Week5_김희숙_예습과제.ipynb

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    "#### CHAPTER 6\n",
    "# 결정 트리\n",
    "\n",
    "1. 결정 트리 학습과 시각화 \n",
    "2. 예측하기\n",
    "3. 클래스 확률 추정\n",
    "4. CART 훈련 알고리즘\n",
    "5. 계산 복잡도\n",
    "6. 지니 불순도 또는 엔트로피?\n",
    "7. 규제 매개변수\n",
    "8. 회귀\n",
    "9. 불안전성\n",
    "\n",
    "##### 결정트리(decision tree)\n",
    "- 분류와 회귀 작업, 다중출력 작업이 가능한 머신러닝 알고리즘\n",
    "- 매우 복잡한 데이터셋도 학습 가능\n",
    "- 랜덤 포레스트(7장)의 기본 구성 요소\n",
    "- 데이터의 전처리가 거의 필요하지 않음 *(특성의 스케일을 맞추거나 평균을 원점에 맞추는 작업이 필요하지 않음)*\n",
    "- 비파라미터 모델"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 결정 트리 학습과 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 공통 모듈 임포트\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 깔끔한 그래프 출력을 위해\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#결정 트리 모델 생성\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data[:, 2:] # 꽃잎 길이와 너비\n",
    "y = iris.target\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42) #DecisionTreeClassifier 훈련\n",
    "tree_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphviz import Source\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "#훈련된 결정 트리 시각화\n",
    "export_graphviz(\n",
    "        tree_clf,\n",
    "        out_file=os.path.join(\"iris_tree.dot\"),\n",
    "        feature_names=iris.feature_names[2:],\n",
    "        class_names=iris.target_names,\n",
    "        rounded=True,\n",
    "        filled=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "import graphviz\n",
    "\n",
    "#위에서 생선된 iris_tree.dot 파일을 graphviz가 읽어서 주피터 노트북상에서 시각화\n",
    "with open(\"iris_tree.dot\") as f:\n",
    "    dot_graph = f.read()\n",
    "graphviz.Source(dot_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "루트 노드(root node)\n",
    "- 깊이가 0인 맨 꼭대기의 노드\n",
    "\n",
    "자식 노드(child node)\n",
    "- 루트 노드를 가지고 있는 노드\n",
    "\n",
    "리프 노드(leaf node)\n",
    "- 자식 노드를 가지고 있지 않은 노드"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 예측하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "꽃잎의 길이가 2.45cm보다 짧은지 검사\n",
    "- 만약 그렇다면 루트 노드에서 왼쪽 자식노드로 이동(주황색 = 리프노드) \n",
    "- 리프 노드이므로 추가적인 검사를 하지 않음\n",
    "- 예측 클래스를 보고 결정 트리가 새로 발견한 곷의 품족을 Iris-Setosa(class=setosa)라고 예측\n",
    "\n",
    "꽃잎의 길이가 2.45cm보다 긴지 검사\n",
    "- 만약 그렇다면 루트 노드에서 오른쪽 자식노드로 이동(하얀색 =/= 리프노드)\n",
    "- 꽃잎의 너비가 1.75cm보다 작은지 검사\n",
    "- 만약 그렇다면 Iris-Versicolor, 그렇지 않다면 Iris-Virginica\n",
    "\n",
    "value 속성\n",
    "- 노드의 value 속성은 각 클래스에 얼마나 많은 훈련 샘플이 있는지 알려줌 \n",
    "gini 속성\n",
    "- 노드의 gini 속성은 불순도(impurity)를 측정함\n",
    "- 한 노드의 모든 샘플이 같은 클래스에 속한다면 gini=0으로 이 노드를 순수하다고 함(주황색)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**지니 불순도(Gini impurity)**\n",
    "$ G_i = 1 - \\sum\\limits_{k=1}^{n}{{p_{i,k}}^2} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#결정 트리의 결정 경계\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    if not iris:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    if plot_training:\n",
    "        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris setosa\")\n",
    "        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris versicolor\")\n",
    "        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"Iris virginica\")\n",
    "        plt.axis(axes)\n",
    "    if iris:\n",
    "        plt.xlabel(\"Petal length\", fontsize=14)\n",
    "        plt.ylabel(\"Petal width\", fontsize=14)\n",
    "    else:\n",
    "        plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "        plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n",
    "    if legend:\n",
    "        plt.legend(loc=\"lower right\", fontsize=14)\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.plot([2.45, 2.45], [0, 3], \"k-\", linewidth=2)\n",
    "plt.plot([2.45, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
    "plt.plot([4.95, 4.95], [0, 1.75], \"k:\", linewidth=2)\n",
    "plt.plot([4.85, 4.85], [1.75, 3], \"k:\", linewidth=2)\n",
    "plt.text(1.40, 1.0, \"Depth=0\", fontsize=15)\n",
    "plt.text(3.2, 1.80, \"Depth=1\", fontsize=13)\n",
    "plt.text(4.05, 0.5, \"(Depth=2)\", fontsize=11)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 굵은 수직선은 루트 노드(깊이 0)의 결정 경계(꽃잎의 길이 =2.45cm)\n",
    "- 왼쪽 영역은 순수 노드(Iris-Setosa만 있음)이기 때문에 너는 나눌 수 없음\n",
    "- 오른쪽 영역은 순수 노드가 아니기 때문에 깊이 1의 오른쪽 노드는 꽃잎 너비=1.75cm 에서 나누어짐\n",
    "- max_depth를 2로 설정했기 때문에 결정 트리는 더 분할 되지 않음\n",
    "- max_depth를 3으로 하면 깊이 2의 두 노드가 각각 결정 경계 추가(점선)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 클래스 확률 추정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 한 샘플이 특정 클래스 k에 속할 확률 추정\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.90740741, 0.09259259]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict_proba([[5, 1.5]]) #길이가 5cm이고 너비가 1.5cm인 꽃\n",
    "#리프 노드는 깊이 2에서 왼쪽 노드이므로 결정 트리는 그에 해당하는 확률을 출력"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 즉, Iris-Setosa는 0%, Iris-Versicolor는 90.7%, Iris-Virginica는 9.3%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict([[5, 1.5]]) #가장 높은 확률을 가진 클래스 넘버 출력"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CART 훈련 알고리즘"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CART 훈련 알고리즘\n",
    "- 사이킷런\n",
    "- 결정 트리를 훈련 시키기 위함\n",
    "- 분류에 대한 CART 비용 함수\n",
    "![dd](https://t1.daumcdn.net/cfile/tistory/99A21C475B9A771E15)\n",
    "- 훈련 세트를 성공적으로 둘로 나누었다면, 같은 방식으로 서브셋을 나누기를 반복\n",
    "- max_depth 의 깊이가 되면 중지하거나 불순도를 줄이는 분할을 찾을 수 없을 때 멈춤 \n",
    "\n",
    "중지 조건 매개변수\n",
    "- min_samples_split: 분할되기 위해 노드가 가져야 하는 최소 샘플의 수\n",
    "- min_samples_leaf:  리프 노드가 가지고 있어야 할 최소 샘플의 수\n",
    "- min_weight_fraction_leaf: 가중치가 부여된 전체 샘플 수에서의 리프 노드가 가지고 있어햐 할 최소 샘플의 수 비율\n",
    "- max_leaf_nodes: 리프 노드의 최대 수"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 계산 복잡도"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **특성과 무관하게 O(log_2(m))**\n",
    "- 예측을 하려면 결정 트리를 루트 노드에서부터 리프 노드까지 탐색해야 함\n",
    "- 일반적으로 결정 트리는 거의 균형을 이루고 있음\n",
    "- 결정 트리를 탐색하기 위해서 약 O(log_2(m))개의 노드를 거쳐야 함\n",
