insulindoseFuzzy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "#탄수화물 섭취량이 적을 때\n",
    "def lowCarbohydrate(f):\n",
    "    lowCarbohydrate, notlowCarbohydrate = 10,12\n",
    "    \n",
    "    if f <= lowCarbohydrate:\n",
    "        return 1\n",
    "    elif f > notlowCarbohydrate:\n",
    "        return 0\n",
    "    elif f > lowCarbohydrate and f <= notlowCarbohydrate:\n",
    "        return ((notlowCarbohydrate - f) / (notlowCarbohydrate - lowCarbohydrate))\n",
    "\n",
    "#탄수화물 섭취량이 보통일 때\n",
    "def mediumCarbohydrate(f):\n",
    "    notmediumCarbohydrate1, mediumCarbohydrate1, mediumCarbohydrate2, notmediumCarbohydrate2 = 10,12,16,18\n",
    "    if f > mediumCarbohydrate1 and f <= mediumCarbohydrate2:\n",
    "        return 1\n",
    "    elif f <= notmediumCarbohydrate1 or f > notmediumCarbohydrate2:\n",
    "        return 0\n",
    "    elif f > notmediumCarbohydrate1 and f <= mediumCarbohydrate1:\n",
    "        return ((f-notmediumCarbohydrate1) / (mediumCarbohydrate1 - notmediumCarbohydrate1))\n",
    "    elif f > mediumCarbohydrate2 and f <= notmediumCarbohydrate2:\n",
    "        return ((notmediumCarbohydrate2 - f) / (notmediumCarbohydrate2 - mediumCarbohydrate2))\n",
    "\n",
    "#탄수화물 섭취량이 많을 때\n",
    "def highCarbohydrate(f):\n",
    "    nothighCarbohydrate, highCarbohydrate = 16,18\n",
    "    \n",
    "    if f > highCarbohydrate:\n",
    "        return 1\n",
    "    elif f <= nothighCarbohydrate:\n",
    "        return 0\n",
    "    elif f > nothighCarbohydrate and f <= highCarbohydrate:\n",
    "        return ((f - nothighCarbohydrate) / (highCarbohydrate - nothighCarbohydrate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24915324100>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 탄수화물 섭취량 그래프로 그리기\n",
    "x = [ i for i in range(31)]\n",
    "    \n",
    "ylowC = [lowCarbohydrate(i) for i in x]\n",
    "ymediumC = [mediumCarbohydrate(i) for i in x]\n",
    "yhighC = [highCarbohydrate(i) for i in x]\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.title('Membership carbohydrate',fontsize = 20)\n",
    "plt.plot(x, ylowC, label = 'Low')\n",
    "plt.plot(x, ymediumC, label = 'Medium')\n",
    "plt.plot(x, yhighC, label = 'High')\n",
    "plt.xlabel('carbohydrate intake (g)')\n",
    "plt.ylabel(r'$\\mu\\ (x)$')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#당화혈색소가 낮을 때\n",
    "def lowBlood(x):\n",
    "    lowBlood , notlowBlood = 6,7\n",
    "    \n",
    "    if x <= lowBlood:\n",
    "        return 1;\n",
    "    elif x > notlowBlood:\n",
    "        return 0\n",
    "    elif x > lowBlood and x <= notlowBlood:\n",
    "        return ((notlowBlood - x) / (notlowBlood - lowBlood))\n",
    "\n",
    "#당화혈색소의 비율이 보통일 때(정상일 때)\n",
    "def mediumBlood(x):\n",
    "    notmediumBlood1, mediumBlood, notmediumBlood2 = 6,7,8\n",
    "    \n",
    "    if x == mediumBlood:\n",
    "        return 1\n",
    "    elif x <= notmediumBlood1 or x > notmediumBlood2:\n",
    "        return 0\n",
    "    elif x > notmediumBlood1 and x <= mediumBlood:\n",
    "        return (x - notmediumBlood1) / (mediumBlood - notmediumBlood1)\n",
    "    elif x > mediumBlood and x <= notmediumBlood2:\n",
    "        return (notmediumBlood2 - x) / (notmediumBlood2 - mediumBlood)\n",
    "\n",
    "#당화혈색소의 비율이 많을 때\n",
    "def highBlood(x):\n",
    "    nothighBlood, highBlood = 7,8\n",
    "    \n",
    "    if x > highBlood:\n",
    "        return 1\n",
    "    elif x <= nothighBlood:\n",
    "        return 0\n",
    "    elif x > nothighBlood and x <= highBlood:\n",
    "        return (x - nothighBlood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24916367160>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#당화혈색소 소속함수 그리는 그래프\n",
    "\n",
    "x = [i for i in range(5,11)]\n",
    "\n",
    "ylowB = [lowBlood(i) for i in x]\n",
    "ymediumB = [mediumBlood(i) for i in x]\n",
    "yhighB = [highBlood(i) for i in x]\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.title('Membership hemoglobin',fontsize = 20) \n",
    "plt.plot(x, ylowB, label = 'Low')\n",
    "plt.plot(x, ymediumB, label = 'Medium')\n",
    "plt.plot(x, yhighB, label = 'High')\n",
