megengine.random.beta¶
- beta(alpha, beta, size=None)¶
Random variable with Beta distribution \(\operatorname{Beta}(\alpha, \beta)\).
The corresponding probability density function is
\[p(x)=\frac{1}{\mathrm{~B}(\alpha, \beta)} x^{\alpha-1}(1-x)^{\beta-1} \quad \text { for } \alpha, \beta>0,\]where \(\mathrm{~B}(\alpha, \beta)\) is the beta function,
\[\mathrm{~B}(\alpha, \beta)=\int_{0}^{1} t^{\alpha-1}(1-t)^{\beta-1} d t.\]- 参数
alpha (
Union[Tensor,float]) – the alpha parameter of the distribution. Must be non-negative.beta (
Union[Tensor,float]) – the beta parameter of the distribution. Must be non-negative.size (
Optional[Iterable[int]]) – the size of output tensor. If alpha and beta are scalars and given size is, e.g., (m, n), then the output shape is (m, n). If alpha or beta is a Tensor and given size is, e.g., (m, n), then the output shape is (m, n) + broadcast(alpha, beta).shape.
- 返回
the output tensor.
实际案例
import megengine as mge import megengine.random as rand x = rand.beta(alpha=2, beta=1, size=(2, 2)) print(x.numpy()) alpha = mge.Tensor([[0.5], [ 3]], dtype="float32") beta = mge.Tensor([0.5,5], dtype="float32") x = rand.beta(alpha=alpha, beta=beta) print(x.numpy()) x = rand.beta(alpha=alpha, beta=beta, size=2) print(x.numpy())
Outputs:
[[0.582565 0.91763186] [0.86963767 0.6088103 ]] [[0.41503012 0.16438372] [0.90159506 0.47588003]] [[[0.55195075 0.01111084] [0.95298755 0.25048104]] [[0.11680304 0.13859665] [0.997879 0.43259275]]]