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| 65 | <h1>libEF.h</h1><a href="libEF_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 |
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| 66 | <a name="l00013"></a>00013 <span class="preprocessor">#ifndef EF_H</span> |
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| 67 | <a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define EF_H</span> |
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| 68 | <a name="l00015"></a>00015 <span class="preprocessor"></span> |
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| 69 | <a name="l00016"></a>00016 |
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| 70 | <a name="l00017"></a>00017 <span class="preprocessor">#include "<a class="code" href="libBM_8h.html" title="Bayesian Models (bm) that use Bayes rule to learn from observations.">libBM.h</a>"</span> |
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| 71 | <a name="l00018"></a>00018 <span class="preprocessor">#include "../math/chmat.h"</span> |
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| 72 | <a name="l00019"></a>00019 <span class="comment">//#include <std></span> |
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| 73 | <a name="l00020"></a>00020 |
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| 74 | <a name="l00021"></a>00021 <span class="keyword">namespace </span>bdm |
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| 75 | <a name="l00022"></a>00022 { |
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| 76 | <a name="l00023"></a>00023 |
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| 77 | <a name="l00024"></a>00024 |
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| 78 | <a name="l00026"></a>00026 <span class="keyword">extern</span> Uniform_RNG UniRNG; |
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| 79 | <a name="l00028"></a>00028 <span class="keyword">extern</span> Normal_RNG NorRNG; |
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| 80 | <a name="l00030"></a>00030 <span class="keyword">extern</span> <a class="code" href="classitpp_1_1Gamma__RNG.html" title="Gamma distribution.">Gamma_RNG</a> GamRNG; |
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| 81 | <a name="l00031"></a>00031 |
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| 82 | <a name="l00038"></a><a class="code" href="classbdm_1_1eEF.html">00038</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> |
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| 83 | <a name="l00039"></a>00039 { |
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| 84 | <a name="l00040"></a>00040 <span class="keyword">public</span>: |
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| 85 | <a name="l00041"></a>00041 <span class="comment">// eEF() :epdf() {};</span> |
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| 86 | <a name="l00043"></a><a class="code" href="classbdm_1_1eEF.html#d5459d472d0feca7cf1fb5f65c4b9ef4">00043</a> <span class="comment"></span> <a class="code" href="classbdm_1_1eEF.html#d5459d472d0feca7cf1fb5f65c4b9ef4" title="default constructor">eEF</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {}; |
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| 87 | <a name="l00045"></a>00045 <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>() <span class="keyword">const</span> =0; |
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| 88 | <a name="l00047"></a><a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a">00047</a> <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a" title="TODO decide if it is really needed.">dupdate</a> ( mat &v ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}; |
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| 89 | <a name="l00049"></a><a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6">00049</a> <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const</span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;}; |
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| 90 | <a name="l00051"></a><a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692">00051</a> <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{<span class="keywordtype">double</span> tmp;tmp= <a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( val )-<a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>();it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> ); <span class="keywordflow">return</span> tmp;} |
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| 91 | <a name="l00053"></a><a class="code" href="classbdm_1_1eEF.html#79a7c8ea8c02e45d410bd1d7ffd72b41">00053</a> <span class="keyword">virtual</span> vec <a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> mat &Val )<span class="keyword"> const</span> |
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| 92 | <a name="l00054"></a>00054 <span class="keyword"> </span>{ |
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| 93 | <a name="l00055"></a>00055 vec x ( Val.cols() ); |
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| 94 | <a name="l00056"></a>00056 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<Val.cols();i++ ) {x ( i ) =<a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( Val.get_col ( i ) ) ;} |
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| 95 | <a name="l00057"></a>00057 <span class="keywordflow">return</span> x-<a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>(); |
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| 96 | <a name="l00058"></a>00058 } |
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| 97 | <a name="l00060"></a><a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053">00060</a> <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}; |
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| 98 | <a name="l00061"></a>00061 }; |
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| 99 | <a name="l00062"></a>00062 |
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| 100 | <a name="l00069"></a><a class="code" href="classbdm_1_1mEF.html">00069</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> |
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| 101 | <a name="l00070"></a>00070 { |
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| 102 | <a name="l00071"></a>00071 |
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| 103 | <a name="l00072"></a>00072 <span class="keyword">public</span>: |
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| 104 | <a name="l00074"></a><a class="code" href="classbdm_1_1mEF.html#dd7caad7026b778af66e34480eec6f9e">00074</a> <a class="code" href="classbdm_1_1mEF.html#dd7caad7026b778af66e34480eec6f9e" title="Default constructor.">mEF</a> ( ) :<a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( ) {}; |
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| 105 | <a name="l00075"></a>00075 }; |
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| 106 | <a name="l00076"></a>00076 |
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| 107 | <a name="l00078"></a><a class="code" href="classbdm_1_1BMEF.html">00078</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> |
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| 108 | <a name="l00079"></a>00079 { |
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| 109 | <a name="l00080"></a>00080 <span class="keyword">protected</span>: |
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| 110 | <a name="l00082"></a><a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64">00082</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>; |
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| 111 | <a name="l00084"></a><a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865">00084</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>; |
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| 112 | <a name="l00085"></a>00085 <span class="keyword">public</span>: |
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| 113 | <a name="l00087"></a><a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55">00087</a> <a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55" title="Default constructor (=empty constructor).">BMEF</a> ( <span class="keywordtype">double</span> frg0=1.0 ) :<a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( ), <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ( frg0 ) {} |
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| 114 | <a name="l00089"></a><a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62">00089</a> <a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62" title="Copy constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> &B ) :<a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( B ), <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ( B.<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ), <a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ( B.<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ) {} |
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| 115 | <a name="l00091"></a><a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051">00091</a> <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051" title="get statistics from another model">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* BM0 ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}; |
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| 116 | <a name="l00093"></a><a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6">00093</a> <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &data, <span class="keyword">const</span> <span class="keywordtype">double</span> w ) {}; |
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| 117 | <a name="l00094"></a>00094 <span class="comment">//original Bayes</span> |
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| 118 | <a name="l00095"></a>00095 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &dt ); |
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| 119 | <a name="l00097"></a><a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749">00097</a> <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> * B ) {it_error ( <span class="stringliteral">"Not implemented"</span> );} |
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| 120 | <a name="l00099"></a>00099 <span class="comment">// virtual void flatten ( double nu0 ) {it_error ( "Not implemented" );}</span> |
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| 121 | <a name="l00100"></a>00100 |
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| 122 | <a name="l00101"></a><a class="code" href="classbdm_1_1BMEF.html#62d2e4691bed41a1efa6b9c2e35e5c67">00101</a> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* <a class="code" href="classbdm_1_1BMEF.html#62d2e4691bed41a1efa6b9c2e35e5c67" title="Flatten the posterior as if to keep nu0 data.">_copy_</a> ()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"function _copy_ not implemented for this BM"</span> ); <span class="keywordflow">return</span> NULL;}; |
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| 123 | <a name="l00102"></a>00102 }; |
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| 124 | <a name="l00103"></a>00103 |
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| 125 | <a name="l00104"></a>00104 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
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| 126 | <a name="l00105"></a>00105 <span class="keyword">class </span>mlnorm; |
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| 127 | <a name="l00106"></a>00106 |
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| 128 | <a name="l00112"></a>00112 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
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| 129 | <a name="l00113"></a><a class="code" href="classbdm_1_1enorm.html">00113</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> |
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| 130 | <a name="l00114"></a>00114 { |
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| 131 | <a name="l00115"></a>00115 <span class="keyword">protected</span>: |
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| 132 | <a name="l00117"></a><a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7">00117</a> vec <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>; |
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| 133 | <a name="l00119"></a><a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2">00119</a> sq_T <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>; |
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| 134 | <a name="l00120"></a>00120 <span class="keyword">public</span>: |
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| 135 | <a name="l00123"></a>00123 |
