sens_sobol — MULTIVAR `sensitivity` op

• 資料種類:design × signal → table

• 呼叫: import fullseye as fs; fs.ledger.sens_sobol(design=None, y=None, n_boot=500, level=0.95, seed=0) -> 'dict'(要直接呼叫實作,import sensitivity; sensitivity.sens_sobol(design=None, y=None, n_boot=500, level=0.95, seed=0) -> 'dict';從台帳取用則 opsmultivar.get("sens_sobol"))

用法

一階與總效應 Sobol' 指數，附自助法信賴區間。

> 以下的詳細說明為原文 —— 摘要與標題已翻譯。

From the outputs on a doe_saltelli design (blocks A, B, A_B^(i)):

• S1_i = (1/N) sum_j f(B)_j (f(A_B^(i))_j - f(A)_j) / V (Saltelli et al. 2010, Table 2 (b));

• ST_i = (1/(2N)) sum_j (f(A)_j - f(A_B^(i))_j)^2 / V (Jansen 1999; Saltelli et al. 2010, Table 2 (f));

• V = variance of the outputs at A and B together.

S1 is the share of output variance a factor explains alone; ST adds every interaction it takes part in. ST ~ 0 means the factor can be fixed anywhere in its range; ST - S1 large means it acts through interactions. Confidence intervals come from n_boot bootstrap resamples of the N rows (the same row index across A, B and A_B^(i)) at level. The sum of S1 (sum_s1) is <= 1 up to estimation error; a value far below 1 says interactions carry the rest.

詳細使用指南

• design_of_experiments 族使用指南

參考(範例資料、文獻)

• 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。

可執行的範例(實際呼叫該運算子並已驗證的樣例)

• sensitivity_tour — py -3.11 examples/sensitivity_tour.py

型別可銜接的下一個運算子(可接受 `table` 作為輸入)

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同類別(`sensitivity`)

doe_morris · sens_morris · doe_saltelli

*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)。*

sens_sobol — MULTIVAR sensitivity op

用法