numpy.random.RandomState.exponential

方法

random.RandomState.exponential(scale=1.0, size=None)

从指数分布中提取样本。

它的概率密度函数是

System Message: WARNING/2 (F（x；\frac 1 \beta）=\frac 1 \beta \exp（-\frac x \beta），)

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对于 x > 0 其他地方为0。 是比例参数，与速率参数成反比 . 速率参数是指数分布的另一种广泛使用的参数化方法。 [3].

指数分布是几何分布的连续模拟。它描述了许多常见的情况，例如在许多暴雨中测得的雨滴大小 [1], 或者页面请求到维基百科的间隔时间 [2].

注解

新代码应该使用 exponential A方法 default_rng() 请参阅 快速启动 .

参数

scale浮点数或类似浮点数的数组

比例参数， . 必须为非负。

sizeint或int的元组，可选

输出形状。如果给定的形状是，例如， (m, n, k) 然后 m * n * k 取样。如果尺寸是 None （默认），如果 scale 是标量。否则， np.array(scale).size 取样。

返回

outndarray或scalar

从参数化指数分布中提取样本。

参见

Generator.exponential

应该用于新代码。

工具书类

1

Peyton Z.Peebles Jr.，“概率、随机变量和随机信号原理”，第4版，2001年，第57页。

2

维基百科，“泊松过程”，https://en.wikipedia.org/wiki/poisson_process

3

维基百科，“指数分布”，https://en.wikipedia.org/wiki/indexive_distribution

numpy.random.RandomState.dirichlet numpy.random.RandomState.f