From d7fc3b52afa148b84f648ee8ef2ab68fd9f901e3 Mon Sep 17 00:00:00 2001
From: jihyeon baek <qormoon@sookmyung.ac.kr>
Date: Mon, 7 Oct 2024 09:02:30 +0000
Subject: [PATCH] =?UTF-8?q?week2=5F2024=5FGDG=5FML=EC=9E=85=EB=AC=B8?=
 =?UTF-8?q?=EC=8A=A4=ED=84=B0=EB=94=94?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...5 2\341\204\214\341\205\256\341\204\216\341\205\241 -1.ipynb" | 1 +
 ...5 2\341\204\214\341\205\256\341\204\216\341\205\241 -2.ipynb" | 1 +
 ...5 2\341\204\214\341\205\256\341\204\216\341\205\241 -3.ipynb" | 1 +
 3 files changed, 3 insertions(+)
 create mode 100644 "week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -1.ipynb"
 create mode 100644 "week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -2.ipynb"
 create mode 100644 "week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -3.ipynb"

diff --git "a/week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -1.ipynb" "b/week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -1.ipynb"
new file mode 100644
index 0000000..c310cf5
--- /dev/null
+++ "b/week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -1.ipynb"	
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{"id":"LGYbZJQsfa4_"},"source":["# k-최근접 이웃 회귀"]},{"cell_type":"markdown","metadata":{"id":"lpY20cgOfa5C"},"source":["<table align=\"left\">\n","  <td>\n","    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/3-1.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n","  </td>\n","</table>"]},{"cell_type":"markdown","metadata":{"id":"i5J2cFzCrDWT"},"source":["## 데이터 준비"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fL3wuWxD0cH6"},"outputs":[],"source":["import numpy as np"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"np5j0UTtJNI_"},"outputs":[],"source":["perch_length = np.array(\n","    [8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0,\n","     21.0, 21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5,\n","     22.5, 22.7, 23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5,\n","     27.3, 27.5, 27.5, 27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0,\n","     36.5, 36.0, 37.0, 37.0, 39.0, 39.0, 39.0, 40.0, 40.0, 40.0,\n","     40.0, 42.0, 43.0, 43.0, 43.5, 44.0]\n","     )\n","perch_weight = np.array(\n","    [5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0,\n","     110.0, 115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0,\n","     130.0, 150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0,\n","     197.0, 218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0,\n","     514.0, 556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0,\n","     820.0, 850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0,\n","     1000.0, 1000.0]\n","     )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cc-Fn23Q4AqQ"},"outputs":[],"source":["import matplotlib.pyplot as plt"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"gE78Nuog4Eg4","outputId":"e4d4e724-059b-46bf-b2d1-05698db75c94"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.scatter(perch_length, perch_weight)\n","plt.xlabel('length')\n","plt.ylabel('weight')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dqSDbM-K4pkB"},"outputs":[],"source":["from sklearn.model_selection import train_test_split"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"seEljNAS4uET"},"outputs":[],"source":["train_input, test_input, train_target, test_target = train_test_split(\n","    perch_length, perch_weight, random_state=42)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sC6HAwnnK4aU","outputId":"9eda8a47-96af-4fde-82c5-3075f9ec8d52"},"outputs":[{"output_type":"stream","name":"stdout","text":["(42,) (14,)\n"]}],"source":["print(train_input.shape, test_input.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Og1eucsRwzIs","outputId":"8ca7574e-8e2b-47ce-acba-54598eded6ff"},"outputs":[{"output_type":"stream","name":"stdout","text":["(4,)\n"]}],"source":["test_array = np.array([1,2,3,4])\n","print(test_array.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y-DXX-xtw8Jb","outputId":"bb84a931-8645-4f07-fa88-7e7903621763"},"outputs":[{"output_type":"stream","name":"stdout","text":["(2, 2)\n"]}],"source":["test_array = test_array.reshape(2, 2)\n","print(test_array.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2z-LC4zrxzWL"},"outputs":[],"source":["# 에러\n","# test_array = test_array.reshape(2, 3)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_GfrND5GKg_z"},"outputs":[],"source":["train_input = train_input.reshape(-1, 1)\n","test_input = test_input.reshape(-1, 1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0c8e0UrkLJDe","outputId":"93e6f0e6-3c50-4cee-afa9-8cdbff50d93a"},"outputs":[{"output_type":"stream","name":"stdout","text":["(42, 1) (14, 1)\n"]}],"source":["print(train_input.shape, test_input.shape)"]},{"cell_type":"markdown","metadata":{"id":"NtmNJ7OqrKy_"},"source":["## 결정 계수\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BcPh-Da44lhx"},"outputs":[],"source":["from sklearn.neighbors import KNeighborsRegressor"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"oe7MTnof45sP","outputId":"43803aea-397c-49d3-b790-e8efb601a8a7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsRegressor()"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":14}],"source":["knr = KNeighborsRegressor()\n","# k-최근접 이웃 회귀 모델을 훈련\n","knr.fit(train_input, train_target)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yEv88u6LIokr","outputId":"6c92e932-cb2f-4ede-d5e3-4cb2d46c93e9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.992809406101064"]},"metadata":{},"execution_count":15}],"source":["knr.score(test_input, test_target)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R8Uju0xGLX3s"},"outputs":[],"source":["from sklearn.metrics import mean_absolute_error"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QKEf3y-5KVQx","outputId":"7e891242-bace-4aff-8cb6-caa1a84b9fc2"},"outputs":[{"output_type":"stream","name":"stdout","text":["19.157142857142862\n"]}],"source":["# 테스트 세트에 대한 예측\n","test_prediction = knr.predict(test_input)\n","# 테스트 세트에 대한 평균 절댓값 오차를 계산\n","mae = mean_absolute_error(test_target, test_prediction)\n","print(mae)"]},{"cell_type":"markdown","metadata":{"id":"pLW8kdDv5asl"},"source":["## 과대적합 vs 과소적합"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZoXIfmiAJaNw","outputId":"6be2f44c-d84e-4734-f462-a219af936920"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9698823289099254\n"]}],"source":["print(knr.score(train_input, train_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Jhu9abILLHjq","outputId":"f5ee736c-c48b-44fd-d54a-d855e0fb05cc"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9804899950518966\n"]}],"source":["# 이웃의 갯수를 3으로 설정합니다\n","knr.n_neighbors = 3\n","# 모델을 다시 훈련합니다\n","knr.fit(train_input, train_target)\n","print(knr.score(train_input, train_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cHedpJWCLVwb","outputId":"d8087f02-fc11-4721-8be6-e298bf804f1c"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9746459963987609\n"]}],"source":["print(knr.score(test_input, test_target))"]},{"cell_type":"markdown","metadata":{"id":"z-oQeMvC2NnY"},"source":["## 