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-// 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) 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_SVD_H
-#define EIGEN_SVD_H
-
-namespace Eigen {
-/** \ingroup SVD_Module
- *
- *
- * \class SVDBase
- *
- * \brief Mother class of SVD classes algorithms
- *
- * \param MatrixType the type of the matrix of which we are computing the SVD decomposition
- * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
- *   \f[ A = U S V^* \f]
- * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
- * the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left
- * and right \em singular \em vectors of \a A respectively.
- *
- * Singular values are always sorted in decreasing order.
- *
- * 
- * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting \a m be the
- * smaller value among \a n and \a p, there are only \a m singular vectors; the remaining columns of \a U and \a V do not correspond to actual
- * singular vectors. Asking for \em thin \a U or \a V means asking for only their \a m first columns to be formed. So \a U is then a n-by-m matrix,
- * and \a V is then a p-by-m matrix. Notice that thin \a U and \a V are all you need for (least squares) solving.
- *  
- * If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is guaranteed to
- * terminate in finite (and reasonable) time.
- * \sa MatrixBase::genericSvd()
- */
-template<typename _MatrixType> 
-class SVDBase
-{
-
-public:
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-  typedef typename MatrixType::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;
-  typedef typename internal::plain_row_type<MatrixType>::type RowType;
-  typedef typename internal::plain_col_type<MatrixType>::type ColType;
-  typedef Matrix<Scalar, DiagSizeAtCompileTime, DiagSizeAtCompileTime,
-		 MatrixOptions, MaxDiagSizeAtCompileTime, MaxDiagSizeAtCompileTime>
-  WorkMatrixType;
-	
-
-
-
-  /** \brief Method performing the decomposition of given matrix using custom options.
-   *
-   * \param matrix the matrix to decompose
-   * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-   *                           By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
-   *                           #ComputeFullV, #ComputeThinV.
-   *
-   * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-   * available with the (non-default) FullPivHouseholderQR preconditioner.
-   */
-  SVDBase& compute(const MatrixType& matrix, unsigned int computationOptions);
-
-  /** \brief Method performing the decomposition of given matrix using current options.
-   *
-   * \param matrix the matrix to decompose
-   *
-   * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
-   */
-  //virtual SVDBase& compute(const MatrixType& matrix) = 0;
-  SVDBase& compute(const MatrixType& matrix);
-
-  /** \returns the \a U matrix.
-   *
-   * For the SVDBase decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
-   * the U matrix is n-by-n if you asked for #ComputeFullU, and is n-by-m if you asked for #ComputeThinU.
-   *
-   * The \a m first columns of \a U are the left singular vectors of the matrix being decomposed.
-   *
-   * This method asserts that you asked for \a U to be computed.
-   */
-  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;
-  }
-
-  /** \returns the \a V matrix.
-   *
-   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
-   * the V matrix is p-by-p if you asked for #ComputeFullV, and is p-by-m if you asked for ComputeThinV.
-   *
-   * The \a m first columns of \a V are the right singular vectors of the matrix being decomposed.
-   *
-   * This method asserts that you asked for \a V to be computed.
-   */
-  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;
-  }
-
-  /** \returns the vector of singular values.
-   *
-   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p, the
-   * returned vector has size \a m.  Singular values are always sorted in decreasing order.
-   */
-  const SingularValuesType& singularValues() const
-  {
-    eigen_assert(m_isInitialized && "SVD is not initialized.");
-    return m_singularValues;
-  }
-
-  
-
-  /** \returns the number of singular values that are not exactly 0 */
-  Index nonzeroSingularValues() const
-  {
-    eigen_assert(m_isInitialized && "SVD is not initialized.");
-    return m_nonzeroSingularValues;
-  }
-
-
-  /** \returns true if \a U (full or thin) is asked for in this SVD decomposition */
-  inline bool computeU() const { return m_computeFullU || m_computeThinU; }
-  /** \returns true if \a V (full or thin) is asked for in this SVD decomposition */
-  inline bool computeV() const { return m_computeFullV || m_computeThinV; }
-
-
-  inline Index rows() const { return m_rows; }
-  inline Index cols() const { return m_cols; }
-
-
-protected:
-  // 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;
-  bool m_computeFullU, m_computeThinU;
-  bool m_computeFullV, m_computeThinV;
-  unsigned int m_computationOptions;
-  Index m_nonzeroSingularValues, m_rows, m_cols, m_diagSize;
-
-
-  /** \brief Default Constructor.
-   *
-   * Default constructor of SVDBase
-   */
-  SVDBase()
-    : m_isInitialized(false),
-      m_isAllocated(false),
-      m_computationOptions(0),
-      m_rows(-1), m_cols(-1)
-  {}
-
-
-};
-
-
-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_SVD_H