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65<h1>exp_family.h</h1><a href="exp__family_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001
66<a name="l00013"></a>00013 <span class="preprocessor">#ifndef EF_H</span>
67<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define EF_H</span>
68<a name="l00015"></a>00015 <span class="preprocessor"></span>
69<a name="l00016"></a>00016
70<a name="l00017"></a>00017 <span class="preprocessor">#include "../base/bdmbase.h"</span>
71<a name="l00018"></a>00018 <span class="preprocessor">#include "../math/chmat.h"</span>
72<a name="l00019"></a>00019
73<a name="l00020"></a>00020 <span class="keyword">namespace </span>bdm
74<a name="l00021"></a>00021 {
75<a name="l00022"></a>00022
76<a name="l00023"></a>00023
77<a name="l00025"></a>00025         <span class="keyword">extern</span> Uniform_RNG UniRNG;
78<a name="l00027"></a>00027         <span class="keyword">extern</span> Normal_RNG NorRNG;
79<a name="l00029"></a>00029         <span class="keyword">extern</span> Gamma_RNG GamRNG;
80<a name="l00030"></a>00030
81<a name="l00037"></a><a class="code" href="classbdm_1_1eEF.html">00037</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
82<a name="l00038"></a>00038         {
83<a name="l00039"></a>00039                 <span class="keyword">public</span>:
84<a name="l00040"></a>00040 <span class="comment">//      eEF() :epdf() {};</span>
85<a name="l00042"></a><a class="code" href="classbdm_1_1eEF.html#d5459d472d0feca7cf1fb5f65c4b9ef4">00042</a> <span class="comment"></span>                        <a class="code" href="classbdm_1_1eEF.html#d5459d472d0feca7cf1fb5f65c4b9ef4" title="default constructor">eEF</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {};
86<a name="l00044"></a>00044                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>() <span class="keyword">const</span> =0;
87<a name="l00046"></a><a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a">00046</a>                         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a" title="TODO decide if it is really needed.">dupdate</a> ( mat &amp;v ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
88<a name="l00048"></a><a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6">00048</a>                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_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;};
89<a name="l00050"></a><a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692">00050</a>                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
90<a name="l00051"></a>00051                                 <span class="keywordtype">double</span> tmp;
91<a name="l00052"></a>00052                                 tmp= <a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( val )-<a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>();
92<a name="l00053"></a>00053                                 it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
93<a name="l00054"></a>00054                                 <span class="keywordflow">return</span> tmp;}
94<a name="l00056"></a><a class="code" href="classbdm_1_1eEF.html#79a7c8ea8c02e45d410bd1d7ffd72b41">00056</a>                         <span class="keyword">virtual</span> vec <a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> mat &amp;Val )<span class="keyword"> const</span>
95<a name="l00057"></a>00057 <span class="keyword">                        </span>{
96<a name="l00058"></a>00058                                 vec x ( Val.cols() );
97<a name="l00059"></a>00059                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;Val.cols();i++ ) {x ( i ) =<a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( Val.get_col ( i ) ) ;}
98<a name="l00060"></a>00060                                 <span class="keywordflow">return</span> x-<a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>();
99<a name="l00061"></a>00061                         }
100<a name="l00063"></a><a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053">00063</a>                         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053" 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> );};
101<a name="l00064"></a>00064         };
102<a name="l00065"></a>00065
103<a name="l00072"></a><a class="code" href="classbdm_1_1mEF.html">00072</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>
104<a name="l00073"></a>00073         {
105<a name="l00074"></a>00074
106<a name="l00075"></a>00075                 <span class="keyword">public</span>:
107<a name="l00077"></a><a class="code" href="classbdm_1_1mEF.html#dd7caad7026b778af66e34480eec6f9e">00077</a>                         <a class="code" href="classbdm_1_1mEF.html#dd7caad7026b778af66e34480eec6f9e" title="Default constructor.">mEF</a> ( ) :<a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( ) {};
108<a name="l00078"></a>00078         };
109<a name="l00079"></a>00079
110<a name="l00081"></a><a class="code" href="classbdm_1_1BMEF.html">00081</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a>
111<a name="l00082"></a>00082         {
112<a name="l00083"></a>00083                 <span class="keyword">protected</span>:
113<a name="l00085"></a><a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64">00085</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;
114<a name="l00087"></a><a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865">00087</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;
115<a name="l00088"></a>00088                 <span class="keyword">public</span>:
116<a name="l00090"></a><a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55">00090</a>                         <a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55" title="Default constructor (=empty constructor).">BMEF</a> ( <span class="keywordtype">double</span> frg0=1.0 ) :<a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( ), <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ( frg0 ) {}
117<a name="l00092"></a><a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62">00092</a>                         <a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62" title="Copy constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> &amp;B ) :<a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( B ), <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ( B.<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ), <a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ( B.<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ) {}
118<a name="l00094"></a><a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051">00094</a>                         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051" title="get statistics from another model">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* BM0 ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
119<a name="l00096"></a><a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6">00096</a>                         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" 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 ) {};
120<a name="l00097"></a>00097                         <span class="comment">//original Bayes</span>
121<a name="l00098"></a>00098                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
122<a name="l00100"></a><a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749">00100</a>                         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> * B ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}
123<a name="l00102"></a>00102 <span class="comment">//      virtual void flatten ( double nu0 ) {it_error ( "Not implemented" );}</span>
124<a name="l00103"></a>00103
125<a name="l00104"></a><a class="code" href="classbdm_1_1BMEF.html#62d2e4691bed41a1efa6b9c2e35e5c67">00104</a>                         <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* <a class="code" href="classbdm_1_1BMEF.html#62d2e4691bed41a1efa6b9c2e35e5c67" title="Flatten the posterior as if to keep nu0 data.">_copy_</a> ()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"function _copy_ not implemented for this BM"</span> ); <span class="keywordflow">return</span> NULL;};
126<a name="l00105"></a>00105         };
127<a name="l00106"></a>00106
128<a name="l00107"></a>00107         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
129<a name="l00108"></a>00108         <span class="keyword">class </span>mlnorm;
130<a name="l00109"></a>00109
131<a name="l00115"></a>00115         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
132<a name="l00116"></a><a class="code" href="classbdm_1_1enorm.html">00116</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
133<a name="l00117"></a>00117         {
134<a name="l00118"></a>00118                 <span class="keyword">protected</span>:
135<a name="l00120"></a><a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7">00120</a>                         vec <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
136<a name="l00122"></a><a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2">00122</a>                         sq_T <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;
137<a name="l00123"></a>00123                 <span class="keyword">public</span>:
138<a name="l00126"></a>00126
139<a name="l00127"></a>00127                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( ),<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ( ) {};
140<a name="l00128"></a>00128                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( <span class="keyword">const</span> vec &amp;<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ) {set_parameters ( mu,R );}
141<a name="l00129"></a>00129                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> );
142<a name="l00130"></a>00130                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#61bd470764020bea6e1ed35000f259e6">from_setting</a>(<span class="keyword">const</span> Setting &amp;root);
143<a name="l00131"></a><a class="code" href="classbdm_1_1enorm.html#38eb17ecb75d94a50b6782fcf735cfea">00131</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#38eb17ecb75d94a50b6782fcf735cfea" title="This method TODO.">validate</a>() {
144<a name="l00132"></a>00132                                 it_assert(<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>.length()==<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.rows(),<span class="stringliteral">"parameters mismatch"</span>);
145<a name="l00133"></a>00133                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>.length();
146<a name="l00134"></a>00134                         }
147<a name="l00136"></a>00136
148<a name="l00139"></a>00139
149<a name="l00141"></a>00141                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 );
150<a name="l00142"></a>00142
151<a name="l00143"></a>00143                         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
152<a name="l00144"></a>00144                         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>;
153<a name="l00145"></a>00145                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
154<a name="l00146"></a>00146                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
155<a name="l00147"></a><a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600">00147</a>                         vec <a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
156<a name="l00148"></a><a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773">00148</a>                         vec <a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> diag ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.to_mat() );}
157<a name="l00149"></a>00149 <span class="comment">//      mlnorm&lt;sq_T&gt;* condition ( const RV &amp;rvn ) const ; &lt;=========== fails to cmpile. Why?</span>
158<a name="l00150"></a>00150                         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" 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="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn ) <span class="keyword">const</span> ;
159<a name="l00151"></a>00151                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" 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="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> ) <span class="keyword">const</span>;
160<a name="l00152"></a>00152 <span class="comment">//                      epdf* marginal ( const RV &amp;rv ) const;</span>
161<a name="l00154"></a>00154 <span class="comment"></span>
162<a name="l00157"></a>00157
163<a name="l00158"></a>00158                         vec&amp; _mu() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
164<a name="l00159"></a>00159                         <span class="keywordtype">void</span> set_mu ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>=mu0;}
165<a name="l00160"></a>00160                         sq_T&amp; _R() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;}
166<a name="l00161"></a>00161                         <span class="keyword">const</span> sq_T&amp; _R()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;}
167<a name="l00163"></a>00163
168<a name="l00164"></a>00164         };
169<a name="l00165"></a>00165         <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a>(enorm&lt;chmat&gt;);
170<a name="l00166"></a>00166         <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a>(enorm&lt;ldmat&gt;);
171<a name="l00167"></a>00167         <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a>(enorm&lt;fsqmat&gt;);
172<a name="l00168"></a>00168
173<a name="l00169"></a>00169
174<a name="l00176"></a><a class="code" href="classbdm_1_1egiw.html">00176</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
175<a name="l00177"></a>00177         {
176<a name="l00178"></a>00178                 <span class="keyword">protected</span>:
177<a name="l00180"></a><a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52">00180</a>                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;
