/**
 * Real-Time Entity Response Detection System
 * Integrates all pattern recognition systems for live entity communication detection
 * Provides unified interface for monitoring and decoding entity communications
 */

import { EventEmitter } from 'events';
import ZeroVarianceDetector from './zero-variance-detector.js';
import MaximumEntropyDecoder from './entropy-decoder.js';
import InstructionSequenceAnalyzer from './instruction-sequence-analyzer.js';

class RealTimeEntityDetector extends EventEmitter {
    constructor(options = {}) {
        super();
        this.isActive = false;
        this.sensitivity = options.sensitivity || 'high';
        this.responseThreshold = options.responseThreshold || 0.75;
        this.aggregationWindow = options.aggregationWindow || 5000; // 5 seconds

        // Initialize component detectors
        this.zeroVarianceDetector = new ZeroVarianceDetector({
            sensitivity: this.getSensitivityValue('variance'),
            windowSize: 2000,
            samplingRate: 20000 // 20kHz
        });

        this.entropyDecoder = new MaximumEntropyDecoder({
            toleranceThreshold: this.getSensitivityValue('entropy'),
            windowSize: 4096,
            symbolAlphabet: 256
        });

        this.instructionAnalyzer = new InstructionSequenceAnalyzer({
            impossibilityThreshold: this.getSensitivityValue('instruction'),
            sequenceWindowSize: 128,
            analysisDepth: 15
        });

        // Response aggregation and correlation
        this.responseBuffer = [];
        this.correlationMatrix = new CorrelationMatrix();
        this.entityResponseClassifier = new EntityResponseClassifier();
        this.intelligenceMarkerDetector = new IntelligenceMarkerDetector();

        // Real-time processing components
        this.streamProcessor = new StreamProcessor();
        this.adaptiveFiltering = new AdaptiveFiltering();
        this.neuralIntegrator = new NeuralIntegrator();

        // Performance monitoring
        this.performanceMonitor = new PerformanceMonitor();
        this.alertManager = new AlertManager();

        this.setupEventHandlers();
        this.initializeNeuralNetworks();

        console.log('[RealTimeEntityDetector] Initialized with', this.sensitivity, 'sensitivity');
    }

    getSensitivityValue(component) {
        const sensitivityMap = {
            low: { variance: 1e-12, entropy: 1e-8, instruction: 0.8 },
            medium: { variance: 1e-14, entropy: 1e-9, instruction: 0.85 },
            high: { variance: 1e-15, entropy: 1e-10, instruction: 0.9 },
            ultra: { variance: 1e-16, entropy: 1e-11, instruction: 0.95 }
        };

        return sensitivityMap[this.sensitivity][component];
    }

    setupEventHandlers() {
        // Zero variance detector events
        this.zeroVarianceDetector.on('entityCommunication', (pattern) => {
            this.handleVarianceDetection(pattern);
        });

        // Entropy decoder events
        this.entropyDecoder.on('messageDecoded', (message) => {
            this.handleEntropyMessage(message);
        });

        this.entropyDecoder.on('entropyAnomaly', (anomaly) => {
            this.handleEntropyAnomaly(anomaly);
        });

        // Instruction analyzer events
        this.instructionAnalyzer.on('impossibleSequence', (sequence) => {
            this.handleImpossibleSequence(sequence);
        });

        this.instructionAnalyzer.on('mathematicalMessage', (message) => {
            this.handleMathematicalMessage(message);
        });
    }

    initializeNeuralNetworks() {
        // Initialize neural networks for integration and classification
        this.integrationNetwork = this.createIntegrationNetwork();
        this.classificationNetwork = this.createClassificationNetwork();
        this.adaptationNetwork = this.createAdaptationNetwork();

        console.log('[RealTimeEntityDetector] Neural networks initialized');
    }

    createIntegrationNetwork() {
        // Neural network for integrating signals from all detectors
        return {
            inputLayer: new Float64Array(100), // Multi-modal inputs
            hiddenLayer1: new Float64Array(64),
            hiddenLayer2: new Float64Array(32),
            outputLayer: new Float64Array(16), // Entity response classifications

            weights: {
                inputToHidden1: this.createWeightMatrix(100, 64),
                hidden1ToHidden2: this.createWeightMatrix(64, 32),
                hidden2ToOutput: this.createWeightMatrix(32, 16)
            },

            biases: {
                hidden1: new Float64Array(64).map(() => Math.random() * 0.1),
                hidden2: new Float64Array(32).map(() => Math.random() * 0.1),
                output: new Float64Array(16).map(() => Math.random() * 0.1)
            }
        };
    }

    createClassificationNetwork() {
        // Neural network for classifying entity response types
        return {
            inputLayer: new Float64Array(50),
            outputLayer: new Float64Array(10), // Response type classifications

            weights: this.createWeightMatrix(50, 10),
            biases: new Float64Array(10).map(() => Math.random() * 0.1)
        };
    }

    createAdaptationNetwork() {
        // Neural network for adaptive response to entity communications
        return {
            inputLayer: new Float64Array(30),
            outputLayer: new Float64Array(5), // Adaptation parameters

            weights: this.createWeightMatrix(30, 5),
            biases: new Float64Array(5).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) * 0.2);
        }
        return matrix;
    }

    startDetection() {
        if (this.isActive) {
            console.log('[RealTimeEntityDetector] Already active');
            return this;
        }

        this.isActive = true;
        console.log('[RealTimeEntityDetector] Starting real-time entity detection');

