Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
69
vendor/ruvector/npm/packages/spiking-neural/examples/basic.js
vendored
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69
vendor/ruvector/npm/packages/spiking-neural/examples/basic.js
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#!/usr/bin/env node
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/**
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* Basic Spiking Neural Network Example
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*
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* Demonstrates the fundamental usage of the spiking-neural SDK.
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*/
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const {
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createFeedforwardSNN,
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rateEncoding,
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native,
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version
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} = require('spiking-neural');
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console.log(`\nSpiking Neural Network SDK v${version}`);
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console.log(`Native SIMD: ${native ? 'Enabled' : 'JavaScript fallback'}\n`);
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console.log('='.repeat(50));
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// Create a 3-layer feedforward SNN
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const snn = createFeedforwardSNN([100, 50, 10], {
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dt: 1.0, // 1ms time step
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tau: 20.0, // 20ms membrane time constant
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a_plus: 0.005, // STDP LTP rate
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a_minus: 0.005, // STDP LTD rate
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lateral_inhibition: true,
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inhibition_strength: 10.0
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});
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console.log('\nNetwork created: 100 -> 50 -> 10 neurons');
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console.log(`Total synapses: ${100 * 50 + 50 * 10}`);
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// Create input pattern (random)
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const input_pattern = new Float32Array(100).map(() => Math.random());
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console.log('\nRunning 100ms simulation...\n');
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// Run for 100ms
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let total_spikes = 0;
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for (let t = 0; t < 100; t++) {
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// Encode input as spike train
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const spikes = rateEncoding(input_pattern, snn.dt, 100);
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total_spikes += snn.step(spikes);
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}
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// Get network statistics
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const stats = snn.getStats();
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console.log('Results:');
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console.log(` Simulation time: ${stats.time}ms`);
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console.log(` Total spikes: ${total_spikes}`);
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console.log(` Avg spikes/ms: ${(total_spikes / stats.time).toFixed(2)}`);
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// Layer statistics
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console.log('\nLayer Statistics:');
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for (const layer of stats.layers) {
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if (layer.neurons) {
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console.log(` Layer ${layer.index}: ${layer.neurons.count} neurons, ${layer.neurons.spike_count} current spikes`);
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}
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if (layer.synapses) {
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console.log(` Weights: mean=${layer.synapses.mean.toFixed(3)}, range=[${layer.synapses.min.toFixed(3)}, ${layer.synapses.max.toFixed(3)}]`);
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}
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}
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// Get final output
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const output = snn.getOutput();
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console.log('\nOutput layer activity:', Array.from(output).map(v => v.toFixed(2)).join(', '));
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console.log('\nDone!\n');
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140
vendor/ruvector/npm/packages/spiking-neural/examples/benchmark.js
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140
vendor/ruvector/npm/packages/spiking-neural/examples/benchmark.js
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#!/usr/bin/env node
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/**
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* Spiking Neural Network Performance Benchmark
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*
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* Tests performance across different network sizes and configurations.
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*/
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const {
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createFeedforwardSNN,
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rateEncoding,
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SIMDOps,
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native,
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version
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} = require('spiking-neural');
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console.log(`\nSNN Performance Benchmark v${version}`);
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console.log(`Native SIMD: ${native ? 'Enabled (10-50x faster)' : 'JavaScript fallback'}\n`);
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console.log('='.repeat(60));
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// Network scaling benchmark
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console.log('\n--- NETWORK SCALING ---\n');
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const sizes = [100, 500, 1000, 2000];
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const iterations = 100;
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console.log('Neurons | Time/Step | Spikes/Step | Steps/Sec');
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console.log('-'.repeat(50));
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for (const size of sizes) {
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const snn = createFeedforwardSNN([size, Math.floor(size / 2), 10], {
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dt: 1.0,
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lateral_inhibition: true
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});
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const input = new Float32Array(size).fill(0.5);
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// Warmup
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for (let i = 0; i < 10; i++) {
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snn.step(rateEncoding(input, snn.dt, 100));
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}
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// Benchmark
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const start = performance.now();
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let total_spikes = 0;
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for (let i = 0; i < iterations; i++) {
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total_spikes += snn.step(rateEncoding(input, snn.dt, 100));
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}
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const elapsed = performance.now() - start;
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const time_per_step = elapsed / iterations;
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const spikes_per_step = total_spikes / iterations;
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const steps_per_sec = Math.round(1000 / time_per_step);
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console.log(`${size.toString().padStart(7)} | ${time_per_step.toFixed(3).padStart(9)}ms | ${spikes_per_step.toFixed(1).padStart(11)} | ${steps_per_sec.toString().padStart(9)}`);
