분류1.ipynb

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  "metadata": {
    "colab": {
      "name": "분류1.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPbOqq7MZjpN9Ie1dhPtFjj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jaesunkkk/esaa_assignment/blob/main/%EB%B6%84%EB%A5%981.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8x30_IZwvzbz",
        "outputId": "3dfac7df-aa0c-420b-a1ca-44c2f19c9ecd"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "dt_clf = DecisionTreeClassifier(random_state=156)\n",
        "iris_data =load_iris()\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris_data.data, iris_data.target,test_size=0.2, random_state=11)\n",
        "dt_clf.fit(X_train, y_train)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=156, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGXeYTi3wAxL"
      },
      "source": [
        "from sklearn.tree import export_graphviz\n",
        "export_graphviz(dt_clf,out_file=\"tree.dot\", class_names=iris_data.target_names,feature_names = iris_data.feature_names, impurity=True, filled=True)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 933
        },
        "id": "QHTgqfOpwFkj",
        "outputId": "7cdb98d4-86da-47b2-9aa4-d3fe48a6b827"
      },
      "source": [
        "!pip install graphviz\n",
        "import graphviz\n",
        "with open(\"tree.dot\") as f:\n",
        "    dot_graph = f.read()\n",
        "graphviz.Source(dot_graph)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (0.10.1)\n"
          ],
          "name": "stdout"
        },
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id=\"edge13\" class=\"edge\">\n<title>12&#45;&gt;13</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M541.5,-222.8796C541.5,-214.6838 541.5,-205.9891 541.5,-197.5013\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"545.0001,-197.298 541.5,-187.2981 538.0001,-197.2981 545.0001,-197.298\"/>\n</g>\n<!-- 16 -->\n<g id=\"node17\" class=\"node\">\n<title>16</title>\n<polygon fill=\"#8139e5\" stroke=\"#000000\" points=\"759,-179.5 642,-179.5 642,-111.5 759,-111.5 759,-179.5\"/>\n<text text-anchor=\"middle\" x=\"700.5\" y=\"-164.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"700.5\" y=\"-149.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 34</text>\n<text text-anchor=\"middle\" x=\"700.5\" y=\"-134.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 0, 34]</text>\n<text text-anchor=\"middle\" x=\"700.5\" y=\"-119.3\" font-family=\"Times,serif\" 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y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 0, 2]</text>\n<text text-anchor=\"middle\" x=\"659.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = virginica</text>\n</g>\n<!-- 13&#45;&gt;15 -->\n<g id=\"edge15\" class=\"edge\">\n<title>13&#45;&gt;15</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M585.4388,-103.9815C595.3598,-94.607 605.8897,-84.6572 615.8008,-75.2921\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"618.3743,-77.6757 623.2389,-68.2637 613.5666,-72.5878 618.3743,-77.6757\"/>\n</g>\n</g>\n</svg>\n"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "D93GVWeQwH4b",
        "outputId": "cbd4beef-aeaf-4ec5-b3cd-7cd057f1a6f9"
      },
      "source": [
        "import seaborn as sns\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "print(\"Feature importances:\\n{0}\".format(np.round(dt_clf.feature_importances_, 3)))\n",
        "for name, value in zip(iris_data.feature_names, dt_clf.feature_importances_):\n",
        "    print('{0} : {1:.3f}'.format(name, value))\n",
        "sns.barplot(x=dt_clf.feature_importances_, y=iris_data.feature_names)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature importances:\n",
            "[0.025 0.    0.555 0.42 ]\n",
            "sepal length (cm) : 0.025\n",
            "sepal width (cm) : 0.000\n",
            "petal length (cm) : 0.555\n",
            "petal width (cm) : 0.420\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7523cad2d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "NCL9cJV4wNC4",
        "outputId": "a1d54547-9d80-45c1-d662-368672184b48"
      },
      "source": [
        "from sklearn.datasets import make_classification\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.title(\"3 Class values with 2 Features Sample data creation\")\n",
        "X_features, y_labels = make_classification(n_features=2, n_redundant=0, n_informative=2, n_classes=3, n_clusters_per_class=1, random_state=0)\n",
        "plt.scatter(X_features[:,0], X_features[:, 1], marker='o', c=y_labels, s=25, edgecolor='k')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7523452350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWGU_SvhwQvz"
      },
      "source": [
        "import numpy as np\n",
        "def visualize_boundary(model, X, y):\n",
        "    fig,ax = plt.subplots()\n",
        "    ax.scatter(X[:, 0], X[:, 1], c=y, s=25, cmap='rainbow', edgecolor='k',\n",
        "               clim=(y.min(), y.max()), zorder=3)\n",
        "    ax.axis('tight')\n",
        "    ax.axis('off')\n",
        "    xlim_start , xlim_end = ax.get_xlim()\n",
        "    ylim_start , ylim_end = ax.get_ylim()\n",
        "    model.fit(X, y)\n",
        "    xx, yy = np.meshgrid(np.linspace(xlim_start,xlim_end, num=200),np.linspace(ylim_start,ylim_end, num=200))\n",
        "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
        "    n_classes = len(np.unique(y))\n",
        "    contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
        "                           levels=np.arange(n_classes + 1) - 0.5,\n",
        "                           cmap='rainbow', clim=(y.min(), y.max()),\n",
        "                           zorder=1)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "1CCaRtNzwW5N",
        "outputId": "ddef9348-b0bb-43ea-b2ed-227ca8df5c9d"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dt_clf = DecisionTreeClassifier().fit(X_features, y_labels)\n",
        "visualize_boundary(dt_clf, X_features, y_labels)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "LZIXHF8vwZsV",
        "outputId": "7eeaccfe-8a00-4325-9a27-0d9db787a018"
      },
      "source": [
        "dt_clf = DecisionTreeClassifier(min_samples_leaf=6).fit(X_features, y_labels)\n",
        "visualize_boundary(dt_clf, X_features, y_labels)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asAbf-kEwbxw",
        "outputId": "e3e3ea57-37e3-4cf1-9628-e79afa7ed70b"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "feature_name_df = pd.read_csv('/content/features.txt', sep='\\s+', header=None, names=['column_index', 'column_name'])\n",
        "feature_name = feature_name_df.iloc[:, 1].values.tolist()\n",
        "print('전체 피처명에서 10개만 추출:', feature_name[:10])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "전체 피처명에서 10개만 추출: ['tBodyAcc-mean()-X', 'tBodyAcc-mean()-Y', 'tBodyAcc-mean()-Z', 'tBodyAcc-std()-X', 'tBodyAcc-std()-Y', 'tBodyAcc-std()-Z', 'tBodyAcc-mad()-X', 'tBodyAcc-mad()-Y', 'tBodyAcc-mad()-Z', 'tBodyAcc-max()-X']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Xx7eAY2Izr8b",
        "outputId": "b09bbc81-0a97-4657-abec-b153248dc246"
      },
      "source": [
        "feature_dup_df = feature_name_df.groupby('column_name').count()\n",
