The fitting package deals with curve fitting for univariate real functions. When a univariate real function y = f(x) does depend on some unknown parameters p0, p1 ... pn-1, curve fitting can be used to find these parameters. It does this by fitting the curve so it remains very close to a set of observed points (x0, y0), (x1, y1) ... (xk-1, yk-1). This fitting is done by finding the parameters values that minimizes the objective function Σ(yi - f(xi))2. This is actually a least-squares problem.
For all provided curve fitters, the operating principle is the same. Users must
Some fitters require that initial values for the parameters are provided by the user, through the withStartPoint method, before attempting to perform the fit. When that's the case the fitter class usually defines a guessing procedure within a ParameterGuesser inner class, that attempts to provide appropriate initial values based on the user-supplied data. When initial values are required but are not provided, the fit method will internally call the guessing procedure.
Fitting of specific functions are provided through the following classes:
The following example shows how to fit data with a polynomial function.
// Collect data. final WeightedObservedPoints obs = new WeightedObservedPoints(); obs.add(-1.00, 2.021170021833143); obs.add(-0.99, 2.221135431136975); obs.add(-0.98, 2.09985277659314); obs.add(-0.97, 2.0211192647627025); // ... Lots of lines omitted ... obs.addt(0.99, -2.4345814727089854); // Instantiate a third-degree polynomial fitter. final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(3); // Retrieve fitted parameters (coefficients of the polynomial function). final double coeff = fitter.fit(obs.toList());