확인문제"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"ICPoeo9c2RLG","outputId":"1dcf21c7-4192-439e-92cb-df65f62a0b09"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAkQAAAHHCAYAAABeLEexAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABWpElEQVR4nO3deXhTZaIG8DdJl3RNF9omZS07ZadArYCoVKgyCIKjKAyIXFAGVMANvAoWlwLjAugMjHrHDdxwFATHCoLACIUCpUIplgKFFkhaoDTpQrfk3D9KQtOkbdpmz/t7njwPOefLyXd6hL5+q0gQBAFEREREHkzs6AoQERERORoDEREREXk8BiIiIiLyeAxERERE5PEYiIiIiMjjMRARERGRx2MgIiIiIo/HQEREREQej4GIiIiIPB4DERE5pTvvvBN33nlnqz/br1+/Zsvt2bMHIpEI3377bau+h4jcBwMREZEHUSqVWLJkCe666y4EBQVBJBJhz549jq4WkcN5OboCRETm7Nixw9FVcEs5OTlYtWoVevTogf79+yMtLc3RVSJyCmwhIiKn5OPjAx8fH0dXwyrKy8sdXQWDuLg4XLt2DadPn8bixYsdXR0ip8FAROShXn31VYhEIpw5cwaPPfYYQkJCIJPJMGvWLFRUVLToWo899hgCAwNx6dIlTJo0CYGBgYiIiMBzzz0HrVZrVFan02HNmjXo27cvpFIpoqKi8MQTT+D69etG5cyNIbpw4QLuv/9+BAQEIDIyEosWLcLPP//caLdPdnY27rrrLvj7+6N9+/ZYvXq12fprtVq89NJLkMvlCAgIwP3334+CggKTcps3b0ZcXBz8/PzQrl07TJ8+HZcuXTL7szh79izuu+8+BAUFYdq0aQCA3NxcTJkyBXK5HFKpFB06dMDUqVOhVqub+xFbTVBQEMLCwuz2fUSugl1mRB7uoYceQkxMDFJSUpCRkYGPPvoIkZGRWLVqVYuuo9VqMW7cOMTHx+Ott97CL7/8grfffhvdunXDvHnzDOWeeOIJfPLJJ5g1axaefvpp5OXl4f3338exY8ewf/9+eHt7m71+eXk57r77biiVSjzzzDOQy+X44osv8Ouvv5otf/36dSQlJWHy5Ml46KGH8O233+LFF19E//79ce+99xqVfeONNyASifDiiy+iqKgIa9asQWJiIjIzM+Hn5wcAhjoPGzYMKSkpKCwsxNq1a7F//34cO3YMISEhhuvV1tZi3LhxGDlyJN566y34+/ujuroa48aNQ1VVFZ566inI5XJcunQJ27dvR0lJCWQyWaM/24qKCotCqkQiQWhoaLPliMgMgYg80vLlywUAwuOPP250/IEHHhDCw8NbdK2ZM2cKAIQVK1YYHR88eLAQFxdneP/f//5XACBs2rTJqFxqaqrJ8dGjRwujR482vH/77bcFAMKWLVsMx27cuCH07t1bACD8+uuvRp8FIHz22WeGY1VVVYJcLhemTJliOPbrr78KAIT27dsLGo3GcPybb74RAAhr164VBEEQqqurhcjISKFfv37CjRs3DOW2b98uABCWLVtm8rNYsmSJ0T0eO3ZMACBs3rzZ/A+xCfpn1dyrc+fOLbru5s2bTX52RJ6KLUREHu7JJ580ej9q1Ch8//330Gg0CA4ObvO1Pv/8c8P7zZs3QyaT4Z577sHVq1cNx+Pi4hAYGIhff/0Vjz76qNlrp6amon379rj//vsNx6RSKebMmYNnn33WpHxgYCCmT59ueO/j44Phw4fj3LlzJmVnzJiBoKAgw/sHH3wQCoUC//nPf/D000/jyJEjKCoqwquvvgqpVGooN378ePTu3Rs//vgjkpOTja5Zv1UMgKEF6Oeff8Z9990Hf39/s/dpzowZMzBy5Mhmy+lbs4io5RiIiDxcp06djN7ru1yuX7/eokAklUoRERFhcq36Y4Nyc3OhVqsRGRlp9hpFRUWNXv/ChQvo1q0bRCKR0fHu3bubLd+hQweTsqGhoTh+/LhJ2R49ehi9F4lE6N69O86fP2/4bgDo1auXyWd79+6N3377zeiYl5cXOnToYHQsJiYGixcvxjvvvINNmzZh1KhRuP/++zF9+vQmu8sAoGvXrujatWuTZYiobRiIiDycRCIxe1wQBKtcpz6dTofIyEhs2rTJ7PmGgaotrHVfreHr6wux2HTOyttvv43HHnsMW7duxY4dO/D0008jJSUFBw8eNAlQ9ZWVlaGsrKzZ75VIJFb9GRJ5EgYiIrKbbt264ZdffsGIESNa3L3TuXNnZGdnQxAEo5afM2fOtLleubm5Ru8FQcCZM2cwYMAAw3cDdWv43H333UZlc3JyDOct0b9/f/Tv3x8vv/wyDhw4gBEjRmDDhg14/fXXG/3MW2+9ZdIlZ07nzp0NrVpE1DKcdk9EdvPQQw9Bq9XitddeMzlXW1uLkpKSRj87btw4XLp0CT/88IPhWGVlJT788MM21+uzzz5DaWmp4f23334LpVJpmI02dOhQREZGYsOGDaiqqjKU++mnn3Dq1CmMHz++2e/QaDSora01Ota/f3+IxWKja5ozY8YM7Ny5s9lXYy1vRNQ8thARkd2MHj0aTzzxBFJSUpCZmYmxY8fC29sbubm52Lx5M9auXYsHH3zQ7GefeOIJvP/++3jkkUfwzDPPQKFQYNOmTYZBzg3HC7VEWFgYRo4ciVmzZqGwsBBr1qxB9+7dMWfOHACAt7c3Vq1ahVmzZmH06NF45JFHDNPuu3TpgkWLFjX7Hbt378aCBQvw5z//GT179kRtbS0+//xzSCQSTJkypcnPWnsMkb416uTJkwCAzz//3DAO6uWXX7ba9xC5EgYiIrKrDRs2IC4uDv/85z/x0ksvwcvLC126dMH06dMxYsSIRj8XGBiI3bt346mnnsLatWsRGBiIGTNm4Pbbb8eUKVOMZn+11EsvvYTjx48jJSUFpaWlGDNmDP7xj38YzQR77LHH4O/vj5UrV+LFF19EQEAAHnjgAaxatcpoDaLGDBw4EOPGjcO2bdtw6dIl+Pv7Y+DAgfjpp59w2223tbrurfHKK68Yvf/Xv/5l+DMDEXkqkWCPEYZERDayZs0aLFq0CBcvXkT79u0dXR0iclEMRETkMm7cuGE0GLuyshKDBw+GVqvF6dOnHVgzInJ17DIjokap1WrcuHGjyTJyudxOtQEmT56MTp06YdCgQVCr1di4cSP++OMPDiYmojZjCxERNeqxxx7Dp59+2mQZe/4TsmbNGnz00Uc4f/48tFotYmNj8cILL+Dhhx+2Wx2IyD0xEBFRo7Kzs3H58uUmyyQmJtqpNkREtsNARERERB6PCzMSERGRx+OgagvodDpcvnwZQUFBbVr8jYiIiOxHEASUlpYiOjra7P6C9TEQWeDy5cvo2LGjo6tBRERErVBQUNDkBsoAA5FFgoKCANT9QIODgx1cGyIiIrKERqNBx44dDb/Hm8JAZAF9N1lwcDADERERkYuxZLgLB1UTERGRx2MgIiIiIo/HQEREREQej4GIiIiIPB4DEREREXk8BiIiIiLyeAxERERE5PEYiIiIiMjjMRARERGRx+NK1URERNQkrU5Ael4xikorERkkxfCYMEjE5ld/bklZZ8JARERERI1KzVIieVs2lOpKwzGFTIrlE2KR1E/R6rLOhl1mREREZFZqlhLzNmYYBRwAUKkrMW9jBlKzlK0q64wYiIiIiMiEVicgeVs2BDPn9MeSt2VDqxNaVNZZMRARERGRifS8YpPWnvoEAEp1JdLziltU1llxDBERERGZKCptPOC0plxLy9obAxERERGZiAySWrVcS8vaGwMRERERmRgeEwaFTAqVutLs2CARALmsblo9gBaVdUYcQ0REREQmJGIRlk+IbbLM8gmxkIhFRmUbrjgkalDWWTEQERERkVlJ/RR4/9HBJscDfCVYP32I0dpCSf0UWD99COQy424xuUxqUtYZscuMiIiIGtU3WgYA8JGI8Wh8R3xy4AKCpN4YGys3KZvUT4F7YuVcqZqIiIjcS97VcgBA14gALLm3D77LuASVuhIHz13D7d3bmZSXiEVI6BZu72q2GbvMiIiIqFHnbgaimHYBkHpL8KeB0QCAf2dccmS1rI6BiIiIiBp1vl4gAoApQ9oDAH7KUqK8qtZh9bI2BiIiIiJqVF6DQDSkUyg6h/ujolqLn0+qHFk1q2IgIiIiokY1DEQikQiTB3cAAHznRt1mDERERERkVmWNFpfVNwDcCkQA8MDgum6z/WevQnnzvKtjICIiIiKzLlyrgCAAQVIvhAX4GI53CvfH8C5hEARgy7HLDqyh9TAQERERkVmGKfftAiASGa8lNPnm4OrvMi5CEMxt2OFaGIiIiIjIrIbjh+q7b4ACvl5i5BaVIeuSxt5VszoGIiIiIjIr72oZAKCLmUAULPXG2L51q1X/O+OiXetlCwxEREREZNb5qxUAzLcQAbe6zX74/TJqtDq71csWGIiIiIjIrHOGMUSBZs+P6t4O7QJ9UVxejb05V+xZNatjICIiIiITpZU1uFpWBQDo0s7fbBkviRiTBtVt5fHdMdfuNmMgIiIiIhP67rJ2gb4Ikno3Wm7ykLpFGn/JLkJJRbVd6mYLDERERERk4tzNAdUxjbQO6cVGB6O3PAjVWh22H1fao2o2wUBEREREJpobUF3flCH6rTxct9uMgYiIiMgNaHUC0s5ew9bMS0g7ew1aXdsWS8wztBCZH1Bd38RB0RCLgIz8EsPaRa7Gy9EVICIiorZJzVIieVs2lOpKwzGFTIrlE2KR1E/RqmveWpSx6S4zAIgMlmJUjwjsPX0F32dcxOKxvVr1nY7EFiIiIiIXlpqlxLyNGUZhCABU6krM25iB1KyWj+sRBKFeIGq+hQiot5XHsUvQtbF1yhEYiIiIiFyUVicgeVs2zMUP/bHkbdkt7j4rLq+GprIWIhHQObz5FiIAGBsrR6CvFy5ev4HD54tb9H3OgIGIiIjIRaXnFZu0DNUnAFCqK5Ge17KAcv5aXetQtMwPUm+JRZ/x85Hgvv51W3ls2HvW4rFM1h771FoODUT79u3DhAkTEB0dDZFIhC1bthidFwQBy5Ytg0KhgJ+fHxITE5Gbm2tUpri4GNOmTUNwcDBCQkIwe/ZslJWVGZU5fvw4Ro0aBalUio4dO2L16tW2vjUiIiKbKyptPAy1ppzeuSuNb+ralA6hfgCAX3Ou4JmvMvHIhwcxctXuRrvtUrOUGLlqNx758KBF5W3JoYGovLwcAwcOxN///nez51evXo1169Zhw4YNOHToEAICAjBu3DhUVt56sNOmTcPJkyexc+dObN++Hfv27cPcuXMN5zUaDcaOHYvOnTvj6NGj+Nvf/oZXX30VH3zwgc3vj4iIyJYig6RWLaenHz/U2ArV5qRmKfHOzlyT442NZbLF2Ke2cOgss3vvvRf33nuv2XOCIGDNmjV4+eWXMXHiRADAZ599hqioKGzZsgVTp07FqVOnkJqaisOHD2Po0KEAgPfeew/33Xcf3nrrLURHR2PTpk2orq7Gv/71L/j4+KBv377IzMzEO++8YxSciIiIXM3wmDAoZFKo1JVmxxGJAMhlUgyPCWvRdfVdZpYOqNaPZTJHuFmP5G3ZuCdWDolY1OzYp4bl7cFpxxDl5eVBpVIhMTHRcEwmkyE+Ph5paWkAgLS0NISEhBjCEAAkJiZCLBbj0KFDhjJ33HEHfHx8DGXGjRuHnJwcXL9+3ex3V1VVQaPRGL2IiIicjUQswvIJsQDqQkR9+vfLJ8S2OFTou8y6WthlZulYpmkfHcT8LzIw7aODNhn71BZOG4hUKhUAICoqyuh4VFSU4ZxKpUJkZKTReS8vL4SFhRmVMXeN+t/RUEpKCmQymeHVsWPHtt8QERGRDST1U2D99CGQy4y7xeQyKdZPH9LidYh0OsHQQtTFwkBk6Rilg+eK8eNxJQ6esyzotHTsU1twYUYzli5disWLFxveazQahiIiInJaSf0UuCdWjvS8YhSVViIyqK6brDXdTYWllais0cFLLDIMkm6OpWOUZiZ0QUw7f+RdrcCnaeetdl1rcNpAJJfXTd0rLCyEQnEr3RYWFmLQoEGGMkVFRUafq62tRXFxseHzcrkchYWFRmX07/VlGvL19YWvr69V7oOIiMgeJGIRErqFt/k6eTe7yzqF+cNbYllHkqVjmZbd7L7T6gTsyFZZfexTWzhtl1lMTAzkcjl27dplOKbRaHDo0CEkJCQAABISElBSUoKjR48ayuzevRs6nQ7x8fGGMvv27UNNTY2hzM6dO9GrVy+Ehoba6W6IiIhcw7mrLesuA1o+lslWY5/awqGBqKysDJmZmcjMzARQN5A6MzMT+fn5EIlEWLhwIV5//XX88MMPOHHiBGbMmIHo6GhMmjQJANCnTx8kJSVhzpw5SE9Px/79+7FgwQJMnToV0dHRAIBHH30UPj4+mD17Nk6ePImvv/4aa9euNeoSIyIiojrnr7ZuDaKWjmWy9tintnJol9mRI0dw1113Gd7rQ8rMmTPxySef4IUXXkB5eTnmzp2LkpISjBw5EqmpqZBKb/3wNm3ahAULFmDMmDEQi8WYMmUK1q1bZzgvk8mwY8cOzJ8/H3FxcWjXrh2WLVvGKfdERERm5LWihUivpWOZrDn2qa1EgiC43g5sdqbRaCCTyaBWqxEcHOzo6hAREdnM3W/vwbkr5dj0P/EY0b2do6vTJi35/e20Y4iIiIjIvmq1OuRfqwDQ8i4zV8dARERERACAi9dvoFYnwNdLDHmw/aa8OwMGIiIiIgIA5F27NaBa7IBxPI7EQEREREQAbq1B5GndZQADEREREd3Ulhlmro6BiIiIiADU3+WegYiIiIg81Dl2mREREZEnq6zR4rL6BgAGIiIiIvJQ+cUVEAQgSOqF8AAfR1fH7hiIiIiIyKi7TCTyrCn3AAMRERERwbMHVAMMRERERATPXoMIYCAiIiIi3FqDiIGIiIiIPFYeu8yIiIjIk5VW1uBKaRUAz1ylGmAgIiIi8ngXrlUAANoF+iBY6u3g2jgGAxEREZGHO+fh44cABiIiIiKPp59h1iWcgYiIiIg8lGENoggGIiIiIvJQ+i6zrh7cZebl6AoQERFR22l1AtLzilFUWonIICmGx4RBIm5+Cw5BEJB3pQyA584wAxiIiIiIXF5qlhLJ27KhVFcajilkUiyfEIukfoomP3u9ogaayloAHENERERELio1S4l5GzOMwhAAqNSVmLcxA6lZyiY/n3e1rnWofYgfpN4Sm9XT2TEQERERuSitTkDytmwIZs7pjyVvy4ZWZ65EnbyrdWsQdWnnb/0KuhAGIiIiIheVnlds0jJUnwBAqa5Eel5xo2X0LUSevAYRwEBERETksopKGw9DlpbTb+rqyeOHAAYiIiIilxUZJG1zOX2XWVcPXoMIYCAiIiJyWcNjwqCQSdHY5HoR6mabDY8JM3tepxNw3rBtR6BtKukiGIiIiIhclEQswvIJsQBgEor075dPiG10PaLC0krcqNFCIhahQ6if7SrqAhiIiIiIXFhSPwXWTx8Cucy4W0wuk2L99CFNrkOkHz/UKcwf3hLPjgRcmJGIiMjFJfVT4J5YeYtXqs7jLvcGDERERERuQCIWIaFbeIs+c54zzAw8u32MiIjIgxlaiDx8hhnAQEREROSx9Lvcx7CFiIGIiIjIE9VqdSgorluDiC1EDEREREQe6VLJDdRoBfh6iaEItmyBR3fGQdVERERuQKsTWjTL7Fy9AdXiZmajeQIGIiIiIheXmqVE8rZso41eFTIplk+IbXQdovOccm+EXWZEREQuLDVLiXkbM0x2vVepKzFvYwZSs5RmP8cZZsbYQkRERG6tpV1JrkSrE5C8LRuCmXP6Y89vPo6zV8ohanDLB85eA8AZZnoMRERE5LZa05XkStLzik1ahhoqrarF337OafR8t0jP3tRVj4GIiIjckr4rqWHrib4rqbl9vlxBUWnTYUgvPiYMncL8TY53aReAIZ1CrFwr18RAREREbqe5riQRgORt2bgnVu6y3WeCIOBYfolFZRcm9mzxth6ehoOqiYjI7TTXlSQAUKorkZ5XbL9KWVF1rQ5LvzuBTw6cb7KcCHVdhMNjwuxSL1fGQERERG7H0q4kS8s5kyulVXj0w4P46nABRCJg8uD2AOrCT33698snxLpsK5g9scuMiIjcTmSQZSsvW1rOEczNjjul1GDOZ0egVFciSOqFdY8Mxl29IjG2b5TJ4HG5Gw0etwcGIiIicjvDY8KgkEmhUleaHUckQl1gcNauJHOz40L8vFFeXYsarYCu7QLw4cyh6BZRN0MsqZ8C98TK3XZ5AXtgICIiIrcjEYuwfEIs5m3MMDnn7F1Jjc2OK7lRAwDoGx2ML+bcBpmft9F5iVjEgdNtwDFERETklpL6KbB++hBIGqxI2C7Ix6FT7mu0OlTWaM2+Kqpq8eoPJ822auldK6tCoC/bM6yNP1EiInJbiX2i6pqEBKBdoA+ullVjYWJPh4Wh7ccvY9HXmajRNhV5mqbSVCE9r5itQVbGFiIiInJbSnUltDoBPl5iPBrfGQCQdnPLCkf4Mj2/TWFIzxVnxzk7thAREZHbKiiuAAB0CPXDyO7tsG5XLtLOXoNOJ0Bs5/FD5VW1OJx3HQDw49Mjza4cnZ5XjNmfHmn2Ws48O85VOXULkVarxSuvvIKYmBj4+fmhW7dueO211yAIt9K1IAhYtmwZFAoF/Pz8kJiYiNzcXKPrFBcXY9q0aQgODkZISAhmz56NsrIye98OERHZWcH1ukDUMdQfgzqGwM9bgmvl1fhDVWr3uqSdvYZqrQ6dwvwRqwhGkNTb5HVnr0goZFKTNYX0uNCi7Th1IFq1ahXWr1+P999/H6dOncKqVauwevVqvPfee4Yyq1evxrp167BhwwYcOnQIAQEBGDduHCorbzUnTps2DSdPnsTOnTuxfft27Nu3D3PnznXELRERkR0VFN8AAHQM84OPlxjxXeuCxIGzV+1el72nrwAARveMgKjh1vM36WfHAVxo0d6cOhAdOHAAEydOxPjx49GlSxc8+OCDGDt2LNLT0wHUtQ6tWbMGL7/8MiZOnIgBAwbgs88+w+XLl7FlyxYAwKlTp5CamoqPPvoI8fHxGDlyJN577z189dVXuHz5sgPvjoiIbC2/+FYLEQCM6NYOALD/jH0DkSAI2HO6CABwZ6+IJsvqZ8fJZcbdYnKZ1C02pHVWTj2G6Pbbb8cHH3yA06dPo2fPnvj999/x22+/4Z133gEA5OXlQaVSITEx0fAZmUyG+Ph4pKWlYerUqUhLS0NISAiGDh1qKJOYmAixWIxDhw7hgQceMPneqqoqVFVVGd5rNBob3iUREdmKocvs5nid27vXzcw6lFeMGq0O3hL7tAvkXS1HQfEN+EjEuK1r87PDuNCi/Tl1IFqyZAk0Gg169+4NiUQCrVaLN954A9OmTQMAqFQqAEBUVJTR56KiogznVCoVIiMjjc57eXkhLCzMUKahlJQUJCcnW/t2iIjIzvRdZvoBzH3kwQgL8EFxeTUyC0owrIt9xuLsyanrLhsWE4oAC9cQ4kKL9uXUXWbffPMNNm3ahC+++AIZGRn49NNP8dZbb+HTTz+16fcuXboUarXa8CooKLDp9xERkfXdqNbiallda7++y0wsFiHhZguNPbvN9OOH7uwZ2UxJchSnDkTPP/88lixZgqlTp6J///74y1/+gkWLFiElJQUAIJfLAQCFhYVGnyssLDSck8vlKCoqMjpfW1uL4uJiQ5mGfH19ERwcbPQiIiLXcvFmd1mQ1Asy/1vbXIzoXjeO6MAZ+6xHVFmjxcFzdd81upnxQ+Q4Th2IKioqIBYbV1EikUCn0wEAYmJiIJfLsWvXLsN5jUaDQ4cOISEhAQCQkJCAkpISHD161FBm9+7d0Ol0iI+Pt8NdEBGRI9Sfcl/fiJvjiI4VXEdFda3N63Hw3DVU1eoQLZOiR2Sgzb+PWsepxxBNmDABb7zxBjp16oS+ffvi2LFjeOedd/D4448DAEQiERYuXIjXX38dPXr0QExMDF555RVER0dj0qRJAIA+ffogKSkJc+bMwYYNG1BTU4MFCxZg6tSpiI6OduDdERGRLdWfcl9fpzB/tA/xw6WSG0jPK8advWzbjWWYbt+r8en25HhOHYjee+89vPLKK/jrX/+KoqIiREdH44knnsCyZcsMZV544QWUl5dj7ty5KCkpwciRI5Gamgqp9NZ0xU2bNmHBggUYM2YMxGIxpkyZgnXr1jniloiIyE4Kis23EIlEIozoHo5vjlzE/jNX7ReIerK7zJmJhPrLPpNZGo0GMpkMarWa44mIiFzE3M+OYEd2IZLv74uZt3cxOrc18xKe+SoTsYpg/OeZUTarQ0FxBUat/hVeYhEylt2DYKl38x8iq2nJ72+nHkNERETUWgXXzXeZAcDtNxdozFZqUFxebbM67LnZOjSkcyjDkJNjICIiIrcjCAIu3uwyM7eJakSQL3pFBQGo22PMVvbm1M1yZneZ82MgIiIit6O+UYPSqroZZB1CTQMRcGvV6v022tesqlaLAzfDVnPbdZDjMRAREZHb0c8wiwjyhdRbYraMrfc1O3r+OiqqtYgI8kWsguNPnR0DERERuZ1baxCZjh/Si+9atzfYhWsVhkUcrUk/fuiOHpxu7woYiIiIyO0YptybGT+kFyT1xsAOMgC2WbV67839y9hd5hoYiIiIyO3kN7IGUUP6bTysPY5Iqb6BnMJSiEXAqB7trHptsg0GIiIicjtNTbmv73bDOKJrsOayfPrWoUEdQxDi72O165LtMBAREZHbuWhhC9GQziGQeotxtawKpwvLrPb9t1an5u72roKBiIiI3IpOJ+CioYWo6UDk6yXBsC5hAKw326xGq8NvuXXX4u72roOBiIiI3EpRaRWqtTpIxCIoZNJmy+vHER2w0jiiY/klKK2qRViADwa0l1nlmmR7DERERORW9FPuo0Ok8JI0/2tOvx7RoXPFqNXq2vz9e0/XrU49qkc7iMWcbu8qGIiIiMitNLbLfWNio4Mh8/NGaVUtjl9St/n79+Rwd3tXxEBERERuRb9KtaWBSCIWIaHrzW08ctvWbVZUWomTlzUAgDsYiFwKAxEREbkVwxpEzUy5r2+ElfY1++/pus/3by9Du0DfNl2L7IuBiIiI3Iph245mZpjVpx9YnXGhBDeqta3+bv12HVyd2vUwEBERkVvRr0HU2C735sS0C4BCJkW1VocjF4pb9b1anYD/5nL8kKtiICIiIrdRXauDUlMJoGVdZiKRyLBq9W+tXI/o+MUSlFTUIFjqhUEdQ1p1DXIcBiIiInIbl0tuQBAAqbcYES0cw6MfR9TajV71s8tG9YiwaLo/ORc+MSIichuG8UOh/hCJWrYGkH4cUdZlNUoqqlv83be262B3mStiICIiIrdhmHLfggHVelHBUnSPCIQgAOt25SLt7DVodU1v+KrVCUg7ew2bDl3A7wUlADjd3lV5OboCRERE1mKYch9q+fghvdQsJS6r6wLVv/afx7/2n4dCJsXyCbFI6qcwWz55WzaU6krDMS+xCJkF15EkMy1Pzo0tRERE5DZaM+UeqAs38zZmoKLBlHuVuhLzNmYgNUtptnz9MAQAtTrBbHlyfgxERETkNloz5V6rE5C8LRvmOsf0x5K3ZRu6z5oqDzPlyTWwy4yIiNxGwXX9GCLLu8zS84pNWnrqEwAo1ZX4+LdziAiW4mpplUXl0/OKkdAt3OJ6kGMxEBERkVsor6pFcXnd7LCWdJkVlTYebup7/T9/tKg+ll6XnAO7zIiIyC3oxw+F+HsjWOpt8ecig6Q2qQ/3MnMtDEREROQWWrrLvd7wmDAoZFK0bNUiC3AIkUthICIiIrdQ0Ipd7gFAIhZh+YRYALBqKLpaXmXFq5GtMRAREZFbuLUGUcsXZUzqp8D66UMgl1mv+8xWXXFkGxxUTUREbuHizTFEHVqxSjVQF4ruiZUjPa8YRaWVuFpahdd+PNXi64gAyGVSDI8Ja1U9yDEYiIiIyC3cGkPU8lWq9SRikWGqvFYn4KPf8qBSV1o8HEjf5bZ8QiwkYquPSiIbYpcZERG5PEEQWr1KdWOaGlukfx/ibzybTS6TYv30IWa3+iDnxhYiIiJyecXl1YZtN9qHtL6FqCH92KKGe5bJb+5xVr+LLTKorpuMLUOuiYGIiIhcnn6FanmwFFJviVWv3XBsUcPgw9Wo3QMDERERubzWTrm3VP2xRfai1QlsfbIjBiIiInJ5bZly74xSs5Qm3XSKm910HJ9kGxxUTURELq+tU+6dSWqWEvM2ZphsIKtSV2LexgykZikdVDP3xkBEREQuzxpT7p2BVicgeVu22Wn++mPJ27Kh1XFfEGtjICIiIpdn7Sn3jpKeV2zSMlSfAECprkR6XrH9KuUhGIiIiMilaXUCLpfcbCFy8UBUVNp4GGpNObIcAxEREbk0laYSNVoB3hIR5MGuvX+YpfufcZ8062MgIiIil6afch8d4ufy09KHx4RBIZOarIytJ0LdbDPuk2Z9DEREROTS9IGok4t3lwGWbRfCfdJsg4GIiIhcmj4QdXCTNYj024XIZcbdYtwnzba4MCMREbk0/bYdtlql2hGa2y6ErK9VLUQrVqxARUWFyfEbN25gxYoVba4UERGRpQrcbJVqPf12IRMHtUdCt3CGIRtrVSBKTk5GWVmZyfGKigokJye3uVJERESWcpc1iMixWhWIBEGASGSaVH///XeEhXHkOxER2UdljRaFmioArr9KNTlWi8YQhYaGQiQSQSQSoWfPnkahSKvVoqysDE8++aTVK0lERGTOpZsLMvr7SBAW4OPg2pAra1EgWrNmDQRBwOOPP47k5GTIZDLDOR8fH3Tp0gUJCQlWryQREZE59ccPmeu5ILJUiwLRzJkzAQAxMTG4/fbb4e3tbZNKERERWcIQiDh+iNqoVWOIRo8eDYlEgtOnT+O3337Dvn37jF7WdOnSJUyfPh3h4eHw8/ND//79ceTIEcN5QRCwbNkyKBQK+Pn5ITExEbm5uUbXKC4uxrRp0xAcHIyQkBDMnj3b7KBwIiJyLe445Z4co1XrEB08eBCPPvooLly4AEEQjM6JRCJotVqrVO769esYMWIE7rrrLvz000+IiIhAbm4uQkNDDWVWr16NdevW4dNPP0VMTAxeeeUVjBs3DtnZ2ZBK6xa1mjZtGpRKJXbu3ImamhrMmjULc+fOxRdffGGVehIRkWO465R7sj+R0DDRWGDQoEHo2bMnkpOToVAoTPpt648taoslS5Zg//79+O9//2v2vCAIiI6OxrPPPovnnnsOAKBWqxEVFYVPPvkEU6dOxalTpxAbG4vDhw9j6NChAIDU1FTcd999uHjxIqKjo5uth0ajgUwmg1qtRnBwsFXujYiI2u5P7/0XWZc0+HDGUNwTG+Xo6pCTacnv71Z1meXm5uLNN99Enz59EBISAplMZvSylh9++AFDhw7Fn//8Z0RGRmLw4MH48MMPDefz8vKgUqmQmJhoOCaTyRAfH4+0tDQAQFpaGkJCQgxhCAASExMhFotx6NAhs99bVVUFjUZj9CIiIudTUMwuM7KOVgWi+Ph4nDlzxtp1MXHu3DmsX78ePXr0wM8//4x58+bh6aefxqeffgoAUKlUAICoKOP/K4iKijKcU6lUiIyMNDrv5eWFsLAwQ5mGUlJSjAJex44drX1rRETURprKGqhv1ABglxm1ncVjiI4fP27481NPPYVnn30WKpUK/fv3N5ltNmDAAKtUTqfTYejQoXjzzTcBAIMHD0ZWVhY2bNhgmPFmC0uXLsXixYsN7zUaDUMREZGT0Y8fCgvwQYAvt+aktrH4v6BBgwZBJBIZDaJ+/PHHDX/Wn7PmoGqFQoHY2FijY3369MG///1vAIBcLgcAFBYWQqG4tftvYWEhBg0aZChTVFRkdI3a2loUFxcbPt+Qr68vfH19rXIPRERkG4buMq5QTVZgcSDKy8uzZT3MGjFiBHJycoyOnT59Gp07dwZQtx6SXC7Hrl27DAFIo9Hg0KFDmDdvHgAgISEBJSUlOHr0KOLi4gAAu3fvhk6nQ3x8vP1uhoiIrIprEJE1WRyI9CHEnhYtWoTbb78db775Jh566CGkp6fjgw8+wAcffACgrlVq4cKFeP3119GjRw/DtPvo6GhMmjQJQF2LUlJSEubMmYMNGzagpqYGCxYswNSpUy2aYUZERM6Jm7qSNbWq0/WHH34we1wkEkEqlaJ79+6IiYlpU8UAYNiwYfj++++xdOlSrFixAjExMVizZg2mTZtmKPPCCy+gvLwcc+fORUlJCUaOHInU1FTDGkQAsGnTJixYsABjxoyBWCzGlClTsG7dujbXj4iIHIdrEJE1tWodIrFYbDKeCDAeRzRy5Ehs2bLFaBFFV8V1iIiInE/iO3txpqgMn88ejlE9IhxdHXJCNl+HaOfOnRg2bBh27twJtVoNtVqNnTt3Ij4+Htu3b8e+fftw7do1w2KJRERE1iQIAi5eZwsRWU+rusyeeeYZfPDBB7j99tsNx8aMGQOpVIq5c+fi5MmTWLNmjdEsNCIiImu5UlaFyhodRCIgOoSzzKjtWhWIzp49a7bpKTg4GOfOnQMA9OjRA1evXm1b7YiIiMzQT7lXBEvh41XX2aHVCUjPK0ZRaSUig6QYHhMGiVjU1GWIDFoViOLi4vD888