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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="keywordflow">return</span> <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>();}
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><a class="code" href="classBMEF.html#c285f29db290d05428bf1aa2cd7c35ad">00095</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="keywordtype">double</span> nu0 ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}
69<a name="l00096"></a>00096 };
70<a name="l00097"></a>00097
71<a name="l00098"></a>00098 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
72<a name="l00099"></a>00099 <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>;
73<a name="l00100"></a>00100
74<a name="l00106"></a>00106 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
75<a name="l00107"></a><a class="code" href="classenorm.html">00107</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> {
76<a name="l00108"></a>00108 <span class="keyword">protected</span>:
77<a name="l00110"></a><a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20">00110</a>         vec <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
78<a name="l00112"></a><a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1">00112</a>         sq_T <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;
79<a name="l00114"></a><a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e">00114</a>         <span class="keywordtype">int</span> <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>;
80<a name="l00115"></a>00115 <span class="keyword">public</span>:
81<a name="l00117"></a>00117         <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> );
82<a name="l00119"></a>00119         <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> );
83<a name="l00121"></a>00121         <span class="comment">//void tupdate ( double phi, mat &amp;vbar, double nubar );</span>
84<a name="l00123"></a>00123 <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 );
85<a name="l00124"></a>00124
86<a name="l00125"></a>00125         vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
87<a name="l00127"></a>00127         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>;
88<a name="l00128"></a>00128         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span> ;
89<a name="l00129"></a>00129         <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>;
90<a name="l00130"></a>00130         <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>;
91<a name="l00131"></a><a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899">00131</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>;}
92<a name="l00132"></a>00132         <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a>* <a class="code" href="classenorm.html#13b7d503c6444eb4db4f359b13ec3bc2" 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 );
93<a name="l00133"></a>00133         <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="classenorm.html#14c05e1d059684b64c455ac16703b1c1" 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> );
94<a name="l00134"></a>00134 <span class="comment">//Access methods</span>
95<a name="l00136"></a><a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac">00136</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>;}
96<a name="l00137"></a>00137
97<a name="l00139"></a><a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b">00139</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;}
98<a name="l00140"></a>00140
99<a name="l00142"></a><a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9">00142</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>;}
100<a name="l00143"></a>00143
101<a name="l00145"></a>00145 <span class="comment">//      mat getR () {return R.to_mat();}</span>
102<a name="l00146"></a>00146 };
103<a name="l00147"></a>00147
104<a name="l00154"></a><a class="code" href="classegiw.html">00154</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> {
105<a name="l00155"></a>00155 <span class="keyword">protected</span>:
106<a name="l00157"></a><a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442">00157</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>;
107<a name="l00159"></a><a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453">00159</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>;
108<a name="l00161"></a><a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e">00161</a>         <span class="keywordtype">int</span> <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>;
109<a name="l00163"></a><a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812">00163</a>         <span class="keywordtype">int</span> <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a>;
110<a name="l00164"></a>00164 <span class="keyword">public</span>:
111<a name="l00166"></a><a class="code" href="classegiw.html#c52a2173c6eb1490edce9c6c7c05d60b">00166</a>         <a class="code" href="classegiw.html#c52a2173c6eb1490edce9c6c7c05d60b" title="Default constructor, assuming.">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 ) : <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 ) {
112<a name="l00167"></a>00167                 <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>();
113<a name="l00168"></a>00168                 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> );
114<a name="l00169"></a>00169                 <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>;
115<a name="l00170"></a>00170         }
116<a name="l00172"></a><a class="code" href="classegiw.html#1a17fdbac6c72b9c3abb97623db466c8">00172</a>         <a class="code" href="classegiw.html#c52a2173c6eb1490edce9c6c7c05d60b" title="Default constructor, assuming.">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 ) : <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 ) {
117<a name="l00173"></a>00173                 <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>();
