algoritmos_ordenacao.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 2,
  "metadata": {
    "colab": {
      "name": "Metodos_ordenação.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3.9.5 64-bit"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "interpreter": {
      "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "source": [
        "from time import time\n",
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "from scipy.interpolate import UnivariateSpline\n",
        "\n",
        "######################    BUBBLE    ######################\n",
        "def bubble_sort(list):\n",
        "    for iter_num in range(len(list)-1,0,-1):\n",
        "        for idx in range(iter_num):\n",
        "            if list[idx]>list[idx+1]:\n",
        "                temp = list[idx]\n",
        "                list[idx] = list[idx+1]\n",
        "                list[idx+1] = temp\n",
        "\n",
        "######################  INSERTION  ######################\n",
        "def insertion_sort(InputList):\n",
        "    for i in range(1, len(InputList)):\n",
        "        j = i-1\n",
        "        nxt_element = InputList[i]\n",
        "        while (InputList[j] > nxt_element) and (j >= 0):\n",
        "            InputList[j+1] = InputList[j]\n",
        "            j=j-1\n",
        "        InputList[j+1] = nxt_element\n",
        "\n",
        "######################   MERGE    ######################\n",
        "def merge_sort(unsorted_list):\n",
        "    if len(unsorted_list) <= 1:\n",
        "        return unsorted_list\n",
        "    middle = len(unsorted_list) // 2\n",
        "    left_list = unsorted_list[:middle]\n",
        "    right_list = unsorted_list[middle:]\n",
        "    left_list = merge_sort(left_list)\n",
        "    right_list = merge_sort(right_list)\n",
        "    lista = merge(left_list, right_list)\n",
        "    return lista\n",
        "\n",
        "def merge(left_half,right_half):\n",
        "    res = []\n",
        "    while len(left_half) != 0 and len(right_half) != 0:\n",
        "        if left_half[0] < right_half[0]:\n",
        "            res.append(left_half[0])\n",
        "            left_half.remove(left_half[0])\n",
        "        else:\n",
        "            res.append(right_half[0])\n",
        "            right_half.remove(right_half[0])\n",
        "    if len(left_half) == 0:\n",
        "        res = res + right_half\n",
        "    else:\n",
        "        res = res + left_half\n",
        "    return res\n",
        "\n",
        "######################   SHELL   ######################\n",
        "def shell_sort(input_list):\n",
        "    gap = len(input_list) // 2\n",
        "    while gap > 0:\n",
        "        for i in range(gap, len(input_list)):\n",
        "            temp = input_list[i]\n",
        "            j = i\n",
        "            while j >= gap and input_list[j - gap] > temp:\n",
        "                input_list[j] = input_list[j - gap]\n",
        "                j = j-gap\n",
        "            input_list[j] = temp\n",
        "        gap = gap//2\n",
        "\n",
        "#####################  SELECTION  #####################\n",
        "def selection_sort(input_list):\n",
        "    for idx in range(len(input_list)):\n",
        "        min_idx = idx\n",
        "        for j in range( idx +1, len(input_list)):\n",
        "            if input_list[min_idx] > input_list[j]:\n",
        "                min_idx = j\n",
        "        input_list[idx], input_list[min_idx] = input_list[min_idx], input_list[idx]\n",
        "\n",
        "\n",
        "#####################  ORDENAÇÃO  #####################\n",
        "vetores = []\n",
        "time_b = []\n",
        "time_m = []\n",
        "time_i = []\n",
        "time_se = []\n",
        "time_sh = []\n",
        "\n",
        "print('Quantidade de vetores sendo ordenados:')\n",
        "t_total = 0\n",
        "n = 100\n",
        "n_max = 10000\n",
        "q_passos = 10\n",
        "\n",
        "def lista_aleatoria(n,n_max):\n",
        "    lista0 = []\n",
        "    np.random.seed(1)\n",
        "    for i in range(n):\n",
        "        y = random.randint(0, n_max + 1)\n",
        "        lista0.append(y)\n",
        "    return lista0\n",
        "while n <= n_max:\n",
