mars.tensor.random.standard_gamma#

mars.tensor.random.standard_gamma(shape, size=None, chunk_size=None, gpu=None, dtype=None)[source]#

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), then m * n * k samples are drawn. If size is None (default), a single value is returned if shape is a scalar. Otherwise, mt.array(shape).size samples are drawn.

• chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension

• gpu (bool, optional) – Allocate the tensor on GPU if True, False as default

• dtype (data-type, optional) – Data-type of the returned tensor.

Returns

out – Drawn samples from the parameterized standard gamma distribution.

Return type

Tensor or scalar

scipy.stats.gamma

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

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. http://mathworld.wolfram.com/GammaDistribution.html

2

Wikipedia, “Gamma distribution”, http://en.wikipedia.org/wiki/Gamma_distribution

Examples

Draw samples from the distribution:

>>> import mars.tensor as mt

>>> shape, scale = 2., 1. # mean and width
>>> s = mt.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.execute(), 50, normed=True)
>>> y = bins**(shape-1) * ((mt.exp(-bins/scale))/ \
...                       (sps.gamma(shape) * scale**shape))
>>> plt.plot(bins, y.execute(), linewidth=2, color='r')
>>> plt.show()