        "print(feature_dup_df[feature_dup_df['column_index'] > 1].count())\n",
        "feature_dup_df[feature_dup_df['column_index'] > 1].head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "column_index    42\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>column_index</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>column_name</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fBodyAcc-bandsEnergy()-1,16</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fBodyAcc-bandsEnergy()-1,24</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fBodyAcc-bandsEnergy()-1,8</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fBodyAcc-bandsEnergy()-17,24</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fBodyAcc-bandsEnergy()-17,32</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              column_index\n",
              "column_name                               \n",
              "fBodyAcc-bandsEnergy()-1,16              3\n",
              "fBodyAcc-bandsEnergy()-1,24              3\n",
              "fBodyAcc-bandsEnergy()-1,8               3\n",
              "fBodyAcc-bandsEnergy()-17,24             3\n",
              "fBodyAcc-bandsEnergy()-17,32             3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DlamXTO0OqH"
      },
      "source": [
        "def get_new_feature_name_df(old_feature_name_df):\n",
        "    feature_dup_df = pd.DataFrame(data=old_feature_name_df.groupby('column_name').cumcount(),columns=['dup_cnt'])\n",
        "    feature_dup_df = feature_dup_df.reset_index()\n",
        "    new_feature_name_df = pd.merge(old_feature_name_df.reset_index(), feature_dup_df, how='outer')\n",
        "    new_feature_name_df['column_name'] = new_feature_name_df[['column_name', 'dup_cnt']].apply(lambda x : x[0]+'_'+str(x[1]) if x[1] >0 else x[0] ,  axis=1)\n",
        "    new_feature_name_df = new_feature_name_df.drop(['index'], axis=1)\n",
        "    return new_feature_name_df"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkgPdSSm0S1b"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "def get_human_dataset( ):\n",
        "    feature_name_df = pd.read_csv('/content/features.txt',sep='\\s+',\n",
        "                        header=None,names=['column_index','column_name'])\n",
        "    new_feature_name_df = get_new_feature_name_df(feature_name_df)\n",
        "    feature_name = new_feature_name_df.iloc[:, 1].values.tolist()\n",
        "    X_train = pd.read_csv('/content/X_train.txt',sep='\\s+', names=feature_name )\n",
        "    X_test = pd.read_csv('/content/X_test.txt',sep='\\s+', names=feature_name)\n",
        "    y_train = pd.read_csv('/content/y_train.txt',sep='\\s+',header=None,names=['action'])\n",
        "    y_test = pd.read_csv('/content/y_test.txt',sep='\\s+',header=None,names=['action'])\n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = get_human_dataset()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oMCXAyyc0ulk",
        "outputId": "7c577d1b-f0e9-48bc-c757-d3d795ab0037"
      },
      "source": [
        "print('## 학습 피처 데이터셋 info()')\n",
        "print(X_train.info())\n",
        "print(y_train['action'].value_counts())"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "## 학습 피처 데이터셋 info()\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7352 entries, 0 to 7351\n",
            "Columns: 561 entries, tBodyAcc-mean()-X to angle(Z,gravityMean)\n",
            "dtypes: float64(561)\n",
            "memory usage: 31.5 MB\n",
            "None\n",
            "6    1407\n",
            "5    1374\n",
            "4    1286\n",
            "1    1226\n",
            "2    1073\n",
            "3     986\n",
            "Name: action, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxJYbVlk2nHE",
        "outputId": "ecb95059-7967-4c31-f686-d4b17a79d9c3"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "dt_clf = DecisionTreeClassifier(random_state=156)\n",
        "dt_clf.fit(X_train , y_train)\n",
        "pred = dt_clf.predict(X_test)\n",
        "accuracy = accuracy_score(y_test , pred)\n",
        "print('결정 트리 예측 정확도: {0:.4f}'.format(accuracy))\n",
        "print('DecisionTreeClassifier 기본 하이퍼 파라미터:\\n', dt_clf.get_params())"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "결정 트리 예측 정확도: 0.8548\n",
            "DecisionTreeClassifier 기본 하이퍼 파라미터:\n",
            " {'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'presort': 'deprecated', 'random_state': 156, 'splitter': 'best'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvSPafgA2slL",
        "outputId": "20c23066-ca16-451b-9c38-5ce00fa6d842"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "params = {\n",
        "    'max_depth' : [ 6, 8 ,10, 12, 16 ,20, 24]\n",
        "}\n",
        "grid_cv = GridSearchCV(dt_clf, param_grid=params, scoring='accuracy', cv=5, verbose=1 )\n",
        "grid_cv.fit(X_train , y_train)\n",
        "print('GridSearchCV 최고 평균 정확도 수치:{0:.4f}'.format(grid_cv.best_score_))\n",
        "print('GridSearchCV 최적 하이퍼 파라미터:', grid_cv.best_params_)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 7 candidates, totalling 35 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:  1.9min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "GridSearchCV 최고 평균 정확도 수치:0.8513\n",
            "GridSearchCV 최적 하이퍼 파라미터: {'max_depth': 16}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "Eejj7XWQ2v_J",
        "outputId": "4e1c270f-e8a7-4aa0-9ae6-5915cace26e0"
      },
      "source": [
        "cv_results_df = pd.DataFrame(grid_cv.cv_results_)\n",
        "cv_results_df[['param_max_depth', 'mean_test_score']]"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>param_max_depth</th>\n",
              "      <th>mean_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>0.850791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>0.851069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>0.851209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>0.844135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16</td>\n",
              "      <td>0.851344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>20</td>\n",
              "      <td>0.850800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>24</td>\n",
              "      <td>0.849440</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  param_max_depth  mean_test_score\n",
              "0               6         0.850791\n",
              "1               8         0.851069\n",
              "2              10         0.851209\n",
              "3              12         0.844135\n",
              "4              16         0.851344\n",
              "5              20         0.850800\n",
              "6              24         0.849440"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lKNhi-U3uxt",
        "outputId": "452b76de-9181-457a-c850-cd4596b1f080"
      },
      "source": [
        "max_depths = [ 6, 8 ,10, 12, 16 ,20, 24]\n",
        "for depth in max_depths:\n",
        "    dt_clf = DecisionTreeClassifier(max_depth=depth, random_state=156)\n",
        "    dt_clf.fit(X_train , y_train)\n",
        "    pred = dt_clf.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test , pred)\n",