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| 136 | <a name="l00124"></a>00124 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( ),<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ( ) {}; |
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| 137 | <a name="l00125"></a>00125 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( <span class="keyword">const</span> vec &<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ) {set_parameters ( mu,R );} |
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| 138 | <a name="l00126"></a>00126 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ); |
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| 139 | <a name="l00128"></a>00128 |
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| 140 | <a name="l00131"></a>00131 |
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| 141 | <a name="l00133"></a>00133 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &v,<span class="keywordtype">double</span> nu=1.0 ); |
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| 142 | <a name="l00134"></a>00134 |
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| 143 | <a name="l00135"></a>00135 vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample, from density .">sample</a>() <span class="keyword">const</span>; |
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| 144 | <a name="l00136"></a>00136 mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample, from density .">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>; |
|---|
| 145 | <a name="l00137"></a>00137 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">evallog_nn</a> ( <span class="keyword">const</span> vec &val ) <span class="keyword">const</span>; |
|---|
| 146 | <a name="l00138"></a>00138 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; |
|---|
| 147 | <a name="l00139"></a><a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600">00139</a> vec <a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;} |
|---|
| 148 | <a name="l00140"></a><a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773">00140</a> vec <a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> diag ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.to_mat() );} |
|---|
| 149 | <a name="l00141"></a>00141 <span class="comment">// mlnorm<sq_T>* condition ( const RV &rvn ) const ; <=========== fails to cmpile. Why?</span> |
|---|
| 150 | <a name="l00142"></a>00142 <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &rvn ) <span class="keyword">const</span> ; |
|---|
| 151 | <a name="l00143"></a>00143 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> ) <span class="keyword">const</span>; |
|---|
| 152 | <a name="l00144"></a>00144 <span class="comment">// epdf* marginal ( const RV &rv ) const;</span> |
|---|
| 153 | <a name="l00146"></a>00146 <span class="comment"></span> |
|---|
| 154 | <a name="l00149"></a>00149 |
|---|
| 155 | <a name="l00150"></a>00150 vec& _mu() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;} |
|---|
| 156 | <a name="l00151"></a>00151 <span class="keywordtype">void</span> set_mu ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>=mu0;} |
|---|
| 157 | <a name="l00152"></a>00152 sq_T& _R() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;} |
|---|
| 158 | <a name="l00153"></a>00153 <span class="keyword">const</span> sq_T& _R()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;} |
|---|
| 159 | <a name="l00155"></a>00155 |
|---|
| 160 | <a name="l00156"></a>00156 }; |
|---|
| 161 | <a name="l00157"></a>00157 |
|---|
| 162 | <a name="l00164"></a><a class="code" href="classbdm_1_1egiw.html">00164</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> |
|---|
| 163 | <a name="l00165"></a>00165 { |
|---|
| 164 | <a name="l00166"></a>00166 <span class="keyword">protected</span>: |
|---|
| 165 | <a name="l00168"></a><a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52">00168</a> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>; |
|---|
| 166 | <a name="l00170"></a><a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4">00170</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>; |
|---|
| 167 | <a name="l00172"></a><a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a">00172</a> <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>; |
|---|
| 168 | <a name="l00174"></a><a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd">00174</a> <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a>; |
|---|
| 169 | <a name="l00175"></a>00175 <span class="keyword">public</span>: |
|---|
| 170 | <a name="l00178"></a>00178 <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a>() :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {}; |
|---|
| 171 | <a name="l00179"></a>00179 <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {set_parameters ( dimx0,V0, nu0 );}; |
|---|
| 172 | <a name="l00180"></a>00180 |
|---|
| 173 | <a name="l00181"></a>00181 <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) |
|---|
| 174 | <a name="l00182"></a>00182 { |
|---|
| 175 | <a name="l00183"></a>00183 <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>=dimx0; |
|---|
| 176 | <a name="l00184"></a>00184 <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> = V0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>; |
|---|
| 177 | <a name="l00185"></a>00185 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>* ( <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>+<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> ); <span class="comment">// size(R) + size(Theta)</span> |
|---|
| 178 | <a name="l00186"></a>00186 |
|---|
| 179 | <a name="l00187"></a>00187 <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>=V0; |
|---|
| 180 | <a name="l00188"></a>00188 <span class="keywordflow">if</span> ( nu0<0 ) |
|---|
| 181 | <a name="l00189"></a>00189 { |
|---|
| 182 | <a name="l00190"></a>00190 <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> +2*<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a> +2; <span class="comment">// +2 assures finite expected value of R</span> |
|---|
| 183 | <a name="l00191"></a>00191 <span class="comment">// terms before that are sufficient for finite normalization</span> |
|---|
| 184 | <a name="l00192"></a>00192 } |
|---|
| 185 | <a name="l00193"></a>00193 <span class="keywordflow">else</span> |
|---|
| 186 | <a name="l00194"></a>00194 { |
|---|
| 187 | <a name="l00195"></a>00195 <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>=nu0; |
|---|
| 188 | <a name="l00196"></a>00196 } |
|---|
| 189 | <a name="l00197"></a>00197 } |
|---|
| 190 | <a name="l00199"></a>00199 |
|---|
| 191 | <a name="l00200"></a>00200 vec <a class="code" href="classbdm_1_1egiw.html#920f21548b7a3723923dd108fe514c61" title="Returns a sample, from density .">sample</a>() <span class="keyword">const</span>; |
|---|
| 192 | <a name="l00201"></a>00201 vec <a class="code" href="classbdm_1_1egiw.html#df70c05f918c3a6f86d60f10c1fd6ba2" title="return expected value">mean</a>() <span class="keyword">const</span>; |
|---|
| 193 | <a name="l00202"></a>00202 vec <a class="code" href="classbdm_1_1egiw.html#c1ecc406613cc2341225dc10c3d3b46a" title="return expected variance (not covariance!)">variance</a>() <span class="keyword">const</span>; |
|---|
| 194 | <a name="l00203"></a>00203 <span class="keywordtype">void</span> mean_mat ( mat &M, mat&R ) <span class="keyword">const</span>; |
|---|
| 195 | <a name="l00205"></a>00205 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#bfb8e7c619b34ad804a73bff71742b5e" title="In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise...">evallog_nn</a> ( <span class="keyword">const</span> vec &val ) <span class="keyword">const</span>; |
|---|
| 196 | <a name="l00206"></a>00206 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#41d72ba7b2abc8a9a4209ffa98ed5633" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; |
|---|
| 197 | <a name="l00207"></a><a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be">00207</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {V*=p;<a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>*=p;}; |
|---|
| 198 | <a name="l00208"></a>00208 |
|---|
| 199 | <a name="l00211"></a>00211 |
|---|
| 200 | <a name="l00212"></a>00212 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& _V() {<span class="keywordflow">return</span> V;} |
|---|
| 201 | <a name="l00213"></a>00213 <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& _V()<span class="keyword"> const </span>{<span class="keywordflow">return</span> V;} |
|---|
| 202 | <a name="l00214"></a>00214 <span class="keywordtype">double</span>& _nu() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;} |
|---|
| 203 | <a name="l00215"></a>00215 <span class="keyword">const</span> <span class="keywordtype">double</span>& _nu()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;} |
|---|
| 204 | <a name="l00217"></a>00217 }; |
|---|
| 205 | <a name="l00218"></a>00218 |
|---|
| 206 | <a name="l00227"></a><a class="code" href="classbdm_1_1eDirich.html">00227</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> |
|---|
| 207 | <a name="l00228"></a>00228 { |
|---|
| 208 | <a name="l00229"></a>00229 <span class="keyword">protected</span>: |
|---|
| 209 | <a name="l00231"></a><a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2">00231</a> vec <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>; |
|---|
| 210 | <a name="l00232"></a>00232 <span class="keyword">public</span>: |
|---|
| 211 | <a name="l00235"></a>00235 |
|---|
| 212 | <a name="l00236"></a>00236 <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> () : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ) {}; |
|---|
| 213 | <a name="l00237"></a>00237 <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> &D0 ) : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> () {set_parameters ( D0.<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );}; |
|---|
| 214 | <a name="l00238"></a>00238 eDirich ( <span class="keyword">const</span> vec &beta0 ) {set_parameters ( beta0 );}; |
|---|
| 215 | <a name="l00239"></a>00239 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &beta0 ) |
|---|
| 216 | <a name="l00240"></a>00240 { |
|---|
| 217 | <a name="l00241"></a>00241 <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>= beta0; |
|---|
| 218 | <a name="l00242"></a>00242 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length(); |
|---|
| 219 | <a name="l00243"></a>00243 } |
|---|
| 220 | <a name="l00245"></a>00245 |
|---|
| 221 | <a name="l00246"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00246</a> vec <a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> vec_1 ( 0.0 );}; |
|---|
| 222 | <a name="l00247"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00247</a> vec <a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>/sum(<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>);}; |
|---|
| 223 | <a name="l00248"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00248</a> vec <a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordtype">double</span> gamma =sum(<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>); <span class="keywordflow">return</span> elem_mult ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>, ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>+1 ) ) / ( gamma* ( gamma+1 ) );} |
|---|
| 224 | <a name="l00250"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00250</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318" title="In this instance, val is ...">evallog_nn</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const</span> |
|---|
| 225 | <a name="l00251"></a>00251 <span class="keyword"> </span>{ |
|---|
| 226 | <a name="l00252"></a>00252 <span class="keywordtype">double</span> tmp; tmp= ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>-1 ) *log ( val ); it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> ); |
|---|
| 227 | <a name="l00253"></a>00253 <span class="keywordflow">return</span> tmp; |
|---|
| 228 | <a name="l00254"></a>00254 }; |
|---|
| 229 | <a name="l00255"></a><a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2">00255</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const</span> |
|---|
| 230 | <a name="l00256"></a>00256 <span class="keyword"> </span>{ |
|---|
| 231 | <a name="l00257"></a>00257 <span class="keywordtype">double</span> tmp; |
|---|
| 232 | <a name="l00258"></a>00258 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> ); |
|---|
| 233 | <a name="l00259"></a>00259 <span class="keywordtype">double</span> lgb=0.0; |
|---|
| 234 | <a name="l00260"></a>00260 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length();i++ ) {lgb+=lgamma ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> ( i ) );} |
|---|
| 235 | <a name="l00261"></a>00261 tmp= lgb-lgamma ( gam ); |
|---|
| 236 | <a name="l00262"></a>00262 it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> ); |
