\section{L\+A\+RS Class Reference} \label{classmlpack_1_1regression_1_1LARS}\index{L\+A\+RS@{L\+A\+RS}} An implementation of \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}, a stage-\/wise homotopy-\/based algorithm for l1-\/regularized linear regression (L\+A\+S\+SO) and l1+l2 regularized linear regression (Elastic Net). \subsection*{Public Member Functions} \begin{DoxyCompactItemize} \item \textbf{ L\+A\+RS} (const bool use\+Cholesky=false, const double lambda1=0.\+0, const double lambda2=0.\+0, const double tolerance=1e-\/16) \begin{DoxyCompactList}\small\item\em Set the parameters to \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}. \end{DoxyCompactList}\item \textbf{ L\+A\+RS} (const bool use\+Cholesky, const arma\+::mat \&gram\+Matrix, const double lambda1=0.\+0, const double lambda2=0.\+0, const double tolerance=1e-\/16) \begin{DoxyCompactList}\small\item\em Set the parameters to \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}, and pass in a precalculated Gram matrix. \end{DoxyCompactList}\item \textbf{ L\+A\+RS} (const arma\+::mat \&data, const arma\+::rowvec \&responses, const bool transpose\+Data=true, const bool use\+Cholesky=false, const double lambda1=0.\+0, const double lambda2=0.\+0, const double tolerance=1e-\/16) \begin{DoxyCompactList}\small\item\em Set the parameters to \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} and run training. \end{DoxyCompactList}\item \textbf{ L\+A\+RS} (const arma\+::mat \&data, const arma\+::rowvec \&responses, const bool transpose\+Data, const bool use\+Cholesky, const arma\+::mat \&gram\+Matrix, const double lambda1=0.\+0, const double lambda2=0.\+0, const double tolerance=1e-\/16) \begin{DoxyCompactList}\small\item\em Set the parameters to \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}, pass in a precalculated Gram matrix, and run training. \end{DoxyCompactList}\item \textbf{ L\+A\+RS} (const \textbf{ L\+A\+RS} \&other) \begin{DoxyCompactList}\small\item\em Construct the \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object by copying the given \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object. \end{DoxyCompactList}\item \textbf{ L\+A\+RS} (\textbf{ L\+A\+RS} \&\&other) \begin{DoxyCompactList}\small\item\em Construct the \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object by taking ownership of the given \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object. \end{DoxyCompactList}\item const std\+::vector$<$ size\+\_\+t $>$ \& \textbf{ Active\+Set} () const \begin{DoxyCompactList}\small\item\em Access the set of active dimensions. \end{DoxyCompactList}\item const arma\+::vec \& \textbf{ Beta} () const \begin{DoxyCompactList}\small\item\em Access the solution coefficients. \end{DoxyCompactList}\item const std\+::vector$<$ arma\+::vec $>$ \& \textbf{ Beta\+Path} () const \begin{DoxyCompactList}\small\item\em Access the set of coefficients after each iteration; the solution is the last element. \end{DoxyCompactList}\item double \textbf{ Compute\+Error} (const arma\+::mat \&matX, const arma\+::rowvec \&y, const bool row\+Major=false) \begin{DoxyCompactList}\small\item\em Compute cost error of the given data matrix using the currently-\/trained \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} model. \end{DoxyCompactList}\item const std\+::vector$<$ double $>$ \& \textbf{ Lambda\+Path} () const \begin{DoxyCompactList}\small\item\em Access the set of values for lambda1 after each iteration; the solution is the last element. \end{DoxyCompactList}\item const arma\+::mat \& \textbf{ Mat\+Utri\+Chol\+Factor} () const \begin{DoxyCompactList}\small\item\em Access the upper triangular cholesky factor. \end{DoxyCompactList}\item \textbf{ L\+A\+RS} \& \textbf{ operator=} (const \textbf{ L\+A\+RS} \&other) \begin{DoxyCompactList}\small\item\em Copy the given \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object. \end{DoxyCompactList}\item \textbf{ L\+A\+RS} \& \textbf{ operator=} (\textbf{ L\+A\+RS} \&\&other) \begin{DoxyCompactList}\small\item\em Take ownership of the given \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object. \end{DoxyCompactList}\item void \textbf{ Predict} (const arma\+::mat \&points, arma\+::rowvec \&predictions, const bool row\+Major=false) const \begin{DoxyCompactList}\small\item\em Predict y\+\_\+i for each data point in the given data matrix using the currently-\/trained \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} model. \end{DoxyCompactList}\item {\footnotesize template$<$typename Archive $>$ }\\void \textbf{ serialize} (Archive \&ar, const unsigned int) \begin{DoxyCompactList}\small\item\em Serialize the \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} model. \end{DoxyCompactList}\item