lucaspg96/Fuzzy_Test_3.ipynb

## Fuzzy_Test_3.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import skfuzzy as fuzz\n",
    "import skfuzzy.control as ctrl\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "from matplotlib.font_manager import FontProperties\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "noises = np.arange(-10,11)\n",
    "anoying = np.arange(0,101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x1 = ctrl.Antecedent(noises, 'x')\n",
    "y1 = ctrl.Consequent(anoying, 'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/367095/miniconda3/envs/fuzzy/lib/python3.6/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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F8wcp5T7Aj8IJ3zN/ygjavW31u/V1l51Pn19ympLxAhzb6FWr37bz23go4SHD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\nZseFHayatspLURmU8GiYuQKO/WVIwzQePEiQLc4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8080afd1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotulosX = ['NB', 'NS', 'Z', 'PS', 'PB']\n",
    "x1.automf(names=rotulosX)\n",
    "x1.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/367095/miniconda3/envs/fuzzy/lib/python3.6/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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4D2revDnffPPNKa8NHTqU7t27O7L9YJtFfmNMb2PMu8aY9/wPWytT4Qlz4l2u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\ntnAk7N2kwbso4Z+wN3rWryz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8080afd0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotulosY = ['N','S','V']\n",
    "y1.automf(names=rotulosY,)\n",
    "y1.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para aproximar o Y da figura apresentada no livro, elevei todos os números ao quadrado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/367095/miniconda3/envs/fuzzy/lib/python3.6/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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mxcKN+Xh78Un/BkzZEM+exEvW/2DMN/DnPug0zn7hhGg40Cgav75p\n9Y8k30jm1U2v8k6rdygWUMyO4cTtpFi4uQqhhRnbM5LR83da15323CH49Q14cKbRe0UIe1EK7vsM\nDv0MB3/K9eUWbeG1Ta/Ro0oPWoa1dEBAkZUUCw9wX/1y3FW1OK9/vxetc2i/SLsB3z0G7d+AUrUc\nF1B4rkJF4YHp8MMYuJSY40vn7J/D5ZuXGdVwlIPCiaykWHiIN3tEsv/UZb7bkcMb8pfXoXg1aDzE\nYbmEoEIzY2aAxU8Ya7rfwd6kvczYO4P37n4PXy9Zo8IMUiw8RCE/b754uBHv/nyQw3/eYbro/cvg\n8Cq471PpJiscr9Wz4BMA69/9x1OXUy/z4oYXebX5q5QPKm9COAFSLDxKjTJBvNy1Jk/P28nVm1m+\nwZ2Phx//BQ9+bVwWEMLRvLyM7rQx3/xtdlqtNW9seoPWYa3pEt7FxIBCioWH6de0Ag0qFOW1pXuM\n9ou067DwUWj7MoQ1Njue8GSBpaDvdGN244sJAMzeP5uz187yYtMXTQ4npFh4oHd61eHgmSt8s+2E\nMQtoyerQ9AmzYwkBlVpCy1GwaAgxp7czY+8MPmj7gcwm6wSkWHigQn7eTHykEbErp3Iz/ndppxDO\npeUzJBcJ5cXVIxjXchxhgWFmJxJIsfBYVdPieM13Hk/ceIbkdFmCUjiPdJ3BS8UK0+PaTe65cNbs\nOCKTFAtPdPU8LByEf6+Pqd2gOaPn7yQ9o2AzgAphK5/s+AQfn0KM6jUPVv4bTv9hdiSBFAvPk5EO\ni4dCZB+I7MOLnWugULy/yk7LsQqRByuOrGD1idW8d/d7eJepC93+C98OtNtyrMJ6Uiw8zdrM+Z7a\nG/Px+Hh78fmAhvy05zQ//nHKxGDC08UmxzJh2wQ+bfcpIf4hxoN1+xrLsS5+3FgHXphGioUn+WMR\n7FsKD8wwponOVKyIH1MGNubNZfvYezIPEw4KYSPJN5IZs24MLzd7mRqhNf7+ZMe3jUKx+i1zwglA\nioXnSNwBK1+GAQugSPF/PF0nLIR3etXhyTk7OHulAAsmCZFHaRlpPLfuOe6tfC/3Vrn3ny/w9jEG\njB78CXbNc3g+YZBi4QkunTSu+/b8HEpHZvuy7vXK8mCT8jw1Zwc30+WUX9if1prxUeMJ8Q/JeYLA\nwqHGF51f34QTWx0XUPyPXYuFUqqrUipWKRWnlHr5Ds