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test KF : estimation of R in KF is not possible! Likelihood of y_t is growing when R -> 0

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16<h1>work/mixpp/bdm/stat/libEF.h</h1><a href="libEF_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001
17<a name="l00013"></a>00013 <span class="preprocessor">#ifndef EF_H</span>
18<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define EF_H</span>
19<a name="l00015"></a>00015 <span class="preprocessor"></span>
20<a name="l00016"></a>00016 <span class="preprocessor">#include &lt;itpp/itbase.h&gt;</span>
21<a name="l00017"></a>00017 <span class="preprocessor">#include "../math/libDC.h"</span>
22<a name="l00018"></a>00018 <span class="preprocessor">#include "<a class="code" href="libBM_8h.html" title="Bayesian Models (bm) that use Bayes rule to learn from observations.">libBM.h</a>"</span>
23<a name="l00019"></a>00019 <span class="preprocessor">#include "../itpp_ext.h"</span>
24<a name="l00020"></a>00020 <span class="comment">//#include &lt;std&gt;</span>
25<a name="l00021"></a>00021
26<a name="l00022"></a>00022 <span class="keyword">using namespace </span>itpp;
27<a name="l00023"></a>00023
28<a name="l00024"></a>00024
29<a name="l00026"></a>00026 <span class="keyword">extern</span> Uniform_RNG UniRNG;
30<a name="l00027"></a>00027 <span class="keyword">extern</span> Normal_RNG NorRNG;
31<a name="l00028"></a>00028 <span class="keyword">extern</span> <a class="code" href="classitpp_1_1Gamma__RNG.html" title="Gamma distribution.">Gamma_RNG</a> GamRNG;
32<a name="l00029"></a>00029
33<a name="l00030"></a>00030
34<a name="l00037"></a><a class="code" href="classeEF.html">00037</a> <span class="keyword">class </span><a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> : <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
35<a name="l00038"></a>00038
36<a name="l00039"></a>00039 <span class="keyword">public</span>:
37<a name="l00041"></a><a class="code" href="classeEF.html#702e24158366430bc24d57c7f64e1e9e">00041</a>         <a class="code" href="classeEF.html#702e24158366430bc24d57c7f64e1e9e" title="default constructor">eEF</a>() :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>() {};
38<a name="l00042"></a>00042
39<a name="l00043"></a>00043         <a class="code" href="classeEF.html#702e24158366430bc24d57c7f64e1e9e" title="default constructor">eEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {};
40<a name="l00044"></a>00044
41<a name="l00045"></a>00045         <span class="keyword">virtual</span> <span class="keywordtype">void</span> tupdate ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ) {};
42<a name="l00046"></a>00046
43<a name="l00047"></a>00047         <span class="keyword">virtual</span> <span class="keywordtype">void</span> dupdate ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 ) {};
44<a name="l00048"></a>00048 };
45<a name="l00049"></a>00049
46<a name="l00050"></a>00050 <span class="keyword">class </span>mEF : <span class="keyword">public</span> <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> {
47<a name="l00051"></a>00051
48<a name="l00052"></a>00052 <span class="keyword">public</span>:
49<a name="l00053"></a>00053         mEF ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( rv0,rvc0 ) {};
50<a name="l00054"></a>00054 };
51<a name="l00055"></a>00055
52<a name="l00061"></a>00061 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
53<a name="l00062"></a>00062
54<a name="l00063"></a><a class="code" href="classenorm.html">00063</a> <span class="keyword">class </span><a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
55<a name="l00064"></a>00064 <span class="keyword">protected</span>:
56<a name="l00066"></a><a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20">00066</a>         vec <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
57<a name="l00068"></a><a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1">00068</a>         sq_T <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;
58<a name="l00070"></a><a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355">00070</a>         sq_T <a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>;
59<a name="l00072"></a><a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1">00072</a>         <span class="keywordtype">bool</span> <a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a>;
60<a name="l00074"></a><a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e">00074</a>         <span class="keywordtype">int</span> <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>;
61<a name="l00075"></a>00075 <span class="keyword">public</span>:
62<a name="l00076"></a>00076         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a>() :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {};
63<a name="l00077"></a>00077
64<a name="l00078"></a>00078         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv );
65<a name="l00079"></a>00079         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> );
