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Python rewrite of the Urb/Homemaker stack
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Bruno Postle 7f82c03f80 Fix programme width default: derive from sqrt(size/proportion) (homemaker-py-can) 
When a programme space has no explicit 'width' key, the fallback to
width_inside [4.0, 1.0] is geometrically impossible for small spaces
(e.g. t3 WC at 3 m²). Now compute target = sqrt(size/proportion),
sigma = max(0.1, target * size_sigma / (2 * size_target)).

Effect on 35-file corpus: 32 files score +1–307% (width quality improves
for correctly-sized small rooms); 5 files lose spurious width fail lines.
Upstream Perl fix tracked as homemaker-py-8fe.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-13 22:23:03 +01:00
.beads Fix programme width default: derive from sqrt(size/proportion) (homemaker-py-can) 2026-06-13 22:23:03 +01:00
.claude Scaffold homemaker-py with validated geometry port 2026-06-10 20:50:20 +01:00
experiments Phase 3 gate (homemaker-py-ccw): scaled search on native fitness 2026-06-13 22:10:38 +01:00
src/homemaker Fix programme width default: derive from sqrt(size/proportion) (homemaker-py-can) 2026-06-13 22:23:03 +01:00
tests Phase 3 gate (homemaker-py-uxz): native fitness 35/35 corpus parity; retire oracle from search 2026-06-13 21:44:42 +01:00
.gitignore bd init: initialize beads issue tracking 2026-06-11 23:27:11 +01:00
AGENTS.md bd init: initialize beads issue tracking 2026-06-11 23:27:11 +01:00
CLAUDE.md Fix stair-fit parity: entrance corners + weighted has_circulation (homemaker-py-w1e/q70) 2026-06-13 20:55:25 +01:00
DESIGN.md Phase 3 gate (homemaker-py-ccw): scaled search on native fitness 2026-06-13 22:10:38 +01:00
pyproject.toml Geometry inner loop: batched full-objective ratio optimiser (CMA-ES) 2026-06-12 09:42:24 +01:00
README.md Scaffold homemaker-py with validated geometry port 2026-06-10 20:50:20 +01:00

README.md

homemaker-py

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

  1. Solver experiment (current): port Urb's geometry, re-solve ratios from programme targets, score the result against the original via the Perl oracle.
  2. Native Python fitness (retire the Perl oracle).
  3. Canonical slicing encoding (normalized Polish expression) + memetic search.

Layout

  • src/homemaker/dom.py — read/write Urb .dom YAML into a Node tree.
  • src/homemaker/geometry.py — faithful port of Urb's top-down geometry.
  • src/homemaker/programme.py — parse patterns.config space requirements.
  • src/homemaker/solver.py — bottom-up ratio solve (scipy).
  • src/homemaker/oracle.py — Phase-1 scaffold: score a .dom via Urb's urb-fitness.pl.

The Perl oracle is the only throwaway component; everything else is permanent.