feat: vendor midstream and sublinear-time-solver libraries

Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/. Co-Authored-By: claude-flow <ruv@ruv.net>
2026-08-09 20:21:43 +00:00 · 2026-03-02 23:32:45 -05:00
parent 14902e6b4e
commit e91bb8a1d5
1600 changed files with 1852646 additions and 0 deletions
@@ -0,0 +1,108 @@
+#!/usr/bin/env node
+/**
+ * Test that WASM modules actually work
+ */
+
+import { WASMAccelerator } from './dist/core/wasm-integration.js';
+import { SublinearSolver } from './dist/core/solver.js';
+
+async function testWASM() {
+  console.log('🧪 Testing WASM Integration\n');
+
+  // Test 1: Initialize WASM
+  console.log('1️⃣ Initializing WASM modules...');
+  const accelerator = new WASMAccelerator();
+  const initialized = await accelerator.initialize();
+
+  if (initialized) {
+    console.log('✅ WASM modules loaded successfully\n');
+  } else {
+    console.log('⚠️ WASM modules not loading - fallback to JS\n');
+  }
+
+  // Test 2: Test PageRank with WASM
+  console.log('2️⃣ Testing PageRank with WASM...');
+  try {
+    const graphReasoner = accelerator.getGraphReasoner();
+    const adjacency = {
+      rows: 4,
+      cols: 4,
+      data: [
+        [0, 1, 1, 0],
+        [1, 0, 0, 1],
+        [0, 1, 0, 1],
+        [1, 0, 1, 0]
+      ],
+      format: 'dense'
+    };
+
+    const ranks = graphReasoner.computePageRank(adjacency, 0.85, 100);
+    console.log(`✅ PageRank computed: [${Array.from(ranks).map(r => r.toFixed(3)).join(', ')}]\n`);
+  } catch (error) {
+    console.log(`❌ PageRank failed: ${error.message}\n`);
+  }
+
+  // Test 3: Test Temporal Neural Solver
+  console.log('3️⃣ Testing Temporal Neural Solver...');
+  try {
+    const temporal = accelerator.getTemporalNeural();
+    const matrix = new Float64Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
+    const vector = new Float64Array([1, 2, 3]);
+
+    const result = temporal.multiplyMatrixVector(matrix, vector, 3, 3);
+    console.log(`✅ Matrix multiplication: [${Array.from(result).map(r => r.toFixed(1)).join(', ')}]\n`);
+  } catch (error) {
+    console.log(`❌ Matrix multiplication failed: ${error.message}\n`);
+  }
+
+  // Test 4: Test Temporal Advantage
+  console.log('4️⃣ Testing Temporal Advantage Prediction...');
+  try {
+    const temporal = accelerator.getTemporalNeural();
+    const matrix = {
+      rows: 3,
+      cols: 3,
+      data: [[2, -1, 0], [-1, 2, -1], [0, -1, 2]],
+      format: 'dense'
+    };
+    const vector = [1, 2, 1];
+
+    const result = await temporal.predictWithTemporalAdvantage(matrix, vector, 10900);
+    console.log(`✅ Temporal Advantage:`);
+    console.log(`   Light travel time: ${result.lightTravelTimeMs.toFixed(2)}ms`);
+    console.log(`   Compute time: ${result.computeTimeMs.toFixed(2)}ms`);
+    console.log(`   Temporal advantage: ${result.temporalAdvantageMs.toFixed(2)}ms`);
+    console.log(`   Solution: [${result.solution.map(x => x.toFixed(3)).join(', ')}]\n`);
+  } catch (error) {
+    console.log(`❌ Temporal prediction failed: ${error.message}\n`);
+  }
+
+  // Test 5: Full solver with WASM
+  console.log('5️⃣ Testing Full Solver with WASM...');
+  try {
+    const solver = new SublinearSolver({
+      method: 'neumann',
+      epsilon: 1e-6,
+      maxIterations: 100
+    });
+
+    const matrix = {
+      rows: 3,
+      cols: 3,
+      data: [[4, -1, 0], [-1, 4, -1], [0, -1, 4]],
+      format: 'dense'
+    };
+    const vector = [3, 2, 3];
+
+    const result = await solver.solve(matrix, vector);
+    console.log(`✅ Solver converged: ${result.converged}`);
+    console.log(`   Iterations: ${result.iterations}`);
+    console.log(`   Solution: [${result.solution.map(x => x.toFixed(3)).join(', ')}]\n`);
+  } catch (error) {
+    console.log(`❌ Solver failed: ${error.message}\n`);
+  }
+
+  console.log('✨ WASM testing complete!');
+}
+
+testWASM().catch(console.error);