spc_mt_sn_ratio — SPC mt op

• Datenarten: signal → table

• Aufruf: import fullseye as fs; fs.ledger.spc_mt_sn_ratio(md) (die Implementierung direkt: import spc; spc.spc_mt_sn_ratio(md); aus dem Register: opsspc.get("spc_mt_sn_ratio"))

Verwendung

> Für diesen Operator gibt es noch keine Übersetzung. Es folgt der Originaltext unverändert.

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.

Referenzen (Beispieldaten, Literatur)

• Katalog der Beispieldaten (Download-URLs / Lizenzen) — 2-D nutzt skimage.data (BSD/Public Domain) plus synthetische Bilder, 3-D nennt Download-URLs echter Datenquellen (Stanford, PDS, …).

• Herkunft und Literatur der Operatoren — die Quellen der Forschung/Verfahren, auf denen diese Operatorfamilie beruht.

• Der kanonische Algorithmus (Autor, Jahr) und seine Anwendungen stehen im Familienleitfaden oben.

Ausführbare Beispiele (verifizierte Samples, die diesen Operator wirklich aufrufen)

• poc_spc — py -3.11 examples/poc_spc.py

Typkompatible Folge-Operatoren (nehmen table als Eingabe)

msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo

Gleiche Kategorie (mt)

spc_mt_unit_space · spc_mt_distance


*Provenance: spc.py — SPC Operator-Registry. Diese Notiz wird von tools/opdocs.py md erzeugt (nicht von Hand bearbeiten).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.