docs/k__means__clustering_8hpp.html

#include <vector>

#include <iostream>


#include "paal/clustering/k_means_clustering.hpp"

#include "paal/utils/functors.hpp"


int main() {

    using Point = std::vector<double>;


    // sample data

    unsigned const int NUMBER_OF_CLUSTER = 2;

    std::vector<Point> points = { { 0, 0 },

                                  { 0, 3 },

                                  { 4, 0 } };

    std::vector<std::pair<Point , int>> point_cluster_pair;

    std::vector<Point> centers(NUMBER_OF_CLUSTER);


    // solution

    paal::get_random_centers(points, NUMBER_OF_CLUSTER, centers.begin());

    paal::k_means(points , centers,

                  back_inserter(point_cluster_pair));


    for (auto i : point_cluster_pair) {

        for(auto && j : i.first) {

            std::cout << j << ",";

        }


        std::cout<< " " << i.second << std::endl;

    }
Functions
template<typename Cluster , typename OutputIterator >
void	paal::centroid_minimalize_w_c_s_s (Cluster &&cluster, OutputIterator out)
	return centroid that minimize within-cluster sum of squares

template<typename Clusters , typename OutputIterator >
void	paal::centroids_minimalize_w_c_s_s (Clusters &&clusters, OutputIterator out)
	centroid minimize within cluster sum of squares More...

template<typename Points , class Centers , class OutputIterator , class Visitor = k_means_visitor>
auto	paal::k_means (Points &&points, Centers &&centers, OutputIterator result, Visitor visitor=Visitor{})
	this is solve k_means_clustering problem and return vector of cluster example: #include <vector> #include <iostream> #include "paal/clustering/k_means_clustering.hpp" #include "paal/utils/functors.hpp" int main() { using Point = std::vector<double>; // sample data unsigned const int NUMBER_OF_CLUSTER = 2; std::vector<Point> points = { { 0, 0 }, { 0, 3 }, { 4, 0 } }; std::vector<std::pair<Point , int>> point_cluster_pair; std::vector<Point> centers(NUMBER_OF_CLUSTER); // solution paal::get_random_centers(points, NUMBER_OF_CLUSTER, centers.begin()); paal::k_means(points , centers, back_inserter(point_cluster_pair)); for (auto i : point_cluster_pair) { for(auto && j : i.first) { std::cout << j << ","; } std::cout<< " " << i.second << std::endl; } More...