failure_confusion — DRIVE `pegfail` op

• Data kinds: any × any → table

• Call: import fullseye as fs; fs.ledger.failure_confusion(true_cls, pred_cls, classes=('nominal', 'chamfer_sliding', 'missed_hole', 'wrong_hole', 'wedging', 'jamming', 'blocked_hole', 'seated', 'unknown')) -> 'dict' (to call the implementation directly, import pegfail; pegfail.failure_confusion(true_cls, pred_cls, classes=('nominal', 'chamfer_sliding', 'missed_hole', 'wrong_hole', 'wedging', 'jamming', 'blocked_hole', 'seated', 'unknown')) -> 'dict'; from the registry, opsdrive.get("failure_confusion"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

混同行列と正答率: 行 = 注入(真)のクラス、列 = 分類。`classes` の外の名は ValueError(綴り壊し)。

返り: `matrix(n, n) int、classes、accuracy、n、per_class_recall`(真の各クラスの再現率、無い行は nan)。

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• poc_peg_failure_recovery — py -3.11 examples/poc_peg_failure_recovery.py

Ops the type connects to (they accept `table` as input)

course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same

Same category (`pegfail`)

insertion_failure_table · insertion_failure_validate · insertion_signature · insertion_failure_classify · insertion_recovery_primitive · jamming_parallelogram_planar · jamming_force_check · wedging_risk

*Provenance: pegfail.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

failure_confusion — DRIVE pegfail op