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File Content:
umath-validation-set-README.txt
Steps to validate transcendental functions: 1) Add a file 'umath-validation-set-<ufuncname>.txt', where ufuncname is name of the function in NumPy you want to validate 2) The file should contain 4 columns: dtype,input,expected output,ulperror a. dtype: one of np.float16, np.float32, np.float64 b. input: floating point input to ufunc in hex. Example: 0x414570a4 represents 12.340000152587890625 c. expected output: floating point output for the corresponding input in hex. This should be computed using a high(er) precision library and then rounded to same format as the input. d. ulperror: expected maximum ulp error of the function. This should be same across all rows of the same dtype. Otherwise, the function is tested for the maximum ulp error among all entries of that dtype. 3) Add file umath-validation-set-<ufuncname>.txt to the test file test_umath_accuracy.py which will then validate your ufunc.
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FILE
FOLDER
Name
Size
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Action
astype_copy.pkl
716 bytes
0644
generate_umath_validation_data.cpp
5842 bytes
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recarray_from_file.fits
8640 bytes
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umath-validation-set-README.txt
967 bytes
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umath-validation-set-arccos.csv
61365 bytes
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umath-validation-set-arccosh.csv
61365 bytes
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umath-validation-set-arcsin.csv
61339 bytes
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umath-validation-set-arcsinh.csv
60289 bytes
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umath-validation-set-arctan.csv
60305 bytes
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umath-validation-set-arctanh.csv
61339 bytes
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umath-validation-set-cbrt.csv
60846 bytes
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umath-validation-set-cos.csv
59122 bytes
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umath-validation-set-cosh.csv
60869 bytes
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umath-validation-set-exp.csv
17491 bytes
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umath-validation-set-exp2.csv
58624 bytes
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umath-validation-set-expm1.csv
60299 bytes
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umath-validation-set-log.csv
11692 bytes
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umath-validation-set-log10.csv
68922 bytes
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umath-validation-set-log1p.csv
60303 bytes
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umath-validation-set-log2.csv
68917 bytes
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umath-validation-set-sin.csv
58611 bytes
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umath-validation-set-sinh.csv
60293 bytes
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umath-validation-set-tan.csv
60299 bytes
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umath-validation-set-tanh.csv
60299 bytes
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