\section{Mean\+Shift$<$ Use\+Kernel, Kernel\+Type, Mat\+Type $>$ Class Template Reference} \label{classmlpack_1_1meanshift_1_1MeanShift}\index{Mean\+Shift$<$ Use\+Kernel, Kernel\+Type, Mat\+Type $>$@{Mean\+Shift$<$ Use\+Kernel, Kernel\+Type, Mat\+Type $>$}} This class implements mean shift clustering. \subsection*{Public Member Functions} \begin{DoxyCompactItemize} \item \textbf{ Mean\+Shift} (const double radius=0, const size\+\_\+t max\+Iterations=1000, const Kernel\+Type kernel=Kernel\+Type()) \begin{DoxyCompactList}\small\item\em Create a mean shift object and set the parameters which mean shift will be run with. \end{DoxyCompactList}\item void \textbf{ Cluster} (const Mat\+Type \&data, arma\+::\+Row$<$ size\+\_\+t $>$ \&assignments, arma\+::mat \¢roids, bool force\+Convergence=true, bool use\+Seeds=true) \begin{DoxyCompactList}\small\item\em Perform mean shift clustering on the data, returning a list of cluster assignments and centroids. \end{DoxyCompactList}\item double \textbf{ Estimate\+Radius} (const Mat\+Type \&data, const double ratio=0.\+2) \begin{DoxyCompactList}\small\item\em Give an estimation of radius based on given dataset. \end{DoxyCompactList}\item const Kernel\+Type \& \textbf{ Kernel} () const \begin{DoxyCompactList}\small\item\em Get the kernel. \end{DoxyCompactList}\item Kernel\+Type \& \textbf{ Kernel} () \begin{DoxyCompactList}\small\item\em Modify the kernel. \end{DoxyCompactList}\item size\+\_\+t \textbf{ Max\+Iterations} () const \begin{DoxyCompactList}\small\item\em Get the maximum number of iterations. \end{DoxyCompactList}\item size\+\_\+t \& \textbf{ Max\+Iterations} () \begin{DoxyCompactList}\small\item\em Set the maximum number of iterations. \end{DoxyCompactList}\item double \textbf{ Radius} () const \begin{DoxyCompactList}\small\item\em Get the radius. \end{DoxyCompactList}\item void \textbf{ Radius} (double radius) \begin{DoxyCompactList}\small\item\em Set the radius. \end{DoxyCompactList}\end{DoxyCompactItemize} \subsection{Detailed Description} \subsubsection*{template$<$bool Use\+Kernel = false, typename Kernel\+Type = kernel\+::\+Gaussian\+Kernel, typename Mat\+Type = arma\+::mat$>$\newline class mlpack\+::meanshift\+::\+Mean\+Shift$<$ Use\+Kernel, Kernel\+Type, Mat\+Type $>$} 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. \begin{DoxyCode} \textcolor{keyword}{extern} arma::mat data; \textcolor{comment}{// Dataset we want to run mean shift on.} arma::Row assignments; \textcolor{comment}{// Cluster assignments.} arma::mat centroids; \textcolor{comment}{// Cluster centroids.} \textcolor{keywordtype}{bool} forceConvergence = \textcolor{keyword}{true}; \textcolor{comment}{// Flag whether to force each centroid seed} to converge regardless of maxIterations. MeanShift<> meanShift(); meanShift.Cluster(dataset, assignments, centroids, forceConvergence); \end{DoxyCode} \begin{DoxyTemplParams}{Template Parameters} {\em Use\+Kernel} & Use kernel or mean to calculate new centroid. If false, Kernel\+Type will be ignored. \\ \hline {\em Kernel\+Type} & The kernel to use. \\ \hline {\em Mat\+Type} & The type of matrix the data is stored in. \\ \hline \end{DoxyTemplParams} Definition at line 51 of file mean\+\_\+shift.\+hpp. \subsection{Constructor \& Destructor Documentation} \mbox{\label{classmlpack_1_1meanshift_1_1MeanShift_a2d412008ae2244778bfd2afb1e292858}} \index{mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}!Mean\+Shift@{Mean\+Shift}} \index{Mean\+Shift@{Mean\+Shift}!mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}} \subsubsection{Mean\+Shift()} {\footnotesize\ttfamily \textbf{ Mean\+Shift} (\begin{DoxyParamCaption}\item[{const double}]{radius = {\ttfamily 0}, }\item[{const size\+\_\+t}]{max\+Iterations = {\ttfamily 1000}, }\item[{const Kernel\+Type}]{kernel = {\ttfamily KernelType()} }\end{DoxyParamCaption})} Create a mean shift object and set the parameters which mean shift will be run with. \begin{DoxyParams}{Parameters} {\em radius} & If distance of two centroids is less than it, one will be removed. If this value isn\textquotesingle{}t positive, an estimation will be given when clustering. \\ \hline {\em max\+Iterations} & Maximum number of iterations allowed before giving up iterations will terminate. \\ \hline {\em kernel} & Optional Kernel\+Type object. \\ \hline \end{DoxyParams} \subsection{Member Function Documentation} \mbox{\label{classmlpack_1_1meanshift_1_1MeanShift_ae6c156618ab407e37eb42f6843e46005}} \index{mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}!Cluster@{Cluster}} \index{Cluster@{Cluster}!mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}} \subsubsection{Cluster()} {\footnotesize\ttfamily void Cluster (\begin{DoxyParamCaption}\item[{const