Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/npm/packages/sona/examples/basic-usage.js

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+/**
+ * Basic SONA Usage Example
+ * Demonstrates core functionality of the SONA engine
+ */
+
+const { SonaEngine } = require('../index.js');
+
+function main() {
+  console.log('🧠 SONA - Self-Optimizing Neural Architecture\n');
+
+  // Create engine with hidden dimension
+  console.log('Creating SONA engine with hidden_dim=256...');
+  const engine = new SonaEngine(256);
+  console.log('✓ Engine created\n');
+
+  // Simulate some inference trajectories
+  console.log('Recording inference trajectories...');
+  for (let i = 0; i < 10; i++) {
+    // Create query embedding
+    const queryEmbedding = Array(256).fill(0).map(() => Math.random());
+
+    // Start trajectory
+    const builder = engine.beginTrajectory(queryEmbedding);
+
+    // Simulate inference steps
+    for (let step = 0; step < 3; step++) {
+      const activations = Array(256).fill(0).map(() => Math.random());
+      const attentionWeights = Array(64).fill(0).map(() => Math.random());
+      const reward = 0.7 + Math.random() * 0.3; // Random reward between 0.7-1.0
+
+      builder.addStep(activations, attentionWeights, reward);
+    }
+
+    // Set route and context
+    builder.setRoute(`model_${i % 3}`);
+    builder.addContext(`context_${i}`);
+
+    // Complete trajectory
+    const quality = 0.75 + Math.random() * 0.25; // Quality between 0.75-1.0
+    engine.endTrajectory(builder, quality);
+  }
+  console.log('✓ Recorded 10 trajectories\n');
+
+  // Apply micro-LoRA transformation
+  console.log('Applying micro-LoRA transformation...');
+  const input = Array(256).fill(1.0);
+  const output = engine.applyMicroLora(input);
+  console.log(`✓ Transformed ${input.length} -> ${output.length} dimensions\n`);
+
+  // Find similar patterns
+  console.log('Finding similar patterns...');
+  const queryEmbedding = Array(256).fill(0).map(() => Math.random());
+  const patterns = engine.findPatterns(queryEmbedding, 5);
+  console.log(`✓ Found ${patterns.length} patterns\n`);
+
+  // Get statistics
+  console.log('Engine statistics:');
+  const stats = engine.getStats();
+  console.log(stats);
+  console.log();
+
+  // Force learning cycle
+  console.log('Running background learning cycle...');
+  const result = engine.forceLearn();
+  console.log(`✓ ${result}\n`);
+
+  console.log('✓ Example completed successfully!');
+}
+
+main();