        "  print(n)\n",
        "\n",
        "  t_t0 = time()\n",
        "\n",
        "  t0 = time()\n",
        "  bubble_sort(lista_aleatoria(n,n_max))\n",
        "  t_bubble = time() - t0\n",
        "\n",
        "  t0 = time()\n",
        "  insertion_sort(lista_aleatoria(n,n_max))\n",
        "  t_insertion = time() - t0\n",
        "\n",
        "  t0 = time()\n",
        "  selection_sort(lista_aleatoria(n,n_max))\n",
        "  t_selection = time() - t0\n",
        "\n",
        "  t0 = time()\n",
        "  merge_sort(lista_aleatoria(n,n_max))\n",
        "  t_merge = time() - t0\n",
        "\n",
        "  t0 = time()\n",
        "  shell_sort(lista_aleatoria(n,n_max))\n",
        "  t_shell = time() - t0\n",
        "\n",
        "  delta_t_t = time() - t_t0\n",
        "  t_total += delta_t_t\n",
        "\n",
        "  vetores.append(n)\n",
        "  time_b.append(t_bubble)\n",
        "  time_i.append(t_insertion)\n",
        "  time_se.append(t_selection)\n",
        "  time_m.append(t_merge)\n",
        "  time_sh.append(t_shell)\n",
        "\n",
        "  if n == 100:\n",
        "    n = 0\n",
        "\n",
        "  n += int(n_max/q_passos)\n",
        "\n",
        "vet = int((q_passos)/10)\n",
        "print_vet = 0\n",
        "for i in range(len(vetores)-1):\n",
        "  if i == print_vet:\n",
        "    print(\"\\nNúmero de vetores:\", vetores[i],'\\nBubble:', time_b[i],'s','\\nInsertion:',time_i[i],'s')\n",
        "    print('Selection:',time_se[i],'s','\\nMerge:',time_m[i],'s','\\nShell:',time_sh[i],'s',)\n",
        "    print_vet += vet\n",
        "\n",
        "i =  len(vetores)-1\n",
        "print(\"\\nNúmero de vetores:\", vetores[i],'\\nBubble:', time_b[i],'s','\\nInsertion:',time_i[i],'s')\n",
        "print('Selection:',time_se[i],'s','\\nMerge:',time_m[i],'s','\\nShell:',time_sh[i],'s',)\n",
        "\n",
        "print('\\nTempo de todas as ordenações:',t_total,'s')\n",
        "\n",
        "######################  GRÁFICO  ######################\n",
        "vetores = np.array(vetores)\n",
        "time_b = np.array(time_b)\n",
        "time_i = np.array(time_i)\n",
        "time_se = np.array(time_se)\n",
        "time_m = np.array(time_m)\n",
        "time_sh = np.array(time_sh)\n",
        "\n",
        "print('\\n')\n",
        "fig, ax = plt.subplots(figsize=(16, 12))\n",
        "plt.xlabel('Quantidade de vetores')\n",
        "plt.ylabel('Tempo (s)')\n",
        "plt.title('Tempo de ordenação em função da quantidade de vetores')\n",
        "ax.grid()\n",
        "\n",
        "xs = np.linspace(min(vetores),max(vetores),1000)\n",
        "spl_b = UnivariateSpline(vetores,time_b)\n",
        "spl_i = UnivariateSpline(vetores,time_i)\n",
        "spl_se = UnivariateSpline(vetores,time_se)\n",
        "spl_m = UnivariateSpline(vetores,time_m)\n",
        "spl_sh = UnivariateSpline(vetores,time_sh)\n",
        "\n",
        "plt.plot(vetores,time_b,\"o\",color='b')\n",
        "plt.plot(vetores,time_i,\"o\",color='r')\n",
        "plt.plot(vetores,time_se,\"o\",color='black')\n",
        "plt.plot(vetores,time_m,\"o\",color='orange')\n",
        "plt.plot(vetores,time_sh,\"o\",color='g')\n",
        "\n",
        "plt.plot(xs,spl_b(xs),\"b\",label=\"Bubble\")\n",
        "plt.plot(xs,spl_i(xs),\"red\",label=\"Insertion\")\n",
        "plt.plot(xs,spl_se(xs),\"black\",label=\"Selection\")\n",
        "plt.plot(xs,spl_m(xs),\"orange\",label=\"Merge\")\n",
        "plt.plot(xs,spl_sh(xs),\"g\",label=\"Shell\")\n",
        "ax.grid()\n",
        "plt.legend(loc=\"best\")\n",
        "ax.grid()\n",
        "\n",
        "#  DETALHE  #\n",
        "ax_insert = fig.add_axes([0.16, 0.43, 0.27, 0.32], facecolor='#f5f5f5')\n",
        "x0, x1 = 10000, 1\n",
        "ax.axvline(x0, ymax = 0.1, color=\"grey\", linestyle=\":\")\n",
        "ax.axvline(x1, ymax=0.1, color=\"grey\", linestyle=\":\")\n",
        "xi = np.linspace(x0, x1, 1000)\n",
        "\n",
        "plt.plot(vetores,time_m,\"o\",color='orange')\n",
        "plt.plot(vetores,time_sh,\"o\",color='g')\n",
        "\n",
        "plt.plot(xs,spl_m(xs),\"orange\",label=\"Merge\")\n",
        "plt.plot(xs,spl_sh(xs),\"g\",label=\"Shell\")\n",
        "plt.legend(loc=\"best\")\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quantidade de vetores sendo ordenados:\n",
