dask.array.random.exponential

dask.array.random.exponential(scale=1.0, size=None, chunks='auto', **kwargs)

Draw samples from an exponential distribution.

This docstring was copied from numpy.random.mtrand.RandomState.exponential.

Some inconsistencies with the Dask version may exist.

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 exponential method of a default_rng() instance instead; please see the Quick Start.

Parameters
scalefloat or array_like of floats

The scale parameter, \(\beta = 1/\lambda\). Must be non-negative.

sizeint or tuple of ints, optional

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.

Returns
outndarray or scalar

Drawn samples from the parameterized exponential distribution.

See also

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