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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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- 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

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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

- Risk Warnings:

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

Content Compliance

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

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Problem description

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mindspore.ops.vander

View Source On Gitee
mindspore.ops.vander(x, N=None)[source]

Generates a Vandermonde matrix. The columns of the output matrix are powers of the input vector. The i-th output column is the input vector raised element-wise to the power of Ni1.

Parameters
  • x (Tensor) – 1-D input array.

  • N (int, optional) – Number of columns in the output. Default: None, N will be assigned as len(x).

Returns

Tensor, the columns are x0,x1,...,x(N1).

Raises
Supported Platforms:

Ascend GPU CPU

Examples

>>> from mindspore import Tensor, ops
>>> a = Tensor([1., 2., 3., 5.])
>>> print(ops.vander(a, N=3))
[[1.   1.   1.]
 [4.   2.   1.]
 [9.   3.   1.]
 [25.  5.   1.]]
>>> a = Tensor([1., 2., 3., 5.])
>>> print(ops.vander(a))
[[1.    1.   1.   1.]
 [8.    4.   2.   1.]
 [27.   9.   3.   1.]
 [125.  25.  5.   1.]]