Bruno Postle
646ee30ab6
- src/homemaker/ → src/homemaker_layout/; all imports updated - pyproject.toml: name = homemaker-layout, entry point updated - .beads/config.yaml: dolt sync.remote updated to homemaker-layout.git - Delete temporary debug/perl scripts from project root - README.md, DESIGN.md: package path references updated - GitHub repo renamed; git remote updated Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
#!/usr/bin/env python3
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"""Why does 2f45907 resist equal-offset recovery? (homemaker-py-1p0)
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Its equal-offset projection adds a failure (2 -> 3) and batched searches stall
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near the projected start, yet DESIGN.md §4.5 reports Nelder-Mead reached
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0.015684 from the same projection. This script checks, for 2f45907 only:
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1. fitness of the three projections (midpoint, a-end, b-end)
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2. scipy Nelder-Mead from the midpoint, maxfev=200 (the §4.5 setup)
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3. CMA-ES with sigma0=0.05 (tighter than the 0.15 default)
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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from scipy.optimize import minimize
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sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
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from homemaker_layout import dom, innerloop # noqa: E402
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URB = Path("/home/bruno/src/urb")
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EX = URB / "examples" / "programme-house"
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NAME = "2f45907abd9accac2a124d311732f749.dom"
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def main() -> None:
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root = dom.load(str(EX / NAME))
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with innerloop.OracleEvaluator(root, EX, URB) as ev:
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a = np.array([b.division[0] for b in ev.free])
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b_ = np.array([b.division[1] for b in ev.free])
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mid = (a + b_) / 2
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for label, x in [("mid", mid), ("a-end", a), ("b-end", b_)]:
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s = ev.evaluate([x])[0]
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print(f"projection {label:6s}: {s.fitness:.6g} fails {s.n_fails}", flush=True)
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# §4.5 reproduction: sequential Nelder-Mead on the same objective
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best = {"f": -1.0, "fails": -1}
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def neg(x: np.ndarray) -> float:
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s = ev.evaluate([np.clip(x, 0.02, 0.98)])[0]
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if s.fitness > best["f"]:
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best.update(f=s.fitness, fails=s.n_fails)
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return -s.fitness
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n0 = ev.n_evals
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minimize(neg, mid, method="Nelder-Mead",
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options={"maxfev": 200, "xatol": 1e-3, "fatol": 1e-12})
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print(f"NM from mid: {best['f']:.6g} fails {best['fails']} "
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f"({ev.n_evals - n0} evals)", flush=True)
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root = dom.load(str(EX / NAME))
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r = innerloop.optimise(root, EX, budget=200, method="cma", sigmas=(0.05,), urb_root=URB)
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print(f"CMA sigma 0.05: {r.fitness:.6g} fails {r.n_fails} ({r.n_evals} evals)")
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if __name__ == "__main__":
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main()
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