Score the prototypical-network single-sample discriminator

Scores each row of X independently with the frozen model from fit_proto_net, in PURE R (no python). Each query is mapped to the per-sample robust CLR over the frozen feature universe (absent universe features carry the neutral rCLR value 0), embedded by the exported MLP forward, and scored by the difference of squared-Euclidean distances to the frozen control and case prototypes. Larger = more case-like.

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 abundances (!any(X_use[i, ] > 0)), return the neutral score 0. A FLAT all-equal-positive composition maps to the rCLR origin but is a VALID specimen and is scored normally. 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_proto_net(model, X, meta = NULL)

Arguments

model: A proto_net_model object from fit_proto_net.
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

Snell J, Swersky K, Zemel R. (2017) Prototypical Networks for Few-shot Learning. NeurIPS 30. arXiv:1703.05175.

Examples

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