Python rewrite of the Urb/Homemaker stack
Bruno Postle
d004e4c937
_assign_adjacency_aware gains fixed_circ (seed the connected-dominating-set from given circulation leaves) and secondary-adjacency-aware room placement: codes with the most non-c adjacency requirements are placed first, each onto the open slot satisfying the most of its requirements against already-typed neighbours (clustering k1<->da1, da1<->o). lift_base_to_storeys(reqs, adjacency_aware=True) grows the upper-floor circulation spine off the inherited vertical core and assigns rooms around it; threaded through driver.search_staged (seed_adjacency_aware) and run_staged_search.py (ADJ env). End-to-end staged harbor, 20000 evals, mean total fails over 3 seeds: ADJ=0 99.0 (reproduces the §11.4 staged lex baseline exactly), ADJ=1 85.3 (-13.7, -14%; best 78). New best harbor configuration overall: staged baseline 99.0 -> single-stage adjacency-aware (§11.6) 90.7 -> staged + adjacency-aware lift 85.3. Staging and adjacency-aware seeding compose. Co-Authored-By: Claude Opus 4.8 <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: 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).✓- Memetic search (current): canonical slicing genome + high-locality operators + Nelder-Mead inner loop.
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/fitness.py— native Python fitness evaluator.src/homemaker_layout/fitness_cmd.py—homemaker-fitnessCLI (drop-in forurb-fitness.pl).src/homemaker_layout/graph.py— leaf-adjacency graph for programme-driven checks.src/homemaker_layout/genome.py— topology genome: base-floor tree + per-storey deltas.src/homemaker_layout/operators.py— high-locality mutation and subtree crossover.src/homemaker_layout/innerloop.py— ratio optimisation inner loop (Nelder-Mead / CMA-ES).src/homemaker_layout/driver.py— memetic search outer loop.src/homemaker_layout/evolve.py—homemaker-evolveCLI entry point.src/homemaker_layout/oracle.py— legacy Perl shim, kept for cross-validation only.