BoundedMultiVariableFunctionFitterM Class

Class MultiVariableFunctionFitter< M > fits a parameterized multivariable function to a set of points where the parameters have inequality constraints.

Inheritance Hierarchy

SystemObject
CenterSpace.NMath.CoreMultiVariableFunctionFitterM
CenterSpace.NMath.CoreBoundedMultiVariableFunctionFitterM

Namespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4

Syntax

C++

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[SerializableAttribute]
public class BoundedMultiVariableFunctionFitter<M> : MultiVariableFunctionFitter<M>
where M : new(), IBoundedNonlinearLeastSqMinimizer

<SerializableAttribute>
Public Class BoundedMultiVariableFunctionFitter(Of M As {New, IBoundedNonlinearLeastSqMinimizer})
	Inherits MultiVariableFunctionFitter(Of M)

[SerializableAttribute]
generic<typename M>
where M : gcnew(), IBoundedNonlinearLeastSqMinimizer
public ref class BoundedMultiVariableFunctionFitter : public MultiVariableFunctionFitter<M>

[<SerializableAttribute>]
type BoundedMultiVariableFunctionFitter<'M when 'M : new() and IBoundedNonlinearLeastSqMinimizer> = 
    class
        inherit MultiVariableFunctionFitter<'M>
    end

Type Parameters

M: [Missing <typeparam name="M"/> documentation for "T:CenterSpace.NMath.Core.BoundedMultiVariableFunctionFitter`1"]

The BoundedMultiVariableFunctionFitterM type exposes the following members.

Constructors

	Name	Description
	BoundedMultiVariableFunctionFitterM(Boolean)	For internal use only.
	BoundedMultiVariableFunctionFitterM(DoubleParameterizedFunctional)	Constructs a BoundedMultiVariableFunctionFitter instance with the given generalized function.
	BoundedMultiVariableFunctionFitterM(DoubleParameterizedFunctional, M)	Constructs a BoundedMultiVariableFunctionFitter instance with the given generalized function and minimizer.
	BoundedMultiVariableFunctionFitterM(FuncDoubleVector, DoubleVector, Double, Int32)	Constructs a BoundedMultiVariableFunctionFitter instance for the given parameterized delegate.
	BoundedMultiVariableFunctionFitterM(FuncDoubleVector, DoubleVector, Double, ActionDoubleVector, DoubleVector, DoubleVector, Int32)	Constructs a BoundedMultiVariableFunctionFitter instance for the given parameterized delegate.

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Properties

	Name	Description
	Function	Gets and sets the parameterized function. (Inherited from MultiVariableFunctionFitterM)
	Minimizer	Gets and sets the function minimizer. (Inherited from MultiVariableFunctionFitterM)

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Methods

	Name	Description
	Clone	Creates a deep copy of this MultiVariableFunctionFitter. (Inherited from MultiVariableFunctionFitterM)
	Fit(DoubleMatrix, DoubleVector, DoubleVector)	Fits a function to the specified points. (Inherited from MultiVariableFunctionFitterM)
	Fit(DoubleMatrix, DoubleVector, DoubleVector, DoubleVector, DoubleVector)	Fits a function to the specified points where the resulting function parameters satisfy the given inequality constriants C# Copy parameterLowerBounds[i] < parameters[i] < parameterUpperBounds[i] for each i.
	ResidualVector	Computes the residual vector from the given data points and solution. (Inherited from MultiVariableFunctionFitterM)

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Fields

	Name	Description
	f_	The parameterized function. (Inherited from MultiVariableFunctionFitterM)
	minimizer_	The minimizer. (Inherited from MultiVariableFunctionFitterM)

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Remarks

In the space of the function parameters, begining at a specified starting point, finds a minimum (possibly local) in the sum of the squared residuals with respect to a set of data points subject to the supplied inequality constraints on the parameters. You must supply at least as many data points to fit as your function has parameters.
For example, the following code fits a function of the form data to the following function:

            F(p, x) = p[0]x[0]x[1]^2 + p[1]sin(x[0]) + p[2]x[1]^3;

to a set of 10 data points, beginning at point (1, 1, 1) in the parameter space. and satisfying the parameter constraints 0 < p[0] < 100 0 < p[1] < 50 0 < p[2] < 75

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DoubleMatrix x = new DoubleMatrix(10, 2);
x[Slice.All, 0] = new DoubleVector("3.6 7.7 9.3 4.1 8.6 2.8 1.3 7.9 10.0 5.4");
x[Slice.All, 1] = new DoubleVector("16.5 150.6 263.1 24.7 208.5 9.9 2.7 163.9 325.0 54.3");
DoubleVector upperBounds = new DoubleVector("100, 50, 75");
DoubleVector lowerBounds = new DoubleVector("0 0 0");
DoubleVector yValues = new DoubleVector("95.09 23.11 60.63 48.59 89.12 76.97 45.68 1.84 82.17 44.47");
DoubleVector initialParameters = new DoubleVector("10 10 10");


Func<DoubleVector, DoubleVector, double> f = delegate(DoubleVector p, DoubleVector xdata)
{
  return Math.Pow(p[0] * xdata[0] * xdata[1], 2.0) + p[1] * Math.Sin(xdata[0]) + Math.Pow(p[2] * xdata[1], 3.0);
};


MultiVariableFunctionFitter<TrustRegionMinimizer> fitter = new MultiVariableFunctionFitter<TrustRegionMinimizer>(f);
DoubleVector solution = fitter.Fit(x, yValues, initialParameters, lowerBounds, upperBounds);

Note that problems can have multiple local minima. Trying different initial parameter points is recommended for better solutions. Also, the initial parameter should satisfy the parameter constraints.

Reference

CenterSpace.NMath.Core Namespace