k-means.hpp File Reference

#include <thrill/api/all_gather.hpp>
#include <thrill/api/cache.hpp>
#include <thrill/api/collapse.hpp>
#include <thrill/api/reduce_by_key.hpp>
#include <thrill/api/sample.hpp>
#include <thrill/api/sum.hpp>
#include <thrill/common/vector.hpp>
#include <cereal/types/vector.hpp>
#include <thrill/data/serialization_cereal.hpp>
#include <limits>
#include <string>
#include <utility>
#include <vector>

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Classes
struct	CentroidAccumulated< Point >
	A point which contains "count" accumulated vectors. More...
struct	ClosestCentroid< Point >
	Assignment of a point to a cluster, which is the input to. More...
class	KMeansModel< Point >
	Model returned by KMeans algorithm containing results. More...

Namespaces
	examples
	examples::k_means

Macros
#define	THRILL_EXAMPLES_K_MEANS_K_MEANS_HEADER

Typedefs
template<size_t D>
using	Point = thrill::common::Vector< D, double >
	Compile-Time Fixed-Dimensional Points. More...
template<typename Point >
using	PointClusterId = std::pair< Point, size_t >
using	VPoint = thrill::common::VVector< double >
	A variable D-dimensional point with double precision. More...

Functions
template<typename Point , typename InStack >
auto	BisecKMeans (const DIA< Point, InStack > &input_points, size_t dimensions, size_t num_clusters, size_t iterations, double epsilon)
	Calculate k-Means using bisecting method. More...
template<typename Point , typename InStack >
auto	KMeans (const DIA< Point, InStack > &input_points, size_t dimensions, size_t num_clusters, size_t iterations, double epsilon=0.0)

Macro Definition Documentation

THRILL_EXAMPLES_K_MEANS_K_MEANS_HEADER

#define THRILL_EXAMPLES_K_MEANS_K_MEANS_HEADER

Definition at line 14 of file k-means.hpp.