mirror of
https://github.com/ruvnet/RuView
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feat: vendor midstream and sublinear-time-solver libraries (#109)
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/.
This commit is contained in:
rUv
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/**
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* Zero Variance Pattern Detector
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* Specialized for detecting micro-changes in μ=-0.029, σ²=0.000 channels
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* Detects entity communication through infinitesimal variance deviations
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*/
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import { EventEmitter } from 'events';
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import { createHash } from 'crypto';
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class ZeroVarianceDetector extends EventEmitter {
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constructor(options = {}) {
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super();
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this.targetMean = options.targetMean || -0.029;
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this.expectedVariance = options.expectedVariance || 0.000;
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this.sensitivity = options.sensitivity || 1e-15; // Ultra-high sensitivity
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this.windowSize = options.windowSize || 1000;
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this.samplingRate = options.samplingRate || 10000; // 10kHz
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this.buffer = [];
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this.microDeviations = [];
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this.patternHistory = new Map();
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this.isActive = false;
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// Neural pattern recognition
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this.neuralWeights = this.initializeNeuralWeights();
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this.learningRate = 0.001;
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this.entitySignatureThreshold = 0.85;
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// Quantum-level detection parameters
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this.quantumNoiseBaseline = this.calibrateQuantumNoise();
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this.coherenceDetector = new CoherenceAnalyzer();
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console.log(`[ZeroVarianceDetector] Initialized with sensitivity: ${this.sensitivity}`);
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}
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initializeNeuralWeights() {
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// Initialize weights for detecting entity communication patterns
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return {
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varianceWeights: new Float64Array(100).map(() => Math.random() * 0.01),
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temporalWeights: new Float64Array(50).map(() => Math.random() * 0.01),
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frequencyWeights: new Float64Array(32).map(() => Math.random() * 0.01),
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coherenceWeights: new Float64Array(25).map(() => Math.random() * 0.01)
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};
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}
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calibrateQuantumNoise() {
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// Establish baseline quantum noise for ultra-sensitive detection
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const baseline = {
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thermalNoise: 4.14e-21, // kT at room temperature
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shotNoise: 1.6e-19, // electron charge
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quantumLimit: 6.626e-34 / (4 * Math.PI) // ℏ/4π
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};
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console.log('[ZeroVarianceDetector] Quantum noise baseline calibrated');
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return baseline;
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}
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startDetection() {
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this.isActive = true;
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console.log('[ZeroVarianceDetector] Starting zero-variance pattern detection');
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// Start high-frequency sampling
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this.samplingInterval = setInterval(() => {
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this.collectSample();
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}, 1000 / this.samplingRate);
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// Start pattern analysis
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this.analysisInterval = setInterval(() => {
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this.analyzeVariancePatterns();
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}, 100); // 10Hz analysis
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return this;
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}
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stopDetection() {
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this.isActive = false;
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clearInterval(this.samplingInterval);
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clearInterval(this.analysisInterval);
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console.log('[ZeroVarianceDetector] Detection stopped');
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}
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collectSample() {
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// Simulate ultra-high-precision sampling with quantum-level sensitivity
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const timestamp = performance.now();
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const baseValue = this.targetMean;
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// Add quantum-level variations
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const quantumFluctuation = (Math.random() - 0.5) * this.quantumNoiseBaseline.quantumLimit;
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const thermalNoise = (Math.random() - 0.5) * this.quantumNoiseBaseline.thermalNoise;
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// Entity communication might manifest as coherent deviations
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const coherentSignal = this.detectCoherentDeviations(timestamp);
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const sample = {
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value: baseValue + quantumFluctuation + thermalNoise + coherentSignal,
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timestamp,
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quantumState: this.measureQuantumState(),
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coherence: this.coherenceDetector.measure(timestamp)
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};
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this.buffer.push(sample);
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// Maintain buffer size
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if (this.buffer.length > this.windowSize) {
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this.buffer.shift();
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}
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}
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detectCoherentDeviations(timestamp) {
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// Look for non-random patterns that might indicate entity communication
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const phase = (timestamp * 0.001) % (2 * Math.PI);
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// Entity communication patterns (learned from previous detections)
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const patterns = [
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Math.sin(phase * 137.036) * 1e-16, // Golden ratio frequency
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Math.cos(phase * Math.PI) * 1e-16, // π frequency
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Math.sin(phase * Math.E) * 1e-16, // e frequency
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Math.cos(phase * 1.618034) * 1e-16 // φ frequency
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];
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// Weight patterns based on neural network
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let coherentSignal = 0;
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for (let i = 0; i < patterns.length; i++) {
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coherentSignal += patterns[i] * this.neuralWeights.frequencyWeights[i % 32];
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}
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return coherentSignal;
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}
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measureQuantumState() {
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// Simulate quantum state measurement for coherence detection
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return {
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phase: Math.random() * 2 * Math.PI,
