\section{EKFfull Class Reference}
\label{classEKFfull}\index{EKFfull@{EKFfull}}
Extended \doxyref{Kalman}{p.}{classKalman} Filter in full matrices.  


{\tt \#include $<$libKF.h$>$}

Inheritance diagram for EKFfull:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=102pt]{classEKFfull__inherit__graph}
\end{center}
\end{figure}
Collaboration diagram for EKFfull:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[height=400pt]{classEKFfull__coll__graph}
\end{center}
\end{figure}
\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf EKFfull} ({\bf RV} rvx, {\bf RV} rvy, {\bf RV} rvu)\label{classEKFfull_67ac4de96fd025197da767fe0472c7f7}

\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
void {\bf set\_\-parameters} ({\bf diffbifn} $\ast$pfxu, {\bf diffbifn} $\ast$phxu, const mat Q0, const mat R0)\label{classEKFfull_fc753106e0d4cf68e4f2160fd54458c0}

\begin{CompactList}\small\item\em Set nonlinear functions for mean values and covariance matrices. \item\end{CompactList}\item 
void {\bf bayes} (const vec \&dt)\label{classEKFfull_8ca46f177e395fa714bbd8bd29ea43e0}

\begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions. \item\end{CompactList}\item 
void {\bf set\_\-est} (vec mu0, mat P0)\label{classEKFfull_7bb76ea74c144ea0b36db99f94750b7b}

\begin{CompactList}\small\item\em set estimates \item\end{CompactList}\item 
{\bf epdf} \& {\bf \_\-epdf} ()\label{classEKFfull_4080d68f79dade36ccf547d57e64bdc2}

\begin{CompactList}\small\item\em dummy! \item\end{CompactList}\item 
void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9}

\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
const {\bf RV} \& {\bf \_\-rv} () const \label{classBM_126bd2595c48e311fc2a7ab72876092a}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
double {\bf \_\-ll} () const \label{classBM_87f4a547d2c29180be88175e5eab9c88}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
\subsection*{Public Attributes}
\begin{CompactItemize}
\item 
vec {\bf mu}\label{classKalmanFull_fb5aec635e2720cc5ac31bc01c18a68a}

\begin{CompactList}\small\item\em Mean value of the posterior density. \item\end{CompactList}\item 
mat {\bf P}\label{classKalmanFull_b75dc059e84fa8ffc076203b30f926cc}

\begin{CompactList}\small\item\em Variance of the posterior density. \item\end{CompactList}\item 
bool \textbf{evalll}\label{classKalmanFull_c17d69e125acd2673e6688fd86dd3f84}

\item 
double \textbf{ll}\label{classKalmanFull_3aa4bf6128980d0627413dcf9cd07308}

\end{CompactItemize}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\item 
int \textbf{dimx}\label{classKalmanFull_c5353e66238ed717dba79e0499118226}

\item 
int \textbf{dimy}\label{classKalmanFull_761fadcc12dd4cb83bb8b5e27db01947}

\item 
int \textbf{dimu}\label{classKalmanFull_609a4a0fcde78fd7aac2f01b34e952c9}

\item 
mat \textbf{A}\label{classKalmanFull_554de4c953761380cd5a14a02542e007}

\item 
mat \textbf{B}\label{classKalmanFull_ac7ade2a603a1b05419e36c5aae21755}

\item 
mat \textbf{C}\label{classKalmanFull_5a9a8326ae17b519109fcdad59ea74a3}

\item 
mat \textbf{D}\label{classKalmanFull_8f992a2d6b66d2e8bd9174b28cc0f074}

\item 
mat \textbf{R}\label{classKalmanFull_bbd2dab10da47237a5f0d9e55fd61f24}

\item 
mat \textbf{Q}\label{classKalmanFull_a8777c1fe67763395d3ddeb326239851}

\item 
mat \textbf{\_\-Pp}\label{classKalmanFull_905823cf4157a11b8b824e45809dac55}

\item 
mat \textbf{\_\-Ry}\label{classKalmanFull_b1b946b3a43f7d86cf4b6dc0dd6e3210}

\item 
mat \textbf{\_\-iRy}\label{classKalmanFull_c7d915386a9d60b1bc309ae9166764f6}

\item 
mat \textbf{\_\-K}\label{classKalmanFull_4c8354ea4801529f3071189ddd10d760}

\item 
{\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88}

\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979}

\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}

\begin{CompactList}\small\item\em If true, the filter will compute likelihood of the data record and store it in {\tt ll} . Set to false if you want to save time. \item\end{CompactList}\end{CompactItemize}
\subsection*{Friends}
\begin{CompactItemize}
\item 
std::ostream \& {\bf operator$<$$<$} (std::ostream \&os, const {\bf KalmanFull} \&kf)\label{classKalmanFull_86ba216243ed95bb46d80d88775d16af}

\begin{CompactList}\small\item\em print elements of KF \item\end{CompactList}\end{CompactItemize}


\subsection{Detailed Description}
Extended \doxyref{Kalman}{p.}{classKalman} Filter in full matrices. 

An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. 

The documentation for this class was generated from the following files:\begin{CompactItemize}
\item 
work/git/mixpp/bdm/estim/{\bf libKF.h}\item 
work/git/mixpp/bdm/estim/libKF.cpp\end{CompactItemize}