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.. currentmodule:: numpy.random
Random Generator
================
The `~Generator` provides access to
a wide range of distributions, and served as a replacement for
:class:`~numpy.random.RandomState`. The main difference between
the two is that ``Generator`` relies on an additional BitGenerator to
manage state and generate the random bits, which are then transformed into
random values from useful distributions. The default BitGenerator used by
``Generator`` is `~PCG64`. The BitGenerator
can be changed by passing an instantized BitGenerator to ``Generator``.
.. autofunction:: default_rng
.. autoclass:: Generator
:members: __init__
:exclude-members: __init__
Accessing the BitGenerator and Spawning
---------------------------------------
.. autosummary::
:toctree: generated/
~numpy.random.Generator.bit_generator
~numpy.random.Generator.spawn
Simple random data
------------------
.. autosummary::
:toctree: generated/
~numpy.random.Generator.integers
~numpy.random.Generator.random
~numpy.random.Generator.choice
~numpy.random.Generator.bytes
Permutations
------------
The methods for randomly permuting a sequence are
.. autosummary::
:toctree: generated/
~numpy.random.Generator.shuffle
~numpy.random.Generator.permutation
~numpy.random.Generator.permuted
The following table summarizes the behaviors of the methods.
+--------------+-------------------+------------------+
| method | copy/in-place | axis handling |
+==============+===================+==================+
| shuffle | in-place | as if 1d |
+--------------+-------------------+------------------+
| permutation | copy | as if 1d |
+--------------+-------------------+------------------+
| permuted | either (use 'out' | axis independent |
| | for in-place) | |
+--------------+-------------------+------------------+
The following subsections provide more details about the differences.
In-place vs. copy
~~~~~~~~~~~~~~~~~
The main difference between `Generator.shuffle` and `Generator.permutation`
is that `Generator.shuffle` operates in-place, while `Generator.permutation`
returns a copy.
By default, `Generator.permuted` returns a copy. To operate in-place with
`Generator.permuted`, pass the same array as the first argument *and* as
the value of the ``out`` parameter. For example,
>>> rng = np.random.default_rng()
>>> x = np.arange(0, 15).reshape(3, 5)
>>> x #doctest: +SKIP
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
>>> y = rng.permuted(x, axis=1, out=x)
>>> x #doctest: +SKIP
array([[ 1, 0, 2, 4, 3], # random
[ 6, 7, 8, 9, 5],
[10, 14, 11, 13, 12]])
Note that when ``out`` is given, the return value is ``out``:
>>> y is x
True
.. _generator-handling-axis-parameter:
Handling the ``axis`` parameter
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
An important distinction for these methods is how they handle the ``axis``
parameter. Both `Generator.shuffle` and `Generator.permutation` treat the
input as a one-dimensional sequence, and the ``axis`` parameter determines
which dimension of the input array to use as the sequence. In the case of a
two-dimensional array, ``axis=0`` will, in effect, rearrange the rows of the
array, and ``axis=1`` will rearrange the columns. For example
>>> rng = np.random.default_rng()
>>> x = np.arange(0, 15).reshape(3, 5)
>>> x
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
>>> rng.permutation(x, axis=1) #doctest: +SKIP
array([[ 1, 3, 2, 0, 4], # random
[ 6, 8, 7, 5, 9],
[11, 13, 12, 10, 14]])
Note that the columns have been rearranged "in bulk": the values within
each column have not changed.
The method `Generator.permuted` treats the ``axis`` parameter similar to
how `numpy.sort` treats it. Each slice along the given axis is shuffled
independently of the others. Compare the following example of the use of
`Generator.permuted` to the above example of `Generator.permutation`:
>>> rng.permuted(x, axis=1) #doctest: +SKIP
array([[ 1, 0, 2, 4, 3], # random
[ 5, 7, 6, 9, 8],
[10, 14, 12, 13, 11]])
In this example, the values within each row (i.e. the values along
``axis=1``) have been shuffled independently. This is not a "bulk"
shuffle of the columns.
Shuffling non-NumPy sequences
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
`Generator.shuffle` works on non-NumPy sequences. That is, if it is given
a sequence that is not a NumPy array, it shuffles that sequence in-place.
For example,
>>> rng = np.random.default_rng()
>>> a = ['A', 'B', 'C', 'D', 'E']
>>> rng.shuffle(a) # shuffle the list in-place
>>> a #doctest: +SKIP
['B', 'D', 'A', 'E', 'C'] # random
Distributions
-------------
.. autosummary::
:toctree: generated/
~numpy.random.Generator.beta
~numpy.random.Generator.binomial
~numpy.random.Generator.chisquare
~numpy.random.Generator.dirichlet
~numpy.random.Generator.exponential
~numpy.random.Generator.f
~numpy.random.Generator.gamma
~numpy.random.Generator.geometric
~numpy.random.Generator.gumbel
~numpy.random.Generator.hypergeometric
~numpy.random.Generator.laplace
~numpy.random.Generator.logistic
~numpy.random.Generator.lognormal
~numpy.random.Generator.logseries
~numpy.random.Generator.multinomial
~numpy.random.Generator.multivariate_hypergeometric
~numpy.random.Generator.multivariate_normal
~numpy.random.Generator.negative_binomial
~numpy.random.Generator.noncentral_chisquare
~numpy.random.Generator.noncentral_f
~numpy.random.Generator.normal
~numpy.random.Generator.pareto
~numpy.random.Generator.poisson
~numpy.random.Generator.power
~numpy.random.Generator.rayleigh
~numpy.random.Generator.standard_cauchy
~numpy.random.Generator.standard_exponential
~numpy.random.Generator.standard_gamma
~numpy.random.Generator.standard_normal
~numpy.random.Generator.standard_t
~numpy.random.Generator.triangular
~numpy.random.Generator.uniform
~numpy.random.Generator.vonmises
~numpy.random.Generator.wald
~numpy.random.Generator.weibull
~numpy.random.Generator.zipf
|
