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17<h1>work/git/mixpp/bdm/stat/libEF.h</h1><a href="libEF_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001
18<a name="l00013"></a>00013 <span class="preprocessor">#ifndef EF_H</span>
19<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define EF_H</span>
20<a name="l00015"></a>00015 <span class="preprocessor"></span>
21<a name="l00016"></a>00016 <span class="preprocessor">#include &lt;itpp/itbase.h&gt;</span>
22<a name="l00017"></a>00017 <span class="preprocessor">#include "../math/libDC.h"</span>
23<a name="l00018"></a>00018 <span class="preprocessor">#include "<a class="code" href="libBM_8h.html" title="Bayesian Models (bm) that use Bayes rule to learn from observations.">libBM.h</a>"</span>
24<a name="l00019"></a>00019 <span class="preprocessor">#include "../itpp_ext.h"</span>
25<a name="l00020"></a>00020 <span class="comment">//#include &lt;std&gt;</span>
26<a name="l00021"></a>00021
27<a name="l00022"></a>00022 <span class="keyword">using namespace </span>itpp;
28<a name="l00023"></a>00023
29<a name="l00024"></a>00024
30<a name="l00026"></a>00026 <span class="keyword">extern</span> Uniform_RNG UniRNG;
31<a name="l00028"></a>00028 <span class="keyword">extern</span> Normal_RNG NorRNG;
32<a name="l00030"></a>00030 <span class="keyword">extern</span> <a class="code" href="classitpp_1_1Gamma__RNG.html" title="Gamma distribution.">Gamma_RNG</a> GamRNG;
33<a name="l00031"></a>00031
34<a name="l00038"></a><a class="code" href="classeEF.html">00038</a> <span class="keyword">class </span><a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> : <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
35<a name="l00039"></a>00039 <span class="keyword">public</span>:
36<a name="l00040"></a>00040 <span class="comment">//      eEF() :epdf() {};</span>
37<a name="l00042"></a><a class="code" href="classeEF.html#7e3c63655e8375c76bf1f421245427a7">00042</a> <span class="comment"></span>        <a class="code" href="classeEF.html#7e3c63655e8375c76bf1f421245427a7" title="default constructor">eEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {};
38<a name="l00044"></a>00044         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classeEF.html#69e5680dac10375d62520d26c672477d" title="logarithm of the normalizing constant, ">lognc</a>() <span class="keyword">const</span> =0;
39<a name="l00046"></a><a class="code" href="classeEF.html#a89bef8996410609004fa019b5b48964">00046</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classeEF.html#a89bef8996410609004fa019b5b48964" title="TODO decide if it is really needed.">dupdate</a> ( mat &amp;v ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
40<a name="l00048"></a><a class="code" href="classeEF.html#48cdd33d0e20d1a1aa45683c956bc61c">00048</a>         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classeEF.html#48cdd33d0e20d1a1aa45683c956bc61c" title="Evaluate normalized log-probability.">evalpdflog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;};
41<a name="l00050"></a><a class="code" href="classeEF.html#6466e8d4aa9dd64698ed288cbb1afc03">00050</a>         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classeEF.html#6466e8d4aa9dd64698ed288cbb1afc03" title="Evaluate normalized log-probability.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{<span class="keywordtype">double</span> tmp;tmp= <a class="code" href="classeEF.html#48cdd33d0e20d1a1aa45683c956bc61c" title="Evaluate normalized log-probability.">evalpdflog_nn</a> ( val )-<a class="code" href="classeEF.html#69e5680dac10375d62520d26c672477d" title="logarithm of the normalizing constant, ">lognc</a>();it_assert_debug(std::isfinite(tmp),<span class="stringliteral">"why?"</span>); <span class="keywordflow">return</span> tmp;}
42<a name="l00052"></a><a class="code" href="classeEF.html#c71faf4b2d153efda14bf1f87dca1507">00052</a>         <span class="keyword">virtual</span> vec <a class="code" href="classeEF.html#6466e8d4aa9dd64698ed288cbb1afc03" title="Evaluate normalized log-probability.">evalpdflog</a> ( <span class="keyword">const</span> mat &amp;Val )<span class="keyword"> const </span>{
43<a name="l00053"></a>00053                 vec x ( Val.cols() );
44<a name="l00054"></a>00054                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;Val.cols();i++ ) {x ( i ) =<a class="code" href="classeEF.html#48cdd33d0e20d1a1aa45683c956bc61c" title="Evaluate normalized log-probability.">evalpdflog_nn</a> ( Val.get_col ( i ) ) ;}
45<a name="l00055"></a>00055                 <span class="keywordflow">return</span> x-<a class="code" href="classeEF.html#69e5680dac10375d62520d26c672477d" title="logarithm of the normalizing constant, ">lognc</a>();
46<a name="l00056"></a>00056         }
47<a name="l00058"></a><a class="code" href="classeEF.html#4f8385dd1cc9740522dc373b1dc3cbf5">00058</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classeEF.html#4f8385dd1cc9740522dc373b1dc3cbf5" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
48<a name="l00059"></a>00059 };
49<a name="l00060"></a>00060
50<a name="l00067"></a><a class="code" href="classmEF.html">00067</a> <span class="keyword">class </span><a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> {
51<a name="l00068"></a>00068
52<a name="l00069"></a>00069 <span class="keyword">public</span>:
53<a name="l00071"></a><a class="code" href="classmEF.html#8bf51fe8654d7b83c8c8afeb19409d4f">00071</a>         <a class="code" href="classmEF.html#8bf51fe8654d7b83c8c8afeb19409d4f" title="Default constructor.">mEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( rv0,rvc0 ) {};
54<a name="l00072"></a>00072 };
55<a name="l00073"></a>00073
56<a name="l00075"></a><a class="code" href="classBMEF.html">00075</a> <span class="keyword">class </span><a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> : <span class="keyword">public</span> <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> {
57<a name="l00076"></a>00076 <span class="keyword">protected</span>:
58<a name="l00078"></a><a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71">00078</a>         <span class="keywordtype">double</span> <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;
59<a name="l00080"></a><a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02">00080</a>         <span class="keywordtype">double</span> <a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;
60<a name="l00081"></a>00081 <span class="keyword">public</span>:
61<a name="l00083"></a><a class="code" href="classBMEF.html#46ac5c919ae647f3a6a38d9faba35f5d">00083</a>         <a class="code" href="classBMEF.html#46ac5c919ae647f3a6a38d9faba35f5d" title="Default constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>, <span class="keywordtype">double</span> frg0=1.0 ) :<a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> ( rv ), <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a> ( frg0 ) {}
62<a name="l00085"></a><a class="code" href="classBMEF.html#3dc6277cafbdc6cbc2db860ff219b33e">00085</a>         <a class="code" href="classBMEF.html#46ac5c919ae647f3a6a38d9faba35f5d" title="Default constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> &amp;B ) :<a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> ( B ), <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a> ( B.<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a> ), <a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ( B.<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ) {}
63<a name="l00087"></a><a class="code" href="classBMEF.html#30bb40eb1fd31869b2e62e79e1ecdcb4">00087</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#30bb40eb1fd31869b2e62e79e1ecdcb4" title="get statistics from another model">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a>* BM0 ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
64<a name="l00089"></a><a class="code" href="classBMEF.html#8f4ecb6e2eaf630155a1fa98f35aa6ad">00089</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#8f4ecb6e2eaf630155a1fa98f35aa6ad" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &amp;data, <span class="keyword">const</span> <span class="keywordtype">double</span> w ) {};
65<a name="l00090"></a>00090         <span class="comment">//original Bayes</span>
66<a name="l00091"></a>00091         <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#8f4ecb6e2eaf630155a1fa98f35aa6ad" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