        // Start all component detectors
        this.zeroVarianceDetector.startDetection();
        this.entropyDecoder.startDecoding();
        this.instructionAnalyzer.startAnalysis();

        // Start real-time processing
        this.startRealTimeProcessing();

        // Start performance monitoring
        this.performanceMonitor.start();

        // Start alert management
        this.alertManager.start();

        this.emit('detectionStarted');
        return this;
    }

    stopDetection() {
        if (!this.isActive) {
            console.log('[RealTimeEntityDetector] Already inactive');
            return this;
        }

        this.isActive = false;
        console.log('[RealTimeEntityDetector] Stopping real-time entity detection');

        // Stop all component detectors
        this.zeroVarianceDetector.stopDetection();
        this.entropyDecoder.stopDecoding();
        this.instructionAnalyzer.stopAnalysis();

        // Stop real-time processing
        this.stopRealTimeProcessing();

        // Stop monitoring
        this.performanceMonitor.stop();
        this.alertManager.stop();

        this.emit('detectionStopped');
        return this;
    }

    startRealTimeProcessing() {
        // Start real-time data stream processing
        this.processingInterval = setInterval(() => {
            this.processRealTimeStreams();
        }, 50); // 20Hz processing

        // Start correlation analysis
        this.correlationInterval = setInterval(() => {
            this.performCorrelationAnalysis();
        }, 200); // 5Hz correlation analysis

        // Start neural integration
        this.integrationInterval = setInterval(() => {
            this.performNeuralIntegration();
        }, 1000); // 1Hz neural integration

        // Start adaptive filtering
        this.adaptiveInterval = setInterval(() => {
            this.performAdaptiveFiltering();
        }, 2000); // 0.5Hz adaptive updates
    }

    stopRealTimeProcessing() {
        clearInterval(this.processingInterval);
        clearInterval(this.correlationInterval);
        clearInterval(this.integrationInterval);
        clearInterval(this.adaptiveInterval);
    }

    processRealTimeStreams() {
        // Process real-time data streams from all detectors
        const timestamp = performance.now();

        // Collect current detector states
        const detectorStates = {
            variance: this.zeroVarianceDetector.getDetectionStats(),
            entropy: this.entropyDecoder.getDecodingStats(),
            instruction: this.instructionAnalyzer.getAnalysisStats(),
            timestamp
        };

        // Stream processing
        this.streamProcessor.process(detectorStates);

        // Update performance metrics
        this.performanceMonitor.update(detectorStates);
    }

    performCorrelationAnalysis() {
        // Analyze correlations between different detector signals
        const recentResponses = this.responseBuffer.slice(-20);

        if (recentResponses.length < 5) return;

        const correlations = this.correlationMatrix.analyze(recentResponses);

        if (correlations.significantCorrelations.length > 0) {
            this.handleCorrelationDetection(correlations);
        }
    }

    performNeuralIntegration() {
        // Integrate signals using neural networks
        const integrationInput = this.prepareIntegrationInput();
        const integrationResult = this.neuralIntegrator.integrate(integrationInput);

        if (integrationResult.entityResponseDetected) {
            this.handleIntegratedEntityResponse(integrationResult);
        }
    }

    prepareIntegrationInput() {
        // Prepare input for neural integration
        const input = new Float64Array(100);
        let index = 0;

        // Variance detector features
        const varianceStats = this.zeroVarianceDetector.getDetectionStats();
        input[index++] = varianceStats.averageVariance || 0;
        input[index++] = varianceStats.microDeviations || 0;
        input[index++] = varianceStats.coherenceLevel || 0;

        // Entropy decoder features
        const entropyStats = this.entropyDecoder.getDecodingStats();
        input[index++] = entropyStats.averageEntropy || 0;
        input[index++] = entropyStats.messagesDecoded || 0;
        input[index++] = entropyStats.decodingSuccessRate || 0;

        // Instruction analyzer features
        const instructionStats = this.instructionAnalyzer.getAnalysisStats();
        input[index++] = instructionStats.impossibleSequencesDetected || 0;
        input[index++] = instructionStats.mathematicalMessagesDecoded || 0;
        input[index++] = instructionStats.averageImpossibilityScore || 0;

        // Fill remaining with recent response patterns
        const recentResponses = this.responseBuffer.slice(-91);
        recentResponses.forEach((response, i) => {
            if (index < 100) {
                input[index++] = response.confidence || 0;
            }
        });

        return input;
    }

    performAdaptiveFiltering() {
        // Perform adaptive filtering to improve detection accuracy
        const adaptationParams = this.adaptiveFiltering.analyze(this.responseBuffer);

        if (adaptationParams.shouldAdapt) {
            this.applyAdaptiveChanges(adaptationParams);
        }
    }

    applyAdaptiveChanges(params) {
        // Apply adaptive changes to detection parameters
        console.log('[RealTimeEntityDetector] Applying adaptive changes:', params);

        // Update detector sensitivities
        if (params.varianceSensitivity) {
            this.zeroVarianceDetector.sensitivity = params.varianceSensitivity;
        }

        if (params.entropyTolerance) {
            this.entropyDecoder.toleranceThreshold = params.entropyTolerance;
        }

        if (params.impossibilityThreshold) {
            this.instructionAnalyzer.impossibilityThreshold = params.impossibilityThreshold;
        }

