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feat(connectome-fly): sparse-Fiedler observer path for N > 1024
Adds src/observer/sparse_fiedler.rs. At n > 1024, compute_fiedler
dispatches to a ruvector-sparsifier-backed sparse Laplacian with
shifted power iteration instead of the dense O(n²) path. Below that
threshold the dense path is unchanged — AC-1 at N=1024 is bit-exact
vs head (verified via ac_1_repeatability).
Memory per detect at sparse path:
old: 2 × n² × 4 B (800 MB at n=10K; 153 GB at n=139K — infeasible)
new: O(n + nnz) × 4 B
- row_ptr: (n+1) × 4 B
- col_idx: 2·nnz × 4 B (symmetric, both directions)
- val: 2·nnz × 4 B
- deg + a handful of n-length f32 workspace vectors for the
matvec + rayleigh-quotient loop
(e.g. at n=10 000 with ~1 M distinct co-firing edges the working
set is ≈ 16–20 MB — four orders of magnitude below the dense
path.)
The hot-path edge accumulator is a HashMap<(u32,u32), f32> keyed by
sorted neuron pair, since every edge gets many τ-coincidence hits per
window and the SparseGraph double-sided adjacency write would pay
that cost twice per update. We canonicalise into
ruvector_sparsifier::SparseGraph at the end (per ADR-154 §13
"sparsify first" pipeline), then export to CSR for matvecs.
Cross-validation: sparse and dense agree within 5 % relative error on
Fiedler value at n=256 on the test fixture. Measured: dense=14.018250
sparse=14.017822 (relative error ≈ 3 × 10⁻⁵).
Scale test: n=10 000 synthetic co-firing, ~60K spikes, completes in
~19 ms on the reference host. Below the ADR-154 §4.2 "≤ 5 ms per
50 ms window" Fiedler target, which is for n ≤ 1024; the n=10K
target is deferred until production-scale calibration.
File sizes: max file = 452 lines (sparse_fiedler.rs); total = 1005
LOC src + tests.
Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruvnet
parent
bd26c4ee41
commit
b805d7158e
4 changed files with 689 additions and 5 deletions
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@ -8,6 +8,13 @@ use crate::lif::Spike;
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use super::eigensolver::{approx_fiedler_power, jacobi_symmetric};
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use super::report::{CoherenceEvent, Report};
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use super::sparse_fiedler::sparse_fiedler;
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/// Active-neuron threshold above which the observer dispatches to the
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/// sparse-Lanczos Fiedler path. Chosen at 1024 so the current
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/// demonstrator (ADR-154, N=1024) keeps its bit-exact AC-1 trace on
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/// the dense path unchanged.
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const SPARSE_FIEDLER_N_THRESHOLD: usize = 1024;
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/// Rolling observer: records spikes, maintains a co-firing window,
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/// runs the Fiedler detector, and produces a final report.
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@ -157,6 +164,14 @@ impl Observer {
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if n < 2 {
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return f32::NAN;
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}
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// Dispatch to the sparse shifted-power-iteration path above
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// the dense-matrix ceiling — avoids the O(n²) adjacency /
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// Laplacian allocation below. Threshold is 1024 so existing
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// demo-scale runs (N=1024 per ADR-154 §3) stay on the dense
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// path and AC-1 remains bit-exact vs head.
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if n > SPARSE_FIEDLER_N_THRESHOLD {
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return sparse_fiedler(&active, &self.cofire_window, SPARSE_FIEDLER_N_THRESHOLD);
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}
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let index_of = |id: NeuronId| -> Option<usize> { active.binary_search(&id).ok() };
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let tau = 5.0_f32;
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let mut a = vec![0.0_f32; n * n];
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@ -3,18 +3,25 @@
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//!
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//! Submodules:
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//!
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//! - `core` — `Observer` and its public API.
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//! - `report` — serializable report + `CoherenceEvent`.
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//! - `eigensolver` — Jacobi full-eigendecomposition for small windows
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//! plus a shifted-power-iteration fallback for
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//! larger ones.
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//! - `core` — `Observer` and its public API.
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//! - `report` — serializable report + `CoherenceEvent`.
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//! - `eigensolver` — Jacobi full-eigendecomposition for small windows
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//! plus a shifted-power-iteration fallback for
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//! larger ones.
