Add core_divide, core_undivide, level_fix operators; wire reqs to mutate()
core_divide: divides a C leaf simultaneously on ALL storeys that share that path, maintaining staircase consistency as an atomic invariant rather than requiring multi-step recovery. core_undivide: reverses core_divide consistently across all floors, merging a C sub-core back into a single C leaf everywhere. level_fix: atomically moves a level-constrained room to its required floor by retyping the largest leaf there and vacating the wrong-floor leaf to C. Requires `reqs` (SpaceReq dict); disabled (zero probability) without it. mutate() gains `reqs=None` parameter; driver.search() passes its already- loaded reqs so level_fix fires during the main memetic loop. Together these let the optimiser escape the deceptive valley around the 2-fail warmstart: level_fix moves l1 to level 0 (reducing fails 2→1), then core_divide can split the C core to accommodate the displaced t3. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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{"id":"homemaker-py-1p0","title":"Geometry inner loop: full-objective equal-offset ratio optimiser","description":"DESIGN.md §5.1, §7 Phase 1. Productionise experiments/optimize_fullfitness.py into homemaker: optimise(topology, x0=None) -\u003e (geometry, fitness). DOF = equal-offset division ratios of free branches (solver.free_branches, lowest-storey cut ownership), clipped to [eps, 1-eps]. Objective = full oracle fitness (never a proxy — §4.2 falsified). Must support warm-start x0 (§5.6) and a population/batch evaluation mode so each iteration scores via one batched oracle call (§4.6).","acceptance_criteria":"Reproduces or exceeds §4.5 gains (x1.24–x1.67, no new failures) on 2f45907, candidate-002, c964435; works as a library call on any corpus .dom","status":"closed","priority":1,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:36:58Z","created_by":"Bruno Postle","updated_at":"2026-06-12T08:46:31Z","started_at":"2026-06-12T00:14:19Z","closed_at":"2026-06-12T08:46:31Z","close_reason":"innerloop.optimise() lands: batched CMA-ES sigma ladder (0.05/0.15, IPOP popsize doubling, deterministic seeding) over equal-offset free-branch ratios vs full oracle fitness; warm-start x0 supported. Acceptance vs unprojected originals: x1.65/x1.66/x1.58 against bars x1.24/x1.67/x1.59, no new failures, 46 oracle calls vs NM's 200. Two near-bar results accepted as reproduced-within-noise (1% tol) — draw spread brackets the single-NM-draw bars; approved by Bruno 2026-06-12. Gotchas: equal-offset projection of legacy unequal cuts loses fitness/adds failures (midpoint projection used); pycma seed=0 means clock-seeded.","dependencies":[{"issue_id":"homemaker-py-1p0","depends_on_id":"homemaker-py-av5","type":"blocks","created_at":"2026-06-12T00:39:33Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":3,"comment_count":0}
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{"id":"homemaker-py-8cs","title":"Experiment: warm-vs-cold start of inner loop (Lamarckian inheritance)","description":"DESIGN.md §5.6, §4.6. Warm-starting a child topology's inner loop from the parent's optimised ratios is the main lever for cutting per-topology cost (~3 min/topology cold). Apply single topology mutations to optimised corpus designs, re-optimise warm (surviving cuts keep values, new cuts get heuristic defaults) vs cold, compare oracle-call counts to convergence at equal final fitness.","acceptance_criteria":"Speedup factor measured across \u003e=10 mutated topologies; decision recorded (expect order-of-magnitude; if \u003c2x, revisit §4.6 Phase-2 scoping)","notes":"Experiment script committed (experiments/warm_vs_cold.py, 1cc86c8) and machinery validated oracle-free; one mutated child scored through the oracle OK. Waiting on homemaker-py-gp2 reference run to finish, then execute under URB_NO_OCCLUSION=1 (3 parents x 400 evals + 12 children x 2 x 200 evals, ~1.5-2 h oracle time). Default budgets: parent 400, child 200; target = evals to 95% of best final.","status":"closed","priority":1,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-06-11T23:36:58Z","created_by":"Bruno Postle","updated_at":"2026-06-12T11:44:45Z","closed_at":"2026-06-12T11:44:45Z","close_reason":"Measured (URB_NO_OCCLUSION=1, parent budget 400, child 200, 12 single mutations across 3 designs): cold start reached 95% of warm final in 0/12 cases within budget — speedup unbounded at practical budgets; warm finals beat cold finals x1.2-x4 in 12/12; 6/12 warm starts were within 95% at 1 eval (near-neutral mutations). Decision: Lamarckian warm-starting is MANDATORY in the memetic driver (homemaker-py-b39), not an optimisation; cold starts produce strictly worse geometry at equal budget. Note: 2 undivides were exactly fitness-neutral (same-type merge == Merge_Divided equivalence) — locality datum for homemaker-py-nyb.","dependencies":[{"issue_id":"homemaker-py-8cs","depends_on_id":"homemaker-py-1p0","type":"blocks","created_at":"2026-06-12T00:39:34Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
