import type { Context } from '../context.js';
type Middleware = (ctx: Context, next: () => Promise<any>) => any;
interface MLOptimizerConfig {
    enabled?: boolean;
    modelPath?: string;
    trainingInterval?: number;
    predictionThreshold?: number;
    features?: {
        performancePrediction?: boolean;
        resourceAllocation?: boolean;
        anomalyDetection?: boolean;
        queryOptimization?: boolean;
        loadBalancing?: boolean;
        autoScaling?: boolean;
    };
    metrics?: {
        collectInterval?: number;
        retentionPeriod?: number;
        aggregationWindow?: number;
    };
    optimization?: {
        targetLatency?: number;
        maxMemory?: number;
        cpuThreshold?: number;
        throughputTarget?: number;
    };
}
interface MetricData {
    timestamp: number;
    path: string;
    method: string;
    duration: number;
    statusCode: number;
    memoryUsage: number;
    cpuUsage: number;
    responseSize: number;
    queryCount?: number;
    cacheHit?: boolean;
}
interface PredictionResult {
    expectedDuration: number;
    confidence: number;
    recommendedAction: 'cache' | 'prefetch' | 'batch' | 'optimize' | 'throttle';
    resourceEstimate: {
        memory: number;
        cpu: number;
        io: number;
    };
}
interface AnomalyAlert {
    severity: 'low' | 'medium' | 'high' | 'critical';
    type: 'latency' | 'memory' | 'cpu' | 'error_rate' | 'throughput';
    message: string;
    metrics: Record<string, number>;
    suggestion: string;
    timestamp: number;
}
interface OptimizationDecision {
    action: string;
    confidence: number;
    impact: number;
    appliedAt: number;
    metadata?: Record<string, any>;
}
/**
 * ML-Powered Performance Optimizer
 *
 * Advanced machine learning capabilities:
 * - Real-time performance prediction using time-series forecasting
 * - Intelligent resource allocation with reinforcement learning
 * - Anomaly detection using statistical models and neural networks
 * - Adaptive query optimization with learned indexes
 * - Smart load balancing based on predicted capacity
 * - Auto-scaling recommendations with cost optimization
 * - Pattern recognition for request clustering
 * - Predictive caching and prefetching
 */
export declare function mlOptimizer(config?: MLOptimizerConfig): Middleware;
export type { MLOptimizerConfig, MetricData, PredictionResult, AnomalyAlert, OptimizationDecision };
export default mlOptimizer;
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