NonlinearCurveFitterT Class

public class NonlinearCurveFitter<T>

Public Class NonlinearCurveFitter(Of T)

generic<typename T>
public ref class NonlinearCurveFitter

type NonlinearCurveFitter<'T> =  class end

Type Parameters

T

The domain of the nonlinear function.

The NonlinearCurveFitterT type exposes the following members.

	Name	Description
	NonlinearCurveFitterT	Constructs a new NonlinearCurveFitter object.
	NonlinearCurveFitterT(NonlinearCurveFittingMethod)	Constructs a new NonlinearCurveFitter object.

	Name	Description
	Algorithm	Gets or sets the nonlinear least squares algorithm that is to be used in the calculations.
	BestFitParameters	Gets the curve parameters corresponding to the best fit.
	Function	Gets or sets the function to be fitted.
	FunctionGradient	Gets or sets the gradient of the function to be fitted.
	InitialGuess	Gets or sets the initial value for the iteration.
	LowerBounds	Gets or sets the vector of lower bounds of the curve parameters.
	Optimizer	Gets the optimizer used to calculate the nonlinear least-squares solution.
	Residuals	Gets the residuals for the observations.
	ScaleVector	Gets or sets the vector used to scale the curve parameters.
	StandardError	Gets the standard error of the curve fit.
	UpperBounds	Gets or sets the vector of upper bounds of the curve parameters.
	WeightFunction	Gets or sets the weight function.
	WeightVector	Gets or sets the weight vector.
	XValues	Gets or sets the vector of x-values.
	YValues	Gets or sets the vector of y-values.

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	Fit	Calculates the least-squares fit.
	GetConfidenceBandwidth(T)	Gets the width of the confidence band around the best-fit curve at the specified point at the 95% confidence level.
	GetConfidenceBandwidth(T, Double)	Gets the width of the confidence band around the best-fit curve at the specified point.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetPredictionBandwidth(T)	Gets the width of the prediction band around the best-fit curve at the specified point at the 95% confidence level.
	GetPredictionBandwidth(T, Double)	Gets the width of the prediction band around the best-fit curve at the specified point.
	GetStandardDeviations	Gets the standard deviations.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	GetVarianceCovarianceMatrix	Returns the variance-covariance matrix of the fit.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

Use the NonlinearCurveFitterT 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 IListT 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