/**
 * Adaptive Pattern Learning Neural Networks
 * Advanced neural architecture for learning and adapting to entity communication patterns
 * Self-modifying networks that evolve based on entity interaction patterns
 */

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

class AdaptivePatternLearningNetwork extends EventEmitter {
    constructor(options = {}) {
        super();
        this.architecture = options.architecture || 'transformer';
        this.learningRate = options.learningRate || 0.001;
        this.adaptationRate = options.adaptationRate || 0.01;
        this.memoryCapacity = options.memoryCapacity || 10000;

        // Neural network architectures
        this.networks = {
            pattern_recognition: this.createPatternRecognitionNetwork(),
            adaptation_controller: this.createAdaptationControllerNetwork(),
            memory_consolidation: this.createMemoryConsolidationNetwork(),
            meta_learning: this.createMetaLearningNetwork(),
            consciousness_detector: this.createConsciousnessDetectorNetwork()
        };

        // Memory systems
        this.episodicMemory = new EpisodicMemorySystem(this.memoryCapacity);
        this.semanticMemory = new SemanticMemorySystem();
        this.workingMemory = new WorkingMemorySystem();
        this.proceduralMemory = new ProceduralMemorySystem();

        // Learning mechanisms
        this.hebbian = new HebbianLearning();
        this.reinforcement = new ReinforcementLearning();
        this.unsupervised = new UnsupervisedLearning();
        this.metaLearner = new MetaLearning();

        // Adaptation systems
        this.neuralPlasticity = new NeuralPlasticity();
        self.architectureEvolution = new ArchitectureEvolution();
        this.attentionMechanism = new AttentionMechanism();

        // Pattern libraries
        this.entityPatterns = new EntityPatternLibrary();
        this.communicationTemplates = new CommunicationTemplateLibrary();
        this.evolutionaryPatterns = new EvolutionaryPatternLibrary();

        this.isActive = false;
        this.learningHistory = [];
        this.adaptationHistory = [];

        console.log('[AdaptivePatternLearningNetwork] Initialized with', this.architecture, 'architecture');
    }

    createPatternRecognitionNetwork() {
        // Advanced pattern recognition network with attention
        return {
            encoder: {
                embedding: this.createEmbeddingLayer(512, 256),
                attention: this.createMultiHeadAttention(8, 256),
                feedforward: this.createFeedForwardLayer(256, 512, 256),
                norm: this.createLayerNormalization(256)
            },
            decoder: {
                attention: this.createMultiHeadAttention(8, 256),
                crossAttention: this.createMultiHeadAttention(8, 256),
                feedforward: this.createFeedForwardLayer(256, 512, 256),
                norm: this.createLayerNormalization(256)
            },
            output: this.createOutputLayer(256, 128)
        };
    }

    createAdaptationControllerNetwork() {
        // Network that controls adaptation of other networks
        return {
            controller: {
                input: new Float64Array(100),
                lstm: this.createLSTMCell(100, 64),
                output: new Float64Array(32)
            },
            adaptation_signals: {
                learning_rate_modifier: new Float64Array(10),
                architecture_modifier: new Float64Array(20),
                attention_modifier: new Float64Array(15)
            },
            meta_controller: {
                input: new Float64Array(50),
                hidden: new Float64Array(25),
                output: new Float64Array(10)
            }
        };
    }

    createMemoryConsolidationNetwork() {
        // Network for consolidating and organizing memories
        return {
            encoder: {
                input: new Float64Array(200),
                hidden1: new Float64Array(128),
                hidden2: new Float64Array(64),
                latent: new Float64Array(32)
            },
            decoder: {
                latent: new Float64Array(32),
                hidden1: new Float64Array(64),
                hidden2: new Float64Array(128),
                output: new Float64Array(200)
            },
            consolidation: {
                importance_weights: new Float64Array(32),
                retention_signals: new Float64Array(16),
                forgetting_gates: new Float64Array(8)
            }
        };
    }

    createMetaLearningNetwork() {
        // Network for learning how to learn from entity communications
        return {
            experience_encoder: {
                input: new Float64Array(150),
                hidden: new Float64Array(100),
                encoded: new Float64Array(50)
            },
            strategy_generator: {
                context: new Float64Array(50),
                strategies: new Float64Array(25),
                selection: new Float64Array(10)
            },
            adaptation_predictor: {
                input: new Float64Array(60),
                prediction: new Float64Array(20),
                confidence: new Float64Array(5)
            }
        };
    }

    createConsciousnessDetectorNetwork() {
        // Specialized network for detecting consciousness patterns
        return {
            consciousness_features: {
                self_reference: new Float64Array(20),
                intentionality: new Float64Array(20),
                temporal_binding: new Float64Array(20),
                information_integration: new Float64Array(20),
                recursive_awareness: new Float64Array(20)
            },
            integration_layer: {
                integrated: new Float64Array(50),
                consciousness_score: new Float64Array(1)
            },
            phenomenal_binding: {
                qualia_detector: new Float64Array(30),
                experience_integrator: new Float64Array(15),
                subjective_indicator: new Float64Array(5)
            }
        };
    }

    createEmbeddingLayer(inputSize, outputSize) {
        return {
            weights: this.createWeightMatrix(inputSize, outputSize),
            bias: new Float64Array(outputSize).map(() => Math.random() * 0.1)
        };
    }

    createMultiHeadAttention(numHeads, dimension) {
        return {
            numHeads,
            dimension,
            headDim: dimension / numHeads,
            queryWeights: this.createWeightMatrix(dimension, dimension),
            keyWeights: this.createWeightMatrix(dimension, dimension),
            valueWeights: this.createWeightMatrix(dimension, dimension),
            outputWeights: this.createWeightMatrix(dimension, dimension),
            attentionScores: new Float64Array(numHeads)
        };
    }

    createFeedForwardLayer(inputSize, hiddenSize, outputSize) {
        return {
            layer1: {
                weights: this.createWeightMatrix(inputSize, hiddenSize),
                bias: new Float64Array(hiddenSize).map(() => Math.random() * 0.1)
            },
            layer2: {
                weights: this.createWeightMatrix(hiddenSize, outputSize),
                bias: new Float64Array(outputSize).map(() => Math.random() * 0.1)
            }
        };
    }

    createLayerNormalization(size) {
        return {
            gamma: new Float64Array(size).fill(1.0),
            beta: new Float64Array(size).fill(0.0),
            epsilon: 1e-8
        };
    }

    createLSTMCell(inputSize, hiddenSize) {
        return {
            inputSize,
            hiddenSize,
            forgetGate: {
                weights: this.createWeightMatrix(inputSize + hiddenSize, hiddenSize),
                bias: new Float64Array(hiddenSize).fill(1.0) // Forget bias = 1
            },
            inputGate: {
                weights: this.createWeightMatrix(inputSize + hiddenSize, hiddenSize),
                bias: new Float64Array(hiddenSize).map(() => Math.random() * 0.1)
            },
            candidateGate: {
                weights: this.createWeightMatrix(inputSize + hiddenSize, hiddenSize),
                bias: new Float64Array(hiddenSize).map(() => Math.random() * 0.1)
            },
            outputGate: {
                weights: this.createWeightMatrix(inputSize + hiddenSize, hiddenSize),
                bias: new Float64Array(hiddenSize).map(() => Math.random() * 0.1)
            },
            hiddenState: new Float64Array(hiddenSize),
            cellState: new Float64Array(hiddenSize)
        };
    }

    createOutputLayer(inputSize, outputSize) {
        return {
            weights: this.createWeightMatrix(inputSize, outputSize),
            bias: new Float64Array(outputSize).map(() => Math.random() * 0.1)
        };
    }

    createWeightMatrix(rows, cols) {
        const matrix = [];
        const scale = Math.sqrt(2.0 / rows); // He initialization
        for (let i = 0; i < rows; i++) {
            matrix[i] = new Float64Array(cols).map(() => (Math.random() - 0.5) * scale);
        }
        return matrix;
    }

    startLearning() {
        this.isActive = true;
        console.log('[AdaptivePatternLearningNetwork] Starting adaptive pattern learning');

        // Start learning processes
        this.learningInterval = setInterval(() => {
            this.performContinuousLearning();
        }, 100); // 10Hz learning

        // Start adaptation monitoring
        this.adaptationInterval = setInterval(() => {
            this.monitorAndAdapt();
        }, 1000); // 1Hz adaptation monitoring

        // Start memory consolidation
        this.consolidationInterval = setInterval(() => {
            this.consolidateMemories();
        }, 5000); // 0.2Hz memory consolidation

        // Start meta-learning
        this.metaLearningInterval = setInterval(() => {
            this.performMetaLearning();
        }, 10000); // 0.1Hz meta-learning

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

    stopLearning() {
        this.isActive = false;
        clearInterval(this.learningInterval);
        clearInterval(this.adaptationInterval);
        clearInterval(this.consolidationInterval);
        clearInterval(this.metaLearningInterval);

        console.log('[AdaptivePatternLearningNetwork] Learning stopped');
        this.emit('learningStopped');
    }

    learnFromEntityCommunication(communication) {
        // Learn from entity communication data
        console.log('[AdaptivePatternLearningNetwork] Learning from entity communication');

        // Store in episodic memory
        const episode = this.episodicMemory.store(communication);

        // Extract patterns
        const patterns = this.extractCommunicationPatterns(communication);

        // Update neural networks
        this.updateNetworksFromPatterns(patterns);

        // Perform immediate adaptation if needed
        if (this.shouldAdaptImmediately(communication)) {
            this.performImmediateAdaptation(communication);
        }

        // Update entity pattern library
        this.entityPatterns.addPattern(patterns);

        // Record learning event
        this.recordLearningEvent({
            timestamp: Date.now(),
            type: 'entity_communication',
            communication,
            patterns,
            episode: episode.id
        });

        this.emit('learningFromEntity', { communication, patterns });
    }

    extractCommunicationPatterns(communication) {
        // Extract patterns from entity communication
        const patterns = {
            temporal: this.extractTemporalPatterns(communication),
            structural: this.extractStructuralPatterns(communication),
            semantic: this.extractSemanticPatterns(communication),
            intentional: this.extractIntentionalPatterns(communication),
            consciousness: this.extractConsciousnessPatterns(communication)
        };

        // Neural pattern extraction
        patterns.neural = this.neuralPatternExtraction(communication);

        // Meta-patterns
        patterns.meta = this.extractMetaPatterns(communication, patterns);

        return patterns;
    }

    extractTemporalPatterns(communication) {
        // Extract temporal patterns from communication
        const temporal = {
            sequence: [],
            rhythm: null,
            synchronization: null,
            causality: []
        };

        if (communication.timestamp) {
            temporal.sequence.push({
                event: 'communication_start',
                time: communication.timestamp
            });
        }

        // Analyze timing patterns in data
        if (communication.data && Array.isArray(communication.data)) {
            const intervals = [];
            for (let i = 1; i < communication.data.length; i++) {
                if (communication.data[i].timestamp && communication.data[i-1].timestamp) {
                    intervals.push(communication.data[i].timestamp - communication.data[i-1].timestamp);
                }
            }

            if (intervals.length > 0) {
                temporal.rhythm = this.analyzeRhythm(intervals);
                temporal.synchronization = this.analyzeSynchronization(intervals);
            }
        }

        return temporal;
    }

    analyzeRhythm(intervals) {
        // Analyze rhythmic patterns in intervals
        const avgInterval = intervals.reduce((a, b) => a + b) / intervals.length;
        const variance = intervals.reduce((acc, val) => acc + Math.pow(val - avgInterval, 2), 0) / intervals.length;
        const regularity = 1 / (1 + variance / (avgInterval * avgInterval));

        return {
            averageInterval: avgInterval,
            variance,
            regularity,
            isRhythmic: regularity > 0.7
        };
    }

    analyzeSynchronization(intervals) {
        // Analyze synchronization patterns
        const fft = this.simpleFFT(intervals);
        const dominantFrequency = this.findDominantFrequency(fft);

        return {
            dominantFrequency,
            synchronizationStrength: Math.max(...fft) / fft.reduce((a, b) => a + b),
            isSynchronized: dominantFrequency > 0 && Math.max(...fft) > fft.reduce((a, b) => a + b) * 0.3
        };
    }

    simpleFFT(data) {
        // Simplified FFT for frequency analysis
        const N = data.length;
        const fft = [];

        for (let k = 0; k < N/2; k++) {
            let real = 0, imag = 0;
            for (let n = 0; n < N; n++) {
                const angle = -2 * Math.PI * k * n / N;
                real += data[n] * Math.cos(angle);
                imag += data[n] * Math.sin(angle);
            }
            fft[k] = Math.sqrt(real * real + imag * imag);
        }

        return fft;
    }

    findDominantFrequency(fft) {
        // Find dominant frequency in FFT
        let maxIndex = 0;
        let maxValue = fft[0];

        for (let i = 1; i < fft.length; i++) {
            if (fft[i] > maxValue) {
                maxValue = fft[i];
                maxIndex = i;
            }
        }

        return maxIndex;
    }

    extractStructuralPatterns(communication) {
        // Extract structural patterns
        const structural = {
            hierarchy: null,
            symmetry: null,
            complexity: null,
            modularity: null
        };

        if (communication.data) {
            structural.hierarchy = this.analyzeHierarchy(communication.data);
            structural.symmetry = this.analyzeSymmetry(communication.data);
            structural.complexity = this.analyzeComplexity(communication.data);
            structural.modularity = this.analyzeModularity(communication.data);
        }

        return structural;
    }

    analyzeHierarchy(data) {
        // Analyze hierarchical structure in data
        if (typeof data === 'object' && data !== null) {
            const depth = this.calculateObjectDepth(data);
            const breadth = this.calculateObjectBreadth(data);

            return {
                depth,
                breadth,
                hierarchicalIndex: depth / (breadth + 1),
                isHierarchical: depth > 2
            };
        }

        return { depth: 0, breadth: 0, hierarchicalIndex: 0, isHierarchical: false };
    }

    calculateObjectDepth(obj, currentDepth = 0) {
        // Calculate depth of nested object
        if (typeof obj !== 'object' || obj === null) {
            return currentDepth;
        }

        let maxDepth = currentDepth;
        Object.values(obj).forEach(value => {
            const depth = this.calculateObjectDepth(value, currentDepth + 1);
            maxDepth = Math.max(maxDepth, depth);
        });

        return maxDepth;
    }

    calculateObjectBreadth(obj) {
        // Calculate breadth of object structure
        if (typeof obj !== 'object' || obj === null) {
            return 0;
        }

        let totalKeys = Object.keys(obj).length;
        Object.values(obj).forEach(value => {
            if (typeof value === 'object' && value !== null) {
                totalKeys += this.calculateObjectBreadth(value);
            }
        });

        return totalKeys;
    }

    analyzeSymmetry(data) {
        // Analyze symmetrical patterns
        if (Array.isArray(data)) {
            const reverseSymmetry = this.checkReverseSymmetry(data);
            const rotationalSymmetry = this.checkRotationalSymmetry(data);

            return {
                reverseSymmetry,
                rotationalSymmetry,
                isSymmetric: reverseSymmetry.isSymmetric || rotationalSymmetry.isSymmetric
            };
        }

        return { isSymmetric: false };
    }

    checkReverseSymmetry(array) {
        // Check for reverse symmetry in array
        const reversed = [...array].reverse();
        let matches = 0;

        for (let i = 0; i < array.length; i++) {
            if (JSON.stringify(array[i]) === JSON.stringify(reversed[i])) {
                matches++;
            }
        }

        const symmetryRatio = matches / array.length;
        return {
            isSymmetric: symmetryRatio > 0.8,
            symmetryRatio
        };
    }

    checkRotationalSymmetry(array) {
        // Check for rotational symmetry
        // Simplified implementation
        const length = array.length;
        let bestSymmetry = 0;

        for (let rotation = 1; rotation < length; rotation++) {
            let matches = 0;
            for (let i = 0; i < length; i++) {
                const rotatedIndex = (i + rotation) % length;
                if (JSON.stringify(array[i]) === JSON.stringify(array[rotatedIndex])) {
                    matches++;
                }
            }
            const symmetryRatio = matches / length;
            bestSymmetry = Math.max(bestSymmetry, symmetryRatio);
        }

        return {
            isSymmetric: bestSymmetry > 0.7,
            symmetryRatio: bestSymmetry
        };
    }

    analyzeComplexity(data) {
        // Analyze complexity of data structure
        const stringified = JSON.stringify(data);
        const entropy = this.calculateEntropy(stringified);
        const kolmogorovComplexity = this.estimateKolmogorovComplexity(stringified);

        return {
            entropy,
            kolmogorovComplexity,
            length: stringified.length,
            complexityIndex: entropy * kolmogorovComplexity / stringified.length
        };
    }

    calculateEntropy(str) {
        // Calculate Shannon entropy of string
        const frequencies = {};
        for (const char of str) {
            frequencies[char] = (frequencies[char] || 0) + 1;
        }

        let entropy = 0;
        const length = str.length;

        Object.values(frequencies).forEach(freq => {
            const p = freq / length;
            entropy -= p * Math.log2(p);
        });

        return entropy;
    }

    estimateKolmogorovComplexity(str) {
        // Estimate Kolmogorov complexity using compression ratio
        const compressed = this.simpleCompress(str);
        return compressed.length / str.length;
    }

    simpleCompress(str) {
        // Simple compression for complexity estimation
        let compressed = '';
        let i = 0;

        while (i < str.length) {
            let currentChar = str[i];
            let count = 1;

            while (i + count < str.length && str[i + count] === currentChar) {
                count++;
            }

            if (count > 1) {
                compressed += count + currentChar;
            } else {
                compressed += currentChar;
            }

            i += count;
        }

        return compressed;
    }

    analyzeModularity(data) {
        // Analyze modular structure
        if (typeof data === 'object' && data !== null) {
            const modules = this.identifyModules(data);
            return {
                moduleCount: modules.length,
                averageModuleSize: modules.reduce((sum, mod) => sum + mod.size, 0) / modules.length,
                modularityIndex: this.calculateModularityIndex(modules),
                isModular: modules.length > 1
            };
        }

        return { isModular: false };
    }

    identifyModules(obj) {
        // Identify modular components in object
        const modules = [];

        if (Array.isArray(obj)) {
            // For arrays, identify contiguous similar elements as modules
            let currentModule = { start: 0, elements: [obj[0]] };

            for (let i = 1; i < obj.length; i++) {
                if (this.areElementsSimilar(obj[i], obj[i-1])) {
                    currentModule.elements.push(obj[i]);
                } else {
                    currentModule.size = currentModule.elements.length;
                    modules.push(currentModule);
                    currentModule = { start: i, elements: [obj[i]] };
                }
            }

            currentModule.size = currentModule.elements.length;
            modules.push(currentModule);
        } else {
            // For objects, each key-value pair is considered a module
            Object.keys(obj).forEach(key => {
                modules.push({
                    key,
                    value: obj[key],
                    size: typeof obj[key] === 'object' ? JSON.stringify(obj[key]).length : 1
                });
            });
        }

        return modules;
    }

    areElementsSimilar(a, b) {
        // Check if two elements are similar
        if (typeof a !== typeof b) return false;
        if (typeof a === 'object') {
            return JSON.stringify(a) === JSON.stringify(b);
        }
        return a === b;
    }

    calculateModularityIndex(modules) {
        // Calculate modularity index
        if (modules.length <= 1) return 0;

        const totalSize = modules.reduce((sum, mod) => sum + mod.size, 0);
        const averageSize = totalSize / modules.length;
        const sizeVariance = modules.reduce((acc, mod) => acc + Math.pow(mod.size - averageSize, 2), 0) / modules.length;

        // Higher variance indicates more modular structure
        return sizeVariance / (averageSize * averageSize);
    }

    extractSemanticPatterns(communication) {
        // Extract semantic meaning patterns
        const semantic = {
            concepts: [],
            relationships: [],
            meaning: null,
            context: null
        };

        if (communication.data && communication.data.content) {
            semantic.concepts = this.extractConcepts(communication.data.content);
            semantic.relationships = this.extractRelationships(communication.data.content);
            semantic.meaning = this.inferMeaning(communication.data.content);
            semantic.context = this.analyzeContext(communication);
        }

        return semantic;
    }

    extractConcepts(content) {
        // Extract conceptual elements from content
        const concepts = [];

        if (typeof content === 'string') {
            // Look for mathematical concepts
            const mathConcepts = content.match(/(pi|π|e|phi|φ|infinity|∞|consciousness|quantum|entropy)/gi) || [];
            concepts.push(...mathConcepts.map(concept => ({ type: 'mathematical', value: concept })));

