03_1_로지스틱회귀.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6004c80a-6c43-41bc-aede-abd6ad6c19cc",
   "metadata": {},
   "source": [
    "## 로지스틱회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f316b4f-67d7-451c-8472-53a18bf38344",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60e1fc89-8124-454c-899a-71c2b78250c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('data/classification_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ccda1c47-5d76-4b8c-9955-7ba26c29b97d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.056844</td>\n",
       "      <td>-0.752741</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.760238</td>\n",
       "      <td>1.558517</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.447763</td>\n",
       "      <td>-1.554927</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.008459</td>\n",
       "      <td>0.529517</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.636547</td>\n",
       "      <td>0.409058</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>-0.129932</td>\n",
       "      <td>-0.797169</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1.426902</td>\n",
       "      <td>1.244639</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>-0.582968</td>\n",
       "      <td>1.630750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>-0.619720</td>\n",
       "      <td>1.630840</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1.938929</td>\n",
       "      <td>0.069963</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          X1        X2  y\n",
       "0  -2.056844 -0.752741  0\n",
       "1   1.760238  1.558517  1\n",
       "2  -0.447763 -1.554927  1\n",
       "3   1.008459  0.529517  1\n",
       "4   0.636547  0.409058  1\n",
       "..       ...       ... ..\n",
       "95 -0.129932 -0.797169  0\n",
       "96  1.426902  1.244639  1\n",
       "97 -0.582968  1.630750  1\n",
       "98 -0.619720  1.630840  0\n",
       "99  1.938929  0.069963  1\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b0641a-dbb0-4234-949d-793d46ab6e4e",
   "metadata": {},
   "source": [
    "## 2차원 평면에 그려보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3730f817-b905-49f4-810c-6d0ecd78c81b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb981d5f-d347-4b1d-acc2-6db3c345e24d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f87a8c7bee0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['X1'],df['X2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4cd643a-d447-4486-ab17-cc42f5b98da5",
   "metadata": {},
   "source": [
    "## 클래스에 따라 색깔 구분하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b81723a2-dbdc-47df-a541-d32dc15e1a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f87d9910eb0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['X1'],df['X2'],c=df['y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381be86a-3b6e-4ddd-b7bc-fdbce8f5fdba",
   "metadata": {},
   "source": [
    "## 로지스틱 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4bf3c89-4c6a-48a1-a3f9-6efb7951043b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "55544107-8e66-4d60-b0ca-2c003c9b1ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-10.        ,  -9.7979798 ,  -9.5959596 ,  -9.39393939,\n",
       "        -9.19191919,  -8.98989899,  -8.78787879,  -8.58585859,\n",
       "        -8.38383838,  -8.18181818,  -7.97979798,  -7.77777778,\n",
       "        -7.57575758,  -7.37373737,  -7.17171717,  -6.96969697,\n",
       "        -6.76767677,  -6.56565657,  -6.36363636,  -6.16161616,\n",
       "        -5.95959596,  -5.75757576,  -5.55555556,  -5.35353535,\n",
       "        -5.15151515,  -4.94949495,  -4.74747475,  -4.54545455,\n",
       "        -4.34343434,  -4.14141414,  -3.93939394,  -3.73737374,\n",
       "        -3.53535354,  -3.33333333,  -3.13131313,  -2.92929293,\n",
       "        -2.72727273,  -2.52525253,  -2.32323232,  -2.12121212,\n",
       "        -1.91919192,  -1.71717172,  -1.51515152,  -1.31313131,\n",
       "        -1.11111111,  -0.90909091,  -0.70707071,  -0.50505051,\n",
       "        -0.3030303 ,  -0.1010101 ,   0.1010101 ,   0.3030303 ,\n",
       "         0.50505051,   0.70707071,   0.90909091,   1.11111111,\n",
       "         1.31313131,   1.51515152,   1.71717172,   1.91919192,\n",
       "         2.12121212,   2.32323232,   2.52525253,   2.72727273,\n",
       "         2.92929293,   3.13131313,   3.33333333,   3.53535354,\n",
       "         3.73737374,   3.93939394,   4.14141414,   4.34343434,\n",
       "         4.54545455,   4.74747475,   4.94949495,   5.15151515,\n",
       "         5.35353535,   5.55555556,   5.75757576,   5.95959596,\n",
       "         6.16161616,   6.36363636,   6.56565657,   6.76767677,\n",
       "         6.96969697,   7.17171717,   7.37373737,   7.57575758,\n",
       "         7.77777778,   7.97979798,   8.18181818,   8.38383838,\n",
       "         8.58585859,   8.78787879,   8.98989899,   9.19191919,\n",
       "         9.39393939,   9.5959596 ,   9.7979798 ,  10.        ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z=np.linspace(-10,10,100)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9393a00b-8065-45d6-9e73-997ceb7db407",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    return 1/(1+np.exp(-1*z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e3df1657-f82e-4d96-a591-049d3aa299d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.53978687e-05, 5.55606489e-05, 6.79983174e-05, 8.32200197e-05,\n",
       "       1.01848815e-04, 1.24647146e-04, 1.52547986e-04, 1.86692945e-04,\n",
       "       2.28478855e-04, 2.79614739e-04, 3.42191434e-04, 4.18766684e-04,\n",
