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Fix: Correct Lapacke bindings for BDCSVD and JacobiSVD to match the updated API
This commit is contained in:
Damiano Franzò
parent
434a2fc4a4
commit
f3e7d64f3d
@ -182,7 +182,9 @@ class BDCSVD : public SVDBase<BDCSVD<MatrixType_, Options_> > {
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* \deprecated Will be removed in the next major Eigen version. Options should
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* be specified in the \a Options template parameter.
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*/
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EIGEN_DEPRECATED BDCSVD(const MatrixType& matrix, unsigned int computationOptions) : m_algoswap(16), m_numIters(0) {
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template <typename Derived>
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EIGEN_DEPRECATED BDCSVD(const MatrixBase<Derived>& matrix, unsigned int computationOptions)
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: m_algoswap(16), m_numIters(0) {
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internal::check_svd_options_assertions<MatrixType, Options>(computationOptions, matrix.rows(), matrix.cols());
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compute_impl(matrix, computationOptions);
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}
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@ -58,7 +58,8 @@ class BDCSVD_LAPACKE : public BDCSVD<MatrixType_, Options> {
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// construct this by moving from a parent object
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BDCSVD_LAPACKE(SVD&& svd) : SVD(std::move(svd)) {}
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void compute_impl_lapacke(const MatrixType& matrix, unsigned int computationOptions) {
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template <typename Derived>
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void compute_impl_lapacke(const MatrixBase<Derived>& matrix, unsigned int computationOptions) {
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SVD::allocate(matrix.rows(), matrix.cols(), computationOptions);
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SVD::m_nonzeroSingularValues = SVD::m_diagSize;
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@ -120,8 +121,8 @@ class BDCSVD_LAPACKE : public BDCSVD<MatrixType_, Options> {
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}
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};
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template <typename MatrixType_, int Options>
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BDCSVD<MatrixType_, Options>& BDCSVD_wrapper(BDCSVD<MatrixType_, Options>& svd, const MatrixType_& matrix,
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template <typename MatrixType_, int Options, typename Derived>
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BDCSVD<MatrixType_, Options>& BDCSVD_wrapper(BDCSVD<MatrixType_, Options>& svd, const MatrixBase<Derived>& matrix,
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int computationOptions) {
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// we need to move to the wrapper type and back
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BDCSVD_LAPACKE<MatrixType_, Options> tmpSvd(std::move(svd));
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@ -134,12 +135,13 @@ BDCSVD<MatrixType_, Options>& BDCSVD_wrapper(BDCSVD<MatrixType_, Options>& svd,
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} // end namespace internal
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#define EIGEN_LAPACKE_SDD(EIGTYPE, EIGCOLROW, OPTIONS) \
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template <> \
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inline BDCSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>& \
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BDCSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>::compute_impl( \
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const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) { \
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return internal::lapacke_helpers::BDCSVD_wrapper(*this, matrix, computationOptions); \
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#define EIGEN_LAPACKE_SDD(EIGTYPE, EIGCOLROW, OPTIONS) \
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template <> \
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template <typename Derived> \
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inline BDCSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>& \
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BDCSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>::compute_impl( \
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const MatrixBase<Derived>& matrix, unsigned int computationOptions) { \
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return internal::lapacke_helpers::BDCSVD_wrapper(*this, matrix, computationOptions); \
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}
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#define EIGEN_LAPACK_SDD_OPTIONS(OPTIONS) \
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@ -40,65 +40,65 @@ namespace Eigen {
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/** \internal Specialization for the data types supported by LAPACKe */
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#define EIGEN_LAPACKE_SVD(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_PREFIX, EIGCOLROW, LAPACKE_COLROW, OPTIONS) \
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template <> \
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inline JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>& \
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JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>::compute_impl( \
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const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) { \
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typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \
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/*typedef MatrixType::Scalar Scalar;*/ \
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/*typedef MatrixType::RealScalar RealScalar;*/ \
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allocate(matrix.rows(), matrix.cols(), computationOptions); \
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\
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/*const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();*/ \
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m_nonzeroSingularValues = diagSize(); \
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\
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lapack_int lda = internal::convert_index<lapack_int>(matrix.outerStride()), ldu, ldvt; \
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lapack_int matrix_order = LAPACKE_COLROW; \
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char jobu, jobvt; \
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LAPACKE_TYPE *u, *vt, dummy; \
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jobu = (m_computeFullU) ? 'A' : (m_computeThinU) ? 'S' : 'N'; \
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jobvt = (m_computeFullV) ? 'A' : (m_computeThinV) ? 'S' : 'N'; \
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if (computeU()) { \
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ldu = internal::convert_index<lapack_int>(m_matrixU.outerStride()); \
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u = (LAPACKE_TYPE*)m_matrixU.data(); \
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} else { \
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ldu = 1; \
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u = &dummy; \
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} \
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MatrixType localV; \
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lapack_int vt_rows = (m_computeFullV) ? internal::convert_index<lapack_int>(cols()) \
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: (m_computeThinV) ? internal::convert_index<lapack_int>(diagSize()) \
