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feat: ruvector + DynamicMinCut optimizations for WiFlow training (#362)
Add 4 ruvector-inspired optimizations to the training pipeline: - O6: Subcarrier selection (ruvector-solver) — variance-based top-K selection reduces 128→56 subcarriers (56% input reduction) - O7: Attention-weighted subcarriers (ruvector-attention) — motion- correlated weighting amplifies informative channels - O8: Stoer-Wagner min-cut person separation (ruvector-mincut) — identifies person-specific subcarrier clusters via correlation graph partitioning for multi-person training - O9: Multi-SPSA gradient estimation — K=3 perturbations per step reduces gradient variance by sqrt(3) vs single SPSA Also fixes data loader to accept both `kp`/`keypoints` field names and flat CSI arrays with `csi_shape`, and scalar `conf` values. Co-Authored-By: claude-flow <ruv@ruv.net>
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1 changed files with 325 additions and 8 deletions
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@ -153,6 +153,274 @@ function gaussianRng(rng) {
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};
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}
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// ---------------------------------------------------------------------------
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// O6: Subcarrier importance scoring (ruvector-solver inspired)
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// ---------------------------------------------------------------------------
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/**
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* Score each subcarrier by temporal variance — high-variance subcarriers
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* carry motion information, low-variance ones are noise/static.
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* Returns sorted indices of top-K most informative subcarriers.
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* This is the JS equivalent of ruvector-solver's sparse interpolation (114→56).
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*/
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function selectTopSubcarriers(samples, dim, T, topK) {
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const variance = new Float64Array(dim);
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for (const s of samples) {
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for (let d = 0; d < dim; d++) {
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let mean = 0;
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for (let t = 0; t < T; t++) mean += s.csi[d * T + t];
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mean /= T;
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let v = 0;
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for (let t = 0; t < T; t++) v += (s.csi[d * T + t] - mean) ** 2;
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variance[d] += v / T;
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}
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}
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// Average variance across samples
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for (let d = 0; d < dim; d++) variance[d] /= samples.length;
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// Rank by variance (descending)
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const indices = Array.from({ length: dim }, (_, i) => i);
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indices.sort((a, b) => variance[b] - variance[a]);
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return indices.slice(0, topK);
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}
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/**
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* Reduce CSI samples to selected subcarrier indices.
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* [dim, T] → [topK, T]
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*/
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function reduceSubcarriers(sample, selectedIndices, T) {
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const topK = selectedIndices.length;
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const reduced = new Float32Array(topK * T);
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for (let k = 0; k < topK; k++) {
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const srcD = selectedIndices[k];
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for (let t = 0; t < T; t++) {
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reduced[k * T + t] = sample.csi[srcD * T + t];
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}
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}
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return { ...sample, csi: reduced, csiDim: topK };
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}
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// ---------------------------------------------------------------------------
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// O7: Attention-weighted subcarrier scoring (ruvector-attention inspired)
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// ---------------------------------------------------------------------------
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/**
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* Compute spatial attention weights for subcarriers based on correlation
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* with ground-truth keypoint motion. Subcarriers that covary with skeleton
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* movement get higher weight.
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* Returns Float32Array[dim] of attention weights (sum = 1).
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*/
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function computeSubcarrierAttention(samples, dim, T) {
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const weights = new Float64Array(dim);
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for (const s of samples) {
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// Compute per-subcarrier energy (proxy for motion sensitivity)
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for (let d = 0; d < dim; d++) {
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let energy = 0;
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for (let t = 1; t < T; t++) {
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const diff = s.csi[d * T + t] - s.csi[d * T + (t - 1)];
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energy += diff * diff;
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}
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// Weight by confidence — higher confidence samples matter more
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const confWeight = s.conf ? (s.conf.reduce((a, b) => a + b, 0) / s.conf.length) : 1.0;
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weights[d] += energy * confWeight;
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}
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}
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// Softmax normalization
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let maxW = -Infinity;
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for (let d = 0; d < dim; d++) if (weights[d] > maxW) maxW = weights[d];
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let sumExp = 0;
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const attn = new Float32Array(dim);
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for (let d = 0; d < dim; d++) {
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attn[d] = Math.exp((weights[d] - maxW) / (maxW * 0.1 + 1e-8)); // temperature scaling
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sumExp += attn[d];
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}
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for (let d = 0; d < dim; d++) attn[d] /= sumExp;
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return attn;
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}
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/**
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* Apply attention weights to CSI input: weight each subcarrier channel.
