kmeans Namespace Reference

Namespace for k-means clustering. More...

Classes
struct	Details
	Additional statistics from the k-means algorithm. More...
class	Initialize
	Base class for initialization algorithms. More...
class	InitializeKmeanspp
	k-means++ initialization of Arthur and Vassilvitskii (2007). More...
struct	InitializeKmeansppOptions
	Options for k-means++ initialization. More...
class	InitializeNone
	No-op "initialization" with existing cluster centers. More...
class	InitializeRandom
	Initialize by sampling random observations without replacement. More...
struct	InitializeRandomOptions
	Options to use for InitializeRandom. More...
class	InitializeVariancePartition
	Implements the variance partitioning method of Su and Dy (2007). More...
struct	InitializeVariancePartitionOptions
	Options for InitializeVariancePartition. More...
class	MockMatrix
	Compile-time interface for matrix data. More...
class	Refine
	Interface for all k-means refinement algorithms. More...
class	RefineHartiganWong
	Implements the Hartigan-Wong algorithm for k-means clustering. More...
struct	RefineHartiganWongOptions
	Options for RefineHartiganWong. More...
class	RefineLloyd
	Implements the Lloyd algorithm for k-means clustering. More...
struct	RefineLloydOptions
	Options for RefineLloyd construction. More...
class	RefineMiniBatch
	Implements the mini-batch algorithm for k-means clustering. More...
struct	RefineMiniBatchOptions
	Options for RefineMiniBatch construction. More...
struct	Results
	Full statistics from k-means clustering. More...
class	SimpleMatrix
	A simple matrix of observations. More...

Functions
template<class Matrix_ , typename Cluster_ , typename Float_ >
void	compute_wcss (const Matrix_ &data, Cluster_ ncenters, const Float_ *centers, const Cluster_ *clusters, Float_ *wcss)
template<class Matrix_ , typename Cluster_ , typename Float_ >
Details< typename Matrix_::index_type >	compute (const Matrix_ &data, const Initialize< Matrix_, Cluster_, Float_ > &initialize, const Refine< Matrix_, Cluster_, Float_ > &refine, Cluster_ num_centers, Float_ *centers, Cluster_ *clusters)
template<class Matrix_ , typename Cluster_ , typename Float_ >
Results< Cluster_, Float_, typename Matrix_::index_type >	compute (const Matrix_ &data, const Initialize< Matrix_, Cluster_, Float_ > &initialize, const Refine< Matrix_, Cluster_, Float_ > &refine, Cluster_ num_centers)
template<typename Task_ , class Run_ >
void	parallelize (int num_workers, Task_ num_tasks, Run_ run_task_range)

Detailed Description

Namespace for k-means clustering.

Function Documentation

compute() [1/2]

template<class Matrix_ , typename Cluster_ , typename Float_ >

Results< Cluster_, Float_, typename Matrix_::index_type > kmeans::compute	(	const Matrix_ &	data,
		const Initialize< Matrix_, Cluster_, Float_ > &	initialize,
		const Refine< Matrix_, Cluster_, Float_ > &	refine,
		Cluster_	num_centers
	)

Overload that allocates the output vectors.

Template Parameters

Matrix_	Matrix type for the input data. This should satisfy the MockMatrix contract.
Cluster_	Integer type for the cluster assignments.
Float_	Floating-point type for the centroids.

Parameters

data	A matrix-like object (see MockMatrix) containing per-observation data.
initialize	Initialization method to use.
refine	Refinement method to use.
num_centers	Number of cluster centers.

Returns

Results of the clustering, including the centroid locations and cluster assignments.

compute() [2/2]

template<class Matrix_ , typename Cluster_ , typename Float_ >

Details< typename Matrix_::index_type > kmeans::compute	(	const Matrix_ &	data,
		const Initialize< Matrix_, Cluster_, Float_ > &	initialize,
		const Refine< Matrix_, Cluster_, Float_ > &	refine,
		Cluster_	num_centers,
		Float_ *	centers,
		Cluster_ *	clusters
	)

Template Parameters

Matrix_	Matrix type for the input data. This should satisfy the MockMatrix contract.
Cluster_	Integer type for the cluster assignments.
Float_	Floating-point type for the centroids.

Parameters

	data	A matrix-like object (see MockMatrix) containing per-observation data.
	initialize	Initialization method to use.
	refine	Refinement method to use.
	num_centers	Number of cluster centers.
[out]	centers	Pointer to an array of length equal to the product of num_centers and data.num_dimensions(). This contains a column-major matrix where rows correspond to dimensions and columns correspond to cluster centers. On output, each column should contain the initial centroid location for its cluster.
[in]	clusters	Pointer to an array of length equal to the number of observations (from data.num_observations()). On output, this will contain the 0-based cluster assignment for each observation.

compute_wcss()

template<class Matrix_ , typename Cluster_ , typename Float_ >

void kmeans::compute_wcss	(	const Matrix_ &	data,
		Cluster_	ncenters,
		const Float_ *	centers,
		const Cluster_ *	clusters,
		Float_ *	wcss
	)

Template Parameters

Matrix_	Matrix type for the input data, satisfying the MockMatrix contract.
Cluster_	Integer type for the cluster assignments.
Float_	Floating-point type for the centers and output.

Parameters

	data	A matrix-like object (see MockMatrix) containing per-observation data.
	ncenters	Number of cluster centers.
[in]	centers	Pointer to an array of length equal to the product of num_centers and data.num_dimensions(). This contains a column-major matrix where rows correspond to dimensions and columns correspond to cluster centers. Each column should contain the initial centroid location for its cluster.
[in]	clusters	Pointer to an array of length equal to the number of observations (from data.num_observations()). This should contain the 0-based cluster assignment for each observation.
[out]	wcss	Pointer to an array of length equal to the number of cluster centers. On output, this will contain the within-cluster sum of squares.

parallelize()

template<typename Task_ , class Run_ >

void kmeans::parallelize	(	int	num_workers,
		Task_	num_tasks,
		Run_	run_task_range
	)

Template Parameters

Task_	Integer type for the number of tasks.
Run_	Function to execute a range of tasks.

Parameters

num_workers	Number of workers.
num_tasks	Number of tasks.
run_task_range	Function to iterate over a range of tasks within a worker.

By default, this is an alias to subpar::parallelize_range(). However, if the KMEANS_CUSTOM_PARALLEL function-like macro is defined, it is called instead. Any user-defined macro should accept the same arguments as subpar::parallelize_range().