/js88+Q0REXb/tlStX8MILL2DYsGEA6rb34GKGRERkC/oB1R1uzjBLzVIieVs2lOpKQxmFTIrlE2KR1E9h9hpE9bVqDNH//d//IS8vDx06dED37t3RvXt3dOjQAefPn8dHH30EACgrK8PLL79s1coSEREBtwJRpzB/pGYpMW9jhlEYAgCVuhLzNmYgNUvpiCqSi2lVC1GvXr2QnZ2NHTt24PTp04Zj99xzD8TiuoylXweIiIjI2vRrEHUI8UPytmyYmy4tABABSN6WjXti5ew+oya1evMXsViMpKQkJCUlWbM+RERETdLpBOSoSgEA1VqdSctQfQIApboS6XnFSOgWbqcakiuyOBCtW7cOc+fOhVQqbXZRw6effrrNFSMiIjLnrR05+P2iGl5iEcICfCz6TFFp46GJCGhBIHr33Xcxbdo0SKVSvPvuu42WE4lEDERERGQTmw5dwD/2nAUArJwyAO0tnHIfGSRtvhB5tFZt7uqIjV6JiMiz/fpHEV7ZkgUAWJTYEw/GdYBWJ0Ahk0KlrjQ7jkgEQC6rm4JP1JRWzTLTq66uRk5ODmpra61VHyIiIhMnLqox/4sM6ATgz3Ed8PSY7gAAiViE5RNiAdSFn/r075dPiOWAampWqwJRRUUFZs+eDX9/f/Tt2xf5+fkAgKeeegorV660agWJiMizFRRX4PFPD6OiWotRPdrhzcn9IRLdCjhJ/RRYP30I5DLjbjG5TIr104dwHSKySKtmmS1duhS///479uzZYzTLLDExEa+++iqWLFlitQoSEZHnUlfUYNYnh3GltAq95UH4x7Qh8JaY/r98Uj8F7omVc6VqarVWBaItW7bg66+/xm233WaU0vv27YuzZ89arXJEROS5qmq1mPv5EZwpKoNCJsUns4YjSOrdaHmJWMSp9dRqreoyu3LlCiIjI02Ol5eXGwUkIiKi1tDpBDy/+TgO5RUjyNcLH88aZtIlRmRNrQpEQ4cOxY8//mh4rw9BH330ERISEqxTMyIicmtanYC0s9ewNfMS0s5eg1Z3a57YWzty8MPvl+ElFmH99Dj0lptuKE5kTa3qMnvzzTdx7733Ijs7G7W1tVi7di2ys7Nx4MAB7N2719p1JCIiN9PUZqzXyquN1hoa2aOdo6pJHqRVLUQjR47E77//jtraWvTv3x87duxAZGQk0tLSEBcXZ+06EhGRG2lqM9YnN2bg5QZrDRHZQ6taiGbMmIG77roLS5YsQbdu3axdJyIiclNandDkZqwAIAjAg/XWGiKyh1YFIh8fH6SkpOB//ud/EB0djdGjR+POO+/E6NGj0aNHD2vXkYiInMD5q+VI3nYSZVWtX4xXc6O2yc1Y9SYOiuYkHbKrVgWijz76CABw6dIl7Nu3D3v37sXbb7+NJ554AgqFAhcvXrRqJYmIyPG+SM/HrzlX7PJdxeXVdvkeIr1WBSK90NBQhIeHIzQ0FCEhIfDy8kJERIS16kZERE4kR1UKAJiR0BkJXVu33k9OYSnW/JLbbDluxkr21qpA9NJLL2HPnj04duwY+vTpg9GjR2PJkiW44447EBoaau06EhGREzhdWBeI7h8YjaFdWrdZ6ti+cnx9uICbsZLTaVUgWrlyJSIiIrB8+XJMnjwZPXv2tHa9iIjIiahv1BjG/vSICmr1dfSbsc7bmAERYBSKuBkrOVKrpt0fO3YM//u//4v09HSMGDEC7du3x6OPPooPPvgAp0+ftnYdiYjIwXJvtg4pZFLI/BrfPsMS3IyVnFGrWogGDhyIgQMH4umnnwYA/P7773j33Xcxf/586HQ6aLVaq1aSiIgcK+dmIOrZhtah+rgZKzmbVgUiQRBw7Ngx7NmzB3v27MFvv/0GjUaDAQMGYPTo0dauIxEROdjpmwOqe8mtE4gAbsZKzqVVgSgsLAxlZWUYOHAgRo8ejTlz5mDUqFEICQmxcvWIiMgZWLuFiMjZtCoQbdy4EaNGjUJwMDfbIyJyd4IgGKbc92IgIjfVqkA0fvx4a9eDiIic1NWyalyvqIFIBHSPDHR0dYhsolWzzIiIyHPo1x/qHOYPPx+Jg2tDZBsMRERE1KTTHD9EHoCBiIiImqQPRNacYUbkbBiIiIioSfoB1WwhInfGQERERI0SBAGnC8sAsIWI3BsDERERNeqyuhJlVbXwlojQJTzA0dUhshkGIiIiapR+hequ7QLh48VfGeS++F83ERE1yrBCNbvLyM0xEBERUaMMe5hFcUFGcm8MRERE1CjuYUaegoGIiIjM0uoE5BZxhhl5BgYiIiIy68K1clTX6iD1FqNjqL+jq0NkUwxERERkln6F6h6RQRCLRQ6uDZFtMRAREZFZOaq67jKOHyJPwEBERERm3drDjDPMyP0xEBERkVmcYUaehIGIiIhMVNVqkXe1HABnmJFnYCAiIiIT566UQ6sTECT1gjxY6ujqENkcAxEREZkwjB+KCoJIxBlm5P4YiIiIyESOinuYkWdhICIiIhP1W4iIPAEDERERmeAMM/I0LhWIVq5cCZFIhIULFxqOVVZWYv78+QgPD0dgYCCmTJmCwsJCo8/l5+dj/Pjx8Pf3R2RkJJ5//nnU1tbaufZERK6hvKoWBcU3AAA9ucs9eQiXCUSHDx/GP//5TwwYMMDo+KJFi7Bt2zZs3rwZe/fuxeXLlzF58mTDea1Wi/Hjx6O6uhoHDhzAp59+ik8++QTLli2z9y0QEbkE/Yau7QJ9ER7o6+DaENmHSwSisrIyTJs2DR9++CFCQ0MNx9VqNf7v//4P77zzDu6++27ExcXh448/xoEDB3Dw4EEAwI4dO5CdnY2NGzdi0KBBuPfee/Haa6/h73//O6qrqx11S0RETuu0iitUk+dxiUA0f/58jB8/HomJiUbHjx49ipqaGqPjvXv3RqdOnZCWlgYASEtLQ//+/REVFWUoM27cOGg0Gpw8edLs91VVVUGj0Ri9iIg8BccPkSfycnQFmvPVV18hIyMDhw8fNjmnUqng4+ODkJAQo+NRUVFQqVSGMvXDkP68/pw5KSkpSE5OtkLtiYhcD2eYkSdy6haigoICPPPMM9i0aROkUvutlLp06VKo1WrDq6CgwG7fTUTkaFyDiDyRUweio0ePoqioCEOGDIGXlxe8vLywd+9erFu3Dl5eXoiKikJ1dTVKSkqMPldYWAi5XA4AkMvlJrPO9O/1ZRry9fVFcHCw0YuIyBNcL69GUWkVAKBHJMcQkedw6kA0ZswYnDhxApmZmYbX0KFDMW3aNMOfvb29sWvXLsNncnJykJ+fj4SEBABAQkICTpw4gaKiIkOZnTt3Ijg4GLGxsXa/JyIiZ6bvLmsf4ocgqbeDa0NkP049higoKAj9+vUzOhYQEIDw8HDD8dmzZ2Px4sUICwtDcHAwnnrqKSQkJOC2224DAIwdOxaxsbH4y1/+gtWrV0OlUuHll1/G/Pnz4evL6aRERPWdvjnlnjvck6dx6kBkiXfffRdisRhTpkxBVVUVxo0bh3/84x+G8xKJBNu3b8e8efOQkJCAgIAAzJw5EytWrHBgrYmInJN+yj1nmJGnEQmCIDi6Es5Oo9FAJpNBrVZzPBERubWH/pmG9LxivPvwQDwwuIOjq0PUJi35/e3UY4iIiMh+BEEwjCFiCxF5GgYiIiICAFwprUJJRQ3EIqBbBGeYkWdhICIiIgC3Vqju0i4AUm+Jg2tDZF8MREREBODWgoxcoZo8EQMREREBAMcPkUdjICIiIgBATiHXICLPxUBERETQ6QTksoWIPBgDERER4VLJDVRUa+EjEaNLuL+jq0NkdwxERERkGFDdNSIAXhL+aiDPw//qiYjIMOWe44fIUzEQERERZ5iRx2MgIiIirkFEHo+BiIjIw9VodTh3pRwAu8zIczEQERF5uAvXylGt1cHfR4L2IX6Org6RQzAQERF5uBxV3YKMPaKCIBaLHFwbIsdgICIi8nCGGWZR3OGePBcDERGRhzut4gwzIgYiIiIPd5prEBExEBERebLKGi3OX7s5w4wtROTBGIiIiDzYmaIy6AQgxN8bEUG+jq4OkcN4OboCRETUelqdgPS8YhSVViIySIrhMWGQtGCmWP0VqkUizjAjz8VARETkolKzlEjelg2lutJwTCGTYvmEWCT1U1h0jVszzNhdRp6NXWZERC4oNUuJeRszjMIQAKjUlZi3MQOpWUqLrmOYYcYB1eTh2EJERORitDoByduyIZg5pz/2wrfHcfH6DYib6QY7cUkNgC1ERAxEREQuJj2v2KRlqCFNZS1e//GURdcTiYCeXJSRPBwDERGRiykqbToM6Q3pFIIOof7NlkvoFo4Qf5+2VovIpTEQERG5mMggqUXlnh/XGwndwm1cGyL3wEHVREQuZnhMGBQyKRobHSRC3Wyz4TFh9qwWkUtjICIicjESsQjLJ8SaHVStD0nLJ8S2aD0iIk/HQERE5IKS+ilw/0DTtYbkMinWTx9i8TpERFSHY4iIiFzQxoMX8MPvdWsNzUzojCGdQ1u1UjUR1WEgIiJyAi3ZguPH40q8sjULAPDU3d3x7Nhe9qwqkVtiICIicrDULCVe/SEbKs2t6fTyYClevd90C47/5l7Bwq+PQRCAR+M7YfE9Pe1dXSK3xDFEREQOlJqlxJMbM4zCEACoNJV4ssEWHJkFJXji86Oo0Qq4r78cr03sxw1ZiayEgYiIyEG0OgFLvjvRZJml352AVifgTFEpZn2cjopqLUZ2b4d3Hx7EsUJEVsQuMyIiBzl47hpKKmqaLHO9ogbbj1/Gyp/+wPWKGgzsIMOGv8TB10tip1oSeQYGIiIiO/sqPR8f/vccrpZVW1T+hW+Po6pWh64RAfh41nAE+vKfbiJr498qIiI7uni9Ast/OImqWp3Fn6mq1aF9iB8+nx2PsADuOUZkCxxDRERkRyn/+QNVtToM7xKGZeNjLfrMsvGx+GXxaLQP8bNx7Yg8FwMREZGdHDh7FT+eUEIsApIn9sXMEV0Q4u/d5GdC/L0xc0QX+PlwzBCRLTEQERHZQa1WhxXbsgEA0+I7o48iGBKxCCsn92/ycysn9+dsMiI7YCAiIrKDL9Pz8YeqFDI/b6PFFJP6KbBh+hDIg32NysuDfbGBe5IR2Q0HVRMR2dj18mq8teM0AODZsT0R2mBgdFI/Be6JlVu8dQcRWR8DERF5hJbsFWZt7+w8DfWNGvSWB+HR4Z3MlpGIRUjoFm6X+hCRKQYiInJ7qVlKJG/LhlJ9a3sMhUyK5RNM9wqztlNKDTYdugAAWD6hL7wkHKlA5Iz4N5OI3FpqlhLzNmYYhSEAUKkrMa/BXmHWVqvVYfE3mdAJQHxMGIbHhNnsu4iobRiIiMhtaXUCkrdlQzBzTn8seVs2tDpzJdomNUuJoW/8glPKUgDAobxijFy126YBjIhaj4GIiNxWel6xSctQfQIApboS6XnFVv1e/Q72Dfcps0erFBG1DgMREbmtotLGw1BryllC3ypljq1bpYio9RiIiMhtRQZJrVrOEo5qlSKitmEgIiK3NTwmDApZ02FHIZNadbCzI1qliKjtGIiIyG1JxCLMGRXTZJllf+pj1fWIrpRWWVTOmq1SRNR2Th2IUlJSMGzYMAQFBSEyMhKTJk1CTk6OUZnKykrMnz8f4eHhCAwMxJQpU1BYWGhUJj8/H+PHj4e/vz8iIyPx/PPPo7a21p63QkQO8vtFNQBA6mX+n7tzVytadD2tTkDa2WvYmnkJaWevGY0FqtXqsPnoxSY/L4L1W6WIqO2cemHGvXv3Yv78+Rg2bBhqa2vx0ksvYezYscjOzkZAQAAAYNGiRfjxxx+xefNmyGQyLFiwAJMnT8b+/fsBAFqtFuPHj4dcLseBAwegVCoxY8YMeHt7480333Tk7RGRjZ27UoZtv18GAHzzZALKq7SGlarPFJXhla1ZeGtHDnpFBSExNqrZ6zW3wOOX6fnIUZXC30eCimotRIDRlH99O9TyCbHcloPIyYgEQXCZqQ5XrlxBZGQk9u7dizvuuANqtRoRERH44osv8OCDDwIA/vjjD/Tp0wdpaWm47bbb8NNPP+FPf/oTLl++jKioun/wNmzYgBdffBFXrlyBj49PU18JANBoNJDJZFCr1QgODrbpPRKR9Tz7ze/4d8ZFJPaJxEczh5mcf3nLCWw8mI8AHwm+nz8CPaOCGr2WfoHHhv9g6mPN3/48AK9tPwX1jRq8NrEvIoJ8HbY6NhHVacnvb6duIWpIra5r+g4Lq2tqPnr0KGpqapCYmGgo07t3b3Tq1MkQiNLS0tC/f39DGAKAcePGYd68eTh58iQGDx5s8j1VVVWoqro1DkCj0djqlojIRvKvVWBL5iUAwFN39zBbZvmEvsgtLMOhvGLM+ewIts4fgRB/0/9Jam6BRxGAZVtPoqJai97yIDwyvBO8JGJu2ErkQpx6DFF9Op0OCxcuxIgRI9CvXz8AgEqlgo+PD0JCQozKRkVFQaVSGcrUD0P68/pz5qSkpEAmkxleHTt2tPLdEJGt/WPPGWh1Au7oGYGBHUPMlvGWiPGPaUPQPsQPF65VYMEXx1Cr1ZmUs2QqfUW1FoDxfmX6DVsnDmqPhG7hDENETsxlAtH8+fORlZWFr776yubftXTpUqjVasOroKDA5t9JRNZz8XoF/p1RN7j5mTHdmywbHuiLD2cMhZ+3BL+duYo3/nPKZOC0SmPZFPnbuoZxx3oiF+USXWYLFizA9u3bsW/fPnTo0MFwXC6Xo7q6GiUlJUatRIWFhZDL5YYy6enpRtfTz0LTl2nI19cXvr6+Vr4LIrKXDXvPokYr4PZu4Yjr3PxsrtjoYLzz0EDM25SBj/efx3cZF6G+cWsmaliAt0Xf+3QjXXNE5PycuoVIEAQsWLAA33//PXbv3o2YGOP1ROLi4uDt7Y1du3YZjuXk5CA/Px8JCQkAgISEBJw4cQJFRUWGMjt37kRwcDBiY2PtcyNEZDcqdSW+OVzXOvT0GMsDyr39FRjfv+5/kuqHIQAoLq8x9xED/VT6+K5sHSJyVU7dQjR//nx88cUX2Lp1K4KCggxjfmQyGfz8/CCTyTB79mwsXrwYYWFhCA4OxlNPPYWEhATcdtttAICxY8ciNjYWf/nLX7B69WqoVCq8/PLLmD9/PluBiNzQhr1nUa3VYXiXMNzWgoCi1Qk4euF6i7+PU+mJ3INTtxCtX78earUad955JxQKheH19ddfG8q8++67+NOf/oQpU6bgjjvugFwux3fffWc4L5FIsH37dkgkEiQkJGD69OmYMWMGVqxY4YhbIiIbKiqtxJfp+QCAoV1CTRZObEp6XjFUmuZXmQ7xN+4+k8ukWD99CKfSE7k4l1qHyFG4DhGRa/ifTw/jl1NFRscsXftna+YlPPNVZrPf8e5DAyGX+XEqPZELcNt1iIiIGrP5SL5JGALqxhTN25jRbCuOpXuLyWV+nElG5IacusuMiMgSWp2AV7dlmz2nbwJP3pbdZPfZ8JgwKGRSNNbWwz3IiNwbAxERubzdp4pQXqVt9LwAQKmuRHpecaNlJGIRlk+om3naMBRx4DSR+2MgIiKX922GZYunFpU2vcBiUj8F1k8fArnMuPuMA6eJ3B/HEBGRU9PqBLP7gemPX7hWjj05Vyy6liXjhJL6KbgHGZEHYiAiIqeVmqU02TE+LMAHgzrKkFmgRnF5tUXXEaGulUenE7A181KzIUe/BxkReQ5Ou7cAp90T2V9qlhLzNmaY3WG+JUSoG0MU4u+NkopbK05bOh2fiFxXS35/cwwRETkdrU5A8rbsVoWhho0++oUU64ch4NZ0/NQsZStrSUTuhF1mROR00vOKjbrJWkInAK+M74N2Qb5oF+iLZ7/JNFtOQF3rUfK2bNwTK+cYISIPxxYiInIKWp2AtLPXsDXzEvafudqma7UL8sXEQe0hFoma3I7Dkun4ROQZ2EJERA6XmqXEqz+ctGgvMUvoZ5M1N81ez9JyROS+GIiIyKFSs5R4cmOGVa6ln02mX03a0u04LC1HRO6LXWZE5DBanYAl352wyrXMrSbN7TiIyFIMRETkMAfPXjOZ/dVa5laT5nYcRGQpdpkRkcPsPW26O705kwZFIzzAB99nXkJx+a0AFRbgjQcGtUdirLzRhRb123E0XOBRznWIiKgeBiIispnGtt2o0erw1eECbDyUb9F1OoT647lxvfDS+NhWbanB7TiIqDkMRERkE+ZmjkUF+WDS4PbYmV2Ec1fLLb7WsM6hANq2pQa34yCipnAMERFZnX7mWMNp9IWl1fjnvjycu1qOsAAfjI2Nsuh6p4vKbFFNIiIDBiIisipLZo75eomx+9nRkMssm+5ecL3CGlUjImoUAxERWZUlM8eqanU4eUmDjqH+Fl3T0nJERK3FQEREVpV2zrJtN9LOXUVveZBFZS0tR0TUWgxERGRVlTU6C0uKUFxRbVFJS8sREbUWZ5kRkdWcKSrF9uNKi8omdAuHWGTZtHdurUFEtsZARERWsfuPQjz9ZSbKqmohEgGC0HjZUH9v3Na1bgq8QiaFSl0Jc8Ub7k1GRGQr7DIjokZpdQLSzl7D1sxLSDt7DdW1OqP3Wp0AQRDwjz1nMPvTIyirqsXwLmFYNaV/k9dNmdwfErGIW2sQkdNgCxERmZWapTTZ7kIsAnT1mnLkwb7oGOaPw+evAwAeje+EVyf0hY+XGMFSb7z6QzZUmlufV5jZLoNbaxCRMxAJQlMN2wQAGo0GMpkMarUawcHBjq4OUZs03E4jrnMojl64brSlxc5sFeZtzDDbjWWOWASsmNgP02/r3OR3NbVdRkvKEhFZoiW/v9lCRORBLGv1kaKyVmtxGAKAEH8fPDK8k8nxlmyXwa01iMiROIaIyEOkZikxb2OGURgCjMMQAKg0lc0urNhQcXk10vOK21pFIiKHYQsRkQtqafeSVicgeVt2i1p9WqqotLL5QkREToqBiMjFmOv2MjdYub70vGKTliFr41pBROTK2GVG5EIa6/ZSqSsxb2MGUrPML4p40Yabo4pQF8i4VhARuTK2EBG5iIqqWizfetJst5f+2PKtJzG0SxgkIhGU6kr8duYK/pt7FQfPXbNJnbhWEBG5CwYiIhfw+cELeG1bNqq1Te8TVlhahaGv/2L2XMPZZE0RAQjx94avlxgqTVWj1+BaQUTkLhiIiJxYda0Or247iS8O5bf4s/4+EsTHhGFUjwjc0bMdcgtL8ddNxwCgycHV+naelMn9cU+svNk1i9gyRETugIGIyEldLavCXzdmIP18MUQiYOrQjvjycEGzn9v0P/FI6BoOkQgQ1ds8tXtkENZPFzW/DlGDVp+GawNxrSAickcMRERO6ORlNeZ+dhSXSm4gyNcLax8ZhNE9I7Hn9JVmN0K9rWs4xI202iT1U7DVh4jIDAYiIifz43Elnt2cicoaHWLaBeDDGXHoHhkEoG7w8ryNGRDBuNurJYObza0IzVYfIvJ0nHZPZCUNd4bXWjqC+SadTsDbO3Iw/4sMVNbocEfPCGz56whDGAJubYQqlxmv+SOXSbF++hAObiYiaiW2EBFZQWsWS6yvrKoWi77OxM7sQgDA3Du64sWk3mZbe8x1e7Gbi4iobbjbvQW42z01Rb9YYsO/SPp40lzLzYVr5Zjz2RGcLiyDj5cYKyf3x+QhHWxWXyIiT8Hd7olayZp7hAmoC0XJ27JxT6zc7HV+y72K+V9kQH2jBpFBvvhgxlAM6hhirdshIiILMRAR3WSLPcIEAEp1JWb+Kx0RQb5G56prdUg9qYJWJ2BQxxD88y9xiArmfmBERI7AQESExru99HuE6bu9BEHAxes3cKygBMfyr2P3H0UWXf+3M1cbPTdlSAe88UA/SL0lbbgDIiJqCwYiB9LqBCjVNxxdDY+n0wGvNLNH2PObj+PboxeRWVCCq2XVLf6O6fGd0aWdv8nxLuEBGNMn0mgBRSIisj8GIge6Vl6Fkat+dXQ1yAKlVbX45VRda5CXWIS+0cEY3CkUAzrIkPKfU7jSSEjSL5aYPLEvZ4ERETkxBiIH8/XiUlCOphME1Gibn2w5aVA0/pLQGX2jZUbdW/4+EszbmAGg9YslEhGRY3HavQU47d69pZ29hkc+PNhsuS/n3Nbois5tXYeIiIisj9PuiVpgeEwYFDJps3uEDY8Ja/QaXCyRiMi1MRCRx5OIRTbbI4yIiFwDB7AQgXuEERF5OrYQEd3Ebi8iIs/lUS1Ef//739GlSxdIpVLEx8cjPT3d0VUiJ6Pv9po4qD0SuoUzDBEReQiPCURff/01Fi9ejOXLlyMjIwMDBw7EuHHjUFRk2UrDRERE5L48JhC98847mDNnDmbNmoXY2Fhs2LAB/v7++Ne//uXoqhEREZGDeUQgqq6uxtGjR5GYmGg4JhaLkZiYiLS0NJPyVVVV0Gg0Ri8iIiJyXx4RiK5evQqtVouoqCij41FRUVCpVCblU1JSIJPJDK+OHTvaq6pERETkAB4RiFpq6dKlUKvVhldBQYGjq0REREQ25BHT7tu1aweJRILCwkKj44WFhZDL5SblfX194evra6/qERERkYN5RAuRj48P4uLisGvXLsMxnU6HXbt2ISEhwYE1IyIiImfgES1EALB48WLMnDkTQ4cOxfDhw7FmzRqUl5dj1qxZjq4aEREROZjHBKKHH34YV65cwbJly6BSqTBo0CCkpqaaDLQmIiIizyMSBMHcBt9Uj1qtRkhICAoKChAcHOzo6hAREZEFNBoNOnbsiJKSEshksibLekwLUVuUlpYCAKffExERuaDS0tJmAxFbiCyg0+lw+fJlBAUFQSSy7t5W+vTq7q1PvE/34Qn3CPA+3Q3v03205B4FQUBpaSmio6MhFjc9j4wtRBYQi8Xo0KGDTb8jODjYbf/jrY/36T484R4B3qe74X26D0vvsbmWIT2PmHZPRERE1BQGIiIiIvJ4DEQO5uvri+XLl7v9yti8T/fhCfcI8D7dDe/TfdjqHjmomoiIiDweW4iIiIjI4zEQERERkcdjICIiIiKPx0BEREREHo+ByEFeffVViEQio1fv3r0dXa0227dvHyZMmIDo6GiIRCJs2bLF6LwgCFi2bBkUCgX8/PyQmJiI3Nxcx1S2lZq7x8cee8zk2SYlJTmmsm2QkpKCYcOGISgoCJGRkZg0aRJycnKMylRWVmL+/PkIDw9HYGAgpkyZgsLCQgfVuOUsucc777zT5Hk++eSTDqpx66xfvx4DBgwwLGSXkJCAn376yXDe1Z+jXnP36Q7P0pyVK1dCJBJh4cKFhmPu8kz1zN2jtZ8nA5ED9e3bF0ql0vD67bffHF2lNisvL8fAgQPx97//3ez51atXY926ddiwYQMOHTqEgIAAjBs3DpWVlXauaes1d48AkJSUZPRsv/zySzvW0Dr27t2L+fPn4+DBg9i5cydqamowduxYlJeXG8osWrQI27Ztw+bNm7F3715cvnwZkydPdmCtW8aSewSAOXPmGD3P1atXO6jGrdOhQwesXLkSR48exZEjR3D33Xdj4sSJOHnyJADXf456zd0n4PrPsqHDhw/jn//8JwYMGGB03F2eKdD4PQJWfp4COcTy5cuFgQMHOroaNgVA+P777w3vdTqdIJfLhb/97W+GYyUlJYKvr6/w5ZdfOqCGbdfwHgVBEGbOnClMnDjRIfWxpaKiIgGAsHfvXkEQ6p6dt7e3sHnzZkOZU6dOCQCEtLQ0R1WzTRreoyAIwujRo4VnnnnGcZWykdDQUOGjjz5yy+dYn/4+BcH9nmVpaanQo0cPYefOnUb35k7PtLF7FATrP0+2EDlQbm4uoqOj0bVrV0ybNg35+fmOrpJN5eXlQaVSITEx0XBMJpMhPj4eaWlpDqyZ9e3ZsweRkZHo1asX5s2bh2vXrjm6Sm2mVqsBAGFhYQCAo0ePoqamxuh59u7dG506dXLZ59nwHvU2bdqEdu3aoV+/fli6dCkqKiocUT2r0Gq1+Oqrr1BeXo6EhAS3fI6A6X3qudOznD9/PsaPH2/07AD3+rvZ2D3qWfN5cnNXB4mPj8cnn3yCXr16QalUIjk5GaNGjUJWVhaCgoIcXT2bUKlUAICoqCij41FRUYZz7iApKQmTJ09GTEwMzp49i5deegn33nsv0tLSIJFIHF29VtHpdFi4cCFGjBiBfv36Aah7nj4+PggJCTEq66rP09w9AsCjjz6Kzp07Izo6GsePH8eLL76InJwcfPfddw6sbcudOHECCQkJqKysRGBgIL7//nvExsYiMzPTrZ5jY/cJuM+zBICvvvoKGRkZOHz4sMk5d/m72dQ9AtZ/ngxEDnLvvfca/jxgwADEx8ejc+fO+OabbzB79mwH1ozaaurUqYY/9+/fHwMGDEC3bt2wZ88ejBkzxoE1a7358+cjKyvLLca5Naaxe5w7d67hz/3794dCocCYMWNw9uxZdOvWzd7VbLVevXohMzMTarUa3377LWbOnIm9e/c6ulpW19h9xsbGus2zLCgowDPPPIOdO3dCKpU6ujo2Yck9Wvt5ssvMSYSEhKBnz544c+aMo6tiM3K5HABMZjoUFhYazrmjrl27ol27di77bBcsWIDt27fj119/RYcOHQzH5XI5qqurUVJSYlTeFZ9nY/doTnx8PAC43PP08fFB9+7dERcXh5SUFAwcOBBr1651q+cINH6f5rjqszx69CiKioowZMgQeHl5wcvLC3v37sW6devg5eWFqKgol3+mzd2jVqs1+UxbnycDkZMoKyvD2bNnoVAoHF0Vm4mJiYFcLseuXbsMxzQaDQ4dOmTUx+9uLl68iGvXrrncsxUEAQsWLMD333+P3bt3IyYmxuh8XFwcvL29jZ5nTk4O8vPzXeZ5NneP5mRmZgKAyz3PhnQ6HaqqqtziOTZFf5/muOqzHDNmDE6cOIHMzEzDa+jQoZg2bZrhz67+TJu7R3PDD9r8PK02PJta5NlnnxX27Nkj5OXlCfv37xcSExOFdu3aCUVFRY6uWpuUlpYKx44dE44dOyYAEN555x3h2LFjwoULFwRBEISVK1cKISEhwtatW4Xjx48LEydOFGJiYoQbN244uOaWa+oeS0tLheeee05IS0sT8vLyhF9++UUYMmSI0KNHD6GystLRVW+RefPmCTKZTNizZ4+gVCoNr4qKCkOZJ598UujUqZOwe/du4ciRI0JCQoKQkJDgwFq3THP3eObMGWHFihXCkSNHhLy8PGHr1q1C165dhTvuuMPBNW+ZJUuWCHv37hXy8vKE48ePC0uWLBFEIpGwY8cOQRBc/znqNXWf7vIsG9NwxpW7PNP66t+jLZ4nA5GDPPzww4JCoRB8fHyE9u3bCw8//LBw5swZR1erzX799VcBgMlr5syZgiDUTb1/5ZVXhKioKMHX11cYM2aMkJOT49hKt1BT91hRUSGMHTtWiIiIELy9vYXOnTsLc+bMEVQqlaOr3WLm7hGA8PHHHxvK3LhxQ/jrX/8qhIaGCv7+/sIDDzwgKJVKx1W6hZq7x/z8fOGOO+4QwsLCBF9fX6F79+7C888/L6jVasdWvIUef/xxoXPnzoKPj48QEREhjBkzxhCGBMH1n6NeU/fpLs+yMQ0Dkbs80/rq36MtnqdIEAShdW1LRERERO6BY4iIiIjI4zEQERERkcdjICIiIiKPx0BEREREHo+BiIiIiDweAxERERF5PAYiIiIi8ngMRETkcu68804sXLjQ0dXAnj17IBKJTPaMIiLXw0BERGQBZwlhRGQbDERERETk8RiIiMilVVVV4bnnnkP79u0REBCA+Ph47Nmzx3D+k08+QUhICH7++Wf06dMHgYGBSEpKglKpNJSpra3F008/jZCQEISHh+PFF1/EzJkzMWnSJADAY489hr1792Lt2rUQiUQQiUQ4f/684fNHjx7F0KFD4e/vj9tvvx05OTl2unsishYGIiJyaQsWLEBaWhq++uorHD9+HH/+85+RlJSE3NxcQ5mKigq89dZb+Pzzz7Fv3z7k5+fjueeeM5xftWoVNm3ahI8//hj79++HRqPBli1bDOfXrl2LhIQEzJkzB0qlEkqlEh07djSc/9///V+8/fbbOHLkCLy8vPD444/b5d6JyHq8HF0BIqLWys/Px8cff4z8/HxER0cDAJ577jmkpqbi448/xptvvgkAqKmpwYYNG9CtWzcAdSFqxYoVhuu89957WLp0KR544AEAwPvvv4///Oc/hvMymQw+Pj7w9/eHXC43qccbb7yB0aNHAwCWLFmC8ePHo7KyElKp1DY3TkRWx0BERC7rxIkT0Gq16Nmzp9HxqqoqhIeHG977+/sbwhAAKBQKFBUVAQDUajUKCwsxfPhww3mJRIK4uDjodDqL6jFgwACjawNAUVEROnXq1PKbIiKHYCAiIpdVVlYGiUSCo0ePQiKRGJ0LDAw0/Nnb29vonEgkgiAIVqtH/euLRCIAsDhMEZFz4BgiInJZgwcPhlarRVFREbp37270Mte1ZY5MJkNUVBQOHz5sOKbVapGRkWFUzsfHB1qt1qr1JyLnwRYiInJZPXv2xLRp0zBjxgy8/fbbGDx4MK5cuYJdu3ZhwIABGD9+vEXXeeqpp5CSkoLu3bujd+/eeO+993D9+nVDaw8AdOnSBYcOHcL58+cRGBiIsLAwW90WETkAW4iIyKV9/PHHmDFjBp599ln06tULkyZNwuHDh1s0fufFF1/EI488ghkzZiAhIQGBgYEYN26c0aDo5557DhKJBLGxsYiIiEB+fr4tboeIHEQkWLMjnYjIDeh0OvTp0wcPPfQQXnvtNUdXh4jsgF1mROTxLly4gB07dmD06NGoqqrC+++/j7y8PDz66KOOrhoR2Qm7zIjI44nFYnzyyScYNmwYRowYgRMnTuCXX35Bnz59HF01IrITdpkRERGRx2MLEREREXk8BiIiIiLyeAxERERE5PEYiIiIiMjjMRARERGRx2MgIiIiIo/HQEREREQej4GIiIiIPB4DEREREXm8/wfZ5+mX0SZ2lAAAAABJRU5ErkJggg==\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["# k-최근접 