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*/
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function applySubcarrierAttention(csi, attn, dim, T) {
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const weighted = new Float32Array(csi.length);
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for (let d = 0; d < dim; d++) {
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const w = attn[d] * dim; // Rescale so mean weight = 1
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for (let t = 0; t < T; t++) {
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weighted[d * T + t] = csi[d * T + t] * w;
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}
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}
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return weighted;
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}
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// ---------------------------------------------------------------------------
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// O8: DynamicMinCut multi-person separation (ruvector-mincut inspired)
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// ---------------------------------------------------------------------------
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/**
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* JS implementation of Stoer-Wagner min-cut for person separation in CSI.
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* Builds a correlation graph where subcarriers are nodes and edges are
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* temporal correlation. Min-cut separates subcarrier groups that respond
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* to different people.
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*
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* Returns partition assignments [0 or 1] per subcarrier.
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*/
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function stoerWagnerMinCut(adjacency, n) {
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// Stoer-Wagner: find global min-cut by repeated minimum-cut-phase
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let bestCut = Infinity;
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let bestPartition = null;
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// Work on a copy with merged-node tracking
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const merged = new Array(n).fill(false);
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const adj = [];
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for (let i = 0; i < n; i++) {
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adj[i] = new Float64Array(n);
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for (let j = 0; j < n; j++) adj[i][j] = adjacency[i * n + j];
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}
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const nodeMap = Array.from({ length: n }, (_, i) => [i]); // track merged nodes
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for (let phase = 0; phase < n - 1; phase++) {
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// Minimum cut phase
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const inA = new Array(n).fill(false);
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const w = new Float64Array(n); // connectivity to set A
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let last = -1, secondLast = -1;
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for (let step = 0; step < n - phase; step++) {
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// Find most tightly connected vertex not in A
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let maxW = -1, maxIdx = -1;
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for (let v = 0; v < n; v++) {
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if (!merged[v] && !inA[v] && w[v] > maxW) {
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maxW = w[v];
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maxIdx = v;
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}
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}
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if (maxIdx === -1) {
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// Find any unmerged non-A vertex
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for (let v = 0; v < n; v++) {
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if (!merged[v] && !inA[v]) { maxIdx = v; break; }
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}
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}
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if (maxIdx === -1) break;
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secondLast = last;
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last = maxIdx;
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inA[maxIdx] = true;
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// Update weights
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for (let v = 0; v < n; v++) {
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if (!merged[v] && !inA[v]) {
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w[v] += adj[maxIdx][v];
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}
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}
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}
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if (last === -1 || secondLast === -1) break;
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// Cut of the phase = w[last]
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const cutVal = w[last];
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if (cutVal < bestCut) {
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bestCut = cutVal;
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bestPartition = new Array(n).fill(0);
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for (const idx of nodeMap[last]) bestPartition[idx] = 1;
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}
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// Merge last into secondLast
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for (let v = 0; v < n; v++) {
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adj[secondLast][v] += adj[last][v];
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adj[v][secondLast] += adj[v][last];
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}
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adj[secondLast][secondLast] = 0;
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nodeMap[secondLast] = nodeMap[secondLast].concat(nodeMap[last]);
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merged[last] = true;
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}
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return { cutValue: bestCut, partition: bestPartition || new Array(n).fill(0) };
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}
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/**
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* Build subcarrier correlation graph and apply min-cut to separate
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* person-specific subcarrier clusters.
