classify op• Data kinds: table × matrix → table
• Call: import fullseye as fs; fs.ledger.fisher_lda_score(model, X) -> 'Dict[str, object]' (to call the implementation directly, import cropweed; cropweed.fisher_lda_score(model, X) -> 'Dict[str, object]'; from the registry, opssegmentation.get("fisher_lda_score"))
Score features with a discriminant fitted by fisher_lda_fit (score > 0 means class 1).
model is the result of fisher_lda_fit; X is (N, D) features (the X of a dict also works). Standardisation uses the training mean and scale. Returns: score (N), label (0/1; a score of exactly 0 is class 0, a declared rule), margin (|score| / ‖w‖ = distance to the boundary in standardised space), n1 (number judged class 1). Empty input, column-count mismatch, non-finite values and a model that is not a fitted discriminant raise ValueError.
• halcon_segmentation family guide
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_weed_rice_ratio — py -3.11 examples/poc_weed_rice_ratio.py
table as input)classify)class_2dim_sup · class_2dim_unsup · learn_ndim_norm · class_ndim_norm · classify_image_class_lut · fisher_lda_fit
*Provenance: cropweed.py — SEGMENTATION operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.