wiring op• 데이터 종류: labels2d × labels2d × table → table
• 호출: import fullseye as fs; fs.ledger.seg_synapse_nri(a, b, synapses, spacing=None, background=0)(구현을 직접 호출하려면 import segcompare; segcompare.seg_synapse_nri(a, b, synapses, spacing=None, background=0), 원장에서 가져오려면 opsemproof.get("seg_synapse_nri"))
> 이 연산자의 설명은 아직 번역이 없습니다. 원문을 그대로 싣습니다.
Neural reconstruction integrity (Reilly et al. 2018) of candidate b against truth a.
NRI scores the pairs of synapse ends that a reconstruction keeps together. Over the 2n ends (synapses as in seg_synapse_partners; ends on background in the truth are dropped, ends on background in the candidate are singletons), a pair of ends that lies in one truth neuron and one candidate object is a true positive; a pair in one truth neuron but different candidate objects is a false negative (a split); a pair in one candidate object but different truth neurons is a false positive (a merge). nri = 2TP / (2TP + FP + FN) with the pair precision and recall.
Identity (checked inside, fail-closed): with n_ij the contingency table of the ends, TP = sum C(n_ij, 2), TP + FN = sum C(s_i, 2), TP + FP = sum C(t_j, 2), so NRI is exactly 1 - adapted_rand_error of seg_rand taken over the ends (the - N form of the pair counts is 2 C(n, 2)). Both are computed and must agree to 1e-12. Returns nri, precision, recall, tp, fp, fn, n_ends.
Raises ValueError: as seg_wiring_variation; fewer than 2 ends.
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• poc_em_wiring_errors — py -3.11 examples/poc_em_wiring_errors.py
table 를 입력으로 받는 것)seg_synapse_partners · seg_wiring_variation · seg_wiring_exposure
wiring)seg_synapse_partners · seg_wiring_variation · seg_wiring_exposure
*Provenance: segcompare.py — EMPROOF 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.