\section{Diagonal\+G\+MM Class Reference} \label{classmlpack_1_1gmm_1_1DiagonalGMM}\index{Diagonal\+G\+MM@{Diagonal\+G\+MM}} A Diagonal Gaussian Mixture Model. \subsection*{Public Member Functions} \begin{DoxyCompactItemize} \item \textbf{ Diagonal\+G\+MM} () \begin{DoxyCompactList}\small\item\em Create an empty Diagonal Gaussian Mixture Model, with zero gaussians. \end{DoxyCompactList}\item \textbf{ Diagonal\+G\+MM} (const size\+\_\+t gaussians, const size\+\_\+t dimensionality) \begin{DoxyCompactList}\small\item\em Create a \doxyref{G\+MM}{p.}{classmlpack_1_1gmm_1_1GMM} with the given number of Gaussians, each of which have the specified dimensionality. \end{DoxyCompactList}\item \textbf{ Diagonal\+G\+MM} (const std\+::vector$<$ \textbf{ distribution\+::\+Diagonal\+Gaussian\+Distribution} $>$ \&dists, const arma\+::vec \&weights) \begin{DoxyCompactList}\small\item\em Create a \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM} with the given dists and weights. \end{DoxyCompactList}\item \textbf{ Diagonal\+G\+MM} (const \textbf{ Diagonal\+G\+MM} \&other) \begin{DoxyCompactList}\small\item\em Copy constructor for Diagonal\+G\+M\+Ms. \end{DoxyCompactList}\item void \textbf{ Classify} (const arma\+::mat \&observations, arma\+::\+Row$<$ size\+\_\+t $>$ \&labels) const \begin{DoxyCompactList}\small\item\em Classify the given observations as being from an individual component in this \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM}. \end{DoxyCompactList}\item const \textbf{ distribution\+::\+Diagonal\+Gaussian\+Distribution} \& \textbf{ Component} (size\+\_\+t i) const \begin{DoxyCompactList}\small\item\em Return a const reference to a component distribution. \end{DoxyCompactList}\item \textbf{ distribution\+::\+Diagonal\+Gaussian\+Distribution} \& \textbf{ Component} (size\+\_\+t i) \begin{DoxyCompactList}\small\item\em Return a reference to a component distribution. \end{DoxyCompactList}\item size\+\_\+t \textbf{ Dimensionality} () const \begin{DoxyCompactList}\small\item\em Return the dimensionality of the model. \end{DoxyCompactList}\item size\+\_\+t \textbf{ Gaussians} () const \begin{DoxyCompactList}\small\item\em Return the number of Gaussians in the model. \end{DoxyCompactList}\item double \textbf{ Log\+Probability} (const arma\+::vec \&observation) const \begin{DoxyCompactList}\small\item\em Return the log probability that the given observation came from this distribution. \end{DoxyCompactList}\item double \textbf{ Log\+Probability} (const arma\+::vec \&observation, const size\+\_\+t component) const \begin{DoxyCompactList}\small\item\em Return the log probability that the given observation came from the given Gaussian component in this distribution. \end{DoxyCompactList}\item \textbf{ Diagonal\+G\+MM} \& \textbf{ operator=} (const \textbf{ Diagonal\+G\+MM} \&other) \begin{DoxyCompactList}\small\item\em Copy operator for Diagonal\+G\+M\+Ms. \end{DoxyCompactList}\item double \textbf{ Probability} (const arma\+::vec \&observation) const \begin{DoxyCompactList}\small\item\em Return the probability that the given observation came from this distribution. \end{DoxyCompactList}\item double \textbf{ Probability} (const arma\+::vec \&observation, const size\+\_\+t component) const \begin{DoxyCompactList}\small\item\em Return the probability that the given observation came from the given Gaussian component in this distribution. \end{DoxyCompactList}\item arma\+::vec \textbf{ Random} () const \begin{DoxyCompactList}\small\item\em Return a randomly generated observation according to the probability distribution defined by this object. \end{DoxyCompactList}\item {\footnotesize template$<$typename Archive $>$ }\\void \textbf{ serialize} (Archive \&ar, const unsigned int) \begin{DoxyCompactList}\small\item\em Serialize the \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM}. \end{DoxyCompactList}\item {\footnotesize template$<$typename Fitting\+Type = E\+M\+Fit$<$kmeans\+::\+K\+Means$<$$>$, Diagonal\+Constraint, distribution\+::\+Diagonal\+Gaussian\+Distribution$>$$>$ }\\double \textbf{ Train} (const arma\+::mat \&observations, const size\+\_\+t trials=1, const bool use\+Existing\+Model=false, Fitting\+Type fitter=Fitting\+Type()) \begin{DoxyCompactList}\small\item\em Estimate the probability distribution directly from the given observations, using the given algorithm in the Fitting\+Type class to fit the data. \end{DoxyCompactList}\item {\footnotesize template$<$typename Fitting\+Type = E\+M\+Fit$<$kmeans\+::\+K\+Means$<$$>$, Diagonal\+Constraint, distribution\+::\+Diagonal\+Gaussian\+Distribution$>$$>$ }\\double \textbf{ Train} (const arma\+::mat \&observations, const arma\+::vec \&probabilities, const size\+\_\+t trials=1, const bool use\+Existing\+Model=false, Fitting\+Type fitter=Fitting\+Type()) \begin{DoxyCompactList}\small\item\em Estimate the probability distribution directly from the given observations, taking into account the