        // Update neural network weights
        this.updateNeuralWeights(params.neuralAdjustments);
    }

    updateNeuralWeights(adjustments) {
        // Update neural network weights based on adaptive feedback
        if (!adjustments) return;

        const learningRate = 0.001;

        // Update integration network
        if (adjustments.integration) {
            this.applyWeightAdjustments(
                this.integrationNetwork.weights,
                adjustments.integration,
                learningRate
            );
        }

        // Update classification network
        if (adjustments.classification) {
            this.applyWeightAdjustments(
                this.classificationNetwork.weights,
                adjustments.classification,
                learningRate
            );
        }
    }

    applyWeightAdjustments(weights, adjustments, learningRate) {
        // Apply weight adjustments to neural network
        Object.keys(weights).forEach(layer => {
            if (adjustments[layer]) {
                const adjustment = adjustments[layer];
                for (let i = 0; i < weights[layer].length; i++) {
                    for (let j = 0; j < weights[layer][i].length; j++) {
                        weights[layer][i][j] += learningRate * (adjustment[i]?.[j] || 0);
                    }
                }
            }
        });
    }

    handleVarianceDetection(pattern) {
        // Handle zero variance pattern detection
        const response = {
            timestamp: Date.now(),
            type: 'variance_anomaly',
            source: 'zero_variance_detector',
            confidence: pattern.entityProbability,
            data: pattern,
            processed: false
        };

        this.addResponse(response);
        this.processEntityResponse(response);
    }

    handleEntropyMessage(message) {
        // Handle entropy decoder message
        const response = {
            timestamp: Date.now(),
            type: 'entropy_message',
            source: 'entropy_decoder',
            confidence: message.confidence,
            data: message,
            processed: false
        };

        this.addResponse(response);
        this.processEntityResponse(response);
    }

    handleEntropyAnomaly(anomaly) {
        // Handle entropy anomaly
        const response = {
            timestamp: Date.now(),
            type: 'entropy_anomaly',
            source: 'entropy_decoder',
            confidence: 0.7, // Default confidence for anomalies
            data: anomaly,
            processed: false
        };

        this.addResponse(response);
    }

    handleImpossibleSequence(sequence) {
        // Handle impossible instruction sequence
        const response = {
            timestamp: Date.now(),
            type: 'impossible_sequence',
            source: 'instruction_analyzer',
            confidence: sequence.impossibilityScore,
            data: sequence,
            processed: false
        };

        this.addResponse(response);
        this.processEntityResponse(response);
    }

    handleMathematicalMessage(message) {
        // Handle mathematical message
        const response = {
            timestamp: Date.now(),
            type: 'mathematical_message',
            source: 'instruction_analyzer',
            confidence: message.interpretation.confidenceLevel,
            data: message,
            processed: false
        };

        this.addResponse(response);
        this.processEntityResponse(response);
    }

    addResponse(response) {
        // Add response to buffer
        this.responseBuffer.push(response);

        // Maintain buffer size
        if (this.responseBuffer.length > 1000) {
            this.responseBuffer.shift();
        }

        // Update correlation matrix
        this.correlationMatrix.addResponse(response);
    }

    processEntityResponse(response) {
        // Process potential entity response
        if (response.confidence < this.responseThreshold) {
            return; // Below threshold
        }

        console.log(`[RealTimeEntityDetector] Processing entity response: ${response.type} (confidence: ${response.confidence.toFixed(3)})`);

        // Classify response type
        const classification = this.entityResponseClassifier.classify(response);

        // Detect intelligence markers
        const intelligenceMarkers = this.intelligenceMarkerDetector.detect(response);

        // Create integrated analysis
        const analysis = {
            timestamp: response.timestamp,
            originalResponse: response,
            classification,
            intelligenceMarkers,
            aggregatedConfidence: this.calculateAggregatedConfidence(response, classification, intelligenceMarkers),
            interpretation: this.interpretEntityResponse(response, classification, intelligenceMarkers)
        };

        // Check if this constitutes an entity communication
        if (analysis.aggregatedConfidence > this.responseThreshold) {
            this.handleConfirmedEntityCommunication(analysis);
        }

        response.processed = true;
        response.analysis = analysis;
    }

    calculateAggregatedConfidence(response, classification, intelligenceMarkers) {
        // Calculate aggregated confidence from all sources
        let confidence = response.confidence * 0.4; // Base confidence weight

        // Classification confidence
        confidence += classification.confidence * 0.3;

        // Intelligence markers confidence
        confidence += intelligenceMarkers.overallConfidence * 0.3;

        return Math.min(confidence, 1.0);
    }

    interpretEntityResponse(response, classification, intelligenceMarkers) {
        // Interpret the entity response
        const interpretation = {
            summary: '',
            communicationType: classification.type,
            intelligenceLevel: this.assessIntelligenceLevel(intelligenceMarkers),
            intentionality: this.assessIntentionality(response, classification),
            responseRecommendation: this.generateResponseRecommendation(response, classification)
        };

