baseline: the published sim-numerics artifact as it stands (490 tests, verify_article.py checks all 72 paper numbers against sweep_full.json)

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-20 22:38:17 +01:00
commit 4a23aacf96
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+"""Stage 17 - the third domain: the step size of an adaptive noise canceller.
+
+Root finding: the method lands second behind Chandrupatla 1997.
+ODE step control: level with a five-constant PI controller, behind the
+three-constant classical one.
+Softening somebody else's acceptance test: fails, three times over.
+
+Active noise control is the domain where the object we have - a step size
+that is a safety factor on an exact step - is exactly the object the field
+argues about, and where the standard answers are openly constant-ridden.
+
+The acoustic paths, the noise regimes and the scenarios come from our earlier
+ANC benchmark (SEM repository, branch feature/sem-anc-benchmark), so the
+environment is one that existed before this question was asked.
+
+Metric: noise reduction in dB on the second half of each run, so that the
+adaptation transient is not counted.  Higher is better.  Ten seeds per cell.
+"""
+from __future__ import annotations
+
+import math
+
+import numpy as np
+
+from anc.bench import (FS, SCENARIOS, make_primary_path, make_secondary_path,
+                       run_nlms)
+from anc.rules import ALL
+
+N = 8000          # one second
+SEEDS = 10
+SNRS = (30.0, 15.0)
+
+
+def main():
+    print(f"{len(SCENARIOS)} scenarios x {len(SNRS)} noise levels x {SEEDS} "
+          f"seeds, {N / FS:g} s each\n")
+    grand = {r.name: [] for r in ALL}
+    for snr in SNRS:
+        print("=" * 88)
+        print(f"reference SNR {snr:g} dB      noise reduction, dB (higher is "
+              f"better)")
+        print("=" * 88)
+        print(f"{'scenario':22s}" + "".join(f"{r.name[:13]:>13s}" for r in ALL))
+        print("-" * 88)
+        for sname, gen in SCENARIOS:
+            row = {r.name: [] for r in ALL}
+            for s in range(SEEDS):
+                rng = np.random.default_rng(1000 + s)
+                noise = gen(N, rng)
+                prim = make_primary_path(rng)
+                sec = make_secondary_path(rng)
+                for r in ALL:
+                    nr = run_nlms(noise, prim, sec, r, snr_db=snr, seed=s)
+                    if math.isfinite(nr):
+                        row[r.name].append(nr)
+                        grand[r.name].append(nr)
+            cells = []
+            for r in ALL:
+                v = row[r.name]
+                cells.append(f"{(sum(v) / len(v)) if v else float('nan'):>13.2f}")
+            print(f"{sname:22s}" + "".join(cells), flush=True)
+        print()
+
+    print("=" * 88)
+    print("OVERALL")
+    print("=" * 88)
+    print(f"{'rule':26s}{'constants':>11s}{'mean NR dB':>13s}"
+          f"{'worst cell':>13s}{'diverged':>11s}")
+    print("-" * 88)
+    for r in ALL:
+        v = grand[r.name]
+        n_bad = SEEDS * len(SCENARIOS) * len(SNRS) - len(v)
+        print(f"{r.name:26s}{r.constants:>11d}"
+              f"{(sum(v) / len(v)) if v else float('nan'):>13.2f}"
+              f"{min(v) if v else float('nan'):>13.2f}{n_bad:>11d}")
+    print("\n'diverged' counts runs whose residual power was not finite - the "
+          "failure mode\na step size that is too large actually produces.")
+
+
+if __name__ == "__main__":
+    main()