mirror of
https://github.com/ruvnet/RuView
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29de574e63
* docs(research): add RuView beyond-SOTA system review (00) First document of the beyond-SOTA research series: capability audit of the current RuView engine with role-to-crate maturity matrix, ruvsense module inventory, gap analysis, and risk register. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * docs(research): add beyond-SOTA architecture design (02, in progress) https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * docs(research): finalize beyond-SOTA architecture (02) https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * docs(research): add benchmark/validation methodology snapshot (03) https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * docs(research): add beyond-SOTA series index with validation results; changelog README index ties the 5 research docs together with the session's measured validation evidence: 2,797 workspace tests / 0 failed, Python proof PASS (bit-exact), and paired pre/post criterion CIR benchmarks. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * perf(signal): precompute CIR warm-start system; hoist tomography solver allocs Exact, determinism-safe optimizations (bit-identical float results): - cir.rs: diag(PhiH Phi)+lambda*I and its CSR matrix depend only on Phi and lambda (fixed at CirEstimator::new) but were rebuilt every frame (O(K*G) pass + CSR allocation). Now built once in new() via build_warm_start_system; summation order unchanged. - tomography.rs: ISTA gradient buffer hoisted out of the 100-iteration loop (fill(0.0) reset) and the Frobenius Lipschitz bound moved from per-reconstruct to construction. Verified: signal 456 tests green; engine 11/11 green including cycle_is_deterministic and witness-stability tests. Criterion paired pre/post: cir_estimate/he40 -3.9% (p<0.01), multiband -1.2/-1.4%. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * fix(worldgraph): bound SemanticState growth with deterministic retention StreamingEngine::process_cycle appended one SemanticState belief per cycle with no eviction — ~1.7M nodes/day at 20 Hz (beyond-SOTA roadmap finding #6). Add WorldGraph::prune_semantic_states(max): deterministic eviction of the oldest beliefs by (valid_from_unix_ms, id); structural nodes (rooms, zones, sensors, anchors, tracks, events) are never eligible. Wire it into the engine after each belief append (DEFAULT_SEMANTIC_RETENTION = 7,200, ~6 min at 20 Hz; set_semantic_retention to tune). The WorldGraph holds current beliefs; durable history is the recorder's job, so no audit data is lost. 3 new tests: end-to-end bounded growth, oldest-only eviction, deterministic equal-timestamp tie-break. Workspace gate: 2,865 passed, 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(sensing-server): route live frames through the governed StreamingEngine Closes the live-trust-path gap (ADR-136 section 8, beyond-SOTA system review): the running server fused live CSI with the bare MultistaticFuser, while the privacy/provenance/witness control plane (ADR-135..146) only ever ran on synthetic in-test frames. The privacy control plane was therefore bypassable on the real path. New engine_bridge module drives StreamingEngine::process_cycle from the server's live NodeState map, reusing the existing NodeState -> MultiBandCsiFrame conversion. It lazily wires each contributing node as a WorldGraph sensor (idempotent), bounds belief growth via the retention cap, and forwards explicit timestamps/calibration ids so the path stays deterministic and replayable. Wired additively into both live ESP32/WiFi fusion sites in main.rs via a split-borrow off the write guard, so person-count behavior is unchanged; the latest BLAKE3 witness is stored on AppState. Every published belief now carries evidence + model + calibration + privacy decision and a deterministic witness. Adds wifi-densepose-engine/-worldgraph/-bfld/-geo deps. 6 new bridge tests (witnessed belief with full provenance, cross-run determinism, idempotent node registration, retention bound, privacy-mode propagation). sensing-server suite 430+128 green; workspace gate 2,904 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(train): falsifiable occupancy benchmark with anti-overfitting gate Makes the presence/person-count "beyond SOTA" claim falsifiable in code instead of aspirational (the unfalsifiability gap from the beyond-SOTA system review). occupancy_bench grades predictions vs ground truth and gates a SOTA claim behind one claim_allowed invariant requiring ALL of: - DataProvenance::Measured — synthetic/mock