GreedyKCenters.h

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/* Author: Mark Moll */
 
#ifndef OMPL_DATASTRUCTURES_GREEDY_K_CENTERS_
#define OMPL_DATASTRUCTURES_GREEDY_K_CENTERS_
 
#include "ompl/util/RandomNumbers.h"
#include <functional>
#include <Eigen/Core>
 
namespace ompl
{
    template <typename _T>
    class GreedyKCenters
    {
    public:
        using DistanceFunction = std::function<double(const _T &, const _T &)>;
        using Matrix = Eigen::MatrixXd;
 
        GreedyKCenters() = default;
 
        virtual ~GreedyKCenters() = default;
 
        void setDistanceFunction(const DistanceFunction &distFun)
        {
            distFun_ = distFun;
        }
 
        const DistanceFunction &getDistanceFunction() const
        {
            return distFun_;
        }
 
        void kcenters(const std::vector<_T> &data, unsigned int k, std::vector<unsigned int> &centers, Matrix &dists)
        {
            // array containing the minimum distance between each data point
            // and the centers computed so far
            std::vector<double> minDist(data.size(), std::numeric_limits<double>::infinity());
 
            centers.clear();
            centers.reserve(k);
            if ((std::size_t)dists.rows() < data.size() || (std::size_t)dists.cols() < k)
                dists.resize(std::max(2u * (std::size_t)dists.rows() + 1u, data.size()), k);
            // first center is picked randomly
            centers.push_back(rng_.uniformInt(0, data.size() - 1));
            for (unsigned i = 1; i < k; ++i)
            {
                unsigned ind = 0;
                const _T &center = data[centers[i - 1]];
                double maxDist = -std::numeric_limits<double>::infinity();
                for (unsigned j = 0; j < data.size(); ++j)
                {
                    if ((dists(j, i - 1) = distFun_(data[j], center)) < minDist[j])
                        minDist[j] = dists(j, i - 1);
                    // the j-th center is the one furthest away from center 0,..,j-1
                    if (minDist[j] > maxDist)
                    {
                        ind = j;
                        maxDist = minDist[j];
                    }
                }
                // no more centers available
                if (maxDist < std::numeric_limits<double>::epsilon())
                    break;
                centers.push_back(ind);
            }
 
            const _T &center = data[centers.back()];
            unsigned i = centers.size() - 1;
            for (unsigned j = 0; j < data.size(); ++j)
                dists(j, i) = distFun_(data[j], center);
        }
 
    protected:
        DistanceFunction distFun_;
 
        RNG rng_;
    };
}  // namespace ompl
 
#endif