{"id":6297,"date":"2023-10-12T14:37:09","date_gmt":"2023-10-12T05:37:09","guid":{"rendered":"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/?p=6297"},"modified":"2023-10-12T14:37:09","modified_gmt":"2023-10-12T05:37:09","slug":"%e3%83%a2%e3%83%b3%e3%83%86%e3%82%ab%e3%83%ab%e3%83%ad%e6%b3%95","status":"publish","type":"post","link":"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/12\/%e3%83%a2%e3%83%b3%e3%83%86%e3%82%ab%e3%83%ab%e3%83%ad%e6%b3%95\/","title":{"rendered":"\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5"},"content":{"rendered":"<p><a href=\"https:\/\/colab.research.google.com\/github\/T-QARD\/t-wave\/blob\/main\/notebooks\/Montecarlo\/MonteCarlo.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"><img decoding=\"async\" src=\"https:\/\/colab.research.google.com\/assets\/colab-badge.svg\" alt=\"Open in Colab\" \/><\/a><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-white ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title ez-toc-toggle\" style=\"cursor:pointer\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/12\/%e3%83%a2%e3%83%b3%e3%83%86%e3%82%ab%e3%83%ab%e3%83%ad%e6%b3%95\/#%E3%83%A2%E3%83%B3%E3%83%86%E3%82%AB%E3%83%AB%E3%83%AD%E6%B3%95%EF%BC%88Monte_Carlo_Method%EF%BC%89%E3%81%A8%E3%81%AF\" >\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\uff08Monte Carlo Method\uff09\u3068\u306f<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/12\/%e3%83%a2%e3%83%b3%e3%83%86%e3%82%ab%e3%83%ab%e3%83%ad%e6%b3%95\/#%E5%AE%9F%E9%9A%9B%E3%81%AB%E5%86%86%E5%91%A8%E7%8E%87%E3%82%92%E6%8E%A8%E5%AE%9A%E3%81%97%E3%81%A6%E3%81%BF%E3%82%8B\" >\u5b9f\u969b\u306b\u5186\u5468\u7387\u3092\u63a8\u5b9a\u3057\u3066\u307f\u308b<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"%E3%83%A2%E3%83%B3%E3%83%86%E3%82%AB%E3%83%AB%E3%83%AD%E6%B3%95%EF%BC%88Monte_Carlo_Method%EF%BC%89%E3%81%A8%E3%81%AF\"><\/span>\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\uff08Monte Carlo Method\uff09\u3068\u306f<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u306f\u78ba\u7387\u7684\u306a\u624b\u6cd5\u3092\u7528\u3044\u3066\u3001\u89e3\u6790\u7684\u306b\u56f0\u96e3\u306a\u6570\u5024\u8a08\u7b97\u3092\u8fd1\u4f3c\u7684\u306b\u884c\u3046\u65b9\u6cd5\u3067\u3059\u3002\u30e9\u30f3\u30c0\u30e0\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u7528\u3044\u3066\u3001\u8907\u96d1\u306a\u6570\u5b66\u7684\u554f\u984c\u3084\u9ad8\u6b21\u5143\u306e\u7a4d\u5206\u3001\u6700\u9069\u5316\u554f\u984c\u306a\u3069\u3092\u89e3\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001\u7269\u7406\u5b66\u3084\u7d71\u8a08\u5b66\u3001\u91d1\u878d\u5de5\u5b66\u306a\u3069\u3001\u69d8\u3005\u306a\u5206\u91ce\u3067\u5e83\u304f\u5229\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u306e\u7cbe\u5ea6\u306f\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306e\u56de\u6570\uff08\u30b9\u30c6\u30c3\u30d7\u6570\uff09\u306b\u4f9d\u5b58\u3057\u3001\u4e00\u822c\u306b\u30b9\u30c6\u30c3\u30d7\u6570\u304c\u5897\u3048\u308b\u307b\u3069\u7cbe\u5ea6\u304c\u9ad8\u307e\u308a\u307e\u3059\u3002<\/p>\n<p>\u5177\u4f53\u4f8b\u3068\u3057\u3066\u3001\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u3092\u7528\u3044\u3066\u5186\u5468\u7387\u3092\u6c42\u3081\u308b\u554f\u984c\u3092\u8003\u3048\u3066\u307f\u307e\u3059\u3002<br \/>\n\u4ee5\u4e0b\u306b\u305d\u306e\u624b\u9806\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n<ol>\n<li>\u4e00\u8fba\u306e\u9577\u3055\u304c1\u306e\u6b63\u65b9\u5f62\u3092\u8003\u3048\u307e\u3059\u3002\u3053\u306e\u6b63\u65b9\u5f62\u306e\u5185\u90e8\u306b\u3001\u6b63\u65b9\u5f62\u306e\u9802\u70b9\u3092\u4e2d\u5fc3\u3068\u3057\u305f\u534a\u5f841\u306e\u5186\u306e\uff14\u5206\u306e\uff11\uff08\u6247\u5f62\uff09\u3092\u63cf\u304d\u307e\u3059\u3002<\/li>\n<li>\u6b21\u306b\u3001\u6b63\u65b9\u5f62\u306e\u5185\u90e8\u306b\u30e9\u30f3\u30c0\u30e0\u306b\u70b9\u3092\u6253\u3061\u307e\u3059\u3002\u3053\u306e\u70b9\u306f\u4e00\u69d8\u5206\u5e03\u306b\u5f93\u3063\u3066\u30e9\u30f3\u30c0\u30e0\u306b\u914d\u7f6e\u3055\u308c\u307e\u3059\u3002<\/li>\n<li>\u305d\u308c\u305e\u308c\u306e\u70b9\u304c\u5186\u306e\u5185\u90e8\u306b\u3042\