이웃 회귀 객체\n","knr = KNeighborsRegressor()\n","# 5에서 45까지 x 좌표\n","x = np.arange(5, 45).reshape(-1, 1)\n","\n","# n = 1, 5, 10일 때 예측 결과 그래프\n","for n in [1, 5, 10]:\n","    # 모델 훈련\n","    knr.n_neighbors = n\n","    knr.fit(train_input, train_target)\n","    # 지정한 범위 x에 대한 예측 구하기\n","    prediction = knr.predict(x)\n","    # 훈련 세트와 예측 결과 그래프 그리기\n","    plt.scatter(train_input, train_target)\n","    plt.plot(x, prediction)\n","    plt.title('n_neighbors = {}'.format(n))\n","    plt.xlabel('length')\n","    plt.ylabel('weight')\n","    plt.show()"]}],"metadata":{"colab":{"collapsed_sections":["pLW8kdDv5asl"],"provenance":[{"file_id":"https://github.com/rickiepark/hg-mldl/blob/master/3-1.ipynb","timestamp":1728289922761}]},"kernelspec":{"display_name":"default:Python","language":"python","name":"conda-env-default-py"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.10"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git "a/week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -2.ipynb" "b/week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -2.ipynb"
new file mode 100644
index 0000000..6f4f5e2
--- /dev/null
+++ "b/week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -2.ipynb"	
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{"id":"V85bCyldfslj"},"source":["# 선형 회귀"]},{"cell_type":"markdown","metadata":{"id":"gm-iQ5NXfsll"},"source":["<table align=\"left\">\n","  <td>\n","    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/3-2.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n","  </td>\n","</table>"]},{"cell_type":"markdown","metadata":{"id":"CgQlTY9VpWeb"},"source":["## k-최근접 이웃의 한계"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HhWInkOKqg6s"},"outputs":[],"source":["import numpy as np\n","\n","perch_length = np.array(\n","    [8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0,\n","     21.0, 21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5,\n","     22.5, 22.7, 23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5,\n","     27.3, 27.5, 27.5, 27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0,\n","     36.5, 36.0, 37.0, 37.0, 39.0, 39.0, 39.0, 40.0, 40.0, 40.0,\n","     40.0, 42.0, 43.0, 43.0, 43.5, 44.0]\n","     )\n","perch_weight = np.array(\n","    [5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0,\n","     110.0, 115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0,\n","     130.0, 150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0,\n","     197.0, 218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0,\n","     514.0, 556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0,\n","     820.0, 850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0,\n","     1000.0, 1000.0]\n","     )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7UVMy686qhP9"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","# 훈련 세트와 테스트 세트\n","train_input, test_input, train_target, test_target = train_test_split(\n","    perch_length, perch_weight, random_state=42)\n","# 훈련 세트와 테스트 세트를 2차원 배열로 변경\n","train_input = train_input.reshape(-1, 1)\n","test_input = test_input.reshape(-1, 1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"k7Uocvq2quTE","outputId":"d6487c94-ed5b-4490-9a92-62460449e9ef"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsRegressor(n_neighbors=3)"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=3)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":3}],"source":["from sklearn.neighbors import KNeighborsRegressor\n","\n","knr = KNeighborsRegressor(n_neighbors=3)\n","# k-최근접 이웃 회귀 모델 훈련\n","knr.fit(train_input, train_target)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CFOp6T9-q0QR","outputId":"42c1bf64-9aa1-4230-f996-6a4193a96617"},"outputs":[{"output_type":"stream","name":"stdout","text":["[1033.33333333]\n"]}],"source":["print(knr.predict([[50]]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"G8FR1q-IuBlU"},"outputs":[],"source":["import matplotlib.pyplot as plt"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"wR9eDDH2q9nP","outputId":"8c9226c2-65dd-445c-fb26-22797cf38f01"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# 50cm 농어의 이웃\n","distances, indexes = knr.kneighbors([[50]])\n","\n","# 훈련 세트의 산점도\n","plt.scatter(train_input, train_target)\n","# 훈련 세트 중 이웃 샘플\n","plt.scatter(train_input[indexes], train_target[indexes], marker='D')\n","# 50cm 농어 데이터\n","plt.scatter(50, 1033, marker='^')\n","plt.xlabel('length')\n","plt.ylabel('weight')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-eljdQI_d0go","outputId":"8a68cc47-e015-48c6-df6e-67d8a704cc97"},"outputs":[{"output_type":"stream","name":"stdout","text":["1033.3333333333333\n"]}],"source":["print(np.mean(train_target[indexes]))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8cw87IL3t_3p","outputId":"8b8c4a78-0061-4011-bde7-e9a19a8e3381"},"outputs":[{"output_type":"stream","name":"stdout","text":["[1033.33333333]\n"]}],"source":["print(knr.predict([[100]]))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"YIMtpZKPfQjc","outputId":"92dda45e-ee93-4c63-d7e4-220252410ff5"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# 100cm 농어의 이웃\n","distances, indexes = knr.kneighbors([[100]])\n","\n","# 훈련 세트의 산점도\n","plt.scatter(train_input, train_target)\n","# 훈련 세트 중 이웃 샘플\n","plt.scatter(train_input[indexes], train_target[indexes], marker='D')\n","# 100cm 농어 데이터\n","plt.scatter(100, 1033, marker='^')\n","plt.xlabel('length')\n","plt.ylabel('weight')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"yFadoVqdpe93"},"source":["## 선형 회귀"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AKnGV8g5uN_U"},"outputs":[],"source":["from sklearn.linear_model import LinearRegression"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"Ypqgx5RRuOWg","outputId":"a5cbeb95-141e-4e8c-a6d7-7d0f2c5e50d4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression()"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":11}],"source":["lr = LinearRegression()\n","# 선형 회귀 모델 훈련\n","lr.fit(train_input, train_target)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_Dh2FgXouRLU","outputId":"8f2960e9-9b1f-492d-8163-7496db48a773"},"outputs":[{"output_type":"stream","name":"stdout","text":["[1241.83860323]\n"]}],"source":["# 50cm 농어에 대한 예측\n","print(lr.predict([[50]]))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Gy_CKIiIMxDu","outputId":"7b6d839c-d282-46cc-9e1c-01137dc6fdf7"},"outputs":[{"output_type":"stream","name":"stdout","text":["[39.01714496] -709.0186449535477\n"]}],"source":["print(lr.coef_, lr.intercept_)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"VumOdzlFuV_5","outputId":"c7c8137d-e4a6-4722-ba3c-4ab7c0158b3e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# 훈련 세트의 산점도\n","plt.scatter(train_input, train_target)\n","# 15에서 50까지 1차 방정식 그래프\n","plt.plot([15, 50], [15*lr.coef_+lr.intercept_, 50*lr.coef_+lr.intercept_])\n","# 50cm 농어 데이터\n","plt.scatter(50, 1241.8, marker='^')\n","plt.xlabel('length')\n","plt.ylabel('weight')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"myWLMckTQZzr","outputId":"f605de5a-354d-4b77-eb71-b3868b3e194c"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.939846333997604\n","0.8247503123313558\n"]}],"source":["print(lr.score(train_input, train_target))\n","print(lr.score(test_input, test_target))"]},{"cell_type":"markdown","metadata":{"id":"h-akkHaaQc0b"},"source":["## 다항 회귀"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wcdsYFxB2q82"},"outputs":[],"source":["train_poly = np.column_stack((train_input ** 2, train_input))\n","test_poly = np.column_stack((test_input ** 2, test_input))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JJ6tf1F96EzF","outputId":"3f9eea99-fa9d-476a-fc12-31edda58132e"},"outputs":[{"output_type":"stream","name":"stdout","text":["(42, 2) (14, 2)\n"]}],"source":["print(train_poly.shape, test_poly.