GMM Practice Data Emitter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import mlab\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 남자 키 분포 ~ N(173.25, 6.57^2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "men = np.random.normal(173.25, 6.57, 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1109927f0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "/lLSWs3m+QyYKanp3gHF7q+fHe+PkM8HknrF548CuzYNS1py4d4GAH1Z8A5dzpWFFwRXK5r/gm4a\n",
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       "C4JzzrnIC4JzzjkA/j/Tovdj3IzCTQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108cf400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(math.ceil(min(men)),\n",
    "                   math.floor(max(men)),\n",
    "                   40)\n",
    "\n",
    "plt.plot(bins, mlab.normpdf(bins, 173.25, 6.57) * 600)\n",
    "plt.hist(men, bins=bins)\n",
    "\n",
    "plt.title('20s men height distribution (2010)')\n",
    "plt.xlabel('height (cm)')\n",
    "plt.ylabel('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 여자 키 분포 ~ N(158.00, 5.05^2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "women = np.random.normal(158.00, 5.05, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x110adb630>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "+2trapLmaP3ClXQI2b0cft+N6Q0Bto2IvcuQbU9gjog4vbvTMiuF9xCs2f0kb0L6HFkLoT92Z2IR\n",
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       "DX9gkYLHHQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a7b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(math.ceil(min(women)),\n",
    "                   math.floor(max(women)),\n",
    "                   30)\n",
    "\n",
    "plt.plot(bins, mlab.normpdf(bins, 158.00, 5.05) * 200)\n",
    "plt.hist(women, bins=bins)\n",
    "\n",
    "plt.title('20s men height distribution (2010)')\n",
    "plt.xlabel('height (cm)')\n",
    "plt.ylabel('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "observations = np.concatenate([men, women])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 관측값 분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11099cf28>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "WuhKKAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ba51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(math.ceil(min(observations)),\n",
    "                   math.floor(max(observations)),\n",
    "                   40)\n",
    "\n",
    "plt.plot(bins, mlab.normpdf(bins, 173.25, 6.57) * 600)\n",
    "plt.plot(bins, mlab.normpdf(bins, 158.00, 5.05) * 200)\n",
    "plt.plot(bins, mlab.normpdf(bins, 173.25, 6.57) * 600 + mlab.normpdf(bins, 158.00, 5.05) * 200)\n",
    "plt.hist(observations, bins=bins)\n",
    "\n",
    "plt.title('20s height distribution (2010)')\n",
    "plt.xlabel('height (cm)')\n",
    "plt.ylabel('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "men_df = pd.DataFrame(men)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "women_df = pd.DataFrame(women)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "observations_df = pd.DataFrame(observations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "men_df.to_csv('men.csv')\n",
    "women_df.to_csv('women.csv')\n",
    "observations_df.to_csv('observations.csv')"
   ]
  },
  {
   "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}