mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-02-03 21:40:53 +00:00
n44144
This commit is contained in:
dopeuni444
@@ -11,6 +11,9 @@ class AgentBuilder {
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this.cognitivePatterns = new Map();
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this.adaptationEngine = new AdaptationEngine();
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this.brainTechVersion = '2025.07.31';
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this.realTimeAnalytics = new RealTimeAnalytics();
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this.neuralOptimizer = new NeuralOptimizer();
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this.cognitiveEnhancer = new CognitiveEnhancer();
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this.loadTemplates();
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this.initializeBrainTech();
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}
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@@ -22,6 +25,8 @@ class AgentBuilder {
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this.neuralNetworks.set('cognitive-mapping', new CognitiveArchitectureMapping());
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this.neuralNetworks.set('adaptive-learning', new AdaptiveLearningSystem());
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this.neuralNetworks.set('brain-interface', new BrainComputerInterface());
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this.neuralNetworks.set('neural-optimizer', this.neuralOptimizer);
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this.neuralNetworks.set('cognitive-enhancer', this.cognitiveEnhancer);
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this.logger.info(`🧠 Brain technology initialized with ${this.neuralNetworks.size} neural networks`);
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} catch (error) {
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@@ -29,6 +34,246 @@ class AgentBuilder {
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}
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}
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// NEW: Real-time analytics system
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async trackAgentPerformance(agentId, performanceData) {
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try {
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await this.realTimeAnalytics.trackPerformance(agentId, performanceData);
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await this.neuralOptimizer.optimizeBasedOnPerformance(agentId, performanceData);
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await this.cognitiveEnhancer.enhanceBasedOnPerformance(agentId, performanceData);
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this.logger.info(`📊 Performance tracked for agent ${agentId}`);
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} catch (error) {
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this.logger.error('Failed to track agent performance:', error);
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}
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}
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// NEW: Advanced neural optimization
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async optimizeAgentNeuralNetworks(agentId) {
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try {
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const agent = await this.getAgent(agentId);
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if (!agent) throw new Error('Agent not found');
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const optimizedNetworks = await this.neuralOptimizer.optimizeNetworks(agent.neuralNetworks);
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const enhancedCognitive = await this.cognitiveEnhancer.enhanceCognitivePatterns(agent.cognitivePatterns);
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await this.updateAgent(agentId, {
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neuralNetworks: optimizedNetworks,
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cognitivePatterns: enhancedCognitive,
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lastOptimized: new Date().toISOString()
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});
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this.logger.info(`🧠 Neural networks optimized for agent ${agentId}`);
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return { optimizedNetworks, enhancedCognitive };
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} catch (error) {
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this.logger.error('Failed to optimize neural networks:', error);
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throw error;
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}
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}
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// NEW: Cognitive enhancement system
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async enhanceAgentCognition(agentId, enhancementType = 'adaptive') {
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try {
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const agent = await this.getAgent(agentId);
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if (!agent) throw new Error('Agent not found');
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const enhancedCognition = await this.cognitiveEnhancer.enhanceCognition(agent, enhancementType);
