code/ch05/ch05.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5장. 차원 축소를 사용한 데이터 압축"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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.jupyter.org/github/rickiepark/python-machine-learning-book-2nd-edition/blob/master/code/ch05/ch05.ipynb\"><img src=\"https://jupyter.org/assets/main-logo.svg\" width=\"28\" />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-2nd-edition/blob/master/code/ch05/ch05.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": {},
   "source": [
    "`watermark`는 주피터 노트북에 사용하는 파이썬 패키지를 출력하기 위한 유틸리티입니다. `watermark` 패키지를 설치하려면 다음 셀의 주석을 제거한 뒤 실행하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last updated: 2020-05-22 \n",
      "\n",
      "numpy 1.18.4\n",
      "scipy 1.4.1\n",
      "matplotlib 3.2.1\n",
      "sklearn 0.23.1\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -u -d -p numpy,scipy,matplotlib,sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 주성분 분석을 통한 비지도 차원 축소"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 주성분 추출 단계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Class label</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class label  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0            1    14.23        1.71  2.43               15.6        127   \n",
       "1            1    13.20        1.78  2.14               11.2        100   \n",
       "2            1    13.16        2.36  2.67               18.6        101   \n",
       "3            1    14.37        1.95  2.50               16.8        113   \n",
       "4            1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "0             5.64  1.04                          3.92     1065  \n",
       "1             4.38  1.05                          3.40     1050  \n",
       "2             5.68  1.03                          3.17     1185  \n",
       "3             7.80  0.86                          3.45     1480  \n",
       "4             4.32  1.04                          2.93      735  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/'\n",
    "                      'machine-learning-databases/wine/wine.data',\n",
    "                      header=None)\n",
    "\n",
    "# UCI 머신 러닝 저장소에서 Wine 데이터셋을 다운로드할 수 없을 때\n",
    "# 다음 주석을 해제하고 로컬 경로에서 데이터셋을 적재하세요.\n",
    "\n",
    "# df_wine = pd.read_csv('wine.data', header=None)\n",
    "\n",
    "df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',\n",
    "                   'Alcalinity of ash', 'Magnesium', 'Total phenols',\n",
    "                   'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',\n",
    "                   'Color intensity', 'Hue',\n",
    "                   'OD280/OD315 of diluted wines', 'Proline']\n",
    "\n",
    "df_wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "70%는 훈련 세트로 30%는 테스트 세트로 나눕니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(X, y, test_size=0.3, \n",
    "                     stratify=y,\n",
    "                     random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터를 표준화합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train_std = sc.fit_transform(X_train)\n",
    "X_test_std = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "공분산 행렬의 고윳값 분해"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "고윳값 \n",
      "[4.84274532 2.41602459 1.54845825 0.96120438 0.84166161 0.6620634\n",
      " 0.51828472 0.34650377 0.3131368  0.10754642 0.21357215 0.15362835\n",
      " 0.1808613 ]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "cov_mat = np.cov(X_train_std.T)\n",
    "eigen_vals, eigen_vecs = np.linalg.eig(cov_mat)\n",
    "\n",
    "print('\\n고윳값 \\n%s' % eigen_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 총분산과 설명된 분산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tot = sum(eigen_vals)\n",
    "var_exp = [(i / tot) for i in sorted(eigen_vals, reverse=True)]\n",
    "cum_var_exp = np.cumsum(var_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.bar(range(1, 14), var_exp, alpha=0.5, align='center',\n",
    "        label='individual explained variance')\n",
    "plt.step(range(1, 14), cum_var_exp, where='mid',\n",
    "         label='cumulative explained variance')\n",
    "plt.ylabel('Explained variance ratio')\n",
    "plt.xlabel('Principal component index')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 특성 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (고윳값, 고유벡터) 튜플의 리스트를 만듭니다\n",
    "eigen_pairs = [(np.abs(eigen_vals[i]), eigen_vecs[:, i])\n",
    "               for i in range(len(eigen_vals))]\n",
    "\n",
    "# 높은 값에서 낮은 값으로 (고윳값, 고유벡터) 튜플을 정렬합니다\n",
    "eigen_pairs.sort(key=lambda k: k[0], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "투영 행렬 W:\n",
      " [[-0.13724218  0.50303478]\n",
      " [ 0.24724326  0.16487119]\n",
      " [-0.02545159  0.24456476]\n",
      " [ 0.20694508 -0.11352904]\n",
      " [-0.15436582  0.28974518]\n",
      " [-0.39376952  0.05080104]\n",
      " [-0.41735106 -0.02287338]\n",
      " [ 0.30572896  0.09048885]\n",
      " [-0.30668347  0.00835233]\n",
      " [ 0.07554066  0.54977581]\n",
      " [-0.32613263 -0.20716433]\n",
      " [-0.36861022 -0.24902536]\n",
      " [-0.29669651  0.38022942]]\n"
     ]
    }
   ],
   "source": [
    "w = np.hstack((eigen_pairs[0][1][:, np.newaxis],\n",
    "               eigen_pairs[1][1][:, np.newaxis]))\n",
    "print('투영 행렬 W:\\n', w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.38299011, 0.45458499])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_std[0].dot(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_pca = X_train_std.dot(w)\n",
    "colors = ['r', 'b', 'g']\n",
    "markers = ['s', 'x', 'o']\n",
    "\n",
    "for l, c, m in zip(np.unique(y_train), colors, markers):\n",
    "    plt.scatter(X_train_pca[y_train == l, 0], \n",
    "                X_train_pca[y_train == l, 1], \n",
    "                c=c, label=l, marker=m)\n",
    "\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 사이킷런의 주성분 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**노트**\n",
    "\n",
    "이어지는 네 개의 셀은 책에 없는 내용입니다. 사이킷런에서 앞의 PCA 구현 결과를 재현하기 위해 추가했습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.36951469, 0.18434927, 0.11815159, 0.07334252, 0.06422108,\n",
       "       0.05051724, 0.03954654, 0.02643918, 0.02389319, 0.01629614,\n",
       "       0.01380021, 0.01172226, 0.00820609])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(1, 14), pca.explained_variance_ratio_, alpha=0.5, align='center')\n",
    "plt.step(range(1, 14), np.cumsum(pca.explained_variance_ratio_), where='mid')\n",
    "plt.ylabel('Explained variance ratio')\n",
    "plt.xlabel('Principal components')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "X_test_pca = pca.transform(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train_pca[:, 0], X_train_pca[:, 1])\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
    "\n",
    "    # 마커와 컬러맵을 준비합니다\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "\n",
    "    # 결정 경계를 그립니다\n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "                           np.arange(x2_min, x2_max, resolution))\n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "\n",
    "    # 클래스 샘플을 표시합니다\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], \n",
    "                    y=X[y == cl, 1],\n",
    "                    alpha=0.6, \n",
    "                    c=cmap.colors[idx],\n",
    "                    edgecolor='black',\n",
    "                    marker=markers[idx], \n",