            "100\n",
            "1000\n",
            "2000\n",
            "3000\n",
            "4000\n",
            "5000\n",
            "6000\n",
            "7000\n",
            "8000\n",
            "9000\n",
            "10000\n",
            "\n",
            "Número de vetores: 100 \n",
            "Bubble: 0.0004401206970214844 s \n",
            "Insertion: 0.0002384185791015625 s\n",
            "Selection: 0.00023818016052246094 s \n",
            "Merge: 0.00023984909057617188 s \n",
            "Shell: 0.00013589859008789062 s\n",
            "\n",
            "Número de vetores: 1000 \n",
            "Bubble: 0.04084420204162598 s \n",
            "Insertion: 0.02120494842529297 s\n",
            "Selection: 0.0198516845703125 s \n",
            "Merge: 0.0027840137481689453 s \n",
            "Shell: 0.0019106864929199219 s\n",
            "\n",
            "Número de vetores: 2000 \n",
            "Bubble: 0.1698451042175293 s \n",
            "Insertion: 0.07616686820983887 s\n",
            "Selection: 0.07754230499267578 s \n",
            "Merge: 0.005354642868041992 s \n",
            "Shell: 0.0043833255767822266 s\n",
            "\n",
            "Número de vetores: 3000 \n",
            "Bubble: 0.38384366035461426 s \n",
            "Insertion: 0.18008017539978027 s\n",
            "Selection: 0.1752793788909912 s \n",
            "Merge: 0.008373737335205078 s \n",
            "Shell: 0.00705265998840332 s\n",
            "\n",
            "Número de vetores: 4000 \n",
            "Bubble: 0.6816797256469727 s \n",
            "Insertion: 0.3346588611602783 s\n",
            "Selection: 0.31537413597106934 s \n",
            "Merge: 0.011530876159667969 s \n",
            "Shell: 0.009512662887573242 s\n",
            "\n",
            "Número de vetores: 5000 \n",
            "Bubble: 1.074972152709961 s \n",
            "Insertion: 0.48247718811035156 s\n",
            "Selection: 0.46988582611083984 s \n",
            "Merge: 0.014771223068237305 s \n",
            "Shell: 0.012574195861816406 s\n",
            "\n",
            "Número de vetores: 6000 \n",
            "Bubble: 1.452115535736084 s \n",
            "Insertion: 0.6677346229553223 s\n",
            "Selection: 0.6908485889434814 s \n",
            "Merge: 0.01849842071533203 s \n",
            "Shell: 0.015159130096435547 s\n",
            "\n",
            "Número de vetores: 7000 \n",
            "Bubble: 2.1408417224884033 s \n",
            "Insertion: 0.9945697784423828 s\n",
            "Selection: 0.9252579212188721 s \n",
            "Merge: 0.021845340728759766 s \n",
            "Shell: 0.018218040466308594 s\n",
            "\n",
            "Número de vetores: 8000 \n",
            "Bubble: 2.6296298503875732 s \n",
            "Insertion: 1.2178139686584473 s\n",
            "Selection: 1.1930994987487793 s \n",
            "Merge: 0.025476455688476562 s \n",
            "Shell: 0.022242069244384766 s\n",
            "\n",
            "Número de vetores: 9000 \n",
            "Bubble: 3.421619415283203 s \n",
            "Insertion: 1.5319249629974365 s\n",
            "Selection: 1.5748448371887207 s \n",
            "Merge: 0.029375314712524414 s \n",
            "Shell: 0.024852991104125977 s\n",
            "\n",
            "Número de vetores: 10000 \n",
            "Bubble: 4.179878234863281 s \n",
            "Insertion: 1.9769206047058105 s\n",
            "Selection: 1.9198253154754639 s \n",
            "Merge: 0.03375101089477539 s \n",
            "Shell: 0.028219223022460938 s\n",
            "\n",
            "Tempo de todas as ordenações: 31.337862014770508 s\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f1775aa28e0>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 2 Axes>"
            ],
            "image/png": 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"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5MY8QeZIGJuT",
        "outputId": "8160c22c-90ca-4bb5-9a48-69413aade672"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "source": [
        "from time import time\n",
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "from scipy.interpolate import UnivariateSpline\n",
        "\n",
        "######################  ORDENAÇÃO MERGE E SHELL  ######################\n",
        "vetores = []\n",
        "time_m = [] \n",
        "time_sh = []\n",
