{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Лабораторная 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Информация о диабете индейцев Пима"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>763</th>\n",
       "      <td>10</td>\n",
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       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import skfuzzy as fuzz\n",
    "import matplotlib as plt\n",
    "from skfuzzy import control as ctrl\n",
    "\n",
    "df = pd.read_csv(\".//scv//diabetes.csv\")\n",
    "print(df.columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаем лингвистические переменные"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Входные: age(возраст) и bmi(ИМТ)\n",
    "\n",
    "Выходные: glucose(давление)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = ctrl.Antecedent(np.arange(df[\"Age\"].min(), df[\"Age\"].max() + 1, 1), \"age\")\n",
    "bmi = ctrl.Antecedent(np.arange(df[\"BMI\"].min(), df[\"BMI\"].max() + 0.1, 0.1), \"bmi\")\n",
    "glucose = ctrl.Consequent(np.arange(df[\"Glucose\"].min(), df[\"Glucose\"].max() + 0.1, 0.1), \"glucose\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Настраиваем их"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib' has no attribute 'axvline'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m age[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmiddle-aged\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m fuzz\u001b[38;5;241m.\u001b[39mtrapmf(age\u001b[38;5;241m.\u001b[39muniverse, [\u001b[38;5;241m18\u001b[39m, \u001b[38;5;241m30\u001b[39m, \u001b[38;5;241m45\u001b[39m, \u001b[38;5;241m60\u001b[39m])\n\u001b[0;32m      3\u001b[0m age[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mold\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m fuzz\u001b[38;5;241m.\u001b[39msmf(age\u001b[38;5;241m.\u001b[39muniverse, \u001b[38;5;241m45\u001b[39m, \u001b[38;5;241m85\u001b[39m)\n\u001b[1;32m----> 5\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxvline\u001b[49m(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m      8\u001b[0m bmi[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlow\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m fuzz\u001b[38;5;241m.\u001b[39mzmf(bmi\u001b[38;5;241m.\u001b[39muniverse, \u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m18\u001b[39m)\n",
      "File \u001b[1;32md:\\5_semester\\AIM\\rep\\AIM-PIbd-31-Razubaev-S-M\\.venv\\Lib\\site-packages\\matplotlib\\_api\\__init__.py:217\u001b[0m, in \u001b[0;36mcaching_module_getattr.<locals>.__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m props:\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m props[name]\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__get__\u001b[39m(instance)\n\u001b[1;32m--> 217\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__module__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'matplotlib' has no attribute 'axvline'"
     ]
    }
   ],
   "source": [
    "age[\"young\"] = fuzz.zmf(age.universe, 7, 30)\n",
    "age[\"middle-aged\"] = fuzz.trapmf(age.universe, [18, 30, 45, 60])\n",
    "age[\"old\"] = fuzz.smf(age.universe, 45, 85)\n",
    "age.view()\n",
    "plt.axvline(x=0)\n",
    "plt.show()\n",
    "\n",
    "bmi[\"low\"] = fuzz.zmf(bmi.universe, 12, 18)\n",
    "bmi[\"normal\"] = fuzz.trapmf(bmi.universe, [16, 19, 24, 27])\n",
    "bmi[\"high\"] = fuzz.smf(bmi.universe, 25, 27)\n",
    "bmi.view()\n",
    "\n",
    "glucose[\"low\"] = fuzz.zmf(glucose.universe, 0, 80)\n",
    "glucose[\"normal\"] = fuzz.trapmf(glucose.universe, [70, 80, 90, 100])\n",
    "glucose[\"high\"] = fuzz.smf(glucose.universe, 90, 180)\n",
    "glucose.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "База нечетких правил:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 16,
     "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": [
    "import numpy as np\n",
    "import skfuzzy as fuzz\n",
    "from skfuzzy import control as ctrl\n",
    "\n",
    "rule1 = ctrl.Rule(age[\"young\"] & bmi[\"low\"], glucose[\"low\"])\n",
    "rule2 = ctrl.Rule(age[\"young\"] & bmi[\"normal\"], glucose[\"normal\"])\n",
    "rule3 = ctrl.Rule(age[\"young\"] & bmi[\"high\"], glucose[\"normal\"])\n",
    "\n",
    "rule4 = ctrl.Rule(age[\"middle-aged\"] & bmi[\"low\"], glucose[\"normal\"])\n",
    "rule5 = ctrl.Rule(age[\"middle-aged\"] & bmi[\"normal\"], glucose[\"normal\"])\n",
    "rule6 = ctrl.Rule(age[\"middle-aged\"] & bmi[\"high\"], glucose[\"high\"])\n",
    "\n",
    "rule7 = ctrl.Rule(age[\"old\"] & bmi[\"low\"], glucose[\"low\"])\n",
    "rule8 = ctrl.Rule(age[\"old\"] & bmi[\"normal\"], glucose[\"normal\"])\n",
    "rule9 = ctrl.Rule(age[\"old\"] & bmi[\"high\"], glucose[\"high\"])\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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\5_semester\\AIM\\rep\\AIM-PIbd-31-Razubaev-S-M\\.venv\\Lib\\site-packages\\skfuzzy\\control\\controlsystem.py:135: 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"
    }
   ],
   "source": [
    "glucose_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",
    "glucose_simulation = ctrl.ControlSystemSimulation(glucose_ctrl)\n",
    "\n",
    "glucose_ctrl.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка расчета выходной переменной"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============\n",