    "                    label=cl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "처음 두 개의 주성분을 사용하여 로지스틱 회귀 분류기를 훈련합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "X_test_pca = pca.transform(X_test_std)\n",
    "\n",
    "lr = LogisticRegression(solver='liblinear', multi_class='auto')\n",
    "lr = lr.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(X_train_pca, y_train, classifier=lr)\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(X_test_pca, y_test, classifier=lr)\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.36951469, 0.18434927, 0.11815159, 0.07334252, 0.06422108,\n",
       "       0.05051724, 0.03954654, 0.02643918, 0.02389319, 0.01629614,\n",
       "       0.01380021, 0.01172226, 0.00820609])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=None)\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 선형 판별 분석을 통한 지도방식의 데이터 압축"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 산포 행렬 계산"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 클래스이 평균 벡터를 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MV 1: [ 0.9066 -0.3497  0.3201 -0.7189  0.5056  0.8807  0.9589 -0.5516  0.5416\n",
      "  0.2338  0.5897  0.6563  1.2075]\n",
      "\n",
      "MV 2: [-0.8749 -0.2848 -0.3735  0.3157 -0.3848 -0.0433  0.0635 -0.0946  0.0703\n",
      " -0.8286  0.3144  0.3608 -0.7253]\n",
      "\n",
      "MV 3: [ 0.1992  0.866   0.1682  0.4148 -0.0451 -1.0286 -1.2876  0.8287 -0.7795\n",
      "  0.9649 -1.209  -1.3622 -0.4013]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.set_printoptions(precision=4)\n",
    "\n",
    "mean_vecs = []\n",
    "for label in range(1, 4):\n",
    "    mean_vecs.append(np.mean(X_train_std[y_train == label], axis=0))\n",
    "    print('MV %s: %s\\n' % (label, mean_vecs[label - 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "클래스 내 산포 행렬을 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "클래스 내의 산포 행렬: 13x13\n"
     ]
    }
   ],
   "source": [
    "d = 13 # 특성의 수\n",
    "S_W = np.zeros((d, d))\n",
    "for label, mv in zip(range(1, 4), mean_vecs):\n",
    "    class_scatter = np.zeros((d, d))  # scatter matrix for each class\n",
    "    for row in X_train_std[y_train == label]:\n",
    "        row, mv = row.reshape(d, 1), mv.reshape(d, 1)  # make column vectors\n",
    "        class_scatter += (row - mv).dot((row - mv).T)\n",
    "    S_W += class_scatter                          # sum class scatter matrices\n",
    "\n",
    "print('클래스 내의 산포 행렬: %sx%s' % (S_W.shape[0], S_W.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "클래스가 균일하게 분포되어 있지 않기 때문에 공분산 행렬을 사용하는 것이 더 낫습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "클래스 레이블 분포: [41 50 33]\n"
     ]
    }
   ],
   "source": [
    "print('클래스 레이블 분포: %s' \n",
    "      % np.bincount(y_train)[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "스케일 조정된 클래스 내의 산포 행렬: 13x13\n"
     ]
    }
   ],
   "source": [
    "d = 13  # 특성의 수\n",
    "S_W = np.zeros((d, d))\n",
    "for label, mv in zip(range(1, 4), mean_vecs):\n",
    "    class_scatter = np.cov(X_train_std[y_train == label].T, bias=True)\n",
    "    S_W += class_scatter\n",
    "print('스케일 조정된 클래스 내의 산포 행렬: %sx%s' % (S_W.shape[0],\n",
    "                                                     S_W.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "클래스 간 산포 행렬을 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "클래스 간의 산포 행렬: 13x13\n"
     ]
    }
   ],
   "source": [
    "mean_overall = np.mean(X_train_std, axis=0)\n",
    "mean_overall = mean_overall.reshape(d, 1)  # 열 벡터로 만들기\n",
    "d = 13  # 특성의 수\n",
    "S_B = np.zeros((d, d))\n",
    "for i, mean_vec in enumerate(mean_vecs):\n",
    "    n = X_train[y_train == i + 1, :].shape[0]\n",
    "    mean_vec = mean_vec.reshape(d, 1)  # 열 벡터로 만들기\n",
    "    S_B += n * (mean_vec - mean_overall).dot((mean_vec - mean_overall).T)\n",
    "\n",
    "print('클래스 간의 산포 행렬: %sx%s' % (S_B.shape[0], S_B.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 새로운 특성 부분 공간을 위해 선형 판별 벡터 선택하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "행렬 $S_W^{-1}S_B$의 일반적인 고윳값 분해 문제를 풉니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "eigen_vals, eigen_vecs = np.linalg.eig(np.linalg.inv(S_W).dot(S_B))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "고윳값의 역순으로 고유 벡터를 정렬합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "내림차순의 고윳값:\n",
      "\n",
      "358.00420701336594\n",
      "177.07768640666222\n",
      "4.021051265375883e-14\n",
      "2.842170943040401e-14\n",
      "2.431086651362812e-14\n",
      "2.431086651362812e-14\n",
      "1.7592380953431885e-14\n",
      "1.4781200190724545e-14\n",
      "1.4781200190724545e-14\n",
      "6.083378287897576e-15\n",
      "5.0182074716589354e-15\n",
      "5.0182074716589354e-15\n",
      "3.212697679208256e-15\n"
     ]
    }
   ],
   "source": [
    "# (고윳값, 고유벡터) 튜플의 리스트를 만듭니다.\n",
    "eigen_pairs = [(np.abs(eigen_vals[i]), eigen_vecs[:, i])\n",
    "               for i in range(len(eigen_vals))]\n",
    "\n",
    "# (고윳값, 고유벡터) 튜플을 큰 값에서 작은 값 순서대로 정렬합니다.\n",
    "eigen_pairs = sorted(eigen_pairs, key=lambda k: k[0], reverse=True)\n",
    "\n",
    "# 고윳값의 역순으로 올바르게 정렬되었는지 확인합니다.\n",
    "\n",
    "print('내림차순의 고윳값:\\n')\n",
    "for eigen_val in eigen_pairs:\n",
    "    print(eigen_val[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tot = sum(eigen_vals.real)\n",
    "discr = [(i / tot) for i in sorted(eigen_vals.real, reverse=True)]\n",
    "cum_discr = np.cumsum(discr)\n",
    "\n",
    "plt.bar(range(1, 14), discr, alpha=0.5, align='center',\n",
    "        label='individual \"discriminability\"')\n",
    "plt.step(range(1, 14), cum_discr, where='mid',\n",
    "         label='cumulative \"discriminability\"')\n",
    "plt.ylabel('\"discriminability\" ratio')\n",
    "plt.xlabel('Linear Discriminants')\n",
    "plt.ylim([-0.1, 1.1])\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "행렬 W:\n",
      " [[-0.1484 -0.4093]\n",
      " [ 0.091  -0.1583]\n",
      " [-0.0168 -0.3536]\n",
      " [ 0.1487  0.322 ]\n",
      " [-0.0165 -0.0813]\n",
      " [ 0.1912  0.0841]\n",
      " [-0.7333  0.2828]\n",
      " [-0.0751 -0.0099]\n",
      " [ 0.002   0.0902]\n",
      " [ 0.2953 -0.2168]\n",
      " [-0.0327  0.274 ]\n",
      " [-0.3539 -0.0133]\n",
      " [-0.3918 -0.5954]]\n"
     ]
    }
   ],
   "source": [
    "w = np.hstack((eigen_pairs[0][1][:, np.newaxis].real,\n",
    "              eigen_pairs[1][1][:, np.newaxis].real))\n",
    "print('행렬 W:\\n', w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 새로운 특성 공간으로 샘플 투영하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_lda = X_train_std.dot(w)\n",
    "colors = ['r', 'b', 'g']\n",
    "markers = ['s', 'x', 'o']\n",
    "\n",
    "for l, c, m in zip(np.unique(y_train), colors, markers):\n",
    "    plt.scatter(X_train_lda[y_train == l, 0],\n",
    "                X_train_lda[y_train == l, 1] * (-1),\n",
    "                c=c, label=l, marker=m)\n",
    "\n",
    "plt.xlabel('LD 1')\n",
    "plt.ylabel('LD 2')\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDA via scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "\n",
    "lda = LDA(n_components=2)\n",
    "X_train_lda = lda.fit_transform(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(solver='liblinear', multi_class='auto')\n",
    "lr = lr.fit(X_train_lda, y_train)\n",
    "\n",
    "plot_decision_regions(X_train_lda, y_train, classifier=lr)\n",
    "plt.xlabel('LD 1')\n",
    "plt.ylabel('LD 2')\n",
    "plt.legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_lda = lda.transform(X_test_std)\n",
