4_决策树.ipynb

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  {
   "cell_type": "markdown",
   "id": "1e1e367f",
   "metadata": {},
   "source": [
    "## 决策树对 iris 数据的分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "126896ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier, export_text, export_graphviz\n",
    "import graphviz\n",
    "from pydotplus.graphviz import graph_from_dot_data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c33a003e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载Iris数据集\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3dcfe247",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据集划分\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "822fca47",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化决策树分类器\n",
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "# 在训练集上训练模型\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c1ad4c4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "# 在测试集上进行预测\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# 计算准确度\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c06eea2",
   "metadata": {},
   "outputs": [
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       "<text text-anchor=\"start\" x=\"506\" y=\"-353.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.056</text>\n",
       "<text text-anchor=\"start\" x=\"500\" y=\"-337.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 35</text>\n",
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       "<text text-anchor=\"start\" x=\"289.88\" y=\"-260.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) ≤ 1.55</text>\n",
       "<text text-anchor=\"start\" x=\"324\" y=\"-243.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
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       "<text text-anchor=\"start\" x=\"291\" y=\"-150.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">sepal length (cm) ≤ 6.95</text>\n",
       "<text text-anchor=\"start\" x=\"330\" y=\"-133.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
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       "<text text-anchor=\"start\" x=\"506\" y=\"-243.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
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      "text/plain": [
       "<graphviz.sources.Source at 0x2208b120550>"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = export_graphviz(clf, out_file=None, feature_names=iris.feature_names, filled=True, rounded=True, special_characters=True)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render(\"iris_decision_tree\")  # 保存为PDF或其他格式\n",
    "graph.format = 'png'\n",
    "graph.render(\"decision_tree\", format='png', cleanup=True)  # 保存为decision_tree.png\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "848dea44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Parameters: {'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}\n"
     ]
    }
   ],
   "source": [
    "# 输出决策树的一些参数\n",
    "print(f'Decision Tree Parameters: {clf.get_params()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7522bb71",
   "metadata": {},
   "source": [
    "查阅资料，各参数的含义如下：\n",
    "* ccp_alpha：定义了用于剪枝的代价复杂性参数的阈值\n",
    "* class_weight：为不同的类别赋予不同的权重\n",
    "* criterion：表示划分数据集的准则。可以选择 \"gini\"（基尼系数）或 \"entropy\"（信息增益）\n",
    "* max_depth：树的最大深度。用于防止树在训练过程中变得过拟合\n",
    "* max_features：寻找最佳分裂时考虑的特征数量。可以是整数（考虑的特征数量）或浮点数（考虑的特征比例）\n",
    "* max_leaf_nodes：指定最大叶子节点数目，用于防止过拟合\n",
    "* min_impurity_decrease：在进行节点分裂时，只有当节点的分裂导致不纯度减少值大于等于指定的阈值时，才进行分裂\n",
    "* min_samples_leaf：叶子节点必须具有的最小样本数\n",
    "* min_samples_split：规定了一个节点在分裂之前必须包含的最小样本数\n",
    "* min_weight_fraction_leaf：叶子节点上的最小样本权重和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9f98955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取训练好的树的深度\n",
    "tree_depth = clf.tree_.max_depth\n",
    "tree_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "550c3210",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义决策树分类器\n",
    "def train_decision_tree(max_depth=None, max_leaf_nodes=None, max_features=None, min_samples_split=2, min_samples_leaf=1):\n",
    "    clf = DecisionTreeClassifier(max_depth=max_depth, max_leaf_nodes=max_leaf_nodes, max_features=max_features, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf)\n",
    "    clf.fit(X_train, y_train)\n",
    "    return clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a430ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cHBxk4cKF4ubmJseOHZNVq1YJAImNjRWz2SwtWrSQJUuWyEMPPSRvvvmmzTny8/MlJiZGWrVqpezbvHmzhIWFSX5+vk0n02vL/LOvyj/de++9MmLECGnVqpXMmTOnSp/pp59+WqnP8dqmFrPZrPSRKtaiRYtSyxX3+7gRq1evlkaNGpX4bAoKCuS9995TOoE/9NBDlW7GWrRokTg5OYlWq5WTJ0+KiIjBYJCTJ0/KV199JXq9Xnx8fMTZ2VkMBoNNx/FrO51HRUXJoEGDlNfFvw//3IYPH15qPd5+++1Sj//0009FRGTLli2i1Wrl+PHjInK1ydbBwcHmHImJiXL+/Hm5cOGCzRYUFCTvvvuuHD16VNni4+Plzz//lL/++svmHFlZWbJp0yYZOnSoaLVaGTNmjGRkZIjVapUrV65IWlqanDlzRqZOnSoAZMyYMSV+HkSVxbBDVI61a9eKXq+XEydOyLlz5+TcuXNy8eJFGTZsmACQXr16SXp6urIlJSVJSkqKUn7AgAHyyiuvKK8vXbokHh4eJULJm2++KQ899JDNvvHjx8t9990nPj4+EhMTI/Pnz5c9e/bIfffdJ87OznL33XeL1WqVhQsXSnBwsAwdOrTEeUVEYmJiRKvVKiOv3N3dJSwsTN566y0BIN7e3sqXbERERIWfyY4dO0Sj0ciBAwfkiy++ECcnpzL7ZFzr+++/l9mzZ0tubq6kp6dLVlaWDBgwQHx8fASANG3aVH744QcxGo1y+fJlmz4iFy9eVPqeXL58WTIyMsRkMikdg/+5Ffd7ul5z5syRkJCQEvs3btwoBoNB6av10EMPldpB2WQySWZmply6dEkpGxcXJ19//bUScET+DjurV69WQtLbb7+tBKjijuPXhp3JkyfLU089JQUFBZKWliaZmZllfg7/7BxdfP7OnTuX6GtTHHZERNq0aSNPPvmkiFwdWejq6mpzjiZNmoiHh0eJUX0AxNHRscR+FxcX6dChg805Nm3apAQtR0dHadq0qbi5uYlWqxWtVivu7u4SGBgoISEhEhoaKgCU0Y1EVcWwQ1SG3NxcadmyZYmOrufPnxeDwSDPP/+8aDQa+fHHH8s8R//+/W3CzqhRowSA3HLLLWI0GqWwsFA2b94sffv2lf/7v/9TjsvLy5OmTZvKnj17lLAjIrJr1y5xdHSUvXv3ire3tyxZskRMJpOcPXu21Cc7IlLmkx0Rkfbt28uvv/4qIle/uP/973+X+5mkp6dL8+bNZdy4ccq++++/X8LDwyU3N7fcskOGDJExY8Yor+Pi4kSr1cq8efMEgLz88stlhq2DBw+KTqeToqIiGTp0qLi7uwuAEl+4Op1Obr755hJf8pmZmXLgwAE5cuSIzVOHa7e4uDjladmUKVNKrUtERITNPTz00EPi4OAgHh4e4uHhoXQOdnNzE2dn5xJf8CJSathZsmSJeHt7y/333y8hISESFBQk999/v/Tu3btE2Bk0aJDMmDFDDh06JAaDQZycnMTBwcHmc3Bzc1NGWP3T22+/XWoH5WvDzoIFC8TT01Py8/Plp59+En9//1J/LtcqDi9eXl4lRvWVxmw2y86dO+XkyZOSnp4uubm5YrFYZOzYsTJ+/PgSx//rX/+S77//vsLzEpWGYYeoDJGRkWIwGCQ5Odlm/3333SfBwcFSWFgow4cPF19fXzl69Gip5xg6dKi88847IiLy1ltvSUBAgNx9993i5+cnnTp1kuTkZBk0aJA0a9ZMCTTFiofbFoedixcvSosWLeTll18WEZGFCxdKixYtlCan8sKOg4ODMuIqKChICTtLliyR4cOHS3JysgQEBIjRaCzz80hLS5MOHTpISEiIzZdvWlqaBAUFSd++fUud40fkarOHTqeTQ4cOiYhIUVGRhIeHy4gRI5T5i5KSksTFxaXUuXu++uorCQwMtNk3ZswYeeSRR5TXv/zyizg5OZU6ZPuHH34QvV4vzs7OSjD55+bu7i4ODg6SmJgoDz74oE2gExHZvXu3ALAZRl+VZqxixYGoeNTRyZMn5bXXXpMhQ4ZIYWGhzJw5U8aMGSOFhYXKSKhrP9c2bdrYDPmPj48XT09PZT4ai8Ui/fr1kwEDBpTa7FOZsJOeni6nTp0SkatPs4KDg8u9p9zcXLn99ttl5MiR0rt3bwkLC7N5qlWW7OxsGTp0qGzfvl3Zd23YiYmJkX79+pX4/yBRVXG5CKJSJCUlYfHixXjiiSdsOqhOmzYN27dvx2effQa9Xo/FixfD1dUVERERJUaxAFdn2J02bRpeeuklzJs3Dxs2bEBAQAAmTZqE0NBQtG/fHv3798fp06dxzz332JS9dhSPxWLB/fffjyZNmuCdd94BAEyYMAG7du0qc3TNtQIDA7F+/Xp88MEHGDFiBM6cOYNnn30WEyZMQFJSEvr06YNnnnkG7u7upZb//vvvcccddyA1NRXff/+9Tcdnf39/bNiwAXv37kW3bt0QHx9fovyiRYvQoUMH3H777QCuLl9R/BkXCwoKwqxZs/DCCy9g5cqVNuVjYmLQqVOnEufct28fpk2bhsOHD+Phhx9GdHQ0unXrVuL6999/PwoLC5GTk4OsrKxSN6PRiIKCAtx00004e/aszYgiq9WK5557Dp07d0Z4eHiFnzdwtSN4YWFhqctWHDt2DEVFRTAYDCgsLMSePXtw1113Qa/XQ6vVQqPRQK/Xl/jZpqam4ujRozafRZs2bTBz5kwMGjQIx44dw9NPP42zZ8/is88+K3U2ZQAwm81ITk5Wtn8ue+Lr64tWrVoBuLouVVm/F8XnevDBB5GRkYH58+dj7dq1uHjxIjp27Ij9+/eXWa6goAAPPfQQfv/99xIjGa+tR1JSEu64445yZ9wmqpDaaYuorjpy5IgyQ7HJZJKxY8eKVqstMbPv0aNHlZl0Bw8eLJ9//rnSZyQuLk66du0qvr6+8scff4iI7ROYxYsXi5OTkzRr1kzeeust2bt3b4nJ8Nzc3GTbtm1y+vRpm4ndiuXm5srvv/8ud955p8ycOdPmvWeffVYaN24st9xyizzyyCPyzjvvyAsvvCDt2rWT/Px8OXfunHTv3l0eeughadu2rcyaNUvi4uKUJwKbNm2S7t27CwDp2rVruf/C3rt3rwQEBIiTk5NMnTpVaUpKS0sTFxcXWbx4sRQWFsozzzwjjo6O8ttvv4mIKE92ihV3SP2///s/sVqtUlBQIE2bNpXFixeXuOahQ4fE0dFRHBwcZMqUKWXWrSry8vLE1dXVpgno9OnT0rRpU1m3bp3NsUOHDq1SR2sREZ1OpzRjPfroo/Ltt98q/cJEbPvsFE/eV/xkZ/HixeLv71/qE5tHHnlEHBwcxNPTU5mosTQzZswotZ6rVq0q9fgXX3xR+vTpU+p7P/30k4SEhEjLli2VJ0EiIufOnZNbb71VAChPbq6d2+js2bPSqVMnCQkJKdHkNXLkSHniiSeU17m5uTJkyBDRaDSydOnSMu+LqDwMO0QVOHv2rAQFBYmrq6t89dVXpR5jNBpl0qRJ4uTkJM8//7wkJibK8OHDRafTSZcuXWxmgB08eLDNMgSnT5+Whx9+WDQajRgMBvn9999tzu3o6Giz5MA/WSwWad26tQQFBcmBAwds3ktPTy8x+/Fnn30mt956q/z+++/Stm1bpc9RWlqazJgxQwYNGiQmk0lERL755htxc3OTd999t8IRWiIiKSkp0r9/f3n88ceVfePHjxe9Xi/p6ely8uRJufXWW20+x127dgkAKSwsVPYtX75c9u7dKyJXRxs5OjrKxYsX5fTp0/Lrr7/KkiVL5OGHHxYPDw+55557ZPjw4WIwGCQkJERGjx4t06ZNkwULFsiRI0cqrHPxZ/j000/LyJEjpX379qLX60tMsGg0Gkt0fB48eLAMHz68xKikCxcuSEpKipw7d84mIBYVFUmnTp2UZTkyMzMlODjYpr9Wcdj573//K7179xZHR0cpLCwUi8UiwcHB8sILL0hWVpbExcXJd999J6+88oqEhoZKQECAPP744xISEiKurq4SEREhU6ZMkbfeektWrlypBKSoqKhSm7Gu7a+1e/du+b//+z+59957RafT2TSbFRQUyMKFCyUsLEw0Go1MnDix1ObLK1euyHPPPSdarVYAyJ133qn8jJcvXy5dunSxWcoiOjpaBg0aJM7OzspSKsUKCwtl8uTJNqPxiKqCYYeoEj7//HObf7mWJS0tTS5fvixFRUXy9NNPlxoS+vXrJy+99FKJssePHy8zTFWkrL4yFanMUN7i4FMVBQUFyn8fO3ZM5s+fr7y+NtSIXB3qjGtmo/6n5ORk5anDhAkTJDg4WEaMGCHvv/++zci37Oxs2bhxo7zyyity7733SnBwsKSlpVW6znPmzJHevXvLlClTZP/+/ZUqM2DAAJs+O9dj3759NiPPCgoKJD8/X/Lz82XMmDGyc+dOEbn6s/rmm2/k+PHjcvLkSfH395cePXrI1KlTZdu2bTa/Z3FxcfL+++/LY489JnfeeadERUUp7x08eFC2bdtmU4f9+/fbPDU0mUzSu3dvefbZZ5U1r64VFRUlo0aNkv/+978V3l9CQoJMnTq1RJ+0f/7uff/99zJgwACZOXNmuX3HiK6HRqQKyxwTERER1TPsoExERER2jWGHiIiI7BrDDhEREdk1hh0iIiKya1xCFlcnDDt//jzc3NzKnISLiIiI6hYRQXZ2Npo2bVrm5JQAww4A4Pz582jWrJna1SAiIqLrkJSUZDPr+T8x7ABwc3MDcPXDKm9adCIiIqo7TCYTmjVrpnyPl4VhB1Cartzd3Rl2iIiI6pmKuqCwgzIRERHZNYYdIiIismsMO0RERGTXGHaIiIjIrjHsEBERkV1j2CEiIiK7xrBDREREdo1hh4iIiOwaww4RERHZNc6gTDXGYhXsO3MZF7Pz4e/mhI4tvaHT1t5Cq7y+utevC3Xg9Rv29etCHXh99X8HABXDTkZGBu666y7ExMSgRYsWFR6/c+dOTJw4Eenp6Xj11Vfx4osvVuo9UseWuAuYuSkeF4z5yr4mHk54c2Bb9G/fhNe38+vXhTrw+g37+nWhDry++r8DxVRpxrp06RIeeOABJCYmVur49PR0DBo0CI8++ij++OMPrF27FjExMRW+R+rYEncBkz47aPMLDgCpxnxM+uwgtsRd4PXt+Pp1oQ68fsO+fl2oA6+v/u/AtVR5sjNixAiMGDECe/bsqdTxa9euRZMmTTB9+nRoNBq88cYbWLFiBXr16lXue1T7LFbBzE3xkFLeK94X9d0RQABtDTzKtFoFUeuP8PoqXb8u1IHXb9jXrwt14PXLv74GwMxN8ejbtnGtNWlpRKS0+tSo06dP4+abb4ZGo8GZM2cqbMYaN24cGjVqhA8//BAAcOHCBfTp0wfx8fHlvlcWs9kMs9msvC5eIt5oNHLV8xv0R0IGHv2ociGWiIgari+e7IwurXxu6BwmkwkeHh4Vfn+r8mTn5ptvrtLxJpMJbdu2VV67u7sjJSWlwvfKMnv2bMycObNKdaDKuZidX/FBAG7ycYa3i2O1X/9yTgHOZuTy+ipdvy7Ugddv2NevC3Xg9St3/cp+X1SHejEaS6/Xw2AwKK+dnJyQm5tb4XtliYqKsunEXPxkh26cv5uh4oMAzBkaesOJvjSVfbLE69fM9etCHXj9hn39ulAHXr9y1/d3c6r2a5elXsyz4+3tjfT0dOV1dnY2HB0dK3yvLAaDAe7u7jYb3TirVbDx8Plyj9Hgam/8ji29a6QOHVt6o4mHE8pqBeb1a/b6daEOvH7Dvn5dqAOvr/7vwD/Vi7ATHh5u05n50KFDCAwMrPA9qj1FFiteXHcIX+5LUvb98xe9+PWbA9vWWKc0nVaDNwe25fVVun5dqAOv37CvXxfqwOur/zvwT3Uq7JhMJhQWFpbYP2jQIOzevRsxMTEoKirC/Pnz0a9fvwrfo9phLrJg8tqD2HDoPPRaDT549E4sG90BjT1sH1E29nDC0tEdanx+hf7tm2Apr6/a9etCHXj9hn39ulAHXl/934FrqTIaS7n4P0ZjtWjRAgsXLsSQIUNKHPvhhx/i+eefh4eHB1xcXLB3714EBARU+F5lVLY3N5WUW1CEpz49gF9PXoKjXosPR3ZARNurn73aM2fy+urPXKp2HXj9hn39ulAHXr9mr1/Z729Vw05VnTp1CkePHkXPnj1L3FR571WEYef6mPILMX7VfuxPzISzow4fjbkL3YJ91a4WERE1EHYZdmoKw07VXc4pwNiV+3AkxQg3Jz1WjeuIsJu81K4WERE1IHV6nh2q3y6a8jHq4704efEKfFwcsWZ8R7Rr6qF2tYiIiErFsENVknQ5F6NX7MXZjFw0dnfCZ090QrC/q9rVIiIiKhPDDlVaQvoVjP54Ly4Y89HMuxE+f6Izmnk7q10tIiKicjHsUKXEnzdhzMq9uHSlAMH+rvhsfKcSQwqJiIjqIoYdqtCf5zIxduU+mPKL0K6pO9Y83hE+rpVbFoKIiEhtDDtUrj8SMvDE6v3IKbCgQ3NPfDKuIzwaOahdLSIiokpj2KEyxRy7iImfHYC5yIpuwT5Y/thdcDHwV4aIiOoXfnNRqf7z1wU8/9WfKLQIItr4Y/HIDnBy0KldLSIioipj2KESvo5Nwivf/gWrAANvb4ro4bfDQVenllEjIiKqNIYdsrH690S8+f1/AQAjwpvhXw/eVutr2RAREVUnhh1SLIk5hXlbjwMAxt/dEq/f3wYaDYMOERHVbww7BBHBu1uPY+kvCQCAZ/u0xgsRrRl0iIjILjDsNHBWq2Dmpv9i9R9nAQCv3ncrJvRopXKtiIiIqg/DTgNWZLFi2ndH8M2BZGg0wDtD2mNUp5vUrhYREVG1YthpoAqKrHj+qz/x45FU6LQazH84FA/eGaR2tYiIiKodw04DlF9owcTPDuCX4+lw0GnwwaMd0L99Y7WrRUREVCMYdhqYK+YijF+1H3vPXIaTgxb/fuwu9LzFT+1qERER1RiGHTtmsQr2nbmMi9n58Hdzwi0Brnh8dSwOJ2XB1aDHyv8LR8eW3mpXk4iIqEYx7NipLXEXMHNTPC4Y85V9eq0GRVaBp7MD1jzeEaFBnupVkIiIqJYw7NihLXEXMOmzg5B/7C+yXt0zpXdrBh0iImowuOCRnbFYBTM3xZcIOtf6+NfTsFjLO4KIiMh+MOzYmX1nLts0XZXmgjEf+85crqUaERERqYthx85czC4/6FT1OCIiovqOYcfO+Ls5VetxRERE9R3Djp3p2NIbTTycUNYSnhoATTycOOSciIgaDIYdO6PTavDmwLalvlccgN4c2BY6LVc0JyKihoFhxw71b98ES0d3gO4feaaxhxOWju6A/u2bqFMxIiIiFXCeHTvVr11jaDUaWETw5sC2uLWxOzq29OYTHSIianAYduzU5ZwCFP5vLp1RnW6Co54P8YiIqGHiN6CdSjVdHVru6+rIoENERA0avwXtVNr/wk6AO4eYExFRw8awY6dSjWYAQGOGHSIiauAYduxUcTNWgAfDDhERNWyqhZ24uDiEh4fDy8sLkZGRECl/YUoRwbvvvovWrVvD19cXTz/9NHJycpT3Bw4cCI1Go2wRERE1fQt1Wtr/1sfikx0iImroVAk7ZrMZAwcORFhYGGJjYxEfH49Vq1aVW2bFihV4//33sXbtWvz222/Yt28fJk6cqLx/4MABHDlyBJmZmcjMzMTGjRtr+C7qtuInOww7RETU0KkSdjZv3gyj0Yjo6Gi0atUKs2bNwooVK8ots2bNGkRGRqJjx44ICQnBzJkzlUCTnJwMEUH79u3h6ekJT09PuLi41Mat1FlpbMYiIiICoFLYOXz4MDp37gxnZ2cAQGhoKOLj48stc+nSJTRv3lx5rdPpoNPpAAD79u2DxWJBUFAQXFxcMGLECGRmZtbcDdQDfLJDRER0lSphx2QyoWXLlsprjUYDnU5XbkC54447sGHDBuX1J598gnvvvRcAcOLECYSFhWHr1q2IjY1FYmIiXn311TLPZTabYTKZbDZ7kl9oQVZuIQCGHSIiIlXCjl6vh8FgsNnn5OSE3NzcMsvMmjUL+/btQ/fu3XH77bfjq6++wjPPPAMAmDZtGjZv3ox27dqhTZs2mDt3Lr755psyzzV79mx4eHgoW7NmzarnxuqI4iYsJwct3BtxkmwiImrYVAk73t7eSE9Pt9mXnZ0NR0fHMsu0aNEC8fHxWL58OZo3b46+ffuie/fupR7r6emJS5cuwWw2l/p+VFQUjEajsiUlJV3/zdRBqdeMxNJouBYWERE1bKqEnfDwcOzZs0d5nZiYCLPZDG9v73LLaTQauLu746effsKcOXOU/cOGDbM53/79+9G4ceMST4+KGQwGuLu722z2JJWzJxMRESlUCTs9evSA0WjEmjVrAABz5sxBREQEdDodTCYTCgsLyyz7zjvv4OGHH0aHDh2UfaGhoXjhhRewd+9e/PDDD5g+fTomT55c4/dRVxU3YzXmSCwiIiJ1Vj3X6/VYvnw5Ro4cicjISFgsFuzcuRPA1eCycOFCDBkypES5U6dO4fPPP0dcXJzN/qioKJw9exZ9+/aFv78/Jk2ahKioqNq4lTqJS0UQERH9TSMVTV1cg1JSUhAbG4uuXbvCz89PrWrAZDLBw8MDRqPRLpq0nl57EP85cgHTH2iL8Xe3rLgAERFRPVTZ729Vh+oEBgYiMDBQzSrYJc6xQ0RE9DcuBGqHlNFYHqV30CYiImpIGHbsjNUquJjN0VhERETFGHbszOXcAhRarnbD8ndj2CEiImLYsTPFTVi+ro5w1PPHS0RExG9DO5PGCQWJiIhsMOzYGY7EIiIissWwY2fS/teMFcDZk4mIiAAw7NgdPtkhIiKyxbBjZ1JNXCqCiIjoWgw7dobNWERERLYYduwMm7GIiIhsMezYkfxCC4x5hQAYdoiIiIox7NiR4gkFnRy0cG+k6hqvREREdQbDjh25tglLo9GoXBsiIqK6gWHHjnD2ZCIiopIYduxIcTNWY47EIiIiUjDs2BGOxCIiIiqJYceOsBmLiIioJIYdO8JmLCIiopIYduxI2v+WiuCTHSIior8x7NgJq1WUZiw+2SEiIvobw46dyMgpQJFVoNEA/m4GtatDRERUZzDs2Inipzo+LgY46PhjJSIiKsZvRTvxd+dkPtUhIiK6FsOOneAcO0RERKVj2LETnGOHiIiodAw7dkJpxmLYISIissGwYyfSsv83xw6HnRMREdlg2LETaXyyQ0REVCqGHTuRygkFiYiISsWwYwfyCy0w5hUCYAdlIiKif2LYsQPFnZMbOejg7qRXuTZERER1C8OOHbi2CUuj0ahcGyIiorqFYccO/D3HDmdPJiIi+ifVwk5cXBzCw8Ph5eWFyMhIiEi5x4sI3n33XbRu3Rq+vr54+umnkZOTo7y/c+dOtGnTBr6+voiOjq7p6tcpnGOHiIiobKqEHbPZjIEDByIsLAyxsbGIj4/HqlWryi2zYsUKvP/++1i7di1+++037Nu3DxMnTgQApKenY9CgQXj00Ufxxx9/YO3atYiJiamFO6kbipuxOMcOERFRSaqEnc2bN8NoNCI6OhqtWrXCrFmzsGLFinLLrFmzBpGRkejYsSNCQkIwc+ZMbNy4EQCwdu1aNGnSBNOnT0fr1q3xxhtvVHg+e5LGdbGIiIjKpErYOXz4MDp37gxnZ2cAQGhoKOLj48stc+nSJTRv3lx5rdPpoNPplPP17t1b6ZzbsWNHHDx4sMxzmc1mmEwmm60+YzMWERFR2VQJOyaTCS1btlReazQa6HQ6ZGZmllnmjjvuwIYNG5TXn3zyCe69995Sz+fu7o6UlJQyzzV79mx4eHgoW7NmzW7gbtSXZuJSEURERGVRZVIWvV4Pg8F25JCTkxNyc3Ph5eVVaplZs2ZhwIAB6N69O0wmE/766y/s2rWr1PMVn6ssUVFRePHFF5XXJpOp3gYeq1XYjEVERFQOVcKOt7c34uLibPZlZ2fD0dGxzDItWrRAfHw8jh07hpdffhkBAQHo3r27cr709PRKn8tgMJQIW/VVRk4BiqwCjQbwc7OPeyIiIqpOqjRjhYeHY8+ePcrrxMREmM1meHt7l1tOo9HA3d0dP/30E+bMmVPm+Q4dOoTAwMDqr3gdVPxUx9fVAAcdp00iIiL6J1W+HXv06AGj0Yg1a9YAAObMmYOIiAjodDqYTCYUFhaWWfadd97Bww8/jA4dOij7Bg0ahN27dyMmJgZFRUWYP38++vXrV+P3URewczIREVH5VOuzs3z5cowcORKRkZGwWCzYuXMngKsjsxYuXIghQ4aUKHfq1Cl8/vnnJZrAfH19sWDBAvTr1w8eHh5wcXFpMEPPlTl2GHaIiIhKpdqqkUOGDMHJkycRGxuLrl27ws/PD8DVJq2yBAcHw2g0lvre5MmTce+99+Lo0aPo2bMn3N3da6LadY7SOdmD/XWIiIhKo+oS2YGBgdXatyY4OBjBwcHVdr76gM1YRERE5WOP1nqOzVhERETlY9ip5/5uxmLYISIiKg3DTj3HZiwiIqLyMezUY3kFFpjyiwBwqQgiIqKyMOzUY8X9dZwddXAzqNrXnIiIqM5i2KnHrm3CKl7xnYiIiGwx7NRjaRyJRUREVCGGnXoslSOxiIiIKsSwU48VN2PxyQ4REVHZGHbqMWWOHXcuFUFERFQWhp16jM1YREREFWPYqcfS2IxFRERUIYadespqFVzMNgPgkx0iIqLyMOzUU5dyzCiyCrQawM+VfXaIiIjKwrBTT6UZrz7V8XU1QK/jj5GIiKgs/Jasp9g5mYiIqHIYduqpVM6eTEREVCkMO/VU2jXrYhEREVHZGHbqKTZjERERVQ7DTj3FRUCJiIgqh2GnnkplMxYREVGlMOzUU383Y3GOHSIiovIw7NRDuQVFyM4vAsBmLCIiooow7NRDxU1YLo46uDk5qFwbIiKiuo1hpx5S5tjhSCwiIqIKMezUQ8Ujsdg5mYiIqGIMO/VQ6v/WxWLYISIiqhjDTj2UxmYsIiKiSmPYqYc4xw4REVHlMezUQ1wElIiIqPIYduqhNK6LRUREVGkMO/WMxSq4mM0OykRERJXFsFPPZFwxw2IVaDWAr6uj2tUhIiKq8xh26pni/jp+bgbodfzxERERVUS1b8u4uDiEh4fDy8sLkZGREJEKy8ybNw8BAQFwd3fHQw89hIyMDOW9gQMHQqPRKFtERERNVl81HIlFRERUNaqEHbPZjIEDByIsLAyxsbGIj4/HqlWryi2za9curF69Grt27cLBgweRn5+PqVOnKu8fOHAAR44cQWZmJjIzM7Fx48Yavgt1pHEkFhERUZWoEnY2b94Mo9GI6OhotGrVCrNmzcKKFSvKLbNv3z7cd999CAkJQXBwMB599FGcOHECAJCcnAwRQfv27eHp6QlPT0+4uLjUxq3UulSOxCIiIqoSVcLO4cOH0blzZzg7OwMAQkNDER8fX26Z9u3b47vvvkNCQgIuXryIFStWoG/fvgCuBiGLxYKgoCC4uLhgxIgRyMzMLPNcZrMZJpPJZqsvipeK4JMdIiKiylEl7JhMJrRs2VJ5rdFooNPpyg0o/fv3R+vWrREcHIyAgADk5ORg2rRpAIATJ04gLCwMW7duRWxsLBITE/Hqq6+Wea7Zs2fDw8ND2Zo1a1Z9N1fDuAgoERFR1agSdvR6PQwGg80+Jycn5Obmlllm3bp1OHv2LI4dO4aMjAy0b98eo0ePBgBMmzYNmzdvRrt27dCmTRvMnTsX33zzTZnnioqKgtFoVLakpKTqubFawGYsIiKiqtGrcVFvb2/ExcXZ7MvOzoajY9nzxnzxxReYNGkSQkJCAAALFy6Eh4cHsrKy4OnpaXOsp6cnLl26BLPZXCJUAYDBYCh1f32QZmQHZSIioqpQ5clOeHg49uzZo7xOTEyE2WyGt7d3mWWKioqQlpamvL5w4QIAwGKxYNiwYTbn279/Pxo3blxvA01ZcsxFyDYXAeCTHSIiospS5clOjx49YDQasWbNGowZMwZz5sxBREQEdDodTCYTGjVqBAcHB5sy3bp1Q3R0NIKCgtCoUSMsXLgQXbp0gY+PD0JDQ/HCCy9g4cKFSE9Px/Tp0zF58mQ1bq1GFTdhuRr0cDWo8qMjIiKqd1T5xtTr9Vi+fDlGjhyJyMhIWCwW7Ny5E8DVkVkLFy7EkCFDbMo8//zzOH/+PN5++21cunQJXbp0UYarR0VF4ezZs+jbty/8/f0xadIkREVF1fZt1bi/m7Ds64kVERFRTdJIZaYuriEpKSmIjY1F165d4efnp1Y1YDKZ4OHhAaPRCHd3d9XqUZHvDibjxXWH0S3YB2uf6Kx2dYiIiFRV2e9vVdtCAgMDERgYqGYV6pVUzp5MRERUZVxJsh5J47pYREREVcawU49wjh0iIqKqY9ipR1JNXCqCiIioqhh26hE2YxEREVUdw049YbEK0q9cfbLDZiwiIqLKq1LYSUxMBACICI4fP17qMSKCIUOGID8//4YrR3+7dMUMi1Wg02rg68p5doiIiCqrSmGnQ4cOAIDCwkIMGzas1GMyMjKwbds2u1uqQW2p/2vC8nM1QKfVqFwbIiKi+qNKYcfFxQWZmZk4e/YsrFYrzp49i9TUVIwZMwanT58GAJw/fx7t2rWDRsMv5OqkzLHDJiwiIqIqqVLY0ev1+O6773D33Xfj1KlT6NixI9566y3Exsaid+/e+PHHHxEbG4u77767purbYKUVDzvnUhFERERVUqWwo9FoMH78eKSlpaFt27b4/fffISJwcnJCTEwMXnrpJcybNw/3339/TdW3wUrlSCwiIqLrUqmwExcXh/vuuw+XL19W9mk0GpumqpYtW2L8+PE4deoUunXrVv01beDYjEVERHR9KhV2CgsL0bp1a7i6uiI7OxtnzpxBQUEBUlJSlGNWrlyJjz/+GPfddx+2bNlSYxVuqP5uxmLYISIiqopKhZ0777wTixYtgoODA7Zu3YrOnTvj5MmTePjhh9GoUSOYTCZs2rQJO3fuxNSpU7Fu3bqarneDw2YsIiKi61PlPjvDhg3D+fPnceuttyI1NRXR0dHQaDRYv349/P390bVrVxw4cKCm6ttgpRUvFcFmLCIioiq5rhmULRYL2rZtq7yeOnWq8t96vR6NGjVCenr6jdeOAABXzEW4Yi4CwCc7REREVaWvysF5eXl44IEHoNFooNfr8eijj8LX1xdNmzbFp59+is6dO6N169ZYunQp/Pz8aqrODU5xE5abQQ8XQ5V+ZERERA1elb4533//fYgIgKvLQuTn5yMnJwcXLlzAjz/+iMjISHh5eeGVV15B165da6TCDVEaR2IRERFdtyqFnYcffrjCYzZs2IDs7OzrrhCVxM7JRERE16/a20SGDBlS3ads8JQ5dhh2iIiIquy6ws6TTz6JhIQE6PWlF+/QoQPmzJlzQxWjvylz7HhwqQgiIqKqqtJorCNHjgAADh06hDfffBOvvvoqkpKS8NprryE9PR2vvfYaoqKisHv3buTk5NRIhRsiNmMRERFdvyo92enZsyfGjBmD3Nxc9OzZEwDg5uaGnj17wsvLS9m3adMmuLi4VH9tG6g0NmMRERFdtyo92WnRogWaNm2KM2fOoGnTpmjSpAkOHz6Mpk2b4vfff0fTpk3RtGlTLFmypKbq2yClKs1YDDtERERVVaknOzt37sSuXbug0+nw8ssv4+uvv8bevXthtVrRtWtX/PHHH+jTpw927NgBANBqr2uuQipFkcWK9OyrsyezGYuIiKjqKhV2XFxcsHHjRiQkJODkyZMAgGXLlgEALl68iH//+984f/48fvjhB/Tt2xfOzs41V+MG5tKVAlgF0Gk18HFlB2UiIqKqqtQjmLvuugv79+9HZGQkevXqhS5dusBoNCIrKwvjxo1DcnIyevXqhblz5+Lmm2/Gxo0ba7reDUZxE5a/mwE6rUbl2hAREdU/le6gnJ2dDavVivfeew+urq5o0qQJUlJS4ODgoBzzwAMP4MyZM/jmm28wePDgGqlwQ1M8Eoudk4mIiK5PpcOOxWLBZ599hmPHjgEAJk2ahCNHjqBly5bKMf/5z38wbdo0fPrpp9Vf0wZKmWOHYYeIiOi6VDrsaLVaJCcnY9q0aXj44Yeh0WgwYcIEjBkzRjkmNDQUL7/8co1UtKHiSCwiIqIbU+mwIyLw8PCAl5cXXnvtNfzyyy/YunUrFi9erBxz6tQpdOzYEX369MHs2bNrpMINTRqbsYiIiG5IpcNOXl4enJyc8Morr+CVV17B2bNnMW3aNGzZsgVLly5Fly5dAABFRUUwGDhqqLqkcqkIIiKiG1LpsNOkSRMkJCQor2+66SZ88cUXWLNmDe699154e3tDRHDmzBkEBQXVSGUbIi4CSkREdGOqPPtf3759AQBWqxVffPEFxowZg23btgEAzGYzWrduXb01bODSuC4WERHRDaly2Dl06BCAq314ijsjv/nmmwAAR0fHMldCp6rLzi9EToEFADsoExERXa9Kh53c3FycOnUKrq6uAACdTgedTgcASh8drVar7KtIXFwcwsPD4eXlhcjISIhIhWXmzZuHgIAAuLu746GHHkJGRoby3s6dO9GmTRv4+voiOjq6srdVpxUPO3dz0sPZkSGSiIjoelQq7Fy+fBk9evTABx98gOzsbLRt2xZt27bFhQsX0LZtW5w6dQpt27ZFmzZtKnVRs9mMgQMHIiwsDLGxsYiPj8eqVavKLbNr1y6sXr0au3btwsGDB5Gfn4+pU6cCANLT0zFo0CA8+uij+OOPP7B27VrExMRUqi51WaqRa2IRERHdqEqFHY1GgyFDhmDRokVo1KgRli5dig8//BA+Pj5YunQpAgMDsXTpUixdurRSF928eTOMRiOio6PRqlUrzJo1CytWrCi3zL59+3DfffchJCQEwcHBePTRR3HixAkAwNq1a9GkSRNMnz4drVu3xhtvvFHh+eoDzrFDRER04yrVNuLl5YVRo0YBABwcHNCzZ08AgJOTE3r27AlXV1dlX2UcPnwYnTt3VhYMDQ0NRXx8fLll2rdvj2eeeQZPPfUU3NzcsGLFCqWz9OHDh9G7d29oNFfXjurYsSOioqLKPJfZbIbZbFZem0ymSte9NqVxJBYREdENq1TYOX/+PG655RZERETAZDLhk08+gYjgypUrWLlyJS5fvqzsKyoqwieffIJx48aVeT6TyWSzzIRGo4FOp0NmZia8vLxKLdO/f3+0bt0awcHBAIDw8HBMmzZNOV/btm2VY93d3ZGSklLm9WfPno2ZM2dW5tZVlcqRWERERDesUs1YTZs2xaFDh9ClSxcYDAaMHz8eL7/8Mrp164ZNmzahQ4cO+P7777Fp0yb0798fX375Zbnn0+v1JSYedHJyQm5ubpll1q1bh7Nnz+LYsWPIyMhA+/btMXr06FLPV9G5oqKiYDQalS0pKakyH0OtU+bYYTMWERHRdav0EJ927dqhXbt2ePXVV7F8+XK8+eab8PPzw/Lly6t8UW9vb8TFxdnsy87OhqOjY5llvvjiC0yaNAkhISEAgIULF8LDwwNZWVnw9vZGenp6pc9lMBjqxSzPXASUiIjoxlV5np1169YhMDAQcXFxmDp1KtatW4eDBw9W6Rzh4eHYs2eP8joxMRFmsxne3t5llikqKkJaWpry+sKFCwCursb+z/MdOnQIgYGBVapTXcRmLCIiohtXpbDz8ccfY9KkSSgqKkJAQABCQkJw4cIFDB8+HOHh4Vi1apVNx9+y9OjRA0ajEWvWrAEAzJkzBxEREdDpdDCZTCgsLCxRplu3bli+fDmWLVuG1atXY8SIEejSpQt8fHwwaNAg7N69GzExMSgqKsL8+fPRr1+/qtxanVNkseLSlaufZQDXxSIiIrp+UknHjx+Xxo0bS2xsbIn3rFarfP3119KqVSvp0KFDpc63fv16adSokfj7+4uPj4/ExcWJiMhNN90k69evL3F8Xl6eTJkyRZo2bSqOjo7Ss2dPOXXqlPL+kiVLxMHBQXx9feWmm26S1NTUyt6aGI1GASBGo7HSZWra+axcuemVH6RV1H/EYrGqXR0iIqI6p7Lf3xqRSkxd/D8mkwnu7u5lvl9QUICEhIRKTy6YkpKC2NhYdO3aFX5+fpWtRplOnTqFo0ePomfPnuXW859MJhM8PDxgNBqrVK4m/XkuEw9++Duaejjh96g+aleHiIiozqns93eV1iCoKAg4OjpWOugAQGBgYLX2rQkODlaGptd3aRyJRUREVC2q3EGZagc7JxMREVUPhp06KtX0v87JDDtEREQ3hGGnjkrjulhERETVgmGnjmIzFhERUfVg2KmjuAgoERFR9WDYqYNERFkXi81YREREN4Zhpw7KNhcht8ACgM1YREREN4phpw5K+19/HXcnPRo56lSuDRERUf3GsFMHsQmLiIio+jDs1EHFI7HYOZmIiOjGMezUQcocOww7REREN4xhpw5iMxYREVH1Ydipg1KNXCqCiIioujDs1EFsxiIiIqo+DDt1EJuxiIiIqg/DTh1TaLHi0hU2YxEREVUXhp06Jj3bDBHAQaeBj4uj2tUhIiKq9xh26pjiJix/NydotRqVa0NERFT/MezUMWnKhIIGlWtCRERkHxh26hh2TiYiIqpeDDt1THHYYedkIiKi6sGwU8cUN2Nxjh0iIqLqwbBTx7AZi4iIqHox7NQxaSbOsUNERFSdGHbqEBFBKpuxiIiIqhXDTh1iyi9CXqEFAJ/sEBERVReGnTqkeAFQdyc9GjnqVK4NERGRfWDYqUOUJix2TiYiIqo2DDt1COfYISIiqn4MO3XIRRM7JxMREVU3hp06hHPsEBERVT+GnTok1cg5doiIiKobw04dksZmLCIiomrHsFOHsBmLiIio+qkWduLi4hAeHg4vLy9ERkZCRMo9fsaMGdBoNCW2X375BQAwcOBAm/0RERG1cBfVp9BixaUrbMYiIiKqbqqEHbPZjIEDByIsLAyxsbGIj4/HqlWryi0zbdo0ZGZmKtvhw4fh5+eHO++8EwBw4MABHDlyRHl/48aNtXAn1Sc92wwRwEGngY+Lo9rVISIishuqhJ3NmzfDaDQiOjoarVq1wqxZs7BixYpyyzg5OcHT01PZFi9ejBdeeAEeHh5ITk6GiKB9+/bK+y4uLrV0N9WjuAnL380JWq1G5doQERHZD70aFz18+DA6d+4MZ2dnAEBoaCji4+MrXf78+fNYv349zpw5AwDYt28fLBYLgoKCkJmZiYEDB2Lp0qXw8vIqtbzZbIbZbFZem0ymG7ib6pFmLJ5Q0KByTYiIiOyLKk92TCYTWrZsqbzWaDTQ6XTIzMysVPlly5Zh5MiRcHV1BQCcOHECYWFh2Lp1K2JjY5GYmIhXX321zPKzZ8+Gh4eHsjVr1uzGbqgasHMyERFRzVAl7Oj1ehgMtk8wnJyckJubW2FZi8WCjz76CBMnTlT2TZs2DZs3b0a7du3Qpk0bzJ07F998802Z54iKioLRaFS2pKSk67+ZasKlIoiIiGqGKs1Y3t7eiIuLs9mXnZ0NR8eKO+bGxMTA19cXbdq0KfMYT09PXLp0CWazuUSoAgCDwVDqfjUVN2Nxjh0iIqLqpcqTnfDwcOzZs0d5nZiYCLPZDG9v7wrLrlu3Dg8++KDNvmHDhtmcb//+/WjcuHGdCzTlYTMWERFRzVAl7PTo0QNGoxFr1qwBAMyZMwcRERHQ6XQwmUwoLCwss+yWLVvQq1cvm32hoaF44YUXsHfvXvzwww+YPn06Jk+eXKP3UN3STJxjh4iIqCao0oyl1+uxfPlyjBw5EpGRkbBYLNi5cyeAq8Fl4cKFGDJkSIlyCQkJOH/+PMLDw232R0VF4ezZs+jbty/8/f0xadIkREVF1catVAsRQSqbsYiIiGqERiqaurgGpaSkIDY2Fl27doWfn59a1YDJZIKHhweMRiPc3d1r/frGvELcPnMbAODY2/3h5KCr9ToQERHVN5X9/lblyU6xwMBABAYGqlmFOqF4AVCPRg4MOkRERNWMC4HWAWzCIiIiqjkMO3WAMscOR2IRERFVO4adOuDvOXbqz1B5IiKi+oJhpw5Q5thhMxYREVG1Y9ipA9LYjEVERFRjGHbqAD7ZISIiqjkMO3VAqpGzJxMREdUUhh2VFVqsyMi5Gna4LhYREVH1Y9hR2cVsM0QAB50G3s4Vr/pOREREVcOwo7LiCQX93Zyg1WpUrg0REZH9YdhRWfFILDZhERER1QyGHZVxqQgiIqKaxbCjMmWOHYYdIiKiGsGwozJljh0PLhVBRERUExh2VFbcjMUnO0RERDWDYUdlaZw9mYiIqEYx7KhIRK5pxmLYISIiqgkMOyoy5RUhv9AKgM1YRERENYVhR0XFT3U8nR3g5KBTuTZERET2iWFHRVztnIiIqOYx7KgojSOxiIiIahzDjor4ZIeIiKjmMeyoqDjsBHAkFhERUY1h2FFRGtfFIiIiqnEMOyriUhFEREQ1j2FHRVwElIiIqOYx7KikoMiKS1cKALAZi4iIqCYx7KjkYvbVpzqOOi28XRxVrg0REZH9YthRSXETlr+7ARqNRuXaEBER2S+GHZWkGs0A2IRFRERU0xh2VMI5doiIiGoHw45K0jh7MhERUa1g2FFJKicUJCIiqhUMOyphMxYREVHtUC3sxMXFITw8HF5eXoiMjISIlHv8jBkzoNFoSmy//PILAGDnzp1o06YNfH19ER0dXQt3cGPYjEVERFQ7VAk7ZrMZAwcORFhYGGJjYxEfH49Vq1aVW2batGnIzMxUtsOHD8PPzw933nkn0tPTMWjQIDz66KP4448/sHbtWsTExNTOzVwHEWEzFhERUS1RJexs3rwZRqMR0dHRaNWqFWbNmoUVK1aUW8bJyQmenp7KtnjxYrzwwgvw8PDA2rVr0aRJE0yfPh2tW7fGG2+8UeH51GTMK4S5yArg6jw7REREVHP0alz08OHD6Ny5M5ydnQEAoaGhiI+Pr3T58+fPY/369Thz5oxyvt69eyuT83Xs2BFRUVFlljebzTCbzcprk8l0Pbdx3Yr763g5O8DJQVer1yYiImpoVHmyYzKZ0LJlS+W1RqOBTqdDZmZmpcovW7YMI0eOhKura6nnc3d3R0pKSpnlZ8+eDQ8PD2Vr1qzZdd7J9SluwuICoERERDVPlbCj1+thMNg23zg5OSE3N7fCshaLBR999BEmTpxY5vkqOldUVBSMRqOyJSUlXcddXD+lczJHYhEREdU4VZqxvL29ERcXZ7MvOzsbjo4VL4gZExMDX19ftGnTxuZ86enplT6XwWAoEbZqE5eKICIiqj2qPNkJDw/Hnj17lNeJiYkwm83w9vausOy6devw4IMPlnu+Q4cOITAwsPoqXM2UOXYYdoiIiGqcKmGnR48eMBqNWLNmDQBgzpw5iIiIgE6ng8lkQmFhYZllt2zZgl69etnsGzRoEHbv3o2YmBgUFRVh/vz56NevX43ew41gMxYREVHtUaUZS6/XY/ny5Rg5ciQiIyNhsViwc+dOAFdHZi1cuBBDhgwpUS4hIQHnz59HeHi4zX5fX18sWLAA/fr1g4eHB1xcXOr00HPOsUNERFR7NFLR1MU1KCUlBbGxsejatSv8/Pxu+HynTp3C0aNH0bNnT7i7u1e6nMlkgoeHB4xGY5XKXa+wt7cjI6cAPz7bHW2b1vz1iIiI7FFlv79VebJTLDAwsFr71gQHByM4OLjazlcTzEUWZOQUAGAzFhERUW3gQqC17KLp6kgsR70WXs4OKteGiIjI/jHs1LI0ZSSWQZnxmYiIiGoOw04tS+Vq50RERLWKYaeWcakIIiKi2sWwU8vS+GSHiIioVjHs1LLU/3VQ5kgsIiKi2sGwU8vS2IxFRERUqxh2alkql4ogIiKqVQw7tUhEOBqLiIioljHs1KKs3EIUFFkBAP7uBpVrQ0RE1DAw7NSi4qc63i6OMOh1KteGiIioYWDYqUWpJnZOJiIiqm0MO7WoeCRWYzZhERER1RqGnVrEkVhERES1j2GnFqWxGYuIiKjWMezUolQjh50TERHVNoadWlS8VEQAm7GIiIhqDcNOLeIioERERLWPYaeWmIssuJxTAIBhh4iIqDYx7NSSi/9rwnLUa+Hp7KBybYiIiBoOhp1acu2aWBqNRuXaEBERNRwMO7WEI7GIiIjUwbBTS5Q5djgSi4iIqFYx7NSSVC4VQUREpAqGnVrCRUCJiIjUwbBTS9K4LhYREZEqGHZqSSonFCQiIlIFw04tEBGkFS8VwbBDRERUqxh2akFmbiEKiqwAGHaIiIhqG8NOLSgeieXj4ghHPT9yIiKi2sRv3lqQxpFYREREqmHYqQWpHIlFRESkGoadWlDcjMUnO0RERLVPtbATFxeH8PBweHl5ITIyEiJS6bIjRozAlClTbPYNHDgQGo1G2SIiIqq7ytctjcPOiYiIVKNK2DGbzRg4cCDCwsIQGxuL+Ph4rFq1qlJlt27dip9//hlvv/22zf4DBw7gyJEjyMzMRGZmJjZu3FgDNb8+fzdjcakIIiKi2qZK2Nm8eTOMRiOio6PRqlUrzJo1CytWrKiwXF5eHiZPnow5c+bA09NT2Z+cnAwRQfv27eHp6QlPT0+4uLjU4B1UDZuxiIiI1KNK2Dl8+DA6d+4MZ2dnAEBoaCji4+MrLPf2228jLy8Per0eP//8s9L0tW/fPlgsFgQFBcHFxQUjRoxAZmZmmecxm80wmUw2W03iUhFERETqUSXsmEwmtGzZUnmt0Wig0+nKDSjnzp1DdHQ0goODce7cOURGRmLo0KEQEZw4cQJhYWHYunUrYmNjkZiYiFdffbXMc82ePRseHh7K1qxZs2q9v2vlF1qQmVsIgH12iIiI1KBK2NHr9TAYbPuvODk5ITc3t8wyq1atQkBAALZv347XX38dv/zyC3bu3Int27dj2rRp2Lx5M9q1a4c2bdpg7ty5+Oabb8o8V1RUFIxGo7IlJSVV273908X/LRNh0Gvh0cihxq5DREREpdOrcVFvb2/ExcXZ7MvOzoajo2OZZZKTk9GnTx8lJLm5uaF169Y4c+ZMiWM9PT1x6dIlmM3mEqEKAAwGQ6n7a8K1c+xoNJpauSYRERH9TZUnO+Hh4dizZ4/yOjExEWazGd7e3mWWadasGfLy8pTXVqsVycnJuOmmmzBs2DCb8+3fvx+NGzeutUBTnlTOnkxERKQqVcJOjx49YDQasWbNGgDAnDlzEBERAZ1OB5PJhMLCwhJlhg8fjk2bNuHbb79FcnIyoqKiYDab0a1bN4SGhuKFF17A3r178cMPP2D69OmYPHlybd9WqdKMnGOHiIhITao0Y+n1eixfvhwjR45EZGQkLBYLdu7cCeDqyKyFCxdiyJAhNmVCQkLw1Vdf4fXXX8exY8fQqlUrbNy4EW5uboiKisLZs2fRt29f+Pv7Y9KkSYiKilLhzkriUhFERETq0khVpi6uZikpKYiNjUXXrl3h5+enVjVgMpng4eEBo9EId3f3aj33058fxH/+uoDpD7TF+LtbVlyAiIiIKqWy39+qPNkpFhgYiMDAQDWrUOPYjEVERKQuLgRaw7hUBBERkboYdmqQiCjz7HA0FhERkToYdmrQ5ZwCFFisAAB/N4YdIiIiNTDs1BCLVbAtPhUA4O6kh07LCQWJiIjUwLBTA7bEXcDdc39G1HdXZ4k25Rfh7rk/Y0vcBZVrRkRE1PAw7FSzLXEXMOmzg7jwv1FYxVKN+Zj02UEGHiIiolrGsFONLFbBzE3xKG3iouJ9MzfFw2JVbWojIiKiBodhpxrtO3O5xBOdawmAC8Z87DtzufYqRURE1MAx7FSji9llB53rOY6IiIhuHMNONars8HIOQyciIqo9DDvVqGNLbzTxcEJZg8w1AJp4OKFjS+/arBYREVGDxrBTjXRaDd4c2BYASgSe4tdvDmzLOXeIiIhqEcNONevfvgmWju6Axh62TVWNPZywdHQH9G/fRKWaERERNUyqrnpur/q3b4K+bRtj35nLuJidD3+3q01XfKJDRERU+xh2aohOq0GXVj5qV4OIiKjBYzMWERER2TWGHSIiIrJrDDtERERk1xh2iIiIyK4x7BAREZFdY9ghIiIiu8awQ0RERHaNYYeIiIjsGsMOERER2TXOoAxARAAAJpNJ5ZoQERFRZRV/bxd/j5eFYQdAdnY2AKBZs2Yq14SIiIiqKjs7Gx4eHmW+r5GK4lADYLVacf78ebi5uUGjsa/FOk0mE5o1a4akpCS4u7urXZ1ax/tv2PcP8DNo6PcP8DOw5/sXEWRnZ6Np06bQasvumcMnOwC0Wi2CgoLUrkaNcnd3t7tf8qrg/Tfs+wf4GTT0+wf4Gdjr/Zf3RKcYOygTERGRXWPYISIiIrvGsGPnDAYD3nzzTRgMBrWrogref8O+f4CfQUO/f4CfQUO/f4AdlImIiMjO8ckOERER2TWGHSIiIrJrDDtERERk1xh27NjGjRtx8803Q6/Xo1OnTjh69KjaVVJN//79sWrVKrWroYpp06Zh4MCBaldDFZ9++imaN28OV1dXREREIDExUe0q1biMjAy0bNnS5