67<a name="l00093"></a><a class="code" href="classBMEF.html#b3689f3ade328d157aa813384a5b153a">00093</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#b3689f3ade328d157aa813384a5b153a" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> * B ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}
68<a name="l00095"></a>00095 <span class="comment">//      virtual void flatten ( double nu0 ) {it_error ( "Not implemented" );}</span>
69<a name="l00096"></a>00096
70<a name="l00097"></a><a class="code" href="classBMEF.html#97f5312efe4a5bedb86d2daec59d8651">00097</a>         <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a>* <a class="code" href="classBMEF.html#97f5312efe4a5bedb86d2daec59d8651" title="Flatten the posterior as if to keep nu0 data.">_copy_</a> ( <span class="keywordtype">bool</span> changerv=<span class="keyword">false</span> ) {it_error ( <span class="stringliteral">"function _copy_ not implemented for this BM"</span> ); <span class="keywordflow">return</span> NULL;};
71<a name="l00098"></a>00098 };
72<a name="l00099"></a>00099
73<a name="l00100"></a>00100 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
74<a name="l00101"></a>00101 <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>;
75<a name="l00102"></a>00102
76<a name="l00108"></a>00108 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
77<a name="l00109"></a><a class="code" href="classenorm.html">00109</a> <span class="keyword">class </span><a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
78<a name="l00110"></a>00110 <span class="keyword">protected</span>:
79<a name="l00112"></a><a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20">00112</a>         vec <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
80<a name="l00114"></a><a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1">00114</a>         sq_T <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;
81<a name="l00116"></a><a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e">00116</a>         <span class="keywordtype">int</span> <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>;
82<a name="l00117"></a>00117 <span class="keyword">public</span>:
83<a name="l00119"></a>00119         <a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f" title="Default constructor.">enorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> );
84<a name="l00121"></a>00121         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> );
85<a name="l00123"></a>00123         <span class="comment">//void tupdate ( double phi, mat &amp;vbar, double nubar );</span>
86<a name="l00125"></a>00125 <span class="comment"></span>        <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 );
87<a name="l00126"></a>00126
88<a name="l00127"></a>00127         vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
89<a name="l00129"></a>00129         mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>;
90<a name="l00130"></a>00130         <span class="keywordtype">double</span> eval ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span> ;
91<a name="l00131"></a>00131         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#c1e3dcba256b0153cfdb286120e110be" title="Evaluate normalized log-probability.">evalpdflog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
92<a name="l00132"></a>00132         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
93<a name="l00133"></a><a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899">00133</a>         vec <a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;}
94<a name="l00134"></a>00134 <span class="comment">//      mlnorm&lt;sq_T&gt;* condition ( const RV &amp;rvn ) const ;</span>
95<a name="l00135"></a>00135         <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classenorm.html#921024bd6d5a0e65f2af2e39bf38dfca" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">condition</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn ) <span class="keyword">const</span> ;
96<a name="l00136"></a>00136 <span class="comment">//      enorm&lt;sq_T&gt;* marginal ( const RV &amp;rv ) const;</span>
97<a name="l00137"></a>00137         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* <a class="code" href="classenorm.html#af50a6102846060bcb23a670bf38117b" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">marginal</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) <span class="keyword">const</span>;
98<a name="l00138"></a>00138 <span class="comment">//Access methods</span>
99<a name="l00140"></a><a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac">00140</a> <span class="comment"></span>        vec&amp; <a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;}
100<a name="l00141"></a>00141
101<a name="l00143"></a><a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b">00143</a>         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b" title="access function">set_mu</a> ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>=mu0;}
102<a name="l00144"></a>00144
103<a name="l00146"></a><a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9">00146</a>         sq_T&amp; <a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;}
104<a name="l00147"></a>00147
105<a name="l00149"></a>00149 <span class="comment">//      mat getR () {return R.to_mat();}</span>
106<a name="l00150"></a>00150 };
107<a name="l00151"></a>00151
108<a name="l00158"></a><a class="code" href="classegiw.html">00158</a> <span class="keyword">class </span><a class="code" href="classegiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
109<a name="l00159"></a>00159 <span class="keyword">protected</span>:
110<a name="l00161"></a><a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442">00161</a>         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;
111<a name="l00163"></a><a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453">00163</a>         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;
112<a name="l00165"></a><a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e">00165</a>         <span class="keywordtype">int</span> <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>;
113<a name="l00167"></a><a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812">00167</a>         <span class="keywordtype">int</span> <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a>;
114<a name="l00168"></a>00168 <span class="keyword">public</span>:
115<a name="l00170"></a><a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9">00170</a>         <a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9" title="Default constructor, if nu0&amp;lt;0 a minimal nu0 will be computed.">egiw</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, mat V0, <span class="keywordtype">double</span> nu0=-1.0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> ( nu0 ) {
116<a name="l00171"></a>00171                 <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>();
117<a name="l00172"></a>00172                 it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>*<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span> );
118<a name="l00173"></a>00173                 <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>;
119<a name="l00174"></a>00174                 <span class="comment">//set mu to have proper normalization and </span>
120<a name="l00175"></a>00175                 <span class="keywordflow">if</span> (nu0&lt;0){
121<a name="l00176"></a>00176                         <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> +2; <span class="comment">// +2 assures finite expected value of R</span>
122<a name="l00177"></a>00177                         <span class="comment">// terms before that are sufficient for finite normalization</span>
123<a name="l00178"></a>00178                 }
124<a name="l00179"></a>00179         }
125<a name="l00181"></a><a class="code" href="classegiw.html#18c1bf6125652a6dcbca68dd02dddd8d">00181</a>         <a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9" title="Default constructor, if nu0&amp;lt;0 a minimal nu0 will be computed.">egiw</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> ( nu0 ) {
126<a name="l00182"></a>00182                 <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>();
127<a name="l00183"></a>00183                 it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>*<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span> );
128<a name="l00184"></a>00184                 <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>;
