const Logger = require('../utils/Logger');

class NeuralOptimizer {
    constructor() {
        this.logger = new Logger();
        this.optimizationHistory = new Map();
        this.optimizationAlgorithms = new Map();
        this.performanceMetrics = new Map();
        this.initializeOptimizationAlgorithms();
    }

    initializeOptimizationAlgorithms() {
        // Initialize various neural optimization algorithms
        this.optimizationAlgorithms.set('gradient-descent', this.gradientDescentOptimization.bind(this));
        this.optimizationAlgorithms.set('genetic-algorithm', this.geneticAlgorithmOptimization.bind(this));
        this.optimizationAlgorithms.set('reinforcement-learning', this.reinforcementLearningOptimization.bind(this));
        this.optimizationAlgorithms.set('adaptive-resonance', this.adaptiveResonanceOptimization.bind(this));
        this.optimizationAlgorithms.set('neural-evolution', this.neuralEvolutionOptimization.bind(this));
        
        this.logger.info(`🧠 Neural optimizer initialized with ${this.optimizationAlgorithms.size} algorithms`);
    }

    async optimizeNetworks(neuralNetworks, optimizationType = 'adaptive') {
        try {
            this.logger.info(`🧠 Starting neural network optimization with type: ${optimizationType}`);
            
            const optimizationResults = {
                timestamp: new Date().toISOString(),
                optimizationType,
                originalNetworks: neuralNetworks,
                optimizedNetworks: {},
                performanceImprovements: {},
                optimizationMetrics: {}
            };

            // Optimize each neural network
            for (const [networkName, network] of Object.entries(neuralNetworks)) {
                const optimizedNetwork = await this.optimizeNetwork(network, optimizationType);
                const performanceImprovement = this.calculatePerformanceImprovement(network, optimizedNetwork);
                
                optimizationResults.optimizedNetworks[networkName] = optimizedNetwork;
                optimizationResults.performanceImprovements[networkName] = performanceImprovement;
            }

            // Calculate overall optimization metrics
            optimizationResults.optimizationMetrics = this.calculateOptimizationMetrics(optimizationResults);

            // Store optimization history
            this.storeOptimizationHistory(optimizationResults);

            this.logger.info(`🧠 Neural network optimization completed`);
            return optimizationResults.optimizedNetworks;
        } catch (error) {
            this.logger.error('Failed to optimize neural networks:', error);
            throw error;
        }
    }

    async optimizeNetwork(network, optimizationType) {
        const algorithm = this.optimizationAlgorithms.get(optimizationType) || this.optimizationAlgorithms.get('adaptive');
        return await algorithm(network);
    }

    async gradientDescentOptimization(network) {
        // Simulate gradient descent optimization
        const optimizedNetwork = {
            ...network,
            layers: network.layers.map(layer => ({
                ...layer,
                weights: this.optimizeWeights(layer.weights, 'gradient-descent'),
                bias: this.optimizeBias(layer.bias, 'gradient-descent'),
                activation: this.optimizeActivation(layer.activation)
            })),
            learningRate: this.optimizeLearningRate(network.learningRate),
            momentum: this.optimizeMomentum(network.momentum),
            optimizationType: 'gradient-descent'
        };

        return optimizedNetwork;
    }

    async geneticAlgorithmOptimization(network) {
        // Simulate genetic algorithm optimization
        const optimizedNetwork = {
            ...network,
            layers: network.layers.map(layer => ({
                ...layer,
                weights: this.optimizeWeights(layer.weights, 'genetic'),
                bias: this.optimizeBias(layer.bias, 'genetic'),
                activation: this.optimizeActivation(layer.activation)
            })),
            population: this.generatePopulation(network),
            fitness: this.calculateFitness(network),
            optimizationType: 'genetic-algorithm'
        };

        return optimizedNetwork;
    }

    async reinforcementLearningOptimization(network) {
        // Simulate reinforcement learning optimization
        const optimizedNetwork = {
            ...network,
            layers: network.layers.map(layer => ({
                ...layer,
                weights: this.optimizeWeights(layer.weights, 'reinforcement'),
                bias: this.optimizeBias(layer.bias, 'reinforcement'),
                activation: this.optimizeActivation(layer.activation)
            })),
            policy: this.optimizePolicy(network),
            valueFunction: this.optimizeValueFunction(network),
            optimizationType: 'reinforcement-learning'
        };

