`numpy.random._generator:Generator`

source: /numpy/random/_generator.cpython-314-x86_64-linux-gnu.so

Signature

class Generator ( bit_generator )

Members

Summary

Container for the BitGenerators.

Extended Summary

Generator exposes 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.

The function numpy.random.default_rng will instantiate a Generator with numpy's default BitGenerator.

No Compatibility Guarantee

Generator does not provide a version compatibility guarantee. In particular, as better algorithms evolve the bit stream may change.

Parameters

bit_generator : BitGenerator: BitGenerator to use as the core generator.

Notes

The Python stdlib module random contains pseudo-random number generator with a number of methods that are similar to the ones available in Generator. It uses Mersenne Twister, and this bit generator can be accessed using MT19937. Generator, besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions to choose from.

Examples

from numpy.random import Generator, PCG64
rng = Generator(PCG64())

✓

rng.standard_normal()

✗

Aliases

numpy.random.Generator

Referenced by

This package

Other packages

scipy tutorial:stats:probability_distributions
scipy tutorial:stats:quasi_monte_carlo
scipy scipy.cluster.vq:kmeans
scipy scipy.cluster.vq:kmeans2
scipy scipy.interpolate._polyint:barycentric_interpolate
scipy scipy.interpolate._polyint:BarycentricInterpolator
scipy scipy.linalg._sketches:clarkson_woodruff_transform
scipy scipy.linalg._sketches:cwt_matrix
scipy scipy.linalg.interpolative
scipy scipy.linalg.interpolative:estimate_rank
scipy scipy.linalg.interpolative:estimate_spectral_norm
scipy scipy.linalg.interpolative:estimate_spectral_norm_diff
scipy scipy.linalg.interpolative:interp_decomp
scipy scipy.linalg.interpolative:svd
scipy scipy.optimize._basinhopping:basinhopping
scipy scipy.optimize._differentialevolution:differential_evolution
scipy scipy.optimize._differentialevolution:DifferentialEvolutionSolver
scipy scipy.optimize._dual_annealing:dual_annealing
scipy scipy.optimize._dual_annealing:VisitingDistribution
scipy scipy.optimize._optimize:check_grad
scipy scipy.sparse._construct:rand
scipy scipy.sparse._construct:random
scipy scipy.sparse._construct:random_array
scipy scipy.sparse.linalg._eigen._svds:svds
scipy scipy.sparse.linalg._eigen.arpack.arpack:eigs
scipy scipy.sparse.linalg._eigen.arpack.arpack:eigsh
scipy scipy.sparse.linalg._svdp:_svdp
scipy scipy.spatial.distance:directed_hausdorff
scipy scipy.spatial.transform._rotation:Rotation.random
scipy scipy.stats._fit:goodness_of_fit
scipy scipy.stats._kde:gaussian_kde.integrate_box
scipy scipy.stats._multicomp:dunnett
scipy scipy.stats._multivariate:invwishart_frozen.__init__
scipy scipy.stats._probability_distribution:_ProbabilityDistribution.sample
scipy scipy.stats._qmc:_van_der_corput_permutations
scipy scipy.stats._qmc:check_random_state
scipy scipy.stats._qmc:Halton
scipy scipy.stats._qmc:LatinHypercube
scipy scipy.stats._qmc:MultinomialQMC
scipy scipy.stats._qmc:MultivariateNormalQMC
scipy scipy.stats._qmc:PoissonDisk
scipy scipy.stats._qmc:QMCEngine
scipy scipy.stats._qmc:Sobol
scipy scipy.stats._qmc:van_der_corput
scipy scipy.stats._resampling:BootstrapMethod
scipy scipy.stats._resampling:MonteCarloMethod
scipy scipy.stats._resampling:permutation_test
scipy scipy.stats._resampling:PermutationMethod
scipy scipy.stats._sensitivity_analysis:sobol_indices