mt op• Datenarten: matrix → table
• Aufruf: import fullseye as fs; fs.ledger.spc_mt_distance(data, mean=None, std=None, inv_corr=None, threshold=3.0) (die Implementierung direkt: import spc; spc.spc_mt_distance(data, mean=None, std=None, inv_corr=None, threshold=3.0); aus dem Register: opsspc.get("spc_mt_distance"))
> Für diesen Operator gibt es noch keine Übersetzung. Es folgt der Originaltext unverändert.
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 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.
• 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.
• poc_mt_hidden_fault — py -3.11 examples/poc_mt_hidden_fault.py
• poc_spc — py -3.11 examples/poc_spc.py
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
mt)spc_mt_unit_space · spc_mt_sn_ratio
*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.