\begin{CompactList}\small\item\em Enum to choose the eventual training source for our network (XML from a file or from a string). \item\end{CompactList}\end{CompactItemize}
\subsection*{Public Member Functions}
\begin{CompactItemize}
\item
{\bf NeuralNet} ()
\begin{CompactList}\small\item\em Empty constructor for the class - it just makes nothing. \item\end{CompactList}\item
\begin{CompactList}\small\item\em It gets the output of the network (note: the layer output should contain an only neuron). \item\end{CompactList}\item
vector$<$ double $>${\bf getOutputs} ()
\begin{CompactList}\small\item\em It gets the output of the network in case the output layer contains more neurons. \item\end{CompactList}\item
\begin{CompactList}\small\item\em It gets the value expected. \item\end{CompactList}\item
void {\bf setExpected} (double {\bf ex})
\begin{CompactList}\small\item\em It sets the value you expect from your network. \item\end{CompactList}\item
void {\bf update} ()
\begin{CompactList}\small\item\em It updates through back-propagation the weights of the synapsis and computes again the output value for {\em epochs\/} times, calling back updateWeights and commitChanges functions. \item\end{CompactList}\item
void {\bf propagate} ()
\begin{CompactList}\small\item\em It propagates values through the network. \item\end{CompactList}\item
void {\bf setInput} (vector$<$ double $>$\&v)
\begin{CompactList}\small\item\em It sets the input for the network. \item\end{CompactList}\item
void {\bf link} ()
\begin{CompactList}\small\item\em It links the layers of the network (input, hidden, output). \item\end{CompactList}\item
\begin{CompactList}\small\item\em Get a training set from a string and copies it to an XML For example, these strings could be training sets for making sums: \char`\"{}2,3;5\char`\"{} - \char`\"{}5,6;11\char`\"{} - \char`\"{}2,2;4\char`\"{} - \char`\"{}4,5:9\char`\"{} This method called on the first string will return an XML such this: '$<$training id=\char`\"{}0\char`\"{}$>$$<$input id=\char`\"{}0\char`\"{}$>$2$<$/input$>$$<$input id=\char`\"{}1\char`\"{}$>$3$<$/input$>$$<$output id=\char`\"{}0\char`\"{}$>$5$<$/output$>$\</training$>$'. \item\end{CompactList}\item
static void {\bf closeXML} (string \&xml)
\begin{CompactList}\small\item\em Closes an open XML document generated by \char`\"{}initXML\char`\"{} and \char`\"{}XMLFromSet\char`\"{}. \item\end{CompactList}\end{CompactItemize}
\begin{CompactList}\small\item\em It updates the weights of the net's synapsis through back-propagation. \item\end{CompactList}\item
void {\bf commitChanges} ({\bf Layer}$\ast$l)
\begin{CompactList}\small\item\em It commits the changes made by \doxyref{updateWeights()}{p.}{classneuralpp_1_1NeuralNet_94169c89a7cd47122ab5dbf1d5c5e108} to the layer l. \item\end{CompactList}\item
\begin{CompactList}\small\item\em Private pointer to function, containing the function to be used as activation function. \item\end{CompactList}\end{CompactItemize}
\subsubsection[source]{\setlength{\rightskip}{0pt plus 5cm}enum {\bf neuralpp::NeuralNet::source}}\label{classneuralpp_1_1NeuralNet_94c36c94060e785ea67a0014c4182f8f}
Enum to choose the eventual training source for our network (XML from a file or from a string).
\subsubsection[NeuralNet]{\setlength{\rightskip}{0pt plus 5cm}neuralpp::NeuralNet::NeuralNet ()\hspace{0.3cm}{\tt [inline]}}\label{classneuralpp_1_1NeuralNet_92b145f2f6f00bf1ba645ce2235882c2}
Empty constructor for the class - it just makes nothing.
