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09/24/08 13:08:07 (16 years ago)
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smidl
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New documentation

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  • doc/html/libEF_8h-source.html

    r162 r171  
    3636<a name="l00040"></a>00040 <span class="comment">//      eEF() :epdf() {};</span> 
    3737<a name="l00042"></a><a class="code" href="classeEF.html#7e3c63655e8375c76bf1f421245427a7">00042</a> <span class="comment"></span>        <a class="code" href="classeEF.html#7e3c63655e8375c76bf1f421245427a7" title="default constructor">eEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {}; 
    38 <a name="l00044"></a>00044         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classeEF.html#69e5680dac10375d62520d26c672477d" title="logarithm of the normalizing constant, ">lognc</a>()<span class="keyword">const</span> =0; 
    39 <a name="l00046"></a><a class="code" href="classeEF.html#fd88bc35550ec8fe9281d358216d0fcf">00046</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classeEF.html#fd88bc35550ec8fe9281d358216d0fcf" title="TODO decide if it is really needed.">tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ) {}; 
    40 <a name="l00048"></a><a class="code" href="classeEF.html#5863718c3b2fb1496dece10c5b745d5c">00048</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classeEF.html#5863718c3b2fb1496dece10c5b745d5c" title="TODO decide if it is really needed.">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 ) {}; 
    41 <a name="l00049"></a>00049 }; 
    42 <a name="l00050"></a>00050  
    43 <a name="l00057"></a><a class="code" href="classmEF.html">00057</a> <span class="keyword">class </span><a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> { 
    44 <a name="l00058"></a>00058  
    45 <a name="l00059"></a>00059 <span class="keyword">public</span>: 
    46 <a name="l00061"></a><a class="code" href="classmEF.html#8bf51fe8654d7b83c8c8afeb19409d4f">00061</a>         <a class="code" href="classmEF.html#8bf51fe8654d7b83c8c8afeb19409d4f" title="Default constructor.">mEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( rv0,rvc0 ) {}; 
    47 <a name="l00062"></a>00062 }; 
    48 <a name="l00063"></a>00063  
    49 <a name="l00069"></a>00069 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    50 <a name="l00070"></a>00070  
    51 <a name="l00071"></a><a class="code" href="classenorm.html">00071</a> <span class="keyword">class </span><a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
    52 <a name="l00072"></a>00072 <span class="keyword">protected</span>: 
    53 <a name="l00074"></a><a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20">00074</a>         vec <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; 
    54 <a name="l00076"></a><a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1">00076</a>         sq_T <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>; 
    55 <a name="l00078"></a><a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e">00078</a>         <span class="keywordtype">int</span> <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>; 
    56 <a name="l00079"></a>00079 <span class="keyword">public</span>: 
    57 <a name="l00080"></a>00080 <span class="comment">//      enorm() :eEF() {};</span> 
    58 <a name="l00082"></a>00082 <span class="comment"></span>        <a class="code" href="classenorm.html#7b5cb487a2570e8109bfdc0df149aa06" title="Default constructor.">enorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ); 
    59 <a name="l00084"></a>00084         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> ); 
    60 <a name="l00086"></a>00086         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a" title="tupdate in exponential form (not really handy)">tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ); 
    61 <a name="l00088"></a>00088         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 ); 
    62 <a name="l00089"></a>00089  
    63 <a name="l00090"></a>00090         vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns the required moment of the epdf.">sample</a>() <span class="keyword">const</span>; 
    64 <a name="l00092"></a>00092         mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns the required moment of the epdf.">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>; 
    65 <a name="l00093"></a>00093         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span> ; 
    66 <a name="l00094"></a>00094         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>; 
    67 <a name="l00095"></a>00095         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; 
    68 <a name="l00096"></a><a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899">00096</a>         vec <a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;} 
    69 <a name="l00097"></a>00097  
    70 <a name="l00098"></a>00098 <span class="comment">//Access methods</span> 
    71 <a name="l00100"></a><a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac">00100</a> <span class="comment"></span>        vec&amp; <a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;} 
    72 <a name="l00101"></a>00101          
    73 <a name="l00103"></a><a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b">00103</a>         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b" title="access function">set_mu</a>(<span class="keyword">const</span> vec mu0) { <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>=mu0;} 
    74 <a name="l00104"></a>00104  
    75 <a name="l00106"></a><a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9">00106</a>         sq_T&amp; <a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;} 
    76 <a name="l00107"></a>00107  
    77 <a name="l00109"></a><a class="code" href="classenorm.html#9b9f58dc86affa23511c246887420658">00109</a>         mat <a class="code" href="classenorm.html#9b9f58dc86affa23511c246887420658" title="access method">getR</a> () {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.to_mat();} 
    78 <a name="l00110"></a>00110 }; 
    79 <a name="l00111"></a>00111  
    80 <a name="l00117"></a><a class="code" href="classegiw.html">00117</a> <span class="keyword">class </span><a class="code" href="classegiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
    81 <a name="l00118"></a>00118 <span class="keyword">protected</span>: 
    82 <a name="l00120"></a><a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442">00120</a>         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (typically known as UD).">ldmat</a> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>; 
    83 <a name="l00122"></a><a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453">00122</a>         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>; 
    84 <a name="l00123"></a>00123 <span class="keyword">public</span>: 
    85 <a name="l00125"></a><a class="code" href="classegiw.html#c52a2173c6eb1490edce9c6c7c05d60b">00125</a>         <a class="code" href="classegiw.html#c52a2173c6eb1490edce9c6c7c05d60b" title="Default constructor.">egiw</a>(<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, mat V0, <span class="keywordtype">double</span> nu0): <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a>(rv), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>(V0), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>(nu0) { 
    86 <a name="l00126"></a>00126                 it_assert_debug(rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>()==<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span>); 
    87 <a name="l00127"></a>00127         } 
    88 <a name="l00128"></a>00128  
    89 <a name="l00129"></a>00129         vec <a class="code" href="classegiw.html#3d2c1f2ba0f9966781f1e0ae695e8a6f" title="Returns the required moment of the epdf.">sample</a>() <span class="keyword">const</span>; 
    90 <a name="l00130"></a>00130         vec <a class="code" href="classegiw.html#6deb0ff2859f41ef7cbdf6a842cabb29" title="return expected value">mean</a>() <span class="keyword">const</span>; 
    91 <a name="l00131"></a>00131         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#425cbc53b377274e28c6add942bab62d" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>; 
    92 <a name="l00132"></a>00132         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#70eb1a0b88459b227f919b425b0d3359" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; 
     38<a name="l00044"></a>00044         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classeEF.html#69e5680dac10375d62520d26c672477d" title="logarithm of the normalizing constant, ">lognc</a>() <span class="keyword">const</span> =0; 
     39<a name="l00046"></a><a class="code" href="classeEF.html#a89bef8996410609004fa019b5b48964">00046</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classeEF.html#a89bef8996410609004fa019b5b48964" title="TODO decide if it is really needed.">dupdate</a> ( mat &amp;v ) {it_error ( <span class="stringliteral">"Not implemneted"</span> );}; 
     40<a name="l00048"></a><a class="code" href="classeEF.html#48cdd33d0e20d1a1aa45683c956bc61c">00048</a>         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classeEF.html#48cdd33d0e20d1a1aa45683c956bc61c" title="Evaluate normalized log-probability.">evalpdflog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>{it_error ( <span class="stringliteral">"Not implemneted"</span> );<span class="keywordflow">return</span> 0.0;}; 
     41<a name="l00050"></a><a class="code" href="classeEF.html#6466e8d4aa9dd64698ed288cbb1afc03">00050</a>         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classeEF.html#6466e8d4aa9dd64698ed288cbb1afc03" title="Evaluate normalized log-probability.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeEF.html#48cdd33d0e20d1a1aa45683c956bc61c" title="Evaluate normalized log-probability.">evalpdflog_nn</a> ( val )-<a class="code" href="classeEF.html#69e5680dac10375d62520d26c672477d" title="logarithm of the normalizing constant, ">lognc</a>();} 
     42<a name="l00052"></a><a class="code" href="classeEF.html#c71faf4b2d153efda14bf1f87dca1507">00052</a>         <span class="keyword">virtual</span> vec <a class="code" href="classeEF.html#6466e8d4aa9dd64698ed288cbb1afc03" title="Evaluate normalized log-probability.">evalpdflog</a> ( <span class="keyword">const</span> mat &amp;Val )<span class="keyword"> const </span>{ 
     43<a name="l00053"></a>00053                 vec x ( Val.cols() ); 
     44<a name="l00054"></a>00054                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;Val.cols();i++ ) {x ( i ) =<a class="code" href="classeEF.html#48cdd33d0e20d1a1aa45683c956bc61c" title="Evaluate normalized log-probability.">evalpdflog_nn</a> ( Val.get_col ( i ) ) ;} 
     45<a name="l00055"></a>00055                 <span class="keywordflow">return</span> x-<a class="code" href="classeEF.html#69e5680dac10375d62520d26c672477d" title="logarithm of the normalizing constant, ">lognc</a>(); 
     46<a name="l00056"></a>00056         } 