l7j4uIQHh4OLy8vREZGVji1fn1X2mfQkP4elnb/12qIfw8ZduxUQkICxo0bhzlz5iAlJQU33XQTnnjiCbWrpYq1a9di69ataldDFXFxcfjwww+xcOFCtatS6xISEvDaa69hw4YNiI+Px0033YT/+7//U7taNerSpUt44IEHbL7kzGYzBg4ciLCwMMTGxiI+Pt6uv+hK+wwa0t/D0u7/Wg317yHDjp06evQoZs2aheHDhyMgIACTJk1CbGys2tWqdZcvX8bUqVMREhKidlVqnYjgqaeewvPPP49WrVqpXZ1a9+eff6Jz587o0KEDmjdvjnHjxuHEiRNqV6tGjRgxAiNGjLDZt3nzZhiNRkRHR6NVq1aYNWsWVqxYoVINa15pn0FD+ntY2v0Xa8h/D7lchJ164IEHbF4fP34cwcHBKtVGPVOnTsWDDz6IvLw8tatS6z766CMcOnQITzzxBH744Qf069cPDg4Oaler1rRt2xY///wz/vzzT9x8881YsmQJ+vbtq3a1atTy5ctx88034/nnn1f2HT58GJ07d4azszMAIDQ0FPHx8SrVsOaV9hk0pL+Hpd1/sYb895BPdhqAgoICzJ8/H5MnT1a7KrUqJiYGO3bswNy5c9WuSq27cuUKXn/9dbRu3RrJycmIjo5Gjx49kJ+fr3bVak3btm0xbNgwdOjQAZ6enti7dy/mz5+vdrVq1M0331xin8lkQsuWLZXXGo0GOp0OmZmZtVm1WlPaZ3Ate/97WNb9N+S/hwDDToPw+uuvw9XVFRMmTFC7KrUmPz8fTz31FJYuXWqXC99V5LvvvkNOTg5+/vlnTJ8+Hdu2bUNWVhbWrFmjdtVqzZ49e7Bp0ybs3bsX2dnZePTRR3HffffZfefcf9Lr9SVmznVyckJubq5KNVIX/x42vL+HAMOO3du+fTuWLVuGzz//vEE1Ybz99tsIDw/H/fffr3ZVVJGcnIxOnTrB29sbwNUvvNDQUJw5c0blmtWer776CiNGjEDHjh3h6uqKd955B6dPn8bhw4fVrlqt8vb2Rnp6us2+7OxsODo6qlQj9fDvYcP8ewiwz45dO336NEaNGoWlS5eibdu2alenVn3++edIT0+Hp6cnACA3Nxfr1q3Dvn378OGHH6pbuVrQrFmzEu3yZ8+eRa9evVSqUe0rKiqyaarJzs5GTk4OLBaLirWqfeHh4fj444+V14mJiTCbzUoQbij497Dh/j0EGHbsVl5eHh544AEMGTIEgwcPxpUrVwAALi4u0Gg0Kteu5v36668oKipSXr/00kvo3Lmz3Q89Lnb//fdjypQpWLZsGR544AF89913OHToEPr376921WpNt27d8Pjjj+O9995DQEAAPv74YwQEBCA0NFTtqtWqHj16wGg0Ys2aNRgzZgzmzJmDiIgI6HQ6tatWa/j3sGH/PQQYduzW1q1bcfToURw9ehQfffSRsv/MmTNo0aKFehWrJUFBQTavXV1d4evrC19fX5VqVLu8vb2xZcsWTJ06FS+++CIaN26ML7/8skH87Is98sgjOH78OBYuXIgLFy6gffv2+O677xpU8wVwtQlz+fLlGDlyJCIjI2GxWLBz5061q1Wr+PewYf89BLjqORFRg5CSkoLY2Fh07doVfn5+aleHqFYx7BAREZFd42gsIiIismsMO0RERGTXGHaIiIjIrjHsEBERkV1j2CGiG3LkyBEkJCSU+b7JZCr1/ZMnT9brpRusVis++OADZGVl3fC5rly5gm+++ebGK0VEpWLYIaJKycjIKHX/+++/j6VLl5ZZ7plnnsGLL74IEYHFYoHZbIbJZEK3bt3w7bffAgAsFgtycnJsyjVr1gy7d+8u87yrV6/G4MGDS+x/8sknERwcjLvvvrvEFhwcjEceeaTCe42NjUXHjh2RlpZW7nEZGRkICwvDtm3boNPp0KJFi1I3jUZTbijKzc3FyJEjsXfv3grrRkRVx0kFiahCJpMJISEh+PjjjzFkyBAAV59saLVaODg4wNnZudRy8+bNw8aNG+Hs7Aw3Nzd4eXnhwQcfRGZmJjQaDZ544glMmDABHh4eyM/Px/Hjx5WFCp2dneHn54eMjAyYTCZlptvmzZsr1/3nApfF5Tp16oTu3buXeO/XX3+t1KSCer0ef/75Z7mTrmm1WsyYMQNBQUEoKiqCn58fEhMTkZmZCS8vLwDA+fPn0bRpU2g0Guj1f/+5veuuu3Dx4kVotX//e9PZ2RmDBw+Gk5MTRARmsxkDBw60mQSPiK4Pww4RVcjd3R2zZ8/GqFGj8Ouvv8LDwwNt27ZFo0aNkJ+fD51Ohw8//BBmsxkPPPAAvvjiC6xfvx4ffvghDh48iNzcXDzyyCOIj4/HwYMH8fTTT+PEiRNYtWoVfv31V5smnOJp7TUaDXQ6HdauXYsPP/wQer0ex44dQ35+PrRaLRwdHaHValFUVGQTJACgsLAQ+fn5Je6jsLCwxLGl0Wq1cHd3L3dJhTNnzqBly5Z44oknkJmZiVGjRiEhIQHdu3fHr7/+CgDo3LkzduzYgXnz5tmcKycnB9988w06duyIY8eOIT09XQlnhw8fxvnz5zFgwIAK60lElcOwQ0SV8uSTTyI+Ph6LFi3C6tWrYTabAQCTJk1CUFAQXnvtNcyfP19ZVfzBBx9Ejx494OXlBY1GozR1zZ07FzNmzICHhwdGjx6NoUOHwmq1oqioCI6Ojli5ciVmzpyJCxcuoHv37vjggw9w7NgxXLx4EZ06dcJTTz2F7du3o6CgANnZ2ejUqRMOHDig1NNsNuPAgQM4d+5ciXtIT09Hx44dbfaZTCZcvHgRbm5uSiDJyMiAwWDApUuXlOPy8/ORm5sLf39/eHp6YvDgwejVqxfee+89eHl5Ydq0abjnnnuwaNEitGrVCgAQHR2NQYMGITY2Fo0aNbK5rtVqhdlsxo4dO7B3714l7Pz222/47bffcO+996KoqKjUp1dEVDWcQZmIKq2oqAgiYtMUNH78eLRt2xZTp07F/PnzceTIEaxevRoAcMsttyAvL8/mqUZxsLj2y99qtaJTp074+uuvAVwNJf7+/jhz5gwuX76M77//Hn369MH8+fOxceNGAMCGDRvw5Zdf4ssvvwQAFBQUQKPRVKqZqvhplIODAzZu3IhHHnnE5omPxWJBfn4+XFxcbOpYUFCA1atXY9SoUbhw4QKGDRuGsLAwLFiwAJ07d8aIESMQGRmJhQsXwsfHB4899hhGjx6NDh064MUXX1TOdeutt2Ly5Mn497//DaPRiNzcXDRv3hzA1aCVk5ODoKAgdO/eHUuWLKn8D4iISsUnO0RUaWazGVlZWfD394dOp4NWq0V2drZNnx2NRqM0F504caLEOYYMGYKJEyeWuwL7jz/+CABYtGgRZs+ejSlTpuC3335DREQEFixYgPbt25cos3LlSrz33ntISEjArbfeCgA4d+4cfH19S/QpMpvNmD59OsaMGYPBgweXaPL65JNP8Pjjj+Po0aNo1qxZqXVs0qQJtm7diqSkJDg4OGDLli3KmlMnT55UOlx/9NFHJZ7q5Ofno2/fvnj22Wfx4YcfYseOHZg7dy4A4IsvvsDJkyexZs2aMj8fIqoaPtkhokpbtmwZ1qxZg5tvvhnr169Ho0aNYDQaodFo4O7ubjPiKi4uDq1atYKnpycCAgKUJy4JCQkICAiAq6srACArKwu9e/e2+XLv0aMHfvvtN9x///246667MGzYMLRr1w5//PEHMjIyMH36dEyfPh1fffWV8mSnmLu7O1auXAkAePfddxEVFYUrV67AyclJ6eTcq1cv+Pj4lHmfQ4cOxb59+zB69GjMmTOn3M/EarVCRODj4wN3d3cUFhYiKysLWq0WPj4+0Gg0yMvLw6JFi/Doo48CAPz8/BAfHw8/Pz/s2LFDeaJVLCwsDE8++WRlfiREVAl8skNElWI2mzF79mzMnz8fDz/8MICrzU033XQTHB0dERsbixYtWpQoZzAYsHXrVrRo0QJ//fUXwsLCMG/ePOUcn3zyic2Q6y1btiA9PR3BwcF45513sGbNGvz5559wdXXFvHnz8O233+LNN99ETExMqfUUEVy5cgXA352d33vvPQwdOhSBgYF4/vnnkZSUVOZ9Hj16FDt27MDvv/+OHj16YOzYsWjTpk2Zxw8ZMgT/+te/kJWVhcLCQtxyyy1o1aoVrFYrevbsWWJYfmFhIa5cuQJfX1/ceuutsFgscHBwgKOjo1L/HTt2IDQ0FJ06dSrzukRUeZxnh4gqZc6cOWjcuLESUgBg1qxZGDBgAMaNG4dJkybBYrGUKFccOBISEjB48GB4eXnh+eefx+HDh7F69WrMmDFD6dALAAcPHsTs2bOVp0UzZszAG2+8gR9//BEnT57EF198ge3bt6N3796l1jM/Px/Lli3DsmXLkJiYCD8/P0yYMAF6vR4tW7bEvffeqwxv/6eCggKMGzcOL774Itq1a4fXX38dY8eOLXVkFwAcOHAA8fHxSrPZa6+9hrvuugsREREYPnw4tmzZgp9//tmmTEJCAho3bgyNRoNjx47h888/h06nw6+//opDhw6hV69e6NWrF4MOUXUSIqIKHDp0SBwdHeXHH39U9n377bfi6ekpCQkJkp6eLs2bN5dRo0ZJbm6uTdmYmBhZv369+Pn5ydKlS2Xw4MHyxhtvSFBQkIwZM0Y2bNggVqtVOd5isYiISEhIiMTHx8uAAQNkypQpIiKybds2WbRokYiIrF+/Xh555JESdfXx8RGj0SiHDx+Wbt26yYMPPigmk0nuuOMOufPOO2X//v2l3mNOTo4MHjxYunbtKoWFhUpdBgwYIP369ZMrV66UKHPffffJ4sWLRURk8eLFEhISIllZWfLcc8/J7Nmz5eeffxYfHx/5+uuvlTJLliyRESNG2Jzn3XfflS5dusi0adPknnvukfz8/DJ+EkR0Pfhkh4gq1LZtWyxcuFCZ+2Xp0qV47LHH8Nlnn+Hmm2+Gr68vNm3ahF9++QW33XYblixZgkuXLuHw4cNYvHgxnn/+eaxbtw4TJ06EiKBLly7466+/4OHhgbFjx+Khhx7CwoULYTablYn2LBYLjEYj7rjjDixYsAAAlE69AJSmqmInTpxAt27doNFoMGLECHz55ZcwmUx46KGHAAD+/v5wdXXFkiVLcPDgQZulKnbs2IG77roLycnJ2LhxozIyS6vV4osvvsClS5fQqVMn/Pnnn0qZ//znP9i1axfGjBmDGTNm4L333sP27dvh4eGhHNOrVy98++23iIyMxO+//w7gaoflQYMGKcekpKTA1dUV//3vf/Huu++iffv22LlzJxITE5Gbm1s9P0Cihk7ttEVE9cvYsWPF399ftm/fXuK9ixcvyuOPPy7NmzeXS5cuSd++feWtt96SnJwc5Zi+ffvKhg0bbMq89957snLlSptzBQYGSlxcXKl1+PTTT8VgMMjSpUuVfVarVRISEmyOi4iIkK+//lq6d+8umzZtEhGRjRs3Sr9+/eTUqVMiIvLII4+IVquV5557rsRTqWJZWVkyaNAg0Wq18vPPP4vI1SdL7777roiIZGRkSFpamnL8U089Ja+++qryuvhJ0eXLl2X48OGSl5cn48aNk5YtW0p4eLjMmDFDjh8/LsnJybJo0SIZPHiwNG3aVABIbGxsqXUiosrjaCwiqpLz58/DwcFBGWZdmoKCAqXDbU0oKiqC1Wqt9DVERBmJ9U8nT57E5cuXK9VH5qeffkJERESV6lqWy5cvw83Nrdx5gbKzs+Hm5lYt1yNqyBh2iIiIyK6xzw4RERHZNYYdIiIismsMO0RERGTXGHaIiIjIrjHsEBERkV1j2CEiIiK7xrBDREREdo1hh4iIiOwaww4RERHZtf8Hvd5gSurq6dIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depth_range = range(1, 16)\n",
    "accuracy_scores = []\n",
    "\n",
    "for depth in depth_range:\n",
    "    clf = train_decision_tree(max_depth=depth)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracy_scores.append(accuracy)\n",
    "\n",
    "# 绘制深度与准确度的关系图\n",
    "plt.plot(depth_range, accuracy_scores, marker='o')\n",
    "plt.title('决策树最大深度与准确度的关系')\n",
    "plt.xlabel('决策树最大深度')\n",
    "plt.ylabel('准确度')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9c68670f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0Rfbll1/Cw8MD3333HSZPnoyQkBCcP3++2Z/z448/Sn/WarX49ddfERAQYFTZmtE+Op0O9913H+677z60b98eixYtkpYDaOo6jBETE4OSkhK8/PLLWLNmDT7//HPs3r1bapm43etoDhsbG6lPS22lpaV48MEHpb4y9W0PPfQQHnroIXz33XfIycmBl5dXnVaLJUuWYMqUKQZBrVOnTrh+/TquX78uhQCNRoMbN26goqJCasmobeLEiVCpVNi9ezcAIC8vD66urti6dSvmzZuHdu3aYevWrdi6dSvatWtnUPb8+fPSyKYrV66gsLAQ3bp1w3vvvYfCwkIUFhbijTfegLe3N5YuXfqn7mPPnj1x5MgRPPjggygpKQEAODs7N1pm5syZ8PT0RGxsLMaPH49Tp041+Tnh4eFYtWoVNm3aBDs7uzqj3/Ly8tC3b18cPnwYQ4YMQUxMTJ33kGXibwFRKygoKMCcOXOg0+ng4+OD7du3A/i9xcjd3R2XL1/G5s2b4ejoiGXLluHIkSNSi4qxfvrpJ7z11lsYOHAgli5dCisrK7zwwgtGle3SpQv+/ve/45VXXkFxcTE6duyI+fPn4+eff5aWCGjqOoxx6tQpHDlyBLGxsbCzs8O6devg4OBg0CpyO9fRHA8++CBWrlwJJycnZGdno1OnTujVqxccHR3rHVnVkGnTphm0fAC3RvGVlpZiwoQJRp1DCAG9Xo+bN2/WaTn57LPP8OKLL2Lw4MGoqKhAUlISQkJCoFKppCDe0Jf6vn37MGXKFGnf1tYWa9euxYABA+Dp6Yni4mK8//77+O9//2vQ2vNHOTk50p9LS0vrhJnw8HAAkDqrNxZ25s6di/j4eHz//ffo27cvTp06hd69e2P58uV47rnnGnzEPHfuXCxbtgyLFi2q9z22trZwcXFBZGQk3n33Xbz99tvNelxN8sWwQ9QKoqOjUVxcjJiYGOTn58Pb2xsff/wx+vTpAwB4/fXXkZqaihdffBFqtRqPPfYYJk2ahB9++EF61GGM119/XRrmHBAQgO3bt9f5n35jli9fjpiYGEydOhVlZWXo3bs3fvjhBzg5ORl1HcaIj4/Hm2++ieeffx7FxcUICgrC9u3bDb7gb/c6jLVy5UpMmDAB/fv3h7OzM9asWfOnzvO///1P6pMD3HoMdvz4cYwbNw6urq7ScSEEsrKy6nwB/3G/9mO82j//hx9+GK6urpg/fz7mzJlTb11qv//nn3/G2bNn0a9fP4P3hIWF4f3338fw4cMhhMDmzZsRGhpa7/lqQl/t6wNQZ0h/jTNnzqBNmzb1zh2Unp6OV199FYcOHcI333yDRx99FACwdetWjB49GmPHjsWSJUvw5ptv4rHHHpP6gpWVleHVV1/F5s2b8d1330kjsoBbw9xr7p+bmxu2bNmCefPmITY2Fv/73/+wYcOGeutJFsZcPaOJ5KKioqLRrbX4+vqKDz74oNU+74+qqqoavQ9/nP6/Iea+DmMtXbpUPPvss6Jv374CgFi7dq3B63q9vs5syP/+97+Ft7d3nVFJ+fn5Ii8vT2RnZ9dZe+rll18W27Ztk/ZHjhwpunXrJi3p8OOPPwpfX19RWloqxo0bJwCIgwcPCiGEGDdunLj//vtFeXm5OHfunNi1a5eYP3++ePTRR4WTk5N4+umnRWRkpGjTpo0ICwsTL7/8spg5c6ZYunSpKCwsFEL8PoNybc8//7zB2lYFBQVi/Pjx4qmnnhLOzs7i2WefNXj/li1bxOOPPy5UKpWIjIw0WEOsRlVVlVi+fLlQq9UCgHB0dBSZmZlCCCGOHj0qAgMDRXJysvT+nTt3iqeeekp4eXmJPn361Dnfp59+Kr744os6x8kysWWH6DZkZmY2ObtuQUEB3N3dW6lG5rNmzRqMHz++wdd79OiB1NTUVqyRaQUGBmLTpk3w9/fH119/jeHDhxu8bmVlVedRzs2bN6FUKtGhQwejP+ezzz4z2P/444+Rl5cnzaUTERGBtLQ02NnZoUOHDli9ejUefvhhAMArr7yCa9euQQiB0aNHo6qqCg888ABeeOEFbN26VWp9yc/Px549e5CUlIS9e/fC1tZWWsU8KCgIX375pUEd3nnnHYPHfO7u7hBCwNnZGXFxcdLsyzWsra2h1WqRkJCAwYMH13udCoUCEydOxDPPPIPVq1dDpVJJo9vCwsKQmppq0AIWHByMwsJCPP3005g4cWKd802ePLnpm0sWQyHEH4Y9EJHRbt68ibS0tEbf89e//vW2OzjfDa5du4aLFy82+LqdnR26devWijUiIrqFYYeIiIhkjUPPiYiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNY49By3JgDLy8uDk5MTZ9skIiK6SwghpBnfG1s/j2EHt9ZT+ePsoERERHR3qL3Abn0YdgBpKvzs7OwmF68jIiKiO4NWq4WPj4/0Pd4Qhh38vi6Ns7Mzww4REdFdpqkuKOygTERERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssYZlE1EXyVwLOMarhSXo52TLcL83ACgyWMhvq44kVUki3J3Yp3kXu5OrFOYnxuUVlxgl4jMx2xh5+rVq3jggQewb98+dO7cucn3HzhwAJMmTUJBQQGmT5+ON954w6jXzGFnaj7mJqQhX1MuHXOxbwMAuF5a0egxKwVQJSCLcndineRe7k6sk6faFrOjAjE4yBNEROagEEKIpt/WsgoLCxEVFYWkpCRkZGQ0GXYKCgrg7++PadOmYfTo0Rg1ahQWLVqEvn37NvqasbRaLdRqNTQazW2vjbUzNR+T151Eq99UojtUTZtO3HM9GXiIqEUZ+/1tlj47o0aNwqhRo4x+//r16+Hp6YmZM2ciICAAs2bNwqpVq5p8rbXpqwTmJqQx6BDVUvP3YW5CGvRV/NtBRK3PLGFnxYoVeO2114x+f0pKCvr16yetahoWFoaTJ082+VpDdDodtFqtwdYSjmVcM3h0RUS3CAD5mnIcy7hm7qoQkQUyS9jp0qVLs96v1Wrh5+cn7Ts7OyM3N7fJ1xoyf/58qNVqafPx8WlWfRpypZhBh6gx/DtCROZwVww9V6lUsLGxkfZtbW1RWlra5GsNiY2NhUajkbbs7OwWqWc7J9sWOQ+RXPHvCBGZw10x9NzNzQ0FBQXSfnFxMaytrZt8rSE2NjYGAamlhPm5wVNti0uacvbbIapFAaCD+veh6UREremuaNkJDQ1FUlKStH/69Gl4eXk1+VprU1opMDsqEMDvI1CILF3N34XZUYGcb4eIzOKOCjtarRYVFRV1jg8dOhSHDh3Cvn37UFlZiUWLFmHQoEFNvmYOg4M8EfdcT3RQGzbXu9i3keYgaezYH78L7uZyd2Kd5F7uTqxTB7Uth50TkVndUY+xgoODsWTJEgwbNszguLu7Oz788EMMGjQIarUaDg4O0vDyxl4zl8FBnhgQ2IEzKN+BdZJ7uTulTl+fyMbb35yBg7USP77VFyrlHfX/KiKyMGaZVPDPSk9Pxy+//ILIyMg6kwc19lpTWnJSQSICym7q0X3WTgDAiRn90dax5fvIEREZ+/19R7XsNMXf3x/+/v7Nfo2IWpedtRJeLnbIvV6G3wpLGHaIyKzYtkxEJtHFwwEA8FvBDTPXhIgsHcMOEZlEF/easFNi5poQkaVj2CEik+ji4QgAuMCwQ0RmxrBDRCYhPcYq5GMsIjIvhh0iMomalp2LV0tRoa8yc22IyJIx7BCRSXg628K2jRUqqwSyrzW+Xh0RkSkx7BCRSVhZKeDnfqt1h52UicicGHaIyGTYb4eI7gQMO0RkMl2rh59nFLJlh4jMh2GHiEyGw8+J6E7AsENEJvP7LMoMO0RkPgw7RGQyftWPsQpv6KAtrzBzbYjIUjHsEJHJONm2QTunW4uAsnWHiMyFYYeITIoLghKRuTHsEJFJ1XRSZssOEZkLww4RmZS0+jnn2iEiM2HYISKT4ogsIjI3lbkrQETy1qV6yYgLV25g66lctHe2RYivK05kFeFKcTnaOdkizM8NAHAs41qjx0xZ7k6sk9zL3Yl1CvNzg9JK8ed/4emOxLBDRCb1c54GAFBRJfD6ptMAACsFUCV+f4+LfRsAwPXSikaPmbLcnVgnuZe7E+vkqbbF7KhADA7yBMmHQgghmn6bvGm1WqjVamg0Gjg7O5u7OkSysTM1H5PXnYTF/yNDd42aNp2453oy8NwFjP3+Zp8dIjIJfZXA3IQ0Bh26q9T8vs5NSIO+ir+9csGwQ0QmcSzjGvI15eauBlGzCQD5mnIcy7hm7qpQC2HYISKTuFLMoEN3N/4OywfDDhGZRDsnW3NXgei28HdYPhh2iMgkwvzc4Km2BQfx0t1GgVujsmqGptPdj2GHiExCaaXA7KhAAGDgobtGze/q7KhAzrcjIww7RGQyg4M8EfdcT3RQGz4O+ON3iIt9G2nOk8aOmbLcnVgnuZe7E+vUQW3LYecyxEkFicikBgd5YkBghzt+5tw7sU5yL3en1OkvMxJRUSWwdNR9eDy4I1t0ZIiTCoKTChIRWbIes3ai5KYe+998BJ2rF66luwMnFSQiIjJCTUuOnv/3ly2GHSIismgq5a2vQs6YLF8MO0REZNFqWnYq9Qw7csWwQ0REFk1V8xiLLTuyZbawk5qaitDQULi6uiI6OhpN9ZMWQmDhwoUICAiAu7s7XnnlFZSUlEivR0VFQaFQSFv//v1NfQlERCQDUstOVZWZa0KmYpawo9PpEBUVhZCQECQnJyMtLQ3x8fGNllm1ahWWLl2K9evX46effsKxY8cwadIk6fUTJ07gzJkzKCoqQlFREbZt22biqyAiIjlgy478mSXsJCYmQqPRYPHixejatSvmzZuHVatWNVpm7dq1iI6ORlhYGLp164a5c+dKgSYnJwdCCAQFBcHFxQUuLi5wcODwQSIiatrvLTsMO3JllrCTkpKC8PBw2NvbAwCCg4ORlpbWaJnCwkJ06tRJ2lcqlVAqlQCAY8eOQa/Xw9vbGw4ODhg1ahSKiopMdwFERCQbKiuOxpI7s4QdrVYLPz8/aV+hUECpVDYaUO677z5s3bpV2l+9ejUGDhwIADh37hxCQkKwa9cuJCcnIzMzE9OnT2/wXDqdDlqt1mAjIiLLZMWWHdkzS9hRqVSwsbExOGZra4vS0tIGy8ybNw/Hjh3Dww8/jHvvvRebNm3Cq6++CgCIiYlBYmIievToge7du+P999/H119/3eC55s+fD7VaLW0+Pj4tc2FERHTX+b3PDjsoy5VZwo6bmxsKCgoMjhUXF8Pa2rrBMp07d0ZaWhpWrFiBTp06YcCAAXj44Yfrfa+LiwsKCwuh0+nqfT02NhYajUbasrOz//zFEBHRXU2aQZlZR7bMEnZCQ0ORlJQk7WdmZkKn08HNza3RcgqFAs7Ozvjhhx+wYMEC6fiIESMMznf8+HF06NChTutRDRsbGzg7OxtsRERkmdiyI39mCTt9+vSBRqPB2rVrAQALFixA//79oVQqodVqUVFR0WDZ9957DyNHjkTPnj2lY8HBwZg6dSqOHj2KHTt2YObMmZgyZYrJr4OIiO5+HI0lfyqzfKhKhRUrVmDMmDGIjo6GXq/HgQMHANwKLkuWLMGwYcPqlEtPT8eGDRuQmppqcDw2NhZZWVkYMGAA2rVrh8mTJyM2NrY1LoWIiO5yKiXn2ZE7hWhq6mITys3NRXJyMiIiIuDh4WGuahi9RDwREcnP2C+O4eC5Anw48l4MD/E2d3WoGYz9/jZLy04NLy8veHl5mbMKRERk4TiDsvxxIVAiIrJo7LMjfww7RERk0TgaS/4YdoiIyKJxBmX5Y9ghIiKLxj478sewQ0REFk3JsCN7DDtERGTRVHyMJXsMO0REZNGUVre+CtmyI18MO0REZNHYsiN/DDtERGTRlBx6LnsMO0REZNHYsiN/DDtERGTRlDULgeoZduSKYYeIiCyaUsGWHblj2CEiIovGSQXlj2GHiIgsmjT0XDDsyBXDDhERWTQV++zIHsMOERFZNCVHY8keww4REVk0FefZkT2GHSIismhs2ZE/hh0iIrJoHI0lfww7RERk0WpGY7FlR74YdoiIyKKxZUf+GHaIiMiiWbHPjuwx7BARkUXjaCz5Y9ghIiKLpuRjLNlj2CEiIovGPjvyx7BDREQWjfPsyB/DDhERWTRpbSyGHdli2CEiIosmzbPDhUBli2GHiIgsGvvsyB/DDhERWbTf++xw6LlcMewQEZFF49Bz+WPYISIii8bRWPLHsENERBatps9OFcOObDHsEBGRRWPLjvyZLeykpqYiNDQUrq6uiI6OhhCN/5IJIbBw4UIEBATA3d0dr7zyCkpKSqTXDxw4gO7du8Pd3R2LFy82dfWJiEgmVNVDz9lnR77MEnZ0Oh2ioqIQEhKC5ORkpKWlIT4+vtEyq1atwtKlS7F+/Xr89NNPOHbsGCZNmgQAKCgowNChQzF69GgcOXIE69evx759+1rhSoiI6G7Hlh35M0vYSUxMhEajweLFi9G1a1fMmzcPq1atarTM2rVrER0djbCwMHTr1g1z587Ftm3bAADr16+Hp6cnZs6ciYCAAMyaNavJ8xEREQGcZ8cSmCXspKSkIDw8HPb29gCA4OBgpKWlNVqmsLAQnTp1kvaVSiWUSqV0vn79+kGhuPULGxYWhpMnTzZ4Lp1OB61Wa7AREZFl4jw78meWsKPVauHn5yftKxQKKJVKFBUVNVjmvvvuw9atW6X91atXY+DAgfWez9nZGbm5uQ2ea/78+VCr1dLm4+NzG1dDRER3M66NJX8qs3yoSgUbGxuDY7a2tigtLYWrq2u9ZebNm4e//e1vePjhh6HVavG///0PBw8erPd8NedqSGxsLN544w1pX6vVMvAQEVko9tmRP7OEHTc3N6SmphocKy4uhrW1dYNlOnfujLS0NJw9exZvvfUW2rdvj4cfflg6X0FBgdHnsrGxqRO2iIjIMimru0AIcWuuHavq8