178<a name="l00182"></a><a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4">00182</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;
179<a name="l00184"></a><a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a">00184</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
180<a name="l00186"></a><a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd">00186</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a>;
181<a name="l00187"></a>00187                 <span class="keyword">public</span>:
182<a name="l00190"></a>00190                         <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a>() :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {};
183<a name="l00191"></a>00191                         <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {set_parameters ( dimx0,V0, nu0 );};
184<a name="l00192"></a>00192
185<a name="l00193"></a>00193                         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 )
186<a name="l00194"></a>00194                         {
187<a name="l00195"></a>00195                                 <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>=dimx0;
188<a name="l00196"></a>00196                                 <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> = V0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
189<a name="l00197"></a>00197                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>* ( <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>+<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> ); <span class="comment">// size(R) + size(Theta)</span>
190<a name="l00198"></a>00198
191<a name="l00199"></a>00199                                 <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>=V0;
192<a name="l00200"></a>00200                                 <span class="keywordflow">if</span> ( nu0&lt;0 )
193<a name="l00201"></a>00201                                 {
194<a name="l00202"></a>00202                                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> +2*<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a> +2; <span class="comment">// +2 assures finite expected value of R</span>
195<a name="l00203"></a>00203                                         <span class="comment">// terms before that are sufficient for finite normalization</span>
196<a name="l00204"></a>00204                                 }
197<a name="l00205"></a>00205                                 <span class="keywordflow">else</span>
198<a name="l00206"></a>00206                                 {
199<a name="l00207"></a>00207                                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>=nu0;
200<a name="l00208"></a>00208                                 }
201<a name="l00209"></a>00209                         }
202<a name="l00211"></a>00211
203<a name="l00212"></a>00212                         vec <a class="code" href="classbdm_1_1egiw.html#920f21548b7a3723923dd108fe514c61" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
204<a name="l00213"></a>00213                         vec <a class="code" href="classbdm_1_1egiw.html#df70c05f918c3a6f86d60f10c1fd6ba2" title="return expected value">mean</a>() <span class="keyword">const</span>;
205<a name="l00214"></a>00214                         vec <a class="code" href="classbdm_1_1egiw.html#c1ecc406613cc2341225dc10c3d3b46a" title="return expected variance (not covariance!)">variance</a>() <span class="keyword">const</span>;
206<a name="l00215"></a>00215
207<a name="l00217"></a>00217                         vec <a class="code" href="classbdm_1_1egiw.html#66d2ba9295c306012b309efcc9e516f0" title="LS estimate of .">est_theta</a>() <span class="keyword">const</span>;
208<a name="l00218"></a>00218
209<a name="l00220"></a>00220                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classbdm_1_1egiw.html#88c321a2051d1afdbb31a098896a717b" title="Covariance of the LS estimate.">est_theta_cov</a>() <span class="keyword">const</span>;
210<a name="l00221"></a>00221
211<a name="l00222"></a>00222                         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>;
212<a name="l00224"></a>00224                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#bfb8e7c619b34ad804a73bff71742b5e" title="In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise...">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
213<a name="l00225"></a>00225                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#41d72ba7b2abc8a9a4209ffa98ed5633" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
214<a name="l00226"></a><a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be">00226</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {V*=p;<a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>*=p;};
215<a name="l00227"></a>00227
216<a name="l00230"></a>00230
217<a name="l00231"></a>00231                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; _V() {<span class="keywordflow">return</span> V;}
218<a name="l00232"></a>00232                         <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; _V()<span class="keyword"> const </span>{<span class="keywordflow">return</span> V;}
219<a name="l00233"></a>00233                         <span class="keywordtype">double</span>&amp; _nu()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
220<a name="l00234"></a>00234                         <span class="keyword">const</span> <span class="keywordtype">double</span>&amp; _nu()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
221<a name="l00235"></a><a class="code" href="classbdm_1_1egiw.html#55b76ec75bd2df5ef9cab3be20a33bbb">00235</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egiw.html#55b76ec75bd2df5ef9cab3be20a33bbb">from_setting</a>(<span class="keyword">const</span> Setting &amp;<span class="keyword">set</span>){
222<a name="l00236"></a>00236                                 <span class="keyword">set</span>.lookupValue(<span class="stringliteral">"nu"</span>,<a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>);
223<a name="l00237"></a>00237                                 <span class="keyword">set</span>.lookupValue(<span class="stringliteral">"dimx"</span>,<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>);
224<a name="l00238"></a>00238                                 mat V;
225<a name="l00239"></a>00239                                 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="The existing instance of type T is initialized with values stored in the Setting...">UI::get</a>(V,<span class="keyword">set</span>,<span class="stringliteral">"V"</span>);
226<a name="l00240"></a>00240                                 set_parameters(<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>, V, <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>);
227<a name="l00241"></a>00241                                 <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a>* <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>=UI::build&lt;RV&gt;(<span class="keyword">set</span>,<span class="stringliteral">"rv"</span>);
228<a name="l00242"></a>00242                                 <a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a>(*rv);
229<a name="l00243"></a>00243                                 <span class="keyword">delete</span> rv;
230<a name="l00244"></a>00244                         }
231<a name="l00246"></a>00246         };
232<a name="l00247"></a>00247         <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a>(egiw);
233<a name="l00248"></a>00248
234<a name="l00257"></a><a class="code" href="classbdm_1_1eDirich.html">00257</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
235<a name="l00258"></a>00258         {
236<a name="l00259"></a>00259                 <span class="keyword">protected</span>:
237<a name="l00261"></a><a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2">00261</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;
238<a name="l00262"></a>00262                 <span class="keyword">public</span>:
239<a name="l00265"></a>00265
240<a name="l00266"></a>00266                         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> () : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ) {};
241<a name="l00267"></a>00267                         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> &amp;D0 ) : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> () {set_parameters ( D0.<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );};
242<a name="l00268"></a>00268                         eDirich ( <span class="keyword">const</span> vec &amp;beta0 ) {set_parameters ( beta0 );};
243<a name="l00269"></a>00269                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 )
244<a name="l00270"></a>00270                         {
245<a name="l00271"></a>00271                                 <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>= beta0;
246<a name="l00272"></a>00272                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length();
247<a name="l00273"></a>00273                         }
248<a name="l00275"></a>00275
249<a name="l00276"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00276</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc" 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 );};
250<a name="l00277"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00277</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>/sum(<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>);};
251<a name="l00278"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00278</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordtype">double</span> gamma =sum(<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>); <span class="keywordflow">return</span> elem_mult ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>, ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>+1 ) ) / ( gamma* ( gamma+1 ) );}
252<a name="l00280"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00280</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318" title="In this instance, val is ...">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>
253<a name="l00281"></a>00281 <span class="keyword">                        </span>{
254<a name="l00282"></a>00282                                 <span class="keywordtype">double</span> tmp; tmp= ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>-1 ) *log ( val );               it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
255<a name="l00283"></a>00283                                 <span class="keywordflow">return</span> tmp;
256<a name="l00284"></a>00284                         };
257<a name="l00285"></a><a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2">00285</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const</span>
258<a name="l00286"></a>00286 <span class="keyword">                        </span>{
259<a name="l00287"></a>00287                                 <span class="keywordtype">double</span> tmp;
260<a name="l00288"></a>00288                                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
261<a name="l00289"></a>00289                                 <span class="keywordtype">double</span> lgb=0.0;
262<a name="l00290"></a>00290                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length();i++ ) {lgb+=lgamma ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> ( i ) );}
263<a name="l00291"></a>00291                                 tmp= lgb-lgamma ( gam );
264<a name="l00292"></a>00292                                 it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
265<a name="l00293"></a>00293                                 <span class="keywordflow">return</span> tmp;
266<a name="l00294"></a>00294                         };
267<a name="l00296"></a><a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324">00296</a>                         vec&amp; <a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;}
268<a name="l00298"></a>00298         };
269<a name="l00299"></a>00299
270<a name="l00301"></a><a class="code" href="classbdm_1_1multiBM.html">00301</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>
271<a name="l00302"></a>00302         {
272<a name="l00303"></a>00303                 <span class="keyword">protected</span>:
273<a name="l00305"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00305</a>                         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;
274<a name="l00307"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00307</a>                         vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;
275<a name="l00308"></a>00308                 <span class="keyword">public</span>:
276<a name="l00310"></a><a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf">00310</a>                         <a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf" title="Default constructor.">multiBM</a> ( ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() )
277<a name="l00311"></a>00311                         {
278<a name="l00312"></a>00312                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>.length() &gt;0 ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
279<a name="l00313"></a>00313                                 <span class="keywordflow">else</span>{<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=0.0;}
280<a name="l00314"></a>00314                         }
281<a name="l00316"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00316</a>                         <a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31" title="Copy constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> &amp;B ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( B.<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() ) {}
