RandomState.power(a, size=None) Draws samples in [0, 1] from a power distribution with positive exponent a - 1

RandomState.permutation(x) Randomly permute a sequence, or return a permuted range. If x

numpy.random.uniform(low=0.0, high=1.0, size=None) Draw samples from a uniform distribution. Samples are uniformly

numpy.random.multinomial(n, pvals, size=None) Draw samples from a multinomial distribution. The multinomial

numpy.random.poisson(lam=1.0, size=None) Draw samples from a Poisson distribution. The Poisson distribution is

numpy.random.normal(loc=0.0, scale=1.0, size=None) Draw random samples from a normal (Gaussian) distribution. The

RandomState.standard_t(df, size=None) Draw samples from a standard Student?s t distribution with df

numpy.random.randn(d0, d1, ..., dn) Return a sample (or samples) from the ?standard normal? distribution. If positive

numpy.random.choice(a, size=None, replace=True, p=None) Generates a random sample from a given 1-D array

RandomState.seed(seed=None) Seed the generator. This method is called when