mindspore.ops.Gamma

class mindspore.ops.Gamma(seed=0, seed2=0)[source]

Produces random positive floating-point values x, distributed according to probability density function:

\[\text{P}(x|α,β) = \frac{\exp(-x/β)}{{β^α}\cdot{\Gamma(α)}}\cdot{x^{α-1}}\]
Parameters

  • seed (int) – Random seed, must be non-negative. Default: 0.

  • seed2 (int) – Random seed2, must be non-negative. Default: 0.

Inputs:

  • shape (tuple) - The shape of random tensor to be generated. Only constant value is allowed.

  • alpha (Tensor) - The α distribution parameter. It must be greater than 0. It is also known as the shape parameter with float32 data type.

  • beta (Tensor) - The β distribution parameter. It must be greater than 0. It is also known as the inverse scale parameter with float32 data type.

Outputs:

Tensor. The shape must be the broadcasted shape of Input “shape” and shapes of alpha and beta. The dtype is float32.

Raises

  • TypeError – If neither seed nor seed2 is an int.

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

  • ValueError – If shape is not a constant value.

Supported Platforms:

Ascend

Examples

>>> shape = (3, 1, 2)
>>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mstype.float32)
>>> beta = Tensor(np.array([1.0]), mstype.float32)
>>> gamma = ops.Gamma(seed=3)
>>> output = gamma(shape, alpha, beta)
>>> result = output.shape
>>> print(result)
(3, 2, 2)