ch11/ch11.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UqHcRqQPlsd3"
      },
      "source": [
        "# 머신 러닝 교과서 3판"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d_mMtNoelsd5"
      },
      "source": [
        "# 11장 - 레이블되지 않은 데이터 다루기: 군집 분석"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQlvShEtlsd5"
      },
      "source": [
        "**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://nbviewer.org/github/rickiepark/python-machine-learning-book-3rd-edition/blob/master/ch11/ch11.ipynb\"><img src=\"https://jupyter.org/assets/share.png\" width=\"60\" />주피터 노트북 뷰어로 보기</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/python-machine-learning-book-3rd-edition/blob/master/ch11/ch11.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECqHPRxylsd6"
      },
      "source": [
        "### 목차"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zROhiCUlsd6"
      },
      "source": [
        "- k-평균 알고리즘을 사용하여 유사한 객체 그룹핑\n",
        "    - 사이킷런을 사용한 k-평균 군집\n",
        "    - k-평균 ++로 초기 클러스터 센트로이드를 똑똑하게 할당\n",
        "    - 직접 군집 vs 간접 군집\n",
        "    - 엘보우 방법을 사용하여 최적의 클러스터 개수 찾기\n",
        "    - 실루엣 그래프로 군집 품질을 정량화\n",
        "- 계층적인 트리로 클러스터 조직화\n",
        "    - 상향식으로 클러스터 묶기\n",
        "    - 거리 행렬에서 계층 군집 수행\n",
        "    - 히트맵에 덴드로그램 연결\n",
        "    - 사이킷런에서 병합 군집 적용\n",
        "- DBSCAN을 사용하여 밀집도가 높은 지역 찾기\n",
        "- 요약"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qa9AH03Alsd6"
      },
      "source": [
        "<br>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:42.673735Z",
          "iopub.status.busy": "2021-10-23T10:43:42.672737Z",
          "iopub.status.idle": "2021-10-23T10:43:42.675419Z",
          "shell.execute_reply": "2021-10-23T10:43:42.674810Z"
        },
        "id": "5e8TX0F_lsd6"
      },
      "outputs": [],
      "source": [
        "from IPython.display import Image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jp38ToZlsd7"
      },
      "source": [
        "# k-평균 알고리즘을 사용하여 유사한 객체 그룹핑"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUiYlcuHlsd7"
      },
      "source": [
        "## 사이킷런을 사용한 k-평균 군집"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:42.682177Z",
          "iopub.status.busy": "2021-10-23T10:43:42.681039Z",
          "iopub.status.idle": "2021-10-23T10:43:43.241935Z",
          "shell.execute_reply": "2021-10-23T10:43:43.242402Z"
        },
        "id": "jbzyHg5Elsd7"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import make_blobs\n",
        "\n",
        "X, y = make_blobs(n_samples=150,\n",
        "                  n_features=2,\n",
        "                  centers=3,\n",
        "                  cluster_std=0.5,\n",
        "                  shuffle=True,\n",
        "                  random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:43.248642Z",
          "iopub.status.busy": "2021-10-23T10:43:43.247389Z",
          "iopub.status.idle": "2021-10-23T10:43:43.612366Z",
          "shell.execute_reply": "2021-10-23T10:43:43.612836Z"
        },
        "id": "ROCmGQl9lsd8",
        "outputId": "2a620a92-20be-47b2-86cb-effc34cc3e31"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(X[:, 0], X[:, 1],\n",
        "            c='white', marker='o', edgecolor='black', s=50)\n",
        "plt.grid()\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/11_01.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:43.618756Z",
          "iopub.status.busy": "2021-10-23T10:43:43.617747Z",
          "iopub.status.idle": "2021-10-23T10:43:44.142684Z",
          "shell.execute_reply": "2021-10-23T10:43:44.143242Z"
        },
        "id": "6t8fxe8hlsd8"
      },
      "outputs": [],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "km = KMeans(n_clusters=3,\n",
        "            init='random',\n",
        "            n_init=10,\n",
        "            max_iter=300,\n",
        "            tol=1e-04,\n",
        "            random_state=0)\n",
        "\n",
        "y_km = km.fit_predict(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:44.147813Z",
          "iopub.status.busy": "2021-10-23T10:43:44.146671Z",
          "iopub.status.idle": "2021-10-23T10:43:44.340066Z",
          "shell.execute_reply": "2021-10-23T10:43:44.340856Z"
        },
        "id": "uH6KDjPglsd8",
        "outputId": "bf7848b4-ec10-48e4-e806-119069976b82"
      },
      "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(X[y_km == 0, 0],\n",
        "            X[y_km == 0, 1],\n",
        "            s=50, c='lightgreen',\n",
        "            marker='s', edgecolor='black',\n",
        "            label='Cluster 1')\n",
        "plt.scatter(X[y_km == 1, 0],\n",
        "            X[y_km == 1, 1],\n",
        "            s=50, c='orange',\n",
        "            marker='o', edgecolor='black',\n",
        "            label='Cluster 2')\n",
        "plt.scatter(X[y_km == 2, 0],\n",
        "            X[y_km == 2, 1],\n",
        "            s=50, c='lightblue',\n",
        "            marker='v', edgecolor='black',\n",
        "            label='Cluster 3')\n",
        "plt.scatter(km.cluster_centers_[:, 0],\n",
        "            km.cluster_centers_[:, 1],\n",
        "            s=250, marker='*',\n",
        "            c='red', edgecolor='black',\n",
        "            label='Centroids')\n",
        "plt.legend(scatterpoints=1)\n",
        "plt.grid()\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/11_02.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9JXA1PRlsd9"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHhc5F78lsd9"
      },
      "source": [
        "## k-평균 ++로 초기 클러스터 센트로이드를 똑똑하게 할당"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Y0eBpr7lsd9"
      },
      "source": [
        "..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "goFnVAfplsd9"
      },
      "source": [
        "## 직접 군집 vs 간접 군집"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9W9ynY7Blsd9"
      },
      "source": [
        "..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDjpKK3-lsd-"
      },
      "source": [
        "## 엘보우 방법을 사용하여 최적의 클러스터 개수 찾기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:44.346360Z",
          "iopub.status.busy": "2021-10-23T10:43:44.344380Z",
          "iopub.status.idle": "2021-10-23T10:43:44.350172Z",
          "shell.execute_reply": "2021-10-23T10:43:44.349355Z"
        },
        "id": "HkOCdPuRlsd-",
        "outputId": "a88e10e9-e02a-42c8-87a5-82e27f57a89a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "왜곡: 72.48\n"
          ]
        }
      ],
      "source": [
        "print('왜곡: %.2f' % km.inertia_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:44.360841Z",
          "iopub.status.busy": "2021-10-23T10:43:44.360151Z",
          "iopub.status.idle": "2021-10-23T10:43:48.568551Z",
          "shell.execute_reply": "2021-10-23T10:43:48.567454Z"
        },
        "id": "1x5O1iKElsd-",
        "outputId": "3509b2f3-34b3-4f14-9b89-1cc0a9876113"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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IdkHPt+wJwQ4AgDaPYBfkBteOs2NmLAAAbR7BLsgN7NpOVou05/BR5bvKzS4HAACYiGAX5OIi7EpPjZNEqx0AAG0dwS4EDOmWKKlm2RMAANB2EexCwM/r2dFiBwBAW0awCwGDu9U8WuyHglIdKas0uRoAAGAWgl0ISIxxqHtSjCQpm3F2AAC0WQS7EEF3LAAAINiFiKGsZwcAQJtHsAsRg7vVBLst+1wqrag2uRoAAGAGgl2ISI2PVOeESHkNacOeI2aXAwAATECwCyG1rXaMswMAoG0i2IWQoUygAACgTSPYhZAhaTVPoNiYV6zyKo/J1QAAgNZGsAsh3RKj1CHWoUqPV5vyis0uBwAAtDKCXQixWCy+9exYqBgAgLbH1GD3yCOPyGKx+G09e/b0HS8vL1dmZqYSExMVExOjCRMmqKCgwO8aubm5GjNmjKKiopSUlKSZM2equrrtLvcx5PgEirWMswMAoM0JM7uA3r1767PPPvO9Dwv7uaTp06fro48+0vvvvy+n06mpU6fq2muv1erVqyVJHo9HY8aMUUpKir7++msdOHBAkyZNkt1u15NPPtnq9xIIalvs1u85omqPV2E2GmUBAGgrTA92YWFhSklJOWm/y+XSa6+9poULF+qyyy6TJL3xxhvq1auX1qxZo2HDhunTTz/Vtm3b9Nlnnyk5OVn9+/fX448/rvvvv1+PPPKIwsPDW/t2THdecqziIsLkLq/W1v1u9escb3ZJAACglZjenLNz506lpqbq7LPP1sSJE5WbmytJWr9+vaqqqjRixAjfuT179lSXLl2UlZUlScrKylLfvn2VnJzsO2fUqFFyu93aunVr695IgLBaLTw3FgCANsrUYDd06FAtWLBAS5cu1csvv6ycnBz96le/UklJifLz8xUeHq74+Hi/zyQnJys/P1+SlJ+f7xfqao/XHjuViooKud1uvy2U1AY7xtkBANC2mNoVO3r0aN/r888/X0OHDlXXrl31j3/8Q5GRkS32vXPmzNGjjz7aYtc3W+0TKL7ZUySv15DVajG5IgAA0BpM74qtKz4+Xj169NCuXbuUkpKiyspKFRcX+51TUFDgG5OXkpJy0izZ2vf1jdurNWvWLLlcLt+Wl5fXvDdisj5nORVpt6n4aJV2FpaaXQ4AAGglARXsSktL9eOPP6pjx44aOHCg7Ha7li9f7ju+Y8cO5ebmKiMjQ5KUkZGhzZs3q7Cw0HfOsmXLFBcXp/T09FN+j8PhUFxcnN8WSuw2qwZ2bSdJWpdz2ORqAABAazE12N13331atWqVdu/era+//lrXXHONbDabfve738npdOr222/XjBkztHLlSq1fv1633nqrMjIyNGzYMEnSyJEjlZ6erptvvlmbNm3SJ598ogcffFCZmZlyOBxm3prpGGcHAEDbY+oYu7179+p3v/udDh8+rA4dOuiiiy7SmjVr1KFDB0nSs88+K6vVqgkTJqiiokKjRo3SSy+95Pu8zWbTkiVLNGXKFGVkZCg6OlqTJ0/WY489ZtYtBYy6M2MNw5DFwjg7AABCncUwDMPsIszmdrvldDrlcrlCplu2vMqj8x/5VJUer1bNvFRdE6PNLgkAADRBY3JKQI2xQ/OJsNt0fienJLpjAQBoKwh2IYyFigEAaFsIdiGMYAcAQNtCsAthA7u2k9Ui5RYdVb6r3OxyAABACyPYhbDYCLt6p9aMs1u3m1Y7AABCHcEuxNU+XoyFigEACH0EuxDHODsAANoOgl2IG9yt5tFiPxSUqqis0uRqAABASyLYhbjEGIfOTYqRJGUzzg4AgJBGsGsD6I4FAKBtINi1AbXBjhY7AABCG8GuDaidGbtln0ulFdUmVwMAAFoKwa4NSI2PVOeESHkNaf2eI2aXAwAAWgjBro0Y0i1REuvZAQAQygh2bcRQJlAAABDyCHZtxODjwW5TnkvlVR6TqwEAAC2BYNdGdEuMUodYhyo9Xm3KKza7HAAA0AIIdm2ExWJhPTsAAEIcwa4N8Y2zYz07AABCEsGuDaltsVu/54iqPV6TqwEAAM2NYNeG9EiKlTPSrqOVHm3d7za7HAAA0MwIdm2I1WrR4G7tJDHODgCAUESwa2Nqu2PXEuwAAAg5BLs2ZkhazRMosncXyes1TK4GAAA0J4JdG9M7NU5R4Ta5jlXph8ISs8sBAADNiGDXxthtVg3syjg7AABCEcGuDRrcjYWKAQAIRQS7NqjuEygMg3F2AACECoJdG9S/c7zCbVYVllRoz+GjZpcDAACaCcGuDYqw29Svs1MS3bEAAIQSgl0bxXp2AACEHoJdG1U7gSJ7N8EOAIBQQbBrowZ2bSerRcotOqoDrmNmlwMAAJpBk4JdQUGBbr75ZqWmpiosLEw2m81vQ+CLjbCrdyrj7AAACCVhTfnQLbfcotzcXD300EPq2LGjLBZLc9eFVjAkLUGb97m0LqdIV/c/y+xyAADAGWpSsPvqq6/05Zdfqn///s1cDlrTkLQEvfZVDi12AACEiCZ1xXbu3JmFbUNA7QSKnYWlKiqrNLkaAABwppoU7J577jk98MAD2r17dzOXg9aUEB2uc5NiJDE7FgCAUNCkrtgbbrhBR48e1TnnnKOoqCjZ7Xa/40VFhIRgMSQtQTsLS7Uup0ijeqeYXQ4AADgDTQp2zz33XDOXAbMMSUvQO2tzGWcHAEAIaFKwmzx5cnPXAZPUPoFi636XSsqrFBthP80nAABAoGpSsJMkj8ejDz74QNu3b5ck9e7dW1dddRXr2AWZjs5IdU6IVF7RMa3fc0SXnpdkdkkAAKCJmhTsdu3apSuvvFL79u3TeeedJ0maM2eOOnfurI8++kjnnHNOsxaJljWkW6LyivYqe3cRwQ4AgCDWpFmxd999t8455xzl5eVpw4YN2rBhg3Jzc5WWlqa77767SYXMnTtXFotF06ZN8+0rLy9XZmamEhMTFRMTowkTJqigoMDvc7m5uRozZoyioqKUlJSkmTNnqrq6ukk1tFVDj3fHMs4OAIDg1qQWu1WrVmnNmjVKSEjw7UtMTNTcuXM1fPjwRl8vOztb//Vf/6Xzzz/fb//06dP10Ucf6f3335fT6dTUqVN17bXXavXq1ZJquoPHjBmjlJQUff311zpw4IAmTZoku92uJ598sim31ibVjrPblOdSeZVHEXa60wEACEZNarFzOBwqKSk5aX9paanCw8Mbda3S0lJNnDhRr776qtq1a+fb73K59Nprr+mZZ57RZZddpoEDB+qNN97Q119/rTVr1kiSPv30U23btk1vv/22+vfvr9GjR+vxxx/Xiy++qMpKFtxtqK6JUUqKdajS49XGvGKzywEAAE3UpGA3duxY3XHHHVq7dq0Mw5BhGFqzZo3uvPNOXXXVVY26VmZmpsaMGaMRI0b47V+/fr2qqqr89vfs2VNdunRRVlaWJCkrK0t9+/ZVcnKy75xRo0bJ7XZr69atTbm1NslisWgw3bEAAAS9JnXFvvDCC5o8ebIyMjJ8ixNXV1frqquu0vPPP9/g67z77rvasGGDsrOzTzqWn5+v8PBwxcfH++1PTk5Wfn6+75y6oa72eO2xU6moqFBFRYXvvdvtbnDNoWpoWoI++u4AT6AAACCINSnYxcfH61//+pd27typ77//XpLUq1cvde/evcHXyMvL0z333KNly5YpIiKiKWU02Zw5c/Too4+26ncGutpxduv3HFGVxyu7rUmNuQAAwERn9Lf3ueeeq3HjxmncuHGNCnVSTVdrYWGhLrjgAoWFhSksLEyrVq3SCy+8oLCwMCUnJ6uyslLFxcV+nysoKFBKSs2jr1JSUk6aJVv7vvac+syaNUsul8u35eXlNar2UNQjKVbOSLuOVnq0dT8tmAAABKMGt9jNmDFDjz/+uKKjozVjxoxfPPeZZ5457fUuv/xybd682W/frbfeqp49e+r+++9X586dZbfbtXz5ck2YMEGStGPHDuXm5iojI0OSlJGRoSeeeEKFhYVKSqpZf23ZsmWKi4tTenr6Kb/b4XDI4XCctsa2xGq1aHC3BH22vUDrcg6rf+d4s0sCAACN1OBg9+2336qqqsr3+kzFxsaqT58+fvuio6OVmJjo23/77bdrxowZSkhIUFxcnO666y5lZGRo2LBhkqSRI0cqPT1dN998s+bNm6f8/Hw9+OCDyszMJLg1wdC02mBXpDsuZpFpAACCTYOD3cqVK+t93ZKeffZZWa1WTZgwQRUVFRo1apReeukl33GbzaYlS5ZoypQpysjIUHR0tCZPnqzHHnusVeoLNbUzY7N3H5HXa8hqtZhcEQAAaAyLYRhGYz9022236fnnn1dsbKzf/rKyMt111116/fXXm63A1uB2u+V0OuVyuRQXF2d2Oaap8njV79FPdbTSo6XTfqWeKW33zwIAgEDRmJzSpMkTb775po4dO3bS/mPHjumtt95qyiURAOw2qwZ2rVkkmvXsAAAIPo0Kdm63Wy6XS4ZhqKSkRG6327cdOXJE//d//+ebxIDgNKRbTXfsWoIdAABBp1Hr2MXHx8tischisahHjx4nHbdYLKwPF+SG1HkChWEYslgYZwcAQLBoVLBbuXKlDMPQZZddpv/93/9VQkKC71h4eLi6du2q1NTUZi8Sradf53iF26w6WFKh3YePKq19tNklAQCABmpUsLvkkktUXV2tyZMna9CgQercuXNL1QWTRNht6tfZqezdR5SdU0SwAwAgiDR68kRYWJj+53/+Rx6PpyXqQQCo7Y5lnB0AAMGlSbNiL7vsMq1ataq5a0GAGJKWKElat/uwyZUAAIDGaFRXbK3Ro0frgQce0ObNmzVw4EBFR/t311111VXNUhzMMbBrO1ktUl7RMe0vPqbU+EizSwIAAA3QpAWKrdZTN/RZLJag66ZlgeKTjfvbV9q8z6Xnb+yvq/ufZXY5AAC0WS2+QLHX6z3lFmyhDvWru+wJAAAIDk0Kdgh9BDsAAIJPk4PdqlWrNG7cOHXv3l3du3fXVVddpS+//LI5a4OJBh9/AsXOwlIdLq0wuRoAANAQTQp2b7/9tkaMGKGoqCjdfffduvvuuxUZGanLL79cCxcubO4aYYKE6HD1SI6RJGXvPmJyNQAAoCGaNHmiV69euuOOOzR9+nS//c8884xeffVVbd++vdkKbA1Mnqjfvy/arHfW5uq24Wl6eFy62eUAANAmtfjkiZ9++knjxo07af9VV12lnJycplwSAah2nF32bsbZAQAQDJoU7Dp37qzly5eftP+zzz7jMWMhpDbYbd3vUkl5lcnVAACA02nSAsX33nuv7r77bm3cuFEXXnihJGn16tVasGCBnn/++WYtEObp6IxUl4Qo5RYd1fo9R3TpeUlmlwQAAH5Bk4LdlClTlJKSoqefflr/+Mc/JNWMu3vvvfd09dVXN2uBMNeQtATlFh3Vupwigh0AAAGuScFOkq655hpdc801zVkLAtCQtAT9z/q9rGcHAEAQaNIYu7PPPluHD5/8gPji4mKdffbZZ1wUAseQ4+vZbdpbrPIqnioCAEAga1Kw2717d72PDquoqNC+ffvOuCgEjq6JUUqKdajKY2hjXrHZ5QAAgF/QqK7YxYsX+15/8skncjqdvvcej0fLly9Xt27dmq04mM9isWhIWoKWfHdA63KKNOzsRLNLAgAAp9CoYDd+/HhJNX/ZT5482e+Y3W5Xt27d9PTTTzdbcQgMQ+sEOwAAELgaFey8Xq8kKS0tTdnZ2Wrfvn2LFIXAMiStppVu/Z4jqvJ4Zbc1+RHDAACgBTXpb+icnJyTQl1xcXFz1IMAdG5SjJyRdh2r8mjLPpfZ5QAAgFNoUrB76qmn9N577/neX3/99UpISNBZZ52lTZs2NVtxCAxWq0WDu/F4MQAAAl2Tgt38+fN9jw5btmyZPvvsMy1dulSjR4/WzJkzm7VABIahxx8vxjg7AAACV5MWKM7Pz/cFuyVLlui3v/2tRo4cqW7dumno0KHNWiACw5A6wc7rNWS1WkyuCAAAnKhJLXbt2rVTXl6eJGnp0qUaMWKEJMkwjHrXt0Pw650ap6hwm9zl1dpRUGJ2OQAAoB5NCnbXXnut/u3f/k2/+c1vdPjwYY0ePVqS9O2336p79+7NWiACQ5jNqoFd20miOxYAgEDVpGD37LPPaurUqUpPT9eyZcsUExMjSTpw4ID+9Kc/NWuBCBy1jxdbxwQKAAACUpPG2Nntdt13330n7Z8+ffoZF4TAVXecnWEYslgYZwcAQCBpcLBbvHixRo8eLbvd7vdosfpcddVVZ1wYAk+/zvEKt1l1sKRCuw8fVVr7aLNLAgAAdTQ42I0fP175+flKSkryPVqsPhaLhQkUISrCblP/zvFat7tI63IOE+wAAAgwDR5j5/V6lZSU5Ht9qo1QF9oGp9VMoFjLBAoAAAJOoydPeL1evf766xo7dqz69Omjvn376uqrr9Zbb70lwzBaokYEkNrnxjIzFgCAwNOoYGcYhq666ir9/ve/1759+9S3b1/17t1bu3fv1i233KJrrrmmpepEgBjYtZ2sFmnvkWPaX3zM7HIAAEAdjZoVu2DBAn3xxRdavny5fv3rX/sdW7FihcaPH6+33npLkyZNatYiEThiHGHqc5ZT3+11KXt3ka7uf5bZJQEAgOMa1WL397//XX/5y19OCnWSdNlll+mBBx7QO++802zFITDVrmfHODsAAAJLo4Ldd999pyuuuOKUx0ePHq1NmzadcVEIbIPrrGcHAAACR6OCXVFRkZKTk095PDk5WUeOHDnjohDYBh9vsdtVWKrDpRUmVwMAAGo1Kth5PB6FhZ16WJ7NZlN1dfUZF4XAlhAdrh7JNY+Ry95NkAcAIFA0avKEYRi65ZZb5HA46j1eUUHrTVsxJC1BPxSUal1Oka7ok2J2OQAAQI1ssZs8ebKSkpLkdDrr3ZKSkho1I/bll1/W+eefr7i4OMXFxSkjI0Mff/yx73h5ebkyMzOVmJiomJgYTZgwQQUFBX7XyM3N1ZgxYxQVFaWkpCTNnDmTVsNW4FvPbvdhkysBAAC1GtVi98YbbzTrl3fq1Elz587VueeeK8Mw9Oabb+rqq6/Wt99+q969e2v69On66KOP9P7778vpdGrq1Km69tprtXr1akk1XcNjxoxRSkqKvv76ax04cECTJk2S3W7Xk08+2ay1wl/tzNht+91yl1cpLsJuckUAAMBiBNjjIhISEvQf//Efuu6669ShQwctXLhQ1113nSTp+++/V69evZSVlaVhw4bp448/1tixY7V//37fpI758+fr/vvv18GDBxUeHt6g73S73XI6nXK5XIqLi2uxews1F89bqdyio3rj1sH69XlJZpcDAEBIakxOafQjxVqKx+PRu+++q7KyMmVkZGj9+vWqqqrSiBEjfOf07NlTXbp0UVZWliQpKytLffv29ZupO2rUKLndbm3duvWU31VRUSG32+23ofGGHF/2JJtlTwAACAimB7vNmzcrJiZGDodDd955pxYtWqT09HTl5+crPDxc8fHxfucnJycrPz9fkpSfn3/S8iu172vPqc+cOXP8xgZ27ty5eW+qjRjCenYAAAQU04Pdeeedp40bN2rt2rWaMmWKJk+erG3btrXod86aNUsul8u35eXltej3haqhx4Pdpr3FKq/ymFwNAABo1OSJlhAeHq7u3btLkgYOHKjs7Gw9//zzuuGGG1RZWani4mK/VruCggKlpNQsr5GSkqJ169b5Xa921mztOfVxOBynXLIFDdclIUrJcQ4VuCv0bW6xMs5JNLskAADaNNNb7E7k9XpVUVGhgQMHym63a/ny5b5jO3bsUG5urjIyMiRJGRkZ2rx5swoLC33nLFu2THFxcUpPT2/12tsai8XiewoF3bEAAJjP1Ba7WbNmafTo0erSpYtKSkq0cOFCff755/rkk0/kdDp1++23a8aMGUpISFBcXJzuuusuZWRkaNiwYZKkkSNHKj09XTfffLPmzZun/Px8Pfjgg8rMzKRFrpUMTUvQku8OaNm2fHVrH6Wk2AgNSUuQzWoxuzQAANocU4NdYWGhJk2apAMHDsjpdOr888/XJ598ot/85jeSpGeffVZWq1UTJkxQRUWFRo0apZdeesn3eZvNpiVLlmjKlCnKyMhQdHS0Jk+erMcee8ysW2pzKj01q+Vs2e/WPe9ulCR1dEZo9rh0XdGno4mVAQDQ9gTcOnZmYB27plm65YCmvL1BJ/4DVNtW9/JNFxDuAAA4Q0G5jh2Ci8dr6NEPt50U6iT59j364TZ5vG3+/xsAAGg1BDs0ybqcIh1wlZ/yuCHpgKucSRUAALQigh2apLDk1KGuKecBAIAzR7BDkyTFRjTreQAA4MwR7NAkQ9IS1NEZoVMtamJRzezY2seOAQCAlkewQ5PYrBbNHlezCPSpwt3scemsZwcAQCsi2KHJrujTUS/fdIFSnP7drRZJ8647n6VOAABoZaY/KxbB7Yo+HfWb9BStyylSobtczyz7QXuKjmrP4aNmlwYAQJtDix3OmM1qUcY5ibp6wFmadWUvSdKCr3fLdbTK5MoAAGhbCHZoViPTk9UzJValFdV6fXWO2eUAANCmEOzQrKxWi+667FxJ0uurc+Qup9UOAIDWQrBDsxvdJ0XnJsWopLxab67ebXY5AAC0GQQ7NDur1aKpl3WXJP33Vzkqrag2uSIAANoGgh1axNjzU3V2+2i5jlXprazdZpcDAECbQLBDi7DVbbX7MkdltNoBANDiCHZoMVf1S1XXxCgVlVXqnbV7zC4HAICQR7BDiwmzWZX565pWu1e++EnHKj0mVwQAQGgj2KFFXTPgLHVqF6lDpZVauC7X7HIAAAhpBDu0KHudVrv5q35UeRWtdgAAtBSCHVrchAs6KdUZoYMlFXovO8/scgAACFkEO7S48DCrphxvtXv58x9VUU2rHQAALYFgh1bx20GdlBIXoXx3ud7/Zq/Z5QAAEJIIdmgVjjCbplx6jqSaVrvKaq/JFQEAEHoIdmg1NwzurKRYh/YVH9M/N9BqBwBAcyPYodVE2G364yU1rXYvfr5LVR5a7QAAaE4EO7SqfxvSRe1jwpVXdEwffLvP7HIAAAgpBDu0qshwm+64+GxJ0osrd6maVjsAAJoNwQ6tbuLQrkqIDtfuw0f14Xf7zS4HAICQQbBDq4t2hOn3v0qTJP1txS55vIbJFQEAEBoIdjDFpIxuio+y66eDZVpCqx0AAM2CYAdTxDjCdPvwn1vtvLTaAQBwxgh2MM3k4d0UGxGmXYWl+nhLvtnlAAAQ9Ah2ME1chF23+VrtdtJqBwDAGSLYwVS3DU9TjCNM3+eX6NNtBWaXAwBAUCPYwVTOKLtuubCbJOmF5TtlGLTaAQDQVAQ7mO72i9IUFW7TtgNuLd9eaHY5AAAELYIdTNcuOlyTMrpJkl5YQasdAABNRbBDQPj9r9IUabfpu70uff7DQbPLAQAgKBHsEBDaxzh007AukqTnP6PVDgCApiDYIWD84eKz5QizamNesb7adcjscgAACDoEOwSMpNgI/dtQWu0AAGgqgh0Cyp2XnKPwMKu+2XNEWT8dNrscAACCCsEOASU5LkI3Du4sqWZdOwAA0HCmBrs5c+Zo8ODBio2NVVJSksaPH68dO3b4nVNeXq7MzEwlJiYqJiZGEyZMUEGB/xMKcnNzNWbMGEVFRSkpKUkzZ85UdXV1a94KmtGdl5wju82iNT8VaS2tdgAANJipwW7VqlXKzMzUmjVrtGzZMlVVVWnkyJEqKyvznTN9+nR9+OGHev/997Vq1Srt379f1157re+4x+PRmDFjVFlZqa+//lpvvvmmFixYoIcfftiMW0IzSI2P1G8H1bTa/W3FLpOrAQAgeFiMABqhfvDgQSUlJWnVqlW6+OKL5XK51KFDBy1cuFDXXXedJOn7779Xr169lJWVpWHDhunjjz/W2LFjtX//fiUnJ0uS5s+fr/vvv18HDx5UeHj4ab/X7XbL6XTK5XIpLi6uRe8RDbP3yFFd+h+fq9pr6H+nZGhg1wSzSwIAwBSNySkBNcbO5XJJkhISav4SX79+vaqqqjRixAjfOT179lSXLl2UlZUlScrKylLfvn19oU6SRo0aJbfbra1bt9b7PRUVFXK73X4bAkundlG6bmAnSdILy2m1AwCgIQIm2Hm9Xk2bNk3Dhw9Xnz59JEn5+fkKDw9XfHy837nJycnKz8/3nVM31NUerz1Wnzlz5sjpdPq2zp07N/PdoDn86dLuslktWvXDQW3MKza7HAAAAl7ABLvMzExt2bJF7777bot/16xZs+RyuXxbXl5ei38nGq9LYpSuGXCWJOlvzJAFAOC0AiLYTZ06VUuWLNHKlSvVqVMn3/6UlBRVVlaquLjY7/yCggKlpKT4zjlxlmzt+9pzTuRwOBQXF+e3ITBl/rq7rBZp+feF2rLPZXY5AAAENFODnWEYmjp1qhYtWqQVK1YoLS3N7/jAgQNlt9u1fPly374dO3YoNzdXGRkZkqSMjAxt3rxZhYWFvnOWLVumuLg4paent86NoMWktY/W1f1rWu1Y1w4AgF9marDLzMzU22+/rYULFyo2Nlb5+fnKz8/XsWPHJElOp1O33367ZsyYoZUrV2r9+vW69dZblZGRoWHDhkmSRo4cqfT0dN18883atGmTPvnkEz344IPKzMyUw+Ew8/bQTDJ/3V0Wi/TptgJt289EFwAATsXUYPfyyy/L5XLp0ksvVceOHX3be++95zvn2Wef1dixYzVhwgRdfPHFSklJ0T//+U/fcZvNpiVLlshmsykjI0M33XSTJk2apMcee8yMW0IL6J4Uo7Hnp0qS/nMlrXYAAJxKQK1jZxbWsQt8PxSUaOSzX0iSPpl2sc5LiTW5IgAAWkfQrmMHnEqP5Fhd2bdmMsx/rmRdOwAA6kOwQ9CY+utzJUlLvtuvXYWlJlcDAEDgIdghaKSnxmlkerIMQ3qRVjsAAE5CsENQufvymla7f23cp5xDZSZXAwBAYCHYIaj0Ocupy3smyUurHQAAJyHYIejcdbzVbtG3+5R7+KjJ1QAAEDgIdgg6/TvH65IeHeTxGrTaAQBQB8EOQal2rN3/btirvCJa7QAAkAh2CFIDu7bTRd3bq9pr6OVVP5pdDgAAAYFgh6BV22r3/jd52l98zORqAAAwH8EOQWtIWoKGnZ2gKo+h+bTaAQBAsENwq221e3ddnvJd5SZXAwCAuQh2CGoZZydqcLd2qvR49V9f0GoHAGjbCHYIahaLRfdc3kOStHBtrgpLaLUDALRdBDsEveHdE3VBl3hVVHv16hc/mV0OAACmIdgh6FksFt9Yu7fX5OpQaYXJFQEAYA6CHULCJT06qF8np45VefTfX+aYXQ4AAKYg2CEk1G21eytrt4rKKk2uCACA1kewQ8i4rGeSeqfG6WilR69/RasdAKDtIdghZNRttVvw9W65jlaZXBEAAK2LYIeQ8pteyeqZEqvSimq9vppWOwBA20KwQ0ixWn9utXt9dY7c5bTaAQDaDoIdQs4VvVN0blKMSsqr9ebq3WaXAwBAqyHYIeRYrRbddbzV7r+/ylFpRbXJFQEA0DoIdghJY/p21NkdouU6VqW3snabXQ4AAK2CYIeQZLNadNdl3SVJ//1ljspotQMAtAEEO4SsceenqltilIrKKvXO2j1mlwMAQIsj2CFkhdmsyvx1TavdK1/8pGOVHpMrAgCgZRHsENLGDzhLnRMidai0UgvX5ZpdDgAALYpgh5Bmt1mVeWlNq938VT+qvIpWOwBA6CLYIeRde0EnnRUfqYMlFXovO8/scgAAaDEEO4S88DCrplx6jiTp5c9/VEU1rXYAgNBEsEObcP2gTkqJi1C+u1zvf7PX7HIAAGgRBDu0CY4wm1+rXWW11+SKAABofgQ7tBk3DO6spFiH9hUf0z830GoHAAg9BDu0GRF2m/54SU2r3Yuf71KVh1Y7AEBoIdihTfm3IV3UPsahvKJjWvTtPrPLAQCgWRHs0KZEhtv0x4vPliS9uHKXqmm1AwCEEIId2pyJw7ooITpcew4f1eJN+80uBwCAZkOwQ5sTFR6mP/yqptXuP1fsksdrmFwRAADNg2CHNunmjK6Kj7Lrp0NlWvIdrXYAgNBAsEObFOMI0+8vSpMk/W3FLnlptQMAhACCHdqsSRd2U1xEmHYVlurjLflmlwMAwBkzNdh98cUXGjdunFJTU2WxWPTBBx/4HTcMQw8//LA6duyoyMhIjRgxQjt37vQ7p6ioSBMnTlRcXJzi4+N1++23q7S0tBXvAsEqLsKu23ytdjtptQMABD1Tg11ZWZn69eunF198sd7j8+bN0wsvvKD58+dr7dq1io6O1qhRo1ReXu47Z+LEidq6dauWLVumJUuW6IsvvtAdd9zRWreAIHfrhWmKdYTp+/wSfbqtwOxyAAA4IxbDMAKimcJisWjRokUaP368pJrWutTUVN1777267777JEkul0vJyclasGCBbrzxRm3fvl3p6enKzs7WoEGDJElLly7VlVdeqb179yo1NbVB3+12u+V0OuVyuRQXF9ci94fA9fSnO/S3FbuU3jFOH919kSwWi9klAQDg05icErBj7HJycpSfn68RI0b49jmdTg0dOlRZWVmSpKysLMXHx/tCnSSNGDFCVqtVa9euPeW1Kyoq5Ha7/Ta0XbcNT1N0uE3bDri1fHuh2eUAANBkARvs8vNrBrMnJyf77U9OTvYdy8/PV1JSkt/xsLAwJSQk+M6pz5w5c+R0On1b586dm7l6BJN20eGadGE3SdILK3YqQBqxAQBotIANdi1p1qxZcrlcvi0vL8/skmCy31+Upki7Td/tdenzHw6aXQ4AAE0SsMEuJSVFklRQ4D+gvaCgwHcsJSVFhYX+XWfV1dUqKirynVMfh8OhuLg4vw1tW2KMQzdndJUkPf8ZrXYAgOAUsMEuLS1NKSkpWr58uW+f2+3W2rVrlZGRIUnKyMhQcXGx1q9f7ztnxYoV8nq9Gjp0aKvXjOD2h1+dLUeYVRvzivXVrkNmlwMAQKOZGuxKS0u1ceNGbdy4UVLNhImNGzcqNzdXFotF06ZN01//+lctXrxYmzdv1qRJk5SamuqbOdurVy9dccUV+sMf/qB169Zp9erVmjp1qm688cYGz4gFanWIdWjiUFrtAADBy9Rg980332jAgAEaMGCAJGnGjBkaMGCAHn74YUnSn//8Z91111264447NHjwYJWWlmrp0qWKiIjwXeOdd95Rz549dfnll+vKK6/URRddpFdeecWU+0Hw++MlZys8zKpv9hxR1k+HzS4HAIBGCZh17MzEOnaoa/a/tujNrD0adnaC3r0jw+xyAABtXEisYweY5c5Lz1G4zao1PxVpLa12AIAgQrADTtDRGanrB3WSJP1txS6TqwEAoOEIdkA9plx6jsKsFn2165DW7ykyuxwAABqEYAfUo1O7KF03sKbV7vnPdirrx8P618Z9yvrxsDzeNj8sFQAQoMLMLgAIVH+6tLv+8U2evth5SF/s/Hldu47OCM0el64r+nQ0sToAAE5Gix1wCtsOuFRf41y+q1xT3t6gpVsOtH5RAAD8AoIdUA+P19CjH26r91ht1nv0w210ywIAAgpdsUA91uUU6YCr/JTHDUkHXOV6aul2DU1LVIdYhzrEOpQY7VB4GP+/BAAwB8EOqEdhyalDXV2vfJGjV77I8dsXH2VXhxiHL+y1r30d41D74z87xDqUEB0um9XSEuU3isdraF1OkQpLypUUG6EhaQkBURcAoPEIdkA9kmIjTn+SpAGd41XtNXSwpEKHSitU7TVUfLRKxUertLOw9Bc/a7VICdE/B8Ca4BfuFwprXzsj7bJYmj9sLd1yQI9+uM2vdZLJIQAQvAh2QD2GpCWoozNC+a5y1TeKziIpxRmh/5lyoa91y+s15DpWpYOlFTpYUuELe7Wva/cfKq3Q4bJKeQ3pUGnN++2nmYdht1n8Wv78WgJPaBmMDrc1KAQu3XJAU97ecNL91U4OefmmCwh3ABBkCHZAPWxWi2aPS9eUtzfIIvmFn9rINHtcul+XpdVqUbvocLWLDleP5NhfvH61x6uiskq/EHiwtEKHSmr3lR8PgZVyHatSlcfQAVf5L477qxVptx0PeuH1dgd3iHWoXVS4Hlm8td7Qahy/x0c/3KbfpKfQLQsAQcRiGEabn9bXmIfrom0JhK7KimqPDpVW1gS9Oi1/fi2Cx38erfQ063c/Mi5dl5yXpMSYcMU6wlqkOxgA8Msak1MIdiLY4ZcF0+SCsopqv+7fE4PfwdJKHSqpUL6rXJ5G/qsfbrMqMSa8ZouuaQVsX+d9Yky42sfU/EyIDpcjzNZCd9k4wfT7A4D6NCan0BULnIbNalHGOYlml9Eg0Y4wRTvC1DUx+hfPy/rxkH736trTXi8p1qGyimqVVXpU6fE2uDtYkmIjwtTheNCrDX6JtWGwThBsHxOuuAi7rC0QtgKhxRUAWhPBDmiDhqQlNmhyyFf3Xyab1aJjlR4dLqvQ4dJKHS6rGft3qPT4++OTQQ7Vee3xGiopr1ZJebV+OlR22nrCrBYlRNcNfjWv64a/umEwwn761kAmhwBoiwh2QBvU2MkhkeE2dQqPUqd2Uae9ttdryF1e5R/+jofBw3XeHz5+3F1erWqvocKSChWWVDSo/uhwmy/4JUY71CHWv1UwIdKuBz/YwuQQAG0OY+zEGDu0XYHQVVlR7VFRWaUv6PkHP/8QeLi0UpUeb7N99zUDUtU71am4SLucdbba9w1dOsZMjCEEQh+TJxqJYIe2LJiCgWEYKqmo9nUB1w1+h0srdKisZnLI7sNlKnA3rPXvl4RZLb6QFxdpV1xEmF8ArC8M1r6OdYS1yLjBugIhmANoeQS7RiLYAaEl68fD+t2ra0573m96JSsy3CZ3eZVcx2o29/GfVZ4z+0+j1SLFRtgVF3lyGPSFxYj6Q2JsRJjCbL/8zOFTjSGsjZKMIQRCB7NiAbRpDX1yyPybB9bbOmkYho5VeeQ+Vu0LfHU39wk/ffuPB8TyKq+8hnz783Ss0fcQ4wirEwLD/IJgbESYXvsqp02MIQymFmUgENBiJ1rsgFBU26Il1T85pCVbtCqqPScEwF8OiHVflzXjItPto8PVPtahGEeYYiLCFBthV4wjTHERYYpxhCk2IkwxdffVOSc2IkyOMKupYwzpagZq0BXbSAQ7IDQFYzCo8nhVUn7qIOg+VqXv9hYr66eiFq/FbrMcD3l2Xzj8ORTajwfBMMXWBkdH3X0/v7afplu5PnQ1Az8j2DUSwQ4IXaHYldfQMYR/Hd9H3RKjVVpRJXd5tUrLq1VaUa2S8iqVVlT79tW+Lz2+9mBpZbWa828GR5hVsRE1Xcixx4NhbTj023e8xTDabtOf//c7HS6rrPd6J66zGMxC8Z9PND/G2AHAccH05JCGaugYwt8N6dKkkOD1GiqrrPaFPXfdQHj89YmhsKS8WiUV1Sotr6oJhxXVvmcXV1R7VVFa84i75mBIOuAq1/gXv1KndlF+obC29bC2O7kmNP7cehgdHhYwwSkYW5QR+GixEy12AIKPmWMIG6ra41VZhUclFT+HvZLyuq9/DocldVoM9x45qrwjjZ9w0lDR4baTxhP+/PN4CPSNQawbEn/uko4JP7PlbOhqRmPQFdtIBDsAwShUW3wa2tWc+etzlBIX4QuFvpbD8mqVVlT5h8eKalVWN9/i1pJ8XcontxDWBMT6upljHGGKCrdp8uvrTvmkFbqacSKCXSMR7AAEq1D8i9PjNXTRUysa/Czjhqqo9vgFwJ9/VvnCX0n5iSHx+PjDOi2Kzfn0k18y/JxEdUmMVozDpujjITIqPEzRDpvvdYyj5n20I0zRjjBF2W0tvjB2Q4Xq/3jU1Vr//hHsGolgBwCBJZC7mk8MiLUhsfR4l7Pv/QljEGt/Hi6rUFlF8y1rc6LocJuiHD+Hvp8DYJiiw38OgTG/cMwXFps4JrEtdDW3ZnAl2DUSwQ4AAk+otvg0tKv5pmFd1CEmQkePT2Qpq6hWWaWn5mfFzxNUao95W+hv80i7zS/o1W0hrA2DdcNhpN2mJ/5vu44crar3ehZJyc4IfTHz1woPa/xSOIGgtYMrwa6RCHYAEJjoam7YvRqGoYpqry/knRj6jlb8/Lq0suZ9fefVDY/VLZUU67BaJLvNqnCbVeFhVtltVtnDLL59dptVdtvx92F19oXV7P/5nJrPOfyOWxV+/LO1+2q+p86+2u+p852+Omw/76vbvV37+6v7Pxx1tcQYSZY7AQCEhFBcrsZmtWj2uHRNeXuDLKq/q3n2uPRGhQKLxaIIu00RdpvaxzjOuMbaoHi0sm4ArFZp3UB4PATWvq49lnO4TDvySxr0PV7j+HI41V6peVbDaRE2q8UX9CyS3OXVpzy3djmedTlFpvyzS7ADAKCVXdGno16+6YKTuppTAqSruW5QTIgOb9RnG9rV/OrNA9Wvc7wqPV5VeQxVebyqrPaq6vj72tc1x49v1Ybv/c/HjePHTnh/4jnVP1+n8qTvq31ds//E1kqP15DHa6i8quETZwpL6m/Ra2kEOwAATHBFn476TXpKyHU1N3QB7ct6JQfsvXq9hqq8JwTM4z+/2X1Ef/7f7057jaTYiFao9GQEOwAATEJXc2CyWi1yWG1yhEk6oWe7a2K0nv3sh9MG1yFpCa1Q6cmCczoKAAAIWLVdzSlO/1arFGdE0C91UhtcpZ+Daq1ACK7MihWzYgEAaAmhOKu5FuvYBTCCHQAAaKxAfPIEY+wAAACaIBDHSDLGDgAAIEQQ7AAAAEIEwQ4AACBEEOwAAABCRMgEuxdffFHdunVTRESEhg4dqnXr1pldEgAAQKsKiWD33nvvacaMGZo9e7Y2bNigfv36adSoUSosLDS7NAAAgFYTEsHumWee0R/+8AfdeuutSk9P1/z58xUVFaXXX3/d7NIAAABaTdAHu8rKSq1fv14jRozw7bNarRoxYoSysrLq/UxFRYXcbrffBgAAEOyCPtgdOnRIHo9HycnJfvuTk5OVn59f72fmzJkjp9Pp2zp37twapQIAALSooA92TTFr1iy5XC7flpeXZ3ZJAAAAZyzoHynWvn172Ww2FRQU+O0vKChQSkpKvZ9xOBxyOBytUR4AAECrCfpgFx4eroEDB2r58uUaP368JMnr9Wr58uWaOnVqg65hGIYkMdYOAAAEnNp8UptXfknQBztJmjFjhiZPnqxBgwZpyJAheu6551RWVqZbb721QZ8vKSmRJMbaAQCAgFVSUiKn0/mL54REsLvhhht08OBBPfzww8rPz1f//v21dOnSkyZUnEpqaqry8vIUGxsri8XSwtWGJrfbrc6dOysvL09xcXFml4NG4vcX3Pj9BTd+f8GtNX5/hmGopKREqamppz3XYjSkXQ84DbfbLafTKZfLxX+YghC/v+DG7y+48fsLboH2+2uTs2IBAABCEcEOAAAgRBDs0CwcDodmz57NMjJBit9fcOP3F9z4/QW3QPv9McYOAAAgRNBiBwAAECIIdgAAACGCYAcAABAiCHZosjlz5mjw4MGKjY1VUlKSxo8frx07dphdFppo7ty5slgsmjZtmtmloIH27dunm266SYmJiYqMjFTfvn31zTffmF0WGsDj8eihhx5SWlqaIiMjdc455+jxxx9v0COjYI4vvvhC48aNU2pqqiwWiz744AO/44Zh6OGHH1bHjh0VGRmpESNGaOfOna1eJ8EOTbZq1SplZmZqzZo1WrZsmaqqqjRy5EiVlZWZXRoaKTs7W//1X/+l888/3+xS0EBHjhzR8OHDZbfb9fHHH2vbtm16+umn1a5dO7NLQwM89dRTevnll/Wf//mf2r59u5566inNmzdPf/vb38wuDadQVlamfv366cUXX6z3+Lx58/TCCy9o/vz5Wrt2raKjozVq1CiVl5e3ap3MikWzOXjwoJKSkrRq1SpdfPHFZpeDBiotLdUFF1ygl156SX/961/Vv39/Pffcc2aXhdN44IEHtHr1an355Zdml4ImGDt2rJKTk/Xaa6/59k2YMEGRkZF6++23TawMDWGxWLRo0SKNHz9eUk1rXWpqqu69917dd999kiSXy6Xk5GQtWLBAN954Y6vVRosdmo3L5ZIkJSQkmFwJGiMzM1NjxozRiBEjzC4FjbB48WINGjRI119/vZKSkjRgwAC9+uqrZpeFBrrwwgu1fPly/fDDD5KkTZs26auvvtLo0aNNrgxNkZOTo/z8fL//jjqdTg0dOlRZWVmtWktYq34bQpbX69W0adM0fPhw9enTx+xy0EDvvvuuNmzYoOzsbLNLQSP99NNPevnllzVjxgz95S9/UXZ2tu6++26Fh4dr8uTJZpeH03jggQfkdrvVs2dP2Ww2eTwePfHEE5o4caLZpaEJ8vPzJUnJycl++5OTk33HWgvBDs0iMzNTW7Zs0VdffWV2KWigvLw83XPPPVq2bJkiIiLMLgeN5PV6NWjQID355JOSpAEDBmjLli2aP38+wS4I/OMf/9A777yjhQsXqnfv3tq4caOmTZum1NRUfn84I3TF4oxNnTpVS5Ys0cqVK9WpUyezy0EDrV+/XoWFhbrgggsUFhamsLAwrVq1Si+88ILCwsLk8XjMLhG/oGPHjkpPT/fb16tXL+Xm5ppUERpj5syZeuCBB3TjjTeqb9++uvnmmzV9+nTNmTPH7NLQBCkpKZKkgoICv/0FBQW+Y62FYIcmMwxDU6dO1aJFi7RixQqlpaWZXRIa4fLLL9fmzZu1ceNG3zZo0CBNnDhRGzdulM1mM7tE/ILhw4eftLzQDz/8oK5du5pUERrj6NGjslr9/wq22Wzyer0mVYQzkZaWppSUFC1fvty3z+12a+3atcrIyGjVWuiKRZNlZmZq4cKF+te//qXY2FjfOAKn06nIyEiTq8PpxMbGnjQeMjo6WomJiYyTDALTp0/XhRdeqCeffFK//e1vtW7dOr3yyit65ZVXzC4NDTBu3Dg98cQT6tKli3r37q1vv/1WzzzzjG677TazS8MplJaWateuXb73OTk52rhxoxISEtSlSxdNmzZNf/3rX3XuuecqLS1NDz30kFJTU30zZ1uNATSRpHq3N954w+zS0ESXXHKJcc8995hdBhroww8/NPr06WM4HA6jZ8+exiuvvGJ2SWggt9tt3HPPPUaXLl2MiIgI4+yzzzb+/d//3aioqDC7NJzCypUr6/07b/LkyYZhGIbX6zUeeughIzk52XA4HMbll19u7Nixo9XrZB07AACAEMEYOwAAgBBBsAMAAAgRBDsAAIAQQbADAAAIEQQ7AACAEEGwAwAACBEEOwAAgBBBsAMAAAgRBDsAIWf37t2yWCzauHGj2aX4fP/99xo2bJgiIiLUv3//Rn8+EO8JQOAh2AFodrfccossFovmzp3rt/+DDz6QxWIxqSpzzZ49W9HR0dqxY4ffg8LNsmDBAsXHx5tdBoBmRrAD0CIiIiL01FNP6ciRI2aX0mwqKyub/Nkff/xRF110kbp27arExMRmrMpcHo9HXq/X7DIAHEewA9AiRowYoZSUFM2ZM+eU5zzyyCMndUs+99xz6tatm+/9LbfcovHjx+vJJ59UcnKy4uPj9dhjj6m6ulozZ85UQkKCOnXqpDfeeOOk63///fe68MILFRERoT59+mjVqlV+x7ds2aLRo0crJiZGycnJuvnmm3Xo0CHf8UsvvVRTp07VtGnT1L59e40aNare+/B6vXrsscfUqVMnORwO9e/fX0uXLvUdt1gsWr9+vR577DFZLBY98sgjp7zOvHnz1L17dzkcDnXp0kVPPPFEvefW1+J2Yovopk2b9Otf/1qxsbGKi4vTwIED9c033+jzzz/XrbfeKpfLJYvF4ldTRUWF7rvvPp111lmKjo7W0KFD9fnnn5/0vYsXL1Z6erocDodyc3P1+eefa8iQIYqOjlZ8fLyGDx+uPXv21Fs7gJZDsAPQImw2m5588kn97W9/0969e8/oWitWrND+/fv1xRdf6JlnntHs2bM1duxYtWvXTmvXrtWdd96pP/7xjyd9z8yZM3Xvvffq22+/VUZGhsaNG6fDhw9LkoqLi3XZZZdpwIAB+uabb7R06VIVFBTot7/9rd813nzzTYWHh2v16tWaP39+vfU9//zzevrpp/X//t//03fffadRo0bpqquu0s6dOyVJBw4cUO/evXXvvffqwIEDuu++++q9zqxZszR37lw99NBD2rZtmxYuXKjk5OQm/7lNnDhRnTp1UnZ2ttavX68HHnhAdrtdF154oZ577jnFxcXpwIEDfjVNnTpVWVlZevfdd/Xdd9/p+uuv1xVXXOG7F0k6evSonnrqKf33f/+3tm7dqoSEBI0fP16XXHKJvvvuO2VlZemOO+5os93ugKkMAGhmkydPNq6++mrDMAxj2LBhxm233WYYhmEsWrTIqPufndmzZxv9+vXz++yzzz5rdO3a1e9aXbt2NTwej2/feeedZ/zqV7/yva+urjaio6ONv//974ZhGEZOTo4hyZg7d67vnKqqKqNTp07GU089ZRiGYTz++OPGyJEj/b47Ly/PkGTs2LHDMAzDuOSSS4wBAwac9n5TU1ONJ554wm/f4MGDjT/96U++9/369TNmz559ymu43W7D4XAYr776ar3Ha+/p22+/NQzDMN544w3D6XT6nXPin29sbKyxYMGCeq9X3+f37Nlj2Gw2Y9++fX77L7/8cmPWrFm+z0kyNm7c6Dt++PBhQ5Lx+eefn/L+ALQOWuwAtKinnnpKb775prZv397ka/Tu3VtW68//uUpOTlbfvn197202mxITE1VYWOj3uYyMDN/rsLAwDRo0yFfHpk2btHLlSsXExPi2nj17SqoZD1dr4MCBv1ib2+3W/v37NXz4cL/9w4cPb9Q9b9++XRUVFbr88ssb/JnTmTFjhn7/+99rxIgRmjt3rt991Wfz5s3yeDzq0aOH35/LqlWr/D4bHh6u888/3/c+ISFBt9xyi0aNGqVx48bp+eef14EDB5rtPgA0HMEOQIu6+OKLNWrUKM2aNeukY1arVYZh+O2rqqo66Ty73e733mKx1LuvMYP4S0tLNW7cOG3cuNFv27lzpy6++GLfedHR0Q2+5pmIjIxs1PkN+bN75JFHtHXrVo0ZM0YrVqxQenq6Fi1adMprlpaWymazaf369X5/Jtu3b9fzzz/vV+uJ3axvvPGGsrKydOGFF+q9995Tjx49tGbNmkbdE4AzR7AD0OLmzp2rDz/8UFlZWX77O3TooPz8fL+A0pzrtNUNFtXV1Vq/fr169eolSbrgggu0detWdevWTd27d/fbGhPm4uLilJqaqtWrV/vtX716tdLT0xt8nXPPPVeRkZENXgqlQ4cOKikpUVlZmW9ffX92PXr00PTp0/Xpp5/q2muv9U0yCQ8Pl8fj8Tt3wIAB8ng8KiwsPOnPJCUl5bQ1DRgwQLNmzdLXX3+tPn36aOHChQ26FwDNh2AHoMX17dtXEydO1AsvvOC3/9JLL9XBgwc1b948/fjjj3rxxRf18ccfN9v3vvjii1q0aJG+//57ZWZm6siRI7rtttskSZmZmSoqKtLvfvc7ZWdn68cff9Qnn3yiW2+99aTAczozZ87UU089pffee087duzQAw88oI0bN+qee+5p8DUiIiJ0//33689//rPeeust/fjjj1qzZo1ee+21es8fOnSooqKi9Je//EU//vijFi5cqAULFviOHzt2TFOnTtXnn3+uPXv2aPXq1crOzvYF227duqm0tFTLly/XoUOHdPToUfXo0UMTJ07UpEmT9M9//lM5OTlat26d5syZo48++uiUtefk5GjWrFnKysrSnj179Omnn2rnzp2+7wLQegh2AFrFY489dlJXaa9evfTSSy/pxRdfVL9+/bRu3bpTzhhtirlz52ru3Lnq16+fvvrqKy1evFjt27eXJF8rm8fj0ciRI9W3b19NmzZN8fHxfuP5GuLuu+/WjBkzdO+996pv375aunSpFi9erHPPPbdR13nooYd077336uGHH1avXr10ww03nDRusFZCQoLefvtt/d///Z/69u2rv//9737LqNhsNh0+fFiTJk1Sjx499Nvf/lajR4/Wo48+Kkm68MILdeedd+qGG25Qhw4dNG/ePEk1XaqTJk3Svffeq/POO0/jx49Xdna2unTpcsq6o6Ki9P3332vChAnq0aOH7rjjDmVmZuqPf/xjo+4fwJmzGCcO0gAAAEBQosUOAAAgRBDsAAAAQgTBDgAAIEQQ7AAAAEIEwQ4AACBEEOwAAABCBMEOAAAgRBDsAAAAQgTBDgAAIEQQ7AAAAEIEwQ4AACBEEOwAAABCxP8HzgpsQ80z1pQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "distortions = []\n",
        "for i in range(1, 11):\n",
        "    km = KMeans(n_clusters=i,\n",
        "                init='k-means++',\n",
        "                n_init=10,\n",
        "                max_iter=300,\n",
        "                random_state=0)\n",
        "    km.fit(X)\n",
        "    distortions.append(km.inertia_)\n",
        "plt.plot(range(1, 11), distortions, marker='o')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('Distortion')\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/11_03.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tgkiKdjnlsd-"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9bk-zc6lsd_"
      },
      "source": [
        "## 실루엣 그래프로 군집 품질을 정량화"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:48.581813Z",
          "iopub.status.busy": "2021-10-23T10:43:48.580611Z",
          "iopub.status.idle": "2021-10-23T10:43:49.457936Z",
          "shell.execute_reply": "2021-10-23T10:43:49.456786Z"
        },
        "id": "yfExDwu-lsd_",
        "outputId": "9cc4708f-cac7-4be3-8a04-3f9b4a1d1305"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "from matplotlib import cm\n",
        "from sklearn.metrics import silhouette_samples\n",
        "\n",
        "km = KMeans(n_clusters=3,\n",
        "            init='k-means++',\n",
        "            n_init=10,\n",
        "            max_iter=300,\n",
        "            tol=1e-04,\n",
        "            random_state=0)\n",
        "y_km = km.fit_predict(X)\n",
        "\n",
        "cluster_labels = np.unique(y_km)\n",
        "n_clusters = cluster_labels.shape[0]\n",
        "silhouette_vals = silhouette_samples(X, y_km, metric='euclidean')\n",
        "y_ax_lower, y_ax_upper = 0, 0\n",
        "yticks = []\n",
        "for i, c in enumerate(cluster_labels):\n",
        "    c_silhouette_vals = silhouette_vals[y_km == c]\n",
        "    c_silhouette_vals.sort()\n",
        "    y_ax_upper += len(c_silhouette_vals)\n",
        "    color = cm.jet(float(i) / n_clusters)\n",
        "    plt.barh(range(y_ax_lower, y_ax_upper), c_silhouette_vals, height=1.0,\n",
        "             edgecolor='none', color=color)\n",
        "\n",
        "    yticks.append((y_ax_lower + y_ax_upper) / 2.)\n",
        "    y_ax_lower += len(c_silhouette_vals)\n",
        "\n",
        "silhouette_avg = np.mean(silhouette_vals)\n",
        "plt.axvline(silhouette_avg, color=\"red\", linestyle=\"--\")\n",
        "\n",
        "plt.yticks(yticks, cluster_labels + 1)\n",
        "plt.ylabel('Cluster')\n",
        "plt.xlabel('Silhouette coefficient')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/11_04.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8jLodeOrlsd_"
      },
      "source": [
        "잘못된 클러스터링:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:49.481805Z",
          "iopub.status.busy": "2021-10-23T10:43:49.480893Z",
          "iopub.status.idle": "2021-10-23T10:43:50.074506Z",
          "shell.execute_reply": "2021-10-23T10:43:50.074981Z"
        },
        "id": "1xzn7tXVlsd_",
        "outputId": "95d75fdf-78bb-47d5-f9bc-f63c6865ab9e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "km = KMeans(n_clusters=2,\n",
        "            init='k-means++',\n",
        "            n_init=10,\n",
        "            max_iter=300,\n",
        "            tol=1e-04,\n",
        "            random_state=0)\n",
        "y_km = km.fit_predict(X)\n",
        "\n",
        "plt.scatter(X[y_km == 0, 0],\n",
        "            X[y_km == 0, 1],\n",
        "            s=50,\n",
        "            c='lightgreen',\n",
        "            edgecolor='black',\n",
        "            marker='s',\n",
        "            label='Cluster 1')\n",
        "plt.scatter(X[y_km == 1, 0],\n",
        "            X[y_km == 1, 1],\n",
        "            s=50,\n",
        "            c='orange',\n",
        "            edgecolor='black',\n",
        "            marker='o',\n",
        "            label='Cluster 2')\n",
        "\n",
        "plt.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1],\n",
        "            s=250, marker='*', c='red', label='Centroids')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/11_05.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:50.079006Z",
          "iopub.status.busy": "2021-10-23T10:43:50.078025Z",
          "iopub.status.idle": "2021-10-23T10:43:50.520424Z",
          "shell.execute_reply": "2021-10-23T10:43:50.519486Z"
        },
        "id": "S32hzlcSlseA",
