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Another big refactoring change:

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* add a new Eigen2Support module including Cwise, Flagged, and some other deprecated stuff
* add a few cwiseXxx functions
* adapt a few modules to use cwiseXxx instead of the .cwise() prefix
This commit is contained in:
Gael Guennebaud
2009-11-18 18:15:19 +01:00
parent 0529ecfe1b
commit e3d890bc5a
48 changed files with 634 additions and 408 deletions
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2
Eigen/src/Eigenvalues/ComplexEigenSolver.h
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@@ -133,7 +133,7 @@ void ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix)
for (int i=0; i<n; i++)
{
int k;
m_eivalues.cwise().abs().end(n-i).minCoeff(&k);
m_eivalues.cwiseAbs().end(n-i).minCoeff(&k);
if (k != 0)
{
k += i;

2
Eigen/src/Eigenvalues/ComplexSchur.h
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@@ -204,7 +204,7 @@ void ComplexSchur<MatrixType>::compute(const MatrixType& matrix, bool skipU)
// compute the shift (the normalization by sf is to avoid under/overflow)
Matrix<Scalar,2,2> t = m_matT.template block<2,2>(iu-1,iu-1);
sf = t.cwise().abs().sum();
sf = t.cwiseAbs().sum();
t /= sf;
c = t.determinant();

6
Eigen/src/Eigenvalues/EigenSolver.h
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@@ -225,7 +225,7 @@ void EigenSolver<MatrixType>::orthes(MatrixType& matH, RealVectorType& ort)
for (int m = low+1; m <= high-1; ++m)
{
// Scale column.
RealScalar scale = matH.block(m, m-1, high-m+1, 1).cwise().abs().sum();
RealScalar scale = matH.block(m, m-1, high-m+1, 1).cwiseAbs().sum();
if (scale != 0.0)
{
// Compute Householder transformation.
@@ -312,7 +312,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
// Store roots isolated by balanc and compute matrix norm
// FIXME to be efficient the following would requires a triangular reduxion code
// Scalar norm = matH.upper().cwise().abs().sum() + matH.corner(BottomLeft,n,n).diagonal().cwise().abs().sum();
// Scalar norm = matH.upper().cwiseAbs().sum() + matH.corner(BottomLeft,n,n).diagonal().cwiseAbs().sum();
Scalar norm = 0.0;
for (int j = 0; j < nn; ++j)
{
@@ -321,7 +321,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
{
m_eivalues.coeffRef(j) = Complex(matH.coeff(j,j), 0.0);
}
norm += matH.row(j).segment(std::max(j-1,0), nn-std::max(j-1,0)).cwise().abs().sum();
norm += matH.row(j).segment(std::max(j-1,0), nn-std::max(j-1,0)).cwiseAbs().sum();
}
// Outer loop over eigenvalue index

6
Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
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@@ -112,7 +112,7 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
*/
MatrixType operatorSqrt() const
{
return m_eivec * m_eivalues.cwise().sqrt().asDiagonal() * m_eivec.adjoint();
return m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint();
}
/** \returns the positive inverse square root of the matrix
@@ -121,7 +121,7 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
*/
MatrixType operatorInverseSqrt() const
{
return m_eivec * m_eivalues.cwise().inverse().cwise().sqrt().asDiagonal() * m_eivec.adjoint();
return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();
}
@@ -287,7 +287,7 @@ struct ei_operatorNorm_selector
{
// FIXME if it is really guaranteed that the eigenvalues are already sorted,
// then we don't need to compute a maxCoeff() here, comparing the 1st and last ones is enough.
return m.eigenvalues().cwise().abs().maxCoeff();
return m.eigenvalues().cwiseAbs().maxCoeff();
}
};