282<a name="l00318"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00318</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a>* mB0 ) {<span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.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="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( mB0 ); <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>=mB-&gt;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;}
283<a name="l00319"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00319</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt )
284<a name="l00320"></a>00320                         {
285<a name="l00321"></a>00321                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 ) {<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
286<a name="l00322"></a>00322                                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
287<a name="l00323"></a>00323                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BM.html#4064b6559d962633e4372b12f4cd204a" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
288<a name="l00324"></a>00324                         }
289<a name="l00325"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00325</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">logpred</a> ( <span class="keyword">const</span> vec &amp;dt )<span class="keyword"> const</span>
290<a name="l00326"></a>00326 <span class="keyword">                        </span>{
291<a name="l00327"></a>00327                                 <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> );
292<a name="l00328"></a>00328                                 vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> = pred.<a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>();
293<a name="l00329"></a>00329
294<a name="l00330"></a>00330                                 <span class="keywordtype">double</span> lll;
295<a name="l00331"></a>00331                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 )
296<a name="l00332"></a>00332                                         {beta*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
297<a name="l00333"></a>00333                                 <span class="keywordflow">else</span>
298<a name="l00334"></a>00334                                         <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
299<a name="l00335"></a>00335                                         <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
300<a name="l00336"></a>00336
301<a name="l00337"></a>00337                                 beta+=dt;
302<a name="l00338"></a>00338                                 <span class="keywordflow">return</span> pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-lll;
303<a name="l00339"></a>00339                         }
304<a name="l00340"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00340</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* B )
305<a name="l00341"></a>00341                         {
306<a name="l00342"></a>00342                                 <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* E=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( B );
307<a name="l00343"></a>00343                                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
308<a name="l00344"></a>00344                                 <span class="keyword">const</span> vec &amp;Eb=E-&gt;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;<span class="comment">//const_cast&lt;multiBM*&gt; ( E )-&gt;_beta();</span>
309<a name="l00345"></a>00345                                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*= ( sum ( Eb ) /sum ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ) );
310<a name="l00346"></a>00346                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
311<a name="l00347"></a>00347                         }
312<a name="l00348"></a>00348                         <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>&amp; posterior()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;};
313<a name="l00349"></a>00349                         <span class="keyword">const</span> eDirich* _e()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &amp;<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;};
314<a name="l00350"></a>00350                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 )
315<a name="l00351"></a>00351                         {
316<a name="l00352"></a>00352                                 <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.set_parameters ( beta0 );
317<a name="l00353"></a>00353                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.lognc();}
318<a name="l00354"></a>00354                         }
319<a name="l00355"></a>00355         };
320<a name="l00356"></a>00356
321<a name="l00366"></a><a class="code" href="classbdm_1_1egamma.html">00366</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
322<a name="l00367"></a>00367         {
323<a name="l00368"></a>00368                 <span class="keyword">protected</span>:
324<a name="l00370"></a><a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa">00370</a>                         vec <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;
325<a name="l00372"></a><a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1">00372</a>                         vec <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;
326<a name="l00373"></a>00373                 <span class="keyword">public</span> :
327<a name="l00376"></a>00376                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a> ( 0 ), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a> ( 0 ) {};
328<a name="l00377"></a>00377                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {set_parameters ( a, b );};
329<a name="l00378"></a>00378                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>=a,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>=b;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>.length();};
330<a name="l00380"></a>00380
331<a name="l00381"></a>00381                         vec <a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
332<a name="l00383"></a>00383 <span class="comment">//      mat sample ( int N ) const;</span>
333<a name="l00384"></a>00384                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egamma.html#a8e11e5a580ff42a1b205974c60768c6" title="TODO: is it used anywhere?">evallog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
334<a name="l00385"></a>00385                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egamma.html#9a66cbd100e8520c769ccb3c451f86f8" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
335<a name="l00387"></a><a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed">00387</a>                         vec&amp; <a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_alpha</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;}
336<a name="l00388"></a>00388                         vec&amp; _beta() {<span class="keywordflow">return</span> beta;}
337<a name="l00389"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00389</a>                         vec <a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,beta );}
338<a name="l00390"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00390</a>                         vec <a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,elem_mult ( beta,beta ) ); }
339<a name="l00391"></a>00391                         
340<a name="l00400"></a><a class="code" href="classbdm_1_1egamma.html#8a6fd1a1c0190f3e0d95a2a1f99aafc1">00400</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egamma.html#8a6fd1a1c0190f3e0d95a2a1f99aafc1">from_setting</a>(<span class="keyword">const</span> Setting &amp;<span class="keyword">set</span>){
341<a name="l00401"></a>00401                                 <a class="code" href="classbdm_1_1egamma.html#8a6fd1a1c0190f3e0d95a2a1f99aafc1">epdf::from_setting</a>(<span class="keyword">set</span>); <span class="comment">// reads rv</span>
342<a name="l00402"></a>00402                                 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="The existing instance of type T is initialized with values stored in the Setting...">UI::get</a>(<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,<span class="keyword">set</span>,<span class="stringliteral">"alpha"</span>);
343<a name="l00403"></a>00403                                 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="The existing instance of type T is initialized with values stored in the Setting...">UI::get</a>(beta,<span class="keyword">set</span>,<span class="stringliteral">"beta"</span>);
344<a name="l00404"></a>00404                                 <a class="code" href="classbdm_1_1egamma.html#c3a8d8ae8ba79d0fb35036711fa7d127" title="This method TODO.">validate</a>();
345<a name="l00405"></a>00405                         }
346<a name="l00406"></a><a class="code" href="classbdm_1_1egamma.html#c3a8d8ae8ba79d0fb35036711fa7d127">00406</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egamma.html#c3a8d8ae8ba79d0fb35036711fa7d127" title="This method TODO.">validate</a>(){
347<a name="l00407"></a>00407                                 it_assert(<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>.length() ==beta.length(), <span class="stringliteral">"parameters do not match"</span>);
348<a name="l00408"></a>00408                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> =<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>.length();
349<a name="l00409"></a>00409                         }
350<a name="l00410"></a>00410         };
351<a name="l00411"></a>00411 <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a>(egamma);
352<a name="l00428"></a><a class="code" href="classbdm_1_1eigamma.html">00428</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a>
353<a name="l00429"></a>00429         {
354<a name="l00430"></a>00430                 <span class="keyword">protected</span>:
355<a name="l00431"></a>00431                 <span class="keyword">public</span> :
356<a name="l00436"></a>00436
357<a name="l00437"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00437</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">egamma::sample</a>();};
358<a name="l00439"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00439</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>,<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-1 );}
359<a name="l00440"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00440</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{vec mea=<a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>(); <span class="keywordflow">return</span> elem_div ( elem_mult ( mea,mea ),<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-2 );}
360<a name="l00441"></a>00441         };
361<a name="l00442"></a>00442         <span class="comment">/*</span>
362<a name="l00444"></a>00444 <span class="comment">        class emix : public epdf {</span>
363<a name="l00445"></a>00445 <span class="comment">        protected:</span>
364<a name="l00446"></a>00446 <span class="comment">                int n;</span>
365<a name="l00447"></a>00447 <span class="comment">                vec &amp;w;</span>
366<a name="l00448"></a>00448 <span class="comment">                Array&lt;epdf*&gt; Coms;</span>
367<a name="l00449"></a>00449 <span class="comment">        public:</span>
368<a name="l00451"></a>00451 <span class="comment">                emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
369<a name="l00452"></a>00452 <span class="comment">                void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
370<a name="l00453"></a>00453 <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>
371<a name="l00454"></a>00454 <span class="comment">                vec sample() {it_error ( "Not implemented" );return 0;}</span>
372<a name="l00455"></a>00455 <span class="comment">        };</span>
373<a name="l00456"></a>00456 <span class="comment">        */</span>
374<a name="l00457"></a>00457
375<a name="l00459"></a>00459
376<a name="l00460"></a><a class="code" href="classbdm_1_1euni.html">00460</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
377<a name="l00461"></a>00461         {
378<a name="l00462"></a>00462                 <span class="keyword">protected</span>:
379<a name="l00464"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00464</a>                         vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
380<a name="l00466"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00466</a>                         vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
381<a name="l00468"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00468</a>                         vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
382<a name="l00470"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00470</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
383<a name="l00472"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00472</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
384<a name="l00473"></a>00473                 <span class="keyword">public</span>:
385<a name="l00476"></a>00476                         <a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {}
386<a name="l00477"></a>00477                         <a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) {set_parameters ( low0,high0 );}
387<a name="l00478"></a>00478                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 )
388<a name="l00479"></a>00479                         {
389<a name="l00480"></a>00480                                 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
390<a name="l00481"></a>00481                                 it_assert_debug ( min ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> );
391<a name="l00482"></a>00482                                 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