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const adaptationMetrics = await this.calculateEnhancedAdaptationMetrics(agent, enhancedCognition);
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await this.updateAgent(agentId, {
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cognitivePatterns: enhancedCognition,
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adaptationMetrics: adaptationMetrics,
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lastEnhanced: new Date().toISOString()
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});
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this.logger.info(`🧠 Cognition enhanced for agent ${agentId}`);
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return { enhancedCognition, adaptationMetrics };
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} catch (error) {
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this.logger.error('Failed to enhance cognition:', error);
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throw error;
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}
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}
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// NEW: Brain-computer interface simulation
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async simulateBrainInterface(agentId, brainSignals) {
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try {
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const brainInterface = this.neuralNetworks.get('brain-interface');
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const processedSignals = await brainInterface.processBrainSignals(brainSignals);
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const agentResponse = await this.generateBrainInterfaceResponse(agentId, processedSignals);
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this.logger.info(`🧠 Brain interface simulation completed for agent ${agentId}`);
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return { processedSignals, agentResponse };
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} catch (error) {
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this.logger.error('Failed to simulate brain interface:', error);
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throw error;
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}
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}
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// NEW: Generate brain interface response
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async generateBrainInterfaceResponse(agentId, processedSignals) {
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const agent = await this.getAgent(agentId);
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if (!agent) throw new Error('Agent not found');
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const response = {
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agentId: agentId,
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responseType: 'brain-interface',
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cognitiveLoad: this.calculateCognitiveLoad(processedSignals),
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neuralResponse: this.generateNeuralResponse(processedSignals),
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adaptationLevel: this.calculateAdaptationLevel(processedSignals),
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timestamp: new Date().toISOString()
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};
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return response;
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}
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// NEW: Calculate cognitive load from brain signals
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calculateCognitiveLoad(brainSignals) {
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const loadFactors = {
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attention: brainSignals.attention || 0,
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memory: brainSignals.memory || 0,
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processing: brainSignals.processing || 0,
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creativity: brainSignals.creativity || 0
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};
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const totalLoad = Object.values(loadFactors).reduce((sum, value) => sum + value, 0);
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return Math.min(totalLoad / 4, 100); // Normalize to 0-100
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}
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// NEW: Generate neural response
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generateNeuralResponse(brainSignals) {
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return {
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pattern: this.analyzeNeuralPattern(brainSignals),
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intensity: this.calculateNeuralIntensity(brainSignals),
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frequency: this.calculateNeuralFrequency(brainSignals),
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coherence: this.calculateNeuralCoherence(brainSignals)
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};
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}
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// NEW: Analyze neural pattern
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analyzeNeuralPattern(brainSignals) {
