devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Make the SVD's output unitary and improved unit tests.

Browse Source
This commit is contained in:
Hauke Heibel
2010-09-24 16:28:20 +02:00
parent 1c54514bfc
commit 053261de88
2 changed files with 19 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
test/svd.cpp
View File

@@ -47,9 +47,9 @@ template<typename MatrixType> void svd(const MatrixType& m)
sigma.diagonal() = svd.singularValues();
matU = svd.matrixU();
//VERIFY_IS_UNITARY(matU);
VERIFY_IS_UNITARY(matU);
matV = svd.matrixV();
//VERIFY_IS_UNITARY(matV);
VERIFY_IS_UNITARY(matV);
VERIFY_IS_APPROX(a, matU * sigma * matV.transpose());
}
@@ -57,10 +57,10 @@ template<typename MatrixType> void svd(const MatrixType& m)
if (rows>=cols)
{
SVD<MatrixType> svd(a);
Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> x = Matrix<Scalar, MatrixType::ColsAtCompileTime, 1>::Random(cols,1);
Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> b = a * x;
Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> result = svd.solve(b);
VERIFY_IS_APPROX(a * result, b);
Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> b = Matrix<Scalar, MatrixType::ColsAtCompileTime, 1>::Random(rows,1);
Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> x = svd.solve(b);
// evaluate normal equation which works also for least-squares solutions
VERIFY_IS_APPROX(a.adjoint()*a*x,a.adjoint()*b);
}