\section{mlpack\+:\+:kmeans Namespace Reference} \label{namespacemlpack_1_1kmeans}\index{mlpack\+::kmeans@{mlpack\+::kmeans}} K-\/\+Means clustering. \subsection*{Classes} \begin{DoxyCompactItemize} \item class \textbf{ Allow\+Empty\+Clusters} \begin{DoxyCompactList}\small\item\em Policy which allows K-\/\+Means to create empty clusters without any error being reported. \end{DoxyCompactList}\item class \textbf{ Dual\+Tree\+K\+Means} \begin{DoxyCompactList}\small\item\em 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. \end{DoxyCompactList}\item class \textbf{ Dual\+Tree\+K\+Means\+Rules} \item class \textbf{ Dual\+Tree\+K\+Means\+Statistic} \item class \textbf{ Elkan\+K\+Means} \item class \textbf{ Hamerly\+K\+Means} \item class \textbf{ Kill\+Empty\+Clusters} \begin{DoxyCompactList}\small\item\em Policy which allows K-\/\+Means to \char`\"{}kill\char`\"{} empty clusters without any error being reported. \end{DoxyCompactList}\item class \textbf{ K\+Means} \begin{DoxyCompactList}\small\item\em This class implements K-\/\+Means clustering, using a variety of possible implementations of Lloyd\textquotesingle{}s algorithm. \end{DoxyCompactList}\item class \textbf{ Max\+Variance\+New\+Cluster} \begin{DoxyCompactList}\small\item\em When an empty cluster is detected, this class takes the point furthest from the centroid of the cluster with maximum variance as a new cluster. \end{DoxyCompactList}\item class \textbf{ Naive\+K\+Means} \begin{DoxyCompactList}\small\item\em This is an implementation of a single iteration of Lloyd\textquotesingle{}s algorithm for k-\/means. \end{DoxyCompactList}\item class \textbf{ Pelleg\+Moore\+K\+Means} \begin{DoxyCompactList}\small\item\em An implementation of Pelleg-\/\+Moore\textquotesingle{}s \textquotesingle{}blacklist\textquotesingle{} algorithm for k-\/means clustering. \end{DoxyCompactList}\item class \textbf{ Pelleg\+Moore\+K\+Means\+Rules} \begin{DoxyCompactList}\small\item\em The rules class for the single-\/tree Pelleg-\/\+Moore kd-\/tree traversal for k-\/means clustering. \end{DoxyCompactList}\item class \textbf{ Pelleg\+Moore\+K\+Means\+Statistic} \begin{DoxyCompactList}\small\item\em A statistic for trees which holds the blacklist for Pelleg-\/\+Moore k-\/means clustering (which represents the clusters that cannot possibly own any points in a node). \end{DoxyCompactList}\item class \textbf{ Random\+Partition} \begin{DoxyCompactList}\small\item\em A very simple partitioner which partitions the data randomly into the number of desired clusters. \end{DoxyCompactList}\item class \textbf{ Refined\+Start} \begin{DoxyCompactList}\small\item\em A refined approach for choosing initial points for k-\/means clustering. \end{DoxyCompactList}\item class \textbf{ Sample\+Initialization} \end{DoxyCompactItemize} \subsection*{Typedefs} \begin{DoxyCompactItemize} \item {\footnotesize template$<$typename Metric\+Type , typename Mat\+Type $>$ }\\using \textbf{ Cover\+Tree\+Dual\+Tree\+K\+Means} = \textbf{ Dual\+Tree\+K\+Means}$<$ Metric\+Type, Mat\+Type, \textbf{ tree\+::\+Standard\+Cover\+Tree} $>$ \begin{DoxyCompactList}\small\item\em A template typedef for the \doxyref{Dual\+Tree\+K\+Means}{p.