data is scorable for regression but never claimable (anti-mock-contamination; the CLAUDE.md Kconfig-bug lesson made structural). - A leak-free EvalSplit — validate() refuses any split where a subject OR environment id appears in both train and test (subject leakage / per-environment overfitting). - n_test >= min_test_samples (small-N guard). - Presence F1 whose bootstrap-CI lower bound (deterministic seeded splitmix64) clears the threshold — not the point estimate. - Count MAE within threshold. The claim string is unreadable except through the gate (NO_CLAIM otherwise), same discipline as the ruview-gamma acceptance gate. What remains is data, not method: a frozen, SHA-pinned, subject/environment-disjoint measured replay set turns the claim into a passing/failing test. Lives in wifi-densepose-train (the eval bounded context, alongside ablation/ eval/metrics). 10 tests cover each refusal path; warning-clean under the crate's missing_docs lint. Workspace gate 2,914 passed / 0 failed. Doc 03 updated. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(engine): per-room adapter provenance + drift-to-recalibration advisor Closes the trust-chain gap where an ~11 KB per-room LoRA adapter (ADR-150 section 3.4) could silently change inference without the witness noticing: provenance carried only "rfenc-v<N>" with no notion of adapter identity. - StreamingEngine::set_room_adapter(AdapterInfo): pins the adapter's content-derived id into provenance model_version ("rfenc-v1+adapter:<id>") — and therefore into the BLAKE3 witness — so swapping or clearing adapter weights always shifts the witness. Engine test proves base -> adapter -> other-adapter -> cleared all witness differently and cleared == base. - RecalibrationAdvisor: recommends re-running the ADR-135 empty-room baseline / refitting the room adapter on sustained low fusion coherence (streak threshold, default 60 cycles ~ 3 s at 20 Hz) or an ADR-142 change-point. Surfaced as TrustedOutput::recalibration_recommended, stored on the sensing-server AppState alongside the witness at both live fusion sites. - Bridge plumbing: EngineBridge::{set_room_adapter, clear_room_adapter} + live-path test that the adapter id flows into the live witness. Scope note (honest): this is the deployable provenance/trigger half of the "retrained model" roadmap item. Fitting the adapter itself runs in the existing external calibration service (aether-arena/calibration/); a trained RF-encoder checkpoint still does not exist in-tree. Engine 15 tests, bridge 7 tests. Workspace gate: 2,918 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * fix(mat): gate api module behind its feature — standalone no-default-features builds pub mod api was unconditional while its only dependency, serde, is optional behind the 'api' feature, so any build without default features failed with 101 unresolved-serde errors (masked in --workspace runs by feature unification). The api module and its create_router/AppState re-export are now cfg(feature = "api")-gated with docsrs annotations. All combos compile: bare --no-default-features (was 101 errors, now 0), --no-default-features --features api, and full default (177 tests pass). Workspace gate: 2,918 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * perf(signal): opt-in FFT operator for the CIR ISTA solver (8-14x measured) Phi is a sub-DFT, so each ISTA mat-vec can run as one length-G FFT (O(G log G)) instead of a dense O(K*G) product — the dominant-latency-hazard finding from the beyond-SOTA optimization roadmap. New CirConfig::fft_operator, default FALSE: the dense path stays the bit-exact witness default. The FFT evaluates the same sums in a different order, so enabling it shifts float results in the last bits and requires regenerating any pinned witness — strictly opt-in per deployment. FftOperator (rustfft, planned once at CirEstimator::new, scratch buffers reused across the ISTA loop) dispatches inside ista_solve: Phi x = scale * forward-FFT(x) sampled at bins (k_idx mod G) Phi^H v = scale * unnormalised inverse-FFT of v scattered into those bins Warm-start and Lipschitz estimation stay dense at construction. Measured (criterion, same run, same machine): ht20: 2.22 ms -> 265 us (8.4x) ht40: 10.26 ms -> 717 us (14.3x) The real HE40 grid (K=484, G=1452) scales further per the O(K*G)/O(G log G) ratio. 