vvsandClaude Opus 5
8830765454
Tables 2-3 regenerated from sweep_full.json by tables_for_md.py with the verifier's own ranking: Chandrupatla's method, missing from both, is FIRST on the canonical set (2785) and second on the wide-bracket set (2423); the proposed rule is 29th (3568) and 10th (3020); Brent 2889/4099 (was 2890/4101). The abstract, §5 text, §5.1, §5.2 (timed configuration named), §7 (records 2527/2523 are under the scheduled projection: 2523 flat for every multiplier 1.25-4, 2527 becomes 2541 at 1.25; under credit the readings give far less, best constant-free total 6494 by 'defect'), §8.4, §8.11 and the conclusion now say so: parity with the tuned methods at the price of one constant, or a constant-free rule with a tighter guarantee that trails on the canonical set. verify_article.py: 279 checks, measured tables recomputed from fresh runs; 762 tests pass twice. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
"""Do the section-7 records depend on the projection's multiplier?
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The plain projection's canonical total is 2806 for every multiplier from 1.25 to 4
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(measure_budget_factor.py). The records 2527 (shape reading, safe) and 2523 (defect
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reading, projection) are quoted with the scheduled projection; whether THEY are flat in
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the multiplier has to be measured, not inferred from the plain rule.
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python -u measure_records_vs_multiplier.py
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"""
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from __future__ import annotations
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from simroot.pure import pure_root
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from stage12_validation import load, per_problem, totals
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FACTORS = (1.25, 1.5, 2.0, 3.0, 4.0)
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RECORDS = {
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"shape reading (2527)": dict(similarity="ratio", scale="derived3", guard="safe",
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prestep="slope", points="shape"),
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"defect reading (2523)": dict(similarity="ratio", scale="derived3", guard="project",
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prestep="defect"),
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"far end by dissimilarity (5511)": dict(similarity="ratio", scale="derived3",
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guard="project", prestep="slope",
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direction="dissimilar"),
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}
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def main():
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probs = load()
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out = {}
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print(f"{'configuration':<34} " + " ".join(f"{f:>13.2f}" for f in FACTORS))
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for name, cfg in RECORDS.items():
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cells = []
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out[name] = {}
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for fac in FACTORS:
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p = per_problem(lambda f, a, b, xt, mx, _c=cfg, _f=fac: pure_root(
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f, a, b, xt, mx, budget_factor=_f, **_c), probs, 200)
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t, _ok = totals(p, probs)
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out[name][fac] = (t["APS"], t["CHA"])
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cells.append(f"{t['APS']:>6d}/{t['CHA']:<6d}")
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print(f"{name:<34} " + " ".join(cells))
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print("\ncells are APS/CHA totals")
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return out
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if __name__ == "__main__":
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main()
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