{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Управление потоком жидкости"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Создание лингвистических переменных\n",
    "\n",
    "Входные X: speed (текущая скорость) и target (желаемая скорость) /\n",
    "Выходные Y: cadence (необходимый каденс)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skfuzzy import control as ctrl\n",
    "\n",
    "speed = ctrl.Antecedent(np.arange(0, 100, 1), \"speed\")\n",
    "target = ctrl.Antecedent(np.arange(0, 100, 1), \"target\")\n",
    "cadence = ctrl.Consequent(np.arange(0, 130, 1), \"cadence\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Формирование нечетких переменных для лингвистических переменных и их визуализация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Методы искусственного интеллекта\\MII_Salin_Oleg_PIbd-33\\.venv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FOdh5eafSuI/MD5MbusU9ET744JEm+H3/RXy28YzscEgWjUY3HSWmXTqMlh2N6Sc3N5KArMuyIyE92X5pOwo0BegW1k12KFQFXLRP5bqvaQjOp+Xgo3WnEObjrFwnC7P/W+DiHuCZVYCtg+xozKOoWIzeePB3RQ02XtiISK9I1HCzwE1hVYAjN3Rbr3Wtg4ea18Cbvx3GnoR02eGQMWVdAda/AzQfAtRqLzsa0+cWCLiHsKhYJQqLC7H1wlZOSZkxJjd0W1ZWVpjxYGO0qOml7CTOHjgWZPVYwNYR6PGu7EjMh1JUzORGDfZe3Yvrhdc5JWXGmNzQXbdp+PLJ5vBwssMLP+zjEnFLcGIVELdM16zPyUt2NGbWqThWV6tEZr8EPMQ1BPW8WERvrpjc0F15Otvj6yEtcelaLkYvjoVGwxUhqpV/HVj1BlCnOxD1kOxozC+5KbgOpJ2WHQlVg0arwaYLm5QpKTF6TeaJyQ1VSN0AN3zyWFOsj0vC7PWnZIdDhrJxmm4bgX4fsadNZQU3FZO5nJoyc0dSjyAlN4VTUmaOyQ1VWLcGARjbqz7mbjyD5Ye45FV1xJvy7nlAlwmAVy3Z0Zgf0aXYtx6TGxVMSXk7eqOpn0hWyVxxKThVykudI3DiahbG/n4Y9QLcEBnoJjsk0ofiImD5SCAwCrjnZdnRmK+Q5kxuzJhoWroxcSO6hHaBjbWN7HCoGjhyQ5Ui5qBnPtgENX2c8dJP+5GVVyg7JNKHXV/ouhEP+BSw4WeeatXdXD0KFHHzWXMUnxmP81nnOSWlAkxuqNKc7G0w78kWyu7hr/96iAXG5i7jHLBpOtD6Rd3IA1WdOH+aQl2CQ2bZldjBxgGtA1vLDoWqickNVUktXxfMGdQU644n4cstZ2WHQ1Ul9kJa+Trg7AN0nSg7GvMXEAXY2HNqykztuLQDLQNawlH0eCKzxuSGqlVgPKJrHXy09iS2nU6RHQ5VxdE/gDPrgX4fAg6sn6o2sU1FYGMmN2YotygX+5P2o11wO9mhkB4wuaFqGdm9HtrX8cWoRbFIysqTHQ5VRk46sGY80GAgENlHdjTqEcyiYnMkEhuxUWaHkA6yQyE9YHJD1WJjbYXZg5rC1sYKIxcdRDHrb8zH+im6wtc+s2RHor6iYtHILy9TdiRUySmpQJdAhHuEyw6F9IDJDVWbr6sDPn2smbK55icb2J3VLJzbARz4Aeg+BXAPkh2NOncIv3xQdiRUCTsu70D74PbsSqwSTG5IL9pE+GB093qYu/E0dpxJlR0O3YkYrRE9bULbAC2ekx2N+vjUARzcOTVlRq7cuIKEzATW26gIkxvSm5fvrYP2tX0xclEskq+z/sZkbfsYyEgA+s8BrPknQO/EOVV2CD8gOxKqxKiNtZU12gS1kR0K6Qn/spHe62/EqC7735iolFPA9o+B9iOBgIayo1Evdio2Kzsv70QT3ybwcPCQHQrpCZMb0is/Nwd89Eg0tp1Oxbc7EmSHQzfTaHTTUR41gE5vyo5G/XU3168AWdyDzdQVaYqw6/IutAvhlJSaMLkhvetUzw9DO4Rj1pqTOHaZK0ZMxsEfgcSdQP/ZgJ2T7Ggso6iYU1Mm72jqUVwvvK4UE5N6MLkhgxjbOxK1/V2V+pvcgmLZ4dCNZGDdJCD6cSCii+xo1M89GHAL4tSUmWy5IKajGvk0kh0K6RGTGzIIB1sbfPpYU1xIz8G0Vcdlh0OiWZ+VDdBzquxILGv0hsmNWdTbtA1qy13AVYbJDRlM3QA3vN2/IX7alYgNcUmyw7Fcp9frtlnoNR1w8ZEdjeUQK6ZErxtR60QmKTM/U5mW4hJw9WFyQwb1ZJswdK3vj3F/HEF6doHscCxPQTawcjQQ3hmIfkx2NJY3cpOfBaRzY1lT3nJBCy1aB3EXcLVhckMGJbp9znywMYo0Grz95xFoxS7UZDybZ+jqbUQRMTuvGn/kRuDUlMnae3UvQlxDlAupC5MbMjh/d0dMu78xVh25imWHuDTWaK4cBmK+ADqPBXxqy47G8jh5Aj51mdyYsH1J+9AyoKXsMEiNyc3nn3+OWrVqwdHREW3atMGePXvuePycOXMQGRkJJycnhIaGYvTo0cjLYzdcU9evSRAGRgdj0p9HcTWT/14GpykGlo8A/CKBdiNkR2O5WFRs0vU2J9NPomUgkxs1kprcLF68GGPGjMGUKVNw4MABREdHo1evXkhOTi73+J9//hnjx49Xjo+Li8M333yjPMdbb71l9Nip8t69rxGc7G3w5u+HOD1laHvmA5djgQGfADZ2sqOx7E7FV48ARaw3MzUHkg4o9TatAlvJDoXUltx8/PHHeOGFF/Dss8+iYcOGmDdvHpydnfHtt9+We/zOnTvRvn17PP7448poT8+ePTF48OC7jvaQafB0tsf7DzVRuhcv2ntBdjjqde0CsOE9oNVQIJSFktJHbooLgKSjsiOh/9ibtBfBLsGst1EpaclNQUEB9u/fj+7du/8bjLW1cj0mJqbcx7Rr1055TEkyEx8fj1WrVqFv3763fZ38/HxkZWWVuZA8XSL98WjLGpi2Mg6Xr+XKDkd9xIjYqjcBBzeg22TZ0VBAFGBtx6kpE7Tv6j5OSamYtOQmNTUVxcXFCAgIKHO7uH716tVyHyNGbN5991106NABdnZ2qF27Nrp06XLHaakZM2bAw8Oj9CLqdEiuif0awsXBBm8t5eopvYtbBpxaDfSdBThyE0Dp7ByBwChuw2BisgqycCL9BIuJVUx6QXFlbN68GdOnT8cXX3yh1OgsWbIEK1euxHvvvXfbx0yYMAGZmZmllwsXOB0im4eTHaY/0BibT6bgjwOXZIejHnmZwKqxQGRfoMFA2dFQiWDuEG5qWG+jfrayXtjX1xc2NjZISirbuVZcDwwMLPcxkyZNwlNPPYXnn39eud64cWNkZ2dj2LBhmDhxojKt9V8ODg7KhUxLtwYBeKBZCN5dfgwd6/oiwN1Rdkjmb/07QMENoO8H7GljanU3+74F8rIAR3fZ0dA//W0CXQJZb6Ni0kZu7O3t0aJFC2zYsKH0No1Go1xv27ZtuY/Jycm5JYERCZLA6Q3zM2VAQ9jb2mDi0qP896uuxN26N9CukwCPGrKjoVt2CNcCV2JlR0I3JTetAlopTUZJnaROS4ll4AsWLMD//vc/ZWn38OHDlZEYsXpKGDJkiDKtVGLAgAH48ssvsWjRIiQkJGDdunXKaI64vSTJIfNaPTX1/kZYH5eE1UfLr7OiChDLjJeP1HXEbf2C7Gjov3zrAvZunJoyoXqbkxknOSWlctKmpYRBgwYhJSUFkydPVoqImzZtijVr1pQWGScmJpYZqXn77beVTFt8vXTpEvz8/JTEZtq0aRL/L6g6ekcFoWfDAExZdgzt6/gq9ThUSTs/BVJPAcM2A9zZ2PSIf5PgpkxuTMTBpIPQaDUsJlY5K62FzQeIpeBi1ZQoLnZ35/y3KRAdi7t/vAUDooMx48HGssMxL2lngS/aAm1eBHrevrCeJFs3GTjyOzDmuOxILN6Hez/EmnNrsO7hdZyWUjGzWi1F6hTo4YhxvSPxy55E7ElIlx2O+RCfS1aMAtwCgC7jZUdDd6u7yboEXOf0qynsJyWmpJjYqBuTGzIJT7SpiRY1vTBhyWHkFxXLDsc8HFoEJGwF+s0G7F1kR0N3LSoWO4Sz341MOYU5iEuPQ/OA5rJDIQNjckMmwdraSpmSSkzPweebzsoOx/RlpwF/vwVEPQzU/bfLN5ko9xDANYB1N5IdST2i1Ns082smOxQyMCY3ZDLqBbjhpc61MW/zWcSn3JAdjmlbOxHQFgO9Z8iOhCpCTIFwh3DpDqUcgpudGyI8I2SHQgbG5IZMyiv31lFqcCb/dYy9b24nfjNw6Beg51TA1V92NFSZTsWXD+hqpUiK2ORYNPFrAmsrvvWpHf+FyaQ42tngnfsaYfuZVCw/fEV2OKanMBdYPgqo2R5o9pTsaKgyQprrtshIj5cdiUUS01GHUw8j2i9adihkBExuyOTcG+mP3o0C8d6K48jKK5QdjmnZ+oFu1U3/OdxiwdyIJosCp6akOJd1Dpn5mYj2Z3JjCZjckEmaPKAhsvOL8PHaU7JDMR1Jx4EdnwAdXwf86smOhirL2Rvwrs3kRpJDyYdgBSs08W0iOxQyAiY3ZJKCPZ0wqntd/BBzDkcvZcoORz6NRrfFgncE0GG07GioqlhULLWYuI5XHbjau8oOhYyAyQ2ZrGfbh6Ouvxsm/XUUGo2FF2Hu/xa4uEc3HWXLXe7Nuu7mymHdfmBk9GLipn5NZYdBRsLkhkyWnY21Ulx8MPEalhy8BIuVdQVY/w7QfAhQq73saKi6IzfF+UAyt2EwJlFrczbzLJr6M7mxFExuyKTdE+GD/k2CMHP1CcstLl49FrB1BHq8KzsSqq7AxoC1LaemJDTvE7hSynIwuSGTN7FfA6W4+NP1p2FxTqwC4pbpmvU5ecmOhqrLzgkIaMRtGCRMSXk5eCHMLUx2KGQkTG7I5AV5OOHVrnXw/c5zOJN8HRYj/zqw6g2gTncg6iHZ0ZC+sKjY6GJTYpVRG26WaTmY3JBZeL5jOEK8nPB/y45bTufijdOA3Ayg30fsaaO2TsUpJ3TJKxlcsaYYR1KOsL+NhWFyQ2bBwdYGk/s3VDoX/33sKlRPfLLfPQ/oMgHwqiU7GtL7DuFa4Moh2ZFYhDPXziCnKIcrpSwMkxsyG90aBKBLpB+mrYpDflExVKu4SNfTJjAKuOdl2dGQvvlFAnYunJoyYn8bWytbNPJtJDsUMiImN2RW3u7XAJev5eG7HeegWru+AJKOAQM+AWxsZUdD+mZto9uKgcmN0YqJI70j4WTrJDsUMiImN2RW6vi74al7auKzjWeQcj0fqpNxDtg0HWj94j/TF6TaZn5cMWW0ZeBiJ3CyLExuyOyM7FYXNtZW+HjdSaiKKJRe+Trg7AN0nSg7GjJ0cpN5AbiRLDsSVcsqyFI2zIzyjZIdChkZkxsyO14u9sq+U4v2XsCxyyrad+roH8CZ9UC/DwEHN9nRkCGVjMpx9MagjqfpOkFH+TC5sTRMbsgsPXlPTYT7umDqijh1LA3PSQfWjAcaDAQi+8iOhgzNIxRw8WPdjYEdSz0GZ1tn1HSvKTsUMjImN2S2+05N6tcQMfFpWHc8CWZv/RSgKB/oM0t2JGQMom8Rm/kZ3LG0Y2jo0xA2ooibLAqTGzJbYll4+zo+eH/NCRQVa2C2zu0ADvwAdJ8CuAfJjoaMpSS5UcPIo4k6mnqU9TYWiskNmS3RSn187wY4m5KN3/dfhFkSozWip01oG6DFc7KjIWN3Ks67BqTHy45EldJy03Al+woa+bC/jSVickNmrXENDwyMDsbs9aeQW2CGjf22zwYyEoD+cwBr/jpa3Iop4fJB2ZGodkpKYPM+y8S/pmT23ugZifTsAny7IwFmJeUUsO0joP1IIKCh7GjI2Jy9Aa9w1t0YMLnxcPBADdcaskMhCZjckNkL83HGE21qYt7ms0qSYxY0Gt10lEcNoNObsqMhWVhUbNCVUmJKijuBWyYmN6QKr3WtI7YixNyNp2EWDv4IJO4E+s8G7NgW3qKTG7GBZnGh7EhURbSHEMXErLexXExuSBV8XB3wUucI/LTrPC6k58Ckia606yYB0Y8DEV1kR0Oy626K8oDkONmRqEpSThLS8tK4UsqCMbkh1XiuQzi8nO3x4VoT35ZhzQTAygboOVV2JCRbYBPdzwKnpvQ+JSVw5MZyMbkh1XC2t8XoHvXwV+xlHL1kotsynF4PHP0d6DUdcPGRHQ3JZu+sKyZncqNXR9OOws/JDwEuAbJDIUmY3JCqPNKiBmr7uWDm6hMwOQXZwMrRQHhnIPox2dGQSRUVc48pQxQTk+VickOqYmtjjbG962P7mVRsO50Ck7J5hq7eRhQRcwUH3dzMLyVOl/ySXoqJxTJw9rexbExuSHV6NgxAi5peyuiNRmMire2vHAZivtAt+/apLTsaMrWRG61Gt2qKqu3i9YvIKshiMbGFY3JDqiP6WrzVtz6OXc7CskOXZYcDaIqB5SMAv0ig3QjZ0ZCp8asP2Dmz7kaP9TYCp6UsG5MbUqUWNb2VERyxcqqgSPKmmnvmA5djgQGfALb2cmMh02NjCwQ1ZXKjx3qbENcQeDl6yQ6FJGJyQ6o1tnckLl3LxeJ9F+QFkXkR2DgVaDUUCG0tLw4y/X43TG70Ii49Dg28G8gOgyRjckOqVcffDfdFB+PzjWeQVyhhU02tFlj5BmDvCnSbbPzXJ/NKbq4lAtmpsiMx+2LiE+knUN+7vuxQSDImN6RqI7rVRfL1PCzak2j8F49bBpxaDfSdBTh6GP/1ybyKigUuCa+WK9lXlGLiBj4cubF0TG5I1SL8XPFg8xr4fPNZ5BYYcfQmLxNYNRaI7As0GGi81yXz5FkTcPbh1FQ1iVEbIdIrUnYoJBmTG1K9EV3rKruFL9x93ngvuv4doOAG0PcD9rShuxM/I9whXC/JjZeDF/yd/WWHQpIxuSHVC/NxVjoXf7n5LLLziwz/ghf2APu+BbpOAjxqGP71SB1KkhtRq0VVUlJvI9pBkGVjckMW4dWudZCVV4gfYgw8elNUACwfCQQ3A1q/YNjXIvV1Ks5NBzLOyY7EbLGYmEowuSGLUMPLGYNaheKrrWdxPa/QcC+081Mg5aSup421jeFeh9S5Ykq4zKLiqsjMz1QKipnckMDkhizGK/fWQU5BMb7fYaBPxmlngS2zgLavAEFNDPMapF4uvrrCYq6YqpKT6SeVr0xuSGByQxYjyMMJj7cOw4Jt8cjM1fPojaiTWDEKcAsAuozX73OT5WBRcbWa9znaOKKme03ZoZAJYHJDFuXlLrWRX6TBN9sT9PvEhxYBCVuBfrMBexf9PjdZVnIjtuooNkLhuwpHbup51YMNp4OJyQ1ZGn93RwxpWxPfbk/AtZwC/Txpdhrw91tA1MNA3e76eU6y3LqbolwgJU52JGY5chPpzf42pMPkhizOi51rQ6PVYv7WeP084dqJgLYY6D1DP89HlisoGrCyZt1NJeUX5yMhM4H1NlSKyQ1ZHF9XBzzdrha+33kOaTfyq/dk8ZuBQ78APacCrmwcRtUkpjT9G7LuppLOZJxBsbaYyQ2VYnJDFmlYxwiINl9fVWf0pjAXWDEaqNkeaPaUPsMjWPoO4Ry5qWx/G2sra9T1qis7FDIRTG7IInm52OOZ9rXw067zyMiuYu3N1g+AzItA/zncYoH0W1ScfBwoyJYdiVklN7Xca8HJ1kl2KGQimNyQxXqufbiygvu7HVVYOZV0HNjxCdDxdcCvniHCI0vuVCxquK4clh2J2WBnYvovJjdksXxcHTC4dZhSe1OprsUajW6LBe8IoMNoQ4ZIlsi/ASBGINipuEI0Wg1OZpxkckNlMLkhizasUwTyCjX4cVcl9pza/y1wcY9uOsrWwZDhkSWysdOtmmJRcYUkZiUityiXy8CpDCY3ZNECPRzxUIsa+GZbAnILiu/+gKwrwPp3gOZDgFrtjREiWSJ2Kq6wExknlK8cuaGbMbkhize8c21cyy3Eor2Jdz949VjdaE2Pd40RGlnyiimxO7hoEEl3dCr9FPyd/OHt6C07FDIhTG7I4oX5OGNgdLDS1K+gSHP7A0+sAuKWAb1nAk5exgyRLA13CK+w0xmnuQScbsHkhuifPaeuZOZhyYGL5R+Qfx1Y9QZQpzsQ9ZCxwyNL4xWuS6DZ7+auTl9jckO3YnJDBKBugBt6NwrEl1vOoqi4nNGbjdOA3Ayg30fsaUOGJ37GWHdzV9mF2bh04xKTG7oFkxuif7xybx2cT8vByiNXyt4h3mD2fAV0mQB41ZIVHlmakuRGNGOicp25dkb5WteTyQ2ZWHLz+eefo1atWnB0dESbNm2wZ8+eOx5/7do1vPLKKwgKCoKDgwPq1auHVatWGS1eUq/GNTzQuZ4fvth0FhrNP28oxUW6njYBjYB7XpYdIllacpOTClyrQKG7BdfbiG0XIjwjZIdCakluNmzYgP79+6N27drKRXy/fv36Sj3H4sWLMWbMGEyZMgUHDhxAdHQ0evXqheTk5HKPLygoQI8ePXDu3Dn8/vvvOHnyJBYsWICQkJCq/m8QlfFq1zo4mXQd6+OSdDfs+gJIOgYM+ASwsZUdHllap2KBU1N3TG7C3MLgYMN+U6SH5OaLL75A79694ebmhpEjRyoXd3d39O3bVxmJqaiPP/4YL7zwAp599lk0bNgQ8+bNg7OzM7799ttyjxe3p6en488//0T79u2VEZ/OnTsrSRGRPrSq5Y3W4d74fNMZaNMTgE3TgdYv6j5FExmTqx/gEcYVU3fAYmLSa3Izffp0zJ49G7/88gtGjBihXH7++WflNnFfRYhRmP3796N79+7/BmNtrVyPiYkp9zHLli1D27ZtlWmpgIAAREVFKa9XXHz75mv5+fnIysoqcyG6k1fvrYNDF68h47cRgLMP0HWi7JDIUnGH8NvSarVcBk76TW5E3YsYufmvnj17IjMzs0LPkZqaqiQlIkm5mbh+9erVch8THx+vTEeJx4k6m0mTJuGjjz7C1KlTb/s6M2bMgIeHR+klNDS0QvGR5epY1xev+B2C95WtQL8PAQc32SGRpRIjhpcP6mq/qIy0vDRcy7+Gep7cuJb0lNwMHDgQS5cuveX2v/76S6m9MRSNRgN/f3/Mnz8fLVq0wKBBgzBx4kRlOut2JkyYoCRcJZcLFy4YLD5SB6vcDIwo+Aariltjn0Mb2eGQpSc3hTlA6knZkZicUxmnlK8cuaHyVKlCUtTHTJs2DZs3b1amiYRdu3Zhx44deP311/Hpp5+WHiumrMrj6+sLGxsbJCX9U7j5D3E9MDCw3MeIFVJ2dnbK40o0aNBAGekR01z29va3PEasqBIXogpbPwX2KMQPHsPhtjUeLWuxrTtJIjbQtLLWFRWLFXtUSkxJOdo4ooZbDdmhkFqSm2+++QZeXl44fvy4cinh6emp3FfCysrqtsmNSETE6ItYdXX//feXjsyI66+++mq5jxFFxKK2Rxwn6nOEU6dOKUlPeYkNUaWd2wEc+AFW/T7Cg2iFcUsO42zKDdT2c5UdGVkiB1fAr76u7kZs1kplkpvanrWVpeBEekluEhISoA9iGfjTTz+Nli1bonXr1pgzZw6ys7OV1VPCkCFDlGXeom5GGD58OD777DNlddZrr72G06dPKwXFt0ugiCqlKF/X0ya0DdDiOdyn0eKDtSfx9bYEzHiwsezoyKKLirkcvLyVUvW8WG9D5ZPauEPUzKSkpGDy5MnK1FLTpk2xZs2a0iLjxMTE0hEaQRQD//333xg9ejSaNGmiJD4i0Rk3bpzE/wtSje2zgYwE4NEfxNI9OFgDz7SrhU82nMbrPevB15XTmySp7ib2F6AwF7Bzkh2NSSjWFCP+Wjz6hfeTHQqZKCutWE9XwVGW9957Dy4uLsr3d+tfY6rEUnCxakoUF4vePESKlFPAvPZAu9eAbpNLb76WU4B2Mzfi+Y4RGNODnxJJgiuHgK86Ac+tBcJY4C6czzqP/kv7Y36P+WgbrKv7JKrSyM3BgwdRWFhY+v3tiDobIrOi0QArRgEeNYBOb5a5y9PZHo+2DMWPMecwvHNtONn/W8xOZBT+DQFbR10zPyY3pfU2AldKUbWTm02bNpX7PZHZi/0JOL8DGPJXucP+QzuE44eYc/h9/wU81ZYbZ5KR2dgBgU1Yd/Of5Mbb0Ru+Tr6yQyETxTJzsmw3koG1bwPRjwMRXco9JNTbGX0bB+Hr7QkoLtlQk0jGDuFUWkxcx7OO7DBIbcmNWNEkugO3a9cOderUQURERJkLkdlYMwGwsgF63r7LtTCsUwTOp+Vg7bHyu2cTGTy5SY8HctJlR2ISuO0CGWS11PPPP48tW7bgqaeeUnrMsM6GzNLp9cDR34H75wEuPnc8tEkNT7QJ98ZXW+PROyqQP/Nk/OXggqi7qfPvfnyWKK8oD4nXE/GM5zOyQyG1JTerV6/GypUrlaZ6RGapIBtYORoI7wxEP1ahh7zYOQLPfb8P+89nsGsxGZd3BODoAVw6aPHJTXxmPDRaDUduSP/TUqI7sbc3/7iTGds8U1dv03+2WOJXoYd0qeePOv6uyugNkVGJn1HW3SjOXjurfBXdiYn0mtyIfjei8V5OTk5VHk4k15XDQMznumXfPhX/A2ltbYUXOoZjfVySsiUDkVGVJDcVa02m6uQmyCUILnYuskMhNUxLNWvWrEydwZkzZ5ROwrVq1VI2s7zZgQMH9Bslkb5oioHlIwC/SKBd5bftuL9ZCD74+xS+2Z6A6Q9wSwYycnKz9QMg8yLgGQpLdTbzLCI8uXCF9JTclGxuSWTW9iwALscCQ9cCtpXfbNXB1gbPttdtySA6FnNLBjKa4H+KisXojQUnNwmZCehco7PsMEgtyc2UKVMMGwmRoYlPvBvfA1oNBUJbV/lpnmgThs82nsEPMee5JQMZj1sA4F5Dt2KqkWV+2MwvzseF6xcQ4cGRGzJAzc2FCxdw8eLF0ut79uzBqFGjMH/+/Ko8HZHhiTqFlW8A9q5l9o6qCrElw6BWui0ZcguK9RYiUcV2CLfcaf9zmeeUlVIsJiaDJDePP/546RYMYjfv7t27KwnOxIkT8e6771blKYkMK24ZcGo10HeWbkltNT3XPhzXcgvxV+wlvYRHVOG6m8sHdbVjFroMXAj3CJcdCqkxuTl69Chat9YN6//6669o3Lgxdu7ciYULF+L777/Xd4xE1ZOXCawaC0T2BRoM1MtThvk4o1v9AHy34xy0Fr56hYyc3BTcAFJPwVKTG7GflIdD9T+gkLpVKbkRu4M7OOgKKdevX4+BA3VvGPXr18eVK1f0GyFRdW14V/eG0PeDCve0qYjn2tfCyaTriIlP09tzEt1RcFPR9MZi+92IZeC1PTglRQZKbho1aoR58+Zh27ZtWLduHXr37q3cfvnyZfj43LmNPZFRXdgD7P0G6DoJ8Kih16duW9sH9QJcldEbIqNwcNO1MbDQupv4a/FcBk6GS27ef/99fPXVV+jSpQsGDx6M6Oho5fZly5aVTlcRSVdUACwfCQQ3A1q/oPenF32fnmmna+p3IZ0NLclILLRTcaGmEOezznPkhgyzt5SoLxA7fycmJqKoqEjZiqHEsGHD4OzsXNmnJDKMnZ8CKSeBYZsBaxuDvMQDzULw/poT+CHmHCb2a2iQ1yC6ZcXU4cVAYR5g5whLIZaAF2mLOHJDhhm5EclNnTp1lFVSNyc2guhW7O/vX9mnJNK/tLPAlllA21eAoCYGexknexs81joUi/ZeQHZ+kcFeh6jMyI2mCLh6BJY2JSWwxw0ZJLmxtrZG3bp1kZbGIkoyUWL10orRuqZnXcYb/OWeuqemktgsOchl4WQE/o0AG3uLm5oSxcSeDp7wduSmzWSgmpuZM2fizTffVJaEE5mcQ4uAhC1Av9mAveE316vh5YxejQLx/Y4EaDRcFk4GJrYNCWyi61RsaXtKeUSU2eOQSK/JzZAhQ5SmfaKQ2MnJCd7e3mUuRNJkpwF/vwVEPQzU7W60l32mXS2cTcnG9jOpRntNsmAWWFQs9pRivQ0ZrKBYmDNnTlUeRmR4aycC2mKg9wyjvmzrcG80DHLH9zvPoVM9P6O+NllocrPnKyA3A3AqW/uoRsWaYiW5ua/2fbJDITUnN08//bT+IyGqrvjNwKFfgIFzAVfjFrYry8Lb18LY3w8jITUb4b6Gnw4jC09uBLEVQ+2uULvLNy4rm2Zy5IYMOi0lnD17Fm+//bbS5yY5OVm5bfXq1Th27FhVn5Ko6gpzdUXENdsDzZ6SEsLA6GB4u9jjfzvZ1I8MzDsCEFsQWMjUlKi3EbhSigya3GzZskXZT2r37t1YsmQJbty4odx+6NAhTJkypSpPSVQ9Wz8AMi8C/efodYuFynC0s8HjrcPw+/6LuJ5XKCUGshDW1kBIM+DSQVjKnlIudi4IcA6QHQqpObkZP348pk6dqmy9YG9vX3p7165dsWvXLn3GR3R3SceBHZ8AHV8H/OpJDeXJe2oit7BYSXCIDF9UvE/X+sBC9pTiSikyaHJz5MgRPPDAA7fcLhr4paZytQgZkUaj22JBDNN3GC07GgR6OKJPVKAyNcVl4WTw5OZGEpB1GWrHPaXIKMmNp6dnubt/Hzx4ECEhIVV5SqKq2f8tcHGPbjrKVrdTvWzPtg/HubQcbD6lq0UjMmhRscrrbkRXfDEtxXobMnhy89hjj2HcuHHKFgximFCj0WDHjh144403lB44REaRdQVY/w7QfAhQqz1MRfMwTzSp4cHdwsmw3AIBt2DVJzdJOUnIKcpBbU9umEkGTm6mT5+O+vXrIzQ0VCkmbtiwITp16oR27dopK6iIjGL1WN1oTY93YUp0u4XXwrbTqTiTfF12OKT2TTRV3qlY1NsIHLkhgyc3ooh4wYIFynLwFStW4KeffsKJEyfw448/wsbGMLsvE5VxcjUQtwzoPdMkm5j1axIEX1cHpakfkWGLig/qas9UnNw42jgi2DVYdiik9iZ+JcLCwpTRG4FV7GQ0+deBlW8AdboDUQ/BFDnY2uCJNmGYvzUeb/aqDw8nO9khkVqTm4LrQNppwC8SaiTqbcI9wmFtVeW2bGSBqvzT8s033yAqKgqOjo7KRXz/9ddf6zc6ovJsnAbkpgP9PpLW06YinrgnDEUaDX7de0F2KKRWwU3FR0tV190oxcRcKUXGSG4mT56MkSNHYsCAAfjtt9+Ui/h+9OjRyn1EBiP+iIs9dbpMALxqwZT5uzmif5Ng/C/mHIq5LJwMwdED8K2r2uRGrJQq6XFDZPBpqS+//FKpuRFbL5QYOHAgmjRpgtdeew3vvmtaBZ6kEsVFup42AY2Ae16GORCFxUsPXsKGuCT0bBQoOxxSbd2NOouK0/LSkFWQxWJiMs7ITWFhIVq2bHnL7S1atEBRUVFVnpLo7nZ9ASQdAwZ8AthUq1zMaKJDPZWl4VwWTgZNbq4eAYryodqVUpyWImMkN0899ZQyevNf8+fPxxNPPFGVpyS6s4xzwOYZQOsX/21eZiaeaR+OmPg0nLiaJTsUUutycE0hcPUo1FhvY2tti1A33cIVooqq8MffMWPGlH4vVkaJ4uG1a9finnvuUW4Tm2gmJiayiR/pn9g7Z+XrgJM30HUizI3YjiHA3QHf7ziHmQ81kR0OqU1AFGBjr6u7qWFeiX9FRm5quddSEhyiyqjwT4zYWuG/U1CC6HUj+Pr6Kpdjx45VKgCiuzr6B3BmPTB4EeDgBnNjZ2ONp+6pibkbz2Bc7/rwcvl3s1miahONLEWCo8Jmftx2gQye3GzatKnKL0JUZTnpwJrxQIOBQGQfmKvBrcPw6cYzWLT3AoZ34coP0jMxVZuwBWojRm4GRQ6SHQaZIXZFItO2foquULLPLJgzH1cH3BcdjB9jzqGoWL3dZElicpN6CsjLhFpcy7uG9Lx0FhNTlVRpIjMvLw9z585VRnOSk5OVjTNvduCA+oZHSYJzO4ADP+ia9bkHwdw9074Wftt/EWuPJ6FvY/P//yETUlJkf/kgENEFapmSEjgtRUZLboYOHaoUEz/88MNo3bo1t14g/ROjNaKnTWgboMVzUINGwR5oHe6tFBYzuSG98qkDOLjriopVktyczTyrbLkgCoqJjJLciM0yV61ahfbt21fl4UR3t302kJEAPPoDYK2e2dNn29XC8IUHcPRSJqJCPGSHQ2ohfkfEVgwqauYXfy0eYW5hsBcrwYgqqUrvGiEhIXBzM79VK2QmUk4B2z4C2otuxA2hJj0aBiDE04m7hZP+qaxTMVdKkdGTm48++gjjxo3D+fPnq/XiRLcQ9VsrRgEeNYBOb0JtbMWy8LY1sSz2MlJvqK+jLElObq5fBrIuQy0rpVhMTEZNbsTWC6KoOCIiQhnB8fb2LnMhqrLYn4DzO4D+swE7J6jRY61ClVmEhbsSZYdCaiwqVsHozY2CG0jKSeLIDRm35kZsmHnp0iVMnz4dAQEBLCgm/biRDKx9G4gerJqiyPJ4Otvj4RY18EPMObzYOQKOdjayQyI1cA8GXAN1RcUN+kMNK6Vqe7InFBkxudm5cydiYmIQHR1dxZclKseaCYCVDdBzGtTu+Q4RWLg7UdkxXDT4I9Lb6I0KOhWL5MYKVgj3CJcdClnStFT9+vWRm5ur/2jIcp1eDxz9Heg1HXDxgdrV8nVBr4aBWLAtHhqNVnY4pKZNNC8d1NWumflKqWDXYDjZqnNqmkw0uZk5cyZef/11bN68GWlpacjKyipzIaqUgmxg5WggvDMQ/RgsxQudIhCfko0NJ5Jlh0JqGrnJzwTSdXv+mXOPG9bbkNGnpXr37q187datW5nbtVqtUn9TXFxcraDIwmyeqau3eepPseU8LEWLml5oWdMLC7bGK0vEiaotuJnuq6i78a0Lc14p1aNmD9lhkKUlN9xEk/TmymEg5nPg3rcAH8srHhSjNy/+uB8HEzPQLMxLdjhk7pw8dd2KRXJjpqOgOYU5uHzjMkduyPjJTefOnav3qkSCphhYPgLwiwTajYAl6t4gAOG+LkrtzRdP/LOUl8iCm/mJYmIttKjrZb4jTyRflfvab9u2DU8++STatWunLAsXfvzxR2zfvl2f8ZGa7VkAXI4FBnwC2Fpmi3Ubays83zEca45exfm0bNnhkFqSm6uHgaICmKPTGaeVlVJcBk5GT27++OMP9OrVC05OTsoO4Pn5uk6rmZmZSu8borvKvAhsfA9oNRQIbQ1L9lDzGkrvm2+2J8gOhdSS3BQXAElHYY5OZZxCqFsoV0qR8ZObqVOnYt68eViwYAHs7OxKbxcbaYpkh+iOtFpg1ZuAvSvQbTIsnWjiN6RtTfy67wIyss3z0zaZkIAowNpOV3djhk5fO80pKZKT3Jw8eRKdOnW65XYPDw9cu3at+lGRusUtB06uAvrOAhy5M7YwpG0tJef7aRf3a6NqsnMEAhqZbd2NmJZickNSkpvAwECcOXPmlttFvY3Yb4rotvIydaM2kX2BBgNlR2MyvF3s8UjLGvhfzDnkFbKVAllmp+K03DSk56Wjjmcd2aGQJSY3L7zwAkaOHIndu3crfW0uX76MhQsX4o033sDw4cP1HyWpx4Z3gYIbQN8PLKqnTUUM7RCBtOwCZUsGomonNykngTzzaqp65pruQzNHbkjKUvDx48dDo9EoTfxycnKUKSoHBwcluXnttdeqHRSp1IU9wN5vgN4zAY8asqMxOeE3bckwqKXYOZzJH1Vnh3AtcCUWCL+1hMCUp6Tsre0R5sb91kjCyI0YrZk4cSLS09Nx9OhR7Nq1CykpKXjvvfeqGQ6pVnEhsHykroNq6xdkR2PyWzJs5JYMVB2iO7G9m9kVFYtiYrEE3Na6Sp+7iUpV6ifoueeeq9Bx3377baWC+Pzzz/HBBx/g6tWryk7jc+fORevWd18evGjRIgwePBj33Xcf/vzzz0q9JhnZzk91w+TDNgPWNrKjMektGcRl/tZ4dOeWDFRV4ncsuKn5JTcsJiYZIzfff/+9svWCWBGVkZFx20tlLF68GGPGjMGUKVOUZeQiuRE9dJKT7/zJ9dy5c8o0WMeOHSv1eiRB2llg8/tA21eAoCayozF5L3SMwJ5z6dh/vnK/S0Tl7hBuJjRajVJzU9eTyQ0ZObkRxcKiUV9CQgLuvfdefPPNN1i6dOktl8r4+OOPlQLlZ599Fg0bNlT65zg7O99x9EdszPnEE0/gnXfeuevqLNFgkLuWSyTWN68YDbgFAF3Gy47GLPRsGIA6/q74YtOtKxKJKlV3k3URuH4V5uDSjUvILcpFHS+ulCIjJzdi+ujKlSsYO3Ysli9fjtDQUDz66KP4+++/lR3BK6ugoAD79+9H9+7d/w3I2lq5HhMTc9vHvfvuu/D398fQoUPv+hozZsxQ+u+UXETMZESHFgEJW4B+swF7F9nRmAVRSPxyl9rYcCIZxy8zGafqFBWLrOGA2UxJCRy5ISkFxWJVlKhzWbduHY4fP45GjRrh5ZdfRq1atXDjxo1KPVdqaqoyChMQULa2QFwX9TflEb10xIiR6I5cERMmTFBGm0ouFy5cqFSMVA3ZacDfbwFRDwN1/01g6e4GRgcj1NsJn2/m6A1VkXsI4BpgNnU3Irlxt3eHv7O/7FDIkjfOVB5sba2snBKjNiJJMbTr16/jqaeeUhIbX1/fCidj7u7uZS5kJGvfBrTFQO8ZsiMxO7Y21nipc22sOnIFZ1Mq96GBSCH6SAU3N5/k5p9tF8R7CpHRkxtRw/LLL7+gR48eqFevHo4cOYLPPvsMiYmJcHV1rdRziQTFxsYGSUlJZW4X10UX5P86e/asUkg8YMAA2NraKpcffvgBy5YtU74X95OJiN8MHPoZ6DkVcOUnsap4uEUN+Ls54MvN/LmmanYqrkLZgJSVUpySIhnJjZh+CgoKwsyZM9G/f39liue3335D3759lVGcyrK3t0eLFi2wYcOG0ttEc0BxvW3btrccX79+fSWZio2NLb0MHDhQKW4W37OexkQU5uqKiGu2B5o9JTsas+Vga6OsnPrz4CVczMiRHQ6Z64opseVJejxMWUFxAc5nnecycJLT50asZAoLC1NWKG3ZskW5lGfJkiUVfk6xDPzpp59Gy5Ytld42c+bMQXZ2trJ6ShgyZAhCQkKUwmBHR0dERUWVebynp6fy9b+3k0RbPwAyLwKDF3OLhWp6vE0YPt90Bl9ticd79/NnnCpJNM0UxNSUT22YqoTMBBRri1HPq57sUMgSkxuRaOh7PnTQoEFKd+PJkycrRcRNmzbFmjVrSouMxXRXVUaFSJKk48COT4BObwJ+/ENVXc72tniufTjmbjqD17rWgb+7o+yQyJw4ewPetXXJTZNHYapOZZxSvoruxET6YKWtyhpuMyb63Igl4WLlFIuL9UyjAb7tBeRdA17aDtg6yI5IFTJzC9Fh5kYMbhOGt/o2kB0OmZs/ngcyzgHPr4ep+nj/x1iTsAZrH14rOxRSCQ6JkP7s/xa4uAfoP4eJjR55ONnhqbY18dOu88jILpAdDpljUfGVw7r93UwUt10gfWNyQ/qRdQVY/w7QfAhQq73saFRnaIdwaLRafLfznOxQyByTm+J8IOkYTHlaiiulSJ+Y3JB+rBmnG63p8a7sSFTJx9UBg1uH4fsdCbieZ7qfwMkEBTYGxC7bJtrvJj0vHck5yajvU192KKQiTG6o+k6uBo7/BfSeCTh5yY5GtYZ1ikBuYTF+2pUoOxQyJ3ZOQEAjk92G4UT6CeVrfS8mN6Q/TG6oevKvAyvfAOp0B6Iekh2NqgV5OCmN/b7ZHo+8QsN3BCcVEZ2KRTM/E3Qy/SScbJ0Q5h4mOxRSESY3VD0bpwG56UC/j9jTxgjElgzp2QVYtIejN1TJupvkON2HERMcuYn0ioS1Fd+OSH/400RVJ4a593wFdJkAeNWSHY1FqOnjomyq+dXWeOQXcfSGKrNDuBa4cgimmNzU9+aUFOkXkxuqmuIiYPkI3Vz+PS/LjsaivNq1LpKy8rBoD3e4pwryiwTsXEyuqDi3KBfnss4xuSG9Y3JDVbPrC93S0gGfADaVanRN1VTH3xX3Nw1RtmVg7Q1ViLUNENzU5JIb0d9Go9UwuSG9Y3JDlSe6nW6eAbR+8Z/hbjK2Ed3qIi27QGnsR1ThTTQvHYSpTUnZWNmgjlcd2aGQyjC5ocoRu3WsfB1w8ga6TpQdjcWq5euCh5qHYN6Ws8gpKJIdDpkD8UEkMxG4kQxTSm7CPcLhYMOO5qRfTG6oco7+AZxZD/T7EHBwkx2NRXuta11cyynEjzEcvaEKKBllNaF+N2IZeANv7pdG+sfkhiouNwNYMx5oMBCI7CM7GosX6u2MR1uFKqM3N/I5ekN34REKuPiZTN1NsaZY2XYh0jtSdiikQkxuqOLWTQaK8oE+s2RHQv949d46yM4vxv+45xTdjehDJZr5mUhycz7rPPKK8zhyQwbB5IYq5twO4MAPQPcpgHuQ7GjoH8GeThjcOhTzt8Yji3tOUUWmpkSnYlE7ZyLbLnDkhgyByQ3dnRitWT4SqNEaaPGc7GjoP16+t46yJPzb7QmyQyFzSG7E9HJGgkkkN0EuQfBw8JAdCqkQkxu6u+2zdX8MRU8ba/7ImJoAd0c8eU9NfLMtAZk5HL2huywHN5GiYnYmJkPiOxXdWcopYNtHQPuRQEBD2dHQHfacKtJosWBbvOxQyJQ5ewNe4dLrbrRaLZMbMigmN3R7Gg2wYhTgUQPo9KbsaOgO/NwcMKRdTXy3I0HZWJPozs385CY3yTnJyMjPYHJDBsPkhm4v9ifg/A6g/2zAzkl2NHQXL3aqrXwVxcVEd6y7uXIYKJY3hXky46TylckNGQqTGyqf6GK6dhIQPRiI6CI7GqoAbxd7PNs+XFkWnnI9X3Y4ZMrJTVEukBwnLYS4tDi427srBcVEhsDkhsq3ZgJgZQ30nCY7EqqE5zuGw9baCl9sPiM7FDJVgU0AKxupU1Ml9TZWovcOkQEwuaFbnV4PHP0d6DUdcPGRHQ1VgqezPV7sHKFsqHkhPUd2OGSK7J11iwMkJjfH0o6hoQ8XKJDhMLmhsgqygZWjgfDOQPRjsqOhKniuQzi8nO3x4VpdXQPRLZROxXKWg6flpuFK9hU08m0k5fXJMjC5obI2z9TV24giYg4ZmyVne1uM7lEPf8VextFLmbLDIVOtu0mJ032YkTBqI0T5RBn9tclyMLmhf4kVFDGf65Z9++hW3pB5eqRFDdT2c8H7a3Qt7oluSW60GuDKIaO/9LHUY/B08ESIa4jRX5ssB5Mb0tEUA8tHAH6RQLsRsqOharK1scbY3vWx7XQqtp1OkR0OmRq/+oCds5S6GzFy08inEYuJyaCY3JDOngXA5VjdFgu29rKjIT3o2TAALWp6YebqE9Bo5G+USCbExhYIamr05EZ0Jj6aepT1NmRwTG4IyLwIbHwPaDUUCG0tOxrSE/HJ+K2+9XHschaWHbosOxwyNRI6FSflJCEtL00ZuSEyJCY3lk6rBVa9Cdi7At0my46G9KxFTW9lBEesnMovKpYdDplacnMtEchONWq9jRDly2JiMiwmN5YubjlwchXQdxbg6CE7GjKAsb0jcflaLn6MOS87FDK1omLBiEvCj6YdhZ+TH/yd/Y32mmSZmNxYsrxM3ahNZF+gwUDZ0ZCB1PF3w6BWofhs0xlk5cnbT4hMjGdNwNnHqFNTYuSG9TZkDExuLNmGd4GCG0DfD9jTRuVGda+HvMJizNt8VnYoZCrE77wYvTFSciOKicVKKfa3IWNgcmOpLuwB9n4DdJ0EeNSQHQ0ZWIC7I57vEIFvdyTgamae7HDIpDoV79fV3hnYhesXkFWQxZEbMgomN5aouBBYPhIIbga0fkF2NGQkwzpHwMnOBrPXnZIdCpkKMXKTmw5cO2+0zsRcKUXGwOTGEu38FEg5qetpY20jOxoyEndHO7zWtS5+238BJ69elx0OmcqKKcEIU1Oiv43oSuzl6GXw1yJicmNp0s4Cm98H2r4CBDWRHQ0Z2ZP31ESYtzOmrjyu1ECQhXPx1RUWG2HFVElnYiJjYHJjScSb2YrRgFsA0GW87GhIAntba7zVt4GyLcOmk8mywyFTYISi4mJNMY6nHWd/GzIaJjeW5PBiIGEL0G82YO8iOxqSpEfDALSr7YOpK+JQWKyRHQ6ZwtSU2HqluMhgL3Eu6xxyi3I5ckNGw+TGUmSnAWsmAFEPA3W7y46GJG/LMKl/Q5xLy2ZjP9KN3BTlAiknDFpvYwUrNPRpaLDXILoZkxtLsfZtQFsM9J4hOxIyAQ2C3DGoVRg+2XAaGdkFssMhmYKiAStrg05NHUk9gloeteAqtnkhMgImN5YgfjNw6Geg51TAlW3PSWdMj3oo1miVBIcsmJii9m9o0OTmUMohNPVrarDnJ/ovJjdqV5irKyKu2R5o9pTsaMiE+Lk54NWudfDjrvM4lcSl4RZN2SHcMCumcgpzcCrjFKL9og3y/ETlYXKjdls/BDIvAv3ncIsFusWz7WspS8P/b9kxLg239E7FyceBghyDTElptBo09efIDRkPkxs1SzoO7JgDdHwd8KsnOxoyQQ62NpjcvyF2nk3D6qNXZYdDMouKRU3e1cN6f+rY5Fi42bsh3CNc789NdDtMbtRKo9FtseAdAXQYLTsaMmH31vdHt/r+mLYyDrkFxbLDIRn8GwC2TgapuxH1Nk38msBaFC0TGQl/2tRq/7fAxT266ShbB9nRkIkTS8NTrufjy81nZIdCMtjY6VZN6Tm5EdNRLCYmGZjcqFHWFWD9O0DzIUCt9rKjITNQy9cFL3QKx7yt8UhM03/dBVlmp+JzmeeUncBZTEzGxuRGjdaM043W9HhXdiRkRl65tw58XOzx7orjskMhWSumMs4BOel6e0oxaiOmoxr7NtbbcxJVBJMbtTm5Gjj+F9B7JuDE3Xep4pztbTGxXwOsj0vChrgk2eGQtB3C9bckPDYlFnU967J5Hxkdkxs1yb8BrHwDqNMdiHpIdjRkhvo1DkLHur6YsuwYi4stjVe47gORHqemxEopTkmRDExu1GTTNCA3Hej3EXvaUJX3nXr3vigkX8/H55tYXGxRxN8MPdbdZOZnIj4znv1tSAomN2ohhpJ3zwO6TAC8asmOhsxYuK8Lhneuja+2nsXZlBuywyFjN/MTyY0eGjoeTtH1zOFKKZKByY0aFBcBy0cAAY2Ae16WHQ2pwPAutRHs6YRJfx5l52JLIkZuclKBzAt6qbfxdvRGDbcaegmNqDKY3KjBri+ApGPAgE8AG1vZ0ZAKONrZ4J2BjZTOxcsOXZYdDhm9qLj6U1OHkg8p9TZiqpPI2JjcmLuM88DmGUDrF3Wfuoj0pEukv1Jg/N6KOGTmFMoOh4zB1R/wCKt2clOkKVL2lGIxMcnC5MaciemClWN0Kxy6TpQdDanQlAENkV9UjBmr42SHQma0Q/iZa2eQU5TDYmKShsmNOTv6B3BmvW51lIOb7GhIhfzdHTG+T30s2nsBu+LTZIdDxkpuLscCmqq3AjiYfBC21rZo5NNIr6ERVRSTG3OVmwGsGQ80GAhE9pEdDanY4FZhaFXLC28tOYK8Qva+UT0xvV2YDaScrPJT7L26V+lK7GjrqNfQiCqKyY25WjcZKMoH+sySHQmpnLW1FWY82BgXMnLY+8YSBDUFxA7eVay7Eavr9iftR8uAlnoPjaiimNyYo/M7gQM/AN2nAO5BsqMhC1DH3w0vd6mDLzefxcmr12WHQ4bk4Ar41a9yciMa96XnpaNlIJMbkofJjbkRozXLRwI1WgMtnpMdDVmQl++tjZo+zhj7x2EUa9j7Rv1FxfurPCVla2XL5n0klUkkN59//jlq1aoFR0dHtGnTBnv27LntsQsWLEDHjh3h5eWlXLp3737H41Vn+2wgPV7X08baJP75yEI42Npg1sNNcPjiNXy7PUF2OGToTsWid1ZhbpWSmyjfKDjbORskNKKKkP7uuHjxYowZMwZTpkzBgQMHEB0djV69eiE5Obnc4zdv3ozBgwdj06ZNiImJQWhoKHr27IlLly5B9VJOAds+AtqPBAIayo6GLFCLmt54rn04Plx7EvHcmkHdRcXaYuDqkUrX2+xL2scpKZLOSiu5t7oYqWnVqhU+++wz5bpGo1ESltdeew3jx4+/6+OLi4uVERzx+CFDhtz1+KysLHh4eCAzMxPu7u4wGxoN8L/+wPUrwPCdgJ2T7IjIQondwvt8shV+bg5YPKytUnBMKlNcCEwPAXq8A9wzvMIPi78Wj/v+ug9fdf8K7ULawRSJ9xjxvkGmydbWVi9draX26i8oKMD+/fsxYcKE0tusra2VqSYxKlMROTk5KCwshLe3d7n35+fnK5ebkxuzFPsTcH4HMOQvJjYklZO9Dd5/qAkGzd+F/8Wcw7Ptw2WHRPpmYwcERVe67qa03sYEm/eJz/HiQ614zyDTJRIbPz8/Jckx2+QmNTVVyaADAgLK3C6unzhxokLPMW7cOAQHBysJUXlmzJiBd955B2btRjKwdhIQPRiI6CI7GiK0ifDBkLY1MWvNSXSt74+aPi6yQyJDTE2d/rtSDxFTUg19G5pkvU1JYiNG7O3t7bnnlQkSCWhGRgauXbsGHx+fav0bmfUuizNnzsSiRYuUOhxRjFweMSokanpuHrkR015mZc0EXd+JntNkR0JUalzv+th4Ihlv/nYYvwy7BzacnlLfiqndXwI56YBz+SPj/31jEiM399e5H6Y4FVWS2Li6usoOh+5A/BuJBEf8m9nY2MAsC4p9fX2V4JOSksrcLq4HBgbe8bEffvihktysXbsWTZo0ue1xDg4Oysm6+WJWTq8Hjv4O9JoOuPjIjoaolIuDLT58JBp7z6dz9ZQalWzEe/lghQ5PyEpAWl4aWgW2gqkpqbERIzZk2koSGpHcVIfU5Eb8oLVo0QIbNmwovU38D4nrbdu2ve3jZs2ahffeew9r1qxBy5YqrsovyAZWjgbCOwPRj8mOhugW90T44PkO4fjg75Ns7qc23hGAo0eFN9Hcd3UfbKxsTLLepgSnokyfvv6NpC8FF1NGonfN//73P8TFxWH48OHIzs7Gs88+q9wvVkDdXHD8/vvvY9KkSfj222+V3jhXr15VLjduqHBZ6uaZunqb/rPFv7jsaIjK9XrPSKW535hfY1FQVL1PW2RCxN8cMXpTwaJikdyIjTJd7Fh/RfJJT24GDRqkTDFNnjwZTZs2RWxsrDIiU1JknJiYiCtXrpQe/+WXXyqrrB5++GEEBQWVXsRzqMqVw0DM50CnNwGf2rKjIbotRzsbfPxoU2Xk5rONp2WHQ/pUktzcpWOIUm+TtJf9bQygS5cuGDVqlOwwzI5JFBS/+uqryqU8olj4ZufOnYPqaYqB5SMAv0ig3QjZ0RDdVeMaHhjRrS4+2XAaXer7o3mYl+yQSF+dird+AGReBDxvvxDjXNY5pOammmS9DVkm6SM3VI49C4DLsbotFmxZAEfm4eUutdE4xAOjFsXiel6h7HBIXyumhMt3rrvZeXknbK1t0dz/n+OJJGNyY2rEJ6SN7wGthgKhrWVHQ1RhtjbW+OSxpki7kY8py47JDof0wS0QcK9x17obkdyIxMYU+9uoiVgiLepQRVd+Z2dn9OnTB6dPny6dGhTN737//ffS40WphyjbKLF9+3ZlBbElNDI0iWkp+oeY1171JmDvCnSbLDsaokoTzfzevS8Kr/92CF0i/TEwOlh2SKSXHcJvP3JTUFyg9Ld5scmLMMetRM5K2COttp+r0um7sp555hklmVm2bJnS1kQ0se3bty+OHz8OOzs7dOrUSSnlePjhh5VESCzScXJyUpri1q9fH1u2bFG2OxKJkdoxuTElccuBk6uAR3/QLcEkMkMPNg/B5lMpmLj0CJqHeaKGl/r/kKq+qFjU3YhaQOtb35APJh9EblEu2oe0h7kRiU3/uduN/rorXuuAqJDK/Y0vSWp27NiBdu10+3YtXLhQaUr7559/4pFHHlGKj7/66ivlvq1bt6JZs2ZKzziR8IjkRnzt3LkzLAGTG1ORl6kbtYnsCzQYKDsaomr1qZh6fxT6frINoxfH4pcX7lGmrMiMR24KbgCppwD/BrfcvePyDvg4+qCeVz2YGzGCIhINGa9bWWIURuy3JDabLiG2KIiMjFTuE0TiMnLkSKSkpCijNCLZKUluhg4dip07d2Ls2LGwBExuTMWGd3V/QPp+wJ42ZPY8nOww57GmGPRVjLKCSvTCITMVJJryWemmpspLbi7tUEZtrMUWMWZGTA1VdgTFlDVu3FjZRHrLli3KZdq0aUpyI/rD7d27V9lkumTUR+3M76dRjS7sAfZ+A3R9G/CoITsaIr1oVcsbY3rUw2ebzmD76VTZ4VBVObrr2lKUU1SckpOCUxmn0C7YMt4wZWrQoAGKioqwe/fu0tvS0tJw8uRJNGzYsHTUtGPHjvjrr79w7NgxdOjQQdmeKD8/X5muEh39XVwso8kikxvZiguB5SOB4KZA62GyoyHSq5e71EGHOr4YtfggkrPyZIdDVXWbTsVilZQVrNA2+Pbb5ZB+1K1bF/fddx9eeOEFZdXToUOH8OSTTyIkJES5vYSYivrll1+UlVJik1Bra2ul0FjU51hKvY3A5Ea2nZ8CKSeBAZ+WW6xHZM6sra0we1BTWFtZYeSiWBRr7tzplky47ibpKFCYd0u9TQOfBvB2vPuu4VR93333nbIfY//+/ZX9F8Xy71WrVikrpUqIBKa4uFhJckqI7/97m9pZacXZsSBZWVnw8PBAZmam/B3C084CX7bTjdj0fE9uLEQGFHM2DU98vUvpYjyqu/kVnlo8UW+z4F5g6HogVNeFuFhTjC6/dsEj9R7BiOam3Uld1JqIIlvRB+bmRIDU+2/FkRtZRE65YjTg6g90GS87GiKDalvbR0lqRHHx1lMpssOhygqIAmzsy3QqjkuPw7X8a2a5BJzUj8mNLIcXAwlbgH6zAXvLKPAiy/bqvXXQuZ4fRiw6iAvp6u+QqipiG5jAJmXqbsQqKbEDeBO/JlJDIyoPkxsZstOANROAqIeBut1lR0NktPqbOYOaws3RFsMX7kdeYbHskKgaRcWimLhNYBvYWXOah0wPkxsZ1r4NaIuB3jNkR0JkVJ7O9vjyiRY4nXQDU/7i/lNml9yknQFyryGrIAuHUg5xSopMFpMbY4vfAhz6Geg5VVdvQ2RhRNO0aQ80xuJ9F/DLnkTZ4VCldwg/iK0Xt6JYW4xONTrJjoqoXOxQbEyFucCKUUDN9kCzp2RHQyTNwy1q4GBiBib/dRR1/V3RshaXEps879qAg4cyNbVRcxlRPlEIdAmUHRVRuThyY0xbPwQyLwL953CLBbJ4UwY0QrNQL7z0035cupYrOxy6G2trIKQZ8i7uw/ZL29GtZjfZERHdFpMbY0k6DuyYA3R8HfBjnw8ie1trfPlkczjY2mDYD/uQW8ACY5MX0gK7UmOVXcC7hnWVHQ3RbTG5MQaNRjcd5R0BdBgtOxoik+Hj6oAFQ1oiPiUbb/x+SOm4SiYsuDk2WOcj3DUUER4RsqMhui0mN8aw/zvgwm7ddJStg+xoiExKw2B3fPxoNFYevoK5G8/IDofuoCioKTY7O6GrGxMbMm1Mbgwt6wqw/v+A5kOAWlw2SVSePo2DlB3EP153Cn/FXpIdDt3GwbwruGZjg25FfOsg08bVUoa2ZpxutKbHu7IjITJpr3Wtg3Np2Xjzt8MI9nRCK66gMjkbEzfCH3ZolBIvOxQyoOLiYlhZWSk7ipsr843cHJxcDRz/C+g9E3Dykh0NkUkTf0xnPtgEzWt6KgXGCanZskOim4h6qA2JG9DVvTasL8XqagnJKNasWYMOHTrA09MTPj4+yq7gZ8+eVe5r164dxo0bV+b4lJQUZdPJrVu3Ktfz8/PxxhtvICQkBC4uLmjTpg02b95cevz333+vPPeyZcvQsGFDODg4IDExEXv37kWPHj3g6+urbDgtdhw/cODf/cWEEydOKLE5Ojoqj12/fr3yu/znn3+WHnPhwgU8+uijymt4e3vjvvvuw7lz5wx6zjhyYyj5N4CVbwB1ugNRD8mOhshsVlB99WRLPPDlDjz3/V4sGd4OXi72ssMi8SaWfgJXsq+ga9SDwKG1QNppwC8SZq0gB0g9ZfzX9a0H2DtX+PDs7GyMGTMGTZo0wY0bNzB58mQ88MADiI2NxRNPPIFZs2Zh5syZSlIhLF68GMHBwejYsaNy/dVXX8Xx48exaNEi5falS5eid+/eOHLkCOrWrasck5OTg/fffx9ff/21kkD5+/sjPj4eTz/9NObOnasktx999BH69u2L06dPw83NTRnhuf/++xEWFobdu3fj+vXreP3112/Z5btXr15o27Yttm3bBltbW0ydOlV5/cOHD8Pe3jC/31ZaC1uekJWVpWSgmZmZcHd3N9wLib2j9n0HvLIL8KpluNchUqHEtBw88MUO1PRxxsLn74GTvY3skCzeZwc/wy8nfsHm+5bBblYEcP88oOlgmAPxBitGM/z8/JQRjVKXY4H5nY0f0LAtQHDTKj88NTVV+X8RyUlAQICSsGzcuLE0mWnXrh06deqkJDxiBCYiIkL5Ko4r0b17d7Ru3RrTp09XRm6effZZJVmKjo6+7etqNBpl9OXnn39WRo/EiNKAAQOUkZnAQF1DRzFyI0Z7RAIlEp+ffvpJSWbi4uJKk6+CggLlecToTs+ePSv2b1VJHLkxhEsHgN3zgO7vMLEhqoIwH2d8+0wrDF6wCy8v3I/5Q1rCzoaz6DKJKanONTrDztlHN/IgNtE0k+TmtsT/h0g0ZLxuJYiREjFaI0ZHRGIjkgxBJCxRUVFKgrBw4UIluUlISEBMTAy++uor5RiRAIkRlnr1yr6mmKoSIzQlxAiKGBm6WVJSEt5++21lCis5OVl5HjHCI15XOHnyJEJDQ0sTG0EkTDc7dOgQzpw5o4z03CwvL690as0QmNzoW3ERsHwEENAIuOdl2dEQma3oUE989VQLZXpq3B+H8dEj0aWf/Mi4TqafxJlrZzCy+chydwg3W2JqqBojKMYiRkdq1qyJBQsWKKMvIrkRSY0YARHE1NSIESOU6SMxqtK4cWPlIohpLBsbG+zfv1/5ejNXV9fS752cnG75/RJTUmlpafjkk0+U1xe1OGJ6qeR1K0K8fosWLZTk67/E6IyhMLnRt91fAknHgOfXAzY8vUTV0bGuHz58JBojF8XCz9UBE/o2kB2SRVoRvwJeDl7/7gIukpsjvwNF+ezdZWAiuRAjJCKxKZl22r59e5ljRIHusGHDlGkikdwMGTKk9L5mzZopIy5i5KXk8RW1Y8cOfPHFF0qdjSCmn8TIUYnIyEjlNjHCI6bHBFGEfLPmzZsrNUCihsegpSD/wXFefco4D2yaDrR+UffLT0TVdl/TEEzu3xBfbY3Hl5sNN4xN5SvWFGNV/Cr0Ce8DO+t/aiCCmwOaQuDqUdnhqZ6Xl5cyfTR//nxlekfU1oji4puJFVCivmXSpElKbcvgwf9OF4rpKDGyIxKeJUuWKNNWe/bswYwZM7By5co7vrYoNv7xxx+V5xRTYuJ5xAhPCVFbU7t2bWWERxQHi2RITGMJJaNA4jFitZVIwERBsXh9Mc0lRpouXrwIQ2Fyoy+iLnvlGN2S764TZUdDpCrPdQjHiG518f6aE/h+R4LscCzK7qu7kZybjAG1B/x7Y2AUIBKdy2WXBZP+iV4zYpWTmFYSU1GjR4/GBx98cMtxIokQ9S1idCYsLKzMfd99952S3IiVTGK0RSRCYoTlv8f91zfffIOMjAxl9OWpp55SEhIxAlNCTHOJomAx9dSqVSs8//zzmDhR9/4nloYLzs7OypJ08VoPPvggGjRogKFDhyo1N4YcyeFqKX0RQ7R/DAUGLwIi++jveYlIIf5UzVh9AvO3xmPmg43xWOs7/2Em/ZiwbQKOph7FsvuXla3JmH+vbin4A/Ng6vS1AofuTozeiL43YpRJjOpUFldLmZLcDGDNeKDBQCY2RAYi3lgn9Kmv7B4+YekRONrZ4P5mIbLDUrWcwhxlldTzjZ+/tZhbTL0nSFhpRCZl6dKlSmGymMISCc3IkSPRvn37KiU2+sTkRh/WTdEV1vWZJTsSIlUTb7DvDGyEvMJijPk1FtbWVhgY/W/vDtIvkdjkFuWiX0S/W+8Uyc3eBUBeJuDoISM8MgHXr19XOiSL5eGitkb0zxHN/mRjclNd53cCB/4H9PsIcA+SHQ2R6omEZuZDTVCs0WLUooMo1mjwQLMassNSpeVnl6NFQAuEuJYzQhbSXPf18kEgoovRYyPTMGTIkDKrs0wFC4qrQ4zWLB8J1GgNtHhOdjREFsPG2gofPBKNh5rXwJhfD+G3fRdkh6Q6yTnJSjHxgIibColv5lMXsHfTNS0lMjEcuamO7bOB9HjgxW3i46TsaIgsLsF5/6EmsLWxxtg/DisjOSwy1h+x/NvWyhY9avUo/wDxNy+kmTqa+ZHqMLmpqpRTwLaPgPYjgYCGsqMhstgpqmn3R8HW2grjlxzBjfwiPN8xQnZYqliZtvTMUtwbdi/c7e+wqlTU3RxabMzQiCqEww1VIfb1WDEK8KgBdHpTdjREsPQE5937GmF4l9qYujIOs9acUN6cqep2XdmF+Mx4DIocdOcDRXJz/TKQddlYoRFVCEduqiL2J+D8DmDIX4Ddv90aiUjeKqpxvevD29ke01bFISOnEFPvj1Kmrqjyfo77GfW86qFlQMs7Hyg6FQui7sadq9bIdHDkprJuJANrJwHRg7lCgMjEvNApArMeboLFexPx6s8HlCXjVDkXsi5gy8UteKLBE3ffqFQkNK6B7FRMJofJTWWtmQBYWQM9p8mOhIjK8WjLUMx7sgU2nUzG4wt2Ie1GvuyQzMovJ3+Bu4M7+obrNku8I5H8qGWHcBPVpUsXjBo16rb3iwRUbIFQUZs3b1Yec+3aNagZk5vKOL0eOPo70Gs64OIjOxoiuo2ejQKxaFhbJKbn4IEvduJsyg3ZIZlNR+Klp5fi4boPw9FWtzfQXYl+N5cO6moRyeiuXLmCPn3YGf+/mNxUVEE2sHI0EN4ZiH5MdjREdBdNQz2x9OX2sLe1xoNf7MSu+DTZIZm8ZWeXKR2J71pIfDMxcpOfCaRzx3YZAgMD4eDgIDsMk8PkpqI2z9TV2/SfrRuKJSKTF+rtjD+Gt0OjYHc8+fVu/BBzjiupbkOj1eDnEz+ja1hXBLlWott6cDPdV05NGYxGo8HYsWPh7e2tJDP/93//d9tpqZ07d6Jp06bKrtwtW7ZU7hPHxMbGlnlOscu4uF/s2t2uXTucPHkSasLVUhVx5TAQ8zlw71uAj9zNwIiocjyc7PC/51pj2so4TP7rGI5eysR790fBwdZGdmgmZdflXUjITMCUtlMq90AnT8Cnjm7FlJmNaotRKvH/bGzhHuFwsq34Stv//e9/GDNmDHbv3o2YmBg888wzyuaUPXqUbbCYlZWFAQMGoG/fvvj5559x/vz529brTJw4UdkDSuy+/dJLL+G5555TdvRWCyY3d6Mp1m2x4BcJtBshOxoiqgI7G2v838BGiArxwFtLj+BU0g2l6DjQo4J1JSonRrO+OfoNGng3QHP/f5Z3V4aZFhWLxGbQikpMwenJ4v6L0dCn4s1fmzRpgilTdEmn2H37s88+w4YNG25JbkRCI0ZpFixYoIzcNGzYEJcuXcILL7xwy3NOmzYNnTt3Vr4fP348+vXrh7y8POVxasDk5m72LNBtDDd0LWBrLzsaIqqGh1vUQF1/V7z00370/XQbPn40Gl0i/WHpYq7EYM/VPZjbde7dl3/fLrk5thQoKjCrv5NiBEUkGjJetzJEcnOzoKAgJCcn33KcmFoSxzrelKC0bt36rs8pnk8QzxkWpo4tTJjc3EnmRWDje0DL54DQ8n9AiMi8RId6YsVrHfD6b4fwzHd78VLn2ni9Zz1ldMdSR20+PfApov2i0bmG7pN8lZKb4gIg6ei/u4WbATE1VJkRFFns7OzKXBcJqKjD0ddzWv2T0Fb3OU2JZf42V4QoOlz1JmDvCnSv5Bw0EZk0H1cHfPt0K0zoUx8LtsXjsfm7cDEjB5ZoQ+IGHEs7hpHNR1Zt1EYIiAKsbc1yakpNIiMjceTIEeTn/9vbae/evbBETG5uJ245cHIV0HcW4OghOxoiMsCeVC92ro1fX2yLq5l56D1nG37dd8GiVlMVa4ox9+BctA1qi1aBrar+RHaOugRHTOGTNI8//rgy+jJs2DDExcXh77//xocffqjcV+XE1UwxuSlPXiaweiwQ2RdoMFB2NERkQC1qemH1qI7oHRWIsb8fxgs/7EPy9TxYghXxK5QNMsWoTbWZaVGxmri7u2P58uXKsm+xHFysiJo8ebJyn1oKhSvKSmtJH1P+WSrn4eGBzMxM5QehXCtfBw4tAl7Zrdv5m4gswrrjSZiw5DCKNVpMHtAQ9zcNUe0n3oLiAgxYOkCpOZl97+zqP+HBhcBfrwDjEwHH2/xtlaSwsBApKSnKsuf/1q+o3cKFC/Hss88q73lOTqa/0bO+/q04cvNfF/YAe78Bur7NxIbIwvRoGIC/R3VCh7p+GL34EJ78ZjcSUrOh1p2/r+ZcxavNXtXPE4qRG2iBK2WbxZFx/fDDD9i+fTsSEhKUBn7jxo3Do48+ahaJjT4xublZcaGup01wU6D1MNnREJGkYuO5g5vh+2dbKXtT9ZqzFZ9uOK2qHcbFVJSotXm8/uOo7amnxqS+dXULMEQzP5Lm6tWrePLJJ9GgQQOMHj0ajzzyCObPnw9Lw2mpm237CNg4DRi2GQgq21eAiCxPbkEx5m48jflb4xHg7ojxfeqjf5Mgs56qKtIUYcjqIbhecB2/Dfit4htkVsT3/QEnL2DQjzAlljwtZW44LaVvaWeBLbOAtq8wsSEihZO9Dcb2ro+1ozuhQZA7XvvlIB76cicOJGbAXH1/7Htl6ffUDlP1m9iU7hDOkRuSj8mNIAavVowGXP2BLuNlR0NEJibCzxVfP90SPz/fBrmFGmWX8aHf71X2qTInpzNO44vYL/BMo2eUpn16J+pusi4C16/CFFnYRIVZ0te/EZMb4fBiIGEL0G82YO8iOxoiMlHt6vgq3Y0/eaypUmjcf+52Zem4OSQ5hZpCTNw+ETXda+KVpq8Y5kWC/+lObGKjNzY2uk1SCwoKZIdCd1FcrKtts7auXnrC7Rey04C/3wKiHgbqdpcdDRGZOBtrK9zXNAT9Ggdh2aHL+GTDaSXJaVfbB893DEeXev5Kg0BTotFqMGXHFGXk5qe+P8HexkD7P4kVpi7+wOUDQP2+MBXijdLZ2VmpuRTs7e3Num5KzaM2WVlZyr8Pk5vqWvs2oCkCes+QHQkRmRFbG2s82LwGBkYHY82xq1iwLQHPfb8PEX4ueLptLaVHjoeznUm8YUzfPR0rE1bi/U7vo5FvI8O9mEgYTLSZn1hIIpQkOGSaRNLp4+NT7eTTsldLpR4EfhgIDJwLNB8iOzQiMmPiT+n+8xn4elsC1sUlKSM8vRsFYlCrULSN8JE2mjNn/xx8c/QbvNvuXTxQ9wHDv+CWD4CYucC487pkx8SI7QlKpj7I9Nja2uplVM1yR24Kc4EVo4Ca7YFmT8mOhojMnPiD3LKWt3IR2zcsOXAJv+69oExdBbg7KIlOn8ZBaFXLW0l8jGHB4QVKYjO21VjjJDYlK6bEFjbp8YCPnnro6JGY7qjulAeZPpP4F/78889Rq1YtZe+LNm3aYM+ePXc8/rfffkP9+vWV4xs3boxVq1ZV/kV3fApkXgT6zzHJTxdEZL783RzxUufa2PB6Z/wxvC36NQ7G2uNJyu7jbaZvwJhfY7HkwEUkZxlmD6uk7CSM2jQKnx78FC83fRlPNTTiB7jgZrqvJjg1RZZD+rTU4sWLMWTIEMybN09JbObMmaMkLydPnoS/v/8tx+/cuROdOnXCjBkz0L9/f/z88894//33ceDAAURFRVV8WmqCF9x7juXSbyIyCo1Gi0MXryn1OdtOpeL4FV3tR11/VzQL80R0qCeia3giMtANdjbWVS4c/vXkr5hzYA4cbRwxoc0E9KrVC0b3aTOgbi+gz0zjvzaRKSQ3IqFp1aoVPvvss9L50NDQULz22msYP/7WxGPQoEHIzs7GihUrSm+75557lB1QRYJU4eRmVjO4j44BbB30/H9ERHR3qTfysfNsGmLOpiL2QiZOJV1XNuy0s7FCmLczwn1dleLkUG9n+Lnaw9fVQbl4ONnB3tYaDrbWyvTW5ezLOJJ6BEdTjiLmSgxOZZzCQ3UfwugWo+HhoCuiNbo/ngcyzgPPr5Pz+mTxpNbciJ4D+/fvx4QJE0pvE3Oh3bt3R0xMTLmPEbePGTOmzG29evVSNggrT35+vnIpIQqJhXZ2+bD5trWe/k+IiKrBCXCtpesnqoUWSVrgag6w87wW2nN3eJyVmFXXFcdqCz2hyauB4syn8eORcPy4ZD1keczaFuNsdqNgvLe0GEid7N86Bzc3t7sWHUtNblJTU5Wq9YCAgDK3i+snTpy47aZg5R0vbi+PmL565513brn92Ojj1YqdiMg0/SY7AEz/50Kkd+/fZm9IS1stJUaFbh7puXbtGmrWrInExMTSvgd096k8MVV44cKFu/5AkQ7PWeXxnFUez1nl8ZyZ/zkTIzd3IzW58fX1VdpiJyUllbldXA8MDCz3MeL2yhzv4OCgXP5LJDam8I9kTsT54jmrHJ6zyuM5qzyes8rjOVP3OZO6FFy0WG7RogU2bNhQepsoKBbX27ZtW+5jxO03Hy+sW7futscTERGRZZE+LSWmjJ5++mm0bNkSrVu3VpaCi9VQzz77rHK/WCYeEhKi1M4II0eOROfOnfHRRx+hX79+WLRoEfbt24f58+dL/j8hIiIiUyA9uRFLu1NSUjB58mSlKFgs6V6zZk1p0bCojbm5m2S7du2U3jZvv/023nrrLdStW1dZKVWRHjeCmKKaMmVKuVNVVD6es8rjOas8nrPK4zmrPJ4zyzhn0vvcEBEREalu+wUiIiIifWFyQ0RERKrC5IaIiIhUhckNERERqYrFJTeff/45atWqBUdHR2XTzj179sgOySSIpfZiA1PR+VHsxn7//fcrO7PfLC8vD6+88gp8fHzg6uqKhx566JaGipZs5syZyn4no0aNKr2N5+xWly5dwpNPPqmcEycnJzRu3Fhp51BCrHEQqyeDgoKU+8Vec6dPn4alElvUTJo0CeHh4cr5qF27Nt577z3lPJWw9HO2detWDBgwAMHBwcrv4H/3GqzI+UlPT8cTTzyhNKnz9PTE0KFDcePGDVjiOSssLMS4ceOU300XFxflGNGW5fLly2ZzziwquVm8eLHSV0csaTtw4ACio6OVTTeTk5Nh6bZs2aK8Ce/atUtpiih+uHv27Kn0HCoxevRoLF++HL/99ptyvPhBf/DBB6XGbSr27t2Lr776Ck2aNClzO89ZWRkZGWjfvj3s7OywevVqHD9+XOlZ5eXlVXrMrFmz8Omnn2LevHnYvXu38sdV/J6KRNESvf/++/jyyy/x2WefIS4uTrkuztHcuXNLj7H0cyb+Tom/5+LDa3kqcn7Em/SxY8eUv38rVqxQ3vyHDRsGSzxnOTk5ynukSKrF1yVLligfdgcOHFjmOJM+Z1oL0rp1a+0rr7xSer24uFgbHBysnTFjhtS4TFFycrL4WKjdsmWLcv3atWtaOzs77W+//VZ6TFxcnHJMTEyM1pJdv35dW7duXe26deu0nTt31o4cOVK5nefsVuPGjdN26NDhtvdrNBptYGCg9oMPPii9TZxHBwcH7S+//KK1RP369dM+99xzZW578MEHtU888YTyPc9ZWeL3a+nSpaXXK3J+jh8/rjxu7969pcesXr1aa2Vlpb106ZLW0s5Zefbs2aMcd/78ebM4ZxYzclNQUID9+/crw5ElRHNAcT0mJkZqbKZI7LoqeHt7K1/FuROjOTefv/r16yMsLMziz58Y8RLdsm8+NwLP2a2WLVumdCN/5JFHlOnPZs2aYcGCBaX3JyQkKM08bz5nYh84MYVsqedMNC4VW86cOnVKuX7o0CFs374dffr0Ua7znN1ZRc6P+CqmVcTPZglxvHiPECM9BOU9QUxfifNkDudMeodiY0lNTVXmrks6H5cQ10+cOCEtLlMk9vcSdSNi+qCk87P44yD2Aiv5wb75/In7LJXY/kMM24ppqf/iObtVfHy8MsUipodFh3Fx3kaMGKGcJ7ENS8l5Ke/31FLP2fjx45VdmUViLDYaFn/Hpk2bpkwJCDxnd1aR8yO+imT7Zra2tsqHO55DKNN3ogZn8ODBpRtnmvo5s5jkhio3EnH06FHl0yHd3oULF5S9zsR8syhQp4olzuKT3vTp05XrYuRG/KyJWgiR3NCtfv31VyxcuFDZdqZRo0aIjY1VPnyIIk+eMzK0wsJCPProo0pRtvhgYi4sZlrK19dX+dTz35Uq4npgYKC0uEzNq6++qhSGbdq0CTVq1Ci9XZwjMbV37dq1Msdb8vkT006iGL158+bKJxZxEUXDonBRfC8+GfKclSVWqzRs2LDMbQ0aNFD2kBNKzgt/T//15ptvKqM3jz32mLJ65amnnlIK1Us2E+Y5u7OKnB/x9b8LS4qKipTVQJZ8Dgv/SWzOnz+vfIgrGbUxh3NmMcmNGPZu0aKFMnd986dIcb1t27awdCIrF4nN0qVLsXHjRmXZ6c3EuRMrXG4+f6J6XrwpWer569atG44cOaJ8ki65iFEJMV1Q8j3PWVliqvO/LQZELUnNmjWV78XPnfjDePM5E1MyYg7fUs+ZWLly8+bBgvigJv5+CTxnd1aR8yO+ig8h4gNLCfF3UJxjUZtjyYnN6dOnsX79eqV1w81M/pxpLciiRYuUCvnvv/9eqfQeNmyY1tPTU3v16lWtpRs+fLjWw8NDu3nzZu2VK1dKLzk5OaXHvPTSS9qwsDDtxo0btfv27dO2bdtWudC/bl4tJfCc3briwtbWVjtt2jTt6dOntQsXLtQ6Oztrf/rpp9JjZs6cqfxe/vXXX9rDhw9r77vvPm14eLg2NzdXa4mefvppbUhIiHbFihXahIQE7ZIlS7S+vr7asWPHlh5j6edMrFg8ePCgchFvax9//LHyfcnKnoqcn969e2ubNWum3b17t3b79u3KCsjBgwdrLfGcFRQUaAcOHKitUaOGNjY2tsx7Qn5+vlmcM4tKboS5c+cqbzb29vbK0vBdu3bJDskkiB/u8i7fffdd6THiD8HLL7+s9fLyUt6QHnjgAeWHnW6f3PCc3Wr58uXaqKgo5YNG/fr1tfPnzy9zv1i6O2nSJG1AQIByTLdu3bQnT57UWqqsrCzlZ0r83XJ0dNRGRERoJ06cWOZNxtLP2aZNm8r9+yUSw4qen7S0NOWN2dXVVevu7q599tlnlQTAEs9ZQkLCbd8TxOPM4ZxZif/IHj0iIiIi0heLqbkhIiIiy8DkhoiIiFSFyQ0RERGpCpMbIiIiUhUmN0RERKQqTG6IiIhIVZjcEBERkaowuSEiIiJVYXJDRGbn//7v/9C0aVPZYRCRiWJyQ0RERKrC5IaIiIhUhckNERmcRqPBrFmzUKdOHTg4OCAsLAzTpk1T7hs3bhzq1asHZ2dnREREYNKkSSgsLCzz+JkzZyIgIABubm4YOnQo8vLybnmNr7/+Gg0aNICjoyPq16+PL774ovS+c+fOwcrKCkuWLMG9996rvFZ0dDRiYmLKPMeOHTvQpUsX5X4vLy/06tULGRkZpf8PM2bMQHh4OJycnJTH//777wY6Y0RULbJ37iQi9Rs7dqyyM/r333+vPXPmjHbbtm3aBQsWKPe999572h07dig7ES9btkzZufn9998vfezixYuVnZy//vpr7YkTJ5Qdsd3c3LTR0dGlx/z000/aoKAg7R9//KGNj49Xvnp7eyuvJ5Tscix2IV+xYoWyI/TDDz+srVmzprawsFA55uDBg8rrDB8+XBsbG6s9evSodu7cudqUlBTl/qlTpyqPX7Nmjfbs2bPa7777Tjl+8+bNRj6bRHQ3TG6IyKCysrKUJKAkmbmbDz74QNuiRYvS623bttW+/PLLZY5p06ZNmeSmdu3a2p9//rnMMSJpEo+9ObkRCVKJY8eOKbfFxcUp1wcPHqxt3759uTHl5eVpnZ2dtTt37ixz+9ChQ5XHEZFpsa3euA8R0Z3FxcUhPz8f3bp1K/f+xYsX49NPP8XZs2dx48YNFBUVwd3dvczjX3rppTKPadu2LTZt2qR8n52drTxWTFe98MILpceI5/Hw8CjzuCZNmpR+HxQUpHxNTk5WprFiY2PxyCOPlBvjmTNnkJOTgx49epS5vaCgAM2aNavE2SAiY2ByQ0QGJepTbkfUvDzxxBN45513lPoWkYwsWrQIH330UYWfXyREwoIFC9CmTZsy99nY2JS5bmdnV/q9qMEpqaW5W5wlr7Fy5UqEhISUuU/UEBGRaWFBMREZVN26dZXEYcOGDbfct3PnTtSsWRMTJ05Ey5YtlWPPnz9f5hhRJLx79+4yt+3atav0e1FoHBwcjPj4eKVg+eaLKP6tKDGqU16MQsOGDZUkJjEx8ZbXCA0NrfBrEJFxcOSGiAxKrF4SK6LGjh0Le3t7tG/fHikpKTh27JiSzIiEQYzWtGrVShkZWbp0aZnHjxw5Es8884yS/IjHLly4UHmsWFlVQoz8jBgxQhn56d27tzINtm/fPmWl05gxYyoU54QJE9C4cWO8/PLLyjSYiFVMfYmpKl9fX7zxxhsYPXq0MtLToUMHZGZmKqurxBTa008/rffzRkTVILvoh4jUr7i4WFltJFYn2dnZacPCwrTTp09X7nvzzTe1Pj4+WldXV+2gQYO0s2fP1np4eJR5/LRp07S+vr7KMU8//bSy+urmgmJh4cKF2qZNm2rt7e2VlVmdOnXSLlmypExBsVgRVSIjI0O5bdOmTaW3iZVP7dq1UwqgPT09tb169VKOEzQajXbOnDnayMhI5f/Bz89PuX/Lli0GPXdEVHlW4j/VSY6IiIiITAlrboiIiEhVmNwQERGRqjC5ISIiIlVhckNERESqwuSGiIiIVIXJDREREakKkxsiIiJSFSY3REREpCpMboiIiEhVmNwQERGRqjC5ISIiIqjJ/wMpWw7jdExn4gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import skfuzzy as fuzz\n",
    "\n",
    "speed[\"slow\"] = fuzz.zmf(speed.universe, 20, 60)\n",
    "speed[\"average\"] = fuzz.trapmf(speed.universe, [10, 30, 60, 80])\n",
    "speed[\"fast\"] = fuzz.smf(speed.universe, 60, 80)\n",
    "speed.view()\n",
    "\n",
    "target[\"slow\"] = fuzz.zmf(target.universe, 20, 60)\n",
    "target[\"average\"] = fuzz.trapmf(target.universe, [10, 30, 60, 80])\n",
    "target[\"fast\"] = fuzz.smf(target.universe, 60, 80)\n",
    "target.view()\n",
    "\n",
    "cadence[\"low\"] = fuzz.zmf(cadence.universe, 0, 80)\n",
    "cadence[\"average\"] = fuzz.trapmf(cadence.universe, [0, 60, 70, 90])\n",
    "cadence[\"high\"] = fuzz.smf(cadence.universe, 80, 110)\n",
    "cadence.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Формирование и визуализация базы нечетких правил\n",
    "\n",
    "В случае ошибки необходимо в файле\n",
    "```\n",
    ".venv/lib/python3.13/site-packages/skfuzzy/control/visualization.py\n",
    "```\n",
    "удалить лишний отступ на 182 строке, должно быть:\n",
    "```python\n",
    "        if not matplotlib_present:\n",
    "            raise ImportError(\"`ControlSystemVisualizer` can only be used \"\n",
    "                              \"with `matplotlib` present in the system.\")\n",
    "\n",
    "        self.ctrl = control_system\n",
    "\n",
    "        self.fig, self.ax = plt.subplots()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rule1 = ctrl.Rule(speed[\"slow\"] & target[\"fast\"], cadence[\"high\"])\n",
    "rule2 = ctrl.Rule(speed[\"slow\"] & target[\"average\"], cadence[\"high\"])\n",
    "rule3 = ctrl.Rule(speed[\"slow\"] & target[\"slow\"], cadence[\"low\"])\n",
    "rule4 = ctrl.Rule(speed[\"average\"] & target[\"fast\"], cadence[\"high\"])\n",
    "rule5 = ctrl.Rule(speed[\"average\"] & target[\"average\"], cadence[\"average\"])\n",
    "rule6 = ctrl.Rule(speed[\"average\"] & target[\"slow\"], cadence[\"low\"])\n",
    "rule7 = ctrl.Rule(speed[\"fast\"] & target[\"fast\"], cadence[\"high\"])\n",
    "rule8 = ctrl.Rule(speed[\"fast\"] & target[\"average\"], cadence[\"low\"])\n",
    "rule9 = ctrl.Rule(speed[\"fast\"] & target[\"slow\"], cadence[\"low\"])\n",
    "\n",
    "rule1.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Создание нечеткой системы и добавление нечетких правил в базу знаний нечеткой системы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cadence_ctrl = ctrl.ControlSystem(\n",
    "    [\n",
    "        rule1,\n",
    "        rule2,\n",
    "        rule3,\n",
    "        rule4,\n",
    "        rule5,\n",
    "        rule6,\n",
    "        rule7,\n",
    "        rule8,\n",
    "        rule9,\n",
    "    ]\n",
    ")\n",
    "\n",
    "cadences = ctrl.ControlSystemSimulation(cadence_ctrl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Пример расчета выходной переменной influx на основе входных переменных level и flow\n",
    "\n",
    "Система также формирует подробный журнал выполнения процесса нечеткого логического вывода"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============\n",
      " Antecedents \n",
      "=============\n",
      "Antecedent: speed                   = 60\n",
      "  - slow                            : 0.0\n",
      "  - average                         : 1.0\n",
      "  - fast                            : 0.0\n",
      "Antecedent: target                  = 20\n",
      "  - slow                            : 1.0\n",
      "  - average                         : 0.5\n",
      "  - fast                            : 0.0\n",
      "\n",
      "=======\n",
      " Rules \n",
      "=======\n",
      "RULE #0:\n",
      "  IF speed[slow] AND target[fast] THEN cadence[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - speed[slow]                                            : 0.0\n",
      "  - target[fast]                                           : 0.0\n",
      "                              speed[slow] AND target[fast] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             cadence[high] : 0.0\n",
      "\n",
      "RULE #1:\n",
      "  IF speed[slow] AND target[average] THEN cadence[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - speed[slow]                                            : 0.0\n",
      "  - target[average]                                        : 0.5\n",
      "                           speed[slow] AND target[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             cadence[high] : 0.0\n",
      "\n",
      "RULE #2:\n",
      "  IF speed[slow] AND target[slow] THEN cadence[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - speed[slow]                                            : 0.0\n",
      "  - target[slow]                                           : 1.0\n",
      "                              speed[slow] AND target[slow] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              cadence[low] : 0.0\n",
      "\n",
      "RULE #3:\n",
