mt op• データ種: matrix → table
• 呼び出し: import fullseye as fs; fs.ledger.spc_mt_unit_space(data) (実装を直接呼ぶなら import spc; spc.spc_mt_unit_space(data)、台帳から引くなら opsspc.get("spc_mt_unit_space"))
既知良品の観測から MT 法の単位空間を作る(MD² の平均はちょうど (n-1)/n)。
> 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 (n, p) — n observations of p`
features, all of which must be normal product. The unit space is the
triple `(mean, std, inv_corr)`: per-feature mean and ddof=1 standard
deviation, and the inverse of the correlation matrix of the standardised
data. Feed it to :func:spc_mt_distance to score new observations.
Returns a dict with `mean, std, corr and inv_corr` arrays, the
unit space's own `md per row, md_sq_mean`, the exact expected value
`md_sq_mean_exact = (n-1)/n, the counts n and p`, how many features
had no spread (`flat_features`), and whether the correlation matrix was
singular enough to need a pseudo-inverse (`pseudo_inverse`).
The mean of `md**2 over the unit space equals (n-1)/n` exactly — the
quantitative form of "a unit space averages a distance of one". It reaches 1
only as `n grows; the deficit is exactly 1/n`.
Raises `ValueError`: a non-2-D / empty *data*, fewer than two
observations (no spread to standardise by), zero features, 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_distance · 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.