sensitivity op• Data kinds: design × signal → table
• Call: import fullseye as fs; fs.ledger.sens_sobol(design=None, y=None, n_boot=500, level=0.95, seed=0) -> 'dict' (implementation को सीधे call करना हो तो import sensitivity; sensitivity.sens_sobol(design=None, y=None, n_boot=500, level=0.95, seed=0) -> 'dict', registry से लेना हो तो opsmultivar.get("sens_sobol"))
first-order और total Sobol' indices, bootstrap confidence intervals सहित।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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.
• design_of_experiments family guide
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• sensitivity_tour — py -3.11 examples/sensitivity_tour.py
table को input के रूप में लेते हैं)—
sensitivity)doe_morris · sens_morris · doe_saltelli
*Provenance: sensitivity.py — MULTIVAR operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.