      " Antecedents \n",
      "=============\n",
      "Antecedent: age                     = 31\n",
      "  - young                           : 0.0\n",
      "  - middle-aged                     : 1.0\n",
      "  - old                             : 0.0\n",
      "Antecedent: bmi                     = 25\n",
      "  - low                             : 0.0\n",
      "  - normal                          : 0.6666666666666666\n",
      "  - high                            : 0.0\n",
      "\n",
      "=======\n",
      " Rules \n",
      "=======\n",
      "RULE #0:\n",
      "  IF age[young] AND bmi[low] THEN glucose[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - age[young]                                             : 0.0\n",
      "  - bmi[low]                                               : 0.0\n",
      "                                   age[young] AND bmi[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              glucose[low] : 0.0\n",
      "\n",
      "RULE #1:\n",
      "  IF age[young] AND bmi[normal] THEN glucose[normal]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - age[young]                                             : 0.0\n",
      "  - bmi[normal]                                            : 0.6666666666666666\n",
      "                                age[young] AND bmi[normal] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                           glucose[normal] : 0.0\n",
      "\n",
      "RULE #2:\n",
      "  IF age[young] AND bmi[high] THEN glucose[normal]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - age[young]                                             : 0.0\n",
      "  - bmi[high]                                              : 0.0\n",
      "                                  age[young] AND bmi[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                           glucose[normal] : 0.0\n",
      "\n",
      "RULE #3:\n",
      "  IF age[middle-aged] AND bmi[low] THEN glucose[normal]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - age[middle-aged]                                       : 1.0\n",
      "  - bmi[low]                                               : 0.0\n",
      "                             age[middle-aged] AND bmi[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                           glucose[normal] : 0.0\n",
      "\n",
      "RULE #4:\n",
      "  IF age[middle-aged] AND bmi[normal] THEN glucose[normal]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - age[middle-aged]                                       : 1.0\n",
      "  - bmi[normal]                                            : 0.6666666666666666\n",
      "                          age[middle-aged] AND bmi[normal] = 0.6666666666666666\n",
      "  Activation (THEN-clause):\n",
      "                                           glucose[normal] : 0.6666666666666666\n",
      "\n",
      "RULE #5:\n",
      "  IF age[middle-aged] AND bmi[high] THEN glucose[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - age[middle-aged]                                       : 1.0\n",
      "  - bmi[high]                                              : 0.0\n",
      "                            age[middle-aged] AND bmi[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             glucose[high] : 0.0\n",
      "\n",
      "RULE #6:\n",
      "  IF age[old] AND bmi[low] THEN glucose[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - age[old]                                               : 0.0\n",
      "  - bmi[low]                                               : 0.0\n",
      "                                     age[old] AND bmi[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              glucose[low] : 0.0\n",
      "\n",
      "RULE #7:\n",
      "  IF age[old] AND bmi[normal] THEN glucose[normal]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - age[old]                                               : 0.0\n",
      "  - bmi[normal]                                            : 0.6666666666666666\n",
      "                                  age[old] AND bmi[normal] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                           glucose[normal] : 0.0\n",
      "\n",
      "RULE #8:\n",
      "  IF age[old] AND bmi[high] THEN glucose[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - age[old]                                               : 0.0\n",
      "  - bmi[high]                                              : 0.0\n",
      "                                    age[old] AND bmi[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             glucose[high] : 0.0\n",
      "\n",
      "\n",
      "==============================\n",
      " Intermediaries and Conquests \n",
      "==============================\n",
      "Consequent: glucose                  = 85.00000000000001\n",
      "  low:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  normal:\n",
      "    Accumulate using accumulation_max : 0.6666666666666666\n",
      "  high:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(85.00000000000001)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glucose_simulation.input[\"age\"] = 31\n",
