gjhernandezp/randomquicksortcompdist.ipynb

## randomquicksortcompdist.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import statistics\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def randomquicksort(alist,times):\n",
    "   comp = [0]\n",
    "   randomquicksorthelper(alist,0,len(alist)-1,comp)\n",
    "   times.append(comp[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def randomquicksorthelper(alist,first,last,comp):\n",
    "   if first<last:\n",
    "\n",
    "       r = randompartition(alist,first,last)\n",
    "       splitpoint = r[0]\n",
    "       tmp = comp.pop()\n",
    "       comp.append(tmp+r[1])\n",
    "    \n",
    "       randomquicksorthelper(alist,first,splitpoint-1,comp)\n",
    "       randomquicksorthelper(alist,splitpoint+1,last,comp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def randompartition(alist,first,last):\n",
    "  \n",
    "   indxpiv = random.randint(first,last)\n",
    "   temp = alist[indxpiv]\n",
    "   alist[indxpiv] = alist[first]\n",
    "   alist[first] = temp\n",
    "   \n",
    "   pivotvalue = alist[first]\n",
    "   compspart = 0\n",
    "\n",
    "   leftmark = first+1\n",
    "   rightmark = last\n",
    "\n",
    "   done = False\n",
    "   while not done:\n",
    "\n",
    "       while leftmark <= rightmark and alist[leftmark] <= pivotvalue:\n",
    "           leftmark = leftmark + 1\n",
    "           compspart = compspart + 1\n",
     "       compspart = compspart + 1\n",
    "\n",
    "       while alist[rightmark] >= pivotvalue and rightmark >= leftmark:\n",
    "           rightmark = rightmark -1\n",
    "           compspart = compspart + 1\n",
    "       compspart = compspart + 1\n",
    "\n",
    "       if rightmark < leftmark:\n",
    "           done = True\n",
    "       else:\n",
    "           temp = alist[leftmark]\n",
    "           alist[leftmark] = alist[rightmark]\n",
    "           alist[rightmark] = temp\n",
    "\n",
    "   temp = alist[first]\n",
    "   alist[first] = alist[rightmark]\n",
    "   alist[rightmark] = temp\n",
    "\n",
    "\n",
    "   return rightmark,compspart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def randomquicksorttimedistrib(s,r):\n",
    "    \n",
    "    # Create an array of 1 .. n  \n",
    "    n=s\n",
    "    runs=r\n",
    "    x = []\n",
    "    for i in range(1,n+1):\n",
    "        x.append(n+1-i)\n",
    "    \n",
    "     # Run quicksort for each permutation\n",
    "    tlist =[]\n",
    "    for p in range(1,runs+1):\n",
    "          y = list(x)\n",
    "          randomquicksort(y,tlist)\n",
    "          \n",
    "        \n",
    "    plt.hist(tlist)\n",
    "    plt.title(\"Number of comparison of Quicksort for all permutaions\")\n",
    "    plt.xlabel(\"Comparisons of elements\")\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"n\",n)\n",
    "    print(\"runs\",runs)\n",
    "    print(\"mean\",statistics.mean(tlist))\n",
    "    print(\"stdev\",statistics.stdev(tlist))\n",
    "    print(\"min\",min(tlist))\n",
    "    print(\"max\",max(tlist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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AYmZmpXBAMTOzUjigmJlZKRxQzMysFA35PRRrHekXABrPj8UxG/gcUAalRh/cmxPEzKyx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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x74f67b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n 100\n",
      "runs 10000\n",
      "mean 647.8568\n",
      "stdev 58.04254118677732\n",
      "min 522\n",
      "max 960\n"
     ]
    }
   ],
   "source": [
    "randomquicksorttimedistrib(100,10000)"
   ]
  },
  {
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import random\n",
	"import statistics\n",
	"import matplotlib.pyplot as plt\n",
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"def randomquicksort(alist,times):\n",
	" comp = [0]\n",
	" randomquicksorthelper(alist,0,len(alist)-1,comp)\n",
	" times.append(comp[0])"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"def randomquicksorthelper(alist,first,last,comp):\n",
	" if first<last:\n",
	"\n",
	" r = randompartition(alist,first,last)\n",
	" splitpoint = r[0]\n",
	" tmp = comp.pop()\n",
	" comp.append(tmp+r[1])\n",
	" \n",
	" randomquicksorthelper(alist,first,splitpoint-1,comp)\n",
	" randomquicksorthelper(alist,splitpoint+1,last,comp)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"def randompartition(alist,first,last):\n",
	" \n",
	" indxpiv = random.randint(first,last)\n",
	" temp = alist[indxpiv]\n",
	" alist[indxpiv] = alist[first]\n",
	" alist[first] = temp\n",
	" \n",
	" pivotvalue = alist[first]\n",
	" compspart = 0\n",
	"\n",
	" leftmark = first+1\n",
	" rightmark = last\n",
	"\n",
	" done = False\n",
	" while not done:\n",
	"\n",
	" while leftmark <= rightmark and alist[leftmark] <= pivotvalue:\n",
	" leftmark = leftmark + 1\n",
	" compspart = compspart + 1\n",
	" compspart = compspart + 1\n",
	"\n",
	" while alist[rightmark] >= pivotvalue and rightmark >= leftmark:\n",
	" rightmark = rightmark -1\n",
	" compspart = compspart + 1\n",
	" compspart = compspart + 1\n",
	"\n",
	" if rightmark < leftmark:\n",
	" done = True\n",
	" else:\n",
	" temp = alist[leftmark]\n",
	" alist[leftmark] = alist[rightmark]\n",
	" alist[rightmark] = temp\n",
	"\n",
	" temp = alist[first]\n",
	" alist[first] = alist[rightmark]\n",
	" alist[rightmark] = temp\n",
	"\n",
	"\n",
	" return rightmark,compspart"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"def randomquicksorttimedistrib(s,r):\n",
	" \n",
	" # Create an array of 1 .. n \n",
	" n=s\n",
	" runs=r\n",
	" x = []\n",
	" for i in range(1,n+1):\n",
	" x.append(n+1-i)\n",
	" \n",
	" # Run quicksort for each permutation\n",
	" tlist =[]\n",
	" for p in range(1,runs+1):\n",
	" y = list(x)\n",
	" randomquicksort(y,tlist)\n",
	" \n",
	" \n",
	" plt.hist(tlist)\n",
	" plt.title(\"Number of comparison of Quicksort for all permutaions\")\n",
	" plt.xlabel(\"Comparisons of elements\")\n",
	" plt.ylabel(\"Frequency\")\n",
	" plt.show()\n",
	" \n",
	" print(\"n\",n)\n",
	" print(\"runs\",runs)\n",
	" print(\"mean\",statistics.mean(tlist))\n",
	" print(\"stdev\",statistics.stdev(tlist))\n",
	" print(\"min\",min(tlist))\n",
	" print(\"max\",max(tlist))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
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AYmZmpXBAMTOzUjigmJlZKRxQzMysFA35PRRrHekXABrPj8UxG/gcUAalRh/cmxPEzKyx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\nRU5ErkJggg==\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x74f67b8>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	},
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"n 100\n",
	"runs 10000\n",
	"mean 647.8568\n",
	"stdev 58.04254118677732\n",
	"min 522\n",
	"max 960\n"
	]
	}
	],
	"source": [
	"randomquicksorttimedistrib(100,10000)"
	]
	},
	{
	"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": "3.4.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}