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09/24/08 13:31:03 (16 years ago)
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  • doc/latex/classegiw.tex

    r162 r172  
     1\hypertarget{classegiw}{ 
    12\section{egiw Class Reference} 
    23\label{classegiw}\index{egiw@{egiw}} 
     4} 
    35Gauss-inverse-Wishart density stored in LD form.   
    46 
    … …  
    2325\begin{CompactItemize} 
    2426\item  
    25 {\bf egiw} ({\bf RV} {\bf rv}, mat V0, double nu0)\label{classegiw_c52a2173c6eb1490edce9c6c7c05d60b} 
     27\hypertarget{classegiw_c52a2173c6eb1490edce9c6c7c05d60b}{ 
     28\hyperlink{classegiw_c52a2173c6eb1490edce9c6c7c05d60b}{egiw} (\hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}, mat V0, double nu0)} 
     29\label{classegiw_c52a2173c6eb1490edce9c6c7c05d60b} 
    2630 
    27 \begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item  
    28 vec {\bf sample} () const  
    29 \begin{CompactList}\small\item\em Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. \item\end{CompactList}\item  
    30 vec {\bf mean} () const \label{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29} 
     31\begin{CompactList}\small\item\em Default constructor, assuming. \item\end{CompactList}\item  
     32\hypertarget{classegiw_1a17fdbac6c72b9c3abb97623db466c8}{ 
     33\hyperlink{classegiw_1a17fdbac6c72b9c3abb97623db466c8}{egiw} (\hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}, \hyperlink{classldmat}{ldmat} V0, double nu0)} 
     34\label{classegiw_1a17fdbac6c72b9c3abb97623db466c8} 
     35 
     36\begin{CompactList}\small\item\em Full constructor for V in \hyperlink{classldmat}{ldmat} form. \item\end{CompactList}\item  
     37\hypertarget{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f}{ 
     38vec \hyperlink{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f}{sample} () const } 
     39\label{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f} 
     40 
     41\begin{CompactList}\small\item\em Returns a sample, $x$ from density $epdf(rv)$. \item\end{CompactList}\item  
     42\hypertarget{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29}{ 
     43vec \hyperlink{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29}{mean} () const } 
     44\label{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29} 
    3145 
    3246\begin{CompactList}\small\item\em return expected value \item\end{CompactList}\item  
    33 double {\bf evalpdflog} (const vec \&val) const \label{classegiw_425cbc53b377274e28c6add942bab62d} 
     47\hypertarget{classegiw_9594f396acc5ad186d1c5b03b0745502}{ 
     48void \textbf{mean\_\-mat} (mat \&M, mat \&R) const } 
     49\label{classegiw_9594f396acc5ad186d1c5b03b0745502} 
    3450 
    35 \begin{CompactList}\small\item\em Compute log-probability of argument {\tt val}. \item\end{CompactList}\item  
    36 double {\bf lognc} () const \label{classegiw_70eb1a0b88459b227f919b425b0d3359} 
     51\item  
     52\hypertarget{classegiw_2ab1e525d692be8272a6f383d60b94cd}{ 
     53double \hyperlink{classegiw_2ab1e525d692be8272a6f383d60b94cd}{evalpdflog\_\-nn} (const vec \&val) const } 
     54\label{classegiw_2ab1e525d692be8272a6f383d60b94cd} 
     55 
     56\begin{CompactList}\small\item\em In this instance, val= \mbox{[}theta, r\mbox{]}. For multivariate instances, it is stored columnwise val = \mbox{[}theta\_\-1 theta\_\-2 ... r\_\-1 r\_\-2 \mbox{]}. \item\end{CompactList}\item  
     57\hypertarget{classegiw_70eb1a0b88459b227f919b425b0d3359}{ 
     58double \hyperlink{classegiw_70eb1a0b88459b227f919b425b0d3359}{lognc} () const } 
     59\label{classegiw_70eb1a0b88459b227f919b425b0d3359} 
    3760 
    3861\begin{CompactList}\small\item\em logarithm of the normalizing constant, $\mathcal{I}$ \item\end{CompactList}\item  
