docs/source/Fan_Speed_Control.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# QFIE For Fan Speed Control\n",
    "\n",
    "This notebook aims to illustrate how QFIE can control efficiently the speed of a Fan according to the input variables 'Core Temperature' and 'Clock Speed'. Finally, the control surface of the system will be obtained both using QFIE and the canonical Mamdani fuzzy system, which configuration is reported in the paper.  \n",
    "\n",
    "Firstly, let us import the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import skfuzzy as fuzz\n",
    "import matplotlib.pyplot as plt\n",
    "import QFIE.FuzzyEngines as FE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The system is a MISO composed of two input variables and one output variable. For each of them three fuzzy sets are considered.\n",
    "\n",
    "- For core temperature the fuzzy sets used are releted to the linguistic terms: 'cold', 'warm' and 'hot'.\n",
    "\n",
    "- For clock speed the fuzzy sets are related to the linguistic terms: 'low', 'normal' and 'turbo'\n",
    "\n",
    "- For the output variable, named 'Fan Speed' two fuzzy sets are used related to the linguistic terms: 'slow' and 'fast\n",
    "\n",
    "By running the following cell, the fuzzy sets used are graphically shown. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x864 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "core_temperature = np.linspace(0, 100, 200)\n",
    "clock_speed = np.linspace(0, 4, 200)\n",
    "fan_speed = np.linspace(0, 6000, 200)\n",
    "\n",
    "\n",
    "\n",
    "t_cold = fuzz.trimf(core_temperature, [0,0,50])\n",
    "t_warm = fuzz.trimf(core_temperature, [30,50,70])\n",
    "t_hot = fuzz.trimf(core_temperature, [50, 100, 100])\n",
    "\n",
    "cs_low = fuzz.trimf(clock_speed, [0,0,1.5])\n",
    "cs_normal = fuzz.trimf(clock_speed, [0.5,2,3.5])\n",
    "cs_turbo = fuzz.trimf(clock_speed, [2.5,4,4])\n",
    "\n",
    "fs_slow = fuzz.trimf(fan_speed, [0, 0, 3500])\n",
    "fs_fast = fuzz.trimf(fan_speed, [2500, 6000, 6000])\n",
    "\n",
    "# Visualize these universes and membership functions\n",
    "fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(8, 12))\n",
    "\n",
    "\n",
    "ax0.plot(core_temperature, t_cold, 'b', linewidth=1.5, label='cold')\n",
    "ax0.plot(core_temperature, t_warm, 'g', linewidth=1.5, label='warm')\n",
    "ax0.plot(core_temperature, t_hot, 'r', linewidth=1.5, label='hot')\n",
    "ax0.set_title('Core Temperature')\n",
    "ax0.legend()\n",
    "\n",
    "ax1.plot(clock_speed, cs_low, 'b', linewidth=1.5, label='low')\n",
    "ax1.plot(clock_speed, cs_normal, 'g', linewidth=1.5, label='normal')\n",
    "ax1.plot(clock_speed, cs_turbo, 'r', linewidth=1.5, label='turbo')\n",
    "ax1.set_title('Clock Speed')\n",
    "ax1.legend()\n",
    "\n",
    "\n",
    "ax2.plot(fan_speed, fs_slow, 'b', linewidth=1.5, label='slow')\n",
    "ax2.plot(fan_speed, fs_fast, 'g', linewidth=1.5, label='fast')\n",
    "ax2.set_title('Fan Speed')\n",
    "ax2.legend()\n",
    "\n",
    "# Turn off top/right axes\n",
    "for ax in (ax0, ax1, ax2):\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.get_xaxis().tick_bottom()\n",
    "    ax.get_yaxis().tick_left()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The rules base of the system is composed of the following rules: \n",
    "\n",
    "- if temp is cold and clock is low then fan is slow\n",
    "- if temp is cold and clock is normal then fan is slow'\n",
    "- if temp is cold and clock is turbo then fan is fast'\n",
    "- if temp is warm and clock is low then fan is slow'\n",
    "- if temp is warm and clock is normal then fan is slow'\n",
    "- if temp is warm and clock is turbo then fan is fast'\n",