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LTlN32tML4Kf","outputId":"469e2197-466c-49a4-b20b-306d4337dd37"},"outputs":[{"output_type":"stream","name":"stdout","text":["[1573.98423528]\n"]}],"source":["lr = LinearRegression()\n","lr.fit(train_poly, train_target)\n","\n","print(lr.predict([[50**2, 50]]))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k_fGreBgNCAc","outputId":"cf36c710-c157-4b0b-f660-a09540a585bc"},"outputs":[{"output_type":"stream","name":"stdout","text":["[  1.01433211 -21.55792498] 116.0502107827827\n"]}],"source":["print(lr.coef_, lr.intercept_)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"c91qVYoPLz1H","outputId":"f8f25204-fd60-4132-f17e-5a32e564c7c2"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# 구간별 직선을 그리기 위해 15에서 49까지 정수 배열 생성\n","point = np.arange(15, 50)\n","# 훈련 세트의 산점도\n","plt.scatter(train_input, train_target)\n","# 15에서 49까지 2차 방정식 그래프\n","plt.plot(point, 1.01*point**2 - 21.6*point + 116.05)\n","# 50cm 농어 데이터\n","plt.scatter([50], [1574], marker='^')\n","plt.xlabel('length')\n","plt.ylabel('weight')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oCGTrZezL3E4","outputId":"d5a93d33-54b7-455b-8028-8a239b84573c"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9706807451768623\n","0.9775935108325122\n"]}],"source":["print(lr.score(train_poly, train_target))\n","print(lr.score(test_poly, test_target))"]}],"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/rickiepark/hg-mldl/blob/master/3-2.ipynb","timestamp":1728290237866}]},"kernelspec":{"display_name":"default:Python","language":"python","name":"conda-env-default-py"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.10"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git "a/week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -3.ipynb" "b/week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -3.ipynb"
new file mode 100644
index 0000000..9f41a59
--- /dev/null
+++ "b/week2/ML \341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 2\341\204\214\341\205\256\341\204\216\341\205\241 -3.ipynb"	
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{"id":"XjTc5n2flYUu"},"source":["# 특성 공학과 규제"]},{"cell_type":"markdown","metadata":{"id":"B8YOr2hElYUv"},"source":["<table align=\"left\">\n","  <td>\n","    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/3-3.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n","  </td>\n","</table>"]},{"cell_type":"markdown","metadata":{"id":"fZwhQU2l8tI6"},"source":["## 데이터 준비"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3kjaTfOqEVwY"},"outputs":[],"source":["import pandas as pd"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8qmTS1RzKRKT","outputId":"62a1790e-34bf-4a28-f135-e66469a5f25b"},"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 8.4   2.11  1.41]\n"," [13.7   3.53  2.  ]\n"," [15.    3.82  2.43]\n"," [16.2   4.59  2.63]\n"," [17.4   4.59  2.94]\n"," [18.    5.22  3.32]\n"," [18.7   5.2   3.12]\n"," [19.    5.64  3.05]\n"," [19.6   5.14  3.04]\n"," [20.    5.08  2.77]\n"," [21.    5.69  3.56]\n"," [21.    5.92  3.31]\n"," [21.    5.69  3.67]\n"," [21.3   6.38  3.53]\n"," [22.    6.11  3.41]\n"," [22.    5.64  3.52]\n"," [22.    6.11  3.52]\n"," [22.    5.88  3.52]\n"," [22.    5.52  4.  ]\n"," [22.5   5.86  3.62]\n"," [22.5   6.79  3.62]\n"," [22.7   5.95  3.63]\n"," [23.    5.22  3.63]\n"," [23.5   6.28  3.72]\n"," [24.    7.29  3.72]\n"," [24.    6.38  3.82]\n"," [24.6   6.73  4.17]\n"," [25.    6.44  3.68]\n"," [25.6   6.56  4.24]\n"," [26.5   7.17  4.14]\n"," [27.3   8.32  5.14]\n"," [27.5   7.17  4.34]\n"," [27.5   7.05  4.34]\n"," [27.5   7.28  4.57]\n"," [28.    7.82  4.2 ]\n"," [28.7   7.59  4.64]\n"," [30.    7.62  4.77]\n"," [32.8  10.03  6.02]\n"," [34.5  10.26  6.39]\n"," [35.   11.49  7.8 ]\n"," [36.5  10.88  6.86]\n"," [36.   10.61  6.74]\n"," [37.   10.84  6.26]\n"," [37.   10.57  6.37]\n"," [39.   11.14  7.49]\n"," [39.   11.14  6.  ]\n"," [39.   12.43  7.35]\n"," [40.   11.93  7.11]\n"," [40.   11.73  7.22]\n"," [40.   12.38  7.46]\n"," [40.   11.14  6.63]\n"," [42.   12.8   6.87]\n"," [43.   11.93  7.28]\n"," [43.   12.51  7.42]\n"," [43.5  12.6   8.14]\n"," [44.   12.49  7.6 ]]\n"]}],"source":["df = pd.read_csv('https://bit.ly/perch_csv_data')\n","perch_full = df.to_numpy()\n","print(perch_full)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PsRC7rvE9SbL"},"outputs":[],"source":["import numpy as np\n","\n","perch_weight = np.array(\n","    [5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0,\n","     110.0, 115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0,\n","     130.0, 150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0,\n","     197.0, 218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0,\n","     514.0, 556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0,\n","     820.0, 850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0,\n","     1000.0, 1000.0]\n","     )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cRKkoWoZ9J0m"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","train_input, test_input, train_target, test_target = train_test_split(perch_full, perch_weight, random_state=42)"]},{"cell_type":"markdown","metadata":{"id":"y5uMFE_8V1tx"},"source":["## 사이킷런의 변환기"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EclugdXmSs-L"},"outputs":[],"source":["from sklearn.preprocessing import PolynomialFeatures"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V5is7cZhKbPU","outputId":"a27d2b55-7547-4add-b3e9-3a97c23eb954"},"outputs":[{"output_type":"stream","name":"stdout","text":["[[1. 2. 3. 4. 6. 9.]]\n"]}],"source":["poly = PolynomialFeatures()\n","poly.fit([[2, 3]])\n","print(poly.transform([[2, 3]]))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bKXkK0oJc4nG","outputId":"ee304884-059a-4e6a-c358-64530cb1129d"},"outputs":[{"output_type":"stream","name":"stdout","text":["[[2. 3. 4. 6. 9.]]\n"]}],"source":["poly = PolynomialFeatures(include_bias=False)\n","poly.fit([[2, 3]])\n","print(poly.transform([[2, 3]]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"__kE6eJdNZfm"},"outputs":[],"source":["poly = PolynomialFeatures(include_bias=False)\n","\n","poly.fit(train_input)\n","train_poly = poly.transform(train_input)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2a_lmkKle4kF","outputId":"15ad4a84-a8e1-4079-8cc6-6bb9ef128e2f"},"outputs":[{"output_type":"stream","name":"stdout","text":["(42, 9)\n"]}],"source":["print(train_poly.