ビンゴゲームのビンゴするまでの回数の統計.ipynb

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  "metadata": {
    "colab": {
      "name": "ビンゴゲームのビンゴするまでの回数の統計.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMo9U9jWOGY3tAy23eG0M7a",
      "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/kawata-yuya/kawata-yuya-Bingo-game-simulator/blob/main/%E3%83%93%E3%83%B3%E3%82%B4%E3%82%B2%E3%83%BC%E3%83%A0%E3%81%AE%E3%83%93%E3%83%B3%E3%82%B4%E3%81%99%E3%82%8B%E3%81%BE%E3%81%A7%E3%81%AE%E5%9B%9E%E6%95%B0%E3%81%AE%E7%B5%B1%E8%A8%88.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "teLf7uA_E2OM"
      },
      "source": [
        "# ビンゴゲームでビンゴになるまでの回数のシュミレーター\n",
        "## ビンゴカードについて\n",
        "リストの要素がビンゴカードに書かれている数字とする。  \n",
        "穴が空いている状態はリストの要素が0(False)で表現する  \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uku9ijMKGUEw"
      },
      "source": [
        "## シュミレーションに必要な関数を定義\n",
        "- bingo_checker\n",
        "- make_a_hole_in_the_bingo_card\n",
        "- bingo_card_printer  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3_EA1ES_MCQ",
        "outputId": "95b4b869-37c6-4735-9c35-640f8ffced2e"
      },
      "source": [
        "import random, time\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "print(\"\"\"### ビンゴカードのルール ###\n",
        "リストの要素がビンゴカードに書かれている数字とする。\n",
        "穴が空いている状態はリストの要素が0(False)で表現する\n",
        "\"\"\")\n",
        "\n",
        "def bingo_checker(bingo_card):\n",
        "    \"\"\"ビンゴシートがビンゴか判定する関数\"\"\"\n",
        "    result = [not(any(i)) for i in bingo_card]          # 縦\n",
        "    result += [not(any(i)) for i in zip(*bingo_card)]   # 横\n",
        "    result += [not(any(bingo_card[i][i] for i in range(5))),\n",
        "               not(any(bingo_card[i][4-i] for i in range(5)))]    # 斜め\n",
        "    return any(result)\n",
        "\n",
        "\n",
        "def make_a_hole_in_the_bingo_card(num, bingo_card):\n",
        "    \"\"\"ビンゴシートに穴をあける関数\"\"\"\n",
        "    if num < 16 and (num in bingo_card[0]):\n",
        "        bingo_card[0] = [i if i != num else 0 for i in bingo_card[0]]\n",
        "    elif num < 31 and (num in bingo_card[1]):\n",
        "        bingo_card[1] = [i if i != num else 0 for i in bingo_card[1]]\n",
        "    elif num < 46 and (num in bingo_card[2]):\n",
        "        bingo_card[2] = [i if i != num else 0 for i in bingo_card[2]]\n",
        "    elif num < 61 and (num in bingo_card[3]):\n",
        "        bingo_card[3] = [i if i != num else 0 for i in bingo_card[3]]\n",
        "    elif (num in bingo_card[4]):\n",
        "        bingo_card[4] = [i if i != num else 0 for i in bingo_card[4]]\n",
        "\n",
        "    return bingo_card\n",
        "\n",
        "\n",
        "def bingo_card_printer(bingo_card):\n",
        "    \"\"\"ビンゴシートを可視化する関数(デバッグ用)\"\"\"\n",
        "    print(\"B I N G O\")\n",
        "    for ax in zip(*bingo_card):\n",
        "        print(str(list(ax))+\",\")\n",
        "    print()\n",
        "\n",
        "\n",
        "def experiment():\n",
        "    \"\"\"ビンゴ大会を実施する関数\"\"\"\n",
        "    number_list = list(range(1, 71))\n",
        "    random.shuffle(number_list)\n",
        "\n",
        "    # ビンゴカードを作成\n",
        "    bingo_card = [[rand_num for rand_num in random.sample(\n",
        "        list(range(i*15+1, i*15+16)), 5)] for i in range(5)]\n",
        "    bingo_card[2][2] = 0\n",
        "\n",
        "    for counter, number in enumerate(number_list, 1):\n",
        "        bingo_card = make_a_hole_in_the_bingo_card(number, bingo_card)\n",
        "        if bingo_checker(bingo_card):\n",
        "            break\n",
        "\n",
