sensitivity op• 数据种类:无 → design(仅由参数决定的算子 —— 不接受图像或数据输入)
• 调用: import fullseye as fs; fs.ledger.doe_morris(r=10, factors=4, levels=4, seed=0, n_candidates=0) -> 'dict'(要直接调用实现,import sensitivity; sensitivity.doe_morris(r=10, factors=4, levels=4, seed=0, n_candidates=0) -> 'dict';从台账取用则 opsmultivar.get("doe_morris"))
Morris 一次一因子筛选设计：r 条各含 k+1 次运行的轨迹 -> design。
> 以下的详细说明为原文 —— 摘要与标题已翻译。
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).
• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。
• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。
• 算法的正典(作者、年份)与用途见上面的族使用指南。
• sensitivity_tour — py -3.11 examples/sensitivity_tour.py
design 作为输入)doe_decode · rsm_fit · doe_main_effects · sens_morris · sens_sobol
sensitivity)sens_morris · doe_saltelli · sens_sobol
*Provenance: sensitivity.py — MULTIVAR 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.