Connect to remote data
Debug
Extend
sizeof
Selecting the collection backend
Use GPUs
Talks & Tutorials
Maintainer Guidelines
dask.array.random.
gamma
(
*
args
,
**
kwargs
)
Draw samples from a Gamma distribution.
This docstring was copied from numpy.random.mtrand.RandomState.gamma.
Some inconsistencies with the Dask version may exist.
Samples are drawn from a Gamma distribution with specified parameters,
shape
(sometimes designated “k”) and
scale
(sometimes designated
“theta”), where both parameters are > 0.
New code should use the
~numpy.random.Generator.gamma
method of a
~numpy.random.Generator
instance instead;
please see the
Quick start
.
Parameters
:
shape
float or array_like of floats
The shape of the gamma distribution. Must be non-negative.
scale
float or array_like of floats, optional
The scale of the gamma distribution. Must be non-negative.
Default is equal to 1.
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
shape
and
scale
are both scalars.
Otherwise,
np.broadcast(shape,
scale).size
samples are drawn.
Returns
:
out
ndarray or scalar
Drawn samples from the parameterized gamma distribution.
See also
scipy.stats.gamma
probability density function, distribution or cumulative density function, etc.
random.Generator.gamma
which should be used for new code.
Notes
The probability density for the Gamma distribution is
\[p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},\]
where
\(k\)
is the shape and
\(\theta\)
the scale,
and
\(\Gamma\)
is the Gamma function.
The Gamma distribution is often used to model the times to failure of
electronic components, and arises naturally in processes for which the
waiting times between Poisson distributed events are relevant.
References
[
1
]
Weisstein, Eric W. “Gamma Distribution.” From MathWorld–A
Wolfram Web Resource.
https://mathworld.wolfram.com/GammaDistribution.html
[
2
]
Wikipedia, “Gamma distribution”,
https://en.wikipedia.org/wiki/Gamma_distribution
Examples
Draw samples from the distribution:
>>> shape, scale = 2., 2. # mean=4, std=2*sqrt(2)
>>> s = np.random.gamma(shape, scale, 1000)
Display the histogram of the samples, along with
the probability density function:
>>> import matplotlib.pyplot as plt
>>> import scipy.special as sps
>>> count, bins, ignored = plt.hist(s, 50, density=True)
>>> y = bins**(shape-1)*(np.exp(-bins/scale) /
... (sps.gamma(shape)*scale**shape))
>>> plt.plot(bins, y, linewidth=2, color='r')
>>> plt.show()
