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bundles / numpy 2.4.4 / numpy / random / RandomState / gamma

cython_function_or_method

numpy.random:RandomState.gamma

Signature

def   gamma ( shape ,  scale = 1.0 ,  size = None )

Summary

Draw samples from a Gamma distribution.

Extended Summary

Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated "k") and scale (sometimes designated "theta"), where both parameters are > 0.

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.

Notes

The probability density for the Gamma distribution is

where is the shape and the scale, and 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.

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
count, bins, ignored = plt.hist(s, 50, density=True)
plt.show()
✓
fig-f7e5ad80c39641f5.png

See also

random.Generator.gamma

which should be used for new code.

scipy.stats.gamma

probability density function, distribution or cumulative density function, etc.

Aliases

  • numpy.random.gamma
  • numpy.random.RandomState.gamma