Python rewrite of the Urb/Homemaker stack
Bruno Postle
27719975b0
Builds path (b) from §26 -- a leaf permanently serving two DIFFERENT compatible programme codes at once, extending leaf-sharing's same-code multiplicity mechanism to different-but-compatible codes. Architect-declared `co_locate` pairs (validated against interchangeable()'s S1-S4 bounds, no transitive closure so the b3v chain problem can't recur), threaded through graph.py's checks via a new leaf_codes() resolver and fitness.py's quality terms (additive size, stricter-of-both width/proportion). Construction-time only, gated behind `multi_use` (default OFF, bit-identical when off). End-to-end A/B (20k evals x 3 seeds x 2 programmes) came back net negative: harbor-house -4.0% but health-centre +24.5% worse (3/3 seeds), because fusing different codes' shape targets via stricter-of-both can impose a tighter joint constraint than either code needed alone, which the tightly- packed health-centre programme can't absorb. Written up as DESIGN.md §33; multi_use stays default OFF, no default-flip recommended. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY |
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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/collapse_cmd.py—homemaker-collapseCLI: finish-time global cell→room relabel of a.dom.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.