mt op• 数据种类:matrix → table
• 调用: import fullseye as fs; fs.ledger.spc_mt_distance(data, mean=None, std=None, inv_corr=None, threshold=3.0)(要直接调用实现,import spc; spc.spc_mt_distance(data, mean=None, std=None, inv_corr=None, threshold=3.0);从台账取用则 opsspc.get("spc_mt_distance"))
> 该算子的说明尚无译文,以下照原文给出。
Score observations against an MT-method unit space.
`data is a 2-D array of shape (m, p). Pass the mean, std` and
`inv_corr from :func:spc_mt_unit_space` to score new product against a
stored unit space; omit all three and the unit space is built from `data`
itself, which is the training case and makes `md_sq_mean` land on
`(m-1)/m`.
Each row's distance is `MD_i = sqrt(z_i' R^-1 z_i / p)` with
`z_i = (x_i - mean) / std. Rows with MD > threshold` are reported in
`flagged; the rest in inside. Dividing the quadratic form by p` is
what puts the unit space at 1 regardless of how many features are used, so a
threshold carries over when features are added or removed.
Returns a dict with the per-row `md and md_sq arrays, flagged`,
`inside, the threshold, md_sq_mean, and n/p`.
Relationship to :func:spc_hotelling_t2 (used as the oracle in the tests):
on the standardised matrix the covariance *is* the correlation, so
`md**2 == t2 / p` exactly for the same data.
Raises `ValueError`: a non-2-D / empty *data*, a non-positive
`threshold, a unit space given only in part, a mean / std` /
`inv_corr whose shape disagrees with p`, a non-positive entry in
`std`, 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_unit_space · 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.