        "outputId": "2201d27f-509f-4b8a-ef2b-d7824b7cf764"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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+RrcujrKypBUrDo0Bn4gp2BUWFh714sRNTU0xFwQAQIelp0vnnON1FUDCxRTsNmzY0OrrhoYGbdiwQY8++qgeeIC/OAEAALwQU7A76aSTjth22mmnqV+/fnr44Yf1ve99r8OFAQAQs4YG6Te/aR5ff72UmeltPUCCxBTsvsrQoUO1du3aeL4lAADRq6+XbryxeTx1KsEOvhFTsPvyRYjNTOXl5Zo9e7a+9a1vxaUwAEglLHwAkAxiCnbdu3c/YvGEmamgoEALFy6MS2EAAACITkzBbkXLEvJ/SktLU69evfTNb35TGRlxnd0FAABAO8WUwsaOHRvvOgAgZTENCyBZtDvY/fGPf2z3m15yySUxFQMAAIDYtTvYXXbZZe3aLxAIcIFiAL5Apw5Asml3sAuHw51ZBwAA8RMKSS33NA+FvK0FSKC0aHZ+4403NHz48CMudyJJBw4c0PHHH6+33norbsUBABCTjAzpoouaHyzqg49EFewee+wxTZs2Td26dTviuby8PP34xz/Wo48+GrfiACBZMQ0LIBlFFezef/99XXDBBV/5/IQJE7Ru3boOFwUAQIc0NEjz5zc/Ghq8rgZImKj607t371bmUW7LkpGRoT179nS4KADwEt04B9TXS9dc0zy+4gpuKQbfiKpjd+yxx+qDDz74yuc3btyo/Pz8DhcFAACA6EUV7CZNmqS7775btbW1RzxXU1Oj4uJiffe7341bcQAAAGi/qKZi77rrLi1evFhDhgzRjTfeqKFDh0qStmzZopKSEjU1NenOO+/slEIBoLMw9QrAFVEFuz59+ujtt9/WDTfcoFmzZsnMJDVflHjixIkqKSlRnz59OqVQAAAAHF3UF/cZOHCgli9frn379mnbtm0yM33rW99Sjx49OqM+AOhUdOsAuCTmqzb26NFDI0eOjGctAAAA6AAuxw3Al+jUOS4Ukp577tAY8AmCHQDAPRkZzdevA3wmqsudAAAAIHnRsQPgJKZafa6xUVqypHl8+eXNHTzAB/hNBwC4p65Omjy5eVxVRbCDb/CbDsAZdOkA+B3n2AEAADiCYAcAAOAIpmIBpCymXgGgNTp2AAAAjqBjByAp0H0DgI4j2AEA3BMMSs88c2gM+ATBDgDgnsxMaepUr6sAEo5gByChmHIFgM5DsAMAuKexUXrllebxxInceQK+wW86gE5BZw6eqquTvvvd5jG3FIOPcLkTAAAARxDsAAAAHEFvGkCbmEoFgNRDxw4AAMARdOwAH6D7BgD+QMcOAADAEXTsAADuCQalX/3q0BjwCYIdkEKYUgXaKTNTKiryugog4ZiKBQAAcAQdOyCJ0aEDYtTUJL31VvP4rLOk9HRv6wEShGAHAHBPba107rnN46oqqUsXb+sBEoSpWAAAAEfQsQM6EVOpAIBEomMHAADgCDp2PkdHCQAAd9CxAwAAcATBDgAAwBFMxbbTz3Un05YAkCoyM6WHHjo0BnyCYAcAcE8wKN12m9dVAAlHsGune/WAAoF7vC4DAAAkEbNir0tohWAHAHBOmsI6ReWSpPXKV5hTyuETBDsAgHOy1Ki1ekqS1EU/U7WCHlcEFyVbt05iVSwAAIAzCHYAAACOYCoWAAAgCsk4BduCjh0AAIAjCHYAAADtlMzdOolgBwAA4AzOsQMAOKdBaZqtsZEx4BcEOwCAcxqUoXt0rtdlwDHJPg0rMRULAADgDDp2AADnBBTWcdorSdqsY2T0MRCjVOjSHY5gBwBwTrYa9aGelMQtxeAv/AkDAADgCIIdAABAG1JtGlYi2AEAADiDYAcAAPAlqditkwh2AAAAziDYAQAAOILLnQAAnNOgND2s0ZEx4BcEOwCAcxqUods1wesygITjzxgAAIDDpOrCCYmOHQDAQQGFNUAHJEmfKY9bisE3CHYAAOdkq1Gf6nFJ3FIM/sKfMAAAAP+UytOwEsEOAADAGQQ7AAAARxDsAAAAHEGwAwAAcATBDgAAwBFc7gQA4JxGpalEIyNjwC8IdgAA59QrQzfqIq/LABKOP2MAAAAcQccOAOAg0zGqliTtVY6kgLflIKml+kWJD0ewAwA4J0cN2qOHJXFLMfgLU7EAAACOINgBAAA4gmAHAADgCM6xAwAAvuPSgonD0bEDAABwBMEOAADAEUzFAgCc06g0zddJkTHQwtUp2BYEOwCAc+qVoWt0uddlAAlHsAMAAM5zvVPXgmAHAHCQKUcNkqRqZYpbisEvOPEAAOCcHDXooObooOZEAh7gBwQ7AAAARxDsAAAAHEGwAwAATvPLwgmJYAcAAOAMgh0AAIAjCHYAAACO4Dp2AADnNCmg5zU8Mgb8gmAHAHBOnTI1WZO9LgMe89OiiRZMxQIAADjC02D35ptv6uKLL1a/fv0UCAT04osvelkOAABASvM02B08eFAnnXSSSkpKvCwDAOCYHNXLNFum2cpRvdflAAnj6Tl2F154oS688EIvSwAAAHBGSi2eqKurU11dXeTriooKD6sBAADJyo8LJ6QUWzwxd+5c5eXlRR4FBQVelwQAAJA0UirYzZo1SwcOHIg8duzY4XVJAAAASSOlpmJDoZBCoZDXZQAAgCTm12lYKcU6dgAAAPhqnnbsqqqqtG3btsjXZWVlKi0tVc+ePTVgwAAPKwMApLImBbRM34qM4Q9+7tS18DTY/eUvf9G5554b+XrGjBmSpClTpmj+/PkeVQUASHV1ytR39QOvywASztNgd84558jMvCwBAADAGZxjBwAAUh7TsM0IdgAA5+SoXlV6QFV6gFuKwVdS6nInAAC0Vxc1eF0COhlduiPRsQMAAHAEHTsAAJAy6NIdHR07AAAARxDsAAAAHEGwAwAAcATn2AEAnBNWQCs1MDIG/IJgBwBwTq0yda6u8boMxBkLJ74eU7EAAACOINgBAAA4gqlYAIBzclSvT/WYJKlQt6haQW8LQrsw1dpxBDsAgJN6qdrrEoCEYyoWAAB4jm5dfBDsAAAAHEGwAwAAcATBDgAAeIpp2Pgh2AEAADiCVbEAAOeEFdBa9YuMkXzo0nUOgh0AwDm1ytTput7rMoCEYyoWAADAEXTsAABAp2C6NfHo2AEAnJOtepXplyrTL5Wteq/LARKGjh0AwDkBSYU6EBmj89GdSw507AAAABxBsAMAAHAEwQ4AAHQI07DJg2AHAADgCBZPAACAqNChS14EOwCAc0zSh+oVGQN+QbADADinRkGNUJHXZQAJR7ADAABfi+nX1MDiCQAAAEfQsQMAOCdb9VqrpyRJIzVNNQp6XFFqokuXegh2AADnBCQdrz2RMeAXTMUCAAA4go4dAABg2tURdOwAAAAcQccOAACfoCvnPjp2AAAAjqBjBwBwjkn6VHmRMejW+QXBDgDgnBoF9Q3d6nUZQMIxFQsAAOAIOnYAADiK6Vf/oWMHAHBOlhr0nn6j9/QbZanB63KAhKFjBwBwTppMI7UzMvYbOnX+RccOAADAEQQ7AAAARzAVCwBACmGaFUdDxw4AAMARdOwAAEgCdOIQDwQ7AICT9ijH6xKAhCPYAQCcU62geut2r8sAEo5gBwBABzCFimTC4gkAAABH0LGLAn+VAUCKqKmRLrywefzSS1J2trf1AAlCsAMAuCccllatOjQGfIKpWAAAAEcQ7AAAABxBsAMAAHAEwQ4AAMARBDsAAABHsCoWAOCmHG4pBv8h2AEA3NOli3TwoNdVAAnHVCwAAIAjCHYAAACOINgBANxTWytddFHzo7bW62qAhOEcOwCAe5qapOXLD40Bn6BjBwAA4AiCHQAAgCMIdgAAAI4g2AEAADiCYAcAAOCIlF4Va2aSpIqKCo8rAQAklcPvOlFRwcpYpLSWnNOSe44mpYNdZWWlJKmgoMDjSgAASatfP68rAOKisrJSeXl5R90nYO2Jf0kqHA5r586dys3NVSAQ6NTPqqioUEFBgXbs2KFu3bp16mchOXDM/Ydj7k8cd/9JtWNuZqqsrFS/fv2Ulnb0s+hSumOXlpam/v37J/Qzu3XrlhK/BIgfjrn/cMz9iePuP6l0zL+uU9eCxRMAAACOINgBAAA4gmDXTqFQSMXFxQqFQl6XggThmPsPx9yfOO7+4/IxT+nFEwAAADiEjh0AAIAjCHYAAACOINgBAAA4gmB3mJKSEhUWFiorK0tnnHGG3nvvvaPu//zzz2vYsGHKysrSCSecoOXLlyeoUsRLNMf8qaee0llnnaUePXqoR48eGjdu3Nf+jiD5RPvvvMXChQsVCAR02WWXdW6B6BTRHvf9+/erqKhI+fn5CoVCGjJkCP+NTzHRHvPHHntMQ4cOVXZ2tgoKCnTrrbeqtrY2QdXGkcHMzBYuXGjBYNB++9vf2ocffmjTpk2z7t272+7du9vcf82aNZaenm4PPfSQffTRR3bXXXdZZmambdq0KcGVI1bRHvOrrrrKSkpKbMOGDbZ582abOnWq5eXl2eeff57gyhGraI95i7KyMjv22GPtrLPOsksvvTQxxSJuoj3udXV1dtppp9mkSZNs9erVVlZWZitXrrTS0tIEV45YRXvMn332WQuFQvbss89aWVmZvfLKK5afn2+33nprgivvOILdP51++ulWVFQU+bqpqcn69etnc+fObXP/yZMn20UXXdRq2xlnnGE//vGPO7VOxE+0x/zLGhsbLTc31xYsWNBZJSLOYjnmjY2NNnr0aHv66adtypQpBLsUFO1xnzdvng0aNMjq6+sTVSLiLNpjXlRUZOedd16rbTNmzLAxY8Z0ap2dgalYSfX19Vq3bp3GjRsX2ZaWlqZx48bpz3/+c5uv+fOf/9xqf0maOHHiV+6P5BLLMf+y6upqNTQ0qGfPnp1VJuIo1mN+7733qnfv3vrRj36UiDIRZ7Ec9z/+8Y8aNWqUioqK1KdPH40YMUJz5sxRU1NTospGB8RyzEePHq1169ZFpms/+eQTLV++XJMmTUpIzfGU0veKjZe9e/eqqalJffr0abW9T58+2rJlS5uv2bVrV5v779q1q9PqRPzEcsy/7Kc//an69et3RMBHcorlmK9evVr/+Z//qdLS0gRUiM4Qy3H/5JNP9MYbb+gHP/iBli9frm3btmn69OlqaGhQcXFxIspGB8RyzK+66irt3btXZ555psxMjY2N+slPfqKf/exniSg5rujYATF48MEHtXDhQi1ZskRZWVlel4NOUFlZqauvvlpPPfWUjjnmGK/LQQKFw2H17t1bv/nNb3TqqafqX//1X3XnnXfq17/+tdeloZOsXLlSc+bM0ZNPPqn169dr8eLFWrZsme677z6vS4saHTtJxxxzjNLT07V79+5W23fv3q2+ffu2+Zq+fftGtT+SSyzHvMUjjzyiBx98UK+99ppOPPHEziwTcRTtMd++fbs+/fRTXXzxxZFt4XBYkpSRkaGtW7dq8ODBnVs0OiyWf+v5+fnKzMxUenp6ZNtxxx2nXbt2qb6+XsFgsFNrRsfEcszvvvtuXX311bruuuskSSeccIIOHjyo66+/XnfeeafS0lKnD5Y6lXaiYDCoU089Va+//npkWzgc1uuvv65Ro0a1+ZpRo0a12l+SXn311a/cH8kllmMuSQ899JDuu+8+vfzyyzrttNMSUSriJNpjPmzYMG3atEmlpaWRxyWXXKJzzz1XpaWlKigoSGT5iFEs/9bHjBmjbdu2RYK8JH388cfKz88n1KWAWI55dXX1EeGtJdhbqt151evVG8li4cKFFgqFbP78+fbRRx/Z9ddfb927d7ddu3aZmdnVV19td9xxR2T/NWvWWEZGhj3yyCO2efNmKy4u5nInKSbaY/7ggw9aMBi0F154wcrLyyOPyspKr74FRCnaY/5lrIpNTdEe988++8xyc3PtxhtvtK1bt9rSpUutd+/edv/993v1LSBK0R7z4uJiy83Ntf/93/+1Tz75xP70pz/Z4MGDbfLkyV59CzEj2B3miSeesAEDBlgwGLTTTz/d3nnnnchzY8eOtSlTprTa/7nnnrMhQ4ZYMBi0448/3pYtW5bgitFR0RzzgQMHmqQjHsXFxYkvHDGL9t/54Qh2qSva4/7222/bGWecYaFQyAYNGmQPPPCANTY2JrhqdEQ0x7yhocFmz55tgwcPtqysLCsoKLDp06fbvn37El94BwXMUq3HCAAAgLZwjh0AAIAjCHYAAACOINgBAAA4gmAHAADgCIIdAACAIwh2AAAAjiDYAQAAOIJgBwAA4AiCHYCoBQIBvfjii5KkTz/9VIFAQKWlpZKklStXKhAIaP/+/Z7V55I1a9bohBNOUGZmpi677LI2t0X7Mz/nnHN0yy23dFrNALxDsAPQyp49e3TDDTdowIABCoVC6tu3ryZOnKg1a9ZE9ikvL9eFF17oYZXt01aASbXgOWPGDJ188skqKyvT/Pnz29w2evRolZeXKy8vr13vuXjxYt13331xrXPq1KmR4AnAOxleFwAguXz/+99XfX29FixYoEGDBmn37t16/fXX9fe//z2yT9++fT2s0F+2b9+un/zkJ+rfv/9Rt0VzTHr27BnXGgEkEa9vVgsgeezbt88k2cqVK4+6nyRbsmSJmZmVlZWZJNuwYYOZma1YscIk2WuvvWannnqqZWdn26hRo2zLli2t3uPJJ5+0QYMGWWZmpg0ZMsR+97vfRZ778nseXtuKFSsi2zZt2mQXXHCBdenSxXr37m0//OEPbc+ePWZmNmXKFJPU6tHyvoc/Wm4E3tTUZHPmzLHCwkLLysqyE0880Z5//vmj/hxqa2vt9ttvt/79+1swGLTBgwfb008/HXl+5cqVNnLkSAsGg9a3b1/76U9/ag0NDZHnj/aZbdX6zDPPtLmt5Wd++A3LV69ebWPHjrXs7Gzr3r27TZgwwf7xj3+YWfMN0G+++eZW38e///u/W79+/SwnJ8dOP/30Vj/nZ555xvLy8uzll1+2YcOGWZcuXWzixIm2c+dOMzMrLi4+oq7DXw8gcQh2ACIaGhqsa9eudsstt1htbe1X7teeYHfGGWfYypUr7cMPP7SzzjrLRo8eHXn94sWLLTMz00pKSmzr1q32H//xH5aenm5vvPFGm+9pdmSw27dvn/Xq1ctmzZplmzdvtvXr19v48ePt3HPPNTOz/fv326hRo2zatGlWXl5u5eXl1tjYaIsWLTJJtnXrVisvL7f9+/ebmdn9999vw4YNs5dfftm2b99uzzzzjIVCoaOG3MmTJ1tBQYEtXrzYtm/fbq+99potXLjQzMw+//xzy8nJsenTp9vmzZttyZIldswxx1hxcXHk9Uf7zMbGRisvL7du3brZY489ZuXl5VZVVXXEturq6iOC3YYNGywUCtkNN9xgpaWl9sEHH9gTTzwRCb1fDnbXXXedjR492t58803btm2bPfzwwxYKhezjjz82s+Zgl5mZaePGjbO1a9faunXr7LjjjrOrrrrKzMwqKytt8uTJdsEFF0R+1nV1dV/5cwPQeQh2AFp54YUXrEePHpaVlWWjR4+2WbNm2fvvv99qn/Z27FosW7bMJFlNTY2ZmY0ePdqmTZvW6j2vuOIKmzRpUpvvaXZksLvvvvtswoQJrd5jx44dkdBmdmSAOby+w7tbtbW1lpOTY2+//XarfX/0ox/ZlVde2ebPaevWrSbJXn311Taf/9nPfmZDhw61cDgc2VZSUmJdu3a1pqamdn9mXl6ePfPMM632+fK2L39PV155pY0ZM6bNusxa/1z++te/Wnp6uv3tb39rtc/5559vs2bNMjOLdAq3bdvW6nvp06dP5OspU6bYpZde+pWfCSAxOMcOQCvf//73ddFFF+mtt97SO++8o5deekkPPfSQnn76aU2dOrXd73PiiSdGxvn5+ZKkL774QgMGDNDmzZt1/fXXt9p/zJgxevzxx9v9/u+//75WrFihrl27HvHc9u3bNWTIkHa/17Zt21RdXa3x48e32l5fX69vf/vbbb6mtLRU6enpGjt2bJvPb968WaNGjVIgEIhsGzNmjKqqqvT555+rsrIy6s9sr9LSUl1xxRXt2nfTpk1qamo64udVV1enf/mXf4l8nZOTo8GDB0e+zs/P1xdffNGhOgHEH8EOwBGysrI0fvx4jR8/Xnfffbeuu+46FRcXRxXsMjMzI+OWcBMOh9v12rS05gX7ZhbZ1tDQ0GqfqqoqXXzxxfrFL35xxOtbgmR7VVVVSZKWLVumY489ttVzoVCozddkZ2dH9Rnx+Mz2iqa2qqoqpaena926dUpPT2/13OGh+fDjKTUf08OPD4DkQLAD8LWGDx8euW5dPBx33HFas2aNpkyZEtm2Zs0aDR8+XJLUq1cvSc2XVWnpXrVcJ6/FKaecokWLFqmwsFAZGW3/pywYDKqpqemIbZJabR8+fLhCoZA+++yzr+zAfdkJJ5ygcDisVatWady4cW1+j4sWLZKZRYLtmjVrlJubq/79+6tHjx5Rf2Z7nXjiiXr99dd1zz33fO2+3/72t9XU1KQvvvhCZ511Vsyf2dbPGkDicR07ABF///vfdd555+m///u/tXHjRpWVlen555/XQw89pEsvvTRun3Pbbbdp/vz5mjdvnv7v//5Pjz76qBYvXqyZM2dKau44fec739GDDz6ozZs3a9WqVbrrrrtavUdRUZH+8Y9/6Morr9TatWu1fft2vfLKK7rmmmsiAaOwsFDvvvuuPv30U+3du1fhcFgDBw5UIBDQ0qVLtWfPHlVVVSk3N1czZ87UrbfeqgULFmj79u1av369nnjiCS1YsKDN76GwsFBTpkzRtddeqxdffFFlZWVauXKlnnvuOUnS9OnTtWPHDt10003asmWL/vCHP6i4uFgzZsxQWlpaTJ/ZXrNmzdLatWs1ffp0bdy4UVu2bNG8efO0d+/eI/YdMmSIfvCDH+jf/u3ftHjxYpWVlem9997T3LlztWzZsnZ/ZmFhoTZu3KitW7dq7969R3RYASSIx+f4AUgitbW1dscdd9gpp5xieXl5lpOTY0OHDrW77rrLqqurI/upHYsnDl+csGHDhsjlRloc7XInZmYfffSRjRo1yrKzs+3kk0+2P/3pT0dcRuPjjz+2yy+/3Lp3727Z2dk2bNgwu+WWWyILFrZu3Wrf+c53LDs7u9Xn33vvvda3b18LBAKRy52Ew2F77LHHbOjQoZaZmWm9evWyiRMn2qpVq77y51VTU2O33nqr5efnWzAYtG9+85v229/+NvL8113upD2fGcviiZbPHj16tIVCIevevbtNnDgx8vyXF5XU19fbz3/+cyssLLTMzEzLz8+3yy+/3DZu3Ghmhy53crglS5bY4f8L+eKLL2z8+PHWtWtXLncCeChgxkkSAAAALmAqFgAAwBEEOwAAAEcQ7AAAABxBsAMAAHAEwQ4AAMARBDsAAABHEOwAAAAcQbADAABwBMEOAADAEQQ7AAAARxDsAAAAHEGwAwAAcMT/B8Ffrkkj0BrqAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cluster_labels = np.unique(y_km)\n",
        "n_clusters = cluster_labels.shape[0]\n",
        "silhouette_vals = silhouette_samples(X, y_km, metric='euclidean')\n",
        "y_ax_lower, y_ax_upper = 0, 0\n",
        "yticks = []\n",
        "for i, c in enumerate(cluster_labels):\n",
        "    c_silhouette_vals = silhouette_vals[y_km == c]\n",
        "    c_silhouette_vals.sort()\n",
        "    y_ax_upper += len(c_silhouette_vals)\n",
        "    color = cm.jet(float(i) / n_clusters)\n",
        "    plt.barh(range(y_ax_lower, y_ax_upper), c_silhouette_vals, height=1.0,\n",
        "             edgecolor='none', color=color)\n",
        "\n",
        "    yticks.append((y_ax_lower + y_ax_upper) / 2.)\n",
        "    y_ax_lower += len(c_silhouette_vals)\n",
        "\n",
        "silhouette_avg = np.mean(silhouette_vals)\n",
        "plt.axvline(silhouette_avg, color=\"red\", linestyle=\"--\")\n",
        "\n",
        "plt.yticks(yticks, cluster_labels + 1)\n",
        "plt.ylabel('Cluster')\n",
        "plt.xlabel('Silhouette coefficient')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/11_06.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cl8H3FMPlseA"
      },
      "source": [
        "<br>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHKX3t9blseA"
      },
      "source": [
        "# 계층적인 트리로 클러스터 조직화"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQtGfN8HlseA"
      },
      "source": [
        "## 상향식으로 클러스터 묶기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:50.527002Z",
          "iopub.status.busy": "2021-10-23T10:43:50.526087Z",
          "iopub.status.idle": "2021-10-23T10:43:50.530189Z",
          "shell.execute_reply": "2021-10-23T10:43:50.530664Z"
        },
        "id": "HDyhiXedlseA",
        "outputId": "e741ac2b-ace1-489d-9c4b-93d16095bf3c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLdsq\" width=\"400\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "Image(url='https://git.io/JLdsq', width=400)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:50.537848Z",
          "iopub.status.busy": "2021-10-23T10:43:50.536936Z",
          "iopub.status.idle": "2021-10-23T10:43:50.709864Z",
          "shell.execute_reply": "2021-10-23T10:43:50.710371Z"
        },
        "id": "mVgWC-WClseB",
        "outputId": "1dc102a3-514c-4e1f-9a7b-9698c4e14863"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             X         Y         Z\n",
