mlpack::meanshift::MeanShift< UseKernel, KernelType, MatType > Class Template Reference

This class implements mean shift clustering. More...

#include <mean_shift.hpp>

Public Member Functions
	MeanShift (const double radius=0, const size_t maxIterations=1000, const KernelType kernel=KernelType())
	Create a mean shift object and set the parameters which mean shift will be run with. More...
double	EstimateRadius (const MatType &data, const double ratio=0.2)
	Give an estimation of radius based on given dataset. More...
void	Cluster (const MatType &data, arma::Row< size_t > &assignments, arma::mat &centroids, bool forceConvergence=true, bool useSeeds=true)
	Perform mean shift clustering on the data, returning a list of cluster assignments and centroids. More...
size_t	MaxIterations () const
	Get the maximum number of iterations.
size_t &	MaxIterations ()
	Set the maximum number of iterations.
double	Radius () const
	Get the radius.
void	Radius (double radius)
	Set the radius.
const KernelType &	Kernel () const
	Get the kernel.
KernelType &	Kernel ()
	Modify the kernel.

Detailed Description

template<bool UseKernel = false, typename KernelType = kernel::GaussianKernel, typename MatType = arma::mat>
class mlpack::meanshift::MeanShift< UseKernel, KernelType, MatType >

This class implements mean shift clustering.

For each point in dataset, apply mean shift algorithm until maximum iterations or convergence. Then remove duplicate centroids.

A simple example of how to run mean shift clustering is shown below.

extern arma::mat data; // Dataset we want to run mean shift on.
arma::Row<size_t> assignments; // Cluster assignments.
arma::mat centroids; // Cluster centroids.
bool forceConvergence = true; // Flag whether to force each centroid seed
to converge regardless of maxIterations.

MeanShift<> meanShift();
meanShift.Cluster(dataset, assignments, centroids, forceConvergence);

Template Parameters

UseKernel	Use kernel or mean to calculate new centroid. If false, KernelType will be ignored.
KernelType	The kernel to use.
MatType	The type of matrix the data is stored in.

Constructor & Destructor Documentation

MeanShift()

template<bool UseKernel, typename KernelType , typename MatType >

mlpack::meanshift::MeanShift< UseKernel, KernelType, MatType >::MeanShift	(	const double	radius = 0,
		const size_t	maxIterations = 1000,
		const KernelType	kernel = KernelType()
	)

Create a mean shift object and set the parameters which mean shift will be run with.

Construct the Mean Shift object.

Parameters

radius	If distance of two centroids is less than it, one will be removed. If this value isn't positive, an estimation will be given when clustering.
maxIterations	Maximum number of iterations allowed before giving up iterations will terminate.
kernel	Optional KernelType object.

Member Function Documentation

Cluster()

template<bool UseKernel, typename KernelType , typename MatType >

void mlpack::meanshift::MeanShift< UseKernel, KernelType, MatType >::Cluster ( const MatType &  data, arma::Row< size_t > &  assignments, arma::mat &  centroids, bool  forceConvergence = true, bool  useSeeds = true  )

inline

Perform mean shift clustering on the data, returning a list of cluster assignments and centroids.

Perform Mean Shift clustering on the data set, returning a list of cluster assignments and centroids.

Template Parameters

MatType

Type of matrix.

Parameters

data	Dataset to cluster.
assignments	Vector to store cluster assignments in.
centroids	Matrix in which centroids are stored.
forceConvergence	Flag whether to force each centroid seed to converge regardless of maxIterations.
useSeeds	Set true to use seeds.

EstimateRadius()

template<bool UseKernel, typename KernelType , typename MatType >

double mlpack::meanshift::MeanShift< UseKernel, KernelType, MatType >::EstimateRadius	(	const MatType &	data,
		const double	ratio = 0.2
	)

Give an estimation of radius based on given dataset.

Parameters

data	Dataset for estimation.
ratio	Percentage of dataset to use for nearest neighbor search.

For each point in dataset, select nNeighbors nearest points and get nNeighbors distances. Use the maximum distance to estimate the duplicate threshhold.

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

• src/mlpack/methods/mean_shift/mean_shift.hpp
• src/mlpack/methods/mean_shift/mean_shift_impl.hpp