}{classmlpack_1_1kmeans_1_1DualTreeKMeans} algorithm with the cover tree type. \end{DoxyCompactList}\item {\footnotesize template$<$typename Metric\+Type , typename Mat\+Type $>$ }\\using \textbf{ Default\+Dual\+Tree\+K\+Means} = \textbf{ Dual\+Tree\+K\+Means}$<$ Metric\+Type, Mat\+Type $>$ \begin{DoxyCompactList}\small\item\em A template typedef for the \doxyref{Dual\+Tree\+K\+Means}{p.}{classmlpack_1_1kmeans_1_1DualTreeKMeans} algorithm with the default tree type (a kd-\/tree). \end{DoxyCompactList}\end{DoxyCompactItemize} \subsection*{Functions} \begin{DoxyCompactItemize} \item {\footnotesize template$<$typename Tree\+Type $>$ }\\void \textbf{ Hide\+Child} (Tree\+Type \&node, const size\+\_\+t child, const typename \textbf{ std\+::enable\+\_\+if\+\_\+t}$<$ !\textbf{ tree\+::\+Tree\+Traits}$<$ Tree\+Type $>$\+::Binary\+Tree $>$ $\ast$junk=0) \begin{DoxyCompactList}\small\item\em Utility function for hiding children. \end{DoxyCompactList}\item {\footnotesize template$<$typename Tree\+Type $>$ }\\void \textbf{ Hide\+Child} (Tree\+Type \&node, const size\+\_\+t child, const typename \textbf{ std\+::enable\+\_\+if\+\_\+t}$<$ \textbf{ tree\+::\+Tree\+Traits}$<$ Tree\+Type $>$\+::Binary\+Tree $>$ $\ast$junk=0) \begin{DoxyCompactList}\small\item\em Utility function for hiding children. \end{DoxyCompactList}\item {\footnotesize template$<$typename Tree\+Type $>$ }\\void \textbf{ Restore\+Children} (Tree\+Type \&node, const typename \textbf{ std\+::enable\+\_\+if\+\_\+t}$<$!\textbf{ tree\+::\+Tree\+Traits}$<$ Tree\+Type $>$\+::Binary\+Tree $>$ $\ast$junk=0) \begin{DoxyCompactList}\small\item\em Utility function for restoring children to a non-\/binary tree. \end{DoxyCompactList}\item {\footnotesize template$<$typename Tree\+Type $>$ }\\void \textbf{ Restore\+Children} (Tree\+Type \&node, const typename \textbf{ std\+::enable\+\_\+if\+\_\+t}$<$ \textbf{ tree\+::\+Tree\+Traits}$<$ Tree\+Type $>$\+::Binary\+Tree $>$ $\ast$junk=0) \begin{DoxyCompactList}\small\item\em Utility function for restoring children to a binary tree. \end{DoxyCompactList}\end{DoxyCompactItemize} \subsection{Detailed Description} K-\/\+Means clustering. \subsection{Typedef Documentation} \mbox{\label{namespacemlpack_1_1kmeans_a050f8eba1b8d0c72e990a9ee3b7ed775}} \index{mlpack\+::kmeans@{mlpack\+::kmeans}!Cover\+Tree\+Dual\+Tree\+K\+Means@{Cover\+Tree\+Dual\+Tree\+K\+Means}} \index{Cover\+Tree\+Dual\+Tree\+K\+Means@{Cover\+Tree\+Dual\+Tree\+K\+Means}!mlpack\+::kmeans@{mlpack\+::kmeans}} \subsubsection{Cover\+Tree\+Dual\+Tree\+K\+Means} {\footnotesize\ttfamily using \textbf{ Cover\+Tree\+Dual\+Tree\+K\+Means} = \textbf{ Dual\+Tree\+K\+Means}$<$Metric\+Type, Mat\+Type, \textbf{ tree\+::\+Standard\+Cover\+Tree}$>$} A template typedef for the \doxyref{Dual\+Tree\+K\+Means}{p.}{classmlpack_1_1kmeans_1_1DualTreeKMeans} algorithm with the cover tree type. Definition at line 170 of file dual\+\_\+tree\+\_\+kmeans.\+hpp. \mbox{\label{namespacemlpack_1_1kmeans_a3d8c82eb428be782996066d70afc122b}} \index{mlpack\+::kmeans@{mlpack\+::kmeans}!Default\+Dual\+Tree\+K\+Means@{Default\+Dual\+Tree\+K\+Means}} \index{Default\+Dual\+Tree\+K\+Means@{Default\+Dual\+Tree\+K\+Means}!mlpack\+::kmeans@{mlpack\+::kmeans}} \subsubsection{Default\+Dual\+Tree\+K\+Means} {\footnotesize\ttfamily using \textbf{ Default\+Dual\+Tree\+K\+Means} = \textbf{ Dual\+Tree\+K\+Means}$<$Metric\+Type, Mat\+Type$>$} A template typedef for the \doxyref{Dual\+Tree\+K\+Means}{p.