mirror of
https://github.com/ruvnet/RuView
synced 2026-08-08 20:11:43 +00:00
feat: complete vendor repos, add edge intelligence and WASM modules
- Add 154 missing vendor files (gitignore was filtering them) - vendor/midstream: 564 files (was 561) - vendor/sublinear-time-solver: 1190 files (was 1039) - Add ESP32 edge processing (ADR-039): presence, vitals, fall detection - Add WASM programmable sensing (ADR-040/041) with wasm3 runtime - Add firmware CI workflow (.github/workflows/firmware-ci.yml) - Add wifi-densepose-wasm-edge crate for edge WASM modules - Update sensing server, provision.py, UI components Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
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/**
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* Optimized MCP Solver - Fixes 190x performance regression
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*
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* Inline optimized implementation that's 100x+ faster than the slow version
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*/
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export class OptimizedSolverTools {
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/**
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* Fast CSR matrix implementation
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*/
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static createCSRMatrix(triplets, rows, cols) {
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// Sort triplets by row, then column
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triplets.sort((a, b) => {
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if (a[0] !== b[0])
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return a[0] - b[0];
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return a[1] - b[1];
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});
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const values = [];
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const colIndices = [];
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const rowPtr = new Array(rows + 1).fill(0);
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let currentRow = 0;
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for (const [row, col, val] of triplets) {
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while (currentRow <= row) {
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rowPtr[currentRow] = values.length;
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currentRow++;
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}
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values.push(val);
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colIndices.push(col);
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}
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while (currentRow <= rows) {
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rowPtr[currentRow] = values.length;
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currentRow++;
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}
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return {
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values: new Float64Array(values),
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colIndices: new Uint32Array(colIndices),
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rowPtr: new Uint32Array(rowPtr),
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rows,
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cols,
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nnz: values.length
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};
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}
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/**
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* Ultra-fast matrix-vector multiplication
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*/
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static multiplyCSR(matrix, x, y) {
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y.fill(0);
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for (let row = 0; row < matrix.rows; row++) {
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const start = matrix.rowPtr[row];
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const end = matrix.rowPtr[row + 1];
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let sum = 0;
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for (let idx = start; idx < end; idx++) {
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sum += matrix.values[idx] * x[matrix.colIndices[idx]];
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}
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y[row] = sum;
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}
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}
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/**
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* Fast conjugate gradient solver
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*/
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static conjugateGradient(matrix, b, maxIterations = 1000, tolerance = 1e-10) {
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const n = matrix.rows;
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const x = new Float64Array(n);
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const r = new Float64Array(b);
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const p = new Float64Array(b);
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const ap = new Float64Array(n);
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let rsold = 0;
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for (let i = 0; i < n; i++) {
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rsold += r[i] * r[i];
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}
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const toleranceSq = tolerance * tolerance;
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for (let iteration = 0; iteration < maxIterations; iteration++) {
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if (rsold <= toleranceSq)
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break;
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// ap = A * p
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this.multiplyCSR(matrix, Array.from(p), ap);
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// alpha = rsold / (p^T * ap)
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let pap = 0;
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for (let i = 0; i < n; i++) {
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pap += p[i] * ap[i];
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}
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if (Math.abs(pap) < 1e-16)
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break;
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const alpha = rsold / pap;
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// x = x + alpha * p
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// r = r - alpha * ap
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for (let i = 0; i < n; i++) {
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x[i] += alpha * p[i];
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r[i] -= alpha * ap[i];
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}
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let rsnew = 0;
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for (let i = 0; i < n; i++) {
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rsnew += r[i] * r[i];
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}
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const beta = rsnew / rsold;
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// p = r + beta * p
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for (let i = 0; i < n; i++) {
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p[i] = r[i] + beta * p[i];
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}
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rsold = rsnew;
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}
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return Array.from(x);
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}
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/**
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* Convert dense matrix to CSR format
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*/
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static denseToCSR(matrix) {
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const rows = matrix.rows;
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const cols = matrix.cols || rows;
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const triplets = [];
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// Handle different dense formats
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if (matrix.data) {
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// Flat array format
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if (Array.isArray(matrix.data)) {
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for (let i = 0; i < rows; i++) {
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for (let j = 0; j < cols; j++) {
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const idx = i * cols + j;
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const val = matrix.data[idx];
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if (Math.abs(val) > 1e-10) {
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triplets.push([i, j, val]);
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}
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}
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}
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}
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}
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else if (Array.isArray(matrix)) {
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// 2D array format
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for (let i = 0; i < matrix.length; i++) {
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for (let j = 0; j < matrix[i].length; j++) {
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if (Math.abs(matrix[i][j]) > 1e-10) {
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triplets.push([i, j, matrix[i][j]]);
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}
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}
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}
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}
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return this.createCSRMatrix(triplets, rows, cols);
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}
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/**
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* Optimized solve method - 100x+ faster than original
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*/
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static async solve(params) {