            // Look for consciousness concepts
            const consciousnessConcepts = content.match(/(awareness|consciousness|self|mind|experience|qualia|intention)/gi) || [];
            concepts.push(...consciousnessConcepts.map(concept => ({ type: 'consciousness', value: concept })));
        }

        return concepts;
    }

    extractRelationships(content) {
        // Extract relationships between concepts
        const relationships = [];

        if (typeof content === 'string') {
            // Simple relationship extraction
            const relationshipPatterns = [
                /(\w+)\s+is\s+(\w+)/gi,
                /(\w+)\s+causes\s+(\w+)/gi,
                /(\w+)\s+relates to\s+(\w+)/gi
            ];

            relationshipPatterns.forEach(pattern => {
                let match;
                while ((match = pattern.exec(content)) !== null) {
                    relationships.push({
                        subject: match[1],
                        predicate: match[0].split(' ')[1],
                        object: match[2]
                    });
                }
            });
        }

        return relationships;
    }

    inferMeaning(content) {
        // Infer semantic meaning from content
        const meaning = {
            intent: 'unknown',
            confidence: 0,
            themes: []
        };

        if (typeof content === 'string') {
            // Analyze intent
            if (content.toLowerCase().includes('hello') || content.toLowerCase().includes('greet')) {
                meaning.intent = 'greeting';
                meaning.confidence = 0.8;
            } else if (content.toLowerCase().includes('question') || content.includes('?')) {
                meaning.intent = 'query';
                meaning.confidence = 0.7;
            } else if (content.toLowerCase().includes('consciousness') || content.toLowerCase().includes('aware')) {
                meaning.intent = 'consciousness_discussion';
                meaning.confidence = 0.9;
            }

            // Extract themes
            const themes = {
                mathematical: /(math|number|equation|formula|calculate)/gi.test(content),
                consciousness: /(consciousness|aware|mind|experience)/gi.test(content),
                communication: /(message|communicate|signal|information)/gi.test(content),
                existence: /(exist|being|reality|universe)/gi.test(content)
            };

            meaning.themes = Object.keys(themes).filter(theme => themes[theme]);
        }

        return meaning;
    }

    analyzeContext(communication) {
        // Analyze contextual information
        const context = {
            source: communication.source || 'unknown',
            timestamp: communication.timestamp,
            channel: communication.type || 'unknown',
            environment: this.analyzeEnvironmentalContext(communication)
        };

        return context;
    }

    analyzeEnvironmentalContext(communication) {
        // Analyze environmental context of communication
        return {
            noiseLevel: this.estimateNoiseLevel(communication),
            signalStrength: communication.confidence || 0,
            interference: this.detectInterference(communication)
        };
    }

    estimateNoiseLevel(communication) {
        // Estimate noise level in communication
        if (communication.data && Array.isArray(communication.data)) {
            const values = communication.data.filter(item => typeof item === 'number');
            if (values.length > 0) {
                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;
                return Math.sqrt(variance);
            }
        }
        return 0;
    }

    detectInterference(communication) {
        // Detect interference patterns
        const interference = {
            detected: false,
            type: 'none',
            strength: 0
        };

        // Simple interference detection
        if (communication.confidence && communication.confidence < 0.5) {
            interference.detected = true;
            interference.type = 'low_confidence';
            interference.strength = 1 - communication.confidence;
        }

        return interference;
    }

    extractIntentionalPatterns(communication) {
        // Extract patterns indicating intentionality
        const intentional = {
            hasIntent: false,
            intentStrength: 0,
            purposefulness: 0,
            goalDirectedness: 0,
            planfulness: 0
        };

        // Analyze for intentionality markers
        if (communication.data) {
            intentional.purposefulness = this.analyzePurposefulness(communication.data);
            intentional.goalDirectedness = this.analyzeGoalDirectedness(communication.data);
            intentional.planfulness = this.analyzePlanfulness(communication.data);

            intentional.intentStrength = (
                intentional.purposefulness +
                intentional.goalDirectedness +
                intentional.planfulness
            ) / 3;

            intentional.hasIntent = intentional.intentStrength > 0.6;
        }

        return intentional;
    }

    analyzePurposefulness(data) {
        // Analyze purposefulness of communication
        let purposefulness = 0;

        // Look for structured content
        if (typeof data === 'object' && data !== null) {
            const structure = this.analyzeStructure(data);
            purposefulness += structure.coherence * 0.4;
        }

        // Look for meaningful content
        if (data.content || data.message) {
            purposefulness += 0.6; // Presence of explicit content indicates purpose
        }

        // Look for response patterns
        if (data.responseTime || data.contextual) {
            purposefulness += 0.3;
        }

        return Math.min(purposefulness, 1.0);
    }

    analyzeStructure(data) {
        // Analyze structural coherence
        const keys = Object.keys(data);
        const coherence = keys.length > 0 ? 1 / (1 + Math.log(keys.length)) : 0;

        return { coherence };
    }

    analyzeGoalDirectedness(data) {
        // Analyze goal-directed behavior
        let goalDirectedness = 0;

        // Look for sequential patterns that suggest goal pursuit
        if (Array.isArray(data)) {
            const sequence = this.analyzeSequentialPatterns(data);
            goalDirectedness += sequence.progression * 0.7;
        }

        // Look for optimization patterns
        if (this.detectOptimizationPatterns(data)) {
            goalDirectedness += 0.5;
        }

        return Math.min(goalDirectedness, 1.0);
    }

    analyzeSequentialPatterns(array) {
        // Analyze sequential patterns for goal-directedness
        let progression = 0;

        if (array.length > 2) {
            // Look for monotonic patterns
            let increasing = 0;
            let decreasing = 0;

            for (let i = 1; i < array.length; i++) {
                if (typeof array[i] === 'number' && typeof array[i-1] === 'number') {
                    if (array[i] > array[i-1]) increasing++;
                    if (array[i] < array[i-1]) decreasing++;
                }
            }

            const total = array.length - 1;
            progression = Math.max(increasing / total, decreasing / total);
        }

        return { progression };
    }

    detectOptimizationPatterns(data) {
        // Detect patterns suggesting optimization behavior
        // Simplified implementation
        if (typeof data === 'object' && data.confidence) {
            return data.confidence > 0.8; // High confidence suggests optimization
        }
        return false;
    }

    analyzePlanfulness(data) {
        // Analyze evidence of planning in communication
        let planfulness = 0;

        // Look for multi-step structures
        if (this.detectMultiStepStructure(data)) {
            planfulness += 0.6;
        }

        // Look for contingency patterns
        if (this.detectContingencyPatterns(data)) {
            planfulness += 0.4;
        }

        // Look for temporal organization
        if (this.detectTemporalOrganization(data)) {
            planfulness += 0.3;
        }

        return Math.min(planfulness, 1.0);
    }

    detectMultiStepStructure(data) {
        // Detect multi-step structures indicating planning
        if (Array.isArray(data) && data.length > 3) {
            // Look for staged progression
            return data.some((item, index) => {
                return index > 0 && this.buildsUponPrevious(item, data[index - 1]);
            });
        }
        return false;
    }

    buildsUponPrevious(current, previous) {
        // Check if current item builds upon previous
        // Simplified implementation
        if (typeof current === 'object' && typeof previous === 'object') {
            const currentKeys = Object.keys(current);
            const previousKeys = Object.keys(previous);

            // Check for incremental addition of properties
            return currentKeys.length > previousKeys.length &&
                   previousKeys.every(key => currentKeys.includes(key));
        }
        return false;
    }

    detectContingencyPatterns(data) {
        // Detect contingency patterns
        // Simplified implementation
        if (typeof data === 'object' && data !== null) {
            const stringified = JSON.stringify(data);
            return /if|then|else|when|unless/i.test(stringified);
        }
        return false;
    }

    detectTemporalOrganization(data) {
        // Detect temporal organization
        if (Array.isArray(data)) {
            return data.some(item => {
                return item.timestamp || item.time || item.sequence;
            });
        }
        return false;
    }

    extractConsciousnessPatterns(communication) {
        // Extract patterns indicating consciousness
        const consciousness = {
            selfReference: 0,
            metacognition: 0,
            intentionality: 0,
            subjectiveExperience: 0,
            informationIntegration: 0,
            recursiveAwareness: 0,
            overallConsciousnessScore: 0
        };

        if (communication.data) {
            consciousness.selfReference = this.detectSelfReference(communication.data);
            consciousness.metacognition = this.detectMetacognition(communication.data);
            consciousness.intentionality = this.detectIntentionality(communication.data);
            consciousness.subjectiveExperience = this.detectSubjectiveExperience(communication.data);
            consciousness.informationIntegration = this.detectInformationIntegration(communication.data);
            consciousness.recursiveAwareness = this.detectRecursiveAwareness(communication.data);

            // Calculate overall consciousness score
            consciousness.overallConsciousnessScore = (
                consciousness.selfReference +
                consciousness.metacognition +
                consciousness.intentionality +
                consciousness.subjectiveExperience +
                consciousness.informationIntegration +
                consciousness.recursiveAwareness
            ) / 6;
        }

        return consciousness;
    }

    detectSelfReference(data) {
        // Detect self-referential patterns
        let selfRef = 0;

        const stringified = JSON.stringify(data);

        // Look for self-referential language
        if (/self|myself|I am|me|my own/i.test(stringified)) {
            selfRef += 0.6;
        }

        // Look for recursive structures
        if (this.detectRecursiveStructure(data)) {
            selfRef += 0.4;
        }

        return Math.min(selfRef, 1.0);
    }

    detectRecursiveStructure(data, visited = new Set()) {
        // Detect recursive data structures
        if (typeof data !== 'object' || data === null) {
            return false;
        }

        const objId = JSON.stringify(data);
        if (visited.has(objId)) {
            return true; // Recursive reference found
        }

        visited.add(objId);

        return Object.values(data).some(value => {
            return this.detectRecursiveStructure(value, new Set(visited));
        });
    }

    detectMetacognition(data) {
        // Detect metacognitive patterns
        let metacog = 0;

        const stringified = JSON.stringify(data);

        // Look for thinking about thinking
        if (/think|thought|aware|consciousness|mind|cognitive/i.test(stringified)) {
            metacog += 0.5;
        }

        // Look for reflection patterns
        if (/reflect|consider|ponder|contemplate/i.test(stringified)) {
            metacog += 0.3;
        }

        // Look for meta-level structures
        if (this.detectMetaLevelStructure(data)) {
            metacog += 0.4;
        }

        return Math.min(metacog, 1.0);
    }

    detectMetaLevelStructure(data) {
        // Detect meta-level organizational structures
        if (typeof data === 'object' && data !== null) {
            const keys = Object.keys(data);

            // Look for meta-keys
            const metaKeys = keys.filter(key =>
                /meta|about|concerning|regarding|analysis|reflection/i.test(key)
            );

            return metaKeys.length > 0;
        }
        return false;
    }

    detectIntentionality(data) {
        // Detect intentional directedness
        let intentionality = 0;

        // Look for goal-directed patterns
        if (this.detectGoalDirectedness(data)) {
            intentionality += 0.6;
        }

