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[33]1\section{KFcondR Class Reference}
2\label{classKFcondR}\index{KFcondR@{KFcondR}}
3\doxyref{Kalman}{p.}{classKalman} Filter with conditional diagonal matrices R and Q. 
4
5
6{\tt \#include $<$libKF.h$>$}
7
8Inheritance diagram for KFcondR:\nopagebreak
9\begin{figure}[H]
10\begin{center}
11\leavevmode
12\includegraphics[width=109pt]{classKFcondR__inherit__graph}
13\end{center}
14\end{figure}
15Collaboration diagram for KFcondR:\nopagebreak
16\begin{figure}[H]
17\begin{center}
18\leavevmode
19\includegraphics[width=400pt]{classKFcondR__coll__graph}
20\end{center}
21\end{figure}
22\subsection*{Public Member Functions}
23\begin{CompactItemize}
24\item 
25{\bf KFcondR} ({\bf RV} rvx, {\bf RV} {\bf rvy}, {\bf RV} {\bf rvu}, {\bf RV} rvR)\label{classKFcondR_d2acbb8e66c7ee592b1a9da5b429a69e}
26
27\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
28void {\bf condition} (const vec \&{\bf R})\label{classKFcondR_8c0721e47879bb8840d829db7a174a7f}
29
30\begin{CompactList}\small\item\em Substitute {\tt val} for {\tt rvc}. \item\end{CompactList}\item 
31void {\bf set\_\-parameters} (const mat \&A0, const mat \&B0, const mat \&C0, const mat \&D0, const {\bf ldmat} \&R0, const {\bf ldmat} \&Q0)\label{classKalman_239b28a0380946f5749b2f8d2807f93a}
32
33\begin{CompactList}\small\item\em Set parameters with check of relevance. \item\end{CompactList}\item 
34void {\bf set\_\-est} (const vec \&mu0, const {\bf ldmat} \&P0)\label{classKalman_80bcf29466d9a9dd2b8f74699807d0c0}
35
36\begin{CompactList}\small\item\em Set estimate values, used e.g. in initialization. \item\end{CompactList}\item 
37void {\bf bayes} (const vec \&dt)\label{classKalman_7750ffd73f261828a32c18aaeb65c75c}
38
39\begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions. \item\end{CompactList}\item 
40void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9}
41
42\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
43{\bf epdf} \& {\bf \_\-epdf} ()\label{classKalman_a213c57aef55b2645e550bed81cfc0d4}
44
45\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
46const {\bf RV} \& {\bf \_\-rv} () const \label{classBM_126bd2595c48e311fc2a7ab72876092a}
47
48\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
49double {\bf \_\-ll} () const \label{classBM_87f4a547d2c29180be88175e5eab9c88}
50
51\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
52const {\bf RV} \& {\bf \_\-rvc} () const \label{classBMcond_3fa60348b2da6b4208bb95b8d146900a}
53
54\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
55\subsection*{Protected Attributes}
56\begin{CompactItemize}
57\item 
58{\bf RV} {\bf rvy}\label{classKalman_7501230c2fafa3655887d2da23b3184c}
59
60\begin{CompactList}\small\item\em Indetifier of output rv. \item\end{CompactList}\item 
61{\bf RV} {\bf rvu}\label{classKalman_44a16ffd5ac1e6e39bae34fea9e1e498}
62
63\begin{CompactList}\small\item\em Indetifier of exogeneous rv. \item\end{CompactList}\item 
64int {\bf dimx}\label{classKalman_39c8c403b46fa3b8c7da77cb2e3729eb}
65
66\begin{CompactList}\small\item\em cache of rv.count() \item\end{CompactList}\item 
67int {\bf dimy}\label{classKalman_ba17b956df1e38b31fbbc299c8213b6a}
68
69\begin{CompactList}\small\item\em cache of rvy.count() \item\end{CompactList}\item 
70int {\bf dimu}\label{classKalman_b0153795a1444b6968a86409c778d9ce}
71
72\begin{CompactList}\small\item\em cache of rvu.count() \item\end{CompactList}\item 