66<a name="l00081"></a>00081         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a" title="tupdate in exponential form (not really handy)">tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar );
67<a name="l00082"></a>00082         <span class="keywordtype">void</span> dupdate ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 );
68<a name="l00083"></a>00083
69<a name="l00084"></a>00084         vec <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">sample</a>();
70<a name="l00085"></a>00085         mat <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">sample</a> ( <span class="keywordtype">int</span> N );
71<a name="l00086"></a>00086         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#93107f05a8e9b34b64853767200121a4" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val );
72<a name="l00087"></a>00087         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#9517594915e897584eaebbb057ed8881" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val );
73<a name="l00088"></a><a class="code" href="classenorm.html#191c1220c3ddd0c5f54e78f19b57ebd5">00088</a>         vec <a class="code" href="classenorm.html#191c1220c3ddd0c5f54e78f19b57ebd5" title="return expected value">mean</a>(){<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;}
74<a name="l00089"></a>00089
75<a name="l00090"></a>00090 <span class="comment">//Access methods</span>
76<a name="l00092"></a><a class="code" href="classenorm.html#3be0cb541ec9b88e5aa3f60307bbc753">00092</a> <span class="comment"></span>        vec* <a class="code" href="classenorm.html#3be0cb541ec9b88e5aa3f60307bbc753" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() {<span class="keywordflow">return</span> &amp;<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;}
77<a name="l00093"></a>00093
78<a name="l00095"></a><a class="code" href="classenorm.html#8725c534863c4fc2bddef0edfb95a740">00095</a>         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#8725c534863c4fc2bddef0edfb95a740" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a> ( sq_T* &amp;pR, sq_T* &amp;piR ) {
79<a name="l00096"></a>00096                 pR=&amp;<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;
80<a name="l00097"></a>00097                 piR=&amp;<a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>;
81<a name="l00098"></a>00098         }
82<a name="l00099"></a>00099
83<a name="l00101"></a><a class="code" href="classenorm.html#c9ca4f2ca42568e40ca146168e7f3247">00101</a>         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#c9ca4f2ca42568e40ca146168e7f3247" title="set cache as inconsistent">_cached</a> ( <span class="keywordtype">bool</span> what ) {<a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a>=what;}
84<a name="l00102"></a>00102 };
85<a name="l00103"></a>00103
86<a name="l00113"></a><a class="code" href="classegamma.html">00113</a> <span class="keyword">class </span><a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
87<a name="l00114"></a>00114 <span class="keyword">protected</span>:
88<a name="l00115"></a>00115         vec alpha;
89<a name="l00116"></a>00116         vec beta;
90<a name="l00117"></a>00117 <span class="keyword">public</span> :
91<a name="l00119"></a><a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1">00119</a>         <a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1" title="Default constructor.">egamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ) {};
92<a name="l00121"></a><a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339">00121</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {alpha=a,beta=b;};
93<a name="l00122"></a>00122         vec <a class="code" href="classegamma.html#0a2186a586432c2c3f22d09c5341890f" title="Returns the required moment of the epdf.">sample</a>();
94<a name="l00123"></a>00123         mat <a class="code" href="classegamma.html#0a2186a586432c2c3f22d09c5341890f" title="Returns the required moment of the epdf.">sample</a> ( <span class="keywordtype">int</span> N );
95<a name="l00124"></a>00124         <span class="keywordtype">double</span> evalpdflog ( <span class="keyword">const</span> vec val );
96<a name="l00126"></a><a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790">00126</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a> ( vec* &amp;a, vec* &amp;b ) {a=&amp;alpha;b=&amp;beta;};
97<a name="l00127"></a><a class="code" href="classegamma.html#6617890ffa40767fc196876534d4119d">00127</a>         vec <a class="code" href="classegamma.html#6617890ffa40767fc196876534d4119d" title="return expected value">mean</a>(){vec pom(alpha); pom/=beta; <span class="keywordflow">return</span> pom;}
98<a name="l00128"></a>00128 };
99<a name="l00129"></a>00129
100<a name="l00131"></a><a class="code" href="classemix.html">00131</a> <span class="keyword">class </span><a class="code" href="classemix.html" title="Weighted mixture of epdfs with external owned components.">emix</a> : <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
101<a name="l00132"></a>00132 <span class="keyword">protected</span>:
102<a name="l00133"></a>00133         <span class="keywordtype">int</span> n;