EPyYZbHWKGhoUhKSpL2MzMzodPp4Obm1mg5hUIBZ2dn/PDDD1iwYEGD5zt9+jS8vLxavuJERCQ7NUPPAbbuyJVZwk6fPn2g0Wiwdu1aAMCCBQvQv39/KJVKaLVaVFRUNFj2vffew8iRI9GzZ0/p2NChQ3Ho0CHs27cPlZWVWLRoEQYNGmTy6yAiorufUvl7S05VE3O+0d3JbH12VqxYgTFjxiA6Ohp6vR4HDhwAcGtk1pIlSzBs2LA65dLT07Fhw4Y6j8Dc3d3x4YcfYtCgQVCr1XBwcODQcyIiMoqq1mMrtuzIk0I0NXWxCeXm5iI5ORkRERHw8PC47fOlp6fjl19+QWRkJJydnY0up9VqoVarodFomlWOiIjufhX6KgS8kwgASJk1EGr7NmauERnL2O9vs7Ts1PDy8mrRvjX+/v7w9/dvsfMREZH81XRQBjjXjlxxIVAiIrJoVlYK1DzJ4lw78sSwQ0REFq9mRBb77MgTww4REVk8JdfHkjWGHSIisngqzqIsaww7RERk8ayklh12UJYjhh0iIrJ4bNmRN4YdIiKyeOyzI28MO0REZPFUDDuyxrBDREQWr2Z9LD7GkieGHSIisng18+ywZUeeGHaIiMji1fTZqdQz7MgRww4REVk89tmRN4YdIiKyeFLLDufZkSWGHSIisngcei5vDDtERGTxlJxUUNYYdoiIyOLV9NmpYtiRJYYdIiKyeGzZkTeGHSIisnicZ0feGHaIiMjisWVH3hh2iIjI4v0+zw6HnssRww4REVk8tuzIG8MOERFZPJWS8+zIGcMOERFZPGV1B2WujSVPDDtERGTxqht22LIjUww7RERk8aSWHYYdWWLYISIiiyfNoCwYduSIYYeIiCyesvo5FvvsyBPDDhERWTzOsyNvDDtERGTxOM+OvDHsEBGRxfu9ZYdhR44YdoiIyOJxNJa8MewQEZHFY8uOvDHsEBGRxbOS+uywg7IcMewQEZHFY8uOvJkt7KSmpiI0NBSurq6Ijo6GMGIipw8++ADt27eHs7Mzhg8fjqtXr0qvRUVFQaFQSFv//v1NWX0iIpIRJcOOrJkl7Oh0OkRFRSEkJATJyclIS0tDfHx8o2UOHjyINWvW4ODBgzh58iTKy8sxbdo06fUTJ07gzJkzKCoqQlFREbZt22biqyAiIrlQcei5rJkl7CQmJkKj0WDx4sXo2rUr5s2bh1WrVjVa5tixY3jsscfQrVs3+Pv7Y/To0Th37hwAICcnB0IIBAUFwcXFBS4uLnBwcGiNSyEiIhlgy468mSXspKSkIDw8HPb29gCA4OBgpKWlNVomKCgI3377LS5cuIArV65g1apVGDBgAIBbQUiv18Pb2xsODg4YNWoUioqKGjyXTqeDVqs12IiIyHKxZUfezBJ2tFot/Pz8pH2FQgGlUtloQBk8eDACAgLg7++P9u3bo6SkBDExMQCAc+fOISQkBLt27UJycjIyMzMxffr0Bs81f/58qNVqafPx8Wm5iyMioruOUnnr61DPtbFkySxhR6VSwcbGxuCYra0tSktLGyyzefNmZGVl4ezZs7h69SqCgoLw3HPPAQBiYmKQmJiIHj16oHv37nj//ffx9ddfN3iu2NhYaDQaacvOzm6ZCyMiorsSW3bkTWWOD3Vzc0NqaqrBseLiYlhbWzdYZuPGjZg8eTK6desGAFiyZAnUajWuX78OFxcXg/e6uLigsLAQOp2uTqgCABsbm3qPExGRZVJyIVBZM0vLTmhoKJKSkqT9zMxM6HQ6uLm5NVimsrISly9flvbz8/MBAHq9HiNGjDA43/Hjx9GhQwcGGiIiMgpbduTNLC07ffr0gUajwdq1azF27FgsWLAA/fv3h1KphFarhZ2dHdq0aWNQplevXli8eDG8vb1hZ2eHJUuW4KGHHkLbtm0RHByMqVOnYsmSJSgoKMDMmTMxZcoUc1waERHdhTgaS97MEnZUKhVWrFiBMWPGIDo6Gnq9HgcOHABwa2TWkiVLMGzYMIMyr7/+OvLy8vDuu++isLAQDz30kDRcPTY2FllZWRgwYADatWuHyZMnIzY2trUvi4iI7lJKtuzImkIYM3WxieTm5iI5ORkRERHw8PAwVzWg1WqhVquh0Wjg7OxstnoQEZF57EzNx6R1J/GAryu+nhxh7uqQkYz9/jZLy04NLy8veHl5mbMKREREUFrd6sLKlh154kKgRERk8bgQqLwx7BARkcVjnx15Y9ghIiKLp+I8O7LGsENERBaPLTvyxrBDREQWT6Vknx05a1bYyczMBAAIIfDrr7/W+x4hBIYNG4by8vLbrhwREVFrkEZjcSFQWWpW2OnZsycAoKKiAiNGjKj3PVevXsXu3bu5VAMREd01lAq27MhZs8KOg4MDioqKkJWVhaqqKmRlZeHSpUsYO3YsfvvtNwBAXl4eevToAUX1Lw4REdGdjn125K1ZYUelUuHbb79F7969kZ6ejrCwMPzrX/9CcnIy+vXrh++//x7Jycno3bu3qepLRETU4mr67FSZb1EBMqFmhR2FQoEJEybg8uXLCAwMxOHDhyGEgK2tLfbt24c333wTH3zwAR5//HFT1ZeIiKjFSS07eg49lyOjwk5qaioee+wxXLt2TTqmUCgMHlX5+flhwoQJSE9PR69evVq+pkRERCbCGZTlzaiwU1FRgYCAADg6OqK4uBgZGRm4efMmcnNzpfd88cUX+Pzzz/HYY49h586dJqswERFRS2OfHXkzKuzcf//9+Oijj9CmTRvs2rUL4eHhOH/+PEaOHAk7OztotVokJCTgwIEDmDZtGjZv3mzqehMREbUYVfXQc7bsyFOz++yMGDECeXl5+Mtf/oJLly5h8eLFUCgU2LJlC9q1a4eIiAicOHHCVPUlIiJqcbVbdgQ7KcvOn5pBWa/XIzAwUNqfNm2a9GeVSgU7OzsUFBTcfu2IiIhaQU2fHQBg4478qJrz5rKyMgwZMgQKhQIqlQqjR4+Gu7s7OnbsiC+//BLh4eEICAhAXFwcPDw8TFVnIiKiFmVVK+xUVlVBaaU0Y22opTUr7CxdulRq3hNCoLy8HCUlJcjPz8f333+P6OhouLq64u2330ZERIRJKkxERNTSarfssN+O/DQr7IwcObLJ92zduhXFxcV/ukJEREStTcmwI2vNCjvGGDZsWEufkoiIyKTYsiNvfyrsvPTSS7hw4QJUqvqL9+zZEwsWLLitihEREbUWpUGfHYYduWnWaKwzZ84AAE6fPo3Zs2dj+vTpyM7OxjvvvIOCggK88847iI2NxaFDh1BSUmKSChMREbU0hUIhBR627MhPs1p2IiMjMXbsWJSWliIyMhIA4OTkhMjISLi6ukrHEhIS4ODg0PK1JSIiMhGllQL6KsGWHRlqVstO586d0bFjR2RkZKBjx47w9PRESkoKOnbsiMOHD6Njx47o2LEjPvnkE1PVl4iIyCSk9bH0DDtyY1TLzoEDB3Dw4EEolUq89dZb+Oqrr3D06FFUVVUhIiICR44cwaOPPor//ve/AAArqz81VyEREZHZ/D6LMlc+lxujwo6DgwO2bduGCxcu4Pz58wCA5cuXAwCuXLmCzz77DHl5edixYwcGDBgAe3t709WYiIjIBLjyuXwZ1QTzwAMP4Pjx44iOjkbfvn3x0EMPQaPR4Pr16xg/fjxycnLQt29fvP/+++jSpQu2bdtm6noTERG1KK58Ll9Gd1AuLi5GVVUV/v3vf8PR0RGenp7Izc1FmzZtpPcMGTIEGRkZ+Prrr/HEE0+YpMJERESmwNFY8mV02NHr9Vi3bh3Onj0LAJg8eTLOnDkDPz8/6T3fffcdYmJi8OWXX7Z8TYmIiExIVd3flGFHfowOO1ZWVsjJyUFMTAxGjhwJhUKBl19+GWPHjpXeExwcjLfeesskFSUiIjIlPsaSL6PDjhACarUarq6ueOedd7B//37s2rULH3/8sfSe9PR0hIWF4dFHH8X8+fNNUmEiIiJTYAdl+TI67JSVlcHW1hZvv/023n77bWRlZSEmJgY7d+5EXFwcHnroIQBAZWUlbGxsTFZhIiIiU+DQc/kyOux4enriwoUL0r6vry82btyItWvXYuDAgXBzc4MQAhkZGfD29jZJZYmIiEyFHZTlq9mz/w0YMAAAUFVVhY0bN2Ls2LHYvXs3AECn0yEgIKBla0hERNQKVEr22ZGrZoed06dPA7jVh6emM/Ls2bMBANbW1g2uhE5ERHQnU9aMxuJyEbJjdNgpLS1Feno6HB0dAQBKpRJKpRIApD46VlZW0rGmpKamIjQ0FK6uroiOjoYQTf9yffDBB2jfvj2cnZ0xfPhwXL16VXrtwIED6N69O9zd3bF48WJjL4uIiAgAUN2ww5YdGTIq7Fy7dg19+vTBsmXLUFxcjMDAQAQGBiI/Px+BgYFIT09HYGAgunfvbtSH6nQ6REVFISQkBMnJyUhLS0N8fHyjZQ4ePIg1a9bg4MGDOHnyJMrLyzFt2jQAQEFBAYYOHYrRo0fjyJEjWL9+Pfbt22dUXYiIiADOsyNnRoUdhUKBYcOG4aOPPoKdnR3i4uLw6aefom3btoiLi4OXlxfi4uIQFxdn1IcmJiZCo9Fg8eLF6Nq1K+bNm4dVq1Y1WubYsWN47LHH0K1bN/j7+2P06NE4d+4cAGD9+vXw9PTEzJkzERAQgFmzZjV5PiIiotqkDspGPGmgu4tRHWxcXV3x7LPPAgDatGmDyMhIAICtrS0iIyPh6OgoHTNGSkoKwsPDpQVDg4ODkZaW1miZoKAgvPrqq5g4cSKcnJywatUqqbN0SkoK+vXrB4Xi1i9qWFgYYmNjGzyXTqeDTqeT9rVardF1JyIiearpoKzn0HPZMSrs5OXl4Z577kH//v2h1WqxevVqCCFw48YNfPHFF7h27Zp0rLKyEqtXr8b48eMbPJ9WqzVYZkKhUECpVKKoqAiurq71lhk8eDACAgLg7+8PAAgNDUVMTIx0vsDAQOm9zs7OyM3NbfDz58+fj7lz5xpz6UREZCGkeXbYQVl2jHqM1bFjR5w+fRoPPfQQbGxsMGHCBLz11lvo1asXEhIS0LNnT2zfvh0JCQkYPHgw/vOf/zR6PpVKVWfiQVtbW5SWljZYZvPmzcjKysLZs2dx9epVBAUF4bnnnqv3fE2dKzY2FhqNRtqys7ONuQ1ERCRjnEFZvoweJ96jRw/06NED06dPx4oVKzB79mx4eHhgxYoVzf5QNzc3pKamGhwrLi6GtbV1g2U2btyIyZMno1u3bgCAJUuWQK1W4/r163Bzc0NBQYHR57KxseEsz0REZIBrY8lXs+fZ2bx5M7y8vJCamopp06Zh8+bNOHnyZLPOERoaiqSkJGk/MzMTOp0Obm5uDZaprKzE5cuXpf38/HwAt1Zj/+P5Tp8+DS8vr2bViYiILBtHY8lXs8LO559/jsmTJ6OyshLt27dHt27dkJ+fj6effhqhoaGIj4836PjbkD59+kCj0WDt2rUAgAULFqB///5QKpXQarWoqKioU6ZXr15YsWIFli9fjjVr1mDUqFF46KGH0LZtWwwdOhSHDh3Cvn37UFlZiUWLFmHQoEHNuTQiIrJwbNmRMWGkX3/9VXTo0EEkJyfXea2qqkp89dVXomvXrqJnz55GnW/Lli3Czs5OtGvXTrRt21akpqYKIYTw9fUVW7ZsqfP+srIy8Y9//EN07NhRWFtbi8jISJGeni69/sknn4g2bdoId3d34evrKy5dumTspQmNRiMACI1GY3QZIiKSl6n/OSV8394hPjuQ3vSb6Y5g7Pe3QgjjJxTQarVwdnZu8PWbN2/iwoULRk8umJubi+TkZERERMDDw8PYajQoPT0dv/zyCyIjIxut5x9ptVqo1WpoNJpmlSMiIvl486sUfH0iB28N7oYpj/ibuzpkBGO/v5u1kFVTQcDa2trooAMAXl5eLdq3xt/fXxqaTkRE1BzSaCwOPZedZndQJiIikiPOoCxfDDtERETgPDtyxrBDREQEQFk99JyjseSHYYeIiAi118Zi2JEbhh0iIiJwbSw5Y9ghIiJC7T47XPVcbhh2iIiIwBmU5Yxhh4iICByNJWcMO0RERACs2LIjWww7REREYMuOnDHsEBER4fd5dhh25Idhh4iICGzZkTOGHSIiItQejcWh53LDsENERAS27MgZww4RERE4z46cMewQERGBa2PJGcMOERERaq16zrWxZIdhh4iICIBSwZYduWLYISIiAkdjyRnDDhEREWqNxmLDjuww7BAREQFQSh2U2bIjNww7RERE+L1lhx2U5Ydhh4iICL/32WEHZflh2CEiIgKg4kKgssWwQ0REBM6gLGcMO0RERODaWHLGsENERATOsyNnDDtERERgB2U5Y9ghIiJCraHnDDuyw7BDREQEtuzIGcMOEREROPRczhh2iIiI8PtyEXyMJT8MO0RERODQczlj2CEiIoJhnx0hGHjkxGxhJzU1FaGhoXB1dUV0dHSTv1hz5syBQqGos+3fvx8AEBUVZXC8f//+rXAVREQkFzUtOwBbd+TGLGFHp9MhKioKISEhSE5ORlpaGuLj4xstExMTg6KiImlLSUmBh4cH7r//fgDAiRMncObMGen1bdu2tcKVEBGRXChrhR3225EXs4SdxMREaDQaLF68GF27dsW8efOwatWqRsvY2trCxcVF2j7++GNMnToVarUaOTk5EEIgKChIet3BwaGVroaIiORAyZYd2VKZ40NTUlIQHh4Oe3t7AEBwcDDS0tKMLp+Xl4ctW7YgIyMDAHDs2DHo9Xp4e3ujqKgIUVFRiIuLg6ura73ldToddDqdtK/Vam/jaoiISA7YsiNfZmnZ0Wq18PPzk/YVCgWUSiWKioqMKr98+XKMGTMGjo6OAIBz584hJCQEu3btQnJyMjIzMzF9+vQGy8+fPx9qtVrafHx8bu+CiIjorlczzw4AVDHsyIpZwo5KpYKNjY3BMVtbW5SWljZZVq/XY+XKlZg0aZJ0LCYmBomJiejRowe6d++O999/H19//XWD54iNjYVGo5G27OzsP38xREQkC7UadtiyIzNmeYzl5uaG1NRUg2PFxcWwtrZusuy+ffvg7u6O7t27N/geFxcXFBYWQqfT1QlVAGBjY1PvcSIislwKhQIqKwUqqwT77MiMWVp2QkNDkZSUJO1nZmZCp9PBzc2tybKbN2/Gk08+aXBsxIgRBuc7fvw4OnTowEBDRETNopQWA60yc02oJZkl7PTp0wcajQZr164FACxYsAD9+/eHUqmEVqtFRUVFg2V37tyJvn37GhwLDg7G1KlTcfToUezYsQMzZ87ElClTTHoNREQkP5xFWZ7M8hhLpVJhxYoVGDNmDKKjo6HX63HgwAEAt4LLkiVLMGzYsDrlLly4gLy8PISGhhocj42NRVZWFgYMGIB27dph8uTJiI2NbY1LISIiGfm9ZYdhR04UwoxzYufm5iI5ORkRERHw8PAwVzWg1WqhVquh0Wjg7OxstnoQEZF59Xx3D66V3MTuqX1wT3snc1eHmmDs97dZWnZqeHl5wcvLy5xVICIikkgtO3q27MgJFwIlIiKqplSwz44cMewQERFV42gseWLYISIiqqZS3go7VebrzkomwLBDRERUjX125Ilhh4iIqBrn2ZEnhh0iIqJqyurFQDnPjrww7BAREVVjy448MewQERFV4wzK8sSwQ0REVO33lh0OPZcThh0iIqJqbNmRJ4YdIiKiakr22ZElhh0iIqJqnGdHnhh2iIiIqkl9djiDsqww7BAREVWrmWeHj7HkhWGHiIiomoodlGWJYYeIiKiasnohUL2eQ8/lhGGHiIioGlt25Ilhh4iIqBqHnssTww4REVE1tuzIE8MOERFRNbbsyBPDDhERUTUuFyFPDDtERETVVNXz7FQx7MgKww4REVE1tuzIE8MOERFRNWm5iCrOsyMnDDtERETV2LIjTww7RERE1VQcjSVLDDtERETVahYCZcuOvDDsEBERVVNJa2Mx7MgJww4REVE19tmRJ4YdIiKiakoFR2PJEcMOERFRNbbsyBPDDhERUbWaPjtVgmFHThh2iIiIqkktO+ygLCsMO0RERNU4z448mS3spKamIjQ0FK6uroiOjoZooslwzpw5UCgUdbb9+/cDAA4cOIDu3bvD3d0dixcvboUrICIiueE8O/JklrCj0+kQFRWFkJAQJCcnIy0tDfHx8Y2WiYmJQVFRkbSlpKTAw8MD999/PwoKCjB06FCMHj0aR44cwfr167Fv377WuRgiIpINtuzIk1nCTmJiIjQaDRYvXoyuXbti3rx5WLVqVaNlbG1t4eLiIm0ff/wxpk6dCrVajfXr18PT0xMzZ85EQEAAZs2a1eT5iIiI/uj30Vgcei4nKnN8aEpKCsLDw2Fvbw8ACA4ORlpamtHl8/LysGXLFmRkZEjn69evHxTV8yOEhYUhNja2wfI6nQ46nU7a12q1f+YyiIhIZtiyI09madnRarXw8/OT9hUKBZRKJYqKiowqv3z5cowZMwaOjo71ns/Z2Rm5ubkNlp8/fz7UarW0+fj4/MkrISIiObHiPDuyZJawo1KpYGNjY3DM1tYWpaWlTZbV6/VYuXIlJk2a1OD5mjpXbGwsNBqNtGVnZ/+JqyAiIrlhy448meUxlpubG1JTUw2OFRcXw9rausmy+/btg7u7O7p3725wvoKCAqPPZWNjUydsERERKRl2ZMksLTuhoaFISkqS9jMzM6HT6eDm5tZk2c2bN+PJJ59s9HynT5+Gl5dXy1WYiIgsgqp66DnDjryYJez06dMHGo0Ga9euBQAsWLAA/fv3h1KphFarRUVFRYNld+7cib59+xocGzp0KA4dOoR9+/ahsrISixYtwqBBg0x6DUREJD9cG0uezPIYS6VSYcWKFRgzZgyio6Oh1+tx4MABALdGZi1ZsgTDhg2rU+7ChQvIy8tDaGiowXF3d3d8+OGHGDRoENRqNRwcHDj0nIiImq1mbSy27MiLWcIOAAwbNgznz59HcnIyIiIi4OHhAeDWI62GdO3aFZWVlfW+NmXKFAwcOBC//PILIiMj4ezsbIpqExGRjHGeHXkyW9gBAC8vrxbtW+Pv7w9/f/8WOx8REVkWaTQWFwKVFS4ESkREVI19duSJYYeIiKgaR2PJE8MOERFRNWX1tyJbduSFYYeIiKiaki07ssSwQ0REVI3LRcgTww4REVE1LhchTww7RERE1VScZ0eWGHaIiIiq1bTsVAmgiq07ssGwQ0REVK1m6DkA6AXDjlww7BAREVVTVq+NBbDfjpww7BAREVWr6bMDcK4dOWHYISIiqqasFXa4PpZ8MOwQERFVUypqt+xwRJZcMOwQERFVs7JSoCbvsM+OfDDsEBER1SLNoszRWLLBsENERFRLTb+dSvbZkQ2GHSIiolpUXAxUdhh2iIiIapFadhh2ZINhh4iIqBaufC4/DDtERES1KLkYqOww7BAREdXClh35YdghIiKqxYp9dmSHYYeIiKgWtuzID8MOERFRLUqGHdlh2CEiIqqF8+zID8MOERFRLZxnR34YdoiIiGpRKWseY3HouVww7BAREdXCtbHkh2GHiIioFo7Gkh+GHSIiolrYZ0d+GHaIiIhq4Wgs+WHYISIiqoUzKMsPww4REVEtv/fZ4WgsuTBb2ElNTUVoaChcXV0RHR0NIYxP0KNGjcI//vEPg2NRUVFQKBTS1r9//5auMhERWYDfZ1A2c0WoxZgl7Oh0OkRFRSEkJATJyclIS0tDfHy8UWV37dqFvXv34t133zU4fuLECZw5cwZFRUUoKirCtm3bTFBzIiKSO7bsyI9Zwk5iYiI0Gg0WL16Mrl27Yt68eVi1alWT5crKyjBlyhQsWLAALi4u0vGcnBwIIRAUFAQXFxe4uLjAwcHBhFdARERyxdFY8mOWsJOSkoLw8HDY29sDAIKDg5GWltZkuXfffRdlZWVQqVTYu3ev9Ojr2LFj0Ov18Pb2hoODA0aNGoWioiKTXgMREckT59mRH7OEHa1WCz8/P2lfoVBAqVQ2GlAuXryIxYsXw9/fHxcvXkR0dDSeeuopCCFw7tw5hISEYNeuXUhOTkZmZiamT5/e4Ll0Oh20Wq3BRkREBADK6qHnbNmRD7OEHZVKBRsbG4Njtra2KC0tbbBMfHw82rdvjz179mDGjBnYv38/Dhw4gD179iAmJgaJiYno0aMHunfvjvfffx9ff/11g+eaP38+1Gq1tPn4+LTYtRER0d2NLTvyY5aw4+bmhoKCAoNjxcXFsLa2brBMTk4OHn30USkkOTk5ISAgABkZGXXe6+LigsLCQuh0unrPFRsbC41GI23Z2dm3cTVERCQnSiXXxpIbs4Sd0NBQJCUlSfuZmZnQ6XRwc3NrsIyPjw/Kysqk/aqqKuTk5MDX1xcjRowwON/x48fRoUOHOq1HNWxsbODs7GywERERAYBSwdFYcmOWsNOnTx9oNBqsXbsWALBgwQL0798fSqUSWq0WFRUVdco8/fTTSEhIwDfffIOcnBzExsZCp9OhV69eCA4OxtSpU3H06FHs2LEDM2fOxJQpU1r7soiISAY4Gkt+VGb5UJUKK1aswJgxYxAdHQ29Xo8DBw4AuDUya8mSJRg2bJhBmW7dumHTpk2YMWMGzp49i65du2Lbtm1wcnJCbGwssrKyMGDAALRr1w6TJ09GbGysGa6MiIjuduyzIz8K0Zypi1tYbm4ukpOTERERAQ8PD3NVA1qtFmq1GhqNho+0iIgs3PzEX/DZgd/wYm8/zBgSaO7qUCOM/f42S8tODS8vL3h5eZmzCkRERAZUfIwlO1wIlIiIqJaaeXb4GEs+GHaIiIhqYcuO/DDsEBER1aLkQqCyY9Y+O0RERHea6qyDjMISHLlwFSG+rjiRVYQrxeVo52SLML9bc8Idy7jW6DFTlrsT62RsuZow2ZoYdoiIiKrtTM3Hp/svAACOZxZh9MokWCmA2k+0XOzbAACul1Y0esyU5e7EOhlTzlNti9lRgRgc5InWZNah53cKDj0nIqKdqfmYvO4kLP5L0YRq2nTinuvZIoHH2O9v9tkhIiKLp68SmJuQxqBjYjX3d25CWquOdmPYISIii3cs4xryNeXmroZFEADyNeU4lnGt1T6TYYeIiCzelWIGndbWmvecYYeIiCxeOydbc1fB4rTmPWfYISIiixfm5wZPtS1af1C05VHg1qismqHqrYFhh4iILJ7SSoHZUbcW/WTgMZ2aezs7KrBV59th2CEiIgIwOMgTcc/1RAe14eOVP34nu9i3keaUaeyYKcvdiXUyplwHtW2LDTtvDk4qSEREVG1wkCcGBHa442Ydbs1zy3EGZU4qCE4qSEREdDfipIJEREREYNghIiIimWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIlnjchEAaiaR1mq1Zq4JERERGavme7upxSAYdgAUFxcDAHx8fMxcEyIiImqu4uJiqNXqBl/n2lgAqqqqkJeXBycnJygUxi1QptVq4ePjg+zsbK6n1Up4z1sf73nr4z1vfbznra+l7rkQAsXFxejYsSOsrBrumcOWHQBWVlbw9vb+U2WdnZ35l6OV8Z63Pt7z1sd73vp4z1tfS9zzxlp0arCDMhEREckaww4RERHJGsPOn2RjY4PZs2fDxsbG3FWxGLznrY/3vPXxnrc+3vPW19r3nB2UiYiISNbYskNERESyxrBDREREssawQ0RERLLGsPMnpKamIjQ0FK6uroiOjm5ymmpqvm3btqFLly5QqVR48MEH8csvvwDgvW8tgwcPRnx8PADe89YSExODqKgoaZ/33TS+/PJLdOrUCY6Ojujfvz8yMzMB8H6bwtWrV+Hn5yfdY6Dx+2zKnwHDTjPpdDpERUUhJCQEycnJSEtLk74UqGVcuHAB48ePx4IFC5CbmwtfX1+8+OKLvPetZP369di1axcA/r63ltTUVHz66adYsmQJAN53U7lw4QLeeecdbN26FWlpafD19cW4ceN4v02gsLAQQ4YMMQg6jd1nk/8MBDXLli1bhKurqygpKRFCCHH69GnRq1cvM9dKXhISEkRcXJy0v3fvXmFtbc173wquXr0q2rdvL7p16yZWr17Ne94KqqqqREREhJg5c6Z0jPfdNL766isxcuRIaf/HH38Unp6evN8m8Oijj4olS5YIACIjI0MI0fjvtal/BmzZaaaUlBSEh4fD3t4eABAcHIy0tDQz10pehgwZgkmTJkn7v/76K/z9/XnvW8G0adPw5JNPIjw8HAB/31vDypUrcfr0afj5+WHHjh2oqKjgfTeRwMBA7N27F6dOnYJGo8Enn3yCAQMG8H6bwIoVK/Daa68ZHGvsPpv6Z8Cw00xarRZ+fn7SvkKhgFKpRFFRkRlrJV83b97EokWLMGXKFN57E9u3bx/++9//4v3335eO8Z6b1o0bNzBjxgwEBAQgJycHixcvRp8+fXjfTSQwMBAjRoxAz5494eLigqNHj2LRokW83ybQpUuXOscau8+m/hkw7DSTSqWqM+Ojra0tSktLzVQjeZsxYwYcHR3x8ssv896bUHl5OSZOnIi4uDiDRfl4z03r22+/RUlJCfbu3YuZM2di9+7duH79Or744gvedxNISkpCQkICjh49iuLiYowePRqPPfYYf89bSWP32dQ/A4adZnJzc0NBQYHBseLiYlhbW5upRvK1Z88eLF++HBs2bECbNm14703o3XffRWhoKB5//HGD47znppWTk4MHH3wQbm5uAG59GQQHB6O8vJz33QQ2bdqEUaNGISwsDI6Ojnjvvffw22+/8fe8lTR2n039M1C1yFksSGhoKD7//HNpPzMzEzqdTvrHilrGb7/9hmeffRZxcXEIDAwEwHtvShs2bEBBQQFcXFwAAKWlpdi8eTM6d+6MiooK6X285y3Lx8cHZWVlBseysrLw4Ycf4t///rd0jPe9ZVRWVho8FikuLkZJSQlUKhWSkpKk47zfptHYv+Em//e9xbo6W4iKigrh4eEh1qxZI4QQYuLEiWLIkCFmrpW8lJaWiu7du4uXXnpJFBcXS9vNmzd5700kOztbZGRkSNvw4cPFBx98IAoKCnjPTejq1atCrVaLuLg4kZ2dLT766CNhY2Mjzp8/z/tuAhs3bhR2dnZi8eLFYv369aJv376iU6dO/LfFhFBrNFZj35+m/m5l2PkTtmzZIuzs7ES7du1E27ZtRWpqqrmrJCtbtmwRAOpsGRkZvPet5PnnnxerV68WQvD33dSOHDkiIiIihJ2dnfDz8xNbtmwRQvC+m0JVVZWYM2eO6NSpk2jTpo24//77RXJyshCC99tUaocdIRq/z6b8GXDV8z8pNzcXycnJiIiIgIeHh7mrY1F471sf77l58L63Lt7v1tHYfTbVz4Bhh4iIiGSNo7GIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoioXpmZmVAoFOauxm3Zv38/OnfubO5qNGncuHGYM2dOs18jIuNwuQgiqlenTp246vMd4NNPP4WVVd3/l+7fvx/jxo1DZmZm61eK6C7DsENE9bKyspLWyiLzsbe3N3cViO56fIxFZKEeeeQRjB8/Hj4+Phg3bhxefvlluLi4YMeOHQDqf4xV81ho+/bt8PX1haurK5YuXWrU5+3evRvdu3eHvb09evXqhQsXLkivffvtt7jnnnvg4OCAvn37Ijc3FwDQuXNnTJo0CR06dMDbb7+NJ554Ah4eHjhx4gTmzJmDv/3tb4iMjIRarcaoUaOg1WqNqsvOnTvx17/+FS4uLnjxxReh0+mk1xYtWgRPT084Oztj9OjRBq81RAiBadOmwd3dHa6urnjttddQM19r586d8c9//hO+vr7w8vLCZ599ZlQda/zxMdalS5egUCjQt29fZGVlQaFQQKFQ4NKlS806L5ElYdghsmAXLlzAsmXLsGbNGvz1r3/FkCFDsG3btkbLXL16FQsWLMB3332HuXPnIjo6us7K3fUZO3YsJkyYgHPnziEoKAgzZswAAFy7dg2jR4/GjBkzkJ6eDjc3N7z33ntSOY1Gg5kzZ2LhwoUYN24cAgMDsXPnTgC3QsuECROQnJyMzMxMzJw506hrfuKJJzB16lScOHECJ06cwAcffAAAOHv2LGJiYrBp0yacPHkS6enpiI+Pb/Kcu3btwurVq/Hf//4Xhw4dwpYtW7B7926D17///nt89tlneP3113Hy5Mkmz9mQ9u3bo6ioCAkJCfDx8UFRURGKiorQvn37P31OIrnjYywiCzZmzBjcd999AICXXnoJV69ebbIPyI0bNxAXF4egoCDcc889eO2113DlyhX4+vo2Ws7Ozg46nQ5qtRrLly9HVVUVAMDJyQkXL16Ek5MTkpOTcfPmTZw7d04q9/zzz8PW1hbt27fHk08+iW3btqGiogIA0KtXL4wdOxYA8Pbbb2Pq1Kn46KOPGq3Hxo0bcf/99+OFF14AAEyaNAmrVq3CjBkzYGtrCwDQ6XTo1KkTjh492ui5al9bVVUVdDodgoKC6rSKRUdHo0ePHujRowcGDhyI7du3o2fPnkad+48UCgVcXFzg6OjIR41ERmLLDpEFq/ly/+OfG+Pq6op7770XAGBtbQ0AMGaJvY0bN2L//v3w9PRE7969pdYNIQRiYmLQsWNHxMTEoLKyEnq9vk696qufj4+P9GcvLy9cvny5yXrk5ubi5MmTcHFxgYuLC6ZNm4aLFy8CuPXI6fPPP0dMTAzatm2LZ555BoWFhU2eMzIyEtOnT8f48ePh7u6OiRMnorS09LbqSUQth2GHiJrF2dm52WVKSkpQUlKCPXv24Nq1a3j44YellpUNGzbgwIEDyMnJweHDhxEVFWX0eWu3Ql28eBGenp5NlvH29sbQoUNx+vRpnD59GikpKdizZw8AICcnB8HBwThx4gQuXryIoqIivPvuu02eMz09HVFRUfj555+RmpqKpKQkLF++/Lbq2RQrKyujQiYRMewQUSuoqqrC448/jnXr1qGwsBBWVlbSY6wbN24AuNV3JzExEe+++67RX+JJSUlYs2YNzp8/j4ULF+Kpp55qsszo0aPx448/4vz58wCAjz76COPHjwcApKWl4W9/+xt++uknlJSUQKFQSPVszN69ezF8+HCcOnUKN2/erFPuww8/RGpqKrZv344ffvgBw4YNM+r6GtOlSxfk5eXhxIkTSE9Px6lTp277nERyxbBDRCbn5OSEdevW4f/+7//QtWtXJCQkIC4uDsCtjssBAQHo3r075s6di4kTJ+Ls2bMoLy9v8rxRUVFYu3YtHnjgAXTt2hWzZ89uskyXLl2wZs0avPHGG+jRowdSU1OxceNGAMDAgQMxceJEjBw5Evfccw+EEHjnnXeaPOf48ePRp08fDBo0CMHBwQgICMDkyZOl15944glERUVh8uTJ+PjjjxEcHNzkOZvi7e2NhQsXYtCgQejZsycOHz582+ckkiuFYDsoEd2F5syZg8zMTKNGS5lT586dER8fj0ceecTcVSGyWGzZIaIWUdPh949bWFiYuat22zZs2NDg9S1ZssTc1SOiJrBlh4haREND1tu0aQMvL6/WrUwLu3HjRoOjslxdXaFWq1u5RkTUHAw7REREJGt8jEVERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREsvb/aEANXe5HGJsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 探究min_samples_split对实验结果的影响\n",
    "split_range = range(2, 100)\n",
    "accuracy_scores_split = []\n",
    "\n",
    "for split_value in split_range:\n",
    "    clf = train_decision_tree(min_samples_split=split_value)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracy_scores_split.append(accuracy)\n",
    "\n",
    "# 绘制min_samples_split与准确度的关系图\n",
    "plt.plot(split_range, accuracy_scores_split, marker='o')\n",
    "plt.title('min_samples_split与准确度的关系')\n",
    "plt.xlabel('min_samples_split')\n",
    "plt.ylabel('准确度')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ecfdfbe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 探究min_samples_leaf对实验结果的影响\n",
    "leaf_range = range(1, 100)\n",
    "accuracy_scores_leaf = []\n",
    "\n",
    "for leaf_value in leaf_range:\n",
    "    clf = train_decision_tree(min_samples_leaf=leaf_value)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracy_scores_leaf.append(accuracy)\n",
    "\n",
    "# 绘制min_samples_leaf与准确度的关系图\n",
    "plt.plot(leaf_range, accuracy_scores_leaf, marker='o')\n",
    "plt.title('min_samples_leaf与准确度的关系')\n",
    "plt.xlabel('min_samples_leaf')\n",
    "plt.ylabel('准确度')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a08020e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 探究max_features对实验结果的影响\n",
    "features_range = range(1, X.shape[1] + 1)\n",
    "accuracy_scores_features = []\n",
    "\n",
    "for feature_value in features_range:\n",
    "    clf = train_decision_tree(max_features=feature_value)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracy_scores_features.append(accuracy)\n",
    "\n",
    "# 绘制max_features与准确度的关系图\n",
    "plt.plot(features_range, accuracy_scores_features, marker='o')\n",
    "plt.title('max_features与准确度的关系')\n",
    "plt.xlabel('max_features')\n",
    "plt.ylabel('准确度')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2e59b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 探究max_leaf_nodes对实验结果的影响\n",
    "leaf_nodes_range = range(2, 21)\n",
    "accuracy_scores_leaf_nodes = []\n",
    "\n",
    "for leaf_nodes_value in leaf_nodes_range:\n",
    "    clf = train_decision_tree(max_leaf_nodes=leaf_nodes_value)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracy_scores_leaf_nodes.append(accuracy)\n",
    "\n",
    "# 绘制max_leaf_nodes与准确度的关系图\n",
    "plt.plot(leaf_nodes_range, accuracy_scores_leaf_nodes, marker='o')\n",
    "plt.title('max_leaf_nodes与准确度的关系')\n",
    "plt.xlabel('max_leaf_nodes')\n",
    "plt.ylabel('准确度')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6c643e2",
   "metadata": {},
   "source": [
    "## 决策树完成数据集的非线性分类（data：LR-testSet2.txt）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f382a747",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "file_path = 'D:\\\\BaiduSyncdisk\\\\机器学习\\\\shangji\\\\data\\\\LR-testSet2.txt'\n",
    "\n",
    "# 读取数据到NumPy数组\n",
    "data_matrix = np.loadtxt(file_path, dtype=float, usecols=(0, 1, 2), delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9c0384c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = data_matrix[:, 0:2]\n",
    "y_data = data_matrix[:, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e328960",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据集划分\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "22fe3d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5c7205a0",
   "metadata": {},
   "outputs": [
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       "<text text-anchor=\"start\" x=\"480.25\" y=\"-536.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"470.12\" y=\"-520.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"469\" y=\"-503.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n",
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       "<!-- 4&#45;&gt;14 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>4&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M509.12,-593.22C509.12,-584.48 509.12,-574.86 509.12,-565.72\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"512.63,-566.01 509.13,-556.01 505.63,-566.01 512.63,-566.01\"/>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>6</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M268.25,-454.25C268.25,-454.25 196,-454.25 196,-454.25 190,-454.25 184,-448.25 184,-442.25 184,-442.25 184,-408.75 184,-408.75 184,-402.75 190,-396.75 196,-396.75 196,-396.75 268.25,-396.75 268.25,-396.75 274.25,-396.75 280.25,-402.75 280.25,-408.75 280.25,-408.75 280.25,-442.25 280.25,-442.25 280.25,-448.25 274.25,-454.25 268.25,-454.25\"/>\n",
       "<text text-anchor=\"start\" x=\"203.25\" y=\"-436.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"193.12\" y=\"-420.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n",
       "<text text-anchor=\"start\" x=\"192\" y=\"-403.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5, 0]</text>\n",
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       "<!-- 5&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>5&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M309.74,-493.22C298.1,-483.22 285.13,-472.07 273.18,-461.8\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"275.71,-459.35 265.84,-455.49 271.15,-464.66 275.71,-459.35\"/>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>7</title>\n",
       "<path fill=\"#eeab7b\" stroke=\"black\" d=\"M382.25,-457.5C382.25,-457.5 310,-457.5 310,-457.5 304,-457.5 298,-451.5 298,-445.5 298,-445.5 298,-405.5 298,-405.5 298,-399.5 304,-393.5 310,-393.5 310,-393.5 382.25,-393.5 382.25,-393.5 388.25,-393.5 394.25,-399.5 394.25,-405.5 394.25,-405.5 394.25,-445.5 394.25,-445.5 394.25,-451.5 388.25,-457.5 382.25,-457.5\"/>\n",
       "<text text-anchor=\"start\" x=\"313.5\" y=\"-441.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"320.25\" y=\"-441.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"327\" y=\"-441.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ 0.244</text>\n",
       "<text text-anchor=\"start\" x=\"309\" y=\"-427.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\n",
       "<text text-anchor=\"start\" x=\"307.12\" y=\"-413.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n",
       "<text text-anchor=\"start\" x=\"306\" y=\"-399.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [6, 2]</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>5&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M346.12,-493.22C346.12,-485.62 346.12,-477.36 346.12,-469.32\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"349.63,-469.33 346.13,-459.33 342.63,-469.33 349.63,-469.33\"/>\n",
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       "<!-- 8 -->\n",
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       "<title>8</title>\n",
       "<path fill=\"#9ccef2\" stroke=\"black\" d=\"M198.25,-357.5C198.25,-357.5 126,-357.5 126,-357.5 120,-357.5 114,-351.5 114,-345.5 114,-345.5 114,-305.5 114,-305.5 114,-299.5 120,-293.5 126,-293.5 126,-293.5 198.25,-293.5 198.25,-293.5 204.25,-293.5 210.25,-299.5 210.25,-305.5 210.25,-305.5 210.25,-345.5 210.25,-345.5 210.25,-351.5 204.25,-357.5 198.25,-357.5\"/>\n",
       "<text text-anchor=\"start\" x=\"129.5\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"136.25\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"143\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ 0.141</text>\n",
       "<text text-anchor=\"start\" x=\"125\" y=\"-327.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"start\" x=\"123.12\" y=\"-313.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"122\" y=\"-299.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 2]</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>7&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M297.64,-398.08C294.76,-396.52 291.91,-394.99 289.12,-393.5 266.84,-381.56 242.27,-368.56 220.73,-357.22\"/>\n",
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       "<!-- 13 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>13</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M382.25,-354.25C382.25,-354.25 310,-354.25 310,-354.25 304,-354.25 298,-348.25 298,-342.25 298,-342.25 298,-308.75 298,-308.75 298,-302.75 304,-296.75 310,-296.75 310,-296.75 382.25,-296.75 382.25,-296.75 388.25,-296.75 394.25,-302.75 394.25,-308.75 394.25,-308.75 394.25,-342.25 394.25,-342.25 394.25,-348.25 388.25,-354.25 382.25,-354.25\"/>\n",
       "<text text-anchor=\"start\" x=\"317.25\" y=\"-336.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"307.12\" y=\"-320.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n",
       "<text text-anchor=\"start\" x=\"306\" y=\"-303.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5, 0]</text>\n",
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       "<!-- 7&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>7&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M346.12,-393.22C346.12,-384.48 346.12,-374.86 346.12,-365.72\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"349.63,-366.01 346.13,-356.01 342.63,-366.01 349.63,-366.01\"/>\n",
       "</g>\n",
       "<!-- 9 -->\n",
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       "<title>9</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M84.25,-254.25C84.25,-254.25 12,-254.25 12,-254.25 6,-254.25 0,-248.25 0,-242.25 0,-242.25 0,-208.75 0,-208.75 0,-202.75 6,-196.75 12,-196.75 12,-196.75 84.25,-196.75 84.25,-196.75 90.25,-196.75 96.25,-202.75 96.25,-208.75 96.25,-208.75 96.25,-242.25 96.25,-242.25 96.25,-248.25 90.25,-254.25 84.25,-254.25\"/>\n",
       "<text text-anchor=\"start\" x=\"19.25\" y=\"-236.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"9.12\" y=\"-220.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-203.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n",
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       "<!-- 8&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>8&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M125.74,-293.22C114.1,-283.22 101.13,-272.07 89.18,-261.8\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"91.71,-259.35 81.84,-255.49 87.15,-264.66 91.71,-259.35\"/>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
       "<path fill=\"#ffffff\" stroke=\"black\" d=\"M198.25,-257.5C198.25,-257.5 126,-257.5 126,-257.5 120,-257.5 114,-251.5 114,-245.5 114,-245.5 114,-205.5 114,-205.5 114,-199.5 120,-193.5 126,-193.5 126,-193.5 198.25,-193.5 198.25,-193.5 204.25,-193.5 210.25,-199.5 210.25,-205.5 210.25,-205.5 210.25,-245.5 210.25,-245.5 210.25,-251.5 204.25,-257.5 198.25,-257.5\"/>\n",
       "<text text-anchor=\"start\" x=\"129.5\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"136.25\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"143\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ 0.175</text>\n",
       "<text text-anchor=\"start\" x=\"133.25\" y=\"-227.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"123.12\" y=\"-213.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"122\" y=\"-199.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 1]</text>\n",
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       "<!-- 8&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>8&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M162.12,-293.22C162.12,-285.62 162.12,-277.36 162.12,-269.32\"/>\n",
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       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>11</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M84.25,-154.25C84.25,-154.25 12,-154.25 12,-154.25 6,-154.25 0,-148.25 0,-142.25 0,-142.25 0,-108.75 0,-108.75 0,-102.75 6,-96.75 12,-96.75 12,-96.75 84.25,-96.75 84.25,-96.75 90.25,-96.75 96.25,-102.75 96.25,-108.75 96.25,-108.75 96.25,-142.25 96.25,-142.25 96.25,-148.25 90.25,-154.25 84.25,-154.25\"/>\n",
       "<text text-anchor=\"start\" x=\"19.25\" y=\"-136.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"9.12\" y=\"-120.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-103.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n",
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       "<!-- 10&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>10&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M125.74,-193.22C114.1,-183.22 101.13,-172.07 89.18,-161.8\"/>\n",
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       "<!-- 12 -->\n",
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       "<title>12</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M198.25,-154.25C198.25,-154.25 126,-154.25 126,-154.25 120,-154.25 114,-148.25 114,-142.25 114,-142.25 114,-108.75 114,-108.75 114,-102.75 120,-96.75 126,-96.75 126,-96.75 198.25,-96.75 198.25,-96.75 204.25,-96.75 210.25,-102.75 210.25,-108.75 210.25,-108.75 210.25,-142.25 210.25,-142.25 210.25,-148.25 204.25,-154.25 198.25,-154.25\"/>\n",
       "<text text-anchor=\"start\" x=\"133.25\" y=\"-136.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"123.12\" y=\"-120.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
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       "<!-- 10&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>10&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M162.12,-193.22C162.12,-184.48 162.12,-174.86 162.12,-165.72\"/>\n",
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       "<!-- 16 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>16</title>\n",
       "<path fill=\"#74baed\" stroke=\"black\" d=\"M827.5,-557.5C827.5,-557.5 738.75,-557.5 738.75,-557.5 732.75,-557.5 726.75,-551.5 726.75,-545.5 726.75,-545.5 726.75,-505.5 726.75,-505.5 726.75,-499.5 732.75,-493.5 738.75,-493.5 738.75,-493.5 827.5,-493.5 827.5,-493.5 833.5,-493.5 839.5,-499.5 839.5,-505.5 839.5,-505.5 839.5,-545.5 839.5,-545.5 839.5,-551.5 833.5,-557.5 827.5,-557.5\"/>\n",
       "<text text-anchor=\"start\" x=\"748.25\" y=\"-541.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"755\" y=\"-541.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n",
       "<text text-anchor=\"start\" x=\"761.75\" y=\"-541.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ &#45;0.013</text>\n",
       "<text text-anchor=\"start\" x=\"746\" y=\"-527.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.355</text>\n",
       "<text text-anchor=\"start\" x=\"740\" y=\"-513.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 52</text>\n",
       "<text text-anchor=\"start\" x=\"734.75\" y=\"-499.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [12, 40]</text>\n",
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       "<!-- 15&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>15&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M783.12,-593.22C783.12,-585.62 783.12,-577.36 783.12,-569.32\"/>\n",
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       "<!-- 45 -->\n",
       "<g id=\"node46\" class=\"node\">\n",
       "<title>45</title>\n",
       "<path fill=\"#eda774\" stroke=\"black\" d=\"M1179.38,-557.5C1179.38,-557.5 1098.88,-557.5 1098.88,-557.5 1092.88,-557.5 1086.88,-551.5 1086.88,-545.5 1086.88,-545.5 1086.88,-505.5 1086.88,-505.5 1086.88,-499.5 1092.88,-493.5 1098.88,-493.5 1098.88,-493.5 1179.38,-493.5 1179.38,-493.5 1185.38,-493.5 1191.38,-499.5 1191.38,-505.5 1191.38,-505.5 1191.38,-545.5 1191.38,-545.5 1191.38,-551.5 1185.38,-557.5 1179.38,-557.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1106.5\" y=\"-541.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"1113.25\" y=\"-541.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"1120\" y=\"-541.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ 0.005</text>\n",
       "<text text-anchor=\"start\" x=\"1102\" y=\"-527.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.355</text>\n",
       "<text text-anchor=\"start\" x=\"1096\" y=\"-513.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 13</text>\n",
       "<text text-anchor=\"start\" x=\"1094.88\" y=\"-499.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [10, 3]</text>\n",
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       "<!-- 15&#45;&gt;45 -->\n",
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       "<title>15&#45;&gt;45</title>\n",
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       "<title>17</title>\n",
       "<path fill=\"#bddef6\" stroke=\"black\" d=\"M749.38,-457.5C749.38,-457.5 668.88,-457.5 668.88,-457.5 662.88,-457.5 656.88,-451.5 656.88,-445.5 656.88,-445.5 656.88,-405.5 656.88,-405.5 656.88,-399.5 662.88,-393.5 668.88,-393.5 668.88,-393.5 749.38,-393.5 749.38,-393.5 755.38,-393.5 761.38,-399.5 761.38,-405.5 761.38,-405.5 761.38,-445.5 761.38,-445.5 761.38,-451.5 755.38,-457.5 749.38,-457.5\"/>\n",
       "<text text-anchor=\"start\" x=\"674.25\" y=\"-441.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"681\" y=\"-441.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n",
       "<text text-anchor=\"start\" x=\"687.75\" y=\"-441.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ &#45;0.148</text>\n",
       "<text text-anchor=\"start\" x=\"676.12\" y=\"-427.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.48</text>\n",
       "<text text-anchor=\"start\" x=\"666\" y=\"-413.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 20</text>\n",
       "<text text-anchor=\"start\" x=\"664.88\" y=\"-399.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [8, 12]</text>\n",
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       "<!-- 16&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>16&#45;&gt;17</title>\n",
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       "<!-- 30 -->\n",
       "<g id=\"node31\" class=\"node\">\n",
       "<title>30</title>\n",
       "<path fill=\"#55abe9\" stroke=\"black\" d=\"M897.38,-457.5C897.38,-457.5 816.88,-457.5 816.88,-457.5 810.88,-457.5 804.88,-451.5 804.88,-445.5 804.88,-445.5 804.88,-405.5 804.88,-405.5 804.88,-399.5 810.88,-393.5 816.88,-393.5 816.88,-393.5 897.38,-393.5 897.38,-393.5 903.38,-393.5 909.38,-399.5 909.38,-405.5 909.38,-405.5 909.38,-445.5 909.38,-445.5 909.38,-451.5 903.38,-457.5 897.38,-457.5\"/>\n",
       "<text text-anchor=\"start\" x=\"824.5\" y=\"-441.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"831.25\" y=\"-441.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"838\" y=\"-441.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ 0.575</text>\n",
       "<text text-anchor=\"start\" x=\"820\" y=\"-427.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.219</text>\n",
       "<text text-anchor=\"start\" x=\"814\" y=\"-413.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 32</text>\n",
       "<text text-anchor=\"start\" x=\"812.88\" y=\"-399.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 28]</text>\n",
       "</g>\n",
       "<!-- 16&#45;&gt;30 -->\n",
       "<g id=\"edge30\" class=\"edge\">\n",
       "<title>16&#45;&gt;30</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M806.74,-493.22C813.08,-484.82 820.03,-475.62 826.69,-466.81\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"829.33,-469.11 832.57,-459.02 823.75,-464.9 829.33,-469.11\"/>\n",
       "</g>\n",
       "<!-- 18 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>18</title>\n",
       "<path fill=\"#7bbeee\" stroke=\"black\" d=\"M561.38,-357.5C561.38,-357.5 480.88,-357.5 480.88,-357.5 474.88,-357.5 468.88,-351.5 468.88,-345.5 468.88,-345.5 468.88,-305.5 468.88,-305.5 468.88,-299.5 474.88,-293.5 480.88,-293.5 480.88,-293.5 561.38,-293.5 561.38,-293.5 567.38,-293.5 573.38,-299.5 573.38,-305.5 573.38,-305.5 573.38,-345.5 573.38,-345.5 573.38,-351.5 567.38,-357.5 561.38,-357.5\"/>\n",
       "<text text-anchor=\"start\" x=\"486.25\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"493\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"499.75\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ &#45;0.349</text>\n",
       "<text text-anchor=\"start\" x=\"484\" y=\"-327.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\n",
       "<text text-anchor=\"start\" x=\"478\" y=\"-313.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\n",
       "<text text-anchor=\"start\" x=\"476.88\" y=\"-299.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 12]</text>\n",
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       "<!-- 17&#45;&gt;18 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>17&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M656.51,-397.07C633.88,-385.28 607.27,-371.4 583.65,-359.09\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"585.56,-356.14 575.07,-354.62 582.32,-362.35 585.56,-356.14\"/>\n",
       "</g>\n",
       "<!-- 29 -->\n",
       "<g id=\"node30\" class=\"node\">\n",
       "<title>29</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M745.25,-354.25C745.25,-354.25 673,-354.25 673,-354.25 667,-354.25 661,-348.25 661,-342.25 661,-342.25 661,-308.75 661,-308.75 661,-302.75 667,-296.75 673,-296.75 673,-296.75 745.25,-296.75 745.25,-296.75 751.25,-296.75 757.25,-302.75 757.25,-308.75 757.25,-308.75 757.25,-342.25 757.25,-342.25 757.25,-348.25 751.25,-354.25 745.25,-354.25\"/>\n",
       "<text text-anchor=\"start\" x=\"680.25\" y=\"-336.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"670.12\" y=\"-320.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"669\" y=\"-303.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 0]</text>\n",
       "</g>\n",
       "<!-- 17&#45;&gt;29 -->\n",
       "<g id=\"edge29\" class=\"edge\">\n",
       "<title>17&#45;&gt;29</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M709.12,-393.22C709.12,-384.48 709.12,-374.86 709.12,-365.72\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"712.63,-366.01 709.13,-356.01 705.63,-366.01 712.63,-366.01\"/>\n",
       "</g>\n",
       "<!-- 19 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>19</title>\n",
       "<path fill=\"#eeab7b\" stroke=\"black\" d=\"M426.25,-257.5C426.25,-257.5 354,-257.5 354,-257.5 348,-257.5 342,-251.5 342,-245.5 342,-245.5 342,-205.5 342,-205.5 342,-199.5 348,-193.5 354,-193.5 354,-193.5 426.25,-193.5 426.25,-193.5 432.25,-193.5 438.25,-199.5 438.25,-205.5 438.25,-205.5 438.25,-245.5 438.25,-245.5 438.25,-251.5 432.25,-257.5 426.25,-257.5\"/>\n",
       "<text text-anchor=\"start\" x=\"355.25\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"362\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n",
       "<text text-anchor=\"start\" x=\"368.75\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ &#45;0.287</text>\n",
       "<text text-anchor=\"start\" x=\"353\" y=\"-227.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\n",
       "<text text-anchor=\"start\" x=\"351.12\" y=\"-213.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"350\" y=\"-199.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 1]</text>\n",
       "</g>\n",
       "<!-- 18&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>18&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M479.32,-293.22C467.14,-284.12 453.71,-274.07 441.05,-264.59\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"443.3,-261.91 433.2,-258.72 439.11,-267.52 443.3,-261.91\"/>\n",
       "</g>\n",
       "<!-- 24 -->\n",
       "<g id=\"node25\" class=\"node\">\n",
       "<title>24</title>\n",
       "<path fill=\"#4ba6e7\" stroke=\"black\" d=\"M561.38,-257.5C561.38,-257.5 480.88,-257.5 480.88,-257.5 474.88,-257.5 468.88,-251.5 468.88,-245.5 468.88,-245.5 468.88,-205.5 468.88,-205.5 468.88,-199.5 474.88,-193.5 480.88,-193.5 480.88,-193.5 561.38,-193.5 561.38,-193.5 567.38,-193.5 573.38,-199.5 573.38,-205.5 573.38,-205.5 573.38,-245.5 573.38,-245.5 573.38,-251.5 567.38,-257.5 561.38,-257.5\"/>\n",
       "<text text-anchor=\"start\" x=\"486.25\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"493\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"499.75\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ &#45;0.145</text>\n",
       "<text text-anchor=\"start\" x=\"484\" y=\"-227.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.153</text>\n",
       "<text text-anchor=\"start\" x=\"478\" y=\"-213.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 12</text>\n",
       "<text text-anchor=\"start\" x=\"476.88\" y=\"-199.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 11]</text>\n",
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       "<!-- 18&#45;&gt;24 -->\n",
       "<g id=\"edge24\" class=\"edge\">\n",
       "<title>18&#45;&gt;24</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M521.12,-293.22C521.12,-285.62 521.12,-277.36 521.12,-269.32\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"524.63,-269.33 521.13,-259.33 517.63,-269.33 524.63,-269.33\"/>\n",
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       "<!-- 20 -->\n",
       "<g id=\"node21\" class=\"node\">\n",