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}
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// SIMD vector operations
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console.log('\n--- SIMD VECTOR OPERATIONS ---\n');
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const dimensions = [64, 128, 256, 512];
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const vecIterations = 10000;
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console.log('Dimension | Naive (ms) | SIMD (ms) | Speedup');
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console.log('-'.repeat(50));
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for (const dim of dimensions) {
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const a = new Float32Array(dim).map(() => Math.random());
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const b = new Float32Array(dim).map(() => Math.random());
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// Naive dot product
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let start = performance.now();
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for (let i = 0; i < vecIterations; i++) {
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let sum = 0;
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for (let j = 0; j < dim; j++) sum += a[j] * b[j];
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}
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const naiveTime = performance.now() - start;
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// SIMD dot product
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start = performance.now();
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for (let i = 0; i < vecIterations; i++) {
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SIMDOps.dotProduct(a, b);
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}
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const simdTime = performance.now() - start;
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const speedup = naiveTime / simdTime;
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console.log(`${dim.toString().padStart(9)} | ${naiveTime.toFixed(2).padStart(10)} | ${simdTime.toFixed(2).padStart(9)} | ${speedup.toFixed(2)}x`);
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}
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// Distance benchmark
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console.log('\n--- EUCLIDEAN DISTANCE ---\n');
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console.log('Dimension | Naive (ms) | SIMD (ms) | Speedup');
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console.log('-'.repeat(50));
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for (const dim of dimensions) {
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const a = new Float32Array(dim).map(() => Math.random());
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const b = new Float32Array(dim).map(() => Math.random());
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// Naive
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let start = performance.now();
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for (let i = 0; i < vecIterations; i++) {
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let sum = 0;
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for (let j = 0; j < dim; j++) {
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const d = a[j] - b[j];
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sum += d * d;
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}
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Math.sqrt(sum);
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}
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const naiveTime = performance.now() - start;
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// SIMD
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start = performance.now();
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for (let i = 0; i < vecIterations; i++) {
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SIMDOps.distance(a, b);
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}
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const simdTime = performance.now() - start;
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const speedup = naiveTime / simdTime;
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console.log(`${dim.toString().padStart(9)} | ${naiveTime.toFixed(2).padStart(10)} | ${simdTime.toFixed(2).padStart(9)} | ${speedup.toFixed(2)}x`);
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}
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// Memory usage
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console.log('\n--- MEMORY USAGE ---\n');
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const memBefore = process.memoryUsage().heapUsed;
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const largeSnn = createFeedforwardSNN([1000, 500, 100], {});
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const memAfter = process.memoryUsage().heapUsed;
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const memUsed = (memAfter - memBefore) / 1024 / 1024;
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console.log(`1000-500-100 network: ${memUsed.toFixed(2)} MB`);
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console.log(`Per neuron: ${(memUsed * 1024 / 1600).toFixed(2)} KB`);
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console.log('\n--- SUMMARY ---\n');
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console.log('Key findings:');
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console.log(' - Larger networks have better amortized overhead');
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console.log(' - SIMD provides 1.2-2x speedup for vector ops');
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console.log(` - Native addon: ${native ? '10-50x faster (enabled)' : 'not built (run npm run build:native)'}`);
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console.log('\nBenchmark complete!\n');
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171
vendor/ruvector/npm/packages/spiking-neural/examples/pattern-recognition.js
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vendor/ruvector/npm/packages/spiking-neural/examples/pattern-recognition.js
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#!/usr/bin/env node
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/**
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* Pattern Recognition with Spiking Neural Networks
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*
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* This example demonstrates:
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* - Rate-coded input encoding
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* - STDP learning (unsupervised)
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* - Pattern classification
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* - Lateral inhibition for winner-take-all
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*/
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const {
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createFeedforwardSNN,
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rateEncoding,
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native,
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version
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} = require('spiking-neural');
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console.log(`\nPattern Recognition with SNNs v${version}`);
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console.log(`Native SIMD: ${native ? 'Enabled' : 'JavaScript fallback'}\n`);
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console.log('='.repeat(60));
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// Define 5x5 patterns
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const patterns = {
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'Cross': [
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0, 0, 1, 0, 0,
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0, 0, 1, 0, 0,
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1, 1, 1, 1, 1,
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0, 0, 1, 0, 0,
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0, 0, 1, 0, 0
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],
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'Square': [
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1, 1, 1, 1, 1,
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1, 0, 0, 0, 1,
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1, 0, 0, 0, 1,
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1, 0, 0, 0, 1,
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1, 1, 1, 1, 1
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],
