root/doc/latex/classbdm_1_1PF.tex @ 270

Revision 270, 10.2 kB (checked in by smidl, 16 years ago)

Changes in the very root classes!
* rv and rvc are no longer compulsory,
* samplecond does not return ll
* BM has drv

Line 
1\hypertarget{classbdm_1_1PF}{
2\section{bdm::PF Class Reference}
3\label{classbdm_1_1PF}\index{bdm::PF@{bdm::PF}}
4}
5{\tt \#include $<$libPF.h$>$}
6
7Inheritance diagram for bdm::PF:\nopagebreak
8\begin{figure}[H]
9\begin{center}
10\leavevmode
11\includegraphics[width=77pt]{classbdm_1_1PF__inherit__graph}
12\end{center}
13\end{figure}
14
15
16\subsection{Detailed Description}
17Trivial particle filter with proposal density equal to parameter evolution model.
18
19Posterior density is represented by a weighted empirical density ({\tt \hyperlink{classbdm_1_1eEmp}{eEmp}} ). \subsection*{Public Member Functions}
20\begin{CompactItemize}
21\item 
22\hypertarget{classbdm_1_1PF_db2ed4517083f83de9d61750a87274de}{
23\hyperlink{classbdm_1_1PF_db2ed4517083f83de9d61750a87274de}{PF} (\hyperlink{classbdm_1_1mpdf}{mpdf} \&par0, \hyperlink{classbdm_1_1mpdf}{mpdf} \&obs0, int n0)}
24\label{classbdm_1_1PF_db2ed4517083f83de9d61750a87274de}
25
26\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
27\hypertarget{classbdm_1_1PF_6f1988db4c3f602d187a6c15ec89cb1e}{
28void \hyperlink{classbdm_1_1PF_6f1988db4c3f602d187a6c15ec89cb1e}{set\_\-est} (const \hyperlink{classbdm_1_1epdf}{epdf} \&epdf0)}
29\label{classbdm_1_1PF_6f1988db4c3f602d187a6c15ec89cb1e}
30
31\begin{CompactList}\small\item\em Set posterior density by sampling from epdf0. \item\end{CompactList}\item 
32void \hyperlink{classbdm_1_1PF_638946eea22d4964bf9350286bb4efd8}{bayes} (const vec \&dt)
33\begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item 
34\hypertarget{classbdm_1_1PF_78a9f6809827be1d9bfe215d03b1c6ed}{
35vec $\ast$ \hyperlink{classbdm_1_1PF_78a9f6809827be1d9bfe215d03b1c6ed}{\_\-\_\-w} ()}
36\label{classbdm_1_1PF_78a9f6809827be1d9bfe215d03b1c6ed}
37
38\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
39\begin{Indent}{\bf Constructors}\par
40\begin{CompactItemize}
41\item 
42virtual \hyperlink{classbdm_1_1BM}{BM} $\ast$ \hyperlink{classbdm_1_1BM_c0f027ff91d8459937c6f60ff8e553ff}{\_\-copy\_\-} ()
43\end{CompactItemize}
44\end{Indent}
45\begin{Indent}{\bf Mathematical operations}\par
46\begin{CompactItemize}
47\item 
48\hypertarget{classbdm_1_1BM_1dee3fddaf021e62d925289660a707dc}{
49virtual void \hyperlink{classbdm_1_1BM_1dee3fddaf021e62d925289660a707dc}{bayesB} (const mat \&Dt)}
50\label{classbdm_1_1BM_1dee3fddaf021e62d925289660a707dc}
51
52\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
53virtual double \hyperlink{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}{logpred} (const vec \&dt) const
54\item 
55\hypertarget{classbdm_1_1BM_0e8ebe61fb14990abe1254bd3dda5fae}{
56vec \hyperlink{classbdm_1_1BM_0e8ebe61fb14990abe1254bd3dda5fae}{logpred\_\-m} (const mat \&dt) const }
57\label{classbdm_1_1BM_0e8ebe61fb14990abe1254bd3dda5fae}
58
59\begin{CompactList}\small\item\em Matrix version of logpred. \item\end{CompactList}\item 
60\hypertarget{classbdm_1_1BM_688d7a2aced1e06aa1c468d73a9e5eba}{
61virtual \hyperlink{classbdm_1_1epdf}{epdf} $\ast$ \hyperlink{classbdm_1_1BM_688d7a2aced1e06aa1c468d73a9e5eba}{epredictor} () const }
62\label{classbdm_1_1BM_688d7a2aced1e06aa1c468d73a9e5eba}
63
64\begin{CompactList}\small\item\em Constructs a predictive density $ f(d_{t+1} |d_{t}, \ldots d_{0}) $. \item\end{CompactList}\item 
65\hypertarget{classbdm_1_1BM_598b25e3f3d96a5bc00a5faeb5b3c912}{
66virtual \hyperlink{classbdm_1_1mpdf}{mpdf} $\ast$ \hyperlink{classbdm_1_1BM_598b25e3f3d96a5bc00a5faeb5b3c912}{predictor} () const }
