/***********************************************************************
 * Software License Agreement (BSD License)
 *
 * Copyright 2011-2024 Jose Luis Blanco (joseluisblancoc@gmail.com).
 *   All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *
 * 1. Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
 *    documentation and/or other materials provided with the distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
 * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
 * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
 * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
 * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
 * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
 * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
 * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *************************************************************************/

#include <nanoflann.hpp>
using namespace nanoflann;

#include <cstdlib>
#include <ctime>
#include <iostream>

#include "KDTreeVectorOfVectorsAdaptor.h"

const int SAMPLES_DIM = 15;

typedef std::vector<std::vector<double>> my_vector_of_vectors_t;

void generateRandomPointCloud(
    my_vector_of_vectors_t& samples, const size_t N, const size_t dim,
    const double max_range = 10.0)
{
    std::cout << "Generating " << N << " random points...";
    samples.resize(N);
    for (size_t i = 0; i < N; i++)
    {
        samples[i].resize(dim);
        for (size_t d = 0; d < dim; d++)
            samples[i][d] = max_range * (rand() % 1000) / (1000.0);
    }
    std::cout << "done\n";
}

void kdtree_demo(const size_t nSamples, const size_t dim)
{
    my_vector_of_vectors_t samples;

    const double max_range = 20;

    // Generate points:
    generateRandomPointCloud(samples, nSamples, dim, max_range);

    // Query point:
    std::vector<double> query_pt(dim);
    for (size_t d = 0; d < dim; d++)
        query_pt[d] = max_range * (rand() % 1000) / (1000.0);

    // construct a kd-tree index:
    // Dimensionality set at run-time (default: L2)
    // ------------------------------------------------------------
    typedef KDTreeVectorOfVectorsAdaptor<my_vector_of_vectors_t, double>
        my_kd_tree_t;

    my_kd_tree_t mat_index(dim /*dim*/, samples, 10 /* max leaf */);

    // do a knn search
    const size_t        num_results = 3;
    std::vector<size_t> ret_indexes(num_results);
    std::vector<double> out_dists_sqr(num_results);

    nanoflann::KNNResultSet<double> resultSet(num_results);

    resultSet.init(&ret_indexes[0], &out_dists_sqr[0]);
    mat_index.index->findNeighbors(resultSet, &query_pt[0]);

    std::cout << "knnSearch(nn=" << num_results << "): \n";
    for (size_t i = 0; i < resultSet.size(); i++)
        std::cout << "ret_index[" << i << "]=" << ret_indexes[i]
                  << " out_dist_sqr=" << out_dists_sqr[i] << std::endl;
}

int main()
{
    // Randomize Seed
    srand(static_cast<unsigned int>(time(nullptr)));
    kdtree_demo(1000 /* samples */, SAMPLES_DIM /* dim */);
}
