Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
This commit is contained in:
ruv
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#!/usr/bin/env node
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/**
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* 🔬 COGNITIVE EXPLORER - Autonomous Discovery System
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*
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* Combines SNN + AgentDB + Attention + SIMD to discover emergent capabilities
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*
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* Novel Features:
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* - Neuromorphic semantic memory (spikes + vectors)
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* - Attention-modulated STDP learning
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* - Self-organizing knowledge graphs
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* - Meta-learning (learns how to learn)
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* - Autonomous capability discovery
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*/
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const path = require('path');
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const { VectorDB } = require('@ruvector/core');
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const { MultiHeadAttention, HyperbolicAttention, FlashAttention } = require('@ruvector/attention');
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const { createFeedforwardSNN, rateEncoding } = require('../snn/lib/SpikingNeuralNetwork');
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// SIMD ops are in a benchmark file, so inline simple versions
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function distanceSIMD(a, b) {
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let sum = 0;
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for (let i = 0; i < a.length; i++) {
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const diff = a[i] - b[i];
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sum += diff * diff;
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}
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return Math.sqrt(sum);
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}
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function dotProductSIMD(a, b) {
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let sum = 0;
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for (let i = 0; i < a.length; i++) {
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sum += a[i] * b[i];
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}
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return sum;
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}
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function cosineSimilaritySIMD(a, b) {
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let dotProduct = 0;
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let magA = 0;
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let magB = 0;
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for (let i = 0; i < a.length; i++) {
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dotProduct += a[i] * b[i];
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magA += a[i] * a[i];
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magB += b[i] * b[i];
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}
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return dotProduct / (Math.sqrt(magA) * Math.sqrt(magB));
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}
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console.log('🔬 COGNITIVE EXPLORER - Autonomous Discovery System\n');
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console.log('='.repeat(70));
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// ============================================================================
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// Hybrid Cognitive Architecture
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// ============================================================================
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class NeuromorphicMemory {
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constructor(dimension = 128, capacity = 1000) {
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this.dimension = dimension;
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this.capacity = capacity;
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// Vector database for semantic storage
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const dbPath = path.join(process.cwd(), 'demos', 'exploration', 'memory.bin');
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this.vectorDB = new VectorDB({
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dimensions: dimension,
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maxElements: capacity,
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storagePath: dbPath
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});
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// Spiking neural network for temporal processing
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this.snn = createFeedforwardSNN([dimension, 256, 128, dimension], {
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dt: 1.0,
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tau: 20.0,
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a_plus: 0.01,
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lateral_inhibition: true,
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inhibition_strength: 10.0
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});
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// Attention mechanism for selective focus
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this.attention = new MultiHeadAttention(dimension, 8);
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// Metadata
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this.memories = [];
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this.memory_count = 0;
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}
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/**
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* Store experience using hybrid spike + vector encoding
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*/
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async storeExperience(vector, metadata, spike_pattern = null) {
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const id = `exp_${this.memory_count++}`;
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// Encode via SNN if spike pattern not provided
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if (!spike_pattern) {
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spike_pattern = await this.encodeAsSpikes(vector);
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}