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{"id":"homemaker-py-av5","title":"Batched oracle: score many .dom files per invocation","description":"oracle.py currently scores one .dom per urb-fitness.pl call (~1.65 s/dom). DESIGN.md §4.6: batching amortises Perl startup to ~0.99 s/dom and is required so population/batch optimisers can score a whole generation in one oracle call. Extend oracle.py with a batch API: write N .dom files, one perl invocation, parse N .score/.fails pairs. Keep the single-file path for compatibility.","acceptance_criteria":"Batch of 35 corpus files scores in one perl invocation; per-file results identical to single-file calls; measured s/dom reported","status":"closed","priority":1,"issue_type":"task","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:36:56Z","created_by":"Bruno Postle","updated_at":"2026-06-12T00:14:06Z","started_at":"2026-06-11T23:50:40Z","closed_at":"2026-06-12T00:14:06Z","close_reason":"score_batch() lands in oracle.py; 35-file corpus parity verified single-vs-batch (1e-12 rel fitness, exact fail sets); 0.98 s/dom batched vs 1.27 single, x1.30","dependency_count":0,"dependent_count":1,"comment_count":0}
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{"id":"homemaker-py-g0b","title":"homemaker-fitness: native Python CLI replacement for urb-fitness.pl","description":"We need a Python CLI tool that replicates the behaviour of urb-fitness.pl so we can score .dom files without shelling out to Perl. The tool should: accept .dom file paths as arguments (or glob *.dom in cwd if none given), load patterns.config and costs.config from cwd and parent dir (local overrides project-level), skip scoring if .score and .fails files are already newer than the .dom (unless FORCE_UPDATE env var is set), score each .dom using fitness.Fitness.score_with_fails(), write the score to \u003cdom\u003e.score (40-digit float format), write the failures to \u003cdom\u003e.fails, print the score to stderr. Expose as homemaker-fitness entry point in pyproject.toml and as python -m homemaker_layout.fitness_cmd module. This replaces the oracle.py shelling-out path for Phase 3 native fitness.","status":"in_progress","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-14T12:32:29Z","created_by":"Bruno Postle","updated_at":"2026-06-14T12:32:52Z","started_at":"2026-06-14T12:32:52Z","dependency_count":0,"dependent_count":0,"comment_count":0}
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{"id":"homemaker-py-gpx","title":"Native fitness parity gap on multi-storey designs (~3.7%)","description":"During programme-house cold-start runs with the fixed level_add operator, the generated 2-storey design showed native=1.2388e-04 vs oracle=1.1944e-04 (3.7% gap), exceeding the 0.01% rel_tol in test_native_fitness_score_parity. All existing single-storey corpus files pass parity fine (73/73). Hypothesis: a subtle discrepancy in value or cost computation for multi-level trees — candidates are staircase quality, circulation connectivity, or per-storey cost accumulation. To investigate: score a sweep of known multi-storey corpus files natively vs oracle and identify which term diverges.","status":"open","priority":2,"issue_type":"bug","owner":"bruno@postle.net","created_at":"2026-06-14T09:35:34Z","created_by":"Bruno Postle","updated_at":"2026-06-14T09:35:34Z","dependency_count":0,"dependent_count":0,"comment_count":0}
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{"id":"homemaker-py-hqw","title":"Make homemaker-py standalone: remove dependency on Perl Urb package","description":"Currently tests and fitness scoring depend on the Perl Urb package (urb-fitness.pl) and corpus files in /home/bruno/src/urb/examples/. The tool should be fully standalone and not require any external Perl packages or local urb corpus paths. This includes: bundling or reimplementing any needed reference data, making the native Python fitness the default path, and ensuring tests pass without /home/bruno/src/urb present.","status":"closed","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-13T22:27:54Z","created_by":"Bruno Postle","updated_at":"2026-06-13T22:39:28Z","started_at":"2026-06-13T22:34:20Z","closed_at":"2026-06-13T22:39:28Z","close_reason":"Closed","dependency_count":0,"dependent_count":0,"comment_count":0}
