megengine.random.gamma¶

gamma(shape, scale=1, size=None)¶

Random variable with Gamma distribution \(\Gamma(k, \theta)\).

The corresponding probability density function is

\[p(x)=x^{k-1} \frac{e^{-x / \theta}}{\theta^{k} \Gamma(k)} \quad \text { for } x>0 \quad k, \theta>0,\]

where \(\Gamma(k)\) is the gamma function,

\[\Gamma(k)=(k-1) ! \quad \text { for } \quad k>0.\]

参数

shape (Union[Tensor, float]) – the shape parameter (sometimes designated “k”) of the distribution. Must be non-negative.
scale (Union[Tensor, float]) – the scale parameter (sometimes designated “theta”) of the distribution. Must be non-negative. Default: 1
size (Optional[Iterable[int]]) – the size of output tensor. If shape and scale are scalars and given size is, e.g., (m, n), then the output shape is (m, n). If shape or scale is a Tensor and given size is, e.g., (m, n), then the output shape is (m, n) + broadcast(shape, scale).shape. The broadcast rules are consistent with numpy.broadcast. Default: None

返回

the output tensor.

实际案例

import megengine as mge
import megengine.random as rand

x = rand.gamma(shape=2, scale=1, size=(2, 2))
print(x.numpy())

shape = mge.Tensor([[ 1],
                    [10]], dtype="float32")
scale = mge.Tensor([1,5], dtype="float32")

x = rand.gamma(shape=shape, scale=scale)
print(x.numpy())

x = rand.gamma(shape=shape, scale=scale, size=2)
print(x.numpy())

Outputs:

[[1.5064533  4.0689363 ]
 [0.71639484 1.4551026 ]]

[[ 0.4352188 11.399335 ]
 [ 9.1888    52.009277 ]]

[[[ 1.1726005   3.9654975 ]
  [13.656933   36.559006  ]]
 [[ 0.25848487  2.5540342 ]
  [11.960409   21.031536  ]]]

megengine.random.beta

megengine.random.poisson