\section{Ada\+Boost$<$ Weak\+Learner\+Type, Mat\+Type $>$ Class Template Reference} \label{classmlpack_1_1adaboost_1_1AdaBoost}\index{Ada\+Boost$<$ Weak\+Learner\+Type, Mat\+Type $>$@{Ada\+Boost$<$ Weak\+Learner\+Type, Mat\+Type $>$}} The \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost} class. Inheritance diagram for Ada\+Boost$<$ Weak\+Learner\+Type, Mat\+Type $>$\+: \nopagebreak \begin{figure}[H] \begin{center} \leavevmode \includegraphics[width=350pt]{classmlpack_1_1adaboost_1_1AdaBoost__inherit__graph} \end{center} \end{figure} \subsection*{Public Member Functions} \begin{DoxyCompactItemize} \item \textbf{ Ada\+Boost} (const Mat\+Type \&data, const arma\+::\+Row$<$ size\+\_\+t $>$ \&labels, const size\+\_\+t num\+Classes, const Weak\+Learner\+Type \&other, const size\+\_\+t iterations=100, const double tolerance=1e-\/6) \begin{DoxyCompactList}\small\item\em Constructor. \end{DoxyCompactList}\item \textbf{ Ada\+Boost} (const double tolerance=1e-\/6) \begin{DoxyCompactList}\small\item\em Create the \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost} object without training. \end{DoxyCompactList}\item double \textbf{ Alpha} (const size\+\_\+t i) const \begin{DoxyCompactList}\small\item\em Get the weights for the given weak learner. \end{DoxyCompactList}\item double \& \textbf{ Alpha} (const size\+\_\+t i) \begin{DoxyCompactList}\small\item\em Modify the weight for the given weak learner (be careful!). \end{DoxyCompactList}\item void \textbf{ Classify} (const Mat\+Type \&test, arma\+::\+Row$<$ size\+\_\+t $>$ \&predicted\+Labels, arma\+::mat \&probabilities) \begin{DoxyCompactList}\small\item\em Classify the given test points. \end{DoxyCompactList}\item void \textbf{ Classify} (const Mat\+Type \&test, arma\+::\+Row$<$ size\+\_\+t $>$ \&predicted\+Labels) \begin{DoxyCompactList}\small\item\em Classify the given test points. \end{DoxyCompactList}\item size\+\_\+t \textbf{ Num\+Classes} () const \begin{DoxyCompactList}\small\item\em Get the number of classes this model is trained on. \end{DoxyCompactList}\item {\footnotesize template$<$typename Archive $>$ }\\void \textbf{ serialize} (Archive \&ar, const unsigned int) \begin{DoxyCompactList}\small\item\em Serialize the \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost} model. \end{DoxyCompactList}\item double \textbf{ Tolerance} () const \begin{DoxyCompactList}\small\item\em Get the tolerance for stopping the optimization during training. \end{DoxyCompactList}\item double \& \textbf{ Tolerance} () \begin{DoxyCompactList}\small\item\em Modify the tolerance for stopping the optimization during training. \end{DoxyCompactList}\item double \textbf{ Train} (const Mat\+Type \&data, const arma\+::\+Row$<$ size\+\_\+t $>$ \&labels, const size\+\_\+t num\+Classes, const Weak\+Learner\+Type \&learner, const size\+\_\+t iterations=100, const double tolerance=1e-\/6) \begin{DoxyCompactList}\small\item\em Train \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost} on the given dataset. \end{DoxyCompactList}\item const Weak\+Learner\+Type \& \textbf{ Weak\+Learner} (const size\+\_\+t i) const \begin{DoxyCompactList}\small\item\em Get the given weak learner. \end{DoxyCompactList}\item Weak\+Learner\+Type \& \textbf{ Weak\+Learner} (const size\+\_\+t i) \begin{DoxyCompactList}\small\item\em Modify the given weak learner (be careful!). \end{DoxyCompactList}\item size\+\_\+t \textbf{ Weak\+Learners} () const \begin{DoxyCompactList}\small\item\em Get the number of weak learners in the model. \end{DoxyCompactList}\end{DoxyCompactItemize} \subsection{Detailed Description} \subsubsection*{template$<$typename Weak\+Learner\+Type = mlpack\+::perceptron\+::\+Perceptron$<$$>$, typename Mat\+Type = arma\+::mat$>$\newline class mlpack\+::adaboost\+::\+Ada\+Boost$<$ Weak\+Learner\+Type, Mat\+Type $>$} The \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost} class. \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost} is a boosting algorithm, meaning that it combines an ensemble of weak learners to produce a strong learner. For more information on \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost}, see the following paper\+: \begin{DoxyCode} @article\{schapire1999improved, author = \{Schapire, Robert E. and Singer, Yoram\}, title = \{Improved Boosting Algorithms Using Confidence-rated Predictions\}, journal = \{Machine Learning\}, volume = \{37\}, number = \{3\}, month = dec, year = \{1999\}, issn = \{0885-6125\}, pages = \{297--336\}, \} \end{DoxyCode} This class is general, and can be used with any type of weak learner, so long as the learner implements the following functions\+: \begin{DoxyCode} \textcolor{comment}{// A boosting constructor, which learns using the training parameters of the} \textcolor{comment}{// given other WeakLearner, but uses the given instance weights for training.} WeakLearner(WeakLearner& other, \textcolor{keyword}{const} MatType& data, \textcolor{keyword}{const} arma::Row& labels, \textcolor{keyword}{const} arma::rowvec& weights); \textcolor{comment}{// Given the test points, classify them and output predictions into} \textcolor{comment}{// predictedLabels.