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[33]1\section{EKF\_\-unQ Class Reference}
[91]2\label{classEKF__unQ}\index{EKF\_\-unQ@{EKF\_\-unQ}}
[33]3Extended \doxyref{Kalman}{p.}{classKalman} filter with unknown {\tt Q}. 
4
5
6Inheritance diagram for EKF\_\-unQ:\nopagebreak
7\begin{figure}[H]
8\begin{center}
9\leavevmode
[91]10\includegraphics[width=95pt]{classEKF__unQ__inherit__graph}
[33]11\end{center}
12\end{figure}
13Collaboration diagram for EKF\_\-unQ:\nopagebreak
14\begin{figure}[H]
15\begin{center}
16\leavevmode
17\includegraphics[width=400pt]{classEKF__unQ__coll__graph}
18\end{center}
19\end{figure}
20\subsection*{Public Member Functions}
21\begin{CompactItemize}
22\item 
23{\bf EKF\_\-unQ} ({\bf RV} rx, {\bf RV} ry, {\bf RV} ru, {\bf RV} rQ)\label{classEKF__unQ_159eaaa5a05c5ceecdaa20956a307244}
24
25\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
26void {\bf condition} (const vec \&Q0)\label{classEKF__unQ_cd06a8c662da244cf61bb5bd39688c99}
27
28\begin{CompactList}\small\item\em Substitute {\tt val} for {\tt rvc}. \item\end{CompactList}\item 
[79]29{\bf EKF\_\-unQ} ({\bf RV} rx, {\bf RV} ry, {\bf RV} ru, {\bf RV} rQ)\label{classEKF__unQ_159eaaa5a05c5ceecdaa20956a307244}
[33]30
[79]31\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
32void {\bf condition} (const vec \&Q0)\label{classEKF__unQ_cd06a8c662da244cf61bb5bd39688c99}
33
34\begin{CompactList}\small\item\em Substitute {\tt val} for {\tt rvc}. \item\end{CompactList}\item 
35void \textbf{bayes} (const vec dt)\label{classEKF__unQ_44b49058c8eb27c7910ae31a1dfd3d21}
36
37\item 
38{\bf EKF\_\-unQ} ({\bf RV} rx, {\bf RV} ry, {\bf RV} ru, {\bf RV} rQ)\label{classEKF__unQ_159eaaa5a05c5ceecdaa20956a307244}
39
40\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
41void {\bf condition} (const vec \&Q0)\label{classEKF__unQ_cd06a8c662da244cf61bb5bd39688c99}
42
43\begin{CompactList}\small\item\em Substitute {\tt val} for {\tt rvc}. \item\end{CompactList}\item 
44void {\bf set\_\-parameters} ({\bf diffbifn} $\ast$pfxu, {\bf diffbifn} $\ast$phxu, const {\bf chmat} Q0, const {\bf chmat} R0)\label{classEKFCh_0216bed270df59fe65d0d62d41f8257c}
45
[33]46\begin{CompactList}\small\item\em Set nonlinear functions for mean values and covariance matrices. \item\end{CompactList}\item 
[79]47void {\bf set\_\-parameters} (const mat \&A0, const mat \&B0, const mat \&C0, const mat \&D0, const {\bf chmat} \&R0, const {\bf chmat} \&Q0)\label{classKalmanCh_92fb227287af05c9f0078d523c7c9793}
[33]48
49\begin{CompactList}\small\item\em Set parameters with check of relevance. \item\end{CompactList}\item 
[79]50void {\bf bayes} (const vec \&dt)\label{classEKFCh_96f6edda324a0b7ef8b4e86cc7af60c1}
[33]51
52\begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions. \item\end{CompactList}\item 
53void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9}
54
55\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
[79]56void {\bf set\_\-est} (const vec \&mu0, const {\bf chmat} \&P0)\label{classKalmanCh_b261b20f6210d4c85131d33302df0adc}
[33]57
58\begin{CompactList}\small\item\em Set estimate values, used e.g. in initialization. \item\end{CompactList}\item 
59{\bf epdf} \& {\bf \_\-epdf} ()\label{classKalman_a213c57aef55b2645e550bed81cfc0d4}
60
61\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
[79]62mat \& {\bf \_\-\_\-K} ()\label{classKalman_980fcd41c6c548c5da7b8b67c8e6da79}
63
64\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
65vec {\bf \_\-dP} ()\label{classKalman_ac9540f3850b74d89a5fe4db6fc358ce}
66
67\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
[33]68const {\bf RV} \& {\bf \_\-rv} () const \label{classBM_126bd2595c48e311fc2a7ab72876092a}
69
70\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
71double {\bf \_\-ll} () const \label{classBM_87f4a547d2c29180be88175e5eab9c88}
72
73\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
74const {\bf RV} \& {\bf \_\-rvc} () const \label{classBMcond_3fa60348b2da6b4208bb95b8d146900a}
75
[79]76\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
77const {\bf RV} \& {\bf \_\-rvc} () const \label{classBMcond_3fa60348b2da6b4208bb95b8d146900a}
78
[33]79\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
80\subsection*{Protected Attributes}
81\begin{CompactItemize}
82\item 
[79]83mat {\bf preA}\label{classKalmanCh_94ee9da75b0e0f632e4a354988ca3798}
84
