agentjs-core/dist/ml/MLPerformanceManager.d.ts

A comprehensive agent-based modeling framework with built-in p5.js visualization

import { MLAgent } from './MLAgent';
import { AgentAction, MLPerformanceMetrics } from './interfaces';
/**
 * Performance manager for ML inference optimization
 */
export declare class MLPerformanceManager {
    private static instance;
    private inferenceQueue;
    private processingBatch;
    private batchSize;
    private maxQueueSize;
    private batchTimeout;
    private timeoutId?;
    private metrics;
    private pendingPromises;
    private constructor();
    /**
     * Get singleton instance
     */
    static getInstance(): MLPerformanceManager;
    /**
     * Queue a prediction request for batch processing
     * @param agent Agent requesting prediction
     * @param modelName Model to use for prediction
     * @param priority Request priority
     * @returns Promise resolving to predicted action
     */
    queuePrediction(agent: MLAgent, modelName: string, priority?: 'high' | 'normal' | 'low'): Promise<AgentAction>;
    /**
     * Process a single prediction immediately (bypassing queue)
     * @param agent Agent requesting prediction
     * @param modelName Model to use
     * @returns Predicted action
     */
    processImmediatePrediction(agent: MLAgent, modelName: string): Promise<AgentAction>;
    /**
     * Add request to queue with priority ordering
     */
    private addToQueue;
    /**
     * Schedule batch processing with timeout
     */
    private scheduleBatchProcessing;
    /**
     * Process a batch of inference requests
     */
    private processBatch;
    /**
     * Group requests by model name for batch processing
     */
    private groupByModel;
    /**
     * Process a batch of requests for a specific model
     */
    private processBatchForModel;
    /**
     * Get agent state for prediction
     */
    private getAgentState;
    /**
     * Update performance metrics
     */
    private updateMetrics;
    /**
     * Get current performance metrics
     */
    getPerformanceMetrics(): MLPerformanceMetrics;
    /**
     * Get queue status
     */
    getQueueStatus(): {
        queueLength: number;
        processing: boolean;
        batchSize: number;
        pendingPromises: number;
    };
    /**
     * Configure batch processing parameters
     */
    configure(options: {
        batchSize?: number;
        maxQueueSize?: number;
        batchTimeout?: number;
    }): void;
    /**
     * Clear the inference queue
     */
    clearQueue(): void;
    /**
     * Get memory usage information
     */
    getMemoryInfo(): {
        framework: any;
        tensorflow: any;
    };
    /**
     * Cleanup resources
     */
    dispose(): void;
}
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