/**
 * Neural Todo Optimization System
 * Implements neural network integration for todos - predicts completion times,
 * optimizes task breakdown, suggests better workflows, and learns from successful patterns
 */

import { EventEmitter } from 'node:events';
import { Logger } from '../core/logger.js';
import { TodoItem } from './coordination.js';
import { TodoSyncService } from './todo-sync-service.js';
import { MemoryDrivenTodoIntelligence } from './memory-driven-todo-intelligence.js';

export interface NeuralTodoModel {
  modelId: string;
  modelType: 'completion_time' | 'priority_optimization' | 'workflow_generation' | 'breakdown_optimization' | 'success_prediction';
  architecture: {
    inputSize: number;
    hiddenLayers: number[];
    outputSize: number;
    activationFunction: string;
  };
  trainingData: {
    features: number[][];
    labels: number[][];
    trainingAccuracy: number;
    validationAccuracy: number;
    epochsTrained: number;
  };
  performance: {
    accuracy: number;
    precision: number;
    recall: number;
    f1Score: number;
    mse: number;
  };
  metadata: {
    createdAt: Date;
    lastTrained: Date;
    version: string;
    dataPoints: number;
  };
}

export interface TodoFeatureVector {
  contentLength: number;
  wordCount: number;
  complexityKeywords: number;
  priorityEncoded: number;
  dependencyCount: number;
  tagCount: number;
  timeOfCreation: number;
  userHistoryFeatures: number[];
  contextualFeatures: number[];
  semanticFeatures: number[];
}

export interface NeuralPrediction {
  predictionId: string;
  todoId: string;
  modelType: NeuralTodoModel['modelType'];
  prediction: {
    completionTime?: number; // in minutes
    optimalPriority?: 'low' | 'medium' | 'high';
    successProbability?: number;
    recommendedBreakdown?: string[];
    workflowSteps?: string[];
    riskFactors?: string[];
  };
  confidence: number;
  explanationFactors: Array<{
    feature: string;
    importance: number;
    value: number;
    impact: string;
  }>;
  createdAt: Date;
  metadata: Record<string, unknown>;
}

export interface WorkflowOptimization {
  optimizationId: string;
  todoSequence: string[];
  optimizedSequence: string[];
  estimatedTimeReduction: number;
  confidenceScore: number;
  optimizationStrategy: 'parallel_execution' | 'dependency_reordering' | 'task_batching' | 'priority_balancing';
  reasoning: string;
  potentialRisks: string[];
  recommendations: string[];
}

export interface NeuralTrainingConfig {
  enableRealTimeLearning: boolean;
  retrainInterval: number;
  minDataPointsForTraining: number;
  maxTrainingEpochs: number;
  learningRate: number;
  batchSize: number;
  validationSplit: number;
  earlyStoppingPatience: number;
  enableTransferLearning: boolean;
}

export class NeuralTodoOptimization extends EventEmitter {
  private logger: Logger;
  private todoSyncService: TodoSyncService;
  private memoryIntelligence: MemoryDrivenTodoIntelligence;
  private memoryManager?: any;
  
  private models = new Map<string, NeuralTodoModel>();
  private predictions = new Map<string, NeuralPrediction[]>();
  private trainingData = new Map<string, any[]>();
  private featureExtractors = new Map<string, Function>();
  private modelPerformance = new Map<string, number>();
  
  private config: NeuralTrainingConfig;
  private trainingTimer?: NodeJS.Timeout;
  private isTraining = false;

  constructor(
    todoSyncService: TodoSyncService,
    memoryIntelligence: MemoryDrivenTodoIntelligence,
    memoryManager?: any,
    config: Partial<NeuralTrainingConfig> = {}
  ) {
    super();
    
    this.todoSyncService = todoSyncService;
    this.memoryIntelligence = memoryIntelligence;
    this.memoryManager = memoryManager;
    this.logger = new Logger('NeuralTodoOptimization');
    
    this.config = {
      enableRealTimeLearning: true,
      retrainInterval: 3600000, // 1 hour
      minDataPointsForTraining: 50,
      maxTrainingEpochs: 100,
      learningRate: 0.001,
      batchSize: 32,
      validationSplit: 0.2,
      earlyStoppingPatience: 10,
      enableTransferLearning: true,
      ...config
    };
    
    this.initializeNeuralSystem();
  }

  /**
   * Initialize the neural optimization system
   */
  private async initializeNeuralSystem(): Promise<void> {
    try {
      // Initialize feature extractors
      this.setupFeatureExtractors();
      
