sensitivity op• 資料種類:無 → design(僅由參數決定的運算子 —— 不接受影像或資料輸入)
• 呼叫: import fullseye as fs; fs.ledger.doe_morris(r=10, factors=4, levels=4, seed=0, n_candidates=0) -> 'dict'(要直接呼叫實作,import sensitivity; sensitivity.doe_morris(r=10, factors=4, levels=4, seed=0, n_candidates=0) -> 'dict';從台帳取用則 opsmultivar.get("doe_morris"))
Morris 一次一因子篩選設計：r 條各含 k+1 次執行的軌跡 -> design。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
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).
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者、年份)與用途見上面的族使用指南。
• sensitivity_tour — py -3.11 examples/sensitivity_tour.py
design 作為輸入)doe_decode · rsm_fit · doe_main_effects · sens_morris · sens_sobol
sensitivity)sens_morris · doe_saltelli · sens_sobol
*Provenance: sensitivity.py — MULTIVAR 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.