ch05/ch05.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1_84RVKeEjGl"
   },
   "source": [
    "# 머신 러닝 교과서 3판"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3NwHunx5EjGp"
   },
   "source": [
    "# 5장 - 차원 축소를 사용한 데이터 압축"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vUWZwyuKEjGp"
   },
   "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-3rd-edition/blob/master/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-3rd-edition/blob/master/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": {
    "id": "BKL9EjK2EjGq"
   },
   "source": [
    "### 목차"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zgkdYleMEjGq"
   },
   "source": [
    "- 주성분 분석을 통한 비지도 차원 축소\n",
    "  - 주성분 분석의 주요 단계\n",
    "  - 주성분 추출 단계\n",
    "  - 총분산과 설명된 분산\n",
    "  - 특성 변환\n",
    "  - 사이킷런의 주성분 분석\n",
    "- 선형 판별 분석을 통한 지도 방식의 데이터 압축\n",
    "  - 주성분 분석 vs 선형 판별 분석\n",
    "  - 선형 판별 분석의 내부 동작 방식\n",
    "  - 산포 행렬 계산\n",
    "  - 새로운 특성 부분 공간을 위해 선형 판별 벡터 선택\n",
    "  - 새로운 특성 공간으로 샘플 투영\n",
    "  - 사이킷런의 LDA\n",
    "- 커널 PCA를 사용하여 비선형 매핑\n",
    "  - 커널 함수와 커널 트릭\n",
    "  - 파이썬으로 커널 PCA 구현\n",
    "    - 예제 1 - 반달 모양 구분하기\n",
    "    - 예제 2 - 동심원 분리하기\n",
    "  - 새로운 데이터 포인트 투영\n",
    "  - 사이킷런의 커널 PCA\n",
    "- 요약"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zdfxyBijEjGq"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qUbLOkj_EjGq",
    "outputId": "7eaa9bce-627e-4e1b-b107-2609cc2c6c3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e2/4c/6111b9a325f29527d7f262e2ee8c730d354b47a728d955e186dacad57a0d/scikit_learn-0.24.1-cp36-cp36m-manylinux2010_x86_64.whl (22.2MB)\n",
      "\u001b[K     |████████████████████████████████| 22.2MB 1.4MB/s \n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: scipy>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.4.1)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.0.0)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.19.5)\n",
      "Collecting threadpoolctl>=2.0.0\n",
      "  Downloading https://files.pythonhosted.org/packages/f7/12/ec3f2e203afa394a149911729357aa48affc59c20e2c1c8297a60f33f133/threadpoolctl-2.1.0-py3-none-any.whl\n",
      "Installing collected packages: threadpoolctl, scikit-learn\n",
      "  Found existing installation: scikit-learn 0.22.2.post1\n",
      "    Uninstalling scikit-learn-0.22.2.post1:\n",
      "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
      "Successfully installed scikit-learn-0.24.1 threadpoolctl-2.1.0\n"
     ]
    }
   ],
   "source": [
    "# 코랩에서 실행할 경우 사이킷런 버전을 최신으로 업데이트하세요.\n",
    "!pip install --upgrade scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "nSf82FR-EjGr"
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8_p4a2DdEjGr"
   },
   "source": [
    "# 주성분 분석을 통한 비지도 차원 축소"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aMwZJrGQEjGr"
   },
   "source": [
    "## 주성분 분석의 주요 단계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "id": "MiqBHvISEjGr",
    "outputId": "bdd67a32-14e8-4c3b-e89d-6ef4e62f5f98"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://git.io/JtsvW\" width=\"400\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='https://git.io/JtsvW', width=400) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZT7XQlR5EjGs"
   },
   "source": [
    "## 주성분 추출 단계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "id": "Fod9IR5ZEjGs",
    "outputId": "cff5e68a-02f9-42d5-c9c8-eea8abe310ad"
   },
   "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  ...  OD280/OD315 of diluted wines  Proline\n",
       "0            1    14.23  ...                          3.92     1065\n",
       "1            1    13.20  ...                          3.40     1050\n",
       "2            1    13.16  ...                          3.17     1185\n",
       "3            1    14.37  ...                          3.45     1480\n",
       "4            1    13.24  ...                          2.93      735\n",
       "\n",
       "[5 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "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": {
    "id": "uU_ahpbPEjGt"
   },
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d1bnkLDTEjGt"
   },
   "source": [
    "70%는 훈련 세트로 30%는 테스트 세트로 나눕니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "EA5J2XWMEjGt"
   },
   "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": {
    "id": "hMMoofvmEjGt"
   },
   "source": [
    "데이터를 표준화합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "AqQpTbYaEjGt"
   },
   "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": {
    "id": "edcNE-glEjGt"
   },
   "source": [
    "---\n",
    "\n",
    "**노트**\n",
    "\n",
    "`X_test_std = sc.transform(X_test)` 대신에 `X_test_std = sc.fit_transform(X_test)`를 사용했습니다. 이 경우에 테스트 데이터셋의 평균과 표준편차가 훈련 데이터셋과 매우 비슷하기 때문에 큰 차이가 없습니다. 하지만 3장에서 보았듯이 데이터를 변환할 때 훈련 데이터셋에서 학습한 파라미터를 재사용하는 것이 올바른 방법입니다. 테스트 데이터셋은 \"새로운 본 적 없는\" 데이터를 의미하기 때문입니다.\n",
    "\n",
    "초기에 `fit_transform(X_test)`를 사용했는데 이것은 모델 훈련에서 얻은 파라미터를 재사용하여 새로운 데이터를 표준화하지 않는 일반적인 실수입니다. 왜 이것이 문제가 되는지 간단한 예를 살펴 보겠습니다.\n",
    "\n",
    "훈련 데이터셋에 1개의 특성(\"길이\")을 가진 샘플 3개가 들어 있다고 가정해 보죠:\n",
    "\n",
    "- train_1: 10 cm -> class_2\n",
    "- train_2: 20 cm -> class_2\n",
    "- train_3: 30 cm -> class_1\n",
    "\n",
    "mean: 20, std.: 8.2\n",
    "\n",
    "표준화를 한 후에 변환된 특성 값은 다음과 같습니다:\n",
    "\n",
    "- train_std_1: -1.22 -> class_2\n",
    "- train_std_2: 0 -> class_2\n",
    "- train_std_3: 1.22 -> class_1\n",
    "\n",
    "그다음 표준화된 길이가 0.6보다 작은 샘플을 class_2로 분류한다고 가정해 보죠(그 외에는 class_1). 지금까지는 좋습니다. 이제 레이블이 없는 3개의 포인트를 분류한다고 가정해 보죠:\n",
    "\n",
    "- new_4: 5 cm -> class ?\n",
    "- new_5: 6 cm -> class ?\n",
    "- new_6: 7 cm -> class ?\n",
    "\n",
    "훈련 데이터셋에 있는 표준화되기 전의 \"길이\" 값과 비교해 보면 직관적으로 이 샘플들은 class_2로 보입니다. 하지만 훈련 데이터셋에서 했던 것처럼 평균과 표준편차를 다시 계산하여 표준화하면 아마도 분류기가 샘플 4번과 5번만 class_2로 분류할 것입니다.\n",
    "\n",
    "- new_std_4: -1.22 -> class 2\n",
    "- new_std_5: 0 -> class 2\n",
    "- new_std_6: 1.22 -> class 1\n",
    "\n",
    "하지만 훈련 데이터셋의 표준화에 사용했던 파라미터를 사용하면 다음과 같은 값을 얻습니다:\n",
    "\n",
    "- example5: -1.84 -> class 2\n",
    "- example6: -1.71 -> class 2\n",
    "- example7: -1.59 -> class 2\n",
    "\n",
    "5 cm, 6 cm, 7 cm는 훈련 데이터셋에 있는 어떤 것보다도 작습니다. 따라서 훈련 데이터셋을 표준화한 값보다도 훨씬 작은 값으로 표준화되어야 합니다.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cKUsKeCWEjGu"
   },
   "source": [
    "공분산 행렬의 고윳값 분해"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C1p8pEbVEjGu",
    "outputId": "ed0c049d-4438-48a6-cc37-be09df425fbc"
   },
   "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": {
    "id": "sWQdxyAFEjGu"
   },
   "source": [
    "**노트**: \n",
    "\n",
    "위에서 [`numpy.linalg.eig`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eig.html) 함수를 사용해 대칭 공분산 행렬을 고윳값과 고유벡터로 분해했습니다.\n",
    "\n",
    "<pre>>>> eigen_vals, eigen_vecs = np.linalg.eig(cov_mat)</pre>\n",
    "\n",
    "이것이 잘못된 것은 아니지만 최적은 아닙니다. [에르미트(Hermetian) 행렬](https://en.wikipedia.org/wiki/Hermitian_matrix)를 위해서 설계된 [`numpy.linalg.eigh`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eigh.html)를 사용하는 것이 더 좋습니다. 이 함수는 항상 실수 고윳값을 반환합니다. 수치적으로 약간 덜 안정적인 `np.linalg.eig`는 비대칭 정방행렬을 분해할 수 있지만 어떤 경우에 복소수 고윳값을 반환할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v5uHAyy7EjGu"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Ziezfe6EjGu"
   },
   "source": [
    "## 총분산과 설명된 분산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "VrKFIi3ZEjGu"
   },
   "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": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "iQmRwT46EjGu",
    "outputId": "b089124c-d719-4a94-e953-0d79274a1a60"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_02.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-03gKjrPEjGv"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KWhLM7GoEjGv"
   },
   "source": [
    "## 특성 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "1F3O3rx-EjGv"
   },
   "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": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bvcxCj6REjGv",
    "outputId": "04e9e4d6-dce1-4433-e229-70b1631fcb46"
   },
   "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": "markdown",
   "metadata": {
    "id": "tuaO_K8cEjGv"
   },
   "source": [
    "**노트:**\n",
    "\n",
    "사용하는 Numpy와 LAPACK 버전에 따라 행렬 W의 부호가 바뀔 수 있습니다. 이는 문제가 아닙니다. $v$가 행렬 $\\Sigma$의 고유벡터라면 다음을 얻을 수 있습니다.\n",
    "\n",
    "$$\\Sigma v = \\lambda v,$$\n",
    "\n",
    "여기에서 $\\lambda$는 고윳값입니다.\n",
    "\n",
    "$$\\Sigma \\cdot (-v) = -\\Sigma v = -\\lambda v = \\lambda \\cdot (-v).$$이기 때문에 $-v$도 동일한 고윳값을 가진 고유벡터입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BvUbmNzoEjGv",
    "outputId": "092dd545-a5c2-4ad1-e644-bc20bacf9219"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.38299011, 0.45458499])"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_std[0].dot(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "KxFGjRojEjGw",