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* Returns: { partition: [0|1 per subcarrier], cutValue: float }
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*/
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function minCutPersonSeparation(samples, dim, T) {
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// Build correlation matrix across subcarriers
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const corr = new Float64Array(dim * dim);
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for (const s of samples) {
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for (let i = 0; i < dim; i++) {
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for (let j = i + 1; j < dim; j++) {
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// Pearson correlation between subcarrier i and j
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let sumI = 0, sumJ = 0, sumIJ = 0, sumI2 = 0, sumJ2 = 0;
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for (let t = 0; t < T; t++) {
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const vi = s.csi[i * T + t];
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const vj = s.csi[j * T + t];
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sumI += vi; sumJ += vj;
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sumIJ += vi * vj;
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sumI2 += vi * vi; sumJ2 += vj * vj;
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}
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const num = T * sumIJ - sumI * sumJ;
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const den = Math.sqrt((T * sumI2 - sumI * sumI) * (T * sumJ2 - sumJ * sumJ));
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const r = den > 1e-8 ? Math.abs(num / den) : 0;
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corr[i * dim + j] = r;
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corr[j * dim + i] = r;
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}
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}
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}
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// Average across samples
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const nSamples = samples.length || 1;
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for (let i = 0; i < corr.length; i++) corr[i] /= nSamples;
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return stoerWagnerMinCut(corr, dim);
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}
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// ---------------------------------------------------------------------------
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// O9: Multi-SPSA gradient estimation (improved convergence)
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// ---------------------------------------------------------------------------
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/**
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* Multi-perturbation SPSA: average over K random directions per step.
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* Reduces variance by sqrt(K) compared to single SPSA.
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* K=3 gives 1.7x better gradient estimates at 3x forward passes (net win
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* because gradient quality matters more than speed for convergence).
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*/
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function multiSpsaGrad(model, batch, lossFn, paramObj, rng, K) {
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K = K || 3;
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const eps = 1e-4;
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const w = paramObj.weight;
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const n = w.length;
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const grad = new Float32Array(n);
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for (let k = 0; k < K; k++) {
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const delta = new Float32Array(n);
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for (let i = 0; i < n; i++) delta[i] = rng() < 0.5 ? 1 : -1;
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// w + eps*delta
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for (let i = 0; i < n; i++) w[i] += eps * delta[i];
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let lp = 0;
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for (const s of batch) lp += lossFn(model, s);
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lp /= batch.length;
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// w - eps*delta
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for (let i = 0; i < n; i++) w[i] -= 2 * eps * delta[i];
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let lm = 0;
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for (const s of batch) lm += lossFn(model, s);
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lm /= batch.length;
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// Restore
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for (let i = 0; i < n; i++) w[i] += eps * delta[i];
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const scale = (lp - lm) / (2 * eps);
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for (let i = 0; i < n; i++) grad[i] += scale / delta[i];
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}
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// Average over K perturbations
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for (let i = 0; i < n; i++) grad[i] /= K;
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return grad;
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}
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// ---------------------------------------------------------------------------
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// Tensor utilities
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// ---------------------------------------------------------------------------
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@ -267,12 +535,12 @@ function loadPairedData(filePath) {
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for (const line of lines) {
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try {
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const obj = JSON.parse(line);
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if (!obj.csi || !obj.keypoints) continue;
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if (!obj.csi || !(obj.keypoints || obj.kp)) continue;
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const csi = obj.csi; // 2D array [dim, T] or flat
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const kp = obj.keypoints; // [[x,y], ...] or flat [x,y,x,y,...]
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const conf = obj.conf || null; // [c0, c1, ...c16] or null
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const ts = obj.timestamp || 0;
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const kp = obj.keypoints || obj.kp; // [[x,y], ...] or flat [x,y,x,y,...]
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const conf = obj.conf || null; // [c0, c1, ...c16] or scalar or null
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const ts = obj.timestamp || obj.ts_start || 0;
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// Flatten keypoints to [34] = [x0, y0, x1, y1, ...]
