identifiability op• 数据种类:matrix → table
• 调用: import fullseye as fs; fs.ledger.mv_residual_pca(R=None, noise_sigma=None, n_perm=100, seed=0) -> 'dict'(要直接调用实现,import sensitivity; sensitivity.mv_residual_pca(R=None, noise_sigma=None, n_perm=100, seed=0) -> 'dict';从台账取用则 opsmultivar.get("mv_residual_pca"))
拟合模型的残差中是否仍有结构？残差矩阵的 PCA 与噪声比较。
> 以下的详细说明为原文 —— 摘要与标题已翻译。
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.
• 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。
• sensitivity_tour — py -3.11 examples/sensitivity_tour.py
table 作为输入)—
identifiability)*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)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.