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mindspore.ops.StandardLaplace

class mindspore.ops.StandardLaplace(seed=0, seed2=0)

Generates random numbers according to the Laplace random number distribution (mean=0, lambda=1). It is defined as:

$$\text{f}(x)=\frac{1}{2}\exp (-|x|)$$

Note

• Random seed: a set of regular random numbers can be obtained through some complex mathematical algorithms, and the random seed determines the initial value of this random number. If the random seed is the same in two separate calls, the random number generated will not change.

• Using the Philox algorithm to scramble seed and seed2 to obtain random seed so that the user doesn't need to worry about which seed is more important.

Warning

The Ascend backend does not support the reproducibility of random numbers, so the seed and seed2 parameter have no effect.

Parameters

• seed (int, optional) – The operator-level random seed, used to generate random numbers, must be non-negative. Default: 0 .

• seed2 (int, optional) – The global random seed, which combines with the operator-level random seed to determine the final generated random number, must be non-negative. Default: 0 .

Inputs:

• shape (Union[tuple[int], Tensor[int]]) - The shape of random tensor to be generated. Only constant value is allowed when the input type is tuple. And the operator supports dynamic shape only when the input type is Tensor.

Outputs:

Tensor. The shape that the input 'shape' denotes. The dtype is float32.

Raises

• TypeError – If seed or seed2 is not an int.

• TypeError – If shape is neither a tuple nor a Tensor.

• ValueError – If seed or seed2 is not a non-negative int.

• ValueError – If shape is a tuple containing non-positive items.

• ValueError – If shape is a Tensor, and the rank of the Tensor is not equal to 1.

Supported Platforms:

Ascend GPU CPU

Examples

>>> from mindspore import ops
>>> shape = (4, 16)
>>> stdlaplace = ops.StandardLaplace(seed=2)
>>> output = stdlaplace(shape)
>>> result = output.shape
>>> print(result)
(4, 16)