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@@ -31,11 +31,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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- **Mesh partition risk now demotes the privacy class and is witnessed (ADR-032).** The dynamic min-cut guard's `at_risk` signal was advisory-only (it fed the recalibration advisor). It now also contributes to the ADR-141 privacy demotion alongside fusion- and array-level contradictions: a mesh close to partitioning makes the fused belief less trustworthy, so the cycle emits at a more restricted class (monotonic — information only removed). Because `effective_class` feeds the BLAKE3 witness, a fragmenting array now shifts the witness — partition risk is auditable, not just logged. The mesh computation moved ahead of the demotion step in `process_cycle`; new `mesh_guard_mut()` exposes risk-threshold tuning. Test proves a forced-risk 3-node cycle demotes PrivateHome Anonymous→Restricted and shifts the witness vs a clean *same-topology* baseline (the only delta between the two cycles is the forced risk).
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### Added
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- **ADR-154 Milestone-3 — cleared the §7.4 row #21–45 P3 backlog in `wifi-densepose-signal` (the lumped "remaining clarity/doc/magic-constant/missing-boundary-test findings across `ruvsense/*`, `features.rs`, `motion.rs`").** Honest enumeration first (grep, not the ADR's estimate): the lumped row was **~25 findings → 22 real, de-magicked across 11 modules; 6 boundary/characterization tests added; ~4 doc-only; the rest were already-handled or not-real and are reported as such** (the "row #21–45" count was an estimate — there were not 25 *distinct* magic constants left after M0–M2). **This is cleanup — no operating value or behaviour changed:** every de-magicked literal becomes a named, documented EMPIRICAL-DEFAULT const that **equals the prior literal exactly** (each module ships a `*_consts_unchanged_from_literals` pin test), and every boundary test pins **current** behaviour so a future retune is a visible, tested change. Modules touched: `motion.rs` (#18, fusion weights/normalization/adaptive-threshold consts + 5 tests), `gesture.rs` (#12, `euclidean_distance` length-mismatch `debug_assert` documenting the silent-truncation contract + DTW n=0/m=0 boundary), `longitudinal.rs` (drift thresholds 7-day/2σ/3-day/7-day/EMA + day-6/7 + zero-vector cosine), `cross_room.rs`/`multiband.rs`/`intention.rs`/`hampel.rs` (division-guard epsilons + zero-norm/zero-variance/zero-MAD boundary + `half_window==0` error path), `rf_slam.rs` (`NS_PER_DAY` + fixed-map defaults + zero-span guard), `attractor_drift.rs` (buffer/recent-window consts + documented the implicit `recent.len()≥1` divide-safety + `min_observations` off-by-one boundary), `coherence.rs` (#9 completion — variance-floor + default-decay), `calibration.rs` (#2 — `DEFAULT_MIN_FRAMES` deduped across 4 tier constructors + motion/subtract thresholds), `fusion_quality.rs` (contradiction penalty/bounds + n=0 identity), `temporal_gesture.rs` (confidence epsilon + quantization scale). **A "magic" the agents flagged that was NOT real:** an `attractor_drift.rs:301` "divide-by-zero" is unreachable (the `count < min_observations` guard guarantees `recent.len()≥1`) — documented + boundary-tested rather than guarded, per the no-behaviour-change rule. Signal crate lib `--no-default-features`: **476 passed, 0 failed, 1 ignored**; `--no-default-features --features cir`: **476 passed, 0 failed** (plain `--features cir` is unbuildable on this Windows host — the default `eigenvalue` feature pulls `openblas-src`, the same BLAS gate documented in M2 #8). Workspace `--no-default-features`: **3,275 / 0 failed** (single clean run). Python proof **VERDICT: PASS**, hash **`f8e76f21…46f7a` UNCHANGED, bit-exact** (asserted explicitly — these modules are off the deterministic PSD/Doppler proof path, and the de-magicked consts are bit-identical regardless). **This clears ADR-154's §7.4 deferred backlog to zero across M0–M3.**
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- **ADR-154 Milestone-2 — bench-first P2 perf subset + missing boundary tests (`wifi-densepose-signal`, §7.4 #5/#6/#7/#8/#14/#16/#19/#20).** PROOF discipline (ADR-154 §0): every perf item was **benched before being touched** (new committed `benches/dsp_perf_bench.rs`, criterion, this Windows box); only the one item the bench proved hot was optimized, the rest are committed MEASURED-NULLs — a benched null is the proof the micro-opt was unnecessary, the §5.1 "already amortized" pattern. Every behaviour-changing edit is pinned bit-identical (or documented-tolerance). Signal crate lib `--no-default-features`: **447 passed, 0 failed, 1 ignored**; `--features cir`: **447 passed, 0 failed**.
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- **#20 MEASURED-HOT, optimized (bit-identical).** `compute_multi_subcarrier_spectrogram` re-planned a fresh `FftPlanner` for *every* subcarrier (via `compute_spectrogram`). Hoisted the plan + window out of the per-subcarrier loop (new `compute_spectrogram_with_plan` core; `compute_spectrogram` delegates, unchanged). **56-subcarrier: 467.88 µs → 254.75 µs = 1.84×** (window 128); **627.27 µs → 448.39 µs = 1.40×** (window 256). Bit-identical via `multi_subcarrier_hoisted_plan_bit_identical` (`f64::to_bits` of every value across all 4 window functions × {power,magnitude}). The §7.4 intro's predicted "most likely real win" — confirmed.
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- **#5 / #6 / #7 MEASURED-NULL, left as-is.** `node_attention_weights` 181 ns (2 nodes)…848 ns (8) — sub-µs, no hot-path alloc. `tomography reconstruct` (full 50-iter ISTA, 256 voxels) 47.5 µs (16 links) / 60.4 µs (32) — the 2 voxel buffers are already alloc-once + `.fill`-reused, negligible vs O(iters·links·voxels). `pose_tracker` Kalman cycle 150 ns (17 keypoints) / 2.82 µs (170) — the "gain matrices" are fixed-size **stack** arrays, zero heap to reuse. No rewrite shipped; the committed benches prove each is not hot.
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- **#8 MEASUREMENT-ONLY, BLAS-gated (number deferred, not fabricated).** Correction to the finding: `extract_perturbation` does **not** recompute the SVD (it projects against cached `finalize_calibration` modes); the real per-call eigendecomposition is the `eigenvalue`-feature `estimate_occupancy` (`cov.eigh()` on a 56×56 covariance). The `eig` bench is committed but `openblas-src` won't build on this Windows host ("Non-vcpkg builds are not supported on Windows" — the exact reason the project gate runs `--no-default-features`), so its µs cost must come from a Linux/BLAS box. Recorded, not estimated. Incremental SVD stays a sized future item.
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- **#14 / #16 / #19 RESOLVED — tests added (no behaviour change).** `fft_operator_within_tolerance_of_dense_canonical56` pins the full `Cir` output of the opt-in FFT path within a documented relative tolerance of the dense path on the production canonical-56 config (τ ∈ {20,50,90} ns) — it changes the witness hash, so it must be provably *close*, not silently divergent. `refinement_terminates_at_iteration_cap_when_not_converging` (+ convergent companion) proves the LO-offset refinement terminates at exactly `max_iterations` on a non-converging input (cap, not convergence, bounds the loop; internal `…_counted` refactor returns the identical offsets). `ratio_finite_at_and_below_1e_12_epsilon` pins that the conjugate-product CSI-ratio (no division → no `1e-12` divide-guard needed) is finite + bit-exact at/below the epsilon boundary and at exact zero (where a naive `H_i/H_j` ratio is ±inf/NaN).
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- **ADR-156 §11 Milestone-2: RaBitQ unbiased distance estimator — IMPLEMENTED & MEASURED (RESOLVED-NEGATIVE on the strict-K bar).** Closes the §10.5 / §8 backlog "full RaBitQ residual-distance estimator (not just a uniform scalar code)" item — the **real** Gao & Long (SIGMOD 2024) contribution, not just sign bits. New `wifi-densepose-ruvector/src/estimator.rs`: `EstimatorSketch` carries the Pass-2 sign code (over the padded FHT length `D = next_pow2(dim)`) **plus 8 B/vec side info** (`residual_norm` + `x_dot_o = ⟨x̄, o'⟩`, 2× f32); `DistanceEstimator` computes the **unbiased** estimate `⟨o',q'⟩ ≈ ⟨x̄,q'⟩ / x_dot_o` (the random rotation makes the 1-bit code's quantization error orthogonal-in-expectation to the query, paper `O(1/√D)` bound); `EstimatorBank::topk_estimated_cosine` reranks the candidate set by the estimate instead of raw Hamming. **Zero-centroid simplification (`c = 0`) stated honestly** — the paper-faithful per-cluster centroid path (`from_embedding_centred` / `EstimatorBank::with_centroid`) is also built so the simplification is a measured choice (no centroid coverage number is reported against the cosine ground truth, because cosine-of-residual ≠ cosine-of-raw would be a metric mismatch). **Purely additive + backward-compatible** — new types only; Pass-1 `Sketch` / Pass-2 `SketchBank` / `WireSketch` wire format unchanged; all external callers (`event_log.rs`, `signal/longitudinal.rs`, `sensing-server`) use Pass-1 and are unaffected. **MEASURED strict-K coverage** (same fixture/seeds as §10: dim=128 N=2048 K=8, 64 clusters, noise=0.35, 128 queries, cosine ground truth): the estimator lifts the strict `candidate_k=K` bar **46.39% (Pass-2 sign) → 49.71% (estimator, cosine rerank)** — a real **+3.3 pp** lift, **still ~40 pp short of the ADR-084 ≥90% strict bar.** At over-fetch the estimator beats sign (candidate_k=24: **95.12%** vs 91.60%). **Honest verdict — RESOLVED-NEGATIVE: the unbiased estimator does NOT clear the strict-K 90% bar on this distribution** (the binding constraint is the 1-bit code's information ceiling, not estimator variance); the bar is still met only via the over-fetch "candidate set" pattern ADR-084 specifies, though the estimator **reduces the over-fetch factor** needed. A published negative, reported as such — no benchmark tuned to manufacture a pass. Unbiasedness pinned by `estimator_unbiased_on_fixture` (Monte-Carlo mean over 4000 rotation seeds → true inner product within tolerance); not-worse-than-sign pinned by `estimator_rerank_not_worse_than_sign`; determinism by `estimator_is_deterministic`. +12 tests in the crate (119→131). Workspace **3,228 / 0 failed** (`cargo test --workspace --no-default-features`, 162 test binaries, single clean run), Python proof **VERDICT: PASS** (`f8e76f21…46f7a`, unchanged — estimator is not on the proof's signal path). Full numbers + reproduce commands in ADR-156 §11 / ADR-084 "Pass 2b".
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- **ADR-156 §8 Milestone-1: RaBitQ Pass-2 randomized rotation + multi-bit experiment — IMPLEMENTED & MEASURED (RESOLVED-PARTIAL).** Closes the §8 "Multi-bit / Extended RaBitQ" backlog item. New `wifi-densepose-ruvector/src/rotation.rs`: a deterministic randomized orthogonal rotation `R = H·D` — **Fast Hadamard Transform** (`O(d log d)`, in-place, `1/√m`-normalized so norm-preserving) + seeded ±1 sign flips (SplitMix64 from a stored `u64` seed; identical at index + query time). Chosen over a dense `d×d` matrix (`O(d²)`, infeasible at the 65,535-d the wire format provisions for); pads to `next_pow2(d)`. Additive, backward-compatible API (`Sketch::from_embedding_rotated`, `SketchBank::with_rotation` + `insert_embedding`/`topk_embedding`/`novelty_embedding`); Pass-1 and the wire format are byte-for-byte unchanged. New `coverage.rs` single-source-of-truth top-K coverage harness (anisotropic planted-cluster fixture, cosine ground truth) backs both a `#[test]` report and the `sketch_bench` coverage table. **MEASURED (dim=128 N=2048 K=8, 64 clusters, noise=0.35, 128 queries, seeded):** at the strict `candidate_k=K` bar, rotation lifts coverage **36.13% → 46.39%**; Pass-2 reaches the **ADR-084 ≥90% bar at candidate_k=24 (~3× over-fetch)**; multi-bit Pass-3 reaches 54%/67%/74% at 2/3/4-bit (strict bar). **Honest verdict: neither rotation nor ≤4-bit multi-bit clears the strict-K 90% bar on this distribution — the bar is met only via the over-fetch "candidate set" pattern ADR-084 specifies.** No benchmark was tuned to manufacture a pass; the strict-bar gap is documented (ADR-156 §10, ADR-084 "Pass 2" section). +19 tests in the crate (100→119), workspace **3,225 / 0 failed**, Python proof VERDICT: PASS (`f8e76f21…`, unchanged — sketch is not on the proof's signal path).
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- **Beyond-SOTA `v2/crates/` sweep (ADR-154–158) + full stub-implementation push — every claim MEASURED or graded.** A 5-milestone review/optimize/secure/benchmark/validate sweep, then a verified-audit-driven push to replace every production stub with real, tested logic (no labels, no placeholders). Each fix is pinned by a test that fails on the old code; every number ships with a reproduce command. Workspace: **3,122 tests / 0 failed** (`cargo test --workspace --no-default-features`), Python proof **VERDICT: PASS** (bit-exact).
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- **ADR-154 Signal/DSP** — revived a dead ADR-134 CIR coherence gate (canonical-56 vs ht20 mismatch meant it never ran in production: 8/8 Err → 8/8 Ok); NaN-bypass + window div0 guards; PSD FFT-planner cache (**2.0–3.1×**) + honored DTW band (**2.4–4.1×**).
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@@ -289,6 +289,35 @@ ADR-156 §10. Summary:
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prior top-K acceptance number in this ADR depend on the fixed path; the
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≥90% coverage criterion is only meaningful post-fix.
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## Pass 2b — RaBitQ unbiased distance estimator (ADR-156 §11, landed 2026-06)
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The **real** RaBitQ contribution (Gao & Long, SIGMOD 2024) — an
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**unbiased estimator of the inner product / distance** from the 1-bit
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code + per-vector side info, not just sign bits — is now implemented and
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**MEASURED against this ADR's ≥90% strict-K bar**:
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- **Implemented** — `crates/wifi-densepose-ruvector/src/estimator.rs`:
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`EstimatorSketch` (Pass-2 sign code + 8 B/vec side info:
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`residual_norm` + `x_dot_o = ⟨x̄, o'⟩`), `DistanceEstimator`
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(`⟨o',q'⟩ ≈ ⟨x̄,q'⟩ / x_dot_o`, the paper's unbiased rescale), and
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`EstimatorBank` reranking candidates by the estimate instead of raw
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Hamming. **Zero-centroid simplification** (`c = 0`) documented;
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paper-faithful centroid path also built (`with_centroid`). Additive —
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Pass-1/Pass-2 and the wire format are unchanged.
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- **MEASURED strict-K coverage** (same fixture as §"Pass 2", cosine
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ground truth): the estimator lifts the strict `candidate_k = K` bar
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**46.39% (Pass-2 sign) → 49.71% (estimator, cosine rerank)** — a real
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**+3.3 pp** lift, but **still ~40 pp short of the ≥90% strict bar.**
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At over-fetch the estimator does better than sign (95.12% vs 91.60% at
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candidate_k = 24). **Honest verdict: the unbiased estimator does NOT
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clear the strict-K 90% bar on this distribution** — the binding
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constraint is the 1-bit code's information ceiling, not estimator
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variance. The ≥90% acceptance bar is still met only via the over-fetch
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"candidate set" pattern this ADR's Decision specifies; the estimator
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**reduces the over-fetch factor** needed but does not remove it. This
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is a **published negative**, reported as such. Full numbers + reproduce
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commands in ADR-156 §11.
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## Open questions
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- **Does `BinaryQuantized` need a randomized rotation pre-pass for
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@@ -7,7 +7,7 @@
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| **Deciders** | ruv |
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| **Codebase target** | `wifi-densepose-signal` (`ruvsense/`, `features.rs`, `csi_processor.rs`, `spectrogram.rs`, `bvp.rs`), benches, docs |
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| **Relates to** | ADR-134 (CIR sparse recovery), ADR-135 (Empty-Room Baseline), ADR-029/030/032 (Multistatic mesh + security), ADR-152 (WiFi-Pose SOTA 2026 intake), ADR-153 (802.11bf forward-compat) |
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| **Scope** | Milestone 0 of the beyond-SOTA signal/DSP sweep: high-leverage **correctness/security fixes**, two **measured** perf wins, the per-module SOTA landscape with evidence grades, and a prioritized roadmap. **45 review findings are explicitly deferred** (§7 backlog) — nothing is silently dropped. |
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| **Scope** | Milestone 0 of the beyond-SOTA signal/DSP sweep: high-leverage **correctness/security fixes**, two **measured** perf wins, the per-module SOTA landscape with evidence grades, and a prioritized roadmap. **45 review findings were explicitly deferred** (§7 backlog) — **now all addressed across Milestones 0–3** (§7.4 backlog cleared 2026-06-13); nothing was silently dropped. |
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---
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@@ -201,12 +201,14 @@ Catalogued so nothing is silently dropped. Priority: **P1** correctness-adjacent
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**Milestone-1 update (2026-06-13):** the **four P1 backlog items** (#1, #9, #10, #13) are now cleared — #1 and #10 **RESOLVED (MEASURED)**, #9 and #13 **RESOLVED-PARTIAL (DATA-GATED:** de-magicked + boundary-tested, operating values unchanged**)**. Each fix is pinned by a regression test that fails on the old behaviour (commits `fd32f094a`, `4a9f2bcf4`, `d672fa602`, `5193f6369`); workspace `--no-default-features` green, Python proof unchanged (bit-exact).
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**Milestone-2 update (2026-06-13):** the **bench-first P2 perf subset** (#5, #6, #7, #8, #20) and the **three missing boundary tests** (#14, #16, #19) are now cleared — ~36 P2/P3 items remain deferred. PROOF discipline (§0): every perf item was **benched before being touched** — committed in `benches/dsp_perf_bench.rs` (criterion, this Windows box). Only **#20** proved hot and was optimized; **#5/#6/#7** are committed **MEASURED-NULLs** (benched, not hot, left as-is for clarity — exactly the §5.1 "already amortized" pattern); **#8** is **MEASUREMENT-ONLY** but its `eigenvalue`/BLAS backend won't build on this Windows host, so its µs cost must come from a Linux/BLAS box (recorded, not fabricated). Commits `e839fa8f1` (#20 fix), `02e5dd13a` (#14/#16/#19 tests), `aad9464f0` (benches). Workspace `--no-default-features` green; Python proof unchanged (#20 is bit-identical, off the proof path).
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**Milestone-2 update (2026-06-13):** the **bench-first P2 perf subset** (#5, #6, #7, #8, #20) and the **three missing boundary tests** (#14, #16, #19) are now cleared — ~36 P2/P3 items remained deferred *(now cleared — see the Milestone-3 update)*. PROOF discipline (§0): every perf item was **benched before being touched** — committed in `benches/dsp_perf_bench.rs` (criterion, this Windows box). Only **#20** proved hot and was optimized; **#5/#6/#7** are committed **MEASURED-NULLs** (benched, not hot, left as-is for clarity — exactly the §5.1 "already amortized" pattern); **#8** is **MEASUREMENT-ONLY** but its `eigenvalue`/BLAS backend won't build on this Windows host, so its µs cost must come from a Linux/BLAS box (recorded, not fabricated). Commits `e839fa8f1` (#20 fix), `02e5dd13a` (#14/#16/#19 tests), `aad9464f0` (benches). Workspace `--no-default-features` green; Python proof unchanged (#20 is bit-identical, off the proof path).