       "<title>20</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M312.25,-154.25C312.25,-154.25 240,-154.25 240,-154.25 234,-154.25 228,-148.25 228,-142.25 228,-142.25 228,-108.75 228,-108.75 228,-102.75 234,-96.75 240,-96.75 240,-96.75 312.25,-96.75 312.25,-96.75 318.25,-96.75 324.25,-102.75 324.25,-108.75 324.25,-108.75 324.25,-142.25 324.25,-142.25 324.25,-148.25 318.25,-154.25 312.25,-154.25\"/>\n",
       "<text text-anchor=\"start\" x=\"247.25\" y=\"-136.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"237.12\" y=\"-120.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"236\" y=\"-103.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 0]</text>\n",
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       "<!-- 19&#45;&gt;20 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>19&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M353.74,-193.22C342.1,-183.22 329.13,-172.07 317.18,-161.8\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"319.71,-159.35 309.84,-155.49 315.15,-164.66 319.71,-159.35\"/>\n",
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       "<!-- 21 -->\n",
       "<g id=\"node22\" class=\"node\">\n",
       "<title>21</title>\n",
       "<path fill=\"#ffffff\" stroke=\"black\" d=\"M426.25,-157.5C426.25,-157.5 354,-157.5 354,-157.5 348,-157.5 342,-151.5 342,-145.5 342,-145.5 342,-105.5 342,-105.5 342,-99.5 348,-93.5 354,-93.5 354,-93.5 426.25,-93.5 426.25,-93.5 432.25,-93.5 438.25,-99.5 438.25,-105.5 438.25,-105.5 438.25,-145.5 438.25,-145.5 438.25,-151.5 432.25,-157.5 426.25,-157.5\"/>\n",
       "<text text-anchor=\"start\" x=\"355.25\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"362\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n",
       "<text text-anchor=\"start\" x=\"368.75\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ &#45;0.265</text>\n",
       "<text text-anchor=\"start\" x=\"361.25\" y=\"-127.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"351.12\" y=\"-113.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"350\" y=\"-99.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 1]</text>\n",
       "</g>\n",
       "<!-- 19&#45;&gt;21 -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>19&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M390.12,-193.22C390.12,-185.62 390.12,-177.36 390.12,-169.32\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"393.63,-169.33 390.13,-159.33 386.63,-169.33 393.63,-169.33\"/>\n",
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       "<!-- 22 -->\n",
       "<g id=\"node23\" class=\"node\">\n",
       "<title>22</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M312.25,-57.5C312.25,-57.5 240,-57.5 240,-57.5 234,-57.5 228,-51.5 228,-45.5 228,-45.5 228,-12 228,-12 228,-6 234,0 240,0 240,0 312.25,0 312.25,0 318.25,0 324.25,-6 324.25,-12 324.25,-12 324.25,-45.5 324.25,-45.5 324.25,-51.5 318.25,-57.5 312.25,-57.5\"/>\n",
       "<text text-anchor=\"start\" x=\"247.25\" y=\"-40.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"237.12\" y=\"-23.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"236\" y=\"-7.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n",
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       "<!-- 21&#45;&gt;22 -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>21&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M352.48,-93.21C341.63,-84.2 329.72,-74.3 318.6,-65.05\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"320.98,-62.48 311.05,-58.78 316.51,-67.86 320.98,-62.48\"/>\n",
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       "<!-- 23 -->\n",
       "<g id=\"node24\" class=\"node\">\n",
       "<title>23</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M426.25,-57.5C426.25,-57.5 354,-57.5 354,-57.5 348,-57.5 342,-51.5 342,-45.5 342,-45.5 342,-12 342,-12 342,-6 348,0 354,0 354,0 426.25,0 426.25,0 432.25,0 438.25,-6 438.25,-12 438.25,-12 438.25,-45.5 438.25,-45.5 438.25,-51.5 432.25,-57.5 426.25,-57.5\"/>\n",
       "<text text-anchor=\"start\" x=\"361.25\" y=\"-40.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"351.12\" y=\"-23.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"350\" y=\"-7.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n",
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       "<!-- 21&#45;&gt;23 -->\n",
       "<g id=\"edge23\" class=\"edge\">\n",
       "<title>21&#45;&gt;23</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M390.12,-93.21C390.12,-85.53 390.12,-77.22 390.12,-69.21\"/>\n",
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       "<!-- 25 -->\n",
       "<g id=\"node26\" class=\"node\">\n",
       "<title>25</title>\n",
       "<path fill=\"#9ccef2\" stroke=\"black\" d=\"M540.25,-157.5C540.25,-157.5 468,-157.5 468,-157.5 462,-157.5 456,-151.5 456,-145.5 456,-145.5 456,-105.5 456,-105.5 456,-99.5 462,-93.5 468,-93.5 468,-93.5 540.25,-93.5 540.25,-93.5 546.25,-93.5 552.25,-99.5 552.25,-105.5 552.25,-105.5 552.25,-145.5 552.25,-145.5 552.25,-151.5 546.25,-157.5 540.25,-157.5\"/>\n",
       "<text text-anchor=\"start\" x=\"469.25\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"476\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n",
       "<text text-anchor=\"start\" x=\"482.75\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ &#45;0.291</text>\n",
       "<text text-anchor=\"start\" x=\"467\" y=\"-127.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"start\" x=\"465.12\" y=\"-113.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"464\" y=\"-99.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 2]</text>\n",
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       "<!-- 24&#45;&gt;25 -->\n",
       "<g id=\"edge25\" class=\"edge\">\n",
       "<title>24&#45;&gt;25</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M515.7,-193.22C514.37,-185.53 512.91,-177.17 511.51,-169.04\"/>\n",
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       "<!-- 28 -->\n",
       "<g id=\"node29\" class=\"node\">\n",
       "<title>28</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M654.25,-154.25C654.25,-154.25 582,-154.25 582,-154.25 576,-154.25 570,-148.25 570,-142.25 570,-142.25 570,-108.75 570,-108.75 570,-102.75 576,-96.75 582,-96.75 582,-96.75 654.25,-96.75 654.25,-96.75 660.25,-96.75 666.25,-102.75 666.25,-108.75 666.25,-108.75 666.25,-142.25 666.25,-142.25 666.25,-148.25 660.25,-154.25 654.25,-154.25\"/>\n",
       "<text text-anchor=\"start\" x=\"589.25\" y=\"-136.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"579.12\" y=\"-120.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 9</text>\n",
       "<text text-anchor=\"start\" x=\"578\" y=\"-103.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 9]</text>\n",
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       "<!-- 24&#45;&gt;28 -->\n",
       "<g id=\"edge28\" class=\"edge\">\n",
       "<title>24&#45;&gt;28</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M552.08,-193.22C561.8,-183.41 572.59,-172.5 582.6,-162.4\"/>\n",
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       "<g id=\"node27\" class=\"node\">\n",
       "<title>26</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M540.25,-57.5C540.25,-57.5 468,-57.5 468,-57.5 462,-57.5 456,-51.5 456,-45.5 456,-45.5 456,-12 456,-12 456,-6 462,0 468,0 468,0 540.25,0 540.25,0 546.25,0 552.25,-6 552.25,-12 552.25,-12 552.25,-45.5 552.25,-45.5 552.25,-51.5 546.25,-57.5 540.25,-57.5\"/>\n",
       "<text text-anchor=\"start\" x=\"475.25\" y=\"-40.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"465.12\" y=\"-23.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
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       "<!-- 25&#45;&gt;26 -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>25&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M504.12,-93.21C504.12,-85.53 504.12,-77.22 504.12,-69.21\"/>\n",
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       "<!-- 27 -->\n",
       "<g id=\"node28\" class=\"node\">\n",
       "<title>27</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M654.25,-57.5C654.25,-57.5 582,-57.5 582,-57.5 576,-57.5 570,-51.5 570,-45.5 570,-45.5 570,-12 570,-12 570,-6 576,0 582,0 582,0 654.25,0 654.25,0 660.25,0 666.25,-6 666.25,-12 666.25,-12 666.25,-45.5 666.25,-45.5 666.25,-51.5 660.25,-57.5 654.25,-57.5\"/>\n",
       "<text text-anchor=\"start\" x=\"589.25\" y=\"-40.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"579.12\" y=\"-23.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"578\" y=\"-7.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2]</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;27 -->\n",
       "<g id=\"edge27\" class=\"edge\">\n",
       "<title>25&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M541.77,-93.21C552.62,-84.2 564.53,-74.3 575.65,-65.05\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"577.74,-67.86 583.2,-58.78 573.27,-62.48 577.74,-67.86\"/>\n",
       "</g>\n",
       "<!-- 31 -->\n",
       "<g id=\"node32\" class=\"node\">\n",
       "<title>31</title>\n",
       "<path fill=\"#48a5e7\" stroke=\"black\" d=\"M897.38,-357.5C897.38,-357.5 816.88,-357.5 816.88,-357.5 810.88,-357.5 804.88,-351.5 804.88,-345.5 804.88,-345.5 804.88,-305.5 804.88,-305.5 804.88,-299.5 810.88,-293.5 816.88,-293.5 816.88,-293.5 897.38,-293.5 897.38,-293.5 903.38,-293.5 909.38,-299.5 909.38,-305.5 909.38,-305.5 909.38,-345.5 909.38,-345.5 909.38,-351.5 903.38,-357.5 897.38,-357.5\"/>\n",
       "<text text-anchor=\"start\" x=\"822.25\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"829\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"835.75\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ &#45;0.559</text>\n",
       "<text text-anchor=\"start\" x=\"820\" y=\"-327.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.133</text>\n",
       "<text text-anchor=\"start\" x=\"814\" y=\"-313.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 28</text>\n",
       "<text text-anchor=\"start\" x=\"812.88\" y=\"-299.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 26]</text>\n",
       "</g>\n",
       "<!-- 30&#45;&gt;31 -->\n",
       "<g id=\"edge31\" class=\"edge\">\n",
       "<title>30&#45;&gt;31</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M857.12,-393.22C857.12,-385.62 857.12,-377.36 857.12,-369.32\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"860.63,-369.33 857.13,-359.33 853.63,-369.33 860.63,-369.33\"/>\n",
       "</g>\n",
       "<!-- 42 -->\n",
       "<g id=\"node43\" class=\"node\">\n",
       "<title>42</title>\n",
       "<path fill=\"#ffffff\" stroke=\"black\" d=\"M1061.25,-357.5C1061.25,-357.5 989,-357.5 989,-357.5 983,-357.5 977,-351.5 977,-345.5 977,-345.5 977,-305.5 977,-305.5 977,-299.5 983,-293.5 989,-293.5 989,-293.5 1061.25,-293.5 1061.25,-293.5 1067.25,-293.5 1073.25,-299.5 1073.25,-305.5 1073.25,-305.5 1073.25,-345.5 1073.25,-345.5 1073.25,-351.5 1067.25,-357.5 1061.25,-357.5\"/>\n",
       "<text text-anchor=\"start\" x=\"996.62\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"1003.38\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n",
       "<text text-anchor=\"start\" x=\"1010.12\" y=\"-341.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ 0.52</text>\n",
       "<text text-anchor=\"start\" x=\"996.25\" y=\"-327.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"986.12\" y=\"-313.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"985\" y=\"-299.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 2]</text>\n",
       "</g>\n",
       "<!-- 30&#45;&gt;42 -->\n",
       "<g id=\"edge42\" class=\"edge\">\n",
       "<title>30&#45;&gt;42</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M909.82,-393.76C927.93,-383.2 948.3,-371.31 966.83,-360.51\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"968.43,-363.62 975.31,-355.56 964.91,-357.58 968.43,-363.62\"/>\n",
       "</g>\n",
       "<!-- 32 -->\n",
       "<g id=\"node33\" class=\"node\">\n",
       "<title>32</title>\n",
       "<path fill=\"#7bbeee\" stroke=\"black\" d=\"M825.25,-257.5C825.25,-257.5 753,-257.5 753,-257.5 747,-257.5 741,-251.5 741,-245.5 741,-245.5 741,-205.5 741,-205.5 741,-199.5 747,-193.5 753,-193.5 753,-193.5 825.25,-193.5 825.25,-193.5 831.25,-193.5 837.25,-199.5 837.25,-205.5 837.25,-205.5 837.25,-245.5 837.25,-245.5 837.25,-251.5 831.25,-257.5 825.25,-257.5\"/>\n",
       "<text text-anchor=\"start\" x=\"756.5\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"763.25\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n",
       "<text text-anchor=\"start\" x=\"770\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ 0.414</text>\n",
       "<text text-anchor=\"start\" x=\"752\" y=\"-227.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\n",
       "<text text-anchor=\"start\" x=\"750.12\" y=\"-213.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"749\" y=\"-199.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 3]</text>\n",
       "</g>\n",
       "<!-- 31&#45;&gt;32 -->\n",
       "<g id=\"edge32\" class=\"edge\">\n",
       "<title>31&#45;&gt;32</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M835.42,-293.22C829.66,-284.91 823.35,-275.82 817.29,-267.09\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"820.29,-265.28 811.72,-259.06 814.54,-269.27 820.29,-265.28\"/>\n",
       "</g>\n",
       "<!-- 37 -->\n",
       "<g id=\"node38\" class=\"node\">\n",
       "<title>37</title>\n",
       "<path fill=\"#42a1e6\" stroke=\"black\" d=\"M947.38,-257.5C947.38,-257.5 866.88,-257.5 866.88,-257.5 860.88,-257.5 854.88,-251.5 854.88,-245.5 854.88,-245.5 854.88,-205.5 854.88,-205.5 854.88,-199.5 860.88,-193.5 866.88,-193.5 866.88,-193.5 947.38,-193.5 947.38,-193.5 953.38,-193.5 959.38,-199.5 959.38,-205.5 959.38,-205.5 959.38,-245.5 959.38,-245.5 959.38,-251.5 953.38,-257.5 947.38,-257.5\"/>\n",