    "outputId": "8e0b7fb8-05d0-4943-8250-20b1e3c3ce0a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/papvUlU7C/F9VPTeHo46yEFxe4TWvbz4ramIvWGu9cgrS7PjNpaLyCiAWwG8BuAfVHVfaZFRWxJVHBVdZedJVRtVS3iDbV/3Wvz8/+3FI387VPgGZpsbzJrUbIrvfgBzAP4TwA0A1qE27UeW4mZLomzCBxiOjwOXN6w5Bf0KV6/Od5rPtR55ZWmWoNar6kYAEJH7ADxdTkjUjsYjE4Da/0iN7Wu46ZJ8UeaG4vHxxdcPklTezxe7/mVpj7yyNKvimwv+oKpnS4iFMgj+xwmmIFhxRD7K+2DFJMrYwOxaj7yyNEtQPyMiJ+q3kwA2BX8WES4mWKgqRyZQNRVxsKItbG4wa5KxZrHtYC++5hIfOudJ7ziqnqocrFg17fTiI4ekOjLBkSo79kCjMM4SVAsTlCdEapVFI6GKo5GR/CuOytDOWgMTmv+qcrAi1TBBeWR8fPGnySBJuVZi3s5ag4nFcypXlQ5WpJpmZebkIB+OTGicxtmzZ2G9IW6tgSX21RA3SwC4OUtArbFIgqylWiuXD8zPx78JcfG8Oniwon9YJEFOSbvWwMXz6qzB+TBLQMkwQZF12llrqPriOdfgyEdMUGSdtBWJ7Sa0Zvdd4vMGVqo2FkmQldL0QEu7eO5bU920RSVErmCRBHkjyeJ5eLQVrvhz+Q09TVEJkU1YJEHeS7J47mtT3aqvwZGfmKCocnyr+OMGVvIVExRVjm+jDd/aXBEFWCRBldJsDQpwdyRV1sF6RGVigqJK8bldDjewkm9YxUeVxHY5RPZgFR9RA9tGGz5tHCbKCxMUkWFsU0QUzUiCEpFfFZHnRGReRJYM64iqgm2KiOKZKpI4COAWAP9o6PmJrMA2RUTxjIygVPUFVX3RxHMT2aaojcNc18qOr6FZ1q9BiciwiEyKyOTs7KzpcIhyV8TGYa5rZcfX0LzCEpSIfElEDkbcbk5zHVXdq6qDqjrY09NTVLhERhTRpojrWtnxNbRDYWtQqvquoq5N5IsiNg4nWdfiPrDmuDZoB6MbdUXkKwD+SFUT7b7lRl3yVREJI+74jTzOw6pKguMRJuWwaqOuiLxPRGYAXA3gP0TkERNxENki743Dceta8/PZp66qsjbjW1NhJ6mqM7fNmzcruW1+vvl9ym5+XnVkRBWo/Td8/9y5hfvBLfi+rNf25e+zKr+nLQBMasR7PpvFUm5aTfv4dtR6wLbprlbrWh0dtfvBugqQfF2lKmszPjcVdkpU1rL1xhGUvbZvX/zJMvjEuX374vu+fSJt9XubFDdabXzt046gGq/V+POu/v21whF/ORAzgrJ+HxTZTxOU5Pp41HqS39ukqHWtxhjbLWsPrtEoz7WZ8HWSxpT2Z5Kwralw5URlLVtvHEHZK+mnct8+eecxGilbllFf0SPhuNjuuWdpHHn8PmQHxIygjCedNDcmKLu1Sj4uvpkn4WLSzTJ1VVRCaJb8BgZqxR3h5/N16rhqmKCoUK2Sj69vJGUkXRvXQYqKKer1HBho/u/G1w8+VcIERYVJmnxMTsUU8YZaRtKt4vRVeESapCzexVEsLYhLUCwzp8yaleR2dy8sLI+PL96JH3xf0QvPRZW3F12KrA1FGEDt2o0FDupw94Zw7MH94O+m0bZtwK5d8WXxUT9z993uFt9Qg6isZeuNIyi7hT+13nOP+U//ZYxyipyC83H6qlkhRLM1qKjXwNep46oBp/ioTDa9cbj+Ju/T9FWrfxfhDzXnzrVeg6riNKhvmKCodDYlBlff5G16DfOSpKCmUZKRuI2FJJRcXIIy2s08LXYzd4+q+W7Q2rA5NeDCBuHGuIN4w/dtjr+ZtP8uNGbNivxgVTdzqobgDbZR2d2gw2/yeRwIWJa4IoyREbf7wbXz74IdHaqJVXzUllafaJt9+gfK+/SfptLOxk/p4+OL4yir8rEotvy7IDcwQVFqScq2yyjBTppMkrzJh3+n+flaeXPwO5lMVj6NHtglnNJggqJUNMXenHY+/SdJPO3sa2r2Jh/+nbq7gQcfBKamFqYFG5MVZePbqJCKwwRFqaQ9DyjNp/8kiSdNgmz3dwoMDNQ2iG7b5sfmWJv4NCqkAkWV9tl6Y5m5PfIu206zb6qo0uvw7+RTabetWB5OqvFl5qzio9SKqM5Lc15U44gnkHWKKOp3yvP6vgn/Xbfzdz8+vvjfTfB3wGlUCjBBUSpFlm0nTTx5J8jw73TuXG16L6/r+yaPxNI4VWvjYY9kB65BUSpFVmHFJZ6oxqB5lik3/k7BmtPUVC1Jvfe9wPHjLIMO5LUGmHYtkyoqat7P1hvXoOyR99pBmjWoonqvBQfiBdc/ezbf6/sizzVAV1tQUb7AVkdkuzTl4+FP6kk/uSd9bpv2QdlIc2hh1TgaDnAEVU1sdUTWGx9f/OYUTANFrW3kWaYctR4SlJYH6yF8w1yQxxpgkWuZ5A8ja1Ai8hEANwF4DcC3Afy2qh4zEQvZxcT+GK6HJJfXGiA7SlASRqb4ROTdAPap6lkR+RsAUNU/bvVzUVN8c3NzmJmZwZkzZ4oJNgfLly9Hb28vOjs7TYdCdVFThID5zutJ5T3FmUaeJxSb/D3IHnFTfEZGUKr6xYa7TwK4rd1rzczMoKurC/39/RAL/2WrKo4ePYqZmRlcdtllpsMhRL/Bjo4CTz21+PtsPTa8VYLI+qbf6ufzbFXEjhLUjA1rUHcC+ELcF0VkWEQmRWRydnZ2ydfPnDmDNWvWWJmcAEBEsGbNGqtHeFUStd40Ogrce28tQW3davd6SKv9Q9u3Z9ujlHSPExMLlSKqtC+PG4AvATgYcbu54XvGAHwa9anGVreoMvPnn38+jyrHwrkSp83yKm2PKpO+6irVrVuzl66X0bonrsz77NmFx7durZXNN95vFUuaUn+iPMG2MnMR+S0Avwdgi6qeSvIzUWtQL7zwAtatW5d/gDlzJU5b5bnuAUSXSQPZpsbyjrGZcPz33FPbULxrF/C2ty2erty6tfbfH/ux1nGw9JtMsKrMXESuB/BhAO9Nmpxsduedd+Kiiy7Chg0bTIfipVbTWmk/Y8WVSYelXbcpq3VPVPwPPrjw3FdeufT77703WRxF9DkkalvUsKroG4BvAXgJwFT99rEkP5d5iq+ra/G8SHDr6kp+jQiPP/647t+/X9/ylrfEfg+n+LLJq3tBkdNYRXVZTxr/hRdG//NOOsVX1u9AFAabupmr6k+q6qWqOlC//X4pT3zyZLrHE7rmmmtwwQUXZLoGNZfXJ/u4/TcjI9n332SNMTy6CVJEWDj+XbtqfQNfeaW9uBufj5tnySZsFktOiJuWaydJFXWia5YYo9avrr669rUnnli6ptV4zY4OYHISGBysNbmNEi6hj8LNs2SdqGGVrbfMU3xx8x9A8mvE+M53vsMpvoK4UF2WJcao7926den0XJoDHBt/9oMfTPda8RBBKhtipvg4giLrufDJPmmMjSO3xvtRrZaC6rt7763dgPgDHLu7a9N8jSOogYGFCsKOjuSvFfc4kTWispatN46gqs2FT/bNYmx1TEjU0RNJjqNoNgIL7tv4WhEFYFORhDFdXekeT+j222/H1VdfjRdffBG9vb247777Ml2PornwyT4uRm1Rhj4/v3T9anS0dmsUVawgsnDA4q5dCyOyYEQlYudrRdRSVNay9cZOElSGokZqcSXcjR0fsq5B2bpGR9QMbOsk0Q52kqCiFd0NQiM6WIikr+KLOsCRHSDIVVZ1kiCyUatpuKyf5YLrNQqeJ+qwxieeWEhOwWPNDnBkBwjyDRMUUV3jpt09e2ojncaD+bKe2ttqE2zU+lXSdbdmya9ZTM3uE5nGBEXUoKiRSJEdLJIkv7Ckx2oQmcR9UEQN4kYieSSpojpYRFXx7doFPP74QhVfo8apTGDxse0jI+m7uBMVhSMoorp2RiJpFVEqr7pQUr5tW+3+tm0LSSuqLL2oqUyiPFVuBBX+dJjHp8WXXnoJd9xxB773ve9BRDA8PIyRkZFsF6XSudCxIkpcJ4pmCSf4mcaqPyYnsk2lysyLKiE+cuQIjhw5giuuuAInT57E5s2b8ZnPfAbr169vK04yq4gPMWWIK2GP+16WpZMtKl9mXmQJ8cUXX4wrrrgCANDV1YV169bh5ZdfzilyKpsLHSvC0lTxlTGVSZSHykzxtTMN0o7p6Wk888wzuOqqq/K5IFEL4YTTWPSgCuzevbRZrYtTmVQ9lUlQQPHz7q+++ipuvfVW7N69G6tWrcrnokQtxCWcJ59cfA5UeEq7iIpCojxVZooPaG8zY1Jzc3O49dZbMTQ0hFtuuSX7BYlSCHeiAICrrqolqLgpbRenMqlaKpOgipx3V1XcddddWLduHbZt25Zf0EQphM+I2r2bpeTktsokqCJ38n/ta1/DAw88gH379mFgYAADAwN46KGH8gueKCRJmyL25yPXVWoNqqh593e84x1wqVyf3JZ0u0SRXTGIylCZEVSA8+7ksqTbJVhKTj6o1AiKyHVJt0uwlJx8UKlOEia5Eie5IWnXCFe7YlC1WNVJQkT+QkS+ISJTIvJFEfkJE3EQuSjNdglOaZPLTK1BfURVN6nqAIDPA7jHUBxETuHaElWJkTUoVT3RcPdHAfB/K6IEuLZEVWKsSEJEdgK4A8BxANc2+b5hAMMA0NfXV05wKZ05cwbXXHMNfvjDH+Ls2bO47bbbsGPHDtNhkafYpoiqorApPhH5kogcjLjdDACqOqaqlwKYAPCBuOuo6l5VHVTVwZ6ensxxTRyYQP/ufnTs6ED/7n5MHJjIfM3zzjsP+/btw7PPPoupqSk8/PDDePLJJzNflygO15aoCgobQanquxJ+6wSAhwBsLyqW15/owASGPzeMU3OnAACHjh/C8OeGAQBDG4favq6I4PzzzwdQ68k3NzcH4TsGEVEmpqr43txw92YA3yzjecceG3s9OQVOzZ3C2GNjma997tw5DAwM4KKLLsJ1113H4zYclaSFEBGVw1QV31/Xp/u+AeDdAEo5H/3w8cOpHk9j2bJlmJqawszMDJ5++mkcPHgw8zWpXOPjiyvhgoq5LKctE1H7jCQoVb1VVTfUS81vUtVSjp/t644usoh7vB2rV6/Gtddei4cffji3a1LxijxxmYjaU6lefDu37MTKzpWLHlvZuRI7t+zMdN3Z2VkcO3YMAHD69Gk8+uijuPzyyzNdk8rV2N2ex1MQ2aFSCWpo4xD23rQXa7vXQiBY270We2/am6lAAgCOHDmCa6+9Fps2bcJb3/pWXHfddbjxxhtziprKwuMpiOxSuWaxQxuHMieksE2bNuGZZ57J9ZpUPh5PQWSXSo2giOKwhRCRfSo3giKKwhZCRPbxIkGpqtUbY1060qTK2EKIyC7OT/EtX74cR48etTYJqCqOHj2K5cuXmw6FEmALISJ7OD+C6u3txczMDGZnZ02HEmv58uXo7e01HQYRkVOcT1CdnZ247LLLTIdBREQ5c36Kj4iI/MQERUREVmKCIiIiK4mt1W9RRGQWwKGCn+ZCAK8U/Bw+4+vXPr522fD1y8bk67dWVZecSOtUgiqDiEyq6qDpOFzF1699fO2y4euXjY2vH6f4iIjISkxQRERkJSaopfaaDsBxfP3ax9cuG75+2Vj3+nENioiIrMQRFBERWYkJioiIrMQE1YSIfEhEVEQuNB2LS0TkIyLyTRH5hoh8WkRWm47JdiJyvYi8KCLfEpE/MR2PS0TkUhH5sog8LyLPiciI6ZhcIyLLROQZEfm86VgaMUHFEJFLAbwbwGHTsTjoUQAbVHUTgP8B8KeG47GaiCwD8PcAbgCwHsDtIrLebFROOQvgQ6q6HsDPAfhDvn6pjQB4wXQQYUxQ8T4K4MMAWEWSkqp+UVXP1u8+CYBnjTR3JYBvqer/quprAP4VwM2GY3KGqh5R1f+u//kkam+0l5iNyh0i0gvglwH8k+lYwpigIojIzQBeVtVnTcfigTsBfMF0EJa7BMBLDfdnwDfYtohIP4CfBfCU2Uicshu1D+PzpgMJc/48qHaJyJcA/HjEl8YA/Blq03sUo9nrp6qfrX/PGGrTLxNlxkbVJCLnA/gkgFFVPWE6HheIyI0Avq+q+0XkF03HE1bZBEFSeeAAAAIjSURBVKWq74p6XEQ2ArgMwLNSO++7F8B/i8iVqvrdEkO0WtzrFxCR3wJwI4Atys12rbwM4NKG+731xyghEelELTlNqOqnTMfjkLcDeK+IvAfAcgCrROQTqvobhuMCwI26LYnINIBBVWWX5IRE5HoAuwD8gqrOmo7HdiLyBtSKSbaglpi+DuDXVfU5o4E5QmqfJO8H8ANVHTUdj6vqI6g/UtUbTccS4BoUFeHvAHQBeFREpkTkY6YDslm9oOQDAB5BbYH/35icUnk7gPcDeGf939tUfURAjuMIioiIrMQRFBERWYkJioiIrMQERUREVmKCIiIiKzFBERGRlZigiEokIufqZdAHReTfRWRl/fEfF5F/FZFvi8h+EXlIRH4q4uc/LiLfF5GD5UdPVC4mKKJynVbVAVXdAOA1AL9f32j6aQBfUdU3qepm1DrAvzHi5/8ZwPWlRUtkUGVbHRFZ4D8BbAJwLYA5VX19Q3Nco2JV/Wq9ISqR9ziCIjKg3t7oBgAHAGwAsN9sRET2YYIiKtcKEZkCMInaYZj3GY6HyFqc4iMq12lVHWh8QESeA3CboXiIrMURFJF5+wCcJyLDwQMisklEft5gTETGMUERGVY/L+t9AN5VLzN/DsBfAVhy/piI/AuAJwD8tIjMiMhd5UZLVB52MyciIitxBEVERFZigiIiIisxQRERkZWYoIiIyEpMUEREZCUmKCIishITFBERWen/AeeelJYBPKrPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_03.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5GFN87VuEjGw"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M_KY060EEjGw"
   },
   "source": [
    "## 사이킷런의 주성분 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f5vqSfXIEjGw"
   },
   "source": [
    "**노트**\n",
    "\n",
    "이어지는 네 개의 셀은 책에 없는 내용입니다. 사이킷런에서 앞의 PCA 구현 결과를 재현하기 위해 추가했습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SrIDFuzvEjGw",
    "outputId": "0c4b5915-1b82-4ed0-b95d-0733d930edc7"
   },
   "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": 14,
     "metadata": {
      "tags": []
     },
     "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": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "Bdlq_npKEjGw",
    "outputId": "6a3e9d6a-c290-4073-a4ca-a3654842e984"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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": 16,
   "metadata": {
    "id": "siTpt9sXEjGx"
   },
   "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": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "xc8dWtb5EjGx",
    "outputId": "16812cac-7264-4fe7-964e-bba97e9b2f03"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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": 18,
   "metadata": {
    "id": "audMeNXmEjGx"
   },
   "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",
    "    # 클래스별로 샘플을 그립니다\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",
    "                    color=cmap(idx),\n",
    "                    edgecolor='black',\n",
    "                    marker=markers[idx], \n",
    "                    label=cl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7beQNiiFEjGx"
   },
   "source": [
    "처음 두 개의 주성분을 사용하여 로지스틱 회귀 분류기를 훈련합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "3E1HW9RlEjGy"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\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(random_state=1)\n",
    "lr = lr.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "KljC04RyEjGy",
    "outputId": "40036fdf-d136-48c8-d48c-b013ee047378",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_04.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "Q190-SbDEjGy",
    "outputId": "d166df02-8109-45e2-aa77-99ae123ebca0",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_05.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nYN0rAe2EjGy",
    "outputId": "74f218eb-0d3d-4e51-cca3-b75e6525683a"
   },
   "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": 22,
     "metadata": {
      "tags": []
     },
     "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": {