     47<a name="l00058"></a><a class="code" href="classeEF.html#4f8385dd1cc9740522dc373b1dc3cbf5">00058</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classeEF.html#4f8385dd1cc9740522dc373b1dc3cbf5" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}; 
     48<a name="l00059"></a>00059 }; 
     49<a name="l00060"></a>00060  
     50<a name="l00067"></a><a class="code" href="classmEF.html">00067</a> <span class="keyword">class </span><a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> { 
     51<a name="l00068"></a>00068  
     52<a name="l00069"></a>00069 <span class="keyword">public</span>: 
     53<a name="l00071"></a><a class="code" href="classmEF.html#8bf51fe8654d7b83c8c8afeb19409d4f">00071</a>         <a class="code" href="classmEF.html#8bf51fe8654d7b83c8c8afeb19409d4f" title="Default constructor.">mEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( rv0,rvc0 ) {}; 
     54<a name="l00072"></a>00072 }; 
     55<a name="l00073"></a>00073  
     56<a name="l00075"></a><a class="code" href="classBMEF.html">00075</a> <span class="keyword">class </span><a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> : <span class="keyword">public</span> <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> { 
     57<a name="l00076"></a>00076 <span class="keyword">protected</span>: 
     58<a name="l00078"></a><a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71">00078</a>         <span class="keywordtype">double</span> <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>; 
     59<a name="l00080"></a><a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02">00080</a>         <span class="keywordtype">double</span> <a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>; 
     60<a name="l00081"></a>00081 <span class="keyword">public</span>: 
     61<a name="l00083"></a><a class="code" href="classBMEF.html#46ac5c919ae647f3a6a38d9faba35f5d">00083</a>         <a class="code" href="classBMEF.html#46ac5c919ae647f3a6a38d9faba35f5d" title="Default constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>, <span class="keywordtype">double</span> frg0=1.0 ) :<a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> ( rv ), <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a> ( frg0 ) {} 
     62<a name="l00085"></a><a class="code" href="classBMEF.html#3dc6277cafbdc6cbc2db860ff219b33e">00085</a>         <a class="code" href="classBMEF.html#46ac5c919ae647f3a6a38d9faba35f5d" title="Default constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> &amp;B ) :<a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> ( B ), <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a> ( B.<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a> ), <a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ( B.<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ) {} 
     63<a name="l00087"></a><a class="code" href="classBMEF.html#30bb40eb1fd31869b2e62e79e1ecdcb4">00087</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#30bb40eb1fd31869b2e62e79e1ecdcb4" title="get statistics from another model">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a>* BM0 ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}; 
     64<a name="l00089"></a><a class="code" href="classBMEF.html#8f4ecb6e2eaf630155a1fa98f35aa6ad">00089</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#8f4ecb6e2eaf630155a1fa98f35aa6ad" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &amp;data, <span class="keyword">const</span> <span class="keywordtype">double</span> w ) {}; 
     65<a name="l00090"></a>00090         <span class="comment">//original Bayes</span> 
     66<a name="l00091"></a>00091         <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#8f4ecb6e2eaf630155a1fa98f35aa6ad" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &amp;dt ); 
     67<a name="l00093"></a><a class="code" href="classBMEF.html#afda119ee86cadadfd2b67335a7cf052">00093</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#afda119ee86cadadfd2b67335a7cf052" title="Flatten the posterior.">flatten</a> ( <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> * B) {it_error ( <span class="stringliteral">"Not implemented"</span> );} 
     68<a name="l00094"></a>00094 }; 
     69<a name="l00095"></a>00095  
     70<a name="l00101"></a>00101 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     71<a name="l00102"></a>00102  
     72<a name="l00103"></a><a class="code" href="classenorm.html">00103</a> <span class="keyword">class </span><a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
     73<a name="l00104"></a>00104 <span class="keyword">protected</span>: 
     74<a name="l00106"></a><a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20">00106</a>         vec <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; 
     75<a name="l00108"></a><a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1">00108</a>         sq_T <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>; 
     76<a name="l00110"></a><a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e">00110</a>         <span class="keywordtype">int</span> <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>; 
     77<a name="l00111"></a>00111 <span class="keyword">public</span>: 
     78<a name="l00112"></a>00112 <span class="comment">//      enorm() :eEF() {};</span> 
     79<a name="l00114"></a>00114 <span class="comment"></span>        <a class="code" href="classenorm.html#7b5cb487a2570e8109bfdc0df149aa06" title="Default constructor.">enorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ); 
     80<a name="l00116"></a>00116         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> ); 
     81<a name="l00118"></a>00118         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a" title="tupdate in exponential form (not really handy)">tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ); 
     82<a name="l00120"></a>00120         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 ); 
     83<a name="l00121"></a>00121  
     84<a name="l00122"></a>00122         vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>; 
     85<a name="l00124"></a>00124         mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>; 
     86<a name="l00125"></a>00125         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span> ; 
     87<a name="l00126"></a>00126         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401" title="Evaluate normalized log-probability.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>; 
     88<a name="l00127"></a>00127         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; 
     89<a name="l00128"></a><a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899">00128</a>         vec <a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;} 
     90<a name="l00129"></a>00129  
     91<a name="l00130"></a>00130 <span class="comment">//Access methods</span> 
     92<a name="l00132"></a><a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac">00132</a> <span class="comment"></span>        vec&amp; <a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;} 
    9393<a name="l00133"></a>00133  
    94 <a name="l00134"></a>00134         <span class="comment">//Access</span> 
    95 <a name="l00136"></a><a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5">00136</a> <span class="comment"></span>        <a class="code" href="classldmat.html" title="Matrix stored in LD form, (typically known as UD).">ldmat</a>&amp; <a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5" title="returns a pointer to the internal statistics. Use with Care!">_V</a>() {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;} 
    96 <a name="l00138"></a><a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe">00138</a>         <span class="keywordtype">double</span>&amp; <a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>() {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;} 
     94<a name="l00135"></a><a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b">00135</a>         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b" title="access function">set_mu</a> ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>=mu0;} 
     95<a name="l00136"></a>00136  
     96<a name="l00138"></a><a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9">00138</a>         sq_T&amp; <a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;} 
    9797<a name="l00139"></a>00139  
    98 <a name="l00140"></a>00140 }; 
    99 <a name="l00141"></a>00141  
    100 <a name="l00151"></a><a class="code" href="classegamma.html">00151</a> <span class="keyword">class </span><a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
    101 <a name="l00152"></a>00152 <span class="keyword">protected</span>: 
    102 <a name="l00154"></a><a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b">00154</a>         vec <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>; 
    103 <a name="l00156"></a><a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790">00156</a>         vec <a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>; 
    104 <a name="l00157"></a>00157 <span class="keyword">public</span> : 
    105 <a name="l00159"></a><a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1">00159</a>         <a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1" title="Default constructor.">egamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ) {}; 
    106 <a name="l00161"></a><a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339">00161</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>=a,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>=b;}; 
    107 <a name="l00162"></a>00162         vec <a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns the required moment of the epdf.">sample</a>() <span class="keyword">const</span>; 
    108 <a name="l00164"></a>00164 <span class="comment">//      mat sample ( int N ) const;</span> 
    109 <a name="l00165"></a>00165         <span class="keywordtype">double</span> <a class="code" href="classegamma.html#de84faac8f9799dfe2777ddbedf997ef" title="TODO: is it used anywhere?">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>; 
    110 <a name="l00166"></a>00166         <span class="keywordtype">double</span> <a class="code" href="classegamma.html#d6dbbdb72360f9e54d64501f80318bb6" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; 
    111 <a name="l00168"></a><a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790">00168</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a> ( vec* &amp;a, vec* &amp;b ) {a=&amp;<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>;b=&amp;<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>;}; 
    112 <a name="l00169"></a><a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a">00169</a>         vec <a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec pom ( <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a> ); pom/=<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>; <span class="keywordflow">return</span> pom;} 
    113 <a name="l00170"></a>00170 }; 
    114 <a name="l00171"></a>00171 <span class="comment">/*</span> 
    115 <a name="l00173"></a>00173 <span class="comment">class emix : public epdf {</span> 