       "<text text-anchor=\"start\" x=\"874.5\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"881.25\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"888\" y=\"-241.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ 0.262</text>\n",
       "<text text-anchor=\"start\" x=\"874.12\" y=\"-227.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.08</text>\n",
       "<text text-anchor=\"start\" x=\"864\" y=\"-213.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 24</text>\n",
       "<text text-anchor=\"start\" x=\"862.88\" y=\"-199.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 23]</text>\n",
       "</g>\n",
       "<!-- 31&#45;&gt;37 -->\n",
       "<g id=\"edge37\" class=\"edge\">\n",
       "<title>31&#45;&gt;37</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M873.08,-293.22C877.19,-285.18 881.67,-276.4 885.99,-267.93\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"889.03,-269.66 890.46,-259.16 882.8,-266.48 889.03,-269.66\"/>\n",
       "</g>\n",
       "<!-- 33 -->\n",
       "<g id=\"node34\" class=\"node\">\n",
       "<title>33</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M768.25,-154.25C768.25,-154.25 696,-154.25 696,-154.25 690,-154.25 684,-148.25 684,-142.25 684,-142.25 684,-108.75 684,-108.75 684,-102.75 690,-96.75 696,-96.75 696,-96.75 768.25,-96.75 768.25,-96.75 774.25,-96.75 780.25,-102.75 780.25,-108.75 780.25,-108.75 780.25,-142.25 780.25,-142.25 780.25,-148.25 774.25,-154.25 768.25,-154.25\"/>\n",
       "<text text-anchor=\"start\" x=\"703.25\" y=\"-136.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"693.12\" y=\"-120.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"692\" y=\"-103.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2]</text>\n",
       "</g>\n",
       "<!-- 32&#45;&gt;33 -->\n",
       "<g id=\"edge33\" class=\"edge\">\n",
       "<title>32&#45;&gt;33</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M770.93,-193.22C765.56,-183.99 759.63,-173.79 754.06,-164.21\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"757.22,-162.69 749.17,-155.81 751.17,-166.21 757.22,-162.69\"/>\n",
       "</g>\n",
       "<!-- 34 -->\n",
       "<g id=\"node35\" class=\"node\">\n",
       "<title>34</title>\n",
       "<path fill=\"#ffffff\" stroke=\"black\" d=\"M882.25,-157.5C882.25,-157.5 810,-157.5 810,-157.5 804,-157.5 798,-151.5 798,-145.5 798,-145.5 798,-105.5 798,-105.5 798,-99.5 804,-93.5 810,-93.5 810,-93.5 882.25,-93.5 882.25,-93.5 888.25,-93.5 894.25,-99.5 894.25,-105.5 894.25,-105.5 894.25,-145.5 894.25,-145.5 894.25,-151.5 888.25,-157.5 882.25,-157.5\"/>\n",
       "<text text-anchor=\"start\" x=\"813.5\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"820.25\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n",
       "<text text-anchor=\"start\" x=\"827\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ 0.546</text>\n",
       "<text text-anchor=\"start\" x=\"817.25\" y=\"-127.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"807.12\" y=\"-113.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"806\" y=\"-99.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 1]</text>\n",
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       "<!-- 32&#45;&gt;34 -->\n",
       "<g id=\"edge34\" class=\"edge\">\n",
       "<title>32&#45;&gt;34</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M807.32,-193.22C812.05,-185.09 817.22,-176.2 822.19,-167.65\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"825.15,-169.53 827.15,-159.12 819.1,-166.01 825.15,-169.53\"/>\n",
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       "<!-- 35 -->\n",
       "<g id=\"node36\" class=\"node\">\n",
       "<title>35</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M827.25,-57.5C827.25,-57.5 755,-57.5 755,-57.5 749,-57.5 743,-51.5 743,-45.5 743,-45.5 743,-12 743,-12 743,-6 749,0 755,0 755,0 827.25,0 827.25,0 833.25,0 839.25,-6 839.25,-12 839.25,-12 839.25,-45.5 839.25,-45.5 839.25,-51.5 833.25,-57.5 827.25,-57.5\"/>\n",
       "<text text-anchor=\"start\" x=\"762.25\" y=\"-40.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"752.12\" y=\"-23.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"751\" y=\"-7.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n",
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       "<!-- 34&#45;&gt;35 -->\n",
       "<g id=\"edge35\" class=\"edge\">\n",
       "<title>34&#45;&gt;35</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M827.96,-93.21C823.2,-85 818,-76.05 813.06,-67.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"816.22,-66.01 808.17,-59.12 810.17,-69.53 816.22,-66.01\"/>\n",
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       "<!-- 36 -->\n",
       "<g id=\"node37\" class=\"node\">\n",
       "<title>36</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M941.25,-57.5C941.25,-57.5 869,-57.5 869,-57.5 863,-57.5 857,-51.5 857,-45.5 857,-45.5 857,-12 857,-12 857,-6 863,0 869,0 869,0 941.25,0 941.25,0 947.25,0 953.25,-6 953.25,-12 953.25,-12 953.25,-45.5 953.25,-45.5 953.25,-51.5 947.25,-57.5 941.25,-57.5\"/>\n",
       "<text text-anchor=\"start\" x=\"876.25\" y=\"-40.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"866.12\" y=\"-23.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"865\" y=\"-7.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n",
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       "<!-- 34&#45;&gt;36 -->\n",
       "<g id=\"edge36\" class=\"edge\">\n",
       "<title>34&#45;&gt;36</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M865.61,-93.21C870.72,-85 876.29,-76.05 881.59,-67.54\"/>\n",
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       "<!-- 38 -->\n",
       "<g id=\"node39\" class=\"node\">\n",
       "<title>38</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M1004.38,-154.25C1004.38,-154.25 923.88,-154.25 923.88,-154.25 917.88,-154.25 911.88,-148.25 911.88,-142.25 911.88,-142.25 911.88,-108.75 911.88,-108.75 911.88,-102.75 917.88,-96.75 923.88,-96.75 923.88,-96.75 1004.38,-96.75 1004.38,-96.75 1010.38,-96.75 1016.38,-102.75 1016.38,-108.75 1016.38,-108.75 1016.38,-142.25 1016.38,-142.25 1016.38,-148.25 1010.38,-154.25 1004.38,-154.25\"/>\n",
       "<text text-anchor=\"start\" x=\"935.25\" y=\"-136.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"921\" y=\"-120.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 18</text>\n",
       "<text text-anchor=\"start\" x=\"919.88\" y=\"-103.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 18]</text>\n",
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       "<!-- 37&#45;&gt;38 -->\n",
       "<g id=\"edge38\" class=\"edge\">\n",
       "<title>37&#45;&gt;38</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M925.32,-193.22C930.69,-183.99 936.62,-173.79 942.19,-164.21\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"945.08,-166.21 947.08,-155.81 939.03,-162.69 945.08,-166.21\"/>\n",
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       "<!-- 39 -->\n",
       "<g id=\"node40\" class=\"node\">\n",
       "<title>39</title>\n",
       "<path fill=\"#61b1ea\" stroke=\"black\" d=\"M1118.25,-157.5C1118.25,-157.5 1046,-157.5 1046,-157.5 1040,-157.5 1034,-151.5 1034,-145.5 1034,-145.5 1034,-105.5 1034,-105.5 1034,-99.5 1040,-93.5 1046,-93.5 1046,-93.5 1118.25,-93.5 1118.25,-93.5 1124.25,-93.5 1130.25,-99.5 1130.25,-105.5 1130.25,-105.5 1130.25,-145.5 1130.25,-145.5 1130.25,-151.5 1124.25,-157.5 1118.25,-157.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1053.62\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x</text>\n",
       "<text text-anchor=\"start\" x=\"1060.38\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"1067.12\" y=\"-141.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> ≤ 0.31</text>\n",
       "<text text-anchor=\"start\" x=\"1045\" y=\"-127.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.278</text>\n",
       "<text text-anchor=\"start\" x=\"1043.12\" y=\"-113.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n",
       "<text text-anchor=\"start\" x=\"1042\" y=\"-99.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 5]</text>\n",
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       "<!-- 37&#45;&gt;39 -->\n",
       "<g id=\"edge39\" class=\"edge\">\n",
       "<title>37&#45;&gt;39</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M959.63,-195.1C979.79,-183.81 1002.92,-170.86 1023.61,-159.27\"/>\n",
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       "<!-- 40 -->\n",
       "<g id=\"node41\" class=\"node\">\n",
       "<title>40</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1059.25,-57.5C1059.25,-57.5 987,-57.5 987,-57.5 981,-57.5 975,-51.5 975,-45.5 975,-45.5 975,-12 975,-12 975,-6 981,0 987,0 987,0 1059.25,0 1059.25,0 1065.25,0 1071.25,-6 1071.25,-12 1071.25,-12 1071.25,-45.5 1071.25,-45.5 1071.25,-51.5 1065.25,-57.5 1059.25,-57.5\"/>\n",
       "<text text-anchor=\"start\" x=\"994.25\" y=\"-40.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"984.12\" y=\"-23.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"983\" y=\"-7.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n",
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       "<!-- 39&#45;&gt;40 -->\n",
       "<g id=\"edge40\" class=\"edge\">\n",
       "<title>39&#45;&gt;40</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1062.64,-93.21C1057.53,-85 1051.96,-76.05 1046.66,-67.54\"/>\n",
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       "<!-- 41 -->\n",
       "<g id=\"node42\" class=\"node\">\n",
       "<title>41</title>\n",
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       "<text text-anchor=\"start\" x=\"1108.25\" y=\"-40.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
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      "text/plain": [
       "<graphviz.sources.Source at 0x2208b2e1290>"
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dot_data = export_graphviz(classifier, out_file=None, filled=True, rounded=True, special_characters=True)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render(\"iris_decision_tree\")  # 保存为PDF或其他格式\n",
    "graph.format = 'png'\n",
    "graph.render(\"decision_tree\", format='png', cleanup=True)  # 保存为decision_tree.png\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "12b5b9b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7083333333333334\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "# 计算准确度\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d377e8e",
   "metadata": {},
   "source": [
    "修改最大深度的参数为4，重新训练并在测试集上测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52ca05c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7916666666666666\n"
     ]
    }
   ],
   "source": [
    "classifier_1 = DecisionTreeClassifier(max_depth=4)\n",
    "classifier_1.fit(X_train, y_train)\n",
    "\n",
    "y_pred = classifier_1.predict(X_test)\n",
    "\n",
    "# 计算准确度\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c736150a",
   "metadata": {},
   "source": [
    "模型在测试集上准确率提高，之前的模型过拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e37b1189",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "# 创建网格点用于绘制决策边界\n",
    "xx, yy = np.meshgrid(np.linspace(-2, 3, 100),\n",
    "                     np.linspace(-2, 2, 100))\n",
    "Z = classifier_1.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "# 绘制数据点和决策边界\n",
    "plt.contourf(xx, yy, Z, alpha=0.8)\n",
    "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred, edgecolors='k')\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('X2')\n",
    "plt.title('Decision Tree Classifier')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f5bd4c1",
   "metadata": {},
   "source": [
    "生成数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9509aaf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "x1, y1 = datasets.make_circles(n_samples=2000, factor=0.5, noise=0.05, random_state=1017)\n",
    "x2, y2 = datasets.make_blobs(n_samples=1000, centers=[[1.2,1.2]], cluster_std=[[.1]], random_state=1017)\n",
    "\n",
    "y2 = np.full_like(y2, 2)\n",
    "\n",
    "# 将向量纵向拼接\n",
    "x_all = np.vstack((x1, x2)) \n",
    "y_all = np.concatenate((y1, y2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cd73f7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据集划分\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_all, y_all, test_size=0.2, random_state=1017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a864cce0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9966666666666667\n"
     ]
    }
   ],
   "source": [
    "cclf = DecisionTreeClassifier()\n",
    "cclf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = cclf.predict(X_test)\n",
    "\n",
    "# 计算准确度\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ebe0f9cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "# 创建网格点用于绘制决策边界\n",
    "xx, yy = np.meshgrid(np.linspace(-2, 3, 100),\n",
    "                     np.linspace(-2, 2, 100))\n",
    "Z = cclf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "# 绘制数据点和决策边界\n",
    "plt.contourf(xx, yy, Z, alpha=0.8)\n",
    "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred, edgecolors='k')\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('X2')\n",
    "plt.title('Decision Tree Classifier')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee1ff021",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}