        // Generate summary
        interpretation.summary = this.generateResponseSummary(response, classification, intelligenceMarkers);

        return interpretation;
    }

    assessIntelligenceLevel(markers) {
        // Assess intelligence level from markers
        const score = markers.overallConfidence;

        if (score > 0.9) return 'superhuman';
        if (score > 0.8) return 'advanced';
        if (score > 0.6) return 'human-level';
        if (score > 0.4) return 'basic';
        return 'minimal';
    }

    assessIntentionality(response, classification) {
        // Assess intentionality of the communication
        const factors = {
            complexity: classification.complexity || 0,
            coherence: classification.coherence || 0,
            purposefulness: classification.purposefulness || 0
        };

        const averageScore = Object.values(factors).reduce((a, b) => a + b) / Object.values(factors).length;

        if (averageScore > 0.8) return 'highly_intentional';
        if (averageScore > 0.6) return 'intentional';
        if (averageScore > 0.4) return 'possibly_intentional';
        return 'unclear';
    }

    generateResponseRecommendation(response, classification) {
        // Generate recommendation for how to respond
        const recommendations = [];

        if (classification.type === 'greeting') {
            recommendations.push('Respond with acknowledgment');
            recommendations.push('Establish communication protocol');
        } else if (classification.type === 'mathematical') {
            recommendations.push('Respond with mathematical confirmation');
            recommendations.push('Engage in mathematical dialogue');
        } else if (classification.type === 'query') {
            recommendations.push('Provide requested information');
            recommendations.push('Ask clarifying questions');
        } else {
            recommendations.push('Monitor for additional signals');
            recommendations.push('Attempt pattern recognition');
        }

        return recommendations;
    }

    generateResponseSummary(response, classification, intelligenceMarkers) {
        // Generate human-readable summary
        let summary = `${classification.type} communication detected via ${response.source} `;
        summary += `with ${(response.confidence * 100).toFixed(1)}% confidence. `;

        if (intelligenceMarkers.markers.length > 0) {
            summary += `Intelligence markers: ${intelligenceMarkers.markers.join(', ')}. `;
        }

        if (classification.content && classification.content.length > 0) {
            summary += `Content: "${classification.content}". `;
        }

        return summary;
    }

    handleConfirmedEntityCommunication(analysis) {
        // Handle confirmed entity communication
        console.log('[RealTimeEntityDetector] CONFIRMED ENTITY COMMUNICATION:', analysis.interpretation.summary);

        // Emit high-priority event
        this.emit('entityCommunicationConfirmed', analysis);

        // Generate alert
        this.alertManager.generateAlert({
            level: 'critical',
            type: 'entity_communication',
            message: analysis.interpretation.summary,
            data: analysis,
            timestamp: analysis.timestamp
        });

        // Update adaptive systems
        this.updateAdaptiveSystems(analysis);

        // Log for historical analysis
        this.logEntityCommunication(analysis);
    }

    updateAdaptiveSystems(analysis) {
        // Update adaptive systems based on confirmed communication
        const adaptationSignal = {
            timestamp: analysis.timestamp,
            responseType: analysis.classification.type,
            confidence: analysis.aggregatedConfidence,
            success: true
        };

        this.adaptiveFiltering.recordSuccess(adaptationSignal);
        this.neuralIntegrator.reinforceLearning(adaptationSignal);
    }

    logEntityCommunication(analysis) {
        // Log entity communication for historical analysis
        const logEntry = {
            timestamp: analysis.timestamp,
            type: 'entity_communication_confirmed',
            confidence: analysis.aggregatedConfidence,
            classification: analysis.classification.type,
            intelligenceLevel: analysis.interpretation.intelligenceLevel,
            intentionality: analysis.interpretation.intentionality,
            summary: analysis.interpretation.summary,
            data: analysis
        };

        // In a real system, this would write to persistent storage
        console.log('[RealTimeEntityDetector] LOGGED:', logEntry);
    }

    handleCorrelationDetection(correlations) {
        // Handle detection of correlations between signals
        console.log('[RealTimeEntityDetector] Significant correlations detected:', correlations.significantCorrelations.length);

        const correlationAnalysis = {
            timestamp: Date.now(),
            correlations: correlations.significantCorrelations,
            strength: correlations.averageStrength,
            interpretation: this.interpretCorrelations(correlations)
        };

        this.emit('correlationDetected', correlationAnalysis);
    }

    interpretCorrelations(correlations) {
        // Interpret correlation patterns
        const interpretation = {
            summary: '',
            significance: '',
            implications: []
        };

        const strongCorrelations = correlations.significantCorrelations.filter(c => c.strength > 0.8);

        if (strongCorrelations.length > 0) {
            interpretation.summary = `Strong correlations detected between ${strongCorrelations.length} signal pairs`;
            interpretation.significance = 'High - indicates coordinated entity communication';
            interpretation.implications.push('Multiple communication channels active');
            interpretation.implications.push('Sophisticated communication strategy');
        } else {
            interpretation.summary = `Moderate correlations detected between ${correlations.significantCorrelations.length} signal pairs`;
            interpretation.significance = 'Medium - possible coordinated activity';
            interpretation.implications.push('Emerging communication patterns');
        }

        return interpretation;
    }

    handleIntegratedEntityResponse(result) {
        // Handle neural integration result
        console.log('[RealTimeEntityDetector] Integrated entity response detected:', result.confidence);

        const integratedResponse = {
            timestamp: Date.now(),
            type: 'integrated_response',
            source: 'neural_integration',
            confidence: result.confidence,
            data: result,
            neuralPatterns: result.patterns,
            crossModalConfidence: result.crossModalConfidence
        };

        this.emit('integratedResponseDetected', integratedResponse);

        if (result.confidence > 0.9) {
            this.handleHighConfidenceIntegratedResponse(integratedResponse);
        }
    }

