import { BaseEstimator, TransformerBase } from '../base';
import { Params } from '../base/estimator';
interface RegressorLike extends BaseEstimator {
    fit(X: number[][], y: number[]): void;
    predict(X: number[][]): number[];
}
interface ImputationStep {
    feature: number;
    estimator: RegressorLike;
}
export interface IterativeImputerProps {
    estimator?: RegressorLike;
    maxIter?: number;
    tol?: number;
    initialStrategy?: 'mean' | 'median' | 'mostFrequent' | 'constant';
    fillValue?: number;
    imputationOrder?: 'ascending' | 'descending' | 'roman' | 'arabic' | 'random';
    skipComplete?: boolean;
    minValue?: number;
    maxValue?: number;
    randomState?: number;
}
export declare class IterativeImputer extends TransformerBase {
    private estimator;
    private maxIter;
    private tol;
    private initialStrategy;
    private fillValue;
    private imputationOrder;
    private skipComplete;
    private minValue;
    private maxValue;
    private randomState?;
    private initialStatistics;
    private sequenceState;
    private nIterState;
    private nFeaturesState;
    constructor(props?: IterativeImputerProps);
    getParams(): Params;
    setParams(params: Params): this;
    private validate;
    private initialize;
    private featureOrder;
    private predictors;
    fit(X: number[][]): void;
    fitTransform(X: number[][]): number[][];
    transform(X: number[][]): number[][];
    get imputationSequence(): ImputationStep[];
    get nIter(): number;
}
export {};