        return optimizedNetwork;
    }

    async adaptiveResonanceOptimization(network) {
        // Simulate adaptive resonance theory optimization
        const optimizedNetwork = {
            ...network,
            layers: network.layers.map(layer => ({
                ...layer,
                weights: this.optimizeWeights(layer.weights, 'adaptive-resonance'),
                bias: this.optimizeBias(layer.bias, 'adaptive-resonance'),
                activation: this.optimizeActivation(layer.activation)
            })),
            vigilance: this.optimizeVigilance(network),
            resonance: this.optimizeResonance(network),
            optimizationType: 'adaptive-resonance'
        };

        return optimizedNetwork;
    }

    async neuralEvolutionOptimization(network) {
        // Simulate neural evolution optimization
        const optimizedNetwork = {
            ...network,
            layers: network.layers.map(layer => ({
                ...layer,
                weights: this.optimizeWeights(layer.weights, 'neural-evolution'),
                bias: this.optimizeBias(layer.bias, 'neural-evolution'),
                activation: this.optimizeActivation(layer.activation)
            })),
            evolutionRate: this.optimizeEvolutionRate(network),
            mutationRate: this.optimizeMutationRate(network),
            optimizationType: 'neural-evolution'
        };

        return optimizedNetwork;
    }

    optimizeWeights(weights, algorithm) {
        // Simulate weight optimization based on algorithm
        const optimizationFactors = {
            'gradient-descent': 0.95,
            'genetic': 0.98,
            'reinforcement': 0.97,
            'adaptive-resonance': 0.96,
            'neural-evolution': 0.99
        };

        const factor = optimizationFactors[algorithm] || 0.95;
        return weights.map(weight => weight * factor);
    }

    optimizeBias(bias, algorithm) {
        // Simulate bias optimization
        const optimizationFactors = {
            'gradient-descent': 0.9,
            'genetic': 0.95,
            'reinforcement': 0.92,
            'adaptive-resonance': 0.94,
            'neural-evolution': 0.96
        };

        const factor = optimizationFactors[algorithm] || 0.9;
        return bias * factor;
    }

    optimizeActivation(activation) {
        // Optimize activation function parameters
        return {
            ...activation,
            threshold: activation.threshold * 0.95,
            slope: activation.slope * 1.05
        };
    }

    optimizeLearningRate(learningRate) {
        return Math.min(learningRate * 1.1, 0.1);
    }

    optimizeMomentum(momentum) {
        return Math.min(momentum * 1.05, 0.9);
    }

    generatePopulation(network) {
        // Generate population for genetic algorithm
        const populationSize = 50;
        const population = [];
        
        for (let i = 0; i < populationSize; i++) {
            population.push({
                id: i,
                network: this.mutateNetwork(network),
                fitness: 0
            });
        }
        
        return population;
    }

    mutateNetwork(network) {
        // Create a mutated version of the network
        return {
            ...network,
            layers: network.layers.map(layer => ({
                ...layer,
                weights: layer.weights.map(weight => weight * (0.9 + Math.random() * 0.2)),
                bias: layer.bias * (0.9 + Math.random() * 0.2)
            }))
        };
    }

    calculateFitness(network) {
        // Calculate fitness score for genetic algorithm
        const complexity = network.layers.length;
        const efficiency = this.calculateNetworkEfficiency(network);
        const accuracy = this.calculateNetworkAccuracy(network);
        
        return (complexity * 0.2 + efficiency * 0.4 + accuracy * 0.4);
    }

    calculateNetworkEfficiency(network) {
        // Calculate network efficiency
        const totalWeights = network.layers.reduce((sum, layer) => sum + layer.weights.length, 0);
        const activeWeights = network.layers.reduce((sum, layer) => 
            sum + layer.weights.filter(w => Math.abs(w) > 0.01).length, 0
        );
        
        return activeWeights / totalWeights;
    }

    calculateNetworkAccuracy(network) {
        // Simulate network accuracy calculation
        return 0.85 + Math.random() * 0.1;
    }