    "- if temp is hot and clock is low then fan is fast'\n",
    "- if temp is hot and clock is normal then fan is fast'\n",
    "- if temp is hot and clock is turbo then fan is fast'\n",
    "\n",
    "By running the following two cells QFIE is properly initialized\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "rules = ['if temp is cold and clock is low then fan is slow',\n",
    "         'if temp is cold and clock is normal then fan is slow',\n",
    "         'if temp is cold and clock is turbo then fan is fast',\n",
    "         'if temp is warm and clock is low then fan is slow',\n",
    "         'if temp is warm and clock is normal then fan is slow',\n",
    "         'if temp is warm and clock is turbo then fan is fast',\n",
    "         'if temp is hot and clock is low then fan is fast',\n",
    "         'if temp is hot and clock is normal then fan is fast',\n",
    "         'if temp is hot and clock is turbo then fan is fast']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "qfie = FE.QuantumFuzzyEngine()\n",
    "\n",
    "qfie.input_variable(name='temp', range=core_temperature)\n",
    "qfie.input_variable(name='clock', range=clock_speed)\n",
    "qfie.output_variable(name='fan', range=fan_speed)\n",
    "\n",
    "qfie.add_input_fuzzysets(var_name='temp', set_names=['cold', 'warm', 'hot'], sets=[t_cold, t_warm, t_hot])\n",
    "qfie.add_input_fuzzysets(var_name='clock', set_names=['low', 'normal', 'turbo'], sets=[cs_low, cs_normal, cs_turbo])\n",
    "qfie.add_output_fuzzysets(var_name='fan', set_names=['slow', 'fast'],sets=[fs_slow,fs_fast])\n",
    "qfie.set_rules(rules)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "To obtain the control surface of QFIE, it is used to fire the rules in 100 different input combinations. In detail, for 10 values of temperature, 10 different values of clock speed are considered. The 100 outputs obtained are collected in a list which will be used at the end of the notebook to plot the control surface. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'temp': 0, 'clock': 0.0}\n",
      "Input values  {'temp': [1.0, 0.0, 0.0], 'clock': [1.0, 0.0, 0.0]}\n",
      "Output Counts {'01': 1.0, '10': 0}\n",
      "{'temp': 0, 'clock': 0.4}\n",
      "Input values  {'temp': [1.0, 0.0, 0.0], 'clock': [0.7333333333333334, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.733125, '10': 0}\n",
      "{'temp': 0, 'clock': 0.8}\n",
      "Input values  {'temp': [1.0, 0.0, 0.0], 'clock': [0.4666666666666667, 0.20000000000000004, 0.0]}\n",
      "Output Counts {'01': 0.67625, '10': 0}\n",
      "{'temp': 0, 'clock': 1.2}\n",
      "Input values  {'temp': [1.0, 0.0, 0.0], 'clock': [0.20000000000000004, 0.4666666666666666, 0.0]}\n",
      "Output Counts {'01': 0.6655, '10': 0}\n",
      "{'temp': 0, 'clock': 1.6}\n",
      "Input values  {'temp': [1.0, 0.0, 0.0], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.732125, '10': 0}\n",
      "{'temp': 0, 'clock': 2.0}\n",
      "Input values  {'temp': [1.0, 0.0, 0.0], 'clock': [0.0, 0.9932998324958124, 0.0]}\n",
      "Output Counts {'01': 0.993, '10': 0}\n",
      "{'temp': 0, 'clock': 2.4}\n",
      "Input values  {'temp': [1.0, 0.0, 0.0], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.73925, '10': 0}\n",
      "{'temp': 0, 'clock': 2.8}\n",
      "Input values  {'temp': [1.0, 0.0, 0.0], 'clock': [0.0, 0.4666666666666668, 0.19999999999999987]}\n",
      "Output Counts {'01': 0.472125, '10': 0.197125}\n",
      "{'temp': 0, 'clock': 3.2}\n",
      "Input values  {'temp': [1.0, 0.0, 0.0], 'clock': [0.0, 0.19999999999999987, 0.4666666666666668]}\n",
      "Output Counts {'01': 0.19975, '10': 0.47025}\n",
      "{'temp': 0, 'clock': 3.6}\n",
      "Input values  {'temp': [1.0, 0.0, 0.0], 'clock': [0.0, 0.0, 0.7333333333333334]}\n",