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const patterns = [];
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if (brainSignals.attention > 70) patterns.push('high-attention');
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if (brainSignals.memory > 70) patterns.push('memory-intensive');
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if (brainSignals.processing > 70) patterns.push('high-processing');
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if (brainSignals.creativity > 70) patterns.push('creative-mode');
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return patterns.length > 0 ? patterns : ['normal-pattern'];
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}
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// NEW: Calculate neural intensity
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calculateNeuralIntensity(brainSignals) {
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const avgIntensity = Object.values(brainSignals).reduce((sum, value) => sum + value, 0) / Object.keys(brainSignals).length;
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return Math.min(avgIntensity, 100);
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}
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// NEW: Calculate neural frequency
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calculateNeuralFrequency(brainSignals) {
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// Simulate neural frequency based on signal patterns
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const frequency = Object.values(brainSignals).reduce((sum, value) => sum + value, 0) / 10;
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return Math.max(1, Math.min(frequency, 100));
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}
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// NEW: Calculate neural coherence
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calculateNeuralCoherence(brainSignals) {
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const values = Object.values(brainSignals);
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const mean = values.reduce((sum, value) => sum + value, 0) / values.length;
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const variance = values.reduce((sum, value) => sum + Math.pow(value - mean, 2), 0) / values.length;
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const coherence = 100 - Math.sqrt(variance);
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return Math.max(0, Math.min(coherence, 100));
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}
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// NEW: Calculate adaptation level
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calculateAdaptationLevel(brainSignals) {
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const adaptationFactors = {
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flexibility: brainSignals.attention || 0,
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learning: brainSignals.memory || 0,
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processing: brainSignals.processing || 0,
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creativity: brainSignals.creativity || 0
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};
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const totalAdaptation = Object.values(adaptationFactors).reduce((sum, value) => sum + value, 0);
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return Math.min(totalAdaptation / 4, 100);
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}
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// NEW: Enhanced adaptation metrics calculation
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async calculateEnhancedAdaptationMetrics(agent, enhancedCognition) {
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const baseMetrics = this.calculateAdaptationMetrics(agent);
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const enhancedMetrics = {
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...baseMetrics,
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cognitiveFlexibility: this.calculateCognitiveFlexibility(enhancedCognition),
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neuralEfficiency: this.calculateNeuralEfficiency(enhancedCognition),
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learningAcceleration: this.calculateLearningAcceleration(enhancedCognition),
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adaptationSpeed: this.calculateAdaptationSpeed(enhancedCognition),
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brainTechCompatibility: this.calculateBrainTechCompatibility(enhancedCognition)
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};
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return enhancedMetrics;
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}
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// NEW: Calculate cognitive flexibility
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calculateCognitiveFlexibility(cognition) {
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const flexibilityFactors = {
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patternRecognition: cognition.patternRecognition || 0,
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problemSolving: cognition.problemSolving || 0,
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creativity: cognition.creativity || 0,
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adaptability: cognition.adaptability || 0
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};
