Legacy random generation — NumPy v2.3.dev0 Manual (original) (raw)

The RandomState provides access to legacy generators. This generator is considered frozen and will have no further improvements. It is guaranteed to produce the same values as the final point release of NumPy v1.16. These all depend on Box-Muller normals or inverse CDF exponentials or gammas. This class should only be used if it is essential to have randoms that are identical to what would have been produced by previous versions of NumPy.

RandomState adds additional information to the state which is required when using Box-Muller normals since these are produced in pairs. It is important to useRandomState.get_state, and not the underlying bit generators_state_, when accessing the state so that these extra values are saved.

Although we provide the MT19937 BitGenerator for use independent ofRandomState, note that its default seeding uses SeedSequencerather than the legacy seeding algorithm. 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 implementRandomState. However, one can reset the state of MT19937using the state of the RandomState:

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

rs = RandomState(12345) mt19937 = MT19937() mt19937.state = rs.get_state() rs2 = RandomState(mt19937)

Same output

rs.standard_normal() rs2.standard_normal()

rs.random() rs2.random()

rs.standard_exponential() rs2.standard_exponential()

class numpy.random.RandomState(seed=None)#

Container for the slow Mersenne Twister pseudo-random number generator. Consider using a different BitGenerator with the Generator container instead.

RandomState and Generator expose a number of methods for generating random numbers drawn from a variety of probability distributions. In addition to the distribution-specific arguments, each method takes a keyword argument size that defaults to None. If size is None, then a single value is generated and returned. If size is an integer, then a 1-D array filled with generated values is returned. If size is a tuple, then an array with that shape is filled and returned.

Compatibility Guarantee

A fixed bit generator using a fixed seed and a fixed series of calls to ‘RandomState’ methods using the same parameters will always produce the same results up to roundoff error except when the values were incorrect.RandomState is effectively frozen and will only receive updates that are required by changes in the internals of Numpy. More substantial changes, including algorithmic improvements, are reserved forGenerator.

Parameters:

seed{None, int, array_like, BitGenerator}, optional

Random seed used to initialize the pseudo-random number generator or an instantized BitGenerator. If an integer or array, used as a seed for the MT19937 BitGenerator. Values can be any integer between 0 and 2**32 - 1 inclusive, an array (or other sequence) of such integers, or None (the default). If seed is None, then the MT19937BitGenerator is initialized by reading data from /dev/urandom(or the Windows analogue) if available or seed from the clock otherwise.

Notes

The Python stdlib module “random” also contains a Mersenne Twister pseudo-random number generator with a number of methods that are similar to the ones available in RandomState. RandomState, besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions to choose from.

Seeding and state#

Simple random data#

Permutations#

Distributions#

Functions in numpy.random#

Many of the RandomState methods above are exported as functions innumpy.random This usage is discouraged, as it is implemented via a globalRandomState instance which is not advised on two counts:

• It uses global state, which means results will change as the code changes
• It uses a RandomState rather than the more modern Generator.

For backward compatible legacy reasons, we will not change this.