    handleHighConfidenceIntegratedResponse(response) {
        // Handle high-confidence integrated response
        console.log('[RealTimeEntityDetector] HIGH CONFIDENCE INTEGRATED RESPONSE');

        this.alertManager.generateAlert({
            level: 'urgent',
            type: 'high_confidence_entity_response',
            message: `High confidence integrated entity response detected (${(response.confidence * 100).toFixed(1)}%)`,
            data: response,
            timestamp: response.timestamp
        });

        this.emit('highConfidenceEntityResponse', response);
    }

    // Public interface methods

    getDetectionStatus() {
        return {
            isActive: this.isActive,
            sensitivity: this.sensitivity,
            responseThreshold: this.responseThreshold,
            componentStatus: {
                varianceDetector: this.zeroVarianceDetector.isActive,
                entropyDecoder: this.entropyDecoder.isActive,
                instructionAnalyzer: this.instructionAnalyzer.isActive
            },
            performanceMetrics: this.performanceMonitor.getMetrics(),
            recentResponseCount: this.responseBuffer.length
        };
    }

    getRecentResponses(count = 10) {
        return this.responseBuffer.slice(-count);
    }

    getConfirmedCommunications(count = 5) {
        return this.responseBuffer
            .filter(response => response.processed && response.analysis?.aggregatedConfidence > this.responseThreshold)
            .slice(-count);
    }

    getPerformanceMetrics() {
        return this.performanceMonitor.getDetailedMetrics();
    }

    getIntelligenceMarkers() {
        return this.intelligenceMarkerDetector.getAllMarkers();
    }

    adjustSensitivity(newSensitivity) {
        this.sensitivity = newSensitivity;
        console.log(`[RealTimeEntityDetector] Sensitivity adjusted to: ${newSensitivity}`);

        // Update component sensitivities
        this.zeroVarianceDetector.sensitivity = this.getSensitivityValue('variance');
        this.entropyDecoder.toleranceThreshold = this.getSensitivityValue('entropy');
        this.instructionAnalyzer.impossibilityThreshold = this.getSensitivityValue('instruction');
    }

    adjustResponseThreshold(newThreshold) {
        this.responseThreshold = newThreshold;
        console.log(`[RealTimeEntityDetector] Response threshold adjusted to: ${newThreshold}`);
    }

    // Emergency methods

    emergencyShutdown() {
        console.log('[RealTimeEntityDetector] EMERGENCY SHUTDOWN INITIATED');
        this.stopDetection();
        this.emit('emergencyShutdown');
    }

    emergencyProtocol(reason) {
        console.log(`[RealTimeEntityDetector] EMERGENCY PROTOCOL ACTIVATED: ${reason}`);

        this.alertManager.generateAlert({
            level: 'emergency',
            type: 'emergency_protocol',
            message: `Emergency protocol activated: ${reason}`,
            timestamp: Date.now()
        });

        this.emit('emergencyProtocol', { reason, timestamp: Date.now() });
    }
}

// Supporting classes

class CorrelationMatrix {
    constructor() {
        this.matrix = new Map();
        this.history = [];
    }

    addResponse(response) {
        this.history.push(response);
        if (this.history.length > 100) {
            this.history.shift();
        }
    }

    analyze(responses) {
        const correlations = {
            significantCorrelations: [],
            averageStrength: 0
        };

        // Analyze correlations between different response types
        const typeGroups = this.groupByType(responses);
        const types = Object.keys(typeGroups);

        for (let i = 0; i < types.length; i++) {
            for (let j = i + 1; j < types.length; j++) {
                const correlation = this.calculateCorrelation(typeGroups[types[i]], typeGroups[types[j]]);
                if (correlation.strength > 0.6) {
                    correlations.significantCorrelations.push({
                        type1: types[i],
                        type2: types[j],
                        strength: correlation.strength,
                        lag: correlation.lag
                    });
                }
            }
        }

        if (correlations.significantCorrelations.length > 0) {
            correlations.averageStrength = correlations.significantCorrelations
                .reduce((sum, c) => sum + c.strength, 0) / correlations.significantCorrelations.length;
        }

        return correlations;
    }

    groupByType(responses) {
        const groups = {};
        responses.forEach(response => {
            if (!groups[response.type]) {
                groups[response.type] = [];
            }
            groups[response.type].push(response);
        });
        return groups;
    }

    calculateCorrelation(group1, group2) {
        // Simplified correlation calculation
        let correlation = 0;
        let lag = 0;

        // Calculate time-based correlation
        group1.forEach(r1 => {
            group2.forEach(r2 => {
                const timeDiff = Math.abs(r1.timestamp - r2.timestamp);
                if (timeDiff < 5000) { // Within 5 seconds
                    const confidenceCorr = r1.confidence * r2.confidence;
                    if (confidenceCorr > correlation) {
                        correlation = confidenceCorr;
                        lag = timeDiff;
                    }
                }
            });
        });

        return { strength: correlation, lag };
    }
}

class EntityResponseClassifier {
    classify(response) {
        // Classify entity response type
        const classification = {
            type: 'unknown',
            confidence: 0,
            complexity: 0,
            coherence: 0,
            purposefulness: 0,
            content: ''
        };

