{"id":1646,"date":"2023-07-25T12:50:55","date_gmt":"2023-07-25T12:50:55","guid":{"rendered":"https:\/\/statorials.org\/ja\/python-%e6%9b%b2%e7%b7%9a%e3%83%95%e3%82%a3%e3%83%83%e3%83%86%e3%82%a3%e3%83%b3%e3%82%af%e3%82%99\/"},"modified":"2023-07-25T12:50:55","modified_gmt":"2023-07-25T12:50:55","slug":"python-%e6%9b%b2%e7%b7%9a%e3%83%95%e3%82%a3%e3%83%83%e3%83%86%e3%82%a3%e3%83%b3%e3%82%af%e3%82%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python-%e6%9b%b2%e7%b7%9a%e3%83%95%e3%82%a3%e3%83%83%e3%83%86%e3%82%a3%e3%83%b3%e3%82%af%e3%82%99\/","title":{"rendered":"Python \u3067\u306e\u66f2\u7dda\u8fd1\u4f3c (\u4f8b\u4ed8\u304d)"},"content":{"rendered":"

<\/p>\n

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Python \u3067\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u66f2\u7dda\u3092\u5f53\u3066\u306f\u3081\u305f\u3044\u5834\u5408\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002<\/span> <\/p>\n

\"\"<\/p>\n

\u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3067\u306f\u3001Python \u3067numpy.polyfit()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u66f2\u7dda\u3092\u30c7\u30fc\u30bf\u306b\u9069\u5408\u3055\u305b\u308b\u65b9\u6cd5\u3068\u3001\u3069\u306e\u66f2\u7dda\u304c\u30c7\u30fc\u30bf\u306b\u6700\u3082\u9069\u5408\u3059\u308b\u304b\u3092\u5224\u65ad\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u306e\u4f5c\u6210\u3068\u8996\u899a\u5316<\/strong><\/span><\/h3>\n

\u307e\u305a\u507d\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f5c\u6210\u3057\u3001\u6b21\u306b\u6563\u5e03\u56f3\u3092\u4f5c\u6210\u3057\u3066\u30c7\u30fc\u30bf\u3092\u8996\u899a\u5316\u3057\u307e\u3057\u3087\u3046\u3002<\/span> <\/p>\n