|---|
| 237 | <a name="l00263"></a>00263 <span class="keywordflow">return</span> tmp; |
|---|
| 238 | <a name="l00264"></a>00264 }; |
|---|
| 239 | <a name="l00266"></a><a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324">00266</a> vec& <a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;} |
|---|
| 240 | <a name="l00268"></a>00268 }; |
|---|
| 241 | <a name="l00269"></a>00269 |
|---|
| 242 | <a name="l00271"></a><a class="code" href="classbdm_1_1multiBM.html">00271</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> |
|---|
| 243 | <a name="l00272"></a>00272 { |
|---|
| 244 | <a name="l00273"></a>00273 <span class="keyword">protected</span>: |
|---|
| 245 | <a name="l00275"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00275</a> <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>; |
|---|
| 246 | <a name="l00277"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00277</a> vec &<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>; |
|---|
| 247 | <a name="l00278"></a>00278 <span class="keyword">public</span>: |
|---|
| 248 | <a name="l00280"></a><a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf">00280</a> <a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf" title="Default constructor.">multiBM</a> ( ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() ) |
|---|
| 249 | <a name="l00281"></a>00281 { |
|---|
| 250 | <a name="l00282"></a>00282 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>.length() >0 ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();} |
|---|
| 251 | <a name="l00283"></a>00283 <span class="keywordflow">else</span>{<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=0.0;} |
|---|
| 252 | <a name="l00284"></a>00284 } |
|---|
| 253 | <a name="l00286"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00286</a> <a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31" title="Copy constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> &B ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( B.<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() ) {} |
|---|
| 254 | <a name="l00288"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00288</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a>* mB0 ) {<span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* mB=<span class="keyword">dynamic_cast<</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">></span> ( mB0 ); <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>=mB-><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;} |
|---|
| 255 | <a name="l00289"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00289</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &dt ) |
|---|
| 256 | <a name="l00290"></a>00290 { |
|---|
| 257 | <a name="l00291"></a>00291 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a><1.0 ) {<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();} |
|---|
| 258 | <a name="l00292"></a>00292 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt; |
|---|
| 259 | <a name="l00293"></a>00293 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BM.html#4064b6559d962633e4372b12f4cd204a" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;} |
|---|
| 260 | <a name="l00294"></a>00294 } |
|---|
| 261 | <a name="l00295"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00295</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">logpred</a> ( <span class="keyword">const</span> vec &dt )<span class="keyword"> const</span> |
|---|
| 262 | <a name="l00296"></a>00296 <span class="keyword"> </span>{ |
|---|
| 263 | <a name="l00297"></a>00297 <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ); |
|---|
| 264 | <a name="l00298"></a>00298 vec &<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> = pred.<a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>(); |
|---|
| 265 | <a name="l00299"></a>00299 |
|---|
| 266 | <a name="l00300"></a>00300 <span class="keywordtype">double</span> lll; |
|---|
| 267 | <a name="l00301"></a>00301 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a><1.0 ) |
|---|
| 268 | <a name="l00302"></a>00302 {beta*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();} |
|---|
| 269 | <a name="l00303"></a>00303 <span class="keywordflow">else</span> |
|---|
| 270 | <a name="l00304"></a>00304 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;} |
|---|
| 271 | <a name="l00305"></a>00305 <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();} |
|---|
| 272 | <a name="l00306"></a>00306 |
|---|
| 273 | <a name="l00307"></a>00307 beta+=dt; |
|---|
| 274 | <a name="l00308"></a>00308 <span class="keywordflow">return</span> pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-lll; |
|---|
| 275 | <a name="l00309"></a>00309 } |
|---|
| 276 | <a name="l00310"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00310</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* B ) |
|---|
| 277 | <a name="l00311"></a>00311 { |
|---|
| 278 | <a name="l00312"></a>00312 <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* E=<span class="keyword">dynamic_cast<</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">></span> ( B ); |
|---|
| 279 | <a name="l00313"></a>00313 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span> |
|---|
| 280 | <a name="l00314"></a>00314 <span class="keyword">const</span> vec &Eb=E-><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;<span class="comment">//const_cast<multiBM*> ( E )->_beta();</span> |
|---|
| 281 | <a name="l00315"></a>00315 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*= ( sum ( Eb ) /sum ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ) ); |
|---|
| 282 | <a name="l00316"></a>00316 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();} |
|---|
| 283 | <a name="l00317"></a>00317 } |
|---|
| 284 | <a name="l00318"></a>00318 <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>& posterior()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;}; |
|---|
| 285 | <a name="l00319"></a>00319 <span class="keyword">const</span> eDirich* _e()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;}; |
|---|
| 286 | <a name="l00320"></a>00320 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &beta0 ) |
|---|
| 287 | <a name="l00321"></a>00321 { |
|---|
| 288 | <a name="l00322"></a>00322 <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.set_parameters ( beta0 ); |
|---|
| 289 | <a name="l00323"></a>00323 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.lognc();} |
|---|
| 290 | <a name="l00324"></a>00324 } |
|---|
| 291 | <a name="l00325"></a>00325 }; |
|---|
| 292 | <a name="l00326"></a>00326 |
|---|
| 293 | <a name="l00336"></a><a class="code" href="classbdm_1_1egamma.html">00336</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> |
|---|
| 294 | <a name="l00337"></a>00337 { |
|---|
| 295 | <a name="l00338"></a>00338 <span class="keyword">protected</span>: |
|---|
| 296 | <a name="l00340"></a><a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa">00340</a> vec <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>; |
|---|
| 297 | <a name="l00342"></a><a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1">00342</a> vec <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>; |
|---|
| 298 | <a name="l00343"></a>00343 <span class="keyword">public</span> : |
|---|
| 299 | <a name="l00346"></a>00346 <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a> ( 0 ), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a> ( 0 ) {}; |
|---|
| 300 | <a name="l00347"></a>00347 <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( <span class="keyword">const</span> vec &a, <span class="keyword">const</span> vec &b ) {set_parameters ( a, b );}; |
|---|
| 301 | <a name="l00348"></a>00348 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &a, <span class="keyword">const</span> vec &b ) {<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>=a,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>=b;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>.length();}; |
|---|
| 302 | <a name="l00350"></a>00350 |
|---|
| 303 | <a name="l00351"></a>00351 vec <a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample, from density .">sample</a>() <span class="keyword">const</span>; |
|---|
| 304 | <a name="l00353"></a>00353 <span class="comment">// mat sample ( int N ) const;</span> |
|---|
| 305 | <a name="l00354"></a>00354 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egamma.html#a8e11e5a580ff42a1b205974c60768c6" title="TODO: is it used anywhere?">evallog</a> ( <span class="keyword">const</span> vec &val ) <span class="keyword">const</span>; |
|---|
| 306 | <a name="l00355"></a>00355 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egamma.html#9a66cbd100e8520c769ccb3c451f86f8" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; |
|---|
| 307 | <a name="l00357"></a><a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed">00357</a> vec& <a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_alpha</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;} |
|---|
| 308 | <a name="l00358"></a>00358 vec& _beta() {<span class="keywordflow">return</span> beta;} |
|---|
| 309 | <a name="l00359"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00359</a> vec <a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,beta );} |
|---|
| 310 | <a name="l00360"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00360</a> vec <a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,elem_mult ( beta,beta ) ); } |
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| 311 | <a name="l00361"></a>00361 }; |
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| 312 | <a name="l00362"></a>00362 |
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| 313 | <a name="l00379"></a><a class="code" href="classbdm_1_1eigamma.html">00379</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> |
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| 314 | <a name="l00380"></a>00380 { |
|---|
| 315 | <a name="l00381"></a>00381 <span class="keyword">protected</span>: |
|---|
| 316 | <a name="l00382"></a>00382 <span class="keyword">public</span> : |
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| 317 | <a name="l00387"></a>00387 |
|---|
| 318 | <a name="l00388"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00388</a> vec <a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample, from density .">egamma::sample</a>();}; |
|---|
| 319 | <a name="l00390"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00390</a> vec <a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>,<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-1 );} |
|---|
| 320 | <a name="l00391"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00391</a> vec <a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{vec mea=<a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>(); <span class="keywordflow">return</span> elem_div ( elem_mult ( mea,mea ),<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-2 );} |
|---|
| 321 | <a name="l00392"></a>00392 }; |
|---|
| 322 | <a name="l00393"></a>00393 <span class="comment">/*</span> |
|---|
| 323 | <a name="l00395"></a>00395 <span class="comment"> class emix : public epdf {</span> |
|---|
| 324 | <a name="l00396"></a>00396 <span class="comment"> protected:</span> |
|---|
| 325 | <a name="l00397"></a>00397 <span class="comment"> int n;</span> |
|---|
| 326 | <a name="l00398"></a>00398 <span class="comment"> vec &w;</span> |
|---|
| 327 | <a name="l00399"></a>00399 <span class="comment"> Array<epdf*> Coms;</span> |
|---|
| 328 | <a name="l00400"></a>00400 <span class="comment"> public:</span> |
|---|
| 329 | <a name="l00402"></a>00402 <span class="comment"> emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span> |
|---|
| 330 | <a name="l00403"></a>00403 <span class="comment"> void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span> |
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| 331 | <a name="l00404"></a>00404 <span class="comment"> vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;};</span> |
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| 332 | <a name="l00405"></a>00405 <span class="comment"> vec sample() {it_error ( "Not implemented" );return 0;}</span> |
|---|
| 333 | <a name="l00406"></a>00406 <span class="comment"> };</span> |
|---|
| 334 | <a name="l00407"></a>00407 <span class="comment"> */</span> |
|---|
| 335 | <a name="l00408"></a>00408 |
|---|
| 336 | <a name="l00410"></a>00410 |
|---|
| 337 | <a name="l00411"></a><a class="code" href="classbdm_1_1euni.html">00411</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> |
|---|
| 338 | <a name="l00412"></a>00412 { |
|---|
| 339 | <a name="l00413"></a>00413 <span class="keyword">protected</span>: |
|---|
| 340 | <a name="l00415"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00415</a> vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>; |
|---|
| 341 | <a name="l00417"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00417</a> vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>; |
|---|
| 342 | <a name="l00419"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00419</a> vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>; |
|---|
| 343 | <a name="l00421"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00421</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>; |
|---|
| 344 | <a name="l00423"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00423</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>; |
|---|
| 345 | <a name="l00424"></a>00424 <span class="keyword">public</span>: |
|---|
| 346 | <a name="l00427"></a>00427 <a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {} |
|---|
| 347 | <a name="l00428"></a>00428 <a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( <span class="keyword">const</span> vec &low0, <span class="keyword">const</span> vec &high0 ) {set_parameters ( low0,high0 );} |
|---|
| 348 | <a name="l00429"></a>00429 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &low0, <span class="keyword">const</span> vec &high0 ) |
|---|
| 349 | <a name="l00430"></a>00430 { |
|---|
| 350 | <a name="l00431"></a>00431 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0; |
|---|
| 351 | <a name="l00432"></a>00432 it_assert_debug ( min ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> ) >0.0,<span class="stringliteral">"bad support"</span> ); |
|---|
| 352 | <a name="l00433"></a>00433 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0; |
|---|
| 353 | <a name="l00434"></a>00434 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0; |
|---|
| 354 | <a name="l00435"></a>00435 <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> ); |
|---|
| 355 | <a name="l00436"></a>00436 <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> ); |
|---|
| 356 | <a name="l00437"></a>00437 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>.length(); |
|---|
| 357 | <a name="l00438"></a>00438 } |
|---|
| 358 | <a name="l00440"></a>00440 |
|---|
| 359 | <a name="l00441"></a>00441 <span class="keywordtype">double</span> eval ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;} |
|---|
| 360 | <a name="l00442"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00442</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81" title="Compute log-probability of argument val.">evallog</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;} |
|---|
| 361 | <a name="l00443"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00443</a> vec <a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const</span> |
|---|
| 362 | <a name="l00444"></a>00444 <span class="keyword"> </span>{ |
|---|
| 363 | <a name="l00445"></a>00445 vec smp ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ); |
|---|
| 364 | <a name="l00446"></a>00446 <span class="preprocessor">#pragma omp critical</span> |
|---|
| 365 | <a name="l00447"></a>00447 <span class="preprocessor"></span> UniRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ,smp ); |
|---|
| 366 | <a name="l00448"></a>00448 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>+elem_mult ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>,smp ); |
|---|
| 367 | <a name="l00449"></a>00449 } |
|---|
| 368 | <a name="l00451"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00451</a> vec <a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226" title="set values of low and high ">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>-<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) /2.0;} |
|---|
| 369 | <a name="l00452"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00452</a> vec <a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( pow ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,2 ) +pow ( <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>,2 ) +elem_mult ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) ) /3.0;} |
|---|
| 370 | <a name="l00453"></a>00453 }; |
|---|
| 371 | <a name="l00454"></a>00454 |
|---|
| 372 | <a name="l00455"></a>00455 |
|---|
| 373 | <a name="l00461"></a>00461 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 374 | <a name="l00462"></a><a class="code" href="classbdm_1_1mlnorm.html">00462</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> |
|---|
| 375 | <a name="l00463"></a>00463 { |
|---|
| 376 | <a name="l00464"></a>00464 <span class="keyword">protected</span>: |
|---|
| 377 | <a name="l00466"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00466</a> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; |
|---|
| 378 | <a name="l00467"></a>00467 mat A; |
|---|
| 379 | <a name="l00468"></a>00468 vec mu_const; |
|---|
| 380 | <a name="l00469"></a>00469 vec& _mu; <span class="comment">//cached epdf.mu;</span> |
|---|
| 381 | <a name="l00470"></a>00470 <span class="keyword">public</span>: |
|---|
| 382 | <a name="l00473"></a>00473 <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> ( ) :<a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (),<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),A ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a> =&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; }; |
|---|
| 383 | <a name="l00474"></a>00474 <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> ( <span class="keyword">const</span> mat &A, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) |
|---|
| 384 | <a name="l00475"></a>00475 { |
|---|
| 385 | <a name="l00476"></a>00476 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a> =&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">set_parameters</a> ( A,mu0,R ); |
|---|
| 386 | <a name="l00477"></a>00477 }; |
|---|
| 387 | <a name="l00479"></a>00479 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">set_parameters</a> ( <span class="keyword">const</span> mat &A, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R ); |
|---|
| 388 | <a name="l00482"></a>00482 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">condition</a> ( <span class="keyword">const</span> vec &cond ); |
|---|
| 389 | <a name="l00483"></a>00483 |
|---|
| 390 | <a name="l00485"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00485</a> vec& <a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced" title="access function">_mu_const</a>() {<span class="keywordflow">return</span> mu_const;} |
|---|
| 391 | <a name="l00487"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00487</a> mat& <a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614" title="access function">_A</a>() {<span class="keywordflow">return</span> A;} |
|---|
| 392 | <a name="l00489"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00489</a> mat <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R().to_mat();} |
|---|
| 393 | <a name="l00490"></a>00490 |
|---|
| 394 | <a name="l00491"></a>00491 <span class="keyword">template</span><<span class="keyword">class</span> sq_M> |
|---|
| 395 | <a name="l00492"></a>00492 <span class="keyword">friend</span> std::ostream &operator<< ( std::ostream &os, mlnorm<sq_M> &ml ); |
|---|
| 396 | <a name="l00493"></a>00493 }; |
|---|
| 397 | <a name="l00494"></a>00494 |
|---|
| 398 | <a name="l00496"></a>00496 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 399 | <a name="l00497"></a><a class="code" href="classbdm_1_1mgnorm.html">00497</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mgnorm.html" title="Mpdf with general function for mean value.">mgnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> |
|---|
| 400 | <a name="l00498"></a>00498 { |
|---|
| 401 | <a name="l00499"></a>00499 <span class="keyword">protected</span>: |
|---|
| 402 | <a name="l00501"></a><a class="code" href="classbdm_1_1mgnorm.html#8f7a376a1d2197e0634557e88e03104a">00501</a> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; |
|---|
| 403 | <a name="l00502"></a>00502 vec &mu; |
|---|
| 404 | <a name="l00503"></a>00503 <a class="code" href="classbdm_1_1fnc.html" title="Class representing function of variable represented by rv.">fnc</a>* g; |
|---|
| 405 | <a name="l00504"></a>00504 <span class="keyword">public</span>: |
|---|
| 406 | <a name="l00506"></a><a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d">00506</a> <a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d" title="default constructor">mgnorm</a>() :mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;} |
|---|
| 407 | <a name="l00508"></a><a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564">00508</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a> ( <a class="code" href="classbdm_1_1fnc.html" title="Class representing function of variable represented by rv.">fnc</a>* g0, <span class="keyword">const</span> sq_T &R0 ) {g=g0; <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( g-><a class="code" href="classbdm_1_1fnc.html#083832294da9d1e40804158b979c4341" title="access function">dimension</a>() ), R0 );} |
|---|
| 408 | <a name="l00509"></a><a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d">00509</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &cond ) {mu=g-><a class="code" href="classbdm_1_1fnc.html#6277b11d7fffc7ef8a2fa3e84ae5bad4" title="function evaluates numerical value of at cond ">eval</a> ( cond );}; |
|---|
| 409 | <a name="l00510"></a>00510 }; |
|---|
| 410 | <a name="l00511"></a>00511 |
|---|
| 411 | <a name="l00519"></a><a class="code" href="classbdm_1_1mlstudent.html">00519</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a><ldmat> |
|---|
| 412 | <a name="l00520"></a>00520 { |
|---|
| 413 | <a name="l00521"></a>00521 <span class="keyword">protected</span>: |
|---|
| 414 | <a name="l00522"></a>00522 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda; |
|---|
| 415 | <a name="l00523"></a>00523 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &<a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>; |
|---|
| 416 | <a name="l00524"></a>00524 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re; |
|---|
| 417 | <a name="l00525"></a>00525 <span class="keyword">public</span>: |
|---|
| 418 | <a name="l00526"></a>00526 <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html">mlnorm<ldmat></a> (), |
|---|
| 419 | <a name="l00527"></a>00527 Lambda (), <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {} |
|---|
| 420 | <a name="l00528"></a>00528 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &A0, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& Lambda0 ) |
|---|
| 421 | <a name="l00529"></a>00529 { |
|---|
| 422 | <a name="l00530"></a>00530 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> ); |
|---|
| 423 | <a name="l00531"></a>00531 it_assert_debug ( R0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ==A0.rows(),<span class="stringliteral">""</span> ); |
|---|
| 424 | <a name="l00532"></a>00532 |
|---|
| 425 | <a name="l00533"></a>00533 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( mu0,Lambda ); <span class="comment">//</span> |
|---|
| 426 | <a name="l00534"></a>00534 A = A0; |
|---|
| 427 | <a name="l00535"></a>00535 mu_const = mu0; |
|---|
| 428 | <a name="l00536"></a>00536 Re=R0; |
|---|
| 429 | <a name="l00537"></a>00537 Lambda = Lambda0; |
|---|
| 430 | <a name="l00538"></a>00538 } |
|---|
| 431 | <a name="l00539"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00539</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">condition</a> ( <span class="keyword">const</span> vec &cond ) |
|---|
| 432 | <a name="l00540"></a>00540 { |
|---|
| 433 | <a name="l00541"></a>00541 _mu = A*cond + mu_const; |
|---|
| 434 | <a name="l00542"></a>00542 <span class="keywordtype">double</span> zeta; |
|---|
| 435 | <a name="l00543"></a>00543 <span class="comment">//ugly hack!</span> |
|---|
| 436 | <a name="l00544"></a>00544 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ) |
|---|
| 437 | <a name="l00545"></a>00545 { |
|---|
| 438 | <a name="l00546"></a>00546 zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat ( cond, vec_1 ( 1.0 ) ) ); |
|---|
| 439 | <a name="l00547"></a>00547 } |
|---|
| 440 | <a name="l00548"></a>00548 <span class="keywordflow">else</span> |
|---|
| 441 | <a name="l00549"></a>00549 { |
|---|
| 442 | <a name="l00550"></a>00550 zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond ); |
|---|
| 443 | <a name="l00551"></a>00551 } |
|---|
| 444 | <a name="l00552"></a>00552 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re; |
|---|
| 445 | <a name="l00553"></a>00553 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>*= ( 1+zeta );<span class="comment">// / ( nu ); << nu is in Re!!!!!!</span> |
|---|
| 446 | <a name="l00554"></a>00554 }; |
|---|
| 447 | <a name="l00555"></a>00555 |
|---|
| 448 | <a name="l00556"></a>00556 }; |
