Files
ruvnet--RuView/v2/crates/rvcsi-ruvector/src/embedding.rs
T
Claude 6432dfbd2d feat(rvcsi): rvcsi-adapter-file (.rvcsi capture/replay) + rvcsi-ruvector (RF memory)
- rvcsi-adapter-file (ADR-095 FR1/FR10, D9): the `.rvcsi` JSONL capture format
  (CaptureHeader line + one CsiFrame per line), FileRecorder, FileReplayAdapter
  (a CsiSource — deterministic replay, preserves timestamps/ordering/validation
  verbatim, carries an unenforced replay_speed for the daemon/CLI), read_all().
  20 unit tests + 1 doctest.
- rvcsi-ruvector (ADR-095 FR8, D8) — standin for the production RuVector binding:
  deterministic embeddings (window_embedding = 32 resampled mean_amplitude bins +
  32 resampled phase_variance bins + [motion_energy, presence_score, quality_score,
  ln1p(frame_count)], L2-normalized, dim 68; event_embedding = 10-wide kind
  one-hot + confidence + ln1p(evidence count), dim 12), cosine_similarity, the
  RfMemoryStore trait + value objects (EmbeddingId/RecordKind/SimilarHit/
  DriftReport), and InMemoryRfMemory + JsonlRfMemory (file-backed append log,
  identical query semantics, latest-baseline-per-room-wins on reopen).
  20 unit tests + 1 doctest.

All rvcsi crates build and test together: core 29, dsp 28, events 18,
adapter-file 20(+1), adapter-nexmon 9, ruvector 20(+1) — 124 unit + 2 doc tests,
0 failures. forbid(unsafe_code) everywhere except rvcsi-adapter-nexmon (FFI).

https://claude.ai/code/session_01CdYAPvRTjcch6YrYf42n1z
2026-05-13 00:03:27 +00:00

273 lines
9.2 KiB
Rust

//! Deterministic, dependency-free embedding functions for RF memory records.
//!
//! [`window_embedding`] turns a [`CsiWindow`] into a fixed-length
//! [`WINDOW_EMBEDDING_DIM`]-vector regardless of subcarrier count;
//! [`event_embedding`] turns a [`CsiEvent`] into a fixed-length
//! [`EVENT_EMBEDDING_DIM`]-vector. [`cosine_similarity`] is the comparison
//! metric used by the [`crate::RfMemoryStore`] implementations.
//!
//! All functions are pure and deterministic — the same input always yields the
//! same bytes, with no clocks, randomness, threads or floating-point
//! reductions whose order could vary.
use rvcsi_core::{CsiEvent, CsiEventKind, CsiWindow};
/// Length of a [`window_embedding`] vector.
///
/// Layout (all indices into the returned `Vec<f32>`):
/// * `0..32` — `mean_amplitude` linearly resampled to 32 bins
/// * `32..64` — `phase_variance` linearly resampled to 32 bins
/// * `64` — `motion_energy`
/// * `65` — `presence_score`
/// * `66` — `quality_score`
/// * `67` — `ln(1 + frame_count)`
///
/// The whole vector is then L2-normalized (left all-zero if its norm is 0,
/// e.g. for an empty window).
pub const WINDOW_EMBEDDING_DIM: usize = 68;
/// Length of an [`event_embedding`] vector.
///
/// Layout:
/// * `0..10` — one-hot of [`CsiEventKind`] in declaration order (see
/// [`kind_index`])
/// * `10` — `confidence`
/// * `11` — `ln(1 + evidence_window_ids.len())`
///
/// Event embeddings are **not** normalized (the one-hot block already gives
/// them a stable scale).
pub const EVENT_EMBEDDING_DIM: usize = 12;
/// Number of bins each per-subcarrier vector is resampled to.
const SUBCARRIER_BINS: usize = 32;
/// Linearly resample `src` (length `n`) to length `m`.
