- Generator.gamma(self, shape, scale=1.0, size=None)
Draws samples from a Gamma distribution.
Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated "k") and scale (sometimes designated "theta"), where both parameters are > 0.
The shape of the gamma distribution. Must be non-negative.
- scalefloat, optional
The scale of the gamma distribution. Must be non-negative. Default is equal to 1.
- sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k), then
m * n * ksamples are drawn.
Drawn samples from the parameterized gamma distribution.
The probability density for the Gamma distribution is
where is the shape and the scale, and is the Gamma function.
If shape is neither a scalar nor None : NotImplementedError occurs.
If scale is neither a scalar nor None * NotImplementedError occurs.
Draw samples from the distribution:
>>> import nlcpy as vp >>> shape, scale = 2., 2. # mean=4, std=2*sqrt(2) >>> s = vp.random.default_rng().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.get(), 50, density=True) >>> y = bins**(shape-1)*(vp.exp(-bins/scale)/(sps.gamma(shape)*scale**shape)) >>> plt.plot(bins, y, linewidth=2, color='r') >>> plt.show()