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/*!
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
*/
#pragma once
#include <pyclustering/interface/pyclustering_package.hpp>
#include <pyclustering/definitions.hpp>
/**
*
* @brief K-Medians result is returned by pyclustering_package that consist sub-packages and this enumerator provides
* named indexes for sub-packages.
*
*/
enum kmedoids_package_indexer {
KMEDOIDS_PACKAGE_INDEX_CLUSTERS = 0,
KMEDOIDS_PACKAGE_INDEX_MEDOIDS,
KMEDOIDS_PACKAGE_SIZE
};
/**
*
* @brief Clustering algorithm K-Medoids returns allocated clusters.
* @details Caller should destroy returned result that is in 'pyclustering_package'.
*
* @param[in] p_sample: input data for clustering.
* @param[in] p_medoids: initial medoids of clusters.
* @param[in] p_tolerance: stop condition - when changes of medians are less then tolerance value.
* @param[in] p_itermax: maximum number of iterations for cluster analysis.
* @param[in] p_metric: pointer to distance metric 'distance_metric' that is used for distance calculation between two points.
* @param[in] p_type: representation of data type ('0' - points, '1' - distance matrix).
*
* @return Returns result of clustering - array of allocated clusters in pyclustering package.
*
*/
extern "C" DECLARATION pyclustering_package * kmedoids_algorithm(const pyclustering_package * const p_sample,
const pyclustering_package * const p_medoids,
const double p_tolerance,
const std::size_t p_itermax,
const void * const p_metric,
const std::size_t p_type);
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