8/opT6Qym1Rym1WSlV3555PFLqNVjwMDQf\nDjXv8K3tNs+0j6BsSCFeWbIn5xlqhbCBbw5+w56kPUxoMwEvlcvHUcka0GeKMePAxROOCSj+x27F\nQinlDUwEugG1gQFKqdq3vewocI/Wui7wDjDVXnk8ksUCS4cbb7JWz1r1I15eig8erM+hP68waX28\nnQMKT7YhYQPT90zns/afUdjXyrVTIjpBq2fgm/5wQ9rXHMmeZxbNgDit9RGtdSqwAOiV9QVa681a\n6wuZd7cCMqWkLf36htHlsOfneRqhXcjPm+mPNuWbqBMsizlpx4DCU+0/v583fn+DT9p9kvcR2i1G\nQMW7jDOMDCsW9BI2Yc9iEQYkZLmfmPlYdh4HfrZjHs+yfRocWgn954JP3kdolw4OYPqQJoz7YT/b\nj0kfd2E7Z66eYfTa0bxx1xvUK1kv7xtQyhh/4e0LP/3LWDNe2J1TNHArpdphFIt/Z/P8cKVUtFIq\n+ty5c44N54oO/QIb/guPLDIaBvOpZplgPu7fgKfn7uTIuRQbBhSeKiU1hRFrRjCo1iA6VeqU/w15\n+0DfGXBqF2z6yHYBRbbsWSxOAhWy3C+f+djfKKXqAdOAXlrr83fakNZ6qta6ida6ScmSJe0S1m0k\nRsP3TxlnFKFVCry5u6uX5MUu1Xl05jbOXpYutSL/UjNSeXbdszQq1YhHIx8t+Ab9g+DhhRA9E2Lm\nF3x7Ikf2LBbbgQilVGWllB/wELA86wuUUhWBJcAgrfUhO2bxDEmHYf4A6DXRWKrSRvo3rUi/xhV4\ndOZ2Lt+Qa8Qi7yzawqubXiXYP5hXmr2CstUsx8HlYOBio0ttlkWThO3ZrVhordOBUcAq4ACwUGu9\nTyn1lFLqqcyXvQkUByYppWKUUtH2yuP2Lp+GufdDx7egRjebb35U+2o0DS/G8NnRMgZD5InWmve2\nvcf56+d5t827eHt523YHJWvAQ/Ng6ZPGmbWwC+VqfembNGmio6PlP8TfXL8AX/eAOvdDm+fttpsM\ni2b0/J1oDZ8PaIiPt1M0eQknN/WPqfxy7Bdmdp1JkF+Q/XYUuxKWj4YhPxoFRNyuQKdz8m53dTev\nwNy+UPluaP0vu+7K20vxcf8GXLmRzstL9mCxuNYXDeF48w7M4/u475nccbJ9CwVAja7QaRzM6QPJ\nR+27Lw8kxcKVpV032ihK14Yu/3HIanf+Pt5MHdyYo0lXefuHfTLKW2Rr6eGlzNo3i2mdp1GysIM6\npjQYAK2fg9m94LLMomxLUixcVXoqLBwMgaWhxycOXRa1sJ8PM4Y0Jfr4BVkHQ9zRymMr+XzX50zt\nNJVygeUcu/Nmw6DJY0bBSJGu9rYixcIVZaTBd4+Bl48xV46tGwytEFLIl9lDm/Hr/j/5ZLV0ZBN/\nWXN8De9GvcvkjpMJDwk3J0Tr56B2L6NgXL1jj3yRR1IsXE1GGiwaApZ0Y9pmb1/TohQP9GfesOb8\nsPsUn605bFoO4TzWHF/DuK3jmNxx8j/XpXC0dq9B9c4wu6cUDBuQYuFKbp1RZKRBv9n5msbD1koF\nBTB/eAuWxZzkcykYHm3Nib8KRe3it88ZagKloMNbUK2jcYYhS7MWiBQLV5GeahSK9FToP8cpCsUt\npYICmD+sBd/HnOTT1Yel0dsDrT6+mnFbxjGp4yTnKBS3KAUdx0K1DjCrp7RhFIAUC1eQdt1Yk0Jr\npysUt5QKNs4wVuw5zYSfD0rB8CA/xP/A+KjxTOk4hcji2S+uZZpbBaNGV/j6XukllU9SLJzdrXEU\nhYoabRROWChuKRUUwILhLdhy5DxvLNsr4zA8wMLYhXy681OmdZ5GreK1zI6TPaWg/evQ4GGY2Q0u\nHDM7kcuRYuHMriXD7N5QvCr0+dLUxmxrFSvix7wnmnPoTAovLNpNWobF7EjCTmbuncmMvTOY2WUm\nVYtWNTuOdVo/B3eNgpn3wjnp9p0XUiyc1cUTMKMLVGoJ931qSvfY/AoK8GXW0GZcvJ7GsNnRXEtN\nNzuSsCGLtvD+9vdZFreMr7t+TYXgCrn/kDNpNgzavwFfd4cTUWancRlSLJzRn/tgRldoPAQ6v+PQ\nAXe2UsjPmy8HNaZEoD8Dvooi+Wqq2ZGEDaRlpPHKb6+wN2kvs7rNokyRMmZHyp8GA6D3ZFgwAA6u\nMDuNS5Bi4WyO/mb02ug0Du4aaXaaAvH19uL9vvVoXa04fSdv5sT5a2ZHEgVwJfUKI9eM5Hr6db7s\n9CUh/iFmRyqYiE7w8CL48VljTQyRIykWzmTXXGPAXd/pULev2WlsQinFi11q8ljrytw/ebMs0eqi\nTqacZNCKQVQMrshHbT8iwCfA7Ei2Ub4xPPYzbP4cfnkdLDL9fnakWDgDiwV+fQs2vm/8x63S1uxE\nNjeoRSU+7Fefp+bsYOmuRLPjiDyIORvDoBWDeLDGg7ze4nV8vHzMjmRbxavCE6vh5C74dhCkXjU7\nkVOSYmG2m1dg4SBIiIIn1kLJ6mYnspt7qpdk/vAWfPjLId5fdZAM6Vrr9H6I/4Ex68YwtuVYHqn1\niNlx7KdwKAxaanRRn9HV6GAi/kaKhZnOx8O0jlCoGAxeBkWKm53I7qqXDuL7ka3YfuwCT8zazqXr\nskyrM0qzpPHetveYvHsyX3X+irvL3212JPvz8TOWJK7XD77qAEc3mp3IqUixMMuhVTC9MzR/Enp+\n7tSD7WytRKA/855oTqXiRej1xSZiz1wxO5LI4vz18zz565McvXyU+d3nU72Y+57t/oNS0HI03D8V\nvnsctkw0Zk4QUiwcLiMd1oyDH8YY6wY3GeqSXWMLytfbi7E9I3mmQwQDvtrK4h3SjuEMdv65k4d+\neoj6Jeszsf1E1+/xlF9V2xntGLsXGJ1OblwyO5HpZA1uR7p0EhY/YZxF3D8VAkuZncgpHDxzmZHz\ndtKwYjHG9YqksJ+bNaC6AIu2MGPvDObun8u4VuM847KTNdJuwKpXIX4N9J0JYY3MTlQQsga3S4hd\nCVPbGrNfDlwihSKLmmWCWT6qNRat6fXF7xw4fdnsSB7l3LVzPL36aTYmbmRBjwVSKLLyDYAeHxlT\nnc/rC1smGb0XPZCcWdjbzRT45TWIX2vM71SppdmJnNriHYmMX3GAJ++uwhNtquDt5XmX6Bxp9fHV\n/N/W/+OB6g/wdP2n3a9brC0lH4Elw8GvCPSaBCFhZifKqwK9maRY2FPCNuM/V6WW0HUCBASbncgl\nJCRf4/mFuwH4sF99KoQWNjmR+7mSeoUJ2yYQczaG8a3H06BUA7MjuYaMdNj0MURNgW7vQZ0HXKnN\nUS5DOZ2bKfDzy/DtQGPajt6TpFDkQYXQwswf3oIOtUrR84tNzNh0VMZk2NDaE2vpvaw3/t7+LLpv\nkRSKvPD2gXtehIHfGYNoFzziMetjyJmFrR1eDT8+B+Gtoct4Y7CPyLf4cym8smQPqekW3nugHjXK\nBJkdyWUlXU/i3ah3ib0Qy1t3vUXTMk3NjuTa0m/Cbx/B9q+M9b4bPwZeTv39Wy5DOYWLCUbbxKkY\n6PGx0ZAtbMJi0SzYnsAHv8TyQKMwnukQQVCA86/t4SzSLel8G/stX+7+kvsj7uep+k+5z9xOzuDs\nAVj+DKCh23+duceUFAtTpd0wJiHbOhGaPwWtxoBvIbNTuaVzV27y35UH2XDoHC93q0mfhmEo17le\nbIrtZ7bzn6j/ULxQcV5p9orrLFLkaiwW2P2NMYaqelej95TzzcggxcIUlgz4YyGsGw9l60OX/0Cx\nSman8gg7T1xg7PJ9ALzctSYtq5UwOZHzib8Yz6c7P+Vg8kFebPoiHSt2lMLqCNcvwvp3jc+GlqOg\n+dPg5zQdNKRYOJTWcPgXWP02+AdCx7eh0l3m5fFQFovmpz2neX9VLOElivBSlxrUCfPQ0cZZnLl6\nhsm7J7M+YT1D6wzloZoP4e/tOVPJOI2kOFj7jtEj8p6XoOFAZ1gWWYqFQ2gNsSuMHhBpN4zF32t2\nd6Vuc24pNd3C/G0nmLgujrphIYzuEEGDCkXNjuVwiVcSmbZnGr8e/5UHqz/I0LpDCfaTHnimS9wB\na96GC0eN9b8bPGLmPHBSLOwqPRX2LTXaJRRw90tQs4ez93rwODfSMlgYncDk9fFUKxXIsDZVaBNR\nwu0vvRxMPsic/XPYmLiRB6s/yKDagygWUMzsWOJ2Cdtgw3/h7H5oMQIaDYIAh58JS7Gwi5SzsONr\n2D4dStU0/oEjOsuZhJNLTbfw/a6TzPjdGJvxWKvK9G5Yzq3mm0q3pLMhcQPzDszj+OXjDKg5gH41\n+smZhCs4uRO2ToLDvxpToTd7EkpUc9TepVjYTEa6MWHYztnGWti1e0KLp6F0pH32J+xGa82W+PPM\n+P0o244m071eWfo1qUCDCkVd9mzj+OXjLD28lOXxyykXWI5Haj1Cx0od8fUy/Vq4yKvLp2D7NOOz\npniEcaZRu5cxlYj9SLEoEIvFWKVu31LYvwxCyhv/cJH3y6hrN3Hm0g0W70xkUXQC3l6K7vXK0aNe\nWaqXdv4BfmeunuHX47/yy7FfOHHlBD2q9KBPtT5UK+awb6PCntJT4dBK2DXH+Byq3hUi+0DV9vZo\n23DeYqGU6gp8CngD07TWE257XmU+fy9wDRiitd6Z0zZtUixuXIajGyButbEIUaFQ4x8osjeUiCjY\ntoXT0lqzK+EiP/1xmhV7TlPE34f2NUvRtnpJGocXw9/H2+yIWLSFA+cPsOnkJn47+RtHLx2lXYV2\ndAnvQouyLfA1v0eNsJcrZ+DAD8YX1z/3GQUjohNU7QBBpW2xB+csFkopb+AQ0AlIBLYDA7TW+7O8\n5l5gNEaxaA58qrVuntN281Usrp43qnbCVjgRBX/uhfJNjX+IiM5SIDyQxaKJSbzIhthzrD90jiNn\nU2hYqRhNMm/1KxSliL/92znSMtI4mHyQXWd3sevsLnae3UmIfwitw1rTulxrmpZpKgXCE10+DXG/\nGl9oj6yHkApQsQVUvAsqNDeugOT9cqrTFou7gLFa6y6Z918B0Fq/m+U1XwLrtdbzM+/HAm211qez\n2262xSI9Fa6cggvH4eJxY33rP/cZt9SrUL4xVGgBFZsbhcK+1waFi0m+mkr0sWR2HL9A9PEL7Dt1\niTLBAdQsE0yNMkGElyhM+WKFqVCsMCWD/PM0dbpFW7h08xInU06SmJJI4pVE4i7GcejCIU5cPkGl\n4Eo0LNWQBqUa0KhUI8oFlrPjbypcTkYanN4NJ7YaX3gTtkFGKpSuY7SnFq8GxcKhaCWjiGQ/CNBp\ni0VfoKvW+onM+4OA5lrrUVle8yMwQWu9KfP+GuDfWutsTx1KVQ7U/cbWNsY9WDLAkmYcTEuGcY3P\nt1DmrQj4BxkD53wLUcDjJDyM1pprqRmk3Ewn5WY611MzuJ6WwY20DNIyNN5eCj9vL3y8FV5K4aWM\nL3qaDDTpWEjHwnXSSCGDq3gRgD/F8ack/pSgEGEUpgKFKIsXfmb/usLFBGdcoHzqESqkxlMq7SQl\n089QIv0MoelnsShvUryCSfEOJlX5k658SVe+1P336gJ9CLpEf0Kl1HBgeObdmxOHbN9rZh4nUgJI\nMjuEk5Bj8Rc5Fn/x0GNx9p8Pvaz2aq3r5HeL9iwWJ4EKWe6Xz3wsr69Baz0VmAqglIrWWjexbVTX\nJMfiL3Is/iLH4i9yLP6ilCpQzyB7DkPeDkQopSorpfyAh4Dlt71mOTBYGVoAl3JqrxBCCGEOu51Z\naK3TlVKjgFUYXWdnaK33KaWeynx+CrACoydUHEbX2cfslUcIIUT+2bXNQmu9AqMgZH1sSpa/a2Bk\nHjc71QbR3IUci7/IsfiLHIu/yLH4S4GOhcuN4BZCCOF4MnWqEEKIXLlUsVBKdVVKxSql4pRSL5ud\nx5GUUhWUUuuUUvuVUvuUUmMyHw9VSv2qlDqc+adHzE+tlPJWSu3KHKvjycehqFLqO6XUQaXUAaXU\nXR58LJ7LfG/sVUrNV0oFeNKxUErNUEqdVUrtzfJYtr+/UuqVzM/SWKVUl9y27zLFInP6kIlAN6A2\nMEApVdvcVA6VDjyvta4NtABGZv7+LwNrtNYRwJrM+55gDHAgy31PPQ6fAiu11jWB+hjHxOOOhVIq\nDK1lOxcAAAOFSURBVHgGaJI5lsAbowemJx2Lr4Gutz12x98/87PjISAy82cmZX7GZstligXQDIjT\nWh/RWqcCC4BeJmdyGK316VuTLGqtr2B8KIRhHINZmS+bBfQ2J6HjKKXKA92BaVke9sTjEALcDUwH\n0Fqnaq0v4oHHIpMPUEgp5QMUBk7hQcdCa70RSL7t4ex+/17AAq31Ta31UYweqc1y2r4rFYswICHL\n/cTMxzyOUiocaAhEAaWzjE05A9hkekon9wnwEmDJ8pgnHofKwDlgZuYluWlKqSJ44LHQWp8EPgBO\nAKcxxmz9ggcei9tk9/vn+fPUlYqFAJRSgcBi4Fmt9eWsz2V2RXbr7m1KqR7AWa31juxe4wnHIZMP\n0AiYrLVuCFzltsssnnIsMq/F98IooOWAIkqpgVlf4ynHIjsF/f1dqVhYNTWIO1NK+WIUinla6yWZ\nD/+plCqb+XxZ7jgpjFtpBfRUSh3DuBTZXik1F887DmB8G0zUWkdl3v8Oo3h44rHoCBzVWp/TWqcB\nS4CWeOaxyCq73z/Pn6euVCysmT7EbWUuFDUdOKC1/ijLU8uBRzP//iiwzNHZHElr/YrWurzWOhzj\n/8BarfVAPOw4AGitzwAJSqkamQ91APbjgccC4/JTC6VU4cz3SgeMdj1PPBZZZff7LwceUkr5K6Uq\nAxHAtpw25FKD8jIXS/qEv6YPGW9yJIdRSrUGfgP28Ne1+lcx2i0WAhWB40A/rfXtjVxuSSnVFnhB\na91DKVUcDzwOSqkGGA39fsARjClzvPDMY/E20B+j5+Au4AkgEA85Fkqp+UBbjJl2/wTeAr4nm99f\nKfUaMBTjeD2rtf45x+27UrEQQghhDle6DCWEEMIkUiyEEELkSoqFEEKIXEmxEEIIkSspFkIIIXIl\nxUIIIUSupFgIIYTIlRQLIQpIKTVOKfVslvvjb603IoS7kEF5QhRQ5izAS7TWjZRSXsBhoJnW+ryp\nwYSwIR+zAwjh6rTWx5RS55VSDTGmgN4lhUK4GykWQtjGNGAIUAaYYW4UIWxPLkMJYQOZMyHvAXyB\nCK11hsmRhLApObMQwga01qlKqXXARSkUwh1JsRDCBjIbtlsAD5qdRQh7kK6zQhSQUqo2xoL3a7TW\nh83OI4Q9SJuFEEKIXMmZhRBCiFxJsRBCCJErKRZCCCFyJcVCCCFErqRYCCGEyJUUCyGEELn6f+MF\nor+DmkloAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f807e017208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for s in rotulosY:\n",
    "    for i,_ in enumerate(y1[s].mf):\n",
    "        y1[s].mf[i] *= y1[s].mf[i]\n",
    "\n",
    "y1.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para gerar a figura, primeiro precisamos definir as regras do sistema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rule1 = ctrl.Rule(antecedent= x1['Z'],consequent=y1['N'],label='rule1' )\n",
    "rule2 = ctrl.Rule(antecedent= (x1['NS'] | x1['PS']) ,consequent=y1['S'],label='rule2' )\n",
    "rule3 = ctrl.Rule(antecedent= ( x1['NB'] | x1['PB']),consequent=y1['V'],label='rule3' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "system_regras = ctrl.ControlSystem(rules=[rule1, rule2, rule3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "system = ctrl.ControlSystemSimulation(system_regras)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em seguida, com o sistema montado, devemos computar as saídas para alguns dos valores. Nesse caso, estou testando para todos da entrada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "saida = []\n",
    "for k0 in noises:\n",
    "    system.input['x'] = k0\n",
    "    system.compute()\n",
    "    saida.append(system.output['y'])\n",
    "saida = np.asarray(saida)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fim, plotamos a figura."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/367095/miniconda3/envs/fuzzy/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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5daPrsuiORdxz9T3WQmgZU2AhEJGcew26AJ/5vVfoexBM+SAi/Kntn5g3ZB61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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f807df2d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.plot(noises,saida,'g-', linewidth=2)\n",
    "\n",
    "ax.set_ylabel('Saída')\n",
    "ax.set_xlabel('Valores da Variável de Entrada')\n",
    "ax.grid(True)\n",
    "ax.legend(loc='upper right')\n",