    39 {\bf ldmat} \& {\bf \_\-V} ()\label{classegiw_533e792e1175bfa06d5d595dc5d080d5} 
     62\hypertarget{classegiw_533e792e1175bfa06d5d595dc5d080d5}{ 
     63\hyperlink{classldmat}{ldmat} \& \hyperlink{classegiw_533e792e1175bfa06d5d595dc5d080d5}{\_\-V} ()} 
     64\label{classegiw_533e792e1175bfa06d5d595dc5d080d5} 
    4065 
    4166\begin{CompactList}\small\item\em returns a pointer to the internal statistics. Use with Care! \item\end{CompactList}\item  
    42 double \& {\bf \_\-nu} ()\label{classegiw_08029c481ff95d24f093df0573879afe} 
     67\hypertarget{classegiw_08029c481ff95d24f093df0573879afe}{ 
     68double \& \hyperlink{classegiw_08029c481ff95d24f093df0573879afe}{\_\-nu} ()} 
     69\label{classegiw_08029c481ff95d24f093df0573879afe} 
    4370 
    4471\begin{CompactList}\small\item\em returns a pointer to the internal statistics. Use with Care! \item\end{CompactList}\item  
    45 virtual void {\bf tupdate} (double phi, mat \&vbar, double nubar)\label{classeEF_fd88bc35550ec8fe9281d358216d0fcf} 
     72\hypertarget{classegiw_036306322a90a9977834baac07460816}{ 
     73void \hyperlink{classegiw_036306322a90a9977834baac07460816}{pow} (double p)} 
     74\label{classegiw_036306322a90a9977834baac07460816} 
     75 
     76\begin{CompactList}\small\item\em Power of the density, used e.g. to flatten the density. \item\end{CompactList}\item  
     77\hypertarget{classeEF_a89bef8996410609004fa019b5b48964}{ 
     78virtual void \hyperlink{classeEF_a89bef8996410609004fa019b5b48964}{dupdate} (mat \&v)} 
     79\label{classeEF_a89bef8996410609004fa019b5b48964} 
    4680 
    4781\begin{CompactList}\small\item\em TODO decide if it is really needed. \item\end{CompactList}\item  
    48 virtual void {\bf dupdate} (mat \&v, double {\bf nu}=1.0)\label{classeEF_5863718c3b2fb1496dece10c5b745d5c} 
     82\hypertarget{classeEF_6466e8d4aa9dd64698ed288cbb1afc03}{ 
     83virtual double \hyperlink{classeEF_6466e8d4aa9dd64698ed288cbb1afc03}{evalpdflog} (const vec \&val) const } 
     84\label{classeEF_6466e8d4aa9dd64698ed288cbb1afc03} 
    4985 
    50 \begin{CompactList}\small\item\em TODO decide if it is really needed. \item\end{CompactList}\item  
    51 virtual mat {\bf sampleN} (int N) const \label{classepdf_54d7dd53a641b618771cd9bee135181f} 
     86\begin{CompactList}\small\item\em Evaluate normalized log-probability. \item\end{CompactList}\item  
     87\hypertarget{classeEF_c71faf4b2d153efda14bf1f87dca1507}{ 
     88virtual vec \hyperlink{classeEF_c71faf4b2d153efda14bf1f87dca1507}{evalpdflog} (const mat \&Val) const } 
     89\label{classeEF_c71faf4b2d153efda14bf1f87dca1507} 
     90 
     91\begin{CompactList}\small\item\em Evaluate normalized log-probability for many samples. \item\end{CompactList}\item  
     92\hypertarget{classepdf_54d7dd53a641b618771cd9bee135181f}{ 
     93virtual mat \hyperlink{classepdf_54d7dd53a641b618771cd9bee135181f}{sampleN} (int N) const } 
     94\label{classepdf_54d7dd53a641b618771cd9bee135181f} 
    5295 
    5396\begin{CompactList}\small\item\em Returns N samples from density $epdf(rv)$. \item\end{CompactList}\item  
    54 virtual double {\bf eval} (const vec \&val) const \label{classepdf_3ea597362e11a0040fe7c990269d072c} 
     97\hypertarget{classepdf_3ea597362e11a0040fe7c990269d072c}{ 
     98virtual double \hyperlink{classepdf_3ea597362e11a0040fe7c990269d072c}{eval} (const vec \&val) const } 
     99\label{classepdf_3ea597362e11a0040fe7c990269d072c} 
    55100 
    56101\begin{CompactList}\small\item\em Compute probability of argument {\tt val}. \item\end{CompactList}\item  
    57 {\bf RV} \& {\bf \_\-rv} ()\label{classepdf_4778ea61ef400813e47750e024e9fc2f} 
     102\hypertarget{classepdf_ca0d32aabb4cbba347e0c37fe8607562}{ 
     103const \hyperlink{classRV}{RV} \& \hyperlink{classepdf_ca0d32aabb4cbba347e0c37fe8607562}{\_\-rv} () const } 