      "  IF speed[average] AND target[fast] THEN cadence[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - speed[average]                                         : 1.0\n",
      "  - target[fast]                                           : 0.0\n",
      "                           speed[average] AND target[fast] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             cadence[high] : 0.0\n",
      "\n",
      "RULE #4:\n",
      "  IF speed[average] AND target[average] THEN cadence[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - speed[average]                                         : 1.0\n",
      "  - target[average]                                        : 0.5\n",
      "                        speed[average] AND target[average] = 0.5\n",
      "  Activation (THEN-clause):\n",
      "                                          cadence[average] : 0.5\n",
      "\n",
      "RULE #5:\n",
      "  IF speed[average] AND target[slow] THEN cadence[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - speed[average]                                         : 1.0\n",
      "  - target[slow]                                           : 1.0\n",
      "                           speed[average] AND target[slow] = 1.0\n",
      "  Activation (THEN-clause):\n",
      "                                              cadence[low] : 1.0\n",
      "\n",
      "RULE #6:\n",
      "  IF speed[fast] AND target[fast] THEN cadence[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - speed[fast]                                            : 0.0\n",
      "  - target[fast]                                           : 0.0\n",
      "                              speed[fast] AND target[fast] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             cadence[high] : 0.0\n",
      "\n",
      "RULE #7:\n",
      "  IF speed[fast] AND target[average] THEN cadence[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - speed[fast]                                            : 0.0\n",
      "  - target[average]                                        : 0.5\n",
      "                           speed[fast] AND target[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              cadence[low] : 0.0\n",
      "\n",
      "RULE #8:\n",
      "  IF speed[fast] AND target[slow] THEN cadence[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - speed[fast]                                            : 0.0\n",
      "  - target[slow]                                           : 1.0\n",
      "                              speed[fast] AND target[slow] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              cadence[low] : 0.0\n",
      "\n",
      "\n",
      "==============================\n",
      " Intermediaries and Conquests \n",
      "==============================\n",
      "Consequent: cadence                  = 35.97074517705886\n",
      "  low:\n",
      "    Accumulate using accumulation_max : 1.0\n",
      "  average:\n",
      "    Accumulate using accumulation_max : 0.5\n",
      "  high:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(35.97074517705886)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cadences.input[\"speed\"] = 60\n",
    "cadences.input[\"target\"] = 20\n",
    "cadences.compute()\n",
    "cadences.print_state()\n",
    "cadences.output[\"cadence\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Визуализация функции принадлежности для выходной переменной influx\n",
    "\n",
    "Функция получена в процессе аккумуляции и используется для дефаззификации значения выходной переменной influx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cadence.view(sim=cadences)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Пример решения задачи регрессии на основе нечеткого логического вывода"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cars_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Price</th>\n",
       "      <th>Prodyear</th>\n",
       "      <th>Engine volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17944</th>\n",
       "      <td>14426</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10224</th>\n",
       "      <td>1568</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15926</th>\n",
       "      <td>3136</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>10976</td>\n",
       "      <td>1995</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15725</th>\n",
       "      <td>21169</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>10663</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8337</th>\n",
       "      <td>45</td>\n",
       "      <td>2007</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14664</th>\n",
       "      <td>34497</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12408</th>\n",
       "      <td>43905</td>\n",
       "      <td>2010</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10796</th>\n",
       "      <td>12858</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15389 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Price  Prodyear  Engine volume\n",
       "17944  14426      2010            1.8\n",
       "10224   1568      2011            2.4\n",
       "15926   3136      2012            3.5\n",
       "559    10976      1995            2.5\n",
       "15725  21169      2012            1.6\n",
       "...      ...       ...            ...\n",
       "574    10663      2011            1.3\n",
       "8337      45      2007            1.3\n",
       "14664  34497      2011            3.0\n",
       "12408  43905      2010            3.5\n",
       "10796  12858      2013            1.6\n",
       "\n",
       "[15389 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'cars_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "    .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>Price</th>\n",
       "      <th>Prodyear</th>\n",
       "      <th>Engine volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9398</th>\n",
       "      <td>3293</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7548</th>\n",
       "      <td>8154</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040</th>\n",
       "      <td>25089</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3395</th>\n",
       "      <td>17346</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6379</th>\n",
       "      <td>65052</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5180</th>\n",
       "      <td>7056</td>\n",
       "      <td>2010</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9497</th>\n",
       "      <td>28225</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13141</th>\n",
       "      <td>11917</td>\n",
       "      <td>2003</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17474</th>\n",
       "      <td>28225</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2493</th>\n",
       "      <td>3136</td>\n",
       "      <td>1998</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3848 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Price  Prodyear  Engine volume\n",
       "9398    3293      2010            1.8\n",
       "7548    8154      2008            3.3\n",
       "3040   25089      2013            1.8\n",
       "3395   17346      2012            1.6\n",
       "6379   65052      2018            2.0\n",
       "...      ...       ...            ...\n",
       "5180    7056      2010            2.0\n",
       "9497   28225      2011            2.0\n",
       "13141  11917      2003            2.4\n",
       "17474  28225      2013            1.8\n",
       "2493    3136      1998            2.8\n",
       "\n",
       "[3848 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from utils import split_stratified_into_train_val_test\n",
    "\n",
    "df = pd.read_csv(\"data/car_price_prediction.csv\")\n",
    "df = df.drop([\"ID\", \"Manufacturer\", \"Model\",  \"Leatherinterior\", \"Fueltype\", \"Cylinders\",\n",
    "          \"Doors\", \"Wheel\", \"Color\", \"Levy\", \"Drive wheels\", \"Airbags\", \"Gear box type\", \"Mileage\"], axis=1)\n",
    "df = df.dropna()\n",
    "df[\"Engine volume\"] = df[\"Engine volume\"].apply(\n",
    "    lambda x: float(x.split()[0])\n",
    ")\n",
    "\n",
    "\n",
    "cars_train, X_val, cars_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
    "    df,\n",
    "    stratify_colname=\"Category\",\n",
    "    target_colname=\"Price\",\n",
    "    frac_train=0.80,\n",
    "    frac_val=0,\n",
    "    frac_test=0.20\n",
    ")\n",
    "\n",
    "cars_train = cars_train.drop(\"Category\", axis=1)\n",
    "cars_test = cars_test.drop(\"Category\", axis=1)\n",