118<a name="l00174"></a>00174                 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> );
119<a name="l00175"></a>00175                 <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>;
120<a name="l00176"></a>00176         }
121<a name="l00177"></a>00177
122<a name="l00178"></a>00178         vec <a class="code" href="classegiw.html#3d2c1f2ba0f9966781f1e0ae695e8a6f" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
123<a name="l00179"></a>00179         vec <a class="code" href="classegiw.html#6deb0ff2859f41ef7cbdf6a842cabb29" title="return expected value">mean</a>() <span class="keyword">const</span>;
124<a name="l00180"></a>00180         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>;
125<a name="l00182"></a>00182         <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>;
126<a name="l00183"></a>00183         <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>;
127<a name="l00184"></a>00184
128<a name="l00185"></a>00185         <span class="comment">//Access</span>
129<a name="l00187"></a><a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5">00187</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>;}
130<a name="l00189"></a><a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe">00189</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>;}
131<a name="l00190"></a>00190         <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 );
132<a name="l00191"></a>00191 };
133<a name="l00192"></a>00192
134<a name="l00201"></a><a class="code" href="classeDirich.html">00201</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> {
135<a name="l00202"></a>00202 <span class="keyword">protected</span>:
136<a name="l00204"></a><a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7">00204</a>         vec <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>;
137<a name="l00205"></a>00205 <span class="keyword">public</span>:
138<a name="l00207"></a><a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af">00207</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> ); };
139<a name="l00209"></a><a class="code" href="classeDirich.html#55cccbc5eb44764dce722567acf5fd58">00209</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> ) {};
140<a name="l00210"></a><a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80">00210</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 );};
141<a name="l00211"></a><a class="code" href="classeDirich.html#4206e1da149d51ff3b663c9241096b73">00211</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> );};
142<a name="l00213"></a><a class="code" href="classeDirich.html#688a24f04be6d80d4769cf0e4ded7acc">00213</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 );};
143<a name="l00214"></a><a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77">00214</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>{
144<a name="l00215"></a>00215                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> );
145<a name="l00216"></a>00216                 <span class="keywordtype">double</span> lgb=0.0;
146<a name="l00217"></a>00217                 <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 ) );}
147<a name="l00218"></a>00218                 <span class="keywordflow">return</span> lgb-lgamma ( gam );
148<a name="l00219"></a>00219         };
149<a name="l00221"></a><a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a">00221</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>;}
150<a name="l00223"></a><a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d">00223</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 ) {
151<a name="l00224"></a>00224                 <span class="keywordflow">if</span> ( beta0.length() !=<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length() ) {
152<a name="l00225"></a>00225                         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> );
153<a name="l00226"></a>00226                         <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() );
154<a name="l00227"></a>00227                 }
155<a name="l00228"></a>00228                 <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>= beta0;
156<a name="l00229"></a>00229         }
157<a name="l00230"></a>00230 };
158<a name="l00231"></a>00231
159<a name="l00233"></a><a class="code" href="classmultiBM.html">00233</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> {
160<a name="l00234"></a>00234 <span class="keyword">protected</span>:
161<a name="l00236"></a><a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5">00236</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>;
162<a name="l00238"></a><a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6">00238</a>         vec &amp;<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>;
163<a name="l00239"></a>00239 <span class="keyword">public</span>:
164<a name="l00241"></a><a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5">00241</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>();}
165<a name="l00243"></a><a class="code" href="classmultiBM.html#b92751adbfb9f259ca8c95232cfd9c09">00243</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() ) {}
166<a name="l00245"></a><a class="code" href="classmultiBM.html#42e36804041e551d3ceea6c75abc0562">00245</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>;}
167<a name="l00246"></a><a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2">00246</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 ) {
168<a name="l00247"></a>00247                 <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>();}
169<a name="l00248"></a>00248                 <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
170<a name="l00249"></a>00249                 <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>;}
171<a name="l00250"></a>00250         }
172<a name="l00251"></a><a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">00251</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>{
