// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Chen-Pang He <jdh8@ms63.hinet.net>
//
// 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_MATRIX_POWER_BASE
#define EIGEN_MATRIX_POWER_BASE

namespace Eigen {

#define EIGEN_MATRIX_POWER_PUBLIC_INTERFACE(Derived) \
  typedef MatrixPowerBase<Derived, MatrixType> Base; \
  using Base::RowsAtCompileTime; \
  using Base::ColsAtCompileTime; \
  using Base::Options; \
  using Base::MaxRowsAtCompileTime; \
  using Base::MaxColsAtCompileTime; \
  typedef typename Base::Scalar     Scalar; \
  typedef typename Base::RealScalar RealScalar; \
  typedef typename Base::RealArray  RealArray;

#define EIGEN_MATRIX_POWER_PROTECTED_MEMBERS(Derived) \
  using Base::m_A; \
  using Base::m_tmp1; \
  using Base::m_tmp2; \
  using Base::m_conditionNumber;

template<typename Derived, typename MatrixType>
class MatrixPowerBaseReturnValue : public ReturnByValue<MatrixPowerBaseReturnValue<Derived,MatrixType> >
{
  public:
    typedef typename MatrixType::RealScalar RealScalar;
    typedef typename MatrixType::Index Index;

    MatrixPowerBaseReturnValue(Derived& pow, RealScalar p) : m_pow(pow), m_p(p)
    { }

    template<typename ResultType>
    inline void evalTo(ResultType& res) const
    { m_pow.compute(res, m_p); }

    template<typename OtherDerived>
    const MatrixPowerProduct<Derived,MatrixType,OtherDerived> operator*(const MatrixBase<OtherDerived>& b) const
    { return MatrixPowerProduct<Derived,MatrixType,OtherDerived>(m_pow, b.derived(), m_p); }

    Index rows() const { return m_pow.rows(); }
    Index cols() const { return m_pow.cols(); }

  private:
    Derived& m_pow;
    const RealScalar m_p;
    MatrixPowerBaseReturnValue& operator=(const MatrixPowerBaseReturnValue&);
};

template<typename Derived, typename MatrixType>
class MatrixPowerBase
{
  private:
    Derived& derived() { return *static_cast<Derived*>(this); }

  public:
    enum {
      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
      Options = MatrixType::Options,
      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
    };
    typedef typename MatrixType::Scalar Scalar;
    typedef typename MatrixType::RealScalar RealScalar;
    typedef typename MatrixType::Index Index;

    explicit MatrixPowerBase(const MatrixType& A) :
      m_A(A),
      m_conditionNumber(0)
    { eigen_assert(A.rows() == A.cols()); }

  #ifndef EIGEN_PARSED_BY_DOXYGEN
    const MatrixPowerBaseReturnValue<Derived,MatrixType> operator()(RealScalar p)
    { return MatrixPowerBaseReturnValue<Derived,MatrixType>(derived(), p); }
  #endif

    void compute(MatrixType& res, RealScalar p)
    { derived().compute(res,p); }

    template<typename OtherDerived, typename ResultType>
    void compute(const OtherDerived& b, ResultType& res, RealScalar p)
    { derived().compute(b,res,p); }
    
    Index rows() const { return m_A.rows(); }
    Index cols() const { return m_A.cols(); }

  protected:
    typedef Array<RealScalar,RowsAtCompileTime,1,ColMajor,MaxRowsAtCompileTime> RealArray;

    typename MatrixType::Nested m_A;
    MatrixType m_tmp1, m_tmp2;
    RealScalar m_conditionNumber;
};

template<typename Derived, typename Lhs, typename Rhs>
class MatrixPowerProduct : public MatrixBase<MatrixPowerProduct<Derived,Lhs,Rhs> >
{
  public:
    typedef MatrixBase<MatrixPowerProduct> Base;
    EIGEN_DENSE_PUBLIC_INTERFACE(MatrixPowerProduct)

