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| author | Pamphile ROY <roy.pamphile@gmail.com> | 2021-04-14 21:03:49 +0200 |
|---|---|---|
| committer | Pamphile ROY <roy.pamphile@gmail.com> | 2021-04-14 21:03:49 +0200 |
| commit | 64bb06f002f2085c172c2f7dc621289d3cd54cbe (patch) | |
| tree | 8376bfcf48abb3af3af6cd6157575c4f013530ad /doc | |
| parent | 04c97a63e1420402b54b89e05082a0ff4cb1c001 (diff) | |
| download | numpy-64bb06f002f2085c172c2f7dc621289d3cd54cbe.tar.gz | |
DOC: revert global seed
Diffstat (limited to 'doc')
| -rw-r--r-- | doc/TESTS.rst.txt | 3 |
1 files changed, 1 insertions, 2 deletions
diff --git a/doc/TESTS.rst.txt b/doc/TESTS.rst.txt index c7e89b5b9..fd8845291 100644 --- a/doc/TESTS.rst.txt +++ b/doc/TESTS.rst.txt @@ -351,8 +351,7 @@ new bugs or regressions, a test that passes most of the time but fails occasionally with no code changes is not helpful. Make the random data deterministic by setting the random number seed before generating it. Use either Python's ``random.seed(some_number)`` or NumPy's -``rng = np.random.default_rng(some_number)``, depending on the source of -random numbers. +``numpy.random.seed(some_number)``, depending on the source of random numbers. Alternatively, you can use `Hypothesis`_ to generate arbitrary data. Hypothesis manages both Python's and Numpy's random seeds for you, and |