103<a name="l00134"></a>00134         vec &amp;w;
104<a name="l00135"></a>00135         Array&lt;epdf*&gt; Coms;
105<a name="l00136"></a>00136 <span class="keyword">public</span>:
106<a name="l00138"></a><a class="code" href="classemix.html#b0ac204af8919c22bce72816ed82019e">00138</a>         <a class="code" href="classemix.html#b0ac204af8919c22bce72816ed82019e" title="Default constructor.">emix</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv, vec &amp;w0): <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>(rv), n(w0.length()), w(w0), Coms(n) {};
107<a name="l00139"></a>00139         <span class="keywordtype">void</span> set_parameters( <span class="keywordtype">int</span> &amp;i, <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* ep){Coms(i)=ep;}
108<a name="l00140"></a><a class="code" href="classemix.html#dcb3f927bf061eac6229850158ca1558">00140</a>         vec <a class="code" href="classemix.html#dcb3f927bf061eac6229850158ca1558" title="return expected value">mean</a>(){vec pom; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0;i&lt;n;i++){pom+=Coms(i)-&gt;mean()*w(i);} <span class="keywordflow">return</span> pom;};
109<a name="l00141"></a><a class="code" href="classemix.html#3eb9a8e12ce1c5c8a3ddb245354b6941">00141</a>         vec <a class="code" href="classemix.html#3eb9a8e12ce1c5c8a3ddb245354b6941" title="Returns the required moment of the epdf.">sample</a>() {it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
110<a name="l00142"></a>00142 };
111<a name="l00143"></a>00143
112<a name="l00145"></a>00145
113<a name="l00146"></a><a class="code" href="classeuni.html">00146</a> <span class="keyword">class </span><a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
114<a name="l00147"></a>00147 <span class="keyword">protected</span>:
115<a name="l00149"></a><a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1">00149</a>         vec <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>;
116<a name="l00151"></a><a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231">00151</a>         vec <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>;
117<a name="l00153"></a><a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4">00153</a>         vec <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>;
118<a name="l00155"></a><a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda">00155</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>,lnk;
119<a name="l00156"></a>00156 <span class="keyword">public</span>:
120<a name="l00157"></a>00157         <a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rv ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {}
121<a name="l00158"></a><a class="code" href="classeuni.html#95f29237feb32fcadf570a181d5a0918">00158</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#95f29237feb32fcadf570a181d5a0918" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val ) {<span class="keywordflow">return</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;}
122<a name="l00159"></a><a class="code" href="classeuni.html#830fca2ffb6786530529c57eabc81666">00159</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#830fca2ffb6786530529c57eabc81666" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) {<span class="keywordflow">return</span> lnk;}
123<a name="l00160"></a><a class="code" href="classeuni.html#0f71562e3e919aba823cb7d9d420ad4c">00160</a>         vec <a class="code" href="classeuni.html#0f71562e3e919aba823cb7d9d420ad4c" title="Returns the required moment of the epdf.">sample</a>() {
124<a name="l00161"></a>00161                 vec smp ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>() ); UniRNG.sample_vector ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>(),smp );
125<a name="l00162"></a>00162                 <span class="keywordflow">return</span> <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>+<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>*smp;
126<a name="l00163"></a>00163         }
127<a name="l00164"></a>00164         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) {
128<a name="l00165"></a>00165                 <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> = high0-low0;
129<a name="l00166"></a>00166                 it_assert_debug ( min ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> );
130<a name="l00167"></a>00167                 <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a> = low0;
131<a name="l00168"></a>00168                 <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a> = high0;
132<a name="l00169"></a>00169                 <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> );
133<a name="l00170"></a>00170                 lnk = log ( <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> );
134<a name="l00171"></a>00171         }
135<a name="l00172"></a><a class="code" href="classeuni.html#34a2f2d23158c40b97b44dfe551a1b3d">00172</a>         vec <a class="code" href="classeuni.html#34a2f2d23158c40b97b44dfe551a1b3d" title="return expected value">mean</a>(){vec pom=<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; pom-=<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; pom/=2.0; <span class="keywordflow">return</span> pom;}