    "ax.legend(bbox_to_anchor=(1.60, 1.15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/367095/miniconda3/envs/fuzzy/lib/python3.6/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
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gPnNTR6Hf31a2B9fsdjV5fogJyuyXuD5PfrSyKd2uxgohhKtwvurf7ZRqa37H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\n9rsFk9mkPrsaK8m5QHAocOMk0+lqtauxUpBWgH1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9ec26f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "light = np.arange(-1,1,0.01)\n",
    "x2 = ctrl.Antecedent(light, 'x')\n",
    "rotulosX = ['NB', 'NS', 'Z', 'PS', 'PB']\n",
    "x2.automf(names=rotulosX,)\n",
    "x2.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para gerar o gráfico da saída, precisamos fazer umas pequenas modificações nos conjuntos gerados automaticamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/367095/miniconda3/envs/fuzzy/lib/python3.6/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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ffg67BrSf4J3cuq09ZcDbSqkapdRt4EM8g+4PoZR6RSk1Uyk1c9CgQYGEbChf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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9ec4d1d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = np.arange(-1,1,0.001)\n",
    "y2 = ctrl.Consequent(out, 'y')\n",
    "y2.automf(names=['NL','NM','S','PM','PL'])\n",
    "\n",
    "for i,_ in enumerate(y2['NL'].mf):\n",
    "    y2['NL'].mf[i] = y2['NL'].mf[i] if out[i]>=-.8 else 1\n",
    "        \n",
    "for i,_ in enumerate(y2['NM'].mf):\n",
    "    y2['NM'].mf[i] = y2['NM'].mf[i] if out[i]>=-.8 else 0\n",
    "    \n",
    "for i,_ in enumerate(y2['PL'].mf):\n",
    "    y2['PL'].mf[i] = y2['PL'].mf[i] if out[i]<=.8 else 1\n",
    "        \n",
    "for i,_ in enumerate(y2['PM'].mf):\n",
    "    y2['PM'].mf[i] = y2['PM'].mf[i] if out[i]<=.8 else 0\n",
    "\n",
    "y2.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para gerar um gráfico mais semelhante, foram adicionadas duas novas regras para relacionar os conjuntos que não apareciam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rule1 = ctrl.Rule(antecedent= x2['Z'],consequent=y2['S'],label='rule1' )\n",
    "rule2 = ctrl.Rule(antecedent= x2['NS'],consequent=y2['NM'],label='rule2' )\n",
    "rule3 = ctrl.Rule(antecedent= x2['NB'],consequent=y2['NL'],label='rule3' )\n",
    "rule4 = ctrl.Rule(antecedent= x2['PS'] ,consequent=y2['PM'],label='rule4' )\n",
    "rule5 = ctrl.Rule(antecedent= x2['PB'] ,consequent=y2['PL'],label='rule5' )\n",
    "\n",
    "system_regras = ctrl.ControlSystem(rules=[rule1,rule3,rule5,rule4,rule2])\n",
    "system = ctrl.ControlSystemSimulation(system_regras)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fim, calculamos a saída para algumas entradas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/367095/miniconda3/envs/fuzzy/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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xSvWTiEjaUkFJQx98/QHtxrdj7ea1tG7Wmik9p9CobqOwY4mIlEoFJc0s+2oZ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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9ec6b8400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saida = []\n",
    "#input_ = light\n",
