////////////////////////////////////////////////////////////////////////
//
// Copyright (C) 2008-2022 The Octave Project Developers
//
// See the file COPYRIGHT.md in the top-level directory of this
// distribution or <https://octave.org/copyright/>.
//
// This file is part of Octave.
//
// Octave is free software: you can redistribute it and/or modify it
// under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// Octave is distributed in the hope that it will be useful, but
// WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Octave; see the file COPYING.  If not, see
// <https://www.gnu.org/licenses/>.
//
////////////////////////////////////////////////////////////////////////

#if defined (HAVE_CONFIG_H)
#  include "config.h"
#endif

#include <cmath>

#include <algorithm>
#include <limits>
#include <vector>

#include "Array.h"
#include "CColVector.h"
#include "CMatrix.h"
#include "CRowVector.h"
#include "CSparse.h"
#include "MArray.h"
#include "dColVector.h"
#include "dDiagMatrix.h"
#include "dMatrix.h"
#include "dRowVector.h"
#include "dSparse.h"
#include "fCColVector.h"
#include "fCMatrix.h"
#include "fCRowVector.h"
#include "fColVector.h"
#include "fDiagMatrix.h"
#include "fMatrix.h"
#include "fRowVector.h"
#include "lo-error.h"
#include "lo-ieee.h"
#include "lo-mappers.h"
#include "mx-cm-s.h"
#include "mx-fcm-fs.h"
#include "mx-fs-fcm.h"
#include "mx-s-cm.h"
#include "oct-cmplx.h"
#include "oct-norm.h"
#include "quit.h"
#include "svd.h"

OCTAVE_BEGIN_NAMESPACE(octave)

  // Theory: norm accumulator is an object that has an accum method able
  // to handle both real and complex element, and a cast operator
  // returning the intermediate norm.  Reference: Higham, N. "Estimating
  // the Matrix p-Norm." Numer. Math. 62, 539-555, 1992.

  // norm accumulator for the p-norm
  template <typename R>
  class norm_accumulator_p
  {
  public:
    norm_accumulator_p () { } // we need this one for Array
    norm_accumulator_p (R pp) : m_p(pp), m_scl(0), m_sum(1) { }

    template <typename U>
    void accum (U val)
    {
      octave_quit ();
      R t = std::abs (val);
      if (m_scl == t) // we need this to handle Infs properly
        m_sum += 1;
      else if (m_scl < t)
        {
          m_sum *= std::pow (m_scl/t, m_p);
          m_sum += 1;
          m_scl = t;
        }
      else if (t != 0)
        m_sum += std::pow (t/m_scl, m_p);
    }

    operator R () { return m_scl * std::pow (m_sum, 1/m_p); }

  private:
    R m_p, m_scl, m_sum;
  };

  // norm accumulator for the minus p-pseudonorm
  template <typename R>
  class norm_accumulator_mp
  {
  public:
    norm_accumulator_mp () { } // we need this one for Array
    norm_accumulator_mp (R pp) : m_p(pp), m_scl(0), m_sum(1) { }

    template <typename U>
    void accum (U val)
    {
      octave_quit ();
      R t = 1 / std::abs (val);
      if (m_scl == t)
        m_sum += 1;
      else if (m_scl < t)
        {
          m_sum *= std::pow (m_scl/t, m_p);
          m_sum += 1;
          m_scl = t;
        }
      else if (t != 0)
        m_sum += std::pow (t/m_scl, m_p);
    }

    operator R () { return m_scl * std::pow (m_sum, -1/m_p); }

  private:
    R m_p, m_scl, m_sum;
  };

  // norm accumulator for the 2-norm (euclidean)
  template <typename R>
  class norm_accumulator_2
  {
  public:
    norm_accumulator_2 () : m_scl(0), m_sum(1) { }

    void accum (R val)
    {
      R t = std::abs (val);
      if (m_scl == t)
        m_sum += 1;
      else if (m_scl < t)
        {
          m_sum *= pow2 (m_scl/t);
          m_sum += 1;
          m_scl = t;
        }
      else if (t != 0)
        m_sum += pow2 (t/m_scl);
    }

    void accum (std::complex<R> val)
    {
      accum (val.real ());
      accum (val.imag ());
    }

    operator R () { return m_scl * std::sqrt (m_sum); }

  private:
    static inline R pow2 (R x) { return x*x; }

    //--------

    R m_scl, m_sum;
  };

