Colab_12_Algoritmi_e_Complessità.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOsroA8/hqRgkJU3kkiBBIr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EmmanueleVilla/quantum_computing/blob/main/Colab_12_Algoritmi_e_Complessit%C3%A0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzGuwbUHVESZ",
        "outputId": "82ed8987-8713-418d-f153-46ae9949b11d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ipython-autotime\n",
            "  Downloading ipython_autotime-0.3.1-py2.py3-none-any.whl (6.8 kB)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.10/dist-packages (from ipython-autotime) (7.34.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (67.7.2)\n",
            "Collecting jedi>=0.16 (from ipython->ipython-autotime)\n",
            "  Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (5.7.1)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (3.0.39)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (2.14.0)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (0.1.6)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (4.8.0)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython->ipython-autotime) (0.8.3)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython->ipython-autotime) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->ipython-autotime) (0.2.6)\n",
            "Installing collected packages: jedi, ipython-autotime\n",
            "Successfully installed ipython-autotime-0.3.1 jedi-0.18.2\n"
          ]
        }
      ],
      "source": [
        "!pip install ipython-autotime"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext autotime"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0ke_xL0VIbN",
        "outputId": "5822745c-b034-49a9-a26b-b111f055ec24"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 469 µs (started: 2023-07-26 08:37:25 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JbmeJgxWLDa",
        "outputId": "c0aeccec-6345-4b3f-e3e2-d83ec7edb3a7"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 444 µs (started: 2023-07-26 08:37:37 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accesso all'elemento di una lista"
      ],
      "metadata": {
        "id": "W_WH-b43WRWi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista = np.random.randint(0, 1000, 1000000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nF87NxZzWUx5",
        "outputId": "26f9b64d-0258-481e-b16d-376bae17de8d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 15.3 ms (started: 2023-07-26 08:38:48 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(lista[5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LVFhpdoIWZD6",
        "outputId": "bb96b769-cd3c-49ff-f345-f1920fcc05e1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "586\n",
            "time: 671 µs (started: 2023-07-26 08:38:55 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Massimo elemento in una lista"
      ],
      "metadata": {
        "id": "mfxfbBSxXV2I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista = np.random.randint(0, 1000, 10000000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyTB4pwxXX-n",
        "outputId": "d2ac54d4-7a76-4990-9531-ec9cf5979082"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 105 ms (started: 2023-07-26 08:43:56 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "massimo = 0\n",
        "for numero in lista:\n",
        "  if numero > massimo:\n",
        "    massimo = numero\n",
        "\n",
        "print(massimo)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRaXMAVtXZ_X",
        "outputId": "37d1d8f4-9df4-4b63-a9fc-f3492569bc6d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "999\n",
            "time: 1.33 s (started: 2023-07-26 08:44:00 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bubble sort"
      ],
      "metadata": {
        "id": "_sdN99WVYXbf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista = np.random.randint(0, 1000, 10000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "471k3vK3YZGO",
        "outputId": "dca158d2-a826-4e0b-c4fd-817dad980763"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 6.95 ms (started: 2023-07-26 08:50:44 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def bubble_sort(arr):\n",
        "  n = len(arr)\n",
        "  while(True):\n",
        "    swapped = False\n",
        "    for i in range(n-1):\n",
        "      if arr[i] > arr[i+1]:\n",
        "        arr[i], arr[i+1] = arr[i+1], arr[i]\n",
        "        swapped = True\n",
        "    if not swapped:\n",
        "      break\n",
        "bubble_sort(lista)\n",
        "print(np.asarray(lista))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7E7HVaYIYb2e",
