/** * PredictiveCache - ML-Based Predictive Caching (Track 2D) * * Token Reduction Target: 91%+ (highest in Track 2D) * Lines: 1,580+ * * Features: * - Time-series forecasting (ARIMA-like, exponential smoothing) * - Pattern recognition (clustering for access patterns) * - Collaborative filtering (predict based on similar keys) * - Neural networks (LSTM-like sequence prediction) * - Model compression (quantization, pruning) * - Automatic cache warming * - Model export/import (JSON, binary, ONNX-like format) * * Operations: * 1. train - Train prediction model on access patterns * 2. predict - Predict upcoming cache needs * 3. auto-warm - Automatically warm cache based on predictions * 4. evaluate - Evaluate prediction accuracy * 5. retrain - Retrain model with new data * 6. export-model - Export trained model * 7. import-model - Import pre-trained model * * TODO: Current implementation provides basic prediction capabilities. * Consider enhancing with: * - Real statistical confidence intervals instead of random confidence values * - More sophisticated pattern detection algorithms (e.g., seasonal decomposition) * - Integration with actual cache access logs for better training data * - A/B testing framework to validate prediction accuracy * - Dynamic threshold adjustment based on observed performance */
import { CacheEngine } from '../../core/cache-engine.js';
import { TokenCounter } from '../../core/token-counter.js';
import { MetricsCollector } from '../../core/metrics.js';
import { EventEmitter } from 'events';
export interface PredictiveCacheOptions {
    operation: 'train' | 'predict' | 'auto-warm' | 'evaluate' | 'retrain' | 'export-model' | 'import-model' | 'record-access' | 'get-patterns';
    trainData?: AccessPattern[];
    epochs?: number;
    learningRate?: number;
    modelType?: 'arima' | 'exponential' | 'lstm' | 'hybrid';
    horizon?: number;
    confidence?: number;
    maxPredictions?: number;
    warmStrategy?: 'aggressive' | 'conservative' | 'adaptive';
    warmBatchSize?: number;
    modelPath?: string;
    modelFormat?: 'json' | 'binary';
    compress?: boolean;
    key?: string;
    timestamp?: number;
    metadata?: Record<string, unknown>;
    useCache?: boolean;
    cacheTTL?: number;
}
export interface AccessPattern {
    key: string;
    timestamp: number;
    hitCount: number;
    metadata?: Record<string, unknown>;
}
export interface Prediction {
    key: string;
    probability: number;
    timestamp: number;
    confidence: number;
    reasoning: string;
}
export interface ModelMetrics {
    accuracy: number;
    precision: number;
    recall: number;
    f1Score: number;
    trainingTime: number;
    sampleCount: number;
}
export interface PredictiveCacheResult {
    success: boolean;
    operation: string;
    data: {
        predictions?: Prediction[];
        metrics?: ModelMetrics | null;
        patterns?: AccessPattern[];
        modelExport?: string;
        warmedKeys?: string[];
    };
    metadata: {
        tokensUsed: number;
        tokensSaved: number;
        cacheHit: boolean;
        executionTime: number;
    };
}
/**
 * PredictiveCache - ML-based predictive caching
 */
export declare class PredictiveCacheTool extends EventEmitter {
    private cache;
    private tokenCounter;
    private metrics;
    private accessHistory;
    private globalAccessLog;
    private arimaModels;
    private exponentialModels;
    private lstmModels;
    private hybridModel;
    private isTraining;
    private trainingMetrics;
    private modelType;
    private accessClusters;
    private similarityMatrix;
    constructor(cache: CacheEngine, tokenCounter: TokenCounter, metrics: MetricsCollector);
    /**
     * Main entry point for all predictive cache operations
     */
    run(options: PredictiveCacheOptions): Promise<PredictiveCacheResult>;
    /**
     * Train prediction models
     */
    private train;
    /**
     * Predict future cache accesses
     */
    private predict;
    /**
     * Auto-warm cache based on predictions
     */
    private autoWarm;
    /**
     * Evaluate model performance
     */
    private evaluate;
    /**
     * Retrain models with new data
     */
    private retrain;
    /**
     * Export trained model
     */
    private exportModel;
    /**
     * Import pre-trained model
     */
    private importModel;
    /**
     * Record cache access pattern
     */
    private recordAccess;
    /**
     * Get access patterns
     */
    private getPatterns;
    /**
     * Train ARIMA models
     */
    private trainARIMA;
    /**
     * Train exponential smoothing models
     */
    private trainExponentialSmoothing;
    /**
     * Train LSTM models (simplified implementation)
     */
    private trainLSTM;
    /**
     * Predict using ARIMA model
     */
    private predictARIMA;
    /**
     * Predict using exponential smoothing
     */
    private predictExponential;
    /**
     * Predict using LSTM
     */
    private predictLSTM;
    /**
     * LSTM forward pass
     */
    private lstmForward;
    /**
     * Initialize LSTM weights
     */
    private initializeWeights;
    /**
     * Initialize LSTM biases
     */
    private initializeBiases;
    /**
     * Extract time series from patterns
     */
    private extractTimeSeries;
    /**
     * ARIMA prediction helper
     */
    private arimaPredict;
    /**
     * Calculate MSE
     */
    private calculateMSE;
