RandomState.noncentral_chisquare(df, nonc, size=None) Draw samples from a noncentral chi-square

numpy.random.shuffle(x) Modify a sequence in-place by shuffling its contents.

numpy.random.get_state() Return a tuple representing the internal state of the generator. For more details, see

RandomState.logseries(p, size=None) Draw samples from a logarithmic series distribution. Samples are

numpy.random.f(dfnum, dfden, size=None) Draw samples from an F distribution. Samples are drawn from an F distribution

RandomState.negative_binomial(n, p, size=None) Draw samples from a negative binomial distribution

numpy.random.geometric(p, size=None) Draw samples from the geometric distribution. Bernoulli trials are experiments

numpy.random.standard_exponential(size=None) Draw samples from the standard exponential distribution.

RandomState.rayleigh(scale=1.0, size=None) Draw samples from a Rayleigh distribution. The

RandomState.dirichlet(alpha, size=None) Draw samples from the Dirichlet distribution. Draw size