// Copyright (c) 1997-2001  ETH Zurich (Switzerland).
// All rights reserved.
//
// This file is part of CGAL (www.cgal.org).
//
// $URL: https://github.com/CGAL/cgal/blob/v5.6.1/Bounding_volumes/include/CGAL/Approximate_min_ellipsoid_d/Approximate_min_ellipsoid_d_impl.h $
// $Id: Approximate_min_ellipsoid_d_impl.h 62a31ba 2022-11-14T17:44:33+01:00 albert-github
// SPDX-License-Identifier: GPL-3.0-or-later OR LicenseRef-Commercial
//
//
// Author(s)     : Kaspar Fischer <fischerk@inf.ethz.ch>


#include <CGAL/Default_diagonalize_traits.h>

#include <CGAL/Approximate_min_ellipsoid_d.h>

namespace CGAL {

  template<class Traits>
  void Approximate_min_ellipsoid_d<Traits>::
  find_lower_dimensional_approximation()
  {
    // (No implementation yet, in accordance with the current
    // specification in doc_tex ...)
  }

  template<class Traits>
  void Approximate_min_ellipsoid_d<Traits>::
  compute_center()
  {
    // According to (****), the computed ellipsoid E* has the representation
    //
    //   E* = { y in R^d | y^T M'/alpha y + 2/alpha y^Tm + (nu/alpha-1) <= 0 }
    //
    // for
    //
    //       [ M'  m  ]
    //   M = [ m^T nu ]
    //
    // where M is the matrix defined via E->matrix(i,j).  We need
    // something in the form
    //
    //    E* = { y | (y - c)^T M'/alpha (y - c) + mu <= 0 }.
    //
    // Expanding the later and comparing with the original form we
    // obtain
    //
    //    c = - M'^{-1} m
    //
    // as the formula for the ellipsoid's center.  Comparing
    // coefficients we also get
    //
    //    mu = nu/alpha-1 - c^T M'/alpha c
    //       = nu/alpha-1 + c^T m / alpha
    //       = (nu + c^Tm)/alpha - 1                               (********)
    //
    // In order to compute c, we will compute the inverse of M' and
    // multiply with m.

    // precondition checking:
    CGAL_APPEL_ASSERT(!has_center);

    // compute M'^{-1}:
    mi.resize(d * d);
    E->compute_inverse_of_submatrix(mi.begin());

    // compute center:
    center_.resize(d);
    for (int i=0; i<d; ++i) {
      FT ci(0);
      for (int j=0; j<d; ++j)
        ci += mi[i+d*j] * E->matrix(d,j);
      center_[i] = -ci;
    }

    // remember that we have computed the center:
    has_center = true;
  }

  template<class Traits>
  void Approximate_min_ellipsoid_d<Traits>::
  compute_axes_2_3()
  {
    // According to (****), the computed ellipsoid E* has the representation
    //
    //   E* = { y in R^d | y^T M'/alpha y + 2/alpha y^Tm + (nu/alpha-1) <= 0 }
    //
    // for
    //
    //       [ M'  m  ]
    //   M = [ m^T nu ]
    //
    // where M is the matrix defined via E->matrix(i,j).  After calling
    // compute_center() (see above), we have in center_ a point c such
    // that
    //
    //   E* = { y | (y - c)^T M'/alpha (y - c) + mu <= 0 }.
    //
    // where mu = nu/alpha-1 - c^T M'/alpha c.
    //
    // Now if we can write M' = U D U^T holds for some diagonal matrix
    // D and an orthogonal matrix U then the length l_i of the ith axes
    // (corresponding to the ith "direction" stored in the ith row of
    // U) can be obtained by plugging (0,...,0,l_i,0,...,0)U^T=y-c into
    // the above equation for E*:
    //
    //   l_i^2 d[i]/alpha = -mu,
    //
    // which gives l_i = sqrt(-mu*alpha/d[i]).

    // precondition checking:
    CGAL_APPEL_ASSERT(!has_axes && lengths_.size() == 0 &&
                      directions_.size() == 0);

    // compute M'^{-1}, if need be:
    if (!has_center)
      compute_center();

    const double alpha = (1+achieved_epsilon()) * (d+1);

    // compute mu according to (********):
    double mu = E->matrix(d,d);
    for (int i=0; i<d; ++i)
      mu += center_[i] * E->matrix(d,i);
    mu = mu/alpha - 1.0;
    const double factor = -mu*alpha;

    // compute Eigendecomposition:
    if (d == 2)
      compute_axes_2(alpha, factor);
    else
      compute_axes_3(alpha, factor);

    // remember that we have computed the axes:
    has_axes = true;
  }

  template<class Traits>
  void Approximate_min_ellipsoid_d<Traits>::
  compute_axes_2(const double /* alpha */, const double factor)
  {
    CGAL_APPEL_ASSERT(d==2);

    // write matrix M' as [ a, b; b, c ]:
    const std::array<double, 3> matrix = {{ E->matrix(0, 0),    // a
                                                    E->matrix(0, 1),    // b
                                                    E->matrix(1, 1) }}; // c
    std::array<double, 4> eigenvectors; // Note: not neces. normalized.
    std::array<double, 2> eigenvalues;  // Note: sorted ascendent.

