﻿DoubleParameterizedFunction Class

# DoubleParameterizedFunction Class

Abstract class representing a parameterized function.
Inheritance Hierarchy
SystemObject
CenterSpace.NMath.CoreDoubleParameterizedFunction
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Namespace:  CenterSpace.NMath.Core
Assembly:  NMath (in NMath.dll) Version: 7.4
Syntax
```[SerializableAttribute]
public abstract class DoubleParameterizedFunction : ICloneable```

The DoubleParameterizedFunction type exposes the following members.

Constructors
NameDescription
DoubleParameterizedFunction
Constructs a DoubleParameterizedFunction object.
DoubleParameterizedFunction(Double)
Constructs a DoubleParameterizedFunction object with the given delta to use in the centeral difference method for approximating the gradient with respect to the parameters.
DoubleParameterizedFunction(DoubleParameterizedFunction)
Copy constructor. Creates a copy of another DoubleParameterizedFunction.
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Properties
NameDescription
CentralDifferenceDelta
Gets and sets the delta used in the centeral difference method for approximating the gradient with respect to the parameters.
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Methods
NameDescription
Clone
Returns a deep copy of the base. Deriving classes must override this method.
Evaluate(DoubleVector, Double)
Evaluates the parameterized function for the given parameter values at the given point.
Evaluate(DoubleVector, DoubleVector, DoubleVector)
Evaluates the parameterized function for the given parameter values at the given set of points.
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 function defines a funtion for each set of parameters. For example:
f(x: a, b) = a*cos(b*x) + b*sin(a*x)
is a parameterized function. For each set of values, a and b, it defines a function of x. For example if a = 1 and b = 2 then
f(x) = cos(2*x) + 2*sin(x).
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(x: a, b) so that the resulting function f(x) best fits the data points.