mv_pca — MULTIVAR `analysis` op

• 数据种类:matrix → table

• 调用: 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'(要直接调用实现,import multivar; multivar.mv_pca(X=None, standardize=True, n_components=None, threshold=0.9, n_parallel=200, parallel_stat='mean', seed=0) -> 'dict';从台账取用则 opsmultivar.get("mv_pca"))

用法

表（样本 x 变量）的主成分，以及数据所支持的成分个数。

> 以下的详细说明为原文 —— 摘要与标题已翻译。

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.

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)

• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py

• poc_simcal_thin_film — py -3.11 examples/poc_simcal_thin_film.py

类型可衔接的下一个算子(可接受 `table` 作为输入)

—

同类别(`analysis`)

mv_pls · rsm_fit · doe_main_effects

*Provenance: multivar.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)。*

mv_pca — MULTIVAR analysis op

用法