      "Output Counts {'01': 0, '10': 0.730125}\n",
      "{'temp': 10, 'clock': 0.0}\n",
      "Input values  {'temp': [0.8, 0.0, 0.0], 'clock': [1.0, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.80825, '10': 0}\n",
      "{'temp': 10, 'clock': 0.4}\n",
      "Input values  {'temp': [0.8, 0.0, 0.0], 'clock': [0.7333333333333334, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.5865, '10': 0}\n",
      "{'temp': 10, 'clock': 0.8}\n",
      "Input values  {'temp': [0.8, 0.0, 0.0], 'clock': [0.4666666666666667, 0.20000000000000004, 0.0]}\n",
      "Output Counts {'01': 0.536, '10': 0}\n",
      "{'temp': 10, 'clock': 1.2}\n",
      "Input values  {'temp': [0.8, 0.0, 0.0], 'clock': [0.20000000000000004, 0.4666666666666666, 0.0]}\n",
      "Output Counts {'01': 0.536875, '10': 0}\n",
      "{'temp': 10, 'clock': 1.6}\n",
      "Input values  {'temp': [0.8, 0.0, 0.0], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.5775, '10': 0}\n",
      "{'temp': 10, 'clock': 2.0}\n",
      "Input values  {'temp': [0.8, 0.0, 0.0], 'clock': [0.0, 0.9932998324958124, 0.0]}\n",
      "Output Counts {'01': 0.799875, '10': 0}\n",
      "{'temp': 10, 'clock': 2.4}\n",
      "Input values  {'temp': [0.8, 0.0, 0.0], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.585125, '10': 0}\n",
      "{'temp': 10, 'clock': 2.8}\n",
      "Input values  {'temp': [0.8, 0.0, 0.0], 'clock': [0.0, 0.4666666666666668, 0.19999999999999987]}\n",
      "Output Counts {'01': 0.3675, '10': 0.16475}\n",
      "{'temp': 10, 'clock': 3.2}\n",
      "Input values  {'temp': [0.8, 0.0, 0.0], 'clock': [0.0, 0.19999999999999987, 0.4666666666666668]}\n",
      "Output Counts {'01': 0.161, '10': 0.38275}\n",
      "{'temp': 10, 'clock': 3.6}\n",
      "Input values  {'temp': [0.8, 0.0, 0.0], 'clock': [0.0, 0.0, 0.7333333333333334]}\n",
      "Output Counts {'01': 0, '10': 0.588125}\n",
      "{'temp': 20, 'clock': 0.0}\n",
      "Input values  {'temp': [0.6, 0.0, 0.0], 'clock': [1.0, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.601125, '10': 0}\n",
      "{'temp': 20, 'clock': 0.4}\n",
      "Input values  {'temp': [0.6, 0.0, 0.0], 'clock': [0.7333333333333334, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.439, '10': 0}\n",
      "{'temp': 20, 'clock': 0.8}\n",
      "Input values  {'temp': [0.6, 0.0, 0.0], 'clock': [0.4666666666666667, 0.20000000000000004, 0.0]}\n",
      "Output Counts {'01': 0.394375, '10': 0}\n",
      "{'temp': 20, 'clock': 1.2}\n",
      "Input values  {'temp': [0.6, 0.0, 0.0], 'clock': [0.20000000000000004, 0.4666666666666666, 0.0]}\n",
      "Output Counts {'01': 0.397625, '10': 0}\n",
      "{'temp': 20, 'clock': 1.6}\n",
      "Input values  {'temp': [0.6, 0.0, 0.0], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.448625, '10': 0}\n",
      "{'temp': 20, 'clock': 2.0}\n",
      "Input values  {'temp': [0.6, 0.0, 0.0], 'clock': [0.0, 0.9932998324958124, 0.0]}\n",
      "Output Counts {'01': 0.60025, '10': 0}\n",
      "{'temp': 20, 'clock': 2.4}\n",
      "Input values  {'temp': [0.6, 0.0, 0.0], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.436625, '10': 0}\n",
      "{'temp': 20, 'clock': 2.8}\n",
      "Input values  {'temp': [0.6, 0.0, 0.0], 'clock': [0.0, 0.4666666666666668, 0.19999999999999987]}\n",
      "Output Counts {'01': 0.279625, '10': 0.115375}\n",
      "{'temp': 20, 'clock': 3.2}\n",
      "Input values  {'temp': [0.6, 0.0, 0.0], 'clock': [0.0, 0.19999999999999987, 0.4666666666666668]}\n",
      "Output Counts {'01': 0.124, '10': 0.283125}\n",
      "{'temp': 20, 'clock': 3.6}\n",
      "Input values  {'temp': [0.6, 0.0, 0.0], 'clock': [0.0, 0.0, 0.7333333333333334]}\n",
      "Output Counts {'01': 0, '10': 0.445125}\n",
      "{'temp': 30, 'clock': 0.0}\n",
