vvsandClaude Opus 5
b677283afc
S-ablation of A' (server): A' with each of the nine similarity functions in the step - tent 4443 on the paper protocol (13032 over 855 problems), the nearest other function e^-|z| 4988 (+12%; +9% over six corpora), quadratic ones 18410-18422 (+315%); ranking as the contraction law predicts; guarantee held with every S. The audit of section 7 rerun on the regenerated sweeps (APS unchanged: triple by shape -493 with the tent, +250 with 1/sqrt(1+z^2); defect -283 vs +215). Text: abstract and section 3 no longer call the similarity scaffolding - the tent making the step Newton's is a property of the similarity chosen; a paragraph in section 7 on the audit; Table 9a; new 8.6 'What is known, and what is new' - Lagouanelle (the order estimate of 7.1 / 10.5), Rall and Hansen-Patrick (8.1), the log-log slope / COC / ACOC (8.2, formula 6), Schroeder and Traub (the split 7 = false position on sign(f)|f|^(1/m)), Brent's acceptance condition (half), Dekker/Brent (tolerance step); what we found no counterpart for is the decision layer by similarity and analogy and the step set by a similarity whose form is decisive. References 13-19. verify_article: Table 9a, the percentages and the audit numbers added; server 446 OK, 0 BAD; regression x2 1226 passed, 0 failed. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
"""The S-ablation of A' on all six corpora (uniform criterion, a sign change within 1e-6):
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A' with each of the nine similarity functions in the step, everything else unchanged. The
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tent row is A' itself. Descriptive: every corpus has been seen.
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python -u sweep_ablation_s.py run -> sweep_ablation_s.json, then the table
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python -u sweep_ablation_s.py --report
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"""
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from __future__ import annotations
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import json
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import sys
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from multiprocessing import Pool
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import sweep_fourth
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from sweep_fourth import evaluate as _eval_one
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from sweep_full import APRIME, PURE_SIMS
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SETS = ("APS", "CHA", "NEW", "RJL", "APP", "D")
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for _s in PURE_SIMS:
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sweep_fourth.CONFIGS[f"S:{_s}"] = ("pure", _s, "derived3", "project", "x", APRIME,
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"recent", "distance", "none", "pattern")
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def evaluate(job):
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name, which = job
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return name, _eval_one((name, which))[1]
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def cost(v):
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return v[0] if v[1] else 200
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def main():
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names = [f"S:{s}" for s in sorted(PURE_SIMS)]
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if "--report" not in sys.argv:
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jobs = [(n, w) for n in names for w in ("seen", "fourth")]
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with Pool(min(12, len(jobs))) as pool:
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out = pool.map(evaluate, jobs)
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R = {}
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for n, res in out:
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R.setdefault(n, {}).update(res)
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with open("sweep_ablation_s.json", "w", encoding="utf8") as fh:
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json.dump(R, fh)
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with open("sweep_ablation_s.json", encoding="utf8") as fh:
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R = json.load(fh)
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tot = {n: {s: sum(cost(v) for v in R[n][s].values()) for s in SETS} for n in R}
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viol = {n: sum(v[2] for s in SETS for v in R[n][s].values()) for n in R}
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print("A' with S in the step - totals per corpus (violations of the 2x bound)")
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for n in sorted(tot, key=lambda k: sum(tot[k].values())):
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print(f" {n[2:]:<11} " + " ".join(f"{s} {tot[n][s]:5d}" for s in SETS)
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+ f" all {sum(tot[n].values()):6d} (viol {viol[n]})")
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print("ablation S done")
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if __name__ == "__main__":
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main()
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