{ "metadata": { "name": "", "signature": "sha256:1208b91f166c53b6cc8748c70aa21201c3d17a376a30c46cc0de44c5873a31c7" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fuzzy Logic Quality Control" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example/development of the fuzzy approach to quality control" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import skfuzzy as fuzz\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Generate universe variables\n", "# * Quality and service on subjective ranges [0, 10]\n", "# * Tip has a range of [0, 25] in units of percentage points\n", "x_spike = np.arange(0, 11, 1)\n", "x_clim = np.arange(0, 11, 1)\n", "x_RoC = np.arange(0, 6, 0.5)\n", "x_tip = np.arange(0.0, 1.01, .01)\n", "\n", "# Generate fuzzy membership functions\n", "spike_lo = fuzz.trapmf(x_spike, [-1e30, -1e30, 0.07, 0.2])\n", "spike_md = fuzz.trapmf(x_spike, [0.07, 0.2, 2, 6])\n", "spike_hi = fuzz.trapmf(x_spike, [2, 6, 1e30, 1e30])\n", "\n", "clim_lo = fuzz.trapmf(x_clim, [-1e30, -1e30, 3, 4])\n", "clim_md = fuzz.trapmf(x_clim, [3, 4, 5, 6])\n", "clim_hi = fuzz.trapmf(x_clim, [5, 6, 1e30, 1e30])\n", "\n", "RoC_lo = fuzz.trapmf(x_RoC, [-1e30, -1e30, 0.5, 1.5])\n", "RoC_md = fuzz.trapmf(x_RoC, [0.5, 1.5, 3, 4])\n", "RoC_hi = fuzz.trapmf(x_RoC, [3, 4, 1e30, 1e30])\n", "\n", "tip_lo = fuzz.trimf(x_tip, [0.0, 0.225, 0.45])\n", "tip_md = fuzz.trimf(x_tip, [0.275, 0.5, 0.725])\n", "tip_hi = fuzz.trimf(x_tip, [0.55, 0.775, 1.0])\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Visualize these universes and membership functions\n", "fig, (ax0, ax1, ax2, ax3) = plt.subplots(nrows=4, figsize=(8, 9))\n", "\n", "ax0.plot(x_spike, spike_lo, 'g', linewidth=1.5, label='Bad')\n", "ax0.plot(x_spike, spike_md, 'y', linewidth=1.5, label='Decent')\n", "ax0.plot(x_spike, spike_hi, 'r', linewidth=1.5, label='Great')\n", "ax0.set_title('Spike')\n", "ax0.legend()\n", "\n", "ax1.plot(x_clim, clim_lo, 'g', linewidth=1.5, label='Poor')\n", "ax1.plot(x_clim, clim_md, 'y', linewidth=1.5, label='Acceptable')\n", "ax1.plot(x_clim, clim_hi, 'r', linewidth=1.5, label='Amazing')\n", "ax1.set_title('Climatology')\n", "ax1.legend()\n", "\n", "ax2.plot(x_RoC, RoC_lo, 'g', linewidth=1.5, label='Low')\n", "ax2.plot(x_RoC, RoC_md, 'y', linewidth=1.5, label='Medium')\n", "ax2.plot(x_RoC, RoC_hi, 'r', linewidth=1.5, label='High')\n", "ax2.set_title('Rate of Change')\n", "ax2.legend()\n", "\n", "ax3.plot(x_tip, tip_lo, 'g', linewidth=1.5, label='Low')\n", "ax3.plot(x_tip, tip_md, 'y', linewidth=1.5, label='Medium')\n", "ax3.plot(x_tip, tip_hi, 'r', linewidth=1.5, label='High')\n", "ax3.set_title('Tip amount')\n", "ax3.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", "plt.tight_layout()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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WrF9/J0uW9GTfvvk1H6akBCZMsMbZvP02jBwJGRkwYoQWN0rVQdOnTycpKYlG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tJGV31S6XffwcyZ5v2mbpWhYGEf2rdvz88//1ypY0NCQli3bp2VE9nGFQscrXUx8BSwBnPR8qXW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\nlJRcwmCIw93dT+9I5eLXwI+Y8BiMEUZ6Lu7JDxN+oEUdfQdD1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"text": [ "" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "spike = 1.0\n", "clim = 5.2\n", "RoC = 0.9" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# We need the activation of our fuzzy membership functions at these values.\n", "# The exact values 6.5 and 9.8 do not exist on our universes...\n", "# This is what fuzz.interp_membership exists for!