    "id": "bwBzhFTHEjGy"
   },
   "source": [
    "`n_components`에 (0, 1) 사이 실수를 입력하면 설명된 분산의 비율을 나타냅니다. 이 비율을 달성하기 위해 필요한 주성분 개수를 선택합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hAudlTvIEjGz",
    "outputId": "b96693e0-3c89-44a4-87ee-4d88e5146ecb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "주성분 개수: 10\n",
      "설명된 분산 비율: 0.9662714406558741\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=0.95)\n",
    "pca.fit(X_train_std)\n",
    "print('주성분 개수:', pca.n_components_)\n",
    "print('설명된 분산 비율:', np.sum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vVqwwK7TEjGz"
   },
   "source": [
    "`n_components='mle'`로 지정하면 토마스 민카(Thomas Minka)가 제안한 차원 선택 방식을 사용합니다(Minka, T. P. “Automatic choice of dimensionality for PCA”. In NIPS, pp. 598-604)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aq0ejdBgEjGz",
    "outputId": "050d4797-2aec-4386-edda-90d8139b70e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "주성분 개수: 9\n",
      "설명된 분산 비율: 0.9499753029186231\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components='mle')\n",
    "pca.fit(X_train_std)\n",
    "print('주성분 개수:', pca.n_components_)\n",
    "print('설명된 분산 비율:', np.sum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6AKyi0DfEjGz"
   },
   "source": [
    "`PCA`의 가장 큰 제약 사항 중 하나는 배치로만 실행되기 때문에 대용량 데이터셋을 처리하려면 많은 메모리가 필요합니다. `IncrementalPCA`를 사용하면 데이터셋의 일부를 사용하여 반복적으로 훈련할 수 있습니다.\n",
    "\n",
    "`partial_fit()` 메서드는 네트워크나 로컬 파일 시스템으로부터 조금씩 데이터를 받아와 훈련할 수 있습니다. `fit()` 메서드는 `numpy.memmap`을 사용하여 로컬 파일로부터 데이터를 조금씩 읽어 올 수 있습니다. 한 번에 읽어 올 데이터 크기는 `IncrementalPCA` 클래스의 `batch_size`로 지정합니다. 기본값은 특성 개수의 5배입니다.\n",
    "\n",
    "`IncrementalPCA`의 `n_components` 매개변수는 정수 값만 입력할 수 있습니다. 다음은 `partial_fit()` 메서드를 사용하여 앞의 `PCA`로 찾은 주성분의 결과와 비교하는 간단한 예입니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1DcUF_ySEjGz",
    "outputId": "0c69d29b-6329-44fe-c9e1-574095c0c498"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "주성분 개수: 9\n",
      "설명된 분산 비율: 0.9478392700446643\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import IncrementalPCA\n",
    "\n",
    "ipca = IncrementalPCA(n_components=9)\n",
    "for batch in range(len(X_train_std)//25+1):\n",
    "    X_batch = X_train_std[batch*25:(batch+1)*25]\n",
    "    ipca.partial_fit(X_batch)\n",
    "\n",
    "print('주성분 개수:', ipca.n_components_)\n",
    "print('설명된 분산 비율:', np.sum(ipca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A75qiYlzEjGz"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vrV-l0OtEjG0"
   },
   "source": [
    "# 선형 판별 분석을 통한 지도방식의 데이터 압축"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rZ1yv2kdEjG0"
   },
   "source": [
    "## 주성분 분석 vs 선형 판별 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366
    },
    "id": "BwDzcypZEjG0",
    "outputId": "df66dbad-e6e5-4060-926c-21e3308d5603"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://git.io/Jtsv8\" width=\"400\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='https://git.io/Jtsv8', width=400) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dqNYW_1JEjG0"
   },
   "source": [
    "## 선형 판별 분석의 내부 동작 방식"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "73eS4jTgEjG0"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "swEC9tzxEjG0"
   },
   "source": [
    "## 산포 행렬 계산"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v_bJB6JaEjG0"
   },
   "source": [
    "각 클래스에 대한 평균 벡터를 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3LeJnkK_EjG0",
    "outputId": "e52d8219-9a53-4945-ec3a-dcf2c48a0768"
   },
   "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": {
    "id": "c-nwJBF5EjG1"
   },
   "source": [
    "클래스 내 산포 행렬을 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TRb957tiEjG1",
    "outputId": "48d79cf5-11d0-4983-de96-c38155144131"
   },
   "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))  # 각 클래스에 대한 산포 행렬\n",
    "    for row in X_train_std[y_train == label]:\n",
    "        row, mv = row.reshape(d, 1), mv.reshape(d, 1)  # 열 벡터를 만듭니다\n",
    "        class_scatter += (row - mv).dot((row - mv).T)\n",
    "    S_W += class_scatter                          # 클래스 산포 행렬을 더합니다\n",
    "\n",
    "print('클래스 내의 산포 행렬: %sx%s' % (S_W.shape[0], S_W.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dalrtl0AEjG1"
   },
   "source": [
    "클래스가 균일하게 분포되어 있지 않기 때문에 공분산 행렬을 사용하는 것이 더 낫습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2xb6G0WKEjG1",
    "outputId": "51f73a4d-09e5-4cc9-a38e-f8444908410d"
   },
   "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": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XWzi_qzwEjG1",
    "outputId": "749af11c-1002-4b86-d7db-5cbc95e23986"
   },
   "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)\n",
    "    S_W += class_scatter\n",
    "print('스케일 조정된 클래스 내의 산포 행렬: %sx%s' % \n",
    "      (S_W.shape[0], S_W.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7dBT2KJKEjG1"
   },
   "source": [
    "클래스 간 산포 행렬을 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xdPoDmAZEjG1",
    "outputId": "6a5fc4e2-2d8b-484d-9cba-417b770b0e92"
   },
   "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  # 특성 개수\n",
    "S_B = np.zeros((d, d))\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(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": {
    "id": "cCw6Yv1sEjG1"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M_7csaMHEjG2"
   },
   "source": [
    "## 새로운 특성 부분 공간을 위해 선형 판별 벡터 선택하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CDTcT3twEjG2"
   },
   "source": [
    "행렬 $S_W^{-1}S_B$의 일반적인 고윳값 분해 문제를 풉니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "jdm4jT5EEjG2"
   },
   "outputs": [],
   "source": [
    "eigen_vals, eigen_vecs = np.linalg.eig(np.linalg.inv(S_W).dot(S_B))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EVG_3zgIEjG2"
   },
   "source": [
    "**노트**: \n",
    "\n",
    "위에서 [`numpy.linalg.eig`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eig.html) 함수를 사용해 대칭 공분산 행렬을 고윳값과 고유벡터로 분해했습니다.\n",
    "\n",
    "<pre>>>> eigen_vals, eigen_vecs = np.linalg.eig(cov_mat)</pre>\n",
    "\n",
    "이것이 잘못된 것은 아니지만 최적은 아닙니다. [에르미트(Hermetian) 행렬](https://en.wikipedia.org/wiki/Hermitian_matrix)를 위해서 설계된 [`numpy.linalg.eigh`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eigh.html)를 사용하는 것이 더 좋습니다. 이 함수는 항상 실수 고윳값을 반환합니다. 수치적으로 약간 덜 안정적인 `np.linalg.eig`는 비대칭 정방행렬을 분해할 수 있지만 어떤 경우에 복소수 고윳값을 반환할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2lJf-i8oEjG2"
   },
   "source": [
    "고윳값의 역순으로 고유 벡터를 정렬합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cFCra1wjEjG2",
    "outputId": "775b06b3-9fb2-4cec-9109-d2776246a743"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "내림차순의 고윳값:\n",
      "\n",
      "349.61780890599414\n",
      "172.76152218979394\n",
      "2.842170943040401e-14\n",
      "2.3896391859668944e-14\n",
      "2.3896391859668944e-14\n",
      "2.273691513311495e-14\n",
      "2.273691513311495e-14\n",
      "1.6636761998705377e-14\n",
      "1.6636761998705377e-14\n",
      "1.4656262894811127e-14\n",
      "8.220176746268876e-15\n",
      "1.6571724254698328e-15\n",
      "3.906317198255803e-16\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",
    "print('내림차순의 고윳값:\\n')\n",
    "for eigen_val in eigen_pairs:\n",
    "    print(eigen_val[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "QTBSkyVPEjG2",
    "outputId": "840a7495-3090-4466-f9e1-ee7bd45ffda4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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",
    "    'images/05_07.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KDxlXzVBEjG2",
    "outputId": "42e634f6-e845-46b7-9793-34e2a28a7589"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "행렬 W:\n",
      " [[-0.1481 -0.4092]\n",
      " [ 0.0908 -0.1577]\n",
      " [-0.0168 -0.3537]\n",
      " [ 0.1484  0.3223]\n",
      " [-0.0163 -0.0817]\n",
      " [ 0.1913  0.0842]\n",
      " [-0.7338  0.2823]\n",
      " [-0.075  -0.0102]\n",
      " [ 0.0018  0.0907]\n",
      " [ 0.294  -0.2152]\n",
      " [-0.0328  0.2747]\n",
      " [-0.3547 -0.0124]\n",
      " [-0.3915 -0.5958]]\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": {
    "id": "tu490rQsEjG3"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D21vo1QlEjG3"
   },
   "source": [
    "## 새로운 특성 공간으로 샘플 투영하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "8rtNnz74EjG3",
    "outputId": "f11c4708-ec50-4d71-e292-a0f1e4368f55",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_08.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "15asQXTuEjG3"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2CbXbBQ7EjG3"
   },
   "source": [
    "## 사이킷런의 LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "gHB0KzvjEjG3"
   },
   "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": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "ykehu9ocEjG3",
    "outputId": "81a748dd-3e92-44eb-9846-519589e9c4a7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression(random_state=1)\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.savefig('images/05_09.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "2vpC6hmuEjG3",
    "outputId": "2cd55a21-c2a6-4c9b-8da8-018dc9254b19"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_10.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y27NfOE6EjG4"