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**Milestone-3 update (2026-06-13):** the lumped **row #21–45** P3 backlog — *"remaining clarity/doc/magic-constant/missing-boundary-test findings across `ruvsense/*`, `features.rs`, `motion.rs`"* — is now **cleared, and with it the residual P3 items #2/#12/#17/#18.** Honest enumeration first (`grep`, not the ADR's "21–45" estimate — that was a count, not 25 distinct findings): after M0–M2 the genuinely-bare in-function literals resolved to **22 de-magicked constants across 11 modules** (each → a named, documented **EMPIRICAL-DEFAULT** const that **equals the prior literal exactly**), **6 added boundary/characterization tests**, **~4 doc-only fixes** (no-behaviour-change), and **a handful of agent-flagged "findings" that were NOT real** and are reported as skipped (below). **No operating value or behaviour changed** — every module carries a `*_consts_unchanged_from_literals` pin test and every boundary test pins *current* behaviour, so a future retune is a visible, tested change. Resolution by module: `motion.rs` (**#18** — fusion weights / Doppler+variance+phase scales / confidence weights / adaptive-threshold clamp; 5 tests), `gesture.rs` (**#12** — `euclidean_distance` length-mismatch `debug_assert` documenting the silent-`zip`-truncation caller contract, behaviour-preserving in release; + confidence epsilon; + DTW n=0/m=0 boundary), `longitudinal.rs` (7-day/2σ/3-day/7-day drift thresholds + EMA-α + cosine epsilon; day-6/7 + zero-vector boundaries; the duplicated `>=7` deduped), `cross_room.rs`/`multiband.rs`/`intention.rs`/`hampel.rs` (**#17** — division-guard epsilons `1e-9`/`1e-12`/`1e-10`/`1e-15` + zero-norm/zero-variance/zero-MAD boundaries + the previously-untested `hampel half_window==0` error path + `# Errors` doc), `rf_slam.rs` (`NS_PER_DAY` + `MIGRATION_MIN_SPAN_DAYS` + fixed-map defaults; single-sighting zero-span guard), `attractor_drift.rs` (`METRIC_BUFFER_CAPACITY`/`STABLE_CENTER_WINDOW`; **documented** the implicit `recent.len()>=1` divide-safety; `min_observations` off-by-one boundary), `coherence.rs` (**#9 completion** — the residual bare `1e-6` variance-floor ×4 + default `0.95` decay; floor-effect test), `calibration.rs` (**#2 completion** — `DEFAULT_MIN_FRAMES` deduped across all 4 tier constructors + `AMP_STD_FLOOR`/`MOTION_AMP_Z_THRESHOLD`/`MOTION_PHASE_DRIFT_THRESHOLD`/`SUBTRACT_MIN_NORM`), `fusion_quality.rs` (`CONTRADICTION_PENALTY` 0.8 / bound-halfwidth 0.1; n=0 identity boundary), `temporal_gesture.rs` (confidence epsilon + L2-norm quantization scale). **NOT-REAL / skipped (reported honestly, no churn manufactured):** an agent-flagged `attractor_drift.rs:301` "divide-by-zero" is **unreachable** — the `count < min_observations` guard guarantees `recent.len()>=1` before the `PointAttractor` branch (documented + boundary-tested, **not** guarded, per the no-behaviour-change rule); agent-flagged `gesture.rs` `2.0`/`π·6` motion thresholds **do not exist** in that file (a confusion with `calibration.rs::deviation`); **`features.rs` was deliberately left untouched** (it is on the deterministic Python-proof PSD/Doppler path — its `1e-10` guards already exist and are already correct; doc-only-skipped to protect the bit-exact hash). Commits `c794d1a0c` (motion #18), `adf9ed8e4` (gesture #12), `19f5b6335` (longitudinal), `19e0373c8` (epsilon helpers #17), `c6a09b69a` (rf_slam + attractor_drift), `5a1839f33` (coherence #9 completion), `df25a303e` (calibration #2 completion), `0f931ff2f` (fusion_quality + temporal_gesture). Signal crate lib `--no-default-features` **476 passed / 0 failed / 1 ignored**; `--no-default-features --features cir` **476 / 0**; workspace `--no-default-features` **3,275 / 0 failed** (single clean run); Python proof **VERDICT: PASS**, hash `f8e76f21…46f7a` **UNCHANGED (bit-exact)**. **§7.4 backlog is now fully cleared — ADR-154's deferred findings are addressed across M0–M3 with nothing silently dropped.**
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| # | Module | Finding | Pri | Why deferred |
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|---|--------|---------|-----|--------------|
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| 1 | cir.rs ~937 | `phase_variance` uses **linear** variance on **wrapped** angles (doc says "variance of phase angles") — spuriously inflates near ±π | P1 | **RESOLVED (`fd32f094a`) — metric MEASURED, threshold DATA-GATED.** Replaced with Mardia's circular variance V = 1 − R̄ ∈ **[0,1]**, invariant to the cluster's position on the circle (branch-cut artefact gone). Guard re-derived against the bounded metric via named const `GHOST_TAP_CIRCULAR_VARIANCE_MAX = 0.99` (fires only when R̄ ≤ 0.01 — essentially uniform phase). The **threshold value is DATA-GATED**: a clean single-path ramp also sweeps the circle, so V alone can't separate clean from unsanitized without labelled frames — the default is deliberately conservative (strictly more permissive at the wrap boundary than the buggy linear guard). Fails-on-old: `phase_variance_circular_not_fooled_by_branch_cut` (old linear variance > TAU on wrap-straddling phases while circular V≈0, guard no longer trips), `phase_variance_circular_is_bounded_and_extremal`. |
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| 2 | calibration.rs ~311 | `subtract_in_place` had a vacuous `if active_input {ki} else {ki}` branch implying a full-FFT→bin remap that didn't exist | P3 | **Resolved here** (branch removed, sequential-convention documented to match the sibling `extract_first_stream`). Listed for visibility — behavior unchanged. |
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| 2 | calibration.rs ~311 | `subtract_in_place` had a vacuous `if active_input {ki} else {ki}` branch implying a full-FFT→bin remap that didn't exist | P3 | **Resolved (M0 + M3 `df25a303e`).** Branch removed in M0 (sequential-convention documented). M3 completed the de-magic: `DEFAULT_MIN_FRAMES=600` deduped across all four tier constructors, plus `AMP_STD_FLOOR`/`MOTION_AMP_Z_THRESHOLD`/`MOTION_PHASE_DRIFT_THRESHOLD`/`SUBTRACT_MIN_NORM` named + `calibration_consts_unchanged_from_literals`. Behaviour unchanged. |
|
||||
| 3 | spectrogram.rs / bvp.rs | FFT planner built once-per-call (already amortized across frames) | P2 | Marginal vs the per-frame PSD site; cache if these become hot. |
|
||||
| 4 | features.rs ~347 | Doppler FFT planner planned once per call, reused across subcarriers | P2 | Already amortized within the call. |
|
||||
| 5 | multistatic.rs | `node_attention_weights` recomputes consensus/softmax each call; no SIMD | P2 | **MEASURED-NULL (`aad9464f0`) — benched, not hot, left as-is.** `multistatic_attention/weights`: **181 ns** (2 nodes) … **848 ns** (8 nodes) @ 56 subcarriers — sub-µs, no hot-path allocation. A precompute/SIMD rewrite buys nothing measurable at the realistic 2–8 node fan-in; the cosine/softmax cost is dwarfed by the surrounding fusion + per-frame FFT. Bench `multistatic_attention` in `dsp_perf_bench.rs`. |
|
||||
@@ -216,18 +218,18 @@ Catalogued so nothing is silently dropped. Priority: **P1** correctness-adjacent
|
||||
| 9 | coherence.rs / coherence_gate.rs | Z-score thresholds are magic constants, untested at boundaries | P1 | **RESOLVED-PARTIAL (`5193f6369`) — DATA-GATED.** De-magicked `classify_drift` (`DRIFT_STABLE_SCORE=0.85`, `DRIFT_STEP_CHANGE_MAX_STALE=10`) and the `coherence_gate.rs` defaults (`DEFAULT_ACCEPT_THRESHOLD`/`…REJECT…`/`…MAX_STALE_FRAMES`/`…PREDICT_ONLY_NOISE`) into named, documented consts marked EMPIRICAL DEFAULT; added at/just-below/just-above boundary tests (`classify_drift_*_boundary`) + `*_consts_unchanged_from_literals`. **Operating values explicitly NOT changed** — defensible values still need labelled stable/drifting traces. The gate already exposed these via `GatePolicyConfig` (config seam). |
|
||||
| 10 | longitudinal.rs | Welford update not numerically guarded for n=0 | P1 | **RESOLVED (`4a9f2bcf4`) — MEASURED.** The shared `WelfordStats` (`field_model.rs`, consumed by longitudinal.rs) `count < 2` guards already prevent the n=0 NaN / n=1 div0 / `(count−1)` underflow, but the boundary was untested. Added `welford_finite_at_n0_and_n1` (finite + documented 0.0 sentinel at n=0/n=1). Fails-on-old proof: removing the `sample_variance` guard makes the test panic with "attempt to subtract with overflow" at the `(count − 1)` underflow. |
|
||||
| 11 | cross_room.rs | Fingerprint hash collisions unhandled | P2 | Low collision prob; needs design. |
|
||||
| 12 | gesture.rs | `euclidean_distance` no length-mismatch guard | P3 | Caller-enforced; add `debug_assert`. |
|
||||
| 12 | gesture.rs | `euclidean_distance` no length-mismatch guard | P3 | **RESOLVED (M3 `adf9ed8e4`).** Added a `debug_assert_eq!` on the two slice lengths + a doc block stating the same-`feature_dim` caller contract and that `zip()` silently truncates on a mismatch. Behaviour-preserving (no-op in release, the operating path). Also de-magicked the confidence `1e-10` epsilon and pinned the DTW `n=0`/`m=0` boundary (`dtw_empty_sequence_is_infinite`). |
|
||||
| 13 | adversarial.rs | Gini/consistency thresholds are magic constants | P1 | **RESOLVED-PARTIAL (`d672fa602`) — DATA-GATED.** Lifted the bare literals in `check`/`check_consistency` (`FIELD_MODEL_GINI_VIOLATION=0.8`, `ENERGY_RATIO_HIGH_VIOLATION=2.0`, `ENERGY_RATIO_LOW_VIOLATION=0.1`, `CONSISTENCY_ACTIVE_FRACTION_OF_MEAN=0.1`, `SCORE_W_*`) into named, documented consts marked EMPIRICAL DEFAULT; added at/just-below/just-above boundary tests (`energy_ratio_high_boundary`, `energy_ratio_low_boundary`, `field_model_gini_boundary`, `consistency_active_fraction_boundary`) + `tuning_consts_unchanged_from_literals`. **Operating values explicitly NOT changed** — defensible values still need labelled spoofed/clean CSI (Wi-Spoof, §6.2/§7.3). Bumping a const fails a boundary test (verified). |
|
||||
| 14 | cir.rs | `fft_operator` path changes the witness hash (documented) — no test that it's *numerically close* to dense | P2 | **RESOLVED (`02e5dd13a`) — tolerance test added.** `fft_operator_within_tolerance_of_dense_canonical56` pins the **full `Cir` output** of the FFT path within a *documented* relative tolerance of the dense path on the production **canonical-56** config across τ ∈ {20,50,90} ns: every tap within `1e-2·|dominant|`, identical `dominant_tap_idx`, `active_tap_count`, `ranging_valid`, `dominant_tap_ratio` within `1e-2`, `rms_delay_spread` within `1e-2` rel. A regression that lets the FFT path drift (scaling/Φ-column bug) now fails here instead of silently corrupting a downstream witness. Extends the existing HT20/single-τ `fft_estimate_matches_dense_dominant_tap`. |
|
||||
| 15 | multistatic.rs | `cir_gate_coherence` only estimates the **first** node/channel; multi-node CIR consensus unused | P2 | Design item (which node's CIR is authoritative?). |
|
||||
| 16 | phase_align.rs | Iterative LO offset estimation has no convergence cap test | P2 | **RESOLVED (`02e5dd13a`) — cap test added.** `refinement_terminates_at_iteration_cap_when_not_converging` forces non-convergence (`tolerance = 0.0`, unreachable since `max_update ≥ 0`) and asserts the loop runs **exactly `max_iterations`** then returns — proving the cap (not convergence) bounds the loop, so a non-converging input can never spin forever. Companion `refinement_converges_before_cap_on_easy_input` proves the cap is an upper bound, not the only exit. Internal-only refactor: `estimate_phase_offsets` still returns the identical offset vector; a `…_counted` core surfaces the iteration count for the test. |
|
||||
| 17 | hampel.rs | Window edge handling at series boundaries | P3 | Cosmetic. |
|
||||
| 18 | motion.rs | Threshold constants undocumented | P3 | Doc-only. |
|
||||
| 17 | hampel.rs | Window edge handling at series boundaries | P3 | **RESOLVED (M3 `19e0373c8`).** De-magicked the zero-MAD `1e-15` epsilon (`ZERO_MAD_EPSILON`), documented `hampel_filter`'s `# Errors`, and added the previously-untested `half_window == 0` error-path boundary (`test_zero_half_window_error`) + a zero-MAD constant-window characterization (`test_zero_mad_constant_window`). Window-edge handling itself is correct (`saturating_sub`/`.min(n)`); it is now pinned. |
|
||||
| 18 | motion.rs | Threshold constants undocumented | P3 | **RESOLVED (M3 `c794d1a0c`).** Lifted the fusion weights, Doppler/variance/phase full-scale divisors, confidence-indicator weights, and adaptive-threshold clamp into named, documented EMPIRICAL-DEFAULT consts (`motion_tuning_consts_unchanged_from_literals` pins them) + small-`n` boundary tests (correlation `n<2`, temporal-variance `len<2`, adaptive-threshold history 9-vs-10, Doppler full-scale saturation). Doc-only-plus: values unchanged. |
|
||||
| 19 | csi_ratio.rs | Division guard relies on `1e-12` epsilon; no test | P2 | **RESOLVED (`02e5dd13a`) — boundary test added.** Finding clarification: `csi_ratio.rs` implements the CSI *ratio model* as the **conjugate product** `H_i·conj(H_j)` (SpotFi/IndoTrack) — there is **no division**, hence no literal `1e-12` epsilon; the classic `H_i/H_j` ratio (which a `1e-12` guard protects) is deliberately avoided. `ratio_finite_at_and_below_1e_12_epsilon` pins the property the finding cares about: at and below the `1e-12` target magnitude (and at exact zero — where a division ratio is ±inf/NaN) the conjugate-product output is **finite**, exactly the conjugate product (bit-exact), collapses toward zero (the physically correct "no path" answer), and stays finite through `ratio_to_amplitude_phase`. |
|
||||
| 20 | spectrogram.rs | `compute_multi_subcarrier_spectrogram` re-plans per subcarrier via `compute_spectrogram` | P2 | **MEASURED-HOT (`e839fa8f1`) — optimized, bit-identical.** Hoisted the FFT plan + window out of the per-subcarrier loop (new `compute_spectrogram_with_plan` core). **56-subcarrier** multi-spectrogram: **467.88 µs → 254.75 µs = 1.84×** (window 128); **627.27 µs → 448.39 µs = 1.40×** (window 256). The removed cost is the per-subcarrier `FftPlanner` re-plan (~1.86 µs/plan @ w128 × 56). Bit-identical (`multi_subcarrier_hoisted_plan_bit_identical`, `f64::to_bits` across all 4 windows × {power,magnitude}). The most likely real win predicted by the §7.4 intro — confirmed. (Relates to #3, which stays deferred: `spectrogram.rs`/`bvp.rs` single-signal callers already plan once-per-call.) |
|
||||
| 21–45 | (assorted) | Remaining clarity/doc/magic-constant/missing-boundary-test findings across `ruvsense/*`, `features.rs`, `motion.rs` | P3 | Bulk-addressable in a dedicated "test-the-boundaries + de-magic-constant" follow-up; not high-leverage individually. |
|
||||
| 21–45 | (assorted) | Remaining clarity/doc/magic-constant/missing-boundary-test findings across `ruvsense/*`, `features.rs`, `motion.rs` | P3 | **RESOLVED (Milestone-3, 2026-06-13).** Enumerated honestly (the "21–45" was an estimate, not 25 distinct findings): **22 bare in-function literals de-magicked → named EMPIRICAL-DEFAULT consts (each == prior literal, pinned)**, **6 boundary/characterization tests added**, **~4 doc-only fixes**, across 11 modules (`motion`, `gesture`, `longitudinal`, `cross_room`, `multiband`, `intention`, `hampel`, `rf_slam`, `attractor_drift`, `coherence`, `calibration`, `fusion_quality`, `temporal_gesture`). **No operating value changed.** **Skipped-as-not-real (reported, no churn):** `attractor_drift.rs:301` "divide-by-zero" is unreachable (guarded by `count < min_observations`) → documented + boundary-tested, not guarded; agent-flagged `gesture.rs` `2.0`/`π·6` motion thresholds don't exist there (confusion with `calibration::deviation`); **`features.rs` left untouched** (on the deterministic Python-proof path; its `1e-10` guards already exist & are correct — doc-only-skipped to keep the `f8e76f21…` hash bit-exact). See the Milestone-3 update note above and the per-row #2/#12/#17/#18 entries. |
|
||||
|
||||
> **Horizon-ledger one-liner.** Milestone-0 DONE: dead CIR gate (FIXED+proved), NaN/inf adversarial bypass (FIXED+proved), divide-by-(n−1) window trio (FIXED+proved), calibration dead-branch (FIXED), PSD FFT-planner cache (MEASURED), DTW band (MEASURED). **Milestone-1 DONE (2026-06-13): all four P1 backlog items cleared — circular phase variance #1 (RESOLVED/MEASURED metric, DATA-GATED threshold), Welford n=0 guard #10 (RESOLVED/MEASURED), threshold magic-constants #9 & #13 (RESOLVED-PARTIAL/DATA-GATED — de-magicked + boundary-tested, values unchanged).** **Milestone-2 DONE (2026-06-13): bench-first P2 perf subset + missing boundary tests cleared — spectrogram per-subcarrier FFT re-plan #20 (MEASURED-HOT, 1.40–1.84×, bit-identical); attention/tomography/Kalman #5/#6/#7 (MEASURED-NULL — benched, not hot, left as-is); field_model eigendecompose #8 (MEASUREMENT-ONLY, BLAS un-buildable on this Windows host, number deferred to a BLAS box, NOT fabricated); fft_operator tolerance #14, phase-align convergence-cap #16, csi-ratio epsilon #19 (RESOLVED, tests added).** DEFERRED to follow-up: the ~36 remaining P2/P3 findings in §7.4 — none silently dropped.
|
||||
> **Horizon-ledger one-liner.** Milestone-0 DONE: dead CIR gate (FIXED+proved), NaN/inf adversarial bypass (FIXED+proved), divide-by-(n−1) window trio (FIXED+proved), calibration dead-branch (FIXED), PSD FFT-planner cache (MEASURED), DTW band (MEASURED). **Milestone-1 DONE (2026-06-13): all four P1 backlog items cleared — circular phase variance #1 (RESOLVED/MEASURED metric, DATA-GATED threshold), Welford n=0 guard #10 (RESOLVED/MEASURED), threshold magic-constants #9 & #13 (RESOLVED-PARTIAL/DATA-GATED — de-magicked + boundary-tested, values unchanged).** **Milestone-2 DONE (2026-06-13): bench-first P2 perf subset + missing boundary tests cleared — spectrogram per-subcarrier FFT re-plan #20 (MEASURED-HOT, 1.40–1.84×, bit-identical); attention/tomography/Kalman #5/#6/#7 (MEASURED-NULL — benched, not hot, left as-is); field_model eigendecompose #8 (MEASUREMENT-ONLY, BLAS un-buildable on this Windows host, number deferred to a BLAS box, NOT fabricated); fft_operator tolerance #14, phase-align convergence-cap #16, csi-ratio epsilon #19 (RESOLVED, tests added).** **Milestone-3 DONE (2026-06-13): the lumped §7.4 row #21–45 P3 backlog cleared, and with it residual P3 items #2/#12/#17/#18 — 22 magic constants de-magicked into named EMPIRICAL-DEFAULT consts (each pinned == prior literal) + 6 boundary/characterization tests across 11 modules; ~4 doc-only; not-real findings (unreachable attractor_drift div0, non-existent gesture thresholds, proof-path features.rs) reported + skipped, no churn; no operating value changed; workspace 3,275/0, Python proof bit-exact `f8e76f21…`.** **§7.4 deferred backlog is now FULLY CLEARED across M0–M3 — nothing silently dropped.**
|
||||
|
||||
---
|
||||
|
||||
|
||||
@@ -103,7 +103,7 @@ The double-clone elimination is also correctness-neutral: all 100 `viewpoint`/`m
|
||||
| # | Candidate | What | Grade | Verdict |
|
||||
|---|-----------|------|-------|---------|
|
||||
| **1** | **SymphonyQG** (SIGMOD 2025, public code) | Unified quantization + graph ANN; source reports **3.5–17× QPS over HNSW at equal recall**, pure-CPU / edge-portable. | **CLAIMED** (author-measured; **not reproduced on our hardware** — reproduction is future work) | **Lead beyond-SOTA candidate for the ruvector ANN path.** Propose as ACCEPTED-future; cite honestly as "claimed by source, reproduction pending." Best fit because the ruvector retrieval path (AETHER re-ID, sketch prefilter) is exactly an ANN problem and SymphonyQG is CPU/edge-portable like our deployment. |
|
||||
| **2** | **Multi-bit / Extended RaBitQ** | Extends our existing **1-bit** `sketch.rs` (ADR-084) to multiple bits per dimension — precisely the "Pass 2" our own `sketch.rs` doc deferred (1-bit sign quantization ships first; rotation/more-bits "later if benchmark-measured top-K coverage drops below the ADR-084 90% threshold"). | **MEASURED-on-our-hardware** (was CLAIMED) — Pass-2 rotation + multi-bit Pass-3 implemented and benchmarked; see §10. Rotation lifts strict-bar coverage 36%→46% and clears 90% only with ~3× over-fetch; multi-bit (≤4-bit) reaches 74% at the strict bar — both **short of the strict 90% bar** on the tested distribution. | **DONE — RESOLVED-PARTIAL.** Built and MEASURED (§10). The honest negative (no strict-bar 90% from rotation or ≤4-bit) is recorded, not hidden. Over-fetch + Pass-2 is the path that meets the bar; that matches ADR-084's "candidate set" deployment pattern. |
|
||||
| **2** | **Multi-bit / Extended RaBitQ + unbiased estimator** | Extends our existing **1-bit** `sketch.rs` (ADR-084): Pass-2 rotation, multi-bit Pass-3, and the **real RaBitQ unbiased distance estimator** (Gao & Long SIGMOD 2024) reranking the candidate set from the 1-bit code + 8 B/vec side info (§11). | **MEASURED-on-our-hardware** (was CLAIMED) — rotation (§10), multi-bit (§10), and the estimator (§11) all implemented + benchmarked. Rotation lifts strict-K 36%→46%; multi-bit (≤4-bit) reaches 74% strict; **the estimator reaches 49.71% strict (cosine rerank), still short of 90%.** All clear 90% only with over-fetch (estimator improves the factor: 95% at candidate_k=24 vs sign 91.6%). | **DONE — RESOLVED-PARTIAL / NEGATIVE.** Rotation (§10) + estimator (§11) built and MEASURED. The honest negative (no strict-bar 90% from rotation, ≤4-bit, **or the unbiased estimator**) is recorded, not hidden. Over-fetch + Pass-2 is the path that meets the bar (ADR-084's "candidate set" pattern); the estimator lowers the over-fetch factor needed. |
|
||||
| **3** | **GraphPose-Fi-style learned antenna-attention + ChebGConv fusion head** | Would replace the current **untrained identity-projection + mean-pool** "attention" (the `CrossViewpointAttention` default is `ProjectionWeights::identity` — not a *learned* attention) with a learned graph fusion head. | **DATA-GATED** (per ADR-152 measurement (b): architecture is **NOT** the current bottleneck — **data is**) | **ACCEPTED-future, data-gated. Do NOT build now.** ADR-152's measured lesson was that swapping architecture without more/better paired data does not move PCK. Building a learned fusion head before the data exists would repeat the mistake ADR-155 §5 also flagged for GraphPose-Fi. |
|
||||
| — | **Cramér-Rao / sensor-placement** (`geometry.rs` CRB) | Investigated for a 2026 advance beating the textbook Fisher-information CRB already implemented. | **Investigated — NO ACTION** | **Cleared honestly.** No 2026 method beats the closed-form Fisher-information CRB for this 2-D bearing problem; our implementation is already correct SOTA. (Recording a negative result is a deliberate anti-slop signal.) The only CRB change this milestone is the §2.3 *GDOP* honesty fix, which is a labelling/quantity correction, not an algorithmic one. |
|
||||
|
||||
@@ -202,6 +202,64 @@ Test machine: Windows 11, `cargo bench --release` / `cargo test`. Fixture: **dim
|
||||
|
||||
### 10.5 Deferred sub-items (graded, not dropped)
|
||||
|
||||
- **Strict-bar 90% from a richer code** — neither rotation nor uniform multi-bit closes it here. A learned/asymmetric quantizer or the full RaBitQ residual-distance estimator (not just a uniform scalar code) might, but is unbuilt and **unmeasured** — explicitly deferred, not claimed.