u308b\u304b\u5916\u90e8\u306b\u3042\u308b\u304b\u3092\u5224\u5b9a\u3057\u307e\u3059\u3002<\/li>\n<li>\u6247\u5f62\u306e\u5185\u90e8\u306b\u3042\u308b\u70b9\u306e\u6570\u3092\u5168\u4f53\u306e\u70b9\u306e\u6570\u3067\u5272\u308a\u307e\u3059\u3002\u3053\u306e\u6bd4\u7387\u30924\u500d\u3059\u308b\u3068\u3001\u5186\u5468\u7387\u306e\u8fd1\u4f3c\u5024\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/li>\n<\/ol>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"\/T-Wave\/wp-content\/uploads\/2023\/06\/MonteCarlo.png\" class=\"aligncenter\" width=\"375\" height=\"395\" \/><\/p>\n<p>\u3053\u306e\u56f3\u3067\u306f\u3001\u4e00\u8fba\u306e\u9577\u3055\u304c1\u306e\u6b63\u65b9\u5f62\u5185\u306b\u30e9\u30f3\u30c0\u30e0\u306b1000\u500b\u306e\u70b9\u3092\u6253\u3063\u3066\u3044\u307e\u3059\uff08\u7070\u8272\u306e\u70b9\uff09\u3002\u305d\u306e\u4e2d\u3067\u3001\u534a\u5f841\u306e\u56db\u5206\u5186\u5185\u306b\u5165\u3063\u305f\u70b9\u3092\u8d64\u8272\u3067\u8868\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u3053\u306e\u56f3\u3092\u898b\u308c\u3070\u3001\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u306e\u7cbe\u5ea6\u304c\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306e\u56de\u6570\uff08\u30b9\u30c6\u30c3\u30d7\u6570\uff09\u306b\u4f9d\u5b58\u3059\u308b\u3053\u3068\u304c\u76f4\u611f\u7684\u306b\u3082\u7406\u89e3\u3067\u304d\u308b\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n<h2><span class=\"ez-toc-section\" id=\"%E5%AE%9F%E9%9A%9B%E3%81%AB%E5%86%86%E5%91%A8%E7%8E%87%E3%82%92%E6%8E%A8%E5%AE%9A%E3%81%97%E3%81%A6%E3%81%BF%E3%82%8B\"><\/span>\u5b9f\u969b\u306b\u5186\u5468\u7387\u3092\u63a8\u5b9a\u3057\u3066\u307f\u308b<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u6b21\u306b\u5148\u307b\u3069\u8ff0\u3079\u305f\u65b9\u6cd5\u306b\u5f93\u3063\u3066\u3001\u5b9f\u969b\u306bPython\u3067\u5186\u5468\u7387\u3092\u63a8\u5b9a\u3057\u3066\u307f\u307e\u3059\u3002<br \/>\n\u4ee5\u4e0b\u304c\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u3067\u5186\u5468\u7387\u3092\u63a8\u5b9a\u3059\u308b\u305f\u3081\u306e\u30b3\u30fc\u30c9\u3067\u3059\u3002<\/p>\n<div class=\"cell border-box-sizing code_cell rendered\" id=\"cell-id=758ebad3-dbb4-42d5-a8d5-37c15c6de450\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\"highlight hl-ipython3\">\n<pre><span><\/span><span class=\"kn\">import<\/span> <span class=\"nn\">random<\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">numpy<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">np<\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">matplotlib.pyplot<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">plt<\/span>\n\n\n<span class=\"c1\"># \u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u3067\u5186\u5468\u7387\u3092\u6c42\u3081\u308b\u95a2\u6570<\/span>\n<span class=\"k\">def<\/span> <span class=\"nf\">estimate_pi<\/span><span class=\"p\">(<\/span><span class=\"n\">num_points<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">points_inside_circle<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">0<\/span>\n\n    <span class=\"k\">for<\/span> <span class=\"n\">_<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">num_points<\/span><span class=\"p\">):<\/span>\n        <span class=\"n\">x<\/span> <span class=\"o\">=<\/span> <span class=\"n\">random<\/span><span class=\"o\">.<\/span><span class=\"n\">uniform<\/span><span class=\"p\">(<\/span><span class=\"mi\">0<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">random<\/span><span class=\"o\">.