BUG: Loss of precision in longdouble min#20872
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seberg merged 3 commits intonumpy:mainfrom Feb 1, 2022
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Generic reuse in the latest changes around `min` works unless the macro is redefined for SIMD. This change avoids `scalar_min_f` for generic comparisons (as it can be redefined) and defines is separately as `scalar_min`
This was referenced Jan 21, 2022
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Thanks! Could you add a test that confirms full precision is preserved here? It would also be nice to "fuzz" it a bit around stranger cases (maybe array size and inclusion of We really need to have a tests that are capable of catching this type of precision loss. |
Adding tests like suggested in numpy#20863
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We've added some tests based on the comments in #20863 |
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Thanks. Could be nice to parametrize this more, but the whole file doesn't parametrize much, so it seems OK.
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* BUG: Loss of precision in longdouble min Generic reuse in the latest changes around `min` works unless the macro is redefined for SIMD. This change avoids `scalar_min_f` for generic comparisons (as it can be redefined) and defines is separately as `scalar_min` * Add tests as requested Adding tests like suggested in numpy#20863 * MAINT: Use `np.longdouble` rather than float128 for min/max tests Co-authored-by: Sebastian Berg <[email protected]>
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* BUG: Loss of precision in longdouble min Generic reuse in the latest changes around `min` works unless the macro is redefined for SIMD. This change avoids `scalar_min_f` for generic comparisons (as it can be redefined) and defines is separately as `scalar_min` * Add tests as requested Adding tests like suggested in numpy#20863 * MAINT: Use `np.longdouble` rather than float128 for min/max tests Co-authored-by: Sebastian Berg <[email protected]>
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Generic reuse in the latest changes around
minworks unless the macro is redefined for SIMD.This change avoids
scalar_min_ffor generic comparisons (as it can be redefined) and defines is separately asscalar_minThis should resolve #20863