Projet_Algo_tri.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import time\n",
    "import numpy \n",
    "import timeit\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "     <h1>\n",
    "  <p style=\"color:#C32F0F\";> On commence par :</p>\n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "     <h1 > \n",
    "         <p style=\"color:#A1A1A1\";> Tri par : Selection </p>\n",
    "         \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tri_selection(tab):\n",
    "   for i in range(len(tab)):\n",
    "      # Trouver le min\n",
    "       min = i\n",
    "       for j in range(i+1, len(tab)):\n",
    "           if tab[min] > tab[j]:\n",
    "               min = j\n",
    "                \n",
    "       tmp = tab[i]\n",
    "       tab[i] = tab[min]\n",
    "       tab[min] = tmp\n",
    "   return tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tab = [98, 22, 15, 32, 2, 74, 63, 70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tri_selection(tab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 > On va maintenant analyser le temps d'exécution de tri par selection </h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeit.timeit( \"tri_selection\",\n",
    "globals=globals(),\n",
    "number=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeit.timeit( \"tri_selection\",\n",
    "globals=globals(),\n",
    "number=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeit.timeit( \"tri_selection\",\n",
    "globals=globals(),\n",
    "number=20000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 > Alors, Voici La courbe de tri par selection </h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randlist(n):\n",
    "    return([randint(1,100) for i in range(n)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "x,y=[],[]\n",
    "for i in range(1,1000,10):\n",
    "    testTimer=timeit.Timer(partial(tri_selection,randlist(i)))\n",
    "    t=testTimer.timeit(number=10)\n",
    "    x.append(i)\n",
    "    y.append(t)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x,y,marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 >On passe maintenant à :  </h1>\n",
    "        \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "     <h1 > \n",
    "         <p style=\"color:#A1A1A1\";> Tri à bulle  </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tri_bulle(tab):\n",
    "    n = len(tab)\n",
    "    # Traverser tous les éléments du tableau\n",
    "    for i in range(n):\n",
    "        for j in range(0, n-i-1):\n",
    "            # échanger si l'élément trouvé est plus grand que le suivant\n",
    "            if tab[j] > tab[j+1] :\n",
    "                tab[j], tab[j+1] = tab[j+1], tab[j]\n",
    "    return tab           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Programme principale pour tester le code ci-dessus\n",
    "tab = [77, 22, 5, 20, 2, 74, 63, 70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 5, 20, 22, 63, 70, 74, 77]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tri_bulle(tab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 >Maintenant , on va analyser le temps d'exécution de Tri a bulle </h1>\n",
    "        <h1> Voila </h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8500000805943273e-05"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit( \"tri_bulle\",\n",
    "globals=globals(),\n",
    "number=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.012009599999146303"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit( \"tri_bulle\",\n",
    "globals=globals(),\n",
    "number=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0032986000005621463"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit( \"tri_bulle\",\n",
    "globals=globals(),\n",
    "number=20000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 > Voici alors, La courbe de Tri a bulle </h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "x,y=[],[]\n",
    "for i in range(1,1000,10):\n",
    "    testTimer=timeit.Timer(partial(tri_bulle,randlist(i)))\n",
    "    t=testTimer.timeit(number=10)\n",
    "    x.append(i)\n",
    "    y.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16f8ad8>]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,marker='*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 >On passe maintenant à :  </h1>\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "     <h1 > \n",
    "         <p style=\"color:#A1A1A1\";> Tri à insertion  </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tri_insertion(liste):\n",
    "    L = list(liste) \n",
    "    N = len(L)\n",
    "    for n in range(1,N):\n",
    "        cle = L[n]\n",
    "        j = n-1\n",
    "        while j>=0 and L[j] > cle:\n",
    "            L[j+1] = L[j] # decalage\n",
    "            j = j-1\n",
    "        L[j+1] = cle\n",
    "    return L\n",
    "    \n",
    "liste = []\n",
    "for k in range(10):\n",
    "    liste.append(random.randint(0,20))\n",
    "liste_triee = tri_insertion(liste)\n",
    "                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 10, 20, 13, 16, 8, 5, 15, 0, 7]\n"
     ]
    }
   ],
   "source": [
    "print(liste)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 5, 7, 8, 10, 13, 15, 16, 20]\n"
     ]
    }
   ],
   "source": [
    "print(liste_triee)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 >on passe à analyser le temps d'exécution de tri insertion </h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voila"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.79999936639797e-06"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit( \"tri_insertion\",\n",
    "globals=globals(),\n",
    "number=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016020799999751034"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit( \"tri_insertion\",\n",
    "globals=globals(),\n",