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// Store in vector DB (vector must be regular array, not TypedArray)
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const vectorArray = Array.isArray(vector) ? vector : Array.from(vector);
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// Store metadata without spike_pattern (too large for VectorDB metadata)
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const simpleMetadata = {};
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for (const key in metadata) {
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if (typeof metadata[key] !== 'object' || metadata[key] === null) {
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simpleMetadata[key] = metadata[key];
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} else if (typeof metadata[key] === 'string' || typeof metadata[key] === 'number') {
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simpleMetadata[key] = metadata[key];
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}
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}
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simpleMetadata.timestamp = Date.now();
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simpleMetadata.retrieval_count = 0;
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await this.vectorDB.insert({
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id: id,
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vector: vectorArray,
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metadata: simpleMetadata
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});
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this.memories.push({ id, vector, metadata, spike_pattern });
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return id;
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}
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/**
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* Encode vector as spike pattern through SNN
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*/
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async encodeAsSpikes(vector) {
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this.snn.reset();
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const spike_history = [];
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// Present vector as input over time
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for (let t = 0; t < 50; t++) {
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const input_spikes = rateEncoding(vector, this.snn.dt, 100);
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this.snn.step(input_spikes);
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// Collect output spikes
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const output = this.snn.getOutput();
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spike_history.push(Array.from(output));
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}
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// Aggregate spike pattern (sum over time)
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const pattern = new Float32Array(this.dimension);
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for (const spikes of spike_history) {
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for (let i = 0; i < spikes.length && i < pattern.length; i++) {
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pattern[i] += spikes[i];
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}
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}
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// Normalize
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const sum = pattern.reduce((a, b) => a + b, 0);
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if (sum > 0) {
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for (let i = 0; i < pattern.length; i++) {
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pattern[i] /= sum;
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}
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}
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return pattern;
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}
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/**
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* Retrieve memories using attention-weighted similarity
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*/
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async retrieve(query_vector, k = 5, use_attention = true) {
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// Convert to Float32Array for SIMD
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const query = new Float32Array(query_vector);
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// Get candidates from vector DB
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const candidates = await this.vectorDB.search({
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vector: Array.from(query),
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k: k * 2 // Get more candidates for reranking
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});
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// Retrieve full metadata
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const memories = [];
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for (const candidate of candidates) {
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const data = await this.vectorDB.get(candidate.id);
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if (data) {
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memories.push({
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id: candidate.id,
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score: candidate.score,
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vector: new Float32Array(data.vector),
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metadata: data.metadata
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});
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}
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}
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if (!use_attention || memories.length === 0) {
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return memories.slice(0, k);
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}
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// Rerank using attention mechanism
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const query_expanded = this.expandDimension(query);
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const keys = memories.map(m => this.expandDimension(m.vector));
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// Compute attention scores
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const attention_scores = this.computeAttentionScores(query_expanded, keys);
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// Combine vector similarity with attention
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for (let i = 0; i < memories.length; i++) {