173<a name="l00252"></a>00252                 <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> );
174<a name="l00253"></a>00253                 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>();
175<a name="l00254"></a>00254
176<a name="l00255"></a>00255                 <span class="keywordtype">double</span> lll;
177<a name="l00256"></a>00256                 <span class="keywordflow">if</span> ( <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>&lt;1.0 )
178<a name="l00257"></a>00257                         {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>();}
179<a name="l00258"></a>00258                 <span class="keywordflow">else</span>
180<a name="l00259"></a>00259                         <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>;}
181<a name="l00260"></a>00260                         <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();}
182<a name="l00261"></a>00261
183<a name="l00262"></a>00262                 beta+=dt;
184<a name="l00263"></a>00263                 <span class="keywordflow">return</span> pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-lll;
185<a name="l00264"></a>00264         }
186<a name="l00265"></a><a class="code" href="classmultiBM.html#3988322f8f51b153622036f461f62a67">00265</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 ) {
187<a name="l00266"></a>00266                 <span class="keyword">const</span> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>* E=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>*<span class="keyword">&gt;</span> ( B );
188<a name="l00267"></a>00267                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
189<a name="l00268"></a>00268                 <span class="keyword">const</span> vec &amp;Eb=<span class="keyword">const_cast&lt;</span><a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>*<span class="keyword">&gt;</span> ( E )-&gt;_beta();
190<a name="l00269"></a>00269                 <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeEF.html#4f8385dd1cc9740522dc373b1dc3cbf5" title="Power of the density, used e.g. to flatten the density.">pow</a> ( sum ( <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ) /sum ( Eb ) );
191<a name="l00270"></a>00270                 <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>();}
192<a name="l00271"></a>00271         }
193<a name="l00272"></a><a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684">00272</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 pointer to the epdf representing posterior density on parameters. Use with...">_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>;};
194<a name="l00273"></a>00273         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 ) {
195<a name="l00274"></a>00274                 <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 );
196<a name="l00275"></a>00275                 <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>();
197<a name="l00276"></a>00276                 <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>();}
198<a name="l00277"></a>00277         }
199<a name="l00278"></a>00278 };
200<a name="l00279"></a>00279
201<a name="l00289"></a><a class="code" href="classegamma.html">00289</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> {
202<a name="l00290"></a>00290 <span class="keyword">protected</span>:
203<a name="l00292"></a><a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b">00292</a>         vec <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>;
204<a name="l00294"></a><a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790">00294</a>         vec <a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>;
205<a name="l00295"></a>00295 <span class="keyword">public</span> :
206<a name="l00297"></a><a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1">00297</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 ) {};
207<a name="l00299"></a><a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339">00299</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;};
208<a name="l00300"></a>00300         vec <a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
209<a name="l00302"></a>00302 <span class="comment">//      mat sample ( int N ) const;</span>
210<a name="l00303"></a>00303         <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>;
211<a name="l00304"></a>00304         <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>;
212<a name="l00306"></a><a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790">00306</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>;};
213<a name="l00307"></a><a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a">00307</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;}
214<a name="l00308"></a>00308 };
215<a name="l00309"></a>00309 <span class="comment">/*</span>
216<a name="l00311"></a>00311 <span class="comment">class emix : public epdf {</span>
217<a name="l00312"></a>00312 <span class="comment">protected:</span>
218<a name="l00313"></a>00313 <span class="comment">        int n;</span>
219<a name="l00314"></a>00314 <span class="comment">        vec &amp;w;</span>
220<a name="l00315"></a>00315 <span class="comment">        Array&lt;epdf*&gt; Coms;</span>
221<a name="l00316"></a>00316 <span class="comment">public:</span>
222<a name="l00318"></a>00318 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
223<a name="l00319"></a>00319 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
224<a name="l00320"></a>00320 <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>
225<a name="l00321"></a>00321 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span>
226<a name="l00322"></a>00322 <span class="comment">};</span>
227<a name="l00323"></a>00323 <span class="comment">*/</span>
228<a name="l00324"></a>00324
229<a name="l00326"></a>00326
230<a name="l00327"></a><a class="code" href="classeuni.html">00327</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> {