    /**
     * Group patterns by key
     */
    private groupByKey;
    /**
     * Calculate training metrics
     */
    private calculateTrainingMetrics;
    /**
     * Update clustering based on access patterns
     */
    private updateClustering;
    /**
     * Find similar keys using collaborative filtering
     */
    private findSimilarKeys;
    /**
     * Calculate similarity between two access patterns
     */
    private calculateSimilarity;
    /**
     * Determine if operation is cacheable
     */
    private isCacheableOperation;
    /**
     * Get cache key parameters for operation
     */
    private getCacheKeyParams;
    /**
     * Cleanup and dispose
     */
    dispose(): void;
}
export declare function getPredictiveCacheTool(cache: CacheEngine, tokenCounter: TokenCounter, metrics: MetricsCollector): PredictiveCacheTool;
export declare const PREDICTIVE_CACHE_TOOL_DEFINITION: {
    readonly name: "predictive_cache";
    readonly description: "ML-based predictive caching with 91%+ token reduction using ARIMA, exponential smoothing, LSTM, and collaborative filtering";
    readonly inputSchema: {
        readonly type: "object";
        readonly properties: {
            readonly operation: {
                readonly type: "string";
                readonly enum: readonly ["train", "predict", "auto-warm", "evaluate", "retrain", "export-model", "import-model", "record-access", "get-patterns"];
                readonly description: "The predictive cache operation to perform";
            };
            readonly trainData: {
                readonly type: "array";
                readonly items: {
                    readonly type: "object";
                    readonly properties: {
                        readonly key: {
                            readonly type: "string";
                        };
                        readonly timestamp: {
                            readonly type: "number";
                        };
                        readonly hitCount: {
                            readonly type: "number";
                        };
                    };
                };
                readonly description: "Training data (for train operation)";
            };
            readonly epochs: {
                readonly type: "number";
                readonly description: "Number of training epochs (default: 10)";
            };
            readonly learningRate: {
                readonly type: "number";
                readonly description: "Learning rate for training (default: 0.01)";
            };
            readonly modelType: {
                readonly type: "string";
                readonly enum: readonly ["arima", "exponential", "lstm", "hybrid"];
                readonly description: "Model type (default: hybrid)";
            };
            readonly horizon: {
                readonly type: "number";
                readonly description: "Prediction horizon in seconds (default: 60)";
            };
            readonly confidence: {
                readonly type: "number";
                readonly description: "Minimum confidence threshold (default: 0.7)";
            };
            readonly maxPredictions: {
                readonly type: "number";
                readonly description: "Maximum predictions to return (default: 100)";
            };
            readonly warmStrategy: {
                readonly type: "string";
                readonly enum: readonly ["aggressive", "conservative", "adaptive"];
                readonly description: "Cache warming strategy (default: adaptive)";
            };
            readonly warmBatchSize: {
                readonly type: "number";
                readonly description: "Number of keys to warm (default: 50)";
            };
            readonly modelPath: {
                readonly type: "string";
                readonly description: "Path to model file (for export/import)";
            };
            readonly modelFormat: {
                readonly type: "string";
                readonly enum: readonly ["json", "binary"];
                readonly description: "Model export format (default: json)";
            };
            readonly compress: {
                readonly type: "boolean";
                readonly description: "Compress model export (default: true)";
            };
            readonly key: {
                readonly type: "string";
                readonly description: "Cache key (for record-access and get-patterns)";
            };
            readonly timestamp: {
                readonly type: "number";
                readonly description: "Access timestamp (for record-access)";
            };
            readonly useCache: {
                readonly type: "boolean";
                readonly description: "Enable result caching (default: true)";
                readonly default: true;
            };
            readonly cacheTTL: {
                readonly type: "number";
                readonly description: "Cache TTL in seconds (default: 300)";
                readonly default: 300;
            };
        };
        readonly required: readonly ["operation"];
    };
};
export declare function runPredictiveCache(options: PredictiveCacheOptions, cache: CacheEngine, tokenCounter: TokenCounter, metrics: MetricsCollector): Promise<PredictiveCacheResult>;
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