Why this note exists

The Chapter 9 generator’s vocabulary is not invented; it is measured off the census. This note records that vocabulary and the descriptive contract under which the generator uses it.

1. The kit and its conditional rules

Ten data-derived geometric primitives (size band × aspect band × rectangular/complex) cover 90.7 per cent of all 12,849 spaces (top three 65.2 per cent). The single largest class is the undimensioned-rectangle placeholder (41.7 per cent): not a true geometric primitive but the spaces without printed dimensions; restricting to the 7,245 spaces with a resolved dimensioned shape, the ten most common dimensioned primitives cover 88.7 per cent of them. Either way the generative kit is small: a whole dwelling is drawn from about seven distinct primitives (kit-reuse ≈ 2.4). Each primitive carries a size signature (for example M-square-rect ≈ 3.6 × 3.2 m, bedroom-dominated; L-oblong-rect ≈ 5.5 × 3.7 m, living; XL-square-rect ≈ 6.0 × 5.8 m, garage). The generator selects and sizes a primitive for a context using the conditional design rules of Appendix H.5 (the empirical distribution of size for a given category × host × graph-role × bedroom-count cell).

2. The generator is descriptive: a lookup-and-redraw sampler, not a fitted model

The conditional rules are empirical conditional distributions tabulated over the complete census. The generator’s primary output for a context is the observed band (median and IQR) a designer dials within: a deterministic lookup. Where a single value is wanted, it is redrawn from the literal observed multiset of that cell, so the generator can emit nothing the corpus did not exhibit in that context; it never synthesises an intermediate value through a fitted curve, and it makes no inferential claim, because over a complete enumeration the band is the population’s own value, not an estimate.¹ This is categorically unlike procedures that manufacture pseudo-samples to estimate sampling variability (none is used) and unlike learned generators that fit weights to a training sample.

3. Two hard constraints on the generator

1. Out-of-corpus guard (required, mechanised). For any context cell the census never observed, the generator flags and refuses to synthesise rather than interpolating a size; interpolation across cells would be inferential. This must be a hard check in the generator contract, not a discipline.
2. Coupling stays on the topology graph. The adjacency vocabulary uses the opening grammar (design rule DR-4) for opening type, but HC-8C V2’s obligatory-versus-preferred split (60 HARD / 49 SOFT) is read from the V14 impact_hm topology graph (15,912 nodes; 15,858 eligible undirected edges; 445 unordered classified pairs). The geometry graph carries a narrower resolved subset and cannot carry this contract.

Thin context cells are reported with their exact observed count and spread, with the wider parent-type band carrying the recommendation: a transparent dial-stability convention, not a sample-size correction.

4. Positioning

The kit extends the shape-grammar tradition by offering a measured generative vocabulary that is sampled rather than an author-specified rule set that is enumerated;² and it differs from learned floor-plan generators (for example the RPLAN/Graph2Plan and House-GAN families) by exposing explicit, inspectable conditional distributions instead of design knowledge locked inside network weights.³ Descriptive throughout; CANDIDATE until operator MR-13.