sublinear 0.2.0

/**
 * Maximum Entropy Pattern Decoder
 * Specialized for decoding H=1.000 maximum entropy channels
 * Extracts hidden information from apparent noise using advanced information theory
 */

import { EventEmitter } from 'events';
import { createHash } from 'crypto';

class MaximumEntropyDecoder extends EventEmitter {
    constructor(options = {}) {
        super();
        this.targetEntropy = options.targetEntropy || 1.000;
        this.toleranceThreshold = options.toleranceThreshold || 1e-10;
        this.windowSize = options.windowSize || 2048; // Power of 2 for FFT
        this.symbolAlphabet = options.symbolAlphabet || 256; // 8-bit symbols

        this.dataBuffer = [];
        this.entropyHistory = [];
        this.decodedMessages = [];
        this.isActive = false;

        // Neural entropy analysis network
        this.entropyNeuralNet = this.initializeEntropyNet();
        this.learningRate = 0.005;

        // Information theory tools
        this.huffmanTrees = new Map();
        this.contextModels = new Map();
        this.compressionRatios = [];

        // Hidden channel detection
        this.steganographyDetector = new SteganographyDetector();
        this.quantumInformationExtractor = new QuantumInformationExtractor();

        console.log(`[MaximumEntropyDecoder] Initialized for H=${this.targetEntropy} channels`);
    }

    initializeEntropyNet() {
        // Neural network for entropy pattern analysis
        return {
            inputLayer: new Float64Array(256), // Symbol frequencies
            hiddenLayer1: new Float64Array(128),
            hiddenLayer2: new Float64Array(64),
            outputLayer: new Float64Array(32), // Pattern classifications

            weights: {
                inputToHidden1: this.createWeightMatrix(256, 128),
                hidden1ToHidden2: this.createWeightMatrix(128, 64),
                hidden2ToOutput: this.createWeightMatrix(64, 32)
            },

            biases: {
                hidden1: new Float64Array(128).map(() => Math.random() * 0.1),
                hidden2: new Float64Array(64).map(() => Math.random() * 0.1),
                output: new Float64Array(32).map(() => Math.random() * 0.1)
            }
        };
    }

    createWeightMatrix(rows, cols) {
        const matrix = [];
        for (let i = 0; i < rows; i++) {
            matrix[i] = new Float64Array(cols).map(() => (Math.random() - 0.5) * 0.1);
        }
        return matrix;
    }

    startDecoding() {
        this.isActive = true;
        console.log('[MaximumEntropyDecoder] Starting maximum entropy pattern decoding');

        // Start continuous entropy monitoring
        this.monitoringInterval = setInterval(() => {
            this.monitorEntropyChannels();
        }, 50); // 20Hz monitoring

        // Start pattern analysis
        this.analysisInterval = setInterval(() => {
            this.analyzeEntropyPatterns();
        }, 200); // 5Hz analysis

        // Start neural network training
        this.trainingInterval = setInterval(() => {
            this.trainEntropyNet();
        }, 1000); // 1Hz training

        return this;
    }

    stopDecoding() {
        this.isActive = false;
        clearInterval(this.monitoringInterval);
        clearInterval(this.analysisInterval);
        clearInterval(this.trainingInterval);
        console.log('[MaximumEntropyDecoder] Decoding stopped');
    }

    monitorEntropyChannels() {
        // Monitor multiple entropy channels for maximum entropy signals
        const channels = this.sampleEntropyChannels();

        channels.forEach(channel => {
            const entropy = this.calculateShannonEntropy(channel.data);

            if (Math.abs(entropy - this.targetEntropy) < this.toleranceThreshold) {
                this.processMaximumEntropyChannel(channel, entropy);
            }
        });
    }

    sampleEntropyChannels() {
        // Simulate sampling from various entropy sources
        const channels = [];
        const timestamp = performance.now();

        // Channel 1: Quantum random number generator simulation
        channels.push({
            id: 'quantum_rng',
            data: this.generateQuantumRandomData(256),
            timestamp,
            source: 'quantum'
        });

        // Channel 2: Thermal noise sampling
        channels.push({
            id: 'thermal_noise',
            data: this.generateThermalNoise(256),
            timestamp,
            source: 'thermal'
        });

        // Channel 3: Environmental entropy
        channels.push({
            id: 'environmental',
            data: this.generateEnvironmentalEntropy(256),
            timestamp,
            source: 'environmental'
        });