392<a name="l00483"></a>00483                                 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
393<a name="l00484"></a>00484                                 <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> );
394<a name="l00485"></a>00485                                 <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> );
395<a name="l00486"></a>00486                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>.length();
396<a name="l00487"></a>00487                         }
397<a name="l00489"></a>00489
398<a name="l00490"></a>00490                         <span class="keywordtype">double</span> eval ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;}
399<a name="l00491"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00491</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81" title="Compute log-probability of argument val.">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;}
400<a name="l00492"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00492</a>                         vec <a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const</span>
401<a name="l00493"></a>00493 <span class="keyword">                        </span>{
402<a name="l00494"></a>00494                                 vec smp ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
403<a name="l00495"></a>00495 <span class="preprocessor">#pragma omp critical</span>
404<a name="l00496"></a>00496 <span class="preprocessor"></span>                                UniRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ,smp );
405<a name="l00497"></a>00497                                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>+elem_mult ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>,smp );
406<a name="l00498"></a>00498                         }
407<a name="l00500"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00500</a>                         vec <a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226" title="set values of low and high ">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>-<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) /2.0;}
408<a name="l00501"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00501</a>                         vec <a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( pow ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,2 ) +pow ( <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>,2 ) +elem_mult ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) ) /3.0;}
409<a name="l00510"></a><a class="code" href="classbdm_1_1euni.html#77f5fef1f006fe056066da23b9e5f042">00510</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1euni.html#77f5fef1f006fe056066da23b9e5f042">from_setting</a>(<span class="keyword">const</span> Setting &amp;<span class="keyword">set</span>){
410<a name="l00511"></a>00511                                 <a class="code" href="classbdm_1_1euni.html#77f5fef1f006fe056066da23b9e5f042">epdf::from_setting</a>(<span class="keyword">set</span>); <span class="comment">// reads rv and rvc</span>
411<a name="l00512"></a>00512                                 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="The existing instance of type T is initialized with values stored in the Setting...">UI::get</a>(<a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,<span class="keyword">set</span>,<span class="stringliteral">"high"</span>);
412<a name="l00513"></a>00513                                 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="The existing instance of type T is initialized with values stored in the Setting...">UI::get</a>(<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>,<span class="keyword">set</span>,<span class="stringliteral">"low"</span>);
413<a name="l00514"></a>00514                         }
414<a name="l00515"></a>00515         };
415<a name="l00516"></a>00516
416<a name="l00517"></a>00517
417<a name="l00523"></a>00523         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
418<a name="l00524"></a><a class="code" href="classbdm_1_1mlnorm.html">00524</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
419<a name="l00525"></a>00525         {
420<a name="l00526"></a>00526                 <span class="keyword">protected</span>:
421<a name="l00528"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00528</a>                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
422<a name="l00529"></a>00529                         mat A;
423<a name="l00530"></a>00530                         vec mu_const;
424<a name="l00531"></a>00531                         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
425<a name="l00532"></a>00532                 <span class="keyword">public</span>:
426<a name="l00535"></a>00535                         <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> ( ) :<a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (),<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),A ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; };
427<a name="l00536"></a>00536                         <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</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 ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() )
428<a name="l00537"></a>00537                         {
429<a name="l00538"></a>00538                                 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">set_parameters</a> ( A,mu0,R );
430<a name="l00539"></a>00539                         };
431<a name="l00541"></a>00541                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" 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 );
432<a name="l00544"></a>00544                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">condition</a> ( <span class="keyword">const</span> vec &amp;cond );
433<a name="l00545"></a>00545
434<a name="l00547"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00547</a>                         vec&amp; <a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced" title="access function">_mu_const</a>() {<span class="keywordflow">return</span> mu_const;}
435<a name="l00549"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00549</a>                         mat&amp; <a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614" title="access function">_A</a>() {<span class="keywordflow">return</span> A;}
436<a name="l00551"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00551</a>                         mat <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R().to_mat();}
437<a name="l00552"></a>00552
438<a name="l00553"></a>00553                         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
439<a name="l00554"></a>00554                         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
440<a name="l00555"></a>00555         };
441<a name="l00556"></a>00556
442<a name="l00558"></a>00558         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
443<a name="l00559"></a><a class="code" href="classbdm_1_1mgnorm.html">00559</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgnorm.html" title="Mpdf with general function for mean value.">mgnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
444<a name="l00560"></a>00560         {
445<a name="l00561"></a>00561                 <span class="keyword">protected</span>:
446<a name="l00563"></a><a class="code" href="classbdm_1_1mgnorm.html#8f7a376a1d2197e0634557e88e03104a">00563</a>                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
447<a name="l00564"></a>00564                         vec &amp;mu;
448<a name="l00565"></a>00565                         <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g;
449<a name="l00566"></a>00566                 <span class="keyword">public</span>:
450<a name="l00568"></a><a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d">00568</a>                         <a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d" title="default constructor">mgnorm</a>() :mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}
451<a name="l00570"></a><a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564">00570</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a> ( <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g0, <span class="keyword">const</span> sq_T &amp;R0 ) {g=g0; <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( g-&gt;<a class="code" href="classbdm_1_1fnc.html#083832294da9d1e40804158b979c4341" title="access function">dimension</a>() ), R0 );}
452<a name="l00571"></a><a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d">00571</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {mu=g-&gt;<a class="code" href="classbdm_1_1fnc.html#6277b11d7fffc7ef8a2fa3e84ae5bad4" title="function evaluates numerical value of  at  cond ">eval</a> ( cond );};
453<a name="l00572"></a>00572
454<a name="l00573"></a>00573
455<a name="l00601"></a><a class="code" href="classbdm_1_1mgnorm.html#d717dacc6a9eb967f8410994dc6dc6f9">00601</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#d717dacc6a9eb967f8410994dc6dc6f9">from_setting</a>( <span class="keyword">const</span> Setting &amp;<span class="keyword">set</span> )
456<a name="l00602"></a>00602                         {       
457<a name="l00603"></a>00603                                 <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g = UI::build&lt;fnc&gt;( <span class="keyword">set</span>, <span class="stringliteral">"g"</span> );
458<a name="l00604"></a>00604
459<a name="l00605"></a>00605                                 mat R;
460<a name="l00606"></a>00606                                 <span class="keywordflow">if</span> ( <span class="keyword">set</span>.exists( <span class="stringliteral">"dR"</span> ) )
461<a name="l00607"></a>00607                                 {
462<a name="l00608"></a>00608                                         vec dR;
463<a name="l00609"></a>00609                                         <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="The existing instance of type T is initialized with values stored in the Setting...">UI::get</a>( dR, <span class="keyword">set</span>, <span class="stringliteral">"dR"</span> );
464<a name="l00610"></a>00610                                         R=diag(dR);
465<a name="l00611"></a>00611                                 }
466<a name="l00612"></a>00612                                 <span class="keywordflow">else</span> 
467<a name="l00613"></a>00613                                         <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="The existing instance of type T is initialized with values stored in the Setting...">UI::get</a>( R, <span class="keyword">set</span>, <span class="stringliteral">"R"</span>);                                 
468<a name="l00614"></a>00614                 
469<a name="l00615"></a>00615                                 <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a>(g,R);
470<a name="l00616"></a>00616                         }
471<a name="l00617"></a>00617
472<a name="l00618"></a>00618                         <span class="comment">/*void mgnorm::to_setting( Setting &amp;set ) const</span>
473<a name="l00619"></a>00619 <span class="comment">                        {       </span>
474<a name="l00620"></a>00620 <span class="comment">                                Transport::to_setting( set );</span>
475<a name="l00621"></a>00621 <span class="comment"></span>
476<a name="l00622"></a>00622 <span class="comment">                                Setting &amp;kilometers_setting = set.add("kilometers", Setting::TypeInt );</span>
477<a name="l00623"></a>00623 <span class="comment">                                kilometers_setting = kilometers;</span>
478<a name="l00624"></a>00624 <span class="comment"></span>
479<a name="l00625"></a>00625 <span class="comment">                                UI::save( passengers, set, "passengers" );</span>
480<a name="l00626"></a>00626 <span class="comment">                        }*/</span>
481<a name="l00627"></a>00627
482<a name="l00628"></a>00628         };
483<a name="l00629"></a>00629
484<a name="l00630"></a>00630         <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a>(mgnorm&lt;chmat&gt;);
485<a name="l00631"></a>00631
486<a name="l00632"></a>00632
487<a name="l00640"></a><a class="code" href="classbdm_1_1mlstudent.html">00640</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>&lt;ldmat&gt;
488<a name="l00641"></a>00641         {
489<a name="l00642"></a>00642                 <span class="keyword">protected</span>:
490<a name="l00643"></a>00643                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
491<a name="l00644"></a>00644                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;<a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>;
492<a name="l00645"></a>00645                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
493<a name="l00646"></a>00646                 <span class="keyword">public</span>:
494<a name="l00647"></a>00647                         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html">mlnorm&lt;ldmat&gt;</a> (),
495<a name="l00648"></a>00648                                         Lambda (),      <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {}
496<a name="l00649"></a>00649                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; Lambda0 )
497<a name="l00650"></a>00650                         {
498<a name="l00651"></a>00651                                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
499<a name="l00652"></a>00652                                 it_assert_debug ( R0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ==A0.rows(),<span class="stringliteral">""</span> );
500<a name="l00653"></a>00653
501<a name="l00654"></a>00654                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( mu0,Lambda ); <span class="comment">//</span>
502<a name="l00655"></a>00655                                 A = A0;
