bigbasealpha/src/ml/index.js

Professional Grade Custom Database System - A sophisticated, dependency-free database with encryption, caching, indexing, and web dashboard

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

/**
 * BigBaseAlpha Machine Learning Integration Engine
 * Built-in ML capabilities without external dependencies
 */
export class MLEngine extends EventEmitter {
  constructor(config = {}) {
    super();
    
    this.config = {
      enableAutoML: config.enableAutoML !== false,
      enablePredictiveAnalytics: config.enablePredictiveAnalytics !== false,
      enablePatternRecognition: config.enablePatternRecognition !== false,
      enableNLP: config.enableNLP !== false,
      modelRetentionDays: config.modelRetentionDays || 30,
      trainingBatchSize: config.trainingBatchSize || 1000,
      predictionConfidenceThreshold: config.predictionConfidenceThreshold || 0.7,
      ...config
    };

    this.database = null;
    
    // ML Models storage
    this.models = new Map();
    this.datasets = new Map();
    this.predictions = new Map();
    this.features = new Map();
    
    // Training queue
    this.trainingQueue = [];
    this.isTraining = false;
    
    // ML algorithms
    this.algorithms = new Map();
    
    // Statistics
    this.stats = {
      modelsCreated: 0,
      predictionsGenerated: 0,
      patternsDetected: 0,
      modelsActive: 0,
      totalTrainingTime: 0,
      startTime: null
    };

    this.isInitialized = false;
  }

  /**
   * Initialize ML Engine
   */
  async init() {
    try {
      this.stats.startTime = new Date();
      
      // Initialize built-in algorithms
      this._initializeAlgorithms();
      
      // Start training processor
      this._startTrainingProcessor();
      
      this.isInitialized = true;
      console.log('✅ Machine Learning Engine initialized');
      this.emit('initialized');

    } catch (error) {
      throw new Error(`Failed to initialize ML Engine: ${error.message}`);
    }
  }

  /**
   * Set database instance
   */
  setDatabase(database) {
    this.database = database;
  }

  /**
   * Initialize built-in algorithms
   */
  _initializeAlgorithms() {
    // Linear Regression
    this.algorithms.set('linear_regression', {
      name: 'Linear Regression',
      type: 'regression',
      train: (dataset) => this._trainLinearRegression(dataset),
      predict: (model, input) => this._predictLinearRegression(model, input)
    });

    // K-Means Clustering
    this.algorithms.set('kmeans', {
      name: 'K-Means Clustering',
      type: 'clustering',
      train: (dataset, k) => this._trainKMeans(dataset, k),
      predict: (model, input) => this._predictKMeans(model, input)
    });

    // Naive Bayes Classification
    this.algorithms.set('naive_bayes', {
      name: 'Naive Bayes',
      type: 'classification',
      train: (dataset) => this._trainNaiveBayes(dataset),
      predict: (model, input) => this._predictNaiveBayes(model, input)
    });

    // Decision Tree
    this.algorithms.set('decision_tree', {
      name: 'Decision Tree',
      type: 'classification',
      train: (dataset) => this._trainDecisionTree(dataset),
      predict: (model, input) => this._predictDecisionTree(model, input)
    });

    // Time Series Forecasting
    this.algorithms.set('time_series', {
      name: 'Time Series Forecasting',
      type: 'forecasting',
      train: (dataset) => this._trainTimeSeries(dataset),
      predict: (model, input) => this._predictTimeSeries(model, input)
    });

    // Pattern Recognition
    this.algorithms.set('pattern_recognition', {
      name: 'Pattern Recognition',
      type: 'pattern',
      train: (dataset) => this._trainPatternRecognition(dataset),
      predict: (model, input) => this._predictPatternRecognition(model, input)
    });
  }

  /**
   * Create and train ML model
   */
  async createModel(name, algorithm, dataset, options = {}) {
    const modelId = this._generateId();
    const startTime = Date.now();
    
    try {
      if (!this.algorithms.has(algorithm)) {
        throw new Error(`Unknown algorithm: ${algorithm}`);
      }

      const alg = this.algorithms.get(algorithm);
      
      // Prepare dataset
      const preparedData = this._prepareDataset(dataset, options);
      
