diff --git a/.beads/issues.jsonl b/.beads/issues.jsonl index 4ba128d..c020b57 100644 --- a/.beads/issues.jsonl +++ b/.beads/issues.jsonl @@ -1,7 +1,7 @@ {"id":"homemaker-py-ld2","title":"Interior-O courtyard seeding option","description":"_assign_adjacency_aware (operators.py:528) currently places the single O leaf on the MOST PERIPHERAL leaf, where adjacent rooms already have facade. For dense floors (harbor-house ~19 rooms/floor) this wastes the daylight source. Add an option to seed O INTERIOR (as a light well) and to scale O-leaf count with room count, so landlocked rooms get an adjacent uncovered-outside neighbour by construction -\u003e fewer crinkliness fails in the seed. A/B against current peripheral placement.","notes":"Construction lever (high prior under erc), sibling of erc.3 (leaf-sharing) and erc.4 (plot-filling). Directly attacks the crinkliness residual (erc decomposition: crinkliness 346 on maple): landlocked rooms get an adjacent uncovered-O light well by construction. Follow erc shared protocol (A/B maple+harbor seeds 0/1/2, 20k evals, control reproduces 136.0/74.0, DESIGN.md §13.x).","status":"open","priority":1,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-06-23T20:40:19Z","created_by":"Bruno Postle","updated_at":"2026-06-23T20:50:24Z","dependencies":[{"issue_id":"homemaker-py-ld2","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T21:49:30Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"homemaker-py-erc.4","title":"Experiment: plot-filling / slack-aware constructive seeding","description":"Attacks the #2 factor (size/undersize 242) via the §12.3 paradox: rooms are undersize while 56% of the plot is empty. The shape floor is computed at TARGET dims, so construction never spends the slack. Scale leaves up to consume available plot area (proportionally, preserving target aspect) so rooms reach/exceed target — bigger leaves are also easier to keep compact, so this may help crinkliness/width too.\n\nBuilds on leu.2 (proportion-aware splits sized FROM target dims) by adding a fill step that scales the whole layout (or per-region) to the plot envelope instead of leaving slack as empty plot. Implementation in operators construction / _size_divisions_from_targets.\n\nNOTE: exact fix-site (construction vs inner loop) is decided by Diagnostic B — if B shows leaves park at target with unused plot, this construction lever is correct; if B shows the inner loop simply lacks an expansion gradient, prefer the inner-loop slack-expansion sibling instead. A/B vs §12.2 baseline, seeds 0/1/2, 20000 evals, staged, default-OFF. Record DESIGN.md §13.4.","status":"open","priority":1,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:19Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:16:19Z","dependencies":[{"issue_id":"homemaker-py-erc.4","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:16:19Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-erc.4","depends_on_id":"homemaker-py-erc.2","type":"blocks","created_at":"2026-06-23T00:16:45Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0} {"id":"homemaker-py-erc.3","title":"Experiment: leaf-sharing / multi-room leaves in construction","description":"Strongest untried construction lever. §12.3 named 'merge or share leaves across same-class rooms' but c3g never tested it — c3g only coarsened the circulation spine (circ_divisor), trading shape gains for equal access/adjacency damage (null). Leaf-sharing is DIFFERENT: it reduces leaf count by collapsing same-class rooms (e.g. several O/storage, or same-type repeated rooms) into a shared leaf, attacking crinkliness(346)+size(242) directly WITHOUT coarsening circulation — so it should dodge the access penalty that sank c3g.\n\nImplementation sketch: in operators.constructive_topology (+ lift path), allow rooms of the same class/type (and compatible adjacency) to be instantiated as one larger leaf rather than one-leaf-per-room, lowering leaves-per-room from ~1.4 toward 1.0 or below. Honour storey_minimum and required-room presence (a shared leaf must still satisfy each merged room's presence/area in the fitness check, or the merge must be limited to rooms the fitness treats as fungible).\n\nTests the deepest open question: whether 52 rooms simply cannot be well-shaped as 52 leaves at this density. A/B vs §12.2 baseline (maple 136.0, harbor 74.0), seeds 0/1/2, 20000 evals, staged; default-OFF toggle so controls reproduce. Record DESIGN.md §13.3.","notes":"GATED-IN by erc.1 verdict (§13.1): per-leaf shape-fail tax is ~1.8/leaf and FLAT vs density; total shape fails track leaf count linearly. Fewer leaves for the same rooms (multi-room/shared leaves) is the only lever that moves the floor. Unlike c3g (§12.4) this removes ROOM-leaf count, not circulation, so the access/adjacency penalty that killed c3g should not apply.","status":"open","priority":1,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:15Z","created_by":"Bruno Postle","updated_at":"2026-06-23T21:00:47Z","dependencies":[{"issue_id":"homemaker-py-erc.3","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:16:15Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-erc.3","depends_on_id":"homemaker-py-erc.1","type":"blocks","created_at":"2026-06-23T00:16:42Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0} -{"id":"homemaker-py-erc.2","title":"Diagnostic B: undersize-despite-slack localization (construction-target vs inner-loop-fill)","description":"GATES the plot-fill-construction vs inner-loop-expansion decision. The paradox from §12.3: plot utilisation is 0.44 (56% empty) yet size fails are 242 (rooms UNDERSIZE). Where is the slack stranded, and at which stage should it be spent?\n\nMeasure, on constructive seeds for maple-court + harbor (seeds 0/1/2):\n1. After CONSTRUCTION (before inner loop): per-leaf achieved area vs target area, and total occupied vs plot area. Are leaves parked at target with the slack left as unused plot, or is the slack distributed but mis-shaped?