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//! - `sparse_fiedler` — sparse shifted-power-iteration path for windows
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//! with more than 1024 active neurons; uses
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//! `ruvector_sparsifier::SparseGraph` as the
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//! canonical scratch edge container so memory per
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//! detect stays `O(n + nnz)` instead of `O(n²)`.
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pub mod core;
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pub mod eigensolver;
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pub mod report;
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pub mod sparse_fiedler;
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pub use core::Observer;
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pub use report::{CoherenceEvent, Report};
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pub use sparse_fiedler::sparse_fiedler;
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#[cfg(test)]
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mod tests {
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452
examples/connectome-fly/src/observer/sparse_fiedler.rs
Normal file
452
examples/connectome-fly/src/observer/sparse_fiedler.rs
Normal file
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@ -0,0 +1,452 @@
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//! Sparse-Fiedler coherence detector path.
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//!
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//! For co-firing windows with more than ~1024 active neurons, the dense
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//! `O(n²)` Laplacian used by `compute_fiedler` stops fitting in cache and
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//! eventually in RAM (n=10 000 → 800 MB per detect call; n=139 000 →
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//! 153 GB — infeasible).
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//!
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//! This module builds a symmetric compressed-sparse-row (CSR) adjacency
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//! matrix directly from the rolling spike window, then estimates the
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//! Fiedler value via shifted power iteration on `L = D − A` without
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//! ever materialising an `n × n` matrix. Memory is `O(n + nnz)` where
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//! `nnz` is the number of distinct co-firing edges inside the window.
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//!
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//! The algorithm mirrors [`super::eigensolver::approx_fiedler_power`]
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//! step-for-step so that at the cross-validation point (n ≤ 1024, both
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//! paths defined) the results agree on the same Laplacian to within
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//! the iterative convergence tolerance:
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//!
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//! 1. Shifted power iteration on `L` with constant-eigenvector
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//! deflation → `λ_max(L)`.
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//! 2. Shifted power iteration on `M = c·I − L` with
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//! `c = 1.1 · λ_max(L) + ε`, again with constant deflation → `μ`.
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//! 3. Return `(λ_max(L) − μ).max(0.0)`.
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//!
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//! `ruvector_sparsifier::SparseGraph` is the canonical sparse-edge
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//! container in the RuVector ecosystem (per ADR-154 §13 follow-up and
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//! `docs/research/connectome-ruvector/05-analysis-layer.md` §3 "sparsify
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//! first" pipeline). For the hot accumulation loop we use a
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//! `HashMap<(u32, u32), f32>` keyed by sorted neuron pair, since every
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//! edge is updated many times per window and the SparseGraph's
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//! double-sided adjacency write is quadratic in the per-edge touch
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//! count. We export into `SparseGraph` once at the end — so downstream
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//! sparsifier consumers still see the canonical shape — and then CSR
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//! from there for the matvec loop.
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use std::collections::{HashMap, VecDeque};
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use ruvector_sparsifier::SparseGraph;
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use crate::connectome::NeuronId;
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use crate::lif::Spike;
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/// Co-firing coincidence window in ms. Matches the dense path in
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/// `super::core::Observer::compute_fiedler`.
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const COFIRE_TAU_MS: f32 = 5.0;
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/// Power-iteration steps for the `λ_max(L)` estimate. Matches the
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/// dense `approx_fiedler_power` path so the two agree on the same
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/// adjacency.
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const POWER_STEPS_LMAX: usize = 32;
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/// Power-iteration steps for the shifted `λ_max(c·I − L)` estimate.
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/// Also matches the dense path.
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const POWER_STEPS_SHIFT: usize = 64;
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/// Relative-tolerance convergence threshold for early exit (same as
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/// the dense path).
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const POWER_TOL: f32 = 1e-4;
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/// Compute the Fiedler value of the co-firing-window Laplacian via a
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/// sparse shifted-power-iteration pipeline (the sparse analogue of
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/// [`super::eigensolver::approx_fiedler_power`]).
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///
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/// `active` is the sorted, deduplicated list of `NeuronId`s whose
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/// spikes lie in the rolling window. `cofire` is the window itself.
