/*    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): Marc Glisse
 *
 *    Copyright (C) 2018 Inria
 *
 *    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 SPARSE_RIPS_COMPLEX_H_
#define SPARSE_RIPS_COMPLEX_H_

#include <gudhi/Debug_utils.h>
#include <gudhi/graph_simplicial_complex.h>
#include <gudhi/choose_n_farthest_points.h>

#include <boost/graph/adjacency_list.hpp>
#include <boost/range/metafunctions.hpp>

#include <vector>

namespace Gudhi {

namespace rips_complex {

// The whole interface is copied on Rips_complex. A redesign should be discussed with all complex creation classes in
// mind.

/**
 * \class Sparse_rips_complex
 * \brief Sparse Rips complex data structure.
 *
 * \ingroup rips_complex
 *
 * \details
 * This class is used to construct a sparse \f$(1+O(\epsilon))\f$-approximation of `Rips_complex`, i.e. a filtered
 * simplicial complex that is multiplicatively \f$(1+O(\epsilon))\f$-interleaved with the Rips filtration.
 *
 * \tparam Filtration_value is the type used to store the filtration values of the simplicial complex.
 */
template <typename Filtration_value>
class Sparse_rips_complex {
 private:
  // TODO(MG): use a different graph where we know we can safely insert in parallel.
  typedef typename boost::adjacency_list<boost::vecS, boost::vecS, boost::undirectedS,
                                         boost::property<vertex_filtration_t, Filtration_value>,
                                         boost::property<edge_filtration_t, Filtration_value>>
      Graph;

  typedef int Vertex_handle;

 public:
  /** \brief Sparse_rips_complex constructor from a list of points.
   *
   * @param[in] points Range of points.
   * @param[in] distance Distance function that returns a `Filtration_value` from 2 given points.
   * @param[in] epsilon Approximation parameter. epsilon must be positive.
   *
   */
  template <typename RandomAccessPointRange, typename Distance>
  Sparse_rips_complex(const RandomAccessPointRange& points, Distance distance, double epsilon) {
    GUDHI_CHECK(epsilon > 0, "epsilon must be positive");
    std::vector<Vertex_handle> sorted_points;
    std::vector<Filtration_value> params;
    auto dist_fun = [&](Vertex_handle i, Vertex_handle j) { return distance(points[i], points[j]); };
    Ker<decltype(dist_fun)> kernel(dist_fun);
    subsampling::choose_n_farthest_points(kernel, boost::irange<Vertex_handle>(0, boost::size(points)), -1, -1,
                                          std::back_inserter(sorted_points), std::back_inserter(params));
    compute_sparse_graph(sorted_points, params, dist_fun, epsilon);
  }

  /** \brief Sparse_rips_complex constructor from a distance matrix.
   *
   * @param[in] distance_matrix Range of range of distances.
   * `distance_matrix[i][j]` returns the distance between points \f$i\f$ and
   * \f$j\f$ as long as \f$ 0 \leqslant i < j \leqslant
   * distance\_matrix.size().\f$
   * @param[in] epsilon Approximation parameter. epsilon must be positive.
   */
  template <typename DistanceMatrix>
  Sparse_rips_complex(const DistanceMatrix& distance_matrix, double epsilon)
      : Sparse_rips_complex(boost::irange<Vertex_handle>(0, boost::size(distance_matrix)),
                            [&](Vertex_handle i, Vertex_handle j) { return distance_matrix[j][i]; }, epsilon) {}

  /** \brief Fills the simplicial complex with the sparse Rips graph and
   * expands it with all the cliques, stopping at a given maximal dimension.
   *
   * \tparam SimplicialComplexForRips must meet `SimplicialComplexForRips` concept.
   *
   * @param[in] complex the complex to fill
   * @param[in] dim_max maximal dimension of the simplicial complex.
   * @exception std::invalid_argument In debug mode, if `complex.num_vertices()` does not return 0.
   *
   */
  template <typename SimplicialComplexForRips>
  void create_complex(SimplicialComplexForRips& complex, int dim_max) {
    GUDHI_CHECK(complex.num_vertices() == 0,
                std::invalid_argument("Sparse_rips_complex::create_complex - simplicial complex is not empty"));

    complex.insert_graph(graph_);
    complex.expansion(dim_max);
  }

 private:
  // choose_n_farthest_points wants the distance function in this form...
  template <class Distance>
  struct Ker {
    typedef std::size_t Point_d;  // index into point range
    Ker(Distance& d) : dist(d) {}
    // Despite the name, this is not squared...
    typedef Distance Squared_distance_d;
    Squared_distance_d& squared_distance_d_object() const { return dist; }
    Distance& dist;
  };

  // PointRange must be random access.
  template <typename PointRange, typename ParamRange, typename Distance>
  void compute_sparse_graph(const PointRange& points, const ParamRange& params, Distance& dist, double epsilon) {
    const int n = boost::size(points);
    graph_.~Graph();
    new (&graph_) Graph(n);
    // for(auto v : vertices(g)) // doesn't work :-(
    typename boost::graph_traits<Graph>::vertex_iterator v_i, v_e;
    for (std::tie(v_i, v_e) = vertices(graph_); v_i != v_e; ++v_i) {
      auto v = *v_i;
      // This whole loop might not be necessary, leave it until someone investigates if it is safe to remove.
      put(vertex_filtration_t(), graph_, v, 0);
    }

    // TODO(MG):
    // - make it parallel
    // - only test near-enough neighbors
    for (int i = 0; i < n; ++i)
      for (int j = i + 1; j < n; ++j) {
        auto&& pi = points[i];
        auto&& pj = points[j];
        auto d = dist(pi, pj);
        auto li = params[i];
        auto lj = params[j];
        GUDHI_CHECK(lj <= li, "Bad furthest point sorting");
        Filtration_value alpha;

        // The paper has d/2 and d-lj/e to match the Cech, but we use doubles to match the Rips
        if (d * epsilon <= 2 * lj)
          alpha = d;
        else if (d * epsilon <= li + lj && (epsilon >= 1 || d * epsilon <= lj * (1 + 1 / (1 - epsilon))))
          alpha = (d - lj / epsilon) * 2;
        else
          continue;

        add_edge(pi, pj, alpha, graph_);
      }
  }

  Graph graph_;
};

}  // namespace rips_complex

}  // namespace Gudhi

#endif  // SPARSE_RIPS_COMPLEX_H_