SVDBase.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.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/.
 
#ifndef EIGEN_SVDBASE_H
#define EIGEN_SVDBASE_H
 
namespace Eigen {
template<typename Derived>
class SVDBase
{
 
public:
  typedef typename internal::traits<Derived>::MatrixType MatrixType;
  typedef typename MatrixType::Scalar Scalar;
  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
  typedef typename MatrixType::StorageIndex StorageIndex;
  typedef Eigen::Index Index; 
  enum {
    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
    DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime),
    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
    MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime,MaxColsAtCompileTime),
    MatrixOptions = MatrixType::Options
  };
 
  typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixUType;
  typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime, MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime> MatrixVType;
  typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType;
  
  Derived& derived() { return *static_cast<Derived*>(this); }
  const Derived& derived() const { return *static_cast<const Derived*>(this); }
 
  const MatrixUType& matrixU() const
  {
    eigen_assert(m_isInitialized && "SVD is not initialized.");
    eigen_assert(computeU() && "This SVD decomposition didn't compute U. Did you ask for it?");
    return m_matrixU;
  }
 
  const MatrixVType& matrixV() const
  {
    eigen_assert(m_isInitialized && "SVD is not initialized.");
    eigen_assert(computeV() && "This SVD decomposition didn't compute V. Did you ask for it?");
    return m_matrixV;
  }
 
  const SingularValuesType& singularValues() const
  {
    eigen_assert(m_isInitialized && "SVD is not initialized.");
    return m_singularValues;
  }
 
  Index nonzeroSingularValues() const
  {
    eigen_assert(m_isInitialized && "SVD is not initialized.");
    return m_nonzeroSingularValues;
  }
  
  inline Index rank() const
  {
    using std::abs;
    using std::max;
    eigen_assert(m_isInitialized && "JacobiSVD is not initialized.");
    if(m_singularValues.size()==0) return 0;
    RealScalar premultiplied_threshold = (max)(m_singularValues.coeff(0) * threshold(), (std::numeric_limits<RealScalar>::min)());
    Index i = m_nonzeroSingularValues-1;
    while(i>=0 && m_singularValues.coeff(i) < premultiplied_threshold) --i;
    return i+1;
  }
  
  Derived& setThreshold(const RealScalar& threshold)
  {
    m_usePrescribedThreshold = true;
    m_prescribedThreshold = threshold;
    return derived();
  }
 
  Derived& setThreshold(Default_t)
  {
    m_usePrescribedThreshold = false;
    return derived();
  }
 
  RealScalar threshold() const
  {
    eigen_assert(m_isInitialized || m_usePrescribedThreshold);
    return m_usePrescribedThreshold ? m_prescribedThreshold
                                    : (std::max<Index>)(1,m_diagSize)*NumTraits<Scalar>::epsilon();
  }
 
  inline bool computeU() const { return m_computeFullU || m_computeThinU; }
  inline bool computeV() const { return m_computeFullV || m_computeThinV; }
 
  inline Index rows() const { return m_rows; }
  inline Index cols() const { return m_cols; }
  
  template<typename Rhs>
  inline const Solve<Derived, Rhs>
  solve(const MatrixBase<Rhs>& b) const
  {
    eigen_assert(m_isInitialized && "SVD is not initialized.");
    eigen_assert(computeU() && computeV() && "SVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice).");
    return Solve<Derived, Rhs>(derived(), b.derived());
  }
  
  #ifndef EIGEN_PARSED_BY_DOXYGEN
  template<typename RhsType, typename DstType>
  EIGEN_DEVICE_FUNC
  void _solve_impl(const RhsType &rhs, DstType &dst) const;
  #endif
 
protected:
  
  static void check_template_parameters()
  {
    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
  }
  
  // return true if already allocated
  bool allocate(Index rows, Index cols, unsigned int computationOptions) ;
 
  MatrixUType m_matrixU;
  MatrixVType m_matrixV;
  SingularValuesType m_singularValues;
  bool m_isInitialized, m_isAllocated, m_usePrescribedThreshold;
  bool m_computeFullU, m_computeThinU;
  bool m_computeFullV, m_computeThinV;
  unsigned int m_computationOptions;
  Index m_nonzeroSingularValues, m_rows, m_cols, m_diagSize;
  RealScalar m_prescribedThreshold;
 
  SVDBase()
    : m_isInitialized(false),
      m_isAllocated(false),
      m_usePrescribedThreshold(false),
      m_computationOptions(0),
      m_rows(-1), m_cols(-1), m_diagSize(0)
  {
    check_template_parameters();
  }
 
 
};
 
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived>
template<typename RhsType, typename DstType>
void SVDBase<Derived>::_solve_impl(const RhsType &rhs, DstType &dst) const
{
  eigen_assert(rhs.rows() == rows());
 
  // A = U S V^*
  // So A^{-1} = V S^{-1} U^*
 
  Matrix<Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime, RhsType::MaxColsAtCompileTime> tmp;
  Index l_rank = rank();
  tmp.noalias() =  m_matrixU.leftCols(l_rank).adjoint() * rhs;
  tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
  dst = m_matrixV.leftCols(l_rank) * tmp;
}
#endif
 
template<typename MatrixType>
bool SVDBase<MatrixType>::allocate(Index rows, Index cols, unsigned int computationOptions)
{
  eigen_assert(rows >= 0 && cols >= 0);
 
  if (m_isAllocated &&
      rows == m_rows &&
      cols == m_cols &&
      computationOptions == m_computationOptions)
  {
    return true;
  }
 
  m_rows = rows;
  m_cols = cols;
  m_isInitialized = false;
  m_isAllocated = true;
  m_computationOptions = computationOptions;
  m_computeFullU = (computationOptions & ComputeFullU) != 0;
  m_computeThinU = (computationOptions & ComputeThinU) != 0;
  m_computeFullV = (computationOptions & ComputeFullV) != 0;
  m_computeThinV = (computationOptions & ComputeThinV) != 0;
  eigen_assert(!(m_computeFullU && m_computeThinU) && "SVDBase: you can't ask for both full and thin U");
  eigen_assert(!(m_computeFullV && m_computeThinV) && "SVDBase: you can't ask for both full and thin V");
  eigen_assert(EIGEN_IMPLIES(m_computeThinU || m_computeThinV, MatrixType::ColsAtCompileTime==Dynamic) &&
           "SVDBase: thin U and V are only available when your matrix has a dynamic number of columns.");
 
  m_diagSize = (std::min)(m_rows, m_cols);
  m_singularValues.resize(m_diagSize);
  if(RowsAtCompileTime==Dynamic)
    m_matrixU.resize(m_rows, m_computeFullU ? m_rows : m_computeThinU ? m_diagSize : 0);
  if(ColsAtCompileTime==Dynamic)
    m_matrixV.resize(m_cols, m_computeFullV ? m_cols : m_computeThinV ? m_diagSize : 0);
 
  return false;
}
 
}// end namespace
 
#endif // EIGEN_SVDBASE_H