    "glucose_simulation.input[\"bmi\"] = 25\n",
    "glucose_simulation.compute()\n",
    "\n",
    "glucose_simulation.print_state()\n",
    "glucose_simulation.output[\"glucose\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализации функции принадлежности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\5_semester\\AIM\\rep\\AIM-PIbd-31-Razubaev-S-M\\.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"
    }
   ],
   "source": [
    "glucose.view(sim=glucose_simulation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценка качества"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>glucose_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>61</td>\n",
       "      <td>31.2</td>\n",
       "      <td>146</td>\n",
       "      <td>156.076444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>38</td>\n",
       "      <td>34.0</td>\n",
       "      <td>124</td>\n",
       "      <td>164.363268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>25</td>\n",
       "      <td>33.7</td>\n",
       "      <td>78</td>\n",
       "      <td>155.611863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>22</td>\n",
       "      <td>28.2</td>\n",
       "      <td>97</td>\n",
       "      <td>142.749562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>21</td>\n",
       "      <td>23.2</td>\n",
       "      <td>99</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>25</td>\n",
       "      <td>53.2</td>\n",
       "      <td>162</td>\n",
       "      <td>155.611863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>24</td>\n",
       "      <td>34.2</td>\n",
       "      <td>111</td>\n",
       "      <td>152.260658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>23</td>\n",
       "      <td>33.6</td>\n",
       "      <td>107</td>\n",
       "      <td>148.066203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>69</td>\n",
       "      <td>26.8</td>\n",
       "      <td>132</td>\n",
       "      <td>158.239802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>23</td>\n",
       "      <td>33.3</td>\n",
       "      <td>113</td>\n",
       "      <td>148.066203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>26</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88</td>\n",
       "      <td>158.305050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>30</td>\n",
       "      <td>42.9</td>\n",
       "      <td>120</td>\n",
       "      <td>164.363268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>23</td>\n",
       "      <td>33.3</td>\n",
       "      <td>118</td>\n",
       "      <td>148.066203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>40</td>\n",
       "      <td>34.5</td>\n",
       "      <td>117</td>\n",
       "      <td>164.363268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>62</td>\n",
       "      <td>27.9</td>\n",
       "      <td>105</td>\n",
       "      <td>156.763261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>33</td>\n",
       "      <td>29.7</td>\n",
       "      <td>173</td>\n",
       "      <td>164.363268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age   BMI  Glucose  glucose_pred\n",
       "115   61  31.2      146    156.076444\n",
       "116   38  34.0      124    164.363268\n",
       "117   25  33.7       78    155.611863\n",
       "118   22  28.2       97    142.749562\n",
       "119   21  23.2       99     85.000000\n",
       "120   25  53.2      162    155.611863\n",
       "121   24  34.2      111    152.260658\n",
       "122   23  33.6      107    148.066203\n",
       "123   69  26.8      132    158.239802\n",
       "124   23  33.3      113    148.066203\n",
       "125   26  55.0       88    158.305050\n",
       "126   30  42.9      120    164.363268\n",
       "127   23  33.3      118    148.066203\n",
       "128   40  34.5      117    164.363268\n",
       "129   62  27.9      105    156.763261\n",
       "130   33  29.7      173    164.363268"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fuzzy_pred(row):\n",
    "    glucose_simulation.input[\"age\"] = row[\"Age\"]\n",
    "    glucose_simulation.input[\"bmi\"] = row[\"BMI\"]\n",
    "    glucose_simulation.compute()\n",
    "    return glucose_simulation.output[\"glucose\"]\n",
    "\n",
    "result = df.copy()\n",
    "\n",
    "result[\"glucose_pred\"] = result.apply(fuzzy_pred, axis=1)\n",
    "\n",
    "result.loc[115:130, [\"Age\", \"BMI\", \"Glucose\", \"glucose_pred\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE': 44.436073098835, 'RMAE': 6.078634343122391, 'R2': -0.9341083419845992}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn import metrics\n",
    "\n",
    "rmetrics = {}\n",
    "rmetrics[\"RMSE\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result[\"Glucose\"], result[\"glucose_pred\"])\n",
    ")\n",
    "rmetrics[\"RMAE\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(result[\"Glucose\"], result[\"glucose_pred\"])\n",
    ")\n",
    "rmetrics[\"R2\"] = metrics.r2_score(\n",
    "    result[\"Glucose\"], result[\"glucose_pred\"]\n",
    ")\n",
    "rmetrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод: данные прогнозируются с низкой точностью и допускается множество ошибок"
   ]
  }
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