    "\n",
    "plot_decision_regions(X_test_lda, y_test, classifier=lr)\n",
    "plt.xlabel('LD 1')\n",
    "plt.ylabel('LD 2')\n",
    "plt.legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "역주 #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3306, 0.4032, 0.2661])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_uniq, y_count = np.unique(y_train, return_counts=True)\n",
    "priors = y_count / X_train_std.shape[0]\n",
    "priors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\sigma_{jk} = \\frac{1}{n} \\sum_{i=1}^n (x_j^{(i)}-\\mu_j)(x_k^{(i)}-\\mu_k)$\n",
    "\n",
    "$m = \\sum_{i=1}^c \\frac{n_i}{n} m_i$\n",
    "\n",
    "$S_W = \\sum_{i=1}^c \\frac{n_i}{n} S_i = \\sum_{i=1}^c \\frac{n_i}{n} \\Sigma_i$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_w = np.zeros((X_train_std.shape[1], X_train_std.shape[1]))\n",
    "for i, label in enumerate(y_uniq):\n",
    "    # 1/n로 나눈 공분산 행렬을 얻기 위해 bias=True로 지정합니다.\n",
    "    s_w += priors[i] * np.cov(X_train_std[y_train == label].T, bias=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ S_B = S_T-S_W = \\sum_{i=1}^{c}\\frac{n_i}{n}(m_i-m)(m_i-m)^T $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_b = np.zeros((X_train_std.shape[1], X_train_std.shape[1]))\n",
    "for i, mean_vec in enumerate(mean_vecs):\n",
    "    n = X_train_std[y_train == i + 1].shape[0]\n",
    "    mean_vec = mean_vec.reshape(-1, 1)\n",
    "    s_b += priors[i] * (mean_vec - mean_overall).dot((mean_vec - mean_overall).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "ei_val, ei_vec = scipy.linalg.eigh(s_b, s_w)\n",
    "ei_vec = ei_vec[:, np.argsort(ei_val)[::-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearDiscriminantAnalysis(solver='eigen')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_eigen = LDA(solver='eigen')\n",
    "lda_eigen.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 클래스 내의 산포 행렬은 covariance_ 속성에 저장되어 있습니다.\n",
    "np.allclose(s_w, lda_eigen.covariance_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sb = np.cov(X_train_std.T, bias=True) - lda_eigen.covariance_\n",
    "np.allclose(Sb, s_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(lda_eigen.scalings_[:, :2], ei_vec[:, :2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(lda_eigen.transform(X_test_std), np.dot(X_test_std, ei_vec[:, :2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 커널 PCA를 사용하여 비선형 매핑하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 파이썬으로 커널 PCA 구현하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist, squareform\n",
    "from numpy import exp\n",
    "from scipy.linalg import eigh\n",
    "import numpy as np\n",
    "\n",
    "def rbf_kernel_pca(X, gamma, n_components):\n",
    "    \"\"\"\n",
    "    RBF 커널 PCA 구현\n",
    "\n",
    "    매개변수\n",
    "    ------------\n",
    "    X: {넘파이 ndarray}, shape = [n_samples, n_features]\n",
    "        \n",
    "    gamma: float\n",
    "      RBF 커널 튜닝 매개변수\n",
    "        \n",
    "    n_components: int\n",
    "      반환할 주성분 개수\n",
    "\n",
    "    반환값\n",
    "    ------------\n",
    "     X_pc: {넘파이 ndarray}, shape = [n_samples, k_features]\n",
    "       투영된 데이터셋\n",
    "\n",
    "    \"\"\"\n",
    "    # MxN 차원의 데이터셋에서 샘플 간의 유클리디안 거리의 제곱을 계산합니다.\n",
    "    sq_dists = pdist(X, 'sqeuclidean')\n",
    "\n",
    "    # 샘플 간의 거리를 정방 대칭 행렬로 변환합니다.\n",
    "    mat_sq_dists = squareform(sq_dists)\n",
    "\n",
    "    # 커널 행렬을 계산합니다.\n",
    "    K = exp(-gamma * mat_sq_dists)\n",
    "\n",
    "    # 커널 행렬을 중앙에 맞춥니다.\n",
    "    N = K.shape[0]\n",
    "    one_n = np.ones((N, N)) / N\n",
    "    K = K - one_n.dot(K) - K.dot(one_n) + one_n.dot(K).dot(one_n)\n",
    "\n",
    "    # 중앙에 맞춰진 커널 행렬의 고윳값과 고유벡터를 구합니다.\n",
    "    # scipy.linalg.eigh 함수는 오름차순으로 반환합니다.\n",
    "    eigvals, eigvecs = eigh(K)\n",
    "    eigvals, eigvecs = eigvals[::-1], eigvecs[:, ::-1]\n",
    "\n",
    "    # 최상위 k 개의 고유벡터를 선택합니다(결과값은 투영된 샘플입니다).\n",
    "    X_pc = np.column_stack([eigvecs[:, i]\n",
    "                            for i in range(n_components)])\n",
    "\n",
    "    return X_pc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 예제 1: 반달 모양 구분하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=100, random_state=123)\n",
    "\n",
    "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='red', marker='^', alpha=0.5)\n",
    "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "scikit_pca = PCA(n_components=2)\n",
    "X_spca = scikit_pca.fit_transform(X)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(7, 3))\n",
    "\n",
    "ax[0].scatter(X_spca[y == 0, 0], X_spca[y == 0, 1],\n",
    "              color='red', marker='^', alpha=0.5)\n",
    "ax[0].scatter(X_spca[y == 1, 0], X_spca[y == 1, 1],\n",
    "              color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "ax[1].scatter(X_spca[y == 0, 0], np.zeros((50, 1)) + 0.02,\n",
    "              color='red', marker='^', alpha=0.5)\n",
    "ax[1].scatter(X_spca[y == 1, 0], np.zeros((50, 1)) - 0.02,\n",
    "              color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "ax[0].set_xlabel('PC1')\n",
    "ax[0].set_ylabel('PC2')\n",
    "ax[1].set_ylim([-1, 1])\n",
    "ax[1].set_yticks([])\n",
    "ax[1].set_xlabel('PC1')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_kpca = rbf_kernel_pca(X, gamma=15, n_components=2)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1,ncols=2, figsize=(7,3))\n",
    "ax[0].scatter(X_kpca[y==0, 0], X_kpca[y==0, 1], \n",
    "            color='red', marker='^', alpha=0.5)\n",
    "ax[0].scatter(X_kpca[y==1, 0], X_kpca[y==1, 1],\n",
    "            color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "ax[1].scatter(X_kpca[y==0, 0], np.zeros((50,1))+0.02, \n",
    "            color='red', marker='^', alpha=0.5)\n",
    "ax[1].scatter(X_kpca[y==1, 0], np.zeros((50,1))-0.02,\n",
    "            color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "ax[0].set_xlabel('PC1')\n",
    "ax[0].set_ylabel('PC2')\n",
    "ax[1].set_ylim([-1, 1])\n",
    "ax[1].set_yticks([])\n",
    "ax[1].set_xlabel('PC1')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 예제 2: 동심원 분리하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "\n",
    "X, y = make_circles(n_samples=1000, random_state=123, noise=0.1, factor=0.2)\n",
    "\n",
    "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='red', marker='^', alpha=0.5)\n",
    "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scikit_pca = PCA(n_components=2)\n",
    "X_spca = scikit_pca.fit_transform(X)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(7, 3))\n",
    "\n",
    "ax[0].scatter(X_spca[y == 0, 0], X_spca[y == 0, 1],\n",
    "              color='red', marker='^', alpha=0.5)\n",
    "ax[0].scatter(X_spca[y == 1, 0], X_spca[y == 1, 1],\n",
    "              color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "ax[1].scatter(X_spca[y == 0, 0], np.zeros((500, 1)) + 0.02,\n",
    "              color='red', marker='^', alpha=0.5)\n",
    "ax[1].scatter(X_spca[y == 1, 0], np.zeros((500, 1)) - 0.02,\n",
    "              color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "ax[0].set_xlabel('PC1')\n",
    "ax[0].set_ylabel('PC2')\n",
    "ax[1].set_ylim([-1, 1])\n",
    "ax[1].set_yticks([])\n",