    116 <a name="l00174"></a>00174 <span class="comment">protected:</span> 
    117 <a name="l00175"></a>00175 <span class="comment">        int n;</span> 
    118 <a name="l00176"></a>00176 <span class="comment">        vec &amp;w;</span> 
    119 <a name="l00177"></a>00177 <span class="comment">        Array&lt;epdf*&gt; Coms;</span> 
    120 <a name="l00178"></a>00178 <span class="comment">public:</span> 
    121 <a name="l00180"></a>00180 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span> 
    122 <a name="l00181"></a>00181 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span> 
    123 <a name="l00182"></a>00182 <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> 
    124 <a name="l00183"></a>00183 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span> 
    125 <a name="l00184"></a>00184 <span class="comment">};</span> 
    126 <a name="l00185"></a>00185 <span class="comment">*/</span> 
    127 <a name="l00186"></a>00186  
     98<a name="l00141"></a><a class="code" href="classenorm.html#9b9f58dc86affa23511c246887420658">00141</a>         mat <a class="code" href="classenorm.html#9b9f58dc86affa23511c246887420658" title="access method">getR</a> () {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.to_mat();} 
     99<a name="l00142"></a>00142 }; 
     100<a name="l00143"></a>00143  
     101<a name="l00150"></a><a class="code" href="classegiw.html">00150</a> <span class="keyword">class </span><a class="code" href="classegiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
     102<a name="l00151"></a>00151 <span class="keyword">protected</span>: 
     103<a name="l00153"></a><a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442">00153</a>         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (typically known as UD).">ldmat</a> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>; 
     104<a name="l00155"></a><a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453">00155</a>         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>; 
     105<a name="l00157"></a><a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e">00157</a>         <span class="keywordtype">int</span> <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; 
     106<a name="l00159"></a><a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812">00159</a>         <span class="keywordtype">int</span> <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a>; 
     107<a name="l00160"></a>00160 <span class="keyword">public</span>: 
     108<a name="l00162"></a><a class="code" href="classegiw.html#c52a2173c6eb1490edce9c6c7c05d60b">00162</a>         <a class="code" href="classegiw.html#c52a2173c6eb1490edce9c6c7c05d60b" title="Default constructor, assuming.">egiw</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, mat V0, <span class="keywordtype">double</span> nu0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> ( nu0 ) { 
     109<a name="l00163"></a>00163                 <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(); 
     110<a name="l00164"></a>00164                 it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>*<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span> ); 
     111<a name="l00165"></a>00165                 <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; 
     112<a name="l00166"></a>00166         } 
     113<a name="l00168"></a><a class="code" href="classegiw.html#1a17fdbac6c72b9c3abb97623db466c8">00168</a>         <a class="code" href="classegiw.html#c52a2173c6eb1490edce9c6c7c05d60b" title="Default constructor, assuming.">egiw</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (typically known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> ( nu0 ) { 
     114<a name="l00169"></a>00169                 <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(); 
     115<a name="l00170"></a>00170                 it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>*<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span> ); 
     116<a name="l00171"></a>00171                 <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="classldmat.html#96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; 
     117<a name="l00172"></a>00172         } 
     118<a name="l00173"></a>00173  
     119<a name="l00174"></a>00174         vec <a class="code" href="classegiw.html#3d2c1f2ba0f9966781f1e0ae695e8a6f" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>; 
     120<a name="l00175"></a>00175         vec <a class="code" href="classegiw.html#6deb0ff2859f41ef7cbdf6a842cabb29" title="return expected value">mean</a>() <span class="keyword">const</span>; 
     121<a name="l00176"></a>00176         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>; 
     122<a name="l00178"></a>00178         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#2ab1e525d692be8272a6f383d60b94cd" title="In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise...">evalpdflog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>; 
     123<a name="l00179"></a>00179         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#70eb1a0b88459b227f919b425b0d3359" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; 
     124<a name="l00180"></a>00180  
     125<a name="l00181"></a>00181         <span class="comment">//Access</span> 
     126<a name="l00183"></a><a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5">00183</a> <span class="comment"></span>        <a class="code" href="classldmat.html" title="Matrix stored in LD form, (typically known as UD).">ldmat</a>&amp; <a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5" title="returns a pointer to the internal statistics. Use with Care!">_V</a>() {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;} 
     127<a name="l00185"></a><a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe">00185</a>         <span class="keywordtype">double</span>&amp; <a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>() {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;} 
     128<a name="l00186"></a>00186         <span class="keywordtype">void</span> <a class="code" href="classegiw.html#036306322a90a9977834baac07460816" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ); 
     129<a name="l00187"></a>00187 }; 
    128130<a name="l00188"></a>00188  
    129 <a name="l00189"></a><a class="code" href="classeuni.html">00189</a> <span class="keyword">class </span><a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { 
    130 <a name="l00190"></a>00190 <span class="keyword">protected</span>: 
    131 <a name="l00192"></a><a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1">00192</a>         vec <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; 
    132 <a name="l00194"></a><a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231">00194</a>         vec <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; 
    133 <a name="l00196"></a><a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4">00196</a>         vec <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>; 
    134 <a name="l00198"></a><a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda">00198</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>; 
    135 <a name="l00200"></a><a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3">00200</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>; 
     131<a name="l00197"></a><a class="code" href="classeDirich.html">00197</a> <span class="keyword">class </span><a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
     132<a name="l00198"></a>00198 <span class="keyword">protected</span>: 
     133<a name="l00200"></a><a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7">00200</a>         vec <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>; 
    136134<a name="l00201"></a>00201 <span class="keyword">public</span>: 
    137 <a name="l00203"></a><a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd">00203</a>         <a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd" title="Defualt constructor.">euni</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {} 
    138 <a name="l00204"></a><a class="code" href="classeuni.html#2723d4992900b5c5495bfa03628195ed">00204</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#2723d4992900b5c5495bfa03628195ed" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;} 
    139 <a name="l00205"></a><a class="code" href="classeuni.html#06af95d514a6623ad4688bd2ad50ad71">00205</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#06af95d514a6623ad4688bd2ad50ad71" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>;} 
    140 <a name="l00206"></a><a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd">00206</a>         vec <a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd" title="Returns the required moment of the epdf.">sample</a>()<span class="keyword"> const </span>{ 
    141 <a name="l00207"></a>00207                 vec smp ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() );  
    142 <a name="l00208"></a>00208 <span class="preprocessor">                #pragma omp critical</span> 
    143 <a name="l00209"></a>00209 <span class="preprocessor"></span>                UniRNG.sample_vector ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(),smp ); 
    144 <a name="l00210"></a>00210                 <span class="keywordflow">return</span> <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>+elem_mult(<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>,smp); 
    145 <a name="l00211"></a>00211         } 
    146 <a name="l00213"></a><a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2">00213</a>         <span class="keywordtype">void</span> <a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2" title="set values of low and high ">set_parameters</a> ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) { 
    147 <a name="l00214"></a>00214                 <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> = high0-low0; 
    148 <a name="l00215"></a>00215                 it_assert_debug ( min ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> ); 
    149 <a name="l00216"></a>00216                 <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a> = low0; 
    150 <a name="l00217"></a>00217                 <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a> = high0; 
    151 <a name="l00218"></a>00218                 <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ); 
    152 <a name="l00219"></a>00219                 <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> ); 
    153 <a name="l00220"></a>00220         } 
    154 <a name="l00221"></a><a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1">00221</a>         vec <a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec pom=<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; pom-=<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; pom/=2.0; <span class="keywordflow">return</span> pom;} 
    155 <a name="l00222"></a>00222 }; 
    156 <a name="l00223"></a>00223  
    157 <a name="l00224"></a>00224  
