{"id":4062,"date":"2023-07-13T20:37:59","date_gmt":"2023-07-13T20:37:59","guid":{"rendered":"https:\/\/statorials.org\/ko\/numpy%e1%84%82%e1%85%b3%e1%86%ab-0%e1%84%80%e1%85%aa-1-%e1%84%89%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%8c%e1%85%a5%e1%86%bc%e1%84%80%e1%85%b2%e1%84%92%e1%85%aa%e1%84%83\/"},"modified":"2023-07-13T20:37:59","modified_gmt":"2023-07-13T20:37:59","slug":"numpy%e1%84%82%e1%85%b3%e1%86%ab-0%e1%84%80%e1%85%aa-1-%e1%84%89%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%8c%e1%85%a5%e1%86%bc%e1%84%80%e1%85%b2%e1%84%92%e1%85%aa%e1%84%83","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/numpy%e1%84%82%e1%85%b3%e1%86%ab-0%e1%84%80%e1%85%aa-1-%e1%84%89%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%8c%e1%85%a5%e1%86%bc%e1%84%80%e1%85%b2%e1%84%92%e1%85%aa%e1%84%83\/","title":{"rendered":"Numpy \ubc30\uc5f4\uc758 \uac12\uc744 0\uacfc 1 \uc0ac\uc774\ub85c \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">NumPy \ubc30\uc5f4\uc758 \uac12\uc744 0\uacfc 1 \uc0ac\uc774\ub85c \uc815\uaddc\ud654\ud558\ub824\uba74 \ub2e4\uc74c \ubc29\ubc95 \uc911 \ud558\ub098\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\ubc29\ubc95 1: NumPy \uc0ac\uc6a9<\/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>\ubc29\ubc95 2: Sklearn \uc0ac\uc6a9<\/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;\">\ub450 \ubc29\ubc95 \ubaa8\ub450 <strong>x\uac00<\/strong> \uc815\uaddc\ud654\ud558\ub824\ub294 NumPy \ubc30\uc5f4\uc758 \uc774\ub984\uc774\ub77c\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uac01 \ubc29\ubc95\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\uc608\uc2dc 1: NumPy\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac12 \uc815\uaddc\ud654<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uacfc \uac19\uc740 NumPy \ubc30\uc5f4\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/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;\">\ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc30\uc5f4\uc758 \uac01 \uac12\uc744 0\uacfc 1 \uc0ac\uc774\ub85c \uc815\uaddc\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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 \ubc30\uc5f4\uc758 \uac01 \uac12\uc740 0\uacfc 1 \uc0ac\uc774\ub85c \uc815\uaddc\ud654\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc791\ub3d9 \ubc29\uc2dd\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub370\uc774\ud130 \uc138\ud2b8\uc758 \ucd5c\uc18c\uac12\uc740 13\uc774\uace0 \ucd5c\ub300\uac12\uc740 71\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>13<\/strong> \uc758 \uccab \ubc88\uc9f8 \uac12\uc744 \uc815\uaddc\ud654\ud558\uae30 \uc704\ud574 \uc774\uc804\uc5d0 \uacf5\uc720\ud55c \uacf5\uc2dd\uc744 \uc801\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>z <sub>i<\/sub> = (x <sub>i<\/sub> \u2013 \ucd5c\uc18c(x)) \/ (\ucd5c\ub300(x) \u2013 \ucd5c\uc18c(x))<\/strong> = (13 \u2013 13) \/ (71 \u2013 13) = <strong>0<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>16<\/strong> \uc758 \ub450 \ubc88\uc9f8 \uac12\uc744 \uc815\uaddc\ud654\ud558\uae30 \uc704\ud574 \ub3d9\uc77c\ud55c \uacf5\uc2dd\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>z <sub>i<\/sub> = (x <sub>i<\/sub> \u2013 \ucd5c\uc18c(x)) \/ (\ucd5c\ub300(x) \u2013 \ucd5c\uc18c(x))<\/strong> = (16 \u2013 13) \/ (71 \u2013 13) = <strong>0.0517<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>19<\/strong> \uc758 \uc138 \ubc88\uc9f8 \uac12\uc744 \uc815\uaddc\ud654\ud558\uae30 \uc704\ud574 \ub3d9\uc77c\ud55c \uacf5\uc2dd\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>z <sub>i<\/sub> = (x <sub>i<\/sub> \u2013 \ucd5c\uc18c(x)) \/ (\ucd5c\ub300(x) \u2013 \ucd5c\uc18c(x))<\/strong> = (19 \u2013 13) \/ (71 \u2013 13) = <strong>0.1034<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub3d9\uc77c\ud55c \uacf5\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc6d0\ub798 NumPy \ubc30\uc5f4\uc758 \uac01 \uac12\uc744 0\uacfc 1 \uc0ac\uc774\ub85c \uc815\uaddc\ud654\ud569\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\uc608\uc2dc 2: sklearn\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac12 \uc815\uaddc\ud654<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\uc774\ubc88\uc5d0\ub3c4 \ub2e4\uc74c\uacfc \uac19\uc740 NumPy \ubc30\uc5f4\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/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> \uc758 <strong>MinMaxScaler()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc30\uc5f4\uc758 \uac01 \uac12\uc744 0\uacfc 1 \uc0ac\uc774\ub85c \uc815\uaddc\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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 \ubc30\uc5f4\uc758 \uac01 \uac12\uc740 0\uacfc 1 \uc0ac\uc774\ub85c \uc815\uaddc\ud654\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774\ub7ec\ud55c \uc815\uaddc\ud654\ub41c \uac12\uc740 \uc774\uc804 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \uacc4\uc0b0\ub41c \uac12\uacfc \uc77c\uce58\ud569\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 NumPy\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ko\/numpy-\u1105\u1175\u1103\u1165\u1107\u1169\u1103\u1173\/\" target=\"_blank\" rel=\"noopener\">NumPy \ubc30\uc5f4\uc758 \uc694\uc18c\ub97c \uc815\ub82c\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/numpy-\u110c\u116e\u11bc\u1107\u1169\u11a8-\u110c\u1166\u1100\u1165\/\" target=\"_blank\" rel=\"noopener\">NumPy \ubc30\uc5f4\uc5d0\uc11c \uc911\ubcf5 \uc694\uc18c\ub97c \uc81c\uac70\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1100\u1161\u110c\u1161\u11bc-\u1107\u1175\u11ab\u1107\u1165\u11ab\u1112\u1161\u11ab-\u1100\u1161\u11b9-numpy\/\" target=\"_blank\" rel=\"noopener\">NumPy \ubc30\uc5f4\uc5d0\uc11c \uac00\uc7a5 \ube48\ubc88\ud55c \uac12\uc744 \ucc3e\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>NumPy \ubc30\uc5f4\uc758 \uac12\uc744 0\uacfc 1 \uc0ac\uc774\ub85c \uc815\uaddc\ud654\ud558\ub824\uba74 \ub2e4\uc74c \ubc29\ubc95 \uc911 \ud558\ub098\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: NumPy \uc0ac\uc6a9 import numpy as np x_norm = (x-np. min (x))\/(np. max (x)-np. min (x)) \ubc29\ubc95 2: Sklearn \uc0ac\uc6a9 from sklearn import preprocessing as pre x = x. reshape (-1, 1) x_norm = pre. MinMaxScaler (). fit_transform (x) \ub450 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-4062","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>NumPy \ubc30\uc5f4\uc758 \uac12\uc744 0\uacfc 1 \uc0ac\uc774\ub85c \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 NumPy \ubc30\uc5f4\uc758 \uac12\uc744 0\uacfc 1 \uc0ac\uc774\uc5d0\uc11c \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95\uc744 \uba87 \uac00\uc9c0 \uc608\uc640 \ud568\uaed8 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\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\/ko\/numpy\u1102\u1173\u11ab-0\u1100\u116a-1-\u1109\u1161\u110b\u1175\u110b\u1166\u1109\u1165-\u110c\u1165\u11bc\u1100\u1172\u1112\u116a\u1103\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NumPy \ubc30\uc5f4\uc758 \uac12\uc744 0\uacfc 1 \uc0ac\uc774\ub85c \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95 - 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