Clusterer¶

template<std::size_t Ndim> class Clusterer¶

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:¶

std::size_t Ndim¶: The number of dimensions of the points to cluster

Public Functions¶

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

Constuct a Clusterer object.

Parameters:¶

DistanceParameter<Ndim> dc¶: Distance threshold for clustering. This parameter can be a single float or an array of floats specifying the distance threshold for each dimension.
float rhoc¶: Density threshold for clustering
DistanceParameter<Ndim> dm = -1.f¶: Minimum distance between clusters. This parameter can be a single float or an array of floats specifying the distance threshold for each dimension.
DistanceParameter<Ndim> seed_dc = -1.f¶: Distance threshold for seed points, if -1.f, dc is used. This parameter can be a single float or an array of floats specifying the distance threshold for each dimension.
int pPBin = 128¶: Number of points per bin, used to determine the tile size

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

Constuct a Clusterer object.

Parameters:¶

Queue &queue¶: The queue to use for the device operations
DistanceParameter<Ndim> dc¶: Distance threshold for clustering. This parameter can be a single float or an array of floats specifying the distance threshold for each dimension.
float rhoc¶: Density threshold for clustering
DistanceParameter<Ndim> dm = -1.f¶: Minimum distance between clusters. This parameter can be a single float or an array of floats specifying the distance threshold for each dimension.
DistanceParameter<Ndim> seed_dc = -1.f¶: Distance threshold for seed points, if the default value -1.f, dc is used. This parameter can be a single float or an array of floats specifying the distance threshold for each dimension.
int pPBin = 128¶: Number of points per bin, used to determine the tile size

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

Set the parameters for the clustering algorithm.

Parameters:¶

DistanceParameter<Ndim> dc¶: Distance threshold for clustering
float rhoc¶: Density threshold for clustering
DistanceParameter<Ndim> dm = -1.f¶: Minimum distance between clusters
DistanceParameter<Ndim> seed_dc = -1.f¶: Distance threshold for seed points, if the defualt value -1.f, dc is used
int pPBin = 128¶: Number of points per bin, used to determine the tile size

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.

Parameters:¶

Queue &queue¶: The queue to use for the device operations
PointsHost &h_points¶: Host points to cluster
const Kernel &kernel = FlatKernel{.5f}¶: The convolutional kernel to use for computing the local densities, default is FlatKernel with height 0.5
std::size_t block_size = 256¶: The size of the blocks to use for clustering, default is 256

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.

Note

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

Parameters:¶

PointsHost &h_points¶: Host points to cluster
const Kernel &kernel = FlatKernel{.5f}¶: The convolutional kernel to use for computing the local densities, default is FlatKernel with height 0.5
std::size_t block_size = 256¶: The size of the blocks to use for clustering, default is 256

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.

Parameters:¶

Queue &queue¶: The queue to use for the device operations
PointsHost &h_points¶: Host points to cluster
PointsDevice &dev_points¶: Device points to cluster
const Kernel &kernel = FlatKernel{.5f}¶: The convolutional kernel to use for computing the local densities, default is FlatKernel with height 0.5
std::size_t block_size = 256¶: The size of the blocks to use for clustering, default is 256

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.

Parameters:¶

Queue &queue¶: The queue to use for the device operations
PointsDevice &dev_points¶: Device points to cluster
const Kernel &kernel = FlatKernel{.5f}¶: The convolutional kernel to use for computing the local densities, default is FlatKernel with height 0.5
std::size_t block_size = 256¶: The size of the blocks to use for clustering, default is 256

template<std::ranges::contiguous_range TRange> void setWrappedCoordinates(const TRange &wrapped_coordinates)¶

Specify which coordinates are periodic.

Parameters:¶

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

template<std::integral... TArgs> void setWrappedCoordinates(TArgs... wrapped_coordinates)¶

Specify which coordinates are periodic.

Template Parameters:¶

std::integral... 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

host_associator getClusters(const PointsHost &h_points)¶

Get the clusters from the host points.

Parameters:¶

const PointsHost &h_points¶: Host points

Returns:¶

An associator mapping clusters and points

AssociationMap<Device> getClusters(Queue &queue, const PointsDevice &d_points)¶

Get the clusters from the device points This function returns an associator object mapping the clusters to the points they contain.

Parameters:¶

const PointsDevice &d_points¶: Device points

Returns:¶

An associator mapping clusters and points