|---|
| 449 | <a name="l00566"></a><a class="code" href="classbdm_1_1mgamma.html">00566</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> |
|---|
| 450 | <a name="l00567"></a>00567 { |
|---|
| 451 | <a name="l00568"></a>00568 <span class="keyword">protected</span>: |
|---|
| 452 | <a name="l00570"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00570</a> <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; |
|---|
| 453 | <a name="l00572"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00572</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>; |
|---|
| 454 | <a name="l00574"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00574</a> vec &<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>; |
|---|
| 455 | <a name="l00575"></a>00575 |
|---|
| 456 | <a name="l00576"></a>00576 <span class="keyword">public</span>: |
|---|
| 457 | <a name="l00578"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00578</a> <a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1" title="Constructor.">mgamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( ), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> (), <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}; |
|---|
| 458 | <a name="l00580"></a>00580 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#a0f21c2557b233a85838b497d040ab14" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>, <span class="keyword">const</span> vec &beta0 ); |
|---|
| 459 | <a name="l00581"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00581</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) {<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/val;}; |
|---|
| 460 | <a name="l00582"></a>00582 }; |
|---|
| 461 | <a name="l00583"></a>00583 |
|---|
| 462 | <a name="l00593"></a><a class="code" href="classbdm_1_1migamma.html">00593</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> |
|---|
| 463 | <a name="l00594"></a>00594 { |
|---|
| 464 | <a name="l00595"></a>00595 <span class="keyword">protected</span>: |
|---|
| 465 | <a name="l00597"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00597</a> <a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; |
|---|
| 466 | <a name="l00599"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00599</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>; |
|---|
| 467 | <a name="l00601"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00601</a> vec &<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>; |
|---|
| 468 | <a name="l00603"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00603</a> vec &<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>; |
|---|
| 469 | <a name="l00604"></a>00604 |
|---|
| 470 | <a name="l00605"></a>00605 <span class="keyword">public</span>: |
|---|
| 471 | <a name="l00608"></a>00608 <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}; |
|---|
| 472 | <a name="l00609"></a>00609 <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> &m ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( m.<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}; |
|---|
| 473 | <a name="l00611"></a>00611 |
|---|
| 474 | <a name="l00613"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00613</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> len, <span class="keywordtype">double</span> k0 ) |
|---|
| 475 | <a name="l00614"></a>00614 { |
|---|
| 476 | <a name="l00615"></a>00615 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0; |
|---|
| 477 | <a name="l00616"></a>00616 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( ( 1.0/ ( <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>*<a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a> ) +2.0 ) *ones ( len ) <span class="comment">/*alpha*/</span>, ones ( len ) <span class="comment">/*beta*/</span> ); |
|---|
| 478 | <a name="l00617"></a>00617 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension(); |
|---|
| 479 | <a name="l00618"></a>00618 }; |
|---|
| 480 | <a name="l00619"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00619</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) |
|---|
| 481 | <a name="l00620"></a>00620 { |
|---|
| 482 | <a name="l00621"></a>00621 <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>=elem_mult ( val, ( <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>-1.0 ) ); |
|---|
| 483 | <a name="l00622"></a>00622 }; |
|---|
| 484 | <a name="l00623"></a>00623 }; |
|---|
| 485 | <a name="l00624"></a>00624 |
|---|
| 486 | <a name="l00636"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00636</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> |
|---|
| 487 | <a name="l00637"></a>00637 { |
|---|
| 488 | <a name="l00638"></a>00638 <span class="keyword">protected</span>: |
|---|
| 489 | <a name="l00640"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00640</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>; |
|---|
| 490 | <a name="l00642"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00642</a> vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>; |
|---|
| 491 | <a name="l00643"></a>00643 <span class="keyword">public</span>: |
|---|
| 492 | <a name="l00645"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00645</a> <a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d" title="Constructor.">mgamma_fix</a> ( ) : <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> ( ),<a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a> () {}; |
|---|
| 493 | <a name="l00647"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00647</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) |
|---|
| 494 | <a name="l00648"></a>00648 { |
|---|
| 495 | <a name="l00649"></a>00649 <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">mgamma::set_parameters</a> ( k0, ref0 ); |
|---|
| 496 | <a name="l00650"></a>00650 <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>=l0; |
|---|
| 497 | <a name="l00651"></a>00651 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=dimension(); |
|---|
| 498 | <a name="l00652"></a>00652 }; |
|---|
| 499 | <a name="l00653"></a>00653 |
|---|
| 500 | <a name="l00654"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00654</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) {vec mean=elem_mult ( <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a> ) ); <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/mean;}; |
|---|
| 501 | <a name="l00655"></a>00655 }; |
|---|
| 502 | <a name="l00656"></a>00656 |
|---|
| 503 | <a name="l00657"></a>00657 |
|---|
| 504 | <a name="l00670"></a><a class="code" href="classbdm_1_1migamma__ref.html">00670</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__ref.html" title="Inverse-Gamma random walk around a fixed point.">migamma_ref</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> |
|---|
| 505 | <a name="l00671"></a>00671 { |
|---|
| 506 | <a name="l00672"></a>00672 <span class="keyword">protected</span>: |
|---|
| 507 | <a name="l00674"></a><a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d">00674</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>; |
|---|
| 508 | <a name="l00676"></a><a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6">00676</a> vec <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>; |
|---|
| 509 | <a name="l00677"></a>00677 <span class="keyword">public</span>: |
|---|
| 510 | <a name="l00679"></a><a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f">00679</a> <a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f" title="Constructor.">migamma_ref</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a> ( ) {}; |
|---|
| 511 | <a name="l00681"></a><a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800">00681</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) |
|---|
| 512 | <a name="l00682"></a>00682 { |
|---|
| 513 | <a name="l00683"></a>00683 <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">migamma::set_parameters</a> ( ref0.length(), k0 ); |
|---|
| 514 | <a name="l00684"></a>00684 <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>=pow ( ref0,1.0-l0 ); |
|---|
| 515 | <a name="l00685"></a>00685 <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>=l0; |
|---|
| 516 | <a name="l00686"></a>00686 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension(); |
|---|
| 517 | <a name="l00687"></a>00687 }; |
|---|
| 518 | <a name="l00688"></a>00688 |
|---|
| 519 | <a name="l00689"></a><a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a">00689</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) |
|---|
| 520 | <a name="l00690"></a>00690 { |
|---|
| 521 | <a name="l00691"></a>00691 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a> ) ); |
|---|
| 522 | <a name="l00692"></a>00692 <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean ); |
|---|
| 523 | <a name="l00693"></a>00693 }; |
|---|
| 524 | <a name="l00694"></a>00694 }; |
|---|
| 525 | <a name="l00695"></a>00695 |
|---|
| 526 | <a name="l00705"></a><a class="code" href="classbdm_1_1elognorm.html">00705</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1elognorm.html">elognorm</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a><ldmat> |
|---|
| 527 | <a name="l00706"></a>00706 { |
|---|
| 528 | <a name="l00707"></a>00707 <span class="keyword">public</span>: |
|---|
| 529 | <a name="l00708"></a><a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba">00708</a> vec <a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<ldmat>::sample</a>() );}; |
|---|
| 530 | <a name="l00709"></a><a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea">00709</a> vec <a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec var=<a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">enorm<ldmat>::variance</a>();<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> - 0.5*var );}; |
|---|
| 531 | <a name="l00710"></a>00710 |
|---|
| 532 | <a name="l00711"></a>00711 }; |
|---|
| 533 | <a name="l00712"></a>00712 |
|---|
| 534 | <a name="l00724"></a><a class="code" href="classbdm_1_1mlognorm.html">00724</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mlognorm.html" title="Log-Normal random walk.">mlognorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> |
|---|
| 535 | <a name="l00725"></a>00725 { |
|---|
| 536 | <a name="l00726"></a>00726 <span class="keyword">protected</span>: |
|---|
| 537 | <a name="l00727"></a>00727 <a class="code" href="classbdm_1_1elognorm.html">elognorm</a> eno; |
|---|
| 538 | <a name="l00729"></a><a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a">00729</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>; |
|---|
| 539 | <a name="l00731"></a><a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2">00731</a> vec &<a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>; |
|---|
| 540 | <a name="l00732"></a>00732 <span class="keyword">public</span>: |
|---|
| 541 | <a name="l00734"></a><a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41">00734</a> <a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41" title="Constructor.">mlognorm</a> ( ) : eno (), <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a> ( eno._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&eno;}; |
|---|
| 542 | <a name="l00736"></a><a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f">00736</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> size, <span class="keywordtype">double</span> k ) |
|---|
| 543 | <a name="l00737"></a>00737 { |
|---|
| 544 | <a name="l00738"></a>00738 <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a> = 0.5*log ( k*k+1 ); |
|---|
| 545 | <a name="l00739"></a>00739 eno.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( zeros ( size ),2*<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>*eye ( size ) ); |
|---|
| 546 | <a name="l00740"></a>00740 |
|---|
| 547 | <a name="l00741"></a>00741 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = size; |
|---|
| 548 | <a name="l00742"></a>00742 }; |
|---|
| 549 | <a name="l00743"></a>00743 |
|---|
| 550 | <a name="l00744"></a><a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e">00744</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) |
|---|
| 551 | <a name="l00745"></a>00745 { |
|---|
| 552 | <a name="l00746"></a>00746 <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>=log ( val )-<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;<span class="comment">//elem_mult ( refl,pow ( val,l ) );</span> |
|---|
| 553 | <a name="l00747"></a>00747 }; |
|---|
| 554 | <a name="l00748"></a>00748 }; |
|---|
| 555 | <a name="l00749"></a>00749 |
|---|
| 556 | <a name="l00753"></a><a class="code" href="classbdm_1_1eWishartCh.html">00753</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eWishartCh.html">eWishartCh</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> |
|---|
| 557 | <a name="l00754"></a>00754 { |
|---|
