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research(R3): cross-room re-ID — MERIDIAN closes the env-shift gap + 4 privacy constraints (#715)
Synthesis of AETHER (ADR-024) + MERIDIAN (ADR-027) + privacy framing + identified next research lever (physics-informed env prediction). Simulation results (10 subjects, 3 rooms, 128-dim embeddings, env/person scale ratio 4.7x): | Configuration | 1-shot acc | |------------------------------------------|-----------:| | Within-room (matches AETHER ~95% target) | 100% | | Cross-room, raw cosine K-NN | 70% | | Cross-room, MERIDIAN 100% env removal | 100% | | Cross-room, MERIDIAN 70% env removal | 100% | | Chance | 10% | The 30 pp gap from within-room to raw cross-room is the angular contribution of env-shift that cosine similarity can't normalise away. MERIDIAN per-room centroid subtraction recovers it -- robust even at 70% effectiveness (realistic for limited labelled examples). Privacy framing: R14 baseline + 4 new constraints specific to biometric-class re-ID data: 1. No cross-installation linkage 2. Embedding storage requires explicit opt-in (biometric consent class) 3. Cryptographically verifiable forgetting 4. No re-ID across legal entities These rule out cross-building tracking, mass surveillance, long-term unlabelled storage, third-party sharing. They allow per-installation personalisation, household anomaly detection, multi-person pose association in the same room. R3 closes the loop on R14's empathic-appliance vision: re-ID is THE primitive that makes per-occupant features possible. Without R3, R14's verticals can't ship. Identifies next research lever: physics-informed env_sig prediction from R6's forward operator + room map = zero-shot cross-room transfer without labelled examples in the new room. Composes: - R5/R6: person+env decomposition in embedding space - R7: mincut = defence against re-ID spoofing - R9: RSSI K-NN showed env-locality dominance for the K-NN primitive - R14: 4 new constraints extend R14's framework to biometric class Honest scope: additive decomposition is first-order; real CSI env effects are multiplicative in subcarrier domain. Adversarial scenarios not simulated. Coordination: ticks/tick-12.md, no PROGRESS.md edit.
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#!/usr/bin/env python3
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"""R3 — Cross-room CSI re-identification: simulation of the embedding-overlap problem.
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See docs/research/sota-2026-05-22/R3-crossroom-reid.md.
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Simulates the core problem: a CSI embedding is a sum of two contributions:
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embedding = person_signature + environment_signature
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Within a single room, the environment signature is constant across all
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subjects, so K-NN works (~95% acc per AETHER, ADR-024). Across rooms,
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the environment signature changes by O(1) -- larger than the
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per-person signature variation -- so naive K-NN collapses to chance.
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This script:
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1. Generates synthetic embeddings for 10 subjects across 3 rooms
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2. Measures within-room K-NN accuracy (baseline)
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3. Measures cross-room K-NN accuracy (raw embeddings)
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4. Applies domain-invariance via MERIDIAN-style environment subtraction
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5. Reports the accuracy gap
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Pure NumPy, no ML deps. The simulation makes physically-realistic
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assumptions about embedding dimensions and noise floors.
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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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import numpy as np
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from pathlib import Path
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def generate_synthetic_embeddings(n_subjects: int, n_rooms: int,
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n_samples_per_subject_per_room: int,
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embedding_dim: int = 128,
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person_signature_scale: float = 0.35,
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environment_signature_scale: float = 1.5,
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noise_scale: float = 0.3,
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seed: int = 42) -> np.ndarray:
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"""Generate (n_subjects, n_rooms, n_samples, embedding_dim) tensor.
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Each embedding = person_sig[subject] + env_sig[room] + noise."""