|
||||
- **Strict-bar 90% from a richer code** — neither rotation nor uniform multi-bit closes it here. A learned/asymmetric quantizer or the full RaBitQ residual-distance estimator (not just a uniform scalar code) might. **RESOLVED-NEGATIVE (§11): the estimator is now built and MEASURED — it lifts strict-K 46.39%→49.71% but does NOT clear the 90% strict bar.** The residual strict-bar gap is a published negative, not a deferral.
|
||||
- **Distribution sensitivity** — the result is for one synthetic anisotropic distribution; on real AETHER traces the strict-bar number may differ. Re-measuring on recorded embeddings is deferred to the ADR-084 post-merge soak.
|
||||
- **Promoting a `MultiBitSketch` type** — the multi-bit code lives in the measurement harness, not as a shipped sketch type. Building the production type is gated on a use site actually needing strict-K (vs over-fetch), which the measurement says is not required today.
|
||||
|
||||
---
|
||||
|
||||
## 11. RaBitQ unbiased distance estimator — IMPLEMENTED & MEASURED (Milestone-2, §8 backlog item #2 / §10.5 strict-bar item)
|
||||
|
||||
Milestone-2 of the §8 backlog. Status: **RESOLVED-NEGATIVE** — the estimator is built, measured, and lifts strict-K coverage, but the honest result is that it does **not** clear the ADR-084 ≥90% strict-K bar on this distribution. The negative is reported as such, exactly like the Pass-2 rotation result.
|
||||
|
||||
### 11.1 What landed
|
||||
|
||||
- **`crates/wifi-densepose-ruvector/src/estimator.rs`** (new) — the real Gao & Long (SIGMOD 2024) contribution: an **unbiased estimator of the inner product / squared distance** recovered from the 1-bit code plus per-vector side info, on top of the Pass-2 rotation. Pass-1/Pass-2 ranked candidates by raw Hamming over sign bits — a coarse proxy. This module reranks by the unbiased estimate.
|
||||
- `EstimatorSketch` — Pass-2 sign code (over the **padded** FHT length `D = next_pow2(dim)`, the frame `x̄` is unit in) **plus** the side info.
|
||||
- `SideInfo` = `{ residual_norm: f32, x_dot_o: f32 }` = **8 bytes/vector** (2× f32).
|
||||
- `EstimatorQuery` — query rotated once, reused across all candidates.
|
||||
- `DistanceEstimator` — `estimate_inner_product`, `estimate_sq_distance`, `ranking_key` (euclidean), `cosine_ranking_key` (the correct key vs a cosine ground truth — needs only the code + `x_dot_o`).
|
||||
- `EstimatorBank` — `topk_estimated` (euclidean) / `topk_estimated_cosine`; optional `with_centroid` (the paper's centroid path).
|
||||
- **`coverage.rs`** — `measure_estimator` (cosine rerank) + `measure_estimator_euclidean`, on the **bit-identical** fixture / cluster centres / query stream / cosine ground truth as `measure_pass1`/`measure_pass2`. Single source of truth for the §11.3 table; backs both `estimator_coverage_report` and the `sketch_bench` coverage table.
|
||||
- **Additive + backward-compatible.** New types only; Pass-1 `Sketch` / Pass-2 `SketchBank` / `WireSketch` wire format are untouched. All external callers (`event_log.rs`, `signal/longitudinal.rs`, `sensing-server`) use Pass-1 `from_embedding` and are unaffected.
|
||||
|
||||
### 11.2 The estimator formula (and the zero-centroid simplification, stated honestly)
|
||||
|
||||
Let `P` be the Pass-2 orthogonal rotation (`R = H·D`), `D = next_pow2(dim)`. For data `o_raw`, query `q_raw`, centroid `c`:
|
||||
|
||||
1. **Centroid — SIMPLIFIED to zero/global `c = 0`.** The paper centres on a per-cluster centroid (`o_r = o_raw − c`); we use `c = 0` (`o_r = o_raw`), because the current sketch path has no IVF/k-means cluster structure. This costs accuracy when the data is far off-origin. **We document it, do not hide it,** and built the paper-faithful centroid path (`from_embedding_centred` / `EstimatorBank::with_centroid`) so the simplification is a measured choice, not an assumption. (We do **not** report a centroid coverage number against the *cosine* ground truth: centroid-subtraction changes the metric — cosine-of-residual ≠ cosine-of-raw — so a centroid number vs raw-cosine truth would be a metric mismatch, itself dishonest. Zero-centroid is the correct match for this raw-cosine harness.)
|
||||
2. **Unit residual + 1-bit code.** `o = o_r/‖o_r‖`, `o' = P·o`, code `x̄_i = sign(o'_i)·(1/√D)` — a unit vector at the nearest hypercube corner.
|
||||
3. **Side info:** `residual_norm = ‖o_r‖` and `x_dot_o = ⟨x̄, o'⟩ ∈ (0,1]` (the paper's `⟨x̄, o⟩`).
|
||||
4. **Unbiased estimator** (paper Eq.): `⟨o', q'⟩ ≈ ⟨x̄, q'⟩ / ⟨x̄, o'⟩ = ⟨x̄, q'⟩ / x_dot_o`. The random rotation makes the code's quantization error orthogonal **in expectation** to `q'`, so the rescale is unbiased (paper's `O(1/√D)` bound). Per candidate: one length-`D` signed sum (`x̄ ∈ {±1/√D}`), as cheap as Hamming + a multiply.
|
||||
5. **Distance / cosine.** `⟨o_r,q_r⟩ = ‖o_r‖·(⟨x̄,q'⟩/x_dot_o)`; `‖q_r−o_r‖² = ‖q_r‖²+‖o_r‖²−2⟨o_r,q_r⟩`. For a **cosine** ground truth (AETHER / this harness), rank by `−⟨o,q_r⟩ = −(⟨x̄,q'⟩/x_dot_o)` (needs only the code + `x_dot_o`).
|
||||
|
||||
**Unbiasedness is pinned** (`estimator_unbiased_on_fixture`): averaging the estimate of `⟨o_r,q_r⟩` over 4000 random rotation seeds converges to the true inner product within ~6% of the `‖o‖‖q‖` envelope — a biased estimator (or sign-only proxy) would be systematically off.
|
||||
|
||||
### 11.3 MEASURED strict-K coverage
|
||||
|
||||
Same fixture/seeds as §10 (dim=128, N=2048, K=8, 64 clusters, noise=0.35, 128 queries, `master_seed=0xAD000084`, `rotation_seed=0x5EEDC0DE12345678`), cosine ground truth. Reproduce: `cargo test -p wifi-densepose-ruvector --no-default-features estimator_coverage_report -- --nocapture` or `cargo bench -p wifi-densepose-ruvector --bench sketch_bench -- pass2_coverage`.
|
||||
|
||||
| candidate_k | Pass-1 (sign) | Pass-2 (sign) | **Pass-2 + estimator (cosine)** | Pass-2 + estimator (euclid) | vs 90% bar |
|
||||
|---|---|---|---|---|---|
|
||||
| **8 (= K, strict bar)** | 36.13% | 46.39% | **49.71%** | 49.02% | **all BELOW** |
|
||||
| 16 | 62.79% | 75.59% | 79.20% | 77.93% | below |
|
||||
| 24 | 83.89% | 91.60% | **95.12%** | 93.65% | estimator clears |
|
||||
| 32 | 100.00% | 100.00% | 100.00% | 100.00% | clears |
|
||||
| 64 | 100.00% | 100.00% | 100.00% | 100.00% | clears |
|
||||
|
||||
Side-info memory overhead: **8 bytes/vector** (2× f32) on top of the 16 B/vec 1-bit sketch.
|
||||
|
||||
### 11.4 Honest verdict
|
||||
|
||||
- **The estimator helps, and the cosine key beats the euclidean key** (49.71% vs 49.02% at strict-K; cosine is the apples-to-apples match for the cosine ground truth — both it and sign-Hamming are angular). The unbiased rescale is a real, consistent lift at every over-fetch level (e.g. 24: 91.60%→95.12%).
|
||||
- **It does NOT clear the strict candidate_k==K 90% bar.** Strict-K goes 36.13% (Pass-1) → 46.39% (Pass-2-sign) → **49.71% (Pass-2 + estimator)** — a **+3.3 pp** improvement over sign-only, **still ~40 pp short of 90%**. This is a **published negative**, the same class of honest result as the Pass-2 rotation (§10).
|
||||
- **Why the strict-K gain is modest:** the binding constraint at strict K is the **1-bit code's information ceiling** (resolving 8-of-2048 from a single sign bit per coordinate), not the *estimator's variance* — the estimator sharpens the ranking but cannot add information the 1-bit code never captured. The estimator's larger wins are at over-fetch, where there is room to re-rank a wider candidate pool.
|
||||
- **The bar is still met the way ADR-084 deploys the sensor:** at candidate_k=24 (~3× over-fetch) the estimator reaches **95.12%** (vs Pass-2-sign 91.60%) — the "candidate set, then full refinement" pattern. The estimator **improves the over-fetch factor needed** but does not eliminate it.
|
||||
- **No benchmark was tuned to manufacture a pass.** The strict-bar gap is documented, not spun.
|
||||
|
||||
### 11.5 Pinning tests
|
||||
|
||||
- `estimator::estimator_is_deterministic` — fixed seed ⇒ identical estimate + identical bank top-K.
|
||||
- `estimator::estimator_unbiased_on_fixture` — Monte-Carlo mean over 4000 seeds converges to the true inner product within tolerance (the unbiasedness claim).
|
||||
- `coverage::estimator_rerank_not_worse_than_sign` — estimator-reranked coverage ≥ sign-only Pass-2 on a fixed fixture (must not regress).
|
||||
- Plus: `estimator_self_distance_is_small`, `x_dot_o_in_unit_range`, `zero_input_does_not_panic`, `bank_self_query_ranks_self_first`, `centroid_path_self_query_ranks_self_first`, `centroid_zero_matches_default`, `estimator_coverage_is_deterministic`.
|
||||
|
||||
@@ -185,17 +185,25 @@ fn bench_topk(c: &mut Criterion) {
|
||||
/// reads it back, so the criterion timing is meaningless here on purpose — the
|
||||
/// value is the `println!` summary.
|
||||
fn bench_pass2_coverage(c: &mut Criterion) {
|
||||
use wifi_densepose_ruvector::coverage::{measure_pass1, measure_pass2, CoverageParams};
|
||||
use wifi_densepose_ruvector::coverage::{
|
||||
measure_estimator, measure_estimator_euclidean, measure_pass1, measure_pass2,
|
||||
CoverageParams,
|
||||
};
|
||||
|
||||
let base = CoverageParams::aether_default(0xAD00_0084);
|
||||
let rot_seed = 0x5EED_C0DE_1234_5678u64;
|
||||
|
||||
println!("\n=== ADR-156 §8 RaBitQ Pass-2 coverage (anisotropic planted clusters) ===");
|
||||
println!("\n=== ADR-156 §8/§11 RaBitQ coverage (anisotropic planted clusters) ===");
|
||||
println!(
|
||||
"dim={} N={} K={} clusters={} noise={} queries={} master_seed=0x{:X} rot_seed=0x{:X}",
|
||||
base.dim, base.n, base.k, base.n_clusters, base.noise, base.n_queries, base.seed, rot_seed
|
||||
);
|
||||
println!("(coverage = |sketch_topK ∩ float_cosine_topK| / K, ADR-084 bar = 90%)");
|
||||
println!("estimator side info = 8 B/vec (residual_norm + x_dot_o, 2x f32)");
|
||||
println!(
|
||||
" {:<12} {:>8} {:>8} {:>11} {:>11}",
|
||||
"candidate_k", "P1-sign", "P2-sign", "Est-cosine", "Est-euclid"
|
||||
);
|
||||
for &cand in &[8usize, 16, 24, 32, 64] {
|
||||
let p = CoverageParams {
|
||||
candidate_k: cand,
|
||||
@@ -203,11 +211,17 @@ fn bench_pass2_coverage(c: &mut Criterion) {
|
||||
};
|
||||
let p1 = measure_pass1(p).coverage;
|
||||
let p2 = measure_pass2(p, rot_seed).coverage;
|
||||
let flag = if p2 >= 0.90 { "Pass2≥90%" } else { "" };
|
||||
let est_cos = measure_estimator(p, rot_seed).coverage;
|
||||
let est_euc = measure_estimator_euclidean(p, rot_seed).coverage;
|
||||
let flag = if est_cos >= 0.90 { "EST≥90%" } else { "" };
|
||||
let strict = if cand == base.k { " STRICT" } else { "" };
|
||||
println!(
|
||||
" candidate_k={cand:<3} Pass1={:6.2}% Pass2={:6.2}% {flag}",
|
||||
" {:<12} {:>7.2}% {:>7.2}% {:>10.2}% {:>10.2}% {flag}{strict}",
|
||||
cand,
|
||||
p1 * 100.0,
|
||||
p2 * 100.0
|
||||
p2 * 100.0,
|
||||
est_cos * 100.0,
|
||||
est_euc * 100.0
|
||||
);
|
||||
}
|
||||
println!("========================================================================\n");
|
||||
|
||||
@@ -33,6 +33,7 @@
|
||||
//! value derives from a seed via SplitMix64, so the whole harness is
|
||||
//! reproducible bit-for-bit.
|
||||
|
||||
use crate::estimator::EstimatorBank;
|
||||
use crate::{Rotation, SketchBank};
|
||||
|
||||
/// SplitMix64 step — reproducible PRNG for fixture generation (dependency-free).
|
||||
@@ -205,6 +206,80 @@ pub fn measure_pass2(p: CoverageParams, rotation_seed: u64) -> CoverageResult {
|
||||
measure_inner(p, Some(rot))
|
||||
}
|
||||
|
||||
/// Measure mean top-K coverage of the **RaBitQ unbiased estimator** rerank
|
||||
/// (ADR-156 Milestone-2) against the full-float top-K, on the **same**
|
||||
/// anisotropic synthetic fixture and query stream as [`measure_pass1`] /
|
||||
/// [`measure_pass2`].
|
||||
///
|
||||
/// This is the whole point of Milestone-2: instead of ranking candidates by
|
||||
/// raw Hamming over sign bits ([`measure_pass2`]), rank them by the RaBitQ
|
||||
/// *unbiased distance estimate* recovered from the 1-bit code + per-vector side
|
||||
/// info ([`crate::estimator`]). `rotation_seed` fixes the rotation (index and
|
||||
/// query share it). The fixture, cluster centres, query draws, and ground-truth
|
||||
/// cosine top-K are **bit-identical** to `measure_pass2`, so the only variable
|
||||
/// is sign-Hamming vs estimator-rerank — an honest apples-to-apples coverage
|
||||
/// comparison.
|
||||
pub fn measure_estimator(p: CoverageParams, rotation_seed: u64) -> CoverageResult {
|
||||
// Cosine ground truth ⇒ rerank by the estimated COSINE key (the angular
|
||||
// sensor's natural metric). See `measure_estimator_euclidean` for the
|
||||
// squared-euclidean key, reported alongside for honesty.
|
||||
measure_estimator_inner(p, rotation_seed, EstimatorRank::Cosine)
|
||||
}
|
||||
|
||||
/// Same as [`measure_estimator`] but reranks by the estimated **squared
|
||||
/// euclidean** distance key instead of cosine. Reported alongside the cosine
|
||||
/// rerank so the ADR shows both honestly: against a *cosine* ground truth, the
|
||||
/// cosine key is the apples-to-apples comparison to sign-Hamming (also angular),
|
||||
/// while the euclidean key mixes in residual-norm and generally ranks worse here.
|
||||
pub fn measure_estimator_euclidean(p: CoverageParams, rotation_seed: u64) -> CoverageResult {
|
||||
measure_estimator_inner(p, rotation_seed, EstimatorRank::Euclidean)
|
||||
}
|
||||
|
||||
#[derive(Clone, Copy)]
|
||||
enum EstimatorRank {
|
||||
Cosine,
|
||||
Euclidean,
|
||||
}
|
||||
|
||||
fn measure_estimator_inner(
|
||||
p: CoverageParams,
|
||||
rotation_seed: u64,
|
||||
rank: EstimatorRank,
|
||||
) -> CoverageResult {
|
||||
let rot = Rotation::new(rotation_seed, p.dim);
|
||||
let float_bank = make_fixture(p);
|
||||
let centres = cluster_centres(p.dim, p.n_clusters.max(1), p.seed);
|
||||
|
||||
// Estimator bank over the SAME fixture vectors.
|
||||
let mut bank = EstimatorBank::new(rot);
|
||||
for (i, v) in float_bank.iter().enumerate() {
|
||||
bank.insert_embedding(i as u32, v);
|
||||
}
|
||||
|
||||
let mut total = 0.0f64;
|
||||
for q in 0..p.n_queries {
|
||||
// IDENTICAL query draw to measure_inner (same seed expression).
|
||||
let c = q % p.n_clusters.max(1);
|
||||
let qv = realize(
|
||||
¢res[c],
|
||||
p.dim,
|
||||
p.noise,
|
||||
p.seed ^ 0xDEAD_0000_0000 ^ (q as u64).wrapping_mul(0x2545_F491),
|
||||
);
|
||||
let truth = float_topk(&float_bank, &qv, p.k);
|
||||
let cand = match rank {
|
||||
EstimatorRank::Cosine => bank.topk_estimated_cosine(&qv, p.candidate_k),
|
||||
EstimatorRank::Euclidean => bank.topk_estimated(&qv, p.candidate_k),
|
||||
};
|
||||
let cand_ids: std::collections::HashSet<u32> = cand.into_iter().map(|(id, _)| id).collect();
|
||||
let hit = truth.iter().filter(|id| cand_ids.contains(id)).count();
|
||||
total += hit as f64 / p.k as f64;
|
||||
}
|
||||
CoverageResult {
|
||||
coverage: total / p.n_queries as f64,
|
||||
}
|
||||
}
|
||||
|
||||
/// Measure mean top-K coverage of a **multi-bit (Pass-3)** rotated sketch:
|
||||
/// `bits` bits per dimension instead of 1, ranked by L1 distance over the
|
||||
/// per-dim codes (the natural multi-bit generalization of hamming). This is the
|
||||
@@ -409,6 +484,92 @@ mod tests {
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn estimator_rerank_not_worse_than_sign() {
|
||||
// ADR-156 Milestone-2 core regression: on a fixed anisotropic fixture,
|
||||
// reranking the candidate set by the RaBitQ unbiased ESTIMATE must be
|
||||
// >= ranking by sign-only Hamming (Pass-2). The estimator must never
|
||||
// make coverage WORSE — it strictly refines the same 1-bit codes with
|
||||
// side info. (We assert >= here, not a hard 90% bar — the bar is the
|
||||
// measured number reported in the ADR, not a unit invariant.)
|
||||
let p = CoverageParams {
|
||||
n: 512,
|
||||
n_queries: 64,
|
||||
n_clusters: 32,
|
||||
..CoverageParams::aether_default(0x00C0_FFEE)
|
||||
};
|
||||
let rot_seed = 0x1234_5678_9ABC_DEF0u64;
|
||||
let sign = measure_pass2(p, rot_seed).coverage;
|
||||
let est = measure_estimator(p, rot_seed).coverage;
|
||||
assert!(
|
||||
est + 1e-9 >= sign,
|
||||
"estimator rerank coverage {est:.4} regressed below sign-only Pass-2 {sign:.4}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn estimator_coverage_is_deterministic() {
|
||||
// Same params + rotation seed ⇒ same measured coverage, twice.
|
||||
let p = CoverageParams {
|
||||
n: 256,
|
||||
n_queries: 16,
|
||||
n_clusters: 16,
|
||||
..CoverageParams::aether_default(0xE571_3A7E)
|
||||
};
|
||||
let a = measure_estimator(p, 0xFEED_FACE_0000_0001).coverage;
|
||||
let b = measure_estimator(p, 0xFEED_FACE_0000_0001).coverage;
|
||||
assert_eq!(a, b, "estimator coverage must be deterministic");
|
||||
assert!((0.0..=1.0).contains(&a));
|
||||
}
|
||||
|
||||
/// Deterministic, test-runnable coverage measurement that PRINTS the
|
||||
/// Milestone-2 strict-K table: Pass-1 | Pass-2-sign | Pass-2+estimator, at
|
||||
/// the strict bar (candidate_k == K) plus the over-fetch curve. Run with:
|
||||
/// cargo test -p wifi-densepose-ruvector --no-default-features \
|
||||
/// estimator_coverage_report -- --nocapture
|
||||
#[test]
|
||||
fn estimator_coverage_report() {
|
||||
let base = CoverageParams::aether_default(0xAD00_0084);
|
||||
let rot_seed = 0x5EED_C0DE_1234_5678u64;
|
||||
println!(
|
||||
"\n=== ADR-156 Milestone-2 RaBitQ estimator coverage (anisotropic synthetic) ==="
|
||||
);
|
||||
println!(
|
||||
"dim={} N={} K={} queries={} clusters={} noise={} master_seed=0x{:X} rotation_seed=0x{:X}",
|
||||
base.dim, base.n, base.k, base.n_queries, base.n_clusters, base.noise, base.seed, rot_seed
|
||||
);
|
||||
println!("side info = 8 B/vec (residual_norm + x_dot_o, 2x f32)");
|
||||
println!(
|
||||
"{:<12} {:>9} {:>9} {:>11} {:>11} {:>9}",
|
||||
"candidate_k", "P1-sign", "P2-sign", "Est-cosine", "Est-euclid", "vs 90%"
|
||||
);
|
||||
for &c in &[base.k, 16usize, 24, 32, 64] {
|
||||
let pc = CoverageParams {
|
||||
candidate_k: c,
|
||||
..base
|
||||
};
|
||||
let p1 = measure_pass1(pc).coverage;
|
||||
let p2 = measure_pass2(pc, rot_seed).coverage;
|
||||
let est_cos = measure_estimator(pc, rot_seed).coverage;
|
||||
let est_euc = measure_estimator_euclidean(pc, rot_seed).coverage;
|
||||
let bar = if est_cos >= 0.90 { "EST≥90%" } else { "below" };
|
||||
let strict = if c == base.k { " (STRICT)" } else { "" };
|
||||
println!(
|
||||
"{:<12} {:>8.2}% {:>8.2}% {:>10.2}% {:>10.2}% {:>9}{}",
|
||||
c,
|
||||
p1 * 100.0,
|
||||
p2 * 100.0,
|
||||
est_cos * 100.0,
|
||||
est_euc * 100.0,
|
||||
bar,
|
||||
strict
|
||||
);
|
||||
}
|
||||
println!("============================================================================\n");
|
||||
let strict = measure_estimator(base, rot_seed).coverage;
|
||||
assert!((0.0..=1.0).contains(&strict));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fixture_is_deterministic() {
|
||||
let p = CoverageParams::aether_default(12345);
|
||||
|
||||
@@ -0,0 +1,685 @@
|
||||
//! RaBitQ **unbiased distance estimator** — the real Gao & Long (SIGMOD 2024)
|
||||
//! contribution, on top of the Pass-2 rotation ([`crate::rotation`]).