       "       5.12469082e-04, 6.27124987e-04, 7.67413430e-04, 9.39055039e-04,\n",
       "       1.14904229e-03, 1.40591988e-03, 1.72012560e-03, 2.10440443e-03,\n",
       "       2.57431039e-03, 3.14881358e-03, 3.85103236e-03, 4.70911357e-03,\n",
       "       5.75728612e-03, 7.03711536e-03, 8.59898661e-03, 1.05038445e-02,\n",
       "       1.28252101e-02, 1.56514861e-02, 1.90885420e-02, 2.32625358e-02,\n",
       "       2.83228820e-02, 3.44451957e-02, 4.18339400e-02, 5.07243606e-02,\n",
       "       6.13831074e-02, 7.41067363e-02, 8.92170603e-02, 1.07052146e-01,\n",
       "       1.27951705e-01, 1.52235823e-01, 1.80176593e-01, 2.11963334e-01,\n",
       "       2.47663801e-01, 2.87185901e-01, 3.30246430e-01, 3.76354517e-01,\n",
       "       4.24816868e-01, 4.74768924e-01, 5.25231076e-01, 5.75183132e-01,\n",
       "       6.23645483e-01, 6.69753570e-01, 7.12814099e-01, 7.52336199e-01,\n",
       "       7.88036666e-01, 8.19823407e-01, 8.47764177e-01, 8.72048295e-01,\n",
       "       8.92947854e-01, 9.10782940e-01, 9.25893264e-01, 9.38616893e-01,\n",
       "       9.49275639e-01, 9.58166060e-01, 9.65554804e-01, 9.71677118e-01,\n",
       "       9.76737464e-01, 9.80911458e-01, 9.84348514e-01, 9.87174790e-01,\n",
       "       9.89496155e-01, 9.91401013e-01, 9.92962885e-01, 9.94242714e-01,\n",
       "       9.95290886e-01, 9.96148968e-01, 9.96851186e-01, 9.97425690e-01,\n",
       "       9.97895596e-01, 9.98279874e-01, 9.98594080e-01, 9.98850958e-01,\n",
       "       9.99060945e-01, 9.99232587e-01, 9.99372875e-01, 9.99487531e-01,\n",
       "       9.99581233e-01, 9.99657809e-01, 9.99720385e-01, 9.99771521e-01,\n",
       "       9.99813307e-01, 9.99847452e-01, 9.99875353e-01, 9.99898151e-01,\n",
       "       9.99916780e-01, 9.99932002e-01, 9.99944439e-01, 9.99954602e-01])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigmoid(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66969daf-2f55-49bc-b19a-192f0a9d2fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f87ca6efa00>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(z,sigmoid(z))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0519e66-f6d6-4fd2-b505-42514bc292b5",
   "metadata": {},
   "source": [
    "## 로지스틱 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "130c1f60-55c1-4838-9064-0be1699b0ef3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f872696c-02a3-4042-89e0-b421329762d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75f003d-597d-4d3d-ab34-fb9b8699a8da",
   "metadata": {},
   "source": [
    "### 데이터 분할"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6f4caae-22c7-47dc-a967-96fb0dfb0a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['X1','X2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81aa4c50-4178-4a25-874a-ee65c571f70e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2aedc921-e597-43d6-928e-160be4edf860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7691452-f951-4b2f-a871-29d80a3bf7ae",
   "metadata": {},
   "source": [
    "### 학습결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "63f7631e-1569-469c-8cc5-23cfb21b5b51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.43054461, -0.36712609]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W=reg.coef_\n",
    "W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fd471a4b-ed2d-4869-8a7d-8ada27764c4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.10689852])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=reg.intercept_\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd5f37fe-653c-45ca-a681-e39c7a3d80e9",
   "metadata": {},
   "source": [
    "## 확률 계산해보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0f7c7300-0516-419f-a380-43be033c4191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(X).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63a54973-af6d-4ba3-b8a5-bbf4283bd7eb",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.61600238],\n",
       "       [ 3.81306237],\n",
       "       [-0.41055481],\n",
       "       [ 2.3636036 ],\n",
       "       [ 1.50387742],\n",
       "       [ 3.74246423],\n",
       "       [ 2.44026099],\n",
       "       [ 5.23731987],\n",
       "       [-3.77290719],\n",
       "       [ 0.46925461],\n",
       "       [-2.78700562],\n",
       "       [ 2.49815958],\n",
       "       [-3.31783252],\n",
       "       [-0.67765404],\n",
       "       [-1.91292558],\n",
       "       [ 5.45082282],\n",
       "       [-0.2311799 ],\n",
       "       [ 5.13228817],\n",
       "       [ 2.08471838],\n",
       "       [ 0.47039452],\n",
       "       [ 1.79713819],\n",
       "       [-2.69713339],\n",
       "       [-5.70749907],\n",
       "       [ 3.53573178],\n",
       "       [-0.32879591],\n",
       "       [-0.19784415],\n",
       "       [ 1.17835724],\n",
       "       [-3.26638161],\n",
       "       [ 1.74801706],\n",
       "       [-2.82291306],\n",
       "       [ 3.65725251],\n",
       "       [-0.77618101],\n",
       "       [ 4.09013878],\n",
       "       [ 3.40722494],\n",
       "       [-1.24078764],\n",
       "       [ 4.70220289],\n",
       "       [ 0.31967246],\n",
       "       [ 2.68163587],\n",
       "       [-0.88801225],\n",
       "       [ 0.8677343 ],\n",
       "       [ 4.06553144],\n",
       "       [-1.92142276],\n",
       "       [ 8.96793756],\n",
       "       [-1.19267962],\n",
       "       [-3.53077716],\n",
       "       [-2.0808738 ],\n",
       "       [ 3.43924025],\n",
       "       [-0.16588498],\n",
       "       [-1.76690732],\n",
       "       [-1.19684774],\n",