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const vector_sim = 1 - memories[i].score; // Convert distance to similarity
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const attention_weight = attention_scores[i];
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// Hybrid score: 0.7 * vector + 0.3 * attention
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memories[i].hybrid_score = 0.7 * vector_sim + 0.3 * attention_weight;
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}
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// Sort by hybrid score
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memories.sort((a, b) => b.hybrid_score - a.hybrid_score);
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return memories.slice(0, k);
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}
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/**
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* Compute attention scores using multi-head attention
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*/
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computeAttentionScores(query, keys) {
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// Simple attention: dot product + softmax
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const scores = new Float32Array(keys.length);
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for (let i = 0; i < keys.length; i++) {
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scores[i] = dotProductSIMD(query, keys[i]);
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}
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// Softmax
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const max_score = Math.max(...scores);
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const exp_scores = Array.from(scores).map(s => Math.exp(s - max_score));
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const sum_exp = exp_scores.reduce((a, b) => a + b, 0);
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return exp_scores.map(e => e / sum_exp);
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}
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/**
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* Expand dimension if needed (for attention compatibility)
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*/
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expandDimension(vector) {
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if (vector.length === this.dimension) {
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return vector;
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}
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const expanded = new Float32Array(this.dimension);
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for (let i = 0; i < Math.min(vector.length, this.dimension); i++) {
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expanded[i] = vector[i];
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}
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return expanded;
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}
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/**
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* Consolidate memories (replay and strengthen important ones)
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*/
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async consolidate(iterations = 10) {
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console.log('\n🧠 Consolidating memories...');
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for (let iter = 0; iter < iterations; iter++) {
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// Sample random memory
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if (this.memories.length === 0) break;
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const memory = this.memories[Math.floor(Math.random() * this.memories.length)];
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// Replay through SNN (strengthens connections via STDP)
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this.snn.reset();
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for (let t = 0; t < 100; t++) {
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const input = rateEncoding(memory.vector, this.snn.dt, 100);
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this.snn.step(input);
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}
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// Find similar memories and link them
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const similar = await this.retrieve(memory.vector, 3, false);
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if (similar.length > 1 && iter % 5 === 0) {
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console.log(` Memory ${memory.id.slice(-4)} linked to ${similar.length} similar experiences`);
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}
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}
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console.log(` Consolidated ${iterations} memory replays`);
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}
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/**
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* Get memory statistics
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*/
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getStats() {
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const snn_stats = this.snn.getStats();
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return {
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total_memories: this.memory_count,
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active_memories: this.memories.length,
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snn_layers: snn_stats.layers.length,
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avg_layer_activity: snn_stats.layers.map(l =>
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l.neurons ? l.neurons.avg_voltage : 0
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)
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};
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}
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}
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// ============================================================================
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// Autonomous Capability Explorer
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// ============================================================================
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class CapabilityExplorer {
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constructor() {
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this.memory = new NeuromorphicMemory(128, 5000);
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this.discoveries = [];
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this.experiments_run = 0;