              "ID_0  6.964692  2.861393  2.268515\n",
              "ID_1  5.513148  7.194690  4.231065\n",
              "ID_2  9.807642  6.848297  4.809319\n",
              "ID_3  3.921175  3.431780  7.290497\n",
              "ID_4  4.385722  0.596779  3.980443"
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "np.random.seed(123)\n",
        "\n",
        "variables = ['X', 'Y', 'Z']\n",
        "labels = ['ID_0', 'ID_1', 'ID_2', 'ID_3', 'ID_4']\n",
        "\n",
        "X = np.random.random_sample([5, 3])*10\n",
        "df = pd.DataFrame(X, columns=variables, index=labels)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4HIYGuwlseB"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJi78cCVlseB"
      },
      "source": [
        "## 거리 행렬에서 계층 군집 수행"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:50.726009Z",
          "iopub.status.busy": "2021-10-23T10:43:50.725265Z",
          "iopub.status.idle": "2021-10-23T10:43:50.729076Z",
          "shell.execute_reply": "2021-10-23T10:43:50.729722Z"
        },
        "id": "nDu2gYTGlseB",
        "outputId": "a25c79dc-9f39-4bd6-f68f-afc0e3751d69"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ID_0      ID_1      ID_2      ID_3      ID_4\n",
              "ID_0  0.000000  4.973534  5.516653  5.899885  3.835396\n",
              "ID_1  4.973534  0.000000  4.347073  5.104311  6.698233\n",
              "ID_2  5.516653  4.347073  0.000000  7.244262  8.316594\n",
              "ID_3  5.899885  5.104311  7.244262  0.000000  4.382864\n",
              "ID_4  3.835396  6.698233  8.316594  4.382864  0.000000"
            ],
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              "        const element = document.querySelector('#df-60dbf544-295a-4cca-a127-ba4046148f9f');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    animation:\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3707694a-74d5-4d1f-a865-cb32206023cf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "from scipy.spatial.distance import pdist, squareform\n",
        "\n",
        "row_dist = pd.DataFrame(squareform(pdist(df, metric='euclidean')),\n",
        "                        columns=labels,\n",
        "                        index=labels)\n",
        "row_dist"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-fKcq82KlseB"
      },
      "source": [
        "함수 설명을 보면 `pdist` 함수에서 계산한 축약된 거리 행렬(상삼각행렬(upper triangular matrix))을 입력 속성으로 사용할 수 있습니다. 아니면 `linkage` 함수에 초기 데이터 배열을 전달하고 `metric='euclidean'` 지표를 매개변수로 사용할 수 있습니다. 앞서 `squareform` 함수로 만든 거리 행렬은 `linkage` 함수가 기대한 값과 다르기 때문에 사용해서는 안됩니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:50.734186Z",
          "iopub.status.busy": "2021-10-23T10:43:50.733251Z",
          "iopub.status.idle": "2021-10-23T10:43:50.752252Z",
          "shell.execute_reply": "2021-10-23T10:43:50.752913Z"
        },
        "id": "wK_FfJMblseB",
        "outputId": "666fd662-3f55-402b-92c4-9c5ea2ba96b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-9dd0eba25b7e>:5: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
            "  row_clusters = linkage(row_dist, method='complete', metric='euclidean')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           row label 1  row label 2   distance  no. of items in clust.\n",
              "cluster 1          0.0          4.0   6.521973                     2.0\n",
              "cluster 2          1.0          2.0   6.729603                     2.0\n",
              "cluster 3          3.0          5.0   8.539247                     3.0\n",
              "cluster 4          6.0          7.0  12.444824                     5.0"
            ],
            "text/html": [
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              "      <td>6.729603</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>cluster 3</th>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-80e16ba0-2042-4d10-baae-024cf9f9f61d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<style>\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "    80% {\n",
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              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bd3383ef-4da9-422f-ae1a-db838e5806ab button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# 1. 잘못된 방식: squareform 거리 행렬\n",
        "\n",
        "from scipy.cluster.hierarchy import linkage\n",
        "\n",
        "row_clusters = linkage(row_dist, method='complete', metric='euclidean')\n",
        "pd.DataFrame(row_clusters,\n",
        "             columns=['row label 1', 'row label 2',\n",
        "                      'distance', 'no. of items in clust.'],\n",
        "             index=['cluster %d' % (i + 1)\n",
        "                    for i in range(row_clusters.shape[0])])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:50.761305Z",
          "iopub.status.busy": "2021-10-23T10:43:50.760115Z",
          "iopub.status.idle": "2021-10-23T10:43:50.767871Z",
          "shell.execute_reply": "2021-10-23T10:43:50.768383Z"
        },
        "id": "wvyaG-RnlseC",
        "outputId": "06eb00dc-38c4-4392-aeaf-d4ecdf1b3168",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           row label 1  row label 2  distance  no. of items in clust.\n",
              "cluster 1          0.0          4.0  3.835396                     2.0\n",
              "cluster 2          1.0          2.0  4.347073                     2.0\n",
              "cluster 3          3.0          5.0  5.899885                     3.0\n",
              "cluster 4          6.0          7.0  8.316594                     5.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e67e0001-f2ff-4c0a-8f27-272d034e265f\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>row label 1</th>\n",
              "      <th>row label 2</th>\n",
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              "      <th>no. of items in clust.</th>\n",
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              "      <th>cluster 1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.835396</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
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              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.347073</td>\n",
              "      <td>2.0</td>\n",
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              "      <th>cluster 3</th>\n",
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              "      <td>5.899885</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>cluster 4</th>\n",
              "      <td>6.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.316594</td>\n",
              "      <td>5.0</td>\n",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e67e0001-f2ff-4c0a-8f27-272d034e265f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e67e0001-f2ff-4c0a-8f27-272d034e265f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e67e0001-f2ff-4c0a-8f27-272d034e265f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-877d78a6-9fc5-4f4f-840e-5280ae189fed\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-877d78a6-9fc5-4f4f-840e-5280ae189fed')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-877d78a6-9fc5-4f4f-840e-5280ae189fed button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# 2. 올바른 방식: 축약된 거리 행렬\n",
        "\n",
        "row_clusters = linkage(pdist(df, metric='euclidean'), method='complete')\n",
        "pd.DataFrame(row_clusters,\n",
        "             columns=['row label 1', 'row label 2',\n",
        "                      'distance', 'no. of items in clust.'],\n",
        "             index=['cluster %d' % (i + 1)\n",
        "                    for i in range(row_clusters.shape[0])])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:50.771975Z",
          "iopub.status.busy": "2021-10-23T10:43:50.771267Z",
          "iopub.status.idle": "2021-10-23T10:43:50.782391Z",
          "shell.execute_reply": "2021-10-23T10:43:50.782863Z"
        },
        "id": "JCuDBOYylseC",
        "outputId": "7bf1d329-00dc-4387-aac5-acb654b1cb53"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           row label 1  row label 2  distance  no. of items in clust.\n",
              "cluster 1          0.0          4.0  3.835396                     2.0\n",
              "cluster 2          1.0          2.0  4.347073                     2.0\n",
              "cluster 3          3.0          5.0  5.899885                     3.0\n",
              "cluster 4          6.0          7.0  8.316594                     5.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e435763-a719-47a9-925c-4309de45c220\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>row label 1</th>\n",
              "      <th>row label 2</th>\n",
              "      <th>distance</th>\n",
              "      <th>no. of items in clust.</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cluster 1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.835396</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster 2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.347073</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster 3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.899885</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster 4</th>\n",
              "      <td>6.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.316594</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e435763-a719-47a9-925c-4309de45c220')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5e435763-a719-47a9-925c-4309de45c220 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5e435763-a719-47a9-925c-4309de45c220');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-16bedbdf-aefe-483e-911b-256f237d0aea\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-16bedbdf-aefe-483e-911b-256f237d0aea')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-16bedbdf-aefe-483e-911b-256f237d0aea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# 3. 올바른 방식: 입력 샘플 행렬\n",
        "\n",
        "row_clusters = linkage(df.values, method='complete', metric='euclidean')\n",
        "pd.DataFrame(row_clusters,\n",
        "             columns=['row label 1', 'row label 2',\n",
        "                      'distance', 'no. of items in clust.'],\n",
        "             index=['cluster %d' % (i + 1)\n",
        "                    for i in range(row_clusters.shape[0])])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:50.786288Z",
          "iopub.status.busy": "2021-10-23T10:43:50.785642Z",
          "iopub.status.idle": "2021-10-23T10:43:50.938661Z",
          "shell.execute_reply": "2021-10-23T10:43:50.939135Z"
        },
        "id": "0mWnk-xulseC",
        "outputId": "8855927b-13ff-429f-8c96-c23927af82cb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from scipy.cluster.hierarchy import dendrogram\n",
        "\n",
        "# 검은색 덴드로그램 만들기 (1/2 부분만)\n",
        "# from scipy.cluster.hierarchy import set_link_color_palette\n",
        "# set_link_color_palette(['black'])\n",
        "\n",
        "row_dendr = dendrogram(row_clusters,\n",
        "                       labels=labels,\n",
        "                       # make dendrogram black (part 2/2)\n",
        "                       # color_threshold=np.inf\n",
        "                       )\n",
        "plt.tight_layout()\n",
        "plt.ylabel('Euclidean distance')\n",
        "# plt.savefig('images/11_11.png', dpi=300,\n",