129<a name="l00185"></a>00185                 <span class="keywordflow">if</span> (nu0&lt;0){
130<a name="l00186"></a>00186                         <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> +2; <span class="comment">// +2 assures finite expected value of R</span>
131<a name="l00187"></a>00187                         <span class="comment">// terms before that are sufficient for finite normalization</span>
132<a name="l00188"></a>00188                 }
133<a name="l00189"></a>00189         }
134<a name="l00190"></a>00190
135<a name="l00191"></a>00191         vec <a class="code" href="classegiw.html#3d2c1f2ba0f9966781f1e0ae695e8a6f" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
136<a name="l00192"></a>00192         vec <a class="code" href="classegiw.html#6deb0ff2859f41ef7cbdf6a842cabb29" title="return expected value">mean</a>() <span class="keyword">const</span>;
137<a name="l00193"></a>00193         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>;
138<a name="l00195"></a>00195         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#2ab1e525d692be8272a6f383d60b94cd" title="In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise...">evalpdflog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
139<a name="l00196"></a>00196         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#70eb1a0b88459b227f919b425b0d3359" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
140<a name="l00197"></a>00197
141<a name="l00198"></a>00198         <span class="comment">//Access</span>
142<a name="l00200"></a><a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5">00200</a> <span class="comment"></span>        <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; <a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5" title="returns a pointer to the internal statistics. Use with Care!">_V</a>() {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;}
143<a name="l00202"></a><a class="code" href="classegiw.html#a46c8a206edf80b357a138d7491780c1">00202</a>         <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; <a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5" title="returns a pointer to the internal statistics. Use with Care!">_V</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;}
144<a name="l00204"></a><a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe">00204</a>         <span class="keywordtype">double</span>&amp; <a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>()  {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
145<a name="l00205"></a>00205         <span class="keyword">const</span> <span class="keywordtype">double</span>&amp; <a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
146<a name="l00206"></a><a class="code" href="classegiw.html#036306322a90a9977834baac07460816">00206</a>         <span class="keywordtype">void</span> <a class="code" href="classegiw.html#036306322a90a9977834baac07460816" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>*=p;<a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>*=p;};
147<a name="l00207"></a>00207 };
148<a name="l00208"></a>00208
149<a name="l00217"></a><a class="code" href="classeDirich.html">00217</a> <span class="keyword">class </span><a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
150<a name="l00218"></a>00218 <span class="keyword">protected</span>:
151<a name="l00220"></a><a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7">00220</a>         vec <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>;
152<a name="l00221"></a>00221 <span class="keyword">public</span>:
153<a name="l00223"></a><a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af">00223</a>         <a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, <span class="keyword">const</span> vec &amp;beta0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ),<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( beta0 ) {it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length(),<span class="stringliteral">"Incompatible statistics"</span> ); };
154<a name="l00225"></a><a class="code" href="classeDirich.html#55cccbc5eb44764dce722567acf5fd58">00225</a>         <a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> &amp;D0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( D0.<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ),<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( D0.<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ) {};
155<a name="l00226"></a><a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80">00226</a>         vec <a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> vec_1 ( 0.0 );};
156<a name="l00227"></a><a class="code" href="classeDirich.html#4206e1da149d51ff3b663c9241096b73">00227</a>         vec <a class="code" href="classeDirich.html#4206e1da149d51ff3b663c9241096b73" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>/sum ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> );};
157<a name="l00229"></a><a class="code" href="classeDirich.html#688a24f04be6d80d4769cf0e4ded7acc">00229</a>         <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#688a24f04be6d80d4769cf0e4ded7acc" title="In this instance, val is ...">evalpdflog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>-1 ) *log ( val );};
158<a name="l00230"></a><a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77">00230</a>         <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{
159<a name="l00231"></a>00231                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> );
160<a name="l00232"></a>00232                 <span class="keywordtype">double</span> lgb=0.0;
161<a name="l00233"></a>00233                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length();i++ ) {lgb+=lgamma ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( i ) );}
162<a name="l00234"></a>00234                 <span class="keywordflow">return</span> lgb-lgamma ( gam );
163<a name="l00235"></a>00235         };
164<a name="l00237"></a><a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a">00237</a>         vec&amp; <a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>()  {<span class="keywordflow">return</span> <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>;}
165<a name="l00239"></a><a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d">00239</a>         <span class="keywordtype">void</span> <a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d" title="Set internal parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;beta0 ) {
166<a name="l00240"></a>00240                 <span class="keywordflow">if</span> ( beta0.length() !=<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length() ) {
167<a name="l00241"></a>00241                         it_assert_debug ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#c114a6f3ff06796cc2f4dacba74291eb" title="Return length (number of entries) of the RV.">length</a>() ==1,<span class="stringliteral">"Undefined"</span> );
168<a name="l00242"></a>00242                         <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#70b24c39c5130b1e4753fa2eef495433" title="access function">set_size</a> ( 0,beta0.length() );
169<a name="l00243"></a>00243                 }
170<a name="l00244"></a>00244                 <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>= beta0;
171<a name="l00245"></a>00245         }
172<a name="l00246"></a>00246 };
173<a name="l00247"></a>00247
174<a name="l00249"></a><a class="code" href="classmultiBM.html">00249</a> <span class="keyword">class </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> {
175<a name="l00250"></a>00250 <span class="keyword">protected</span>:
176<a name="l00252"></a><a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5">00252</a>         <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>;
177<a name="l00254"></a><a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6">00254</a>         vec &amp;<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>;
178<a name="l00255"></a>00255 <span class="keyword">public</span>:
179<a name="l00257"></a><a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5">00257</a>         <a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>, <span class="keyword">const</span> vec beta0 ) : <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> ( rv ),<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ( rv,beta0 ),<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>._beta() ) {<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();}