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/// `n_threshold` is the active-neuron count above which this sparse
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/// path is dispatched — only used here for the degenerate-case check;
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/// the caller is responsible for the dispatch itself.
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///
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/// Returns `NaN` if the window is too small to form a Laplacian, or
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/// `0.0` if the graph is trivially disconnected.
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pub fn sparse_fiedler(active: &[NeuronId], cofire: &VecDeque<Spike>, _n_threshold: usize) -> f32 {
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let n = active.len();
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if n < 2 || cofire.len() < 2 {
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return f32::NAN;
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}
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// Phase 1 — build sparse adjacency from the co-firing window.
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let Some(csr) = build_sparse_laplacian(active, cofire, n) else {
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return f32::NAN;
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};
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// Phase 2 — power iteration on L → λ_max(L). Mirrors the dense
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// path's 32-step loop.
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let lambda_max = power_iter_lmax(&csr);
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if !lambda_max.is_finite() || lambda_max <= 0.0 {
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return 0.0;
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}
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// Phase 3 — 64-step shifted power iteration on c·I − L → μ.
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let c = lambda_max * 1.1 + 1e-3;
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let mu = power_iter_shifted(&csr, c);
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(lambda_max - mu).max(0.0)
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}
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// ---------------------------------------------------------------------
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// CSR construction
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// ---------------------------------------------------------------------
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/// Dense-ish representation of a symmetric sparse matrix in CSR form,
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/// plus a degree vector for fast Laplacian matvecs. `val` entries are
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/// the edge weights of the co-firing graph (not the negated Laplacian
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/// off-diagonals), so `(L·x)[i] = deg[i]·x[i] − Σ_{j ∈ nbrs(i)}
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/// val[j] · x[col[j]]`.
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struct LaplacianCsr {
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n: usize,
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row_ptr: Vec<u32>,
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col_idx: Vec<u32>,
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val: Vec<f32>,
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deg: Vec<f32>,
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}
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impl LaplacianCsr {
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fn nnz(&self) -> usize {
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self.col_idx.len()
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}
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}
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/// Build the symmetric weighted adjacency of the co-firing graph as
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/// CSR.
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///
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/// Accumulation pass uses a `HashMap<(u32, u32), f32>` keyed by sorted
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/// neuron pair — cheaper than `SparseGraph` for many-hit edges because
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/// each update is a single hash probe instead of two adjacency-map
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/// writes. We then export into a `SparseGraph` so downstream
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/// sparsifier consumers see the canonical shape, and finally convert
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/// to CSR for the matvec loop.
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fn build_sparse_laplacian(
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active: &[NeuronId],
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cofire: &VecDeque<Spike>,
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n: usize,
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) -> Option<LaplacianCsr> {
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// `active` is assumed sorted by the caller — binary-search to map
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// NeuronId back to a dense row index in `[0, n)`.
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let lookup = |id: NeuronId| active.binary_search(&id).ok();
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// --- Accumulation. Each τ-coincident spike pair contributes +1. ---
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let mut acc: HashMap<(u32, u32), f32> = HashMap::with_capacity(cofire.len());
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let spikes: Vec<Spike> = cofire.iter().copied().collect();
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for (i, sa) in spikes.iter().enumerate() {
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let Some(ai) = lookup(sa.neuron) else {
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continue;
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};
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for sb in &spikes[i + 1..] {
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if (sb.t_ms - sa.t_ms).abs() > COFIRE_TAU_MS {
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break;
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}
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let Some(bi) = lookup(sb.neuron) else {
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continue;
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};
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if ai == bi {
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continue;
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}
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let (u, v) = if ai < bi {
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(ai as u32, bi as u32)
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} else {
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(bi as u32, ai as u32)
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};
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*acc.entry((u, v)).or_insert(0.0) += 1.0;
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}
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}
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if acc.is_empty() {
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return None;
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}
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// --- Canonicalise via SparseGraph (matches the sparsifier
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// pipeline API: `insert_or_update_edge` guarantees undirected
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// storage and duplicate-rejection). ---
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let mut graph = SparseGraph::with_capacity(n);
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for (&(u, v), &w) in &acc {
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let _ = graph.insert_or_update_edge(u as usize, v as usize, w as f64);
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}
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if graph.num_edges() == 0 {
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return None;
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}
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// --- CSR export. ---
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let (rp_f64, ci_f64, vals_f64, exported_n) = graph.to_csr();
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let mut row_ptr: Vec<u32> = rp_f64.iter().map(|x| *x as u32).collect();
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// `to_csr` returns the graph's vertex count, which may be < n if
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// the last few neurons have no edges. Pad with empty rows so the
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// caller can index by `ai ∈ [0, n)` safely.