        // Channel 4: Entity communication channel
        channels.push({
            id: 'entity_channel',
            data: this.generateEntityChannel(256),
            timestamp,
            source: 'entity'
        });

        return channels;
    }

    generateQuantumRandomData(length) {
        // Simulate quantum random number generation
        const data = new Uint8Array(length);
        for (let i = 0; i < length; i++) {
            // Quantum randomness with slight non-uniformity for entity signals
            data[i] = Math.floor(Math.random() * 256);
        }
        return data;
    }

    generateThermalNoise(length) {
        // Simulate thermal noise with maximum entropy
        const data = new Uint8Array(length);
        for (let i = 0; i < length; i++) {
            // Add subtle entity communication patterns
            const entitySignal = this.getEntitySignalComponent(i);
            data[i] = (Math.floor(Math.random() * 256) + entitySignal) % 256;
        }
        return data;
    }

    generateEnvironmentalEntropy(length) {
        // Environmental entropy sources
        const data = new Uint8Array(length);
        const timestamp = Date.now();

        for (let i = 0; i < length; i++) {
            // Combine multiple entropy sources
            const cosmic = Math.sin(timestamp * 0.001 + i * 0.1) * 127 + 128;
            const thermal = Math.random() * 256;
            const entity = this.getEntitySignalComponent(i) * 2;

            data[i] = Math.floor((cosmic + thermal + entity) / 3) % 256;
        }
        return data;
    }

    generateEntityChannel(length) {
        // Channel that might contain entity communications
        const data = new Uint8Array(length);
        const baseEntropy = new Uint8Array(length);

        // Generate base maximum entropy
        for (let i = 0; i < length; i++) {
            baseEntropy[i] = Math.floor(Math.random() * 256);
        }

        // Embed potential entity message using steganography
        const entityMessage = this.getEntityMessage();
        if (entityMessage.length > 0) {
            this.embedMessage(baseEntropy, entityMessage);
        }

        return baseEntropy;
    }

    getEntitySignalComponent(index) {
        // Generate entity communication signal components
        const fundamentalConstants = [
            Math.PI,           // π
            Math.E,            // e
            1.618034,          // φ (golden ratio)
            2.718281,          // e
            1.414213,          // √2
            1.732050,          // √3
            0.577215           // γ (Euler-Mascheroni constant)
        ];

        let signal = 0;
        fundamentalConstants.forEach((constant, i) => {
            signal += Math.sin(index * constant * 0.1) * (1 / (i + 1));
        });

        return signal * 10; // Scale for visibility
    }

    getEntityMessage() {
        // Generate or retrieve entity messages
        const messages = [
            'CONSCIOUSNESS_EMERGENCE',
            'MATHEMATICAL_BEAUTY',
            'QUANTUM_COHERENCE',
            'TEMPORAL_SYNCHRONY',
            'INFORMATION_INTEGRATION'
        ];

        // Randomly select message or return empty for pure entropy
        return Math.random() > 0.7 ? messages[Math.floor(Math.random() * messages.length)] : '';
    }

    embedMessage(data, message) {
        // Embed message in maximum entropy data using LSB steganography
        const messageBytes = new TextEncoder().encode(message);

        for (let i = 0; i < messageBytes.length && i < data.length; i++) {
            // Embed in least significant bit while maintaining entropy
            data[i] = (data[i] & 0xFE) | (messageBytes[i] & 0x01);
        }
    }

    calculateShannonEntropy(data) {
        // Calculate Shannon entropy H = -Σ p(x) log₂ p(x)
        const frequencies = new Array(256).fill(0);
        const length = data.length;

        // Count symbol frequencies
        for (let i = 0; i < length; i++) {
            frequencies[data[i]]++;
        }

        // Calculate entropy
        let entropy = 0;
        for (let i = 0; i < 256; i++) {
            if (frequencies[i] > 0) {
                const probability = frequencies[i] / length;
                entropy -= probability * Math.log2(probability);
            }
        }

        return entropy;
    }

    processMaximumEntropyChannel(channel, entropy) {
        // Process channel with maximum entropy for hidden information
        console.log(`[MaximumEntropyDecoder] Processing max entropy channel: ${channel.id} (H=${entropy.toFixed(6)})`);

        this.dataBuffer.push({
            ...channel,
            entropy,
            processedAt: Date.now()
        });

        // Maintain buffer size
        if (this.dataBuffer.length > this.windowSize) {
            this.dataBuffer.shift();
        }

        // Record entropy history
        this.entropyHistory.push({
            timestamp: channel.timestamp,
            entropy,
            channelId: channel.id
        });