      "Input values  {'temp': [0.4, 0.005276381909547677, 0.0], 'clock': [1.0, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.397, '10': 0}\n",
      "{'temp': 30, 'clock': 0.4}\n",
      "Input values  {'temp': [0.4, 0.005276381909547677, 0.0], 'clock': [0.7333333333333334, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.300375, '10': 0}\n",
      "{'temp': 30, 'clock': 0.8}\n",
      "Input values  {'temp': [0.4, 0.005276381909547677, 0.0], 'clock': [0.4666666666666667, 0.20000000000000004, 0.0]}\n",
      "Output Counts {'01': 0.27525, '10': 0}\n",
      "{'temp': 30, 'clock': 1.2}\n",
      "Input values  {'temp': [0.4, 0.005276381909547677, 0.0], 'clock': [0.20000000000000004, 0.4666666666666666, 0.0]}\n",
      "Output Counts {'01': 0.266625, '10': 0}\n",
      "{'temp': 30, 'clock': 1.6}\n",
      "Input values  {'temp': [0.4, 0.005276381909547677, 0.0], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.2975, '10': 0}\n",
      "{'temp': 30, 'clock': 2.0}\n",
      "Input values  {'temp': [0.4, 0.005276381909547677, 0.0], 'clock': [0.0, 0.9932998324958124, 0.0]}\n",
      "Output Counts {'01': 0.410875, '10': 0}\n",
      "{'temp': 30, 'clock': 2.4}\n",
      "Input values  {'temp': [0.4, 0.005276381909547677, 0.0], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.296375, '10': 0}\n",
      "{'temp': 30, 'clock': 2.8}\n",
      "Input values  {'temp': [0.4, 0.005276381909547677, 0.0], 'clock': [0.0, 0.4666666666666668, 0.19999999999999987]}\n",
      "Output Counts {'01': 0.183875, '10': 0.07975}\n",
      "{'temp': 30, 'clock': 3.2}\n",
      "Input values  {'temp': [0.4, 0.005276381909547677, 0.0], 'clock': [0.0, 0.19999999999999987, 0.4666666666666668]}\n",
      "Output Counts {'01': 0.082875, '10': 0.192}\n",
      "{'temp': 30, 'clock': 3.6}\n",
      "Input values  {'temp': [0.4, 0.005276381909547677, 0.0], 'clock': [0.0, 0.0, 0.7333333333333334]}\n",
      "Output Counts {'01': 0, '10': 0.296375}\n",
      "{'temp': 40, 'clock': 0.0}\n",
      "Input values  {'temp': [0.2, 0.5, 0.0], 'clock': [1.0, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.697625, '10': 0}\n",
      "{'temp': 40, 'clock': 0.4}\n",
      "Input values  {'temp': [0.2, 0.5, 0.0], 'clock': [0.7333333333333334, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.511875, '10': 0}\n",
      "{'temp': 40, 'clock': 0.8}\n",
      "Input values  {'temp': [0.2, 0.5, 0.0], 'clock': [0.4666666666666667, 0.20000000000000004, 0.0]}\n",
      "Output Counts {'01': 0.478375, '10': 0}\n",
      "{'temp': 40, 'clock': 1.2}\n",
      "Input values  {'temp': [0.2, 0.5, 0.0], 'clock': [0.20000000000000004, 0.4666666666666666, 0.0]}\n",
      "Output Counts {'01': 0.458875, '10': 0}\n",
      "{'temp': 40, 'clock': 1.6}\n",
      "Input values  {'temp': [0.2, 0.5, 0.0], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.51775, '10': 0}\n",
      "{'temp': 40, 'clock': 2.0}\n",
      "Input values  {'temp': [0.2, 0.5, 0.0], 'clock': [0.0, 0.9932998324958124, 0.0]}\n",
      "Output Counts {'01': 0.70275, '10': 0}\n",
      "{'temp': 40, 'clock': 2.4}\n",
      "Input values  {'temp': [0.2, 0.5, 0.0], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.514625, '10': 0}\n",
      "{'temp': 40, 'clock': 2.8}\n",
      "Input values  {'temp': [0.2, 0.5, 0.0], 'clock': [0.0, 0.4666666666666668, 0.19999999999999987]}\n",
      "Output Counts {'01': 0.332625, '10': 0.139875}\n",
      "{'temp': 40, 'clock': 3.2}\n",
      "Input values  {'temp': [0.2, 0.5, 0.0], 'clock': [0.0, 0.19999999999999987, 0.4666666666666668]}\n",
      "Output Counts {'01': 0.135, '10': 0.332375}\n",
      "{'temp': 40, 'clock': 3.6}\n",
      "Input values  {'temp': [0.2, 0.5, 0.0], 'clock': [0.0, 0.0, 0.7333333333333334]}\n",
