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Support more interpolation methods in `cupy.percentile` and `cupy.quantile`

See original GitHub issue

Since NumPy 1.22, numpy.percentile and numpy.quantile have new methods to estimate the percentile/quantile.

https://numpy.org/devdocs/reference/generated/numpy.quantile.html https://numpy.org/devdocs/reference/generated/numpy.percentile.html

methods that needs to be implemented:

inverted_cdf
averaged_inverted_cdf
closest_observation
interpolated_inverted_cdf
hazen
weibull
median_unbiased
normal_unbiased

Issue Analytics

State:
Created 2 years ago
Comments:9 (5 by maintainers)

Top GitHub Comments

1reaction

takagicommented, Aug 17, 2022

Thanks @pri1311! Would you make a PR for your change? I’ll review it next week as I have days off this week.

0reactions

pri1311commented, Aug 14, 2022

@takagi commit - https://github.com/cupy/cupy/compare/master...pri1311:cupy:interpolation

Branch - https://github.com/pri1311/cupy/tree/interpolation

Weird thing though: When I changed the shape of the array from a = testing.shaped_random((10, 2, 3, 2), xp, dtype) ap = a.copy() to a = testing.shaped_random((4, 3, 10, 2, 8), xp, dtype) in def test_quantile_overwrite it no longer fails.

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cupy.percentile — CuPy 11.3.0 documentation

method (str) – Interpolation method when a quantile lies between two data points. linear interpolation is used by default. Supported interpolations ...

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latest PDF - CuPy Documentation

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cupy.interp# ... One-dimensional linear interpolation. ... This function may synchronize if left or right is not already on the device. ... Created using...