    "\n",
    "- 훈련 알고리즘은 각 노드에서 모든 훈련 샘플의 모든 특성을 비교\n",
    "- **각 노드에서 모든 샘플의 모든 특성을 비교하면 훈련 복잡도는 O(nxmlog_2(m))**\n",
    "- 훈련 세트가 수천 개 이하의 샘플 정도로 작을 경우 사이킷런은 (presort=TRUE 로 지정) 미리 데이터를 정렬하여 훈련 속도를 높일 수 있음"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 지니 불순도 또는 엔트로피?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "엔트로피 불순도\n",
    "- criterion 매개변수를 \"entropy\"로 지정\n",
    "- 엔트로피는 분자의 무질서함을 측정하는 것(열역학의 개념)\n",
    "- 분자가 안정, 질서 정연: 엔트로피 = 0\n",
    "- 즉, 어떤 세트가 한 클래스의 샘플만 담고 있다면 엔프로피 = 0\n",
    "\n",
    "$ H_i = -\\sum\\limits_{k=1 \\atop p_{i,k} \\ne 0}^{n}{{p_{i,k}}\\log_2(p_{i,k})} $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "지니 불순도 vs 엔트로피 불순도 \n",
    "- 큰 차이 없음\n",
    "- 비슷한 트리 만듦\n",
    "- 단, **지니 불순도가 조금 더 계산이 빠름. 따라서 기본값 추천**\n",
    "- 그러나, 다른 트리가 만들어지는 경우 지니 불순도는 가장 빈도 높은 클래스를 한쪽 가지로 고립시키는 경향이 있음\n",
    "- 반면, **엔트로피는 조금 더 균형 잡힌 트리를 만듦**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 규제 매개변수"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "비파라미터 모델 \n",
    "- 훈련되기 전에 파라미터 수가 결정되지 않는 모델\n",
    "- 모델 구조가 데이터에 맞춰져서 고정되지 않고 자유로움\n",
    "\n",
    "파라미터 모델\n",
    "- 미리 정의된 모델 파라미터 수를 가지므로 자유도가 제한되고 과대적합 될 위험이 적은 모델\n",
    "- 과소적합될 위험은 커짐\n",
    "- ex. 선형 모델\n",
    "\n",
    "max_depth\n",
    "- 결정 트리의 최대 깊이 제어\n",
    "- 값을 줄이면 모델을 규제하게 되고 과대적합의 위험을 감소시킴"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**결정 트리의 형태 제한**\n",
    "\n",
    "중지 조건 매개변수\n",
    "- min_samples_split: 분할되기 위해 노드가 가져야 하는 최소 샘플의 수\n",
    "- min_samples_leaf:  리프 노드가 가지고 있어야 할 최소 샘플의 수\n",
    "- min_weight_fraction_leaf: 가중치가 부여된 전체 샘플 수에서의 리프 노드가 가지고 있어햐 할 최소 샘플의 수 비율\n",
    "- max_leaf_nodes: 리프 노드의 최대 수\n",
    "//\n",
    "- max_features: 각 노드에서 분할에 사용할 특성의 최대 수\n",
    "\n",
    "=> min_ or max_로 시작하는 매개변수를 증가 or 감소시키면 모델에 규제가 커짐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)\n",
    "\n",
    "deep_tree_clf1 = DecisionTreeClassifier(random_state=42)\n",
    "deep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\n",
    "deep_tree_clf1.fit(Xm, ym)\n",
    "deep_tree_clf2.fit(Xm, ym)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.4, -1, 1.5], iris=False)\n",
    "plt.title(\"No restrictions\", fontsize=16)\n",
    "plt.sca(axes[1])\n",
    "plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.4, -1, 1.5], iris=False)\n",
    "plt.title(\"min_samples_leaf = {}\".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 왼쪽 결정 트리는 기본 매개변수 사용하여 훈련(규제X) -> 과대적합\n",
    "- 오른쪽 결정 트리는 min_samples_leaf = 4 로 지정(규제O) -> 성능 good\n",
    "\n",
    "**가지치기 알고리즘**\n",
    "- 제한 없이 결정 트리를 훈련 시키고 불필요한 노드를 제거하는 알고리즘\n",
    "- 순도를 높이는 것이 통계적으로 큰 효과가 없는 경우\n",
    "- 카이제곱 검정을 이용"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2차식으로 만든 데이터셋 + 잡음\n",
    "np.random.seed(42)\n",
    "m = 200\n",
    "X = np.random.rand(m, 1)\n",
    "y = 4 * (X - 0.5) ** 2\n",
    "y = y + np.random.randn(m, 1) / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "#잡음이 섞인 2차 함수 형태의 데이터셋\n",
    "tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(\n",
    "        tree_reg,\n",
    "        out_file=os.path.join(\"regression_tree.dot\"),\n",
    "        feature_names=[\"x1\"],\n",
    "        rounded=True,\n",
    "        filled=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " -->\r\n",
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       " viewBox=\"0.00 0.00 482.00 269.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
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       "<text text-anchor=\"middle\" x=\"234.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 &lt;= 0.197</text>\r\n",
       "<text text-anchor=\"middle\" x=\"234.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.098</text>\r\n",
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       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x20ffd643970>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "\n",
    "#위에서 생선된 iris_tree.dot 파일을 graphviz가 읽어서 주피터 노트북상에서 시각화\n",
    "with open(\"regression_tree.dot\") as f:\n",
    "    dot_graph = f.read()\n",
    "graphviz.Source(dot_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 앞에서 만든 분류 트리와 매우 비슷\n",
    "- value = 0.111인 리프 노드: 110개의 훈련 샘플의 평균 타깃값이 예측값 \n",
    "- mse = 0.015: 예측값을 사용하여 110개의 샘플에 대한 평균제곱오차 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v/I3/nMmpp0rUdoFQFb0j//nPf/Z675prrmHAgAH8+Mc/zrVZeUtVlbFKgjhzyTHjdJ561ngm///+9a7jx/D7rD6/2yfkF3bam1calr8OnUs0Rjfa22VarJv4aKrj8ccfD/R26J544glWrVrFbbfdxn777eeFaWlpbGyksbGR+vp6ioqC3SHvoybhOWacTps2vtMHX37J0ufM9mAFv2ToT4bf7ROcwU6bdRMn2pvrGqa1zsutArROsUUSbKnK99qKi7Ves0a7CaCNr8hf/Oc//0n+pt3rmKstCyKRiB48eLCeOHFi1762tjY9btw4feSRR+qOjg7LdT377LO6pKREl5WV6QEDBugxY8bob3zjG7qxsTErG2NZtGiRHjt2bI99q1ev1uFw2LFj3HzzzfqYY47RxcXFPa6LU6xZo/Xcud232Jo1Wvftq3U4bPxNdOsBjdoH2uPEVlFRkfb69O2r9Sw1X2vQG//nmrTXNNf4Vb8EwW3i9cvc57aG5W8PXZ8+MHx40k0NH07b4OE0lxt/Vez7o0fDMcfAqFE9PzdwoFH3wQfDc8/JmFCBoJTi+OOPp7GxEeOeg1/+8pc0NTWxYMECwtHll6xyyy230NzczK5du1i9ejWjRo3i+OOP57HHHnPDfFcYO3Yst912myszDc3hxFtuMf42NCSeOVbImHE6Z5xt9NBd99ITjh9j+vTpjtfpJH63T8gvrLa3RPoFudGw/HXoxo+Hjz9OuZVu+5gBu42/sfsblr3HvK+9RMPvNvT8zN13UwPUlJaKM5cMJ/rT1qyBvn2NdSr79jVeZ1tnlhx//PHs2rWL9evXs2XLFmbPns20adOYOHFiVvWOGjWKOXPmMGPGDK666iq01uzZs4frrruOMWPGMHjwYM4880zefvvtrs9UV1dz9dVXM2XKFMrKyhg/fjyrVq0CoKGhgcsuu4x3332XsrIyiouLmTJlStdnH3nkEcaOHcvAgQP52te+lvEswYsvvpipU6cydOjQrM4/EYmEz5w5Fg7L7FeTqiqYdJbh0E05+OCu/amGdWpqaiw74bHtxo/43T7BGey0WTex2t6SOW450bBsuvf8vKUbskhGym7R3/42p8MIuTyWHVIOuTpFoj5rD/nb3/6mAb1kyRJ9ySWX6NLSUv3OO+/YrufZZ5/Vs2fP7rX/qaee0oB+44039PTp0/XZZ5+tN2/erFtbW/WPfvQjffjhh+u2tjattdannnqqLisr00899ZRub2/Xv/3tb3Vpaal+7733tNY9h1zNNrR69WoN6G9/+9u6ublZb968WR966KF6zpw5XTacffbZeuDAgUm3hx9+uJfdt956a8ZDrsm+4mT3YLomQQENuXZx333GI0tNTcprF3ONCl6/hGDh53Zkd2jVbQ0LdpS0C6RMChjK3w5N3+GzZIHHHXccoVCIBx54gH/84x9cf/31HHLIIY7Vf9BBBwHwySefsGzZMt5//32GDRsGwK233sqCBQt48cUXOemkkwCYNm0akyZNAuDCCy/k3nvvZdmyZdx0000pj3P77bdTVlZGWVkZ06ZN6xFsvHLlSsfOJx2psqknm/bvsybhD6ITXVRdHXrhQkeTmiqlMH5j/Inf7RPyi/j2lkzDUqUtcVvDPHHolFKDgQeBycBW4Eat9bIE5UqBXwLnAsXAP4DLtNYb3bItZVJAm7FSQv4wYMAAvvCFL/D8888zfPhwbr7Z2SSuH330EQCh6EPD0Ucf3eP99vZ2Pvzww67Xo0eP7vH+6NGju+pIRjgcZv+Y3Iz9+/f3LDFrOsdDnDeLRB06PWMG4GxSU787S363T8gv9M03w0EHQUsLAMfsg49ao2/uhb6nAX2A4mKq+ven6rPP4KftyarrTXExh8HYbGz0qsvpHqANGAZcCNyrlBqfoNz3gSrgaGAEsAO4203DTO969uwEa7BJD11Bc9xxxwEwb948ysvLHa37kUce4cADD2TcuHEAvPXWW+zcubNr27NnD1//+te7ym/YsKHH5zds2NDVyxfKsJ2eddZZXb13ibaHH344s5NLQCbxJJK2JAFRh25ZNMYypX7ZZNmyXs/YvsLv9gl5xM9/zrKf/AQ2boRdu2DXLvq27mIw3VvfVmM/W7fC++8bf6NlLW1btzIABmVjZs49FKVUf+A84BatdYvW+u/An4FvJig+BnhSa/2J1nof8AiQyPFzlKRJAaWHrmBpb2+nvr6eyspKLrroIsfq/fDDD7n11ltZvHgxv/zlLxk2bBjTp0/niiuuYONGoyN6586dPPbYY7REnwwBHn/8cZ555hk6Ozv53e9+R2NjY5fDN3z4cLZs2cLu3btt2bJq1SpaWlqSbhdeeGFX2fb2dvbt20dHRweRSIR9+/bR2tqaovae2HE8Ghrg8ssNpy9+5ljBE3XoVsb0zjqV1DSXQ/CZ4Hf7hDzisccIQmvzYsh1HNChtW6K2fcycGqCsg8Cv1RKjQB2YvTmrXLdwhjMlAnV1VCVpuejR1kZLsorfv7zn/Pee+/x8MMPo5TKqq7Zs2fz05/+FKUUQ4YM4YQTTmDNmjVdPYD3338/c+fOpbq6ms2bNzNo0CBOPvlkJk+e3FXHJZdcwp133sk555zDwQcfzPLlyxkzZgwAp512GpMmTep67QYzZ85kyZIlXa/79u3LqFGjevUcxhJ/f1gZVjXjVPbt656snOsFr32N2UNXWZnwbTuaFF/W7z1gfrdPyCMmTmTZmjWYg/xWfwFSBQVk9yuSGC8cujIgvutgF5BoDOst4ENgI9AJvApcmaxipVQNUAMwcuTIrA2ND3pc9+MQEwAGDEhbNtvhDsF7tm/fzpNPPskrr7zCz372M6699tquVSMyZfjw4Wl7svr168ecOXOYM2dO0jJDhw5lwYIFCd8rLi5m+fLlAFRUVABGqpOOjo4e5Wpra60bHsfixYtZvHix5fKZ3h9mrJ3pzCmVv2lLMtKvqEM39R//YEXcW4mu+YQJExJWk6js3LlTWbEivlb/MHWqv+0TnCFZm80lL4/9b37IbP5ACTsYzJDBUFqSpHCfPjBoEOzYQfP2Vpqbezp2IUj8+T592L1hw85s7PTCoWsB4j2iAUCi6Ox7gFJgCPAZ8L8YPXRfTlSx1roOqAOorKzMOmI2PnD7ldfDrAU48cS0ZaUHIfg8+eSTTJ8+nc997nNcc8013H777V6bZJu1a9d6bQKQ+f0RG+RfVAQXXwwzZuTnvZWRfkXDQGpi8tCZJLrmydpDorJ+yP2VCr/bJziDHzRs7b8i1ABvMJ7jwuuYfZ0R1pCO12MelGL1a0QS/XpLqXeysdMLh64JKFJKHaa1fiu674vA6wnKHgPcrLXeDqCUuhu4TSk1VGu91e6B7Q6Jxs8YO+qL0SHXzs60ZfOxB6HQ+PrXv95jIoKQOZneH6lSABQivTQs2kNXba5iE4Oda56o7JFHpviAD6gWkRVyROWECGOA9YR8rV85d+i01p8ppR7FcMy+g+G0nQOckKD4v4AZSql6YA9wBbApU2fO7pBP/JdxREt0UkQkkrZsof/wCO5RH8B1r6zeH4keuiSFiUFCDYs6dCPq63sNcdjRpERly8tHeJbWxgojRvjbPiF/OHp8J+XA+oNCPPMH/+qXV4mFrwAeArYA24DLtdavK6VOBlZprcui5a4D7sKIpSsBXsPISWebTId8enwZz4SMQMann04Y7Cg/PILfMCdw+CFnV/z9ES9+EoeamsQaZkh4czThdDzx1zxVe4gv63dnye/2Cc7gCw2LRIwHpgNDXcOlftQvTxy66BDqtAT7n8eYNGG+3oYxszVrHBkSlTx0gmCZVCEO8eK3YAEsXw6trUYHuJWHrkKbVZ5QwzoNCa/7+9+pGTzYemUNDWkvWl1dna/j1PxunxBseuhLJEIdUBONWU3kvC1d2j0b3yv9KpilvxwZEs1hHjrJsyUEmXRPq7G9Ta2tcOWVxv+RiPHclO6hK1H9+U5CDbvpLwA0dnZSs2OH9cpOPRWeey6lEDY2NjriMFnwHTPCKfsEIZ54ffnn/AiNQE20Uye+t3zpUli0qHs2fjhsX7+cuEcKqssp64SbOeqhM7/s2NeCECQSDQ/GErtSRCjU05k7/fT0Apeu/nyll4ZF1+Kts1tRe3vai1ZXZ7vWLmI1y61E0NnYJwipiNeXf68zeuhMHyB+pRsAMyuUUvDtb3ujXwXl0GVNjhw688uOfe03/BCTFTQK6ZolWtordumu2JUi7rkHSkuNsqWlUFub/qErk6XD8pLzzweg2u7niovTXrRsZpHGapZbDrfMchXcIl5fjj26k2rg/Q9DvfTrmWeMVCRm+T59jNd26neqKRfMkGsqEgU3JhyajRlydWsYAbq/7L17u1/7iXA4THt7OyXmo4lgifb2dsIFsnxc/PAgJB5iMO+ho46yFw4hs8qj1NTwzjtw9X0/obWorStZaWsbtLZDaXFcAtPNmwF4bcHfODLNRcsm8XSsZrnlcGdjnyCkIl5f1L8i1AJvvxdm6sTe+gX29Mg1/dJa5+VWUVGhrbBmjdZ9+2odDht/Fy7s+XrNmu6yj974T42R9LnXe06zZo3uOpbf2LRpk/7000+9NiNwfPrpp3rTpk05PaZf2tDcucY9BcbfuXOdPwbQqH2gPU5sdvUrFNrYpUnxmmbqVKymDCnZnVa/Nm7caMmGZJjHcksns7VPCAZ+0LDfXfyk3gj6SSa5pl9aZ69hBT/kGj+WvXx54rHthgaYd0eYhcDNHExrq7tDoX7ucRg8eDA7duxg69attLW1FdRQol201rS1tbF161Z27NjBYDszER1g4cKFLFy4MKfHTIQMkbqDqV+RSGWXXiWLz1m6FO6kHwsB3dbG0qWp665Msj6sXdzSMqfsE/yNHzTsi0dFqAS0zcTCuabgh1zNH5rWViOY8Zhj4Pnne6c3qa+HjkiIGuDfDOaONLNY8pnS0lJGjhzJ9u3b2bBhA50JVs4QugmHw5SXlzNy5EhKS0tzemy/zAKUIVJ36NavTSgFQ4YYw9fJUjRNp4xh7OFHtKete9OmTa7Z7QR+t09wBj9o2BGHR9gEvDMuxDOL/atfBe/QVVUZObDMtAl332283rat5w9PdTU8VhKCViiik1/9yr9fai4oLS3lgAMO4IADDvDaFCEgJEu8XWj55JzE1K/LL59PJDKLq682HOdEzvOMGdB2nxFQV1bcljZwe/78+cyaNctF67PD7/YJeURnJ/OBWYeFGJsgp6Zf9KvgHTownLdIpDuh6bZt3Qvvxn5ZdQ+GqfsGtA3ZxpU1vd/3+ssUhHjM1A5+eMpNdK/4Ibt60Nm2DbTe1COhaWxqk9jr/ueB+yjeBX98uI1jU00Aw/89YH63T3AGX2hYxOih274rzMJ5/lodogfZBOD5ebMaVKx16iDi2P0vLftPjwDNZJ9zCnwQDCoEG7+0oWT3ipOTJSjASRFap9ah+Pe62sN//iP6JQQCX7SjRx/VGvSfQtN63C9OT/bKVsMKflIE9M4pY3rY8cHFLzb2TDnhRXLT2tpalFIopQiFQuy333586Utf4uabb2ZzNCWB0zQ1NVFbW8vOnTt77F+8eDFKKVpaWhw/ptaauXPncvDBB9O3b19OOeUU/v3vfzt+HCE3JLtXZLJE9lRVwciRFb30C3pf9y7a2tLqV0VFhfvGZ4Hf7RPyiEiECow4+tj7xW/6JUOuURLF98SvnXjc8aGU7+fqyxw4cCB//etfAdi1axfr1q3j3nvvpa6ujr/+9a+OC11TUxM//vGP+da3vsWgQYMcrTsZt99+O7Nnz+ZnP/sZn//857nzzjs5/fTTee211xg+fHhObBCcI9m9IpMlnOHhh+tIdNvHX3cztyXt7Wn1y+8rMfjdPiGP6OykDng/FCKsuu8Xv+mXOHQpiP+yjh3W7dDFZovO9ZdZVFTE8ccf3/X6jDPO4PLLL+eUU07hggsu4M033wx0Att9+/Zx++23c+ONN3LllVcCUFVVxejRo/nVr37FnDlzPLZQsEuqeyXZZAnBOuXl5Qn3x1/3E04w9i99oI3DLkqtX8nq9At+t0/IIyIRyoFTTgsze2LP+8VX+pXNeK2fNzsxKFZpXL4hZ4mFtU4cO3DrrbfqIUOGJCy/atUqDehVq1ZprbXeu3evvv766/VBBx2kS0pK9NFHH62feOKJHp8ZNWqUnjVrlr7tttv0sGHDdP/+/fX06dP1zp07tdZar169ussOcxs1apTWWutFixZpQL/yyiv69NNP1/369dOHH364Xr58eVbn/cwzz2hAv/HGGz32X3zxxXrChAlZ1V1oJGpD+QoFGkOntdaHHXaY1WukAf1fodVpNcxqnemO5RbZ2icEA19o2MMP68NA669/3dXDZKthEkNng4YXuy+XHxcEr66upqioiBdeeAGA888/n8WLF3PTTTexYsUKvvSlL/HVr361Vyza7373O55++mnuv/9+7rzzTp544gm+853vADBhwgR+/vOfA/Doo4/S0NDAY4891uPz06dP56tf/SqPPfYYhx12GBdccAEfffRR1/uRSISOjo6UW2wuO7OH8bDDDutxnCOOOII333zTseslCPlCU1OTrfKhSHtaDbNbZ67xu31CHhGJ0AQ5W889U/xtnc+oOrH7cvkhADKePn36MHToUD755BOeeeYZnnjiCf74xz9y+eWXM3nyZB544AFOOOEEfvKTn/T43N69e3niiSeYOnUqNTU13HPPPSxfvpw33niDAQMGcPjhhwNw7LHHcvzxx3Psscf2+Pw111zDVVddxeTJk1m8eDGRSISVK1d2vX/bbbdRXFycchs7dmxX+R07dlBWVtZr2Hi//fZjz549tPWI7hYEwe66pn1DbWk1zO9rpfrdPiGPiBhrufrdoZMYOhtUHBdGAy1lw3j1KR+Nm8dg9NrC008/zfDhwznxxBPp6Ojoen/ixIksXry4x2cmTZpEWVlZ1+tzzz0XrTX/+te/OOKII9Iec/LkyV3/DxkyhM997nM9euhqamqYMmVKyjpyvYJCoWC2B0EA0OecA3/6E//f9HZuvMKfGiYIsfhCw8wRJJ/HpotDF0PaJMFR77ysbyRjIXQzEfG+ffvYtm0bw4YNY+PGjWzevJni4uJe5eJ7vj73uc/1eN2vXz/Kysr4+OOPLR03fuZrSUkJ+/bt63o9fPjwXseIRynV9f9+++1HS0sLnZ2dPWzdsWMH/fr1o6SkxJJdglAomL1VafUleu+cP7UN0uhPsh4wvyRTlx46IWdID12wsJTx2XQu4tYutSpwbmeVXr16NR0dHVRVVfHss89y4IEH8vjjj6f93JYtW3q83rNnDy0tLY4t63Xbbbfx4x//OGWZUaNGsWHDBgA+//nP09nZydtvv9013AtGbN3nP/95R2wShHxi3LhxLFnSlF5fzAe8mLCFZPo1bty4XnFqfsqMn8g+QXCFSIRxQJM4dMEgUZLNXkIVClEBsGsXa6O77AicpWNkyM6dO/nBD37AoYceyumnn45Sivnz51NWVpbWCfrb3/5GS0tL17DrY489hlKKyspKgK4esdheNzvYHXI94YQTGDBgAH/84x/54Q9/CBhO5ooVK3yxhFWQMHMSrl27Nk1JIcisXLmS5cvT60vFk08CsLa9HUitX7FxsCZuaphdEtkn5B++0LBIhJUgPXRBwVKS4FCIddCjh86OwDmViLijo6NrJmtzczNr167l3nvvZc+ePfz1r38lHA4zadIkzjjjDCZNmsQPfvADxo8fz+7du/n3v//Nvn37mDdvXld9ffv25eyzz+b666/n448/5vrrr+fcc8/lC1/4AkBXL9nChQu54IIL6NevH0cddZRle0eMGMGIESMsl+/Tpw833HADs2fPZr/99utKLByJRLjqqqss1yPAunXrvDZByAHNzc2W9GXdtm3GP9EeulT61dzc3OvzXiVTT0Qi+4T8wxcaFonQDOLQBQVLSYITBETaETinEhHv2rWLqqoqlFIMGDCAQw89lG984xtcddVVXasoKKV49NFHmTt3LgsWLOCDDz5g8ODBHHPMMb2cogsuuIDy8nIuueQSWlpa+OpXv8q9997b9f6oUaP4+c9/zl133cXdd9/NQQcd1DU86hY33HADkUiEefPmsW3bNiorK/nb3/7GsGHDXD2uIASRmpoa1q5da11foj10qfTLrDMWP2XGT2SfILhCZyc1wFqfT4pQvphB4gKVlZW6sbHR2Ur37EH17w/0nHnjVpCwOVHAze9o9OjRnH/++V255oT8IhdtKBGZ3BPZ3kdKqbVa60r7n/QfrugXMe1h8GAYOhRaWmj7rI32diO8rsScQ1VaCl/6Evzv/2Ysal61PSG/8KId9dKiu+6C738frrrK+N/KZzIgWw2THjo7JPHOzaU/Ghpg3jzvn1wFwUti47KKiuDii2HGDG8nDOU7s2bNYv78+akLNTR0/799u7EBJdGtV50bNzL/iSfgued8+WVYOmdBsEki/frf0gi/AuYnGXL1i375e0DYb6QYPze/0FtuMf7GaqcgFBKxcVmtrbBwYfp7IlEsl2AdSzGqNi/qCDCGZn36ZdiJyxUEqyTSr/vuiRj3QxIfwC/6JT10dkjh0Plp9pcd3I6FE3zCF74AO3YY//fpA4MGGa9bWx0/1LVtcFEnRMwdGtReKD8DGJL42OZnNKA6YcjPgcQjG0k5EsY7cgIBZNasWekLpQjwjR/MUsAsMMZh/bYkThRL5ywINjHjSvftA62Njc5O435IMkrnl8lC4tDZIRRipvm/1hCTDHfIEMPf09r72V+CYDLzpJPg73+HN97I2TFLgYQZDJujm4XPqO0ZHbeP/U/lByNGjGDTpk2pC1VVMfOrX4WXX4Z+/Yw4uahjvecz2NlsOHb92MdgdjIiFGKTT4dbweI5C4Fn5syZ6Qs5iDnxZ+lSeOgh6OiAEBFGaNiUpFPHL5OFPHPolFKDgQeBycBW4Eat9bIkZScAC4AJwGfAXK31L3Nkaqwh1CnV7bZHHbqGBrj6aqN3LhSCBQt8q4FCgVGXYKWQXKDSF3HkM4KB1QkUdX/6U699ZrhIa8jQrxWXruTMe6bSeNppvhYyNyaNCP6jrq7O1foTTWYw4+KPPRauvBJUR4RGYOPHIQ5MUo/5GS/xMobuHqANGAZcCNyrlOo1ZKKUGgr8FVgIDAEOBZ7KoZ09MT30SNeAUtdwayRi+HlmqidB8Jyzz3atap1gs/O+4BzZrJgQr1879xhTJJp27nTGOJeQVSKEbEkX+75tm3FfKB2hCdjwob+nHXjSQ6eU6g+cBxyptW4B/q6U+jPwTeCGuOLXAk9qrR+Ovm4Fcjd+FMfaaK9cRWenMQWGnuPnRUXwwQdGw/DaWxeEtUcfDUBFKATmeroOxdA1fwbN0WE6BZSXw4D+3e+3tcG27UYsnQIGlUP/JDF0TtC6eXNmS5nkAbW1tdRbiMQ287aZ2feht359vNXo1a19+23S1+gdVs9ZCDaJ2qxTpIt9r6427olwp7GW62/H+DsPnVdDruOADq117CPWy8CpCcoeD7yqlFqD0Tv3IvBdrfUH8QWVUjVADcDIkSMdNxqgsqMDAB3TQxc/5n7//bBkif2py35Z9FrIHyonTwZADx0KH3/saN2vx0/Vf7Jnuy0FVtbBd79rPOWWdsAzy9xr268p9bo7NeeGbPTLqmNjLucXm9MrXr8ee6KEa4A/j7Q/xyRWw9xGnLnCIFGbdQorkxm0hjCd1AMfKn/30HllXRmwO27fLqA8QdmDgIuA7wMjgfeA3yWqVGtdp7Wu1FpX7r///g6am4CY5b/AEMWRI43dmUxdlrQngqso5yPUTEdg9uzkDy8vvWTcD5GIpCNJRzb6le0ax7H6tS9i9NBd8Y69Ic14DXMbWddZyJZ0GlZfb0yKUESoAd57398OnVc9dC3AgLh9A0g8B24v8JjW+l8ASqkfA1uVUgO11rvcNTMFMT10JtlMXfZLHhshT3HBoYPUgcANDUaPj/lgXVQks7/dwuzFyAZTvzpbSyACXy6zN2k4XsPcxolzFoRUGjZkiPFTHyJCJdC3v78dOq+sawKKlFKHxez7IpBoyOQVesZT+yO2OoFDZ6XHIhmmmIbDkvZEcAEPFpWur+/uyFbKyLguoQTu4ERvlalfl11l9NBdNSTRgEly4jXMbaSHTnCbbdsM6QxFe+g+2+dvh86THjqt9WdKqUeB25RS3wGOAc4BTkhQfBGwXCl1F4bDdwvwd09756DXkKtJplOX/ZLHRshTXOqhS0V8j/WMGTk3oWAoLy+nuTlJkj8bVFVB1dAS+CWUv/lmsrSBST8bq2EnJFJzB3HqnAUhGdXVRrrGon0RyjW8NlYmRSTjCuAhYAuwDbhca/26UupkYJXWugxAa/2sUuom4AmgH/B3YLpHNneToIcuW/yQx0bIUzzooZOHlNzhaILdaO7CTQcmy7iVnFxqmCQVFtzG1DD1g042PQ/lY6SHLiFa6+3AtAT7n8eYNBG7717g3txYZpGKCmOdwzS0tkFrO5QWQ6mVYYjYlBImkgNFyJYc9NClStApuEt9fT1Tp051prLoeGn9Z58x1Mcz7x09Z0EguYbxpQgrnoepHjwY20GW/rJDQwNduck//DBtcQ2URDfzdUY/q6eeCj5egkfwL41/+hOcc47rPXQN8SlMbMaQCtlRV1dnybmxtLpCtIfuvuYWVvv4O7V6zkKwydWKICk1LBKhDv87dP62zm/U11MBWE1vqBJsGdHeLtNehYyoGD/eaK8u99DJLG1vWbFihaVyFRUV6RO0Rnvolof7+vo7tXrOQrCx1GYdIKWGRSKsAGPGj48Rh84O1dW2vlDHlj4qLpZpr0JmmDlDMnToGhpg3rz0eRFllra3TJ/uYFhxtIfu4vYWX3+njp5zFtTX11NUJINdfsSqfkEaDevsNAL3fd5DJ63QDlVV1Jx9Nrz8MnWQdukiRRYxdO+/3x1HJ8OtQobU3HwzAHUZCJGVYdTYmBOZAOEdU6ZMsVTOTPWRcsHzaA/dVK35no+/0/hzrq6upqGhgZKSEkKhEEOGDOHEE0/k6quvdqyHZ/HixcyZM4e3337bkfri2bJlC9dddx3PPfcc27ZtY/jw4VxyySXccMMNKA9mqvsBS202AXb1K+UkrkiEKeB7hw6tdV5uFRUV2g2IdrbFs2aN1nPnGn8d4Re/SHosQbBKVxs67DDbn507V+twWGsw/s6d2/P9NWu07tvXeK9v38Rt3/H7IgVAo/aB9jixeapfkYjxpYPWHR2OH8stTj31VD179uyu1xs2bNA333yzLioq0o8++qgjx1i0aJEeO3Zsj32rV6/W4XDYkfrfeecdPW/ePP3uu+/qSCSiX331VT1q1Cg9f/58R+oPIpm2Iyf16+NplxoV3XtvhmdhjWw1zOfuZjCws2yX5S7gAn0aE1wigyfLdMOoqWJOGhrg8suNz8hydu6TTe9NL/16QUFJiRHzm2Amv51hLDdJd86jRo1izpw5zJgxg6uuugqtNXv27OG6665jzJgxDB48mDPPPLNHb1t1dTVXX301U6ZMoaysjPHjx7Nq1SoAGhoauOyyy3j33XcpKyujrKysx3qyjzzyCGPHjmXgwIF87WtfyyhH3iGHHMINN9zAmDFjUEpx5JFHcsEFF8i6tRngpH79ZUXEuB8khi7/sRoQbmu9Vr937QrBIoMf/HQrnyQTTLOdL1woEyVyhfFwnxkJ9au42Ij5jXPo/LTmtNVzvuCCC9i4cSPr169n5syZvPnmm7zwwgts3ryZL3/5y0yZMoX2mPN88MEH+f73v8/OnTu56aabOPfcc9mwYQNVVVXcd999HHLIIbS0tNDS0kJ1tNF3dnby1FNP8fLLL9PU1MRLL73EXXfd1VXnlClTGDRoUNJt2bJlCW2PRCLU19fzxS9+MfMLVaA4qV86EjHuB5//LvvbuoBgNSDc1kxA6aETnCRDIaqqghtvTBw/lUwwzXauY+Zj+DGoPp9I5hBYIaF+lZSwDHotyuqn2cxWz/mggw4C4JNPPmHZsmX8+te/ZtiwYZSUlHDrrbfy8ccf8+KLL3aVnzZtGpMmTaKoqIgLL7yQyspKS8e6/fbbKSsrY9iwYUybNq1Huo2VK1eyc+fOpFuyCR7XXnstO3bs4LrrrrN0rkJPHNOvUKdxP/jcoZNJEQ5gNSN+/FJIsU8EvT7r84YjBAyLDwgJ22IKEiUOjm3nRUXGGq4zZvgvqD6fWLlyZcazPpPp10pg+rHH9uilu7YNLuqM5tTshCE/h9afp5n45VJidKvn/NFHHwEQimrq0Ucf3eP99vZ2PozJKzp69Oge748ePbqrjmSEw2H233//rtf9+/fPelmya6+9llWrVvHMM88wcODArOoqFNzSrzPej3D1Kpju899lcegcIlVG/FQzAZPOxPF5wxEChgWHzqnkwLLkV+7JpocO4vSroQF27DB6JKKOjjm4WQIcEPvB7d37zXK9WppLidGtnvMjjzzCgQceyLhx4wB46623ejhf8WzYsKHX66985StAt1Nol7POOovnn38+6fsLFy7kwgsvBIxh1ksvvZSGhgaee+45hg8fntExCw1X9eubEemhy0cmTJgAWH8SSNTIbryx+/1EQxhVVciQq+AIE444At54I60QNTRAba2RiScSiWuLGSBLfuWWqVOnWkq0a0m/ouOoUwGzxqzUyEyM7nCDSHfOH374IQ888ACLFy/mkUceYdiwYUyfPp0rrriCBQsWcOCBB7Jz505Wr17NpEmTKCszVpx8/PHHeeaZZ6iuruYPf/gDjY2N/OY3vwFg+PDhbNmyhd27dzNgwADLtpoTK9LR0dHBN7/5Td58803q6+sZOnSo5WPkK2abTYXr+hWJGPeDzydFiENnk7Vr19p6EkjqsEWJ79794IPoCIXPnwSEYLD24YdhwoSUDwhmezbFMBSSmLegYebqSocl/aquhqIiajo6gMQJ0c3WlGxaQo/W5lJi9ETnPHv2bH7605+ilGLIkCGccMIJrFmzhuOOOw6A+++/n7lz51JdXc3mzZsZNGgQJ598MpMnT+6q45JLLuHOO+/knHPO4eCDD2b58uWMGTMGgNNOO41JkyYxZswYOjs7+dOf/uToOf3jH//g97//PaWlpT2Gfk8++WTLTmG+sfZXv4I77oDRoxPmfm1tg1Hb4cGYfe2dfdh/0SBYuCNtvlhL7NhBDcDq1eCThNaJEIcuA9I5abEki5uL5aKLYPNm+Mtf4P77YckSePVqcegEBzAje1M8IJjt2XTmTj/deNpN93RrN15FcI9qGw5TWv2qqoL/+z+qf/ITeO012ppb2ba9ezh1yODuWLm2Ntiz12g7+6K/mwoYsl8EdmwxdriUGD3+nK2k9ujXrx9z5sxhzpw5ScsMHTqUBQsWJHyvuLiY5cuX99rfEXV+TWpra9PakohTTz01qxnLeUdDA5x0ktHAktArDCCKeit11bFX2UoPdDXAAw/Al74EFh+gco04dBlgxUkzSRVPFPukHAoZAmt2F7+5XoZcBQcwhTBFD118e7bqzDkRryI4