opendp 0.16.0

A library of differential privacy algorithms for the statistical analysis of sensitive private data.
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# type: ignore
class InverseCDF(CanonicalRV):
    # type Edge = RBig

    def inverse_cdf(self, uniform: RBig, _refinements: usize, _R) -> RBig | None:
        f_inv = quantile_cnd(
            uniform, self.tradeoff, self.fixed_point
        )  # `\label{f_inv}`
        return f_inv * self.scale + self.shift