# Эксперимент: Ellipsoidal-Preserving Spherical Feature Stabilizer (#1419)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a NumPy/SciPy MVP of the normalized n=3 intersection-body map using Fibonacci-sphere quadrature and a local Funk-transform approximation. The numerical linearization reproduced the claimed phenomenon: degree-2 multiplier 0.992 (near neutral), while degree-4 and degree-6 multipliers were 0.547 and 0.371, below the predicted high-mode factor 0.5625; the mixed high-mode ratio was 0.550. The toy classification was saturated at 1.0 accuracy for both baseline and idea, so no downstream accuracy win was demonstrated.",
  "metrics": {
    "baseline": "Raw spherical feature classifier accuracy: 1.000",
    "idea": "Stabilized feature classifier accuracy: 1.000; degree-2 ratio 0.992, degree-4 ratio 0.547, degree-6 ratio 0.371, mixed high-mode ratio 0.550 versus predicted 0.5625"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "This is a small S^2 NumPy diagnostic, not a spherical CNN or ModelNet40 experiment. The Funk transform is approximated by a finite local-strip neighbor matrix, so the exact degree-2 identity is only approximate. The toy classification task was too easy to measure an accuracy improvement, training-speed gain, FLOP comparison, robustness benefit, or parameter-matched Gaussian-smoothing baseline."
}