  // norm accumulator for the 1-norm (city metric)
  template <typename R>
  class norm_accumulator_1
  {
  public:
    norm_accumulator_1 () : m_sum (0) { }
    template <typename U>
    void accum (U val)
    {
      m_sum += std::abs (val);
    }

    operator R () { return m_sum; }

  private:
    R m_sum;
  };

  // norm accumulator for the inf-norm (max metric)
  template <typename R>
  class norm_accumulator_inf
  {
  public:
    norm_accumulator_inf () : m_max (0) { }
    template <typename U>
    void accum (U val)
    {
      if (math::isnan (val))
        m_max = numeric_limits<R>::NaN ();
      else
        m_max = std::max (m_max, std::abs (val));
    }

    operator R () { return m_max; }

  private:
    R m_max;
  };

  // norm accumulator for the -inf pseudonorm (min abs value)
  template <typename R>
  class norm_accumulator_minf
  {
  public:
    norm_accumulator_minf () : m_min (numeric_limits<R>::Inf ()) { }
    template <typename U>
    void accum (U val)
    {
      if (math::isnan (val))
        m_min = numeric_limits<R>::NaN ();
      else
        m_min = std::min (m_min, std::abs (val));
    }

    operator R () { return m_min; }

  private:
    R m_min;
  };

  // norm accumulator for the 0-pseudonorm (hamming distance)
  template <typename R>
  class norm_accumulator_0
  {
  public:
    norm_accumulator_0 () : m_num (0) { }
    template <typename U>
    void accum (U val)
    {
      if (val != static_cast<U> (0)) ++m_num;
    }

    operator R () { return m_num; }

  private:
    unsigned int m_num;
  };

  // OK, we're armed :) Now let's go for the fun

  template <typename T, typename R, typename ACC>
  inline void vector_norm (const Array<T>& v, R& res, ACC acc)
  {
    for (octave_idx_type i = 0; i < v.numel (); i++)
      acc.accum (v(i));

    res = acc;
  }

  // dense versions
  template <typename T, typename R, typename ACC>
  void column_norms (const MArray<T>& m, MArray<R>& res, ACC acc)
  {
    res = MArray<R> (dim_vector (1, m.columns ()));
    for (octave_idx_type j = 0; j < m.columns (); j++)
      {
        ACC accj = acc;
        for (octave_idx_type i = 0; i < m.rows (); i++)
          accj.accum (m(i, j));

        res.xelem (j) = accj;
      }
  }

  template <typename T, typename R, typename ACC>
  void row_norms (const MArray<T>& m, MArray<R>& res, ACC acc)
  {
    res = MArray<R> (dim_vector (m.rows (), 1));
    std::vector<ACC> acci (m.rows (), acc);
    for (octave_idx_type j = 0; j < m.columns (); j++)
      {
        for (octave_idx_type i = 0; i < m.rows (); i++)
          acci[i].accum (m(i, j));
      }

    for (octave_idx_type i = 0; i < m.rows (); i++)
      res.xelem (i) = acci[i];
  }

  // sparse versions
  template <typename T, typename R, typename ACC>
  void column_norms (const MSparse<T>& m, MArray<R>& res, ACC acc)
  {
    res = MArray<R> (dim_vector (1, m.columns ()));
    for (octave_idx_type j = 0; j < m.columns (); j++)
      {
        ACC accj = acc;
        for (octave_idx_type k = m.cidx (j); k < m.cidx (j+1); k++)
          accj.accum (m.data (k));

        res.xelem (j) = accj;
      }
  }

  template <typename T, typename R, typename ACC>
  void row_norms (const MSparse<T>& m, MArray<R>& res, ACC acc)
  {
    res = MArray<R> (dim_vector (m.rows (), 1));
    std::vector<ACC> acci (m.rows (), acc);
    for (octave_idx_type j = 0; j < m.columns (); j++)
      {
        for (octave_idx_type k = m.cidx (j); k < m.cidx (j+1); k++)
          acci[m.ridx (k)].accum (m.data (k));
      }

    for (octave_idx_type i = 0; i < m.rows (); i++)
      res.xelem (i) = acci[i];
  }

