Эксперимент: Calibrated Compact-Support Anomaly Score (#416)

{ "worked": true, "confidence": 9, "verdict": "Built the calibrated compact-support anomaly scorer with Cholesky Mahalanobis geometry, exact normalization, calibrated gamma, hard outside-support scores, and a stable barrier option. Across R²={7.5,11,25,85} in d=5, numerical normalization was 1.0, sampled E[r²] was 5.003, 5.003, 4.998, 5.000 versus predicted 5, and boundary slopes matched predictions within 1.2e-6; affine radius error was 7.1e-15. The mechanism is real, but the equal-feature OOD comparison was exactly tied with Gaussian Mahalanobis (AUROC 0.7885 and anomaly acceptance 0.5445 at 95% nominal coverage), as expected from monotonicity in r².", "metrics": { "baseline": "Gaussian Mahalanobis: AUROC=0.788464; anomaly acceptance at 95% nominal threshold=0.5445.", "idea": "Compact calibrated score: AUROC=0.788464; anomaly acceptance at 95% nominal threshold=0.5445. Math checks: normalization≈1, E[r²]≈5 for all four radii, boundary slope absolute errors 7.6e-9 to 1.2e-6, affine radius error=7.1e-15." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 compact_anomaly.py", "files": [ "compact_anomaly.py", "results.json" ], "limitations": "Only a synthetic equal-feature anomaly experiment was tested; no CIFAR-10, MVTec, learned encoder, robust covariance estimation, training-time barrier optimization, runtime/FLOP study, or real-world calibration evaluation was performed. The Monte Carlo covariance check used identity covariance and exact sampling rather than estimating parameters from finite nominal data." }