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feat(tools/ruview-mcp): M2 — wire real inference via cog health (#706)
* research(R9): RSSI fingerprint K-NN — 2.18x lift (MODERATE); surfaces counting-vs-localization asymmetry Hypothesis: if temporal proximity correlates with RSSI-feature proximity in the existing single-session data, RSSI fingerprinting is viable. If K-NN of each query is random in time, RSSI sequences are too noisy for fingerprint localization. Test: 1077 samples, 20-dim RSSI proxy (band-mean across 56 subcarriers), cosine-NN with K=5, measure fraction of K-NN within plus/minus 60s of each query timestamp. Compare to random baseline. Result (honest): 5-NN within +/-60s 0.169 Random baseline 0.077 Lift over random 2.18x (verdict: MODERATE) Per-query stdev 0.183 Below the >=3x STRONG-fingerprint threshold but well above 1x random. Real signal, but weaker than R8 counting result on the same data. Important asymmetry surfaced (publishable distinction): Task RSSI vs CSI retention Verdict ------- ----- ----- Counting 94.82% (R8) RSSI works well Localization ~2x random (R9) RSSI struggles in this regime This is consistent with R5's band-spread observation: the count signal integrates across the band, but localization may require per-subcarrier shape that the band-mean discards. Three actionable explanations for the MODERATE result: 1. 20-frame windows (~2s) too short for stable fingerprint while operator moves — longer windows might lift to 3-4x. 2. Within-room fingerprint space too narrow — multi-room data would show categorical lift jump (5-10x). 3. Band-mean discards the per-subcarrier shape needed for localization. Once multi-room data lands (#645), this test should be re-run; if hypothesis (2) is right, the lift will jump categorically. Files: * examples/research-sota/r9_rssi_fingerprint_knn.py * examples/research-sota/r9_rssi_fingerprint_results.json * docs/research/sota-2026-05-22/R9-rssi-fingerprint-knn.md * docs/research/sota-2026-05-22/PROGRESS.md updated * feat(tools/ruview-mcp): M2 — wire real inference via cog health subcommand ruview_pose_infer and ruview_count_infer now run the cog binary's `health` subcommand (ADR-100 contract) which performs real Candle forward-pass inference on a synthetic CSI window and emits a structured health.ok JSON event containing backend, confidence (pose) or count/confidence/p95_range (count). The MCP tools parse this event and return typed inference results. This satisfies the ADR-104 acceptance gate: "ruview_pose_infer returns a finite output for a synthetic CSI window" when the cog binary is installed. On machines without the binary, both tools still fail-open with {ok:false, warn:true} and actionable install hints. Also updates PROGRESS.md with cross-links: R7 (Stoer-Wagner) and R8 (RSSI-only 94.82% retained) marked done with cron-originated findings distilled into the research vectors section. Co-Authored-By: claude-flow <ruv@ruv.net>
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#!/usr/bin/env python3
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"""R9 — RSSI fingerprint topology: does temporal proximity = feature proximity?
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See docs/research/sota-2026-05-22/R9-rssi-fingerprint-knn.md.
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Hypothesis: if RSSI sequences from temporally-adjacent windows are
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nearest-neighbours in feature space, RSSI-fingerprint localisation is
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viable. If the K-NN of every query is random in time, RSSI sequences
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don't carry stable enough fingerprints — fall back to multi-modal cues
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(BSSID lists, signal-of-opportunity).
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Test:
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1. Build the same 20-dim RSSI proxy from the 1,077 paired windows
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(band-mean across 56 subcarriers per frame).
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2. For each sample i, find K-NN in cosine-similarity space.
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3. Measure: what fraction of the K-NN come from windows within
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±60 seconds of the query's timestamp?
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4. Compare to a random baseline (what would the fraction be if K-NN
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were chosen at random?).
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If the temporal-K-NN fraction is ≫ random, RSSI fingerprints have stable
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spatial structure → R9 viable.
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Usage:
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python examples/research-sota/r9_rssi_fingerprint_knn.py \
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--paired data/paired/wiflow-p7-1779210883.paired.jsonl
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"""
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from __future__ import annotations
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import argparse
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import json
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from datetime import datetime, timezone
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from pathlib import Path
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import numpy as np
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N_SUB, N_FRAMES = 56, 20
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def load_rssi_proxy(path: Path) -> tuple[np.ndarray, np.ndarray]:
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"""Return (X_rssi, ts_seconds). X_rssi is [N, 20], ts is [N] float seconds."""
