don't index Eigen

1 files changed, 0 insertions, 138 deletions

diff --git a/eigen/lapack/svd.cpp b/eigen/lapack/svd.cpp
deleted file mode 100644
index 77b302b..0000000
--- a/eigen/lapack/svd.cpp
+++ /dev/null
@@ -1,138 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#include "lapack_common.h"
-#include <Eigen/SVD>
-
-// computes the singular values/vectors a general M-by-N matrix A using divide-and-conquer
-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,
-                         EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int * /*iwork*/, int *info))
-{
-  // TODO exploit the work buffer
-  bool query_size = *lwork==-1;
-  int diag_size = (std::min)(*m,*n);
-  
-  *info = 0;
-        if(*jobz!='A' && *jobz!='S' && *jobz!='O' && *jobz!='N')  *info = -1;
-  else  if(*m<0)                                                  *info = -2;
-  else  if(*n<0)                                                  *info = -3;
-  else  if(*lda<std::max(1,*m))                                   *info = -5;
-  else  if(*lda<std::max(1,*m))                                   *info = -8;
-  else  if(*ldu <1 || (*jobz=='A' && *ldu <*m)
-                   || (*jobz=='O' && *m<*n && *ldu<*m))           *info = -8;
-  else  if(*ldvt<1 || (*jobz=='A' && *ldvt<*n)
-                   || (*jobz=='S' && *ldvt<diag_size)
-                   || (*jobz=='O' && *m>=*n && *ldvt<*n))         *info = -10;
-  
-  if(*info!=0)
-  {
-    int e = -*info;
-    return xerbla_(SCALAR_SUFFIX_UP"GESDD ", &e, 6);
-  }
-  
-  if(query_size)
-  {
-    *lwork = 0;
-    return 0;
-  }
-  
-  if(*n==0 || *m==0)
-    return 0;
-  
-  PlainMatrixType mat(*m,*n);
-  mat = matrix(a,*m,*n,*lda);
-  
-  int option = *jobz=='A' ? ComputeFullU|ComputeFullV
-             : *jobz=='S' ? ComputeThinU|ComputeThinV
-             : *jobz=='O' ? ComputeThinU|ComputeThinV
-             : 0;
-
-  BDCSVD<PlainMatrixType> svd(mat,option);
-  
-  make_vector(s,diag_size) = svd.singularValues().head(diag_size);
-
-  if(*jobz=='A')
-  {
-    matrix(u,*m,*m,*ldu)   = svd.matrixU();
-    matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
-  }
-  else if(*jobz=='S')
-  {
-    matrix(u,*m,diag_size,*ldu)   = svd.matrixU();
-    matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint();
-  }
-  else if(*jobz=='O' && *m>=*n)
-  {
-    matrix(a,*m,*n,*lda)   = svd.matrixU();
-    matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
-  }
-  else if(*jobz=='O')
-  {
-    matrix(u,*m,*m,*ldu)        = svd.matrixU();
-    matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint();
-  }
-    
-  return 0;
-}
-
-// computes the singular values/vectors a general M-by-N matrix A using two sided jacobi algorithm
-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,
-                         EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int *info))
-{
-  // TODO exploit the work buffer
-  bool query_size = *lwork==-1;
-  int diag_size = (std::min)(*m,*n);
-  
-  *info = 0;
-        if( *jobu!='A' && *jobu!='S' && *jobu!='O' && *jobu!='N') *info = -1;
-  else  if((*jobv!='A' && *jobv!='S' && *jobv!='O' && *jobv!='N')
-           || (*jobu=='O' && *jobv=='O'))                         *info = -2;
-  else  if(*m<0)                                                  *info = -3;
-  else  if(*n<0)                                                  *info = -4;
-  else  if(*lda<std::max(1,*m))                                   *info = -6;
-  else  if(*ldu <1 || ((*jobu=='A' || *jobu=='S') && *ldu<*m))    *info = -9;
-  else  if(*ldvt<1 || (*jobv=='A' && *ldvt<*n)
-                   || (*jobv=='S' && *ldvt<diag_size))            *info = -11;
-  
-  if(*info!=0)
-  {
-    int e = -*info;
-    return xerbla_(SCALAR_SUFFIX_UP"GESVD ", &e, 6);
-  }
-  
-  if(query_size)
-  {
-    *lwork = 0;
-    return 0;
-  }
-  
-  if(*n==0 || *m==0)
-    return 0;
-  
-  PlainMatrixType mat(*m,*n);
-  mat = matrix(a,*m,*n,*lda);
-  
-  int option = (*jobu=='A' ? ComputeFullU : *jobu=='S' || *jobu=='O' ? ComputeThinU : 0)
-             | (*jobv=='A' ? ComputeFullV : *jobv=='S' || *jobv=='O' ? ComputeThinV : 0);
-  
-  JacobiSVD<PlainMatrixType> svd(mat,option);
-  
-  make_vector(s,diag_size) = svd.singularValues().head(diag_size);
-  {
-        if(*jobu=='A') matrix(u,*m,*m,*ldu)           = svd.matrixU();
-  else  if(*jobu=='S') matrix(u,*m,diag_size,*ldu)    = svd.matrixU();
-  else  if(*jobu=='O') matrix(a,*m,diag_size,*lda)    = svd.matrixU();
-  }
-  {
-        if(*jobv=='A') matrix(vt,*n,*n,*ldvt)         = svd.matrixV().adjoint();
-  else  if(*jobv=='S') matrix(vt,diag_size,*n,*ldvt)  = svd.matrixV().adjoint();
-  else  if(*jobv=='O') matrix(a,diag_size,*n,*lda)    = svd.matrixV().adjoint();
-  }
-  return 0;
-}