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in all decs, make the compute() methods return *this

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(implements feature request #18)
This commit is contained in:
Benoit Jacob 2009-08-15 23:12:39 -04:00
parent 65fe5f76fd
commit ee982709d3
9 changed files with 40 additions and 23 deletions
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7
Eigen/src/Cholesky/LDLT.h
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@ -123,7 +123,7 @@ template<typename MatrixType> class LDLT
template<typename Derived>
bool solveInPlace(MatrixBase<Derived> &bAndX) const;
void compute(const MatrixType& matrix);
LDLT& compute(const MatrixType& matrix);
protected:
/** \internal
@ -142,7 +142,7 @@ template<typename MatrixType> class LDLT
/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
*/
template<typename MatrixType>
void LDLT<MatrixType>::compute(const MatrixType& a)
LDLT<MatrixType>& LDLT<MatrixType>::compute(const MatrixType& a)
{
ei_assert(a.rows()==a.cols());
const int size = a.rows();
@ -154,7 +154,7 @@ void LDLT<MatrixType>::compute(const MatrixType& a)
m_transpositions.setZero();
m_sign = ei_real(a.coeff(0,0))>0 ? 1:-1;
m_isInitialized = true;
return;
return *this;
}
RealScalar cutoff = 0, biggest_in_corner;
@ -235,6 +235,7 @@ void LDLT<MatrixType>::compute(const MatrixType& a)
}
m_isInitialized = true;
return *this;
}
/** Computes the solution x of \f$ A x = b \f$ using the current decomposition of A.

8
Eigen/src/Cholesky/LLT.h
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@ -105,7 +105,7 @@ template<typename MatrixType, int _UpLo> class LLT
template<typename Derived>
bool solveInPlace(MatrixBase<Derived> &bAndX) const;
void compute(const MatrixType& matrix);
LLT& compute(const MatrixType& matrix);
protected:
/** \internal
@ -213,9 +213,12 @@ template<typename MatrixType> struct LLT_Traits<MatrixType,UpperTriangular>
};
/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
*
*
* \returns a reference to *this
*/
template<typename MatrixType, int _UpLo>
void LLT<MatrixType,_UpLo>::compute(const MatrixType& a)
LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const MatrixType& a)
{
assert(a.rows()==a.cols());
const int size = a.rows();
@ -223,6 +226,7 @@ void LLT<MatrixType,_UpLo>::compute(const MatrixType& a)
m_matrix = a;
m_isInitialized = Traits::inplace_decomposition(m_matrix);
return *this;
}
/** Computes the solution x of \f$ A x = b \f$ using the current decomposition of A.

7
Eigen/src/LU/LU.h
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@ -111,8 +111,10 @@ template<typename MatrixType> class LU
*
* \param matrix the matrix of which to compute the LU decomposition.
* It is required to be nonzero.
*
* \returns a reference to *this
*/
void compute(const MatrixType& matrix);
LU& compute(const MatrixType& matrix);
/** \returns the LU decomposition matrix: the upper-triangular part is U, the
* unit-lower-triangular part is L (at least for square matrices; in the non-square
@ -377,7 +379,7 @@ LU<MatrixType>::LU(const MatrixType& matrix)
}
template<typename MatrixType>
void LU<MatrixType>::compute(const MatrixType& matrix)
LU<MatrixType>& LU<MatrixType>::compute(const MatrixType& matrix)
{
m_originalMatrix = &matrix;
m_lu = matrix;
@ -447,6 +449,7 @@ void LU<MatrixType>::compute(const MatrixType& matrix)
std::swap(m_q.coeffRef(k), m_q.coeffRef(cols_transpositions.coeff(k)));
m_det_pq = (number_of_transpositions%2) ? -1 : 1;
return *this;
}
template<typename MatrixType>

5
Eigen/src/LU/PartialLU.h
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@ -87,7 +87,7 @@ template<typename MatrixType> class PartialLU
*/
PartialLU(const MatrixType& matrix);
void compute(const MatrixType& matrix);
PartialLU& compute(const MatrixType& matrix);
/** \returns the LU decomposition matrix: the upper-triangular part is U, the
* unit-lower-triangular part is L (at least for square matrices; in the non-square
@ -350,7 +350,7 @@ void ei_partial_lu_inplace(MatrixType& lu, IntVector& row_transpositions, int& n
}
template<typename MatrixType>
void PartialLU<MatrixType>::compute(const MatrixType& matrix)
PartialLU<MatrixType>& PartialLU<MatrixType>::compute(const MatrixType& matrix)
{
m_lu = matrix;
m_p.resize(matrix.rows());
@ -369,6 +369,7 @@ void PartialLU<MatrixType>::compute(const MatrixType& matrix)
std::swap(m_p.coeffRef(k), m_p.coeffRef(rows_transpositions.coeff(k)));
m_isInitialized = true;
return *this;
}
template<typename MatrixType>

5
Eigen/src/QR/EigenSolver.h
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@ -118,7 +118,7 @@ template<typename _MatrixType> class EigenSolver
return m_eivalues;
}
void compute(const MatrixType& matrix);
EigenSolver& compute(const MatrixType& matrix);
private:
@ -189,7 +189,7 @@ typename EigenSolver<MatrixType>::EigenvectorType EigenSolver<MatrixType>::eigen
}
template<typename MatrixType>
void EigenSolver<MatrixType>::compute(const MatrixType& matrix)
EigenSolver<MatrixType>& EigenSolver<MatrixType>::compute(const MatrixType& matrix)
{
assert(matrix.cols() == matrix.rows());
int n = matrix.cols();
@ -205,6 +205,7 @@ void EigenSolver<MatrixType>::compute(const MatrixType& matrix)
hqr2(matH);
m_isInitialized = true;
return *this;
}
// Nonsymmetric reduction to Hessenberg form.