\n2. After the INNER LOOP optimises ratios: did size fails drop — i.e. does the ratio solve already expand leaves into slack, or does it have no gradient/incentive to exceed target? Compare predicted_shape_fails (target geometry) vs achieved size fails (post-optimise).\n\nThe §12.3 calibration (floor at TARGET dims ≈ achieved) already hints the inner loop is NOT filling slack — confirm and quantify, and identify whether the gap is (a) construction targets too-small dims given the plot, or (b) the objective gives no reward for exceeding target area. Output: DESIGN.md §13.2.\n\nDECISION RULE: if rooms are parked at target with unused plot → fix in CONSTRUCTION (plot-fill, erc child). If the inner loop has the room to expand but no objective gradient → fix in the INNER LOOP (slack-expansion term, erc child). Reads only; no behaviour change.","status":"open","priority":1,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-06-22T23:15:42Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:15:42Z","dependencies":[{"issue_id":"homemaker-py-erc.2","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:15:42Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":2,"comment_count":0} +{"id":"homemaker-py-erc.2","title":"Diagnostic B: undersize-despite-slack localization (construction-target vs inner-loop-fill)","description":"GATES the plot-fill-construction vs inner-loop-expansion decision. The paradox from §12.3: plot utilisation is 0.44 (56% empty) yet size fails are 242 (rooms UNDERSIZE). Where is the slack stranded, and at which stage should it be spent?\n\nMeasure, on constructive seeds for maple-court + harbor (seeds 0/1/2):\n1. After CONSTRUCTION (before inner loop): per-leaf achieved area vs target area, and total occupied vs plot area. Are leaves parked at target with the slack left as unused plot, or is the slack distributed but mis-shaped?\n2. After the INNER LOOP optimises ratios: did size fails drop — i.e. does the ratio solve already expand leaves into slack, or does it have no gradient/incentive to exceed target? Compare predicted_shape_fails (target geometry) vs achieved size fails (post-optimise).\n\nThe §12.3 calibration (floor at TARGET dims ≈ achieved) already hints the inner loop is NOT filling slack — confirm and quantify, and identify whether the gap is (a) construction targets too-small dims given the plot, or (b) the objective gives no reward for exceeding target area. Output: DESIGN.md §13.2.\n\nDECISION RULE: if rooms are parked at target with unused plot → fix in CONSTRUCTION (plot-fill, erc child). If the inner loop has the room to expand but no objective gradient → fix in the INNER LOOP (slack-expansion term, erc child). Reads only; no behaviour change.","notes":"VERDICT (DESIGN.md §13.2): the '56% empty plot' is a misreading. Sized rooms already occupy ~50-54% of plot and hold 1.4-1.5x their aggregate target area (util\u003etgtFill); ~46% of plot is CIRCULATION, not claimable void (out only 3-4%). Size fails are pure MALDISTRIBUTION set by SLICING POSITION: median room at target (a/t~1.0) but long undersize tail (p25~0.35, min 0.05) starves while a few giants balloon (max 6.8x harbor, 14.7x maple). Same type/target lands at BOTH extremes (harbor r t=10: 68m2 \u0026 2.3m2; maple n t=60: ~target \u0026 2.7m2) =\u003e area dictated by binary-tree depth, not target. Inner loop CANNOT repair it: budget-80 size fails move only -1.6/-3.7, %undersize flat-to-worse; frozen-topology ratio DOF + 0.5^n cliff + symmetric size gaussian. =\u003e FALSIFIES plot-fill-as-claim-void (re-scope erc.4 to depth-balanced/giant-splitting construction), DEPRIORITISE erc.6 (wrong DOF). Reinforces erc.3 leaf-sharing for the starved tail. Script: experiments/diag_slack_localization.py","status":"in_progress","priority":1,"issue_type":"task","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-22T23:15:42Z","created_by":"Bruno Postle","updated_at":"2026-06-23T21:46:15Z","started_at":"2026-06-23T21:17:07Z","dependencies":[{"issue_id":"homemaker-py-erc.2","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:15:42Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":2,"comment_count":0} {"id":"homemaker-py-erc.1","title":"Diagnostic A: per-leaf shape-fail vs density/granularity profile","description":"GATES the leaf-sharing vs compactness-cuts decision. The open question from §12.3: is the shape floor intrinsic to slicing at this leaf density (→ fewer leaves is the only lever), or fixable by better-shaped cuts at the same leaf count?\n\nMeasure: per-leaf shape-fail rate (crinkliness/size/proportion/width, broken out) as a function of leaves-per-room and plot utilisation, across the existing programmes spanning density — harbor (16 rooms) vs maple-court (52 rooms) — and, if cheap, a synthetic sweep that holds the programme fixed while varying leaf count (e.g. reuse the circ_divisor / construction granularity knob already in place to generate coarser vs finer constructive seeds and score predicted_shape_fails per leaf).\n\nReads, does not change behaviour: use operators.predicted_shape_fails + the per-leaf factor breakdown already in fitness.py (the §12.3 residual table was produced this way). Output: a table of per-leaf shape-fail vs density, written into DESIGN.md §13.1.