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1\section{PF Class Reference}
2\label{classPF}\index{PF@{PF}}
3A Particle Filter prototype. 
4
5
6{\tt \#include $<$libPF.h$>$}
7
8Inheritance diagram for PF:\nopagebreak
9\begin{figure}[H]
10\begin{center}
11\leavevmode
12\includegraphics[width=49pt]{classPF__inherit__graph}
13\end{center}
14\end{figure}
15Collaboration diagram for PF:\nopagebreak
16\begin{figure}[H]
17\begin{center}
18\leavevmode
19\includegraphics[width=38pt]{classPF__coll__graph}
20\end{center}
21\end{figure}
22\subsection*{Public Member Functions}
23\begin{CompactItemize}
24\item 
25ivec {\bf resample} (RESAMPLING\_\-METHOD method=SYSTEMATIC)\label{classPF_a0e26b2f6a5884aca49122f3e4f0cf19}
26
27\begin{CompactList}\small\item\em Returns indexes of particles that should be resampled. The ordering MUST guarantee inplace replacement. (Important for MPF.). \item\end{CompactList}\item 
28\textbf{PF} (vec w)\label{classPF_c37f95f0c1661c7f1e3fccb31d39de73}
29
30\item 
31void \textbf{bayes} (const vec \&dt, bool evell)\label{classPF_eb06bd7d4325f22f54233967295793b9}
32
33\item 
34virtual void {\bf bayes} (const vec \&dt)=0
35\begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item 
36void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9}
37
38\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
39{\bf epdf} $\ast$ {\bf \_\-epdf} ()\label{classBM_a5b8f6c8a872738cfaa30ab010e8c077}
40
41\begin{CompactList}\small\item\em Returns a pointer to the \doxyref{epdf}{p.}{classepdf} representing posterior density on parameters. Use with care! \item\end{CompactList}\end{CompactItemize}
42\subsection*{Public Attributes}
43\begin{CompactItemize}
44\item 
45double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979}
46
47\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
48bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}
49
50\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}
51\subsection*{Protected Attributes}
52\begin{CompactItemize}
53\item 
54int \textbf{n}\label{classPF_2c2f44ed7a4eaa42e07bdb58d503f280}
55
56\item 
57vec \textbf{w}\label{classPF_f6bc92f7979af4513b06b161497ba868}
58
59\item 
60Uniform\_\-RNG \textbf{URNG}\label{classPF_3568ca7c3b3175d98b548f496b4c34dd}
61
62\end{CompactItemize}
63
64
65\subsection{Detailed Description}
66A Particle Filter prototype.
67
68Bayesian Filtering equations hold.
69
70\subsection{Member Function Documentation}
71\index{PF@{PF}!bayes@{bayes}}
72\index{bayes@{bayes}!PF@{PF}}
73\subsubsection{\setlength{\rightskip}{0pt plus 5cm}virtual void BM::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  [pure virtual, inherited]}}\label{classBM_a892eff438aab2dd1a9e2efcb7fb5bdf}
74
75
76Incremental Bayes rule.
77
78\begin{Desc}
79\item[Parameters:]
80\begin{description}
81\item[{\em dt}]vector of input data \end{description}
82\end{Desc}
83
84
85The documentation for this class was generated from the following files:\begin{CompactItemize}
86\item 
87work/mixpp/bdm/estim/{\bf libPF.h}\item 
88work/mixpp/bdm/estim/libPF.cpp\end{CompactItemize}
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