---
title: "world-tabular-rules — Mova code model"
description: "Mova code model of world/src/tabular.rs: components with their labels, obernants and the model text."
url: "https://groupants.elamur.ai/free/en/mova/world/src/tabular.rs.groupant/rules.groupant/"
---
Elamur: [Mova](https://md.mova.elamur.ai/free/en/) · [Dictionary](https://md.dictionary.elamur.ai/free/en/) · [Research](https://md.research.elamur.ai/free/en/) · **Groupants**

This page as HTML: <https://groupants.elamur.ai/free/en/mova/world/src/tabular.rs.groupant/rules.groupant/>

# world-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

- **kind:** groupant_fragment
- **status:** partial source bound model
- **source:** [`world/src/tabular.rs`](https://github.com/ElamurAI/mova-free/blob/main/world/src/tabular.rs)
- **source SHA-256:** `7ce9d08ca2a14933…`
- **covers:** Cond, List, covers, learn, learn_k, learn_kc, predict, learn_rare; orchestration run/main_args (bytes 8206–20918)
- **part of:** [world-tabular](https://md.groupants.elamur.ai/free/en/mova/world/src/tabular.rs.groupant/)
- **repository folder:** [`world/src/tabular.rs.groupant/rules.groupant`](https://github.com/ElamurAI/mova-free/tree/main/world/src/tabular.rs.groupant/rules.groupant)
- **repository commit:** `855da7bd4a7b`

> **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](#forward) |
| **ast** | partial | `../syntax.json` |
| **composition** | partial source model | [composition](#composition) |
| **cost** | partial source model | [cost](#cost) |
| **descriptant** | partial source model | [epoch-memory](#epoch-memory) |
| **invariant** | partial source model | [invariant](#invariant) |
| **tests** | partial source model | [checks](#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](#behavior-complement) |
| `world-tabular-rules-reverse-reconstruction` | reverse_reconstruction | partial source model | [reverse-reconstruction](#reverse-reconstruction) |
| `world-tabular-rules-rules-coupling` | constraint_relation | partial source model | [rules-coupling](#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(H*N log N); identifier branch recomputes an alphabetic count for every row O(H*N^2). Group expansion bounds groups to <=12, plus linear duplicate membership scans. Let A attributes and R rules: candidate generation O(R*N*sum_{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.