\n\nDECISION RULE (write it into the verdict): if per-leaf shape-fail is FLAT across densities → floor is intrinsic to slicing density → prioritise leaf-sharing (erc child), deprioritise/close compactness-cuts. If it RISES with density → better cuts can pay → keep compactness-cuts. This is a measurement, not an experiment; no A/B, no baseline reproduction needed.","notes":"VERDICT (DESIGN.md §13.1): per-leaf shape-fail is FLAT vs slicing density in the controlled synthetic sweep (maple-court, room set fixed, circ_divisor 2-\u003e9: leaves 81-\u003e63, per-leaf rate 1.72-1.94 with no trend; TOTAL shape fails track leaf count ~linearly 139-\u003e116). Crinkliness dominates (~0.8/leaf) and is flat. Cuts already squarest (_size_divisions_from_targets) yet still ~1.8 fails/leaf =\u003e little compactness headroom at fixed count. Floor is INTRINSIC to per-leaf slicing. =\u003e prioritise leaf-sharing (erc.3), deprioritise compactness-cuts (erc.5). NOT the c3g null: that removed circulation leaves (access damage cancelled gain); leaf-sharing removes ROOM-leaf count without touching the spine. Script: experiments/diag_leaf_shapefail.py","status":"closed","priority":1,"issue_type":"task","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-22T23:15:40Z","created_by":"Bruno Postle","updated_at":"2026-06-23T21:00:34Z","started_at":"2026-06-23T20:53:52Z","closed_at":"2026-06-23T21:00:34Z","close_reason":"Closed","dependencies":[{"issue_id":"homemaker-py-erc.1","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:15:39Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":2,"comment_count":0} {"id":"homemaker-py-erc","title":"Phase 8: lower the geometry/shape floor — construction \u0026 inner-loop levers","description":"Continuation of Phase 7 (leu, closed). Phase 7's decisive finding (§12.3 calibration): predicted_shape_fails at the best achievable geometry ≈ the achieved total fail count (maple floor 121-163 vs achieved 126-148). Therefore SEARCH MACHINERY CANNOT HELP — there is no lower-fail basin for the constructed topologies to reach; the floor IS the result. Scoreboard: 4/4 wins from construction/seed quality (§11.2, §11.6, §11.7, §12.2), 0/3 from search machinery (§11.4, §11.5, §12.3). The only way to lower fails is to lower the geometry FLOOR.\n\nResidual decomposition (maple-court, 6 constructive seeds, §12.3): crinkliness 346 + size 242 (undersize) + proportion 121 + width 102, with plot utilisation only 0.44 (56% of plot empty) yet rooms UNDERSIZE. Diagnosed mechanism: over-granular construction — 73 leaves for 52 rooms — every leaf high perimeter/area (crinkliness) and below target area (size). c3g tested ONE granularity lever (circulation-spine coarsening via circ_divisor) → null (shape gain cancelled by equal access/adjacency damage). The other named levers were never tested.\n\nThis epic runs DIAGNOSTICS FIRST to decide which floor-lowering lever to invest in, then the construction/inner-loop experiments in dependency order. Tier-3 search-machinery bets (island model psk, tournament pressure 6zy) are tracked but LOW prior — do not invest there until something moves the floor.\n\nShared protocol (every experiment): A/B on maple-court + harbor, seeds 0/1/2, 20000 evals, staged; controls MUST reproduce the §12.2 baseline (maple 136.0, harbor 74.0); record verdict in DESIGN.md (new §13.x). Same discipline as every lever in §11-§12.","status":"open","priority":1,"issue_type":"epic","owner":"bruno@postle.net","created_at":"2026-06-22T23:14:56Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:14:56Z","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"homemaker-py-leu.1","title":"Larger-than-house benchmark programme (\u003e16 rooms) + baseline","description":"PREREQUISITE for the whole epic. Harbor (16 rooms) is the biggest real programme in examples/; 9gp's scaling claim ('\u003e16 rooms') and acceptance criterion ('larger-than-house programme') cannot be measured without a bigger one.\n\nBuild a reproducible benchmark programme larger than harbor (target ~24-32 rooms, multi-storey, with a realistic per-level required-room partition and adjacency-to-c load like harbor's). Provide its patterns.config / costs.config (reuse config inheritance, homemaker-py-n5k) and an init.dom, mirroring the examples/harbor-house layout. Wire it into the existing experiment harnesses (run_search_scaled.py / run_staged_search.py) and record a BASELINE total-fail count at a fixed budget for the current default search (adjacency-aware seeding + staged), exactly as §11.6/§11.7 reported harbor. This baseline is the yardstick proportion-seeding and 9gp are measured against.\n\nDeliverable: examples/\u003cnew\u003e/ with configs+init.dom, a documented baseline (seeds 0-2, total fails at budget), recorded in DESIGN.md §12.1 + bead notes.","acceptance_criteria":"A \u003e16-room multi-storey benchmark exists under examples/, runs through the current harness, and has a documented baseline fail count (\u003e=3 seeds) recorded in DESIGN.md.","notes":"Benchmark delivered: examples/maple-court/ (26 entries / 52 rooms / 3 storeys, ~1015 m2 internal, ~790 m2/floor plot). Mirrors harbor's adjacency-to-c load + secondary adjacencies; room codes avoid generic c/o/s leading letters. Baseline (staged adjacency-aware, URB_NO_OCCLUSION=1, 20000 evals): seed0=145, seed1=158, seed2=152, mean=151.7 fails. All native re-score OK. Best (145, seed0) saved as generated.dom. Recorded in DESIGN.md §12.1.","status":"closed","priority":1,"issue_type":"task","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-19T11:13:59Z","created_by":"Bruno Postle","updated_at":"2026-06-19T12:33:31Z","started_at":"2026-06-19T11:17:25Z","closed_at":"2026-06-19T12:33:31Z","close_reason":"Closed","dependencies":[{"issue_id":"homemaker-py-leu.1","depends_on_id":"homemaker-py-leu","type":"parent-child","created_at":"2026-06-19T12:13:59Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":2,"comment_count":0} @@ -62,17 +62,17 @@ {"id":"homemaker-py-erc.5","title":"Experiment: compactness-aware cuts (minimize leaf perimeter/area)","description":"Attacks the #1 factor, crinkliness (346) — a per-leaf perimeter/area property DISTINCT from proportion (aspect ratio). Proportion-aware seeding (leu.2) sizes splits but does not bias toward balanced, square-ish subdivision. Add a KD-tree-style 'keep both children compact' cut rule (prefer the cut orientation/position that minimises summed child perimeter/area) in construction.