Pythagoras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import random\n",
    "import numpy\n",
    "from deap import tools\n",
    "from app.problem import pset, creator, toolbox\n",
    "from app.ourMods import draw, eaNigel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen\tnevals\tmedian \tstd    \tmin    \n",
      "0  \t100   \t544.033\t792139 \t225.229\n",
      "1  \t100   \t270.229\t1415.45\t225.229\n",
      "2  \t100   \t270.229\t1152.39\t220.771\n",
      "3  \t100   \t270.229\t320468 \t220.771\n",
      "4  \t100   \t270.229\t409.416\t220.771\n",
      "5  \t100   \t270.229\t404938 \t220.771\n",
      "6  \t100   \t270.229\t211949 \t211.711\n",
      "7  \t100   \t270.229\t1757.59\t203.14 \n",
      "8  \t100   \t270.229\t145744 \t203.14 \n",
      "9  \t100   \t270.229\t1047.81\t203.14 \n",
      "10 \t100   \t270.229\t114025 \t203.14 \n",
      "11 \t100   \t270.229\t213848 \t203.14 \n",
      "12 \t100   \t270.229\t410458 \t210.03 \n",
      "13 \t100   \t270.229\t2986.93\t225.229\n",
      "14 \t100   \t265.229\t830.31 \t132.927\n",
      "15 \t100   \t266.979\t91913.7\t132.927\n",
      "16 \t100   \t270.229\t129292 \t132.927\n",
      "17 \t100   \t270.229\t113976 \t132.927\n",
      "18 \t100   \t235.229\t2853.61\t132.927\n",
      "19 \t100   \t225.229\t155153 \t132.927\n",
      "20 \t100   \t225.229\t404367 \t132.927\n",
      "21 \t100   \t225.229\t1983.25\t132.927\n",
      "22 \t100   \t225.229\t1438.55\t132.927\n",
      "23 \t100   \t225.229\t278.686\t132.927\n",
      "24 \t100   \t225.229\t216478 \t132.927\n",
      "25 \t100   \t225.229\t2699.62\t132.927\n",
      "26 \t100   \t225.229\t114012 \t132.927\n",
      "27 \t100   \t225.229\t360476 \t132.927\n",
      "28 \t100   \t225.229\t113996 \t132.927\n",
      "29 \t100   \t225.229\t1229.6 \t132.927\n",
      "30 \t100   \t225.229\t299949 \t132.927\n",
      "31 \t100   \t225.229\t299977 \t132.927\n",
      "32 \t100   \t214.472\t90792.1\t132.927\n",
      "33 \t100   \t225.229\t145681 \t132.927\n",
      "34 \t100   \t225.229\t113943 \t132.927\n",
      "35 \t100   \t225.229\t189213 \t132.927\n",
      "36 \t100   \t225.229\t414937 \t132.927\n",
      "37 \t100   \t181.55 \t688.241\t132.927\n",
      "38 \t100   \t218    \t848.296\t132.927\n",
      "39 \t100   \t225.229\t169840 \t132.927\n",
      "40 \t100   \t224.553\t767.579\t132.927\n",
      "41 \t100   \t225.68 \t192859 \t132.927\n",
      "42 \t100   \t228.581\t4178.29\t132.927\n",
      "43 \t100   \t255.229\t180062 \t132.927\n",
      "44 \t100   \t239.179\t208443 \t132.927\n",
      "45 \t100   \t225.229\t574088 \t132.927\n",
      "46 \t100   \t225.229\t2223.73\t132.927\n",
      "47 \t100   \t225.229\t183799 \t132.927\n",
      "48 \t100   \t225.229\t3204.03\t132.927\n",
      "49 \t100   \t225.229\t1020.86\t132.927\n",
      "50 \t100   \t225.229\t693.541\t132.927\n",
      "51 \t100   \t225.229\t145715 \t132.927\n",
      "52 \t100   \t225.229\t1422.57\t132.927\n",
      "53 \t100   \t210.771\t3205.04\t132.927\n",
      "54 \t100   \t225.229\t157528 \t132.927\n",
      "55 \t100   \t210.771\t1430.63\t132.927\n",
      "56 \t100   \t210.771\t114309 \t132.927\n",
      "57 \t100   \t197.998\t2452.56\t132.927\n",
      "58 \t100   \t223.889\t92488.1\t132.927\n",
      "59 \t100   \t225.229\t360450 \t132.927\n",
      "60 \t100   \t218    \t752.89 \t132.927\n",
      "61 \t100   \t210.771\t113980 \t132.927\n",
      "62 \t100   \t225.229\t611.293\t132.927\n",
      "63 \t100   \t225.229\t12538.8\t132.927\n",
      "64 \t100   \t225.229\t145622 \t132.927\n",
      "65 \t100   \t225.229\t1061.07\t132.927\n",
      "66 \t100   \t216.034\t1090.14\t132.927\n",
      "67 \t100   \t222.93 \t113998 \t132.927\n",
      "68 \t100   \t225.229\t744.086\t132.927\n",
      "69 \t100   \t225.229\t84765.7\t132.927\n",
      "70 \t100   \t225.229\t4104.25\t136.599\n",
      "71 \t100   \t225.229\t800.408\t136.599\n",
      "72 \t100   \t270.229\t73605.3\t136.599\n",
      "73 \t100   \t255.229\t343110 \t136.599\n",
      "74 \t100   \t270.229\t1335.4 \t40     \n",
      "40.0\n"
     ]
    },
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105bd7cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop = toolbox.population(n=100)\n",
    "hof = tools.HallOfFame(1)\n",
    "stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
    "stats.register(\"median\", numpy.median)\n",
    "stats.register(\"std\", numpy.std)\n",
    "stats.register(\"min\", numpy.min)\n",
    "pop, logbook = eaNigel(pop, toolbox, 40, 400, stats, halloffame=hof)\n",
    "print(hof[0].fitness.values[0])\n",
    "draw(hof[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}