ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t > Class Template Reference

#include <hdbscan.hpp>

Collaboration diagram for ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >:

Public Member Functions
	CondensedHierarchy (const raft::handle_t &handle_, size_t n_leaves_)
	CondensedHierarchy (const raft::handle_t &handle_, size_t n_leaves_, int n_edges_, value_idx *parents_, value_idx *children_, value_t *lambdas_, value_idx *sizes_)
	CondensedHierarchy (const raft::handle_t &handle_, size_t n_leaves_, int n_edges_, int n_clusters_, rmm::device_uvector< value_idx > &&parents_, rmm::device_uvector< value_idx > &&children_, rmm::device_uvector< value_t > &&lambdas_, rmm::device_uvector< value_idx > &&sizes_)
void	condense (value_idx *full_parents, value_idx *full_children, value_t *full_lambdas, value_idx *full_sizes, value_idx size=-1)
value_idx	get_cluster_tree_edges ()
value_idx *	get_parents ()
value_idx *	get_children ()
value_t *	get_lambdas ()
value_idx *	get_sizes ()
value_idx	get_n_edges ()
int	get_n_clusters ()
value_idx	get_n_leaves () const

Detailed Description

template<typename value_idx, typename value_t>
class ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >

The Condensed hierarchicy is represented by an edge list with parents as the source vertices, children as the destination, with attributes for the cluster size and lambda value.

Template Parameters

value_idx
value_t

Constructor & Destructor Documentation

CondensedHierarchy() [1/3]

template<typename value_idx , typename value_t >

ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::CondensedHierarchy	(	const raft::handle_t &	handle_,
		size_t	n_leaves_
	)

Constructs an empty condensed hierarchy object which requires condense() to be called in order to populate the state.

Parameters

handle_
n_leaves_

CondensedHierarchy() [2/3]

template<typename value_idx , typename value_t >

ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::CondensedHierarchy	(	const raft::handle_t &	handle_,
		size_t	n_leaves_,
		int	n_edges_,
		value_idx *	parents_,
		value_idx *	children_,
		value_t *	lambdas_,
		value_idx *	sizes_
	)

Constructs a condensed hierarchy object with existing arrays which already contain a condensed hierarchy.

Parameters

handle_
n_leaves_
n_edges_
parents_
children_
lambdas_
sizes_

CondensedHierarchy() [3/3]

template<typename value_idx , typename value_t >

ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::CondensedHierarchy	(	const raft::handle_t &	handle_,
		size_t	n_leaves_,
		int	n_edges_,
		int	n_clusters_,
		rmm::device_uvector< value_idx > &&	parents_,
		rmm::device_uvector< value_idx > &&	children_,
		rmm::device_uvector< value_t > &&	lambdas_,
		rmm::device_uvector< value_idx > &&	sizes_
	)

Constructs a condensed hierarchy object by moving rmm::device_uvector. Used to construct cluster trees

Parameters

handle_
n_leaves_
n_edges_
n_clusters_
parents_
children_
lambdas_
sizes_

Member Function Documentation

condense()

template<typename value_idx , typename value_t >

void ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::condense	(	value_idx *	full_parents,
		value_idx *	full_children,
		value_t *	full_lambdas,
		value_idx *	full_sizes,
		value_idx	size = -1
	)

To maintain a high level of parallelism, the output from Condense::build_condensed_hierarchy() is sparse (the cluster nodes inside any collapsed subtrees will be 0).

This function converts the sparse form to a dense form and renumbers the cluster nodes into a topological sort order. The renumbering reverses the values in the parent array since root has the largest value in the single-linkage tree. Then, it makes the combined parent and children arrays monotonic. Finally all of the arrays of the dendrogram are sorted by parent->children->sizes (e.g. topological). The root node will always have an id of 0 and the largest cluster size.

Ths single-linkage tree dendrogram is a binary tree and parents/children can be found with simple indexing arithmetic but the condensed tree no longer has this property and so the tree now relies on either special indexing or the topological ordering for efficient traversal.

get_children()

template<typename value_idx , typename value_t >

value_idx* ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::get_children ( )

inline

get_cluster_tree_edges()

template<typename value_idx , typename value_t >

value_idx ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::get_cluster_tree_edges

(

)

get_lambdas()

template<typename value_idx , typename value_t >

value_t* ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::get_lambdas ( )

inline

get_n_clusters()

template<typename value_idx , typename value_t >

int ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::get_n_clusters ( )

inline

get_n_edges()

template<typename value_idx , typename value_t >

value_idx ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::get_n_edges ( )

inline

get_n_leaves()

template<typename value_idx , typename value_t >

value_idx ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::get_n_leaves ( ) const

inline

get_parents()

template<typename value_idx , typename value_t >

value_idx* ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::get_parents ( )

inline

get_sizes()

template<typename value_idx , typename value_t >

value_idx* ML::HDBSCAN::Common::CondensedHierarchy< value_idx, value_t >::get_sizes ( )

inline

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

• include/cuml/cluster/hdbscan.hpp