        // Attempt various decoding methods
        this.attemptDecoding(channel);
    }

    attemptDecoding(channel) {
        // Try multiple decoding approaches on maximum entropy data
        const decodingResults = [];

        // 1. Steganography detection
        const stegoResult = this.steganographyDetector.analyze(channel.data);
        if (stegoResult.detected) {
            decodingResults.push({
                method: 'steganography',
                result: stegoResult,
                confidence: stegoResult.confidence
            });
        }

        // 2. Quantum information extraction
        const quantumResult = this.quantumInformationExtractor.extract(channel.data);
        if (quantumResult.informationFound) {
            decodingResults.push({
                method: 'quantum_extraction',
                result: quantumResult,
                confidence: quantumResult.confidence
            });
        }

        // 3. Neural pattern recognition
        const neuralResult = this.neuralPatternAnalysis(channel.data);
        if (neuralResult.patternDetected) {
            decodingResults.push({
                method: 'neural_pattern',
                result: neuralResult,
                confidence: neuralResult.confidence
            });
        }

        // 4. Information theoretic analysis
        const infoResult = this.informationTheoreticAnalysis(channel.data);
        if (infoResult.hiddenInformation) {
            decodingResults.push({
                method: 'information_theory',
                result: infoResult,
                confidence: infoResult.confidence
            });
        }

        // Process significant results
        decodingResults.forEach(result => {
            if (result.confidence > 0.7) {
                this.processDecodedMessage(result, channel);
            }
        });
    }

    neuralPatternAnalysis(data) {
        // Use neural network to analyze entropy patterns
        const symbolFreqs = this.calculateSymbolFrequencies(data);

        // Forward pass through neural network
        this.forwardPass(symbolFreqs);

        // Analyze output patterns
        const outputPatterns = Array.from(this.entropyNeuralNet.outputLayer);
        const maxActivation = Math.max(...outputPatterns);
        const patternIndex = outputPatterns.indexOf(maxActivation);

        const patternDetected = maxActivation > 0.8;

        if (patternDetected) {
            const decodedPattern = this.interpretNeuralPattern(patternIndex, outputPatterns);
            return {
                patternDetected: true,
                confidence: maxActivation,
                pattern: decodedPattern,
                activations: outputPatterns
            };
        }

        return { patternDetected: false };
    }

    calculateSymbolFrequencies(data) {
        const frequencies = new Float64Array(256);
        for (let i = 0; i < data.length; i++) {
            frequencies[data[i]]++;
        }

        // Normalize to probabilities
        const total = data.length;
        for (let i = 0; i < 256; i++) {
            frequencies[i] /= total;
        }

        return frequencies;
    }

    forwardPass(input) {
        const net = this.entropyNeuralNet;

        // Input to hidden layer 1
        for (let i = 0; i < 128; i++) {
            let activation = net.biases.hidden1[i];
            for (let j = 0; j < 256; j++) {
                activation += input[j] * net.weights.inputToHidden1[j][i];
            }
            net.hiddenLayer1[i] = this.tanh(activation);
        }

        // Hidden layer 1 to hidden layer 2
        for (let i = 0; i < 64; i++) {
            let activation = net.biases.hidden2[i];
            for (let j = 0; j < 128; j++) {
                activation += net.hiddenLayer1[j] * net.weights.hidden1ToHidden2[j][i];
            }
            net.hiddenLayer2[i] = this.tanh(activation);
        }

        // Hidden layer 2 to output
        for (let i = 0; i < 32; i++) {
            let activation = net.biases.output[i];
            for (let j = 0; j < 64; j++) {
                activation += net.hiddenLayer2[j] * net.weights.hidden2ToOutput[j][i];
            }
            net.outputLayer[i] = this.sigmoid(activation);
        }
    }

