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allaun
670e7617c3
- Rename python/build_corpus250.py → python/build_manifold.py - Rename emitCorpus250 → emitManifold, corpus250 → allFixtures - Schema: avm_rrc_corpus250_v1 → avm_rrc_manifold_v1 - Classify.lean now delegates to ClassifyN (generic n-dim module) - ClassifyN.hashTable8: extracted 119-entry hash table from old Classify - build_manifold.py now emits ClassifyN.classifyProxy hashTable8 + classifyExact 8 - build_pist_matrices_250.py: added pistMatrixDim constant - SilverSight docs/AGENTS.md updated for renames - Research Stack AGENTS.md updated for toolchain references - Authentik deployed on neon-64gb (port 30001, working) - cross_domain_significance.py: statistical significance test (all phases <6σ with n=3) - setup_authentik.sh: fixed image, password, port, key sharing Build: 3307 jobs, 0 errors (lake build)
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
#!/usr/bin/env python3
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"""
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cross_domain_significance.py — Statistical significance test for
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RRC cross-domain 1/n residual signatures.
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Tests whether observed deviations from predicted 1/n scaling or
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1/7 meta-solid threshold are statistically significant at ≥6σ.
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"""
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import json, math
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from pathlib import Path
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from datetime import datetime, timezone
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ROOT = Path(__file__).resolve().parents[1]
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def gaussian_p_value(z: float) -> float:
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"""Two-tailed p-value from z-score via complementary error function."""
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return math.erfc(abs(z) / math.sqrt(2))
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def sigma_from_p(p: float) -> float:
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"""Convert p-value to sigma level (two-tailed) via inverse erfc approximation."""
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if p <= 0:
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return float('inf')
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if p >= 1:
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return 0.0
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t = math.sqrt(-2 * math.log(p if p <= 0.5 else 1 - p))
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c = (2.515517, 0.802853, 0.010328)
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d = (1.432788, 0.189269, 0.001308)
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return t - (c[0] + c[1]*t + c[2]*t**2) / (1 + d[0]*t + d[1]*t**2 + d[2]*t**3)
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def main():
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sig_path = ROOT / "signatures" / "cross_domain_signatures.json"
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sigs = json.loads(sig_path.read_text())
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entries = sigs["signatures"]
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phases = {}
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for e in entries:
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phases.setdefault(e.get("phase", 0), []).append(e)
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results = []
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for phase_num in sorted(phases):
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group = phases[phase_num]
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domain = group[0].get("domain", "Unknown")
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deviations = [abs(e["deviation"]) for e in group if "deviation" in e]
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residuals = [e["residual_mhz"] for e in group if "residual_mhz" in e]
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values = deviations or residuals
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n = len(values)
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if n < 2:
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results.append({
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"phase": phase_num, "domain": domain,
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"n": n, "status": "insufficient_data"
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})
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continue
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mean_val = sum(values) / n
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var = sum((v - mean_val)**2 for v in values) / (n - 1) if n > 1 else 0
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se = math.sqrt(var / n)
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z = mean_val / se if se > 0 else 0.0
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p = gaussian_p_value(z)
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sigma = sigma_from_p(p)
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results.append({
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"phase": phase_num,
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"domain": domain,
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"n": n,
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"mean_deviation": round(mean_val, 6),
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"std_err": round(se, 6),
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"z_score": round(z, 4),
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"sigma_level": round(sigma, 2) if sigma < 1e6 else "inf",
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"passes_6sigma": bool(sigma >= 6.0),
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})
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summary = {
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"schema": "cross_domain_significance_v1",
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"generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
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"source": "cross_domain_signatures.json",
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"total_entries": len(entries),
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"phases": results,
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}
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out_path = ROOT / "signatures" / "cross_domain_significance.json"
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out_path.write_text(json.dumps(summary, indent=2) + "\n")
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print(f"Wrote {out_path}")
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for r in results:
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s = r.get("sigma_level")
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if s is not None and s != "inf":
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tag = "✅" if r["passes_6sigma"] else "❌"
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print(f" Phase {r['phase']} ({r['domain']}): σ={s} {tag}")
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else:
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tag = "✅" if r.get("status") == "insufficient_data" else "❌"
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print(f" Phase {r['phase']} ({r['domain']}): σ={s} {tag}")
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if __name__ == "__main__":
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main()
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