analysis op• Data kinds: matrix → table
• Call: 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' (implementation को सीधे call करना हो तो import multivar; multivar.mv_pca(X=None, standardize=True, n_components=None, threshold=0.9, n_parallel=200, parallel_stat='mean', seed=0) -> 'dict', registry से लेना हो तो opsmultivar.get("mv_pca"))
table (samples x variables) के principal components, और data कितने components का समर्थन करता है।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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.
• 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।
• 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 को input के रूप में लेते हैं)—
analysis)mv_pls · rsm_fit · doe_main_effects
*Provenance: multivar.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.