analysis op• 資料種類:matrix → table
• 呼叫: import fullseye as fs; fs.ledger.mv_pca(X=None, standardize=True, n_components=None, threshold=0.9, n_parallel=200, parallel_stat='mean', seed=0) -> 'dict'(要直接呼叫實作,import multivar; multivar.mv_pca(X=None, standardize=True, n_components=None, threshold=0.9, n_parallel=200, parallel_stat='mean', seed=0) -> 'dict';從台帳取用則 opsmultivar.get("mv_pca"))
表（樣本 x 變數）的主成分，以及資料所支持的成分個數。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
PCA is the SVD of the centred (by default also unit-variance) matrix Xc = U S V^T: scores U S, loadings V, eigenvalues S^2/(n-1) (the covariance or correlation eigenvalues — the tests check this identity).
The number of components is reported three ways, because each fails differently:
• cumulative — the smallest count reaching threshold of the variance (a convention, not a test: with many noisy columns it keeps noise);
• parallel — Horn's parallel analysis (Horn 1965): keep component i while its eigenvalue exceeds the parallel_stat ("mean" as in Horn, or "p95") of the i-th eigenvalue of n_parallel normal random tables of the same shape treated the same way;
• mdl — the MDL criterion of Wax & Kailath 1985 (mathspectral.n_sources_mdl, reused) on the centred data; it assumes white noise of equal variance in every column, which standardising breaks, so it is computed only with standardize=False (homogeneous columns: pixels, features in one unit) and is None otherwise.
n_components fixes how many scores/loadings to return (default: the parallel count, at least 1). Accepts a matrix or a feat_image_vector table.
• 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。
• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py
• poc_simcal_thin_film — py -3.11 examples/poc_simcal_thin_film.py
table 作為輸入)—
analysis)mv_pls · rsm_fit · doe_main_effects
*Provenance: multivar.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.