NumPy参考 >例行程序 >Random sampling (numpy.random) >Legacy Generator (RandomState) > numpy.random.exponential
numpy.random.
exponential
(scale=1.0, size=None)¶Draw samples from an exponential distribution.
Its probability density function is
for x > 0
and 0 elsewhere. is the scale parameter,
which is the inverse of the rate parameter .
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].
Note
New code should use the exponential
method of a default_rng()
instance instead; see random-quick-start.
The scale parameter, . Must be non-negative.
Output shape. If the given shape is, e.g., (m, n, k)
, then
m * n * k
samples are drawn. If size is None
(default),
a single value is returned if scale
is a scalar. Otherwise,
np.array(scale).size
samples are drawn.
Drawn samples from the parameterized exponential distribution.
See also
Generator.exponential
which should be used for new code.
References
Peyton Z. Peebles Jr., “Probability, Random Variables and Random Signal Principles”, 4th ed, 2001, p. 57.
Wikipedia, “Poisson process”, https://en.wikipedia.org/wiki/Poisson_process
Wikipedia, “Exponential distribution”, https://en.wikipedia.org/wiki/Exponential_distribution