Elamur Groupantsworld-tabular-rules
The groupant beside world/src/tabular.rs in the Mova repository: what the code computes, what it never does, how to go back from its outputs, its invariants, cost and checks.
Facts
Implementation material. A Mova code model describes this code at one source revision: partial models, finite checks and their stated scope. It is not a reviewed general exposition, and its labels are shown as written, never raised.
Components
| component | label | where |
|---|---|---|
| approximant | partial source model | forward |
| ast | partial | ../syntax.json |
| composition | partial source model | composition |
| cost | partial source model | cost |
| descriptant | partial source model | epoch-memory |
| invariant | partial source model | invariant |
| tests | partial source model | checks |
Obernants
The typed relations around the forward model: the behaviour complement (what the code never does), the reverse reconstruction (every input compatible with an output) and specialised views.
| id | type | label | where |
|---|---|---|---|
world-tabular-rules-behavior-complement | behavior_complement | partial source model | behavior-complement |
world-tabular-rules-reverse-reconstruction | reverse_reconstruction | partial source model | reverse-reconstruction |
world-tabular-rules-rules-coupling | constraint_relation | partial source model | rules-coupling |
Model text
Cond, List, covers, learn, learn_k, learn_kc, predict, learn_rare; orchestration run/main_args
forward
Greedy covering removes matched rows, stores support counts, then prediction selects first match. run/main_args adds IO, alignment and nested CV around the same helpers.
behavior-complement
Universe U consists of input/output pairs for the scoped deterministic observations with fixed dependency state and successful allocation. N={(x,y) in U | y != F(x)} is the exact complement of the stated model. Correspondence with Rust is a source-derived hypothesis, supported only on named finite tests; unknown IO/dependency outcomes are not declared impossible.
reverse-reconstruction
B(y)={x in D | F(x)=y}, retaining all compatible state/input antecedents. x belongs to B(F(x)); unique recovery requires injectivity, which the lossy projections lack. Bounded fibers are complete only within their enumerated input domain.
rules-coupling
Type: constraint_relation. Rules are not invertible training summaries: many datasets induce the same conditions/default. IO, CV quality and CLI argument parsing are source-derived gaps. Domain is the named function scope at this source binding. Status: partial source-derived relation; native evidence only where explicitly named, otherwise a falsifiable prediction.
invariant
World arrays have one entry per row; constructor links are symmetric, self-free and duplicate-free. Row attributes are deterministic BTreeMaps. Predictions use first-match priority and boolean default; no perfect-training-fit invariant. Numeric sorting requires absence of NaN for nonpanic behavior.
composition
Greedy covering removes matched rows, stores support counts, then prediction selects first match. run/main_args adds IO, alignment and nested CV around the same helpers. Rules are not invertible training summaries: many datasets induce the same conditions/default. IO, CV quality and CLI argument parsing are source-derived gaps.
cost
For N rows, H columns and B bytes, splitting is O(B). read_world numeric sorting O(HN log N); identifier branch recomputes an alphabetic count for every row O(HN^2). Group expansion bounds groups to <=12, plus linear duplicate membership scans. Let A attributes and R rules: candidate generation O(RNsum_{j=1..min(k,3)} choose(A,j)) times BTreeMap comparison costs; rare learning stops at 14 and only enumerates up to pairs. run invokes repeated learners over 10 outer/inner folds and 15 settings. IO costs unmeasured.
checks
cargo test -p world --lib wave8_models -- --test-threads=1. File-specific filters md::wave8_models and tabular::wave8_models. Tests use native pure functions and synthetic World/Text records, never an annotator or teacher. Evidence and negative controls are in root evidence.md.
epoch-memory
2026-10-30 first epoch. Prediction: independent bounded encoding/projection observations agree exactly in debug and release, while a compiled production mutation fails the named oracle. Existing lossy behavior is retained explicitly. No numerical surrogate is attached: categorical encoding/report fidelity lacks a justified approximate metric.
limitations
read_csv does not support quoted multiline records or validate width/quotes. run can panic on empty combined rows or malformed teacher/test indices, and output IDs are not CSV escaped. Numeric NaN may panic in partial_cmp unwrap. Features are inferred jointly on train/test, despite the old module prose claiming train-only. No live teacher, training corpus, file IO or CLI run is exercised.
This page in Markdown → https://md.groupants.elamur.ai/free/en/mova/world/src/tabular.rs.groupant/rules.groupant/