      "Output Counts {'01': 0, '10': 0.516375}\n",
      "{'temp': 50, 'clock': 0.0}\n",
      "Input values  {'temp': [0.0025125628140703427, 0.9874371859296484, 0.0025125628140703427], 'clock': [1.0, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.991625, '10': 0.001375}\n",
      "{'temp': 50, 'clock': 0.4}\n",
      "Input values  {'temp': [0.0025125628140703427, 0.9874371859296484, 0.0025125628140703427], 'clock': [0.7333333333333334, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.723125, '10': 0.002}\n",
      "{'temp': 50, 'clock': 0.8}\n",
      "Input values  {'temp': [0.0025125628140703427, 0.9874371859296484, 0.0025125628140703427], 'clock': [0.4666666666666667, 0.20000000000000004, 0.0]}\n",
      "Output Counts {'01': 0.66, '10': 0.00175}\n",
      "{'temp': 50, 'clock': 1.2}\n",
      "Input values  {'temp': [0.0025125628140703427, 0.9874371859296484, 0.0025125628140703427], 'clock': [0.20000000000000004, 0.4666666666666666, 0.0]}\n",
      "Output Counts {'01': 0.662375, '10': 0.0015}\n",
      "{'temp': 50, 'clock': 1.6}\n",
      "Input values  {'temp': [0.0025125628140703427, 0.9874371859296484, 0.0025125628140703427], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.730875, '10': 0.0025}\n",
      "{'temp': 50, 'clock': 2.0}\n",
      "Input values  {'temp': [0.0025125628140703427, 0.9874371859296484, 0.0025125628140703427], 'clock': [0.0, 0.9932998324958124, 0.0]}\n",
      "Output Counts {'01': 0.983, '10': 0.0025}\n",
      "{'temp': 50, 'clock': 2.4}\n",
      "Input values  {'temp': [0.0025125628140703427, 0.9874371859296484, 0.0025125628140703427], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.720375, '10': 0.0025}\n",
      "{'temp': 50, 'clock': 2.8}\n",
      "Input values  {'temp': [0.0025125628140703427, 0.9874371859296484, 0.0025125628140703427], 'clock': [0.0, 0.4666666666666668, 0.19999999999999987]}\n",
      "Output Counts {'01': 0.465875, '10': 0.204125}\n",
      "{'temp': 50, 'clock': 3.2}\n",
      "Input values  {'temp': [0.0025125628140703427, 0.9874371859296484, 0.0025125628140703427], 'clock': [0.0, 0.19999999999999987, 0.4666666666666668]}\n",
      "Output Counts {'01': 0.192125, '10': 0.462375}\n",
      "{'temp': 50, 'clock': 3.6}\n",
      "Input values  {'temp': [0.0025125628140703427, 0.9874371859296484, 0.0025125628140703427], 'clock': [0.0, 0.0, 0.7333333333333334]}\n",
      "Output Counts {'01': 0, '10': 0.724}\n",
      "{'temp': 60, 'clock': 0.0}\n",
      "Input values  {'temp': [0.0, 0.5, 0.2], 'clock': [1.0, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.493375, '10': 0.204}\n",
      "{'temp': 60, 'clock': 0.4}\n",
      "Input values  {'temp': [0.0, 0.5, 0.2], 'clock': [0.7333333333333334, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.368, '10': 0.15475}\n",
      "{'temp': 60, 'clock': 0.8}\n",
      "Input values  {'temp': [0.0, 0.5, 0.2], 'clock': [0.4666666666666667, 0.20000000000000004, 0.0]}\n",
      "Output Counts {'01': 0.320375, '10': 0.13925}\n",
      "{'temp': 60, 'clock': 1.2}\n",
      "Input values  {'temp': [0.0, 0.5, 0.2], 'clock': [0.20000000000000004, 0.4666666666666666, 0.0]}\n",
      "Output Counts {'01': 0.337125, '10': 0.133375}\n",
      "{'temp': 60, 'clock': 1.6}\n",
      "Input values  {'temp': [0.0, 0.5, 0.2], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.366125, '10': 0.156}\n",
      "{'temp': 60, 'clock': 2.0}\n",
      "Input values  {'temp': [0.0, 0.5, 0.2], 'clock': [0.0, 0.9932998324958124, 0.0]}\n",
      "Output Counts {'01': 0.491625, '10': 0.20275}\n",
      "{'temp': 60, 'clock': 2.4}\n",
      "Input values  {'temp': [0.0, 0.5, 0.2], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.375, '10': 0.143875}\n",
      "{'temp': 60, 'clock': 2.8}\n",