<\/span><span class=\"n\">uniform<\/span><span class=\"p\">(<\/span><span class=\"mi\">0<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">distance<\/span> <span class=\"o\">=<\/span> <span class=\"n\">x<\/span><span class=\"o\">**<\/span><span class=\"mi\">2<\/span> <span class=\"o\">+<\/span> <span class=\"n\">y<\/span><span class=\"o\">**<\/span><span class=\"mi\">2<\/span>\n        <span class=\"k\">if<\/span> <span class=\"n\">distance<\/span> <span class=\"o\">&lt;=<\/span> <span class=\"mi\">1<\/span><span class=\"p\">:<\/span>\n            <span class=\"n\">points_inside_circle<\/span> <span class=\"o\">+=<\/span> <span class=\"mi\">1<\/span>\n\n    <span class=\"k\">return<\/span> <span class=\"mi\">4<\/span> <span class=\"o\">*<\/span> <span class=\"n\">points_inside_circle<\/span> <span class=\"o\">\/<\/span> <span class=\"n\">num_points<\/span>\n\n\n<span class=\"c1\"># \u30b9\u30c6\u30c3\u30d7\u6570\u3068\u7cbe\u5ea6\u306e\u95a2\u4fc2\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u95a2\u6570<\/span>\n<span class=\"k\">def<\/span> <span class=\"nf\">plot_accuracy1<\/span><span class=\"p\">():<\/span>\n    <span class=\"n\">steps<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span> <span class=\"mi\">100<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1000<\/span><span class=\"p\">,<\/span> <span class=\"mi\">10000<\/span><span class=\"p\">,<\/span> <span class=\"mi\">100000<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1000000<\/span><span class=\"p\">]<\/span>\n    <span class=\"n\">estimates<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"n\">estimate_pi<\/span><span class=\"p\">(<\/span><span class=\"n\">step<\/span><span class=\"p\">)<\/span> <span class=\"k\">for<\/span> <span class=\"n\">step<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">steps<\/span><span class=\"p\">]<\/span>\n\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">plot<\/span><span class=\"p\">(<\/span><span class=\"n\">steps<\/span><span class=\"p\">,<\/span> <span class=\"n\">estimates<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">xscale<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"log\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">axhline<\/span><span class=\"p\">(<\/span><span class=\"n\">y<\/span><span class=\"o\">=<\/span><span class=\"mf\">3.14159<\/span><span class=\"p\">,<\/span> <span class=\"n\">color<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"r\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">linestyle<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"--\"<\/span><span class=\"p\">)<\/span>  <span class=\"c1\"># \u771f\u306e\u5186\u5468\u7387<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">xlabel<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"\u30b9\u30c6\u30c3\u30d7\u6570\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">ylabel<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"\u63a8\u5b9a\u5024\uff08\u8d64\u306e\u7834\u7dda\uff1a\u5186\u5468\u7387\uff09\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">title<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"\u63a8\u5b9a\u5024\u306e\u30b9\u30c6\u30c3\u30d7\u6570\u306b\u3088\u308b\u5909\u5316\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">show<\/span><span class=\"p\">()<\/span>\n\n\n<span class=\"k\">def<\/span> <span class=\"nf\">plot_accuracy2<\/span><span class=\"p\">():<\/span>\n    <span class=\"n\">steps<\/span> <span class=\"o\">=<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"mi\">10000<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1010000<\/span><span class=\"p\">,<\/span> <span class=\"mi\">10000<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">estimates<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"n\">estimate_pi<\/span><span class=\"p\">(<\/span><span class=\"n\">step<\/span><span class=\"p\">)<\/span> <span class=\"k\">for<\/span> <span class=\"n\">step<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">steps<\/span><span class=\"p\">]<\/span>\n\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">plot<\/span><span class=\"p\">(<\/span><span class=\"n\">steps<\/span><span class=\"p\">,<\/span> <span class=\"n\">estimates<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">axhline<\/span><span class=\"p\">(<\/span><span class=\"n\">y<\/span><span class=\"o\">=<\/span><span class=\"mf\">3.141592<\/span><span class=\"p\">,<\/span> <span class=\"n\">color<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"r\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">linestyle<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"--\"<\/span><span class=\"p\">)<\/span>  <span