3\u884c5\u5217\u306e\u4e71\u6570\u914d\u5217a\u3092\u4f5c\u6210\u3059\u308b\u3002
\n\u3053\u3053\u3067\u306f\u898b\u3084\u3059\u3044\u3088\u3046\u306b\u5404\u8981\u7d20\u3092100\u500d\u3057\u305f\u5f8c\u306b\u6574\u6570\u5316\u3057\u3066\u3044\u308b\u3002<\/p>\n
>>> import numpy as np\r\n>>> a = np.random.random((3,5)) * 100\r\n>>> a\r\narray([[ 61.95240209, 19.78842754, 19.66424701, 5.21288021, 10.02728863],\r\n [ 75.68367629, 66.49158917, 1.50172285, 61.8935986 , 15.05438068],\r\n [ 66.41067826, 38.32497801, 2.42314341, 18.44271156, 22.53073152]])\r\n>>> a = a.astype(int)\r\n>>> a\r\narray([[61, 19, 19, 5, 10],\r\n [75, 66, 1, 61, 15],\r\n [66, 38, 2, 18, 22]])\r\n<\/pre>\n\u914d\u5217\u8981\u7d20\u306e\u6700\u5c0f\u5024\u3068\u6700\u5927\u5024\u3092\u53d6\u5f97\u3059\u308b\u3002<\/p>\n
>>> vmin = a.min()\r\n>>> vmin\r\n1\r\n>>> vmax = a.max()\r\n>>> vmax\r\n75\r\n<\/pre>\n\u914d\u5217a\u306e\u5168\u8981\u7d20\u306e\u5024\u3092\u3001\u6700\u5c0f\u5024\uff5e\u6700\u5927\u5024\u306e\u7bc4\u56f2\u3067\u6b63\u898f\u5316\u3057\u305f\u914d\u5217b\u3092\u4f5c\u6210\u3059\u308b\u3002
\n\u203bnumpy\u3067\u306f\u3001MATLAB,octave\u306e\u3088\u3046\u306b(\u30d9\u30af\u30bf),(\u30b9\u30ab\u30e9)\u9593\u306e\u56db\u5247\u6f14\u7b97\u304c\u3067\u304d\u308b\u3002<\/span><\/p>\n>>> b = (a - vmin).astype(float) \/ (vmax - vmin).astype(float)\r\n>>> b\r\narray([[ 0.81081081, 0.24324324, 0.24324324, 0.05405405, 0.12162162],\r\n [ 1. , 0.87837838, 0. , 0.81081081, 0.18918919],\r\n [ 0.87837838, 0.5 , 0.01351351, 0.22972973, 0.28378378]])\r\n<\/pre>\n
\n","protected":false},"excerpt":{"rendered":"3\u884c5\u5217\u306e\u4e71\u6570\u914d\u5217a\u3092\u4f5c\u6210\u3059\u308b\u3002 \u3053\u3053\u3067\u306f\u898b\u3084\u3059\u3044\u3088\u3046\u306b\u5404\u8981\u7d20\u3092100\u500d\u3057\u305f\u5f8c\u306b\u6574\u6570\u5316\u3057\u3066\u3044\u308b\u3002 >>> import numpy as np >>> a = np.random.ra\u2026 \u7d9a\u304d\u3092\u8aad\u3080 »<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[],"class_list":["post-241","post","type-post","status-publish","format-standard","hentry","category-numpy"],"views":15489,"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/www.dogrow.net\/python\/wp-json\/wp\/v2\/posts\/241","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dogrow.net\/python\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dogrow.net\/python\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dogrow.net\/python\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dogrow.net\/python\/wp-json\/wp\/v2\/comments?post=241"}],"version-history":[{"count":7,"href":"https:\/\/www.dogrow.net\/python\/wp-json\/wp\/v2\/posts\/241\/revisions"}],"predecessor-version":[{"id":2667,"href":"https:\/\/www.dogrow.net\/python\/wp-json\/wp\/v2\/posts\/241\/revisions\/2667"}],"wp:attachment":[{"href":"https:\/\/www.dogrow.net\/python\/wp-json\/wp\/v2\/media?parent=241"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dogrow.net\/python\/wp-json\/wp\/v2\/categories?post=241"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dogrow.net\/python\/wp-json\/wp\/v2\/tags?post=241"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}