The Clusterer class is the interface for running the clustering algorithm. It provides methods to set up the clustering parameters, initializes the internal buffers and runs the clustering algorithm on host or device points. More...

#include <Clusterer.hpp>

Public Member Functions
	Clusterer (float dc, float rhoc, float dm, float seed_dc=-1.f, int pPBin=128)
	Constuct a Clusterer object.

	Clusterer (Queue &queue, float dc, float rhoc, float dm, float seed_dc=-1.f, int pPBin=128)
	Constuct a Clusterer object.

	Clusterer (Queue &queue, TilesDevice *tile_buffer, float dc, float rhoc, float dm, float seed_dc=-1.f, int pPBin=128)
	Constuct a Clusterer object.

void	setParameters (float dc, float rhoc, float dm, float seed_dc=-1.f, int pPBin=128)
	Set the parameters for the clustering algorithm.

template<concepts::convolutional_kernel Kernel = FlatKernel>
void	make_clusters (Queue &queue, PointsHost &h_points, const Kernel &kernel=FlatKernel{.5f}, std::size_t block_size=256)
	Construct the clusters from host points.

template<concepts::convolutional_kernel Kernel = FlatKernel>
void	make_clusters (PointsHost &h_points, const Kernel &kernel=FlatKernel{.5f}, std::size_t block_size=256)
	Construct the clusters from host points.

template<concepts::convolutional_kernel Kernel = FlatKernel>
void	make_clusters (Queue &queue, PointsHost &h_points, PointsDevice &dev_points, const Kernel &kernel=FlatKernel{.5f}, std::size_t block_size=256)
	Construct the clusters from host and device points.

template<concepts::convolutional_kernel Kernel = FlatKernel>
void	make_clusters (PointsHost &h_points, PointsDevice &dev_points, const Kernel &kernel=FlatKernel{.5f}, std::size_t block_size=256)
	Construct the clusters from host and device points.

template<concepts::convolutional_kernel Kernel = FlatKernel>
void	make_clusters (Queue &queue, PointsDevice &dev_points, const Kernel &kernel=FlatKernel{.5f}, std::size_t block_size=256)
	Construct the clusters from device points.

void	setWrappedCoordinates (const std::array< uint8_t, Ndim > &wrappedCoordinates)
	Specify which coordinates are periodic.

void	setWrappedCoordinates (std::array< uint8_t, Ndim > &&wrappedCoordinates)
	Specify which coordinates are periodic.

template<typename... TArgs>
void	setWrappedCoordinates (TArgs... wrappedCoordinates)
	Specify which coordinates are periodic.

std::vector< std::vector< int > >	getClusters (const PointsHost &h_points)
	Get the clusters from the host points.

Detailed Description

template<uint8_t Ndim>
class clue::Clusterer< Ndim >

The Clusterer class is the interface for running the clustering algorithm. It provides methods to set up the clustering parameters, initializes the internal buffers and runs the clustering algorithm on host or device points.

Template Parameters

Ndim	The number of dimensions of the points to cluster

Constructor & Destructor Documentation

◆ Clusterer() [1/3]

template<uint8_t Ndim>

clue::Clusterer< Ndim >::Clusterer	(	float	dc,
		float	rhoc,
		float	dm,
		float	seed_dc = -1.f,
		int	pPBin = 128 )

Constuct a Clusterer object.

Parameters

dc	Distance threshold for clustering
rhoc	Density threshold for clustering
dm	Minimum distance between clusters
seed_dc	Distance threshold for seed points, if -1.f, dc is used
pPBin	Number of points per bin, used to determine the tile size

◆ Clusterer() [2/3]

template<uint8_t Ndim>

clue::Clusterer< Ndim >::Clusterer	(	Queue &	queue,
		float	dc,
		float	rhoc,
		float	dm,
		float	seed_dc = -1.f,
		int	pPBin = 128 )

Constuct a Clusterer object.

Parameters

queue	The queue to use for the device operations
dc	Distance threshold for clustering
rhoc	Density threshold for clustering
dm	Minimum distance between clusters
seed_dc	Distance threshold for seed points, if the default value -1.f, dc is used
pPBin	Number of points per bin, used to determine the tile size

◆ Clusterer() [3/3]

template<uint8_t Ndim>

clue::Clusterer< Ndim >::Clusterer	(	Queue &	queue,
		TilesDevice *	tile_buffer,
		float	dc,
		float	rhoc,
		float	dm,
		float	seed_dc = -1.f,
		int	pPBin = 128 )

Constuct a Clusterer object.

Parameters

queue	The queue to use for the device operations
tile_buffer	Buffer to pre-allocated tiles
dc	Distance threshold for clustering
rhoc	Density threshold for clustering
dm	Minimum distance between clusters
seed_dc	Distance threshold for seed points, if the default value -1.f, dc is used
pPBin	Number of points per bin, used to determine the tile size

Member Function Documentation

◆ getClusters()

template<uint8_t Ndim>

std::vector< std::vector< int > > clue::Clusterer< Ndim >::getClusters ( const PointsHost & h_points )

Get the clusters from the host points.

Parameters

h_points Host points to cluster

Returns: A vector of clusters, where each cluster is a vector of point indices

◆ make_clusters() [1/5]

template<uint8_t Ndim>

template<concepts::convolutional_kernel Kernel = FlatKernel>

void clue::Clusterer< Ndim >::make_clusters	(	PointsHost &	h_points,
		const Kernel &	kernel = FlatKernel{.5f},
		std::size_t	block_size = 256 )

Construct the clusters from host points.