    "ax[1].set_xlabel('PC1')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAADQCAYAAAATQIhnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABf3ElEQVR4nO29eXRc5Znu+7wqqUqDrcG2BltCNp7AWBHCMWAIsU2YQtMMcfsk6dWXsNrJ5SaEk8XN4SYEQtonBA6hfbvpHBJyWR1Oh053EuIWYAKEY0OME4zBxsjCE54wsmzLsq15KJWG7/7x1Nd7q1SjplJJ72+tWlW1a9fe39526fne93sHMcZAURRFUZTUJS3ZA1AURVEUZWSomCuKoihKiqNiriiKoigpjoq5oiiKoqQ4KuaKoiiKkuKkJ3sA48msWbPMvHnzkj0MRUlJ3n///XPGmMJEv6e/O0UZGfH89qaUmM+bNw+7du1K9jAUJSURkU+G8z393SnKyIjnt6dudkVRFEVJcVTMFUVRFCXFUTFXFEVRlBRnSq2ZK1OT2lqguhqoqwN8PsAYIBAAysuBNWu4j/3cbqusTO6YFUVREkHFXJl0uMXb6wVOngTmzwfa27nd7wdycoA5c4A33wRyc4EFC4CyMqC5GdiwAbj/fhV0RVFSBxVzJaWprQV+9jNgxw6KdFoaRXvOHKCqCvjgA6CtDcjKonD7/YDHQ8v89GngxAmK+AUXANu2Aa2tnAA8/TQf7vO4rfeKCmDvXrXmFUWZGKiYKylFqMt8717g3DkK9NmzQGcnkJnJ5x07gJ4eWt7vvQd0dwMZGXSz9/RQ0Ht7gY8+Aj7+mJ/l59Nq37yZ56qsBDZuBB55hPtmZQFvvQU0NgLz5gFXX+1Y87fdpgKvKEpy0AA4JWWoraVoNjfTmt69GzhwAEhPBzo6KOJpaUB/v/Pe7+d329oo4n4/0NUF9PVxP2P4uq+PYt3ZCZw5w+9WV1PIv/lNWvDNzcCRI7ToMzKApiZOGAIBTijuvRd4/nng6FHg8GGOtbY2ufdMUZSpgVrmyoQh1JUdGpx27Bgt47o6usNPn6YIt7XR0hYBBgYo2MYA2dl0uTc3U6h7eyngFvdrY/hobeWE4Px5WuAvvMDvTZsGtLQ4kwD7yMwEdu6k2Pf28lg7dwJ//jMF//33gV//Wi10RVHGFrXMlQlBqNXd3Aw89BDw4IPOtro6WsLnztF1bte+rQC3tlLQrXV+5AiPXVRE692Kt4hzXhGukff20g3f3083e08Prf62NmD6dG4fGOA5AZ43M5OP+np+Py2N5wwE+Lq3Fzh4ELjxRuDrX1crXVGUsUPFXJkQVFcDBQV8pKXxubGR6+B228CA41Lv7HRSzDo6aI1bt3lODkVahOvq111HK72ggNayx8Pj2QfAfT0ePjo7+T47m8fLzaVFLsL3AJ+nT6eQnz/PiURTE8fg8ThegrQ0Tghefpmehm98Q0VdUZTRR93syrgSzpVeWcn3ZWWD9+3pGfw+M5Pr3a2ttKJFKLR+P7d5PLSy8/P53TlzKMwHD/Lz9HQKtM01b2/nZMAYfmaMY9kXFwOFhRTrtDRa9+fOUbjT0xn4dvw4x52ezmO2tjpr8PZY6ekcXyAAlJbS/b59O3DhhYy21yA5RVFGA7XMlXEjnCvdBomVl1MM3fh8fFhKSmhdW4u8s5NWeU8PRTwvD7jkEop5WRnF9+RJoKaGAtvZyUdOjmOhe70U/exsbp89G1iyhOctK+NaeUUFMGMGzz1vHnDLLRxvZycnEz4fJxmWgQFn/H19FPScHFrtJ09S5JubB1+/oijKSEiqZS4inwfwTwA8AP7ZGPN4yOc+AM8B+DSA8wC+ZIw5HvysEsD/ByAXwACAy40x/vEbvZIoblc64DxXV9NC3bCB7/PyKOxFRRTu5mZuKy2lJZyTQ/G0rnURintTEwU/K4vfaWigoPb2Urj7+mhlnz4NLFwIXHYZBdvn44TgnXdo/VtL3eMBHn6Y6WY+H3DttRT2LVuYs37mDCcDAPe16+oAJwsAt02fDsyaxfNmZfFa2toGX79a54qijISkibmIeAD8FMANAOoB7BSRTcaY/a7dvgqg2RizUES+DODHAL4kIukAfgXgTmPMHhGZCaB3nC9BSZCaGopsWxsF7eKLKdh1dfw8O5sR5CLAihXAo49yu3XLL1oEfOELrM5m18zt2vjAAN9//DEt7fZ2usrb2pyo8+xsR/xnzgQee4zH37CBwrpiBcfY1ATccAPXtysrgbVruV9tLQPyjh7l+T0eTgI8Hh7fBs4NDPDR309R93o58ejupnvdFq75/e/pxg8EOPG45x4VdUVRhkcyLfMrABwxxhwDABH5DYDbAbjF/HYA64OvNwJ4SkQEwI0Aao0xewDAGHN+vAatxEe4imkff0whzcujsL3zDrB0KV3VDz3EgDeA4ldfz9eVlUMF7le/olWckUEhtFHqaWm0vpuaaI1nZvJ8hYVO8FpXF13x8+fzO9XVFPy6Orrnb7458jp2dTUD8nJzHW/B+fMU7t5enqOvjxMUv5/r6g0NjHBvaODkor+fEw2b7+7xOIVo6us5wVBBVxQlUZIp5qUATrje1wO4MtI+xpg+EWkFMBPAYgBGRF4HUAjgN8aYJ8Z+yEo82LVxGz3+2mvAE084wWGZmY57e98+4KKLKHi5uRRIv5/vH3mEYh8aLFdVRfHr7XWiy21Uus0Dz852LGKPh2vf9nVpKT+zY6yspFu/uTl6QFpdHS3vvDxeg60I5/c7OeYZGVzHtxOSq68GrriCBW5sgJzX63gWOjp4jLY2BtSpy11RlOGQqtHs6QCuAXA5gC4Ab4jI+8aYN0J3FJG7AdwNAOXl5eM6yKlCqBXe0ECR7OlhXnhnp1M3PT2dgnvsGK3jiy5itbTp0ylqAJ87OoA33qDL3J13bou0tLXRIg51s3u9PG5nJycLxcUMhDOGQrtsGccgEnn93j6HTiLKyzn58Pu5Bl5fz7HaAjLp6byO9naea2CAueqrV3MCsnUr78knn/AeeDycwGRl8f3Jk5ykrF+vJWEVRUmMZEaznwRwget9WXBb2H2C6+R5YCBcPYBtxphzxpguAK8CWBbuJMaYZ4wxy40xywsLC0f5EpSNG4E77xxcxnTLFgrewYMUX1s7XcSJIk9Pp6i1tFB4Q2lp4X42xzwQYCDa5s0Uy5wc7mcMRdPr5bGnTaNglpTQKp43j+vjlZXAVVfRSr7/fsfCdpOXxzXzSBH3FRV8/9FHDGbLznauyaa95eQw8j0jgxOO1lYuCbz8MqvEnT/vBMsZ49wXgM8HDvAeHj3Ke3rnnbzHiqIo0UimmO8EsEhELhQRL4AvA9gUss8mAHcFX68F8KYxxgB4HcCnRCQ7KPKrMHitXRkHamvpCrfr0n4/BTcri6LY2kox7+lxrOH+fqdeel8fXd4A16K7uylw3d0UeLcj5cABpzlKZiYFMyvLKfSSkUHRTkvjsZYsoVVeVQWsW0er+Ic/5LGefJKegaNHB1/P0aMc965dwJ49TsGaggJ2Ztu0Cbj8crZLtfXYi4r4eUkJxwRw7M3NwKlTXL/fvJkiboPwsrO5X28vPRC9vU5OvcfDe+j3O2v9jzyi6WuKokQnaWJujOkDcC8ozAcAPG+M2SciPxSR24K7/QLATBE5AuDbAB4IfrcZwD+AE4IaALuNMa+M8yVMeaqrKUR5eRSdrCwKltdL8fJ6Kcy2IEtenlNJDaCretEiCqRN5bK55kVFjHa3NDTQWrcV33p6KNw+H4+bk+OMY9o0CqHN5V6zZmiO+5w5DMA7fJiW/eHDfC/CcXV3s7jLmTM87o4dFO1Fi4Bbb2WEe2kp3erz5/MYdXVOHXjrMejvpzveWuPnz/MavF4nYK6/nx6EggLeq8xM3ksbLNjb67j/FUVRwpHUNXNjzKugi9y97Qeu134A/yXCd38Fpqcp44h7ffyDD5wAMLvenZlJi/aGGyhoW7bQQraR6mlpTpnUa67htgULaM1WVTlrxdOnA88955yjoYHimJFBAWxvd9bMMzMpsqtXU5RPnqSAlpcDX/0qXezr1w9eI1+8mM8nT3JCcPIkU9NOnaKQ2+s5cMARXrdb/sABjjEQ4Dr8wYO8Lr/fqfc+Zw4Fv7OT47UV4dwu9spK4KabOInZupX7ZWY657EWuk3fUxRFCUeqBsApScAdpV5WxmCwxkbHqs7MpChlZDg52lb833qLqWmBAIXyM59hihjA71RVUXDd51m6lCK7P7iAkp9PcbMuaWvlt7ZSBA8fpkD+5CdDg8bClYtduJBjfvZZuuLLyhiQ9s47/Nzn4/U1N1PoW1udyYC9zrw8TlYKCphq1t3tlILNyXGWGjo6HGvdRuBnZbFwjZ14PPwwXeqtrU5Uf3MzJwZtbbw/GhCnKEo4VMyVuAmt4HbZZU6K2JkzdIP7fMB3vuMITmieuHtCMDDgpIR99avhz7N4MYXMRocDnBTYoLnycidvfe9eCmI4sSsv53ns2AGe267L289LShgod/AghbyoiAFzwOAKdVZglwXDLktKeP22rntWFsdkq9nl5vKzri6K+bRpHOfPfz50rI88Qu9GVpazrm4M1/qffpr397/9tzj/0RRFmRKomCsRCU05q6kZLJQlJbSe33iDNc3nzqVbec8efjecqFZWUhzdx7WucEuoFZ2XRxHs7wduv53bXnuNzzff7OzX3ExBtxXb3FRUUCR7e2k1l5bS5f3Zz9LiranhJKGigm5/n4/Hu/9+Z2zucS9bxuh0n4+TjDlzuH3pUm63leAWLOBxLrqI4m6t7jlzHFe/m7Vrub26GnjxRU4AbC56bi6P8aMf8V6Hu05FUaYmKuZKWEJd6s3NFLvs7MEidOIE34eKarR87XBV3dyEWtFLlnA9OTfXsebPn2drUzd5eeHXlmtrGYleUUGX9tmztKK/8hVut4VjsrM5GTh1ipZwfr5zHeHG7Z7sLF7M69u7l16ElhZ+326357GWug3MC4c9T10da8BnZTmtVG3A3OOPq5griuKgYq6EJVxTlIoKFmw5epSWp89H4fuLvxj8XZuvfezY4MnAhg2DLV1LuNKvmzY5x/J6ub5dWuqsL19//eCOasBgt3mka1m0iEsCf/4z8Hd/R6GcOxdYvpzCK0JBXr2a54427nCTkrVrB18PwOOG80YA0QvElJdz0tLfz1gDm4aXng4cOhTZ+6EoytRDxVwJS10dLcytWx3XsE3Zcq8722ptblpbaZnOnRu+wlqkNXQr+ps2AbfdRlG1Qvfoo+G/Bwy2dt1r7+5rsW77M2e4LHD+PK3cjAyWUW1rAz73OU4WentjjzsS4a7HTgZsgF+s/Wzg4P79zhq7x+O0frWR9l/5CnDHHRoUpyiKirkShtpaPg4fZqBWSQlFfOtWvrYu9TNngD/9CXj1VVqfVVWM3G5upos5XIW1UDd4pLaoe/cOFr9Q4ll7t7jd9gcOMHjO5nOL0MLv7GTQ29mzXFOPNW57n0KXEaK1eXVH97/4Is+7bBld6KHlZDdsYIW82bMZ0W/7ons8FPh587hfNM+BoihTBxVzZRDWYuzpodVqi54UFdFita7tM2dYVCU72xG/V19lJHdZGS3zI0cGr6+Hc4OHSxmLJJ6hxFp7t7h7pdvCM2lpDEI7f96pF2/T7GxVumjjjmRZt7UNHZO9Hvd3AFra27ez7GxxsbOfnRAEAgyg6+nhuG3L14wM5t1/8gnvcUkJo9yffjr2vVAUZXKSzHKuygTECkl6Ot3kmZkMOmtv57p1dzf327mT5Uxt7nh5Oa14WwiltJRV0w4d4vfd1djclJc79cu3bgVeegl4/fWh6+EjwVrxBQVOQZfCQk5QLgh2BxgY4PuHH+a1NzdHH7fbAreWdUEBRddWsQN4Xa+/zq5p3/oWJw0FBfRcdHXxHr7wAq/9yBHej7o6CrvNNZ87l6Vi7b9LIMDtGRkU+BMnOB4t+aooUxe1zJVBWEvZ5m7Pm0fBaGtjedW9eynQR49SGP1+CtTLL3Mtt7GRzxdfzHztkyc5IQjnBq+tpYX5yis8V0kJLf22NgrUaAZ4WSt+zRrgwQc5/q4uCnFuLicqdl3epoZFc99H8ijk51P8Ad6bbdv4euVKLkk0NfF8hYVM4fN6+XlLC49pJw3NzYzi376d92/WLO7T0uJY58bQW9Lfz+PedRdT93QNXVGmHmqZK4Pw+WhJnj5NN+758xRar5dW98MPO6lbfj+tcZ+PotLW5tRjf+cduofdVqob63L2+WgRp6fzuAMDwKpVdC+PRT3yykrgscd4jkCA4169enCAnS3/+uyzfI60Dh96bbaSnfUCvPcehXvVKq59FxVx8mDX5svKnAY0+fmc/OzdSzG2ld9WrOCxe3pYE37WLP479PU51eRsDfiursFd3hRFmTqoZT6FCBewFWopnzhBUbYNPqwFvmQJI8zXruXaeHs7xT4jg9HsaWlOrfSsLArL1q0U5XDR2m43dUcHrc/+flryAMUKGJmVGel6I1VeSwT3OnxoNL07T7ysjPcG4D18+216L7xe3qdZs7hmDlD8//AH4N//nSI/MMB9VqxwyuMePMjlC4sxPL7HQ8FPNPpeUZTJgVrmU4TQrmHhLLjqaorvqlUUkpYWNhO5+GLWUt+0ifuXl9P6njuXYh4IUHy8XqdjmE39uuyywWvK1tq268INDRTC7m4KUnMzU8X6+mi1D9fKjOd64znG+vWs275+/eDvVlZycrNnD/DrX/P5ttuG5om7rffiYuBTn6KFDnDiY4X8jTeYl5+WRpf56dOc3FxyyeB+7/fey/uUlua46AFOhmyDlngDCBVFmTwkVcxF5PMi8pGIHBGRB8J87hOR3wY/f1dE5oV8Xi4iHSJy/7gNOkWJFLDldmVbgS0poft80SIK+cDA4P3XrHEi3W1wVk4ORTMnh5Z9by8nBiUlzvHdImOF7uBBJxq+o4PHTU+nG3rZMqeXeCRRHcn1RiPWZMBWlbv0UuCv/5rPdrJjse5ydzCdbQTz3HMs8er1Mp+8tdUpCOPzMXagq4ueiv5+Bs+tW0c3/NVX033f3++s+RcXc9ljLAIIFUWZ+CRNzEXEA+CnAG4GcAmAvxaRS0J2+yqAZmPMQgD/CODHIZ//A4DXxnqsqYrbsnzpJf6xdxNqwbktSdvty+938sXt/pWVLFhy+jQt0oEBilJ6Ott5rlzJtK+LLhp8PneKlxW6xkaKbFERxcnrpRAVFDgCtWVL4ha2nZhEut5oVjcQezIQz2TBHUVfX89nu8zg/uzUKVrpc+fSq5Ge7rRKbWgAPvyQ98lef2YmcMUVwN13A3/7t1xvP3OGE7CMjMEBhIqiTA2SuWZ+BYAjxphjACAivwFwO4D9rn1uB7A++HojgKdERIwxRkTuAPAxAJcTUrGEa1e6bRuDvYqLuU9o/rR7HTg3l58b43QGs/vX1lLEr7/eqXXe18cyrLZ72owZPCfASPHQCm1WzL71Lac7mc9H6xRwqpzV1AAzZw4uwnLuHL83f374tX8gepe0WNXXAGe9u6GB3gPb+cweL1w0u7WM3Wv00XDnydvGMZmZ9GoAvBd+P7fZ4LmCAl53IMDXdXW8Px4P7wvgtHLVdXNFmTok081eCuCE6319cFvYfYwxfQBaAcwUkWkAvgvgv8c6iYjcLSK7RGTX2bNnR2XgqUCo5XjZZdy+e3fk/Gm3tVhQQCEvLaUb+PnnGdBWUeEce9Ei4NprgS9+kVXhZs2iiFx6KbuRVVRQ0GtrB1ul7vP95Cesi37ppUyDO3qU4mn7k58/zwhxy5kzQy3VUEu9tpb7vfIKRfL06cHXG49VXV7OvO933uF6vp3cfPyxEzcQmk++bRs9C3ZcDz4IPPRQbK/CmjUU67Y2LlP09NDFnp3tBBZefLGzf14e97ER92Vl3HfuXHpDPB6642tqhvVfR1GUFCRVA+DWA/hHY0xHrB2NMc8YY5YbY5YXhtbpnMSEuplLSuj+trXPt22jcIRi07JefJFpaCdPOiVObQOUmprwLuwdOwaL5KJF9ARUVUVO8bITiECAxy0pcYrT7N1Lr4AN7AJYrObMGQaJbdvmWKhWiK3V7fU6XdXeeIPiZycT0Vzw1v1eU8PvWcvY7+fkxk5mQtfDd+/mcdwBf2fPOssI0dbtKyuZGrd6Ncc9YwbjDZYudRrPuGMPQj0qto96VpaTTZCWxu2KokwNkulmPwngAtf7suC2cPvUi0g6gDwA5wFcCWCtiDwBIB/AgIj4jTFPjfmoJzg2HeuDD2gVX3aZIwTt7fxDv3Qp3eNvvcWiJA8/PLSdZm0t8NRTdPkWFTGtqriY4lVXR0EJdWEbE1899lAqK3nsW24ZfMzmZoqwuwjL4cNcF547l4K/fTtTt+w5Qmujz57tuNvtZCKSC95Gz9t9P/yQ+4nwHi5bxkmNtXjb2nje/HyOc+XKwaLb0zP0Wm1HufXrh6bMhSvH6g7Ei9RQJj+fk5vubmfiMTDA7YqiTA2SaZnvBLBIRC4UES+ALwPYFLLPJgB3BV+vBfCmIZ81xswzxswD8CSAx1TIB//hv+IKis1bbzlu5l276L597TW6kK0l98gjQ93UGzbQqrSd0rZvp0XsrnIWWvJ0xYrwhVTCtSUNJZK1HAgMLsJiG79Mn87xZ2ZSHO05YgW+AeGjzJubORlxexYuuICBfCUlTqzBrl10vT//PL8zZw5d8CtWDPYgAJwchEaVHz1KV328AX3RgugsVVVMecvK4r95Vhbfu5cnFEWZ3CRNzINr4PcCeB3AAQDPG2P2icgPReS24G6/ANfIjwD4NoAh6WuKg9sqnT2bApSbC7z7Lq3E7m6KuRWdkye5vtrbO9j1a49TVMTvWdE8cGBolTO3wNxzT3iRjBUIBoSvqHb0KHOvn3yS7+fOBf7yLznm7m6Kr81pt+eIVJnNPaGIJJCBwOCJgE3La2zkhOg//oOu974+3hO/n96Pvj5OikKv3dZ/d2/buZNR6tu28WELvURLmYtVkW7NGt6TSy9llbhLL+X7eO67oiiTAzHGJHsM48by5cvNrl27kj2MUcVd5Wz3buDKKynkloEBClZ5OS3yY8cYLCXiRE0vXMg12mef5ft162g1HjjAeuL9/RSv9HTgmmsG99wOrbAGRK8yF+06rIs7L49C/s47FNTOTq4/NzVx2WDaNIpiezsnGVdfDfzud+GPY93S998fe2zf+AbvoRX1JUv4/UOHuP7c3c2x5OTwnpSVUTQzM3n/7ruPx6+p4f75+VwS+Phjpyf8iROclNjJgN9Pq76317n/wyFWdb/RQETeN8YsT/R7k/F3pyjjSTy/PS3nmsJESj9btcpZu7VWaV0dLerjx2kN+nxOffXS0sGWa3k5BezQIbrZOzrovs3IcKqcRUvvWr8+8WsJ7U9+8iSF3DZqKSykgL79NlOxior4aG+nyNqmLJH6nAODx3v4MHDnncCFF/K+VFQ4pWynT3fK0S5cCFx+OQPTtm3jhKKvz0kFmzuXE41rr3XE89gxbnc3Wvnc5yjytoZ6dvbg9DvbI364xNsOVlGUyUmqRrMrGJpiZfPBP/hgqJu7vJyiuGoVRbyri9Z5aSktbrdLds0aTgxEKJzFxVw/vv56RpiHO3eiFdbC4XYnz59PKzgz01nbLymhiNoGKdnZ4ZuyhHNLu8d79iyvw+0af+QRpylKdjaPn5vL+9PT47Qkzc2lmNuJUGsrJzlur4Q9z0cfcf/cXOD991nLvr+flvq5c+GXCUaTjRu51GKzCjZuHP1zKIoyMVDLPAWxLtV/+zcGYF1yCQW3uJgR1e+957jW3e07N2ygC/6OO2gNnj/Pmuv33DM0//vCCylytumKjeS2gWSRWoCOVk3w8nKmurmzCf1+ehQKC9nq0zIwEPu87vEeOODUMm9ro/D29tILsHix49VwL1G4W5IWFdFC7++nGD/88NBiM4BTaKajgwKeleVY4+fO8Zz5+cANN4y+Vb1xI/Cd7/D8s2fT7f+d7/Cz0MwFRVFSHxXzFMPt3p4zh4KxfTvXjYuLKVC33z7U1R3qfr755ujrqlVVQ9O3mpsdd3y0CmujwZo17B528CDfp6dzrXrWrKER4vGc1z1eK7LuUrWFhbTYwx3XVsYrKOD6dk0NXfu33jp0IuQ+j+0J39BAEZ89m8sc2dm0+EVY5OUb3xjJnQrPU0/xGm16mn1+6ikVc0WZjKibPcVwu3GXLKFlKMIqbbGix+Pp022JlL5ljx3r89HA1mZvb3fquFvLPNHzusdrq7n5/U5ltbIyusvDHdcd/d7by4lQdTXbqIaLLLffvegiWv6dnbwWj4cFYWbP5lKBTbsbi7Xukyd5nW5yc50Ws4qiTC7UMk8x3G7ckhLgqqvoNj51ikFYbrf6SIgUSGaPHevzkVJdzXX6M2cohra/eXs78L3vcc07kfO6x1tQQLfz0qVO6pjHQ3d5pOPGG2AWel9WrXLy+3NzuVxhi++4C9mMNqWlTkS9pa2N2xVFmXyomKcYoe7tkhJaq9deO7wo8mjEErCxjKC2Ee12OQGgF8IGrw03Yt6ONzSVywr3aLigQ+9LaLqctdzdVdxGm3vvddbIc3Mp5G1twPe/P3bnVBQleaiYpxjuzmaRyntOBEaa9xwpAM4dhDeS841nKtdoejHivU47KXnqKU6KSksp5LperiiTEy0ak4KMR4GQkRCtcEu846ytZR64CI9hC6xUVDDVym2Zj+R8E/1euhmN+zoStGiMoiQHLRozSZnoBUJCm53Y53j6a7vFdfFi9k0PBGiRL1xIV3FDA6vUWfEd7vni6Ws+XMZikjCS+6ooyuRGxVyJi0TEabg56KHi2trK1K4LLnCq1tnuZjZwbcMGCnzoWOI531iJ41hNEsY6t19RlNRFU9OUmLi7sUXr9GV7gX/wAfD667SgLdFywe337rqLVdN6epyqcgsWMPr72Wf5vGDB0KpzLS3D69YWT4e14TCS6nj2Xqxbx2f3PY6niYyiKFMTFXMlJvGIU6z2q5Fywd3fs+VN33nHmQi4xTWS+EZqyRor93w0xDGc+A53khBr0jQeuf2KoqQmSXWzi8jnAfwTAA+AfzbGPB7yuQ/AcwA+DeA8gC8ZY46LyA0AHgfgBRAA8P8YY94c18FPQiJ1QXvxRT7n57PISknJUHEKdVmvXs0OZO++y/KxkaK33d/Lz6dbPTOTld9KSgaLa6Sqc1VVztp5ItHi7swAv98pcXvDDU7jlmj3yOdjc5YFCwaLb04Ox5VodbxYbv+xzu1XFCV1SZqYi4gHwE8B3ACgHsBOEdlkjNnv2u2rAJqNMQtF5MsAfgzgSwDOAbjVGHNKRCrAnuhaDmMEhFvnfeghWsq2w1p3N63mq67iNrc4ha7nFhcDN93E2uY28jzcZMH9PVv73Oej6zw0HztaWt5wggKtOP7sZ+xTPnMmcN117JAWbo079B69/jo9EGVljscC4DJBc/PgcR47xvQwd+Be6HhragbXw1+yZGgq3kQPflQUJTkk081+BYAjxphjxpgAgN8AuD1kn9sB/DL4eiOA60REjDEfGGNOBbfvA5AVtOIVF9HWX0MJ50pvbGSRlmXLKFAAm4u8/DLwyiuszmaPGctlHcmFbIPaAE4Arr6a6WgAx+AWVHdZ1fr6oZ8Ph8pKegBuuYVlWmfPjrzGHXqPAgG2S7X14wGKsC3TascZCDiTokgxB7W1Tt/z3FxOnLZvZ193XRNXFCUWyXSzlwI44XpfD+DKSPsYY/pEpBXATNAyt/wVgN3GmJ5wJxGRuwHcDQDlU+ivYqIR1eEipa2AW5HdudNp4zltGt3oDz4IPPZY7GI2kVzIoVas18ua5pHGORzLNFYkfrxR4qH75eWxTKt7EmMnMO5xrl/P64oWNV9dzfKy+/bR5Z+ZyXuzdy/w3e+O3rUqijI5SekAOBFZCrre/69I+xhjnjHGLDfGLC90lxOb5LjFs7GR+dq7dgHf+lZ4Cz2cZe3z8XHmDOu/f/wxG41Mm+a0CT16lG7qWFZzpKCwUCt2NKxtN/FE4scbCBe635IlrBXv9UYPSIsnIK6ujnn0V13FDmvW1X7hhYkV2ol1rYl4axRFSR2SaZmfBHCB631ZcFu4fepFJB1AHhgIBxEpA/ACgK8YY46O/XBTC2tFNjRwndv27963j2Jzww1svWmFIpxlXVTE5z/8gVZiSwtd4D09bH4ybRrdxzt28HvRrOZoLVNHcx041DJtaBjqETh3jpOa+fO5T0UFsGnT4GsPVyI39B55vRTg0tLw/ePd137oEJvhtLbyu3PmsChO6P0pKXEmSqH3KxaxAujGskiOoijJJZmW+U4Ai0TkQhHxAvgygE0h+2wCcFfw9VoAbxpjjIjkA3gFwAPGmLfHa8CphLUiDx6kiPf3U+C8Xrbh3L17sNUWzrJ+9FG62NvbaZF7PHz09lKYLHaNOxrjkVYVzjLdsoVua8uZM8CHH9JbYffZtAm47bbY3oFI9+jnP4/eVraigml6H35ID8ef/wz87ndcEx9u2tlwUuJGkv+uKMrEJmmWeXAN/F4wEt0D4FljzD4R+SGAXcaYTQB+AeBfReQIgCZQ8AHgXgALAfxARH4Q3HajMaZxfK9i4mKtyMZGYNYsusMBBnhZN679Qx6tzeeRI7Rgs7Np1be0sB1pQwODtDwe4HOfiz2e4aRVJbL+W1tLa7uxkR6FJUs4EZk5k1His2dzvwMHKGRFRYMj0G0nNnvOJ58Mf87heBG2bOGEp72dk6q0NL5/7z1mDDz6aGL3J5KFnZ0dPSVOK8gpyuQlqXnmxphXAbwasu0Hrtd+AP8lzPd+BOBHYz7AFMaKgxW4/n5g7ly6xru7+Uc8nj/ktg9PRwePIULLEaDFO2MGg8Ai5WWHjileIdy4EXjkEXoBCgvp2g91CVvhranhen5HB13eNhL86quZg/7GGxS8vDzei/R05stb7H0IFcl33wWefppxA/Pns61ool3HamuZp2/X2jMy+NzXx6WK48cH55HHM7l58UWOadmywROSQGBoSpx7uSDaUoeiKKlNSgfAKdGprAR+8hNg+XIKucdDofP7abnG84d8xQpalKdP012fnk4BycqikM+ezaIpo+mqra2lkItQyP1+Ws7nz3Nysm4d1/sffNBxR4tQzJubObbMTFrhmZmMD7Cu8aIiur3tujTg3Ae3G/rAAcYC2IlLSwv7g2/cmNh1bNjASRDAY/X0ONY5wDHV1MR/LCvWxnDCsn8/sHUr3fg7dkRfLtAKcooyedFGK5Mcd2GULVvodl6xgmvn8fRBv+ceCsO773IyANDqmz+flc5s1PVoumqrqx2LXITi3NXF1LhZs4CVKwcXbLFj6OtjXnx2Ni3XxsahLULdohhqvT75pOOG3rmT98imiOXnc/tTT8VvndvJwdy5FF07Mejr43XZXPX6+qHfjRbIZyvlDQxQxOfN41hFuP4fLa1PK8gpyuRELfMpQGUlg7Sqq1kYpbc3/hSwykrmkS9dSku8qIiR2NOm0WK2gjiartq6Oscit7S1UQTtWre7YIvtdz5zJl9nZTFivaho6DVGS6Fzp561t3NC0NdHQQdYzOVkaL5FjOvIy6NnpLDQWbKweDx0u7uL7wCxA/kuvpivm5ocq7+nB7jsstgBbZWVjA247z6+f/JJTVFTlMmAWuZTiOGmgFl3/YYNFLe9e+l2Hhhgapa1bEerYEl5OcVq3z6+z8ykuGZkUBS3bqXbPy2NAvupTwHbtnGSkpvLyUZ5eeKFZ9ypZ9Onc007Lc0Jnmtr45p8Itdh081uvplW85kzFPXMTFrYM2fSkxBaQCY0xcwdyCfCe3HuHJ/7+5mfXlLCf5NEW81qipqipD5qmStxYS3axYvpYs/P5/OiRdwOxNcmNR7WrOHa/NKlFL2zZylaixYxX7u7m6La0cHAt9df5yQDoPju28e1Y5tbHW+RFLfVfuGFPGZ+PsW2pYVifu+93Dee47rXqIuKgGuv5bEuuQS4/HL2aU9LY5BeaAGZ0BSzqirGDBw+DLz9tjO2OXM4ibEk2tBFU9QUZXIgJtT3N4lZvny52bVrV7KHMaEZrnW9fv3QSGn7PrSjWTzHDB1HRYUTFJeXR3f3iRNOhH1+Ps9lG6XY81oL1L0+Hq8FunEj18hPnuTkwUazuy3bWMcNvY79+1kSNxDgd4uK+L6nh93l7L0Kdy8DAQbm2fS7wkJObuw9qaqK7/rWreOE6+xZHs/Wgy8ocDrkhUNE3jfGLI995wajvztFGRnx/PbUza78JyNxv0bKYa6pYcewRI8ZzhX+q1/x+w0NfNh1+4EBrj97vRSnlSs5nlgV0SLdA7f4/uQnQ/dN5Lih1+G+x34/lwcAjtnem9tuC1+R7rbbWM3P6+VnM2cy/W7/fhbxufba+ALaystp4e/dS89Hbi7P0dISX4qhoigTD3WzK//JSNyvkeqbt7SMnku3qooPW/hm2jQeMz2dYt7RwXNaV3M8NdHdxFPbHEj8uG7crvz33qOQrlo1uFvb3r1Dg/SswPt8FHPbjtYY3pO/+ZvIFehCWbOG5xChmPv9PM7SpepqV5RUJaaYi0iuiCwIs13n75OMkYhUpBzm/PzhHzPSORobKeY9PRQ2r5di1NnppNytWRN/AxVLdTXd9nv2sM3rnj18HypwiR43lEOHGMR39Cg9DE1Nzmf23tioc1smdu9eino87WhjUVnJzISWFh73zBnGQixcqNXgFCVViSrmIvJFAAcB/IeI7BORy10f/8tYDkwZf0YiUpFSvqqqRiZ84c5RVERr3BgGgM2bR2Hr66PYWRd+6ATj8GGKaE1N+KC1mhrWT+/udnqKf/jh0KIuIym+snEji89Yj0VXF7B5M13l0e6NnWjZdrT9/VzLDwScOIF4Aw5razmByM9nLEJxMScYR44ksRrc175GV0Eij9xcFkJ4+GEGVHR0JGnwipJ8Yq2ZPwjg08aY0yJyBVgn/XvGmBcAyNgPTxlPYvUkj0WklK+RHDPcOWyaXH8/Jw5nzzIt6+GHBxd0cRdJsSVfly510ulC1+5bWpzqdoBj/be0DB1DpOIrsQIIn3qKGpSfzwmJLRjz7rt0tUe6N+5SrMXFXGKYO5fHsalzQPR4APc+FRW0yt290/ftAx54IOY/wdjwi18k/p32duCXv+SMLj+fM8dbbx3tkSlKShBLzD3GmNMAYIx5T0SuBfB7EbkAwNQJg58ijEWFsLE+ps/HwK9IEfJ2grF+PcUvWtBafj4t1u5uZy15YMCp/hbuuG7iCSA