3 new tests: FFT<->dense matvec equivalence to float tolerance on ht20 and he40 grids; end-to-end dominant-tap agreement on a single-path frame; all default configs keep FFT off. New cir_estimate_fft bench group. Workspace gate: 2,921 passed / 0 failed (default path bit-exact, witnesses unchanged). https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(core): canonical frame decoder — capture-to-claim replay (ADR-136) The encode half of the ADR-136 frame contract existed (ComplexSample, to_canonical_bytes, witness_hash) but there was no decoder: a captured canonical frame could be witnessed but never reconstructed, blocking replay-from-capture. CsiFrame::from_canonical_bytes is the exact inverse: same id, metadata, complex payload, and witness hash (tested as the round-trip law AC7 — the replayed frame re-encodes byte-identically). Amplitude/phase are recomputed from the payload (projections, not independent state). Every malformed-input class fails closed (AC8): header truncation -> Truncated, payload truncation -> PayloadMismatch, unknown discriminants, non-UTF-8 device id, trailing bytes. Nil calibration uuid decodes as None per the documented encoding. Core: 36 tests pass. Workspace gate: 2,937 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(engine): dynamic min-cut mesh partition guard (ruvector-mincut) Maintains an exact min-cut over the live mesh coupling graph — nodes are sensing nodes, coupling is the product of fusion attention weights — and surfaces per cycle, as TrustedOutput::mesh: - cut value: the global "how close is the array to partitioning" number, a structural measure per-node heuristics miss; - weak side: which specific nodes would split off (failure/jamming triage, feeds ADR-032 posture); - at-risk flag: counts as a structural event for the drift->recalibration advisor (alongside ADR-142 change-points). Degenerate cases fail toward risk: a node with zero coupling is reported as already partitioned (cut 0, that node as the weak side). Measured cost policy (criterion, 12-node mesh — the honest part): - weights quantized (1/64) + change-gated: steady-state cycles do ZERO graph work and reuse the cached cut (~7.3 us, ~23x cheaper than building); - on any real change a full exact rebuild (~171 us) is used, because ONE DynamicMinCut delete+insert measured ~240 us — the subpolynomial machinery amortizes on much larger graphs, so rebuild-on-change is the measured optimum at mesh scale (one-edge case -28% after switching policy); - full process_cycle with the guard: ~33 us for 4 nodes vs the 50 ms budget. 9 mesh_guard tests (weak-node detection, steady-state zero updates, sub-quantum gating, join/drop rebuild, determinism, disconnection) + an engine-level wiring test (down-weighted node -> weak side -> recalibration). Engine 24 tests; workspace gate 2,946 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(engine): mesh partition risk demotes privacy + enters the witness (ADR-032) Completes the mesh-guard integration: its at_risk signal was advisory-only (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; mesh_guard_mut exposes risk-threshold tuning. Test: a forced-risk 3-node cycle demotes PrivateHome Anonymous->Restricted and shifts the witness vs a clean baseline. Engine 25 tests; workspace gate 2,947 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * fix: public-PR review findings — privacy-path honesty, gate holes, mesh-guard cliff - sensing-server: engine errors logged+counted (no silent swallow), trust state exposed via status surface, privacy-demotion claims aligned with the actual parallel-audit-path behavior - occupancy_bench: vacuous-F1 hole closed (degenerate test sets fail with their own criterion); CI-lower-bound test made probative - mesh_guard: quantization scaled to observed coupling range — >=65-node balanced meshes no longer permanently at_risk (regression test) - engine: both wiring tests made probative (same-topology witness compare, deterministic risk-crossing fixture) - mat: axum/tokio optional behind api; real serde feature (api enables it) - core: canonical decoder strict (non-zero reserved bytes and nil UUID rejected — injective on accepted domain, forged-bytes tests) - CHANGELOG: un-spliced the FFT/adapter bullet mangle Co-Authored-By: claude-flow <ruv@ruv.net> * chore: strip private-track references for public PR Reword the occupancy-benchmark changelog bullet to drop a cross-reference to the private research track, and restore the WorldGraph retention bullet header that was glued onto the preceding MAT bullet. Co-Authored-By: claude-flow <ruv@ruv.net> * chore: lockfile refresh for cherry-picked feature set Co-Authored-By: claude-flow <ruv@ruv.net> --------- Co-authored-by: Claude <noreply@anthropic.com>
365 lines
15 KiB
Rust
365 lines
15 KiB
Rust
//! Mesh partition guard: dynamic min-cut over the live multistatic node graph.
