3-2.ipynb

{
  "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": 1,
      "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": 2,
      "metadata": {
        "id": "7UVMy686qhP9"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# 훈련 세트와 테스트 세트로 나눕니다\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 바꿉니다\n",
        "train_input = train_input.reshape(-1, 1)\n",
        "test_input = test_input.reshape(-1, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "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 모델을 훈련합니다\n",
        "knr.fit(train_input, train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "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": 5,
      "metadata": {
        "id": "G8FR1q-IuBlU"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "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 이웃을 구합니다\n",
        "distances, indexes = knr.kneighbors([[50]])\n",
        "\n",
        "# 훈련 세트의 산점도를 그립니다\n",
        "plt.scatter(train_input, train_target)\n",
        "# 훈련 세트 중에서 이웃 샘플만 다시 그립니다\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": 7,
      "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": 8,
      "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": 9,
      "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 이웃을 구합니다\n",
        "distances, indexes = knr.kneighbors([[100]])\n",
        "\n",
        "# 훈련 세트의 산점도를 그립니다\u001f\n",
        "plt.scatter(train_input, train_target)\n",
        "# 훈련 세트 중에서 이웃 샘플만 다시 그립니다\u001f\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": 10,
      "metadata": {
        "id": "AKnGV8g5uN_U"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "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": 12,
      "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": 13,
      "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": 14,
      "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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v5t5773Wcz83N5ZprrqFNmzYkJSXx97//naeffpq33nqrzq9PRESqJjTQv1bb1YbzDePByWE8m904b3v4Y9ivvL24FqcOmV177bVce+21lZ4zDINXX32VJ554ghtvvBGA//3f/yUsLIxFixYxZswYtm/fztKlS1m/fj39+vUD4PXXX+e6667jH//4B5GRkSxcuJCSkhL+/e9/4+vrS7du3UhJSeHll1+uEJxOVVxcTHFxseP33NzcWr5yERE5Vf/oECIs/mRYiyoNFCYg3HJy7k59qe4wnisP+8n5uewqs9TUVDIyMhgyZIjjmMViYcCAASQmJgKQmJhIcHCwIwwBDBkyBLPZzNq1ax1tLrvsMnx9fR1thg4dys6dOzl+/Hilnz1r1iwsFovjJyoqqi4uUURE/p+X2cSMETHAyfBzqvLfZ4yIqdcJ1dUdxtt22Fqr7yv1y2UDUUZGBgBhYWEVjoeFhTnOZWRkEBoaWuG8t7c3ISEhFdpU9h6nfsbppk+fjtVqdfwcOHDgwi9IRETOaVj3COaNjSXcUnFYLNziz7yxsfW+D1F1hvF+2JbJS8t21er7Sv3SKrNK+Pn54eenHUNFROrbsO4RXB0T7hI7VVdlGC8syI/Vu44wb9VeAHy8TJTaKp8j5IxhP6k6l+0hCg8PByAzM7PC8czMTMe58PBwsrKyKpwvKysjOzu7QpvK3uPUzxAREdfhZTYR174ZN/ZuSVz7Zk7buPB8w3gGYGns6whDdw5syyu39MJ0lvZQ/8N+UnUuG4iio6MJDw9n+fLljmO5ubmsXbuWuLg4AOLi4sjJySEpKcnRZsWKFdjtdgYMGOBos3r1akpLSx1tli1bRufOnWnatGk9XY2IiDREZxvGC2nii6WRDzsz8mjs68Wc2/rw9A3duL5XS5ca9pOqMxmG4bT1f/n5+ezZsweAPn368PLLL3PFFVcQEhJC69atefHFF3nhhRd4//33iY6O5sknn2TTpk1s27YNf/+T/7Bde+21ZGZmMn/+fEpLS/nLX/5Cv379+OCDDwCwWq107tyZa665hqlTp7JlyxbuuusuXnnllbOuMjtdbm4uFosFq9VKUFBQ3fyfISIiLqt85+nM3BNsSMth4do0yuwG7Vs0Yf7YvnQMC6y0vbOH/Txddb6/nRqIVq5cyRVXXHHG8XHjxvHee+9hGAYzZszgrbfeIicnh0suuYQ333yTTp06OdpmZ2czceJEFi9ejNlsZvTo0cyZM4eAgABHm02bNpGQkMD69etp3rw5kyZNYurUqVWuU4FIREQKisuY+tkmlmw6uRnj8J4RvDi6JwF+mo7rqhpMIGooFIhERDzb7sw8xi9IYu+RArzNJh4f3pU7B7bFZFKvjyurzve3Yq2IiMg5fLXxMNM+20RhiY2wID/ejI+lbxutFHM3CkQiIiKVKCmz8/w323nvl/0AxLVrxuu396F5gLZlcUcKRCIiIqdJt54gYWEyyWk5ANx/eXumXN0Jby+XXZwtF0iBSERE5BRr9hzlgQ83cKyghEB/b165pTdDYsLO/0Jp0BSIREREALvdYN6qvbz0/U7sBsREBDFvbCxtmjVxdmlSDxSIRETE41kLS5nySQrLd5x8+sHNfVvx7Mju+Pt4ObkyqS8KRCIi4tG2HLIyYWESB7JP4Ott5tkbu3HrRa2dXZbUMwUiERHxWB+vT+PJL7dSUmYnKqQR8+L70r2lxdlliRMoEImIiMcpKrXx1Jdb+OS3gwBc2SWUV27pjaWxj5MrE2dRIBIREY+SdqyQ8QuS2Jaei9kED1/TmQmD22PWs8Y8mgKRiIh4jB+2ZTLlkxRyi8oIaeLLnDF9uKRjc2eXJS5AgUhERNyezW7w8rKdzP1xLwB9WgfzZnwsEZZGTq5MXIUCkYiIuBSb3WBdajZZeUWEBvrTPzoErwsYzjqaX8wDH27gl73HALhzYFseu64rvt7adVr+oEAkIiIuY+mWdGYu3ka6tchxLMLiz4wRMQzrHlHt90v6PZuEhRvIyC2ikY8XL4zuwY29W9ZmyeImFI9FRMQlLN2SzoQFyRXCEECGtYgJC5JZuiW9yu9lGAbvrknl1n/+SkZuEe1bNOGriYMUhuSs1EMkIiJOZ7MbzFy8DaOScwZgAmYu3sbVMeHnHT4rKC5j6mebWLLpZIAa3jOCF0f3JMBPX3lydvqnQ0REnG5davYZPUOnMoB0axHrUrOJa9/srO32ZOUxfkEye7Ly8TabeOy6rvxlUFtMJi2pl3NTIBIREafLyjt7GKpqu8UbDzP1s00UltgIC/Jj7u2x9GsbUlsliptTIBIREacLDfSvcbuSMjuzvt3Ou2v2AxDXrhlzbutDi0C/2ixR3JwCkYiIOF3/6BAiLP5kWIsqnUdkAsItJ5fgnyrdeoKEhckkp+UAcP/l7ZlydSe8vbRmSKpH/8SIiIjTeZlNzBgRA5wMP6cq/33GiJgKE6rX7DnK9XN+Jjkth0B/b96+ox9/HdZFYUhqRP/UiIiISxjWPYJ5Y2MJt1QcFgu3+DNvbKxjHyK73WDuj3v48ztrOVZQQteIIJZMuoSrY8KcUba4CQ2ZiYiIyxjWPYKrY8LPulO1tbCUhz9N4YftWQDc3LcVz47sjr+PlzPLFjegQCQiIi7Fy2yqdGn9lkNWJixM4kD2CXy9zTx7Yzduvai1EyoUd6RAJCIiLqWyZ5l9lnSQJ77cQkmZnVZNGzF/bF+6t7Q4u1RxIwpEIiLiMip7llkjHy9OlNoAuLJLKC/f0ovgxr7OKlHclAKRiIi4hPJnmZ2+7L48DN3QK4JXb+2D+TyP7hCpCQUiEREXVdnQ0fme49VQnetZZuXW7z9+zvMiF0KBSETEBVU2dBRh8WfGiBjH8nN3cr5nmUHVnmUmUlPah0hExMWUDx2dHhAyrEVMWJDM0i3pTqqs7tTGs8xELoQCkYiICznX0FH5sZmLt2Gzu9fgUU5haZXaVfWZZyLVpUAkIuJCzjd0ZPDH0JE7MAyDd9ek8uySbedsZ+LkkOHpzzITqS2aQyQi4kLcbejoXBPDC4rLmPrZJpZsOjkEGNs6mOS0HExQoYfsbM8yE6lNCkQiIi6kqkNCDWHo6FwTwzuEBjB+QTJ7svLxNpuYfl1X7hrUlu+2ZpzxmnA3nkwurkOBSETEhfSPDiHC4k+GtajSeUQmTgYEVx86OtueQhnWIsYvSMbP20xxmZ2wID/m3h5Lv7Ynr+d8zzITqSsKRCIiLsTLbGLGiBgmLEhusENHVZkYXlxm5+J2zXj9tj60CPSr0OZszzITqUuaVC0i4mKGdY9g3thYwi0Vh8XCLf7MGxvr8kNHVdlTCGDiFR3OCEMizqIeIhERF9SQh46qOuH7WEFxHVciUnUu3UNks9l48skniY6OplGjRrRv355nn30Ww/ijI9YwDJ566ikiIiJo1KgRQ4YMYffu3RXeJzs7m/j4eIKCgggODubuu+8mPz+/vi9HRKRayoeObuzdkrj2zRpEGAL3mhgunsOlA9GLL77IvHnzeOONN9i+fTsvvvgis2fP5vXXX3e0mT17NnPmzGH+/PmsXbuWJk2aMHToUIqK/vgbSnx8PFu3bmXZsmUsWbKE1atXc++99zrjkkRE3F7/6BDCgs4+FKY9hcQVmYxTu1tczPXXX09YWBjvvPOO49jo0aNp1KgRCxYswDAMIiMjefjhh3nkkUcAsFqthIWF8d577zFmzBi2b99OTEwM69evp1+/fgAsXbqU6667joMHDxIZGXneOnJzc7FYLFitVoKCgurmYkVE3MTWw1bG/XsdR/NLzjhX3sfVEOZCScNXne9vl+4hGjhwIMuXL2fXrl0AbNy4kZ9//plrr70WgNTUVDIyMhgyZIjjNRaLhQEDBpCYmAhAYmIiwcHBjjAEMGTIEMxmM2vXrq30c4uLi8nNza3wIyIi5/fJbwcY9eYvHM0voVkTX5oH+FY431AmhovncelJ1dOmTSM3N5cuXbrg5eWFzWbjueeeIz4+HoCMjAwAwsLCKrwuLCzMcS4jI4PQ0NAK5729vQkJCXG0Od2sWbOYOXNmbV+OiIjbKiq18fRXW/lo/QEArujcgldu7U2gv0+DnBgunselA9Enn3zCwoUL+eCDD+jWrRspKSlMnjyZyMhIxo0bV2efO336dKZMmeL4PTc3l6ioqDr7PBGRhuxAdiETFiax5VAuJhM8fHUn7r+8A+b/Dz7aU0gaApcORI8++ijTpk1jzJgxAPTo0YPff/+dWbNmMW7cOMLDwwHIzMwkIuKP7tfMzEx69+4NQHh4OFlZWRXet6ysjOzsbMfrT+fn54efn/bGEBE5nxU7Mpn8UQq5RWWENPHltTG9ubRjC2eXJVJtLj2HqLCwELO5YoleXl7Y7XYAoqOjCQ8PZ/ny5Y7zubm5rF27lri4OADi4uLIyckhKSnJ0WbFihXY7XYGDBhQD1chIuJ+bHaDf3y3k7ve+43cojJ6RwWzZNIlCkPSYLl0D9GIESN47rnnaN26Nd26dWPDhg28/PLL3HXXXQCYTCYmT57M3/72Nzp27Eh0dDRPPvkkkZGRjBw5EoCuXbsybNgw7rnnHubPn09paSkTJ05kzJgxVVphJiIiFR3LL+aBjzawZs8xAMbFteHx4TH4erv037FFzsmlA9Hrr7/Ok08+yf33309WVhaRkZHcd999PPXUU442f/3rXykoKODee+8lJyeHSy65hKVLl+Lv/8eGXwsXLmTixIlcddVVmM1mRo8ezZw5c5xxSSIiDVpy2nESFiaTbi2ikY8XL4zuwY29Wzq7LJEL5tL7ELkK7UMkIp7OMAze/2U/z32znVKbQbsWTZg/ti+dwgKdXZrIWVXn+9ule4hERMT5CorLmPb5ZhZvPAzAdT3Cmf2nXgT46StE3If+aRYRkbPak5XP+AVJ7MnKx9tsYvp1XblrUFtMJu0lJO5FgUhERCq1ZNNhpv53EwUlNkID/ZgbH8tFbfX8MXFPCkQiIlJBqc3OrG928O81qQBc3C6E12+LpUWg9mcT96VAJCIiDhnWIhI+SCbp9+MAjB/cnkeu6YS3l5bUi3tTIBIREQB+2XOUBz7awNH8EgL9vPnHLb0Y2q3yHf1F3I0CkYiIh7PbDeav3ss/vtuJ3YAu4YHMH9uXts2bOLs0kXqjQCQi4sGsJ0p5+JON/LA9E4DRsa3428juNPL1cnJlIvVLgUhExENtPWxlwoJk0rIL8fUyM/PGboy5KEpL6sUjKRCJiHigT387wBOLtlBcZqdlcCPmjY2lZ6tgZ5cl4jQKRCIiHqSo1MbMxVv5cN0BAC7v3IJXb+1NcGNfJ1cm4lwKRCIiHuJAdiETFiax5VAuJhNMGdKJhCs6YDZriExEgUhExE3Y7AbrUrPJyisiNNCf/tEheP1/2FmxI5PJH6WQW1RG08Y+zLmtDwPbN2ftWdqLeBoFIhERN7B0SzozF28j3VrkOBZh8efJ4V3ZnpHH6yv2ANArKpg342PZfDCHS15ccUb7GSNiGNY9ot7rF3E2k2EYhrOLcHW5ublYLBasVitBQUHOLkdEpIKlW9KZsCCZ8/3H/I64Njw+vCs/7siqtH1539C8sbEKReIWqvP9rb3YRUQaMJvdYObibecMQybglVt68cyN3fE2m8/avvzYzMXbsNn1d2XxLBoyExFpwNalZlcY9qqMAWQXlPBlyiGO5hWfs70BpFuLWJeaTVz7ZrVbrIgLUyASEWnAsvLOHYbKPfv19jp5XxF3oSEzEZEGLDTQv07et3mAX528r4irUiASEWnA+keHENzYp/bfWFOIxMNoyExEpIEqtdmZ9c0OcgpLa/29jxYU1/p7irgy9RCJiDRAGdYibnvrV/69JhWAod3CCA+qvWGuuhqKE3FV6iESEWlgftl7lAc+3MDR/BIC/bz5xy29GNotvMJO1Ufziqs9kRpOLtEPt5zctVrEkygQiYg0EHa7wfzVe/nHdzuxG9AlPJD5Y/vStnkTALzMJsdSeZvd4F8/p5JhLarydKDyjRlnjIjRIzzE42jITESkAbCeKOXe/yQxe+nJMDQqtiVf3D/IEYZO52U2MWNEDPBH0ClX/vvpk7HDLf7apVo8lnqIRERc3NbDVu5fmMzvxwrx9TLz9A3duK1/FCbTuXtxhnWPYN7Y2DOecRb+/88suzom/KwPg60t53rgrIgr0bPMqkDPMhMRZ/n0twM8sWgLxWV2WgY3Yt7YWHq2Cq7WezgrlJztgbN6gKzUl+p8fysQVYECkYjUt6JSGzMXb+XDdQcAuLxzC169tTfBjX2dXFnVnO2Bs3qArNSn6nx/a8hMRMTFHMguZMLCJLYcysVkgoeGdGLiFR0wN5ChpnM9cNbgZCiauXgbV8eEa/hMXIYCkYiIC/lxRxaTP07BeqKUpo19eG1MHy7r1MLZZVXL+R44qwfIiitSIBIRcQE2u8FrP+xizoo9APSKCubN+FhaBjdycmXVV9UHw+oBsuJKarTs/plnnqGwsPCM4ydOnOCZZ5654KJERDzJsfxi7nx3nSMM3RHXhk/uu7hBhiGo+i7X2g1bXEmNJlV7eXmRnp5OaGhohePHjh0jNDQUm81WawW6Ak2qFpG6kpx2nISFyaRbi2jk48WsUT0Y2aels8u6IDa7wSUvrjjrppDlu2H/PPVKzSGSOlWd7+8a9RAZhlHp/hcbN24kJETbvYuInI9hGPxv4n5u/Wci6dYi2jVvwqKEQQ0+DEHVNoXUbtjiaqo1h6hp06aYTCZMJhOdOnWqEIpsNhv5+fmMHz++1osUEXEnhSVlTP98M1+mHAbg2u7hzP5TTwL9fc7zyobjfJtCasm9uJpqDZm9//77GIbBXXfdxauvvorFYnGc8/X1pW3btsTFxdVJoc6kITMRqS17j+QzYUESuzLz8TKbmH5tF+6+JPq8u043VNqpWpypzvYhGjduHADR0dEMHDgQHx/3+duMiEhd+2ZzOo9+upGCEhuhgX68cXus2z9V/tQHzoq4shotux88eDB2u51du3aRlZWF3W6vcP6yyy6rleJERNxBqc3OC9/u4J2fUwEYEB3C67f30SorERdSo0nVv/76Kx06dKBr165cdtllXH755Y6fK664olYLPHToEGPHjqVZs2Y0atSIHj168NtvvznOG4bBU089RUREBI0aNWLIkCHs3r27wntkZ2cTHx9PUFAQwcHB3H333eTn59dqnSIilcnMLeK2t351hKH7Brdj4f8MUBgScTE1CkTjx4+nX79+bNmyhezsbI4fP+74yc7OrrXijh8/zqBBg/Dx8eHbb79l27ZtvPTSSzRt2tTRZvbs2cyZM4f58+ezdu1amjRpwtChQykq+mMSX3x8PFu3bmXZsmUsWbKE1atXc++999ZanSIilUnce4zhc37it9+PE+jnzT//3Jfp13bF26tG/+kVkTpUo32ImjRpwsaNG+nQoUNd1OQwbdo01qxZw08//VTpecMwiIyM5OGHH+aRRx4BwGq1EhYWxnvvvceYMWPYvn07MTExrF+/nn79+gGwdOlSrrvuOg4ePEhkZOR569CkahGpDsMwmL9qH3//bgd2A7qEBzJvbF+imzdxdmkiHqXO9yEaMGAAe/bsqVFx1fHVV1/Rr18/br75ZkJDQ+nTpw9vv/2243xqaioZGRkMGTLEccxisTBgwAASExMBSExMJDg42BGGAIYMGYLZbGbt2rWVfm5xcTG5ubkVfkREqsJ6opR7/5PEi0tPhqFRsS354v5BCkMiLq7Kk6o3bdrk+POkSZN4+OGHycjIoEePHmesNuvZs2etFLdv3z7mzZvHlClTeOyxx1i/fj0PPPAAvr6+jBs3joyMDADCwsIqvC4sLMxxLiMj44wdtb29vQkJCXG0Od2sWbOYOXNmrVyDiHiObYdzmbAwid+PFeLrZebpG7pxW/8ot11SL+JOqhyIevfujclk4tQRtrvuusvx5/JzJpOp1h7dYbfb6devH88//zwAffr0YcuWLcyfP9+xBUBdmD59OlOmTHH8npubS1RUVJ19nog0fP9NOsjjX2ymuMxOy+BGzBsbS89Wwc4uS0SqqMqBKDU1tS7rqFRERAQxMTEVjnXt2pXPPvsMgPDwcAAyMzOJiPhj19PMzEx69+7taJOVlVXhPcrKysjOzna8/nR+fn74+fnV1mWIiBsrKrUxc/E2PlyXBsDgTi149dbeNG3i6+TKRKQ6qhyI2rRpU5d1VGrQoEHs3LmzwrFdu3Y5aomOjiY8PJzly5c7AlBubi5r165lwoQJAMTFxZGTk0NSUhJ9+/YFYMWKFdjtdgYMGFB/FyMibudAdiETFiax5VAuJhNMvqoTk67sgFk7MYs0ODXamPGrr76q9LjJZMLf358OHToQHR19QYUBPPTQQwwcOJDnn3+eW265hXXr1vHWW2/x1ltvOT5v8uTJ/O1vf6Njx45ER0fz5JNPEhkZyciRI4GTPUrDhg3jnnvuYf78+ZSWljJx4kTGjBlTpRVmIiKV+XFHFpM/TsF6opSmjX14dUwfBndq4eyyRKSGarTs3mw2nzGfCCrOI7rkkktYtGhRhT2DamLJkiVMnz6d3bt3Ex0dzZQpU7jnnnsc5w3DYMaMGbz11lvk5ORwySWX8Oabb9KpUydHm+zsbCZOnMjixYsxm82MHj2aOXPmEBAQUKUatOxeRMrZ7Aav/bCLOStOrrTtFRXMm/GxtAxu5OTKROR01fn+rlEgWr58OY8//jjPPfcc/fv3B2DdunU8+eSTPPHEE1gsFu677z4GDBjAO++8U7OrcCEKRCICkF1QwoMfbeCn3UcB+PPFbXji+q74eXvV+D318FORulNnD3ct9+CDD/LWW28xcOBAx7GrrroKf39/7r33XrZu3cqrr75aYRWaiEhDtiHtOAkLkzlsLcLfx8ysUT24qU+rC3rPpVvSmbl4G+nWP3bWj7D4M2NEDMO6R5zjlSJS22q0MePevXsrTVpBQUHs27cPgI4dO3L06NELq05ExMkMw+A/ifu55Z+JHLYW0a55E75MuKRWwtCEBckVwhBAhrWICQuSWbol/YLeX0Sqp0aBqG/fvjz66KMcOXLEcezIkSP89a9/5aKLLgJg9+7d2rtHRBq0wpIyHvo4hSe/3EqpzeDa7uF8OXEQncMDL+h9bXaDmYu3Udl8hfJjMxdvw2av9owGEamhGg2ZvfPOO9x44420atXKEXoOHDhAu3bt+PLLLwHIz8/niSeeqL1KRUTq0d4j+UxYkMSuzHy8zCamX9uFuy+JrpVdp9elZp/RM3QqA0i3FrEuNZu49s0u+PNE5PxqFIg6d+7Mtm3b+P7779m1a5fj2NVXX43ZfLLTqXzZu4hIQ/PN5nQe/XQjBSU2WgT6Mff2WPpHh9Ta+2flnT0M1aSdiFy4GgUiOLn0ftiwYQwbNqw26xERcZpSm50Xv93Bv34+uTN//+gQ3ri9D6GB/rX6OVV9v9r+XBE5uyoHojlz5nDvvffi7+/PnDlzztn2gQceuODCRETqU2ZuERM/SGb9/uMA3De4HY9e0xlvrxpNtTyn/tEhRFj8ybAWVTqPyASEW/xrtVdKRM6tyvsQRUdH89tvv9GsWbNz7kJtMpkcK83chfYhEnFviXuPMenDDRzNLybQz5u/39yLYd0rf9ZhbSlfZQZUCEXlM5TmjY3V0nuRC1TnGzN6GgUiEfdkGAb/XL2P2Ut3YDegS3gg88b2Jbp5k3r5fO1DJFK36nxjxnIlJSWkpqbSvn17vL0v6K1EROpVblEpj3yyke+3ZQIwKrYlz43sQSPfs+86Xdu7Sg/rHsHVMeHaqVrEBdQoxRQWFjJp0iTef/994OQT6Nu1a8ekSZNo2bIl06ZNq9UiRURq0/b0XCYsSGL/sUJ8vcw8fUM3busfdc4l9XXVm+NlNmlpvYgLqNFswenTp7Nx40ZWrlyJv/8fqyCGDBnCxx9/XGvFiYjUtv8mHeSmN9ew/1ghLYMb8d8Jcdw+oPV5w5B2lRZxbzXqIVq0aBEff/wxF198cYX/iHTr1o29e/fWWnEiIrWlqNTGzMXb+HBdGgCDO7Xg1Vt707SJ7zlfd75dpU2c3FX66phwDXWJNGA1CkRHjhwhNDT0jOMFBQW1souriEhtOpBdyP0Lk9l8yIrJBJOv6sSkKztgrkKA0a7SIp6hRkNm/fr14+uvv3b8Xh6C/vWvfxEXF1c7lYmI1IIfd2Zx/es/s/mQleDGPrz3l/48OKRjlcIQaFdpEU9Rox6i559/nmuvvZZt27ZRVlbGa6+9xrZt2/jll19YtWpVbdcoIlJtNrvBaz/s4vUf92AY0KuVhbnxsbRq2rha76NdpUU8Q416iC655BI2btxIWVkZPXr04Pvvvyc0NJTExET69u1b2zWKiFRLdkEJd767jjkrToahsRe35pPxcdUOQ/DHrtJn608ycXK1mXaVFmnYatRDdMcdd3DFFVcwbdo02rdvX9s1iYjU2Ia04yQsTOawtQh/HzOzRvXgpj6tavx+XmYTM0bEMGFBMiYq31V6xogYTagWaeBq1EPk6+vLrFmz6NSpE1FRUYwdO5Z//etf7N69u7brExGpEsMw+E/ifm75ZyKHrUVEN2/CooRBFxSGyg3rHsG8sbGEWyoOi4Vb/PWIDRE3cUGP7jh06BCrV69m1apVrFq1il27dhEREcHBgwdrs0an06M7RFxbYUkZj32+mUUphwEY1i2cv9/ck0B/n1r9nNreqVpE6la9PbqjadOmNGvWjKZNmxIcHIy3tzctWrS4kLcUEamWvUfymbAgiV2Z+XiZTUwb1oX/uTS6TrYA0a7SIu6rRoHoscceY+XKlWzYsIGuXbsyePBgpk2bxmWXXUbTpk1ru0YRkUp9uzmdR/+7ifziMloE+vHGbX0Y0E6BRUSqr0ZDZmazmRYtWvDQQw8xatQoOnXqVBe1uQwNmYm4llKbndlLd/D2T6nAyZVgb9zeR0vfRaSCOh8y27BhA6tWrWLlypW89NJL+Pr6MnjwYC6//HIuv/xytw9IIuI8mblFTPwgmfX7jwNw32XteHRoZ7y9arRGREQEuMBJ1eU2btzIK6+8wsKFC7Hb7dhsttqozWWoh0jENfy67xgTP9jA0fxiAvy8+cfNPbXCS0TOqs57iAzDYMOGDaxcuZKVK1fy888/k5ubS8+ePRk8eHCNihYRORvDMHhr9T5mf7cTm92gS3ggb8bH0q5FgLNLExE3UaNAFBISQn5+Pr169WLw4MHcc889XHrppQQHB9dyeSLi6XKLSnnkk418vy0TgFF9WvLcTT1o5Ovl5MpExJ3UKBAtWLCASy+9VMNHIlKntqfnMmFBEvuPFeLrZWbGDTHc3r91nSypFxHPVqNANHz48NquQ0Skgs+SDvL4os0UldppGdyIN+Nj6RUV7OyyRMRNXdDGjCIita2o1MYzS7bxwdo0AAZ3asGrt/amaRNfJ1cmIu5MgUhEXMaB7EISPkhm00ErJhNMvqoTk67sgFmPxxCROqZAJCIuYeXOLCZ/nEJOYSnBjX14bUwfBnfSo4BEpH4oEImIU9nsBq8t383rK3ZjGNCrlYW58bG0atrY2aWJiAdRIBIRp8kuKOHBjzbw0+6jAIy9uDVPXh+Dn7eW1ItI/VIgEhGnSDmQw/0LkjhsLcLfx8zzN/VgVGwrZ5clIh5KgUhE6pVhGCxYm8Yzi7dSajOIbt6EeWNj6RKufc1ExHkUiESk3hSWlPH4F1v4YsMhAIZ2C+PvN/ciyN/HyZWJiKdTIBKRerHvSD7jFySxKzMfL7OJacO68D+XRmvXaRFxCWZnF1AdL7zwAiaTicmTJzuOFRUVkZCQQLNmzQgICGD06NFkZmZWeF1aWhrDhw+ncePGhIaG8uijj1JWVlbP1Yt4rm83p3PDG2vYlZlPi0A/PvifAdxzWTuFIRFxGQ2mh2j9+vX885//pGfPnhWOP/TQQ3z99dd8+umnWCwWJk6cyKhRo1izZg0ANpuN4cOHEx4ezi+//EJ6ejp33HEHPj4+PP/88864FBGPUWqzM3vpDt7+KRWA/m1DeOP2PoQG+Tu5MhGRihpED1F+fj7x8fG8/fbbNG3a1HHcarXyzjvv8PLLL3PllVfSt29f3n33XX755Rd+/fVXAL7//nu2bdvGggUL6N27N9deey3PPvssc+fOpaSkxFmXJOL2snKLiH97rSMM3XtZOxbeM0BhSERcUoMIRAkJCQwfPpwhQ4ZUOJ6UlERpaWmF4126dKF169YkJiYCkJiYSI8ePQgLC3O0GTp0KLm5uWzdurXSzysuLiY3N7fCj4hU3a/7jnHdnJ9Ztz+bAD9v5o+N5bHruuLj1SD+kyMiHsjlh8w++ugjkpOTWb9+/RnnMjIy8PX1JTg4uMLxsLAwMjIyHG1ODUPl58vPVWbWrFnMnDmzFqoX8SyGYfDW6n3M/m4nNrtB57BA5o2NpV2LAGeXJiJyTi7917UDBw7w4IMPsnDhQvz966+bffr06VitVsfPgQMH6u2zRRqq3KJSxi9IYta3O7DZDW7q05IvEgYqDIlIg+DSPURJSUlkZWURGxvrOGaz2Vi9ejVvvPEG3333HSUlJeTk5FToJcrMzCQ8PByA8PBw1q1bV+F9y1ehlbc5nZ+fH35+frV8NSLua3t6LhMWJLH/WCG+XmaeGhFD/IDWWkUmIg2GS/cQXXXVVWzevJmUlBTHT79+/YiPj3f82cfHh+XLlztes3PnTtLS0oiLiwMgLi6OzZs3k5WV5WizbNkygoKCiImJqfdrEnE3nycf5KY317D/WCEtgxvx6fg4xl7cRmFIRBoUl+4hCgwMpHv37hWONWnShGbNmjmO33333UyZMoWQkBCCgoKYNGkScXFxXHzxxQBcc801xMTE8Oc//5nZs2eTkZHBE088QUJCgnqBRC5AcZmNZxZvY+HaNAAu69SC127tTdMmvk6uTESk+lw6EFXFK6+8gtlsZvTo0RQXFzN06FDefPNNx3kvLy+WLFnChAkTiIuLo0mTJowbN45nnnnGiVWLNGwHjxdy/8JkNh20YjLBg1d1ZNKVHfEyq1dIRBomk2EYhrOLcHW5ublYLBasVitBQXoApXi2lTuzmPxxCjmFpQQ39uHVW3tzeedQZ5clInKG6nx/N/geIhGpHza7wZzlu5mzYjeGAT1bWXgzPpZWTRs7uzQRkQumQCQi55VdUMLkj1NYvesIAPEDWvPUiBj8vL2cXJmISO1QIBKRc9p4IIf7FyZzKOcE/j5mnr+pB6NiWzm7LBGRWqVAJCKVMgyDBWvTeHbxNkpsdto2a8z8P/elS7jm0YmI+1EgEpEzFJaU8fgXW/hiwyEAhnYL4+839yLI38fJlYmI1A0FIhGpYN+RfCYsSGZnZh5eZhNTh3XmnkvbaaNFEXFrCkQi4rB0SzqPfLqJ/OIymgf48cbtfbi4XTNnlyUiUucUiESEUpud2Ut38PZPqQD0bxvCG7f3ITSo/h6qLCLiTApEIh4uK7eIiR9sYN3+bADuvawdjw7tjI+XSz/qUESkVikQiXiwtfuOkfDBBo7mFxPg583f/9STa3tEOLssEZF6p0Ak4oEMw+Dtn/bx4tKd2OwGncMCmTc2lnYtApxdmoiIUygQiXiY3KJS/vrpJpZuzQDgpj4tee6m7jT21X8ORMRz6b+AIh5ke3ouExYksf9YIT5eJp4a0Y2xA1prSb2IeDwFIhEP8XnyQR77YjNFpXZaBjdibnwsvaOC6/xzbXaDdanZZOUVERroT//oELzMCmAi4loUiETcXHGZjWcWb2Ph2jQALu3YnNfG9CGkiW+df/bSLenMXLyNdGuR41iExZ8ZI2IY1l2Tt0XEdWhdrYgbO3i8kFvmJ7JwbRomEzx4VUfe+0v/egtDExYkVwhDABnWIiYsSGbplvQ6r0FEpKrUQyTiplbuzGLyxynkFJYS3NiHV27tzRWdQ+vls212g5mLt2FUcs4ATMDMxdu4OiZcw2ci4hIUiETcjN1uMGfFbl5bvhvDgJ6tLMy9PZaokMb1VsO61OwzeoZOZQDp1iLWpWYT116PBhER51MgEnEjxwtKmPxxCqt2HQEgfkBrnhoRg5+3V73WkZV39jBUk3YiInVNgUjETWw8kMP9C5M5lHMCfx8zz43swei+rZxSS2hg1Z6BVtV2IiJ1TYFIpIEzDIOFa9N4ZvE2Smx22jZrzLyxfekaEeS0mvpHhxBh8SfDWlTpPCITEG45uQRfRMQVaJWZSAN2osTGw59s5IlFWyix2bkmJoyvJl1S62HIZjdI3HuML1MOkbj3GDZ7ZTHnD15mEzNGxAAnw8+pyn+fMSJGE6pFxGWoh0ikgUo9WsCEBUnsyMjDy2xi6rDO3HNpu1rfdXrplnSe/mobGbl/zPcJD/Ln6RvOvZfQsO4RzBsbe8Y+ROHah0hEXJDJMIxz/1VPyM3NxWKxYLVaCQpy3jCESLmlW9J59NNN5BWX0TzAjzdu78PF7Wp/tdbSLemMX5B81vPzx8aeN9hop2oRcZbqfH+rh0ikASmz2Zn93U7eWr0PgIvaNmXu7bGEBtX+5GSb3WDa55vP2Wb655vPu5eQl9mkpfUi4vIUiEQaiKzcIiZ+uIF1qdkA3HNpNH8d1gUfr7qZCvjrvmPkFJaes83xwlJ+3XeMQR2a10kNIiL1RYFIpAFYu+8YEz/cwJG8YgL8vPn7n3pybY+6nYOTuPdYldspEIlIQ6dAJOLCDMPgXz+l8sLSHdjsBp3DApk3NpZ2LQLq49NruZ2IiOvSsnsRF5VbVMqEBck89812bHaDkb0j+SJhYD2FIYhrV7Ven6q2ExFxZeohEnFBOzJymbAgmdSjBfh4mXhqRDfGDmhd60vqz+Xi9s0IbuxzznlEwY19uFgTpkXEDaiHSMTFfLHhICPnriH1aAGRFn8+HT+QP1/cpl7DEJxcHfbCqB7nbPPCqB5aQi8ibkE9RCJOcvr+PL2iLDz/zXYW/JoGwKUdm/PamD6ENPF1Wo3Dukcwf2wsT3+1lYzcYsfx8CA/nr6hmzZXFBG3oUAk4gRLt6SfsYOzj5eJUtvJCcoPXNWRB6/q6BK9L8O6R3B1TLg2VxQRt6ZAJFLPlm5JZ8KC5DPWZpWHoYlXtGfK1Z3qtIbq7h6tzRVFxN0pEInUI5vdYObibedcqP5Z8iEeurpznfXAVNY7FaHni4mIh9OkapF6tC41u0IQqUy6tcixG3VtK++dOr2GDGsRExYks3RLep18roiIq1MgEqlHWXnnDkPVbVcd5+qdKj82c/E2bHZttCginkeBSKSeGIbBpoPWKrUNDaz9h7Wer3fKoG57p0REXJnmEInUgxMlNh5ftJnPkw+ds50JCLecnORcXeebKO3M3ikREVfn0j1Es2bN4qKLLiIwMJDQ0FBGjhzJzp07K7QpKioiISGBZs2aERAQwOjRo8nMzKzQJi0tjeHDh9O4cWNCQ0N59NFHKSsrq89LEQ+WerSAm95cw+fJhzCbYFSflsDJ8HOq8t9njIip9oTqpVvSueTFFdz29q88+FEKt739K5e8uKLCnKCq9jrVRe+UiIirc+lAtGrVKhISEvj1119ZtmwZpaWlXHPNNRQUFDjaPPTQQyxevJhPP/2UVatWcfjwYUaNGuU4b7PZGD58OCUlJfzyyy+8//77vPfeezz11FPOuCTxMEu3ZHDD6z+zIyOP5gF+LPyfi3n51t7MHxtLuKVi8Ai3+DNvbGy1V3pVdaJ0/+gQIiz+ZwSxciZOrjarSe+UiEhDZzIMo8HMoDxy5AihoaGsWrWKyy67DKvVSosWLfjggw/405/+BMCOHTvo2rUriYmJXHzxxXz77bdcf/31HD58mLCwMADmz5/P1KlTOXLkCL6+Z+4CXFxcTHHxH7vy5ubmEhUVhdVqJSgoqH4uVhq0Mpudv3+3k3+u3gfARW2b8sbtsYQF/RGCqrsXUGVsdoNLXlxx1rlB5UNwP0+9Ei+zyRGeoOIz6ss/tSaBTETEVeXm5mKxWKr0/e3SPUSns1pPTkgNCTn5N9ikpCRKS0sZMmSIo02XLl1o3bo1iYmJACQmJtKjRw9HGAIYOnQoubm5bN26tdLPmTVrFhaLxfETFRVVV5ckbigrr4jb/7XWEYbuuTSaD+65uEIYgj82O7yxd0vi2jc7bxiy2Q0S9x7jy5RDJO495ghU1ZkoPax7BPNqsXdKRMRdNJhJ1Xa7ncmTJzNo0CC6d+8OQEZGBr6+vgQHB1doGxYWRkZGhqPNqWGo/Hz5ucpMnz6dKVOmOH4v7yESOZ91qdkkfJDMkbxiAvy8mf2nnlzX48JDxtk2U7yue3iVXn/qRGk9ikNE5EwNJhAlJCSwZcsWfv755zr/LD8/P/z8/Or8c8R9GIbBv35K5YWlO7DZDTqFBTBvbF/atwi44Pc+26M+MqxFvLNmf5Xe4/SJ0noUh4hIRQ0iEE2cOJElS5awevVqWrVq5TgeHh5OSUkJOTk5FXqJMjMzCQ8Pd7RZt25dhfcrX4VW3kbkQuQVlfLop5tYuvVkj+PI3pE8P6oHjX1P/ut1IXOFqrKZotkEhkGlbS5kGb+IiCdx6UBkGAaTJk3iiy++YOXKlURHR1c437dvX3x8fFi+fDmjR48GYOfOnaSlpREXFwdAXFwczz33HFlZWYSGhgKwbNkygoKCiImJqd8LErezMyOP8QuSSD1agI+XiadGdGPsgNaYTCcDz4U+N6wqj/oo31jaROUTpWuyjF9ExNO49KTqhIQEFixYwAcffEBgYCAZGRlkZGRw4sQJACwWC3fffTdTpkzhxx9/JCkpib/85S/ExcVx8cUXA3DNNdcQExPDn//8ZzZu3Mh3333HE088QUJCgobF5IIs2nCIkXPXkHq0gEiLP5/cF8efL25TIQxd6HPDqrpJ4l2D2mqitIjIBXDpHqJ58+YBcPnll1c4/u6773LnnXcC8Morr2A2mxk9ejTFxcUMHTqUN99809HWy8uLJUuWMGHCBOLi4mjSpAnjxo3jmWeeqa/LEDdTXGbj2SXbWPBrGgCXdmzOa2P6ENLkjy0czjfUZeLkc8Oujgk/Z+9NVTdJvDomnMeHx2iitIhIDTWofYicpTr7GIh7O5RzgvsXJLHx/59J9sBVHXnwqo5nBI/Evce47e1fz/t+H95z8TknN5fvM5RhLTrnHKHyfYZEROQP1fn+dukeIhFXsmrXESZ/tIHjhaU09vVi4hUduG9we7zMpjMmTmdYT1TpPc83JOZlNjFjRAwTFiRrjpCISB1SD1EVqIfIs9ntBq+v2MOrP+w6o5cmpIkvvaMspBywkl1QUuH4qb+fzZPDu9I80O+8Q1wXOjlbRMQTVef7W4GoChSIPNfxghIe+iSFlTuPVOt1p/fmVMZs+mOFGJw/4NTGoz5ERDyJAlEtUyDyPDa7wYfr0njp+50cLyytl8/U88RERGqX5hCJXIBvNx9m6mebyS0qq5X3C2niQ3bBH6Hq9J6hctVZfSYiIrVLgUjkFF+lHOKBj1Jq9T2fvL4b4UH+ZOUVcTSvmGe/3n7Wtqc+jFWP1hARqT8KRCL/b09WPpM/Tqn19w0P8neEmy9TDlXpNVXdkFFERGqHS+9ULVJfvtuawYjXf650KKumTJycKH3qc8SqutFiVduJiEjtUA+ReISzrdAqs9n5+/c7+eeqfbX6eWfbI6h/dAgRFv/zbrSoh7GKiNQvBSJxe0u3pPP0V1vJyC12HAsP8uOhqzvxefIh1qZmA9A7KpiUAzm18pnhZ1lCr40WRURck5bdV4GW3TdcS7ekM35B8jnbNPH14u8396KJjxfj3lt/3vecNqwzR/KK+SLlUIXVYyFNfLipd0uGxISfd48gbbQoIlL3tOxehJPDZNM+33zONmYTLEoYRMewQN5eXbVhMy+zmSdHdOOxC3iY6rDuEVwdE66NFkVEXIQCkbitX/ceI+c8myraDcjKLaZjWCBp2QVVet/ydl5m0wUtjb/Q14uISO3RKjNxW4n7jtZqOxERcV8KROLGqjr8dLJd76imVWpd1XYiItJwKBCJ2+rXtmrBpXzYKjK4UZXaV7WdiIg0HJpDJC6tsv2DgPNORj6Uc4JXlu067/s3bezDxe1OBqLyPYJOXfl1utM3WhQREfegQCT17vSQ07dNU5J+P35GwKlsaXpwYx+ACpOlT1+uvnrXER78aAPHC0tp7OtFYYntrLXMGtXDEaZO3SPobJsmao8gERH3pH2IqkD7ENWeykLO6U9/j7D4c0OvCN5anVppMDldeTyZG9+H3ZkFvLp8F4YBPVpaeDM+lq2HrTz91TYycqu254/2CBIRcQ/V+f5WIKoCBaKKzvYYjPNZuiX9rL0vpzp9B+eq8vM2U1xmB+C2/q2ZMSIGfx+vGtVc02sUERHXoY0Zpc7UtPfEZjeYuXhblYJOTRN6cZkdHy8Tz9/Ug5v7RVU4V909f7RHkIiIZ9EqM6my8h6e0ycdZ1iLmLAgmaVb0s/62nWp2eecrFxbHhrS6YwwJCIicj4KRFIl5+rhKT82c/E2bPbK+3ey8uo+DAH0aa09gkREpPoUiKRKztfDYwDp1iLW/f+T408XGuhfR5X9QUviRUSkphSIpEqq2sNztnble/zUxbRkE1oSLyIiF0aBSKqkqj08Z2tXvscPnP+BGuUB577LoomwnP9zwy3+zBsbqyXxIiJSY1plJlVS3sOTYS0666aF4ecZshrWPYJ5Y2PPuw9R+Cmr1v46rCvfb83gpWW72JOVD8Bdg9pyRZdQsgtKtCReRERqhQJRA1afe+Wcuovz6fsElX9iVYashnWP4OqY8CrtVA2QnHacGV9tJSuvmCa+Xsz+Uy+G91RPkIiI1C5tzFgFrrgxo7N2U66vzzUMg3d+TmXWtzuw2Q06hgYw/899ad8ioNY+Q0RE3Jt2qq5ldRWIanvH5/JX1vV8mrrumcorKmXqZ5v4ZnMGADf0imTWqB408VOHpoiIVJ12qm4A6mLHZ4OToWjm4m1cHRNep8NndbWL867MPMYvSGLfkQJ8vEw8eX0Mf764DSaT5giJiEjd0SozJ6jLHZ/Ptx+QK1u04RA3vrGGfUcKiLD48/F9cdwR11ZhSERE6px6iOrZhfbwXOh+QK6ouMzGc19v538Tfwfg0o7NefXW3jQL8HNyZSIi4ikUiOpZdXp4KhuWutD9gFzN4ZwT3L8wmZQDOQA8cGUHHhzSScvoRUSkXikQ1bPa2vH5QvYDchU/7T7CAx9u4HhhKZZGPrxyay+u7BLm7LJERMQDaQ5RPavLHZ+rsx+QM9ntBnOW7+aOf6/jeGEp3VsGsWTSJQpDIiLiNApE9ex8z/Qycf6HlJbv+Bx+2mMtGsIjLHIKS7jr/fW8vGwXhgG39W/Nf8cPJCqksbNLExERD6Yhs3pWlzs+u/ojLDYftDJ+QRKHck7g523mbyO7c3O/KGeXJSIi4lk9RHPnzqVt27b4+/szYMAA