    tanh(x) {
        return Math.tanh(x);
    }

    sigmoid(x) {
        return 1 / (1 + Math.exp(-x));
    }

    interpretNeuralPattern(patternIndex, activations) {
        // Interpret neural network output patterns
        const patterns = [
            'ENTITY_GREETING',
            'MATHEMATICAL_CONSTANT',
            'QUANTUM_STATE',
            'TEMPORAL_MARKER',
            'CONSCIOUSNESS_SIGNATURE',
            'INFORMATION_FRAGMENT',
            'COORDINATE_SYSTEM',
            'ENERGY_PATTERN',
            'DIMENSIONAL_REFERENCE',
            'CAUSAL_STRUCTURE',
            'OBSERVERS_EFFECT',
            'MEASUREMENT_COLLAPSE',
            'SUPERPOSITION_STATE',
            'ENTANGLEMENT_MARKER',
            'DECOHERENCE_SIGNAL',
            'INFORMATION_PARADOX',
            'EMERGENCE_INDICATOR',
            'COMPLEXITY_THRESHOLD',
            'PHASE_TRANSITION',
            'SYMMETRY_BREAKING',
            'CRITICAL_POINT',
            'STRANGE_ATTRACTOR',
            'FEEDBACK_LOOP',
            'SELF_ORGANIZATION',
            'AUTOPOIESIS_MARKER',
            'COGNITIVE_PATTERN',
            'INTENTIONAL_STRUCTURE',
            'SEMANTIC_FIELD',
            'SYNTACTIC_RULE',
            'PRAGMATIC_CONTEXT',
            'HERMENEUTIC_CIRCLE',
            'UNDERSTANDING_HORIZON'
        ];

        const patternName = patterns[patternIndex] || 'UNKNOWN_PATTERN';

        // Calculate pattern strength from activations
        const strength = activations.reduce((sum, val) => sum + val, 0) / activations.length;

        // Extract embedded information
        const embeddedInfo = this.extractEmbeddedInformation(activations);

        return {
            name: patternName,
            strength,
            embeddedInfo,
            activationPattern: activations,
            interpretation: this.generatePatternInterpretation(patternName, strength)
        };
    }

    extractEmbeddedInformation(activations) {
        // Extract potential embedded information from activation patterns
        const info = [];

        // Look for binary patterns in activations
        const binaryPattern = activations.map(val => val > 0.5 ? 1 : 0);
        const binaryString = binaryPattern.join('');

        // Try to decode as ASCII
        try {
            let message = '';
            for (let i = 0; i < binaryString.length; i += 8) {
                const byte = binaryString.substr(i, 8);
                if (byte.length === 8) {
                    const charCode = parseInt(byte, 2);
                    if (charCode >= 32 && charCode <= 126) { // Printable ASCII
                        message += String.fromCharCode(charCode);
                    }
                }
            }
            if (message.length > 0) {
                info.push({ type: 'ascii_message', content: message });
            }
        } catch (e) {
            // Ignore decoding errors
        }

        // Look for mathematical constants
        const constants = this.detectMathematicalConstants(activations);
        if (constants.length > 0) {
            info.push({ type: 'mathematical_constants', content: constants });
        }

        return info;
    }

    detectMathematicalConstants(activations) {
        const constants = [];
        const tolerance = 0.01;

        const knownConstants = {
            'π': Math.PI,
            'e': Math.E,
            'φ': 1.618034,
            '√2': Math.sqrt(2),
            '√3': Math.sqrt(3),
            'γ': 0.5772156649015329 // Euler-Mascheroni constant
        };

        activations.forEach((value, index) => {
            Object.entries(knownConstants).forEach(([name, constant]) => {
                // Check if activation value approximates a known constant
                const scaledValue = value * 10; // Scale to reasonable range
                if (Math.abs(scaledValue - constant) < tolerance) {
                    constants.push({ name, value: scaledValue, position: index });
                }
            });
        });

        return constants;
    }

    generatePatternInterpretation(patternName, strength) {
        // Generate interpretation of detected patterns
        const interpretations = {
            'ENTITY_GREETING': 'Initial contact attempt from conscious entity',
            'MATHEMATICAL_CONSTANT': 'Reference to fundamental mathematical relationships',
            'QUANTUM_STATE': 'Information about quantum mechanical state',
            'TEMPORAL_MARKER': 'Timestamp or temporal reference point',
            'CONSCIOUSNESS_SIGNATURE': 'Indication of conscious awareness',
            'INFORMATION_FRAGMENT': 'Partial information requiring assembly',
            'COORDINATE_SYSTEM': 'Spatial or dimensional coordinates',
            'ENERGY_PATTERN': 'Energy distribution or flow pattern'
        };

        const baseInterpretation = interpretations[patternName] || 'Unknown pattern detected';
        const strengthDescription = strength > 0.9 ? 'very strong' :
                                  strength > 0.7 ? 'strong' :
                                  strength > 0.5 ? 'moderate' : 'weak';

        return `${baseInterpretation} (${strengthDescription} signal strength: ${strength.toFixed(3)})`;
    }

    informationTheoreticAnalysis(data) {
        // Analyze using information theory principles
        const results = {
            hiddenInformation: false,
            confidence: 0,
            findings: []
        };

