/**
 * Logistic regression
 */
export class LogisticRegression {
    _W: Matrix<number>;
    _b: number;
    _output(x: any): any;
    /**
     * Fit model.
     * @param {Array<Array<number>>} x Training data
     * @param {Array<1 | -1>} y Target values
     * @param {number} [iteration] Iteration count
     * @param {number} [rate] Learning rate
     * @param {number} [l1] L1 regularization strength
     * @param {number} [l2] L2 regularization strength
     */
    fit(x: Array<Array<number>>, y: Array<1 | -1>, iteration?: number, rate?: number, l1?: number, l2?: number): void;
    /**
     * Returns predicted values.
     * @param {Array<Array<number>>} points Sample data
     * @returns {Array<1 | -1>} Predicted values
     */
    predict(points: Array<Array<number>>): Array<1 | -1>;
}
/**
 * Multinomial logistic regression
 */
export class MultinomialLogisticRegression {
    /**
     * @param {number[]} [classes] Initial class labels
     */
    constructor(classes?: number[]);
    _classes: number[];
    _W: Matrix<number>;
    _b: Matrix<number>;
    _output(x: any): any;
    /**
     * Fit model.
     * @param {Array<Array<number>>} train_x Training data
     * @param {*[]} train_y Target values
     * @param {number} [iteration] Iteration count
     * @param {number} [rate] Learning rate
     * @param {number} [l1] L1 regularization strength
     * @param {number} [l2] L2 regularization strength
     */
    fit(train_x: Array<Array<number>>, train_y: any[], iteration?: number, rate?: number, l1?: number, l2?: number): void;
    /**
     * Returns predicted categories.
     * @param {Array<Array<number>>} points Sample data
     * @returns {*[]} Predicted values
     */
    predict(points: Array<Array<number>>): any[];
}
import Matrix from '../util/matrix.js';