+2psvm5IrO6RhyhY2wPNn5avNurwNWD9V+r8++EUpXPoEG6af24nvpE64EKQ5cxpb06IPdQmwboo055CtG/DUzsqfHaIIZMaW19poA07F0K0lKACPztLr4tNT+tu6jzQBoqmkRQN/hpWP+upwjzTjtKOv6cpgRhh49MTAU6YoBzj1o9QZOfuWtM3KAejzpPQlo+9/qIIM4Nhq4NhK6Togzbcx8DnpV4mlTe2P1/2iWSyNdLCwkUZYhcwxfLt/cZ70vA99LvULAoB2w6Xg4xNs2HORpKRA6vR+6DMgL0Xa5uIPDH0V6D65PNDcPCW9hy5uKm8ydfID+s6S3ke2rrIU3yA0GmD1ECDpvFRres/b8pVFFKVJALWdZLXsvcq/3H17Stcdfetfq3DjEPDTBOnfbuRSYNB8w5a7PopygR/vkzpMO7eRaoeTL5SFm7Pln08VKSwAjy6Arzbc9JSGkFvZmrr0VWnUUh+lPW9LtenWTsCYj4Aek1tcbQ8Dj55afODJTQGOrZJ+hWlnKLX3kkJO7ycBG+fGHb8oV6rKPvJleZWwnQcQGib1z7Cu5jVPvw78OQ+IPSTdbjsQuP9L449cyUuTFm088V15p+xuD0kdLJ3bSG3m9t5s8mqMkkLg9I/AwU/La9Wc2wBDXgGCH6n513J2ohSAT/5Q/uvb0lZ6TL+nm+ZUAxc3SQtfWjkA88/JH95KCqTRjAmnpdqO1ChpEsQ72bqXhx9tEHLyq/plmxkndVLOT5P+H036Tr4v5NzbwA/3Sp8td1JYSnPdVHw+nkGApbWpS6mf21HApv9IQQ6Qaj7vWy7fNCEyYODRU4sNPGnRUgiJWFfe58a1vVQD0+MRw/9nLymUmo8Ofy59EALSL5OQ/0jhytZV+sV7fLU0mqokX/pCG7lU+jVvypCRHgPsfRc4v7HqfUorwKk14Ny2PAQ5ty2/be/Z4n5l1UtpkdS/6+CnQE6CtM3JT+rcHvxo/Zs6Swqlf5djq6Rf6Vrt7wb6PQN0GNk0AqlGLY2OSr0CDH0NGL5Q7hJVVZwHJF+s3NSTElk+GqkiG9cKtUDBgGdXaa6dpPPS7Se3y187kh4D/Pak1DRVsVnKs2vDZ3mWm7pUqiXd/4H0I83WDbj3/6T10pp7Z38w8OjN7AKPKErVvyUF0pdEqfZvoTR0VKOW7leXSH81aumNrttWdr+mwv26fcsuKZfKllwo+/XWqrc04qrzOONPbqUukSYeO/RpeYdhSzug9wzpg1XbQdp/sFSr4xpg3PLUJiFCqp1Ki5ZCWtbN6j/sK7KwloJPpUBUdtvFX/5f8aZWWiwF3QOfANk3pW2OraR+Ir0eb/gXjShKzSXHVgGXtwIo+9hy6yDV+ARPBVT2BnkKRhHxC7D5acDGBXj+XPU1neaopABIvgQknikPQimR1U9TAUi1ubP3As5+Ji1mi5N4TuqgXfFHgINveU2cby/pur2nTAU0DgYePRkt8KTHSE0jFYNLSaEUXLSXircr7lddNbIxdLxX6jvQdqDpayU0GuDy38CBj4Gkc+XbreylvjO9nzK/X+rqUql2IiNWCkCZZX+1t7NvQffFWxOnNkCrXtIaOtoPoeY4D5C6RKo9PPCxtKgjADj4SEHnrumGnQIgPUYa4XXmv9J8JYA0Guiux4GQ2VLQNAeiKDVDxB4GDn4ivV/k6tdiSKVF0o+ohIjyEU3JF6V+MI/9AbQNlbuELUNpkVTbc+GPsh+T1XwWOfiWf+749pLCUBNuAmPg0ZPRAo925tTGsrCWvhwsrKXmFIVSanNWWEgdOxXai2XZfRZSPwiFxR37Vrhf5SCNmDLmWj31JYrAtT1A+JdSTc/oZU13fonSYqkWo6ZApO2zcie3DuUBqNVdgHd348yFoqXRAHm3pS/c7ASp1srKHlA5Su8N7cXKXv/QqS6VhgHv/1B6/oDUJ2zQAqD3E8btF1GUI9WcHFsFpEdL27RT+rfpL43A8+lhul+5GrX0xR97uOxypPLoHwcfYN5p+Zt6jKG0WKqRNudatuasKEdqTkyIkPr4JEYAqVdRbQhybFUefrQ1Qk0kBDHw6MlogSfrljSBmoWNFFgsy/5a2Egf+hYVLhVvV9zPQsX+IM1JYVb5B1DCaemvtj9TRYKibIRIr7LaoF7SnEb1qRURRSA/XQpe2QlSM1z2Len9mH1Lup2TKH0Z1YeVfeUApAtE2nBUYZu6BDj2DZARIz3WzlOajqDPk3WPyjMkjUZacuTYSiD636r323tJ4ce7uxSAvHsALgGNr1FUl0hNC7GHpHATF14+EEDLwlqaMNN/ENBzGpt6yHSKcqT3Z2JE+edQ2jVUH4JaS+HHuY30Q9vCWmp+Vqqkz6Eq27R/a9lmYW3wIfwMPHoyuz481LLkpZZ9+JQFoFunq68JUlhKNXLaWiB777IaGm2YuVn2N6F8sdRaCdIXv1Mr6cOrKEdqDirKkS7aIeANYesuNdP0mSl/7UXKZWmoddI56RdvTb9yreylUOlTFoS8ewCeXWoPmaVF0r+XLuAcK18ssuJx2/SXZo1uO0j692uqHWSp+dGGIG0tUEJE5bXADMn3LmDOXoMekoFHTww8ZHayEysHoIQzQEG6fsew85DmS3FsLYUax1bS6DLHVtJte++av3hFUfoyL8qR5iUqqnjJrRyMinPLw1JJgdTZPGS2cZvkGqM4T+p0m3RWCkCJ56T+J6WFVfdVWEgTzWkDkHd3qfkv9ghw47A0KaJ2hKOWtXNZuBkg9Y3z7tF8JkmklqEwW/qBkBAhNX2ri8v6lxZJ7/fSovJt6uKyfqjFVe/T7q/98dQmFHhqe62n1hcDj54YeMjsiaLU9FUxBBVkSoGmujDj4Gv+c4iYE3Wp9Ks28VxZTdA56XphZt2PtfMor71pO0Aa4mxune2J5KTRlAUftcH7dDHw6ImBh4iqEEWpv1PS+fLmsMRzAMSyJqqB0sW9I/vZEcmksd/frHslIhIEqUOxsx/QeazcpSEiI2BdLBERETV7ZhF4vvrqK/j7+8Pa2hr9+vXD8ePHa91/48aN6Ny5M6ytrdG9e3ds27bNRCUlIiKipkj2wLNhwwYsWLAAixcvxunTpxEcHIxRo0YhJSWl2v2PHDmCqVOnYubMmThz5gwmTpyIiRMn4sKFC9XuT0RERCR7p+V+/fqhb9++WLFiBQBAo9HAz88P8+bNw2uvvVZl/ylTpiAvLw9btmzRbevfvz969uyJVatW1Xk+dlomIiJqehr7/S1rDU9xcTFOnTqFkSNH6rYpFAqMHDkS4eHh1T4mPDy80v4AMGrUqBr3LyoqQnZ2dqULERERtSyyBp7U1FSo1Wp4eVVe1t7LywtJSdWvQ5SUlKTX/suWLYOTk5Pu4ufHqd2JiIhaGtn78BjbwoULkZWVpbvEx8fLXSQiIiIyMVnn4XF3d4dSqURycnKl7cnJyfD29q72Md7e3nrtr1KpoFLVYxFGIiIiarZkreGxsrJC7969sWfPHt02jUaDPXv2IDQ0tNrHhIaGVtofAHbt2lXj/kRERESyz7S8YMECzJgxA3369EFISAiWL1+OvLw8PPnkkwCA6dOno1WrVli2bBkA4Pnnn8fQoUPxySefYNy4cVi/fj1OnjyJ1atXy/k0iIiIyIzJHnimTJmC27dvY9GiRUhKSkLPnj2xfft2XcfkuLg4KCoszjdgwACsW7cOb775Jl5//XV07NgRmzdvRrdu3eR6CkRERGTmZJ+Hx9Q4Dw8REVHT06Tn4SEiIiIyBQYeIiIiavZk78NjatoWPM64TERE1HRov7cb2hOnxQWenJwcAOCMy0RERE1QTk4OnJyc9H5ci+u0rNFokJCQAAcHBwiCYNBjZ2dnw8/PD/Hx8ewQbUJ83eXB110efN3lwdddHhVfdwcHB+Tk5MDX17fS6O36anE1PAqFAq1btzbqORwdHfkfQgZ83eXB110efN3lwdddHtrXvSE1O1rstExERETNHgMPERERNXsMPAakUqmwePFiLlZqYnzd5cHXXR583eXB110ehnzdW1ynZSIiImp5WMNDREREzR4DDxERETV7DDxERETU7DHwEBERUbPHwGMgX331Ffz9/WFtbY1+/frh+PHjchepWVuyZAkEQah06dy5s9zFanYOHDiA8ePHw9fXF4IgYPPmzZXuF0URixYtgo+PD2xsbDBy5EhcvXpVnsI2I3W97k888USV9//o0aPlKWwzsmzZMvTt2xcODg7w9PTExIkTERUVVWmfwsJChIWFwc3NDfb29pg0aRKSk5NlKnHzUJ/XfdiwYVXe808//bRe52HgMYANGzZgwYIFWLx4MU6fPo3g4GCMGjUKKSkpchetWQsKCkJiYqLucujQIbmL1Ozk5eUhODgYX331VbX3f/jhh/jiiy+watUqHDt2DHZ2dhg1ahQKCwtNXNLmpa7XHQBGjx5d6f3/yy+/mLCEzdP+/fsRFhaGo0ePYteuXSgpKcG9996LvLw83T4vvPAC/v77b2zcuBH79+9HQkICHnzwQRlL3fTV53UHgNmzZ1d6z3/44Yf6nUikRgsJCRHDwsJ0t9Vqtejr6ysuW7ZMxlI1b4sXLxaDg4PlLkaLAkDctGmT7rZGoxG9vb3Fjz76SLctMzNTVKlU4i+//CJDCZunO193URTFGTNmiBMmTJClPC1JSkqKCEDcv3+/KIrS+9vS0lLcuHGjbp/IyEgRgBgeHi5XMZudO193URTFoUOHis8//3yjjssankYqLi7GqVOnMHLkSN02hUKBkSNHIjw8XMaSNX9Xr16Fr68v2rVrh2nTpiEuLk7uIrUoMTExSEpKqvTed3JyQr9+/fjeN4F9+/bB09MTgYGBeOaZZ5CWliZ3kZqdrKwsAICrqysA4NSpUygpKan0nu/cuTPatGnD97wB3fm6a/38889wd3dHt27dsHDhQuTn5+t13Ba3eKihpaamQq1Ww8vLq9J2Ly8vXL58WaZSNX/9+vXD2rVrERgYiMTERCxduhSDBw/GhQsX4ODgIHfxWoSkpCQAqPa9r72PjGP06NF48MEHERAQgOjoaLz++usYM2YMwsPDoVQq5S5es6DRaDB//nwMHDgQ3bp1AyC9562srODs7FxpX77nDae61x0AHn30UbRt2xa+vr44d+4cXn31VURFReGPP/6o97EZeKhJGjNmjO56jx490K9fP7Rt2xa//vorZs6cKWPJiIzvkUce0V3v3r07evTogfbt22Pfvn0YMWKEjCVrPsLCwnDhwgX2DTSxml73OXPm6K53794dPj4+GDFiBKKjo9G+fft6HZtNWo3k7u4OpVJZpZd+cnIyvL29ZSpVy+Ps7IxOnTrh2rVrchelxdC+v/nel1+7du3g7u7O97+BzJ07F1u2bMHevXvRunVr3XZvb28UFxcjMzOz0v58zxtGTa97dfr16wcAer3nGXgaycrKCr1798aePXt02zQaDfbs2YPQ0FAZS9ay5ObmIjo6Gj4+PnIXpcUICAiAt7d3pfd+dnY2jh07xve+id28eRNpaWl8/zeSKIqYO3cuNm3ahH///RcBAQGV7u/duzcsLS0rveejoqIQFxfH93wj1PW6VyciIgIA9HrPs0nLABYsWIAZM2agT58+CAkJwfLly5GXl4cnn3xS7qI1Wy+99BLGjx+Ptm3bIiEhAYsXL4ZSqcTUqVPlLlqzkpubW+kXVExMDCIiIuDq6oo2bdpg/vz5+L//+z907NgRAQEBeOutt+Dr64uJEyfKV+hmoLbX3dXVFUuXLsWkSZPg7e2N6OhovPLKK+jQoQNGjRolY6mbvrCwMKxbtw5//vknHBwcdP1ynJycYGNjAycnJ8ycORMLFiyAq6srHB0dMW/ePISGhqJ///4yl77pqut1j46Oxrp16zB27Fi4ubnh3LlzeOGFFzBkyBD06NGj/idq1Bgv0vnyyy/FNm3aiFZWVmJISIh49OhRuYvUrE2ZMkX08fERraysxFatWolTpkwRr127Jnexmp29e/eKAKpcZsyYIYqiNDT9rbfeEr28vESVSiWOGDFCjIqKkrfQzUBtr3t+fr547733ih4eHqKlpaXYtm1bcfbs2WJSUpLcxW7yqnvNAYhr1qzR7VNQUCA+++yzoouLi2hrays+8MADYmJionyFbgbqet3j4uLEIUOGiK6urqJKpRI7dOggvvzyy2JWVpZe5xHKTkZERETUbLEPDxERETV7DDxERETU7DHwEBERUbPHwENERETNHgMPERERNXsMPERERNTsMfAQERFRs8fAQ0QtniAI2Lx5s9zFICIjYuAhIlk98cQTEAShymX06NFyF42ImhGupUVEshs9ejTWrFlTaZtKpZKpNETUHLGGh4hkp1Kp4O3tXeni4uICQGpuWrlyJcaMGQMbGxu0a9cOv/32W6XHnz9/HnfffTdsbGzg5uaGOXPmIDc3t9I+P/zwA4KCgqBSqeDj44O5c+dWuj81NRUPPPAAbG1t0bFjR/z111/GfdJEZFIMPERk9t566y1MmjQJZ8+exbRp0/DII48gMjISAJCXl4dRo0bBxcUFJ06cwMaNG7F79+5KgWblypUICwvDnDlzcP78efz111/o0KFDpXMsXboUkydPxrlz5zB27FhMmzYN6enpJn2eRGREBl/2lIhIDzNmzBCVSqVoZ2dX6fLuu++KoiitpPz0009Xeky/fv3EZ555RhRFUVy9erXo4uIi5ubm6u7funWrqFAodCuI+/r6im+88UaNZQAgvvnmm7rbubm5IgDxn3/+MdjzJCJ5sQ8PEclu+PDhWLlyZaVtrq6uuuuhoaGV7gsNDUVERAQAIDIyEsHBwbCzs9PdP3DgQGg0GkRFRUEQBCQkJGDEiBG1lqFHjx6663Z2dnB0dERKSkpDnxIRmRkGHiKSnZ2dXZUmJkOxsbGp136WlpaVbguCAI1GY4wiEZEM2IeHiMze0aNHq9zu0qULAKBLly44e/Ys8vLydPcfPnwYCoUCgYGBcHBwgL+/P/bs2WPSMhOReWENDxHJrqioCElJSZW2WVhYwN3dHQCwceNG9OnTB4MGDcLPP/+M48eP4/vvvwcATJs2DYsXL8aMGTOwZMkS3L59G/PmzcPjjz8OLy8vAMCSJUvw9NNPw9PTE2PGjEFOTg4OHz6MefPmmfaJEpFsGHiISHbbt2+Hj49PpW2BgYG4fPkyAGkE1fr16/Hss8/Cx8cHv/zyC7p27QoAsLW1xY4dO/D888+jb9++sLW1xaRJk/Dpp5/qjjVjxgwUFhbis88+w0svvQR3d3c89NBDpnuCRCQ7QRRFUe5CEBHVRBAEbNq0CRMnTpS7KETUhLEPDxERETV7DDxERETU7LEPDxGZNba6E5EhsIaHiIiImj0GHiIiImr2GHiIiIio2WPgISIiomaPgYeIiIiaPQYeIiIiavYYeIiIiKjZY+AhIiKiZo+Bh4iIiJq9/wcsqoM7Y5aL+QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://ir.cs.ui.ac.id/alfan/ml/second/ML-ANN-Keras.pdf"
      ],
      "metadata": {
        "id": "lORnQbKEXsE6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### d. Evaluate the performance of the above two architectures on the test set by finding the accuracy, precision, recall and F1-Score values. And provide a detailed explanation of the results."
      ],
      "metadata": {
        "id": "cae3g5KJnFNd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "pred = model.predict(X_test)\n",
        "pred = np.argmax(pred, axis=1)\n",
        "print(classification_report(y_test, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gej0KVVqoxAq",