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if exported_n < n {
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let last = *row_ptr.last().unwrap_or(&0);
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row_ptr.resize(n + 1, last);
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}
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let col_idx: Vec<u32> = ci_f64.iter().map(|x| *x as u32).collect();
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let val: Vec<f32> = vals_f64.iter().map(|x| *x as f32).collect();
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// Degree from CSR row sums — matches `Σ_j A[i,j]` (A symmetric, so
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// weighted degree = row sum).
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let mut deg = vec![0.0_f32; n];
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for i in 0..n {
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let s = row_ptr[i] as usize;
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let e = row_ptr[i + 1] as usize;
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let mut d = 0.0_f32;
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for k in s..e {
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d += val[k];
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}
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deg[i] = d;
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}
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Some(LaplacianCsr {
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n,
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row_ptr,
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col_idx,
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val,
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deg,
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})
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}
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// ---------------------------------------------------------------------
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// Lanczos matvecs
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// ---------------------------------------------------------------------
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/// `y ← L·x` where `L = D − A`, using the CSR adjacency `a`.
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fn mat_vec_l(csr: &LaplacianCsr, x: &[f32], y: &mut [f32]) {
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debug_assert_eq!(x.len(), csr.n);
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debug_assert_eq!(y.len(), csr.n);
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for i in 0..csr.n {
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let s = csr.row_ptr[i] as usize;
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let e = csr.row_ptr[i + 1] as usize;
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let mut acc = csr.deg[i] * x[i];
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for k in s..e {
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let j = csr.col_idx[k] as usize;
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acc -= csr.val[k] * x[j];
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}
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y[i] = acc;
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}
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}
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// ---------------------------------------------------------------------
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// Shifted power iteration — sparse analogue of
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// `super::eigensolver::approx_fiedler_power`.
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//
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// The dense path does:
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// - 32 power-iteration steps on L with constant-deflation → λ_max(L)
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// - 64 power-iteration steps on (c·I − L) with c = 1.1·λ_max + ε
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// → μ (≈ λ_max(c·I − L))
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// - return (λ_max − μ).max(0)
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//
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// We do the same, but each matvec `L·x` uses the CSR adjacency instead
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// of an `n × n` scan. Every numerical choice (seed pattern, step
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// counts, tolerance, deflation order) is kept identical to the dense
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// reference so the cross-validation test at n ≤ 1024 agrees within
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// 5 % relative error.
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// ---------------------------------------------------------------------
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fn power_iter_lmax(csr: &LaplacianCsr) -> f32 {
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let n = csr.n;
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// Same seeding polynomial as the dense path's λ_max estimate.
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let mut x: Vec<f32> = (0..n).map(|i| ((i * 31 + 7) as f32).sin()).collect();
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deflate_const(&mut x);
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normalize(&mut x);
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let mut w = vec![0.0_f32; n];
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let mut lambda_max = 0.0_f32;
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for _ in 0..POWER_STEPS_LMAX {
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mat_vec_l(csr, &x, &mut w);
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deflate_const(&mut w);
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normalize(&mut w);
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// Rayleigh quotient: w · L · w.
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let mut lw = vec![0.0_f32; n];
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mat_vec_l(csr, &w, &mut lw);
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let lam = dot(&w, &lw);
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let converged = (lam - lambda_max).abs() < POWER_TOL * lam.abs().max(1.0);
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lambda_max = lam;
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std::mem::swap(&mut x, &mut w);
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if converged {
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break;
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}
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}
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lambda_max
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}
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fn power_iter_shifted(csr: &LaplacianCsr, c: f32) -> f32 {
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let n = csr.n;
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// Same seed polynomial as the dense path's shifted loop.
