KDTreeVectorOfVectorsAdaptor.h

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#pragma once
 
#include "nanoflann.hpp"
#include <vector>
 
// ===== This example shows how to use nanoflann with these types of containers:
// using my_vector_of_vectors_t = std::vector<std::vector<double> > ;
//
// The next one requires #include <Eigen/Dense>
// using my_vector_of_vectors_t = std::vector<Eigen::VectorXd> ;
// =============================================================================
 
template <class VectorOfVectorsType, typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2,
          typename IndexType = size_t>
struct KDTreeVectorOfVectorsAdaptor
{
    using self_t = KDTreeVectorOfVectorsAdaptor<VectorOfVectorsType, num_t, DIM, Distance>;
    using metric_t = typename Distance::template traits<num_t, self_t>::distance_t;
    using index_t = nanoflann::KDTreeSingleIndexAdaptor<metric_t, self_t, DIM,
                                                        IndexType>; // nanoflann::KDTreeSingleIndexAdaptor,
                                                                    // nanoflann::KDTreeSingleIndexDynamicAdaptor
 
    index_t* index = nullptr;
 
    KDTreeVectorOfVectorsAdaptor(const size_t /* dimensionality */, const VectorOfVectorsType& mat,
                                 const int leaf_max_size = 10)
        : m_data(mat)
    {
        assert(mat.size() != 0 && mat[0].size() != 0);
        const size_t dims = mat[0].size();
        if (DIM > 0 && static_cast<int>(dims) != DIM)
            throw std::runtime_error("Data set dimensionality does not match the 'DIM' template "
                                     "argument");
        index = new index_t(static_cast<int>(dims), *this /* adaptor */,
                            nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size));
    }
 
    ~KDTreeVectorOfVectorsAdaptor() { delete index; }
 
    const VectorOfVectorsType& m_data;
 
    inline void query(const num_t* query_point, const size_t num_closest, IndexType* out_indices,
                      num_t* out_distances_sq) const
    {
        nanoflann::KNNResultSet<num_t, IndexType> resultSet(num_closest);
        resultSet.init(out_indices, out_distances_sq);
        index->findNeighbors(resultSet, query_point);
    }
 
    const self_t& derived() const { return *this; }
    self_t& derived() { return *this; }
 
    // Must return the number of data points
    inline size_t kdtree_get_point_count() const { return m_data.size(); }
 
    // Returns the dim'th component of the idx'th point in the class:
    inline num_t kdtree_get_pt(const size_t idx, const size_t dim) const { return m_data[idx][dim]; }
 
    // Optional bounding-box computation: return false to default to a standard
    // bbox computation loop.
    // Return true if the BBOX was already computed by the class and returned
    // in "bb" so it can be avoided to redo it again. Look at bb.size() to
    // find out the expected dimensionality (e.g. 2 or 3 for point clouds)
    template <class BBOX> bool kdtree_get_bbox(BBOX& /*bb*/) const { return false; }
 
}; // end of KDTreeVectorOfVectorsAdaptor