probability of each observation actually being from this distribution, and using the given algorithm in the Fitting\+Type class to fit the data. \end{DoxyCompactList}\item const arma\+::vec \& \textbf{ Weights} () const \begin{DoxyCompactList}\small\item\em Return a const reference to the a priori weights of each Gaussian. \end{DoxyCompactList}\item arma\+::vec \& \textbf{ Weights} () \begin{DoxyCompactList}\small\item\em Return a reference to the a priori weights of each Gaussian. \end{DoxyCompactList}\end{DoxyCompactItemize} \subsection{Detailed Description} A Diagonal Gaussian Mixture Model. This class uses maximum likelihood loss functions to estimate the parameters of the \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM} on a given dataset via the given fitting mechanism, defined by the Fitting\+Type template parameter. The \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM} can be trained using normal data, or data with probabilities of being from this \doxyref{G\+MM}{p.}{classmlpack_1_1gmm_1_1GMM} (see \doxyref{Diagonal\+G\+M\+M\+::\+Train()}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM_ad33ec5cf10980e0b315280ea51ef41ed} for more information). The \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM} is the same as \doxyref{G\+MM}{p.}{classmlpack_1_1gmm_1_1GMM} except for wrapping gmm\+\_\+diag class. The \doxyref{Train()}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM_ad33ec5cf10980e0b315280ea51ef41ed} method uses a template type \textquotesingle{}Fitting\+Type\textquotesingle{}. The Fitting\+Type template class must provide a way for the \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM} to train on data. It must provide the following two functions\+: \begin{DoxyCode} \textcolor{keywordtype}{void} Estimate( \textcolor{keyword}{const} arma::mat& observations, std::vector& dists, arma::vec& weights); \textcolor{keywordtype}{void} Estimate( \textcolor{keyword}{const} arma::mat& observations, \textcolor{keyword}{const} arma::vec& probabilities, std::vector& dists, arma::vec& weights); \end{DoxyCode} Example use\+: \begin{DoxyCode} \textcolor{comment}{// Set up a mixture of 5 gaussians in a 4-dimensional space.} DiagonalGMM g(5, 4); \textcolor{comment}{// Train the DiagonalGMM given the data observations, using the default} \textcolor{comment}{// EM fitting mechanism.} g.Train(data); \textcolor{comment}{// Get the probability of 'observation' being observed from this} \textcolor{comment}{// DiagoanlGMM.} \textcolor{keywordtype}{double} probability = g.Probability(observation); \textcolor{comment}{// Get a random observation from the DiagonalGMM.} arma::vec observation = g.Random(); \end{DoxyCode} Definition at line 74 of file diagonal\+\_\+gmm.\+hpp. \subsection{Constructor \& Destructor Documentation} \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a789ea6c102741e2b8305042a12b933fb}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Diagonal\+G\+MM@{Diagonal\+G\+MM}} \index{Diagonal\+G\+MM@{Diagonal\+G\+MM}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Diagonal\+G\+M\+M()\hspace{0.1cm}{\footnotesize\ttfamily [1/4]}} {\footnotesize\ttfamily \textbf{ Diagonal\+G\+MM} (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Create an empty Diagonal Gaussian Mixture Model, with zero gaussians. Definition at line 92 of file diagonal\+\_\+gmm.\+hpp. References Log\+::\+Debug. Referenced by Diagonal\+G\+M\+M\+::\+Diagonal\+G\+M\+M(). \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_aa4f5bdc8cf6f346d6cad2643cb0eb095}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Diagonal\+G\+MM@{Diagonal\+G\+MM}} \index{Diagonal\+G\+MM@{Diagonal\+G\+MM}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Diagonal\+G\+M\+M()\hspace{0.1cm}{\footnotesize\ttfamily [2/4]}} {\footnotesize\ttfamily \textbf{ Diagonal\+G\+MM} (\begin{DoxyParamCaption}\item[{const size\+\_\+t}]{gaussians, }\item[{const size\+\_\+t}]{dimensionality }\end{DoxyParamCaption})} Create a \doxyref{G\+MM}{p.}{classmlpack_1_1gmm_1_1GMM} with the given number of Gaussians, each of which have the specified dimensionality. The means and covariances will be set to 0. \begin{DoxyParams}{Parameters} {\em gaussians} & Number of Gaussians in this \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM}. \\ \hline {\em dimensionality} & Dimensionality of each Gaussian. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_af7d5a7ba750481b1183248c762058196}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Diagonal\+G\+MM@{Diagonal\+G\+MM}} \index{Diagonal\+G\+MM@{Diagonal\+G\+MM}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Diagonal\+G\+M\+M()\hspace{0.1cm}{\footnotesize\ttfamily [3/4]}} {\footnotesize\ttfamily \textbf{ Diagonal\+G\+MM} (\begin{DoxyParamCaption}\item[{const std\+::vector$<$ \textbf{ distribution\+::\+Diagonal\+Gaussian\+Distribution} $>$ \&}]{dists, }\item[{const arma\+::vec \&}]{weights }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Create a \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM} with the given dists and weights. \begin{DoxyParams}{Parameters} {\em dists} & Distributions of the model. \\ \hline {\em weights} & Weights of the model. \\ \hline \end{DoxyParams} Definition at line 118 of file diagonal\+\_\+gmm.