Python rewrite of the Urb/Homemaker stack
Bruno Postle
c3635634e8
Interchangeable codes (similar size/width/proportion, compatible level/stack, no adjacency edge) form equivalence classes derived from the programme. With --superpose (default off), each fitness eval COLLAPSES every superposed leaf to its best in-class usage via an optimal supply->demand assignment (brute force <=C! within cap C=4, scipy Hungarian beyond), then scores the condensed types. Because collapse re-types on the unmerged tree before all checks, counts / adjacency / quality are unchanged downstream -- no Node field, no graph/operator changes -- and default OFF is bit-identical. - programme.py: derive_interchange_classes + interchangeable (S1-S4, locked thresholds R_SIZE=1.5/R_WIDTH=1.3/R_PROP=1.5, CLASS_CAP=4) - fitness.py: collapse_superposition, _best_assignment, _usage_quality; superpose/superpose_class_cap conf knobs; collapse hooked into _evaluate_full - driver.py/evolve.py: superpose flag plumbed beside leaf_sharing; --superpose - tests/test_superposition.py: 17 tests (derivation, assignment, end-to-end) Closes homemaker-py-9o5 (build); validation A/B is homemaker-py-xi7. 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.