mindspore.ops.gamma
- mindspore.ops.gamma(shape, alpha, beta, seed=None)[source]
Generates random numbers according to the Gamma random number distribution.
- Parameters
shape (tuple) – The shape of random tensor to be generated.
alpha (Tensor) – The \(\alpha\) distribution parameter. It should be greater than 0 with float32 data type.
beta (Tensor) – The \(\beta\) distribution parameter. It should be greater than 0 with float32 data type.
seed (int, optional) – Seed is used as entropy source for the random number engines to generate pseudo-random numbers, must be non-negative. Default:
None
, which will be treated as0
.
- Returns
Tensor. The shape should be equal to the broadcasted shape between the input shape and shapes of alpha and beta. The dtype is float32.
- Raises
- Supported Platforms:
Ascend
Examples
>>> 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 ]]]