180<a name="l00259"></a><a class="code" href="classmultiBM.html#b92751adbfb9f259ca8c95232cfd9c09">00259</a>         <a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a> &amp;B ) : <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ( <a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>,B.<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ),<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>._beta() ) {}
181<a name="l00261"></a><a class="code" href="classmultiBM.html#42e36804041e551d3ceea6c75abc0562">00261</a>         <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#42e36804041e551d3ceea6c75abc0562" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a>* mB0 ) {<span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>* mB=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( mB0 ); <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>=mB-&gt;<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>;}
182<a name="l00262"></a><a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2">00262</a>         <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
183<a name="l00263"></a>00263                 <span class="keywordflow">if</span> ( <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>&lt;1.0 ) {<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>*=<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();}
184<a name="l00264"></a>00264                 <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
185<a name="l00265"></a>00265                 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
186<a name="l00266"></a>00266         }
187<a name="l00267"></a><a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">00267</a>         <span class="keywordtype">double</span> <a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">logpred</a> ( <span class="keyword">const</span> vec &amp;dt )<span class="keyword"> const </span>{
188<a name="l00268"></a>00268                 <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> );
189<a name="l00269"></a>00269                 vec &amp;<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> = pred.<a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>();
190<a name="l00270"></a>00270
191<a name="l00271"></a>00271                 <span class="keywordtype">double</span> lll;
192<a name="l00272"></a>00272                 <span class="keywordflow">if</span> ( <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>&lt;1.0 )
193<a name="l00273"></a>00273                         {beta*=<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();}
194<a name="l00274"></a>00274                 <span class="keywordflow">else</span>
195<a name="l00275"></a>00275                         <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
196<a name="l00276"></a>00276                         <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();}
197<a name="l00277"></a>00277
198<a name="l00278"></a>00278                 beta+=dt;
199<a name="l00279"></a>00279                 <span class="keywordflow">return</span> pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-lll;
200<a name="l00280"></a>00280         }
201<a name="l00281"></a><a class="code" href="classmultiBM.html#3988322f8f51b153622036f461f62a67">00281</a>         <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#3988322f8f51b153622036f461f62a67" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a>* B ) {
202<a name="l00282"></a>00282                 <span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>* E=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( B );
203<a name="l00283"></a>00283                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
204<a name="l00284"></a>00284                 <span class="keyword">const</span> vec &amp;Eb=E-&gt;<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>;<span class="comment">//const_cast&lt;multiBM*&gt; ( E )-&gt;_beta();</span>
205<a name="l00285"></a>00285                 <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>*= ( sum ( Eb ) /sum ( <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ) );
206<a name="l00286"></a>00286                 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();}
207<a name="l00287"></a>00287         }
208<a name="l00288"></a><a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684">00288</a>         <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>&amp; <a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684" title="Returns a reference to the epdf representing posterior density on parameters.">_epdf</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>;};
209<a name="l00289"></a><a class="code" href="classmultiBM.html#66a0fa6966e40bb6c3e7ba22d26e9d35">00289</a>         <span class="keyword">const</span> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>* <a class="code" href="classmultiBM.html#66a0fa6966e40bb6c3e7ba22d26e9d35" title="Returns a pointer to the epdf representing posterior density on parameters. Use with...">_e</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &amp;<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>;};
210<a name="l00290"></a>00290         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 ) {
211<a name="l00291"></a>00291                 <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d" title="Set internal parameters.">set_parameters</a> ( beta0 );
212<a name="l00292"></a>00292                 <a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a> = <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classepdf.html#ca0d32aabb4cbba347e0c37fe8607562" title="access function, possibly dangerous!">_rv</a>();
213<a name="l00293"></a>00293                 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();}
214<a name="l00294"></a>00294         }
215<a name="l00295"></a>00295 };
216<a name="l00296"></a>00296
217<a name="l00306"></a><a class="code" href="classegamma.html">00306</a> <span class="keyword">class </span><a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
218<a name="l00307"></a>00307 <span class="keyword">protected</span>:
219<a name="l00309"></a><a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b">00309</a>         vec <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>;
220<a name="l00311"></a><a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790">00311</a>         vec <a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>;
221<a name="l00312"></a>00312 <span class="keyword">public</span> :
222<a name="l00314"></a><a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1">00314</a>         <a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1" title="Default constructor.">egamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ) {};
223<a name="l00316"></a><a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339">00316</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>=a,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>=b;};
224<a name="l00317"></a>00317         vec <a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
225<a name="l00319"></a>00319 <span class="comment">//      mat sample ( int N ) const;</span>
226<a name="l00320"></a>00320         <span class="keywordtype">double</span> <a class="code" href="classegamma.html#de84faac8f9799dfe2777ddbedf997ef" title="TODO: is it used anywhere?">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
227<a name="l00321"></a>00321         <span class="keywordtype">double</span> <a class="code" href="classegamma.html#d6dbbdb72360f9e54d64501f80318bb6" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
228<a name="l00323"></a><a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790">00323</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a> ( vec* &amp;a, vec* &amp;b ) {a=&amp;<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>;b=&amp;<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>;};
229<a name="l00324"></a><a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a">00324</a>         vec <a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec pom ( <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a> ); pom/=<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>; <span class="keywordflow">return</span> pom;}
230<a name="l00325"></a>00325 };
231<a name="l00326"></a>00326 <span class="comment">/*</span>
232<a name="l00328"></a>00328 <span class="comment">class emix : public epdf {</span>