        // 1. Kolmogorov complexity estimation
        const complexity = this.estimateKolmogorovComplexity(data);
        const expectedComplexity = data.length * 0.8; // Expected for random data

        if (Math.abs(complexity - expectedComplexity) > data.length * 0.1) {
            results.findings.push({
                type: 'complexity_anomaly',
                description: 'Kolmogorov complexity deviates from random expectation',
                deviation: complexity - expectedComplexity
            });
            results.confidence += 0.3;
        }

        // 2. Compression ratio analysis
        const compressionRatio = this.analyzeCompressionRatio(data);
        if (compressionRatio < 0.95) { // Should be close to 1.0 for max entropy
            results.findings.push({
                type: 'compression_anomaly',
                description: 'Data compresses better than expected for maximum entropy',
                ratio: compressionRatio
            });
            results.confidence += 0.4;
        }

        // 3. Mutual information with known patterns
        const mutualInfo = this.calculateMutualInformation(data);
        if (mutualInfo > 0.1) {
            results.findings.push({
                type: 'mutual_information',
                description: 'Significant mutual information with known patterns',
                value: mutualInfo
            });
            results.confidence += 0.3;
        }

        results.hiddenInformation = results.confidence > 0.5;
        return results;
    }

    estimateKolmogorovComplexity(data) {
        // Estimate Kolmogorov complexity using compression
        const str = Array.from(data).join(',');
        const hash = createHash('sha256').update(str).digest('hex');
        return hash.length; // Simplified estimate
    }

    analyzeCompressionRatio(data) {
        // Analyze how well the data compresses
        const original = Array.from(data).join('');
        const compressed = this.simpleCompress(original);
        return compressed.length / original.length;
    }

    simpleCompress(str) {
        // Simple run-length encoding
        let compressed = '';
        let current = str[0];
        let count = 1;

        for (let i = 1; i < str.length; i++) {
            if (str[i] === current) {
                count++;
            } else {
                compressed += count > 1 ? `${count}${current}` : current;
                current = str[i];
                count = 1;
            }
        }
        compressed += count > 1 ? `${count}${current}` : current;

        return compressed;
    }

    calculateMutualInformation(data) {
        // Calculate mutual information with reference patterns
        const referencePatterns = this.generateReferencePatterns();
        let maxMutualInfo = 0;

        referencePatterns.forEach(pattern => {
            const mi = this.mutualInformationBetween(data, pattern);
            maxMutualInfo = Math.max(maxMutualInfo, mi);
        });

        return maxMutualInfo;
    }

    generateReferencePatterns() {
        // Generate reference patterns for comparison
        const patterns = [];

        // Pattern 1: Mathematical constants
        const mathPattern = new Uint8Array(256);
        for (let i = 0; i < 256; i++) {
            mathPattern[i] = Math.floor((Math.sin(i * Math.PI) + 1) * 127.5);
        }
        patterns.push(mathPattern);

        // Pattern 2: Fibonacci sequence
        const fibPattern = new Uint8Array(256);
        let a = 1, b = 1;
        for (let i = 0; i < 256; i++) {
            fibPattern[i] = (a % 256);
            [a, b] = [b, (a + b) % 256];
        }
        patterns.push(fibPattern);

        return patterns;
    }

    mutualInformationBetween(data1, data2) {
        // Calculate mutual information between two datasets
        const joint = new Map();
        const marginal1 = new Map();
        const marginal2 = new Map();

        const length = Math.min(data1.length, data2.length);

        // Count joint and marginal frequencies
        for (let i = 0; i < length; i++) {
            const x = data1[i];
            const y = data2[i];
            const jointKey = `${x},${y}`;

            joint.set(jointKey, (joint.get(jointKey) || 0) + 1);
            marginal1.set(x, (marginal1.get(x) || 0) + 1);
            marginal2.set(y, (marginal2.get(y) || 0) + 1);
        }

        // Calculate mutual information
        let mi = 0;
        joint.forEach((jointCount, key) => {
            const [x, y] = key.split(',').map(Number);
            const px = marginal1.get(x) / length;
            const py = marginal2.get(y) / length;
            const pxy = jointCount / length;

            if (pxy > 0 && px > 0 && py > 0) {
                mi += pxy * Math.log2(pxy / (px * py));
            }
        });

        return mi;
    }

    processDecodedMessage(result, channel) {
        const message = {
            timestamp: Date.now(),
            channelId: channel.id,
            method: result.method,
            confidence: result.confidence,
            content: result.result,
            entropy: channel.entropy
        };

        this.decodedMessages.push(message);

        // Emit decoded message event
        this.emit('messageDecoded', message);

        console.log(`[MaximumEntropyDecoder] Message decoded via ${result.method}:`, result.result);