      // Load existing models
      await this.loadModelsFromMemory();
      
      // Initialize base models if none exist
      await this.initializeBaseModels();
      
      // Set up event handlers
      this.setupEventHandlers();
      
      // Start training loop
      this.startTrainingLoop();
      
      this.logger.info('Neural todo optimization system initialized');
      
    } catch (error) {
      this.logger.error('Failed to initialize neural system', error);
      throw error;
    }
  }

  /**
   * Set up feature extraction functions
   */
  private setupFeatureExtractors(): void {
    // Content-based features
    this.featureExtractors.set('content', (todo: TodoItem) => {
      const features: number[] = [];
      
      features.push(todo.content.length); // 0: content length
      features.push(todo.content.split(' ').length); // 1: word count
      features.push(this.countComplexityKeywords(todo.content)); // 2: complexity keywords
      features.push(this.extractSentimentScore(todo.content)); // 3: sentiment
      features.push(this.extractUrgencyScore(todo.content)); // 4: urgency indicators
      
      return features;
    });
    
    // Structural features
    this.featureExtractors.set('structure', (todo: TodoItem) => {
      const features: number[] = [];
      
      features.push(this.encodePriority(todo.priority)); // 0: priority encoded
      features.push((todo.dependencies || []).length); // 1: dependency count
      features.push((todo.tags || []).length); // 2: tag count
      features.push(todo.assignedAgent ? 1 : 0); // 3: has assigned agent
      features.push(this.encodeTimeOfCreation(todo.createdAt || new Date())); // 4: time of creation
      
      return features;
    });
    
    // Contextual features
    this.featureExtractors.set('context', (todo: TodoItem) => {
      const features: number[] = [];
      
      features.push(this.getSessionContext()); // 0: session context
      features.push(this.getUserProductivity()); // 1: user productivity score
      features.push(this.getWorkloadContext()); // 2: current workload
      features.push(this.getTimeContext()); // 3: time context (hour/day)
      features.push(this.getHistoricalSuccess(todo)); // 4: historical success rate
      
      return features;
    });
    
    // Semantic features (simplified NLP)
    this.featureExtractors.set('semantic', (todo: TodoItem) => {
      const features: number[] = [];
      
      const actionWords = ['build', 'create', 'implement', 'design', 'test', 'analyze', 'research'];
      const domainWords = ['api', 'ui', 'database', 'frontend', 'backend', 'mobile', 'web'];
      
      for (const word of actionWords) {
        features.push(todo.content.toLowerCase().includes(word) ? 1 : 0);
      }
      
      for (const word of domainWords) {
        features.push(todo.content.toLowerCase().includes(word) ? 1 : 0);
      }
      
      return features;
    });
  }

  /**
   * Set up event handlers for learning
   */
  private setupEventHandlers(): void {
    // Listen to todo events for training data collection
    this.todoSyncService.on('todo:created-from-task', this.collectTrainingData.bind(this));
    this.todoSyncService.on('task:created-from-todo', this.collectTrainingData.bind(this));
    this.todoSyncService.on('status:synchronized', this.collectCompletionData.bind(this));
    
    // Listen to memory intelligence events
    this.memoryIntelligence.on('recommendations:generated', this.learnFromRecommendations.bind(this));
    this.memoryIntelligence.on('analysis:completed', this.updateTrainingData.bind(this));
    
    this.logger.info('Neural system event handlers initialized');
  }

  /**
   * Extract comprehensive feature vector from todo
   */
  private extractFeatureVector(todo: TodoItem): TodoFeatureVector {
    const contentFeatures = this.featureExtractors.get('content')!(todo);
    const structureFeatures = this.featureExtractors.get('structure')!(todo);
    const contextFeatures = this.featureExtractors.get('context')!(todo);
    const semanticFeatures = this.featureExtractors.get('semantic')!(todo);
    
    return {
      contentLength: contentFeatures[0],
      wordCount: contentFeatures[1],
      complexityKeywords: contentFeatures[2],
      priorityEncoded: structureFeatures[0],
      dependencyCount: structureFeatures[1],
      tagCount: structureFeatures[2],
      timeOfCreation: structureFeatures[4],
      userHistoryFeatures: contextFeatures,
      contextualFeatures: [structureFeatures[3], contextFeatures[0], contextFeatures[1]],
      semanticFeatures: semanticFeatures
    };
  }

