Python rewrite of the Urb/Homemaker stack
Bruno Postle
517a825505
Previously level_add copied the top storey exactly, duplicating all named programme rooms and immediately triggering space-count failures for every room on the new floor. The lex outer-search comparison (-n_fails, score) then always rejected the multi-storey child because its fail count was far higher than the single-storey parent. Fix: retype all named-room leaves on the new storey to generic C or O before admitting the child. The outer search then retypes them incrementally via the normal retype operator. This allows level_add to produce designs with the same fail count as the parent (storey_minimum fail removed, no duplication fails added), making the multi-storey transition visible to the lex selector. Result on programme-house cold start (init.dom, 100k evals, 4 workers): before: 6 fails, single-storey, stuck after 40k evals after: 4 fails, two-storey, still improving at 100k Also adds examples/harbor-house/ from urb/examples for future runs. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> |
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|---|---|---|
| .beads | ||
| .claude | ||
| examples | ||
| experiments | ||
| src/homemaker_layout | ||
| tests | ||
| .gitignore | ||
| AGENTS.md | ||
| CLAUDE.md | ||
| DESIGN.md | ||
| pyproject.toml | ||
| README.md | ||
homemaker-layout
Programme-driven building-layout search over slicing trees. A clean-room Python successor to the Perl Urb project, intended to eventually be 100% Python.
Why a rewrite
Urb represents a building as a binary slicing tree where room sizes are derived top-down from division ratios. That makes room area an emergent property of every cut above it, which:
- gives the genome low locality (a cut near the root rescales every descendant),
- makes target room sizes nearly impossible to hit, so the gaussian size penalty dominates fitness, and
- defeats crossover (transplanted subtrees lose their proportions).
homemaker inverts this: leaves carry target dimensions from the programme and division ratios are solved bottom-up for a fixed topology. The evolutionary search then only explores topology + types + adjacency.
Phase plan
- Solver experiment (current): port Urb's geometry, re-solve ratios from programme targets, score the result against the original via the Perl oracle.
- Native Python fitness (retire the Perl oracle).
- Canonical slicing encoding (normalized Polish expression) + memetic search.
Layout
src/homemaker_layout/dom.py— read/write Urb.domYAML into aNodetree.src/homemaker_layout/geometry.py— faithful port of Urb's top-down geometry.src/homemaker_layout/programme.py— parsepatterns.configspace requirements.src/homemaker_layout/solver.py— bottom-up ratio solve (scipy).src/homemaker_layout/oracle.py— Phase-1 scaffold: score a.domvia Urb'surb-fitness.pl.
The Perl oracle is the only throwaway component; everything else is permanent.