91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
"""Detectability check for PREREG_empty_step_2026-09-21.md: empty step (E) and repeating
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pattern with the period chosen from the data (R). Method untouched - states come from the
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behaviour-neutral trace hook.
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python -u diag_empty_step.py
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"""
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from __future__ import annotations
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from scipy.optimize._tstutils import _APS_TESTS_DICTS, _CHANDRUPATLA_TESTS_DICTS
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from diag_contraction_similarity import CONFIGS, FLAT, auc
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from simroot.pure import pure_root
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XTOL, MAXFEV, ACC = 1e-12, 200, 1e-6
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def ratio_sim(a, b):
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hi, lo = max(a, b), min(a, b)
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return lo / hi if hi > 0.0 else 1.0
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def features(f, a, b, r, cfg):
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"""(S_E, S_R) at every traced state; S_R is None until two repeats fit the history."""
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st = {"trace": []}
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x, _n, conv = pure_root(f, a, b, XTOL, MAXFEV, stats=st, **cfg)
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if not (conv and abs(float(x) - r) <= ACC):
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return []
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out, rhos = [], []
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for s in st["trace"]:
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if s["xp2"] is None or s["fx"] == 0.0 or s["fp"] == 0.0:
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continue
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rho = abs(s["fx"] / s["fp"])
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if not (rho == rho) or rho == float("inf"):
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continue
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rhos.append(rho)
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s_e = ratio_sim(abs(s["fx"]), abs(s["fp"]))
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s_r = None
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k = len(rhos)
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best = None
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for p in range(2, k // 2 + 1): # at least two repeats must fit the history
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sims = [ratio_sim(rhos[j], rhos[j - p]) for j in range(p, k)]
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v = sum(sims) / len(sims)
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if best is None or v > best:
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best = v
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s_r = best
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out.append((s_e, s_r))
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return out
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def main():
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out = {}
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for cname, cfg in CONFIGS.items():
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groups = {"APS": [], "CHA fun1": [], "CHA flat fun3-6": [], "CHA other": []}
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for d in _APS_TESTS_DICTS:
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a, b = (float(v) for v in d["bracket"])
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f = (lambda ff, aa: lambda t: ff(t, *aa))(d["f"], d.get("args", ()))
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groups["APS"] += features(f, a, b, float(d["root"]), cfg)
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for d in _CHANDRUPATLA_TESTS_DICTS:
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a, b = (float(v) for v in d["bracket"])
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fam = str(d["ID"]).split(".")[0]
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g = ("CHA flat fun3-6" if fam in FLAT else "CHA fun1" if fam == "fun1"
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else "CHA other")
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groups[g] += features(d["f"], a, b, float(d["root"]), cfg)
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print(f"\n== {cname}")
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print(f" {'group':<18} {'states':>7} {'E: S_E > 1/2':>13} {'R: S_R > 1/2':>13}"
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f" {'(R defined)':>12}")
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res = {"rates": {}, "auc": {}}
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for g, v in groups.items():
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n = max(len(v), 1)
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vr = [x[1] for x in v if x[1] is not None]
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e_rate = 100.0 * sum(x[0] > 0.5 for x in v) / n
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r_rate = 100.0 * sum(y > 0.5 for y in vr) / max(len(vr), 1)
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res["rates"][g] = (len(v), e_rate, len(vr), r_rate)
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print(f" {g:<18} {len(v):7d} {e_rate:12.1f}% {r_rate:12.1f}% {len(vr):12d}")
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flat = groups["CHA flat fun3-6"]
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print(" AUC, flat states against: E R")
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for g in ("APS", "CHA fun1", "CHA other"):
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ae = auc([x[0] for x in flat], [x[0] for x in groups[g]])
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ar = auc([x[1] for x in flat if x[1] is not None],
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[x[1] for x in groups[g] if x[1] is not None])
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res["auc"][g] = (ae, ar)
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print(f" {g:<30} {ae:.3f} {ar:.3f}")
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out[cname] = res
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print("\nbars: m>=2 fires 0.2% canonical / 5.8% flat; successive-ratio similarity AUC"
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" 0.28-0.42")
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return out
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if __name__ == "__main__":
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main()
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