import { Matrix } from 'ml-matrix';
import type { Data2D, ParameterizedFunction } from './types.ts';
/**
 * Difference of the matrix function over the parameters
 * @param data Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
 * @param evaluatedData - Array of previous evaluated function values
 * @param params - Array of previous parameter values
 * @param gradientDifference - The step size to approximate the jacobian matrix
 * @param centralDifference - If true the jacobian matrix is approximated by central differences otherwise by forward differences
 * @param paramFunction - The parameters and returns a function with the independent variable as a parameter
 */
export default function gradientFunction(data: Data2D, evaluatedData: Float64Array, params: number[], gradientDifference: number[], paramFunction: ParameterizedFunction, centralDifference: boolean): Matrix;
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