231<a name="l00328"></a>00328 <span class="keyword">protected</span>:
232<a name="l00330"></a><a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1">00330</a>         vec <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>;
233<a name="l00332"></a><a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231">00332</a>         vec <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>;
234<a name="l00334"></a><a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4">00334</a>         vec <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>;
235<a name="l00336"></a><a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda">00336</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;
236<a name="l00338"></a><a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3">00338</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>;
237<a name="l00339"></a>00339 <span class="keyword">public</span>:
238<a name="l00341"></a><a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd">00341</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 ) {}
239<a name="l00342"></a><a class="code" href="classeuni.html#2723d4992900b5c5495bfa03628195ed">00342</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#2723d4992900b5c5495bfa03628195ed" title="Compute probability of argument val.">eval</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#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;}
240<a name="l00343"></a><a class="code" href="classeuni.html#06af95d514a6623ad4688bd2ad50ad71">00343</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>;}
241<a name="l00344"></a><a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd">00344</a>         vec <a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{
242<a name="l00345"></a>00345                 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>() );
243<a name="l00346"></a>00346 <span class="preprocessor">#pragma omp critical</span>
244<a name="l00347"></a>00347 <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 );
245<a name="l00348"></a>00348                 <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 );
246<a name="l00349"></a>00349         }
247<a name="l00351"></a><a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2">00351</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 ) {
248<a name="l00352"></a>00352                 <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> = high0-low0;
249<a name="l00353"></a>00353                 it_assert_debug ( min ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> );
250<a name="l00354"></a>00354                 <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a> = low0;
251<a name="l00355"></a>00355                 <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a> = high0;
252<a name="l00356"></a>00356                 <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> );
253<a name="l00357"></a>00357                 <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> );
254<a name="l00358"></a>00358         }
255<a name="l00359"></a><a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1">00359</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;}
256<a name="l00360"></a>00360 };
257<a name="l00361"></a>00361
258<a name="l00362"></a>00362
259<a name="l00368"></a>00368 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
260<a name="l00369"></a><a class="code" href="classmlnorm.html">00369</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> {
261<a name="l00371"></a>00371         <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>;
262<a name="l00372"></a>00372         mat A;
263<a name="l00373"></a>00373         vec mu_const;
264<a name="l00374"></a>00374         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
265<a name="l00375"></a>00375 <span class="keyword">public</span>:
266<a name="l00377"></a>00377         <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> );
267<a name="l00379"></a>00379         <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 );
268<a name="l00381"></a>00381         vec <a class="code" href="classmlnorm.html#1bd939dbf8ec7b8066d3f18abba6822b" title="Generate one sample of the posterior.">samplecond</a> (<span class="keyword">const</span> vec &amp;cond, <span class="keywordtype">double</span> &amp;lik );
269<a name="l00383"></a>00383         mat <a class="code" href="classmlnorm.html#1bd939dbf8ec7b8066d3f18abba6822b" title="Generate one sample of the posterior.">samplecond</a> (<span class="keyword">const</span> vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n );
270<a name="l00385"></a>00385         <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 );
271<a name="l00386"></a>00386 };
272<a name="l00387"></a>00387
273<a name="l00397"></a><a class="code" href="classmgamma.html">00397</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> {
274<a name="l00398"></a>00398 <span class="keyword">protected</span>:
275<a name="l00400"></a><a class="code" href="classmgamma.html#612dbf35c770a780027619aaac2c443e">00400</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>;
276<a name="l00402"></a><a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687">00402</a>         <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>;
277<a name="l00404"></a><a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691">00404</a>         vec* <a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>;
278<a name="l00405"></a>00405
279<a name="l00406"></a>00406 <span class="keyword">public</span>:
280<a name="l00408"></a>00408         <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> );
281<a name="l00410"></a>00410         <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> );
282<a name="l00411"></a><a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97">00411</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;};
283<a name="l00412"></a>00412 };
