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the Index types change.

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As discussed on the list (too long to explain here).
This commit is contained in:
Benoit Jacob
2010-05-30 16:00:58 -04:00
parent faa3ff3be6
commit aaaade4b3d
158 changed files with 3137 additions and 2878 deletions
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27
Eigen/src/SVD/SVD.h
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@@ -46,6 +46,7 @@ template<typename _MatrixType> class SVD
typedef _MatrixType MatrixType;
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Index Index;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
@@ -79,7 +80,7 @@ template<typename _MatrixType> class SVD
* according to the specified problem \a size.
* \sa JacobiSVD()
*/
SVD(int rows, int cols) : m_matU(rows, rows),
SVD(Index rows, Index cols) : m_matU(rows, rows),
m_matV(cols,cols),
m_sigma(std::min(rows, cols)),
m_workMatrix(rows, cols),
@@ -143,13 +144,13 @@ template<typename _MatrixType> class SVD
template<typename ScalingType, typename RotationType>
void computeScalingRotation(ScalingType *positive, RotationType *unitary) const;
inline int rows() const
inline Index rows() const
{
ei_assert(m_isInitialized && "SVD is not initialized.");
return m_rows;
}
inline int cols() const
inline Index cols() const
{
ei_assert(m_isInitialized && "SVD is not initialized.");
return m_cols;
@@ -182,7 +183,7 @@ template<typename _MatrixType> class SVD
MatrixType m_workMatrix;
RowVector m_rv1;
bool m_isInitialized;
int m_rows, m_cols;
Index m_rows, m_cols;
};
/** Computes / recomputes the SVD decomposition A = U S V^* of \a matrix
@@ -194,8 +195,8 @@ template<typename _MatrixType> class SVD
template<typename MatrixType>
SVD<MatrixType>& SVD<MatrixType>::compute(const MatrixType& matrix)
{
const int m = m_rows = matrix.rows();
const int n = m_cols = matrix.cols();
const Index m = m_rows = matrix.rows();
const Index n = m_cols = matrix.cols();
m_matU.resize(m, m);
m_matU.setZero();
@@ -203,14 +204,14 @@ SVD<MatrixType>& SVD<MatrixType>::compute(const MatrixType& matrix)
m_matV.resize(n,n);
m_workMatrix = matrix;
int max_iters = 30;
Index max_iters = 30;
MatrixVType& V = m_matV;
MatrixType& A = m_workMatrix;
SingularValuesType& W = m_sigma;
bool flag;
int i=0,its=0,j=0,k=0,l=0,nm=0;
Index i=0,its=0,j=0,k=0,l=0,nm=0;
Scalar anorm, c, f, g, h, s, scale, x, y, z;
bool convergence = true;
Scalar eps = NumTraits<Scalar>::dummy_precision();
@@ -426,9 +427,9 @@ SVD<MatrixType>& SVD<MatrixType>::compute(const MatrixType& matrix)
// sort the singular values:
{
for (int i=0; i<n; i++)
for (Index i=0; i<n; i++)
{
int k;
Index k;
W.tail(n-i).maxCoeff(&k);
if (k != 0)
{
@@ -459,11 +460,11 @@ struct ei_solve_retval<SVD<_MatrixType>, Rhs>
{
ei_assert(rhs().rows() == dec().rows());
for (int j=0; j<cols(); ++j)
for (Index j=0; j<cols(); ++j)
{
Matrix<Scalar,MatrixType::RowsAtCompileTime,1> aux = dec().matrixU().adjoint() * rhs().col(j);
for (int i = 0; i < dec().rows(); ++i)
for (Index i = 0; i < dec().rows(); ++i)
{
Scalar si = dec().singularValues().coeff(i);
if(si == RealScalar(0))
@@ -471,7 +472,7 @@ struct ei_solve_retval<SVD<_MatrixType>, Rhs>
else
aux.coeffRef(i) /= si;
}
const int minsize = std::min(dec().rows(),dec().cols());
const Index minsize = std::min(dec().rows(),dec().cols());
dst.col(j).head(minsize) = aux.head(minsize);
if(dec().cols()>dec().rows()) dst.col(j).tail(cols()-minsize).setZero();
dst.col(j) = dec().matrixV() * dst.col(j);