class=\"c1\"># \u771f\u306e\u5186\u5468\u7387<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">xlabel<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"\u30b9\u30c6\u30c3\u30d7\u6570\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">ylabel<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"\u63a8\u5b9a\u5024\uff08\u8d64\u306e\u7834\u7dda\uff1a\u5186\u5468\u7387\uff09\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">title<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"\u63a8\u5b9a\u5024\u306e\u30b9\u30c6\u30c3\u30d7\u6570\u306b\u3088\u308b\u5909\u5316\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">show<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\" id=\"cell-id=af237204-bcd9-4861-8643-a1c42d187cd2\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\"highlight hl-ipython3\">\n<pre><span><\/span><span class=\"kn\">import<\/span> <span class=\"nn\">japanize_matplotlib<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\" id=\"cell-id=f7bf1223-a029-4f6e-b201-438f9241fc74\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\"highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">plot_accuracy1<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_png output_subarea\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\" id=\"cell-id=a33193e1-bf54-442e-81b4-9ae717c217ba\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\"highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">plot_accuracy2<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_png output_subarea\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\" id=\"cell-id=8b63487c-de53-498e-9ede-4c82e2f591cd\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\"highlight hl-ipython3\">\n<pre><span><\/span><span class=\"c1\"># \u30b9\u30c6\u30c3\u30d7\u6570\u3068\u8aa4\u5dee\u306e\u95a2\u4fc2\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u95a2\u6570<\/span>\n<span class=\"k\">def<\/span> <span class=\"nf\">plot_error<\/span><span class=\"p\">():<\/span>\n    <span class=\"n\">steps<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span> <span class=\"mi\">100<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1000<\/span><span class=\"p\">,<\/span> <span class=\"mi\">10000<\/span><span class=\"p\">,<\/span> <span class=\"mi\">100000<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1000000<\/span><span class=\"p\">]<\/span>\n    <span class=\"n\">true_pi<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">pi<\/span>\n    <span class=\"n\">errors<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"nb\">abs<\/span><span class=\"p\">(<\/span><span class=\"n\">estimate_pi<\/span><span class=\"p\">(<\/span><span class=\"n\">step<\/span><span class=\"p\">)<\/span> <span class=\"o\">-<\/span> <span class=\"n\">true_pi<\/span><span class=\"p\">)<\/span> <span class=\"k\">for<\/span> <span class=\"n\">step<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">steps<\/span><span class=\"p\">]<\/span>\n\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">plot<\/span><span class=\"p\">(<\/span><span class=\"n\">steps<\/span><span class=\"p\">,<\/span> <span class=\"n\">errors<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">xscale<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"log\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">yscale<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"log\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">xlabel<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"\u30b9\u30c6\u30c3\u30d7\u6570\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">ylabel<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"\u8aa4\u5dee\u306e\u7d76\u5bfe\u5024\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">title<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"\u5186\u5468\u7387\u3068\u63a8\u5b9a\u5024\u306e\u8aa4\u5dee\u306e\u30b9\u30c6\u30c3\u30d7\u6570\u306b\u3088\u308b\u5909\u5316\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">show<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\" id=\"cell-id=e95362d9-0da6-4188-9364-255a36a8dd32\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\"highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">plot_error<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_png