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//!
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//! The fusion mesh (nodes = sensing nodes, edge weights = fusion coupling
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//! derived from per-node attention weights) changes *incrementally* at cycle
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//! rate — one node's coupling drifts, a node joins or drops. This module
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//! maintains a [`ruvector_mincut::DynamicMinCut`] over that graph and exposes,
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//! per cycle:
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//!
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//! - the **min-cut value** — the cheapest set of couplings whose loss splits
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//! the mesh in two: a principled, global "how close is the array to
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//! partitioning" number (vs per-node heuristics that miss multi-node
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//! structure);
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//! - the **weak side** — which specific nodes are about to partition (feeds
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//! failure/jamming triage, ADR-032 posture);
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//! - an **at-risk flag** consumed by the engine: it counts as a structural
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//! event for the drift→recalibration advisor.
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//!
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//! ## Cost model (the optimization)
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//!
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//! Weights are quantized (default 1/64; a *nonzero* coupling below one quantum
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//! saturates to quantum 1 so a live coupling is never erased — see
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//! [`MeshGuard::weight_quantum`]) and updates are **change-gated**: an
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//! edge is touched only when its quantized weight actually moves, so the
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//! steady-state cycle applies *zero* graph updates and reuses the cached cut —
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//! O(active-changes) per cycle, not O(n²) rebuilds. The exact (deterministic)
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//! algorithm is used; mesh sizes are ≤ tens of nodes, far inside its budget.
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use std::collections::BTreeMap;
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use ruvector_mincut::{DynamicMinCut, MinCutBuilder};
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/// Per-cycle report from the mesh guard.
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#[derive(Debug, Clone, PartialEq)]
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pub struct MeshPartitionReport {
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/// Current min-cut value over the coupling graph (higher = more robust).
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pub cut_value: f64,
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/// True when the mesh has ≥ `min_nodes` nodes and the cut value fell to or
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/// below the risk threshold — the array is close to splitting.
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pub at_risk: bool,
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/// The smaller side of the min-cut partition (node ids): the nodes that
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/// would be isolated if the weak couplings failed.
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pub weak_side: Vec<u8>,
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/// Incremental edge updates applied this cycle (0 in steady state).
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pub updates_applied: usize,
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}
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/// Dynamic min-cut guard over the live mesh.
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pub struct MeshGuard {
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mincut: Option<DynamicMinCut>,
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/// Node set the structure was built over (sorted). A change forces rebuild.
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nodes: Vec<u8>,
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/// Quantized edge weights currently installed, keyed `(u, v)` with `u < v`.
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edges: BTreeMap<(u8, u8), i64>,
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/// Weight quantum: weights are snapped to multiples of this before
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/// comparison/installation, gating out sub-quantum jitter.
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///
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/// Policy: a **nonzero** coupling below one quantum saturates to quantum 1
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/// instead of quantizing to 0 — quantization never erases a live coupling.
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/// (Without the floor, a balanced mesh of ≥ 65 nodes — attention weights
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/// ~1/n ⇒ couplings ~1/n < 1/64 — had every edge erased and was reported
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/// permanently "already partitioned"/at-risk.) Exact zero stays zero: a
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/// truly absent coupling *is* a partition. Relative weakness below one
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/// quantum is not resolved; lower this quantum if that resolution matters.
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pub weight_quantum: f64,
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/// Cut value at or below which the mesh counts as at partition risk.
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pub risk_threshold: f64,
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/// Minimum node count for risk to be meaningful (a 2-node mesh always has
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/// a trivial cut; default 3).
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pub min_nodes: usize,
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}
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impl Default for MeshGuard {
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fn default() -> Self {
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Self {
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mincut: None,
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nodes: Vec::new(),
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edges: BTreeMap::new(),
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weight_quantum: 1.0 / 64.0,
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risk_threshold: 0.25,
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min_nodes: 3,
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}
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}
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}
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impl MeshGuard {
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/// Quantize a raw weight to the guard's grid (floor; weights are ≥ 0).