8edIR32nTnKhyK9SR7k3oRCtajflY1NWxBG9urhPNnpfH8ycl+O1rXxv+d7u6uE7g9QIvvMD/DNOmjd7YFCVFiCXm7SKywPYLD1roqwG8CGDpSE8uIp8H8E9g17R/NsY8HvK5D8BzAD4N9jH/kjHmePCz7wH4KoB+AN8yxrw+0vHETXs7O3Dcc8+k+cMRalHed9/IOppF+jxWV7J4SbQATjwdw6qqaKEeOMB/4unTWcO+qiq+c8QT5V5aSkvfThCmTePywIUXcsIRidBJka3EF67GfCyslV9czAeQeO/0UWU4Vrmbzk7OwD75BPjjH9U6V6YkscT8Gwhxpxtj2oMi/MWRnFhEPAB+CuAGAPUAdorIJmPMftduXwXQbIxZKCJfBvBjAF8SkUvAdqhLAcwBsEVEFhtj+kcypqi0twM//jGLdn/hC8D/+l9crP3iiG7DhCDUojx8GLjzTgpMVVV4oY5mgUb63EZkJ6PyWDwdwyoqgOeeo9CWl7NIzEcfxV4SsBOUf/s3CuySJY7Ihk4Y7r2Xa+YALeO2Nj6+//3Y1+CewESrMR+LkS6njCojscotAwO8iLNnx9Q6D1f7YO/e8O/b27msc+YMJ2u9vdzW28vl/unTgSuu4P0/fJhzkd5eXoIu/U9ORIaWdAYoI3//94l3ZAwlVjR7J4DiMNuvALBjZKfGFQCOGGOOGWMCAH4D4PaQfW4H8Mvg640ArhMRCW7/jTGmxxjzMYAjweONnPZ2RhSFC7Z59FHgf/9vtuw6eBD40pdYS/Q73+HjxhsZnpxiuC3Ks2edtCUb2BUaWBUrhc1+HggAr74K/Pa3wGuvseNZX19yKo/FE7S2dy/Loubn879Bfj7f790b+bhuUZ0zh5ryzjvOf4PQCcPatcATT/DYp0/z+YknEv8hR6sxP5bfHXVGapVbWlupgtY6H2VC0xYPHeJP/vBhZwL8ne9we1sb/0x89JEzxzh/nr8HY5y5x+bNzERoa2OdANvGV5mcRKrPdvw4cPfdiXVkDEcsy/xJAN8Ls70t+NlI/FmlAE643tcDuDLSPsaYPhFpBTAzuH1HyHfDVs0WkbsB3A0A5bF8kLt3A5/+dNwXAMDJ7bH9RX/6U0bWphBuF7RtG5qZyT8y4VzFsVzWdXUsv/rHP3IN2uejeJ87B+zaxXlRJMt1rIhn7d6uJS9a5GyLVevcPbG55BK2JRXhffT5wlu8a9eOfBZurynRSnrWyzLcOv0Tlv5+rl80NTHacZRd7aFLKKdOOY13Fi3ic24utx8/zt9PVxeXbaIV2ezt5ZADgVEdrpJitLcn1pExHLHEvNgY82HoRmPMhyIyb/inHT+MMc8AeAZgWcmoO99zz/BOcu4cxTw3F3j+eeCb3xy8mDnBcbugbWlP2xENGCq4sVzW5eW0xDs7KWgZGfyj5fNxv4MHndszko5riZaejSVgtjLayZNOUFhp6WBxD8U9sbFpY5Eqso1WI5p4SYmGKilSTjp0Amt/Jzbt0v2+vZ3tgTs7OceIdom2x73tLaBMTQYGEuvIGI5Ybvb8KJ9ljezUOAngAtf7suC2sPuISDqAPDAQLp7vJsbu3cwPGg7G8Nc4MEBz9qc/HdFQxhu3C9r+QfL7nUjpUMGN5LKuqGAQV00N3Y2dnZzj9Pbyj1pZGS2QxsbE87NDibdaWyJUVNBF3tLCNc2WFr6vqIj8nfJyFn/ZuhV46SVa5GVlQyuyjcV4Y6ENVUaP0BoMeXn8qbsnvPb99OkUaBH+/5coSbxpaZzkpmth7SlNWlpiHRnDHiPG57tE5P8M3SgiXwPw/shOjZ0AFonIhSLiBQPaNoXsswnAXcHXawG8adgZZhOAL4uIT0QuBLAIwHsjGs1wrXI3fj9F/fnnU2rt3L2GWlDglPYsKgovuOHWXG1wW3MzP7fC3dZGy7ysjBOFsjIeN5612midyUKFKhDgGuVXvjL8/tx79wIrVgxeM1+xIvKaeW0tc7M3baKIi0SeAAxHWBPp+BaOkVT0UwYTOoGdM8dpiTswwOe2Nm5fvtzpPe/zRRfzjAxW47P19pWpyfTpTkfG4RJrPngfgBdE5G/giPdyAF4AXxjJiYNr4PcCeB1MTXvWGLNPRH4IYJcxZhOAX4CFao4AaAIFH8H9ngewH0AfgG+OKJJ9JFa5m0CAYm6t8xRaOw+NlI6VFx7qsl6/fvCa4jXXMJinvZ3iDfC2LFzIOMJ41nqjuYhraritrY3WT1ubk+41XHdyXR3Ht3ixsy3Smrkdn71HLS1cK50/35kAuNe/4kmNS+T64yGeCH4lPkJjLhYvdmrc19VxKeYLX3De33ijE82ekxM7mt0uRWk0++RlrKPZo4q5MeYMgKuDxWKsrfGKMebNkZ32P4//KoBXQ7b9wPXaD+C/RPjuowAeHY1x4L/+11E5DAYG6FsWYSRUijKc4KhQsSou5h+0rVs5xxEBVq9mIkA8x46Wsw3wD6Vt/XnkCN2aPh87icXTHS0ciYhfdTXnbZ98wh9oVhYtrGnTwtc4T1RYh9PxLZR4UtDGex0/lQn3uwj9AzwagY2KMhyiutlFJFNE7gPwVwACAJ4eLSGfUNjC2MMhLY2q4vMBn/sclaupCXjjjdEbX5JIxM0brq57Zibw139NK/qDD1hnJ16hiOYirq7mMoAxzsqGx0Orxq7zD8ednEjN9ZoaWmEeD9c7e3sZC9DQEF6kE63nHu364/13iZWClox1fEVRxoZYbvZfAugF8CcANwNYArreJxfNzfHve911NMdCmTt3Ugi4JVE3b6KFSGIV4LCR7+EsWesOz81lZLwNNJo2LXKUfDwWaCKlZ20d99mzKZQeD7e3t4e/7kTL2kay5G1DlXj/XcItn9jqe2fOjNz6VxRlYhBLzC8xxnwKAETkFxhpkNlkYBIJdjQSdfMmIlYbNzKcoLcXKCykqDz3HNeabROUEyco0vPnD50cVFfzdUkJH0uW0J2fm+sU5HALajwV6xJ1Nds67h4Pg58aGng90YL6Elm+iDQ5yskZngDbe2Cb0+zYweeyMk6c8vLo1Sgq0gA5RUlFYol5r30RDFgb4+EoE4VEA7aA+IuYPPIIhbqwkG7ygwcpjqdOMbCooIDFW3p6+Drc5MAtdF4vJwGlpRSo0H1jrb8PJ9CsqorCanPSFy1yctJHw6qNNDl68kneNzfxLCnYXu1793L5IyuLK0LHjnHJorubUfhLlw4OAFQUJTWIJeaXikhb8LUAyAq+FwDGGJM7pqNTkkasgK3hBk5VV9OCzcriaoXfT5d1Zydr7wCOhVhfH775SDihixYhHzox2b+fyQvNzcC//AsFbP58fhbO0g13rdZyvvTSwZbzcHLmIxFucjTcCPW6Ot5PK+THjzOiuq2NXoWFCydAG1RFUYZN1AA4Y4zHGJMbfEw3xqS7XquQT2KiBWyNJHCqrs4R8t6g36evj2Le2ck0nTffZIR6NIGybU2ffZZjqq6OHBDmDs7bv581sbu6KIhtbcDbbw+OgXRbupGuFUhOffNEA+ks5eUMEMzM5Hu/38lx7u93Cp5ceKGulytKKhJVzJWpS7RI6JFUFisvpwVo6ehwci/Tgv8bz55lDfd4rNx4JhZuAXz3XUafezx0V+fmUtTec0WDuC3daNfqnlCsXz8+IphIkxR31Lvt3tXayvvt8dDNPmsWPSG3386lg3jbvSqKMrHQIoJKRCKtgQ9nPd2yZg3wq18xD72zkxZiWprT17u/n5a7SGSBcru8GxpiB4S53fLWRV1Y6HTJ7O52LN3Q4LmRXOtYEW9sgjsWoLWVkfenT1PES0oYwNfXB1x0kTPZSUobVEVRRoyKuZIwI6ksVlkJXH898869Xq6TZ2XxdUYGqyF1dYXvIhUuKn3LFmYLugkntlYAt27lGr0V8mnTuH9WVvjguXiuNZH4gfEq0hIa9NfT40yciooYcHj55Zw09fRQ6KOlyimKMrFRMVcSJtGc8lDuuccR5bQ0RlQbQ2uxu5u52qtWDf1euKj0mTNZwGX2bGe/aBOLe+9l32mALva2NlqnGzaEr94V61oTyccfry5mtbXAiy/ydX4+vRCHDjEFLTOTQXvNzfx3UPFWlMmBrpkrCZPIum2s719wAcVm9myun9fXO7WpQwPZwlVFq6oCzp+PPyBs7VrgiScocqdP8/mb32TKVrgAuljX+vTTbPCybRsfgUD4+IHaWuBb32IswJ49rBbX08Pv3nVX7Ap78VZ9sxMGn4/eju5u4E9/4vKFCK9Xu6cpyuRDTIr0Ex4Nli9fbnbt2pXsYSgh1NZSFDdvpqVdVUULsrl5sHCuXz/U5d3cTAEtLh6e69ptLbst73gmJ7W1PNeMGXTT+/18rFjBSP377qNg1tSwlnxbGycvPT1O0cH8fI5/1arI501kjPYeBQJsD5CZSc+H18tgt6uvphdk506mp11wAccbj5UuIu8bY5bHd2cd9HenKCMjnt+eutmVpFNZSTG+5ZbBQg0MDmSL5PIeiZs60Up37jXvY8coliJ8ZGVxn5oaYNkyR4Cbm/l5dzeDzmbNopVvz2et5UjnTWSMNmAvLY3CfeCA0yLWCvmbbzJjwMYqvPUWx/PYY+p2V5RUJSludhGZISKbReRw8Lkgwn53Bfc5LCJ3Bbdli8grInJQRPaJyOPjO3plLIin9/ZI3fvDPa8lNA2usZEi2dxMV//HH1PgDx1iYJ8VYJvDXVhIEe3uptvbBqUtWRL9vHV13G/rVuCll/js94ff151TX1zMY0+fzqWLjRv5/fPnuSRhj93WRitd3e6KkrokyzJ/AMAbxpjHReSB4PvvuncQkRkA/g7sn24AvC8imwD0ANhgjPmjiHgBvCEiNxtjXhvfS1BGk3gj5IfTnjXR8x45wtKy69Y5bnuAa96NjYwGX7KEzy0tFPTGRgqk10sR/eADut8BinR3N9/b6nf9/VzXvvpq7h/pegHuZ2vP5+byWNu2saVsKBUVTt37rCym7nV0MCahp4eTiYEBlqK1zf76+liWtqZm9O6roijjS7IC4G4HO7Ih+HxHmH1uArDZGNNkjGkGsBnA540xXcaYPwKAMSYAYDeAsjDfV1KI4VY2i5dIAWSh5z10iE1ISkudiPMHHwQeeoiCPWsWxXT7dor5wAAFcu5cloSdNQv47GedKHuAwu/3U6yLixlNfuWVdMV7vbGvN1JYS+j22lpg0yaWpy0spNXd3MzX5eWsG19YSBHv62MqoG234PVyYqIoSmqSLDEvNsacDr5uAFAcZp9SACdc7+uD2/4TEckHcCuAiK3MRORuEdklIrvOnj07okErY8dYuNAt0arEhZ731CngqqsofLbi29mzjkXe00OLNzOT2z71KaeaWlaWY2m7o+wLC2kxG+O43h97jPXk7Xl7emgtP/nk0Gj1QABYuZLHb2ujVZ+dzQZ+7n3t2npuLkV6YIDn7Ox0jmVbxPb2Oo+eHn4nP3/k91pRlOQwZm52EdkCoCTMRw+53xhjjIgkHFIvIukAfg3gJ8aYY5H2M8Y8A+AZgFG1iZ5HGTnxFkoZbRe6JVYAmfu869YNrfhmy89edhk7iwF0Tzc28nruuIPv3a76zEzghhucrm+LFgHf/W74HuruaPXCwqH553YpYPVqlmXdvp1iPWfO4H3r6mht79jB80+fTo9AUxNd7dOmsZStHWd/P5/nzWMVuEWLRuFmK4qSFMZMzI0x10f6TETOiMhsY8xpEZkNoDHMbicBrHa9LwOw1fX+GQCHjTFPjny0ylgRrVAKMDbV0EInDzU1Q48bKdgs3Bq6z8fnkhJa7QcPOpa6vY6RRNnHmmy4o/j376eQG0P3vXvf8nLgtdeczmizZtF13t9Pj0NZGQvyXHQRrfDQXvGj2fFNUZTxJVlu9k0A7gq+vgvAS2H2eR3AjSJSEIx2vzG4DSLyIwB5AO4b+6EqIyFSo5Knnx5+57Vo2MnD4cPA0aPA88/Tmn7//cH7RQo2C7d2X1hI4W5u5vOllwLLlwM/+Ylj1Y9kiSBWRL37+KdO8bOrrnJc5nbfNWvo2jfGaaZSVMROaH6/k8/+058OdvGPV8c3RVHGjmRFsz8O4HkR+SqATwB8EQBEZDmArxtjvmaMaRKRRwDsDH7nh8FtZaCr/iCA3cIInqeMMf887lehxCRSo5KXX+Y6cLz53fFSXU1LdO9eWqiFhQz22r6d1uiCBZHLz1qLvq2N487P59r3Y485x7bW/mc/y/dPPul4FdavH96Y44nkdy8FRNq3spKu/d27nXQ4G2RXUDB0fCreijJ5SIqYG2POA7guzPZdAL7mev8sgGdD9qkHIGM9RmV0iCRUxsSX351oY5K6Olqb1tUMMC2rt5elU/fvZzT6tGnAD35AsbbuZbscUFk52PXs7r5mxzSaNdYTqXUfa99vfCN8tTjthqYokxutza6MKZFSzlascIqbWMJ1I0vUFV9ezujzzExnm9/PHO+WFuCSS2ip9vU5BV42bKDbP94e7SPp5x6ORNz0sfYdy6wARVEmLlrOVRlT3L3ErXVtrcRY1miipVYBTh7+/d8ZpAYwejsnhwI+cyaLo2Rl8dHdzTXoSy9lEZZbbx18rGgV2Ua7x3kikfyx9h2rrABFUSYuKubKmBNJXMKJvHu/4YpmSQnTsWxBlN5eCvdnPgO89x5zqgFa762tPKYxfB1Pj/aR9HNXFEUZC1TMlaQRy4IcjmhWVzMffOlSNhlpbaVV3t9P8fZ4WK61v5+vZ8/mPitWOJ3M/H6ms50/D1x/vVNcxjLSfu7JJNEYBEVRUgNdM1cmLMMp8WrTvIqLWWTl9tuBm26ihX/0KAW6p4dpWk1NdMe/9hpF+/77uf2NYD3B665jjnnoOv1EXJeOp9/5xo3AnXcyXe/oUSdeYKTpgIqiJB+1zKcgqWKdRVpvH441X1XF6mlnz9Klfu4cLfXsbAr2pk1OAZiZMynqH33kFGYJXaePd116PO51uOj6hx5ifflAgOe1DVhEmK7n9wP79tGDMdJ0QEVRko+K+RRjtNOqEj13osKWaDBXNBf4k0/SSt+2jZZ7VhaFva3NKWSzeTMj3213su3b6YIfTnCbvdf9/bTgd+wAXngBePhhYO3a2Pck3vsVGigYCHApobGR19vcTCE/d44BgQ0NTrnXkycHR/4ripKaqJt9ihGaVmUt0K98JbJ7Nh4XbizcaWYZGXRtr1nDvOhox0v03NFc4LbXd2urI2B+P0U/L49iO3MmrVcRp6FKTc3wgtvcBWz8fqdj2SOP8Dqipd4lkpYXWkHuwAEKdSDgpM61tzM1r6eHnojeXnoqTpzQwD1FmQyomE8x3H/4bdMO20oznGAMJ9c7HHYS0dND0QRoAe/eHfl4wz13ZSWF/9ln+WytWbsG7/XS6u7upshefLFTyOaCC4Djx+mCPn6cDUrOnx9e3fLQAjYivPe9vbwf0fLVE8llt5MUi33tFviBAQb8GcMc+/R0p6Oa1mRXlNRH3exTDPea8oEDjoWanx8+jzucC9da8nfcEf8asE0z27bNETe3izvcuq09dyDA79nI9Kef5iNRrNVu3ekzZ9KF7vPxnixaRCu6qIjj6uykmF977fDWx71ertEXFjr7WAvduu2jpd7Fk5ZXW8tJ2ebNvK8+HycQxnDcFutpsNfW1cVJwiWX6Hq5okwG1DKfYrgjxFta+EffWqfAUMFI1JKPRCwXd6TiLH4/z9ndzXVsYyhcw43ArqykmFdXAzffzGvas4cC9/HHPF92NtuCzp9P4Z01K/Zxw3kRTp6kFWytfusJKC3l/Qi1qAEn9S7aZ6Hn9HoZ4NfQwLXyWbPoZt+7Fzh9mmPJzQU+/WlOYGbMYODbddex8YqiKKmPivkUw72mbNeG3R24QgXDLSrWkhdxLPl4y5hGcnEvWRK9OEtNzWA3tQgFabilU933Yc0aim13N13qn3zCNe7+fop7Vhabwdh+5tH42c+ADz4Afvtb4JlngLffpoBWVVHIz57lufr7WbimoYER5pFS7+JJy3N7TY4fp1Vu7+/y5Tz/u+9y/IsXMxWtuxu44gpWvUtPVxe7okwWkiLmIjJDRDaLyOHgc0GE/e4K7nNYRO4K8/kmEdk79iOeXNg15V/+kr2tfb7IgpGoJR/tnPffz4IuTU3ctmIFxSdS7nhoS087AaiqSiy6PFIQ3c9+xrXx+npasD09vEaA+emrV3MiEStArLYWeOUVHiMtjWvTx49TtAMB4F//lRZwaytLy9r89U2bgNtuCx+sF08uu/Vc/P73wIcfcgLi8XB54NAh3qd58+hSnzOH5wWYRx8IJD83XlGU0SNZa+YPAHjDGPO4iDwQfP9d9w4iMgPA3wFYDsAAeF9ENhljmoOfrwHQMb7DnlzEk8ft3iceSz7W+X7+88Fry7NnR84dr6xkMZcPPnBael52GYVw9uz4zhktFe8Pf6CbXYQR9unpFMIjRzi5ibeyW3U1909P5+TEYiPIKyuZCnfLLRyHde03NvIe2L7o4a4/mth6vcBbb/He+HyORyE7m2lo1dWcPFx9tRPzMHu2EzOhQq4ok4dkifntAFYHX/8SwFaEiDmAmwBsNsY0AYCIbAbweQC/FpFpAL4N4G4Az4/DeCct8eRx231sDre15IdbxjSR3PF77hlZS89IzVqefpqiClDIBwboBs/M5HN9fXxFagAK8rRpFO7eXidS3DZyWbeOE5IrrnDiDjIzubbd2Dj8PH9bez4QoIC3tfH8nZ200Pv66CnYu5cudzsBG2lTGEVRJh7JEvNiY8zp4OsGAMVh9ikFcML1vj64DQAeAfD/AuiKdSIRuRsUfZRrQu2ISLQi22hUPxtOFTg3kZq1vPwyBdXvp5CnpVGAAwFg7lymtcVLeTnd9V4vrXF7TBHHI7BvHyPyc3KcGIDubkaXR4rmD4f7nn7wAdfdd+6kgOfnO4Kens40OzvJOHhweN4URVFSgzETcxHZAqAkzEcPud8YY4yImASOWwVggTHm/xaRebH2N8Y8A+AZAFi+fHnc51HCE8uqtmJTU8Po8KVLgYULR1ZpbiQtPSOVdzWG0epHjzpWuV3vXr06/uPX1jKY7ZNPeFyfj4Lt9/Ocy5bxuJddRpf4J59QZOvrKebz53PfeCzl0CWDfftodV9+OdfIMzPZoz03l1b/5Zfze2+/TQ/ASLwpiqJMbMZMzI0x10f6TETOiMhsY8xpEZkNoDHMbifhuOIBoAx0x18FYLmIHAfHXyQiW40xq6EkFbfYNDfTMt23b7CLd7zrgEcq77piBa3Y9nZatf393Meuea9bF9ubYK+3r4+pYNbFLULh/PSnuVYO8PpXrmSzk6NHaTFfeCEnD9u2xZciVl3Nc+3Zw+vwePi8fTsnEKdPO2vmGRn8TnEx8KlPMU0ukaUDRVFSi2S52TcBuAvA48Hnl8Ls8zqAx1yR7jcC+F5wDf1pAAha5r9XIR974nGZP/00C8oEAhSW0lK6e62LNxlrtW43fU0NXc75+RS/tjamcJ086aSOFRfTui4qiu1NsOvxe/Ywd7u0lNa2daGfOEE3uCUzkwLe2ckJjnXzA876dzRqamh5Z2Xx+01NLGqTlsbJwcAA0/amTeO1vP02hdzjiRxkpyjK5CBZeeaPA7hBRA4DuD74HiKyXET+GQCCov0IgJ3Bxw9tMJwyvsRTVrW2lsVcjKHQeDx0KduiKUDy1mpt8F5uLgW3uZku70OHaE0vWAB88YvA5z5Ht3g8JVQBp6COuxBOZibd7n19zMt/7TWncIu9fytXUpATzWVvaeG4bM59ezsnHtnZ/L7NNkhP54Slr48TFU1BU5TJT1Isc2PMeQDXhdm+C8DXXO+fBRAxFMkYcxxARaTPldEhnpKu1dW0CgEKyuzZzLWur2dOuhWzeNZqx6JtqLvpSWYmK7u1tlLQ//Vfefx162iRu4nmTbDr8Xl5jkXe1MRtmZksDwswr9v2S6+u5udLllDsW1tpcS9bFvs+WPd9dzeP39lJwQ4EOCnJzuZzQwMnCCtX8v6rkCvK5EcrwCkxCVfStbOTbuSf/pTrvf/yLwzs8vspNjk5dK0HAs5EIB4LcbQau7iPt3498G//Bvz5zxTDcE1PgPhKqLqxBXXmzOE1t7Tw/uTns1CLCK/fdmKzHoJjx4CtW7lPRgYt9BMnYje4aWriPbZWfU6O42LPyaGw2xrsBw9q1LqiTCVUzJWYhJZ0HRhworEzMhg01tgIvP8+y4a6xeZLXwJefHFw97JoJNItLBYbNwJ33gk8/zxFu7mZ4+wIlhoKbXoSTwlVN3Y9PjeXot3QQHd3WvBX5fEMrSdfWUlXf26uY02vWkVXv/saw92HpUt53y+9FLj1VuAzn+F58/M5Yejo4Hj9fgYeHj2q5VoVZaqgXdOUmLgjwltaaCEGAhRrr9fJz25vpzDedJMTNX7PPYmdK1JeeKzAuVCXdEUF8O1vO4FtAwNOg5izZym0fj9F1Fqv8eS0h55n+nTgnXcouBddxHtz+jS9EllZ/I67nnxlJe/VTTc5og9wfKENbkLvw8KFtOYLCvj5okXA5z/P1y0tXCu3BX283viC6hRFmRyomCsxCS3pGghwzTY7m5/39TnR2YHAyFKgIuWFR3MXhyvZ+u1vO61AAY7bGE44jGHN8gULhjYbiZbTHnqeQ4cY4FZURPH2+2mZ+/0cw8yZfO33MxXOinU81xhpn6oqejlCx/TRR5z0iPB8V19NQR/vVEBFUZKDutmVuHA3Z5kzh+71vj66iq2Y5+ezQcmzz8bvVg8lkqu7ogL4+tcpZpddBnzjG84acziXdGMjRVuE26yVaq3kBQu4JHDbbfx+aBOWcISe59QpRzztWnVBAa31QMCJVr/qqsENW+Jx58ezj/UStLU5RWuysijkxcVatlVRphIq5kpCVFYCDz9MoejooGAWFlLQCwtHvkYbrlvYbbcxwO6tt2htZmQwgOyhhyho7gA9S18fn8O5mquqOOFYs4ady+INtgs9T2srhbvLVVTYlmqdM8eJKG9rowX/61/z3D/7WeRuadHug3uf2lrgwQcZF7BjB89x5gy9BLZQjQbAKcrUQd3sSsKsXUur9umnKSTGANdcw/XxUGu8ttbZr7OTbu2BAYreihXhvxPq6l6/nuvcubmD16EbG2mZhnNJp6czFc261627PT2dggoMTbnr6aG7+q676GGoqGAqm10f9/kokHb/vDxeU0+Pky5mBf7hh/ndmhpg/356MAoL+b233qJAP/ZYdO9FNJe/bd/a3Mzr7OvjOF57jePLzNSyrYoylVAxV4ZFZSVFOhq1tbSejxyhW7qxkVZsVhZTrOIRtdpaRsPX1Tk1xwG2+Gxro4B9//u0sAGniEtREc9n07VssFlJieM9cAeZNTQwkM3no/AfOgQ89xxd5AsWUBhPnOCx5s/neebM4bVNm0Y3d38/hfSxx5wJz9at/K7Xy0nMtGk8xtmzsdezo+Xb28lRTw8nKFlZfN3Vxfv65S9r2VZFmUqomCtjRnU1BTU3ly7ggQGn3WhHB93B0UTNBnf5fLQ0u7vZvAWggHk8/P4DD3AdPSfHCb77h39gCdODBxn0JsKJwD/8g3Mut0V/8KBTxS0/n+vhubmsoLZoEfdZsICCaaPJ8/JYnjUQoEj7fI71/fWvA1u2cGJh677X13PykJPD7eHWs0Mb1VRUOJMJW1oW4OSjqYnX5fE4Pdnt2r07SE5RlMmPirkyZtTVUfzy8hgk1t/vuL/9fsctHSlIy7rBly2jmJ05Q8vTGB4nLc1JAaur47N7XXnx4qGWLUChq6uj+J44QbFsaaH13NPD8+3YQTG3+fVnztBdfuoU8Dd/A9x3H4/t9Q527x8+DDzyCK9txgxOQJqbOd6MDHoUPB6eO3Q9266Df/IJhby/n+NbvpwThsZGBuqVlHDScPYsv9fTwwkSwEmFXVJQFGXqoGKujBm2z7cV7q4up9VoZiYty6YmYPduCmxo2VbrBm9spBj6/Y7b3OOhOPp8TpGa0L7g7jXn2lquM2/Zwmh2O7ZTp2jldnXxWO5IcJu7/fvfs2pbRsbgQLm2tqEehfp6ro+npXEyMHs23eEdHbT4Ozv5vQULhgYL2nXw9nZn4tPWxoIzGRmO56CzkxZ7XR1fDwzwnDk5FPMVK8bgH1NRlAmNirkyZqxZw6pwR45QaFpaKMhZWRSqujpar1deGb5DWXk516737aOFPGsWn21JVp/PsXTz8ganYrnXm71eussbG3m+ri6KelkZxVaEBV+Mcda27Xp4RgYt34wMCmx7u1Oitq5ucEAcQGu5sJDH7O6muC5YQGu7t5djXbXKCfxzj/PVV3kOn88JaLMueoDj7u/n5CMnh4VntmzhPTWGcQhz5yZeqEdRlNRHxVwZMyorgUcfdaLZi4qcaPaWForPNdc4vc6BwZb19OnASy9R2IyhgPl8FLSeHgpvZyfF7LLLnFSs0OIur79OC9ce48QJfv/4cYptIMDzzZ3riPTixRTfujqK+vTpnEx4PCxpu3IlLe3mZn7XBt5lZDjlWt95h595PBT0iy4aPFnZuJEueRvp3tPD65k5kwV52tud+2InAmlptPKPHQMuv5zlcnfv5ndt0xsNelOUqUdSxFxEZgD4LYB5AI4D+KIxpjnMfncB+H7w7Y+MMb8MbvcCeArAagADAB4yxvzHmA9cSZhIUe/r1lFo3SVN3Zb1xo1s4mJTy3p7OQEoLaUb/JNPKNDWAv7gA04WHn00fJe36dPpAj93zlm/t01h8vN5jt27GRlvxXDdOlq/tj95Vhb3a211qrHZjnF2Xf7hhxlZ7/PR3b19O70CRUVOxTx7fd/8Jq9r+nRHqG1r05kzB69926UJgBa618s1/KqqoZMERVGmHsmyzB8A8IYx5nEReSD4/rvuHYKC/3cAlgMwAN4XkU1B0X8IQKMxZrGIpAGYMb7DV0ZKrJKmTz1FC76nxwl4Gxigizk/n+7x5ma+Tg/+L7biV1PDz9raKJJnzlA0OzqcynXWfW29BMePU3DdngE7xosvdqxs64pvbgY++9nwqWM28K6mhiJ88820zFtb6TG47TbHIp82jeNpbKTYd3Q47nSPh+ezrnePxymlW1rK9f5rr9UUNEVRkifmt4NWNQD8EsBWhIg5gJsAbDbGNAGAiGwG8HkAvwawDsDFAGCMGQBwbsxHrIwq7uYt1kXtLnJy7JizHm6bpNiI7cOHableeinX2y3NzQwi+/hjWt2trRTqgQEnf7ynh8e0BWXS0pya7fn5FODQMRYU0MquqWHA3g03ANddRwvcXQ/eveZvy99a1z3gPD/1FIXcdk7LyHCi3gcGKOoZGRRuY5x0Pvv+mmuASy7h8TQFTVEUIHnlXIuNMaeDrxsAFIfZpxTACdf7egClIpIffP+IiOwWkd+JSLjvAwBE5G4R2SUiu87aXB4l6cQqV5qW5lik7g5gaWkUSJ+PbvFf/5qFWRoaOCnYsYPBa42NFOiMDCcSfvp0HiMriw+bn52eTgHNzqb4h46xp4eFWD75hPsaw8CzWK1aw5WZzcvjRKWzk+dqbaUHoa2NFrrPxyYw5eVcA7/lFua5izB47y//kkIerTWroihTjzGzzEVkC4CSMB895H5jjDEikkhmbDqAMgDbjTHfFpFvA9gA4M5wOxtjngHwDAAsX75cM3AnENHKlV54IQW6t9cR0LQ0Cm5uLicAAwMUw44OBr2VltJ67eri6+PHnXagNpUNoDhbS13EaeU6MEDr3FJbS3f466/zO7m5HMtbb/Gcf/EXg8ecl0frff16PtfU8LwXXEBXfUkJe4z39NBV3tvLR2cnvy9CV/+sWbTU6+vpnt+1a3DUe0GButYVRRnMmIm5Meb6SJ+JyBkRmW2MOS0iswE0htntJBxXPEAB3wrgPIAuANYG+h0ArUA9yVi1ipbq7t0UaBGuL9sgsMJCBpbZYLGMDPYRnzHDWcPu6BgsmL29FNZTp2ipZ2dTnPv6uAZ/0UW0ggGnFO077zg5352dPMecORT+mhp+z3L0KF382dm0vo3hGM+dYwT8woWMjDeG6/giTkMYgBOMri6OOyeHaW7XXsvPok18FEVRkuVm3wTgruDruwC8FGaf1wHcKCIFIlIA4EYArxtjDICX4Qj9dQD2j+1wlfFmzRpa53fcwXXxvDwK9sqVFNcZMyiwXi/fZ2bSor38cgpiaytFvqeHgm/d9Tk5dF0vXMjzFBfzPF1dbIxSUcHtthStiNPIpLeXxzp3jhOL8+edde5Dh+h6b29nBLutm56Tw+N1dzPn3tZR9/n4OeBE9AcCPM/Zs06am7rSFUWJh2QFwD0O4HkR+SqATwB8EQBEZDmArxtjvmaMaRKRRwDsDH7nhzYYDgyW+1cReRLAWQB/O66jV8Ycu15dXU3hu+QS5ofb9fDWVgrfokVcC7epYwsW0PJuauK2uXNpJQO0opcvp7t7yRLgT3+i5dzQQHf4rFnA//yfFOU33nBy023ke1oahbupifsuWQLs2UOL3C4HlJXRCm9tdbq1GeOknhnjtExNS+P29HQnMK+vj9/Nz2eam1rjiqLEQ1LE3BhzHrSoQ7fvAvA11/tnATwbZr9PAKwcyzEqySfUtWzXjQsKnJxzj4ei7fezpnprK1307vzv6dM5CVi82DlWaytd3U1NFOtAgAFuhw9zMjBnDsW8pWVwFTYrwK2tdJmfOOEUjvF4nFz03t7B12KbzNg2sO6o+vR0rsfb81RVsUmMCrmiKPGiFeCUlCFarfUVK5z8bxsc5t53wwZ+ZtPgjh6lVW5zt0UosGlpDDyrqqLr3AqsLV4zMMDz9fWx97lb6G3FOBt5b79nLW7bAtUWx7HnzcykoM+axcmACrmiKImSrDVzRRkRlZXAz39O6/vmmymkoelt7n1D0+AuuIDib5uiuAvP2H7jAIUY4Oc5OVyrB2i1AzyG7eBmLW1rbQNOLrvXy2Pn5/P1ypU8XnExPQfZ2RRyda0rijIc1DJXUpp4o7xD91u3zumclpHhiLgNrmttdXqXd3fTmraR7+3tFGjbwc02Y7F4vVznt/ntdt08N9epBX/FFRzD3r1DK8gpiqIkioq5MiUpL6fQHj/uCK4tn5qXR+HNymKwWnk5n/1+fteuve/dS0G3vcp7evh63jxa7jk5dO0bw/0uv5yudLf3YO3aZFy9oiiTDXWzK1OSNWsovFdcQWvZ66WAX3kl17ALCoDPfIbub6+Xgl5SwvcPPMCHDVqzZVYzM7l2/5vfAE8+yej5zEyKelUVz6UNURRFGQvUMlemJKGpby0tXM+2ndDcbUqfeorBcqWl7KrmtqYff5ypb3Yd3HZdq6xUq1tRlPFDjLvP4iRn+fLlZteuXckehqKkJCLyvjFmeaLf09+dooyMeH576mZXFEVRlBRHxVxRFEVRUhwVc0VRFEVJcabUmrmInAVrwSeTWQDOJXkM8ZAK40yFMQKTZ5xzjTGFiR50gvzugNT4d0iFMQI6ztEknjHG/O1NKTGfCIjIruEEEY03qTDOVBgjoOOcKKTC9aXCGAEd52gyWmNUN7uiKIqipDgq5oqiKIqS4qiYjz/PJHsAcZIK40yFMQI6zolCKlxfKowR0HGOJqMyRl0zVxRFUZQURy1zRVEURUlxVMwVRVEUJcVRMR8DRGSGiGwWkcPB54II+90V3OewiNwV3JYtIq+IyEER2Scij4/y2D4vIh+JyBEReSDM5z4R+W3w83dFZJ7rs+8Ft38kIjeN5rhGa5wicoOIvC8iHwafPzcRx+n6vFxEOkTk/ok4RhGpFJF3gv8XPxSRzLEa52igv73kjXM8f3up8Lsb6TgT/u0ZY/Qxyg8ATwB4IPj6AQA/DrPPDADHgs8FwdcFALIBXBvcxwvgTwBuHqVxeQAcBTA/eOw9AC4J2eceAD8Pvv4ygN8GX18S3N8H4MLgcTxjdP9GMs7LAMwJvq4AcHIM/52HPU7X5xsB/A7A/RNtjGBXxVoAlwbfzxyrf/NRvF797SVvnOPy20uF390o3MuEf3tqmY8NtwP4ZfD1LwHcEWafmwBsNsY0GWOaAWwG8HljTJcx5o8AYIwJANgNoGyUxnUFgCPGmGPBY/8mONZIY98I4DoRkeD23xhjeowxHwM4EjzeWDDscRpjPjDGnApu3wcgS0R8E22cACAidwD4ODjOsWIkY7wRQK0xZg8AGGPOG2P6x3Cso4H+9pI0znH87aXC726k40z4t6diPjYUG2NOB183ACgOs08pgBOu9/XBbf+JiOQDuBXAG6M0rpjndO9jjOkD0ArOCuP57mgxknG6+SsAu40xPRNtnCIyDcB3Afz3MRrbiMcIYDEAIyKvi8huEfnOGI91NNDfXvLG6WYsf3up8Lsb0TgxjN9e+qgMeQoiIlsAlIT56CH3G2OMEZGE8/9EJB3ArwH8xBhzbHijnLqIyFIAPwZnuBOR9QD+0RjTETQYJiLpAK4BcDmALgBvCPsqj5bADQv97U1sJvhvbz0m/u8OGMZvT8V8mBhjro/0mYicEZHZxpjTIjIbQGOY3U4CWO16XwZgq+v9MwAOG2OeHPloB53zgpBznoywT33wj1oegPNxfncijBMiUgbgBQBfMcYcHaMxjnScVwJYKyJPAMgHMCAifmPMUxNojPUAthljzgGAiLwKYBlGz1odFvrbm/K/vVT43Y10nIn/9sZq8X8qPwD8PQYH4TwRZp8Z4LpNQfDxMYAZwc9+BOA/AKSN8rjSwWCfC+EEZCwN2eebGByQ8Xzw9VIMDsI5hrELwhnJOPOD+68Zh3/nYY8zZJ/1GLsAuJHcywJw3Tg7eJwtAG4Z6/s6wuvV317yxjkuv71U+N2Nwr1M+Lc3rj+0qfIA1zzeAHA4+I9g/1AsB/DPrv3WgcEsRwD8bXBbGQAD4ACAmuDja6M4tr8AcAiMsnwouO2HAG4Lvs4EozyPAHgPwHzXdx8Kfu8jjFKU72iPE8D3AXS67l0NgKKJNs6QY4z1H5WR/Jv/H2Cg0F6EEcaJ9tDf3tT47aXC724U/s0T+u1pOVdFURRFSXE0ml1RFEVRUhwVc0VRFEVJcVTMFUVRFCXFUTFXFEVRlBRHxVxRFEVRUhwVc2VcEJF+EakRkb0i8jsRyQ5uLxGR34jI0WCnpVdFZHHwsz+ISIuI/D65o1eU1EV/e1MDFXNlvOg2xlQZYyoABAB8PdhQ4AUAW40xC4wxnwbwPTj1tP8ewJ3JGa6iTBr0tzcFUDFXksGfACwEcC2AXmPMz+0Hxpg9xpg/BV+/AaA9OUNUlEmJ/vYmKSrmyrgSrD98M4APwZ7H7yd3RIoyNdDf3uRGxVwZL7JEpAbALgB1AH6R3OEoypRBf3tTAO2apowX3caYKvcGEdkHYG1yhqMoUwb97U0B1DJXksmbAHwicrfdICKVIvLZJI5JUaYC+tubZKiYK0nDsMvPFwBcH0yP2QfgfwBoAAAR+RPYUeg6EakXkZuSN1pFmTzob2/yoV3TFEVRFCXFUctcURRFUVIcFXNFURRFSXFUzBVFURQlxVExVxRFUZQUR8VcURRFUVIcFXNFURRFSXFUzBVFURQlxfn/Aak59c0N7TLBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_kpca = rbf_kernel_pca(X, gamma=15, n_components=2)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(7, 3))\n",
    "ax[0].scatter(X_kpca[y == 0, 0], X_kpca[y == 0, 1],\n",
    "              color='red', marker='^', alpha=0.5)\n",
    "ax[0].scatter(X_kpca[y == 1, 0], X_kpca[y == 1, 1],\n",
    "              color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "ax[1].scatter(X_kpca[y == 0, 0], np.zeros((500, 1)) + 0.02,\n",
    "              color='red', marker='^', alpha=0.5)\n",
    "ax[1].scatter(X_kpca[y == 1, 0], np.zeros((500, 1)) - 0.02,\n",
    "              color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "ax[0].set_xlabel('PC1')\n",
    "ax[0].set_ylabel('PC2')\n",
    "ax[1].set_ylim([-1, 1])\n",
    "ax[1].set_yticks([])\n",
    "ax[1].set_xlabel('PC1')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 새로운 데이터 포인트 투영하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist, squareform\n",
    "from numpy import exp\n",
    "from scipy.linalg import eigh\n",
    "import numpy as np\n",
    "\n",
    "def rbf_kernel_pca(X, gamma, n_components):\n",
    "    \"\"\"\n",
    "    RBF 커널 PCA 구현\n",
    "\n",
    "    매개변수\n",
    "    ------------\n",
    "    X: {넘파이 ndarray}, shape = [n_samples, n_features]\n",
    "        \n",
    "    gamma: float\n",
    "      RBF 커널 튜닝 매개변수\n",
    "        \n",
    "    n_components: int\n",
    "      반환할 주성분 개수\n",
    "\n",
    "    Returns\n",
    "    ------------\n",
    "     alphas: {넘파이 ndarray}, shape = [n_samples, k_features]\n",
    "       투영된 데이터셋\n",
    "     \n",
    "     lambdas: list\n",
    "       고윳값\n",
    "\n",
    "    \"\"\"\n",
    "    # MxN 차원의 데이터셋에서 샘플 간의 유클리디안 거리의 제곱을 계산합니다.\n",
    "    sq_dists = pdist(X, 'sqeuclidean')\n",
    "\n",
    "    # 샘플 간의 거리를 정방 대칭 행렬로 변환합니다.\n",
    "    mat_sq_dists = squareform(sq_dists)\n",
    "\n",
    "    # 커널 행렬을 계산합니다.\n",
    "    K = exp(-gamma * mat_sq_dists)\n",
    "\n",
    "    # 커널 행렬을 중앙에 맞춥니다.\n",
    "    N = K.shape[0]\n",
    "    one_n = np.ones((N, N)) / N\n",
    "    K = K - one_n.dot(K) - K.dot(one_n) + one_n.dot(K).dot(one_n)\n",
    "\n",
    "    # 중앙에 맞춰진 커널 행렬의 고윳값과 고유 벡터를 구합니다.\n",
    "    # scipy.linalg.eigh 함수는 오름차순으로 반환합니다.\n",
    "    eigvals, eigvecs = eigh(K)\n",
    "    eigvals, eigvecs = eigvals[::-1], eigvecs[:, ::-1]\n",
    "\n",
    "    # 최상위 k 개의 고유 벡터를 선택합니다(투영 결과).\n",
    "    alphas = np.column_stack([eigvecs[:, i]\n",
    "                              for i in range(n_components)])\n",
    "\n",
    "    # 고유 벡터에 상응하는 고윳값을 선택합니다.\n",
    "    lambdas = [eigvals[i] for i in range(n_components)]\n",
    "\n",
    "    return alphas, lambdas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_moons(n_samples=100, random_state=123)\n",
    "alphas, lambdas = rbf_kernel_pca(X, gamma=15, n_components=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.8713, 0.0093])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new = X[25]\n",
    "x_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0788])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_proj = alphas[25] # 원본 투영\n",
    "x_proj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0788])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def project_x(x_new, X, gamma, alphas, lambdas):\n",
    "    pair_dist = np.array([np.sum((x_new - row)**2) for row in X])\n",
    "    k = np.exp(-gamma * pair_dist)\n",
    "    return k.dot(alphas / lambdas)\n",
    "\n",
    "# 새로운 데이터포인트를 투영합니다.\n",
    "x_reproj = project_x(x_new, X, gamma=15, alphas=alphas, lambdas=lambdas)\n",
    "x_reproj "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(alphas[y == 0, 0], np.zeros((50)),\n",
    "            color='red', marker='^', alpha=0.5)\n",
    "plt.scatter(alphas[y == 1, 0], np.zeros((50)),\n",
    "            color='blue', marker='o', alpha=0.5)\n",
    "plt.scatter(x_proj, 0, color='black',\n",
    "            label='original projection of point X[25]', marker='^', s=100)\n",
    "plt.scatter(x_reproj, 0, color='green',\n",
    "            label='remapped point X[25]', marker='x', s=500)\n",
    "plt.legend(scatterpoints=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 사이킷런의 커널 PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import KernelPCA\n",
    "\n",
    "X, y = make_moons(n_samples=100, random_state=123)\n",
    "scikit_kpca = KernelPCA(n_components=2, kernel='rbf', gamma=15)\n",
    "X_skernpca = scikit_kpca.fit_transform(X)\n",
    "\n",
    "plt.scatter(X_skernpca[y == 0, 0], X_skernpca[y == 0, 1],\n",
    "            color='red', marker='^', alpha=0.5)\n",
    "plt.scatter(X_skernpca[y == 1, 0], X_skernpca[y == 1, 1],\n",
    "            color='blue', marker='o', alpha=0.5)\n",
    "\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "TensorFlow 2.3 on Python 3.6 (CUDA 10.1)",
   "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": "3.6.9"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}