opendp 0.16.0

A library of differential privacy algorithms for the statistical analysis of sensitive private data.
Documentation 
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# type: ignore
def sample_geometric_exp_fast(x: RBig) -> int:
    if x == 0:
        return 0

    s, t = x.into_parts()

    while True:
        u = sample_uniform_ubig_below(t)  # |\label{line:U}|
        d = sample_bernoulli_exp(Rational(u, t))  # |\label{line:D}|
        if d:
            break

    v = sample_geometric_exp_slow(1)  # |\label{line:V}|
    z = u + t * v
    return z // s