anomaly_patch op• Data kinds: video → table
• Call: import fullseye as fs; fs.ledger.patchdist_fit(stack, patch=8, stride=4, scales=(1.0, 2.0, 4.0), groups=('gradient', 'gabor', 'lbp'), n_orient=4, shrinkage='auto', n_dims=None, seed=0, ridge=1e-06, pool='position') (implementation को सीधे call करना हो तो import anomaly_patch; anomaly_patch.patchdist_fit(stack, patch=8, stride=4, scales=(1.0, 2.0, 4.0), groups=('gradient', 'gabor', 'lbp'), n_orient=4, shrinkage='auto', n_dims=None, seed=0, ridge=1e-06, pool='position'), registry से लेना हो तो opsspc.get("patchdist_fit"))
अच्छे images से हर grid position पर patch feature का normal distribution सीखना (shrinkage घोषित, singular covariance अस्वीकृत) (table)।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
stack is (n, H, W) or (n, H, W, 3) of good images, n >= 2, registered well enough that a grid cell sees the same content. The feature is patch_features (same parameters). At every cell the mean and the covariance (maximum likelihood, divided by n) of the D-dimensional feature are estimated; n <= D makes the covariance singular, so the shrinkage toward tr(S)/D * I is declared: "auto" is the Ledoit–Wolf analytic intensity per position, a number in (0, 1] fixes it, "none" (or 0) is accepted only when n - 1 >= D and the covariance is positive definite and otherwise refused. n_dims keeps a seeded random subset of the D dimensions (the memory trick of the original method), ridge (relative to the median variance, applied unless shrinkage="none") is a numerical floor against exactly constant channels. pool="position" (default) estimates one distribution per grid cell, right when the content is registered; pool="global" estimates ONE distribution from the patches of all cells and images (n * gh * gw samples, overlapping patches are not independent), right for a stationary texture whose content at a cell differs from image to image and which has far more samples than dimensions.
Returns the model: per-position mean and precision (inverse of the shrunk covariance), shrinkage_map and its mean shrinkage_mean, n_train, dim, dims (kept channels), the feature parameters and image_shape. Pass it to patchdist_score.
Raises ValueError for fewer than two images, a singular covariance with shrinkage="none", a constant feature that stays singular, or invalid parameters.
• 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_patch_anomaly — py -3.11 examples/poc_patch_anomaly.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
anomaly_patch)patch_features · patchdist_score · patchbank_fit · patchbank_score
*Provenance: anomaly_patch.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.