megengine.random.gamma¶
- gamma(shape, scale=1, size=None)¶
服从伽玛分布 \(\Gamma(k, \theta)\) 的随机变量。
对应的概率密度函数为
\[p(x)=x^{k-1} \frac{e^{-x / \theta}}{\theta^{k} \Gamma(k)} \quad \text { for } x>0 \quad k, \theta>0,\]其中 \(\Gamma(k)\) 是 gamma 函数，
\[\Gamma(k)=(k-1) ! \quad \text { for } \quad k \quad \text{is positive integer}.\]- 参数
shape (Union[Tensor, float]) – the shape parameter (sometimes designated “k”) of the distribution. Must be positive.
scale (Union[Tensor, float]) – the scale parameter (sometimes designated “theta”) of the distribution. Must be positive. 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
tensor. The random variable with Gamma distribution.
- 返回类型
Return type
实际案例
>>> import megengine.random as rand >>> x = rand.gamma(shape=2, scale=1, size=(2, 2)) >>> x.numpy() array([[0.97447544, 1.5668875 ], [1.0069491 , 0.3078318 ]], dtype=float32) >>> shape = mge.Tensor([[ 1], ... [10]], dtype="float32") >>> scale = mge.Tensor([1,5], dtype="float32") >>> x = rand.gamma(shape=shape, scale=scale) >>> x.numpy() array([[ 0.11312152, 3.0799196 ], [10.973469 , 29.596972 ]], dtype=float32) >>> x = rand.gamma(shape=shape, scale=scale, size=2) >>> x.numpy() array([[[4.35868073e+00, 1.22415285e+01], [1.02696848e+01, 4.19773598e+01]],
- [[7.73875117e-02, 6.06766164e-01],
[1.22881927e+01, 8.13445740e+01]]], dtype=float32)