    158 <a name="l00230"></a>00230 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    159 <a name="l00231"></a><a class="code" href="classmlnorm.html">00231</a> <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { 
    160 <a name="l00233"></a>00233         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; 
    161 <a name="l00234"></a>00234         mat A; 
    162 <a name="l00235"></a>00235         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span> 
    163 <a name="l00236"></a>00236 <span class="keyword">public</span>: 
    164 <a name="l00238"></a>00238         <a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5" title="Constructor.">mlnorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ); 
    165 <a name="l00240"></a>00240         <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#b6749030c5d5abcb3eb6898f74cea3c0" title="Set A and R.">set_parameters</a> ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> sq_T &amp;R ); 
    166 <a name="l00242"></a>00242         vec <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik ); 
    167 <a name="l00244"></a>00244         mat <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n ); 
    168 <a name="l00246"></a>00246         <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( vec &amp;cond ); 
    169 <a name="l00247"></a>00247 }; 
     135<a name="l00203"></a><a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af">00203</a>         <a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, <span class="keyword">const</span> vec &amp;beta0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ),<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( beta0 ) {it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length(),<span class="stringliteral">"Incompatible statistics"</span> ); }; 
     136<a name="l00205"></a><a class="code" href="classeDirich.html#55cccbc5eb44764dce722567acf5fd58">00205</a>         <a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> &amp;D0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( D0.<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ),<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( D0.<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ) {}; 
     137<a name="l00206"></a><a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80">00206</a>         vec <a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> vec_1 ( 0.0 );}; 
     138<a name="l00207"></a><a class="code" href="classeDirich.html#4206e1da149d51ff3b663c9241096b73">00207</a>         vec <a class="code" href="classeDirich.html#4206e1da149d51ff3b663c9241096b73" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>/sum ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> );}; 
     139<a name="l00209"></a><a class="code" href="classeDirich.html#688a24f04be6d80d4769cf0e4ded7acc">00209</a>         <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#688a24f04be6d80d4769cf0e4ded7acc" title="In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise...">evalpdflog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>-1 ) *log ( val );}; 
     140<a name="l00210"></a><a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77">00210</a>         <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{ 
     141<a name="l00211"></a>00211                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ); 
     142<a name="l00212"></a>00212                 <span class="keywordtype">double</span> lgb=0.0; 
     143<a name="l00213"></a>00213                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length();i++ ) {lgb+=lgamma ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( i ) );} 
     144<a name="l00214"></a>00214                 <span class="keywordflow">return</span> lgb-lgamma ( gam ); 
     145<a name="l00215"></a>00215         }; 
     146<a name="l00217"></a><a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a">00217</a>         vec&amp; <a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>() {<span class="keywordflow">return</span> <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>;} 
     147<a name="l00218"></a>00218 }; 
     148<a name="l00219"></a>00219  
     149<a name="l00221"></a><a class="code" href="classmultiBM.html">00221</a> <span class="keyword">class </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> { 
     150<a name="l00222"></a>00222 <span class="keyword">protected</span>: 
     151<a name="l00224"></a><a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5">00224</a>         <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>; 
     152<a name="l00225"></a>00225         vec &amp;beta; 
     153<a name="l00226"></a>00226 <span class="keyword">public</span>: 
     154<a name="l00228"></a><a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5">00228</a>         <a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>, <span class="keyword">const</span> vec beta0 ) : <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> ( rv ),<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ( rv,beta0 ),beta ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>._beta() ) {<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} 
     155<a name="l00230"></a><a class="code" href="classmultiBM.html#b92751adbfb9f259ca8c95232cfd9c09">00230</a>         <a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a> &amp;B ) : <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ( <a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>,B.beta ),beta ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>._beta() ) {} 
     156<a name="l00231"></a>00231  
     157<a name="l00232"></a>00232         <span class="keywordtype">void</span> set_statistics ( <span class="keyword">const</span> <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a>* mB0 ) {<span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>* mB=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( mB0 ); beta=mB-&gt;<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6">beta</a>;} 
     158<a name="l00233"></a><a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2">00233</a>         <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) { 
     159<a name="l00234"></a>00234                 <span class="keywordflow">if</span> ( <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>&lt;1.0 ) {beta*=<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} 
     160<a name="l00235"></a>00235                 beta+=dt; 
     161<a name="l00236"></a>00236                 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;} 
     162<a name="l00237"></a>00237         } 
     163<a name="l00238"></a><a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">00238</a>         <span class="keywordtype">double</span> <a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">logpred</a> ( <span class="keyword">const</span> vec &amp;dt )<span class="keyword"> const </span>{ 
     164<a name="l00239"></a>00239                 <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ); 
     165<a name="l00240"></a>00240                 vec &amp;beta = pred.<a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>(); 
     166<a name="l00241"></a>00241                  
     167<a name="l00242"></a>00242                 <span class="keywordtype">double</span> lll; 
     168<a name="l00243"></a>00243                 <span class="keywordflow">if</span> ( <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>&lt;1.0 ) 
     169<a name="l00244"></a>00244                         {beta*=<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} 
     170<a name="l00245"></a>00245                 <span class="keywordflow">else</span> 
     171<a name="l00246"></a>00246                         <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;} 
     172<a name="l00247"></a>00247                         <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} 
    170173<a name="l00248"></a>00248  
    171 <a name="l00258"></a><a class="code" href="classmgamma.html">00258</a> <span class="keyword">class </span><a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { 
    172 <a name="l00259"></a>00259 <span class="keyword">protected</span>: 
    173 <a name="l00261"></a><a class="code" href="classmgamma.html#612dbf35c770a780027619aaac2c443e">00261</a>         <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; 
    174 <a name="l00263"></a><a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687">00263</a>         <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>; 
    175 <a name="l00265"></a><a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691">00265</a>         vec* <a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>; 
    176 <a name="l00266"></a>00266  
    177 <a name="l00267"></a>00267 <span class="keyword">public</span>: 
    178 <a name="l00269"></a>00269         <a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6" title="Constructor.">mgamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ); 
    179 <a name="l00271"></a>00271         <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a> ); 
    180 <a name="l00272"></a><a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97">00272</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {*<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/val;}; 
    181 <a name="l00273"></a>00273 }; 
    182 <a name="l00274"></a>00274  
    183 <a name="l00286"></a><a class="code" href="classmgamma__fix.html">00286</a> <span class="keyword">class </span><a class="code" href="classmgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> { 
    184 <a name="l00287"></a>00287 <span class="keyword">protected</span>: 
    185 <a name="l00289"></a><a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6">00289</a>         <span class="keywordtype">double</span> <a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>; 
    186 <a name="l00291"></a><a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0">00291</a>         vec <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>; 
    187 <a name="l00292"></a>00292 <span class="keyword">public</span>: 
    188 <a name="l00294"></a><a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80">00294</a>         <a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80" title="Constructor.">mgamma_fix</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ) : <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> ( rv,rvc ),<a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a> ( rv.count() ) {}; 
    189 <a name="l00296"></a><a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1">00296</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) { 
    190 <a name="l00297"></a>00297                 <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">mgamma::set_parameters</a> ( k0 ); 
    191 <a name="l00298"></a>00298                 <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>=l0; 
    192 <a name="l00299"></a>00299         }; 
    193 <a name="l00300"></a>00300  
    194 <a name="l00301"></a><a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460">00301</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {vec mean=elem_mult ( <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>,pow ( val,<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a> ) ); *<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/mean;}; 