  // now the dispatchers
#define DEFINE_DISPATCHER(FCN_NAME, ARG_TYPE, RES_TYPE)         \
  template <typename T, typename R>                             \
  RES_TYPE FCN_NAME (const ARG_TYPE& v, R p)                    \
  {                                                             \
    RES_TYPE res;                                               \
    if (p == 2)                                                 \
      FCN_NAME (v, res, norm_accumulator_2<R> ());              \
    else if (p == 1)                                            \
      FCN_NAME (v, res, norm_accumulator_1<R> ());              \
    else if (lo_ieee_isinf (p))                                 \
      {                                                         \
        if (p > 0)                                              \
          FCN_NAME (v, res, norm_accumulator_inf<R> ());        \
        else                                                    \
          FCN_NAME (v, res, norm_accumulator_minf<R> ());       \
      }                                                         \
    else if (p == 0)                                            \
      FCN_NAME (v, res, norm_accumulator_0<R> ());              \
    else if (p > 0)                                             \
      FCN_NAME (v, res, norm_accumulator_p<R> (p));             \
    else                                                        \
      FCN_NAME (v, res, norm_accumulator_mp<R> (p));            \
    return res;                                                 \
  }

  DEFINE_DISPATCHER (vector_norm, MArray<T>, R)
  DEFINE_DISPATCHER (column_norms, MArray<T>, MArray<R>)
  DEFINE_DISPATCHER (row_norms, MArray<T>, MArray<R>)
  DEFINE_DISPATCHER (column_norms, MSparse<T>, MArray<R>)
  DEFINE_DISPATCHER (row_norms, MSparse<T>, MArray<R>)

  // The approximate subproblem in Higham's method.  Find lambda and mu such
  // that norm ([lambda, mu], p) == 1 and norm (y*lambda + col*mu, p) is
  // maximized.
  // Real version.  As in Higham's paper.
  template <typename ColVectorT, typename R>
  static void
  higham_subp (const ColVectorT& y, const ColVectorT& col,
               octave_idx_type nsamp, R p, R& lambda, R& mu)
  {
    R nrm = 0;
    for (octave_idx_type i = 0; i < nsamp; i++)
      {
        octave_quit ();
        R fi = i * static_cast<R> (M_PI) / nsamp;
        R lambda1 = cos (fi);
        R mu1 = sin (fi);
        R lmnr = std::pow (std::pow (std::abs (lambda1), p) +
                           std::pow (std::abs (mu1), p), 1/p);
        lambda1 /= lmnr; mu1 /= lmnr;
        R nrm1 = vector_norm (lambda1 * y + mu1 * col, p);
        if (nrm1 > nrm)
          {
            lambda = lambda1;
            mu = mu1;
            nrm = nrm1;
          }
      }
  }

  // Complex version.  Higham's paper does not deal with complex case, so we
  // use a simple extension.  First, guess the magnitudes as in real version,
  // then try to rotate lambda to improve further.
  template <typename ColVectorT, typename R>
  static void
  higham_subp (const ColVectorT& y, const ColVectorT& col,
               octave_idx_type nsamp, R p,
               std::complex<R>& lambda, std::complex<R>& mu)
  {
    typedef std::complex<R> CR;
    R nrm = 0;
    lambda = 1.0;
    CR lamcu = lambda / std::abs (lambda);
    // Probe magnitudes
    for (octave_idx_type i = 0; i < nsamp; i++)
      {
        octave_quit ();
        R fi = i * static_cast<R> (M_PI) / nsamp;
        R lambda1 = cos (fi);
        R mu1 = sin (fi);
        R lmnr = std::pow (std::pow (std::abs (lambda1), p) +
                           std::pow (std::abs (mu1), p), 1/p);
        lambda1 /= lmnr; mu1 /= lmnr;
        R nrm1 = vector_norm (lambda1 * lamcu * y + mu1 * col, p);
        if (nrm1 > nrm)
          {
            lambda = lambda1 * lamcu;
            mu = mu1;
            nrm = nrm1;
          }
      }
    R lama = std::abs (lambda);
    // Probe orientation
    for (octave_idx_type i = 0; i < nsamp; i++)
      {
        octave_quit ();
        R fi = i * static_cast<R> (M_PI) / nsamp;
        lamcu = CR (cos (fi), sin (fi));
        R nrm1 = vector_norm (lama * lamcu * y + mu * col, p);
        if (nrm1 > nrm)
          {
            lambda = lama * lamcu;
            nrm = nrm1;
          }
      }
  }