    optimizePolicy(network) {
        // Optimize policy for reinforcement learning
        return {
            epsilon: Math.max(0.01, network.policy?.epsilon * 0.95 || 0.1),
            gamma: Math.min(0.99, network.policy?.gamma * 1.02 || 0.9),
            alpha: Math.min(0.1, network.policy?.alpha * 1.05 || 0.01)
        };
    }

    optimizeValueFunction(network) {
        // Optimize value function for reinforcement learning
        return {
            discount: Math.min(0.99, network.valueFunction?.discount * 1.01 || 0.9),
            learningRate: Math.min(0.1, network.valueFunction?.learningRate * 1.1 || 0.01)
        };
    }

    optimizeVigilance(network) {
        // Optimize vigilance parameter for adaptive resonance
        return Math.min(0.9, network.vigilance * 1.05 || 0.7);
    }

    optimizeResonance(network) {
        // Optimize resonance parameter for adaptive resonance
        return Math.min(0.95, network.resonance * 1.02 || 0.8);
    }

    optimizeEvolutionRate(network) {
        // Optimize evolution rate for neural evolution
        return Math.min(0.1, network.evolutionRate * 1.1 || 0.01);
    }

    optimizeMutationRate(network) {
        // Optimize mutation rate for neural evolution
        return Math.min(0.1, network.mutationRate * 1.05 || 0.05);
    }

    calculatePerformanceImprovement(originalNetwork, optimizedNetwork) {
        const originalMetrics = this.calculateNetworkMetrics(originalNetwork);
        const optimizedMetrics = this.calculateNetworkMetrics(optimizedNetwork);
        
        return {
            efficiency: (optimizedMetrics.efficiency - originalMetrics.efficiency) / originalMetrics.efficiency,
            accuracy: (optimizedMetrics.accuracy - originalMetrics.accuracy) / originalMetrics.accuracy,
            speed: (optimizedMetrics.speed - originalMetrics.speed) / originalMetrics.speed,
            overall: this.calculateOverallImprovement(originalMetrics, optimizedMetrics)
        };
    }

    calculateNetworkMetrics(network) {
        return {
            efficiency: this.calculateNetworkEfficiency(network),
            accuracy: this.calculateNetworkAccuracy(network),
            speed: this.calculateNetworkSpeed(network),
            complexity: network.layers.length
        };
    }

    calculateNetworkSpeed(network) {
        // Simulate network speed calculation
        const totalOperations = network.layers.reduce((sum, layer) => 
            sum + layer.weights.length * layer.neurons, 0
        );
        return 1 / (1 + totalOperations / 1000); // Normalize to 0-1
    }

    calculateOverallImprovement(originalMetrics, optimizedMetrics) {
        const weights = {
            efficiency: 0.3,
            accuracy: 0.4,
            speed: 0.3
        };
        
        const efficiencyImprovement = (optimizedMetrics.efficiency - originalMetrics.efficiency) / originalMetrics.efficiency;
        const accuracyImprovement = (optimizedMetrics.accuracy - originalMetrics.accuracy) / originalMetrics.accuracy;
        const speedImprovement = (optimizedMetrics.speed - originalMetrics.speed) / originalMetrics.speed;
        
        return (
            efficiencyImprovement * weights.efficiency +
            accuracyImprovement * weights.accuracy +
            speedImprovement * weights.speed
        );
    }

    calculateOptimizationMetrics(optimizationResults) {
        const improvements = Object.values(optimizationResults.performanceImprovements);
        
        return {
            averageImprovement: improvements.reduce((sum, imp) => sum + imp.overall, 0) / improvements.length,
            maxImprovement: Math.max(...improvements.map(imp => imp.overall)),
            minImprovement: Math.min(...improvements.map(imp => imp.overall)),
            optimizationSuccess: improvements.filter(imp => imp.overall > 0).length / improvements.length
        };
    }

    storeOptimizationHistory(optimizationResults) {
        const historyKey = `${optimizationResults.optimizationType}-${Date.now()}`;
        this.optimizationHistory.set(historyKey, optimizationResults);
        
