handson-ml2-06.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"ykEYEggsOKcC"},"source":["# 6장 결정트리"]},{"cell_type":"markdown","metadata":{"id":"2dJaalOROKcE"},"source":["## 주요 내용"]},{"cell_type":"markdown","metadata":{"id":"nqJC2hkcOKcF"},"source":["- 결정트리 분류모델 작동법\n","- CART: 결정트리 알고리즘\n","- 결정트리 회귀모델 작동법"]},{"cell_type":"markdown","metadata":{"id":"QwMcvupXOKcG"},"source":["__참고:__\n","핵심 설명과 코드는 &#x1f511;로 표시되었으며 굳이 알아둘 필요가 없는 코드는 &#x270b;로 표시되었다."]},{"cell_type":"markdown","metadata":{"id":"_OO7JBKjOKcG"},"source":["## 기본 설정"]},{"cell_type":"markdown","metadata":{"id":"cvARVLmsOKcG"},"source":["- 필수 모듈 불러오기\n","- 그래프 출력 관련 기본 설정 지정"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"rLKk_qjmOKcG","executionInfo":{"status":"ok","timestamp":1701867967247,"user_tz":-540,"elapsed":675,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["# 파이썬 ≥3.5 필수 (파이썬 3.7 추천)\n","import sys\n","assert sys.version_info >= (3, 5)\n","\n","# 사이킷런 ≥0.20 필수\n","import sklearn\n","assert sklearn.__version__ >= \"0.20\"\n","\n","# 공통 모듈 임포트\n","import numpy as np\n","import os\n","\n","# 노트북 실행 결과를 동일하게 유지하기 위해\n","np.random.seed(42)\n","\n","# 깔끔한 그래프 출력을 위해\n","%matplotlib inline\n","import matplotlib as mpl\n","import matplotlib.pyplot as plt\n","mpl.rc('axes', labelsize=14)\n","mpl.rc('xtick', labelsize=12)\n","mpl.rc('ytick', labelsize=12)\n","\n","# 그림을 저장할 위치\n","PROJECT_ROOT_DIR = \".\"\n","CHAPTER_ID = \"decision_trees\"\n","IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n","os.makedirs(IMAGES_PATH, exist_ok=True)\n","\n","def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n","    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n","    print(\"Saving figure\", fig_id)\n","    if tight_layout:\n","        plt.tight_layout()\n","    plt.savefig(path, format=fig_extension, dpi=resolution)"]},{"cell_type":"markdown","metadata":{"id":"tHplh9baOKcI"},"source":["## 6.1 결정트리 학습과 시각화 (p. 229)"]},{"cell_type":"markdown","metadata":{"id":"WaKhJeYnOKcI"},"source":["`DecisionTreeClassifier` 클래스는 결정트리 알고리즘을 활용한 분류 모델을 지원한다.\n","아래 코드는 붓꽃 데이터의 꽃잎 길이와 꽃잎 너비 특성만을 이용한 결정트리 모델의 훈련방법을 보여준다.\n","\n","- `max_depth=2`: 가지분할을 최대 2번까지만 허용하는 규제"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"Mc3E1oA9OKcI","outputId":"64a9e31d-24d9-4f5c-b9a6-c09c7cf74447","colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"status":"ok","timestamp":1701867975845,"user_tz":-540,"elapsed":777,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(max_depth=2, random_state=42)"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":2}],"source":["from sklearn.datasets import load_iris\n","from sklearn.tree import DecisionTreeClassifier\n","\n","iris = load_iris()\n","X = iris.data[:, 2:] # 꽃잎의 길이와 너비\n","y = iris.target\n","\n","tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n","tree_clf.fit(X, y)"]},{"cell_type":"markdown","metadata":{"id":"5mD4JfwoOKcJ"},"source":["__주의사항: *아래 코드는 파이썬 3.8을 사용하는 경우 필요한 모듈을 설치할 수 없기에 실행하지 말아야 한다. 이어지는 코드의 실행과는 무관한 그래프 지원 보조코드이다. 하만 다행이도 파썬 3.7 버전을 사용하는 구글 코랩에서는 잘 실행된다.*__"]},{"cell_type":"markdown","metadata":{"id":"D_imWpXxOKcJ"},"source":["아래 코드는 `graphviz` 모듈의 기능을 이용하여 학습된 결정트리 분류모델의 훈련결과를 보여준다.\n","각각의 노드(node)에서 훈련과정에 사용된 값과 속성을 확인할 수 있다.\n","\n","- 첫째줄: 노드 분할과정에 사용된 특성과 특성 임곗값\n","- `gini`: 노드의 지니 불순도\n","- `samples`: 노드에 포함된 훈련 샘플 개수\n","- `value`: 노드에 포함된 샘플들의 클래스 비율\n","- `class`: 가장 높은 비율의 클래스. 동율이면 작은 인덱스의 클래스 지정"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"5DiCcT8KOKcJ","outputId":"1277cd6e-0c54-4b9e-f259-f301ff4eefe2","colab":{"base_uri":"https://localhost:8080/","height":439},"executionInfo":{"status":"ok","timestamp":1701867982248,"user_tz":-540,"elapsed":326,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"image/svg+xml":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"351pt\" height=\"314pt\"\n viewBox=\"0.00 0.00 351.00 314.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 310)\">\n<title>Tree</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-310 347,-310 347,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#ffffff\" stroke=\"black\" d=\"M209.5,-306C209.5,-306 65.5,-306 65.5,-306 59.5,-306 53.5,-300 53.5,-294 53.5,-294 53.5,-235 53.5,-235 53.5,-229 59.5,-223 65.5,-223 65.5,-223 209.5,-223 209.5,-223 215.5,-223 221.5,-229 221.5,-235 221.5,-235 221.5,-294 221.5,-294 221.5,-300 215.5,-306 209.5,-306\"/>\n<text text-anchor=\"middle\" x=\"137.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) &lt;= 2.45</text>\n<text text-anchor=\"middle\" x=\"137.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.667</text>\n<text text-anchor=\"middle\" x=\"137.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 150</text>\n<text text-anchor=\"middle\" x=\"137.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [50, 50, 50]</text>\n<text text-anchor=\"middle\" x=\"137.5\" 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setosa</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"black\" d=\"M110.09,-222.91C102.49,-211.65 94.23,-199.42 86.59,-188.11\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"89.39,-186 80.89,-179.67 83.59,-189.91 89.39,-186\"/>\n<text text-anchor=\"middle\" x=\"76.14\" y=\"-200.51\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<path fill=\"#ffffff\" stroke=\"black\" d=\"M286,-187C286,-187 147,-187 147,-187 141,-187 135,-181 135,-175 135,-175 135,-116 135,-116 135,-110 141,-104 147,-104 147,-104 286,-104 286,-104 292,-104 298,-110 298,-116 298,-116 298,-175 298,-175 298,-181 292,-187 286,-187\"/>\n<text text-anchor=\"middle\" x=\"216.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) &lt;= 1.75</text>\n<text text-anchor=\"middle\" x=\"216.5\" y=\"-156.8\" 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fill=\"#4de88e\" stroke=\"black\" d=\"M196,-68C196,-68 99,-68 99,-68 93,-68 87,-62 87,-56 87,-56 87,-12 87,-12 87,-6 93,0 99,0 99,0 196,0 196,0 202,0 208,-6 208,-12 208,-12 208,-56 208,-56 208,-62 202,-68 196,-68\"/>\n<text text-anchor=\"middle\" x=\"147.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.168</text>\n<text text-anchor=\"middle\" x=\"147.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 54</text>\n<text text-anchor=\"middle\" x=\"147.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 49, 5]</text>\n<text text-anchor=\"middle\" x=\"147.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M190.81,-103.73C185.29,-94.97 179.45,-85.7 173.91,-76.91\"/>\n<polygon fill=\"black\" stroke=\"black\" 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class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M241.82,-103.73C247.26,-94.97 253.01,-85.7 258.48,-76.91\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"261.52,-78.64 263.82,-68.3 255.57,-74.95 261.52,-78.64\"/>\n</g>\n</g>\n</svg>\n","text/plain":["<graphviz.sources.Source at 0x7b5a548f2b00>"]},"metadata":{},"execution_count":3}],"source":["# 파이썬 3.8인 경우 실행하지 말것.\n","# 현재(2021년 5월) 구글 코랩에서는 잘 실행됨.\n","\n","from graphviz import Source\n","from sklearn.tree import export_graphviz\n","\n","export_graphviz(\n","        tree_clf,\n","        out_file=os.path.join(IMAGES_PATH, \"iris_tree.dot\"),\n","        feature_names=iris.feature_names[2:],\n","        class_names=iris.target_names,\n","        rounded=True,\n","        filled=True\n","    )\n","\n","Source.from_file(os.path.join(IMAGES_PATH, \"iris_tree.dot\"))"]},{"cell_type":"markdown","metadata":{"id":"dvg9bJ-FOKcK"},"source":["## 6.2 예측하기 (p. 231)"]},{"cell_type":"markdown","metadata":{"id":"6VRNnW5HOKcK"},"source":["아래 코드는 결정트리 분류 모델의 클래스 예측결과를 그래프로 그린다."]},{"cell_type":"code","execution_count":4,"metadata":{"id":"kcuSZ6tUOKcK","executionInfo":{"status":"ok","timestamp":1701867990318,"user_tz":-540,"elapsed":392,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["from matplotlib.colors import ListedColormap\n","\n","def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):\n","    x1s = np.linspace(axes[0], axes[1], 100)\n","    x2s = np.linspace(axes[2], axes[3], 100)\n","    x1, x2 = np.meshgrid(x1s, x2s)\n","    X_new = np.c_[x1.ravel(), x2.ravel()]\n","    y_pred = clf.predict(X_new).reshape(x1.shape)\n","    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n","    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n","    if not iris:\n","        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n","        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n","    if plot_training:\n","        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris setosa\")\n","        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris versicolor\")\n","        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"Iris virginica\")\n","        plt.axis(axes)\n","    if iris:\n","        plt.xlabel(\"Petal length\", fontsize=14)\n","        plt.ylabel(\"Petal width\", fontsize=14)\n","    else:\n","        plt.xlabel(r\"$x_1$\", fontsize=18)\n","        plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n","    if legend:\n","        plt.legend(loc=\"lower right\", fontsize=14)"]},{"cell_type":"markdown","metadata":{"id":"19u4LNtaOKcK"},"source":["아래 코드는 가지분할을 최대 3번까지 허용해서 얻어진 모델의 훈련샘플들에 대한 예측결과를 보여준다.\n","\n","- 1차 분할 기준: 꽃잎 길이=2.45cm\n","- 2차 분할 기준: 꽃잎 너비=1.75cm\n","- 3차 분할 기준: (우상단) 꽃잎 길이=4.85cm, (우하단) 꽃잎 길이=4.95cm"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"BySdqL1oOKcK","outputId":"44ed775a-52c7-4e12-8ddc-fd5f0ac7db48","colab":{"base_uri":"https://localhost:8080/","height":425},"executionInfo":{"status":"ok","timestamp":1701867997180,"user_tz":-540,"elapsed":1125,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saving figure decision_tree_decision_boundaries_plot\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.figure(figsize=(8, 4))\n","plot_decision_boundary(tree_clf, X, y)\n","\n","plt.plot([2.45, 2.45], [0, 3], \"k-\", linewidth=2)        # 1차 분할 기준선\n","plt.plot([2.45, 7.5], [1.75, 1.75], \"k--\", linewidth=2)  # 2차 분할 기준선\n","plt.plot([4.95, 4.95], [0, 1.75], \"k:\", linewidth=2)     # 3차 분할 기준선 (우하단)\n","plt.plot([4.85, 4.85], [1.75, 3], \"k:\", linewidth=2)     # 3차 분할 기준선 (우상단)\n","\n","plt.text(1.40, 1.0, \"Depth=0\", fontsize=15)\n","plt.text(3.2, 1.80, \"Depth=1\", fontsize=13)\n","plt.text(4.05, 0.5, \"(Depth=2)\", fontsize=11)\n","\n","save_fig(\"decision_tree_decision_boundaries_plot\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"iAarHAwcOKcL"},"source":["학습된 결정트리의 모양은 다음과 같다."]},{"cell_type":"markdown","metadata":{"id":"4dy9QTDdOKcL"},"source":["__주의사항: *아래 코드는 파이썬 3.8을 사용하는 경우 필요한 모듈을 설치할 수 없기에 실행하지 말아야 한다. 이어지는 코드의 실행과는 무관한 그래프 지원 보조코드이다.*__"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"ykWAXJCjOKcL","outputId":"2fb08eee-3f64-4d9b-aefa-65b0ade5b81a","colab":{"base_uri":"https://localhost:8080/","height":598},"executionInfo":{"status":"ok","timestamp":1701868003148,"user_tz":-540,"elapsed":627,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"image/svg+xml":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"553pt\" height=\"433pt\"\n viewBox=\"0.00 0.00 553.00 433.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 429)\">\n<title>Tree</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 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화이트박스 vs. 블랙박스"]},{"cell_type":"markdown","metadata":{"id":"tYSsL6BFOKcL"},"source":["결정트리 모델처럼 훈련과 예측 과정은 명료하게 추적할 수 있는 모델을 화이트박스(white box) 모델이라 부른다.\n","반면에 앞으로 다룰 예정인 랜덤 포레스트, 신경망 등의 모델은 블랙박스(black box) 모델이라 부른다.\n","이유는 그런 모델들의 훈련과 예측 과정을 정확히 설명할 수 있기 때문이다."]},{"cell_type":"markdown","metadata":{"id":"Zsll02d1OKcL"},"source":["## 6.3 클래스 확률 추정 (p. 234)"]},{"cell_type":"markdown","metadata":{"id":"qgZM4upiOKcL"},"source":["입력 샘플의 클래스 확률은 샘플이 속한 리프 노드(leaf node)의 클래스 비율을 사용하며,\n","가장 높은 비율의 클래스를 예측값으로 사용한다.\n","따라서 노드에 속한 모든 샘플의 클래스 확률은 동일하다."]},{"cell_type":"markdown","metadata":{"id":"q8ieVn17OKcL"},"source":["아래 코드는 꽃잎 길이와 너비가 각각 5cm, 1.5cm인 붗꽃의 품종별 확률과 예측값을 보여준다.\n","\n","- `maximal_depth=2`인 경우 예측값: 버시컬러"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"F54P_25oOKcM","outputId":"7a99d746-0ebd-41ad-ef72-b21b1891974f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701868013148,"user_tz":-540,"elapsed":279,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.        , 0.90740741, 0.09259259]])"]},"metadata":{},"execution_count":7}],"source":["tree_clf.predict_proba([[5, 1.5]])"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"XzskXi65OKcM","outputId":"d8bdd363-3a84-44f3-d68f-a37c3d434c50","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701868016836,"user_tz":-540,"elapsed":307,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1])"]},"metadata":{},"execution_count":8}],"source":["tree_clf.predict([[5, 1.5]])"]},{"cell_type":"markdown","metadata":{"id":"xsezdYogOKcM"},"source":["- `maximal_depth=3`인 경우 예측값: 버지니카"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"eCfokUjkOKcM","outputId":"cf33f3da-e64d-4ecb-c2f8-f9ea764d4a78","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701868020285,"user_tz":-540,"elapsed":292,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.        , 0.33333333, 0.66666667]])"]},"metadata":{},"execution_count":9}],"source":["tree_clf_d3.predict_proba([[5, 1.5]])"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"hsF1A6y-OKcN","outputId":"4c25ee11-bf35-4a1c-cfe4-a0ed099e23d0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701868023895,"user_tz":-540,"elapsed":397,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([2])"]},"metadata":{},"execution_count":10}],"source":["tree_clf_d3.predict([[5, 1.5]])"]},{"cell_type":"markdown","metadata":{"id":"GqM9fnxUOKcN"},"source":["## 6.7 규제 매개변수 (p. 237)"]},{"cell_type":"markdown","metadata":{"id":"vy_wJdCaOKcN"},"source":["결정트리 모델의 규제는 기본적으로 분할을 제한하는 다양한 방식으로 이루어진다.\n","아래 코드는 `min_samples_leaf=4` 규제를 사용하는 모델(아래 그림 오른편)과\n","어느 규제도 사용하지 않은 모델(아래 그림 왼편)의 차이를 보여준다.\n","사용되는 훈련세트는 moons 데이터셋이다.\n","\n","- 왼편 결정트리: 과대적합 발생\n","- 오른편 결정트리: `min_samples_leaf=4` 규제 사용. 즉, 리프노드에 최소 4개 이상의 샘플이 포함되어 있어야 함."]},{"cell_type":"code","execution_count":11,"metadata":{"id":"eu-nBruiOKcN","outputId":"3a055ad8-82e8-408c-e094-2b9f0d17c866","colab":{"base_uri":"https://localhost:8080/","height":425},"executionInfo":{"status":"ok","timestamp":1701868031759,"user_tz":-540,"elapsed":2317,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saving figure min_samples_leaf_plot\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["from sklearn.datasets import make_moons\n","Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)\n","\n","deep_tree_clf1 = DecisionTreeClassifier(random_state=42)  # 규제 없음\n","deep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)  # 규제 사용\n","deep_tree_clf1.fit(Xm, ym)\n","deep_tree_clf2.fit(Xm, ym)\n","\n","fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n","plt.sca(axes[0])\n","plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.4, -1, 1.5], iris=False)\n","plt.title(\"No restrictions\", fontsize=16)\n","plt.sca(axes[1])\n","plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.4, -1, 1.5], iris=False)\n","plt.title(\"min_samples_leaf = {}\".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\n","plt.ylabel(\"\")\n","\n","save_fig(\"min_samples_leaf_plot\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"TRaez9nxOKcO"},"source":["## 6.8 회귀 (p. 239)"]},{"cell_type":"markdown","metadata":{"id":"JkqevW-WOKcO"},"source":["결정트리 회귀모델 알고리즘은 분류모델과 거의 동일한 알고리즘을 사용한다.\n","아래 코드는 2차 다항식을 이용하여 생성된 훈련 세트에 대한 결정트리 회귀모델 훈련을 보여준다."]},{"cell_type":"code","execution_count":12,"metadata":{"id":"x2Rq2hpWOKcO","executionInfo":{"status":"ok","timestamp":1701868036274,"user_tz":-540,"elapsed":311,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["# 2차 다항식 훈련세트 + 잡음\n","np.random.seed(42)\n","\n","m = 200\n","X = np.random.rand(m, 1)\n","y = 4 * (X - 0.5) ** 2\n","y = y + np.random.randn(m, 1) / 10"]},{"cell_type":"markdown","metadata":{"id":"3dOuOLBzOKcO"},"source":["아래 코드는 `max_depth`를 2 또는 3으로 지정하고 결정트리 회귀모델을 훈련시킨 결과를 보여준다."]},{"cell_type":"code","execution_count":13,"metadata":{"id":"ZB229f1qOKcO","outputId":"7a97580d-bff8-49c5-b94c-cc0068f02fa9","colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"status":"ok","timestamp":1701868039602,"user_tz":-540,"elapsed":12,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeRegressor(max_depth=3, random_state=42)"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before 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1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3, random_state=42)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":13}],"source":["from sklearn.tree import DecisionTreeRegressor\n","\n","tree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)  # max_depth=2\n","tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)  # max_depth=3\n","tree_reg1.fit(X, y)\n","tree_reg2.fit(X, y)"]},{"cell_type":"markdown","metadata":{"id":"tr-gQ5VxOKcP"},"source":["아래 함수는 훈련세트와 노드별 예측값(빨강 직선)을 그린다."]},{"cell_type":"code","execution_count":14,"metadata":{"id":"YQVo9kR-OKcP","executionInfo":{"status":"ok","timestamp":1701868043623,"user_tz":-540,"elapsed":285,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel=\"$y$\"):\n","    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n","    y_pred = tree_reg.predict(x1)\n","    plt.axis(axes)\n","    plt.xlabel(\"$x_1$\", fontsize=18)\n","    if ylabel:\n","        plt.ylabel(ylabel, fontsize=18, rotation=0)\n","    plt.plot(X, y, \"b.\")\n","    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")"]},{"cell_type":"markdown","metadata":{"id":"kKDb8ARBOKcP"},"source":["- 왼편 그림: `max_depth=2`인 경우\n","- 오른편 그림: `max_depth=3`인 경우"]},{"cell_type":"code","execution_count":15,"metadata":{"id":"2kWNGn9fOKcP","outputId":"c4a7e286-d9ec-4f73-f153-cf0ba3513dc6","colab":{"base_uri":"https://localhost:8080/","height":425},"executionInfo":{"status":"ok","timestamp":1701868054494,"user_tz":-540,"elapsed":1293,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saving figure tree_regression_plot\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n","\n","# 왼편 그림\n","plt.sca(axes[0])\n","plot_regression_predictions(tree_reg1, X, y)\n","for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n","    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n","plt.text(0.21, 0.65, \"Depth=0\", fontsize=15)\n","plt.text(0.01, 0.2, \"Depth=1\", fontsize=13)\n","plt.text(0.65, 0.8, \"Depth=1\", fontsize=13)\n","plt.legend(loc=\"upper center\", fontsize=18)\n","plt.title(\"max_depth=2\", fontsize=14)\n","\n","# 오른편 그림\n","plt.sca(axes[1])\n","plot_regression_predictions(tree_reg2, X, y, ylabel=None)\n","for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n","    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n","for split in (0.0458, 0.1298, 0.2873, 0.9040):\n","    plt.plot([split, split], [-0.2, 1], \"k:\", linewidth=1)\n","plt.text(0.3, 0.5, \"Depth=2\", fontsize=13)\n","plt.title(\"max_depth=3\", fontsize=14)\n","\n","save_fig(\"tree_regression_plot\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"p0bJTGbYOKcQ"},"source":["왼편 그래프에 해당하는 결정트리 회귀모델은 다음과 같다."]