///
/// * `n == 0` → `vec![0.0; m]`
/// * `n == 1` → `vec![src[0]; m]`
/// * otherwise, for each output index `j`: `pos = j * (n-1) / (m-1)`,
/// `lo = floor(pos)`, `frac = pos - lo`, value `src[lo] * (1 - frac) +
/// src[min(lo+1, n-1)] * frac`.
fn resample_linear(src: &[f32], m: usize) -> Vec<f32> {
let n = src.len();
if n == 0 {
return vec![0.0; m];
}
if n == 1 {
return vec![src[0]; m];
}
if m == 0 {
return Vec::new();
}
if m == 1 {
// Degenerate target: just take the first sample (avoids /0 below).
return vec![src[0]];
}
let mut out = Vec::with_capacity(m);
let denom = (m - 1) as f32;
let span = (n - 1) as f32;
for j in 0..m {
let pos = j as f32 * span / denom;
let lo = pos.floor() as usize;
let frac = pos - lo as f32;
let hi = (lo + 1).min(n - 1);
out.push(src[lo] * (1.0 - frac) + src[hi] * frac);
}
out
}
/// L2 norm of a slice (`0.0` for an empty slice).
fn l2_norm(v: &[f32]) -> f32 {
v.iter().map(|x| x * x).sum::<f32>().sqrt()
}
/// In-place L2 normalization; leaves `v` unchanged if its norm is `0` or
/// non-finite.
fn l2_normalize(v: &mut [f32]) {
let norm = l2_norm(v);
if norm.is_finite() && norm > 0.0 {
for x in v.iter_mut() {
*x /= norm;
}
}
}
/// Build the deterministic embedding for a [`CsiWindow`].
///
/// The returned vector has length [`WINDOW_EMBEDDING_DIM`]; see that constant's
/// docs for the exact bin layout. The result is L2-normalized (or all-zero for
/// an empty window — i.e. `subcarrier_count == 0` and `frame_count == 0`).
pub fn window_embedding(w: &CsiWindow) -> Vec<f32> {
let mut out = Vec::with_capacity(WINDOW_EMBEDDING_DIM);
out.extend(resample_linear(&w.mean_amplitude, SUBCARRIER_BINS));
out.extend(resample_linear(&w.phase_variance, SUBCARRIER_BINS));
out.push(w.motion_energy);
out.push(w.presence_score);
out.push(w.quality_score);
out.push((w.frame_count as f32).ln_1p());
debug_assert_eq!(out.len(), WINDOW_EMBEDDING_DIM);
l2_normalize(&mut out);
out
}
/// Fixed index of a [`CsiEventKind`] in the one-hot block of an event
/// embedding — the variant declaration order in `rvcsi_core`.
fn kind_index(k: CsiEventKind) -> usize {
match k {
CsiEventKind::PresenceStarted => 0,
CsiEventKind::PresenceEnded => 1,
CsiEventKind::MotionDetected => 2,
CsiEventKind::MotionSettled => 3,
CsiEventKind::BaselineChanged => 4,
CsiEventKind::SignalQualityDropped => 5,
CsiEventKind::DeviceDisconnected => 6,
CsiEventKind::BreathingCandidate => 7,
CsiEventKind::AnomalyDetected => 8,
CsiEventKind::CalibrationRequired => 9,
}
}
/// Build the deterministic embedding for a [`CsiEvent`].
///
/// The returned vector has length [`EVENT_EMBEDDING_DIM`]; see that constant's
/// docs for the exact layout. Not normalized.
pub fn event_embedding(e: &CsiEvent) -> Vec<f32> {
let mut out = vec![0.0_f32; EVENT_EMBEDDING_DIM];
out[kind_index(e.kind)] = 1.0;
out[10] = e.confidence;
out[11] = (e.evidence_window_ids.len() as f32).ln_1p();
out
}
/// Cosine similarity of two equal-length vectors.
///
/// Returns `0.0` if the lengths differ or either vector is all-zero (or has a
/// non-finite norm); otherwise `dot(a, b) / (||a|| * ||b||)` clamped to
/// `[-1.0, 1.0]`.
pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
if a.len() != b.len() || a.is_empty() {
return 0.0;
}
let na = l2_norm(a);
let nb = l2_norm(b);
if !(na.is_finite() && nb.is_finite()) || na == 0.0 || nb == 0.0 {
return 0.0;
}
let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
(dot / (na * nb)).clamp(-1.0, 1.0)
}
#[cfg(test)]
mod tests {
use super::*;
use rvcsi_core::{EventId, SessionId, SourceId, WindowId};
fn window() -> CsiWindow {
CsiWindow {
window_id: WindowId(7),
session_id: SessionId(1),
source_id: SourceId::from("emb-test"),
start_ns: 1_000,
end_ns: 2_000,
frame_count: 12,
mean_amplitude: vec![1.0, 2.0, 3.0, 4.0, 5.0],
phase_variance: vec![0.1, 0.2, 0.1, 0.3, 0.2],
motion_energy: 0.42,
presence_score: 0.8,
quality_score: 0.9,
}
}
fn event(kind: CsiEventKind) -> CsiEvent {
CsiEvent::new(
EventId(3),
kind,
SessionId(1),
SourceId::from("emb-test"),
5_000,
0.75,
vec![WindowId(1), WindowId(2)],
)
}
#[test]
fn resample_edge_cases() {
assert_eq!(resample_linear(&[], 4), vec![0.0; 4]);
assert_eq!(resample_linear(&[2.5], 3), vec![2.5, 2.5, 2.5]);
// identity-ish: 3 -> 3 keeps endpoints
let r = resample_linear(&[0.0, 1.0, 2.0], 3);
assert!((r[0] - 0.0).abs() < 1e-6);
assert!((r[1] - 1.0).abs() < 1e-6);
assert!((r[2] - 2.0).abs() < 1e-6);
// upsample 2 -> 5 is a straight line
let r = resample_linear(&[0.0, 4.0], 5);
assert!((r[2] - 2.0).abs() < 1e-6);
}
#[test]
fn window_embedding_is_deterministic_and_unit_length() {
let w = window();
let a = window_embedding(&w);
let b = window_embedding(&w);
assert_eq!(a, b);
assert_eq!(a.len(), WINDOW_EMBEDDING_DIM);
let norm = l2_norm(&a);
assert!((norm - 1.0).abs() < 1e-5, "norm was {norm}");
}
#[test]
fn empty_window_embeds_to_zero() {
let mut w = window();
w.mean_amplitude.clear();
w.phase_variance.clear();
w.motion_energy = 0.0;
w.presence_score = 0.0;
w.quality_score = 0.0;
w.frame_count = 0;
let e = window_embedding(&w);
assert_eq!(e.len(), WINDOW_EMBEDDING_DIM);
assert!(e.iter().all(|x| *x == 0.0));
}
#[test]
fn window_embedding_length_independent_of_subcarrier_count() {
let mut a = window();
a.mean_amplitude = vec![1.0; 56];
a.phase_variance = vec![0.1; 56];
let mut b = window();
b.mean_amplitude = vec![1.0; 234];
b.phase_variance = vec![0.1; 234];
assert_eq!(window_embedding(&a).len(), window_embedding(&b).len());
}
#[test]
fn event_embedding_layout() {
let e = event(CsiEventKind::MotionDetected);
let v = event_embedding(&e);
assert_eq!(v.len(), EVENT_EMBEDDING_DIM);
assert_eq!(v[kind_index(CsiEventKind::MotionDetected)], 1.0);
// exactly one hot in the first 10
assert_eq!(v[..10].iter().filter(|x| **x == 1.0).count(), 1);
assert!((v[10] - 0.75).abs() < 1e-6);
assert!((v[11] - (2.0_f32).ln_1p()).abs() < 1e-6);
// a different kind lights a different bin
let v2 = event_embedding(&event(CsiEventKind::AnomalyDetected));
assert_eq!(v2[kind_index(CsiEventKind::AnomalyDetected)], 1.0);
assert_ne!(v, v2);
}
#[test]
fn cosine_basic_identities() {
let v = window_embedding(&window());
assert!((cosine_similarity(&v, &v) - 1.0).abs() < 1e-5);
let neg: Vec<f32> = v.iter().map(|x| -x).collect();
assert!((cosine_similarity(&v, &neg) + 1.0).abs() < 1e-5);
// mismatched lengths -> 0
assert_eq!(cosine_similarity(&v, &v[..3]), 0.0);
// all-zero -> 0
assert_eq!(cosine_similarity(&[0.0; 4], &[1.0; 4]), 0.0);
assert_eq!(cosine_similarity(&[], &[]), 0.0);
}
}