    CGAL::Default_diagonalize_traits<double, 2>::diagonalize_selfadjoint_covariance_matrix
      (matrix, eigenvalues, eigenvectors);

    // normalize eigenvectors:
    double l1=1.0/std::sqrt(eigenvectors[2]*eigenvectors[2]+
                            eigenvectors[3]*eigenvectors[3]);
    double l2=1.0/std::sqrt(eigenvectors[0]*eigenvectors[0]+
                            eigenvectors[1]*eigenvectors[1]);

    // store axes lengths:
    lengths_.push_back(std::sqrt(factor/eigenvalues[1]));
    lengths_.push_back(std::sqrt(factor/eigenvalues[0]));

    // store directions:
    directions_.resize(2);
    directions_[0].push_back(eigenvectors[2]*l1);
    directions_[0].push_back(eigenvectors[3]*l1);
    directions_[1].push_back(eigenvectors[0]*l2);
    directions_[1].push_back(eigenvectors[1]*l2);
  }

  template<class Traits>
  void Approximate_min_ellipsoid_d<Traits>::
  compute_axes_3(const double /* alpha */, const double factor)
  {
    CGAL_APPEL_ASSERT(d==3);

    // write matrix M' as
    //
    //        [ a b c ]
    //   M' = [ b d e ]
    //        [ c e f ]
    //
    const std::array<double, 6> matrix = {{ E->matrix(0, 0),   // a
                                                    E->matrix(0, 1),   // b
                                                    E->matrix(0, 2),   // c
                                                    E->matrix(1, 1),   // d
                                                    E->matrix(1, 2),   // e
                                                    E->matrix(2, 2) }}; // f

    std::array<double, 9> eigenvectors; // Note: not necessarily normalized.
    std::array<double, 3> eigenvalues;  // Note: sorted ascendent.

    CGAL::Default_diagonalize_traits<double, 3>::diagonalize_selfadjoint_covariance_matrix
      (matrix, eigenvalues, eigenvectors);

    // normalize eigenvectors:
    double l1 = 1.0/std::sqrt(eigenvectors[0] * eigenvectors[0]+  // x^2
                              eigenvectors[1] * eigenvectors[1]+  // y^2
                              eigenvectors[2] * eigenvectors[2]); // z^2
    double l2 = 1.0/std::sqrt(eigenvectors[3] * eigenvectors[3]+  // x^2
                              eigenvectors[4] * eigenvectors[4]+  // y^2
                              eigenvectors[5] * eigenvectors[5]); // z^2
    double l3 = 1.0/std::sqrt(eigenvectors[6] * eigenvectors[6]+  // x^2
                              eigenvectors[7] * eigenvectors[7]+  // y^2
                              eigenvectors[8] * eigenvectors[8]); // z^2

    // store axes lengths:
    for (int i=0; i<3; ++i)
      lengths_.push_back(std::sqrt(factor/eigenvalues[i]));

    // store directions:
    directions_.resize(3);
    directions_[0].push_back(eigenvectors[6]*l3);
    directions_[0].push_back(eigenvectors[7]*l3);
    directions_[0].push_back(eigenvectors[8]*l3);
    directions_[1].push_back(eigenvectors[3]*l2);
    directions_[1].push_back(eigenvectors[4]*l2);
    directions_[1].push_back(eigenvectors[5]*l2);
    directions_[2].push_back(eigenvectors[0]*l1);
    directions_[2].push_back(eigenvectors[1]*l1);
    directions_[2].push_back(eigenvectors[2]*l1);
  }

  template<class Traits>
  void Approximate_min_ellipsoid_d<Traits>::
  write_eps(const std::string& name)
  {
    CGAL_APPEL_ASSERT(d==2 && !is_degenerate());

    namespace Impl = Approximate_min_ellipsoid_d_impl;
    Impl::Eps_export_2 epsf(name,2.0);
    epsf.set_label_mode(Impl::Eps_export_2::None);

    // output the input points:
    for (unsigned int k=0; k<P.size(); ++k) {
      C_it pk = tco.cartesian_begin(P[k]);
      const double u = to_double(*pk++);
      const double v = to_double(*pk);
      epsf.write_circle(u,v,0.0);
    }

    // output center:
    Center_coordinate_iterator c = center_cartesian_begin();
    const double cx = *c;
    const double cy = *++c;
    epsf.write_circle(cx, cy, 0.0, true); // (Note: last arg says to color.)

    // output axes:
    Axes_lengths_iterator axes = axes_lengths_begin();
    const double a1 = *axes;
    const double a2 = *++axes;
    Axes_direction_coordinate_iterator d1 = axis_direction_cartesian_begin(0);
    const double d1_x = *d1;
    const double d1_y = *++d1;
    Axes_direction_coordinate_iterator d2 = axis_direction_cartesian_begin(1);
    const double d2_x = *d2;
    const double d2_y = *++d2;
    epsf.write_line(cx, cy, cx+a1*d1_x, cy+a1*d1_y, true);
    epsf.write_line(cx, cy, cx+a2*d2_x, cy+a2*d2_y, true);

    // output the inscribed ellipse:
    const double alpha_inv = 1.0/((1+achieved_epsilon())*(d+1));
    epsf.set_stroke_mode(Impl::Eps_export_2::Dashed);
    epsf.write_ellipse(to_double(defining_matrix(0,0)*alpha_inv),
                       to_double(defining_matrix(1,1)*alpha_inv),
                       to_double(defining_matrix(0,1)*alpha_inv),
                       to_double(defining_vector(0)*alpha_inv),
                       to_double(defining_vector(1)*alpha_inv),
                       to_double(defining_scalar()*alpha_inv-1.0));
  }

}

