FunctionBasis<T>.LeastSquaresFit Method

Definition

Namespace: Numerics.NET.Curves.Generic
Assembly: Numerics.NET.Generic (in Numerics.NET.Generic.dll) Version: 9.0.3

Overload List

LeastSquaresFit(Vector<T>, Vector<T>)	Gets the least squares fit of target data in terms of the components of the FunctionBasis.
LeastSquaresFit(T[], T[], T[])	Gets the least squares fit of target data in terms of the components of the FunctionBasis.
LeastSquaresFit(T[], T[], Int32)	Gets the least squares fit of target data in terms of the components of the FunctionBasis.
LeastSquaresFit(Vector<T>, Vector<T>, Vector<T>)	Gets the least squares fit of target data in terms of the components of the FunctionBasis.

LeastSquaresFit(Vector<T>, Vector<T>)

Gets the least squares fit of target data in terms of the components of the FunctionBasis.

public virtual LinearCombination<T> LeastSquaresFit(
	Vector<T> xValues,
	Vector<T> yValues
)

Visual Basic

Public Overridable Function LeastSquaresFit ( 
	xValues As Vector(Of T),
	yValues As Vector(Of T)
) As LinearCombination(Of T)

C++/CLI

public:
virtual LinearCombination<T>^ LeastSquaresFit(
	Vector<T>^ xValues, 
	Vector<T>^ yValues
)

abstract LeastSquaresFit : 
        xValues : Vector<'T> * 
        yValues : Vector<'T> -> LinearCombination<'T> 
override LeastSquaresFit : 
        xValues : Vector<'T> * 
        yValues : Vector<'T> -> LinearCombination<'T>

Parameters

xValues Vector<T>: A vector containing the data points for the fit.
yValues Vector<T>: A vector containing the data values corresponding to the data points in xValues.

Return Value

LinearCombination<T>
A LinearCombination that is the least squares fit of the data in terms of this FunctionBasis.

Exceptions

ArgumentNullException	xValues is null. -or- yValues is null.
[!:Mathematics.TotalLossOfPrecisionException]	The solution of the least squares problem could not be found because roundoff error caused a total loss of precision.
DimensionMismatchException	The xValues and yValues do not have the same length.
ArgumentException	The number of data points is less than the number of basis functions.

LeastSquaresFit(T[], T[], T[])

Gets the least squares fit of target data in terms of the components of the FunctionBasis.

public virtual LinearCombination<T> LeastSquaresFit(
	T[] xValues,
	T[] yValues,
	T[] weights
)

Visual Basic

Public Overridable Function LeastSquaresFit ( 
	xValues As T(),
	yValues As T(),
	weights As T()
) As LinearCombination(Of T)

C++/CLI

public:
virtual LinearCombination<T>^ LeastSquaresFit(
	array<T>^ xValues, 
	array<T>^ yValues, 
	array<T>^ weights
)

abstract LeastSquaresFit : 
        xValues : 'T[] * 
        yValues : 'T[] * 
        weights : 'T[] -> LinearCombination<'T> 
override LeastSquaresFit : 
        xValues : 'T[] * 
        yValues : 'T[] * 
        weights : 'T[] -> LinearCombination<'T>

Parameters

xValues T[]: A T array containing the data points for the fit.
yValues T[]: A T array containing the data values corresponding to the data points. in xValues.
weights T[]: A T array containing the weights to assign to the residual error corresponding to the data values in yValues.

Return Value

LinearCombination<T>
A LinearCombination that is the least squares fit of the data in terms of this FunctionBasis.

Exceptions

ArgumentNullException	xValues is null. -or- yValues is null.
TotalLossOfPrecisionException	The solution of the least squares problem could not be found because roundoff error caused a total loss of precision.
DimensionMismatchException	The xValues and yValues do not have the same length.
ArgumentException	The number of data points is less than the number of basis functions.