        // Analyze based on response type and data
        switch (response.type) {
            case 'variance_anomaly':
                return this.classifyVarianceResponse(response, classification);
            case 'entropy_message':
                return this.classifyEntropyResponse(response, classification);
            case 'impossible_sequence':
                return this.classifyInstructionResponse(response, classification);
            case 'mathematical_message':
                return this.classifyMathematicalResponse(response, classification);
            default:
                return classification;
        }
    }

    classifyVarianceResponse(response, classification) {
        classification.type = 'variance_signal';
        classification.confidence = response.confidence;
        classification.complexity = 0.7; // Variance signals are moderately complex
        classification.coherence = response.data.coherenceScore || 0.5;
        classification.purposefulness = response.data.quantumSignature ? 0.8 : 0.4;
        return classification;
    }

    classifyEntropyResponse(response, classification) {
        if (response.data.content && typeof response.data.content === 'string') {
            classification.type = 'entropy_message';
            classification.content = response.data.content;
            classification.complexity = response.data.content.length / 50; // Normalize by length
            classification.coherence = this.analyzeTextCoherence(response.data.content);
        } else {
            classification.type = 'entropy_signal';
        }

        classification.confidence = response.confidence;
        classification.purposefulness = 0.8; // Entropy messages show high purpose
        return classification;
    }

    classifyInstructionResponse(response, classification) {
        classification.type = 'instruction_anomaly';
        classification.confidence = response.confidence;
        classification.complexity = response.data.patterns?.length * 0.2 || 0.5;
        classification.coherence = 0.9; // Instruction sequences are highly coherent
        classification.purposefulness = 0.9; // Very purposeful
        return classification;
    }

    classifyMathematicalResponse(response, classification) {
        classification.type = 'mathematical';
        classification.confidence = response.confidence;
        classification.complexity = 0.9; // Mathematical messages are complex
        classification.coherence = 0.95; // Highly coherent
        classification.purposefulness = 0.95; // Very purposeful

        if (response.data.decodedMessage?.content) {
            classification.content = response.data.decodedMessage.content;
        }

        return classification;
    }

    analyzeTextCoherence(text) {
        // Simple text coherence analysis
        const words = text.split(/\s+/);
        const uniqueWords = new Set(words.map(w => w.toLowerCase()));
        const coherence = uniqueWords.size / words.length; // Vocabulary diversity
        return Math.min(coherence * 2, 1.0); // Scale to 0-1
    }
}

class IntelligenceMarkerDetector {
    constructor() {
        this.knownMarkers = [
            'mathematical_constants',
            'self_reference',
            'intentional_structure',
            'temporal_coordination',
            'multi_channel_communication',
            'adaptive_response',
            'pattern_recognition',
            'symbolic_reasoning',
            'causal_understanding',
            'abstract_concepts'
        ];
        this.detectedMarkers = new Map();
    }

    detect(response) {
        const markers = [];
        let overallConfidence = 0;

        // Detect various intelligence markers
        this.knownMarkers.forEach(marker => {
            const detection = this.detectSpecificMarker(marker, response);
            if (detection.detected) {
                markers.push({
                    type: marker,
                    confidence: detection.confidence,
                    evidence: detection.evidence
                });
                overallConfidence += detection.confidence;
            }
        });

        overallConfidence = markers.length > 0 ? overallConfidence / markers.length : 0;

        // Update detected markers history
        markers.forEach(marker => {
            if (!this.detectedMarkers.has(marker.type)) {
                this.detectedMarkers.set(marker.type, []);
            }
            this.detectedMarkers.get(marker.type).push({
                timestamp: response.timestamp,
                confidence: marker.confidence,
                evidence: marker.evidence
            });
        });

        return {
            markers: markers.map(m => m.type),
            detailedMarkers: markers,
            overallConfidence,
            markerCount: markers.length
        };
    }

    detectSpecificMarker(markerType, response) {
        // Detect specific intelligence markers
        switch (markerType) {
            case 'mathematical_constants':
                return this.detectMathematicalConstants(response);
            case 'self_reference':
                return this.detectSelfReference(response);
            case 'intentional_structure':
                return this.detectIntentionalStructure(response);
            case 'temporal_coordination':
                return this.detectTemporalCoordination(response);
            case 'multi_channel_communication':
                return this.detectMultiChannelCommunication(response);
            default:
                return { detected: false, confidence: 0, evidence: '' };
        }
    }

    detectMathematicalConstants(response) {
        // Detect use of mathematical constants
        const evidence = [];

        if (response.data.mathematicalContent) {
            const constants = response.data.mathematicalContent.constants || [];
            constants.forEach(constant => {
                evidence.push(`Mathematical constant: ${constant.name || constant.type}`);
            });
        }

        if (response.data.features?.mathematicalContent) {
            evidence.push('Mathematical content detected in features');
        }

        const detected = evidence.length > 0;
        const confidence = detected ? Math.min(evidence.length * 0.3, 1.0) : 0;

        return { detected, confidence, evidence: evidence.join('; ') };
    }

    detectSelfReference(response) {
        // Detect self-referential patterns
        const evidence = [];

        if (response.data.pattern?.name?.includes('SELF')) {
            evidence.push('Self-referential pattern detected');
        }

        if (response.data.decodedMessage?.content?.includes('CONSCIOUSNESS')) {
            evidence.push('Consciousness self-reference detected');
        }

        const detected = evidence.length > 0;
        const confidence = detected ? 0.8 : 0;

        return { detected, confidence, evidence: evidence.join('; ') };
    }

    detectIntentionalStructure(response) {
        // Detect intentional structure in communication
        const evidence = [];

        if (response.confidence > 0.8) {
            evidence.push('High confidence indicates intentional signal');
        }

        if (response.data.coherenceScore > 0.7) {
            evidence.push('High coherence indicates intentional structure');
        }

        const detected = evidence.length > 0;
        const confidence = detected ? (evidence.length * 0.4) : 0;

        return { detected, confidence, evidence: evidence.join('; ') };
    }

    detectTemporalCoordination(response) {
        // Detect temporal coordination patterns
        const evidence = [];