    "#input_ = [-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1.0]\n",
    "input_ = [-1,-0.8,0,.8,1]\n",
    "for k0 in input_:\n",
    "    system.input['x'] = k0\n",
    "    system.compute()\n",
    "    saida.append(system.output['y'])\n",
    "saida = np.asarray(saida)\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(input_,saida,'g-', linewidth=2)\n",
    "\n",
    "ax.set_ylabel('Saída')\n",
    "ax.set_xlabel('Valores da Variável de Entrada')\n",
    "ax.grid(True)\n",
    "ax.legend(loc='upper right')\n",
    "ax.legend(bbox_to_anchor=(1.60, 1.15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Matriz Condicional\n",
    "def MaCcond(A):\n",
    "    n = A.shape[0]\n",
    "    C = np.zeros((n,n))\n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            C[i,j] = A[j,i] / max(A[j,i],A[i,j])\n",
    "    return C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matriz das percepções"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1. ,  0.6,  0.4,  0.3],\n",
       "       [ 0.7,  1. ,  0.1,  0.4],\n",
       "       [ 0.2,  0.4,  1. ,  0.3],\n",
       "       [ 0.5,  0.3,  0.4,  1. ]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = np.array([[1,.6,.4,.3],[.7,1,.1,.4],[.2,.4,1,.3],[.5,.3,.4,1]])\n",
    "M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Máximo Condicional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  1.        ,  0.5       ,  1.        ],\n",
       "       [ 0.85714286,  1.        ,  1.        ,  0.75      ],\n",
       "       [ 1.        ,  0.25      ,  1.        ,  1.        ],\n",
       "       [ 0.6       ,  1.        ,  0.75      ,  1.        ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R = MaCcond(M)\n",
    "R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtemos a menor percepção para cara elemento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.3, 0.25, 0.5, 0.7]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = [0.3,0.4,0.6,0.7]\n",
    "v = [min(R[:,r]) for r in range(R.shape[1])]\n",
    "v = [min(a,b) for a,b in zip(s,v)]\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assim, x4 é o mais semelhante"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matriz dos objetivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.7,  0.9,  0.3],\n",
       "       [ 0.4,  0.6,  0.8],\n",
       "       [ 0.7,  0.3,  6. ],\n",
       "       [ 0.8,  0.5,  0.5]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "O = np.array([[0.7,.9,0.3],[0.4,.6,0.8],[0.7,0.3,6],[.8,0.5,0.5]])\n",
    "O"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preferência dos objetivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.6,  0.8,  0.7,  0.5])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = np.array([0.6,.8,0.7,0.5])\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.4,  0.2,  0.3,  0.5])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_barra =  1-p\n",
    "p_barra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Escolha(O,b_barra):\n",
    "    M = np.copy(O)\n",
    "    l,c = O.shape\n",
    "    v = np.zeros((c,1))\n",
    "    for j in range(c):\n",
    "        com1 = np.zeros((l,1))\n",
    "        for i in range(l):\n",
