Click or drag to resize

DoubleComplexLeastSquares Class

Class DoubleComplexLeastSquares computes the minimum-norm solution to a linear system Ax = y.
Inheritance Hierarchy
SystemObject
  CenterSpace.NMath.CoreDoubleComplexLeastSquares

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

The DoubleComplexLeastSquares type exposes the following members.

Constructors
  NameDescription
Public methodDoubleComplexLeastSquares(DoubleComplexMatrix, DoubleComplexVector)
Constructs a least squares solution for the given linear system Ax = y.
Public methodDoubleComplexLeastSquares(DoubleComplexMatrix, DoubleComplexVector, Boolean)
Constructs a least squares solution for the given linear system Ax = y, optionally adding an intercept parameter to the model.
Public methodDoubleComplexLeastSquares(DoubleComplexMatrix, DoubleComplexVector, Double)
Constructs a least squares solution for the given linear system Ax = y using the specified tolerance to compute the effective rank.
Public methodDoubleComplexLeastSquares(DoubleComplexMatrix, DoubleComplexVector, Boolean, Double)
Constructs a least squares solution for the given linear system Ax = y, optionally adding an intercept parameter, and using the specified tolerance to compute the effective rank.
Top
Properties
  NameDescription
Public propertyRank
Gets the effective rank of the matrix A.
Public propertyResiduals
Gets the vector of residuals. If y is the right-hand side of the least squares equation Ax = y, and we denote by yhat the vector Ax where x is the computed least squares solution, then the vector of residuals r is the vector whose ith component is r[i] = y[i] - yhat[i].
Public propertyResidualSumOfSquares
Gets the residual sum of squares. If y is the right-hand side of the least squares equation Ax = y, and we denote by yhat the vector Ax where x is the computed least squares solution, then the residual sum of squares is defined to be (y[0] - yhat[0])^2 + (y[1] - yhat[1])^2 + ... + (y[m-1] - yhat[m-1])^2.
Public propertyTolerance
Gets the tolerance used to compute the effective rank of the input matrix A.
Public propertyX
Gets the least squares solution x for the least squares problem Ax = y.
Public propertyYhat
Gets the predicted value of y by computing yHat = Ax, where x is the calculated solution to the least squares problem Ax = y.
Top
Methods
  NameDescription
Public methodClone
Creates a deep copy of this least squares.
Top
Remarks
In a least squares problem, we assume a linear model for a quantity y that depends on one or more independent variables a1, a2,...,an; that is, y = x0 + x1*a1 + ... + xn*an. x0 is called the intercept parameter.
The goal of a least squares problem is to solve for the best values of x0, x1,...,xn. Several observations of the independent values ai are recorded, along with the corresponding values of the dependent variable y. If m observations are performed, and for the ith observation we denote the values of the independent variables ai1, ai2,...ain and the corresponding dependent value of y as yi, then we form the linear system Ax = y, where A = (aij) and y = (yi). The least squares solution is the value of x that minimizes ||Ax - y||.
Note that if the model contains a non-zero intercept parameter, then the first column of A is all ones. Class DoubleComplexLeastSquares uses a complete orthogonal factorization of A to compute the solution. Matrix A is rectangular and may be rank deficient.
See Also