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/// Nonzero sub-quantum weights saturate to quantum 1 — see the
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/// [`Self::weight_quantum`] policy (review finding: sub-quantum couplings
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/// must not produce a false "already partitioned").
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fn quantize(&self, w: f64) -> i64 {
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let w = w.max(0.0);
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let q = (w / self.weight_quantum).floor() as i64;
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if q == 0 && w > 0.0 {
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1
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} else {
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q
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}
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}
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/// Update the guard with this cycle's mesh: `nodes` are the contributing
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/// node ids and `coupling(i, j)` returns the fusion coupling between
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/// `nodes[i]` and `nodes[j]` (symmetric, ≥ 0).
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///
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/// Returns `None` for meshes of fewer than 2 nodes (no cut exists).
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pub fn update(
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&mut self,
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nodes: &[u8],
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coupling: impl Fn(usize, usize) -> f64,
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) -> Option<MeshPartitionReport> {
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if nodes.len() < 2 {
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// Mesh degenerated: drop state so a later rebuild starts clean.
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self.mincut = None;
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self.nodes.clear();
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self.edges.clear();
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return None;
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}
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let mut sorted: Vec<u8> = nodes.to_vec();
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sorted.sort_unstable();
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sorted.dedup();
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// Desired quantized edge set for this cycle.
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let mut desired: BTreeMap<(u8, u8), i64> = BTreeMap::new();
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for i in 0..nodes.len() {
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for j in (i + 1)..nodes.len() {
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let (a, b) = if nodes[i] < nodes[j] {
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(nodes[i], nodes[j])
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} else {
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(nodes[j], nodes[i])
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};
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if a == b {
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continue;
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}
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let q = self.quantize(coupling(i, j));
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desired.insert((a, b), q);
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}
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}
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// Change detection: count quantized-weight moves vs the installed set.
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let changed = if self.mincut.is_none() || self.nodes != sorted {
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usize::MAX // node set changed / first cycle: rebuild unconditionally
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} else {
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desired
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.iter()
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.filter(|(k, &q)| self.edges.get(k).copied().unwrap_or(0) != q)
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.count()
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};
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let mut updates = 0usize;
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if changed > 0 {
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// Measured policy (criterion, 12-node mesh): a full exact rebuild
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// is ~170 µs while ONE DynamicMinCut delete+insert is ~240 µs —
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// the incremental machinery's overheads target much larger graphs.
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// At mesh scale the optimum is: change-gate aggressively (the
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// steady state below is ~7 µs and covers almost every cycle) and
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// rebuild whenever anything actually moved.
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let edges: Vec<(u64, u64, f64)> = desired
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.iter()
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.filter(|(_, &q)| q > 0)
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.map(|(&(a, b), &q)| {
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(u64::from(a), u64::from(b), q as f64 * self.weight_quantum)
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})
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.collect();
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updates = if changed == usize::MAX { edges.len() } else { changed };
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self.mincut = MinCutBuilder::new().exact().with_edges(edges).build().ok();
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self.nodes = sorted;
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self.edges = desired;
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}
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// changed == 0: steady state — zero graph work, cached cut reused.
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// Nodes with no positive coupling never enter the cut structure (zero
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// edges are not installed) — they are already partitioned. Report them
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// as the degenerate cut before consulting the structure.
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let mut isolated: Vec<u8> = self
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.nodes
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.iter()
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.copied()
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.filter(|&v| {
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!self
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.edges
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.iter()
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.any(|(&(a, b), &q)| q > 0 && (a == v || b == v))
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})
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.collect();
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if !isolated.is_empty() {
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isolated.sort_unstable();
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return Some(MeshPartitionReport {
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cut_value: 0.0,
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at_risk: self.nodes.len() >= self.min_nodes,
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weak_side: isolated,
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updates_applied: updates,
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});
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}
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let mc = self.mincut.as_ref()?;
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// A disconnected coupling graph is the degenerate cut: value 0.