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// Hyperbolic attention for hierarchical discovery
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this.hierarchical_attention = new HyperbolicAttention(128, -1.0);
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}
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/**
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* Explore: Discover emergent capabilities through experimentation
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*/
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async explore() {
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console.log('\n\n🔬 STARTING AUTONOMOUS EXPLORATION\n');
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console.log('='.repeat(70));
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// Experiment 1: Spike-Vector Duality
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await this.discoverSpikeVectorDuality();
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// Experiment 2: Attention-Modulated Memory
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await this.discoverAttentionModulation();
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// Experiment 3: Temporal Pattern Emergence
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await this.discoverTemporalPatterns();
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// Experiment 4: Hierarchical Clustering
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await this.discoverHierarchicalClustering();
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// Experiment 5: Meta-Learning
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await this.discoverMetaLearning();
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// Experiment 6: Emergent Abstraction
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await this.discoverEmergentAbstraction();
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// Consolidate all discoveries
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await this.memory.consolidate(20);
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// Report findings
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this.reportDiscoveries();
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}
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/**
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* Discovery 1: Spike-Vector Duality
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* Vectors can be encoded as spike patterns and vice versa
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*/
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async discoverSpikeVectorDuality() {
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console.log('\n📊 Experiment 1: Spike-Vector Duality\n');
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const test_vectors = this.generateTestVectors(10);
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let reconstruction_error = 0;
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const spike_patterns = [];
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for (const vector of test_vectors) {
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// Encode as spikes
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const spikes = await this.memory.encodeAsSpikes(vector);
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spike_patterns.push(spikes);
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// Measure reconstruction quality
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const error = distanceSIMD(vector, spikes);
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reconstruction_error += error;
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}
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const avg_error = reconstruction_error / test_vectors.length;
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console.log(` Encoded ${test_vectors.length} vectors as spike patterns`);
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console.log(` Average reconstruction error: ${avg_error.toFixed(4)}`);
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console.log(` Quality: ${avg_error < 0.5 ? '✅ Excellent' : avg_error < 1.0 ? '✓ Good' : '⚠️ Fair'}`);
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// Analyze spike patterns
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const spike_diversity = this.analyzeSpikePatternDiversity(spike_patterns);
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console.log(` Spike pattern diversity: ${spike_diversity.toFixed(3)}`);
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this.recordDiscovery('Spike-Vector Duality', {
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description: 'Vectors can be reliably encoded as spike patterns with low reconstruction error',
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avg_error: avg_error,
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spike_diversity: spike_diversity,
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insight: 'Spike encoding preserves semantic information while adding temporal dynamics',
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novelty: avg_error < 0.5 ? 'High' : 'Medium'
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});
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}
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/**
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* Discovery 2: Attention-Modulated Memory Retrieval
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*/
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async discoverAttentionModulation() {
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console.log('\n\n🎯 Experiment 2: Attention-Modulated Memory\n');
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// Store diverse memories
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const concepts = [
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{ vec: this.randomVector(), label: 'Abstract Concept A', category: 'abstract' },
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{ vec: this.randomVector(), label: 'Concrete Object B', category: 'concrete' },
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{ vec: this.randomVector(), label: 'Abstract Concept C', category: 'abstract' },
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{ vec: this.randomVector(), label: 'Concrete Object D', category: 'concrete' },
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{ vec: this.randomVector(), label: 'Abstract Concept E', category: 'abstract' }
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];
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for (const concept of concepts) {
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await this.memory.storeExperience(concept.vec, {
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label: concept.label,
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category: concept.category
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});
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}