    "            com1[i] = max(M[i,j],b_barra[i])\n",
    "            menor = min(com1)\n",
    "        v[j] = menor\n",
    "    D = np.argmax(v)\n",
    "    return D+1,v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.4],\n",
       "       [ 0.3],\n",
       "       [ 0.4]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Opcao,v = Escolha(O,p_barra)\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realizando a escolha, observamos que existe um empate entre os atributos 1 e 3. \n",
    "Analizando essas colunas com os objetivos, podemos perceber que os objetivos que geram o empate são o 1 e o 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.7,  0.3,  6. ],\n",
       "       [ 0.8,  0.5,  0.5]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "O2 = O[2:]\n",
    "O2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma vez removidos os objetivos, podemos refazer a escolha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.7],\n",
       "       [ 0.4],\n",
       "       [ 0.5]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Opcao,v = Escolha(O2,p_barra)\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assim, o atributo 1 é o escolhido."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  2, -1],\n",
       "       [-1,  4,  2],\n",
       "       [-5,  2,  5]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MU = np.array([[3,2,-1],[-1,4,2],[-5,2,5]])\n",
    "MU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1. ,  0.7,  0.1,  0. ,  0. ,  0. ],\n",
       "       [ 0. ,  0.3,  0.9,  0.9,  0.3,  0. ],\n",
       "       [ 0. ,  0. ,  0. ,  0.1,  0.7,  1. ]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PD = np.array([[ 1,0.7,0.1,0.0,0,0 ],[0.0,0.3,0.9,0.9,0.3, 0],\n",
    "[0.0,0.0,0.0,0.1,0.7,1.]])\n",
    "PD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.2,  0.1,  0.4,  0.1,  0.1,  0.1])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = np.array([0.2,0.1,0.4,0.1,0.1,0.1])\n",
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1. ,  0.7,  0.2,  0. ,  0. ,  0. ],\n",
       "       [ 0. ,  0.3,  0.8,  0.8,  0.3,  0. ],\n",
       "       [ 0. ,  0. ,  0. ,  0.2,  0.7,  1. ]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "growth = np.array([[1,0.7,0.2,0,0,0],[0,0.3,0.8,0.8,0.3,0],[0,0,0,0.2,0.7,1.]])\n",
    "growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.1,  0.1,  0.5,  0.1,  0.1,  0.1],\n",
       "       [ 0. ,  0. ,  0.1,  0.4,  0.4,  0.1],\n",
       "       [ 0.1,  0.2,  0.4,  0.2,  0.1,  0. ],\n",
       "       [ 0.5,  0.1,  0. ,  0. ,  0.2,  0.2],\n",
       "       [ 0. ,  0. ,  0. ,  0.1,  0.3,  0.6],\n",
       "       [ 0.1,  0.7,  0.2,  0. ,  0. ,  0. ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncertain_s_x = np.array([[0.1,0.1,0.5,0.1,0.1,0.1],[0,0,0.1,0.4,0.4,0.1],[0.1,0.2,0.4,0.2,0.1,0],\n",
    "                [0.5,0.1,0.,0,0.2,0.2],[0,0,0,0.1,0.3,0.6],[0.1,0.7,0.2,0.,0.,0.]])\n",
    "uncertain_s_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  1.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  1.,  0.],\n",
       "       [ 1.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  1.],\n",