233<a name="l00329"></a>00329 <span class="comment">protected:</span>
234<a name="l00330"></a>00330 <span class="comment">        int n;</span>
235<a name="l00331"></a>00331 <span class="comment">        vec &amp;w;</span>
236<a name="l00332"></a>00332 <span class="comment">        Array&lt;epdf*&gt; Coms;</span>
237<a name="l00333"></a>00333 <span class="comment">public:</span>
238<a name="l00335"></a>00335 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
239<a name="l00336"></a>00336 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
240<a name="l00337"></a>00337 <span class="comment">        vec mean(){vec pom; for(int i=0;i&lt;n;i++){pom+=Coms(i)-&gt;mean()*w(i);} return pom;};</span>
241<a name="l00338"></a>00338 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span>
242<a name="l00339"></a>00339 <span class="comment">};</span>
243<a name="l00340"></a>00340 <span class="comment">*/</span>
244<a name="l00341"></a>00341
245<a name="l00343"></a>00343
246<a name="l00344"></a><a class="code" href="classeuni.html">00344</a> <span class="keyword">class </span><a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
247<a name="l00345"></a>00345 <span class="keyword">protected</span>:
248<a name="l00347"></a><a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1">00347</a>         vec <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>;
249<a name="l00349"></a><a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231">00349</a>         vec <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>;
250<a name="l00351"></a><a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4">00351</a>         vec <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>;
251<a name="l00353"></a><a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda">00353</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;
252<a name="l00355"></a><a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3">00355</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>;
253<a name="l00356"></a>00356 <span class="keyword">public</span>:
254<a name="l00358"></a><a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd">00358</a>         <a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd" title="Defualt constructor.">euni</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {}
255<a name="l00359"></a>00359         <span class="keywordtype">double</span> eval ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;}
256<a name="l00360"></a><a class="code" href="classeuni.html#06af95d514a6623ad4688bd2ad50ad71">00360</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#06af95d514a6623ad4688bd2ad50ad71" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>;}
257<a name="l00361"></a><a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd">00361</a>         vec <a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{
258<a name="l00362"></a>00362                 vec smp ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() );
259<a name="l00363"></a>00363 <span class="preprocessor">#pragma omp critical</span>
260<a name="l00364"></a>00364 <span class="preprocessor"></span>                UniRNG.sample_vector ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(),smp );
261<a name="l00365"></a>00365                 <span class="keywordflow">return</span> <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>+elem_mult ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>,smp );
262<a name="l00366"></a>00366         }
263<a name="l00368"></a><a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2">00368</a>         <span class="keywordtype">void</span> <a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2" title="set values of low and high ">set_parameters</a> ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) {
264<a name="l00369"></a>00369                 <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> = high0-low0;
265<a name="l00370"></a>00370                 it_assert_debug ( min ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> );
266<a name="l00371"></a>00371                 <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a> = low0;
267<a name="l00372"></a>00372                 <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a> = high0;
268<a name="l00373"></a>00373                 <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> );
269<a name="l00374"></a>00374                 <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> );
270<a name="l00375"></a>00375         }
271<a name="l00376"></a><a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1">00376</a>         vec <a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec pom=<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; pom-=<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; pom/=2.0; <span class="keywordflow">return</span> pom;}
272<a name="l00377"></a>00377 };
273<a name="l00378"></a>00378
274<a name="l00379"></a>00379
275<a name="l00385"></a>00385 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
276<a name="l00386"></a><a class="code" href="classmlnorm.html">00386</a> <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> {
277<a name="l00387"></a>00387 <span class="keyword">protected</span>:
278<a name="l00389"></a><a class="code" href="classmlnorm.html#b76ee2171ace4fb3ff95a131ae8fc421">00389</a>         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
279<a name="l00390"></a>00390         mat A;
280<a name="l00391"></a>00391         vec mu_const;
281<a name="l00392"></a>00392         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
282<a name="l00393"></a>00393 <span class="keyword">public</span>:
283<a name="l00395"></a>00395         <a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837" title="Constructor.">mlnorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> );
284<a name="l00397"></a>00397         <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999" title="Set A and R.">set_parameters</a> ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R );
285<a name="l00398"></a>00398 <span class="comment">//      //!Generate one sample of the posterior</span>
286<a name="l00399"></a>00399 <span class="comment">//      vec samplecond (const vec &amp;cond, double &amp;lik );</span>
287<a name="l00400"></a>00400 <span class="comment">//      //!Generate matrix of samples of the posterior</span>
288<a name="l00401"></a>00401 <span class="comment">//      mat samplecond (const vec &amp;cond, vec &amp;lik, int n );</span>
289<a name="l00403"></a>00403 <span class="comment"></span>        <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &amp;cond );
290<a name="l00404"></a>00404
291<a name="l00406"></a><a class="code" href="classmlnorm.html#2732ae47835dd25d5784bf08fde0a546">00406</a>         vec&amp; <a class="code" href="classmlnorm.html#2732ae47835dd25d5784bf08fde0a546" title="access function">_mu_const</a>() {<span class="keywordflow">return</span> mu_const;}
292<a name="l00408"></a><a class="code" href="classmlnorm.html#65ec3840c21b21102896bfd2282b47b3">00408</a>         mat&amp; <a class="code" href="classmlnorm.html#65ec3840c21b21102896bfd2282b47b3" title="access function">_A</a>() {<span class="keywordflow">return</span> A;}
293<a name="l00410"></a><a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63">00410</a>         mat <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R().to_mat();}
294<a name="l00411"></a>00411
295<a name="l00412"></a>00412         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
296<a name="l00413"></a>00413         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
297<a name="l00414"></a>00414 };
298<a name="l00415"></a>00415
299<a name="l00418"></a><a class="code" href="classmlstudent.html">00418</a> <span class="keyword">class </span><a class="code" href="classmlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>&lt;ldmat&gt; {
300<a name="l00419"></a>00419 <span class="keyword">protected</span>:
301<a name="l00420"></a>00420         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
302<a name="l00421"></a>00421         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;<a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>;
303<a name="l00422"></a>00422         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
304<a name="l00423"></a>00423 <span class="keyword">public</span>:
305<a name="l00424"></a>00424         <a class="code" href="classmlstudent.html">mlstudent</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;ldmat&gt;</a> ( rv0,rvc0 ),