double \textbf{ Train} (const arma\+::mat \&data, const arma\+::rowvec \&responses, arma\+::vec \&beta, const bool transpose\+Data=true) \begin{DoxyCompactList}\small\item\em Run \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}. \end{DoxyCompactList}\item double \textbf{ Train} (const arma\+::mat \&data, const arma\+::rowvec \&responses, const bool transpose\+Data=true) \begin{DoxyCompactList}\small\item\em Run \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}. \end{DoxyCompactList}\end{DoxyCompactItemize} \subsection{Detailed Description} An implementation of \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}, a stage-\/wise homotopy-\/based algorithm for l1-\/regularized linear regression (L\+A\+S\+SO) and l1+l2 regularized linear regression (Elastic Net). Let $ X $ be a matrix where each row is a point and each column is a dimension and let $ y $ be a vector of responses. The Elastic Net problem is to solve \[ \min_{\beta} 0.5 || X \beta - y ||_2^2 + \lambda_1 || \beta ||_1 + 0.5 \lambda_2 || \beta ||_2^2 \] where $ \beta $ is the vector of regression coefficients. If $ \lambda_1 > 0 $ and $ \lambda_2 = 0 $, the problem is the L\+A\+S\+SO. If $ \lambda_1 > 0 $ and $ \lambda_2 > 0 $, the problem is the elastic net. If $ \lambda_1 = 0 $ and $ \lambda_2 > 0 $, the problem is ridge regression. If $ \lambda_1 = 0 $ and $ \lambda_2 = 0 $, the problem is unregularized linear regression. Note\+: This algorithm is not recommended for use (in terms of efficiency) when $ \lambda_1 $ = 0. For more details, see the following papers\+: \begin{DoxyCode} @article\{efron2004least, title=\{Least angle regression\}, author=\{Efron, B. and Hastie, T. and Johnstone, I. and Tibshirani, R.\}, journal=\{The Annals of statistics\}, volume=\{32\}, number=\{2\}, pages=\{407--499\}, year=\{2004\}, publisher=\{Institute of Mathematical Statistics\} \} \end{DoxyCode} \begin{DoxyCode} @article\{zou2005regularization, title=\{Regularization and variable selection via the elastic net\}, author=\{Zou, H. and Hastie, T.\}, journal=\{Journal of the Royal Statistical Society Series B\}, volume=\{67\}, number=\{2\}, pages=\{301--320\}, year=\{2005\}, publisher=\{Royal Statistical Society\} \} \end{DoxyCode} Definition at line 89 of file lars.\+hpp. \subsection{Constructor \& Destructor Documentation} \mbox{\label{classmlpack_1_1regression_1_1LARS_a286c84aa2fd218969a77cc297985c482}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!L\+A\+RS@{L\+A\+RS}} \index{L\+A\+RS@{L\+A\+RS}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{L\+A\+R\+S()\hspace{0.1cm}{\footnotesize\ttfamily [1/6]}} {\footnotesize\ttfamily \textbf{ L\+A\+RS} (\begin{DoxyParamCaption}\item[{const bool}]{use\+Cholesky = {\ttfamily false}, }\item[{const double}]{lambda1 = {\ttfamily 0.0}, }\item[{const double}]{lambda2 = {\ttfamily 0.0}, }\item[{const double}]{tolerance = {\ttfamily 1e-\/16} }\end{DoxyParamCaption})} Set the parameters to \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}. Both lambda1 and lambda2 default to 0. \begin{DoxyParams}{Parameters} {\em use\+Cholesky} & Whether or not to use Cholesky decomposition when solving linear system (as opposed to using the full Gram matrix). \\ \hline {\em lambda1} & Regularization parameter for l1-\/norm penalty. \\ \hline {\em lambda2} & Regularization parameter for l2-\/norm penalty. \\ \hline {\em tolerance} & Run until the maximum correlation of elements in (X$^\wedge$T y) is less than this. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1regression_1_1LARS_a7d239923bcafc8aae2fe6920a5816936}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!L\+A\+RS@{L\+A\+RS}} \index{L\+A\+RS@{L\+A\+RS}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{L\+A\+R\+S()\hspace{0.1cm}{\footnotesize\ttfamily [2/6]}} {\footnotesize\ttfamily \textbf{ L\+A\+RS} (\begin{DoxyParamCaption}\item[{const bool}]{use\+Cholesky, }\item[{const arma\+::mat \&}]{gram\+Matrix, }\item[{const double}]{lambda1 = {\ttfamily 0.0}, }\item[{const double}]{lambda2 = {\ttfamily 0.0}, }\item[{const double}]{tolerance = {\ttfamily 1e-\/16} }\end{DoxyParamCaption})} Set the parameters to \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}, and pass in a precalculated Gram matrix. Both lambda1 and lambda2 default to 0. \begin{DoxyParams}{Parameters} {\em use\+Cholesky} & Whether or not to use Cholesky decomposition when solving linear system (as opposed to using the full Gram matrix). \\ \hline {\em gram\+Matrix} & Gram matrix. \\ \hline {\em lambda1} & Regularization parameter for l1-\/norm penalty. \\ \hline {\em lambda2} & Regularization parameter for l2-\/norm penalty. \\ \hline {\em tolerance} & Run until the maximum correlation of elements in (X$^\wedge$T y) is less than this. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1regression_1_1LARS_a70e31765ec8b1f41c244bc096f662e95}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!L\+A\+RS@{L\+A\+RS}} \index{L\+A\+RS@{L\+A\+RS}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{L\+A\+R\+S()\hspace{0.1cm}{\footnotesize\ttfamily [3/6]}} {\footnotesize\ttfamily \textbf{ L\+A\+RS} (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{data, }\item[{const arma\+::rowvec \&}]{responses, }\item[{const bool}]{transpose\+Data = {\ttfamily true}, }\item[{const bool}]{use\+Cholesky = {\ttfamily false}, }\item[{const double}]{lambda1 = {\ttfamily 0.0}, }\item[{const double}]{lambda2 = {\ttfamily 0.0}, }\item[{const double}]{tolerance = {\ttfamily 1e-\/16} }\end{DoxyParamCaption})} Set the parameters to \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} and run training. Both lambda1 and lambda2 are set by default to 0. \begin{DoxyParams}{Parameters} {\em data} & Input data. \\ \hline {\em responses} & A vector of targets. \\ \hline {\em transpose\+Data} & Should be true if the input data is column-\/major and false otherwise. \\ \hline {\em use\+Cholesky} & Whether or not to use Cholesky decomposition when solving linear system (as opposed to using the full Gram matrix). \\ \hline {\em lambda1} & Regularization parameter for l1-\/norm penalty. \\ \hline {\em lambda2} & Regularization parameter for l2-\/norm penalty. \\ \hline {\em tolerance} & Run until the maximum correlation of elements in (X$^\wedge$T y) is less than this. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1regression_1_1LARS_a063aa7d400bd5974d940d19299536b39}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!L\+A\+RS@{L\+A\+RS}} \index{L\+A\+RS@{L\+A\+RS}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{L\+A\+R\+S()\hspace{0.1cm}{\footnotesize\ttfamily [4/6]}} {\footnotesize\ttfamily \textbf{ L\+A\+RS} (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{data, }\item[{const arma\+::rowvec \&}]{responses, }\item[{const bool}]{transpose\+Data, }\item[{const bool}]{use\+Cholesky, }\item[{const arma\+::mat \&}]{gram\+Matrix, }\item[{const double}]{lambda1 = {\ttfamily 0.0}, }\item[{const double}]{lambda2 = {\ttfamily 0.0}, }\item[{const double}]{tolerance = {\ttfamily 1e-\/16} }\end{DoxyParamCaption})} Set the parameters to \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}, pass in a precalculated Gram matrix, and run training. Both lambda1 and lambda2 are set by default to 0. \begin{DoxyParams}{Parameters} {\em data} & Input data. \\ \hline {\em responses} & A vector of targets. \\ \hline {\em transpose\+Data} & Should be true if the input data is column-\/major and false otherwise. \\ \hline {\em use\+Cholesky} & Whether or not to use Cholesky decomposition when solving linear system (as opposed to using the full Gram matrix). \\ \hline {\em gram\+Matrix} & Gram matrix. \\ \hline {\em lambda1} & Regularization parameter for l1-\/norm penalty. \\ \hline {\em lambda2} & Regularization parameter for l2-\/norm penalty. \\ \hline {\em tolerance} & Run until the maximum correlation of elements in (X$^\wedge$T y) is less than this. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1regression_1_1LARS_a0cc9d048eafa5549851dfbad14e2034a}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!L\+A\+RS@{L\+A\+RS}} \index{L\+A\+RS@{L\+A\+RS}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{L\+A\+R\+S()\hspace{0.1cm}{\footnotesize\ttfamily [5/6]}} {\footnotesize\ttfamily \textbf{ L\+A\+RS} (\begin{DoxyParamCaption}\item[{const \textbf{ L\+A\+RS} \&}]{other }\end{DoxyParamCaption})} Construct the \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object by copying the given \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object. \begin{DoxyParams}{Parameters} {\em other} & \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object to copy. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1regression_1_1LARS_a7aa7ae2d7c44de23b571d63999228a80}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!L\+A\+RS@{L\+A\+RS}} \index{L\+A\+RS@{L\+A\+RS}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{L\+A\+R\+S()\hspace{0.1cm}{\footnotesize\ttfamily [6/6]}} {\footnotesize\ttfamily \textbf{ L\+A\+RS} (\begin{DoxyParamCaption}\item[{\textbf{ L\+A\+RS} \&\&}]{other }\end{DoxyParamCaption})} Construct the \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object by taking ownership of the given \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object. \begin{DoxyParams}{Parameters} {\em other} & \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object to take ownership of. \\ \hline \end{DoxyParams} \subsection{Member Function Documentation} \mbox{\label{classmlpack_1_1regression_1_1LARS_a1775b52aa7ab1b47251df16bfa84969a}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!Active\+Set@{Active\+Set}} \index{Active\+Set@{Active\+Set}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{Active\+Set()} {\footnotesize\ttfamily const std\+::vector$<$size\+\_\+t$>$\& Active\+Set (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Access the set of active dimensions. Definition at line 253 of file lars.