[140] | 1 | \section{EKFful\_\-unQR Class Reference} |
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| 2 | \label{classEKFful__unQR}\index{EKFful\_\-unQR@{EKFful\_\-unQR}} |
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| 3 | Extended \doxyref{Kalman}{p.}{classKalman} filter with unknown {\tt Q} and {\tt R}. |
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[99] | 4 | |
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| 5 | |
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[140] | 6 | {\tt \#include $<$ekf\_\-templ.h$>$} |
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[99] | 7 | |
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[140] | 8 | Inheritance diagram for EKFful\_\-unQR:\nopagebreak |
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[99] | 9 | \begin{figure}[H] |
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| 10 | \begin{center} |
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| 11 | \leavevmode |
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[140] | 12 | \includegraphics[width=102pt]{classEKFful__unQR__inherit__graph} |
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[99] | 13 | \end{center} |
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| 14 | \end{figure} |
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[140] | 15 | Collaboration diagram for EKFful\_\-unQR:\nopagebreak |
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[99] | 16 | \begin{figure}[H] |
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| 17 | \begin{center} |
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| 18 | \leavevmode |
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[140] | 19 | \includegraphics[height=400pt]{classEKFful__unQR__coll__graph} |
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[99] | 20 | \end{center} |
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| 21 | \end{figure} |
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| 22 | \subsection*{Public Member Functions} |
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| 23 | \begin{CompactItemize} |
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| 24 | \item |
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[140] | 25 | {\bf EKFful\_\-unQR} ({\bf RV} rx, {\bf RV} ry, {\bf RV} ru, {\bf RV} rQR)\label{classEKFful__unQR_873e3a72264f6e777c4443220db15d79} |
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[99] | 26 | |
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| 27 | \begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item |
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[140] | 28 | void {\bf condition} (const vec \&QR0)\label{classEKFful__unQR_b586e534c8a8d219561b0c020329d412} |
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| 29 | |
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| 30 | \begin{CompactList}\small\item\em Substitute {\tt val} for {\tt rvc}. \item\end{CompactList}\item |
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[99] | 31 | void {\bf set\_\-parameters} ({\bf diffbifn} $\ast$pfxu, {\bf diffbifn} $\ast$phxu, const mat Q0, const mat R0)\label{classEKFfull_fc753106e0d4cf68e4f2160fd54458c0} |
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| 32 | |
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| 33 | \begin{CompactList}\small\item\em Set nonlinear functions for mean values and covariance matrices. \item\end{CompactList}\item |
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| 34 | void {\bf bayes} (const vec \&dt)\label{classEKFfull_8ca46f177e395fa714bbd8bd29ea43e0} |
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| 35 | |
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| 36 | \begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions. \item\end{CompactList}\item |
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[140] | 37 | void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9} |
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| 38 | |
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| 39 | \begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item |
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[99] | 40 | void {\bf set\_\-est} (vec mu0, mat P0)\label{classEKFfull_7bb76ea74c144ea0b36db99f94750b7b} |
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| 41 | |
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| 42 | \begin{CompactList}\small\item\em set estimates \item\end{CompactList}\item |
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| 43 | {\bf epdf} \& {\bf \_\-epdf} ()\label{classEKFfull_4080d68f79dade36ccf547d57e64bdc2} |
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| 44 | |
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| 45 | \begin{CompactList}\small\item\em dummy! \item\end{CompactList}\item |
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| 46 | const {\bf RV} \& {\bf \_\-rv} () const \label{classBM_126bd2595c48e311fc2a7ab72876092a} |
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| 47 | |
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| 48 | \begin{CompactList}\small\item\em access function \item\end{CompactList}\item |
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| 49 | double {\bf \_\-ll} () const \label{classBM_87f4a547d2c29180be88175e5eab9c88} |
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| 50 | |
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[140] | 51 | \begin{CompactList}\small\item\em access function \item\end{CompactList}\item |
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| 52 | const {\bf RV} \& {\bf \_\-rvc} () const \label{classBMcond_3fa60348b2da6b4208bb95b8d146900a} |
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| 53 | |
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[99] | 54 | \begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize} |
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| 55 | \subsection*{Public Attributes} |
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| 56 | \begin{CompactItemize} |
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| 57 | \item |
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| 58 | vec {\bf mu}\label{classKalmanFull_fb5aec635e2720cc5ac31bc01c18a68a} |
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| 59 | |
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| 60 | \begin{CompactList}\small\item\em Mean value of the posterior density. \item\end{CompactList}\item |
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| 61 | mat {\bf P}\label{classKalmanFull_b75dc059e84fa8ffc076203b30f926cc} |
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| 62 | |
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| 63 | \begin{CompactList}\small\item\em Variance of the posterior density. \item\end{CompactList}\item |
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| 64 | bool \textbf{evalll}\label{classKalmanFull_c17d69e125acd2673e6688fd86dd3f84} |
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| 65 | |
