103 lines
4.6 KiB
Python
103 lines
4.6 KiB
Python
"""
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========================
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Random Number Generation
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========================
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==================== =========================================================
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Utility functions
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==============================================================================
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random Uniformly distributed values of a given shape.
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bytes Uniformly distributed random bytes.
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random_integers Uniformly distributed integers in a given range.
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random_sample Uniformly distributed floats in a given range.
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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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seed Seed the random number generator.
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==================== =========================================================
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==================== =========================================================
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Compatibility functions
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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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randint Uniformly distributed integers in a given range.
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==================== =========================================================
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==================== =========================================================
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Univariate 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 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 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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# To get sub-modules
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from info import __doc__, __all__
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from mtrand import *
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# Some aliases:
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ranf = random = sample = random_sample
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__all__.extend(['ranf','random','sample'])
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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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"""
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return RandomState()
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from numpy.testing import Tester
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test = Tester().test
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bench = Tester().bench
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