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Apply clang-format to lapack/blas directories
This commit is contained in:
Antonio Sanchez
committed by
Antonio Sánchez
Antonio Sánchez
parent
4eac211e96
commit
186f8205db
203
lapack/svd.inc
203
lapack/svd.inc
@@ -11,128 +11,135 @@
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#include <Eigen/SVD>
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// computes the singular values/vectors a general M-by-N matrix A using divide-and-conquer
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EIGEN_LAPACK_FUNC(gesdd,(char *jobz, int *m, int* n, Scalar* a, int *lda, RealScalar *s, Scalar *u, int *ldu, Scalar *vt, int *ldvt, Scalar* /*work*/, int* lwork,
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EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int * /*iwork*/, int *info))
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{
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EIGEN_LAPACK_FUNC(gesdd, (char *jobz, int *m, int *n, Scalar *a, int *lda, RealScalar *s, Scalar *u, int *ldu,
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Scalar *vt, int *ldvt, Scalar * /*work*/, int *lwork,
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EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar * /*rwork*/) int * /*iwork*/, int *info)) {
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// TODO exploit the work buffer
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bool query_size = *lwork==-1;
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int diag_size = (std::min)(*m,*n);
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bool query_size = *lwork == -1;
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int diag_size = (std::min)(*m, *n);
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*info = 0;
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if(*jobz!='A' && *jobz!='S' && *jobz!='O' && *jobz!='N') *info = -1;
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else if(*m<0) *info = -2;
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else if(*n<0) *info = -3;
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else if(*lda<std::max(1,*m)) *info = -5;
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else if(*lda<std::max(1,*m)) *info = -8;
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else if(*ldu <1 || (*jobz=='A' && *ldu <*m)
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|| (*jobz=='O' && *m<*n && *ldu<*m)) *info = -8;
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else if(*ldvt<1 || (*jobz=='A' && *ldvt<*n)
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|| (*jobz=='S' && *ldvt<diag_size)
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|| (*jobz=='O' && *m>=*n && *ldvt<*n)) *info = -10;
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if(*info!=0)
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{
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if (*jobz != 'A' && *jobz != 'S' && *jobz != 'O' && *jobz != 'N')
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*info = -1;
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else if (*m < 0)
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*info = -2;
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else if (*n < 0)
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*info = -3;
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else if (*lda < std::max(1, *m))
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*info = -5;
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else if (*lda < std::max(1, *m))
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*info = -8;
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else if (*ldu < 1 || (*jobz == 'A' && *ldu < *m) || (*jobz == 'O' && *m < *n && *ldu < *m))
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*info = -8;
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else if (*ldvt < 1 || (*jobz == 'A' && *ldvt < *n) || (*jobz == 'S' && *ldvt < diag_size) ||
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(*jobz == 'O' && *m >= *n && *ldvt < *n))
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*info = -10;
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if (*info != 0) {
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int e = -*info;
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return xerbla_(SCALAR_SUFFIX_UP"GESDD ", &e, 6);
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return xerbla_(SCALAR_SUFFIX_UP "GESDD ", &e, 6);
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}
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if(query_size)
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{
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if (query_size) {
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*lwork = 0;
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return 0;
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}
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if(*n==0 || *m==0)
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return 0;
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PlainMatrixType mat(*m,*n);
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mat = matrix(a,*m,*n,*lda);
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int option = *jobz=='A' ? ComputeFullU|ComputeFullV
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: *jobz=='S' ? ComputeThinU|ComputeThinV
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: *jobz=='O' ? ComputeThinU|ComputeThinV
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: 0;
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BDCSVD<PlainMatrixType> svd(mat,option);
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make_vector(s,diag_size) = svd.singularValues().head(diag_size);
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if (*n == 0 || *m == 0) return 0;
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if(*jobz=='A')
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{
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matrix(u,*m,*m,*ldu) = svd.matrixU();
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matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
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PlainMatrixType mat(*m, *n);
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mat = matrix(a, *m, *n, *lda);
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int option = *jobz == 'A' ? ComputeFullU | ComputeFullV
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: *jobz == 'S' ? ComputeThinU | ComputeThinV
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: *jobz == 'O' ? ComputeThinU | ComputeThinV
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: 0;
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BDCSVD<PlainMatrixType> svd(mat, option);
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make_vector(s, diag_size) = svd.singularValues().head(diag_size);
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if (*jobz == 'A') {
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matrix(u, *m, *m, *ldu) = svd.matrixU();
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matrix(vt, *n, *n, *ldvt) = svd.matrixV().adjoint();
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} else if (*jobz == 'S') {
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matrix(u, *m, diag_size, *ldu) = svd.matrixU();
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matrix(vt, diag_size, *n, *ldvt) = svd.matrixV().adjoint();
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} else if (*jobz == 'O' && *m >= *n) {
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matrix(a, *m, *n, *lda) = svd.matrixU();
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matrix(vt, *n, *n, *ldvt) = svd.matrixV().adjoint();
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} else if (*jobz == 'O') {
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matrix(u, *m, *m, *ldu) = svd.matrixU();
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matrix(a, diag_size, *n, *lda) = svd.matrixV().adjoint();
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}
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else if(*jobz=='S')
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{
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matrix(u,*m,diag_size,*ldu) = svd.matrixU();
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matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint();
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}
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else if(*jobz=='O' && *m>=*n)
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{
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matrix(a,*m,*n,*lda) = svd.matrixU();
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matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
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}