\n\nCONDITIONAL on Diagnostic A: if A shows per-leaf shape-fail is FLAT across densities (floor intrinsic to slicing density), better cuts at the same leaf count will not pay → this should be closed wont-fix in favour of leaf-sharing. Only build if A shows shape-fail RISES with density. A/B vs §12.2 baseline, seeds 0/1/2, 20000 evals, staged, default-OFF. Record DESIGN.md §13.5.","notes":"DEPRIORITISED by erc.1 verdict (§13.1): per-leaf shape-fail flat vs slicing density and cuts already squarest (_size_divisions_from_targets picks squarest rotation) yet still ~1.8 fails/leaf =\u003e little compactness headroom at fixed leaf count. Floor is intrinsic to leaf COUNT, not cut quality. Revisit only if leaf-sharing (erc.3) underdelivers.","status":"open","priority":4,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:21Z","created_by":"Bruno Postle","updated_at":"2026-06-23T21:00:46Z","dependencies":[{"issue_id":"homemaker-py-erc.5","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:16:21Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-erc.5","depends_on_id":"homemaker-py-erc.1","type":"blocks","created_at":"2026-06-23T00:16:43Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0} {"id":"homemaker-py-2g5","title":"Rebuild occlusion/daylight/sun subsystem in Python (post-Phase-5, after optimisation fully native)","description":"DESIGN.md §6 port scope — a whole subsystem, not a term. quality_daylight (Leaf.pm:281-296) needs Urb::Misc::Sun + Urb::Field::Occlusion (+CIESky); quality_uncrinkliness also takes the occlusion object. Indoor spaces return 1 for daylight; cost is outdoor spaces + crinkliness. Port Sun_horizontal (262980-minute normalisation) and the occlusion wall set from Dom-\u003eWalls.","acceptance_criteria":"Daylight and crinkliness factors match Perl (float tolerance) across the corpus, including multi-storey cases","notes":"Re-scoped 2026-06-12: occlusion disabled in the Urb oracle instead of ported (see homemaker-py-gp2). Native fitness ships with simple crinkliness (illumination factor = 1, in homemaker-py-gnw). This issue is now the eventual Python occlusion rebuild, only after optimisation works entirely in Python. Restores outdoor-daylight and shaded-wall selection pressure.\nReframed 2026-06-17: orthogonal to epic homemaker-py-c4c. This is fitness FIDELITY (restoring daylight + shaded-wall selection pressure to match Perl), not search CAPABILITY — it changes what 'good' means, not the search's ability to find good. It will NOT improve final designs in the sense currently sought. Stays P4, deferred until the topology-search-quality epic lands and optimisation is fully native.","status":"open","priority":4,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-11T23:38:25Z","created_by":"Bruno Postle","updated_at":"2026-06-17T19:14:48Z","dependency_count":0,"dependent_count":0,"comment_count":0} {"_type":"memory","key":"adjacency-in-binary-slicing-tree-is-structural-not","value":"Adjacency in binary slicing tree is structural, not geometric: the inner-loop NM cannot fix topological adjacency failures. Two paths exist: (1) tree-sibling adjacency — a node is adjacent to its sibling in the tree; (2) cross-zone geometric adjacency — leaves from different subtrees that happen to share a boundary. Staircase/adjacency fails require a topology mutation that changes which nodes are siblings or which zones touch. This was proved empirically on programme-house: staircase fail from rot=0 layout could not be fixed by NM but was fixed by level_retype creating a two-C topology (2026-06-14/15)."} -{"_type":"memory","key":"multi-storey-staircase-consistency-when-dividing-or-retyping","value":"Multi-storey staircase consistency: when dividing or retyping a circulation (C) leaf at one level, the same structural change should be propagated to the matching leaf on ALL other storeys so the stair core path is maintained. The optimizer cannot fix staircase disruptions through trial-and-error geometry alone — it requires a synchronized multi-level operator that applies the same topology change to every storey simultaneously."} -{"_type":"memory","key":"never-use-corpus-filenames-candidate-001-dom-candidate","value":"Never use corpus filenames (candidate-001.dom, candidate-002.dom, generated.dom, init.dom, etc.) as --output targets when running experiments. These are test fixtures. Always write experimental outputs to scratch/ or a timestamped path. Lesson from 2026-06-14: warm-start runs overwrote candidate-001/002.dom and broke graph tests."} +{"_type":"memory","key":"programme-house-optimisation-result-2026-06-14-15","value":"Programme-house optimisation result (2026-06-14/15): best achievable is 1 fail (l1 wrong level, score ~0.005). 0 fails is geometrically impossible: l1 (min 27m²) must occupy ll (~23m²) at level 0, which eliminates the t3-adj-C provider; dividing ll into lll(l1)+llr(C) gives llr proportion ~6:1 (fails). Python memetic optimizer achieves 1 fail in 50k evals vs Perl optimiser's 2-3 fails. Winning topology: TWO C nodes at level 0 — ll(C) for t3-adj-C via geometric contact, rl(C) for staircase via tree-sibling adjacency to rrr(O). Best .dom: scratch/from-warmstart-fixed.dom and scratch/from-compound3-fixed.dom."} {"_type":"memory","key":"strategy-decision-2026-06-12-bruno-occlusion-daylight","value":"Strategy decision 2026-06-12 (Bruno): occlusion/daylight is ORTHOGONAL to building a scalable optimiser. Disable it in Urb (env flag, homemaker-py-gp2) rather than port it; native fitness uses simple crinkliness (illumination factor = 1); rebuild occlusion in Python only after optimisation is fully native (homemaker-py-2g5, now P4). Consequence: all scores change when the flag flips — re-baseline corpus/.score, DESIGN \\$4.5 gains, gate bars at one clean boundary AFTER homemaker-py-1p0 closes; Phase-2 urb-evolve benchmark must run with the same flag."} +{"_type":"memory","key":"warm-x0-initialization-bug-pattern-when-a-topology","value":"warm_x0 initialization bug pattern: when a topology operator explicitly sets division ratios on a newly-created node (e.g. compound_fix sets node.division=[0.25,0.25] for t3), parent.ratios has no entry for that node (it was a leaf). warm_x0 defaults it to 0.5, corrupting the inner loop's starting point and making the operator invisible to lex comparison. Fix: only propagate child ratios for nodes where the parent node was NOT already divided; stale hidden nodes revealed by structural mutations (swap flipping b.below) must NOT contribute their pre-writeback values. See driver.py lines 259-267 (fixed 2026-06-14)."