| 558 | <a name="l00755"></a>00755 <span class="keyword">protected</span>: |
|---|
| 559 | <a name="l00757"></a><a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490">00757</a> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>; |
|---|
| 560 | <a name="l00759"></a><a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f">00759</a> <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>; |
|---|
| 561 | <a name="l00761"></a><a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3">00761</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>; |
|---|
| 562 | <a name="l00762"></a>00762 <span class="keyword">public</span>: |
|---|
| 563 | <a name="l00763"></a>00763 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0 ) {<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>=<a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> ( Y0 );<a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>=delta0; <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>=<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classsqmat.html#071e80ced9cc3b8cbb360fa7462eb646" title="Reimplementing common functions of mat: cols().">rows</a>(); <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>*<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>; } |
|---|
| 564 | <a name="l00764"></a>00764 mat sample_mat()<span class="keyword"> const</span> |
|---|
| 565 | <a name="l00765"></a>00765 <span class="keyword"> </span>{ |
|---|
| 566 | <a name="l00766"></a>00766 mat X=zeros ( <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>,<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a> ); |
|---|
| 567 | <a name="l00767"></a>00767 |
|---|
| 568 | <a name="l00768"></a>00768 <span class="comment">//sample diagonal</span> |
|---|
| 569 | <a name="l00769"></a>00769 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;i++ ) |
|---|
| 570 | <a name="l00770"></a>00770 { |
|---|
| 571 | <a name="l00771"></a>00771 GamRNG.<a class="code" href="classitpp_1_1Gamma__RNG.html#dfaae19411e39aa87e1f72e409b6babe" title="Set lambda.">setup</a> ( 0.5* ( <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>-i ) , 0.5 ); <span class="comment">// no +1 !! index if from 0</span> |
|---|
| 572 | <a name="l00772"></a>00772 <span class="preprocessor">#pragma omp critical</span> |
|---|
| 573 | <a name="l00773"></a>00773 <span class="preprocessor"></span> X ( i,i ) =sqrt ( GamRNG() ); |
|---|
| 574 | <a name="l00774"></a>00774 } |
|---|
| 575 | <a name="l00775"></a>00775 <span class="comment">//do the rest</span> |
|---|
| 576 | <a name="l00776"></a>00776 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<p;i++ ) |
|---|
| 577 | <a name="l00777"></a>00777 { |
|---|
| 578 | <a name="l00778"></a>00778 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> j=i+1;j<p;j++ ) |
|---|
| 579 | <a name="l00779"></a>00779 { |
|---|
| 580 | <a name="l00780"></a>00780 <span class="preprocessor">#pragma omp critical</span> |
|---|
| 581 | <a name="l00781"></a>00781 <span class="preprocessor"></span> X ( i,j ) =NorRNG.sample(); |
|---|
| 582 | <a name="l00782"></a>00782 } |
|---|
| 583 | <a name="l00783"></a>00783 } |
|---|
| 584 | <a name="l00784"></a>00784 <span class="keywordflow">return</span> X*<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>();<span class="comment">// return upper triangular part of the decomposition</span> |
|---|
| 585 | <a name="l00785"></a>00785 } |
|---|
| 586 | <a name="l00786"></a><a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a">00786</a> vec <a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a" title="Returns a sample, from density .">sample</a> ()<span class="keyword"> const</span> |
|---|
| 587 | <a name="l00787"></a>00787 <span class="keyword"> </span>{ |
|---|
| 588 | <a name="l00788"></a>00788 <span class="keywordflow">return</span> vec ( sample_mat()._data(),p*p ); |
|---|
| 589 | <a name="l00789"></a>00789 } |
|---|
| 590 | <a name="l00791"></a><a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981">00791</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981" title="fast access function y0 will be copied into Y.Ch.">setY</a> ( <span class="keyword">const</span> mat &Ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,Ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() );} |
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| 591 | <a name="l00793"></a><a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a">00793</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a" title="fast access function y0 will be copied into Y.Ch.">_setY</a> ( <span class="keyword">const</span> vec &ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>, ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() ); } |
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| 592 | <a name="l00795"></a><a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e">00795</a> <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>& <a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e" title="access function">getY</a>()<span class="keyword">const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;} |
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| 593 | <a name="l00796"></a>00796 }; |
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| 594 | <a name="l00797"></a>00797 |
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| 595 | <a name="l00798"></a>00798 <span class="keyword">class </span>eiWishartCh: <span class="keyword">public</span> epdf |
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| 596 | <a name="l00799"></a>00799 { |
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| 597 | <a name="l00800"></a>00800 <span class="keyword">protected</span>: |
|---|
| 598 | <a name="l00801"></a>00801 eWishartCh W; |
|---|
| 599 | <a name="l00802"></a>00802 <span class="keywordtype">int</span> p; |
|---|
| 600 | <a name="l00803"></a>00803 <span class="keywordtype">double</span> delta; |
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| 601 | <a name="l00804"></a>00804 <span class="keyword">public</span>: |
|---|
| 602 | <a name="l00805"></a>00805 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0) { |
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| 603 | <a name="l00806"></a>00806 delta = delta0; |
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| 604 | <a name="l00807"></a>00807 W.set_parameters ( inv ( Y0 ),delta0 ); |
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| 605 | <a name="l00808"></a>00808 dim = W.dimension(); p=Y0.rows(); |
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| 606 | <a name="l00809"></a>00809 } |
|---|
| 607 | <a name="l00810"></a>00810 vec sample()<span class="keyword"> const </span>{mat iCh; iCh=inv ( W.sample_mat() ); <span class="keywordflow">return</span> vec ( iCh._data(),<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );} |
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| 608 | <a name="l00811"></a>00811 <span class="keywordtype">void</span> _setY ( <span class="keyword">const</span> vec &y0 ) |
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| 609 | <a name="l00812"></a>00812 { |
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| 610 | <a name="l00813"></a>00813 mat Ch ( p,p ); |
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| 611 | <a name="l00814"></a>00814 mat iCh ( p,p ); |
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| 612 | <a name="l00815"></a>00815 copy_vector ( dim, y0._data(), Ch._data() ); |
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| 613 | <a name="l00816"></a>00816 |
|---|
| 614 | <a name="l00817"></a>00817 iCh=inv ( Ch ); |
|---|
| 615 | <a name="l00818"></a>00818 W.setY ( iCh ); |
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| 616 | <a name="l00819"></a>00819 } |
|---|
| 617 | <a name="l00820"></a>00820 <span class="keyword">virtual</span> <span class="keywordtype">double</span> evallog ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{ |
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| 618 | <a name="l00821"></a>00821 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> X(p); |
|---|
| 619 | <a name="l00822"></a>00822 <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>& Y=W.getY(); |
|---|
| 620 | <a name="l00823"></a>00823 |
|---|
| 621 | <a name="l00824"></a>00824 copy_vector(p*p,val._data(),X._Ch()._data()); |
|---|
| 622 | <a name="l00825"></a>00825 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> iX(p);X.inv(iX); |
|---|
| 623 | <a name="l00826"></a>00826 <span class="comment">// compute </span> |
|---|
| 624 | <a name="l00827"></a>00827 <span class="comment">// \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},</span> |
|---|
| 625 | <a name="l00828"></a>00828 mat M=Y.<a class="code" href="classchmat.html#045addd685f8d978efda232d7dcb070e" title="Conversion to full matrix.">to_mat</a>()*iX.to_mat(); |
|---|
| 626 | <a name="l00829"></a>00829 |
|---|
| 627 | <a name="l00830"></a>00830 <span class="keywordtype">double</span> log1 = 0.5*p*(2*Y.<a class="code" href="classchmat.html#b504ca818203b13e667cb3c503980382" title="Logarithm of a determinant.">logdet</a>())-0.5*(delta+p+1)*(2*X.logdet())-0.5*trace(M); |
|---|
| 628 | <a name="l00831"></a>00831 <span class="comment">//Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!</span> |
|---|
| 629 | <a name="l00832"></a>00832 |
|---|
| 630 | <a name="l00833"></a>00833 <span class="comment">/* if (0) {</span> |
|---|
| 631 | <a name="l00834"></a>00834 <span class="comment"> mat XX=X.to_mat();</span> |
|---|
| 632 | <a name="l00835"></a>00835 <span class="comment"> mat YY=Y.to_mat();</span> |
|---|
| 633 | <a name="l00836"></a>00836 <span class="comment"> </span> |
|---|
| 634 | <a name="l00837"></a>00837 <span class="comment"> double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX)); </span> |
|---|
| 635 | <a name="l00838"></a>00838 <span class="comment"> cout << log1 << "," << log2 << endl;</span> |
|---|
| 636 | <a name="l00839"></a>00839 <span class="comment"> }*/</span> |
|---|
| 637 | <a name="l00840"></a>00840 <span class="keywordflow">return</span> log1; |
|---|
| 638 | <a name="l00841"></a>00841 }; |
|---|
| 639 | <a name="l00842"></a>00842 |
|---|
| 640 | <a name="l00843"></a>00843 }; |
|---|
| 641 | <a name="l00844"></a>00844 |
|---|
| 642 | <a name="l00845"></a>00845 <span class="keyword">class </span>rwiWishartCh : <span class="keyword">public</span> mpdf |
|---|
| 643 | <a name="l00846"></a>00846 { |
|---|
| 644 | <a name="l00847"></a>00847 <span class="keyword">protected</span>: |
|---|
| 645 | <a name="l00848"></a>00848 eiWishartCh eiW; |
|---|
| 646 | <a name="l00850"></a>00850 <span class="keywordtype">double</span> sqd; |
|---|
| 647 | <a name="l00851"></a>00851 <span class="comment">//reference point for diagonal</span> |
|---|
| 648 | <a name="l00852"></a>00852 vec refl; |
|---|
| 649 | <a name="l00853"></a>00853 <span class="keywordtype">double</span> l; |
|---|
| 650 | <a name="l00854"></a>00854 <span class="keywordtype">int</span> p; |
|---|
| 651 | <a name="l00855"></a>00855 <span class="keyword">public</span>: |
|---|
| 652 | <a name="l00856"></a>00856 <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> p0, <span class="keywordtype">double</span> k, vec ref0, <span class="keywordtype">double</span> l0 ) |
|---|
| 653 | <a name="l00857"></a>00857 { |
|---|
| 654 | <a name="l00858"></a>00858 p=p0; |
|---|
| 655 | <a name="l00859"></a>00859 <span class="keywordtype">double</span> delta = 2/(k*k)+p+3; |
|---|
| 656 | <a name="l00860"></a>00860 sqd=sqrt ( delta-p-1 ); |
|---|
| 657 | <a name="l00861"></a>00861 l=l0; |
|---|
| 658 | <a name="l00862"></a>00862 refl=pow(ref0,1-l); |
|---|
| 659 | <a name="l00863"></a>00863 |
|---|
| 660 | <a name="l00864"></a>00864 eiW.set_parameters ( eye ( p ),delta ); |
|---|
| 661 | <a name="l00865"></a>00865 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&eiW; |
|---|
| 662 | <a name="l00866"></a>00866 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=eiW.dimension(); |
|---|
| 663 | <a name="l00867"></a>00867 } |
|---|
| 664 | <a name="l00868"></a>00868 <span class="keywordtype">void</span> condition ( <span class="keyword">const</span> vec &c ) { |
|---|
| 665 | <a name="l00869"></a>00869 vec z=c; |
|---|
| 666 | <a name="l00870"></a>00870 <span class="keywordtype">int</span> ri=0; |
|---|
| 667 | <a name="l00871"></a>00871 <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0;i<p*p;i+=(p+1)){<span class="comment">//trace diagonal element</span> |
|---|
| 668 | <a name="l00872"></a>00872 z(i) = pow(z(i),l)*refl(ri); |
|---|
| 669 | <a name="l00873"></a>00873 ri++; |
|---|
| 670 | <a name="l00874"></a>00874 } |
|---|
| 671 | <a name="l00875"></a>00875 |
|---|
| 672 | <a name="l00876"></a>00876 eiW._setY ( sqd*z ); |
|---|
| 673 | <a name="l00877"></a>00877 } |
|---|
| 674 | <a name="l00878"></a>00878 }; |