\n", "spike_level_lo = fuzz.interp_membership(x_spike, spike_lo, spike)\n", "spike_level_md = fuzz.interp_membership(x_spike, spike_md, spike)\n", "spike_level_hi = fuzz.interp_membership(x_spike, spike_hi, spike)\n", "\n", "print(\"Qual level: %s\" % spike)\n", "print(spike_level_lo, spike_level_md, spike_level_hi)\n", "\n", "\n", "clim_level_lo = fuzz.interp_membership(x_clim, clim_lo, clim)\n", "clim_level_md = fuzz.interp_membership(x_clim, clim_md, clim)\n", "clim_level_hi = fuzz.interp_membership(x_clim, clim_hi, clim)\n", "\n", "print(\"Serv level: %s\" % clim)\n", "print(clim_level_lo, clim_level_md, clim_level_hi)\n", "\n", "\n", "RoC_level_lo = fuzz.interp_membership(x_RoC, RoC_lo, RoC)\n", "RoC_level_md = fuzz.interp_membership(x_RoC, RoC_md, RoC)\n", "RoC_level_hi = fuzz.interp_membership(x_RoC, RoC_hi, RoC)\n", "\n", "print(\"Rate of Change: %s\" % RoC)\n", "print(RoC_level_lo, RoC_level_md, RoC_level_hi)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Qual level: 1.0\n", "(0.0, 1.0, 0.0)\n", "Serv level: 5.2\n", "(0.0, 0.79999999999999982, 0.20000000000000018)\n", "Rate of Change: 0.9\n", "(0.59999999999999998, 0.40000000000000002, 0.0)\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rule Low" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Now we take our rules and apply them. Rule 1 concerns bad food OR service.\n", "# The OR operator means we take the maximum of these two.\n", "active_rule1 = np.mean((spike_level_lo, clim_level_lo, RoC_level_lo), axis=0)\n", "print(\"active_rule1: %s, %s -> %s\" % (spike_level_lo, clim_level_lo, active_rule1))\n", "\n", "# Now we apply this by clipping the top off the corresponding output\n", "# membership function with `np.fmin`\n", "tip_activation_lo = np.fmin(active_rule1, tip_lo) # removed entirely to 0\n", "print(\"tip_activation_lo: %s\" % tip_activation_lo)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "active_rule1: 0.0, 0.0 -> 0.2\n", "tip_activation_lo: [ 0. 0.04444444 0.08888889 0.13333333 0.17777778 0.2 0.2\n", " 0.2 0.2 0.2 0.2 0.2 0.2 0.2\n", " 0.2 0.2 0.2 0.2 0.2 0.2 0.2\n", " 0.2 0.2 0.2 0.2 0.2 0.2 0.2\n", " 0.2 0.2 0.2 0.2 0.2 0.2 0.2\n", " 0.2 0.2 0.2 0.2 0.2 0.2\n", " 0.17777778 0.13333333 0.08888889 0.04444444 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. ]\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# For rule 2 we connect acceptable service to medium tipping\n", "\n", "active_rule2 = np.mean((spike_level_md, clim_level_md, RoC_level_md), axis=0)\n", "print(\"active_rule1: %s, %s -> %s\" % (spike_level_md, clim_level_md, active_rule2))\n", "\n", "#tip_activation_md = np.fmin(clim_level_md, tip_md)\n", "tip_activation_md = np.fmin(active_rule2, tip_md)\n", "#print(\"Medium\")\n", "#print(\"clim_level_md: %s\" % clim_level_md)\n", "#print(tip_md)\n", "print(\"tip_activation_md: %s\" % tip_activation_md)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "active_rule1: 1.0, 0.8 -> 0.733333333333\n", "tip_activation_md: [ 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0.02222222 0.06666667 0.11111111 0.15555556 0.2 0.24444444\n", " 0.28888889 0.33333333 0.37777778 0.42222222 0.46666667 0.51111111\n", " 0.55555556 0.6 0.64444444 0.68888889 0.73333333 0.73333333\n", " 0.73333333 0.73333333 0.73333333 0.73333333 0.73333333 0.73333333\n", " 0.73333333 0.73333333 0.73333333 0.73333333 0.73333333 0.68888889\n", " 0.64444444 0.6 0.55555556 0.51111111 0.46666667 0.42222222\n", " 0.37777778 0.33333333 0.28888889 0.24444444 0.2 0.15555556\n", " 0.11111111 0.06666667 0.02222222 