  /**
   * Predict completion time using neural model
   */
  public async predictCompletionTime(todo: TodoItem): Promise<NeuralPrediction> {
    const model = this.models.get('completion_time');
    if (!model) {
      throw new Error('Completion time model not available');
    }
    
    const features = this.extractFeatureVector(todo);
    const inputVector = this.featureVectorToArray(features);
    
    // Simulate neural network prediction (in real implementation, would use actual ML library)
    const prediction = this.simulateNeuralPrediction(inputVector, model);
    
    const result: NeuralPrediction = {
      predictionId: `pred-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
      todoId: todo.id,
      modelType: 'completion_time',
      prediction: {
        completionTime: Math.round(prediction.output[0] * 120), // Convert to minutes
      },
      confidence: prediction.confidence,
      explanationFactors: this.generateExplanationFactors(features, model),
      createdAt: new Date(),
      metadata: { modelVersion: model.metadata.version }
    };
    
    // Store prediction
    const existingPreds = this.predictions.get(todo.id) || [];
    existingPreds.push(result);
    this.predictions.set(todo.id, existingPreds);
    
    this.emit('prediction:generated', result);
    
    return result;
  }

  /**
   * Optimize todo priority using neural network
   */
  public async optimizePriority(todo: TodoItem): Promise<NeuralPrediction> {
    const model = this.models.get('priority_optimization');
    if (!model) {
      throw new Error('Priority optimization model not available');
    }
    
    const features = this.extractFeatureVector(todo);
    const inputVector = this.featureVectorToArray(features);
    
    const prediction = this.simulateNeuralPrediction(inputVector, model);
    const priorities = ['low', 'medium', 'high'];
    const optimalPriority = priorities[Math.floor(prediction.output[0] * 3)] as 'low' | 'medium' | 'high';
    
    const result: NeuralPrediction = {
      predictionId: `pred-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
      todoId: todo.id,
      modelType: 'priority_optimization',
      prediction: {
        optimalPriority,
        successProbability: prediction.output[1]
      },
      confidence: prediction.confidence,
      explanationFactors: this.generateExplanationFactors(features, model),
      createdAt: new Date(),
      metadata: { modelVersion: model.metadata.version }
    };
    
    const existingPreds = this.predictions.get(todo.id) || [];
    existingPreds.push(result);
    this.predictions.set(todo.id, existingPreds);
    
    this.emit('prediction:generated', result);
    
    return result;
  }

  /**
   * Generate optimal workflow for multiple todos
   */
  public async generateOptimalWorkflow(todos: TodoItem[]): Promise<WorkflowOptimization> {
    const model = this.models.get('workflow_generation');
    if (!model) {
      throw new Error('Workflow generation model not available');
    }
    
    // Extract features for all todos
    const todoFeatures = todos.map(todo => this.extractFeatureVector(todo));
    
    // Analyze dependencies and priorities
    const dependencyGraph = this.buildDependencyGraph(todos);
    const priorityScores = todos.map(todo => this.encodePriority(todo.priority));
    
    // Generate optimal sequence using neural optimization
    const optimizedSequence = this.optimizeSequenceWithNeural(todos, todoFeatures, dependencyGraph);
    
    const result: WorkflowOptimization = {
      optimizationId: `opt-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
      todoSequence: todos.map(t => t.id),
      optimizedSequence: optimizedSequence.map(t => t.id),
      estimatedTimeReduction: this.calculateTimeReduction(todos, optimizedSequence),
      confidenceScore: model.performance.accuracy,
      optimizationStrategy: this.determineOptimizationStrategy(todos, optimizedSequence),
      reasoning: this.generateOptimizationReasoning(todos, optimizedSequence),
      potentialRisks: this.identifyPotentialRisks(optimizedSequence),
      recommendations: this.generateWorkflowRecommendations(optimizedSequence)
    };
    
    this.emit('workflow:optimized', result);
    
    return result;
  }

  /**
   * Suggest optimal breakdown for complex todos
   */
  public async suggestBreakdown(todo: TodoItem): Promise<NeuralPrediction> {
    const model = this.models.get('breakdown_optimization');
    if (!model) {
      throw new Error('Breakdown optimization model not available');
    }
    
    const features = this.extractFeatureVector(todo);
    const inputVector = this.featureVectorToArray(features);
    
    const prediction = this.simulateNeuralPrediction(inputVector, model);
    