       "       [ 1.13047254],\n",
       "       [ 3.04565176],\n",
       "       [ 0.91782128],\n",
       "       [-4.35865716],\n",
       "       [-1.89906972],\n",
       "       [ 1.9369803 ],\n",
       "       [-1.38463496],\n",
       "       [ 1.15181198],\n",
       "       [ 2.70909908],\n",
       "       [-2.66372065],\n",
       "       [-1.68614391],\n",
       "       [-1.51238418],\n",
       "       [-3.17027817],\n",
       "       [-3.37982576],\n",
       "       [ 0.40538261],\n",
       "       [-1.08964408],\n",
       "       [-1.18113371],\n",
       "       [-3.89821355],\n",
       "       [-3.71388497],\n",
       "       [-4.90257046],\n",
       "       [ 0.52252097],\n",
       "       [ 4.73345224],\n",
       "       [ 3.36691676],\n",
       "       [ 3.44267597],\n",
       "       [-1.26119197],\n",
       "       [ 0.3334597 ],\n",
       "       [-5.5765385 ],\n",
       "       [-1.94135531],\n",
       "       [-7.189738  ],\n",
       "       [ 3.55031808],\n",
       "       [ 1.98756157],\n",
       "       [-3.61513624],\n",
       "       [-2.49908783],\n",
       "       [ 2.86637533],\n",
       "       [ 3.87321635],\n",
       "       [ 5.29185002],\n",
       "       [-3.36566122],\n",
       "       [-3.41886354],\n",
       "       [-5.7989079 ],\n",
       "       [-2.95359463],\n",
       "       [-1.77684131],\n",
       "       [ 5.52567937],\n",
       "       [ 4.23952716],\n",
       "       [-5.50118117],\n",
       "       [ 2.43474212],\n",
       "       [ 0.08375556],\n",
       "       [ 3.11810899],\n",
       "       [-1.90872215],\n",
       "       [-1.99808153],\n",
       "       [ 4.79386608]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (100, 2) * (2, 1)\n",
    "# 로지스틱 회귀로 구한 계수(weight)와 상수를 이용해 선형회귀식 세우기\n",
    "np.dot(X,W.T)+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9cd5505b-a753-485d-91e5-9e9741eaa137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.79536428e-03],\n",
       "       [9.78396557e-01],\n",
       "       [3.98779096e-01],\n",
       "       [9.14009457e-01],\n",
       "       [8.18152067e-01],\n",
       "       [9.76852847e-01],\n",
       "       [9.19846332e-01],\n",
       "       [9.94713613e-01],\n",
       "       [2.24686977e-02],\n",
       "       [6.15207317e-01],\n",
       "       [5.80304202e-02],\n",
       "       [9.24012699e-01],\n",
       "       [3.49644700e-02],\n",
       "       [3.36785099e-01],\n",
       "       [1.28652535e-01],\n",
       "       [9.95725578e-01],\n",
       "       [4.42461057e-01],\n",
       "       [9.94131604e-01],\n",
       "       [8.89408990e-01],\n",
       "       [6.15477130e-01],\n",
       "       [8.57800212e-01],\n",
       "       [6.31427205e-02],\n",
       "       [3.30997536e-03],\n",
       "       [9.71687526e-01],\n",
       "       [4.18533625e-01],\n",
       "       [4.50699667e-01],\n",
       "       [7.64652303e-01],\n",
       "       [3.67426783e-02],\n",
       "       [8.51702521e-01],\n",
       "       [5.60984835e-02],\n",
       "       [9.74845753e-01],\n",
       "       [3.15143549e-01],\n",
       "       [9.83538602e-01],\n",
       "       [9.67929571e-01],\n",
       "       [2.24298915e-01],\n",
       "       [9.91006356e-01],\n",
       "       [5.79244426e-01],\n",
       "       [9.35934283e-01],\n",
       "       [2.91520200e-01],\n",
       "       [7.04274034e-01],\n",
       "       [9.83135422e-01],\n",
       "       [1.27702994e-01],\n",
       "       [9.99872585e-01],\n",
       "       [2.32780030e-01],\n",
       "       [2.84490994e-02],\n",
       "       [1.10969732e-01],\n",
       "       [9.68908637e-01],\n",
       "       [4.58623594e-01],\n",
       "       [1.45927355e-01],\n",
       "       [2.32036460e-01],\n",
       "       [7.55926094e-01],\n",
       "       [9.54594429e-01],\n",
       "       [7.14597968e-01],\n",
       "       [1.26339007e-02],\n",
       "       [1.30213800e-01],\n",
       "       [8.74020023e-01],\n",
       "       [2.00265636e-01],\n",
       "       [7.59841728e-01],\n",
       "       [9.37561430e-01],\n",
       "       [6.51483638e-02],\n",
       "       [1.56283626e-01],\n",
       "       [1.80585726e-01],\n",
       "       [4.02996557e-02],\n",
       "       [3.29319433e-02],\n",
       "       [5.99980199e-01],\n",
       "       [2.51685306e-01],\n",
       "       [2.34848413e-01],\n",
       "       [1.98750760e-02],\n",
       "       [2.38022523e-02],\n",
       "       [7.37270596e-03],\n",
       "       [6.27737063e-01],\n",
       "       [9.91280643e-01],\n",
       "       [9.66654450e-01],\n",
       "       [9.69011970e-01],\n",
       "       [2.20768769e-01],\n",
       "       [5.82600936e-01],\n",
       "       [3.77136980e-03],\n",
       "       [1.25499037e-01],\n",
       "       [7.53718197e-04],\n",
       "       [9.72086059e-01],\n",
       "       [8.79484925e-01],\n",
       "       [2.62079180e-02],\n",
       "       [7.59221512e-02],\n",
       "       [9.46158998e-01],\n",
       "       [9.79632088e-01],\n",
       "       [9.94992757e-01],\n",
       "       [3.33860443e-02],\n",
       "       [3.17111055e-02],\n",
       "       [3.02170457e-03],\n",
       "       [4.95668938e-02],\n",
       "       [1.44693606e-01],\n",
       "       [9.96032641e-01],\n",
       "       [9.85790417e-01],\n",
       "       [4.06535256e-03],\n",
       "       [9.19438487e-01],\n",
       "       [5.20926657e-01],\n",
       "       [9.57633574e-01],\n",
       "       [1.29124480e-01],\n",
       "       [1.19404496e-01],\n",
