identifiability op• 데이터 종류: matrix → table
• 호출: import fullseye as fs; fs.ledger.mv_confounding(S=None, names=None, threshold=10.0, log_params=False, max_subset=None) -> 'dict'(구현을 직접 호출하려면 import sensitivity; sensitivity.mv_confounding(S=None, names=None, threshold=10.0, log_params=False, max_subset=None) -> 'dict', 원장에서 가져오려면 opsmultivar.get("mv_confounding"))
공선성 지수 gamma_K(Brun, Reichert & Künsch 2001): 데이터가 정할 수 있는 파라미터는 무엇인가.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
S is the sensitivity matrix (observations x parameters): column j = the change of every output per change of parameter j (finite differences of a simulation, or a Jacobian), ideally already scaled to be dimensionless (Brun: s_ij = dtheta_j / sc_i * dy_i/dtheta_j). Each column is normalised to unit length, s~_j = s_j / ||s_j||, and for a parameter subset K
gamma_K = 1 / sqrt( min eigenvalue of S~_K^T S~_K ).
gamma_K = 1 for orthogonal columns; it grows when a change in one parameter can be compensated by the others (Brun et al. use 10-15 as the critical range; threshold defaults to 10). Returned: delta_msqr (importance, sqrt(mean s_ij^2) on the given scale), gamma of the full set and of every subset up to max_subset (all subsets for <= 14 parameters), the confounded_pairs (gamma > threshold), the largest identifiable subsets, the undetermined_direction of the full set (eigenvector of the smallest eigenvalue: moving the parameters along it barely changes the output), and per confounded_groups entry (the union of the minimal confounded subsets) the combinations the data determined (eigenvalue of the group's S~^T S~ >= 1/threshold^2) and those undetermined; parameters in no group are determined_individually. With log_params=True (columns are sensitivities to log parameters) the combinations are written as products of powers — e.g. n^1 * d^1 when only the product of refractive index and thickness reaches the data. Columns that are all zero are reported as no_influence and left out of gamma.
• design_of_experiments 패밀리 가이드
• 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_simcal_thin_film — py -3.11 examples/poc_simcal_thin_film.py
• sensitivity_tour — py -3.11 examples/sensitivity_tour.py
table 를 입력으로 받는 것)—
identifiability)*Provenance: sensitivity.py — MULTIVAR 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.