        "outputId": "203b1031-b458-4589-bc08-13efdb0d9cb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 191ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.85      0.85        20\n",
            "           1       0.73      0.73      0.73        11\n",
            "\n",
            "    accuracy                           0.81        31\n",
            "   macro avg       0.79      0.79      0.79        31\n",
            "weighted avg       0.81      0.81      0.81        31\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "pred = ann3.predict(X_test)\n",
        "pred = np.argmax(pred, axis=1)\n",
        "print(classification_report(y_test, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8AsC7JVmbbxR",
        "outputId": "ea0f2a87-7da4-4c1b-cc8b-9f41bd2e037b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 75ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      1.00      0.91        20\n",
            "           1       1.00      0.64      0.78        11\n",
            "\n",
            "    accuracy                           0.87        31\n",
            "   macro avg       0.92      0.82      0.84        31\n",
            "weighted avg       0.89      0.87      0.86        31\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari hasil classification report diatas dapat disimpulkan bahwa model ann3 atau model hyperparameter tunning lebinh baik daripada model pertama"
      ],
      "metadata": {
        "id": "FR5uM2f5LZN0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### f. Recording video"
      ],
      "metadata": {
        "id": "Sx4ctnXhT2Lh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://binusianorg-my.sharepoint.com/personal/alisha_saadiya_binus_ac_id/_layouts/15/guestaccess.aspx?share=EfXhXqUzlF5AiYy8mTLXUIQBCDzFwvxHq5AVo4hE8VHa2g&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJPbmVEcml2ZUZvckJ1c2luZXNzIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXciLCJyZWZlcnJhbFZpZXciOiJNeUZpbGVzTGlua0RpcmVjdCJ9fQ&e=gwH0mf"
      ],
      "metadata": {
        "id": "BIO37UEdT6Dj"
      }
    }
  ]
}