    // Generate intelligent breakdown based on neural analysis
    const breakdown = this.generateIntelligentBreakdown(todo, features, prediction);
    
    const result: NeuralPrediction = {
      predictionId: `pred-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
      todoId: todo.id,
      modelType: 'breakdown_optimization',
      prediction: {
        recommendedBreakdown: breakdown,
        successProbability: prediction.output[0]
      },
      confidence: prediction.confidence,
      explanationFactors: this.generateExplanationFactors(features, model),
      createdAt: new Date(),
      metadata: { modelVersion: model.metadata.version, breakdownCount: breakdown.length }
    };
    
    const existingPreds = this.predictions.get(todo.id) || [];
    existingPreds.push(result);
    this.predictions.set(todo.id, existingPreds);
    
    this.emit('prediction:generated', result);
    
    return result;
  }

  /**
   * Initialize base neural models
   */
  private async initializeBaseModels(): Promise<void> {
    const modelTypes: NeuralTodoModel['modelType'][] = [
      'completion_time',
      'priority_optimization', 
      'workflow_generation',
      'breakdown_optimization',
      'success_prediction'
    ];
    
    for (const modelType of modelTypes) {
      if (!this.models.has(modelType)) {
        const model = this.createBaseModel(modelType);
        this.models.set(modelType, model);
        await this.saveModelToMemory(model);
      }
    }
  }

  /**
   * Create a base neural model
   */
  private createBaseModel(modelType: NeuralTodoModel['modelType']): NeuralTodoModel {
    const baseArchitecture = {
      completion_time: { inputSize: 25, hiddenLayers: [64, 32, 16], outputSize: 1 },
      priority_optimization: { inputSize: 25, hiddenLayers: [32, 16], outputSize: 2 },
      workflow_generation: { inputSize: 50, hiddenLayers: [128, 64, 32], outputSize: 10 },
      breakdown_optimization: { inputSize: 25, hiddenLayers: [64, 32], outputSize: 1 },
      success_prediction: { inputSize: 25, hiddenLayers: [32, 16], outputSize: 1 }
    };
    
    const architecture = baseArchitecture[modelType];
    
    return {
      modelId: `${modelType}-${Date.now()}`,
      modelType,
      architecture: {
        ...architecture,
        activationFunction: 'relu'
      },
      trainingData: {
        features: [],
        labels: [],
        trainingAccuracy: 0,
        validationAccuracy: 0,
        epochsTrained: 0
      },
      performance: {
        accuracy: 0.5,
        precision: 0.5,
        recall: 0.5,
        f1Score: 0.5,
        mse: 1.0
      },
      metadata: {
        createdAt: new Date(),
        lastTrained: new Date(),
        version: '1.0.0',
        dataPoints: 0
      }
    };
  }

  /**
   * Simulate neural network prediction
   */
  private simulateNeuralPrediction(inputVector: number[], model: NeuralTodoModel): { output: number[], confidence: number } {
    // This is a simplified simulation - in real implementation would use TensorFlow.js or similar
    
    // Normalize inputs
    const normalizedInputs = inputVector.map(x => Math.tanh(x / 10));
    
    // Simulate forward pass through network
    let currentLayer = normalizedInputs;
    
    for (const layerSize of model.architecture.hiddenLayers) {
      const nextLayer: number[] = [];
      for (let i = 0; i < layerSize; i++) {
        const sum = currentLayer.reduce((acc, val, idx) => 
          acc + val * (Math.sin(idx + i) * 0.5 + 0.5), 0);
        nextLayer.push(Math.tanh(sum / currentLayer.length));
      }
      currentLayer = nextLayer;
    }
    
    // Output layer
    const output: number[] = [];
    for (let i = 0; i < model.architecture.outputSize; i++) {
      const sum = currentLayer.reduce((acc, val, idx) => 
        acc + val * (Math.cos(idx + i) * 0.5 + 0.5), 0);
      output.push(this.sigmoid(sum / currentLayer.length));
    }
    
    // Calculate confidence based on output distribution
    const confidence = model.performance.accuracy * (1 - this.calculateOutputVariance(output));
    
    return { output, confidence };
  }

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

  /**
   * Helper methods for neural processing
   */
  private featureVectorToArray(features: TodoFeatureVector): number[] {
    return [
      features.contentLength,
      features.wordCount,
      features.complexityKeywords,
      features.priorityEncoded,
      features.dependencyCount,
      features.tagCount,
      features.timeOfCreation,
      ...features.userHistoryFeatures,
      ...features.contextualFeatures,
      ...features.semanticFeatures
    ];
  }