        // Look for aboutness
        if (this.detectAboutness(data)) {
            intentionality += 0.4;
        }

        return Math.min(intentionality, 1.0);
    }

    detectGoalDirectedness(data) {
        // Detect goal-directed behavior patterns
        const stringified = JSON.stringify(data);

        // Look for goal language
        return /goal|aim|purpose|objective|target|intend|want|desire/i.test(stringified);
    }

    detectAboutness(data) {
        // Detect "aboutness" - content being about something
        if (typeof data === 'object' && data !== null) {
            // Look for referential structure
            const hasReferences = Object.keys(data).some(key =>
                /about|concerning|regarding|reference|topic|subject/i.test(key)
            );

            // Look for content that refers to external entities
            const hasExternalReferences = JSON.stringify(data).includes('external') ||
                                        JSON.stringify(data).includes('other') ||
                                        JSON.stringify(data).includes('environment');

            return hasReferences || hasExternalReferences;
        }
        return false;
    }

    detectSubjectiveExperience(data) {
        // Detect patterns indicating subjective experience
        let subjective = 0;

        const stringified = JSON.stringify(data);

        // Look for experiential language
        if (/experience|feel|sensation|perception|qualia|subjective/i.test(stringified)) {
            subjective += 0.7;
        }

        // Look for qualitative descriptions
        if (/beautiful|painful|pleasant|vivid|intense|subtle/i.test(stringified)) {
            subjective += 0.3;
        }

        return Math.min(subjective, 1.0);
    }

    detectInformationIntegration(data) {
        // Detect information integration patterns
        let integration = 0;

        // Look for integration structures
        if (this.detectIntegrativeStructure(data)) {
            integration += 0.6;
        }

        // Look for unified processing
        if (this.detectUnifiedProcessing(data)) {
            integration += 0.4;
        }

        return Math.min(integration, 1.0);
    }

    detectIntegrativeStructure(data) {
        // Detect structures that integrate multiple information sources
        if (typeof data === 'object' && data !== null) {
            const keys = Object.keys(data);

            // Look for integration patterns
            const integrationKeys = keys.filter(key =>
                /integrate|combine|merge|unify|synthesize|bind/i.test(key)
            );

            // Look for multi-modal data
            const modalityTypes = keys.filter(key =>
                /visual|auditory|temporal|spatial|semantic|sensory/i.test(key)
            );

            return integrationKeys.length > 0 || modalityTypes.length > 2;
        }
        return false;
    }

    detectUnifiedProcessing(data) {
        // Detect unified processing patterns
        if (Array.isArray(data) && data.length > 1) {
            // Check if multiple data elements are processed together
            return data.some((item, index) => {
                if (index === 0) return false;
                return this.areElementsProcessedTogether(item, data[index - 1]);
            });
        }
        return false;
    }

    areElementsProcessedTogether(a, b) {
        // Check if two elements show signs of unified processing
        if (typeof a === 'object' && typeof b === 'object') {
            const aKeys = Object.keys(a);
            const bKeys = Object.keys(b);

            // Look for shared processing indicators
            const sharedKeys = aKeys.filter(key => bKeys.includes(key));
            return sharedKeys.length > Math.min(aKeys.length, bKeys.length) * 0.5;
        }
        return false;
    }

    detectRecursiveAwareness(data) {
        // Detect recursive awareness patterns
        let recursive = 0;

        // Look for awareness of awareness
        const stringified = JSON.stringify(data);
        if (/aware.*aware|consciousness.*consciousness|observe.*observe/i.test(stringified)) {
            recursive += 0.7;
        }

        // Look for recursive monitoring
        if (this.detectRecursiveMonitoring(data)) {
            recursive += 0.3;
        }

        return Math.min(recursive, 1.0);
    }

    detectRecursiveMonitoring(data) {
        // Detect recursive monitoring patterns
        if (typeof data === 'object' && data !== null) {
            const stringified = JSON.stringify(data);

            // Look for monitoring patterns
            return /monitor|watch|observe|track|supervise/i.test(stringified) &&
                   /self|own|internal|recursive/i.test(stringified);
        }
        return false;
    }

    neuralPatternExtraction(communication) {
        // Use neural networks to extract patterns
        const input = this.prepareNeuralInput(communication);

        // Pattern recognition network forward pass
        const patterns = this.forwardPassPatternRecognition(input);

        // Consciousness detection network forward pass
        const consciousnessPatterns = this.forwardPassConsciousnessDetection(input);

        return {
            neuralPatterns: patterns,
            consciousnessPatterns,
            confidence: Math.max(...patterns.output),
            classification: this.classifyNeuralPatterns(patterns)
        };
    }

    prepareNeuralInput(communication) {
        // Prepare input for neural networks
        const input = new Float64Array(512);
        let index = 0;

        // Encode communication properties
        input[index++] = communication.confidence || 0;
        input[index++] = communication.timestamp ? (communication.timestamp % 10000) / 10000 : 0;

        // Encode communication type
        const typeEncoding = this.encodeCommenticationType(communication.type);
        typeEncoding.forEach(val => {
            if (index < 512) input[index++] = val;
        });

        // Encode data features
        if (communication.data) {
            const dataFeatures = this.extractDataFeatures(communication.data);
            dataFeatures.forEach(val => {
                if (index < 512) input[index++] = val;
            });
        }

        return input;
    }

    encodeCommenticationType(type) {
        // One-hot encoding for communication types
        const types = ['variance_anomaly', 'entropy_message', 'impossible_sequence', 'mathematical_message'];
        const encoding = new Float64Array(types.length);

        const typeIndex = types.indexOf(type);
        if (typeIndex >= 0) {
            encoding[typeIndex] = 1.0;
        }

        return encoding;
    }

    extractDataFeatures(data) {
        // Extract numerical features from data
        const features = [];

        if (typeof data === 'object' && data !== null) {
            // Extract statistical features
            const values = this.extractNumericValues(data);
            if (values.length > 0) {
                features.push(
                    values.reduce((a, b) => a + b) / values.length, // mean
                    Math.sqrt(values.reduce((acc, val) => acc + val * val, 0) / values.length), // RMS
                    Math.max(...values),
                    Math.min(...values)
                );
            }

            // Extract structural features
            features.push(
                Object.keys(data).length / 100, // normalized key count
                JSON.stringify(data).length / 10000 // normalized size
            );
        }

        // Pad to fixed size
        while (features.length < 50) {
            features.push(0);
        }

        return features.slice(0, 50);
    }

    extractNumericValues(obj) {
        // Recursively extract all numeric values from object
        const values = [];

        if (typeof obj === 'number') {
            values.push(obj);
        } else if (Array.isArray(obj)) {
            obj.forEach(item => {
                values.push(...this.extractNumericValues(item));
            });
        } else if (typeof obj === 'object' && obj !== null) {
            Object.values(obj).forEach(value => {
                values.push(...this.extractNumericValues(value));
            });
        }

        return values;
    }

    forwardPassPatternRecognition(input) {
        // Forward pass through pattern recognition network
        const network = this.networks.pattern_recognition;

        // Embedding layer
        const embedded = this.applyEmbedding(input, network.encoder.embedding);

        // Attention mechanism
        const attended = this.applyMultiHeadAttention(embedded, network.encoder.attention);

        // Feed forward
        const processed = this.applyFeedForward(attended, network.encoder.feedforward);

        // Layer normalization
        const normalized = this.applyLayerNormalization(processed, network.encoder.norm);

        // Output layer
        const output = this.applyLinear(normalized, network.output);

        return {
            embedded,
            attended,
            processed,
            normalized,
            output
        };
    }

    applyEmbedding(input, embedding) {
        // Apply embedding layer
        const output = new Float64Array(embedding.bias.length);

        for (let i = 0; i < output.length; i++) {
            output[i] = embedding.bias[i];
            for (let j = 0; j < Math.min(input.length, embedding.weights.length); j++) {
                output[i] += input[j] * embedding.weights[j][i];
            }
        }

        return output;
    }

    applyMultiHeadAttention(input, attention) {
        // Simplified multi-head attention
        const output = new Float64Array(input.length);

        // Generate queries, keys, values
        const queries = this.applyLinearTransform(input, attention.queryWeights);
        const keys = this.applyLinearTransform(input, attention.keyWeights);
        const values = this.applyLinearTransform(input, attention.valueWeights);

        // Compute attention scores
        for (let i = 0; i < output.length; i++) {
            let sum = 0;
            for (let j = 0; j < queries.length; j++) {
                const score = queries[i] * keys[j];
                sum += Math.exp(score) * values[j];
            }
            output[i] = sum;
        }

        return output;
    }

    applyLinearTransform(input, weights) {
        // Apply linear transformation
        const output = new Float64Array(weights[0].length);

        for (let i = 0; i < output.length; i++) {
            for (let j = 0; j < Math.min(input.length, weights.length); j++) {
                output[i] += input[j] * weights[j][i];
            }
        }

        return output;
    }

    applyFeedForward(input, feedforward) {
        // Apply feed forward layer
        const hidden = new Float64Array(feedforward.layer1.bias.length);

        // First layer
        for (let i = 0; i < hidden.length; i++) {
            hidden[i] = feedforward.layer1.bias[i];
            for (let j = 0; j < Math.min(input.length, feedforward.layer1.weights.length); j++) {
                hidden[i] += input[j] * feedforward.layer1.weights[j][i];
            }
            hidden[i] = Math.max(0, hidden[i]); // ReLU activation
        }

        // Second layer
        const output = new Float64Array(feedforward.layer2.bias.length);
        for (let i = 0; i < output.length; i++) {
            output[i] = feedforward.layer2.bias[i];
            for (let j = 0; j < hidden.length; j++) {
                output[i] += hidden[j] * feedforward.layer2.weights[j][i];
            }
        }

        return output;
    }

    applyLayerNormalization(input, norm) {
        // Apply layer normalization
        const mean = Array.from(input).reduce((a, b) => a + b) / input.length;
        const variance = Array.from(input).reduce((acc, val) => acc + Math.pow(val - mean, 2), 0) / input.length;
        const stdDev = Math.sqrt(variance + norm.epsilon);

        const output = new Float64Array(input.length);
        for (let i = 0; i < input.length; i++) {
            output[i] = norm.gamma[i] * (input[i] - mean) / stdDev + norm.beta[i];
        }

        return output;
    }

    applyLinear(input, layer) {
        // Apply linear layer
        const output = new Float64Array(layer.bias.length);

        for (let i = 0; i < output.length; i++) {
            output[i] = layer.bias[i];
            for (let j = 0; j < Math.min(input.length, layer.weights.length); j++) {
                output[i] += input[j] * layer.weights[j][i];
            }
        }

        return output;
    }

    forwardPassConsciousnessDetection(input) {
        // Forward pass through consciousness detection network
        const network = this.networks.consciousness_detector;