w4gRIywH4VvSr6oqRjz3HM3NzZQC78c47yOi33Mv/QoZzS0chjuu3Amz9+uqyw3snLMQUOrrUzpzkHk4gN3PjQCaAZYvD7ZDp5S6D7gUOFBrvSnuvcOBV4H7tNbfc95Ef2Ems1yzRlvunUgWTxT7pKy1IYpmiofDj5AeOiF71Je+BICOtttEvWqZTGKw00stuI9Vx8aOfsXWmUjDUunXiae4H2skzlwBUF3dY3g/Wa+a3d62VGEEyehqbeedZ+EI3mC1h64Bw6E7Dng87r1fALuBW50zy/9YDfpONSwV/6S8YAFs22bsP7RJeugEBwmFUvaq2Z3EYKeXWnCfuro6y3F0YO37NutMpmGp9OvLR7v/QGr3nK0gKzL4jJgG17rf59i2I5Rw6L/Xr6W5JvqOxDF0bW0kDSNIRp1S1NTW+rZ3Dqw7dC9E//Zw6JRSZwNnAd/VWu9w1rTgk25YKmXPyNvSQyc4iFIsXQr79hm9KU7MApPUJP6hsbHRceemsbGRo46qSfkQkLQN7HO/h86Ncxb8y30n/JZr/jIJrY1h/dnX9cwYYYdkYQSpaKyp8X17s+rQNWFkHDrO3KGUKgbuBF4DFjpvWvCxMiwV/6RsPg2f3yoOneAczZ+FWLSoe45EOJx9r5qkJvEPdXV1rtQ5b15qDUumX6ed5L5D58Y5C/7lr0+FPNWvILQ3Sw6d1lorpV4ATlRKKW1Mw/k+MA44XWvd6aaRQcXusFRsj976kAy5Cs6xq1lhTsRTCr79bXHG8onq6mrHhwurq6uZN6/esobF6lefYvcfSN04Z8G/dHQav4le6VcQ2pudWa4vAF8BDldKbQduAR7XWj/jimV5gN1hqR49ehHpoROcY8CgECWfdv8wz5iRuJykIgkmmabJSFenHQ2L1a99Oka/tHYlUbob5yz4l1BRiHCnd/oVhPZmx6FriP49DjgFYxh6luMW5Rl2unVje/TCIQXtaT8iCJYYMECl/WGWVCTBxVzWyo06rWpYzxEJxd690TfMXCYu2ScUBvN/EWLFLu/0KwjtzY5D908gAnwHOBH4mdb6XVes8jELF9oLF7TzxBD7NPzfkRC//WGmVgqCwcJZs2D+fFAq7Q+zpCIJLpWVlWzatCltOTv6VVlZyfLlmzLSr+pqOOGE6Budna44dFbPWQg2Cw85BN59lyOPDnHkScnLua1fQWhvlh06rfVupdR/gJOBzcBPXLPKx9iZ5ZLJE0PXj+5yGXIVsqfm7LMNh87CUnKSiiS4WP2hsaNfy5dvyly/Yul0J8Ta7z+ugjPUDBsG776bVsPc1q8gtDe7XsM/o39v1FpLVsc0JHpisIwLMSdCAWJOC7PQnsweltmzZbg1aMyfP9/xOmfPnp+5fsWSJtN/prhxzoIPsbDaDbivX0Fob5Z76KJpSqqBRmCJWwb5HXPqspUn3ayeGCz0qAhCOupWrACgxmJ7StTDIhMl/I/V3gM7+jVw4CZnejykh07IgrrNmwFrGuamfgWhvSmrCwErpW7EGGY9QWv9QrryXlNZWakbGxsdr9dcOsfqdcu4Mf35z6hzzrF1LEGIp6u9TpwITz+dtnx8e7UaNuAHp08ptVZrXenN0Z0l8PoVe6zt22G//ex9WBCidLWjf/4ToksZpiK2zUJw9Auy17CUPXRKqcHAGcDRwPXAnU45c9G6HwQmA1sxhnGXpShfArwMlGutD3LChlxgN3mh2bDOLZIhV8E53ns/xOaG1G0xkfNmJdBYZsd6T0VFBWvXrnWlzkz0q0dvnks9dG6cs+BfFi0J8fkOexp20UXO6VcQ2lu6IdczgGXAFow1W29w8Nj3AG3AMOAY4Aml1Mta69eTlL8e+BQod9AGXxHbsP4VliFXwTneelsxbWJqZyveeVu61NhfFFWJZMNuMjvWe9xaKcIO8T+MXbgUQxeEzP2Cc/z6XsXrD1nXsNZWWLfOOf0KQntL6TVorX+ntVZa62Fa6+udWhFCKdUfOA+4RWvdorX+O/Bn4JtJyo8BvgHMc+L4fiW2YbV3SA+d4BwRVNrAdjPmMxw2RPChh+D++415FTNnJhfS2M/J7FhvKC93/jnXbp3xP4xduNRD58Y5C/6lPRKyrGGhkPEc0djonH4Fob151Q00DujQWjfF7HsZGJ+k/N3ATcDeJO8DoJSqUUo1KqUaP/30U2cszSGxDStUJD10gnNoQmmdrdhZYhdfbPwOm9vIkcmfimV2rDNko19Tpkxx3B67dcb/MHbhkkPnxjkL/kWFrGvY6ad3O3VO6VcQ2pvlSRGOHlSpk4E/aq2Hx+ybCVyota6OK3suUKO1PkspVQ381koMnV+Ciu1ixqCc0/cpxl9zhqvHEvIfs72+dfhX+HTRE5adrSDGxcmkiPTkSr+MxMLRY73/vvGLKggZYLbZuu+9ypEXHGlJh4KoX5C9hnnVDdQCDIjbNwDokdsuOjR7B/C9HNnlHyQPneAghx4WsiVo0usWLNxay9URXOqhC8LamoJzzLzUuoa5oV9BaG92lv5ykiagSCl1mNb6rei+LwLxEyIOA0YDz0e99BJgoFJqM3C81npDbsztxs3estiniucTTIrwy9RqITjoxx+HadOSPiCYbWrIENi2rWfbsjtDW/A/udKvREOuol9CJujDD4f161NqmDmBa8aMwtYvTxw6rfVnSqlHgduUUt/BmOV6DnBCXNHXgINjXp8A/AqYgDHjNa/oEVSsezp0Qe1CFjwm+gPe9HaIbXFpS8w21dpqxJqEQlBaKm0riPihhy7ppIhIxBX9CkKPiZA9e/dE6Au89HKIY4/o+V5DA5x2mqFhYEzkcmuWfRDam5eR91cAfTFSovwOuFxr/bpS6mSlVAuA1rpDa73Z3IDtQCT62p1+fA+JDSoOx+Why2oZMaFgWf+GkTLiP28oJk40BNDEbFNmVolIRNpWUBk3bpzndaaaFOGGfrlxzoK/aGiATR8ZAjXjW6Ee+gXdGmbS3u6efgWhvXnm0Gmtt2utp2mt+2utR5pJhbXWz2uty5J8pt7rpMIVFRVUVFRkXU9DA8yb1/MHNnbcf/4ven41khpCyISv/Ox6KkictiR2ij8Yf6VtBZOVK1daKmdHv1LVmU6/nnkmpnBnpyv6ZfWcheBSXw/n6A+pAFrbQ72cNbNdmRQXu6dfQWhvXsXQBZZ169ZlXUeq4Yeucf/ne/bQmWIpMSiCHd7d8S6QOG1JbJtKFEMnBIfm5ub0hbCnX8nqtKRfsUQiruiX1XMWgkt1NdyE0QX3+eJQL2etqgpWr04cQ+c0QWhv4tB5gKXM1AkWIi7EIE/BGb5wpOKZut7tR9pUflBTU+P4skTJ6rS9Mkh0UoTTbc2Ncxb8RWx7eXiZYkKC9pMrDQtCexOHzgPMbmLzCTdhF3ECh04Q0pFsJuERXwiBOG55ixs/NMnqtKRfsbiUtsTvP65CZiTTsAmV3v4mBqG9idfgAZZy5MRM0Y4PBI0nUTyLUHiYQ2G33EKvCRCS1zC/mTVrVs7qtJvja8lDna5omBvnLHhLSg3zuJMjCO1Neug8Il038auvdzfeiSkWVU8UzwLOxqpI/qhgkHImofT45jUjRozIaZ12hrnuXxhh3WLrGrZggbV4TqvnLPoVHPysYW7cY04jDp1PWbuuu0clVZxK/A2wdCksWeJcvifJfxccUg6Fpeihkx+84JPLHjrbRDota1hrK3z3u0b6xHR6Y8U+0a9gkYmG5Uq/pIcuD5k5c2ZOjhMbL5AsTqWhAT74AIqKusuBzYDlNNgOgBZcJ5mAJZpJOLO62tiRQgzlBy/4jBgxgk2bNqUtZ0e/rNaZjpJQZ0INi12lxPwRD4UMrYnNiZisPVqxT/TLn9jSsD59YN++hD10udQvp+4HNxGHziZ1dXU5Oc7Rx3Q33kSNNLYhh8Mwc6YxZRt69tBlm5PHdgC04CrpBCx+KKzu4osNdUwyXCE/ePlBY2OjpXJ29Mtqnem45OIIP7kk8Sol8cOsQ4bA1Vdb0xsr9ol++Q/bGta/f1KHLpf65dT94Cbi0PmVmB6VdMMUACNHdpdzMt+T5L/zF7YFzFwGIkkPnfzg5QdNTU2Ox/g4VeeFF3T2mmEd3463bYMbbzTeO+ooa3pjxT7RL/9hW8PM9YcTOHS51C837jGnEYfOJubUZSdWi0hJmgDQVA3Z6bw8kqvMP9gVsLXvvQdARZL2JD94+UFtbS31FtY8sqNfVutMS4K0JU7ol1X7RL/8hW0Ni67tlUjDcqlfjt0PLqK06f3mGZWVldqNLlIV7elw/bq99hrqqKNSHkuC2QsTO997V3u9+GJj5eo8Rim1Vmtd6bUdThB4/Yo91l/+Amed1et90a/CJSMN27EDBg1y2zRPyVbDJJeBX7GQN6yqyhimqKqSXHSFROz3bhlJW5LX1NTU+LfOJImFs9UvN85ZyA1B1LAgtDcZcvUrNhqvzFQU0iKJhfOaykrnOyYdqzPNShGZ6pcb5yz4GI8duiC0N3ls9ys2foBTJmMUBBCHLs8JYg+dSab6FYQeE8FBpIcuLdJD51dsNF4rQaaxMQtQGLErEqMTgwy55jXl5eU0Nzf7s05zpnUSMtWvOXPK+ewzZ8/Zb4iGxeCxhrlxjzmNOHR+xUbjTTfTJz5nnVLQ0ZHfw7P5NgydtbBLD11e40bCU8fqTNNDl6l+FRdvoqEh2Pd1KvJJwxxxTD3WML8nFQYZcvUvNhtvqiDT2CGN9vbCGJ7Np2HolAtWW0V66PIaN9IpOFZnGocOMtWv+kDf1+nIFw1zRL/Acw3ze8oSEIfONo2NjbnJGO1g4zWHNMJhKC7u/j+fE8nGnnPQzzMbYW+87joaIe0DgsySDjZWV4Cwo1+OrYqTZsg1Hcn0C+oCfV+nI180LFvHtFEpQ8NS/CbmQr9ytUpUNsiQq01cTyhsYnOWa6ru7PghDcj/uIx8SZibaL1eK8JutokLOg5mDKQVw3wZ2ilUVqxYYamcHf2yWmdaLMxyzUy/VuR1O80XDRsyxJAfre3rV3U1VKVYKcIsmwv9cux+cBOtdV5uFRUVOtC8/74GtPEVJWfNGq379tU6HDb+rlmTI/sE14n9bktKtL7ssu7vd80arefOTfx9x37u2vACrUFv+n/fS3qcuXONsmD8nTvXpRNyGaBR+0B7nNjs6tfXv/51W+VzUWeXfj34YNIy2eiXG+csOIv5/YZCWhcVab1wYc/3EmlYjzbRJ2IIEyRtG7nSr1y0t2w1TIZcbVJTU2Nr+nJ9fT1FRRl0hFrsocuXOAuhN7HfbWdn93q96WJSYj/3VOfvqQGWP6qSDkfky9BOITNlyhRL5ezol9U605JiyDUb/XLMPsE1zO83EjG8sm3bjP2pNKxHzGRrhBpgJsnj73KlX4Fob9l4g37e4p9wTz31VF1SUqLLysr0gAED9JgxY/Q3vvEN3djYaNeDTtprtmjRIj127Nge+1avXq3D4bCtY2ittf7oo4THuvnmm/Uxxxyji4uL9cSJE13toUvVCyQkx6nrluy7TfdEan5Oqe72+gt1Tcon13z4ringHjqrpNIv1441YIDWQ4Z0bwccoPVhh2l9wAG6dcAQvYXurXXAkJ5lk20HHKD1V7+atsHmQ7v2Ai81LPYzfYrau9pRqt63fPmes9Wwgoqhu+WWW/jhD38IwPvvv8/999/P8ccfzx/+8AfOPfdcj62LI0kP3dixY7ntttt48sknefPNN12Ls5C4qsxw8rol+27T5e0yP7d0Kdx3n7FPhVXKJ1dZwDzYKKUwfg98UmdsV8ru3UmLlQD7x+5IXrQX6s9/Rq9aBc89l7DxioZlhtcaFvuZ/QdGmPldY3+q3rdc6Jcb95jTFOyQ66hRo5gzZw4zZszgqquuQmvNnj17uO666xgzZgyDBw/mzDPP5O233+76THVMayorK2P8+PGsWrUKgIaGBi677DLeffddysrKKCsr6zHN+ZFHHmHs2LEMHDiQr33ta+kTFCZx6C6++GKmTp3K0KFDu/ZltC5eGmQoNzOcvm6JvltT8GbPTi62VVVw773dr6tPC8mPWR7jxg9NVnXmQDA0GHlMkhxLNCwz/KBh5me+8+3u4foFC7x1yP3uzEEBO3QmF1xwARs3bmT9+vXMnDmTN998kxdeeIHNmzfz5S9/mSlTptDe3t7rczt37uSmm27i3HPPZcOGDVRVVXHfffdxyCGH0NLSQktLS5cD2NnZyVNPPcXLL79MU1MTL730EnfddVdXXVOmTGHQoEE9t3Hjut5ftmyZ69chHomryoxcXTcrTnxsJ8kzq5PH0AnBxw2NyKrOHAjGMjDymCQ5lmhYZvhJw15s6Hborr7a27RKXvwO26XgHbqDDjoIgE8++YRly5bx61//mmHDhlFSUsKtt97Kxx9/zIsvvtjrc0VFRVx44YVUVlZa+qJvv/12ysrKGDZsGNOmTeuRC2rlypXs3Lmz5/bOO13vT58+3YEztYeVXiChN366brFP1h2dIemhyGNWrlzprzpjG/6oUTB8ePc2ejQcc0zv/Va36OjEyqKipMOtpgl+uReDhJ+u2/PPdTt0XveyunGPOY1nMXRKqcHAg8BkYCtwo9a6l2eklLoeuAgYFS33a631z5yy46OPPgIgFB3iPProo3u8397ezocffpj086NHj+6qIxnhcJj99++OFOnfv3/GQ67JcGPNP4mryoxsrpuT32Psk3W6GDoh2Piuhy6WDRvSFrHV7t98E444gmWHHJK2sGhYZmR63Zz+HTr5pO5hTq97WYPQQ+flpIh7gDZgGHAM8IRS6mWt9etx5RQwA3gFGAs8pZT6UGv9eyeMeOSRRzjwwAMZFx3ifOutt3o4X/EMHjyY0aNHd73esGEDX/nKV4Bup9AuZ511Fs8//3zS92trH6a29sJe+3ftMrJjDxlidEfbDWKVhZ/dIdFC4lausdNB3FVVcOTQEZRs3cTXpysOku84b5k6daqlxKcTJkxwvM50JFtv1bxPbOtXcTEAZ7//ISfNE/1yg0w0zI1JKF/+UoQJgFYh7vG4t9Cp+8FNPHHolFL9gfOAI7XWLcDflVJ/Br4J3BBbVmt9R8zL9UqpPwEnAlk5dB9++CEPPPAAixcv5pFHHmHYsGFMnz6dK664ggULFnDggQeyc+dOVq9ezaRJkygrKwOgra2NO+64g87OTv7whz/Q2NjIb37zGwCGDx/Oli1b2L17NwMGDLBsizmxIpZ//m0XX548CIA77riQM84wGnN7ezudnZ1s2NDBunUR1q3bF519U0ok0t0tnWvnQTBItpC4lWucKBg52+/k1e9fbiR7OrjgoyvyGqu55dauXet4nYmIjXWaOLF324+9T5Qy8pRZ1q+oQ3d+az9m3iL65TSZapgb+kUkwlqAgQPA4+83m/shV3il8uOADq11U8y+l4HxqT6klFLAyUB8L575fo1SqlEp1fjpp5/2en/27NmUl5czYMAATjnlFN5++23WrFnDeeedB8D999/P4YcfTnV1NeXl5Rx11FH88Y9/RMWsg3nJJZdw5513MnDgQG677TaWL1/OmDFjADjttNOYNGkSY8aMYdCgQTz33HO2Lkosz/+9+5ixsQMzZ86kb9++LFnyEyKR1UQifensPJxw2F4Qq8wAc4fkC4mnv8auBCObSV3TrOUqeE86/UpFtQtjUdnUGdvWE7X92PskEsGefkUTtZ9MSPTLBTLVsHzXLzfuMafxasi1jN4Zh3YB5Wk+V4vhhC5K9KbWug6oA6isrOwxx7jewh3fr18/5syZw5w5c5KWGTp0KAsWLEj4XnFxMcuXL++1v6Ojo8fr2tratLacdEq3rx17cyxevJhLL13M0qXw0EPGjVZSYkzp3rbN+vBDulxm4P6QbD4O+cZe1/in23R64EpOQXOqfYbhAELuSKVf6RgxYkT6uFybZFNnbFuPb/vm+sThcPf7tvQr2kN3DJ+mdB5EvzIjUw3Ld/1y4x5znGyyEme6AccCe+L2zQJWpPjMlcB7wEFWjuFGpvVTTz01d5nWP/us61ix2a9j18YLh7WeNk33et9qxmyr64G6sUZsLtegzXUW8TVrjHVXL7vMWLswm2Nna3tXe7311swqCBDIShFpyZl+xRwrkX6Fw1oXF2t93HHW1vfswY4dWoNu7z+gIPTLPF4QNSxruz/5JKdt1muy1TCvHLr+GBMiDovZtxS4PUn5bwMfAYdYPUbgHbo9exIea+5cw5mLrlesi4t7LtjulMi4veBxrhZUzrXw2jlmOrFzwvauNvTjH9v/cMAoZIduYaxXlPoaWdYvq3XaOVbsfQ/G8nRm27bc3ltatAa9sLg46bHzRb+0Dq6GOWL3xx/7xqHL9n6wQrYa5kk/ptb6M+BR4DalVH+l1InAOcBv4ssqpS4E5gKTtNbv5tbSnlgZtnWMJF3M1dU93+rs7I5rcDIuzu3kkrlKXulFrKCVY6ZanNpOPZbxQQyK4B6xeS39XKd535vNUevutm25vUdj6BrjQlkSHSfo+gXB1TBH7I5E0pfJEW7cD07j5cD0FUBfYAvwO+ByrfXrSqmTlVItMeXmAEOAfymlWqLbfR7Ym1uS/ABXVcE99xhhJKEQlJZ2i4nTInPRRTBzpjszyHKVvNKLbPHV1cZvjlLG30THtCJ2jtouDl1eU1dXF4g6zfv+0ksN7Ypt25bbezSGrs7s6EtCPugXBFfDHLE7xfeba9y4H5zGszx0WuvtwLQE+5/HmDRhvh6TQ7P8Q4og0JoaOOqo3sGnTgWlxqc0mTEjeblsjpWLpJ+uBOpawNShZHpkZVKKo7b7IKhYcI/q6mrHRxDcqBO67/sZM3q3bUvtPRSCUIjqSIT6zs6uHjsTq/plls30/spV0uKgapgjdvuoh86t+8FRshmv9fMW+KDizs6sjpVNMKqV+JD4+Ij//V+tJ0/uGeBcqFx2mREblC6+xs1AZ7PurjbkZpCPT6CAY+hWr15tqZwdTbFapxPHSkTK+6O0VK8Grffs6fWW1fi2dBM0CpU1awwtN2O1vdAws97G5Rt8E0OX7f1ghWw1zMuVIoRUxA6RaW1ryKyhAU47rfvJafVq609HZkoB86E3We9RbHf7vn1wRzT981NPGX9ravJ3Wn8qGhpg0aLup9pwOPlQg1tLhMX2UHQhPXR5jbnSjd/rtEpaDSsqYlxrq5FPI+5zVvQLempYZyf885/GBoZ+mfUVkoaZ2tHaanSOhUKpr6EbS4TF6tfSIv/00Hl5P1hFHDqbLFy4MDcHysKhW7rUuCHB+Lt0qXHTpBOn+AzhU6caa2EnIra7XceFsixfbgwJF+JKFPX13b8xSsG3v+38eadb5SP2h+o8JjOZpySGLs+prKxk06ZNacvZ0S+rdbpBOg27PlRMJbCpvb3rM/H6NXMmHHtsd2xX/H1oatjevT33L1/e/UBaaBpmaofpzJ1+OtTWOnvedvSrU0dYCMb6cB7j5f1gFXHobOLJ8h/m3ZUFVsQp9kbSGp54wjj0kiW9y8fGR+zc2d1DB3DeeS4tAxMA4uNKUsXvZEq6axtrw0nqKGo6npIeujzH6g+NHf3y249XrIZd0lnMJjCWMogSe1+YpFoj1tSwO+6Axx/v3h9dOKggNSxev5x25sCefpUUaWpagUGDnDUiA/x2PyRCVD4I6CRRqUmYMaM7NYDpVKSbkRQ7VBEOG7//5lBEslmYVVVw443w05/CwoUwebLxt6bGm5lZuaahAebN6zldPxez34YMMb7bZMMhsTZ87fxo25Eeurxm/vz5gajTKuk0rJ1i5kOXQxevXyUlRj3pZpJXVcFjj/XWLyhMDfObfv1mSXTI1QcPpF7eD1aRHjqbmFOXc9pTZ3OmT1VVd16n2OHVZDOSkg1VxD7dphOzmppuITRt8GJmVq5I1ePp5uy3hgbjezHXv1ywIPGxTBvqTn8ZgBpx6PIaq70HdvTLyx6JdBrWGSlikwY6OhLql9kzvmSJNQ2L1y/ThkLUMD/pF29GjLXwdu/Gg7GxHgShh87z2VxubYGf5Rp7rL17Hakv2YykZLPCcr3cTJDIJlO82zOQY+lqQ7/4hf2DBQwKeJarVTzRLwcx7529B441boKmppT3hGhYcjLVsFzql379dd/Mcs0F2WqY9NAFAYdy8ZhPQvFBwsnyCWX7pJbPM8Ss5JFLRLaB1pkeV4Zc85uKigrWrl3r+zqdIlJUTAWwtr095T3h1kzyfCATLcm5fmn/JBb28/1gIg5dEBgxomekb4Z0dML4vTDefN0XisJQBewG2oqLCPftQ+lZexMer6PT2IrCxpbJsRJSVAR9+hjTzRw4T1tkeGzzmnWEjZuoaLK1zx3bBpuj6UQ69hZRNLkPhK0fO9Pj0tAA3/++xcJC0AjKShHZEOtMTIkUG0NxHR22hkatOmmFMMM1kyHlbCeK2D6mjxIL++1+SIQ4dH4lNtJ+1y5HqiwCBsTu2NvzvSKAfak/b7XBpDpWvmDnepj0iW5dtCQp6PBxeeQR+K//6h0oJOQF5eXlgagzG3pOiiiiHLomRVjpibPjpGXjuASpZ89uD6adHrZk1yHZSFFCfOTQ+e1+SIQ4dH7F70uMCMHDTLAl5B1TpkyhqanJ93VmQ6wz0RkpZoqGppi0Jemw46R5FVLhJk44mlZ72FJdB1vXyEcOnd/uh0SIQ+dXXJojny4iIVmkVaLPpYvKiv2MlQiuTI6RK+zYZve8E30m1fEyvk5mgi0h73Djh8ZvP16xzsThvy+m6RX4zUPt7H0Ftm1L76jYcdIyneHq19x1DQ3GebS3G7NLv/MdYyZwKocs2bkn69WL/Uyq62DrGvkohs5v90MilPbRBXOSyspK3djY6Hi9Khpcnovr1nWsUaO606Y7RGsbbNsOsc8/7fRh/8MGUda2I+HxWtugtR1Ki6G0xEFj+vSBQYPYu3kHOza3ojEclCGDHT5OimOzI/E5m+zYBZ/FDBsrYGgC+8zraukcUpw3JK/HzjHU5s0A6NgEW3mKUmqt1rrSazucwK5+1dbWUltbm7acHf2yWqcTx7JLy+ET+HnTS9QwvEvDQqS/31qKB9H+6Q76hVpd0Zak96ZFnXGFPn14f/cgOrbvoJTuYye7XrY0LMlnBpbDluY+7GQQg9jBiMGtGekX7e2obdsA0GvWeOodZ3s/WCFbDZMeOpt44gBv2OB4lXfOg1tu6RmLHw7D7IuNZMGJKI1ubtEX+HfMU94IHzzZmtx0Odx3X/drpeAn1/W+VrHXNRyG2QnKxLMgyWfeT3ItSkn+Xjz5+bgmZEpePMA3NNCv6SUADmBzz/e2p/5omUsmmZQCB8S8VmnsyRUjk72RwL4Sep5Dumua8DPNMDrmZex1sHuNulrsqafCc8/5o8vTp4hDV6CYww9WF2F2kky6871mxgx48MHulYaKixNfq0xibzJJG+PX6yR4gxs9B273RmRMfT0KqPXajiT4JUwkFjs2ZWK/3c9kdI3a2z0dw/bt/RCDOHQFSmyMyJAh8NJLuTluroKGnZ5pVlVlPBwuXWq8ThZ/ki72JpFd+Z6RXnCfcePGOR7j40adjlBdjSoqYlxHB+sTvO2mQ5XLON9MYnGt1peu3mTHTmWT0/b2ItlTdI7w7f0Qgzh0NqmoqADwfYJBK5i9POZyLG1txlI5bs7MSramrFPOTEOD4XQ99JBxDCedRqu9YqmChnO9XFg+tVchOStXrrRUzk57sFpnzqmqgv/7P1b+6Eeot96itbnVWkyWA3FszZ9Bc3P3scrLYUB/C/HFNo7d2gZ79hob6c7JCtFjKxvnncghSxf71uszDsUNVmzfDn37snbVKk+fdn17P8QgDp1N1q1b57UJjpPLmVnxw4tDhjjXY2c6TPv2dU+O8tNMMy9mwOVjexV609zcbKmcnfZgtU5PqKqi+fbboaIio7jVTHk9/qHsSWN