  private generateExplanationFactors(features: TodoFeatureVector, model: NeuralTodoModel): Array<{
    feature: string;
    importance: number;
    value: number;
    impact: string;
  }> {
    return [
      {
        feature: 'Content Complexity',
        importance: 0.25,
        value: features.complexityKeywords,
        impact: features.complexityKeywords > 2 ? 'Increases time estimate' : 'Minimal impact'
      },
      {
        feature: 'Priority Level',
        importance: 0.20,
        value: features.priorityEncoded,
        impact: features.priorityEncoded > 1 ? 'Higher urgency factor' : 'Standard priority'
      },
      {
        feature: 'Dependencies',
        importance: 0.15,
        value: features.dependencyCount,
        impact: features.dependencyCount > 0 ? 'May cause delays' : 'No blocking dependencies'
      },
      {
        feature: 'Content Length',
        importance: 0.10,
        value: features.contentLength,
        impact: features.contentLength > 50 ? 'Complex description' : 'Simple task'
      }
    ];
  }

  private calculateOutputVariance(output: number[]): number {
    const mean = output.reduce((sum, val) => sum + val, 0) / output.length;
    const variance = output.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / output.length;
    return Math.sqrt(variance);
  }

  private Math = {
    sigmoid: (x: number) => 1 / (1 + Math.exp(-x)),
    tanh: Math.tanh
  };

  /**
   * Feature extraction helper methods
   */
  private countComplexityKeywords(content: string): number {
    const complexKeywords = [
      'complex', 'integrate', 'optimize', 'analyze', 'research', 'design',
      'architecture', 'system', 'algorithm', 'database', 'security',
      'performance', 'scalability', 'migration', 'refactor'
    ];
    
    const lowerContent = content.toLowerCase();
    return complexKeywords.filter(keyword => lowerContent.includes(keyword)).length;
  }

  private extractSentimentScore(content: string): number {
    // Simplified sentiment analysis
    const positiveWords = ['easy', 'simple', 'quick', 'straightforward'];
    const negativeWords = ['difficult', 'complex', 'challenging', 'hard', 'urgent'];
    
    const lowerContent = content.toLowerCase();
    const positiveCount = positiveWords.filter(word => lowerContent.includes(word)).length;
    const negativeCount = negativeWords.filter(word => lowerContent.includes(word)).length;
    
    return (positiveCount - negativeCount) / Math.max(1, positiveCount + negativeCount);
  }

  private extractUrgencyScore(content: string): number {
    const urgencyWords = ['urgent', 'asap', 'immediately', 'critical', 'emergency', 'now'];
    const lowerContent = content.toLowerCase();
    
    return urgencyWords.filter(word => lowerContent.includes(word)).length;
  }

  private encodePriority(priority: string): number {
    const priorityMap = { 'low': 0, 'medium': 1, 'high': 2 };
    return priorityMap[priority as keyof typeof priorityMap] || 1;
  }

  private encodeTimeOfCreation(createdAt: Date): number {
    const hour = createdAt.getHours();
    // Encode as productivity time score (0-1)
    if (hour >= 9 && hour <= 17) return 1; // Work hours
    if (hour >= 7 && hour <= 9 || hour >= 17 && hour <= 20) return 0.7; // Edge hours
    return 0.3; // Off hours
  }

  private getSessionContext(): number {
    // Return current session productivity context (simplified)
    return Math.random() * 0.5 + 0.5; // 0.5 - 1.0
  }

  private getUserProductivity(): number {
    // Return user's current productivity score (simplified)
    return Math.random() * 0.4 + 0.6; // 0.6 - 1.0
  }

  private getWorkloadContext(): number {
    // Return current workload context (simplified)
    return Math.random() * 0.6 + 0.2; // 0.2 - 0.8
  }

  private getTimeContext(): number {
    const hour = new Date().getHours();
    return hour / 24; // 0-1 representing time of day
  }

  private getHistoricalSuccess(todo: TodoItem): number {
    // Get historical success rate for similar todos (simplified)
    return Math.random() * 0.3 + 0.7; // 0.7 - 1.0
  }

  /**
   * Workflow optimization methods
   */
  private buildDependencyGraph(todos: TodoItem[]): Map<string, string[]> {
    const graph = new Map<string, string[]>();
    
    for (const todo of todos) {
      const dependencies = todo.dependencies || [];
      graph.set(todo.id, dependencies);
    }
    
    return graph;
  }

  private optimizeSequenceWithNeural(
    todos: TodoItem[], 
    features: TodoFeatureVector[], 
    dependencyGraph: Map<string, string[]>
  ): TodoItem[] {
    // Simplified neural-based sequence optimization
    // In real implementation, would use reinforcement learning
    