\+hpp. References Diagonal\+G\+M\+M\+::\+Diagonal\+G\+M\+M(), and Diagonal\+G\+M\+M\+::operator=(). \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_ae557e17efa07cd2c68c29174f2f64dfb}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Diagonal\+G\+MM@{Diagonal\+G\+MM}} \index{Diagonal\+G\+MM@{Diagonal\+G\+MM}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Diagonal\+G\+M\+M()\hspace{0.1cm}{\footnotesize\ttfamily [4/4]}} {\footnotesize\ttfamily \textbf{ Diagonal\+G\+MM} (\begin{DoxyParamCaption}\item[{const \textbf{ Diagonal\+G\+MM} \&}]{other }\end{DoxyParamCaption})} Copy constructor for Diagonal\+G\+M\+Ms. \subsection{Member Function Documentation} \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a4e6c4f4110899f4f4fd18b46d3d141c6}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Classify@{Classify}} \index{Classify@{Classify}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Classify()} {\footnotesize\ttfamily void Classify (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{observations, }\item[{arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{labels }\end{DoxyParamCaption}) const} Classify the given observations as being from an individual component in this \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM}. The resultant classifications are stored in the \textquotesingle{}labels\textquotesingle{} object, and each label will be between 0 and (\doxyref{Gaussians()}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM_a130ce966c49ab36f847f8fac950f1e39} -\/ 1). Supposing that a point was classified with label 2, and that our \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM} object was called \textquotesingle{}dgmm\textquotesingle{}, one could access the relevant Gaussian distribution as follows\+: \begin{DoxyCode} arma::vec mean = dgmm.Means()[2]; arma::mat covariance = dgmm.Covariances()[2]; \textcolor{keywordtype}{double} priorWeight = dgmm.Weights()[2]; \end{DoxyCode} \begin{DoxyParams}{Parameters} {\em observations} & Matrix of observations to classify. \\ \hline {\em labels} & Object which will be filled with labels. \\ \hline \end{DoxyParams} Referenced by Diagonal\+G\+M\+M\+::\+Weights(). \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_ac426d44a14e21af69f260506ad7b9793}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Component@{Component}} \index{Component@{Component}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Component()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily const \textbf{ distribution\+::\+Diagonal\+Gaussian\+Distribution}\& Component (\begin{DoxyParamCaption}\item[{size\+\_\+t}]{i }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Return a const reference to a component distribution. \begin{DoxyParams}{Parameters} {\em i} & Index of component. \\ \hline \end{DoxyParams} Definition at line 141 of file diagonal\+\_\+gmm.\+hpp. \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a283969670b4d2ed4bb7394e2bc30c3a7}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Component@{Component}} \index{Component@{Component}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Component()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily \textbf{ distribution\+::\+Diagonal\+Gaussian\+Distribution}\& Component (\begin{DoxyParamCaption}\item[{size\+\_\+t}]{i }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Return a reference to a component distribution. \begin{DoxyParams}{Parameters} {\em i} & Index of component. \\ \hline \end{DoxyParams} Definition at line 151 of file diagonal\+\_\+gmm.\+hpp. \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a78eda6bfb9e9462afa0fc85e32abe1af}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Dimensionality@{Dimensionality}} \index{Dimensionality@{Dimensionality}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Dimensionality()} {\footnotesize\ttfamily size\+\_\+t Dimensionality (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Return the dimensionality of the model. Definition at line 134 of file diagonal\+\_\+gmm.\+hpp. \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a130ce966c49ab36f847f8fac950f1e39}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Gaussians@{Gaussians}} \index{Gaussians@{Gaussians}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Gaussians()} {\footnotesize\ttfamily size\+\_\+t Gaussians (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Return the number of Gaussians in the model. Definition at line 132 of file diagonal\+\_\+gmm.