284<a name="l00413"></a>00413
285<a name="l00425"></a><a class="code" href="classmgamma__fix.html">00425</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> {
286<a name="l00426"></a>00426 <span class="keyword">protected</span>:
287<a name="l00428"></a><a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6">00428</a>         <span class="keywordtype">double</span> <a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>;
288<a name="l00430"></a><a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0">00430</a>         vec <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>;
289<a name="l00431"></a>00431 <span class="keyword">public</span>:
290<a name="l00433"></a><a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80">00433</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() ) {};
291<a name="l00435"></a><a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1">00435</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 ) {
292<a name="l00436"></a>00436                 <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">mgamma::set_parameters</a> ( k0 );
293<a name="l00437"></a>00437                 <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;
294<a name="l00438"></a>00438         };
295<a name="l00439"></a>00439
296<a name="l00440"></a><a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460">00440</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;};
297<a name="l00441"></a>00441 };
298<a name="l00442"></a>00442
299<a name="l00444"></a><a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212">00444</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 };
300<a name="l00450"></a><a class="code" href="classeEmp.html">00450</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> {
301<a name="l00451"></a>00451 <span class="keyword">protected</span> :
302<a name="l00453"></a><a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd">00453</a>         <span class="keywordtype">int</span> <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;
303<a name="l00455"></a><a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8">00455</a>         vec <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;
304<a name="l00457"></a><a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a">00457</a>         Array&lt;vec&gt; <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;
305<a name="l00458"></a>00458 <span class="keyword">public</span>:
306<a name="l00460"></a><a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4">00460</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> ) {};
307<a name="l00462"></a>00462         <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 );
308<a name="l00464"></a>00464         <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 );
309<a name="l00466"></a><a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b">00466</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>;};
310<a name="l00468"></a><a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575">00468</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>;};
311<a name="l00470"></a>00470         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 );
312<a name="l00472"></a><a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12">00472</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;}
313<a name="l00474"></a><a class="code" href="classeEmp.html#23e7358995400865ad2e278945922fb3">00474</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;}
314<a name="l00475"></a><a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d">00475</a>         vec <a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d" title="return expected value">mean</a>()<span class="keyword"> const </span>{
315<a name="l00476"></a>00476                 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>() );
316<a name="l00477"></a>00477                 <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 );}
317<a name="l00478"></a>00478                 <span class="keywordflow">return</span> pom;
318<a name="l00479"></a>00479         }
319<a name="l00480"></a>00480 };
320<a name="l00481"></a>00481
321<a name="l00482"></a>00482
322<a name="l00484"></a>00484
323<a name="l00485"></a>00485 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
324<a name="l00486"></a><a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f">00486</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() ) {};
325<a name="l00487"></a>00487
326<a name="l00488"></a>00488 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
327<a name="l00489"></a><a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af">00489</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 ) {
328<a name="l00490"></a>00490 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
329<a name="l00491"></a>00491         <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> = mu0;
330<a name="l00492"></a>00492         <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> = R0;
331<a name="l00493"></a>00493 };
332<a name="l00494"></a>00494
333<a name="l00495"></a>00495 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
334<a name="l00496"></a><a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2">00496</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 ) {
335<a name="l00497"></a>00497         <span class="comment">//</span>
336<a name="l00498"></a>00498 };
337<a name="l00499"></a>00499
338<a name="l00500"></a>00500 <span class="comment">// template&lt;class sq_T&gt;</span>
339<a name="l00501"></a>00501 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
340<a name="l00502"></a>00502 <span class="comment">//      //</span>
341<a name="l00503"></a>00503 <span class="comment">// };</span>
342<a name="l00504"></a>00504
343<a name="l00505"></a>00505 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
344<a name="l00506"></a><a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5">00506</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>{