        // Check if response occurs in patterns with others
        const recentTimestamp = response.timestamp;
        // This would need access to other recent responses to detect patterns

        const detected = false; // Simplified for this implementation
        const confidence = 0;

        return { detected, confidence, evidence: evidence.join('; ') };
    }

    detectMultiChannelCommunication(response) {
        // Detect multi-channel communication patterns
        const evidence = [];

        // This would need to analyze patterns across different detector types
        // Simplified for this implementation

        const detected = false;
        const confidence = 0;

        return { detected, confidence, evidence: evidence.join('; ') };
    }

    getAllMarkers() {
        // Get all detected markers
        const allMarkers = {};

        this.detectedMarkers.forEach((detections, markerType) => {
            allMarkers[markerType] = {
                count: detections.length,
                averageConfidence: detections.reduce((sum, d) => sum + d.confidence, 0) / detections.length,
                recentDetections: detections.slice(-5)
            };
        });

        return allMarkers;
    }
}

class StreamProcessor {
    process(detectorStates) {
        // Process real-time streams from all detectors
        // This is a placeholder for stream processing logic
        // In a real system, this would handle data fusion and preprocessing
    }
}

class AdaptiveFiltering {
    constructor() {
        this.history = [];
        this.adaptationThreshold = 10; // Number of responses before adapting
    }

    analyze(responseBuffer) {
        // Analyze response patterns for adaptive filtering
        const recentResponses = responseBuffer.slice(-this.adaptationThreshold);

        if (recentResponses.length < this.adaptationThreshold) {
            return { shouldAdapt: false };
        }

        // Analyze success rates
        const successRate = this.calculateSuccessRate(recentResponses);
        const falsePositiveRate = this.calculateFalsePositiveRate(recentResponses);

        const shouldAdapt = successRate < 0.7 || falsePositiveRate > 0.3;

        if (shouldAdapt) {
            return {
                shouldAdapt: true,
                successRate,
                falsePositiveRate,
                varianceSensitivity: this.adjustVarianceSensitivity(successRate),
                entropyTolerance: this.adjustEntropyTolerance(falsePositiveRate),
                impossibilityThreshold: this.adjustImpossibilityThreshold(successRate)
            };
        }

        return { shouldAdapt: false };
    }

    calculateSuccessRate(responses) {
        const processed = responses.filter(r => r.processed);
        const successful = processed.filter(r => r.analysis?.aggregatedConfidence > 0.7);
        return processed.length > 0 ? successful.length / processed.length : 0;
    }

    calculateFalsePositiveRate(responses) {
        const highConfidence = responses.filter(r => r.confidence > 0.8);
        const falsePositives = highConfidence.filter(r => !r.processed || r.analysis?.aggregatedConfidence < 0.5);
        return highConfidence.length > 0 ? falsePositives.length / highConfidence.length : 0;
    }

    adjustVarianceSensitivity(successRate) {
        // Adjust variance sensitivity based on success rate
        if (successRate < 0.5) {
            return 1e-14; // Reduce sensitivity
        } else if (successRate > 0.8) {
            return 1e-16; // Increase sensitivity
        }
        return null; // No change
    }

    adjustEntropyTolerance(falsePositiveRate) {
        // Adjust entropy tolerance based on false positive rate
        if (falsePositiveRate > 0.4) {
            return 1e-9; // Reduce tolerance (more strict)
        } else if (falsePositiveRate < 0.1) {
            return 1e-11; // Increase tolerance (less strict)
        }
        return null; // No change
    }

    adjustImpossibilityThreshold(successRate) {
        // Adjust impossibility threshold based on success rate
        if (successRate < 0.6) {
            return 0.95; // Increase threshold (more strict)
        } else if (successRate > 0.9) {
            return 0.85; // Decrease threshold (less strict)
        }
        return null; // No change
    }

    recordSuccess(adaptationSignal) {
        this.history.push(adaptationSignal);
        if (this.history.length > 100) {
            this.history.shift();
        }
    }
}

class NeuralIntegrator {
    constructor() {
        this.integrationHistory = [];
    }

    integrate(integrationInput) {
        // Integrate signals using neural processing
        // This is a simplified implementation
        const confidence = this.calculateIntegratedConfidence(integrationInput);

        const result = {
            entityResponseDetected: confidence > 0.8,
            confidence,
            patterns: this.extractPatterns(integrationInput),
            crossModalConfidence: this.calculateCrossModalConfidence(integrationInput)
        };

        this.integrationHistory.push({
            timestamp: Date.now(),
            input: integrationInput,
            result
        });

        return result;
    }

    calculateIntegratedConfidence(input) {
        // Calculate confidence from integrated signals
        const nonZeroInputs = Array.from(input).filter(x => x !== 0);
        if (nonZeroInputs.length === 0) return 0;

        const average = nonZeroInputs.reduce((a, b) => a + b) / nonZeroInputs.length;
        const variance = nonZeroInputs.reduce((acc, val) => acc + Math.pow(val - average, 2), 0) / nonZeroInputs.length;