    MatrixPowerProduct(Derived& pow, const Rhs& b, RealScalar p) :
      m_pow(pow),
      m_b(b),
      m_p(p)
    { eigen_assert(pow.cols() == b.rows()); }

    template<typename ResultType>
    inline void evalTo(ResultType& res) const
    { m_pow.compute(m_b, res, m_p); }

    inline Index rows() const { return m_pow.rows(); }
    inline Index cols() const { return m_b.cols(); }

  private:
    Derived& m_pow;
    typename Rhs::Nested m_b;
    const RealScalar m_p;
};

template<typename Derived>
template<typename MatrixPower, typename Lhs, typename Rhs>
Derived& MatrixBase<Derived>::lazyAssign(const MatrixPowerProduct<MatrixPower,Lhs,Rhs>& other)
{
  other.evalTo(derived());
  return derived();
}

namespace internal {

template<typename Derived, typename MatrixType>
struct traits<MatrixPowerBaseReturnValue<Derived, MatrixType> >
{ typedef MatrixType ReturnType; };

template<typename Derived, typename _Lhs, typename _Rhs>
struct traits<MatrixPowerProduct<Derived,_Lhs,_Rhs> >
{
  typedef MatrixXpr XprKind;
  typedef typename remove_all<_Lhs>::type Lhs;
  typedef typename remove_all<_Rhs>::type Rhs;
  typedef typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
  typedef Dense StorageKind;
  typedef typename promote_index_type<typename Lhs::Index, typename Rhs::Index>::type Index;

  enum {
    RowsAtCompileTime = traits<Lhs>::RowsAtCompileTime,
    ColsAtCompileTime = traits<Rhs>::ColsAtCompileTime,
    MaxRowsAtCompileTime = traits<Lhs>::MaxRowsAtCompileTime,
    MaxColsAtCompileTime = traits<Rhs>::MaxColsAtCompileTime,
    Flags = (MaxRowsAtCompileTime==1 ? RowMajorBit : 0)
	  | EvalBeforeNestingBit | EvalBeforeAssigningBit | NestByRefBit,
    CoeffReadCost = 0
  };
};

template<int IsComplex>
struct recompose_complex_schur
{
  template<typename ResultType, typename MatrixType>
  static inline void run(ResultType& res, const MatrixType& T, const MatrixType& U)
  { res.noalias() = U * (T.template triangularView<Upper>() * U.adjoint()); }
};

template<>
struct recompose_complex_schur<0>
{
  template<typename ResultType, typename MatrixType>
  static inline void run(ResultType& res, const MatrixType& T, const MatrixType& U)
  { res.noalias() = (U * (T.template triangularView<Upper>() * U.adjoint())).real(); }
};

template<typename Scalar, int IsComplex = NumTraits<Scalar>::IsComplex>
struct matrix_power_unwinder
{
  static inline Scalar run(const Scalar& eival, const Scalar& eival0, int unwindingNumber)
  { return numext::atanh2(eival-eival0, eival+eival0) + Scalar(0, M_PI*unwindingNumber); }
};

template<typename Scalar>
struct matrix_power_unwinder<Scalar,0>
{
  static inline Scalar run(Scalar eival, Scalar eival0, int)
  { return numext::atanh2(eival-eival0, eival+eival0); }
};

template<typename T>
inline int binary_powering_cost(T p, int* squarings)
{
  int applyings=0, tmp;

  frexp(p, squarings);
  --*squarings;

  while (std::frexp(p, &tmp), tmp > 0) {
    p -= std::ldexp(static_cast<T>(0.5), tmp);
    ++applyings;
  }
  return applyings;
}

inline int matrix_power_get_pade_degree(float normIminusT)
{
  const float maxNormForPade[] = { 2.8064004e-1f /* degree = 3 */ , 4.3386528e-1f };
  int degree = 3;
  for (; degree <= 4; ++degree)
    if (normIminusT <= maxNormForPade[degree - 3])
      break;
  return degree;
}

inline int matrix_power_get_pade_degree(double normIminusT)
{
  const double maxNormForPade[] = { 1.884160592658218e-2 /* degree = 3 */ , 6.038881904059573e-2, 1.239917516308172e-1,
      1.999045567181744e-1, 2.789358995219730e-1 };
  int degree = 3;
  for (; degree <= 7; ++degree)
    if (normIminusT <= maxNormForPade[degree - 3])
      break;
  return degree;
}