    "plt.xlabel('glycated hemoglobin (%)')\n",
    "plt.ylabel(r'$\\mu\\ (x)$')\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FUZZIFICATION 하는 함수를 각각 구한다\n",
    "# 각각의 소속함수에 대해 low/medium/high의 값을 배열에 저장함\n",
    "\n",
    "def fuzzificationCarbohydrate(carbohydrateValue):\n",
    "    carbohydrateSet = []\n",
    "    carbohydrateSet.append(lowCarbohydrate(carbohydrateValue))\n",
    "    carbohydrateSet.append(mediumCarbohydrate(carbohydrateValue))\n",
    "    carbohydrateSet.append(highCarbohydrate(carbohydrateValue))\n",
    "    return carbohydrateSet\n",
    "\n",
    "def fuzzificationBlood(bloodValue):\n",
    "    bloodSet = []\n",
    "    bloodSet.append(lowBlood(bloodValue))\n",
    "    bloodSet.append(mediumBlood(bloodValue))\n",
    "    bloodSet.append(highBlood(bloodValue))\n",
    "    return bloodSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "#INFERENCE\n",
    "# 출력값에 정한 규칙을 대입한다.\n",
    "# and 연산이므로 min의 값을 입력값으로 받는다.\n",
    "\n",
    "def inference(carbohydrateSet, bloodSet):\n",
    "    inferenceSet = []\n",
    "    verylowSet, lowSet, mediumSet, highSet, veryhighSet = [], [], [], [],[]\n",
    "\n",
    "    # 당화혈색소가 낮으면 verylow (1번 규칙)\n",
    "    verylowSet.append(bloodSet[0])\n",
    "    # 탄수화물 섭취량이 적고 당화혈색소가 정상이면 인슐린을 매우 적게 투여한다 (2번 규칙)\n",
    "    verylowSet.append(min(carbohydrateSet[0], bloodSet[1]))\n",
    "    \n",
    "    # 탄수화물 섭취량이 보통이고 당화혈 색소가 정상이면  인슐린을 적게 투여한다 (4번 규칙)\n",
    "    lowSet.append(min(carbohydrateSet[1], bloodSet[1]))\n",
    "    \n",
    "    # 탄수화물 섭취량이 적고 당화혈색소가 높으면 인슐린을 보통만큼 투여한다 (3번 규칙)\n",
    "    mediumSet.append(min(carbohydrateSet[0], bloodSet[2]))\n",
    "    # 탄수화물 섭취량이 보통이고 당화혈 색소가 높으면 인슐린을 보통만큼 투여한다 (5번 규칙)\n",
    "    mediumSet.append(min(carbohydrateSet[1], bloodSet[2]))\n",
    "    \n",
    "    #탄수화물 섭취량이 많고 당화혈 색소가 정상이면 인슐린을 많이 투여한다(6번 규칙)\n",
    "    highSet.append(min(carbohydrateSet[2], bloodSet[1]))\n",
    "    \n",
    "    #탄수화물 섭취량이 많고 당화혈 색소가 높으면 인슐린을 매우 많이 투여한다 (7번 규칙)\n",
    "    veryhighSet.append(min(carbohydrateSet[2], bloodSet[2]))\n",
    "    \n",
    "    inferenceSet.append(max(verylowSet))\n",
    "    inferenceSet.append(max(lowSet))\n",
    "    inferenceSet.append(max(mediumSet))\n",
    "    inferenceSet.append(max(highSet))\n",
    "    inferenceSet.append(max(veryhighSet))\n",
    "    \n",
    "    return inferenceSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DEFUZZIFICATION\n",
    "# 스게노를 이용\n",
    "def defuzzification(inferenceSet):\n",
    "    multiplier = (inferenceSet[0]*10) + (inferenceSet[1]*20) + (inferenceSet[2]*30) + (inferenceSet[3]*40) + (inferenceSet[4]*50)\n",
    "    divider = inferenceSet[0] + inferenceSet[1] + inferenceSet[2] + inferenceSet[3] + inferenceSet[4]\n",
    "    return multiplier/divider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x249163d8d00>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#스게노 그래프\n",
    "xVerylow = [10,10]\n",
    "xLow = [20,20]\n",
    "xMedium = [30,30]\n",
    "xHigh = [40,40]\n",
    "xVeryHigh = [50,50]\n",
    "\n",
    "y = [0,1]\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.title('Defuzzification insullin',fontsize = 20)\n",
    "plt.plot(xVerylow,y,label='VeryLow')\n",
    "plt.plot(xLow,y,label='Low')\n",
    "plt.plot(xMedium,y,label='Medium')\n",
    "plt.plot(xHigh,y,label='High')\n",
    "plt.plot(xVeryHigh,y,label='VeryHigh')\n",
    "plt.xlabel('insulin dose')\n",
    "plt.ylabel(r'$\\mu\\ output$')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "탄수화물 섭취량을 입력하세요: 15\n",
      "당화혈색소 값을 입력하세요: 8\n",
      "result:  30.0\n"
     ]
    }
   ],
   "source": [
    "#계산 \n",
    "insulinDose = []\n",
    "\n",
    "fuzziCarbohydrate = int(input(\"탄수화물 섭취량을 입력하세요: \"))\n",
    "fuzzificationCarbohydrate(fuzziCarbohydrate)\n",
    "\n",
    "fuzziBlood = int(input(\"당화혈색소 값을 입력하세요: \"))\n",
    "fuzzificationBlood(fuzziBlood)\n",
    "\n",
    "inferensi = inference(fuzzificationCarbohydrate(fuzziCarbohydrate),fuzzificationBlood(fuzziBlood))\n",
    "insulinDose.extend([defuzzification(inferensi)])\n",
    "print(\"result: \" , insulinDose[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}