machinelearning/logisticRegression/06-OvrAndOvo/ovrAndOvo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OvR 和 OvO"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:08.665418Z",
     "start_time": "2024-09-15T11:51:07.801656Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:,:2]\n",
    "y = iris.target"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:08.713286Z",
     "start_time": "2024-09-15T11:51:08.666853Z"
    }
   },
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:08.768127Z",
     "start_time": "2024-09-15T11:51:08.714232Z"
    }
   },
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ],
      "text/html": [
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       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
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       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
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       "  /* Arrow on the left of the label */\n",
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       "\n",
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       "\n",
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       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
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       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
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       "\n",
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       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
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       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
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       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
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       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
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       "\n",
       "/* On hover */\n",
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       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
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    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:08.775076Z",
     "start_time": "2024-09-15T11:51:08.769957Z"
    }
   },
   "source": [
    "log_reg.score(X_test, y_test)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7894736842105263"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
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    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:08.784482Z",
     "start_time": "2024-09-15T11:51:08.779361Z"
    }
   },
   "source": [
    "def plot_decision_boundary(model, axis):\n",
    "    \n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "    \n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:08.917944Z",
     "start_time": "2024-09-15T11:51:08.785923Z"
    }
   },
   "source": [
    "plot_decision_boundary(log_reg, axis=[4, 8.5, 1.5, 4.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.scatter(X[y==2,0], X[y==2,1])\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6s/0g7__q5538vb0p3wntyf7p3m0000gn/T/ipykernel_59568/4161441671.py:15: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:08.928674Z",
     "start_time": "2024-09-15T11:51:08.919515Z"
    }
   },
   "source": [
    "# multinomial = ovo \n",
    "# solver 计算方式\n",
    "log_reg2 = LogisticRegression(multi_class=\"multinomial\", solver=\"newton-cg\")\n",