    // Sort by neural-predicted priority scores
    const scoredTodos = todos.map((todo, idx) => ({
      todo,
      score: this.calculateNeuralPriorityScore(features[idx]),
      dependencies: dependencyGraph.get(todo.id) || []
    }));
    
    // Topological sort considering dependencies and neural scores
    const optimized = this.topologicalSortWithScores(scoredTodos);
    
    return optimized.map(item => item.todo);
  }

  private calculateNeuralPriorityScore(features: TodoFeatureVector): number {
    // Simplified neural scoring
    return (
      features.priorityEncoded * 0.3 +
      features.complexityKeywords * 0.2 +
      (1 / Math.max(1, features.dependencyCount)) * 0.2 +
      features.userHistoryFeatures[0] * 0.3
    );
  }

  private topologicalSortWithScores(scoredTodos: Array<{
    todo: TodoItem;
    score: number;
    dependencies: string[];
  }>): Array<{ todo: TodoItem; score: number; dependencies: string[] }> {
    // Simplified topological sort with scoring
    return scoredTodos.sort((a, b) => {
      // First sort by dependencies (fewer dependencies first)
      if (a.dependencies.length !== b.dependencies.length) {
        return a.dependencies.length - b.dependencies.length;
      }
      // Then by neural score (higher score first)
      return b.score - a.score;
    });
  }

  private calculateTimeReduction(original: TodoItem[], optimized: TodoItem[]): number {
    // Estimate time reduction from optimization (simplified)
    const parallelismGain = this.calculateParallelismGain(original, optimized);
    const dependencyOptimization = this.calculateDependencyOptimization(original, optimized);
    
    return Math.round((parallelismGain + dependencyOptimization) * 100) / 100;
  }

  private calculateParallelismGain(original: TodoItem[], optimized: TodoItem[]): number {
    // Simplified parallelism calculation
    const parallelizableTasks = optimized.filter(todo => 
      (todo.dependencies || []).length === 0
    ).length;
    
    return Math.min(30, parallelizableTasks * 5); // Max 30% time reduction
  }

  private calculateDependencyOptimization(original: TodoItem[], optimized: TodoItem[]): number {
    // Simplified dependency optimization calculation
    return Math.random() * 10 + 5; // 5-15% improvement
  }

  private determineOptimizationStrategy(
    original: TodoItem[], 
    optimized: TodoItem[]
  ): WorkflowOptimization['optimizationStrategy'] {
    const parallelTasks = optimized.filter(t => (t.dependencies || []).length === 0).length;
    
    if (parallelTasks > original.length * 0.5) {
      return 'parallel_execution';
    } else if (optimized.some(t => t.priority === 'high')) {
      return 'priority_balancing';
    } else {
      return 'dependency_reordering';
    }
  }

  private generateOptimizationReasoning(original: TodoItem[], optimized: TodoItem[]): string {
    const strategy = this.determineOptimizationStrategy(original, optimized);
    
    const reasoningMap = {
      'parallel_execution': 'Identified multiple independent tasks that can be executed in parallel',
      'dependency_reordering': 'Reordered tasks to minimize dependency wait times',
      'task_batching': 'Grouped similar tasks together for better context switching',
      'priority_balancing': 'Balanced high and low priority tasks for optimal workflow'
    };
    
    return reasoningMap[strategy];
  }

  private identifyPotentialRisks(optimized: TodoItem[]): string[] {
    const risks: string[] = [];
    
    const highPriorityCount = optimized.filter(t => t.priority === 'high').length;
    if (highPriorityCount > optimized.length * 0.7) {
      risks.push('High concentration of urgent tasks may cause burnout');
    }
    
    const dependentTasks = optimized.filter(t => (t.dependencies || []).length > 0).length;
    if (dependentTasks > optimized.length * 0.6) {
      risks.push('Many dependent tasks may cause cascading delays');
    }
    
    return risks;
  }

  private generateWorkflowRecommendations(optimized: TodoItem[]): string[] {
    const recommendations: string[] = [];
    
    recommendations.push('Consider time-boxing each task for better focus');
    recommendations.push('Set up regular progress check-ins for dependent tasks');
    recommendations.push('Prepare fallback plans for high-risk tasks');
    
    return recommendations;
  }

  private generateIntelligentBreakdown(
    todo: TodoItem, 
    features: TodoFeatureVector, 
    prediction: { output: number[], confidence: number }
  ): string[] {
    const breakdown: string[] = [];
    const complexity = features.complexityKeywords;
    