},{"cell_type":"code","execution_count":16,"metadata":{"id":"pEmBEzt2OKcQ","outputId":"6ea830e7-b35f-4522-9037-15dd5dafee93","colab":{"base_uri":"https://localhost:8080/","height":379},"executionInfo":{"status":"ok","timestamp":1701868059312,"user_tz":-540,"elapsed":447,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"image/svg+xml":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"666pt\" height=\"269pt\"\n viewBox=\"0.00 0.00 666.00 269.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 265)\">\n<title>Tree</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-265 662,-265 662,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#f6d6be\" stroke=\"black\" d=\"M392,-261C392,-261 265,-261 265,-261 259,-261 253,-255 253,-249 253,-249 253,-205 253,-205 253,-199 259,-193 265,-193 265,-193 392,-193 392,-193 398,-193 404,-199 404,-205 404,-205 404,-249 404,-249 404,-255 398,-261 392,-261\"/>\n<text text-anchor=\"middle\" x=\"328.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 &lt;= 0.197</text>\n<text text-anchor=\"middle\" x=\"328.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">squared_error = 0.098</text>\n<text text-anchor=\"middle\" x=\"328.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 200</text>\n<text text-anchor=\"middle\" x=\"328.5\" y=\"-200.8\" 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feature_names=[\"x1\"],\n","        rounded=True,\n","        filled=True\n","    )\n","\n","Source.from_file(os.path.join(IMAGES_PATH, \"regression_tree.dot\"))"]},{"cell_type":"markdown","metadata":{"id":"0DutrQ7tOKcQ"},"source":["### 회귀모델 규제"]},{"cell_type":"markdown","metadata":{"id":"M1KntRP0OKcQ"},"source":["결정트리 회귀모델의 규제는\n","CART 알고리즘이 노드 분할에 사용하는 기준(`criterion`) 하이퍼파라미터에 사용되는 값을 제외하고\n","분류모델의 경우와 완전히 동일하다.\n","\n","- 분류모델의 비용함수 기준: 지니 불순도(`gini`) 또는 엔트로피(`entropy`)\n","- 회귀모델의 비용함수 기준: 평균제곱오차(`mse`), 프리드먼 평균제곱오차(`friedman_mse`),\n","    평균절대오차(`mae`), 쁘와송(`poisson`)"]},{"cell_type":"markdown","metadata":{"id":"3UBL2LYsOKcQ"},"source":["아래 코드는 `min_samples_leaf=10` 규제를 사용하는 회귀모델(아래 그림 오른편)과\n","어느 규제도 사용하지 않은 회귀모델(아래 그림 왼편)의 차이를 보여준다.\n","\n","- 왼편 결정트리: 과대적합 발생\n","- 오른편 결정트리: `min_samples_leaf=10` 규제 사용. 즉, 리프노드에 최소 10개 이상의 샘플이 포함되어 있어야 함."]},{"cell_type":"code","execution_count":17,"metadata":{"id":"JPlVZ9XfOKcQ","executionInfo":{"status":"ok","timestamp":1701868066539,"user_tz":-540,"elapsed":390,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["tree_reg1 = DecisionTreeRegressor(random_state=42)\n","tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n","tree_reg1.fit(X, y)\n","tree_reg2.fit(X, y)\n","\n","x1 = np.linspace(0, 1, 500).reshape(-1, 1)\n","y_pred1 = tree_reg1.predict(x1)\n","y_pred2 = tree_reg2.predict(x1)"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"Fx59ANBtOKcR","outputId":"1b16233b-f541-4f43-808a-ff2de2b7217d","colab":{"base_uri":"https://localhost:8080/","height":425},"executionInfo":{"status":"ok","timestamp":1701868072243,"user_tz":-540,"elapsed":1426,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saving figure tree_regression_regularization_plot\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA94AAAGGCAYAAACNL1mYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADPCklEQVR4nOydeXwTZf7HP5P0hJ6UthRCi0gALxaPghxVxKMe4LGCeFL8FVoWPHBBFC9AFEXwXLVCjbaiol10XUFXvEAr5SiL6KogRSkQKG0phRZ6J/P7YzLJzGQmmdxHv+/Xq68mz1xP2sk8z+f5XgzLsiwIgiAIgiAIgiAIgvAJmkB3gCAIgiAIgiAIgiDCGRLeBEEQBEEQBEEQBOFDSHgTBEEQBEEQBEEQhA8h4U0QBEEQBEEQBEEQPoSEN0EQBEEQBEEQBEH4EBLeBEEQBEEQBEEQBOFDSHgTBEEQBEEQBEEQhA8h4U0QBEEQBEEQBEEQPoSEN0EQBEEQBEEQBEH4EBLeBEEELePGjQPDMD47/4ABAzBgwACfnZ8gCKK7s2nTJjAMg0WLFgW6KyFLSUkJGIZBSUlJoLsiIlBjaFVVFW666SZkZGRAo9EgKSnJ730gCHcg4U0QPqa6uhoMw4BhGOTm5srus3XrVjAMg2nTpvm3cz5m2rRpYBgG1dXV3fL6BEEQBEF4D5PJhBtvvBGff/45rrvuOjzxxBN4+OGHvXb+devW4d5778WYMWPQs2dPVYtG7e3tePLJJ6HX6xETE4O+ffuioKAAdXV1XusXER5EBLoDBNGd+PLLL/Htt99i/Pjxge5KSPDOO++gpaXFZ+f/5ptvfHZugiAIAhgxYgR2796N3r17B7orRBiwf/9+/Pbbb5gxYwZWrVrl9fM///zz+O6775CQkIC+ffti3759Dvc3m8244YYbsGHDBlx88cW4+eabUVVVhTfffBPffPMNtm7ditTUVK/3kwhNyOJNEH5iwIAB0Gg0eOihh8CybKC7ExJkZmZi6NChPjv/mWeeiTPPPNNn5ycIguju9OjRA0OHDiXhTXiFI0eOAAD69u3rk/MvWbIEe/fuxYkTJ7BkyRKn+5eWlmLDhg247bbbUFFRgWeffRYfffQRXn/9dfz555947LHHfNJPIjQh4U0QfmLIkCG46667sGPHDpSVlak+7sCBA8jPz0e/fv0QFRUFnU6H/Px8HDx4UPU5Fi1aBIZhsGnTJpSUlOCCCy5Ajx49MG7cOOs+zc3NWLhwIc455xzExsYiKSkJubm5+OGHH+zOV1NTg/vvvx96vd6671lnnYWZM2fi5MmTALiFhtLSUgDAGWecYXW3F16Tf3/48GFMnToVffr0gUajwaZNmwA4jvH+97//jauuugopKSmIiYnBgAEDcNddd+GXX35RfX2l+LTTp09j4cKFGDp0KGJiYtCrVy9cd9112Lx5s8O/7fvvv4/hw4cjNjYWGRkZuP/++9Ha2mp3zEcffYRLL70UaWlpVre0K664Ah999JHsZyUIgvA3wtjsiooKXHbZZYiPj0dqaipmzZplfbZ99tlnGDVqFHr27In09HTMnz8fXV1dsucRwj9/T506hfvvvx99+/ZFdHQ0hg0bhrVr17rdbzXjEwDs3bsX8+fPxwUXXGAdRwYPHoyHH34Yp06dsjsvPx61t7fjkUceQWZmJmJjY3HhhRfi66+/BgCcPHkSs2fPRt++fRETE4NRo0Zh+/btdufiP/uJEydQWFiIPn36ICYmBueffz7WrFnj0ufdv38/pk+fjszMTERHRyMjIwPTpk3DgQMH7PbduXMnJk2aZN03NTUV2dnZePrpp126pjNYlsVbb72FMWPGICEhAT169MBFF12Et956y27fI0eOYOHChbj44ouRlpaG6OhoDBgwALNmzbJz1R4wYAAuvfRSAMDixYut47o38wfk5ORAr9erzi9TXFwMAHjmmWdExxQWFmLgwIF47733ZOcBRPeEXM0Jwo88+eST+OCDD/DYY4/hr3/9KyIjIx3uv3fvXowdOxb19fWYOHEizjnnHPzyyy946623sG7dOvzwww8YPHiw6usvX74cGzduxA033ICrrroKWq0WAHD8+HFccskl+PXXXzFmzBjMnDkTTU1N+Pe//43LLrsM//znP3HjjTcCAFpaWjBmzBhUV1fjqquuwk033YSOjg7s378fq1evxrx585CYmIg5c+agpKQEP/30E+6//35r8hOp0G1oaMCoUaPQq1cv3HrrrWhra0NCQoLDzzF37ly88MIL6NWrF2688UakpaXh0KFD+Prrr3HhhRfi3HPPVX19KW1tbRg/fjy2b9+OCy64AHPmzEFtbS0+/PBDbNiwAWvWrMHkyZPtjnv11VfxxRdf4IYbbsD48ePxxRdf4JVXXsGxY8fw3nvvWfcrKirCrFmzkJGRgZtuugkpKSk4evQotm/fjn/961+4+eabHfaPIAjCn2zbtg3Lli1Dbm4uCgsLsXHjRhQVFaGpqQkTJ07EtGnTcMMNN2DUqFH47LPPsHz5csTFxeGJJ55weu7Ozk5cddVVaGxsxM0334yWlhZ88MEHuOWWW/DFF1/gqquucqmvascnAPj4449hMBhw2WWXYdy4cTCbzdi6dSuWLVuG7777Dt9//73sGD1lyhT873//w/XXX4/W1la89957mDBhAjZv3oyCggJ0dHRg8uTJqK+vx4cffoirr74a+/fvt16Xp6OjA1dccQVOnTqFu+66C6dPn0ZZWRluv/12HDt2DPfee6/Tz7tt2zbk5ubi9OnTmDBhAvR6Paqrq/Hee+/hP//5D7Zs2YKBAwcCAHbt2oXRo0dDq9XihhtuQFZWFk6cOIHffvsNq1atwqOPPurS31oJlmVxxx13YM2aNdDr9bj99tsRFRWFr776Cvn5+fjtt9+wYsUK6/7ff/89nn/+eVx++eUYOXIkIiMj8eOPP6KoqAgbNmzAzp07rX+7OXPmYNeuXSgtLcWll15qXUgXLqj7k7a2Nmzbtg1DhgxBVlaWaBvDMLjyyiuxcuVK7NixAzk5OQHpIxFksARB+JT9+/ezANjc3FyWZVl23rx5LAD2H//4h3WfLVu2sADYvLw80bGXXXYZC4BduXKlqP21115jAbDjx49X1YeFCxeyANiePXuyP//8s93222+/nQXAFhcXi9pra2vZ/v37s6mpqWxrayvLsiz76aefsgDYOXPm2J2nubmZbWtrs77Py8tjAbD79++X7RcAFgB79913s11dXXbbL730Ulb6mFq3bh0LgD3vvPPYY8eOibZ1dnayR48eVX39rKwsNisrS9S2ePFiFgB7xx13sGaz2dq+c+dONioqik1KSmKbmpqs7fzfNjExkd2zZ4+1vaWlhR08eDCr0WjYw4cPW9svuOACNioqiq2trbXrj/TzEARBBIqNGzdan9GffPKJtb2jo4MdNmwYyzAM27t3b3b79u3WbU1NTWxaWhrbq1cvtqOjQ3SehQsXis6flZXFAmBvuOEGtr293dr+9ddfi8ZMV3BlfDIajaLr8vBjwLvvvitq58ejsWPHsqdOnbK2f/jhhywANikpiZ08eTLb2dlp3bZs2TIWAPv888/LfvZLLrlE1IdDhw6xvXv3ZqOjo1mj0Whtf/vtt1kA7Ntvv21t6+joYAcMGMDGx8ezO3fuFJ2/vLyc1Wq17IQJE6xtf//73+3+lzzujj1yY+iqVaus4zp/D7Asy7a3t7MTJ05kAbA7duywttfW1rLNzc125y4tLWUBsE899ZSoXel+YlluvrVw4UKXfhyxZs0axWuxLMv+8ssvLADR31nIihUrWACswWBweB2i+0Cu5gThZx555BEkJSVhyZIlsu5sPAcPHsTGjRtx9tlnY8aMGaJtM2fOxNChQ/Htt9/i0KFDqq9dUFCA8847T9R27NgxfPjhhxg/fjymT58u2paWloYHH3wQ9fX1Vlc6ntjYWLvzx8XFITo6WnV/ACAqKgrPPfec1frujNdffx0A8PLLLyMlJUW0LSIiAunp6S5dX0ppaSkiIyPx7LPPitzGzj//fOTl5eHEiRP45JNP7I67//77MWTIEOv72NhY3HbbbTCbzfjvf/8r2jcyMlLWkiL9PARBEIHmsssuww033GB9HxkZiUmTJoFlWUycOBHZ2dnWbfHx8ZgwYQKOHz8Oo9Go6vwvvvgioqKirO8vv/xyZGVlobKy0u0+qxmf+PAtKffccw8A2I15PE8//TR69uxpfT9p0iRERkbixIkTWLFiBSIibM6kt912GwDgp59+kj3X0qVLRX3Q6XS4//770d7ejg8++MDRR8T69etRXV2NBx98EOeff75o29ixY3HDDTfg888/R1NTk2ib3N/Gm2PPq6++ip49e+K1114TjXNRUVFWl3ahO31aWhri4uLsznPXXXchISFB8f8gR3V1NRYvXuzSjyfwoQtSbwYe3ntPGOJAdG/I1Zwg/ExycjIefvhhPPzww1ixYoVibNKuXbsAAJdeeqldrJFGo8Ell1yCPXv2YNeuXejfv7+qa48YMcKurbKyEiaTCe3t7bJ9qaqqAgDs2bMHEyZMwCWXXIKMjAw8++yz+OmnnzBhwgRceumlOOuss9yquX3GGWe4lHRn+/btiI6OtsZ5eZOmpib8+eefOOuss6DT6ey2X3bZZSguLsauXbtw1113ibZdeOGFdvvz5zhx4oS17dZbb8X8+fNx7rnn4vbbb8dll12GsWPHOnWvJwiCCATDhw+3a8vIyHC67ciRIzjjjDMcnjspKUl2H51Ohy1btrjcV1fGJ5Zl8fbbb6OkpAS//PILTp48CbPZbN3OJ/GSIv3MGo0GaWlpaGlpQWZmpmib8G8hJSIiAqNGjbJr512Sf/zxR4efdevWrQCA33//XXbsPnr0KMxmM/bu3YuLLroIt9xyC1566SXcdNNNmDJlCq688kpccskl6Nevn8PruEJLSwv+97//oW/fvli2bJnd9s7OTgDcfELIxx9/jJUrV2Lnzp1obGyEyWSyblP6P8gxbtw4Sl5LBDUkvAkiANx333149dVX8fzzz2PWrFmy+/Cr1EoWXH5Al65mO0LuXMePHwcAbN68WTZ5GM/p06cBcCu7W7duxRNPPIF169bh888/BwD0798fDz/8sOLncaVPjjh58iT69esHjcb7Djue/M3lhDNv+RBOIubNm4eUlBQUFRXh+eeft1pIrrvuOrz44otOJ6oEQRD+xNGzzdE2XmQ5QslSGBERIRLBanFlfOLH4f79++P6669HRkaG1SK+ePFitLe3y15D6TO7+rfo3bu37DjGjz/OrKT82C3MISIHP3aPHDkSmzZtwtKlS/H+++/j7bffBgBkZ2dj2bJluOyyyxyeRw2NjY1gWRaHDx92aE3m+wRw5bvmzZuH1NRUXHXVVdDpdFar/EsvvaT4fwgG+PtX6X/FzxWU7nOi+0HCmyACQGxsLBYvXoz8/HwsXrzYznoK2Ab32tpa2XMcPXpUtJ8a5CzS/PFz584VJTxxRGZmJkpKSmA2m/Hzzz/jyy+/xCuvvILZs2cjOTnZ6l7nbp8ckZSUZF3J97b49sXfXArDMPi///s//N///R8aGhpQXl6ONWvWoKysDFVVVfj5559Vu90TBEEQYtSMT3V1dXjttdcwbNgwbNmyBT169LAef/ToUY9dkNVw7Ngx2XGMH3+ciTV+HFq3bh0mTJig6po5OTn4z3/+g9bWVmzbtg3r1q3D66+/juuuuw6//PKLNRGbu/B9uvDCC7Fjxw6n+3d1dWHJkiXIyMjArl27kJaWZt3Gsiyee+45l65fXV2NkpISl47xJCP6wIEDodForJ6BUvh2vV7v9jWI8IKEN0EEiLy8PLzwwgsoLi6WdTfj3dm+//57sCwrEqgsy+L7778X7ecu2dnZYBjGLbc+jUaD4cOHY/jw4Rg1ahQuueQSfPrpp1bhzQtIocXXU0aMGIHPP/8c3333ndMVelevn5CQgIEDB2Lfvn04fPiwnQseX+bM0785T0pKCm688UbceOONOHbsGL799lvs27dPFCtOEARBuI6j8enPP/8Ey7K44oorRKIbAMrLy/3Sv66uLmzZsgVjxoyRvb40blvKyJEjAQBbtmxRLbx5YmNjMW7cOIwbNw5JSUl44okn8NVXX6GwsNCl80iJj4/HWWedhd27d+PEiRPWaiJKHDt2DCdPnsTll18uEt0AsGPHDpfLcPEx3q7gifCOjY3FiBEjsHXrVhw4cECU2ZxlWXz11Vfo2bMnLrroIrevQYQXlFyNIAKEVqvF0qVL0dnZKfvgz8zMxGWXXYZff/3VrvblqlWrsHv3bowfP151fLcSffr0wS233IKKigosX75cNj5q27ZtaGlpAQD8+uuvshZhvi0mJsba1qtXLwBwKQGcM2bPng2AS2bGu9rxdHV1ifrmzvXz8vLQ2dmJBQsWiP4WP//8M0pKSpCYmGgtreYOmzZtsvsbd3Z2Wj+L8O9HEARBqEft+MQLpIqKCpFLu9FoxIIFC/zQU45HHnkEHR0douu//PLLiI6Oxq233urw2BtuuAGZmZl44YUXrAvxQjo7O/HDDz9Y32/ZsgVtbW12+8mN3Z5w3333oaWlBTNmzBC5lPPs378f1dXVALjEarGxsdi5c6d1jgFwLutqyqlJ4WO8XfnxlIKCAgCwmzOsXLkSf/75J+644w7ZhHZE94Qs3gQRQK6//nqMHTtWNDgKKSoqwtixYzFjxgysW7cOZ599Nn799Vd8+umnSE1NRVFRkVf68frrr+P333/H/PnzsXr1aowaNQpJSUk4dOgQduzYgaqqKtTU1KBHjx746quv8OCDD2LMmDEYPHgwUlJS8Oeff+LTTz9FTEyMVRgDwPjx47FixQoUFBTg5ptvRs+ePZGVlSXrWq+Wa6+9FvPmzcOKFSug1+tx0003IS0tDYcPH8Y333yDefPmYc6cOW5ff/78+fjss8+wevVq7N69G5dffjnq6urw4YcfoqurC8XFxYiPj3e7/zfeeCMSEhJw8cUXIysrC52dnfjqq6/w22+/YdKkSXa1QAmCIAh1qB2fMjIycPPNN+Ojjz7CRRddhMsvvxy1tbVYv349Lr/8cvzxxx8+72tGRgZOnz6NYcOGYeLEidY63g0NDXjllVecJj2Ljo7G2rVrcc011+DSSy/F+PHjcd5554FhGBw4cADl5eVISUmxJjJbtmwZNm7ciEsuuQRnnHEGYmJisHPnTnzzzTcYOHAgbrrpJq98rsLCQmzduhWlpaXYvHkzrrjiCvTt2xe1tbXYs2cPtm3bhvfffx8DBgyARqPBrFmz8Pzzz+Mvf/kLJk6ciKamJvznP/9BVlYW+vbt65U+ucInn3xirVyyf/9+axu/WDB06FA8/PDD1v3z8vLw4YcfYs2aNdi/fz8uvfRS7Nu3Dx9//DHOOOMMPPXUU/7+CEQw47fCZQTRTZHW8ZayefNma61UaR1vlmXZ6upq9u6772YzMjLYiIgINiMjg7377rvZ6upq1X3ga01v3LhRcZ+Wlhb2ueeeYy+88EK2Z8+ebGxsLHvGGWewN954I/vOO+9Ya5P+9ttv7P3338+ef/75bEpKChsdHc0OHDiQzcvLY3/99Ve78z733HOsXq9nIyMjWQDspZdeat0mfS9Fro43z0cffcRedtllbGJiIhsdHc0OGDCAveuuu9hffvlF9fXlapCyLMueOnWKffzxx9nBgwdba3dfc801bHl5ud2+jv62crVXX3/9dfb6669ns7Ky2JiYGDYlJYUdMWIEW1RUJKp5ShAEEUgc1UuWe7bxSJ+Jjup4yz1/Wdbxs98RroxPzc3N7Ny5c9kBAwaw0dHRrF6vZ5csWcJ2dHTIjk2O+uTos8idi9//+PHjbEFBAZuens5GR0ezf/nLX9j333/f7hyO/t5Go5G9//77Wb1ez0ZHR7MJCQnsWWedxU6fPp395ptvrPt98cUX7NSpU9khQ4aw8fHxbFxcHHv22WezjzzyCFtfXy/bd2c4+twffvghe8UVV7DJyclsZGQk269fP3bcuHHs888/L7peR0cH+/TTT1v7n5mZyc6dO5dtbm6WPb+j+9Ib8Pev0o/cnKWtrY1dtGgRe+aZZ7JRUVFsnz592OnTp7NHjx71SR+J0IVhWcq7TxAEQRAEQRD+YMCAAQBgtaISBNE9oBhvgiAIgiAIgiAIgvAhJLwJgiAIgiAIgiAIwodQcjWCIAiCIAgiKNm0aZO1lKMjhg8f7lHFCQIoKSlR5f5+4403eq2sJkF0J0h4EwRBEARBEEHJpk2bVNVmzsvLCxnhHayx3SUlJfjuu++c7jdgwAAS3gThBpRcjSAIgiAIgiAIgiB8CMV4EwRBEARBEARBEIQPIVdzFZjNZhw5cgTx8fFgGCbQ3SEIgiAIESzLorm5GX379oVGE5xr6jSWEgRBEMGMr8dSEt4qOHLkCPr37x/obhAEQRCEQw4dOgSdThfobshCYylBEAQRCvhqLCXhrYL4+HgA3D8hISEB2LsXyM7mNt5+O/DJJ0BLCzBkCPCvf4E9+2xI1/K7oMGxb39Bnwv7+bXvBEEQRPjT1NSE/v37W8erYMRuLHXC4cPAOecAwkw0Gg3wyy9APxpKCYIgCC/j67GUhLcKeJe4hIQEbrIQFWXbGBcH9OjBCe+ODuDoUYWzmHHwp1okXHaW7ztMEARBdEuC2YXbbix1wn//KxbdAGA2A7W1wFk0lBIEQRA+wldjaXAGggU7HR2211FRQGws97q1FdDrwdrZu4EuaJE+ZpCfOkgQBEEQoY1ez1m4hWi1wCAaSgmCIIgQhIS3OzgS3jodmAfniXY3g8GWvJXIyA7OuDuCIAgiiDEagbIyoKgIjUuL8MsTZaipNAa6Vz5HpwNWreLENsD9XrmSaycIgiAIdzEagY0bud/+hFzN3cGR8AaAq68Gli+37nJq1kPIeS3fjx0kCIIgwgKDAZgxw+pznWz5MS9hUJ5XjJyS8B5b8vOB3Fxg3z7O0k2imyAIgvAEgwEoKOBClzQaboE3309DKVm83aG93fZaKLw7OgCTCejqEu2e0KeHHztHEARBhAVGo0h0C9GAxajSwm5j+R43jkQ3QRAE4RlGo010A9zvwkL/Wb5JeLuDksUbANraOPEtpLPTP/0iCIIgwoeqKlnRzRMBE2o37/NjhwiCIAgidKmqsoluHpOJ86ryByS83UEovKOjxcK7tZWEN0EQBOE5ej3gILMqJe0kCIIgCPUEOmknCW93cGTxlhPeEtdzgiAIgnCKTgcUFcluoqSdBEEQBOEagU7aScLbHVwV3mTxJgiCINzhzjtlm5seejrsE6sRBEEQhLfJzweqq7ms5tXV/kusBlBWc/cg4U0QBEH4AwWPqaT+CX7uCEEQBEGEBzpdYBJ2kvB2BwfC++R765CYlSzen4Q34SVYlkVnZyfM0swQRMDQaDSIjIwE4yAWlyDcRilUSbrAGyYYjVzyG72espgTBEEQ4QUJb3eQlBM79O1e9Le8TVz+OFgwEE3BKcab8BCTyYRjx46hubkZnbSQE3RERkYiPj4evXv3hpYPHCIIb6D0fQ9D4R3I2qoEQRAE4WtIeLuDwOJ9vLoJ/X75QrSZgaT8CwklwgNMJhMOHTqE9vZ2JCYmIi4uDlqtliysQQDLsjCZTDh16hROnDiB1tZW9O/fn8Q34T2UFm7DbEFXqbZqbi5ZvgmCIIjwgIS3OwiFd9Ux9HK2PwlvwgOOHTuG9vZ2ZGZmIlaYT4AIGuLi4pCYmIiDBw/i2LFjSE9PD3SXiHBBKLCjo20eV2Fm8XZUW5WEN0EQBBEOUFZzdxAI7+ThA2CCE8sjCW/CTViWRXNzMxITE0l0BzmxsbFISEhAc3MzWJZ1fgBBqEEovGNibK/DTHgHurYqQRAEQfiaoBfep06dwsKFC3H11VejV69eYBgGJSUlqo8/ceIECgoKkJqaip49e+Kyyy7Dzp07PeuUQHinDOuHirxiCBfq7abcJLwJN+ns7ERnZyfi4uIC3RVCBfHx8db/GUF4BeG9FB1tex1mwjvQtVUJgiAIwtcEvfA+duwYnnzySezevRt/+ctfXDrWbDbjuuuuw/vvv4977rkHzz33HOrq6jBu3DhUVVW53ylJVvOckny0TsqzNjGTJ4v3D7NYPMJ/8NnLKWY4NOD/T5R1nvAa3cTiDQS2tipBEARB+Jqgj/HOyMhATU0N+vTpgx07diA7O1v1sWvXrkVFRQX++c9/YtKkSQCAW265BYMHD8bChQvx/vvvu9cpaTkxAD37JdnaIiPF+5P1i/AQSqQWGtD/ifA60hhvnjAU3kDgaqsSBEEQhK8Jeot3dHQ0+vTp49axa9euRXp6Ov76179a21JTU3HLLbfg3//+N9qFZcFcQVJODAAQESG/HSDhTRAEQbiHkvAmTyqCIAiCCCmCXnh7wo8//ogLLrgAGknGlhEjRqClpQV79+5178QyFm8S3gRBEITX6SYx3gRBEAQR7gS9q7kn1NTU4JJLLrFrz8jIAAAcOXIE5513nt329vZ2kTW8qalJvIOc8BbG4EqFN1kmCIIgCDUYjaivqEIV9MgcrYMuhGO8nY6lBEEQBOEjjEauVKVeHzwhTGFt8W5tbUW00EJgIcYyeWltbZU97plnnkFiYqL1p3///tZtRiPQ8OdJ284NDdxvsngTRMiye/duLFq0CEVFRYHuCtGdMRjAZmYidcp4jJyShcWZBnz+aegKb2dj6caN3G+CIAiC8CYGA5CVBYwfz/02GALdI46wFt6xsbGycdxtbW3W7XIsWLAAJ0+etP4cOnQIAPDOO8DiTAN67dhg2/mii7j/plB4Cy3iAAlvgghiurq6cOedd2Lx4sWYNWsWPvnkk0B3ieiOGI1gCwrAWGrAa2FGEVuINS8cse0TYq7mjsbSYJwQEQRBEKGP0QgUFAB8gRmzGSgsDI6F3rAW3nxGdCl8W9++fWWPi46ORkJCgugHAJ699zBWstMhylvMstx/8/RpWxtZvAkiZHj22Wexc+dOPP300xg4cCBmzpyJ48ePB7pbRHejqgqMpAxdBEzoxx6yNYSY8FYaS++7LzgnRARBEEToU1VlG2N4TCZg377A9EdIWAvv4cOHY+fOnXY1dbdt24YePXpg8ODBLp3vDPwh/wczmYDGRtt7i0XdCsV4E0RQ8ssvv2DJkiWYNGkSHnnkEZSVleHEiRO49957A901oruh14NlxCNMF7Q4xqTZGoSu5iE8rliM+laCZUJEEARBhD56PSDJqw2tFhg0KDD9ERI2wrumpgZ79uxBp8C6PGnSJNTW1uLjjz+2th07dgz//Oc/MXHiRNn4b0fsx5lg5TZotUB6uu09WbwJIugxmUy4++67odPp8OabbwIALrzwQqxYsQLvv/8+Pv300wD3kOhW6HRg7r9P1PQ3ZiVum5lkawgxi7cS0nL3wTIhIgiCIEIfnQ5YtcqW91qrBVauDI4EayGR1fzVV1/FiRMncOQIF+u2bt06GC1+affeey8SExOxYMEClJaWYv/+/RgwYAAATnhffPHFuPvuu/Hbb7+hd+/eeP3112EymbB48WKX+/HwP/rhj3vPxCD8YWvUaLj/ptDVnGK8CSLo0Wq1qKystGu/5557cM899wSgR0S3Z9w44KWXrG8XHsyH7ocPAD7nX4glV1PilVeAOXO4jxBMEyKCIAgiPMjPB3JzOW+qQYOCZ4wJCeG9YsUKHDhwwPr+448/tlqx77zzTiQmJsoep9Vq8fnnn+PBBx/EK6+8gtbWVmRnZ6OkpARDhgxxuR9TpwKxL0UAfwDm6BhoVr8DjBrF/Tdff922I1m8CYIgCFeRVNrQ6SB2KQ8Ti/fUqcCNNwbfhIggCIIIH3S64BtfQkJ4V1dXO92npKQEJSUldu3Jycl48803ra6knhLZxCVd0mT0ASZPtm1wVE7Mw1i8YKxDRxAEQXiZU6fs20K4jrcjAjEhorGUIAiCCCQhIbyDBpYF+GzHvXqJt/GBBIBXLd4Ggy0lvkbDxSzk57t9OoIgCCJYaW62bwtDizfefBPgy3k2NgJ1dUBaGpCcrNwm156SAowerUpF01hKEARBBBoS3q5w8qRtspOSIt7myOLtpvBWqkOXm0ur9QThKrW1tejTpw8A4IsvvkBubq7ivvfccw9ee+01jBo1Cps3bwYjzQZFEL5ATngLxw+h8A7hrOaYO9d752IYoLjYoYqmsZQgCIIIBsImq7lfEJYMk1q8IxysYbgpvIO5Dh1BhBrp6ekYOHAgAK6koBI//fQT3njjDWg0GvzjH/8g0U34j+5i8fYmLOu0EDiNpQRBEEQwQMLbFXg3c8CxxVsKy9qP+ioI5jp0BBGKjBkzBoBj4X3vvffCZDJh+vTpuPDCC/3VNYJwLrzDKMbbqzhR0TSWEgRBEMEACW9XWLPG+rK5VSK0hTHecrhh9Q7mOnQEEYqMHj0agLLwfvfdd1FeXo7k5GQ8/fTT/uwaQTh3NSfhLY8TFU1jKUEQBBEMkPB2heJi68u4t19B+TSDbZsjizfgtrt5fj5QXQ1s3Mj9pmQwRMAwGrkb0YFLZ7DDW7wbGhqwT2Iha25uxvz58wEAS5YsQe/evf3eP6KbQ67mrsMwqlQ0jaUEQRBEoKHkam7CABhVWoia2bnIyNb5THgDwVmHjuhmhElK4HPOOQeJiYk4efIktm3bhkECK9nixYtRU1ODYcOGYebMmQHsJdFtEQrvyEjudxgK7zl4AZHgsponoRHpqMfdD6UieYAgq3l9PZCaap/VvL4eqK0F3n+fa3v4YdXPIhpLCYIgiEBCwtsDImBC7eZ96oR3KGegJYKfiy4Cjh71zblNJvG5zWZg+nTgscech1i4S58+wI4dXj+tRqPByJEj8eWXX2Lr1q244447AAC7d+/GK6+8AgB49dVXofXV5yIIRwiFNz+mKMV4h/CY8jbyASSI2i64Ghg3TuUJPvvMJrz5smQEQRAEEeSQ8PaALmiRPsZiMfNBjDdBqOboUeDwYf9fMwQZM2YMvvzyS1Gc93333YfOzk7cfvvtyMnJCWDviO5M57FGRFrfdIp/A2Fj8WYYLucoj8uJziIjba9pbCUIgiAEGI1cNQu9Pvi8nEh4u4kJGmzJW4mcbMt/1Ieu5gThFEt9ap8gtXgLr+lLi7eP4BOs/fTTT2hvb8f69evx9ddfIy4uDsuXL/fZdQnCEeXTDBh74E9bQ1cXp07D0NX8lVeAOXO4j+BWojMS3gRBEIQMwR4ZScLbFV54AY1tsTjSnoJe142yiW6AhDcRWHzgli3CYOBq5QpnysH0JHOBiy++GFqtFh0dHdi8eTPmzp0LAHjsscfQt2/fAPeO6I7UVBoxurQA0orxNRX7kSEU3lFRNnNxCAvvqVOBG2/kKoANGuSGRYKEN0EQBCHBaLSJboD7XVgI5OYGj+WbhLcr5OcjOSEByXLbKMabCGfy87knl9sz5eAhLi4O5513Hnbt2oX8/HwcOHAAer0eDzzwQKC7RnRTjpZXIQNmu/a6H34XC++ICG7hq6srpIU34GGis6go2+uODq/0hyAIgghtqqpsopvHZOKmrsEybaVyYt6CYryJcEen47IfBcvTywP4smLV1dUAgJdffhlRwsk8QfiRPjl6mGSG47SLMsVjR2SkbawJceHtEWTxJgiCICTo9Zx7uRCXc4j4GBLe3sKJxXvfK5+hoagspGsgE0S4wMd5A8DEiRNxzTXXBLA3RHcnI1uHrXf8w759WJrYW4q3eAPd0ovKaOTqcB9tIOFNEARBiNHpuJhufph0K4eIjyHh7S2cCO9Bby5AyqwpYPv3R9PMB1FRZiQNThABItZSgig6OhovvvhigHtDEMCYl6fYN3Z0KAvvbmbxXrECyMoCxo8Hxl1l8045fZKEN0EQBMGRnw9UV3OLtNXVwZeOiIS3t3AW422BAZCwcgVGTsnC4kwDDAbfdosgCDEmkwmLFi0CADz44IM488wzA9shggCA1lb7ts5OsUU3IsI21nQj4b18OfDgg7bYvXbWZvFet7aDxlGCIAjCSjBHRpLw9hYullXSwowithBPFpDlmyD8ySuvvIKff/4ZAwYMwIIFCwLdHaKbUVNpxI8vbERNpeTB39Zmv3Nnp9ji3Q1jvI1G4KGHxG2dtmrniEAnCgspiosgCKI7wYcehdqzn4S3t1Bp8RYdAhPOMO/Dvn0+6A9BEHasWbMGDz30EBiGQXFxMXr06BHoLhHdiM13FiF9RCbOnzseaSOyUD5NYKpVI7y7oat5VRVXPU2IUHhHotOatZYgCIIIfwwGW+hRVhZCyuuJhLe3cEN4d0GL/ZpBQZVtjyDCjc8++wwDBgxAYmIibr/9dnR2duKxxx7DFVdcEeiuEd2ImkojRr03GxpwKlILM0aVFtos30qu5t1ceMtlqe2ALcY7Ch1Bl7WWIAiC8A1KtbpDxfJNwttbyAhvM6NFF+Rd0FkAf2NW4olVuqCMQSCIcGHz5s04cOAAurq6cP755+PNN9/Ek08+GehuEd2Mo+VVVtHNEwETajdbTLVKFm9pjHc3y2ouzVKr0QATbrRZvKPQGXRZawmCIAjf4KhWdyjgupmWkEcmxlsTHYna76vQ8eKryFqzTLStaWg2Fn6VT5MFgvAxS5cuxdKlSwPdDaKb0ydHb9fWBS3Sx1hMtXLCW5rVvBvGeANcVtrcXG5iNWgQoEuNBGK4bWNHduLyIMtaSxAEQfgG3gtKKL5DyeuJLN7eQs7VXKtFRrYOWflX2m1KPCeTRDdBEEQ3ISNb/MA3g8GWvJW2drUx3t0wqzkgzlJrrLVZvKM1VE6MIAiiuxAKtbodQcLbWygIbwDAli12m+p3HPBxhwiCIIhg5fS02cgpEZhqKcZbFQYDkHWGxhrGVX+kI8A9IgiCIPxJsNfqdgQJb2+hJLyNRrBPPGG3qfeBHfblZAiCIIhuQXysRDi7GuPdDYW3MKkOn9n8yIHOkEmqQxAEQXiHYK7V7QgS3t5CTnhHRABVVWCktVAAMIAtqQ5BEAQR/iQk2F7X1oq3yQjv6pJv0XngiK3hyJFuLbyFSXV44R2JzpBJqkMQBEH4l2Cr903C21vIJFeDVgvo9WAZxm4TC9iS6hAEQRDhT3y89WXr99vR+NBSoKyMmxHIuJoPKFuByJqDtobMTODECe51N8lqLkRYWowvKRaJzpBJqkMQBEF4D2eiOhjrfZPw9hZKwlunA1NcDLNEfDelnmmXbIcgCIIIYwRpWGOPGZH83KPAlCmcoC4vd348ywKHDnGvu6HFW5hUh7d4Z/TqCDlXQ4IgCMIznInqYK33TcLbW2g0tqV4Hl6M5+dDc/AgGorKwIIT4ImZSbKnqak04pcnytBQVBb4u4MgCILwGuamZvkNLAt8/LGLJzNzx3Uz+KQ6yWmc8I6Lls9qHmzuhQRBEIR3UCOqg7XeNwlvbyKN8xZawXU6pMycDCbWUny0wz4Ta/k0A9JHZOLcJVOQMmsK2MzM4PCLIAIO2w0n2KEI/Z8IR7BymcutG924d6Szim6CTgfExHOu5qLkcxaC0b2QIAiC8A5qRLUwNIknGOp9k/D2JlJ3czn38+ho7nd7u6i5ptKI0aUzoIFt8sWwLNiCIPCLIAKGxvLUMHVDt9JQhP8/aaRPeyLsqV1fiapZL6B2faX8DmYztGYH32OZXCB2aDTcbIKnOz8XIi21vCXCO1jdCwmCIAjnqPFWUiOqg7Xed0jMDtvb2/HQQw+hb9++iI2NxciRI/HVV1+pOvbrr7/GZZddht69eyMpKQkjRozA6tWrfdNRRxZvnijLKr1EeB8tr4IWMtnPzUHgF0EEjMjISERGRuLUqVOB7gqhgubmZuv/jOg+/JE9BWkTR0BfNBdpE0dg75hp9jsJspbL2rYvuUT23I2PPw8UFXFJ2A4cAPr1s20k4W3nPRas7oUEQRCEY9R6K6kV1cFY71umBlbwMW3aNKxduxZz5syBXq9HSUkJrr32WmzcuBFjx45VPO7TTz/FjTfeiFGjRmHRokVgGAZlZWWYOnUqjh07hgceeMC7HVUjvHmLt2Sy0CdHDzPsV0JYjRZMoP0iiIDBMAzi4+Nx4sQJJCYmIjY2NtBdIhRobW1FU1MTkpKSwKixXhJhQe36SgzcUQb+P84A0FeUonb9bKRPyLbtKHAzb7twFI6eexUy//k8tC2ngLQ0YMAA4Lvv7M6fvPgBsTVcOK50w8zmVhQs3rwlRCi+g8G9kCAIggBnyl63Dvj9d27sS04GADQ2AvsebcRy1KEOaThhTsZ/pwN/rQWS0QjU1Yn2z29sxOS8OtQzaeg1MBnJHQCKLCcS7KsDoAOACvtzWC8sbHcUEuYFgl54b9++HR988AGWL1+OefPmAQCmTp2Kc889F/Pnz0dFRYXisa+++ioyMjLw7bffItoieAsLCzF06FCUlJQEVnhLLN4Z2TocPXM0+vxh+zwsw4BZFQR+EURA6d27N1pbW3Hw4EEkJCQgPj4eWq2WxF0QwLIsTCYTmpub0dTUhOjoaPTu3TvQ3SL8SNPn5UiXtDEAmr/YLBbeLS3Wl7ED+uKMkkXAL+uB//4XOHYMOH1a/gLS77lwXOnOFm/ee6yri4uPt/ydeEtIYSH35wkW90KCIIhuz4svAn//u+ymZADPyG14VPl0CZafUCLohffatWuh1WpRUFBgbYuJiUF+fj4eeeQRHDp0CP3795c9tqmpCcnJyVbRDQARERG+mxirifFWcDUHgD4jBwAC4c385z9Abq4XO0iEIlqtFv3798exY8fQ3NyME3wdXyJoiIyMRFJSEnr37g2t3PeeCFsSrhkLtggQymMWQPzVY8Q7ClfRec+VjAzut9kMHDwIVQgXeLuz8BaGc3R1id7n53ND5759nKWbRDdBEESAMRoVRXd3IuiF948//ojBgwcjIUG8pjFixAgAwK5duxSF97hx47Bs2TI8/vjjyMvLA8MweP/997Fjxw6UlZV5v7NSi7f0PaDoai7bFhfnnX4RIY9Wq0V6ejrS0tLQ2dkJczfNZhyMaDQaREZGkgdCNyX98nNF71kAVaPzMFho7QYcC28Apv/9Crklm/JpBuSUCALTyOLNIRTeHR3i9+DENglugiCIIKGqKtA9CAqCXnjX1NQgQzA54eHbjhw5onjs448/jv379+Ppp5/GU089BQDo0aMHPvroI9xwww2Kx7W3t6NdYJFuampS11lXXM07OzkrhzAtn1R4V1cDhw+jcV8DDrelIGXiaGRkezaTMBqBgxVG6FGF1NF6mpmEGAzDIIr3miAIwn8YjWhYV4GaGoifxcePi3YzJyVj8OYS++PlhPfRo9Ymbau8q/mo0kLUzM61XS+EhLfbY6kahM9BmZJichiN3NxP74Whz5vnIgiCCHuEFTm6MUGf1by1tVXkKs4TExNj3a5EdHQ0Bg8ejEmTJmHNmjV49913cdFFF+HOO+/E1q1bFY975plnkJiYaP1Rsqjb4UpWc8BeaEvf33knMGUKkh+dhXOXTEH6iEyUT3O/IKnBACzpvwojp2Qhdcp4mDOpwClBEIRTDAaw/TORMmuK/bNYIrwVS4ZJhTefYMYJETChdrMgJXcICW+3x1I1CC3cKoS3N2t7U51wgiAIF9HpcLzfeYHuRcAJeot3bGysaMWcp81SmsVRlud77rkHW7duxc6dO611dW+55Racc845uP/++7Ft2zbZ4xYsWIC/C+IQmpqa1E0YXKnjDXBC27KAYH3vAA1Ye+uHSoxGYPEMI6ox01orXMOawRYWgsnNpSV7giAIOYxGsNOni2K4Rc/ilkbx/kqLwcL2Hj1Uu911QYv0MYKU3CGU1dztsVQNUldzByjV9nZn6PPmuQiCILoLRiOw+3AfXIn/AQD+gb/hCPpj/tJkW5Lxxkagvh5ITbXPPC5tl7Tt+R148SUgCY1IQz3qkIoTsJ2jFxqxYHo9EgY6OXdrq09j0YNeeGdkZODw4cN27TU1NQCAvn37yh7X0dEBg8GA+fPnW0U3wCVBuuaaa/Dqq6+io6ND1m03Ojpa1sruFFdczQH7BGtOJg+AzfrhqvCuqgLOZKusopuH4Quc0oyBIAjCnqoqyEXvW5/FWRLh3dnJCWLpeCC1eOv1XCZuVraqNwBOdG/JW4kc4fM+hCzebo+lanDB4u2otrerQ583z0UQBNFdqKoCtLAtFj+EFWhFD+SOAsaN8/z8cUbgzVfsn8+ArbpFgpo63k1NPhXeQe9qPnz4cOzdu9cuNoy3Vg8fPlz2uIaGBnR1dcEkMzHhk1PJbfMI6URLbjLgiqu5DHbWD5Xo9cAfjH18BUsFTgmCIJTR6yEnja3PYomrOQB5q7dUeOt0QHGxfbkwACwYHLxlHuq3V4sTqwHdJqu50Qhs3Mj9lsWFGG++trcQd4c+b56LIAiiu6DXAxEC4d2FCK8+O/lSkvzatFYLLF/OjSPV1Vy1i2Ag6IX3pEmTYDKZsGrVKmtbe3s73n77bYwcOdLqtnbw4EHs2bPHuk9aWhqSkpLwr3/9Cx0CQXvq1CmsW7cOQ4cOdeim7g5t+w6J3rObN9sHf3lg8TaDwZa8lW4lWNPpgIXF4uPMGi0YKnBKEAShjE4HRrIcL3oWNzbaHyMV3kYjTv3ne/vt+flcGbGyMjQuLcLvDxShoagMzKGDyPxwufyzPoQs3u6iKobaBYu33ITM3aHPm+ciCILoLuh0wJAzbcKb1US49ex0tCibn8+JbF5sz5vHWdOD6fkc9K7mI0eOxOTJk7FgwQLU1dVh0KBBKC0tRXV1NQyC0Xjq1Kn47rvvwFrc9rRaLebNm4fHHnsMF198MaZOnQqTyQSDwQCj0Yh3333Xq/2sXV+JtNNiywcDgC0oEMdQeyC8T+ffj5w33V+yyc8HMN32XlO9H/BmshuCIIhwRK8HNm2yvm18bQ1yrh8DlJUBX31lv79QeBsMYKfPQJzAbs7OfwhMr17cQ1mnAyZPRjIgiEZzQJgLb9Ux1C7EeAPere1NdcIJgiBcJz2ZWyRlGQb7D2jsnp3OqkUYDLbxQaPhFkGlluxgLyUZ9MIbAN555x08/vjjWL16NRobGzFs2DCsX78el1xyicPjHn30UZxxxhl4+eWXsXjxYrS3t2PYsGFYu3Ytbr75Zq/2senzcqTLtDNmszj4ywNX8/hELzsopMv1mCAIovtSU2nE0fIq9MnR2yzOEotqyu4fgHtuU47PbmkBjEY0vrMOSY/OsosRZ8CCLXAvseWpNi3i+DdvvMG5nqeliRN1hjCqY6iFY+mqVdz7tDT7pDl1ddZ2HQAdAFSI262kpACjR6v6nwT75I4gCCKQyIpoS0JQJjLS7vnpSFQbjUBFBTBjhm3Y9TSxZaBKQoaE8I6JicHy5cuxfPlyxX02CawRQm6//XbcfvvtPuqZjYRrc8AWwW6CxWo0YIQBDJ4kVzstX+fVbTo7xZMXX2P55nizLjlBEIS3KM8rxph3CpEBFiZoUJ63iouxlroyv/qqw/OcWrwCPT98y6EFmzG7npHLYAAGvrcXl/ENr72m+thQgY+hFopv2ThAQWgZioq81wGG4WLvgyUg0AFUS5wgiGBEUUTzlTgkObEceTpt2CDeJsTdxJZqLOe+IuhjvEOF9AnZqBqdJ0rCw4IBs2qV+I7wRHifOuVZJ6XWGRW1T72GwQBkZnq1LjlBEIS3qKk0Ysw7hdbKD1qYuZJhlUaXn5U9P3xLNhO6EFbjWlYZviTkpdjkUl9CDVUx1EajvIu/N2BZbsanmNUtOKBa4gRBBCNKItpohKLwVvJ02rJFWXQD7iW2dNg/P0DC24sM3lyCunXbsT9vIY4vLQJz6KD9EoonWc09Fd7Smq/+Et5Go9g/xIK1Fm5lcE9wCIIIf46W25db5EuGufqsdCq6GQ2YVa5llbGVhAx/pAly7CwRKmuguw1vRglSAj1xJAiCUMJRuJCS8FaqFsGyjkW3O8nZHPbPD4SEq3kokT4hG5iQrbxDIC3e0vP7S3hXVSnGQrpbl5wgCMKb9MmxL7doLRn2jfhZae4ZB81p15/HLIDGpUXoddcEl2cLfElIE8tAK1vgLLxwGEOtoga6RwR5fTCqJU4QRLDiMFzIojtM2kh8v9EWJsN7OhUWcs8yXlSPHi1/rjVrgFGj3HveqQ5n8hHdYfE8uBAI76Z/fyNeopYT3sJVIZkY75pKI358YaM6q3GghDc/SZLB3brkBEEQ3kS6+CcqGSZ5VopE9+zZaFxahJqrpjq9BvPXv6LXgpluzRb4kpAzmWKYnNrUwxxLDXRWYVzxBJZhgr4+GNUSJwgiWHEYLmSxeNfUR9iFych5Oimda/Jk9x/RgS4JSRZvP/PH2p040/I6YdULYItfBFNcDPzf/8kL77g4rr2lxc7iXfnXpbjoX48iAxAnAlIiUMJbpwMWLQIWLhQ18xPbHLJ2EwQRZLTclm99nrad6oRizvC77kLyyJFcLbAv33F80pwcj/rElbHKx/YtuRjcsAUpjfuA+nogNZXLav73v3t0/lDCgHwsRi5GYgvOxD7cfkU9ho1PFWUpP1HdiKZ99YgflIrkAZJs5/zfLTkZ9UtXIvXQLgDAxexWFGAEgjm1mpJ1KIjXCgiC6EYolVw0dXRBC6DLIj+lmcnlPJ18Ub4xkCUhSXj7kZpKI87YUSZqY1hLWZnx42WPYU+cAMNbyXnhbSlTc9G/HrXaPayJgGbnKrttB0p4A8BNN9kJ75MLX0DOomCe3hAE0V2J6xNvfd3W5EB49+rF/e7Rw/lJhaFGbqLTAbrJOgCTxRuamrqN8LbGOLM6HLL8HVZsBKrftk2gDAagYLnzrLVGI/Dd337AHdgFAGhAL49K1PgLqiVOEEQwIyeiTe1i4Q2oC5PxRfnGQJWEJFdzP8Il77GHMZvEpVGE2wCwfCz46dPW7ODJMrVhrYmAlAik8DaZ7JqSz3BUbIcgCCI4iI108KzkhXdsrPMTeUF4E86T47iSfOzll4E22P4v0WgP9txqVnQ6YNw4Et0EQYQGESw3lnYi0trW3cJkSHj7kT45etnYPBYATpxQPM56RHOzbHZwHqfx0kEmvBVTFRIEQQQR0YyDZ2VSEvdbjfCOUbSbEy4gF+Os0QA9e3Kv1WatNRqB558HOmCrNhKFjm43ESQIgvAHGhMX481bvLtjmAwJbz+Ska1DRZ59YhwGAHv77db3Ulltfd/W5jCLa/lFDzjODk7CmyAIwnWUnpVJSbYMLX5yNSfsk+MA3HBy8cWcU5ja5GN8wY12icX7gQe610SQIAjCL1iSqw05J0K5XKQFo5FLtBZuZRJJePuZnJJ87Hl7m524FkrxamRZt7MAmIwM6zYl2W0GMPT1+x1fnIQ3QRCE6yg9KxMSbK+//tr5eUh4e438fGDLFrHA5l3KAeWstcLJHC/QhcI7lmnH/U6GUoIgCMIN+Dre2giH1SANBi7juTTzeThAwjsAdBw/5bAYTNpNY7GzaDtOLHwRzPbtOBaZbt2mdNyhYRPE1m6jEY1Li7B/6hOoXV9puXCQCW9f1WAlCILwJkrPyoMHuRmB0Qj2mWedn4dczb3KqVPKLuVypWmkk7kNGziB3sXYXM3nz+mws3aHq+WFIAjCb5jN1gf2jp8jFUW1Kzk6QhES3gGAi/VWpmdSFC6cmY2kRXNQgwz0OrhLdj+hbM3KybK9ee01sP37I/nRWThj9RKkTRyBvWOmBVZ4y1m3yeJNEESwIlgY7Gpz8KwsKAAqKsAo+iMJIIu3V3HmUi5MPqY0mcvNBe6Za/u/XDO+XXS+cLa8EARB+A3e2g37cmJCUa02R0eoQsI7AHCx3m9CUXZG2VbflTKhAxLrd0MD99toBHvPPaJtDAB9RSkay/8nPoHgS+BzyNWcIIgQxdTqQHibzQDDgGUc+TFZIOHtVaSx3o4S9TiazCWm2sZctNuEd7hbXgiCIPyGjPAG7EW12hwdoQoJ7wCRU5KP2u2HcFo3xH6jQHg7tI4L70xeeFdVybqjMwBat/0sbgy0qzkJb4IgQoAIOHhWajTAqFFgiothlohvVvo0JldzryPnUi6Hw8mccEFE4BkW7pYXgiAIv6EgvKWi2pUF1VCEhHcAycjWoefIc+03CIS3onWcYYA33rDOJFp/3M3Fcuv1sg6PLIAef9GLG0l4EwRBOEVr4p6VJxEvfr4yDDdD0OmA/HxoDh5EQ1EZfn+gCA1FZWAemi8+EVm8fYKaetYOJ3OC/8vuXe1Wi3a4W14IgiD8hkh4c3W8lUS12gXVUCTC+S6ET0lOtm8TCG+As47XzM5F05r1yGjei4QLBwMTJgA6Hbpm3YsIcztijxkRM3EE9o7Ow+BzzwV++UV0jqqL78Lg8zLF1wm08KbkagRBBCvC55PlWdljcH/sfKAEg/Z+hsTBfazPYSs6HVJmTkYK//5FiU8yCe+Akp/PxXTv28eJZ+u/TjDmvvRcO95cwYn0/Hzud2EhN4SFm+WFIAjCbwg0x2VXRmDjI5LnsASdLjyftSS8A01Skn2bRHgDnOU7I3umqK12fSXSumzxaHwsd9tfLoLUoXHwJ8uBL74QN1JyNYIgCOdYnpWRsZG4cGY2gGx1x0mFNgnvgCM3mWs4HW1dLIlChyjxmqJYJwiCINQjsHjHxkdg3LjAdSWQkPAONCos3ko0fV6OdEkbA4CtO26/c0ND8JUTI+FNEEQowD8rIyNdO04a000x3kFJTYNNeEeDW8zmY7l5oU6CmyAIwgOECZ0juq/8pBjvQKPS4i1HwrU5dvHcLABNYk/7nY8fJ+FNEAShFv75ZDLZ3M49Fd5k8Q5K+mTaxtwocOMkxXITBEF4EaHwdnUsDSNIeAcaD4R3+oRsVI3Os4pvFkDV6DxEx8tYVcjiTRAEoR5+kiB8TpLwDkt697P9X6LRTrHcBEEQ3kY4lpLFmwgYcq7mp0+rPnzw5hKY0jMAAOaEJAzeXCJfn9tLwrum0ohfnihDQ1GZa8VMKbkaQRChBP8c9WSVXiq0+ZTahM8wGrlMuC7V2hb8n+6+rd0vWXTd6idBEESoQq7mAEh4B57ycrsm9pFHAYNB9SkiEuMBAFrGImTlBHVDA9DeLm5zUXiXTzMgfUQmzl0yBSmzpoDNzFTfT7J4EwQR7AjrcPvC4k34FIMByMoCxo/nfqseRgVeZpl9Onxu6Xa7nwRBEKEKCW8AJLwDi9EI9tln7ZoZsGALCtUvhfOTQX6CKCOom3+t9sjiXVNpxOjSGdAIosoZ1oV+UlZzgiBCCRLeIYXRCBQU2IYVPjO5qmFU6JkgXaAWnN8bFmqP+kkQBBGqUIw3ABLegaWqCoyCuzVjtqRUVQO/Ws9PEGVczXuWFuHQJzvEjS4I76PlVdDapXJzoZ9k8SYIIpSQW8gk4R20VFXZDykmtcOoE+HtTQu1R/0kCIIIVcjiDYCEd2DR68EKXRsFsBoXUqoKLd4sKyuoNWDR79cvxY0uCO8+OXrIyWTV/aQYb4IgQglvWLwpmZrf0OsBjWRGozozuTChqcQzzNsWao/6SRAEEapQcjUAJLwDi04HprgYZon4ZhkNmFUupFQVTgZNJkVBrZFarF0Q3hnZOtScfaWkn4z6fpLFmyCIYIdivEMWnQ5YtcqWv86lzOQOLN7etlB71E+CIIhQhSzeAIDu+8mDhfx8aHJz0bB+C47tbUDvwSlImTDKtVFYOBns7LTe3CwAoaQ3gxGLbxeTq/Ubpwd++8r6ntmwAbjySgdHCCDhTRBEsCP0wiHhHXLk5wO5uZwoHjTIhWHUgfDmLdTC4cpTC7Xb/SQIgghVPIzxNhq5hVC9PrSfmSS8gwGdDikzJyPF3eOlwtsyUWyL643YU8cAcCK8fuBIpP+5VbyvK0hj31Jc6DElVyMIIhgxGoGKCjQ0AL1Y1rpY2XasGbtf2AhdeidS+X1dnCxsnftPXCx4Xz7NgJwSH9ep6ubodG5Myhy4mvMW6sJCbv2Yt1ADXLI1dyeBbvWTIAgiyJEKZP79eUe70JvfyUWLt8FgC/nRaLhnsq9LPvoKcjUPBxSEd2xWOlhLMFnn0POgMYutzke//sW160iFt5wVWwmyeBMEEWwYDEBmJjCFK5Eo9BCK3vodzp87Hr3uvMbW6ILwrqk0IrtsnqhtVGkhaiopfXXQ4SS5Wn4+UF3NCe3qaq6NyoERBEGIkSainDbN9n56nnsx3uFWCYKEdzggnAx2dNjcOSIiwMTGAgCYjg70rq4UHZZW9YNrk0BvC29KrkYQRKAwGoEZMxSfQ7wI1wrTSrogvLlKEOLFxQiYULuZ0lcHHUKLt0I5MZ0OGDeOex1Ok0CCIAhvICeQS0tt7zWsezHe4VYJgoR3OCCcNAgs3oiMtMYYmpqaIc2frgHr2iSQLN4EQYQLVVWuL/65ILz75OhhkgyxXdAifQylrw46IiJsifUkruZSwm0SSBAE4Q3kno1CIuBejHe4VYIIiRjv9vZ2PPHEE1i9ejUaGxsxbNgwPPXUU7hSZWKvDz/8EC+99BJ+/vlnREZG4uyzz8ZTTz2F8ePH+7jnfkJ4A7e32+58gfCOaj8lm2zNpUmgVHjL1AtXhIQ3QRDBhF7PiS1XxLcLk4WMbB3K81ZhVGkhImBCF7TYkrcSOdkU2Bt0MAy3gN3ezpltiops2xobgbo6IC0NSE7G8EZgJoBENCINdahDGpqQjL9UANgt2X/QIGD0aArmJggi7JFLRClEJLxdsHgr5dkI1cdqSAjvadOmYe3atZgzZw70ej1KSkpw7bXXYuPGjRg7dqzDYxctWoQnn3wSkyZNwrRp09DZ2YlffvkFhw8f9lPv/YBwMtjaansdEWEV3prmJrvDjmcOR4Yrk0CyeBMEES7odMA//gHcc4/D3UTVIFxMrpZTko+a2bmo3bwP6WMGkegOVgwG2/hWUwPMmqW4azKAIrkNjyocwDBAcXHoZgIiCIJQgZxAvvNOYPVqbrofzXTCWljJxeRq4VQJIuiF9/bt2/HBBx9g+fLlmDePS1QzdepUnHvuuZg/fz4qKioUj926dSuefPJJPP/883jggQf81WX/oyS8BRZvOXpm9nLtOp4IbzmRTTHeBEH4gZpKIxrWVSAjA0iZKLBA3nSTU+G9EoX4G97g3rhRAiUjW+faAifhX/hYf1/BstxMNDc3tGeLBEF0e5yV9JIK5A0bOOENuG/x5gmXShBBH+O9du1aaLVaFBQUWNtiYmKQn5+PLVu24NChQ4rHvvTSS+jTpw/uv/9+sCyLU6dO+aPL/kc4GWxpEbcLs7VK6Ky3t4I7hCzeBEGEGOXTDEgfkYlzl3CZy9nMTFsa6uZmp8fHQ7CPG8KbCHLcifV3FQoCJwgixJFmLFeq5qCUiFKUXK0bj6VBL7x//PFHDB48GAkJCaL2ESNGAAB27dqleOw333yD7OxsvPLKK0hNTUV8fDwyMjLw6quv+rLL/kdJeAtczWUPazrGxbKVlalLyUrCmyCIEKKm0ogxpdNtruIAGJYFW2BJQ61CeP8Fu2xvTp/2QS+JgMLH+vuSUM4ERBBEt8edkl7SZGueWrzDhaD/5DU1NcjIyLBr59uOHDkie1xjYyOOHTuGzZs349tvv8XChQuRmZmJt99+G/feey8iIyNRWFgoe2x7ezvaBSKzqclFy7C/cWTxdiC8Y2v222LZ1MShuZhcTeSSQsKbIAg/c7S8CvajB8CYLRZIFZbO8/Cr9TX75JNgMjMpXlclITGW6nTc2OegtJzHeJgJyJl7J0EQhC9xVM1B6ZkkTbZGwpsj6D95a2sromXcpWMsgrJVGNMsgHcrb2howAcffIApU6YAACZNmoTzzjsPTz31lKLwfuaZZ7B48WJvdN8/uCm8RaiJQ3PB4m0w2FbHNBpg51Um/EW6EwlvgiB8SJ8cvV01BwBgNVowgwYBO3e6dD4GAFtQCIbidVURMmMpH5i4ZQvQ0CDe1tgI1NcDqalAcjIAYH818MayRqShHnVIxQkkQwNg6VIgGY3A779zBWwBYNIkjxZqpGPpqlW07kMQhH+Ry1juzJFHpwOWLQMefJB7H4lO67aGkxFI8VFfg52gF96xsbGiFXOetrY263al4wAgMjISkyZNsrZrNBpMmTIFCxcuxMGDB5GZmWl37IIFC/D3v//d+r6pqQn9+/f36HP4FGEdbxdcze1wtnylUnjLuaR8tcFsL7wpuRpBED4kI1uH433PQa8jAqs1w4BZZbFAfvedy+e0WstJeDslpMZSnQ6YPFnVrtUbgeeW2bdPGWWJbTx40Ca8PXBjV3LvpHUfgiD8iU4HvPusEf9+qAID2H1IRx3GTEyDbh23GCktu8hz4+5GaCxlF0dgu7W9bt9JEt7BSkZGhmzpr5qaGgBA3759ZY/r1asXYmJikJSUBK1WK9qWlpYGgHNHlxPe0dHRslb2oMVRVnPJoC9n/bHibPlKpfCWc0lhWHI1JwjC//TKSgAEEUnMtm1Adjb3RkWMtxSrtZxwSsiNpSpxav0RGgQUvPLU4I57J0EQhNcxGHDbQzNwm9Bg9onlxwGDAPxdpn3o8zOAoeiW7jtBn1xt+PDh2Lt3r11s2LZt26zb5dBoNBg+fDjq6+vR0dEh2sbHhaempnq/w4HAheRqXVE9YJKT3gzjPA5NZYw3PykREsGQ8CYIIgBI44p797a9dlF4s4zGZi0nui18vVp+TV+rlQyfQuEtHJNdRG4spTxtBEH4Fb7kohe9VBk+vFVNYucwI+iF96RJk2AymbBq1SprW3t7O95++22MHDnS6rZ28OBB7NmzR3TslClTYDKZUMq7fIFzUX/vvfdw9tlnK1rLQw4XYrwj01NQt/0gfl1YhlOT77ZtWLTI8coTy6q2eMtNSq64jIQ3QRABQCq8O21xZq4I718nLQRz8EC3XKEn7MnPB6qrgY0bud+i28JLFm+nAp8gCMLX+KrkYjctsxj0ruYjR47E5MmTsWDBAtTV1WHQoEEoLS1FdXU1DIIiclOnTsV3330HVnBzFBYW4s0338Ts2bOxd+9eZGZmYvXq1Thw4ADWrVsXiI/jGxy5mktr5cXFISNbh4zsycBaBvjn21x7jx6Or9HVZf/Fc5Bcjc9Vs28ftzqvW2oCvpXsRDHeBEH4GkfCW2WW7S5o0Wv+dFI8hAidTuGW0Gq53CsdHR4Jb0BmLKVbkCAIf6LX++a83dR9J+iFNwC88847ePzxx7F69Wo0NjZi2LBhWL9+PS655BKHx8XGxuLbb7/F/Pnz8dZbb+H06dMYPnw4PvvsM+Tm5vqp937AlTrecXG218LYO4k7vh0yCe6c1fEWTUqonBhBEP6GZT22eHdBiy15K5GTTYqHcIHYWG5c9cDVnEdR4BMEQfganQ4YPRqoqPDeOTWabuu+ExLCOyYmBsuXL8fy5csV99m0aZNse1paGkpKSnzTsWDBlXJiQuEtzIYuEN6yNUPlhLeTOt4i5EQ2CW+CIHzJ6dP2njWdnYDRiIZ1FYj8ficSHBxuvHE2tI88TKKbcJ0ePYCTJ9F2ohXHjN1yfkkQRLiQlWUT3nPncvN3S4nFxkag4Y9GpDH1SBiYKspqjsZGNP1Zj3qkotfAZG5TSgowalS3fSiGhPAmnKBUTswN4a1YM9QNi7fTfUl4EwThS+RcyT/+GOyzz6oqZaLLPRcg0U24QVNXLBIANNe1IiuL6m8TBBHCCL1iH3gA6NcPgEUzPCajGQQkWH4IjqBPrkaowF1Xc6Hwbm9XrBlqNIKEN0EQoYeM8GaffVa5pKIU4TOSIFRiNAIH67kEaz3QIh5LCYIgQg2h8LaMiw41A6EICe9wwFFyNRdivB3VDEVbm/11PRXelFyNIAi1GI1oKCrDL0+UoaZS5cguI7xVi26AhDfhFlVVQAu4hKWxaAXAdtcEvgRBBCFGI1eRQbVIlhHeDjUDoQgJ73DASzHeDmuGehrjTRZvgiDcxWCAuX8mUmZNwblLpiB9RCbKpxmcHydn8XblutKqEAShAr0eaAVn8daARRQ6umsCX4IgggyDgQvZHj+e+21QMZSKNIDFaOdQMxCKuCy8a2trwTAMGIbBhg0bHO57zz33gGEYjB49WlTmi/AyLriaN58UiF2J8HZYM9RTV3NKrkYQhDsYjWBnzIBGIJk1YDGqtNC55VvO4n3eeaovffyAunJjRPdBjaVIpwPOPMdWyzte09JdE/gSBBFEuO0eLkzAXMtpDoeaQXA9lyzr3QCXhXd6ejoGDhwIANi2bZvifj/99BPeeOMNaDQa/OMf/wDDuOTgR7iCI4v399+Ldo1762WbpUgS4w1wSRGqq7kvSnW1IEkCxXgTBBEIqqrAyCzcRsCE2s2Ofdr2vrDevlGQcfVI7jQ0Li0C7rtP9vikhwrVWdaJboErliLd4B7W1z9va6XEagRBBBx33cPrj3DCuwtaZA3UWp99ipoBblrWuwFuuZqPGTMGgGPhfe+998JkMmH69Om48MIL3esdoQ4l4X3qFNjXXhPtygA2S5FCHW+dDhg3TrI6L7dcRTHeBEH4Gr0erMzCbRe0SB+j7NNWU2nEoM0ldu0ddcetr/tOvw7JC2YCl10mew7VlnUi7HHZUhRrs3j3TW6120yWIIIg/I077uFGI3CkmtMIHYiye/ZJNYPRCJSVATNmUOI1OdwS3qNHjwagLLzfffddlJeXIzk5GU8//bT7vSPUoSS8GxpkEwlZLUUKdbztMBiAO+6wa/5xB8V4EwThY3Q6MEuWiJpYAFvyViLDQamvo+VVIvd0HnPDSdsby+Lj/177TvE8aizrRPjjsqWoh83iLRqXQZYggiACgxr3cClVVUAUbMIbUH728c+2KVPsbWuUeI3DI4t3Q0MD9kn+is3NzZg/fz4AYMmSJejdu7eHXSScIhTQwqzm/fo5thSpEd5GI7dsJUPF+kb1q1ckvAmCcJeJE0Vv23JvRE6JY9/dPjl6mGWWHiPQaXsTHY2aSiPO/vplxfM4s6wT3QOXLUUCi7dwXKYSPARBBBJH7uFy6PU24d0ObrFa7tknfbZJocRrHBHuHHTOOecgMTERJ0+exLZt2zBI8JdcvHgxampqMGzYMMycOdNrHSUcILR4CzONp6WBKS6GecYMaCxLTyZosCVvJXKydWKRLhfDDXBLXQou4clowL59KhPGkPAmCMIJtesr0fR5ORKuzUH6hGzbBsnzKban8zXjjGwd6gdchNTqSlF7RIfguRcVhaPlVchQyHUuel4S3RreUlRYyA1nTi1FQuG9Zg3w9ddAXR3am9Mww8zlGUhCI9JQhzpTGk4/nwwMtuzf2AjU1QFpaaKcBLLtKSnA6NGUuY0gCCtGIzd91+vlHw06nfpHhk4HnE5uBxo5i7fSs0/OK4hHjWW9u+CW8NZoNBg5ciS+/PJLbN26FXdY3JB3796NV155BQDw6quvQsv7MhC+RankTWQkcOed0OTmomH9Fhw9CvS6bpRtEunE4l1TacTpd7fgTMjXvj2FBLvVK8WJM2U1JwjCAXvHTIO+ohTpANgiYO/oPAzmY7SlC4NtbarOmTooEaiWNJ46ZXsdHY0+Of1hAgOtRHybGQ2OfboVOcLnGNGtyc8HcnM5d8lBg5xMIvfssb1+6SXryzMBvCG3/0tyjSphGKC42LnpiiCIsMdgsFmeNRpuwdDTR0PPCE4j9O4bhept8s8+3itIOLXXarl1x1GjSHTzuF3HWy7B2n333YfOzk7cfvvtyMnJ8bx3hDocCW8A0OmQMnMyzlk0WRwTqdXaAj0kwrt8mgHpI/pj0FuPyopuABh9Razoi7R3dB7SJo6Avmgu0iaOwN4x02wbKbkaQRAK1K6vhL6i1PqsYQDoK0pRu95irXZTeOPYMfs24bMoOhoZ2TpU5BXDJHjSsYwGmuJV4sVDgoBC8lEpRiPw6af+6hI3lpK/OkF0e3wWymLRCLEJUYrPPqX48cmTSXQLcVt48wnWfvrpJ7S3t+Ojjz7C119/jbi4OCxfvtxrHSRUoCS8T5xwfixv9RYI75pKI0aXTpe9OYRS+dyhtgls7fpK6Le8ozxxJldzgiCkGI1oKCpD/aJ/2C3wMQCav9jMvfGm8BZief7llOSjbvtB/LqwDA1FZWAOHiDrYTfG44zjVVVe7Y8qKHMRQXRr+Gzi7pQLcwqvEYSesjK4Gj/eHXHL1RwALr74Ymi1WnR0dGDz5s2YO3cuAOCxxx5D3759vdZBQgVr18q3T5/OrYQ7uvOjorhYb8HElot5lEc0ORaI6abPy5Eus2/zF5s5qxEJb4IghBgMME+fgRSwSAG3qCd8vrAA4q/mPKvshHerfXkmWRoaHG8XlFTMyNYhI3uyuvMSYYtX3DT1es79259eXZS5iCC6LcLnlhSvPBpUCm/Atfjx7ojbFu+4uDicd955AID8/HwcOHAAer0eDzzwgNc6R6jAaARrySJvhxr3M37iKbB498nRK6Qagjitq0BMJ44+x/7yAE4fa+Fq4JLwJgiCx2gEO2OGqNyXVHRXjc6zuXpLLdxqLN4tLc4FukB4E4TX3DR1OqC4WLaqiE9gGMpcRBDdFEfZxL2S1Mxkss3hacz0GLct3gAX571r1y5UV1cDAF5++WVEqVgNIbxIVRUYR6vqvI+J0rdOxtU8I1uHloR09GiqFe/LMMDChdwPwGVQNxqBigrEfFNud2oGwF8+fBTmDx9DU+8BSJDuQDHeBNE9cfbcAjD4h7dtb9S4mlueRWho4LI///GHdZM5PhGa5pP2x9AkghDgqFa3yxPX/Hwwubn45qktWLuyAQloRDrqMfamVIy4ypKRvLERqK8HUlOtWcq//BL49l+NSEU9jiEVg0cmY9s2bjEqGY24/Yp6DBufCnz/PfDFF9x53nsPuO02jz47QRChiVI28RdfBCZN8sJ6nEAftLFRiPHwdN0dj4T36NGj8dprrwEAJk6ciGuuucYrnSJcQK8HyzDKk1hnPiYywhssix5dzQCA1qQ0dE66AwkXDgYmTACam23Ce88eoH9/ALAX1QI0YBF/bL/9BrJ4E0T3RK8HCwaMgm8NA3Dimi/J5Ex4v/kmMGOG4uUYOdENqHKbI7oPSll53XbT1Olw+RuTMeQxdZnQDQag4N+AGdw69yOPANOf4d7zrNgIVL8N6NrbbcJbWHKMIIhuhdJzyyuiG8A7b3ZgquX1dxVRMBoodtsT3HY1B4BYy6QoOjoaL774olc6RLiITsfV6pZzadNonPuY8BNP4cT2xAnOTRNA7EXDkFD8AjBzJneeCMFaTUWF6m5aeyfsJwlvgghfLInT9uUvRVP+HGDpUi7zi9HIPbcefsjx8ZZnEADHwpv3s3OAosMvWbwJAUpZeT2dvKrJhC51F2VZ4JlnHCRKEo7FcqFcBEGEBc6SPfrqucVf+8H7bYa5dkRRAQUPcdvibTKZsGjRIgDAgw8+iDPPPNNbfSJcJT/fWqv72N4GpKUCyYNS1BXOk4nxrvviv0jj30hX0t2szW5NnBQZabsWCW+CCE8kidNE8DWHc3OBZ59VPkdLC5BiOdpRcrWqKvfDVkh4ExJcqtXtBKORuz31eufnkXMXNZvtc7RZLfBbBdO3ri73O0kQRNCiNtmjN59bQqqqgAjWpg86EOV++A0BwAOL9yuvvIKff/4ZAwYMwIIFC7zZJ8IdLLW6h7wwE8kLZqovnCdxNS+fZkDv26+0bmb/+U/um8/jhvBmAbQkWjLdC0ufkfAmiPBDJnGaCD7p4+HDjs9z+rTttZzFm1cjer17/dRo3F5IJMIbVbW6nWAwAFlZwPjx3G/hMCoH7y4qRKsFli1TsGQJ710S3gQRdria7NEbzy0pej0Qw4iFNxVQ8Ay3hPeaNWvw0EMPgWEYFBcXo0ePHt7uF+EveOFtNqNm6wGMLp0huikYAGyB4JsuHOwVMrayYICCAmtG187BZ6NnRoL98ZRcjSDCC6MRJ1escpo4DSYTcPCg430cuZoDNs8ZnY5TNw4wyzmbk7Wb8BHuZEdXchd98EGFurjkak4QYU1FhY9qcruATgcse9I2/nYw0VRAwUNUu5p/9tlnmD17NhobG9HU1AQAePzxx3HFFVf4rHOEHxAkF6rb9BsyZKxUjFngVyIc7AWT69PX3Azj0CvQe3AKUiZwLu7MF18ABw+CMRrR2TsFkQA3m+CzQJDFmyDCB4MB7PTpSFSzr1YL9O7teB9nwrutzSae+/a1tS9cyLXX16MxKhVHYgah97AMpN+cIz6ehDfhI9zNjq7kLsr/rqoSvCeLN0GELbyLuZRAWJsnXd8BPG55fXsU4iixmkeoFt6bN2/GgQMH0KNHD5x//vmYPXs28imtXegjmHymZWfCDHs3CFajBcN/0xVcM3tOvBxD/jZT1HayLQqJACJbmsAe5BZroNGQ8CaIcMPiXq66avHKlUBcnON9hMJbrnxYWxuQaJH5JwVZy//2NyA9HQCQbPmRta6T8CZ8hCfZ0XU6e3EuG+dJFm+CCEuU6nKryZfsEwQ5oOKSqRKIp6h2NV+6dClYlsXp06exc+dOEt3hgsDinXFeKur0YqsQy2jArBJ805ViImPElf1qKo1IqLP5w/ATchNfJwUg4U0Q4YKTutytYwWeUbfeypn2hCUMZTh2wEGMNyAW4ydO2F4nytjchbkleKiUGOEjvJllWMltvbGZkqsRRDiiVJf7gw9cK+PlLBu6aoRjNY2bHuNROTEiDBB+iTo60GeATUAfX24Ac/CA+JuuUngfLa+StX6ZTKwtgwzFeBNEeGCpyy0Hq9Ei9tlFtgZ+4c2J8H6gsMWWkEpOeAszm/MW7+hou2cRAHnhTRZvwofk5yvEZruIktt67TFyNSeIcEQp0eKoUerP4WpyR4eQ8PYqJLy7O8Iv0YsvwrRtOwDAHNsDvebebb9Er1J498nRyyY00sRE2Z4oZPEmiPBApwMzs9Cu2eoxIyw3ybuQOxHeMWyLLSGVWou3nLUbIOFN+B2+lJinpX2UJuFpfcnVnCDCEU89ZtxJ7ugQ4VhN46bHkPDu7hw4YHv9wgvQNnGWI6a1BeV3v2W/f4RCWgDJlzEjW4f9F022Pzw2moQ3QYQjw4dbX9ZccScaispsHjPCyhcqhXcPtNgyuDoT3rzFOylJ/mTkak74EW9am5Qm4b1SyeJNEOGKJx4zjpI7uoVw/KVx02NIeHdnjEZg82bZTQyA0aUFqKmULJGptHgDwJkzr7Lfj0+uBpDwJohwoqHB+jJj9s1ImTnZtkQfG2vbjxfenZ0OT9cTp20JqRwJb7MZsFTaIIs3EWi8bm2CwiSckqsRRFjjbl1uJS8Zt7Ohk6u5VyHh3Z3ha5MooIUZtZslS2QuCG/Ex8ucVEvJ1QgiHDl+3PY6JUW8LTLSJn5VWrzjmBabe50j4d3cbMsXoWTxlvPUIeFN+ACvW5ss2E3CIyi5GkEQ9ngzuSMAEt5eRnU5MSIM0es5EayQ5MwEDdLHSJbIpMtoPHLCOyHBvo2v4w1QcjWCCCcEFm/06mW/vUcPziVcpfC+N78F8bx7nUw5sebVHyN+506xopGzbAPccy4iQixQSHgTPsCTUmIuQXW8CSL8MBqBigrxeAoAjY1AXR2QlgYkJztub2xEfl0d/jo3DXUdyUhLBZI7ACwV79vYCNTWAX2iG5HU7uDchw7Z2k4Lqo0QbhESwru9vR1PPPEEVq9ejcbGRgwbNgxPPfUUrrzySpfOc+WVV+Lrr7/G7Nmz8eqrr/qotyGETgcUFwMzZtiJYDMYVOStQk62zBKZdAILuGbxJldzggg/HFm8AZeFd7xWUMdbxuId/36x/UGff84F1MoFxEVGip9btHJP+ADe2lRYyFm6PbY2KUGu5gQRXhgMwPTpXjtdsuXH3e2yPPooJ9CppLTbhITwnjZtGtauXYs5c+ZAr9ejpKQE1157LTZu3IixY8eqOsfHH3+MLVu2+LinIUh+PpCbC2zZAuzbh6Y/61ETPxgJt02QF90AN5OQCm856xEJb4LoNrQfOALrU0DJ4g2oFt5tW3cipqwMGD1a3tVcicJC7pkmVTqRkeISZCRWCB/BD6v79nme1VwRsngTRPhgNHJGsGCHZYHCQtQMy8WeUzro9T56voUxQR/jvX37dnzwwQd45plnsHz5chQUFODbb79FVlYW5s+fr+ocbW1tmDt3Lh566CEf9zZE0emAyZOBBQuQUPwChrwwExlKohuQj/NW62re1kYx3gQRZpRPMyDqpx0AABZAecFq+52kwttJcrWYnyqBKVOAzEzO3U0tCgG1XS1id3X2P//xsLgpQSjjbmIkRxiNXII1oxFk8SaIcKKqKnTCL00m3DFyn3dqhHdDgl54r127FlqtFgUFBda2mJgY5OfnY8uWLTgkjD1Q4LnnnoPZbMa8efN82dXug1rhLWPxZququGRIQOg8ZAiCUKSm0ojRpQWwLKeBATCqtNC+IgIvvFtbuUU3JxZvKywLtraWe6lmf5mA2tr1ldB2ia/HAGALCjxLN00QfkJaouzzLym5GkGEDXo9WN4oFeR0QYu9LDfGeqNqQ3cj6IX3jz/+iMGDByNBYj0dMWIEAGDXrl0Ojz948CCeffZZLFu2DLHCkjaE+8hlCJYR3saT9sKbAcAKywARBBHSHC2vghbi73IETPYVEXr2tL1ua1MvvAEIpyMOnxoajWxAbdPn5ZCb0jBms+fppgnCx8iVKFvxIrmaE0TYoNPh97+vDHQvnMIyGhRiJQ7DNsZ6o2pDdyLoY7xramqQkZFh1863HTlyxOHxc+fOxfnnn49bb71V9TXb29vRLogpbOJrxBIcchZvmRjvqgNRSIcGkZKpsnUCTMKbIEKePjl6mKARie8uaO0rIvAWb4DLjOqC8Obhnx0/37YU/c9LRjIagfp6IDWVs3KPGiXr25twbQ7YItiJb1ajAeP1dNMED42l3kGuRFm7mVzNCSKciPvbVOB5zrt3L87E85gHDYClSyEe66SZxyXtJ6ob8dayetQiFScs6dM0ABLQiDTUo87SzgCYMR04M6URSR0qzp2SgqMDRqHkYp1oBdwnVRvCmKAX3q2trYiWEXUxFgtrqzBZjoSNGzfio48+wrZt21y65jPPPIPFixe71tHuhFR4a7WyVnC9HnaiG+DcRRmAhDdBhAEZ2Tr89/qFuPDThQC4ighb8lbaJ2cUCu+WFkXhbQYDjQOncgZAj16xSF4wU3Uf0ydkY+/oPOgrSq3imwUDZtUqygzjQ2gs9Q5yJcqg0domv2TxJoiQR5duy3tyCFkwaGdi5Uog2cUE4kkAEvXAfEFlhQceAJ5bYb/vrXcASePUnzsDfqraEMYEvat5bGysaMWcp83irqzkPt7V1YX77rsPd911F7Kzs1265oIFC3Dy5Enrj5o48m6FVHjLxXdD/ovIAmB4l1MS3gQRFlw4J8f6uuWumcgpkZkpqBTeJ55/C78/UKQovVkA8VePcbmPgzeXoG7dduzPW4jjS4vAHDpIJVF8DI2l3oEvUcYPvVotMP8RsngTRFghWEA7PzsS1dXuD1H5+UB1NZeMsboauP9+W0EhHnct1dJz0zDqGkFv8c7IyMDhw4ft2mtqagAAffv2lT3unXfewe+//46VK1eiurpatK25uRnV1dVIS0tDD+Fk0EJ0dLSslZ2woFJ4S2kccx2SX1wI3HIL52pKydUIIjywJD8DgLi/nCm/j1R4K2Q173VeP/T6+5XAOZFceRXBc4IFUDU6D4MnuLaYypM+IRtw81jCdWgs9R75+cCwYcAPPwBjxwLZEVrgKctGsngTROgjGBN7pUeil4dWZJ1ObADzpqVaem5CPUEvvIcPH46NGzeiqalJlGCNdx8fPny47HEHDx5EZ2cnxoyxt4y88847eOedd/Cvf/0LN954oy+6Hd5I3cpVCu/EzZ+j/LWbkCOt4200AhUVQEMDGhuBhj8akRrfhsQ7JgIueisQBBEABMIb6eny+wiF93vvoWPPH4iS24+PA+aLIW/ZghP/3YfGo+3oMek6t0U3QYQyBoMtwZpGA/zziQj8ld8oI7yNRi42nOrsEkSIIFyMjoxUfRj/XY+LA06dUv7O80Pqvn2cpZueC4Eh6IX3pEmTsGLFCqxatcpaDqy9vR1vv/02Ro4cif79+wPghHZLSwuGDh0KALj11ltlRflNN92Ea6+9FjNmzMDIkSP99jnCCpUW75pKI/rAltBIAxajSgvRqeuPSICbQRgMIqtWsuUHANiXl4DJywNKSrz+EQiC8CJqhPfvv9tev/iivOgGgMmTgeJibpag0wGTJyNpMhe3RhDdEbms5k88KRDeEldzqUhftYrcQQki6HFDeAu/6zyOvvNkqQ48QS+8R44cicmTJ2PBggWoq6vDoEGDUFpaiurqahgEVdunTp2K7777DqxFwA0dOtQqwqWcccYZZOn2BKnwVnAlPFpeBWk++giY0NbWyQnvri47V1IhDAC2tBTM7Nlk+SaIIOb0/jr0dLSD0QisW6fuZCzL+cPl5tIMgSAgn9W8wyxfTkxOpNPXiSBCAIHwPt0Z6XhMhf13nYe+88FN0CdXAzjX8Dlz5mD16tW477770NnZifXr1+OSSy4JdNe6Jyot3nyZISFd0ELb0yLUu7qcxnkzALB5s5sdJQjC1xgMwL4PKq3v2dyruUYhVVWunZQKgxKEFT6ruRBWI59cTU6k09eJIIKftWtswvuf/4q0G0alyH3Xeeg7H7yEhPCOiYnB8uXLUVNTg7a2Nmzfvh25ubmifTZt2mS1djuCZVm8+uqrvupq90Aa481Iq+NyZGTrUJG3Cl3ghHoXtNiStxKRPaIdHieEBQCZOH2CIAKP0QgsnmHEMPxsbWNYM9jCQm4jj16v6vtuhQqDEt0Eo5HLDiz8ukiRy2r+1DPyFm85kU5fJ4IIboxG4KmFNuHdgUhIh1Epct91HvrOBy8hIbyJ4KLtD/GTgP3vf+0tXBZySvJRv70au17ciPrt1VyZIeGTYupUxeuwABfjTW7mBBGUVFUBZ7JVkEpqRrrcrtNxcdsK4tssPINGQ4VBiW6BwQBkZQHjx3O/HVm4pCV8ptwhWAAXCG85kU5fJ4IIbqqqAC1rE96diHRqtZZ+13noOx/cBH2MNxFc1K6vRNrp46I2BgBbUABGIaAkI1uHjGxBuzCr+cCBtva//Q2N/Yehx7OPIbqpAUyPHpRYjSCCGL0e+IPRg2UhEt+sVgtGutwuyFKOF1/kflvYVnYQgxu2ICUFwKhRNGMgwh53YrFFiZFqlet4U/Zigggt9Hogmum0uHlywluN1Vr4Xe/Zk6vUS9/54IaEN+ESTZ+XQy5nMWM2c998Nd923uplNgNHjtjap09H8gUXAGteB/7XQHW+CSLI0emAhcU6NE5PQi+cAACYNVpolJbbLVnKsWOHSHiPmqwDMNk/nSaIIMBRLLaqSbNW3tWch7IXE0TooNMBjzzYCTzHvTcxkaqt1vRdDy1IeBMukXBtDtgi2LmWshqNvYVLCd7izbJi4d23L/ebL6MgLK1AEIR/qazkMpHHxADJyWhsBGrrgNQhKUiZONo60ufnA6b5EcBxwJSWAe1/tzufBfTu7YcPQBDBCx+fKRTfLsVlRihbvAmCCF74utvSetsTcm3Ce8asSCRQCcCwhIQ34RLpE7Kxd3Qe9BWlVvHNggGzapX6JTeL8DZ3mWD67y6utBjDAKmp3HZeePNZzxXiQo1G4GCFEXpUIXW07QlWU2lEw7oKZGRAJBAIglDJLbcA//ynqCnZ8gMA7GwGDF9rG4C2s537nZKk7vtGwpvo5vDxmYWFnG52OS5TYPE+Xt+FFqP7Q52cEFASBwRBuI+w7rZdvW2BsSkhRV0dbyL0IOFNuMzgzSWoXT8bLWs/Q+KQPuh11wSXRub64xqkAtCYuqCpsSRqY1nu6VNSYhPeAPcgioqyO4fBAGydYcAbbAG0MMPMaKApXoXycmBM6QxkWAJlpALBLxg54V//e4OddZAggp7KSjvRLYVhWbAFhba8Du2c8EZ0tLprkPAmCM9isQUW713/NeHKLMkkXiVyQgBwIA78DC0AEOGC07wOQi/PSBLe4QoJb8It0idkAxNczzZuNAL11Z1IldtYWgrMni0W2jLCmy9hVM3OgMYisDWsGeyMAoxmWWsbICMQfI3BAHb6DKSARYqlKSDinyDcpbxc1W6M2RKQ2q8f0NHBNZLwJgiXcDc+03g0AvxhEehSlZzN7hwKQsBstqVYcee83sKhdZAgQgyneR3cFN60OBVaUDkxwq9UVQHRaFfeYfNme4u3zDnOZKtEAhvg6gdrYZ+QzSoQfI3RCHbGDDB2/eLEv8OCjAThZ2oqjfjxhY2oqZTclzk5qo5nNZaAVF50A6qFd+Vz34rel09zUEeJIAg7qv60uZpHgEuu5qz8kN05FISANK+pq+f1BkqLAjSMEsGG0ciV+XN2b8rV3RbldXBDeLtSkpAIDkh4E35Frwda0EN5hzFjZIW38MHGlzCSwjIamO1aBQLB11RVgVHIxO438U8QKiifZkDaiCycP3c80kZkiYVvdjZXl8QBLMOAWbVS7GYOqBLeNZVGXPDJE6K2UaWF9gsABEEooh+igdmSaUULLrmamuRs0rFUTghI06q4lPTNSziyDhJEsOCK8JXW3bbL6+Ci8KbFqdCEhDfhV3Q6QDcoVn5jXh436ZcIb+mDbcMGroSREDOjAVO8CnWDxoraRQLB1+j1YO3yvVv64S/xTxBOqKk0YnQplxsBALQw2wvfHpbFscREYOlSoKgIrRePs25m/v1vm8+nUHjL5GOQcrS8ynptngiYULuZZtQEoRadDtYZfAS6VCVnkxtL5YRAcbEDceAnnFoHCSLAuCN88/OB6mpu8au6WhI64aLwpsWp0IRivAm/0ydDA1geDKxWC+axx4DrruNENyCavNcc6JB9sFVXi8+p+fTfwIQJ6LNpE7DvB2s78913ql1nPUanAzNvLrBihaiZy/oegJkLQchwtLwKGQrCNyPbco+2tHC/dTpgwQIAQGxVFbB1E9eenGw72EWLd58cPUzQiMR3F7RIH0MzaoJwBU1kBGDqwpAzTaje5HiIURIJ1dXcjzTBm9tJ37yEx1nfCcLHOI3ZVkAxr4OLwtvjkoREQCCLN+F/BH5sTM+ewKJFNtENiB44B/Z1qlvRS0zkfgvjTQEgJQV+Zdw4uybmxRcoIwwRNPTJ0dtlQhAJX5a1Ce8egrAQadJDHhdjvDOydajIW4UuaK3X3pK30ib6CYJQhyWzeVxMl1NB6kwkjBsnFgNybf7GoXWQIAKM170yXBTeTl3XiaCELN6E/xE+qeRcUwUPnAH9OtWt6PFWt3ZJ4japEPc1vGARkpTk3z4QhAMysnVoi0tBzKkGAIAZDLbkrUQOL3zb2mzZlYSx3kpJD120eANATkk+ambnonbzPqSPGWS7NkEQ6uFn3F1dTncNVeuYu1nfCcLXeN0rQziuRqiTZx6VJCQCAlm8Cf/jgvDuk9Ipv6LXT2yzO2Zs415IhbZMVnSfcvq0fZvJ5N8+EIQTYnrHW1+351yBnBKBKUm4eCS0eHtReAPcAsDwOePI0k0Q7sJPzlWMMc6sY2ozMxMEYcOrXhlulhMLBu8UQj1k8Sb8j1B4yz1chGK8o0N+Ra9NbNme9X9tyDUB+VKLt7+Ft5zFW+rfRxCBRhDuEZssSXYoXDxSsngLF7jcFN4EQXiICxZvQNk6RvWyCcJ9vOaV4abwJkILsngT/kdYq8SJxZt/EElX9I7sPSU6JIptQ2Eh0N4ssXj729VczuJNwpsIJZQs3kox3iS8CSIwuGDx5pGOpVSSiCCCBBLe3QIS3oT/Eda6Vim8pRz4pVn0PgZtMJmA9uYgtHiTqzkRbAgXv6S159VYvEl4E0Tg4YW3Sou3HFSSiCCCBBLe3QJyNSf8j7OHiwrhfUaq2OIdgzZotUCMxoHF22gEKipw4r/70LSvDnEXDkGvqRO5pX+jEQ3rKlBTA6RMHO1+3Cm5mnuE0QgcrDBCjyqkjtZT0FIg8FOMN0EQHuKiq7kcniRdMxo54a7Xiy3o0jbC/9D/IQQh4d0tIOFN+B+hGHZm8VZwFe8TJxbesUw7Vq4EolYoWLwNBmDGDIBlkQQgCQA+BthHZ4PJmwpz6TtIAYsUAOYlDMrzisUJp9RCruZuYzAAW2cY8AZbAC3MMDMaaIop2NAjLAtK9b83ID3NUn47JcXxRF2N8KYYb4IIPG64mktxNzOzXFw4QLHiwQDF7PsGny9mkPDuFpDwJvyPs4eLUiypkGaxq/lj89qQmA9gqYzF22gEpk+XPQ0DFmxpqSjmQgMWo0oLUTM713XLN7mau4XRCCyeYUQ1OwMaS5VpDWsGW1gIJjeXluzdwWCAefoM64KSImpdzdXEeMstpBEE4Ru8YPEGXC9JJBcXXlBge83/LizkzkuPb/+hFLNP/wfP8MtiBgnvbgHFeBP+xxWLt5LwPiW2eCdGW8qJyWU1r6py2B1Gpi0CJtRudiPIjSzeblFVBZzJVllFNw9DwYbuYTSCnTHD7u8pi1R4k6s5QYQGXrB487hSkkguLtxspljxYIBi9r2P3xIQkvDuFpDwJvyPN4S3xOKNNoU63h0dnF+QA+SkSRe0SB+jIshNCsV4u4VeD/zB2P+fWLXBhoSYqiowUkGtFleTqwm/c24Ib6ofTBBu4oXkau7Ax4UL0Wjs2+jx7X/k/jf0f/AMvy1meCi8aSwNDUh4E/7HleRqcjHeRiPw3XfitjYHFm+dDsjOlu0KC4DJy7Nr25K30r0Ea3IWb3I1d4pOBywsFv+9zRotGDXBhoQ9er0aW7c8KizeRz//LxqXFgFlZUBtrW0fF4W3wQBkZQHjx3O/DQZ3O00Q3RAvuJq7M1nn48L5y2u13HtpGz2+/Y/c/4b+D54RF+ebxQy7754HwpvG0tCBhDfhf4Ri2tUYb4MB6N8fePttUfPRDT/Zn1t4fL9+1qaTd99nfc2MH293ro7hI91LrAaQxdsDpPFSmgPVlBHGTcof26B+Z0eu5gox3n02fYjkR2cBU6aAXbjQto8LwpvqBxOEhwhdzd3wcPFksp6fD1RXc8Khupp7L9dG+B/6P3gPgwG4+GL7rP+eLmbIffdaTronvGksDS1IeBP+RyimXXE1Nxq5zOQypFX9gJrth+wt3rwQ5y3iABIXz7Nt12jsrAXRcR7E1lCMt/fo2zfQPQhJaiqNGF1aIMpdYALQmp5laxDMGFpPtGLPnCI05c8Bli4Fvv/etp/gfm7444Ts9YTX+fX172T3kYNiEQnCQ3izJuDyOOONybpcXLgrseKE76D/g+dIvyMAN2XcskV+MUOt94hScsLP/m2b737wkfp5KI2loQUJb8L/uBvjXVWluKqvAYva8r322/njhYI8MdH2ur3d3kruiWs4ZTX3Hn6OWwwXjpZXQQvxKKwF0JUgyG0uuCdjKzZi6MuzkPDWy8CjjwJffmnb77bbrGawht+OOr320G9fQ02lupk7xSIShIfwFm/A5XGGJusE4RilJIJy9hVXvEeUzhsJ23x33oJI1YtgNJaGFlROjPA/7gpvB0nSWADp2f2VryWweCMuzvZaTnh7YKE2nWyGVtpIFm/36OoK6fJUNZVGHC2vQp8cvXv5AtykT44eJmhE4rsLWkSkJgJ8gn+pZ4gSLGutRdPrgjOc7q6FGbWb96n6vO7WDyYIwoLQ4v3AA1xIVXKyra2xEairA9LSbO2WtuHRaZiJZGsuiCQ0Ih11+Mt/0oDdTs6hpn3IEGDiRLe/0D6vmUyEDIG6F3hBK3UzlwpaV0u4yZ0XEAvvNnMk9u1T93lpLA0tSHgT/seVOt5CUazTAVOmAB9+aHfIiYyzkXFeqvK1eOEdGck98WJiuLa2Nq8J7/JpBoxtqLffQBZv9whhi3d53psY/U4hMmCGCRqU561yP2+Ai2Rk6/DT1Q/iL18sAwCYwWBL3krkNH9u20mt8AasZrDew52P4iZoXKoG4Gr9YIIgBAgTG77+ukuHJgMoktvwnCcdkjBrFvDmmy4HGfulZjIREgTyXlAraB15j8iNaXLnNZuBSNY2NzZrIl2yWNNYGjqQqznhf5wlV3NUTkxg9W6+62/W18mDUuTFBH8tfltMDPebTwLlJYu3XFytJ+cjELLCu6bSiDHvFFgtzlqYMaq0ULULtjf4y32XWl+fmjGHE/3CxGdy1QKU4Jf4Jd/VzvS+dpnTf574mMvWfYpFJAg3MBqB334LdC+cU1DgUuA4JYoieILhXlCTqM4dV2/peYuLgSiBxfuFf0S6PCbSWBoakMWb8D/COGxX63g3NFhfxt/3f8C7b3Dnk7NcC4/nLd68+HAkvN2wUB8tr0IGFAQ2CW/3CFHhzd0LYkkaAZNqF2yv0NRkfZkw1HJNofCWfq+U0GhsS/zNzaJNkZeOBZYs4VxKLfS741LpGQiC8AVVVc73CQbMZmXTnwyuWg+J8CVY7gUdjNDVVQC7LfNPSZiFDsB3twKfv9+I3qjDMaTh2inJ0K2D7P58m66uDro0LrQjH0Br3wPAEW7ztGtqATgP7yJCDxLeRGDxQHgjJYWzYLe2csLaHYu3l1zN5eJqrZCruXuEqPDuk2Ofi6ALWpdcsD1GKJITErjfDkp9dcTGo+2O6UgYmGqbGKSkAKNG2WY4Uu+U2Fhg8GBRU8qtV6L8P/5zqyeIbouDnCdBhUbjUpYntXG1RPgTFPfCm28qVtMRMtbyY+V9y48LxApes2eeCaa4mGIswpCgdzVvb2/HQw89hL59+yI2NhYjR47EV1995fS4jz/+GFOmTMHAgQPRo0cPDBkyBHPnzsWJEyd832lCPXLCWynGG5AX3oC85RpQtngLj5MKdjeEd0a2DjsmLbO+F9k7yeLtHiEqvDOydTALgg74GGt/JlgTCe/4eO63A+Ed1ac3EopfABYsAGbO5H4mTxabFaTf1R497NznA+FWTxDdEp2OEwXBzqpVLpkn+fhXPm8cJYrqvgT8XuB93QMAw7JgCyjGIhwJeov3tGnTsHbtWsyZMwd6vR4lJSW49tprsXHjRowdO1bxuIKCAvTt2xd33nknMjMz8b///Q+vvvoqPv/8c+zcuROxsbGKxxJ+xNUYb154R0QA8fEwRcZAC8B0tA7anTvtz+VOjLebFuqRT18PrH0QANDVfwAiD1VzG0h4u0eICm8A0MREWxd7Wm+81ScW4JpKI06+tw59m39HwkWSDMJywpu/9+Xo0cP5BWUs3pxbvRi/u9UTRHeFz6i0fj2wdy+QmmqfYby+Xtwu1wbgRHUjmvbVI35QKpIHODmHo/bqamCZZRH66qvdsthRoiiCxx/3gmLWdAclbP0BY6YYi3AkqIX39u3b8cEHH2D58uWYN28eAGDq1Kk499xzMX/+fFRUVCgeu3btWowbN07UduGFFyIvLw/vvfcepk+f7suuEyo5WduCRGmjgvCuXV+JXr//iUgASEyE4S0Gt9Q1IR6AtukE2KlT7ZObSS3ePnI1ByAq7hiZ2gvghTe5mrtHCAtvREYKMukrW5rdpeKO13Dx+/fYRO9bEGcQFsR4q7F4uyW8e/RQLF3mV7d6gujGGKFD1ZCZ0E9QPz+XCg2DAShY7qXM0fX1NuEtt7CuEp2O9AZhw1f612HW9ACHc7AaLRiKsQg7gtrVfO3atdBqtSgQuHrExMQgPz8fW7ZswaFDhxSPlYpuALjpppsAALt37/Z6Xwn3iP/HMyifZhA3ygjvvWOmIW3iCES2coKCbWjA0emPIg42sSubUbyzkxO+vIiTupqbzUBLi/gYsxk1lUb88kQZGorK1Lv6CM/Dix3+GoTrhLDwbjPb7uF//7MDBgMAoxENRWXYM6cIjUuLgKIioMyF+8tCTaURF79/j/zDm88g7KKrubvCOyNbh4q8VeiyVK/vgtb/bvUE0U0xGICsLGD8eO63weD6McuXezlztGT8Nhq5zM3kMUu4g9I97o37ymnWdJ0OmDrVo/67C8towKyiGItwJKgt3j/++CMGDx6MBD45kIURI0YAAHbt2oX+/furPt/Ro0cBAL179/ZeJwmXqKk0ilxTNWC5mNDZubbJunDg7uhA7fpK6CtKRcKaAfAwlsqLbSEdHeIYbqnFG7DL1txZbUSfEf2t/WRnM+qSXAgs3oiLs70m4e0eISq8jUYg4nQk+ljeJ+AkNs4w4P/Y6UgBkCI9gGG4WiIqTUxy7t1W+AzCLiZXUyW8pTHelnCdnJJ81MzORe3mfUgfMwg5JLoJwucoiYbcXOW5utwxDz/s5czRgufEkeoOZGVRPW7CPZTu8cZG4KGHPL+vVGVNHz4ceOcd7vXddwMjRrgefqEy5KOxEairB3oPTkHKhFEkusOUoBbeNTU1yMiwn2LybUeOHHHpfMuWLYNWq8WkSZMc7tfe3o52gVhrErptEh6hKiZUOMHv7ETT5+VIlzmXFlwSM4fiu7NTLLyl5cQA+zJJna2i93ySC8bRjAZQtniTq7k6pL5kISq8q6qAQbAtHqXjKN5gZyjfpyzrfMYsoE+OXvm+5zMIu2rxVpPzQsbizZORrSMrN2EHjaW+w51SS3LH8OLFa5mjBc+JfXs6rUEoahYGCEKI0j3Oi27As/tKVdZ04Vh6883Adde5dhEXSLb8EOFNULuat7a2IlpmshhjsVq2trbabVPi/fffh8FgwNy5c6F3ErfxzDPPIDEx0frjilWdcAwfEyrELiZUMHC3/7wHrcdOQS68x8xosBaTZbdZ6eiwxdoC8hZvFZNBa5ILBzT+LAh9CDZXc4ub8778pTg55wmgsjLQPbJH+ncKUeGt1wNRsOUNSEU9tI7vUtuMWQUZ2TqcyDhbfiOfQVguxtvT5Gp8allXjiG6NTSW+g5eNAhxJJiNRs7AxjD2xyxb5sXM0RE2e47wOQiof8wFu3t6sPcvXJC7x6VCGXBp+BShKmu63CI2QXhAUAvv2NhY0Wo5T5tFSKnNTF5eXo78/Hzk5ubi6aefdrr/ggULcPLkSeuPo1hywjVUxYSWlVlfRu/+CcP+udDOuseCgaZ4FUYdKsOR6xzUWOzsFAtvaYw3YGfxloPVODYBlE8zIHHh/db3xs3Vto2BFt6rVoHt3x8ps6Zg0FuPIvHlJWBHjACmTQtsv6TICO+aSiN+fGFjSJWn0umAXlGnrO97oMWZ7HbZxJR8Zi/ra5afSev1Nn87/p6OiJD38pCiRkRLZ+xUGYJwAo2lvsOVUkt8nOyUKdx7/qvMHzNvHpeMfONG7ndurgfCkmGsi+eREFclUfOYcydu3Z8Ee/+UCMXFArl7/NlnXVtwckZ+vvjet3NZJ+FNeJmgFt4ZGRmoqamxa+fb+vbt6/QcP/30E66//nqce+65WLt2LSIinHvXR0dHIyEhQfRDeI+cknzUb6/Grhc3on57tbjUktEIzJ2reGydbjiOLy0Cc+ggkJ8PnQ7o5+g2UBPj7cTizYJxmOSiptKIMaUzoBHIq76/fW3bIZCu5kYj2MJCu4ULBgBbWhpclm/J3+nnJf9G2ogsnD93PNJGZNkn4QtWTCZEdtjCDlI0J9B+8aXK+2s0rpuY+MUkjQZMiiVqXFABoLOmnutKTA/bLNtT4e2NY4huBY2lvsWpaIB9nCzLco+EoiLxMTodMG4csGGDF4SlRXif0a/TJUu602RXASbY+6dEqC4WALZ7vKwMeP994LbbvF/bm7/35c7RUkvCm/AuQS28hw8fjr1799rFhW3bts263RF//PEHrr76aqSlpeHzzz9HnDDhFRFQMrJ1GD5nnH1cqJO6ianGXfj190jbE9JoBFv8pvKFTp2St3i7ILyZ9951mLnjaHmVSHQDEL8PpMW7qkoxtpgBgM2b/dgZJ0iE97lfLLeWqdLCzCXhCwXLtyRLvtbcheit31nf789biI4zh9h2+PVX8f1lNAJlZWhcWoQ9c4rkM+sLy+PxAtgSenNg+PWIPMrtrznVhL1jpnHbHQlvR9uUIOFNEAHHkWgAlOO6Z8/mRLYQrwlLS56WXnEdThcGnPXVXTdiXxDs/ZMjVBcLhGzYANx6K+exkZXFtblyX7lr7TcYgK8/sXmvvfcpCW/Cc4JaeE+aNAkmkwmrVq2ytrW3t+Ptt9/GyJEjrfFiBw8exJ49e0THHj16FFdddRU0Gg02bNiA1NRUv/adcBO93t6lVQADiAVYVRUYR468p07JW7xdcTV3smDTZ6y9j5Mojj2QwluvV/zrsAAwZowfO+MEyd9JupjBJ+ELek6dsmsS3tFnvPwAooYK7hneYg1wI33//sCUKUh+dBaGvjwLKbOmgM3MFJsp+PwWsbE2l+/WVtSur0TmT+tE19VXlKJ2faVjce1OvV1yNSeIoEcuThaQF2BeE5b886Sz0+nCgLO+epTozcsEe//kCMXFAiFKCweAuvvKXWs/f9042OaHMx+MD6kFCyI4CWrhPXLkSEyePBkLFizA/PnzsWrVKowfPx7V1dV47rnnrPtNnToVZ511lujYq6++Gn/++SfuvPNO/PDDD3j33XetP1999ZW/PwqhFp2OK63kQHyLBJheb4txlUOrdW7xdia8nSTxy9Bp7dqqcgRLsIF0NdfpwIwfb9fMAmDy8oDsbP/3SQknfye7JHzBirCsnARTRCRX3ku48MMvDBmNwAz5fAV8Zn3rqC+0eAuEd9Pn5bJhBc1fbHYsvFWE4NhBFm+CCHr4OFk58S0VYF4Tlrzw7uhwvJ9MX4UxvN5wI/YmrsTVBwuhuFggxJOFA0+s/fx14y3C2wQNTpljQ2bBggheglp4A8A777yDOXPmYPXq1bjvvvvQ2dmJ9evX45JLLnF43E8//QQAeO6553DXXXeJftQkWCMCSH4+cPAgUFQka60VCTCdDkxxMcwS8S06zsU63nYIhbsM/5v2vF3b0Acn2t44snhbso3/8kSZ79yozzjDrolJSABKSnxzPXdxILxZwD4JX7AiY/Hm0XR1Am+9Jb7/+PvLSZiFKLM+vxgkdDXv7ERC7ii77wwLIP7qMY6zmpPwJoiwJT8f2LrVuQDzmrDkS4J2djreT4LBYKsrrtFwItyV+sz+SCCmJq4+mAjFxQIhSh4batLTeCLa+evywvsU4qDVMiGzYEEEL0EvvGNiYrB8+XLU1NSgra0N27dvR25urmifTZs2gZVMWFmWVfzZtGmTHz8B4RY6HTBzJpg33xSJahM09gIsPx+agwfRUFSG3x/gYmKZXlzW5/b6kzj+s2AUdqecmAOLd02lEed89aJde4NRcIyS8DYYYO6fiZRZU3DukilIH5GJ8mkG1FQaUTGnDDvyFeJ7XUXYf3dciv2FgwWK9ovHiZPwBTMOhDcDgC0oEC8y8AtDTsIsRJn1ebEudDUHkD7+XLCM7bHOAqganYf0Cdlet3gf3dPo8jEEQQSG7Gx1AkxOWLosaAWu5mqRs04+/LD6a8q5FFvSZaDMC8OoEFfc54OBUFssEKLTceXupCxY4Px/6om1n1+w4IV3M+K9vmARipnmCc9xw8xBEH4kPx+a3Fw0rN+Co0eBXteNQo6c1VOnQ8rMyeCjZdvnFiAaQPTxo4h8cLptP3fKickI75pKI46WV6H1YD0yZOzyx6oarX2RteQajWBniDOha8BidOkMMKWwnfMtgJ3NgCkudn+0FPY/Ph44ftxlS4RfcGDxjklyYK0NNvbvd7iZMZvFXhT8a50OmD9fdpYhyqzPsvKu5gDQ3AwNy81cW9MHoOnNMgyeYAkn8HKMd+p1I1Cetyp0FkQIopuTn8+VCtu3jxMfSiJCp7NtMxhsglij4cSI06HIBVdzo5GzTNbXK1snnYkdOdEufA9wa5qeDKOhjvB/GjIYjWhYV4G/bGnAQ2hEGupQhzScQDJgAk4/DyC1EairA9LSgORk27GNjdDV1WHL9Wko+SQZZnCWxmkTG6FbIdm/Uf4c+Y2NMEfUAV1A+oAYr947bn2viLCAhDcR/EhEtTNqKo3o03LC+l6UpIsXoR5YvMunGTC6tAAZMMNkSe0mtVMmjxoK8IZwOUtuVRUYGbdirYyI5+N7mdxc0chZU8kNSrr2fUhqtwwagwYBo0eLR1hh/xMSOOHd1eX4MwcCRzHeDlywg4o33wQ7Y4b1fpC7N1iNBoww2aMwFGLUKOvLrrQMRNRxpROZxYtso3Jnp+2ekli8UVdnfRk7fAhiJwhi+B0I75OHTyHR8SdDTaURGYL31kzzs3NDIwSAIAiXBJhSjKxkKLJHpau5UHwwDPcjfNSrtU4qZW4XwrLKfefFv14fguI0XDEYYJ4+AylgcSWAK+X2ecn5aUZYfqx84lo3eIN5ZPUf3A2roI5duYfc/l4RYUHQu5oThKscLVcuoYXHH+cengIRYmpocHxCgXCtqTRidGmBoMwVKxuHbo4VxL/KCUpnSeEkiOJ7AZRPXYX0Ef1x7pIpSHruUeDll4FHH+XqbShlwAZsdSiDUXg7ioUPBeFtNIItKBDdewzE+QY4y/UqQCi8hdZvwb0YMfw8W7vQQ0O4vzDGGxAJb7uaow6Ed/wLi5zWST9aXmXXFjKZ5gmCcBlXYmRFbrMqLN5y9cUBsSv8Aw+o66dSHLAUub6Hco3rsEXGIzAoKCiQ9Qt39R4K9UzzhGeQ8CbCjr6D45Qf1/yy97ffWpu0ztyuBULnaHmVVXTzyH2JUidebHsjJyh1OjCCzPwAJ9CUpKcwvrem0ogxqwuVv7z8Z+QHCKHw5kujsWxgs63LEWz9cRUFLwYGwK8X5eH40iIwhw5yK+ZCESy0eAsXgfr1s72Wc00H7F3NHQlvB8nVNGCd1knvk6MXl8lDCGWaJwjCZZQqafbsKX4vFR5HGy0WbwfjjJz4YFlgzRpg3jxu24oV6oSMXAIxOaQW9HCocR2WKIylAcdstlPH7txDoZ5pnvAMEt5E2JHe85SyxRsATCawH36o/oQC4SonPswyVxOJcyVL7qRJordtV0zElttftduNBWzxveDEv9MvrnD5VJgBWxjLG2xW7xCxeNdUGvHjCxvtRapeD1bmXuiCFr1efwq9Fsy0+ZEpWbCFwrtvX9troTgXLqRIXc1ra22vXbB4A86t1xnZOlTkrUIXuFltF7Shk2meIAiXUcoTKayYKCc89uwTjDMKC9tK4mPAAOCFF2yPfLViWJpA7M03xbkqNRr7ZHJkeQwsisnF9Ppgs3VzaDR26riiwvV7KNQzzROeQcKbCD+cuXFrNI6FuZTWVsBoRH3ZRuzfD3x98xuizSf6nu34eCVLrmRCEhunxZiXbrHbjbnlFlFcUZ8cFYOScPmUF2qxscEtvEPA4l0+zYC0EVk4f+54pI3IErtn63Rg7pkt2l82Cz8gX04M4OLvedRavNW6mq9Zo/CpONRYr3NK8lG/vRq7XtyI+u3VlFiNIMIYNZY5OfHaAcE4I+NuzsfDLltmLz5OnXJfDAuzjfNVSfms5gcO2IfnkuUxcDh0z9bpwNx+e8D6JgvDcGpZoI4NBuC22+x3VXMPhXKmecIzKLkaEX7wtb1nzIBGaim1FAdlH3pIvSvTL7+A7Z+JVLDoBQ3uwWsQFrTrNTgVOOLgeCVLrtQS0NEBtLTY7yfJOJ2RrUN7bCKiW08qX1O4fCoU3sKyUcGW2TzIk6vVVBoxptQWdyabXGzcOOBVzmvh6BV3gF36rHwWfqHF22LNrqk0Irb8JyTx7XyCIkBZeDtIriYS3kYjMGuW4mfjrdeyfZWQka0jKzdBdAN4y1xhIfd4lrPM8eJVOMx1QvDskowz0mzOzz7LlTrjs6wbjfbnc1cM63TA5MmefT7C+6hKLnbBBcD77wMADl87HT3PG4ikjnouP4okeznqxe2NjUDDH41IY+qRMND5/rJtwvbBg4EJE0Q3hvQz8Mh5VigRkpnmCY8h4U2EJ4IyZMf2NiAtFUgelMJljdbpwPTqBfN0dck72O3brRZyLcz4B+4R7yAQQl3QIgIm628A6oV3e7u88JZaDNrbraK7pe8gdNz1f0hq+JPzrQOAq68WL58Kaz4Hs8U7yF3Nj5ZX2ZWO492zrUJU8L/qM3EEoCRQJRbv8mkGkagHAMyYYXut5GoujfEWuponJNheV1U5/BtWPrAGOS84mKESBNEtcVaCTE68njMsEvjRsoNgnJMTXAsWcBY//rz+FsNqS6wR3sORi7/17y8Y5/r97QZO+KrAYAAKHvN9mS65zwAAH3zgeLGHIEh4E+GLozJk+fnQXHklzIP00HQ6rjUqdUu3CmoeXthGRqJ+85+o3bwP6Rf1R0aOZYleyZIrFb5KFm9edBmNXEDR7t22vp19FpKeXcD55/HCWyrolSzewSa8g9zVvE+O3q7Nzj1bKJCFFmspAov3yb21GF262H4RSCiUHVm8ha7mSjHeer19rR7BZxhw2yi7doIgCMC5ZY4Xr1u2cI+YtNVRNuEtWIxUJbjgXzFMpcT8j5yXhJ1Xg3ScU4E/y3QpfYZRNJQSTqAYb6L7kpmJlrh0lw/jk0tZ4cVWTAwysnUYPmccMi7Osm03mwGjEQ1FZdgzpwgNRWXcCCFn8ZbUDAfATVwMBq5M2JQpwKJF1k0xX6/j4ox79rQFq50UuKCzbHi4mgcBUvdq2eRiQu8ER8JbYPE+tfewbP12EWot3kqu5jodUFwszjYEBzHoBEEQLrBhA3DrrdwQ9a/18snVXImpFsZrC1FMyOUGVEosMKhKLibNZaICfybLowRphLuQxZvottRUGtGn8ZDLxy1gnsVy9kFbAz9ACAcH4eyipgbm/plIAWu1vrOzGTAPCs4BKFu8m5o4t2OFUlXWOOOEBODECW5/4Tn548jV3Ksc/+BL5EwZL25UK7wF90p8v3guc72ji6ktJ3bElmyg4XCb2NtDYJZq3NeAI+0p6LxoFLp66mA00oSBIAj3kFoalZKrqXIjNxqBdeuA338H0tJEMbfbv2zE5k/qUIs0lCEZd9wOjD2nkVtwlOyLRsftjdFp2PFcMviAniRzI5qm16FxbxqSBzg5D982ZAgwcSI9PN3AqVeDG8JblSVdJWo8IaSeHqNHu34dovtBwpvotnAxu65hiozG37+bBIwWiGZhuS4egWWR/eMPO9cShmXBrlghFltKMd5NTQ6FpzXOWE54S0tPOXI1t7iyN+5rQG0dkM7PM1JSuBHF15OLILd4S0k7Q6bIrdAy7ah8l+BeSUiJxol0PZJqq8T7CGcQjlzNy8tt7wUzjuTZt6F8+ylx5nFLtqFkAB8bgIIbfB8LRxBEeCO1NHY4SK7mUHAtWgQsXqx4nRGWHyvvu9/nZABFchuec/FEs2ZxYV4yD09yY3eMwxAGqWeXyvN5Iz+ANAGgo7Fxwwb1+xIEQMKb6MZwNbkZ526+ArRxPZCRKhGszc3cb6HQYhircFKyZDJ2NVg65F3NtVrF+FxAEGdssCTTErqaOxLewgmRwWC1qieDm5SIO8twrsq+HFGCJKu50QgcrDBCjyqkjnYwYxIWs+URWHeONUXhfxsVJl3Ce6W9HUm9o4BawBQRhaYnX+YSAV50ETBwoHUfK8L/aWsr8Nprst3TgLXPui74jP6KhSMIIryRWho7Ie9qziMruIxGh6I7qCkosHt4uiLewhWPFh4EC8w1jTHYozSWSvA0P4ArYyONo4Q7UIw30W3JyNahIq8YJleqejOM/USCF2DSVVmLu7mSZGQZyddPyeINAC+/LNssitFNTOQaW1ttfZQKbzlXc6NR0ZXd1lmWG1G8EVinhL9dzY1GrsBrURGwdCkwZw6237QUW/v/FaOm9EfqlPEw9++PwxMLUVMp87mdCO+8GVHKsYPCe6WtDTh2DACgzUhH8oKZXFrUAQNsnhNtbdb+nv7kS9uxhw6p8oaQ4s9YOIIgwhtpvGsXY7N4/3dLh3XYcBifXVUl0xgimM2ih6eSIPPl8Bls8Glpxo/nfi9f7mJsvkB4Z18S61IcvlJ+ACFK96IrYyONo4Q7kPAmujU5Jfmo234Qvy4sQ/u5Fzjd39zYqJyUTEF4M8KyThZYMGBm/U3cqBTj3dEhrk8xbBgalxbh14VlqNt+wOZKLLwO726uxuLtpNSUFV+PKP50NX/jDaB/fy4T0KxZwKOPAi+/jBGfPIpJ+Jd1KUYDoN/6VUgfkcklsRPiRHi3spxVW3bSJSknhoYG7nWKICqbYWz31JEj1uR6Pdd9aN2FVbB289hlXbfgSpIjgiAIZ+Tnc2XBNm4E8mfaFngfuLcTWVnAtGlOEpnp7atGhAwajejh2d0FmXQtn2WB+fNdTGInEN4tLDcOemsBw1FSPVfGRhpHCXcg4U10ezKydThn0WSYk2ULj4nQsCyObdkrv1EqvPnlf8beos78bSa3JCvEUR1voYA++2wkL5iJcxZNFrsQ8xZvQJ3w5i3efKkpZ/h6RPGXxdtoBP72N+f7CeDdtkWcOmW/o8AlXBjnaDfpEt4r9fW2/0WK5B7k9zt8WDG5Hsswsv8/RxnLKSMrQRDehrc0shE24R2FDpjNQGmpEwuwTidePA4lVq0SPTy7uyCrqFAeslWLZ8HcpQ228dLTBQxn3giujI00jhLuQDHeBGGhvbEVaqpFNm/5Fb3lNkiTafEjrzAZFk9UlL3lXCnGu71dXYZP4aSluBiIi8PpzT+iJ9/W1SU+lhd7Oh2wZAnw2GPy5wU4YefrEcVfFm83XRrt6rc7sXgLhbfdpEt4rxw+bHvdW3JnOUrQZoFhWc5lHgAaGtDYCBxpT0Gv60Yhx0GZMH/WyiUIovvQ0BwFfjSKhLyHmF39bpa1LTz36wfccguQmmqfkby+Xtwu1+aD9saoVNS2J3NJR9EIPPUU19/0dLvgbW8l+QpX5Gq32yGY87TDNg56uoChppa8K2MjjaOEq5DwJggL0anxzncCkNxHoUyUgqu5KDEWT0eHfB1vJYu3GuF94IDt9TPPAIBNdANgV64Ec/XVtgbh9a+/3iq8Wy8ajYM5d2HAf4oQvednbvv27VyyL1/ir+Rqbro0dkErFt9OhLdJEwWYFSZdwv+hUHgrWbwdwGq0YEaNsl5ANjmeAg6zyhIEQbhBr3SbxVtJeNsJqOZm22Lw0KHACy/4sIeuIZsoTVcC7N0rP76jewuy0aMd5oNVJ54tcx6TNhIMtICXFjDUlhxzZWykcZRwBXI1JwgLsb3F5aHMkE+MFvnrTvkTKLmay9HRYV/Oy2y2ZUiX7utMeBuNXF0LBzAAWOE+wusLBGPsyPMx5IWZiL7gXNv2pCSH5/YGDbtrfX4NANwIOXGi092E/3vebVuEnPAWTMI++zoaGzdycY922WyFluxa2+dubotU3k+uj4wGzCoypRAEETwk9ha7mmu1QF6e2CX3gQckB/F5LgD7BcgAouSa3BFlWdZWSogKdUm+fIHDJHZ+QKfjnO6E0XZ8NJRq8WyZ82h7xlpzB8iOpRKcfXZyDycCDVm8CYJHIpRPLnoJydeOxsk1n+HU6k/Q79hPAICe69fKH69k8ZZDzuINcHW4hf0xmexd0GNlHOJVuk8zwiVoBeFtzXzeU2AvlxOZHlK7vhItZeuQODQDVT+eRvba+V6/hiJnnQWsW8e9fuABzr3Q4mbYiGQcaU/BoLKnEL2bs/jXVfyBnIuzgNLptnPIxXgL/o59MqPQ50yF669ZI9sc99bLKDedZ0uYJ7PIcvqam2EcegV6D05ByoRRNGMgCCK4iLJ5hT35WCdeKuQeU089xRXoeP55YMUKzqhtLbN1/Ljt+CAS3kquya3owQUT8YvoEYGbTgvLdgVLXWmpxR9w0frPGxtiYlRblNWWcOvO3ghE4CHhTRA8EqGcPCQdyM5GCzKQ8aKK+qLeFt7JyVyZKTUWbz5BmhOXbJZhrOL7WE0n9pZZ6lVrBNYGftLkQ+G9d8w06CtKrdnDRwCOi7p5u5yYcCFjyhRg5EjrW6ur9g8rgd1cW8awVHtXeCeu5sLJpwgHyd0YQFx7W+Z/3fP2GzHkzjvlz00QBBFoBGUrz9Z3AgJh88ILtse5qO6x0OLdq5efOuocJdfk6OQe1veHq1qw92iCe/WqPUQqNllW4e8bAHEpFcwu9YEfo1WEWwGu19Qm93AiUJDwJggeqVC2rGAfLa9ChprjpW7BjlzN29tlhXf7oaO2NCJxcZzw7uoSu7PJDUS8b5eDetwsowHz15uAjz4CAHzxfx/iDlxvyYytsQlfHwvv2vWVItENOBHdgG+Ft5wHAWBfa1uaOdyJq7mim7iT8m187e2MbJ38OVJTFY8lCIIIOMJFxw0bOFVUV4f25jTMMNsyUCShEWmmOrQvSgPMglTVjsZOlQitwJ4ILKVEaTHrbMI7+5wW1LAJfrcwy4lNKaoSmQUjAou3GtQkTSOIYICEN0HwSAd7i/Duk6OHCQy0shHfAly1eEtjvAFE/brL+pqtrrYJUr48mNx1eHj/qS1buHi5xkY0/VmPeqQi6cJBnFtyWZlVeN+J96yHMqxgxOInTT1sEwtHcWyu0vR5OdK9djY3cUd4S10J3bV4O/FOENXelvtfk/AmCCKYqaiwvX7/fevLMwG8Ibe/tK7zkiVAZqbbClaty7FaZF2Tv7UtTMew3PjobwuznNiUErJlzFwU3mqTphFEoCHhTRA8ChbvjGwdyvOKMaZ0uuNshF5wNVe0AqsR3gA32k+ebH2bYPmxoiYOzccx3glXjgRbpMLKLcTR7MJoRMO6CtT/3oDUISlImTja+axH6LqvVnhLrc9OYrwVhbcD7wQ+iZu1DBhZvAmCCCWMRuCttzw7B8u6rWBddTlWi51rsmBhuids46M/raxyYpOfdpiVKmo4wVueAh7BsrYxWml8lqC2hFtQfD6iW0PCmyB4FIQ3AOSU5ONE78NIen6h8vGuZjWXi/FW4uRJ22uVA5EskZHO9/Gxq3n6uWmuH6RUasxggHn6DKSABZ+Oh53NgCkudmzicNXi3doq9gAA0PLrH2hfWoTkQSlc/RSdTuxqriS8ATvvBMXa2zU1dodW3fII9FtWK5+bIAgiUKhM9OkUNxWs31yOBeNBD9g8wvxpZVUSm+4mDvO2p4AaZIWwcBxVafEGnCdNC8TnIwgpJLwJgseB8AaApMF9HB8vtU66k1xNCaHwdmEgskPB4s0CjmO8vehqfvzzrXA5dY6MWz6MRrAzZkAjCQFgWBZsQSEYRyYOd1zNJf+vHrUH0OPRWZaLMpwVm7d4azTOvQsE3gmytbeNRrA7dth5Bgza+i5q19+H9AnZjs9PEAThb1Qm+nSKmwrWby7HgvExXtMCuGlh9gSjERg4kFu/PX1aLDZd7YOvPAUcoSiEnSWTdYBS0rRAfD6CkIPqeBMEj0KMtxUnA8DJ2jZxg6t1vCWIpi1qXc2doSAGmT6CRQUfupqXTzMgac5U1w+U+1tVVYnLowlgzBYThxJC4a3095QK74MHlc/Hu0by7ueOrN1qqaqSdcdnADR/sdnz8xMEQXgbPpRGmozSBVhG47aC9VudZoHF+wPDadV1pr2FwQBkZQHjxwMXXwz88Ydnn9GRp4AvUBLCRiM8Et5K+PvzEYQSZPEmCB4nFm9nA0D8CwtR3tDPVoPZkcVbIas5ALQnpeLUM68h5ZsyYK2lZri3hLcrruZC12ovCO+aSiNGl85wb7VPTnjr9aLyaEJYjRaMIxMHL7yjopT/T1LhbTQ67qPJZPs7eUN46/ViTwQLLID4q8d4fn6CIAhfIJPoE/X1/9/evYdHUd3/A39PQggJEMIlQGC5k0hFLFqTEFAutRYRlEefANoLwS4CXkq9cPlhSwOCKELBr1IRMCWg8kVArYq0goLCN2IAgdpWLgEJYSEQCAkJ5gLJnt8fy2z2Mrs7uzuzmU3er+fhIZmd2T05C/nsZ845n2OrT9HWYW6Pw/H/+29bvPMuUIL2yEM6smBCoDlsSPZpdoiPHWIrMXy4Dq/hgT+jt2rXNHudKWCx2ArmyVu+lZYCxcVAx47u76fK41ePATOtpeiIYhSjI8rQFqgDauaVOle491U9TiUWXyOjYOJNJPOVeHvaHkq+HC57MAc41Tw6uReip40DDu6oP6jVGm9P058d11DrtMbbti1bgNMPldZ4m0yQ/vxnYL7zHusCgLTaxxCHnHh760vXxLtrV+9tdJzh4OPfiiomE6S33oJ1cn1RPwEgf3AmkjnNnIiMzKXQpzcWCzCsB2DPiQKvreb08rpOIdZpKZYaatex+7Om2WNxss+yvW5TGqh+AF5WesC1wv0nn9h+kCCnEqgtvkakN041J5IFOdUcqN+DWfH5HHmbai5XrXZM3nRe4+3UFp3WeHe+KwmB3ru+frVa+QGFYQZp4kTfQTqQxLudj5Xpq1bVfxrSYsQbAMxmRJw5g9JFK3EqMwvFn+xDcm6ONs9NRGQAYTkNWKftNtWQR28duY7eep3K7YHZbJsub582P9KiS9LtN18NV8nt52NhNWoAYZF419TUYPbs2ejSpQtiYmKQlpaGHTt2+L4QwNmzZzF+/HjEx8cjLi4OY8eOxQ8//KBziyksBTnVHHDZgznQ4mpy4u2YvOk91dxxRFuLNd4Wi23P8JUrgUWLUG5+GuXvfoKzPZynSKsN59Yfq5QfKCtzP6ZmbaGarUq8FFerGjEKx598tf7xQYNsUVwurqZV4g0AJhPazpmGXjnzWFCNiBodNYmk4Wi8FEsteer4yy97X8ce6M0Mk8l2P9tkuvEkDZ10A5rehXH6+YgaQFhMNZ80aRK2bNmCp59+GklJScjJycF9992HXbt24c477/R43dWrVzFixAhcuXIFzz//PKKiorB8+XIMGzYMhw8fRvv27T1eS01QkIm32x7MRky8PY14K+0/7fjB4j//QemilbhQDHTqCLRFqS0Zvf9+IMUlGcx2n5om7yfuGMLL5ixG/D/+Fzh82GezI6M8JNNKibeaD0GBjHg7vF8xP+uP5CV/AP42x/Zc8vsjb4OiZeJNRNSIheU0YMcb019+CbRqpXqNc2kpcKEY6BxdivgalWulS0vx753F2PBFR5SiLSQA2x4E+rQrRYIoRtyFjsDK+ue4raAU81GNT3A/DsAWo/2+mZGU5He36MLwd2GI1DN84r1v3z5s3LgRS5YswYwZMwAAEydOxC233IJZs2bh66+/9njtG2+8gfz8fOzbtw8pN5KDUaNG4ZZbbsFf/vIXLFq0KCQ/A4UJX1PNvazbPTl5EWKn/NZ5D2ZvU829FFezjziHMvF2JL/u++/XH/vuO7T97gn3La8WLAAyM4GcHNv3Fu9T0xzT5/hfjwb2/kNds+V7GBYLSj75GhePlSDhpvZof6HA/eQQJN729yghwVbt/OJF2/fyDQwt1ngTETURWhVEU1tMLGi7dtV/vW2b7Y9KittHqjAAwEuOBz70fG48gD8DmIsFyEEmHovMcbuZ4bOvTCbgN78B3nkngNZqJCLwCvdERmT4xHvLli2IjIzElClT7MdatGgBs9mM559/HmfOnEG3bt08XpuSkmJPugGgX79+uPvuu7Fp0yYm3uTMdYTadVq2l4S3zx8fAXq6BAZfI94e1niLRS9B6t3bOfGuqKj/OpjiamqrmlsswPTp6p5z3TrgySdtI9/+TE1r3lz9z1JbC2RnQ0yejPYA5LkqSlW/fSbe16/XF2vzdhPDn8S7pMQ2r0+PqeZERE1AsAXR/CkmFhSLBViyRIcn1p4EYBLW4b6/P+m0VEl1X910U/3X06YBP/2pqir1AR93PNa3L5CezqSbGhXDJ96HDh1CcnIy4uLinI6npqYCAA4fPqyYeFutVnz33Xf43e9+5/ZYamoqtm/fjoqKCrRu3VqfhlP4CWaqeYcOvp8PsE3frqz0OtVcgoCYMhXSnP+n/FolJUCgyyTUjHhHRfm/tis315Z4+zM1LTpafeJ97RrEY4+5JdmKE9DlvbQ9cdzD258Rb8fp+I6JNwBYrSib/ifEy4vqjLAujoioiVCzxZZmo+FGWfuskgSg04lc4MaUc3+2I7PP5gKAiRNtibDOQjZrgagBGL64WlFRERITE92Oy8fOnTuneN3ly5dRU1MT0LU1NTUoLy93+kNNQBBTzZ3We3l6PgBwvNHjpRKqZK2z7YGqpF8/2+3qQKgd8U5KUlek7IYrx4twwrwI5VlLlW9CeHodPxJvpf26Ffka8Q408Xa8USKPaDtUm4//a/0kQPHtt4G/R0SNBGMphYqvYmLZ2UCPHsDPf277O6hfz37GR0MYUl/Y1K/Ca5cu1X+tNrYHQdP3iciADJ94V1VVIVoh4Wlx40NxVZVytWP5eCDXvvTSS2jTpo39j6ep7NTIBDPirRSElUa8VSbeQoqA2LnTw4Mi8O011K7xNpmANWtUf7hos/IV9P3bHxH3t/9xDtS//jWwaBHqlG5aNG+ufr26EBDK49vuQpF4R0XZ+t/DzREJgJgyRZMtUIjCFWMpqWGx2JZMB/Pr0ltl9EC21vLKz/jY4B54wKkIql9V5EOYeGv+PhEZkOGnmsfExKBGrhTsoPrGdkAxHj44y8cDuXbOnDl49tln7d+Xl5fzA0NTEETivWdSNu7KcVkg5SvxdkgQ6yAh8kbNbyFFQHruWWDpUs9tlW9P+zsPy5/ianK1m717gZISW6HVi0Cn5qWIX/y8utfbuBEoKEDkqlXA6dPur6N2xNtqhTT998Brr3k+p2tX4OzZ+n51KMTWSS4OW1oKHD3q9Lwe+Uq88/O9NlmyWgN7j4gaCcZS8kWrddneKqPv2uV5hDfgX88u8RGA6rXMTrH0mu14KdpizvO2uiXxKEVHXMQlJGDWorb1scuf9dPvvWertg7YOsVHX738cn1Ic+qTG1PNRWQkvjwUj6Rk/UKat5F4hlFqLAyfeCcmJuLs2bNux4uKigAAXbp0UbyuXbt2iI6Otp/nz7XR0dGKI+XUyAUx1Tx93VQUPTkSib6qmjsm3g5rkYv35OPy5wfRuTPQfoxtDZX4y188T68OdHsNNVPNHc8xmYBx4wA4VGLdtQtYrPL15KipVEvB3+JqQ4Z4TLwFgJrSSrQAbIl3djaskx9Dewh4XQ2/ZYvtk5/SJz1fibc83dDDeyQiIiBxCxRqwhhLyRu/1hqr4KkyujzC65jUabJDlUN89IdSVfPDu4BVCueOTLftO+23Zs3qE++3366/8X1jqzJzx4546IW2KL4IFBQAX84sxSEUYzs6YvSv2uLOO2+ce/w4AOBiXVv8/G5J16J1ur1PRAZi+MR74MCB2LVrF8rLy50KrOXl5dkfVxIREYEBAwbgwIEDbo/l5eWhd+/eLKxGznyNeEdE2BIuhaJozVCHC7knnBNvpRHvVq3qv3YY8U5M74nEO/s4nSqtWQPrY48hwjWxC2Z7DX9GvD3xkXA6kaOm0v+1qCj1ibcQzpXdXUgAoitLbd/cKMQWAZVrwj190vOVeMvTDRW2TxOQIK1ezdv0REQe6DHCqVQZPRz2Cdc86fz++/qvN260/XEh3wC4CcBIxwc23PjjIAGX8Dtk429Wc1A3R7wJh/eJKFiGX+OdkZGBuro6rF692n6spqYGa9euRVpamn3aWmFhIY46TiG9ce3+/fudku9jx45h586dGBfAXUpq5Hwl3oDH6ea1iESnIS4RUu0ab0lSHh03mxFRWIiSlZtw7JmVKF20Eti0yXbnOtDbzVok3mrXtzneIHBNvKOibNf7szVaWZnXhx1bo7oQG+C5qozDe1117rLydmJms207sU2bUDZ7EQoznsHlRSshnSnUaR8bIqLGwa+1xkEym20ju7t22f422q9nOemUPwoElXRaLMCrr2rZPEgAVmMKusLiuRCbjyapWcdv9PeJKFiGH/FOS0vDuHHjMGfOHBQXF6Nv375Yt24dCgoKkO1Q7nDixIn46quvIBw+cD/xxBNYs2YNRo8ejRkzZiAqKgrLli1Dp06d8NxzzzXEj0NG5muqOWBLxlxGXmsRib2Zq3BXikuEVEqmY2Prv5ZHvL1N/zaZ0H7aOO/Tpf2htqq5Lwrrvy//UIoEXERcb4X9Nx1H+oH6afv+JN4OFcSVOO7prbi/tycePuntnbEF8sYpLXb9AycrWsM+J0FhOn78OCBe7WsSETVxoR7hDHafcL15mirvN522O4uEFX1xAucjTX7dHPF3Hb/R3yeiYBg+8QaA9evXY+7cuXj77bdRWlqKW2+9FVu3bsXQoUO9Xte6dWt8+eWXeOaZZ7Bw4UJYrVYMHz4cy5cvR4K8/y6RzPXWu9KItct6xX+/8AE63JvinnQrXF8XGYXqsuuwbzwm7wutZhRaK2r38VZDaf23J64j3nJyr9GItwBQ3qEX2lw6BQCQfvEL4PPPfT+nh2n7RfstSH2v/uacBKDXgc31J6i5OUFERF75m2wacY9nLdukSdLpz3IwP9QhAqci+vp1c0TrdfxE4S4sEu8WLVpgyZIlWLJkicdzvpSLSLgwmUzYvHmz4mNETlwTbaWp1C5TzQc8Ndy5kqiX54usu46YD991P09toquFTZt8n6NHUukp8Va7nRjgccS7tkVLlGzehU6fvGW7lQ7g2tETkH+Kwoxn0Pr2ZPfKsK6j8g7O78lHIpwXHzqtGQ/le0ZE1IipTTa1qoCuJSO2yVv9kUAJSUL+c6uR+weTXwkzK5UTOQuLxJsoJJSmhrtyTRS9JWAKz6e64JceLBZAzRILPUbgXaeaBzLi7SHxbpbQDp3GpACv/bH+6S0F9q+vF1jQdvMy9a8DoPNdSahDBCIdkm8rpPr3j4k3EVHIeBo5vfVW2wYhDTECbsTRXPvo+0gzTIUu250Bfm1Ltm97Kf7vw4s4hmT8A2OQ1c8Es58/FyuVEzlj4k0kU5pa7sp1axxvSaqa5wMgrlyBNGkSkJOj6vyAqVn31by576JpgfA04r1nj/L5StPkPE01b9HC9mljxw7Fh3sf2IwLW/fbknOVElNM2JO5GkPWTbZXoCzuk47OJ7+2fcPEm4goZDyNnA4a1HCjzUYbzXUffTfBbA6skLDFAqQ/ifpbzyKwmwqsVE7kzPBVzYlCRk2i7DLiXXT4gudz1Yygw7Z+WKxbB+zfr+r8gMnrvrzRK6FUSrwtFojXVyieLpRuaHgqrtaihe0TkAcSgIp/5qpsaL27csyo/NUU+/fta87WP8jEm4goaGqrXStVQAfcR5t9PY+WQlmV3RdPo++B9oe3mwr+kiuVb9oEbNhgS96Jmiom3kQyNYn35ctO33ZK74U9k7KVz1U54g3cqMCd639y6Bc124DpVTRMaap5fj4kD1PvrZLCTQtvibeXmwoCQOt7h/jR2HrnCmrsX0dZTtu/vpRfGtDzERGRTXY20KMH8POf2/7O9hBKAffttpTCa6CJYaA03QIsSFomyoD2NxU++wx4+GFgwgTbe71kibobLkSNDRNvIpmvEWqLBeLIEadDERBIXzcVRfsVoodL1Crtnw7hYZMrAQBDAksO/eKw73TpopW42u0nzo/rlXi7jHjXSNFAUhKEp5sAMQpF17wl3h5uKggA+YMz/ZpmLivab0Gfr9crPtZu8oOeb7gQEZFXgYzQOu7x/M03xhhtboh9p5VmCWidKGt5U0HpvZ41S90NF6LGhok3kczXCHV+vmLa3Ax1uJCrcFvZJZFvO+JnkN5aA6tCcihlZgIp/ieHAbmxDVjbOdPQqp9LFNVh708AbqP5pw9dRvZnJkhr3PsDACJj3YuuiVIPo8zy9H+HmwplsxfhVGYWij/Zh+TcnICafH5PPiI9jMh7veFCREReBTpCazIBw4fbwqVRRpvlNgH6j+J6miWgJlFWO61fptVNBaX3WtYQSwSIGhKLqxHJfCXeN0ZoJZfktBaR6DRE4bay6/MlJABmMyJGjkTJ1r0o+/YEOrSuQZtHRocu6XZRcqQY7R2+F8XF2hd6s1gg/uc1p5sWScjHPVMsGHnaVnm1ZOtetH98fP0JCiPv9n6PjgZq6qeAO627v3FTIX4cEB9ks22VzSWPybd8wyVRaQ93IiLySItq1/7uAa6nUGwr5quKurf+CLR9WuwrrvReO+L2YtSUcMSbSOZrqrnJfYS2DhHYm7lKOflyTbw7dLA/T/tp49BnzRy0WTavwZLuov0WtLP8y+mYLoXe8vMhCeeIKwHoZT1hG9240R9OXKvHOxCOSTfg317gfkhMMeHrzDWo8/C4xxsuRETklVZTmeXR5oZM2rQubOaJmlkCSv2h1L4pU/Sv5+rYJsf32hW3F6OmhIk3kUxNMTSzGRGFhShZuQn/zdqE4n2ncVeOh9vGrlFGTrwN4vwe5anzmhd6S0qCcOlbKySciujrOdh6WWvu1madEm/AVtm8eN8ZnL1/GqxQecOFiIh8aoj10XrQurCZJ4Gu41Zqn9Vq24otVOurHd/rJUuMsUSAqCFwqjmRTG0V8hsjtO19nefyfCWXhO9rQsg2lRpwvQktAEhaFnozmSCtWAE88YT90Lf4Gf682uQ52PpT5C3GfT24lhJTTMDHKwHLH1GydS/OnwfajU7HXUy6iYiC4s9UZovFlkQmJRkrUdNi2rwage6J7Wmqt+tUdb3J7/Xw4bYK50ZYIkAUahzxJpKp3HdbNZe9pds++YihKmHbplK/BcdYrFuht2nTnL69+cGbvI9uOCTePsu96Tji7eTGDZf+88ZxpJuIKIT82Xos1EK5rVggswTk9hlhCzbHNjX0EgGihsDEm0jmx77bPlksEHv2OD+9ASth35VjxoV9Z3DsmZW48kwWpH37tC2sJnOpXN6yrec13ACA69ftX9a1aOU9+Q5V4k1ERCEXqjXUwQjltHlfSatS9XKz2ThbsBE1ZUy8iWSe9pQOhL9bjzWgxBQTblo2LbSF3lymkruOXohvv7V/3Sw6EvmDM+3Jt1sSzsSbiKjRCtUa6mAZYRTX28wAI23BRtRUMfEmknna6yIQN7Yec9WkK2E79ofDtH6LBZj/mPPQhVPP1dYiOTcHxZ/sw4knl6P4k33Oz8vEm4io0Qq0qFhTo2ZmQGMpaEcUrph4E8m0TLz93XqsKYiNrf+6qsr+ZX4+0EfkK1xgI25MO+80JgV9VzyNTmNcRuWZeBMRNVqhXEMdzjzNDNi713nquRFG5omaKlY1J5JpmXgDtq3HRo5kJWxZy5bAjz/avq6stB9OSgJOSkmoExGIhMJ7cO0a9kzK9rxtGxNvIqJGzWy2Vd9mJWzPlKqXR0QAEyYAQti+Xr2ao9xEDYkj3kQy4bN+tv9YCbue44i3nIDD9gEqa40J06TVqL2xuZnjOyEB3ovSRUVp31YiIjIUjtR6pzQzQIj6jzZGLEpH1NQw8SaSaT3iTc5atrR/WX3+stNDZjOQVWjG/k0FOPTbZW6F6bwWpdOyKB4REZHBKVUuB5zXcG/Y4D6eYMSidERNCRNvIhkTb11VnK8f5Y7en+u2p7nJBKSPM6Hz78ehzuVXk9eidFpuA0dERGRgvvY0l2cGDB7MonRERsNPrEQyPaaaEwCgaL8FrUoK7N97mz6emGLC15n1085rEem9KB1HvImIqAnwZ09zFqUjMh4WVyOSccRbN+f35CPR5Zg8fVwpob4rx4yiJ0fiQu4JdBrS12tRutJTpWircXuJiIiMxtue5koJtZqidBaL7XmTkpiUE+mNiTeRjIm3bjrflYQ6OFct97WneWKKSVVBujYvPIc9p+M8Vz0nIiJqBJQql/uaPm4yeU6os7PrR9BZ9ZxIf5xqTiRzmWrOyp/a8Xv6uBeu09MjILxXPSciopDzVACMAqfl9HF/pq0TkTaYeBPJdu92+nZ+92y3oiUUuLtyzLi4rwCHl+/CxX0FAY9Qn9+T73bMa9VzIiIKKV8FwChwjpXLCwoCH6H2Nm2diPQhCcGKUr6Ul5ejTZs2uHLlCuLi4hq6OaQHiwWie3dIDv8dahGJPhEFyD1t4ronAynab0HH1B5u09Yv7ivgXunUZIVDnAqHNlLwLBZbsu06HbqggGuIjYTvE5E7veMUR7yJACA/3ynpBmyjqL2sJ3j312C0nLZORETa4khqeGDVc6LQY3E1IgBISoKIiIBkdR5FPRXRl3teGpA/Vc+JiCh0AikARg1DTdVzItIOR7yJAMBkgrR6NawR9aOoj0ur8OfVnGZuVIkpJgx8ejhHuomIDIQjqeHFZAKGD+f7QxQKHPEmkpnNiBg5Ehf3nsAJ9EVWOpNuIiIif3EklYjIHRNvIkcmExLGmZDQ0O0gIiIKY972jyYiaoo41ZyIiIiIiIhIR4ZPvMvKyjBlyhQkJCSgZcuWGDFiBA4ePOjzOqvVipycHDzwwAPo1q0bWrZsiVtuuQULFy5EdXV1CFpOREREREREZPDE22q1YvTo0diwYQOeeuopvPLKKyguLsbw4cORn5/v9drKyko8+uijuHjxIqZNm4ZXX30VqampyMrKwqhRo8Dty4mIiIiIiCgUDL3Ge8uWLfj666+xefNmZGRkAADGjx+P5ORkZGVlYcOGDR6vbd68OXJzczF48GD7scceeww9e/ZEVlYWvvjiC/ziF7/Q/WcgIiIiIiKips3QI95btmxBp06d8NBDD9mPJSQkYPz48fjoo49QU1Pj8drmzZs7Jd2yBx98EABw5MgR7RtMRERERERE5MLQifehQ4dw++23IyLCuZmpqamorKzE8ePH/X7O8+fPAwA6dOigSRuJiIiIiIiIvDH0VPOioiIMHTrU7XhiYiIA4Ny5cxgwYIBfz/nKK68gLi4Oo0aN8nhOTU2N02j6lStXAADl5eV+vRYREVEoyPHJSPVLGEuJiCic6B1LQ5Z4W61WXLt2TdW50dHRkCQJVVVViI6Odnu8RYsWAICqqiq/2rBo0SJ8/vnneOONNxAfH+/xvJdeegnz5893O96tWze/Xo+IiCiUSkpK0KZNm4ZuBgDGUiIiCk96xdKQJd67d+/GiBEjVJ175MgR9OvXDzExMYrruOXtwGJiYlS//nvvvYc//elPMJvNePzxx72eO2fOHDz77LP278vKytCjRw8UFhYa5gNNuCsvL0e3bt1w5swZxMXFNXRzGg32q/bYp9pjn2rvypUr6N69O9q1a9fQTbFjLNUf/y9pj32qD/ar9tin2tM7loYs8e7Xrx/Wrl2r6lx5KnliYiKKiorcHpePdenSRdXz7dixAxMnTsTo0aPx5ptv+jw/OjpacaS9TZs2/Ietsbi4OPapDtiv2mOfao99qj3XmigNibE0dPh/SXvsU32wX7XHPtWeXrE0ZIl3586dMWnSJL+uGThwIPbs2QOr1erUAXl5eYiNjUVycrLP58jLy8ODDz6IO+64A5s2bUKzZoZe1k5ERERERESNjHFujSvIyMjAhQsX8MEHH9iPXbp0CZs3b8b999/vdCf95MmTOHnypNP1R44cwejRo9GzZ09s3brVr6npRERERERERFow9PBvRkYGBg0ahEcffRTff/89OnTogDfeeAN1dXVuBVvuvvtuAEBBQQEAoKKiAiNHjkRpaSlmzpyJTz/91On8Pn36ID09XVU7oqOjkZWVpThljgLDPtUH+1V77FPtsU+1Fw59Gg5tDDfsU+2xT/XBftUe+1R7evepJIy094gCOXH++9//jqqqKqSkpGDp0qW44447nM7r2bMngPrEu6CgAL169fL4vJmZmcjJydGp1UREREREREQ2hk+8iYiIiIiIiMKZodd4ExEREREREYU7Jt5EREREREREOmrSiXdNTQ1mz56NLl26ICYmBmlpadixY4eqa8+ePYvx48cjPj4ecXFxGDt2LH744QedW2x8gfbpBx98gAkTJqB3796IjY3FTTfdhOeeew5lZWX6NzoMBPNv1dE999wDSZLw1FNP6dDK8BJsn7733ntIT09Hy5YtER8fj8GDB2Pnzp06ttj4gunTzz//HCNGjECHDh0QHx+P1NRUvP322zq32PiuXr2KrKws3HvvvWjXrh0kSfKrPklZWRmmTJmChIQEtGzZEiNGjMDBgwc1bSNjqfYYS7XHOKoPxlLtMZZqzzCxVDRhDz/8sGjWrJmYMWOGWLVqlUhPTxfNmjUTe/bs8XpdRUWFSEpKEh07dhSLFy8Wy5YtE926dRMmk0lcunQpRK03pkD7tH379mLAgAFi7ty5Ys2aNWL69OmiefPmol+/fqKysjJErTeuQPvV0fvvvy9atmwpAIgnn3xSx9aGh2D6NCsrS0iSJMaNGyfefPNN8frrr4upU6eK9evXh6DlxhVon3700UdCkiQxePBg8frrr4sVK1aIoUOHCgBi2bJlIWq9MZ06dUoAEN27dxfDhw8XAMTatWtVXVtXVycGDx4sWrZsKebNmydWrFghbr75ZtG6dWtx/PhxzdrIWKo9xlLtMY7qg7FUe4yl2jNKLG2yiXdeXp4AIJYsWWI/VlVVJfr06SPS09O9Xrt48WIBQOzbt89+7MiRIyIyMlLMmTNHtzYbXTB9umvXLrdj69atEwDEmjVrtG5qWAmmXx3P79mzp3jhhRf4gUEE16d79+4VkiQ1+SDmKpg+veeee0SXLl1EdXW1/dj169dFnz59xK233qpbm8NBdXW1KCoqEkIIsX//fr8+LLz33nsCgNi8ebP9WHFxsYiPjxePPPKIJu1jLNUeY6n2GEf1wViqPcZSfRglljbZxHvmzJkiMjJSXLlyxen4okWLBABRWFjo8dqUlBSRkpLidvyXv/yl6NOnj+ZtDRfB9KmS8vJyAUA8++yzWjYz7GjRr/Pnzxfdu3cXlZWV/MAgguvTCRMmiMTERFFXVyesVquoqKjQu7lhIZg+TUtLE/3791c8npaWpnlbw5W/HxbGjRsnOnXqJOrq6pyOT5kyRcTGxjp9OAsUY6n2GEu1xziqD8ZS7TGW6q8hY2mTXeN96NAhJCcnIy4uzul4amoqAODw4cOK11mtVnz33Xdu+4jL1548eRIVFRWatzccBNqnnpw/fx4A0KFDB03aF66C7dfCwkK8/PLLWLx4MWJiYvRqZlgJpk+/+OILpKSk4LXXXkNCQgJat26NxMRErFixQs8mG14wfTp8+HD897//xdy5c3HixAmcPHkSCxYswIEDBzBr1iw9m92oHTp0CLfffjsiIpxDfWpqKiorK3H8+HFNXoOxVFuMpdpjHNUHY6n2GEuNR8tY2kzrxoWLoqIiJCYmuh2Xj507d07xusuXL6OmpsbntTfddJOGrQ0PgfapJ4sXL0ZkZCQyMjI0aV+4CrZfn3vuOdx22214+OGHdWlfOAq0T0tLS3Hp0iXk5uZi586dyMrKQvfu3bF27Vr8/ve/R1RUFKZOnapr240qmH+nc+fOxalTp/Diiy9i4cKFAIDY2Fi8//77GDt2rD4NbgKKioowdOhQt+OO78mAAQOCfg3GUm0xlmqPcVQfjKXaYyw1Hi1jaZNNvKuqqhAdHe12vEWLFvbHPV0HIKBrG7tA+1TJhg0bkJ2djVmzZiEpKUmzNoajYPp1165deP/995GXl6db+8JRoH169epVAEBJSQk2btyICRMmAAAyMjIwYMAALFy4sMl+WAjm32l0dDSSk5ORkZGBhx56CHV1dVi9ejV+85vfYMeOHRg0aJBu7W7MtPydrPVrMJZ6xliqPcZRfTCWao+x1Hi0/J3cZBPvmJgY1NTUuB2vrq62P+7pOgABXdvYBdqnrvbs2QOz2YyRI0fixRdf1LSN4SjQfq2trcX06dPx29/+FikpKbq2MdwE+/8/KirKafQoIiICEyZMQFZWFgoLC9G9e3cdWm1swfz/f+qpp/DNN9/g4MGD9qlc48ePR//+/fGHP/yBH3gDpNXvZD1eg7HUM8ZS7TGO6oOxVHuMpcajZSxtsmu8ExMTUVRU5HZcPtalSxfF69q1a4fo6OiArm3sAu1TR//617/wwAMP4JZbbsGWLVvQrFmTvTdkF2i/rl+/HseOHcPUqVNRUFBg/wMAFRUVKCgoQGVlpW7tNrJg/v+3aNEC7du3R2RkpNNjHTt2BGCbQtcUBdqn165dQ3Z2NkaPHu20fioqKgqjRo3CgQMHcO3aNX0a3chp8TtZr9dgLPWMsVR7jKP6YCzVHmOp8WgZS5ts4j1w4EAcP34c5eXlTsflu0EDBw5UvC4iIgIDBgzAgQMH3B7Ly8tD79690bp1a83bGw4C7VPZyZMnce+996Jjx47Ytm0bWrVqpVdTw0qg/VpYWIjr169jyJAh6NWrl/0PYPsw0atXL2zfvl3XthtVMP//Bw4ciIsXL7oFMHndVUJCgvYNDgOB9mlJSQlqa2tRV1fn9tj169dhtVoVHyPfBg4ciIMHD8JqtTodz8vLQ2xsLJKTkzV5DcZSbTGWao9xVB+MpdpjLDUeTWOpP+XXG5NvvvnGbZ+86upq0bdvX6eS+6dPnxZHjhxxuvbll18WAMT+/fvtx44ePSoiIyPF7Nmz9W+8QQXTp0VFRaJ3796iS5cu4tSpU6FqclgItF+PHDkiPvzwQ7c/AMR9990nPvzwQ3Hu3LmQ/ixGEcy/1eXLlwsAYvXq1fZjVVVVonfv3uLmm2/Wv/EGFWif1tbWivj4eJGcnCxqamrsxysqKoTJZBL9+vULzQ8QBrxtgXLu3Dlx5MgRce3aNfuxjRs3uu09evHiRREfHy8mTJigSZsYS7XHWKo9xlF9MJZqj7FUfw0ZS5ts4i2EbV+2Zs2aiZkzZ4pVq1aJwYMHi2bNmomvvvrKfs6wYcOE6/2J8vJy0adPH9GxY0fxyiuviOXLl4tu3bqJLl26iOLi4lD/GIYSaJ/+9Kc/FQDErFmzxNtvv+30Z/v27aH+MQwn0H5VAu4/KoQIvE8rKytF//79RVRUlJgxY4Z47bXXREpKioiMjBTbtm0L9Y9hKIH26cKFCwUAcdttt4nly5eLpUuXip/85CcCgHjnnXdC/WMYzuuvvy4WLFggHn/8cQFAPPTQQ2LBggViwYIFoqysTAghRGZmpgDglGzV1taKQYMGiVatWon58+eLv/71r6J///6idevW4ujRo5q1j7FUe4yl2mMc1QdjqfYYS/VhhFjapBPvqqoqMWPGDNG5c2cRHR0tUlJSxD//+U+nczz9Ej5z5ozIyMgQcXFxolWrVmLMmDEiPz8/VE03rED7FIDHP8OGDQvhT2BMwfxbdcUPDDbB9OmFCxdEZmamaNeunYiOjhZpaWlu1zZFwfTpu+++K1JTU0V8fLyIiYkRaWlpYsuWLaFquqH16NHD4+9H+cOB0ocFIYS4fPmyMJvNon379iI2NlYMGzbMaYRZC4yl2mMs1R7jqD4YS7XHWKoPI8RSSQgh1E9MJyIiIiIiIiJ/NNniakREREREREShwMSbiIiIiIiISEdMvImIiIiIiIh0xMSbiIiIiIiISEdMvImIiIiIiIh0xMSbiIiIiIiISEdMvImIiIiIiIh0xMSbiIiIiIiISEdMvImIiIiIiIh0xMSbiIiIiIiISEdMvImIiIiIiIh0xMSbiIiIiIiISEdMvIlIU7m5uZAkCZIkYdOmTYrn5OXloVWrVpAkCTNnzgxxC4mIiIyNsZSo8ZGEEKKhG0FEjcvYsWPx8ccfo1+/fvjPf/6DyMhI+2PHjh3DkCFDUFJSgszMTKxduxaSJDVga4mIiIyHsZSoceGINxFp7qWXXkJkZCSOHj2Kd955x3783LlzGDlyJEpKSjBmzBi89dZb/KBARESkgLGUqHHhiDcR6WLy5MnIzs5Gr169cOzYMfz4448YOnQo/v3vf+POO+/E9u3bERMT09DNJCIiMizGUqLGg4k3Eeni7NmzSEpKQlVVFZYvX44PP/wQu3fvxoABA7B7927Ex8c3dBOJiIgMjbGUqPHgVHMi0kXXrl0xffp0AMAzzzyD3bt3o2fPnvjss88UPyhcvXoV8+bNw5gxY9C5c2dIkoRJkyaFttFEREQGwlhK1Hgw8SYi3UyfPh0REbZfM+3atcP27duRmJioeO6lS5cwf/58HDx4EHfccUcom0lERGRYjKVEjUOzhm4AETVOtbW1mDp1KqxWKwCgsrLS6zq0xMREWCwWdO3aFdXV1VyzRkRETR5jKVHjwRFvItKcEAKTJ0/G1q1bkZCQgF69eqG6uhpZWVker4mOjkbXrl1D2EoiIiLjYiwlalyYeBOR5mbNmoV169ahVatW+PTTT/Hiiy8CANatW4fvv/++gVtHRERkfIylRI0LE28i0tTSpUuxdOlSREVF4YMPPkBKSgoefvhh3Hrrrairq8OcOXMauolERESGxlhK1Pgw8SYizaxfvx6zZs2CJEnIycnBPffcAwCQJAkLFiwAAHz88cfIzc1tyGYSEREZFmMpUePExJuINLFt2zaYzWYIIbBs2TL86le/cnr8gQceQFpaGgBg9uzZDdFEIiIiQ2MsJWq8mHgTUdD27t2LcePGoba2FrNnz8bTTz+teJ68Pi03NxcfffRRCFtIRERkbIylRI0btxMjoqClp6fjxx9/9Hne3XffDSFECFpEREQUXhhLiRo3jngTERERERER6Ygj3kRkGCtWrEBZWRlqa2sBAN999x0WLlwIABg6dCiGDh3akM0jIiIyPMZSImOSBOeqEJFB9OzZE6dPn1Z8LCsrC/PmzQttg4iIiMIMYymRMTHxJiIiIiIiItIR13gTERERERER6YiJNxEREREREZGOmHgTERERERER6YiJNxEREREREZGOmHgTERERERER6YiJNxEREREREZGOmHgTERERERER6YiJNxEREREREZGOmHgTERERERER6YiJNxEREREREZGOmHgTERERERER6YiJNxEREREREZGO/j9vFnTaHz0loQAAAABJRU5ErkJggg==\n"},"metadata":{}}],"source":["fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n","\n","# 왼편 그림\n","plt.sca(axes[0])\n","plt.plot(X, y, \"b.