NonlinearCurveFitter<T> Class

public class NonlinearCurveFitter<T>

Public Class NonlinearCurveFitter(Of T)

generic<typename T>
public ref class NonlinearCurveFitter

type NonlinearCurveFitter<'T> = class end

Inheritance

Object  →  NonlinearCurveFitter<T>

Type Parameters

T

The domain of the nonlinear function.

Use the NonlinearCurveFitter<T> class to fit data to a nonlinear function by the method of least squares. The function's domain can be of any type, so it is possible to fit surfaces or arbitrary data. The data is supplied as IList<T> objects through the XValues and a Vector for the YValues properties.

The Fit() method performs the actual curve fit. This method returns a delegate represents the function that best fits the supplied data. The Optimizer property returns the MultidimensionalOptimizer object that was used to find the least squares solution. To verify that the algorithm terminated normally, you can inspect the Optimizer's Status property, which is of type AlgorithmStatus. A value of Normal indicates that the algorithm terminated normally. However, it is still possible that the algorithm didn't converge to the actual best fit. A visual inspection is always recommended.

The parameters of the fitted curve can be retrieved through the BestFitParameters property. The standard deviations associated with each parameter are available through the GetStandardDeviations() method.

By default, the observations are unweighted. You can supply a weighting method in two ways. You can set the WeightFunction property to a function of two variables delegate that computes the weight for each observation. The WeightFunctions class provides predefined delegates for the most common weight functions. Alternatively, you can set the individual weights by setting the WeightVector property to a Vector that contains the weight for each individual observation.

Reference