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 crate::transformations::Pairwise;

use super::*;

#[test]
fn test_make_bounded_deviations() -> Fallible<()> {
    let arg = vec![1., 2., 3., 4., 5.];

    let input_domain = VectorDomain::new(AtomDomain::new_closed((0., 10.))?).with_size(5);
    let input_metric = SymmetricDistance;
    let transformation_sample =
        make_sum_of_squared_deviations::<Pairwise<_>>(input_domain.clone(), input_metric.clone())?;
    let ret = transformation_sample.invoke(&arg)?;
    let expected = 10.;
    assert_eq!(ret, expected);
    assert!(transformation_sample.check(&1, &(100. / 5.))?);

    let transformation_pop =
        make_sum_of_squared_deviations::<Pairwise<_>>(input_domain, input_metric)?;
    let ret = transformation_pop.invoke(&arg)?;
    let expected = 10.0;
    assert_eq!(ret, expected);
    assert!(transformation_pop.check(&1, &(100. * 4. / 25.))?);

    Ok(())
}