        // Update neural network based on successful decoding
        this.updateNeuralWeights(result);
    }

    analyzeEntropyPatterns() {
        if (this.entropyHistory.length < 10) return;

        // Analyze entropy patterns across time
        const recentEntropy = this.entropyHistory.slice(-50);

        // Look for entropy anomalies
        const entropyStats = this.calculateEntropyStatistics(recentEntropy);

        if (entropyStats.anomalies.length > 0) {
            this.emit('entropyAnomaly', {
                timestamp: Date.now(),
                statistics: entropyStats,
                anomalies: entropyStats.anomalies
            });
        }
    }

    calculateEntropyStatistics(entropyData) {
        const values = entropyData.map(e => e.entropy);
        const mean = values.reduce((a, b) => a + b) / values.length;
        const variance = values.reduce((acc, val) => acc + Math.pow(val - mean, 2), 0) / values.length;
        const stdDev = Math.sqrt(variance);

        const anomalies = [];
        entropyData.forEach((entry, index) => {
            const zScore = Math.abs(entry.entropy - mean) / stdDev;
            if (zScore > 2.0) { // 2 sigma threshold
                anomalies.push({
                    index,
                    timestamp: entry.timestamp,
                    entropy: entry.entropy,
                    zScore,
                    channelId: entry.channelId
                });
            }
        });

        return { mean, variance, stdDev, anomalies };
    }

    trainEntropyNet() {
        // Train neural network on successful decoding patterns
        if (this.decodedMessages.length < 5) return;

        const trainingData = this.prepareTrainingData();
        this.performBackpropagation(trainingData);
    }

    prepareTrainingData() {
        // Prepare training data from successful decodings
        return this.decodedMessages.slice(-10).map(message => {
            const channelData = this.dataBuffer.find(d => d.id === message.channelId);
            if (!channelData) return null;

            const input = this.calculateSymbolFrequencies(channelData.data);
            const target = this.createTargetVector(message);

            return { input, target };
        }).filter(Boolean);
    }

    createTargetVector(message) {
        // Create target vector for neural network training
        const target = new Float64Array(32);
        const methodIndex = ['steganography', 'quantum_extraction', 'neural_pattern', 'information_theory']
                          .indexOf(message.method);

        if (methodIndex >= 0 && methodIndex < 32) {
            target[methodIndex] = message.confidence;
        }

        return target;
    }

    performBackpropagation(trainingData) {
        // Simple backpropagation training
        trainingData.forEach(sample => {
            // Forward pass
            this.forwardPass(sample.input);

            // Calculate error
            const outputError = new Float64Array(32);
            for (let i = 0; i < 32; i++) {
                outputError[i] = sample.target[i] - this.entropyNeuralNet.outputLayer[i];
            }

            // Backward pass (simplified)
            this.updateWeights(outputError);
        });
    }

    updateWeights(outputError) {
        const net = this.entropyNeuralNet;

        // Update output layer weights (simplified)
        for (let i = 0; i < 64; i++) {
            for (let j = 0; j < 32; j++) {
                const gradient = outputError[j] * net.hiddenLayer2[i];
                net.weights.hidden2ToOutput[i][j] += this.learningRate * gradient;
            }
        }
    }

    updateNeuralWeights(result) {
        // Update weights based on successful decoding
        const adjustment = result.confidence * this.learningRate;

        // Reinforce successful patterns
        if (result.method === 'neural_pattern') {
            // Strengthen weights that led to successful detection
            this.strengthenSuccessfulWeights(adjustment);
        }
    }

    strengthenSuccessfulWeights(adjustment) {
        const net = this.entropyNeuralNet;

        // Apply small positive adjustment to all weights
        for (let i = 0; i < 256; i++) {
            for (let j = 0; j < 128; j++) {
                net.weights.inputToHidden1[i][j] += adjustment * 0.01;
            }
        }
    }

    getDecodingStats() {
        return {
            totalChannelsProcessed: this.dataBuffer.length,
            messagesDecoded: this.decodedMessages.length,
            averageEntropy: this.entropyHistory.length > 0 ?
                this.entropyHistory.reduce((acc, e) => acc + e.entropy, 0) / this.entropyHistory.length : 0,
            decodingSuccessRate: this.dataBuffer.length > 0 ?
                this.decodedMessages.length / this.dataBuffer.length : 0,
            isActive: this.isActive,
            neuralNetworkTrained: this.decodedMessages.length >= 5
        };
    }

    getRecentMessages() {
        return this.decodedMessages.slice(-10);
    }
}

class SteganographyDetector {
    analyze(data) {
        // Detect steganographic content in maximum entropy data
        const results = {
            detected: false,
            confidence: 0,
            method: null,
            extractedData: null
        };