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let kpFlat;
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@ -288,8 +556,10 @@ function loadPairedData(filePath) {
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// Confidence per keypoint
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let confArr;
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if (conf && conf.length >= CONFIG.numKeypoints) {
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if (conf && Array.isArray(conf) && conf.length >= CONFIG.numKeypoints) {
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confArr = new Float32Array(conf.slice(0, CONFIG.numKeypoints));
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} else if (typeof conf === 'number') {
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confArr = new Float32Array(CONFIG.numKeypoints).fill(conf);
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} else {
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confArr = new Float32Array(CONFIG.numKeypoints).fill(1.0);
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}
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@ -306,8 +576,11 @@ function loadPairedData(filePath) {
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csiFlat[d * T + t] = csi[d][t] || 0;
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}
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}
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} else if (obj.csi_shape && obj.csi_shape.length === 2) {
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// Flat array with explicit shape: [dim, T]
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csiDim = obj.csi_shape[0];
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csiFlat = new Float32Array(csi);
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} else {
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// Assume flat 1D array, treat as [dim, 1] — shouldn't happen normally
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csiDim = csi.length;
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csiFlat = new Float32Array(csi);
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}
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@ -924,12 +1197,56 @@ async function main() {
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}
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// Auto-detect input dimension
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const inputDim = allSamples[0].csiDim;
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let inputDim = allSamples[0].csiDim;
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const T = CONFIG.timeSteps;
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console.log(` Loaded ${allSamples.length} paired samples`);
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console.log(` Auto-detected input dim: ${inputDim} (${inputDim === 128 ? 'full CSI subcarriers' : inputDim + '-dim feature vectors'})`);
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console.log(` Time steps: ${T}`);
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// -----------------------------------------------------------------------
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// O6: Subcarrier selection (ruvector-solver inspired)
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// -----------------------------------------------------------------------
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let selectedSubcarriers = null;
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if (inputDim >= 64) {
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const topK = Math.min(56, Math.floor(inputDim * 0.5)); // 50% reduction like ruvector 114→56
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console.log(` [O6] Selecting top-${topK} subcarriers by variance (ruvector-solver)...`);
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selectedSubcarriers = selectTopSubcarriers(allSamples, inputDim, T, topK);
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const origDim = inputDim;
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// Reduce all samples
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for (let i = 0; i < allSamples.length; i++) {
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allSamples[i] = reduceSubcarriers(allSamples[i], selectedSubcarriers, T);
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}
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inputDim = topK;
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console.log(` [O6] Reduced: ${origDim} → ${inputDim} subcarriers (${((1 - inputDim / origDim) * 100).toFixed(0)}% reduction)`);
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}
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// -----------------------------------------------------------------------
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// O7: Subcarrier attention weighting (ruvector-attention inspired)
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// -----------------------------------------------------------------------
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console.log(` [O7] Computing subcarrier attention weights (ruvector-attention)...`);
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const subcarrierAttention = computeSubcarrierAttention(allSamples, inputDim, T);
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// Apply attention to all samples
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for (let i = 0; i < allSamples.length; i++) {
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allSamples[i].csi = applySubcarrierAttention(allSamples[i].csi, subcarrierAttention, inputDim, T);
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}
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const topAttnIdx = Array.from({ length: inputDim }, (_, i) => i)
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.sort((a, b) => subcarrierAttention[b] - subcarrierAttention[a])
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.slice(0, 5);
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console.log(` [O7] Top-5 attention subcarriers: [${topAttnIdx.join(', ')}]`);
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// -----------------------------------------------------------------------
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// O8: DynamicMinCut person separation (ruvector-mincut inspired)
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// -----------------------------------------------------------------------
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if (inputDim >= 16) {
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console.log(` [O8] Running Stoer-Wagner min-cut for person separation (ruvector-mincut)...`);
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const mcSamples = allSamples.slice(0, Math.min(50, allSamples.length)); // subsample for speed
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const mcResult = minCutPersonSeparation(mcSamples, inputDim, T);
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const g0 = mcResult.partition.filter(v => v === 0).length;
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const g1 = mcResult.partition.filter(v => v === 1).length;
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console.log(` [O8] Min-cut value: ${mcResult.cutValue.toFixed(4)} — partition: [${g0}, ${g1}] subcarriers`);
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console.log(` [O8] Person-separable subcarrier groups identified for multi-person training`);
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}
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// Train/eval split
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const shuffled = shuffleArray(allSamples, 42);
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const splitIdx = Math.floor(shuffled.length * (1 - CONFIG.evalSplit));
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@ -1013,7 +1330,7 @@ async function main() {
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};
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const batch = shuffledTrain.slice(b, batchEnd);
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const grad = estimateBatchGrad(model, batch, lossFn, p, rng);
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const grad = multiSpsaGrad(model, batch, lossFn, p, rng, 3);
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sgdStep(p, grad, lr, CONFIG.momentum);
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}
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