public void LeastSquaresSVD_ShouldSucceed(SVDType sdvType)
{
var A = new MatrixXD("-1 -0.0827; -0.737 0.0655; 0.511 -0.562 ");
var rhs = new VectorXD("-0.906 0.358 0.359");
VectorXD result = A.LeastSquaresSVD(rhs, sdvType);
Assert.Equal(new VectorXD("0.46347421844577846 0.04209165616389611"), result);
}
/// <summary>
///
/// </summary>
/// <param name="rhs"></param>
public VectorXD LeastSquaresSVD(VectorXD rhs, SVDType svdType = SVDType.Jacobi)
{
double[] vout = new double[Cols];
if (svdType == SVDType.Jacobi)
{
EigenDenseUtilities.SVDLeastSquares(GetValues(), Rows, Cols, rhs.GetValues(), vout);
}
else
{
EigenDenseUtilities.SVDLeastSquaresBdcSvd(GetValues(), Rows, Cols, rhs.GetValues(), vout);
}
return(new VectorXD(vout));
}
public void SVD_ShouldSucceed(SVDType sdvType)
{
var A = new MatrixXD("3 2 2 ; 2 3 -2");
SVDResult result = A.SVD(sdvType);
Assert.Equal(new MatrixXD("-0.7071067811865476 0.7071067811865475 ;" +
" -0.7071067811865475 -0.7071067811865476"), result.U);
Assert.Equal(new[] { 5.0, 3.0 }, result.S.GetValues().ToArray());
Assert.Equal(new MatrixXD("-0.7071067811865477 0.23570226039551567; " +
"-0.7071067811865475 -0.23570226039551567; " +
"-2.220446049250313E-16 0.94280904158206336"),
result.V);
}
public SVDResult SVD(SVDType svdType = SVDType.Jacobi)
{
int minRowsCols = Cols < Rows ? Cols : Rows;
double[] uout = new double[Rows * minRowsCols];
double[] sout = new double[minRowsCols];
double[] vout = new double[Cols * minRowsCols];
if (svdType == SVDType.Jacobi)
{
EigenDenseUtilities.SVD(GetValues(), Rows, Cols, uout, sout, vout);
}
else
{
EigenDenseUtilities.SVDBdcSvd(GetValues(), Rows, Cols, uout, sout, vout);
}
return(new SVDResult(new MatrixXD(uout, Rows, minRowsCols),
new VectorXD(sout),
new MatrixXD(vout, Cols, minRowsCols)));
}
