numpy.random.standard_gamma¶

numpy.random.
standard_gamma
(shape, size=None)¶ Draw samples from a standard Gamma distribution.
Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated “k”) and scale=1.
Parameters:  shape : float or array_like of floats
Parameter, should be > 0.
 size : int or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifshape
is a scalar. Otherwise,np.array(shape).size
samples are drawn.
Returns:  out : ndarray or scalar
Drawn samples from the parameterized standard gamma distribution.
See also
scipy.stats.gamma
 probability density function, distribution or cumulative density function, etc.
Notes
The probability density for the Gamma distribution is
p(x) = x^{k1}\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. http://mathworld.wolfram.com/GammaDistribution.html [2] Wikipedia, “Gamma distribution”, http://en.wikipedia.org/wiki/Gamma_distribution Examples
Draw samples from the distribution:
>>> shape, scale = 2., 1. # mean and width >>> s = np.random.standard_gamma(shape, 1000000)
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**(shape1) * ((np.exp(bins/scale))/ \ ... (sps.gamma(shape) * scale**shape)) >>> plt.plot(bins, y, linewidth=2, color='r') >>> plt.show()