        "    print('max_depth = {0} 정확도: {1:.4f}'.format(depth , accuracy))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "max_depth = 6 정확도: 0.8558\n",
            "max_depth = 8 정확도: 0.8707\n",
            "max_depth = 10 정확도: 0.8673\n",
            "max_depth = 12 정확도: 0.8646\n",
            "max_depth = 16 정확도: 0.8575\n",
            "max_depth = 20 정확도: 0.8548\n",
            "max_depth = 24 정확도: 0.8548\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sghjJPG732Dc",
        "outputId": "c8d3d0c2-6309-4529-b180-4f601cc90d22"
      },
      "source": [
        "best_df_clf = grid_cv.best_estimator_\n",
        "pred1 = best_df_clf.predict(X_test)\n",
        "accuracy = accuracy_score(y_test , pred1)\n",
        "print('결정 트리 예측 정확도:{0:.4f}'.format(accuracy))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "결정 트리 예측 정확도:0.8575\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "CNBKjj_X36wj",
        "outputId": "98f52506-0537-4d60-d22d-13cae2c05f8c"
      },
      "source": [
        "import seaborn as sns\n",
        "ftr_importances_values = best_df_clf.feature_importances_\n",
        "ftr_importances = pd.Series(ftr_importances_values, index=X_train.columns  )\n",
        "ftr_top20 = ftr_importances.sort_values(ascending=False)[:20]\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.title('Feature importances Top 20')\n",
        "sns.barplot(x=ftr_top20 , y = ftr_top20.index)\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "kV3uwaqq3-J0",
        "outputId": "aef734ed-9dba-4398-bd81-146cad274a63"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "cancer = load_breast_cancer()\n",
        "data_df = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
        "data_df.head(3)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.8</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.6</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.9</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.8</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.0</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.5</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
              "0        17.99         10.38  ...          0.4601                  0.11890\n",
              "1        20.57         17.77  ...          0.2750                  0.08902\n",
              "2        19.69         21.25  ...          0.3613                  0.08758\n",
              "\n",
              "[3 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDetRbDj4Bgc",
        "outputId": "854f5a69-0fb5-4158-88b9-85c782d2e54a"
      },
      "source": [
        "lr_clf = LogisticRegression()\n",
        "knn_clf = KNeighborsClassifier(n_neighbors=8)\n",
        "vo_clf = VotingClassifier( estimators=[('LR',lr_clf),('KNN',knn_clf)] , voting='soft' )\n",
        "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.2 , random_state= 156)\n",
        "vo_clf.fit(X_train , y_train)\n",
        "pred = vo_clf.predict(X_test)\n",
        "print('Voting 분류기 정확도: {0:.4f}'.format(accuracy_score(y_test , pred)))\n",
        "\n",
        "\n",
        "classifiers = [lr_clf, knn_clf]\n",
        "for classifier in classifiers:\n",
        "    classifier.fit(X_train , y_train)\n",
        "    pred = classifier.predict(X_test)\n",
        "    class_name= classifier.__class__.__name__\n",
        "    print('{0} 정확도: {1:.4f}'.format(class_name, accuracy_score(y_test , pred)))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Voting 분류기 정확도: 0.9474\n",
            "LogisticRegression 정확도: 0.9386\n",
            "KNeighborsClassifier 정확도: 0.9386\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9uRzfZV4F-p",
        "outputId": "f23cbae7-e541-4ac0-bda1-e21904a0c305"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "X_train, X_test, y_train, y_test = get_human_dataset()\n",
        "rf_clf = RandomForestClassifier(random_state=0)\n",
        "rf_clf.fit(X_train , y_train)\n",
        "pred = rf_clf.predict(X_test)\n",
        "accuracy = accuracy_score(y_test , pred)\n",
        "print('랜덤 포레스트 정확도: {0:.4f}'.format(accuracy))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "랜덤 포레스트 정확도: 0.9253\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jYzFWo54Ihm",
        "outputId": "78c1fe12-364e-45e2-cbe1-394d16e8ba40"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "params = {\n",
        "    'n_estimators':[100],\n",
        "    'max_depth' : [6, 8, 10, 12], \n",
        "    'min_samples_leaf' : [8, 12, 18 ],\n",
        "    'min_samples_split' : [8, 16, 20]\n",
        "}\n",
        "rf_clf = RandomForestClassifier(random_state=0, n_jobs=-1)\n",
        "grid_cv = GridSearchCV(rf_clf , param_grid=params , cv=2, n_jobs=-1 )\n",
        "grid_cv.fit(X_train , y_train)\n",
        "\n",
        "print('최적 하이퍼 파라미터:\\n', grid_cv.best_params_)\n",
        "print('최고 예측 정확도: {0:.4f}'.format(grid_cv.best_score_))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "최적 하이퍼 파라미터:\n",
            " {'max_depth': 10, 'min_samples_leaf': 8, 'min_samples_split': 8, 'n_estimators': 100}\n",
            "최고 예측 정확도: 0.9180\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJDfr-PJ4NZQ",
        "outputId": "88f2d4a8-4730-42cc-87c4-b3f8bdb56e2d"
      },
      "source": [
        "rf_clf1 = RandomForestClassifier(n_estimators=300, max_depth=10, min_samples_leaf=8, \\\n",
        "                                 min_samples_split=8, random_state=0)\n",
        "rf_clf1.fit(X_train , y_train)\n",
        "pred = rf_clf1.predict(X_test)\n",
        "print('예측 정확도: {0:.4f}'.format(accuracy_score(y_test , pred)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "예측 정확도: 0.9165\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "bidpTEWD4QO_",
        "outputId": "6b100f10-6234-4858-b787-6d4b575ee49f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "ftr_importances_values = rf_clf1.feature_importances_\n",
        "ftr_importances = pd.Series(ftr_importances_values,index=X_train.columns  )\n",
        "ftr_top20 = ftr_importances.sort_values(ascending=False)[:20]\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.title('Feature importances Top 20')\n",
