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