output_subarea\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u95a2\u6570estimate_pi\u3067\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u306b\u3088\u308b\u63a8\u5b9a\u3092\u884c\u3044\u3001\u95a2\u6570plot_accuracy\u3067\u63a8\u5b9a\u7d50\u679c\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u3044\u307e\u3059\u3002<br \/>\n\u5b9f\u884c\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<p>\u5b9f\u884c\u3059\u308b\u3068\u4ee5\u4e0b\u306e\u69d8\u306a\u30b0\u30e9\u30d5\u304c\u51fa\u529b\u3055\u308c\u307e\u3059\u3002\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u306b\u3088\u308b\u63a8\u5b9a\u306a\u306e\u3067\u3001\u5b9f\u884c\u6bce\u306b\u7d50\u679c\u304c\u7570\u306a\u308a\u307e\u3059\u3002<br \/>\n\u30b0\u30e9\u30d5\u3092\u898b\u308b\u969b\u306b\u306f\u30b0\u30e9\u30d5\u306e\u6a2a\u8ef8\u304c\u5bfe\u6570\u76ee\u76db\u308a\u3067\u3042\u308b\u3053\u3068\u306b\u7559\u610f\u3057\u3066\u4e0b\u3055\u3044\u3002<br \/>\n<img loading=\"lazy\" decoding=\"async\" src=\"\/T-Wave\/wp-content\/uploads\/2023\/06\/19dc05cf2ac174e0877720b0564a3d87.png\" alt=\"null\" class=\"aligncenter\" width=\"431\" height=\"343\" \/><br \/>\n\u30b9\u30c6\u30c3\u30d7\u6570\u304c\u5897\u3048\u308b\u3054\u3068\u306b\u63a8\u5b9a\u5024\u304c\u5186\u5468\u7387\u306b\u8fd1\u3065\u3044\u3066\u3044\u308b\u3053\u3068\u304c\u898b\u3066\u53d6\u308c\u307e\u3059\u3002<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u306f\u78ba\u7387\u7684\u306a\u624b\u6cd5\u3092\u7528\u3044\u3066\u3001\u89e3\u6790\u7684\u306b\u56f0\u96e3\u306a\u6570\u5024\u8a08\u7b97\u3092\u8fd1\u4f3c\u7684\u306b\u884c\u3046\u65b9\u6cd5\u3067\u3059\u3002\u30e9\u30f3\u30c0\u30e0\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u7528\u3044\u3066\u3001\u8907\u96d1\u306a\u6570\u5b66\u7684\u554f\u984c\u3084\u9ad8\u6b21\u5143\u306e\u7a4d\u5206\u3001\u6700\u9069\u5316\u554f\u984c\u306a\u3069\u3092\u89e3\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001\u7269\u7406\u5b66\u3084\u7d71\u8a08\u5b66\u3001\u91d1\u878d\u5de5\u5b66\u306a\u3069\u3001\u69d8\u3005\u306a\u5206\u91ce\u3067\u5e83\u304f\u5229\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u306e\u7cbe\u5ea6\u306f\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306e\u56de\u6570\uff08\u30b9\u30c6\u30c3\u30d7\u6570\uff09\u306b\u4f9d\u5b58\u3057\u3001\u4e00\u822c\u306b\u30b9\u30c6\u30c3\u30d7\u6570\u304c\u5897\u3048\u308b\u307b\u3069\u7cbe\u5ea6\u304c\u9ad8\u307e\u308a\u307e\u3059\u3002<\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-6297","post","type-post","status-publish","format-standard","hentry","category-glossary"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5 - T-QARD Harbor<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/12\/\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5 - T-QARD Harbor\" \/>\n<meta property=\"og:description\" content=\"\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u306f\u78ba\u7387\u7684\u306a\u624b\u6cd5\u3092\u7528\u3044\u3066\u3001\u89e3\u6790\u7684\u306b\u56f0\u96e3\u306a\u6570\u5024\u8a08\u7b97\u3092\u8fd1\u4f3c\u7684\u306b\u884c\u3046\u65b9\u6cd5\u3067\u3059\u3002\u30e9\u30f3\u30c0\u30e0\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u7528\u3044\u3066\u3001\u8907\u96d1\u306a\u6570\u5b66\u7684\u554f\u984c\u3084\u9ad8\u6b21\u5143\u306e\u7a4d\u5206\u3001\u6700\u9069\u5316\u554f\u984c\u306a\u3069\u3092\u89e3\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001\u7269\u7406\u5b66\u3084\u7d71\u8a08\u5b66\u3001\u91d1\u878d\u5de5\u5b66\u306a\u3069\u3001\u69d8\u3005\u306a\u5206\u91ce\u3067\u5e83\u304f\u5229\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u306e\u7cbe\u5ea6\u306f\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306e\u56de\u6570\uff08\u30b9\u30c6\u30c3\u30d7\u6570\uff09\u306b\u4f9d\u5b58\u3057\u3001\u4e00\u822c\u306b\u30b9\u30c6\u30c3\u30d7\u6570\u304c\u5897\u3048\u308b\u307b\u3069\u7cbe\u5ea6\u304c\u9ad8\u307e\u308a\u307e\u3059\u3002\" 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