503<a name="l00656"></a>00656                                 mu_const = mu0;
504<a name="l00657"></a>00657                                 Re=R0;
505<a name="l00658"></a>00658                                 Lambda = Lambda0;
506<a name="l00659"></a>00659                         }
507<a name="l00660"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00660</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">condition</a> ( <span class="keyword">const</span> vec &amp;cond )
508<a name="l00661"></a>00661                         {
509<a name="l00662"></a>00662                                 _mu = A*cond + mu_const;
510<a name="l00663"></a>00663                                 <span class="keywordtype">double</span> zeta;
511<a name="l00664"></a>00664                                 <span class="comment">//ugly hack!</span>
512<a name="l00665"></a>00665                                 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() )
513<a name="l00666"></a>00666                                 {
514<a name="l00667"></a>00667                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat ( cond, vec_1 ( 1.0 ) ) );
515<a name="l00668"></a>00668                                 }
516<a name="l00669"></a>00669                                 <span class="keywordflow">else</span>
517<a name="l00670"></a>00670                                 {
518<a name="l00671"></a>00671                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
519<a name="l00672"></a>00672                                 }
520<a name="l00673"></a>00673                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
521<a name="l00674"></a>00674                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>*= ( 1+zeta );<span class="comment">// / ( nu ); &lt;&lt; nu is in Re!!!!!!</span>
522<a name="l00675"></a>00675                         };
523<a name="l00676"></a>00676
524<a name="l00677"></a>00677         };
525<a name="l00687"></a><a class="code" href="classbdm_1_1mgamma.html">00687</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
526<a name="l00688"></a>00688         {
527<a name="l00689"></a>00689                 <span class="keyword">protected</span>:
528<a name="l00691"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00691</a>                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
529<a name="l00693"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00693</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
530<a name="l00695"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00695</a>                         vec &amp;<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
531<a name="l00696"></a>00696
532<a name="l00697"></a>00697                 <span class="keyword">public</span>:
533<a name="l00699"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00699</a>                         <a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1" title="Constructor.">mgamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( ), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> (), <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
534<a name="l00701"></a>00701                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#a0f21c2557b233a85838b497d040ab14" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>, <span class="keyword">const</span> vec &amp;beta0 );
535<a name="l00702"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00702</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff" 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="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/val;};
536<a name="l00712"></a><a class="code" href="classbdm_1_1mgamma.html#da2af0f327e5452bee71d1bf97452ae4">00712</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#da2af0f327e5452bee71d1bf97452ae4">from_setting</a>(<span class="keyword">const</span> Setting &amp;<span class="keyword">set</span>){
537<a name="l00713"></a>00713                                 <a class="code" href="classbdm_1_1mgamma.html#da2af0f327e5452bee71d1bf97452ae4">mpdf::from_setting</a>(<span class="keyword">set</span>); <span class="comment">// reads rv and rvc</span>
538<a name="l00714"></a>00714                                 vec betatmp; <span class="comment">// ugly but necessary</span>
539<a name="l00715"></a>00715                                 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="The existing instance of type T is initialized with values stored in the Setting...">UI::get</a>(betatmp,<span class="keyword">set</span>,<span class="stringliteral">"beta"</span>);
540<a name="l00716"></a>00716                                 <span class="keyword">set</span>.lookupValue(<span class="stringliteral">"k"</span>,<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>);
541<a name="l00717"></a>00717                                 <a class="code" href="classbdm_1_1mgamma.html#a0f21c2557b233a85838b497d040ab14" title="Set value of k.">set_parameters</a>(<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>,betatmp);
542<a name="l00718"></a>00718                         }
543<a name="l00719"></a>00719         };
544<a name="l00720"></a>00720         <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a>(mgamma);
545<a name="l00721"></a>00721         
546<a name="l00731"></a><a class="code" href="classbdm_1_1migamma.html">00731</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
547<a name="l00732"></a>00732         {
548<a name="l00733"></a>00733                 <span class="keyword">protected</span>:
549<a name="l00735"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00735</a>                         <a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
550<a name="l00737"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00737</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
551<a name="l00739"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00739</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
552<a name="l00741"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00741</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
553<a name="l00742"></a>00742
554<a name="l00743"></a>00743                 <span class="keyword">public</span>:
555<a name="l00746"></a>00746                         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
556<a name="l00747"></a>00747                         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> &amp;m ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( m.<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
557<a name="l00749"></a>00749
558<a name="l00751"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00751</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> len, <span class="keywordtype">double</span> k0 )
559<a name="l00752"></a>00752                         {
560<a name="l00753"></a>00753                                 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
561<a name="l00754"></a>00754                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( ( 1.0/ ( <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>*<a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a> ) +2.0 ) *ones ( len ) <span class="comment">/*alpha*/</span>, ones ( len ) <span class="comment">/*beta*/</span> );
562<a name="l00755"></a>00755                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
563<a name="l00756"></a>00756                         };
564<a name="l00757"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00757</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
565<a name="l00758"></a>00758                         {
566<a name="l00759"></a>00759                                 <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>=elem_mult ( val, ( <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>-1.0 ) );
567<a name="l00760"></a>00760                         };
568<a name="l00761"></a>00761         };
569<a name="l00762"></a>00762
570<a name="l00763"></a>00763
571<a name="l00775"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00775</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a>
572<a name="l00776"></a>00776         {
573<a name="l00777"></a>00777                 <span class="keyword">protected</span>:
574<a name="l00779"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00779</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
575<a name="l00781"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00781</a>                         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
576<a name="l00782"></a>00782                 <span class="keyword">public</span>:
577<a name="l00784"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00784</a>                         <a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d" title="Constructor.">mgamma_fix</a> ( ) : <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> ( ),<a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a> () {};
578<a name="l00786"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00786</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
579<a name="l00787"></a>00787                         {
580<a name="l00788"></a>00788                                 <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">mgamma::set_parameters</a> ( k0, ref0 );
581<a name="l00789"></a>00789                                 <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>=l0;
582<a name="l00790"></a>00790                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=dimension();
583<a name="l00791"></a>00791                         };
584<a name="l00792"></a>00792
585<a name="l00793"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00793</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7" 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="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a> ) ); <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/mean;};
586<a name="l00794"></a>00794         };
587<a name="l00795"></a>00795
588<a name="l00796"></a>00796
589<a name="l00809"></a><a class="code" href="classbdm_1_1migamma__ref.html">00809</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__ref.html" title="Inverse-Gamma random walk around a fixed point.">migamma_ref</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a>
590<a name="l00810"></a>00810         {
591<a name="l00811"></a>00811                 <span class="keyword">protected</span>:
592<a name="l00813"></a><a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d">00813</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>;
593<a name="l00815"></a><a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6">00815</a>                         vec <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>;
594<a name="l00816"></a>00816                 <span class="keyword">public</span>:
595<a name="l00818"></a><a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f">00818</a>                         <a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f" title="Constructor.">migamma_ref</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a> ( ) {};
596<a name="l00820"></a><a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800">00820</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
597<a name="l00821"></a>00821                         {
598<a name="l00822"></a>00822                                 <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">migamma::set_parameters</a> ( ref0.length(), k0 );
599<a name="l00823"></a>00823                                 <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
600<a name="l00824"></a>00824                                 <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>=l0;
601<a name="l00825"></a>00825                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
602<a name="l00826"></a>00826                         };
603<a name="l00827"></a>00827
604<a name="l00828"></a><a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a">00828</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
605<a name="l00829"></a>00829                         {
606<a name="l00830"></a>00830                                 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a> ) );
607<a name="l00831"></a>00831                                 <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
608<a name="l00832"></a>00832                         };
609<a name="l00833"></a>00833
610<a name="l00854"></a>00854                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#9e7e0f7d2aa9ecca8ec1af8cbcb5ef1d">from_setting</a>( <span class="keyword">const</span> Setting &amp;<span class="keyword">set</span> );
611<a name="l00855"></a>00855
612<a name="l00856"></a>00856                         <span class="comment">// TODO dodelat void to_setting( Setting &amp;set ) const;</span>
613<a name="l00857"></a>00857         };
614<a name="l00858"></a>00858
615<a name="l00859"></a>00859
616<a name="l00860"></a>00860         <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a>(migamma_ref);
617<a name="l00861"></a>00861
618<a name="l00871"></a><a class="code" href="classbdm_1_1elognorm.html">00871</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1elognorm.html">elognorm</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a>&lt;ldmat&gt;
619<a name="l00872"></a>00872         {
620<a name="l00873"></a>00873                 <span class="keyword">public</span>:
621<a name="l00874"></a><a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba">00874</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;ldmat&gt;::sample</a>() );};
622<a name="l00875"></a><a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea">00875</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec var=<a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">enorm&lt;ldmat&gt;::variance</a>();<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> - 0.5*var );};
623<a name="l00876"></a>00876
