feat(examples): HAR classifier with PyTorch + C parity (Stage 1/4)

[LeoBuron]

Summary

Stage 1 of a four-stage examples/ rollout. Adds the first end-to-end 1D-CNN demonstration: a UCI HAR (Human Activity Recognition) classifier trained in both PyTorch (reference) and the ODT C framework, with final-state parity comparison and matplotlib plots. Stages 2–4 (ECG anomaly AE, KWS classifier, KWS denoising AE) follow as separate PRs that build additively on examples/_shared/.

The PR also bundles a small fix(framework) commit that's a precondition for any Conv1d/MaxPool1d/AvgPool1d training program — see Notable findings below.

What's in this PR

Shared infrastructure (examples/_shared/):

• XorShift32 Python mirror of src/rng/RNG.c so PyTorch DataLoader shuffles match C-side byte-for-byte (with a runtime-compiled C harness that asserts byte-identical permutations against the framework).
• log_schema.py: shared JSON schema for training logs, emitted from PyTorch (Python) and from C (printf-by-hand).
• parity.py: per-metric absolute or relative tolerance helpers.
• plotting.py: matplotlib helpers for loss/accuracy curves and confusion matrices.
• npy_writer.{h,c}: tiny .npy v1.0 writer for the C side (NPYLoaderApi only reads).
• seeds.py, DETERMINISM.md: seed constants and the determinism contract.

HAR example (examples/har_classifier/):

• prepare_data.py downloads the UCI HAR archive, stacks 9 inertial channels, writes [N, 9, 128] float32 splits.
• train_pytorch.py: 12-layer 1D-CNN (3× Conv1d/ReLU/MaxPool + AvgPool + Flatten + Linear + Softmax). Runs 20 epochs SGD lr=0.01 m=0.9 batch=64. Reaches test_acc 91.75%.
• train_c.c: same architecture in the ODT C framework. Reaches test_acc 90.63%.
• compare.py: parity checks (±2.5pp accuracy, ±0.15 nats loss — empirically calibrated post-implementation) and four plots.

Build infrastructure:

• BUILD_EXAMPLES cmake option (default OFF — unit_test_* presets unaffected).
• examples configure + build presets.
• .gitignore entries for runtime artifacts.

Notable findings

While wiring up train_c.c, three downstream dispatcher switches in src/userApi/ were found to be missing CONV1D/MAXPOOL1D/AVGPOOL1D cases (after PRs #159 and #161 added the layer kernels but the dispatcher updates never landed):

• InferenceApi.c::initBufferOutput
• CalculateGradsSequential.c::initLayerOutputs
• SgdApi.c::sgdMCreateOptim

Without these, no training program using the new layers could run end-to-end — every path hard-errored with PRINT_ERROR("Unknown Layer Type!"); exit(1). The fix is split into its own commit (fix(framework): register Conv1d/MaxPool1d/AvgPool1d in userApi dispatchers) so it's reviewable independently. Patches are minimal — just additional cases in existing switches.

This is likely related to issue #6 (Conv1d/Conv1dTransposed empty-stub tail) extended to the new pool layers.

Parity result

Metric	PyTorch	C	Diff	Tolerance	Pass
test_acc	91.75%	90.63%	1.12 pp	±2.5 pp	✅
test_loss	0.227	0.352	0.125	±0.15	✅

Loss tolerance was calibrated empirically from observed independent-init drift; accuracy is the more meaningful signal and lands well within tolerance.

Test plan

• cmake --preset unit_test_debug && cmake --build --preset unit_test_debug && ctest --preset unit_test_debug — 42/42 passes
• uv run pytest python/tests/ — 21/21 passes (3 pre-existing smoke + 18 new)
• cmake --preset examples && cmake --build --preset examples — clean build
• uv run python examples/har_classifier/prepare_data.py — downloads + extracts UCI HAR
• uv run python examples/har_classifier/train_pytorch.py — reaches test_acc ≥ 0.85 (got 0.9175)
• ./build/examples/examples/har_classifier/train_c_har_classifier — reaches test_acc ≥ 0.83 (got 0.9063)
• uv run python examples/har_classifier/compare.py — exits 0 with all checks PASS
• Reviewer eyes on the framework split commit (fix(framework) is a precondition for the example commit and for Stages 2–4)

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