e92a96ac99
The owner supplied the provenance the analysis was missing. The constant is Christopher Alexander, A Pattern Language 159, "Light on Two Sides of Every Room", and it changes what the numbers mean. 1/crink = A/(L*h) is floor area per metre of ILLUMINATED wall over storey height. It is not room depth -- it equals depth only for a room lit on one side. So 5/6 * h = 2.5 m is 2.5 m of room depth PER WINDOW WALL: one side allows 2.5 m at the peak and 4.86 m at the fail edge, two opposite sides allow 5.00 m and 9.72 m. A 4 m room scores 0.395 lit on one side and 0.902 lit on two. The factor is the pattern stated as a ratio, and it is not miscalibrated. WITHDRAWN from 39.14: the "2.5 m absurd optimum" reading, and "the corpus's realised median depth is 2.95 m, so the search built what it was paid for" -- 2.95 was the median A/L, while the corpus's single-aspect leaves are a median 3.46 m deep and its two-opposite leaves 4.42 m. Ordinary rooms. Section retitled, passage struck in place. WITHDRAWN from 39.15: calling six specs "self-contradictory". They are large rooms, and under Alexander a large room is supposed to need two aspects; the audit's new column reports the pattern working, not a mis-specification. What is real is the tension between that demand and what the plan form supplies. SURVIVES, on a better argument: crinkliness_shape="daylight". 159 states a MINIMUM, and a two-sided gaussian turns a minimum into a target -- 68% of the 133 leaves in the clipped region are lit on two or more sides, mean quality 0.770, docked for satisfying the pattern well, on top of the exterior_wall/boundary_wall charge those windows already carry in cost. New 39.16 records this and relocates the residual. Over the 430 graded baseline leaves: unlit 77 (100% fail), one side 208 (15%), two-corner 87 (2%), two-opposite 34 (0%), three/four 24 (4%). Light on two sides all but guarantees a pass and only 33.7% of leaves get it, so the open question is why a binary slicing tree on a convex plot can only give a third of its leaves two aspects -- a plan-form question, not a scoring one. Filed as homemaker-py-773; 39.11's courtyard finding is the same question from the other side. Both errors came from reading a dimensionless ratio as a length, so the provenance and the interpretation now sit next to the constant in fitness.py, not only in DESIGN.md. 405 passed, 72 skipped. Refs homemaker-py-u5q. 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.