@bonginkan/maria
Version:
MARIA OS v5.9.5 – Self-Evolving Organizational Intelligence OS | Speed Improvement Phase 3: LLM Optimization + Command Refactoring | Performance Measurement + Run Evidence System | Zero ESLint/TypeScript Errors | 人とAIが役割を持ち、学び、進化し続けるための仕事のOS | GraphRAG ×
59 lines (58 loc) • 1.78 kB
TypeScript
import { EventEmitter } from "node:events";
export interface AnomalyConfig {
windowSize?: number;
threshold?: number;
minDataPoints?: number;
modelType?: "isolation-forest" | "autoencoder" | "lstm";
updateInterval?: number;
}
export interface MetricPoint {
timestamp: number;
value: number;
metadata?: Record<string, unknown>;
}
export interface AnomalyResult {
isAnomaly: boolean;
score: number;
expectedRange: [number, number];
actualValue: number;
timestamp: number;
metric: string;
severity: "low" | "medium" | "high" | "critical";
}
export declare class AnomalyDetector extends EventEmitter {
private windowSize;
private threshold;
private minDataPoints;
private modelType;
private models;
private dataBuffers;
private statistics;
private updateInterval;
private updateTimer?;
constructor(config?: AnomalyConfig);
private startUpdateTimer;
addDataPoint(metric: string, point: MetricPoint): Promise<AnomalyResult | null>;
private detectAnomaly;
private calculateStatistics;
private isolationForestDetection;
private buildIsolationTree;
private getPathLength;
private averagePathLength;
private randomSample;
private autoencoderDetection;
private createAutoencoderModel;
private lstmDetection;
private createLSTMModel;
private updateModels;
getAnomalyHistory(metric: string, limit?: number): MetricPoint[];
getStatistics(metric: string): {
mean: number;
std: number;
min: number;
max: number;
} | undefined;
evaluateMetrics(): Promise<Map<string, AnomalyResult[]>>;
dispose(): void;
}
export declare function getAnomalyDetector(config?: AnomalyConfig): AnomalyDetector;