NonlinearModelOfFitEnsemble Class

Adapts a FitEnsemble to the requirements of a Levenberg-Marquardt fitting procedure. This means, the adapter makes the FitEnsemble compatible with the Levenberg-Marquardt algorithm.

Inheritance Hierarchy

SystemObject
Altaxo.Calc.Optimization.ObjectiveFunctionsNonlinearObjectiveFunctionNonAllocatingBase
Altaxo.Calc.Regression.NonlinearNonlinearModelOfFitEnsemble

Namespace: Altaxo.Calc.Regression.Nonlinear
Assembly: AltaxoBase (in AltaxoBase.dll) Version: 4.8.3572.0 (4.8.3572.0)

Syntax

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public class NonlinearModelOfFitEnsemble : NonlinearObjectiveFunctionNonAllocatingBase

The NonlinearModelOfFitEnsemble type exposes the following members.

Constructors

	Name	Description
	NonlinearModelOfFitEnsemble	Constructor of the adapter.

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Properties

	Name	Description
	DegreeOfFreedom	Get the degree of freedom. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	FunctionEvaluations	Get the number of calls to function. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	Gradient	Get the Gradient vector. G = J'(y - f(x; p)) (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	Hessian	Get the approximated Hessian matrix. H = J'J (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	IsFixedByUser	Gets whether parameters are fixed or free (by the user). (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	IsFixedByUserOrBoundary	Gets or sets an array of the same length as the parameter array. If an element in this vector is , that parameter is either fixed by the user or fixed because the corresponding parameter has reached a boundary. This array is updated only at the end of the minimization process. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	IsGradientSupported	Gets a value indicating whether the gradient can be provided by the model. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	IsHessianSupported	Gets a value indicating whether the Hessian can be provided by the model. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	JacobianEvaluations	Get the number of calls to jacobian. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	ModelValues	Get the y-values of the fitted model that correspond to the independent values. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	NegativeGradient	Gets the negative gradient vector. -G = -J'(y - f(x; p)). (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	NumberOfObservations	Gets the number of observations. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	NumberOfParameters	Gets the number of unknown parameters. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	ObservedY	Get the y-values of the observations. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	Point	Get the values of the parameters. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	SigmaSquare	Gets Chi²/(N-F+1). (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	Value	Get the residual sum of squares. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	Weights	Gets or sets the values of the weights for the observations. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)

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Methods

	Name	Description
	CopyParametersBackTo	Copies the current model parameters back to the specified parameter set.
	CopyParametersForFitElement	Copies the current parameters for the fit element with the provided index into the provided array.
	CreateNew	Creates a new instance of the objective model with identical configuration but independent state. (Overrides NonlinearObjectiveFunctionNonAllocatingBaseCreateNew)
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object)
	EvaluateAt(IReadOnlyListDouble)	Evaluates the model at the given parameter vector and invalidates cached dependent values. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	EvaluateAt(VectorDouble)	Evaluates the model at the given parameter vector, updating dependent values. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	EvaluateFunction	Evaluates the objective function value and updates cached values. (Overrides NonlinearObjectiveFunctionNonAllocatingBaseEvaluateFunction)
	EvaluateJacobian	Evaluates the Jacobian and updates cached Jacobian-derived values (gradient and Hessian). (Overrides NonlinearObjectiveFunctionNonAllocatingBaseEvaluateJacobian)
	EvaluateModelValues(VectorDouble, Boolean)	Calculates the fitting values.
	EvaluateModelValues(IReadOnlyListDouble, VectorDouble, Boolean)	Calculates the fitting values.
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object)
	Fork	Creates a forked copy of the model with independent mutable state. (Overrides NonlinearObjectiveFunctionNonAllocatingBaseFork)
	GetDependentVariablesInUse	Returns the array of indices of dependent variables that are currently in use (i.e. associated with a data column).
	GetHashCode	Serves as the default hash function. (Inherited from Object)
	GetType	Gets the Type of the current instance. (Inherited from Object)
	GetValidNumericRows	Returns the collection of valid numeric rows for the given fit element.
	GetWeights	Stores the weights for the fit differences in an array. The data are stored from FitElement_0 to FitElement_n. For FitElements with more than one dependent variable in use, the data are stored interleaved.
	HasToUseWeights	Returns true if any of the fit elements use scaling weights. In this case we have to calculate the weights for all fit elements and include them in the fitting procedures.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object)
	NumericalJacobian	Numerically approximates the Jacobian at the specified parameter vector. (Overrides NonlinearObjectiveFunctionNonAllocatingBaseNumericalJacobian(VectorDouble, VectorDouble, Int32))
	SetParameters(IReadOnlyListDouble, IReadOnlyListBoolean)	Sets model parameters and optional fixed flags for individual parameters. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	SetParameters(VectorDouble, ListBoolean)	Sets the model parameters and optional fixed flags for individual parameters. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	ToObjectiveFunction	Converts this model to an objective function suitable for minimizers. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	ToString	Returns a string that represents the current object. (Inherited from Object)

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Fields

	Name	Description
	_accuracyOrder	The desired accuracy order to evaluate the jacobian by numerical approximaiton. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_coefficients	Coefficients for the model. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_f1	Temporary function-evaluation vectors used for numerical differentiation. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_f2	Temporary function-evaluation vectors used for numerical differentiation. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_f3	Temporary function-evaluation vectors used for numerical differentiation. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_f4	Temporary function-evaluation vectors used for numerical differentiation. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_f5	Temporary function-evaluation vectors used for numerical differentiation. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_f6	Temporary function-evaluation vectors used for numerical differentiation. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_functionValue	The residual sum of squares, residuals * residuals. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_hasFunctionValue	Indicates if the function value has been computed. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_hasJacobianValue	Indicates if the jacobian has been computed. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_hessianValue	The Hessian matrix. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_jacobianValue	The Jacobian matrix. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_jacobianValueTransposed	The Jacobian matrix, transposed. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_negativeGradientValue	The Gradient vector. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	_residuals	The weighted error values. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)
	L	Gets or sets the Cholesky factorization of the weights = LL'. (Inherited from NonlinearObjectiveFunctionNonAllocatingBase)

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Reference

Altaxo.Calc.Regression.Nonlinear Namespace