inline int matrix_power_get_pade_degree(long double normIminusT)
{
#if   LDBL_MANT_DIG == 53
  const int maxPadeDegree = 7;
  const double maxNormForPade[] = { 1.884160592658218e-2L /* degree = 3 */ , 6.038881904059573e-2L, 1.239917516308172e-1L,
      1.999045567181744e-1L, 2.789358995219730e-1L };
#elif LDBL_MANT_DIG <= 64
  const int maxPadeDegree = 8;
  const double maxNormForPade[] = { 6.3854693117491799460e-3L /* degree = 3 */ , 2.6394893435456973676e-2L,
      6.4216043030404063729e-2L, 1.1701165502926694307e-1L, 1.7904284231268670284e-1L, 2.4471944416607995472e-1L };
#elif LDBL_MANT_DIG <= 106
  const int maxPadeDegree = 10;
  const double maxNormForPade[] = { 1.0007161601787493236741409687186e-4L /* degree = 3 */ ,
      1.0007161601787493236741409687186e-3L, 4.7069769360887572939882574746264e-3L, 1.3220386624169159689406653101695e-2L,
      2.8063482381631737920612944054906e-2L, 4.9625993951953473052385361085058e-2L, 7.7367040706027886224557538328171e-2L,
      1.1016843812851143391275867258512e-1L };
#else
  const int maxPadeDegree = 10;
  const double maxNormForPade[] = { 5.524506147036624377378713555116378e-5L /* degree = 3 */ ,
      6.640600568157479679823602193345995e-4L, 3.227716520106894279249709728084626e-3L,
      9.619593944683432960546978734646284e-3L, 2.134595382433742403911124458161147e-2L,
      3.908166513900489428442993794761185e-2L, 6.266780814639442865832535460550138e-2L,
      9.134603732914548552537150753385375e-2L };
#endif
  int degree = 3;
  for (; degree <= maxPadeDegree; ++degree)
    if (normIminusT <= maxNormForPade[degree - 3])
      break;
  return degree;
}

} // namespace internal

template<typename MatrixType>
class MatrixPowerTriangularAtomic
{
  private:
    enum {
      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime
    };
    typedef typename MatrixType::Scalar Scalar;
    typedef typename MatrixType::RealScalar RealScalar;
    typedef typename MatrixType::Index Index;
    typedef Array<Scalar,RowsAtCompileTime,1,ColMajor,MaxRowsAtCompileTime> ArrayType;

    const MatrixType& m_A;

    static void computePade(int degree, const MatrixType& IminusT, MatrixType& res, RealScalar p);
    void compute2x2(MatrixType& res, RealScalar p) const;
    void computeBig(MatrixType& res, RealScalar p) const;

  public:
    explicit MatrixPowerTriangularAtomic(const MatrixType& T);
    void compute(MatrixType& res, RealScalar p) const;
};

template<typename MatrixType>
MatrixPowerTriangularAtomic<MatrixType>::MatrixPowerTriangularAtomic(const MatrixType& T) :
  m_A(T)
{ eigen_assert(T.rows() == T.cols()); }

template<typename MatrixType>
void MatrixPowerTriangularAtomic<MatrixType>::compute(MatrixType& res, RealScalar p) const
{
  res.resizeLike(m_A);
  switch (m_A.rows()) {
    case 0:
      break;
    case 1:
      res(0,0) = std::pow(m_A(0,0), p);
      break;
    case 2:
      compute2x2(res, p);
      break;
    default:
      computeBig(res, p);
  }
}

template<typename MatrixType>
void MatrixPowerTriangularAtomic<MatrixType>::computePade(int degree, const MatrixType& IminusT, MatrixType& res, RealScalar p)
{
  int i = degree<<1;
  res = (p-degree) / ((i-1)<<1) * IminusT;
  for (--i; i; --i) {
    res = (MatrixType::Identity(IminusT.rows(), IminusT.cols()) + res).template triangularView<Upper>()
	.solve((i==1 ? -p : i&1 ? (-p-(i>>1))/(i<<1) : (p-(i>>1))/((i-1)<<1)) * IminusT).eval();
  }
  res += MatrixType::Identity(IminusT.rows(), IminusT.cols());
}

// this function assumes that res has the correct size (see bug 614)
template<typename MatrixType>
void MatrixPowerTriangularAtomic<MatrixType>::compute2x2(MatrixType& res, RealScalar p) const
{
  using std::abs;
  using std::pow;
  
