{"id":2496,"date":"2023-07-22T00:36:10","date_gmt":"2023-07-22T00:36:10","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d9%85%d8%b5%d9%81%d9%88%d9%81%d8%a9-%d8%a7%d9%84%d8%aa%d8%b7%d8%a8%d9%8a%d8%b9-numpy\/"},"modified":"2023-07-22T00:36:10","modified_gmt":"2023-07-22T00:36:10","slug":"%d9%85%d8%b5%d9%81%d9%88%d9%81%d8%a9-%d8%a7%d9%84%d8%aa%d8%b7%d8%a8%d9%8a%d8%b9-numpy","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d9%85%d8%b5%d9%81%d9%88%d9%81%d8%a9-%d8%a7%d9%84%d8%aa%d8%b7%d8%a8%d9%8a%d8%b9-numpy\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u062a\u0637\u0628\u064a\u0639 \u0645\u0635\u0641\u0648\u0641\u0629 numpy: \u0645\u0639 \u0627\u0644\u0623\u0645\u062b\u0644\u0629"},"content":{"rendered":"<p><\/p>\n<hr>\n<p style=\";text-align:right;direction:rtl\"><span style=\"color: #000000;\">\u062a\u0639\u0646\u064a <strong>\u062a\u0633\u0648\u064a\u0629<\/strong> \u0627\u0644\u0645\u0635\u0641\u0648\u0641\u0629 \u062a\u063a\u064a\u064a\u0631 \u062d\u062c\u0645 \u0627\u0644\u0642\u064a\u0645 \u0628\u062d\u064a\u062b \u064a\u0643\u0648\u0646 \u0646\u0637\u0627\u0642 \u0642\u064a\u0645 \u0627\u0644\u0635\u0641\u0648\u0641 \u0623\u0648 \u0627\u0644\u0623\u0639\u0645\u062f\u0629 \u0628\u064a\u0646 0 \u06481.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0623\u0633\u0647\u0644 \u0637\u0631\u064a\u0642\u0629 \u0644\u062a\u0637\u0628\u064a\u0639 \u0642\u064a\u0645 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy \u0647\u064a \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.normalize.html\" target=\"_blank\" rel=\"noopener\">\u0627\u0644\u062a\u0637\u0628\u064a\u0639 ()<\/a> \u0645\u0646 \u062d\u0632\u0645\u0629 sklearn\u060c \u0648\u0627\u0644\u062a\u064a \u062a\u0633\u062a\u062e\u062f\u0645 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0627\u0644\u0623\u0633\u0627\u0633\u064a \u0627\u0644\u062a\u0627\u0644\u064a:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> normalize\n\n<span style=\"color: #008080;\">#normalize rows of matrix\n<\/span>normalize(x, axis= <span style=\"color: #008000;\">1<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')\n\n<span style=\"color: #008080;\">#normalize columns of matrix\n<span style=\"color: #000000;\">normalize(x, axis= <span style=\"color: #008000;\">0<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')<\/span>\n<\/span><\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u0648\u0636\u062d \u0627\u0644\u0623\u0645\u062b\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0647\u0630\u0627 \u0639\u0645\u0644\u064a\u064b\u0627.<\/span><\/p>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0645\u062b\u0627\u0644 1: \u062a\u0633\u0648\u064a\u0629 \u0635\u0641\u0648\u0641 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646 \u0644\u062f\u064a\u0646\u0627 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create matrix\n<\/span>x = np. <span style=\"color: #3366ff;\">arange<\/span> (0, 36, 4). <span style=\"color: #3366ff;\">reshape<\/span> (3,3)\n\n<span style=\"color: #008080;\">#view matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x)\n\n[[ 0 4 8]\n [12 16 20]\n [24 28 32]]\n<\/span><\/span><\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u062a\u0633\u0648\u064a\u0629 \u0635\u0641\u0648\u0641 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy:<\/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: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> normalize\n\n<span style=\"color: #008080;\">#normalize matrix by rows\n<\/span>x_normed = normalize(x, axis= <span style=\"color: #008000;\">1<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')\n\n<span style=\"color: #008080;\">#view normalized matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x_normed)\n\n[[0. 