345<a name="l00507"></a>00507         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
346<a name="l00508"></a>00508         NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
347<a name="l00509"></a>00509         vec smp = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
348<a name="l00510"></a>00510
349<a name="l00511"></a>00511         smp += <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
350<a name="l00512"></a>00512         <span class="keywordflow">return</span> smp;
351<a name="l00513"></a>00513 };
352<a name="l00514"></a>00514
353<a name="l00515"></a>00515 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
354<a name="l00516"></a><a class="code" href="classenorm.html#60f0f3bfa53d6e65843eea9532b16d36">00516</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>{
355<a name="l00517"></a>00517         mat X ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N );
356<a name="l00518"></a>00518         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
357<a name="l00519"></a>00519         vec pom;
358<a name="l00520"></a>00520         <span class="keywordtype">int</span> i;
359<a name="l00521"></a>00521
360<a name="l00522"></a>00522         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
361<a name="l00523"></a>00523                 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
362<a name="l00524"></a>00524                 pom = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
363<a name="l00525"></a>00525                 pom +=<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
364<a name="l00526"></a>00526                 X.set_col ( i, pom );
365<a name="l00527"></a>00527         }
366<a name="l00528"></a>00528
367<a name="l00529"></a>00529         <span class="keywordflow">return</span> X;
368<a name="l00530"></a>00530 };
369<a name="l00531"></a>00531
370<a name="l00532"></a>00532 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
371<a name="l00533"></a><a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0">00533</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0" title="Compute probability of argument val.">enorm&lt;sq_T&gt;::eval</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
372<a name="l00534"></a>00534         <span class="keywordtype">double</span> pdfl,e;
373<a name="l00535"></a>00535         pdfl = <a class="code" href="classeEF.html#6466e8d4aa9dd64698ed288cbb1afc03" title="Evaluate normalized log-probability.">evalpdflog</a> ( val );
374<a name="l00536"></a>00536         e = exp ( pdfl );
375<a name="l00537"></a>00537         <span class="keywordflow">return</span> e;
376<a name="l00538"></a>00538 };
377<a name="l00539"></a>00539
378<a name="l00540"></a>00540 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
379<a name="l00541"></a><a class="code" href="classenorm.html#c1e3dcba256b0153cfdb286120e110be">00541</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>{
380<a name="l00542"></a>00542         <span class="comment">// 1.83787706640935 = log(2pi)</span>
381<a name="l00543"></a>00543         <span class="keywordflow">return</span>  -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>
382<a name="l00544"></a>00544 };
383<a name="l00545"></a>00545
384<a name="l00546"></a>00546 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
385<a name="l00547"></a><a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8">00547</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>{
386<a name="l00548"></a>00548         <span class="comment">// 1.83787706640935 = log(2pi)</span>
387<a name="l00549"></a>00549         <span class="keywordflow">return</span> 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() );
388<a name="l00550"></a>00550 };
389<a name="l00551"></a>00551
390<a name="l00552"></a>00552 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
391<a name="l00553"></a><a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837">00553</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>() ) {
392<a name="l00554"></a>00554         <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>;
393<a name="l00555"></a>00555 }
394<a name="l00556"></a>00556
395<a name="l00557"></a>00557 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
396<a name="l00558"></a><a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999">00558</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 ) {
397<a name="l00559"></a>00559         <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 );
398<a name="l00560"></a>00560         A = A0;
399<a name="l00561"></a>00561         mu_const = mu0;
400<a name="l00562"></a>00562 }
401<a name="l00563"></a>00563
402<a name="l00564"></a>00564 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
403<a name="l00565"></a><a class="code" href="classmlnorm.html#1bd939dbf8ec7b8066d3f18abba6822b">00565</a> vec <a class="code" href="classmlnorm.html#1bd939dbf8ec7b8066d3f18abba6822b" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> (<span class="keyword">const</span>  vec &amp;cond, <span class="keywordtype">double</span> &amp;lik ) {
404<a name="l00566"></a>00566         this-&gt;<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> ( cond );
405<a name="l00567"></a>00567         vec smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample();
406<a name="l00568"></a>00568         lik = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp );
407<a name="l00569"></a>00569         <span class="keywordflow">return</span> smp;
408<a name="l00570"></a>00570 }
409<a name="l00571"></a>00571
410<a name="l00572"></a>00572 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
411<a name="l00573"></a><a class="code" href="classmlnorm.html#06a3600a414b4b0f006ce9440f462817">00573</a> mat <a class="code" href="classmlnorm.html#1bd939dbf8ec7b8066d3f18abba6822b" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> (<span class="keyword">const</span> vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n ) {
