fullseye

sens_morris — MULTIVAR sensitivity op

• データ種: design × signal → table
• 呼び出し: import fullseye as fs; fs.ledger.sens_morris(design=None, y=None, negligible=0.05) -> 'dict' (実装を直接呼ぶなら import sensitivity; sensitivity.sens_morris(design=None, y=None, negligible=0.05) -> 'dict'、台帳から引くなら opsmultivar.get("sens_morris"))

使い方

Morris elementary effects -> mu, mu, sigma per factor (Morris 1991; mu Campolongo et al. 2007).

design must come from :func: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.

詳しい使い方ガイド

• design_of_experiments ファミリ ガイド

参考(サンプルデータ・文献)

• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。

実行できる例(この op を実際に呼ぶ検証済みサンプル)

• sensitivity_tour — py -3.11 examples/sensitivity_tour.py

型が繋がる次の op(table を入力に取れる)

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同カテゴリ(sensitivity)

doe_morris · doe_saltelli · sens_sobol

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Provenance: sensitivity.py — MULTIVAR operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.

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