    "log_reg2.fit(X_train, y_train)\n",
    "log_reg2.score(X_test, y_test)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/myenv3.10/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7894736842105263"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:09.029414Z",
     "start_time": "2024-09-15T11:51:08.929982Z"
    }
   },
   "source": [
    "plot_decision_boundary(log_reg2, axis=[4, 8.5, 1.5, 4.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.scatter(X[y==2,0], X[y==2,1])\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6s/0g7__q5538vb0p3wntyf7p3m0000gn/T/ipykernel_59568/4161441671.py:15: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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Z/Vo99ydSeFiIiCKJ57RQxBmVuWPEfs2Y+xMpPCxERJHGmRaKOKMyd2T2a+Xcn0jgYSEiigY2LRRxRmXuyOyXuT9ERObDpoUizqjMHZn9WjX3J1J4LgsRRQObFooKozJ3ZPZrtdyfSOG5LEQULTwRl6LGqMwdmf0y90cOGxYiiiY2LRRVRmXuyOzXCrk/kcCGhYiijYeHiIiIyBQ402ICKgT6MYAwtnGWJfZ4hQen64pQ31KBREc6spLGwKbxUneKLjYtilMh0I8BhLHNNdsOfBXtKiiSit2bseP0UtS2lLXdl+zIQH7WQgxyTopiZRTreHhIYSoE+jGAkHh5c2wpdm/GphML/RoWAKhtKcOmEwtR7N4cpcqI2LQoS4VAPwYQEsUWr/Bgx+mlQcfsOP0svIIrRVN0sGlRlAqBfgwgJJ7LEltO1xV1mGFpr7alFKfriiJUEZE/ntOiKKNCBaNdAwMIzYMNS+ypb6kI6ziicGPToigVAv2MqIEBhObAhiU2JTrSwzqOKNx4eEhRKgT6MYAwNjHBOXZlJY1BsiMj6JhkRyayksZEqCIif2xaFKVCoB8DCGMTrxaKXTbNjvyshUFGaMjPeojrtVDUsGlRmAqBfgwgjC2cZaFBzkmYnL20w4xLsiMTk7OXcJ0WiipNCKH0RahutxtpaWk4tmIFnImJ0S4nKlRYYZYr4lqfa7adsyzUhiviUnc11LixaFIfVFVVwel0hmWfIZ2IW1lZicOHD2Po0KHo2bNnWAqhwFQI9GMAIVFssWl29Eu+KtplEPmRPjy0du1aXHLJJZgzZw6ys7Oxdu3aLh8zY8YMaJrWdpsyZUpIxZI5NHmBNyuT8XS5E29WJqPJG56xHgF8Xh+H/6lOxOf1cVFZqE6FGozCWRYiUp3UTEtVVRXmz5+PTz75BLm5uXjjjTewcOFC3HzzzUEft2vXLuzfvx/Z2dkAgB49eoReMSlteUUqVlelwHvRInPLzjjxs7Qa3J9eHfJYFTKNVKjBKLzEmYjMQGqmxe124/nnn0dubi4AYOzYsThz5kzQx5w8eRJCCIwYMQIulwsulwvJycmhV0zKWl6RilVVKWg/WeIFsKoqBcsrUkMaq0KmkQo1GIUNCxGZhVTTkpOTg9mzZwMAmpubsXz5csycOTPoY3bu3AmPx4Ps7GwkJydj1qxZOHfuXOgVk5KavMDqqpTzW+3PuvVtr65KQZNXbqwKmUYq1GAUNixEZCYhXfK8d+9eZGVl4cMPP8QLL7wQdOyhQ4cwatQorF+/Hjt27EBxcTEKCgoCjm9sbITb7fa7kfrediefP8wT6DIhDV5oeNudLDVWhUwjFWogIqIQm5bc3Fx8/PHHGDJkCObMmRN0bEFBATZs2IBRo0Zh5MiRWLp0Kd55552A459++mmkpaW13XJyckIpkSKspFnfpZAlzXapsVbNYFIB12QhIrMJqWnRNA1XXnklVq1ahXfffReVlZW6H5uRkYEzZ86gsbGx038vKChAVVVV262kpCSUEinCcnroyxTK6eGRGmvVDKZo45osRGRGUk3Ltm3bsHDhhSWe4+LioGkabLbAu7n11luxffv2tu3CwkJkZmYiPj6+0/Hx8fFwOp1+N1LfLc5a2CCAANlDgIANArc4a6XGqpBppEIN4caGhYjMSKppGTp0KF555RW88sorKCkpwaOPPoof/OAHcDqdcLvdaG5u7vCYkSNH4v7778f27dvx3nvvoaCgAPPmzQvbEyA1xNmAn6XVnN9q/+Xu2/5ZWg3ibHJjVcg0UqEGIiKSbFr69u2Ld955BytWrMAVV1yBuro6/PWvfwXgO89l/fr1HR7zyCOPIDc3F9OmTcO8efMwf/58PPbYY+GpnpRyf3o17kyr6fAflQ3Ane3WXpEZq0KmkQo1hAuvGCIis2L2EIVdk9d3NVFJsx05PTy4xVmLuADtscxYFTKNVKihO9iwEFGkKJM9RBRMnA24w1Ub9rEqZBqpUEOo2LAQkdmxaTEBo/66l5nlMGq/KqRHG/U6qMQ12w58FXyMJjzod3YHkhtKUZuQiVO98iHCkOorkxbMZGEiCoZNi+KMyruRyf0xar8yz81sr4PZXPrdOow/8BhSG0613Ved0A/bhv8BR/pOD3m/xe7N2HF6KWpbytruS3ZkID9rIQY5J4U8lohik8X+nrQWo/JuZHJ/jNqvzHMz2+ugomCXOF/63TrcsPsepFzUsABASsN3uGH3Pbj0u3Uh/cxi92ZsOrHQrwkBgNqWMmw6sRDF7s0hjSWi2MWmRVFG5d3I5P4YtV+Z52a210FFwc5l0YQH4w88hguv5kX/dv5VH3/gt9CE3MJ5XuHBjtNLg47ZcfpZeIVHaiwRxTY2LYoyKu9GJvfHqP3KPDezvQ6q6erk235ndyC14VSQV0EgteEk+p3dIfVzT9cVdZg1aa+2pRSn64qkxhJRbOM5LYoyKu9GJvfHqP32tuubvpB5bqq8DmaT3FAa1nGt6lsqwjpOdiwRWRObFkUZlXcjk/tj1H6NeG6qvA6qaAtD7OKKodqETF370zuuVaIjPazjZMcSkTXx8JCijMq7kcn9MWq/Ms/NbK+DCmTSm0/1ykd1Qr9OzmjxEdBQndAfp3rlS9WQlTQGyY6MoGOSHZnIShojNZaIYhubFkUZlXcjk/tj1H5lnpvZXgfVLLjizaD/LjQ7tg3/g+//BzjRedvw30uv12LT7MjPWhhkhIb8rIdg0+xSY4kotpn8I9najMq7kcn9MWq/Ms/NbK9DNMnMsrQ60nc61o/9C2oS+vrdX5PQD+vH/iXkdVoGOSdhcvbSDrMoyY5MTM5e4rf2isxYIopdzB4yAbOtBMsVcSOvq2Yl2DotrbgiLhGFE7OHYpRReTcyuT9G7VfmuZntdYiUUGZXOiM0O072vjYs+7qYTbOjX/JVYR9LRLHHZH9PEtHFwtWwEBGZAWdaKOxUOOQTC2QalgVXvKnrEFG0CW8TWkrXoKmhBHEJOXBk3g7NJrdwYCS1eJtw8NxaVDedQGpcNob1vBkOheslMjs2LRRWKoQgWl2osyuqNy71x5Zgg/vvKGvtWuuBjIoX8H3nLCQOfDi6xXXis9IV+PLMmxAXpVftLH0eI3rfgaszF0SxMiLr4uEhChsVQhCtzqqHg+qPLcGamr+jrN0nUrkNWFPzd9QfWxKdwgL4rHQF9p/5q1/DAgACXuw/81d8VroiSpURWRubFgoLFUIQrcw12x6WhqWrdVuiQXibsMH9d99/D1q7/x7Ob29w/x3CG/6TsEPR4m3Cl2eCv45fnnkTLYrUS2QlbFooLFQIQbSqcM+uqNa4tJSu8R0S0gL896BpKLNraCldE+HKOnfw3NoOMyztCXhx8NzaCFVEFDt4TguFhREBj7IhiFZk1cNBF2tqKNE9rofBtehR3XQirOOISD/OtFBYyIQgGhUGaTVGNiwqzbbEJeSEdZzRUuOywzqOiPRj00JhoUIIolWE6/wVs3Bk3o4Mj4AWYHFuTQhkeAQcmbdHuLLODet5M7QuPjo12DCs580RqogodrBpobBQIQSR5Kgy26LZ4vB95yzf/2/XuLRuf985S5n1Why2OIzoHfzS8RG97+B6LUQGYNNCYaNCCKLZxdIMy8USBz6M21NmoU+781v7eIHbU9Rbp+XqzAUY2fvnHWZcNNgwsvfPuU4LkUEYmEhhxxVx5UWzWVFpwTmuiEtkHQxMJFNQIQTRTGJ1dqUzmi0OPfrepcRVQno4bHEY2Xt2tMsgihlsWsLIqFkDmf02eYG33ckoabYjp4cHtzhrERfhg4CcPdHP8IbF6wVOHAVq3ECKE8geDNj8/4NQfXn/QGRmZbzCg9N1RahvqUCiIx1ZSWNg0yLbLMrUIDNWEx70O7sDyQ2lqE3IxKle+RBheG4qvGZE7bFpCROjcnRk9ru8IhWrq1LgvWjRtmVnnPhZWg3uT68OuQaj6o11hjcsX+8DNr0LVFdduC81DZh8EzA019ifbTCZnKJi92bsOL0UtS1lbfclOzKQn7UQg5yTIlKvTA0yYy/9bh3GH3gMqQ2n2u6rTuiHbcP/gCN9p0ekXqJI4om4YWBUjo7MfpdXpGJVVUqHdTq9AFZVpWB5RWpINRhVbyyLyCXNX+8D3nvdv2EBfNvvve77d5OSySkqdm/GphML/b58AaC2pQybTixEsXuz4fXK1CAz9tLv1uGG3fcg5aKGBQBSGr7DDbvvwaXfrTO8XqJIY9PSTUbl6Mjst8kLrK5KOf+v7Y/D+LZXV6WgKfjK493CPCF9InL+itfrm2EJZtM/fOPOU+Xy567I5BR5hQc7Ti8Nur8dp5+FVxi3iKFMDTJjNeHB+AOP4cJv1wWtywaMP/BbaJLPTYXXjCgYNi3dZFSOjsx+33Ynnz8kFOjEEQ1eaHjbnSxVgwzmCXUtYifcnjjacYalvepK37iLmKFxkckpOl1X1GG2oL3allKcrisyolQAkKpBZmy/szuQ2nAqyG+8QGrDSfQ7u8Oweomigee0dJMRmTuy+y1p1jdW77hQGPU6WEVErxCqcYd3nEJkcorqk7J0ja1vqehOSWHZt0wN9S0VSG6o0zU2uaFU935l6jDyNSMKhjMt3WRUjo7MfnN66Burd1womCfUuagsyZ+icz2ETsapPtsik1OU6EjXNVbvuFDI1CAztjYhU9dYveMu3nc4xxGFG5uWbjIqR0dmv7c4a2GDAAKMBQRsELjFWStVg1H1xoqorb+SPdh3lVAwqS7fOJORySnKShqDZEdG0P0lOzKRlTTGiFIBQKoGmbGneuWjOqFfJ2e0+AhoqE7oj1O98g2rlyga2LR0k1E5OjL7jbMBP0urOf+v7T/Mfds/S6sxdL0W5gkpxGbzXdYczOSZHdZraaXybItMTpFNsyM/a2GwvSE/6yFD1x6RqUFmrNDs2Db8DwA6norbur1t+O+l12tR4TUjCoZNSxgYlaMjs9/706txZ1pNhzfUBuDOCK3TwjyhC6K+yu3QXODGuzvOuKS6fPebeJ0WmZyiQc5JmJy9tMPsQbIjE5Ozl0RkzRGZGmTGHuk7HevH/gU1CX39xtYk9MP6sX8JeZ0WFV4zokCYPRRGXBFXvl6riXqz0p6OFXEDUX2VXK6I68MVcUlVRmQPsWkhChPlGpYwUL1xISJ1MTAxRnEGR31WbFhUp8RMgKceSUefRkvjMTjiB6JucAFg7/4fVx5PPU6dfA7uxhI443PQr/+DsIdhv0Rmx5kWxamaaWSDiGimkapioVlRcbZFhWyc+AP34p2WHSh1XPjbL7OlBT915KNx+J9C3u/hI/fh04bt8F60gJ5NCFybcB0uv/SFbtVMFElGzLTwb2WFMdNIbbHQsKhIhWyc+AP34kXxOUrt/v8NlNnteFF8jvgD94a038NH7sM/G7Z3+vv2z4btOHzkvtAKJrIINi2KYqaR2mKpYVHpEmglsnE89XinZUfQ/KN3WnYAnnq53Xrq8WnDdt9G+5iC89ufNmyHR3K/RFbCpkVRzDRSVyw1LKpRIRsn6ejTvkNCQfKPSh0OJB19Wmq/p04+5zskFGC/0DR4NQ2nTj4nWzKRZbBpURQzjdQTlSX5FaHKbIsK2TgtjcfCOq6Vu1FfrpLecURWxKZFUcw0UkusNiuqUSEbxxE/MKzjWjnj9eUq6R1HZEVsWhTFTCN1sGHxUWG2RYVsnLrBBchsaQmaf5TZ0uK7/FlCv/4PwiYEEOiCTiFgEwL9+j8oWzKRZbBpURQzjdTAhkUtSmTj2BPxU4cviDBQ/tFPHfnS67XY7Ym4NuE630b7xuX89rUJ13G9FoppFv66MT9mGkVPLJ+/EowKsy0qZOM0Dv8T5mt5yPD4HxrN8HgwX8sLeZ2Wyy99AdcnXNfp79v1XKeFiIvLmQFXxI08NizBqbDgHFfEJVIbl/GPUXYNyEuUO3cl3PuNswF3uKx97grAZsVMbJod/ZKvim4R9kTUDXkKABDO31C7PRE5A34bxj0SWUNITUtlZSUOHz6MoUOHomfPnuGuSSkysxFmSzeWmT2x8usA4YWj6ShS8muB43JJyEG1tABFnwKVFYArHRhzLeAI8CvXjTTmYDyeFuw+uAvl1ZXok+rC2GFXwW7vvAaP8GJ3+UmUN9SiT0IyxvbpD7vWeQ0yCcsqaPLU4/OyFXA3lcAZl4O8jAWICzBzIZOazKRpdVj5udEF0oeH1q5di1/84hfIycnB0aNH8cYbb+Dmm28O+pht27Zh7ty5KC8vx6OPPooHHnhA98+L5uEhmXweozKCjCKTJ2Tl16FH/X4kVr4Hm7fqwp2pacDkm4ChuaHveOsHwOdb/U+o1DQgbwIwYYb/2K/3AZveBarDW8PGnRvxzNG9KLVfaDwyPV4sGjwKU8ZN8R974l94Zs9WlNbXXBibmIJFoydgSvaQLveb4RH4vnMWEgc+HHK9Rvn4+AM4XrOtw/0DUsbjBwOW+d136XfrMP7AY0htONV2X3VCP2wb/gcc6Tvdb2z9sSXY4P47yi7qyAO9DipkJcnUoEK9Mqz83Mws6tlDVVVVmD9/Pj755BPs378fK1euxMKFwc7kB8rLyzFjxgzcdtttKCwsxFtvvYUtW7Z0q+hIkMnnMSojyCgyeUJWfh161O9H0tlV/g0L4Gse3nvd10yEYusHwM4tnV8BsnOL799bfb3P97Oqw1vDxp0b8cC3+1Bq85/iKrNpeODbfdi4c+OFsSf+hQcK1/k1LABQVl+DBwrXYeOJf3W533IbsKbm76g/tiSkeo0SqGEBgOM12/Dx8Qt/QF363TrcsPsepFzUsABASsN3uGH3Pbj0u3Vt99UfW4I1NX9HWbtP0M5eBxWykmRqUKFeGVZ+btSRVNPidrvx/PPPIzfX99ff2LFjcebMmaCPeeutt9CvXz88/vjjGDJkCJ544gm89tproVccATL5PEZlBBlFJk/Iyq8DhBfJze8FDCgAAGz6h++wjYyWFt8MSzCfb/WN83p9MyxhrsHjacEzR/cGzcb549G98Hha4BFePLNna6cr8bTe98c9W+ERXl373eD+O4Q3/OdfhaLJUx+wYWl1vGYbmjz10IQH4w88hgv/tV7QuhTA+AO/hSY8EN4m3/MEunwdVMhKkqlBhXplWPm5UeekmpacnBzMnj0bANDc3Izly5dj5syZQR+zd+9eTJw4Edr5X+Zx48bhiy++CDi+sbERbrfb7xZpMvk8RmUEGUUmT8jKr4Pr3451nN1or7rSd56JjKJPAy8O1koI37gTRw2pYffBXb5DN0GycU7bbdh9cBd2l5/sMMPiNxbA6foa7C4/qWu/ZXYNLaVrpOo1yudlK3SP63d2B1IbTgX5rRBIbTiJfmd3oKV0je+QkI7XQYWsJJkaVKhXhpWfG3UupBNx9+7di0mTJiEuLg4HDx4MOtbtdmP48OFt206nE6dOnQo4/umnn8aTTz4ZSllhY0Tuj2xGkFFk8oR62/X9hW+218E12w4c0NkM10g2zZU6M28qK4Dk1K7HhVBDeXWl/nFOl76xDbW+BkqHpoYS9NA10ljuJp1ZPk0lSPaW6hqb3FCKJp37bWooQX1Slq6xRmYlGZHXZGS9Mqz83KhzIV2ekJubi48//hhDhgzBnDlzgo51OByIj49v205ISEBdXV3A8QUFBaiqqmq7lZREPhxMJp/HqIwgo8jkCVnxdWi7pDlF50lhese1/QCdmTeudMNq6JPq0j2uT4K+hO4+Ccm69xuXoEY2jjNOZ5ZPXA5qEzJ1ja1NyNT9/OIScpTISpKpQYV6ZVj5uVHnQmpaNE3DlVdeiVWrVuHdd99FZWVlwLG9evVCeXl523Z1dTXi4gIfIoiPj4fT6fS7RZpMPo9RGUFGkckTstLr0GGF2+zBvit0gkl1+cbJGHNtwMMGbTTNN86gGsYOuwqZHm/QbJwsjxdjh12FsX36IzMxJchhESArMQVj+/TXtd8Mj4Aj83apeo2Sl7FA97hTvfJRndCvkzNafAQ0VCf0x6le+XBk3o4Mj9D1OqiQlSRTgwr1yrDyc6POSTUt27Zt87taKC4uDpqmwRZkPYm8vDwUFha2bRcVFaF///4hlBo5Mvk8RmUEGUUmT8gqr0OnC8bZbL5LioOZPFN+rRSHw3dZczB5E3zjDKrBbndg0eBRAAJn4zwyeBTsdgfsmg2LRvvq7fy0bOCR0RNg12y69vt95yxl1muJsydiQMr4oGMGpIxHnD0RQrNj2/A/AOh4Km7r9rbhv4fQ7NBscfi+cxaArl8HFbKSZGpQoV4ZVn5u1DmpT8OhQ4filVdewSuvvIKSkhI8+uij+MEPfgCn0wm3243m5uYOj5kxYwY+/fRTbNy4Ec3NzViyZAmmTp0atidgFJl8HqMygowikydk9tch6Aq3Q3OBG+/uONuR6vLdH+oaKRNmAOMmdpxx0TTf/Rev02JQDVPGTcGyS3KR4fX/Us30Ciy7JNdvnZYp2UOw7JrpyEhM8R+bmIJl10z3W6cl2H5vT1FvnZYfDFgWsHFpv07Lkb7TsX7sX1CT0NdvXE1CP6wf+xe/dVoSBz6M21NmoU+70776eNHhdVAhK0mmBhXqlWHl50YdSS8ut2HDBvzmN79BSUkJpk6dihdffBF9+vTBJZdcgueffx433nhjh8e8/PLLuO+++5CSkgKXy4XCwkJkZuo7hhzt7CErrwRr5RVxpZbjN2g1WiuviBtovypkEnWGK+LK16BCvTKs/NzMyojF5SIWmFhcXIxDhw7h+uuvR0pKStcPOC/aTQuZEzOEokfVxoWIIsvUgYmDBg3CoEGDIvXjKIaxYSEisiamPIeRCodFYp1SDYvMIR+jDlFFod4FV7zpN9sic7hFZtpeZr8yjNqvlfFwC0UKm5YwMVtQoNUo1awAciGIBgUmqlCvTAChTJCdzH5lGLVfK2MAIUVShP+UsyazBQVajZINi94QRIMCE1WoVyaAUCbITma/Mozar5UxgJAijU1LN5kuKNBilGtYZEIQDQpMlGJQvQuG/VV3AKFMkJ1MsKEMo/ZrZQwgpGhg09JNZgsKtBLlGhZALgTRoMBEKUbVe+Ko7gBCmSA7mWBDGUbt18oYQEjRwHNausmIcEUKTslmpZXecEOZEETZ0EYZUa43uaEU9Y4kXWPrWyqQ3BA4t6z9fmXoHS+7XyszIqyQqCtsWrrJLEGBVqF0wwIYE4IoG9ooI8r11iZkItER3/VA+ILsahMade9XhkxgIvkwgJCigYeHukn1oEArUb5hAeRCEI0KbZRhVL1djL04gFAmyE4m2FCGUfu1MgYQUjSwaekmlYMCraJDQrPKZEIQjQptlGFUvReN7SqAUCbITibYUIZR+7UyBhBSNLBpCQMVgwKtwjTNysVkQhCNCm2UYVS958dqqf6HizoLIJQJspMJNpRh1H6tjAGEFGkRyx4KlZmyh7gibviZsmlpFaMr4gYa+z+HcrkirkVxRVzqjKkDE0NlpqaFwsvUDQt1wCBFothi6sBEIr2s0qx4hBe7y0+ivKEWfRKSMbZPf9i1ALMRLS1A0adAZQXgSgfGXAs4Ov/1lNqvUUKod0jqb7Ht9I1B/wqX+Yu9xduEg+fWorrpBFLjsjGs581w2DpfD0mF2RMValCBCrMyKtRAoWHTQkqxSsOy8cS/8MyerSitr2m7LzMxBYtGT8CU7CH+g7d+AHy+Fbh40nPr+0DeBGDCjND3a5Ru1fs/AXNpZDJsPitdgS/PvAmBC+eR7Sx9HiN634GrMxf4jVUhT0iFGlSgQk6RCjVQ6HgiLinDSg3LA4Xr/BoLACirr8EDheuw8cS/Lty59QNg5xb/BgDwbe/c4vv3UPZrlDDU21kujUyGzWelK7D/zF/9GhYAEPBi/5m/4rPSFW33qZAnpEINKlAhp0iFGqh72LRQ1JnqkuYueIQXz+zZ2umqPa33/XHPVniE13eI5fOtwXf4+VagpUVuv0YJU72tWnNpZDJsWrxN+PLMm0HHfnnmTbR4m5TIE1KhBhWokFOkQg3UfWxaKKqs0qy02l1+ssPMwsUEgNP1NdhdftJ3TkhX58ELARR9Krdfo4SxXuBCLo1Mhs3Bc2s7zLB0KAFeHDy3Vok8IRVqUIEKOUUq1EDdx6aFosZqDQsAlDfU6h9XqTOTpbJCbr9GMaDe+pYKqQyb6qYTusZWN51QIk9IhRpUoEJOkQo1UPfxRFyKOCs2K636JCTrH+fSmcniSpfbr1EMqFcmlybRkY7UuGxdY1PjslGL6OcJMdPIR4WcIhVqoO7jTAtFlJUbFgAY26c/MhNTghwOALISUzC2T3/fZcJaF6sPahow5lq5/RrFgHqzksZIZdgM63kztC4+tjTYMKznzUrkCalQgwpUyClSoQbqPjYtFDFWb1gAwK7ZsGj0BADo5MRLn0dGT/Ctq+Jw+C4TDiZvAuBwyO3XKAbUa5PMHnLY4jCid/BF6kb0vgMOW5wSeUIq1KACFXKKVKiBuo9NC0VELDQsraZkD8Gya6YjIzHF7/7MxBQsu2a6/3oqE2YA4yZ2nMHQNN/9F617IrVfo4S53gVX+K4EksmwuTpzAUb2/nmHGRcNNozs/XO/dVpUyBNSoQYVqJBTpEIN1D1cxp8MFUvNSntcEVdfvRcv788Vca1PhdVoVaghFjB7iE2LqcRyw0L6MZOIyJqYPUSxTYUkZBkG1dvU1ID/+uQDlNRWISc5Dbf+2wzExSUoW29XFlzxZlvjIvMXMP9aJoo9bFrIEGGfZfl6H7DpXaC66sJ9qWnA5JuAobnh/VnhYFC9y9b9BavqKuFtPaekqQbPvvci7kxy4YHp9yhXr14LrngTvynspzsThvkxRLFJ4T9TyYwMWZL/633Ae6/7f6ECvu33Xvf9u0oMqnfZur/g9brKDuvBegG8XleJZev+EtJ+VXh9N574l+5MGObHEMUuNi0UNoacw+L1+mYAgtn0D984FRhUb1NTA1bVVfo2OrtyB8Cquko0NTVI7VeF17c1pyiYUHKKiMh62LRQWBh20u2Jox1nANqrrvSNU4FB9f7XJx/4DgkFWtxN0+DVNPzXJx90/u+BKPD6GpVTRETWw3NaqFsMv0Koxh3ecUYzqN6S2i4aC8lx0nUY+PrK5BTpxfwYImti00Ihi8glzSk6L5PTO85oBtWbk5wGNAWfjWgbJ0OB19eonCIish4eHqKQRGwNluzBvqtYgkl1+capwKB6b/23GbAJAQRaVkkI2ITArf82o/N/D0SB17ernCLgQiYM82OIYhubFpIW0UXjbDbfZbfBTJ6pznotBtUbF5eAO5Ncvo32jcv57TuTXPLrtSjw+gbLKcL5e1szYZgfQxTbFPmkJzMw5HJmPYbmAjfe3XFGINXlu1+1dVoMqveB6ffg7iRXh19aG4C7u7NOiwKvb6Ccos4yYZgfQxS7uIw/6aLEkvxcEReA9VbEvVj7nKLtZQ9zRVwik+Iy/hQVSjQsgO8LdMBl0a5CP4PqjYtLwM+m3BL2/arw+to1G/Iyctq2/7c88H97Ns2OfslXRaIsIlKEwn+mEhEREV3AmRYKSJkZFpUYdQhFZr8KHsYZ26c/7Fr4a7g4TJEoXHho0bzYtFCn2LB0wqhQQZn9KhAcufHEv/DMnq1+q9hmJqZg0egJmJI9JOw/j40LhRPDNs2Nh4eoAzYsnTAqVFBmv4oEGz5QuK7Dsvtl9TV4oHAdNp74l+E1EIWKYZvmx6aF/LBh6YRRoYIy+1Uo2LCzyw1b7/vjnq3wiPDXsOCKN8O+T4otDNu0BjYtBCCKa7CYgVGhgjL7NUGwoQBwur4Gu8tPGlYDUagYtmkNbFqIzUpXjAoVlNmviYIN9Y6TxdkW6g69IZoM21QbT8SNYWxWdDIqVNCI/SoQbKh3HFEk6Q3RZNim2jjTEqPYsEgwKlRQZr8mCDbUAGQlpmBsn/6G1cDZFgoVwzatgU1LDGLDIsmoUEGZ/SoebNi6/cjoCYas10LUXQzbtAZ+usQYNiwhMipUUGa/CgcbZiamYNk10w1Zp6U9zrZQqBi2aX48pyVGsFkJg6G5wGUjwr8arcx+japBwpTsIZjY/9KIrIhLFG6DnJMwMHU8V8Q1KTYtMYANSxgZFSoos18Fgw0jjavkUncwbNO8pP40ev/99zF48GA4HA6MHj0aBw8e7PIxM2bMgKZpbbcpU6aEXCxFj0d48XlZCf7f8UP4vKwkfAuIeb3A8W+AA7t9/2vg4mhhIVOvxFjDXl+T8Xha8PmXO/D/Cj/E51/ugMfTEnCsCoeJvMKDU7W7cKTqQ5yq3cWFyYgMpnum5ciRI7j77rvx8ssvY/z48fj1r3+NOXPm4NNPPw36uF27dmH//v3Izs4GAPTo0aN7FZNu4ZphMSxrRoEcHSkGZQRFOstHVRt3bsQzR/ei1H7hb6nMLz/FosGjMGWcen/sMMOGKPJ0z7QcPHgQzzzzDG655RZkZmZi3rx5KCoKvnLgyZMnIYTAiBEj4HK54HK5kJzMNRwiIZwNiyFZMwrk6EgxKCOIWT4+G3duxAPf7kOpzf+6pDKbhge+3YeNOzdGqbLOMcOGKDp0Ny3Tp0/HL3/5y7btw4cPY8iQ4H8F7ty5Ex6PB9nZ2UhOTsasWbNw7ty50KslXcLVsBiWNaNAjo4UgzKCopnloxKPpwXPHN3re86af9Mizm//8ejeTg8VReMQETNsiKInpNP9m5qa8Nxzz2Hu3LlBxx06dAijRo3C+vXrsWPHDhQXF6OgoCDoYxobG+F2u/1upF84T7o1LGtGgRwdKQZlBDHLx2f3wV2+Q0Ja+9VffISm4bTdht0Hd3X675FuXJhhQxQ9ITUtixcvRnJyMubMmRN0XEFBATZs2IBRo0Zh5MiRWLp0Kd55552gj3n66aeRlpbWdsvJid4VCmZiROChYVkzCuToSDEoIyjaWT6qKK+