306<a name="l00425"></a>00425                         Lambda ( rv0.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),
307<a name="l00426"></a>00426                         <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a> ( <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {}
308<a name="l00427"></a>00427         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; Lambda0) {
309<a name="l00428"></a>00428                 <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),Lambda );
310<a name="l00429"></a>00429                 A = A0;
311<a name="l00430"></a>00430                 mu_const = mu0;
312<a name="l00431"></a>00431                 Re=R0;
313<a name="l00432"></a>00432                 Lambda = Lambda0;
314<a name="l00433"></a>00433         }
315<a name="l00434"></a><a class="code" href="classmlstudent.html#d153460ae0180f4bc7f28301f5cde876">00434</a>         <span class="keywordtype">void</span> <a class="code" href="classmlstudent.html#d153460ae0180f4bc7f28301f5cde876" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
316<a name="l00435"></a>00435                 _mu = A*cond + mu_const;
317<a name="l00436"></a>00436                 <span class="keywordtype">double</span> zeta;
318<a name="l00437"></a>00437                 <span class="comment">//ugly hack!</span>
319<a name="l00438"></a>00438                 <span class="keywordflow">if</span> ((cond.length()+1)==Lambda.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()){
320<a name="l00439"></a>00439                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat(cond, vec_1(1.0)) );
321<a name="l00440"></a>00440                 } <span class="keywordflow">else</span> {
322<a name="l00441"></a>00441                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
323<a name="l00442"></a>00442                 }
324<a name="l00443"></a>00443                 <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a> = Re;
325<a name="l00444"></a>00444                 <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>*=( 1+zeta );<span class="comment">// / ( nu ); &lt;&lt; nu is in Re!!!!!!</span>
326<a name="l00445"></a>00445         };
327<a name="l00446"></a>00446
328<a name="l00447"></a>00447 };
329<a name="l00457"></a><a class="code" href="classmgamma.html">00457</a> <span class="keyword">class </span><a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> {
330<a name="l00458"></a>00458 <span class="keyword">protected</span>:
331<a name="l00460"></a><a class="code" href="classmgamma.html#612dbf35c770a780027619aaac2c443e">00460</a>         <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
332<a name="l00462"></a><a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687">00462</a>         <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>;
333<a name="l00464"></a><a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691">00464</a>         vec* <a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>;
334<a name="l00465"></a>00465
335<a name="l00466"></a>00466 <span class="keyword">public</span>:
336<a name="l00468"></a>00468         <a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6" title="Constructor.">mgamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> );
337<a name="l00470"></a>00470         <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a> );
338<a name="l00471"></a><a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97">00471</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {*<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/val;};
339<a name="l00472"></a>00472 };
340<a name="l00473"></a>00473
341<a name="l00485"></a><a class="code" href="classmgamma__fix.html">00485</a> <span class="keyword">class </span><a class="code" href="classmgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> {
342<a name="l00486"></a>00486 <span class="keyword">protected</span>:
343<a name="l00488"></a><a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6">00488</a>         <span class="keywordtype">double</span> <a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>;
344<a name="l00490"></a><a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0">00490</a>         vec <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>;
345<a name="l00491"></a>00491 <span class="keyword">public</span>:
346<a name="l00493"></a><a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80">00493</a>         <a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80" title="Constructor.">mgamma_fix</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ) : <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> ( rv,rvc ),<a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a> ( rv.count() ) {};
347<a name="l00495"></a><a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1">00495</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) {
348<a name="l00496"></a>00496                 <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">mgamma::set_parameters</a> ( k0 );
349<a name="l00497"></a>00497                 <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>=l0;
350<a name="l00498"></a>00498         };
351<a name="l00499"></a>00499
352<a name="l00500"></a><a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460">00500</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {vec mean=elem_mult ( <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>,pow ( val,<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a> ) ); *<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/mean;};
353<a name="l00501"></a>00501 };
354<a name="l00502"></a>00502
355<a name="l00504"></a><a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212">00504</a> <span class="keyword">enum</span> <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
356<a name="l00510"></a><a class="code" href="classeEmp.html">00510</a> <span class="keyword">class </span><a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
357<a name="l00511"></a>00511 <span class="keyword">protected</span> :
358<a name="l00513"></a><a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd">00513</a>         <span class="keywordtype">int</span> <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;
359<a name="l00515"></a><a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8">00515</a>         vec <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;
360<a name="l00517"></a><a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a">00517</a>         Array&lt;vec&gt; <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;
361<a name="l00518"></a>00518 <span class="keyword">public</span>:
362<a name="l00520"></a><a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4">00520</a>         <a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4" title="Default constructor.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0 ,<span class="keywordtype">int</span> n0 ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ( n0 ),<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ),<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ) {};
363<a name="l00522"></a>00522         <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#eab03bd3381aaea11ce34d5a26556353" title="Set samples and weights.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
364<a name="l00524"></a>00524         <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#e31bc9e6196173c3480b06a761a3e716" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
365<a name="l00526"></a><a class="code" href="classeEmp.html#a4215f6a5a04d07b43f7ebaa942e15f1">00526</a>         <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#a4215f6a5a04d07b43f7ebaa942e15f1" title="Set sample.">set_n</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ){<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>.set_size(n0,copy);<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>.set_size(n0,copy);};
366<a name="l00528"></a><a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b">00528</a>         vec&amp; <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;};
367<a name="l00530"></a><a class="code" href="classeEmp.html#d6f4ae1a67ecd2bff8b9f176ee261afc">00530</a>         <span class="keyword">const</span> vec&amp; <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;};
368<a name="l00532"></a><a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575">00532</a>         Array&lt;vec&gt;&amp; <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;};