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| 66 | \item |
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| 67 | double \textbf{ll}\label{classKalmanFull_3aa4bf6128980d0627413dcf9cd07308} |
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| 68 | |
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| 69 | \end{CompactItemize} |
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| 70 | \subsection*{Protected Attributes} |
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| 71 | \begin{CompactItemize} |
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| 72 | \item |
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| 73 | int \textbf{dimx}\label{classKalmanFull_c5353e66238ed717dba79e0499118226} |
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| 74 | |
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| 75 | \item |
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| 76 | int \textbf{dimy}\label{classKalmanFull_761fadcc12dd4cb83bb8b5e27db01947} |
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| 77 | |
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| 78 | \item |
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| 79 | int \textbf{dimu}\label{classKalmanFull_609a4a0fcde78fd7aac2f01b34e952c9} |
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| 80 | |
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| 81 | \item |
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| 82 | mat \textbf{A}\label{classKalmanFull_554de4c953761380cd5a14a02542e007} |
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| 83 | |
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| 84 | \item |
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| 85 | mat \textbf{B}\label{classKalmanFull_ac7ade2a603a1b05419e36c5aae21755} |
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| 86 | |
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| 87 | \item |
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| 88 | mat \textbf{C}\label{classKalmanFull_5a9a8326ae17b519109fcdad59ea74a3} |
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| 89 | |
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| 90 | \item |
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| 91 | mat \textbf{D}\label{classKalmanFull_8f992a2d6b66d2e8bd9174b28cc0f074} |
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| 92 | |
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| 93 | \item |
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| 94 | mat \textbf{R}\label{classKalmanFull_bbd2dab10da47237a5f0d9e55fd61f24} |
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| 95 | |
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| 96 | \item |
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| 97 | mat \textbf{Q}\label{classKalmanFull_a8777c1fe67763395d3ddeb326239851} |
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| 98 | |
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| 99 | \item |
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| 100 | mat \textbf{\_\-Pp}\label{classKalmanFull_905823cf4157a11b8b824e45809dac55} |
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| 101 | |
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| 102 | \item |
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| 103 | mat \textbf{\_\-Ry}\label{classKalmanFull_b1b946b3a43f7d86cf4b6dc0dd6e3210} |
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| 104 | |
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| 105 | \item |
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| 106 | mat \textbf{\_\-iRy}\label{classKalmanFull_c7d915386a9d60b1bc309ae9166764f6} |
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| 107 | |
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| 108 | \item |
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| 109 | mat \textbf{\_\-K}\label{classKalmanFull_4c8354ea4801529f3071189ddd10d760} |
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| 110 | |
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| 111 | \item |
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[140] | 112 | bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129} |
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[99] | 113 | |
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[140] | 114 | \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}\item |
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[99] | 115 | double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979} |
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| 116 | |
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| 117 | \begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item |
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[140] | 118 | {\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88} |
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[99] | 119 | |
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[140] | 120 | \begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item |
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| 121 | {\bf RV} {\bf rvc}\label{classBMcond_9ba793c8ec453f04d372d17195ed8dec} |
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| 122 | |
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| 123 | \begin{CompactList}\small\item\em Identificator of the conditioning variable. \item\end{CompactList}\end{CompactItemize} |
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[99] | 124 | \subsection*{Friends} |
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| 125 | \begin{CompactItemize} |
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| 126 | \item |
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| 127 | std::ostream \& {\bf operator$<$$<$} (std::ostream \&os, const {\bf KalmanFull} \&kf)\label{classKalmanFull_86ba216243ed95bb46d80d88775d16af} |
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| 128 | |
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| 129 | \begin{CompactList}\small\item\em print elements of KF \item\end{CompactList}\end{CompactItemize} |
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| 130 | |
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| 131 | |
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| 132 | \subsection{Detailed Description} |
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[140] | 133 | Extended \doxyref{Kalman}{p.}{classKalman} filter with unknown {\tt Q} and {\tt R}. |
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[99] | 134 | |
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[140] | 135 | The documentation for this class was generated from the following file:\begin{CompactItemize} |
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[99] | 136 | \item |
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[145] | 137 | work/git/mixpp/bdm/estim/{\bf ekf\_\-templ.h}\end{CompactItemize} |
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