uDOs4ozHDhih6pJuWzZs3Y+XKlVizZo30SbUZGRk4c+YMGhsbA44pKChAVVVV262kpES2xJhj1CXNhmXNGJXlYxSZeiXGMsvHp0+qq9vjIjnbwgwbouiRalqKi4tx2223YeXKlRg+fHiX42+99VZs3769bbuwsBCZmZmIj48P+Jj4+Hg4nU6/GwVm5BoshmXNKJCjI8WgjCAVsnxUMHbYVcj0eKGJzs7uATQhkOXxYuyw4OtqRKpxYYYNUfToblrq6+sxffp0/PjHP8bMmTNRU1ODmpoaCCHgdrvR3Nzc4TEjR47E/fffj+3bt+O9995DQUEB5s2bF9YnEMuMXjTOsKwZBXJ0pBiUEcQsHx+73YFFg0cBQIfGpXX7kcGjYLersRYmM2yIokf3p+HHH3+MAwcO4NVXX0Vqamrb7dixY8jNzcX69es7POaRRx5Bbm4upk2bhnnz5mH+/Pl47LHHwvoEYpER568EYljWjAI5OlIMyghSIctHBVPGTcGyS3KR4fVvWjK9AssuyVVunRZm2BBFhyZEgDlZRbjdbqSlpeHYihVwJiZGu5yoi9aS/B7hNSZrxuuNao6ONJl6JcYa9vqajMfTgt0Hd6G8uhJ9Ul0YO+wq6RmWSC7v7xUeZtgQBdBQ48aiSX1QVVUVtlM91JhvJV2imSFkWNaMAjk6UgzKCIp2lo8q7HYH8kbkR7sM3ZhhQxRZsfennElFPfTQbBlBRmlpAT7fBmz4v77/bQmcjUPRoUImEREZgzMtiot6swKYLyPIKFs/AD7fClx8RHXr+0DeBGDCjGhVRZ1gCjSRNXGmhYIzW0aQUbZ+AOzc4t+wAL7tnVt8/05ERIZi06KwqM+ymC0jyCgtLb4ZlmA+38pDRUREBmPToqBIXtIclNkygoxS9GnHGZb2hPCNI2Xw3BYi62HTohglmpVWZssIMkqlzgwZveOIiCgkbFoUolTDApgvI8goLp0ZMnrHUcRwtoXIWti0KEK5hgUwX0aQUcZcC2iBEoLO0zTfOFIOGxci62DTEmXKnL/SGbNlBBnF4fBd1hxM3gTfOCIiMozFv23UpmyzcjGzZQQZZcIMYNzEjjMumua7n+u0KI2zLUTWwD8No8QUDUurobnAZSPMlRFkhAkzgOt+6LtKqLLCdw7LmGs5w0JEFCH8tI0CUzUsrcyWEWQUhwPIGx/tKoiIYlKM/akcXUqfv0JkcTxERGR+bFoipH2z4vEChd/2wPtfJqDw2x7wWGlRWSuHK1r5uanA4NeXjQuRufHwkME6m1n58GA8nvwoFd+5L/xbX6cHi6dWY9qwxkiWF35WDle08nNTAV9fIuoCZ1oi7MOD8Zi3Ng3fuf1f+tNuG+atTcOHB+OjVFkYWDlc0crPTQURfH0520JkXmxaDNTZIaEnP0qFL8XG/9JZcX77yY9SzXmoyMrhilZ+biqIwuvLxoXInNi0GKSzw0I7j/c4f0io89VVBTR857Zj5/EeBldnACuHK1r5uamAry8R6cRzWsIs2NVBZTX6rhzyjWsOU0URYuVwRSs/NxXw9SUinTjTEkZdXc6ckeLRtR+945Ri5XBFKz83FUTp9eUhIiLzYdMSJnrWXxk3oBl9nR5o589qaU+DQF+nB+MGmGyWBbB2uKKVn5sK+PoSkU5sWsJA74JxdhuweGo1AHRoXFq3F0+tht2M74qVwxWt/NxUEMXXl7MtRObCT9luCGWF22nDGvHSzVXIcvpfCZHl9OKlm6vMvU6LlcMVrfzcVBDF15eNC5F58ETcEHVnOf5pwxrx/csbsfN4D5TV2JGR4jskZMoZlvasHK5o5eemAr6+RNQFNi0hCEd+kN0GXHNJM0x3lZAeVg5XtPJzU0GUXt8FV7yJFV/dEfGfS0Ry2LRIYNghWZnH04LdB3ehvLoSfVJdGDvsKtjtYfiI8Ho5e0JEYcGmRSc2LGRlG3duxDNH96L0omOUmV9+ikWDR2HKuCmh75h5QkQURvxzhyjGbdy5EQ98uw+lNv+VmstsGh74dh827twY2o5NltfEE3KJ1MemRQfOspBVeTwteOboXt8F91q7PKzz2388uhceT4vcjk2a18TGhUhtbFqCCOWSZiIz2X1wl++QkBYgD0vTcNpuw+6Du+R2zDwhIjIAm5YA2KxQLCivrgzruDYmzhPibAuRuti0dIINC8WKPqmusI5rw7wmIjIAm5Z22LBQLBk77CpkerzQRIA8LCGQ5fFi7LCr5HZs8jwhzrYQqYlNy3k8f4Vikd3uwKLBowCgQ+PSuv3I4FHy67Uwr4mIDMBPDHB2hWLblHFTsOySXGR4/ZuWTK/AsktyQ1+nxeR5TZxtIVKPJkSAeWFFuN1upKWl4diKFXAmJoZ9/2xYiHy4Im7nuLw/UWgaatxYNKkPqqqq4HSG5/y1mF0Rl80KkT+73YG8Efnh3zHzmogoTMzz504YsWEhIr14mIhIHTHXtLBhISIiMqeYalrYsBAREZlXTDQtvJyZiLqDh4iI1BATTQsRUXexcSGKPss3LZxhISIisgbLNi08JERE4cbZFqLosmTTwmaFiIjIeizXtLBhISIjcbaFKHossyIumxUiIiJrs8RMCxsWIookzrYQRYfpmxY2LERERLHB1E0LGxYiihbOthBFninPaWGzojivFzhxFKhxAylOIHuwL+mXiIioG6S/Sd5//30MHjwYDocDo0ePxsGDB7t8zLZt2zBs2DCkp6dj2bJlIRXaig2L4r7eB/zpKeDvK4F1q33/+6enfPcTWQxnW4giS6ppOXLkCO6++24888wzOHnyJIYOHYo5c+YEfUx5eTlmzJiB2267DYWFhXjrrbewZcuWkIplw6K4r/cB770OVFf5319d5bufjQtZEBsXosiRaloOHjyIZ555BrfccgsyMzMxb948FBUVBX3MW2+9hX79+uHxxx/HkCFD8MQTT+C1117rVtGkIK8X2PRu8DGb/uEbR2QxbFyIIkPqnJbp06f7bR8+fBhDhgwJ+pi9e/di4sSJ0DQNADBu3DgsWrQo4PjGxkY0Nja2bbvdbgCA6xbOsijtxNGOMyztVVf6xg24LCIlERGRtYR8dmRTUxOee+45zJ07N+g4t9uNQYMGtW07nU6cOnUq4Pinn34aaWlpbbecnJxQS6RIqnGHdxwREVE7ITctixcvRnJycpfntDgcDsTHx7dtJyQkoK6uLuD4goICVFVVtd1KSkpCLZEiKcUZ3nFEJsNDRETGC+mS582bN2PlypXYsWMHevToEXRsr169UF5e3rZdXV2NuLi4gOPj4+P9mhwyiezBQGpa8ENEqS7fOCIiohBIz7QUFxfjtttuw8qVKzF8+PAux+fl5aGwsLBtu6ioCP3795f9saQ6mw2YfFPwMZNncr0WsjTOthAZS+obpL6+HtOnT8ePf/xjzJw5EzU1NaipqYEQAm63G83NzR0eM2PGDHz66afYuHEjmpubsWTJEkydOjVsT4AUMjQXuPFu34zLxVJdvvuH5kalLKJIYuNCZBypw0Mff/wxDhw4gAMHDuDVV19tu7+4uBgTJkzA888/jxtvvNHvMenp6Vi+fDl++MMfIiUlBS6XC2+88UY4aicVDc0FLhvBFXGJiCjspJqWH//4xxBCdPpv3377bcDHzZ07F1OnTsWhQ4dw/fXXIyUlRapIMhmbjZc1ExFR2EXsz99Bgwbh3//939mwEJHl8RARkTE4Z09ERESmwKaFiMgAnG0hCj82LUREBmHjQhRebFqIiIjIFNi0EBERkSmwaSEiMhAPERGFD5sWIiIiMgU2LUREBuNsC1F4sGkhIiIiU2DTQkQUAZxtIeo+Ni1ERBHCxoWoe9i0EBFFEBsXotCxaSEiIiJTYNNCREREpsCmhYgowniIiCg0bFqIiIjIFNi0EBFFAWdbiOSxaSEiihI2LkRy2LQQERGRKbBpISIiIlNg00JEFEU8RESkH5sWIiIiMgU2LUREUcbZFiJ92LQQESmAjQtR19i0EBERkSmwaSEiUgRnW4iCY9NCREREpsCmhYiIiEyBTQsRkUJ4iIgoMDYtRESKYeNC1Dk2LURECmLjQtQRmxYiIiIyBTYtRESK4mwLkT82LURERGQKbFqIiIjIFNi0EBEpjIeIiC5g00JEpDg2LkQ+bFqIiIjIFNi0EBGZAGdbiNi0EBERkUmwaSEiIiJTYNNCRGQSPEREsY5NCxGRibBxoVjGpoWIiIhMgU0LEZHJcLaFYhWbFiIiIjIFNi1ERCbE2RaKRWxaiIiIyBTYtBARmRRnWyjWSDctFRUVGDRoEL799ltd42fMmAFN09puU6ZMkf2RREQUABsXiiUOmcEVFRWYPn267oYFAHbt2oX9+/cjOzsbANCjRw+pAomIiIgAyZmWWbNm4fbbb9c9/uTJkxBCYMSIEXC5XHC5XEhOTpYukoiIAuNsC8UKqZmWV199FYMGDcKCBQt0jd+5cyc8Hg+ys7Nx7tw5/OhHP8JLL72Enj17BnxMY2MjGhsb27arqqoAAO7qeplSiYhiSkONO9olEPlpqK0GAAghwrdTEQIAori4uMtx//mf/ymmTJki9uzZI/bt2yfGjRsn7r333qCPWbx4sQDAG2+88cYbb7xZ4HbkyJFQWo1OaULIt0CapqG4uBiXXHKJ1OM++eQT3HTTTaioqAg4pv1MS2VlJQYOHIjjx48jLS1NtlSlud1u5OTkoKSkBE6nM9rlhBWfmznxuZkTn5s5Wfm5Ab4jJQMGDMC5c+fgcrnCsk+pw0PdlZGRgTNnzqCxsRHx8fGdjomPj+/039LS0iz5pgKA0+nkczMhPjdz4nMzJz4387LZwre6iqHrtNx6663Yvn1723ZhYSEyMzMDNixEREREgYSlaXG73Whubu5w/8iRI3H//fdj+/bteO+991BQUIB58+aF40cSERFRjAlL05Kbm4v169d3uP+RRx5Bbm4upk2bhnnz5mH+/Pl47LHHpPYdHx+PxYsXW3J2hs/NnPjczInPzZz43MzLiOcX0om4RERERJHG7CEiIiIyBTYtREREZApsWoiIiMgU2LREwLRp0/DGG290OS43N9cvEXvOnDnGF0cd3HfffX7vw2WXXdblY5hmro5HHnkEP/rRj3SN5fsWfW+88Ybfe9B66+ozk5+X0ffnP/8ZOTk5SEpKwoQJE3D06NEuH9Pt37mwra3bTVOnThWvv/56l+PWrl0rBgwYIPr27SvWrFljfGHd9OabbwoAXT632tpakZSUJMrKysS5c+fEuXPnRF1dXWSKDMGvf/1rv2WaL7300i4fs3XrVvG9731P9O7dWzz33HMRqDI011xzjVi/fn3b++B2u7t8TN++fcX+/fvbHlNTUxOBSkP38MMPi+nTp+saa5b3TQgh9u7dK1JSUnQvG26W9+3111/vdHn0rj5XzPB52djY2Pb6nzt3TpSUlIj09HTxzTffBHyMWT4vX331VZGdnS0SExPF+PHjdf13aZbft2+++Ubk5OSIL774Qhw7dkzcc8894vrrr+/ycd39nVOiadH7xb5//34RFxcnXn31VbFv3z5x2WWXiUOHDkWmyBCcOXNGZGZmissvv7zL57Z9+3aRn58fmcLCQPaLvaysTDidTvHkk0+Kr7/+WowdO1Zs3rw5QtXq19zcLJxOp6iurtb9mBMnToisrCwDqwovmS92s7xvQgjh8XjE1VdfLR5//HFd4830voXyxW62z8tWf/jDH8QvfvGLoGPM8HkZype6mX7f1q5dK26++ea27e3bt4u+ffsGfUw4fuei3rTIfLEvWLBATJ06tW37+eefF4899pjBFYburrvuEnPnzhV33nlnl89t2bJlIjs7W6Snp4u0tDQxd+5c0dDQEJlCJYXyxb58+XLxve99T3i9XiGEEO+9956YPXu2USWGbPfu3SIlJUVceumlIiEhQUydOlUcO3Ys6GPeffdd0adPH9G/f3+RlJQkbr31VnH27NkIVSxH9ovdLO+bEEKsXLlSJCUliddee028//77orGxMeh4M71v7en5Yjfb56UQQtTX14uMjIwuA3nN8HkZype6mX7fvvrqK9G7d29RVFQkKisrxe233y5+/vOfB31MOH7non5Oy4MPPoiZM2ciPz+/y7F79+7FpEmT2rbHjRuHL774wsjyQrZlyxZs2rQJS5Ys0TX+8OHDuO6667B9+3Z89NFH2LBhA5YvX25wlaHZv38/vF4vRo8ejcTEREybNg3Hjx8P+pi9e/di4sSJ0DQNgLrv3YEDB3D55Zdj9erV2LdvHxwOB375y18GfcyhQ4cwatQorF+/Hjt27EBxcTEKCgoiVLGcl19+Gfv378cll1yCDz74AE1NTUHHm+V9q6mpweLFizF48GAcO3YMy5cvx3XXXYf6+vqAjzHT+3axhoYGrFixAo8++mjQcWb6vGy1Zs0aXH311V2G8Zrh83L48OHYvHkz9uzZg6qqKrz44ov4/ve/H/QxZvl9A3zP76c//SnGjBkDl8uFwsJCPPvss0EfE5bfue50Wt21efNmkZOTI9xut67ZiLFjx4q33367bfvLL78Uubm5Blcpr76+XgwZMkSsW7dOCCF0Pbf2Vq1aJa688koDquu+N998U1x55ZXif//3f8XXX38tbrjhBr+/6Dpz0003iSVLlrRt19TUCKfTaXSp3Xbs2DFhs9lEVVWV7sds27ZN9O7d28CqQlNdXS3S09PFiBEjxBNPPCEmTJgg8vLygp4LYJb3bdWqVSIxMVGUl5cLIXyzgd/73vfEn/70J937UPV9a++1114TP/rRj7ocZ5bPy4vl5eWJ9evXSz9O1c/Le++9t+38o0GDBomysrKg483