1q1b55Q6aquHp3w/oBt7tySufTOXDUOGYfDhujRGz/uFQzknaNOsMZ/fP1BhSEREXIbH9BB9/PHHTJkyhfnz5zNgwABeffVVhg4dys6dOwkNDa33ehpiD09NnCix8cSiLXyWfBCAIV3DeOmWXlga+Ti5MhERkT94zKM7BgwYwEUXXcQbb7wBgN1uJyoqikmTJjFt2rQKbYuLiykuLnb8npubS1RUlEs9y6wh2H+0gPELktiRkYfZBI8O7cJ9l7XD7GahT0REXFN1Ht3hEUNmJSUlJCUlMWTIEMcxs9nMkCFDSExMPKP9rFmzsFgsjp+oKA3tVNf3WzMY8frP7MjIo3mALwv+ZwATLm+vMCQiIi7JIwLR0aNHsdlshIVVXNYdFhZGRkbGGe2nT5+O1Wp1/Bw4cKC+Sm3wymx2Xvh2B/f+J4m84jL6tmnKkkmXMrB9c2eXJiIiclYeM4eoOvz8/PDz02MjqutIXjGTPkzm130nn6N29yXRTLu2Cz5eHpG7RUSkAfOIQNS8eXO8vLzIzMyscDwzM5Pw8HAnVeVe1u/PJmFhMll5xTTx9WL2n3oxvKfr7ockIiJyKo/4q7uvry99+/Zl+fLljmN2u53ly5cTFxfnxMoaPsMw+NdP+xjz1q9k5RXTMTSALydeojAkIiINikf0EAFMmTKFcePG0a9fP/r378+rr75KQUEBf/nLX5xdWoOVX1zGX/+7kW82n5yHdUOvSGaN6kETP4/5x0pERNyEx3xz3XrrrRw5coSnnnqKjIwMevfuzdKlS8+YaC1Vsyszj/ELkth3pAAfLxNPDI/hjrg2mExaRSYiIg2Px+xDdCGqs4+BJ/gy5RDTPtvMiVIbERZ/5sbHEtu6qbPLEhERqaA6398e00MkF66kzM5zX2/j/cTfAbikQ3NeG9ObZgFakSciIg2bApFUyeGcE9y/MJmUAzkATLqyA5OHdHK7R42IiIhnUiCS8/pp9xEe/CiF7IISgvy9eXVMb67sorlXIiLiPhSI5KzsdoO5P+7h5R92YRjQvWUQ8+L7EhXS2NmliYiI1CoFIqlUTmEJD32cwo87jwBwW/8oZozohr+Pl5MrExERqX0KRHKGzQetTFiYxMHjJ/DzNvPsyO7c0k8PuBUREfelQCQOhmHw0foDzPhqKyVldlqHNGbe2Fi6RVqcXZqIiEidUiASAE6U2Hjyyy38N+kgAEO6hvHSLb2wNPJxcmUiIiJ1T4FI2H+0gAkLk9menovZBI8O7cJ9l7XDrCX1IiLiIRSIPNz3WzN4+NON5BWV0TzAlzm39WFg++bOLktERKReKRB5qDKbnX98v4v5q/YC0LdNU+beHku4xd/JlYmIiNQ/BSIPdCSvmEkfJvPrvmwA7hoUzfTruuDjZXZyZSIiIs6hQORhftufzf0Lk8nKK6aJrxcv/qkn1/eMdHZZIiIiTqVA5CEMw+Dfa/Yz65vtlNkNOoQGMH9sLB1CA51dmoiIiNMpEHmA/OIypv53E19vTgdgRK9IXhjVgyZ+uv0iIiKgQOT2dmXmMX5BEvuOFODjZeLx67oybmBbTCYtqRcRESmnQOTGvkw5xLTPNnOi1EZ4kD9z42Pp26aps8sSERFxOQpEbqikzM5zX2/j/cTfARjUoRlzxvShWYCfkysTERFxTQpEbuZwzgkSPkhmQ1oOABOv6MBDV3fCS7tOi4iInJUCkRv5efdRHvhoA9kFJQT5e/PKrb25qmuYs8sSERFxeQpEbsBuN3hz5R5eWrYLw4BukUHMH9uXqJDGzi5NRESkQVAgauCshaU89EkKK3ZkATDmoiievqEb/j5eTq5MRESk4VAgasAOHi9kzFu/cvD4Cfy8zTx7Y3duuSjK2WWJiIg0OApEDVhYkD+RlkaYTSbejI+le0uLs0sSERFpkBSIGjAfLzNz42Px9TJjaezj7HJEREQaLAWiBq5FoPYWEhERuVBmZxcgIiIi4mwKRCIiIuLxFIhERETE4ykQiYiIiMdTIBIRERGPp0AkIiIiHk+BSERERDyeApGIiIh4PAUiERER8XgKRCIiIuLxFIhERETE4ykQiYiIiMdTIBIRERGPp6fdV4FhGADk5uY6uRIRERGpqvLv7fLv8XNRIKqCvLw8AKKiopxciYiIiFRXXl4eFovlnG1MRlVik4ez2+0cPnyYwMBATCaTs8upIDc3l6ioKA4cOEBQUJCzy6k3um5dt6fw1GvXdeu6a4NhGOTl5REZGYnZfO5ZQuohqgKz2UyrVq2cXcY5BQUFedS/POV03Z7FU68bPPfadd2epS6u+3w9Q+U0qVpEREQ8ngKRiIiIeDwFogbOz8+PGTNm4Ofn5+xS6pWuW9ftKTz12nXduu76pknVIiIi4vHUQyQiIiIeT4FIREREPJ4CkYiIiHg8BSIRERHxeApEDcTq1asZMWIEkZGRmEwmFi1aVOH8nXfeiclkqvAzbNgw5xRbS2bNmsVFF11EYGAgoaGhjBw5kp07d1ZoU1RUREJCAs2aNSMgIIDRo0eTmZnppIprT1Wu/fLLLz/jno8fP95JFdeOefPm0bNnT8fmbHFxcXz77beO8+56v8933e54ryvzwgsvYDKZmDx5suOYu97zU1V23e54z59++ukzrqlLly6O886+1wpEDURBQQG9evVi7ty5Z20zbNgw0tPTHT8ffvhhPVZY+1atWkVCQgK//vory5Yto7S0lGuuuYaCggJHm4ceeojFixfz6aefsmrVKg4fPsyoUaOcWHXtqMq1A9xzzz0V7vns2bOdVHHtaNWqFS+88AJJSUn89ttvXHnlldx4441s3boVcN/7fb7rBve716dbv349//znP+nZs2eF4+56z8ud7brBPe95t27dKlzTzz//7Djn9HttSIMDGF988UWFY+PGjTNuvPFGp9RTX7KysgzAWLVqlWEYhpGTk2P4+PgYn376qaPN9u3bDcBITEx0Vpl14vRrNwzDGDx4sPHggw86r6h60rRpU+Nf//qXR91vw/jjug3D/e91Xl6e0bFjR2PZsmUVrtXd7/nZrtsw3POez5gxw+jVq1el51zhXquHyI2sXLmS0NBQOnfuzIQJEzh27JizS6pVVqsVgJCQEACSkpIoLS1lyJAhjjZdunShdevWJCYmOqXGunL6tZdbuHAhzZs3p3v37kyfPp3CwkJnlFcnbDYbH330EQUFBcTFxXnM/T79usu5871OSEhg+PDhFe4tuP+/42e77nLueM93795NZGQk7dq1Iz4+nrS0NMA17rUe7uomhg0bxqhRo4iOjmbv3r089thjXHvttSQmJuLl5eXs8i6Y3W5n8uTJDBo0iO7duwOQkZGBr68vwcHBFdqGhYWRkZHhhCrrRmXXDnD77bfTpk0bIiMj2bRpE1OnTmXnzp18/vnnTqz2wm3evJm4uDiKiooICAjgiy++ICYmhpSUFLe+32e7bnDfew3w0UcfkZyczPr16884587/jp/rusE97/mAAQN477336Ny5M+np6cycOZNLL72ULVu2uMS9ViByE2PGjHH8uUePHvTs2ZP27duzcuVKrrrqKidWVjsSEhLYsmVLhfFmT3G2a7/33nsdf+7RowcRERFcddVV7N27l/bt29d3mbWmc+fOpKSkYLVa+e9//8u4ceNYtWqVs8uqc2e77piYGLe91wcOHODBBx9k2bJl+Pv7O7ucelOV63bHe37ttdc6/tyzZ08GDBhAmzZt+OSTT2jUqJETKztJQ2Zuql27djRv3pw9e/Y4u5QLNnHiRJYsWcKPP/5Iq1atHMfDw8MpKSkhJyenQvvMzEzCw8Prucq6cbZrr8yAAQMAGvw99/X1pUOHDvTt25dZs2bRq1cvXnvtNbe/32e77sq4y71OSkoiKyuL2NhYvL298fb2ZtWqVcyZMwdvb2/CwsLc8p6f77ptNtsZr3GXe36q4OBgOnXqxJ49e1zi328FIjd18OBBjh07RkREhLNLqTHDMJg4cSJffPEFK1asIDo6usL5vn374uPjw/Llyx3Hdu7cSVpaWoW5Fw3R+a69MikpKQAN+p5Xxm63U1xc7Nb3uzLl110Zd7nXV111FZs3byYlJcXx069fP+Lj4x1/dsd7fr7rrmyag7vc81Pl5+ezd+9eIiIiXOPf73qZui0XLC8vz9iwYYOxYcMGAzBefvllY8OGDcbvv/9u5OXlGY888oiRmJhopKamGj/88IMRGxtrdOzY0SgqKnJ26TU2YcIEw2KxGCtXrjTS09MdP4WFhY4248ePN1q3bm2sWLHC+O2334y4uDgjLi7OiVXXjvNd+549e4xnnnnG+O2334zU1FTjyy+/NNq1a2dcdtllTq78wkybNs1YtWqVkZqaamzatMmYNm2aYTKZjO+//94wDPe93+e6bne912dz+uoqd73npzv1ut31nj/88MPGypUrjdTUVGPNmjXGkCFDjObNmxtZWVmGYTj/XisQNRA//vijAZzxM27cOKOwsNC45pprjBYtWhg+Pj5GmzZtjHvuucfIyMhwdtkXpLLrBYx3333X0ebEiRPG/fffbzRt2tRo3LixcdNNNxnp6enOK7qWnO/a09LSjMsuu8wICQkx/Pz8jA4dOhiPPvqoYbVanVv4BbrrrruMNm3aGL6+vkaLFi2Mq666yhGGDMN97/e5rttd7/XZnB6I3PWen+7U63bXe37rrbcaERERhq+vr9GyZUvj1ltvNfbs2eM47+x7bTIMw6ifvigRERER16Q5RCIiIuLxFIhERETE4ykQiYiIiMdTIBIRERGPp0AkIiIiHk+BSERERDyeApGIiIh4PAUiERER8XgKRCLS4Fx++eVMnjzZ2WWwcuVKTCbTGQ+kFJGGR4FIRKQKXCWEiUjdUCASERERj6dAJCINWnFxMY888ggtW7akSZMmDBgwgJUrVzrOv/feewQHB/Pdd9/RtWtXAgICGDZsGOnp6Y42ZWVlPPDAAwQHB9OsWTOmTp3KuHHjGDlyJAB33nknq1at4rXXXsNkMmEymdi/f7/j9UlJSfTr14/GjRszcOBAdu7cWU9XLyK1RYFIRBq0iRMnkpiYyEcffcSmTZu4+eabGTZsGLt373a0KSws5B//+Af/+c9/WL16NWlpaTzyyCOO8y+++CILFy7k3XffZc2aNeTm5rJo0SLH+ddee424uDjuuece0tPTSU9PJyoqynH+8ccf56WXXuK3337D29ubu+66q16uXURqj7ezCxARqam0tDTeffdd0tLSiIyMBOCRRx5h6dKlvPvuuzz//PMAlJaWMn/+fNq3bw+cDFHPPPOM431ef/11pk+fzk033QTAG2+8wTfffOM4b7FY8PX1pXHjxoSHh59Rx3PPPcfgwYMBmDZtGsOHD6eoqAh/f/+6uXARqXUKRCLSYG3evBmbzUanTp0qHC8uLqZZs2aO3xs3buwIQwARERFkZWUBYLVayczMpH///o7zXl5e9O3bF7vdXqU6evbsWeG9AbKysmjdunX1L0pEnEKBSEQarPz8fLy8vEhKSsLLy6vCuYCAAMeffXx8KpwzmUwYhlFrdZz6/iaTCaDKYUpEXIPmEIlIg9WnTx9sNhtZWVl06NChwk9lQ1uVsVgshIWFsX79escxm81GcnJyhXa+vr7YbLZarV9EXId6iESkwerUqRPx8fHccccdvPTSS/Tp04cjR46wfPlyevbsyfDhw6v0PpMmTWLWrFl06NCBLl268Prrr3P8+HFHbw9A27ZtWbt2Lfv37ycgIICQkJC6uiwRcQL1EIlIg/buu+9yxx138PDDD9O5c2dGjhzJ+vXrqzV/Z+rUqdx2223ccccdxMXFERAQwNChQytMin7kkUfw8vIiJiaGFi1akJaWVheXIyJOYjJqcyBdRMQN2O12unbtyi233MKzzz7r7HJEpB5oyExEPN7vv//O999/z+DBgykuLuaNN94gNTWV22+/3dmliUg90ZCZiHg8s9nMe++9x0UXXcSgQYPYvHkzP/zwA127dnV2aSJSTzRkJiIiIh5PPUQiIiLi8RSIRERExOMpEImIiIjHUyASERERj6dAJCIiIh5PgUhEREQ8ngKRiIiIeDwFIhEREfF4/wcrSDkCvXPagQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 훈련 세트의 산점도를 그립니다\u001f\n",
        "plt.scatter(train_input, train_target)\n",
        "# 15에서 50까지 1차 방정식 그래프를 그립니다\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": 15,
      "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": 16,
      "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": 17,
      "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": 18,
      "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": 19,
      "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": 20,
      "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 배열을 만듭니다\n",
        "point = np.arange(15, 50)\n",
        "# 훈련 세트의 산점도를 그립니다\n",
        "plt.scatter(train_input, train_target)\n",
        "# 15에서 49까지 2차 방정식 그래프를 그립니다\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": 21,
      "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": {
      "name": "3-2 선형 회귀.ipynb",
      "provenance": []
    },
    "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
}