plan op• Data kinds: कोई नहीं → design (केवल arguments से तय होने वाला op — यह कोई image या data input नहीं लेता)
• Call: import fullseye as fs; fs.ledger.doe_lhs(n=10, factors=3, seed=0, maximin=0, centered=False) -> 'dict' (implementation को सीधे call करना हो तो import doe; doe.doe_lhs(n=10, factors=3, seed=0, maximin=0, centered=False) -> 'dict', registry से लेना हो तो opsmultivar.get("doe_lhs"))
Latin hypercube design (McKay, Beckman व Conover 1979) -> design।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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 family guide
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• 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 को input के रूप में लेते हैं)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 operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.