01_시간복잡도(Big O).ipynb

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  "metadata": {
    "colab": {
      "name": "자료구조_1_시간복잡도.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "132gDNb07OOw1QldSKid9SyhY-BR9vDhx",
      "authorship_tag": "ABX9TyMoNYzXcpFCWMZl6EME6Qik",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sejin-sim/Data_structure/blob/master/%EC%9E%90%EB%A3%8C%EA%B5%AC%EC%A1%B0_1_%EC%8B%9C%EA%B0%84%EB%B3%B5%EC%9E%A1%EB%8F%84.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "We1Re5JPreQt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "b341e51e-90f0-4357-aa1a-f9331aea1d74"
      },
      "source": [
        "# 알고리즘 A\n",
        "\n",
        "# n = 1일때\n",
        "n = 1\n",
        "\n",
        "sum = n * n     # 대입 1 + 연산 1\n",
        "\n",
        "print(sum)\n",
        "\n",
        "# n = 100일때\n",
        "n = 100\n",
        "\n",
        "sum = n * n\n",
        "print(sum)\n",
        "\n",
        "# n의 개수와 관계없이 곱하기라는 행위 한번으로 계산이 끝난다.\n",
        "# n     1   2  3  100  \n",
        "# T(n)  2   2  2   2   \n",
        "\n",
        "# T(n)= 2 즉 O(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXEwYA7us37Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "bffd66f9-19b1-445e-fedf-504965ffa1a5"
      },
      "source": [
        "# 알고리즘 B\n",
        "\n",
        "# n = 1일때\n",
        "n = 1\n",
        "k = 0\n",
        "\n",
        "sum = 0                  # 대입 1 \n",
        "for i in range(1, n+1):\n",
        "  sum += n               # 대입 n + 연산 n\n",
        "\n",
        "\n",
        "  k += 1                 # 연산 횟수 test를 위해\n",
        "\n",
        "print(\"sum : {}, k : {}\".format(sum, k))\n",
        "\n",
        "# n = 100일때\n",
        "n = 100\n",
        "k = 0\n",
        "\n",
        "sum = 0\n",
        "for i in range(1, n+1):\n",
        "  sum += n\n",
        "  k += 1 # 연산 횟수 test를 위해\n",
        "\n",
        "print(\"sum : {}, k : {}\".format(sum, k))\n",
        "\n",
        "# n의 갯수만큼 돌아간다.\n",
        "# n     1    100  \n",
        "# k     1    100\n",
        "# T(n)    2n+1   \n",
        "\n",
        "# T(n)= 2n+1, 즉 O(n)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sum : 1, k : 1\n",
            "sum : 10000, k : 100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7K8x96auOkY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "25430ca1-b797-473c-ebbc-e098f62e5b2e"
      },
      "source": [
        "# 알고리즘 C\n",
        "\n",
        "# n = 1일때\n",
        "n = 1\n",
        "k = 0\n",
        "\n",
        "sum = 0                        # 대입 1\n",
        "for i in range(1, n+1):\n",
        "  for j in range(1, n+1):\n",
        "    sum += n                   # 대입 n^2 + 연산 n^2\n",
        "    k += 1                     # 연산 횟수 test를 위해\n",
        "\n",
        "print(\"sum : {}, k : {}\".format(sum, k))\n",
        "\n",
        "# n = 100일때\n",
        "n = 100\n",
        "k = 0\n",
        "\n",
        "sum = 0\n",
        "for i in range(1, n+1):\n",
        "  for j in range(1, n+1):\n",
        "    sum += n\n",
        "    k += 1                      # 연산 횟수 test를 위해\n",
        "\n",
        "print(\"sum : {}, k : {}\".format(sum, k))\n",
        "\n",
        "# n^2의 갯수만큼 돌아간다\n",
        "# n     1   100  \n",
        "# k     1   10000 = 100^2\n",
        "# T(n)    1 + n^2 + n^2 = 2n^2 +1   \n",
        "\n",
        "# T(n)= 2n^2+1, 즉 O(n^2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sum : 1, k : 1\n",
            "sum : 1000000, k : 10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzeS2V9iwz7Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "ef50a410-cc1a-4c71-8759-7c5bfabf0ec0"
      },
      "source": [
        "# 그래프로 그리기\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "x = range(4)\n",
        "A_values = [2 for n in x]\n",
        "B_values = [(2*n)+1 for n in x]\n",
        "C_values = [(2*n**2)+1 for n in x]\n",
        "\n",
        "plt.plot(x, A_values, 'g')\n",
        "plt.plot(x, B_values, 'r')\n",
        "plt.plot(x, C_values)\n",
        "\n",
        "plt.legend(['A = 2', 'B = 2n+1', 'C = 2n^2+1'])\n",
        "plt.xlabel('T(n)')\n",
        "plt.ylabel('n')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}