spc_mt_sn_ratio — SPC `mt` op

• 資料種類:signal → table

• 呼叫: import fullseye as fs; fs.ledger.spc_mt_sn_ratio(md)(要直接呼叫實作,import spc; spc.spc_mt_sn_ratio(md);從台帳取用則 opsspc.get("spc_mt_sn_ratio"))

> 該運算子的說明尚無譯文,以下照原文給出。

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.

• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。

• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。

• 演算法的正典(作者・年份)與用途見上面的族使用指南。

• poc_spc — py -3.11 examples/poc_spc.py

*Provenance: spc.py — SPC 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*

spc_mt_sn_ratio — SPC mt op