    195 <a name="l00302"></a>00302 }; 
    196 <a name="l00303"></a>00303  
    197 <a name="l00305"></a><a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212">00305</a> <span class="keyword">enum</span> <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 }; 
    198 <a name="l00311"></a><a class="code" href="classeEmp.html">00311</a> <span class="keyword">class </span><a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { 
    199 <a name="l00312"></a>00312 <span class="keyword">protected</span> : 
    200 <a name="l00314"></a><a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd">00314</a>         <span class="keywordtype">int</span> <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>; 
    201 <a name="l00316"></a><a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8">00316</a>         vec <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>; 
    202 <a name="l00318"></a><a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a">00318</a>         Array&lt;vec&gt; <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>; 
    203 <a name="l00319"></a>00319 <span class="keyword">public</span>: 
    204 <a name="l00321"></a><a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4">00321</a>         <a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4" title="Default constructor.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0 ,<span class="keywordtype">int</span> n0 ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ( n0 ),<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ),<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ) {}; 
    205 <a name="l00323"></a>00323         <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#6606a656c1b28114f7384c25aaf80e8d" title="Set sample.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;w0, <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 ); 
    206 <a name="l00325"></a><a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b">00325</a>         vec&amp; <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;}; 
    207 <a name="l00327"></a><a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575">00327</a>         Array&lt;vec&gt;&amp; <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;}; 
    208 <a name="l00329"></a>00329         ivec <a class="code" href="classeEmp.html#77268292fc4465cb73ddbfb1f2932a59" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC ); 
    209 <a name="l00331"></a><a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12">00331</a>         vec <a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;} 
    210 <a name="l00333"></a><a class="code" href="classeEmp.html#23e7358995400865ad2e278945922fb3">00333</a>         <span class="keywordtype">double</span> <a class="code" href="classeEmp.html#23e7358995400865ad2e278945922fb3" title="inherited operation : NOT implemneted">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;} 
    211 <a name="l00334"></a><a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d">00334</a>         vec <a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d" title="return expected value">mean</a>()<span class="keyword"> const </span>{ 
    212 <a name="l00335"></a>00335                 vec pom=zeros ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ); 
    213 <a name="l00336"></a>00336                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( i ) *<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( i );} 
    214 <a name="l00337"></a>00337                 <span class="keywordflow">return</span> pom; 
    215 <a name="l00338"></a>00338         } 
    216 <a name="l00339"></a>00339 }; 
    217 <a name="l00340"></a>00340  
    218 <a name="l00341"></a>00341  
    219 <a name="l00343"></a>00343  
    220 <a name="l00344"></a>00344 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    221 <a name="l00345"></a><a class="code" href="classenorm.html#7b5cb487a2570e8109bfdc0df149aa06">00345</a> <a class="code" href="classenorm.html#7b5cb487a2570e8109bfdc0df149aa06" title="Default constructor.">enorm&lt;sq_T&gt;::enorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), mu ( rv.count() ),R ( rv.count() ),dim ( rv.count() ) {}; 
    222 <a name="l00346"></a>00346  
    223 <a name="l00347"></a>00347 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    224 <a name="l00348"></a><a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af">00348</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">enorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) { 
    225 <a name="l00349"></a>00349 <span class="comment">//Fixme test dimensions of mu0 and R0;</span> 
    226 <a name="l00350"></a>00350         <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> = mu0; 
    227 <a name="l00351"></a>00351         <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> = R0; 
    228 <a name="l00352"></a>00352 }; 
    229 <a name="l00353"></a>00353  
    230 <a name="l00354"></a>00354 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    231 <a name="l00355"></a><a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2">00355</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) { 
    232 <a name="l00356"></a>00356         <span class="comment">//</span> 
    233 <a name="l00357"></a>00357 }; 
    234 <a name="l00358"></a>00358  
    235 <a name="l00359"></a>00359 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    236 <a name="l00360"></a><a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a">00360</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a" title="tupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ) { 
    237 <a name="l00361"></a>00361         <span class="comment">//</span> 
    238 <a name="l00362"></a>00362 }; 
    239 <a name="l00363"></a>00363  
    240 <a name="l00364"></a>00364 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    241 <a name="l00365"></a><a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5">00365</a> vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns the required moment of the epdf.">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const </span>{ 
    242 <a name="l00366"></a>00366         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> ); 
    243 <a name="l00367"></a>00367         NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x ); 
    244 <a name="l00368"></a>00368         vec smp = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); 
     174<a name="l00249"></a>00249                 beta+=dt; 
     175<a name="l00250"></a>00250                 <span class="keywordflow">return</span> pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-lll; 
     176<a name="l00251"></a>00251         } 
     177<a name="l00252"></a><a class="code" href="classmultiBM.html#58257073a90aab5d1aafbc9b805d324a">00252</a>         <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#58257073a90aab5d1aafbc9b805d324a" title="Flatten the posterior.">flatten</a> (<a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a>* B ) { 
     178<a name="l00253"></a>00253                 <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>* E=<span class="keyword">dynamic_cast&lt;</span><a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>*<span class="keyword">&gt;</span>(B); 
     179<a name="l00254"></a>00254                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span> 
     180<a name="l00255"></a>00255                 <span class="keyword">const</span> vec &amp;Eb=E-&gt;<a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>(); 
     181<a name="l00256"></a>00256                 <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeEF.html#4f8385dd1cc9740522dc373b1dc3cbf5" title="Power of the density, used e.g. to flatten the density.">pow</a> ( sum(beta)/sum(Eb) ); 
     182<a name="l00257"></a>00257                 <span class="keywordflow">if</span>(<a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a>){<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} 
     183<a name="l00258"></a>00258         } 
     184<a name="l00259"></a><a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684">00259</a>         <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>&amp; <a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684" title="Returns a pointer to the epdf representing posterior density on parameters. Use with...">_epdf</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>;}; 
     185<a name="l00261"></a>00261 }; 
     186<a name="l00262"></a>00262  
     187<a name="l00272"></a><a class="code" href="classegamma.html">00272</a> <span class="keyword">class </span><a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
     188<a name="l00273"></a>00273 <span class="keyword">protected</span>: 
     189<a name="l00275"></a><a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b">00275</a>         vec <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>; 
     190<a name="l00277"></a><a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790">00277</a>         vec <a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>; 
     191<a name="l00278"></a>00278 <span class="keyword">public</span> : 
     192<a name="l00280"></a><a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1">00280</a>         <a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1" title="Default constructor.">egamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ) {}; 
     193<a name="l00282"></a><a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339">00282</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>=a,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>=b;}; 
     194<a name="l00283"></a>00283         vec <a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>; 
     195<a name="l00285"></a>00285 <span class="comment">//      mat sample ( int N ) const;</span> 
     196<a name="l00286"></a>00286         <span class="keywordtype">double</span> <a class="code" href="classegamma.html#de84faac8f9799dfe2777ddbedf997ef" title="TODO: is it used anywhere?">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>; 
     197<a name="l00287"></a>00287         <span class="keywordtype">double</span> <a class="code" href="classegamma.html#d6dbbdb72360f9e54d64501f80318bb6" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; 
     198<a name="l00289"></a><a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790">00289</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a> ( vec* &amp;a, vec* &amp;b ) {a=&amp;<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>;b=&amp;<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>;}; 
     199<a name="l00290"></a><a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a">00290</a>         vec <a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec pom ( <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a> ); pom/=<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>; <span class="keywordflow">return</span> pom;} 
     200<a name="l00291"></a>00291 }; 