LeastSquaresFit(T[], T[], Int32)

Gets the least squares fit of target data in terms of the components of the FunctionBasis.

public virtual LinearCombination<T> LeastSquaresFit(
	T[] xValues,
	T[] yValues,
	int numberOfDataPoints
)

Visual Basic

Public Overridable Function LeastSquaresFit ( 
	xValues As T(),
	yValues As T(),
	numberOfDataPoints As Integer
) As LinearCombination(Of T)

C++/CLI

public:
virtual LinearCombination<T>^ LeastSquaresFit(
	array<T>^ xValues, 
	array<T>^ yValues, 
	int numberOfDataPoints
)

abstract LeastSquaresFit : 
        xValues : 'T[] * 
        yValues : 'T[] * 
        numberOfDataPoints : int -> LinearCombination<'T> 
override LeastSquaresFit : 
        xValues : 'T[] * 
        yValues : 'T[] * 
        numberOfDataPoints : int -> LinearCombination<'T>

Parameters

xValues T[]: An array containing the data points for the fit.
yValues T[]: A array containing the data values corresponding to the data points in xValues.
numberOfDataPoints Int32: The number of actual data points.

Return Value

LinearCombination<T>
A LinearCombination that is the least squares fit of the data in terms of this FunctionBasis.

Remarks

Use this method to calculate a LinearCombination of the basis functions in this instance through a set of points using the least squares method.

Only the first numberOfDataPoints values in xValues and yValues are used.

Exceptions

ArgumentNullException

xValues is null.

-or-

yValues is null.

ArgumentOutOfRangeException

numberOfDataPoints is less than the number of basis functions.

-or-

numberOfDataPoints is greater than the length of xValues

-or-

numberOfDataPoints is greater than the length of yValues

TotalLossOfPrecisionException

The solution of the least squares problem could not be found because roundoff error caused a total loss of precision.

LeastSquaresFit(Vector<T>, Vector<T>, Vector<T>)

Gets the least squares fit of target data in terms of the components of the FunctionBasis.

public virtual LinearCombination<T> LeastSquaresFit(
	Vector<T> xValues,
	Vector<T> yValues,
	Vector<T>? weights
)

Visual Basic

Public Overridable Function LeastSquaresFit ( 
	xValues As Vector(Of T),
	yValues As Vector(Of T),
	weights As Vector(Of T)
) As LinearCombination(Of T)

C++/CLI

public:
virtual LinearCombination<T>^ LeastSquaresFit(
	Vector<T>^ xValues, 
	Vector<T>^ yValues, 
	Vector<T>^ weights
)

abstract LeastSquaresFit : 
        xValues : Vector<'T> * 
        yValues : Vector<'T> * 
        weights : Vector<'T> -> LinearCombination<'T> 
override LeastSquaresFit : 
        xValues : Vector<'T> * 
        yValues : Vector<'T> * 
        weights : Vector<'T> -> LinearCombination<'T>

Parameters

xValues Vector<T>: A vector containing the data points for the fit.
yValues Vector<T>: A vector containing the data values corresponding to the data points in xValues.
weights Vector<T>: A vector containing the weights to assign to the residual error corresponding to the data values in yValues.

Return Value

LinearCombination<T>
A LinearCombination that is the least squares fit of the data in terms of this FunctionBasis.

Exceptions

ArgumentNullException	xValues is null. -or- yValues is null.
[!:Mathematics.TotalLossOfPrecisionException]	The solution of the least squares problem could not be found because roundoff error caused a total loss of precision.
DimensionMismatchException	The xValues and yValues do not have the same length.
ArgumentException	The number of data points is less than the number of basis functions.

FunctionBasis<T>.LeastSquaresFit Method

Definition

Overload List

LeastSquaresFit(Vector<T>, Vector<T>)

Parameters

Return Value

Exceptions

LeastSquaresFit(T[], T[], T[])

Parameters

Return Value

Exceptions

LeastSquaresFit(T[], T[], Int32)

Parameters

Return Value

Remarks

Exceptions

LeastSquaresFit(Vector<T>, Vector<T>, Vector<T>)

Parameters

Return Value

Exceptions

See Also

Reference