\item[{\em in\_\-size}]Size of the input layer \item[{\em hidden\_\-size}]Size of the hidden layer \item[{\em out\_\-size}]Size of the output layer \item[{\em l}]learn rate (get it after doing some experiments, but generally try to keep its value quite low to be more accurate) \item[{\em e}]Epochs (cycles) to execute (the most you execute, the most the network can be accurate for its purpose) \end{description}
\item[{\em in\_\-size}]Size of the input layer \item[{\em hidden\_\-size}]Size of the hidden layer \item[{\em out\_\-size}]Size of the output layer \item[{\em actv}]Activation function to use (default: f(x)=x) \item[{\em l}]learn rate (get it after doing some experiments, but generally try to keep its value quite low to be more accurate) \item[{\em e}]Epochs (cycles) to execute (the most you execute, the most the network can be accurate for its purpose) \end{description}
\subsubsection[getOutputs]{\setlength{\rightskip}{0pt plus 5cm}vector$<$double$>$ neuralpp::NeuralNet::getOutputs ()}\label{classneuralpp_1_1NeuralNet_a6b8bf3800b43b58843c65fc431207ae}
It gets the output of the network in case the output layer contains more neurons.
\begin{Desc}
\item[Returns:]A vector containing the output values of the network \end{Desc}
\subsubsection[update]{\setlength{\rightskip}{0pt plus 5cm}void neuralpp::NeuralNet::update ()}\label{classneuralpp_1_1NeuralNet_b0bd1daadb06980dff1f50d33a7c098e}
It updates through back-propagation the weights of the synapsis and computes again the output value for {\em epochs\/} times, calling back updateWeights and commitChanges functions.
\subsubsection[propagate]{\setlength{\rightskip}{0pt plus 5cm}void neuralpp::NeuralNet::propagate ()}\label{classneuralpp_1_1NeuralNet_c129c180647362da963758bfd1ba6890}
It propagates values through the network.
Use this when you want to give an already trained network some new values the get to the output \index{neuralpp::NeuralNet@{neuralpp::NeuralNet}!setInput@{setInput}}
\subsubsection[link]{\setlength{\rightskip}{0pt plus 5cm}void neuralpp::NeuralNet::link ()}\label{classneuralpp_1_1NeuralNet_46f23f462318a4ffc037a4e806364c3f}
It links the layers of the network (input, hidden, output).
Don't use unless you exactly know what you're doing, it is already called by the constructor \index{neuralpp::NeuralNet@{neuralpp::NeuralNet}!save@{save}}
Train a network using a training set loaded from an XML file.
A sample XML file is available in examples/adder.xml \begin{Desc}
\item[Parameters:]
\begin{description}
\item[{\em xml}]XML file containing our training set \item[{\em src}]Source type from which the XML will be loaded (from a file [default] or from a string) \end{description}
Get a training set from a string and copies it to an XML For example, these strings could be training sets for making sums: \char`\"{}2,3;5\char`\"{} - \char`\"{}5,6;11\char`\"{} - \char`\"{}2,2;4\char`\"{} - \char`\"{}4,5:9\char`\"{} This method called on the first string will return an XML such this: '$<$training id=\char`\"{}0\char`\"{}$>$$<$input id=\char`\"{}0\char`\"{}$>$2$<$/input$>$$<$input id=\char`\"{}1\char`\"{}$>$3$<$/input$>$$<$output id=\char`\"{}0\char`\"{}$>$5$<$/output$>$\</training$>$'.
\begin{Desc}
\item[Parameters:]
\begin{description}
\item[{\em id}]ID for the given training set (0,1,..,n) \item[{\em set}]String containing input values and expected outputs \end{description}
\subsubsection[actv\_\-f]{\setlength{\rightskip}{0pt plus 5cm}double($\ast${\bf neuralpp::NeuralNet::actv\_\-f})(double)\hspace{0.3cm}{\tt [private]}}\label{classneuralpp_1_1NeuralNet_c1469e6afd87d85b82f14bc246f82457}
Private pointer to function, containing the function to be used as activation function.
\subsubsection[input]{\setlength{\rightskip}{0pt plus 5cm}{\bf Layer}$\ast${\bf neuralpp::NeuralNet::input}}\label{classneuralpp_1_1NeuralNet_e2b4e8405f9d25edab395d61502bdba9}
\subsubsection[hidden]{\setlength{\rightskip}{0pt plus 5cm}{\bf Layer}$\ast${\bf neuralpp::NeuralNet::hidden}}\label{classneuralpp_1_1NeuralNet_bbdaa1b6c0a1a95d2b18cd25fda2a266}
\subsubsection[output]{\setlength{\rightskip}{0pt plus 5cm}{\bf Layer}$\ast${\bf neuralpp::NeuralNet::output}}\label{classneuralpp_1_1NeuralNet_fa9b2dbcbb39d0fc70f790ac24069a74}