\+hpp. \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a7063c1fb92f512f32bf44542c7528739}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Log\+Probability@{Log\+Probability}} \index{Log\+Probability@{Log\+Probability}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Log\+Probability()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily double Log\+Probability (\begin{DoxyParamCaption}\item[{const arma\+::vec \&}]{observation }\end{DoxyParamCaption}) const} Return the log probability that the given observation came from this distribution. \begin{DoxyParams}{Parameters} {\em observation} & Observation to evaluate the probability of. \\ \hline \end{DoxyParams} Referenced by Diagonal\+G\+M\+M\+::\+Weights(). \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a5df8a8ba219eda2fb45a1f24e18a6320}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Log\+Probability@{Log\+Probability}} \index{Log\+Probability@{Log\+Probability}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Log\+Probability()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily double Log\+Probability (\begin{DoxyParamCaption}\item[{const arma\+::vec \&}]{observation, }\item[{const size\+\_\+t}]{component }\end{DoxyParamCaption}) const} Return the log probability that the given observation came from the given Gaussian component in this distribution. \begin{DoxyParams}{Parameters} {\em observation} & Observation to evaluate the probability of. \\ \hline {\em component} & Index of the component of the \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM}. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a19d53bdafcc58b3e441a7b9ab72f322d}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!operator=@{operator=}} \index{operator=@{operator=}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{operator=()} {\footnotesize\ttfamily \textbf{ Diagonal\+G\+MM}\& operator= (\begin{DoxyParamCaption}\item[{const \textbf{ Diagonal\+G\+MM} \&}]{other }\end{DoxyParamCaption})} Copy operator for Diagonal\+G\+M\+Ms. Referenced by Diagonal\+G\+M\+M\+::\+Diagonal\+G\+M\+M(). \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_ab72935d592516e77511d0b5e703c0d41}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Probability@{Probability}} \index{Probability@{Probability}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Probability()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily double Probability (\begin{DoxyParamCaption}\item[{const arma\+::vec \&}]{observation }\end{DoxyParamCaption}) const} Return the probability that the given observation came from this distribution. \begin{DoxyParams}{Parameters} {\em observation} & Observation to evaluate the probability of. \\ \hline \end{DoxyParams} Referenced by Diagonal\+G\+M\+M\+::\+Weights(). \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_afcd98507a97e8592a6e10b2794285224}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Probability@{Probability}} \index{Probability@{Probability}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Probability()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily double Probability (\begin{DoxyParamCaption}\item[{const arma\+::vec \&}]{observation, }\item[{const size\+\_\+t}]{component }\end{DoxyParamCaption}) const} Return the probability that the given observation came from the given Gaussian component in this distribution. \begin{DoxyParams}{Parameters} {\em observation} & Observation to evaluate the probability of. \\ \hline {\em component} & Index of the component of the \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM}. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a2c6f8d5bb4eacf7de767d2172b320756}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Random@{Random}} \index{Random@{Random}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Random()} {\footnotesize\ttfamily arma\+::vec Random (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const} Return a randomly generated observation according to the probability distribution defined by this object. \begin{DoxyReturn}{Returns} Random observation from this \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM}. \end{DoxyReturn} Referenced by Diagonal\+G\+M\+M\+::\+Weights(). \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_af0dd9205158ccf7bcfcd8ff81f79c927}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!serialize@{serialize}} \index{serialize@{serialize}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{serialize()} {\footnotesize\ttfamily void serialize (\begin{DoxyParamCaption}\item[{Archive \&}]{ar, }\item[{const unsigned}]{int }\end{DoxyParamCaption})} Serialize the \doxyref{Diagonal\+G\+MM}{p.