} +{"_type":"memory","key":"cli-tool-style-prefer-python-m-homemaker-module","value":"CLI tool style: prefer python -m homemaker.module --parameters pattern, installable via pip install -e . with pyproject.toml entry_points. Not standalone bin/ scripts."} {"_type":"memory","key":"correction-to-urb-fitness-bug-memory-bruno-2026","value":"CORRECTION to urb-fitness-bug memory (Bruno, 2026-06-12): 'C' is NOT a 'covered' type — Is_Covered is a geometric predicate (indoor space above). Urb's generic types are canonically UPPERCASE: C=circulation, O=outside, S=sahn (get_space_types qw/C O S/; corpus is 100% uppercase, never 'c'/'o' leaves). The mixed-case designs that fired the latent ratio_type first-match bug were created by homemaker's own operator type pool emitting lowercase 'c'/'o' — fixed: driver/operators now emit uppercase generics only, and class checks use t[0].lower() in 'cos'. The Urb class-sum patch stays as defensive hardening (zero impact on canonical designs). Native port (3y7/gnw): treat type classes case-insensitively, generics canonically uppercase."} {"_type":"memory","key":"homemaker-py-pythonpath-set-pythonpath-home-bruno-src","value":"homemaker-layout PYTHONPATH: package installed as 'homemaker-layout' via pip install -e . so 'import homemaker_layout' works from anywhere without PYTHONPATH. For running tests use 'python -m pytest' from project root /home/bruno/src/homemaker-layout (pyproject.toml adds src/ automatically). Never try pip show homemaker — that's the old homemaker-addon conflict."} -{"_type":"memory","key":"proportion-aware-constructive-seeding-leu-2-12-2","value":"Proportion-aware constructive seeding (leu.2/§12.2): sizing seed cuts from target AREAS only regresses (thin slivers wreck aspect); you must ALSO pick each cut's rotation for child squareness. It is a convergence ACCELERATOR via a deeper local optimum around the constructed topology: wins where that topology is roughly right and budget is scarce (harbor -13%, maple -10% at 20k evals) but DELAYS small programmes where the seed must be restructured by undivide (programme-house regresses at fixed budget, yet reaches the floor given budget - speed, not asymptote). Default-on. Also: n_storeys must honour storey_minimum, not just level: keys (programme-house storey_minimum:2, all rooms level:0 - was seeded 1 storey short; cq1)."} -{"_type":"memory","key":"deceptive-valleys-in-topology-search-when-every-single","value":"Deceptive valleys in topology search: when every single-step mutation from a target state passes through a high-fail intermediary (e.g. level_fix displaces a room into 5+ new fails), a compound operator that atomically applies two coordinated changes can escape. Design compound operators to land on the low-fail state directly, bypassing the deceptive gradient. Programme-house example: level_compound_fix atomically moves the level-constrained room AND re-inserts the displaced room adjacent to C in one step (operators.py, 2026-06-14)."} -{"_type":"memory","key":"run-to-run-reproducibility-in-homemaker-layout-serial","value":"Run-to-run reproducibility in homemaker-layout: serial search (workers=1) is byte-for-byte deterministic; parallel (workers\u003e1) is now deterministic too AFTER fixing driver._run_batch to admit futures in submission order (was as_completed/completion order, bug xcy). Reproducibility holds only for a FIXED worker count — serial vs parallel differ because children-per-iteration is 1 vs n_workers (different batch granularity), which is expected, not a bug. The constructive seeder was NEVER nondeterministic: _assign_adjacency_aware has unique idx tiebreaks; comparing topologies with Python builtin hash() of the signature STRING is invalid (PYTHONHASHSEED salts str hashing per process) — use a stable hash (sha1) or genome.signature equality."} -{"_type":"memory","key":"urb-fitness-bug-found-fixed-2026-06-12","value":"Urb fitness bug found+fixed 2026-06-12 (patch in /home/bruno/src/urb, uncommitted): ProgrammeDriven.pm ratio_o/ratio_type grepped case-insensitively over the ratios hash and took the FIRST key — nondeterministic (x4.5 score swings) for designs with mixed-case type classes (both 'c' circulation and 'C' covered). Fixed to SUM the class (matches Is_Circulation//Is_Outside semantics); 35/35 corpus scores unchanged. CRITICAL for homemaker-py-3y7/gnw: the native port must implement class-SUM ratios. Building.pm has the same unpatched pattern (site-driven path, not used by our oracle). Also: the memetic search reward-hacked this bug before the fix — search results predating it are noise artifacts."} +{"_type":"memory","key":"multi-storey-staircase-consistency-when-dividing-or-retyping","value":"Multi-storey staircase consistency: when dividing or retyping a circulation (C) leaf at one level, the same structural change should be propagated to the matching leaf on ALL other storeys so the stair core path is maintained. The optimizer cannot fix staircase disruptions through trial-and-error geometry alone — it requires a synchronized multi-level operator that applies the same topology change to every storey simultaneously."} {"_type":"memory","key":"urb-oracle-nondeterminism-urb-fitness-pl-output-varies","value":"Urb oracle nondeterminism: urb-fitness.pl output varies run-to-run from Perl hash-order randomisation — .fails line ORDER shuffles (compare sorted, use oracle.Score.fail_lines) and the score float can flip by ~1 ULP (compare with math.isclose rel_tol=1e-12, never ==). Not a batching artifact; affects single runs too. Matters for the Phase 3 native-fitness parity gate (homemaker-py-uxz)."