0.33333333 0.66666667]\n [0.25 0.33333333 0.41666667]\n [0.28571429 0.33333333 0.38095238]]<\/strong><\/span><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0627\u062d\u0638 \u0623\u0646 \u0627\u0644\u0642\u064a\u0645 \u0627\u0644\u0645\u0648\u062c\u0648\u062f\u0629 \u0641\u064a \u0643\u0644 \u0635\u0641 \u062a\u0636\u064a\u0641 \u0627\u0644\u0622\u0646 \u0645\u0627 \u064a\u0635\u0644 \u0625\u0644\u0649 \u0648\u0627\u062d\u062f.<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u062c\u0645\u0648\u0639 \u0627\u0644\u0633\u0637\u0631 \u0627\u0644\u0623\u0648\u0644: 0 + 0.33 + 0.67 = <strong>1<\/strong><\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u062c\u0645\u0648\u0639 \u0627\u0644\u0633\u0637\u0631 \u0627\u0644\u062b\u0627\u0646\u064a: 0.25 + 0.33 + 0.417 = <strong>1<\/strong><\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u062c\u0645\u0648\u0639 \u0627\u0644\u0635\u0641 \u0627\u0644\u062b\u0627\u0644\u062b: 0.2857 + 0.3333 + 0.3809 = <strong>1<\/strong><\/span><\/li>\n<\/ul>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0645\u062b\u0627\u0644 2: \u062a\u0633\u0648\u064a\u0629 \u0623\u0639\u0645\u062f\u0629 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646 \u0644\u062f\u064a\u0646\u0627 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create matrix\n<\/span>x = np. <span style=\"color: #3366ff;\">arange<\/span> (0, 36, 4). <span style=\"color: #3366ff;\">reshape<\/span> (3,3)\n\n<span style=\"color: #008080;\">#view matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x)\n\n[[ 0 4 8]\n [12 16 20]\n [24 28 32]]\n<\/span><\/span><\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u062a\u0633\u0648\u064a\u0629 \u0635\u0641\u0648\u0641 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy:<\/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: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> normalize\n\n<span style=\"color: #008080;\">#normalize matrix by columns\n<\/span>x_normed = normalize(x, axis= <span style=\"color: #008000;\">0<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')\n\n<span style=\"color: #008080;\">#view normalized matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x_normed)\n\n[[0. 0.08333333 0.13333333]\n [0.33333333 0.33333333 0.33333333]\n [0.66666667 0.58333333 0.53333333]]<\/strong><\/span><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0627\u062d\u0638 \u0623\u0646 \u0627\u0644\u0642\u064a\u0645 \u0627\u0644\u0645\u0648\u062c\u0648\u062f\u0629 \u0641\u064a \u0643\u0644 \u0639\u0645\u0648\u062f \u062a\u0636\u064a\u0641 \u0627\u0644\u0622\u0646 \u0645\u0627 \u064a\u0635\u0644 \u0625\u0644\u0649 \u0648\u0627\u062d\u062f.<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u062c\u0645\u0648\u0639 \u0627\u0644\u0639\u0645\u0648\u062f \u0627\u0644\u0623\u0648\u0644: 0 + 0.33 + 0.67 = <strong>1<\/strong><\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u062c\u0645\u0648\u0639 \u0627\u0644\u0639\u0645\u0648\u062f \u0627\u0644\u062b\u0627\u0646\u064a: 0.083 + 0.333 + 0.583 = <strong>1<\/strong><\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u062c\u0645\u0648\u0639 \u0627\u0644\u0639\u0645\u0648\u062f \u0627\u0644\u062b\u0627\u0644\u062b: 0.133 + 0.333 + 0.5333 = <strong>1<\/strong><\/span><\/li>\n<\/ul>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u0634\u0631\u062d \u0627\u0644\u0628\u0631\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0639\u0645\u0644\u064a\u0627\u062a \u0627\u0644\u0634\u0627\u0626\u0639\u0629 \u0627\u0644\u0623\u062e\u0631\u0649 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <a href=\"https:\/\/statorials.org\/ar\/\u062a\u0637\u0628\u064a\u0639-\u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a-\u0641\u064a-\u0628\u064a\u062b\u0648\u0646\/\" target=\"_blank\" rel=\"noopener\">\u0643\u064a\u0641\u064a\u0629 \u062a\u0637\u0628\u064a\u0639 \u0627\u0644\u0645\u0635\u0641\u0648\u0641\u0627\u062a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a><br \/> <a href=\"https:\/\/statorials.org\/ar\/\u062a\u0637\u0628\u064a\u0639-\u0627\u0654\u0639\u0645\u062f\u0629-dataframe-\u0627\u0644\u0628\u0627\u0646\u062f\u0627\/\" target=\"_blank\" rel=\"noopener\">\u0643\u064a\u0641\u064a\u0629 \u062a\u0637\u0628\u064a\u0639 \u0627\u0644\u0623\u0639\u0645\u062f\u0629 \u0641\u064a Pandas DataFrame<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u062a\u0639\u0646\u064a \u062a\u0633\u0648\u064a\u0629 \u0627\u0644\u0645\u0635\u0641\u0648\u0641\u0629 \u062a\u063a\u064a\u064a\u0631 \u062d\u062c\u0645 \u0627\u0644\u0642\u064a\u0645 \u0628\u062d\u064a\u062b \u064a\u0643\u0648\u0646 \u0646\u0637\u0627\u0642 \u0642\u064a\u0645 \u0627\u0644\u0635\u0641\u0648\u0641 \u0623\u0648 \u0627\u0644\u0623\u0639\u0645\u062f\u0629 \u0628\u064a\u0646 0 \u06481. \u0623\u0633\u0647\u0644 \u0637\u0631\u064a\u0642\u0629 \u0644\u062a\u0637\u0628\u064a\u0639 \u0642\u064a\u0645 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy \u0647\u064a \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 \u0627\u0644\u062a\u0637\u0628\u064a\u0639 () \u0645\u0646 \u062d\u0632\u0645\u0629 sklearn\u060c \u0648\u0627\u0644\u062a\u064a \u062a\u0633\u062a\u062e\u062f\u0645 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0627\u0644\u0623\u0633\u0627\u0633\u064a \u0627\u0644\u062a\u0627\u0644\u064a: from sklearn. preprocessing import normalize #normalize rows of matrix normalize(x, axis= 1 , norm=&#8217; l1 &#8216;) #normalize columns of [&hellip;]<\/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":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u0643\u064a\u0641\u064a\u0629 \u062a\u0637\u0628\u064a\u0639 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy (\u0645\u0639 \u0623\u0645\u062b\u0644\u0629) \u2013 \u0627\u0644\u0625\u062d\u0635\u0627\u0626\u064a\u0627\u062a<\/title>\n<meta name=\"description\" content=\"\u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u062a\u0633\u0648\u064a\u0629 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy\u060c \u0645\u0639 \u0639\u062f\u0629 \u0623\u0645\u062b\u0644\u0629.\" \/>\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\/ar\/\u0645\u0635\u0641\u0648\u0641\u0629-\u0627\u0644\u062a\u0637\u0628\u064a\u0639-numpy\/\" \/>\n<meta property=\"og:locale\" content=\"az_AZ\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u0643\u064a\u0641\u064a\u0629 \u062a\u0637\u0628\u064a\u0639 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy (\u0645\u0639 \u0623\u0645\u062b\u0644\u0629) \u2013 \u0627\u0644\u0625\u062d\u0635\u0627\u0626\u064a\u0627\u062a\" \/>\n<meta property=\"og:description\" content=\"\u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u062a\u0633\u0648\u064a\u0629 \u0645\u0635\u0641\u0648\u0641\u0629 NumPy\u060c \u0645\u0639 \u0639\u062f\u0629 \u0623\u0645\u062b\u0644\u0629.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ar\/\u0645\u0635\u0641\u0648\u0641\u0629-\u0627\u0644\u062a\u0637\u0628\u064a\u0639-numpy\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-22T00:36:10+00:00\" \/>\n<meta name=\"author\" content=\"\u062f\u0643\u062a\u0648\u0631 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