3 Commits

Author	SHA1	Message	Date
forbes-0023	0b5813b5a9	feat: parameterized assembly templates and complexity tiers ... Add 4 new topology generators to SyntheticAssemblyGenerator: - generate_tree_assembly: random spanning tree with configurable branching - generate_loop_assembly: closed ring producing overconstrained data - generate_star_assembly: hub-and-spoke topology - generate_mixed_assembly: tree + loops with configurable edge density Each accepts joint_types as JointType | list[JointType] for per-joint type sampling. Add complexity tiers (simple/medium/complex) with predefined body count ranges via COMPLEXITY_RANGES dict and ComplexityTier type alias. Update generate_training_batch with 7-way generator selection, complexity_tier parameter, and generator_type field in output dicts. Extract private helpers (_random_position, _random_axis, _select_joint_type, _create_joint) to reduce duplication. 44 generator tests, 130 total — all passing. Closes #7	2026-02-02 14:38:05 -06:00
forbes-0023	dc742bfc82	test: add unit tests for datagen modules ... - test_types.py: JointType enum values/count, dataclass defaults/isolation - test_pebble_game.py: DOF accounting, rigidity, classification, edge results - test_jacobian.py: Jacobian shape per joint type, rank, parallel axis degeneracy - test_analysis.py: demo scenarios (revolute, fixed, triangle, parallel axes) - test_generator.py: chain/rigid/overconstrained generation, training batch Bug fixes found during testing: - JointType enum: duplicate int values caused aliasing (SLIDER=REVOLUTE etc). Changed to (ordinal, dof) tuple values with a .dof property. - pebble_game.py: .value -> .dof for constraint count - analysis.py: classify from effective DOF (not raw pebble game with virtual ground body skew) 105 tests, all passing. Closes #6	2026-02-02 14:08:22 -06:00
forbes-0023	363b49281b	build: phase 0 infrastructure setup ... - Project structure: solver/, freecad/, export/, configs/, scripts/, tests/, docs/ - pyproject.toml with dependency groups: core, train, freecad, dev - Hydra configs: dataset (synthetic, fusion360), model (baseline, gat), training (pretrain, finetune), export (production) - Dockerfile with CUDA+PyG GPU and CPU-only targets - docker-compose.yml for train, test, data-gen services - Makefile with targets: train, test, lint, format, type-check, data-gen, export, check - Pre-commit hooks: ruff, mypy, conventional commits - Gitea Actions CI: lint, type-check, test on push/PR - README with setup and usage instructions	2026-02-02 13:26:38 -06:00