numpy.random.exponential(scale=1.0, size=None) Draw samples from an exponential distribution. Its probability

numpy.random.noncentral_f(dfnum, dfden, nonc, size=None) Draw samples from the noncentral F distribution. Samples

numpy.random.rand(d0, d1, ..., dn) Random values in a given shape. Create an array of the given shape and populate

RandomState.exponential(scale=1.0, size=None) Draw samples from an exponential distribution. Its

RandomState.randint(low, high=None, size=None, dtype='l') Return random integers from low (inclusive)

numpy.random.sample(size=None) Return random floats in the half-open interval [0.0, 1.0). Results are from the

RandomState.get_state() Return a tuple representing the internal state of the generator. For more details

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

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

RandomState.lognormal(mean=0.0, sigma=1.0, size=None) Draw samples from a log-normal distribution. Draw