mt op• Data kinds: signal → table
• Call: import fullseye as fs; fs.ledger.spc_mt_sn_ratio(md) (implementation को सीधे call करना हो तो import spc; spc.spc_mt_sn_ratio(md), registry से लेना हो तो opsspc.get("spc_mt_sn_ratio"))
abnormal-sample distances का larger-the-better SN ratio, decibels में।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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.
• 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।
• poc_spc — py -3.11 examples/poc_spc.py
table को input के रूप में लेते हैं)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 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.