/*!

@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause

*/

#pragma once


#include <pyclustering/cluster/bsas.hpp>
#include <pyclustering/cluster/mbsas_data.hpp>

#include <pyclustering/utils/metric.hpp>


using namespace pyclustering::utils::metric;


namespace pyclustering {

namespace clst {


/*!

@class    mbsas mbsas.hpp pyclustering/cluster/mbsas.hpp

@brief    Class represents MBSAS (Modified Basic Sequential Algorithmic Scheme).
@details  Interface of MBSAS algorithm is the same as for BSAS. This algorithm performs clustering in two steps.
           The first - is determination of amount of clusters. The second - is assignment of points that were not
           marked as a cluster representatives to clusters.

*/
class mbsas : public bsas {
public:
    /*!

    @brief    Default constructor of the clustering algorithm.

    */
    mbsas() = default;

    /*!

    @brief    Creates MBSAS algorithm using specified parameters.

    @param[in] p_amount: amount of clusters that should be allocated.
    @param[in] p_threshold: threshold of dissimilarity (maximum distance) between points.
    @param[in] p_metric: metric for distance calculation between points.

    */
    mbsas(const std::size_t p_amount,
          const double p_threshold,
          const distance_metric<point> & p_metric = distance_metric_factory<point>::euclidean());

public:
    /*!
    
    @brief    Performs cluster analysis of an input data.
    
    @param[in]  p_data: input data for cluster analysis.
    @param[out] p_result: clustering result of an input data.
    
    */
    void process(const dataset & p_data, mbsas_data & p_result);
};


}

}