numpy.random.RandomState.exponential¶
-
RandomState.
exponential
(scale=1.0, size=None)¶ Draw samples from an exponential distribution.
Its probability density function is
f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),
for
x > 0
and 0 elsewhere. \beta is the scale parameter, which is the inverse of the rate parameter \lambda = 1/\beta. The rate parameter is an alternative, widely used parameterization of the exponential distribution [3].The exponential distribution is a continuous analogue of the geometric distribution. It describes many common situations, such as the size of raindrops measured over many rainstorms [1], or the time between page requests to Wikipedia [2].
Parameters: - scale : float or array_like of floats
The scale parameter, \beta = 1/\lambda.
- size : int or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifscale
is a scalar. Otherwise,np.array(scale).size
samples are drawn.
Returns: - out : ndarray or scalar
Drawn samples from the parameterized exponential distribution.
References
[1] (1, 2) Peyton Z. Peebles Jr., “Probability, Random Variables and Random Signal Principles”, 4th ed, 2001, p. 57. [2] (1, 2) Wikipedia, “Poisson process”, http://en.wikipedia.org/wiki/Poisson_process [3] (1, 2) Wikipedia, “Exponential distribution”, http://en.wikipedia.org/wiki/Exponential_distribution