        "    return counter"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "### ビンゴカードのルール ###\n",
            "リストの要素がビンゴカードに書かれている数字とする。\n",
            "穴が空いている状態はリストの要素が0(False)で表現する\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQWSlktoFkom"
      },
      "source": [
        "## シュミレーションの実行\n",
        "\n",
        "\n",
        "---\n",
        "### 注意\n",
        "変数:number_of_samples  \n",
        "に実験回数を代入してください。  \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7s3V6rQ0CSVK",
        "outputId": "1996dd58-de35-4b55-efbd-d13efcfbd5c0"
      },
      "source": [
        "# 実験の実行\n",
        "number_of_samples = 1000000           # サンプル数(任意)\n",
        "\n",
        "print(\"集めるサンプル数をnumber_of_samplesに代入してください。\")\n",
        "\n",
        "try:\n",
        "    if not(result_list): \n",
        "        result_list = {i: 0 for i in range(1, 76)}\n",
        "except:\n",
        "    result_list = {i: 0 for i in range(1, 76)}\n",
        "\n",
        "# メイン計算\n",
        "print(\"--計算中--\")\n",
        "time.sleep(0.25)\n",
        "for _ in tqdm(range(number_of_samples)):\n",
        "    result_list[experiment()] += 1\n",
        "time.sleep(0.25)\n",
        "print(\"--計算終了--\")\n",
        "\n",
        "print(\", \".join(\"{}:{}\".format(*i) for i in result_list.items()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "集めるサンプル数をnumber_of_samplesに代入してください。\n",
            "--計算中--\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1000000/1000000 [04:42<00:00, 3535.44it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "--計算終了--\n",
            "1:0, 2:0, 3:0, 4:4, 5:14, 6:39, 7:71, 8:103, 9:237, 10:338, 11:522, 12:676, 13:973, 14:1346, 15:1665, 16:2090, 17:2625, 18:3266, 19:4135, 20:4901, 21:6041, 22:7023, 23:8198, 24:9630, 25:10701, 26:12390, 27:14253, 28:15966, 29:17982, 30:19938, 31:22169, 32:24104, 33:25832, 34:28531, 35:30488, 36:32010, 37:33843, 38:35500, 39:37144, 40:38095, 41:38774, 42:39737, 43:39941, 44:39773, 45:39643, 46:38431, 47:37357, 48:35599, 49:33530, 50:31543, 51:28795, 52:26252, 53:23161, 54:20180, 55:17322, 56:14683, 57:11939, 58:9300, 59:7076, 60:5211, 61:3704, 62:2369, 63:1504, 64:772, 65:383, 66:146, 67:32, 68:0, 69:0, 70:0, 71:0, 72:0, 73:0, 74:0, 75:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pS6ZB1iaGctu"
      },
      "source": [
        "## シュミレートした結果をグラフ化"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "zurEEv70Cgkc",
        "outputId": "1433cb83-7293-46b5-e2ae-753a245b54ec"
      },
      "source": [
        "# 実験結果のグラフ化\n",
        "result_list_sum = sum(result_list.values())\n",
        "result_list = {i:(j/result_list_sum) for i, j in result_list.items()}\n",
        "result_list_mode = {i:j for i, j in result_list.items() if j == max(result_list.values())}\n",
        "result_list_mode = sum(result_list_mode.keys())/len(result_list_mode)\n",
        "\n",
        "# グラフの設定\n",
        "plt.figure(figsize=(16, 9), dpi=75)\n",
        "plt.plot(list(result_list.keys()), list(result_list.values()))\n",
        "plt.xlabel(\"Number of times it took to do bingo\")\n",
        "plt.ylabel(\"Probability of bingo by that number of times\")\n",
        "plt.grid(which = \"major\", axis = \"x\", color = \"gray\", alpha = 0.8, linestyle = \"--\", linewidth = 0.5)\n",
        "plt.grid(which = \"major\", axis = \"y\", color = \"gray\", alpha = 0.8, linestyle = \"--\", linewidth = 0.5)\n",
        "plt.title(\"Statistics on the number of times it takes to bingo\")\n",
        "plt.vlines(x=result_list_mode, ymin=0, ymax=0.042, color = \"gray\", linewidth=0.7)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1200x675 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}