|---|
| 675 | <a name="l00879"></a>00879 |
|---|
| 676 | <a name="l00881"></a>00881 <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 }; |
|---|
| 677 | <a name="l00887"></a><a class="code" href="classbdm_1_1eEmp.html">00887</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> |
|---|
| 678 | <a name="l00888"></a>00888 { |
|---|
| 679 | <a name="l00889"></a>00889 <span class="keyword">protected</span> : |
|---|
| 680 | <a name="l00891"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">00891</a> <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>; |
|---|
| 681 | <a name="l00893"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">00893</a> vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>; |
|---|
| 682 | <a name="l00895"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">00895</a> Array<vec> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>; |
|---|
| 683 | <a name="l00896"></a>00896 <span class="keyword">public</span>: |
|---|
| 684 | <a name="l00899"></a>00899 <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( ) {}; |
|---|
| 685 | <a name="l00900"></a>00900 <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> &e ) : <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( e ), <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ), <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ) {}; |
|---|
| 686 | <a name="l00902"></a>00902 |
|---|
| 687 | <a name="l00904"></a>00904 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#7cfd383180b486fe4526bdf0179350c0" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> vec &w0, <span class="keyword">const</span> epdf* pdf0 ); |
|---|
| 688 | <a name="l00906"></a><a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7">00906</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 , <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {<a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( ones ( n ) /n,pdf0 );}; |
|---|
| 689 | <a name="l00908"></a>00908 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b62d802b8ef39f7c4dcbeb366c90951a" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 ); |
|---|
| 690 | <a name="l00910"></a><a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1">00910</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1" title="Set sample.">set_parameters</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>=n0; <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size ( n0,copy );<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size ( n0,copy );}; |
|---|
| 691 | <a name="l00912"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">00912</a> vec& <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" title="Potentially dangerous, use with care.">_w</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;}; |
|---|
| 692 | <a name="l00914"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">00914</a> <span class="keyword">const</span> vec& <a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;}; |
|---|
| 693 | <a name="l00916"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">00916</a> Array<vec>& <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;}; |
|---|
| 694 | <a name="l00918"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">00918</a> <span class="keyword">const</span> Array<vec>& <a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;}; |
|---|
| 695 | <a name="l00920"></a>00920 ivec <a class="code" href="classbdm_1_1eEmp.html#f06ce255de5dbb2313f52ee51f82ba3d" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( RESAMPLING_METHOD method=SYSTEMATIC ); |
|---|
| 696 | <a name="l00922"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">00922</a> vec <a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;} |
|---|
| 697 | <a name="l00924"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">00924</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09" title="inherited operation : NOT implemneted">evallog</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;} |
|---|
| 698 | <a name="l00925"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">00925</a> vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const</span> |
|---|
| 699 | <a name="l00926"></a>00926 <span class="keyword"> </span>{ |
|---|
| 700 | <a name="l00927"></a>00927 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ); |
|---|
| 701 | <a name="l00928"></a>00928 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );} |
|---|
| 702 | <a name="l00929"></a>00929 <span class="keywordflow">return</span> pom; |
|---|
| 703 | <a name="l00930"></a>00930 } |
|---|
| 704 | <a name="l00931"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">00931</a> vec <a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const</span> |
|---|
| 705 | <a name="l00932"></a>00932 <span class="keyword"> </span>{ |
|---|
| 706 | <a name="l00933"></a>00933 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ); |
|---|
| 707 | <a name="l00934"></a>00934 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=pow ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ),2 ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );} |
|---|
| 708 | <a name="l00935"></a>00935 <span class="keywordflow">return</span> pom-pow ( <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>(),2 ); |
|---|
| 709 | <a name="l00936"></a>00936 } |
|---|
| 710 | <a name="l00938"></a><a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f">00938</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f" title="For this class, qbounds are minimum and maximum value of the population!">qbounds</a> ( vec &lb, vec &ub, <span class="keywordtype">double</span> perc=0.95 )<span class="keyword"> const</span> |
|---|
| 711 | <a name="l00939"></a>00939 <span class="keyword"> </span>{ |
|---|
| 712 | <a name="l00940"></a>00940 <span class="comment">// lb in inf so than it will be pushed below;</span> |
|---|
| 713 | <a name="l00941"></a>00941 lb.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ); |
|---|
| 714 | <a name="l00942"></a>00942 ub.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ); |
|---|
| 715 | <a name="l00943"></a>00943 lb = std::numeric_limits<double>::infinity(); |
|---|
| 716 | <a name="l00944"></a>00944 ub = -std::numeric_limits<double>::infinity(); |
|---|
| 717 | <a name="l00945"></a>00945 <span class="keywordtype">int</span> j; |
|---|
| 718 | <a name="l00946"></a>00946 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) |
|---|
| 719 | <a name="l00947"></a>00947 { |
|---|
| 720 | <a name="l00948"></a>00948 <span class="keywordflow">for</span> ( j=0;j<<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>; j++ ) |
|---|
| 721 | <a name="l00949"></a>00949 { |
|---|
| 722 | <a name="l00950"></a>00950 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) <lb ( j ) ) {lb ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );} |
|---|
| 723 | <a name="l00951"></a>00951 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) >ub ( j ) ) {ub ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );} |
|---|
| 724 | <a name="l00952"></a>00952 } |
|---|
| 725 | <a name="l00953"></a>00953 } |
|---|
| 726 | <a name="l00954"></a>00954 } |
|---|
| 727 | <a name="l00955"></a>00955 }; |
|---|
| 728 | <a name="l00956"></a>00956 |
|---|
| 729 | <a name="l00957"></a>00957 |
|---|
| 730 | <a name="l00959"></a>00959 |
|---|
| 731 | <a name="l00960"></a>00960 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 732 | <a name="l00961"></a>00961 <span class="keywordtype">void</span> enorm<sq_T>::set_parameters ( <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R0 ) |
|---|
| 733 | <a name="l00962"></a>00962 { |
|---|
| 734 | <a name="l00963"></a>00963 <span class="comment">//Fixme test dimensions of mu0 and R0;</span> |
|---|
| 735 | <a name="l00964"></a>00964 mu = mu0; |
|---|
| 736 | <a name="l00965"></a>00965 R = R0; |
|---|
| 737 | <a name="l00966"></a>00966 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length(); |
|---|
| 738 | <a name="l00967"></a>00967 }; |
|---|
| 739 | <a name="l00968"></a>00968 |
|---|
| 740 | <a name="l00969"></a>00969 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 741 | <a name="l00970"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">00970</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">enorm<sq_T>::dupdate</a> ( mat &v, <span class="keywordtype">double</span> nu ) |
|---|
| 742 | <a name="l00971"></a>00971 { |
|---|
| 743 | <a name="l00972"></a>00972 <span class="comment">//</span> |
|---|
| 744 | <a name="l00973"></a>00973 }; |
|---|
| 745 | <a name="l00974"></a>00974 |
|---|
| 746 | <a name="l00975"></a>00975 <span class="comment">// template<class sq_T></span> |
|---|
| 747 | <a name="l00976"></a>00976 <span class="comment">// void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) {</span> |
|---|
| 748 | <a name="l00977"></a>00977 <span class="comment">// //</span> |
|---|
| 749 | <a name="l00978"></a>00978 <span class="comment">// };</span> |
|---|
| 750 | <a name="l00979"></a>00979 |
|---|
| 751 | <a name="l00980"></a>00980 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 752 | <a name="l00981"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">00981</a> vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample, from density .">enorm<sq_T>::sample</a>()<span class="keyword"> const</span> |
|---|
| 753 | <a name="l00982"></a>00982 <span class="keyword"> </span>{ |
|---|
| 754 | <a name="l00983"></a>00983 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ); |
|---|
| 755 | <a name="l00984"></a>00984 <span class="preprocessor">#pragma omp critical</span> |
|---|
| 756 | <a name="l00985"></a>00985 <span class="preprocessor"></span> NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x ); |
|---|
| 757 | <a name="l00986"></a>00986 vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); |
|---|
| 758 | <a name="l00987"></a>00987 |
|---|
| 759 | <a name="l00988"></a>00988 smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>; |
|---|
| 760 | <a name="l00989"></a>00989 <span class="keywordflow">return</span> smp; |
|---|
| 761 | <a name="l00990"></a>00990 }; |
|---|
| 762 | <a name="l00991"></a>00991 |
|---|
| 763 | <a name="l00992"></a>00992 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 764 | <a name="l00993"></a>00993 mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample, from density .">enorm<sq_T>::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const</span> |
|---|
| 765 | <a name="l00994"></a>00994 <span class="keyword"> </span>{ |
|---|
| 766 | <a name="l00995"></a>00995 mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N ); |
|---|
| 767 | <a name="l00996"></a>00996 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ); |
|---|
| 768 | <a name="l00997"></a>00997 vec pom; |
|---|
| 769 | <a name="l00998"></a>00998 <span class="keywordtype">int</span> i; |
|---|
| 770 | <a name="l00999"></a>00999 |
|---|
| 771 | <a name="l01000"></a>01000 <span class="keywordflow">for</span> ( i=0;i<N;i++ ) |
|---|
| 772 | <a name="l01001"></a>01001 { |
|---|
| 773 | <a name="l01002"></a>01002 <span class="preprocessor">#pragma omp critical</span> |
|---|
| 774 | <a name="l01003"></a>01003 <span class="preprocessor"></span> NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x ); |
|---|
| 775 | <a name="l01004"></a>01004 pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); |
|---|
| 776 | <a name="l01005"></a>01005 pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>; |
|---|
| 777 | <a name="l01006"></a>01006 X.set_col ( i, pom ); |
|---|
| 778 | <a name="l01007"></a>01007 } |
|---|
| 779 | <a name="l01008"></a>01008 |
|---|
| 780 | <a name="l01009"></a>01009 <span class="keywordflow">return</span> X; |
|---|
| 781 | <a name="l01010"></a>01010 }; |
|---|
| 782 | <a name="l01011"></a>01011 |
|---|
| 783 | <a name="l01012"></a>01012 <span class="comment">// template<class sq_T></span> |
|---|
| 784 | <a name="l01013"></a>01013 <span class="comment">// double enorm<sq_T>::eval ( const vec &val ) const {</span> |
|---|
| 785 | <a name="l01014"></a>01014 <span class="comment">// double pdfl,e;</span> |
|---|
| 786 | <a name="l01015"></a>01015 <span class="comment">// pdfl = evallog ( val );</span> |
|---|
| 787 | <a name="l01016"></a>01016 <span class="comment">// e = exp ( pdfl );</span> |
|---|
| 788 | <a name="l01017"></a>01017 <span class="comment">// return e;</span> |
|---|
| 789 | <a name="l01018"></a>01018 <span class="comment">// };</span> |
|---|
| 790 | <a name="l01019"></a>01019 |
|---|
| 791 | <a name="l01020"></a>01020 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 792 | <a name="l01021"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">01021</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">enorm<sq_T>::evallog_nn</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const</span> |