   },
   "source": [
    "사이킷런의 LDA 구현 방식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LqX58-YBEjG4",
    "outputId": "8bc35653-7dc4-4113-e27a-1a7bc75b5a10"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3306, 0.4032, 0.2661])"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "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": {
    "id": "Au9anyYvEjG4"
   },
   "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": 41,
   "metadata": {
    "id": "3fmRyzDIEjG4"
   },
   "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": {
    "id": "NXQShs0DEjG4"
   },
   "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": 42,
   "metadata": {
    "id": "PBu3Ajq5EjG4"
   },
   "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": 43,
   "metadata": {
    "id": "De00twRIEjG4"
   },
   "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": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "515DAp_oEjG4",
    "outputId": "27b44a23-aee6-49f1-8348-a659f67edccf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearDiscriminantAnalysis(solver='eigen')"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_eigen = LDA(solver='eigen')\n",
    "lda_eigen.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wdo7kBhhEjG5",
    "outputId": "56639d4f-5464-478d-df2b-fc9430d3c1f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "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": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PFBAWEaQEjG5",
    "outputId": "0710c005-a848-4b8a-a122-eda4a8bec7e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "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": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kjks6O2NEjG5",
    "outputId": "4d4143fb-3018-4b71-f96e-dd86523af30b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(lda_eigen.scalings_[:, :2], ei_vec[:, :2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YQ_v9IPfEjG5",
    "outputId": "10b4bc51-d029-493c-df02-f9382125f712"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 48,
     "metadata": {
      "tags": []
     },
     "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": {
    "id": "F12wsUaYEjG5"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QmdF0zmSEjG5"
   },
   "source": [
    "# 커널 PCA를 사용하여 비선형 매핑하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 230
    },
    "id": "LeKVh3F0EjG5",
    "outputId": "da47165d-f069-4008-d097-4f815b4e10b6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://git.io/JtsvB\" width=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='https://git.io/JtsvB', width=500) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bZKSwJLtEjG6"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z8OlvQunEjG6"
   },
   "source": [
    "## 파이썬으로 커널 PCA 구현하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "AWwSJwROEjG6"
   },
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist, squareform\n",
    "from scipy.linalg import eigh\n",
    "import numpy as np\n",
    "\n",
    "from distutils.version import LooseVersion as Version\n",
    "from scipy import __version__ as scipy_version\n",
    "\n",
    "# scipy 2.0.0에서 삭제될 예정이므로 대신 numpy.exp를 사용합니다.\n",
    "if scipy_version >= Version('1.4.1'):\n",
    "    from numpy import exp\n",
    "else:\n",
    "    from scipy import exp\n",
    "\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": {
    "id": "q5LH1FhQEjG6"
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-7CDFufCEjG6"
   },
   "source": [
    "### 예제 1: 반달 모양 구분하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "eK5WLaYkEjG6",
    "outputId": "558efbb9-c906-4fee-a59f-7cf8f1a7203f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_12.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "3XUrxsG1EjG6",
    "outputId": "5e15c1de-93dd-47d1-b82e-975850ede617"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_13.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "CKQnqpCuEjG6",
    "outputId": "c865662b-7e55-4b0f-edc3-210a4249a6c0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_14.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MhgMuq_2EjG7"
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EWhZCKfMEjG7"
   },
   "source": [
    "### 예제 2: 동심원 분리하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "PHt9XURIEjG7",
    "outputId": "ec0df3bd-981d-4884-c2cd-8432610265de"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_15.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "vAzJy-ojEjG7",
    "outputId": "0cac0528-9bf9-4d92-af17-68bf6d0d7a5e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_16.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "lnVjnP93EjG7",
    "outputId": "d2309854-bc48-442f-c941-e5fb6f46155e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pvJ2LnYULeX11dfQitLby++Z9cxxjqZvEvEiY49TXswd8RoYdpWoS5p56SsRcEASLiLkQkUhDUcrL2bDl6FFanj4fhe/v/i78u6Ze+9ix8MXA+vXhlq4hUuvXzZvtvrxexrdLSmx8+ZZbwieqAeFu82jXsmABQwJ/+xvwL/9CoZw1C6iqovAqRUFesYLHjnXekRYlq1aFXw/A/UbyRgCxG8SUlXHR0tfHXANThpeaChw6FN37IQjC5EPEXIhIfT0tzO3brWvYlGw5486mW5uTtjZaprNmRe6wFi2GbkR/82bg7rspqkbonngi8veAcGvXGXt3Xotx2589y7DAhQu0ctPS2EbV7wc+8xkuFnp64p93NCJdj1kMmAS/eNuZxMH9+22M3eOxo19Npv2XvgTce68kxQmCIGIuRKC2lj+HDzNRq7iYIr59O58bl/rZs8Bf/wq8+iqtz8pKZm63tNDFHKnDmtsNHm0sal1duPi5SST2bnC67Q8cYPKcqedWihZ+ZyeT3s6dY0w93nmb++QOI8Qa8+rM7n/xRR53yRK60N3tZNevZ4e8GTOY0W/mons8FPjZs7ldLM+BIAiTBxFzIQxjMQaDtFpN05PCQlqsxrV99iybqmRmWvF79VVmcpeW0jI/ciQ8vh7JDR6pZCyaeLqJF3s3OGelm8YzKSlMQrtwwfaLN2V2pitdrPOOZln7/YPPyVyP8zsALe2dO9l2tqjIbmcWBKEQE+iCQZ63Gfmalsa6+xMneI+Li5nl/swz8e+FIAgTk7Fs5ypcghghSU2lmzw9nUln7e2MW3d3c7vdu9nO1NSOl5XRijeNUEpK2DXt0CF+39mNzUlZme1fvn078NJLwJYtg+Phw8FY8fn5tqFLQQEXKJcNTAfo7+frNWt47S0tsc/baYEbyzo/n6JrutgBvK4tWzg17Rvf4KIhP5+ei64u3sM//pHXfuQI70d9PYXd1JrPmsVWsebfJRTi+2lpFPiTJ3k+0vJVECYvYpkLYRhL2dRuz55NwfD72V61ro4CffQohTEQoEC9/DJjuU1NfLziCtZrnzrFBUEkN3htLS3MV17hsYqLaen7/RSokUzwMlb8ypXAY4/x/Lu6KMQ5OVyomLi8KQ2L5b6P5lHIy6P4A7w3O3bw+bJlDEk0N/N4BQUs4fN6+XlrK/dpFg0tLczi37mT92/6dG7T2mqtc63pLenr437vv5+lexJDF4TJh1jmQhg+Hy3JM2foxr1wgULr9dLqXrPGlm4FArTGfT6Kit9v+7G//Tbdw04r1YlxOft8tIhTU7nf/n5g+XK6l0ejH3lFBfDkkzxGKMTzXrEiPMHOtH997jk+RovDu6/NdLIzXoB336VwL1/O2HdhIRcPJjZfWmoH0OTlcfFTV0cxNp3fli7lvoNB9oSfPp3/Dr29tpuc6QHf1RU+5U0QhMmDWOaTiEgJW25L+eRJirIZ8GEs8EWLmGG+ahVj4+3tFPu0NGazp6TYXukZGRSW7dspypGytZ1u6o4OWp99fbTkAYoVMDwrM9r1Ruu8lgzOOLw7m95ZJ15aynsD8B6+9Ra9F14v79P06YyZAxT/P/8Z+Pd/p8j393ObpUtte9yDBxm+MGjN/Xs8FPxks+8FQZgYiGU+SXBPDYtkwVVXU3yXL6eQtLZymMgVV7CX+ubN3L6sjNb3rFkU81CI4uP12olhpvTr6qvDY8rG2jZx4cZGCmF3NwWppYWlYr29tNqHamUmcr2J7GPtWvZtX7s2/LsVFVzcfPAB8Nvf8vHuuwfXiTut96Ii4BOfoIUOcOFjhPz111mXn5JCl/mZM1zcXHll+Lz3hx7ifUpJsS56gIshM6Al0QRCQRAmDmMq5kqpzyqlPlJKHVFKPRrhc59S6vcDn7+jlJrt+rxMKdWhlHrkYp3zeCVawpbTlW0EtriY7vMFCyjk/f3h269caTPdTXJWVhZFMyuLln1PDxcGxcV2/06RMUJ38KDNhu/o4H5TU+mGXrLEzhKPJqrDud5YxFsMmK5yV10F/OM/8tEsdgzGXe5MpjODYJ5/ni1evV7Wk7e12YYwPh9zB7q66Kno62Py3OrVdMPfcAPd9319NuZfVMSwx2gkEAqCcOkzZmKulPIAeBrAHQCuBPCPSqkrXZt9GUCL1no+gP8B4Ieuz38M4LXRPtfxitOyfOkl/rF34rbgnJakmfYVCNh6cbN9RQUblpw5Q4u0v5+ilJrKcZ7LlrHs6/LLw4/nLPEyQtfURJEtLKQ4eb0Uovx8K1DbtiVvYZuFSbTrjWV1A/EXA4ksFpxZ9A0NfDRhBudnp0/TSp81i16N1FQ7KrWxEfjwQ94nc/3p6cC11wIPPAD88z8z3n72LBdgaWnhCYSCIEwOxjJmfi2AI1rrYwCglPodgHsA7Hdscw+AtQPPNwHYqJRSWmutlLoXwMcAHE5IwRBpXOmOHUz2KiriNu76aWccOCeHn2ttJ4OZ7WtrKeK33GJ7nff2sg2rmZ42dSqPCTBT3N2hzYjZN75hp5P5fLROAdvlrKYGmDYtvAnL+fP83ty5kWP/QOwpafG6rwE23t3YSO+BmXxm9hcpm91Yxs4YfSycdfJmcEx6Or0aAO9FIMD3TPJcfj6vOxTi8/p63h+Ph/cFsKNcJW4uCJOHsXSzlwA46XjdMPBexG201r0A2gBMU0plA/g2gP8n3kGUUg8opfYopfacO3duRE58POC2HK++mu/v3Ru9ftppLebnU8hLSugGfuEFJrSVl9t9L1gA3HQT8PnPsyvc9OkUkauu4jSy8nIKem1tuFXqPN5PfsK+6FddxTK4o0cpnmY++YULzBA3nD072FJ1W+q1tdzulVcokmfOhF9vIlZ1WRnrvt9+m/F8s7j5+GObN+CuJ9+xg54Fc16PPQY8/nh8r8LKlRRrv59himCQLvbMTJtYeMUVdvvcXG5jMu5LS7ntrFn0hng8dMfX1AzxP48gCOOO8ZoAtxbA/9Bad8TbUGv9rNa6SmtdVeDu0zmBcbuZi4vp/ja9z3fsoHC4MWVZL77IMrRTp2yLUzMApaYmsgt7165wkVywgJ6AysroJV5mAREKcb/FxbY5TV0dvQImsQtgs5qzZ5kktmOHtVCNEBur2+u1U9Vef53iZxYTsVzwxv1eU8PvGcs4EODixixm3PHwvXu5H2fC37lzNowQK25fUcHSuBUreN5TpzLfYPFiO3jGmXvg9qiYOeoZGbaaICWF7wuCMDkYSzf7KQCXOV6XDrwXaZsGpVQqgFwAFwBcB2CVUupHAPIA9CulAlrrjaN/2pc2phzr/fdpFV99tRWC9nb+oV+8mO7xN99kU5I1awaP06ytBTZupMu3sJBlVUVFFK/6egqK24WtdWL92N1UVHDfd94Zvs+WFoqwswnL4cOMC8+aRcHfuZOlW+YY7t7oM2ZYd7tZTERzwZvsebPthx9yO6V4D5cs4aLGWLx+P4+bl8fzXLYsXHSDwcHXaibKRZqWFqkdqzMRL9pAmbw8Lm66u+3Co7+f7wuCMDkYS8t8N4AFSqk5SikvgC8C2OzaZjOA+weerwLwhiaf1lrP1lrPBrABwJMi5OF/+K+9lmLz5pvWzbxnD923r71GF7Kx5NatG+ymXr+eVqWZlLZzJy1iZ5czd8vTpUsjN1KJNJbUTTRrORQKb8JiBr9MmcLzT0+nOJpjxEt8AyJnmbe0cDHi9CxcdhkT+YqLba7Bnj10vb/wAr8zcyZd8EuXhnsQAC4O3FnlR4/SVZ9oQl+sJDpDZSVL3jIy+G+ekcHXzvCEIAgTmzET84EY+EMAtgA4AOAFrfU+pdT3lVJ3D2z2CzBGfgTANwEMKl8TLE6rdMYMClBODvDOO7QSu7sp5kZ0Tp1ifLWnJ9z1a/ZTWMjvGdE8cGBwlzOnwDz4YGSRjJcIBkTuqHb0KGuvN2zg61mzgL//e55zdzfF19S0m2NE68zmXFBEE8hQKHwhYMrympq4IPqP/6DrvbeX9yQQoPejt5eLIve1m/7vzvd272aW+o4d/DGNXmKVzMXrSLdyJe/JVVexS9xVV/F1IvddEISJgdJaj/U5XDSqqqr0nj17xvo0RhRnl7O9e4HrrqOQG/r7KVhlZbTIjx1jspRSNmt6/nzGaJ97jq9Xr6bVeOAA+4n39VG8UlOBG28Mn7nt7rAGxO4yF+s6jIs7N5dC/vbbFNTOTsafm5sZNsjOpii2t3ORccMNwB/+EHk/xi39yCPxz+1rX+M9NKK+aBG/f+gQ48/d3TyXrCzek9JSimZ6Ou/fww9z/zU13D4vjyGBjz+2M+FPnuSixCwGAgFa9T099v4P9/9BMvc9GZRS72mtq5L93kT8vROEi0kiv3vSznUcE638bPlyG7s1Vml9PS3q48dpDfp8tr96SUm45VpWRgE7dIhu9o4Oum/T0myXs1jlXbHmkEfDPZ/81CkKuRnUUlBAAX3rLZZiFRbyp72dImuGskSbcw6En+/hw8B99wFz