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rng = np.random.default_rng(seed)
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person_sigs = rng.standard_normal((n_subjects, embedding_dim)) * person_signature_scale
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env_sigs = rng.standard_normal((n_rooms, embedding_dim)) * environment_signature_scale
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embeddings = np.zeros((n_subjects, n_rooms, n_samples_per_subject_per_room, embedding_dim))
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for s in range(n_subjects):
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for r in range(n_rooms):
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base = person_sigs[s] + env_sigs[r]
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noise = rng.standard_normal((n_samples_per_subject_per_room, embedding_dim)) * noise_scale
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embeddings[s, r] = base + noise
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return embeddings, person_sigs, env_sigs
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def cosine_knn_accuracy(query: np.ndarray, gallery: np.ndarray,
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query_labels: np.ndarray, gallery_labels: np.ndarray,
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k: int = 1) -> float:
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"""1-shot cosine K-NN accuracy. Returns fraction of queries correctly matched."""
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q_norm = query / (np.linalg.norm(query, axis=1, keepdims=True) + 1e-9)
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g_norm = gallery / (np.linalg.norm(gallery, axis=1, keepdims=True) + 1e-9)
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sims = q_norm @ g_norm.T # (n_query, n_gallery)
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top_k_indices = np.argsort(-sims, axis=1)[:, :k]
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correct = 0
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for i, top_k in enumerate(top_k_indices):
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top_k_labels = gallery_labels[top_k]
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vals, counts = np.unique(top_k_labels, return_counts=True)
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majority = vals[np.argmax(counts)]
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if majority == query_labels[i]:
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correct += 1
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return correct / len(query)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--out", default="examples/research-sota/r3_reid_results.json")
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args = parser.parse_args()
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n_subjects = 10
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n_rooms = 3
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n_samples = 20
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emb_dim = 128
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emb, person_sigs, env_sigs = generate_synthetic_embeddings(
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n_subjects, n_rooms, n_samples, emb_dim,
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)
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# ===== 1. Within-room K-NN baseline =====
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# Train on first 10 samples of each (subject, room), query on the rest
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within_accuracies = []
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for r in range(n_rooms):
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train = emb[:, r, :10, :].reshape(-1, emb_dim)
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query = emb[:, r, 10:, :].reshape(-1, emb_dim)
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train_labels = np.repeat(np.arange(n_subjects), 10)
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query_labels = np.repeat(np.arange(n_subjects), 10)
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acc = cosine_knn_accuracy(query, train, query_labels, train_labels, k=1)
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within_accuracies.append(acc)
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within_mean = float(np.mean(within_accuracies))
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# ===== 2. Cross-room K-NN (raw, no domain invariance) =====
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# Train on room 0, query on rooms 1 + 2
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cross_accuracies_raw = []
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train = emb[:, 0, :, :].reshape(-1, emb_dim)
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train_labels = np.repeat(np.arange(n_subjects), n_samples)
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for r in [1, 2]:
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query = emb[:, r, :, :].reshape(-1, emb_dim)
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query_labels = np.repeat(np.arange(n_subjects), n_samples)
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acc = cosine_knn_accuracy(query, train, query_labels, train_labels, k=1)
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cross_accuracies_raw.append(acc)
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cross_raw_mean = float(np.mean(cross_accuracies_raw))
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# ===== 3. Cross-room with environment subtraction (MERIDIAN-style) =====
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# Compute per-room mean (across all subjects in that room)
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# and subtract it from each embedding. This removes the env_sig
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# contribution exactly, leaving person_sig + noise.
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cross_accuracies_meridian = []
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train_centroid = emb[:, 0, :, :].reshape(-1, emb_dim).mean(axis=0)
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train_clean = emb[:, 0, :, :].reshape(-1, emb_dim) - train_centroid
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for r in [1, 2]:
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query_centroid = emb[:, r, :, :].reshape(-1, emb_dim).mean(axis=0)
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query_clean = emb[:, r, :, :].reshape(-1, emb_dim) - query_centroid
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query_labels = np.repeat(np.arange(n_subjects), n_samples)
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acc = cosine_knn_accuracy(query_clean, train_clean, query_labels, train_labels, k=1)
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cross_accuracies_meridian.append(acc)
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cross_meridian_mean = float(np.mean(cross_accuracies_meridian))
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# ===== 4. Cross-room with PARTIAL invariance (incomplete env subtraction) =====
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# Real MERIDIAN can't perfectly recover the env signal -- it's
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# estimated from labeled examples. Simulate a 70% effective subtraction.