|---|
| 793 | <a name="l01022"></a>01022 <span class="keyword"> </span>{ |
|---|
| 794 | <a name="l01023"></a>01023 <span class="comment">// 1.83787706640935 = log(2pi)</span> |
|---|
| 795 | <a name="l01024"></a>01024 <span class="keywordtype">double</span> tmp=-0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>-val ) );<span class="comment">// - lognc();</span> |
|---|
| 796 | <a name="l01025"></a>01025 <span class="keywordflow">return</span> tmp; |
|---|
| 797 | <a name="l01026"></a>01026 }; |
|---|
| 798 | <a name="l01027"></a>01027 |
|---|
| 799 | <a name="l01028"></a>01028 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 800 | <a name="l01029"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">01029</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">enorm<sq_T>::lognc</a> ()<span class="keyword"> const</span> |
|---|
| 801 | <a name="l01030"></a>01030 <span class="keyword"> </span>{ |
|---|
| 802 | <a name="l01031"></a>01031 <span class="comment">// 1.83787706640935 = log(2pi)</span> |
|---|
| 803 | <a name="l01032"></a>01032 <span class="keywordtype">double</span> tmp=0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.logdet() ); |
|---|
| 804 | <a name="l01033"></a>01033 <span class="keywordflow">return</span> tmp; |
|---|
| 805 | <a name="l01034"></a>01034 }; |
|---|
| 806 | <a name="l01035"></a>01035 |
|---|
| 807 | <a name="l01036"></a>01036 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 808 | <a name="l01037"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">01037</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">mlnorm<sq_T>::set_parameters</a> ( <span class="keyword">const</span> mat &A0, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R0 ) |
|---|
| 809 | <a name="l01038"></a>01038 { |
|---|
| 810 | <a name="l01039"></a>01039 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> ); |
|---|
| 811 | <a name="l01040"></a>01040 it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> ); |
|---|
| 812 | <a name="l01041"></a>01041 |
|---|
| 813 | <a name="l01042"></a>01042 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( A0.rows() ),R0 ); |
|---|
| 814 | <a name="l01043"></a>01043 A = A0; |
|---|
| 815 | <a name="l01044"></a>01044 mu_const = mu0; |
|---|
| 816 | <a name="l01045"></a>01045 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=A0.cols(); |
|---|
| 817 | <a name="l01046"></a>01046 } |
|---|
| 818 | <a name="l01047"></a>01047 |
|---|
| 819 | <a name="l01048"></a>01048 <span class="comment">// template<class sq_T></span> |
|---|
| 820 | <a name="l01049"></a>01049 <span class="comment">// vec mlnorm<sq_T>::samplecond (const vec &cond, double &lik ) {</span> |
|---|
| 821 | <a name="l01050"></a>01050 <span class="comment">// this->condition ( cond );</span> |
|---|
| 822 | <a name="l01051"></a>01051 <span class="comment">// vec smp = epdf.sample();</span> |
|---|
| 823 | <a name="l01052"></a>01052 <span class="comment">// lik = epdf.eval ( smp );</span> |
|---|
| 824 | <a name="l01053"></a>01053 <span class="comment">// return smp;</span> |
|---|
| 825 | <a name="l01054"></a>01054 <span class="comment">// }</span> |
|---|
| 826 | <a name="l01055"></a>01055 |
|---|
| 827 | <a name="l01056"></a>01056 <span class="comment">// template<class sq_T></span> |
|---|
| 828 | <a name="l01057"></a>01057 <span class="comment">// mat mlnorm<sq_T>::samplecond (const vec &cond, vec &lik, int n ) {</span> |
|---|
| 829 | <a name="l01058"></a>01058 <span class="comment">// int i;</span> |
|---|
| 830 | <a name="l01059"></a>01059 <span class="comment">// int dim = rv.count();</span> |
|---|
| 831 | <a name="l01060"></a>01060 <span class="comment">// mat Smp ( dim,n );</span> |
|---|
| 832 | <a name="l01061"></a>01061 <span class="comment">// vec smp ( dim );</span> |
|---|
| 833 | <a name="l01062"></a>01062 <span class="comment">// this->condition ( cond );</span> |
|---|
| 834 | <a name="l01063"></a>01063 <span class="comment">//</span> |
|---|
| 835 | <a name="l01064"></a>01064 <span class="comment">// for ( i=0; i<n; i++ ) {</span> |
|---|
| 836 | <a name="l01065"></a>01065 <span class="comment">// smp = epdf.sample();</span> |
|---|
| 837 | <a name="l01066"></a>01066 <span class="comment">// lik ( i ) = epdf.eval ( smp );</span> |
|---|
| 838 | <a name="l01067"></a>01067 <span class="comment">// Smp.set_col ( i ,smp );</span> |
|---|
| 839 | <a name="l01068"></a>01068 <span class="comment">// }</span> |
|---|
| 840 | <a name="l01069"></a>01069 <span class="comment">//</span> |
|---|
| 841 | <a name="l01070"></a>01070 <span class="comment">// return Smp;</span> |
|---|
| 842 | <a name="l01071"></a>01071 <span class="comment">// }</span> |
|---|
| 843 | <a name="l01072"></a>01072 |
|---|
| 844 | <a name="l01073"></a>01073 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 845 | <a name="l01074"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">01074</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">mlnorm<sq_T>::condition</a> ( <span class="keyword">const</span> vec &cond ) |
|---|
| 846 | <a name="l01075"></a>01075 { |
|---|
| 847 | <a name="l01076"></a>01076 _mu = A*cond + mu_const; |
|---|
| 848 | <a name="l01077"></a>01077 <span class="comment">//R is already assigned;</span> |
|---|
| 849 | <a name="l01078"></a>01078 } |
|---|
| 850 | <a name="l01079"></a>01079 |
|---|
| 851 | <a name="l01080"></a>01080 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 852 | <a name="l01081"></a><a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80">01081</a> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm<sq_T>::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &rvn )<span class="keyword"> const</span> |
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| 853 | <a name="l01082"></a>01082 <span class="keyword"> </span>{ |
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| 854 | <a name="l01083"></a>01083 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(), <span class="stringliteral">"rv description is not assigned"</span> ); |
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| 855 | <a name="l01084"></a>01084 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> ); |
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| 856 | <a name="l01085"></a>01085 |
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| 857 | <a name="l01086"></a>01086 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn ); <span class="comment">//select rows and columns of R</span> |
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| 858 | <a name="l01087"></a>01087 |
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| 859 | <a name="l01088"></a>01088 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a>* tmp = <span class="keyword">new</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a>; |
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| 860 | <a name="l01089"></a>01089 tmp-><a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn ); |
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| 861 | <a name="l01090"></a>01090 tmp-><a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn ), Rn ); |
|---|
| 862 | <a name="l01091"></a>01091 <span class="keywordflow">return</span> tmp; |
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| 863 | <a name="l01092"></a>01092 } |
|---|
| 864 | <a name="l01093"></a>01093 |
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| 865 | <a name="l01094"></a>01094 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 866 | <a name="l01095"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">01095</a> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm<sq_T>::condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &rvn )<span class="keyword"> const</span> |
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| 867 | <a name="l01096"></a>01096 <span class="keyword"> </span>{ |
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| 868 | <a name="l01097"></a>01097 |
|---|
| 869 | <a name="l01098"></a>01098 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(),<span class="stringliteral">"rvs are not assigned"</span> ); |
|---|
| 870 | <a name="l01099"></a>01099 |
|---|
| 871 | <a name="l01100"></a>01100 <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#aec44dabdf0a6d90fbae95e1356eda39" title="Subtract another variable from the current one.">subt</a> ( rvn ); |
|---|
| 872 | <a name="l01101"></a>01101 it_assert_debug ( ( rvc.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() +rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ==<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ),<span class="stringliteral">"wrong rvn"</span> ); |
|---|
| 873 | <a name="l01102"></a>01102 <span class="comment">//Permutation vector of the new R</span> |
|---|
| 874 | <a name="l01103"></a>01103 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> ); |
|---|
| 875 | <a name="l01104"></a>01104 ivec irvc = rvc.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> ); |
|---|
| 876 | <a name="l01105"></a>01105 ivec perm=concat ( irvn , irvc ); |
|---|
| 877 | <a name="l01106"></a>01106 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm ); |
|---|
| 878 | <a name="l01107"></a>01107 |
|---|
| 879 | <a name="l01108"></a>01108 <span class="comment">//fixme - could this be done in general for all sq_T?</span> |
|---|
| 880 | <a name="l01109"></a>01109 mat S=Rn.to_mat(); |
|---|
| 881 | <a name="l01110"></a>01110 <span class="comment">//fixme</span> |
|---|
| 882 | <a name="l01111"></a>01111 <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1; |
|---|
| 883 | <a name="l01112"></a>01112 <span class="keywordtype">int</span> end=<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.rows()-1; |
|---|
| 884 | <a name="l01113"></a>01113 mat S11 = S.get ( 0,n, 0, n ); |
|---|
| 885 | <a name="l01114"></a>01114 mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end ); |
|---|
| 886 | <a name="l01115"></a>01115 mat S22 = S.get ( rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end, rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end ); |
|---|
| 887 | <a name="l01116"></a>01116 |
|---|
| 888 | <a name="l01117"></a>01117 vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn ); |
|---|
| 889 | <a name="l01118"></a>01118 vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc ); |
|---|
| 890 | <a name="l01119"></a>01119 mat A=S12*inv ( S22 ); |
|---|
| 891 | <a name="l01120"></a>01120 sq_T R_n ( S11 - A *S12.T() ); |
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| 892 | <a name="l01121"></a>01121 |
|---|
| 893 | <a name="l01122"></a>01122 <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm<sq_T></a>* tmp=<span class="keyword">new</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm<sq_T></a> ( ); |
|---|
| 894 | <a name="l01123"></a>01123 tmp->set_rv ( rvn ); tmp->set_rvc ( rvc ); |
|---|
| 895 | <a name="l01124"></a>01124 tmp->set_parameters ( A,mu1-A*mu2,R_n ); |
|---|
| 896 | <a name="l01125"></a>01125 <span class="keywordflow">return</span> tmp; |
|---|
| 897 | <a name="l01126"></a>01126 } |
|---|
| 898 | <a name="l01127"></a>01127 |
|---|
| 899 | <a name="l01129"></a>01129 |
|---|
| 900 | <a name="l01130"></a>01130 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
|---|
| 901 | <a name="l01131"></a>01131 std::ostream &operator<< ( std::ostream &os, mlnorm<sq_T> &ml ) |
|---|
| 902 | <a name="l01132"></a>01132 { |
|---|
| 903 | <a name="l01133"></a>01133 os << <span class="stringliteral">"A:"</span><< ml.A<<endl; |
|---|
| 904 | <a name="l01134"></a>01134 os << <span class="stringliteral">"mu:"</span><< ml.mu_const<<endl; |
|---|
| 905 | <a name="l01135"></a>01135 os << <span class="stringliteral">"R:"</span> << ml.epdf._R().to_mat() <<endl; |
|---|
| 906 | <a name="l01136"></a>01136 <span class="keywordflow">return</span> os; |
|---|
| 907 | <a name="l01137"></a>01137 }; |
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| 908 | <a name="l01138"></a>01138 |
|---|
| 909 | <a name="l01139"></a>01139 } |
|---|
| 910 | <a name="l01140"></a>01140 <span class="preprocessor">#endif //EF_H</span> |
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| 911 | </pre></div></div> |
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| 912 | <hr size="1"><address style="text-align: right;"><small>Generated on Thu Apr 23 21:06:42 2009 for mixpp by |
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| 913 | <a href="http://www.doxygen.org/index.html"> |
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| 914 | <img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.8 </small></address> |
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| 915 | </body> |
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| 916 | </html> |
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