        "sns.barplot(x=ftr_top20 , y = ftr_top20.index)\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jbd8J3A40AsT9qbJgL5Vs42jgWuAyqsAPSWsCFwB72x4B7FvuPRmYZ3sz2+8GbmvxHJsCewFbAV8DnrO9OXAXcFCpMw44umQ4PwecV8p/AWxd6l8OnFBrd2NgZ+A9wJclDSzvYP3y3A0dwPbNg7L9U+AJ4H8oGdfOSDpcUoekjseeebZVtYiIiGVKfnqlhyRdBWwI3G97r1J8TclAAQwEzpE0EngZ2KiU/ww4S9IKwC7AHbbndzEDeyFV0HQ1cAhwWCnfDZhc7r8SOFnSscDWpd0HAWw/XuqPpgoWKeVPtOhvsu2ngaclzaMKJgFmAu+WtArVNPMVtXGvUP77NmCipLWBQcCDtXavt/0C8IKkOcBawAKgeSp2DrBOi7GdC6xo+w8trmN7HFUwyoh3vDX7AEZERJDMXnfcC2zROLE9BjgYWL1Wp55GOg54BBgBtFMFPth+nipTtzNVxm9iVx3bngK0SRoFDLA9q1waC4yWNBuYCqwBfKAnDyVpTJkuni6psY/eC7UqC2rnC6j+H4PlgCdL5rHx11hL9x3gHNubAZ8CBtfaqrf7cmlrflMdyvn8Mr7xZWw/rY1hQU+eMSIiIhLstfI0sGo5vhTYTtJHa9dXWsi9Q4CHbS8ADgQG1K5NpMrQbQ+87svTpn4bflDGMB6qtXvl/nVtt9luAz5DFQDeDewgab1StxGQ3lzqUMqH2b6qFrB1LOR5XmH7KeBBSfuWdiRpRO25/16OP96Ntp4ABpQp6YaNgFnl+iFlbB/pztgiIiKicwn2OmH7MWCKpFnAaVTTpkeUDwzuAk6i+kq0M+cBH5c0g2qtWj3rdxPwfuAW2y92cu+1QCPj1li7dgkwjGptHsAY4LYyLdrwE2B34CngcGBS6b+RPTwdGCZpVinfsVsvonMHAJ8o7dwL7FHKT6Wa3p0KPNrNtm4C3lc735HaOsaIiIhYfLKztOnNTNI+wB62D+zrsfQ2SVsAxzWeTdIdVM/aak1ht414x1t944mfXtxmIiIigKVjuzRJU223N5fnA403MUnfAT4M9MupTNvTVP1A9QCqNZDf6o1ALyIiIl6VzF70S+3t7e7o6NZSxIiIiH6hVWYva/YiIiIi+rEEexERERH9WNbsRb/0/Jw/cd+5e3RdMSIi+oWNP/OTristo5LZi4iIiOjHEuxFRERE9GMJ9haDpKGSjqydbyjpOkkPSJpaflZkh17qq13S2eV4lKRtu3nf8pLmSjqjN8bRXZJOkzS6G/X2lHRKOT5K0qFN1wdLuk/SZrWyz0v6Xu+POiIiov9JsLd4hgJHQhWUUO3+MM72cNtbAkcD6zffJKnHayVtd9g+ppyOAroV7AE7AfcD+0pST/tdVLZPsX1LN6qeQLXrCMBFVO+s3s7zwLHAeWV7trcCRwAn9uZ4IyIi+qsEe4vnDGC4pOnAQ8Bdtq9pXLQ9y/bFAJJOlTRB0hRggqQ2SXdKmlb+ti31Lpe0a6MNSRdL2qdk866T1EYV7BzX2FZN0oOSBpb6q9XPqfbMPQv4K7BNrd1dSr8zJN1aylaRNF7STEn3SNq7+YElHSzpakk3S5pdsnHHS/qtpLsb+/E2xl2OZ0v6SulvpqSNS/lGwAu2Hy3v6zlgtqT31Pu0fQPwMHAQcCZwan58OSIionsS7C2eE4EHbI8EJgDTuqi/CTDa9lhgDrCT7S2A/YCzS52JwMcAJA0CPkhtv1jbs4HzgTNtj7R9J3A70AgQ9wcm2X6pZBtHU+25exlV4IekNYELgL1tjwD2LfeeDMyzvZntdwO3tXiOTYG9gK2ArwHP2d4cuIsqIOvMo+VZvwt8rpRtx+vfWQewPa93bOlrTdsTOutA0uGSOiR1PPFMZ1sPR0RELHsS7C0hkq6SNEvSpFrxNbbnl+OBwAWSZgJXUAWCAD8DdpS0AtVWaXfU7mnlQuCQcnwIML4c7wZMLvdfCexZtibburT7IIDtx0v90cC5jUYXkj2bbPtp23OBeVTBJMBMoK3FPY33MLVWZ21gblO9OcA6zTfb/gdV8PndFu1je5ztdtvtw1YZ1KpaRETEMiXBXu+5F9iicWJ7DHAw1Z6vDc/Wjo8DHgFGAO3AoHLf81SZup2pMn4Tu+rY9hSgTdIoYIDtWeXSWGC0pNlUQdYawAd68lCSxpTp4umSGluwvFCrsqB2voDWv93YqPNyrc58YHBTvcHAfElvr/V7RK39BT0Zf0RExLIuwd7ieRpYtRxfCmwn6aO16yst5N4hwMO2FwAHAgNq1yZSZei2B27oot+GH5QxjIdq7V65f13bbbbbgM9QBYB3AztIWq/UbQSkN5c6lPJhtq8q08Ujbff2ZrO/BzZoKtsImGX7oVq/5/dyvxEREcuMBHuLwfZjwBRJs4DTqKZNj5D0Z0l3AScBp7e4/Tzg45JmABvz2qzfTcD7gVtsd7b47FqgkXFrrG+7BBhGtTYPYAxwm+16Fu4nwO7AU8DhwKTSfyN7eDowrEw/zwB27NaLWHR3AJs3fSW8HVXQGREREb1Atvt6DNELypeve9g+sK/H0hOSzgKutX2LpM2B43vjGTZdd6h//IX3L/4AIyJiqZDt0kDSVNvtzeXZG7cfkPQdqo85PtLXY1kEXwfeW47fQvVFcERERPSSZPaiX2pvb3dHR28vMYyIiHjzapXZy5q9iIiIiH4swV5EREREP5Y1e9EvPf3oH7n9gl27rhgREUu9UYdd33WlZVgyexERERH9WIK9iIiIiH4swd4SIqmt/NhyV/XWlnRdOd5L0q21a+8rP5z8hk23S7pQ0ibl+IvdqN8myZJOr5W9RdJLks5ZAuO7RdKw3m43IiKiv0qw1/eOBy4AsD0JeEHS/5M0kGqXjSNt/9+iNCxpQNe1Xsv2J23/rpx2GewVDwL1BXL7Uu0VvCRMAI5cQm1HRET0Own2OiHpaklTJd0r6fBS9oykr0maIeluSWuV8uHlfKak0yU900l7AyR9Q9JvJN0j6VO1y3vz2v1vj6LatuxU4De2f9lJeytJ+pGk30m6StKvJLXXxvnNst3ZNpJOKf3OkjROlY0l/brWXpukmeX4dkntks4AViyZxUsknSbp2No9X5P0b+X0OeD3jTEA+wE/qtVdU9KVZRy/kbRdKX+PpLsk/VbSLyW9s5QfLGmSpBsk/VHSf9Ue/xqq/X0jIiKiGxLsde5Q21sC7cAxktYAVgbutj2Cak/Xw0rds4CzbG8G/K1Fe58A5tneCtgKOEzSepLWA56o719r+89Ue9UeBXyhRXtHlvs2odpxYsvatZWBX9keYfsXwDm2t7K9KbAisJvt+4BBpX+ogrOJtTawfSIw3/ZI2wcAFwEHAUhaDtgf+GHtlsuB/SW9HXgZ+Eft2lnAmeX59wYuLOX3Advb3hw4hWo3jYaRZVybAfuVdrH9BLBC+TeJiIiILuSnVzp3jKQx5fjtwIbAi8B1pWwqsFM53gbYsxxfCvx3J+19CHh32b8WYEhp8xlgbr1imXrdqVx7B/BoJ+29jyqAwvYsSffUrr0MXFk731HSCcBKwOpU06vXUmXe9gPOKP/dr5N+XmF7tqTHyv61awG/tf2YpFVLlRuArwKP0BQ4AqOBTSQ1zleTtEp5D/8jaUPAwMDaPbfanlfeye/Ku3ioXJsDrAM8Vu+kZGEPB1hr9cELe5yIiIhlRoK9JpJGUQUn29h+TtLtwGDgJb+6t9zL9OzdCTja9o1NfW1e2q47EpgJnAScK2mbWr/d8bztl0v7g6nW/bXbfkjSqbX+JgJXSJoE2PYfu9H2hcDBwD9TZfpeYftFSVOBzwKbAB+tXV4O2Nr28/V7ygcck22PkdQG3F67/ELtuPl9DwbmNw/O9jhgHMA724ZkH8CIiAgyjduZIVRTpM9J2hjYuov6d1NNTUI1tdmZG4FPl48ukLSRpJWB+4G2RiVJ/0z1wcYJtm8A/g58slx7j6QflKpTgI+V8k2opjo70wjsHi2ZtEZmEdsPUAVRJ/P6TFzDS40xF1cBu1BNRd/YSf1vAl+w/XhT+U3A0bXnHFkOh5RnhCqI7JKq9OA/A7O7Uz8iImJZl2Dv9W4Alpf0e6opzru7qH8scHyZSt0AmNdJnQuB3wHTVP0cy/eA5W0/CzwgaYNS71vAf9luTO0eC3xJ0urAuryazToPWLNMb55ONTX7un5tP0n1pe8squDsN01VJgL/Su1jiibjgHskXVLaexGYDPyokT1s6u9e2//TSTvHAO3l45TfAUeU8v8C/kPSb+l+pnRLqrWTi/SFckRExLJGPZshjGaSVqL6kMGS9gfG2t6jB/ePAba0fVIX9b4BTLB9T1nXN9D285KGA7cA7yzB2BJTPsyYBuzbzWnfJTGGs4BrbN+6sHrvbBvi733pfW/QqCIioi9lu7SKpKm225vLs2Zv8W0JnFOmF58EDu3Jzbav6s6XpbY/XztdCZhcplhF9Vt8SzrQ24TqA5Wr+irQK2Z1FehFRETEq5LZi36pvb3dHR0dfT2MiIiIN0yrzF7W7EVERET0Ywn2IiIiIvqxBHsRERER/Vg+0Ih+6YlH/8iPx+/S18OIiIglbJ9Dbui60jIumb2IiIiIfizBXkREREQ/lmBvCZLUVnbM6Kre2pKuk/RPkmaXbdMa186V9O9LdqSvGctpkkaX42PLj0Z3dY8l/bB2vrykuZKuWwLju1zShr3dbkRERH+VYO/N4XjgAttzqLZo+28ASVsA2zfOe6rstNEjtk+xfUs5PZbqB5y78iywqaQVy/lOvLrnbW/7LnDCEmo7IiKi30mw14KkqyVNlXSvpMNL2TOSviZphqS7Ja1VyoeX85mSTpf0TCftDZD0DUm/KXvEfqp2eW+qPXmh2o92uKQdgXOBo2y/1NTWcpLOk3SfpJsl/VTSPuXabEn/KWkasK+kw0qfMyRdKWklSUMk/aVsf4aklSU9JGmgpIsl7SPpGGAdqp06Jks6VNK3a2M4TNKZtWH9FNi1HI8FLqvVXVnSRZJ+Lem3kvYo5W2S7pQ0rfxtW8pHSbpd0o/LM15SdigBuBMYLSkfF0VERHRDgr3WDrW9JdAOHFO2NFsZuNv2COAO4LBS9yzgLNubAX9r0d4ngHm2twK2Ag6TtJ6k9YAnbL8AYHsB8GngSuAPtu/opK29gDZgE+BAYJum64/Z3sL25cAk21uVMf8e+ITtecB04P2l/m7AjfWg0vbZwD+AHW3vCPwI2L1s0QZwCHBRrc/Lgf0lDQbeDfyqdu1LwG223wPsCHxD0srAHGAn21sA+wFn1+7ZnCqzuAmwPrBd7f38CRjR/FIkHS6pQ1LHU88s0d3jIiIilhoJ9lo7RtIM4G7g7cCGwItU+8MCTKUKuKAKtq4ox5e2aO9DwEGSplMFQmuUNtcG5tYr2p4OzALOa9HW+4ArbC+w/b/A5KbrE2vHm5bs2UzgAOBdtTr7leP9m+55HdvPALcBu0naGBhoe2bt+j1U72MsVZav7kPAieXZbwcGA+sCA4ELytiuoArsGn5t+28luJvOq+8aqiBxnU7GOM52u+321VYZtLDHiYiIWGZkKqwTkkYBo4FtbD8n6XaqAOUlv7qZ8Mv07P0JONr2jU19bV7abrag/C2KZ2vHFwN72p4h6WBgVCm/Bvi6pNWBLakCua5cCHwRuA8Y38n1a6jWF46iCmYbBOxt+w/1ypJOBR6hytItBzxfu/xC7bj5XQ8G5ndjvBEREcu8ZPY6N4RqavW5ksXauov6d1Otu4MqS9aZG4FPN6ZBJW1UpjLv57VZq05JequkW8vpFGDvsnZvLV4N4DqzKvBw6feARmHJ1P2Gagr6Otsvd3Lv0+X+xj2/ospy/j9qa/JqLgK+Us/4FTcCRzfW3ZUAF6r3/HDJ3h0IdPeDko2oMp8RERHRhQR7nbsBWF7S76m+jr27i/rHAsdLugfYAJjXSZ0Lgd8B08rPsXwPWN72s8ADkjbooo+1gf8rx1dSrQ38HfBDYFqLPgFOppo2nkKVkaubCPwrradwxwE3SKpPE/8ImGL7iebKZdr17OZy4KtUU7b3SLq3nEM1Tf3xMl2+Ma/NSHaqBLfzy/R1REREdEGvzkrGoiq/RTfftiXtD4y1vUcP7h8DbGn7pEzzWs0AACAASURBVIXUOQr4q+1ryvkqtp8pH478GtjujQiAym/nnWn71i4rL5n+jwOesv39hdUb3jbE//nl5u9WIiKiv8l2aa+SNNV2e3N51uz1ji2Bc8o05ZPAoT252fZVJWhbWJ1zmoqukzQUGAR8dUkHeqWvXwMz+irQK54EJvRh/xEREUuVZPaiX2pvb3dHR0dfDyMiIuIN0yqzlzV7EREREf1Ygr2IiIiIfixr9qJfmvvYH/nehJ37ehgREbEIPnXgjV1Xim5LZi8iIiKiH0uwFxEREdGPLZPBnqShko6snW8o6TpJD0iaKmmypB16qa92SWeX41GStu3mfctLmivpjN4Yx5uNpD0lnVKOj5LU6c/VSDq7Ua+cf0nSuW/UOCMiIpZ2y2SwBwwFjgSQNBi4Hhhne7jtLYGjgfWbb5LU4zWOtjtsH1NORwHdCvaAnai2Utu3sc3Ym9GivJPiBKodNKDaZu3oFvVOAg6WtL6k9YFPAl9axD4jIiKWOctqsHcGMFzSdOAh4K7GzhQAtmfZvhhA0qmSJkiaAkyQ1CbpTknTyt+2pd7lknZttCHpYkn7lGzedZLagCOA4yRNl7S9pAdre+WuVj8HxlLtW/tXYJtau7uUfmc09sqVtIqk8ZJmSrpHUmOfXmr3DZD0DUm/KXU+VcpHSbpd0o8l3SfpktoetltK+nnJdt4oae1Sfrukb0vqAP5N0lalzemlj1ml3h2SRtbG8AtJIyRtBLxg+9Hyvp8DZkt6T/O4bT9FFdydU/5Osf1kd/6RIyIiYtkN9k4EHrA9kmo3hmld1N8EGG17LDAH2Mn2FsB+QGMv2InAxwAkDQI+SJUxBMD2bOB8qq3GRtq+E7gdaASI+wOTbL9Uso2jgWuBy6gCPyStCVwA7G17BLBvufdkYJ7tzWy/G7itk2f4RKmzFbAVcJik9cq1zan2992EKqO5XQk6vwPsU7KdFwFfq7U3yHa77W8C44FPlff5cq3O94GDy9g3AgbbngFsx+vfeQewfSfjxvZlwDBgNdstd8+QdLikDkkdzzz9YqtqERERy5RlNdhrSdJVkmZJmlQrvsb2/HI8ELhA0kzgCqoACeBnwI6SVgA+DNxRu6eVC4FDyvEhVEETwG7A5HL/lcCekgYAW5d2HwSw/XipPxp4ZR2b7Sc66etDwEElm/krYA1gw3Lt17b/ZnsBMB1oA94JbArcXO45CXhbrb2J8Mo2aqvavquUX1qrcwWwWwkcDwUuLuVrA3ObxjcHWKeTcSPpbeWedSSt0lmd8tzjSgDavsqqg1pVi4iIWKbkd/bgXuCVjzFsj5HUDvx3rc6ztePjgEeAEVTB8vPlvucl3Q7sTJXxu7yrjm1PKdPCo4ABtmeVS2OB90maXc7XAD7Qk4eSNAb4cjn9JCDgaNs3NtUbBbxQK3qZ6n8uBNxrexs692yL8lfYfk7SzcAeVFnPLcul+cCQpuqDgfmS3k6V0QQ43/b5VNPZXwb+pfz38131HREREZVlNbP3NLBqOb6Uatryo7XrKy3k3iHAwyULdiAwoHZtIlWGbnvghi76bfhBGcN4qNbulfvXtd1muw34DFUAeDewQ2P6VdLqpY2bSx1K+TDbV5Xp4pG2O4AbgU/X1ghuJGnlhTznH4A1JW1T6g+U9K7mSmX93NOS3luK9m+qciHVVPdvahnH3wMbNNXbCJhl+6HauM+X9GHgn8p7+iqwl6RNiIiIiG5ZJoM9248BU8qHBKdRTZseIenPku6imrI8vcXt5wEflzQD2JjXZrhuAt4P3GK7s0Vj1wJjGh9olLJLqNajXVbOxwC32a5n234C7A48BRwOTCr9TyzXTweGlennGcCOnfR9IfA7YFp57u+xkMxuGf8+wH+WNqfT+kviT1BNbU8HVgbm1dqZWsY9vlb/DmDzpq+Mt6MKWl9R1i5+GzjSlWepsnrntBp3REREvJZs9/UYlmmS9gH2sH1gX49lUUlaxfYz5fhEYG3b/1bO16H6EGXjkg1t3HMWcK3tWyRtDhzfm+/gHesN8RdP27q3mouIiDdQtktbNJKm2m5vLs+avT4k6TtUH3N8pK/Hsph