sensitivity 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_morris(r=10, factors=4, levels=4, seed=0, n_candidates=0) -> 'dict' (implementation को सीधे call करना हो तो import sensitivity; sensitivity.doe_morris(r=10, factors=4, levels=4, seed=0, n_candidates=0) -> 'dict', registry से लेना हो तो opsmultivar.get("doe_morris"))
Morris one-at-a-time screening plan: k+1 runs की r trajectories -> design।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
Each trajectory starts at a random point of the levels-level grid on the unit cube and moves one factor at a time by delta = levels / (2 (levels - 1)) (Morris 1991; with an even levels every grid level is equally likely to be visited), so r (k+1) runs give r elementary effects per factor. n_candidates > r draws that many trajectories and keeps r of them that are spread out: the trajectory distance of Campolongo et al. 2007 (sum over point pairs of their Euclidean distances), chosen greedily — a pair of the farthest trajectories first, then repeatedly the trajectory that adds the most distance (an approximation of their exhaustive search, stated in meta['selection']).
The coded matrix is 2u - 1 of the unit-cube points; meta keeps the trajectory bookkeeping that sens_morris needs (changed factor and sign per step).
• 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।
• sensitivity_tour — py -3.11 examples/sensitivity_tour.py
design को input के रूप में लेते हैं)doe_decode · rsm_fit · doe_main_effects · sens_morris · sens_sobol
sensitivity)sens_morris · doe_saltelli · sens_sobol
*Provenance: sensitivity.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.