/j30rHdKw6D63z8kqubzOsaxTyri4Hou4r++HKCGvDRC8x3SywuHM4ugaGmDevJ65kJNhDi/Onm383bYtcY9dJpgOk+nMKZW7uEArZHudBSEZNS7MYHajTicx7fNSv6qqko86ZIKfNazQ9cvv9wNID51AdjFcmcTExQ8vZrQ2aQJie/+KiuDiizPPteQGEisnuIUbQ+p+H6Y37fNavzJeXzkBftawQtcvv98PgDGNPR+3iooK7QYYPeGu1O3lsTJl7lytw2Gtwfg7d679OtasMT63Zk329lita80arfv2NWzu2zfzYzthu5PnH08Q2pBTAI3aB9rjxGZXv6699lqr18hye7BapxPHyoRs7dPaGf3SOrga5nf90to/GuZEe0tHthomPXRCL+w89TnxdOrUhAA7djsRz+bEjF0/LxUkBIcRI0YEok4nSWWfVS1wqnfNCQ2LtdlKbFq2Gib6ZQ+/3w8gQ65CHHZvUL90wyezO5mwOyHkTjiFfl0qSAgWs2bNCkSdTpLMPjsa5nf9Mt9zQ8NEv+zh9/sBxKGzzcyZM702wVUyuUH9kOQ2WWByqjQh2Qq5E06hk/E3icj39ioYjBgxgk2bNqUtZ6c9WK3TK5LZZ1fD/Kpf5gOpWxoWBP0C/2iY3+8HEIfONnV1dV6b4Cq5uEHdIJHd6YQ9WyF3wil0u4cg39urYGB13Vc77cGNtbCdJJl9QdSwZDa7qWFB0C/wj4b5/X4AceiEOPwyBGEHc0hiwQIjDUqs3W4LuxNP937oIRCCTVNTk+MxPm7U6STJ7AuahqXSL7edU9Ev6/j9fgBQxsQKDw6s1GDgQWAysBW4UWu9LEE5BdwOfCe66wHgBp3G8MrKSu2GR21OXTYzrruJcer+XlDb7Wnz6epPFy+T6PO5TlfiJblsr16jlFqrta702g4nsKtf1dXV1FtIgGanPVitMxlu61e29kFutCDVMazE+8V/vpD0C/yjYU60t3RkrWHZTJHNZgN+BzwClAEnAbuA8QnKXQqsBw4CDgT+A1yWrn5JW+I+TqX+yKZ+u2kH3LbZb/i9DTkJBZy2xCqiX93kQgvSHUP0Kz1+b0dOkq2GebJShFKqP3AecIvWukVr/Xfgz8A3ExS/CJivtf5Ia70RmA98K2fGCklJFN9hJ+t6JvXHYzd7uZNZ3eNx8twFwQ6FvFJEpritX8mOEYvoV3Dw+/0A3sXQjQM6tNZNMfteBk5NUHZ89L3YcuNdtE2wiJvrsiaq3xS7+CEHO/EybsWkFFI+JsF/VFY6P9LsRp1Okq19butXomNUV4t+BRW/3w/gnUNXBuyO27cLKE9SdldcuTKllIp2UXahlKoBagBGjhzpnLUFhJ34jHgxcjonUSKxSyY8Vo/jVsB0IeVjEtwhG/2SHrpurGqY2/qV6Bgg+hVU/H4/gHcOXQswIG7fAKDZQtkBQEu8Mwegta4D6sAIKnbG1MLBT+uyJqvfCeExy5vDFU5MlghiqgTBX2SjX+Xl5TQ3J5LPzHGjTidJZF8midHd1K/4Y8yb54x+mQ+38+Y5M1lC9Cs9fr8fwDuHrgkoUkodprV+K7rvi8DrCcq+Hn3vn2nKCVmSrbOUi3QBTghPItGH7IYcgpYqQcgv3Eh46vckqk4kFY4lKPoFvTVswQK4+mrRLzfx+/0AHjl0WuvPlFKPArcppb4DHAOcA5yQoPhS4Fql1F8wZrvMAu7Ola2FhJ/WZU1Vf7bCkyyw2OoPQbIn4ULJxyT4j/r6eqZOner7Op0kkX3ZalgQ9At6a9jy5aJfbuP3+wG8TSx8BfAQsAXYBlyutX5dKXUysEprXRYttxA4BHg1+vqB6D5PCEK26EwJylNatsKTTPSt/BAELXg4n9ur0E1dXZ2lHxs77cFqnV6RyL4gaJgTjlO8hp13Hjz/fP7pF/hHw/x+P4CHiYXdxq3EwrnEb4mF3Upo6UWizEwTDs+bB7fcYjwJh8MwezbceGNubBZSU8iJhf2I6Je7ZJJwWPTL32SrYbL0l2AJt57s3Kw3lbjFPyVbFWUJHhb8yPTp01m2rNdCO76r00ns2Jdv+gU9NUz0y338fj+AOHS2Macu+2XB4FwRG7PR2gq1tcaWrXi5MV3ersjaKR+EIZ1YCrW9FhpTpkyxVM5Oe7Bap1fYsS9eZ5YudeYeFv1yH79omN/vB8C7pb/c3mTpnMxYs8ZYfiZ+SRlzyZlQyFimJhRyZukZN5aysbucTibL7yS6Rn7EizbkFcjSX2kpdP0Kh7UuKdG6tNQZzQmifpl2i4b5j2w1THrohC5SPemZT3a1tfD00xCJuJc8OFPbzTrsDivYKR97jcJh+Pa3YcaMYDzpCvmLUgrj98DfdTpJvH1W9Ku+Hj74AO6/35letaDpl3msiRONkZZQCO65BwKQM9dz/H4/gAy5CjGkGz6oqjIcOiuzqeyQLNGvVRIJuR2RtSPKsdeosxMWLoQlS4IxW0zIX9z4ofH7j1e8fVb0y5w8sGSJcxqWLNGvVXKpX2CUa201HsojEbjySjjqKNGvdPj9fgBkyNUu5PGQhdXhA6e767MdtrAy5OCUzaatShnHszPM4QW5bkNeQgEPuT788MNWr5Hl9mC1TieOlQnx9tnRET9pWC71y6yrqKhbv0Ih/+qX1v7RsGzvBytkq2EhL5xIwZ+YT3qzZ6cPrL3xRnfXEbSDOeQQDid+4jafgH/4QzjlFMgmtta8RpdeCqWlyY8pCLlk5cqVgajTSeLts6pfZlm/aFgu9QuMc77nHiguNoZcS0tFv6zg9/sBJA+dbXKZW8lveZzcwomp/6mm7c+bZ4hhJGK8Li6G555zZjaa32eLFUobAslDZwXRL3fIVsNEv5JTSO1I8tDlmAkTJnhtgie4efM7EVicKvt6dbXxJGoKYmenM+kFgrBUTqG210Jj6tSprFixIm05O+3Bap1eYdc+P2uY6Fdy/KJhfr8fQBw626xdu9ZrE3JOLpaLcVNczCGGK680xLCQhhgKsb0WIjUWpynaaQ9W6/QKO/YFWcMKWb/APxrm9/sBxKETLOBG8sxcEPtEXlNjzOQKwhCDINil2oVfeDfqdBI79gVRw0S//IXf7wcQh06wQBCXi0n2RC5CKOQjI0aMoLm52fd1Ookd+4KmYaJf/sPv9wMgs1ztopTqCtIsFOzMHvMLiZYqa2jw2qrcU4jttRCx+kNjpz34/cfLjn1B0zDRr278omF+vx9AHDrBIk5P83cb84ncDCZ++mnjibdQRVHIb9xY59LrtTPTYde+IGmY6Jf/8Pv9AOLQCXmCmaXdFDzzifz007tFMZMcd4IQBNxIceJGnU7id/vsEqthol/+IwjtTfLQ2UTyOPmPVDPYcjG7zc8UUhuSPHTpEf3yJ8l0qtD1CwqrHWWrYdJDJwSeVFnak8XOxPfoCUKQkVmuwSaZhol++YcgtDeZ5SoEnnQz2OJnh8lTr5Bv1NbWBqJOJ/G7fXZIpWGiX/4gCO1NeugKlHx6wrM7gy3btWMFwW+MGzcuEHU6ye7d4wpSw0S/vMHv9wNID51tFi5c6LUJWZOPT3h2cjQFLSdVNuRDexXSU1lZyaZNm9KWs9MerNbpBQ0NcM45lYTDmwpOwwpJv8A/Gubn+8FEHDqbBGH5j3QEMWu6kzixdmxQyIf2KqTH6g+Nnfbg5x+v+noIhzcVpIYVkn6BfzTMz/eDiQy5FiDmE144XBhPeIkIUk4qQUjH/PnzA1GnU1RXg1LzC1bDRL9yj5/vBxPpobOJmVzQL08NmVBoT3iFTD60VyE9VnsP7LQHP/dIVFXB+edv4uijRcPyHb9omJ/vBxPJQ2cTyeMkBIlCakOShy49ol9C0CikdiR56ARBEAqcioqKQNTpJH63T8gvgtDexKETBEEIOLKWqyC4SxDamzh0giAIAae8vDwQdTqJ3+0T8osgtLecO3RKqcFKqceUUp8ppd5XSk1PUfZ6pdRrSqlmpdR7Sqnrc2mrIAhCEJgyZUog6nQSv9sn5BdBaG9ezHK9B2gDhgHHAE8opV7WWr+eoKwCZgCvAGOBp5RSH2qtf58rYwVBEPxOU1NTIOp0Er/bJ+QXQWhvOe2hU0r1B84DbtFat2it/w78GfhmovJa6zu01uu01h1a6/XAn4ATc2exIAiC/5G1XAXBXYLQ3nKatkQpdSzwD611v5h91wGnaq2npvmsAtYBC7XW9yUpUwOYyWqOBF5zxHBvGQps9doIh5Bz8Sf5ci6Ha639H+iShDzVL8if9gX5cy75ch6QX+eSlYblesi1DNgdt28XYOUEajF6FBclK6C1rgPqAJRSjfmQkypfzgPkXPxKvpyLUsr5xG05JB/1C+Rc/Ei+nAfk37lk83lHh1yVUvVKKZ1k+zvQAgyI+9gAoDlNvVdixNKdrbVuddJmQRAEQRCEoONoD53WujrV+9EYuiKl1GFa67eiu78IJJoQYX7m28ANwCla64+cslUQBEEQBCFfyOmkCK31Z8CjwG1Kqf5KqROBc4DfJCqvlLoQmAtM0lq/a/Nw/s8CaI18OQ+Qc/Er+XIu+XIeIOfiV/LlXPLlPEDOpYucr+WqlBoMPARMArYBN2itl0XfOxlYpbUui75+DzgIiB1m/a3W+rKcGi0IgiAIguBjcu7QCYIgCIIgCM4iS38JgiAIgiAEHHHoBEEQBEEQAk5gHTqra8Iqg58qpbZFt59GkxT7Bhvn4vu1be2s1RstX6KUekMp5asZzDbXHJ6glPo/pVSLUuoTpdT3c2lrOmy0r1Kl1H3Rc9iulFqhlDow1/YmQyl1pVKqUSnVqpRanKbsNUqpzUqp3Uqph5RSpTky0xKiX6JfbpMvGpYv+gXua1hgHTp6rgl7IXCvUmp8gnI1wDSM9ChHA1OBS3Nko1Wsnou5tu1+wJnAlUqpC3JmpTWsnovJ9cCnuTDMJpbOQyk1FPgrsBAYAhwKPJVDO61g9Tv5PlCFcZ+MAHYAd+fKSAtsAuZgTKpKilLqDIxURxOBUcAhwI9dt84eol+iX26TLxqWL/oFbmuY1jpwG9Af4wseF7PvN8DtCcquAWpiXl8CvOD1OWRyLgk+exdwt9fnkOm5AGOAN4CzgI+8tj/D9jUX+I3XNjt0LvcCd8S8PhtY7/U5JLBzDrA4xfvLgLkxrycCm722O8PvRPTLp+fiV/3KoI35VsPyUb+itrmiYUHtoRsHdGitm2L2vQwk8trHR99LV84r7JxLF9Fhl5NJkZTZA+yey93ATcBetw2ziZ3zOB7YrpRao5TaEu3mH5kTK61h51weBE5USo1QSvXDeBpelQMbnSbRPT9MKTXEI3viEf0S/XKbfNGwQtQvyFDDgurQ2VkTtiz6Xmy5Mh/FoWS6vm0tada29QDL56KUOhcIa60fy4VhNrHznRwEXITR3T8SeA/4navW2cPOubwFfAhsjH7mCOA2V61zh0T3PFhbMzoXiH6JfrlNvmhYIeoXZKhhQXXo7KwJG192ANCio/2YPsD2+rbKv2vbWjoXZSwBdwfwvRzZZRc738le4DGt9b+01vsw4hxOUEoNdNlGq9g5l3uAUow4mv4Yq7oE8Qk30T0PadaMziGiX6JfbpMvGlaI+gUZalhQHbomomvCxuxLtibs69H30pXzCjvnEru27UTtv7VtrZ7LYcBo4Hml1GaMG++A6Iye0bkwNA12vpNXgNgfV7/80JrYOZdjMOI6tkd/aO8GjosGTQeJRPf8J1rrbR7ZE4/ol+iX2+SLhhWifkGmGuZ1cGAWQYW/x+gW7g+ciNElOT5BucswAlcPxJj58jpwmdf2Z3guFwKbgSO8tjmbcwGKgOEx239jzP4ZjjGMEYjziJb7L4zZVMcAxcAvgOe9tj/Dc1kELAcGRs/lJmCj1/bHtZs+wDyMwOg+QFGCcmdG75MvAIOAZ7EQpO/T70T0y2fnEgT9svm9+FrD8kW/YtqOaxrm+QlmcWEGA48