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* add a HouseholderSequence class (not good enough yet for Triadiagonalization and HessenbergDecomposition)

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* rework a bit AnyMatrixBase, and mobe it to a separate file
This commit is contained in:
Gael Guennebaud
2009-09-16 14:35:42 +02:00
parent 77f858f6ab
commit 49dd5d7847
12 changed files with 339 additions and 103 deletions
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8
test/jacobisvd.cpp
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@@ -36,14 +36,14 @@ template<typename MatrixType, unsigned int Options> void svd(const MatrixType& m
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime> MatrixUType;
typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime> MatrixVType;
typedef Matrix<Scalar, RowsAtCompileTime, 1> ColVectorType;
typedef Matrix<Scalar, ColsAtCompileTime, 1> InputVectorType;
MatrixType a;
if(pickrandom) a = MatrixType::Random(rows,cols);
else a = m;
@@ -53,7 +53,7 @@ template<typename MatrixType, unsigned int Options> void svd(const MatrixType& m
sigma.diagonal() = svd.singularValues().template cast<Scalar>();
MatrixUType u = svd.matrixU();
MatrixVType v = svd.matrixV();
VERIFY_IS_APPROX(a, u * sigma * v.adjoint());
VERIFY_IS_UNITARY(u);
VERIFY_IS_UNITARY(v);
@@ -98,7 +98,7 @@ void test_jacobisvd()
}
CALL_SUBTEST(( svd<MatrixXf,0>(MatrixXf(300,200)) ));
CALL_SUBTEST(( svd<MatrixXcd,AtLeastAsManyColsAsRows>(MatrixXcd(100,150)) ));
CALL_SUBTEST(( svd_verify_assert<Matrix3f>() ));
CALL_SUBTEST(( svd_verify_assert<Matrix3d>() ));
CALL_SUBTEST(( svd_verify_assert<MatrixXf>() ));