\")\n","plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n","plt.axis([0, 1, -0.2, 1.1])\n","plt.xlabel(\"$x_1$\", fontsize=18)\n","plt.ylabel(\"$y$\", fontsize=18, rotation=0)\n","plt.legend(loc=\"upper center\", fontsize=18)\n","plt.title(\"No restrictions\", fontsize=14)\n","\n","# 오른편 그림\n","plt.sca(axes[1])\n","plt.plot(X, y, \"b.\")\n","plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n","plt.axis([0, 1, -0.2, 1.1])\n","plt.xlabel(\"$x_1$\", fontsize=18)\n","plt.title(\"min_samples_leaf={}\".format(tree_reg2.min_samples_leaf), fontsize=14)\n","\n","save_fig(\"tree_regression_regularization_plot\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"wS8QIKf8OKcR"},"source":["## 6.9 불안정성 (p. 241)"]},{"cell_type":"markdown","metadata":{"id":"My2yVgK8OKcR"},"source":["결정트리 모델의 훈련과 사용은 매우 쉽고, 성능도 강력하다.\n","하지만 x, y 등의 축(axis)에 수직인 결정경계만 사용하기 때문에 회전, 샘플 삭제/추가 등 훈련세트의 작은 변화에\n","매우 민감하다."]},{"cell_type":"markdown","metadata":{"id":"FURsti7wOKcR"},"source":["### 회전 민감도 예제 1"]},{"cell_type":"markdown","metadata":{"id":"DJzTGFdtOKcR"},"source":["아래 코드는 결정트리 모델의 회전 민감도를 보여준다.\n","두 개의 특성을 가지며, 임의로 생성된 100개의 훈련 샘플을 시계방향으로 45도 회전시킨 후\n","훈련시킨 모델의 결정경계가 직선에서 계단식으로 변화되는 것을 볼 수 있다.\n","결국 일반화 성능이 보다 약한 모델이 생성되었다."]},{"cell_type":"markdown","metadata":{"id":"SUY-tzCCOKcR"},"source":["- 두 개의 특성을 갖는 100개의 훈련 샘플 생성. 둘째 특성값의 부호에 따라 두 클래스로 분류."]},{"cell_type":"code","execution_count":19,"metadata":{"id":"sLv49IlMOKcS","executionInfo":{"status":"ok","timestamp":1701868080241,"user_tz":-540,"elapsed":403,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["np.random.seed(6)\n","Xs = np.random.rand(100, 2) - 0.5\n","ys = (Xs[:, 0] > 0).astype(np.float32) * 2"]},{"cell_type":"markdown","metadata":{"id":"a35W62wwOKcS"},"source":["- 시계방향으로 45도 회전시키기"]},{"cell_type":"code","execution_count":20,"metadata":{"id":"oH4aOD30OKcS","executionInfo":{"status":"ok","timestamp":1701868083832,"user_tz":-540,"elapsed":321,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["angle = np.pi / 4  # 회전 각도 45\n","\n","# 훈련 세트 회전시키기\n","rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n","Xsr = Xs.dot(rotation_matrix)"]},{"cell_type":"markdown","metadata":{"id":"f063D6CuOKcS"},"source":["* 결정트리 모델 비교"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"dRZyf7H1OKcS","outputId":"3fd7a2fe-858c-40ce-bd59-309f7caed962","colab":{"base_uri":"https://localhost:8080/","height":425},"executionInfo":{"status":"ok","timestamp":1701868089370,"user_tz":-540,"elapsed":1201,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saving figure sensitivity_to_rotation_plot\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA90AAAGGCAYAAABmGOKbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABwa0lEQVR4nO3deXyV1Z0/8M+Tm+QmLEkwC9yrqBBEBoVSxySCihZroeJSlMWxU8DBZWqtY5WlOKMpbsiiWMfRiloWq6OIMDrgVGlRiaDBX0XDDJskIkuCMUgIkOQmuTm/P8ITctfc5dnO83zer1debe6W89xgzv2e7/d8jyKEECAiIiIiIiIizaWYPQAiIiIiIiIiu2LQTURERERERKQTBt1EREREREREOmHQTURERERERKQTBt1EREREREREOmHQTURERERERKQTBt1EREREREREOmHQTURERERERKSTVLMHIKv29nZUV1ejd+/eUBTF7OEQERFpRgiB48ePw+v1IiUlufV5zpdERGRXsc6XDLoTVF1djf79+5s9DCIiIt0cOHAAZ511VlKvwfmSiIjsrrv5kkF3gnr37g0A+Oabz5GV1cvk0RDZT2trC1aseB6HD3+H/9txNlIKv4aS2QJvQT/cNe0upKelmz1EIts60XACJeeUdM51yeB8SUREdtXQcALnnHNRt/Mlg+4EqSVyWVm9kJWV/IcSIgrU2tqCzEw33O40pKW5keJOg+IWSM9MR++s3gy6iQygRTk450siIrK77uZLNlIjIiIiIiIi0gmDbiIiIiIiIiKdMOgmIiIiIiIi0gmDbiIiIiIiIiKdMOgmIiIiIiIi0gmDbiIiIiIiIiKdMOgmIiIiIiIi0gmDbiIiIiIiIiKdMOgmIiIiIiIi0gmDbiIiIiIiIiKdMOgmIiIiIiIi0gmDbiIiIiIiIiKdMOgmIiIiIiIi0omUQbfP58OcOXPg9XqRmZmJkpISbNiwIebnv/HGGxg5ciR69uyJnJwcjBo1Chs3btRxxEREREREROREUgbd06dPx1NPPYWf//zn+P3vfw+Xy4VrrrkGH3/8cbfP/d3vfod/+Id/QP/+/fHUU0/h0UcfxfDhw3Ho0CEDRk5EREREREROkmr2AOK1detWvP7661i0aBFmzpwJAJg6dSouvPBCzJ49G1u2bIn43E8//RQPP/wwnnzySfzmN78xashERERERETkUNJlulevXg2Xy4U77rij87aMjAzMmDEDn3zyCQ4cOBDxuU8//TT69euHf/mXf4EQAidOnDBiyERERERERORQ0gXd27Ztw+DBg5GVlRVwe3FxMQDgiy++iPjcv/71rygqKsIzzzyD/Px89O7dGx6PB88++6yeQyYiIiIikoYQftTXb0Ft7VrU12+BEH6zh0QkNenKy2tqauDxeEJuV2+rrq4O+7yjR4+irq4OmzdvxsaNG1FaWoqzzz4by5Ytw69//WukpaXhzjvvjPhzfT4ffD5f5/cNDQ1JXgkREZH9cL4kkltd3Xrs3fsgWlpqOm9LT/dg0KBHkJc33sSREclLukx3U1MT3G53yO0ZGRmd94ejlpIfOXIEL730EmbOnInJkydj/fr1GDp0KB599NGoP3f+/PnIzs7u/Orfv3+SV0JERGQ/nC+J5FVXtx47dtweEHADQEvLYezYcTvq6tabNDIiuUkXdGdmZgasoKuam5s774/0PABIS0vDxIkTO29PSUnBlClTcPDgQezfvz/iz507dy6OHTvW+RVt7zgREZFTcb4kkpMQfuzd+yAAEe5eAEBl5UMsNSdKgHTl5R6PJ+zxXjU1HStyXq837PPOOOMMZGRkICcnBy6XK+C+goICAB0l6GeffXbY57vd7rAZdiIiIjqN8yWRnI4dKw/JcAcS8PmqcexYOXJyRhk2LiI7kC7TPWLECOzZsydkj1h5eXnn/eGkpKRgxIgR+O6779DS0hJwn7oPPD8/X/sBExERERFZXEvLt5o+johOky7onjhxIvx+P5YuXdp5m8/nw7Jly1BSUtK5d2z//v3YtWtXwHOnTJkCv9+PFStWdN7W3NyMV199FUOHDo2YJSciIiIisrP09L6aPo6ITpOuvLykpASTJk3C3LlzUVtbi0GDBmHFihXYt28fXn755c7HTZ06FR999BGEOL0v5c4778RLL72EX/3qV9izZw/OPvtsvPLKK/jmm2/w3//932ZcDhERERGR6bKzS5Ce7kFLy2GE39etwO32IDu7xOihEUlPuqAbAFauXIkHH3wQr7zyCo4ePYrhw4dj3bp1GD16dNTnZWZmYuPGjZg9ezb++Mc/4uTJkxgxYgTWr1+PsWPHGjR6IiIiIiJrURQXBg16BDt23A5AQWDgrQAACgsfhqK4wj2diKKQMujOyMjAokWLsGjRooiP+fDDD8PeXlBQgOXLl+szMCIiIiJyLCH8pxqSfYv09L7Izi6RKkjNyxuPoUNfDDmn2+32oLDwYZ7TTZQgKYNuIiIiIiIrqatbHxKspqd7MGjQI1IFq3l545GbO07qxQMiq2HQTURERESUhLq69afKsgP3Qre0HMaOHbdj6NAXpQq8FcXFY8GINCRd93IiIiIiIqsQwo+9ex9E+OZjHbdVVj4EIfyGjksWQvhRX78FtbVrUV+/he8T2RIz3URERERECeoow66J8ggBn68ax46VM3scxC4l+UTdYaabiIiIiChBLS3favo4p1BL8oMXLNSS/Lq69SaNjEh7DLqJiIiIiBKUnt5X08c5AUvyyWkYdBMRERERJSg7uwTp6R6oZ1mHUuB2e5GdXWLksCwtnpJ8Ijtg0E1ERERElCBFcWHQoEfU74LvBQAUFj7MI7e6YEk+OQ2DbiIiIiKiJOTljcfQoS8iPb1fwO1ut0e648KSEWsncpbkk9OwezkRERERUZLy8sYjN3fcqdLpb5Ge3hfZ2SWOyXDH04lcLclvaTmM8Pu6FbjdHpbkk20w001EREREpAFFcSEnZxQKCiYgJ2eUowLueDqRsySfnIZBNxERERERJSTRTuQsyScnYXk5EcVMCL9jy+aspqyqDKXvlWLe2Hm4fODlZg+HiIgcKp5O5Dk5owLucXpJPjkHg24iikk8e7Wog16BsRACCzYuwN66vViwcQEuG3AZFCXSUTVERET6SbYTuVqST2RnLC8n0kisHTtlFO9eLQoNjIUIV3aXmE1Vm1BRUwEAqKipwKaqTZq9NhERUTzYiZyoewy6iTRQV7ce5eVFqKi4Cbt23YWKiptQXl5ki2A00b1aMiurKsOY58egrKos4dfQKzAWQmDRB4vgOlV651JcWPTBIk2DeiIiolipnchDG6KpFLjdXnYiJ0dj0E2UJLtngePZq2UHWmSo9QyM1WDef2qRwy/8tsh2a7HQQUSUCDtXqhmBnciJusegmygJTsgCJ7tXSzZaZKj1CoyDg3mV7NluPUvxiYiisXOlmpHYiZwoOgbdRElwQhbYSXu1tMhQ6xkYBwfzKtmz3dyjTkRmsHulmtHy8sajpOQzDB/+FoYMeQ7Dh7+F4uKtDLiJwKCbbMSM8jAnZIGdtFdLiwy1XoGxGswrEX4PChQps93co05EZnBCpZoZ1E7kBQUTkJMziiXlRKcw6CZbMKs8zAlZYKfs1dIiQ61nYNzib0F1QzVE2A+IgIBATUMNWvwtcb+2mey6R52IrM0JlWp64154otjxnG6SnloeFrxarZaH6bmXSM0Ct7QcDvn5HRS43R7ps8DqXq3gc7rdbg8KCx+2RelY1xLnrroGgVcUXhH1NeIJjN2p7rjG5051Y92MdTjSeCTiY/J65sX9umbqutDRtTJAXegYPXA0zx8ncigh/KcC42+Rnt4X2dklmi7uOqFSTU91detDPhOkp3swaNAjtvhMQKQ1Bt0kte7LwxRUVj6E3NxxumRi1SxwR9CvBI3DPllgoCPwzs0dp+uHILN0zVCHC5jVDHV3QaDegbE32wtvtjeh51qRFgsdRGQ/RgR0TqhU04uZyQ4iWTHoJqnFUx6WkzNKlzE4IQusUvdq2Y2WGWq7BcZ60Wqhg4jsxaiAzimValozO9lBJCsG3SQ1q5SH2TkL7AR2LN22Oj1L8YlITkYGdE6qVNOSFZIdRDJi0E1SM7s8TO89Z2QcJ2Woy6rKUPpeKeaNnYfLB15uyhi40EFEwYwO6JxUqaYVqyQ7iGTDoJukZmZ5GJuIkIyEEFiwcQH21u3Fgo0LcNmAy0wr33bSQgcRdc+MgE6mSjUrLPSbnewgkhWDbpKaWeVhbCJCsuravIzNyojISswK6GToV2KFhX4h/BDCj9TUHLS11Ud4FPfCE4XDc7pJemp5WHp6v4Db3W6PLsFv93vOgMrKh3heJVlO8Fnk8ZxBTkSkp64BXWQK0tJy4fPVmHoutNHnU6sL/cGl9+pCf13del1/vhB+fPPNk9iy5UJs3z45asANcC88UTjMdJMtGFkexiYiJKvgI7p4NBcRWUG4LG54Aq2tR7B7990AzNnSZXTG2exu4XV167Fnzyy0tR3t9rFO2AtvhRJ/khMz3WQbanlYQcEE5OSM0u2PIJuIkIyCs9wqGbLdZVVlGPP8GJRVlZk9FCLSWKQsbiyMyvSqzMg4x7PQrzX1ersLuFNTczBs2CoUF2+1dcBdV7ce5eVFqKi4Cbt23YWKiptQXl5k2L8/kpuUQbfP58OcOXPg9XqRmZmJkpISbNiwIe7Xufrqq6EoCu6++24dRkl2xSYiJCM1y+0PKoPsmu22ouDGb1ZeHCCi+ETP4nZITc2By9Un0isAMGZLl1lby8xa6I/ld6Nqa6uHorhsnfE1u8Sf5Cdl0D19+nQ89dRT+PnPf47f//73cLlcuOaaa/Dxxx/H/Bpr1qzBJ598ouMoya7Ujunq3qVQCtxuL5uIkGWoWW4lwr9ZBYpls93hGr8RkT10n8XtCOj8/miZVv0yvV3FmnE+dOhlTQNvsxb6Y/nddGXn6j728iEtSBd0b926Fa+//jrmz5+PRYsW4Y477sDGjRtxzjnnYPbs2TG9RnNzM+6//37MmTNH59GSHakd0099F3wvADYRIWtp8beguqEaIkLGQkCgpqEGLf4Wg0cWHRu/EdmbloGa3kFfrK9fVVWqacmxWQv98b6fdq7uM7PEn+xDukZqq1evhsvlwh133NF5W0ZGBmbMmIEHHngABw4cQP/+/aO+xsKFC9He3o6ZM2fioYce0nvIZENqx/TgZipOaCJC8nGnurFuxjocaTwS8TF5PfPgTnUbOKrusfEbkb1pGajpHfTF8/paHh9q1tGo8Vyv3av72MuHtCBd0L1t2zYMHjwYWVlZAbcXFxcDAL744ouoQff+/fvxxBNP4I9//CMyMzN1HSvZm5Ed04mS5c32wpvtNXsYMeua5e66D13Ndo8eOBqKEinzQ0QyULO4LS2HEb50t+PM5/b2drS2fhv1MXoHfd2PtSttu4qbsdAfz/XavbqPvXxIC9IF3TU1NfB4PCG3q7dVV1dHff7999+PH/7wh7j55pvj+rk+nw8+n6/z+4aGhrieT/akdkwPh8dKECUuOMutYrbb+jhfUqxizeICMDzTG99Yw9Hu+NCO88v7YMCAf0Vr6xGkpeV2LjTodd2xXG9qah8MHrzI9tV9sS4O2TnbT8mTbk93U1MT3O7QEsiMjIzO+yP54IMP8NZbb+Hpp5+O++fOnz8f2dnZnV/dlbCTs/FYCaLEydz4jThfOpkQftTXb0Ft7VrU12+JqbGUmsVNT+8XcLvb7eksz47lMUaINI5oki057vp5Yvfuu1FVVYqvv34MbW1HdV9oiHS9qal9cM45szBy5HbbB9wAe/mQNqTLdGdmZgasoKuam5s77w+nra0N99xzD37xi1+gqKgo7p87d+5c3HfffZ3fNzQ08IMEhaUeKxG8GqrlHi8iO4un8ZvV9qET50unqqtbH1L+nJ7uwaBBj3Q758WyXcsqW7ry8sZDiHbs2TMTfn/3VRzJlBxb4fOEVd53s7GXDyVLuqDb4/Hg0KFDIbfX1HT8B+D1ht+zuHLlSuzevRsvvPAC9u3bF3Df8ePHsW/fPhQUFKBHjx5hn+92u8Nm2Im66v5YCe32eBHZlayN36gD50vn0SI4jLZdK57H6K2ubj127rwT3ZeXJ1dybKXPE1Z4362ACxCUDOmC7hEjRuCDDz5AQ0NDQDO18vLyzvvD2b9/P1pbW3HppZeG3Ldy5UqsXLkSa9euxc9+9jM9hk0OEc+xEpzAiCKTrfEbkVNpGRxavRdK9GsNlUzJMT9PWBMXIChR0gXdEydOxOLFi7F06VLMnDkTQEfTlmXLlqGkpKSzhG3//v1obGzEkCFDAAA333xz2IB8woQJuOaaa3D77bejpIQNECg5PFaCiIicRKvgMJnydKN0f60d0tJycd55C5Iat56fJ6y+uEFkR9IF3SUlJZg0aRLmzp2L2tpaDBo0CCtWrMC+ffvw8ssvdz5u6tSp+Oijjzob7QwZMqQzAA82YMAAZrhJEzxWgoiInESL4NAKe5djEeu1Dhw4L+nx6vV5QobFDSI7kq57OdBRDn7vvffilVdewT333IPW1lasW7cOo0ePNnto5HDqsRKh3S1VCtLScuHz1cTc2ZWIiMiqkg0Ouy9PByorH7LEfBnrtbrdoUfbxiuWzxNutzeuPePq4kZwtl5d3OAJK0T6kTLozsjIwKJFi1BTU4Pm5mZs3boVY8eODXjMhx9+GNNxMkIIPPvss3oNlRwm+rESACDQ2noEu3ffzWPEiIhIeskGh/GUp0eTyHFl8dIjEI5E62OqZFrcILIjKYNuIiuL5xzPlpYari47TFlVGcY8PwZlVWVmD4WIKGnJBodalaerZ1nv2nWXLova6j7o/PxrET5w1f68Zi3PJ9dqcYOIEiPdnm4iGXQ9VqKu7n9QXf1SlEcLHiPmEEIILNi4AHvr9mLBxgW4bMBlUJRIGRMiIjkkc4ZxsuXpRuwHD7cPuiNv1d75nV7nNWt1TBUbvRKZi0E3kU4UxYXs7BLs3HlHt4/lsR/OsKlqEypqKgAAFTUV2FS1CVcUXmHyqIiIkpdocKiWbLe0HEakDHKk866NOMs6UlCvfn/mmbcjN3ecrh3AtTimyoqNXtvb29HS0mLYzyPSQ3Nzc0yPY9BNpKNjx8rR2nokpsdyddnehBBY9MEiuBQX/MIPl+LCog8WYfTA0cx2E5EtJBIcquXpHYGtgsDgNnrJtt5nWccS1NfVrcfAgaWWr1RLZnFDD998U4l33nkDJ0+eMOTnEemluTm2hSMG3UQ6iieQ5jFi9tY1yw0AfuFntpuICImXp+tdMq13UG+kZBY3tNTW1oYPPvgfbNq0Bfv2nQGfr0DXn0ekt9ZWX0yPY9BNpKNYA+m0tFzDVpfJeMFZbhWz3R2N5UrfK8W8sfNw+cDLzR4OEZkkkfJ0vUum7bYPOpm991o4cuQ7vPnmSuzceQwHDnrQnnsEyhmxBSxEVtUa4xYJBt2kO7XjZzINQGR1upwr2ko5MGjQfMe8J04UnOVWOT3bzcZyRNRVvOXpepdMW3EfdLK0aswWr+rqA3j11RdRWZmGw98WQBm4Dyk9Gvk3n6SX4muN6XEMuklX4Tp+pqd7MGjQI7qvqFpBYDlX+HPjzzrrl8jPv87YgZFh1Cy3AgUizL8BBYpjs91sLEdEiep6hNehQy+GeUTyJdNW2wetFS0as8Xr0KH9aG5uwvHjORCpbUhx++BKTUGf7Bzk9sk1dCxEWvI1sbycTGbEMR4yiFTOlZaWi0GD5jPgtrkWfwuqG6rDBtwAICBQ01CDFn8L3Klug0dnHjaWI6JEGXWEl1X2QduNoihQFAUpLgXjrhyHkhFyLVoQdXW84Th+/6vfd/s4Bt2kCyOO8ZCJWeVcZD53qhvrZqzDkcbIXezzeuY5KuAG2FiOiBJj9BFeZu+DJiJ7YNBNurBTx0+tmFHORdbgzfbCm+01exiWwcZyRJQIs47w4sI5ESWLQTfpwm4dP4lIO2wsR0SJMHNB34iFcyc3niWyOwbdpAs7dvwkouSxsRwRJcrOC/pObzxLZHcpZg+A7Ent+Kk2GgmlwO32Wq7jpxB+1NdvQW3tWtTXb4HoUvpKRMmLp7FcvMqqyjDm+TEoqypLdphEZEF2XdBX96kHZ/HVxrN1desNGQc/AxHph5lu0oWMHT+5ykykP70ay/HMbyL7s+MRXlZpPMvPQET6YqabdKN2/ExP7xdwu9vtsdxxYWatMnNVmZzIm+3FMM+wiF+eLE/crxnuzG8ishd1Qf/Ud8H3ArDegn534tmnrherZNopOaz2sjZmuklXMnT8NGuVWZZVZTZ2Iavjmd9EzmG3I7zM3qdulUw7JYfVXtbHoJt0Z/WjsszohhrpnFF1VdkqlQCyLAyQs/HMbyJnkWFBP1Zm71PnEa/2EK7ai/OftbC8nBzP6FXm7leVgcrKh0wvNWe5Gcmga5a7KzXbLUT4hm1EJDd1Qb+gYAJyckZJGXAD5jeeNeIzkBB++P3/i/z8r+Hx7IeitCf8WhQqeB7k/GdNDLrJ8YxeZbbC/q3uyLIwQKSu7vuD/i12zXYTEVmV2fvU9f4MVFe3HuXlRWhp+R2GDt2M669/A7dNfR6FBQcTej0KFTwPcv6zJgbd5HhGrzKbvX8rFjIsDBB1PfM7HPXMb672E5GVmdl4Vs/PQJEq5nr1Oo7xIz7BwFwG3slitZc8uKebHM/o483M3r8VCxkWBojiOfM73iPIiIiMZNY+db0+A0WrmFMUQAjgssIv4GPFXFKCe5qo2NvEehh0EyG+bqjJdvOW4ZxRGRYGiPQ685uIyAxmNZ7VoyN8dxVzigL0zmhCu38XADZoS0TXaq9wi89qtRdP8rAGBt1Ep8SyyqxFN2+jM+uJ6H5hAABS0NoaOdghMoI32wtvttfsYRARSU2rTLuamKirWxfT4xVxNJHhErSt9iqrKkPpe6WYN3YeLh94uR7DdTwG3URdRFtl1vKYLz3PGdXiXO3AhYFI2rFz551QlJSQ8fJsbyIiIrkkm2kPl5jojlD6JPzznE6rai+e8W0MBt1EMei+m7eCysqHkJs7LubgUo/9W1qeq52XNx5/93cvYOfOfwYQ+XiP4Ovm2d72wtVvIiLqTqTERCRCACd8mWjtPUTfgdmcFtVePOPbGOxeThQDvbp5a3nOqB7naqel5SJawB183Tzb216CV7/ZBZWIiIJFT0yEe3zH/35cOQJgFZypeMa3cRh0E8XA6t289TpXO57r5tne1lNWVYYxz49BWVVZQs8Pt/pNRETUVfeJiUDHT/TG+i9GourIWTqOimLBM76Nw6CbDCeEH/X1W1Bbuxb19VukCMKs3s1br0x8PNfNs72tJdksNVe/iYgoFrEu0B88OBjvvDMFL6/8JSprGXCbjWd8G4tBNxmqrm49ysuLUFFxE3btugsVFTehvLwoqbJjI4J4tZu32l08lAK322vaMV96ZeLjuW6rVwMkKtlssVmSzVJz9ZuIiGIR6wJ9c3Mv9OhxEmd6D0CJsRSd9BM8z6s43+uDQTcZRo/9vnoE8eGo3bxPfRd8LwBzj/nSKxMfz3VbvRogEbLuaU42S83VbyIiilX3C/QAkIJBgz7Hj3+8DpMn/CduvWI9BuYeNGqIFKTrGd/hqGd8c77XjpRBt8/nw5w5c+D1epGZmYmSkhJs2LCh2+etWbMGU6ZMwcCBA9GjRw+cf/75uP/++1FfX6//oB1Oj/2+sQTxWmbB1WO+0tP7BdzudnviOi5MD3pm4mO9bqtXAyRC1j3NyWapufpNRLKQcctaIqx8ndEX6FWBTVl7uZswbugnSG/lljMzxHPGN2lDyiPDpk+fjtWrV+Pee+/Feeedh+XLl+Oaa67BBx98gMsuuyzi8+644w54vV784z/+I84++2xs374dzz77LN599118/vnnyMzMNPAqnCWe/b6xnBEZyxFee/bMgqL8G1pbD3fek+zRVXoc86WFwHO1FQS+L8ln4mO5br3HYLSu2V6/8HdmeUcPHG3p8yuDx62KdfxdV7/DTcbq6rfV34doeAwaWZUQfsvNL2aL9p5ocURlPO+5Wb8fGY7iVBfoQ8/pTkG4U1AUpaOLec/mZRDiV47/d240rc74pthJF3Rv3boVr7/+OhYtWoSZM2cCAKZOnYoLL7wQs2fPxpYtWyI+d/Xq1bjyyisDbvv7v/97TJs2Da+++ipuu+02PYfuaFrv940liG9rOxrm9Tuy4MlkptVjvqwm0oTndntQWPhw0hNzLNet9xiM1DXLDQRmea18fmXwuFWxjj+e1W8ZJ+PgLQOXDbhM2sUDshcZAiujRXtPAIQ9FzqeeT6e99ys30+k86+1+DyjteAF+paW71BVVRrx8YoCuMQRNB/bisyckQaOlABtzvim2EkXdK9evRoulwt33HFH520ZGRmYMWMGHnjgARw4cAD9+/cP+9zggBsAJkyYgGnTpmHnzp16DZmg/Z7jxJtxdWTBKysfQm7uONutrFohE2+FMSQr2WyxWbTIUtt99TvclgErL6KQM8gUWBkl+ntyG1JT+4Tc1yG2eT6e99ys308sVX1W+zzTdYG+tnZtTM9pa6nVc0hkEFaRRSdd0L1t2zYMHjwYWVlZAbcXFxcDAL744ouIQXc4hw93lB7n5eVpN0gKoe73bWk5jPCThwK32xPzft/kmnHFV8ouGytk4q0whmQkmy02i1ZZaruufsu6ZYDsTcbASm+x9IEJV83W9THR5vl43nMApv1+tN6aZ7RYP6ulphfoPBLSG6vIuidd0F1TUwOPxxNyu3pbdXV1XK+3YMECuFwuTJw4MerjfD4ffD5f5/cNDQ1x/Ryn03q/b/dBfPd8vmgTGTmVzHuazchSy7SyLeuWAdlwvoyP7IGVHrp/T2ITqSounve843XM+f3IfhRnd5/VhADaU3KRkV1s/OBIU6wi65503cubmprgdod+YMzIyOi8P1avvfYaXn75Zdx///0477zzoj52/vz5yM7O7vyKJ5tOHbTs/h1bp8zoqqpKNT9ajOSnZUdPM8749mZ7McwzLOKXJyt00TJRMh2pxmPQjMP5Mj6yB1Z60OpaI2Va43nPzfz9yH4UZ+BntUDqn9yTGbc6poLDrpI9ptQppMt0Z2ZmBqygq5qbmzvvj0VZWRlmzJiBsWPH4rHHHuv28XPnzsV9993X+X1DQwM/SCRAy/2+kZt2eeH3N6Kt7RiiZcFbW783fK8cO9Nan1bZYieUWsm0si3rlgEZcb6Mj+yBlR6Sv9boW9b0eM/1+P1ovTXPDOpntR075gA4Pa+eaM7Ex1UjUDTSumOn2LCKLDbSBd0ejweHDh0Kub2mpiPo8nq734f45Zdf4vrrr8eFF16I1atXIzW1+7fB7XaHzbBT/LTc7xspiD9y5M+nStmjMXavHDvTykOLPc0yBaSJkGl/tMxbBmTE+TI+dgistBbLe5KamoO2tvpT38e3ZS3e99ys349djuLMyxuPjIze+PTTF1FXl4Hvj/fG4R6tcGUqKDJ7cJQUWRvPmkG68vIRI0Zgz549IXvEysvLO++PprKyEuPGjUNBQQHeffdd9OrVS6+hkkHUIL6gYAJyckZBUVydK6upqWd08+zAfVt6UTufBu8LUzufsszdWHqXfTuh1EpdVFAn2a4r21aj5ZYBou4I4Ud9/RbU1q5Fff0WiC4fRMOJvl1KnsBKS7G8J4MHL0p4y1o877nZvx8tt+aZSVFcOHasHyor/w4HD50DkeDWQLKW4M8CKit/JjCLdJnuiRMnYvHixVi6dGnnOd0+nw/Lli1DSUlJZwnb/v370djYiCFDhnQ+9/Dhw/jJT36ClJQUvPfee8jPzzflGsgYeXnj4fc3Y/fuu7t9rJ575diZ1lqMKPu2e6mVbCvbdj8Gjawj0YqmyNulPCgsfFiawEpLsb4niW5Zi+c9N/v3Y4ejOMl+WEUWH+mC7pKSEkyaNAlz585FbW0tBg0ahBUrVmDfvn14+eWXOx83depUfPTRRwGZpXHjxqGqqgqzZ8/Gxx9/jI8//rjzvr59++Lqq6829FpIf253bE2j9Nwrx8601qJ32bdsAWkiZNwfbddj0Mg6kj3LmYFVqFjek2S2rMXznpv9+5H9KE6yH62OKXUK6YJuAFi5ciUefPBBvPLKKzh69CiGDx+OdevWYfTo0VGf9+WXXwIAFi5cGHLfFVdcwaDbhmI5Wiw1NQdC+CGEX5fJk51prcOIfcgyBqTxkHllW6bjzUguWlU0MbAKpfd7Es/r8/dDdBqryOIjZdCdkZGBRYsWYdGiRREf8+GHH4bcZqf9lBSb6E1IOrS11WP79sm6NTVjZ1rr0LvsW+aANFayrmw7oZs8mYcVTUTkRKwii52UQTeZT6ajryLtxQoWawlgvNiZ1hqMKPuWNSCNh6wr23bvJk/mYkUTERFFw6Cb4hZPoxirBOfqXqz6+i3YufOOLseMdKVPUzO7HPkhOyPKvmUNSOMl28q2TMebkZxY0URERNEw6Ka4xNMoxmrnUqtHf4QPuFX6lACa3fnU6Yws+5YtIHUCu3eTJ/OxoomIiKKR7pxuMk/3jWKAysqHIITfsudSm1kCmJc3HiUln2H48LcwZMhzGD78LRQXb2XAbQCe0+xcwWemq+x4djqZx+yznInIPsqqyjDm+TEoqyozeyikIWa6KWaxNoqpr99i2XOpzS4BNKLzqVVK+hPRdewpKWcAaNfkdZ1S9k2h7N5NnqyDFU1ElCw2/bQvBt0Us1izv/X1WyzbxdXuJYBWK+mPR7ixDx7cC21tPwRwbtKvz7Jv53FCN3myFrPPciYiubHpp32xvJxiFmv2t7n5QEyPM6OLq51LAK1a0h+LSGNPTT2B4cPLMGjQLpNGRjLjtgIyg1rRVFAwATk5o6ScT4jIeMHboWTZBsVy+Ngw000x6z5L3OG7796K6fXM6uJqxxLA7vfbm1fS351oY1cUQAjgyis24OtN44wfHCWsrKoMpe+VYt7Yebh84OWmjIHbCoiISBYyNv1kOXzsGHRTzKIffRXXK5lewm23EsBY99ubUdLfne7GrihAVlYDzsz9Doca+xg4MkqUlSZhbisgkpfMPUqI4hF8tKXK6kdcshw+dgy6KS6RssSxs04JtxFNzYxiZlf2ZMU6pp7uZqBR58GQJjgJE9mLGcGvzD1K7IqLIPqRseln8EKB1RcIzMagm+KmZokPHXoZVVWlcT1XqxJu/uEPZHZX9mTEOqaTvgydR0Ja4CRMTuCkOciM4Fft8xFcUaf2KBk69EUG3gbjIoh+ZG36KWM5vJkYdFNCFMWF9PT8mB7bv/+96NlzsGYfTPiHP5TMXdm7G7sQwPHjWTh0JB/IbDN+gBQXTsJkd06ag8wIfmXuUWJXXATRVzxNP63Sg0TWcngzMeimhMWaoezT53LNyrj5hz+86PvtrVPSH060sasNOz/86GoIJSWk3zxZCydhsjsnzUFmBb8y9yixIy6C6E/Gpp8ylsObjUeGUcLUDGXo0VsqBW63V7Psavd/+IHKyocgunzYdxJ1v316er+A291uj+U/CEYae2trb1RUXI69e4eYNLLTeCRG99RJ2B/032DXSZhIVk6bg+IJfrUkc48SOzLr34HTeLO9GOYZFvHLk+Uxe4idupbDh6OWw1v9qDOjMeimhBl95jX/8HcvL288Sko+w/Dhb2HIkOcwfPhbKC7eaumAWxU89qFDX8dXX81Abe3ZZg8tpBs3J5JQnITJ7pw2B5kV/Mrco8SO7LQIwsVzbcRTDk+nJRR0b968GYqiQFEUrFq1KuxjysvL0atXLyiKglmzZiU1SLKuZLOrQvhRX78FtbVrUV+/JWqGwE5/+PWkdmUvKJiAnJxRUpV7dR17dvZIdPcnyqgJNFw3bgrESZjszmlzkFnBr9FVdBSd2YsgWs3zXDzXjloOv/629RG/1t22zlLl8FaQ0J7uSy+9FNdffz3eeecdlJaW4qabboLLdfqD/e7duzF+/HicPHkS06ZNw8KFCzUbMFlPomdex9uMxuw//GQtRp0FzW7csZFxTxpRPJw2B5nVoFPmHiV2ZGajVi3neR5lqS1vthfebK/Zw5BKwuXl8+fPh8vlwq5du/CnP/2p8/bq6mqMHTsWR44cwbXXXouXXnqJH0wdIN7sqtqMJrhUT21GU1e3PuQ53a9+A2lpufD5arrNmpP8jMo+B+9T5v7kyGTak0YUL6dlYI3eQtaVzD1K7MbMfwdazfNdF8+B0809me0mIyUcdA8dOhTTp08HAMybNw+tra2or6/HuHHj8M033+Cyyy7DqlWrkJrKBukUKNFmNNH/8HdobT2C3bvvRkXFTSgvLwobvJP8jJpAg3+OihM2kfni2Z6kBTODD7OYGfzK3KPEbsz4d6DlPM/Fc+ew8r79pBqpzZs3D5mZmfj666/xH//xH7jhhhuwfft2DBs2DP/93/+NzMxMrcZJNpJMM5pIf/jDiZY1J7mVfV1myATKbtzasvJkSHKpq1uP8vIiVFTchF277jJsodWJGVgzg1+Ze5TYjdH/DrQKlLl47hxW37efVNB95pln4p577gEA/OY3v8GmTZtw7rnn4r333kNOTk7I4z///HPcd999+MEPfoCsrCzk5+dj9OjR+K//+q9khkEmSiTTkGwzmq5/+M8//1mkpeVGGh0Aex3hQh1/VJdsWqL7BMpu3Nqy+mRI8khke5KWnJiBZfBLgHH/DrQMlLl47hxWb3qbdO33Pffcg0WLFqG9vR1nnHEG3n//fXg84fftLVy4EBs2bMCNN96Iu+66C01NTfjP//xPTJgwAQ8++CAefvjhZIdDBoq3EdrpxyTfjEb9w19fvwWtrZEbN3XNmufkjIrp5xpNCH/cTeic7EDrAWw/sj3k9q4TqBbNUeLpxs3mYN1jExvSQvfbkxRUVj6E3Nxxuv4dVecgIlVbWxs++ug97Ny5HUK0mz0cy2lr86OtTaCtLQVIbQWUjv9ew+k6X3QV7zzfdfE83FyuLp6zMar8ZGh6m1TQ3dbWhjvvvBPt7R1/XBobG6OWlP/617/G8uXLkZGREXDbZZddhvnz5+Pee+/FGWeckcyQyCBqpiH4g4+aaYhWZqdlJ0zZj3BJdOHCqQQEyk+WGzKBshu3dmSYDEkO8WxPYlBMRjly5Du8+eYK7Np1DEeP9oYQXDgPlYbGpt5oyz6OlL7fAikCLlc6cnrnBDxKy0CZi+fOEbxQo3UiRgsJB91CCNx2221Yt24d8vPz0atXL3z99dcoLS3Fyy+/HPY5l156achtLpcLN954Iz799FPs2bMHl1xySaJDIoMkm2nQ8jgQmY9wSWbhwqna0Y7j7ccNm0CtdiRGWVUZSt8rxbyx83D5wMvNHk7MZJgMSQ6yL7SSvQgh8Le/fYL/+Z/1qKzMQn1THyDr+Kksrr185z6I/+2zGRcevRT5vrMSeg0l9yRSejVCUYDMHpmYcu0kDB44OOAxWgbKXDx3huCFfZXVFvgTDrpnz56NFStWoFevXli/fj327t2LW265BStWrMD999+PoUOHxvxa1dXVAID8/PxEh0MG0iLToDajCc7yut0eFBY+HHOwaeb5kcmwSomkbFxwYVLOJNx4w41IS00L+xi7TqBGnUuuNVkmQ5KDzAutZC/t7e1Yu/Y1bNv2v9izpx9aMhuRMugA4GqP2AtEVgICu1ybcSKlHrvyNqOv/0cJX6OSomBg/wGYOvEX6JPdJ+R+rQNlqy2ek/a02o6gt4SC7sWLF2Px4sVIS0vDmjVrUFRUhIsvvhhPPPEEKioqMHfuXLz99tsxvdahQ4ewbNkylJSUoLCwMJHhkMG0yjTk5Y1Hbu64pPYza5k1NxJLJBPX29UbF/a7EOlp6WYPxVCy7omWZTIkOci60Er2U1//Paqq9uDYsQy0tKRDOesgFJeAO8ONTLe9Tu855D+Eel89AKA+pR6NmSdxpuvMuF/H5XJh5N9fgh9f+mOkpETu5cxAmWIl0779uIPulStXYvbs2VAUBcuXL8fVV18NAFAUBY888ghuuOEGvPPOO9i8eXPYcvKuGhsbMWHCBPh8PixdujSxKyDDaZlp0KIZjVZZc72Ea5TGEkmKh6x7omWaDEkOsi60kn0J0fHvTlEUKCnAgP7n4q5f3GXyqLQjhMB1L18H1+HT88+3ZxzGSzNe5N9tMp1M+/bjCrrfffddzJgxo+PIniVLcMsttwTcf/3116OkpATl5eWYM2cOPv7444iv1dLSghtvvBGff/45Vq1aheHDhyd2BWQ4K2YatMia6yFSozSP5+cxPZ8lkgTIuydapsmQ5GH1hVbq4NSTOewWiMo6/0Qja38UCiXTvv2Yg+5PPvkEkyZNQltbG+bMmYN777037OMee+wx/PjHP8bmzZvx9ttv44Ybbgh5TGtrKyZPnoz3338fy5Ytw4033pjwBZDxrJppsNoRLtEapX3zzWKkpvZBW1t9yP0dWCJJHWTeEy3TZEhysepCK3XgyRz2IPP8E4ms/VEoMlm2I8QcdI8cORInT57s9nFXXXVV1EPr/X4/brnlFrz99tt4/vnnMW3atFiHQBbCTEN0sTRKO806CxdkPbLviZZlMiT5WG2hlTrwZA77kH3+CUfW/igkv8hdDHTQ3t6OadOmYfXq1ViyZAn++Z//OaHX8fl8mDNnDrxeLzIzM1FSUoINGzbE9NxDhw5h8uTJyMnJQVZWFm644QZUVVUlNA6ny8sbj5KSzzB8+FsYMuQ5DB/+FoqLt3IyRWyN0trajuKcc2YiPb1fwD1ut4cfSghA4J7ocNQ90dEWOomIjNL9gjNQWfkQRJesKVmTHeefrpl74HTGXqZrIHklfGRYImbNmoVXX30VI0eORF5eHv70pz8F3D9q1CgMHDiw29eZPn06Vq9ejXvvvRfnnXceli9fjmuuuQYffPABLrvssojPO3HiBH70ox/h2LFjeOCBB5CWloYlS5bgiiuuwBdffIHc3Nykr9FpmGkIL9YGaJmZA1BS8hlLJCks7okmIpnwZA77sOP8Y8f96U4i+158Q4Puv/3tbwA69od/8sknIfcvW7as26B769ateP3117Fo0SLMnDkTADB16lRceOGFmD17NrZs2RLxuc899xy++uorbN26FUVFRQCAn/70p7jwwgvx5JNP4vHHH0/00ogCxNPhnQsXFAn3RBORTHgyh33Ybf6x4/50J7HDXnxDg+4PP/ww6ddYvXo1XC4X7rjjjs7bMjIyMGPGDDzwwAM4cOAA+vfvH/G5RUVFnQE3AAwZMgRXXXUVVq1axaCbNGPFDu8kp2T3RMu+MkxE8tDySFEyn516cthxf7qT2GEvvqF7urWwbds2DB48GFlZWQG3FxcXAwC++OKLsM9rb29HRUUFLr744pD7iouLUVlZiePHj2s+XrIXIfyor9+C2tq1qK/fEnFfmtrh/dR3wfcCYKM00l/wyjD3rRGRntQF59B5T6XA7fZywZkMZcf96U5il7340gXdNTU18Hg8Ibert1VXV4d93vfffw+fz5fQc4GO5m0NDQ0BX+QsdXXrUV5ehIqKm7Br112oqLgJ5eVFqKtbH/bxaod3Nkojs4RbGSbSG+dL5+KCs3nKqsow5vkxKKsqM3soMTNqzPHsTyfrUT/LqNsCulYnyMTQ8nItNDU1we0O3T+SkZHReX+k5wFI6LkAMH/+fMybNy/u8ZI9JHoECs+S1U6t6zC2u/6GMS0/MnsoUgjev8Z9a2QUzpfOxiNFjSfjflcjx2y1/enc9hU7O+3Fly7ozszMhM/nC7m9ubm58/5IzwOQ0HMBYO7cubjvvvs6v29oaIi4d5zsJZYztysrH0Ju7riwwTQbpSVPQGCnuwInlOP49OSn0pUUmYFdWsksnC+JC87GknG/q9Fjtsr+dBkXSMxkp7340pWXezwe1NSEHkeh3ub1hv8P6owzzoDb7U7ouUBHhjwrKyvgi+xPCD8OHXo55iNQSB/f9a5Cvet7AECtvxZlX1ujfM6q5XzB+59Usu6DIrlwviTg9IJzQcEE5OSMYsCtExn3u8o4Zq1w21fs7LYXX7qge8SIEdizZ0/IHrHy8vLO+8NJSUnBsGHD8P/+3/8Lua+8vBwDBw5E7969NR8vyUvdw11VVRrT43kEij4EBHZ5PwJExx9dBQqWbFpi+h9ZKzcpC97/pJJ1HxQREYUn435XGcesBScvNiTCbnvxpQu6J06cCL/fj6VLl3be5vP5sGzZMpSUlHSWsO3fvx+7du0Kee5nn30WEHjv3r0bGzduxKRJk4y5AJKCuoc7eoY7EI9A0cfXSg2O9awBlI4/ugIC2w9vN31ytupqtd1WhomIKDwZq5pkHLNWnLrYkCh1L/7629ZH/Fp32zppzoqXLuguKSnBpEmTMHfuXMyePRtLly7FmDFjsG/fPixcuLDzcVOnTsXf/d3fBTz3rrvuQmFhIcaPH49Fixbh6aefxtVXX42+ffvi/vvvN/pSyKKi7+EOh0eg6EUIgY9cX3ZmuVUpSoqpk7OVV6vttjJMREThyVjVJOOYtWDXxQa9t9l5s70Y5hkW8cuTFXoqlVVJ10gNAFauXIkHH3wQr7zyCo4ePYrhw4dj3bp1GD16dNTn9e7dGx9++CF+85vf4NFHH0V7ezuuvPJKLFmyBPn5+QaNnqyuo/FLrBluHoGip52t36Am5fuQ29tFu6kNNKzcpMxqXVqJiEh7Xauawi2yqlVNVuruLOOYtWKnhmAqNoWLj5RBd0ZGBhYtWoRFixZFfMyHH34Y9vazzjoLb775pk4jIzuIZ282j0DRjxAC605uVhvEhzBrcpbh+AqrdGklIrIjIfw4ceIz5ObuRWNjLxw6lGv4GOKparLKIquMY9aC1osNVjlyTMau+WaSMugm0lOse7MHDpyHM8+cwQy3Tlr8LTjafjxswA2YNznbcbWaiIhiU1e3vvMM8kGDgEGDgIsv/hAf7vwhvj6RZ9g4ZKxqknHMWtByscEq2eXgBISVEg9WxaCbKEh2dgnS0z1oaTmM8Pu6FbjdHgbcOnOnujE75+f4uvYgvtrrQcrZB6G4W1GQl49bfnYL0lLTDJ+cnVwaR0TkdGqT1eDPBr16Hce1RZvw7v+WADjfsPHIWNUk45iTpeVig1Wyy1beZmdVDLqJgiiKC4MGPXJqYlUQOLlyD7eR+rh6o0mcge+aPEhpb4SCFuSn5uPCfhciPS3d8PE4tTSOiMjpojVZVRRACGD0eRX4pPZKw8dG1qfFYoNVsssybLOzIgbdRGHk5Y3H0KEvdpaQqbiH29mcWhpHROR03TVZVRSgd0YT+rgPGDgqchKrZJe5zS4xDLqJIsjLG4/c3HGnJtpvkZ7eF9nZJcxwO5wTS+OIiJwu1iarbtcJnUdCTmSV7DK32SWOQTdRFIriQk7OKLOHQUREMRDCr/tCqRE/g6wn1iarPn8vnUdCTmSV7DK32SWOQTcR2YZVjtEgIuN17SqtSk/3YNCgRzTbEmTEz4iEwb65umuyKgRwwpeJo77+mv9szm3OZqXsMrfZJY5BNxHZglWO0SAi40XqKt3Schg7dtyOoUNfTDooNuJnRPvZkYJ9boMyRrQmq+LU/9301XCkZado+nM5t5HVssvcZpcYBt1EZAtWOUbDKZh5IauI1lW64zYFlZUPITd3XMLBqBE/I5Lowf5tSE3tg7a2o523G5V5d6JITVaPH8/CR7tG4OsTeRiSre3P5NxGzC7bA4NuIgOxPFAfVjlGwymYeSEr6a6rNCDg81Xj2LHyhHt0GPEzwr5qt8E+AgJuwJjMu5OpTVYPHHgff/nLKzhwoBe++GI4lIHfIKV3o6Y/i3MbqZhdlp+2NTBEFFFd3XqUlxehouIm7Np1FyoqbkJ5eRHq6tabPTTpqZkAtaNn18YipL1wmRczlFWVYczzY1BWVWbKzydriLWrdKyPM+tnhNN9sB9ORzBeWfkQRJcux6QdRXGhV68iHDkyCN99dyaE0OfjNOc2Ivtg0E1kALU8MPjDk5qRYOCduK6ZgK7UjIAQ4fdA0WnxBK/B77dZ73Nwtp2/Z+eKtat0rI8z62eEk3gQfzrzTnKy2tzGRU5j8f22HwbdRDqLpTyQGYnEBWcCVMwIxCbe4NUqmRerZNvJfGpX6Y7mVuEocLu9yM4usfTPCCfZIF7rzDsZx0pzGxc5jWWH95uLBqEYdBPpLJ69gBSfrsdohKMeoyHjhGWUeIJXq2RerJJtJ2tQu0qf+i74XgBAYeHDSfXPMOJnhNN9sB+d1pl3MobV5jYuchpL9vfbDosGemDQTaQzs/YCOkE8x2hQqHiDV6tkXqySbSfrULtKp6f3C7jd7fZo1lDMiJ8RLHqwH/WZumTeuyOEH/X1W1Bbuxb19VtYwZUgK81tXOQ0lh3eb9kXDfTC7uVEOjNrL6AT8BiN5HSdGIHA4DX4SJqumZdwHwTVzIveXXWDu/mq2NWX1K7Sep4QYcTPCPczwx1TdfqosMAzo/XMvEcT7SxxJ3VR/7rpa4x5fkxSxylaaW6LZ54wgt2Pq7Ta+x0vdtyPjEE3kc7U8sCWlsMIv69bgdvtMTwjYSQ9j0rjMRqJiTd4jSfzoucHweAPJCrZPpiQPhTFpemRXWb9jGCRgv0jR/4cEui63R4UFj5saKAb/SxxuY4vS2a+EhD46PuPUNNSk/RxilaY26y2yGn34yqt9n4nQvZFAz0x6CbSmVoe2PGBxBoZCSMx+2FN8QavVsi8WCXbTmSGcMG+GZn3YN03C1VQWfkQcnPHhYxLzwXZRCQ7X9WiFjWnnhtvoGHFDK7VFjnDlS3bKZCz2vsdLzssGuiJe7qJDGDGXkArsMJRaeygGSrRJj3ebC+GeYZF/PJkeXQdt5X2ORJZhRqMFxRMQE7OKMOD1kSbhdbVrUd5eREqKm7Crl13oaLiJpSXF5l2hGay85WAwI6U/+v8uxrPXlwrNp6yWjM3O+x1jsZq73cirNL3xaqY6SYyiBUyEkZKJvuh3RjsXYqWKKuUisfLCtl2IgqUSLNQq5WjazFffZdag3qlvvP7eLKTVszgWm2esHvZstXe73ixEq17DLqJDGTGXkCzxJP90Os9seIHGSuQOXi1wj5HIjot3mahVliQDZbsfCUgsDPjSyhCgVBOX1csZbVWbTxlpXnCCWXLVnq/EyH7ooERGHQTkS7MPirNqh9krMKOwWs8eyKtuH+S7MVq+5X1Em+zUCssyAZLdr76LqsK9amhwVIs2VgrZ3CtMk/Ivtc5VlZ5vxMh+6KBERh0E5EuzD4qzcofZEh78Wwl4LYD0puTGkjG2yzU7AXZcJKZrwQEdnk/UJP0IaKV1Tohg5ssli3LQ+ZFAyOwkRoR6ULNfoT9FAKgI/vh1eWotOCGKyq7NV6h08JtJdDisUTRCOFHff0W1NauRX39Fgjht0QDSaPF0yzU7AXZcJKZr9rRjqb0hohPjdbgkY2nuscGmmQXzHQTkS70PCpt877NeOQvj0QsDXZKKRp1iGcrAbcdkFYiZbPb25tgpf3KRom1WWi85ehGSGa+csGF0TtnoHXAV0jp0Yxz+5+LiddMDHhMuLJaZnBjw7JlsgtmuokiCJfBoPjocVSaEAKLP1wc8WgVOxy7QfEJzhZFyxLF81iiSCJns2vQ1lYf5Znhj88KeITEc08sx5epAe6p74LvBZD4gmwykpmvMluzkePPRQ76oJ+7X0zHKTKDGzuzj6vUCo8wdTZmuonCcNJ+PL1pfVTagdYD2H5kO4DwHcnZQVN+8TQ5i2dPJPdPkhaid9+OTaT9yk6Ze9QAN/ha3W4PCgsfNu1ajTzakxlcZ2EvEWLQTRTEaueH2oFWR6UJCJSfLEeKkoJ20R42WOIHGbnF+8Eknq0E3HZAWui++3b3wu1XdtrcY2SAGw8jj/Zk4ynn4BGmxPJyoi66Pz8UqKx8SKpyPzupVWpR669Fu2gHELk02C6laE4UT5OzeLYScNsBaSW5rtrhG3I5de6JpRydqCsZS7SDm7uyqaszMegm3ci4Ly2e80PJWAICO1P+LyRo4uRlH/F+MIlnKwH3T5JWEu+qHXm/Muceou4FV0LJMu+zlwgBLC8nnci6L82K54dSh+9ch1Gv1IfcztJg+4j3bPV4txJw2wFpIZbu26mpOVAUN1pbD3feGm2/Muceou7JWKJtp14i8fRboVBSBt319fWYPXs21q5di8bGRhQXF+PJJ5/ERRddFPV57e3tWLlyJdasWYNt27bh+++/x4ABA3DzzTdj5syZyMjIMOgK7E3mfWlWPD+UTmW53dvVE3dC8GgV+SX6wSSePZHcP0laiOV4qcGDF8W1X5lzD1F0sh73aJdeImwElzzpysvb29sxfvx4vPbaa7j77ruxcOFC1NbW4sorr8RXX30V9bmNjY249dZb8d133+Gf//mf8fTTT6O4uBilpaX46U9/Kk2ZipXJvi9NzWCEjewARNqPpwUZy/H1oL4PdXVvo0ePAwDa0a740aQ0Rvy1sDRYfsHldyqW4ZEVxXK8VDz7lc2ce4hkIGOJtp16icTTb4XCky7TvXr1amzZsgVvvvkmJk6cCACYPHkyBg8ejNLSUrz22msRn5ueno7Nmzdj1KjTXSlvv/12nHvuuSgtLcVf//pX/PjHP9b9Guwsnn1pRnUHjUcsGQw9zg+VtRxfa8Hvw4ABgMfTA03NP0Vb49VoyTiBgrx83PKzW5CWmhbwXJYGy6vrB5Nwe65ZyUBWpGX3bbPmHiIZyFqibZcjTGWtMrAaKYPuvn374sYbb+y8LT8/H5MnT8af/vQn+Hw+uN3h/+Gmp6cHBNyqCRMmoLS0FDt37mTQnSQ77Esz+vxQmcvxtRTpfXC7G3HdtW9B+dulqDrWF/mp+biw34VIT0s3Z6CkObt8MCHn0fJ4KaueXR2OEH7LHfVF9iVribZdjjCNt98KhSdd0L1t2zZcdNFFSEkJrIwvLi7G0qVLsWfPHgwbNiyu1zx8uKPRSV5enmbjdCq77Esz6vzQ7svxFVRWPoTc3HG2/kAT7X1QFEAI4MoLtuHrLWONHxzpzi4fTIiSZdWzq7tiZRYZSfZKKNl7ichaZWBF0gXdNTU1GD16dMjtHk/HubvV1dVxB90LFy5EVlYWfvrTn0Z8jM/ng8/n6/y+oaEhrp/hFLF0dXW7PVLsS9MygxGJ7OX4WunufVAUoHdmI7w5dQDknbwoMtk/mNBpnC+TE8/cY3TGmZVZZDRZKqHs2tlb1ioDKzI16G5vb0dLS2yNj9xuNxRFQVNTU9jycbXzeFNTU1xjePzxx/GXv/wFzz33HHJyciI+bv78+Zg3b15cr+1E3JcWHzuU42sh1uvr6W7GSZ3H4gR2/XBA1sD50hhGZ5xZmUVmkKESStbO3t19FpC9ysBqTO1evmnTJmRmZsb0tXv3bgBAZmZmwAq6qrm5ufP+WL3xxhv4t3/7N8yYMQO//OUvoz527ty5OHbsWOfXgQMH4rhSZ4mlqyt1sEs5frJivb6TPh7rl6zgDwdadE0tqyrDmOfHoKyqTIMRkuw4X+pPzTgHVwipGee6uvWa/8x4KrOItOTN9mKYZ1jEL0+Wx9TxydjZO5bPAvFUGVD3TM10DxkyBMuWLYvpsWr5uMfjQU1N6B999TavN7byxA0bNmDq1KkYP348/vCHP3T7eLfbHbFBG4WSYV+aFdipHD8Z3b0PQgAnmnuguj4P3n6hz6fYhftwkExpmKwr/KQfzpf6MivjzMosolCydvaO5bOADFUGMjE16O7Xrx+mT58e13NGjBiBsrIytLe3BzRTKy8vR48ePTB48OBuX6O8vBwTJkzAxRdfjFWrViE1Vbqt7VIwYk+07FiO3yHa+6Auvn74fz+EiHiGrZyMLvPW48OB1kE8EUVnVi8QVmYRhZKxs3c8nwXYb0U7ppaXJ2LixIn49ttvsWbNms7b6urq8Oabb+K6664LWF2vrKxEZWVlwPN37tyJ8ePH49xzz8W6deviKkcn0gPL8TtEeh+am3vgv9fdhMrD/U0amT70KPPujvrhQO1A2vXDQSK6TtzA6W6mRlwLkVOZlXFWK5IQcfFTgdvtRVbWxaiv34La2rWor98C0aXjMZGdBM+BKqvPhVp/FqDYSJfinThxIi655BLceuut2LFjB/Ly8vDcc8/B7/eHNG656qqrAAD79u0DABw/fhxjx47F0aNHMWvWLKxfH7jnqbCwECNHjjTkOuyKZ3cmhuX4Hbq+D01Nh/D++2XYuzcVe/cORMp5ld2/gESMzhDrceyHjCv8RLIzK+McS2VWfv4N2Lr1Eh4nRo4gY2dvHgFmHumCbpfLhXfffRezZs3CM888g6amJhQVFWH58uU4//zzoz73yJEjnQ1dfvvb34bcP23aNAbdSeDZnclhOX4H9X3o2bMFjY37ANhvf6AZe8C0/nDAiZvIHGb2AlErkoLnerfbg/z8G3Dw4B9CxsTjxMiOZO3sLeNCgV1IV14OAH369MFLL72Euro6nDx5Eh9++CEuvvjikMft27evM8sNAOeeey6EEBG/li9fbtxF2IwZnVSJZGV0aVfXDwfhqB8O4imFC74GFcvUiPSlZpxPfRd8LwB9e4Hk5Y1HSclnGD78LQwZ8hyGD38LRUWfoLb2vxC5uRtQWfkQS83JNmTs7K3HZwGKnXSZbrIent1JFDszMsTxfDiIpQupLCv8PI+c7Cpaxrmw8GHdM8rBlVn19VtMae5GZBYZO3tr/VmA4sOgm5JmVidVIhmZUdql9YcDGSZuHmVGdmelXiA8ToycSLbO3jIuFNgJg25KGidbotiYmSHW8sOBDBM3jzIjJ7BKLxAeJ0YkB9kWCuyEQTcljZMtUWxkyBDHysoTtxmN6oiczMzmbiQfbv0hJ2LQTUnjZEsUGxkyxHbAo8ysqabmIE6c6GX2MEgnZ5xxLw4fnhPhXoE+ff4FNTXVho5JT8ePHzN7CFLi1h9yKgbdlLRYzu7Us5MqkUysnCG2Ax5lZl0rViyF251u9jBIR3l5V+C88z5DRkZj523NzT3w1VdF+OCD/wPwf+YNTgetrcD33xdA9DgBuH1mD0cK3PpDTsWgmzRhdidV0p8Qfks07CGKhmeQWtf27WchLY1VHPbWHxs3jsKZZx5Az54ncPJkLxw61B9CSHlCbbcEBETf7+DKPQIlBUhNS8WIvxuh6c+wUyk2t/6QkzHoJs1YqZMqaauubn3Igkp6ugeDBj3CBRWyDFmOMnMqMfBrCHea2cMgnQkABwDAnwFktAGFX5s8Ih2ltCMlrRWKoqBPTh/84qafY9A5gzR7ebuVYnPrDzkZg27SlFU6qZJ26urWn9o6EBjEtLQcxo4dt2Po0BcZeJMl2KlRnR2lZLYgxR3+d0MkK1eKC8OGXIibr78ZPTJ7aPradirFTmbrj52y/eRcDLqJHCqWcnEh/Ni790GEb5AnACiorHwIubnjWNFApmOjOmu79OKRyOiRYfYwiDQ1eMBgDBsyTPMMtN1KsRPd+mO3bD85F4NuIgeKtVy8IyivCfcSpwj4fNU4dqycFQ5kCWxUZ103/OQG9M7qbfYwiKRgp1LsZLb+2CnbT85mz84WZHlC+FFfvwW1tWtRX78FokupEelLLRcPDqbVcvG6uvVdbvs2pteM9XFEREQUXdcsd1dqtlsIubZpxLP1J+D2oPdB1usnApjpJhOwKZd54i0XT0/vG9Prxvo4IiIiis5upzAkuvXHTtl+IgbdZCg25TJXvOXi2dklSE/3oKXlMMIH6grcbg+ys0t0GjEREZFz2PUUhni3/iTTeI3IilheTobpPssKVFY+xFJzHcVbLq4oLgwa9MipW4Mnt47vCwsfZhM1IiIiDSRaim03apbbH/SZsGu2m0gmzHSTYdiUy3yJlIvn5Y3H0KEvhmwJcLs9KCx8mJUJ1InHuhCREez8t4anMNg320/OxqCbDMOmXOZLtFw8L288cnPHdXvEGDkXj3UhIiM44W+N009hiCfbb+fFB7IXBt1kGDblMp9aLt6xr15BYOAdvVxcUVysQKCIeKwLERmBf2vsj9l+siMG3WQYNuWyBpaLk9aCG96w0Q0R6YF/a5zD6dl+sh82UiPDsCmXdeTljUdJyWcYPvwtDBnyHIYPfwvFxVsZcFNCghvesNENEemBf2u0V1ZVhjHPj0FZVZnZQyGyNQbdZCg1y5qe3i/gdrfbw+PCDKaWixcUTEBOzigudlBCumaeulIzUEIEVrXwAx4RJSLevzXUveD98XwPifTDoJsMxywrkX3Ec6wLP+ARUaJ4hJT2wu2PJyJ9MOgmUzDL2kEIP+rrt6C2di3q67fwjHKSStdjXcJRj3VRg2t+wCOiRMT7t4a6F1w5wIoBIn2xkRqRSerq1oc0M0tP92DQoEeY9ScpxHOsS7ornQ2QiCghPEJKe10XQYHAigF2gyfSHoNuIhPU1a0/dWxX4AeIlpbD2LHjdu5vJynEc6zLR5Uf8QMeESWER0hpK7gLvIqLoUT6YdBNZAAh/Dh2rBwtLd8iLS0Pe/c+iPDHpgkACiorH0Ju7jjHlt2TPGI51oUf8IgoWTxCSjvBWW6VkxZDy6rKUPpeKeaNnYfLB15u9nDIAbinm0hndXXrUV5ehIqKm7Br113Yvn1yQEl5KAGfrxrHjpUbNkYiPbEBEhGRNXB/PJt6kjkYdBPpSC0jjx5kh9fS8q0OI9IWG8FRd/gBj4jIOuLZH29XbOpJZmB5OZFOhPBHKSPvXnp6X20HpDE2gqNYsAESEZF1OH1/fPB2J25zIqMw6CbSScce7vgz3IACt9uD7OwSzceklUQawXXd156e3hfZ2SXcs+4ATv+AR0RkNU7eH8+u7WQWBt1EOkmsPLxjlbWw8GHLBqTRM/jhG8FZMSvOJirGcfIHPCIisgY29SQzSbmnu76+HnfccQfy8/PRs2dP/OhHP8Lnn38e9+u0trZi6NChUBQFixcv1mGk5GSJlIe73R7LHxfWfQY/sBFcpH3tala8rm69jqONMEI2USEiIopbWVUZxjw/BmVVZWYPJW5s6klmki7obm9vx/jx4/Haa6/h7rvvxsKFC1FbW4srr7wSX331VVyv9e///u/Yv3+/TiN1BjbSiiw7uwTp6R4gQgMpQEF6ugfDhq3CkCHPYfjwt1BcvNXSATcQewa/peXbGLLiQGXlQ4b/u2ETFSIiovgYvWCtZYDPpp5kNumC7tWrV2PLli1Yvnw5SktL8atf/QoffvghXC4XSktLY36d2tpaPPzww5gzZ46Oo7W34KOwKipuQnl5kSmZSytSFBcGDXpE/S74XgDAoEGPoE+fy1FQMAE5OaMsW1LeVawZ/PT0vnFnxY3QtbwMOF1WxomWiMhaZM6q2pGRC9ZaB/js2k5mk25P9+rVq9G3b1/ceOONnbfl5+dj8uTJ+NOf/gSfzwe3u/uGPL/97W9x/vnn4x//8R/x0EMP6TlkW0qkkZYT5eWNx9ChL4bsZ3a7PSgsfFjK90jN4Le0HEb4DPbpRnDfffdOTK9p5PFobKJCRGR9wUHXZQMu435bExnd9TtcgJ/MHM2mnmQ26YLubdu24aKLLkJKSmCSvri4GEuXLsWePXswbNiwqK+xdetWrFixAh9//DH/gCcgkUZaTpaXNx65ueNs07lbzeB3LLooCPx3ENgILp6suBHYRIWISA5aB12UHCMXrPUK8NnUk8wkXXl5TU0NPB5PyO3qbdXV1VGfL4TAr3/9a0yZMgUjR46M+ef6fD40NDQEfDmVFUuGrU5RXMjJGSVVGXk0agY/Pb1fwO3BjeBi2dfudnsNOx6NTVSI9Mf5kpLFbUDWEvz7UOn1ewmeq+Odo7ktgazI1KC7vb0dzc3NMX2p/0E3NTWFLR/PyMjovD+a5cuXY/v27ViwYEFcY50/fz6ys7M7v/r37x/X8+0knkZaZF95eeNRUvIZhg9/K2IjuFj2tRt1PJodmqjwgwTJgPMlJSvZoIu0ZeSCdbIBPk8nIasyNejetGkTMjMzY/ravXs3ACAzMxM+ny/ktZqbmzvvj6ShoQFz587FrFmz4v4QMHfuXBw7dqzz68CBA3E9306sVjJM5oklgx9rVlxvsjdR4QcJkgXnS0qG0VlVis7oBetkA3yeTkJWZeqe7iFDhmDZsmUxPVYtH/d4PKipCS1tVm/zeiPv1Vi8eDFaWlowZcoU7Nu3DwBw8OBBAMDRo0exb98+eL1epKenhzzX7XbH1KDNCeJppCUjIfy22X9tFVbY1y57ExXubyRZcL6kZATvHVax6aU54lmwTnb+7Brgh/t5aoAfaW+30c3eiOJhatDdr18/TJ8+Pa7njBgxAmVlZWhvbw9oplZeXo4ePXpg8ODBEZ+7f/9+HD16FBdccEHIfY8//jgef/xxbNu2DSNGjIhrTE4TTyMt2dTVrQ/pNJ6e7sGgQY9I2WncStSsuJlkbaLCDxLJK6sqQ+l7pZg3dh4uH3i52cMhojCSDbpIe0YuWCcb4PN0ErIy6bqXT5w4EatXr8aaNWswceJEAEBdXR3efPNNXHfddQGr65WVlQCAwsJCAMA999yDn/3sZwGvV1tbizvvvBPTp0/HDTfcgAEDBhhzIZKz41FYVj0GjZl34geJ5PDoISI5GJlVpdgZtWCdTIAf7+kkXIglo0kZdF9yySW49dZbsWPHDuTl5eG5556D3+/HvHnzAh571VVXAUBnKflFF12Eiy66KOAx6n0XXHBBSEBO0VmhZFgrVj0GjZl34jFnyWNpPpEcrLQNiEGZORIN8OPZlsCFWDKDdEG3y+XCu+++i1mzZuGZZ55BU1MTioqKsHz5cpx//vlmD89xrFAyrIV4jkEz6nr1yLwzay4f7m9MDkvzieRihW1ADMrkEu+2BC7EkhmkC7oBoE+fPnjppZfw0ksvRX2cmsWO5txzz2UnTLLcMWh6ZN6ZNZcP9zcmj6X5RBQvBmVyiWdbQrornQuxZAopg24irRlxDFo8WWatM+9W3a9O0XF/Y3JYmk9E8WJ1jHzi2ZbwUeVHXIglUzDoJoL+x6DFm2XWMvNu1f3qMjJ6j5+V9jcGk2G/I0vziSherI6RUyzbErgQS2ZK6f4hRPanHoN26rvgewEkfgyammUOzlyrWea6uvUhz9Ey8x5P1pwiC97jZ9S2FG+2F8M8wyJ+ebI8hoyjK7Pei3h0Lc0PRy3Nt+LYicgcXYOyrtSgTNa/F2VVZRjz/BiUVZWZPRRTqQsqXQNuIHBhhUgvDLqJTlGPQUtP7xdwu9vtSbj8uvssM1BZ+RBE0ASgZt5DFwBUCtxub0yZd6vtV5dVuD1+TiXDexFPaT4REWDPoEyGRVIjcCGWzMbycqIutD4GLdG92WrmvWMftoLAoD2+zLsR+9Xtjnv8TpPlvbByaT4RWY9dG1eyKVwH9kghszHoJgqi5TFoPl+0gPu0cFlmNfMevBfc7fagsPDhmDPveu9XdwLu8TtNpvfCCkcPEZEc7BiUybJIagQuxJLZGHSTbVjtDOq6uvWoqiqN6bGRssxaZN61zJo7ERuvnMb3gojsyo5BmUyLpEbgQiyZiUE32YLVzqCOdERXqO6zzFpk3rXKmjsRO2CfxveCiOzMTkEZF0mJrIWN1Eh6iXQH11P05mmhjMoy5+WNR0nJZxg+/C0MGfIchg9/C8XFWxlwR8HGK6fxvSAikocdm8IRyYxBN0kt0e7geuq+eVqHtLTchLuiJ0rNmhcUTEBOziiWlHeDHbBP43tBRCQHLpISWQ/Ly0lqiXYH11OsR28NHDiPWWaLs+Mev0TxvSAikoNsTeHKqspQ+l4p5o2dh8sHXm72cIh0waCbpGbFM6hjPXrL7fboPBLSgp32+CWL7wURkfV1XSQVQuA3b/8GX9V9hfPyzsOSG5ZAURTLLJIGnyN+2YDLuNecbInl5SQ1K55BrR7RhQhlXR3N07w8oouIiIh04c32YphnGI42HcVXdV8BAL6q+wpHm45imGcYPFnWWPgPd444kR0x6CapWTHAVY/oUn9+8HgAHtFFRERE+urawRw43bncKnu5rT4+Ii0x6CapWTXAVY/oSk/vF3C72+0xvHkaEREROU9wB3OrdS63+viItMQ93SQ9q55BnZc3Hrm54041e/sW6el9kZ1dwgw3ERER6crq53RbfXxEWmPQTbZg1QBXPaKLiIiIyChd90p31TWbfEXhFSaMrIPVx0ekNZaXk23wDGoiIiJyOquf02318RHpgUE3EREREZFNxHNOtxmsPj4iPbC8nIiIiIjIJrqe0x2Jmed0W318RHpg0E1EREREZCPebC+82V6zhxGR1cdHpDWWlxMRERERaaisqgxjnh+Dsqoys4dCRBbAoJuIiIiISCNCCCzYuAB76/ZiwcYFbAhGRAy6iYiIiIi00vU4LPX4KyJyNgbdREREREQaUI/Dcp06ttSluHj8FREx6CYiIiIi0oKa5fYLPwDAL/zMdhMRg24iIiIiomQFZ7lVzHYTEYNuIiIiIqIkBWe5Vcx2ExGDbiIiIiKiJKhZbgVK2PsVKMx2EzkYg24iIiIioiS0+FtQ3VANgfBBtYBATUMNWvwtBo+MiKwg1ewBJKK+vh6zZ8/G2rVr0djYiOLiYjz55JO46KKLYnp+e3s7XnjhBbzwwgvYvXs3evTogR/84AdYsmQJfvCDH+g8eiIiIiKyE3eqG+tmrMORxiMRH5PXMw/uVLeBoyIiq5Au6G5vb8f48ePx5ZdfYtasWcjLy8Nzzz2HK6+8En/7299w3nnndfsa//RP/4RXX30VU6dOxd13342TJ09i27ZtqK2tNeAKiIiIiMhuvNleeLO9Zg+DiCxIuqB79erV2LJlC958801MnDgRADB58mQMHjwYpaWleO2116I+f9WqVVixYgXWrFmDCRMmGDFkIiIiIiIicijp9nSvXr0affv2xY033th5W35+PiZPnoy3334bPp8v6vOfeuopFBcXY8KECWhvb8fJkyf1HjIRERERERE5lHRB97Zt23DRRRchJSVw6MXFxWhsbMSePXsiPrehoQFbt25FUVERHnjgAWRnZ6NXr14YOHAgVq1apffQiYiIiIiIyGGkKy+vqanB6NGjQ273eDwAgOrqagwbNizscysrKyGEwOuvv47U1FQsXLgQ2dnZ+P3vf4+bb74ZWVlZGDduXNjn+ny+gCx6Q0ODBldDRERkL5wviYiIApkadLe3t6OlJbajE9xuNxRFQVNTE9zu0M6PGRkZAICmpqaIr3HixAkAwJEjR/Dpp5+ipKQEAHD99ddjwIABePTRRyMG3fPnz8e8efNiGisREZFTcb4kIiIKZGp5+aZNm5CZmRnT1+7duwEAmZmZYfdtNzc3d94fiXrfgAEDOgNuAOjVqxeuu+46bN26FW1tbWGfO3fuXBw7dqzz68CBAwlfNxERkV1xviQiIgpkaqZ7yJAhWLZsWUyPVcvHPR4PampqQu5Xb/N6Ix/VoN7Xt2/fkPsKCgrQ2tqKkydPIjs7O+R+t9sdNsNOREREp3G+JCIiCmRq0N2vXz9Mnz49rueMGDECZWVlaG9vD2imVl5ejh49emDw4MERn+v1etGvXz8cOnQo5L7q6mpkZGSgd+/ecY2HiIiIiMjKyqrKUPpeKeaNnYfLB15u9nCIHEe67uUTJ07Et99+izVr1nTeVldXhzfffBPXXXddwOp6ZWUlKisrA54/ZcoUHDhwABs2bAh4/ttvv40xY8aEdEUnIiIiIpKVEAILNi7A3rq9WLBxAYQQZg+JyHGk614+ceJEXHLJJbj11luxY8cO5OXl4bnnnoPf7w9p3HLVVVcBAPbt29d529y5c7Fq1SrcdNNNuO+++5CdnY0//OEPaG1txeOPP27kpRARERER6WpT1SZU1FQAACpqKrCpahOuKLzC5FEROYt0aV2Xy4V3330XU6ZMwTPPPINZs2YhLy8PGzduxPnnn9/t8/v27YuPP/4YP/7xj7FkyRL867/+K8466yx89NFH+MEPfmDAFRARERER6U8IgUUfLIJLcQEAXIoLiz5YxGw3kcGky3QDQJ8+ffDSSy/hpZdeivq4rhnurgYOHBhQnk5EREREZDdds9wA4Bd+ZruJTCBdppuIiIiIiKILznKrmO0mMh6DbiIiIiIim1Gz3H7hD7i9a7abiIzBoJuIiIiIyEbULLcCJez9ChRmu4kMxKCbiIiIiMhGWvwtqG6ohkD4oFpAoKahBi3+FoNHRuRMUjZSIyIiIiKi8NypbqybsQ5HGo9EfExezzy4U90GjorIuRh0ExERERHZjDfbC2+21+xhEBFYXk5ERERERESkGwbdRERERERERDph0E1ERERERESkEwbdRERERERERDph0E1ERERERESkEwbdRERERERERDph0E1ERERERESkEwbdRERERERERDpJNXsAshJCAAAaGk6YPBIie2ptbUFTkw8+XytaW31I8bVCSWlFS1MLjjccR3pautlDJLKtE6fmNnWuS4b6Gic4XxIRkc3EOl8qQosZ1YEOHjyI/v37mz0MIiIi3Rw4cABnnXVWUq/B+ZKIiOyuu/mSQXeC2tvbUV1djd69e0NRFEN/dkNDA/r3748DBw4gKyvL0J9tNl47r53X7hy8dvOuXQiB48ePw+v1IiUluZ1oZs6XgPnvpZl47bx2Xrtz8NrNufZY50uWlycoJSUl6dX/ZGVlZTnuPyoVr53X7jS8dl670bKzszV5HSvMlwD/HfHanYfXzmt3GrOuPZb5ko3UiIiIiIiIiHTCoJuIiIiIiIhIJwy6JeR2u1FaWgq32232UAzHa+e1Ow2vnddOyXHye8lr57U7Da+d125VbKRGREREREREpBNmuomIiIiIiIh0wqCbiIiIiIiISCcMuomIiIiIiIh0wqBbEvX19bjjjjuQn5+Pnj174kc/+hE+//zzmJ/f3t6O559/HiNGjEBmZiZyc3MxZswYfPnllzqOWhvJXruqtbUVQ4cOhaIoWLx4sQ4j1V6i197e3o7ly5fj+uuvR//+/dGzZ09ceOGFePTRR9Hc3GzAyGPj8/kwZ84ceL1eZGZmoqSkBBs2bIjpuYcOHcLkyZORk5ODrKws3HDDDaiqqtJ5xNpJ9NrXrFmDKVOmYODAgejRowfOP/983H///aivr9d/0BpJ5vfe1dVXXw1FUXD33XfrMEp9JHvtb7zxBkaOHImePXsiJycHo0aNwsaNG3UcsZycOmdyvrTvfAlwzuScyTlT6jlTkOX5/X4xatQo0bNnT/G73/1OPPvss2Lo0KGid+/eYs+ePTG9xrRp00Rqaqr4p3/6J/Hiiy+Kp59+WkybNk28//77Oo8+OVpcu+rJJ58UPXv2FADEokWLdBqxdpK59uPHjwsA4pJLLhGPPvqoWLp0qbj11ltFSkqKuPLKK0V7e7tBVxHdzTffLFJTU8XMmTPFCy+8IEaOHClSU1NFWVlZ1OcdP35cnHfeeaKgoEAsWLBAPPXUU6J///7irLPOEnV1dQaNPjmJXntubq4YNmyYePDBB8WLL74o7rnnHpGeni6GDBkiGhsbDRp9chK99q7eeuutzv+ef/WrX+k4Wm0lc+2lpaVCURQxadIk8Yc//EH8+7//u7jzzjvFypUrDRi5PJw6Z3K+tPd8KQTnTM6ZnDNlnjMZdEvgjTfeEADEm2++2XlbbW2tyMnJEf/wD/8Q8/PXrFmj5zB1key1q7799luRnZ0tHn74YWk+RCRz7T6fT2zevDnk9nnz5gkAYsOGDZqPN17l5eUhv4umpiZRWFgoRo4cGfW5CxYsEADE1q1bO2/buXOncLlcYu7cubqNWSvJXPsHH3wQctuKFSsEAPHiiy9qPVTNJXPtXR9/7rnndv73LMsHiGSu/ZNPPhGKooinnnpK72FKz6lzJudL+86XQnDO5JzJOVMIuedMBt0SmDRpkujbt6/w+/0Bt99xxx2iR48eorm5OerzS0pKRHFxsRCiYzX4xIkTuo1Va8leu+rWW28VxcXFoqqqSpoPEVpde1cVFRUCgHjmmWe0GmbCZs2aJVwulzh27FjA7Y8//rgAIPbv3x/xuUVFRaKoqCjk9p/85CeisLBQ87FqLZlrD6ehoUEAEPfdd5+Ww9SFFtc+b948cfbZZ4vGxkapPkAkc+1TpkwRHo9H+P1+0d7eLo4fP673cKXl1DmT86V950shOGdyzuScqZJ1zuSebgls27YNF110EVJSAn9dxcXFaGxsxJ49eyI+t6GhAVu3bkVRUREeeOABZGdno1evXhg4cCBWrVql99CTlsy1q7Zu3YoVK1bg6aefhqIoeg1Vc1pce7DDhw8DAPLy8jQZYzK2bduGwYMHIysrK+D24uJiAMAXX3wR9nnt7e2oqKjAxRdfHHJfcXExKisrcfz4cc3Hq6VErz0SK/1eu5Pste/fvx9PPPEEFixYgMzMTL2GqYtkrv2vf/0rioqK8MwzzyA/Px+9e/eGx+PBs88+q+eQpeTUOZPzpX3nS4BzJudMzpkqWedMBt0SqKmpgcfjCbldva26ujricysrKyGEwOuvv44//vGPWLhwIV599VXk5+fj5ptvxp///Gfdxq2FZK4dAIQQ+PWvf40pU6Zg5MiRuoxRL8leezgLFy5EVlYWfvrTnyY9vmQlen3ff/89fD6f5u+NkbT+3S5YsAAulwsTJ07UZHx6Svba77//fvzwhz/EzTffrMv49JTotR89ehR1dXXYvHkzHnzwQfz2t7/FG2+8gREjRuDXv/41XnjhBV3HLRunzpmcL+07XwKcMzlnBuKcKd+cmWrKT3Ww9vZ2tLS0xPRYt9sNRVHQ1NQEt9sdcn9GRgYAoKmpKeJrnDhxAgBw5MgRfPrppygpKQEAXH/99RgwYAAeffRRjBs3Lt7LSIjR1w4Ay5cvx/bt27F69er4B6whM6492OOPP46//OUveO6555CTkxPXc/WQ6PWpt2v53hhNy9/ta6+9hpdffhmzZ8/Geeedp9kY9ZLMtX/wwQd46623UF5ertv49JTotXf9O/76669jypQpAICJEydi2LBhePTRR3HnnXfqNGpzOXXO5HzJ+TIY50zOmV1xzpRvzmSm22CbNm1CZmZmTF+7d+8GAGRmZsLn84W8lnqURbRyEfW+AQMGdH54AIBevXrhuuuuw9atW9HW1qblJUZk9LU3NDRg7ty5mDVrFvr376/PRcXI6GsP9sYbb+Df/u3fMGPGDPzyl7/U5qKSlOj1qbdr9d6YQavfbVlZGWbMmIGxY8fiscce03SMekn02tva2nDPPffgF7/4BYqKinQdo16S/TeflpYWkJlJSUnBlClTcPDgQezfv1+HEZvPqXMm50vOl8E4Z3LO7IpzpnxzJjPdBhsyZAiWLVsW02PV8gmPx4OampqQ+9XbvF5vxNdQ7+vbt2/IfQUFBWhtbcXJkyeRnZ0d05iSYfS1L168GC0tLZgyZQr27dsHADh48CCAjtKTffv2wev1Ij09PZ7LSIjR197Vhg0bMHXqVIwfPx5/+MMfYhyx/jweDw4dOhRye3fXd8YZZ8Dtdmvy3pgl0Wvv6ssvv8T111+PCy+8EKtXr0Zqqhx/zhO99pUrV2L37t144YUXOv97Vh0/fhz79u1DQUEBevToofmYtZLMv/mMjAzk5OTA5XIF3FdQUACg42/a2WefrfGIzefUOZPzJefLYJwzOWd2xTlTwjnT3D5uFIuJEyeG7cp5++23x9SVs1+/fqJ///4ht//iF78QGRkZIa9rJclc+7Rp0wSAqF/btm3T+QoSl+zvXQghPv30U9GzZ08xatQoy51HOXPmzLBdKR977LFuu1JefPHFYTuxXn311WLgwIGaj1VryVy7EELs3btX9OvXTwwePFjU1tbqOVTNJXrtpaWl3f73vHbtWgOuIHHJ/N4vueQS4XK5hM/nC7j9wQcfFADEoUOHdBmzjJw6Z3K+tO98KQTnTM6ZnDNVss6ZDLol8Prrr4ecP/ndd9+JnJwcMWXKlIDH7t27V+zduzfgtn/5l38RAMT7778f8PysrCxxzTXX6Dv4JCVz7X/729/E2rVrA75eeOEFAUBMnz5drF27VtTX1xt2LfFK9ve+Y8cOkZubKy644ALx/fffGzLmeHz66achx9E0NzeLQYMGiZKSks7bvvnmG7Fz586A5z7xxBMCgPjss886b9u1a5dwuVxizpw5+g8+Sclce01NjRg4cKDwer3i66+/NmrImkn02nfu3Bny3/PatWsFAHHNNdeItWvXiurqakOvJV7J/N6XLFkiAIilS5d23tbU1CQGDhwohg4dqv/gJeLUOZPzpX3nSyE4Z3LO5JwphNxzJoNuCbS1tYlLLrlE9OrVS8ybN0/8x3/8h7jgggtE7969xa5duwIee84554hzzjkn4LbDhw8Lj8cjevfuLUpLS8VTTz0lBg8eLDIzM8UXX3xh4JXEL9lrD/b1119Lc+5oMtfe0NAg+vfvL1JSUsQTTzwhXnnllYCvLVu2GHw14U2aNEmkpqaKWbNmiRdeeEGMGjVKpKamio8++qjzMVdccYUILsppaGgQhYWFoqCgQCxcuFAsWbJE9O/fX3i9XmlWsRO99h/84AcCgJg9e3bI77VrkGBliV57OJDozFEhEr/2xsZGccEFF4i0tDQxc+ZM8cwzz4iioiLhcrnEu+++a/RlWJpT50zOl/aeL4XgnMk5k3OmzHMmg25JfP/992LGjBkiNzdX9OjRQ1xxxRUBK5aqSBNpZWWlmDBhgsjKyhKZmZlizJgxYuvWrQaMPHnJXntXMn2IECLxa1evM9LXtGnTjLuIKJqamsTMmTNFv379hNvtFkVFReLPf/5zwGMiTSQHDhwQEydOFFlZWaJXr17i2muvFV999ZVRQ09aotce7fd6xRVXGHgFiUvm9x5Mtg8QyVz7t99+K6ZNmybOOOMM4Xa7RUlJSchzqYNT50zOl/adL4XgnMk5k3OmzHOmIoQQICIiIiIiIiLN8cgwIiIiIiIiIp0w6CYiIiIiIiLSCYNuIiIiIiIiIp0w6CYiIiIiIiLSCYNuIiIiIiIiIp0w6CYiIiIiIiLSCYNuIiIiIiIiIp0w6CYiIiIiIiLSCYNuIiIiIiIiIp0w6CYiIiIiIiLSCYNuIiIiIiIiIp0w6CYiIiIiIiLSCYNuIjLV5s2boSgKFEXBqlWrwj6mvLwcvXr1gqIomDVrlsEjJCIiMh/nSyJ5KUIIYfYgiMjZbrjhBrzzzjsYMmQI/vd//xcul6vzvt27d+PSSy/FkSNHMG3aNCxbtgyKopg4WiIiInNwviSSEzPdRGS6+fPnw+VyYdeuXfjTn/7UeXt1dTXGjh2LI0eO4Nprr8VLL73EDxBERORYnC+J5MRMNxFZwm233YaXX34ZAwYMwO7du3Hy5EmMHj0a27dvx2WXXYb3338fmZmZZg+TiIjIVJwvieTDoJuILOHQoUM477zz0NTUhCVLlmDt2rXYtGkThg0bhk2bNiEnJ8fsIRIREZmO8yWRfFheTkSWcOaZZ+Kee+4BAPzmN7/Bpk2bcO655+K9994L+wHixIkT+N3vfodrr70W/fr1g6IomD59urGDJiIiMhjnSyL5MOgmIsu45557kJLS8WfpjDPOwPvvvw+PxxP2sXV1dZg3bx4+//xzXHzxxUYOk4iIyFScL4nkkmr2AIiIAKCtrQ133nkn2tvbAQCNjY1R96R5PB4cPHgQZ555Jpqbm7l/jYiIHIHzJZF8mOkmItMJIXDbbbdh3bp1yM/Px4ABA9Dc3IzS0tKIz3G73TjzzDMNHCUREZG5OF8SyYlBNxGZbvbs2VixYgV69eqF9evX47HHHgMArFixAjt27DB5dERERNbA+ZJITgy6ichUixcvxuLFi5GWloY1a9agqKgIN998M4YPHw6/34+5c+eaPUQiIiLTcb4kkheDbiIyzcqVKzF79mwoioLly5fj6quvBgAoioJHHnkEAPDOO+9g8+bNZg6TiIjIVJwvieTGoJuITPHuu+9ixowZEELgqaeewi233BJw//XXX4+SkhIAwJw5c8wYIhERkek4XxLJj0E3ERnuk08+waRJk9DW1oY5c+bg3nvvDfs4da/a5s2b8fbbbxs4QiIiIvNxviSyBx4ZRkSGGzlyJE6ePNnt46666ioIIQwYERERkfVwviSyB2a6iYiIiIiIiHTCTDcRSevZZ59FfX092traAAAVFRV49NFHAQCjR4/G6NGjzRweERGRJXC+JDKXIliLQkSSOvfcc/HNN9+Eva+0tBS/+93vjB0QERGRBXG+JDIXg24iIiIiIiIinXBPNxEREREREZFOGHQTERERERER6YRBNxEREREREZFOGHQTERERERER6YRBNxEREREREZFOGHQTERERERER6YRBNxEREREREZFOGHQTERERERER6YRBNxEREREREZFOGHQTERERERER6YRBNxEREREREZFOGHQTERERERER6eT/A3/BUNfelCfFAAAAAElFTkSuQmCC\n"},"metadata":{}}],"source":["# 왼편 그래프 모델\n","tree_clf_s = DecisionTreeClassifier(random_state=42)\n","tree_clf_s.fit(Xs, ys)\n","\n","# 오른편 그래프 모델\n","tree_clf_sr = DecisionTreeClassifier(random_state=42)\n","tree_clf_sr.fit(Xsr, ys)\n","\n","# 그래프 그리기\n","fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n","# 왼편 그래프\n","plt.sca(axes[0])\n","plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n","\n","# 오른편 그래프\n","plt.sca(axes[1])\n","plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n","plt.ylabel(\"\")\n","\n","save_fig(\"sensitivity_to_rotation_plot\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"mejFRqk7OKcS"},"source":["### 회전 민감도 예제 2"]},{"cell_type":"markdown","metadata":{"id":"tLWHfpITOKcT"},"source":["아래 코드는 붗꽃 데이터를 시계방향으로 20도 회전한 후에 훈련시키면 분류 모델의 결정경계가\n","이전과 많이 다름을 잘 보여준다."]},{"cell_type":"code","execution_count":22,"metadata":{"id":"6JHj0GyxOKcT","executionInfo":{"status":"ok","timestamp":1701868093377,"user_tz":-540,"elapsed":287,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["# 붓꽃 데이터 다시 불러오기\n","iris = load_iris()\n","X = iris.data[:, 2:] # 꽃잎의 길이와 너비\n","y = iris.target"]},{"cell_type":"code","execution_count":23,"metadata":{"id":"5ypP-EmiOKcT","outputId":"4ce2839c-67bc-4308-ea68-1f65b06775d1","colab":{"base_uri":"https://localhost:8080/","height":332},"executionInfo":{"status":"ok","timestamp":1701868097220,"user_tz":-540,"elapsed":351,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x300 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# 시계방향으로 20도 회전\n","angle = np.pi / 180 * 20\n","rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n","Xr = X.dot(rotation_matrix)\n","\n","tree_clf_r = DecisionTreeClassifier(random_state=42)\n","tree_clf_r.fit(Xr, y)\n","\n","plt.figure(figsize=(8, 3))\n","plot_decision_boundary(tree_clf_r, Xr, y, axes=[0.5, 7.5, -1.0, 1], iris=False)\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"W6pHfnFFOKcT"},"source":["### 샘플 민감도 예제"]},{"cell_type":"markdown","metadata":{"id":"zqE65dtuOKcU"},"source":["아래 코드는 훈련세트에 포함된 버시컬러 품종 중에서 가장 넓은 꽃잎을 가진\n","샘플을 삭제한 후에 훈련시킨 결정트리 분류모델의 결정경계를 보여준다.\n","이전과 많이 다름에 주의하라."]},{"cell_type":"markdown","metadata":{"id":"TDZaFOQGOKcU"},"source":["- 버시컬러 샘플 중 가장 넓은 꽃잎 너비 확인: 1.8cm"]},{"cell_type":"code","execution_count":24,"metadata":{"id":"4znCwP0iOKcU","outputId":"ea7b1aa2-4381-497e-9d3c-0c02acde6eaa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701868101823,"user_tz":-540,"elapsed":295,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[4.8, 1.8]])"]},"metadata":{},"execution_count":24}],"source":["X[(X[:, 1]==X[:, 1][y==1].max()) & (y==1)] # 가장 넓은 꽃잎을 갖는 버시컬러 샘플의 특성 확인"]},{"cell_type":"markdown","metadata":{"id":"k-vaJPBqOKcU"},"source":["- 가장 넓은 버시컬러 샘플 제거 후 훈련"]},{"cell_type":"code","execution_count":25,"metadata":{"id":"nz9V5wkIOKcU","outputId":"9fbebf98-950e-4b5e-b324-62cc0adc5734","colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"status":"ok","timestamp":1701868105139,"user_tz":-540,"elapsed":319,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(max_depth=2, random_state=40)"],"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=40)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":25}],"source":["not_widest_versicolor = (X[:, 1]!=1.8) | (y==2)  # y==2는 버지니카 품종은 그대로 둠.\n","\n","X_tweaked = X[not_widest_versicolor]\n","y_tweaked = y[not_widest_versicolor]\n","\n","tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\n","tree_clf_tweaked.fit(X_tweaked, y_tweaked)"]},{"cell_type":"markdown","metadata":{"id":"uqfppTtYOKcU"},"source":["결정경계 기준값으로 꽃잎의 길이는 전혀 사용되지 않는다."]},{"cell_type":"code","execution_count":26,"metadata":{"id":"y1rPCOh-OKcV","outputId":"1732ef08-7740-4c0c-a984-cc7fb74b52f4","colab":{"base_uri":"https://localhost:8080/","height":425},"executionInfo":{"status":"ok","timestamp":1701868109156,"user_tz":-540,"elapsed":858,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saving figure decision_tree_instability_plot\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.figure(figsize=(8, 4))\n","plot_decision_boundary(tree_clf_tweaked, X_tweaked, y_tweaked, legend=False)\n","plt.plot([0, 7.5], [0.8, 0.8], \"k-\", linewidth=2)\n","plt.plot([0, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n","plt.text(1.0, 0.9, \"Depth=0\", fontsize=15)\n","plt.text(1.0, 1.80, \"Depth=1\", fontsize=13)\n","\n","save_fig(\"decision_tree_instability_plot\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"8f2HJp50OKcV"},"source":["## 연습문제 해답"]},{"cell_type":"markdown","metadata":{"id":"RV9VgpIsOKcV"},"source":["## 1. to 6."]},{"cell_type":"markdown","metadata":{"id":"bazbH1nXOKcV"},"source":["책 부록 A 참조."]},{"cell_type":"markdown","metadata":{"id":"n1TQxSn9OKcV"},"source":["## 7."]},{"cell_type":"markdown","metadata":{"id":"PTROVTQaOKcW"},"source":["_Exercise: train and fine-tune a Decision Tree for the moons dataset._"]},{"cell_type":"markdown","metadata":{"id":"-7GkA2T6OKcW"},"source":["a. Generate a moons dataset using `make_moons(n_samples=10000, noise=0.4)`."]},{"cell_type":"markdown","metadata":{"id":"sWW1-o-iOKcW"},"source":["Adding `random_state=42` to make this notebook's output constant:"]},{"cell_type":"code","execution_count":27,"metadata":{"id":"EgZokwmbOKcW","executionInfo":{"status":"ok","timestamp":1701868117684,"user_tz":-540,"elapsed":318,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["from sklearn.datasets import make_moons\n","\n","X, y = make_moons(n_samples=10000, noise=0.4, random_state=42)"]},{"cell_type":"markdown","metadata":{"id":"UPKBTSRGOKcW"},"source":["b. Split it into a training set and a test set using `train_test_split()`."]},{"cell_type":"code","execution_count":28,"metadata":{"id":"zxx1KKMQOKcW","executionInfo":{"status":"ok","timestamp":1701868121605,"user_tz":-540,"elapsed":314,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"]},{"cell_type":"markdown","metadata":{"id":"XMsD5Z1gOKcW"},"source":["c. Use grid search with cross-validation (with the help of the `GridSearchCV` class) to find good hyperparameter values for a `DecisionTreeClassifier`. Hint: try various values for `max_leaf_nodes`."]},{"cell_type":"code","execution_count":29,"metadata":{"id":"Q2R9qUwyOKcX","outputId":"1a2310fd-acef-4d25-f9be-2a5c79433210","colab":{"base_uri":"https://localhost:8080/","height":136},"executionInfo":{"status":"ok","timestamp":1701868134732,"user_tz":-540,"elapsed":9108,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 3 folds for each of 294 candidates, totalling 882 fits\n"]},{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42),\n","             param_grid={'max_leaf_nodes': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n","                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n","                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n","                                            31, ...],\n","                         'min_samples_split': [2, 3, 4]},\n","             verbose=1)"],"text/html":["<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42),\n","             param_grid={&#x27;max_leaf_nodes&#x27;: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n","                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n","                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n","                                            31, ...],\n","                         &#x27;min_samples_split&#x27;: [2, 3, 4]},\n","             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42),\n","             param_grid={&#x27;max_leaf_nodes&#x27;: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n","                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n","                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n","                                            31, ...],\n","                         &#x27;min_samples_split&#x27;: [2, 3, 4]},\n","             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":29}],"source":["from sklearn.model_selection import GridSearchCV\n","\n","params = {'max_leaf_nodes': list(range(2, 100)), 'min_samples_split': [2, 3, 4]}\n","grid_search_cv = GridSearchCV(DecisionTreeClassifier(random_state=42), params, verbose=1, cv=3)\n","\n","grid_search_cv.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":30,"metadata":{"id":"soC36uLOOKcX","outputId":"3bce680b-6e94-4ada-8f87-39f0a5a9ca6a","colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"status":"ok","timestamp":1701868138639,"user_tz":-540,"elapsed":325,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(max_leaf_nodes=17, random_state=42)"],"text/html":["<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_leaf_nodes=17, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_leaf_nodes=17, random_state=42)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":30}],"source":["grid_search_cv.best_estimator_"]},{"cell_type":"markdown","metadata":{"id":"ZvuuvLKYOKcX"},"source":["d. Train it on the full training set using these hyperparameters, and measure your model's performance on the test set. You should get roughly 85% to 87% accuracy."]},{"cell_type":"markdown","metadata":{"id":"NrfJ9fbgOKcX"},"source":["By default, `GridSearchCV` trains the best model found on the whole training set (you can change this by setting `refit=False`), so we don't need to do it again. We can simply evaluate the model's accuracy:"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"pkJ_pYRdOKcX","outputId":"6aea6f15-c6ef-48ec-c106-b0f1b1fa6ca7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701868142825,"user_tz":-540,"elapsed":309,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8695"]},"metadata":{},"execution_count":31}],"source":["from sklearn.metrics import accuracy_score\n","\n","y_pred = grid_search_cv.predict(X_test)\n","accuracy_score(y_test, y_pred)"]},{"cell_type":"markdown","metadata":{"id":"nNJ2YT1VOKcX"},"source":["## 8."]},{"cell_type":"markdown","metadata":{"id":"Aro3L4nJOKcY"},"source":["_Exercise: Grow a forest._"]},{"cell_type":"markdown","metadata":{"id":"ROKV_petOKcY"},"source":["a. Continuing the previous exercise, generate 1,000 subsets of the training set, each containing 100 instances selected randomly. Hint: you can use Scikit-Learn's `ShuffleSplit` class for this."]},{"cell_type":"code","execution_count":32,"metadata":{"id":"yWYIcQJMOKcY","executionInfo":{"status":"ok","timestamp":1701868149410,"user_tz":-540,"elapsed":726,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["from sklearn.model_selection import ShuffleSplit\n","\n","n_trees = 1000\n","n_instances = 100\n","\n","mini_sets = []\n","\n","rs = ShuffleSplit(n_splits=n_trees, test_size=len(X_train) - n_instances, random_state=42)\n","for mini_train_index, mini_test_index in rs.split(X_train):\n","    X_mini_train = X_train[mini_train_index]\n","    y_mini_train = y_train[mini_train_index]\n","    mini_sets.append((X_mini_train, y_mini_train))"]},{"cell_type":"markdown","metadata":{"id":"vW4woRY1OKcY"},"source":["b. Train one Decision Tree on each subset, using the best hyperparameter values found above. Evaluate these 1,000 Decision Trees on the test set. Since they were trained on smaller sets, these Decision Trees will likely perform worse than the first Decision Tree, achieving only about 80% accuracy."]},{"cell_type":"code","execution_count":33,"metadata":{"id":"SnwB2FfKOKcY","outputId":"95289b0c-6568-452d-fa29-29b76b50270c","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701868155758,"user_tz":-540,"elapsed":2007,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.805471"]},"metadata":{},"execution_count":33}],"source":["from sklearn.base import clone\n","\n","forest = [clone(grid_search_cv.best_estimator_) for _ in range(n_trees)]\n","\n","accuracy_scores = []\n","\n","for tree, (X_mini_train, y_mini_train) in zip(forest, mini_sets):\n","    tree.fit(X_mini_train, y_mini_train)\n","\n","    y_pred = tree.predict(X_test)\n","    accuracy_scores.append(accuracy_score(y_test, y_pred))\n","\n","np.mean(accuracy_scores)"]},{"cell_type":"markdown","metadata":{"id":"vqtiThQZOKcY"},"source":["c. Now comes the magic. For each test set instance, generate the predictions of the 1,000 Decision Trees, and keep only the most frequent prediction (you can use SciPy's `mode()` function for this). This gives you _majority-vote predictions_ over the test set."]},{"cell_type":"code","execution_count":34,"metadata":{"id":"r-MsH1PpOKcY","executionInfo":{"status":"ok","timestamp":1701868160809,"user_tz":-540,"elapsed":308,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["Y_pred = np.empty([n_trees, len(X_test)], dtype=np.uint8)\n","\n","for tree_index, tree in enumerate(forest):\n","    Y_pred[tree_index] = tree.predict(X_test)"]},{"cell_type":"code","execution_count":35,"metadata":{"id":"YHhq7i95OKcZ","executionInfo":{"status":"ok","timestamp":1701868164130,"user_tz":-540,"elapsed":308,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[],"source":["from scipy.stats import mode\n","\n","y_pred_majority_votes, n_votes = mode(Y_pred, axis=0)"]},{"cell_type":"markdown","metadata":{"id":"NVJhO5KCOKcZ"},"source":["d. Evaluate these predictions on the test set: you should obtain a slightly higher accuracy than your first model (about 0.5 to 1.5% higher). Congratulations, you have trained a Random Forest classifier!"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"ZCFju7PxOKcZ","outputId":"9aba40cf-3f2d-4ef7-8198-bef3a2203fd5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701868168751,"user_tz":-540,"elapsed":277,"user":{"displayName":"TESUNG JAE","userId":"00210078617188020195"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.872"]},"metadata":{},"execution_count":36}],"source":["accuracy_score(y_test, y_pred_majority_votes.reshape([-1]))"]},{"cell_type":"code","source":[],"metadata":{"id":"l0bcmbZfP2I8"},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"},"nav_menu":{"height":"309px","width":"468px"},"toc":{"navigate_menu":true,"number_sections":true,"sideBar":true,"threshold":6,"toc_cell":false,"toc_section_display":"block","toc_window_display":false},"colab":{"provenance":[{"file_id":"https://github.com/codingalzi/handson-ml2/blob/master/notebooks/handson-ml2-06.ipynb","timestamp":1701867793180}],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}