Composer half of the ground-truth bead: examples/harbor-house/drawings/
harbor-house 1.svg turned out to be a Bonsai/Blender render of 3m.dom's own
IFC (32 IfcSpace paths == 3m.dom's upper-storey leaf count), not a human
trace, so no usable reference exists yet -- this builds and tests the
pipeline that will consume one once traced. compose.py parses storey-N
Inkscape layers of cut-lines + labels against a boundary-stub .dom (plot/
height/elevation only, no room shapes to keep aligned across storeys) and
recursively detects guillotine cuts, mirroring geometry.py's own
division-line algebra; non-slicible regions and label mismatches are
reported by location rather than guessed at. homemaker-compose CLI added.
Renamed dom._link to public dom.link since compose.py needs to re-link from
outside dom.py. Full design writeup in DESIGN.md sec 37.3; actual human
tracing of harbor-house/programme-house is tracked as follow-up under
2g7.1, still open.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
Follow-up to the previous commit: user flagged that quality_width/
quality_proportion's "stricter of both" (max target, min sigma) combination
for a fused leaf's two codes was an ad hoc hack. Tried two more principled
alternatives and A/B'd all three against the harbor-house/health-centre
example programmes (20k evals x 3 seeds each):
1. stricter-of-both (original) -> health-centre +24.5% worse
2. precision-weighted Gaussian product -> health-centre -13.9% better
3. mixture (max of two Gaussians) -> health-centre +20.4% worse
Landed #2 (fitness._gaussian_product): combining two Gaussian evidence
sources about the same quantity via precision-weighting gives an
intermediate target with a narrower spread, unlike the naive max/min hack.
#3's building block (_clipped_gaussian) is kept, documented, and unit-tested
as a recorded negative alternative -- somewhat counterintuitively, the more
philosophically appealing "let the leaf collapse toward whichever code fits"
mixture model was empirically worse, because max() lets a leaf score 1.0 by
satisfying only the weaker of the two codes' targets.
multi_use stays default OFF -- the precision-weighted result improves both
example programmes on average but isn't the clean sweep needed for a
default flip (harbor-house loses 1/3 seeds). DESIGN.md §33 rewritten with
the full three-way comparison.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
Builds path (b) from §26 -- a leaf permanently serving two DIFFERENT
compatible programme codes at once, extending leaf-sharing's same-code
multiplicity mechanism to different-but-compatible codes. Architect-declared
`co_locate` pairs (validated against interchangeable()'s S1-S4 bounds, no
transitive closure so the b3v chain problem can't recur), threaded through
graph.py's checks via a new leaf_codes() resolver and fitness.py's quality
terms (additive size, stricter-of-both width/proportion). Construction-time
only, gated behind `multi_use` (default OFF, bit-identical when off).
End-to-end A/B (20k evals x 3 seeds x 2 programmes) came back net negative:
harbor-house -4.0% but health-centre +24.5% worse (3/3 seeds), because
fusing different codes' shape targets via stricter-of-both can impose a
tighter joint constraint than either code needed alone, which the tightly-
packed health-centre programme can't absorb. Written up as DESIGN.md §33;
multi_use stays default OFF, no default-flip recommended.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