    if (complexity > 3 || features.contentLength > 100) {
      // High complexity - detailed breakdown
      breakdown.push(`Research and plan: ${todo.content}`);
      breakdown.push(`Design approach for: ${todo.content}`);
      breakdown.push(`Implement core functionality: ${todo.content}`);
      breakdown.push(`Test and validate: ${todo.content}`);
      breakdown.push(`Document and finalize: ${todo.content}`);
    } else if (complexity > 1 || features.contentLength > 50) {
      // Medium complexity - moderate breakdown
      breakdown.push(`Plan and start: ${todo.content}`);
      breakdown.push(`Complete implementation: ${todo.content}`);
      breakdown.push(`Review and finish: ${todo.content}`);
    } else {
      // Low complexity - minimal breakdown
      breakdown.push(`Execute: ${todo.content}`);
      breakdown.push(`Verify completion: ${todo.content}`);
    }
    
    return breakdown;
  }

  /**
   * Training and learning methods
   */
  private async collectTrainingData(data: any): Promise<void> {
    try {
      // Extract training data from todo/task events
      const trainingPoint = this.extractTrainingPoint(data);
      
      if (trainingPoint) {
        const modelType = this.determineModelType(data);
        const existing = this.trainingData.get(modelType) || [];
        existing.push(trainingPoint);
        this.trainingData.set(modelType, existing);
        
        // Trigger retraining if enough data
        if (existing.length >= this.config.minDataPointsForTraining) {
          await this.scheduleRetraining(modelType);
        }
      }
      
    } catch (error) {
      this.logger.error('Failed to collect training data', error);
    }
  }

  private async collectCompletionData(data: any): Promise<void> {
    try {
      // Collect completion data for success prediction models
      const completionPoint = this.extractCompletionPoint(data);
      
      if (completionPoint) {
        const existing = this.trainingData.get('success_prediction') || [];
        existing.push(completionPoint);
        this.trainingData.set('success_prediction', existing);
      }
      
    } catch (error) {
      this.logger.error('Failed to collect completion data', error);
    }
  }

  private extractTrainingPoint(data: any): any {
    // Extract relevant training features and labels from event data
    // This would be more sophisticated in a real implementation
    return {
      features: this.extractEventFeatures(data),
      label: this.extractEventLabel(data),
      timestamp: new Date()
    };
  }

  private extractCompletionPoint(data: any): any {
    return {
      features: this.extractEventFeatures(data),
      success: data.status === 'completed' ? 1 : 0,
      timestamp: new Date()
    };
  }

  private extractEventFeatures(data: any): number[] {
    // Simplified feature extraction from event data
    return [Math.random(), Math.random(), Math.random()]; // Placeholder
  }

  private extractEventLabel(data: any): number {
    // Extract label for supervised learning
    return Math.random(); // Placeholder
  }

  private determineModelType(data: any): string {
    // Determine which model should learn from this data
    if (data.completionTime) return 'completion_time';
    if (data.priority) return 'priority_optimization';
    return 'success_prediction';
  }

  private async scheduleRetraining(modelType: string): Promise<void> {
    if (this.isTraining) return;
    
    setTimeout(async () => {
      await this.retrainModel(modelType);
    }, 1000); // Delay to allow batch collection
  }

  private async retrainModel(modelType: string): Promise<void> {
    if (this.isTraining) return;
    
    try {
      this.isTraining = true;
      this.logger.info(`Retraining model: ${modelType}`);
      
      const model = this.models.get(modelType);
      const trainingData = this.trainingData.get(modelType);
      
      if (!model || !trainingData || trainingData.length < this.config.minDataPointsForTraining) {
        return;
      }
      
      // Simulate training process
      await this.simulateTraining(model, trainingData);
      
      // Update model performance
      model.metadata.lastTrained = new Date();
      model.metadata.dataPoints = trainingData.length;
      
      // Save updated model
      await this.saveModelToMemory(model);
      
      this.emit('model:retrained', { modelType, performance: model.performance });
      
    } catch (error) {
      this.logger.error(`Failed to retrain model ${modelType}`, error);
    } finally {
      this.isTraining = false;
    }
  }

  private async simulateTraining(model: NeuralTodoModel, trainingData: any[]): Promise<void> {
    // Simulate neural network training
    const epochs = Math.min(this.config.maxTrainingEpochs, 50);
    
    for (let epoch = 0; epoch < epochs; epoch++) {
      // Simulate training epoch
      const accuracy = 0.5 + (epoch / epochs) * 0.4 + Math.random() * 0.1;
      model.performance.accuracy = Math.min(0.95, accuracy);
      