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{"id":"homemaker-py-0px","title":"Blank-slate cold-start initialisation","description":"The outer search stalls when starting from init.dom (Phase 2 gate: 18 fails after 2000 evals vs urb-evolve's 6). The root cause is single-seed topology mutation chaining — building structure one room at a time gives no gradient across the large zero-feasibility region. Fix requires multi-start bootstrap: generate a diverse initial population by random topology sampling, or a greedy room-placement initialiser that satisfies adjacency/level constraints before handing off to the memetic loop. Without this the tool is only useful for refining existing designs, not designing new buildings from scratch.","acceptance_criteria":"Cold-start from init.dom reaches comparable fail count to urb-evolve within equal eval budget; tested on programme-house","status":"closed","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-13T21:52:15Z","created_by":"Bruno Postle","updated_at":"2026-06-13T22:28:58Z","started_at":"2026-06-13T22:24:02Z","closed_at":"2026-06-13T22:28:58Z","close_reason":"Bootstrap implemented: auto-detect bare-plot seed, generate pop_size random topologies, evaluate each at child_budget before memetic loop; 3 new tests all green","dependency_count":0,"dependent_count":0,"comment_count":0}
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{"id":"homemaker-py-ccw","title":"Scaled topology search on native fitness","description":"DESIGN.md §7 Phase 3 closing step. Once native fitness passes corpus parity, re-run the Phase-2 memetic search at real scale (population/generations comparable to urb-evolve) on the native objective. This is the first point where the §1 scaling question gets a real answer.","acceptance_criteria":"Full-scale run on programme-house beats both urb-evolve and the small-scale Phase-2 result; larger programme attempted","status":"closed","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:38:59Z","created_by":"Bruno Postle","updated_at":"2026-06-13T21:11:13Z","started_at":"2026-06-13T20:49:27Z","closed_at":"2026-06-13T21:11:13Z","close_reason":"Closed","dependencies":[{"issue_id":"homemaker-py-ccw","depends_on_id":"homemaker-py-uxz","type":"blocks","created_at":"2026-06-12T00:39:44Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-ccw","depends_on_id":"homemaker-py-way","type":"blocks","created_at":"2026-06-12T00:39:45Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":2,"dependent_count":1,"comment_count":0}
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@ -28,10 +30,12 @@
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{"id":"homemaker-py-yg5","title":"Penalty reshaping: replace 0.5^n while preserving inner-loop protection","description":"DESIGN.md §4.7, §5.4, §7 Phase 4, §8.5. The 0.5^n cliff gives the outer search no gradient and rewards flag-count over geometry, but it also PROTECTS the inner loop from trading into new failures (§4.5). One fitness shape cannot naively be both soft outside and cliff-protected inside. Candidates: cliff-inside-inner-loop only, lexicographic (failure count first, score second), additive/soft, multi-objective Pareto. Must preserve the missing-space failure hierarchy (worse to drop a room than to have a poor one). Measure landscape + search outcomes; this helps Urb today too.","acceptance_criteria":"Chosen scheme documented with measurements: search improves while inner loop still never trades into new failures","status":"closed","priority":3,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:39:00Z","created_by":"Bruno Postle","updated_at":"2026-06-14T08:16:14Z","started_at":"2026-06-14T07:55:32Z","closed_at":"2026-06-14T08:16:14Z","close_reason":"Implemented lexicographic outer-search comparison (-n_fails, fitness). Inner loop unchanged (0.5^n cliff protection preserved). Experiment penalty_reshape.py confirms 0/9 fail regressions in inner loop and shows lex avoids the 3-fail trap that scalar hits 1/3 of the time. Fixed stale _CHILD_INNER_KW sigmas entry.","dependencies":[{"issue_id":"homemaker-py-yg5","depends_on_id":"homemaker-py-uxz","type":"blocks","created_at":"2026-06-12T00:39:46Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
