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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2011-2016 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 "KDTreeVectorOfVectorsAdaptor.h"
#include <ctime>
#include <cstdlib>
#include <iostream>
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 */ );
mat_index.index->buildIndex();
// 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], nanoflann::SearchParams(10));
std::cout << "knnSearch(nn="<<num_results<<"): \n";
for (size_t i = 0; i < num_results; 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 */);
}