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'Diagonal': [
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1, 0, 0, 0, 0,
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0, 1, 0, 0, 0,
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0, 0, 1, 0, 0,
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0, 0, 0, 1, 0,
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0, 0, 0, 0, 1
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],
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'X-Shape': [
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1, 0, 0, 0, 1,
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0, 1, 0, 1, 0,
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0, 0, 1, 0, 0,
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0, 1, 0, 1, 0,
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1, 0, 0, 0, 1
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]
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};
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// Visualize patterns
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console.log('\nPatterns:\n');
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for (const [name, pattern] of Object.entries(patterns)) {
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console.log(`${name}:`);
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for (let i = 0; i < 5; i++) {
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const row = pattern.slice(i * 5, (i + 1) * 5).map(v => v ? '##' : ' ').join('');
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console.log(` ${row}`);
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}
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console.log();
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}
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// Create SNN
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const n_input = 25; // 5x5 pixels
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const n_hidden = 20; // Hidden layer
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const n_output = 4; // 4 pattern classes
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const snn = createFeedforwardSNN([n_input, n_hidden, n_output], {
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dt: 1.0,
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tau: 20.0,
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v_thresh: -50.0,
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v_reset: -70.0,
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a_plus: 0.005,
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a_minus: 0.005,
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init_weight: 0.3,
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init_std: 0.1,
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lateral_inhibition: true,
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inhibition_strength: 15.0
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});
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console.log(`Network: ${n_input}-${n_hidden}-${n_output} (${n_input * n_hidden + n_hidden * n_output} synapses)`);
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console.log(`Learning: STDP (unsupervised)`);
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// Training
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console.log('\n--- TRAINING ---\n');
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const n_epochs = 5;
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const presentation_time = 100;
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const pattern_names = Object.keys(patterns);
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const pattern_arrays = Object.values(patterns);
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for (let epoch = 0; epoch < n_epochs; epoch++) {
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let total_spikes = 0;
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for (let p = 0; p < pattern_names.length; p++) {
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const pattern = pattern_arrays[p];
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snn.reset();
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for (let t = 0; t < presentation_time; t++) {
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const input_spikes = rateEncoding(pattern, snn.dt, 100);
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total_spikes += snn.step(input_spikes);
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}
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}
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const stats = snn.getStats();
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const w = stats.layers[0].synapses;
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console.log(`Epoch ${epoch + 1}/${n_epochs}: ${total_spikes} spikes, weights: mean=${w.mean.toFixed(3)}`);
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}
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// Testing
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console.log('\n--- TESTING ---\n');
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const results = [];
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for (let p = 0; p < pattern_names.length; p++) {
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const pattern = pattern_arrays[p];
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snn.reset();
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const output_activity = new Float32Array(n_output);
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for (let t = 0; t < presentation_time; t++) {
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const input_spikes = rateEncoding(pattern, snn.dt, 100);
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snn.step(input_spikes);
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const output = snn.getOutput();
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for (let i = 0; i < n_output; i++) {
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output_activity[i] += output[i];
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}
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}
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const winner = Array.from(output_activity).indexOf(Math.max(...output_activity));
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const total = output_activity.reduce((a, b) => a + b, 0);
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const confidence = total > 0 ? (output_activity[winner] / total * 100) : 0;
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results.push({ pattern: pattern_names[p], winner, confidence });
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console.log(`${pattern_names[p].padEnd(10)} -> Neuron ${winner} (${confidence.toFixed(1)}% confidence)`);
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}
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// Noise test
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console.log('\n--- ROBUSTNESS (20% noise) ---\n');
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function addNoise(pattern, noise_level = 0.2) {
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return pattern.map(v => Math.random() < noise_level ? 1 - v : v);
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}
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for (let p = 0; p < pattern_names.length; p++) {
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const noisy_pattern = addNoise(pattern_arrays[p], 0.2);
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snn.reset();
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const output_activity = new Float32Array(n_output);
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for (let t = 0; t < presentation_time; t++) {
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const input_spikes = rateEncoding(noisy_pattern, snn.dt, 100);
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snn.step(input_spikes);
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const output = snn.getOutput();
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for (let i = 0; i < n_output; i++) {
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output_activity[i] += output[i];
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}
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}
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const winner = Array.from(output_activity).indexOf(Math.max(...output_activity));
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const correct = winner === results[p].winner;
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console.log(`${pattern_names[p].padEnd(10)} -> Neuron ${winner} ${correct ? '✓' : '✗'}`);
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}
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console.log('\nDone!\n');
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