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const totalFlexibility = Object.values(flexibilityFactors).reduce((sum, value) => sum + value, 0);
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return Math.min(totalFlexibility / 4, 100);
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}
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// NEW: Calculate neural efficiency
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calculateNeuralEfficiency(cognition) {
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const efficiencyFactors = {
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processingSpeed: cognition.processingSpeed || 0,
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memoryEfficiency: cognition.memoryEfficiency || 0,
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energyOptimization: cognition.energyOptimization || 0,
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synapticStrength: cognition.synapticStrength || 0
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};
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const totalEfficiency = Object.values(efficiencyFactors).reduce((sum, value) => sum + value, 0);
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return Math.min(totalEfficiency / 4, 100);
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}
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// NEW: Calculate learning acceleration
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calculateLearningAcceleration(cognition) {
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const accelerationFactors = {
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learningRate: cognition.learningRate || 0,
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retentionRate: cognition.retentionRate || 0,
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transferLearning: cognition.transferLearning || 0,
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metaLearning: cognition.metaLearning || 0
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};
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const totalAcceleration = Object.values(accelerationFactors).reduce((sum, value) => sum + value, 0);
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return Math.min(totalAcceleration / 4, 100);
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}
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// NEW: Calculate adaptation speed
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calculateAdaptationSpeed(cognition) {
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const speedFactors = {
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responseTime: cognition.responseTime || 0,
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adaptationRate: cognition.adaptationRate || 0,
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flexibility: cognition.flexibility || 0,
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resilience: cognition.resilience || 0
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};
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const totalSpeed = Object.values(speedFactors).reduce((sum, value) => sum + value, 0);
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return Math.min(totalSpeed / 4, 100);
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}
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// NEW: Calculate brain tech compatibility
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calculateBrainTechCompatibility(cognition) {
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const compatibilityFactors = {
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neuralInterface: cognition.neuralInterface || 0,
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cognitiveMapping: cognition.cognitiveMapping || 0,
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adaptiveLearning: cognition.adaptiveLearning || 0,
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brainComputerInterface: cognition.brainComputerInterface || 0
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};
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const totalCompatibility = Object.values(compatibilityFactors).reduce((sum, value) => sum + value, 0);
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return Math.min(totalCompatibility / 4, 100);
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}
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async loadTemplates() {
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try {
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// Load agent templates from the collection
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@@ -65,7 +310,9 @@ class AgentBuilder {
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brainTech = true,
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neuralComplexity = 'medium',
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cognitiveEnhancement = true,
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adaptiveBehavior = true
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adaptiveBehavior = true,
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realTimeAnalytics = true,
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neuralOptimization = true
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} = config;
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// Validate configuration
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@@ -74,7 +321,7 @@ class AgentBuilder {
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// Generate agent ID
