datafit/types/lib.d.ts

Simple curve-fitting algorithm

import { Datum, F, Summary, VariableType } from './types';
/**
 * Minimize the sum of squared errors to fit a set of data
 * points to a curve with a set of unknown parameters.
 * @param f The model function for curve fitting.
 * @param data The entire dataset, as an array of points.
 * @param params_initial The initial guess for function
 * parameters, which defaults to an array filled with zeroes.
 * @param iterations The number of parameter sets to generate.
 * @param maxDeviation The relative standard parameter deviation.
 * This is a number [0.0-1.0] and affects the standard deviation
 * on the first iteration. Every subsequent iteration has a
 * decayed standard deviation until the final iteration.
 * @returns The set of parameters and error for the best fit.
 * @example
 * ```ts
 * // Define model function
 * function f(x: number, a2: number = -0.5, a1: number = 3.9, a0: number = -1.2): number {
 *     return a2 * x ** 2 + a1 * x + a0;
 * }
 * // Construct a data set
 * const data: Datum<number>[] = [0, 2, 4].map(x => ({ x: x, y: f(x) }));
 * // Compute best-fit summary
 * const summary = fit(f, data);
 * ```
 */
export declare function fit<T extends VariableType>(f: F<T>, data: Datum<T>[], params_initial?: number[], iterations?: number, maxDeviation?: number): Summary<T>;