      "Input values  {'temp': [0.0, 0.5, 0.2], 'clock': [0.0, 0.4666666666666668, 0.19999999999999987]}\n",
      "Output Counts {'01': 0.234625, '10': 0.2255}\n",
      "{'temp': 60, 'clock': 3.2}\n",
      "Input values  {'temp': [0.0, 0.5, 0.2], 'clock': [0.0, 0.19999999999999987, 0.4666666666666668]}\n",
      "Output Counts {'01': 0.10425, '10': 0.369625}\n",
      "{'temp': 60, 'clock': 3.6}\n",
      "Input values  {'temp': [0.0, 0.5, 0.2], 'clock': [0.0, 0.0, 0.7333333333333334]}\n",
      "Output Counts {'01': 0, '10': 0.50475}\n",
      "{'temp': 70, 'clock': 0.0}\n",
      "Input values  {'temp': [0.0, 0.005276381909547851, 0.39999999999999997], 'clock': [1.0, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.0045, '10': 0.404125}\n",
      "{'temp': 70, 'clock': 0.4}\n",
      "Input values  {'temp': [0.0, 0.005276381909547851, 0.39999999999999997], 'clock': [0.7333333333333334, 0.0, 0.0]}\n",
      "Output Counts {'01': 0.00375, '10': 0.29375}\n",
      "{'temp': 70, 'clock': 0.8}\n",
      "Input values  {'temp': [0.0, 0.005276381909547851, 0.39999999999999997], 'clock': [0.4666666666666667, 0.20000000000000004, 0.0]}\n",
      "Output Counts {'01': 0.003875, '10': 0.266375}\n",
      "{'temp': 70, 'clock': 1.2}\n",
      "Input values  {'temp': [0.0, 0.005276381909547851, 0.39999999999999997], 'clock': [0.20000000000000004, 0.4666666666666666, 0.0]}\n",
      "Output Counts {'01': 0.002875, '10': 0.258}\n",
      "{'temp': 70, 'clock': 1.6}\n",
      "Input values  {'temp': [0.0, 0.005276381909547851, 0.39999999999999997], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.003625, '10': 0.293}\n",
      "{'temp': 70, 'clock': 2.0}\n",
      "Input values  {'temp': [0.0, 0.005276381909547851, 0.39999999999999997], 'clock': [0.0, 0.9932998324958124, 0.0]}\n",
      "Output Counts {'01': 0.004375, '10': 0.40575}\n",
      "{'temp': 70, 'clock': 2.4}\n",
      "Input values  {'temp': [0.0, 0.005276381909547851, 0.39999999999999997], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0.004, '10': 0.2915}\n",
      "{'temp': 70, 'clock': 2.8}\n",
      "Input values  {'temp': [0.0, 0.005276381909547851, 0.39999999999999997], 'clock': [0.0, 0.4666666666666668, 0.19999999999999987]}\n",
      "Output Counts {'01': 0.001875, '10': 0.271375}\n",
      "{'temp': 70, 'clock': 3.2}\n",
      "Input values  {'temp': [0.0, 0.005276381909547851, 0.39999999999999997], 'clock': [0.0, 0.19999999999999987, 0.4666666666666668]}\n",
      "Output Counts {'01': 0.000625, '10': 0.26775}\n",
      "{'temp': 70, 'clock': 3.6}\n",
      "Input values  {'temp': [0.0, 0.005276381909547851, 0.39999999999999997], 'clock': [0.0, 0.0, 0.7333333333333334]}\n",
      "Output Counts {'01': 0, '10': 0.293375}\n",
      "{'temp': 80, 'clock': 0.0}\n",
      "Input values  {'temp': [0.0, 0.0, 0.6], 'clock': [1.0, 0.0, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.596125}\n",
      "{'temp': 80, 'clock': 0.4}\n",
      "Input values  {'temp': [0.0, 0.0, 0.6], 'clock': [0.7333333333333334, 0.0, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.4365}\n",
      "{'temp': 80, 'clock': 0.8}\n",
      "Input values  {'temp': [0.0, 0.0, 0.6], 'clock': [0.4666666666666667, 0.20000000000000004, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.3935}\n",
      "{'temp': 80, 'clock': 1.2}\n",
      "Input values  {'temp': [0.0, 0.0, 0.6], 'clock': [0.20000000000000004, 0.4666666666666666, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.398}\n",
      "{'temp': 80, 'clock': 1.6}\n",
      "Input values  {'temp': [0.0, 0.0, 0.6], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.439125}\n",
      "{'temp': 80, 'clock': 2.0}\n",
      "Input values  {'temp': [0.0, 0.0, 0.6], 'clock': [0.0, 0.9932998324958124, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.60675}\n",
