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#ifndef PCL_SURFACE_IMPL_MARCHING_CUBES_RBF_H_
#define PCL_SURFACE_IMPL_MARCHING_CUBES_RBF_H_

#include <pcl/surface/marching_cubes_rbf.h>
#include <pcl/common/common.h>
#include <pcl/common/vector_average.h>
#include <pcl/Vertices.h>
#include <pcl/kdtree/kdtree_flann.h>

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointNT>
pcl::MarchingCubesRBF<PointNT>::~MarchingCubesRBF ()
{
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointNT> void
pcl::MarchingCubesRBF<PointNT>::voxelizeData ()
{
  // Initialize data structures
  const unsigned int N = static_cast<unsigned int> (input_->size ());
  Eigen::MatrixXd M (2*N, 2*N),
                  d (2*N, 1);

  for (unsigned int row_i = 0; row_i < 2*N; ++row_i)
  {
    // boolean variable to determine whether we are in the off_surface domain for the rows
    bool row_off = (row_i >= N) ? 1 : 0;
    for (unsigned int col_i = 0; col_i < 2*N; ++col_i)
    {
      // boolean variable to determine whether we are in the off_surface domain for the columns
      bool col_off = (col_i >= N) ? 1 : 0;
      M (row_i, col_i) = kernel (Eigen::Vector3f (input_->points[col_i%N].getVector3fMap ()).cast<double> () + Eigen::Vector3f (input_->points[col_i%N].getNormalVector3fMap ()).cast<double> () * col_off * off_surface_epsilon_,
                                 Eigen::Vector3f (input_->points[row_i%N].getVector3fMap ()).cast<double> () + Eigen::Vector3f (input_->points[row_i%N].getNormalVector3fMap ()).cast<double> () * row_off * off_surface_epsilon_);
    }

    d (row_i, 0) = row_off * off_surface_epsilon_;
  }

  // Solve for the weights
  Eigen::MatrixXd w (2*N, 1);

  // Solve_linear_system (M, d, w);
  w = M.fullPivLu ().solve (d);

  std::vector<double> weights (2*N);
  std::vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d> > centers (2*N);
  for (unsigned int i = 0; i < N; ++i)
  {
    centers[i] = Eigen::Vector3f (input_->points[i].getVector3fMap ()).cast<double> ();
    centers[i + N] = Eigen::Vector3f (input_->points[i].getVector3fMap ()).cast<double> () + Eigen::Vector3f (input_->points[i].getNormalVector3fMap ()).cast<double> () * off_surface_epsilon_;
    weights[i] = w (i, 0);
    weights[i + N] = w (i + N, 0);
  }

  for (int x = 0; x < res_x_; ++x)
    for (int y = 0; y < res_y_; ++y)
      for (int z = 0; z < res_z_; ++z)
      {
        const Eigen::Vector3f point_f = (size_voxel_ * Eigen::Array3f (x, y, z) 
            + lower_boundary_).matrix ();
        const Eigen::Vector3d point = point_f.cast<double> ();

        double f = 0.0;
        std::vector<double>::const_iterator w_it (weights.begin());
        for (std::vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d> >::const_iterator c_it = centers.begin ();
             c_it != centers.end (); ++c_it, ++w_it)
          f += *w_it * kernel (*c_it, point);

        grid_[x * res_y_*res_z_ + y * res_z_ + z] = float (f);
      }
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointNT> double
pcl::MarchingCubesRBF<PointNT>::kernel (Eigen::Vector3d c, Eigen::Vector3d x)
{
  double r = (x - c).norm ();
  return (r * r * r);
}

#define PCL_INSTANTIATE_MarchingCubesRBF(T) template class PCL_EXPORTS pcl::MarchingCubesRBF<T>;

#endif    // PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_