        "\n",
        "print('Quantidade de vetores sendo ordenados:')\n",
        "t_total = 0\n",
        "n = 10000\n",
        "n_max = 1000000\n",
        "q_passos = 5\n",
        "\n",
        "def lista_aleatoria(n,n_max):\n",
        "    lista0 = []\n",
        "    np.random.seed(1)\n",
        "    for i in range(n):\n",
        "        y = random.randint(0, n_max + 1)\n",
        "        lista0.append(y)\n",
        "    return lista0\n",
        "\n",
        "while n <= n_max:\n",
        "  print(n)\n",
        "  t_t0 = time()\n",
        "\n",
        "  t0 = time()\n",
        "  merge_sort(lista_aleatoria(n,n_max))\n",
        "  t_merge = time() - t0\n",
        "\n",
        "  t0 = time()\n",
        "  shell_sort(lista_aleatoria(n,n_max))\n",
        "  t_shell = time() - t0\n",
        "\n",
        "\n",
        "  delta_t_t = time() - t_t0\n",
        "  t_total += delta_t_t\n",
        "\n",
        "  vetores.append(n)\n",
        "  time_m.append(t_merge)\n",
        "  time_sh.append(t_shell)\n",
        "\n",
        "  if n == 10000:\n",
        "    n = 0\n",
        "\n",
        "  n += int(n_max/q_passos)\n",
        "\n",
        "vet = int((q_passos)/5)\n",
        "print_vet = 0\n",
        "for i in range(len(vetores)-1):\n",
        "  if i == print_vet:\n",
        "    print(\"\\nNúmero de vetores:\", vetores[i])\n",
        "    print('Merge:',time_m[i],'s','\\nShell:',time_sh[i],'s',)\n",
        "    print_vet += vet\n",
        "\n",
        "i =  len(vetores)-1\n",
        "print(\"\\nNúmero de vetores:\", vetores[i])\n",
        "print('Merge:',time_m[i],'s','\\nShell:',time_sh[i],'s',)\n",
        "\n",
        "print('\\nTempo de todas as ordenações:',t_total,'s')\n",
        "\n",
        "######################     GRÁFICO     ######################\n",
        "vetores = np.array(vetores)\n",
        "time_m = np.array(time_m)\n",
        "time_sh = np.array(time_sh)\n",
        "\n",
        "print('\\n')\n",
        "fig, ax = plt.subplots(figsize=(14, 8))\n",
        "plt.xlabel('Quantidade de vetores')\n",
        "plt.ylabel('Tempo (s)')\n",
        "plt.title('Tempo de ordenação em função da quantidade de vetores')\n",
        "ax.grid()\n",
        "\n",
        "xs = np.linspace(min(vetores),max(vetores),1000)\n",
        "spl_m = UnivariateSpline(vetores,time_m)\n",
        "spl_sh = UnivariateSpline(vetores,time_sh)\n",
        "\n",
        "plt.plot(vetores,time_m,\"o\",color='orange')\n",
        "plt.plot(vetores,time_sh,\"o\",color='g')\n",
        "\n",
        "plt.plot(xs,spl_m(xs),\"orange\",label=\"Merge\")\n",
        "plt.plot(xs,spl_sh(xs),\"g\",label=\"Shell\")\n",
        "plt.legend(loc=\"best\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quantidade de vetores sendo ordenados:\n",
            "10000\n",
            "200000\n",
            "400000\n",
            "600000\n",
            "800000\n",
            "1000000\n",
            "\n",
            "Número de vetores: 10000\n",
            "Merge: 0.05227351188659668 s \n",
            "Shell: 0.03235006332397461 s\n",
            "\n",
            "Número de vetores: 200000\n",
            "Merge: 3.1328320503234863 s \n",
            "Shell: 1.1107027530670166 s\n",
            "\n",
            "Número de vetores: 400000\n",
            "Merge: 11.042633056640625 s \n",
            "Shell: 2.578446388244629 s\n",
            "\n",
            "Número de vetores: 600000\n",
            "Merge: 24.279353380203247 s \n",
            "Shell: 4.1274144649505615 s\n",
            "\n",
            "Número de vetores: 800000\n",
            "Merge: 42.46733856201172 s \n",
            "Shell: 6.354367017745972 s\n",
            "\n",
            "Número de vetores: 1000000\n",
            "Merge: 65.88241028785706 s \n",
            "Shell: 8.170181035995483 s\n",
            "\n",
            "Tempo de todas as ordenações: 169.23033046722412 s\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f177582b850>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ],
            "image/png": 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"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "metadata": {
        "id": "jXfjroeAWI1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "de8b731a-7107-4721-b3d3-a658bfd38f0d"
      }
    }
  ]
}