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let mut x: Vec<f32> = (0..n).map(|i| ((i * 19 + 11) as f32).cos()).collect();
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deflate_const(&mut x);
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normalize(&mut x);
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let mut lx = vec![0.0_f32; n];
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let mut mu = 0.0_f32;
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for _ in 0..POWER_STEPS_SHIFT {
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mat_vec_l(csr, &x, &mut lx);
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// y = (c·I − L) · x = c·x − L·x
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let mut y: Vec<f32> = (0..n).map(|i| c * x[i] - lx[i]).collect();
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deflate_const(&mut y);
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normalize(&mut y);
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// Rayleigh quotient of (c·I − L) at y: y · (c·y − L·y).
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mat_vec_l(csr, &y, &mut lx);
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let mut m2 = 0.0_f32;
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for i in 0..n {
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m2 += y[i] * (c * y[i] - lx[i]);
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}
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let converged = (m2 - mu).abs() < POWER_TOL * m2.abs().max(1.0);
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mu = m2;
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x = y;
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if converged {
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break;
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}
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}
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mu
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}
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// ---------------------------------------------------------------------
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// Small vector kernels
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// ---------------------------------------------------------------------
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fn dot(a: &[f32], b: &[f32]) -> f32 {
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debug_assert_eq!(a.len(), b.len());
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let mut s = 0.0_f32;
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for i in 0..a.len() {
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s += a[i] * b[i];
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}
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s
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}
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|
||||
fn norm(x: &[f32]) -> f32 {
|
||||
x.iter().map(|v| v * v).sum::<f32>().sqrt()
|
||||
}
|
||||
|
||||
fn normalize(x: &mut [f32]) {
|
||||
let nrm = norm(x);
|
||||
if nrm > 1e-20 {
|
||||
let inv = 1.0 / nrm;
|
||||
for v in x.iter_mut() {
|
||||
*v *= inv;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn deflate_const(x: &mut [f32]) {
|
||||
if x.is_empty() {
|
||||
return;
|
||||
}
|
||||
let m: f32 = x.iter().sum::<f32>() / x.len() as f32;
|
||||
for v in x.iter_mut() {
|
||||
*v -= m;
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------
|
||||
// Expose the LaplacianCsr nnz for tests / diagnostics.
|
||||
// ---------------------------------------------------------------------
|
||||
|
||||
/// Return `(n, nnz)` of the CSR Laplacian this path would build for
|
||||
/// the given window. Intended for diagnostics only; has the same
|
||||
/// memory cost as one matvec so callers should not invoke it from
|
||||
/// hot paths.
|
||||
pub fn estimate_sparse_extent(
|
||||
active: &[NeuronId],
|
||||
cofire: &VecDeque<Spike>,
|
||||
) -> Option<(usize, usize)> {
|
||||
let n = active.len();
|
||||
let csr = build_sparse_laplacian(active, cofire, n)?;
|
||||
Some((csr.n, csr.nnz()))
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn id(i: u32) -> NeuronId {
|
||||
NeuronId(i)
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tiny_two_cluster_graph_returns_finite_fiedler() {
|
||||
// Two tight clusters of four neurons each, weakly bridged by a
|
||||
// single cross-pair. We assert the path returns a finite, non-
|
||||
// negative value and does not panic on small-but-valid input.
|
||||
// Whether the value clears the `max(0.0)` floor depends on the
|
||||
// shifted-power-iteration convergence at this scale and is not
|
||||
// algorithmically guaranteed — the cross-validation at N=256
|
||||
// in `tests/sparse_fiedler_10k.rs` is the correctness check.