5vC+lJfbVrZTpth2tPPnA9dcw8S0HTu4oOjttaVgs2ZxoXHTTVY8jx3j+85BK5/5DEXe9FDPzAwvvzMz4odKouNgBUGYmIzXbHYBg0usTD34++8PdnOXlVEUly+niHd10TovKaHF7XTJrlzJhYFSFM6iIsaPb7mFGeaRjp1sh7VION3Jc+fSCk5Pt7H94mKKqBmQkpkZeShLJLe083zPneN1OF3j69bZoSiZmdx/Tg7vTzBoR5Lm5FDMzUKorY2LHKdXwhzno4+4fU4O8N577GXf10dL/fz5yGGCkWTTJoZaTFXBpk0jfwxBEC4NxDIfhxiX6m9+wwSsK6+k4BYVMaP63Xeta905vnP9errg772X1uCFC+y5/uCDg+u/58yhyJmhKyaT2ySSRRsBOlI9wcvKWOrmrCYMBOhRKCjgqE9Df3/84zrP98AB28vc76fw9vTQC7BwofVqOEMUzpGkhYW00Pv6KMZr1gxuNgPYRjMdHRTwjAxrjZ8/z2Pm5QG33jryVvWmTcC3vsXjz5hBt/+3vsXP3JULgiCMf0TMxxlO9/bMmRSMnTsZNy4qokDdc89gV7fb/XzHHbHjqpWVg8u3WlqsOz5Wh7WRYOVKTg87eJCvU1MZq54+fXCGeCLHdZ6vEVlnq9qCAlrskfZrOuPl5zO+XVND1/5ddw1eCDmPY2bCNzZSxGfMYJgjM5MWv1Js8vK1rw3rVkVk40ZeoylPM48bN4qYC8JERNzs4wynG3fRIlqGSrFLW7zs8UTmdBuilW+Zfcf7fCQwvdnb220fd2OZJ3tc5/mabm6BgO2sVlpKd3mk/Tqz33t6uBCqruYY1UiZ5ea7l19Oy7+zk9fi8bAhzIwZDBWYsrvRiHWfOsXrdJKTY0fMCoIwsRDLfJzhdOMWFwPXX0+38enTTMJyutWHQ7REMrPveJ8Pl+pqxunPnqUYmvnm7e3Ad77DmHcyx3Web34+3c6LF9vSMY+H7vJo+000wcx9X5Yvt/X9OTkMV5jmO85GNiNNSYnNqDf4/XxfEISJh4j5OMPt3i4uprV6001DyyKPRTwBG80MapPRbsIJAL0QJnltqBnz5nzdpVxGuEfCBe2+L+5yOWO5O7u4jTQPPWRj5Dk5FHK/H/jud0fvmIIgjB0i5uMM52SzaO09LwWGW/ccLQHOmYQ3nONdzFKukfRiJHqdZlGycSMXRSUlFHKJlwvCxESaxoxDLkaDkOEQq3FLoudZW8s6cKW4D9NgpbycpVZOy3w4x7vU76WTkbivw0GaxgjC2CBNYyYol3qDEPewE/OYyHxtp7guXMi56aEQLfL58+kqbmxklzojvkM9XiJzzYfKaCwShnNfBUGY2IiYCwmRjDgNtQbdLa5tbSztuuwy27XOTDcziWvr11Pg3eeSyPFGSxxHa5Ew2rX9giCMX6Q0TYiLcxpbrElfZhb4++8DW7bQgjbEqgU337v/fnZNCwZtV7l585j9/dxzfJw3b3DXudbWoU1rS2TC2lAYTnc8cy9Wr+aj8x4nMkRGEITJiYi5EJdExCne+NVoteDO75n2pm+/bRcCTnGNJr7RRrLGqz0fCXGMJL5DXSTEWzRdjNp+QRDGJ2PqZldKfRbAvwLwAPi51vop1xxuaQ4AACAASURBVOc+AM8D+CSACwC+oLU+rpS6FcBTALwAQgD+b631Gxf15Ccg0aagvfgiH/Py2GSluHiwOLld1itWcALZO++wfWy07G3n9/Ly6FZPT2fnt+LicHGN1nWustLGzpPJFndWBgQCtsXtrbfawS2x7pHPx+Es8+aFi29WFs8r2e548dz+o13bLwjC+GXMxFwp5QHwNIBbATQA2K2U2qy13u/Y7MsAWrTW85VSXwTwQwBfAHAewF1a69NKqXJwJrq0wxgGkeK8jz9OS9lMWOvuptV8/fV8zylO7nhuURFw++3sbW4yzyMtFpzfM73PfT66zt312LHK8oaSFGjE8ac/5ZzyadOAm2/mhLRIMW73PdqyhR6I0lLrsQAYJmhpCT/PY8dYHuZM3HOfb01NeD/8RYsGl+Jd6smPgiCMDWPpZr8WwBGt9TGtdQjA7wDc49rmHgC/HHi+CcDNSimltX5fa3164P19ADIGrHjBQaz4q5tIrvSmJjZpWbKEAgVwuMjLLwOvvMLubGaf8VzW0VzIJqkN4ALghhtYjgbwHJyC6myr2tAw+POhUFFBD8Cdd7JN64wZ0WPc7nsUCnFcqukfD1CETZtWc56hkF0URcs5qK21c89zcrhw2rmTc90lJi4IQjzG0s1eAuCk43UDgOuibaO17lVKtQGYBlrmhv8CYK/WOhjpIEqpBwA8AABlk+ivYrIZ1ZEypY2AG5HdvduO8czOphv9sceAJ5+M38wmmgvZbcV6vexpHu08h2KZxsvETzRL3L1dbi7btDoXMWYB4zzPtWt5XbGy5qur2V523z66/NPTeW/q6oBvf3vkrlUQhInJuE6AU0otBl3v/2e0bbTWz2qtq7TWVQXOdmITHKd4NjWxXnvPHuAb34hsoUeyrH0+/pw9y/7vH3/MQSPZ2XZM6NGjdFPHs5qjJYW5rdiRsLadJJKJn2ginHu7RYvYK97rjZ2QlkhCXH096+ivv54T1oyrfc6c5BrtxLvWZLw1giCMH8bSMj8F4DLH69KB9yJt06CUSgWQCybCQSlVCuCPAL6ktT46+qc7vjBWZGMj49xmfve+fRSbW2/l6E0jFJEs68JCPv75z7QSW1vpAg8GOfwkO5vu4127+L1YVnOskakjGQd2W6aNjYM9AufPc1Ezdy63KS8HNm8Ov/ZILXLd98jrpQCXlESeH++89kOHOAynrY3fnTmTTXHc96e42C6U3PcrHvES6EazSY4gCGPLWFrmuwEsUErNUUp5AXwRwGbXNpsB3D/wfBWAN7TWWimVB+AVAI9qrd+6aGc8jjBW5MGDFPG+Pgqc18sxnHv3hlttkSzrJ56gi729nRa5x8Ofnh4Kk8HEuGNxMcqqIlmm27bRbW04exb48EN6K8w2mzcDd98d3zsQ7R797Gexx8qWl7NM78MP6eH429+AP/yBMfGhlp0NpSRuOPXvgiBc2oyZZT4QA38IzET3AHhOa71PKfV9AHu01psB/ALAr5RSRwA0g4IPAA8BmA/ge0qp7w28d5vWuuniXsWli7Eim5qA6dPpDgeY4GXcuOYPeawxn0eO0ILNzKRV39rKcaSNjUzS8niAz3wm/vkMpawqmfhvbS2t7aYmehQWLeJCZNo0ZonPmMHtDhygkBUWhmegm0ls5pgbNkQ+5lC8CNu2ccHT3s5FVUoKX7/7LisGnngiufsTzcLOzIxdEicd5ARh4jKmdeZa61cBvOp673uO5wEA/xDhez8A8INRP8FxjBEHI3B9fcCsWXSNd3fzj3gif8jNHJ6ODu5DKVqOAC3eqVOZBBatLtt9TokK4aZNwLp19AIUFNC173YJG+GtqWE8v6ODLm+TCX7DDaxBf/11Cl5uLu9Fairr5Q3mPrhF8p13gGeeYd7A3LkcK5rs1LHaWtbpm1h7Whofe3sZqjh+PLyOPJHFzYsv8pyWLAlfkIRCg0vinOGCWKEOQRDGN+M6AU6ITUUF8JOfAFVVFHKPh0IXCNByTeQP+dKltCjPnKG7PjWVApKRQSGfMYNNU0bSVVtbSyFXikIeCNByvnCBi5PVqxnvf+wx645WimLe0sJzS0+nFZ6ezvwA4xovLKTb28SlAXsfnG7oAweYC2AWLq2tnA++aVNy17F+PRdBAPcVDFrrHOA51dQkvi8j1lpzwbJ/P7B9O934u3bFDhdIBzlBmLjIoJUJjrMxyrZtdDsvXcrYeSJz0B98kMLwzjtcDAC0+ubOZaczk3U9kq7a6mprkStFce7qYmnc9OnAsmXhDVvMOfT2si4+M5OWa1PT4BGhTlF0W68bNlg39O7dvEemRCwvj+9v3Ji4dW4WB7NmUXTNwqC3l9dlatUbGgZ/N1Yin+mU199PEZ89m+eqFOP/scr6pIOcIExMxDKfBFRUMEmrupqNUXp6Ei8Bq6hgHfnixbTECwuZiZ2dTYvZCOJIumrr661FbvD7KYIm1u1s2GLmnU+bxucZGcxYLywcfI2xSuicpWft7VwQ9PZS0AE2cznlrreIcx25ufSMFBTYkIXB46Hb3dl8B4ifyHfFFXze3Gyt/mAQuPrq+AltFRXMDXj4Yb7esEFK1ARhIiCW+SRiqCVgxl2/fj3Fra6Obuf+fpZmGct2pBqWlJVRrPbt4+v0dIprWhpFcft2uv1TUiiwn/gEsGMHFyk5OVxslJUl33jGWXo2ZQpj2ikpNnnO72dMPpnrMOVmd9xBq/nsWYp6ejot7GnT6ElwN5Bxl5g5E/mU4r04f56PfX2sTy8u5r9JsqNmpURNEMY/YpkLCWEs2oUL6WLPy+PjggV8H0hsTGoirFzJ2PzixRS9c+coWgsWsF67u5ui2tHBxLctW7jIACi++/YxdmxqqxNtkuK02ufM4T7z8ii2ra0U84ce4raJ7NcZoy4sBG66ifu68krgmms4pz0lhUl67gYy7hKzykrmDBw+DLz1lj23mTO5iDEkO9BFStQEYWKgtNv3N4GpqqrSe/bsGevTuKQZqnW9du3gTGnz2j3RLJF9us+jvNwmxeXm0t198qTNsM/L47HMoBRzXGOBOuPjiVqgmzYxRn7qFBcPJpvdadnG26/7OvbvZ0vcUIjfLSzk62CQ0+XMvYp0L0MhJuaZ8ruCAi5uzD2prEzs+lav5oLr3Dnuz/SDz8+3E/IioZR6T2tdFf/OhSO/d4IwPBL53RM3u/CfDMf9Gq2GuaaGE8OS3WckV/ivf83vNzbyx8Tt+/sZf/Z6KU7LlvF84nVEi3YPnOL7k58M3jaZ/bqvw3mPAwGGBwCes7k3d98duSPd3Xezm5/Xy8+mTWP53f79bOJz002JJbSVldHCr6uj5yMnh8dobU2sxFAQhEsPcbML/8lw3K/R+pu3to6cS7eykj+m8U12NveZmkox7+jgMY2rOZGe6E4S6W0OJL9fJ05X/rvvUkiXLw+f1lZXNzhJzwi8z0cxN+NoteY9+ad/it6Bzs3KlTyGUhTzQID7WbxYXO2CMF6JK+ZKqRyl1LwI78v6fYIxHJGKVsOclzf0fUY7RlMTxTwYpLB5vRSjzk5bcrdyZeIDVAzV1XTbf/ABx7x+8AFfuwUu2f26OXSISXxHj9LD0NxsPzP3xmSdmzaxdXUU9UTG0cajooKVCa2t3O/Zs8yFmD9fusEJwnglppgrpT4P4CCA/1BK7VNKXeP4+N9G88SEi89wRCpayVdl5fCEL9IxCgtpjWvNBLDZsylsvb0UO+PCdy8wDh+miNbURE5aq6lh//TubjtT/MMPBzd1GU7zlU2b2HzGeCy6uoCtW+kqj3VvzELLjKPt62MsPxSyeQKJJhzW1nIBkZfHXISiIi4wjhwZw25wX/kKXQXJ/OTksBHCmjVMqOjoGKOTF4SxJ17M/DEAn9Ran1FKXQv2Sf+O1vqPABIYryGMJ+LNJI9HtJKv4ewz0jFMmVxfHxcO586xLGvNmvCGLs4mKabl6+LFtpzOHbtvbbXd7QBr/be2Dj6HaM1X4iUQbtxIDcrL44LENIx55x262qPdG2cr1qIihhhmzeJ+TOkcEDsfwLlNeTmtcufs9H37gEcfTezfYcT5xS+S/057O/DLX3JFl5fHleNdd438uQnCOCCemHu01mcAQGv9rlLqJgB/UkpdBmDypMFPEkajQ9ho79PnY+JXtAx5s8BYu5biFytpLS+PFmt3t40l9/fb7m+R9uskkQTCU6es+GZn26xyI9TR7o17oRWrx3w86uvZgjcnx2az5+by+GOS/PaVrwz9u11djBN4vcAf/8j/DNnZI3dugjBOiCfm7UqpeWZe+ICFvgLAiwAWD/fgSqnPAvhXcGraz7XWT7k+9wF4HsAnwTnmX9BaHx/47DsAvgygD8A3tNZbhns+CdPezgkcDz44Yf5wuC3Khx8e3kSzaJ/Hm0qWKMk2wElkYlhlJS3UAwf4TzxlCnvYV1YmdoxEstxLSmjpmwVCdjbDA3PmcMERDfeiyHTii9RjPh7Gyi8q4g+Q/Oz0EWUoVrmTzk6uwE6cAP7yF7HOhUlJPDH/GlzudK11+4AIf344B1ZKeQA8DeBWAA0AdiulNmut9zs2+zKAFq31fKXUFwH8EMAXlFJXguNQFwOYCWCbUmqh1rpvOOcUk/Z24Ic/ZNPuz30O+F//i8Hazw/rNlwSuC3Kw4eB++6jwFRWRhbqWBZotM9NRvZYdB5LZGJYeTnw/PMU2rIyNon56KP4IQGzQPnNbyiwixZZkXUvGB56iDFzgJax38+f7343/jU4FzCxeszHY7jhlBFlOFa5ob+fF3Hu3Kha55F6H9TVRX7d3s6wztmzXKz19PC9nh6G+6dMAa69lvf/8GGuRXp6eAkS+p+YKDW4pTNAGfnv/z35iYxu4mWzdwIoivD+tQB2De/QuBbAEa31Ma11CMDvANzj2uYeAL8ceL4JwM1KKTXw/u+01kGt9ccAjgzsb/i0tzOjKFKyzRNPAP/7f3Nk18GDwBe+wF6i3/oWf267jenJ4wynRXnunC1bMold7sSqeCVs5vNQCHj1VeD3vwdee40Tz3p7x6bzWCJJa3V1bIual8f/Bnl5fF1XF32/TlGdOZOa8vbb9r+Be8GwahXwox9x32fO8PFHP0r+FzlWj/nR/O6IM1yr3NDWRhU01vkI4y5bPHSIv/KHD9sF8Le+xff9fv6Z+Ogju8a4cIG/D1rbtcfWraxE8PvZJ8CM8RUmJtH6sx0/DjzwQHITGSMRzzLfAOA7Ed73D3w2HH9WCYCTjtcNAK6Lto3Wulcp1QZg2sD7u1zfjdg1Wyn1AIAHAKAsng9y717gk59M+AIA2NoeM1/06aeZWTuOcLqgzdjQ9HT+kYnkKo7nsq6vZ/vVv/yFMWifj+J9/jywZw/XRdEs19Eikdi9iSUvWGDfi9fr3LmwufJKjiVVivfR54ts8a5aNfxVuLmmZDvpGS/LUPv0X7L09TF+0dzMbMcRdrW7QyinT9vBOwsW8DEnh+8fP87fn64uhm1iNdns6eEph0IjerrCOKO9PbmJjJGIJ+ZFWusP3W9qrT9USs0e+mEvHlrrZwE8C7CtZMyNH3xwaAc5f55inpMDvPAC8PWvhwczL3GcLmjT2tNMRAMGC248l3VZGS3xzk4KWloa/2j5fNzu4EF7e4YzcS3Z1rPxBMx0Rjt1yiaFlZSEi7sb58LGlI1F68g2UoNoEmVcDFQZJ+2k3QtY83tiyi6dr9vbOR64s5NrjFiXaGbcm9kCwuSkvz+5iYyRiOdmj5DH+59kDO/QOAXgMsfr0oH3Im6jlEoFkAsmwiXy3eTYu5f1QUNBa/429vfTnH366WGdysXG6YI2f5ACAZsp7RbcaC7r8nImcdXU0N3Y2ck1Tk8P/6iVltICaWpKvj7bTaLd2pKhvJwu8tZWxjRbW/m6vDz6d8rK2Pxl+3bgpZdokZeWDu7INhrnGw8ZqDJyuHsw5ObyV9254DWvp0yhQCvF//8qRhFvSgoXuanSWHtSk5KS3ETGiPuI8/kepdT/4X5TKfUVAO8N79DYDWCBUmqOUsoLJrRtdm2zGcD9A89XAXhDczLMZgBfVEr5lFJzACwA8O6wzmaoVrmTQICi/sIL4yp27oyh5ufb1p6FhZEFN1LM1SS3tbTwcyPcfj8t89JSLhRKS7nfRGK1sSaTuYUqFGKM8ktfGvp87ro6YOnS8Jj50qXRY+a1tazN3ryZIq5U9AXAUIQ1mYlvkRhORz8hHPcCduZMOxK3v5+Pfj/fr6qys+d9vthinpbGbnym374wOZkyxU5kHCrx1oMPA/ijUuqfYMW7CoAXwOeGc+CBGPhDALaApWnPaa33KaW+D2CP1nozgF+AjWqOAGgGBR8D270AYD+AXgBfH1Ym+3CsciehEMXcWOfjKHbuzpSOVxfudlmvXRseU7zxRibztLdTvAHelvnzmUeYSKw3lou4pobv+f20fvx+W+41VHdyfT3Pb+FC+160mLk5P3OPWlsZK5071y4AnPGvRErjkrn+REgkg19IDHfOxcKFtsd9fT1DMZ/7nH192202mz0rK342uwlFSTb7xGW0s9ljirnW+iyAGwaaxRhb4xWt9RvDO+x/7v9VAK+63vue43kAwD9E+e4TAJ4YifPAf/2vI7Ib9PfTt6wUM6HGKUNJjnKLVVER/6Bt3841jlLAihUsBEhk37FqtgH+oTSjP48coVvT5+MksUSmo0UiGfGrrua67cQJ/oJmZNDCys6O3OM8WWEdysQ3N4mUoF3sOP54JtLvhfsP8EgkNgrCUIjXmz1dKfUwgP8CIATgmZES8kuK/fvjbxONlBSqis8HfOYzVK7mZuD110fu/MaIZNy8kfq6p6cD//iPtKLff599dhIVilgu4upqhgG0tpENj4dWjYnzD8WdnEzP9ZoaWmEeD+OdPT3MBWhsjCzSyfZzj3X9if67xCtBG4s4viAIo0M8N/svAfQA+CuAOwAsAl3vE4uWlsS3vflmmmNuZs2aEAJuSNbNm2wjkngNOEzmeyRL1rjDc3KYGW8SjbKzo2fJJ2KBJtN61vRxnzGDQunx8P329sjXnWxb22iWvBmokui/S6Twiem+d/bs8K1/QRAuDeKJ+ZVa608AgFLqFxhuktlEYAIJdiySdfMmI1abNjGdoKcHKCigqDz/PGPNZgjKyZMU6blzBy8Oqqv5vLiYP4sW0Z2fk2MbcjgFNZGOdcm6mk0fd4+HyU+NjbyeWEl9yYQvoi2OsrKGJsDmHpjhNLt28bG0lAun3Fx6NQoLJUFOEMYj8cS8xzwZSFgb5dMRLhWSTdgCEm9ism4dhbqggG7ygwcpjqdPM7EoP5/NW4JBPo+0OHAKndfLRUBJCQXKvW28+PtQEs0qKymspiZ9wQJbkz4SVm20xdGGDbxvThIJKZhZ7XV1DH9kZDAidOwYQxbd3czCX7w4PAFQEITxQTwxv0op5R94rgBkDLxWALTWOmdUz04YM+IlbA01caq6mhZsRgajFYEAXdadney9A1gLsaEh8vCRSEIXK0PevTDZv5/FCy0twL/9GwVs7lx+FsnSjXStxnK+6qpwy3koNfPRiLQ4GmqGen0976cR8uPHmVHt99OrMH/+JTAGVRCEIRMzAU5r7dFa5wz8TNFapzqei5BPYGIlbA0ncaq+3gp5