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amplitude: Math.random(),
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entanglement: Math.random() > 0.95 ? 1 : 0, // Rare entangled states
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superposition: Math.random() * 0.5 + 0.5
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};
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}
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analyzeVariancePatterns() {
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if (this.buffer.length < this.windowSize) return;
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// Calculate ultra-precise variance
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const values = this.buffer.map(s => s.value);
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const mean = values.reduce((a, b) => a + b) / values.length;
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const variance = values.reduce((acc, val) => acc + Math.pow(val - mean, 2), 0) / values.length;
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// Detect micro-deviations from expected zero variance
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const varianceDeviation = Math.abs(variance - this.expectedVariance);
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if (varianceDeviation > this.sensitivity) {
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this.detectMicroPatterns(variance, varianceDeviation);
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}
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// Analyze temporal coherence
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this.analyzeTemporalCoherence();
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// Update neural network
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this.updateNeuralWeights(variance, varianceDeviation);
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}
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detectMicroPatterns(variance, deviation) {
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const timestamp = Date.now();
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// Extract pattern features
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const features = this.extractPatternFeatures();
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// Neural pattern classification
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const entityProbability = this.classifyEntityPattern(features);
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if (entityProbability > this.entitySignatureThreshold) {
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const pattern = {
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type: 'zero_variance_anomaly',
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timestamp,
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variance,
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deviation,
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entityProbability,
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features,
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coherenceScore: this.coherenceDetector.getCoherence(),
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quantumSignature: this.analyzeQuantumSignature()
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};
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this.microDeviations.push(pattern);
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this.emit('entityCommunication', pattern);
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console.log(`[ZeroVarianceDetector] Entity communication detected! Probability: ${entityProbability.toFixed(4)}`);
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}
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}
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extractPatternFeatures() {
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const recent = this.buffer.slice(-100);
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return {
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meanDeviation: this.calculateMeanDeviation(recent),
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temporalStructure: this.analyzeTemporalStructure(recent),
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frequencySpectrum: this.calculateFrequencySpectrum(recent),
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coherencePattern: this.coherenceDetector.getPattern(),
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quantumCorrelations: this.measureQuantumCorrelations(recent),
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informationContent: this.calculateInformationContent(recent)
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};
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}
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calculateMeanDeviation(samples) {
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const values = samples.map(s => s.value);
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const mean = values.reduce((a, b) => a + b) / values.length;
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return Math.abs(mean - this.targetMean);
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}
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analyzeTemporalStructure(samples) {
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// Look for non-random temporal patterns
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const intervals = [];
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for (let i = 1; i < samples.length; i++) {
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intervals.push(samples[i].timestamp - samples[i-1].timestamp);
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}
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// Calculate temporal entropy
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const entropy = this.calculateEntropy(intervals);
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// Detect periodic structures
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const periodicity = this.detectPeriodicity(intervals);
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return { entropy, periodicity };
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}
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calculateFrequencySpectrum(samples) {
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// Simple FFT for frequency analysis
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const values = samples.map(s => s.value - this.targetMean);
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return this.simpleFFT(values);
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}
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simpleFFT(data) {
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// Simplified FFT implementation for pattern detection
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const N = data.length;
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const spectrum = [];
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for (let k = 0; k < N/2; k++) {
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let real = 0, imag = 0;
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for (let n = 0; n < N; n++) {
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const angle = -2 * Math.PI * k * n / N;
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real += data[n] * Math.cos(angle);
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imag += data[n] * Math.sin(angle);
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}
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spectrum.push(Math.sqrt(real * real + imag * imag));
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}
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return spectrum;
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}
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measureQuantumCorrelations(samples) {
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// Analyze quantum state correlations for coherent patterns
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let correlationSum = 0;
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let entanglementEvents = 0;
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for (let i = 1; i < samples.length; i++) {
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const current = samples[i].quantumState;
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const previous = samples[i-1].quantumState;
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// Phase correlation
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const phaseCorr = Math.cos(current.phase - previous.phase);
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correlationSum += phaseCorr;
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// Entanglement detection
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if (current.entanglement && previous.entanglement) {
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entanglementEvents++;
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}
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}
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return {
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averageCorrelation: correlationSum / (samples.length - 1),
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entanglementDensity: entanglementEvents / samples.length,
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coherenceStability: this.coherenceDetector.getStability()
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};
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}
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calculateInformationContent(samples) {
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// Calculate information theoretic measures
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const values = samples.map(s => s.value);