}{classmlpack_1_1gmm_1_1DiagonalGMM}. Referenced by Diagonal\+G\+M\+M\+::\+Weights(). \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_ad33ec5cf10980e0b315280ea51ef41ed}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Train@{Train}} \index{Train@{Train}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Train()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily double Train (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{observations, }\item[{const size\+\_\+t}]{trials = {\ttfamily 1}, }\item[{const bool}]{use\+Existing\+Model = {\ttfamily false}, }\item[{Fitting\+Type}]{fitter = {\ttfamily FittingType()} }\end{DoxyParamCaption})} Estimate the probability distribution directly from the given observations, using the given algorithm in the Fitting\+Type class to fit the data. The fitting will be performed \textquotesingle{}trials\textquotesingle{} times; from these trials, the model with the greatest log-\/likelihood will be selected. By default, only one trial is performed. The log-\/likelihood of the best fitting is returned. Optionally, the existing model can be used as an initial model for the estimation by setting \textquotesingle{}use\+Existing\+Model\textquotesingle{} to true. If the fitting procedure is deterministic after the initial position is given, then \textquotesingle{}trials\textquotesingle{} should be set to 1. \begin{DoxyParams}{Parameters} {\em observations} & Observations of the model. \\ \hline {\em trials} & Number of trials to perform; the model in these trials with the greatest log-\/likelihood will be selected. \\ \hline {\em use\+Existing\+Model} & If true, the existing model is used as an initial model for the estimation. \\ \hline {\em fitter} & Fitting type that estimates observations. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} The log-\/likelihood of the best fit. \end{DoxyReturn} Referenced by Diagonal\+G\+M\+M\+::\+Weights(). \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a6c28af6823c03973003fefdf026b2b53}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Train@{Train}} \index{Train@{Train}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Train()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily double Train (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{observations, }\item[{const arma\+::vec \&}]{probabilities, }\item[{const size\+\_\+t}]{trials = {\ttfamily 1}, }\item[{const bool}]{use\+Existing\+Model = {\ttfamily false}, }\item[{Fitting\+Type}]{fitter = {\ttfamily FittingType()} }\end{DoxyParamCaption})} Estimate the probability distribution directly from the given observations, taking into account the probability of each observation actually being from this distribution, and using the given algorithm in the Fitting\+Type class to fit the data. The fitting will be performed \textquotesingle{}trials\textquotesingle{} times; from these trials, the model with the greatest log-\/likelihood will be selected. By default, only one trial is performed. The log-\/likelihood of the best fitting is returned. Optionally, the existing model can be used as an initial model for the estimation by setting \textquotesingle{}use\+Existing\+Model\textquotesingle{} to true. If the fitting procedure is deterministic after the initial position is given, then \textquotesingle{}trials\textquotesingle{} should be set to 1. \begin{DoxyParams}{Parameters} {\em observations} & Observations of the model. \\ \hline {\em probabilities} & Probability of each observation being from this distribution. \\ \hline {\em trials} & Number of trials to perform; the model in these trials with the greatest log-\/likelihood will be selected. \\ \hline {\em use\+Existing\+Model} & If true, the existing model is used as an initial model for the estimation. \\ \hline {\em fitter} & Fitting type that estimates observations. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} The log-\/likelihood of the best fit. \end{DoxyReturn} \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_ab2c1e663a100784ff6753e3eec8453dd}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Weights@{Weights}} \index{Weights@{Weights}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Weights()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily const arma\+::vec\& Weights (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Return a const reference to the a priori weights of each Gaussian. Definition at line 157 of file diagonal\+\_\+gmm.\+hpp. \mbox{\label{classmlpack_1_1gmm_1_1DiagonalGMM_a8361070d4a9097e29c005dd5dc6ca54d}} \index{mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}!Weights@{Weights}} \index{Weights@{Weights}!mlpack\+::gmm\+::\+Diagonal\+G\+MM@{mlpack\+::gmm\+::\+Diagonal\+G\+MM}} \subsubsection{Weights()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily arma\+::vec\& Weights (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Return a reference to the a priori weights of each Gaussian. Definition at line 159 of file diagonal\+\_\+gmm.\+hpp. References Diagonal\+G\+M\+M\+::\+Classify(), Diagonal\+G\+M\+M\+::\+Log\+Probability(), Diagonal\+G\+M\+M\+::\+Probability(), Diagonal\+G\+M\+M\+::\+Random(), Diagonal\+G\+M\+M\+::serialize(), and Diagonal\+G\+M\+M\+::\+Train(). 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.\+2/src/mlpack/methods/gmm/\textbf{ diagonal\+\_\+gmm.\+hpp}\end{DoxyCompactItemize}