      // Early stopping simulation
      if (accuracy > 0.9) break;
    }
    
    model.trainingData.epochsTrained = epochs;
    model.trainingData.trainingAccuracy = model.performance.accuracy;
    model.trainingData.validationAccuracy = model.performance.accuracy * 0.95;
  }

  /**
   * Event handlers
   */
  private async learnFromRecommendations(data: any): Promise<void> {
    try {
      // Learn from memory intelligence recommendations
      const { recommendations } = data;
      
      for (const rec of recommendations) {
        await this.incorporateRecommendationFeedback(rec);
      }
      
    } catch (error) {
      this.logger.error('Failed to learn from recommendations', error);
    }
  }

  private async updateTrainingData(data: any): Promise<void> {
    try {
      // Update training data based on memory intelligence analysis
      const { metrics } = data;
      
      // Use metrics to improve model accuracy
      await this.adjustModelParameters(metrics);
      
    } catch (error) {
      this.logger.error('Failed to update training data', error);
    }
  }

  private async incorporateRecommendationFeedback(recommendation: any): Promise<void> {
    // Incorporate feedback from memory intelligence recommendations
    // This helps neural models learn from higher-level intelligence
  }

  private async adjustModelParameters(metrics: any): Promise<void> {
    // Adjust neural model parameters based on overall system metrics
    for (const [modelType, model] of this.models) {
      if (metrics.patternAccuracy) {
        // Adjust model confidence based on pattern accuracy
        const adjustment = metrics.patternAccuracy > 0.8 ? 1.05 : 0.95;
        model.performance.accuracy *= adjustment;
        model.performance.accuracy = Math.min(0.95, Math.max(0.3, model.performance.accuracy));
      }
    }
  }

  /**
   * Memory management
   */
  private async loadModelsFromMemory(): Promise<void> {
    try {
      if (!this.memoryManager) {
        this.logger.warn('No memory manager available for model loading');
        return;
      }
      
      const storedModels = await this.memoryManager.get('neural_todo_models') || {};
      
      for (const [modelType, modelData] of Object.entries(storedModels)) {
        this.models.set(modelType, modelData as NeuralTodoModel);
      }
      
      this.logger.info(`Loaded ${this.models.size} neural models from memory`);
      
    } catch (error) {
      this.logger.error('Failed to load models from memory', error);
    }
  }

  private async saveModelToMemory(model: NeuralTodoModel): Promise<void> {
    try {
      if (!this.memoryManager) return;
      
      const storedModels = await this.memoryManager.get('neural_todo_models') || {};
      storedModels[model.modelType] = model;
      
      await this.memoryManager.store('neural_todo_models', storedModels);
      
    } catch (error) {
      this.logger.error('Failed to save model to memory', error);
    }
  }

  private startTrainingLoop(): void {
    if (!this.config.enableRealTimeLearning) return;
    
    this.trainingTimer = setInterval(async () => {
      await this.runPeriodicTraining();
    }, this.config.retrainInterval);
  }

  private async runPeriodicTraining(): Promise<void> {
    try {
      for (const [modelType, model] of this.models) {
        const trainingData = this.trainingData.get(modelType);
        
        if (trainingData && trainingData.length >= this.config.minDataPointsForTraining) {
          await this.retrainModel(modelType);
        }
      }
      
    } catch (error) {
      this.logger.error('Failed to run periodic training', error);
    }
  }

  /**
   * Public API methods
   */
  public getModel(modelType: NeuralTodoModel['modelType']): NeuralTodoModel | undefined {
    return this.models.get(modelType);
  }

  public getAllModels(): NeuralTodoModel[] {
    return Array.from(this.models.values());
  }

  public getPredictions(todoId: string): NeuralPrediction[] {
    return this.predictions.get(todoId) || [];
  }

  public getModelPerformance(): Map<string, number> {
    const performance = new Map<string, number>();
    
    for (const [modelType, model] of this.models) {
      performance.set(modelType, model.performance.accuracy);
    }
    
    return performance;
  }

  public async forceRetrain(modelType: NeuralTodoModel['modelType']): Promise<void> {
    await this.retrainModel(modelType);
  }

  public async shutdown(): Promise<void> {
    if (this.trainingTimer) {
      clearInterval(this.trainingTimer);
    }
    
    // Save all models to memory
    for (const model of this.models.values()) {
      await this.saveModelToMemory(model);
    }
    
    this.logger.info('Neural todo optimization system shutdown complete');
  }
}