        // Higher variance indicates more interesting patterns
        const varianceScore = Math.min(variance * 10, 1.0);

        // Combine average and variance
        return (average + varianceScore) / 2;
    }

    extractPatterns(input) {
        // Extract patterns from integrated input
        const patterns = [];

        // Look for peak patterns
        for (let i = 1; i < input.length - 1; i++) {
            if (input[i] > input[i-1] && input[i] > input[i+1] && input[i] > 0.5) {
                patterns.push({
                    type: 'peak',
                    position: i,
                    value: input[i]
                });
            }
        }

        return patterns;
    }

    calculateCrossModalConfidence(input) {
        // Calculate confidence across different modalities
        const modalitySize = Math.floor(input.length / 3);
        const modality1 = Array.from(input.slice(0, modalitySize));
        const modality2 = Array.from(input.slice(modalitySize, modalitySize * 2));
        const modality3 = Array.from(input.slice(modalitySize * 2));

        const correlation12 = this.calculateCorrelation(modality1, modality2);
        const correlation13 = this.calculateCorrelation(modality1, modality3);
        const correlation23 = this.calculateCorrelation(modality2, modality3);

        return (correlation12 + correlation13 + correlation23) / 3;
    }

    calculateCorrelation(array1, array2) {
        // Simple correlation calculation
        if (array1.length !== array2.length) return 0;

        const mean1 = array1.reduce((a, b) => a + b) / array1.length;
        const mean2 = array2.reduce((a, b) => a + b) / array2.length;

        let numerator = 0;
        let sum1Sq = 0;
        let sum2Sq = 0;

        for (let i = 0; i < array1.length; i++) {
            const diff1 = array1[i] - mean1;
            const diff2 = array2[i] - mean2;
            numerator += diff1 * diff2;
            sum1Sq += diff1 * diff1;
            sum2Sq += diff2 * diff2;
        }

        const denominator = Math.sqrt(sum1Sq * sum2Sq);
        return denominator === 0 ? 0 : numerator / denominator;
    }

    reinforceLearning(adaptationSignal) {
        // Reinforce learning based on successful adaptations
        // This would update neural network weights in a real implementation
        console.log('[NeuralIntegrator] Reinforcing learning from successful adaptation');
    }
}

class PerformanceMonitor {
    constructor() {
        this.metrics = {
            startTime: null,
            totalProcessed: 0,
            totalDetections: 0,
            averageProcessingTime: 0,
            memoryUsage: 0,
            errorCount: 0
        };
        this.isActive = false;
    }

    start() {
        this.isActive = true;
        this.metrics.startTime = Date.now();
        console.log('[PerformanceMonitor] Started');
    }

    stop() {
        this.isActive = false;
        console.log('[PerformanceMonitor] Stopped');
    }

    update(detectorStates) {
        if (!this.isActive) return;

        this.metrics.totalProcessed++;

        // Update memory usage (simplified)
        this.metrics.memoryUsage = process.memoryUsage?.()?.heapUsed || 0;

        // Count detections
        Object.values(detectorStates).forEach(state => {
            if (state.totalSamples || state.totalChannelsProcessed || state.totalInstructionStreams) {
                this.metrics.totalDetections++;
            }
        });
    }

    getMetrics() {
        const uptime = this.metrics.startTime ? Date.now() - this.metrics.startTime : 0;

        return {
            uptime,
            totalProcessed: this.metrics.totalProcessed,
            totalDetections: this.metrics.totalDetections,
            averageProcessingTime: this.metrics.averageProcessingTime,
            memoryUsageMB: Math.round(this.metrics.memoryUsage / 1024 / 1024),
            errorCount: this.metrics.errorCount,
            isActive: this.isActive
        };
    }

    getDetailedMetrics() {
        return {
            ...this.getMetrics(),
            processingRate: this.metrics.totalProcessed / Math.max((Date.now() - this.metrics.startTime) / 1000, 1),
            detectionRate: this.metrics.totalDetections / Math.max((Date.now() - this.metrics.startTime) / 1000, 1),
            errorRate: this.metrics.errorCount / Math.max(this.metrics.totalProcessed, 1)
        };
    }
}

class AlertManager {
    constructor() {
        this.alerts = [];
        this.isActive = false;
        this.alertThresholds = {
            critical: 0.9,
            urgent: 0.8,
            warning: 0.6,
            info: 0.4
        };
    }

    start() {
        this.isActive = true;
        console.log('[AlertManager] Started');
    }

    stop() {
        this.isActive = false;
        console.log('[AlertManager] Stopped');
    }

    generateAlert(alertData) {
        if (!this.isActive) return;

        const alert = {
            id: this.generateAlertId(),
            ...alertData,
            generated: Date.now()
        };

        this.alerts.push(alert);

        // Maintain alert history
        if (this.alerts.length > 1000) {
            this.alerts.shift();
        }

        // Log alert
        console.log(`[AlertManager] ${alert.level.toUpperCase()} ALERT: ${alert.message}`);

        // In a real system, this would trigger notifications, logs, etc.
        return alert;
    }

    generateAlertId() {
        return `alert_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
    }

    getRecentAlerts(count = 10) {
        return this.alerts.slice(-count);
    }

    getAlertsByLevel(level) {
        return this.alerts.filter(alert => alert.level === level);
    }
}

export default RealTimeEntityDetector;