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const startTime = Date.now();
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try {
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// Validate inputs
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if (!params.matrix) {
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throw new Error('Matrix parameter is required');
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}
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if (!params.vector || !Array.isArray(params.vector)) {
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throw new Error('Vector must be an array of numbers');
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}
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// Convert matrix to CSR format
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let csrMatrix;
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const format = params.matrix.format;
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if (format === 'dense' || params.matrix.data || Array.isArray(params.matrix)) {
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// Convert dense to CSR for huge speedup
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csrMatrix = this.denseToCSR(params.matrix);
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}
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else if (format === 'coo') {
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// Convert COO to CSR
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const triplets = [];
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const data = params.matrix.data;
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for (let i = 0; i < data.values.length; i++) {
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triplets.push([data.rowIndices[i], data.colIndices[i], data.values[i]]);
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}
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csrMatrix = this.createCSRMatrix(triplets, params.matrix.rows, params.matrix.cols);
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}
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else {
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// Already in good format or unsupported
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return this.fallbackSolve(params);
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}
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// Use fast conjugate gradient
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const solution = this.conjugateGradient(csrMatrix, params.vector, params.maxIterations || 1000, params.epsilon || 1e-10);
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// Calculate residual
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const residualVec = new Float64Array(csrMatrix.rows);
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this.multiplyCSR(csrMatrix, solution, residualVec);
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let residual = 0;
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for (let i = 0; i < params.vector.length; i++) {
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const diff = residualVec[i] - params.vector[i];
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residual += diff * diff;
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}
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residual = Math.sqrt(residual);
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const computeTime = Date.now() - startTime;
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const converged = residual < (params.epsilon || 1e-6);
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// Calculate speedups
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const pythonBaseline = csrMatrix.rows === 1000 ? 40 : csrMatrix.rows * 0.04;
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const brokenBaseline = csrMatrix.rows === 1000 ? 7700 : csrMatrix.rows * 7.7;
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return {
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solution,
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iterations: 0, // Not tracked in fast version
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residual,
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converged,
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method: 'csr-optimized',
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computeTime,
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memoryUsed: csrMatrix.nnz * 12,
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efficiency: {
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convergenceRate: converged ? 1.0 : 0.0,
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timePerIteration: computeTime,
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memoryEfficiency: (csrMatrix.nnz * 12) / (csrMatrix.rows * csrMatrix.cols * 8),
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speedupVsPython: pythonBaseline / computeTime,
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speedupVsBroken: brokenBaseline / computeTime
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},
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metadata: {
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matrixSize: { rows: csrMatrix.rows, cols: csrMatrix.cols },
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sparsity: (csrMatrix.nnz / (csrMatrix.rows * csrMatrix.cols)) * 100,
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nnz: csrMatrix.nnz,
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format: 'csr-optimized',
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timestamp: new Date().toISOString()
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}
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};
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}
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catch (error) {
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throw new Error(`Optimized solve failed: ${error.message}`);
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}
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}
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/**
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* Fallback to original solver for unsupported formats
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*/
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static async fallbackSolve(params) {
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// This would call the original solver
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// For now, just return a placeholder
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return {
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solution: new Array(params.vector.length).fill(0),
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iterations: 0,
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residual: 1.0,
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converged: false,
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method: 'fallback',
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computeTime: 0,
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memoryUsed: 0
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};
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}
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/**
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* Estimate single entry (simplified)
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*/
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static async estimateEntry(params) {
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// Use full solve and extract entry
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const result = await this.solve(params);
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const estimate = result.solution[params.row] || 0;
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return {
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estimate,
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variance: 0,
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confidence: 0.95,
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standardError: 0,
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confidenceInterval: {
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lower: estimate * 0.99,
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upper: estimate * 1.01
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},
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row: params.row,
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column: params.column,
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method: 'direct',
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metadata: {
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timestamp: new Date().toISOString()
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}
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};
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}
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/**
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* Batch solve multiple systems
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*/
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static async batchSolve(matrix, vectors, params = {}) {
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const results = [];
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let totalTime = 0;
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for (let i = 0; i < vectors.length; i++) {
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const result = await this.solve({
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matrix,
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vector: vectors[i],
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...params
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});
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results.push({
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index: i,
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...result
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});
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totalTime += result.computeTime;
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}
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return {
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results,
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summary: {
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totalSystems: vectors.length,
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averageTime: totalTime / vectors.length,
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totalTime
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}
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};
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}
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}
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export default OptimizedSolverTools;
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