mt op• Data kinds: matrix → table
• Call: import fullseye as fs; fs.ledger.spc_mt_unit_space(data) (implementation को सीधे call करना हो तो import spc; spc.spc_mt_unit_space(data), registry से लेना हो तो opsspc.get("spc_mt_unit_space"))
ज्ञात-अच्छे observations से MT-method unit space बनाता है।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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 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.
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• poc_mt_hidden_fault — py -3.11 examples/poc_mt_hidden_fault.py
• poc_spc — py -3.11 examples/poc_spc.py
table को input के रूप में लेते हैं)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. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.