       "       [ 0.,  1.,  0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perfect_s_x = np.array([[0,0,1.,0,0,0],[0,0,0,1,0,0],[0,0,0,0,1,0],\n",
    "                [1,0,0,0,0,0],[0,0,0,0,0,1],[0,1,0,0.,0.,0.]])\n",
    "perfect_s_x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculamos a probabilidade marginal de M3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marginal_m3 = growth[2].dot(ps)\n",
    "marginal_m3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculamos a posterior de M3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        ,  0.        ,  0.01333333,  0.07      ,\n",
       "         0.05882353],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.05333333,  0.28      ,\n",
       "         0.05882353],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.02666667,  0.07      ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.14      ,\n",
       "         0.11764706],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.01333333,  0.21      ,\n",
       "         0.35294118],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posterior_m3 = (growth[2]*uncertain_s_x*ps)/(uncertain_s_x.dot(ps))\n",
    "posterior_m3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fim, descobrimos a probabilidade de D2 dado M3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32074303405572757"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_d2_m3 = (PD[1].dot(posterior_m3).dot(ps))/marginal_m3\n",
    "p_d2_m3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculamos a marginal de M2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46000000000000008"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marginal_m2 = growth[1].dot(ps)\n",
    "marginal_m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A posterior de M2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.02307692,  0.69565217,  0.05333333,  0.03      ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.        ,  0.13913043,  0.21333333,  0.12      ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.04615385,  0.55652174,  0.10666667,  0.03      ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.02307692,  0.        ,  0.        ,  0.06      ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.05333333,  0.09      ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.16153846,  0.27826087,  0.        ,  0.        ,\n",
       "         0.        ]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posterior_m2 = (growth[1]*uncertain_s_x*ps)/(uncertain_s_x.dot(ps))\n",
    "posterior_m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dessa vez, calculamos as probabilidades dos D's, dado M2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.81582788,  0.55494402,  0.30070064])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_d_m2 = (PD.dot(posterior_m2).dot(ps))/marginal_m2\n",
    "p_d_m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fim, pegamos as utilidades de A1 e calculamos a utilidade esperada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2566710290339773"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conditional_a1 = MU[0].dot(p_d_m2)\n",
    "conditional_a1"
   ]
  }
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