  ArrayType logTdiag = m_A.diagonal().array().log();
  res.coeffRef(0,0) = pow(m_A.coeff(0,0), p);

  for (Index i=1; i < m_A.cols(); ++i) {
    res.coeffRef(i,i) = pow(m_A.coeff(i,i), p);
    if (m_A.coeff(i-1,i-1) == m_A.coeff(i,i)) {
      res.coeffRef(i-1,i) = p * pow(m_A.coeff(i,i), p-1);
    }
    else if (2*abs(m_A.coeff(i-1,i-1)) < abs(m_A.coeff(i,i)) || 2*abs(m_A.coeff(i,i)) < abs(m_A.coeff(i-1,i-1))) {
      res.coeffRef(i-1,i) = (res.coeff(i,i)-res.coeff(i-1,i-1)) / (m_A.coeff(i,i)-m_A.coeff(i-1,i-1));
    }
    else {
      int unwindingNumber = std::ceil((numext::imag(logTdiag[i]-logTdiag[i-1]) - M_PI) / (2*M_PI));
      Scalar w = internal::matrix_power_unwinder<Scalar>::run(m_A.coeff(i,i), m_A.coeff(i-1,i-1), unwindingNumber);
      res.coeffRef(i-1,i) = RealScalar(2) * std::exp(RealScalar(0.5)*p*(logTdiag[i]+logTdiag[i-1])) * std::sinh(p * w)
          / (m_A.coeff(i,i) - m_A.coeff(i-1,i-1));
    }
    res.coeffRef(i-1,i) *= m_A.coeff(i-1,i);
  }
}

template<typename MatrixType>
void MatrixPowerTriangularAtomic<MatrixType>::computeBig(MatrixType& res, RealScalar p) const
{
  const int digits = std::numeric_limits<RealScalar>::digits;
  const RealScalar maxNormForPade = digits <=  24? 4.3386528e-1f:                           // sigle precision
				    digits <=  53? 2.789358995219730e-1:                    // double precision
				    digits <=  64? 2.4471944416607995472e-1L:               // extended precision
				    digits <= 106? 1.1016843812851143391275867258512e-1L:   // double-double
						   9.134603732914548552537150753385375e-2L; // quadruple precision
  MatrixType IminusT, sqrtT, T = m_A.template triangularView<Upper>();
  RealScalar normIminusT;
  int degree, degree2, numberOfSquareRoots = 0;
  bool hasExtraSquareRoot = false;

  /* FIXME
   * For singular T, norm(I - T) >= 1 but maxNormForPade < 1, leads to infinite
   * loop.  We should move 0 eigenvalues to bottom right corner.  We need not
   * worry about tiny values (e.g. 1e-300) because they will reach 1 if
   * repetitively sqrt'ed.
   *
   * [ T  A ]^p   [ T^p  (T^-1 T^p A) ]
   * [      ]   = [                   ]
   * [ 0  0 ]     [  0         0      ]
   */
  for (Index i=0; i < m_A.cols(); ++i)
    eigen_assert(m_A(i,i) != RealScalar(0));

  while (true) {
    IminusT = MatrixType::Identity(m_A.rows(), m_A.cols()) - T;
    normIminusT = IminusT.cwiseAbs().colwise().sum().maxCoeff();
    if (normIminusT < maxNormForPade) {
      degree = internal::matrix_power_get_pade_degree(normIminusT);
      degree2 = internal::matrix_power_get_pade_degree(normIminusT/2);
      if (degree - degree2 <= 1 || hasExtraSquareRoot)
	break;
      hasExtraSquareRoot = true;
    }
    MatrixSquareRootTriangular<MatrixType>(T).compute(sqrtT);
    T = sqrtT.template triangularView<Upper>();
    ++numberOfSquareRoots;
  }
  computePade(degree, IminusT, res, p);

  for (; numberOfSquareRoots; --numberOfSquareRoots) {
    compute2x2(res, std::ldexp(p,-numberOfSquareRoots));
    res = res.template triangularView<Upper>() * res;
  }
  compute2x2(res, p);
}

} // namespace Eigen

#endif // EIGEN_MATRIX_POWER