import { BaseEstimator, RegressorBase } from '../base';
import { Params } from '../base/estimator';
export interface HuberRegressorProps {
    epsilon?: number;
    maxIter?: number;
    alpha?: number;
    tol?: number;
    fitIntercept?: boolean;
}
export declare class HuberRegressor extends RegressorBase {
    private epsilon;
    private maxIter;
    private alpha;
    private tol;
    private fitIntercept;
    private coefState;
    private interceptState;
    private scaleState;
    private outliersState;
    private nIterState;
    constructor(props?: HuberRegressorProps);
    getParams(): Params;
    fit(X: number[][], y: number[]): void;
    predict(X: number[][]): number[];
    get coef(): number[];
    get intercept(): number;
    get scale(): number;
    get outliers(): boolean[];
    get nIter(): number;
}
interface RegressorLike extends BaseEstimator {
    fit(X: number[][], y: number[]): void;
    predict(X: number[][]): number[];
}
export interface RANSACRegressorProps {
    estimator?: RegressorLike;
    minSamples?: number;
    residualThreshold?: number | null;
    maxTrials?: number;
    stopProbability?: number;
    randomState?: number;
}
export declare class RANSACRegressor extends RegressorBase {
    private estimator;
    private minSamples?;
    private residualThreshold;
    private maxTrials;
    private stopProbability;
    private randomState?;
    private estimatorState?;
    private inlierMaskState;
    private nTrialsState;
    constructor(props?: RANSACRegressorProps);
    getParams(): Params;
    fit(X: number[][], y: number[]): void;
    predict(X: number[][]): number[];
    get estimatorFitted(): RegressorLike;
    get inlierMask(): boolean[];
    get nTrials(): number;
}
export interface TheilSenRegressorProps {
    fitIntercept?: boolean;
    maxSubpopulation?: number;
    nSubsamples?: number | null;
    maxIter?: number;
    tol?: number;
    randomState?: number;
}
export declare class TheilSenRegressor extends RegressorBase {
    private fitIntercept;
    private maxSubpopulation;
    private nSubsamples;
    private maxIter;
    private tol;
    private randomState?;
    private coefState;
    private interceptState;
    private nIterState;
    constructor(props?: TheilSenRegressorProps);
    getParams(): Params;
    fit(X: number[][], y: number[]): void;
    predict(X: number[][]): number[];
    get coef(): number[];
    get intercept(): number;
    get nIter(): number;
}
export interface QuantileRegressorProps {
    quantile?: number;
    alpha?: number;
    fitIntercept?: boolean;
    maxIter?: number;
    tol?: number;
}
export declare class QuantileRegressor extends RegressorBase {
    private quantile;
    private alpha;
    private fitIntercept;
    private maxIter;
    private tol;
    private coefState;
    private interceptState;
    private nIterState;
    constructor(props?: QuantileRegressorProps);
    getParams(): Params;
    fit(X: number[][], y: number[]): void;
    predict(X: number[][]): number[];
    get coef(): number[];
    get intercept(): number;
    get nIter(): number;
}
export {};