Score the 1D wavelet-scattering discriminator (forced row-by-row)

Scores each row of X independently with the frozen model from fit_inv_scatter. The ScatteringTorch1D operator is REBUILT in python from the stored config (no python pointer survives a fit), then each query is mapped to the per-sample robust CLR over the frozen feature universe (absent universe features carry the neutral rCLR value 0), zero-padded to the frozen shape, scattered in float64 ONE ROW AT A TIME, standardized with the FROZEN training statistics, and passed through the FROZEN ridge-logistic linear head. The score is $\mathrm{intercept} + \sum_d w_d \cdot \mathrm{std\_coeff}_d(x)$; larger = more case-like. Forcing the $n=1$ scattering path uniformly (float64) makes a row's coefficients – hence its score – bit-identical whether scored alone or in any batch.

Queries with fewer than model$hp$min_features universe features present (a column-overlap floor), or empty positive support over the frozen universe on the (pre-rCLR) aligned ORIGINAL abundances (!any(X_use[i, ] > 0)), return the neutral score 0. A FLAT all-equal-positive composition maps to the rCLR origin (all-zero signal) but is a VALID specimen and is scored normally (NOT floored). The score of a row depends only on that row and the frozen model, and is exactly invariant to per-specimen positive scaling.

Usage

score_inv_scatter(model, X, meta = NULL)

Arguments

model: An inv_scatter_model object from fit_inv_scatter.
X: Numeric matrix (samples $\times$ features) of non-negative abundances with named feature columns.
meta: Optional per-sample metadata. Accepted for interface uniformity and ignored by this method.

Value

Plain finite numeric vector of length nrow(X); larger values are more case-like.

References

Anden J, Mallat S. (2014) Deep Scattering Spectrum. IEEE TSP 62(16):4114-4128.

Examples

if (FALSE) { # \dontrun{
model <- fit_inv_scatter(X, y)
score_inv_scatter(model, X[1, , drop = FALSE])
} # }