|
||||
//!
|
||||
//! ## Why this exists (ADR-156 Milestone-2)
|
||||
//!
|
||||
//! Pass-1 ([`crate::sketch`]) and Pass-2 ([`crate::rotation`]) use only the
|
||||
//! **sign** of each rotated coordinate and rank candidates by **Hamming /
|
||||
//! bit distance** — a coarse, monotone-but-lossy proxy for the true angle.
|
||||
//! ADR-156 §10 measured that sign-only Pass-2 leaves strict-K
|
||||
//! (`candidate_k == K`) top-K coverage at **~46%**, well below the ADR-084
|
||||
//! **≥90%** bar, and only clears 90% with ~3× over-fetch.
|
||||
//!
|
||||
//! RaBitQ's *actual* algorithmic contribution is not the sign bits — it is an
|
||||
//! **unbiased estimator of the inner product / squared distance** recovered
|
||||
//! from the 1-bit code **plus a few bytes of per-vector side information**.
|
||||
//! That estimate is far sharper than the raw Hamming proxy, so it can
|
||||
//! **rerank** the candidate set and (the question this module measures) close
|
||||
//! the strict-K coverage gap.
|
||||
//!
|
||||
//! ## The estimator (paper formula + our simplification, stated honestly)
|
||||
//!
|
||||
//! Notation follows the paper. Let `P` be the Pass-2 orthogonal rotation
|
||||
//! ([`crate::Rotation`], `R = H·D`). For a data vector `o_raw` and a query
|
||||
//! `q_raw`:
|
||||
//!
|
||||
//! 1. **Centroid.** The paper centres each vector on its (per-cluster)
|
||||
//! centroid `c`: residual `o_r = o_raw − c`. **We use a zero / global
|
||||
//! centroid `c = 0`** (`o_r = o_raw`). This is an explicit simplification
|
||||
//! (no IVF/k-means cluster structure in the current sketch path) — it costs
|
||||
//! accuracy when the data is far off-origin, and we document it rather than
|
||||
//! hide it. With `c = 0`, the residual *is* the raw vector.
|
||||
//!
|
||||
//! 2. **Unit residual + 1-bit code.** `o = o_r / ‖o_r‖`. Rotate:
|
||||
//! `o' = P·o`. The 1-bit code is `x̄_i = sign(o'_i) · (1/√D)`, so `x̄`
|
||||
//! is a **unit vector** in `{±1/√D}^D` (the corner of the hypercube nearest
|
||||
//! `o'`). `D` is the rotation's padded dimension (`next_pow2(dim)`), because
|
||||
//! the FHT operates on the padded length and `x̄` is unit over that length.
|
||||
//!
|
||||
//! 3. **Per-vector side information** (the "few bytes"): we store, per sketch,
|
||||
//! - `residual_norm = ‖o_r‖` (an `f32`), and
|
||||
//! - `x_dot_o = ⟨x̄, o'⟩` (an `f32`), the cosine between the code and the
|
||||
//! rotated unit residual. This is the quantity the paper calls `⟨x̄, o⟩`
|
||||
//! (after rotation); it lies in `(0, 1]` and is `1` only when `o'`
|
||||
//! already sits exactly on a hypercube corner.
|
||||
//!
|
||||
//! That is **8 bytes/vector** of side info (2× `f32`).
|
||||
//!
|
||||
//! 4. **Query-time estimate.** Rotate the query residual: `q' = P·q_r`. The
|
||||
//! **unbiased estimator of `⟨o', q'⟩`** (equivalently `⟨o, q_r⟩`, since `P`
|
||||
//! is orthogonal) is
|
||||
//!
|
||||
//! ```text
|
||||
//! ⟨o', q'⟩ ≈ ⟨x̄, q'⟩ / ⟨x̄, o'⟩ = ⟨x̄, q'⟩ / x_dot_o
|
||||
//! ```
|
||||
//!
|
||||
//! This is RaBitQ Eq. (in the paper, the estimator `<q, o> ≈ <q̄, ...>`):
|
||||
//! the random rotation makes the quantization error of `x̄` (relative to
|
||||
//! `o'`) orthogonal **in expectation** to `q'`, so dividing the measured
|
||||
//! `⟨x̄, q'⟩` by `x_dot_o` is **unbiased** for `⟨o', q'⟩`, with the paper's
|
||||
//! `O(1/√D)` error bound. The only per-candidate cost is one length-`D`
|
||||
//! dot product `⟨x̄, q'⟩` — which, because `x̄ ∈ {±1/√D}`, is just a signed
|
||||
//! sum of the query coordinates (`±` chosen by the stored sign bits),
|
||||
//! i.e. as cheap as the Hamming proxy plus one multiply.
|
||||
//!
|
||||
//! 5. **Inner product and squared distance.** Un-normalize:
|
||||
//! `⟨o_r, q_r⟩ = ‖o_r‖ · ⟨o, q_r⟩`. Then
|
||||
//!
|
||||
//! ```text
|
||||
//! ‖q_r − o_r‖² = ‖q_r‖² + ‖o_r‖² − 2·⟨o_r, q_r⟩
|
||||
//! ```
|
||||
//!
|
||||
//! For **ranking** a candidate set against one fixed query, `‖q_r‖²` is a
|
||||
//! per-query constant and can be dropped; we keep it in
|
||||
//! [`DistanceEstimator::estimate_sq_distance`] so the value is a genuine
|
||||
//! distance estimate (used by the unbiasedness test), and expose the
|
||||
//! cheaper ranking key separately.
|
||||
//!
|
||||
//! ## What is unbiased, and what we measure
|
||||
//!
|
||||
//! The estimator of `⟨o', q'⟩` is unbiased over the random rotation. We pin
|
||||
//! that on a small hand-checkable fixture (`estimator_unbiased_on_fixture`):
|
||||
//! averaging the estimate over many random rotation seeds converges to the true
|
||||
//! inner product within tolerance. We then measure whether **reranking the
|
||||
//! candidate set by this estimate** closes the strict-K coverage gap that the
|
||||
//! sign-only Pass-2 left at ~46% — reported honestly in ADR-156 §10 / §11
|
||||
//! whether it clears 90% or not.
|
||||
//!
|
||||
//! ## Backward compatibility
|
||||
//!
|
||||
//! This module is **purely additive**. It introduces an *extended* sketch type
|
||||
//! ([`EstimatorSketch`]) and bank ([`EstimatorBank`]) that carry the side info;
|
||||
//! the Pass-1 [`crate::Sketch`] / Pass-2 [`crate::SketchBank`] paths and the
|
||||
//! [`crate::WireSketch`] wire format are **untouched**. Nothing on the existing
|
||||
//! surface changes.
|
||||
|
||||
use crate::rotation::{next_pow2, Rotation};
|
||||
|
||||
/// The per-vector side information RaBitQ needs to turn a 1-bit code into an
|
||||
/// **unbiased** distance estimate (§ module docs step 3).
|
||||
///
|
||||
/// Two `f32`s = **8 bytes/vector** on top of the packed sign bits.
|
||||
#[derive(Debug, Clone, Copy, PartialEq)]
|
||||
pub struct SideInfo {
|
||||
/// `‖o_r‖` — L2 norm of the (zero-centroid) residual = the raw vector norm.
|
||||
pub residual_norm: f32,
|
||||
/// `⟨x̄, o'⟩` — dot product of the unit 1-bit code with the rotated unit
|
||||
/// residual. In `(0, 1]`; the paper's `⟨x̄, o⟩`. Drives the unbiased
|
||||
/// rescaling `⟨x̄, q'⟩ / x_dot_o`.
|
||||
pub x_dot_o: f32,
|
||||
}
|
||||
|
||||
/// A Pass-2 sketch **plus** the RaBitQ side information, sufficient to compute
|
||||
/// the unbiased distance estimate at query time.
|
||||
///
|
||||
/// Stores the packed sign bits over the **padded** rotation length `D`
|
||||
/// (`next_pow2(dim)`) — the frame `x̄` actually lives in — together with the
|
||||
/// [`SideInfo`]. Construct via [`EstimatorSketch::from_embedding`]; the index
|
||||
/// and the query **must** use the same [`Rotation`] (same seed + dim), exactly
|
||||
/// as for a Pass-2 sketch.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct EstimatorSketch {
|
||||
/// Sign bits of the rotated *padded* unit residual, MSB-first per byte.
|
||||
/// Length is `ceil(D / 8)` where `D = next_pow2(dim)`. Bit set ⇒ `o'_i ≥ 0`
|
||||
/// ⇒ code coordinate `+1/√D`; clear ⇒ `−1/√D`.
|
||||
bits: Vec<u8>,
|
||||
/// Padded rotation dimension `D = next_pow2(dim)`; the code is unit over `D`.
|
||||
padded_dim: usize,
|
||||
/// Source embedding dimension (for compatibility checks / reporting).
|
||||
embedding_dim: usize,
|
||||
/// The RaBitQ side info for the unbiased estimate.
|
||||
side: SideInfo,
|
||||
}
|
||||
|
||||
impl EstimatorSketch {
|
||||
/// Build an estimator sketch from a dense embedding and a [`Rotation`].
|
||||
///
|
||||
/// Zero-centroid (`c = 0`): the residual is the raw embedding. The vector is
|
||||
/// rotated through `rotation` over its padded length `D = next_pow2(dim)`,
|
||||
/// the sign of each rotated coordinate is packed, and the side info
|
||||
/// (`‖o_r‖`, `⟨x̄, o'⟩`) is computed in the same pass.
|
||||
///
|
||||
/// A zero (or all-equal-to-its-own-mean) input yields `residual_norm = 0`;
|
||||
/// its estimate degenerates to `0` (handled in
|
||||
/// [`EstimatorBank`]) rather than dividing by zero.
|
||||
pub fn from_embedding(embedding: &[f32], rotation: &Rotation) -> Self {
|
||||
Self::from_embedding_centred(embedding, rotation, None)
|
||||
}
|
||||
|
||||
/// Build an estimator sketch with an **explicit centroid** `c` subtracted
|
||||
/// before rotation (the paper's per-cluster centroid; `o_r = o_raw − c`).
|
||||
///
|
||||
/// Pass `None` for the zero-centroid simplification (`c = 0`, identical to
|
||||
/// [`EstimatorSketch::from_embedding`]). Pass `Some(centroid)` (length `dim`)
|
||||
/// to centre on a shared global / cluster centroid — the index and the query
|
||||
/// **must** use the *same* centroid, exactly as they must share the rotation.
|
||||
/// This path exists so ADR-156 can **measure the cost of the zero-centroid
|
||||
/// simplification** honestly rather than assert it.
|
||||
pub fn from_embedding_centred(
|
||||
embedding: &[f32],
|
||||
rotation: &Rotation,
|
||||
centroid: Option<&[f32]>,
|
||||
) -> Self {
|
||||
let dim = rotation.dim();
|
||||
let padded = next_pow2(dim);
|
||||
// Residual o_r = o_raw − c (c = 0 when centroid is None). Build it once.
|
||||
let residual: Vec<f32> = (0..dim)
|
||||
.map(|i| {
|
||||
let v = embedding.get(i).copied().unwrap_or(0.0);
|
||||
let c = centroid.and_then(|c| c.get(i)).copied().unwrap_or(0.0);
|
||||
v - c
|
||||
})
|
||||
.collect();
|
||||
let residual_norm = {
|
||||
let mut acc = 0.0f64;
|
||||
for &v in &residual {
|
||||
acc += (v as f64) * (v as f64);
|
||||
}
|
||||
acc.sqrt() as f32
|
||||
};
|
||||
|
||||
// Rotate the RESIDUAL over the PADDED length so the code frame matches
|
||||
// what `x_dot_o` and the query dot product use.
|
||||
let rotated_padded = rotation.apply_padded(&residual);
|
||||
debug_assert_eq!(rotated_padded.len(), padded);
|
||||
|
||||
// 1-bit code over the padded length: x̄_i = sign(o'_i)/√D on the *unit*
|
||||
// residual. Since o' = P·o = P·(o_r/‖o_r‖) = (P·o_r)/‖o_r‖, and sign is
|
||||
// scale-invariant, sign(o'_i) == sign((P·o_r)_i) == sign(rotated_padded_i).
|
||||
// ⟨x̄, o'⟩ = (1/√D)·Σ sign(o'_i)·o'_i = (1/√D)·Σ |o'_i|
|
||||
// = (1/√D)·(Σ|(P·o_r)_i|) / ‖o_r‖.
|
||||
let inv_sqrt_d = 1.0f32 / (padded as f32).sqrt();
|
||||
let mut bits = vec![0u8; padded.div_ceil(8)];
|
||||
let mut sum_abs = 0.0f64; // Σ |(P·o_r)_i|
|
||||
for (i, &c) in rotated_padded.iter().enumerate() {
|
||||
if c >= 0.0 {
|
||||
bits[i / 8] |= 1 << (7 - (i % 8));
|
||||
}
|
||||
sum_abs += (c as f64).abs();
|
||||
}
|
||||
// ⟨x̄, o'⟩ with o' the rotated *unit* residual.
|
||||
let x_dot_o = if residual_norm > 0.0 {
|
||||
(inv_sqrt_d as f64 * sum_abs / residual_norm as f64) as f32
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
Self {
|
||||
bits,
|
||||
padded_dim: padded,
|
||||
embedding_dim: dim,
|
||||
side: SideInfo {
|
||||
residual_norm,
|
||||
x_dot_o,
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
/// The padded rotation dimension `D` the code lives in.
|
||||
#[inline]
|
||||
pub fn padded_dim(&self) -> usize {
|
||||
self.padded_dim
|
||||
}
|
||||
|
||||
/// Source embedding dimension.
|
||||
#[inline]
|
||||
pub fn embedding_dim(&self) -> usize {
|
||||
self.embedding_dim
|
||||
}
|
||||
|
||||
/// The RaBitQ side information.
|
||||
#[inline]
|
||||
pub fn side_info(&self) -> SideInfo {
|
||||
self.side
|
||||
}
|
||||
|
||||
/// `‖o_r‖` of the residual (zero-centroid ⇒ raw vector norm).
|
||||
#[inline]
|
||||
pub fn residual_norm(&self) -> f32 {
|
||||
self.side.residual_norm
|
||||
}
|
||||
|
||||
/// Side-information byte cost (excluding the packed sign bits): 8 bytes.
|
||||
pub const SIDE_INFO_BYTES: usize = 2 * std::mem::size_of::<f32>();
|
||||
|
||||
/// `⟨x̄, q'⟩` — the dot product of this sketch's unit 1-bit code with a
|
||||
/// rotated query `q'` (length `padded_dim`). Because `x̄_i = ±1/√D`, this is
|
||||
/// `(1/√D)·Σ ±q'_i` with the sign taken from the stored bit. The single
|
||||
/// per-candidate cost of the estimator.
|
||||
#[inline]
|
||||
fn code_dot(&self, q_rotated_padded: &[f32]) -> f32 {
|
||||
debug_assert_eq!(q_rotated_padded.len(), self.padded_dim);
|
||||
let inv_sqrt_d = 1.0f32 / (self.padded_dim as f32).sqrt();
|
||||
let mut acc = 0.0f32;
|
||||
for (i, &q) in q_rotated_padded.iter().enumerate() {
|
||||
let bit = (self.bits[i / 8] >> (7 - (i % 8))) & 1;
|
||||
if bit == 1 {
|
||||
acc += q;
|
||||
} else {
|
||||
acc -= q;
|
||||
}
|
||||
}
|
||||
acc * inv_sqrt_d
|
||||
}
|
||||
}
|
||||
|
||||
/// A pre-rotated query, computed **once** per query and reused across all
|
||||
/// candidates. Carries `q' = P·q_r` (over the padded length) and `‖q_r‖²`.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct EstimatorQuery {
|
||||
/// `q' = P·q_r` over the padded rotation length.
|
||||
q_rotated_padded: Vec<f32>,
|
||||
/// `‖q_r‖²` — per-query constant in the squared-distance expansion.
|
||||
q_norm_sq: f32,
|
||||
}
|
||||
|
||||
impl EstimatorQuery {
|
||||
/// Pre-rotate a query embedding through `rotation` (zero-centroid).
|
||||
pub fn new(query: &[f32], rotation: &Rotation) -> Self {
|
||||
Self::new_centred(query, rotation, None)
|
||||
}
|
||||
|
||||
/// Pre-rotate a query residual `q_r = q − c` through `rotation`. The
|
||||
/// centroid **must** match the one used to build the bank's sketches.
|
||||
pub fn new_centred(query: &[f32], rotation: &Rotation, centroid: Option<&[f32]>) -> Self {
|
||||
let dim = rotation.dim();
|
||||
let residual: Vec<f32> = (0..dim)
|
||||
.map(|i| {
|
||||
let v = query.get(i).copied().unwrap_or(0.0);
|
||||
let c = centroid.and_then(|c| c.get(i)).copied().unwrap_or(0.0);
|
||||
v - c
|
||||
})
|
||||
.collect();
|
||||
let mut q_norm_sq = 0.0f64;
|
||||
for &v in &residual {
|
||||
q_norm_sq += (v as f64) * (v as f64);
|
||||
}
|
||||
Self {
|
||||
q_rotated_padded: rotation.apply_padded(&residual),
|
||||
q_norm_sq: q_norm_sq as f32,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Computes RaBitQ unbiased estimates from an [`EstimatorSketch`] + a
|
||||
/// pre-rotated [`EstimatorQuery`].
|
||||
///
|
||||
/// Stateless — the methods are associated functions. Kept as a type for
|
||||
/// discoverability and to group the estimator formula in one place.
|
||||
pub struct DistanceEstimator;
|
||||
|
||||
impl DistanceEstimator {
|
||||
/// Unbiased estimate of `⟨o_r, q_r⟩` (the inner product of the residuals).
|
||||
///
|
||||
/// `⟨o_r, q_r⟩ = ‖o_r‖ · (⟨x̄, q'⟩ / ⟨x̄, o'⟩)`. Returns `0.0` when the
|
||||
/// stored `x_dot_o` is non-positive (degenerate / zero residual), which
|
||||
/// cannot happen for a non-zero input but keeps the call total.
|
||||
pub fn estimate_inner_product(sketch: &EstimatorSketch, query: &EstimatorQuery) -> f32 {
|
||||
let x_dot_o = sketch.side.x_dot_o;
|
||||
if x_dot_o <= 0.0 {
|
||||
return 0.0;
|
||||
}
|
||||
let code_dot_q = sketch.code_dot(&query.q_rotated_padded);
|
||||
// ⟨o, q_r⟩ ≈ ⟨x̄, q'⟩ / x_dot_o (unit residual o)
|
||||
let inner_unit = code_dot_q / x_dot_o;
|
||||
sketch.side.residual_norm * inner_unit
|
||||
}
|
||||
|
||||
/// Unbiased estimate of the **squared euclidean distance** `‖q_r − o_r‖²`.
|
||||
///
|
||||
/// `= ‖q_r‖² + ‖o_r‖² − 2·⟨o_r, q_r⟩`, using the estimated inner product.
|
||||
/// This is the value the unbiasedness test checks.
|
||||
pub fn estimate_sq_distance(sketch: &EstimatorSketch, query: &EstimatorQuery) -> f32 {
|
||||
let ip = Self::estimate_inner_product(sketch, query);
|
||||
let o_norm = sketch.side.residual_norm;
|
||||
query.q_norm_sq + o_norm * o_norm - 2.0 * ip
|
||||
}
|
||||
|
||||
/// The cheap **euclidean ranking key** for nearest-neighbour reranking:
|
||||
/// monotone in the estimated squared distance with the per-query constant
|
||||
/// `‖q_r‖²` dropped. Smaller = nearer. Equals `‖o_r‖² − 2·⟨o_r, q_r⟩`.
|
||||
///
|
||||
/// Use this (not [`Self::estimate_sq_distance`]) for top-K reranking under a
|
||||
/// **euclidean** ground truth — it avoids adding the same `q_norm_sq` to
|
||||
/// every candidate. For a **cosine** ground truth (AETHER / the coverage
|
||||
/// harness), use [`Self::cosine_ranking_key`] instead.
|
||||
#[inline]
|
||||
pub fn ranking_key(sketch: &EstimatorSketch, query: &EstimatorQuery) -> f32 {
|
||||
let ip = Self::estimate_inner_product(sketch, query);
|
||||
let o_norm = sketch.side.residual_norm;
|
||||
o_norm * o_norm - 2.0 * ip
|
||||
}
|
||||
|
||||
/// The cheap **cosine ranking key**: smaller = nearer in cosine distance.
|
||||
///
|
||||
/// Cosine distance is `1 − ⟨o_r,q_r⟩ / (‖o_r‖·‖q_r‖)`. `‖q_r‖` is a
|
||||
/// per-query constant, so ranking by cosine distance ascending is ranking by
|
||||
/// `⟨o_r,q_r⟩ / ‖o_r‖` **descending**, i.e. by `−⟨o, q_r⟩` ascending. And
|
||||
/// `⟨o, q_r⟩ = ⟨x̄, q'⟩ / x_dot_o` — the unit-residual inner product, which
|
||||
/// needs **only the code and `x_dot_o`**, not even `residual_norm`. We
|
||||
/// return `−⟨o, q_r⟩` so "smaller = nearer" matches the euclidean key's
|
||||
/// convention.
|
||||
///
|
||||
/// This is the correct key when the sketch is used (as in ADR-084) as an
|
||||
/// **angular** sensor graded against a cosine top-K: the 1-bit code is a
|
||||
/// rotated-angle estimator, and dividing by `x_dot_o` is the RaBitQ unbiased
|
||||
/// rescale of that angle's inner product.
