sensitivity op• 資料種類:design × signal → table
• 呼叫: import fullseye as fs; fs.ledger.sens_morris(design=None, y=None, negligible=0.05) -> 'dict'(要直接呼叫實作,import sensitivity; sensitivity.sens_morris(design=None, y=None, negligible=0.05) -> 'dict';從台帳取用則 opsmultivar.get("sens_morris"))
Morris 基本效應 -> 各因子的 mu、mu*、sigma（Morris 1991；mu* 見 Campolongo 等 2007）。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
design must come from doe_morris (its trajectory bookkeeping is used) and y holds the model output for its rows in order. For trajectory t and step s that moves factor i by ±delta: EE_i = (y_s - y_{s-1}) / (±delta) in unit-cube units, and also per natural unit of the factor (ee_natural; on a log-scale factor the step is in log10 units).
mu (mean EE) can cancel when effects change sign; mu_star (mean |EE|) does not and is the ranking measure; sigma (standard deviation, ddof = 1) is large when the effect depends on where the other factors are (non-linear or interacting). class per factor: negligible (mu* below negligible x the largest mu*), linear/additive (sigma < 0.1 mu*: the effect is the same everywhere — a convention of this op, stated), otherwise non-linear or interacting.
• 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
table 作為輸入)—
sensitivity)doe_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.