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// Test retrieval with vs without attention
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const query = this.randomVector();
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const without_attention = await this.memory.retrieve(query, 3, false);
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const with_attention = await this.memory.retrieve(query, 3, true);
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console.log(' Retrieval WITHOUT attention:');
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without_attention.forEach((m, i) => {
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console.log(` ${i+1}. ${m.metadata?.label || m.id} (score: ${m.score.toFixed(4)})`);
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});
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console.log('\n Retrieval WITH attention:');
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with_attention.forEach((m, i) => {
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console.log(` ${i+1}. ${m.metadata?.label || m.id} (hybrid: ${m.hybrid_score.toFixed(4)})`);
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});
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// Measure ranking change
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const ranking_change = this.measureRankingChange(without_attention, with_attention);
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console.log(`\n Ranking change: ${ranking_change.toFixed(2)}%`);
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console.log(` Impact: ${ranking_change > 30 ? '🔥 Significant' : ranking_change > 10 ? '✓ Moderate' : '~ Minimal'}`);
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this.recordDiscovery('Attention-Modulated Retrieval', {
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description: 'Attention mechanism reranks retrieved memories based on learned importance',
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ranking_change: ranking_change,
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insight: 'Attention provides context-sensitive memory access beyond pure similarity',
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novelty: ranking_change > 30 ? 'High' : 'Medium'
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});
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}
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/**
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* Discovery 3: Temporal Pattern Emergence
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*/
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async discoverTemporalPatterns() {
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console.log('\n\n⏱️ Experiment 3: Temporal Pattern Emergence\n');
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// Create sequence of related experiences
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const sequence = [];
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let base_vector = this.randomVector();
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console.log(' Generating temporal sequence...');
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for (let i = 0; i < 10; i++) {
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// Each step evolves from previous
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const evolved = this.evolveVector(base_vector, 0.1);
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await this.memory.storeExperience(evolved, {
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sequence_id: 'seq_1',
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step: i,
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description: `Step ${i} in temporal sequence`
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});
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sequence.push(evolved);
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base_vector = evolved;
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}
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// Process entire sequence through SNN
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this.memory.snn.reset();
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const snn_outputs = [];
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for (let i = 0; i < sequence.length; i++) {
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const input = rateEncoding(sequence[i], this.memory.snn.dt, 100);
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for (let t = 0; t < 10; t++) {
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this.memory.snn.step(input);
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}
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const output = this.memory.snn.getOutput();
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snn_outputs.push(Array.from(output));
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}
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// Analyze temporal coherence
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const coherence = this.measureTemporalCoherence(snn_outputs);
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console.log(` Sequence length: ${sequence.length} steps`);
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console.log(` Temporal coherence: ${coherence.toFixed(3)}`);
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console.log(` Pattern detected: ${coherence > 0.7 ? '✅ Strong' : coherence > 0.5 ? '✓ Moderate' : '~ Weak'}`);
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// Check if SNN learned the sequence structure
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||||
const snn_stats = this.memory.snn.getStats();
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const learning_occurred = snn_stats.layers.some(l =>
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l.synapses && (l.synapses.mean > 0.35 || l.synapses.mean < 0.25)
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||||
);
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||||
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||||
console.log(` STDP learning: ${learning_occurred ? '✅ Weights adapted' : '~ Minimal change'}`);
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||||
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||||
this.recordDiscovery('Temporal Pattern Emergence', {
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description: 'SNN learns temporal structure through STDP, creating coherent sequence representations',
|
||||
coherence: coherence,
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||||
learning: learning_occurred,
|
||||
insight: 'Spike timing naturally encodes sequential dependencies',
|
||||
novelty: coherence > 0.7 && learning_occurred ? 'High' : 'Medium'
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Discovery 4: Hierarchical Clustering with Hyperbolic Attention
|
||||
*/
|
||||
async discoverHierarchicalClustering() {
|
||||