       "       [9.91787619e-01]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 로지스틱 함수에 적용하기\n",
    "sigmoid(np.dot(X,W.T)+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "58bb1b82-767e-40b4-bfb3-1148c798a256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.79536428e-03, 9.78396557e-01, 3.98779096e-01, 9.14009457e-01,\n",
       "       8.18152067e-01, 9.76852847e-01, 9.19846332e-01, 9.94713613e-01,\n",
       "       2.24686977e-02, 6.15207317e-01, 5.80304202e-02, 9.24012699e-01,\n",
       "       3.49644700e-02, 3.36785099e-01, 1.28652535e-01, 9.95725578e-01,\n",
       "       4.42461057e-01, 9.94131604e-01, 8.89408990e-01, 6.15477130e-01,\n",
       "       8.57800212e-01, 6.31427205e-02, 3.30997536e-03, 9.71687526e-01,\n",
       "       4.18533625e-01, 4.50699667e-01, 7.64652303e-01, 3.67426783e-02,\n",
       "       8.51702521e-01, 5.60984835e-02, 9.74845753e-01, 3.15143549e-01,\n",
       "       9.83538602e-01, 9.67929571e-01, 2.24298915e-01, 9.91006356e-01,\n",
       "       5.79244426e-01, 9.35934283e-01, 2.91520200e-01, 7.04274034e-01,\n",
       "       9.83135422e-01, 1.27702994e-01, 9.99872585e-01, 2.32780030e-01,\n",
       "       2.84490994e-02, 1.10969732e-01, 9.68908637e-01, 4.58623594e-01,\n",
       "       1.45927355e-01, 2.32036460e-01, 7.55926094e-01, 9.54594429e-01,\n",
       "       7.14597968e-01, 1.26339007e-02, 1.30213800e-01, 8.74020023e-01,\n",
       "       2.00265636e-01, 7.59841728e-01, 9.37561430e-01, 6.51483638e-02,\n",
       "       1.56283626e-01, 1.80585726e-01, 4.02996557e-02, 3.29319433e-02,\n",
       "       5.99980199e-01, 2.51685306e-01, 2.34848413e-01, 1.98750760e-02,\n",
       "       2.38022523e-02, 7.37270596e-03, 6.27737063e-01, 9.91280643e-01,\n",
       "       9.66654450e-01, 9.69011970e-01, 2.20768769e-01, 5.82600936e-01,\n",
       "       3.77136980e-03, 1.25499037e-01, 7.53718197e-04, 9.72086059e-01,\n",
       "       8.79484925e-01, 2.62079180e-02, 7.59221512e-02, 9.46158998e-01,\n",
       "       9.79632088e-01, 9.94992757e-01, 3.33860443e-02, 3.17111055e-02,\n",
       "       3.02170457e-03, 4.95668938e-02, 1.44693606e-01, 9.96032641e-01,\n",
       "       9.85790417e-01, 4.06535256e-03, 9.19438487e-01, 5.20926657e-01,\n",
       "       9.57633574e-01, 1.29124480e-01, 1.19404496e-01, 9.91787619e-01])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict_proba(X)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc1bc15c-c388-4e23-bdaf-a13c84de4b30",
   "metadata": {},
   "source": [
    "## 정확도 측정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "33a0cc68-775b-42b9-8a79-6079774f3af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8042d575-5c0f-462e-b6ba-4cdcb674b9a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2     False\n",
       "3      True\n",
       "4      True\n",
       "      ...  \n",
       "95    False\n",
       "96     True\n",
       "97    False\n",
       "98     True\n",
       "99     True\n",
       "Name: y, Length: 100, dtype: bool"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y==y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2bbb049f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 브로드캐스팅의 성질에 의해 각각의 값이 같은지 비교\n",
    "np.sum(y==y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "beb07301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정확도 계산\n",
    "np.sum(y==y_pred)/y.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8259ed2e-d4dc-4e2d-ad29-806f27376f08",
   "metadata": {},
   "source": [
    "## 의사결정의 경계선"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc684ba3-82f7-441e-b04b-fd2ecb17e8f3",
   "metadata": {},
   "source": [
    "$\n",
    "h(x)=w_{0}+w_{1}x_{1}+w_{2}x_{2}  \n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a8e7f5",
   "metadata": {},
   "source": [
    "$\n",
    "0=w_{0}+w_{1}x_{1}+w_{2}x_{2}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8e5396",
   "metadata": {},
   "source": [
    "$\n",
    "x_{2}=-\\dfrac{w_{0}+w_{1}x_{1}}{w_{2}}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36492ca7-21c8-4c2e-a1bd-d932dfb7de27",
   "metadata": {},
   "source": [
    "* x1의 범위에 따라 x2의 변화를 위의 관계식에 따라 그린다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "97fbf423-8a86-445d-85fa-0554c27dcea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.7481009063383444, 3.6079049245195742)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['X1'].min(), df['X1'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "926e58a6-211f-4b0d-868d-f56ec215a5cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.74810091, -2.6183865 , -2.4886721 , -2.35895769, -2.22924329,\n",
       "       -2.09952888, -1.96981448, -1.84010007, -1.71038567, -1.58067126,\n",
       "       -1.45095686, -1.32124245, -1.19152805, -1.06181365, -0.93209924,\n",
       "       -0.80238484, -0.67267043, -0.54295603, -0.41324162, -0.28352722,\n",
       "       -0.15381281, -0.02409841,  0.105616  ,  0.2353304 ,  0.36504481,\n",
       "        0.49475921,  0.62447362,  0.75418802,  0.88390243,  1.01361683,\n",
       "        1.14333124,  1.27304564,  1.40276004,  1.53247445,  1.66218885,\n",
       "        1.79190326,  1.92161766,  2.05133207,  2.18104647,  2.31076088,\n",
       "        2.44047528,  2.57018969,  2.69990409,  2.8296185 ,  2.9593329 ,\n",
       "        3.08904731,  3.21876171,  3.34847612,  3.47819052,  3.60790492])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# x1이 가질 수 있는 값\n",
    "x1=np.linspace(df['X1'].min(), df['X1'].max())\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1a30ff24-67d4-4407-aa8c-d2c3cb964fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.43054461, -0.36712609]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 로지스틱 회귀로 얻었던 파라미터\n",