67\label{classbdm_1_1BM_598b25e3f3d96a5bc00a5faeb5b3c912}
68
69\begin{CompactList}\small\item\em Constructs a conditional density 1-step ahead predictor. \item\end{CompactList}\end{CompactItemize}
70\end{Indent}
71\begin{Indent}{\bf Access to attributes}\par
72\begin{CompactItemize}
73\item 
74\hypertarget{classbdm_1_1BM_ff2d8755ba0b3def927d31305c03b09c}{
75const \hyperlink{classbdm_1_1RV}{RV} \& \textbf{\_\-drv} () const }
76\label{classbdm_1_1BM_ff2d8755ba0b3def927d31305c03b09c}
77
78\item 
79\hypertarget{classbdm_1_1BM_f135ae6dce7e9f30c9f88229c7930b96}{
80void \textbf{set\_\-drv} (const \hyperlink{classbdm_1_1RV}{RV} \&rv)}
81\label{classbdm_1_1BM_f135ae6dce7e9f30c9f88229c7930b96}
82
83\item 
84\hypertarget{classbdm_1_1BM_5be65d37dedfe33a3671e7065f523a70}{
85double \textbf{\_\-ll} () const }
86\label{classbdm_1_1BM_5be65d37dedfe33a3671e7065f523a70}
87
88\item 
89\hypertarget{classbdm_1_1BM_236b3abbcc93594fc97cd86d82c1a83f}{
90void \textbf{set\_\-evalll} (bool evl0)}
91\label{classbdm_1_1BM_236b3abbcc93594fc97cd86d82c1a83f}
92
93\item 
94\hypertarget{classbdm_1_1BM_963258c4c2dd05be001003b19aceefef}{
95virtual const \hyperlink{classbdm_1_1epdf}{epdf} \& \textbf{\_\-epdf} () const =0}
96\label{classbdm_1_1BM_963258c4c2dd05be001003b19aceefef}
97
98\item 
99\hypertarget{classbdm_1_1BM_4ed0f8b880e606316ae800f3a011c3a6}{
100virtual const \hyperlink{classbdm_1_1epdf}{epdf} $\ast$ \textbf{\_\-e} () const =0}
101\label{classbdm_1_1BM_4ed0f8b880e606316ae800f3a011c3a6}
102
103\end{CompactItemize}
104\end{Indent}
105\subsection*{Protected Attributes}
106\begin{CompactItemize}
107\item 
108\hypertarget{classbdm_1_1PF_eeafaf9b8ad75fe62ee9fd6369e3f7fe}{
109int \hyperlink{classbdm_1_1PF_eeafaf9b8ad75fe62ee9fd6369e3f7fe}{n}}
110\label{classbdm_1_1PF_eeafaf9b8ad75fe62ee9fd6369e3f7fe}
111
112\begin{CompactList}\small\item\em number of particles; \item\end{CompactList}\item 
113\hypertarget{classbdm_1_1PF_dc049265b9086cad7071f98d00a2b9af}{
114\hyperlink{classbdm_1_1eEmp}{eEmp} \hyperlink{classbdm_1_1PF_dc049265b9086cad7071f98d00a2b9af}{est}}
115\label{classbdm_1_1PF_dc049265b9086cad7071f98d00a2b9af}
116
117\begin{CompactList}\small\item\em posterior density \item\end{CompactList}\item 
118\hypertarget{classbdm_1_1PF_f5149d5522d1095d39240c4c607f61a3}{
119vec \& \hyperlink{classbdm_1_1PF_f5149d5522d1095d39240c4c607f61a3}{\_\-w}}
120\label{classbdm_1_1PF_f5149d5522d1095d39240c4c607f61a3}
121
122\begin{CompactList}\small\item\em pointer into {\tt \hyperlink{classbdm_1_1eEmp}{eEmp}} \item\end{CompactList}\item 
123\hypertarget{classbdm_1_1PF_914bd66025692c4018dbd482cb3c47c1}{
124Array$<$ vec $>$ \& \hyperlink{classbdm_1_1PF_914bd66025692c4018dbd482cb3c47c1}{\_\-samples}}
125\label{classbdm_1_1PF_914bd66025692c4018dbd482cb3c47c1}
126
127\begin{CompactList}\small\item\em pointer into {\tt \hyperlink{classbdm_1_1eEmp}{eEmp}} \item\end{CompactList}\item 
128\hypertarget{classbdm_1_1PF_cf3a1b2a407012e47ac878e3aa2fbf34}{
129\hyperlink{classbdm_1_1mpdf}{mpdf} \& \hyperlink{classbdm_1_1PF_cf3a1b2a407012e47ac878e3aa2fbf34}{par}}
130\label{classbdm_1_1PF_cf3a1b2a407012e47ac878e3aa2fbf34}
131
132\begin{CompactList}\small\item\em Parameter evolution model. \item\end{CompactList}\item 
133\hypertarget{classbdm_1_1PF_c58b8fa634272c3f48845a9020ba55aa}{
134\hyperlink{classbdm_1_1mpdf}{mpdf} \& \hyperlink{classbdm_1_1PF_c58b8fa634272c3f48845a9020ba55aa}{obs}}
135\label{classbdm_1_1PF_c58b8fa634272c3f48845a9020ba55aa}
136
137\begin{CompactList}\small\item\em Observation model. \item\end{CompactList}\item 
138\hypertarget{classbdm_1_1BM_c400357e37d27a4834b2b1d9211009ed}{