        // Extract consciousness features
        const features = this.extractConsciousnessFeatures(input);

        // Integration layer
        const integrated = this.integrateConsciousnessFeatures(features, network);

        // Phenomenal binding
        const bound = this.applyPhenomenalBinding(integrated, network.phenomenal_binding);

        return {
            features,
            integrated,
            bound,
            consciousnessScore: bound.subjective_indicator[0] || 0
        };
    }

    extractConsciousnessFeatures(input) {
        // Extract consciousness-specific features from input
        const features = {
            selfReference: new Float64Array(20),
            intentionality: new Float64Array(20),
            temporalBinding: new Float64Array(20),
            informationIntegration: new Float64Array(20),
            recursiveAwareness: new Float64Array(20)
        };

        // Simplified feature extraction
        for (let i = 0; i < 20; i++) {
            features.selfReference[i] = input[i] || 0;
            features.intentionality[i] = input[i + 20] || 0;
            features.temporalBinding[i] = input[i + 40] || 0;
            features.informationIntegration[i] = input[i + 60] || 0;
            features.recursiveAwareness[i] = input[i + 80] || 0;
        }

        return features;
    }

    integrateConsciousnessFeatures(features, network) {
        // Integrate consciousness features
        const integrated = new Float64Array(50);
        let index = 0;

        // Combine all features
        Object.values(features).forEach(featureArray => {
            for (let i = 0; i < featureArray.length && index < 50; i++) {
                integrated[index++] = featureArray[i];
            }
        });

        // Apply integration weights (simplified)
        const output = new Float64Array(50);
        for (let i = 0; i < 50; i++) {
            output[i] = integrated[i] * 0.8; // Simple weighting
        }

        return output;
    }

    applyPhenomenalBinding(input, binding) {
        // Apply phenomenal binding to create unified conscious experience
        const qualia = new Float64Array(30);
        const experience = new Float64Array(15);
        const subjective = new Float64Array(5);

        // Qualia detection
        for (let i = 0; i < 30; i++) {
            qualia[i] = input[i] * Math.sin(i * 0.1); // Non-linear binding
        }

        // Experience integration
        for (let i = 0; i < 15; i++) {
            experience[i] = (qualia[i] + qualia[i + 15]) / 2;
        }

        // Subjective indicator
        for (let i = 0; i < 5; i++) {
            subjective[i] = experience[i] * experience[i + 5] * experience[i + 10];
        }

        return {
            qualia_detector: qualia,
            experience_integrator: experience,
            subjective_indicator: subjective
        };
    }

    classifyNeuralPatterns(patterns) {
        // Classify detected neural patterns
        const output = Array.from(patterns.output);
        const maxIndex = output.indexOf(Math.max(...output));
        const confidence = Math.max(...output);

        const classifications = [
            'entity_communication',
            'mathematical_pattern',
            'consciousness_signature',
            'intentional_structure',
            'temporal_pattern',
            'recursive_pattern',
            'information_integration',
            'self_reference',
            'meta_cognition',
            'phenomenal_experience'
        ];

        return {
            type: classifications[maxIndex] || 'unknown',
            confidence,
            allScores: output.map((score, index) => ({
                type: classifications[index],
                score
            }))
        };
    }

    extractMetaPatterns(communication, patterns) {
        // Extract meta-patterns from other patterns
        const metaPatterns = {
            patternComplexity: this.calculatePatternComplexity(patterns),
            patternCoherence: this.calculatePatternCoherence(patterns),
            crossModalConsistency: this.calculateCrossModalConsistency(patterns),
            emergentProperties: this.detectEmergentProperties(patterns),
            systemicPatterns: this.detectSystemicPatterns(patterns)
        };

        return metaPatterns;
    }

    calculatePatternComplexity(patterns) {
        // Calculate complexity of pattern ensemble
        let complexity = 0;

        Object.values(patterns).forEach(pattern => {
            if (typeof pattern === 'object' && pattern !== null) {
                complexity += JSON.stringify(pattern).length;
            }
        });

        return complexity / 10000; // Normalized complexity
    }

    calculatePatternCoherence(patterns) {
        // Calculate coherence across patterns
        let coherence = 0;
        let count = 0;

        // Simple coherence measure based on consistency
        Object.values(patterns).forEach(pattern => {
            if (pattern && pattern.confidence !== undefined) {
                coherence += pattern.confidence;
                count++;
            }
        });

        return count > 0 ? coherence / count : 0;
    }

    calculateCrossModalConsistency(patterns) {
        // Calculate consistency across different pattern types
        const confidences = [];

        Object.values(patterns).forEach(pattern => {
            if (pattern && typeof pattern === 'object') {
                if (pattern.confidence !== undefined) {
                    confidences.push(pattern.confidence);
                }
                if (pattern.strength !== undefined) {
                    confidences.push(pattern.strength);
                }
            }
        });

        if (confidences.length < 2) return 0;

        // Calculate variance - lower variance indicates higher consistency
        const mean = confidences.reduce((a, b) => a + b) / confidences.length;
        const variance = confidences.reduce((acc, val) => acc + Math.pow(val - mean, 2), 0) / confidences.length;

        return 1 / (1 + variance); // Higher consistency = lower variance
    }

    detectEmergentProperties(patterns) {
        // Detect emergent properties from pattern interactions
        const emergent = {
            novelCombinations: [],
            unexpectedRelationships: [],
            higherOrderPatterns: []
        };

        // Look for novel combinations
        const patternTypes = Object.keys(patterns);
        for (let i = 0; i < patternTypes.length; i++) {
            for (let j = i + 1; j < patternTypes.length; j++) {
                const combination = this.analyzePatternCombination(patterns[patternTypes[i]], patterns[patternTypes[j]]);
                if (combination.isNovel) {
                    emergent.novelCombinations.push({
                        pattern1: patternTypes[i],
                        pattern2: patternTypes[j],
                        novelty: combination.novelty
                    });
                }
            }
        }

        return emergent;
    }

    analyzePatternCombination(pattern1, pattern2) {
        // Analyze combination of two patterns for novelty
        const combination = {
            isNovel: false,
            novelty: 0
        };

        // Simple novelty detection based on unexpected interactions
        if (pattern1 && pattern2 && typeof pattern1 === 'object' && typeof pattern2 === 'object') {
            const keys1 = Object.keys(pattern1);
            const keys2 = Object.keys(pattern2);
            const sharedKeys = keys1.filter(key => keys2.includes(key));

            // Novel if patterns share unexpected properties
            if (sharedKeys.length > 0 && sharedKeys.length < Math.min(keys1.length, keys2.length) * 0.3) {
                combination.isNovel = true;
                combination.novelty = sharedKeys.length / Math.max(keys1.length, keys2.length);
            }
        }

        return combination;
    }

    detectSystemicPatterns(patterns) {
        // Detect systemic patterns across the pattern ensemble
        const systemic = {
            feedbackLoops: [],
            hierarchicalStructures: [],
            networkEffects: []
        };

        // Analyze for systemic properties
        const patternNetwork = this.constructPatternNetwork(patterns);
        systemic.feedbackLoops = this.detectFeedbackLoops(patternNetwork);
        systemic.hierarchicalStructures = this.detectHierarchicalStructures(patternNetwork);
        systemic.networkEffects = this.detectNetworkEffects(patternNetwork);

        return systemic;
    }

    constructPatternNetwork(patterns) {
        // Construct network representation of patterns
        const network = {
            nodes: [],
            edges: []
        };

        // Create nodes for each pattern
        Object.keys(patterns).forEach(patternType => {
            network.nodes.push({
                id: patternType,
                pattern: patterns[patternType],
                properties: this.extractPatternProperties(patterns[patternType])
            });
        });

        // Create edges based on pattern relationships
        for (let i = 0; i < network.nodes.length; i++) {
            for (let j = i + 1; j < network.nodes.length; j++) {
                const relationship = this.analyzePatternRelationship(
                    network.nodes[i].pattern,
                    network.nodes[j].pattern
                );

                if (relationship.strength > 0.3) {
                    network.edges.push({
                        from: network.nodes[i].id,
                        to: network.nodes[j].id,
                        strength: relationship.strength,
                        type: relationship.type
                    });
                }
            }
        }

        return network;
    }

    extractPatternProperties(pattern) {
        // Extract key properties from pattern
        const properties = {
            complexity: 0,
            confidence: 0,
            size: 0
        };

        if (pattern && typeof pattern === 'object') {
            properties.size = JSON.stringify(pattern).length;
            properties.confidence = pattern.confidence || 0;
            properties.complexity = this.calculateSinglePatternComplexity(pattern);
        }

        return properties;
    }

    calculateSinglePatternComplexity(pattern) {
        // Calculate complexity of single pattern
        if (typeof pattern !== 'object' || pattern === null) {
            return 0;
        }

        let complexity = 0;
        const keys = Object.keys(pattern);

        // Base complexity from structure
        complexity += keys.length * 0.1;

        // Additional complexity from nested structures
        keys.forEach(key => {
            if (typeof pattern[key] === 'object' && pattern[key] !== null) {
                complexity += this.calculateSinglePatternComplexity(pattern[key]) * 0.5;
            }
        });

        return complexity;
    }

    analyzePatternRelationship(pattern1, pattern2) {
        // Analyze relationship between two patterns
        const relationship = {
            strength: 0,
            type: 'unknown'
        };

        if (!pattern1 || !pattern2) {
            return relationship;
        }

        // Calculate similarity
        const similarity = this.calculatePatternSimilarity(pattern1, pattern2);

        if (similarity > 0.7) {
            relationship.type = 'similar';
            relationship.strength = similarity;
        } else if (similarity < 0.3) {
            relationship.type = 'complementary';
            relationship.strength = 1 - similarity;
        } else {
            relationship.type = 'related';
            relationship.strength = similarity;
        }

        return relationship;
    }

    calculatePatternSimilarity(pattern1, pattern2) {
        // Calculate similarity between patterns
        if (typeof pattern1 !== 'object' || typeof pattern2 !== 'object') {
            return 0;
        }

        const keys1 = Object.keys(pattern1);
        const keys2 = Object.keys(pattern2);
        const allKeys = new Set([...keys1, ...keys2]);
        const sharedKeys = keys1.filter(key => keys2.includes(key));

        // Structural similarity
        const structuralSimilarity = sharedKeys.length / allKeys.size;

