/*    This file is part of the Gudhi Library. The Gudhi library
 *    (Geometric Understanding in Higher Dimensions) is a generic C++
 *    library for computational topology.
 *
 *    Author(s):       Siargey Kachanovich
 *
 *    Copyright (C) 2015  INRIA Sophia Antipolis-Méditerranée (France)
 *
 *    This program 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.
 *
 *    This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
 */

#ifndef LANDMARK_CHOICE_BY_FURTHEST_POINT_H_
#define LANDMARK_CHOICE_BY_FURTHEST_POINT_H_

#include <boost/range/size.hpp>

#include <limits>  // for numeric_limits<>
#include <iterator>
#include <algorithm>  // for sort
#include <vector>

namespace Gudhi {

namespace witness_complex {

  typedef std::vector<int> typeVectorVertex;

  /** 
   *  \ingroup witness_complex
   *  \brief Landmark choice strategy by iteratively adding the furthest witness from the
   *  current landmark set as the new landmark. 
   *  \details It chooses nbL landmarks from a random access range `points` and
   *  writes {witness}*{closest landmarks} matrix in `knn`.
   *
   *  The type KNearestNeighbors can be seen as 
   *  Witness_range<Closest_landmark_range<Vertex_handle>>, where
   *  Witness_range and Closest_landmark_range are random access ranges 
   *  
   */

  template <typename KNearestNeighbours,
  typename Point_random_access_range>
  void landmark_choice_by_furthest_point(Point_random_access_range const &points,
                                         int nbL,
                                         KNearestNeighbours &knn) {
    int nb_points = boost::size(points);
    assert(nb_points >= nbL);
    // distance matrix witness x landmarks
    std::vector<std::vector<double>> wit_land_dist(nb_points, std::vector<double>());
    // landmark list
    typeVectorVertex chosen_landmarks;

    knn = KNearestNeighbours(nb_points, std::vector<int>());
    int current_number_of_landmarks = 0;  // counter for landmarks
    double curr_max_dist = 0;  // used for defining the furhest point from L
    const double infty = std::numeric_limits<double>::infinity();  // infinity (see next entry)
    std::vector< double > dist_to_L(nb_points, infty);  // vector of current distances to L from points

    // TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety
    // or better yet std::uniform_int_distribution
    int rand_int = rand() % nb_points;
    int curr_max_w = rand_int;  // For testing purposes a pseudo-random number is used here

    for (current_number_of_landmarks = 0; current_number_of_landmarks != nbL; current_number_of_landmarks++) {
      // curr_max_w at this point is the next landmark
      chosen_landmarks.push_back(curr_max_w);
      unsigned i = 0;
      for (auto& p : points) {
        double curr_dist = euclidean_distance(p, *(std::begin(points) + chosen_landmarks[current_number_of_landmarks]));
        wit_land_dist[i].push_back(curr_dist);
        knn[i].push_back(current_number_of_landmarks);
        if (curr_dist < dist_to_L[i])
          dist_to_L[i] = curr_dist;
        ++i;
      }
      curr_max_dist = 0;
      for (i = 0; i < dist_to_L.size(); i++)
        if (dist_to_L[i] > curr_max_dist) {
          curr_max_dist = dist_to_L[i];
          curr_max_w = i;
        }
    }
    for (int i = 0; i < nb_points; ++i)
      std::sort(std::begin(knn[i]),
                std::end(knn[i]),
                [&wit_land_dist, i](int a, int b) {
                  return wit_land_dist[i][a] < wit_land_dist[i][b]; });
  }

}  // namespace witness_complex

}  // namespace Gudhi

#endif  // LANDMARK_CHOICE_BY_FURTHEST_POINT_H_