Python rewrite of the Urb/Homemaker stack
Bruno Postle
bf3ff43837
Same-code rooms collapse into fewer, larger SHARED leaves so the ~1.8/leaf shape tax (§13.1) is paid once per group. Multiplicity k is recovered from area (k=clamp(round(area/target),1,max_share)) — no genome change — and used in two default-OFF sites: graph.check_space_counts counts coverage (Σk vs req.count) so one leaf covers several rooms without a missing fail, and fitness.quality_size centres on k×target (σ scaled by k). Construction: operators._share_rooms groups instances; _size_divisions_from_targets sizes shared leaves to k×target via leaf_mult. Floor probe (experiments/diag_leaf_sharing.py, harbor+maple, seeds 0/1/2, +innerloop): total fails −27% harbor / −16% maple at share3, shape factors fall ~linearly with leaf count (confirms §13.1). Cap: 17–44 missing fails leak because depth maldistribution (§13.2) keeps shared leaves below k×target so round() undercounts; inner loop can't close it. Net still positive. Default-OFF reproduces baseline exactly (214 tests pass). Driver plumbing + staged 20k A/B remain; §13.3 records the next design fork. 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.