5
Eigen/src/QR/QR.h
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@ -101,7 +101,7 @@ template<typename MatrixType> class HouseholderQR
*/
const MatrixType& matrixQR() const { return m_qr; }
void compute(const MatrixType& matrix);
HouseholderQR& compute(const MatrixType& matrix);
protected:
MatrixType m_qr;
@ -112,7 +112,7 @@ template<typename MatrixType> class HouseholderQR
#ifndef EIGEN_HIDE_HEAVY_CODE
template<typename MatrixType>
void HouseholderQR<MatrixType>::compute(const MatrixType& matrix)
HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const MatrixType& matrix)
{
m_qr = matrix;
m_hCoeffs.resize(matrix.cols());
@ -175,6 +175,7 @@ void HouseholderQR<MatrixType>::compute(const MatrixType& matrix)
}
}
m_isInitialized = true;
return *this;
}
template<typename MatrixType>

12
Eigen/src/QR/SelfAdjointEigenSolver.h
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@ -90,9 +90,9 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
compute(matA, matB, computeEigenvectors);
}
void compute(const MatrixType& matrix, bool computeEigenvectors = true);
SelfAdjointEigenSolver& compute(const MatrixType& matrix, bool computeEigenvectors = true);
void compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true);
SelfAdjointEigenSolver& compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true);
/** \returns the computed eigen vectors as a matrix of column vectors */
MatrixType eigenvectors(void) const
@ -182,7 +182,7 @@ static void ei_tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, int st
* \sa SelfAdjointEigenSolver(MatrixType,bool), compute(MatrixType,MatrixType,bool)
*/
template<typename MatrixType>
void SelfAdjointEigenSolver<MatrixType>::compute(const MatrixType& matrix, bool computeEigenvectors)
SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::compute(const MatrixType& matrix, bool computeEigenvectors)
{
#ifndef NDEBUG
m_eigenvectorsOk = computeEigenvectors;
@ -195,7 +195,7 @@ void SelfAdjointEigenSolver<MatrixType>::compute(const MatrixType& matrix, bool
{
m_eivalues.coeffRef(0,0) = ei_real(matrix.coeff(0,0));
m_eivec.setOnes();
return;
return *this;
}
m_eivec = matrix;
@ -240,6 +240,7 @@ void SelfAdjointEigenSolver<MatrixType>::compute(const MatrixType& matrix, bool
m_eivec.col(i).swap(m_eivec.col(k+i));
}
}
return *this;
}
/** Computes the eigenvalues of the generalized eigen problem
@ -250,7 +251,7 @@ void SelfAdjointEigenSolver<MatrixType>::compute(const MatrixType& matrix, bool
* \sa SelfAdjointEigenSolver(MatrixType,MatrixType,bool), compute(MatrixType,bool)
*/
template<typename MatrixType>
void SelfAdjointEigenSolver<MatrixType>::
SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::
compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors)
{
ei_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows());
@ -282,6 +283,7 @@ compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors
for (int i=0; i<m_eivec.cols(); ++i)
m_eivec.col(i) = m_eivec.col(i).normalized();
}
return *this;
}
#endif // EIGEN_HIDE_HEAVY_CODE

5
Eigen/src/SVD/JacobiSquareSVD.h
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@ -67,7 +67,7 @@ template<typename MatrixType, bool ComputeU, bool ComputeV> class JacobiSquareSV
compute(matrix);
}
void compute(const MatrixType& matrix);
JacobiSquareSVD& compute(const MatrixType& matrix);
const MatrixUType& matrixU() const
{
@ -95,7 +95,7 @@ template<typename MatrixType, bool ComputeU, bool ComputeV> class JacobiSquareSV
};
template<typename MatrixType, bool ComputeU, bool ComputeV>
void JacobiSquareSVD<MatrixType, ComputeU, ComputeV>::compute(const MatrixType& matrix)
JacobiSquareSVD<MatrixType, ComputeU, ComputeV>& JacobiSquareSVD<MatrixType, ComputeU, ComputeV>::compute(const MatrixType& matrix)
{
MatrixType work_matrix(matrix);
int size = matrix.rows();
@ -164,5 +164,6 @@ sweep_again:
}
m_isInitialized = true;
return *this;
}
#endif // EIGEN_JACOBISQUARESVD_H

9
Eigen/src/SVD/SVD.h
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@ -97,7 +97,7 @@ template<typename MatrixType> class SVD
return m_matV;
}
void compute(const MatrixType& matrix);
SVD& compute(const MatrixType& matrix);
template<typename UnitaryType, typename PositiveType>
void computeUnitaryPositive(UnitaryType *unitary, PositiveType *positive) const;
@ -138,9 +138,11 @@ template<typename MatrixType> class SVD
/** Computes / recomputes the SVD decomposition A = U S V^* of \a matrix
*
* \note this code has been adapted from Numerical Recipes, third edition.
*
* \returns a reference to *this
*/
template<typename MatrixType>
void SVD<MatrixType>::compute(const MatrixType& matrix)
SVD<MatrixType>& SVD<MatrixType>::compute(const MatrixType& matrix)
{
const int m = matrix.rows();
const int n = matrix.cols();
@ -157,7 +159,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
SingularValuesType& W = m_sigma;
bool flag;
int i,its,j,jj,k,l,nm;
int i,its,j,k,l,nm;
Scalar anorm, c, f, g, h, s, scale, x, y, z;
bool convergence = true;
Scalar eps = precision<Scalar>();
@ -392,6 +394,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
m_matU = A.block(0,0,m,m);
m_isInitialized = true;
return *this;
}
/** \returns the solution of \f$ A x = b \f$ using the current SVD decomposition of A.