mt op• 数据种类:matrix → table
• 调用: import fullseye as fs; fs.ledger.spc_mt_unit_space(data)(要直接调用实现,import spc; spc.spc_mt_unit_space(data);从台账取用则 opsspc.get("spc_mt_unit_space"))
> 该算子的说明尚无译文,以下照原文给出。
Build an MT-method unit space from known-good observations.
data is a 2-D array of shape (n, p) — n observations of p features, all of which must be normal product. The unit space is the triple (mean, std, inv_corr): per-feature mean and ddof=1 standard deviation, and the inverse of the correlation matrix of the standardised data. Feed it to spc_mt_distance to score new observations.
Returns a dict with mean, std, corr and inv_corr arrays, the unit space's own md per row, md_sq_mean, the exact expected value md_sq_mean_exact = (n-1)/n, the counts n and p, how many features had no spread (flat_features), and whether the correlation matrix was singular enough to need a pseudo-inverse (pseudo_inverse).
The mean of md**2 over the unit space equals (n-1)/n exactly — the quantitative form of "a unit space averages a distance of one". It reaches 1 only as n grows; the deficit is exactly 1/n.
Raises ValueError: a non-2-D / empty *data*, fewer than two observations (no spread to standardise by), zero features, or non-finite input.
• 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。
• poc_mt_hidden_fault — py -3.11 examples/poc_mt_hidden_fault.py
• poc_spc — py -3.11 examples/poc_spc.py
table 作为输入)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
mt)spc_mt_distance · spc_mt_sn_ratio
*Provenance: spc.py — SPC 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.