Score the GASF-image CNN single-sample discriminator (forced row-by-row)

Scores each row of X independently with the frozen model from fit_img_gasfcnn. The python CNN is REBUILT from the exported R-side state_dict (load_state_dict, eval() -> FROZEN BatchNorm, float64), 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), rendered to its GASF image under the FROZEN bounds, and forwarded through the CNN ONE ROW AT A TIME. The score is the eval-mode logit; larger = more case-like. Forcing the $n=1$ forward path uniformly (float64) makes a row's logit 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 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_img_gasfcnn(model, X, meta = NULL)

Arguments

model: An img_gasfcnn_model object from fit_img_gasfcnn.
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

Wang Z, Oates T. (2015) Imaging Time-Series to Improve Classification and Imputation. IJCAI; arXiv:1506.00327.

Examples

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