412<a name="l00574"></a>00574         <span class="keywordtype">int</span> i;
413<a name="l00575"></a>00575         <span class="keywordtype">int</span> dim = <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>();
414<a name="l00576"></a>00576         mat Smp ( dim,n );
415<a name="l00577"></a>00577         vec smp ( dim );
416<a name="l00578"></a>00578         this-&gt;<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> ( cond );
417<a name="l00579"></a>00579
418<a name="l00580"></a>00580         <span class="keywordflow">for</span> ( i=0; i&lt;n; i++ ) {
419<a name="l00581"></a>00581                 smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample();
420<a name="l00582"></a>00582                 lik ( i ) = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp );
421<a name="l00583"></a>00583                 Smp.set_col ( i ,smp );
422<a name="l00584"></a>00584         }
423<a name="l00585"></a>00585
424<a name="l00586"></a>00586         <span class="keywordflow">return</span> Smp;
425<a name="l00587"></a>00587 }
426<a name="l00588"></a>00588
427<a name="l00589"></a>00589 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
428<a name="l00590"></a><a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40">00590</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 ) {
429<a name="l00591"></a>00591         _mu = A*cond + mu_const;
430<a name="l00592"></a>00592 <span class="comment">//R is already assigned;</span>
431<a name="l00593"></a>00593 }
432<a name="l00594"></a>00594
433<a name="l00595"></a>00595 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
434<a name="l00596"></a><a class="code" href="classenorm.html#14c05e1d059684b64c455ac16703b1c1">00596</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="classenorm.html#14c05e1d059684b64c455ac16703b1c1" 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 ) {
435<a name="l00597"></a>00597         ivec irvn = rvn.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4" title="generate indeces into">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> );
436<a name="l00598"></a>00598
437<a name="l00599"></a>00599         sq_T Rn ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>,irvn );
438<a name="l00600"></a>00600         <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 );
439<a name="l00601"></a>00601         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 );
440<a name="l00602"></a>00602         <span class="keywordflow">return</span> tmp;
441<a name="l00603"></a>00603 }
442<a name="l00604"></a>00604
443<a name="l00605"></a>00605 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
444<a name="l00606"></a><a class="code" href="classenorm.html#13b7d503c6444eb4db4f359b13ec3bc2">00606</a> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a>* <a class="code" href="classenorm.html#13b7d503c6444eb4db4f359b13ec3bc2" 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 ) {
445<a name="l00607"></a>00607
446<a name="l00608"></a>00608         <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 );
447<a name="l00609"></a>00609         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> );
448<a name="l00610"></a>00610         <span class="comment">//Permutation vector of the new R</span>
449<a name="l00611"></a>00611         ivec irvn = rvn.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4" title="generate indeces into">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> );
450<a name="l00612"></a>00612         ivec irvc = rvc.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4" title="generate indeces into">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> );
451<a name="l00613"></a>00613         ivec perm=concat ( irvn , irvc );
452<a name="l00614"></a>00614         sq_T Rn ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>,perm );
453<a name="l00615"></a>00615
454<a name="l00616"></a>00616         <span class="comment">//fixme - could this be done in general for all sq_T?</span>
455<a name="l00617"></a>00617         mat S=<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.to_mat();
456<a name="l00618"></a>00618         <span class="comment">//fixme</span>
457<a name="l00619"></a>00619         <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;
458<a name="l00620"></a>00620         <span class="keywordtype">int</span> end=<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.rows()-1;
459<a name="l00621"></a>00621         mat S11 = S.get ( 0,n, 0, n );
460<a name="l00622"></a>00622         mat S12 = S.get ( rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(), end, 0, n );
461<a name="l00623"></a>00623         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 );
462<a name="l00624"></a>00624
463<a name="l00625"></a>00625         vec mu1 = <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvn );
464<a name="l00626"></a>00626         vec mu2 = <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvc );
465<a name="l00627"></a>00627         mat A=S12*inv ( S22 );
466<a name="l00628"></a>00628         sq_T R_n ( S11 - A *S12.T() );
467<a name="l00629"></a>00629
468<a name="l00630"></a>00630         <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 );
469<a name="l00631"></a>00631
470<a name="l00632"></a>00632         tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
471<a name="l00633"></a>00633         <span class="keywordflow">return</span> tmp;
472<a name="l00634"></a>00634 }
473<a name="l00635"></a>00635
474<a name="l00637"></a>00637
475<a name="l00638"></a>00638
476<a name="l00639"></a>00639 <span class="preprocessor">#endif //EF_H</span>
477</pre></div></div>
478<hr size="1"><address style="text-align: right;"><small>Generated on Wed Oct 15 15:57:09 2008 for mixpp by&nbsp;
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480<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>
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