An algorithm for an exact Lloyd iteration which simply uses dual-tree nearest-neighbor search to find the nearest centroid for each point in the dataset. More...

Public Types
template < typename TreeMetricType , typename IgnoredStatType , typename TreeMatType >
using	NNSTreeType = TreeType< TreeMetricType, DualTreeKMeansStatistic, TreeMatType >

typedef TreeType< MetricType, DualTreeKMeansStatistic, MatType >	Tree
	Convenience typedef. More...

Public Member Functions
	DualTreeKMeans (const MatType &dataset, MetricType &metric)
	Construct the DualTreeKMeans object, which will construct a tree on the points. More...

	~DualTreeKMeans ()
	Delete the tree constructed by the DualTreeKMeans object. More...

size_t	DistanceCalculations () const
	Return the number of distance calculations. More...

size_t &	DistanceCalculations ()
	Modify the number of distance calculations. More...

double	Iterate (const arma::mat &centroids, arma::mat &newCentroids, arma::Col< size_t > &counts)
	Run a single iteration of the dual-tree nearest neighbor algorithm for k-means, updating the given centroids into the newCentroids matrix. More...

Detailed Description

template
<
typename
MetricType
,
typename
MatType
,
template
<
typename
TreeMetricType
,
typename
TreeStatType
,
typename
TreeMatType
>
class
TreeType
=
tree::KDTree
>

class mlpack::kmeans::DualTreeKMeans< MetricType, MatType, TreeType >

An algorithm for an exact Lloyd iteration which simply uses dual-tree nearest-neighbor search to find the nearest centroid for each point in the dataset.

The conditions under which this will perform best are probably limited to the case where k is close to the number of points in the dataset, and the number of iterations of the k-means algorithm will be few.

Definition at line 41 of file dual_tree_kmeans.hpp.

Member Typedef Documentation

◆ NNSTreeType

using NNSTreeType = TreeType<TreeMetricType, DualTreeKMeansStatistic, TreeMatType>

Definition at line 51 of file dual_tree_kmeans.hpp.

◆ Tree

typedef TreeType<MetricType, DualTreeKMeansStatistic, MatType> Tree

Convenience typedef.

Definition at line 45 of file dual_tree_kmeans.hpp.

Constructor & Destructor Documentation

◆ DualTreeKMeans()

DualTreeKMeans	(	const MatType &	dataset,
		MetricType &	metric
	)

Construct the DualTreeKMeans object, which will construct a tree on the points.

◆ ~DualTreeKMeans()

~DualTreeKMeans ( )

Delete the tree constructed by the DualTreeKMeans object.

Member Function Documentation

◆ DistanceCalculations() [1/2]

size_t DistanceCalculations ( ) const

inline

Return the number of distance calculations.

Definition at line 77 of file dual_tree_kmeans.hpp.

◆ DistanceCalculations() [2/2]

size_t& DistanceCalculations ( )

inline

Modify the number of distance calculations.

Definition at line 79 of file dual_tree_kmeans.hpp.

References mlpack::kmeans::HideChild(), and mlpack::kmeans::RestoreChildren().

◆ Iterate()

double Iterate	(	const arma::mat &	centroids,
		arma::mat &	newCentroids,
		arma::Col< size_t > &	counts
	)

Run a single iteration of the dual-tree nearest neighbor algorithm for k-means, updating the given centroids into the newCentroids matrix.

Parameters

centroids	Current cluster centroids.
newCentroids	New cluster centroids.
counts	Current counts, to be overwritten with new counts.

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/kmeans/dual_tree_kmeans.hpp

Public Types

Public Member Functions