      "{'temp': 80, 'clock': 2.4}\n",
      "Input values  {'temp': [0.0, 0.0, 0.6], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.439}\n",
      "{'temp': 80, 'clock': 2.8}\n",
      "Input values  {'temp': [0.0, 0.0, 0.6], 'clock': [0.0, 0.4666666666666668, 0.19999999999999987]}\n",
      "Output Counts {'01': 0, '10': 0.393625}\n",
      "{'temp': 80, 'clock': 3.2}\n",
      "Input values  {'temp': [0.0, 0.0, 0.6], 'clock': [0.0, 0.19999999999999987, 0.4666666666666668]}\n",
      "Output Counts {'01': 0, '10': 0.402625}\n",
      "{'temp': 80, 'clock': 3.6}\n",
      "Input values  {'temp': [0.0, 0.0, 0.6], 'clock': [0.0, 0.0, 0.7333333333333334]}\n",
      "Output Counts {'01': 0, '10': 0.43475}\n",
      "{'temp': 90, 'clock': 0.0}\n",
      "Input values  {'temp': [0.0, 0.0, 0.8], 'clock': [1.0, 0.0, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.79325}\n",
      "{'temp': 90, 'clock': 0.4}\n",
      "Input values  {'temp': [0.0, 0.0, 0.8], 'clock': [0.7333333333333334, 0.0, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.585625}\n",
      "{'temp': 90, 'clock': 0.8}\n",
      "Input values  {'temp': [0.0, 0.0, 0.8], 'clock': [0.4666666666666667, 0.20000000000000004, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.53475}\n",
      "{'temp': 90, 'clock': 1.2}\n",
      "Input values  {'temp': [0.0, 0.0, 0.8], 'clock': [0.20000000000000004, 0.4666666666666666, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.523375}\n",
      "{'temp': 90, 'clock': 1.6}\n",
      "Input values  {'temp': [0.0, 0.0, 0.8], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.5795}\n",
      "{'temp': 90, 'clock': 2.0}\n",
      "Input values  {'temp': [0.0, 0.0, 0.8], 'clock': [0.0, 0.9932998324958124, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.7945}\n",
      "{'temp': 90, 'clock': 2.4}\n",
      "Input values  {'temp': [0.0, 0.0, 0.8], 'clock': [0.0, 0.7333333333333334, 0.0]}\n",
      "Output Counts {'01': 0, '10': 0.6005}\n",
      "{'temp': 90, 'clock': 2.8}\n",
      "Input values  {'temp': [0.0, 0.0, 0.8], 'clock': [0.0, 0.4666666666666668, 0.19999999999999987]}\n",
      "Output Counts {'01': 0, '10': 0.529125}\n",
      "{'temp': 90, 'clock': 3.2}\n",
      "Input values  {'temp': [0.0, 0.0, 0.8], 'clock': [0.0, 0.19999999999999987, 0.4666666666666668]}\n",
      "Output Counts {'01': 0, '10': 0.529625}\n",
      "{'temp': 90, 'clock': 3.6}\n",
      "Input values  {'temp': [0.0, 0.0, 0.8], 'clock': [0.0, 0.0, 0.7333333333333334]}\n",
      "Output Counts {'01': 0, '10': 0.5815}\n"
     ]
    }
   ],
   "source": [
    "temp_list, speed_list = [t for t in range(0, 100, 10)], [s/2.5 for s in range(0, 10, 1)]\n",
    "f_quantum = []\n",
    "for t in temp_list:\n",
    "    for s in speed_list:\n",
    "        qfie.build_inference_qc({'temp':t, 'clock':s}, draw_qc=False)\n",
    "        f_quantum.append(qfie.execute('qasm_simulator', 8000, plot_histo=False)[0])  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "____\n",
    "Then, the classical Mamdani system is implemented by using the python library Sci-kit Fuzzy. The following cells initializes the system as required from the library.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from skfuzzy import control as ctrl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "temp = ctrl.Antecedent(core_temperature, 'temp')\n",
    "speed = ctrl.Antecedent(clock_speed , 'speed')\n",
    "fan = ctrl.Consequent(fan_speed, 'fan')\n",
    "\n",
    "temp['cold'] = t_cold\n",
    "temp['warm'] = t_warm\n",
    "temp['hot'] = t_hot \n",
    "\n",
    "speed['low'] = cs_low\n",
    "speed['normal'] = cs_normal\n",
    "speed['turbo'] = cs_turbo \n",
    "\n",
