numpy.random.Generator.weibull

метод

Generator.weibull(a, size=None)

Генерировать выборки из распределения Вейбулла.

Генерировать выборки из распределения Вейбулла с заданным параметром формы a.

<img alt="X = (-ln(U))^{1/a}" 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

Здесь U выбирается из равномерного распределения на интервале (0,1].

Более распространённый 2-параметрический закон Вейбулла, включая параметр масштаба просто

<img alt="X = \lambda(-ln(U))^{1/a}" class="math" 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

.

Параметры

afloat or array_like of floats

Параметр формы распределения. Должен быть неотрицательным.

sizeint or tuple of ints, optional

Форма вывода. Если заданная форма, например, (m, n, k), то m * n * k образцов будут сгенерированы. Если размер равен None (по умолчанию), возвращается одно значение, если a является скалярным. В противном случае np.array(a).size образцов будут сгенерированы.

Возвращает

outndarray or scalar

Сгенерированные образцы из параметризованного распределения Вейбулла.

См. также

scipy.stats.weibull_max, scipy.stats.weibull_min, scipy.stats.genextreme, gumbel

Примечания

Распределение Вейбулла (или распределение III типа асимптотического экстремального значения для наименьших значений, распределение III типа типа SEV или распределение Розина-Раммлера) является одним из класса распределений обобщённых экстремальных значений (GEV), используемых в задачах моделирования экстремальных значений. Этот класс включает распределения Гумбеля и Фреше.

Плотность вероятности для распределения Вейбулла равна

<img alt="p(x) = \frac{a} {\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a}," src="data:image/svg+xml;base64,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

где форма, и масштаб.

Функция имеет свой пик (моду) в точке .

Когда a = 1, распределение Вейбулла сводится к экспоненциальному распределению.

Литература

1

Валодди Вейбулл, Королевский технологический университет, Стокгольм, 1939 «Статистическая теория прочности материалов», Труды Шведской инженерной академии № 151, 1939, Издательство литографии Генерального штаба, Стокгольм.

2

Валодди Вейбулл, «Функция статистического распределения с широкой областью применения», Журнал прикладной механики ASME, статья 1951 г.

3

Википедия, «Распределение Вейбулла», https://en.wikipedia.org/wiki/Weibull_distribution

Примеры

Генерация выборок из распределения:

>>> rng = np.random.default_rng()
>>> a = 5. # shape
>>> s = rng.weibull(a, 1000)

Отображение гистограммы выборок вместе с функцией плотности вероятности:

>>> import matplotlib.pyplot as plt
>>> x = np.arange(1,100.)/50.
>>> def weib(x,n,a):
...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)

>>> count, bins, ignored = plt.hist(rng.weibull(5.,1000))
>>> x = np.arange(1,100.)/50.
>>> scale = count.max()/weib(x, 1., 5.).max()
>>> plt.plot(x, weib(x, 1., 5.)*scale)
>>> plt.show()