Эксперимент: Clustered alpha-smoothing mixture wrapper (#865)

{ "worked": false, "confidence": 8, "verdict": "Built a fixed-seed NumPy implementation of global coordinate-wise trimming and clustered trimmed mixture aggregation, with mixture weights and retained-sample bookkeeping. Weight normalization matched the mathematical claim to 2.2e-16, and clustering preserved two modes while improving error at q=0.05 contamination (0.027 versus 0.126 for global trimming and 0.351 for the ordinary mean). However, the separation sweep did not show the predicted global error scaling, and clustered trimming became worse than global trimming at q>=0.10, so the required mechanism predictions were not sufficiently confirmed.", "metrics": { "baseline": "Ordinary mean error: 0.011, 0.351, 0.706, 1.062, 1.768 for contamination q=0,.05,.10,.15,.25; global-trim error: 0.011, 0.126, 0.275, 0.563, 1.342.", "idea": "Clustered-trim center error: 0.017, 0.027, 0.401, 0.785, 1.470 for q=0,.05,.10,.15,.25; two components were retained with average smallest mixture weight 0.50, 0.476, 0.449, 0.425, 0.373. Weight-sum maximum absolute error was 2.22e-16." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 clustered_alpha_experiment.py", "files": [ "clustered_alpha_experiment.py", "results.json", "run_output.txt" ], "limitations": "This was a synthetic 2-D experiment, not a neural stochastic predictor or trajectory benchmark. Clustering used known-separation nearest-center assignments rather than learned k-means, covariance/Gaussian sampling was not benchmarked, runtime/FLOPs/Wasserstein distance and safety metrics were not tested, and contamination was injected across modes rather than exclusively within one cluster." }