     201<a name="l00292"></a>00292 <span class="comment">/*</span> 
     202<a name="l00294"></a>00294 <span class="comment">class emix : public epdf {</span> 
     203<a name="l00295"></a>00295 <span class="comment">protected:</span> 
     204<a name="l00296"></a>00296 <span class="comment">        int n;</span> 
     205<a name="l00297"></a>00297 <span class="comment">        vec &amp;w;</span> 
     206<a name="l00298"></a>00298 <span class="comment">        Array&lt;epdf*&gt; Coms;</span> 
     207<a name="l00299"></a>00299 <span class="comment">public:</span> 
     208<a name="l00301"></a>00301 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span> 
     209<a name="l00302"></a>00302 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span> 
     210<a name="l00303"></a>00303 <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> 
     211<a name="l00304"></a>00304 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span> 
     212<a name="l00305"></a>00305 <span class="comment">};</span> 
     213<a name="l00306"></a>00306 <span class="comment">*/</span> 
     214<a name="l00307"></a>00307  
     215<a name="l00309"></a>00309  
     216<a name="l00310"></a><a class="code" href="classeuni.html">00310</a> <span class="keyword">class </span><a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { 
     217<a name="l00311"></a>00311 <span class="keyword">protected</span>: 
     218<a name="l00313"></a><a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1">00313</a>         vec <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; 
     219<a name="l00315"></a><a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231">00315</a>         vec <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; 
     220<a name="l00317"></a><a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4">00317</a>         vec <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>; 
     221<a name="l00319"></a><a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda">00319</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>; 
     222<a name="l00321"></a><a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3">00321</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>; 
     223<a name="l00322"></a>00322 <span class="keyword">public</span>: 
     224<a name="l00324"></a><a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd">00324</a>         <a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd" title="Defualt constructor.">euni</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {} 
     225<a name="l00325"></a><a class="code" href="classeuni.html#2723d4992900b5c5495bfa03628195ed">00325</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#2723d4992900b5c5495bfa03628195ed" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;} 
     226<a name="l00326"></a><a class="code" href="classeuni.html#06af95d514a6623ad4688bd2ad50ad71">00326</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#06af95d514a6623ad4688bd2ad50ad71" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>;} 
     227<a name="l00327"></a><a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd">00327</a>         vec <a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{ 
     228<a name="l00328"></a>00328                 vec smp ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ); 
     229<a name="l00329"></a>00329 <span class="preprocessor">#pragma omp critical</span> 
     230<a name="l00330"></a>00330 <span class="preprocessor"></span>                UniRNG.sample_vector ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(),smp ); 
     231<a name="l00331"></a>00331                 <span class="keywordflow">return</span> <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>+elem_mult ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>,smp ); 
     232<a name="l00332"></a>00332         } 
     233<a name="l00334"></a><a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2">00334</a>         <span class="keywordtype">void</span> <a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2" title="set values of low and high ">set_parameters</a> ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) { 
     234<a name="l00335"></a>00335                 <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> = high0-low0; 
     235<a name="l00336"></a>00336                 it_assert_debug ( min ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> ); 
     236<a name="l00337"></a>00337                 <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a> = low0; 
     237<a name="l00338"></a>00338                 <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a> = high0; 
     238<a name="l00339"></a>00339                 <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ); 
     239<a name="l00340"></a>00340                 <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> ); 
     240<a name="l00341"></a>00341         } 
     241<a name="l00342"></a><a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1">00342</a>         vec <a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec pom=<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; pom-=<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; pom/=2.0; <span class="keywordflow">return</span> pom;} 
     242<a name="l00343"></a>00343 }; 
     243<a name="l00344"></a>00344  
     244<a name="l00345"></a>00345  
     245<a name="l00351"></a>00351 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     246<a name="l00352"></a><a class="code" href="classmlnorm.html">00352</a> <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { 
     247<a name="l00354"></a>00354         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; 
     248<a name="l00355"></a>00355         mat A; 
     249<a name="l00356"></a>00356         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span> 
     250<a name="l00357"></a>00357 <span class="keyword">public</span>: 
     251<a name="l00359"></a>00359         <a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5" title="Constructor.">mlnorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ); 
     252<a name="l00361"></a>00361         <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#b6749030c5d5abcb3eb6898f74cea3c0" title="Set A and R.">set_parameters</a> ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> sq_T &amp;R ); 
     253<a name="l00363"></a>00363         vec <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik ); 
     254<a name="l00365"></a>00365         mat <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n ); 
     255<a name="l00367"></a>00367         <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( vec &amp;cond ); 
     256<a name="l00368"></a>00368 }; 
    245257<a name="l00369"></a>00369  
    246 <a name="l00370"></a>00370         smp += <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; 
    247 <a name="l00371"></a>00371         <span class="keywordflow">return</span> smp; 
    248 <a name="l00372"></a>00372 }; 
    249 <a name="l00373"></a>00373  
    250 <a name="l00374"></a>00374 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    251 <a name="l00375"></a><a class="code" href="classenorm.html#60f0f3bfa53d6e65843eea9532b16d36">00375</a> mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns the required moment of the epdf.">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{ 
    252 <a name="l00376"></a>00376         mat X ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N ); 
    253 <a name="l00377"></a>00377         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> ); 
    254 <a name="l00378"></a>00378         vec pom; 
    255 <a name="l00379"></a>00379         <span class="keywordtype">int</span> i; 
    256 <a name="l00380"></a>00380  
    257 <a name="l00381"></a>00381         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) { 
    258 <a name="l00382"></a>00382                 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x ); 
    259 <a name="l00383"></a>00383                 pom = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); 
    260 <a name="l00384"></a>00384                 pom +=<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; 
    261 <a name="l00385"></a>00385                 X.set_col ( i, pom ); 
    262 <a name="l00386"></a>00386         } 
     258<a name="l00379"></a><a class="code" href="classmgamma.html">00379</a> <span class="keyword">class </span><a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { 
     259<a name="l00380"></a>00380 <span class="keyword">protected</span>: 
     260<a name="l00382"></a><a class="code" href="classmgamma.html#612dbf35c770a780027619aaac2c443e">00382</a>         <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; 
     261<a name="l00384"></a><a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687">00384</a>         <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>; 
     262<a name="l00386"></a><a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691">00386</a>         vec* <a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>; 
    263263<a name="l00387"></a>00387  
    264 <a name="l00388"></a>00388         <span class="keywordflow">return</span> X; 
    265 <a name="l00389"></a>00389 }; 
    266 <a name="l00390"></a>00390  
    267 <a name="l00391"></a>00391 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    268 <a name="l00392"></a><a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0">00392</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0" title="Compute probability of argument val.">enorm&lt;sq_T&gt;::eval</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{ 
    269 <a name="l00393"></a>00393         <span class="keywordtype">double</span> pdfl,e; 
    270 <a name="l00394"></a>00394         pdfl = <a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401" title="Compute log-probability of argument val.">evalpdflog</a> ( val ); 
    271 <a name="l00395"></a>00395         e = exp ( pdfl ); 
    272 <a name="l00396"></a>00396         <span class="keywordflow">return</span> e; 
    273 <a name="l00397"></a>00397 }; 
    274 <a name="l00398"></a>00398  
    275 <a name="l00399"></a>00399 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    276 <a name="l00400"></a><a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401">00400</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401" title="Compute log-probability of argument val.">enorm&lt;sq_T&gt;::evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{ 
    277 <a name="l00401"></a>00401         <span class="comment">// 1.83787706640935 = log(2pi)</span> 
    278 <a name="l00402"></a>00402         <span class="keywordflow">return</span>  -0.5* (  +<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>-val ) ) - <a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">lognc</a>(); 
    279 <a name="l00403"></a>00403 }; 
    280 <a name="l00404"></a>00404  
    281 <a name="l00405"></a>00405 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    282 <a name="l00406"></a><a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8">00406</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const </span>{ 