Mat\+Type \&}]{data, }\item[{arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{assignments, }\item[{arma\+::mat \&}]{centroids, }\item[{bool}]{force\+Convergence = {\ttfamily true}, }\item[{bool}]{use\+Seeds = {\ttfamily true} }\end{DoxyParamCaption})} Perform mean shift clustering on the data, returning a list of cluster assignments and centroids. \begin{DoxyTemplParams}{Template Parameters} {\em Mat\+Type} & Type of matrix. \\ \hline \end{DoxyTemplParams} \begin{DoxyParams}{Parameters} {\em data} & Dataset to cluster. \\ \hline {\em assignments} & Vector to store cluster assignments in. \\ \hline {\em centroids} & Matrix in which centroids are stored. \\ \hline {\em force\+Convergence} & Flag whether to force each centroid seed to converge regardless of max\+Iterations. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1meanshift_1_1MeanShift_a18e16a5764b4b09cd46402c5ac40e711}} \index{mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}!Estimate\+Radius@{Estimate\+Radius}} \index{Estimate\+Radius@{Estimate\+Radius}!mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}} \subsubsection{Estimate\+Radius()} {\footnotesize\ttfamily double Estimate\+Radius (\begin{DoxyParamCaption}\item[{const Mat\+Type \&}]{data, }\item[{const double}]{ratio = {\ttfamily 0.2} }\end{DoxyParamCaption})} Give an estimation of radius based on given dataset. \begin{DoxyParams}{Parameters} {\em data} & Dataset for estimation. \\ \hline {\em ratio} & Percentage of dataset to use for nearest neighbor search. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1meanshift_1_1MeanShift_a917492b75cc17298bc58c3d28e2944fb}} \index{mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}!Kernel@{Kernel}} \index{Kernel@{Kernel}!mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}} \subsubsection{Kernel()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily const Kernel\+Type\& Kernel (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the kernel. Definition at line 105 of file mean\+\_\+shift.\+hpp. \mbox{\label{classmlpack_1_1meanshift_1_1MeanShift_ab8d1bedeac8344d80e50d819790a117a}} \index{mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}!Kernel@{Kernel}} \index{Kernel@{Kernel}!mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}} \subsubsection{Kernel()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily Kernel\+Type\& Kernel (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Modify the kernel. Definition at line 107 of file mean\+\_\+shift.\+hpp. \mbox{\label{classmlpack_1_1meanshift_1_1MeanShift_a420770944a5b0c7a852c4ec372c4a2d1}} \index{mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}!Max\+Iterations@{Max\+Iterations}} \index{Max\+Iterations@{Max\+Iterations}!mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}} \subsubsection{Max\+Iterations()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily size\+\_\+t Max\+Iterations (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the maximum number of iterations. Definition at line 95 of file mean\+\_\+shift.\+hpp. \mbox{\label{classmlpack_1_1meanshift_1_1MeanShift_acda675ab4ab86b95c92bc33bc391a61b}} \index{mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}!Max\+Iterations@{Max\+Iterations}} \index{Max\+Iterations@{Max\+Iterations}!mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}} \subsubsection{Max\+Iterations()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily size\+\_\+t\& Max\+Iterations (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Set the maximum number of iterations. Definition at line 97 of file mean\+\_\+shift.\+hpp. \mbox{\label{classmlpack_1_1meanshift_1_1MeanShift_a02d5a1a7e8669d3c22506ecd9a811df8}} \index{mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}!Radius@{Radius}} \index{Radius@{Radius}!mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}} \subsubsection{Radius()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily double Radius (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the radius. Definition at line 100 of file mean\+\_\+shift.\+hpp. \mbox{\label{classmlpack_1_1meanshift_1_1MeanShift_ae12840857c3632026e7c513e0f19e52d}} \index{mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}!Radius@{Radius}} \index{Radius@{Radius}!mlpack\+::meanshift\+::\+Mean\+Shift@{mlpack\+::meanshift\+::\+Mean\+Shift}} \subsubsection{Radius()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily void Radius (\begin{DoxyParamCaption}\item[{double}]{radius }\end{DoxyParamCaption})} Set the radius. The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} \item /var/www/mlpack.\+ratml.\+org/mlpack.\+org/\+\_\+src/mlpack-\/3.\+3.\+0/src/mlpack/methods/mean\+\_\+shift/\textbf{ mean\+\_\+shift.\+hpp}\end{DoxyCompactItemize}