Commit Graph

4 Commits

Author SHA1 Message Date
arsen da4c123df9 feat(adr-120): windowed temporal classifier (W-MLP) — 53.53% → 90.40%
Adds WindowedMlpModel: 440 → 64 ReLU → n_classes, stacks last 20
frames × 22 features as input. Captures temporal patterns that
frame-level classifiers physically cannot see (walking cadence,
sit-stand cycles, gesture rhythm).

AppStateInner gets feature_window: VecDeque<[f64; 22]> (cap 20)
auto-pushed at the 3 tick sites before adaptive_override. The
classify_window API flattens the buffer (oldest first) + current
frame's features → 440-d input → softmax over classes. Cold-start
(<20 frames) falls back to frame-level MLP.

AdaptiveModel now carries all three classifiers side-by-side:
LogReg (ADR-118), MLP (ADR-119), W-MLP (this). classify_window
picks W-MLP first; legacy classify() picks MLP > LogReg.

Result on the same 6-node, 7-class, 151,329-frame dataset:
  LogReg:   49.58%
  MLP:      53.53%
  W-MLP:    90.40%  (+36.87 pts over MLP, +50.0 pts over original
                     2-node 15-feature LogReg baseline)

Per-class W-MLP accuracy:
  absent          100% (was 41%)
  present_still   100% (was 99%, saturated)
  transition       86% (was 36%)  — sit/stand cadence captured
  waving           90% (was 38%)  — gesture cadence captured
  present_moving   82% (was 33%)  — walking step cadence captured
  active           74% (was 30%)  — jumping bursts captured

Loss broke through frame-level plateau (1.15 → 0.25). Caveat:
90.4% is training-set accuracy; ~28k weights on ~30k windowed
samples means some overfitting likely. Held-out test set
recommended as follow-up.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-18 01:02:38 +07:00
arsen 9433070864 feat(adr-119): MLP classifier (22→32→6) replaces LogReg fallback
Single-hidden-layer perceptron (~3k params, ReLU + softmax) trained via
manual backprop (no external ML crate). SGD + momentum 0.9 + weight
decay 1e-4 + cosine LR decay, 30 epochs over 151,329 frames.

AdaptiveModel carries both LogReg and MLP weights side-by-side;
classify() prefers MLP via is_trained() check, falls back to LogReg
when loading legacy 15-feature models.

Result on same 6-node 7-class dataset:
  LogReg (ADR-118):   49.58%
  MLP    (this):      53.53%   (+3.95 pts)

Per-class gains concentrated on motion classes — exactly where
non-linear feature combinations matter:
  absent          +1   (40% → 41%)
  present_still   tied (99% → 99%, class-imbalance ceiling)
  transition      +7   (29% → 36%)
  active          +8   (22% → 30%)
  waving          +4   (34% → 38%)
  present_moving  +9   (24% → 33%)

Cumulative session improvement vs 2-node 15-feature baseline:
  40.4% → 53.53% (+13.1 pts).

Loss flatlines at 1.15 around epoch 10 — frame-level information
ceiling for the 22-feature representation. Next big lever is
temporal context (windowed LSTM/TCN), documented in Out-of-scope.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-18 00:48:19 +07:00
arsen e86f650681 feat(adr-118): feature decorrelation + multi-node extractor
Audit on 6-node training data (151,329 frames) found 21 multicollinear
pairs (|r|>0.85), one dead feature (amp_min constant 0), and only node[0]
used in 8 of 15 features. Top per-feature F-stat = 15,497 but accuracy
stuck at 44.4% — classifier couldn't extract the signal that physical
sensors were already capturing.

Refactor:
- Drop 8 dead/redundant features (amp_min, amp_range, breath_bp,
  spec_pow, motion_bp, amp_mean, amp_max, amp_iqr, amp_kurt).
- Keep 4 globals: variance, mean_rssi, dom_hz, change_pts.
- Add per-node features × all 6 nodes: amp_std, amp_skew, amp_entropy.
- New N_FEATURES = 22 (was 15). Z-score normalisation kept.

API change: features_from_runtime now takes &[(u8, &[f64])] — caller
must supply per-node amplitudes. New helper current_per_node_amps()
reads AMP_HIST.nbvi_history.back() for all live nodes.

Old data/adaptive_model.json removed (incompatible 15-feature schema).

Retrain result on same 151k frames:
  44.4% → 49.58% accuracy (+5.2 pts)
Total improvement vs 2-node baseline (40.4%): +9.2 pts.

Live confidence distribution now meaningful (0.30-0.85) vs pre-fix
near-uniform 0.04-0.10. Sensor placement matters: n6 (near door, far
from AP) sep_ratio=0.60 best; n1/n5 (near AP) ~0.01-0.06 nearly dead.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-18 00:35:08 +07:00
rUv f49c722764
chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427)
The Rust port lived two directories deep (rust-port/wifi-densepose-rs/)
without any sibling under rust-port/ that warranted the extra level.
Move the whole workspace up to v2/ to match v1/ (Python) at the same
depth and shorten every cd / build command across the repo.

git mv preserves history for all tracked files. 60 files updated for
path references (CI workflows, ADRs, docs, scripts, READMEs, internal
.claude-flow state). Two manual fixes for relative-cd paths in
CLAUDE.md and ADR-043 that became wrong after the depth change
(cd ../.. → cd ..).

Validated:
- cargo check --workspace --no-default-features → clean (after target/
  nuke; the gitignored target/ was carried by the OS rename and had
  hard-coded old paths in build scripts)
- cargo test --workspace --no-default-features → 1,539 passed, 0 failed,
  8 ignored (same totals as pre-rename)
- ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm)

After-merge follow-up: contributors should `rm -rf v2/target` once and
let cargo regenerate from the new path.
2026-04-25 21:28:13 -04:00