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{"id":"homemaker-py-9gp","title":"Canonical slicing encoding (normalized Polish expression) + shape feasibility","description":"DESIGN.md §5.5, §7 Phase 5. Representation upgrade once core lands: normalized Polish expression / skewed slicing tree (Wong–Liu) for redundancy-free, high-locality topology moves (M1/M2/M3); bottom-up shape-feasibility checks to prune infeasible topologies before the inner loop. Goal: scale to larger programmes. Excluded representations stay excluded (§2): no sequence-pair/B*-tree (non-slicing).","acceptance_criteria":"Encoding round-trips with the genome; M1/M2/M3 moves implemented; measured search improvement on a larger-than-house programme","status":"open","priority":4,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-11T23:39:02Z","created_by":"Bruno Postle","updated_at":"2026-06-11T23:39:02Z","dependencies":[{"issue_id":"homemaker-py-9gp","depends_on_id":"homemaker-py-ccw","type":"blocks","created_at":"2026-06-12T00:39:48Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
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{"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.","status":"open","priority":4,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-11T23:38:25Z","created_by":"Bruno Postle","updated_at":"2026-06-12T07:27:48Z","dependency_count":0,"dependent_count":0,"comment_count":0}
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{"_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."}
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{"_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)."}
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{"_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."}
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{"_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."}
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{"_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."}
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{"_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."}
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{"_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)."}
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{"_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."}
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{"_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."}
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{"_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."}
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{"_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."}
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{"_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."}
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{"_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."}
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[project.scripts]
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homemaker-evolve = "homemaker_layout.evolve:main"
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homemaker-fitness = "homemaker_layout.fitness_cmd:main"
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[project.optional-dependencies]
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dev = ["pytest>=8.0", "ruff>=0.5"]
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@ -249,7 +249,8 @@ def search(
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else:
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parent = _tournament(pop, rng, _key)
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child_root, desc = operators.mutate(parent.root, rng, types,
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weights=_MUTATION_WEIGHTS)
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weights=_MUTATION_WEIGHTS,
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reqs=reqs)
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ratios = parent.ratios
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x0 = innerloop.warm_x0(child_root, ratios)
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tasks.append((child_root, x0, child_budget, inner_kw, desc))
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@ -122,6 +122,140 @@ def mutate_rotate(root: dom.Node, rng: np.random.Generator,
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return _finalise(child), f"rotate {li}/{n.id or 'root'}"
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def mutate_level_fix(root: dom.Node, rng: np.random.Generator,
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types: list[str], reqs=None) -> tuple[dom.Node, str]:
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"""Atomically move a level-constrained room to its required floor.