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let cut_value = if mc.is_connected() { mc.min_cut_value() } else { 0.0 };
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let (side_a, side_b) = mc.partition();
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let weak_raw = if side_a.len() <= side_b.len() { side_a } else { side_b };
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let mut weak_side: Vec<u8> = weak_raw.into_iter().map(|v| v as u8).collect();
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weak_side.sort_unstable();
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let at_risk = self.nodes.len() >= self.min_nodes && cut_value <= self.risk_threshold;
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Some(MeshPartitionReport { cut_value, at_risk, weak_side, updates_applied: updates })
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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/// Triangle with one weakly-attached node: the cut isolates that node and
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/// the cut value equals its total coupling.
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#[test]
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fn weakly_attached_node_is_the_weak_side() {
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let mut g = MeshGuard::default();
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let nodes = [0u8, 1, 2];
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// 0–1 strongly coupled; node 2 hangs on by 0.05 + 0.05.
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let w = |i: usize, j: usize| match (i.min(j), i.max(j)) {
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(0, 1) => 1.0,
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_ => 0.05,
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};
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let r = g.update(&nodes, w).expect("3-node mesh");
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assert!(r.cut_value <= 0.13, "cut {} should be ~0.10", r.cut_value);
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assert_eq!(r.weak_side, vec![2]);
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assert!(r.at_risk, "weak coupling must flag partition risk");
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}
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#[test]
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fn strong_mesh_is_not_at_risk() {
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let mut g = MeshGuard::default();
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let r = g.update(&[0, 1, 2, 3], |_, _| 0.9).expect("mesh");
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assert!(r.cut_value > g.risk_threshold);
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assert!(!r.at_risk);
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}
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#[test]
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fn two_node_mesh_reports_but_never_risks() {
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let mut g = MeshGuard::default();
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let r = g.update(&[0, 1], |_, _| 0.01).expect("2-node mesh");
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// Trivial cut exists but min_nodes=3 keeps the flag off.
|
||
assert!(!r.at_risk);
|
||
}
|
||
|
||
#[test]
|
||
fn fewer_than_two_nodes_yields_none() {
|
||
let mut g = MeshGuard::default();
|
||
assert!(g.update(&[7], |_, _| 1.0).is_none());
|
||
assert!(g.update(&[], |_, _| 1.0).is_none());
|
||
}
|
||
|
||
/// The optimization contract: identical weights on the next cycle apply
|
||
/// zero updates; a sub-quantum wiggle also applies zero; a real change
|
||
/// applies exactly the changed edges.
|
||
#[test]
|
||
fn steady_state_applies_zero_updates() {
|
||
let mut g = MeshGuard::default();
|
||
let nodes = [0u8, 1, 2, 3];
|
||
let first = g.update(&nodes, |_, _| 0.5).unwrap();
|
||
assert_eq!(first.updates_applied, 6); // cold build installs all edges
|
||
|
||
let second = g.update(&nodes, |_, _| 0.5).unwrap();
|
||
assert_eq!(second.updates_applied, 0);
|
||
|
||
// Sub-quantum jitter (quantum is 1/64 ≈ 0.0156) is gated out.
|
||
let third = g.update(&nodes, |_, _| 0.5 + 0.004).unwrap();
|
||
assert_eq!(third.updates_applied, 0);
|
||
|
||
// One genuinely changed edge touches exactly one edge.
|
||
let fourth = g
|
||
.update(&nodes, |i, j| if (i.min(j), i.max(j)) == (0, 1) { 0.1 } else { 0.5 })
|
||
.unwrap();
|
||
assert_eq!(fourth.updates_applied, 1);
|
||
}
|
||
|
||
/// Node set changes force a clean rebuild (drop/join handled correctly).
|
||
#[test]
|
||
fn node_join_and_drop_rebuild() {
|
||
let mut g = MeshGuard::default();
|
||
g.update(&[0, 1, 2], |_, _| 0.8).unwrap();
|
||
// Node 3 joins.
|
||
let joined = g.update(&[0, 1, 2, 3], |_, _| 0.8).unwrap();
|
||
assert_eq!(joined.updates_applied, 6); // rebuild over 4 nodes
|
||
// Node 0 drops.
