{"id":4060,"date":"2023-07-13T20:37:59","date_gmt":"2023-07-13T20:37:59","guid":{"rendered":"https:\/\/statorials.org\/ja\/numpy-%e3%81%af-0-%e3%81%a8-1-%e3%81%ae%e9%96%93%e3%81%a6%e3%82%99%e6%ad%a3%e8%a6%8f%e5%8c%96%e3%81%97%e3%81%be%e3%81%99\/"},"modified":"2023-07-13T20:37:59","modified_gmt":"2023-07-13T20:37:59","slug":"numpy-%e3%81%af-0-%e3%81%a8-1-%e3%81%ae%e9%96%93%e3%81%a6%e3%82%99%e6%ad%a3%e8%a6%8f%e5%8c%96%e3%81%97%e3%81%be%e3%81%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/numpy-%e3%81%af-0-%e3%81%a8-1-%e3%81%ae%e9%96%93%e3%81%a6%e3%82%99%e6%ad%a3%e8%a6%8f%e5%8c%96%e3%81%97%e3%81%be%e3%81%99\/","title":{"rendered":"Numpy\u914d\u5217\u306e\u5024\u30920\u30681\u306e\u9593\u3067\u6b63\u898f\u5316\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">NumPy \u914d\u5217\u306e\u5024\u3092 0 \u304b\u3089 1 \u306e\u9593\u3067\u6b63\u898f\u5316\u3059\u308b\u306b\u306f\u3001\u6b21\u306e\u3044\u305a\u308c\u304b\u306e\u65b9\u6cd5\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 1: NumPy \u3092\u4f7f\u7528\u3059\u308b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n\nx_norm = (x-np. <span style=\"color: #3366ff;\">min<\/span> (x))\/(np. <span style=\"color: #3366ff;\">max<\/span> (x)-np. <span style=\"color: #3366ff;\">min<\/span> (x))\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 2: Sklearn \u3092\u4f7f\u7528\u3059\u308b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> preprocessing <span style=\"color: #008000;\">as<\/span> pre\n\nx = x. <span style=\"color: #3366ff;\">reshape<\/span> (-1, 1)\n\nx_norm = pre. <span style=\"color: #3366ff;\">MinMaxScaler<\/span> (). <span style=\"color: #3366ff;\">fit_transform<\/span> (x)<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3069\u3061\u3089\u306e\u30e1\u30bd\u30c3\u30c9\u3082\u3001 <strong>x \u304c<\/strong>\u6b63\u898f\u5316\u3059\u308b NumPy \u914d\u5217\u306e\u540d\u524d\u3067\u3042\u308b\u3068\u60f3\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u5404\u30e1\u30bd\u30c3\u30c9\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>\u4f8b 1: NumPy \u3092\u4f7f\u7528\u3057\u3066\u5024\u3092\u6b63\u898f\u5316\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e NumPy \u914d\u5217\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#create NumPy array\n<\/span>x = np. <span style=\"color: #3366ff;\">array<\/span> ([13, 16, 19, 22, 23, 38, 47, 56, 58, 63, 65, 70, 71])\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u914d\u5217\u5185\u306e\u5404\u5024\u3092 0 \u3068 1 \u306e\u9593\u3067\u6b63\u898f\u5316\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#normalize all values to be between 0 and 1\n<\/span>x_norm = (x-np. <span style=\"color: #3366ff;\">min<\/span> (x))\/(np. <span style=\"color: #3366ff;\">max<\/span> (x)-np. <span style=\"color: #3366ff;\">min<\/span> (x))\n\n<span style=\"color: #008080;\">#view normalized array\n<\/span><span style=\"color: #008000;\">print<\/span> (x_norm)\n\n[0. 0.05172414 0.10344828 0.15517241 0.17241379 0.43103448\n 0.5862069 0.74137931 0.77586207 0.86206897 0.89655172 0.98275862\n 1. ]\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">NumPy \u914d\u5217\u306e\u5404\u5024\u306f\u30010 \u304b\u3089 1 \u306e\u9593\u306b\u306a\u308b\u3088\u3046\u306b\u6b63\u898f\u5316\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305d\u306e\u4ed5\u7d44\u307f\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6700\u5c0f\u5024\u306f 13 \u3067\u3001\u6700\u5927\u5024\u306f 71 \u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6700\u521d\u306e\u5024<strong>13 \u3092<\/strong>\u6b63\u898f\u5316\u3059\u308b\u306b\u306f\u3001\u4ee5\u524d\u306b\u5171\u6709\u3057\u305f\u5f0f\u3092\u9069\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>z <sub>i<\/sub> = (x <sub>i<\/sub> \u2013 min(x)) \/ (max(x) \u2013 min(x))<\/strong> = (13 \u2013 13) \/ (71 \u2013 13) = <strong>0<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">2 \u756a\u76ee\u306e\u5024<strong>16 \u3092<\/strong>\u6b63\u898f\u5316\u3059\u308b\u306b\u306f\u3001\u540c\u3058\u5f0f\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>z <sub>i<\/sub> = (x <sub>i<\/sub> \u2013 \u6700\u5c0f(x)) \/ (\u6700\u5927(x) \u2013 \u6700\u5c0f(x))<\/strong> = (16 \u2013 13) \/ (71 \u2013 13) = <strong>0.0517<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">3 \u756a\u76ee\u306e\u5024<strong>19 \u3092<\/strong>\u6b63\u898f\u5316\u3059\u308b\u306b\u306f\u3001\u540c\u3058\u5f0f\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>z <sub>i<\/sub> = (x <sub>i<\/sub> \u2013 \u6700\u5c0f(x)) \/ (\u6700\u5927(x) \u2013 \u6700\u5c0f(x))<\/strong> = (19 \u2013 13) \/ (71 \u2013 13) = <strong>0.1034<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3068\u540c\u3058\u5f0f\u3092\u4f7f\u7528\u3057\u3066\u3001\u5143\u306e NumPy \u914d\u5217\u306e\u5404\u5024\u3092 0 \u304b\u3089 1 \u306e\u9593\u3067\u6b63\u898f\u5316\u3057\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b 2: sklearn \u3092\u4f7f\u7528\u3057\u3066\u5024\u3092\u6b63\u898f\u5316\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u3082\u3046\u4e00\u5ea6\u3001\u6b21\u306e NumPy \u914d\u5217\u304c\u3042\u308b\u3068\u4eee\u5b9a\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#create NumPy array\n<\/span>x = np. <span style=\"color: #3366ff;\">array<\/span> ([13, 16, 19, 22, 23, 38, 47, 56, 58, 63, 65, 70, 71])\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\"><strong>sklearn<\/strong>\u306e<strong>MinMaxScaler()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u914d\u5217\u5185\u306e\u5404\u5024\u3092 0 \u3068 1 \u306e\u9593\u3067\u6b63\u898f\u5316\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> preprocessing <span style=\"color: #008000;\">as<\/span> pre\n\n<span style=\"color: #008080;\">#reshape array so that it works with sklearn\n<\/span>x = x. <span style=\"color: #3366ff;\">reshape<\/span> (-1, 1)\n\n<span style=\"color: #008080;\">#normalize all values to be between 0 and 1\n<\/span>x_norm = pre. <span style=\"color: #3366ff;\">MinMaxScaler<\/span> (). <span style=\"color: #3366ff;\">fit_transform<\/span> (x)\n\n<span style=\"color: #008080;\">#view normalized array\n<\/span><span style=\"color: #008000;\">print<\/span> (x_norm)\n\n[[0. ]\n [0.05172414]\n [0.10344828]\n [0.15517241]\n [0.17241379]\n [0.43103448]\n [0.5862069]\n [0.74137931]\n [0.77586207]\n [0.86206897]\n [0.89655172]\n [0.98275862]\n [1. ]]<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">NumPy \u914d\u5217\u306e\u5404\u5024\u306f\u30010 \u304b\u3089 1 \u306e\u9593\u306b\u306a\u308b\u3088\u3046\u306b\u6b63\u898f\u5316\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089\u306e\u6b63\u898f\u5316\u3055\u308c\u305f\u5024\u306f\u3001\u524d\u306e\u65b9\u6cd5\u3092\u4f7f\u7528\u3057\u3066\u8a08\u7b97\u3055\u308c\u305f\u5024\u3068\u4e00\u81f4\u3059\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\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\/\u304d\u3099\u3053\u3061\u306a\u3044\u30ea\u30fc\u30bf\u3099\u30fc\u30db\u3099\u30fc\u30c8\u3099\/\" target=\"_blank\" rel=\"noopener\">NumPy \u914d\u5217\u306e\u8981\u7d20\u3092\u4e26\u3079\u66ff\u3048\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/numpy-\u91cd\u8907\u3092\u524a\u9664\/\" target=\"_blank\" rel=\"noopener\">NumPy\u914d\u5217\u304b\u3089\u91cd\u8907\u8981\u7d20\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u6700\u3082\u983b\u5ea6\u306e\u9ad8\u3044\u5024-numpy\/\" target=\"_blank\" rel=\"noopener\">NumPy\u914d\u5217\u3067\u6700\u3082\u983b\u5ea6\u306e\u9ad8\u3044\u5024\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>NumPy \u914d\u5217\u306e\u5024\u3092 0 \u304b\u3089 1 \u306e\u9593\u3067\u6b63\u898f\u5316\u3059\u308b\u306b\u306f\u3001\u6b21\u306e\u3044\u305a\u308c\u304b\u306e\u65b9\u6cd5\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002 \u65b9\u6cd5 1: NumPy \u3092\u4f7f\u7528\u3059\u308b import numpy as np x_norm = (x-np. min (x))\/ 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