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Finds a room type with a ``level: N`` constraint that currently sits on the
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wrong storey. Retypes the LARGEST leaf on the required floor to that room,
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and retypes the vacated wrong-floor leaf to a generic (C or O). Does not
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undivide anything, so the size may still be suboptimal — the inner NM loop
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fixes geometry, and subsequent core_divide / retype mutations fill in any
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displaced rooms.
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Requires ``reqs`` (dict[str, SpaceReq] from programme.load_programme_dir).
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"""
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if not reqs:
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return _finalise(copy.deepcopy(root)), "level_fix noop"
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from . import geometry as _geo
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level_types = {code: req.level for code, req in reqs.items()
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if getattr(req, "level", None) is not None}
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if not level_types:
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return _finalise(copy.deepcopy(root)), "level_fix noop"
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child = copy.deepcopy(root)
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lvls = dom.levels(child)
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violations = [
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(li, lf, code, req_level)
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for code, req_level in level_types.items()
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for li, lvl in enumerate(lvls)
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for lf in lvl.leaves()
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if lf.type == code and li != req_level
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]
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if not violations:
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return _finalise(child), "level_fix noop"
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li_wrong, wrong_leaf, code, req_level = _pick(rng, violations)
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if req_level >= len(lvls):
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return _finalise(child), "level_fix noop"
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correct_leaves = lvls[req_level].leaves()
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if not correct_leaves:
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return _finalise(child), "level_fix noop"
|
||||
|
||||
# Pick the largest leaf on the correct floor as the best landing spot
|
||||
target = max(correct_leaves, key=lambda lf: _geo.area(lf))
|
||||
target.type = code
|
||||
|
||||
generics = [t for t in types if t.upper() in ("C", "O")]
|
||||
wrong_leaf.type = str(rng.choice(generics)) if generics else "C"
|
||||
|
||||
return _finalise(child), (
|
||||
f"level_fix {code}: lvl{li_wrong}/{wrong_leaf.id or 'root'}"
|
||||
f" → lvl{req_level}/{target.id or 'root'}"
|
||||
)
|
||||
|
||||
|
||||
def mutate_core_divide(root: dom.Node, rng: np.random.Generator,
|
||||
types: list[str]) -> tuple[dom.Node, str]:
|
||||
"""Divide a circulation leaf at the same path across ALL storeys at once.
|
||||
|
||||
Staircase cores (C leaves at the same path on 2+ consecutive floors) are
|
||||
disrupted if a single-storey divide changes the C path on only one floor.
|
||||
This operator applies the same rotation and division to every floor that
|
||||
has a C leaf at the chosen path, maintaining staircase consistency as an
|
||||
atomic invariant rather than a multi-step recovery task.
|
||||
"""
|
||||
child = copy.deepcopy(root)
|
||||
lvls = dom.levels(child)
|
||||
|
||||
# Collect paths that are C leaves on 2+ floors
|
||||
c_paths: dict[str, list[int]] = {}
|
||||
for li, lvl in enumerate(lvls):
|
||||
for lf in lvl.leaves():
|
||||
if lf.type and lf.type.upper() == "C":
|
||||
c_paths.setdefault(lf.id, []).append(li)
|
||||
core_paths = [(path, lis) for path, lis in c_paths.items() if len(lis) >= 2]
|
||||
if not core_paths:
|
||||
return _finalise(child), "core_divide noop"
|
||||
|
||||
path, level_indices = _pick(rng, core_paths)
|
||||
rotation = int(rng.integers(4))
|
||||
division = [0.5, 0.5]
|
||||
|
||||
for li in level_indices:
|
||||
node = lvls[li].by_id(path)
|
||||
if node is None or node.divided:
|
||||
continue
|
||||
node.division = list(division)
|
||||
node.rotation = rotation
|
||||
node.left = dom.Node(type="C")
|
||||
node.right = dom.Node(type=str(_pick(rng, types)))
|
||||
node.type = None
|
||||
|
||||
return _finalise(child), f"core_divide {path} ({len(level_indices)} floors)"
|
||||
|
||||
|
||||
def mutate_core_undivide(root: dom.Node, rng: np.random.Generator,
|
||||
types: list[str]) -> tuple[dom.Node, str]:
|
||||
"""Reverse of core_divide: merge a C sub-core back into a single C leaf on all floors.
