import { SVR } from './svr';
import { Params } from '../base/estimator';
import { KernelMatrix, KernelType, SVRSolution } from './smo';
export interface NuSVRProps {
    kernel?: KernelType;
    degree?: number;
    gamma?: number | 'scale' | 'auto';
    coef0?: number;
    tol?: number;
    C?: number;
    /**
     * upper bound on the fraction of training errors (points outside the
     * tube), lower bound on the fraction of support vectors
     */
    nu?: number;
    /** hard limit on SMO pair updates; -1 (default) = run until convergence */
    maxIter?: number;
}
/**
 * nu-Support Vector Regression (libsvm solve_nu_svr semantics): `nu`
 * replaces `epsilon` and directly trades off support-vector fraction against
 * points outside the tube; the tube half-width epsilon becomes a variable of
 * the optimization (exposed after fit as `fittedEpsilon`).
 */
export declare class NuSVR extends SVR {
    private nu;
    /** tube half-width found by the optimizer (libsvm's -r); 0 before fit */
    protected fittedEpsilon: number;
    constructor(props?: NuSVRProps);
    getParams(): Params;
    protected solveDual(K: KernelMatrix, y: number[]): SVRSolution;
    /** epsilon-tube half-width chosen by the nu-SVR optimization */
    getFittedEpsilon(): number;
}