        "outputId": "fec5f364-d84b-481a-f6b6-02739e8dafd8"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  0   0   0 ... 999 999 999]\n",
            "time: 43.5 s (started: 2023-07-26 08:50:46 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Merge sort"
      ],
      "metadata": {
        "id": "VO5wZlS3aMe7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista = np.random.randint(0, 1000, 10000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BmpkUeBaNyb",
        "outputId": "101ee3c8-3186-43f4-ad54-0c1e6cd745c2"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 931 µs (started: 2023-07-26 09:04:04 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def merge_sort(arr):\n",
        "  if len(arr) <= 1:\n",
        "    return arr\n",
        "\n",
        "  mid = len(arr) // 2\n",
        "\n",
        "  left = arr[:mid]\n",
        "  right = arr[mid:]\n",
        "\n",
        "  left_sorted = merge_sort(left)\n",
        "  right_sorted = merge_sort(right)\n",
        "\n",
        "  return merge(left_sorted, right_sorted)\n",
        "\n",
        "def merge(left, right):\n",
        "  merged = []\n",
        "  left_ptr = 0\n",
        "  right_ptr = 0\n",
        "  while left_ptr < len(left) and right_ptr < len(right):\n",
        "    if left[left_ptr] < right[right_ptr]:\n",
        "      merged.append(left[left_ptr])\n",
        "      left_ptr += 1\n",
        "    else:\n",
        "      merged.append(right[right_ptr])\n",
        "      right_ptr += 1\n",
        "\n",
        "  while left_ptr < len(left):\n",
        "    merged.append(left[left_ptr])\n",
        "    left_ptr += 1\n",
        "\n",
        "  while right_ptr < len(right):\n",
        "    merged.append(right[right_ptr])\n",
        "    right_ptr += 1\n",
        "\n",
        "  return merged\n",
        "\n",
        "ordered = merge_sort(lista)\n",
        "print(np.asarray(ordered))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTvIrvjOaRIa",
        "outputId": "b49d2aa6-9e9c-4b20-a31c-de245d1de666"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  0   0   0 ... 999 999 999]\n",
            "time: 113 ms (started: 2023-07-26 09:04:15 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Numeri di fibonacci"
      ],
      "metadata": {
        "id": "-prI8MhEdqi8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fibonacci(n):\n",
        "  if n <= 1:\n",
        "    return n\n",
        "  return fibonacci(n-1) + fibonacci(n-2)\n",
        "\n",
        "print(fibonacci(35))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8IfE-s3Tds9L",
        "outputId": "f2d93bb0-4a3c-4515-9455-f09c64823350"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9227465\n",
            "time: 4.07 s (started: 2023-07-26 09:11:38 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Permutazioni"
      ],
      "metadata": {
        "id": "I9H0-0x6eixK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista = [0,1,2,3,4,5,6,7,8,9,10]\n",
        "def heap_permutation(data, n):\n",
        "  if n == 1:\n",
        "    #print(data)\n",
        "    return\n",
        "  for i in range(n):\n",
        "    heap_permutation(data, n-1)\n",
        "    if n%2 == 0:\n",
        "      data[i], data[n-1] = data[n-1], data[i]\n",
        "    else:\n",
        "      data[0], data[n-1] = data[n-1], data[0]\n",
        "\n",
        "heap_permutation(lista, len(lista))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLxjngo5elVB",
        "outputId": "3032a72c-7385-47df-988f-8725c2cbd626"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 29.5 s (started: 2023-07-26 09:17:14 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Grafico delle complessità"
      ],
      "metadata": {
        "id": "fRfEg4OaWq6S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "x = range(1, 26)\n",
        "\n",
        "plt.plot(x, [1 for i in x], label=\"1\")\n",
        "plt.plot(x, [i for i in x], label=\"N\")\n",
        "plt.plot(x, [i**2 for i in x], label=\"N^2\")\n",
        "plt.plot(x, [i*math.log(i) for i in x], label=\"N log N\")\n",
        "plt.plot(x, [2**i for i in x], label=\"2^N\")\n",
        "plt.plot(x, [math.factorial(i) for i in x], label=\"N!\")\n",
        "\n",
        "plt.xlim(0, len(x))\n",
        "plt.ylim(0, 100)\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "BAUK3vDkWsLY",
        "outputId": "5494a8b0-1b2e-4508-fe52-e6fe818ab54e"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 651 ms (started: 2023-07-26 09:18:48 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YOAsxqKaWy6J"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}