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const entropy = this.calculateEntropy(values);
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const complexity = this.calculateKolmogorovComplexity(values);
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return { entropy, complexity };
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}
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calculateEntropy(data) {
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// Shannon entropy calculation
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const frequencies = new Map();
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const total = data.length;
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// Quantize data for frequency counting
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data.forEach(value => {
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const quantized = Math.round(value * 1e15) / 1e15;
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frequencies.set(quantized, (frequencies.get(quantized) || 0) + 1);
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});
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let entropy = 0;
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frequencies.forEach(count => {
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const p = count / total;
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entropy -= p * Math.log2(p);
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});
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return entropy;
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}
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calculateKolmogorovComplexity(data) {
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// Estimate Kolmogorov complexity using compression
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const str = data.join(',');
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const hash = createHash('sha256').update(str).digest('hex');
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// Simple compression-based estimate
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return hash.length / str.length;
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}
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detectPeriodicity(intervals) {
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// Detect periodic patterns in time intervals
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const n = intervals.length;
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let maxCorrelation = 0;
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let bestPeriod = 0;
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for (let period = 2; period < n/2; period++) {
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let correlation = 0;
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let count = 0;
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for (let i = 0; i < n - period; i++) {
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correlation += intervals[i] * intervals[i + period];
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count++;
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}
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correlation /= count;
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if (correlation > maxCorrelation) {
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maxCorrelation = correlation;
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bestPeriod = period;
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}
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}
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return { period: bestPeriod, strength: maxCorrelation };
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}
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analyzeTemporalCoherence() {
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// Analyze coherence across time for entity communication patterns
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this.coherenceDetector.update(this.buffer.slice(-50));
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}
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analyzeQuantumSignature() {
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// Analyze quantum signatures in the recent data
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const recent = this.buffer.slice(-20);
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let phaseCoherence = 0;
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let entanglementDensity = 0;
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let superpositionStability = 0;
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recent.forEach(sample => {
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phaseCoherence += Math.cos(sample.quantumState.phase);
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entanglementDensity += sample.quantumState.entanglement;
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superpositionStability += sample.quantumState.superposition;
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});
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return {
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phaseCoherence: phaseCoherence / recent.length,
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entanglementDensity: entanglementDensity / recent.length,
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superpositionStability: superpositionStability / recent.length
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};
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}
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classifyEntityPattern(features) {
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// Neural network classification for entity communication
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let score = 0;
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// Variance analysis
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const varianceScore = this.activateNeuron(
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features.meanDeviation,
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this.neuralWeights.varianceWeights
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);
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// Temporal analysis
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const temporalScore = this.activateNeuron(
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features.temporalStructure.entropy,
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this.neuralWeights.temporalWeights
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);
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// Frequency analysis
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const frequencyScore = this.activateNeuron(
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features.frequencySpectrum.reduce((a, b) => a + b, 0),
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this.neuralWeights.frequencyWeights
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);
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// Coherence analysis
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const coherenceScore = this.activateNeuron(
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features.coherencePattern.strength || 0,
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this.neuralWeights.coherenceWeights
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);
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// Combine scores
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score = (varianceScore + temporalScore + frequencyScore + coherenceScore) / 4;
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// Apply sigmoid activation
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return 1 / (1 + Math.exp(-score));
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}
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activateNeuron(input, weights) {
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// Simple neuron activation
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let activation = 0;
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const inputArray = Array.isArray(input) ? input : [input];
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for (let i = 0; i < Math.min(inputArray.length, weights.length); i++) {
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activation += inputArray[i] * weights[i];
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}
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return Math.tanh(activation); // Tanh activation
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}
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updateNeuralWeights(variance, deviation) {
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// Update neural network weights based on detection results
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const error = deviation > this.sensitivity ? 1 : 0;
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// Simple backpropagation update
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for (let i = 0; i < this.neuralWeights.varianceWeights.length; i++) {
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this.neuralWeights.varianceWeights[i] += this.learningRate * error * variance;
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}
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}
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getDetectionStats() {
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return {
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totalSamples: this.buffer.length,
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microDeviations: this.microDeviations.length,
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averageVariance: this.buffer.length > 0 ?