Parameters

h_points	Host points to cluster
kernel	The convolutional kernel to use for computing the local densities, default is FlatKernel with height 0.5
block_size	The size of the blocks to use for clustering, default is 256

Note: This method creates a temporary queue for the operations on the device

◆ make_clusters() [2/5]

template<uint8_t Ndim>

template<concepts::convolutional_kernel Kernel = FlatKernel>

void clue::Clusterer< Ndim >::make_clusters	(	PointsHost &	h_points,
		PointsDevice &	dev_points,
		const Kernel &	kernel = FlatKernel{.5f},
		std::size_t	block_size = 256 )

Construct the clusters from host and device points.

Parameters

h_points	Host points to cluster
dev_points	Device points to cluster
kernel	The convolutional kernel to use for computing the local densities, default is FlatKernel with height 0.5
block_size	The size of the blocks to use for clustering, default is 256

Note: This method creates a temporary queue for the operations on the device

◆ make_clusters() [3/5]

template<uint8_t Ndim>

template<concepts::convolutional_kernel Kernel = FlatKernel>

void clue::Clusterer< Ndim >::make_clusters	(	Queue &	queue,
		PointsDevice &	dev_points,
		const Kernel &	kernel = FlatKernel{.5f},
		std::size_t	block_size = 256 )

Construct the clusters from device points.

Parameters

queue	The queue to use for the device operations
dev_points	Device points to cluster
kernel	The convolutional kernel to use for computing the local densities, default is FlatKernel with height 0.5
block_size	The size of the blocks to use for clustering, default is 256

◆ make_clusters() [4/5]

template<uint8_t Ndim>

template<concepts::convolutional_kernel Kernel = FlatKernel>

void clue::Clusterer< Ndim >::make_clusters	(	Queue &	queue,
		PointsHost &	h_points,
		const Kernel &	kernel = FlatKernel{.5f},
		std::size_t	block_size = 256 )

Construct the clusters from host points.

Parameters

queue	The queue to use for the device operations
h_points	Host points to cluster
kernel	The convolutional kernel to use for computing the local densities, default is FlatKernel with height 0.5
block_size	The size of the blocks to use for clustering, default is 256

◆ make_clusters() [5/5]

template<uint8_t Ndim>

template<concepts::convolutional_kernel Kernel = FlatKernel>

void clue::Clusterer< Ndim >::make_clusters	(	Queue &	queue,
		PointsHost &	h_points,
		PointsDevice &	dev_points,
		const Kernel &	kernel = FlatKernel{.5f},
		std::size_t	block_size = 256 )

Construct the clusters from host and device points.

Parameters

queue	The queue to use for the device operations
h_points	Host points to cluster
dev_points	Device points to cluster
kernel	The convolutional kernel to use for computing the local densities, default is FlatKernel with height 0.5
block_size	The size of the blocks to use for clustering, default is 256

◆ setParameters()

template<uint8_t Ndim>

void clue::Clusterer< Ndim >::setParameters	(	float	dc,
		float	rhoc,
		float	dm,
		float	seed_dc = -1.f,
		int	pPBin = 128 )

Set the parameters for the clustering algorithm.

Parameters

dc	Distance threshold for clustering
rhoc	Density threshold for clustering
dm	Minimum distance between clusters
seed_dc	Distance threshold for seed points, if the defualt value -1.f, dc is used
pPBin	Number of points per bin, used to determine the tile size

◆ setWrappedCoordinates() [1/3]

template<uint8_t Ndim>

void clue::Clusterer< Ndim >::setWrappedCoordinates ( const std::array< uint8_t, Ndim > & wrappedCoordinates )

Specify which coordinates are periodic.

Parameters

wrappedCoordinates Array of wrapped coordinates, where 1 means periodic and 0 means non-periodic

◆ setWrappedCoordinates() [2/3]

template<uint8_t Ndim>

void clue::Clusterer< Ndim >::setWrappedCoordinates ( std::array< uint8_t, Ndim > && wrappedCoordinates )

Specify which coordinates are periodic.

Parameters

wrappedCoordinates Array of wrapped coordinates, where 1 means periodic and 0 means non-periodic

◆ setWrappedCoordinates() [3/3]

template<uint8_t Ndim>

template<typename... TArgs>

void clue::Clusterer< Ndim >::setWrappedCoordinates ( TArgs... wrappedCoordinates )

Specify which coordinates are periodic.

Template Parameters

TArgs Types of the wrapped coordinates, should be convertible to uint8_t

Parameters

wrappedCoordinates Wrapped coordinates, where 1 means periodic and 0 means non-periodic

The documentation for this class was generated from the following file:

include/CLUEstering/core/Clusterer.hpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ Clusterer() [1/3]

◆ Clusterer() [2/3]

◆ Clusterer() [3/3]

Member Function Documentation

◆ getClusters()

◆ make_clusters() [1/5]

◆ make_clusters() [2/5]

◆ make_clusters() [3/5]

◆ make_clusters() [4/5]

◆ make_clusters() [5/5]

◆ setParameters()

◆ setWrappedCoordinates() [1/3]

◆ setWrappedCoordinates() [2/3]

◆ setWrappedCoordinates() [3/3]