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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-Means result is returned by pyclustering_package that consist sub-packages and this enumerator provides
* named indexes for sub-packages.
*
*/
enum kmeans_package_indexer {
KMEANS_PACKAGE_INDEX_CLUSTERS = 0,
KMEANS_PACKAGE_INDEX_CENTERS,
KMEANS_PACKAGE_INDEX_EVOLUTION_CLUSTERS,
KMEANS_PACKAGE_INDEX_EVOLUTION_CENTERS,
KMEANS_PACKAGE_INDEX_WCE,
KMEANS_PACKAGE_SIZE
};
/**
*
* @brief Clustering algorithm K-Means returns allocated clusters.
* @details Caller should destroy returned result in 'pyclustering_package'.
*
* @param[in] p_sample: input data for clustering.
* @param[in] p_centers: initial cluster centers.
* @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_observe: if 'true' then evolution of cluster and center changes are collected to result.
* @param[in] p_metric: pointer to distance metric 'distance_metric' that is used for distance calculation between two points.
*
* @return Returns result of clustering - array of allocated clusters, if 'p_observe' is 'true' then package contains
* evolution of cluster and center changes.
*
*/
extern "C" DECLARATION pyclustering_package * kmeans_algorithm(const pyclustering_package * const p_sample,
const pyclustering_package * const p_initial_centers,
const double p_tolerance,
const std::size_t p_itermax,
const bool p_observe,
const void * const p_metric);
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