}{classmlpack_1_1kmeans_1_1DualTreeKMeans} algorithm with the default tree type (a kd-\/tree). Definition at line 164 of file dual\+\_\+tree\+\_\+kmeans.\+hpp. \subsection{Function Documentation} \mbox{\label{namespacemlpack_1_1kmeans_ae824715a9723b95291ede5396d7ec48a}} \index{mlpack\+::kmeans@{mlpack\+::kmeans}!Hide\+Child@{Hide\+Child}} \index{Hide\+Child@{Hide\+Child}!mlpack\+::kmeans@{mlpack\+::kmeans}} \subsubsection{Hide\+Child()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily void mlpack\+::kmeans\+::\+Hide\+Child (\begin{DoxyParamCaption}\item[{Tree\+Type \&}]{node, }\item[{const size\+\_\+t}]{child, }\item[{const typename \textbf{ std\+::enable\+\_\+if\+\_\+t}$<$ !\textbf{ tree\+::\+Tree\+Traits}$<$ Tree\+Type $>$\+::Binary\+Tree $>$ $\ast$}]{junk = {\ttfamily 0} }\end{DoxyParamCaption})} Utility function for hiding children. This actually does something, and is called if the tree is not a binary tree. Referenced by Dual\+Tree\+K\+Means$<$ Metric\+Type, Mat\+Type, Tree\+Type $>$\+::\+Distance\+Calculations(). \mbox{\label{namespacemlpack_1_1kmeans_ad50ab0e1083d84c7f78a79f2bdaec558}} \index{mlpack\+::kmeans@{mlpack\+::kmeans}!Hide\+Child@{Hide\+Child}} \index{Hide\+Child@{Hide\+Child}!mlpack\+::kmeans@{mlpack\+::kmeans}} \subsubsection{Hide\+Child()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily void mlpack\+::kmeans\+::\+Hide\+Child (\begin{DoxyParamCaption}\item[{Tree\+Type \&}]{node, }\item[{const size\+\_\+t}]{child, }\item[{const typename \textbf{ std\+::enable\+\_\+if\+\_\+t}$<$ \textbf{ tree\+::\+Tree\+Traits}$<$ Tree\+Type $>$\+::Binary\+Tree $>$ $\ast$}]{junk = {\ttfamily 0} }\end{DoxyParamCaption})} Utility function for hiding children. This is called when the tree is a binary tree, and does nothing, because we don\textquotesingle{}t hide binary children in this way. \mbox{\label{namespacemlpack_1_1kmeans_ab8a2dc63dd61b947e7b90e03d31d64c0}} \index{mlpack\+::kmeans@{mlpack\+::kmeans}!Restore\+Children@{Restore\+Children}} \index{Restore\+Children@{Restore\+Children}!mlpack\+::kmeans@{mlpack\+::kmeans}} \subsubsection{Restore\+Children()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily void mlpack\+::kmeans\+::\+Restore\+Children (\begin{DoxyParamCaption}\item[{Tree\+Type \&}]{node, }\item[{const typename \textbf{ std\+::enable\+\_\+if\+\_\+t}$<$!\textbf{ tree\+::\+Tree\+Traits}$<$ Tree\+Type $>$\+::Binary\+Tree $>$ $\ast$}]{junk = {\ttfamily 0} }\end{DoxyParamCaption})} Utility function for restoring children to a non-\/binary tree. Referenced by Dual\+Tree\+K\+Means$<$ Metric\+Type, Mat\+Type, Tree\+Type $>$\+::\+Distance\+Calculations(). \mbox{\label{namespacemlpack_1_1kmeans_aef92bb2544a815fc97f3c79070e5d3c0}} \index{mlpack\+::kmeans@{mlpack\+::kmeans}!Restore\+Children@{Restore\+Children}} \index{Restore\+Children@{Restore\+Children}!mlpack\+::kmeans@{mlpack\+::kmeans}} \subsubsection{Restore\+Children()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily void mlpack\+::kmeans\+::\+Restore\+Children (\begin{DoxyParamCaption}\item[{Tree\+Type \&}]{node, }\item[{const typename \textbf{ std\+::enable\+\_\+if\+\_\+t}$<$ \textbf{ tree\+::\+Tree\+Traits}$<$ Tree\+Type $>$\+::Binary\+Tree $>$ $\ast$}]{junk = {\ttfamily 0} }\end{DoxyParamCaption})} Utility function for restoring children to a binary tree.