139\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1BM_c400357e37d27a4834b2b1d9211009ed}{drv}}
140\label{classbdm_1_1BM_c400357e37d27a4834b2b1d9211009ed}
141
142\begin{CompactList}\small\item\em Random variable of the data (optional). \item\end{CompactList}\item 
143\hypertarget{classbdm_1_1BM_4064b6559d962633e4372b12f4cd204a}{
144double \hyperlink{classbdm_1_1BM_4064b6559d962633e4372b12f4cd204a}{ll}}
145\label{classbdm_1_1BM_4064b6559d962633e4372b12f4cd204a}
146
147\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
148\hypertarget{classbdm_1_1BM_faff0ad12556fe7dc0e2807d4fd938ee}{
149bool \hyperlink{classbdm_1_1BM_faff0ad12556fe7dc0e2807d4fd938ee}{evalll}}
150\label{classbdm_1_1BM_faff0ad12556fe7dc0e2807d4fd938ee}
151
152\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 computational time. \item\end{CompactList}\end{CompactItemize}
153
154
155\subsection{Member Function Documentation}
156\hypertarget{classbdm_1_1PF_638946eea22d4964bf9350286bb4efd8}{
157\index{bdm::PF@{bdm::PF}!bayes@{bayes}}
158\index{bayes@{bayes}!bdm::PF@{bdm::PF}}
159\subsubsection[bayes]{\setlength{\rightskip}{0pt plus 5cm}void bdm::PF::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  \mbox{[}virtual\mbox{]}}}}
160\label{classbdm_1_1PF_638946eea22d4964bf9350286bb4efd8}
161
162
163Incremental Bayes rule.
164
165\begin{Desc}
166\item[Parameters:]
167\begin{description}
168\item[{\em dt}]vector of input data \end{description}
169\end{Desc}
170
171
172Implements \hyperlink{classbdm_1_1BM_60b1779a577367c369a932cabd3a6188}{bdm::BM}.
173
174Reimplemented in \hyperlink{classbdm_1_1MPF_286d040770d08bd7ff416cea617b1b14}{bdm::MPF$<$ BM\_\-T $>$}.
175
176References bdm::mpdf::\_\-e(), \_\-samples, \_\-w, est, bdm::epdf::evallog(), bdm::mpdf::evallogcond(), n, obs, par, bdm::eEmp::resample(), and bdm::mpdf::samplecond().\hypertarget{classbdm_1_1BM_c0f027ff91d8459937c6f60ff8e553ff}{
177\index{bdm::PF@{bdm::PF}!\_\-copy\_\-@{\_\-copy\_\-}}
178\index{\_\-copy\_\-@{\_\-copy\_\-}!bdm::PF@{bdm::PF}}
179\subsubsection[\_\-copy\_\-]{\setlength{\rightskip}{0pt plus 5cm}virtual {\bf BM}$\ast$ bdm::BM::\_\-copy\_\- ()\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
180\label{classbdm_1_1BM_c0f027ff91d8459937c6f60ff8e553ff}
181
182
183Copy function required in vectors, Arrays of \hyperlink{classbdm_1_1BM}{BM} etc. Have to be DELETED manually! Prototype:
184
185\begin{Code}\begin{verbatim} BM* _copy_(){return new BM(*this);} 
186\end{verbatim}
187\end{Code}
188
189 
190
191Reimplemented in \hyperlink{classbdm_1_1ARX_60c40b5c6abc4c7e464b4ccae64a5a61}{bdm::ARX}.\hypertarget{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}{
192\index{bdm::PF@{bdm::PF}!logpred@{logpred}}
193\index{logpred@{logpred}!bdm::PF@{bdm::PF}}
194\subsubsection[logpred]{\setlength{\rightskip}{0pt plus 5cm}virtual double bdm::BM::logpred (const vec \& {\em dt}) const\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
195\label{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}
196
197
198Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.
199
200Reimplemented in \hyperlink{classbdm_1_1ARX_080a7e531e3aa06694112863b15bc6a4}{bdm::ARX}, \hyperlink{classbdm_1_1MixEF_da724da464a75e07521941e430929efa}{bdm::MixEF}, and \hyperlink{classbdm_1_1multiBM_e157b607c1e3fa91d42aeea44458e2bf}{bdm::multiBM}.
201
202Referenced by bdm::BM::logpred\_\-m().
203
204The documentation for this class was generated from the following files:\begin{CompactItemize}
205\item 
206\hyperlink{libPF_8h}{libPF.h}\item 
207libPF.cpp\end{CompactItemize}
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