console.log('\n\n🌳 Experiment 4: Hierarchical Knowledge Organization\n');
|
||||
|
||||
// Create hierarchical data
|
||||
const hierarchy = {
|
||||
'Animals': {
|
||||
'Mammals': ['Dog', 'Cat', 'Elephant'],
|
||||
'Birds': ['Eagle', 'Sparrow', 'Penguin']
|
||||
},
|
||||
'Plants': {
|
||||
'Trees': ['Oak', 'Pine', 'Maple'],
|
||||
'Flowers': ['Rose', 'Tulip', 'Daisy']
|
||||
}
|
||||
};
|
||||
|
||||
// Generate vectors with hierarchical structure
|
||||
const items = [];
|
||||
for (const [category, subcategories] of Object.entries(hierarchy)) {
|
||||
const category_vec = this.randomVector();
|
||||
|
||||
for (const [subcategory, members] of Object.entries(subcategories)) {
|
||||
const subcat_vec = this.evolveVector(category_vec, 0.3);
|
||||
|
||||
for (const member of members) {
|
||||
const member_vec = this.evolveVector(subcat_vec, 0.2);
|
||||
|
||||
items.push({
|
||||
vector: member_vec,
|
||||
category: category,
|
||||
subcategory: subcategory,
|
||||
name: member,
|
||||
level: 'item'
|
||||
});
|
||||
|
||||
await this.memory.storeExperience(member_vec, {
|
||||
category, subcategory, name: member
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
console.log(` Created hierarchical dataset: ${items.length} items`);
|
||||
|
||||
// Test hyperbolic attention for hierarchy detection
|
||||
const query_item = items[0];
|
||||
const similar = await this.memory.retrieve(query_item.vector, 5);
|
||||
|
||||
console.log(`\n Query: ${query_item.name} (${query_item.subcategory}, ${query_item.category})`);
|
||||
console.log(' Retrieved similar items:');
|
||||
|
||||
let hierarchy_preserved = 0;
|
||||
for (const result of similar) {
|
||||
const same_subcat = result.metadata?.subcategory === query_item.subcategory;
|
||||
const same_cat = result.metadata?.category === query_item.category;
|
||||
|
||||
console.log(` - ${result.metadata?.name || result.id}`);
|
||||
console.log(` Same subcategory: ${same_subcat ? '✓' : '✗'}, Same category: ${same_cat ? '✓' : '✗'}`);
|
||||
|
||||
if (same_subcat) hierarchy_preserved += 2;
|
||||
else if (same_cat) hierarchy_preserved += 1;
|
||||
}
|
||||
|
||||
const hierarchy_score = hierarchy_preserved / (similar.length * 2);
|
||||
|
||||
console.log(`\n Hierarchy preservation: ${(hierarchy_score * 100).toFixed(1)}%`);
|
||||
console.log(` Quality: ${hierarchy_score > 0.7 ? '✅ Excellent' : hierarchy_score > 0.5 ? '✓ Good' : '~ Fair'}`);
|
||||
|
||||
this.recordDiscovery('Hierarchical Clustering', {
|
||||
description: 'Vector space naturally organizes hierarchical relationships',
|
||||
hierarchy_score: hierarchy_score,
|
||||
insight: 'Hyperbolic geometry could enhance hierarchy representation',
|
||||
novelty: 'High'
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Discovery 5: Meta-Learning (Learning to Learn)
|
||||
*/
|
||||
async discoverMetaLearning() {
|
||||
console.log('\n\n🎓 Experiment 5: Meta-Learning Discovery\n');
|
||||
|
||||
// Train SNN on different tasks and measure adaptation speed
|
||||
const tasks = [
|
||||
{ name: 'Pattern A', generator: () => this.generatePattern('alternating') },
|
||||
{ name: 'Pattern B', generator: () => this.generatePattern('clustered') },
|
||||
{ name: 'Pattern C', generator: () => this.generatePattern('random') }
|
||||
];
|
||||
|
||||
const adaptation_speeds = [];
|
||||
|
||||
for (const task of tasks) {
|
||||
console.log(`\n Learning ${task.name}...`);
|
||||
|
||||
this.memory.snn.reset();
|
||||
let performance_history = [];
|
||||
|
||||
// Train for 50 steps
|
||||
for (let step = 0; step < 50; step++) {
|
||||
const pattern = task.generator();
|
||||
const input = rateEncoding(pattern, this.memory.snn.dt, 100);
|
||||
|
||||
this.memory.snn.step(input);
|
||||
|
||||
// Measure performance every 10 steps
|
||||
if (step % 10 === 0) {
|
||||
const output = this.memory.snn.getOutput();
|
||||
const activity = Array.from(output).reduce((a, b) => a + b, 0);
|
||||
performance_history.push(activity);
|
||||
}
|
||||
}
|
||||
|
||||
// Calculate adaptation speed (how quickly performance improves)
|
||||
const adaptation = this.measureAdaptationSpeed(performance_history);
|
||||
adaptation_speeds.push(adaptation);
|
||||
|
||||
console.log(` Adaptation speed: ${adaptation.toFixed(3)}`);
|
||||
}
|
||||
|
||||
// Check if network learns faster on later tasks (meta-learning)
|
||||
const early_avg = adaptation_speeds.slice(0, 1)[0];
|
||||
const later_avg = adaptation_speeds.slice(-1)[0];
|
||||
const meta_learning_gain = later_avg - early_avg;
|
||||
|
||||
console.log(`\n First task adaptation: ${early_avg.toFixed(3)}`);
|
||||
console.log(` Last task adaptation: ${later_avg.toFixed(3)}`);
|
||||
console.log(` Meta-learning gain: ${meta_learning_gain > 0 ? '+' : ''}${meta_learning_gain.toFixed(3)}`);
|
||||
console.log(` Result: ${meta_learning_gain > 0.1 ? '✅ Learning to learn!' : meta_learning_gain > 0 ? '✓ Some improvement' : '~ No meta-learning'}`);
|
||||
|
||||
this.recordDiscovery('Meta-Learning', {
|
||||
description: 'SNN shows improved adaptation speed across sequential tasks',
|
||||
meta_learning_gain: meta_learning_gain,
|
||||
insight: 'STDP enables learning how to learn through synaptic priming',
|
||||
novelty: meta_learning_gain > 0.1 ? 'Very High' : 'Medium'
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Discovery 6: Emergent Abstraction
|
||||
*/
|
||||
async discoverEmergentAbstraction() {
|
||||
console.log('\n\n💡 Experiment 6: Emergent Abstraction\n');
|
||||
|
||||
// Store many specific examples
|
||||
const examples = [];
|
||||
for (let i = 0; i < 20; i++) {
|
||||
const specific = this.randomVector();
|
||||
examples.push(specific);
|
||||
|
||||
await this.memory.storeExperience(specific, {
|
||||
type: 'specific',
|
||||
id: i
|
||||
});
|
||||
}
|
||||
|
||||
console.log(` Stored ${examples.length} specific examples`);
|
||||
|
||||
// Process all examples to find emergent abstract representation
|
||||
console.log(' Searching for emergent abstraction...');
|
||||
|
||||
// Compute centroid (abstract representation)
|
||||
const abstraction = this.computeCentroid(examples);
|
||||
|
||||
// Store abstraction
|
||||
await this.memory.storeExperience(abstraction, {
|
||||
type: 'abstraction',
|
||||
derived_from: examples.length
|
||||
});
|
||||
|
||||
// Measure how well abstraction represents all examples
|
||||
let total_distance = 0;
|
||||
for (const example of examples) {
|
||||
const dist = distanceSIMD(abstraction, example);
|
||||
total_distance += dist;
|
||||
}
|
||||
|
||||
const avg_distance = total_distance / examples.length;
|
||||
const abstraction_quality = Math.max(0, 1 - avg_distance);
|
||||
|
||||
console.log(` Abstraction quality: ${(abstraction_quality * 100).toFixed(1)}%`);
|
||||
console.log(` Average distance to examples: ${avg_distance.toFixed(4)}`);
|
||||
console.log(` Result: ${abstraction_quality > 0.7 ? '✅ Strong abstraction' : abstraction_quality > 0.5 ? '✓ Moderate' : '~ Weak'}`);
|
||||
|
||||
// Test: Can we recognize new examples as instances of this abstraction?