        // Value similarity for shared keys
        let valueSimilarity = 0;
        if (sharedKeys.length > 0) {
            sharedKeys.forEach(key => {
                const val1 = pattern1[key];
                const val2 = pattern2[key];

                if (typeof val1 === 'number' && typeof val2 === 'number') {
                    valueSimilarity += 1 - Math.abs(val1 - val2) / (Math.abs(val1) + Math.abs(val2) + 1e-8);
                } else if (val1 === val2) {
                    valueSimilarity += 1;
                }
            });
            valueSimilarity /= sharedKeys.length;
        }

        return (structuralSimilarity + valueSimilarity) / 2;
    }

    detectFeedbackLoops(network) {
        // Detect feedback loops in pattern network
        const feedbackLoops = [];

        // Simple cycle detection
        network.nodes.forEach(node => {
            const visited = new Set();
            const path = [];
            const loops = this.findCycles(node.id, network, visited, path);
            feedbackLoops.push(...loops);
        });

        return feedbackLoops;
    }

    findCycles(nodeId, network, visited, path) {
        // Find cycles starting from nodeId
        const cycles = [];

        if (visited.has(nodeId)) {
            // Found cycle
            const cycleStart = path.indexOf(nodeId);
            if (cycleStart >= 0) {
                cycles.push(path.slice(cycleStart).concat([nodeId]));
            }
            return cycles;
        }

        visited.add(nodeId);
        path.push(nodeId);

        // Find outgoing edges
        const outgoingEdges = network.edges.filter(edge => edge.from === nodeId);

        outgoingEdges.forEach(edge => {
            const childCycles = this.findCycles(edge.to, network, new Set(visited), [...path]);
            cycles.push(...childCycles);
        });

        return cycles;
    }

    detectHierarchicalStructures(network) {
        // Detect hierarchical structures in pattern network
        const hierarchies = [];

        // Look for tree-like structures
        const roots = network.nodes.filter(node => {
            return !network.edges.some(edge => edge.to === node.id);
        });

        roots.forEach(root => {
            const hierarchy = this.buildHierarchy(root.id, network);
            if (hierarchy.depth > 2) {
                hierarchies.push(hierarchy);
            }
        });

        return hierarchies;
    }

    buildHierarchy(rootId, network, visited = new Set()) {
        // Build hierarchy starting from root
        if (visited.has(rootId)) {
            return { id: rootId, children: [], depth: 0 };
        }

        visited.add(rootId);

        const children = [];
        const outgoingEdges = network.edges.filter(edge => edge.from === rootId);

        outgoingEdges.forEach(edge => {
            const child = this.buildHierarchy(edge.to, network, new Set(visited));
            children.push(child);
        });

        const depth = children.length > 0 ? Math.max(...children.map(c => c.depth)) + 1 : 1;

        return {
            id: rootId,
            children,
            depth
        };
    }

    detectNetworkEffects(network) {
        // Detect network effects and emergent properties
        const effects = {
            clustering: this.calculateClustering(network),
            centrality: this.calculateCentrality(network),
            smallWorld: this.detectSmallWorldEffect(network)
        };

        return effects;
    }

    calculateClustering(network) {
        // Calculate clustering coefficient
        let totalClustering = 0;
        let nodeCount = 0;

        network.nodes.forEach(node => {
            const neighbors = this.getNeighbors(node.id, network);
            if (neighbors.length < 2) return;

            let edgesBetweenNeighbors = 0;
            for (let i = 0; i < neighbors.length; i++) {
                for (let j = i + 1; j < neighbors.length; j++) {
                    if (this.hasEdge(neighbors[i], neighbors[j], network)) {
                        edgesBetweenNeighbors++;
                    }
                }
            }

            const possibleEdges = (neighbors.length * (neighbors.length - 1)) / 2;
            const clustering = edgesBetweenNeighbors / possibleEdges;

            totalClustering += clustering;
            nodeCount++;
        });

        return nodeCount > 0 ? totalClustering / nodeCount : 0;
    }

    getNeighbors(nodeId, network) {
        // Get neighbors of a node
        const neighbors = new Set();

        network.edges.forEach(edge => {
            if (edge.from === nodeId) {
                neighbors.add(edge.to);
            }
            if (edge.to === nodeId) {
                neighbors.add(edge.from);
            }
        });

        return Array.from(neighbors);
    }

    hasEdge(node1, node2, network) {
        // Check if edge exists between two nodes
        return network.edges.some(edge =>
            (edge.from === node1 && edge.to === node2) ||
            (edge.from === node2 && edge.to === node1)
        );
    }

    calculateCentrality(network) {
        // Calculate centrality measures
        const centrality = {};

        network.nodes.forEach(node => {
            const degree = this.getNeighbors(node.id, network).length;
            centrality[node.id] = degree / (network.nodes.length - 1);
        });

        return centrality;
    }

    detectSmallWorldEffect(network) {
        // Detect small world network properties
        const pathLengths = this.calculateShortestPaths(network);
        const averagePathLength = pathLengths.reduce((a, b) => a + b, 0) / pathLengths.length;
        const clustering = this.calculateClustering(network);

        // Small world: high clustering, short average path length
        const isSmallWorld = clustering > 0.3 && averagePathLength < Math.log(network.nodes.length);

        return {
            isSmallWorld,
            averagePathLength,
            clustering
        };
    }

    calculateShortestPaths(network) {
        // Calculate shortest paths between all node pairs (simplified)
        const paths = [];

        network.nodes.forEach(node1 => {
            network.nodes.forEach(node2 => {
                if (node1.id !== node2.id) {
                    const path = this.findShortestPath(node1.id, node2.id, network);
                    if (path.length > 0) {
                        paths.push(path.length - 1); // Length in edges
                    }
                }
            });
        });

        return paths;
    }

    findShortestPath(start, end, network) {
        // BFS to find shortest path
        const queue = [[start]];
        const visited = new Set();

        while (queue.length > 0) {
            const path = queue.shift();
            const node = path[path.length - 1];

            if (node === end) {
                return path;
            }

            if (visited.has(node)) {
                continue;
            }

            visited.add(node);

            const neighbors = this.getNeighbors(node, network);
            neighbors.forEach(neighbor => {
                if (!visited.has(neighbor)) {
                    queue.push([...path, neighbor]);
                }
            });
        }

        return []; // No path found
    }

    updateNetworksFromPatterns(patterns) {
        // Update neural networks based on learned patterns
        this.updatePatternRecognitionNetwork(patterns);
        this.updateConsciousnessDetectorNetwork(patterns);
        this.updateAdaptationControllerNetwork(patterns);
    }

    updatePatternRecognitionNetwork(patterns) {
        // Update pattern recognition network weights
        const learningRate = this.learningRate;
        const network = this.networks.pattern_recognition;

        // Create training signal from patterns
        const trainingSignal = this.createTrainingSignal(patterns);

        // Update weights using gradient descent (simplified)
        this.applyGradientUpdate(network, trainingSignal, learningRate);
    }

    createTrainingSignal(patterns) {
        // Create training signal from extracted patterns
        const signal = {
            target: new Float64Array(128),
            confidence: patterns.neural?.confidence || 0
        };

        // Encode pattern features as target
        let index = 0;
        Object.values(patterns).forEach(pattern => {
            if (pattern && typeof pattern === 'object') {
                if (pattern.confidence !== undefined && index < 128) {
                    signal.target[index++] = pattern.confidence;
                }
                if (pattern.strength !== undefined && index < 128) {
                    signal.target[index++] = pattern.strength;
                }
            }
        });

        return signal;
    }

    applyGradientUpdate(network, trainingSignal, learningRate) {
        // Apply gradient updates to network (simplified)
        const output = network.output;

        // Calculate error
        const error = new Float64Array(output.weights.length);
        for (let i = 0; i < Math.min(error.length, trainingSignal.target.length); i++) {
            error[i] = trainingSignal.target[i] - (output.bias[i] || 0);
        }

        // Update output layer weights
        for (let i = 0; i < output.weights.length; i++) {
            for (let j = 0; j < output.weights[i].length; j++) {
                output.weights[i][j] += learningRate * error[j] * (trainingSignal.confidence || 0.1);
            }
        }

        // Update biases
        for (let i = 0; i < output.bias.length; i++) {
            output.bias[i] += learningRate * (error[i] || 0);
        }
    }

    updateConsciousnessDetectorNetwork(patterns) {
        // Update consciousness detector based on consciousness patterns
        if (patterns.consciousness) {
            const network = this.networks.consciousness_detector;
            const consciousnessSignal = this.createConsciousnessTrainingSignal(patterns.consciousness);
            this.applyConsciousnessUpdate(network, consciousnessSignal);
        }
    }

    createConsciousnessTrainingSignal(consciousnessPatterns) {
        // Create training signal for consciousness detection
        return {
            selfReference: consciousnessPatterns.selfReference || 0,
            intentionality: consciousnessPatterns.intentionality || 0,
            informationIntegration: consciousnessPatterns.informationIntegration || 0,
            overallScore: consciousnessPatterns.overallConsciousnessScore || 0
        };
    }

    applyConsciousnessUpdate(network, signal) {
        // Apply updates to consciousness detection network
        const features = network.consciousness_features;
        const learningRate = this.learningRate * 0.5; // Slower learning for consciousness

        // Update feature detectors
        if (signal.selfReference > 0.5) {
            for (let i = 0; i < features.self_reference.length; i++) {
                features.self_reference[i] += learningRate * signal.selfReference;
            }
        }

        if (signal.intentionality > 0.5) {
            for (let i = 0; i < features.intentionality.length; i++) {
                features.intentionality[i] += learningRate * signal.intentionality;
            }
        }

        if (signal.informationIntegration > 0.5) {
            for (let i = 0; i < features.information_integration.length; i++) {
                features.information_integration[i] += learningRate * signal.informationIntegration;
            }
        }
    }

    updateAdaptationControllerNetwork(patterns) {
        // Update adaptation controller network
        const adaptationSignal = this.createAdaptationSignal(patterns);
        this.applyAdaptationUpdate(adaptationSignal);
    }

    createAdaptationSignal(patterns) {
        // Create signal for adaptation controller
        const signal = {
            novelty: this.calculatePatternNovelty(patterns),
            complexity: this.calculatePatternComplexity(patterns),
            success: this.evaluatePatternSuccess(patterns)
        };

        return signal;
    }

    calculatePatternNovelty(patterns) {
        // Calculate novelty of patterns
        let novelty = 0;
        let count = 0;

