identifiability opmatrix → tableimport fullseye as fs; fs.ledger.mv_confounding(S=None, names=None, threshold=10.0, log_params=False, max_subset=None) -> 'dict' (実装を直接呼ぶなら import sensitivity; sensitivity.mv_confounding(S=None, names=None, threshold=10.0, log_params=False, max_subset=None) -> 'dict'、台帳から引くなら opsmultivar.get("mv_confounding"))Collinearity index gamma_K (Brun, Reichert & Künsch 2001): which parameters the data can fix.
S is the sensitivity matrix (observations x parameters): column j = the change of
every output per change of parameter j (finite differences of a simulation, or a
Jacobian), ideally already scaled to be dimensionless (Brun: s_ij = dtheta_j / sc_i *
dy_i/dtheta_j). Each column is normalised to unit length, s~_j = s_j / ||s_j||, and for
a parameter subset K
gamma_K = 1 / sqrt( min eigenvalue of S~_K^T S~_K ).
gamma_K = 1 for orthogonal columns; it grows when a change in one parameter can be
compensated by the others (Brun et al. use 10-15 as the critical range; threshold
defaults to 10). Returned: delta_msqr (importance, sqrt(mean s_ij^2) on the given
scale), gamma of the full set and of every subset up to max_subset (all subsets
for <= 14 parameters), the confounded_pairs (gamma > threshold), the largest
identifiable subsets, the undetermined_direction of the full set (eigenvector of
the smallest eigenvalue: moving the parameters along it barely changes the output),
and per confounded_groups entry (the union of the minimal confounded subsets) the
combinations the data determined (eigenvalue of the group’s S~^T S~ >=
1/threshold^2) and those undetermined; parameters in no group are
determined_individually.
With log_params=True (columns are sensitivities to log parameters) the
combinations are written as products of powers — e.g. n^1 * d^1 when only the
product of refractive index and thickness reaches the data. Columns that are all zero
are reported as no_influence and left out of gamma.
py -3.11 examples/poc_simcal_thin_film.pypy -3.11 examples/sensitivity_tour.pytable を入力に取れる)—
identifiability)Provenance: sensitivity.py — MULTIVAR 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.