624<a name="l00877"></a>00877         };
625<a name="l00878"></a>00878
626<a name="l00890"></a><a class="code" href="classbdm_1_1mlognorm.html">00890</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlognorm.html" title="Log-Normal random walk.">mlognorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>
627<a name="l00891"></a>00891         {
628<a name="l00892"></a>00892                 <span class="keyword">protected</span>:
629<a name="l00893"></a>00893                         <a class="code" href="classbdm_1_1elognorm.html">elognorm</a> eno;
630<a name="l00895"></a><a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a">00895</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;
631<a name="l00897"></a><a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2">00897</a>                         vec &amp;<a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>;
632<a name="l00898"></a>00898                 <span class="keyword">public</span>:
633<a name="l00900"></a><a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41">00900</a>                         <a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41" title="Constructor.">mlognorm</a> ( ) : eno (), <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a> ( eno._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eno;};
634<a name="l00902"></a><a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f">00902</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> size, <span class="keywordtype">double</span> k )
635<a name="l00903"></a>00903                         {
636<a name="l00904"></a>00904                                 <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a> = 0.5*log ( k*k+1 );
637<a name="l00905"></a>00905                                 eno.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( zeros ( size ),2*<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>*eye ( size ) );
638<a name="l00906"></a>00906
639<a name="l00907"></a>00907                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = size;
640<a name="l00908"></a>00908                         };
641<a name="l00909"></a>00909
642<a name="l00910"></a><a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e">00910</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
643<a name="l00911"></a>00911                         {
644<a name="l00912"></a>00912                                 <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>=log ( val )-<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;<span class="comment">//elem_mult ( refl,pow ( val,l ) );</span>
645<a name="l00913"></a>00913                         };
646<a name="l00914"></a>00914
647<a name="l00933"></a>00933                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#49e45ea13a869da607ef9be7a229128a">from_setting</a>( <span class="keyword">const</span> Setting &amp;<span class="keyword">set</span> );
648<a name="l00934"></a>00934
649<a name="l00935"></a>00935                         <span class="comment">// TODO dodelat void to_setting( Setting &amp;set ) const;</span>
650<a name="l00936"></a>00936
651<a name="l00937"></a>00937         };
652<a name="l00938"></a>00938         
653<a name="l00939"></a>00939         <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a>(mlognorm);
654<a name="l00940"></a>00940
655<a name="l00944"></a><a class="code" href="classbdm_1_1eWishartCh.html">00944</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eWishartCh.html">eWishartCh</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
656<a name="l00945"></a>00945         {
657<a name="l00946"></a>00946                 <span class="keyword">protected</span>:
658<a name="l00948"></a><a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490">00948</a>                         <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;
659<a name="l00950"></a><a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f">00950</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;
660<a name="l00952"></a><a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3">00952</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>;
661<a name="l00953"></a>00953                 <span class="keyword">public</span>:
662<a name="l00954"></a>00954                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0 ) {<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>=<a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> ( Y0 );<a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>=delta0; <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>=<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classsqmat.html#071e80ced9cc3b8cbb360fa7462eb646" title="Reimplementing common functions of mat: cols().">rows</a>(); <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>*<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>; }
663<a name="l00955"></a>00955                         mat sample_mat()<span class="keyword"> const</span>
664<a name="l00956"></a>00956 <span class="keyword">                        </span>{
665<a name="l00957"></a>00957                                 mat X=zeros ( <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>,<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a> );
666<a name="l00958"></a>00958
667<a name="l00959"></a>00959                                 <span class="comment">//sample diagonal</span>
668<a name="l00960"></a>00960                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;i++ )
669<a name="l00961"></a>00961                                 {
670<a name="l00962"></a>00962                                         GamRNG.setup ( 0.5* ( <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>-i ) , 0.5 ); <span class="comment">// no +1 !! index if from 0</span>
671<a name="l00963"></a>00963 <span class="preprocessor">#pragma omp critical</span>
672<a name="l00964"></a>00964 <span class="preprocessor"></span>                                        X ( i,i ) =sqrt ( GamRNG() );
673<a name="l00965"></a>00965                                 }
674<a name="l00966"></a>00966                                 <span class="comment">//do the rest</span>
675<a name="l00967"></a>00967                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;p;i++ )
676<a name="l00968"></a>00968                                 {
677<a name="l00969"></a>00969                                         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> j=i+1;j&lt;p;j++ )
678<a name="l00970"></a>00970                                         {
679<a name="l00971"></a>00971 <span class="preprocessor">#pragma omp critical</span>
680<a name="l00972"></a>00972 <span class="preprocessor"></span>                                                X ( i,j ) =NorRNG.sample();
681<a name="l00973"></a>00973                                         }
682<a name="l00974"></a>00974                                 }
683<a name="l00975"></a>00975                                 <span class="keywordflow">return</span> X*<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>();<span class="comment">// return upper triangular part of the decomposition</span>
684<a name="l00976"></a>00976                         }
685<a name="l00977"></a><a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a">00977</a>                         vec <a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a" title="Returns a sample,  from density .">sample</a> ()<span class="keyword"> const</span>
686<a name="l00978"></a>00978 <span class="keyword">                        </span>{
687<a name="l00979"></a>00979                                 <span class="keywordflow">return</span> vec ( sample_mat()._data(),p*p );
688<a name="l00980"></a>00980                         }
689<a name="l00982"></a><a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981">00982</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981" title="fast access function y0 will be copied into Y.Ch.">setY</a> ( <span class="keyword">const</span> mat &amp;Ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,Ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() );}
690<a name="l00984"></a><a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a">00984</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a" title="fast access function y0 will be copied into Y.Ch.">_setY</a> ( <span class="keyword">const</span> vec &amp;ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>, ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() ); }
691<a name="l00986"></a><a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e">00986</a>                         <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; <a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e" title="access function">getY</a>()<span class="keyword">const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;}
692<a name="l00987"></a>00987         };
693<a name="l00988"></a>00988
694<a name="l00989"></a>00989         <span class="keyword">class </span>eiWishartCh: <span class="keyword">public</span> epdf
695<a name="l00990"></a>00990         {
696<a name="l00991"></a>00991                 <span class="keyword">protected</span>:
697<a name="l00992"></a>00992                         eWishartCh W;
698<a name="l00993"></a>00993                         <span class="keywordtype">int</span> p;
699<a name="l00994"></a>00994                         <span class="keywordtype">double</span> delta;
700<a name="l00995"></a>00995                 <span class="keyword">public</span>:
701<a name="l00996"></a>00996                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0) {
702<a name="l00997"></a>00997                                 delta = delta0;
703<a name="l00998"></a>00998                                 W.set_parameters ( inv ( Y0 ),delta0 );
704<a name="l00999"></a>00999                                 dim = W.dimension(); p=Y0.rows();
705<a name="l01000"></a>01000                         }
706<a name="l01001"></a>01001                         vec sample()<span class="keyword"> const </span>{mat iCh; iCh=inv ( W.sample_mat() ); <span class="keywordflow">return</span> vec ( iCh._data(),<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );}
707<a name="l01002"></a>01002                         <span class="keywordtype">void</span> _setY ( <span class="keyword">const</span> vec &amp;y0 )
708<a name="l01003"></a>01003                         {
709<a name="l01004"></a>01004                                 mat Ch ( p,p );
710<a name="l01005"></a>01005                                 mat iCh ( p,p );
711<a name="l01006"></a>01006                                 copy_vector ( dim, y0._data(), Ch._data() );
712<a name="l01007"></a>01007                                 
713<a name="l01008"></a>01008                                 iCh=inv ( Ch );
714<a name="l01009"></a>01009                                 W.setY ( iCh );
715<a name="l01010"></a>01010                         }
716<a name="l01011"></a>01011                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> evallog ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
717<a name="l01012"></a>01012                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> X(p);
718<a name="l01013"></a>01013                                 <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; Y=W.getY();
719<a name="l01014"></a>01014                                 
720<a name="l01015"></a>01015                                 copy_vector(p*p,val._data(),X._Ch()._data());
721<a name="l01016"></a>01016                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> iX(p);X.inv(iX);
722<a name="l01017"></a>01017                                 <span class="comment">// compute  </span>
723<a name="l01018"></a>01018 <span class="comment">//                              \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},</span>
724<a name="l01019"></a>01019                                 mat M=Y.<a class="code" href="classchmat.html#045addd685f8d978efda232d7dcb070e" title="Conversion to full matrix.">to_mat</a>()*iX.to_mat();
725<a name="l01020"></a>01020                                 
726<a name="l01021"></a>01021                                 <span class="keywordtype">double</span> log1 = 0.5*p*(2*Y.<a class="code" href="classchmat.html#b504ca818203b13e667cb3c503980382" title="Logarithm of a determinant.">logdet</a>())-0.5*(delta+p+1)*(2*X.logdet())-0.5*trace(M);
727<a name="l01022"></a>01022                                 <span class="comment">//Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!</span>
728<a name="l01023"></a>01023                                 
729<a name="l01024"></a>01024 <span class="comment">/*                              if (0) {</span>
730<a name="l01025"></a>01025 <span class="comment">                                        mat XX=X.to_mat();</span>
731<a name="l01026"></a>01026 <span class="comment">                                        mat YY=Y.to_mat();</span>
732<a name="l01027"></a>01027 <span class="comment">                                        </span>
733<a name="l01028"></a>01028 <span class="comment">                                        double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX)); </span>
734<a name="l01029"></a>01029 <span class="comment">                                        cout &lt;&lt; log1 &lt;&lt; "," &lt;&lt; log2 &lt;&lt; endl;</span>
735<a name="l01030"></a>01030 <span class="comment">                                }*/</span>
736<a name="l01031"></a>01031                                 <span class="keywordflow">return</span> log1;                           
737<a name="l01032"></a>01032                         };
738<a name="l01033"></a>01033                         
739<a name="l01034"></a>01034         };
740<a name="l01035"></a>01035
741<a name="l01036"></a>01036         <span class="keyword">class </span>rwiWishartCh : <span class="keyword">public</span> mpdf
