import { TransformerBase } from '../base/transformer';
import { Params } from '../base/estimator';
export interface LocallyLinearEmbeddingProps {
    nNeighbors?: number;
    nComponents?: number;
    reg?: number;
    randomState?: number;
}
export declare class LocallyLinearEmbedding extends TransformerBase {
    private nNeighbors;
    private nComponents;
    private reg;
    private randomState?;
    private trainX;
    private trainY;
    constructor(props?: LocallyLinearEmbeddingProps);
    /** @deprecated positional form; prefer the props-object constructor */
    constructor(nNeighbors?: number, nComponents?: number, reg?: number, randomState?: number);
    getParams(): Params;
    private static dot;
    private static matVecMul;
    private static normalize;
    private static outer;
    private static cloneMatrix;
    private static powerIteration;
    private distance;
    private neighbors;
    private computeWeights;
    fit(X: number[][]): void;
    transform(X: number[][]): number[][];
    /**
     * Fit and return the training embedding directly (not `transform(X)`,
     * which would re-estimate reconstruction weights out-of-sample).
     */
    fitTransform(X: number[][]): number[][];
}