sens_sobol — MULTIVAR `sensitivity` op

• 数据种类:design × signal → table

• 调用: import fullseye as fs; fs.ledger.sens_sobol(design=None, y=None, n_boot=500, level=0.95, seed=0) -> 'dict'(要直接调用实现,import sensitivity; sensitivity.sens_sobol(design=None, y=None, n_boot=500, level=0.95, seed=0) -> 'dict';从台账取用则 opsmultivar.get("sens_sobol"))

用法

一阶与总效应 Sobol' 指数，附自助法置信区间。

> 以下的详细说明为原文 —— 摘要与标题已翻译。

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.

详细使用指南

• design_of_experiments 族使用指南

参考(示例数据、文献)

• 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。

可运行的示例(实际调用该算子并已验证的样例)

• sensitivity_tour — py -3.11 examples/sensitivity_tour.py

类型可衔接的下一个算子(可接受 `table` 作为输入)

—

同类别(`sensitivity`)

doe_morris · sens_morris · doe_saltelli

*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)。*

sens_sobol — MULTIVAR sensitivity op

用法