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 * Copyright 2011-2022 Jose Luis Blanco (joseluisblancoc@gmail.com).
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#include <cstdlib>
#include <ctime>
#include <iostream>
#include <nanoflann.hpp>

#include "utils.h"

template <typename num_t>
void kdtree_demo(const size_t N)
{
    PointCloud_Quat<num_t> cloud;

    // Generate points:
    generateRandomPointCloud_Quat(cloud, N);

    num_t query_pt[4] = {0.5, 0.5, 0.5, 0.5};

    // construct a kd-tree index:
    using my_kd_tree_t = nanoflann::KDTreeSingleIndexAdaptor<
        nanoflann::SO3_Adaptor<num_t, PointCloud_Quat<num_t>>,
        PointCloud_Quat<num_t>, 4 /* dim */
        >;

    dump_mem_usage();

    my_kd_tree_t index(4 /*dim*/, cloud, {10 /* max leaf */});
    index.buildIndex();

    dump_mem_usage();
    {
        // do a knn search
        const size_t                   num_results = 1;
        size_t                         ret_index;
        num_t                          out_dist_sqr;
        nanoflann::KNNResultSet<num_t> resultSet(num_results);
        resultSet.init(&ret_index, &out_dist_sqr);
        index.findNeighbors(
            resultSet, &query_pt[0], nanoflann::SearchParams(10));

        std::cout << "knnSearch(nn=" << num_results << "): \n";
        std::cout << "ret_index=" << ret_index
                  << " out_dist_sqr=" << out_dist_sqr << std::endl;
    }
}

int main()
{
    // Randomize Seed
    srand(static_cast<unsigned int>(time(nullptr)));
    kdtree_demo<float>(1000000);
    kdtree_demo<double>(1000000);
    return 0;
}
