Signature	Description	Parameters
#include <DataFrame/DataFrameMLVisitors.h> template<typename T, typename I = unsigned long> struct AffinityPropVisitor;	This is a “single action visitor”, meaning it is passed the whole data vector in one call and you must use the single_act_visit() interface. This functor class finds clusters in data using Affinity Propagation algorithm. The constructor takes three parameters 1. Number of iterations 2. A function to calculate distance between to data points of type T with a default value 3. Damping factor used in the algorithm. The default is 0.9. (1 – damping factor) prevents numerical oscillations. explicit AffinityPropVisitor( size_type num_of_iter, distance_func f = [](const value_type &x, const value_type &y) -> double { return ((x - y) * (x - y)); }, double damping_factor = 0.9); The result type is VectorPtrView of type T containing the centers of clusters. There is also a get_clusters() method that returns an vector of VectorPtrView’s which contain the data clustered around the centers.	T: Column data type I: Index type

static void test_affinity_propagation()  {

    std::cout << "\nTesting affinity propagation visitor ..." << std::endl;

    const size_t            item_cnt = 50;
    MyDataFrame             df;
    RandGenParams<double>   p;
    std::vector<double>     final_col;
    std::vector<double>     col_data;

    p.seed = 3575984165U;

    p.min_value = 0;
    p.max_value = 10;
    col_data = gen_uniform_real_dist<double>(item_cnt, p);
    final_col.insert(final_col.end(), col_data.begin(), col_data.end());

    p.min_value = 20;
    p.max_value = 30;
    col_data = gen_uniform_real_dist<double>(item_cnt, p);
    final_col.insert(final_col.end(), col_data.begin(), col_data.end());

    p.min_value = 40;
    p.max_value = 50;
    col_data = gen_uniform_real_dist<double>(item_cnt, p);
    final_col.insert(final_col.end(), col_data.begin(), col_data.end());

    p.min_value = 60;
    p.max_value = 70;
    col_data = gen_uniform_real_dist<double>(item_cnt, p);
    final_col.insert(final_col.end(), col_data.begin(), col_data.end());

    p.min_value = 80;
    p.max_value = 90;
    col_data = gen_uniform_real_dist<double>(item_cnt, p);
    final_col.insert(final_col.end(), col_data.begin(), col_data.end());

    df.load_data(MyDataFrame::gen_sequence_index(0, item_cnt * 5, 1), std::make_pair("col1", final_col));
    df.shuffle<1, double>({"col1"}, false);

    KMeansVisitor<5, double>    km_visitor(1000);
    AffinityPropVisitor<double> ap_visitor(50);

    df.single_act_visit<double>("col1", km_visitor);
    df.single_act_visit<double>("col1", ap_visitor);

    // Using the calculated means, separate the given column into clusters
    const auto  k_means = km_visitor.get_result();
    const auto  results = ap_visitor.get_clusters(df.get_index(), df.get_column<double>("col1"));

    for (auto iter : k_means)  {
        std::cout << iter << ", ";
    }
    std::cout << "\n\n" << std::endl;
    for (auto iter : results)  {
        for (auto iter2 : iter)  {
            std::cout << iter2 << ", ";
        }
        std::cout << "\n" << std::endl;
    }
}