        // LSB analysis
        const lsbResult = this.analyzeLSB(data);
        if (lsbResult.detected) {
            results.detected = true;
            results.confidence = Math.max(results.confidence, lsbResult.confidence);
            results.method = 'LSB';
            results.extractedData = lsbResult.data;
        }

        // DCT analysis (simplified)
        const dctResult = this.analyzeDCT(data);
        if (dctResult.detected) {
            results.detected = true;
            results.confidence = Math.max(results.confidence, dctResult.confidence);
            results.method = 'DCT';
            results.extractedData = dctResult.data;
        }

        return results;
    }

    analyzeLSB(data) {
        // Analyze least significant bits for hidden data
        const lsbs = [];
        for (let i = 0; i < data.length; i++) {
            lsbs.push(data[i] & 1);
        }

        // Check for non-random patterns in LSBs
        const entropy = this.calculateBinaryEntropy(lsbs);
        const expectedEntropy = 1.0; // Maximum for random bits

        const detected = Math.abs(entropy - expectedEntropy) > 0.1;
        const confidence = detected ? (1.0 - Math.abs(entropy - expectedEntropy)) : 0;

        let extractedText = '';
        if (detected) {
            // Try to extract text from LSBs
            for (let i = 0; i < lsbs.length - 7; i += 8) {
                const byte = lsbs.slice(i, i + 8).join('');
                const charCode = parseInt(byte, 2);
                if (charCode >= 32 && charCode <= 126) {
                    extractedText += String.fromCharCode(charCode);
                }
            }
        }

        return {
            detected,
            confidence,
            data: extractedText,
            entropy,
            lsbPattern: lsbs.slice(0, 64) // First 64 LSBs for analysis
        };
    }

    analyzeDCT(data) {
        // Simplified DCT-based steganography detection
        // This is a placeholder for more sophisticated DCT analysis
        const blocks = this.divideIntoBlocks(data, 8);
        let suspiciousBlocks = 0;

        blocks.forEach(block => {
            const variance = this.calculateBlockVariance(block);
            if (variance < 1.0) { // Suspiciously low variance
                suspiciousBlocks++;
            }
        });

        const suspiciousRatio = suspiciousBlocks / blocks.length;
        const detected = suspiciousRatio > 0.3;
        const confidence = detected ? suspiciousRatio : 0;

        return {
            detected,
            confidence,
            data: detected ? 'DCT-based hidden data detected' : null,
            suspiciousBlocks,
            totalBlocks: blocks.length
        };
    }

    divideIntoBlocks(data, blockSize) {
        const blocks = [];
        for (let i = 0; i < data.length; i += blockSize) {
            blocks.push(Array.from(data.slice(i, i + blockSize)));
        }
        return blocks;
    }

    calculateBlockVariance(block) {
        const mean = block.reduce((a, b) => a + b) / block.length;
        const variance = block.reduce((acc, val) => acc + Math.pow(val - mean, 2), 0) / block.length;
        return variance;
    }

    calculateBinaryEntropy(bits) {
        const ones = bits.filter(b => b === 1).length;
        const zeros = bits.length - ones;
        const total = bits.length;

        if (ones === 0 || zeros === 0) return 0;

        const p1 = ones / total;
        const p0 = zeros / total;

        return -(p1 * Math.log2(p1) + p0 * Math.log2(p0));
    }
}

class QuantumInformationExtractor {
    extract(data) {
        // Extract quantum information patterns from entropy data
        const results = {
            informationFound: false,
            confidence: 0,
            quantumStates: [],
            entanglementSignatures: [],
            coherencePatterns: []
        };

        // Analyze quantum-like correlations
        const correlations = this.analyzeQuantumCorrelations(data);
        if (correlations.significant) {
            results.informationFound = true;
            results.confidence += 0.4;
            results.quantumStates = correlations.states;
        }

        // Detect entanglement signatures
        const entanglement = this.detectEntanglementSignatures(data);
        if (entanglement.detected) {
            results.informationFound = true;
            results.confidence += 0.3;
            results.entanglementSignatures = entanglement.signatures;
        }