    "w0=b\n",
    "w1=W[0][0]\n",
    "w2=W[0][1]\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8e5396",
   "metadata": {},
   "source": [
    "$\n",
    "x_{2}=-\\dfrac{w_{0}+w_{1}x_{1}}{w_{2}}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0078bfcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x1값에 따른 x2값\n",
    "x2 = -(w0+w1*x1)/w2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c9490da9-b429-49d7-9a26-9e01d4080487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.3129195942741365, 3.455157807261014)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PAS2+Tn3R6neh9Eqswzg170Y2ff8jftR3KdLjuaYN5tiO+AutTvDsm3CsRudgEnkbCQVDvHb/u3zw5CdEI1H2P3kSx1w+DW+6p9WvPfaAEUTCsYt1ngwPFz9yNn/8vJbfvi9ix/FDOPjM/WMWIVWV7+Z9z3fzvic7L4t9j59Idm7TyrsefelhzH76EypLq4iErV0lnnQ3x1w+jfTsutd5+JKnePexOYQCYWx2G8/c8BIXPnw2B53WsoS1eaGzG9h6YCXE+uVcFZI4x62RNTVJPP6WSIhAqACNbkRssZUYNboBLbsd/B9aD3gOhIxLofzOmjEVcIEtB0k/K2lxGx2bSeRtQFW55tDb+eGLH2sPrrx05xvMf2chD86/Dbu9dY97d+uVw+m3nsjT175AKBAiGlU86W7GHjCCfY/fE5ut4TnbcCjMddPuYMnnP+Kv8OP2unji6ue57d2r2WXSTo1eu0efbjyS/3f+fdMrLJz9Hdl5WRx7+TT2PnaPOp+35PNlvPvY3NpF0U1bCR849zF2nzquRVsWC4p8pLvsDOmZgUROQqvfpu7UioBkgXNss8dukP89GqyLXMsWd6pENYyuP66mYmF483ihAuj2BFT9G6KrwbWXtVAb5w+B0TUlJZGLyJPAVKBYVYcnY8zO5PvPl7F0/vI6pw+D/hArf/yDr979lj2m7drqMRx9yVRG7r0zs5+ZR3WFn0lHTWDXKaO2msQBPnzqExb/b1lt04lNX8NNx9zDi6sebdIfobx+Pbh05jlb/Zx5L30et2mE3WHnq/e+5YBT9m70OvXlF/oY0S8Hu03AtjOadSOUzwDsQBRsuUi3x5q1+Ngo9ROzMFmfvSfY4syRBz629onXeX0YousQ3YB0eyB5cRqdSrLuyJ8GHgKeTdJ4ncqyr38mHIr95a6u8LP0y+VtksgBth8zmO3HDG7Wa2Y/My+mcxBAoDrALwW/scPY5pWibYjNZrNaeMe5mW3JiUx/KMLS1WVM32vz12tLOwL1HgyhRVYDCcdOLT/t2RD3PlDxCLElZAEcIC4k+6741w3/bNUNr0+rIfwTcFByYzU6jaTciqjqp4BZQq9n41of33yQTzQaxRnnpKQ7zUVe/+aXcm1LDd6x61aea4H9TtwTlyf2exSJRNnt0DHNHu/7P0oJRzWmUJaIB3GNt2qRJDuJY9U4wXtkTes4qfnnBPu2kP4XJPcDxNXAVI5jW5C0OIOmgb15f4CNrqXN5shFZDowHWDAgI5Xz6M5VJVHLn2atx/5CJfHSSgYJhIKIyJseQDL7rCz7wkTUxhp46acuR8/F6yIqWeSluVl8Mjk1bTecfz2HHXJVF699x00EsVmt6HA3546j8xuze/Uk1/oA0i4dK3V6zKKSNN/VSTrBvAcjPrfAmyIdxriasK7Lvf+INk10zObFqft1jy+Z3ILoje6ijZL5Ko6E5gJ1snOtrpuKsx+eh7vPT6XUCBEKGAdrrE7bHjS3YTDEQToOSCXa164pEVJqi1NPmUvvnx7AQtnf0coEMbpdmCz25jx2l+TekcOcPrNJ3DAKXvz5dsLcXmcTDpqN7r3btmCXn6Rj745XnpmtmxXkEZL0bIba3aPRFDn2JqytY3fGYsIuHdD3M2r6y7igh4vo2UzIPCJ9aB7HyRrhvWcYTTA7FppBbP+8U7MHWwkHCUcCvPQV7eTnp1OzwG5LXprHwlH+HbOInzFZQyftCN9tm3iwZIWstvt3PDq5fwwfzlvPvQBvy0ppEefbpQUrWeHcdslPZn322Ebjrks8c5MBYW+Ft+Nqyq64VRrzpqaU66hBdbJ0Lw5rXokX+w9kW7/qu16n9SFWKPTMom8FVT64vdEtNlteDO8LT7kUrhsFZfvO4NAdQCNKpFwhIPP3J/zHjijVeZ7tzTr3nf45oN8/JUBViwuZMnny9jzrd244pkLWvW6LVFc7meVr5rTJw5q2QChhRD5ndokDtSWra2ahWScmYQot84kcKM5kvLTIiIvAF8CQ0VkpYi0/k96O7bboWOwO2O35aVlpdFrUMuSuKpy3WF34Cv2UVVWTXWFn6A/xIdPf8Knr85PNOSt+v6LH2uT+Cb+ygD/mzWfn779tVWv3RIFNfPjLa54GP6N+Edh/RBZ3sKo2i/VCOr/hGjpLUQrHrEONRkdSrJ2rZygqn1U1amq/VQ1OaXzOqiTrzuarO4ZtbswbHYb7jQXlz12TounIn77vogNazbG5Bd/ZYC3H/4w0ZC3Kn/O4jrVCzcJByN8O2dxQmP7qwIE/bFjJyK/yIfDJgzbxmocraqs+2MDpevKmjaAY/sGnvCCo+Mck1ANodGNtdM08T8niG44BfVdAtXPQsVDaMlBaOCzNozUSJSZWmkF3Xt34/Hv7+Pthz8kf+4S+gzuxZEXH5pQ951AVaDBPwINdchJloxu6TjdjpiqiE63g8xuLaspXvTjKv5+xr/48ZufAWHM5BFc9sS59OiT+GnFgkIfO/XJwuO0s+zrn7jj5AcoXrkeVNlh3HZc/fzF9Nzatk/nCHDuBKElbO4KZANbGuKNrbDY3qhG0YoHoOpp0DBIOpp5Oba0Y2I/t+pVCH3P5jo01tervkuh5xfN2q1jpI6ZiGslWd0zOemao/n7xzO47PFzE26hNmT0tog9dh7c7XU1awtjJBKhfGMF0WjDd2n17XP8RMQWZw5ehElHT2jyOJtUllZy0cRrWTr/JyLhaM0C7ndcMuk6IpF4DRyaLhJVFq30MXpADhvWbORvk29i1c9rCPlDhAJhls7/iUv3vj7udYL+INWVfkQE6fYkpB0Hkgl4wD0Z6fEaYmvfu4wAK4lXPlVzuCgIuhHKbkb9cd65+d8kbjExQjUJ3ugITCLvIBxOB1c8cwHuNBeOmvl3T7qbATv1ZepfGq/cp6q8/Pe3OCr3DI7tczZH5Z3BGw+9R1Mai3Trmc2M1/5GenYaaVle0jK9ZHRL55a3rmzR9sm5z31GyB+qc+1IOEppSRkLPvyu2eNt6aficiqDEUb1z+HDpz6JKRYWjUQpW19O/twltY9tLC7luml3MC37VI7odhrn73YVv/2wDlvWddh6LcTWexG2bg8h9j4JxdYWVMPWnXhMcvZbCT5GQy391KoMaXQI5n1TB7L7YeOY+d09vPf4HNav2siuU0Yx6egJOF2N/8K98eD7PDvj5drj9hXBME9c+TzuNDcHn9F4c4JxB47klbWP88MXy0Fg2B5DcThb9uNTtPwP/HGO/YdDYVb/urZFY26yaaFzVP8cXv95TdwmGdGIUlK0zvr/0SiX7n09q39ZW5v0ly/4mUv2uo5nf3ooaf1FNbIeIivAPgCxt14NdrQCtIHGIHEWMSXtOLRssVUGoM4T2eBovCia0T6YRN7BbLNdb866/eRmv+65W2fF1EzxVwX4942vNimRAzhdTkbuM6zZ165vh7GD8WR48FfUrUdid9gZPCKx06IFRT6yvU62zU1nl712Zt7LX+KvrF/3xJorB6sF3vo/NtS5c1eFcCDMh09/wjGXTUsoHtUIWnYdVL8F4gYNoJ7JNfVWWuGQj2RZdWR0Y+xzjh1iH/McCoHPwP8+1l24A3Ag3R5u9S2tRvKYqZVOTFX54q1vuP7wOyktib9jY8PqOL/wLbR+9UZWr1hbO2Xy+9KV1uGlktI6n7f3MbuTnZtZZ4um0+1k4LD+TSqNu4lGy1H/XDTwOVpzF5pf6GNU/xxEhL2P3Z3ufXJwujbfr7i9LsZMHsF2IwcBsPqXtWgkdnopUB2kaNkfTY6lwRgrH4Xqd7Dmqsut//V/jJbfk/DY8YjYIPNvgLfeMx4k869xP9+WcyfS41Uk60ok61ak5/+smjFGh2HuyDuxe89+hHkvfb7VXS39dkh83re4sISbj7uPXwp+w2YTMntkkJ6VxprfinE4HYQCIaadN4Xpd52CiODyuHjoq9t54qrn+Oy1r7E7bEw+dW9Ou/G4Jt8FRqtehbIbt5jHtVOR9ijLi8uZMrw3AG6vm4e+up3nbpnFp698idPj5NDpB3DkRYfUjjN45ECrrlU9nnQ3Q3dtWmXHaDTa8LbSymeJrYToh6oX0cwrW+Wu15Z2FGrLQCsehMhqcOyAZF6OuBouPibOHcAZ547d6BCkKYtdyTZu3DhdsGBBm1+3K/nlu9+4aOI1cfd/b+L2urj+1csZn0CPzmg0yqlDzqekaH1tM4h4POluLvzX2S2qK16fhn5C1x9F/QT5RdEITp51Ok+fviv7DG3aPLSqcvl+M1j21U+18+l2h51uvbN5cuk/ajs4aWgZWn4fhBeBrTeScR4fvmDj6eteZP0fG8nt253Tbz2BA0/dp8740TXDqHtCdBNBen1vtvcZzSIiC1V1XP3HzdRKJ/XtnMVx27uB1fh4h7GDufGNvyWUxAEKPl5C2fryrSZxsPa6z7r3nYSutYlWzyJecixY0xcgpnTt1ogIt713NUdceAg5PbPJyElnvxP35J9f37FFEv8R3XA8BOdBdD2Ev+fDmTN46IKZrP/Dmppat2oDD/zf48z5z3/rXsDZwPfXMdQkcSNpzE9SJ5WenYbD6SAcrJvMXR4nZ9x6IkddMjUp11m3agMabdq7urIN5Um5JlrG5jKvm323ph+Duis5ac1bRHR73Zx1x8mcdUf8RWStuK9mV8fmr/OZO3sQqKobQ6AqwFPXvsjkkze/65Csa9ANJ9S0dotgdSdyIVkzmhWjYWyNuSNvBb//UMQjlz3Dnac9yH9f/qLBO+PWNOmo+CVURSSpNdB33G17ok1I5HaHvUUNIuIR92SgbgMGVchf3Z/RA3ok5Rp1hBaxZRJXhXWr42/5LFm5vs7H4twJ6fEWeI8GxzDwTkNyZ211vrohGvyG6PqTiRZPJLrhdDSY2J57o/Mwd+RJNvf5/3Hf2Y8QDoWJhKN89tpXvP7g+9w99/om7fdOlsxuGdz81pXMOPLu2l0kqnDN8xe1uMZ3PAN27Mse08bx5dsLa7c32h12IhGr7rqq9S4gLSuNk6+zjohrZB1a/ZLVvswxAkk7GrFlNf2i7r3BNdaqUqhVgPBHeW/WVWUyemDTF281XIhWvwbRDYh7b6v2t8TpQWrrXdNL0yICvfqFWLsy9s6/d5yiaOIYgGTf3OS44sYamIduvJDadYFgCbphIXR/HHGNT2hso+Mzi51J5K8KcEyvM2N2iXjS3Zz3jzOYcsZ+bR5TKBhiyWfLiEai7DJpJ1ye+NMOpevK+N+sr/BX+tl1yigG7ty/ydeIRCK888hs3n54NoHqIHsfszu7TR3L+0/MZfWvxYzadxiHn38w6dlpzHnmZeY88xxOZ5SDT17HxIMDiC0NyX0NsTe9DrlqBPwfov73QNJ499fDuODlMt46fyIj+uU0+vpo9UdQehlWo+MwkAbOXZDuTyL1TjSq/2PUdzFbLq5+8kYe913Wl0D15t8fd5qLvz55Hnsfu0fd14cLITAPxAOeAxBb8/+QRksOqCmtW49jGLbc15s9ntExNbTYaRJ5En07ZxE3Hv13qspia1eMmbwLd86+PgVRNW7+Owu55bh7QYRoOILYbRx2zgGcc8+fk3aNaDTKlVNuYekXi/HXlGv3pEXY9wgfF9/9B7gPxJZAl/ib3/mB/8z/ncUzDsLl2PqMoWoALZ4AWlnvGS+SdT2SdlRs/FUvQ/ndNXPdCt4j+e8H+/HUta9Q/HsJvQb15IxbT2TvY3av+7ryB6DyMesDsYEqknM/4mn6H3XVMLp2GHE7U+PE1tvUROkqGkrkZmolidxp7gZrl3gz6x/QaB3+qgA2mzR4511fdaWfW0+4j0B13W2K786cw+6H7ZqUk5wACz78jqXzl9cmcStWO3NndePI6SUM2OHThMYvKPIxvG92o0kcgGABcTePU41WvxU3kdvSjkW9R1pTLLZsRLzsexzse1zD2yk1+B1UPgHUvEOr+dFQ3yXQ8/NmFOCy15zWjLNYbOvexDGMzswsdibRThO2Jy1Owvaku5n6lwNb9dqFy1Zx4R7XcHjOqUzLPpVrD7udjWt9jb7u248WYbPH/hgEqgJ89O//xnlFy3zzYQH+itiDSQJ893kG0PLj6sFwlMWrSpu+7VBcxL+7xZr+aOhl4kDsvRFp2h9lrX6T2iReZyAbBJr+vRURSD+L2NOaXkj/S5PHMTovk8iTyGazceu7V5OVm0lalhdvhgenx8kRFx7CuANHttp1K3yVXDTxGpZ9tdwqCxuKsODD77i4CWVhG9pxokqje8ObIycvq85R+U1sdiWzm0CcWtlNtWxNGcFwNG5HINUgGlmD6hbvOJwjIG4y9iJpx7U4jlhh4v7BUCXe9smtcu1hLboiWFsYPZAxHUk7qcXRaWQNWv2mtQagyW3uYbQtM7WSZNuNHMSLKx/l248WUb6xkpH7DCOvXytsidvCnP98SigQqtM9KBKOsHGtj2/nLGbXg0Y1+Noxk3chEopNKp50N/ufNKlZcfirAvgr/WTnZsUcPT/g1L154fbXNvdpqGGzw4RDd0AyLmzWtbZUUOQD6h4EUtWa5gpPWolT7Gj6dCT9HGtnSreZ6IbTgTBoFIhaf0zc+7Y4jvrEcwjqfzO2siARcDf9e6v+T8B3Edbdfc1/ZHEgnkNbfMQ/Wv4PqHwcxI51P2eH7k8jzuRMpRltyyTyVuB0Odnt0LFtcq2NxaUsX/hL3KP4kXCUNY2UhU3PSuOvT53HXaf/E41ECYcjuDwu9jluImMmj2hSDJVlVdw3/RG+ePMbAHr06c4lj53DmP13qf2cnv1zue6lS7ntpH8AoNEInnQbN712Gmn9Jjf1y40rv9BHboabvjmb77K18gmofJLautwKVDyCSiaSfjLiHA49P7N2k0R94NodcSTW/COGazfwHAbVb2PteLFb/7JmNLpzRTUCgdlo1dsQ/Iy65QgioFVo+b1ICxaINfBFzfcmUOcNg248C/I+i78F02jXkrJrRUSmAP/A+kl9XFXv2Nrnd9ZdK21p1c+rue3E+1mxuJBoRIlGojELrZ50D7d/cA3DJ+7Y6HjFRev470tfUF1RzW6HjmXorkOaHMtf97+R779YRigQrn3Mnebmn1/fHrONMRQMsXT+TzhcDnYcP6TFPUy3tO/f5zGkZwaPnbp5MT+6djyoL/aTbT2x9WzbfpQaLEADc0G8iGdqo38wVKOo71wIflWzT74BkoOt19fNjie68SIIvB9nvHSk20zEtWuzxzTaRqvtWhHrz/c/gQOAlcA3IvKWqv6Q6NhGfMFAiEsmXYevpKzB4/Euj5PBIwcwbI+hTRqzZ/9cjrm88drbRT+u4vErn2PRpz+Q3SOT/U/Zm6Xzl9dJ4gChQIhX732Hyx4/t87jTpeTEXslr0TqxsogK9ZVcvTYfrWPqUbjJ3GwaqW0MXGNQlyjmv6C4GeNJ3GAFuxHB7YyrjR+TaNdSsbUynjgZ1X9FUBEXgT+BHT6RF5d6efr9/IJVAUYe+DIpDQOboov31qAvyoQk8RtdhsOlwNvupsDTtunWWVhm2LNb8Wcv9tVVJf7UVUqNlbywm2vEW9BLxqJkj93Me89NodJR09oUUu4pihY6QOos9ApYkPtAxs4QNP0dxqpov6Pm5BQvZB+ZovGF++haPBrYtrBaQSc41CttkrgVr9hNW/2TEEyL2nRQSajbSQjkfcFirb4eCUQU+hDRKYD0wEGDEjyXGQK5H+8mBsOv8u6iVElGo5y2k3Hc2wT7moTVVK0jlAgXguzKEdccHCDxZ8S9dKdbxCoCtaZwokXxyZrC0t4+NKnefjSp7nl7auStid9SwWFPkSIOc0pmVejvouoO7fsQTKvSnoMSWfLwvrVDMd50gMopJ2MeFu408dzKFTNgvDimj8YdsAJWTeCpKEbjoPQUmq3Tla/igY/h9z3W6erkZGwZGw/jHfLF3OLpqozVXWcqo7Ly4utR9GR+KsC3HD4XVRX+Kku9+OvCBD0h3h2xkssX/hLq19/6K5D4vbL9GZ42GlC6zUH+P6LH+MWALM77Tg9cerIqFW+1l8ZYMZRdxMOxUtMiSko8rFDz0wy3HW/H+LZF+k2E5xjQbqDc1fr+L17jwZGaj/EeyTx77G80G0m0vMzbFl/bfG7LREn0v0pJPtO8BwOaaciua9hSzscQgsgvJy6+9/D1pSU/8MWXc9ofclI5CuBLVe0+gGJ98hqx775oCDuL1HIH2L2M/Na/frD99yRHcZth8u7+e7I6XbSZ3AvJkxtvd0y/YduE/frttmEk645im2264XDGX/HQzQc5fsvfkxqPKpKQZEv7v5xAHFPwNbjBWy95mPr8RziilkjapfEMQiybgE81olOSQfJQro/ic09AbFlJ34NcSCeg7Dl3IUt6ypk05RTaKk1nVKfVqGhxQlf12gdyUjk3wDbi8i2Yr3vOh54KwnjtlshfxCNNy8cVQJbaauWLCLC7R9cy0nXHEmfwb3oNTCPoy+dyv2f3Yzd0Xpbx4674nBc3rp33i6Pk/EHj+Gka47imZ8eYnRDWxaFBg9TttSKdZWUVodiTnRqZA0a+AqNbH3rZXtmS5uG9PwSyb4HyXkI6fkF4mqDLa32/lu0z9uCeK0/MJ2UhpahVS9aPWC14enC9irhOXJVDYvI+cCHWJNtT6pqp67iM+aAETENG8A6RDPp6N3jvGKzYCDEh09+zCcvfo43w8PUcw5kwtSxzX6b7HI7OfHqozjx6ti6IK1lh7Hbcd3Ll/HA/z3GhjU+RIR9jp/IBQ+dVfs5B566N4s//SGmAqSIsPMeyZ32qT0IVHNHrhpES68A/0dbdKw/EMm+M6aiYUcgtnTwJO+AUpO4J4HkgPrZfPpUAJe1J76TUQ1btW82lUwQO0gadH+uQ/3hSsqBIFV9D3gvGWN1BDl52Uy/+xQev+I/hIJhopEongwP46eMZtcpoxp8XTgU5rJ9bmDF4t9rD/As+vQHpp5zIH+5+9Q2ij4xux0yhv+s+Bdl68vxZnhiinPtdczufPrqfL75IB9/VQCX24nYbFz70qVJr8eeX+gj3WVn+56ZAGj5/eCfi9WxvuaAlH8Oan8AybwsqdfurEQc0OMFtPQqawskgHNnJPsOxJaZ2uBagVa9DIFPqV0UV6xpJN8FSO7bqQytWUwZ2wT8uuh3Zj87D3+Fnz2PnMDYA0Zs9c76kxc/596zH465W3V5nDy17B/0HNCxF4E3UVV++HI5Cz/6jsxuGexz/ES69Ux8Xre+wx78jAy3gxemTwAgunYMaEXsJ0omtl4Lk379zk61GjTSjCqNHU903TQIL4vzjBvJ+wCx923zmLbGlLFtBYNHDOScv5/W5M//+r1vY5I4WB11Fn26lMknd5xEvmJJIRvXlrLD2MFk5KTXeU5EGLbH0K0eRgpUB/jsta8pKVrH0PFDGLXv8GZNL/lDEZauLmP6XoM3P9jQ3uuYuuPtm0ZWoWV3WAeDxAPe45CM/2vzrX8i3vh70jqTBouF2bbyXPtjEnkbyumVbbVBq7eFT0TI6pG8t60/zF/O7Kc/wV8ZYK9jdmfC1LENHoX/36z5PDvjZYqL1jF4xEDOuuPkrSbg9as3cs0ht7Hqp9XYnXZCgRAnXdu8ufrCZau4ZK/rCPlDBKqDuLwuths5kDtnX4fb627SGEtWlRKOat2FTucuEIrTx9LZepUn49FgPur/0Fo09ByChJagVS8CYfAejqSd1GBS1qgPXXckaCkQtf4IVT6Bhpch3R5p06+jS/AcCpUziSk3bMsG+6BURNQipoxtGzrkrP3jbs9zeZyMmbxLnFds3cbiUn77vojgFodyXrjjdf42+Ubee3wuc5/7H7ef9A9uOuYeotHYkrTvPzGXO097iN++L6KqrJolny3jigNv2uo2wRuOuIvfvi/EXxWgsrSKoD/EC7e/zlfvNn3q4rYT7qd8fQXVFX6ikSj+Cj8/ffsrr/y96Zud6i90AkjWDdZCFZu+x9bClWS1XWemaOkMdOOfoeopq7rg+iPQshuswzfhpVB+H7rhVKsoVhxa9VJNtcQt/3sFIPAFGm79MwpdjaSfAY5BbG7m7bZ+ZnLuTeqp6NZmEnkb6j+0L399+ny8mZ7aeuU9B+Ry19wb4h7waUhlWRXXTruDkwaey4V7XM0xPc/knZkfsW7Vev5z0yvW6cua4/v+ygALZ3/Hwo8W1RkjGo3yxFXP1TZM3iRQFeTxq56Le93Vv65lxeJCIuG6fxT8lQFm3f9uk2LfsGYjhctWxRT4ClaHmP1M05st5Bf56JvjpWfm5kYQ4hyO9HgTvMeAYyR4j0V6vNVmpVk1mA/Vr9ck4k01x6PUPaHph9CyhhtLhAqoexq1hjgglNx9+Ia1M0h6vIZk3wbeEyDjfCT3ow5z5mATM7XSxvY+Znd2P2wsy77+GU+6m+3HDG72X/7bT/oH385ZTCgQqj0i/8ilz7Bq+eq43X78lQE+f/2rOnXJKzZWUlUe21sUYMXiODVKgPKNFTgc9volxQHwlZQ2Kfatra03Z+G9oNBX5258E3EMRLJvavI4yaT+2cRNwjGq0ODX8ft2OnaAwP+IKdxOpObO0Ug2ESd4D0G8h6Q6lBYzd+Qp4PK4GLHXzuwwdrtmJ/ENazbWJvEtBaoCLJhdgMSZC7fZbTE9Q9OyvNgbeBfQs39u3McHDY9fI8fpdrDHtPilT/1VAea99BnvPfYha34rpkefbvTdvjf1v2yXx8nkU/aKO0Z9xeV+VvmqGd3U1m5tRZw07VfKDbae8YdIOyHOgRwnOHZAnMmrGml0LiaRdzC+4jIcrvinN4P+IPGOTzpcDg76c92DJQ6ngyMvOgR3Wt3FRXeam1NnHBt3fJfbyf89cDruNFftHyCXx0W3XjkcefGhMZ+/5LN8jutzCveedS8PX/woZ+z0fzxx5T+4+vmLSc9Jx5NuXdub4WHgsP4c+9c/Nfr1g3U3DjR4ND/ZVBVVf6PvGMRzGNCEvfJiR7zxv1ax90a6/wccw6gtZuU5EOn2ZLPjNroOM7XSwfTboU/c6Qm7087YA0ayz3ETuW5aTV8PtQ4hnXvfnxk0rH/Ma0678TgQ4fX73yUcDJOWncZZd5zEnkfEFK+sddBp+zJgx3689o93KSlax/hDxjDt3INitiCGgiGum3YbVeWwefER3njwU0ZPHsVzvz3MvJe+YN3K9QzddTvGTRmF3d608gL5RT4cNmHYNsnfm74lVUUrH7N2NWgF2HLRzCuweeOfcBTn9mjmpVB+D9Y9kgBhkExra6SI1bwh537E3nD7P3EOQ3JfR9XqKtQRT6UabcscCOqA3vzn+zx2xeaFSrvDTlqWl0fy76Zn/1yC/iALP1pEsDrI6Mm7kNV961sbI+EIVeXVpGenJaVjD8CC99/m5uOeoqqifnJW9j06j6tffrjFY58wcz6VwTBvnb9nQjFaP/uKSPyvOVrxL6h8tF7PTQ+Scx/i2b/hcSNrahYzneDZDyQbIr9Y9b4d2zd4PcNojDkQ1In86byD6TO4Ny/d9QbrVm1g9H67cOI1R9bObbs8LnY/rOmr7naHPemNH0LVqxs4TCL4q5q2MLpiSSFvP/whJUXr2XXKaA44bW9cXjeLVvo4aouOQM2l0Uq0/DaofhMIoc4xSNaNiHNzLRjViLV9MKZxsh+tuH+riVzsvSHtuLoPdoCGFkbHZRJ5ChUXlvDDl8vp1juHXSbt1Ky74fEHj2b8waNbMbrEjNh3TyLh2PrVnrQI+x3X+HbA/82az52nPkgwEEKjytfv5/P8bbP468c3URmMxFQ8bA7d+JeabX41O0NCC9ENx0PuB4i9ZhFSK0AbqGQZWdnia3cEGi2D8E9g793ujqgb8ZlEngKqyr8ufor3HpuDw+lAVcnKzeTuuTfQZ9teqQ6vQRW+Sr58ewHhYJhdDx5N7jbdG/zc9G47c/H9mdx3UTmRMETCNjxpEUbsEWDP48/f6nXCoTD3nP0wgerNW/CikSjr/9jINRf9G0buzNCcpp0ArU9DyyC0mJjtfRpEq15EMi+0PpZMqw54vN6frXB3rRoE//vWFkZbDpJ2HOJsoCRwK1FVtOJ+qHwSxGV9T1y7IjkPdOp6K52BSeQpMO+lL/jgyY8J+kME/dY2wkBVgBlH3M2jBX9PcXTxffHmN9x6wn1oVFEUQTjrzpM48qKpDb5m/7NmMnTXu5j99H8p9ym7Tx3IuGnXYXfG3964yYrFhURDsSdRAf7Aga06wGU7X8iw3Xfgyn9fSF6/Hiz96ifenfkR5esr2PPI3djn+D3iV1uMrACxxdncE7ROXtYQsaEZl0D5HdTtbelBMi7favzNpRpEN5xiHRSiGrCh1W9bC6vpJyX1WlvlfwsqnwYCm9+NBL9GS69Euj3UdnEYzWYSeQq8+c/3Y4pnRaPKqp9Ws+rn1fQd0qfZY25Ys5HFny4lPSed0fsNT2qDifKNFdx87D2EQ3WPlT/6138zZvLIuDtiAERc9B91LWfef22zrufN9BIKxi/u798mD8/qEiLBMEs+W8ale13PERcfwpNXv0DQb51o/XbuIt5+5EPumXdjbDJ3bG8tOsZwg3N4nUds6ScQlXSofAAixeAYjGT+DXE3vKunRfzvbJHEwToN6ofyO1DvtDYrH6uVjxPTkJkgBOah0fJOWca2szDL5ynQ0IlKm8MWtzpiY/5z8yucPPg87jn7EW4+5h6O7/eXBk9ntsS8Fz+PSeJgtW97+e43knadTfpt34cecaZtoi4HwdwcPH+UWB9HopSuK+Oxv/6bQFWgTlmCFYsL+eSFz2PGEMcQcI0DtpyaERC3dRinHlvaNGx5c7D1XoQt941W6fmp1R8Qm0CxDgYFv0n69RoU3djAEzbQsraLw2g2k8hTYK+jJuCK06zY4XQ0eHfbkG/nLualO98k5A9RXV5NVXk1vuJSrjrktriFslpi+cJfG3zu10WFSblGfbd/cE3MqVd/71wQwbN6Xe1j4VAYaaAswaevfhl3bOn2L0g70ZoHxwmuSUiPVxFbw3P+rcqWTYM9zNtybtq1B3FTgi0DbM1/l2i0HZPIU+DIi6fSa1DP2pONdocdt9fFX586r9lTIu88Mht/VexdfFVZFcu++ikp8Q4eObDB5/oMbp3F2f5D+3L33Btwp7lweV04XQ78fax67Z7VJbWfJyLYbLFJUETI7B4/CYp4sGVdha3XQmy9v8fW/fGUtvWStOMBT5wnvOBsgz6dmy6XedHmP27WI1ZcmTPM3vd2zsyRp0BappeHF97J3Oc+45sP8uk5IJepfzmA/kObv9Wrqix+MwURoaq8KQWcGrf/iZN49LJnY+qo2x02jopzND9ZRu4zjFeLn+Sb9/Op2FjJNf/9DdeGMux+a8eJy+ti2B5D+f2HlbWt8zZxeV1M/cuBrRZbMolrLJp5IZTfb1U5BBAP0u1JRFqvmXZMHPa+kPsOWvkEBL8G+wAk46w23z1jNF9CJztF5BhgBrATMF5Vm3Rc05zsTJ73n5jLvy56Kuau3J3m5pU1j+HN8DbwyuaZ/ew87v/Lo0RCUaIaxelyMvWcA/i/+05PyviNUVV2vWUOPX2lZLzwIU6Xg4PP3I9j/3Y4q5b/wRUH3YK/MoAIhINhTr/1BI6+pGM1C9boRmtOXDLANd7qn2kYW2joZGeiiXwnrCX2R4HLTSJve8FAiMv2uYHflhTirwxgswlOt5PzHzqTKafHKZOagOLCEv778pf4qwPsftg4hozaNqnjb83KjVXseecn3PSnYZy6+6CY5yORCIs/XUpVWTXDJ+3YaFmC1qbhnyH8C9i3rXNi1DAS0SpH9FV1ac3giQxjJMDldnLfpzfx6Stf8vmb35CTl8Wh0w9g8IiG57VbqueAPI65fFrSx22KTR2BRvfvFvd5u93OqH2Hx32uLan60Y3n1dxZO0DDqHMk0u1RxJbW+ACG0QJt9t5NRKYD0wEGDIhf19poGYfTwX4nTmK/EyelOpRWk1/ow+2wsWOf9r2XWcvvteaXCWw+dBQqQMtvR7JvTmVojdLoBrTqeQguBMd2SNqpiMP8rnYEjS5Fi8gcEVkS51/TikfXUNWZqjpOVcfl5XWcbvFG+1BQ5GN432yccbYativVrxLTyJcAVL/RrA5IbU0jq9CSg6HiEQh+DlXPo+unocGm92I1UqfRO3JVndwWgRhGQ4LhKEtWlXLKhORPFyVdQ4W2CKIawCqb2/gCtGoEoiVW3RWJszUxybT8HtBSNjd9DlvTQqXXIHkftPr1jcS089sbw4Bla8oIhKNxe3S2O67xxB7uEZAcKB6Drh1DdN3haOiHBoeIVs1CiyegJQeia3clWnq9VVSrNQX+x+YkvoVIIRptWtlhI3USSuQicoSIrAR2B94Vkdi6pYaRoE0LnYmUrm0rknV9zaGaTSUAXICAlgNhIALhH9ANJ6GRtTGv18A8KLup5u7YT+20TFnd+XWNrEID89HIupgxAFSjqDbjZK80tBBrlS8w2reEErmqvq6q/VTVraq9VPWgZAVmGJsUFPrIy3TTNyc5e+KbQzWKhhajwQVNuisWx7ZI7geQPh3c+4J3GlYyD9cbOIRWvRR7vYp/Elt3xW8l82glqtVEN05HS6agvvPQkn2Ill5rTcUAGllLdOM56Nph6NphRDdMtzoWNSbtZGJPlzrBvX+bTO0YiTEnDox2L7/Ix6j+OW2+zVVDS60mFFpGbQ/O7Lu22h0IQOy5SOYF1hjV76D+9xsom7s89sWRPxoY1AbRjWjlvyDwJXVKzVa/jdq3hfRT0PXHQXQtUHMKN/g/dP0xkDcXImvR8lsh8Jl1l+09Esm8DBEPkn4GGv4R/B9Yz2kInDsj2bc27ZtlpJRJ5Ea75qsKsmJdJUcn0NqtJawa4afFNJZQ3yWQ+y7iaGJxM+eODZTN9YAzTocn50gIzCUm86sdjRRD9RvE3N1TDVXPgKMvRH3UJnGw/r9WoNVvWE2hNy1oahCqXkRDy5Ae/0bEjuT8HY1cAqEfwd7PHGTqQMxiZyflrwrw1bsLmf/OwrhFtTqK2oNAbb3QGfgvEK8mehitntXkYcQxBNwTqFs21waShqQdHfv5GRdbxbLqLJh6wJYOG/9MbBKvoeUQXoE1r17/uUrwv1/Tf3TLefMAhL6rs/Aq9r6IZz+TxDsYc0feCX317kJuPeF+pKYqYDQS5ernL25WQ+b2Ir/QhwiM6JfTtheO+iDuYmEYovEXGBsiOQ+hFf+C6pes6RDXXkjWFYgtO/ZznTtA9xfR8vsglA+2HqAC0d9oMIkj1m4Zx/bWHwGtrPd0OmgVcZO82CH8Mzh3btbXZLQvJpF3MhuLS7n5uHtjqgHeevx9PPvLQ3TvHf+Ie3tVUORjh56ZZLjb+EfVNZ642/EkDXHv1ayhRFxI5sWQeXHTXmDvV9OOrgoiAeI2najlsColZl4B9gFg61nTHDq0+Xlbd3BNgtAPxBxW0ig42q5mjtE6zNRKJ/PpK1/GWVizqgf+9+X4jRbaK1WloMjX9tMqgDgGgvfommmOTbzg2AnciRcj0/BKohvPJbpmF6JrxxItuwVVK2Gr75Kafd1Btp7EXZB2MpL7LuIYjIgD6fEieKZa2wnFC56pSI9XrO5HUrMVcsvXO3cAR+pr1BiJMXfknUx1eXXctmzhUKTBFnOpoBpBK5+Bqv9YUwHuSdYOCvvmTjQr1lVSWh1K2f5xyboe3LujVS+C+sFzGJJ2VMLlZTVaiq4/aouFxwBUvYSGfoSceyA4HyuJb40T0k7ElnV13Zht3ZCcO4E7Y1/S4yXUdzmEfwAEHMMg51+m6F0nYO7IO5lxU0bhcMU2I3C6HYw/OM4uiRaqLKviqWtf4M9DL2T6yMt4+5HZRCLxdmfEp6VXQ8X9EF0JuhH876DrDkejG2o/p/YgUIpOdIoI4jkQW/cnsfV4Hlv6CYi4Eh5Xq2Y1sPC4CA1+ZfXq3CoP2LojGec077rB7yH8K9ZduUJ4GWz8c+07AaPjMom8kxkyalv2P3mv2jZyAJ50N/sevyfbjxmclGsE/UEumHA1r9zzNqt+Ws2KxYU8evmz3HnKg016vUb+AP+71F18i4JWoVUv1D6SX+gj3WVn+57tu+Jhs4UXE3/hUWoWJeP9QbSDfQi4JkLGBdZ0SjN6jKr6ofyGmutumnurhvDvcQ8mGR2LmVrphC5+eDp7Hj6ej/79KarKAafsza5TRiVt/P++