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feat: End-to-end training pipeline with RuVector signal intelligence #49

Merged
ruvnet merged 7 commits from feat/windows-wifi-enhanced-fidelity into main 2026-03-01 13:10:26 +08:00
Conversation 2 Commits 7 Files Changed 59 +19597 -1708

7 Commits

Author SHA1 Message Date
ruv
3e245ca8a4 Implement feature X to enhance user experience and optimize performance 2026-03-01 00:08:44 -05:00
ruv
45f0304d52 fix: Review fixes for end-to-end training pipeline 
- Snapshot best-epoch weights during training and restore before
  checkpoint/RVF export (prevents exporting overfit final-epoch params)
- Add CsiToPoseTransformer::zeros() for fast zero-init when weights
  will be overwritten, avoiding wasteful Xavier init during gradient
  estimation (~2*param_count transformer constructions per batch)
- Deduplicate synthetic data generation in main.rs training mode

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 23:58:20 -05:00
ruv
4cabffa726 Implement feature X to enhance user experience and optimize performance 2026-02-28 23:51:23 -05:00
ruv
3e06970428 feat: Training mode, ADR docs, vitals and wifiscan crates 
- Add --train CLI flag with dataset loading, graph transformer training,
  cosine-scheduled SGD, PCK/OKS validation, and checkpoint saving
- Refactor main.rs to import training modules from lib.rs instead of
  duplicating mod declarations
- Add ADR-021 (vital sign detection), ADR-022 (Windows WiFi enhanced
  fidelity), ADR-023 (trained DensePose pipeline) documentation
- Add wifi-densepose-vitals crate: breathing, heartrate, anomaly
  detection, preprocessor, and temporal store
- Add wifi-densepose-wifiscan crate: 8-stage signal intelligence
  pipeline with netsh/wlanapi adapters, multi-BSSID registry,
  attention weighting, spatial correlation, and breathing extraction

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 23:50:20 -05:00
ruv
add9f192aa feat: Docker images, RVF export, and README update 
- Add docker/ folder with Dockerfile.rust (132MB), Dockerfile.python (569MB),
  and docker-compose.yml
- Remove stale root-level Dockerfile and docker-compose files
- Implement --export-rvf CLI flag for standalone RVF package generation
- Generate wifi-densepose-v1.rvf (13KB) with model weights, vital config,
  SONA profile, and training provenance
- Update README with Docker pull/run commands and RVF export instructions
- Update test count to 542+ and fix Docker port mappings
- Reply to issues #43, #44, #45 with Docker/RVF availability

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 23:44:30 -05:00
ruv
fc409dfd6a feat: ADR-023 full DensePose training pipeline (Phases 1-8) 
Implement complete WiFi CSI-to-DensePose neural network pipeline:

Phase 1 - Dataset loaders: .npy/.mat v5 parsers, MM-Fi + Wi-Pose
  loaders, subcarrier resampling (114->56, 30->56), DataPipeline
Phase 2 - Graph transformer: COCO BodyGraph (17 kp, 16 edges),
  AntennaGraph, multi-head CrossAttention, GCN message passing,
  CsiToPoseTransformer full pipeline
Phase 4 - Training loop: 6-term composite loss (MSE, cross-entropy,
  UV regression, temporal consistency, bone length, symmetry),
  SGD+momentum, cosine+warmup scheduler, PCK/OKS metrics, checkpoints
Phase 5 - SONA adaptation: LoRA (rank-4, A*B delta), EWC++ Fisher
  regularization, EnvironmentDetector (3-sigma drift), temporal
  consistency loss
Phase 6 - Sparse inference: NeuronProfiler hot/cold partitioning,
  SparseLinear (skip cold rows), INT8/FP16 quantization with <0.01
  MSE, SparseModel engine, BenchmarkRunner
Phase 7 - RVF pipeline: 6 new segment types (Index, Overlay, Crypto,
  WASM, Dashboard, AggregateWeights), HNSW index, OverlayGraph,
  RvfModelBuilder, ProgressiveLoader (3-layer: A=instant, B=hot, C=full)
Phase 8 - Server integration: --model, --progressive CLI flags,
  4 new REST endpoints, WebSocket pose_keypoints + model_status

229 tests passing (147 unit + 48 bin + 34 integration)
Benchmark: 9,520 frames/sec (105μs/frame), 476x real-time at 20 Hz
7,832 lines of pure Rust, zero external ML dependencies

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 23:22:15 -05:00
ruv
1192de951a feat: ADR-021 vital sign detection + RVF container format (closes #45) 
Implement WiFi CSI-based vital sign detection and RVF model container:

- Pure-Rust radix-2 DIT FFT with Hann windowing and parabolic interpolation
- FIR bandpass filter (windowed-sinc, Hamming) for breathing (0.1-0.5 Hz)
  and heartbeat (0.8-2.0 Hz) band isolation
- VitalSignDetector with rolling buffers (30s breathing, 15s heartbeat)
- RVF binary container with 64-byte SegmentHeader, CRC32 integrity,
  6 segment types (Vec, Manifest, Quant, Meta, Witness, Profile)
- RvfBuilder/RvfReader with file I/O and VitalSignConfig support
- Server integration: --benchmark, --load-rvf, --save-rvf CLI flags
- REST endpoint /api/v1/vital-signs and WebSocket vital_signs field
- 98 tests (32 unit + 16 RVF integration + 18 vital signs integration)
- Benchmark: 7,313 frames/sec (136μs/frame), 365x real-time at 20 Hz

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 22:52:19 -05:00