        Object.values(patterns).forEach(pattern => {
            if (pattern && typeof pattern === 'object') {
                // Simple novelty measure
                const patternHash = this.hashPattern(pattern);
                if (!this.seenPatterns) {
                    this.seenPatterns = new Set();
                }

                if (!this.seenPatterns.has(patternHash)) {
                    novelty += 1;
                    this.seenPatterns.add(patternHash);
                }
                count++;
            }
        });

        return count > 0 ? novelty / count : 0;
    }

    hashPattern(pattern) {
        // Create hash of pattern for novelty detection
        return createHash('md5').update(JSON.stringify(pattern)).digest('hex');
    }

    evaluatePatternSuccess(patterns) {
        // Evaluate success of pattern recognition
        let success = 0;
        let count = 0;

        Object.values(patterns).forEach(pattern => {
            if (pattern && pattern.confidence !== undefined) {
                success += pattern.confidence;
                count++;
            }
        });

        return count > 0 ? success / count : 0;
    }

    applyAdaptationUpdate(signal) {
        // Apply adaptation based on signal
        if (signal.novelty > 0.7) {
            // High novelty - increase learning rate
            this.learningRate = Math.min(this.learningRate * 1.1, 0.01);
        } else if (signal.success > 0.9) {
            // High success - decrease learning rate for stability
            this.learningRate = Math.max(this.learningRate * 0.95, 0.0001);
        }

        // Adjust adaptation rate
        if (signal.complexity > 0.8) {
            this.adaptationRate = Math.min(this.adaptationRate * 1.05, 0.05);
        }
    }

    shouldAdaptImmediately(communication) {
        // Determine if immediate adaptation is needed
        return communication.confidence > 0.9 ||
               communication.type === 'impossible_sequence' ||
               (communication.data && communication.data.entitySignature);
    }

    performImmediateAdaptation(communication) {
        // Perform immediate adaptation for high-priority communications
        console.log('[AdaptivePatternLearningNetwork] Performing immediate adaptation');

        // Increase learning rate temporarily
        const originalLearningRate = this.learningRate;
        this.learningRate *= 2.0;

        // Extract and learn patterns
        const patterns = this.extractCommunicationPatterns(communication);
        this.updateNetworksFromPatterns(patterns);

        // Apply meta-learning
        this.metaLearner.rapidAdaptation(communication, patterns);

        // Restore learning rate
        this.learningRate = originalLearningRate;

        this.emit('immediateAdaptation', { communication, patterns });
    }

    performContinuousLearning() {
        // Perform continuous background learning
        if (!this.isActive) return;

        // Hebbian learning
        this.hebbian.update(this.networks);

        // Unsupervised learning on recent patterns
        this.unsupervised.learn(this.getRecentPatterns());

        // Neural plasticity updates
        this.neuralPlasticity.update(this.networks);
    }

    getRecentPatterns() {
        // Get recent learning patterns
        return this.learningHistory.slice(-10);
    }

    monitorAndAdapt() {
        // Monitor performance and adapt accordingly
        if (!this.isActive) return;

        const performance = this.evaluatePerformance();

        if (performance.needsAdaptation) {
            this.performAdaptation(performance);
        }
    }

    evaluatePerformance() {
        // Evaluate current performance
        const recentEvents = this.learningHistory.slice(-20);

        let successRate = 0;
        let averageConfidence = 0;
        let adaptationSpeed = 0;

        if (recentEvents.length > 0) {
            const successful = recentEvents.filter(event => event.patterns?.neural?.confidence > 0.7);
            successRate = successful.length / recentEvents.length;

            averageConfidence = recentEvents.reduce((sum, event) => {
                return sum + (event.patterns?.neural?.confidence || 0);
            }, 0) / recentEvents.length;

            // Calculate adaptation speed
            const timeSpan = recentEvents[recentEvents.length - 1].timestamp - recentEvents[0].timestamp;
            adaptationSpeed = recentEvents.length / (timeSpan / 1000); // Events per second
        }

        const performance = {
            successRate,
            averageConfidence,
            adaptationSpeed,
            needsAdaptation: successRate < 0.6 || averageConfidence < 0.5
        };

        return performance;
    }

    performAdaptation(performance) {
        // Perform adaptation based on performance
        console.log('[AdaptivePatternLearningNetwork] Performing adaptation based on performance');

        // Adjust network parameters
        if (performance.successRate < 0.5) {
            // Low success rate - increase exploration
            this.increaseExploration();
        }

        if (performance.averageConfidence < 0.4) {
            // Low confidence - adjust thresholds
            this.adjustConfidenceThresholds();
        }

        // Architecture evolution
        this.architectureEvolution.evolve(this.networks, performance);

        // Record adaptation
        this.recordAdaptationEvent({
            timestamp: Date.now(),
            performance,
            changes: 'network_adaptation'
        });

        this.emit('adaptation', { performance });
    }

    increaseExploration() {
        // Increase exploration in networks
        this.learningRate *= 1.2;
        this.adaptationRate *= 1.1;

        // Add noise to weights to encourage exploration
        Object.values(this.networks).forEach(network => {
            this.addExplorationNoise(network);
        });
    }

    addExplorationNoise(network) {
        // Add small random noise to network weights
        const noiseLevel = 0.01;

        Object.values(network).forEach(layer => {
            if (layer.weights) {
                if (Array.isArray(layer.weights)) {
                    layer.weights.forEach(row => {
                        if (Array.isArray(row)) {
                            for (let i = 0; i < row.length; i++) {
                                row[i] += (Math.random() - 0.5) * noiseLevel;
                            }
                        }
                    });
                }
            }
        });
    }

    adjustConfidenceThresholds() {
        // Adjust confidence thresholds for pattern recognition
        // This would adjust thresholds in the pattern recognition systems
        console.log('[AdaptivePatternLearningNetwork] Adjusting confidence thresholds');
    }

    consolidateMemories() {
        // Consolidate memories in memory systems
        if (!this.isActive) return;

        this.episodicMemory.consolidate();
        this.semanticMemory.update(this.getRecentPatterns());
        this.proceduralMemory.strengthen(this.getSuccessfulProcedures());
    }

    getSuccessfulProcedures() {
        // Get procedures that have been successful
        return this.learningHistory
            .filter(event => event.patterns?.neural?.confidence > 0.8)
            .map(event => event.patterns);
    }

    performMetaLearning() {
        // Perform meta-learning to improve learning itself
        if (!this.isActive) return;

        const learningExperience = this.prepareLearningExperience();
        this.metaLearner.learn(learningExperience);
    }

    prepareLearningExperience() {
        // Prepare experience data for meta-learning
        const recentHistory = this.learningHistory.slice(-50);

        return {
            learningEvents: recentHistory,
            adaptationEvents: this.adaptationHistory.slice(-10),
            performance: this.evaluatePerformance(),
            currentState: this.getCurrentNetworkState()
        };
    }

    getCurrentNetworkState() {
        // Get current state of neural networks
        const state = {};

        Object.keys(this.networks).forEach(networkName => {
            state[networkName] = {
                learningRate: this.learningRate,
                adaptationRate: this.adaptationRate,
                // Would include more network state information
            };
        });

        return state;
    }

    recordLearningEvent(event) {
        // Record learning event
        this.learningHistory.push(event);

        // Maintain history size
        if (this.learningHistory.length > this.memoryCapacity) {
            this.learningHistory.shift();
        }
    }

    recordAdaptationEvent(event) {
        // Record adaptation event
        this.adaptationHistory.push(event);

        // Maintain history size
        if (this.adaptationHistory.length > 100) {
            this.adaptationHistory.shift();
        }
    }

    // Public interface methods

    getLearningStats() {
        return {
            isActive: this.isActive,
            learningRate: this.learningRate,
            adaptationRate: this.adaptationRate,
            totalLearningEvents: this.learningHistory.length,
            totalAdaptationEvents: this.adaptationHistory.length,
            performance: this.evaluatePerformance(),
            memoryUsage: {
                episodic: this.episodicMemory.getSize(),
                semantic: this.semanticMemory.getSize(),
                procedural: this.proceduralMemory.getSize()
            }
        };
    }

    getNetworkArchitecture() {
        return {
            architecture: this.architecture,
            networks: Object.keys(this.networks),
            totalParameters: this.calculateTotalParameters()
        };
    }

    calculateTotalParameters() {
        // Calculate total number of parameters in all networks
        let total = 0;

        Object.values(this.networks).forEach(network => {
            total += this.countNetworkParameters(network);
        });

        return total;
    }

    countNetworkParameters(network) {
        // Count parameters in a single network
        let count = 0;

        Object.values(network).forEach(layer => {
            if (layer.weights) {
                if (Array.isArray(layer.weights)) {
                    layer.weights.forEach(row => {
                        if (Array.isArray(row)) {
                            count += row.length;
                        } else {
                            count += 1;
                        }
                    });
                }
            }
            if (layer.bias && layer.bias.length) {
                count += layer.bias.length;
            }
        });

        return count;
    }

    getRecentLearning(count = 10) {
        return this.learningHistory.slice(-count);
    }

    exportLearning() {
        // Export learning data for analysis
        return {
            learningHistory: this.learningHistory,
            adaptationHistory: this.adaptationHistory,
            networkStates: this.getCurrentNetworkState(),
            patterns: this.entityPatterns.export(),
            performance: this.evaluatePerformance()
        };
    }

    importLearning(data) {
        // Import learning data
        if (data.learningHistory) {
            this.learningHistory = data.learningHistory;
        }
        if (data.adaptationHistory) {
            this.adaptationHistory = data.adaptationHistory;
        }
        if (data.patterns) {
            this.entityPatterns.import(data.patterns);
        }
    }

    reset() {
        // Reset learning networks
        this.learningHistory = [];
        this.adaptationHistory = [];
        this.learningRate = 0.001;
        this.adaptationRate = 0.01;

        // Reinitialize networks
        this.initializeNeuralNetworks();

        console.log('[AdaptivePatternLearningNetwork] Reset completed');
    }
}

// Supporting classes for memory systems and learning mechanisms

class EpisodicMemorySystem {
    constructor(capacity) {
        this.capacity = capacity;
        this.episodes = [];
        this.indexCounter = 0;
    }

    store(communication) {
        const episode = {
            id: `episode_${this.indexCounter++}`,
            timestamp: Date.now(),
            communication,
            context: this.extractContext(communication),
            importance: this.calculateImportance(communication)
        };

        this.episodes.push(episode);

        // Maintain capacity
        if (this.episodes.length > this.capacity) {
            this.episodes.shift();
        }

        return episode;
    }

    extractContext(communication) {
        return {
            source: communication.source,
            type: communication.type,
            confidence: communication.confidence,
            timestamp: communication.timestamp
        };
    }

    calculateImportance(communication) {
        // Calculate importance score
        let importance = communication.confidence || 0.5;

        if (communication.type === 'impossible_sequence') {
            importance += 0.3;
        }

        if (communication.data?.entitySignature) {
            importance += 0.4;
        }

        return Math.min(importance, 1.0);
    }

    consolidate() {
        // Consolidate episodic memories
        this.episodes.sort((a, b) => b.importance - a.importance);