    "display(\"cars_train\", cars_train)\n",
    "display(\"cars_test\", cars_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Инициализация лингвистических переменных и автоматическое формирование нечетких переменных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26307500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Методы искусственного интеллекта\\MII_Salin_Oleg_PIbd-33\\.venv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prodyear = ctrl.Antecedent(np.arange(1885, 2025, 1), \"prodyear\")\n",
    "engine_volume = ctrl.Antecedent(cars_train[\"Engine volume\"].sort_values().unique(), \"engine_volume\")\n",
    "print(cars_train[\"Price\"].max())\n",
    "price = ctrl.Consequent(np.arange(cars_train[\"Price\"].min() // 100, \n",
    "                                  cars_train[\"Price\"].max() // 100, 1), \"price\")\n",
    "\n",
    "prodyear[\"old\"] = fuzz.zmf(prodyear.universe, 1885, 2020)\n",
    "prodyear[\"new\"] = fuzz.smf(prodyear.universe, 2015, 2020)\n",
    "prodyear.view()\n",
    "engine_volume.automf(3, variable_type=\"quant\")\n",
    "engine_volume.view()\n",
    "price[\"very cheap\"] = fuzz.trimf(price.universe, [0, 50, 100])\n",
    "price[\"cheap\"] = fuzz.trimf(price.universe, [80, 120, 200])\n",
    "price[\"average\"] = fuzz.trapmf(price.universe, [150, 250, 500, 1000])\n",
    "price[\"expensive\"] = fuzz.trimf(price.universe, [800, 1500, 2000])\n",
    "price[\"very expensive\"] = fuzz.smf(price.universe, 1800, 2500)\n",
    "price.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Нечеткие правила"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule11 = ctrl.Rule(\n",
    "    engine_volume[\"low\"] & prodyear[\"old\"],\n",
    "    price[\"very cheap\"],\n",
    ")\n",
    "rule12 = ctrl.Rule(\n",
    "    engine_volume[\"average\"] & prodyear[\"old\"],\n",
    "    price[\"cheap\"],\n",
    ")\n",
    "rule13 = ctrl.Rule(\n",
    "    engine_volume[\"high\"] & prodyear[\"old\"],\n",
    "    price[\"average\"],\n",
    ")\n",
    "rule21 = ctrl.Rule(\n",
    "    engine_volume[\"low\"] & prodyear[\"new\"],\n",
    "    price[\"average\"],\n",
    ")\n",
    "rule22 = ctrl.Rule(\n",
    "    engine_volume[\"average\"] & prodyear[\"new\"],\n",
    "    price[\"expensive\"],\n",
    ")\n",
    "rule23 = ctrl.Rule(\n",
    "    engine_volume[\"high\"] & prodyear[\"new\"],\n",
    "    price[\"very expensive\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Создание нечеткой системы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[IF engine_volume[low] AND prodyear[old] THEN price[very cheap]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF engine_volume[average] AND prodyear[old] THEN price[cheap]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF engine_volume[high] AND prodyear[old] THEN price[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF engine_volume[low] AND prodyear[new] THEN price[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF engine_volume[average] AND prodyear[new] THEN price[expensive]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF engine_volume[high] AND prodyear[new] THEN price[very expensive]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzzy_rules = [\n",
    "    rule11,\n",
    "    rule12,\n",
    "    rule13,\n",
    "    rule21,\n",
    "    rule22, \n",
    "    rule23,\n",
    "]\n",
    "\n",
    "price_cntrl = ctrl.ControlSystem(fuzzy_rules)\n",
    "\n",
    "sim = ctrl.ControlSystemSimulation(price_cntrl)\n",
    "\n",
    "fuzzy_rules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Пример использования полученной нечеткой системы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============\n",
      " Antecedents \n",
      "=============\n",
      "Antecedent: engine_volume           = 1.5\n",
      "  - low                             : 0.85\n",
      "  - average                         : 0.15\n",
      "  - high                            : 0.0\n",
      "Antecedent: prodyear                = 2016\n",
      "  - old                             : 0.0017558299039780523\n",
      "  - new                             : 0.08000000000000002\n",
      "\n",
      "=======\n",
      " Rules \n",
      "=======\n",
      "RULE #0:\n",
      "  IF engine_volume[low] AND prodyear[old] THEN price[very cheap]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - engine_volume[low]                                     : 0.85\n",
      "  - prodyear[old]                                          : 0.0017558299039780523\n",
      "                      engine_volume[low] AND prodyear[old] = 0.0017558299039780523\n",
      "  Activation (THEN-clause):\n",
      "                                         price[very cheap] : 0.0017558299039780523\n",
      "\n",
      "RULE #1:\n",
      "  IF engine_volume[average] AND prodyear[old] THEN price[cheap]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - engine_volume[average]                                 : 0.15\n",
      "  - prodyear[old]                                          : 0.0017558299039780523\n",
      "                  engine_volume[average] AND prodyear[old] = 0.0017558299039780523\n",
      "  Activation (THEN-clause):\n",
      "                                              price[cheap] : 0.0017558299039780523\n",
      "\n",
      "RULE #2:\n",
      "  IF engine_volume[high] AND prodyear[old] THEN price[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - engine_volume[high]                                    : 0.0\n",
      "  - prodyear[old]                                          : 0.0017558299039780523\n",
      "                     engine_volume[high] AND prodyear[old] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            price[average] : 0.0\n",
      "\n",
      "RULE #3:\n",
      "  IF engine_volume[low] AND prodyear[new] THEN price[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - engine_volume[low]                                     : 0.85\n",
      "  - prodyear[new]                                          : 0.08000000000000002\n",
      "                      engine_volume[low] AND prodyear[new] = 0.08000000000000002\n",
      "  Activation (THEN-clause):\n",
      "                                            price[average] : 0.08000000000000002\n",
      "\n",
      "RULE #4:\n",
      "  IF engine_volume[average] AND prodyear[new] THEN price[expensive]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - engine_volume[average]                                 : 0.15\n",
      "  - prodyear[new]                                          : 0.08000000000000002\n",
      "                  engine_volume[average] AND prodyear[new] = 0.08000000000000002\n",
      "  Activation (THEN-clause):\n",
      "                                          price[expensive] : 0.08000000000000002\n",
      "\n",
      "RULE #5:\n",
      "  IF engine_volume[high] AND prodyear[new] THEN price[very expensive]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - engine_volume[high]                                    : 0.0\n",
      "  - prodyear[new]                                          : 0.08000000000000002\n",
      "                     engine_volume[high] AND prodyear[new] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                     price[very expensive] : 0.0\n",
      "\n",
      "\n",
      "==============================\n",
      " Intermediaries and Conquests \n",
      "==============================\n",
      "Consequent: price                    = 1065.2447541258305\n",
      "  very cheap:\n",
      "    Accumulate using accumulation_max : 0.0017558299039780523\n",
      "  cheap:\n",
      "    Accumulate using accumulation_max : 0.0017558299039780523\n",
      "  average:\n",
      "    Accumulate using accumulation_max : 0.08000000000000002\n",
      "  expensive:\n",
      "    Accumulate using accumulation_max : 0.08000000000000002\n",
      "  very expensive:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(1065.2447541258305)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.input[\"prodyear\"] = 2016\n",
    "sim.input[\"engine_volume\"] = 1.5\n",
    "sim.compute()\n",
    "sim.print_state()\n",
    "display(sim.output[\"price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Функция для автоматизации вычисления целевой переменной Y на основе вектора признаков X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fuzzy_pred(row):\n",
    "    sim.input[\"prodyear\"] = row[\"Prodyear\"]\n",
    "    sim.input[\"engine_volume\"] = row[\"Engine volume\"]\n",