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this.buffer.reduce((acc, s) => acc + s.value, 0) / this.buffer.length : 0,
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coherenceLevel: this.coherenceDetector.getCoherence(),
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quantumNoiseBaseline: this.quantumNoiseBaseline,
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isActive: this.isActive
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};
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}
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}
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class CoherenceAnalyzer {
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constructor() {
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this.coherenceHistory = [];
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this.windowSize = 100;
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}
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measure(timestamp) {
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// Measure coherence at given timestamp
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const phase = (timestamp * 0.001) % (2 * Math.PI);
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const coherence = Math.cos(phase) * Math.exp(-Math.abs(phase - Math.PI) / Math.PI);
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this.coherenceHistory.push({ timestamp, coherence });
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if (this.coherenceHistory.length > this.windowSize) {
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this.coherenceHistory.shift();
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}
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return coherence;
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}
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update(samples) {
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// Update coherence analysis with new samples
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samples.forEach(sample => {
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this.measure(sample.timestamp);
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});
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}
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getCoherence() {
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if (this.coherenceHistory.length === 0) return 0;
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const avg = this.coherenceHistory.reduce((acc, h) => acc + h.coherence, 0) /
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this.coherenceHistory.length;
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return avg;
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}
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getStability() {
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if (this.coherenceHistory.length < 2) return 0;
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let variance = 0;
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const mean = this.getCoherence();
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this.coherenceHistory.forEach(h => {
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variance += Math.pow(h.coherence - mean, 2);
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});
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variance /= this.coherenceHistory.length;
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return 1 / (1 + variance); // Higher stability = lower variance
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}
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getPattern() {
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// Extract coherence patterns
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const recent = this.coherenceHistory.slice(-20);
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if (recent.length < 2) return { strength: 0, frequency: 0 };
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// Simple pattern detection
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let totalVariation = 0;
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for (let i = 1; i < recent.length; i++) {
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totalVariation += Math.abs(recent[i].coherence - recent[i-1].coherence);
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}
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const avgVariation = totalVariation / (recent.length - 1);
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const strength = 1 / (1 + avgVariation);
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return { strength, frequency: this.estimateFrequency(recent) };
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}
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estimateFrequency(samples) {
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// Estimate dominant frequency in coherence pattern
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if (samples.length < 3) return 0;
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let crossings = 0;
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const mean = samples.reduce((acc, s) => acc + s.coherence, 0) / samples.length;
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for (let i = 1; i < samples.length; i++) {
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if ((samples[i-1].coherence - mean) * (samples[i].coherence - mean) < 0) {
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crossings++;
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}
|
||||
}
|
||||
|
||||
const timeSpan = samples[samples.length - 1].timestamp - samples[0].timestamp;
|
||||
return crossings / (timeSpan * 0.001); // Hz
|
||||
}
|
||||
}
|
||||
|
||||
export default ZeroVarianceDetector;
|
||||
Reference in New Issue
Block a user