opendp 0.14.2-dev.20260401.2

A library of differential privacy algorithms for the statistical analysis of sensitive private data.
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use super::*;

#[test]
fn test_make_sum() -> Fallible<()> {
    macro_rules! test_sum {
        ($bounds:expr, $data:expr, $expected:expr, $metric:expr) => {{
            let input_space = (
                VectorDomain::new(AtomDomain::new_closed($bounds)?),
                SymmetricDistance,
            );
            let transformation = (input_space >> then_sum())?;
            let ret = transformation.invoke(&$data)?;
            assert_eq!(ret, $expected);
        }};
    }
    test_sum!((0, 10), vec![1, 2, 3, 4, 5], 15, SymmetricDistance);
    test_sum!((0, 10), vec![1, 2, 3, 4, 5], 15, InsertDeleteDistance);
    test_sum!((0., 10.), vec![1., 2., 3., 4., 5.], 15., SymmetricDistance);
    test_sum!(
        (0., 10.),
        vec![1., 2., 3., 4., 5.],
        15.,
        InsertDeleteDistance
    );
    Ok(())
}