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"""
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========================
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Random Number Generation
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========================
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Use ``default_rng()`` to create a `Generator` and call its methods.
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=============== =========================================================
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Generator
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--------------- ---------------------------------------------------------
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Generator       Class implementing all of the random number distributions
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default_rng     Default constructor for ``Generator``
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=============== =========================================================
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============================================= ===
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BitGenerator Streams that work with Generator
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--------------------------------------------- ---
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MT19937
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PCG64
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PCG64DXSM
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Philox
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SFC64
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============================================= ===
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============================================= ===
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Getting entropy to initialize a BitGenerator
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--------------------------------------------- ---
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SeedSequence
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============================================= ===
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Legacy
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------
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For backwards compatibility with previous versions of numpy before 1.17, the
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various aliases to the global `RandomState` methods are left alone and do not
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use the new `Generator` API.
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==================== =========================================================
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Utility functions
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-------------------- ---------------------------------------------------------
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random               Uniformly distributed floats over ``[0, 1)``
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bytes                Uniformly distributed random bytes.
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permutation          Randomly permute a sequence / generate a random sequence.
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shuffle              Randomly permute a sequence in place.
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choice               Random sample from 1-D array.
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==================== =========================================================
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==================== =========================================================
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Compatibility
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functions - removed
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in the new API
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-------------------- ---------------------------------------------------------
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rand                 Uniformly distributed values.
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randn                Normally distributed values.
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ranf                 Uniformly distributed floating point numbers.
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random_integers      Uniformly distributed integers in a given range.
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                     (deprecated, use ``integers(..., closed=True)`` instead)
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random_sample        Alias for `random_sample`
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randint              Uniformly distributed integers in a given range
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seed                 Seed the legacy random number generator.
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==================== =========================================================
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==================== =========================================================
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Univariate
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distributions
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-------------------- ---------------------------------------------------------
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beta                 Beta distribution over ``[0, 1]``.
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binomial             Binomial distribution.
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chisquare            :math:`\\chi^2` distribution.
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exponential          Exponential distribution.
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f                    F (Fisher-Snedecor) distribution.
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gamma                Gamma distribution.
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geometric            Geometric distribution.
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gumbel               Gumbel distribution.
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hypergeometric       Hypergeometric distribution.
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laplace              Laplace distribution.
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logistic             Logistic distribution.
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lognormal            Log-normal distribution.
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logseries            Logarithmic series distribution.
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negative_binomial    Negative binomial distribution.
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noncentral_chisquare Non-central chi-square distribution.
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noncentral_f         Non-central F distribution.
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normal               Normal / Gaussian distribution.
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pareto               Pareto distribution.
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poisson              Poisson distribution.
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power                Power distribution.
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rayleigh             Rayleigh distribution.
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triangular           Triangular distribution.
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uniform              Uniform distribution.
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vonmises             Von Mises circular distribution.
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wald                 Wald (inverse Gaussian) distribution.
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weibull              Weibull distribution.
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zipf                 Zipf's distribution over ranked data.
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==================== =========================================================
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==================== ==========================================================
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Multivariate
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distributions
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-------------------- ----------------------------------------------------------
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dirichlet            Multivariate generalization of Beta distribution.
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multinomial          Multivariate generalization of the binomial distribution.
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multivariate_normal  Multivariate generalization of the normal distribution.
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==================== ==========================================================
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==================== =========================================================
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Standard
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distributions
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-------------------- ---------------------------------------------------------
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standard_cauchy      Standard Cauchy-Lorentz distribution.
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standard_exponential Standard exponential distribution.
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standard_gamma       Standard Gamma distribution.
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standard_normal      Standard normal distribution.
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standard_t           Standard Student's t-distribution.
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==================== =========================================================
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==================== =========================================================
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Internal functions
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-------------------- ---------------------------------------------------------
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get_state            Get tuple representing internal state of generator.
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set_state            Set state of generator.
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==================== =========================================================
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"""
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__all__ = [
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    'beta',
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    'binomial',
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    'bytes',
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    'chisquare',
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    'choice',
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    'dirichlet',
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    'exponential',
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    'f',
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    'gamma',
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    'geometric',
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    'get_state',
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    'gumbel',
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    'hypergeometric',
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    'laplace',
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    'logistic',
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    'lognormal',
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    'logseries',
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    'multinomial',
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    'multivariate_normal',
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    'negative_binomial',
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    'noncentral_chisquare',
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    'noncentral_f',
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    'normal',
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    'pareto',
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    'permutation',
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    'poisson',
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    'power',
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    'rand',
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    'randint',
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    'randn',
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    'random',
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    'random_integers',
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    'random_sample',
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    'ranf',
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    'rayleigh',
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    'sample',
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    'seed',
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    'set_state',
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    'shuffle',
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    'standard_cauchy',
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    'standard_exponential',
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    'standard_gamma',
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    'standard_normal',
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    'standard_t',
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    'triangular',
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    'uniform',
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    'vonmises',
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    'wald',
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    'weibull',
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    'zipf',
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]
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# add these for module-freeze analysis (like PyInstaller)
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from . import _bounded_integers, _common, _pickle
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from ._generator import Generator, default_rng
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from ._mt19937 import MT19937
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from ._pcg64 import PCG64, PCG64DXSM
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from ._philox import Philox
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from ._sfc64 import SFC64
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from .bit_generator import BitGenerator, SeedSequence
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from .mtrand import *
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__all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937',
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            'Philox', 'PCG64', 'PCG64DXSM', 'SFC64', 'default_rng',
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            'BitGenerator']
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def __RandomState_ctor():
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    """Return a RandomState instance.
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    This function exists solely to assist (un)pickling.
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    Note that the state of the RandomState returned here is irrelevant, as this
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    function's entire purpose is to return a newly allocated RandomState whose
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    state pickle can set.  Consequently the RandomState returned by this function
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    is a freshly allocated copy with a seed=0.
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    See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
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    """
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    return RandomState(seed=0)
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from numpy._pytesttester import PytestTester
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test = PytestTester(__name__)
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del PytestTester
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