megengine.random.beta¶
- beta(alpha, beta, size=None)¶
服从贝塔分布 \(\operatorname{Beta}(\alpha, \beta)\) 的随机变量。
对应的概率密度函数为
\[p(x)=\frac{1}{\mathrm{~B}(\alpha, \beta)} x^{\alpha-1}(1-x)^{\beta-1} \quad \text { for } \alpha, \beta>0,\]其中 \(\mathrm{~B}(\alpha, \beta)\) 是 Beta 函数，
\[\mathrm{~B}(\alpha, \beta)=\int_{0}^{1} t^{\alpha-1}(1-t)^{\beta-1} d t.\]- 参数
alpha (Union[Tensor, float]) – the alpha parameter of the distribution. Must be positive.
beta (Union[Tensor, float]) – the beta parameter of the distribution. Must be positive.
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. Default: None.
- 返回
tensor. The random variable with Beta distribution.
- 返回类型
Return type
实际案例
>>> import megengine.random as rand >>> x = rand.beta(alpha=2, beta=1, size=(2, 2)) >>> x.numpy() array([[0.6172312 , 0.9789006 ], [0.50004643, 0.9775796 ]], dtype=float32) >>> alpha = mge.Tensor([[0.5], ... [ 3]], dtype="float32") >>> beta = mge.Tensor([0.5,5], dtype="float32") >>> x = rand.beta(alpha=alpha, beta=beta) >>> x.numpy() array([[0.0075407 , 0.1275094 ], [0.96331763, 0.22299217]], dtype=float32) >>> x = rand.beta(alpha=alpha, beta=beta, size=2) >>> x.numpy() array([[[0.46863747, 0.13819647], [0.8646759 , 0.16014215]],
- [[0.0682759 , 0.04448463],
[0.97733796, 0.19206746]]], dtype=float32)