<\/p>\n
Aby znormalizowa\u0107 warto\u015bci tablicy NumPy w zakresie od 0 do 1, mo\u017cesz u\u017cy\u0107 jednej z nast\u0119puj\u0105cych metod:<\/span><\/p>\n Metoda 1: U\u017cyj NumPy<\/strong><\/span><\/p>\n Metoda 2: U\u017cyj Sklearna<\/strong><\/span><\/p>\n Obie metody zak\u0142adaj\u0105, \u017ce x<\/strong> jest nazw\u0105 tablicy NumPy, kt\u00f3r\u0105 chcesz znormalizowa\u0107.<\/span><\/p>\n Poni\u017csze przyk\u0142ady pokazuj\u0105, jak zastosowa\u0107 ka\u017cd\u0105 metod\u0119 w praktyce.<\/span><\/p>\n Za\u0142\u00f3\u017cmy, \u017ce mamy nast\u0119puj\u0105c\u0105 tablic\u0119 NumPy:<\/span><\/p>\n Mo\u017cemy u\u017cy\u0107 nast\u0119puj\u0105cego kodu, aby znormalizowa\u0107 ka\u017cd\u0105 warto\u015b\u0107 w tablicy od 0 do 1:<\/span><\/p>\n Ka\u017cda warto\u015b\u0107 w tablicy NumPy zosta\u0142a znormalizowana tak, aby zawiera\u0142a si\u0119 w przedziale od 0 do 1.<\/span><\/p>\n Oto jak to dzia\u0142a\u0142o:<\/span><\/p>\n Minimalna warto\u015b\u0107 w zbiorze danych to 13, a maksymalna warto\u015b\u0107 to 71.<\/span><\/p>\n Aby znormalizowa\u0107 pierwsz\u0105 warto\u015b\u0107 13<\/strong> , zastosowaliby\u015bmy udost\u0119pnion\u0105 wcze\u015bniej formu\u0142\u0119:<\/span><\/p>\n Aby znormalizowa\u0107 drug\u0105 warto\u015b\u0107 16<\/strong> , u\u017cyliby\u015bmy tego samego wzoru:<\/span><\/p>\n Aby znormalizowa\u0107 trzeci\u0105 warto\u015b\u0107 19<\/strong> , u\u017cyliby\u015bmy tego samego wzoru:<\/span><\/p>\n U\u017cywamy tej samej formu\u0142y do normalizacji ka\u017cdej warto\u015bci w oryginalnej tablicy NumPy w zakresie od 0 do 1.<\/span><\/p>\n Za\u0142\u00f3\u017cmy ponownie, \u017ce mamy nast\u0119puj\u0105c\u0105 tablic\u0119 NumPy:<\/span><\/p>\n Mo\u017cemy u\u017cy\u0107 funkcji MinMaxScaler()<\/strong> sklearn<\/strong> , aby znormalizowa\u0107 ka\u017cd\u0105 warto\u015b\u0107 w tablicy od 0 do 1:<\/span><\/p>\n Ka\u017cda warto\u015b\u0107 w tablicy NumPy zosta\u0142a znormalizowana tak, aby zawiera\u0142a si\u0119 w przedziale od 0 do 1.<\/span><\/p>\n Nale\u017cy pami\u0119ta\u0107, \u017ce te znormalizowane warto\u015bci odpowiadaj\u0105 warto\u015bciom obliczonym przy u\u017cyciu poprzedniej metody.<\/span><\/p>\n Poni\u017csze samouczki wyja\u015bniaj\u0105, jak wykonywa\u0107 inne typowe zadania w NumPy:<\/span><\/p>\n Jak zam\u00f3wi\u0107 elementy w tablicy NumPy<\/a> Aby znormalizowa\u0107 warto\u015bci tablicy NumPy w zakresie od 0 do 1, mo\u017cesz u\u017cy\u0107 jednej z nast\u0119puj\u0105cych metod: Metoda 1: U\u017cyj NumPy import numpy as np x_norm = (x-np. min (x))\/(np. max (x)-np. min (x)) Metoda 2: U\u017cyj Sklearna from sklearn import preprocessing as pre x = x. reshape (-1, 1) x_norm = pre. MinMaxScaler (). […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-4063","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"\n import<\/span> numpy as<\/span> np\n\nx_norm = (x-np. min<\/span> (x))\/(np. max<\/span> (x)-np. min<\/span> (x))\n<\/strong><\/pre>\n from<\/span> sklearn import<\/span> preprocessing as<\/span> pre\n\nx = x. reshape<\/span> (-1, 1)\n\nx_norm = pre. MinMaxScaler<\/span> (). fit_transform<\/span> (x)<\/strong><\/pre>\n Przyk\u0142ad 1: Normalizuj warto\u015bci za pomoc\u0105 NumPy<\/strong><\/span><\/h2>\n
import<\/span> numpy as<\/span> np\n\n#create NumPy array\n<\/span>x = np. array<\/span> ([13, 16, 19, 22, 23, 38, 47, 56, 58, 63, 65, 70, 71])\n<\/strong><\/span><\/pre>\n #normalize all values to be between 0 and 1\n<\/span>x_norm = (x-np. min<\/span> (x))\/(np. max<\/span> (x)-np. min<\/span> (x))\n\n#view normalized array\n<\/span>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\n
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Przyk\u0142ad 2: Normalizuj warto\u015bci za pomoc\u0105 sklearn<\/strong><\/span><\/h2>\n
import<\/span> numpy as<\/span> np\n\n#create NumPy array\n<\/span>x = np. array<\/span> ([13, 16, 19, 22, 23, 38, 47, 56, 58, 63, 65, 70, 71])\n<\/strong><\/span><\/pre>\n from<\/span> sklearn import<\/span> preprocessing as<\/span> pre\n\n#reshape array so that it works with sklearn\n<\/span>x = x. reshape<\/span> (-1, 1)\n\n#normalize all values to be between 0 and 1\n<\/span>x_norm = pre. MinMaxScaler<\/span> (). fit_transform<\/span> (x)\n\n#view normalized array\n<\/span>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 Dodatkowe zasoby<\/strong><\/span><\/h2>\n
Jak usun\u0105\u0107 zduplikowane elementy z tablicy NumPy<\/a>
Jak znale\u017a\u0107 najcz\u0119stsz\u0105 warto\u015b\u0107 w tablicy NumPy<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"