8000 feat: add complex dtype support for `mean` by kgryte · Pull Request #850 · data-apis/array-api · GitHub
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merged 2 commits into from
Dec 12, 2024
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feat: add complex dtype support for mean #850

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feat: add complex dtype support `mean`
kgryte Oct 31, 2024
095e8d0
docs: add note concerning the use of `isnan`
kgryte Dec 12, 2024
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docs: add note concerning the use of isnan
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@kgryte
kgryte committed Dec 12, 2024
commit 095e8d0ad891d5972ff7704948be8549e87a38ae
5 changes: 4 additions & 1 deletion src/array_api_stubs/_draft/statistical_functions.py
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Expand Up @@ -137,11 +137,14 @@ def mean(
- If ``N`` is ``0``, the arithmetic mean is ``NaN``.
- If ``x_i`` is ``NaN``, the arithmetic mean is ``NaN`` (i.e., ``NaN`` values propagate).

For complex floating-point operands, real-valued floating-point special cases must independently apply to the real and imaginary component operations involving real numbers. For example, let ``a = real(x_i)`` and ``b = imag(x_i)``, and
For complex floating-point operands, real-valued floating-point special cases should independently apply to the real and imaginary component operations involving real numbers. For example, let ``a = real(x_i)`` and ``b = imag(x_i)``, and

- If ``N`` is ``0``, the arithmetic mean is ``NaN + NaN j``.
- If ``a`` is ``NaN``, the real component of the result is ``NaN``.
- Similarly, if ``b`` is ``NaN``, the imaginary component of the result is ``NaN``.

.. note::
Array libraries, such as NumPy, PyTorch, and JAX, currently deviate from this specification in their handling of components which are ``NaN`` when computing the arithmetic mean. In general, consumers of array libraries implementing this specification should use :func:`~array_api.isnan` to test whether the result of computing the arithmetic mean over an array have a complex floating-point data type is ``NaN``, rather than relying on ``NaN`` propagation of individual components.
"""


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