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DoubleParameterizedFunctional Class

Abstract class representing a parameterized functional.
Inheritance Hierarchy

Namespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4
Syntax
[SerializableAttribute]
public abstract class DoubleParameterizedFunctional : ICloneable

The DoubleParameterizedFunctional type exposes the following members.

Constructors
 NameDescription
Public methodDoubleParameterizedFunctional(DoubleParameterizedFunctional) Copy constructor. Constructs a deep copy of the given DoubleParameterizedFunctional object.
Public methodDoubleParameterizedFunctional(Int32) Constructs a DoubleParameterizedFunctional object with the specified domain dimension.
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Properties
 NameDescription
Public propertyCentralDifferenceDelta Gets and sets the delta used in the centeral difference method for approximating the gradient with respect to the parameters.
Public propertyXDimension Gets and sets the dimension of the domain of the functional.
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Methods
 NameDescription
Public methodClone Returns a deep copy of the base. Deriving classes must override this method.
Public methodEvaluate(DoubleVector, DoubleVector) Evaluates the parameterized function for the given parameter values at the given point.
Public methodEvaluate(DoubleVector, DoubleMatrix, DoubleVector) Evaluates the parameterized function for the given parameter values at the given set of points.
Public methodGradientWithRespectToParams Method for calculating the gradient with respect to the parameters while keeping x fixed at the specified value.
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Remarks
A parameterized functional defines a funtion mapping a vector to a real value for each set of parameters. For example:
f(x1, x2: a, b) = a*cos(b*x1) + b*sin(a*x2)
is a parameterized function. For each set of values, a and b, it defines a function of x1, x2. For example if a = 1 and b = 2 then
f(x1, x2) = cos(2*x1) + 2*sin(x2)
Parameterized function are used by curve fitting routines and solve the following problem -
Suppose I have a set of points (xi, yi), i = 1...n, and I want to determine the values of a and b in the above parameterized function f(x1, x2: a, b) described above so that f fits the data points.
See Also