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/*!
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
*/
#pragma once
#include <memory>
#include <vector>
#include <pyclustering/cluster/cluster_data.hpp>
namespace pyclustering {
namespace clst {
/*!
@class dbscan_data dbscan_data.hpp pyclustering/cluster/dbscan_data.hpp
@brief Clustering results of DBSCAM algorithm that consists of information about allocated
clusters and noise (points that are not related to any cluster).
*/
class dbscan_data : public cluster_data {
private:
clst::noise m_noise;
public:
/*!
@brief Default constructor that creates empty clustering data.
*/
dbscan_data() = default;
/*!
@brief Copy constructor of DBSCAN clustering data.
@param[in] p_other: another DBSCAN clustering data.
*/
dbscan_data(const dbscan_data & p_other) = default;
/*!
@brief Move constructor of DBSCAN clustering data.
@param[in] p_other: another clustering data.
*/
dbscan_data(dbscan_data && p_other) = default;
/*!
@brief Default destructor that destroys DBSCAN clustering data.
*/
virtual ~dbscan_data() = default;
public:
/*!
@brief Returns reference to outliers represented by indexes.
*/
clst::noise & noise() { return m_noise; }
/*!
@brief Returns constant reference to outliers represented by indexes.
*/
const clst::noise & noise() const { return m_noise; }
};
}
}
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