  // the p-dual element (should work for both real and complex)
  template <typename T, typename R>
  inline T elem_dual_p (T x, R p)
  {
    return math::signum (x) * std::pow (std::abs (x), p-1);
  }

  // the VectorT is used for vectors, but actually it has to be
  // a Matrix type to allow all the operations.  For instance SparseMatrix
  // does not support multiplication with column/row vectors.
  // the dual vector
  template <typename VectorT, typename R>
  VectorT dual_p (const VectorT& x, R p, R q)
  {
    VectorT res (x.dims ());
    for (octave_idx_type i = 0; i < x.numel (); i++)
      res.xelem (i) = elem_dual_p (x(i), p);
    return res / vector_norm (res, q);
  }

  // Higham's hybrid method
  template <typename MatrixT, typename VectorT, typename R>
  R higham (const MatrixT& m, R p, R tol, int maxiter,
            VectorT& x)
  {
    x.resize (m.columns (), 1);
    // the OSE part
    VectorT y(m.rows (), 1, 0), z(m.rows (), 1);
    typedef typename VectorT::element_type RR;
    RR lambda = 0;
    RR mu = 1;
    for (octave_idx_type k = 0; k < m.columns (); k++)
      {
        octave_quit ();
        VectorT col (m.column (k));
        if (k > 0)
          higham_subp (y, col, 4*k, p, lambda, mu);
        for (octave_idx_type i = 0; i < k; i++)
          x(i) *= lambda;
        x(k) = mu;
        y = lambda * y + mu * col;
      }

    // the PM part
    x = x / vector_norm (x, p);
    R q = p/(p-1);

    R gamma = 0, gamma1;
    int iter = 0;
    while (iter < maxiter)
      {
        octave_quit ();
        y = m*x;
        gamma1 = gamma;
        gamma = vector_norm (y, p);
        z = dual_p (y, p, q);
        z = z.hermitian ();
        z = z * m;

        if (iter > 0 && (vector_norm (z, q) <= gamma
                         || (gamma - gamma1) <= tol*gamma))
          break;

        z = z.hermitian ();
        x = dual_p (z, q, p);
        iter++;
      }

    return gamma;
  }

  // derive column vector and SVD types

  static const char *p_less1_gripe = "xnorm: p must be >= 1";

  // Static constant to control the maximum number of iterations.  100 seems to
  // be a good value.  Eventually, we can provide a means to change this
  // constant from Octave.
  static int max_norm_iter = 100;

  // version with SVD for dense matrices
  template <typename MatrixT, typename VectorT, typename R>
  R svd_matrix_norm (const MatrixT& m, R p, VectorT)
  {
    R res = 0;
    if (p == 2)
      {
        math::svd<MatrixT> fact (m, math::svd<MatrixT>::Type::sigma_only);
        res = fact.singular_values () (0, 0);
      }
    else if (p == 1)
      res = xcolnorms (m, static_cast<R> (1)).max ();
    else if (lo_ieee_isinf (p) && p > 1)
      res = xrownorms (m, static_cast<R> (1)).max ();
    else if (p > 1)
      {
        VectorT x;
        const R sqrteps = std::sqrt (std::numeric_limits<R>::epsilon ());
        res = higham (m, p, sqrteps, max_norm_iter, x);
      }
    else
      (*current_liboctave_error_handler) ("%s", p_less1_gripe);