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const agentId = uuidv4();
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// Create agent structure with brain technology
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// Create agent structure with enhanced brain technology
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const agent = {
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id: agentId,
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name,
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@@ -90,37 +337,39 @@ class AgentBuilder {
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neuralComplexity,
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cognitiveEnhancement,
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adaptiveBehavior,
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realTimeAnalytics,
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neuralOptimization,
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brainTechVersion: this.brainTechVersion,
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neuralNetworks: this.initializeAgentNeuralNetworks(config),
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cognitivePatterns: this.analyzeCognitivePatterns(config),
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adaptationMetrics: this.calculateAdaptationMetrics(config),
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realTimeData: [],
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performanceHistory: [],
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optimizationHistory: [],
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enhancementHistory: [],
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createdAt: new Date().toISOString(),
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version: '2.0.0',
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version: '3.0.0',
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status: 'active'
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};
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// Generate system prompt based on type and configuration with brain tech
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// Initialize adaptive system
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await this.initializeAdaptiveSystem(agent);
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// Generate system prompt with brain technology
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agent.systemPrompt = await this.generateSystemPrompt(agent);
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// Generate tools configuration with neural enhancement
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// Generate tools configuration
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agent.toolsConfig = await this.generateToolsConfig(agent);
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// Generate memory configuration with cognitive enhancement
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if (memory) {
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agent.memoryConfig = await this.generateMemoryConfig(agent);
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}
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// Generate memory configuration
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agent.memoryConfig = await this.generateMemoryConfig(agent);
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// Initialize adaptive learning system
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if (adaptiveBehavior) {
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agent.adaptiveSystem = await this.initializeAdaptiveSystem(agent);
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}
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// Save agent configuration
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// Save agent
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await this.saveAgent(agent);
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this.logger.info(`🧠 Created brain-enhanced agent: ${name} (${agentId})`);
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return agent;
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this.logger.info(`🧠 Agent "${name}" created with advanced brain technology`);
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return agent;
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} catch (error) {
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this.logger.error('Failed to create agent:', error);
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throw error;
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485
AI_Agent_Builder_Framework/src/core/NeuralOptimizer.js
Normal file
485
AI_Agent_Builder_Framework/src/core/NeuralOptimizer.js
Normal file
@@ -0,0 +1,485 @@
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const Logger = require('../utils/Logger');
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class NeuralOptimizer {
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constructor() {
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this.logger = new Logger();
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this.optimizationHistory = new Map();
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this.optimizationAlgorithms = new Map();
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this.performanceMetrics = new Map();
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this.initializeOptimizationAlgorithms();
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}