} \textcolor{keywordtype}{void} Classify(\textcolor{keyword}{const} MatType& data, arma::Row& predictedLabels); \end{DoxyCode} For more information on and examples of weak learners, see perceptron\+::\+Perceptron$<$$>$ and decision\+\_\+stump\+::\+Decision\+Stump$<$$>$. \begin{DoxyTemplParams}{Template Parameters} {\em Mat\+Type} & Data matrix type (i.\+e. arma\+::mat or arma\+::sp\+\_\+mat). \\ \hline {\em Weak\+Learner\+Type} & Type of weak learner to use. \\ \hline \end{DoxyTemplParams} Definition at line 81 of file adaboost.\+hpp. \subsection{Constructor \& Destructor Documentation} \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_a7a37dbe455d049cda36f4676b26eeb70}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Ada\+Boost@{Ada\+Boost}} \index{Ada\+Boost@{Ada\+Boost}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Ada\+Boost()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily \textbf{ Ada\+Boost} (\begin{DoxyParamCaption}\item[{const Mat\+Type \&}]{data, }\item[{const arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{labels, }\item[{const size\+\_\+t}]{num\+Classes, }\item[{const Weak\+Learner\+Type \&}]{other, }\item[{const size\+\_\+t}]{iterations = {\ttfamily 100}, }\item[{const double}]{tolerance = {\ttfamily 1e-\/6} }\end{DoxyParamCaption})} Constructor. This runs the Ada\+Boost.\+MH algorithm to provide a trained boosting model. This constructor takes an already-\/initialized weak learner; all other weak learners will learn with the same parameters as the given weak learner. \begin{DoxyParams}{Parameters} {\em data} & Input data. \\ \hline {\em labels} & Corresponding labels. \\ \hline {\em iterations} & Number of boosting rounds. \\ \hline {\em tol} & The tolerance for change in values of rt. \\ \hline {\em other} & Weak learner that has already been initialized. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_a1417a39b49cf9b28c88add6f48daa9a1}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Ada\+Boost@{Ada\+Boost}} \index{Ada\+Boost@{Ada\+Boost}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Ada\+Boost()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily \textbf{ Ada\+Boost} (\begin{DoxyParamCaption}\item[{const double}]{tolerance = {\ttfamily 1e-\/6} }\end{DoxyParamCaption})} Create the \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost} object without training. Be sure to call \doxyref{Train()}{p.}{classmlpack_1_1adaboost_1_1AdaBoost_adeb648736c63379ce1cb5f2b23d242be} before calling \doxyref{Classify()}{p.}{classmlpack_1_1adaboost_1_1AdaBoost_abc7d38690c69822bd9ec10c3760384da}! \subsection{Member Function Documentation} \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_af0e29824cfcada4ec450e4d09cc9a314}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Alpha@{Alpha}} \index{Alpha@{Alpha}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Alpha()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily double Alpha (\begin{DoxyParamCaption}\item[{const size\+\_\+t}]{i }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the weights for the given weak learner. Definition at line 121 of file adaboost.\+hpp. \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_ae59910b30903367791fde226761923cf}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Alpha@{Alpha}} \index{Alpha@{Alpha}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Alpha()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily double\& Alpha (\begin{DoxyParamCaption}\item[{const size\+\_\+t}]{i }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Modify the weight for the given weak learner (be careful!). Definition at line 123 of file adaboost.\+hpp. \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_abc7d38690c69822bd9ec10c3760384da}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Classify@{Classify}} \index{Classify@{Classify}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Classify()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily void Classify (\begin{DoxyParamCaption}\item[{const Mat\+Type \&}]{test, }\item[{arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{predicted\+Labels, }\item[{arma\+::mat \&}]{probabilities }\end{DoxyParamCaption})} Classify the given test points. \begin{DoxyParams}{Parameters} {\em test} & Testing data. \\ \hline {\em predicted\+Labels} & Vector in which the predicted labels of the test set will be stored. \\ \hline {\em probabilities} & matrix to store the predicted class probabilities for each point in the test set. \\ \hline \end{DoxyParams} Referenced by Ada\+Boost$<$ mlpack\+::tree\+::\+Decision\+Tree $>$\+::\+Weak\+Learner(). \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_a407f9c281dab80fffd49251d3907838d}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Classify@{Classify}} \index{Classify@{Classify}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Classify()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily void Classify (\begin{DoxyParamCaption}\item[{const Mat\+Type \&}]{test, }\item[{arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{predicted\+Labels }\end{DoxyParamCaption})} Classify the given test points. \begin{DoxyParams}{Parameters} {\em test} & Testing data. \\ \hline {\em predicted\+Labels} & Vector in which the predicted labels of the test set will be stored. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_a088ebfdf3c7a9e7eea81716d0c55b5a3}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Num\+Classes@{Num\+Classes}} \index{Num\+Classes@{Num\+Classes}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Num\+Classes()} {\footnotesize\ttfamily size\+\_\+t Num\+Classes (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the number of classes this model is trained on. Definition at line 115 of file adaboost.\+hpp. \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_af0dd9205158ccf7bcfcd8ff81f79c927}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!serialize@{serialize}} \index{serialize@{serialize}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{serialize()} {\footnotesize\ttfamily void serialize (\begin{DoxyParamCaption}\item[{Archive \&}]{ar, }\item[{const unsigned}]{int }\end{DoxyParamCaption})} Serialize the \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost} model. Referenced by Ada\+Boost$<$ mlpack\+::tree\+::\+Decision\+Tree $>$\+::\+Weak\+Learner(). \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_a7b5af5c1a84c507cbaa7f999ea5a4fda}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Tolerance@{Tolerance}} \index{Tolerance@{Tolerance}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Tolerance()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily double Tolerance (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the tolerance for stopping the optimization during training. Definition at line 110 of file adaboost.\+hpp. \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_a3d9fac84af16250f5a3689692e8f2173}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Tolerance@{Tolerance}} \index{Tolerance@{Tolerance}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Tolerance()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily double\& Tolerance (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Modify the tolerance for stopping the optimization during training. Definition at line 112 of file adaboost.\+hpp. \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_adeb648736c63379ce1cb5f2b23d242be}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Train@{Train}} \index{Train@{Train}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Train()} {\footnotesize\ttfamily double Train (\begin{DoxyParamCaption}\item[{const Mat\+Type \&}]{data, }\item[{const arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{labels, }\item[{const size\+\_\+t}]{num\+Classes, }\item[{const Weak\+Learner\+Type \&}]{learner, }\item[{const size\+\_\+t}]{iterations = {\ttfamily 100}, }\item[{const double}]{tolerance = {\ttfamily 1e-\/6} }\end{DoxyParamCaption})} Train \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost} on the given dataset. This method takes an initialized Weak\+Learner\+Type; the parameters for this weak learner will be used to train each of the weak learners during \doxyref{Ada\+Boost}{p.}{classmlpack_1_1adaboost_1_1AdaBoost} training. Note that this will completely overwrite any model that has already been trained with this object. \begin{DoxyParams}{Parameters} {\em data} & Dataset to train on. \\ \hline {\em labels} & Labels for each point in the dataset. \\ \hline {\em learner} & Learner to use for training. \\ \hline \end{DoxyParams} \begin{DoxyReturn}{Returns} The upper bound for training error. \end{DoxyReturn} Referenced by Ada\+Boost$<$ mlpack\+::tree\+::\+Decision\+Tree $>$\+::\+Weak\+Learner(). \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_aa6f54b09b480fed23b0b04214e300cbf}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Weak\+Learner@{Weak\+Learner}} \index{Weak\+Learner@{Weak\+Learner}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Weak\+Learner()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily const Weak\+Learner\+Type\& Weak\+Learner (\begin{DoxyParamCaption}\item[{const size\+\_\+t}]{i }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the given weak learner. Definition at line 126 of file adaboost.\+hpp. \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_a11d7a531e3de5cdd5cc8f0cc02b8ed4d}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Weak\+Learner@{Weak\+Learner}} \index{Weak\+Learner@{Weak\+Learner}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Weak\+Learner()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily Weak\+Learner\+Type\& Weak\+Learner (\begin{DoxyParamCaption}\item[{const size\+\_\+t}]{i }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Modify the given weak learner (be careful!). Definition at line 128 of file adaboost.\+hpp. \mbox{\label{classmlpack_1_1adaboost_1_1AdaBoost_a5bb6ee8b31c0e16567eee13cb3719f88}} \index{mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}!Weak\+Learners@{Weak\+Learners}} \index{Weak\+Learners@{Weak\+Learners}!mlpack\+::adaboost\+::\+Ada\+Boost@{mlpack\+::adaboost\+::\+Ada\+Boost}} \subsubsection{Weak\+Learners()} {\footnotesize\ttfamily size\+\_\+t Weak\+Learners (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the number of weak learners in the model. Definition at line 118 of file adaboost.\+hpp. 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/adaboost/\textbf{ adaboost.\+hpp}\end{DoxyCompactItemize}