|
||||
let active: Vec<NeuronId> = (0..8).map(id).collect();
|
||||
let mut cofire: VecDeque<Spike> = VecDeque::new();
|
||||
for k in 0..10 {
|
||||
let t = k as f32 * 10.0;
|
||||
for i in 0..4 {
|
||||
cofire.push_back(Spike {
|
||||
t_ms: t + i as f32 * 0.1,
|
||||
neuron: id(i),
|
||||
});
|
||||
}
|
||||
}
|
||||
for k in 0..10 {
|
||||
let t = k as f32 * 10.0 + 0.5;
|
||||
for i in 4..8 {
|
||||
cofire.push_back(Spike {
|
||||
t_ms: t + (i - 4) as f32 * 0.1,
|
||||
neuron: id(i),
|
||||
});
|
||||
}
|
||||
}
|
||||
for k in 0..3 {
|
||||
let t = k as f32 * 30.0 + 2.0;
|
||||
cofire.push_back(Spike {
|
||||
t_ms: t,
|
||||
neuron: id(1),
|
||||
});
|
||||
cofire.push_back(Spike {
|
||||
t_ms: t + 0.2,
|
||||
neuron: id(5),
|
||||
});
|
||||
}
|
||||
let f = sparse_fiedler(&active, &cofire, 0);
|
||||
assert!(f.is_finite(), "sparse fiedler returned non-finite: {f}");
|
||||
assert!(f >= 0.0, "fiedler must be non-negative (PSD), got {f}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn disconnected_window_returns_zero_or_nan() {
|
||||
// Fewer than two coincident spikes — no edges at all.
|
||||
let active = vec![id(0)];
|
||||
let cofire: VecDeque<Spike> = vec![Spike {
|
||||
t_ms: 0.0,
|
||||
neuron: id(0),
|
||||
}]
|
||||
.into();
|
||||
let f = sparse_fiedler(&active, &cofire, 0);
|
||||
assert!(
|
||||
f.is_nan(),
|
||||
"single-neuron window should return NaN, got {f}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn memory_extent_is_linear_in_nnz() {
|
||||
// 512 neurons, ~1500 spikes → nnz bounded well below n².
|
||||
let n: usize = 512;
|
||||
let active: Vec<NeuronId> = (0..n).map(|i| id(i as u32)).collect();
|
||||
let mut cofire: VecDeque<Spike> = VecDeque::new();
|
||||
for k in 0..60 {
|
||||
let t = k as f32 * 3.0;
|
||||
for i in 0..25 {
|
||||
cofire.push_back(Spike {
|
||||
t_ms: t + i as f32 * 0.05,
|
||||
neuron: id(((k + i) % n) as u32),
|
||||
});
|
||||
}
|
||||
}
|
||||
let Some((rn, nnz)) = estimate_sparse_extent(&active, &cofire) else {
|
||||
panic!("expected edges")
|
||||
};
|
||||
assert_eq!(rn, n);
|
||||
// nnz stored both directions — symmetric. Bound is O(n·k) not
|
||||
// n²; empirically here ≪ n².
|
||||
assert!(nnz < (n * n) / 2, "nnz {nnz} not sparse for n={n}");
|
||||
}
|
||||
}
|
||||
210
examples/connectome-fly/tests/sparse_fiedler_10k.rs
Normal file
210
examples/connectome-fly/tests/sparse_fiedler_10k.rs
Normal file
|
|
@ -0,0 +1,210 @@
|
|||
//! Scale + correctness tests for the sparse-Fiedler observer path.
|
||||
//!
|
||||
//! The dense-Laplacian Fiedler path used at `n ≤ 1024` allocates
|
||||
//! `2 · n² · 4 B`, which is 8 MB at N=1024 but 800 MB at N=10 000 and
|
||||
//! 153 GB at N=139 000 (FlyWire v783). The sparse path this file
|
||||
//! exercises allocates `O(n + nnz)` instead.
|
||||
//!
|
||||
//! Tests:
|
||||
//!
|
||||
//! 1. `sparse_fiedler_scales_to_10k` — synthesises a 30 000-spike
|
||||
//! co-firing window over ~2 000 active neurons (N=10 000) and
|
||||
//! asserts a finite non-NaN Fiedler value returned in < 200 ms.
|
||||
//! 2. `sparse_vs_dense_within_five_percent` — at N=256, runs both the
|
||||
//! dense shifted-power-iteration path and the sparse path on the
|
||||
//! same adjacency and asserts agreement within 5 % relative error.
|
||||
|
||||
use std::collections::VecDeque;
|
||||
use std::time::Instant;
|
||||
|
||||
use connectome_fly::observer::eigensolver::approx_fiedler_power;
|
||||
use connectome_fly::observer::sparse_fiedler::sparse_fiedler;
|
||||
use connectome_fly::{NeuronId, Spike};
|
||||
|
||||
#[test]
|
||||
fn sparse_fiedler_scales_to_10k() {
|
||||
// Construct a co-firing window at N=10 000 with 2 000 active
|
||||
// neurons organised as two "chains": neurons fire in sequence so
|
||||
// τ-coincidence links only consecutive neurons (bounded degree),
|
||||
// not all-to-all (unbounded degree). This keeps λ_max modest so
|
||||
// the shifted power iteration has room to resolve λ_2 above the
|
||||
// f32 noise floor.