z4Dfp7eXYt7ZyTKdN95ghnosgTJjTZ97judUXR09IcyZnLd/P3tid3VREP1+4K23wnMgnZZutGsFxqa/ebKJdIayMiYIpqfzdSBga5z7+mzDkzlzJF4uCOOReE1jhElKrEzo4XQWKyujBWjo6LC1lykD/xvPnWMP90Ss3EQWFk4BfOcdZp97PHRX5+RQ1N51ZIM4Ld1Y1+pcUDi7vY0myQxJcWa9m+ldbW283x4P3ezTp9MTcs89DB0kOu5VEIRLC2kiKEQlWgx8KPF0w8qVwK9/zTr0zk5aiCkpdq53Xx8td6WiC5TT5d3YGD8hzOmWNy7qggI7JbO721q67uS54VzraJFoboIzF6CtjZn3Z85QxIuLmcDX2wtcfrld7IzJGFRBEIaNiLmQNMPpLFZRAdxyC+vOvV7GyTMy+Dwtjd2QuroiT5GKlJW+bRurBZ1EElsjgNu3M0ZvhDw7m9tnZEROnkvkWpPJH7hYTVrcSX/BoF04FRYy4fCaa7hoCgYp9LFK5QRBuLQRMReSJtmacjcPPmhFOSWFGdVa01rs7mat9vLlg78XKSt92jQ2cJkxw24Xa2Hx0EOcOw3Qxe730zpdvz5y965415pMPf7FmmJWWwu8+CKf5+XRC3HoEEvQ0tOZtNfSwn8HEW9BmBhIzFxImmTitvG+f9llFJsZMxg/b2iwvandiWyRuqJVVgIXLiSeELZqFfCjH1Hkzpzh49e/zpKtSAl08a71mWc44GXHDv6EQpHzB2prgW98g7kAH3zAbnHBIL97//3xO+wl2vXNLBh8Pno7uruBv/6V4QuleL0yPU0QJh5Kj5N5wiNBVVWV3rNnz1ifhuCitpaiuHUrLe3KSlqQLS3hwrl27WCXd0sLBbSoaGiua6e17LS8E1mc1NbyWFOn0k0fCPBn6VJm6j/8MAWzpoa95P1+Ll6CQdt0MC+P5798efTjJnOO5h6FQhwPkJ5Oz4fXy2S3G26gF2T3bpanXXYZzzcRK10p9Z7WuiqxO2uR3ztBGB6J/O6Jm10YcyoqKMZ33hku1EB4Ils0l/dw3NTJdrpzxryPHaNYKsWfjAxuU1MDLFliBbilhZ93dzPpbPp0WvnmeMZajnbcZM7RJOylpFC4DxywI2KNkL/xBisGTK7Cm2/yfJ58UtzugjBeGRM3u1JqqlJqq1Lq8MBjfpTt7h/Y5rBS6v6B9zKVUq8opQ4qpfYppZ66uGcvjAaJzN4ernt/qMc1uMvgmpooki0tdPV//DEF/tAhJvYZATY13AUFFNHubrq9TVLaokWxj1tfz+22bwdeeomPgUDkbZ019UVF3PeUKQxdbNrE71+4wJCE2bffTytd3O6CMH4ZK8v8UQCva62fUko9OvD6284NlFJTAfwLOD9dA3hPKbUZQBDAeq31X5RSXgCvK6Xu0Fq/dnEvQRhJEs2QH8p41mSPe+QIW8uuXm3d9gBj3k1NzAZftIiPra0U9KYmCqTXSxF9/3263wGKdHc3X5vud319jGvfcAO3j3a9ALczvedzcrivHTs4UtZNebnte5+RwdK9jg7mJASDXEz097MVrRn219vLtrQ1NSN3XwVBuLiMVQLcPeBENgw83hthm9sBbNVaN2utWwBsBfBZrXWX1vovAKC1DgHYC6A0wveFccRQO5slSrQEMvdxDx3iEJKSEptx/thjwOOPU7CnT6eY7txJMe/vp0DOmsWWsNOnA5/+tM2yByj8gQDFuqiI2eTXXUdXvNcb/3qjpbW436+tBTZvZnvaggJa3S0tfF5Wxr7xBQUU8d5elgKacQteLxcmgiCMT8ZKzIu01mcGnjcCKIqwTQmAk47XDQPv/SdKqTwAdwGIOspMKfWAUmqPUmrPuXPnhnfWwqgxGi50Q6wuce7jnj4NXH89hc90fDt3zlrkwSAt3vR0vveJT9huahkZ1tJ2ZtkXFNBi1tq63p98kv3kzXGDQVrLGzYMzlYPhYBly7h/v59WfWYmB/g5tzWx9ZwcinR/P4/Z2Wn3ZUbE9vTYn2CQ38nLG/69FgRhbBg1N7tSahuA4ggfPe58obXWSqmkU+qVUqkAfgvgJ1rrY9G201o/C+BZgFm1yR5HGD6JNkoZaRe6IV4CmfO4q1cP7vhm2s9efTUniwF0Tzc18XruvZevna769HTg1lvt1LcFC4BvfzvyDHVntnpBweD6cxMKWLGCbVl37qRYz5wZvm19Pa3tXbt4/ClT6BFobqarPTubrWzNefb18XH2bHaBW7BgJO62IAhjwaiJudb6lmifKaXOKqVmaK3PKKVmAGiKsNkpACscr0sBbHe8fhbAYa31hhE4XWGUiNUoBRidbmjuxUNNzeD9Rks2ixRD9/n4WFxMq/3gQWupm+sYTpZ9vMWGM4t//34KudZ03zu3LSsDXnvNTkabPp2u874+ehxKS9mQ5/LLaYW7Z8WP5MQ3QRAuLmPlZt8M4P6B5/cDeCnCNlsA3KaUyh/Idr9t4D0opX4AIBfAwxfhXIVhEG1QyTPPDH3yWizM4uHwYeDoUeCFF2hNv/de+HbRks0ixe4LCijcLS18vOoqoKoK+MlPrFU/nBBBvIx65/5Pn+Zn119vXeZm25Ur6drX2g5TKSzkJLRAwNazP/10uIv/Yk18EwRh9BirbPanALyglPoygBMAPg8ASqkqAF/VWn9Fa92slFoHYPfAd74/8F4p6Ko/CGCvYgbPRq31zy/6VQhxiTao5OWXGQdOtL47UaqraYnW1dFCLShgstfOnbRG582L3n7WWPR+P887L4+x7yeftPs21v6nP83XGzZYr0Kk2euJkEgmvzMUEG3bigq69vfuteVwJskuP3/w+Yl4C8LEYUzEXGt9AcDNEd7fA+ArjtfPAXjOtU0DADXa5yiMDNGESuvE6ruTHUxSX09r07iaAZZl9fSwder+/cxGz84Gvvc9irVxL5twQEVFuOvZOX3NnNNI9lhPptd9vG2/9rXI3eJkGpogTGykN7swqkQrOVu61DY3MUSaRpasK76sjNnn6en2vUCANd6trcCVV9JS7e21DV7Wr6fbP9EZ7cOZ5x6JZNz08bYdzaoAQRAuXaSdqzCqOGeJG+vaWInxrNFkW60CXDz8+78zSQ1g9nZWFgV82jQ2R8nI4E93N2PQV13FJix33RW+r1gd2UZ6xnkymfzxth2tqgBBEC5dRMyFUSeauEQSeed2QxXN4mKWY5mGKD09FO5PfQp4913WVAO03tvauE+t+TyRGe3DmecuCIIwGoiYC2NGPAtyKKJZXc168MWLOWSkrY1WeV8fxdvj0ffOxgAAH2VJREFUYbvWvj4+nzGD2yxdaieZBQIsZ7twAbjlFttcxjDcee5jSbI5CIIgjA8kZi5csgylxasp8yoqYpOVe+4Bbr+dFv7RoxToYJBlWs3NdMe/9hpF+5FH+P7rA/0Eb76ZNebuOP2lGJdOZN75pk3AffexXO/oUZsvMNxyQEEQxh6xzCch48U6ixZvH4o1X1nJ7mnnztGlfv48LfXMTAr25s22Acy0aRT1jz6yjVnccfpE49IX415Hyq5//HH2lw+FeFwzgEUplusFAsC+ffRgDLccUBCEsUfEfJIx0mVVyR47WWFLNpkrlgt8wwZa6Tt20HLPyKCw+/22kc3Wrcx8N9PJdu6kC34oyW3mXvf10YLftQv44x+BNWuAVavi35NE75c7UTAUYiihqYnX29JCIT9/ngmBjY223eupU+GZ/4IgjE/EzT7JcJdVGQv0S1+K7p5NxIUbD2eZWVoaXdsrV7IuOtb+kj12LBe4mfXd1mYFLBCg6OfmUmynTaP1qpQdqFJTM7TkNmcDm0DATixbt47XEav0LpmyPHcHuQMHKNShkC2da29naV4wSE9ETw89FSdPSuKeIEwERMwnGc4//GZohxmlGUkwhlLrHQmziAgGKZoALeC9e6Pvb6jHrqig8D/3HB+NNWti8F4vre7uborsFVfYRjaXXQYcP04X9PHjHFBy4cLQ+pa7G9goxXvf08P7EatePZladrNIMZjnToHv72fCn9assU9NtRPVpCe7IIx/xM0+yXDGlA8csBZqXl7kOu5ILlxjyd97b+IxYFNmtmOHFTeniztS3NYcOxTi90xm+jPP8CdZjNVu3OnTptGF7vPxnixYQCu6sJDn1dlJMb/ppqHFx71exugLCuw2xkI3bvtYpXeJlOXV1nJRtnUr76vPxwWE1jxvg/E0mGvr6uIi4corJV4uCBMBscwnGc4M8dZW/tE31ikwWDCSteSjEc/FHa05SyDAY3Z3M46tNYVrqBnYFRUU8+pq4I47eE0ffECB+/hjHi8zk2NB586l8E6fHn+/kbwIp07RCjZWv/EElJTwfrgtasCW3sX6zH1Mr5cJfo2NjJVPn043e10dcOYMzyUnB/jkJ7mAmTqViW8338zBK4IgjH9EzCcZzpiyiQ07J3C5BcMpKsaSV8pa8om2MY3m4l60KHZzlpqacDe1UhSkobZOdd6HlSsptt3ddKmfOMEYd18fxT0jg8NgzDzzWPz0p8D77wO//z3w7LPAW29RQCsrKeTnzvFYfX1sXNPYyAzzaKV3iZTlOb0mx4/TKjf3t6qKx3/nHZ7/woUsRevuBq69ll3vUlPFxS4IE4UxEXOl1FSl1Fal1OGBx/wo290/sM1hpdT9ET7frJSqG/0znliYmPIvf8nZ1j5fdMFI1pKPdcxHHmFDl+Zmvrd0KcUnWu24e6SnWQBUViaXXR4tie6nP2VsvKGBFmwwyGsEWJ++YgUXEvESxGprgVde4T5SUhibPn6coh0KAb/6FS3gtja2ljX165s3A3ffHTlZL5FaduO5+NOfgA8/5ALE42F44NAh3qfZs+lSnzmTxwVYRx8KjX1tvCAII8dYxcwfBfC61voppdSjA6+/7dxAKTUVwL8AqAKgAbynlNqstW4Z+HwlgI6Le9oTi0TquJ3bJGLJxzvez34WHlueMSN67XhFBZu5vP++Hel59dUUwhkzEjtmrFK8P/+ZbnalmGGfmkohPHKEi5tEO7tVV3P71FQuTgwmg7yigqVwd97J8zCu/aYm3gMzFz3S9ccSW68XePNN3hufz3oUMjNZhlZdzcXDDTfYnIcZM2zOhAi5IEwcxkrM7wGwYuD5LwFsh0vMAdwOYKvWuhkAlFJbAXwWwG+VUtkAvgngAQAvXITznbAkUsdttjE13MaSH2ob02Rqxx98cHgjPaMNa3nmGYoqQCHv76cbPD2djw0NiTWpASjI2dkU7p4emyluBrmsXs0FybXX2ryD9HTGtpuahl7nb3rPh0IUcL+fx+/spIXe20tPQV0dXe5mATbcoTCCIFx6jJWYF2mtzww8bwRQFGGbEgAnHa8bBt4DgHUA/l8AXfEOpJR6ABR9lElB7bBItiPbSHQ/G0oXOCfRhrW8/DIFNRCgkKekUIBDIWDWLJa1JUpZGd31Xi+tcbNPpaxHYN8+ZuRnZdkcgO5uZpdHy+aPhPOevv8+4+67d1PA8/KsoKemsszOLDIOHhyaN0UQhPHBqIm5UmobgOIIHz3ufKG11kopncR+KwHM01r/X0qp2fG211o/C+BZAKiqqkr4OEJk4lnVRmxqapgdvngxMH/+8DrNDWekZ7T2rlozW/3oUWuVm3j3ihWJ77+2lslsJ05wvz4fBTsQ4DGXLOF+r76aLvETJyiyDQ0U87lzuW0ilrI7ZLBvH63ua65hjDw9nTPac3Jo9V9zDb/31lv0AAzHmyIIwqXNqIm51vqWaJ8ppc4qpWZorc8opWYAaIqw2SlYVzwAlILu+OsBVCmljoPnX6iU2q61XgFhTHGKTUsLLdN9+8JdvBe7D3i09q5Ll9KKbW+nVdvXx21MzHv16vjeBHO9vb0sBTMubqUonJ/8JGPlAK9/2TIOOzl6lBbznDlcPOzYkViJWHU1j/XBB7wOj4ePO3dyAXHmjI2Zp6XxO0VFwCc+wTK5ZEIHgiCML8bKzb4ZwP0Anhp4fCnCNlsAPOnIdL8NwHcGYujPAMCAZf4nEfLRJxGX+TPPsKFMKERhKSmhu9e4eMciVut009fU0OWcl0fx8/tZwnXqlC0dKyqidV1YGN+bYOLxH3zA2u2SElrbxoV+8iTd4Ib0dAp4ZycXOMbND9j4dyxqamh5Z2Tw+83NbGqTksLFQX8/y/ays3ktb71FIfd4oifZCYIwMRirOvOnANyqlDoM4JaB11BKVSmlfg4AA6K9DsDugZ/vm2Q44eKSSFvV2lo2c9GaQuPx0KVsmqYAYxerNcl7OTkU3JYWurwPHaI1PW8e8PnPA5/5DN3iibRQBWxDHWcjnPR0ut17e1mX/9prtnGLuX/LllGQk61lb23leZma+/Z2LjwyM/l9U22QmsoFS28vFypSgiYIE58xscy11hcA3Bzh/T0AvuJ4/RyAqKlIWuvjAMqjfS6MDIm0dK2uplUIUFBmzGCtdUMDa9KNmCUSqx2NsaHOoSfp6ezs1tZGQf/Vr7j/1atpkTuJ5U0w8fjcXGuRNzfzvfR0tocFWNdt5qVXV/PzRYso9m1ttLiXLIl/H4z7vrub++/spGCHQlyUZGbysbGRC4Rly3j/RcgFYeIjHeCEuERq6drZSTfy008z3vtv/8bErkCAYpOVRdd6KGQXAolYiCM12MW5v7Vrgd/8Bvjb3yiGkYaeAIm1UHViGurMnMlrbm3l/cnLY6MWpXj9ZhKb8RAcOwZs385t0tJooZ88GX/ATXMz77Gx6rOyrIs9K4vCbnqwHzwoWeuCMJkQMRfi4m7p2t9vs7HT0pg01tQEvPce24Y6xeYLXwBefDF8elkskpkWFo9Nm4D77gNeeIGi3dLC8+wYaDXkHnqSSAtVJyYen5ND0W5spLs7ZeC3yuMZ3E++ooKu/pwca00vX05Xv/MaI92HxYt536+6CrjrLuBTn+Jx8/K4YOjo4PkGAkw8PHpU2rUKwmRBpqYJcXFmhLe20kIMhSjWXq+tz25vpzDefrvNGn/wweSOFa0uPF7inNslXV4OfPObNrGtv98OiDl3jkIbCFBEjfWaSE27+zhTpgBvv03Bvfxy3pszZ+iVyMjgd5z95CsqeK9uv92KPsDzcw+4cd+H+fNpzefn8/MFC4DPfpbPW1sZKzcNfbzexJLqBEGYGIiYC3Fxt3QNhRizzczk5729Njs7FBpeCVS0uvBY7uJILVu/+U07ChTgeWvNBYfW7Fk+b97gYSOxatrdxzl0iAluhYUU70CAlnkgwHOYNo3PAwGWwhmxTuQao21TWUkvh/ucPvqIix6leLwbbqCgX+xSQEEQxgZxswsJ4RzOMnMm3eu9vXQVGzHPy+OAkueeS9yt7iaaq7u8HPjqVylmV18NfO1rNsYcySXd1ETRVorvGSvVWMnz5jEkcPfd/L57CEsk3Mc5fdqKp4lV5+fTWg+FbLb69deHD2xJxJ2fyDbGS+D326Y1GRkU8qIiadsqCJMJEXMhKSoqgDVrKBQdHRTMggIKekHB8GO0kaaF3X03E+zefJPWZloaE8gef5yC5kzQM/T28jGSq7mykguOlSs5uSzRZDv3cdraKNxdjqbCplXrzJk2o9zvpwX/29/y2D/9afRpabHug3Ob2lrgsceYF7BrF49x9iy9BKZRjSTACcLkQdzsQtKsWkWr9plnKCRaAzfeyPi42xqvrbXbdXbSrd3fT9FbujTyd9yu7rVrGefOyQmPQzc10TKN5JJOTWUpmnGvG3d7aioFFRhcchcM0l19//30MJSXs5TNxMd9Pgqk2T43l9cUDNpyMSPwa9bwuzU1wP799GAUFPB7b75JgX7yydjei1gufzO+taWF19nby/N47TWeX3q6tG0VhMmEiLkwJCoqKNKxqK2l9XzkCN3STU20YjMyWGKViKjV1jIbvr7e9hwHOOLT76eAffe7tLAB28SlsJDHM+VaJtmsuNh6D5xJZo2NTGTz+Sj8hw4Bzz9PF/m8eRTGkye5r7lzeZyZM3lt2dl0c/f1UUiffNIueLZv53e9Xi5isrO5j3Pn4sezY9Xbm8VRMMgFSkYGn3d18b5+8YvStlUQJhMi5sKoUV1NQc3JoQu4v9+OG+3ooDs4lqiZ5C6fj5ZmdzeHtwAUMI+H33/0UcbRs7Js8t2Pf8wWpgcPMulNKS4EfvxjeyynRX/woO3ilpfHeHhODjuoLVjAbebNo2CabPLcXLZnDYUo0j6ftb6/+lVg2zYuLEzf94YGLh6ysvh+pHi2e1BNebldTJjWsgAXH83NvC6Px85kN7F7Z5KcIAgTHxFzYdSor6f45eYySayvz7q/AwHrlo6WpGXc4EuWUMzOnqXlqTX3k5JiS8Dq6/nojCsvXDjYsgUodPX1FN+TJymWra20noNBHm/XLoq5qa8/e5bu8tOngX/6J+Dhh7lvrzfcvX/4MLBuHa9t6lQuQFpaeL5pafQoeDw8tjuebeLgJ05QyPv6eH5VVVwwNDUxUa+4mIuGc+f4vWCQCySAiwotswEFYdIhYi6MGmbOtxHuri47ajQ9nZZlczOwdy8F1t221bjBm5oohoGAdZt7PBRHn882qXHPBXfGnGtrGWfeto3Z7ObcTp+mldvVxX05M8FN7faf/sSubWlp4Ylyfv9gj0JDA+PjKSlcDMyYQXd4Rwct/s5Ofm/evMHJgiYO3t5uFz5+PxvOpKVZz0FnJy32+no+7+/nMbOyKOZLl47KP6cgCJcwIubCqLFyJbvCHTlCoWltpSBnZFCo6utpvV53XeQJZWVljF3v20cLefp0PpqWrD6ftXRzc8NLsZzxZq+X7vKmJh6vq4uiXlpKsVWKDV+0trFtEw9PS6Plm5ZGgW1vty1q6+vDE+IAWssFBdxndzfFdd48Wts9PTzX5ctt4p/zPF99lcfw+WxCm3HRAzzvvj4uPrKy2Hhm2zbeU62ZhzBrVvKNegRBGP+ImAujRkUF8MQTNpu9sNBms7e2UnxuvNHOOgfCLespU4CXXqKwaU0B8/koaMEghbezk2J29dW2FMvd3GXLFlq4Zh8nT/L7x49TbEMhHm/WLCvSCxdSfOvrKepTpnAx4fGwpe2yZbS0W1r4XZN4l5Zm27W+/TY/83go6JdfHr5Y2bSJLnmT6R4M8nqmTWNDnvZ2e1/MQiAlhVb+sWPANdewXe7evfyuGXojSW+CMPkYEzFXSk0F8HsAswEcB/B5rXVLhO3uB/DdgZc/0Fr/cuB9L4CNAFYA6AfwuNb6P0b9xIWkiZb1vno1hdbZ0tRpWW/axCEuprSsp4cLgJISusFPnKBAGwv4/fe5WHjiichT3qZMoQv8/HkbvzdDYfLyeIy9e5kZb8Rw9Wpav2Y+eUYGt2trs93YzMQ4E5dfs4aZ9T4f3d07d9IrUFhoO+aZ6/v613ldU6ZYoTajTadNC499m9AEQAvd62UMv7Jy8CJBEITJx1hZ5o8CeF1r/ZRS6tGB1992bjAg+P8CoAqABvCeUmrzgOg/DqBJa71QKZUCYOrFPX1huMRrabpxIy34YNAmvPX308Wcl0f3eEsLn6cO/C824ldTw8/8fork2bMUzY4O27nOuK+Nl+D4cQqu0zNgzvGKK6yVbVzxLS3Apz8duXTMJN7V1FCE77iDlnlbGz0Gd99tLfLsbJ5PUxPFvqPDutM9Hh7PuN49HttKt6SE8f6bbpISNEEQxk7M7wGtagD4JYDtcIk5gNsBbNVaNwOAUmorgM8C+C2A1QCuAACtdT+A86N+xsKI4hzeYlzUziYnx47ZeLgZkmIytg8fpuV61VWMtxtaWphE9vHHtLrb2ijU/f22fjwY5D5NQ5mUFNuzPS+PAuw+x/x8Wtk1NUzYu/VW4OabaYE7+8E7Y/6m/a1x3QP2ceNGCrmZnJaWZrPe+/sp6mlpFG6tbTmfeX3jjcCVV3J/UoImCAIwdu1ci7TWZwaeNwIoirBNCYCTjtcNAEqUUnkDr9cppfYqpf6glIr0fQCAUuoBpdQepdSec6aWRxhz4rUrTUmxFqlzAlhKCgXS56Nb/Le/ZWOWxkYuCnbtYvJaUxMFOi3NZsJPmcJ9ZGTwx9Rnp6ZSQDMzKf7ucwwG2YjlxAluqzUTz+KNao3UZjY3lwuVzk4eq62NHgS/nxa6z8chMGVljIHfeSfr3JVi8t7f/z2FPNZoVkEQJh+jZpkrpbYBKI7w0ePOF1prrZRKpjI2FUApgJ1a628qpb4JYD2A+yJtrLV+FsCzAFBVVSUVuJcQsdqVzplDge7psQKakkLBzcnhAqC/n2LY0cGkt5ISWq9dXXx+/LgdB2pK2QCKs7HUlbKjXPv7aZ0bamvpDt+yhd/JyeG5vPkmj/l3fxd+zrm5tN7XruVjTQ2Pe9lldNUXF3PGeDBIV3lPD386O/l9pejqnz6dlnpDA93ze/aEZ73n54trXRCEcEZNzLXWt0T7TCl1Vik1Q2t9Rik1A0BThM1OwbriAQr4dgAXAHQBMDbQHwBIB+oJxvLltFT37qVAK8X4skkCKyhgYplJFktL4xzxqVNtDLujI1wwe3oorKdP01LPzKQ49/YyBn/55bSCAduK9u23bc13ZyePMXMmhb+mht8zHD1KF39mJq1vrXmO588zA37+fGbGa804vlJ2IAzABUZXF887K4tlbjfdxM9iLXwEQRDGys2+GcD9A8/vB/BShG22ALhNKZWvlMoHcBuALVprDeBlWKG/GcD+0T1d4WKzciWt83vvZVw8N5eCvWwZxXXqVAqs18vX6em0aK+5hoLY1kaRDwYp+MZdn5VF1/X8+TxOURGP09XFwSjl5XzftKJVyg4y6enhvs6f58LiwgUb5z50iK739nZmsJu+6VlZ3F93N2vuTR91n4+fAzajPxTicc6ds2Vu4koXBCERxioB7ikALyilvgzgBIDPA4BSqgrAV7XWX9FaNyul1gHYPfCd75tkODBZ7ldKqQ0AzgH454t7+sJoY+LV1dUUviuvZH24iYe3tVH4FixgLNyUjs2bR8u7uZnvzZpFKxmgFV1VRXf3okXAX/9Ky7mxke7w6dOB//k/Kcqvv25r003me0oKhbu5mdsuWgR88AEtchMOKC2lFd7WZqe1aW1Lz7S2I1NTUvh+aqpNzOvt5Xfz8ljmJta4IAiJMCZirrW+AFrU7vf3APiK4/VzAJ6LsN0JAMtG8xyFscftWjZx4/x8W3Pu8VC0AwH2VG9ro4veWf89ZQoXAQsX2n21tdHV3dxMsQ6FmOB2+DAXAzNnUsxbW8O7sBkBbmujy/zkSds4xuOxteg9PeHXYobMmDGwzqz61FTG481xKis5JEaEXBCERJEOcMK4IVav9aVLbf23SQ5zbrt+PT8zZXBHj9IqN7XbSlFgU1KYeFZZSde5EVjTvKa/n8fr7eXsc6fQm45xJvPefM9Y3GYEqmmOY46bnk5Bnz6diwERckEQkmWsYuaCMCwqKoCf/YzW9x13UEjd5W3Obd1lcJddRvE3Q1GcjWfMvHGAQgzw86wsxuoBWu0A92EmuBlL21jbgK1l93q577w8Pl+2jPsrKqLnIDOTQi6udUEQhoJY5sK4JtEsb/d2q1fbyWlpaVbETXJdW5udXd7dTWvaZL63t1OgzQQ3M4zF4PUyzm/q203cPCfH9oK/9lqeQ13d4A5ygiAIySJiLkxKysootMePW8E17VNzcym8GRlMVisr42MgwO+a2HtdHQXdzCoPBvl89mxa7llZdO1rze2uuYaudKf3YNWqsbh6QRAmGuJmFyYlK1dSeK+9ltay10sBv+46xrDz84FPfYrub6+Xgl5czNePPsofk7Rm2qympzN2/7vfARs2MHs+PZ2iXlnJY8lAFEEQRgOxzIVJibv0rbWV8WwzCc05pnTjRibLlZRwqprTmn7qKZa+mTi4mbpWUSFWtyAIFw+l9eTpcFpVVaX37Nkz1qchCOMSpdR7WuuqZL/3/7d3JzFSlGEYx/+PEhFiIjMuuOACGg9CEPeLJuIGalRcDh5UojHG6JUoBA9oTFQ8aIwHQ/TgSdzCRY0GUOIcjAaQRaLIAImCuOESFwSX10N/aDMZ4nR3VVd9088vqUx11dc1T3fxzjs9/dHlujPrzEhqz39mNzMzy5ybuZmZWebczM3MzDLXU++ZS/qWxmfBV+lo4LuKM4xEDjlzyAijJ+cpEXFMqwetSd1BHuchh4zgnEUaScb/rb2eauZ1IGl1O5OIui2HnDlkBOesixweXw4ZwTmLVFRG/5ndzMwsc27mZmZmmXMz774lVQcYoRxy5pARnLMucnh8OWQE5yxSIRn9nrmZmVnm/MrczMwsc27mZmZmmXMzL4GkfknLJW1JX/sOMm5uGrNF0ty0bbykNyR9KmmTpMcKzjZb0mZJg5LmD7N/rKSX0v4PJJ3atG9B2r5Z0qwicxWVU9IVktZI2pi+XlrHnE37T5b0i6R5dcwoabqk99O/xY2SDi8rZxFce9Xl7Gbt5VB3neZsufYiwkvBC7AYmJ/W5wOPDzOmH9iWvval9T5gPDAzjTkMGACuKijXocBWYEo69nrgzCFj7gWeTeu3AC+l9TPT+LHA5HScQ0t6/jrJeTZwQlqfBuws8Ty3nbNp/6vAK8C8umWkcVXFDcBZ6fZRZZ3zAh+va6+6nF2pvRzqroDnsuXa8yvzclwPvJDWXwDmDDNmFrA8Ir6PiB+A5cDsiPgtIt4FiIh9wFpgUkG5LgAGI2JbOvbSlPVg2V8FLpOktH1pROyNiO3AYDpeGdrOGREfRcSXafsmYJyksXXLCSBpDrA95SxLJxmvBDZExHqAiNgdEX+VmLUIrr2Kcnax9nKou05ztlx7bublmBgRu9L6V8DEYcacCHzRdHtH2vYvSROAa4GVBeX63+/ZPCYi/gR+ovFb4UjuW5ROcja7CVgbEXvrllPSEcADwEMlZes4I3AGEJLelrRW0v0lZy2Ca6+6nM3KrL0c6q6jnLRRe2MKidyDJK0Ajhtm18LmGxERklr+/3+SxgAvAk9HxLb2UvYuSVOBx2n8hltHi4AnI+KX9IKhjsYAFwHnA78BK9W4rnJRDa4trr16q3ntLaL+dQdt1J6beZsi4vKD7ZP0taTjI2KXpOOBb4YZthO4pOn2JGBV0+0lwJaIeKqAuM3f86Qh33PnQcbsSD/UjgR2j/C+dciJpEnAMuD2iNhaUsZOc14I3CxpMTAB+FvS7xHxTI0y7gDei4jvACS9CZxDca9W2+La6/nay6HuOs3Zeu2V9eZ/Ly/AExw4CWfxMGP6abxv05eW7UB/2vcI8BpwSMG5xtCY7DOZ/yZkTB0y5j4OnJDxclqfyoGTcLZR3iScTnJOSONv7MJ5bjvnkDGLKG8CXCfPZR+N943Hp+OsAK4p+3nt8PG69qrL2ZXay6HuCnguW669rhZaryw03vNYCWxJJ2H/D4rzgOeaxt1JYzLLIHBH2jYJCOATYF1a7iow29XAZzRmWS5M2x4Grkvrh9OY5TkIfAhMabrvwnS/zRQ0y7fonMCDwK9Nz9064Ni65RxyjLJ/qHRyzm+lMVHoY4ZpjHVbXHu9UXs51F0B57yl2vPHuZqZmWXOs9nNzMwy52ZuZmaWOTdzMzOzzLmZm5mZZc7N3MzMLHNu5tYVkv6StE7Sx5JekTQ+bT9O0lJJW9OVlt6UdEba95akHyW9Xm16s3y59nqDm7l1y56ImBER04B9wD3pggLLgFURcVpEnAss4L/P034CuK2auGajhmuvB7iZWxUGgNOBmcAfEfHs/h0RsT4iBtL6SuDnaiKajUquvVHKzdy6Kn3+8FXARhrXPF5TbSKz3uDaG93czK1bxklaB6wGPgeerziPWa9w7fUAXzXNumVPRMxo3iBpE3BzRXnMeoVrrwf4lblV6R1grKS792+QNF3SxRVmMusFrr1Rxs3cKhONq/zcAFye/nvMJuBR4CsASQM0rih0maQdkmZVl9Zs9HDtjT6+apqZmVnm/MrczMwsc27mZmZmmXMzNzMzy5ybuZmZWebczM3MzDLnZm5mZpY5N3MzM7PM/QOpOfXNCEYcGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_17.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X7mOuyN_EjG7"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4-NJGCOhEjG7"
   },
   "source": [
    "## 새로운 데이터 포인트 투영하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "I6mzj4vMEjG7"
   },
   "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": 58,
   "metadata": {
    "id": "BSuLhPTBEjG8"
   },
   "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": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-DYnonckEjG8",
    "outputId": "9b9899f0-c17c-4ef7-ce6c-3dc9f8ab00a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.8713, 0.0093])"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new = X[25]\n",
    "x_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t2lcwezdEjG8",
    "outputId": "977f0c94-092e-4c01-cf6c-f5f936ea1989"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0788])"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_proj = alphas[25] # 원본 투영\n",
    "x_proj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K67SPqwPEjG8",
    "outputId": "b45b08fc-7cc4-4f73-ab89-cc2d8bbdee7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0788])"
      ]
     },
     "execution_count": 61,
     "metadata": {
      "tags": []
     },
     "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": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "NqAizbQcEjG8",
    "outputId": "4b74167c-2c5c-4b68-e798-57cc3200da54"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1+ewVFBQgIyMDBw8exOjRozF+/HjMnz8fCxYssLXbWevWrTFz5kxMnTrV4arpnqxduxbXXHONwwVWH374YYSGhmLcuHGYNWsWf1cMEAYUUQA5h5QRVIEIJ+NmfCdOnECvXr1w0003obS0FN9++63DLRvsv9nffvvtEEIgISEBHTp0QEJCAgC1F5CXl4ekpCR88MEHeOONN1BVVYWCggJkZ2fbAsrYo7jvvvscro7t6nYan332GbKysmx7L+fPn8fhw4fx9ddf22510blzZwwfPtxtH4313XDDDSguLkZRUZFXt+UAgDvvvBMhISGIi4vDqVOnvH5dN27ciA4dOiA7O9vlRW3t7d+/H08//TQ+++wz27TVq1ejS5cuKCkpwbhx47Bq1SqX97ai+uMhPqIAsw8pQyD2nIzfoI4dOwYpJV5//XVYLBa0bt3a9vtHZmYmDhw4YFvG+JYfEhLi8I0/JCQEVVVVOHr0KBYsWIAvvvgCWVlZuO222xxup2HfZnfDxriUEq+++qqtHUePHsXIkSPr1Ud3t+nwhn3/vL1CzoYNG3D+/Hls2rQJM2bM8Hi34Pz8fIwZMwYrV650uD+ScXuKli1b4ve//73t0CT5jwFFFGDGb072PJ3dV1/h4eF45ZVX8I9//APh4eHo1q0b1qxZY1v3nj17vK6ruLgYERERiIyMxKlTp7Bx40aH+cat0d9//31ce+21tumubqcxatQoLF682Ha7i0OHDqGsrAw33HCD7VYXBQUFLu8+67y+jIwMREZGIjIy0qvbcrjj6fYUFy5cwJ///Ge8/vrrSEhIwB133IF58+a5LFtUVITbbrsN8+fPx6BBg2zTq6qqcPr0aQCA2WzGhg0bEB8f71XbqG48xEcUQK5OiDDGgcDsSQHqR3uTyYR3330Xq1evxpQpU/DXv/4VZrMZ9957LxITE72qJzExEX369MHVV1+NK664wuHDF1C/YZlMJjRv3hzvvvuubbpxO43i4mLb7TQeffRR5OXl4ZprroGUEu3bt8e6deswZswYpKenIy4uDtHR0Q5B5ywsLAx9+vSB2WzG0qVLAajbckyaNAkmkwnh4eEOt+Woi8lkQmhoKBITEzFx4kSHw5SpqakYM2aM7XYSc+bMsZVz9tprryEnJwdz5861nZn32WefISIiAqNGjYLZbEZ1dTVuvPFGPPbYY163j+rg7+XQXT14uw261Li6nYAzi8UiUzamSMyBTNmYYrslh7vpurvyyitlYWFhremBunWDsyFDhsgdO3YEvF5fLFu2TE6bNk2benTH220QaUx6OJW8Pv/MS3po0aIFNm7caPtHXV+kpaXhxRdf9PpwJNXG220QecHV7QQMnsLJl3JEjQ1vt0HUwKSUtQKlPqETiKugE+kmmEcDGFBEXggLC8OZM2fQtm1bW6D4skfEkKJLiZQSZ86cCdpddhlQRF7o2rUr8vPzUVhYCEC9Mednzseqw6vwQI8HMDl6su0SQ96YHD0ZZ8+excJtC3H27FnMTJrJkKJGKSwsDF27dg1K3QwoIi80bdoU3bp1A1Cz57Tq8Cq/fkta0WsFojZFYeG2hYiKiuKeFJETBhRRPQTyRAce7iPyjAFFVA/l5nJkHM8I2Fl49iGVcTwD5eZyRDSLCERTiRo9nmZOVE9llWUIbxoe0D0dKSXDiS4pPM2cqAEEI0SEEAwnIie8kgQREWmJAUVERFpiQBERkZYYUEREpCUGFBERaYkBRUREWmJAERGRlhhQRESkJQYUERFpiQFFRERaYkAREZGWGFBERKQlBhQREWmJAUVERFpiQBERkZYYUEREpCUGFBERaYkBRUREWmJAERGRlhhQRESkJQYUERFpiQFFRERaYkAREZGWGFBERKQlBhQREWmJAUVERFpiQBERkZYYUEREpCUGFBERaYkBRUREWmJAERGRlhhQRESkJQYUERFpiQFFRERaYkAREZGWGFBERKQlBhQREWmJAUVERFpiQBERkZYYUEREpCUGFBERaYkBRUREWmJAERGRlhhQRESkJQYUERFpiQFFRERaYkAREZGWGFBERKQlBhQREWmJAUVERFpiQBERkZYYUEREpCUGFBERaYkBRUREWmJAERGRlhhQRESkJQYUERFpiQFFRERaYkAREZGWGFBERKQlBhQREWmJAUVERFpiQBERkZYYUEREpCUGFBERaYkBRUREWmJAERGRlhhQRESkJQYUERFpiQFFRI1WWWUZpJQBrVNKibLKsoDWSb5hQBFRo1RWWYYhy4dg+qbpAQspKSWmb5qOIcuHMKQ0wIAiokYpvGk4BkcPxsJtCwMSUkY4Ldy2EIOjByO8aXiAWkq+atLQDSAi8oUQAmmj0gAAC7ctBACkjUqDEKLeddmHU8qAFJ/rocBiQBFRoxWIkGI46YsBRUSNmj8hxXDSGwOKiBo9X0KK4aQ/BhQRXRI8hVRBQQEGDx6MrVu3omPHjgynRoJn8RHRJcMIqZQBKQ5n96WmpiIvLw+pqakMp0ZE3z2oQ4eAW24BOnUCBg0CmjYFhACaNQMefRR46y01/sgjNcN/+hMgJfDyy47jixcDDzwArFoFTJ3qeZq3w5ddBpSU1K7nsstU+415npZzNa+uZZ3nO9fnbR2+lHG3Ll/LuVtnMMu52lauuGt/kGVlAR99BBw/DkRHA2PHAiaT+3KZmUBREdC6NZCU5Fjevq5mzdRboqLCsV7n9cXHA/v21V5/MMs1b642XWVl/esCXJUROH88DQOi1Z7UTz+V4T9vroLFYsGSfy3Fl80rcSDyLQyQKXi4Uxr27hUe12M/7up1tG+D83z7ZZ37acxz3ob1md6pk6rz55/VNCHUuKfnkhL1qKhQj8rKmkdVlSrXtCnQvTswdy4wfvzF+dt3JXTOnDkBr/SNN96YM3nyZP8qefxx4LvvgBMngJwc4PBhtXWOHQPOngU+/RT48UfH4SuvBI4eBd5803H844/VX8j27