    "number=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0012671000004047528"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit( \"tri_insertion\",\n",
    "globals=globals(),\n",
    "number=20000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 > On va visualiser la courbe de tri par insertion </h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "x,y=[],[]\n",
    "for i in range(1,1000,10):\n",
    "    testTimer=timeit.Timer(partial(tri_insertion,randlist(i)))\n",
    "    t=testTimer.timeit(number=10)\n",
    "    x.append(i)\n",
    "    y.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x5355d18>]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 >à une autre Tri :</h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "     <h1 > \n",
    "         <p style=\"color:#A1A1A1\";> Tri par fusion  </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fusion(L1,L2):\n",
    "    n1 = len(L1)\n",
    "    n2 = len(L2)\n",
    "    L12 = [0]*(n1+n2)\n",
    "    i1 = 0\n",
    "    i2 = 0\n",
    "    i = 0\n",
    "    while i1<n1 and i2<n2:\n",
    "        if L1[i1] < L2[i2]:\n",
    "            L12[i] = L1[i1]\n",
    "            i1 += 1\n",
    "        else:\n",
    "            L12[i] = L2[i2]\n",
    "            i2 += 1\n",
    "        i += 1\n",
    "    while i1<n1:\n",
    "        L12[i] = L1[i1]\n",
    "        i1 += 1\n",
    "        i += 1\n",
    "    while i2<n2:\n",
    "        L12[i] = L2[i2]\n",
    "        i2 += 1\n",
    "        i += 1 \n",
    "    return L12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = fusion([1,6,10],[0,7,8,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 6, 7, 8, 9, 10]\n"
     ]
    }
   ],
   "source": [
    "print(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 >On analyse le temps d'exécution de tri par fusion </h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.1000013122102246e-06"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit( \"fusion\",\n",
    "globals=globals(),\n",
    "number=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005556999998589163"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit( \"fusion\",\n",
    "globals=globals(),\n",
    "number=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0018013999997492647"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit( \"fusion\",\n",
    "globals=globals(),\n",
    "number=20000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 > ==> La courbe de Tri fusion </h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "fusion() missing 1 required positional argument: 'L2'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-202-d9a21cb87381>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mtestTimer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTimer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpartial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfusion\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrandlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtestTimer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\timeit.py\u001b[0m in \u001b[0;36mtimeit\u001b[1;34m(self, number)\u001b[0m\n\u001b[0;32m    175\u001b[0m         \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 177\u001b[1;33m             \u001b[0mtiming\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    178\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\timeit.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(_it, _timer, _stmt)\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: fusion() missing 1 required positional argument: 'L2'"
     ]
    }
   ],
   "source": [
    "from functools import partial\n",
    "x,y=[],[]\n",
    "for i in range(1,1000,10):\n",
    "    testTimer=timeit.Timer(partial(fusion,randlist(i)))\n",
    "    t=testTimer.timeit(number=10)\n",
    "    x.append(i)\n",
    "    y.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x,y,marker='^')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 >On passe à: </h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "     <h1 > \n",
    "         <p style=\"color:#A1A1A1\";> Tri Rapide  </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tri_rapide(L):\n",
    "    n = len(L)\n",
    "    pivot = L[n-1]\n",
    "    i = 0\n",
    "    j = 0\n",
    "    while j < n-1:\n",
    "        if L[j] <= pivot:\n",
    "            L[i],L[j] = L[j],L[i]\n",
    "            i += 1\n",
    "        j += 1\n",
    "    L[n-1],L[i] = L[i],L[n-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "liste = []\n",
    "for k in range(11):\n",
    "    liste.append(random.randint(0,40))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(liste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tri_rapide (liste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(liste)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 >On va analyser le temps d'exécution de Tri rapide </h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeit.timeit( \"tri_rapide\",\n",
    "globals=globals(),\n",
    "number=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeit.timeit( \"tri_rapide\",\n",
    "globals=globals(),\n",
    "number=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeit.timeit( \"tri_rapide\",\n",
    "globals=globals(),\n",
    "number=20000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<html>\n",
    "    <body>\n",
    "        <h1 > On constate la courbe de tri rapide suivante :</h1>\n",
    "    </body>\n",
    "    </html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "x,y=[],[]\n",
    "for i in range(1,1000,10):\n",
    "    testTimer=timeit.Timer(partial(tri_rapide,randlist(i)))\n",
    "    t=testTimer.timeit(number=10)\n",
    "    x.append(i)\n",
    "    y.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x,y,marker='p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}