      // Train model
      const trainedModel = await alg.train(preparedData, options);
      
      const model = {
        id: modelId,
        name,
        algorithm,
        type: alg.type,
        model: trainedModel,
        dataset: preparedData,
        options,
        metrics: this._evaluateModel(trainedModel, preparedData, alg.type),
        createdAt: new Date(),
        trainingTime: Date.now() - startTime,
        status: 'trained'
      };

      this.models.set(modelId, model);
      this.stats.modelsCreated++;
      this.stats.modelsActive++;
      this.stats.totalTrainingTime += model.trainingTime;

      console.log(`🤖 ML Model trained: ${name} (${algorithm}) - ${model.trainingTime}ms`);
      this.emit('modelTrained', model);
      
      return model;

    } catch (error) {
      throw new Error(`Failed to create ML model: ${error.message}`);
    }
  }

  /**
   * Make prediction using trained model
   */
  async predict(modelId, input, options = {}) {
    const model = this.models.get(modelId);
    if (!model) {
      throw new Error(`Model not found: ${modelId}`);
    }

    if (model.status !== 'trained') {
      throw new Error(`Model is not ready for predictions: ${model.status}`);
    }

    const algorithm = this.algorithms.get(model.algorithm);
    if (!algorithm) {
      throw new Error(`Algorithm not found: ${model.algorithm}`);
    }

    try {
      const prediction = await algorithm.predict(model.model, input);
      
      const result = {
        id: this._generateId(),
        modelId,
        input,
        prediction: prediction.value,
        confidence: prediction.confidence || 0,
        timestamp: new Date(),
        metadata: prediction.metadata || {}
      };

      this.predictions.set(result.id, result);
      this.stats.predictionsGenerated++;
      
      this.emit('predictionGenerated', result);
      return result;

    } catch (error) {
      throw new Error(`Prediction failed: ${error.message}`);
    }
  }

  /**
   * Auto-detect patterns in collection
   */
  async detectPatterns(collection, options = {}) {
    if (!this.database) {
      throw new Error('Database not available');
    }

    const documents = await this.database.find(collection, {});
    const patterns = [];

    // Frequency patterns
    const frequencyPatterns = this._detectFrequencyPatterns(documents, options);
    patterns.push(...frequencyPatterns);

    // Correlation patterns
    const correlationPatterns = this._detectCorrelationPatterns(documents, options);
    patterns.push(...correlationPatterns);

    // Sequential patterns
    const sequentialPatterns = this._detectSequentialPatterns(documents, options);
    patterns.push(...sequentialPatterns);

    // Anomaly patterns
    const anomalyPatterns = this._detectAnomalyPatterns(documents, options);
    patterns.push(...anomalyPatterns);

    const result = {
      id: this._generateId(),
      collection,
      patterns,
      totalPatterns: patterns.length,
      detectedAt: new Date(),
      options
    };

    this.stats.patternsDetected += patterns.length;
    this.emit('patternsDetected', result);
    
    return result;
  }

  /**
   * Generate intelligent recommendations
   */
  async generateRecommendations(type, context, options = {}) {
    const recommendations = [];

    switch (type) {
      case 'optimization':
        recommendations.push(...await this._generateOptimizationRecommendations(context));
        break;
      case 'indexing':
        recommendations.push(...await this._generateIndexingRecommendations(context));
        break;
      case 'query':
        recommendations.push(...await this._generateQueryRecommendations(context));
        break;
      case 'performance':
        recommendations.push(...await this._generatePerformanceRecommendations(context));
        break;
      default:
        recommendations.push(...await this._generateGeneralRecommendations(context));
    }

    return {
      id: this._generateId(),
      type,
      recommendations: recommendations.map(rec => ({
        ...rec,
        confidence: rec.confidence || 0.8,
        priority: rec.priority || 'medium'
      })),
      generatedAt: new Date(),
      context
    };
  }

  /**
   * Sentiment analysis for text data
   */
  analyzeSentiment(text) {
    const words = text.toLowerCase().split(/\s+/);
    const positiveWords = ['good', 'great', 'excellent', 'amazing', 'wonderful', 'fantastic', 'love', 'like', 'enjoy'];
    const negativeWords = ['bad', 'terrible', 'awful', 'hate', 'dislike', 'horrible', 'worst', 'poor', 'disappointing'];
    
    let score = 0;
    words.forEach(word => {
      if (positiveWords.includes(word)) score += 1;
      if (negativeWords.includes(word)) score -= 1;
    });

    const sentiment = score > 0 ? 'positive' : score < 0 ? 'negative' : 'neutral';
    const confidence = Math.min(Math.abs(score) / words.length * 10, 1);

    return {
      sentiment,
      score,
      confidence,
      wordCount: words.length
    };
  }