2lfTvVP/z9Bde/QL3IKoveI+vB3rF14HG1O9bqL4ojoiIiF6WzF70S+3t7e7o6OjrYURERLxhWmX2lsk1exERERHLigR7EREREf1Y1uxFv/SPJ/7IqT/aua+HERERLZz6sXyE8UZJZi8iIiKiH0uwFxEREdGPJdjrJklDJR1ZO99Q0nWSHpA0VdJkSTssrI0e9NUu6exyPEpSqx8zbr5veUlzJZ3RG+NYkiR9u/G+JF0uacNO6gwo73aHWtlNkvZ9I8caERGxNEuw131DgSPhlZ0drgfG2R5ue0vgaGD95psk9XhdpO0O28eU01G03rmi2U7A/cC+TbtTvKlIWgPY2vYdpei7wAnN9Wy/TPXOzynbtY0FFti+4o0bbURExNItwV73nQEML1uCPQTcZfuaxkXbs2xfDCDpVEkTJE0BJkhqk3SnpGnlb9tS73JJuzbakHSxpH1KNu86SW3AEcBxjS3WJD1Y2992tfo51f65ZwF/BbaptbtL6XeGpFtL2SqSxkuaKekeSXs3P7CkgyVdLelmSbMlHSXpeEm/lXR3Y29eSYdJ+k1p/0pJK5Xyn5QfVkbSpyRdUprem9fuHXwnMLqzwNj2r4C7gFOpfoj5qK7+oSIiIuJVCfa670TgAdsjgQnAtC7qbwKMtj0WmAPsZHsLYD/g7FJnIvAxAEmDgA9SZQwBsD0bOB840/ZI23dSbT3WCBD3BybZfqlkG0dT7b97GVXgh6Q1gQuAvW2PABpToCcD82xvZvvdwG0tnmNTYC9gK6rdMJ6zvTlVAHZQqTPJ9lal/d9T7ZUL1T6+p5R9gD9Llf2Eah/cqbXnXAD8CRjRYgz/DhwLXGr7Ty3qRERERCcS7PUCSVdJmiVpUq34Gtvzy/FA4AJJM4ErqAJBgJ8BO0pagWrbtDtq97RyIXBIOT4EGF+OdwMml/uvBPaUNADYurT7IIDtx0v90cC5jUZtP9Giv8m2n7Y9F5hHFUwCzATayvGmJXM5EzgAeFdp8xHgFGAy8Nla32sDc5v6mQOs02IMO5S+N21xHQBJh0vqkNTx3FMvLqxqRETEMiPB3qK5F9iicWJ7DNV+sKvX6jxbOz4OeIQqc9UODCr3PU+VqduZKuM3sauObU8B2iSNAgbYnlUujaWaCp1NlTVbA/hATx5K0pgyXTxdUmO7lRdqVRbUzhfw6u80XgwcZXsz4CvA4No9mwGP8dpAbn5THcr5/OYxSFoZ+K/yLP8kqeU+wrbH2W633b7SaoO6+9gRERH9WoK97nsaWLUcXwpsJ+mjtesrLeTeIcDDZbryQGBA7dpEqgzd9rx2HVtn/Tb8oIxhPFRr98r969pus90GfIYqALwb2EHSeqVuIyC9udShlA+zfVWZLh5puycby64KPFzWDh5Qa/M9VBnLzYHPNcZANdW7QVMbGwGzOhnDKcCPbN9H9bHGmWXKOiIiIrohwV432X4MmCJpFnAa1bTpEZL+LOku4CTg9Ba3nwd8XNIMYGNem/W7CXg/cIvtzuYerwUa2a7tS9klwDCqtXkAY4DbbNezcD8Bdgeeolo7N6n038geng4MK9PPM4Adu/UiOncy8CtgCnAfQJmavgA41PY/qNbsXVS+Er6e6itjSt21gPm2/7feqKR3lWf7GoDt3wI3Al9YjLFGREQsU2S7r8cQPSRpH2AP2wf29VgWlaRfALvZflLSccBTtr/fW+2vM3yID/+PrXuruYiI6GXZLq33SZpqu725PHvjLmUkfYdqarTl2rWlxGeBdYEny9+Evh1ORERE/5TMXvRL7e3t7ujoybLDiIiIpVurzF7W7EVERET0Ywn2IiIiIvqxrNmLfumPTz7Ah3/yuh3gIiLiDfSzPa7s6yEEyexFRERE9GsJ9iIiIiL6sQR7EREREf1Ygr0mkoZKOrJ2vqGk6yQ9IGmqpMmSduilvtolnV2OR0natpv3LS9prqQzemMcfUHSipJ+LmmApDUldbZVXOMd3StpUDkfXnYtWe2NHXFERMTSKcHe6w2l2oOVsgfr9cA428NtbwkcDazffJOkHn/sYrvD9jHldBTQrWAP2Am4H9i3bD+2NDoUmGT7ZdtzqfbW3a65Utkf9+fA50rRucCXbD/1xg01IiJi6ZVg7/XOAIZLmg48BNxl+5rGRduzbF8MIOlUSRMkTQEmSGqTdKekaeVv21Lvckm7NtqQdLGkfUo27zpJbcARwHGNPXAlPShpYKm/Wv0cGAucBfwV2KbW7i6l3xmSbi1lq0gaL2mmpHskve4TVUkHS7pa0s2SZks6StLxkn4r6W5Jq5d6wyXdUDKcd0rauJTvLulXpf4tZa/bxvu5SNLtJRt3TK3bA6j27224upR15ovAYZJOAJa3fVmLehEREdEkP73yeicCm9oeKelbwF+6qL8J8D7b8yWtBOxk+3lJGwKXAe3AROBjwPVlOvKDwKeB9wLYni3pfOAZ2/8NIOl2YFeqIGh/qizYSyXbOBr4FFUWcizwS0lrAhcAO9h+sBGgAScD82xvVtod1uI5NgU2BwYDfwK+YHtzSWcCBwHfBsYBR9j+o6T3AucBHwB+AWxt25I+CZxAtR0awMbAjsCqwB8kfRcQsL7t2bX+O4DTOxtY2T/3jNLfJi3Gj6TDgcMBBq+5YqtqERERy5QEez0g6SpgQ+B+23uV4mtszy/HA4FzJI0EXgY2KuU/A86StAKwC3BHCQ4X1t2FVEHT1cAhwGGlfDdgcrn/SuBkSccCW5d2HwSw/XipP5oqWKSUP9Giv8m2nwaeljQPuLaUzwTeLWkVqmnmK2rjXqH8923ARElrA4OAB2vtXm/7BeAFSXOAtYAFVPvh1s0B1lnI+/gw8AhVsPeHzirYHkcVkDJkg2HZBzAiIoJM43blXmCLxontMcDBwOq1Os/Wjo+jCkhGUGX0BpX7ngduB3YG9qPK9C2U7SlAm6RRwADbs8qlscBoSbOBqcAaVNm1bpM0pkwXT5fU2EPvhVqVBbXzBVT/T8FywJO2R9b+/qXU+Q5wTskefooqO9hQb/fl0tb8pjqU8/llfOPL2H5azncDhlC9v2+UDGpERER0Q4K913uaasoR4FJgO0kfrV1fWKAxBHjY9gLgQGBA7dpEqgzd9kBnX57W+234QRnDeKjW7pX717XdZrsN+AxVAHg3sIOk9UrdRkB6c6lDKR9m+6pawNaxkOd5Rfkg4kFJ+5Z2JGlE7bn/Xo4/3o22ngAGlCnpho2AWeX6IWVsH5G0IvAt4DO2Z1Kt8/tSd8YcERERCfZex/ZjwBRJs4DTqKZNjygfGNwFnESLtWVUa8o+LmkG1Vq1etbvJuD9wC22X+zk3muBRsZt+1J2CTCMau0fwBjgtjIt2vATYHfgKar1apNK/43s4enAMEmzSvmO3XoRnTsA+ERp515gj1J+KtX07lTg0W62dRPwvtr5jlRfPjc7GbjK9u9qfY0tayIjIiKiC7KztOnNStI+wB62D+zrsfQ2SVsAxzWeTdIdVM/aak1hjwzZYJi3/WaPZrcjIqKXZW/cN5akqbbbm8vzgcablKTvUH2U8JG+HsuSYHuaqh+oHkC1BvJbvRXoAWw4dHj+l0xERAQJ9t60bB/d12NY0mxfVA7nUn11HBEREb0sa/YiIiIi+rEEexERERH9WKZxo1/645MP85GrWn00HRHR//10zEl9PYR4k0hmLyIiIqIfS7AXERER0Y8l2OshSUMlHVk731DSdZIekDS1/JzIDr3UV7uks8vxKEnbdvO+5SXNlXRGb4xjSZL07cb7knR5848lS3qXpPvLThqNsusljX2jxxoREbE0SrDXc0OBIwHKdl/XA+NsD7e9JXA0sH7zTZJ6vD7SdoftY8rpKKBbwR6wE3A/sK8k9bTfN4qkNYCtbd9Rir4LnFCvY/teYBJlizRJewIDbV9GREREdCnBXs+dAQyXNB14CLjL9jWNi7Zn2b4YQNKpkiZImgJMkNQm6U5J08rftqXe5ZJ2bbQh6WJJ+5Rs3nWS2oAjgOMa26lJelDSwFJ/tfo51V65ZwF/BbaptbtL6XeGpFtL2SqSxkuaKekeSXs3P7CkgyVdLelmSbMlHSXpeEm/lXR3Yx9eSYdJ+k1p/0pJK5Xyn0g6qBx/StIlpem9ee0+wXcCozsJjE+jClxHlvf/GSIiIqJbEuz13InAA7ZHAhOAaV3U3wQYbXssMAfYyfYWwH7A2aXOROBjAJIGAR+ktk+s7dnA+cCZtkfavhO4HWgEiPsDk2y/VLKNo6n22r2MKvBD0prABcDetkcA+5Z7Twbm2d7M9ruB21o8x6bAXsBWwNeA52xvDtwFHFTqTLK9VWn/98AnSvnhwCllz9/PUmU/AbYDptaecwHwJ2BEvWPbzwGfA+4ALrf9x84GKOlwSR2SOl586tnOqkRERCxzEuz1IklXSZolaVKt+Brb88vxQOACSTOBK6gCQYCfATtKWoFqi7Q7ave0ciFwSDk+BBhfjncDJpf7rwT2LFuSbV3afRDA9uOl/mjg3EajC9mybLLtp23PBeZRBZMAM4G2crxpyVzOBA4A3lXafAQ4BZgMfLbW99pUu2fUzQHWae7c9rXAk8B5LcaH7XG22223D1pt5VbVIiIilikJ9hbPvcAWjRPbY4CDqfZ6bainmI4DHqHKXLUDg8p9z1Nl6namyvhN7Kpj21OANkmjgAG2Z5VLY6mmQmdTZc3WAD7Qk4eSNKZMF0+X1NhQ+YValQW18wW8+nuNFwNH2d4M+AowuHbPZsBjvDaQm99Uh3I+v8UYFpS/iIiI6KYEez33NLBqOb4U2E7SR2vXV1rIvUOAh8t05YHAgNq1iVQZuu157Tq2zvpt+EEZw3io1u6V+9e13Wa7jWp921jgbmAHSeuVuo2A9GZqa+AkDbN9VZkuHmm7YyHP02xV4OGydvCAWpvvocpYbg58rjEGqqneDZra2AiYtRhjiIiIiJoEez1k+zFgiqRZVB8O7AYcIenPku4CTgJabd1wHvBxSTOAjXlt1u8m4P3ALbZf7OTea4FGtmv7UnYJMIxqbR7AGOA22/Us3E+A3YGnqNbOTSr9N7KHpwPDyvTzDGDHbr2Izp0M/AqYAtwHUKamLwAOtf0PqjV7F5WvhK+n+sqYUnctYL7t/12MMURERESNbPf1GGIRSdoH2MP2gX09lkUl6RfAbraflHQc8JTt7y9uu0M2eKu3+8anF3+AERFLqWyXtuyRNNV2e3N59sZdSkn6DtXU6Ef6eiyL6bPAulQfXzxJ9YVzRERE9JJk9qJfam9vd0dHlvpFRMSyo1VmL2v2IiIiIvqxBHsRERER/VjW7EW/9Mcn57LrpO/29TAilhrX75UPmiL6q2T2IiIiIvqxBHsRERER/dgyHexJGirpyNr5hpKuk/SApKmSJkvaoZf6apd0djkeJWnbbt63vKS5ks7ojXG82UjaU9Ip5fgoSYc2XR8s6T5Jm9XKPi/pe2/0WCMiIpZGy3SwBwwFjoQqqKDa0WGc7eG2twSOBtZvvklSj9c62u6wfUw5HQV0K9gDdgLuB/Ytu068KS3KOylOoNpZBOAiqnf+irJv8LHAeaq8FTgCOHFRxxoREbEsWdaDvTOA4ZKmAw8Bd9m+pnHR9izbFwNIOlXSBElTgAmS2iTdKWla+du21Ltc0q6NNiRdLOn/s3ff4XZV1frHv6+hBCmhiAgqHkqQS0uAA5duaIKAQiQYcimCFAGlKbafIKigWK5KETCAQUKEXCRBivROqCdAGlGukFwRkCKBUEIL7++POTZZ2dmnJJxwkp3xeZ48rDXXXHPNtcLzMBhzrT0GRTbvGkktlGDl+FrpM0lTop4skpar7lPq2p4B/APYsjLurnHdcZJuibZlJA2TNEHSeEl719+wpF6SfiHpwejz1WgfIOl2SX+KTNqIWnApaVNJd0S28wZJq0b77ZJ+I6kNOFbSZjHmI3GNidHvTkn9K3O4W1I/SesAb9p+IZ7368DUqKX7HtvXA88ABwK/Bk6xPa2rf8kppZTSomxR/xr3u8AGtvtL+hXwf530Xw/YxvYMSR8Gdrb9hqS+lPq0rZSas18CrpW0BLAjcCTwnwC2p0o6D3jV9i+hBE3A7sCVwL7AKNtvR7ZxJ+CrlCzkEOAeSStT6s1uZ3uKpBVjficBL9veMMZdocE9HBJ9Nou6tWMk3RjHNgbWB56m1LfdWtL9wFmUsmzPSxoMnAbUlluXqP2AYwR3h9m+t27Z+ULgIOC4CPB62x4n6WDgobr5tQHbAg/UtR8Xbf9rO6tspJRSSl20qGf22iVptKSJkkZVmq+yPSO2FwfOlzQBuJwSCAJcB2wfgdTngDsr57TnAuDg2D4YGBbbewC3xflXAHtJ6gVsEeNOAbD9YvTfCfhtbdB2sl+fBQ6MbOb9wEpA3zj2gO1/2n4XeARoAT4NbADcFOecCHyiMt5IKO8/Asvavjfa/1jpczmwR2QrvwJcFO2rAs/Xze85YLX6Sdt+GrgVaPf3VCQdLqlNUttbL7/aXreUUkppkbKoZ/aqJgHvfYxhe6CkVuCXlT6vVbaPB54F+lGC5jfivDciU7cLMBi4rLML2x4Ty8IDgF62J8ahIcA2kqbG/krADnNzU5IGAifH7qGAgKNt31DXbwDwZqVpJuXfDwGTbG9JY6+10/4e269LugnYk5L13DQOzQD61HXvDcyQ9Eng6mg7z/Z5wLvxp73rDAWGAvRZ+1NZBzCllFIiM3uvAMvG9h8py5ZfqBz/cAfn9gGeiSzYAUCvyrGRlAzdtsD1nVy35uKYwzAo7+7F+avbbrHdAnyNEgDeB2wnaY3oW1vGvSn6EO0r2B5tu3/8aQNuAI6svCO4jqSlO7jPvwErS9oy+i8uaf36TrZfAl6R9J/RtG9dlwuAM4EHKxnHycDadf3WASbafrIy7/M6mF9KKaWUOrBIB3u2/015Z20i8CPKsukRkp6QdC9lyfLUdk4/B/iypHHAusye4boR+Axws+23Gpx7NTCw9oFGtI0AVqC8+wcwELjVdjXb9mfg88B04HBgVFx/ZBw/FVghlp/HAds3uPYFwKPAQ3Hfv6ODDG/MfxDwsxjzEdr/kvgQytL2I8DSwMuVccbGvIdV+t8JbFz7ECRsTQlaU0oppdQNZOdq14JA0iDKRxAH9PRc5pWkZWy/GtvfBVa1fWzsrwbcDqwb2dDaOWcAV9u+WdLGwDe64xn0WftT3ubn+essKXVVlktLaeEnaWzto8mqfGdvASDpLMrHHLv19Fzep90lfY/y79X/Ub7ARdKBlC94v1EN9MJPiC+VgY9QvihOKaWUUjfJzF5qSq2trW5ra+vpaaSUUkofmPYye4v0O3sppZRSSs0ug72UUkoppSaW7+ylpvT3aS+yx59G9PQ0UvrAXTNov56eQkppAZOZvZRSSimlJpbBXkoppZRSE3tfwZ6kYyRNlvSUpOfjR4InSfqTpI6qTzQaq0vFTCX9Jq43T3OXdIqkE7r7nCh3NrGjPvNK0u1Ruq294xdJmhLP/xFJ98yPeXRV/B1tF9uXSerbTr+VJN0m6VVJZ3cw3o6SHop7u1tSfdWNlFJKKbXj/Wb2jgJ2Br4PjIzSVusDb1HqwnarCPAGAk9SKlTMd5IWlvcav1UpL9ZehYu5IqlX573mOGclYAvbd0bTucC32+n+BuV39ToLvs8F9rPdn1JS7sS5nVdKKaW0qJrnYE/SecCawHWUMl+19sUopbKmxX6LpFsljZd0i6TVo30NSfdKmiDp1Mr5F0vaq7I/QtKesTsAmET5j/+QSp9VJI2WNC7+bBXtB8Z1x0ka3uAe1pJ0vaSxku6StG60XyTpPEn3Az+vO+cwSddJWqrBY1ks5ju5mt2U9ANJD0YZs6G18mCRsfuZpAckPVYrnSZpqciITZY0Glgq2nvF3CbGczu+k7+jUyT9Pq7zhKRjKsf2j+s+Iul3tcAusmz/rVIabUtJh8TcHpB0vqSzJS0bmcRafd3lKvt7M3s94LuAnRoFzbZfs303JejriIHlYrsP8HQn/VNKKaUU5jnYs30E5T+621MCu8EqNVGfAlak1H8FOAv4g+2NKPVfz4z2M4BzbW8IPFMZ+kJmVV7oQ6nDem0cG0KpHTuaUq1h8Wg/E7jDdj9gE2CSpPUpGaAdov3YBrcxFDja9qaU7NI5lWOfALay/Y1ag6SvU+rn7mV7RoPxPg2cY/s/KHVgj4r2s21vZnsDSuC2R+WcxWxvDhwHnBxtRwKvxzgnA5tGe3/g47Y3iOdWrTP7i8oybvUz1HWBXYDNgZMlLS7pPyiZ160jWzYTqH3CtzRwfzyzJyiZty0oNWvXBbD9CqX02e5xzr7AKNtvR7+xtYtHxYy/A/0aPK+uOhT4i6R/AgcAp7+PsVJKKaVFSnd+oDEyAoePAROAb0X7lpSlN4DhwDaxvTUlcKu1A2D7DqCvpJUpwd0Vtt+RtASlnNiVtqcD91OCGIAdKNk+bM+0/XK0XW77hWh/sTpZSctQAsnLI0j9HbBqpcvltmdW9g+klDQbZPvNdp7Bk7bHxPYllXvdXtL9kibEvNavnDMq/jkWaInt7eJ8bI8Hxkf7E8Caks6StCsloKypLuNWf3vhWttvxnN4DlgF2JESQD4Y974jJUsLJfC7IrY3pwTRL0Ygd3ll3AuAg2P7YGYFnqsCz9c9l+eA1Zh3xwO72f5EXOdXjTpJOlxSm6S2t6ZPb9QlpZRSWuR0+9e4LvXXrqYELJ12b6f9YmB/ShDx+2jbBVgemCBpKiWQGtLw7K75EPBSJUDqH5m0mtfq+k+gBGOfAJD0yUom7Yh27seSelMyhoMiG3c+0LvSpxY4zqST3z20PY2SQLt+2wAAIABJREFUIbsdOIIScHWmGpjWriFKtrV235+2fUr0eaMuyG1vLmOAFkkDgF62ax+nzGD2+yP2Z0gaWHlm7X5wUhVBfz/b90fTSEqQ3mhOQ2232m5dYrnlGnVJKaWUFjnz66dXtgEej+17KMt8UJYK74rtMXXtVRdRljWx/Wi0DQEOtd1iuwVYA9g53ou7hbL0WXuvrQ9wK7BPfDCApBWrF4js4BRJ+8RxSepoqfFh4KvAVZJWs/1kJVg6L/qsLmnL2P4v4G5mBT4vRDZxUAfXqLkzzkfSBsBGsf0R4EO2r6AsUW/ShbEauQUYJOmjMe6Kkj7VoN+DwGckrRDv3O1dd/xiSta2upw8Gaj/WnYdYKLt0ZVn1mHhWpV3NzenvCLQR9I6cWjnuEZKKaWUuqA7g73BkbEZD2wM/DjajwYOjvYDmPXu3LHA12Jp8+PVgWw/S/kP+jCACOh2Zda7e9h+jRJMfT7G2j7GGgusZ3sScBpwR3xsUFv6W4xZ2a79gEPi+CSg9iFIQ/ExwQnAtRF41ftb3NNkykcr59p+iZLNmwjcQAmgOnMusEyM8yNmvQP3ceD2WHq9BPhe5ZzqO3uPxLJ3e/fxKCVYvDH+Xm5i9iXsWr+ngJ8AD1CC86nAy5UuI+I+L620XUv5kAYoH88AM2z/q9FcIkv7K+AgSf+UtF4c2gh42vY7wGHAFfH3dACzXhFIKaWUUidUVl0XLBHcTQA2iffvunPs0cD5tv/SneM2K0nL2H41Mnujgd/bHh3HBgF72j6g7py7gT1svxRfDE+3feFcXHM54ELb+8zrvJdfa01v87Mfd94xpSaT5dJSWnRJGmt7jtekFrgKGpJ2omT1zpoPgd4E4F3gxu4ct8mdEpnEicAU4EoASWdRvoptFFF9E1g9tl8C/jA3F7Q9/f0EeimllFKaZYHM7KX0frW2trqtrcPXAlNKKaWmstBk9lJKKaWUUvfJYC+llFJKqYllsJdSSiml1MQ6/BHflBZWf5/2Ml/409Wdd0xpPrpq0Od7egoppZSZvZRSSimlZpbBXkoppZRSE8tgr4skLS/pqMp+X0nXSHpc0lhJt0nqSj3grlyrVdKZsT1AUsNasA3OW0zS85JO7455zE+SflN7XpIuk9S3nX6n1VUGeUzSzCg9l1JKKaVOZLDXdcsDRwFI6k0pCzbU9lq2N6WUhVuz/qSoPDFXbLfZPiZ2BwBdCvYodWMfo9QE1txe94MS9Yq3sH1nNJ0LfLtRX9vfr9TT7U8pN/dT269+QNNNKaWUFmoZ7HXd6cBaUU3iSeBe21fVDtqeaPsiAEmnSBouaQwwXFKLpLskPRR/top+l0navTaGpIskDYps3jWSWoAjgOMjq7WtpCmSFo/+y1X3gSHAGcA/gC0r4+4a1x0n6ZZoW0bSMEkTJI2XtHf9DUs6SNKVkm6SNFXS1yV9Q9LDku6TtGL0O0zSgzH+FVHuDkl/lnRgbH9V0ogYem/g+sql7gJ26iwwlrQ/sDZwSkf9UkoppTRLBntd913g8cguDQce6qT/esBOtocAzwE7294EGAycGX1GAl8CkLQEsCMlYwiA7anAecCvI7N1F3A7UAsQ9wVG2X47so07AVcDl1ICPyStDJwP7G27H1ArQ3YS8LLtDW1vBNzazn1sAHwR2Aw4DXjd9sbAvcCB0WeU7c1i/MnAIdF+OPADSdtSSqgdHe1bA2Mr9/ku8HegX3sPMwLf04H9bL/TTp/DJbVJantrerdW2ksppZQWWhnsdQNJoyVNlDSq0nyV7RmxvThwftTmvZwSCAJcB2wvaUngc8CdlXPacwFwcGwfDAyL7T2A2+L8K4C9JPUCtohxpwDYfjH67wT8tjao7WntXO8226/Yfh54mRJMAkwAWmJ7g8hcTgD2A9aPMZ8FfgDcBnyzcu1VgefrrvMcsFqjCcR9XAKcZPvv7cwT20Ntt9puXWK5Pu11SymllBYpGezNm0nAJrUd2wOBg4AVK31eq2wfDzxLyVy1AkvEeW9QMnW7UDJ+Izu7sO0xQIukAUAv2xPj0BDKUuhUStZsJWCHubkpSQMrH0LUauu9WenybmX/XWb9TuNFwNdtbwj8EOhdOWdD4N/MHsjNqOtD7M9oZw4nAs/YHkZKKaWU5koGe133CrBsbP8R2FrSFyrHP9zBuX0owcq7wAFAr8qxkZQM3bbM/h5bo+vWXBxzGAbl3b04f3XbLbZbgK9RAsD7gO0krRF9awHpTdGHaF/B9ujKxxBtHdxPvWWBZ+Ldwf0qY25OyVhuDJxQmwNlqXftujHWASbWz0HSFpRA+vC5mE9KKaWUQgZ7XWT738AYSROBH1GWTY+Q9ISkeynZp1PbOf0c4MuSxgHrMnvW70bgM8DNtt9qcO7VQC3btW20jQBWoLybBzAQuNV2NQv3Z+DzwHRKoDQqrl/LHp4KrBDLz+OA7bv0IBo7CbgfGAP8FSCWps8HvmL7aco7e7+Pr4SvpXxlTPRdBZhh+18Nxv4hJZC+re4nWNZ6H/NNKaWUFhmy3dNzSHNJ0iBgT9sH9PRc5pWku4E9bL8k6Xhguu0Lu2v85dfq6+1+9qvuGi6leZLl0lJKHyRJY2231rdnbdyFjKSzKEuju/X0XN6nbwKrAy/Fn+E9O52UUkqpOWVmLzWl1tZWt7XNzWuHKaWU0sKtvcxevrOXUkoppdTEMthLKaWUUmpi+c5eakqPT3uVgVfc3dPTSE1q9N7b9PQUUkqpyzKzl1JKKaXUxDLYSymllFJqYk0R7ElaXtJRlf2+kq6R9LiksZJuk7RdN12rVdKZsT1A0lZdPG8xSc9LOr075jE3JH1M0mWV5/EXSevM41jHSJosaYSkJSXdHD9yPDiO/0nSmrF9s6QV2hlnWN2PJE+V9GyDfvtJGi9pgqR7JPWbl3mnlFJKi6qmCPaA5YGjACT1plRoGGp7LdubAkcDa9afJGmu31m03Wb7mNgdAHQp2AN2Bh4D9okqEvNdBJgCRgO3V57H94BV5nHYo4Cdbe9HKYNGlDYbKWl9Sr3eJ6Lv8Og/B9sH18qiUeoM/wP4foOuU4DPRN3dHwND53HeKaWU0iKpWYK904G1JD0CPAnca/uq2kHbE21fBCDpFEnDJY0BhktqkXSXpIfiz1bR7zJJu9fGkHSRpEGRzbtGUgtwBHB8rZSZpClRHxZJy1X3KXVqz6AENVtWxt01rjtO0i3RtkxkviZEVmvv+hvuYN4Dov0q4FFKGbS3bZ9XeR7jbN+l4hdRMm1CLTsX43xL0oNx/R9G23mUoPk6Sd8BLgE2q5Qv249Spq3mqrjvzvw/4HnbF9QfsH2P7Wmxex/wiS6Ml1JKKaXQLF/jfhfYwHZ/Sb8C/q+T/usB29ieIenDlEzVG5L6UurNtlJqyH4JuFbSEsCOwJHAfwLYnhrBz6u2fwkg6XZgd+BKYF9glO23I9u4E/BVShZyCHCPpJUp9WO3sz1F0ooxv5OAlyObRTtLoc+1M28ombINYsxjgLHtPIcvAv2BfsBHgAcl3QlsCPQFNgcEXCVpO9tHSNoV2N72C5LuB06wvUfMc2tm1evF9rRY6l0pagvPQdLmwKEx584cAlzX3kFJh1PqALPUR+Y1cZlSSik1l2bJ7LVL0ujIXI2qNF9le0ZsLw6cL2kCcDklEIQSVGwvaUlKebI7K+e05wLg4Ng+GBgW23sAt8X5VwB7SeoFbBHjTgGw/WL03wn4bW3QSmarqr15AzxQG7MT2wCX2p5p+1ngDmAz4LPx52HgIWBdSvDXmVWB5+vangNWa9RZ0jKU7OAhlXtvSNL2lGDvO+31sT3Udqvt1iWXW74L000ppZSaX7Nk9qomAe99jGF7oKRW4JeVPq9Vto8HnqVktz4EvBHnvRGZul2AwcBlnV3Y9phYXh1AeXdtYhwaAmwjaWrsrwTsMDc3JWkgcHLsHkoJIOeYd4P7mwQMmptrUbJ5P7X9u7k8bwbQu66tNzBD0teAw6JtN9tPA2cBf7Z9S4eTkTaiBNKfay9DmFJKKaXGmiWz9wqwbGz/Edha0hcqxz/cwbl9gGdsvwscAPSqHBtJydBtC1zfyXVrLo45DIPy7l6cv7rtFtstwNcoAeB9wHaS1oi+tWXcm6IP0b6C7dG1Dxpst3Uy76pbgSVjibM23kaStgXuAgZL6hVLytsBDwA3AF+JzBuSPi7po+2MXzUZWLtyHQEfA6ba/m1l/k9LGkQJVOf4KEPSQEk/je3VgVHAAbYf68IcUkoppVTRFMFeZHvGSJoI/IiS9TpC0hOS7gVOBE5t5/RzgC9LGkdZrqxmxW4EPgPcbPutBudeDQysfaARbSOAFZj17tpA4Fbbb1bO+zPweWA65R2zUXH9kXH8VGCFWH4eR/nIYm7m/R7bjjnspPLTK5OAnwL/onylOx4YRwkKv237X7ZvpASs98Yy8Z+YM6ht5FrKF8o1mwL32X6nQd/TgJWBBzT7T7AsBawVzwbgB5RM6DlxvK0L80gppZRSUIkFUneJjNWetg/o6bl80CJQuw3Y2vZMSWdQ3o/scJm2wTiXAMfbrn//r8tWWGtdD/j5HB/3ptQtslxaSmlBJGms7db69mZ8Z6/HSDqL8jHHbj09l54QXzefDHyc8hMzE+c20Itx9u/2yaWUUkqLqMzspabU2trqtrZc8U0ppbToaC+z1xTv7KWUUkoppcYy2EsppZRSamL5zl5qSk+89CaDR/29p6eRFiIjv7h2551SSmkhlJm9lFJKKaUmlsFeSimllFITy2BvLkg6RtJkSU9Jej5+5HeSpD9J6qhKR6OxXu1iv9/E9ebp70rSKZIsqVrZ4rhom+OLnZ4W8z2hsv8bSdvF9mWSulKjN6WUUkohg725cxSwM6XE18go/bU+8Balfm63igBvIPAkpZLHvJoA7FvZ34dSM3eBJmklYAvbd0bTucC3e3BKKaWU0kIng70uknQesCZwHaUcWq19MWBpYFrst0i6VdJ4SbdEbVckrSHpXkkTJJ1aOf9iSXtV9kdI2jN2B1CCsnMptXRrfVaRNFrSuPizVbQfGNcdJ2l4ZfpXAntGn7WAl4EXKuOdK6ktspQ/rLTvJumvksZKOlPSNe08m6mSflorZyZpE0k3RHm2I6LPMvE8HopnsGfl/O9LekzS3cCnK0Pvzew1ie+ilH3LD4tSSimlLspgr4tsHwE8TalTOw0YLOkR4ClgRUqdXICzgD/Y3ohSJ/fMaD8DONf2hsAzlaEvBA4CkNQH2IpSYxZKgHcppYbt7pIWj/YzgTts9wM2ASZJWp9SA3iHaD+2co3pwJOSNqBk+EYyu+/HjzBuBHxG0kaSegO/Az5ne1NKHduO/MN2f0pAdhEwCNgCqAWPbwADbW8Sz/C/VWwac+pPqTyyWWXMrYGxtR3b7wJ/B/p1MpeUUkophQz25t3ICG4+Rlkm/Va0bwn8MbaHA7UimltTArdaOwC27wD6SlqZEtxdYfsdSUtQgp8rbU8H7gd2idN2oGT7sD3T9svRdrntF6L9xbr5XkYJqvaiBI9VX5L0EPAwsD6wHrAu8ITtKdHnUjp2VfxzAnC/7Veitu2bkpYHBPxE0njgZkpJtVWAbYHRtl+P+7yqMuaqQH193OeA1RpNQNLhkVlse/Pl+ttPKaWUFk0Z7L1PLvXmrga260r3dtovBvYHDgZ+H227AMsDEyRNpQSNQxqe3TXXAAdQMnDTa42S1gBOAHaMbOS1QO+OBool2kckXVBpfjP++W5lu7a/GLAfJTu4aQTJz3Z2HWBGgz69o30OtofabrXdumSfFTsZOqWUUlo0ZLDXPbYBHo/te5j1McR+lGVNgDF17VUXAccB2H402oYAh9pusd0CrAHsHF/93gIcCSCpVyz/3grsEx81IGm2aMf268B3gNPqrr0c8BrwsqRVgM9F+9+ANSW1xP57H6DY3iU+Tjm03Scypz7Ac7bflrQ98KlovxPYS9JSkpYFPl85ZzJQ/0u36wAT5+K6KaWU0iItg715NziyW+OBjYEfR/vRwMHRfgCz3p07FviapAmUJcz32H6WEtgMA4iAbldmvbuH7deAuynB0LHA9jHWWGA925MogdwdksYBv6qfsO3LbD9U1zaOsnz7V8ry85hon0H5+vh6SWOBVygfdsyrEUBrzPnAuB4xn5HAOMrHLw9WzrmW8pEKUD5MAWbY/tf7mEdKKaW0SFFZhUw9KYK7CcAm8f7dAkHSMrZflSTgt8D/2v71BzyHu4E9bL8k6Xhguu0LOztvxbU39M4/r381MaX2Zbm0lNLCTtLY+OByNpnZ62GSdqJk9c5akAK9cFh8cTyJsgz7ux6YwzeB1WP7JeAPPTCHlFJKaaGVmb3UlFpbW93W1tbT00gppZQ+MJnZSymllFJaBGWwl1JKKaXUxDLYSymllFJqYlljNDWl5156m9+Ofranp5EWYF8buEpPTyGllD4QmdlLKaWUUmpiGeyllFJKKTWxDPbmE0ktkjot6yVpVUnXxPZpUZWj9ucxSTMlLTP/Z/zefC6QtF5s/78u9G+RZEmnVto+IultSWfPh/ndLGmF7h43pZRSalYZ7PW8bwDnA9j+ftSc7W+7P6V02E9tvzovA0vqNbfn2D60Up+302AvTAF2r+zvQ/kh5vlhOKWMW0oppZS6IIO9BiRdKWmspEmSDo+2VyPzNk7SfVGnFUlrxf4ESadKmiMwk9RL0i8kPShpvKSvVg7vDVzf4Jz9gbWBUxoc+7Ck/5H0qKTRku6X1FqZ539HfdwtJf0grjtR0lAV60p6oDJeS9SsRdLtklolnQ4sFRnGEZJ+JOm4yjmnSarV/X0dmFybAzAY+J9K35UlXRHzeFDS1tG+uaR7JT0s6R5Jn472gySNknS9pP+V9PPK7V8FDGn4F5dSSimlOWSw19hXbG8KtALHSFoJWBq4z3Y/4E7gsOh7BnCG7Q2Bf7Yz3iHAy7Y3AzajlCFbQ9IawDTbb1Y7S2oBTgf2s/1Og/GOivPWA04CNq0cWxq433Y/23cDZ9vezPYGwFKUOrN/BZaI60MJzkZWL2D7u8CMyDLuB/weODDm9yFgX+CSyimXAftK+iQwE3i6cuwM4Ndx/3sDF0T7X4FtbW8M/AD4SeWc/jGvDYHBMS62pwFLxt/JbCQdLqlNUtur019s8NhSSimlRU/+9Epjx0gaGNufBPoCbwHXRNtYYOfY3hLYK7b/CPyywXifBTaSNCj2+8SYrwLPVzvG0uslwEm2/97O/LahBFDYnihpfOXYTOCKyv72kr4NfBhYkbK8ejUl8zaYElQOjj/tsj1V0r8lbQysAjxs+9+Slo0u1wM/Bp6lLnAEdgLWk1TbXy7eQ+wD/EFSX8DA4pVzbqnVCpb0KPAp4Mk49hywGvDvujkOBYYCrL52v6wDmFJKKZHB3hwkDaAEJ1vafl3S7UBv4G3PKiQ8k7l7dgKOtn1D3bU2jrGrTgSesT1sHqYP8IbtmTF+b+AcoNX2k5JOqVxvJHC5pFGAbf9vF8a+ADgI+Bgl0/ce229JGgt8E1gP+ELl8IeALWy/UT0nPuC4zfbAyGbeXjlczXbWP+/ewIwuzDellFJa5OUy7pz6UJZIX5e0LrBFJ/3voyxNQlnabOQG4EhJiwNIWkfS0sBjQEutk6QtKMHU4fUDxPttF8fuGOBL0b4eZamzkVpg90Jk0mqZRWw/TgmiTmLOTFzN27U5h9HArpSl6Bsa9P9v4Du269dQbwSOrtxL/9jsAzwV2we1M4fZqKQHPwZM7Ur/lFJKaVGXwd6crgcWkzSZssR5Xyf9jwO+EUupawMvN+hzAfAo8JDKz7H8DljM9mvA45LWjn4/pCy33qbZf4JlLWB1ZmWzzgFWjuXNUylLs3Nc1/ZLlC99J1KCswfruowE9qfyMUWdocB4SSNivLeA24D/qWUP6643yfYfGoxzDNAaH6c8ChwR7T8HfirpYbqeKd2U8u5ko3cZU0oppVRHs1Ym07yQ9GHKhwyWtC8wxPaec3H+QGBT2yd20u8XwHDb4+O9vsVtvxGB4M3ApyMYm2/iw4yHgH26uOw7P+ZwBnCV7Vs66rf62v38nV/c+AHNKi2MslxaSqnZSBpru7W+Pd/Ze/82Bc6O5cWXgK/Mzcm2Rzf6srRBv29VdmvZv8Up7wMe9QEEeutRPlAZ3VOBXpjYWaCXUkoppVkys5eaUmtrq9va2np6GimllNIHpr3MXr6zl1JKKaXUxDLYSymllFJqYvnOXmpKL097h+tGvtDT00gLkM8N/khPTyGllHpEZvZSSimllJpYBnsppZRSSk2sqYI9SctLOqqy31fSNZIelzRW0m2Stuuma7VKOjO2B0jaqovnLSbpeUmnd8c85oakj0m6rPI8/iJpnXkc6xhJkyWNkLSkpJvjB6AHx/E/SVoztm+WtELd+TtLujd+sgZJvSQ9XP8cJa0b/d6UdMK83XlKKaW06GqqYA9YHjgK3qsLey0w1PZatjellOxas/4kSXP97qLtNtvHxO4AoEvBHrAzpUzaPrVAZ36LAFOUcme3V57H94B5/WXZo4Cdbe8HbAxgu7/tkZLWB3rZfiL6Do/+77F9E/B/wCHRdDTQZvueuuu8SKnA8ct5nGdKKaW0SGu2YO90YC1JjwBPAvfavqp20PZE2xcBSDpF0nBJY4Dhklok3SXpofizVfS7TNLutTEkXSRpUGTzrpHUQin/dXxktraVNKVSB3e56j4wBDgD+AewZWXcXeO64yTdEm3LSBomaUKUGqvV4KVyXnvzHhDtV1FKtW0PvG37vMrzGGf7LhW/kDQxrjW4Mv63JD0Y1/9htJ1HCZqvk/Qd4BJgs0ppt/2AP1emeVXcd73jge9FcPh14Dv1HWw/Z/tB4O0G56eUUkqpE832Ne53gQ1s95f0K0rmqCPrAdvYnhFlz3aOEmR9gUuBVkr92C8B10paAtgROBL4TwDbUyP4edX2LwEk3Q7sDlwJ7AuMsv12ZBt3Ar5KyUIOAe6RtDKlhu12tqdIWjHmdxLwsu0NY9zZlkLDc+3MG2CTeB5TJB0DjG3nOXwR6A/0Az4CPCjpTmBDoC+wOaVSx1WStrN9hKRdge1tvyDpfuAE23vEPLeOeRDPaFos9a5k+9+V9mck/Qa4FzjG9ovtzK9LJB0OHA7w0Y984v0MlVJKKTWNZsvstUvS6Mhcjao0X2V7RmwvDpwvaQJwOSUQBLgO2F7SksDngDsr57TnAuDg2D4YGBbbewC3xflXAHup1LndIsadAlAJenYCflsb1Pa0Btdqb94AD9TG7MQ2wKW2Z9p+FrgD2Az4bPx5mFITd11K8NeZVYHn69qeA1Zr0Pe3lCXfi7owbodsD7Xdart1ueU6rUCXUkopLRKaLbNXNQl472MM2wMltTL7u1+vVbaPB56lZLc+BLwR570RmbpdgMHAZZ1d2PaYWF4dQAlkJsahIcA2kqbG/krADnNzU5IGAifH7qGUAHKOeTe4v0nAoLm5FiWb91Pbv5vL82YAvevaegMzJH0NOCzadrP9tKSs2ZdSSinNJ82W2XsFWDa2/whsLekLleMf7uDcPsAztt8FDgB6VY6NpGTotgWu7+S6NRfHHIZBeXcvzl/ddovtFuBrlADwPmA7SWtE39oy7k3Rh2hfwfbo+BCiv+22TuZddSuwZCx11sbbSNK2wF3A4PgidmVKkPwAcAPwFUnLRP+PS/poO+NXTQbWrlxHwMeAqbZ/W5n/041OljRQ0k+7cJ2UUkopdaKpgr14H2yMpInAjyhZryMkPSHpXuBE4NR2Tj8H+LKkcZTlympW7EbgM8DNtt9qcO7VwMDaBxrRNgJYgVnvrg0EbrX9ZuW8PwOfB6ZT3jUbFdcfGcdPBVaI5edxlI8s5mbe77HtmMNOKj+9Mgn4KfAvyle644FxlKDw27b/ZftGSsB6bywT/4k5g9pGrqV8oVyzKXCf7Xe6cC7AWpRnUvu5mH8C3wBOlPTPCJxTSiml1AUqMUDqbpIGAXvaPqCn5/JBk7QUcBuwte2Zks6gvB95SxfPvwQ43nb9e39d1net/j7zJzfP6+mpCWW5tJRSs5M01nZrfXszv7PXYySdRfmYY7eenktPiK+bTwY+TvmJmYldDfTi/P3n2+RSSimlRUxm9lJTam1tdVtbW09PI6WUUvrAtJfZa6p39lJKKaWU0uwy2EsppZRSamL5zl5qSq+/8A4PX/BcT08jLSA2PrQrvxiUUkrNKTN7KaWUUkpNLIO9lFJKKaUm1mmwJ+kYSZMlPSXp+fjh4EmS/iSpo4oUjcZ6tYv9fhPXm6dgVNIpkk7o7nOiBNrEjvrMK0m3Rzm39o5fJGlKPP9HJN0zP+bRVfF3tF1sXyapYc1cSStJuk3Sq5LO7mC8r0v6uyRL+kjdsQGVf+/u6N47SSmllJpbV4Kpo4Cdge8DI6PM1frAW5Rasd0qAryBwJOUqhXznaSF5d3Fb1VKjW3VHQNKaq+8WkfnrARsYfvOaDoX+HY73d8ATgI6C77HADsB/1d3reUpVUK+EP/e7TO3800ppZQWZR0Ge5LOA9YErqOU/qq1LwYsDUyL/RZJt0oaL+kWSatH+xqS7pU0QdKplfMvlrRXZX+EpD1jdwAwiRJADKn0WUXSaEnj4s9W0X5gXHecpOEN7mEtSddLGivpLknrRvtFks6TdD/w87pzDpN0XVSCqLdYzHdyNbsp6QeSHozSZkOjHmwtY/czSQ9IeqxWTk3SUpERmyxpNLBUtPeKuU2M53Z8J39Hp0j6fVznCUnHVI7tH9d9RNLvaoFdZNn+W6XE2paSDom5PSDpfElnS1o2MomLxznLVfb3ZvYawXdRyrDNETTbfs323ZSgr122H7Y9tcGh/wJG2f5H9MuvLlJKKaW50GGwZ/sI4GlKTdZpwGBJjwBPAStSasICnAX8wfZGlJqq9q33AAAasklEQVSwZ0b7GcC5tjcEnqkMfSFwEICkPsBWlHqqUAK8Syn1WnevBRsx5h22+wGbAJMkrU+pd7tDtB/b4DaGAkfb3pSSXTqncuwTwFa2v1FrkPR1Sk3dvWzPaDDep4FzbP8HpX7rUdF+tu3NbG9ACdz2qJyzmO3NgeOAk6PtSOD1GOdkSv1YgP7Ax21vEM9tWGWcX1SWcUdU2tcFdgE2B06WtLik/6BkXre23R+YCewX/ZcG7o9n9gQl87YFsHWMhe1XgNuB3eOcfSlB19vRb2zt4rbfBf4O9GvwvN6vdSj1gW+PgP3A+XCNlFJKqWnN7TtxIyNw+BgwAfhWtG8J/DG2hwPbxPbWlMCt1g6A7TuAvpJWpgR3V9h+R9ISlBJjV9qeDtxPCWIAdqBk+7A90/bL0Xa57Rei/cXqZCUtQwkkL48g9XfAqpUul9ueWdk/kFLmbJDtN9t5Bk/aHhPbl1TudXtJ90uaEPNav3LOqPjnWKAltreL87E9Hhgf7U8Aa0o6S9KulICyprqMu1+l/Vrbb8ZzeA5YBdiREkA+GPe+IyVLCyXwuyK2N6cE0S9GIHd5ZdwLgINj+2BmBZ6rAvV1a58DVqP7LUa5j90p/y6cJGmdRh0lHS6pTVLbtFf+PR+mklJKKS185ukDCJcaa1dTApZOu7fTfjGwPyWI+H207QIsD0yQNJUSSA1peHbXfAh4qRIg9Y9MWs1rdf0nUIKxTwBI+mQlk3ZEO/djSb0pGcNBkY07H+hd6VMLHGfSyW8b2p5GyZDdDhxBCbg6Uw1Ma9cQJdtau+9P2z4l+rxRF+S2N5cxQIukAUAv27WPU2Yw+/0R+zMkDaw8s3Y/OJkL/wRuiOXgF4A7aSeDaHuo7VbbrSssu1I3XDqllFJa+L2fn17ZBng8tu+hLPNBWSq8K7bH1LVXXURZ1sT2o9E2BDjUdovtFmANYOd4L+4WytJn7b22PsCtwD7xwQCSVqxeILKDUyTtE8clqaOlxoeBrwJXSVrN9pOVYOm86LO6pC1j+7+Au5kV+LwQ2cRBHVyj5s44H0kbABvF9keAD9m+grJEvUkXxmrkFmCQpI/GuCtK+lSDfg8Cn5G0Qrxzt3fd8YspWdvqcvJkYO26fusAE22PrjyzDovTqry7uXkn9/FnYBtJi8W/B/8Z108ppZRSF8xtsDc4MjbjgY2BH0f70cDB0X4As96dOxb4Wixtfrw6kO1nKf/RHgYQ/yHflVnv7mH7NUow9fkYa/sYayywnu1JwGnAHfGxwa/i1MWYle3aDzgkjk8Cah+CNBQfE5wAXKu6nwAJf4t7mkz5aOVc2y9RsnkTgRsoAVRnzgWWiXF+xKx34D4O3B5Lr5cA36ucU31n75FY9m7vPh6lBIs3xt/LTcy+hF3r9xTwE+ABSnA+FXi50mVE3OellbZrKR/SAOXjGWCG7X81mktkaX8FHCTpn5LWi0MbUd4Jrf3Ezz8pWdXxki6I+U2mfAwyPuZ4QSXDmFJKKaVOqKzI9sCFS3A3Adgk3r/rzrFHA+fb/kt3jtusJC1j+9XI7I0Gfm97dBwbBOxp+4C6c+4G9rD9UnwxPN32hXNxzeWAC23Pl59SWa+lv0eceOP8GDothLJcWkppUSBprO05XqHqkQoaknaiZPXOmg+B3gTgXSD/S991p0QmcSIwBbgSQNJZwOnMyuBWfRNYPbZfAv4wNxe0PX1+BXoppZRSmqXHMnspzU+tra1ua+vwlcGUUkqpqSxQmb2UUkoppfTByGAvpZRSSqmJZbCXUkoppdTEOvyB35QWVm//622e+flTPT2N9AFZ9dsf77xTSiktojKzl1JKKaXUxDLYSymllFJqYot0sBdVGyZLekrS81GVYpKkP8WPPs/NWK92sd9v4nrz9OwlnSLJktautB0Xbd1Ri3aBEiXubpW0nKQlJN0