        "#            bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idphq9RAlseC"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TnLnGfmHlseC"
      },
      "source": [
        "## 히트맵에 덴드로그램 연결"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:50.943824Z",
          "iopub.status.busy": "2021-10-23T10:43:50.942728Z",
          "iopub.status.idle": "2021-10-23T10:43:51.212395Z",
          "shell.execute_reply": "2021-10-23T10:43:51.212871Z"
        },
        "id": "QRwT02vLlseC",
        "outputId": "6b6c1baf-7a77-4b66-e692-0b742b4dc3b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-eb84f823cd67>:21: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  axm.set_xticklabels([''] + list(df_rowclust.columns))\n",
            "<ipython-input-18-eb84f823cd67>:22: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  axm.set_yticklabels([''] + list(df_rowclust.index))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(8, 8), facecolor='white')\n",
        "axd = fig.add_axes([0.09, 0.1, 0.2, 0.6])\n",
        "\n",
        "# 노트: matplotlib < v1.5.1일 때는 use orientation='right'를 사용하세요\n",
        "row_dendr = dendrogram(row_clusters, orientation='left')\n",
        "\n",
        "# 군집에 맞게 데이터를 재정렬합니다.\n",
        "df_rowclust = df.iloc[row_dendr['leaves'][::-1]]\n",
        "\n",
        "axd.set_xticks([])\n",
        "axd.set_yticks([])\n",
        "\n",
        "# 덴드로그램의 축을 제거합니다.\n",
        "for i in axd.spines.values():\n",
        "    i.set_visible(False)\n",
        "\n",
        "# 히트맵을 출력합니다.\n",
        "axm = fig.add_axes([0.23, 0.1, 0.6, 0.6])  # x-위치, y-위치, 너비, 높이\n",
        "cax = axm.matshow(df_rowclust, interpolation='nearest', cmap='hot_r')\n",
        "fig.colorbar(cax)\n",
        "axm.set_xticklabels([''] + list(df_rowclust.columns))\n",
        "axm.set_yticklabels([''] + list(df_rowclust.index))\n",
        "\n",
        "# plt.savefig('images/11_12.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zDlCPSiwlseD"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8MZjtwiClseD"
      },
      "source": [
        "## 사이킷런에서 병합 군집 적용"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:51.217250Z",
          "iopub.status.busy": "2021-10-23T10:43:51.216279Z",
          "iopub.status.idle": "2021-10-23T10:43:51.222174Z",
          "shell.execute_reply": "2021-10-23T10:43:51.222622Z"
        },
        "id": "-VnTWJbDlseD",
        "outputId": "c1d604a3-4a3b-4520-e922-646cbe720a2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "클러스터 레이블: [1 0 0 2 1]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.cluster import AgglomerativeClustering\n",
        "\n",
        "# 사이킷런 1.2버전에서 `affinity` 매개변수가 deprecated되고\n",
        "# 1.4버전에서 삭제되므로 대신 `metric`을 사용합니다.\n",
        "ac = AgglomerativeClustering(n_clusters=3,\n",
        "                             metric='euclidean',\n",
        "                             linkage='complete')\n",
        "labels = ac.fit_predict(X)\n",
        "print('클러스터 레이블: %s' % labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:51.226598Z",
          "iopub.status.busy": "2021-10-23T10:43:51.225638Z",
          "iopub.status.idle": "2021-10-23T10:43:51.231732Z",
          "shell.execute_reply": "2021-10-23T10:43:51.231152Z"
        },
        "id": "EVhHCtwllseD",
        "outputId": "bb12169b-6d20-4361-ce49-eec565a3b14f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "클러스터 레이블: [0 1 1 0 0]\n"
          ]
        }
      ],
      "source": [
        "# 사이킷런 1.2버전에서 `affinity` 매개변수가 deprecated되고\n",
        "# 1.4버전에서 삭제되므로 대신 `metric`을 사용합니다.\n",
        "ac = AgglomerativeClustering(n_clusters=2,\n",
        "                             metric='euclidean',\n",
        "                             linkage='complete')\n",
        "labels = ac.fit_predict(X)\n",
        "print('클러스터 레이블: %s' % labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "601GNGpJlseD"
      },
      "source": [
        "<br>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SeFVp46blseD"
      },
      "source": [
        "# DBSCAN을 사용하여 밀집도가 높은 지역 찾기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:51.236816Z",
          "iopub.status.busy": "2021-10-23T10:43:51.236177Z",
          "iopub.status.idle": "2021-10-23T10:43:51.241115Z",
          "shell.execute_reply": "2021-10-23T10:43:51.241536Z"
        },
        "id": "jF3K1zW-lseE",
        "outputId": "18c39023-5ba0-4fcf-b543-a931d1af8526"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLdsY\" width=\"500\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "Image(url='https://git.io/JLdsY', width=500)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:51.274506Z",
          "iopub.status.busy": "2021-10-23T10:43:51.272206Z",
          "iopub.status.idle": "2021-10-23T10:43:51.403204Z",
          "shell.execute_reply": "2021-10-23T10:43:51.404107Z"
        },
        "id": "mOADI80ulseE",
        "outputId": "86a9dec3-8388-4b10-c1e5-9472277ec69b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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qA3peABBRRwzsKMyoQEZv8ZaKUHp/o1Y8itN3geZjUV9rsSYfW0s5R7wXHB2pDeh5AUBEHXEqlsKY+xXJqClTN/WApZPEC4YJeWeiYODpmv/9xMBfDnvLElE0DOwoLN5ARq8FF3ZZuGHklKleTe3JfcTAX0lYyAsAIuqMU7EUFk8go1exXTsV7TV6ylTP6TtyF6m8PRHr2BGRFPaKZa/YMLGfqlwg07mfqpZemdHotR89iccEIOK4rDwm8g52niAiQF3cwsCOgV0EtYGMGAxK5eZJBYOde6SOOKc3Ln/8I9X7MYOdRhGJiMh71MQtnIqlCGpLN2hZORotUEpPSURTy3FV+zELp0yJiMgpGNhRF2oCGbULLqSmW2MFdVoeT2/xllgh0qrz6DYvKogoFgZ2FJXSQEbNggs1rZLifTwiN4iVBsBRZCKKhoEdxUXNylGlrZKiYdFe8ppYXWDuWFGFXj2749DRE+HbmfdJRADr2FGc1BTb1TqNyqK95DVyXWAARAR1QHz9i4nIPRjYUdykiu32TumOX1zaD2k9EtEWEhRPo6andI/4vWPRXrsULyYykpbRbfGdMP+97XxfEHkYp2JJFx0XXKzZHsDq6n1oajmOFzfsxosbdiMrLRkPjL9A0bTtx/deiS1fH+ySO8SyI+QVWke3rVw9TkT2wBE70k23BB+avz+Olzbs7rLKNdB8DHe+9hmuG9YegMWatk08JaFLz00x36jzKAann8iN4l0kZNXqcSKyHgM70o2SvKB3P6/H4hvV9UhVsl9OP5GbiIuStGaUcvU4kXcZHtgtXrwY/fr1Q3JyMvLz87F582bJba+44gr4fL4uP+PHjw9vc+utt3b5e3FxsdFPgxRQWqy4d0oi1s8eg5Ulo/D0pDysLBmF9bPHSE6nqimCTOQGsRYlxeJDe3oCV48TeZehOXavv/46Zs2ahSVLliA/Px9PPfUUioqKsHPnTvTp06fL9m+99RaOHz85hdfY2Ihhw4bh5z//ecR2xcXFeOmll8K/JyUlGfckSDE1xYrVFPxVWwSZyA2kusCIZU58iN72j6vHibzN0MDuiSeeQElJCaZNmwYAWLJkCT744AMsW7YMc+bM6bJ9enrkVeaqVavQs2fPLoFdUlIS/H6/cQdOmqgpVmyH/RJZSUlHCakuMGu2BxS3/SMibzEssDt+/Di2bNmCuXPnhm9LSEhAYWEhKisrFe3jxRdfxKRJk5CSkhJx+7p169CnTx/07t0bY8aMwe9//3ucfrr06E9raytaW1vDvweDQZXPhpRQU6zYDvslsoqaFd7RRrfZv5iIpBiWY3fgwAG0tbUhMzMz4vbMzEwEAgHZ+2/evBk1NTW47bbbIm4vLi7GK6+8gvLycpSWluLjjz/GNddcg7a2Nsl9LVy4EGlpaeGfvn37antSFJOaYsV22C+RFfRa4S0GfB1XjxMR2XZV7IsvvoghQ4Zg5MiREbdPmjQJ1113HYYMGYKJEyfi/fffx6effop169ZJ7mvu3Llobm4O/3zzzTcGH713SRUrjrXq1cr9EplJ7QpvFuQmIrUMm4rNyMhAt27d0NDQEHF7Q0ODbH5cS0sLVq1ahYcfflj2cQYMGICMjAzs2rULV111VdRtkpKSuMDCREZNE3H6iZxOzQrv5u+PsyA3EalmWGCXmJiIESNGoLy8HBMnTgQAhEIhlJeXY+bMmTHv++abb6K1tRU33XST7ON8++23aGxsRFYWP+jsRM2qVzvsl8gMSldur9kewEsbdncZ2ROnazlKTURSDJ2KnTVrFpYuXYqXX34ZO3bswIwZM9DS0hJeJXvLLbdELK4Qvfjii5g4cWKXBRFHjhzBvffei40bN2L37t0oLy/HhAkTMGjQIBQVFRn5VIiI4qZ05fbq6n2Kpms5VUtEnRla7uSGG27Ad999hwcffBCBQAB5eXkoKysLL6jYs2cPEhIiY8udO3di/fr1+Pvf/95lf926dcPWrVvx8ssv49ChQ8jOzsbVV1+NBQsWcKqViGxPyQrv3indu7Tk60icrn224ius+vQbTtUSUQSfIAieu8QLBoNIS0tDc3MzUlNTrT4cIvIQcVUsEL3A8C8u7YcXN+zWtG9xH5yqJXIXNXGLbVfFEhG5kdwK78Ic7cXX2TuZiAydiiUioq5irfBuCwkxp2vldFxZy4VGRN7DwI4MpaRtEpEXSa3wFgtyz1hRFbUfrNJgj72TibyJgR0ZRk3bJCI6SZyujdYPdtLFZ+PJtf+S3Qd7JxN5EwM7CtNzdE1MEGcdLiJtpKZrAWDVp3vYO5mIomJgRwD0HV2Ta5vkQ3ty99gcP6dliWKQmq6NNVUr/p3vLSJv4qpY0q0puUhN2yQiUs+I3sksdkzkDhyx8zgjRteUJm0zuZtIOz17JzMflsg9OGLncUaMrilN2mZyN1F8xKnaCXlnomDg6ZqDOj1H7InIWgzsPM6I0TWxbZLUV4wP7aMBTO4mkmfkFKnciD3AYsdETsOpWI+LZ3RNahWtXB0ugMndREoYPUWqZsSexY6JnIGBnccpaUoerXSC3BdOrDpczNshkmdGyaA12wOKtmM+LJFzMLDzOC2ja0q/cPRM7ibyEjNKBpXV1GPZht2KtmU+LJFzMMfOozrm7aT1SMTiG5WVTlCbk6NHcjeR1xhdMkh8HyvBfFgiZ+GInQdJTaM+MP4C9E5Jijm6xpwcIuMZXTJI7n3cEfNhiZyFI3YeE6u0wZ2vfYbm74/HHF1jjToi4xldMkjp+3P6pf2YD0vkMAzsPESP0gasUUdkPKNLBil9fxbm+DXtn4isw8DOQ/TI22GNOiLjiYuaAHR5r+lRMojvYyL3YmDnIXpMoxr9hUNE7YzoByvi+5jIvbh4wkP0mkZljToicxhZMojvYyJ38gmC4LleMcFgEGlpaWhubkZqaqrVh2OatpCA0aUVssWI188eo+iLQ6rzBBE5B9/HRPanJm7hiJ2H6N3qS6xRR0TOxfcxkbswx85jjMzbISIiImtxxM6D4snb4bQNERGRfTGw8ygt0y9SHSuYaE1ERGQPnIolRWJ1rJixogplNfUWHRkRERGJGNiRLD06VhAREZHxGNiRLD06VhAREZHxGNiRLD06VhAREZHxGNiRLL06VhAREZGxGNiRLCUNw09PSUSg+XtU1jYy146IiMgiDOxIVqyG4UB7jl1jy3H89o3PMXnpRowureAqWSIiIgswsCNFpDpWRMMSKERERNbwCYLguXkzNc10KZLYeSIQPIYF729DU8uJqNv50N6mbP3sMexMQUREFAc1cQtH7EgVsWOFPzVZMqgDWAKFiIjICgzsSBOWQCEiIrIfBnakCUugEBER2c8pVh8AOZNYAiXQfCxqqzExx25k/3SzD42ILCLm4O4/fAx9Tmt//zPHlshcDOxIE7EEyowVVfABEcGd+DE+79ocfqgTuYCSgK2sph7z39se0X4wKy0Z867NQXFultmHTORZXBXLVbFx4Yc5kbspeY+X1dRjxooqydH7524azs8DojioiVsY2Dk4sLPLtIddjoOI9CUVsInv7uduGo6xOX6MLq2ICPw6692zO/55/1h+LhBppCZu4VSsQ2kZKTMqABNLoJD5GFSTUdpCAua/tz3qKJyA9uBu/nvbcVpS95hBHQAcPHoCfyr/CnePPc+IQyWiDhjYOZDUVbTY8SHatEe8U6YMIOyH0+BkpM11TTEDNrFWZeW/Dyja39PlX+G8zFMxbmi2TkdIRNGw3InDyF1FA+1X0W2hk1uIgWDnD2mlrb/KauoxurQCk5duxF2rqtkP1gbi/TclkqO8BqWyCzwBwK9e+4znJpHBGNg5jNKraLHjg5ZAsCMGEPYT778pEdB+HlXWNuKd6r2orG3scr4orUF5SgLQq0d3xY/Lc5PIWJyKdRilV9F/+zHgCoUExYFg5zw5pTk2Y3P8nJY1kZrgnrmPFI2SafyR/dPhT01GIBj7M2fVp9/g1kv64anyrxQ9Ns9NciInpSMxsHMYpVfRr1R+jVcqv1Z8JR0tYGQAYU9s50bxUJqj2y3Bh8kjz8aTa/8Vc3+BYCsu7peOXj2749BR6f7RHfHcJCeJdSE0Nsdvu4DP8KnYxYsXo1+/fkhOTkZ+fj42b94sue3y5cvh8/kifpKTIwMZQRDw4IMPIisrCz169EBhYSG++krZlaIbiB0flJ42h75X9kEbLWBkAGFPbOdGWqmdxu+X0VPRfg+0tOLR/xii+Dh4bpJTxEpHumNFFUb8fo3t8s8NDexef/11zJo1C/PmzUNVVRWGDRuGoqIi7N+/X/I+qampqK+vD/98/fXXEX9/7LHH8Mwzz2DJkiXYtGkTUlJSUFRUhGPHvBFciB0fAKUpy7H50H7lEa31FwMIe5IL7mP9m5K3qc3RVfMZUJybhf+5cThiDVbw3CQnUXIh1HmU2g7554YGdk888QRKSkowbdo05OTkYMmSJejZsyeWLVsmeR+fzwe/3x/+yczMDP9NEAQ89dRTuP/++zFhwgQMHToUr7zyCvbt24fVq1cb+VRspTg3C8/dNBz+tPgCKrnWXwwg7ClWcM92bhSL2lF4tZ8B44Zm4dnJF0puC/DcJOeQuxCKxg4L2AwL7I4fP44tW7agsLDw5IMlJKCwsBCVlZWS9zty5AjOOecc9O3bFxMmTMC2bdvCf6urq0MgEIjYZ1paGvLz82Pu042Kc7OwfvYYrCwZhVsKzlF0n875dv605JitfhhA2JdUcC/3b0repnYUXstnwLih2Vhy03Bk8dwkh9OaZtR55Ntshi2eOHDgANra2iJG3AAgMzMTX375ZdT7DB48GMuWLcPQoUPR3NyMRYsW4ZJLLsG2bdtw1llnIRAIhPfReZ/i36JpbW1Fa2tr+PdgMKj1adlKx44Pr1R+LbM1sHjKcCT4fKqSPMUAonPiqJ+FcC1XnJtly8Rdsi9xBC7QfEyyr6u/0yi8ls8AnpvkBvGmGVmVf26rVbEFBQUoKCgI/37JJZfgggsuwPPPP48FCxZo3u/ChQsxf/58PQ7RlpR+WI8acLqmD1Z+SNsX27mRGuII3IwVVfABEZ8XsUbhtXwG8Nwkp5P7bpVjVf65YVOxGRkZ6NatGxoaGiJub2hogN/vV7SP7t2748ILL8SuXbsAIHw/tfucO3cumpubwz/ffPONmqdiCrliobGYMWUqfkhPyDsTBQO1BYhkb/Gcg+QcWqfx+RlAXqN1saLV+eeGjdglJiZixIgRKC8vx8SJEwEAoVAI5eXlmDlzpqJ9tLW14YsvvsC4ceMAAP3794ff70d5eTny8vIAtE+rbtq0CTNmzJDcT1JSEpKSkuJ6PkbSo+cnp0wpHuw76y0chSdSRuq7VazbqGbk2yw+QRAMuyx//fXXMXXqVDz//PMYOXIknnrqKbzxxhv48ssvkZmZiVtuuQVnnnkmFi5cCAB4+OGHMWrUKAwaNAiHDh3C448/jtWrV2PLli3IyWmPmktLS/Hoo4/i5ZdfRv/+/fHAAw9g69at2L59e5ead1KCwSDS0tLQ3NyM1NRUo56+IlLFQsXTQW2ysZOqY5M96H0OEhG5TbTv1jXbA6ZdEKuJWwzNsbvhhhvw3Xff4cEHH0QgEEBeXh7KysrCix/27NmDhISTs8EHDx5ESUkJAoEAevfujREjRuAf//hHOKgDgPvuuw8tLS24/fbbcejQIYwePRplZWWKgzo7MaJlF/NaSA22jSMikhftu9WuI9+GjtjZlV1G7CprGzF56UbZ7VaWjELBwNM5Gke6U3sOEnXEzyQic9hmxI5iU1MslDlQZAS2jSOt+JlEZE+G94olaUqXQu8+0CLZq87q1iXkbGwbR1rE6p/JzyTyAjtXEeCInYWU1p9buXkPc6DIEFoK1pK3MS+TvM7uo9UcsbOQkvpzky4+G4FgK6RY3bqEnI1t40gtuf6Z/EwiN3PCaDUDO4vJFQvtl9FT0X6YA0Vase8sqcG8TPIqudFqoH202uppWU7F2kCsJdOVtY2K9sEcKIqHXZftk/0wL5O8Ss1otZVVBBjY2YRU/TnmQJFZWAORlOBnEnmVU0arORVrc8yBIr3YeRUXOQc/k8irnDJazRE7B2AfWIqX3VdxkbPwM4m8yCmj1ew8YXGvWDVY5Z20YC9YMgo/k8hrxM9TABGfqUZ/nqqJWxjY2SSw4wckGaEtJGB0aYVkwq94hbl+9hieb2QKftaR01kxA8KWYg7DaTIyilNWcZE38LOOjGTWRYPdqwgwsLOY1DSZWOxQj2FdXiF7l1NWcZH7mfFZR95l9kWDnasIMLCzkBmteXiF7G1OWcVF7sY2ZGQksy4anDJIwsDOQkZPk/EKmZyyiovcp+OX4IHDrUwJIEOYddHgpEESBnYW0jpNpuSqgVfIBJysOTZjRRV8iL6KizXHSG/RvgSVYEoAqWVGHrHTBkkY2FlIyzSZ0qsGJs2TiDXHyExSX4JKMCWA1DI6j9iJgyQM7CykZpqsLSTg2YpdeHLtv7psF+2qgUnz1JHdV3GRO8T6EoyFKQGkldF5xE4cJGFgZyGl02Rrtgfw0LvbEAi2Rt1PtKsGJs1TZ3KruJySGEz2JfclKEUAUwJIG6PziJ04SMLAzmJy02QAFE1rdL5qYNI8qeGkxGCyLzt9uZE3GJ1H7MRBEgZ2NiA1TQYAo0srVE1rBILHUFnbiP2Hj2HSxX3x5NqvmDRPMTktMZjsS+uXmx3zlMg5jMwjlhskAYD0lO4YcU5vzY+hN7YUs0lLsWgqaxsxeelGVfdJT+mOppYT4d979ewOADh09ORtHIkhEVuOkZ7E8ynWl2AsK0tG2SZPiZzHqHQSqf6wHbGlGCmiZVqjY1AHAM0/BnS/LTwP/TJ6MneKAJz8ANyw6zvHJQaTfXWcFtOCU7kUD6O6QUiNCHZkpxmOBEsfnWLSY85evLpY9eke/HRoNgoGns6gzuPKauoxurQCk5duxLMf1Sq6D79wSani3CwsvnE4tHzMRPvMawsJqKxtxDvVe1FZ24i2kOcmmcgGinOz8PG9VyI9JTHq38Wzcv572y0/RzliZ2NK5vYBID0lEU0txyX/zlEXEmmtMWanxGCyv94piVDz3Sa1mIuLeshOtnx90BHftRyxszFxWgM4ueChs98WnosHxl+gaH8cdfE2LTXGfGj/IuXqaVJDzWeN1GIu8SKk89SXOOVVVlOvx6ESKeaU0icM7GxOnNv3p0WOmGSlJWPJTcNxV+F58Kf1ULQvjrp4m9oaY1w9TVqp+azxpyV3yUuSq/YP2GPKi7zFKaVPOBXrAHJdA1izjpRQexXJlmOklZI0kl49umPxlOEYNaBr3q8Tq/2T+znlu5Yjdg4hrvaZkHdmlwUQsaZsOepCIqVXkdfk+rGyZBTWzx7DoI40kftM8gF49PohuHRQRtTPJadMeZG3OOW7loGdS0hN2Uab5iBvEq825T5yymoCaP7+uOUfTuRs8XwmOWXKi7zHCd+1LFBs4wLFWrDfJ8VSVlOPO2RqjLEoMelJy2eSXKFjnqNkNbO/a9XELQzsXBbYEcl5eu2/8OTar2S3YxcAspJUtX/xq9MuoyNEZlATt3Aqlshj+mWkKNqO+UtkJSdMeRHZEVfFEnkM85fIKeQqAhAZzYnpTQzsXMCJJx5ZxylL9okA4/p/EslxaucTBnYO59QTj6zTsVG7D9Hzl+ywZJ+IyCpS7RfFzid2Tgdgjp2DseUOacX8JSKi6Jze+YQjdg4ld+L50H7ijc3xc+SFotIzf4npAETkFk7vfMLAzqGcfuKRPeiRv8R0ACJyE6d3PuFUrEM5/cQjd2A6ABG5jdMrBzCwcyinn3jkfE7PQyEiikau/aIP7bMSdq0cwMDOoZx+4pG9tIUEVNY24p3qvaisbVQUjKlJByAicgqxcgCALt+xTqgcwBw7h2LJCtKL1hw5pgNQvJQuuuHiHDKbWDmg82ej3wH5wwzsHMzJJx7ZQzy1mpgOQPFQekHBxTlkNvFCovWHEBb9fBggAAdaWh1zUeETBMFzCTBqmuk6Aa9mSYu2kIDRpRWS06liB4r1s8dIjqKMLq2Q7WAhdX/yLqkLCvEsES8olG5HpJdYFxJWtrdTE7dwxM4F2HKHtIi3ZA7TAUgLpTU4x5yfyVqdZKpYMxh3rKhCr57dcejoifDtdh055uIJIo/SI0eOHSxILaUXFP9buZuLc8g0Slb5dwzqAPuWdTI8sFu8eDH69euH5ORk5OfnY/PmzZLbLl26FP/v//0/9O7dG71790ZhYWGX7W+99Vb4fL6In+LiYqOfBpHr6JUjV5ybhfWzx2BlySg8PSkPK0tGYf3sMQzqKCqlFxRfNx3VdX9EschdcERj17JOhgZ2r7/+OmbNmoV58+ahqqoKw4YNQ1FREfbv3x91+3Xr1mHy5Mn46KOPUFlZib59++Lqq6/G3r17I7YrLi5GfX19+GflypVGPg0iV5IrmQMACT7gYMtx2X2J6QAT8s5EwcDTOTVGkpReUJyT3lPX/ZF3KSnnpPUCwY4jx4bm2D3xxBMoKSnBtGnTAABLlizBBx98gGXLlmHOnDldtn/11Vcjfn/hhRfwl7/8BeXl5bjlllvCtyclJcHv9xt56IbiYgeyg445clJCAnDna1W4e/956JfRk+crxU28oJBbdHNzQT+8sL5OdjvW6qRYlK6qjvcCwU4jx4aN2B0/fhxbtmxBYWHhyQdLSEBhYSEqKysV7ePo0aM4ceIE0tMj37jr1q1Dnz59MHjwYMyYMQONjY26HrsepK4QymrqMbq0ApOXbsRdq6oxeelGjC6tsN0cPXlDcW4WFt94IWLFaQKAJ9f+i+cr6UJp8dfEUxIcXSSWrKem5aGSGYxY7DRybFhgd+DAAbS1tSEzMzPi9szMTAQCAUX7mD17NrKzsyOCw+LiYrzyyisoLy9HaWkpPv74Y1xzzTVoa2uT3E9rayuCwWDEj5GkgreFf93OvppkO71TkqAmPYTnK8VL6aIbLs4hrdS2PIx1wRGLHbs82bbcyaOPPopVq1Zh3bp1SE4++aaeNGlS+P+HDBmCoUOHYuDAgVi3bh2uuuqqqPtauHAh5s+fb/gxA9LLpeubj+H5T+qi3odL98lKaqcQeL6SHopzsxTVBVO6HVFHWso5SRX9792zOw4ePeGYsk6GBXYZGRno1q0bGhoaIm5vaGiQzY9btGgRHn30UaxduxZDhw6Nue2AAQOQkZGBXbt2SQZ2c+fOxaxZs8K/B4NB9O3bV+EzUS7WFYIcuZphREbRMoXA85X0oLQGJ2t1klpayzlJXUis2R5wTJcnwwK7xMREjBgxAuXl5Zg4cSIAIBQKoby8HDNnzpS832OPPYZHHnkEH374IS666CLZx/n222/R2NiIrCzpFzYpKQlJSUmqn4NaWpZLd2anBEzyBrlk9lh4vhKRHcVTzinahYSTRo4NLXcya9YsLF26FC+//DJ27NiBGTNmoKWlJbxK9pZbbsHcuXPD25eWluKBBx7AsmXL0K9fPwQCAQQCARw5cgQAcOTIEdx7773YuHEjdu/ejfLyckyYMAGDBg1CUVGRkU9FET2+5DJSjA9AiTrSmlsC2CthmIhIJLcYQktunFPKOhka2N1www1YtGgRHnzwQeTl5aG6uhplZWXhBRV79uxBff3JBOznnnsOx48fx3/+538iKysr/LNo0SIAQLdu3bB161Zcd911OO+88zB9+nSMGDEC//d//2fKiJwcPb7k7nnzcyalk+mkktSl2DFhmNxBSc0xIjlKVl9PuvhsvL91n+vOM58gCO55NgqpaaarhlxTdFHnBMzOfwPY3Jqs0bHG4u4DLXhy7VeSCcM8R0lv0WqOpad0x+8n5GLc0GwLj4ycKto51btndwiAI/q+itTELQzsdAzsgJOrYoHoX4a3X9Yf71TvQyDYKrkPsfDm+tljbDvUS96gtLgnUbykKgqIfnlZf8wdl2PqMZE7RLtg7czuF6wM7GQYGdgB8l+GG3YdwJQXNsnuZ2XJKK4EI8uxUwoZTZztkFt89j83Dse4ofb70iVnkDvP7DyooiZusW0dOyeTWz1z4Ij0aF1HXHFIdtBxhRiDPDKC0ooC96/+Amk9u+PAkVaef6Saltp2TsTAziCx6i7FswybyCqcliWjKL2IbTp6ImK2g+cfqaG1tp3TGLoqlqIzYhk2kZGkei7WNx/DHSuq8Net+yw6MnIDrRexbG9HanhlUIWBnQWUNsHmFAPZgZKOKjNXfoa/buWXK2kzsn860lO6q75ftJ6fRFLkBlWA9lXYI87pbdoxGYGBnUXY3JqcQkn+U0gAfvUaR05Im24JPvx+Qq6m+3bMiyKKRUkx9qaWE7j88Y8c/VnGHDsLOalFCXmXmnyT+e9tx9gcP89hUm3c0Gz88ttDeP6TOk33d3peFJlDHFTpnC/ckTjF79RBFo7YWcwpLUrIu9Tkm3DkhOIxd1wO/ufG4UhPSVR9X6fnRZF5inOz8PG9V0qeZ06f4ueInUVYNoKcQsxLUVKOAuDICcVn3NAsFOWenMnISEnCPW9+joZg9I4+Yu0xLjYjNbZ8fRBNLccl/+7k0icM7CzAshHkJGJeyh0/dlSRw5ETilfnclEPXZeDGSuqJNvbcbEZqeXm0iecijWZVNkILtsnOyvOzcL/3HghYn13skwPGYWLzUhvbi59whE7E8UqGyGg/YuRyedkV+OGZuNZ+PCr17qO3HHkhIzGxWakJzHFJNDsvil+jtiZSE07EyI7Gjc0C0tuGo4sjpyQBbjYjPTi5nqyHLEzkZvn9Mk7OHJCRG4gVfrE7/CcdwZ2JnLznD55S6xeyERETtHxQjUQPIamI61IT0lEWo9EtIUER16wMrAzkZvn9ImIiJyoW4IPzd8fx2NlX7qiWgVz7Ezk5jl9IiIiJ3JbtQoGdibjsn0iIiJ7kKtWAbRXq9jw1QG8U70XlbWNtu9G4RMEwd5HaIBgMIi0tDQ0NzcjNTXVkmNg5wkiIiJrVdY2YvLSjaruY8UUrZq4hTl2FmHyORERkbW0VKEQp2jtOsvGqVgiIiLyJC1VKDpO0dpxWpaBHREREXmSWK1CbSKUnRsKMLCzkbaQgMraRsckaBIRETlZrGoVStixoQBz7GyirKa+S/Vrp9bQISJSiwvKyCpSHSiUsGNDAa6KtWhVbEdiDZ3O/xDiR5pdEzSJiPTAC1uyg44XFxmnJuGeN6rREGyN2VBg/ewxplyAqIlbOBVrMaU1dDgtS0Ru5LbisORcYrWKCXln4tJBGXjoup8AcF5DAQZ2Fttc1xRz6NfOCZpERPHghS3ZmVMbCjDHzmJKEy/tmKBJRBQPNRe2rPtJVijOzcLYHL+j8j8Z2FlMaeKlHRM0iZjwTvHghS05gdMaCjCws5hYQyfQfCxmgubI/ulmHxpRTEx4p3jxwpZIf8yxs1isGjp2T9Ak72LCO+lBrjisD+0XC7ywJVKOgZ0NODVBk7xJacL7hq8OsNg2xaT2wpZF3InksY6dDerYiZivRE5QWduIyUs3qroPp2gpFiXT+pz6Jy9TE7cwsLNRYKcVA0Iy0zvVe3HXqmpV92GxbZIT63OMRdzJ69TELVw84XC8iiWzaUlkF9D+JTz/ve0Ym+PnhQd1IbXyUG7qn+cVUSTm2DkYE9jJCnIJ71I6F9tmvhQpwSLuROpwxM6heBVLVhET3mesqIIPiHoOxrL/8LGoI83pKYmYmJeNsTl+phNQGGvdkR68lLLEwM6hWLGdrCSu5O4cnCmx+8BRPLX2X10CwqaW41i2YTeWbdjNdAKXkvpyjfWly1p3FK94U5acFhQysHMoXsWS1Tq32sk4NQn3vFGNhmCrZLHtzNQkrNy8R3aUr/7HdAImxbuH1JfrdcOy8O7n9ZJfuiziTvGQWngTUPgZ48Q8dubYORSvYskOxIT3CXln4tJBGXjoup8AkK5JNnnk2QgElV9ssAG8O0jlA9c3H8Pzn9TFzBNmEXfSSmnNTanPGKfmsTOwM4AZSeGs2E52JFdsu/n7E4r3xaR4d4j15Sql85cui7iTFvEsvIk3KLQSp2J1ZtawbawEdl7FkpU6T9GKOSlrtgewbMNu1ftjOoGzyX25SumcJyx1XvEzjqTEk7Lk5Dx2BnY6incuXy2pBHa/zef/yf061yQTr361YDqBs8UbmHe8v1StO6Jo4klZcnIeOwM7nVhVfoRXseQEWkZtmBTvDvEG5gzsSSulC29CIQHvVO+N+P50ch47AzudWDlsy6tYsju1V7VSDeB5AeM8cl+uUhjYU7zkUpYEAN+faMOUFzeFbxdTp8bm+B27GpuLJ3Ti5GFbIqOpvartnBRfVlOP0aUVmLx0I+5aVY3JSzdidGmFbVel0UmxVrVKYZ4w6UVq4U2vnt0BAIeORi7oElOn1mwPOHY1NgM7nTh52JbIaEpWcZ+ekogn/2sYVpaMwvrZYyKCOieWHKCTpL5cs9KS8cvL+iOLq13JQMW5WVg/ewxWlozC05Py8Opt+Ug6JXr403HF69gcvyNXY/sEQTB0re7ixYvx+OOPIxAIYNiwYfjTn/6EkSNHSm7/5ptv4oEHHsDu3btx7rnnorS0FOPGjQv/XRAEzJs3D0uXLsWhQ4dw6aWX4rnnnsO5556r+JiCwSDS0tLQ3NyM1NTUuJ6fqC0kYHRpheyw7frZY2wZ4RMZTQzQgOiruKN9UIrvK6k0B76vnEVL5wkivVXWNmLy0o2y260sGYWCgafb4vxUE7cYOmL3+uuvY9asWZg3bx6qqqowbNgwFBUVYf/+/VG3/8c//oHJkydj+vTp+OyzzzBx4kRMnDgRNTU14W0ee+wxPPPMM1iyZAk2bdqElJQUFBUV4dgxa6c4WUSTKLZYtcgW33gh0nokdqn9yAbw7tKxoHXBwNPDn4dStxMZQW3qlNPOT0NH7PLz83HxxRfj2WefBQCEQiH07dsXv/71rzFnzpwu299www1oaWnB+++/H75t1KhRyMvLw5IlSyAIArKzs3HPPffgd7/7HQCgubkZmZmZWL58OSZNmqTouIwYsRM5sf0Ikd5iXeF2/tvBluNY8EH090zrDyHctapa9vGenpSHCXlnGvV0iMhF1I7Y2YGauMWwVbHHjx/Hli1bMHfu3PBtCQkJKCwsRGVlZdT7VFZWYtasWRG3FRUVYfXq1QCAuro6BAIBFBYWhv+elpaG/Px8VFZWSgZ2ra2taG1tDf8eDAa1Pi1ZLD9CXid3cdNxFXdZTT3ufE269uPdhecpekzmrhKRUm7vP2zYVOyBAwfQ1taGzMzMiNszMzMRCASi3icQCMTcXvyvmn0CwMKFC5GWlhb+6du3r+rno4bThm2J9KJmoYOSlj2rPt0Df2oSW+cRkW7cnjrliVWxc+fORXNzc/jnm2++sfqQiFxHbW9Fpflzk0eeDcCdH8CkjRn9uMnd3Nx/2LCp2IyMDHTr1g0NDQ0Rtzc0NMDv90e9j9/vj7m9+N+GhgZkZWVFbJOXlyd5LElJSUhKStLyNIhIIbVFupUmMPfLSGHrPApjHjPpxa2pU4YFdomJiRgxYgTKy8sxceJEAO2LJ8rLyzFz5syo9ykoKEB5eTnuvvvu8G1r1qxBQUEBAKB///7w+/0oLy8PB3LBYBCbNm3CjBkzjHoqRKSA2pVmamo/Fgw83ZUfwKSO2f24yf3c2LnJ0JZis2bNwtSpU3HRRRdh5MiReOqpp9DS0oJp06YBAG655RaceeaZWLhwIQDgrrvuwuWXX44//vGPGD9+PFatWoV//vOf+POf/wwA8Pl8uPvuu/H73/8e5557Lvr3748HHngA2dnZ4eCRiKyhtki32gRmN34Ak3JW9eMmchpDA7sbbrgB3333HR588EEEAgHk5eWhrKwsvPhhz549SEg4meZ3ySWX4LXXXsP999+P//7v/8a5556L1atXIzc3N7zNfffdh5aWFtx+++04dOgQRo8ejbKyMiQnc1UckZW0BGqx+jgCzJ+jk6zsx03OZocCw2YyvPOEHRlZx47Iy7R0l2DOFCnxTvVe1jQk1dzy+WKLOnZE5D3iSjM1Cx3cmsBM+mI/blLLqzmZDOyISFdaAjXmz5EctxeVJX15OSfTE3XsiMhcLNJNenN7UVnSl5f7TDOwIyIiR3BzUVnSl9ryS27CqVgiInIM5mSSEl7OyWRgR0REjsKcTJLj5ZxMTsVagH0OibTj+4eI5Hg5J5MjdiZzS00dIivw/UNESmkpv+QGLFBsYoFiqZo6sYq3ErmZmorwfP8QkRZu6DzBAsU25OWaOkTRqBl94/uHiLTyWk4mc+xM4uWaOkSdiaNvnd8TYkX4spr6iNv5/iFyHubDWoMjdibxck0doo60jL7x/UPkLFblw7ph2jVeDOxM4uWaOkQdqRl9E6dP+P4hcg6rerRycVU7TsWaRKypI3Xd4EP7CejGmjpEHWkZfeP7h8gZ5EbkgfYReb2nZdWmd7gZAzuTeLmmDlFHWkbf+P4hcgYr8mGtCibtioGdidjnkKh99K1Xz+4xt+nVs3uX0Te+f4jsz4h8WLlFGFxcFYk5diZjn0MieVLvBr5/iOxN73xYJXlzXFwViYGdBbxWU4eoo811TTh09ETMbQ4ePRGxeKIjvn+I7EvPHq1KF2FwcVUkTsUSkal4dU3kXnrlw8rlzQk4mTfHxVWRGNgRkal4dU3kbnrkw8rlzQHteXNPrtmJzXVNeGA8F1eJOBVLRKbSc6qGiOwp3nzYQFDZiP2zH9Xi2Y9qkZWWjNsv6493P6+PCAj9Hqxjx8COiEwlTtXMWFEFHxAR3Hnx6prIreLJh2060qpq+0DzMfz5kzosvvFC9E5J8vTiKk7FEpHpWLqEiGJJT0lUtb14gbjggx0Y2T8dE/LORMHA0z0X1AEcsSMii7B0CRFJ8af1UH2faO0IvYiBHRFZRu1UDRt8E3mDmIsrt4AiGq+vqGdgR0SOwAbfRN7RMRdXbSMwr6+oZ44dEdkeG3wTeY+Yi5uVpixQ81q9OikcsSMiW5MrVOpDe6HSsTl+TssSuUznXNzdB1rw5NqvuKI+BgZ2RGRrahp8ezlhmshNYuXTDvaf1iUtw4v16qQwsCMiW2MLMiJvkcun5Yr62BjYEZGt6dmCjKtqibQz4/0j5tN2Tr0Q82nFOpfxFD92OwZ2RGRrerUg46paIu3MeP8wn1YfXBVLRLYmlj0AtDf45qpaIu3Mev+oyaclaQzsiMj24mlBJjcKALSPArSF1FbLInI/M98/zKfVB6diicgRtCZMc1UtkXZmvn/0zKf1MgZ2ROQYWhKmOQpApJ2Z7x+98mm9jlOxRORquw+0KNqOowBEXZk5iqZHPi0xsCMiF2sLCVi5eY/sdmxDRBSdOIomFUrp3cYrnnxaasepWCJyrc11TQgEW2W3m3Tx2RwFIIpCHEWbsaLKtDZeLEAcHwZ2RORaSvN++mX0NPhIiJxLHEUzs40XCxBrx8COiFyLq+yI9NF5FC0jJQnwAQeOtKKytpEjajbCwI6IXIur7Ij0I46ildXU43f/3+e6daFgqz99MbAjItcyIj+IX0LkZUp7uarZH1v96csnCILnyq0Hg0GkpaWhubkZqampVh8OERlMry8PfgmRl7WFBIwurZAsWCyOgK+fPUbRxY5UkCjek6tgT1ITt3DEjogcR+2omR6r7PQeqSByGj27UMi1KvOhvVXZ2Bw/R8RVYmBHRI6iddQsnlV2/BIi0rcLBVv9GYcFionIMcRRs85fCOKoWVlNvSGPq+ZLiMit9FxlzlZ/xjEssGtqasKUKVOQmpqKXr16Yfr06Thy5EjM7X/9619j8ODB6NGjB84++2z85je/QXNzc8R2Pp+vy8+qVauMehpEZBNyo2ZA+6hZW0j/tGF+CRHp24WCpYiMY1hgN2XKFGzbtg1r1qzB+++/j08++QS333675Pb79u3Dvn37sGjRItTU1GD58uUoKyvD9OnTu2z70ksvob6+PvwzceJEo54GEdmEFaNmbSEBlbWN+KpB+qK0I34JkZvp2cvV7FZlXmJIjt2OHTtQVlaGTz/9FBdddBEA4E9/+hPGjRuHRYsWITs7u8t9cnNz8Ze//CX8+8CBA/HII4/gpptuwg8//IBTTjl5qL169YLf7zfi0InIpsweNYuWyyeF9fDIK/TqQmFFqzKvMCSwq6ysRK9evcJBHQAUFhYiISEBmzZtws9+9jNF+xGX9XYM6gDgzjvvxG233YYBAwbgjjvuwLRp0+Dz8R+fyM3MnLqRWgEbDb+EyGv06uVqRasyLzAksAsEAujTp0/kA51yCtLT0xEIBBTt48CBA1iwYEGX6duHH34YY8aMQc+ePfH3v/8dv/rVr3DkyBH85je/kdxXa2srWltPNgIPBoMqng0R2YFZXSRi5fJFwy8h8iK9ernqFSTSSaoCuzlz5qC0tDTmNjt27IjrgID2wGv8+PHIycnBQw89FPG3Bx54IPz/F154IVpaWvD444/HDOwWLlyI+fPnx31cRGQds6Zu5HL5RDOvHIhLB53BLyGiOCkJEtnxRTlVgd0999yDW2+9NeY2AwYMgN/vx/79+yNu/+GHH9DU1CSbG3f48GEUFxfjtNNOw9tvv43u3bvH3D4/Px8LFixAa2srkpKSom4zd+5czJo1K/x7MBhE3759Y+6XiOzHjKkbpTl652aexvpaRCZgxxd1VAV2Z5xxBs444wzZ7QoKCnDo0CFs2bIFI0aMAABUVFQgFAohPz9f8n7BYBBFRUVISkrCu+++i+Rk+VyZ6upq9O7dWzKoA4CkpKSYfyci5zB66oZlGIjsgx1f1DMkx+6CCy5AcXExSkpKsGTJEpw4cQIzZ87EpEmTwiti9+7di6uuugqvvPIKRo4ciWAwiKuvvhpHjx7FihUrEAwGw7lwZ5xxBrp164b33nsPDQ0NGDVqFJKTk7FmzRr84Q9/wO9+9zsjngYR2ZRe+T3RmJXLR0SxseOLNoa1FHv11Vcxc+ZMXHXVVUhISMD111+PZ555Jvz3EydOYOfOnTh69CgAoKqqCps2bQIADBo0KGJfdXV16NevH7p3747Fixfjt7/9LQRBwKBBg/DEE0+gpKTEqKdBRC4llbPDMgxE9sC2Y9r4BEHQv0y7zQWDQaSlpYXLqRCRtyjJ2WFeD5G13qnei7tWVctu9/SkPEzIO9P4A7KQmrjFsBE7IiI7UpqzwzIMRNZivqs2DOyIyDPU5uwYmctHRLEx31Ubw3rFEhHZjRX9ZolIGz1703oJAzsi8oy125V1vtGr3ywRxUesXelPi5xu9acls9SJBE7FEpEntIUEvF29V9G2zNkhsg/mu6rDwI6IPGFzXROaWk7Ibnd6SiJzdohshvmuynEqlog8Qen06oS8bI4EEJFjccSOiDxB6fTq2JzY/ayJnESqEDe5FwM7IvIEudIJQHsBYk7DkluwyLY3cSqWiDxBrnSCDyydQO4hFuLuXN5HLMRdVlNv0ZGR0RjYEZFnsHQCeYFcIW6gvRB3W0iIuE9lbSPeqd6LytrGiL+Rs3Aqlog8haUTyO3UFOIuGHi6oVO2zPEzHwM7IvIcu5dO4JchxUPpCvD9h49J9k6ubz6GO1ZU4beF52LmmHM1nX/M8bMGAzsiIhvhlyHFS+kK8IxTk/C7Nz+XXEwEAE+u/QorN3+Dh65Td/5JBYxijh9TH4zDHDsiIptgwjvpQVwBLjXG5kP7xQIExJyyFQWC6s4/LTl+pB8GdkRENsAvQ9KL3ApwoH0FuNqeyErPPzU5fqQ/BnZERFGYvUqQX4akJyUrwJtajiven5rzT02OH+mPOXZERJ1oyXOLd8GDWV+GXJhhLitfb7kV4OmnJqnep5LzT2mOn9LtSB0GdkREHWhJ+tZjwYMZX4ZcmGEuK1/vzgHlT4d27YHsT1V/Lik5/+S6vPjQPnLILi/G4FQsEdGPtOS56bXgQWnCu9YvQy7MMJeVr/dft9bj4kfWYvLSjbhrVTUmL92I0aUVXR5TPOeU6Hz+xUpVUJrjx5FiYzCwIyL6kdo8Nz0XPBj5ZciFGeay8vVe+Nft+NVrVV3y5+qjBJTiOSe21JPS+fwrq6nH6NKKmIEju7xYh4EdEdGP1Oa56b3gwagvQy7MMJdVr/dft+7D85/UxXzczgGl1DnXUcfzT81IZHFuFtbPHoOVJaPw9KQ8rCwZhfWzxzCoMxhz7IiIfqQ2z82IBQ9GtDzjKkVzaXm9411k0RYScP87NbLbdWwlJup8zmWkJAE+4MCR1ohjkRuJ9KE9cByb4w8fu927vLgRAzsioh+pTfrWe8FD5y/3a3KzsOXrg3h/6764AjyuUjSX2tdbj0UWm+ua0NRyQtG20QJPJQGY2h60ZA0GdkREPxJzjmasqIIPiAjuouW56bn6L9qXe4IP6JiGpXVFJVcpmkvN661X6y01o61aA3iO/DoDc+yIiDpQk+em14IHqbylzrn1WldUcpWiPKUFqZVsp/T1BqDbIgulwdrpKYmaA3iO/DoDR+yIiDpRk+cmBoKdR9v8CkfXYuUtdSaVx6T0OcVznG6mdCq0rKYeD727DYFga/g2f2oSHrruJ11ePyWvd2Vto25Tm+IooVzv1wUTcjUH8Bz5dQafIAieW98eDAaRlpaG5uZmpKamWn04ROQCWpPfK2sbMXnpRtWPt7JklKY8JnaeiCQ1FSq+Ih1Xg96xokpyP0skpkxjvd7vVO/FXauqZY/x6Ul5mJB3pubnIvrlZf0xd1yO7H6UPAYQPVWBpUyMoSZu4YgdEZEOtK7+05qPpPV+XKV4ktJVnmPOz8Sct76Iua85b30RdRQ11uut99Sm1Chhekp3/H5CLsYNzVa0Hy2PwZFf+2BgR0RkkbaQgAOHW+U3jELrSluvj9B1pHSV58v/2I1DR2OvOD109AQ21jbi0nMzFD++EVObRpTLseIxSDsGdkREFoiW16VEvCttta6sdWOAqHTU89PdjYq2q/z3AVWBndpV2Gr2a/SoLEd+7YuBHRGRyeRyoaRoWWkbbxkNcV9WNbM3ktJRz56JSr8q1Qe6nNokvTGwIyIykZpVsJ3r2Omx0lbtylo9A0S7UToVev3ws7C6ep/s/rSOYHFqk/TEwI6IyERyeV2iB8ZfgJsL+mHL1wdVf9nr1SFAzwDRjpROhV4yKAO9enaPmWfXu2d3jBqgfWqSU5ukFxYoJiIykdK8rozTkpB4SgIKBp6OCXlnomDg6YqDJ706BFjVzN5MSgpSd0vw4dH/GBJzPwv/Y0hcwa3SAslEcjhiR0RkIjOq9+v1GEa3kLLLggwlU6HFuVlYctNwPPTudgSC+uYaujWHkazBwI6IyERmVO/X6zGMDELtFswomQo1IhfOzTmMZA1OxRIRmciMvq16PYYYIEpt5UN7MKY2CJXqjau1F66ZxABQ7fR4NHI5jIDyXrFEIgZ2REQmU5LXZYfHUBMgKs0RYzBzkhdyGMl8nIolIoqD1jwxPaf1pI5Bj8dQUmdNzbSq0mBm478bkeDz6TLlaZdcvs6MzmEkb2JgR0SkUbx5YnqUuJA7Bj0eI1aAqDZHTGmQcuerVTj0/cnyIlrz7+yWy9eRGQtpyHt8giC4f7y7k2AwiLS0NDQ3NyM1NdXqwyEiB5IKaMRxIDOS3q0+hraQgNGlFZIjcOIijfWzx4RHyCprGzF56UbVj6XlOZnx+nQcDcxISQJ8wIEjrYpGBsXXT26RS8fXj7xJTdzCETsiIpXsULjXDsegpRCy3IrdWPtS85zMeH3k+v3KjQwa1SuWvI2LJ4iIVLJD0rsdjkFLjlisBRly1Dwno18fqZW9HSlZ5WvGQhryFo7YERGpZIekdzscg9YcMakFGXJtu0RKnpORr4/Sfr9KRwbZK5b0xMCOiEglOyS92+EYRpzTGwk+IFZlEp8P+OGHENpCQpdODp2DmVBIwJQXN8k+rpLnZOTro7TfL6C8Ly97xZJeGNgREalkRvcIJxzDlq8PxgzqAEAQgJtf2hw136xzMNMWEnR7Tka+PlpG+ViyhMzCHDsiIpXM6B4Ri7gSc1yuXzJoMfoYAHXBipJ8My2vq1RhZCP/jbSM8hkxcqq0KDR5i2GBXVNTE6ZMmYLU1FT06tUL06dPx5EjR2Le54orroDP54v4ueOOOyK22bNnD8aPH4+ePXuiT58+uPfee/HDDz8Y9TSIiKKyKum9rKYeo0srMHnpRry4YTcAoHNsYlbivZpgRWlXCTWva8fX4q5V1Zi8dCNGl1aEg0ej/o3kWq11pLXtmhy5507eZVgdu2uuuQb19fV4/vnnceLECUybNg0XX3wxXnvtNcn7XHHFFTjvvPPw8MMPh2/r2bNnuGZLW1sb8vLy4Pf78fjjj6O+vh633HILSkpK8Ic//EHxsbGOHRHpxcyuBrHqsgkAfnFpP4zN8ZuWeC9Xh03KypJRsvlkcq+rmhp14r4Czd+jqeU40k9Ngj81vn8r8fEBSD53o+oJWl2/kMynJm4xJLDbsWMHcnJy8Omnn+Kiiy4CAJSVlWHcuHH49ttvkZ2dHfV+V1xxBfLy8vDUU09F/fvf/vY3/PSnP8W+ffuQmZkJAFiyZAlmz56N7777DomJiYqOj4EdETmNlmLAZlAS4HT29KQ8TMg7U/NjanktjOhAEW8dOy3seh6QsdTELYZMxVZWVqJXr17hoA4ACgsLkZCQgE2bYq94evXVV5GRkYHc3FzMnTsXR48ejdjvkCFDwkEdABQVFSEYDGLbtm2S+2xtbUUwGIz4ISJyEjPq1mnJ2ZKa7owl3nwzta+FVM05JXl/sRTnZmH97DFYWTIKT0/Kw6vT8/Hqbfl4elIeVpaMwvrZY3QfObND/UKyN0NWxQYCAfTp0yfygU45Benp6QgEApL3u/HGG3HOOecgOzsbW7duxezZs7Fz50689dZb4f12DOoAhH+Ptd+FCxdi/vz5Wp8OEZHljK5bF8+Illi6ZOO/G7v0eO1Ir5W6al4LoztQmF2mxA71C8neVI3YzZkzp8vihs4/X375peaDuf3221FUVIQhQ4ZgypQpeOWVV/D222+jtrZW8z4BYO7cuWhubg7/fPPNN3Htj4jIbEbWZdNjRKtbgg+XDsrAo9cPgQ/GrhZW81q4bYTLDvULyd5Ujdjdc889uPXWW2NuM2DAAPj9fuzfvz/i9h9++AFNTU3w+/2KHy8/Px8AsGvXLgwcOBB+vx+bN2+O2KahoQEAYu43KSkJSUlJih+XiMhujKrLpveIllRXCb+O+WZqXov3t+5TtE+njHDZoX4h2ZuqwO6MM87AGWecIbtdQUEBDh06hC1btmDEiBEAgIqKCoRCoXCwpkR1dTUAICsrK7zfRx55BPv37w9P9a5ZswapqanIyclR81SIiBzFqIbxaka0lE45Gt0iS81r4bYRLqPOA3IPQxZPXHDBBSguLkZJSQk2b96MDRs2YObMmZg0aVJ4RezevXtx/vnnh0fgamtrsWDBAmzZsgW7d+/Gu+++i1tuuQWXXXYZhg4dCgC4+uqrkZOTg5tvvhmff/45PvzwQ9x///248847OSJHRK5nRF02o3K2xNyzCXlnomDg6boHGkpfCyU153r37O6oES6raiiSMxjWUuzVV1/FzJkzcdVVVyEhIQHXX389nnnmmfDfT5w4gZ07d4ZXvSYmJmLt2rV46qmn0NLSgr59++L666/H/fffH75Pt27d8P7772PGjBkoKChASkoKpk6dGlH3jojIzfQeDXPyiJaS10Ic4brjx5Is0Rw8egJrtgccFRAZPSpKzmVYgWI7Yx07IqJ2ckWG3VAXrS0kYMTv1+DQ0dirdZ38HMndLK9jR0REzmB131szbK5rkgzqAOetjCWKhYEdEZHHuT1ni7XfyEsMy7EjIiLncHPOlpPzCInUYmBHREQAzO+iYBbWfiMv4VQsERG5mhfyCIlEDOyIiMj13J5HSCTiVCwREVmiLSSYmtPn5jxCIhEDOyIiMl1ZTX2XfrJZOvaTlWJVHqHZQSx5FwM7IiIyVVlNPWasqOqykCHQfAwzVlS5bmrUqiCWvIk5dkREZJq2kID5722PujpVvG3+e9vRFnJHUyQxiO0Y1AEng9iymnqLjozcioEdERGZZnNdU5cgpyM3dYHwWhBL9sDAjoiITOOlLhBeCmLJPhjYERGRabzUBcJLQSzZBwM7IiIyjdgFQmo9qA/tCwvc0AXCS0Es2QcDOyIiMo2XukB4KYgl+2BgR0REpvJKFwgvBbFkHz5BEDy3HCcYDCItLQ3Nzc1ITU21+nCIiDzJK0V7WceO4qUmbmFgx8COiIgM5pUgloyhJm5h5wkiIiKDWdXKjLyHOXZERERELsHAjoiIiMglGNgRERERuQQDOyIiIiKXYGBHRERE5BIM7IiIiIhcgoEdERERkUswsCMiIiJyCQZ2RERERC7BwI6IiIjIJRjYEREREbmEJ3vFCoIAoL2pLhEREZGdifGKGL/E4snA7vDhwwCAvn37WnwkRERERMocPnwYaWlpMbfxCUrCP5cJhULYt28fTjvtNPh8PqsPB0B7NN63b1988803SE1NtfpwHIuvoz74OuqDr6M++Drqg6+jfsx+LQVBwOHDh5GdnY2EhNhZdJ4csUtISMBZZ51l9WFElZqayjecDvg66oOvoz74OuqDr6M++Drqx8zXUm6kTsTFE0REREQuwcCOiIiIyCUY2NlEUlIS5s2bh6SkJKsPxdH4OuqDr6M++Drqg6+jPvg66sfOr6UnF08QERERuRFH7IiIiIhcgoEdERERkUswsCMiIiJyCQZ2RERERC7BwM5CjzzyCC655BL07NkTvXr1UnQfQRDw4IMPIisrCz169EBhYSG++uorYw/U5pqamjBlyhSkpqaiV69emD59Oo4cORLzPldccQV8Pl/Ezx133GHSEdvD4sWL0a9fPyQnJyM/Px+bN2+Ouf2bb76J888/H8nJyRgyZAj++te/mnSk9qbmdVy+fHmX8y45OdnEo7WnTz75BNdeey2ys7Ph8/mwevVq2fusW7cOw4cPR1JSEgYNGoTly5cbfpx2p/Z1XLduXZfz0efzIRAImHPANrVw4UJcfPHFOO2009CnTx9MnDgRO3fulL2fXT4jGdhZ6Pjx4/j5z3+OGTNmKL7PY489hmeeeQZLlizBpk2bkJKSgqKiIhw7dszAI7W3KVOmYNu2bVizZg3ef/99fPLJJ7j99ttl71dSUoL6+vrwz2OPPWbC0drD66+/jlmzZmHevHmoqqrCsGHDUFRUhP3790fd/h//+AcmT56M6dOn47PPPsPEiRMxceJE1NTUmHzk9qL2dQTaK9V3PO++/vprE4/YnlpaWjBs2DAsXrxY0fZ1dXUYP348rrzySlRXV+Puu+/Gbbfdhg8//NDgI7U3ta+jaOfOnRHnZJ8+fQw6Qmf4+OOPceedd2Ljxo1Ys2YNTpw4gauvvhotLS2S97HVZ6RAlnvppZeEtLQ02e1CoZDg9/uFxx9/PHzboUOHhKSkJGHlypUGHqF9bd++XQAgfPrpp+Hb/va3vwk+n0/Yu3ev5P0uv/xy4a677jLhCO1p5MiRwp133hn+va2tTcjOzhYWLlwYdfv/+q//EsaPHx9xW35+vvDLX/7S0OO0O7Wvo9L3upcBEN5+++2Y29x3333CT37yk4jbbrjhBqGoqMjAI3MWJa/jRx99JAAQDh48aMoxOdX+/fsFAMLHH38suY2dPiM5YucgdXV1CAQCKCwsDN+WlpaG/Px8VFZWWnhk1qmsrESvXr1w0UUXhW8rLCxEQkICNm3aFPO+r776KjIyMpCbm4u5c+fi6NGjRh+uLRw/fhxbtmyJOI8SEhJQWFgoeR5VVlZGbA8ARUVFnj3vAG2vIwAcOXIE55xzDvr27YsJEyZg27ZtZhyuq/B81FdeXh6ysrIwduxYbNiwwerDsZ3m5mYAQHp6uuQ2djonTzH9EUkzMe8hMzMz4vbMzEzP5kQEAoEu0wannHIK0tPTY74mN954I8455xxkZ2dj69atmD17Nnbu3Im33nrL6EO23IEDB9DW1hb1PPryyy+j3icQCPC860TL6zh48GAsW7YMQ4cORXNzMxYtWoRLLrkE27Ztw1lnnWXGYbuC1PkYDAbx/fffo0ePHhYdmbNkZWVhyZIluOiii9Da2ooXXngBV1xxBTZt2oThw4dbfXi2EAqFcPfdd+PSSy9Fbm6u5HZ2+oxkYKezOXPmoLS0NOY2O3bswPnnn2/SETmT0tdRq445eEOGDEFWVhauuuoq1NbWYuDAgZr3SxRLQUEBCgoKwr9fcskluOCCC/D8889jwYIFFh4ZedHgwYMxePDg8O+XXHIJamtr8eSTT+J///d/LTwy+7jzzjtRU1OD9evXW30oijGw09k999yDW2+9NeY2AwYM0LRvv98PAGhoaEBWVlb49oaGBuTl5Wnap10pfR39fn+XRPUffvgBTU1N4ddLifz8fADArl27XB/YZWRkoFu3bmhoaIi4vaGhQfI18/v9qrb3Ai2vY2fdu3fHhRdeiF27dhlxiK4ldT6mpqZytC5OI0eOdFQQY6SZM2eGF+TJjajb6TOSOXY6O+OMM3D++efH/ElMTNS07/79+8Pv96O8vDx8WzAYxKZNmyJGAdxA6etYUFCAQ4cOYcuWLeH7VlRUIBQKhYM1JaqrqwEgImB2q8TERIwYMSLiPAqFQigvL5c8jwoKCiK2B4A1a9a47rxTQ8vr2FlbWxu++OILT5x3euL5aJzq6mrPn4+CIGDmzJl4++23UVFRgf79+8vex1bnpOnLNSjs66+/Fj777DNh/vz5wqmnnip89tlnwmeffSYcPnw4vM3gwYOFt956K/z7o48+KvTq1Ut45513hK1btwoTJkwQ+vfvL3z//fdWPAVbKC4uFi688EJh06ZNwvr164Vzzz1XmDx5cvjv3377rTB48GBh06ZNgiAIwq5du4SHH35Y+Oc//ynU1dUJ77zzjjBgwADhsssus+opmG7VqlVCUlKSsHz5cmH79u3C7bffLvTq1UsIBAKCIAjCzTffLMyZMye8/YYNG4RTTjlFWLRokbBjxw5h3rx5Qvfu3YUvvvjCqqdgC2pfx/nz5wsffvihUFtbK2zZskWYNGmSkJycLGzbts2qp2ALhw8fDn/+ARCeeOIJ4bPPPhO+/vprQRAEYc6cOcLNN98c3v7f//630LNnT+Hee+8VduzYISxevFjo1q2bUFZWZtVTsAW1r+OTTz4prF69Wvjqq6+EL774QrjrrruEhIQEYe3atVY9BVuYMWOGkJaWJqxbt06or68P/xw9ejS8jZ0/IxnYWWjq1KkCgC4/H330UXgbAMJLL70U/j0UCgkPPPCAkJmZKSQlJQlXXXWVsHPnTvMP3kYaGxuFyZMnC6eeeqqQmpoqTJs2LSI4rquri3hd9+zZI1x22WVCenq6kJSUJAwaNEi49957hebmZouegTX+9Kc/CWeffbaQmJgojBw5Uti4cWP4b5dffrkwderUiO3feOMN4bzzzhMSExOFn/zkJ8IHH3xg8hHbk5rX8e677w5vm5mZKYwbN06oqqqy4KjtRSy70flHfO2mTp0qXH755V3uk5eXJyQmJgoDBgyI+Jz0KrWvY2lpqTBw4EAhOTlZSE9PF6644gqhoqLCmoO3kWivYefvYjt/RvoEQRBMGx4kIiIiIsMwx46IiIjIJRjYEREREbkEAzsiIiIil2BgR0REROQSDOyIiIiIXIKBHREREZFLMLAjIiIicgkGdkREREQuwcCOiIiIyCUY2BERERG5BAM7IiIiIpdgYEdERETkEv8/QxOas3Oii/cAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.datasets import make_moons\n",
        "\n",
        "X, y = make_moons(n_samples=200, noise=0.05, random_state=0)\n",
        "plt.scatter(X[:, 0], X[:, 1])\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/11_14.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CUCHqITlseE"
      },
      "source": [
        "K-평균과 계층 군집:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:51.435318Z",
          "iopub.status.busy": "2021-10-23T10:43:51.433727Z",
          "iopub.status.idle": "2021-10-23T10:43:52.183064Z",
          "shell.execute_reply": "2021-10-23T10:43:52.183584Z"
        },
        "id": "JCnbs2-ElseE",
        "outputId": "c93430cf-8ea8-4bf6-c81b-4e1f73552479"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))\n",
        "\n",
        "km = KMeans(n_clusters=2, n_init=10, random_state=0)\n",
        "y_km = km.fit_predict(X)\n",
        "ax1.scatter(X[y_km == 0, 0], X[y_km == 0, 1],\n",
        "            edgecolor='black',\n",
        "            c='lightblue', marker='o', s=40, label='cluster 1')\n",
        "ax1.scatter(X[y_km == 1, 0], X[y_km == 1, 1],\n",
        "            edgecolor='black',\n",
        "            c='red', marker='s', s=40, label='cluster 2')\n",
        "ax1.set_title('K-means clustering')\n",
        "\n",
        "# 사이킷런 1.2버전에서 `affinity` 매개변수가 deprecated되고\n",
        "# 1.4버전에서 삭제되므로 대신 `metric`을 사용합니다.\n",
        "ac = AgglomerativeClustering(n_clusters=2,\n",
        "                             metric='euclidean',\n",
        "                             linkage='complete')\n",
        "y_ac = ac.fit_predict(X)\n",
        "ax2.scatter(X[y_ac == 0, 0], X[y_ac == 0, 1], c='lightblue',\n",
        "            edgecolor='black',\n",
        "            marker='o', s=40, label='Cluster 1')\n",
        "ax2.scatter(X[y_ac == 1, 0], X[y_ac == 1, 1], c='red',\n",
        "            edgecolor='black',\n",
        "            marker='s', s=40, label='Cluster 2')\n",
        "ax2.set_title('Agglomerative clustering')\n",
        "\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/11_15.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpDTGZZXlseE"
      },
      "source": [
        "DBSCAN:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T10:43:52.214893Z",
          "iopub.status.busy": "2021-10-23T10:43:52.193827Z",
          "iopub.status.idle": "2021-10-23T10:43:52.358940Z",
          "shell.execute_reply": "2021-10-23T10:43:52.359384Z"
        },
        "id": "hVZeyHtSlseE",
        "outputId": "f394b28f-c545-4604-8394-48fa765faa11"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.cluster import DBSCAN\n",
        "\n",
        "db = DBSCAN(eps=0.2, min_samples=5, metric='euclidean')\n",
        "y_db = db.fit_predict(X)\n",
        "plt.scatter(X[y_db == 0, 0], X[y_db == 0, 1],\n",
        "            c='lightblue', marker='o', s=40,\n",
        "            edgecolor='black',\n",
        "            label='Cluster 1')\n",
        "plt.scatter(X[y_db == 1, 0], X[y_db == 1, 1],\n",
        "            c='red', marker='s', s=40,\n",
        "            edgecolor='black',\n",
        "            label='Cluster 2')\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/11_16.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QU22iAUFlseF"
      },
      "source": [
        "<br>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HQ-KoH3lseF"
      },
      "source": [
        "# 요약"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWjScp3hlseF"
      },
      "source": [
        "..."
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "colab": {
      "name": "ch11.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}