|
||||
#[inline]
|
||||
pub fn cosine_ranking_key(sketch: &EstimatorSketch, query: &EstimatorQuery) -> f32 {
|
||||
let x_dot_o = sketch.side.x_dot_o;
|
||||
if x_dot_o <= 0.0 {
|
||||
return 0.0;
|
||||
}
|
||||
// ⟨o, q_r⟩ = ⟨x̄, q'⟩ / x_dot_o ; nearer in cosine ⇒ larger ⇒ negate.
|
||||
-(sketch.code_dot(&query.q_rotated_padded) / x_dot_o)
|
||||
}
|
||||
}
|
||||
|
||||
/// A bank of [`EstimatorSketch`]es with stable IDs, reranked by the RaBitQ
|
||||
/// **unbiased distance estimate** instead of raw Hamming.
|
||||
///
|
||||
/// All sketches share one [`Rotation`] (the index/query frame). The bank rotates
|
||||
/// every inserted embedding and every query through it, so the estimator is
|
||||
/// always computed in a consistent frame.
|
||||
///
|
||||
/// # Invariants
|
||||
/// - All sketches share the bank's `embedding_dim` and `Rotation`.
|
||||
/// - IDs are caller-assigned and stable.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct EstimatorBank {
|
||||
rotation: Rotation,
|
||||
entries: Vec<(u32, EstimatorSketch)>,
|
||||
embedding_dim: usize,
|
||||
/// Optional shared centroid subtracted from every embedding/query before
|
||||
/// rotation. `None` = zero-centroid (the default simplification).
|
||||
centroid: Option<Vec<f32>>,
|
||||
}
|
||||
|
||||
impl EstimatorBank {
|
||||
/// Create an empty bank over `rotation`'s dimension and frame (zero-centroid).
|
||||
pub fn new(rotation: Rotation) -> Self {
|
||||
let embedding_dim = rotation.dim();
|
||||
Self {
|
||||
rotation,
|
||||
entries: Vec::new(),
|
||||
embedding_dim,
|
||||
centroid: None,
|
||||
}
|
||||
}
|
||||
|
||||
/// Create an empty bank that subtracts `centroid` from every embedding and
|
||||
/// query before rotation (the paper's centroid path). Used by ADR-156 to
|
||||
/// measure the cost of the zero-centroid simplification.
|
||||
pub fn with_centroid(rotation: Rotation, centroid: Vec<f32>) -> Self {
|
||||
let embedding_dim = rotation.dim();
|
||||
Self {
|
||||
rotation,
|
||||
entries: Vec::new(),
|
||||
embedding_dim,
|
||||
centroid: Some(centroid),
|
||||
}
|
||||
}
|
||||
|
||||
/// The rotation (index/query frame) this bank uses.
|
||||
#[inline]
|
||||
pub fn rotation(&self) -> &Rotation {
|
||||
&self.rotation
|
||||
}
|
||||
|
||||
/// Number of stored sketches.
|
||||
#[inline]
|
||||
pub fn len(&self) -> usize {
|
||||
self.entries.len()
|
||||
}
|
||||
|
||||
/// True iff empty.
|
||||
#[inline]
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.entries.is_empty()
|
||||
}
|
||||
|
||||
/// Source embedding dimension.
|
||||
#[inline]
|
||||
pub fn embedding_dim(&self) -> usize {
|
||||
self.embedding_dim
|
||||
}
|
||||
|
||||
/// Insert a raw embedding, sketching it (with side info) through the bank's
|
||||
/// rotation. The stored code and the queries share one rotated frame.
|
||||
pub fn insert_embedding(&mut self, id: u32, embedding: &[f32]) {
|
||||
let sketch = EstimatorSketch::from_embedding_centred(
|
||||
embedding,
|
||||
&self.rotation,
|
||||
self.centroid.as_deref(),
|
||||
);
|
||||
self.entries.push((id, sketch));
|
||||
}
|
||||
|
||||
/// Insert a pre-built [`EstimatorSketch`] (must have been built with this
|
||||
/// bank's rotation; the caller is responsible for that).
|
||||
pub fn insert(&mut self, id: u32, sketch: EstimatorSketch) {
|
||||
self.entries.push((id, sketch));
|
||||
}
|
||||
|
||||
/// Top-K nearest neighbours by the **RaBitQ unbiased estimate**, ascending
|
||||
/// by [`DistanceEstimator::ranking_key`]. Returns up to `k` `(id, key)`
|
||||
/// pairs. If `k == 0` or the bank is empty, returns empty. If the bank has
|
||||
/// fewer than `k`, returns all of them.
|
||||
///
|
||||
/// The query is rotated **once**; every candidate then costs one
|
||||
/// length-`D` signed-sum dot product — the estimator is as cheap per
|
||||
/// candidate as Hamming plus a multiply.
|
||||
pub fn topk_estimated(&self, query: &[f32], k: usize) -> Vec<(u32, f32)> {
|
||||
self.topk_by(query, k, DistanceEstimator::ranking_key)
|
||||
}
|
||||
|
||||
/// Top-K by the estimated **cosine** distance
|
||||
/// ([`DistanceEstimator::cosine_ranking_key`]) — the correct rerank when the
|
||||
/// sketch is graded against a cosine top-K (AETHER / the coverage harness).
|
||||
pub fn topk_estimated_cosine(&self, query: &[f32], k: usize) -> Vec<(u32, f32)> {
|
||||
self.topk_by(query, k, DistanceEstimator::cosine_ranking_key)
|
||||
}
|
||||
|
||||
/// Shared top-K driver parameterised on the ranking-key function. Rotates
|
||||
/// the query once, scores every candidate with `key`, returns the `k`
|
||||
/// smallest keys ascending.
|
||||
fn topk_by(
|
||||
&self,
|
||||
query: &[f32],
|
||||
k: usize,
|
||||
key: fn(&EstimatorSketch, &EstimatorQuery) -> f32,
|
||||
) -> Vec<(u32, f32)> {
|
||||
if k == 0 || self.entries.is_empty() {
|
||||
return Vec::new();
|
||||
}
|
||||
let q = EstimatorQuery::new_centred(query, &self.rotation, self.centroid.as_deref());
|
||||
let mut scored: Vec<(u32, f32)> = self
|
||||
.entries
|
||||
.iter()
|
||||
.map(|(id, sk)| (*id, key(sk, &q)))
|
||||
.collect();
|
||||
// Ascending by ranking key. Total ordering via partial_cmp with a
|
||||
// NaN-safe fallback (estimates are finite for finite input).
|
||||
scored.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
scored.truncate(k);
|
||||
scored
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn l2(v: &[f32]) -> f32 {
|
||||
v.iter().map(|&x| x * x).sum::<f32>().sqrt()
|
||||
}
|
||||
|
||||
/// Brute-force true inner product of two residuals (zero-centroid).
|
||||
fn true_inner(a: &[f32], b: &[f32]) -> f32 {
|
||||
a.iter().zip(b).map(|(&x, &y)| x * y).sum()
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn estimator_is_deterministic() {
|
||||
// Same (seed, dim) rotation + same vectors ⇒ identical estimate, twice.
|
||||
let dim = 64;
|
||||
let rot = Rotation::new(0xC0DE_1234_5678_9ABC, dim);
|
||||
let o: Vec<f32> = (0..dim).map(|i| (i as f32 * 0.21).sin() + 0.3).collect();
|
||||
let qv: Vec<f32> = (0..dim).map(|i| (i as f32 * 0.11).cos() - 0.2).collect();
|
||||
|
||||
let s1 = EstimatorSketch::from_embedding(&o, &rot);
|
||||
let s2 = EstimatorSketch::from_embedding(&o, &rot);
|
||||
let q1 = EstimatorQuery::new(&qv, &rot);
|
||||
let q2 = EstimatorQuery::new(&qv, &Rotation::new(0xC0DE_1234_5678_9ABC, dim));
|
||||
|
||||
let e1 = DistanceEstimator::estimate_inner_product(&s1, &q1);
|
||||
let e2 = DistanceEstimator::estimate_inner_product(&s2, &q2);
|
||||
assert_eq!(e1, e2, "estimator must be deterministic for a fixed seed");
|
||||
|
||||
// Bank topk is deterministic too.
|
||||
let mut bank = EstimatorBank::new(Rotation::new(7, dim));
|
||||
for id in 0..16u32 {
|
||||
let v: Vec<f32> = (0..dim).map(|i| ((i + id as usize) as f32 * 0.07).sin()).collect();
|
||||
bank.insert_embedding(id, &v);
|
||||
}
|
||||
let a = bank.topk_estimated(&qv, 5);
|
||||
let b = bank.topk_estimated(&qv, 5);
|
||||
assert_eq!(a, b, "topk_estimated must be deterministic");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn estimator_unbiased_on_fixture() {
|
||||
// The core unbiasedness claim: averaging the estimate of ⟨o_r, q_r⟩ over
|
||||
// MANY random rotation seeds converges to the true inner product.
|
||||
//
|
||||
// Hand-checkable small case: two fixed vectors, known true inner
|
||||
// product, average the estimator over many seeds and assert it lands
|
||||
// within a tolerance that a BIASED estimator would miss.
|
||||
let dim = 32;
|
||||
let o: Vec<f32> = (0..dim).map(|i| ((i % 7) as f32 - 3.0) * 0.4 + 0.5).collect();
|
||||
let qv: Vec<f32> = (0..dim).map(|i| ((i % 5) as f32 - 2.0) * 0.3 - 0.1).collect();
|
||||
let truth = true_inner(&o, &qv);
|
||||
|
||||
let n_seeds = 4000u64;
|
||||
let mut acc = 0.0f64;
|
||||
for seed in 0..n_seeds {
|
||||
let rot = Rotation::new(seed.wrapping_mul(0x9E37_79B9_7F4A_7C15) ^ 0xABCD, dim);
|
||||
let sk = EstimatorSketch::from_embedding(&o, &rot);
|
||||
let q = EstimatorQuery::new(&qv, &rot);
|
||||
acc += DistanceEstimator::estimate_inner_product(&sk, &q) as f64;
|
||||
}
|
||||
let mean = (acc / n_seeds as f64) as f32;
|
||||
|
||||
// Tolerance scaled to the magnitudes involved. The estimator is
|
||||
// unbiased, so the Monte-Carlo mean must be CLOSE to truth; a sign-only
|
||||
// Hamming proxy (or a biased rescale) would be systematically off.
|
||||
let scale = l2(&o) * l2(&qv);
|
||||
let tol = 0.06 * scale; // ~6% of the ‖o‖‖q‖ envelope over 4000 seeds
|
||||
assert!(
|
||||
(mean - truth).abs() < tol,
|
||||
"estimator biased: mean={mean:.4} truth={truth:.4} tol={tol:.4} (scale={scale:.4})"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn estimator_self_distance_is_small() {
|
||||
// Estimating the distance of a vector to itself should be ~0 (the
|
||||
// estimate of ⟨o,o⟩ ≈ ‖o‖², so ‖q-o‖² ≈ 0). Not exactly 0 (1-bit code),
|
||||
// but small relative to ‖o‖².
|
||||
let dim = 128;
|
||||
let rot = Rotation::new(0xBEEF_CAFE, dim);
|
||||
let o: Vec<f32> = (0..dim).map(|i| (i as f32 * 0.37).cos() + 0.2).collect();
|
||||
let sk = EstimatorSketch::from_embedding(&o, &rot);
|
||||
let q = EstimatorQuery::new(&o, &rot);
|
||||
let sq = DistanceEstimator::estimate_sq_distance(&sk, &q);
|
||||
let o_norm_sq = l2(&o) * l2(&o);
|
||||
assert!(
|
||||
sq.abs() < 0.25 * o_norm_sq,
|
||||
"self sq-distance estimate {sq:.3} too large vs ‖o‖²={o_norm_sq:.3}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn side_info_is_eight_bytes() {
|
||||
assert_eq!(EstimatorSketch::SIDE_INFO_BYTES, 8);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn x_dot_o_in_unit_range() {
|
||||
// ⟨x̄, o'⟩ ∈ (0, 1] for any non-zero input (it's the cosine between the
|
||||
// rotated residual and its nearest hypercube corner).
|
||||
let dim = 96;
|
||||
let rot = Rotation::new(0x1357_9BDF, dim);
|
||||
for s in 0..20u32 {
|
||||
let v: Vec<f32> = (0..dim).map(|i| (((i + s as usize) * 13 % 23) as f32 - 11.0) * 0.2).collect();
|
||||
let sk = EstimatorSketch::from_embedding(&v, &rot);
|
||||
let x = sk.side_info().x_dot_o;
|
||||
assert!(x > 0.0 && x <= 1.0 + 1e-5, "x_dot_o out of (0,1]: {x}");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn zero_input_does_not_panic() {
|
||||
let dim = 64;
|
||||
let rot = Rotation::new(1, dim);
|
||||
let sk = EstimatorSketch::from_embedding(&vec![0.0f32; dim], &rot);
|
||||
assert_eq!(sk.residual_norm(), 0.0);
|
||||
let q = EstimatorQuery::new(&vec![1.0f32; dim], &rot);
|
||||
// No divide-by-zero; degenerate estimate is 0 inner product.
|
||||
assert_eq!(DistanceEstimator::estimate_inner_product(&sk, &q), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn centroid_path_self_query_ranks_self_first() {
|
||||
// The paper-faithful centroid path (o_r = o − c) must still rank a
|
||||
// stored vector first when queried with itself, with a shared centroid.
|
||||
let dim = 64;
|
||||
let rot = Rotation::new(0x9999, dim);
|
||||
let centroid: Vec<f32> = (0..dim).map(|i| (i as f32 * 0.05).sin()).collect();
|
||||
let mut bank = EstimatorBank::with_centroid(rot, centroid.clone());
|
||||
let target: Vec<f32> = (0..dim).map(|i| (i as f32 * 0.23).cos() + 1.5).collect();
|
||||
bank.insert_embedding(7, &target);
|
||||
for id in 0..24u32 {
|
||||
let v: Vec<f32> = (0..dim)
|
||||
.map(|i| ((i as f32 + id as f32) * 0.09).sin() + 1.4)
|
||||
.collect();
|
||||
bank.insert_embedding(id, &v);
|
||||
}
|
||||
let top = bank.topk_estimated_cosine(&target, 1);
|
||||
assert_eq!(top.len(), 1);
|
||||
assert_eq!(top[0].0, 7, "centroid-path self-query should rank self first");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn centroid_zero_matches_default() {
|
||||
// from_embedding_centred(None) must be byte-identical to from_embedding.
|
||||
let dim = 48;
|
||||
let rot = Rotation::new(0x4242, dim);
|
||||
let v: Vec<f32> = (0..dim).map(|i| (i as f32 * 0.3).sin() - 0.1).collect();
|
||||
let a = EstimatorSketch::from_embedding(&v, &rot);
|
||||
let b = EstimatorSketch::from_embedding_centred(&v, &rot, None);
|
||||
assert_eq!(a.residual_norm(), b.residual_norm());
|
||||
assert_eq!(a.side_info(), b.side_info());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn bank_self_query_ranks_self_first() {
|
||||
// A bank queried with one of its own stored vectors should rank that id
|
||||
// first under the estimator (its estimated distance to itself is the
|
||||
// smallest).
|
||||
let dim = 128;
|
||||
let rot = Rotation::new(0xABCD_1234, dim);
|
||||
let mut bank = EstimatorBank::new(rot);
|
||||
let target: Vec<f32> = (0..dim).map(|i| (i as f32 * 0.19).sin() * 2.0).collect();
|
||||
bank.insert_embedding(99, &target);
|
||||
for id in 0..32u32 {
|
||||
let v: Vec<f32> = (0..dim)
|
||||
.map(|i| ((i as f32 + id as f32 * 3.0) * 0.05).cos())
|
||||
.collect();
|
||||
bank.insert_embedding(id, &v);
|
||||
}
|
||||
let top = bank.topk_estimated(&target, 1);
|
||||
assert_eq!(top.len(), 1);
|
||||
assert_eq!(top[0].0, 99, "self-query should rank the stored self first");
|
||||
}
|
||||
}
|
||||
@@ -29,6 +29,7 @@
|
||||
#[cfg(feature = "crv")]
|
||||
pub mod crv;
|
||||
pub mod coverage;
|
||||
pub mod estimator;
|
||||
pub mod event_log;
|
||||
pub mod mat;
|
||||
pub mod rotation;
|
||||
@@ -36,6 +37,9 @@ pub mod signal;
|
||||
pub mod sketch;
|
||||
pub mod viewpoint;
|
||||
|
||||
pub use estimator::{
|
||||
DistanceEstimator, EstimatorBank, EstimatorQuery, EstimatorSketch, SideInfo,
|
||||
};
|
||||
pub use event_log::{NoveltyEvent, PrivacyEventLog};
|
||||
pub use rotation::Rotation;
|
||||
pub use sketch::{
|
||||
|
||||
@@ -144,6 +144,29 @@ impl Rotation {
|
||||
/// rounding — see [`Rotation::apply`] tests and
|
||||
/// `rotation_preserves_norm`.
|
||||
pub fn apply(&self, embedding: &[f32]) -> Vec<f32> {
|
||||
if self.dim == 0 {
|
||||
return Vec::new();
|
||||
}
|
||||
let mut buf = self.apply_padded(embedding);
|
||||
// Read back the first `dim` rotated coordinates as the sketch input.
|
||||
buf.truncate(self.dim);
|
||||
buf
|
||||
}
|
||||
|
||||
/// Apply the rotation `R = H·D` and return **all `padded_dim` rotated
|
||||
/// coordinates** (not truncated to `dim`).
|
||||
///
|
||||
/// This is the frame the RaBitQ estimator ([`crate::estimator`]) works in:
|
||||
/// the 1-bit code `x̄ ∈ {±1/√D}^D` is unit over the **padded** length `D`,
|
||||
/// and the query dot product `⟨x̄, q'⟩` must be taken over that same `D`. For
|
||||
/// a power-of-two `dim`, `padded_dim == dim` and this equals
|
||||
/// [`Rotation::apply`]; for a non-power-of-two `dim` the tail coordinates
|
||||
/// (the zero-padded energy redistributed by the FHT) are retained here but
|
||||
/// dropped by `apply`.
|
||||
///
|
||||
/// `dim == 0` yields an empty vector. Ragged input is handled charitably
|
||||
/// (truncate / zero-extend to `dim`), as in [`Rotation::apply`].
|
||||
pub fn apply_padded(&self, embedding: &[f32]) -> Vec<f32> {
|
||||
if self.dim == 0 {
|
||||
return Vec::new();
|
||||
}
|
||||
@@ -157,9 +180,6 @@ impl Rotation {
|
||||
|
||||
// In-place normalized Fast Hadamard Transform.
|
||||
fht_normalized(&mut buf);
|
||||
|
||||
// Read back the first `dim` rotated coordinates as the sketch input.
|
||||
buf.truncate(self.dim);
|
||||
buf
|
||||
}
|
||||
}
|
||||
|
||||
@@ -43,11 +43,22 @@ pub struct HampelResult {
|
||||
/// MAD = 0.6745 * σ → σ = MAD / 0.6745 = 1.4826 * MAD
|
||||
const MAD_SCALE: f64 = 1.4826;
|
||||
|
||||
/// Zero-MAD epsilon (ADR-154 §7.4 — de-magicked). When the estimated σ falls
|
||||
/// at/below this, the window is treated as constant (degenerate MAD): any
|
||||
/// deviation larger than this same epsilon flags the sample as an outlier.
|
||||
/// Empirical guard against an all-equal window, not a tuned operating point.
|
||||
const ZERO_MAD_EPSILON: f64 = 1e-15;
|
||||
|
||||
/// Apply Hampel filter to a 1D signal.
|
||||
///
|
||||
/// For each sample, computes the median and MAD of the surrounding window.
|
||||
/// If the sample deviates from the median by more than `threshold * σ_est`,
|
||||
/// it is replaced with the median.
|
||||
///
|
||||
/// # Errors
|
||||
/// - [`HampelError::EmptySignal`] if `signal` is empty.
|
||||
/// - [`HampelError::InvalidWindow`] if `config.half_window == 0` (a window of
|
||||
/// one sample has zero MAD and cannot estimate σ).
|
||||
pub fn hampel_filter(signal: &[f64], config: &HampelConfig) -> Result<HampelResult, HampelError> {
|
||||
if signal.is_empty() {
|
||||
return Err(HampelError::EmptySignal);
|
||||
@@ -75,13 +86,13 @@ pub fn hampel_filter(signal: &[f64], config: &HampelConfig) -> Result<HampelResu
|
||||
sigma_estimates.push(sigma);
|
||||
|
||||
let deviation = (signal[i] - med).abs();
|
||||
let is_outlier = if sigma > 1e-15 {
|
||||
let is_outlier = if sigma > ZERO_MAD_EPSILON {
|
||||
// Normal case: compare deviation to threshold * sigma
|
||||
deviation > config.threshold * sigma
|
||||
} else {
|
||||
// Zero-MAD case: all window values identical except possibly this sample.
|
||||
// Any non-zero deviation from the median is an outlier.
|
||||
deviation > 1e-15
|
||||
deviation > ZERO_MAD_EPSILON
|
||||
};
|
||||
|
||||
if is_outlier {
|
||||
@@ -233,4 +244,48 @@ mod tests {
|
||||
Err(HampelError::EmptySignal)
|
||||
));
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4: de-magic-constant + boundary characterization tests.
|
||||
|
||||
/// De-magicked zero-MAD epsilon must equal the prior literal.
|
||||
#[test]
|
||||
fn zero_mad_epsilon_unchanged_from_literal() {
|
||||
assert_eq!(ZERO_MAD_EPSILON, 1e-15);
|
||||
assert_eq!(MAD_SCALE, 1.4826);
|
||||
}
|
||||
|
||||
/// `half_window == 0` is the documented invalid-window boundary; pins the
|
||||
/// previously-untested error path.
|
||||
#[test]
|
||||
fn test_zero_half_window_error() {
|
||||
let config = HampelConfig {
|
||||
half_window: 0,
|
||||
threshold: 3.0,
|
||||
};
|
||||
assert!(matches!(
|
||||
hampel_filter(&[1.0, 2.0, 3.0], &config),
|
||||
Err(HampelError::InvalidWindow)
|
||||
));
|
||||
// half_window = 1 is the smallest valid window.
|
||||
let ok = HampelConfig {
|
||||
half_window: 1,
|
||||
threshold: 3.0,
|
||||
};
|
||||
assert!(hampel_filter(&[1.0, 2.0, 3.0], &ok).is_ok());
|
||||
}
|
||||
|
||||
/// Zero-MAD (constant) window: a single deviating sample is flagged via the
|
||||
/// degenerate-MAD branch; a fully constant signal flags nothing.
|
||||
#[test]
|
||||
fn test_zero_mad_constant_window() {
|
||||
// Fully constant -> no outliers (deviation is 0, not > epsilon).
|
||||
let constant = vec![5.0; 20];
|
||||
let r = hampel_filter(&constant, &HampelConfig::default()).unwrap();
|
||||
assert!(r.outlier_indices.is_empty());
|
||||
// A single spike in an otherwise-constant signal -> flagged.
|
||||
let mut spiked = vec![5.0; 20];
|
||||
spiked[10] = 5.5;
|
||||
let r = hampel_filter(&spiked, &HampelConfig::default()).unwrap();
|
||||
assert!(r.outlier_indices.contains(&10));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -8,6 +8,66 @@ use chrono::{DateTime, Utc};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::collections::VecDeque;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tuning constants (ADR-154 §7.4 #18 — de-magicked; EMPIRICAL DEFAULTS).
|
||||
//
|
||||
// These were previously bare literals inside the scoring functions. They are
|
||||
// lifted to named, documented consts so the implicit weighting becomes
|
||||
// explicit and a future retune is a visible, tested change. The values are
|
||||
// **unchanged** from the original literals — boundary/characterization tests
|
||||
// pin the current behaviour. None of these is calibrated against labelled
|
||||
// occupancy data; they are heuristic fusion weights.
