/**
 * Computes the coefficients of a polynomial regression for the given `samples`
 * (each a [x,y] tuple). The `degree` param defines the degree of the polynomial
 * and the number of returned coefficients (+1).
 *
 * @remarks
 * The resulting coeffs can be then used with {@link polynomial} to evaluate the
 * curve (i.e. used to make predictions).
 *
 * @param samples
 * @param degree
 */
export declare const polynomialRegression: (samples: number[][], degree: number) => number[];
/**
 * Computes polynomial for `x` and given `coeffs` (in order of increasing
 * exponents).
 *
 * @remarks
 * See {@link polynomialRegression} for computing coefficients.
 *
 * The number of given coefficients defines the degree (+1) of the polynomial,
 * i.e. a cubic function will require 4 coeffs, with the y-intercept being the
 * first coeff.
 *
 * @param x
 * @param coeffs
 */
export declare const polynomial: (x: number, coeffs: number[]) => number;
/**
 * Takes an augmented matrix (in column-major order) and uses Gaussian
 * elimination to compute solution coefficients for its system of linear
 * equations `Ax = b`.
 *
 * @remarks
 * References:
 * - https://en.wikipedia.org/wiki/Gaussian_elimination
 * - https://www.geeksforgeeks.org/dsa/gaussian-elimination/
 *
 * @param mat
 * @param degree
 */
export declare const gaussianElimination: (mat: number[][], degree: number) => number[];
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