136<a name="l00173"></a>00173 };
137<a name="l00174"></a>00174
138<a name="l00175"></a>00175
139<a name="l00181"></a>00181 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
140<a name="l00182"></a><a class="code" href="classmlnorm.html">00182</a> <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> mEF {
141<a name="l00183"></a>00183         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
142<a name="l00184"></a>00184         vec* _mu; <span class="comment">//cached epdf.mu;</span>
143<a name="l00185"></a>00185         mat A;
144<a name="l00186"></a>00186 <span class="keyword">public</span>:
145<a name="l00188"></a>00188         <a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5" title="Constructor.">mlnorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv,<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc );
146<a name="l00189"></a>00189         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> sq_T &amp;R );
147<a name="l00191"></a>00191         vec <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik );
148<a name="l00193"></a>00193         mat <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n );
149<a name="l00194"></a>00194         <span class="keywordtype">void</span> condition ( vec &amp;cond );
150<a name="l00195"></a>00195 };
151<a name="l00196"></a>00196
152<a name="l00206"></a><a class="code" href="classmgamma.html">00206</a> <span class="keyword">class </span><a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> mEF {
153<a name="l00207"></a>00207         <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
154<a name="l00208"></a>00208         <span class="keywordtype">double</span> k;
155<a name="l00209"></a>00209         vec* _beta;
156<a name="l00210"></a>00210
157<a name="l00211"></a>00211 <span class="keyword">public</span>:
158<a name="l00213"></a>00213         <a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6" title="Constructor.">mgamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc );
159<a name="l00214"></a>00214         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">double</span> k );
160<a name="l00216"></a>00216         vec <a class="code" href="classmgamma.html#9f40dc43885085fad8e3d6652b79e139" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik );
161<a name="l00218"></a>00218         mat <a class="code" href="classmgamma.html#9f40dc43885085fad8e3d6652b79e139" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n );
162<a name="l00219"></a>00219         <span class="keywordtype">void</span> condition ( <span class="keyword">const</span> vec &amp;val ) {*_beta=k/val;};
163<a name="l00220"></a>00220 };
164<a name="l00221"></a>00221
165<a name="l00223"></a><a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212">00223</a> <span class="keyword">enum</span> <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
166<a name="l00229"></a><a class="code" href="classeEmp.html">00229</a> <span class="keyword">class </span><a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
167<a name="l00230"></a>00230 <span class="keyword">protected</span> :
168<a name="l00232"></a><a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd">00232</a>         <span class="keywordtype">int</span> <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;
169<a name="l00233"></a>00233         vec w;
170<a name="l00234"></a>00234         Array&lt;vec&gt; samples;
171<a name="l00235"></a>00235 <span class="keyword">public</span>:
172<a name="l00236"></a>00236         <a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0 ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ) {};
173<a name="l00237"></a>00237         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;w0, <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
174<a name="l00239"></a><a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b">00239</a>         vec&amp; <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> w;};
175<a name="l00240"></a>00240         Array&lt;vec&gt;&amp; _samples() {<span class="keywordflow">return</span> samples;};
176<a name="l00242"></a>00242         ivec <a class="code" href="classeEmp.html#77268292fc4465cb73ddbfb1f2932a59" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC );
177<a name="l00243"></a><a class="code" href="classeEmp.html#c9b44099a400579b88aff9f5afaf9c13">00243</a>         vec <a class="code" href="classeEmp.html#c9b44099a400579b88aff9f5afaf9c13" title="Returns the required moment of the epdf.">sample</a>() {it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
178<a name="l00244"></a><a class="code" href="classeEmp.html#de42454cb65a17fd8662d207dd59aacf">00244</a>         vec <a class="code" href="classeEmp.html#de42454cb65a17fd8662d207dd59aacf" title="return expected value">mean</a>(){vec pom;
179<a name="l00245"></a>00245                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++){pom+=samples(i)*w(i);}