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: 1; \
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if (computeV()) { \
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localV.resize(vt_rows, cols()); \
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ldvt = internal::convert_index<lapack_int>(localV.outerStride()); \
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vt = (LAPACKE_TYPE*)localV.data(); \
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} else { \
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ldvt = 1; \
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vt = &dummy; \
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} \
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Matrix<LAPACKE_RTYPE, Dynamic, Dynamic> superb; \
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superb.resize(diagSize(), 1); \
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MatrixType m_temp; \
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m_temp = matrix; \
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lapack_int info = LAPACKE_##LAPACKE_PREFIX##gesvd( \
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matrix_order, jobu, jobvt, internal::convert_index<lapack_int>(rows()), \
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internal::convert_index<lapack_int>(cols()), (LAPACKE_TYPE*)m_temp.data(), lda, \
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(LAPACKE_RTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data()); \
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/* Check the result of the LAPACK call */ \
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if (info < 0 || !m_singularValues.allFinite()) { \
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m_info = InvalidInput; \
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} else if (info > 0) { \
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m_info = NoConvergence; \
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} else { \
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m_info = Success; \
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if (computeV()) m_matrixV = localV.adjoint(); \
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} \
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/* for(int i=0;i<diagSize();i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--; \
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* m_singularValues.coeffRef(i)=RealScalar(0);}*/ \
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m_isInitialized = true; \
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return *this; \
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#define EIGEN_LAPACKE_SVD(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_PREFIX, EIGCOLROW, LAPACKE_COLROW, OPTIONS) \
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template <> \
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template <typename Derived> \
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inline JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>& \
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JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>::compute_impl( \
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const MatrixBase<Derived>& matrix, unsigned int computationOptions) { \
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/*typedef MatrixType::Scalar Scalar;*/ \
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/*typedef MatrixType::RealScalar RealScalar;*/ \
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allocate(matrix.rows(), matrix.cols(), computationOptions); \
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\
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/*const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();*/ \
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m_nonzeroSingularValues = diagSize(); \
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\
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lapack_int lda = internal::convert_index<lapack_int>(matrix.outerStride()), ldu, ldvt; \
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lapack_int matrix_order = LAPACKE_COLROW; \
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char jobu, jobvt; \
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LAPACKE_TYPE *u, *vt, dummy; \
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jobu = (m_computeFullU) ? 'A' : (m_computeThinU) ? 'S' : 'N'; \
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jobvt = (m_computeFullV) ? 'A' : (m_computeThinV) ? 'S' : 'N'; \
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if (computeU()) { \
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ldu = internal::convert_index<lapack_int>(m_matrixU.outerStride()); \
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u = (LAPACKE_TYPE*)m_matrixU.data(); \
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} else { \
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ldu = 1; \
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u = &dummy; \
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} \
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MatrixType localV; \
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lapack_int vt_rows = (m_computeFullV) ? internal::convert_index<lapack_int>(cols()) \
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: (m_computeThinV) ? internal::convert_index<lapack_int>(diagSize()) \
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: 1; \
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if (computeV()) { \
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localV.resize(vt_rows, cols()); \
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ldvt = internal::convert_index<lapack_int>(localV.outerStride()); \
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vt = (LAPACKE_TYPE*)localV.data(); \
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} else { \
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ldvt = 1; \
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vt = &dummy; \
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} \
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Matrix<LAPACKE_RTYPE, Dynamic, Dynamic> superb; \
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superb.resize(diagSize(), 1); \
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MatrixType m_temp; \
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m_temp = matrix; \
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lapack_int info = LAPACKE_##LAPACKE_PREFIX##gesvd( \
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matrix_order, jobu, jobvt, internal::convert_index<lapack_int>(rows()), \
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internal::convert_index<lapack_int>(cols()), (LAPACKE_TYPE*)m_temp.data(), lda, \
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(LAPACKE_RTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data()); \
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/* Check the result of the LAPACK call */ \
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if (info < 0 || !m_singularValues.allFinite()) { \
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m_info = InvalidInput; \
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} else if (info > 0) { \
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m_info = NoConvergence; \
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} else { \
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m_info = Success; \
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if (computeV()) m_matrixV = localV.adjoint(); \
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} \
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/* for(int i=0;i<diagSize();i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--; \
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* m_singularValues.coeffRef(i)=RealScalar(0);}*/ \
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m_isInitialized = true; \
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return *this; \
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}
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#define EIGEN_LAPACK_SVD_OPTIONS(OPTIONS) \
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