mt op• 数据种类:signal → table
• 调用: import fullseye as fs; fs.ledger.spc_mt_sn_ratio(md)(要直接调用实现,import spc; spc.spc_mt_sn_ratio(md);从台账取用则 opsspc.get("spc_mt_sn_ratio"))
> 该算子的说明尚无译文,以下照原文给出。
Larger-the-better SN ratio of abnormal-sample distances, in decibels.
MT-method item selection asks which features earn their place: run the unit space with a subset of features, score the *abnormal* samples, and keep the subset whose distances are largest. The figure of merit is the larger-the-better signal-to-noise ratio
eta = -10 log10( (1/n) sum_i 1 / MD_i^2 )
an inverse-square average, so one abnormal sample that the subset fails to separate drags the whole score down — which is the behaviour wanted when the cost of a miss dominates. The caller drives the subsets (classically by an orthogonal array); this operator is the closed-form score for one subset.
md is a 1-D array of distances for the abnormal samples. Returns a dict with sn_ratio_db, the harmonic_mean_md_sq it came from, and n.
For a constant MD == d the sum collapses and eta == 20 log10(d) exactly — pinned in the tests, and the reason a subset that puts every abnormal sample at distance 10 scores exactly 20 dB.
Raises ValueError: an empty or non-1-D *md*, a non-positive entry (a zero distance would divide by zero: an abnormal sample sitting exactly on the unit-space centre means the subset separates nothing), 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_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_distance
*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.