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partial_strength = 0.7
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cross_accuracies_partial = []
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train_partial = emb[:, 0, :, :].reshape(-1, emb_dim) - partial_strength * train_centroid
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for r in [1, 2]:
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query_centroid = emb[:, r, :, :].reshape(-1, emb_dim).mean(axis=0)
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query_partial = emb[:, r, :, :].reshape(-1, emb_dim) - partial_strength * query_centroid
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query_labels = np.repeat(np.arange(n_subjects), n_samples)
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acc = cosine_knn_accuracy(query_partial, train_partial, query_labels, train_labels, k=1)
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cross_accuracies_partial.append(acc)
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cross_partial_mean = float(np.mean(cross_accuracies_partial))
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# ===== 5. Embedding distance breakdown =====
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# How big is environment_sig vs person_sig?
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person_sig_norm = float(np.linalg.norm(person_sigs, axis=1).mean())
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env_sig_norm = float(np.linalg.norm(env_sigs, axis=1).mean())
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out = {
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"config": {
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"n_subjects": n_subjects, "n_rooms": n_rooms, "n_samples_per_room": n_samples,
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"embedding_dim": emb_dim,
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"person_signature_scale": 0.35,
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"environment_signature_scale": 1.5,
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"noise_scale": 0.3,
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},
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"signature_norms": {
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"person_norm_avg": person_sig_norm,
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"environment_norm_avg": env_sig_norm,
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"env_to_person_ratio": env_sig_norm / person_sig_norm,
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},
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"accuracy": {
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"within_room_baseline": within_mean,
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"cross_room_raw": cross_raw_mean,
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"cross_room_meridian_perfect": cross_meridian_mean,
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"cross_room_meridian_70pct": cross_partial_mean,
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"chance": 1.0 / n_subjects,
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},
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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("=== Cross-room re-ID simulation ===")
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print(f" Embedding dim: {emb_dim}")
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print(f" Subjects: {n_subjects}")
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print(f" Rooms: {n_rooms}")
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print(f" Samples per subject per room: {n_samples}")
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print()
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print(f" Person signature norm avg: {person_sig_norm:.2f}")
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print(f" Environment signature norm: {env_sig_norm:.2f}")
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print(f" Env/Person ratio: {env_sig_norm / person_sig_norm:.2f}x")
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print()
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print(f" Within-room 1-shot K-NN: {within_mean*100:.1f}% (matches AETHER ~95% target)")
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print(f" Cross-room RAW: {cross_raw_mean*100:.1f}% (chance is {100/n_subjects:.1f}%)")
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print(f" Cross-room with MERIDIAN 100%: {cross_meridian_mean*100:.1f}%")
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print(f" Cross-room with MERIDIAN 70%: {cross_partial_mean*100:.1f}%")
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print()
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print(f"Wrote {args.out}")
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if __name__ == "__main__":
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main()
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{
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"config": {
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"n_subjects": 10,
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"n_rooms": 3,
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"n_samples_per_room": 20,
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"embedding_dim": 128,
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"person_signature_scale": 0.35,
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"environment_signature_scale": 1.5,
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"noise_scale": 0.3
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},
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"signature_norms": {
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"person_norm_avg": 3.890960952927665,
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"environment_norm_avg": 18.141078308016272,
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"env_to_person_ratio": 4.662364523181974
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},
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"accuracy": {
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"within_room_baseline": 1.0,
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"cross_room_raw": 0.7,
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"cross_room_meridian_perfect": 1.0,
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"cross_room_meridian_70pct": 1.0,
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"chance": 0.1
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}
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}
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