\section{Linear\+S\+V\+M\+Function$<$ Mat\+Type $>$ Class Template Reference} \label{classmlpack_1_1svm_1_1LinearSVMFunction}\index{Linear\+S\+V\+M\+Function$<$ Mat\+Type $>$@{Linear\+S\+V\+M\+Function$<$ Mat\+Type $>$}} The hinge loss function for the linear S\+VM objective function. \subsection*{Public Member Functions} \begin{DoxyCompactItemize} \item \textbf{ Linear\+S\+V\+M\+Function} (const Mat\+Type \&dataset, const arma\+::\+Row$<$ size\+\_\+t $>$ \&labels, const size\+\_\+t num\+Classes, const double lambda=0.\+0001, const double delta=1.\+0, const bool fit\+Intercept=false) \begin{DoxyCompactList}\small\item\em Construct the Linear S\+VM objective function with given parameters. \end{DoxyCompactList}\item const arma\+::sp\+\_\+mat \& \textbf{ Dataset} () const \begin{DoxyCompactList}\small\item\em Get the dataset. \end{DoxyCompactList}\item arma\+::sp\+\_\+mat \& \textbf{ Dataset} () \begin{DoxyCompactList}\small\item\em Modify the dataset. \end{DoxyCompactList}\item double \textbf{ Evaluate} (const arma\+::mat \¶meters) \begin{DoxyCompactList}\small\item\em Evaluate the hinge loss function for all the datapoints. \end{DoxyCompactList}\item double \textbf{ Evaluate} (const arma\+::mat \¶meters, const size\+\_\+t first\+Id, const size\+\_\+t batch\+Size=1) \begin{DoxyCompactList}\small\item\em Evaluate the hinge loss function on the specified datapoints. \end{DoxyCompactList}\item {\footnotesize template$<$typename Grad\+Type $>$ }\\double \textbf{ Evaluate\+With\+Gradient} (const arma\+::mat \¶meters, Grad\+Type \&gradient) const \begin{DoxyCompactList}\small\item\em Evaluate the gradient of the hinge loss function, following the Linear\+Function\+Type requirements on the Gradient function followed by evaluation of the hinge loss function on all the datapoints. \end{DoxyCompactList}\item {\footnotesize template$<$typename Grad\+Type $>$ }\\double \textbf{ Evaluate\+With\+Gradient} (const arma\+::mat \¶meters, const size\+\_\+t first\+Id, Grad\+Type \&gradient, const size\+\_\+t batch\+Size=1) const \begin{DoxyCompactList}\small\item\em Evaluate the gradient of the hinge loss function, following the Linear\+Function\+Type requirements on the Gradient function followed by evaluation of the hinge loss function on the specified datapoints. \end{DoxyCompactList}\item bool \textbf{ Fit\+Intercept} () const \begin{DoxyCompactList}\small\item\em Gets the intercept flag. \end{DoxyCompactList}\item void \textbf{ Get\+Ground\+Truth\+Matrix} (const arma\+::\+Row$<$ size\+\_\+t $>$ \&labels, arma\+::sp\+\_\+mat \&ground\+Truth) \begin{DoxyCompactList}\small\item\em Constructs the ground truth label matrix with the passed labels. \end{DoxyCompactList}\item {\footnotesize template$<$typename Grad\+Type $>$ }\\void \textbf{ Gradient} (const arma\+::mat \¶meters, Grad\+Type \&gradient) \begin{DoxyCompactList}\small\item\em Evaluate the gradient of the hinge loss function following the Linear\+Function\+Type requirements on the Gradient function. \end{DoxyCompactList}\item {\footnotesize template$<$typename Grad\+Type $>$ }\\void \textbf{ Gradient} (const arma\+::mat \¶meters, const size\+\_\+t first\+Id, Grad\+Type \&gradient, const size\+\_\+t batch\+Size=1) \begin{DoxyCompactList}\small\item\em Evaluate the gradient of the hinge loss function, following the Linear\+Function\+Type requirements on the Gradient function. \end{DoxyCompactList}\item const arma\+::mat \& \textbf{ Initial\+Point} () const \begin{DoxyCompactList}\small\item\em Return the initial point for the optimization. \end{DoxyCompactList}\item arma\+::mat \& \textbf{ Initial\+Point} () \begin{DoxyCompactList}\small\item\em Modify the initial point for the optimization. \end{DoxyCompactList}\item double \& \textbf{ Lambda} () \begin{DoxyCompactList}\small\item\em Sets the regularization parameter. \end{DoxyCompactList}\item double \textbf{ Lambda} () const \begin{DoxyCompactList}\small\item\em Gets the regularization parameter. \end{DoxyCompactList}\item size\+\_\+t \textbf{ Num\+Functions} () const \begin{DoxyCompactList}\small\item\em Return the number of functions. \end{DoxyCompactList}\item void \textbf{ Shuffle} () \begin{DoxyCompactList}\small\item\em Shuffle the dataset. \end{DoxyCompactList}\end{DoxyCompactItemize} \subsection*{Static Public Member Functions} \begin{DoxyCompactItemize} \item static void \textbf{ Initialize\+Weights} (arma\+::mat \&weights, const size\+\_\+t feature\+Size, const size\+\_\+t num\+Classes, const bool fit\+Intercept=false) \begin{DoxyCompactList}\small\item\em Initialize Linear S\+VM weights (trainable parameters) with the given parameters. \end{DoxyCompactList}\end{DoxyCompactItemize} \subsection{Detailed Description} \subsubsection*{template$<$typename Mat\+Type = arma\+::mat$>$\newline class mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function$<$ Mat\+Type $>$} The hinge loss function for the linear S\+VM objective function. This is used by various ensmallen optimizers to train the linear S\+VM model. Definition at line 28 of file linear\+\_\+svm\+\_\+function.