f5612ce06c
39.16 relocated the crinkliness residual to a plan-form question. Four measurements answer it, and two of them refute the premises 773 was filed on. The search DOES build courtyards -- harbor 8 (273 m2), maple 16 (404 m2), health-centre 13 (107 m2) over three seeds -- and they work: of 524 lit edges 44% come from the plot wall, 30% from a courtyard, 26% from a perimeter void, and a courtyard supplies at least one side for 53% of the two-aspect leaves. No operator is missing. (The shape-curve DP does NOT model exposure -- shapecurve.py:25 -- but per 38.24 it fires ~8 times in 500k evals, so that gap is not what is costing anything.) The answer is per-storey. Comparing each storey's demand, sum A_i/(1.6202*h), with the lit wall its leaves actually hold: every harbor and maple ground floor is below 1.0 and every top floor above 1.2, and the ratio predicts the fail rate almost exactly -- above ~1.2 near-zero fails, below 1.0 40-55% of the storey. health-centre and programme-house sit at 1.6-4.2 throughout and fail essentially nothing. That corrects 39.11, which divided demand evenly across storeys and concluded harbor and maple were frontage-feasible "with room to spare". Programmes pin rooms to level 0 and the ground floor cannot set itself back to buy perimeter: harbor's pinned 347 m2 needs 71.4 m against the plot's 53.0 m, maple's 414 m2 needs 85.2 m against 55.0 m, while health-centre and programme-house have 51.0 and 14.2 m spare. Same ordering as the corpus fail counts, and fixed before any search runs. The averaged check is not just weaker: on maple it asks for a 22 m2 courtyard where the ground floor needs 57 m2. New third _preflight check, advisory like the others, silent on the two programmes with slack. tests/test_evolve_preflight.py covers all three checks and asserts the ground-floor figure exceeds the averaged one -- if they ever agree, one has stopped earning its place. 39.11 annotated in place. Also recorded, not acted on: the open space is on the wrong storey (harbor puts 50 m2 of courtyard on the starved ground floor and 223 m2 on the surplus first floor), because value_rate pays an outside leaf above ground value_supported = 300 -- a room's rate -- against a cost of 110, with nothing tying its value to whether it illuminates anything. Filed as homemaker-py-ecx. 411 passed, 72 skipped. Refs homemaker-py-773. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MJ84Feep79Hhm3E4zZJmnB |
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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: canonical slicing genome + high-locality operators + Nelder-Mead inner loop.✓Penalty reshaping: lexicographic✓(-n_fails, fitness)outer-search comparison.Representation upgrade: canonical slicing encoding + bottom-up shape feasibility, scaled to larger programmes.✓- Search-quality experiments (current): a long running series of
opt-in levers tried against the
harbor-house,health-centre, andprogramme-houseexample corpora — leaf-sharing, finish-time cell→room collapse, ruin-and-recreate LNS, 2-opt polish, multi-use/co-located leaves, adjacency-graph and bubble-diagram fitness signals, and more. Most of these are negative/null results kept as opt-in flags or reference code rather than defaults. SeeDESIGN.md§11 onward for the full, numbered experiment log with methodology and results for each.
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.src/homemaker_layout/bubble.py— 3D bubble-diagram adjacency fitness-signal prototype (DESIGN.md §27); validated null, not wired intofitness.py— reference only.
Room codes and reserved names
Leaf types live in three namespaces that share a first character. Only the first is enforced; the other two are conventions the fitness function reads, so a room's spelling can change how it is scored.
1. Generic structural types — C, O, S (reserved). The leaves the
search itself creates: C circulation, O outside, S sahn (an outside court
that also serves as circulation). Always uppercase. A programme code spelled
exactly C, O or S is rejected at load.
2. Programme room codes — anything else, lowercase. k1, b1, cr1,
of, and single-character codes like r or t. These may start with any
letter: since DESIGN.md §39.4 the generic tests match C/O/S exactly, so
naming a room cr1 no longer makes it circulation. (Before that fix it did —
and silently dropped it from the required-space check entirely.)
3. Access requirements — the usage: attribute. Every space declares one
of living, kitchen, bedroom, toilet, utility, none. Mandatory, no
fallback, and a missing or unknown value is a load error. It replaced a
first-character convention (b/t/l/k) under which a room silently
inherited another room's connectivity rules from its spelling — la1 "Laundry
Room" was trimmed as a living room (DESIGN.md §39.7).
spaces:
la1:
usage: utility # controlled, drives engine behaviour
name: Laundry Room # free text, building-specific
A usage value exists only where the engine treats it differently, so the vocabulary is closed: a new access class means new code, not new config. Check a programme with:
python experiments/audit_programme_config.py
which reports reserved-name collisions, the usage class each code picks up, and whether each room's size/width/proportion/crinkliness targets are mutually satisfiable at all.