        // Keep only last 100 optimization histories
        if (this.optimizationHistory.size > 100) {
            const keys = Array.from(this.optimizationHistory.keys());
            const oldestKey = keys[0];
            this.optimizationHistory.delete(oldestKey);
        }
    }

    async optimizeBasedOnPerformance(agentId, performanceData) {
        try {
            this.logger.info(`🧠 Optimizing neural networks based on performance for agent ${agentId}`);
            
            // Store performance data
            if (!this.performanceMetrics.has(agentId)) {
                this.performanceMetrics.set(agentId, []);
            }
            this.performanceMetrics.get(agentId).push(performanceData);
            
            // Analyze performance patterns
            const performancePatterns = this.analyzePerformancePatterns(agentId);
            
            // Determine optimization strategy
            const optimizationStrategy = this.determineOptimizationStrategy(performancePatterns);
            
            // Apply optimization
            const optimizationResult = await this.applyPerformanceBasedOptimization(agentId, optimizationStrategy);
            
            this.logger.info(`🧠 Performance-based optimization completed for agent ${agentId}`);
            return optimizationResult;
        } catch (error) {
            this.logger.error('Failed to optimize based on performance:', error);
            throw error;
        }
    }

    analyzePerformancePatterns(agentId) {
        const performanceData = this.performanceMetrics.get(agentId) || [];
        
        if (performanceData.length === 0) return {};
        
        const patterns = {
            responseTimeTrend: this.calculateTrend(performanceData.map(d => d.responseTime)),
            accuracyTrend: this.calculateTrend(performanceData.map(d => d.accuracy)),
            efficiencyTrend: this.calculateTrend(performanceData.map(d => d.efficiency)),
            adaptationTrend: this.calculateTrend(performanceData.map(d => d.adaptation))
        };
        
        return patterns;
    }

    calculateTrend(values) {
        if (values.length < 2) return 'stable';
        
        const firstHalf = values.slice(0, Math.floor(values.length / 2));
        const secondHalf = values.slice(Math.floor(values.length / 2));
        
        const firstAvg = firstHalf.reduce((sum, val) => sum + val, 0) / firstHalf.length;
        const secondAvg = secondHalf.reduce((sum, val) => sum + val, 0) / secondHalf.length;
        
        const difference = secondAvg - firstAvg;
        const threshold = 0.05;
        
        if (difference > threshold) return 'improving';
        if (difference < -threshold) return 'declining';
        return 'stable';
    }

    determineOptimizationStrategy(performancePatterns) {
        const strategy = {
            type: 'adaptive',
            focus: [],
            intensity: 'medium'
        };
        
        if (performancePatterns.responseTimeTrend === 'declining') {
            strategy.focus.push('speed-optimization');
            strategy.intensity = 'high';
        }
        
        if (performancePatterns.accuracyTrend === 'declining') {
            strategy.focus.push('accuracy-optimization');
            strategy.intensity = 'high';
        }
        
        if (performancePatterns.efficiencyTrend === 'declining') {
            strategy.focus.push('efficiency-optimization');
        }
        
        if (performancePatterns.adaptationTrend === 'declining') {
            strategy.focus.push('adaptation-optimization');
        }
        
        if (strategy.focus.length === 0) {
            strategy.focus.push('general-optimization');
            strategy.intensity = 'low';
        }
        
        return strategy;
    }

    async applyPerformanceBasedOptimization(agentId, strategy) {
        // Apply optimization based on performance strategy
        const optimizationResult = {
            agentId,
            strategy,
            timestamp: new Date().toISOString(),
            optimizations: []
        };
        
        for (const focus of strategy.focus) {
            const optimization = await this.applyFocusOptimization(focus, strategy.intensity);
            optimizationResult.optimizations.push(optimization);
        }
        
        return optimizationResult;
    }

    async applyFocusOptimization(focus, intensity) {
        const optimizationFactors = {
            'low': 0.95,
            'medium': 0.9,
            'high': 0.85
        };
        
        const factor = optimizationFactors[intensity] || 0.9;
        
        return {
            focus,
            intensity,
            factor,
            description: `Applied ${focus} optimization with ${intensity} intensity`
        };
    }
}

module.exports = NeuralOptimizer; 