numpy.random.RandomState.gamma

метод

RandomState.gamma(shape, scale=1.0, size=None)

Генерирует выборки из гамма-распределения.

Выборка производится из гамма-распределения со заданными параметрами, shape (иногда обозначается как “k”) и scale (иногда обозначается как “theta”), где оба параметра больше 0.

Параметры:

shape : float or array_like of floats Форма гамма-распределения. Должно быть больше нуля. scale : float or array_like of floats, optional Масштаб гамма-распределения. Должно быть больше нуля. По умолчанию равно 1. (m, n, k) Форма возвращаемого массива. Если заданная форма, например, (m, n, k), то генерируется m * n * k выборок. Если размерность None (по умолчанию), то возвращается одно значение, если shape и scale являются скалярами. В противном случае генерируются np.broadcast(shape, scale).size выборок.

Возвращает:

out : ndarray or scalar Сгенерированные выборки из гамма-распределения с заданными параметрами.

См. также

scipy.stats.gamma

функция плотности вероятности, распределение или функция кумулятивной плотности и т.д.

Примечания

Плотность вероятности для гамма-распределения:

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

где является формой, а масштаба, а является функцией Гамма.

Распределение Гамма часто используется для моделирования времени до отказа электронных компонентов, и возникает естественным образом в процессах, для которых время ожидания между событиями, распределёнными по Пуассону, имеют отношение.

Ссылки

[1]	Weisstein, Eric W. “Распределение Гамма”. Из MathWorld – ресурса Wolfram Web. http://mathworld.wolfram.com/GammaDistribution.html

[2]	Википедия, «Распределение Гамма», https://en.wikipedia.org/wiki/Gamma_distribution

Примеры

Генерация выборок из распределения:

>>> shape, scale = 2., 2.  # mean=4, std=2*sqrt(2)
>>> s = np.random.gamma(shape, scale, 1000)

Отображение гистограммы выборок вместе с функцией плотности вероятности:

>>> import matplotlib.pyplot as plt
>>> import scipy.special as sps
>>> count, bins, ignored = plt.hist(s, 50, density=True)
>>> y = bins**(shape-1)*(np.exp(-bins/scale) /
...                      (sps.gamma(shape)*scale**shape))
>>> plt.plot(bins, y, linewidth=2, color='r')
>>> plt.show()