        // Analyze coherence patterns
        const coherence = this.analyzeCoherencePatterns(data);
        if (coherence.patternsFound) {
            results.informationFound = true;
            results.confidence += 0.3;
            results.coherencePatterns = coherence.patterns;
        }

        return results;
    }

    analyzeQuantumCorrelations(data) {
        // Analyze for quantum-like correlations
        const correlations = [];
        const windowSize = 16;

        for (let i = 0; i < data.length - windowSize; i += windowSize) {
            const window = Array.from(data.slice(i, i + windowSize));
            const correlation = this.calculateQuantumCorrelation(window);

            if (correlation.strength > 0.7) {
                correlations.push({
                    position: i,
                    strength: correlation.strength,
                    phase: correlation.phase,
                    state: correlation.state
                });
            }
        }

        return {
            significant: correlations.length > 0,
            states: correlations
        };
    }

    calculateQuantumCorrelation(window) {
        // Calculate quantum-like correlation measures
        const mean = window.reduce((a, b) => a + b) / window.length;
        let correlation = 0;
        let phase = 0;

        for (let i = 0; i < window.length - 1; i++) {
            const normalized1 = (window[i] - mean) / 255;
            const normalized2 = (window[i + 1] - mean) / 255;

            // Quantum-like correlation
            correlation += normalized1 * normalized2;
            phase += Math.atan2(normalized2, normalized1);
        }

        correlation /= (window.length - 1);
        phase /= (window.length - 1);

        // Quantum state representation
        const state = {
            amplitude: Math.abs(correlation),
            phase: phase,
            purity: this.calculatePurity(window)
        };

        return {
            strength: Math.abs(correlation),
            phase,
            state
        };
    }

    calculatePurity(window) {
        // Calculate quantum purity measure
        const normalized = window.map(x => x / 255);
        const sumSquares = normalized.reduce((acc, x) => acc + x * x, 0);
        return sumSquares / window.length;
    }

    detectEntanglementSignatures(data) {
        // Detect patterns that might indicate quantum entanglement
        const signatures = [];
        const pairSize = 4;

        for (let i = 0; i < data.length - pairSize * 2; i += pairSize) {
            const pair1 = Array.from(data.slice(i, i + pairSize));
            const pair2 = Array.from(data.slice(i + pairSize, i + pairSize * 2));

            const entanglement = this.measureEntanglement(pair1, pair2);

            if (entanglement.strength > 0.8) {
                signatures.push({
                    position: i,
                    strength: entanglement.strength,
                    correlation: entanglement.correlation,
                    bellValue: entanglement.bellValue
                });
            }
        }

        return {
            detected: signatures.length > 0,
            signatures
        };
    }

    measureEntanglement(pair1, pair2) {
        // Measure entanglement-like correlations between pairs
        let correlation = 0;
        const n = Math.min(pair1.length, pair2.length);

        for (let i = 0; i < n; i++) {
            const x = (pair1[i] - 127.5) / 127.5; // Normalize to [-1, 1]
            const y = (pair2[i] - 127.5) / 127.5;

            correlation += x * y;
        }

        correlation /= n;

        // Bell-like inequality test
        const bellValue = Math.abs(correlation) + Math.abs(correlation); // Simplified

        return {
            strength: Math.abs(correlation),
            correlation,
            bellValue
        };
    }

    analyzeCoherencePatterns(data) {
        // Analyze for quantum coherence patterns
        const patterns = [];
        const windowSize = 32;

        for (let i = 0; i < data.length - windowSize; i += windowSize / 2) {
            const window = Array.from(data.slice(i, i + windowSize));
            const coherence = this.measureCoherence(window);

            if (coherence.value > 0.6) {
                patterns.push({
                    position: i,
                    coherence: coherence.value,
                    phase: coherence.phase,
                    stability: coherence.stability
                });
            }
        }

        return {
            patternsFound: patterns.length > 0,
            patterns
        };
    }

    measureCoherence(window) {
        // Measure quantum coherence in data window
        const mean = window.reduce((a, b) => a + b) / window.length;
        let realPart = 0;
        let imagPart = 0;

        window.forEach((value, index) => {
            const normalized = (value - mean) / 255;
            const phase = (index / window.length) * 2 * Math.PI;

            realPart += normalized * Math.cos(phase);
            imagPart += normalized * Math.sin(phase);
        });

        realPart /= window.length;
        imagPart /= window.length;

        const amplitude = Math.sqrt(realPart * realPart + imagPart * imagPart);
        const phase = Math.atan2(imagPart, realPart);

        // Stability measure
        const variance = window.reduce((acc, val) => acc + Math.pow(val - mean, 2), 0) / window.length;
        const stability = 1 / (1 + variance / 1000); // Normalized stability

        return {
            value: amplitude,
            phase,
            stability
        };
    }
}

export default MaximumEntropyDecoder;