 import<\/span> pandas as<\/span> pd\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' x<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\n                   ' y<\/span> ': [3, 14, 23, 25, 23, 15, 9, 5, 9, 13, 17, 24, 32, 36, 46]})\n\n#create scatterplot of x vs. y\n<\/span>plt. scatter<\/span> (df. x<\/span> , df. y<\/span> )<\/strong> <\/pre>\n
\"\"<\/p>\n
\u30b9\u30c6\u30c3\u30d7 2: \u8907\u6570\u306e\u30ab\u30fc\u30d6\u3092\u8abf\u6574\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306b\u3001\u3044\u304f\u3064\u304b\u306e\u591a\u9805\u5f0f\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u306b\u9069\u5408\u3055\u305b\u3001\u540c\u3058\u30d7\u30ed\u30c3\u30c8\u3067\u5404\u30e2\u30c7\u30eb\u306e\u66f2\u7dda\u3092\u8996\u899a\u5316\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/span> <\/p>\n
 import<\/span> numpy as<\/span> np\n\n#fit polynomial models up to degree 5\n<\/span>model1 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 1))\nmodel2 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 2))\nmodel3 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 3))\nmodel4 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 4))\nmodel5 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 5))\n\n#create scatterplot\n<\/span>polyline = np. linspace<\/span> (1, 15, 50)\nplt. scatter<\/span> (df. x<\/span> , df. y<\/span> )\n\n#add fitted polynomial lines to scatterplot \n<\/span>plt. plot<\/span> (polyline, model1(polyline), color=' green<\/span> ')\nplt. plot<\/span> (polyline, model2(polyline), color=' red<\/span> ')\nplt. plot<\/span> (polyline, model3(polyline), color=' purple<\/span> ')\nplt. plot<\/span> (polyline, model4(polyline), color=' blue<\/span> ')\nplt. plot<\/span> (polyline, model5(polyline), color=' orange<\/span> ')\nplt. show<\/span> ()\n<\/span><\/span><\/strong><\/pre>\n
\"\"<\/p>\n
\u3053\u306e\u5024\u306f\u3001\u4e88\u6e2c\u5909\u6570\u306e\u6570\u3092\u8abf\u6574\u3057\u305f\u3001\u30e2\u30c7\u30eb\u5185\u306e\u4e88\u6e2c\u5909\u6570\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u308b\u5fdc\u7b54\u5909\u6570\u306e\u5909\u52d5\u306e\u30d1\u30fc\u30bb\u30f3\u30c6\u30fc\u30b8\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #define function to calculate adjusted r-squared\ndef<\/span> adjR(x, y, degree):\n    results = {}\n    coeffs = np. polyfit<\/span> (x, y, degree)\n    p = np. poly1d<\/span> (coeffs)\n    yhat = p(x)\n    ybar = np. sum<\/span> (y)\/len(y)\n    ssreg = np. sum<\/span> ((yhat-ybar)**2)\n    sstot = np. sum<\/span> ((y - ybar)**2)\n    results[' r_squared<\/span> '] = 1- (((1-(ssreg\/sstot))*(len(y)-1))\/(len(y)-degree-1))\n\n    return<\/span> results<\/span>\n\n#calculated adjusted R-squared of each model\n<\/span>adjR(df. x<\/span> , df. y<\/span> , 1)\nadjR(df. x<\/span> , df. y<\/span> , 2)\nadjR(df. x<\/span> , df. y<\/span> , 3)\nadjR(df. x<\/span> , df. y<\/span> , 4)\nadjR(df. x<\/span> , df. y<\/span> , 5)\n\n{'r_squared': 0.3144819}\n{'r_squared': 0.5186706}\n{'r_squared': 0.7842864}\n{'r_squared': 0.9590276}\n{'r_squared': 0.9549709}\n<\/strong><\/pre>\n
\u7d50\u679c\u304b\u3089\u3001\u8abf\u6574\u3055\u308c\u305f R \u4e8c\u4e57\u304c\u6700\u3082\u9ad8\u3044\u30e2\u30c7\u30eb\u306f\u3001\u8abf\u6574\u3055\u308c\u305f R \u4e8c\u4e57\u304c0.959<\/strong>\u3067\u3042\u308b 4 \u6b21\u591a\u9805\u5f0f\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u30b9\u30c6\u30c3\u30d7 3: \u6700\u7d42\u7684\u306a\u66f2\u7dda\u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/span><\/h3>\n
\u6700\u5f8c\u306b\u30014 \u6b21\u591a\u9805\u5f0f\u30e2\u30c7\u30eb\u306e\u66f2\u7dda\u3092\u4f7f\u7528\u3057\u3066\u6563\u5e03\u56f3\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
 #fit fourth-degree polynomial\n<\/span>model4 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 4))\n\n#define scatterplot\n<\/span>polyline = np. linspace<\/span> (1, 15, 50)\nplt. scatter<\/span> (df. x<\/span> , df. y<\/span> )\n\n#add fitted polynomial curve to scatterplot\n<\/span>plt. plot<\/span> (polyline, model4(polyline), ' --<\/span> ', color=' red<\/span> ')\nplt. show<\/span> ()\n<\/strong><\/pre>\n
\"\"<\/p>\n
 print()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u306e\u884c\u306e\u65b9\u7a0b\u5f0f\u3092\u53d6\u5f97\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 print<\/span> (model4)\n\n          4 3 2\n-0.01924x + 0.7081x - 8.365x + 35.82x - 26.52\n<\/strong><\/pre>\n
\u66f2\u7dda\u306e\u65b9\u7a0b\u5f0f\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n
 y = -0.01924x 4<\/sup> + 0.7081x 3<\/sup> \u2013 8.365x 2<\/sup> + 35.82x \u2013 26.52<\/span><\/p>\n
\u3053\u306e\u65b9\u7a0b\u5f0f\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u30e2\u30c7\u30eb\u5185\u306e\u4e88\u6e2c\u5909\u6570\u306b\u57fa\u3065\u3044\u3066\u5fdc\u7b54\u5909\u6570<\/a>\u306e\u5024\u3092\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001 x<\/em> = 4 \u306e\u5834\u5408\u3001 y<\/em> = 23.32<\/strong>\u3068\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n
 y = -0.0192(4) 4<\/sup> + 0.7081(4) 3<\/sup> \u2013 8.365(4) 2<\/sup> + 35.82(4) \u2013 26.52 = 23.32<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
\u591a\u9805\u5f0f\u56de\u5e30\u306e\u6982\u8981
<\/a>Python \u3067\u591a\u9805\u5f0f\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Python \u3067\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u66f2\u7dda\u3092\u5f53\u3066\u306f\u3081\u305f\u3044\u5834\u5408\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002 \u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3067\u306f\u3001Python \u3067numpy.polyfit()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u66f2\u7dda\u3092\u30c7\u30fc\u30bf\u306b\u9069\u5408\u3055\u305b\u308b\u65b9\u6cd5\u3068\u3001\u3069\u306e\u66f2\u7dda\u304c\u30c7\u30fc\u30bf\u306b\u6700\u3082\u9069 […]<\/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-1646","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nPython \u3067\u306e\u66f2\u7dda\u8fd1\u4f3c (\u4f8b\u4ed8\u304d) - Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u66f2\u7dda\u3092\u8fd1\u4f3c\u3059\u308b\u65b9\u6cd5\u3092\u3044\u304f\u3064\u304b\u306e\u4f8b\u3068\u3068\u3082\u306b\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ja\/python-\u66f2\u7dda\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30af\u3099\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python \u3067\u306e\u66f2\u7dda\u8fd1\u4f3c (\u4f8b\u4ed8\u304d) - 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