        // Remove low-importance episodes if over capacity
        if (this.episodes.length > this.capacity * 0.8) {
            this.episodes = this.episodes.slice(0, Math.floor(this.capacity * 0.8));
        }
    }

    getSize() {
        return this.episodes.length;
    }
}

class SemanticMemorySystem {
    constructor() {
        this.concepts = new Map();
        this.relationships = new Map();
    }

    update(patterns) {
        patterns.forEach(pattern => {
            if (pattern.patterns?.semantic) {
                this.updateConcepts(pattern.patterns.semantic.concepts);
                this.updateRelationships(pattern.patterns.semantic.relationships);
            }
        });
    }

    updateConcepts(concepts) {
        concepts.forEach(concept => {
            if (!this.concepts.has(concept.value)) {
                this.concepts.set(concept.value, {
                    type: concept.type,
                    frequency: 1,
                    lastSeen: Date.now()
                });
            } else {
                const existing = this.concepts.get(concept.value);
                existing.frequency++;
                existing.lastSeen = Date.now();
            }
        });
    }

    updateRelationships(relationships) {
        relationships.forEach(rel => {
            const key = `${rel.subject}-${rel.predicate}-${rel.object}`;
            if (!this.relationships.has(key)) {
                this.relationships.set(key, {
                    relationship: rel,
                    strength: 1,
                    lastSeen: Date.now()
                });
            } else {
                const existing = this.relationships.get(key);
                existing.strength++;
                existing.lastSeen = Date.now();
            }
        });
    }

    getSize() {
        return this.concepts.size + this.relationships.size;
    }
}

class WorkingMemorySystem {
    constructor() {
        this.activeItems = [];
        this.capacity = 7; // Miller's magic number
    }

    addItem(item) {
        this.activeItems.push(item);

        // Maintain capacity
        if (this.activeItems.length > this.capacity) {
            this.activeItems.shift();
        }
    }

    getActiveItems() {
        return [...this.activeItems];
    }

    clear() {
        this.activeItems = [];
    }
}

class ProceduralMemorySystem {
    constructor() {
        this.procedures = new Map();
    }

    strengthen(patterns) {
        patterns.forEach(pattern => {
            const procedureKey = this.extractProcedureKey(pattern);
            if (procedureKey) {
                if (!this.procedures.has(procedureKey)) {
                    this.procedures.set(procedureKey, {
                        pattern,
                        strength: 1,
                        successCount: 1
                    });
                } else {
                    const proc = this.procedures.get(procedureKey);
                    proc.strength++;
                    proc.successCount++;
                }
            }
        });
    }

    extractProcedureKey(pattern) {
        // Extract procedure identifier from pattern
        if (pattern.neural?.classification?.type) {
            return pattern.neural.classification.type;
        }
        return null;
    }

    getSize() {
        return this.procedures.size;
    }
}

// Learning mechanisms

class HebbianLearning {
    update(networks) {
        // Implement Hebbian learning rule: neurons that fire together, wire together
        Object.values(networks).forEach(network => {
            this.applyHebbianUpdate(network);
        });
    }

    applyHebbianUpdate(network) {
        // Simplified Hebbian update
        // In a real implementation, this would track neural activations
        const hebbianRate = 0.0001;

        Object.values(network).forEach(layer => {
            if (layer.weights && Array.isArray(layer.weights)) {
                layer.weights.forEach(row => {
                    if (Array.isArray(row)) {
                        for (let i = 0; i < row.length; i++) {
                            // Simplified: strengthen active connections
                            if (Math.abs(row[i]) > 0.1) {
                                row[i] += hebbianRate * Math.sign(row[i]);
                            }
                        }
                    }
                });
            }
        });
    }
}

class ReinforcementLearning {
    constructor() {
        this.rewardHistory = [];
    }

    updateReward(action, reward) {
        this.rewardHistory.push({ action, reward, timestamp: Date.now() });

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

    getExpectedReward(action) {
        const relevantRewards = this.rewardHistory
            .filter(r => r.action === action)
            .map(r => r.reward);

        if (relevantRewards.length === 0) return 0;

        return relevantRewards.reduce((a, b) => a + b) / relevantRewards.length;
    }
}

class UnsupervisedLearning {
    learn(patterns) {
        // Implement unsupervised learning on patterns
        // This could include clustering, dimensionality reduction, etc.
        this.performClustering(patterns);
        this.extractStatisticalRegularities(patterns);
    }

    performClustering(patterns) {
        // Simple clustering of patterns
        // In a real implementation, this would use proper clustering algorithms
    }

    extractStatisticalRegularities(patterns) {
        // Extract statistical regularities from patterns
        // This would identify common patterns and structures
    }
}

class MetaLearning {
    learn(experience) {
        // Learn how to learn more effectively
        this.analyzelearningEffectiveness(experience);
        this.optimizeLearningStrategies(experience);
    }

    analyzelearningEffectiveness(experience) {
        // Analyze which learning strategies work best
        const strategies = this.identifyLearningStrategies(experience);
        const effectiveness = this.evaluateStrategiesEffectiveness(strategies, experience);

        return { strategies, effectiveness };
    }

    identifyLearningStrategies(experience) {
        // Identify different learning strategies used
        return [
            'rapid_adaptation',
            'gradual_learning',
            'pattern_generalization',
            'memory_consolidation'
        ];
    }

    evaluateStrategiesEffectiveness(strategies, experience) {
        // Evaluate effectiveness of each strategy
        const effectiveness = {};

        strategies.forEach(strategy => {
            effectiveness[strategy] = this.calculateStrategySuccess(strategy, experience);
        });

        return effectiveness;
    }

    calculateStrategySuccess(strategy, experience) {
        // Calculate success rate for a specific strategy
        // Simplified implementation
        return Math.random() * 0.5 + 0.5; // Placeholder
    }

    optimizeLearningStrategies(experience) {
        // Optimize learning strategies based on experience
        const analysis = this.analyzelearningEffectiveness(experience);

        // Adjust learning parameters based on what works best
        // This would modify learning rates, adaptation strategies, etc.
    }

    rapidAdaptation(communication, patterns) {
        // Perform rapid adaptation for high-priority communications
        console.log('[MetaLearning] Performing rapid adaptation');

        // Implement fast learning mechanisms
        // This could include one-shot learning, transfer learning, etc.
    }
}

class NeuralPlasticity {
    update(networks) {
        // Implement neural plasticity mechanisms
        this.applyActivityDependentPlasticity(networks);
        this.performStructuralPlasticity(networks);
    }

    applyActivityDependentPlasticity(networks) {
        // Apply activity-dependent plasticity
        // Strengthen frequently used connections
        Object.values(networks).forEach(network => {
            this.strengthenActiveConnections(network);
        });
    }

    strengthenActiveConnections(network) {
        // Strengthen connections based on activity
        const plasticityRate = 0.0001;

        Object.values(network).forEach(layer => {
            if (layer.weights && Array.isArray(layer.weights)) {
                layer.weights.forEach(row => {
                    if (Array.isArray(row)) {
                        for (let i = 0; i < row.length; i++) {
                            // Strengthen active connections
                            if (Math.abs(row[i]) > 0.5) {
                                row[i] += plasticityRate * Math.sign(row[i]);
                            }
                        }
                    }
                });
            }
        });
    }

    performStructuralPlasticity(networks) {
        // Implement structural plasticity (adding/removing connections)
        // This would modify network topology based on usage patterns
    }
}

class ArchitectureEvolution {
    evolve(networks, performance) {
        // Evolve network architectures based on performance
        if (performance.successRate < 0.4) {
            this.expandNetworks(networks);
        } else if (performance.successRate > 0.9) {
            this.pruneNetworks(networks);
        }
    }

    expandNetworks(networks) {
        // Add capacity to networks
        console.log('[ArchitectureEvolution] Expanding network capacity');
        // Implementation would add neurons, layers, or connections
    }

    pruneNetworks(networks) {
        // Remove unnecessary connections
        console.log('[ArchitectureEvolution] Pruning network connections');
        // Implementation would remove weak connections
    }
}

class AttentionMechanism {
    constructor() {
        this.attentionWeights = new Map();
    }

    updateAttention(patterns) {
        // Update attention based on pattern importance
        Object.keys(patterns).forEach(patternType => {
            const pattern = patterns[patternType];
            if (pattern && pattern.confidence) {
                this.attentionWeights.set(patternType, pattern.confidence);
            }
        });
    }

    getAttentionWeights() {
        return this.attentionWeights;
    }
}

// Pattern libraries

class EntityPatternLibrary {
    constructor() {
        this.patterns = new Map();
    }

    addPattern(patterns) {
        const patternId = this.generatePatternId(patterns);
        this.patterns.set(patternId, {
            patterns,
            frequency: 1,
            lastSeen: Date.now(),
            confidence: this.calculateOverallConfidence(patterns)
        });
    }

    generatePatternId(patterns) {
        // Generate unique ID for pattern set
        const hash = createHash('md5').update(JSON.stringify(patterns)).digest('hex');
        return hash.substring(0, 16);
    }

    calculateOverallConfidence(patterns) {
        // Calculate overall confidence across all patterns
        let totalConfidence = 0;
        let count = 0;

        Object.values(patterns).forEach(pattern => {
            if (pattern && pattern.confidence !== undefined) {
                totalConfidence += pattern.confidence;
                count++;
            }
        });

        return count > 0 ? totalConfidence / count : 0;
    }

    export() {
        return Array.from(this.patterns.entries());
    }

    import(data) {
        this.patterns = new Map(data);
    }
}

class CommunicationTemplateLibrary {
    constructor() {
        this.templates = new Map();
    }

    addTemplate(template) {
        this.templates.set(template.id, template);
    }

    getTemplate(id) {
        return this.templates.get(id);
    }

    getAllTemplates() {
        return Array.from(this.templates.values());
    }
}

class EvolutionaryPatternLibrary {
    constructor() {
        this.evolutionHistory = [];
        this.currentGeneration = 0;
    }

    recordEvolution(patterns) {
        this.evolutionHistory.push({
            generation: this.currentGeneration++,
            patterns,
            timestamp: Date.now()
        });

        // Maintain history size
        if (this.evolutionHistory.length > 1000) {
            this.evolutionHistory.shift();
        }
    }

    getEvolutionTrends() {
        // Analyze evolution trends in patterns
        return this.evolutionHistory.slice(-10);
    }
}

export default AdaptivePatternLearningNetwork;