DnwEfANOj+0/GGJIwyymM7vHt0e0Ookue+WWzcS7vAe0Y3bHmdp/X9mdyLnGfqcZ/s8QsnwdwOUbcxg5gBXCw1/Zn2L6GAA8DW4CdwN+B47y2P8a+Wozeg9itFiPupwUYGVP2WuATjFiaRUCp1/Zn+J2IfvnwXOI+4zv9snsuftawfNGvqI2uapis5SoIgiAIghBwghpDJwiCIAiCIEQRh04QBEEQBCHgiEMnCIIgCIIQcMShEwRBEARBCDji0AmCIAiCIAQccegEQRAEQRACjjh0giAIgiAIAUccOkEQBEEQhIAjDp0gCIIgCELAEYdOCBxKqb5KqY+UUh8opUrj3ntAKdWplLrAK/sEQRCSIfoluIU4dELg0FrvBW4FDgauMPcrpeYBlwBXaa1/75F5giAISRH9EtxC1nIVAolSKgy8DHwOOAT4DvAL4Fat9W1e2iYIgpAK0S/BDcShEwKLUmoKsAJ4FjgN+JXW+nveWiUIgpAe0S/BacShEwKNUmodcCzwe2C6jmvQSqmvAd8DjgG2aq1H59pGQRCERIh+CU4iMXRCYFFK/Q/wxejL5ngxjLID+BVwc84MEwRBSIPol+A00kMnBBKl1GSM4YoVQDvw/4CjtNZvJCk/DVggT7iCIHiN6JfgBtJDJwQOpdSXgUeBfwAXAj8EIsA8L+0SBEFIh+iX4Bbi0AmBQin1BeAvQBMwTWvdqrV+B3gQOEcpdaKnBgqCICRB9EtwE3HohMCglBoJPIkRV3KW1np3zNuzgb3AHV7YJgiCkArRL8Ftirw2QBCsorX+ACMZZ6L3NgH9cmuRIAiCNUS/BLcRh07Ia6IJPIujm1JK9QG01rrVW8sEQRBSI/ol2EEcOiHf+SawKOb1XuB9YLQn1giCIFhH9EuwjKQtEQRBEARBCDgyKUIQBEEQBCHgiEMnCIIgCIIQcMShEwRBEARBCDji0AmCIAiCIAQccegEQRAEQRACjjh0giAIgiAIAUccOkEQBEEQhIDz/wMyUChwzTLQCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#위 모델의 예측 plot\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel=\"$y$\"):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
    "    y_pred = tree_reg.predict(x1)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "    if ylabel:\n",
    "        plt.ylabel(ylabel, fontsize=18, rotation=0)\n",
    "    plt.plot(X, y, \"b.\")\n",
    "    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_regression_predictions(tree_reg1, X, y)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "plt.text(0.21, 0.65, \"Depth=0\", fontsize=15)\n",
    "plt.text(0.01, 0.2, \"Depth=1\", fontsize=13)\n",
    "plt.text(0.65, 0.8, \"Depth=1\", fontsize=13)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"max_depth=2\", fontsize=14)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_regression_predictions(tree_reg2, X, y, ylabel=None)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "for split in (0.0458, 0.1298, 0.2873, 0.9040):\n",
    "    plt.plot([split, split], [-0.2, 1], \"k:\", linewidth=1)\n",
    "plt.text(0.3, 0.5, \"Depth=2\", fontsize=13)\n",
    "plt.title(\"max_depth=3\", fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 각 영역의 예측값은 항상 그 영역에 있는 타깃값의 평균\n",
    "- 알고리즘은 가능한 한 많은 샘플이 가까이 있도록 영역을 분할함"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CART 알고리즘은 훈련 세트 불순도를 최소화하는 방향으로 분할하는대신 평균제곱오차(MSE)를 최소화하도록 분할하는 것을 제외하고는 앞서 설명한 것과 거의 비슷하게 작동\n",
    "#### 알고리즘이 최소화하기 위한 비용함수\n",
    "![nn](https://t1.daumcdn.net/cfile/tistory/9963233F5C625EA82B)\n",
    "\n",
    "- 분류, 회귀작업에서 결정 트리 과대적합되기 쉬움"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#규제 유무 차이에 따른 plot\n",
    "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "x1 = np.linspace(0, 1, 500).reshape(-1, 1)\n",
    "y_pred1 = tree_reg1.predict(x1)\n",
    "y_pred2 = tree_reg2.predict(x1)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", fontsize=18, rotation=0)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"No restrictions\", fontsize=14)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.title(\"min_samples_leaf={}\".format(tree_reg2.min_samples_leaf), fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 왼쪽 그래프는 기본 매개변수를 사용하여 규제가 없는 예측 -> 훈련 세트에 크게 과대적합\n",
    "- 오른쪽 그래프는 min_samples_leaf=10 을 지정하여 규제 존재"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 불안전성"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결정 트리 - 제한 사항\n",
    "- 결정 트리는 계단 모양의 결정 경계를 만듦(모든 분할은 축에 수직)\n",
    "- 훈련 세트의 회전에 민감\n",
    "- 훈련 데이터에 있는 작은 변화에도 매우 민감\n",
    "- 이러한 문제를 해결하기 위해 훈련 데이터를 더 좋은 방향으로 회전시키는 PCA 기법을 사용(8장)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(6)\n",
    "Xs = np.random.rand(100, 2) - 0.5\n",
    "ys = (Xs[:, 0] > 0).astype(np.float32) * 2\n",
    "\n",
    "angle = np.pi / 4\n",
    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]]) #데이터 셋 45도 회전\n",
    "Xsr = Xs.dot(rotation_matrix)\n",
    "\n",
    "tree_clf_s = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_s.fit(Xs, ys)\n",
    "tree_clf_sr = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_sr.fit(Xsr, ys)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
    "plt.sca(axes[1])\n",
    "plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "X = iris.data[:, 2:] # 꽃잎 길이와 너비\n",
    "y = iris.target\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
    "tree_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.8, 1.8]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[(X[:, 1]==X[:, 1][y==1].max()) & (y==1)] # widest Iris versicolor flower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=40)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_widest_versicolor = (X[:, 1]!=1.8) | (y==2)\n",
    "X_tweaked = X[not_widest_versicolor]\n",
    "y_tweaked = y[not_widest_versicolor]\n",
    "\n",
    "tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\n",
    "tree_clf_tweaked.fit(X_tweaked, y_tweaked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#훈련 세트에서 가장 넓은 Iris-Versicolor(꽃잎 길이 4. 8cm, 너비 1.8cm)제거\n",
    "#결정 트리 훈련\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundary(tree_clf_tweaked, X_tweaked, y_tweaked, legend=False)\n",
    "plt.plot([0, 7.5], [0.8, 0.8], \"k-\", linewidth=2)\n",
    "plt.plot([0, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
    "plt.text(1.0, 0.9, \"Depth=0\", fontsize=15)\n",
    "plt.text(1.0, 1.80, \"Depth=1\", fontsize=13)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아래 그래프와 차이 \n",
    "![asda](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#회전\n",
    "angle = np.pi / 180 * 20\n",
    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n",
    "Xr = X.dot(rotation_matrix)\n",
    "\n",
    "tree_clf_r = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_r.fit(Xr, y)\n",
    "\n",
    "plt.figure(figsize=(8, 3))\n",
    "plot_decision_boundary(tree_clf_r, Xr, y, axes=[0.5, 7.5, -1.0, 1], iris=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}