simcal_validate — SIMCAL `check` op

• 数据种类:calresult × simmodel → calresult

• 调用: import fullseye as fs; fs.ledger.simcal_validate(result=None, holdout=None, locked=None, tau=None, dims=None, n_boot=200, alpha=0.001, seed=0) -> 'dict'(要直接调用实现,import simcalcheck; simcalcheck.simcal_validate(result=None, holdout=None, locked=None, tau=None, dims=None, n_boot=200, alpha=0.001, seed=0) -> 'dict';从台账取用则 opssimcal.get("simcal_validate"))

用法

在保留图像上的过拟合闸门；任一失败即拒绝该标定（撤回估计值）。

> 以下的详细说明为原文 —— 摘要与标题已翻译。

holdout (and locked): observed simmodels of the same knobs (simcal_observe with split='holdout' / 'locked') or image lists for the fitted model's conditions. Errors are reduced chi^2 (1 = the residuals are exactly the noise). Gates, each recorded in validation.gates with its value and limit:

1. beats_initial — holdout error at the estimate < holdout error at the initial knobs;

2. holdout_over_train — holdout / train <= 1 + tau, tau = the bootstrap 95 % relative spread of the train error over its pixels (n_boot resamples) unless given;

3. beats_gain_offset — better than the initial simulation re-scaled by a fitted gain and offset only (the cheapest model that could explain the images);

4. estimate_plausible — every estimate inside its range, not within 1 % of an end, and within 3 sd of its prior when one is given;

5. knobs_within_dims — free knobs <= output directions above noise (dims, or computed from a Latin hypercube over the knobs with sensitivity.mv_residual_pca);

6. fit_consistent_with_noise — the absolute gate: on the train images (nu = pixels − free knobs) and on the holdout images (nu = pixels), chi^2 of the noise-normalised residuals has an upper-tail probability P(chi^2_nu >= chi^2) of at least alpha (default 1e-3). Gates 1-5 are relative comparisons and pass a fit that is 60 times the noise everywhere (a thin film in the wrong fringe order, measured); this one does not. Judged on p, not on chi^2/nu, because chi^2/nu concentrates on 1 for large nu (both are returned). The variance is the noise model's plus, for a stochastic model, its sample variance. A failure means: the fit is bad for this noise model — a local minimum, a model discrepancy, or a noise estimate that is too small. ★It does NOT detect a noise model that is too large: over-estimating the variance by 4x drives p to 1 and the gate passes (measured) — that direction is visible only as chi^2/nu << 1.

locked is scored once (errors.locked) and is not used by any gate; a result that already carries a LOCKED score is refused (the robust.py discipline: the untouched number is untouched only once).

详细使用指南

• sim_to_real_calibration 族使用指南

参考(示例数据、文献)

• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。

• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。

• 算法的正典(作者、年份)与用途见上面的族使用指南。

可运行的示例(实际调用该算子并已验证的样例)

• poc_pocket_interreflection — py -3.11 examples/poc_pocket_interreflection.py

• poc_simcal_thin_film — py -3.11 examples/poc_simcal_thin_film.py

• simcal_checks_tour — py -3.11 examples/simcal_checks_tour.py

类型可衔接的下一个算子(可接受 `calresult` 作为输入)

simcal_identifiability · simcal_uncertainty · simcal_discrepancy · simcal_report

同类别(`check`)

simcal_identifiability · simcal_uncertainty · simcal_discrepancy

*Provenance: simcalcheck.py — SIMCAL 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*

simcal_validate — SIMCAL check op

用法