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initializeOptimizationAlgorithms() {
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// Initialize various neural optimization algorithms
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this.optimizationAlgorithms.set('gradient-descent', this.gradientDescentOptimization.bind(this));
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this.optimizationAlgorithms.set('genetic-algorithm', this.geneticAlgorithmOptimization.bind(this));
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this.optimizationAlgorithms.set('reinforcement-learning', this.reinforcementLearningOptimization.bind(this));
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this.optimizationAlgorithms.set('adaptive-resonance', this.adaptiveResonanceOptimization.bind(this));
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this.optimizationAlgorithms.set('neural-evolution', this.neuralEvolutionOptimization.bind(this));
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this.logger.info(`🧠 Neural optimizer initialized with ${this.optimizationAlgorithms.size} algorithms`);
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}
|
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async optimizeNetworks(neuralNetworks, optimizationType = 'adaptive') {
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try {
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this.logger.info(`🧠 Starting neural network optimization with type: ${optimizationType}`);
|
||||
|
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const optimizationResults = {
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timestamp: new Date().toISOString(),
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optimizationType,
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originalNetworks: neuralNetworks,
|
||||
optimizedNetworks: {},
|
||||
performanceImprovements: {},
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||||
optimizationMetrics: {}
|
||||
};
|
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|
||||
// Optimize each neural network
|
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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;
|
||||
408
AI_Agent_Builder_Framework/src/core/RealTimeAnalytics.js
Normal file
408
AI_Agent_Builder_Framework/src/core/RealTimeAnalytics.js
Normal file
@@ -0,0 +1,408 @@
|
||||
const Logger = require('../utils/Logger');
|
||||
|
||||
class RealTimeAnalytics {
|
||||
constructor() {
|
||||
this.logger = new Logger();
|
||||
this.performanceData = new Map();
|
||||
this.analyticsHistory = [];
|
||||
this.realTimeMetrics = new Map();
|
||||
this.performanceThresholds = {
|
||||
responseTime: 1000, // ms
|
||||
accuracy: 0.8, // 80%
|
||||
efficiency: 0.7, // 70%
|
||||
adaptation: 0.6 // 60%
|
||||
};
|
||||
}
|
||||
|
||||
async trackPerformance(agentId, performanceData) {
|
||||
try {
|
||||
const timestamp = new Date().toISOString();
|
||||
const enhancedData = {
|
||||
...performanceData,
|
||||
timestamp,
|
||||
agentId,
|
||||
metrics: this.calculatePerformanceMetrics(performanceData),
|
||||
insights: this.generatePerformanceInsights(performanceData),
|
||||
recommendations: this.generatePerformanceRecommendations(performanceData)
|
||||
};
|
||||
|
||||
// Store performance data
|
||||
if (!this.performanceData.has(agentId)) {
|
||||
this.performanceData.set(agentId, []);
|
||||
}
|
||||
this.performanceData.get(agentId).push(enhancedData);
|
||||
|
||||
// Update real-time metrics
|
||||
this.updateRealTimeMetrics(agentId, enhancedData);
|
||||
|
||||
// Store in analytics history
|
||||
this.analyticsHistory.push(enhancedData);
|
||||
|
||||
// Keep only last 1000 entries for performance
|
||||
if (this.analyticsHistory.length > 1000) {
|
||||
this.analyticsHistory = this.analyticsHistory.slice(-1000);
|
||||
}
|
||||
|
||||
this.logger.info(`📊 Performance tracked for agent ${agentId}`);
|
||||
return enhancedData;
|
||||
} catch (error) {
|
||||
this.logger.error('Failed to track performance:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
calculatePerformanceMetrics(performanceData) {
|
||||
const metrics = {
|
||||
responseTime: this.calculateResponseTime(performanceData),
|
||||
accuracy: this.calculateAccuracy(performanceData),
|
||||
efficiency: this.calculateEfficiency(performanceData),
|
||||
adaptation: this.calculateAdaptation(performanceData),
|
||||
cognitiveLoad: this.calculateCognitiveLoad(performanceData),
|
||||
neuralEfficiency: this.calculateNeuralEfficiency(performanceData)
|
||||
};
|
||||
|
||||
return metrics;
|
||||
}
|
||||
|
||||
calculateResponseTime(performanceData) {
|
||||
const responseTime = performanceData.responseTime || 0;
|
||||
return Math.min(responseTime, 10000); // Cap at 10 seconds
|
||||
}
|
||||
|
||||
calculateAccuracy(performanceData) {
|
||||
const accuracy = performanceData.accuracy || 0;
|
||||
return Math.max(0, Math.min(accuracy, 1)); // Normalize to 0-1
|
||||
}
|
||||
|
||||
calculateEfficiency(performanceData) {
|
||||
const efficiency = performanceData.efficiency || 0;
|
||||
return Math.max(0, Math.min(efficiency, 1)); // Normalize to 0-1
|
||||
}
|
||||
|
||||
calculateAdaptation(performanceData) {
|
||||
const adaptation = performanceData.adaptation || 0;
|
||||
return Math.max(0, Math.min(adaptation, 1)); // Normalize to 0-1
|
||||
}
|
||||
|
||||
calculateCognitiveLoad(performanceData) {
|
||||
const cognitiveLoad = performanceData.cognitiveLoad || 0;
|
||||
return Math.max(0, Math.min(cognitiveLoad, 100)); // Normalize to 0-100
|
||||
}
|
||||
|
||||
calculateNeuralEfficiency(performanceData) {
|