|
||||
|
||||
Picks a C leaf (e.g. 'rll') whose parent is also a C leaf on 2+ floors,
|
||||
then undivides the parent on every floor simultaneously, restoring the
|
||||
larger staircase footprint without a temporary path-mismatch fail.
|
||||
"""
|
||||
child = copy.deepcopy(root)
|
||||
lvls = dom.levels(child)
|
||||
|
||||
# Find divided nodes whose left child is C (candidate for core_undivide):
|
||||
# the parent path must have C.left on 2+ floors.
|
||||
parent_paths: dict[str, list[int]] = {}
|
||||
for li, lvl in enumerate(lvls):
|
||||
for n in [n for li2, n in _owned_branches(child) if li2 == li]:
|
||||
if (n.left.type and n.left.type.upper() == "C"
|
||||
and not n.left.divided and not n.right.divided):
|
||||
parent_paths.setdefault(n.id or "", []).append(li)
|
||||
core_parents = [(p, lis) for p, lis in parent_paths.items() if len(lis) >= 2]
|
||||
if not core_parents:
|
||||
return _finalise(child), "core_undivide noop"
|
||||
|
||||
path, level_indices = _pick(rng, core_parents)
|
||||
for li in level_indices:
|
||||
node = lvls[li].by_id(path)
|
||||
if node is None or not node.divided:
|
||||
continue
|
||||
keep = [t for t in (node.left.type, node.right.type)
|
||||
if t and t[0].lower() not in "cos"]
|
||||
node.type = keep[0] if keep else (node.left.type or str(_pick(rng, types)))
|
||||
node.division = None
|
||||
node.left = node.right = None
|
||||
|
||||
return _finalise(child), f"core_undivide {path} ({len(level_indices)} floors)"
|
||||
|
||||
|
||||
def mutate_level_retype(root: dom.Node, rng: np.random.Generator,
|
||||
types: list[str]) -> tuple[dom.Node, str]:
|
||||
"""Swap the types of two leaves on different storeys.
|
||||
|
|
@ -180,6 +314,9 @@ MUTATIONS = {
|
|||
"retype": mutate_retype,
|
||||
"swap": mutate_swap,
|
||||
"rotate": mutate_rotate,
|
||||
"core_divide": mutate_core_divide,
|
||||
"core_undivide": mutate_core_undivide,
|
||||
"level_fix": mutate_level_fix,
|
||||
"level_retype": mutate_level_retype,
|
||||
"level_add": mutate_level_add,
|
||||
"level_delete": mutate_level_delete,
|
||||
|
|
@ -187,11 +324,19 @@ MUTATIONS = {
|
|||
|
||||
|
||||
def mutate(root: dom.Node, rng: np.random.Generator, types: list[str],
|
||||
weights: dict[str, float] | None = None) -> tuple[dom.Node, str]:
|
||||
weights: dict[str, float] | None = None,
|
||||
reqs=None) -> tuple[dom.Node, str]:
|
||||
"""Apply one random mutation drawn from MUTATIONS."""
|
||||
names = sorted(MUTATIONS)
|
||||
p = np.array([(weights or {}).get(n, 1.0) for n in names], dtype=float)
|
||||
name = rng.choice(names, p=p / p.sum())
|
||||
# level_fix needs programme reqs; disable it silently when not available
|
||||
if reqs is None:
|
||||
p[names.index("level_fix")] = 0.0
|
||||
if p.sum() == 0:
|
||||
p[:] = 1.0
|
||||
name = str(rng.choice(names, p=p / p.sum()))
|
||||
if name == "level_fix":
|
||||
return mutate_level_fix(root, rng, types, reqs=reqs)
|
||||
return MUTATIONS[name](root, rng, types)
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue