mindspore.ops.gamma
- mindspore.ops.gamma(shape, alpha, beta, seed=None)[源代码]
根据伽马分布生成随机数。
- 参数:
shape (tuple) - 指定生成随机数的shape。任意维度的Tensor。
alpha (Tensor) - \(\alpha\) 分布的参数。应该大于0且数据类型为float32。
beta (Tensor) - \(\beta\) 分布的参数。应该大于0且数据类型为float32。
seed (int,可选) - 随机数生成器的种子,必须是非负数,默认为
None
,将视为0
。
- 返回:
Tensor。shape是输入 shape 、 alpha 、 beta 广播后的shape。数据类型为float32。
- 异常:
TypeError - shape 不是tuple。
TypeError - alpha 或 beta 不是Tensor。
TypeError - seed 的数据类型不是int。
TypeError - alpha 或 beta 的数据类型不是float32。
- 支持平台:
Ascend
样例:
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> # case 1: alpha_shape is (2, 2) >>> shape = (3, 1, 2) >>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32) >>> beta = Tensor(np.array([1.0]), mindspore.float32) >>> output = ops.gamma(shape, alpha, beta, seed=5) >>> result = output.shape >>> print(result) (3, 2, 2) >>> # case 2: alpha_shape is (2, 3), so shape is (3, 1, 3) >>> shape = (3, 1, 3) >>> alpha = Tensor(np.array([[1, 3, 4], [2, 5, 6]]), mindspore.float32) >>> beta = Tensor(np.array([1.0]), mindspore.float32) >>> output = ops.gamma(shape, alpha, beta, seed=5) >>> result = output.shape >>> print(result) (3, 2, 3) >>> # case 3: beta_shape is (1, 2), the output is different. >>> shape = (3, 1, 2) >>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32) >>> beta = Tensor(np.array([1.0, 2]), mindspore.float32) >>> output = ops.gamma(shape, alpha, beta, seed=5) >>> print(output) [[[ 2.2132034 5.8855834] [ 3.8825176 8.6066265]] [[ 3.3981476 7.5805717] [ 3.7190282 19.941492 ]] [[ 2.9512358 2.5969937] [ 3.786061 5.160872 ]]] >>> # case 4: beta_shape is (2, 1), the output is different. >>> shape = (3, 1, 2) >>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32) >>> beta = Tensor(np.array([[1.0], [2.0]]), mindspore.float32) >>> output = ops.gamma(shape, alpha, beta, seed=5) >>> print(output) [[[ 5.6085486 7.8280783] [ 15.97684 16.116285]] [[ 1.8347423 1.713663] [ 3.2434065 15.667398]] [[ 4.2922077 7.3365674] [ 5.3876944 13.159832 ]]]