mt op• 데이터 종류: matrix → table
• 호출: import fullseye as fs; fs.ledger.spc_mt_distance(data, mean=None, std=None, inv_corr=None, threshold=3.0)(구현을 직접 호출하려면 import spc; spc.spc_mt_distance(data, mean=None, std=None, inv_corr=None, threshold=3.0), 원장에서 가져오려면 opsspc.get("spc_mt_distance"))
> 이 연산자의 설명은 아직 번역이 없습니다. 원문을 그대로 싣습니다.
Score observations against an MT-method unit space.
`data is a 2-D array of shape (m, p). Pass the mean, std` and
`inv_corr from :func: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 :func: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.
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• poc_mt_hidden_fault — py -3.11 examples/poc_mt_hidden_fault.py
• poc_spc — py -3.11 examples/poc_spc.py
table 를 입력으로 받는 것)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 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.