FibonacciTime.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FibonacciTime",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AuFp4-lIVthX"
      },
      "source": [
        "# **Laboratorio 10**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RBO1Sm4nfKWV"
      },
      "source": [
        "**PARTE 2.** ***Fibonacci time*** \r\n",
        "\r\n",
        "\r\n",
        "Plot of the *recursive Fibonacci time* [0,50] and the iterative Fibonacci time [0,500] including the line y=n to compare in a Colab notebook and upload the notebook in ***FibonacciTime.ipynb*** in the group’s github."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "633ge8eW5OWt",
        "outputId": "22010556-f043-460e-ba3c-c8f14102c3ea"
      },
      "source": [
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkXRJRxlgD4B"
      },
      "source": [
        "def recursiveFibonacci(n):\r\n",
        "  if n <= 1:\r\n",
        "    return n\r\n",
        "  else:\r\n",
        "    return (recursiveFibonacci(n-1) + recursiveFibonacci(n-2))"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSBHYbuBgHgT"
      },
      "source": [
        "import time\r\n",
        "\r\n",
        "numbers = [m for m in range (51)]\r\n",
        "times = [-1 for n in range (51)]\r\n",
        "\r\n",
        "start_time = time.time()\r\n",
        "for i in range (len(numbers)):\r\n",
        "  recursiveFibonacci(numbers[i])\r\n",
        "  print(i)\r\n",
        "  times[i] = (time.time() - start_time)\r\n",
        "  #debido a que el tiempo de ejecución fue amplio, se guardaron los datos en un txt,\r\n",
        "  #posteriormente para su uso simplemente se copia y pega la información\r\n",
        "  with open(\"/content/drive/MyDrive/2020-2S/ALGORITMOS/time_data.txt\", 'a') as f:\r\n",
        "            f.write(str(times[i])+\",\")\r\n",
        "  start_time = time.time()\r\n",
        "\r\n",
        "#print(times)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CRyoageGgMtV"
      },
      "source": [
        "def iterativeFibonacci(n):\r\n",
        "    a, b = 0, 1\r\n",
        "    for i in range(n):\r\n",
        "        a, b = b, a + b\r\n",
        "    return a"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2sRSoJ8gP5t"
      },
      "source": [
        "import time\r\n",
        "\r\n",
        "numbers = [m for m in range (51)]\r\n",
        "times = [-1 for n in range (51)]\r\n",
        "\r\n",
        "start_time = time.time()\r\n",
        "for i in range (len(numbers)):\r\n",
        "  iterativeFibonacci(numbers[i])\r\n",
        "  times[i] = (time.time() - start_time)\r\n",
        "  start_time = time.time()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "TKMyBm5tgTY7",
        "outputId": "abecc945-2278-4a80-f65b-c32f4c480171"
      },
      "source": [
        "from pylab import * \r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "#los datos fueron guardados en un archivo txt y estos fueron los resultados\r\n",
        "data_time = [0.0002162456512451172,0.0003001689910888672,0.0011801719665527344,0.0007374286651611328,0.0007405281066894531,8.630752563476562e-05,0.00010251998901367188,0.00011134147644042969,0.00013184547424316406,0.0001361370086669922,0.00011992454528808594,0.0001671314239501953,0.00018453598022460938,0.00020241737365722656,0.0003247261047363281,0.0003533363342285156,0.0004725456237792969,0.0007014274597167969,0.0010178089141845703,0.002119302749633789,0.0024995803833007812,0.004230499267578125,0.009592533111572266,0.01047658920288086,0.016532421112060547,0.02631664276123047,0.04415488243103027,0.06951022148132324,0.11139917373657227,0.1834416389465332,0.29152584075927734,0.4796013832092285,0.7538888454437256,1.2162978649139404,1.9676363468170166,3.177314281463623,5.1462156772613525,8.310202360153198,13.465224504470825,21.773308515548706,35.29947519302368,57.15582513809204,92.663809299469,150.65571522712708,242.96133470535278,392.32102823257446,635.5977056026459,1025.6046669483185,1659.4229621887207,2684.43709731102,4344.018990039825]\r\n",
        "\r\n",
        "plt.plot(data_time)\r\n",
        "plt.plot(times)\r\n",
        "plt.plot(arange(50.))\r\n",
        "plt.title(\"time comparison\")\r\n",
        "plt.ylabel('time')\r\n",
        "plt.xlabel('n fibonacci')\r\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}