    "fan['slow'] = fs_slow\n",
    "fan['fast'] = fs_fast\n",
    "\n",
    "rule1 = ctrl.Rule(temp['cold'] & speed['low'], fan['slow'])\n",
    "rule2 = ctrl.Rule(temp['cold'] & speed['normal'], fan['slow'])\n",
    "rule3 = ctrl.Rule(temp['cold'] & speed['turbo'], fan['fast'])\n",
    "rule4 = ctrl.Rule(temp['warm'] & speed['low'], fan['slow'])\n",
    "rule5 = ctrl.Rule(temp['warm'] & speed['normal'], fan['slow'])\n",
    "rule6 = ctrl.Rule(temp['warm'] & speed['turbo'], fan['fast'])\n",
    "rule7 = ctrl.Rule(temp['hot'] & speed['low'], fan['fast'])\n",
    "rule8 = ctrl.Rule(temp['hot'] & speed['normal'], fan['fast'])\n",
    "rule9 = ctrl.Rule(temp['hot'] & speed['turbo'], fan['fast'])\n",
    "\n",
    "fan_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6, rule7, rule8, rule9])\n",
    "fan_sys = ctrl.ControlSystemSimulation(fan_ctrl)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "By running the following cell, the same combinations of inputs are passed to the classical control which computes the 100 output. All the output values are stored in a list. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "f_classical = []\n",
    "for t in temp_list:\n",
    "    for s in speed_list:\n",
    "        fan_sys.input['temp']=t    \n",
    "        fan_sys.input['speed']=s\n",
    "\n",
    "        fan_sys.compute()\n",
    "        f_classical.append(fan_sys.output['fan'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "At this point, both the QFIE and Classical system' control surfaces can be plotted. Firstly, the following cell shows the former surface. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 0, 'Fan Speed')"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "X, Y, = temp_list, speed_list\n",
    "plotx,ploty, = np.meshgrid(np.linspace(np.min(X),np.max(X),10), np.linspace(np.min(Y),np.max(Y),10))\n",
    "Z = np.zeros_like(plotx)\n",
    "counter = 0 \n",
    "for i in range(len(temp_list)):\n",
    "    for j in range(len(speed_list)):\n",
    "        Z[i,j]=f_quantum[counter]\n",
    "        counter+=1\n",
    "        \n",
    "zfig = plt.figure()\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "ax.plot_surface(plotx, ploty, Z, rstride=1, cstride=1, cmap='viridis',\n",
    "                       linewidth=0.4, antialiased=True)\n",
    "ax.set_xlabel('Temperature')\n",
    "ax.set_ylabel('Clock')\n",
    "ax.set_zlabel('Fan Speed')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Then, the classical surface is displayed by running the following cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 0, 'Fan Speed')"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, Y, = temp_list, speed_list\n",
    "plotx,ploty, = np.meshgrid(np.linspace(np.min(X),np.max(X),10), np.linspace(np.min(Y),np.max(Y),10))\n",
    "Z = np.zeros_like(plotx)\n",
    "counter = 0 \n",
    "for i in range(len(temp_list)):\n",
    "    for j in range(len(speed_list)):\n",
    "        Z[i,j]=f_classical[counter]\n",
    "        counter+=1\n",
    "zfig = plt.figure()\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "ax.plot_surface(plotx, ploty, Z, rstride=1, cstride=1, cmap='viridis',\n",
    "                       linewidth=0.4, antialiased=True)\n",
    "ax.set_xlabel('Temperature')\n",
    "ax.set_ylabel('Clock')\n",
    "ax.set_zlabel('Fan Speed')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The two surfaces show how QFIE is able to control in a very similar way to a classical Mamdani fuzzy control the Fan Speed system, despite the computational advantage in firing the fuzzy rules in a parallel way."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "general",
   "language": "python",
   "name": "general"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}