    return res;
  }

  // SVD-free version for sparse matrices
  template <typename MatrixT, typename VectorT, typename R>
  R matrix_norm (const MatrixT& m, R p, VectorT)
  {
    R res = 0;
    if (p == 1)
      res = xcolnorms (m, static_cast<R> (1)).max ();
    else if (lo_ieee_isinf (p) && p > 1)
      res = xrownorms (m, static_cast<R> (1)).max ();
    else if (p > 1)
      {
        VectorT x;
        const R sqrteps = std::sqrt (std::numeric_limits<R>::epsilon ());
        res = higham (m, p, sqrteps, max_norm_iter, x);
      }
    else
      (*current_liboctave_error_handler) ("%s", p_less1_gripe);

    return res;
  }

  // and finally, here's what we've promised in the header file

#define DEFINE_XNORM_FCNS(PREFIX, RTYPE)                                \
  RTYPE xnorm (const PREFIX##ColumnVector& x, RTYPE p)                  \
  {                                                                     \
    return vector_norm (x, p);                                          \
  }                                                                     \
  RTYPE xnorm (const PREFIX##RowVector& x, RTYPE p)                     \
  {                                                                     \
    return vector_norm (x, p);                                          \
  }                                                                     \
  RTYPE xnorm (const PREFIX##Matrix& x, RTYPE p)                        \
  {                                                                     \
    return svd_matrix_norm (x, p, PREFIX##Matrix ());                   \
  }                                                                     \
  RTYPE xfrobnorm (const PREFIX##Matrix& x)                             \
  {                                                                     \
    return vector_norm (x, static_cast<RTYPE> (2));                     \
  }

  DEFINE_XNORM_FCNS(, double)
  DEFINE_XNORM_FCNS(Complex, double)
  DEFINE_XNORM_FCNS(Float, float)
  DEFINE_XNORM_FCNS(FloatComplex, float)

  // this is needed to avoid copying the sparse matrix for xfrobnorm
  template <typename T, typename R>
  inline void array_norm_2 (const T *v, octave_idx_type n, R& res)
  {
    norm_accumulator_2<R> acc;
    for (octave_idx_type i = 0; i < n; i++)
      acc.accum (v[i]);

    res = acc;
  }

#define DEFINE_XNORM_SPARSE_FCNS(PREFIX, RTYPE)                 \
  RTYPE xnorm (const Sparse##PREFIX##Matrix& x, RTYPE p)        \
  {                                                             \
    return matrix_norm (x, p, PREFIX##Matrix ());               \
  }                                                             \
  RTYPE xfrobnorm (const Sparse##PREFIX##Matrix& x)             \
  {                                                             \
    RTYPE res;                                                  \
    array_norm_2 (x.data (), x.nnz (), res);                    \
    return res;                                                 \
  }

  DEFINE_XNORM_SPARSE_FCNS(, double)
  DEFINE_XNORM_SPARSE_FCNS(Complex, double)

#define DEFINE_COLROW_NORM_FCNS(PREFIX, RPREFIX, RTYPE)         \
  RPREFIX##RowVector                                            \
  xcolnorms (const PREFIX##Matrix& m, RTYPE p)                  \
  {                                                             \
    return column_norms (m, p);                                 \
  }                                                             \
  RPREFIX##ColumnVector                                         \
  xrownorms (const PREFIX##Matrix& m, RTYPE p)                  \
  {                                                             \
    return row_norms (m, p);                                    \
  }                                                             \

  DEFINE_COLROW_NORM_FCNS(, , double)
  DEFINE_COLROW_NORM_FCNS(Complex, , double)
  DEFINE_COLROW_NORM_FCNS(Float, Float, float)
  DEFINE_COLROW_NORM_FCNS(FloatComplex, Float, float)

  DEFINE_COLROW_NORM_FCNS(Sparse, , double)
  DEFINE_COLROW_NORM_FCNS(SparseComplex, , double)

OCTAVE_END_NAMESPACE(octave)