85\begin{CompactList}\small\item\em pre array (triangular matrix) \item\end{CompactList}\item 
86mat {\bf postA}\label{classKalmanCh_0d31a26dc72b5846cfe5af3ccb63ac87}
87
88\begin{CompactList}\small\item\em post array (triangular matrix) \item\end{CompactList}\item 
[33]89{\bf RV} {\bf rvy}\label{classKalman_7501230c2fafa3655887d2da23b3184c}
90
91\begin{CompactList}\small\item\em Indetifier of output rv. \item\end{CompactList}\item 
92{\bf RV} {\bf rvu}\label{classKalman_44a16ffd5ac1e6e39bae34fea9e1e498}
93
94\begin{CompactList}\small\item\em Indetifier of exogeneous rv. \item\end{CompactList}\item 
95int {\bf dimx}\label{classKalman_39c8c403b46fa3b8c7da77cb2e3729eb}
96
97\begin{CompactList}\small\item\em cache of rv.count() \item\end{CompactList}\item 
98int {\bf dimy}\label{classKalman_ba17b956df1e38b31fbbc299c8213b6a}
99
100\begin{CompactList}\small\item\em cache of rvy.count() \item\end{CompactList}\item 
101int {\bf dimu}\label{classKalman_b0153795a1444b6968a86409c778d9ce}
102
103\begin{CompactList}\small\item\em cache of rvu.count() \item\end{CompactList}\item 
104mat {\bf A}\label{classKalman_5e02efe86ee91e9c74b93b425fe060b9}
105
106\begin{CompactList}\small\item\em Matrix A. \item\end{CompactList}\item 
107mat {\bf B}\label{classKalman_dc87704284a6c0bca13bf51f4345a50a}
108
109\begin{CompactList}\small\item\em Matrix B. \item\end{CompactList}\item 
110mat {\bf C}\label{classKalman_86a805cd6515872d1132ad0d6eb5dc13}
111
112\begin{CompactList}\small\item\em Matrix C. \item\end{CompactList}\item 
113mat {\bf D}\label{classKalman_d69f774ba3335c970c1c5b1d182f4dd1}
114
115\begin{CompactList}\small\item\em Matrix D. \item\end{CompactList}\item 
[79]116{\bf chmat} {\bf Q}\label{classKalman_9b69015c800eb93f3ee49da23a6f55d9}
[33]117
118\begin{CompactList}\small\item\em Matrix Q in square-root form. \item\end{CompactList}\item 
[79]119{\bf chmat} {\bf R}\label{classKalman_11d171dc0e0ab111c56a70f98b97b3ec}
[33]120
121\begin{CompactList}\small\item\em Matrix R in square-root form. \item\end{CompactList}\item 
[79]122{\bf enorm}$<$ {\bf chmat} $>$ {\bf est}\label{classKalman_5568c74bac67ae6d3b1061dba60c9424}
[33]123
124\begin{CompactList}\small\item\em posterior density on \$x\_\-t\$ \item\end{CompactList}\item 
[79]125{\bf enorm}$<$ {\bf chmat} $>$ {\bf fy}\label{classKalman_e580ab06483952bd03f2e651763e184f}
[33]126
127\begin{CompactList}\small\item\em preditive density on \$y\_\-t\$ \item\end{CompactList}\item 
128mat {\bf \_\-K}\label{classKalman_d422f51467c7a06174af2476d2826132}
129
130\begin{CompactList}\small\item\em placeholder for \doxyref{Kalman}{p.}{classKalman} gain \item\end{CompactList}\item 
[79]131vec \& {\bf \_\-yp}\label{classKalman_764bbc95238eda11fc81c5ebd0b1dcfd}
[33]132
133\begin{CompactList}\small\item\em cache of fy.mu \item\end{CompactList}\item 
[79]134{\bf chmat} \& {\bf \_\-Ry}\label{classKalman_45c9f928d2d62e0c884900fb3380f904}
[33]135
136\begin{CompactList}\small\item\em cache of fy.R \item\end{CompactList}\item 
[79]137vec \& {\bf \_\-mu}\label{classKalman_fe803a81d2d847b0b1db3c6b29c18061}
[33]138
139\begin{CompactList}\small\item\em cache of est.mu \item\end{CompactList}\item 
[79]140{\bf chmat} \& {\bf \_\-P}\label{classKalman_9fb808cc94a4c2652e1fb93be9bb7dcf}
[33]141
142\begin{CompactList}\small\item\em cache of est.R \item\end{CompactList}\item 
143{\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88}
144
145\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
146double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979}
147
148\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
149bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}
150
151\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 
152{\bf RV} {\bf rvc}\label{classBMcond_9ba793c8ec453f04d372d17195ed8dec}
153
154\begin{CompactList}\small\item\em Identificator of the conditioning variable. \item\end{CompactList}\end{CompactItemize}
155
156
157\subsection{Detailed Description}
158Extended \doxyref{Kalman}{p.}{classKalman} filter with unknown {\tt Q}.
159
[79]160The documentation for this class was generated from the following files:\begin{CompactItemize}
[33]161\item 
[79]162work/mixpp/pmsm/pmsm\_\-sim.cpp\item 
163work/mixpp/pmsm/pmsm\_\-sim2.cpp\item 
164work/mixpp/pmsm/pmsm\_\-unkQpf.cpp\end{CompactItemize}
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