flopscope.

flopscope.numpy.random.RandomState.weibull

fnp.random.RandomState.weibull(self, a, size=None)

Draw samples from a Weibull distribution.

Adapted from NumPy docs np.random.RandomState.weibull

Arearandom
Typecounted
NumPy Refnp.random.RandomState.weibull
Cost
numel(output)\text{numel}(\text{output})
Flopscope Context

Legacy Weibull sampler; cost = numel(output).

Draw samples from a 1-parameter Weibull distribution with the given shape parameter a.

X=(−ln(U))1/aX = (-ln(U))^{1/a}

Here, U is drawn from the uniform distribution over (0,1].

The more common 2-parameter Weibull, including a scale parameter λ\lambda is just X=λ(−ln(U))1/aX = \lambda(-ln(U))^{1/a}.

Note.

New code should use the weibull method of a Generator instance instead; please see the random-quick-start.

Parameters

a:float or array_like of floats

Shape parameter of the distribution. Must be nonnegative.

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, flops.array(a).size samples are drawn.

Returns

out:ndarray or scalar

Drawn samples from the parameterized Weibull distribution.

See also

Notes

The Weibull (or Type III asymptotic extreme value distribution for smallest values, SEV Type III, or Rosin-Rammler distribution) is one of a class of Generalized Extreme Value (GEV) distributions used in modeling extreme value problems. This class includes the Gumbel and Frechet distributions.

The probability density for the Weibull distribution is

p(x)=aλ(xλ)a−1e−(x/λ)a,p(x) = \frac{a} {\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a},

where aa is the shape and λ\lambda the scale.

The function has its peak (the mode) at λ(a−1a)1/a\lambda(\frac{a-1}{a})^{1/a}.

When a = 1, the Weibull distribution reduces to the exponential distribution.

References

footnote
1

Waloddi Weibull, Royal Technical University, Stockholm,
1939 "A Statistical Theory Of The Strength Of Materials",
Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,
Generalstabens Litografiska Anstalts Forlag, Stockholm.
footnote
2

Waloddi Weibull, "A Statistical Distribution Function of
Wide Applicability", Journal Of Applied Mechanics ASME Paper
1951.
footnote
3

Wikipedia, "Weibull distribution",
https://en.wikipedia.org/wiki/Weibull_distribution

Examples

Draw samples from the distribution:

>>> a = 5. # shape
>>> s = flops.random.weibull(a, 1000)

Display the histogram of the samples, along with the probability density function:

>>> import matplotlib.pyplot as plt
>>> x = flops.arange(1,100.)/50.
>>> def weib(x,n,a):
... return (a / n) * (x / n)**(a - 1) * flops.exp(-(x / n)**a)
>>> count, bins, ignored = plt.hist(flops.random.weibull(5.,1000))
>>> x = flops.arange(1,100.)/50.
>>> scale = count.max()/weib(x, 1., 5.).max()
>>> plt.plot(x, weib(x, 1., 5.)*scale)
>>> plt.show()