| SVDRegressionCalculation Class |
Class SVDRegressionCalculation computes linear regression parameters by
the method of least squares using a singular value decomposition.
Inheritance Hierarchy Namespace: CenterSpace.NMath.CoreAssembly: NMath (in NMath.dll) Version: 7.4
Syntax [SerializableAttribute]
public class SVDRegressionCalculation : IRegressionCalculation,
ICloneable
<SerializableAttribute>
Public Class SVDRegressionCalculation
Implements IRegressionCalculation, ICloneable
[SerializableAttribute]
public ref class SVDRegressionCalculation : IRegressionCalculation,
ICloneable
[<SerializableAttribute>]
type SVDRegressionCalculation =
class
interface IRegressionCalculation
interface ICloneable
end
The SVDRegressionCalculation type exposes the following members.
Constructors Properties | Name | Description |
---|
| Cols |
Gets the number of columns in the matrix.
|
| Fail |
Gets the status of the singular value decomposition.
|
| IsGood |
Returns true if the singular value decomposition may be used to
solve least squares problems; otherwise false.
|
| Rank |
Gets the numerical rank of the matrix.
|
| RankAvailable |
Returns the rank if it was calculated as a byproduct of the parameter
calculation.
|
| Rows |
Gets the number of rows in the matrix.
|
| Tolerance |
Gets and sets the tolerance for computing the numerical rank and SVD tuncation.
In truncation all singular values less than Tolerance are set to zero and
solutions will be computed using the truncated SVD.
If the Tolerance is set to 0 no truncation will be performed. The
default tolerance is 0, i.e. no truncation.
|
| XTXInv |
Gets the matrix formed by taking the inverse of the product of the
transpose of the regression matrix with itself, if available.
|
| XTXInvAvailable |
Gets a boolean indicating whether or not the matrix formed by taking
the inverse of the product of the transpose of the regression matrix
with itself is avaialble as part of the decomposition.
|
TopMethods | Name | Description |
---|
| CalculateParameters(DoubleMatrix, DoubleVector) |
Calculates the parameters for the regression using a singular value decomposition
of the regression matrix to solve the least squares problem.
|
| CalculateParameters(DoubleMatrix, DoubleVector, Boolean) |
Calculates the parameters for the regression using a singular value decomposition
of the regression matrix to solve the least squares problem.
|
| Clone |
Creates a deep copy of this regression calculator instance.
|
| Factor(DoubleMatrix) |
Factors a given matrix so that it may be used to solve least squares problems.
|
| Factor(DoubleMatrix, Double) |
Factors a given matrix so that it may be used to solve least squares problems.
The specified tolerance is used in computing the numerical rank of the matrix.
|
| OnSerializing |
Checks for soln_ to be null, instantiates if so
|
| Solve |
Computes the solution to the least squares problem Ax = b.
|
TopRemarks
Class SVDRegressionCalculation finds the minimal norm solution to the
overdetermined linear system:
That is, this class finds the vector
x that minimizes the 2-norm
of the residual vector
Ax - b.
See Also