    "    sim.compute()\n",
    "    return sim.output[\"price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Тестирование нечеткой системы на обучающей выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Price</th>\n",
       "      <th>Prodyear</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>PricePred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14122</th>\n",
       "      <td>35124</td>\n",
       "      <td>2019</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1083.035198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5666</th>\n",
       "      <td>26657</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.8</td>\n",
       "      <td>99.933398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13023</th>\n",
       "      <td>4200</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.6</td>\n",
       "      <td>99.704172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1838</th>\n",
       "      <td>15530</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1065.244754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3112</th>\n",
       "      <td>32302</td>\n",
       "      <td>2013</td>\n",
       "      <td>2.5</td>\n",
       "      <td>99.959694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9204</th>\n",
       "      <td>5959</td>\n",
       "      <td>2015</td>\n",
       "      <td>2.5</td>\n",
       "      <td>99.979430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3100</th>\n",
       "      <td>22580</td>\n",
       "      <td>2017</td>\n",
       "      <td>2.0</td>\n",
       "      <td>954.577176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12754</th>\n",
       "      <td>9408</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.0</td>\n",
       "      <td>99.947365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14803</th>\n",
       "      <td>6586</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.5</td>\n",
       "      <td>848.532066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4756</th>\n",
       "      <td>6586</td>\n",
       "      <td>1999</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99.638970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9505</th>\n",
       "      <td>20385</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.5</td>\n",
       "      <td>99.947365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17586</th>\n",
       "      <td>40769</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.0</td>\n",
       "      <td>99.881685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13728</th>\n",
       "      <td>8154</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.5</td>\n",
       "      <td>99.762966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>10036</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99.734373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18262</th>\n",
       "      <td>251</td>\n",
       "      <td>2019</td>\n",
       "      <td>2.5</td>\n",
       "      <td>831.412760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Price  Prodyear  Engine volume    PricePred\n",
       "14122  35124      2019            5.7  1083.035198\n",
       "5666   26657      2011            1.8    99.933398\n",
       "13023   4200      2001            1.6    99.704172\n",
       "1838   15530      2016            2.0  1065.244754\n",
       "3112   32302      2013            2.5    99.959694\n",
       "9204    5959      2015            2.5    99.979430\n",
       "3100   22580      2017            2.0   954.577176\n",
       "12754   9408      2012            3.0    99.947365\n",
       "14803   6586      2018            2.5   848.532066\n",
       "4756    6586      1999            2.0    99.638970\n",
       "9505   20385      2012            2.5    99.947365\n",
       "17586  40769      2008            3.0    99.881685\n",
       "13728   8154      2003            1.5    99.762966\n",
       "944    10036      2002            2.0    99.734373\n",
       "18262    251      2019            2.5   831.412760"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_train = cars_train.copy()\n",
    "\n",
    "result_train[\"PricePred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
    "\n",
    "result_train.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Тестирование нечеткой системы на тестовой выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Price</th>\n",
       "      <th>Prodyear</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>PricePred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9398</th>\n",
       "      <td>3293</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.8</td>\n",
       "      <td>9991.779367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7548</th>\n",
       "      <td>8154</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.3</td>\n",
       "      <td>9988.168542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040</th>\n",
       "      <td>25089</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.8</td>\n",
       "      <td>9995.969432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3395</th>\n",
       "      <td>17346</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.6</td>\n",
       "      <td>9994.736527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6379</th>\n",
       "      <td>65052</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80674.546145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5180</th>\n",
       "      <td>7056</td>\n",
       "      <td>2010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9991.779367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9497</th>\n",
       "      <td>28225</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9993.339772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13141</th>\n",
       "      <td>11917</td>\n",
       "      <td>2003</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9976.296566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17474</th>\n",
       "      <td>28225</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.8</td>\n",
       "      <td>9995.969432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2493</th>\n",
       "      <td>3136</td>\n",
       "      <td>1998</td>\n",
       "      <td>2.8</td>\n",
       "      <td>9960.397848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3848 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Price  Prodyear  Engine volume     PricePred\n",
       "9398    3293      2010            1.8   9991.779367\n",
       "7548    8154      2008            3.3   9988.168542\n",
       "3040   25089      2013            1.8   9995.969432\n",
       "3395   17346      2012            1.6   9994.736527\n",
       "6379   65052      2018            2.0  80674.546145\n",
       "...      ...       ...            ...           ...\n",
       "5180    7056      2010            2.0   9991.779367\n",
       "9497   28225      2011            2.0   9993.339772\n",
       "13141  11917      2003            2.4   9976.296566\n",
       "17474  28225      2013            1.8   9995.969432\n",
       "2493    3136      1998            2.8   9960.397848\n",
       "\n",
       "[3848 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_test = cars_test.copy()\n",
    "\n",
    "result_test[\"PricePred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
    "result_test[\"PricePred\"] = result_test[\"PricePred\"].apply(lambda x: x * 100)\n",
    "\n",
    "display(result_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Оценка результатов на основе метрик для задачи регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE_train': 36049.32401271262,\n",
       " 'RMSE_test': 36049.32401271262,\n",
       " 'RMAE_test': 142.4884853198348,\n",
       " 'R2_test': -1.5218160662039941}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "rmetrics = {}\n",
    "\n",
    "rmetrics[\"RMSE_train\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_test[\"Price\"], result_test[\"PricePred\"])\n",
    ")\n",
    "rmetrics[\"RMSE_test\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_test[\"Price\"], result_test[\"PricePred\"])\n",
    ")\n",
    "rmetrics[\"RMAE_test\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(result_test[\"Price\"], result_test[\"PricePred\"])\n",
    ")\n",
    "rmetrics[\"R2_test\"] = metrics.r2_score(\n",
    "    result_test[\"Price\"], result_test[\"PricePred\"]\n",
    ")\n",
    "\n",
    "rmetrics"
   ]
  }
 ],
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