73mat {\bf A}\label{classKalman_5e02efe86ee91e9c74b93b425fe060b9}
74
75\begin{CompactList}\small\item\em Matrix A. \item\end{CompactList}\item 
76mat {\bf B}\label{classKalman_dc87704284a6c0bca13bf51f4345a50a}
77
78\begin{CompactList}\small\item\em Matrix B. \item\end{CompactList}\item 
79mat {\bf C}\label{classKalman_86a805cd6515872d1132ad0d6eb5dc13}
80
81\begin{CompactList}\small\item\em Matrix C. \item\end{CompactList}\item 
82mat {\bf D}\label{classKalman_d69f774ba3335c970c1c5b1d182f4dd1}
83
84\begin{CompactList}\small\item\em Matrix D. \item\end{CompactList}\item 
85{\bf ldmat} {\bf Q}\label{classKalman_9b69015c800eb93f3ee49da23a6f55d9}
86
87\begin{CompactList}\small\item\em Matrix Q in square-root form. \item\end{CompactList}\item 
88{\bf ldmat} {\bf R}\label{classKalman_11d171dc0e0ab111c56a70f98b97b3ec}
89
90\begin{CompactList}\small\item\em Matrix R in square-root form. \item\end{CompactList}\item 
91{\bf enorm}$<$ {\bf ldmat} $>$ {\bf est}\label{classKalman_5568c74bac67ae6d3b1061dba60c9424}
92
93\begin{CompactList}\small\item\em posterior density on \$x\_\-t\$ \item\end{CompactList}\item 
94{\bf enorm}$<$ {\bf ldmat} $>$ {\bf fy}\label{classKalman_e580ab06483952bd03f2e651763e184f}
95
96\begin{CompactList}\small\item\em preditive density on \$y\_\-t\$ \item\end{CompactList}\item 
97mat {\bf \_\-K}\label{classKalman_d422f51467c7a06174af2476d2826132}
98
99\begin{CompactList}\small\item\em placeholder for \doxyref{Kalman}{p.}{classKalman} gain \item\end{CompactList}\item 
100vec $\ast$ {\bf \_\-yp}\label{classKalman_5188eb0329f8561f0b357af329769bf8}
101
102\begin{CompactList}\small\item\em cache of fy.mu \item\end{CompactList}\item 
103{\bf ldmat} $\ast$ {\bf \_\-Ry}\label{classKalman_e17dd745daa8a958035a334a56fa4674}
104
105\begin{CompactList}\small\item\em cache of fy.R \item\end{CompactList}\item 
106{\bf ldmat} $\ast$ {\bf \_\-iRy}\label{classKalman_8a35bd14afa5a2d9bbd23ad333bec874}
107
108\begin{CompactList}\small\item\em cache of fy.iR \item\end{CompactList}\item 
109vec $\ast$ {\bf \_\-mu}\label{classKalman_d1f669b5b3421a070cc75d77b55ba734}
110
111\begin{CompactList}\small\item\em cache of est.mu \item\end{CompactList}\item 
112{\bf ldmat} $\ast$ {\bf \_\-P}\label{classKalman_b3388218567128a797e69b109138271d}
113
114\begin{CompactList}\small\item\em cache of est.R \item\end{CompactList}\item 
115{\bf ldmat} $\ast$ {\bf \_\-iP}\label{classKalman_13fec2c93d8a132201e28b70270acf5c}
116
117\begin{CompactList}\small\item\em cache of est.iR \item\end{CompactList}\item 
118{\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88}
119
120\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
121double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979}
122
123\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
124bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}
125
126\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 
127{\bf RV} {\bf rvc}\label{classBMcond_9ba793c8ec453f04d372d17195ed8dec}
128
129\begin{CompactList}\small\item\em Identificator of the conditioning variable. \item\end{CompactList}\end{CompactItemize}
130
131
132\subsection{Detailed Description}
133\doxyref{Kalman}{p.}{classKalman} Filter with conditional diagonal matrices R and Q.
134
135The documentation for this class was generated from the following files:\begin{CompactItemize}
136\item 
137work/mixpp/bdm/estim/{\bf libKF.h}\item 
138work/mixpp/bdm/estim/libKF.cpp\end{CompactItemize}
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