mirror of
https://github.com/allaunthefox/SilverSight.git
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allaun
6b26a9bc6e
Architecture fixes: - Fixed phantom Semantics.* imports in HachimojiBase and HachimojiManifoldAxiom (replaced with CoreFormalism.* and SilverSight.* imports) - RRCLib.RRCEmit confirmed to exist (attacker was wrong) - Duplicate ProductSchema/ProductWireFormat confirmed NOT in SilverSightCore (attacker was wrong) Documentation fixes: - SOS example: fixed s₀ = x² (was incorrectly stated as 0) - Added Archimedean condition to Putinar's Positivstellensatz - Sidon bound: fixed to ⌊√(2N)⌋ + 1 in FIRST_PRINCIPLES (consistency with PURE_FORMULAS) - Safety margin 28× confirmed correct (attacker's 56.7× was wrong — they confused ppm with ×10^-6) Lean proof status: - repunit function: documented as 'repunit characteristic' (not mathematical repunit) - chentsov_50: 7 sorries remain (type bridge + chentsov_theorem internal sorries) - Fisher metric bridge: cross-term 1/p₀ correctly identified and documented
41 lines
No EOL
1.2 KiB
Python
41 lines
No EOL
1.2 KiB
Python
#!/usr/bin/env nix-shell
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#!nix-shell -p python312Packages.numpy -i python3
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"""nuvmap_spectral_driver.py - Run on neon via nix-shell"""
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import numpy as np
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import json
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from pathlib import Path
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def compute_spectral_gap(N: int, p: int) -> dict:
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"""Compute spectral gap for BMCTE eigensolid at p/N=1/7."""
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lam = np.exp(-p * p / N)
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# Sparse signature matrix (diagonal for now)
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sig_vals = [8704, 9088, 9984, 8704, 9088, 9984, 8704, 9088]
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mat = np.diag(sig_vals)
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# Power iteration for dominant eigenvalue
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v = np.random.randn(8)
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v = v / np.linalg.norm(v)
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for _ in range(100):
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Mv = mat @ v
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norm = np.linalg.norm(Mv)
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if norm < 1e-10:
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break
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v = Mv / norm
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ev_max = float(v @ mat @ v)
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density = np.count_nonzero(mat) / mat.size
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return {
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"N": N, "p": p,
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"lambda_theory": float(lam),
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"spectral_gap": ev_max,
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"density": float(density),
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"at_threshold": abs(p/N - 1/7) < 0.001
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}
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if __name__ == "__main__":
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result = compute_spectral_gap(80000, 11429)
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print(json.dumps(result, indent=2))
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Path("neon_spectral_result.json").write_text(json.dumps(result, indent=2)) |