AIM-PIbd-31-Bakalskaya-E-D/lab_7/lab_7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"margin: 15px;\">\n",
    "\n",
    "\n",
    "<ul>\n",
    "<li>Выбрать входные и выходные переменные.</li>\n",
    "<li>Выполнить настройку параметров лингвистических переменных: определить\n",
    "количество термов, типов и параметров функций принадлежности</li>\n",
    "<li>Сформировать базу нечетких правил.</li>\n",
    "<li>Выполнить оценку качества полученной нечеткой системы</li>\n",
    "</ul>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import skfuzzy.control as control\n",
    "import skfuzzy as fuzzy\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# считаем данные и поределим входные и выходные переменные\n",
    "data = pd.read_csv(\"./csv/option4.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"margin: 15px;\">\n",
    "Так как мы предсказываем инсульт, то входными переменными будут самые, пожалуй, важные критерии - возраст, уровень сахара в крови, ИМТ, гипертония (ее наличие/отсутствие) и сердечный приступ (тоже его наличие/отсутствие)<br><br>а вот ВЫходной переменной будет, естесственно, сам инсульт (наличие/отсутствие)\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = control.Antecedent(np.arange(0, 101, 1), 'age') # возраст от 0 до 100 с шагом 1 год и т.д.\n",
    "glucose = control.Antecedent(np.arange(50, 301, 1), 'glucose')\n",
    "bmi = control.Antecedent(np.arange(10, 50, 0.1), 'bmi')\n",
    "hypertension = control.Antecedent(np.arange(0, 2, 1), 'hypertension')\n",
    "heart_disease = control.Antecedent(np.arange(0, 2, 1), 'heart_disease')\n",
    "\n",
    "# а теперь выходная переменная (Consequent)\n",
    "stroke_risk = control.Consequent(np.arange(0, 1.1, 0.1), 'stroke_risk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"margin: 15px; text-align: center;\">\n",
    "НАКОНЕЦ Я УСТАНОВИЛА ВСЕ ПАКЕТЫ етить его\n",
    "</p>\n",
    "\n",
    "<p style=\"margin: 15px;\">\n",
    "теперь самое время определить нечеткие переменные, которые сложатся... в лингвистические\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# возраст\n",
    "age['young'] = fuzzy.zmf(age.universe, 0, 30)\n",
    "age['middle'] = fuzzy.trapmf(age.universe, [18, 20, 30, 40])\n",
    "age['old'] = fuzzy.trapmf(age.universe, [40, 50, 60, 70])\n",
    "age['aged'] = fuzzy.smf(age.universe, 60, 100)  \n",
    "age.view()\n",
    "# plt.show()\n",
    "\n",
    "# сахар\n",
    "glucose['low'] = fuzzy.zmf(glucose.universe, 50, 80)\n",
    "glucose['normal'] = fuzzy.trapmf(glucose.universe, [70, 80, 90, 100])\n",
    "glucose['hight'] = fuzzy.smf(glucose.universe, 100, 300)\n",
    "glucose.view()\n",
    "\n",
    "# ИМТ\n",
    "bmi['low'] = fuzzy.zmf(bmi.universe, 0, 19)\n",
    "bmi['normal'] = fuzzy.trimf(bmi.universe, [18, 20, 25])\n",
    "bmi['hight'] = fuzzy.smf(bmi.universe, 25, 50)\n",
    "bmi.view()\n",
    "\n",
    "# гипертония\n",
    "# пердечный сриступ\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}