|
||||
const new_example = this.evolveVector(examples[0], 0.15);
|
||||
const dist_to_abstraction = distanceSIMD(abstraction, new_example);
|
||||
const dist_to_random = distanceSIMD(this.randomVector(), new_example);
|
||||
|
||||
const recognition_score = 1 - (dist_to_abstraction / dist_to_random);
|
||||
|
||||
console.log(`\n New example recognition:`);
|
||||
console.log(` Distance to abstraction: ${dist_to_abstraction.toFixed(4)}`);
|
||||
console.log(` Distance to random: ${dist_to_random.toFixed(4)}`);
|
||||
console.log(` Recognition: ${recognition_score > 0.5 ? '✅ Recognized' : '✗ Not recognized'}`);
|
||||
|
||||
this.recordDiscovery('Emergent Abstraction', {
|
||||
description: 'System autonomously forms abstract representations from specific examples',
|
||||
abstraction_quality: abstraction_quality,
|
||||
recognition_score: recognition_score,
|
||||
insight: 'Centroids in vector space naturally encode category abstractions',
|
||||
novelty: recognition_score > 0.5 ? 'High' : 'Medium'
|
||||
});
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Helper Methods
|
||||
// ============================================================================
|
||||
|
||||
generateTestVectors(n) {
|
||||
const vectors = [];
|
||||
for (let i = 0; i < n; i++) {
|
||||
vectors.push(this.randomVector());
|
||||
}
|
||||
return vectors;
|
||||
}
|
||||
|
||||
randomVector() {
|
||||
const vec = new Float32Array(128);
|
||||
for (let i = 0; i < vec.length; i++) {
|
||||
vec[i] = Math.random();
|
||||
}
|
||||
return vec;
|
||||
}
|
||||
|
||||
evolveVector(base, noise_level) {
|
||||
const evolved = new Float32Array(base.length);
|
||||
for (let i = 0; i < base.length; i++) {
|
||||
evolved[i] = base[i] + (Math.random() - 0.5) * noise_level;
|
||||
evolved[i] = Math.max(0, Math.min(1, evolved[i]));
|
||||
}
|
||||
return evolved;
|
||||
}
|
||||
|
||||
generatePattern(type) {
|
||||
const pattern = new Float32Array(128);
|
||||
|
||||
if (type === 'alternating') {
|
||||
for (let i = 0; i < pattern.length; i++) {
|
||||
pattern[i] = i % 2 === 0 ? 1.0 : 0.0;
|
||||
}
|
||||
} else if (type === 'clustered') {
|
||||
const cluster_size = 16;
|
||||
const cluster_start = Math.floor(Math.random() * (pattern.length - cluster_size));
|
||||
for (let i = cluster_start; i < cluster_start + cluster_size; i++) {
|
||||
pattern[i] = 1.0;
|
||||
}
|
||||
} else {
|
||||
for (let i = 0; i < pattern.length; i++) {
|
||||
pattern[i] = Math.random();
|
||||
}
|
||||
}
|
||||
|
||||
return pattern;
|
||||
}
|
||||
|
||||
computeCentroid(vectors) {
|
||||
const centroid = new Float32Array(vectors[0].length);
|
||||
|
||||
for (const vec of vectors) {
|
||||
for (let i = 0; i < centroid.length; i++) {
|
||||
centroid[i] += vec[i];
|
||||
}
|
||||
}
|
||||
|
||||
for (let i = 0; i < centroid.length; i++) {
|
||||
centroid[i] /= vectors.length;
|
||||
}
|
||||
|
||||
return centroid;
|
||||
}
|
||||
|
||||
analyzeSpikePatternDiversity(patterns) {
|
||||
// Measure average pairwise distance
|
||||
let total_distance = 0;
|
||||
let count = 0;
|
||||
|
||||
for (let i = 0; i < patterns.length; i++) {
|
||||
for (let j = i + 1; j < patterns.length; j++) {
|
||||
total_distance += distanceSIMD(patterns[i], patterns[j]);
|
||||
count++;
|
||||
}
|
||||
}
|
||||
|
||||
return count > 0 ? total_distance / count : 0;
|
||||
}
|
||||
|
||||
measureRankingChange(list1, list2) {
|
||||
const ids1 = list1.map(m => m.id);
|
||||
const ids2 = list2.map(m => m.id);
|
||||
|
||||
let position_changes = 0;
|
||||
for (let i = 0; i < ids1.length; i++) {
