Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

Please select the submission type

Problem type

Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

Please select the type of question

Problem description

Describe the bug so that we can quickly locate the problem.

Submit

Document feedback

mindspore.ops.Gamma

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

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

$$\text{P}(x|\alpha ,\beta )=\frac{\exp (-x/\beta )}{{\beta }^{\alpha }\cdot \mathrm{\Gamma }(\alpha )}\cdot x^{\alpha -1}$$

Note

• Random seed: A set of regular random numbers can be obtained through some complex mathematical algorithms, and the random seed is the initial value of this random number. If the random seed is the same, the random number obtained will not change.

• Global random seed and operator-level random seed are not set: Use the default value as the random seed.

• Global random seed is set, but operator-level random seed is not set: A global random seed will splice with a randomly generated seed.

• Global random seed is not set, operator-level random seed is set: The default global random seed is used, and splices with the operator-level random seed.

• Both Global random and operator-level random seed are set: The global random seed will splice with the operator-level random seed.

Parameters

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

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

Inputs:

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

• alpha (Tensor) - α is the shape parameter of Gamma distribution, which mainly determines the shape of the curve. It must be greater than 0. The data type is float32.

• beta (Tensor) - β is the inverse scale parameter of the Gamma distribution, which mainly determines how steep the curve is. It must be greater than 0. The data type is float32.

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 data type of seed or seed2 is not int.

• TypeError – If alpha or beta is not a Tensor.

• TypeError – If data type of alpha or beta is not float32.

• 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)