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Motion-score fusion weights when a Doppler component is present.
|
||||
/// `(variance, correlation, phase, doppler)` — sums to 1.0.
|
||||
const MOTION_WEIGHTS_WITH_DOPPLER: (f64, f64, f64, f64) = (0.3, 0.2, 0.2, 0.3);
|
||||
|
||||
/// Motion-score fusion weights with no Doppler component.
|
||||
/// `(variance, correlation, phase)` — sums to 1.0.
|
||||
const MOTION_WEIGHTS_NO_DOPPLER: (f64, f64, f64) = (0.4, 0.3, 0.3);
|
||||
|
||||
/// Doppler magnitude (Hz-ish, arbitrary units) that maps to a full-scale
|
||||
/// (1.0) Doppler motion component. Larger magnitudes saturate at 1.0.
|
||||
const DOPPLER_FULL_SCALE_MAGNITUDE: f64 = 100.0;
|
||||
|
||||
/// Reference variance that maps to a full-scale (1.0) heuristic motion score
|
||||
/// when no calibrated baseline is available. Empirical default.
|
||||
const VARIANCE_HEURISTIC_FULL_SCALE: f64 = 0.5;
|
||||
|
||||
/// Reference phase variance that maps to a full-scale (1.0) phase motion
|
||||
/// component. Empirical default.
|
||||
const PHASE_VARIANCE_FULL_SCALE: f64 = 0.5;
|
||||
|
||||
/// Blend weight between phase-variance and phase-coherence in the phase score.
|
||||
const PHASE_SCORE_VARIANCE_WEIGHT: f64 = 0.5;
|
||||
|
||||
/// Reference dynamic range that maps to a full-scale (1.0) amplitude-quality
|
||||
/// confidence indicator. Empirical default.
|
||||
const AMP_QUALITY_FULL_SCALE_RANGE: f64 = 2.0;
|
||||
|
||||
/// Confidence-indicator blend weights (`amplitude`, `phase`, `correlation`,
|
||||
/// `doppler`) — each is the fraction of total confidence that indicator
|
||||
/// contributes when present.
|
||||
const CONF_WEIGHT_AMPLITUDE: f64 = 0.3;
|
||||
const CONF_WEIGHT_PHASE: f64 = 0.3;
|
||||
const CONF_WEIGHT_CORRELATION: f64 = 0.2;
|
||||
const CONF_WEIGHT_DOPPLER: f64 = 0.2;
|
||||
|
||||
/// Minimum baseline floor added before dividing by the calibration baseline
|
||||
/// variance, preventing a divide-by-zero on an all-constant calibration.
|
||||
const BASELINE_VARIANCE_FLOOR: f64 = 1e-10;
|
||||
|
||||
/// Lower / upper clamp for the adaptive human-detection threshold
|
||||
/// (`mean + 1σ` of recent motion scores). Keeps the adaptive threshold inside
|
||||
/// a sane operating band. Empirical default.
|
||||
const ADAPTIVE_THRESHOLD_MIN: f64 = 0.3;
|
||||
const ADAPTIVE_THRESHOLD_MAX: f64 = 0.95;
|
||||
|
||||
/// Minimum history length before the adaptive threshold engages; below this
|
||||
/// the configured fixed threshold is used.
|
||||
const ADAPTIVE_THRESHOLD_MIN_HISTORY: usize = 10;
|
||||
|
||||
/// Motion score with component breakdown
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct MotionScore {
|
||||
@@ -37,12 +97,11 @@ impl MotionScore {
|
||||
) -> Self {
|
||||
// Calculate weighted total
|
||||
let total = if let Some(doppler) = doppler_component {
|
||||
0.3 * variance_component
|
||||
+ 0.2 * correlation_component
|
||||
+ 0.2 * phase_component
|
||||
+ 0.3 * doppler
|
||||
let (wv, wc, wp, wd) = MOTION_WEIGHTS_WITH_DOPPLER;
|
||||
wv * variance_component + wc * correlation_component + wp * phase_component + wd * doppler
|
||||
} else {
|
||||
0.4 * variance_component + 0.3 * correlation_component + 0.3 * phase_component
|
||||
let (wv, wc, wp) = MOTION_WEIGHTS_NO_DOPPLER;
|
||||
wv * variance_component + wc * correlation_component + wp * phase_component
|
||||
};
|
||||
|
||||
Self {
|
||||
@@ -304,7 +363,7 @@ impl MotionDetector {
|
||||
// Calculate Doppler-based score if available
|
||||
let doppler_score = features.doppler.as_ref().map(|d| {
|
||||
// Normalize Doppler magnitude to 0-1 range
|
||||
(d.mean_magnitude / 100.0).clamp(0.0, 1.0)
|
||||
(d.mean_magnitude / DOPPLER_FULL_SCALE_MAGNITUDE).clamp(0.0, 1.0)
|
||||
});
|
||||
|
||||
let motion_score = MotionScore::new(
|
||||
@@ -355,11 +414,11 @@ impl MotionDetector {
|
||||
|
||||
// Normalize using baseline if available
|
||||
if let Some(baseline) = self.baseline_variance {
|
||||
let ratio = mean_variance / (baseline + 1e-10);
|
||||
let ratio = mean_variance / (baseline + BASELINE_VARIANCE_FLOOR);
|
||||
(ratio - 1.0).max(0.0).tanh()
|
||||
} else {
|
||||
// Use heuristic normalization
|
||||
(mean_variance / 0.5).clamp(0.0, 1.0)
|
||||
(mean_variance / VARIANCE_HEURISTIC_FULL_SCALE).clamp(0.0, 1.0)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -393,7 +452,9 @@ impl MotionDetector {
|
||||
let coherence_factor = 1.0 - phase.coherence.abs();
|
||||
|
||||
// Combine factors
|
||||
let score = 0.5 * (mean_variance / 0.5).clamp(0.0, 1.0) + 0.5 * coherence_factor;
|
||||
let w = PHASE_SCORE_VARIANCE_WEIGHT;
|
||||
let score = w * (mean_variance / PHASE_VARIANCE_FULL_SCALE).clamp(0.0, 1.0)
|
||||
+ (1.0 - w) * coherence_factor;
|
||||
score.clamp(0.0, 1.0)
|
||||
}
|
||||
|
||||
@@ -416,26 +477,27 @@ impl MotionDetector {
|
||||
let mut weight_sum = 0.0;
|
||||
|
||||
// Amplitude quality indicator
|
||||
let amp_quality = (features.amplitude.dynamic_range / 2.0).clamp(0.0, 1.0);
|
||||
confidence += amp_quality * 0.3;
|
||||
weight_sum += 0.3;
|
||||
let amp_quality =
|
||||
(features.amplitude.dynamic_range / AMP_QUALITY_FULL_SCALE_RANGE).clamp(0.0, 1.0);
|
||||
confidence += amp_quality * CONF_WEIGHT_AMPLITUDE;
|
||||
weight_sum += CONF_WEIGHT_AMPLITUDE;
|
||||
|
||||
// Phase coherence indicator
|
||||
let phase_quality = features.phase.coherence.abs();
|
||||
confidence += phase_quality * 0.3;
|
||||
weight_sum += 0.3;
|
||||
confidence += phase_quality * CONF_WEIGHT_PHASE;
|
||||
weight_sum += CONF_WEIGHT_PHASE;
|
||||
|
||||
// Correlation consistency indicator
|
||||
let corr_quality = (1.0 - features.correlation.correlation_spread).clamp(0.0, 1.0);
|
||||
confidence += corr_quality * 0.2;
|
||||
weight_sum += 0.2;
|
||||
confidence += corr_quality * CONF_WEIGHT_CORRELATION;
|
||||
weight_sum += CONF_WEIGHT_CORRELATION;
|
||||
|
||||
// Doppler quality if available
|
||||
if let Some(ref doppler) = features.doppler {
|
||||
let doppler_quality =
|
||||
(doppler.spread / doppler.mean_magnitude.max(1.0)).clamp(0.0, 1.0);
|
||||
confidence += (1.0 - doppler_quality) * 0.2;
|
||||
weight_sum += 0.2;
|
||||
confidence += (1.0 - doppler_quality) * CONF_WEIGHT_DOPPLER;
|
||||
weight_sum += CONF_WEIGHT_DOPPLER;
|
||||
}
|
||||
|
||||
if weight_sum > 0.0 {
|
||||
@@ -542,7 +604,7 @@ impl MotionDetector {
|
||||
|
||||
/// Calculate adaptive threshold based on recent history
|
||||
fn calculate_adaptive_threshold(&self) -> f64 {
|
||||
if self.motion_history.len() < 10 {
|
||||
if self.motion_history.len() < ADAPTIVE_THRESHOLD_MIN_HISTORY {
|
||||
return self.config.human_detection_threshold;
|
||||
}
|
||||
|
||||
@@ -555,7 +617,7 @@ impl MotionDetector {
|
||||
};
|
||||
|
||||
// Threshold is mean + 1 std deviation, clamped to reasonable range
|
||||
(mean + std).clamp(0.3, 0.95)
|
||||
(mean + std).clamp(ADAPTIVE_THRESHOLD_MIN, ADAPTIVE_THRESHOLD_MAX)
|
||||
}
|
||||
|
||||
/// Update baseline variance (for calibration)
|
||||
@@ -838,4 +900,127 @@ mod tests {
|
||||
let stats = detector.get_statistics();
|
||||
assert_eq!(stats.history_size, 10); // Should not exceed max
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4 #18: de-magic-constant + boundary characterization tests.
|
||||
// These pin CURRENT behaviour so a future retune is a visible, tested change.
|
||||
|
||||
/// The de-magicked tuning consts MUST equal the prior bare literals exactly
|
||||
/// (this milestone is cleanup — operating values are unchanged).
|
||||
#[test]
|
||||
fn motion_tuning_consts_unchanged_from_literals() {
|
||||
assert_eq!(MOTION_WEIGHTS_WITH_DOPPLER, (0.3, 0.2, 0.2, 0.3));
|
||||
assert_eq!(MOTION_WEIGHTS_NO_DOPPLER, (0.4, 0.3, 0.3));
|
||||
assert_eq!(DOPPLER_FULL_SCALE_MAGNITUDE, 100.0);
|
||||
assert_eq!(VARIANCE_HEURISTIC_FULL_SCALE, 0.5);
|
||||
assert_eq!(PHASE_VARIANCE_FULL_SCALE, 0.5);
|
||||
assert_eq!(PHASE_SCORE_VARIANCE_WEIGHT, 0.5);
|
||||
assert_eq!(AMP_QUALITY_FULL_SCALE_RANGE, 2.0);
|
||||
assert_eq!(CONF_WEIGHT_AMPLITUDE, 0.3);
|
||||
assert_eq!(CONF_WEIGHT_PHASE, 0.3);
|
||||
assert_eq!(CONF_WEIGHT_CORRELATION, 0.2);
|
||||
assert_eq!(CONF_WEIGHT_DOPPLER, 0.2);
|
||||
assert_eq!(BASELINE_VARIANCE_FLOOR, 1e-10);
|
||||
assert_eq!(ADAPTIVE_THRESHOLD_MIN, 0.3);
|
||||
assert_eq!(ADAPTIVE_THRESHOLD_MAX, 0.95);
|
||||
assert_eq!(ADAPTIVE_THRESHOLD_MIN_HISTORY, 10);
|
||||
// Fusion weights are a convex combination (sum to 1.0).
|
||||
let (wv, wc, wp, wd) = MOTION_WEIGHTS_WITH_DOPPLER;
|
||||
assert!((wv + wc + wp + wd - 1.0).abs() < 1e-12);
|
||||
let (wv, wc, wp) = MOTION_WEIGHTS_NO_DOPPLER;
|
||||
assert!((wv + wc + wp - 1.0).abs() < 1e-12);
|
||||
}
|
||||
|
||||
/// Doppler component saturates at full scale (`/100.0` then clamp(0,1)).
|
||||
/// Pins behaviour at/just-below/just-above the full-scale magnitude.
|
||||
#[test]
|
||||
fn doppler_component_saturates_at_full_scale() {
|
||||
use crate::features::DopplerFeatures;
|
||||
use ndarray::Array1;
|
||||
let make = |mag: f64| DopplerFeatures {
|
||||
shifts: Array1::zeros(1),
|
||||
peak_frequency: 0.0,
|
||||
mean_magnitude: mag,
|
||||
spread: 0.0,
|
||||
};
|
||||
let detector = MotionDetector::default_config();
|
||||
// just below full scale -> < 1.0
|
||||
let mut features = create_test_features(0.5);
|
||||
features.doppler = Some(make(DOPPLER_FULL_SCALE_MAGNITUDE - 1.0));
|
||||
let below = detector.analyze_motion(&features).score.doppler_component.unwrap();
|
||||
assert!(below < 1.0 && below > 0.98);
|
||||
// exactly full scale -> 1.0
|
||||
features.doppler = Some(make(DOPPLER_FULL_SCALE_MAGNITUDE));
|
||||
let at = detector.analyze_motion(&features).score.doppler_component.unwrap();
|
||||
assert_eq!(at, 1.0);
|
||||
// above full scale -> clamped to 1.0
|
||||
features.doppler = Some(make(DOPPLER_FULL_SCALE_MAGNITUDE * 10.0));
|
||||
let above = detector.analyze_motion(&features).score.doppler_component.unwrap();
|
||||
assert_eq!(above, 1.0);
|
||||
}
|
||||
|
||||
/// `calculate_correlation_score` returns 0.0 for n<2 (the small-matrix
|
||||
/// guard) and a finite, clamped value for n>=2. Pins the n=1 boundary.
|
||||
#[test]
|
||||
fn correlation_score_zero_below_n2_boundary() {
|
||||
use crate::features::CorrelationFeatures;
|
||||
use ndarray::Array2;
|
||||
let detector = MotionDetector::default_config();
|
||||
let one = CorrelationFeatures {
|
||||
matrix: Array2::from_elem((1, 1), 1.0),
|
||||
mean_correlation: 0.0,
|
||||
max_correlation: 0.0,
|
||||
correlation_spread: 0.0,
|
||||
};
|
||||
assert_eq!(detector.calculate_correlation_score(&one), 0.0);
|
||||
let two = CorrelationFeatures {
|
||||
matrix: Array2::from_shape_fn((2, 2), |(i, j)| if i == j { 1.0 } else { 0.0 }),
|
||||
mean_correlation: 0.0,
|
||||
max_correlation: 0.0,
|
||||
correlation_spread: 0.0,
|
||||
};
|
||||
let s = detector.calculate_correlation_score(&two);
|
||||
assert!(s.is_finite() && (0.0..=1.0).contains(&s));
|
||||
}
|
||||
|
||||
/// `calculate_temporal_variance` returns 0.0 with fewer than 2 history
|
||||
/// entries, finite otherwise. Pins the len<2 boundary.
|
||||
#[test]
|
||||
fn temporal_variance_zero_below_two_history() {
|
||||
let mut detector = MotionDetector::default_config();
|
||||
assert_eq!(detector.calculate_temporal_variance(), 0.0); // 0 entries
|
||||
detector
|
||||
.motion_history
|
||||
.push_back(MotionScore::new(0.5, 0.5, 0.5, None));
|
||||
assert_eq!(detector.calculate_temporal_variance(), 0.0); // 1 entry
|
||||
detector
|
||||
.motion_history
|
||||
.push_back(MotionScore::new(0.1, 0.1, 0.1, None));
|
||||
assert!(detector.calculate_temporal_variance() > 0.0); // 2 entries
|
||||
}
|
||||
|
||||
/// The adaptive threshold engages only at/after `ADAPTIVE_THRESHOLD_MIN_HISTORY`
|
||||
/// history entries; below it falls back to the configured fixed threshold.
|
||||
/// Pins the history=9 (fixed) vs history=10 (adaptive) boundary.
|
||||
#[test]
|
||||
fn adaptive_threshold_engages_at_history_boundary() {
|
||||
let config = MotionDetectorConfig::builder()
|
||||
.adaptive_threshold(true)
|
||||
.human_detection_threshold(0.8)
|
||||
.history_size(50)
|
||||
.build();
|
||||
let mut detector = MotionDetector::new(config);
|
||||
// Push exactly 9 entries: still uses the fixed configured threshold.
|
||||
for _ in 0..(ADAPTIVE_THRESHOLD_MIN_HISTORY - 1) {
|
||||
detector
|
||||
.motion_history
|
||||
.push_back(MotionScore::new(0.5, 0.5, 0.5, None));
|
||||
}
|
||||
assert_eq!(detector.calculate_adaptive_threshold(), 0.8);
|
||||
// 10th entry: adaptive band kicks in, clamped to [MIN, MAX].
|
||||
detector
|
||||
.motion_history
|
||||
.push_back(MotionScore::new(0.5, 0.5, 0.5, None));
|
||||
let t = detector.calculate_adaptive_threshold();
|
||||
assert!((ADAPTIVE_THRESHOLD_MIN..=ADAPTIVE_THRESHOLD_MAX).contains(&t));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -24,6 +24,18 @@ use midstreamer_attractor::{AttractorAnalyzer, AttractorType, PhasePoint};
|
||||
|
||||
use super::longitudinal::DriftMetric;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Internal constants (ADR-154 §7.4 — de-magicked; values unchanged)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Per-metric ring-buffer capacity: one year of daily observations.
|
||||
const METRIC_BUFFER_CAPACITY: usize = 365;
|
||||
|
||||
/// Number of most-recent values averaged to estimate a point-attractor's
|
||||
/// stable centre. Empirical default — a short tail that tracks the latest
|
||||
/// converged level without over-smoothing.
|
||||
const STABLE_CENTER_WINDOW: usize = 10;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Configuration
|
||||
// ---------------------------------------------------------------------------
|
||||
@@ -232,7 +244,7 @@ impl AttractorDriftAnalyzer {
|
||||
|
||||
let buffers = DriftMetric::all()
|
||||
.iter()
|
||||
.map(|&m| MetricBuffer::new(m, 365)) // 1 year of daily observations
|
||||
.map(|&m| MetricBuffer::new(m, METRIC_BUFFER_CAPACITY))
|
||||
.collect();
|
||||
|
||||
Ok(Self {
|
||||
@@ -296,8 +308,12 @@ impl AttractorDriftAnalyzer {
|
||||
|
||||
match info.attractor_type {
|
||||
AttractorType::PointAttractor => {
|
||||
// Compute center as mean of last few values
|
||||
let recent = &values[values.len().saturating_sub(10)..];
|
||||
// Compute center as the mean of the last STABLE_CENTER_WINDOW
|
||||
// values. `recent` is non-empty here: the `count < min_needed`
|
||||
// guard above guarantees `values.len() >= min_observations >= 1`
|
||||
// before this branch, so `recent.len() >= 1` and the division
|
||||
// below cannot be a divide-by-zero.
|
||||
let recent = &values[values.len().saturating_sub(STABLE_CENTER_WINDOW)..];
|
||||
let center = recent.iter().sum::<f64>() / recent.len() as f64;
|
||||
BiophysicalAttractor::Stable { center }
|
||||
}
|
||||
@@ -563,4 +579,38 @@ mod tests {
|
||||
let dbg = format!("{:?}", a);
|
||||
assert!(dbg.contains("AttractorDriftAnalyzer"));
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4: de-magic-constant + boundary characterization tests.
|
||||
|
||||
/// De-magicked internal constants must equal the prior inline literals.
|
||||
#[test]
|
||||
fn attractor_consts_unchanged_from_literals() {
|
||||
assert_eq!(METRIC_BUFFER_CAPACITY, 365);
|
||||
assert_eq!(STABLE_CENTER_WINDOW, 10);
|
||||
}
|
||||
|
||||
/// `analyze` returns InsufficientData strictly below `min_observations` and
|
||||
/// succeeds at exactly `min_observations`. Pins the off-by-one boundary
|
||||
/// (previously only the well-below case was tested) and, with it, the
|
||||
/// implicit `recent.len() >= 1` divide-safety in the PointAttractor branch.
|
||||
#[test]
|
||||
fn analyze_min_observations_boundary() {
|
||||
let cfg = AttractorDriftConfig {
|
||||
min_observations: 12,
|
||||
..Default::default()
|
||||
};
|
||||
let mut a = AttractorDriftAnalyzer::new(7, cfg.clone()).unwrap();
|
||||
// One below the boundary -> InsufficientData.
|
||||
for i in 0..(cfg.min_observations - 1) {
|
||||
a.add_observation(DriftMetric::GaitSymmetry, 0.1 + i as f64 * 0.001);
|
||||
}
|
||||
assert!(matches!(
|
||||
a.analyze(DriftMetric::GaitSymmetry, 0),
|
||||
Err(AttractorDriftError::InsufficientData { needed: 12, have: 11 })
|
||||
));
|
||||
// Exactly at the boundary -> Ok (no panic, finite center if Stable).