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else if(*jobz=='O')
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{
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matrix(u,*m,*m,*ldu) = svd.matrixU();
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matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint();
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}
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return 0;
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}
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// computes the singular values/vectors a general M-by-N matrix A using two sided jacobi algorithm
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EIGEN_LAPACK_FUNC(gesvd,(char *jobu, char *jobv, int *m, int* n, Scalar* a, int *lda, RealScalar *s, Scalar *u, int *ldu, Scalar *vt, int *ldvt, Scalar* /*work*/, int* lwork,
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EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int *info))
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{
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EIGEN_LAPACK_FUNC(gesvd, (char *jobu, char *jobv, int *m, int *n, Scalar *a, int *lda, RealScalar *s, Scalar *u,
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int *ldu, Scalar *vt, int *ldvt, Scalar * /*work*/, int *lwork,
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EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar * /*rwork*/) int *info)) {
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// TODO exploit the work buffer
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bool query_size = *lwork==-1;
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int diag_size = (std::min)(*m,*n);
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bool query_size = *lwork == -1;
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int diag_size = (std::min)(*m, *n);
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*info = 0;
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if( *jobu!='A' && *jobu!='S' && *jobu!='O' && *jobu!='N') *info = -1;
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else if((*jobv!='A' && *jobv!='S' && *jobv!='O' && *jobv!='N')
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|| (*jobu=='O' && *jobv=='O')) *info = -2;
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else if(*m<0) *info = -3;
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else if(*n<0) *info = -4;
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else if(*lda<std::max(1,*m)) *info = -6;
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else if(*ldu <1 || ((*jobu=='A' || *jobu=='S') && *ldu<*m)) *info = -9;
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else if(*ldvt<1 || (*jobv=='A' && *ldvt<*n)
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|| (*jobv=='S' && *ldvt<diag_size)) *info = -11;
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if(*info!=0)
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{
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if (*jobu != 'A' && *jobu != 'S' && *jobu != 'O' && *jobu != 'N')
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*info = -1;
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else if ((*jobv != 'A' && *jobv != 'S' && *jobv != 'O' && *jobv != 'N') || (*jobu == 'O' && *jobv == 'O'))
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*info = -2;
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else if (*m < 0)
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*info = -3;
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else if (*n < 0)
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*info = -4;
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else if (*lda < std::max(1, *m))
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*info = -6;
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else if (*ldu < 1 || ((*jobu == 'A' || *jobu == 'S') && *ldu < *m))
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*info = -9;
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else if (*ldvt < 1 || (*jobv == 'A' && *ldvt < *n) || (*jobv == 'S' && *ldvt < diag_size))
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*info = -11;
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if (*info != 0) {
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int e = -*info;
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return xerbla_(SCALAR_SUFFIX_UP"GESVD ", &e, 6);
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return xerbla_(SCALAR_SUFFIX_UP "GESVD ", &e, 6);
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}
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if(query_size)
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{
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if (query_size) {
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*lwork = 0;
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return 0;
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}
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if(*n==0 || *m==0)
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return 0;
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PlainMatrixType mat(*m,*n);
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mat = matrix(a,*m,*n,*lda);
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int option = (*jobu=='A' ? ComputeFullU : *jobu=='S' || *jobu=='O' ? ComputeThinU : 0)
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| (*jobv=='A' ? ComputeFullV : *jobv=='S' || *jobv=='O' ? ComputeThinV : 0);
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JacobiSVD<PlainMatrixType> svd(mat,option);
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make_vector(s,diag_size) = svd.singularValues().head(diag_size);
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if (*n == 0 || *m == 0) return 0;
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PlainMatrixType mat(*m, *n);
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mat = matrix(a, *m, *n, *lda);
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int option = (*jobu == 'A' ? ComputeFullU
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: *jobu == 'S' || *jobu == 'O' ? ComputeThinU
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: 0) |
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(*jobv == 'A' ? ComputeFullV
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: *jobv == 'S' || *jobv == 'O' ? ComputeThinV
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: 0);
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JacobiSVD<PlainMatrixType> svd(mat, option);
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make_vector(s, diag_size) = svd.singularValues().head(diag_size);
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{
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if(*jobu=='A') matrix(u,*m,*m,*ldu) = svd.matrixU();
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else if(*jobu=='S') matrix(u,*m,diag_size,*ldu) = svd.matrixU();
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else if(*jobu=='O') matrix(a,*m,diag_size,*lda) = svd.matrixU();
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if (*jobu == 'A')
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matrix(u, *m, *m, *ldu) = svd.matrixU();
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else if (*jobu == 'S')
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matrix(u, *m, diag_size, *ldu) = svd.matrixU();
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else if (*jobu == 'O')
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matrix(a, *m, diag_size, *lda) = svd.matrixU();
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}
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{
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if(*jobv=='A') matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
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else if(*jobv=='S') matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint();
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else if(*jobv=='O') matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint();
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if (*jobv == 'A')
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matrix(vt, *n, *n, *ldvt) = svd.matrixV().adjoint();
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else if (*jobv == 'S')
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matrix(vt, diag_size, *n, *ldvt) = svd.matrixV().adjoint();
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else if (*jobv == 'O')
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matrix(a, diag_size, *n, *lda) = svd.matrixV().adjoint();
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}
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return 0;
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}
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