import * as tf from '@tensorflow/tfjs';
import { io } from '@tensorflow/tfjs-core';
import LearningInterface from './base';
import DataManager from '../utils/data_manager';
declare class DataModel implements LearningInterface {
    epochs: number;
    batchSize: number;
    learningRate: number;
    validateRate: number;
    isTrainedDone: boolean;
    limitSize: number;
    isRunning: boolean;
    isReady: boolean;
    model: tf.LayersModel | null;
    dataManager: DataManager;
    constructor({ epochs, batchSize, limitSize, learningRate, validateRate, }?: {
        epochs?: number;
        batchSize?: number;
        limitSize?: number;
        learningRate?: number;
        validateRate?: number;
    });
    onProgress(progress: number): void;
    onLoss(loss: number): void;
    onTrainBegin(log: any): void;
    onTrainEnd(log: any): void;
    onEpochEnd(epoch: number, logs: any): void;
    addData(label: string, data: number[]): Promise<void>;
    augmentData(data: number[]): number[];
    normalizeData(data: number[]): number[];
    train(): Promise<tf.History>;
    infer(data: number[]): Promise<Map<string, number>>;
    saveModel(handlerOrURL: io.IOHandler | string, config?: io.SaveConfig): Promise<void>;
    running(): boolean;
    ready(): boolean;
    load({ jsonURL, labels }: {
        jsonURL: string;
        labels: Array<string>;
    }): Promise<void>;
}
export default DataModel;