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 make_count(
    input_domain: VectorDomain[AtomDomain[TIA]], input_metric: SymmetricDistance
):
    output_domain = AtomDomain.default(TO)  # |\label{line:output-domain}|

    def function(arg: Vec[TIA]) -> TO:  # |\label{line:TO-output}|
        size = arg.len()  # |\label{line:size}|
        try:  # |\label{line:try-catch}|
            return TO.exact_int_cast(size)  # |\label{line:exact-int-cast}|
        except FailedCast:
            return TO.MAX_CONSECUTIVE  # |\label{line:except-return}|

    output_metric = AbsoluteDistance(TO)

    stability_map = StabilityMap.new_from_constant(
        TO.one()
    )  # |\label{line:stability-map}|

    return Transformation(
        input_domain,
        output_domain,
        function,
        input_metric,
        output_metric,
        stability_map,
    )