742<a name="l01037"></a>01037         {
743<a name="l01038"></a>01038                 <span class="keyword">protected</span>:
744<a name="l01039"></a>01039                         eiWishartCh eiW;
745<a name="l01041"></a>01041                         <span class="keywordtype">double</span> sqd;
746<a name="l01042"></a>01042                         <span class="comment">//reference point for diagonal</span>
747<a name="l01043"></a>01043                         vec refl;
748<a name="l01044"></a>01044                         <span class="keywordtype">double</span> l;
749<a name="l01045"></a>01045                         <span class="keywordtype">int</span> p;
750<a name="l01046"></a>01046                 <span class="keyword">public</span>:
751<a name="l01047"></a>01047                         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> p0, <span class="keywordtype">double</span> k, vec ref0, <span class="keywordtype">double</span> l0  )
752<a name="l01048"></a>01048                         {
753<a name="l01049"></a>01049                                 p=p0;
754<a name="l01050"></a>01050                                 <span class="keywordtype">double</span> delta = 2/(k*k)+p+3;
755<a name="l01051"></a>01051                                 sqd=sqrt ( delta-p-1 );
756<a name="l01052"></a>01052                                 l=l0;
757<a name="l01053"></a>01053                                 refl=pow(ref0,1-l);
758<a name="l01054"></a>01054                                 
759<a name="l01055"></a>01055                                 eiW.set_parameters ( eye ( p ),delta );
760<a name="l01056"></a>01056                                 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eiW;
761<a name="l01057"></a>01057                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=eiW.dimension();
762<a name="l01058"></a>01058                         }
763<a name="l01059"></a>01059                         <span class="keywordtype">void</span> condition ( <span class="keyword">const</span> vec &amp;c ) {
764<a name="l01060"></a>01060                                 vec z=c;
765<a name="l01061"></a>01061                                 <span class="keywordtype">int</span> ri=0;
766<a name="l01062"></a>01062                                 <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0;i&lt;p*p;i+=(p+1)){<span class="comment">//trace diagonal element</span>
767<a name="l01063"></a>01063                                         z(i) = pow(z(i),l)*refl(ri);
768<a name="l01064"></a>01064                                         ri++;
769<a name="l01065"></a>01065                                 }
770<a name="l01066"></a>01066
771<a name="l01067"></a>01067                                 eiW._setY ( sqd*z );
772<a name="l01068"></a>01068                         }
773<a name="l01069"></a>01069         };
774<a name="l01070"></a>01070
775<a name="l01072"></a>01072         <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
776<a name="l01078"></a><a class="code" href="classbdm_1_1eEmp.html">01078</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
777<a name="l01079"></a>01079         {
778<a name="l01080"></a>01080                 <span class="keyword">protected</span> :
779<a name="l01082"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">01082</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
780<a name="l01084"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">01084</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
781<a name="l01086"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">01086</a>                         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
782<a name="l01087"></a>01087                 <span class="keyword">public</span>:
783<a name="l01090"></a>01090                         <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( ) {};
784<a name="l01092"></a><a class="code" href="classbdm_1_1eEmp.html#a3daf6363455af099921715e1233c076">01092</a>                         <a class="code" href="classbdm_1_1eEmp.html#a3daf6363455af099921715e1233c076" title="copy constructor">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> &amp;e ) : <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( e ), <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ), <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ) {};
785<a name="l01094"></a>01094
786<a name="l01096"></a>01096                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#7cfd383180b486fe4526bdf0179350c0" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
787<a name="l01098"></a><a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7">01098</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 , <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {<a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( ones ( n ) /n,pdf0 );};
788<a name="l01100"></a>01100                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b62d802b8ef39f7c4dcbeb366c90951a" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
789<a name="l01102"></a><a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1">01102</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1" title="Set sample.">set_parameters</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>=n0; <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size ( n0,copy );<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size ( n0,copy );};
790<a name="l01104"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">01104</a>                         vec&amp; <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
791<a name="l01106"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">01106</a>                         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
792<a name="l01108"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">01108</a>                         Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
793<a name="l01110"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">01110</a>                         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
794<a name="l01112"></a>01112                         ivec <a class="code" href="classbdm_1_1eEmp.html#f06ce255de5dbb2313f52ee51f82ba3d" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( RESAMPLING_METHOD method=SYSTEMATIC );
795<a name="l01114"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">01114</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270" 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;}
796<a name="l01116"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">01116</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09" title="inherited operation : NOT implemneted">evallog</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;}
797<a name="l01117"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">01117</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const</span>
798<a name="l01118"></a>01118 <span class="keyword">                        </span>{
799<a name="l01119"></a>01119                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
800<a name="l01120"></a>01120                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
801<a name="l01121"></a>01121                                 <span class="keywordflow">return</span> pom;
802<a name="l01122"></a>01122                         }
803<a name="l01123"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">01123</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const</span>
804<a name="l01124"></a>01124 <span class="keyword">                        </span>{
805<a name="l01125"></a>01125                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
806<a name="l01126"></a>01126                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=pow ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ),2 ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
807<a name="l01127"></a>01127                                 <span class="keywordflow">return</span> pom-pow ( <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>(),2 );
808<a name="l01128"></a>01128                         }
809<a name="l01130"></a><a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f">01130</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f" title="For this class, qbounds are minimum and maximum value of the population!">qbounds</a> ( vec &amp;lb, vec &amp;ub, <span class="keywordtype">double</span> perc=0.95 )<span class="keyword"> const</span>
810<a name="l01131"></a>01131 <span class="keyword">                        </span>{
811<a name="l01132"></a>01132                                 <span class="comment">// lb in inf so than it will be pushed below;</span>
812<a name="l01133"></a>01133                                 lb.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
813<a name="l01134"></a>01134                                 ub.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
814<a name="l01135"></a>01135                                 lb = std::numeric_limits&lt;double&gt;::infinity();
815<a name="l01136"></a>01136                                 ub = -std::numeric_limits&lt;double&gt;::infinity();
816<a name="l01137"></a>01137                                 <span class="keywordtype">int</span> j;
817<a name="l01138"></a>01138                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ )
818<a name="l01139"></a>01139                                 {
819<a name="l01140"></a>01140                                         <span class="keywordflow">for</span> ( j=0;j&lt;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>; j++ )
820<a name="l01141"></a>01141                                         {
821<a name="l01142"></a>01142                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &lt;lb ( j ) ) {lb ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
822<a name="l01143"></a>01143                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &gt;ub ( j ) ) {ub ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
823<a name="l01144"></a>01144                                         }
824<a name="l01145"></a>01145                                 }
825<a name="l01146"></a>01146                         }
826<a name="l01147"></a>01147         };
827<a name="l01148"></a>01148
828<a name="l01149"></a>01149
829<a name="l01151"></a>01151
830<a name="l01152"></a>01152         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
831<a name="l01153"></a>01153         <span class="keywordtype">void</span> enorm&lt;sq_T&gt;::set_parameters ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 )
832<a name="l01154"></a>01154         {
833<a name="l01155"></a>01155 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
834<a name="l01156"></a>01156                 mu = mu0;
835<a name="l01157"></a>01157                 R = R0;
836<a name="l01158"></a>01158                 <a class="code" href="classbdm_1_1root.html#1c314bd6d6dacb8ba78ea5eb88fd9516" title="This method TODO.">validate</a>();
837<a name="l01159"></a>01159         };
838<a name="l01160"></a>01160
839<a name="l01161"></a>01161         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
840<a name="l01162"></a><a class="code" href="classbdm_1_1enorm.html#61bd470764020bea6e1ed35000f259e6">01162</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#61bd470764020bea6e1ed35000f259e6">enorm&lt;sq_T&gt;::from_setting</a>(<span class="keyword">const</span> Setting &amp;<span class="keyword">set</span>){
841<a name="l01163"></a>01163                 <a class="code" href="classbdm_1_1enorm.html#61bd470764020bea6e1ed35000f259e6">epdf::from_setting</a>(<span class="keyword">set</span>); <span class="comment">//reads rv</span>
842<a name="l01164"></a>01164                 
843<a name="l01165"></a>01165                 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="The existing instance of type T is initialized with values stored in the Setting...">UI::get</a>(<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">set</span>,<span class="stringliteral">"mu"</span>);
844<a name="l01166"></a>01166                 mat Rtmp;<span class="comment">// necessary for conversion</span>
845<a name="l01167"></a>01167                 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="The existing instance of type T is initialized with values stored in the Setting...">UI::get</a>(Rtmp,<span class="keyword">set</span>,<span class="stringliteral">"R"</span>);
846<a name="l01168"></a>01168                 <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>=Rtmp; <span class="comment">// conversion</span>
847<a name="l01169"></a>01169                 <a class="code" href="classbdm_1_1enorm.html#38eb17ecb75d94a50b6782fcf735cfea" title="This method TODO.">validate</a>();
848<a name="l01170"></a>01170         }
849<a name="l01171"></a>01171
850<a name="l01172"></a>01172         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
851<a name="l01173"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">01173</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu )
852<a name="l01174"></a>01174         {
853<a name="l01175"></a>01175                 <span class="comment">//</span>
854<a name="l01176"></a>01176         };
855<a name="l01177"></a>01177
856<a name="l01178"></a>01178 <span class="comment">// template&lt;class sq_T&gt;</span>
857<a name="l01179"></a>01179 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
858<a name="l01180"></a>01180 <span class="comment">//      //</span>
859<a name="l01181"></a>01181 <span class="comment">// };</span>
860<a name="l01182"></a>01182
861<a name="l01183"></a>01183         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
862<a name="l01184"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">01184</a>         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const</span>
863<a name="l01185"></a>01185 <span class="keyword">        </span>{