    283 <a name="l00407"></a>00407         <span class="comment">// 1.83787706640935 = log(2pi)</span> 
    284 <a name="l00408"></a>00408         <span class="keywordflow">return</span> -0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.logdet()); 
    285 <a name="l00409"></a>00409 }; 
    286 <a name="l00410"></a>00410  
    287 <a name="l00411"></a>00411 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    288 <a name="l00412"></a><a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5">00412</a> <a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5" title="Constructor.">mlnorm&lt;sq_T&gt;::mlnorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0,<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> ( rv0,rvc0 ),<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),A ( rv0.count(),rv0.count() ),<a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>(<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>()) { <a class="code" href="classmpdf.html#7aa894208a32f3487827df6d5054424c" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; 
    289 <a name="l00413"></a>00413 } 
    290 <a name="l00414"></a>00414  
    291 <a name="l00415"></a>00415 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    292 <a name="l00416"></a><a class="code" href="classmlnorm.html#b6749030c5d5abcb3eb6898f74cea3c0">00416</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#b6749030c5d5abcb3eb6898f74cea3c0" 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> sq_T &amp;R0 ) { 
    293 <a name="l00417"></a>00417         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),R0 ); 
    294 <a name="l00418"></a>00418         A = A0; 
    295 <a name="l00419"></a>00419 } 
    296 <a name="l00420"></a>00420  
    297 <a name="l00421"></a>00421 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    298 <a name="l00422"></a><a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18">00422</a> vec <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik ) { 
    299 <a name="l00423"></a>00423         this-&gt;<a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( cond ); 
    300 <a name="l00424"></a>00424         vec smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample(); 
    301 <a name="l00425"></a>00425         lik = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp ); 
    302 <a name="l00426"></a>00426         <span class="keywordflow">return</span> smp; 
    303 <a name="l00427"></a>00427 } 
    304 <a name="l00428"></a>00428  
    305 <a name="l00429"></a>00429 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    306 <a name="l00430"></a><a class="code" href="classmlnorm.html#215fb88cc8b95d64cdefd6849abdd1e8">00430</a> mat <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n ) { 
    307 <a name="l00431"></a>00431         <span class="keywordtype">int</span> i; 
    308 <a name="l00432"></a>00432         <span class="keywordtype">int</span> dim = <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(); 
    309 <a name="l00433"></a>00433         mat Smp ( dim,n ); 
    310 <a name="l00434"></a>00434         vec smp ( dim ); 
    311 <a name="l00435"></a>00435         this-&gt;<a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( cond ); 
    312 <a name="l00436"></a>00436  
    313 <a name="l00437"></a>00437         <span class="keywordflow">for</span> ( i=0; i&lt;n; i++ ) { 
    314 <a name="l00438"></a>00438                 smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample(); 
    315 <a name="l00439"></a>00439                 lik ( i ) = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp ); 
    316 <a name="l00440"></a>00440                 Smp.set_col ( i ,smp ); 
    317 <a name="l00441"></a>00441         } 
    318 <a name="l00442"></a>00442  
    319 <a name="l00443"></a>00443         <span class="keywordflow">return</span> Smp; 
    320 <a name="l00444"></a>00444 } 
    321 <a name="l00445"></a>00445  
    322 <a name="l00446"></a>00446 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    323 <a name="l00447"></a><a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195">00447</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">mlnorm&lt;sq_T&gt;::condition</a> ( vec &amp;cond ) { 
    324 <a name="l00448"></a>00448         _mu = A*cond; 
    325 <a name="l00449"></a>00449 <span class="comment">//R is already assigned;</span> 
    326 <a name="l00450"></a>00450 } 
    327 <a name="l00451"></a>00451  
    328 <a name="l00453"></a>00453  
    329 <a name="l00454"></a>00454  
    330 <a name="l00455"></a>00455 <span class="preprocessor">#endif //EF_H</span> 
     264<a name="l00388"></a>00388 <span class="keyword">public</span>: 
     265<a name="l00390"></a>00390         <a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6" title="Constructor.">mgamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ); 
     266<a name="l00392"></a>00392         <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a> ); 
     267<a name="l00393"></a><a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97">00393</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {*<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/val;}; 
     268<a name="l00394"></a>00394 }; 
     269<a name="l00395"></a>00395  
     270<a name="l00407"></a><a class="code" href="classmgamma__fix.html">00407</a> <span class="keyword">class </span><a class="code" href="classmgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> { 
     271<a name="l00408"></a>00408 <span class="keyword">protected</span>: 
     272<a name="l00410"></a><a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6">00410</a>         <span class="keywordtype">double</span> <a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>; 
     273<a name="l00412"></a><a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0">00412</a>         vec <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>; 
     274<a name="l00413"></a>00413 <span class="keyword">public</span>: 
     275<a name="l00415"></a><a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80">00415</a>         <a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80" title="Constructor.">mgamma_fix</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ) : <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> ( rv,rvc ),<a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a> ( rv.count() ) {}; 
     276<a name="l00417"></a><a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1">00417</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) { 
     277<a name="l00418"></a>00418                 <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">mgamma::set_parameters</a> ( k0 ); 
     278<a name="l00419"></a>00419                 <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>=l0; 
     279<a name="l00420"></a>00420         }; 
     280<a name="l00421"></a>00421  
     281<a name="l00422"></a><a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460">00422</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {vec mean=elem_mult ( <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>,pow ( val,<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a> ) ); *<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/mean;}; 
     282<a name="l00423"></a>00423 }; 
     283<a name="l00424"></a>00424  
     284<a name="l00426"></a><a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212">00426</a> <span class="keyword">enum</span> <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 }; 
     285<a name="l00432"></a><a class="code" href="classeEmp.html">00432</a> <span class="keyword">class </span><a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { 
     286<a name="l00433"></a>00433 <span class="keyword">protected</span> : 
     287<a name="l00435"></a><a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd">00435</a>         <span class="keywordtype">int</span> <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>; 
     288<a name="l00437"></a><a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8">00437</a>         vec <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>; 
     289<a name="l00439"></a><a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a">00439</a>         Array&lt;vec&gt; <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>; 
     290<a name="l00440"></a>00440 <span class="keyword">public</span>: 
     291<a name="l00442"></a><a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4">00442</a>         <a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4" title="Default constructor.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0 ,<span class="keywordtype">int</span> n0 ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ( n0 ),<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ),<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ) {}; 
     292<a name="l00444"></a>00444         <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#6606a656c1b28114f7384c25aaf80e8d" title="Set sample.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;w0, <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 ); 
     293<a name="l00446"></a><a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b">00446</a>         vec&amp; <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;}; 
     294<a name="l00448"></a><a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575">00448</a>         Array&lt;vec&gt;&amp; <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;}; 
     295<a name="l00450"></a>00450         ivec <a class="code" href="classeEmp.html#77268292fc4465cb73ddbfb1f2932a59" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC ); 
     296<a name="l00452"></a><a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12">00452</a>         vec <a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;} 
     297<a name="l00454"></a><a class="code" href="classeEmp.html#23e7358995400865ad2e278945922fb3">00454</a>         <span class="keywordtype">double</span> <a class="code" href="classeEmp.html#23e7358995400865ad2e278945922fb3" title="inherited operation : NOT implemneted">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;} 
     298<a name="l00455"></a><a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d">00455</a>         vec <a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d" title="return expected value">mean</a>()<span class="keyword"> const </span>{ 
     299<a name="l00456"></a>00456                 vec pom=zeros ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ); 
     300<a name="l00457"></a>00457                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( i ) *<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( i );} 
     301<a name="l00458"></a>00458                 <span class="keywordflow">return</span> pom; 
     302<a name="l00459"></a>00459         } 
     303<a name="l00460"></a>00460 }; 
     304<a name="l00461"></a>00461  
     305<a name="l00462"></a>00462  
     306<a name="l00464"></a>00464  
