usage.cpp

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// This example is in the public domain
 
#include "nabo/nabo.h"
#include <iostream>
 
int main()
{
        using namespace Nabo;
        using namespace Eigen;
        using namespace std;
        
        // 100 points in 3D
        MatrixXf M = MatrixXf::Random(3, 100);
        // 50 query points
        MatrixXf q = MatrixXf::Random(3, 50);
        
        // Create a kd-tree for M, note that M must stay valid during the lifetime of the kd-tree.
        NNSearchF* nns = NNSearchF::createKDTreeLinearHeap(M);
        
        // The output of the query are a matrix of indices to columns of M and 
        // a matrix of squared distances corresponding to these indices.
        // These matrix must have the correct size when passed to the search function.
        MatrixXi indices;
        MatrixXf dists2;
        
        // Look for the 5 nearest neighbours of each query point, 
        // We do not want approximations but we want to sort by the distance,
        indices.resize(5, q.cols());
        dists2.resize(5, q.cols());
        nns->knn(q, indices, dists2, 5, 0, NNSearchF::SORT_RESULTS);
        
        // Look for the 3 nearest neighbours of each query point, use the data themselves for the query.
        // We do not want approximations but we want to sort by the distance,
        // and we want to allow self-match.
        indices.resize(3, M.cols());
        dists2.resize(3, M.cols());
        nns->knn(M, indices, dists2, 3, 0, NNSearchF::SORT_RESULTS | NNSearchF::ALLOW_SELF_MATCH);
        
        // Make sure that the closest return points correspond to the points from M.
        for (int i = 0; i < 100; ++i)
        {
                // The query is the data itself and we allow self-match.
                if (indices.coeff(0, i) != i)
                        cerr << "Oups, something went wrong: " << indices.coeff(0, i) << " " <<  i << endl;
        }
        
        // Now look for the 2 nearset neighbours of each query point.
        // We do allow 10% approximation but do not want to allow self-match.
        // We do not care about sorting either.
        indices.resize(2, M.cols());
        dists2.resize(2, M.cols());
        nns->knn(M, indices, dists2, 2, 0.1, 0);
        for (int i = 0; i < 50; ++i)
        {
                // The query is the data itself but we forbide self-match.
                if (indices.coeff(0, i) == i)
                        cerr << "Oups, something went wrong: " << indices.coeff(0, i) << " " <<  i << endl;
        }
        
        // Cleanup the kd-tree.
        delete nns;
        
        return 0;
}