||||
const neuralEfficiency = performanceData.neuralEfficiency || 0;
|
||||
return Math.max(0, Math.min(neuralEfficiency, 100)); // Normalize to 0-100
|
||||
}
|
||||
|
||||
generatePerformanceInsights(performanceData) {
|
||||
const insights = [];
|
||||
const metrics = this.calculatePerformanceMetrics(performanceData);
|
||||
|
||||
if (metrics.responseTime > this.performanceThresholds.responseTime) {
|
||||
insights.push('Response time exceeds optimal threshold - consider optimization');
|
||||
}
|
||||
|
||||
if (metrics.accuracy < this.performanceThresholds.accuracy) {
|
||||
insights.push('Accuracy below target threshold - review decision-making patterns');
|
||||
}
|
||||
|
||||
if (metrics.efficiency < this.performanceThresholds.efficiency) {
|
||||
insights.push('Efficiency below optimal level - consider resource optimization');
|
||||
}
|
||||
|
||||
if (metrics.adaptation < this.performanceThresholds.adaptation) {
|
||||
insights.push('Adaptation rate below target - enhance learning mechanisms');
|
||||
}
|
||||
|
||||
if (metrics.cognitiveLoad > 80) {
|
||||
insights.push('High cognitive load detected - consider load balancing');
|
||||
}
|
||||
|
||||
if (metrics.neuralEfficiency < 60) {
|
||||
insights.push('Neural efficiency below optimal - review network architecture');
|
||||
}
|
||||
|
||||
return insights;
|
||||
}
|
||||
|
||||
generatePerformanceRecommendations(performanceData) {
|
||||
const recommendations = [];
|
||||
const metrics = this.calculatePerformanceMetrics(performanceData);
|
||||
|
||||
if (metrics.responseTime > this.performanceThresholds.responseTime) {
|
||||
recommendations.push('Implement response time optimization algorithms');
|
||||
recommendations.push('Consider parallel processing for complex tasks');
|
||||
}
|
||||
|
||||
if (metrics.accuracy < this.performanceThresholds.accuracy) {
|
||||
recommendations.push('Enhance decision-making algorithms');
|
||||
recommendations.push('Implement additional validation layers');
|
||||
}
|
||||
|
||||
if (metrics.efficiency < this.performanceThresholds.efficiency) {
|
||||
recommendations.push('Optimize resource allocation');
|
||||
recommendations.push('Implement caching mechanisms');
|
||||
}
|
||||
|
||||
if (metrics.adaptation < this.performanceThresholds.adaptation) {
|
||||
recommendations.push('Enhance adaptive learning algorithms');
|
||||
recommendations.push('Implement real-time feedback loops');
|
||||
}
|
||||
|
||||
if (metrics.cognitiveLoad > 80) {
|
||||
recommendations.push('Implement cognitive load balancing');
|
||||
recommendations.push('Add task prioritization mechanisms');
|
||||
}
|
||||
|
||||
if (metrics.neuralEfficiency < 60) {
|
||||
recommendations.push('Optimize neural network architecture');
|
||||
recommendations.push('Implement neural efficiency monitoring');
|
||||
}
|
||||
|
||||
return recommendations;
|
||||
}
|
||||
|
||||
updateRealTimeMetrics(agentId, enhancedData) {
|
||||
const currentMetrics = this.realTimeMetrics.get(agentId) || {};
|
||||
const newMetrics = {
|
||||
...currentMetrics,
|
||||
lastUpdate: enhancedData.timestamp,
|
||||
performanceScore: this.calculatePerformanceScore(enhancedData.metrics),
|
||||
trend: this.calculatePerformanceTrend(agentId, enhancedData),
|
||||
alerts: this.generatePerformanceAlerts(enhancedData.metrics)
|
||||
};
|
||||
|
||||
this.realTimeMetrics.set(agentId, newMetrics);
|
||||
}
|
||||
|
||||
calculatePerformanceScore(metrics) {
|
||||
const weights = {
|
||||
responseTime: 0.2,
|
||||
accuracy: 0.3,
|
||||
efficiency: 0.2,
|
||||
adaptation: 0.15,
|
||||
cognitiveLoad: 0.1,
|
||||
neuralEfficiency: 0.05
|
||||
};
|
||||
|
||||
const normalizedResponseTime = Math.max(0, 1 - (metrics.responseTime / 10000));
|
||||
const score = (
|
||||
normalizedResponseTime * weights.responseTime +
|
||||
metrics.accuracy * weights.accuracy +
|
||||
metrics.efficiency * weights.efficiency +
|
||||
metrics.adaptation * weights.adaptation +
|
||||
(1 - metrics.cognitiveLoad / 100) * weights.cognitiveLoad +
|
||||
(metrics.neuralEfficiency / 100) * weights.neuralEfficiency
|
||||
);
|
||||
|
||||
return Math.max(0, Math.min(score, 1));
|
||||
}
|
||||
|
||||
calculatePerformanceTrend(agentId, currentData) {
|
||||
const agentHistory = this.performanceData.get(agentId) || [];
|
||||
if (agentHistory.length < 2) return 'stable';
|
||||
|
||||
const recentScores = agentHistory.slice(-5).map(data =>
|
||||
this.calculatePerformanceScore(data.metrics)
|
||||
);
|
||||
|
||||
const trend = this.calculateTrendFromScores(recentScores);
|
||||
return trend;
|
||||
}
|
||||
|
||||
calculateTrendFromScores(scores) {
|
||||
if (scores.length < 2) return 'stable';
|
||||
|
||||
const firstHalf = scores.slice(0, Math.floor(scores.length / 2));
|
||||
const secondHalf = scores.slice(Math.floor(scores.length / 2));
|
||||
|
||||
const firstAvg = firstHalf.reduce((sum, score) => sum + score, 0) / firstHalf.length;
|
||||
const secondAvg = secondHalf.reduce((sum, score) => sum + score, 0) / secondHalf.length;
|
||||
|
||||
const difference = secondAvg - firstAvg;
|
||||
const threshold = 0.05;
|
||||
|
||||
if (difference > threshold) return 'improving';
|
||||
if (difference < -threshold) return 'declining';
|
||||
return 'stable';
|
||||
}
|
||||
|
||||
generatePerformanceAlerts(metrics) {
|
||||
const alerts = [];
|
||||
|
||||
if (metrics.responseTime > this.performanceThresholds.responseTime) {
|
||||
alerts.push({
|
||||
type: 'warning',
|
||||
message: 'Response time exceeds threshold',
|
||||
metric: 'responseTime',
|
||||
value: metrics.responseTime
|
||||
});
|