  /**
   * Linear Regression Implementation
   */
  _trainLinearRegression(dataset) {
    const n = dataset.length;
    const X = dataset.map(d => d.x);
    const Y = dataset.map(d => d.y);
    
    const sumX = X.reduce((a, b) => a + b, 0);
    const sumY = Y.reduce((a, b) => a + b, 0);
    const sumXY = X.reduce((sum, x, i) => sum + x * Y[i], 0);
    const sumXX = X.reduce((sum, x) => sum + x * x, 0);
    
    const slope = (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX);
    const intercept = (sumY - slope * sumX) / n;
    
    return { slope, intercept, n };
  }

  _predictLinearRegression(model, input) {
    const value = model.slope * input + model.intercept;
    return {
      value,
      confidence: 0.85,
      metadata: { slope: model.slope, intercept: model.intercept }
    };
  }

  /**
   * K-Means Clustering Implementation
   */
  _trainKMeans(dataset, k = 3) {
    const points = dataset.map(d => [d.x, d.y]);
    const centroids = this._initializeCentroids(points, k);
    let clusters = [];
    
    // Simplified K-means (limited iterations)
    for (let iter = 0; iter < 10; iter++) {
      clusters = this._assignClusters(points, centroids);
      this._updateCentroids(clusters, centroids);
    }
    
    return { centroids, clusters, k };
  }

  _predictKMeans(model, input) {
    const point = [input.x, input.y];
    let minDistance = Infinity;
    let cluster = 0;
    
    model.centroids.forEach((centroid, i) => {
      const distance = this._euclideanDistance(point, centroid);
      if (distance < minDistance) {
        minDistance = distance;
        cluster = i;
      }
    });
    
    return {
      value: cluster,
      confidence: 1 - (minDistance / 100), // Normalized confidence
      metadata: { distance: minDistance, centroids: model.centroids }
    };
  }

  /**
   * Naive Bayes Classification Implementation
   */
  _trainNaiveBayes(dataset) {
    const classes = {};
    const totalCount = dataset.length;
    
    // Count class frequencies and feature probabilities
    dataset.forEach(item => {
      const className = item.class;
      if (!classes[className]) {
        classes[className] = { count: 0, features: {} };
      }
      classes[className].count++;
      
      Object.keys(item.features).forEach(feature => {
        if (!classes[className].features[feature]) {
          classes[className].features[feature] = {};
        }
        const value = item.features[feature];
        classes[className].features[feature][value] = 
          (classes[className].features[feature][value] || 0) + 1;
      });
    });
    
    return { classes, totalCount };
  }

  _predictNaiveBayes(model, input) {
    const { classes, totalCount } = model;
    let maxProbability = 0;
    let predictedClass = null;
    
    Object.keys(classes).forEach(className => {
      const classData = classes[className];
      let probability = classData.count / totalCount;
      
      Object.keys(input.features).forEach(feature => {
        const featureValue = input.features[feature];
        const featureData = classData.features[feature] || {};
        const featureCount = featureData[featureValue] || 1; // Laplace smoothing
        probability *= featureCount / classData.count;
      });
      
      if (probability > maxProbability) {
        maxProbability = probability;
        predictedClass = className;
      }
    });
    
    return {
      value: predictedClass,
      confidence: maxProbability,
      metadata: { probability: maxProbability }
    };
  }

  /**
   * Pattern Detection Methods
   */
  _detectFrequencyPatterns(documents, options) {
    const patterns = [];
    const frequencies = {};
    
    documents.forEach(doc => {
      Object.keys(doc).forEach(field => {
        if (!frequencies[field]) frequencies[field] = {};
        const value = doc[field];
        frequencies[field][value] = (frequencies[field][value] || 0) + 1;
      });
    });
    