ZP/6cUkoppS5YpIM94ChgZ+D7wMio57o+8BYwuLsvFgHeQOBJ4DPvY6gJzKo5DLAPpRTcAut9BGi7AePiR5jfotT87fa/m5RSSqlZLbLBnqTzgDWB6yi1X2vtiwFLA9NivyUyS+Ml3SJp9WhfQ9K9kiZIOrVy/sWS9qrsj5BUq8c7gBKUnQsMqfRZRdJoSePiz1bRfmBcd5yk4ZXpX0nU+JW0FqWW7QuV8c6V1BZZyh9W2neT9FdJYyWdKemadp7NppLuiH43SFo12m+X9DNJD0h6TNK20d5L0i8kPRjz/Wq0D5B0l6SrgEclfUjSOTGHmyT9RdIgSTtIurJy/Z2j5B2U2sZ/rrv3/RrNO6WUUkpzWmSDPdtHAE8D21MCu8FRMuwpYEXg6uh6FvAH2xsBI4Azo/0M4FzbGwLPVIa+EDgIQFIfYCvg2jg2BLiUUn92d0mLR/uZwB22+wGbAJMkrQ+cCOwQ7cdWrjEdeFLSBpQM38i62/t+/IL2RsBnJG0kqTfwO+BztjcFVm70XGJOZwGDot/vgdMqXRazvTlwHHBytB0CvGx7M2Az4DBJa8SxTYBjba8DfBFoAdYDDgC2jD63AetKqs3p4LguwNbA2Mr1J8Y1Gs398Ahy2/792r8bdUkppZQWOYtssNfASNv9gY9Rlkm/Fe1bAn+M7eHANrG9NSVwq7UDYPsOoG8ELkOAK2y/I2kJypLklbanA/cDu8RpO1CyfdieGfWCdwAut/1CtL9YN9/LKIHeXpTgsepLkh4CHgbWpwRX6wJP2J4SfS6lsU8DGwA3RfB7IvCJyvFR8c+xlMAN4LPAgdH/fmAloG8ce6ByzW3int61/S9KkIdLzb7hwP6Slqc88+vinBVtv1K7uO2ZwFuSlq2fuO2htlttt6609Ert3F5KKaW0aMkX3evYtqSrgaOB0zvr3k77xcD+lGDs4GjbBVgemCAJ4MPADKDhUmoXXAP8AmizPT3GJDJqJwCb2Z4m6SKgd0cDSboBWAVoo2QsJ9nesp3ub8Y/ZzLr3x8BR9u+oW7cAcBrXbyfYZRs6huUgPCdaH9H0odsv1vpu2T0SymllFInMrPX2DbA47F9D7M+htgPuCu2x9S1V11EWebE9qPRNgQ41HaL7RZgDWDn+Or3FuBIeO/9tz7ArcA+klaK9hWrF7D9OvAdZl9iBViOEmC9LGkV4HPR/jdgTUktsf/eRw62d4mPUw6NfitL2jKuu3gsKXfkBuDI2rK0pHUkLd2g3xhg73h3bxXKO4y1OTxNWVY/kRL41fyN8m4lMfZKwAu23+5kTimllFIiM3tVgyVtQwmA/0m8d0fJ8A2T9C3geWZl6o4F/ijpO8z+AQG2n5U0mfIxARHQ7QocUenzmqS7gc/HWEMlHULJmB1p+15JpwF3SJpJWZI9qO46l9XfhO1xkh4G/kr56ndMtM+QdBRwvaTXgAcbPQTbb0kaBJwZQediwG/o+GvfCyhLug+ppBifpywv17sC2BF4NOb2EOXjkpoRwMq2J1farqUEhX+P/e2Z9Q5kSimllDqh8rpU6k4R3E0ANon37xYIkpax/WoEZL8F/tf2r3toDisBDwBbx/t7SDobeNj2hZX+qwIX29459kcB37X9WEfX6feJfr7+mL/Mt/tIC5Ysl5ZSSiBpbHygOZtcxu1mknYCJgNnLUiBXjgsPqKYBPShfJ37Qbsm5nAX8ONKoDeW8vXwJdXOtp8Bzlf8qDLlA5cOA72UUkopzZKZvdSUWltb3dbW1tPTSCmllD4wmdlLKaWUUloEZbCXUkoppdTE8mvc1JTefvZ1nv3N2M47poXaKsdt2tNTSCmlBV5m9lJKKaWUmlgGeymllFJKTSyDvQpJy8cPD9f2+0q6RtLjksZKuk3Sdt10rVZJZ8b2AElbdfG8xSQ9L6mzUm4LLElLSbojqoWsLOn6dvr1kvRI3Z8XJI38oOecUkopLawy2Jvd8sBRAJJ6Uyo1DLW9lu1NKdU01qw/SdJcv/tou832MbE7AOhSsAfsDDxGKaWmub3uAuIrwCjbM20/Dzwjaev6TnG8f+0PpfTbDODHH/B8U0oppYVWBnuzOx1YK37090ngXttX1Q7anmj7IgBJp0gaLmkMMFxSi6S7JD0Uf7aKfpdJ2r02hqSLJA2KbN41Uav2COD4yFxtK2lKpc7sctV9So3dM4B/AFtWxt01rjtO0i3RtoykYZImSBovae/6G5Z0kKQrJd0kaaqkr0v6hqSHJd1Xq8kraS1J10eG8y5J60b75yXdH/1vjpq3tefze0m3S3pC0jGVy+7H7CXmrmTO+sL18xTwB+AXtid21DellFJKs2SwN7vvAo9HFmk4pXZrR9YDdrI9BHgO2Nn2JsBg4MzoMxL4EkBUgNiRSm1X21OB84BfRwbrLuB2oBYg7kvJgr0d2cadgKuBSymBH5JWBs4H9rbdD9gnzj0JeNn2hrY3Am5t5z42AL4IbAacBrxue2PgXuDA6DMUODoynCcA50T73cAW0f8y4NuVcdcFdgE2B06WtHg8gzXjvmvagG3bmVvN8cA7wFntdZB0uKQ2SW0vvjatk+FSSimlRUP+9EoXSRoN9AUes/3FaL7K9ozYXhw4W1J/YCawTrRfB5whaUlgV+BO2zM6WYG9gBI0XQkcDBwW7XsAt8X5VwAnSToO2CLGnQJg+8XovxMlWCTa24uAbrP9CvCKpJcpwSSU+r4bSVqGssx8eWXeS8Y/PwGMjBq2SwBTKuNea/tN4E1JzwGrAO8CL9Vd/zlgtfYehqR+wHHAZu6g5IvtoZSglH6fXC9Lw6SUUkpkZq8jk4BNaju2BwIHAStW+rxW2T4eeBboB7RSAh9sv0HJ1O1Cyfh1+nGB7TFAi6QBQK/KsuUQYCdJU4GxwErADnNzU5IGVj52qJVUebPS5d3K/ruU/yH4EPBS9f052/8Rfc4Czra9IfBVoHdlrOq4M2OsGXV9iP0ZMb9hMbe/xP5SwAjgSNvPzs29ppRSSimDvXqvAMvG9h+BrSV9oXL8wx2c2wd4xva7wAFAr8qxkZQM3bZAoy9Pq9etuTjmMAzKu3tx/uq2W2y3AF+jBID3AdtJWiP61gLSm6IP0b6C7dGVgK1LxWNtTwemSNonxlFk22r3/VRsf7kLY00DesWSdM06wMQ4fnDMbbc49kvgDtvXklJKKaW5lsFehe1/A2MkTQR+RFk2PSI+MLgXOBE4tZ3TzwG+LGkc5V21atbvRuAzwM2232pw7tVALeNWe3dtBLAC5d08gIHArbEsWvNn4PPAdOBwYFRcv5Y9PBVYQdLEaN++Sw+isf2AQ2KcScCe0X4KZXl3LPBCF8e6Edimsr89lfcYayStRvk6eoe6n18ZMY/3kFJKKS1y1MErUKkHSRoE7Gn7gJ6eS3eTtAlwfO3eJN1Juddu+6qi3yfX843fHN5dw6UFVJZLSymlWSSNtd1a354faCyAJJ1F+U253TrruzCy/ZDKD1T3orwD+avuDPRSSimlNEtm9lJTam1tdVtbl15JTCmllJpCe5m9fGcvpZRSSqmJZbCXUkoppdTE8p291JTeeW46z519Y09PI80HH/36Z3t6CimltFDJzF5KKaWUUhPLYC+llFJKqYk1RbAnaXlJR1X2+0q6RtLjksbGz3xs103XapX+f3v3HqxVVYdx/PsECt4l0LylXMJxvBTmEU2R8H5NJCl1DB3LkHRixsZMU8dybLSpaUbFxtAZzVswmsQpssgLZYTCAT0COTgop8QbXhoVA7z064+9Xt0e3nPOe8553/NezvOZ2eN+915r7bV/LngXa+/1Lt2Y9idIOqzEfAMlvSbp+nLUozsk7SJpVi4ef5S0d9c5i5Y1XdIzku6RNEjSQ+mHjs9I5++XNDLtPyRpSAflFJZFK2xtkjZbDk3SPpIWSdok6ZKe1NnMzKw/a4jOHrAj2UoLpGW45gEzI2JURBwEfBcY2T6TpG6/sxgRLRExPX2cAJTU2QOOBZ4FviZJ3b1uT6QOpoA5wIJcPC4HPtPDYi8Ejo2Is4EDAdLyZrMl7Ue2lu/zKe1dKf1mcsuijSFbg/jfwBVFkr4JTCdbNs3MzMy6qVE6e9cDoyQ9BbwALIqI5sLJiFgREXcASPqRpLskLQTukjRc0mOSlqXtsJRulqSTC2VIukPS5DSa9wdJw4FpwMWFZc4krZG0RUq/ff4z2Rq2N5B1ar6UK/eEdN1WSQ+nY9umka/lkp6WdHr7G+6k3hPS8Wbgn2RLkb0fEbfk4tEaEY+lNW5/lpZTW14YnUvlfF/SknT9H6djt5B1mh+U9APgbuDgdP+jyJZUm5urZnO67678EHgtIm5rfyIi1kXEEuD9EsoxMzOzdhplNu5lwP4RMUbSL4B/dZF+X2BcRGyQtDXZSNVGSaPJ1qJtIltf9uvAPElbAkcD3wEOAYiIttT5WR8RPweQtAA4GfgdcCbwQES8n0YbjwEuIBuFPAv4h6SdgFuB8RGxRtKnU/2uAt6KiANSucUeha7roN6QjZTtn8qcDiztIA5fBcYAXwCGAUvS0mUHAKOBsYCAZknjI2KapBOAIyPidUlPAJdExCmpnofz8Vq+RMR/0qPeoWnd4c1IGgucn+psZmZmZdYoI3sdkjQnjVw9kDvcHBEb0v4WwK2SlgP3kXUEAR4EjpQ0iGzpsr/l8nTkNuC8tH8ecHvaPwV4NOX/LXCasqXCDk3lrgGIiDdT+mOAmwuFdrCUWEf1BlhcKLML44DfRMSHEfEq8FfgYOC4tD0JLAP2Iev8dWVX4LV2x9YBuxVLLGlbstHBb+XuvcckTZXUIqnljfVv9bY4MzOzhtAoI3t5K4GPJmNExCRJTXzyna93c/sXA6+SjW59CtiY8m1MI3XHA2cAs7q6cEQsTI9XJ5C9u7YinToLGCepLX0eChzVnZuSNAm4On08n6wDuVm9i9zfSmByd65FNpp3XUT8qpv5NgCD2x0bDGyQdBHw7XTspIh4CbgJmBsRD3fzOkVFxExgJsCYPff2OoBmZmY0zsjeO8B2af9e4HBJp+bOb91J3h2AlyPif8AUYEDu3GyyEbojgD91cd2CO1Mdbofs3b2Uf8+IGB4Rw4GLyDqAjwPjJY1IaQuPcf+S0pCOD4mIOYUJDRHR0kW98x4BBkmamivv85KOAB4DzpA0ID1SHg8sBv4MfDONvCFpd0k7d1B+3jPA53LXEbAL0BYRN+fq/5KkyWQd1c0mZUiaJOm6Eq5nZmZmXWiIzl56H2yhpBXANWSjXtMkPS9pEXAlcG0H2X8JnCuplexxZX5UbD7wZeChiHivSN7fA5MKEzTSsXuAIXz87tok4JGI2JTLNxf4CvA2MBV4IF1/djp/LTAkPX5uJZtk0Z16fyQiItXhGGU/vbISuA54hWyW7tNAK1mn8NKIeCUi5pN1WBelx8T3s3mntph5ZDOUCw4CHo+ID4qk/QmwE7BYn/wJlq2AUSk2hZ+NWQt8D7hS0trUgTYzM7MSKOsLWLmkEauJETGl2nXpa6mj9ihweER8KOkGsvcju/WYVtLdwMUR0f79v5KN2XPvmH/pjJ5mtxrm5dLMzIqTtDQimtofb8R39qpG0k1kkzlOqnZdqiHNbr4a2J3sJ2ZW9OR9vIj4RtkrZ2Zm1k95ZM8aUlNTU7S0tFS7GmZmZn2mo5G9hnhnz8zMzMyK88ieNSRJ7wCrql2PGjAMeL3alagyx8AxKHAcHIOCRo3DXhGxU/uDfmfPGtWqYkPZ/Y2klv4eB8fAMShwHByDgv4WBz/GNTMzM2tg7uyZmZmZNTB39qxRzax2BWqE4+AYgGNQ4Dg4BgX9Kg6eoGFmZmbWwDyyZ2ZmZtbA3NmzuiDpBEmrJK2WdFmR84MkzU7nn5A0PHfu8nR8laTjSy2z1lQoBm2Slqd1ieviV6h7GgdJQyU9Kmm9pBnt8hyU4rBa0o2S1Dd30zMVisGCVGZhneqd++ZueqYXMThW0tL0/3uppKNyeeqqHUDF4tBf2sLY3D22SppUapl1JyK8eavpDRgAPAeMBLYEWoF926W5ELgl7Z8JzE77+6b0g4ARqZwBpZRZS1slYpDOtQHDqn1/fRSHbYBxwDRgRrs8i4FDAQEPAidW+16rEIMFQFO1768PYnAgsFva3x94sR7bQYXj0F/awtbAwLS/K7CO7Cfp6ur7oZTNI3tWD8YCqyPi+Yh4D5gFTGyXZiLw67R/P3B0+lf5RGBWRGyKiDXA6lReKWXWkkrEoB71OA4R8W5E/B3YmE8saVdg+4h4PLK/9e8ETqvoXfRO2WNQh3oTgycj4qV0fCWwVRr5qbd2ABWIQ5/Uurx6E4P/RsQH6fhgoDCJod6+H7rkzp7Vg92BF3Kf16ZjRdOkP7xvAUM7yVtKmbWkEjGA7C+3+ekxztQK1LvcehOHzspc20WZtaQSMSi4PT3SuqrGH2GWKwanA8siYhP11w6gMnEo6BdtQdIhklYCy4Fp6Xy9fT90yZ09s/5tXER8ETgRuEjS+GpXyKrm7Ig4ADgibVOqXJ+KkrQf8FPggmrXpZo6iEO/aQsR8URE7AccDFwuaXC161QJ7uxZPXgR+Gzu8x7pWNE0kgYCOwBvdJK3lDJrSSViQEQU/rsOmEPtP97tTRw6K3OPLsqsJZWIQb4tvAPcS223hV7FQNIeZO39nIh4Lpe+ntoBVCYO/aotFETEM8B60vuLJZRZV9zZs3qwBBgtaYSkLclesG1ul6YZODftTwYeSe/dNANnpndyRgCjyV7CLqXMWlL2GEjaRtJ2AJK2AY4DVvTBvfRGb+JQVES8DLwt6dD0uOocYG75q142ZY+BpIGShqX9LYBTqO220OMYSNoRmAdcFhELC4nrsB1ABeLQz9rCiNT5Q9JewD5kk9bq7fuha9WeIeLNWykbcBLwLNkMqSvSsWuAU9P+YOA+sskHi4GRubxXpHyryM2uK1ZmLW/ljgHZTLPWtK2shxiUIQ5twJtk/4JfS5phBzSRfaE9B8wg/eB8rW7ljgHZLN2lwNOpLdxAmrFdq1tPYwBcCbwLPJXbdq7HdlCJOPSztjAl3eNTwDLgtM7KrOfNK2iYmZmZNTA/xjUzMzNrYO7smZmZmTUwd/bMzMzMGpg7e2ZmZmYNzJ09MzMzswbmzp6ZmZlZA3Nnz8zMzKyBubNnZmZm1sD+D4/aaDdGw0/RAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}