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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

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Correctness

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- 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

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

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Problem description

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mindspore.ops.heaviside

mindspore.ops.heaviside(input, values)[source]

Computes the Heaviside step function for each element in input.

 heaviside ( input, values )={0, if input <0 values,  if input =01, if input >0
Parameters
  • input (Tensor) – The input tensor. With real number data type.

  • values (Tensor) – The values to use where input is zero. Values can be broadcast with input . input should have the same dtype with values .

Returns

Tensor, has the same type as input and values.

Raises
  • TypeError – If input or values is not Tensor.

  • TypeError – If data type input and values is different.

  • ValueError – If shape of two inputs are not broadcastable.

Supported Platforms:

Ascend GPU CPU

Examples

>>> input = Tensor(np.array([-5., 1., 0., 2., 0.]))
>>> values = Tensor(np.array([3.]))
>>> y = ops.heaviside(input, values)
>>> print(y)
[0. 1. 3. 1. 3.]