||||
}
|
||||
|
||||
if (metrics.accuracy < this.performanceThresholds.accuracy) {
|
||||
alerts.push({
|
||||
type: 'error',
|
||||
message: 'Accuracy below threshold',
|
||||
metric: 'accuracy',
|
||||
value: metrics.accuracy
|
||||
});
|
||||
}
|
||||
|
||||
if (metrics.cognitiveLoad > 90) {
|
||||
alerts.push({
|
||||
type: 'critical',
|
||||
message: 'Critical cognitive load detected',
|
||||
metric: 'cognitiveLoad',
|
||||
value: metrics.cognitiveLoad
|
||||
});
|
||||
}
|
||||
|
||||
return alerts;
|
||||
}
|
||||
|
||||
async getAgentAnalytics(agentId, timeRange = '24h') {
|
||||
try {
|
||||
const agentData = this.performanceData.get(agentId) || [];
|
||||
const filteredData = this.filterDataByTimeRange(agentData, timeRange);
|
||||
|
||||
const analytics = {
|
||||
agentId,
|
||||
timeRange,
|
||||
dataPoints: filteredData.length,
|
||||
averageMetrics: this.calculateAverageMetrics(filteredData),
|
||||
trends: this.calculateTrends(filteredData),
|
||||
insights: this.generateAnalyticsInsights(filteredData),
|
||||
recommendations: this.generateAnalyticsRecommendations(filteredData)
|
||||
};
|
||||
|
||||
return analytics;
|
||||
} catch (error) {
|
||||
this.logger.error('Failed to get agent analytics:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
filterDataByTimeRange(data, timeRange) {
|
||||
const now = new Date();
|
||||
const timeRanges = {
|
||||
'1h': 60 * 60 * 1000,
|
||||
'6h': 6 * 60 * 60 * 1000,
|
||||
'24h': 24 * 60 * 60 * 1000,
|
||||
'7d': 7 * 24 * 60 * 60 * 1000,
|
||||
'30d': 30 * 24 * 60 * 60 * 1000
|
||||
};
|
||||
|
||||
const rangeMs = timeRanges[timeRange] || timeRanges['24h'];
|
||||
const cutoffTime = new Date(now.getTime() - rangeMs);
|
||||
|
||||
return data.filter(entry => new Date(entry.timestamp) >= cutoffTime);
|
||||
}
|
||||
|
||||
calculateAverageMetrics(data) {
|
||||
if (data.length === 0) return {};
|
||||
|
||||
const metrics = ['responseTime', 'accuracy', 'efficiency', 'adaptation', 'cognitiveLoad', 'neuralEfficiency'];
|
||||
const averages = {};
|
||||
|
||||
metrics.forEach(metric => {
|
||||
const values = data.map(entry => entry.metrics[metric]).filter(val => val !== undefined);
|
||||
if (values.length > 0) {
|
||||
averages[metric] = values.reduce((sum, val) => sum + val, 0) / values.length;
|
||||
}
|
||||
});
|
||||
|
||||
return averages;
|
||||
}
|
||||
|
||||
calculateTrends(data) {
|
||||
if (data.length < 2) return {};
|
||||
|
||||
const trends = {};
|
||||
const metrics = ['responseTime', 'accuracy', 'efficiency', 'adaptation', 'cognitiveLoad', 'neuralEfficiency'];
|
||||
|
||||
metrics.forEach(metric => {
|
||||
const values = data.map(entry => entry.metrics[metric]).filter(val => val !== undefined);
|
||||
if (values.length >= 2) {
|
||||
trends[metric] = this.calculateTrendFromScores(values);
|
||||
}
|
||||
});
|
||||
|
||||
return trends;
|
||||
}
|
||||
|
||||
generateAnalyticsInsights(data) {
|
||||
const insights = [];
|
||||
const averageMetrics = this.calculateAverageMetrics(data);
|
||||
|
||||
if (averageMetrics.responseTime > this.performanceThresholds.responseTime) {
|
||||
insights.push('Consistently high response times detected');
|
||||
}
|
||||
|
||||
if (averageMetrics.accuracy < this.performanceThresholds.accuracy) {
|
||||
insights.push('Accuracy consistently below target threshold');
|
||||
}
|
||||
|
||||
if (averageMetrics.cognitiveLoad > 80) {
|
||||
insights.push('Sustained high cognitive load observed');
|
||||
}
|
||||
|
||||
return insights;
|
||||
}
|
||||
|
||||
generateAnalyticsRecommendations(data) {
|
||||
const recommendations = [];
|
||||
const averageMetrics = this.calculateAverageMetrics(data);
|
||||
|
||||
if (averageMetrics.responseTime > this.performanceThresholds.responseTime) {
|
||||
recommendations.push('Implement response time optimization');
|
||||
recommendations.push('Consider parallel processing architecture');
|
||||
}
|
||||
|
||||
if (averageMetrics.accuracy < this.performanceThresholds.accuracy) {
|
||||
recommendations.push('Enhance decision-making algorithms');
|
||||
recommendations.push('Implement additional validation layers');
|
||||
}
|
||||
|
||||
if (averageMetrics.cognitiveLoad > 80) {
|
||||
recommendations.push('Implement cognitive load balancing');
|
||||
recommendations.push('Add task prioritization mechanisms');
|
||||
}
|
||||
|
||||
return recommendations;
|
||||
}
|
||||
|
||||
async exportAnalytics(agentId, format = 'json') {
|
||||
try {
|
||||
const analytics = await this.getAgentAnalytics(agentId, '30d');
|
||||
|
||||
if (format === 'json') {
|
||||
return JSON.stringify(analytics, null, 2);
|
||||
} else if (format === 'csv') {
|
||||
return this.convertToCSV(analytics);
|
||||
} else {
|
||||
throw new Error(`Unsupported format: ${format}`);
|
||||
}
|
||||
} catch (error) {
|
||||
this.logger.error('Failed to export analytics:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
convertToCSV(analytics) {
|
||||
const headers = ['timestamp', 'responseTime', 'accuracy', 'efficiency', 'adaptation', 'cognitiveLoad', 'neuralEfficiency'];
|
||||
const rows = analytics.dataPoints.map(data => [
|
||||
data.timestamp,
|
||||
data.metrics.responseTime,
|
||||
data.metrics.accuracy,
|
||||
data.metrics.efficiency,
|
||||
data.metrics.adaptation,
|
||||
data.metrics.cognitiveLoad,
|
||||
data.metrics.neuralEfficiency
|
||||
]);
|
||||
|
||||
const csvContent = [headers.join(','), ...rows.map(row => row.join(','))].join('\n');
|
||||
return csvContent;
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = RealTimeAnalytics;
|
||||
Reference in New Issue
Block a user