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. The format is \((N,*)\) where \(*\) means, any number of additional dimensions.

  • alpha (Tensor) – The alpha α distribution parameter. It should be greater than 0 with float32 data type.

  • beta (Tensor) – The beta β distribution parameter. It should be greater than 0 with float32 data type.

  • seed (int) – 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 as 0.

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

  • TypeError – If shape is not a tuple.

  • TypeError – If neither alpha nor beta is a Tensor.

  • TypeError – If seed is not an int.

  • TypeError – If dtype of alpha and beta is not float32.

Supported Platforms:

Ascend

Examples

>>> # 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)
>>> result = output.shape
>>> print(output)
[[[ 2.2132034  5.8855834]]
 [ 3.3981476  7.5805717]
[[ 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)
>>> result = output.shape
>>> 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 ]]]