xorbits.numpy.random.RandomState = <function RandomState>[source]#


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 for Generator.


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 MT19937 BitGenerator is initialized by reading data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise.


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.

See also

Generator, MT19937, numpy.random.BitGenerator

This docstring was copied from numpy.random.