mt op• データ種: matrix → table
• 呼び出し: import fullseye as fs; fs.ledger.spc_mt_distance(data, mean=None, std=None, inv_corr=None, threshold=3.0) (実装を直接呼ぶなら import spc; spc.spc_mt_distance(data, mean=None, std=None, inv_corr=None, threshold=3.0)、台帳から引くなら opsspc.get("spc_mt_distance"))
MT 法の単位空間からの距離で観測を採点する(MD² = T²/p)。
> 以下の詳細説明は原文のままです —— 要約と見出しは訳出済み。
data is a 2-D array of shape (m, p). Pass the mean, std and inv_corr from 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 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.
• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。
• poc_mt_hidden_fault — py -3.11 examples/poc_mt_hidden_fault.py
• poc_spc — py -3.11 examples/poc_spc.py
table を入力に取れる)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 operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.