|
||||
//
|
||||
// Community A: neurons 0..1000, sequential spacing 0.5 ms.
|
||||
// Community B: neurons 1000..2000, sequential spacing 0.5 ms.
|
||||
// Bridge: neurons 500 ↔ 1500 co-fire on a few bursts.
|
||||
let cluster_size = 1000_u32;
|
||||
let step_ms = 0.5_f32; // intra-cluster spike spacing (~10 neighbours within τ)
|
||||
let n_bursts = 30_u32;
|
||||
let bridge_count = 5_u32;
|
||||
let n_total: u32 = 10_000;
|
||||
|
||||
let mut window: VecDeque<Spike> = VecDeque::new();
|
||||
for b in 0..n_bursts {
|
||||
let t_a = b as f32 * 2000.0;
|
||||
let t_b = t_a + 1000.0;
|
||||
for i in 0..cluster_size {
|
||||
window.push_back(Spike {
|
||||
t_ms: t_a + i as f32 * step_ms,
|
||||
neuron: NeuronId(i),
|
||||
});
|
||||
}
|
||||
for i in 0..cluster_size {
|
||||
window.push_back(Spike {
|
||||
t_ms: t_b + i as f32 * step_ms,
|
||||
neuron: NeuronId(cluster_size + i),
|
||||
});
|
||||
}
|
||||
for k in 0..bridge_count {
|
||||
let src = (k * 37) % cluster_size;
|
||||
let dst = cluster_size + (k * 41) % cluster_size;
|
||||
let t_bridge = t_a + 500.0 + k as f32 * 2.0;
|
||||
window.push_back(Spike {
|
||||
t_ms: t_bridge,
|
||||
neuron: NeuronId(src),
|
||||
});
|
||||
window.push_back(Spike {
|
||||
t_ms: t_bridge + 0.1,
|
||||
neuron: NeuronId(dst),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
let mut active: Vec<NeuronId> = (0..2 * cluster_size).map(NeuronId).collect();
|
||||
active.sort();
|
||||
active.dedup();
|
||||
|
||||
assert!(
|
||||
active.len() >= 1500,
|
||||
"synth: expected ≥ 1500 active neurons, got {}",
|
||||
active.len()
|
||||
);
|
||||
assert!(
|
||||
window.len() >= 25_000,
|
||||
"synth: expected ≥ 25k spikes, got {}",
|
||||
window.len()
|
||||
);
|
||||
// Guard: we built this fixture to live beyond the dense 1024
|
||||
// threshold so the sparse dispatch is the one actually exercised.
|
||||
assert!(
|
||||
active.len() > 1024,
|
||||
"synth: n_active {} ≤ 1024 — dense path would be used",
|
||||
active.len()
|
||||
);
|
||||
let _ = n_total; // documentation anchor for future flywire scale
|
||||
|
||||
// Drive the sparse path directly — we bypass the Observer so we
|
||||
// control the scale test independently of the detect cadence.
|
||||
let t0 = Instant::now();
|
||||
let fiedler = sparse_fiedler(&active, &window, 1024);
|
||||
let dt = t0.elapsed();
|
||||
|
||||
assert!(
|
||||
fiedler.is_finite() && !fiedler.is_nan(),
|
||||
"sparse fiedler returned non-finite: {fiedler}"
|
||||
);
|
||||
// Fiedler is the second-smallest eigenvalue of a PSD matrix — ≥ 0.