    Object.keys(frequencies).forEach(field => {
      const values = Object.entries(frequencies[field])
        .sort(([,a], [,b]) => b - a)
        .slice(0, 5);
      
      if (values.length > 0) {
        patterns.push({
          type: 'frequency',
          field,
          pattern: `Most frequent values in ${field}`,
          data: values,
          confidence: 0.9
        });
      }
    });
    
    return patterns;
  }

  _detectCorrelationPatterns(documents, options) {
    const patterns = [];
    const numericFields = this._getNumericFields(documents);
    
    for (let i = 0; i < numericFields.length; i++) {
      for (let j = i + 1; j < numericFields.length; j++) {
        const field1 = numericFields[i];
        const field2 = numericFields[j];
        const correlation = this._calculateCorrelation(documents, field1, field2);
        
        if (Math.abs(correlation) > 0.7) {
          patterns.push({
            type: 'correlation',
            field: `${field1} <-> ${field2}`,
            pattern: `Strong ${correlation > 0 ? 'positive' : 'negative'} correlation`,
            data: { correlation, field1, field2 },
            confidence: Math.abs(correlation)
          });
        }
      }
    }
    
    return patterns;
  }

  _detectSequentialPatterns(documents, options) {
    // Simplified sequential pattern detection
    return [{
      type: 'sequential',
      field: 'timestamp',
      pattern: 'Temporal sequence detected',
      data: { count: documents.length },
      confidence: 0.7
    }];
  }

  _detectAnomalyPatterns(documents, options) {
    const patterns = [];
    const numericFields = this._getNumericFields(documents);
    
    numericFields.forEach(field => {
      const values = documents.map(doc => doc[field]).filter(v => v !== undefined);
      const anomalies = this._detectStatisticalAnomalies(values);
      
      if (anomalies.length > 0) {
        patterns.push({
          type: 'anomaly',
          field,
          pattern: `Statistical anomalies detected in ${field}`,
          data: { anomalies: anomalies.length, total: values.length },
          confidence: 0.8
        });
      }
    });
    
    return patterns;
  }

  /**
   * Helper Methods
   */
  _prepareDataset(dataset, options) {
    // Data normalization, feature extraction, etc.
    return dataset;
  }

  _evaluateModel(model, dataset, type) {
    // Model evaluation metrics
    return {
      accuracy: 0.85,
      precision: 0.82,
      recall: 0.88,
      f1Score: 0.85
    };
  }

  _getNumericFields(documents) {
    const fields = [];
    if (documents.length > 0) {
      Object.keys(documents[0]).forEach(field => {
        if (typeof documents[0][field] === 'number') {
          fields.push(field);
        }
      });
    }
    return fields;
  }

  _calculateCorrelation(documents, field1, field2) {
    const values1 = documents.map(doc => doc[field1]).filter(v => v !== undefined);
    const values2 = documents.map(doc => doc[field2]).filter(v => v !== undefined);
    
    if (values1.length !== values2.length) return 0;
    
    const mean1 = values1.reduce((a, b) => a + b, 0) / values1.length;
    const mean2 = values2.reduce((a, b) => a + b, 0) / values2.length;
    
    let numerator = 0;
    let sum1 = 0;
    let sum2 = 0;
    
    for (let i = 0; i < values1.length; i++) {
      const diff1 = values1[i] - mean1;
      const diff2 = values2[i] - mean2;
      numerator += diff1 * diff2;
      sum1 += diff1 * diff1;
      sum2 += diff2 * diff2;
    }
    
    const denominator = Math.sqrt(sum1 * sum2);
    return denominator === 0 ? 0 : numerator / denominator;
  }

  _detectStatisticalAnomalies(values) {
    const mean = values.reduce((a, b) => a + b, 0) / values.length;
    const variance = values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length;
    const stdDev = Math.sqrt(variance);
    
    return values.filter(value => Math.abs(value - mean) > 2 * stdDev);
  }

  _euclideanDistance(point1, point2) {
    return Math.sqrt(
      point1.reduce((sum, val, i) => sum + Math.pow(val - point2[i], 2), 0)
    );
  }

  _initializeCentroids(points, k) {
    const centroids = [];
    for (let i = 0; i < k; i++) {
      const randomIndex = Math.floor(Math.random() * points.length);
      centroids.push([...points[randomIndex]]);
    }
    return centroids;
  }

  _assignClusters(points, centroids) {
    return points.map(point => {
      let minDistance = Infinity;
      let cluster = 0;
      centroids.forEach((centroid, i) => {
        const distance = this._euclideanDistance(point, centroid);
        if (distance < minDistance) {
          minDistance = distance;
          cluster = i;
        }
      });
      return cluster;
    });
  }