/CUlResIBTZvzwtUBfjizW/4felKBu7UyJ7v0DJrvjbmpGTAKqFao6DIx4h+Odjj9OTs0Bw7AnOIrZKoiGNHNONCqHig5q4dwA6SjnR/ErH3btk1Q4uJf9/mt7Ympv+5ZeMa7YJJ5J2QiLDrlNHsOiV5Uylbyv94Mf7K2L3pYhOWzv+p8URu7wsabyudo3YHhT8UYenqMs7eKznvItoTSTsGrXys5g/Zprtjp3XH7RyBzTUStQ+0PidSAu7dkYzzWp7EwWofF28XDoDEboM0OhaTyI1m6zUoD6fbQShQNxnbbDZy+zb+dl+cQ1HnThBaQt1DN04k7VQAlqwqJRxVRneAQlnNJbbu0ONFtPQGCC0E7OA5BMm6vnbhUTyTEU8SK0g7dgRbHkQKqbOtSbxI+knJu46REmaO3Gi2Q86ajN1Rd0HVZhPSc9IYvX/TtrJJt8esolI4rX/2gUj3x61tf6R+obO1iWMIth7PIb2WIL0WY8u5G7G13lqAiFjfc1tv64CQZGBtXzwTce/datc12oa5I++k1v5ewhsPvc+KxYXsNGF7pp17EN165SRl7Lx+Pbjlnau445QHKN9QSTQaZdCw/lz/ymXY7bE7ZuIRWxbS7SFrx4T6QeoWxcov8tE3x0vPzM5deS/RrYzNupZjEOR9Yr0LiG4E5xjEnttm1zdaj6Si/dS4ceN0wYIFbX7druLHBb/w1/1mEAqECYfCuDxOXF4XD311O32H9Gn09U2lqvzxyxpcHhd5/XokbVyAiXd8zKgBOfzzxDFJHdcwOjIRWaiqMbU2zNRKJ3T/Xx6lusJPOGTNYQf9ISpLq3j08meTeh0Roe+QPklP4sXlflb5qjvl/LhhtAaTyDuZoD/Ir4t+j3lco0r+3MUpiKj5Cgp9QMfoCGQY7YFJ5J2M3WHH7oj/n9WT3jHmm/OLfDhswvC+ZlucYTSFSeSdjN1hZ5/jJuJ01z3m7fa6OOycA1IUVfMUFPrYqU8WHmfTFk4No6tLtPny3SKyTEQWicjrIpKTpLiMBJz/4JnsvPsOuL0u0rK8uDxOxh86hhOvOSrVoTUqElUWrUxNxUPD6KgS3fv0EXCVqoZF5E7gKuCKxMMyEpGW6eXvH8/g9x+KWPXzGrYdPoA+g3ulOqwm+am4nMpgxMyPG0YzJJTIVXX2Fh/OB2J7VxkpM3Dn/gzcuYm9JdsJs9BpGM2XzDnyM4D3G3pSRKaLyAIRWVBSUpLEyxqdSUGRj2yvk21z01MdimF0GI0mchGZIyJL4vz70xafcw1WQ8HnGhpHVWeq6jhVHZeXl5ec6I1OJ7/Qx6j+OZ2+2YEGPie67k9Wh6CSyUSr3kx1SEYH1ujUiqputXKPiJwGTAX211QcEzU6jYpAmOXF5UwZnkCVvw5AA/PRjedSW5M8Ughl1xPVSmzpJ6Y0NqNjSnTXyhSsxc1pqlqVnJCMrmrRSh+qdPodK1rxd2IbS1RDxf2oNlBq1jC2ItE58oeATOAjESkQkUeSEJPRReV3lYXO8C/xH9dK0PK2jcXoFBLdtTIkWYEYRkGRj8G56eSkJd4Xs12z94Pwj7GPi6emvKxhNI852Wm0C6pKQZGv89+NA5JxMVC/XIIX0qcjYk6zGs1nErnRLqzyVVNSHui0jSS2JJ79Ifs2q8kDNqvVWsYFSPr0VIdmdFCmsYTRLmzqCDS6f7fUBtJGbN6pqOdQrFZ3zk6/3dJoXSaRG0mnwQVoxWMQXQXO8UjG2Yh96w0tCgp9uB02duzTeu3O2hsreXfy9QCjTZhEbiRVtPodKL2a2u114V9R/1vQ4w3E0a/B1+UX+RjeNxun3cz2GUZzmd8aI2lUw1B2E3X3SIdBK9CKBxt8XSgSZcmqUtMRyDBayCRyI3kif4AG4jwRheCXDb5s2epyAuFol1joNIzWYBK5kTy2bCDSwHMN9/XML9oIdIGDQIbRSkwiN5JGbNng3ofYBTwvkn52g68rKPSRl+mmb463NcMzjE7LJHIjqST7DnDtDrhrTil6IOMcxHtIg6/ZdBDIbMEzjJYxu1aMpBJbBtL9MTSyBqIlYB+M2BquLe6rCvLrukqOHtfwjhbDMLbOJHKjVYi9N9gbL0e76SCQmR83jJYzUytGSuUX+hCBEf1yUh2KYXRYJpEbKVVQ5GNor0wy3ObNoWG0lEnkRsp0pYqHhtGaTCI3UmbFukpKq0MmkRtGgkwiN1KmtuLhgK5R8dAwWotJ5EbKFBT5SHfZGdLTdMUxjESYRG6kTH6hjxH9crDbzEEgw0hEQolcRG4WkUU1jZdni8g2yQrM6Nz8oQhLV5cx2hTKMoyEJXpHfreqjlDVUcA7wPWJh2R0BUtWlRKOqlnoNIwkSCiRq2rZFh+mA5pYOEZXUXui09yRG0bCEj6FISK3AqcCpcC+W/m86cB0gAEDBiR6WaODyy/y0TfHS8/M+t3kDcNorkbvyEVkjogsifPvTwCqeo2q9geeA85vaBxVnamq41R1XF5eXvK+AqNDKij0mbtxw0iSRu/IVXVyE8d6HngXuCGhiIxOr7jczypfNadPHJTqUAyjU0h018r2W3w4DViWWDhGV1BQ6AMwO1YMI0kSnSO/Q0SGAlHgd+CcxEMyOrv8Ih8OmzBsm+xUh2IYnUJCiVxVj0pWIEZdqsrX733L+09+TDgYZvLJezHp6AnY7fZUh5awgkIfO2+ThcfZ8b8Ww2gPTO3QdurBC57go2fm4a+0utJ/N+97Pn7hM258/W8duiVaJKosWunjqLGmI5BhJIs5ot8O/b50JR8+9UltEgfwVwbIn7uYgk+WpDCyxP1UXE5lMGIOAhlGEplE3g7lz1kMGnu2yl8Z4JsPCto+oCTavNBpKh4aRrKYRN4OpeekYXPEzh873Q4yu3fsSoEFRT5y0pwM6pGW6lAMo9Mwibwdmnj4eOJNg4vNxv4nTWr7gJIov9DHyH45HXqe3zDaG5PI26G0TC+3vXs1md0zSMvykpblxZvh4ZrnL6Zn/9xUh9diFYEwy4vLzf5xw0gys2ulnRq+5068vPoxlny2jEg4wvA9d8Ttdac6rIQsKvKhilnoNIwkM4m8HXM4HYzad3iqw0ia/E0VD00iN4ykMlMrRpspKPIxODednDRXqkMxjE7FJHKjTagq+YU+czduGK3AJHKjTazyVbOuImAWOg2jFZhEbrSJ2o5A/c1BIMNINpPIjTaRX+jD7bCxY5/MVIdiGJ2OSeRGmygo8rFL32ycdvMjZxjJZn6rjFYXDEdZsqrULHQaRisxidxodcvWlBEIR02PTsNoJSaRG61u00KnqXhoGK3DJHKj1RUU+sjLdLNNtifVoRhGp2QSudHq8ousg0Cm4qFhtI6kJHIRuVxEVEQ6bmk+o1X4qoKsWFdpDgIZRitKOJGLSH/gAKAw8XCMzqbAFMoyjFaXjDvy+4C/AbG9yYwuL7/QhwiM6JeT6lAMo9NKKJGLyDRglap+14TPnS4iC0RkQUlJSSKXNTqQgiIfQ3tlkuE2FZMNo7U0+tslInOA3nGeuga4GjiwKRdS1ZnATIBx48aZu/cuQFUpKPJx8PB4Pz6GYSRLo4lcVSfHe1xEdgG2Bb6r2Y3QD/hWRMar6pqkRml0SCvWVVJaHTLz44bRylr8fldVFwM9N30sIr8B41R1XRLiMjoBcxDIMNqG2UdutJr8Qh/pLjtDemakOhTD6NSStgKlqoOSNZbRORQU+RjRLwe7zRwEMozWZO7IjVbhD0VYurrMHAQyjDZgErnRKpasKiUcVbPQaRhtwCRyo1XUnug0d+SG0epEte23dItICfB7m18YcoH2vKvGxJcYE19iTHyJaYv4BqpqXv0HU5LIU0VEFqjquFTH0RATX2JMfIkx8SUmlfGZqRXDMIwOziRywzCMDq6rJfKZqQ6gESa+xJj4EmPiS0zK4utSc+SGYRidUVe7IzcMw+h0TCI3DMPo4LpcIheRm0VkkYgUiMhsEdkm1TFtSUTuFpFlNTG+LiI5qY5pSyJyjIh8LyJREWk3W8FEZIqI/CgiP4vIlamOZ0si8qSIFIvIklTHEo+I9BeRT0Rkac1/24tSHdOWRMQjIl+LyHc18d2Y6pjiERG7iOSLyDttfe0ul8iBu1V1hKqOAt4Brk9xPPV9BAxX1RHAcuCqFMdT3xLgSODTVAeyiYjYgX8CBwM7AyeIyM6pjaqOp4EpqQ5iK8LAZaq6EzABOK+dff8CwH6qOhIYBUwRkQmpDSmui4Clqbhwl0vkqlq2xYfptLNeo6o6W1XDNR/Ox2rY0W6o6lJV/THVcdQzHvhZVX9V1SDwIvCnFMdUS1U/BTakOo6GqOpqVf225v+XYyWjvqmNajO1VNR86Kz5165+b0WkH3Ao8Hgqrt/lEjmAiNwqIkXASbS/O/ItnQG8n+ogOoC+QNEWH6+kHSWijkREBgGjga9SHEodNdMWBUAx8JGqtqv4gPuxmtBHU3HxTpnIRWSOiCyJ8+9PAKp6jar2B54Dzm9v8dV8zjVYb3mfa4/xtTPxCp63qzu2jkBEMoBZwMX13rmmnKpGaqZD+wHjRWR4ikOqJSJTgWJVXZiqGDpla/OG+ozG8TzwLnBDK4YTo7H4ROQ0YCqwv6Zgo38zvn/txUqg/xYf9wP+SFEsHZKIOLGS+HOq+lqq42mIqvpEZB7WmkN7WTyeCEwTkUMAD5AlIv9R1ZPbKoBOeUe+NSKy/RYfTgOWpSqWeERkCnAFME1Vq1IdTwfxDbC9iGwrIi7geOCtFMfUYYjVPf0JYKmq3pvqeOoTkbxNu7dExAtMph393qrqVarar6ZL2vHAx22ZxKELJnLgjpppgkXAgVgrze3JQ0Am8FHNFslHUh3QlkTkCBFZCewOvCsiH6Y6pprF4fOBD7EW6l5W1e9TG9VmIvIC8CUwVERWisiZqY6pnonAKcB+NT9zBTV3l+1FH+CTmt/Zb7DmyNt8i197Zo7oG4ZhdHBd8Y7cMAyjUzGJ3DAMo4MzidwwDKODM4ncMAyjgzOJ3DAMo4MzidwwDKODM4ncMAyjg/t/I2bT0YL/ZIcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['X1'],df['X2'],c=df['y'])\n",
    "# 의사결정 경계선\n",
    "plt.plot(x1,x2)\n",
    "plt.xlim(X['X1'].min()-1,X['X1'].max()+1)\n",
    "plt.ylim(X['X2'].min()-1,X['X2'].max()+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9f0a5069-479c-46ff-8f9f-d00b71cff418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f87cb343730>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['X1'],df['X2'],c=reg.predict(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4d251d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}