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. ]\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rule High" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# For rule 3 we connect high service OR high food with high tipping\n", "active_rule3 = np.fmax(RoC_level_hi, np.fmax(spike_level_hi, clim_level_hi))\n", "tip_activation_hi = np.fmin(active_rule3, tip_hi)\n", "\n", "print(\"High\")\n", "print(\"active_rule3: %s, %s -> %s\" % (spike_level_hi, clim_level_hi, active_rule3))\n", "print(\"tip_activation_hi: %s\" % tip_activation_hi)\n", "\n", "tip0 = np.zeros_like(x_tip)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "High\n", "active_rule3: 0.0, 0.2 -> 0.2\n", "tip_activation_hi: [ 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0.04444444 0.08888889 0.13333333 0.17777778 0.2 0.2 0.2\n", " 0.2 0.2 0.2 0.2 0.2 0.2 0.2\n", " 0.2 0.2 0.2 0.2 0.2 0.2 0.2\n", " 0.2 0.2 0.2 0.2 0.2 0.2 0.2\n", " 0.2 0.2 0.2 0.2 0.2 0.2 0.2\n", " 0.2 0.2 0.2 0.2 0.2 0.17777778\n", " 0.13333333 0.08888889 0.04444444 0. ]\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# Visualize this\n", "fig, ax0 = plt.subplots(figsize=(8, 3))\n", "\n", "ax0.fill_between(x_tip, tip0, tip_activation_lo, facecolor='g', alpha=0.7)\n", "ax0.plot(x_tip, tip_lo, 'g', linewidth=0.5, linestyle='--', )\n", "ax0.fill_between(x_tip, tip0, tip_activation_md, facecolor='y', alpha=0.7)\n", "ax0.plot(x_tip, tip_md, 'y', linewidth=0.5, linestyle='--')\n", "ax0.fill_between(x_tip, tip0, tip_activation_hi, facecolor='r', alpha=0.7)\n", "ax0.plot(x_tip, tip_hi, 'r', linewidth=0.5, linestyle='--')\n", "ax0.set_title('Output membership activity')\n", "\n", "# Turn off top/right axes\n", "for ax in (ax0,):\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", "plt.tight_layout()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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sRwscP5ubM5eJIybaYm4VFZzeeuuP1NfDiSe2tzqKaqWkpGg6derK4sXXUlpa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\n6c12mi1wRCQCeAoYBfQDxorICY3aXAD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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# Aggregate all three output membership functions together\n", "aggregated = np.fmax(tip_activation_lo,\n", " np.fmax(tip_activation_md, tip_activation_hi))\n", "\n", "# Calculate defuzzified result\n", "#tip = fuzz.defuzz(x_tip, aggregated, 'centroid')\n", "tip = fuzz.defuzz(x_tip, aggregated, 'bisector')\n", "tip_activation = fuzz.interp_membership(x_tip, aggregated, tip) # for plot\n", "\n", "print tip" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.5\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# Visualize this\n", "fig, ax0 = plt.subplots(figsize=(8, 3))\n", "\n", "ax0.plot(x_tip, tip_lo, 'b', linewidth=0.5, linestyle='--', )\n", "ax0.plot(x_tip, tip_md, 'g', linewidth=0.5, linestyle='--')\n", "ax0.plot(x_tip, tip_hi, 'r', linewidth=0.5, linestyle='--')\n", "ax0.fill_between(x_tip, tip0, aggregated, facecolor='Orange', alpha=0.7)\n", "ax0.plot([tip, tip], [0, tip_activation], 'k', linewidth=1.5, alpha=0.9)\n", "ax0.set_title('Aggregated membership and result (line)')\n", "\n", "# Turn off top/right axes\n", "for ax in (ax0,):\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", "plt.tight_layout()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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hQwxPe4rnzqlKUvlE23FinogwtmNTPl/1Ll8s/MJ2HP+99x6cc46zYrGyr04d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\nYzxVK4Wn4IiLMzzV5V0G//f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