xorbits.numpy.random.exponential#
- xorbits.numpy.random.exponential(scale=1.0, size=None)[source]#
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.
Note
New code should use the ~numpy.random.Generator.exponential method of a ~numpy.random.Generator instance instead; please see the random-quick-start.
- Parameters
scale (float or array_like of floats) – The scale parameter, \(\beta = 1/\lambda\). Must be non-negative.
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 – Drawn samples from the parameterized exponential distribution.
- Return type
ndarray or scalar
See also
random.Generator.exponential
which should be used for new code.
References
- 1
Peyton Z. Peebles Jr., “Probability, Random Variables and Random Signal Principles”, 4th ed, 2001, p. 57.
- 2
Wikipedia, “Poisson process”, https://en.wikipedia.org/wiki/Poisson_process
- 3
Wikipedia, “Exponential distribution”, https://en.wikipedia.org/wiki/Exponential_distribution
This docstring was copied from numpy.random.