RandomState(seed=None)
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 the internals of Numpy. More substantial changes, including algorithmic improvements, are reserved for Generator
.
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.
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 :None:None:`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.
Container for the slow Mersenne Twister pseudo-random number generator. Consider using a different BitGenerator with the Generator container instead.
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.utils.misc.create_random_state
scipy.optimize._optimize.check_grad
scipy.optimize._basinhopping.basinhopping
dask.array.random.RandomState.tomaxint
networkx.utils.decorators.np_random_state
scipy.optimize._basinhopping.RandomDisplacement
scipy.linalg._sketches.cwt_matrix
scipy.optimize._dual_annealing.StrategyChain
numpy.random.mtrand.RandomState
scipy.optimize._dual_annealing.dual_annealing
scipy.sparse.linalg._eigen._svds.svds
scipy.sparse._construct.random
scipy.optimize._differentialevolution.DifferentialEvolutionSolver
scipy.optimize._basinhopping.Metropolis
numpy.random
networkx.convert_matrix.from_pandas_edgelist
scipy.optimize._qap.quadratic_assignment
scipy.linalg._sketches.clarkson_woodruff_transform
scipy.optimize._dual_annealing.VisitingDistribution
scipy._lib._util.check_random_state
scipy.spatial.distance.directed_hausdorff
scipy.linalg.interpolative.seed
networkx.utils.decorators.random_state
scipy.optimize._differentialevolution.differential_evolution
scipy.sparse._construct.rand
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