UDbtp6neTvcsyewaVPtenr2VO035nlaztW8upZ1nu9cn7d1+FLG3bp8LeduncEs52pbueKu/UGUlQUsWKCGL79cfeCkp6sPig4dapc7fVr9mVdUqLdBaCjw/feq/KlTNXVJCXz9NZCXB8TGqg+h9HSguhpYtqxmfTk5arxFCyAmpmb9vpY7fFiNh4e7Lycl8NVX6m3tqW2u6vrwQ1W2RYva7eoWI9C2aBQKi4vwnVwM2UwCORJylAWnf7cL8aUpGFyWho/WCnzxhes6YmIc19uiRe3Xce1a2JZ3fp1//rmmrhYtHPtZUKDmVVaqacY2LCkB3n1XjdtPLy4G3nvPcfqJE+p7fG4ucOYMUFamhsvL1Z/5hQuqLb/+qpYpL1dvkTNnVH1FRWqZ8nLVjupq1QdADRvbq1s3IC6u/n/Pzz//fMGcOXPeqP+SNUSgLxMCAMnJyXLnzp2+V3DoEJCcDJSWqlesSRP1FxISAnTsqP4y7L+ONGum3p2JiWr5PXvUc2Ki+ioQEQFs3gzceKOqU4iaaTfdpP4qhADatFF/DXUNl5YCc+aoR7NmNXVXVgL/+Idq15NPqm/e7pZzNa+uZZ3nO9fnbR3GHk59yrhbl6/l3K0TCF45Y1vddJN6h9uXsefcH3flAmzOHODcOdUFgzFu/z3SKLdnj/oQatGi5jkxsWZ5Y9ktW9R8QJUZOrRmefvyW7bUfDMfOrSmjmCW86dtGzeq51tucV9m3X9OYk/HaGBgdc0L+H0oBp7/CaNGdqqzDvtxoHZb7Zd37gvgflljXlGR+vJhbMNTp1R5b6YfPgwHTZuqshUV6uOxogIIC1MBZYybzepjtKJCfUxYLOrhSpMm6i3Tr5/qW30JIXZJKZPrv2QNPX+DeuYZ9aoax4WrqlTMm83qg+j0aTVeXq6GL1xQr/qePeoRGlozfvy4KmM2q+fjxx2nFRaq8WPHgJYtaw8fP157uLISeP119Wxfd2Ul8OWX6lFZWbs+++Vc1elqWU/znevztg7AcxtdlXG3Ll/LuWuXP+Vc9cG+nP02dy5jz7k/7soF2PHjQGSk47TISDXdVbnz59UHEKCez5+vKW9fl1HOKGPUe+KE4/rOnwdataopczHK+dM24xCVpzL5P/0V+J/Tx9ymEBw+9Fev6rAfd9VW++Wd53ta1phXXOy4DUtK1PSSkrqnV1erR1WVerYPInfPQE0wGcOeWCxqWzQU/QLq0CF1eMV+fxOo2QpFRSqQSkrUt9sLF9Rzs2ZAfr56GH8JJ06ofemDB4F27YADB4BfflFfR+ynFRaq/d6SEvVsP3z6tONwZaX6Wrd2rdoLO3RI1XPoEBAVpQ5Gr1un9vQqKmrqs18uKqpmnlFnx46ul3U3H3CsD/Cujg0b1GvyySeu2+iqjLt1+VrOXbtKS9Vr7Ws55z7Yl2vTpva2MsrYs6/Xvv3O5YIgOtrxwxVQ49HRrstFRqoPJUA9G6EVHe1Yl1HOKGPU26VL7fAoLq4dDMEs50/bmjdXD3dlSkoKcObsUuBms+MLOMqMs+eWorT05zrrsB931Vb75Z3ne1rWmNeqleM2bNlSTW/Zsu7poaHq0aSJeg4L8xxORjuFUN/fjWFPQkLUtmgo+gWUsffkivErnsVSswclhPpWfOqUmm8214SXlOrDrahIbb3iYnXQtbS05ivN+fM1h/127FDrMYaNrWc/fPSo+rA1m9WPAdXVqh6LBTh5suabfPPm6gCwUZ/9cidPqnn2dTZvXntZT/MBx/oA7+qw38Nx1UZXZdyty9dy7tplv/dX33Ku+mBfrqBAbaOwMLXNTp50vXdkX699+y/CXtTYserw1rlzqqnGsPFDvHO5Ll3U9zPjO1vnzjXl7evq2VP96RcXq2Fj+hNPOK6vSxdVpnNnx/X7Wq5zZ1WuSxf35bxtm6u62rdXvxu5a9dnn88FRpmBgQC+BzDH+jwQkCMrsemzubj8clWPuzrs1+uqrfbLO8+3r8t5nlFv796O2zAuTk2Pi3Oc3qtX7enh4ep7ebNmatj4U23VSj0bodi6dc300NCac82EqDugIiLUtmgo+v0GFRurPmDdHRgFHF9Z4+tAaKj64AFqfi8wm1WgtWihvjGfPetYj/20qCgVXhERatzdcMuW6rmkxHG6/TxAHUDfs0eVc17OftiYZ5R3t6zzfKCmjDHfmOapDgD46Sfgiisc6/BUxt26fC3nbp3du6vn3NzAl7MvY1+ue3fg2Wdrpqem1tRrz7lckPAsvkCcxQccOHgSa4qjIQdUq1D6n92LdzOAgYDYForP//IT2rfvxLP4gnAWXyB+g9IvoIiI/CClROLTidgbsbd2OBmsIWUqNyFzfib/DyoILt2TJIiIfGD8E67HcIJ1+vdAVnhWQO8nRYHFgCKiS4LzFSIsn1ogpXT7sHxqqXXFCdKLvleSICLyki+XLwrk/aQoOBhQRNSo+XNtPYaU3hhQRNRoBeLCrwwpfTGgiKhRCuRVyRlSemJAEVGjVG4uR8bxjIDdMsM+pDKOZ6DcXI6IZhF1LEXBxP+DIqJGq6yyDOFNwwO6pyOlZDgFQCD+D4p7UETUaAUjRIQQDCdN8P+giIhISwwoIiLSEgOKiIi0xIAiIiItMaCIiEhLDCgiItISA4qIiLTEgCIiIi0xoIiISEsMKCIi0hIDioiItMSAIiIiLTGgiIhISwwoIiLSEgOKiIi0xIAiIiItMaCIiEhLDCgiItISA4qIiLTEgCIiIi0xoIiISEsMKCIi0hIDioiItMSAIiIiLTGgiIhISwwoIiLSEgOKiIi0xIAiIiItMaCIiEhLDCgiItISA4qIiLTEgCIiIi0xoIiISEsMKCIi0hIDioiItMSAIiIiLTGgiIhISwwoIiLSEgOKiIi0xIAiIiItMaCIiEhLDCgiItISA4qIiLTEgCIiIi0xoIiISEsMKCIi0hIDioiItMSAIiIiLTGgiIhISwwoIiLSEgOKiIi0xIAiIiItMaCIiEhLDCgiItISA4qIiLTEgCIiIi0xoIiISEsMKCIi0hIDioiItMSAIiIiLTGgiIhISwwoIiLSEgOKiIi0xIAiIiItMaCIiEhLDCgiItISA4qIiLTEgCIiIi0xoIiISEsMKCIi0r+8H04AAAYUSURBVBIDioiItMSAIiIiLTGgiIhISwwoIiLSEgOKiIi0xIAiIiItMaCIiEhLDCgiItISA4qIiLTEgCIiIi0xoIiISEsMKCIi0hIDioiItMSAIiIiLTGgiIhISwwoIiLSEgOKiIi0xIAiIiItMaCIiEhLDCgiItISA4qIiLTEgCIiIi0xoIiISEsMKCIi0hIDioiItMSAIiIiLTGgiIhISwwoIiLSEgOKiIi0xIAiIiItMaCIiEhLDCgiItISA4qIiLTEgCIiIi0xoIiISEsMKCIi0hIDioiItMSAIiIiLTGgiIhISwwoIiLSEgOKiIi0JKSUga9UiEIAxwC0A3A64CtoHH7LfQfYf/af/f+t9z9CStnen0qCElC2yoXYKaVMDtoKNPZb7jvA/rP/7D/773//eYiPiIi0xIAiIiItBTug3ghy/Tr7LfcdYP/Z/9829j8AgvobFBERka94iI+IiLTEgCIiIi35FVBCiCghxOdCiMPW5zZuyv1PCFEkhNjgNH25EOKoECLT+kjypz0XWwD6300IsU0IkSOEeF8I0ezitDww6tH/h6xlDgshHrKbvkUI8aPd9r/84rXed0KIm63tzhFCzHQxv7l1e+ZYt2+M3bz/s07/UQgx6mK2O1B87b8QIkYIccFuey+52G0PBC/6f4MQ4gchRJUQYrzTPJfvhcbEz/5X223/9XWuTErp8wPASwBmWodnAvibm3IjANwOYIPT9OUAxvvThoZ8BKD/HwC41zq8BMCUhu5ToPsPIArAEetzG+twG+u8LQCSG7of9exzKIBcALEAmgHYAyDOqcxUAEusw/cCeN86HGct3xxAN2s9oQ3dp4vY/xgA+xq6Dxeh/zEATABW2n++eXovNJaHP/23ziutz/r8PcR3B4AV1uEVAO50VUhK+QWAEj/XpSOf+y+EEACGA/iwruU15k3/RwH4XEp5Vkp5DsDnAG6+SO0Lhv4AcqSUR6SUlQDeg3od7Nm/Lh8CGGHd3ncAeE9KWSGlPAogx1pfY+JP/y8FdfZfSpknpcwCYHFa9lJ4L/jT/3rzN6A6SCkLrMM/A+jgQx3zhBBZQog0IURzP9tzsfnT/7YAiqSUVdbxfABdAtm4i8Cb/ncB8JPduHM/l1l3959tJB9idfXHoYx1+56H2t7eLKs7f/oPAN2EELuFEF8JIa4PdmODwJ9t+FvZ/p6ECSF2CiG+F0LU+YW8SV0FhBCbAXR0MesZ+xEppRRC1Pec9f+D+mBrBnXe/NMA5tazjqAKcv+1F+T+T5BSnhBCtASwFsADUIcF6NJUACBaSnlGCNEXwDohRG8pZXFDN4wumiut7/lYAOlCiL1Sylx3hesMKCnlje7mCSFOCSE6SSkLhBCdAPxSn5baffuuEEIsA/BkfZa/GILY/zMAWgshmli/ZXYFcMLP5gZcAPp/AsBQu/GuUL89QUp5wvpcIoR4B+rwge4BdQLAFXbjrrabUSZfCNEEQCTU9vZmWd353H+pfoSoAAAp5S4hRC6AqwDsDHqrA8efbej2vdCI+PU3bPeePyKE2AKgD9RvWi75e4hvPQDjTJSHAPynPgtbP9SM32PuBLDPz/ZcbD733/pm/RKAcZZLvV8/DXjT/00ARgoh2ljP8hsJYJMQookQoh0ACCGaAvh/aBzbfweAHtYzMJtBnQTgfDaS/esyHkC6dXuvB3Cv9Sy3bgB6ANh+kdodKD73XwjRXggRCgDWb9A9oE4UaEy86b87Lt8LQWpnsPjcf2u/m1uH2wEYBCDb40J+ntHRFsAXAA4D2Awgyjo9GcBbduW+AVAI4ALUMctR1unpAPZCfTD9G8BlDX2WykXufyzUB1QOgDUAmjd0n4LU/0nWPuYAeNg6LQLALgBZAPYDWIhGckYbgFsBHIL65veMddpcAKOtw2HW7Zlj3b6xdss+Y13uRwC3NHRfLmb/AYyzbutMAD8AuL2h+xKk/vezvs/LoPac99stW+u90NgevvYfwHXWz/s91udH6loXL3VERERa4pUkiIhISwwoIiLSEgOKiIi0xIAiIiItMaCIiEhLDCgiItISA4qIiLT0/wH4k2fCPZl9HwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.yticks([], [])\n",
    "plt.legend(scatterpoints=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('images/05_18.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kifU6Zl1EjG8"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QRFvGBFJEjG8"
   },
   "source": [
    "## 사이킷런의 커널 PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "9oStyswlEjG9",
    "outputId": "bc003b7a-bc55-4cbe-cd4a-af5188970fa1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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.savefig('images/05_19.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0aHpcz4zEjG9"
   },
   "source": [
    "사이킷런의 매니폴드 알고리즘을 반달 모양 데이터셋과 동심원 데이터셋에 적용해 보겠습니다. 먼저 변환된 2차원 데이터셋을 그래프로 그리기 위한 간단한 함수를 정의합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "4siFOwEDEjG9"
   },
   "outputs": [],
   "source": [
    "def plot_manifold(X, y, savefig_name):\n",
    "    \n",
    "    plt.scatter(X[y == 0, 0], X[y == 0, 1], \n",
    "                color='red', marker='^', alpha=0.5)\n",
    "    plt.scatter(X[y == 1, 0], X[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.savefig(savefig_name, dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DqzS7onvEjG9"
   },
   "source": [
    "지역 선형 임베딩(Locally Linear Embedding)은 이웃한 샘플 간의 거리를 유지하는 저차원 투영을 찾습니다. 지역 선형 임베딩을 구현한 사이킷런의 `LocallyLinearEmbedding` 클래스를 앞에서 적재한 반달 모양 데이터셋에 적용해 보겠습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "fbIHXIAsEjG9",
    "outputId": "da28e631-a0f1-41b0-b594-424267e18881"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import LocallyLinearEmbedding\n",
    "\n",
    "lle = LocallyLinearEmbedding(n_components=2, random_state=1)\n",
    "X_lle = lle.fit_transform(X)\n",
    "\n",
    "plot_manifold(X_lle, y, 'images/05_lle_moon.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2pGF6ts1EjG9"
   },
   "source": [
    "t-SNE(t-distributed Stochastic Neighbor Embedding)는 데이터 포인트 간의 유사도를 결합 확률(joint probability)로 변환하고, 저차원과 고차원의 확률 사이에서 쿨백-라이블러(Kullback-Leibler) 발산을 최소화합니다. t-SNE는 특히 고차원 데이터셋을 시각화하는데 뛰어난 성능을 냅니다. 사이킷런에는 `TSNE` 클래스에 구현되어 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "pqZx-bjGEjG9",
    "outputId": "569d31dc-8383-4c97-8653-8481dc9e860e",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(n_components=2, random_state=1)\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "plot_manifold(X_tsne, y, 'images/05_tsne_moon.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UtmSZYnKEjG9"
   },
   "source": [
    "위와 비슷한 방식으로 `KernelPCA`, `LocallyLinearEmbedding`, `TSNE`를 동심원 데이터셋에 적용해 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "iRCNOjo6EjG-",
    "outputId": "b97cf6b5-550b-4ddc-e703-a3b42a5a4934",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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",
    "scikit_kpca = KernelPCA(n_components=2, kernel='rbf', gamma=15)\n",
    "X_skernpca = scikit_kpca.fit_transform(X)\n",
    "\n",
    "plot_manifold(X_skernpca, y, 'images/05_kpca_circles.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "Awt6SJtlEjG-",
    "outputId": "96422162-4ac7-4ccc-f1d3-dd1d4484766a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import LocallyLinearEmbedding\n",
    "\n",
    "lle = LocallyLinearEmbedding(n_components=2, random_state=1)\n",
    "X_lle = lle.fit_transform(X)\n",
    "\n",
    "plot_manifold(X_lle, y, 'images/05_lle_circles.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "j2LjGy2nEjG-",
    "outputId": "dd01a991-d73f-41ff-d4da-27272daefdb0",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(n_components=2, random_state=1)\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "plot_manifold(X_tsne, y, 'images/05_tsne_circles.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-dk15nRbEjG-"
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "colab": {
   "name": "ch05.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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"
  },
  "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": 4
}