<\/p>\n
\u0130ki NumPy dizisinin de\u011ferlerini kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: \u0130ki NumPy dizisinin \u00f6\u011fe d\u00fczeyinde e\u015fit olup olmad\u0131\u011f\u0131n\u0131 test edin<\/strong><\/span><\/p>\n Y\u00f6ntem 2: \u0130ki NumPy dizisinin \u00f6\u011fe d\u00fczeyinde (bir tolerans dahilinde) e\u015fit olup olmad\u0131\u011f\u0131n\u0131 test edin<\/strong><\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler her y\u00f6ntemin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, iki NumPy dizisinin \u00f6\u011fe d\u00fczeyinde e\u015fit olup olmad\u0131\u011f\u0131n\u0131 test etmek i\u00e7in array_equal()<\/strong> i\u015flevinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/span><\/p>\n Her iki NumPy dizisi de ayn\u0131 uzunlu\u011fa ve ayn\u0131 konumlarda ayn\u0131 de\u011ferlere sahip oldu\u011fundan i\u015flev True de\u011ferini<\/strong> d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n Ancak, iki NumPy dizisi ayn\u0131 de\u011ferlere ancak farkl\u0131 konumlara sahipse i\u015flev False de\u011ferini<\/strong> d\u00f6nd\u00fcr\u00fcr:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, iki NumPy dizisinin \u00f6\u011fe d\u00fczeyinde 2<\/strong> tolerans de\u011feri dahilinde e\u015fit olup olmad\u0131\u011f\u0131n\u0131 test etmek i\u00e7in allclose()<\/strong> i\u015flevinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Her NumPy dizisi aras\u0131ndaki e\u015fle\u015fen \u00f6\u011felerin t\u00fcm\u00fc birbirinin 2’si dahilinde oldu\u011fundan i\u015flev True de\u011ferini<\/strong> d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n \u00d6rne\u011fin her dizinin \u00fc\u00e7\u00fcnc\u00fc ve d\u00f6rd\u00fcnc\u00fc konumlar\u0131ndaki elemanlar\u0131n farkl\u0131 oldu\u011funu g\u00f6r\u00fcyoruz ancak her \u00e7iftin birbirinden 2 de\u011fer uzakta olmas\u0131 nedeniyle fonksiyon true de\u011ferini d\u00f6nd\u00fcr\u00fcyor.<\/span><\/p>\n Ancak mutlak tolerans (atol) arg\u00fcman\u0131n\u0131 1<\/strong> olarak de\u011fi\u015ftirirsek fonksiyon False<\/strong> de\u011ferini d\u00f6nd\u00fcrecektir:<\/span><\/p>\n Her NumPy dizisinin \u00fc\u00e7\u00fcnc\u00fc konumundaki kar\u015f\u0131l\u0131k gelen \u00f6\u011feler birbirinden 1 saniye uzakta olmad\u0131\u011f\u0131 i\u00e7in i\u015flev False de\u011ferini<\/b> d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n Not<\/strong> : array_equal<\/a> ve allclose<\/a> i\u015flevlerinin tam a\u00e7\u0131klamas\u0131 i\u00e7in NumPy belgelerine bak\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimler NumPy’de di\u011fer genel g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini a\u00e7\u0131klamaktad\u0131r:<\/span><\/p>\n NumPy dizisindeki \u00f6\u011feler nas\u0131l ta\u015f\u0131n\u0131r<\/a> \u0130ki NumPy dizisinin de\u011ferlerini kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz: Y\u00f6ntem 1: \u0130ki NumPy dizisinin \u00f6\u011fe d\u00fczeyinde e\u015fit olup olmad\u0131\u011f\u0131n\u0131 test edin #test if array A and array B are element-wise equal n.p. array_equal (A,B) Y\u00f6ntem 2: \u0130ki NumPy dizisinin \u00f6\u011fe d\u00fczeyinde (bir tolerans dahilinde) e\u015fit olup olmad\u0131\u011f\u0131n\u0131 test edin #test if array A and array […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-3197","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n #test if array A and array B are element-wise equal<\/span>\nn.p. array_equal<\/span> (A,B)\n<\/strong><\/pre>\n #test if array A and array B are element-wise equal (within absolute tolerance of 2)<\/span>\nn.p. allclose<\/span> (A, B, atol= 2<\/span> )\n<\/strong><\/pre>\n \u00d6rnek 1: \u0130ki NumPy dizisinin \u00f6\u011fe d\u00fczeyinde e\u015fit olup olmad\u0131\u011f\u0131n\u0131 test edin<\/strong><\/span><\/h3>\n
import<\/span> numpy as<\/span> np\n<\/span>\n#create two NumPy arrays\n<\/span>A = np. array<\/span> ([1, 4, 5, 7, 10])\nB = np. array<\/span> ([1, 4, 5, 7, 10])\n\n#test if arrays are element-wise equal\n<\/span>n.p. array_equal<\/span> (A,B)\n\nTrue<\/strong><\/pre>\n import<\/span> numpy as<\/span> np\n<\/span>\n#create two NumPy arrays with same values but in different positions\n<\/span>A = np. array<\/span> ([1, 4, 5, 7, 10])\nB = np. array<\/span> ([1, 4, 7, 5, 10])\n\n#test if arrays are element-wise equal\n<\/span>n.p. array_equal<\/span> (A,B)\n\nFalse<\/strong><\/pre>\n \u00d6rnek 2: \u0130ki NumPy dizisinin \u00f6\u011fe d\u00fczeyinde (tolerans dahilinde) e\u015fit olup olmad\u0131\u011f\u0131n\u0131 test edin<\/strong><\/span><\/h3>\n
import<\/span> numpy as<\/span> np\n<\/span>\n#create two NumPy arrays\n<\/span>A = np. array<\/span> ([1, 4, 5, 7, 10])\nB = np. array<\/span> ([1, 4, 7, 8, 10])\n\n#test if arrays are element-wise equal (within absolute tolerance of 2)\n<\/span>n.p. allclose<\/span> (A, B, atol= 2<\/span> )\n\nTrue<\/strong><\/pre>\n import<\/span> numpy as<\/span> np\n<\/span>\n#create two NumPy arrays\n<\/span>A = np. array<\/span> ([1, 4, 5, 7, 10])\nB = np. array<\/span> ([1, 4, 7, 8, 10])\n\n#test if arrays are element-wise equal (within absolute tolerance of 1)\n<\/span>n.p. allclose<\/span> (A, B, atol= 1<\/span> )\n\nFalse<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
NumPy’de eleman olu\u015fumlar\u0131 nas\u0131l say\u0131l\u0131r?<\/a>
NumPy dizisinin modu nas\u0131l hesaplan\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"