\+hpp. \mbox{\label{classmlpack_1_1regression_1_1LARS_a853eded6fe21f0293d997b3bc7905857}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!Beta@{Beta}} \index{Beta@{Beta}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{Beta()} {\footnotesize\ttfamily const arma\+::vec\& Beta (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Access the solution coefficients. Definition at line 260 of file lars.\+hpp. \mbox{\label{classmlpack_1_1regression_1_1LARS_ac597124267a151fc199119835fd4a520}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!Beta\+Path@{Beta\+Path}} \index{Beta\+Path@{Beta\+Path}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{Beta\+Path()} {\footnotesize\ttfamily const std\+::vector$<$arma\+::vec$>$\& Beta\+Path (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Access the set of coefficients after each iteration; the solution is the last element. Definition at line 257 of file lars.\+hpp. \mbox{\label{classmlpack_1_1regression_1_1LARS_a007f162efb479883303b4d0d2bacdcb4}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!Compute\+Error@{Compute\+Error}} \index{Compute\+Error@{Compute\+Error}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{Compute\+Error()} {\footnotesize\ttfamily double Compute\+Error (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{matX, }\item[{const arma\+::rowvec \&}]{y, }\item[{const bool}]{row\+Major = {\ttfamily false} }\end{DoxyParamCaption})} Compute cost error of the given data matrix using the currently-\/trained \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} model. Only $\vert$$\vert$y-\/beta$\ast$\+X$\vert$$\vert$2 is used to calculate cost error. \begin{DoxyParams}{Parameters} {\em matX} & Column-\/major input data (or row-\/major input data if row\+Major = true). \\ \hline {\em y} & responses A vector of targets. \\ \hline {\em row\+Major} & Should be true if the data points matrix is row-\/major and false otherwise. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} The minimum cost error. \end{DoxyReturn} Referenced by L\+A\+R\+S\+::\+Mat\+Utri\+Chol\+Factor(). \mbox{\label{classmlpack_1_1regression_1_1LARS_a1d857e3dd6d8bde441e0df7ce4d8fb62}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!Lambda\+Path@{Lambda\+Path}} \index{Lambda\+Path@{Lambda\+Path}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{Lambda\+Path()} {\footnotesize\ttfamily const std\+::vector$<$double$>$\& Lambda\+Path (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Access the set of values for lambda1 after each iteration; the solution is the last element. Definition at line 264 of file lars.\+hpp. \mbox{\label{classmlpack_1_1regression_1_1LARS_aa1cedc65d70665851c31a900685dc061}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!Mat\+Utri\+Chol\+Factor@{Mat\+Utri\+Chol\+Factor}} \index{Mat\+Utri\+Chol\+Factor@{Mat\+Utri\+Chol\+Factor}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{Mat\+Utri\+Chol\+Factor()} {\footnotesize\ttfamily const arma\+::mat\& Mat\+Utri\+Chol\+Factor (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Access the upper triangular cholesky factor. Definition at line 267 of file lars.\+hpp. References L\+A\+R\+S\+::\+Compute\+Error(), and L\+A\+R\+S\+::serialize(). \mbox{\label{classmlpack_1_1regression_1_1LARS_ac19238b303d9bcb7b8adf9fde6bd2b80}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!operator=@{operator=}} \index{operator=@{operator=}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{operator=()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily \textbf{ L\+A\+RS}\& operator= (\begin{DoxyParamCaption}\item[{const \textbf{ L\+A\+RS} \&}]{other }\end{DoxyParamCaption})} Copy the given \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object. \begin{DoxyParams}{Parameters} {\em other} & \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object to copy. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1regression_1_1LARS_a24105afcc8594b088d865c2a7171e860}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!operator=@{operator=}} \index{operator=@{operator=}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{operator=()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily \textbf{ L\+A\+RS}\& operator= (\begin{DoxyParamCaption}\item[{\textbf{ L\+A\+RS} \&\&}]{other }\end{DoxyParamCaption})} Take ownership of the given \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object. \begin{DoxyParams}{Parameters} {\em other} & \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} object to take ownership of. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1regression_1_1LARS_af2f52669db2906eea3d0a0cb28d9c62a}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!Predict@{Predict}} \index{Predict@{Predict}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{Predict()} {\footnotesize\ttfamily void Predict (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{points, }\item[{arma\+::rowvec \&}]{predictions, }\item[{const bool}]{row\+Major = {\ttfamily false} }\end{DoxyParamCaption}) const} Predict y\+\_\+i for each data point in the given data matrix using the currently-\/trained \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} model. \begin{DoxyParams}{Parameters} {\em points} & The data points to regress on. \\ \hline {\em predictions} & y, which will contained calculated values on completion. \\ \hline {\em row\+Major} & Should be true if the data points matrix is row-\/major and false otherwise. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1regression_1_1LARS_af0dd9205158ccf7bcfcd8ff81f79c927}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!serialize@{serialize}} \index{serialize@{serialize}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{serialize()} {\footnotesize\ttfamily void serialize (\begin{DoxyParamCaption}\item[{Archive \&}]{ar, }\item[{const unsigned}]{int }\end{DoxyParamCaption})} Serialize the \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} model. Referenced by L\+A\+R\+S\+::\+Mat\+Utri\+Chol\+Factor(). \mbox{\label{classmlpack_1_1regression_1_1LARS_a6d3b55be8a7673b24b91100d71f88b83}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!Train@{Train}} \index{Train@{Train}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{Train()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily double Train (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{data, }\item[{const arma\+::rowvec \&}]{responses, }\item[{arma\+::vec \&}]{beta, }\item[{const bool}]{transpose\+Data = {\ttfamily true} }\end{DoxyParamCaption})} Run \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}. The input matrix (like all mlpack matrices) should be column-\/major -- each column is an observation and each row is a dimension. However, because \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} is more efficient on a row-\/major matrix, this method will (internally) transpose the matrix. If this transposition is not necessary (i.\+e., you want to pass in a row-\/major matrix), pass \textquotesingle{}false\textquotesingle{} for the transpose\+Data parameter. \begin{DoxyParams}{Parameters} {\em data} & Column-\/major input data (or row-\/major input data if row\+Major = true). \\ \hline {\em responses} & A vector of targets. \\ \hline {\em beta} & Vector to store the solution (the coefficients) in. \\ \hline {\em transpose\+Data} & Set to false if the data is row-\/major. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} minimum cost error($\vert$$\vert$y-\/beta$\ast$\+X$\vert$$\vert$2 is used to calculate error). \end{DoxyReturn} \mbox{\label{classmlpack_1_1regression_1_1LARS_a124534427646a88375731cb50e0adacc}} \index{mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}!Train@{Train}} \index{Train@{Train}!mlpack\+::regression\+::\+L\+A\+RS@{mlpack\+::regression\+::\+L\+A\+RS}} \subsubsection{Train()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily double Train (\begin{DoxyParamCaption}\item[{const arma\+::mat \&}]{data, }\item[{const arma\+::rowvec \&}]{responses, }\item[{const bool}]{transpose\+Data = {\ttfamily true} }\end{DoxyParamCaption})} Run \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}. The input matrix (like all mlpack matrices) should be column-\/major -- each column is an observation and each row is a dimension. However, because \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS} is more efficient on a row-\/major matrix, this method will (internally) transpose the matrix. If this transposition is not necessary (i.\+e., you want to pass in a row-\/major matrix), pass \textquotesingle{}false\textquotesingle{} for the transpose\+Data parameter. \begin{DoxyParams}{Parameters} {\em data} & Input data. \\ \hline {\em responses} & A vector of targets. \\ \hline {\em transpose\+Data} & Should be true if the input data is column-\/major and false otherwise. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} minimum cost error($\vert$$\vert$y-\/beta$\ast$\+X$\vert$$\vert$2 is used to calculate error). \end{DoxyReturn} 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/lars/\textbf{ lars.\+hpp}\end{DoxyCompactItemize}