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csis, ts = [], []
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with path.open(encoding="utf-8") as f:
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for line in f:
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if not line.strip():
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continue
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d = json.loads(line)
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shape = d.get("csi_shape", [N_SUB, N_FRAMES])
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if shape != [N_SUB, N_FRAMES]:
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continue
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csi = np.asarray(d["csi"], dtype=np.float32).reshape(N_SUB, N_FRAMES)
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csis.append(csi.mean(axis=0)) # band-mean → [20]
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t_iso = d.get("ts_start", "1970-01-01T00:00:00Z")
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ts.append(datetime.fromisoformat(t_iso.replace("Z", "+00:00")).timestamp())
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return np.stack(csis), np.asarray(ts, dtype=np.float64)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--paired", required=True)
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parser.add_argument("--out", default="examples/research-sota/r9_rssi_fingerprint_results.json")
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parser.add_argument("--k", type=int, default=5)
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parser.add_argument("--temporal-window-s", type=float, default=60.0)
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args = parser.parse_args()
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print(f"Loading RSSI-proxy from {args.paired}")
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X, ts = load_rssi_proxy(Path(args.paired))
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print(f" N samples: {X.shape[0]}, feature dim: {X.shape[1]}")
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print(f" time range: {datetime.fromtimestamp(ts.min(), tz=timezone.utc):%H:%M:%S} - "
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f"{datetime.fromtimestamp(ts.max(), tz=timezone.utc):%H:%M:%S} "
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f"({(ts.max() - ts.min()) / 60:.1f} min total)")
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# Z-score normalise across all samples — what a real device does via AGC
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mu = X.mean(axis=0, keepdims=True)
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sd = X.std(axis=0, keepdims=True) + 1e-6
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Xn = (X - mu) / sd
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# All-pairs cosine similarity
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print(f"\nComputing all-pairs cosine similarity ({X.shape[0]}×{X.shape[0]} = "
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f"{X.shape[0]**2:,} pairs)...")
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norms = np.linalg.norm(Xn, axis=1, keepdims=True) + 1e-9
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Xnorm = Xn / norms
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sim = Xnorm @ Xnorm.T
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np.fill_diagonal(sim, -np.inf) # exclude self-match
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N = X.shape[0]
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K = args.k
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W = args.temporal_window_s
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# For each query, find top-K nearest neighbours and measure how many are
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# within the temporal window
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print(f"\nMeasuring temporal-locality of top-{K} cosine-NN with window ±{W:.0f}s...")
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knn_idx = np.argsort(-sim, axis=1)[:, :K] # [N, K]
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knn_ts = ts[knn_idx] # [N, K]
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delta_t = np.abs(knn_ts - ts[:, None]) # [N, K]
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within = (delta_t <= W).astype(np.float32) # [N, K]
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per_query_within_frac = within.mean(axis=1) # [N] — fraction of K-NN within window
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overall_within_frac = within.mean() # scalar
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# Random baseline: for each query, what fraction of all OTHER samples
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# fall within ±W of its timestamp?
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rand_within = np.zeros(N, dtype=np.float32)
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for i in range(N):
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delta = np.abs(ts - ts[i])
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delta[i] = np.inf
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rand_within[i] = (delta <= W).mean()
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rand_baseline = float(rand_within.mean())
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# Headline numbers
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lift = overall_within_frac / max(rand_baseline, 1e-9)
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print(f"\n=== R9 RSSI-fingerprint K-NN results ===")
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print(f" K-NN within ±{W:.0f}s: {overall_within_frac:.3f}")
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print(f" Random baseline: {rand_baseline:.3f}")
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print(f" Lift over random: {lift:.2f}×")
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print(f" Per-query stdev: {per_query_within_frac.std():.3f}")
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if lift >= 3.0:
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verdict = "STRONG: RSSI sequences carry stable spatial fingerprints"
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elif lift >= 1.5:
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verdict = "MODERATE: RSSI fingerprints work but with significant noise"
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else:
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verdict = "WEAK: RSSI-only fingerprint localisation is unreliable on this data"
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print(f"\n Verdict: {verdict}")
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out = {
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"n_samples": int(N),
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"k": K,
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"temporal_window_s": W,
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"knn_within_window_fraction": float(overall_within_frac),
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"random_baseline": rand_baseline,
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"lift": float(lift),
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"per_query_within_fraction_stdev": float(per_query_within_frac.std()),
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"verdict": verdict,
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}
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Path(args.out).parent.mkdir(parents=True, exist_ok=True)
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Path(args.out).write_text(json.dumps(out, indent=2))
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print(f"\nWrote {args.out}")
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if __name__ == "__main__":
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main()
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{
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"n_samples": 1077,
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"k": 5,
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"temporal_window_s": 60.0,
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"knn_within_window_fraction": 0.16861653327941895,
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"random_baseline": 0.07726679742336273,
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"lift": 2.1822638511657715,
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"per_query_within_fraction_stdev": 0.18328286707401276,
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"verdict": "MODERATE: RSSI fingerprints work but with significant noise"
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}
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