afcajamarcar/quick2.ipynb

## quick2.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def partition(alist,first,last):\n",
    "   pivotvalue = alist[first]\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",
    "\n",
    "       while alist[rightmark] >= pivotvalue and rightmark >= leftmark:\n",
    "           rightmark = rightmark -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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def quickSortHelper(alist,first,last):\n",
    "   if first<last:\n",
    "\n",
    "       splitpoint = partition(alist,first,last)\n",
    "\n",
    "       quickSortHelper(alist,first,splitpoint-1)\n",
    "       quickSortHelper(alist,splitpoint+1,last)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def quickSort(alist):\n",
    "   quickSortHelper(alist,0,len(alist)-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def insertion_sort(A):\n",
    "    j = 1\n",
    "    while j < len(A):\n",
    "        key = A[j]\n",
    "        i = j - 1\n",
    "        while (i >= 0) and (A[i] > key):\n",
    "            A[i + 1] = A[i]\n",
    "            i = i -1\n",
    "        A[i + 1] = key\n",
    "        j = j + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time \n",
    "import gc\n",
    "\n",
    "def calc_time(sort_function, array):\n",
    "    timesum = 0\n",
    "    gc.collect()\n",
    "    for i in range(10):\n",
    "        array_copy = list(array)\n",
    "        tic = time.time();\n",
    "        sort_function(array_copy)\n",
    "        toc = time.time();\n",
    "        timesum += toc - tic\n",
    "    return timesum/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random as rnd\n",
    "\n",
    "def exper_analysis(sizes):\n",
    "    results_quick = []\n",
    "    results_insertion = []\n",
    "    for i in sizes:\n",
    "        list1 = range(i)\n",
    "        rnd.shuffle(list1)\n",
    "        results_quick.append(calc_time(quickSort, list1))\n",
    "        results_insertion.append(calc_time(insertion_sort, list1))        \n",
    "    return (results_quick, results_insertion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x77313d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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D+Na3vsX7779PU1MTK1asYMaMGTm3W9NVi/RuCjhZmDJl/9sSq6pyu525GHV09V974vW9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x74bdbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as pl\n",
    "%matplotlib inline\n",
    "pl.clf()\n",
    "sizes = np.arange(1,100,1)\n",
    "(y1,y2) = np.array(exper_analysis(sizes))\n",
    "pl.plot(sizes, y1, 'k.', label = 'Quick sort')\n",
    "pl.plot(sizes, y2, 'bx', label = 'Insertion sort')\n",
    "pl.xlabel('Array size')\n",
    "pl.ylabel('Seconds')\n",
    "pl.legend(loc = 'upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"def partition(alist,first,last):\n",
	" pivotvalue = alist[first]\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",
	"\n",
	" while alist[rightmark] >= pivotvalue and rightmark >= leftmark:\n",
	" rightmark = rightmark -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"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"def quickSortHelper(alist,first,last):\n",
	" if first<last:\n",
	"\n",
	" splitpoint = partition(alist,first,last)\n",
	"\n",
	" quickSortHelper(alist,first,splitpoint-1)\n",
	" quickSortHelper(alist,splitpoint+1,last)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"def quickSort(alist):\n",
	" quickSortHelper(alist,0,len(alist)-1)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"def insertion_sort(A):\n",
	" j = 1\n",
	" while j < len(A):\n",
	" key = A[j]\n",
	" i = j - 1\n",
	" while (i >= 0) and (A[i] > key):\n",
	" A[i + 1] = A[i]\n",
	" i = i -1\n",
	" A[i + 1] = key\n",
	" j = j + 1"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import time \n",
	"import gc\n",
	"\n",
	"def calc_time(sort_function, array):\n",
	" timesum = 0\n",
	" gc.collect()\n",
	" for i in range(10):\n",
	" array_copy = list(array)\n",
	" tic = time.time();\n",
	" sort_function(array_copy)\n",
	" toc = time.time();\n",
	" timesum += toc - tic\n",
	" return timesum/10"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import random as rnd\n",
	"\n",
	"def exper_analysis(sizes):\n",
	" results_quick = []\n",
	" results_insertion = []\n",
	" for i in sizes:\n",
	" list1 = range(i)\n",
	" rnd.shuffle(list1)\n",
	" results_quick.append(calc_time(quickSort, list1))\n",
	" results_insertion.append(calc_time(insertion_sort, list1)) \n",
	" return (results_quick, results_insertion)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.legend.Legend at 0x77313d0>"
	]
	},
	"execution_count": 8,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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S5FlsZpvNbJ2ZHd9VWTMbamZrzOw5M3vQzIYk1TfGzN4ysytLt2ciIpKLkgYc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\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x74bdbd0>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"import numpy as np\n",
	"import pylab as pl\n",
	"%matplotlib inline\n",
	"pl.clf()\n",
	"sizes = np.arange(1,100,1)\n",
	"(y1,y2) = np.array(exper_analysis(sizes))\n",
	"pl.plot(sizes, y1, 'k.', label = 'Quick sort')\n",
	"pl.plot(sizes, y2, 'bx', label = 'Insertion sort')\n",
	"pl.xlabel('Array size')\n",
	"pl.ylabel('Seconds')\n",
	"pl.legend(loc = 'upper left')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 2",
	"language": "python",
	"name": "python2"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 2
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython2",
	"version": "2.7.10"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}