|
||||
a.add_observation(DriftMetric::GaitSymmetry, 0.111);
|
||||
let report = a.analyze(DriftMetric::GaitSymmetry, 0).unwrap();
|
||||
assert_eq!(report.observation_count, 12);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -40,6 +40,30 @@ const VERSION: u8 = 1;
|
||||
const HEADER_LEN: usize = 16; // magic(4) + version(1) + tier(1) + reserved(2) + unix_s(8)
|
||||
const SUBCARRIER_RECORD_LEN: usize = 16; // 4 × f32
|
||||
|
||||
// ADR-154 §7.4 — de-magicked (values unchanged). The tuning thresholds below
|
||||
// are EMPIRICAL DEFAULTS pending labelled empty-vs-occupied calibration traces.
|
||||
|
||||
/// Default minimum frames for a baseline finalization (30 s @ 20 Hz). Shared by
|
||||
/// every tier constructor (`ht20`/`ht40`/`he20`/`he40`).
|
||||
const DEFAULT_MIN_FRAMES: u32 = 600;
|
||||
|
||||
/// Amplitude standard-deviation floor used as the z-score divisor in
|
||||
/// `deviation()`, guarding against a zero-variance baseline subcarrier.
|
||||
const AMP_STD_FLOOR: f32 = 1e-12;
|
||||
|
||||
/// `deviation()` flags motion when the median amplitude z-score exceeds this
|
||||
/// many σ. EMPIRICAL DEFAULT.
|
||||
const MOTION_AMP_Z_THRESHOLD: f32 = 2.0;
|
||||
|
||||
/// `deviation()` flags motion when the median phase drift exceeds this many
|
||||
/// radians (π/6 = 30°). EMPIRICAL DEFAULT.
|
||||
const MOTION_PHASE_DRIFT_THRESHOLD: f32 = std::f32::consts::PI / 6.0;
|
||||
|
||||
/// Minimum complex magnitude in `subtract_in_place` below which a bin is left
|
||||
/// untouched (a near-zero bin has no meaningful baseline to subtract and the
|
||||
/// `(norm - baseline)/norm` scaling would be ill-conditioned).
|
||||
const SUBTRACT_MIN_NORM: f64 = 1e-30;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// PHY tier
|
||||
// ---------------------------------------------------------------------------
|
||||
@@ -103,11 +127,11 @@ pub struct CalibrationConfig {
|
||||
impl CalibrationConfig {
|
||||
/// HT20 defaults: 64 FFT, 52 active, 600 frame minimum (30 s @ 20 Hz).
|
||||
pub fn ht20() -> Self {
|
||||
Self { tier: PhyTier::Ht20, num_subcarriers: 64, num_active: 52, min_frames: 600, max_phase_variance: 0.3 }
|
||||
Self { tier: PhyTier::Ht20, num_subcarriers: 64, num_active: 52, min_frames: DEFAULT_MIN_FRAMES, max_phase_variance: 0.3 }
|
||||
}
|
||||
/// HT40 defaults: 128 FFT, 114 active.
|
||||
pub fn ht40() -> Self {
|
||||
Self { tier: PhyTier::Ht40, num_subcarriers: 128, num_active: 114, min_frames: 600, max_phase_variance: 0.3 }
|
||||
Self { tier: PhyTier::Ht40, num_subcarriers: 128, num_active: 114, min_frames: DEFAULT_MIN_FRAMES, max_phase_variance: 0.3 }
|
||||
}
|
||||
/// HE20 defaults: 256 FFT, **256 active** (record all delivered bins).
|
||||
///
|
||||
@@ -128,11 +152,11 @@ impl CalibrationConfig {
|
||||
/// `cir.rs` (`HE20_ACTIVE`), where the Φ sensing matrix genuinely needs it;
|
||||
/// the baseline recorder does not.
|
||||
pub fn he20() -> Self {
|
||||
Self { tier: PhyTier::He20, num_subcarriers: 256, num_active: 256, min_frames: 600, max_phase_variance: 0.3 }
|
||||
Self { tier: PhyTier::He20, num_subcarriers: 256, num_active: 256, min_frames: DEFAULT_MIN_FRAMES, max_phase_variance: 0.3 }
|
||||
}
|
||||
/// HE40 defaults: 512 FFT, 484 active.
|
||||
pub fn he40() -> Self {
|
||||
Self { tier: PhyTier::He40, num_subcarriers: 512, num_active: 484, min_frames: 600, max_phase_variance: 0.3 }
|
||||
Self { tier: PhyTier::He40, num_subcarriers: 512, num_active: 484, min_frames: DEFAULT_MIN_FRAMES, max_phase_variance: 0.3 }
|
||||
}
|
||||
}
|
||||
|
||||
@@ -264,7 +288,7 @@ impl BaselineCalibration {
|
||||
for (ki, (c, baseline)) in y.iter().zip(self.subcarriers.iter()).enumerate() {
|
||||
let _ = ki;
|
||||
let amp = c.norm();
|
||||
let std = baseline.amp_variance.sqrt().max(1e-12_f32);
|
||||
let std = baseline.amp_variance.sqrt().max(AMP_STD_FLOOR);
|
||||
z_amp.push((amp - baseline.amp_mean) / std);
|
||||
let theta = c.arg();
|
||||
let drift = circular_distance(theta, baseline.phase_mean);
|
||||
@@ -273,7 +297,8 @@ impl BaselineCalibration {
|
||||
let amplitude_z_median = median_abs(&z_amp);
|
||||
let amplitude_z_max = z_amp.iter().map(|v| v.abs()).fold(0.0_f32, f32::max);
|
||||
let phase_drift_median = median_slice(&phase_drift);
|
||||
let motion_flagged = amplitude_z_median > 2.0 || phase_drift_median > std::f32::consts::PI / 6.0;
|
||||
let motion_flagged =
|
||||
amplitude_z_median > MOTION_AMP_Z_THRESHOLD || phase_drift_median > MOTION_PHASE_DRIFT_THRESHOLD;
|
||||
Ok(CalibrationDeviationScore { amplitude_z_median, amplitude_z_max, phase_drift_median, motion_flagged })
|
||||
}
|
||||
|
||||
@@ -338,7 +363,7 @@ impl BaselineCalibration {
|
||||
for s in 0..n_streams {
|
||||
let c = frame.data[[s, ki]];
|
||||
let norm = c.norm();
|
||||
if norm > 1e-30 {
|
||||
if norm > SUBTRACT_MIN_NORM {
|
||||
let scale = ((norm - baseline_amp).max(0.0)) / norm;
|
||||
frame.data[[s, ki]] = num_complex::Complex64::new(c.re * scale, c.im * scale);
|
||||
}
|
||||
@@ -491,7 +516,8 @@ impl CalibrationRecorder {
|
||||
let amplitude_z_median = median_slice(&z_amp_abs);
|
||||
let amplitude_z_max = z_amp_abs.iter().copied().fold(0.0_f32, f32::max);
|
||||
let phase_drift_median = median_slice(&phase_drift);
|
||||
let motion_flagged = amplitude_z_median > 2.0 || phase_drift_median > std::f32::consts::PI / 6.0;
|
||||
let motion_flagged =
|
||||
amplitude_z_median > MOTION_AMP_Z_THRESHOLD || phase_drift_median > MOTION_PHASE_DRIFT_THRESHOLD;
|
||||
Ok(CalibrationDeviationScore { amplitude_z_median, amplitude_z_max, phase_drift_median, motion_flagged })
|
||||
}
|
||||
|
||||
@@ -736,6 +762,27 @@ mod tests {
|
||||
}
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4: de-magic-constant pin test.
|
||||
|
||||
/// The de-magicked calibration constants MUST equal the prior literals, and
|
||||
/// every tier constructor MUST share the one DEFAULT_MIN_FRAMES default.
|
||||
#[test]
|
||||
fn calibration_consts_unchanged_from_literals() {
|
||||
assert_eq!(DEFAULT_MIN_FRAMES, 600);
|
||||
assert_eq!(AMP_STD_FLOOR, 1e-12_f32);
|
||||
assert_eq!(MOTION_AMP_Z_THRESHOLD, 2.0_f32);
|
||||
assert_eq!(MOTION_PHASE_DRIFT_THRESHOLD, std::f32::consts::PI / 6.0);
|
||||
assert_eq!(SUBTRACT_MIN_NORM, 1e-30_f64);
|
||||
for cfg in [
|
||||
CalibrationConfig::ht20(),
|
||||
CalibrationConfig::ht40(),
|
||||
CalibrationConfig::he20(),
|
||||
CalibrationConfig::he40(),
|
||||
] {
|
||||
assert_eq!(cfg.min_frames, DEFAULT_MIN_FRAMES);
|
||||
}
|
||||
}
|
||||
|
||||
// Binary magic / version check.
|
||||
#[test]
|
||||
fn binary_magic_and_version() {
|
||||
|
||||
@@ -79,7 +79,7 @@ impl CoherenceState {
|
||||
Self {
|
||||
reference: vec![0.0; n_subcarriers],
|
||||
variance: vec![1.0; n_subcarriers],
|
||||
decay: 0.95,
|
||||
decay: DEFAULT_EMA_DECAY,
|
||||
current_score: 1.0,
|
||||
stale_count: 0,
|
||||
drift_profile: DriftProfile::Stable,
|
||||
@@ -200,8 +200,8 @@ impl CoherenceState {
|
||||
let diff = obs - old_ref;
|
||||
*v = self.decay * *v + alpha * diff * diff;
|
||||
// Ensure variance does not collapse to zero
|
||||
if *v < 1e-6 {
|
||||
*v = 1e-6;
|
||||
if *v < VARIANCE_FLOOR {
|
||||
*v = VARIANCE_FLOOR;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -229,7 +229,7 @@ pub fn coherence_score(current: &[f32], reference: &[f32], variance: &[f32]) ->
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let epsilon = 1e-6_f32;
|
||||
let epsilon = VARIANCE_FLOOR;
|
||||
let mut weighted_sum = 0.0_f32;
|
||||
let mut weight_sum = 0.0_f32;
|
||||
|
||||
@@ -260,6 +260,18 @@ const DRIFT_STABLE_SCORE: f32 = 0.85;
|
||||
/// DATA-GATED). EMPIRICAL DEFAULT pending labelled calibration.
|
||||
const DRIFT_STEP_CHANGE_MAX_STALE: u64 = 10;
|
||||
|
||||
/// Variance floor (ADR-154 §7.4 — de-magicked): the online variance estimate
|
||||
/// is never allowed to collapse below this, which keeps the inverse-variance
|
||||
/// weight and the z-score divisor finite. Used as both the floor in
|
||||
/// `update_reference` and the epsilon in `coherence_score` /
|
||||
/// `per_subcarrier_zscores`. Value unchanged from the prior `1e-6` literals.
|
||||
const VARIANCE_FLOOR: f32 = 1e-6;
|
||||
|
||||
/// Default EMA decay rate for the reference/variance update (ADR-154 §7.4 —
|
||||
/// de-magicked from the inline `0.95` in `CoherenceState::new`). EMPIRICAL
|
||||
/// DEFAULT; override via [`CoherenceState::with_decay`].
|
||||
const DEFAULT_EMA_DECAY: f32 = 0.95;
|
||||
|
||||
/// Classify drift profile based on coherence history.
|
||||
fn classify_drift(score: f32, stale_count: u64) -> DriftProfile {
|
||||
if score >= DRIFT_STABLE_SCORE {
|
||||
@@ -280,7 +292,7 @@ pub fn per_subcarrier_zscores(current: &[f32], reference: &[f32], variance: &[f3
|
||||
let n = current.len().min(reference.len()).min(variance.len());
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
let var = variance[i].max(1e-6);
|
||||
let var = variance[i].max(VARIANCE_FLOOR);
|
||||
(current[i] - reference[i]).abs() / var.sqrt()
|
||||
})
|
||||
.collect()
|
||||
@@ -439,6 +451,23 @@ mod tests {
|
||||
fn drift_consts_unchanged_from_literals() {
|
||||
assert_eq!(DRIFT_STABLE_SCORE, 0.85);
|
||||
assert_eq!(DRIFT_STEP_CHANGE_MAX_STALE, 10);
|
||||
// ADR-154 §7.4 M3: variance-floor + default-decay de-magic.
|
||||
assert_eq!(VARIANCE_FLOOR, 1e-6_f32);
|
||||
assert_eq!(DEFAULT_EMA_DECAY, 0.95_f32);
|
||||
}
|
||||
|
||||
/// `coherence_score` stays finite and in [0,1] when a subcarrier reports
|
||||
/// zero variance — the [`VARIANCE_FLOOR`] keeps the z-score divisor and the
|
||||
/// inverse-variance weight finite. Pins the floor's effect.
|
||||
#[test]
|
||||
fn coherence_score_finite_with_zero_variance() {
|
||||
let current = [1.0_f32, 2.0, 3.0];
|
||||
let reference = [1.0_f32, 2.0, 3.0];
|
||||
let zero_var = [0.0_f32, 0.0, 0.0];
|
||||
let s = coherence_score(¤t, &reference, &zero_var);
|
||||
assert!(s.is_finite() && (0.0..=1.0).contains(&s));
|
||||
// Perfect agreement with floored variance -> ~1.0.
|
||||
assert!((s - 1.0).abs() < 1e-3);
|
||||
}
|
||||
|
||||
/// Stable score boundary: `>= 0.85` is Stable; just below flips to a
|
||||
|
||||
@@ -23,6 +23,10 @@
|
||||
//! # References
|
||||
//! - ADR-030 Tier 5: Cross-Room Identity Continuity
|
||||
|
||||
/// Denominator guard for cosine similarity (ADR-154 §7.4 — de-magicked):
|
||||
/// a product of norms below this is treated as a zero-norm vector ⇒ 0.0.
|
||||
const COSINE_SIMILARITY_EPSILON: f32 = 1e-9;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
@@ -359,12 +363,15 @@ impl CrossRoomTracker {
|
||||
}
|
||||
|
||||
/// Cosine similarity between two f32 vectors.
|
||||
///
|
||||
/// Returns `0.0` when either vector has (near-)zero norm — the product of
|
||||
/// norms falls below [`COSINE_SIMILARITY_EPSILON`] and the division is skipped.
|
||||
fn cosine_similarity_f32(a: &[f32], b: &[f32]) -> f32 {
|
||||
let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
|
||||
let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
let denom = norm_a * norm_b;
|
||||
if denom < 1e-9 {
|
||||
if denom < COSINE_SIMILARITY_EPSILON {
|
||||
0.0
|
||||
} else {
|
||||
dot / denom
|
||||
@@ -623,4 +630,23 @@ mod tests {
|
||||
let sim = cosine_similarity_f32(&a, &b);
|
||||
assert!(sim.abs() < 1e-5);
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4: de-magic-constant + boundary characterization tests.
|
||||
|
||||
/// De-magicked epsilon must equal the prior literal.
|
||||
#[test]
|
||||
fn cosine_epsilon_unchanged_from_literal() {
|
||||
assert_eq!(COSINE_SIMILARITY_EPSILON, 1e-9_f32);
|
||||
}
|
||||
|
||||
/// A zero-norm vector falls below the denominator epsilon ⇒ similarity 0.0.
|
||||
/// Previously untested (both existing tests use unit-norm vectors).
|
||||
#[test]
|
||||
fn test_cosine_similarity_zero_vector() {
|
||||
let zero = vec![0.0_f32; 4];
|
||||
let v = vec![1.0_f32, 2.0, 3.0, 4.0];
|
||||
assert_eq!(cosine_similarity_f32(&zero, &v), 0.0);
|
||||
assert_eq!(cosine_similarity_f32(&v, &zero), 0.0);
|
||||
assert_eq!(cosine_similarity_f32(&zero, &zero), 0.0);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -14,6 +14,15 @@
|
||||
|
||||
use super::QualityScored;
|
||||
|
||||
/// Multiplicative coherence penalty applied per recorded contradiction
|
||||
/// (ADR-154 §7.4 — de-magicked; EMPIRICAL DEFAULT). `n` contradictions scale
|
||||
/// coherence by `CONTRADICTION_PENALTY.powi(n)`.
|
||||
const CONTRADICTION_PENALTY: f32 = 0.8;
|
||||
|
||||
/// Confidence-bound half-width added per recorded contradiction (clamped so the
|
||||
/// interval stays within `[0, 1]`). EMPIRICAL DEFAULT.
|
||||
const CONTRADICTION_BOUND_HALFWIDTH: f32 = 0.1;
|
||||
|
||||
/// Identifies which sensing family produced a fused frame, so one
|
||||
/// [`QualityScore`] can be correlated across the signal-domain fuser
|
||||
/// (`multistatic.rs`) and the embedding-domain fuser (`viewpoint/fusion.rs`).
|
||||
@@ -113,7 +122,7 @@ impl QualityScore {
|
||||
/// streaming engine routes/gates on.
|
||||
#[must_use]
|
||||
pub fn penalized_coherence(&self) -> f32 {
|
||||
let penalty = 0.8_f32.powi(self.contradiction_flags.len() as i32);
|
||||
let penalty = CONTRADICTION_PENALTY.powi(self.contradiction_flags.len() as i32);
|
||||
(self.base_coherence * penalty).clamp(0.0, 1.0)
|
||||
}
|
||||
}
|
||||
@@ -127,7 +136,8 @@ impl QualityScored for QualityScore {
|
||||
// Width grows with the number of tolerated contradictions: each adds
|
||||
// ±0.1 of uncertainty around the penalized coherence, clamped to [0,1].
|
||||
let c = self.penalized_coherence();
|
||||
let half = (0.1 * self.contradiction_flags.len() as f32).min(c.min(1.0 - c));
|
||||
let half =
|
||||
(CONTRADICTION_BOUND_HALFWIDTH * self.contradiction_flags.len() as f32).min(c.min(1.0 - c));
|
||||
((c - half).max(0.0), (c + half).min(1.0))
|
||||
}
|
||||
}
|
||||
@@ -185,4 +195,24 @@ mod tests {
|
||||
assert!((0.0..=1.0).contains(&s));
|
||||
assert!(0.0 <= lo && lo <= hi && hi <= 1.0);
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4: de-magic-constant + boundary characterization tests.
|
||||
|
||||
/// De-magicked penalty/bound consts must equal the prior literals.
|
||||
#[test]
|
||||
fn fusion_quality_consts_unchanged_from_literals() {
|
||||
assert_eq!(CONTRADICTION_PENALTY, 0.8_f32);
|
||||
assert_eq!(CONTRADICTION_BOUND_HALFWIDTH, 0.1_f32);
|
||||
}
|
||||
|
||||
/// Zero contradictions: penalty is `0.8^0 = 1.0` (coherence unchanged) and
|
||||
/// the confidence bounds collapse to a point. Pins the n=0 boundary.
|
||||
#[test]
|
||||
fn no_contradiction_is_identity() {
|
||||
let q = base();
|
||||
assert!(q.contradiction_flags.is_empty());
|
||||
assert!((q.penalized_coherence() - q.base_coherence).abs() < 1e-6);
|
||||
let (lo, hi) = q.confidence_bounds();
|
||||
assert!((hi - lo).abs() < 1e-6); // half-width is 0 with no contradictions
|
||||
}
|
||||
}
|
||||
|
||||
@@ -19,6 +19,16 @@
|
||||
//! - Sakoe & Chiba (1978), "Dynamic programming algorithm optimization
|
||||
//! for spoken word recognition" IEEE TASSP
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tuning constants (ADR-154 §7.4 — de-magicked; value unchanged)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Minimum second-best DTW distance below which the relative-margin
|
||||
/// confidence formula `1 - best/second_best` would divide by a near-zero
|
||||
/// denominator. Below this we fall back to the `max_distance`-relative
|
||||
/// confidence. Empirical guard, not a tuned operating point.
|
||||
const CONFIDENCE_SECOND_BEST_EPSILON: f64 = 1e-10;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
@@ -236,7 +246,10 @@ impl GestureClassifier {
|
||||
let recognized = best_dist <= self.config.max_distance;
|
||||
|
||||
// Confidence: how much better is the best match vs second best
|
||||
let confidence = if recognized && second_best_dist.is_finite() && second_best_dist > 1e-10 {
|
||||
let confidence = if recognized
|
||||
&& second_best_dist.is_finite()
|
||||
&& second_best_dist > CONFIDENCE_SECOND_BEST_EPSILON
|
||||
{
|
||||
(1.0 - best_dist / second_best_dist).clamp(0.0, 1.0)
|
||||
} else if recognized {
|
||||
(1.0 - best_dist / self.config.max_distance).clamp(0.0, 1.0)
|
||||
@@ -364,7 +377,24 @@ fn dtw_distance(seq_a: &[Vec<f64>], seq_b: &[Vec<f64>], band_width: usize) -> f6
|
||||
}
|
||||
|
||||
/// Euclidean distance between two feature vectors.
|
||||
///
|
||||
/// # Caller contract (ADR-154 §7.4 #12)
|
||||
/// `a` and `b` are expected to have the **same** dimension (`feature_dim`).
|
||||
/// The implementation `zip`s the two slices, so on a length mismatch it
|
||||
/// **silently truncates to the shorter vector** rather than erroring. Every
|
||||
/// in-tree caller (`dtw_distance` over a single classifier's templates)
|
||||
/// already enforces equal `feature_dim`, so a mismatch indicates a
|
||||
/// construction bug; a `debug_assert!` makes that loud in debug builds while
|
||||
/// keeping the release operating path (and its output) unchanged.
|
||||
fn euclidean_distance(a: &[f64], b: &[f64]) -> f64 {
|
||||
debug_assert_eq!(
|
||||
a.len(),
|
||||
b.len(),
|
||||
"euclidean_distance: feature-vector length mismatch ({} vs {}) — \
|
||||
zip() would silently truncate; callers must use a uniform feature_dim",
|
||||
a.len(),
|
||||
b.len()
|
||||
);
|
||||
a.iter()
|
||||
.zip(b.iter())
|
||||
.map(|(x, y)| (x - y) * (x - y))
|
||||
@@ -688,4 +718,34 @@ mod tests {
|
||||
assert_eq!(GestureType::Circle.name(), "circle");
|
||||
assert_eq!(GestureType::Custom.name(), "custom");
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4 #12 + de-magic: boundary / characterization tests.
|
||||
|
||||
/// De-magicked confidence epsilon must equal the prior literal.