     307<a name="l00465"></a>00465 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     308<a name="l00466"></a><a class="code" href="classenorm.html#7b5cb487a2570e8109bfdc0df149aa06">00466</a> <a class="code" href="classenorm.html#7b5cb487a2570e8109bfdc0df149aa06" title="Default constructor.">enorm&lt;sq_T&gt;::enorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), mu ( rv.count() ),R ( rv.count() ),dim ( rv.count() ) {}; 
     309<a name="l00467"></a>00467  
     310<a name="l00468"></a>00468 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     311<a name="l00469"></a><a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af">00469</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">enorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) { 
     312<a name="l00470"></a>00470 <span class="comment">//Fixme test dimensions of mu0 and R0;</span> 
     313<a name="l00471"></a>00471         <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> = mu0; 
     314<a name="l00472"></a>00472         <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> = R0; 
     315<a name="l00473"></a>00473 }; 
     316<a name="l00474"></a>00474  
     317<a name="l00475"></a>00475 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     318<a name="l00476"></a><a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2">00476</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) { 
     319<a name="l00477"></a>00477         <span class="comment">//</span> 
     320<a name="l00478"></a>00478 }; 
     321<a name="l00479"></a>00479  
     322<a name="l00480"></a>00480 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     323<a name="l00481"></a><a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a">00481</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a" title="tupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ) { 
     324<a name="l00482"></a>00482         <span class="comment">//</span> 
     325<a name="l00483"></a>00483 }; 
     326<a name="l00484"></a>00484  
     327<a name="l00485"></a>00485 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     328<a name="l00486"></a><a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5">00486</a> vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const </span>{ 
     329<a name="l00487"></a>00487         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> ); 
     330<a name="l00488"></a>00488         NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x ); 
     331<a name="l00489"></a>00489         vec smp = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); 
     332<a name="l00490"></a>00490  
     333<a name="l00491"></a>00491         smp += <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; 
     334<a name="l00492"></a>00492         <span class="keywordflow">return</span> smp; 
     335<a name="l00493"></a>00493 }; 
     336<a name="l00494"></a>00494  
     337<a name="l00495"></a>00495 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     338<a name="l00496"></a><a class="code" href="classenorm.html#60f0f3bfa53d6e65843eea9532b16d36">00496</a> mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{ 
     339<a name="l00497"></a>00497         mat X ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N ); 
     340<a name="l00498"></a>00498         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> ); 
     341<a name="l00499"></a>00499         vec pom; 
     342<a name="l00500"></a>00500         <span class="keywordtype">int</span> i; 
     343<a name="l00501"></a>00501  
     344<a name="l00502"></a>00502         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) { 
     345<a name="l00503"></a>00503                 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x ); 
     346<a name="l00504"></a>00504                 pom = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); 
     347<a name="l00505"></a>00505                 pom +=<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; 
     348<a name="l00506"></a>00506                 X.set_col ( i, pom ); 
     349<a name="l00507"></a>00507         } 
     350<a name="l00508"></a>00508  
     351<a name="l00509"></a>00509         <span class="keywordflow">return</span> X; 
     352<a name="l00510"></a>00510 }; 
     353<a name="l00511"></a>00511  
     354<a name="l00512"></a>00512 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     355<a name="l00513"></a><a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0">00513</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0" title="Compute probability of argument val.">enorm&lt;sq_T&gt;::eval</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{ 
     356<a name="l00514"></a>00514         <span class="keywordtype">double</span> pdfl,e; 
     357<a name="l00515"></a>00515         pdfl = <a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401" title="Evaluate normalized log-probability.">evalpdflog</a> ( val ); 
     358<a name="l00516"></a>00516         e = exp ( pdfl ); 
     359<a name="l00517"></a>00517         <span class="keywordflow">return</span> e; 
     360<a name="l00518"></a>00518 }; 
     361<a name="l00519"></a>00519  
     362<a name="l00520"></a>00520 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     363<a name="l00521"></a><a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401">00521</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401" title="Evaluate normalized log-probability.">enorm&lt;sq_T&gt;::evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{ 
     364<a name="l00522"></a>00522         <span class="comment">// 1.83787706640935 = log(2pi)</span> 
     365<a name="l00523"></a>00523         <span class="keywordflow">return</span>  -0.5* ( +<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>-val ) ) - <a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">lognc</a>(); 
     366<a name="l00524"></a>00524 }; 
     367<a name="l00525"></a>00525  
     368<a name="l00526"></a>00526 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     369<a name="l00527"></a><a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8">00527</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const </span>{ 
     370<a name="l00528"></a>00528         <span class="comment">// 1.83787706640935 = log(2pi)</span> 
     371<a name="l00529"></a>00529         <span class="keywordflow">return</span> -0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.logdet() ); 
     372<a name="l00530"></a>00530 }; 
     373<a name="l00531"></a>00531  
     374<a name="l00532"></a>00532 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     375<a name="l00533"></a><a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5">00533</a> <a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5" title="Constructor.">mlnorm&lt;sq_T&gt;::mlnorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0,<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> ( rv0,rvc0 ),<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),A ( rv0.count(),rv0.count() ),<a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a> ( <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() ) { 
     376<a name="l00534"></a>00534         <a class="code" href="classmpdf.html#7aa894208a32f3487827df6d5054424c" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; 
     377<a name="l00535"></a>00535 } 
     378<a name="l00536"></a>00536  
     379<a name="l00537"></a>00537 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     380<a name="l00538"></a><a class="code" href="classmlnorm.html#b6749030c5d5abcb3eb6898f74cea3c0">00538</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#b6749030c5d5abcb3eb6898f74cea3c0" 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> sq_T &amp;R0 ) { 
     381<a name="l00539"></a>00539         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),R0 ); 
     382<a name="l00540"></a>00540         A = A0; 
     383<a name="l00541"></a>00541 } 
     384<a name="l00542"></a>00542  
     385<a name="l00543"></a>00543 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     386<a name="l00544"></a><a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18">00544</a> vec <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik ) { 
     387<a name="l00545"></a>00545         this-&gt;<a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( cond ); 
     388<a name="l00546"></a>00546         vec smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample(); 
     389<a name="l00547"></a>00547         lik = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp ); 
     390<a name="l00548"></a>00548         <span class="keywordflow">return</span> smp; 
     391<a name="l00549"></a>00549 } 
     392<a name="l00550"></a>00550  
     393<a name="l00551"></a>00551 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     394<a name="l00552"></a><a class="code" href="classmlnorm.html#215fb88cc8b95d64cdefd6849abdd1e8">00552</a> mat <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n ) { 
     395<a name="l00553"></a>00553         <span class="keywordtype">int</span> i; 
     396<a name="l00554"></a>00554         <span class="keywordtype">int</span> dim = <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(); 
     397<a name="l00555"></a>00555         mat Smp ( dim,n ); 
     398<a name="l00556"></a>00556         vec smp ( dim ); 
     399<a name="l00557"></a>00557         this-&gt;<a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( cond ); 
     400<a name="l00558"></a>00558  
     401<a name="l00559"></a>00559         <span class="keywordflow">for</span> ( i=0; i&lt;n; i++ ) { 
     402<a name="l00560"></a>00560                 smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample(); 
     403<a name="l00561"></a>00561                 lik ( i ) = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp ); 
     404<a name="l00562"></a>00562                 Smp.set_col ( i ,smp ); 
     405<a name="l00563"></a>00563         } 
     406<a name="l00564"></a>00564  
     407<a name="l00565"></a>00565         <span class="keywordflow">return</span> Smp; 
     408<a name="l00566"></a>00566 } 
     409<a name="l00567"></a>00567  
     410<a name="l00568"></a>00568 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
     411<a name="l00569"></a><a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195">00569</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">mlnorm&lt;sq_T&gt;::condition</a> ( vec &amp;cond ) { 
     412<a name="l00570"></a>00570         _mu = A*cond; 
     413<a name="l00571"></a>00571 <span class="comment">//R is already assigned;</span> 
     414<a name="l00572"></a>00572 } 
     415<a name="l00573"></a>00573  
     416<a name="l00575"></a>00575  
     417<a name="l00576"></a>00576  
     418<a name="l00577"></a>00577 <span class="preprocessor">#endif //EF_H</span> 
    331419</pre></div></div> 
    332 <hr size="1"><address style="text-align: right;"><small>Generated on Thu Sep 4 19:28:00 2008 for mixpp by&nbsp; 
     420<hr size="1"><address style="text-align: right;"><small>Generated on Tue Sep 23 16:00:45 2008 for mixpp by&nbsp; 
    333421<a href="http://www.doxygen.org/index.html"> 
    334422<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>