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/*!
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
*/
#pragma once
#include <pyclustering/cluster/somsc_data.hpp>
#include <pyclustering/nnet/som.hpp>
namespace pyclustering {
namespace clst {
/*!
@class somsc somsc.hpp pyclustering/cluster/somsc.hpp
@brief The class represents a simple clustering algorithm based on the self-organized feature map.
@details This algorithm uses amount of clusters that should be allocated as a size of SOM map. Captured
objects by neurons are considered as clusters. The algorithm is designed to process data with Gaussian
distribution that has spherical forms.
*/
class somsc {
private:
std::size_t m_amount_clusters = 0;
std::size_t m_epoch = 0;
public:
/**
*
* @brief Default constructor to create algorithm instance.
*
*/
somsc() = default;
/**
*
* @brief Default copy constructor to create algorithm instance.
*
*/
somsc(const somsc & p_other) = default;
/**
*
* @brief Default move constructor to create algorithm instance.
*
*/
somsc(somsc && p_other) = default;
/**
*
* @brief Creates algorithm with specified parameters.
*
* @param[in] p_amount_clusters: amount of clusters that should be allocated.
* @param[in] p_epoch: maximum iterations for SOM learning process.
*
*/
somsc(const std::size_t p_amount_clusters, const std::size_t p_epoch = 100);
/**
*
* @brief Default destructor to destroy algorithm instance.
*
*/
~somsc() = default;
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 (consists of allocated clusters).
*
*/
void process(const dataset & p_data, somsc_data & p_result);
};
}
}
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