anomaly_patch op• Data kinds: image2d × table → table
• Call: import fullseye as fs; fs.ledger.patchbank_score(image, model, k=1, smooth=4.0) (to call the implementation directly, import anomaly_patch; anomaly_patch.patchbank_score(image, model, k=1, smooth=4.0); from the registry, opsspc.get("patchbank_score"))
Score one image by the distance of each patch to its k nearest good patches in the bank (table).
model comes from patchbank_fit; image must have the training images' shape. The distance (Euclidean, in standardised feature space; the mean over the k nearest) is computed exactly by brute force, interpolated to the image size and smoothed by a Gaussian of smooth px. Returns map (H, W), grid (raw distances), image_score (maximum of the smoothed map), k and smooth.
Raises ValueError for a model that is not a patchbank_fit result, a different image shape, k larger than the bank, or a negative / non-finite smooth.
• 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_patch_anomaly — py -3.11 examples/poc_patch_anomaly.py
table as input)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
anomaly_patch)patch_features · patchdist_fit · patchdist_score · patchbank_fit
*Provenance: anomaly_patch.py — SPC 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.