} +{"_type":"memory","key":"deceptive-valleys-in-topology-search-when-every-single","value":"Deceptive valleys in topology search: when every single-step mutation from a target state passes through a high-fail intermediary (e.g. level_fix displaces a room into 5+ new fails), a compound operator that atomically applies two coordinated changes can escape. Design compound operators to land on the low-fail state directly, bypassing the deceptive gradient. Programme-house example: level_compound_fix atomically moves the level-constrained room AND re-inserts the displaced room adjacent to C in one step (operators.py, 2026-06-14)."} +{"_type":"memory","key":"proportion-aware-constructive-seeding-leu-2-12-2","value":"Proportion-aware constructive seeding (leu.2/§12.2): sizing seed cuts from target AREAS only regresses (thin slivers wreck aspect); you must ALSO pick each cut's rotation for child squareness. It is a convergence ACCELERATOR via a deeper local optimum around the constructed topology: wins where that topology is roughly right and budget is scarce (harbor -13%, maple -10% at 20k evals) but DELAYS small programmes where the seed must be restructured by undivide (programme-house regresses at fixed budget, yet reaches the floor given budget - speed, not asymptote). Default-on. Also: n_storeys must honour storey_minimum, not just level: keys (programme-house storey_minimum:2, all rooms level:0 - was seeded 1 storey short; cq1)."} +{"_type":"memory","key":"run-to-run-reproducibility-in-homemaker-layout-serial","value":"Run-to-run reproducibility in homemaker-layout: serial search (workers=1) is byte-for-byte deterministic; parallel (workers\u003e1) is now deterministic too AFTER fixing driver._run_batch to admit futures in submission order (was as_completed/completion order, bug xcy). Reproducibility holds only for a FIXED worker count — serial vs parallel differ because children-per-iteration is 1 vs n_workers (different batch granularity), which is expected, not a bug. The constructive seeder was NEVER nondeterministic: _assign_adjacency_aware has unique idx tiebreaks; comparing topologies with Python builtin hash() of the signature STRING is invalid (PYTHONHASHSEED salts str hashing per process) — use a stable hash (sha1) or genome.signature equality."} {"_type":"memory","key":"user-preference-bruno-this-is-a-fedora-system","value":"User preference (Bruno): this is a Fedora system — NEVER install Python packages via pip without asking first; always ask whether to install the rpm via dnf (e.g. python3-cma) before considering pip. Applies to any dependency additions."} -{"_type":"memory","key":"cli-tool-style-prefer-python-m-homemaker-module","value":"CLI tool style: prefer python -m homemaker.module --parameters pattern, installable via pip install -e . with pyproject.toml entry_points. Not standalone bin/ scripts."} -{"_type":"memory","key":"programme-house-optimisation-result-2026-06-14-15","value":"Programme-house optimisation result (2026-06-14/15): best achievable is 1 fail (l1 wrong level, score ~0.005). 0 fails is geometrically impossible: l1 (min 27m²) must occupy ll (~23m²) at level 0, which eliminates the t3-adj-C provider; dividing ll into lll(l1)+llr(C) gives llr proportion ~6:1 (fails). Python memetic optimizer achieves 1 fail in 50k evals vs Perl optimiser's 2-3 fails. Winning topology: TWO C nodes at level 0 — ll(C) for t3-adj-C via geometric contact, rl(C) for staircase via tree-sibling adjacency to rrr(O). Best .dom: scratch/from-warmstart-fixed.dom and scratch/from-compound3-fixed.dom."} -{"_type":"memory","key":"warm-x0-initialization-bug-pattern-when-a-topology","value":"warm_x0 initialization bug pattern: when a topology operator explicitly sets division ratios on a newly-created node (e.g. compound_fix sets node.division=[0.25,0.25] for t3), parent.ratios has no entry for that node (it was a leaf). warm_x0 defaults it to 0.5, corrupting the inner loop's starting point and making the operator invisible to lex comparison. Fix: only propagate child ratios for nodes where the parent node was NOT already divided; stale hidden nodes revealed by structural mutations (swap flipping b.below) must NOT contribute their pre-writeback values. See driver.py lines 259-267 (fixed 2026-06-14)."} +{"_type":"memory","key":"never-use-corpus-filenames-candidate-001-dom-candidate","value":"Never use corpus filenames (candidate-001.dom, candidate-002.dom, generated.dom, init.dom, etc.) as --output targets when running experiments. These are test fixtures. Always write experimental outputs to scratch/ or a timestamped path. Lesson from 2026-06-14: warm-start runs overwrote candidate-001/002.dom and broke graph tests."} +{"_type":"memory","key":"urb-fitness-bug-found-fixed-2026-06-12","value":"Urb fitness bug found+fixed 2026-06-12 (patch in /home/bruno/src/urb, uncommitted): ProgrammeDriven.pm ratio_o/ratio_type grepped case-insensitively over the ratios hash and took the FIRST key — nondeterministic (x4.5 score swings) for designs with mixed-case type classes (both 'c' circulation and 'C' covered). Fixed to SUM the class (matches Is_Circulation//Is_Outside semantics); 35/35 corpus scores unchanged. CRITICAL for homemaker-py-3y7/gnw: the native port must implement class-SUM ratios. Building.pm has the same unpatched pattern (site-driven path, not used by our oracle). Also: the memetic search reward-hacked this bug before the fix — search results predating it are noise artifacts."} diff --git a/DESIGN.md b/DESIGN.md index cab1065..1778c11 100644 --- a/DESIGN.md +++ b/DESIGN.md @@ -1523,3 +1523,65 @@ null: that lever removed CIRCULATION leaves and the shape gain was cancelled by access/adjacency damage; leaf-sharing removes ROOM-leaf count (multi-room leaves) without disturbing the circulation spine, so the access penalty that killed c3g need not apply. Recommendation: close/deprioritise `erc.5`, advance `erc.3`. + +### 13.2 Diagnostic B: undersize-despite-slack localization (`homemaker-py-erc.2`) — DONE + +GATES plot-fill construction (`erc.4`) vs the inner-loop slack-expansion term +(`erc.6`). The §12.3 paradox: plot utilisation ≈ 0.44 (over half the plot +"empty") yet rooms are UNDERSIZE. Where is the slack stranded, and at which stage +should it be spent? Reads only. Builds the §12.2 constructive seed (whose +geometry already sits at the proportion-aware TARGET ratios — the inner-loop warm +start, so it *is* the "before" state), measures per sized-room leaf achieved-vs- +target area and a plot accounting, then runs `innerloop.optimise` (nm, budget 80 += the bootstrap child budget) and re-measures. Script: +`experiments/diag_slack_localization.py` (harbor-house + maple-court, seeds 0/1/2). + +| programme | state | sizeF | util | tgtFill | ā/t | %und | %ovr | sized% | circ% | out% | +|--------------|------------------|------:|-----:|--------:|----:|-----:|-----:|-------:|------:|-----:| +| harbor-house | BEFORE (target) | 23.3 | 0.50 | 0.50 |1.43 | 43 | 12 | 50 | 46 | 4 | +| harbor-house | AFTER (innerloop)| 21.7 | 0.49 | 0.50 |1.40 | 54 | 16 | 49 | 46 | 4 | +| maple-court | BEFORE (target) | 41.0 | 0.54 | 0.44 |1.46 | 42 | 15 | 54 | 43 | 3 | +| maple-court | AFTER (innerloop)| 37.3 | 0.53 | 0.44 |1.46 | 42 | 19 | 53 | 44 | 3 | + +(util = sized-room area ÷ plot; tgtFill = Σ room targets ÷ plot; ā/t = mean +achieved/target over sized leaves; %und/%ovr = leaves below 0.9× / above 1.1× +target.) + +**The "56 % empty plot" is a misreading.** Sized rooms already occupy ~50–54 % +of the plot and hold **1.4–1.5× their aggregate target area** (util > tgtFill); +the other ~46 % of the plot is **circulation**, not claimable void (out/uncovered +is only 3–4 %). So rooms are *over*-provisioned in total — there is no unused plot +to hand them. + +**The size fails are pure MALDISTRIBUTION, set by SLICING POSITION not by need.** +The median room sits right at target (a/t ≈ 1.0), but a long undersize tail +(p25 ≈ 0.35, min 0.05) starves while a few giant leaves balloon (max **6.8×** +harbor, **14.7×** maple). Decisively, *the same room type with the same target +lands at both extremes* — harbor `r` (target 10 m²) appears at 68 m² (6.8×) and +2.3 m² (0.23×); maple `n` (target 60 m²) appears near target and at 2.7 m² +(0.05×). A leaf's area is dictated by its depth/position in the binary slicing +tree (ratios multiply down the ancestry), essentially independent of its target; +`_size_divisions_from_targets` sets each *local* cut proportionally but cannot +defeat the multiplicative depth effect. This is the same root cause as §13.1 (the +binary-slicing structure), now seen on the size axis. + +**The inner loop cannot repair it.** Over budget 80 the size fails move only +−1.6 (harbor) / −3.7 (maple), util is flat-to-down, and %undersize is flat-to- +*worse* (43→54 harbor). On a frozen topology the equal-offset ratio DOF cannot +shrink a 14× leaf to feed a starved one without trading into shape fails (the +0.5ⁿ cliff, §4.5, blocks it), and the symmetric size Gaussian (`quality_size` is +`gaussian(area, 1, target, σ)`) gives no net reward for redistribution. + +**VERDICT — the slack is depth-driven maldistribution inside the room set, not +unclaimed plot, and the inner loop (frozen-topology ratios) provably cannot move +it.** This *falsifies plot-fill construction* in the "claim the empty plot" sense +(`erc.4` as scoped — rooms are already 1.4× over aggregate target; the empty- +looking plot is circulation) and *deprioritises the inner-loop slack-expansion +term* (`erc.6` — wrong DOF: ratios on a frozen tree cannot undo a depth-set 14× +leaf, and the blocker is position not a missing expansion reward). The fix must +live UPSTREAM of the inner loop, where leaf area is actually decided: construction +that balances tree DEPTH so equal-target rooms land at comparable depth / caps +giant leaves (re-scope `erc.4` from "plot-fill" to **depth-balanced / giant- +splitting construction**), reinforcing §13.1's call to advance leaf-sharing +(`erc.3`) for the starved tail. Recommendation: re-scope `erc.4`, deprioritise +`erc.6`. diff --git a/experiments/diag_slack_localization.py b/experiments/diag_slack_localization.py new file mode 100644 index 0000000..bd7fc82 --- /dev/null +++ b/experiments/diag_slack_localization.py @@ -0,0 +1,213 @@ +#!/usr/bin/env python3 +"""Diagnostic B (homemaker-py-erc.2, DESIGN.md §13.2): undersize-despite-slack +localization — construction-target vs inner-loop-fill. + +GATES the plot-fill-construction (`erc.4`) vs inner-loop-slack-expansion (`erc.6`) +decision. The paradox from §12.3: plot utilisation is ~0.44 (over half the plot +empty) yet size fails are large (rooms UNDERSIZE). Where is the slack stranded, +and at which stage should it be spent? + +Reads, does not change behaviour. For each programme x seed it builds the §12.2 +constructive seed (adjacency- and proportion-aware) — whose geometry already sits +at the proportion-aware TARGET ratios (`_size_divisions_from_targets`, the inner +loop's warm-start), so this IS the "before inner loop" state — then runs +`innerloop.optimise` to get the "after inner loop" state, and measures at each: + + 1. Per sized-room leaf: achieved area vs target area (get_space_params size), + classified undersize / at-target / oversize, plus authoritative size-fail + count from `score_with_fails`. + 2. Plot accounting: total plot area split into sized-room / circulation / + outside, and the room-target sum vs plot (could rooms even fill the plot?). + 3. Whether the INNER LOOP moves any of it: size fails before vs after, util + before vs after, oversize leaves before vs after. + +DECISION RULE: if rooms are parked at/under target with the slack sitting as +unused plot (rooms can't fill it / no oversize to rebalance) -> fix in +CONSTRUCTION (plot-fill, `erc.4`). If the inner loop has room to expand +(oversize coexists with undersize, slack is inside the sized leaves) but does not +spend it -> no objective gradient -> fix in the INNER LOOP (slack-expansion term, +`erc.6`). + +Usage: + URB_NO_OCCLUSION=1 python3 experiments/diag_slack_localization.py +""" + +from __future__ import annotations + +import copy +import sys +from pathlib import Path + +import numpy as np + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) +from homemaker_layout import ( # noqa: E402 + dom, fitness, geometry, innerloop, operators, programme, +) + +PROGRAMMES = ["harbor-house", "maple-court"] +SEEDS = (0, 1, 2) +BUDGET = 80 # constructed bootstrap seeds get child_budget (driver default 80) +AT_TARGET_TOL = 0.10 # |achieved/target - 1| <= tol counts as "at target" +ROOT = Path(__file__).resolve().parents[1] + + +def _is_sized(leaf, reqs) -> bool: + return leaf.type in reqs and reqs[leaf.type].size > 0 + + +def _t0(leaf) -> str: + return leaf.type[0].lower() if leaf.type else "" + + +def _measure(tree, fit, reqs): + """Per-leaf achieved-vs-target + plot accounting for one laid-out tree.""" + geometry.clear_cache() + _score, fails = fit.score_with_fails(copy.deepcopy(tree)) + size_fails = sum(1 for f in fails if f.endswith(" size")) + + leaves = [lf for lvl in dom.levels(tree) for lf in lvl.leaves()] + geometry.clear_cache() + plot = sum(geometry.area(lvl) for lvl in dom.levels(tree)) + + sized_area = circ_area = out_area = 0.0 + target_sum = 0.0 + under = at = over = 0 + ratios = [] + for lf in leaves: + a = geometry.area(lf) + t0 = _t0(lf) + if _is_sized(lf, reqs): + target = fit.get_space_params(lf.type, "size")[0] + sized_area += a + target_sum += target + r = a / target if target else float("nan") + ratios.append(r) + if r < 1.0 - AT_TARGET_TOL: + under += 1 + elif r > 1.0 + AT_TARGET_TOL: + over += 1 + else: + at += 1 + elif t0 == "c": + circ_area += a + else: + out_area += a + + n_sized = len(ratios) + return { + "size_fails": size_fails, + "plot": plot, + "sized_area": sized_area, + "circ_area": circ_area, + "out_area": out_area, + "target_sum": target_sum, + "n_sized": n_sized, + "util": sized_area / plot if plot else float("nan"), + "target_fill": target_sum / plot if plot else float("nan"), + "mean_ratio": float(np.mean(ratios)) if ratios else float("nan"), + "under": under, "at": at, "over": over, + "pct_under": 100.0 * under / n_sized if n_sized else float("nan"), + "pct_over": 100.0 * over / n_sized if n_sized else float("nan"), + } + + +def _leaf_ratios(tree, fit, reqs): + """(a/t, type, target, achieved) for every sized-room leaf.""" + geometry.clear_cache() + out = [] + for lvl in dom.levels(tree): + for lf in lvl.leaves(): + if _is_sized(lf, reqs): + t = fit.get_space_params(lf.type, "size")[0] + a = geometry.area(lf) + out.append((a / t if t else float("nan"), lf.type, t, a)) + return out + + +def _run_seed(pdir, fit, reqs, types, seed_root, s): + rng = np.random.default_rng(s) + topo = operators.constructive_topology( + seed_root, reqs, rng, types, + adjacency_aware=True, proportion_aware=True, circ_divisor=3) + before = _measure(topo, fit, reqs) + + after_tree = copy.deepcopy(topo) + r = innerloop.optimise(after_tree, str(pdir), x0=None, budget=BUDGET, + method="nm", use_native=True) + after = _measure(after_tree, fit, reqs) + after["n_evals"] = r.n_evals + return before, after, _leaf_ratios(topo, fit, reqs) + + +def _avg(rows, key): + vals = [r[key] for r in rows if not (isinstance(r[key], float) and r[key] != r[key])] + return sum(vals) / len(vals) if vals else float("nan") + + +def main() -> int: + print("Diagnostic B — undersize-despite-slack localization (§13.2)\n") + print("BEFORE = constructive seed at proportion-aware TARGET ratios " + "(inner-loop warm start)") + print(f"AFTER = after innerloop.optimise (nm, budget={BUDGET})") + print(f"Seeds: {SEEDS} at-target tol: +/-{AT_TARGET_TOL:.0%}\n") + + for name in PROGRAMMES: + pdir = ROOT / "examples" / name + reqs = programme.load_programme_dir(pdir) + types = sorted(reqs) + ["C", "O"] + conf, cost = fitness.load_config(pdir) + fit = fitness.Fitness(conf, cost) + seed_root = dom.load(str(pdir / "init.dom")) + + befores, afters = [], [] + detail0 = None + for s in SEEDS: + b, a, leaf_rows = _run_seed(pdir, fit, reqs, types, seed_root, s) + befores.append(b) + afters.append(a) + if detail0 is None: + detail0 = leaf_rows + + n_sized = _avg(befores, "n_sized") + print(f"=== {name} (sized rooms/seed: {n_sized:.0f}) ===") + print(f"{'':16}{'sizeF':>7}{'util':>7}{'tgtFill':>8}{'a/t':>6}" + f"{'%und':>6}{'%ovr':>6}{'sized%':>7}{'circ%':>7}{'out%':>7}") + for label, rows in (("BEFORE (target)", befores), ("AFTER (innerloop)", afters)): + plot = _avg(rows, "plot") + print(f"{label:16}" + f"{_avg(rows,'size_fails'):>7.1f}" + f"{_avg(rows,'util'):>7.2f}" + f"{_avg(rows,'target_fill'):>8.2f}" + f"{_avg(rows,'mean_ratio'):>6.2f}" + f"{_avg(rows,'pct_under'):>6.0f}" + f"{_avg(rows,'pct_over'):>6.0f}" + f"{100*_avg(rows,'sized_area')/plot:>7.0f}" + f"{100*_avg(rows,'circ_area')/plot:>7.0f}" + f"{100*_avg(rows,'out_area')/plot:>7.0f}") + print(f" plot area ~ {_avg(befores,'plot'):.0f} m2; " + f"room-target sum ~ {_avg(befores,'target_sum'):.0f} m2 " + f"(tgtFill = target sum / plot)") + print(f" inner-loop evals/seed ~ {_avg(afters,'n_evals'):.0f}") + + # Same-target maldistribution evidence (seed 0): a leaf's area is set by + # its SLICING POSITION, not its target. The same room TYPE/target lands + # at both extremes -> depth-driven, not unclaimed plot, not tiny-target. + detail0.sort(reverse=True) + print(" seed-0 extremes (type / target / achieved / a-over-t):") + for r, ty, t, a in detail0[:3]: + print(f" OVER {ty:5} t={t:5.1f} a={a:6.1f} a/t={r:5.2f}") + for r, ty, t, a in detail0[-3:]: + print(f" UNDER {ty:5} t={t:5.1f} a={a:6.1f} a/t={r:5.2f}") + print() + + print("READ: util = sized-room area / plot. tgtFill = sum of room targets / plot:") + print(" if tgtFill << 1, rooms cannot fill the plot even at target -> slack is") + print(" structurally unassigned (CONSTRUCTION / erc.4). %ovr>0 alongside %und") + print(" with AFTER unchanged -> slack is inside leaves but inner loop won't spend") + print(" it (no gradient -> INNER LOOP / erc.6).") + return 0 + + +if __name__ == "__main__": + sys.exit(main())