numpy.random.f()

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

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numpy.random.hypergeometric()

numpy.random.hypergeometric(ngood, nbad, nsample, size=None) Draw samples from a Hypergeometric distribution.

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RandomState.gumbel()

RandomState.gumbel(loc=0.0, scale=1.0, size=None) Draw samples from a Gumbel distribution. Draw samples

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RandomState.shuffle()

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

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numpy.random.noncentral_f()

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

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numpy.random.dirichlet()

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

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numpy.random.triangular()

numpy.random.triangular(left, mode, right, size=None) Draw samples from the triangular distribution. The triangular

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numpy.random.random_integers()

numpy.random.random_integers(low, high=None, size=None) Random integers of type np.int between low

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numpy.random.permutation()

numpy.random.permutation(x) Randomly permute a sequence, or return a permuted range. If x is a

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RandomState.binomial()

RandomState.binomial(n, p, size=None) Draw samples from a binomial distribution. Samples are drawn from

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