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X6GUwfXTfKbl","outputId":"a47e9c30-9730-497c-e897-8ff248cc9074"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['x0', 'x1', 'x2', 'x0^2', 'x0 x1', 'x0 x2', 'x1^2', 'x1 x2',\n","       'x2^2'], dtype=object)"]},"metadata":{},"execution_count":40}],"source":["poly.get_feature_names_out()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DJMPxe2mgbOo"},"outputs":[],"source":["test_poly = poly.transform(test_input)"]},{"cell_type":"markdown","metadata":{"id":"PdDAslHzNk3H"},"source":["## 다중 회귀 모델 훈련하기"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"E9ygy-8WOvIP","outputId":"73ec508e-d9f9-4147-bad6-21109aafd93d"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9903183436982125\n"]}],"source":["from sklearn.linear_model import LinearRegression\n","\n","lr = LinearRegression()\n","lr.fit(train_poly, train_target)\n","print(lr.score(train_poly, train_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GKKyfFcAd7zm","outputId":"80944369-a45b-4e14-c38e-0652efe39723"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9714559911594111\n"]}],"source":["print(lr.score(test_poly, test_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2fDt5mrReMwU"},"outputs":[],"source":["poly = PolynomialFeatures(degree=5, include_bias=False)\n","\n","poly.fit(train_input)\n","train_poly = poly.transform(train_input)\n","test_poly = poly.transform(test_input)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hcM8R4VHSzR8","outputId":"ab384f3c-04d9-41b3-b5c7-f6ac6de6df9e"},"outputs":[{"output_type":"stream","name":"stdout","text":["(42, 55)\n"]}],"source":["print(train_poly.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UffVFVTGP8xj","outputId":"c76714a5-18eb-46a1-d29f-6f26c5a83a19"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9999999999996433\n"]}],"source":["lr.fit(train_poly, train_target)\n","print(lr.score(train_poly, train_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GtITdlYFg7AY","outputId":"c2c167cf-e680-44a5-a7ff-71efe48b9a03"},"outputs":[{"output_type":"stream","name":"stdout","text":["-144.40579436844948\n"]}],"source":["print(lr.score(test_poly, test_target))"]},{"cell_type":"markdown","metadata":{"id":"K2YMPSelQBpO"},"source":["## 규제"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hCC7wKy3QQrE"},"outputs":[],"source":["from sklearn.preprocessing import StandardScaler\n","\n","ss = StandardScaler()\n","ss.fit(train_poly)\n","\n","train_scaled = ss.transform(train_poly)\n","test_scaled = ss.transform(test_poly)"]},{"cell_type":"markdown","metadata":{"id":"qyLI7JQsJ7RQ"},"source":["## 릿지"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LdNuDNQGQipv","outputId":"11558a6c-0c97-4b1f-f345-933018b18221"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9896101671037343\n"]}],"source":["from sklearn.linear_model import Ridge\n","\n","ridge = Ridge()\n","ridge.fit(train_scaled, train_target)\n","print(ridge.score(train_scaled, train_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"p5mXLecwhdnF","outputId":"0e2ddf45-68a9-4f8b-8ab2-e4b9f5360087"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9790693977615387\n"]}],"source":["print(ridge.score(test_scaled, test_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wXd3_Kq6hlbM"},"outputs":[],"source":["import matplotlib.pyplot as plt\n","\n","train_score = []\n","test_score = []"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9MvIvQOrhfqC"},"outputs":[],"source":["alpha_list = [0.001, 0.01, 0.1, 1, 10, 100]\n","for alpha in alpha_list:\n","    # 릿지 모델 생성\n","    ridge = Ridge(alpha=alpha)\n","    # 릿지 모델을 훈련\n","    ridge.fit(train_scaled, train_target)\n","    # 훈련 점수와 테스트 점수를 저장\n","    train_score.append(ridge.score(train_scaled, train_target))\n","    test_score.append(ridge.score(test_scaled, test_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"95DjrJxlhiow","outputId":"a004ea2c-a338-448d-9dbd-2b68aff09892"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.plot(np.log10(alpha_list), train_score)\n","plt.plot(np.log10(alpha_list), test_score)\n","plt.xlabel('alpha')\n","plt.ylabel('R^2')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5S5vhi-vhjzT","outputId":"ac52eb28-1bf2-434f-d21f-b123a6d1f0cd"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9903815817570367\n","0.9827976465386928\n"]}],"source":["ridge = Ridge(alpha=0.1)\n","ridge.fit(train_scaled, train_target)\n","\n","print(ridge.score(train_scaled, train_target))\n","print(ridge.score(test_scaled, test_target))"]},{"cell_type":"markdown","metadata":{"id":"jUph9pH_KA9_"},"source":["## 라쏘"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ymu-jmekh0IK","outputId":"dab813ee-5531-4905-93b2-52d16c8e8b21"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.989789897208096\n"]}],"source":["from sklearn.linear_model import Lasso\n","\n","lasso = Lasso()\n","lasso.fit(train_scaled, train_target)\n","print(lasso.score(train_scaled, train_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t3jO37UMh2iI","outputId":"232674e2-4d08-4ae2-bba0-040a72a53e44"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9800593698421883\n"]}],"source":["print(lasso.score(test_scaled, test_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uoL2oJ6Ih4Jw","outputId":"30dffbb7-9b86-42e5-fa32-5953a4b8efa3","scrolled":true},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+04, tolerance: 5.183e+02\n","  model = cd_fast.enet_coordinate_descent(\n","/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.297e+04, tolerance: 5.183e+02\n","  model = cd_fast.enet_coordinate_descent(\n"]}],"source":["train_score = []\n","test_score = []\n","\n","alpha_list = [0.001, 0.01, 0.1, 1, 10, 100]\n","for alpha in alpha_list:\n","    # 라쏘 모델을 생성\n","    lasso = Lasso(alpha=alpha, max_iter=10000)\n","    # 라쏘 모델을 훈련\n","    lasso.fit(train_scaled, train_target)\n","    # 훈련 점수와 테스트 점수를 저장\n","    train_score.append(lasso.score(train_scaled, train_target))\n","    test_score.append(lasso.score(test_scaled, test_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"7rkH8Dvzh9UI","outputId":"a1b647d7-25d5-4206-edc8-1bf07a279d9d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.plot(np.log10(alpha_list), train_score)\n","plt.plot(np.log10(alpha_list), test_score)\n","plt.xlabel('alpha')\n","plt.ylabel('R^2')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t4uFD9Flh_Dw","outputId":"6946c4fd-97af-44b4-b99a-facb65d14661"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9888067471131867\n","0.9824470598706695\n"]}],"source":["lasso = Lasso(alpha=10)\n","lasso.fit(train_scaled, train_target)\n","\n","print(lasso.score(train_scaled, train_target))\n","print(lasso.score(test_scaled, test_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z_bQc3s8Uoai","outputId":"5ad2a313-3880-4078-ac63-4019049b5ec4"},"outputs":[{"output_type":"stream","name":"stdout","text":["40\n"]}],"source":["print(np.sum(lasso.coef_ == 0))"]}],"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/rickiepark/hg-mldl/blob/master/3-3.ipynb","timestamp":1728290385918}]},"kernelspec":{"display_name":"default:Python","language":"python","name":"conda-env-default-py"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.10"}},"nbformat":4,"nbformat_minor":0}
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