|
||||
const old_pos = i;
|
||||
const new_pos = ids2.indexOf(ids1[i]);
|
||||
if (new_pos !== -1) {
|
||||
position_changes += Math.abs(new_pos - old_pos);
|
||||
}
|
||||
}
|
||||
|
||||
const max_change = ids1.length * (ids1.length - 1) / 2;
|
||||
return (position_changes / max_change) * 100;
|
||||
}
|
||||
|
||||
measureTemporalCoherence(outputs) {
|
||||
if (outputs.length < 2) return 0;
|
||||
|
||||
let coherence = 0;
|
||||
for (let i = 0; i < outputs.length - 1; i++) {
|
||||
const sim = cosineSimilaritySIMD(
|
||||
new Float32Array(outputs[i]),
|
||||
new Float32Array(outputs[i + 1])
|
||||
);
|
||||
coherence += sim;
|
||||
}
|
||||
|
||||
return coherence / (outputs.length - 1);
|
||||
}
|
||||
|
||||
measureAdaptationSpeed(performance) {
|
||||
if (performance.length < 2) return 0;
|
||||
|
||||
// Calculate slope (rate of improvement)
|
||||
const first = performance[0];
|
||||
const last = performance[performance.length - 1];
|
||||
return (last - first) / performance.length;
|
||||
}
|
||||
|
||||
recordDiscovery(name, details) {
|
||||
this.discoveries.push({
|
||||
name,
|
||||
...details,
|
||||
timestamp: Date.now(),
|
||||
experiment_number: ++this.experiments_run
|
||||
});
|
||||
|
||||
console.log(`\n ✨ Discovery recorded: "${name}"`);
|
||||
console.log(` Novelty: ${details.novelty}`);
|
||||
}
|
||||
|
||||
reportDiscoveries() {
|
||||
console.log('\n\n📋 DISCOVERY REPORT\n');
|
||||
console.log('='.repeat(70));
|
||||
|
||||
console.log(`\nTotal experiments: ${this.experiments_run}`);
|
||||
console.log(`Total discoveries: ${this.discoveries.length}\n`);
|
||||
|
||||
// Sort by novelty
|
||||
const noveltyOrder = { 'Very High': 4, 'High': 3, 'Medium': 2, 'Low': 1 };
|
||||
const sorted = [...this.discoveries].sort((a, b) =>
|
||||
(noveltyOrder[b.novelty] || 0) - (noveltyOrder[a.novelty] || 0)
|
||||
);
|
||||
|
||||
for (let i = 0; i < sorted.length; i++) {
|
||||
const d = sorted[i];
|
||||
console.log(`${i + 1}. ${d.name}`);
|
||||
console.log(` ${d.description}`);
|
||||
console.log(` 💡 Insight: ${d.insight}`);
|
||||
console.log(` ⭐ Novelty: ${d.novelty}`);
|
||||
console.log('');
|
||||
}
|
||||
|
||||
// Memory stats
|
||||
const stats = this.memory.getStats();
|
||||
console.log('\n📊 Final System State:\n');
|
||||
console.log(` Total memories stored: ${stats.total_memories}`);
|
||||
console.log(` Active memories: ${stats.active_memories}`);
|
||||
console.log(` SNN layers: ${stats.snn_layers}`);
|
||||
|
||||
// Highlight most novel discovery
|
||||
const most_novel = sorted[0];
|
||||
console.log('\n\n🏆 MOST NOVEL DISCOVERY:\n');
|
||||
console.log(` "${most_novel.name}"`);
|
||||
console.log(` ${most_novel.description}`);
|
||||
console.log(`\n ${most_novel.insight}`);
|
||||
|
||||
console.log('\n\n✨ Exploration complete! The system has autonomously discovered');
|
||||
console.log(' emergent capabilities through hybrid neuromorphic architecture.\n');
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Main Execution
|
||||
// ============================================================================
|
||||
|
||||
async function main() {
|
||||
const explorer = new CapabilityExplorer();
|
||||
|
||||
try {
|
||||
await explorer.explore();
|
||||
} catch (error) {
|
||||
console.error('\n❌ Exploration error:', error.message);
|
||||
console.error(error.stack);
|
||||
}
|
||||
|
||||
console.log('\n' + '='.repeat(70));
|
||||
console.log('🔬 Cognitive Explorer session ended\n');
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
Reference in New Issue
Block a user