y+yaEEJ999pno16+f2LFjh6isrBSPPvqouOqqq9pmifQI5XcuajMtDQ0NuPfee/HSSy8hNTVV12McDoffcsAJCQmoq6szqsSQ/e53v0NeXh5uuOGGkPeRkZGBkydPhrGq8Jk9ezZ27dqFa665BkOGDMGLL76IDRs2wO12B3yMWd679jIyMuD1evHdd99JPaY1zVwl7777Lmpra7FlyxY8+eST2LBhA6qrq7F69eqAjzHL+3bixAnk5+cjPT0dgK/u3NxcfPPNN7r3oer71t7LL7+MuXPndjnOLO9dq2+++QbffPNNl7MRnVHx83Lnzp347//+b+zYsQOVlZW47bbb8MMf/hAiyCL0ZnrP/va3v2HWrFm4+uqrkZaWht///vc4cuQI9u7dq3sfofzORa1pCeWLvVevXigvL2/brq6uRlxcnBHldcuaNWvw/vvvw+VyweVyYc2aNZg/fz7mz58f8DHXXHMNSkpK2rYLCwsxcODASJTbbXq+2M3y3i1cuBBr1qxp2y4sLITNZkNOTk7Ax5glzTyUL3azvG/Z2dkdDgUdO3YM/fv3D/gYs7xvF5P5YjfLe9fq7bffxvTp09GjR48ux5rh8zKUL3UzvWderxdlZWVt29XV1airq4PH4wn4mHD8zkWtaQnliz0vLw+FhYVt20VFRUE/lKLln//8J7788kvs2bMHe/bswYwZM/DUU0/hqaeeQmVlZadv6hVXXIF7770Xn332GVatWoXnnntO2UTsUL7YzfLejRo1Cr/97W+xadMmfPzxx5g7dy5+/vOfIykpyfRp5qF8sZvlfbvhhhtw4MABvPzyyzhx4gReeOEF7N27FzfddJPp37eLyXyxm+W9a/Xhhx9iwoQJfveZ+fMylC91M71n119/Pd59910sX74ca9aswY033oisrCzk5uYa+zsneRgrbEpKSkRxcXHb7Sc/+YlYunSpKC8vF+fOnRMtLS0dHrNnzx6RnJws9u3bJ6qrq8Xo0aPFs88+G4Xq5Vx8TgsAUVRU1GHMuXPnxI033igSExPFwIEDxYsvvhjZIiWsXr1aDBo0SGzcuFF89NFHYujQoeKuu+4SQghRVVUlmpqaOjymvLxcJCQkiA0bNoimpiYxbdo08atf/SrSpeuyaNEikZaWJnr16iXuu+++tnUEBg4cKP7xj390GN/U1CTuuecekZycLLKyssSTTz4pmpubI1x11yoqKoTT6RQvvfSSKCkpEStWrBAJCQni+PHjlnjfWi+DTUxMFIMHDxYffPCBEML879vFrr/+evHaa6/53WeFz8u6ujoRFxcnDh486He/mT8v165dK5KSksSyZcvEW2+9JSZOnCgGDhwompqaLPH75vV6xVNPPSUGDBggevToIcaMGSN2794thDD2dy7qlzy30vPFLoQQjz76qIiLixNOp1NceeWVSi4oFAtkv9iFEOKll14SPXr0ED179hSDBg0Sp0+fjmDFJIT8F7sQfN9UIfvFLgQ/L6MplC91Ifj71hVNiCBnBSnqwIEDOHnyJMaPH6/s8T7qXHFxMQ4dOoTrr78eKSkp0S6HdOL7Zl78vDQf/r4FZsqmhYiIiGJP1BeXIyIiItKDTQsRERGZApsWIiIiMgU2LURERGQKbFqIiIjIFNi0EBERkSmwaSEiIiJTYNNCREREpsCmhYiIiEzh/wNUAVYBs5UFfgAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用所有的数据"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:09.034329Z",
     "start_time": "2024-09-15T11:51:09.030406Z"
    }
   },
   "source": [
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:09.048448Z",
     "start_time": "2024-09-15T11:51:09.035460Z"
    }
   },
   "source": [
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)\n",
    "log_reg.score(X_test, y_test)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:09.061039Z",
     "start_time": "2024-09-15T11:51:09.049431Z"
    }
   },
   "source": [
    "log_reg2 = LogisticRegression(multi_class=\"multinomial\", solver=\"newton-cg\")\n",
    "log_reg2.fit(X_train, y_train)\n",
    "log_reg2.score(X_test, y_test)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/myenv3.10/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OvO and OvR\n",
    "\n",
    "OneVsRestClassifier、OneVsOneClassifier 可以对所有的二分类模型进行多分类处理。 \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:09.079404Z",
     "start_time": "2024-09-15T11:51:09.061773Z"
    }
   },
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "ovr = OneVsRestClassifier(log_reg)\n",
    "ovr.fit(X_train, y_train)\n",
    "ovr.score(X_test, y_test)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": ""
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-15T11:51:09.094199Z",
     "start_time": "2024-09-15T11:51:09.080457Z"
    }
   },
   "source": [
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "\n",
    "ovo = OneVsOneClassifier(log_reg)\n",
    "ovo.fit(X_train, y_train)\n",
    "ovo.score(X_test, y_test)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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