plan op• 데이터 종류: 없음 → design(인자만으로 정해지는 연산자 —— 이미지나 데이터 입력을 받지 않습니다)
• 호출: import fullseye as fs; fs.ledger.doe_lhs(n=10, factors=3, seed=0, maximin=0, centered=False) -> 'dict'(구현을 직접 호출하려면 import doe; doe.doe_lhs(n=10, factors=3, seed=0, maximin=0, centered=False) -> 'dict', 원장에서 가져오려면 opsmultivar.get("doe_lhs"))
라틴 하이퍼큐브 설계(McKay, Beckman & Conover 1979) -> design.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
Each factor's range is cut into n equal strata and every stratum holds exactly one run, so each factor is sampled evenly however many factors there are (a random design of the same size leaves strata empty). Construction (McKay et al. 1979, section 2): for factor j draw an independent random permutation pi_j of 0..n-1 and put run i at u_ij = (pi_j(i) + U_ij) / n with U_ij ~ Uniform[0, 1) (centered=True uses the stratum mid-point, U = 1/2). Coded value = 2u - 1.
maximin=m (m >= 2) draws m independent hypercubes and keeps the one with the largest smallest inter-point distance (each candidate is still a Latin hypercube, so the stratum property is never traded away); 0 or 1 = one plain draw.
Parameters: n runs (2..1e6), factors (int or factor list, see module docstring), seed (int, reproducible), maximin (0..1000 candidates), centered.
Returns a design whose meta holds strata_ok (every factor has one point per stratum — the defining property, checked on the returned matrix) and min_distance (in the unit cube).
• 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.
• doe_plan_tour — py -3.11 examples/doe_plan_tour.py
• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py
• sensitivity_tour — py -3.11 examples/sensitivity_tour.py
• simcal_checks_tour — py -3.11 examples/simcal_checks_tour.py
design 를 입력으로 받는 것)doe_decode · rsm_fit · doe_main_effects · sens_morris · sens_sobol
plan)doe_orthogonal_array · doe_fractional_factorial · doe_central_composite · doe_box_behnken
*Provenance: doe.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.