180<a name="l00246"></a>00246                 <span class="keywordflow">return</span> pom;
181<a name="l00247"></a>00247         }
182<a name="l00248"></a>00248 };
183<a name="l00249"></a>00249
184<a name="l00250"></a>00250
185<a name="l00252"></a>00252
186<a name="l00253"></a>00253 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
187<a name="l00254"></a>00254 <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::enorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a>(), mu ( rv.count() ),R ( rv.count() ),_iR ( rv.count() ),cached ( false ),dim ( rv.count() ) {};
188<a name="l00255"></a>00255
189<a name="l00256"></a>00256 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
190<a name="l00257"></a>00257 <span class="keywordtype">void</span> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) {
191<a name="l00258"></a>00258 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
192<a name="l00259"></a>00259         mu = mu0;
193<a name="l00260"></a>00260         R = R0;
194<a name="l00261"></a>00261         <span class="keywordflow">if</span> ( _iR.rows() !=R.rows() ) _iR=R; <span class="comment">// memory allocation!</span>
195<a name="l00262"></a>00262         R.inv ( _iR ); <span class="comment">//update cache</span>
196<a name="l00263"></a>00263         cached=<span class="keyword">true</span>;
197<a name="l00264"></a>00264 };
198<a name="l00265"></a>00265
199<a name="l00266"></a>00266 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
200<a name="l00267"></a>00267 <span class="keywordtype">void</span> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) {
201<a name="l00268"></a>00268         <span class="comment">//</span>
202<a name="l00269"></a>00269 };
203<a name="l00270"></a>00270
204<a name="l00271"></a>00271 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
205<a name="l00272"></a><a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a">00272</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a" title="tupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ) {
206<a name="l00273"></a>00273         <span class="comment">//</span>
207<a name="l00274"></a>00274 };
208<a name="l00275"></a>00275
209<a name="l00276"></a>00276 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
210<a name="l00277"></a><a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023">00277</a> vec <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">enorm&lt;sq_T&gt;::sample</a>() {
211<a name="l00278"></a>00278         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
212<a name="l00279"></a>00279         NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
213<a name="l00280"></a>00280         vec smp = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
214<a name="l00281"></a>00281
215<a name="l00282"></a>00282         smp += <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
216<a name="l00283"></a>00283         <span class="keywordflow">return</span> smp;
217<a name="l00284"></a>00284 };
218<a name="l00285"></a>00285
219<a name="l00286"></a>00286 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
220<a name="l00287"></a>00287 mat <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N ) {
221<a name="l00288"></a>00288         mat X ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N );
222<a name="l00289"></a>00289         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
223<a name="l00290"></a>00290         vec pom;
224<a name="l00291"></a>00291         <span class="keywordtype">int</span> i;
225<a name="l00292"></a>00292
226<a name="l00293"></a>00293         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
227<a name="l00294"></a>00294                 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
228<a name="l00295"></a>00295                 pom = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
229<a name="l00296"></a>00296                 pom +=<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
230<a name="l00297"></a>00297                 X.set_col ( i, pom );
231<a name="l00298"></a>00298         }
232<a name="l00299"></a>00299
233<a name="l00300"></a>00300         <span class="keywordflow">return</span> X;
234<a name="l00301"></a>00301 };
235<a name="l00302"></a>00302
236<a name="l00303"></a>00303 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
237<a name="l00304"></a><a class="code" href="classenorm.html#93107f05a8e9b34b64853767200121a4">00304</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#93107f05a8e9b34b64853767200121a4" title="Compute probability of argument val.">enorm&lt;sq_T&gt;::eval</a> ( <span class="keyword">const</span> vec &amp;val ) {
238<a name="l00305"></a>00305         <span class="keywordtype">double</span> pdfl,e;
239<a name="l00306"></a>00306         pdfl = <a class="code" href="classenorm.html#9517594915e897584eaebbb057ed8881" title="Compute log-probability of argument val.">evalpdflog</a> ( val );
240<a name="l00307"></a>00307         e = exp ( pdfl );
241<a name="l00308"></a>00308         <span class="keywordflow">return</span> e;
242<a name="l00309"></a>00309 };
243<a name="l00310"></a>00310