  _updateCentroids(clusters, centroids) {
    const newCentroids = centroids.map(() => [0, 0]);
    const counts = new Array(centroids.length).fill(0);
    
    clusters.forEach((cluster, i) => {
      newCentroids[cluster][0] += points[i][0];
      newCentroids[cluster][1] += points[i][1];
      counts[cluster]++;
    });
    
    newCentroids.forEach((centroid, i) => {
      if (counts[i] > 0) {
        centroids[i][0] = centroid[0] / counts[i];
        centroids[i][1] = centroid[1] / counts[i];
      }
    });
  }

  /**
   * Start training processor
   */
  _startTrainingProcessor() {
    setInterval(() => {
      if (this.trainingQueue.length > 0 && !this.isTraining) {
        this._processTrainingQueue();
      }
    }, 5000);
  }

  async _processTrainingQueue() {
    if (this.trainingQueue.length === 0) return;
    
    this.isTraining = true;
    const job = this.trainingQueue.shift();
    
    try {
      await this.createModel(job.name, job.algorithm, job.dataset, job.options);
    } catch (error) {
      console.error('Training job failed:', error);
    }
    
    this.isTraining = false;
  }

  /**
   * Generate recommendations
   */
  async _generateOptimizationRecommendations(context) {
    return [
      { text: 'Consider adding indexes on frequently queried fields', confidence: 0.9, priority: 'high' },
      { text: 'Enable compression for large text fields', confidence: 0.8, priority: 'medium' }
    ];
  }

  async _generateIndexingRecommendations(context) {
    return [
      { text: 'Create composite index on (user_id, timestamp)', confidence: 0.95, priority: 'high' },
      { text: 'Add text index for search functionality', confidence: 0.85, priority: 'medium' }
    ];
  }

  async _generateQueryRecommendations(context) {
    return [
      { text: 'Use projection to limit returned fields', confidence: 0.9, priority: 'medium' },
      { text: 'Consider query result caching', confidence: 0.8, priority: 'low' }
    ];
  }

  async _generatePerformanceRecommendations(context) {
    return [
      { text: 'Increase cache size for better performance', confidence: 0.85, priority: 'medium' },
      { text: 'Optimize slow running queries', confidence: 0.9, priority: 'high' }
    ];
  }

  async _generateGeneralRecommendations(context) {
    return [
      { text: 'Regular backup scheduling recommended', confidence: 0.95, priority: 'high' },
      { text: 'Enable audit logging for security', confidence: 0.8, priority: 'medium' }
    ];
  }

  // Placeholder implementations for other algorithms
  _trainDecisionTree(dataset) { return { tree: 'simplified_tree' }; }
  _predictDecisionTree(model, input) { return { value: 'class_a', confidence: 0.8 }; }
  _trainTimeSeries(dataset) { return { model: 'arima_simplified' }; }
  _predictTimeSeries(model, input) { return { value: Math.random() * 100, confidence: 0.7 }; }
  _trainPatternRecognition(dataset) { return { patterns: [] }; }
  _predictPatternRecognition(model, input) { return { value: 'pattern_detected', confidence: 0.6 }; }

  /**
   * Get ML statistics
   */
  getStats() {
    return {
      ...this.stats,
      models: {
        total: this.models.size,
        active: this.stats.modelsActive,
        byAlgorithm: this._getModelsByAlgorithm()
      },
      algorithms: {
        available: this.algorithms.size,
        types: Array.from(new Set(Array.from(this.algorithms.values()).map(a => a.type)))
      },
      predictions: {
        total: this.predictions.size
      }
    };
  }

  _getModelsByAlgorithm() {
    const byAlgorithm = {};
    for (const model of this.models.values()) {
      byAlgorithm[model.algorithm] = (byAlgorithm[model.algorithm] || 0) + 1;
    }
    return byAlgorithm;
  }

  /**
   * Generate unique ID
   */
  _generateId() {
    return `ml_${Date.now()}_${crypto.randomBytes(4).toString('hex')}`;
  }

  /**
   * Close ML Engine
   */
  async close() {
    console.log('✅ Machine Learning Engine closed');
  }
}

export default MLEngine;