Classes
struct	CentroidAccumulated
	A point which contains "count" accumulated vectors. More...

struct	ClosestCentroid
	Assignment of a point to a cluster, which is the input to. More...

class	KMeansModel
	Model returned by KMeans algorithm containing results. More...

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)

Typedef Documentation

◆ Point

using Point = thrill::common::Vector<D, double>

Compile-Time Fixed-Dimensional Points.

Examples:: examples/tutorial/k-means_step1.cpp, examples/tutorial/k-means_step2.cpp, examples/tutorial/k-means_step3.cpp, examples/tutorial/k-means_step4.cpp, examples/tutorial/k-means_step5.cpp, and examples/tutorial/k-means_step6.cpp.

Definition at line 39 of file k-means.hpp.

◆ PointClusterId

using PointClusterId = std::pair<Point, size_t>

Definition at line 45 of file k-means.hpp.

◆ VPoint

using VPoint = thrill::common::VVector<double>

A variable D-dimensional point with double precision.

Definition at line 42 of file k-means.hpp.

Function Documentation

◆ BisecKMeans()

auto examples::k_means::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.

initial cluster size

model that is steadily updated and returned to the calling function

Definition at line 258 of file k-means.hpp.

References ClosestCentroid< Point >::center, ClosestCentroid< Point >::cluster_id, and KMeans().

Referenced by RunKMeansFile(), and RunKMeansGenerated().

◆ KMeans()

auto examples::k_means::KMeans	(	const DIA< Point, InStack > &	input_points,
		size_t	dimensions,
		size_t	num_clusters,
		size_t	iterations,
		double	epsilon = `0.0`
	)

Calculate k-Means using Lloyd's Algorithm. The DIA centroids is both an input and an output parameter. The method returns a std::pair<Point2D, size_t> = Point2DClusterId into the centroids for each input point.

Definition at line 175 of file k-means.hpp.

References DIA< ValueType_, Stack_ >::Cache(), ClosestCentroid< Point >::center, ClosestCentroid< Point >::cluster_id, Vector< D, Type >::DistanceSquare(), and CentroidAccumulated< Point >::p.

Referenced by BisecKMeans(), RunKMeansFile(), and RunKMeansGenerated().

Classes

Typedefs