244<a name="l00311"></a>00311 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
245<a name="l00312"></a><a class="code" href="classenorm.html#9517594915e897584eaebbb057ed8881">00312</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#9517594915e897584eaebbb057ed8881" title="Compute log-probability of argument val.">enorm&lt;sq_T&gt;::evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) {
246<a name="l00313"></a>00313         <span class="keywordflow">if</span> ( !<a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a> ) {<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.inv ( <a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a> );<a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a>=<span class="keyword">true</span>;}
247<a name="l00314"></a>00314
248<a name="l00315"></a>00315         <span class="keywordflow">return</span> -0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.cols() *0.79817986835811504957 +<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.logdet() +<a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>.qform ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>-val ) );
249<a name="l00316"></a>00316 };
250<a name="l00317"></a>00317
251<a name="l00318"></a>00318
252<a name="l00319"></a>00319 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
253<a name="l00320"></a><a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5">00320</a> <a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5" title="Constructor.">mlnorm&lt;sq_T&gt;::mlnorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0,<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :mEF ( rv0,rvc0 ),<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ),A ( rv0.count(),rv0.count() ) {
254<a name="l00321"></a>00321         _mu = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu();
255<a name="l00322"></a>00322 }
256<a name="l00323"></a>00323
257<a name="l00324"></a>00324 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
258<a name="l00325"></a>00325 <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> sq_T &amp;R0 ) {
259<a name="l00326"></a>00326         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( rv.count() ),R0 );
260<a name="l00327"></a>00327         A = A0;
261<a name="l00328"></a>00328 }
262<a name="l00329"></a>00329
263<a name="l00330"></a>00330 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
264<a name="l00331"></a><a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18">00331</a> vec <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik ) {
265<a name="l00332"></a>00332         this-&gt;condition ( cond );
266<a name="l00333"></a>00333         vec smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample();
267<a name="l00334"></a>00334         lik = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp );
268<a name="l00335"></a>00335         <span class="keywordflow">return</span> smp;
269<a name="l00336"></a>00336 }
270<a name="l00337"></a>00337
271<a name="l00338"></a>00338 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
272<a name="l00339"></a><a class="code" href="classmlnorm.html#215fb88cc8b95d64cdefd6849abdd1e8">00339</a> mat <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n ) {
273<a name="l00340"></a>00340         <span class="keywordtype">int</span> i;
274<a name="l00341"></a>00341         <span class="keywordtype">int</span> dim = rv.count();
275<a name="l00342"></a>00342         mat Smp ( dim,n );
276<a name="l00343"></a>00343         vec smp ( dim );
277<a name="l00344"></a>00344         this-&gt;condition ( cond );
278<a name="l00345"></a>00345
279<a name="l00346"></a>00346         <span class="keywordflow">for</span> ( i=0; i&lt;n; i++ ) {
280<a name="l00347"></a>00347                 smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample();
281<a name="l00348"></a>00348                 lik ( i ) = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp );
282<a name="l00349"></a>00349                 Smp.set_col ( i ,smp );
283<a name="l00350"></a>00350         }
284<a name="l00351"></a>00351
285<a name="l00352"></a>00352         <span class="keywordflow">return</span> Smp;
286<a name="l00353"></a>00353 }
287<a name="l00354"></a>00354
288<a name="l00355"></a>00355 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
289<a name="l00356"></a>00356 <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::condition</a> ( vec &amp;cond ) {
290<a name="l00357"></a>00357         *_mu = A*cond;
291<a name="l00358"></a>00358 <span class="comment">//R is already assigned;</span>
292<a name="l00359"></a>00359 }
293<a name="l00360"></a>00360
294<a name="l00362"></a>00362
295<a name="l00363"></a>00363
296<a name="l00364"></a>00364 <span class="preprocessor">#endif //EF_H</span>
297</pre></div><hr size="1"><address style="text-align: right;"><small>Generated on Thu Feb 28 16:54:40 2008 for mixpp by&nbsp;
298<a href="http://www.doxygen.org/index.html">
299<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.3 </small></address>
300</body>
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