     104\label{classepdf_ca0d32aabb4cbba347e0c37fe8607562} 
    58105 
    59 \begin{CompactList}\small\item\em access function, possibly dangerous! \item\end{CompactList}\end{CompactItemize} 
     106\begin{CompactList}\small\item\em access function, possibly dangerous! \item\end{CompactList}\item  
     107\hypertarget{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}{ 
     108void \hyperlink{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}{\_\-renewrv} (const \hyperlink{classRV}{RV} \&in\_\-rv)} 
     109\label{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5} 
     110 
     111\begin{CompactList}\small\item\em modifier function - useful when copying epdfs \item\end{CompactList}\end{CompactItemize} 
    60112\subsection*{Protected Attributes} 
    61113\begin{CompactItemize} 
    62114\item  
    63 {\bf ldmat} {\bf V}\label{classegiw_f343d03ede89db820edf44a6297fa442} 
     115\hypertarget{classegiw_f343d03ede89db820edf44a6297fa442}{ 
     116\hyperlink{classldmat}{ldmat} \hyperlink{classegiw_f343d03ede89db820edf44a6297fa442}{V}} 
     117\label{classegiw_f343d03ede89db820edf44a6297fa442} 
    64118 
    65119\begin{CompactList}\small\item\em Extended information matrix of sufficient statistics. \item\end{CompactList}\item  
    66 double {\bf nu}\label{classegiw_4a2f130b91afe84f6d62fed289d5d453} 
     120\hypertarget{classegiw_4a2f130b91afe84f6d62fed289d5d453}{ 
     121double \hyperlink{classegiw_4a2f130b91afe84f6d62fed289d5d453}{nu}} 
     122\label{classegiw_4a2f130b91afe84f6d62fed289d5d453} 
    67123 
    68124\begin{CompactList}\small\item\em Number of data records (degrees of freedom) of sufficient statistics. \item\end{CompactList}\item  
    69 {\bf RV} {\bf rv}\label{classepdf_74da992e3f5d598da8850b646b79b9d9} 
     125\hypertarget{classegiw_3d5c719f15a5527a6c62c2a53160148e}{ 
     126int \hyperlink{classegiw_3d5c719f15a5527a6c62c2a53160148e}{xdim}} 
     127\label{classegiw_3d5c719f15a5527a6c62c2a53160148e} 
     128 
     129\begin{CompactList}\small\item\em Dimension of the output. \item\end{CompactList}\item  
     130\hypertarget{classegiw_c70d13d86e0d9f0acede3e1dc0368812}{ 
     131int \hyperlink{classegiw_c70d13d86e0d9f0acede3e1dc0368812}{nPsi}} 
     132\label{classegiw_c70d13d86e0d9f0acede3e1dc0368812} 
     133 
     134\begin{CompactList}\small\item\em Dimension of the regressor. \item\end{CompactList}\item  
     135\hypertarget{classepdf_74da992e3f5d598da8850b646b79b9d9}{ 
     136\hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}} 
     137\label{classepdf_74da992e3f5d598da8850b646b79b9d9} 
    70138 
    71139\begin{CompactList}\small\item\em Identified of the random variable. \item\end{CompactList}\end{CompactItemize} 
    … …  
    75143Gauss-inverse-Wishart density stored in LD form.  
    76144 
    77 More?...  
    78  
    79 \subsection{Member Function Documentation} 
    80 \index{egiw@{egiw}!sample@{sample}} 
    81 \index{sample@{sample}!egiw@{egiw}} 
    82 \subsubsection[sample]{\setlength{\rightskip}{0pt plus 5cm}vec egiw::sample () const\hspace{0.3cm}{\tt  [virtual]}}\label{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f} 
    83  
    84  
    85 Returns the required moment of the \doxyref{epdf}{p.}{classepdf}.  
    86  
    87 Returns a sample, $x$ from density $epdf(rv)$  
    88  
    89 Implements {\bf epdf} \doxyref{}{p.}{classepdf_8019654e494bf5e458f6fb947e11b262}. 
     145For $p$-variate densities, given rv.count() should be $p\times$ V.rows().  
    90146 
    91147The documentation for this class was generated from the following files:\begin{CompactItemize} 
    92148\item  
    93 work/git/mixpp/bdm/stat/{\bf libEF.h}\item  
     149work/git/mixpp/bdm/stat/\hyperlink{libEF_8h}{libEF.h}\item  
    94150work/git/mixpp/bdm/stat/libEF.cpp\end{CompactItemize}