fit_box()

BoxExtent MayaFlux::Kinesis::fit_box ( std::span< const Eigen::VectorXd >  samples, double  coverage = 0.9, double  softness_fraction = 0.15  )

inline

Fit a box extent covering a central fraction of a sample set.

Parameters

samples	Observations, all of the same dimension
coverage	Fraction of observations the bounds should contain per axis, in 0..1. One takes the full min and max
softness_fraction	Soft margin per axis, as a fraction of that axis's fitted width

Returns

Bounds at the symmetric quantiles implied by coverage

Quantiles rather than min and max so one badly performed demonstration does not widen the extent to include everything between it and the others. A coverage of one recovers the min and max behaviour for callers who want it.

Definition at line 284 of file FeatureExtent.hpp.

{
    if (samples.empty())
        return {};
 
    const Eigen::Index n = samples.front().size();
    BoxExtent extent {
        .lower = Eigen::VectorXd::Zero(n),
        .upper = Eigen::VectorXd::Zero(n),
        .softness = Eigen::VectorXd::Zero(n)
    };
 
    const double tail = 0.5 * (1.0 - std::clamp(coverage, 0.0, 1.0));
    std::vector<double> axis;
    axis.reserve(samples.size());
 
    for (Eigen::Index i = 0; i < n; ++i) {
        axis.clear();
        for (const auto& s : samples)
            axis.push_back(s(i));
        std::ranges::sort(axis);
 
        const auto last = static_cast<double>(axis.size() - 1);
        const auto lo_idx = static_cast<size_t>(std::floor(tail * last));
        const auto hi_idx = static_cast<size_t>(std::ceil((1.0 - tail) * last));
 
        extent.lower(i) = axis[lo_idx];
        extent.upper(i) = axis[hi_idx];
        extent.softness(i) = std::max(
            (extent.upper(i) - extent.lower(i)) * softness_fraction, 1e-9);
    }
 
    return extent;
}

References MayaFlux::Kinesis::BoxExtent::lower.