369<a name="l00534"></a><a class="code" href="classeEmp.html#bd48c1c36e2e9e78dbcea7df66dcbf25">00534</a>         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;};
370<a name="l00536"></a>00536         ivec <a class="code" href="classeEmp.html#77268292fc4465cb73ddbfb1f2932a59" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC );
371<a name="l00538"></a><a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12">00538</a>         vec <a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
372<a name="l00540"></a><a class="code" href="classeEmp.html#23e7358995400865ad2e278945922fb3">00540</a>         <span class="keywordtype">double</span> <a class="code" href="classeEmp.html#23e7358995400865ad2e278945922fb3" title="inherited operation : NOT implemneted">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;}
373<a name="l00541"></a><a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d">00541</a>         vec <a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d" title="return expected value">mean</a>()<span class="keyword"> const </span>{
374<a name="l00542"></a>00542                 vec pom=zeros ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() );
375<a name="l00543"></a>00543                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( i ) *<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( i );}
376<a name="l00544"></a>00544                 <span class="keywordflow">return</span> pom;
377<a name="l00545"></a>00545         }
378<a name="l00546"></a>00546 };
379<a name="l00547"></a>00547
380<a name="l00548"></a>00548
381<a name="l00550"></a>00550
382<a name="l00551"></a>00551 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
383<a name="l00552"></a><a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f">00552</a> <a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f" title="Default constructor.">enorm&lt;sq_T&gt;::enorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), mu ( rv.count() ),R ( rv.count() ),dim ( rv.count() ) {};
384<a name="l00553"></a>00553
385<a name="l00554"></a>00554 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
386<a name="l00555"></a><a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af">00555</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">enorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) {
387<a name="l00556"></a>00556 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
388<a name="l00557"></a>00557         <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> = mu0;
389<a name="l00558"></a>00558         <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> = R0;
390<a name="l00559"></a>00559 };
391<a name="l00560"></a>00560
392<a name="l00561"></a>00561 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
393<a name="l00562"></a><a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2">00562</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) {
394<a name="l00563"></a>00563         <span class="comment">//</span>
395<a name="l00564"></a>00564 };
396<a name="l00565"></a>00565
397<a name="l00566"></a>00566 <span class="comment">// template&lt;class sq_T&gt;</span>
398<a name="l00567"></a>00567 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
399<a name="l00568"></a>00568 <span class="comment">//      //</span>
400<a name="l00569"></a>00569 <span class="comment">// };</span>
401<a name="l00570"></a>00570
402<a name="l00571"></a>00571 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
403<a name="l00572"></a><a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5">00572</a> vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const </span>{
404<a name="l00573"></a>00573         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
405<a name="l00574"></a>00574         NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
406<a name="l00575"></a>00575         vec smp = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
407<a name="l00576"></a>00576
408<a name="l00577"></a>00577         smp += <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
409<a name="l00578"></a>00578         <span class="keywordflow">return</span> smp;
410<a name="l00579"></a>00579 };
411<a name="l00580"></a>00580
412<a name="l00581"></a>00581 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
413<a name="l00582"></a><a class="code" href="classenorm.html#60f0f3bfa53d6e65843eea9532b16d36">00582</a> mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{
414<a name="l00583"></a>00583         mat X ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N );
415<a name="l00584"></a>00584         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
416<a name="l00585"></a>00585         vec pom;
417<a name="l00586"></a>00586         <span class="keywordtype">int</span> i;
418<a name="l00587"></a>00587
419<a name="l00588"></a>00588         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
420<a name="l00589"></a>00589                 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
421<a name="l00590"></a>00590                 pom = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
422<a name="l00591"></a>00591                 pom +=<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
423<a name="l00592"></a>00592                 X.set_col ( i, pom );
424<a name="l00593"></a>00593         }
425<a name="l00594"></a>00594
426<a name="l00595"></a>00595         <span class="keywordflow">return</span> X;
427<a name="l00596"></a>00596 };
428<a name="l00597"></a>00597
429<a name="l00598"></a>00598 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
430<a name="l00599"></a>00599 <span class="keywordtype">double</span> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::eval</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
431<a name="l00600"></a>00600         <span class="keywordtype">double</span> pdfl,e;
432<a name="l00601"></a>00601         pdfl = <a class="code" href="classeEF.html#6466e8d4aa9dd64698ed288cbb1afc03" title="Evaluate normalized log-probability.">evalpdflog</a> ( val );
433<a name="l00602"></a>00602         e = exp ( pdfl );
434<a name="l00603"></a>00603         <span class="keywordflow">return</span> e;
435<a name="l00604"></a>00604 };
436<a name="l00605"></a>00605
437<a name="l00606"></a>00606 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
438<a name="l00607"></a><a class="code" href="classenorm.html#c1e3dcba256b0153cfdb286120e110be">00607</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#c1e3dcba256b0153cfdb286120e110be" title="Evaluate normalized log-probability.">enorm&lt;sq_T&gt;::evalpdflog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
439<a name="l00608"></a>00608         <span class="comment">// 1.83787706640935 = log(2pi)</span>
440<a name="l00609"></a>00609         <span class="keywordtype">double</span> tmp=-0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>-val ) );<span class="comment">// - lognc();</span>
441<a name="l00610"></a>00610         <span class="keywordflow">return</span>  tmp;
442<a name="l00611"></a>00611 };
443<a name="l00612"></a>00612
444<a name="l00613"></a>00613 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
445<a name="l00614"></a><a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8">00614</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const </span>{
446<a name="l00615"></a>00615         <span class="comment">// 1.83787706640935 = log(2pi)</span>
447<a name="l00616"></a>00616         <span class="keywordtype">double</span> tmp=0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.logdet() );
448<a name="l00617"></a>00617         <span class="keywordflow">return</span> tmp;
449<a name="l00618"></a>00618 };
450<a name="l00619"></a>00619
451<a name="l00620"></a>00620 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
452<a name="l00621"></a><a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837">00621</a> <a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837" title="Constructor.">mlnorm&lt;sq_T&gt;::mlnorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> ( rv0,rvc0 ),<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),A ( rv0.count(),rv0.count() ),<a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a> ( <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() ) {
453<a name="l00622"></a>00622         <a class="code" href="classmpdf.html#7aa894208a32f3487827df6d5054424c" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
