"""Control for Claim 3 (Theorem 2): relax the cluster-noise condition.

Theorem 2 asserts that after K*T GD iterations the misclassification error is
uniformly small *across all K tasks*, under sigma = Theta(1/(polylog(d) sqrt d)).
On the prescribed grid (exp3_regime.py, `claim3` block) the measured error is
identically 0 at every one of the 32 configurations, which supports the claim
but has no discriminating power: a broken measurement would look the same.

This control sweeps the one condition Theorem 2 places on the data -- the
cluster noise level sigma = sigma_c / sqrt(d) -- from the paper's sigma_c = 0.1
up to sigma_c = 4.0, holding everything else at the claim-3 base point.  If the
audit is measuring what Theorem 2 describes, error must stay at 0 while the
condition holds and rise once it is violated.

Deliberately single-process and small: it runs alongside the exp4/exp5 sweeps
without adding to peak memory.
"""

import json
import os
import sys
import time

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import clcore as C  # noqa: E402

OUT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "results")
os.makedirs(OUT, exist_ok=True)

D, M, N, K, T, ETA = 50, 1000, 1000, 6, 200, 8.0
SIGMAS = [0.1, 0.25, 0.5, 1.0, 2.0, 4.0]
SEEDS = [0, 1, 2]

if __name__ == "__main__":
    t0 = time.time()
    recs = []
    total = len(SIGMAS) * len(SEEDS)
    for i, sc in enumerate(SIGMAS):
        for s in SEEDS:
            r = C.continual_run(d=D, m=M, K=K, n=N, T=T, eta=ETA,
                                sigma_c=sc, loss_name="hinge", seed=s,
                                n_test=2000)
            recs.append(dict(
                sigma_c=sc, seed=s, d=D, m=M, n=N, K=K, T=T, eta=ETA,
                # error of every task k measured at the final iterate w_{K-1}
                train_err_end=[float(r["err_at"][K - 1, k]) for k in range(K)],
                test_err_end=[float(r["test_err_at"][K - 1, k]) for k in range(K)],
                # error of task k right after it was trained (no forgetting yet)
                train_err_own=[float(r["err_at"][k, k]) for k in range(K)],
                train_loss_end=[float(r["loss_at"][K - 1, k]) for k in range(K)],
            ))
            print(f"  {len(recs)}/{total}  sigma_c={sc}  seed={s}  "
                  f"{time.time() - t0:.0f}s", flush=True)
    path = os.path.join(OUT, "exp6_noise.json")
    with open(path, "w") as f:
        json.dump(recs, f)
    print("wrote exp6_noise.json", f"{time.time() - t0:.0f}s", flush=True)