// Copyright (c) 2005  INRIA Sophia-Antipolis (France).
// All rights reserved.
//
// This file is part of CGAL (www.cgal.org).
//
// $URL$
// $Id$
// SPDX-License-Identifier: GPL-3.0-or-later OR LicenseRef-Commercial
//
// Author(s) : Pierre Alliez and Sylvain Pion and Ankit Gupta

#ifndef CGAL_LINEAR_LEAST_SQUARES_FITTING_POINTS_2_H
#define CGAL_LINEAR_LEAST_SQUARES_FITTING_POINTS_2_H

#include <CGAL/license/Principal_component_analysis.h>


#include <CGAL/basic.h>
#include <CGAL/centroid.h>
#include <iterator>
#include <cmath>

namespace CGAL {

namespace internal {

// Fits a line to a 2D point set.
// Returns a fitting quality (1 - lambda_min/lambda_max):
//  1 is best  (zero variance orthogonally to the fitting line);
//  0 is worst (isotropic case, returns a line with horizontal
//              direction by default).
template < typename InputIterator, 
           typename K,
	   typename DiagonalizeTraits>
typename K::FT
linear_least_squares_fitting_2(InputIterator first,
                               InputIterator beyond, 
                               typename K::Line_2& line,   // best fit line
                               typename K::Point_2& c,     // centroid
                               const typename K::Point_2*,// used for indirection
                               const K&,                   // kernel
			       const CGAL::Dimension_tag<0>& tag,
			       const DiagonalizeTraits&)
{
  // types
  typedef typename K::FT       FT;
  typedef typename K::Line_2   Line;
  typedef typename K::Point_2  Point;
  typedef typename K::Vector_2 Vector;

	// if internally double, declare a kernel

  // precondition: at least one element in the container.
  CGAL_precondition(first != beyond);

  // compute centroid
  c = centroid(first,beyond,K(),tag);

  // assemble covariance matrix as a semi-definite matrix. 
  // Matrix numbering:
  // 0 1
  //   2

  typename DiagonalizeTraits::Covariance_matrix covariance = {{ 0., 0., 0. }};

  for(InputIterator it = first;
      it != beyond;
      it++)
  {
    const Point& p = *it;
    Vector d = p - c; // centered data point
    covariance[0] += d.x() * d.x();
    covariance[1] += d.x() * d.y();
    covariance[2] += d.y() * d.y();
  }

  // solve for eigenvalues and eigenvectors.
  // eigen values are sorted in ascending order, 
  // eigen vectors are sorted in accordance.
  typename DiagonalizeTraits::Vector eigen_values = {{ 0. , 0. }};
  typename DiagonalizeTraits::Matrix eigen_vectors = {{ 0., 0., 0. }};
  DiagonalizeTraits::diagonalize_selfadjoint_covariance_matrix
    (covariance, eigen_values, eigen_vectors);

  // check unicity and build fitting line accordingly
  if(eigen_values[0] != eigen_values[1])
  {
    // regular case
    line = Line(c, Vector (eigen_vectors[2], eigen_vectors[3]));
    return (FT)1.0 - eigen_values[0] / eigen_values[1];
  } 
  else
  {
    // isotropic case (infinite number of directions)
    // by default: assemble a line that goes through 
    // the centroid and with a default horizontal vector.
    line = Line(c, Vector(FT(1), FT(0)));
    return (FT)0.0;
  } 
} // end linear_least_squares_fitting_2 for point set

} // end namespace internal

} //namespace CGAL

#endif // CGAL_LINEAR_LEAST_SQUARES_FITTING_POINTS_2_H