454<a name="l00623"></a>00623 }
455<a name="l00624"></a>00624
456<a name="l00625"></a>00625 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
457<a name="l00626"></a><a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999">00626</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999" title="Set A and R.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) {
458<a name="l00627"></a>00627         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),R0 );
459<a name="l00628"></a>00628         A = A0;
460<a name="l00629"></a>00629         mu_const = mu0;
461<a name="l00630"></a>00630 }
462<a name="l00631"></a>00631
463<a name="l00632"></a>00632 <span class="comment">// template&lt;class sq_T&gt;</span>
464<a name="l00633"></a>00633 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
465<a name="l00634"></a>00634 <span class="comment">//      this-&gt;condition ( cond );</span>
466<a name="l00635"></a>00635 <span class="comment">//      vec smp = epdf.sample();</span>
467<a name="l00636"></a>00636 <span class="comment">//      lik = epdf.eval ( smp );</span>
468<a name="l00637"></a>00637 <span class="comment">//      return smp;</span>
469<a name="l00638"></a>00638 <span class="comment">// }</span>
470<a name="l00639"></a>00639
471<a name="l00640"></a>00640 <span class="comment">// template&lt;class sq_T&gt;</span>
472<a name="l00641"></a>00641 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
473<a name="l00642"></a>00642 <span class="comment">//      int i;</span>
474<a name="l00643"></a>00643 <span class="comment">//      int dim = rv.count();</span>
475<a name="l00644"></a>00644 <span class="comment">//      mat Smp ( dim,n );</span>
476<a name="l00645"></a>00645 <span class="comment">//      vec smp ( dim );</span>
477<a name="l00646"></a>00646 <span class="comment">//      this-&gt;condition ( cond );</span>
478<a name="l00647"></a>00647 <span class="comment">//</span>
479<a name="l00648"></a>00648 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
480<a name="l00649"></a>00649 <span class="comment">//              smp = epdf.sample();</span>
481<a name="l00650"></a>00650 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
482<a name="l00651"></a>00651 <span class="comment">//              Smp.set_col ( i ,smp );</span>
483<a name="l00652"></a>00652 <span class="comment">//      }</span>
484<a name="l00653"></a>00653 <span class="comment">//</span>
485<a name="l00654"></a>00654 <span class="comment">//      return Smp;</span>
486<a name="l00655"></a>00655 <span class="comment">// }</span>
487<a name="l00656"></a>00656
488<a name="l00657"></a>00657 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
489<a name="l00658"></a><a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40">00658</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
490<a name="l00659"></a>00659         _mu = A*cond + mu_const;
491<a name="l00660"></a>00660 <span class="comment">//R is already assigned;</span>
492<a name="l00661"></a>00661 }
493<a name="l00662"></a>00662
494<a name="l00663"></a>00663 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
495<a name="l00664"></a><a class="code" href="classenorm.html#af50a6102846060bcb23a670bf38117b">00664</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* <a class="code" href="classenorm.html#af50a6102846060bcb23a670bf38117b" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm&lt;sq_T&gt;::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const </span>{
496<a name="l00665"></a>00665         ivec irvn = rvn.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> );
497<a name="l00666"></a>00666
498<a name="l00667"></a>00667         sq_T Rn ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>,irvn );
499<a name="l00668"></a>00668         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* tmp = <span class="keyword">new</span> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> ( rvn );
500<a name="l00669"></a>00669         tmp-&gt;<a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">set_parameters</a> ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvn ), Rn );
501<a name="l00670"></a>00670         <span class="keywordflow">return</span> tmp;
502<a name="l00671"></a>00671 }
503<a name="l00672"></a>00672
504<a name="l00673"></a>00673 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
505<a name="l00674"></a><a class="code" href="classenorm.html#921024bd6d5a0e65f2af2e39bf38dfca">00674</a> <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classenorm.html#921024bd6d5a0e65f2af2e39bf38dfca" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const </span>{
506<a name="l00675"></a>00675
507<a name="l00676"></a>00676         <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#b9d175c327c21488b1e2fb756a84e149" title="Subtract another variable from the current one.">subt</a> ( rvn );
508<a name="l00677"></a>00677         it_assert_debug ( ( rvc.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() +rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),<span class="stringliteral">"wrong rvn"</span> );
509<a name="l00678"></a>00678         <span class="comment">//Permutation vector of the new R</span>
510<a name="l00679"></a>00679         ivec irvn = rvn.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> );
511<a name="l00680"></a>00680         ivec irvc = rvc.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> );
512<a name="l00681"></a>00681         ivec perm=concat ( irvn , irvc );
513<a name="l00682"></a>00682         sq_T Rn ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>,perm );
514<a name="l00683"></a>00683
515<a name="l00684"></a>00684         <span class="comment">//fixme - could this be done in general for all sq_T?</span>
516<a name="l00685"></a>00685         mat S=Rn.to_mat();
517<a name="l00686"></a>00686         <span class="comment">//fixme</span>
518<a name="l00687"></a>00687         <span class="keywordtype">int</span> n=rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>()-1;
519<a name="l00688"></a>00688         <span class="keywordtype">int</span> end=<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.rows()-1;
520<a name="l00689"></a>00689         mat S11 = S.get ( 0,n, 0, n );
521<a name="l00690"></a>00690         mat S12 = S.get ( 0, n , rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(), end );
522<a name="l00691"></a>00691         mat S22 = S.get ( rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(), end, rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(), end );
523<a name="l00692"></a>00692
524<a name="l00693"></a>00693         vec mu1 = <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvn );
525<a name="l00694"></a>00694         vec mu2 = <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvc );
526<a name="l00695"></a>00695         mat A=S12*inv ( S22 );
527<a name="l00696"></a>00696         sq_T R_n ( S11 - A *S12.T() );
528<a name="l00697"></a>00697
529<a name="l00698"></a>00698         <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a>* tmp=<span class="keyword">new</span> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a> ( rvn,rvc );
530<a name="l00699"></a>00699
531<a name="l00700"></a>00700         tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
532<a name="l00701"></a>00701         <span class="keywordflow">return</span> tmp;
533<a name="l00702"></a>00702 }
534<a name="l00703"></a>00703
535<a name="l00705"></a>00705
536<a name="l00706"></a>00706 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
537<a name="l00707"></a>00707 std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml ) {
538<a name="l00708"></a>00708         os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
539<a name="l00709"></a>00709         os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
540<a name="l00710"></a>00710         os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
541<a name="l00711"></a>00711         <span class="keywordflow">return</span> os;
542<a name="l00712"></a>00712 };
543<a name="l00713"></a>00713
544<a name="l00714"></a>00714 <span class="preprocessor">#endif //EF_H</span>
545</pre></div></div>
546<hr size="1"><address style="text-align: right;"><small>Generated on Wed Nov 12 20:46:06 2008 for mixpp by&nbsp;
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548<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>
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