\+hpp. \subsection{Constructor \& Destructor Documentation} \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_af445da901fbdbaf4a6624eb7c59a219f}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Linear\+S\+V\+M\+Function@{Linear\+S\+V\+M\+Function}} \index{Linear\+S\+V\+M\+Function@{Linear\+S\+V\+M\+Function}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Linear\+S\+V\+M\+Function()} {\footnotesize\ttfamily \textbf{ Linear\+S\+V\+M\+Function} (\begin{DoxyParamCaption}\item[{const Mat\+Type \&}]{dataset, }\item[{const arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{labels, }\item[{const size\+\_\+t}]{num\+Classes, }\item[{const double}]{lambda = {\ttfamily 0.0001}, }\item[{const double}]{delta = {\ttfamily 1.0}, }\item[{const bool}]{fit\+Intercept = {\ttfamily false} }\end{DoxyParamCaption})} Construct the Linear S\+VM objective function with given parameters. \begin{DoxyParams}{Parameters} {\em dataset} & Input training data, each column associate with one sample \\ \hline {\em labels} & Labels associated with the feature data. \\ \hline {\em num\+Classes} & Number of classes for classification. \\ \hline {\em lambda} & L2-\/regularization constant. delta Margin of difference between correct class and other classes. \\ \hline {\em fit\+Intercept} & Intercept term flag. \\ \hline \end{DoxyParams} \subsection{Member Function Documentation} \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a63f50b704711fa3661dc1ae565f51811}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Dataset@{Dataset}} \index{Dataset@{Dataset}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Dataset()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily const arma\+::sp\+\_\+mat\& Dataset (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the dataset. Definition at line 167 of file linear\+\_\+svm\+\_\+function.\+hpp. \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_ae7b442671b21a94780f17a6624e1f31d}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Dataset@{Dataset}} \index{Dataset@{Dataset}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Dataset()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily arma\+::sp\+\_\+mat\& Dataset (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Modify the dataset. Definition at line 169 of file linear\+\_\+svm\+\_\+function.\+hpp. \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a1ca0efaedbc2e7e7542c89901cdcf2ee}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Evaluate@{Evaluate}} \index{Evaluate@{Evaluate}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Evaluate()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily double Evaluate (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{parameters }\end{DoxyParamCaption})} Evaluate the hinge loss function for all the datapoints. \begin{DoxyParams}{Parameters} {\em paramters} & The parameters of the S\+VM. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} The value of the loss function for the entire dataset. \end{DoxyReturn} \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a3b3e9347c2b7e25c47f3fba3baf12d19}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Evaluate@{Evaluate}} \index{Evaluate@{Evaluate}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Evaluate()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily double Evaluate (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{parameters, }\item[{const size\+\_\+t}]{first\+Id, }\item[{const size\+\_\+t}]{batch\+Size = {\ttfamily 1} }\end{DoxyParamCaption})} Evaluate the hinge loss function on the specified datapoints. \begin{DoxyParams}{Parameters} {\em parameters} & The parameters of the S\+VM. \\ \hline {\em first\+Id} & Index of the datapoints to use for function evaluation. \\ \hline {\em batch\+Size} & Size of batch to process. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} The value of the loss function for the given parameters. \end{DoxyReturn} \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a1e5f0be6eadfa83bbea6f6152d04f735}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Evaluate\+With\+Gradient@{Evaluate\+With\+Gradient}} \index{Evaluate\+With\+Gradient@{Evaluate\+With\+Gradient}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Evaluate\+With\+Gradient()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily double Evaluate\+With\+Gradient (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{parameters, }\item[{Grad\+Type \&}]{gradient }\end{DoxyParamCaption}) const} Evaluate the gradient of the hinge loss function, following the Linear\+Function\+Type requirements on the Gradient function followed by evaluation of the hinge loss function on all the datapoints. \begin{DoxyTemplParams}{Template Parameters} {\em Grad\+Type} & Type of the gradient matrix. \\ \hline \end{DoxyTemplParams} \begin{DoxyParams}{Parameters} {\em parameters} & The parameters of the S\+VM. \\ \hline {\em gradient} & Linear matrix to output the gradient into. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} The value of the loss function at the given parameters. \end{DoxyReturn} \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a116bf353d6114dbaddc9bcad60f690b1}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Evaluate\+With\+Gradient@{Evaluate\+With\+Gradient}} \index{Evaluate\+With\+Gradient@{Evaluate\+With\+Gradient}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Evaluate\+With\+Gradient()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily double Evaluate\+With\+Gradient (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{parameters, }\item[{const size\+\_\+t}]{first\+Id, }\item[{Grad\+Type \&}]{gradient, }\item[{const size\+\_\+t}]{batch\+Size = {\ttfamily 1} }\end{DoxyParamCaption}) const} Evaluate the gradient of the hinge loss function, following the Linear\+Function\+Type requirements on the Gradient function followed by evaluation of the hinge loss function on the specified datapoints. \begin{DoxyTemplParams}{Template Parameters} {\em Grad\+Type} & Type of the gradient matrix. \\ \hline \end{DoxyTemplParams} \begin{DoxyParams}{Parameters} {\em parameters} & The parameters of the S\+VM. \\ \hline {\em first\+Id} & Index of the datapoint to use for the gradient and function evaluation. \\ \hline {\em gradient} & Linear matrix to output the gradient into. \\ \hline {\em batch\+Size} & Size of the batch to process. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} The value of the loss function at the given parameters. \end{DoxyReturn} \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a2878e0828ecdc1d486b0f43a81f95059}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Fit\+Intercept@{Fit\+Intercept}} \index{Fit\+Intercept@{Fit\+Intercept}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Fit\+Intercept()} {\footnotesize\ttfamily bool Fit\+Intercept (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Gets the intercept flag. Definition at line 177 of file linear\+\_\+svm\+\_\+function.\+hpp. References Linear\+S\+V\+M\+Function$<$ Mat\+Type $>$\+::\+Num\+Functions(). \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_afb090fbee5f880dd69edde4cdd0797cd}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Get\+Ground\+Truth\+Matrix@{Get\+Ground\+Truth\+Matrix}} \index{Get\+Ground\+Truth\+Matrix@{Get\+Ground\+Truth\+Matrix}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Get\+Ground\+Truth\+Matrix()} {\footnotesize\ttfamily void Get\+Ground\+Truth\+Matrix (\begin{DoxyParamCaption}\item[{const arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{labels, }\item[{arma\+::sp\+\_\+mat \&}]{ground\+Truth }\end{DoxyParamCaption})} Constructs the ground truth label matrix with the passed labels. \begin{DoxyParams}{Parameters} {\em labels} & Labels associated with the training data. \\ \hline {\em ground\+Truth} & Pointer to arma\+::mat which stores the computed matrix. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a306147f4dffda5b0972a12de487ece8e}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Gradient@{Gradient}} \index{Gradient@{Gradient}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Gradient()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily void Gradient (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{parameters, }\item[{Grad\+Type \&}]{gradient }\end{DoxyParamCaption})} Evaluate the gradient of the hinge loss function following the Linear\+Function\+Type requirements on the Gradient function. \begin{DoxyTemplParams}{Template Parameters} {\em Grad\+Type} & Type of the gradient matrix. \\ \hline \end{DoxyTemplParams} \begin{DoxyParams}{Parameters} {\em parameters} & The parameters of the S\+VM. \\ \hline {\em gradient} & Linear matrix to output the gradient into. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_ac193621da3a050a935bacdc988b990b8}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Gradient@{Gradient}} \index{Gradient@{Gradient}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Gradient()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily void Gradient (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{parameters, }\item[{const size\+\_\+t}]{first\+Id, }\item[{Grad\+Type \&}]{gradient, }\item[{const size\+\_\+t}]{batch\+Size = {\ttfamily 1} }\end{DoxyParamCaption})} Evaluate the gradient of the hinge loss function, following the Linear\+Function\+Type requirements on the Gradient function. \begin{DoxyTemplParams}{Template Parameters} {\em Grad\+Type} & Type of the gradient matrix. \\ \hline \end{DoxyTemplParams} \begin{DoxyParams}{Parameters} {\em parameters} & The parameters of the S\+VM. \\ \hline {\em first\+Id} & Index of the datapoint to use for the gradient evaluation. \\ \hline {\em gradient} & Linear matrix to output the gradient into. \\ \hline {\em batch\+Size} & Size of the batch to process. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_af39490b2ae62f54b6a4c25a3d92ab0ce}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Initialize\+Weights@{Initialize\+Weights}} \index{Initialize\+Weights@{Initialize\+Weights}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Initialize\+Weights()} {\footnotesize\ttfamily static void Initialize\+Weights (\begin{DoxyParamCaption}\item[{arma\+::mat \&}]{weights, }\item[{const size\+\_\+t}]{feature\+Size, }\item[{const size\+\_\+t}]{num\+Classes, }\item[{const bool}]{fit\+Intercept = {\ttfamily false} }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} Initialize Linear S\+VM weights (trainable parameters) with the given parameters. \begin{DoxyParams}{Parameters} {\em weights} & This will be filled with the initialized model weights. \\ \hline {\em feature\+Size} & The number of features in the training set. \\ \hline {\em num\+Classes} & Number of classes for classification. \\ \hline {\em fit\+Intercept} & If true, an intercept is fitted. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} Initialized model weights. \end{DoxyReturn} \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a380e98a13444e16f7cd2984e23b32e2e}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Initial\+Point@{Initial\+Point}} \index{Initial\+Point@{Initial\+Point}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Initial\+Point()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily const arma\+::mat\& Initial\+Point (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Return the initial point for the optimization. Definition at line 162 of file linear\+\_\+svm\+\_\+function.\+hpp. \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_ab1582fd207a6b9a400cf27c710c5af06}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Initial\+Point@{Initial\+Point}} \index{Initial\+Point@{Initial\+Point}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Initial\+Point()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily arma\+::mat\& Initial\+Point (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Modify the initial point for the optimization. Definition at line 164 of file linear\+\_\+svm\+\_\+function.\+hpp. \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_aaf66629b989a326453647f42443c6a0c}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Lambda@{Lambda}} \index{Lambda@{Lambda}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Lambda()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily double\& Lambda (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Sets the regularization parameter. Definition at line 172 of file linear\+\_\+svm\+\_\+function.\+hpp. \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a53535041275cedd0ec3de67ca032aa94}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Lambda@{Lambda}} \index{Lambda@{Lambda}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Lambda()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily double Lambda (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Gets the regularization parameter. Definition at line 174 of file linear\+\_\+svm\+\_\+function.\+hpp. \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a1fa76af34a6e3ea927b307f0c318ee4b}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Num\+Functions@{Num\+Functions}} \index{Num\+Functions@{Num\+Functions}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Num\+Functions()} {\footnotesize\ttfamily size\+\_\+t Num\+Functions (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const} Return the number of functions. Referenced by Linear\+S\+V\+M\+Function$<$ Mat\+Type $>$\+::\+Fit\+Intercept(). \mbox{\label{classmlpack_1_1svm_1_1LinearSVMFunction_a2697cc8b37d7bca7c055228382a9b208}} \index{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}!Shuffle@{Shuffle}} \index{Shuffle@{Shuffle}!mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function@{mlpack\+::svm\+::\+Linear\+S\+V\+M\+Function}} \subsubsection{Shuffle()} {\footnotesize\ttfamily void Shuffle (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} Shuffle the dataset. 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.\+1/src/mlpack/methods/linear\+\_\+svm/\textbf{ linear\+\_\+svm\+\_\+function.\+hpp}\end{DoxyCompactItemize}