|
||||
assert!(
|
||||
fiedler >= 0.0,
|
||||
"negative fiedler on valid window: {fiedler}"
|
||||
);
|
||||
eprintln!(
|
||||
"sparse_fiedler_scales_to_10k: n_active={} spikes={} fiedler={:.5} \
|
||||
elapsed={:?}",
|
||||
active.len(),
|
||||
window.len(),
|
||||
fiedler,
|
||||
dt
|
||||
);
|
||||
assert!(
|
||||
dt.as_millis() < 200,
|
||||
"sparse fiedler took {:?} — target < 200 ms on reference host",
|
||||
dt
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sparse_vs_dense_within_five_percent() {
|
||||
// Build a connected ring-with-chords window at N=256:
|
||||
// - every neuron in the active set fires once per tick
|
||||
// - τ-window captures neighbours in the firing order
|
||||
// - each tick is offset to avoid cross-tick τ-coupling
|
||||
// The resulting co-firing graph is a path (dominant) plus a
|
||||
// handful of chord edges where tick boundaries overlap. This is
|
||||
// well-connected, so λ_2(L) > 0, and small enough (n=256) for the
|
||||
// dense shifted-power path to be stable.
|
||||
let n_active = 256_u32;
|
||||
let mut window: VecDeque<Spike> = VecDeque::new();
|
||||
// 8 ticks, each tick fires all neurons in order with a 0.1 ms
|
||||
// inter-spike interval. Ticks are 200 ms apart so τ=5 ms does not
|
||||
// couple across ticks.
|
||||
for tick in 0..8 {
|
||||
let t0 = tick as f32 * 200.0;
|
||||
for i in 0..n_active {
|
||||
window.push_back(Spike {
|
||||
t_ms: t0 + i as f32 * 0.1,
|
||||
neuron: NeuronId(i),
|
||||
});
|
||||
}
|
||||
}
|
||||
// Plus a handful of chord edges: (i, i+17 mod n) for every 4th i.
|
||||
for i in (0..n_active).step_by(4) {
|
||||
let j = (i + 17) % n_active;
|
||||
let t = 2000.0 + i as f32 * 0.01;
|
||||
window.push_back(Spike {
|
||||
t_ms: t,
|
||||
neuron: NeuronId(i),
|
||||
});
|
||||
window.push_back(Spike {
|
||||
t_ms: t + 0.05,
|
||||
neuron: NeuronId(j),
|
||||
});
|
||||
}
|
||||
|
||||
let mut active: Vec<NeuronId> = (0..n_active).map(NeuronId).collect();
|
||||
active.sort();
|
||||
active.dedup();
|
||||
|
||||
// --- Dense reference (same construction as Observer::compute_fiedler). ---
|
||||
let n = active.len();
|
||||
let index_of = |id: NeuronId| -> Option<usize> { active.binary_search(&id).ok() };
|
||||
let tau = 5.0_f32;
|
||||
let mut a = vec![0.0_f32; n * n];
|
||||
let spikes: Vec<_> = window.iter().copied().collect();
|
||||
for (i, sa) in spikes.iter().enumerate() {
|
||||
let Some(ai) = index_of(sa.neuron) else {
|
||||
continue;
|
||||
};
|
||||
for sb in &spikes[i + 1..] {
|
||||
if (sb.t_ms - sa.t_ms).abs() > tau {
|
||||
break;
|
||||
}
|
||||
if let Some(bi) = index_of(sb.neuron) {
|
||||
if ai != bi {
|
||||
a[ai * n + bi] += 1.0;
|
||||
a[bi * n + ai] += 1.0;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
let dense = approx_fiedler_power(&a, n);
|
||||
|
||||
// --- Sparse under test. ---
|
||||
let sparse = sparse_fiedler(&active, &window, 1024);
|
||||
|
||||
eprintln!("sparse_vs_dense: n={n} dense={dense:.6} sparse={sparse:.6}");
|
||||
assert!(
|
||||
dense.is_finite() && sparse.is_finite(),
|
||||
"one path returned non-finite (dense={dense}, sparse={sparse})"
|
||||
);
|
||||
|
||||
// Relative error vs dense reference. Both paths are iterative
|
||||
// eigensolvers on the same symmetric Laplacian so the comparable
|
||||
// quantity is the same: `λ_2(L)`.
|
||||
let denom = dense.abs().max(1e-6);
|
||||
let rel = (sparse - dense).abs() / denom;
|
||||
assert!(
|
||||
rel <= 0.05,
|
||||
"sparse-vs-dense relative error {rel:.4} > 0.05 (dense={dense:.6}, sparse={sparse:.6})"
|
||||
);
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue