mxnet.np.random.weibull

weibull(a, size=None, device=None, out=None)

Draw samples from a 1-parameter Weibull distribution with given parameter a via inversion.

Parameters

  • a (float or array_like of floats) – Shape of the distribution. Must be non-negative.

  • size (int 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 a is a scalar. Otherwise, np.array(a).size samples are drawn.

Returns

out – Drawn samples from the 1-parameter Weibull distribution.

Return type

ndarray or scalar

Examples

>>> np.random.weibull(a=5)
array(0.9553641)
>>> np.random.weibull(a=5, size=[2,3])
array([[1.0466299 , 1.1320982 , 0.98415005],
      [1.1430776 , 0.9532727 , 1.1344457 ]])
>>> np.random.weibull(a=np.array([2,3])
array([0.98843634, 1.0125613 ])
The Weibull distribution is one of a class of Generalized Extreme
Value (GEV) distributions. This class includes the Gumbel and Frechet
distributions.
The probability density for the Weibull distribution is
f(x) = \frac{a}{\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a},
where a is the shape and \lambda the scale. The generated 1-parameter Weibull
sample has the scale parameter \lambda = 1.
The Weibull distribution is commonly used in reliability engineering to
model time to failure, in modeling particle sizes, in information retrieval
to model dwell time on pages, in quantitative finance to model risk etc.