{"id":3510,"date":"2023-07-17T02:21:36","date_gmt":"2023-07-17T02:21:36","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e5%87%b9%e5%87%b8%e3%81%ae%e3%81%82%e3%82%8b%e6%9c%80%e5%b0%8f%e3%82%a8%e3%83%83%e3%82%b7%e3%82%99%e3%81%ae%e8%aa%bf%e6%95%b4\/"},"modified":"2023-07-17T02:21:36","modified_gmt":"2023-07-17T02:21:36","slug":"%e5%87%b9%e5%87%b8%e3%81%ae%e3%81%82%e3%82%8b%e6%9c%80%e5%b0%8f%e3%82%a8%e3%83%83%e3%82%b7%e3%82%99%e3%81%ae%e8%aa%bf%e6%95%b4","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e5%87%b9%e5%87%b8%e3%81%ae%e3%81%82%e3%82%8b%e6%9c%80%e5%b0%8f%e3%82%a8%e3%83%83%e3%82%b7%e3%82%99%e3%81%ae%e8%aa%bf%e6%95%b4\/","title":{"rendered":"Numpy \u3067\u6700\u5c0f\u4e8c\u4e57\u6cd5\u30d5\u30a3\u30c3\u30c8\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d)"},"content":{"rendered":"<hr>\n<p><span style=\"color: #000000;\"><strong>\u6700\u5c0f\u4e8c\u4e57\u6cd5\u306f\u3001<\/strong>\u7279\u5b9a\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u6700\u3082\u3088\u304f\u9069\u5408\u3059\u308b\u56de\u5e30\u76f4\u7dda\u3092\u898b\u3064\u3051\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3067\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">NumPy \u306e<strong>linalg.lstsq(<\/strong> ) \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u6700\u5c0f\u4e8c\u4e57\u8abf\u6574\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u306f\u3001\u3053\u306e\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: X \u3068 Y \u306e\u5024\u3092\u5165\u529b\u3057\u307e\u3059<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e NumPy \u914d\u5217\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u304b\u3089\u59cb\u3081\u307e\u3057\u3087\u3046\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#define x and y arrays\n<\/span>x = np. <span style=\"color: #3366ff;\">array<\/span> ([6, 7, 7, 8, 12, 14, 15, 16, 16, 19])\n\ny = np. <span style=\"color: #3366ff;\">array<\/span> ([14, 15, 15, 17, 18, 18, 19, 24, 25, 29])\n<\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u6700\u5c0f\u4e8c\u4e57\u8abf\u6574\u3092\u5b9f\u884c\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u6700\u5c0f\u4e8c\u4e57\u8fd1\u4f3c\u3092\u5b9f\u884c\u3057\u3001\u30c7\u30fc\u30bf\u306b\u6700\u3082\u3088\u304f\u300c\u9069\u5408\u300d\u3059\u308b\u884c\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform least squares fitting\n<\/span>n.p. <span style=\"color: #3366ff;\">linalg<\/span> . <span style=\"color: #3366ff;\">lstsq<\/span> (np. <span style=\"color: #3366ff;\">vstack<\/span> ([x, np. <span style=\"color: #3366ff;\">ones<\/span> ( <span style=\"color: #008000;\">len<\/span> (x))]). <span style=\"color: #3366ff;\">T<\/span> , y, rcond= <span style=\"color: #008000;\">None<\/span> )[ <span style=\"color: #008000;\">0<\/span> ]\n\narray([0.96938776, 7.76734694])\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u7d50\u679c\u306f\u3001\u6700\u9069\u306a\u76f4\u7dda\u306e<strong>\u50be\u304d<\/strong>\u3068<strong>\u5207\u7247\u306e<\/strong>\u5024\u3092\u542b\u3080\u30c6\u30fc\u30d6\u30eb\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/span><\/p>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u6b21\u306e\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u52fe\u914d: <strong>0.969<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\">\u76d7\u5841\u6570: <strong>7,767<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089 2 \u3064\u306e\u5024\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u6700\u9069\u306a\u76f4\u7dda\u3092\u6c42\u3081\u308b\u65b9\u7a0b\u5f0f\u3092\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0177 = 7.767 + 0.969x<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u7d50\u679c\u3092\u89e3\u91c8\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6700\u9069\u306a\u7dda\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">x \u304c 0 \u306e\u5834\u5408\u3001y \u306e\u5e73\u5747\u5024\u306f<strong><span id=\"interceptOut\">7.767<\/span><\/strong>\u3067\u3059\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">x \u304c 1 \u5358\u4f4d\u5897\u52a0\u3059\u308b\u3054\u3068\u306b\u3001y \u306f\u5e73\u5747<strong>0.969<\/strong>\u5897\u52a0\u3057\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6700\u9069\u76f4\u7dda\u3092\u4f7f\u7528\u3057\u3066\u3001x \u306e\u5024\u306b\u57fa\u3065\u3044\u3066 y \u306e\u5024\u3092\u4e88\u6e2c\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001 xa \u306e\u5024\u304c 10 \u306e\u5834\u5408\u3001 y \u306e\u5024\u306f<strong>17.457<\/strong>\u306b\u306a\u308b\u3068\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u0177 = 7.767 + 0.969x<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0177 = 7.767 + 0.969(10)<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0177 = 17.457<\/span><\/li>\n<\/ul>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001NumPy \u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30bf\u30b9\u30af\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ja\/numpy\u306f\u914d\u5217\u304b\u3089\u8981\u7d20\u3092\u524a\u9664\u3057\u307e\u3059\/\" target=\"_blank\" rel=\"noopener\">NumPy\u914d\u5217\u304b\u3089\u7279\u5b9a\u306e\u8981\u7d20\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/numpy\u914d\u5217\u306e\u6700\u5927\u5024\u306e\u30a4\u30f3\u30c6\u3099\u30c3\u30af\u30b9\u3092\u53d6\u5f97\/\" target=\"_blank\" rel=\"noopener\">NumPy\u914d\u5217\u306e\u6700\u5927\u5024\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u53d6\u5f97\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u304d\u3099\u3053\u3061\u306a\u3044\u5857\u308a\u3064\u3075\u3099\u3057\u914d\u5217\/\" target=\"_blank\" rel=\"noopener\">NumPy \u914d\u5217\u306b\u5024\u3092\u5165\u529b\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6700\u5c0f\u4e8c\u4e57\u6cd5\u306f\u3001\u7279\u5b9a\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u6700\u3082\u3088\u304f\u9069\u5408\u3059\u308b\u56de\u5e30\u76f4\u7dda\u3092\u898b\u3064\u3051\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3067\u3059\u3002 NumPy \u306elinalg.lstsq( ) \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u6700\u5c0f\u4e8c\u4e57\u8abf\u6574\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002 \u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u306f\u3001 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-3510","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>NumPy \u3067\u6700\u5c0f\u4e8c\u4e57\u8abf\u6574\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u3042\u308a) - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001NumPy \u3067\u6700\u5c0f\u4e8c\u4e57\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u6319\u3052\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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