864<a name="l01186"></a>01186                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
865<a name="l01187"></a>01187 <span class="preprocessor">#pragma omp critical</span>
866<a name="l01188"></a>01188 <span class="preprocessor"></span>                NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
867<a name="l01189"></a>01189                 vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
868<a name="l01190"></a>01190
869<a name="l01191"></a>01191                 smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
870<a name="l01192"></a>01192                 <span class="keywordflow">return</span> smp;
871<a name="l01193"></a>01193         };
872<a name="l01194"></a>01194
873<a name="l01195"></a>01195         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
874<a name="l01196"></a>01196         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const</span>
875<a name="l01197"></a>01197 <span class="keyword">        </span>{
876<a name="l01198"></a>01198                 mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
877<a name="l01199"></a>01199                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
878<a name="l01200"></a>01200                 vec pom;
879<a name="l01201"></a>01201                 <span class="keywordtype">int</span> i;
880<a name="l01202"></a>01202
881<a name="l01203"></a>01203                 <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ )
882<a name="l01204"></a>01204                 {
883<a name="l01205"></a>01205 <span class="preprocessor">#pragma omp critical</span>
884<a name="l01206"></a>01206 <span class="preprocessor"></span>                        NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
885<a name="l01207"></a>01207                         pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
886<a name="l01208"></a>01208                         pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
887<a name="l01209"></a>01209                         X.set_col ( i, pom );
888<a name="l01210"></a>01210                 }
889<a name="l01211"></a>01211
890<a name="l01212"></a>01212                 <span class="keywordflow">return</span> X;
891<a name="l01213"></a>01213         };
892<a name="l01214"></a>01214
893<a name="l01215"></a>01215 <span class="comment">// template&lt;class sq_T&gt;</span>
894<a name="l01216"></a>01216 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
895<a name="l01217"></a>01217 <span class="comment">//      double pdfl,e;</span>
896<a name="l01218"></a>01218 <span class="comment">//      pdfl = evallog ( val );</span>
897<a name="l01219"></a>01219 <span class="comment">//      e = exp ( pdfl );</span>
898<a name="l01220"></a>01220 <span class="comment">//      return e;</span>
899<a name="l01221"></a>01221 <span class="comment">// };</span>
900<a name="l01222"></a>01222
901<a name="l01223"></a>01223         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
902<a name="l01224"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">01224</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>
903<a name="l01225"></a>01225 <span class="keyword">        </span>{
904<a name="l01226"></a>01226                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
905<a name="l01227"></a>01227                 <span class="keywordtype">double</span> tmp=-0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>-val ) );<span class="comment">// - lognc();</span>
906<a name="l01228"></a>01228                 <span class="keywordflow">return</span>  tmp;
907<a name="l01229"></a>01229         };
908<a name="l01230"></a>01230
909<a name="l01231"></a>01231         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
910<a name="l01232"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">01232</a>         <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const</span>
911<a name="l01233"></a>01233 <span class="keyword">        </span>{
912<a name="l01234"></a>01234                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
913<a name="l01235"></a>01235                 <span class="keywordtype">double</span> tmp=0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.logdet() );
914<a name="l01236"></a>01236                 <span class="keywordflow">return</span> tmp;
915<a name="l01237"></a>01237         };
916<a name="l01238"></a>01238
917<a name="l01239"></a>01239         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
918<a name="l01240"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">01240</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" 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 )
919<a name="l01241"></a>01241         {
920<a name="l01242"></a>01242                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
921<a name="l01243"></a>01243                 it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
922<a name="l01244"></a>01244
923<a name="l01245"></a>01245                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( A0.rows() ),R0 );
924<a name="l01246"></a>01246                 A = A0;
925<a name="l01247"></a>01247                 mu_const = mu0;
926<a name="l01248"></a>01248                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=A0.cols();
927<a name="l01249"></a>01249         }
928<a name="l01250"></a>01250
929<a name="l01251"></a>01251 <span class="comment">// template&lt;class sq_T&gt;</span>
930<a name="l01252"></a>01252 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
931<a name="l01253"></a>01253 <span class="comment">//      this-&gt;condition ( cond );</span>
932<a name="l01254"></a>01254 <span class="comment">//      vec smp = epdf.sample();</span>
933<a name="l01255"></a>01255 <span class="comment">//      lik = epdf.eval ( smp );</span>
934<a name="l01256"></a>01256 <span class="comment">//      return smp;</span>
935<a name="l01257"></a>01257 <span class="comment">// }</span>
936<a name="l01258"></a>01258
937<a name="l01259"></a>01259 <span class="comment">// template&lt;class sq_T&gt;</span>
938<a name="l01260"></a>01260 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
939<a name="l01261"></a>01261 <span class="comment">//      int i;</span>
940<a name="l01262"></a>01262 <span class="comment">//      int dim = rv.count();</span>
941<a name="l01263"></a>01263 <span class="comment">//      mat Smp ( dim,n );</span>
942<a name="l01264"></a>01264 <span class="comment">//      vec smp ( dim );</span>
943<a name="l01265"></a>01265 <span class="comment">//      this-&gt;condition ( cond );</span>
944<a name="l01266"></a>01266 <span class="comment">//</span>
945<a name="l01267"></a>01267 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
946<a name="l01268"></a>01268 <span class="comment">//              smp = epdf.sample();</span>
947<a name="l01269"></a>01269 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
948<a name="l01270"></a>01270 <span class="comment">//              Smp.set_col ( i ,smp );</span>
949<a name="l01271"></a>01271 <span class="comment">//      }</span>
950<a name="l01272"></a>01272 <span class="comment">//</span>
951<a name="l01273"></a>01273 <span class="comment">//      return Smp;</span>
952<a name="l01274"></a>01274 <span class="comment">// }</span>
953<a name="l01275"></a>01275
954<a name="l01276"></a>01276         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
955<a name="l01277"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">01277</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond )
956<a name="l01278"></a>01278         {
957<a name="l01279"></a>01279                 _mu = A*cond + mu_const;
958<a name="l01280"></a>01280 <span class="comment">//R is already assigned;</span>
959<a name="l01281"></a>01281         }
960<a name="l01282"></a>01282
961<a name="l01283"></a>01283         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
962<a name="l01284"></a><a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80">01284</a>         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" 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="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
963<a name="l01285"></a>01285 <span class="keyword">        </span>{
964<a name="l01286"></a>01286                 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(), <span class="stringliteral">"rv description is not assigned"</span> );
965<a name="l01287"></a>01287                 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
966<a name="l01288"></a>01288
967<a name="l01289"></a>01289                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn ); <span class="comment">//select rows and columns of R</span>
968<a name="l01290"></a>01290
969<a name="l01291"></a>01291                 <a class="code" href="classbdm_1_1enorm.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="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>;
970<a name="l01292"></a>01292                 tmp-&gt;<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
971<a name="l01293"></a>01293                 tmp-&gt;<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn ), Rn );
972<a name="l01294"></a>01294                 <span class="keywordflow">return</span> tmp;
973<a name="l01295"></a>01295         }
974<a name="l01296"></a>01296
975<a name="l01297"></a>01297         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
976<a name="l01298"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">01298</a>         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" 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="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
977<a name="l01299"></a>01299 <span class="keyword">        </span>{
978<a name="l01300"></a>01300
979<a name="l01301"></a>01301                 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(),<span class="stringliteral">"rvs are not assigned"</span> );
980<a name="l01302"></a>01302
981<a name="l01303"></a>01303                 <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#aec44dabdf0a6d90fbae95e1356eda39" title="Subtract another variable from the current one.">subt</a> ( rvn );
982<a name="l01304"></a>01304                 it_assert_debug ( ( rvc.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() +rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ==<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ),<span class="stringliteral">"wrong rvn"</span> );
983<a name="l01305"></a>01305                 <span class="comment">//Permutation vector of the new R</span>
984<a name="l01306"></a>01306                 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
985<a name="l01307"></a>01307                 ivec irvc = rvc.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
986<a name="l01308"></a>01308                 ivec perm=concat ( irvn , irvc );
987<a name="l01309"></a>01309                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
988<a name="l01310"></a>01310
989<a name="l01311"></a>01311                 <span class="comment">//fixme - could this be done in general for all sq_T?</span>
990<a name="l01312"></a>01312                 mat S=Rn.to_mat();
991<a name="l01313"></a>01313                 <span class="comment">//fixme</span>
992<a name="l01314"></a>01314                 <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
993<a name="l01315"></a>01315                 <span class="keywordtype">int</span> end=<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.rows()-1;
994<a name="l01316"></a>01316                 mat S11 = S.get ( 0,n, 0, n );
995<a name="l01317"></a>01317                 mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
996<a name="l01318"></a>01318                 mat S22 = S.get ( rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end, rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
997<a name="l01319"></a>01319
998<a name="l01320"></a>01320                 vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
999<a name="l01321"></a>01321                 vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
1000<a name="l01322"></a>01322                 mat A=S12*inv ( S22 );
1001<a name="l01323"></a>01323                 sq_T R_n ( S11 - A *S12.T() );
1002<a name="l01324"></a>01324
1003<a name="l01325"></a>01325                 <a class="code" href="classbdm_1_1mlnorm.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="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a> ( );
1004<a name="l01326"></a>01326                 tmp-&gt;set_rv ( rvn ); tmp-&gt;set_rvc ( rvc );
1005<a name="l01327"></a>01327                 tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
1006<a name="l01328"></a>01328                 <span class="keywordflow">return</span> tmp;
1007<a name="l01329"></a>01329         }
1008<a name="l01330"></a>01330
1009<a name="l01332"></a>01332
1010<a name="l01333"></a>01333         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
1011<a name="l01334"></a>01334         std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml )
1012<a name="l01335"></a>01335         {
1013<a name="l01336"></a>01336                 os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
1014<a name="l01337"></a>01337                 os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
1015<a name="l01338"></a>01338                 os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
1016<a name="l01339"></a>01339                 <span class="keywordflow">return</span> os;
1017<a name="l01340"></a>01340         };
1018<a name="l01341"></a>01341
1019<a name="l01342"></a>01342 }
1020<a name="l01343"></a>01343 <span class="preprocessor">#endif //EF_H</span>
1021</pre></div></div>
1022<hr size="1"><address style="text-align: right;"><small>Generated on Wed Jul 1 13:05:55 2009 for mixpp by&nbsp;
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1024<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.8 </small></address>
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