variation op• 資料種類:table × video → table
• 呼叫: import fullseye as fs; fs.ledger.varmodel_calibrate(model, good_images, far=None)(要直接呼叫實作,import varmodel; varmodel.varmodel_calibrate(model, good_images, far=None);從台帳取用則 opsspc.get("varmodel_calibrate"))
用未參與擬合的良品重新求容許帶半寬 k(用於不滿足常態假設的真實影像,table)。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
model is a varmodel_fit table; good_images (m, H, W) are good samples that were NOT used to fit it. Each is scored (varmodel_score, align="translate") and the pooled |z| of all valid pixels gives the empirical (1 - far) quantile, which replaces k (far defaults to the model's declared rate). Defects are never looked at: the threshold still comes from the good distribution, only measured instead of assumed. Real images break the per-pixel independent-normal model (residual misregistration, compression, lighting drift); the closed-form k then under-states the false alarms and this is the honest fix.
Returns a copy of the model with k / lower / upper replaced and k_declared (the closed-form k), k_source = held-out quantile, calibration_n_images, calibration_n_pixels and calibration_far_declared_k (the false-alarm rate the closed-form k actually had on these good images).
Raises ValueError on an empty / malformed / non-finite set, a shape mismatch, a model without the fitted keys, far outside its range, or fewer than 10/far calibration pixels (the quantile would rest on fewer than 10 exceedances).
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者、年份)與用途見上面的族使用指南。
• poc_good_sample_variation_model — py -3.11 examples/poc_good_sample_variation_model.py
table 作為輸入)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
variation)varmodel_alignment · varmodel_band_k · varmodel_fit · varmodel_score
*Provenance: varmodel.py — SPC 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.