8000 DOC: np.random documentation cleanup and expansion. by rkern · Pull Request #13849 · numpy/numpy · GitHub
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DOC: np.random documentation cleanup and expansion. #13849

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Merged
merged 9 commits into from
Jun 28, 2019
Merged

DOC: np.random documentation cleanup and expansion. #13849

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ed72319
DOC: np.random documentation cleanup and expansion.
rkern Jun 27, 2019
ffbc6b1
DOC: Expand on the recommendations.
rkern Jun 27, 2019
6b3e58a
DOC: typo
rkern Jun 27, 2019
73ae703
DOC: fix reference links
mattip Jun 27, 2019
4bdaa9b
Merge pull request #1 from mattip/make-refs
rkern Jun 27, 2019
5b0d1b5
DOC: clean up examples.
rkern Jun 27, 2019
e53abb5
DOC: Fix description of PCG64 jump.
rkern Jun 27, 2019
ecae5c3
DOC: Use old title of np.random section
rkern Jun 28, 2019
4945048
DOC: Make sure canonical numpy.random target is the index.
rkern Jun 28, 2019
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DOC: clean up examples.
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@rkern
rkern committed Jun 27, 2019
commit 5b0d1b56e334bc9b83e1ba6bf5a557788a8fd5bf
2 changes: 1 addition & 1 deletion doc/source/reference/random/bit_generators/index.rst
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Expand Up @@ -81,7 +81,7 @@ user, which is up to you.
# If the user did not provide a seed, it should return `None`.
seed = get_user_seed()
ss = SeedSequence(seed)
print(f'seed = {ss.entropy}')
print('seed = {}'.format(ss.entropy))
bg = PCG64(ss)

.. end_block
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17 changes: 9 additions & 8 deletions doc/source/reference/random/legacy.rst
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Expand Up @@ -23,27 +23,28 @@ Although we provide the `~mt19937.MT19937` BitGenerator for use independent of
rather than the legacy seeding algorithm. `~mtrand.RandomState` will use the
legacy seeding algorithm. The methods to use the legacy seeding algorithm are
currently private as the main reason to use them is just to implement
`~mtrand.RandomState`.
`~mtrand.RandomState`. However, one can reset the state of `~mt19937.MT19937`
using the state of the `~mtrand.RandomState`:

.. code-block:: python

from numpy.random import MT19937
from numpy.random import RandomState

# Use same seed
rs = RandomState(12345)
mt19937 = MT19937(12345)
lg = RandomState(mt19937)
mt19937 = MT19937()
mt19937.state = rs.get_state()
rs2 = RandomState(mt19937)

# Different output, sorry.
# Same output
rs.standard_normal()
lg.standard_normal()
rs2.standard_normal()

rs.random()
lg.random()
rs2.random()

rs.standard_exponential()
lg.standard_exponential()
rs2.standard_exponential()


.. currentmodule:: numpy.random.mtrand
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