|
||
let dropped = g.update(&[1, 2, 3], |_, _| 0.8).unwrap();
|
||
assert_eq!(dropped.updates_applied, 3);
|
||
assert!(!dropped.at_risk);
|
||
}
|
||
|
||
/// Determinism: same inputs, same report (cut value + weak side).
|
||
#[test]
|
||
fn reports_are_deterministic() {
|
||
let run = || {
|
||
let mut g = MeshGuard::default();
|
||
let w = |i: usize, j: usize| match (i.min(j), i.max(j)) {
|
||
(0, 1) => 0.9,
|
||
(1, 2) => 0.6,
|
||
_ => 0.07,
|
||
};
|
||
g.update(&[0, 1, 2], w).unwrap()
|
||
};
|
||
let a = run();
|
||
let b = run();
|
||
assert_eq!(a.cut_value.to_bits(), b.cut_value.to_bits());
|
||
assert_eq!(a.weak_side, b.weak_side);
|
||
}
|
||
|
||
/// Regression (review finding 3): a balanced mesh of ≥ 65 nodes has every
|
||
/// pairwise coupling at ~1/n < quantum (1/64). The old floor-to-zero
|
||
/// quantization erased all edges and reported the mesh permanently
|
||
/// "already partitioned" (cut 0, at_risk). Nonzero sub-quantum couplings
|
||
/// now saturate to one quantum, so the mesh reports a healthy cut.
|
||
#[test]
|
||
fn large_balanced_mesh_is_not_at_risk() {
|
||
let mut g = MeshGuard::default();
|
||
let nodes: Vec<u8> = (0..70u8).collect();
|
||
// Attention-weight product coupling: (1/n)·(1/n)·n = 1/n ≈ 0.0143 < 1/64.
|
||
let n = nodes.len() as f64;
|
||
let r = g.update(&nodes, |_, _| 1.0 / n).expect("70-node mesh");
|
||
assert!(
|
||
r.cut_value > 0.0,
|
||
"live couplings must not quantize to zero"
|
||
);
|
||
// Min cut isolates one node: 69 edges × one quantum (1/64) ≈ 1.08,
|
||
// well above the 0.25 default risk threshold.
|
||
assert!(r.cut_value > g.risk_threshold);
|
||
assert!(
|
||
!r.at_risk,
|
||
"balanced large mesh must not be at partition risk"
|
||
);
|
||
assert!(r.weak_side.len() < nodes.len(), "no false full partition");
|
||
}
|
||
|
||
/// Sub-quantum couplings saturate to one quantum but exact zero is still a
|
||
/// real partition (the floor must not invent couplings).
|
||
#[test]
|
||
fn sub_quantum_saturates_but_zero_stays_zero() {
|
||
let mut g = MeshGuard::default();
|
||
// 0.001 < 1/64 everywhere: connected, tiny cut, flagged at risk
|
||
// (cut = 2 × 1/64 ≈ 0.031 ≤ 0.25) — but NOT "already partitioned".
|
||
let r = g.update(&[0, 1, 2], |_, _| 0.001).expect("mesh");
|
||
assert!(r.cut_value > 0.0);
|
||
assert!(r.at_risk);
|
||
// Exact zero to node 2: degenerate cut 0, node 2 isolated.
|
||
let mut g2 = MeshGuard::default();
|
||
let r2 = g2
|
||
.update(&[0, 1, 2], |i, j| if i == 2 || j == 2 { 0.0 } else { 0.5 })
|
||
.expect("mesh");
|
||
assert_eq!(r2.cut_value, 0.0);
|
||
assert_eq!(r2.weak_side, vec![2]);
|
||
}
|
||
|
||
/// A fully partitioned mesh (zero coupling to one node) reports cut 0.
|
||
#[test]
|
||
fn disconnected_mesh_is_cut_zero() {
|
||
let mut g = MeshGuard::default();
|
||
let w = |i: usize, j: usize| {
|
||
if i == 2 || j == 2 { 0.0 } else { 0.9 }
|
||
};
|
||
let r = g.update(&[0, 1, 2], w).unwrap();
|
||
assert_eq!(r.cut_value, 0.0);
|
||
assert!(r.at_risk);
|
||
assert_eq!(r.weak_side, vec![2]);
|
||
}
|
||
}
|