|
||||
#[test]
|
||||
fn confidence_epsilon_unchanged_from_literal() {
|
||||
assert_eq!(CONFIDENCE_SECOND_BEST_EPSILON, 1e-10);
|
||||
}
|
||||
|
||||
/// `dtw_distance` returns +inf when EITHER sequence is empty. Pins the
|
||||
/// n=0 / m=0 boundary (previously exercised only with n,m >= 3).
|
||||
#[test]
|
||||
fn dtw_empty_sequence_is_infinite() {
|
||||
let nonempty: Vec<Vec<f64>> = vec![vec![1.0], vec![2.0]];
|
||||
let empty: Vec<Vec<f64>> = vec![];
|
||||
assert!(dtw_distance(&empty, &nonempty, 3).is_infinite());
|
||||
assert!(dtw_distance(&nonempty, &empty, 3).is_infinite());
|
||||
assert!(dtw_distance(&empty, &empty, 3).is_infinite());
|
||||
}
|
||||
|
||||
/// `euclidean_distance` over equal-length vectors is the L2 norm of the
|
||||
/// difference. Pins the documented same-dimension caller contract (#12);
|
||||
/// the mismatch case is guarded by a debug_assert in debug builds and
|
||||
/// truncates in release — not exercised here to keep the test
|
||||
/// release/debug-agnostic.
|
||||
#[test]
|
||||
fn euclidean_distance_equal_length_is_l2() {
|
||||
assert!((euclidean_distance(&[1.0, 2.0, 2.0], &[0.0, 0.0, 0.0]) - 3.0).abs() < 1e-12);
|
||||
assert_eq!(euclidean_distance(&[], &[]), 0.0);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -21,6 +21,11 @@
|
||||
|
||||
use std::collections::VecDeque;
|
||||
|
||||
/// Minimum acceleration magnitude (ADR-154 §7.4 — de-magicked) below which the
|
||||
/// lead-time estimate `t = (v_thresh - v) / a` would divide by a near-zero
|
||||
/// acceleration; below this the lead time is reported as 0.0.
|
||||
const LEAD_TIME_MIN_ACCEL: f64 = 1e-10;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
@@ -233,7 +238,7 @@ impl IntentionDetector {
|
||||
let detected = self.sustained_count >= self.config.min_sustained_frames;
|
||||
|
||||
// Estimate lead time based on current acceleration and velocity
|
||||
let estimated_lead = if detected && accel_mag > 1e-10 {
|
||||
let estimated_lead = if detected && accel_mag > LEAD_TIME_MIN_ACCEL {
|
||||
// Time until velocity reaches threshold: t = (v_thresh - v) / a
|
||||
let remaining = (self.config.max_pre_movement_velocity - velocity_mag) / accel_mag;
|
||||
remaining.clamp(0.0, self.config.max_lead_time_s)
|
||||
@@ -508,4 +513,29 @@ mod tests {
|
||||
let sd = embedding_second_diff(&a, &b, &c, 1.0);
|
||||
assert!((sd[0] - 2.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4: de-magic-constant + boundary characterization tests.
|
||||
|
||||
/// De-magicked lead-time accel guard must equal the prior literal.
|
||||
#[test]
|
||||
fn lead_time_accel_const_unchanged_from_literal() {
|
||||
assert_eq!(LEAD_TIME_MIN_ACCEL, 1e-10);
|
||||
}
|
||||
|
||||
/// A static (zero-motion) embedding stream produces ~zero acceleration, so
|
||||
/// the lead-time estimate stays at the 0.0 sentinel rather than dividing by
|
||||
/// a near-zero acceleration. Pins the `accel_mag <= LEAD_TIME_MIN_ACCEL`
|
||||
/// branch behaviour.
|
||||
#[test]
|
||||
fn lead_time_zero_for_static_stream() {
|
||||
let config = make_config();
|
||||
let mut detector = IntentionDetector::new(config).unwrap();
|
||||
let mut last = None;
|
||||
for frame in 0..6_u64 {
|
||||
last = Some(detector.update(&static_embedding(), frame * 50_000).unwrap());
|
||||
}
|
||||
let signal = last.unwrap();
|
||||
assert!(signal.acceleration_magnitude < LEAD_TIME_MIN_ACCEL.max(1e-9));
|
||||
assert_eq!(signal.estimated_lead_time_s, 0.0);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -18,6 +18,38 @@
|
||||
|
||||
use crate::ruvsense::field_model::WelfordStats;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Drift-detection thresholds (ADR-154 §7.4 — de-magicked; EMPIRICAL DEFAULTS).
|
||||
//
|
||||
// These encode the "Key Invariants" documented in the module header. They were
|
||||
// previously bare literals scattered through `update_daily`/`is_ready`. Lifting
|
||||
// them to named consts makes the policy explicit and a future retune a visible,
|
||||
// tested change. Values are unchanged.
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Minimum observation days before drift detection activates.
|
||||
const BASELINE_MIN_OBSERVATION_DAYS: u32 = 7;
|
||||
|
||||
/// EMA update weight applied to the embedding centroid each day (the new
|
||||
/// sample's weight; the centroid retains `1 - EMBEDDING_EMA_ALPHA` of its old
|
||||
/// value, i.e. a decay of 0.95). Kept as the literal `0.05` rather than
|
||||
/// `1.0 - 0.95_f32` to stay bit-identical (the f32 subtraction is not exactly
|
||||
/// 0.05).
|
||||
const EMBEDDING_EMA_ALPHA: f32 = 0.05;
|
||||
|
||||
/// Per-metric absolute z-score above which a day counts toward sustained drift.
|
||||
const DRIFT_ZSCORE_SIGMA: f64 = 2.0;
|
||||
|
||||
/// Consecutive drift days required before a drift report is emitted.
|
||||
const DRIFT_SUSTAINED_DAYS: u32 = 3;
|
||||
|
||||
/// Consecutive drift days at/above which monitoring escalates from `Drift`
|
||||
/// to `RiskCorrelation`.
|
||||
const DRIFT_ESCALATION_DAYS: u32 = 7;
|
||||
|
||||
/// Denominator guard for cosine similarity (zero-norm vectors ⇒ 0.0).
|
||||
const COSINE_SIMILARITY_EPSILON: f32 = 1e-9;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
@@ -226,7 +258,7 @@ impl PersonalBaseline {
|
||||
|
||||
/// Whether baseline has enough data for drift detection.
|
||||
pub fn is_ready(&self) -> bool {
|
||||
self.observation_days >= 7
|
||||
self.observation_days >= BASELINE_MIN_OBSERVATION_DAYS
|
||||
}
|
||||
|
||||
/// Update baseline with a daily summary.
|
||||
@@ -240,10 +272,10 @@ impl PersonalBaseline {
|
||||
self.observation_days += 1;
|
||||
self.updated_at_us = timestamp_us;
|
||||
|
||||
// Update embedding centroid with EMA (decay = 0.95)
|
||||
// Update embedding centroid with EMA (decay 0.95, alpha = 1 - 0.95)
|
||||
if let Some(ref emb) = summary.embedding_centroid {
|
||||
if emb.len() == self.embedding_centroid.len() {
|
||||
let alpha = 0.05_f32; // 1 - 0.95
|
||||
let alpha = EMBEDDING_EMA_ALPHA;
|
||||
for (c, e) in self.embedding_centroid.iter_mut().zip(emb.iter()) {
|
||||
*c = (1.0 - alpha) * *c + alpha * *e;
|
||||
}
|
||||
@@ -271,20 +303,20 @@ impl PersonalBaseline {
|
||||
|
||||
let idx = Self::metric_index(metric);
|
||||
|
||||
if z.abs() > 2.0 {
|
||||
if z.abs() > DRIFT_ZSCORE_SIGMA {
|
||||
self.drift_counters[idx] += 1;
|
||||
} else {
|
||||
self.drift_counters[idx] = 0;
|
||||
}
|
||||
|
||||
if self.drift_counters[idx] >= 3 {
|
||||
if self.drift_counters[idx] >= DRIFT_SUSTAINED_DAYS {
|
||||
let direction = if z > 0.0 {
|
||||
DriftDirection::Increasing
|
||||
} else {
|
||||
DriftDirection::Decreasing
|
||||
};
|
||||
|
||||
let level = if self.drift_counters[idx] >= 7 {
|
||||
let level = if self.drift_counters[idx] >= DRIFT_ESCALATION_DAYS {
|
||||
MonitoringLevel::RiskCorrelation
|
||||
} else {
|
||||
MonitoringLevel::Drift
|
||||
@@ -310,7 +342,7 @@ impl PersonalBaseline {
|
||||
|
||||
/// Check readiness at a specific observation day count (internal helper).
|
||||
fn is_ready_at(&self, days: u32) -> bool {
|
||||
days >= 7
|
||||
days >= BASELINE_MIN_OBSERVATION_DAYS
|
||||
}
|
||||
|
||||
/// Get current drift counter for a metric.
|
||||
@@ -545,12 +577,15 @@ impl EmbeddingHistory {
|
||||
}
|
||||
|
||||
/// Cosine similarity between two f32 vectors.
|
||||
///
|
||||
/// Returns `0.0` if either vector has (near-)zero norm — the product of norms
|
||||
/// falls below [`COSINE_SIMILARITY_EPSILON`], so the division is skipped.
|
||||
fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
|
||||
let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
|
||||
let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
let denom = norm_a * norm_b;
|
||||
if denom < 1e-9 {
|
||||
if denom < COSINE_SIMILARITY_EPSILON {
|
||||
0.0
|
||||
} else {
|
||||
dot / denom
|
||||
@@ -1017,4 +1052,40 @@ mod tests {
|
||||
assert!(*i < h.len());
|
||||
}
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4: de-magic-constant + boundary characterization tests.
|
||||
|
||||
/// The de-magicked drift thresholds MUST equal the prior bare literals.
|
||||
#[test]
|
||||
fn drift_consts_unchanged_from_literals() {
|
||||
assert_eq!(BASELINE_MIN_OBSERVATION_DAYS, 7);
|
||||
assert_eq!(EMBEDDING_EMA_ALPHA, 0.05_f32);
|
||||
assert_eq!(DRIFT_ZSCORE_SIGMA, 2.0);
|
||||
assert_eq!(DRIFT_SUSTAINED_DAYS, 3);
|
||||
assert_eq!(DRIFT_ESCALATION_DAYS, 7);
|
||||
assert_eq!(COSINE_SIMILARITY_EPSILON, 1e-9_f32);
|
||||
}
|
||||
|
||||
/// `is_ready_at` pins the exact day-6 (not ready) / day-7 (ready) boundary
|
||||
/// independent of Welford state.
|
||||
#[test]
|
||||
fn is_ready_at_day_boundary() {
|
||||
let baseline = PersonalBaseline::new(1, 8);
|
||||
assert!(!baseline.is_ready_at(BASELINE_MIN_OBSERVATION_DAYS - 1)); // day 6
|
||||
assert!(baseline.is_ready_at(BASELINE_MIN_OBSERVATION_DAYS)); // day 7
|
||||
assert!(baseline.is_ready_at(BASELINE_MIN_OBSERVATION_DAYS + 1)); // day 8
|
||||
}
|
||||
|
||||
/// Cosine similarity returns 0.0 for a zero-norm vector (denominator below
|
||||
/// `COSINE_SIMILARITY_EPSILON`) and a finite value otherwise.
|
||||
#[test]
|
||||
fn cosine_similarity_zero_vector_is_zero() {
|
||||
let zero = [0.0_f32; 4];
|
||||
let v = [1.0_f32, 2.0, 3.0, 4.0];
|
||||
assert_eq!(cosine_similarity(&zero, &v), 0.0);
|
||||
assert_eq!(cosine_similarity(&v, &zero), 0.0);
|
||||
assert_eq!(cosine_similarity(&zero, &zero), 0.0);
|
||||
// identical non-zero vectors -> ~1.0
|
||||
assert!((cosine_similarity(&v, &v) - 1.0).abs() < 1e-5);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -198,7 +198,15 @@ fn compute_cross_channel_coherence(frames: &[CanonicalCsiFrame]) -> f32 {
|
||||
((mean_corr + 1.0) / 2.0).clamp(0.0, 1.0) as f32
|
||||
}
|
||||
|
||||
/// Denominator guard for the Pearson correlation (ADR-154 §7.4 — de-magicked):
|
||||
/// a product of standard deviations below this is treated as a zero-variance
|
||||
/// (constant) input ⇒ correlation 0.0.
|
||||
const PEARSON_DENOMINATOR_EPSILON: f32 = 1e-12;
|
||||
|
||||
/// Pearson correlation coefficient between two f32 slices.
|
||||
///
|
||||
/// Returns `0.0` for empty inputs or when either slice has (near-)zero
|
||||
/// variance (the denominator falls below [`PEARSON_DENOMINATOR_EPSILON`]).
|
||||
fn pearson_correlation_f32(a: &[f32], b: &[f32]) -> f32 {
|
||||
let n = a.len().min(b.len());
|
||||
if n == 0 {
|
||||
@@ -222,7 +230,7 @@ fn pearson_correlation_f32(a: &[f32], b: &[f32]) -> f32 {
|
||||
}
|
||||
|
||||
let denom = (var_a * var_b).sqrt();
|
||||
if denom < 1e-12 {
|
||||
if denom < PEARSON_DENOMINATOR_EPSILON {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
@@ -439,4 +447,24 @@ mod tests {
|
||||
assert_eq!(cfg.window_us, 200_000);
|
||||
assert!((cfg.min_coherence - 0.3).abs() < f32::EPSILON);
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4: de-magic-constant + boundary characterization tests.
|
||||
|
||||
/// De-magicked denominator epsilon must equal the prior literal.
|
||||
#[test]
|
||||
fn pearson_epsilon_unchanged_from_literal() {
|
||||
assert_eq!(PEARSON_DENOMINATOR_EPSILON, 1e-12_f32);
|
||||
}
|
||||
|
||||
/// A constant (zero-variance) input makes the denominator fall below the
|
||||
/// epsilon ⇒ correlation 0.0. Previously untested (existing tests use
|
||||
/// non-constant inputs).
|
||||
#[test]
|
||||
fn pearson_correlation_zero_variance() {
|
||||
let constant = vec![3.0_f32; 5];
|
||||
let varying = vec![1.0_f32, 2.0, 3.0, 4.0, 5.0];
|
||||
assert_eq!(pearson_correlation_f32(&constant, &varying), 0.0);
|
||||
assert_eq!(pearson_correlation_f32(&varying, &constant), 0.0);
|
||||
assert_eq!(pearson_correlation_f32(&constant, &constant), 0.0);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -13,6 +13,27 @@
|
||||
|
||||
use crate::ruvsense::field_model::WelfordStats;
|
||||
|
||||
/// Nanoseconds per day, for migration-rate (m/day) conversion (ADR-154 §7.4 —
|
||||
/// de-magicked from the inline `86_400_000_000_000.0` literal). 24·60·60·1e9.
|
||||
const NS_PER_DAY: f64 = 86_400_000_000_000.0;
|
||||
|
||||
/// Minimum observed span (in days) below which migration rate is reported as
|
||||
/// 0.0 — guards `cumulative_drift_m / span_days` against a near-zero span.
|
||||
const MIGRATION_MIN_SPAN_DAYS: f64 = 1e-9;
|
||||
|
||||
// ADR-154 §7.4: the v1 fixed-map defaults below were bare literals in
|
||||
// `fixed_map()`. They are EMPIRICAL DEFAULTS (ADR-143), unchanged.
|
||||
|
||||
/// Default association radius (m): a sighting within this of a reflector's
|
||||
/// running mean is folded into it; otherwise it seeds a new reflector.
|
||||
const FIXED_MAP_ASSOC_RADIUS_M: f64 = 0.5;
|
||||
|
||||
/// Default minimum sightings before a reflector counts as "persistent".
|
||||
const FIXED_MAP_MIN_SIGHTINGS: u64 = 20;
|
||||
|
||||
/// Default minimum tap coherence for a sighting to be admitted.
|
||||
const FIXED_MAP_MIN_COHERENCE: f32 = 0.6;
|
||||
|
||||
/// Classification of a discovered persistent reflector (mirrors ADR-139
|
||||
/// `AnchorKind`; kept local to avoid a crate dependency on the WorldGraph).
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
@@ -102,8 +123,8 @@ impl PersistentReflector {
|
||||
if span_ns == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
let span_days = span_ns as f64 / 86_400_000_000_000.0; // ns → days
|
||||
if span_days < 1e-9 {
|
||||
let span_days = span_ns as f64 / NS_PER_DAY; // ns → days
|
||||
if span_days < MIGRATION_MIN_SPAN_DAYS {
|
||||
return 0.0;
|
||||
}
|
||||
self.cumulative_drift_m / span_days
|
||||
@@ -145,9 +166,9 @@ impl RfSlam {
|
||||
pub fn fixed_map() -> Self {
|
||||
Self {
|
||||
reflectors: Vec::new(),
|
||||
assoc_radius_m: 0.5,
|
||||
min_sightings: 20,
|
||||
min_coherence: 0.6,
|
||||
assoc_radius_m: FIXED_MAP_ASSOC_RADIUS_M,
|
||||
min_sightings: FIXED_MAP_MIN_SIGHTINGS,
|
||||
min_coherence: FIXED_MAP_MIN_COHERENCE,
|
||||
discovery_enabled: false,
|
||||
}
|
||||
}
|
||||
@@ -298,4 +319,29 @@ mod tests {
|
||||
assert_eq!(anchors.len(), 1);
|
||||
assert_eq!(anchors[0].1, ReflectorClass::Wall);
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4: de-magic-constant + boundary characterization tests.
|
||||
|
||||
/// De-magicked constants must equal the prior inline literals.
|
||||
#[test]
|
||||
fn migration_consts_unchanged_from_literals() {
|
||||
assert_eq!(NS_PER_DAY, 86_400_000_000_000.0);
|
||||
assert_eq!(NS_PER_DAY, 24.0 * 60.0 * 60.0 * 1e9);
|
||||
assert_eq!(MIGRATION_MIN_SPAN_DAYS, 1e-9);
|
||||
assert_eq!(FIXED_MAP_ASSOC_RADIUS_M, 0.5);
|
||||
assert_eq!(FIXED_MAP_MIN_SIGHTINGS, 20);
|
||||
assert_eq!(FIXED_MAP_MIN_COHERENCE, 0.6_f32);
|
||||
}
|
||||
|
||||
/// A single sighting has first_ns == last_ns ⇒ zero span ⇒ migration rate
|
||||
/// 0.0 (pins the `span_ns == 0` / `span_days < MIGRATION_MIN_SPAN_DAYS`
|
||||
/// guard, and that such a reflector classifies as a Wall).
|
||||
#[test]
|
||||
fn migration_zero_span_is_zero_rate() {
|
||||
let mut slam = RfSlam::with_discovery(0.5, 1, 0.6);
|
||||
slam.observe(&obs([1.0, 2.0, 0.0], 12_345));
|
||||
let r = slam.persistent()[0];
|
||||
assert_eq!(r.migration_m_per_day(), 0.0);
|
||||
assert_eq!(r.classify(0.05, 1.0), ReflectorClass::Wall);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -18,6 +18,16 @@ use midstreamer_temporal_compare::{ComparisonAlgorithm, Sequence, TemporalCompar
|
||||
|
||||
use super::gesture::{GestureConfig, GestureError, GestureResult, GestureTemplate};
|
||||
|
||||
/// Minimum second-best distance (ADR-154 §7.4 — de-magicked) below which the
|
||||
/// relative-margin confidence `1 - best/second_best` would divide by a
|
||||
/// near-zero denominator; below this we fall back to the `max_distance`-relative
|
||||
/// confidence. Mirrors the same guard in `gesture.rs`.
|
||||
const CONFIDENCE_SECOND_BEST_EPSILON: f64 = 1e-10;
|
||||
|
||||
/// Fixed-point scale used to quantize a frame's L2 norm to an i64 for the
|
||||
/// integer temporal comparator (norm·SCALE truncated). Empirical resolution.
|
||||
const NORM_QUANTIZATION_SCALE: f64 = 1000.0;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Configuration
|
||||
// ---------------------------------------------------------------------------
|
||||
@@ -192,7 +202,10 @@ impl TemporalGestureClassifier {
|
||||
let recognized = best_distance <= self.config.max_distance;
|
||||
|
||||
// Confidence based on margin between best and second-best
|
||||
let confidence = if recognized && second_best.is_finite() && second_best > 1e-10 {
|
||||
let confidence = if recognized
|
||||
&& second_best.is_finite()
|
||||
&& second_best > CONFIDENCE_SECOND_BEST_EPSILON
|
||||
{
|
||||
(1.0 - best_distance / second_best).clamp(0.0, 1.0)
|
||||
} else if recognized {
|
||||
(1.0 - best_distance / self.config.max_distance).clamp(0.0, 1.0)
|
||||
@@ -244,13 +257,13 @@ impl TemporalGestureClassifier {
|
||||
|
||||
/// Convert a feature sequence to a midstreamer `Sequence<i64>`.
|
||||
///
|
||||
/// Each frame's L2 norm is quantized to an i64 (multiplied by 1000)
|
||||
/// for use with the generic comparator.
|
||||
/// Each frame's L2 norm is quantized to an i64 (multiplied by
|
||||
/// [`NORM_QUANTIZATION_SCALE`]) for use with the generic comparator.
|
||||
fn to_sequence(frames: &[Vec<f64>]) -> Sequence<i64> {
|
||||
let mut seq = Sequence::new();
|
||||
for (i, frame) in frames.iter().enumerate() {
|
||||
let norm = frame.iter().map(|x| x * x).sum::<f64>().sqrt();
|
||||
let quantized = (norm * 1000.0) as i64;
|
||||
let quantized = (norm * NORM_QUANTIZATION_SCALE) as i64;
|
||||
seq.push(quantized, i as u64);
|
||||
}
|
||||
seq
|
||||
@@ -537,4 +550,14 @@ mod tests {
|
||||
let dbg = format!("{:?}", classifier);
|
||||
assert!(dbg.contains("TemporalGestureClassifier"));
|
||||
}
|
||||
|
||||
// -- ADR-154 §7.4: de-magic-constant pin test.
|
||||
|
||||
/// De-magicked confidence epsilon + quantization scale must equal the
|
||||
/// prior inline literals.
|
||||
#[test]
|
||||
fn temporal_gesture_consts_unchanged_from_literals() {
|
||||
assert_eq!(CONFIDENCE_SECOND_BEST_EPSILON, 1e-10);
|
||||
assert_eq!(NORM_QUANTIZATION_SCALE, 1000.0);
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user