Untitled


{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitness de cada ejecución"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fi = np.array([\n",
    "    -0.31, -0.31, -0.31, -0.31, -0.31,\n",
    "    -0.31, -0.31, -0.31, -0.31, -0.31,\n",
    "    -0.31, -0.31, -0.31, -0.31, -0.31,\n",
    "    -0.31, -0.31, -0.31, -0.31, -0.3099,\n",
    "    -0.31, -0.31, -0.31, -0.31, -0.31,\n",
    "    -0.31, -0.31, -0.31, -0.31, -0.31,\n",
    "])\n",
    "\n",
    "fi = pd.Series(fi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    30.000000\n",
       "mean     -0.309997\n",
       "std       0.000018\n",
       "min      -0.310000\n",
       "25%      -0.310000\n",
       "50%      -0.310000\n",
       "75%      -0.310000\n",
       "max      -0.309900\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align}\n",
    "\\text{Media Aritmética} & = -0.309997 \\\\\n",
    "\\text{Mediana} & = -0.31 \\\\\n",
    "\\text{Desvío estándar} & = 0.000018 \\\\\n",
    "\\text{Porcentaje de hits} & = 96.66\\% \\\\\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6HRoTndV01urQ2aEx0dmdSR8AAMDC7PQBAAAszKQPAABgYXb6jtBZq0Nnh8ZEZzWdtXTW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\nPgAAgIW56AMAAFjY/wd2ywtp05mZogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec4fd882b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.plot(fi, np.zeros_like(fi), '.')\n",
    "\n",
    "plt.xticks(np.arange(-0.311, -0.309, 0.0001))\n",
    "plt.yticks([])\n",
    "\n",
    "plt.box(on=None)\n",
    "plt.grid(axis='x')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitness de cada generación en una única ejecución"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "generations = np.array([\n",
    "-0.020617341450352322, -0.17218436635844103, -0.29171144429682233, -0.30690217467697584, -0.30877010611165284,\n",
    "-0.30921687705788786, -0.3099544523171261, -0.3099544523171261, -0.3099544523171261, -0.3099749808318596,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788  -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "-0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788, -0.3100200890711788,\n",
    "])\n",
    "\n",
    "generations = pd.Series(generations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    199.000000\n",
       "mean      -0.309312\n",
       "std        0.031711\n",
       "min       -0.620040\n",
       "25%       -0.310020\n",
       "50%       -0.310020\n",
       "75%       -0.310020\n",
       "max       -0.020617\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generations.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0m/xaRLwhIh7ay6m21+qSmY/JzHt2t3nPGOPefR1wI1f1MTczTyLiloi4c91jbe5q/yw6\nVt8QEVcOJj6z+7GLbvPMlc+1tU6Xq/LYR/vJzLw9Ip5e/NQvXfnKGGNk5iN+CiEzHxcR3x8R5edc\nrzcTujw2Ip4XET85xrgrM98Y5+PlX55+2D2a9Hj5njHGZzPz6yLi9sz82O5vitelyzbJzO+LiM+N\nMe4+pv+kc8ZjZYzxUETcvJvg/kVmno4x7ph+2D2a+DH3SRHx9oh4/W7ydF2b1eXIdX/dD59aH3uv\n6b++R71wGmO89JF+brdk9/Qxxn2Z+fUR8blHuatXRMTfjDEeuMZzHpQJXT4TEZ8ZY9y1e/3t8ej7\nLdeFGY+XMcZnd98/kJnviPMx63V74TShyXdHxKt2uwmPj4ivyczfH2O8ZqUj78XEjy0xxngwM98Z\nEd8V1/gP2h2KGV0y86si4k8i4g/GGH+20lH3aubj5Yj9Y0TcdMXrN8X5nzWPdptn7n7smHW6XJXL\nfKru1oh47e7l10bEoz1BXx0Rb7vE+7qeXNhljHFfRHw6M5+z+6GXRMRH9nO8zVzYJTOfkJlP3r38\nxIh4WUQc7VfARO+x8otjjJvGGM+K8093/9X1ftHU0HmsfO2Xv3oqM786Il4aEXfv7YTb6HTJiPi9\niLh3jPHGPZ5tS1fzZ9Exe39EPDszT3af5fmhOG9zpVsj4jUREXn+ldyfv+LTnMeq0+XLejukY4xr\n+hYRT42Id0fEJyLiXRFx4+7HnxER77zidk+MiH+OiCdf6/u6nr5dRZfviIi7IuKDEfGnEfGUrc++\ndZeI+MaIuGf37cMR8Qtbn3vrJg+7/Ysi4tatz30IXeL8K+k+sHusfCgi3rD1uQ+kywvjfBfunji/\nkLw7Il6+9dm37rJ7/W0R8U8R8V9xvvPyo1uffYUWr4iIj0fEJ7/88TMiXhcRr7viNm/a/fwHI+J5\nW5/5ELrE+aeBPx0RD8b5FxD8Q0Q86ZHuz3+5AgDQ5F8OBwBocuEEANDkwgkAoMmFEwBAkwsnAIAm\nF04AAE0unAAAmlw4AQA0/S8yrNWJUpI7sQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec4fabcc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5), frameon=False)\n",
    "\n",
    "plt.plot(generations, np.zeros_like(generations), '.')\n",
    "\n",
    "plt.xticks(np.arange(-0.7, 0.2, 0.1))\n",
    "plt.yticks([])\n",
    "\n",
    "plt.box(on=None)\n",
    "plt.grid(axis='x')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}