root/doc/latex/classKalmanCh.tex @ 74

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Matrix in Cholesky decomposition, Square-root Kalman and many bug fixes

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1\section{KalmanCh Class Reference}
2\label{classKalmanCh}\index{KalmanCh@{KalmanCh}}
3\doxyref{Kalman}{p.}{classKalman} filter in square root form. 
4
5
6{\tt \#include $<$libKF.h$>$}
7
8Inheritance diagram for KalmanCh:\nopagebreak
9\begin{figure}[H]
10\begin{center}
11\leavevmode
12\includegraphics[width=55pt]{classKalmanCh__inherit__graph}
13\end{center}
14\end{figure}
15Collaboration diagram for KalmanCh:\nopagebreak
16\begin{figure}[H]
17\begin{center}
18\leavevmode
19\includegraphics[height=400pt]{classKalmanCh__coll__graph}
20\end{center}
21\end{figure}
22\subsection*{Public Member Functions}
23\begin{CompactItemize}
24\item 
25{\bf KalmanCh} ({\bf RV} rvx0, {\bf RV} rvy0, {\bf RV} rvu0)\label{classKalmanCh_d11f110cccaa66177514632d37b086bb}
26
27\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
28{\bf KalmanCh} (const {\bf KalmanCh} \&K0)\label{classKalmanCh_7ae89bdef6b886e421e0895173d3c76d}
29
30\begin{CompactList}\small\item\em Copy constructor. \item\end{CompactList}\item 
31void {\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}
32
33\begin{CompactList}\small\item\em Set parameters with check of relevance. \item\end{CompactList}\item 
34void {\bf set\_\-pred} (const vec \&mu0, const {\bf chmat} \&P0)\label{classKalmanCh_7fe9b65d626971542e35213d11b4b98d}
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{classKalmanCh_cca758192846940409822b9bd778d4e1}
38
39\begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions The following equality hold::\[ \left[\begin{array}{cc} R^{0.5}\\ P_{t|t-1}^{0.5}C' & P_{t|t-1}^{0.5}CA'\\ & Q^{0.5}\end{array}\right]<\mathrm{orth.oper.}>=\left[\begin{array}{cc} R_{y}^{0.5} & KA'\\ & P_{t+1|t}^{0.5}\\ \\\end{array}\right]\]. \item\end{CompactList}\item 
40{\bf epdf} \& {\bf \_\-epdf} ()\label{classKalmanCh_221892e744e7020ac5735978803d357d}
41
42\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
43{\bf epdf} \& {\bf \_\-pred} ()\label{classKalmanCh_1d62f16738a29f25c468acd3b1289ec7}
44
45\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
46void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9}
47
48\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
49const {\bf RV} \& {\bf \_\-rv} () const \label{classBM_126bd2595c48e311fc2a7ab72876092a}
50
51\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
52double {\bf \_\-ll} () const \label{classBM_87f4a547d2c29180be88175e5eab9c88}
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{classKalmanCh_e1b7369670041b75231242f7293c4f90}
59
60\begin{CompactList}\small\item\em Indetifier of output rv. \item\end{CompactList}\item 
61{\bf RV} {\bf rvu}\label{classKalmanCh_1cebd72aaff9f146b9b2400cc69e1b1a}
62
63\begin{CompactList}\small\item\em Indetifier of exogeneous rv. \item\end{CompactList}\item 
64int {\bf dimx}\label{classKalmanCh_5c34f0f5cc4e1dd1be4b4489d2fc83d0}
65
66\begin{CompactList}\small\item\em cache of rv.count() \item\end{CompactList}\item 
67int {\bf dimy}\label{classKalmanCh_c8302d61db0512d8a0d4f34715117445}
68
69\begin{CompactList}\small\item\em cache of rvy.count() \item\end{CompactList}\item 
70int {\bf dimu}\label{classKalmanCh_bac7284585a271e28f928acabd990146}
71
72\begin{CompactList}\small\item\em cache of rvu.count() \item\end{CompactList}\item 
73mat {\bf A}\label{classKalmanCh_2ca9afe2042fbda38af3da8e7d8d9d3c}
74
75\begin{CompactList}\small\item\em Matrix A. \item\end{CompactList}\item 
76mat {\bf B}\label{classKalmanCh_5a5ee93d6d32f45b5d15e87764efd529}
77
78\begin{CompactList}\small\item\em Matrix B. \item\end{CompactList}\item 
79mat {\bf C}\label{classKalmanCh_3c1ce2fe15dae5c0882a99c34b19cc40}
80
81\begin{CompactList}\small\item\em Matrix C. \item\end{CompactList}\item 
82mat {\bf D}\label{classKalmanCh_50d3e4c9e411f8c96b87a1883b7e7154}
83
84\begin{CompactList}\small\item\em Matrix D. \item\end{CompactList}\item 
85{\bf chmat} {\bf Q}\label{classKalmanCh_eb21fe7ab03feb24005c331070d28040}
86
87\begin{CompactList}\small\item\em Matrix Q in square-root form. \item\end{CompactList}\item 
88{\bf chmat} {\bf R}\label{classKalmanCh_6716e9f9208fc7191b38fd801810b30a}
89
90\begin{CompactList}\small\item\em Matrix R in square-root form. \item\end{CompactList}\item 
91mat {\bf preA}\label{classKalmanCh_94ee9da75b0e0f632e4a354988ca3798}
92
93\begin{CompactList}\small\item\em pre array (triangular matrix) \item\end{CompactList}\item 
94mat {\bf postA}\label{classKalmanCh_0d31a26dc72b5846cfe5af3ccb63ac87}
95
96\begin{CompactList}\small\item\em post array (triangular matrix) \item\end{CompactList}\item 
97{\bf enorm}$<$ {\bf chmat} $>$ {\bf pred}\label{classKalmanCh_c0ffeeb2ca028ae2c1b1e166d4015804}
98
99\begin{CompactList}\small\item\em predictive density on \$x\_\-t\$ \item\end{CompactList}\item 
100{\bf enorm}$<$ {\bf chmat} $>$ {\bf fy}\label{classKalmanCh_9086baae040b9c4e5cc42a445853862a}
101
102\begin{CompactList}\small\item\em predictive density on \$y\_\-t\$ \item\end{CompactList}\item 
103vec $\ast$ \textbf{\_\-mu}\label{classKalmanCh_0c77c0c0489b7b04d8d10e208cd704bf}
104
105\item 
106{\bf chmat} $\ast$ \textbf{\_\-P}\label{classKalmanCh_feb8eedfd3d2c635e8b47ce71dd55b84}
107
108\item 
109vec $\ast$ \textbf{\_\-yp}\label{classKalmanCh_759d473aaae1cc270a99a8c4e4c68eea}
110
111\item 
112{\bf chmat} $\ast$ \textbf{\_\-Ry}\label{classKalmanCh_5a0c1b6761bfaed74a3cafa7a4ae4d2a}
113
114\item 
115{\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88}
116
117\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
118double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979}
119
120\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
121bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}
122
123\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}
124
125
126\subsection{Detailed Description}
127\doxyref{Kalman}{p.}{classKalman} filter in square root form.
128
129The documentation for this class was generated from the following files:\begin{CompactItemize}
130\item 
131work/mixpp/bdm/estim/{\bf libKF.h}\item 
132work/mixpp/bdm/estim/libKF.cpp\end{CompactItemize}
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