import { BaseEstimator } from '../base';
import { Params } from '../base/estimator';
interface PLSProps {
    nComponents?: number;
    scale?: boolean;
    maxIter?: number;
    tol?: number;
}
type Targets = number[] | number[][];
declare abstract class BasePLS extends BaseEstimator {
    protected nComponents: number;
    protected scale: boolean;
    protected maxIter: number;
    protected tol: number;
    protected mode: 'A' | 'B';
    protected canonical: boolean;
    protected xMean: number[];
    protected yMean: number[];
    protected xStd: number[];
    protected yStd: number[];
    protected xWeightsState: number[][];
    protected yWeightsState: number[][];
    protected xLoadingsState: number[][];
    protected yLoadingsState: number[][];
    protected xRotationsState: number[][];
    protected yRotationsState: number[][];
    protected nIterState: number[];
    protected yWas1d: boolean;
    constructor(props: PLSProps, mode: 'A' | 'B', canonical: boolean);
    getParams(): Params;
    private normalize;
    private pseudoInverse;
    fit(X: number[][], y: Targets): void;
    transform(X: number[][]): number[][];
    predict(X: number[][]): number[] | number[][];
    score(X: number[][], y: Targets): number;
    get xWeights(): number[][];
    get yWeights(): number[][];
    get xLoadings(): number[][];
    get yLoadings(): number[][];
    get xRotations(): number[][];
    get nIter(): number[];
}
export interface PLSRegressionProps extends PLSProps {
}
export declare class PLSRegression extends BasePLS {
    constructor(props?: PLSRegressionProps);
}
export interface CCAProps extends PLSProps {
}
export declare class CCA extends BasePLS {
    constructor(props?: CCAProps);
}
export {};