identifiability op• 資料種類:matrix → table
• 呼叫: import fullseye as fs; fs.ledger.mv_residual_pca(R=None, noise_sigma=None, n_perm=100, seed=0) -> 'dict'(要直接呼叫實作,import sensitivity; sensitivity.mv_residual_pca(R=None, noise_sigma=None, n_perm=100, seed=0) -> 'dict';從台帳取用則 opsmultivar.get("mv_residual_pca"))
擬合模型的殘差中是否仍有結構？殘差矩陣的 PCA 與雜訊比較。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
R is (observations x variables) — e.g. one row per image, one column per pixel or feature, holding measured minus simulated. It is not centred: a bias common to every observation is itself a structure the model lacks.
A component counts as structural when its singular value exceeds the noise edge:
• with noise_sigma (e.g. the temporal noise from an EMVA 1288 characterisation): the largest singular value of an n x p matrix of i.i.d. noise is about sigma (sqrt(n) + sqrt(p)) (Marchenko-Pastur edge), used as the threshold;
• without it: permutation parallel analysis (Buja & Eyuboglu 1992) — each column is shuffled independently n_perm times, which keeps every column's distribution but destroys structure shared across columns, and component i must beat the 95th percentile of the shuffled i-th singular values. (It cannot see a structure that lives inside single columns, e.g. a constant bias column — use noise_sigma for that.)
Returns the singular values, the threshold(s), n_structural, the share of the residual energy in the structural components, and their loadings (reshape a loading to the image shape to see *where* the model is wrong) and scores.
• 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)—
identifiability)*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.