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mindspore.ops.gamma

mindspore.ops.gamma(shape, alpha, beta, seed=None)

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 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

>>> 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 ]]]