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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8 | Inheritance diagram for PF:\nopagebreak |
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9 | \begin{figure}[H] |
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10 | \begin{center} |
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11 | \leavevmode |
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12 | \includegraphics[width=49pt]{classPF__inherit__graph} |
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13 | \end{center} |
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14 | \end{figure} |
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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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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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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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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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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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59 | \item |
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60 | Uniform\_\-RNG \textbf{URNG}\label{classPF_3568ca7c3b3175d98b548f496b4c34dd} |
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61 | |
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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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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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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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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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81 | \item[{\em dt}]vector of input data \end{description} |
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82 | \end{Desc} |
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83 | |
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84 | |
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85 | The documentation for this class was generated from the following files:\begin{CompactItemize} |
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86 | \item |
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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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