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15<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
16<a name="l00013"></a>00013 <span class="preprocessor">#ifndef EF_H</span>
17<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define EF_H</span>
18<a name="l00015"></a>00015 <span class="preprocessor"></span>
19<a name="l00016"></a>00016 <span class="preprocessor">#include &lt;itpp/itbase.h&gt;</span>
20<a name="l00017"></a>00017 <span class="preprocessor">#include "../math/libDC.h"</span>
21<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>
22<a name="l00019"></a>00019 <span class="comment">//#include &lt;std&gt;</span>
23<a name="l00020"></a>00020
24<a name="l00021"></a>00021 <span class="keyword">using namespace </span>itpp;
25<a name="l00022"></a>00022
26<a name="l00028"></a><a class="code" href="classeEF.html">00028</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> {
27<a name="l00029"></a>00029
28<a name="l00030"></a>00030 <span class="keyword">public</span>:
29<a name="l00031"></a>00031         <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 ) {};
30<a name="l00032"></a>00032         <span class="keyword">virtual</span> <span class="keywordtype">void</span> dupdate( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 ) {};
31<a name="l00033"></a>00033 };
32<a name="l00034"></a>00034
33<a name="l00035"></a>00035 <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> {
34<a name="l00036"></a>00036
35<a name="l00037"></a>00037 <span class="keyword">public</span>:
36<a name="l00038"></a>00038
37<a name="l00039"></a>00039 };
38<a name="l00040"></a>00040
39<a name="l00046"></a>00046 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
40<a name="l00047"></a><a class="code" href="classenorm.html">00047</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> {
41<a name="l00048"></a>00048         <span class="keywordtype">int</span> dim;
42<a name="l00049"></a>00049         vec mu;
43<a name="l00050"></a>00050         sq_T R;
44<a name="l00051"></a>00051 <span class="keyword">public</span>:
45<a name="l00052"></a>00052         <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, vec &amp;mu, sq_T &amp;R );
46<a name="l00053"></a>00053         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a>();
47<a name="l00055"></a>00055         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#2a1a522504c7788dfd7fb733157ee39e" title="tupdate used in KF">tupdate</a>( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar );
48<a name="l00056"></a>00056         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#d1b0faf61260de09cf63bf823add5b32" title="dupdate used in KF">dupdate</a>( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 );
49<a name="l00058"></a>00058         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#2a1a522504c7788dfd7fb733157ee39e" title="tupdate used in KF">tupdate</a>();
50<a name="l00060"></a>00060         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#d1b0faf61260de09cf63bf823add5b32" title="dupdate used in KF">dupdate</a>();
51<a name="l00061"></a>00061         
52<a name="l00062"></a>00062         vec <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">sample</a>();
53<a name="l00063"></a>00063         mat <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">sample</a>(<span class="keywordtype">int</span> N);
54<a name="l00064"></a>00064         <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 );
55<a name="l00065"></a>00065         Normal_RNG RNG;
56<a name="l00066"></a>00066 };
57<a name="l00067"></a>00067
58<a name="l00071"></a>00071 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
59<a name="l00072"></a>00072 <span class="keyword">class </span>mlnorm : <span class="keyword">public</span> mEF {
60<a name="l00073"></a>00073         <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>;
61<a name="l00074"></a>00074         mat A;
62<a name="l00075"></a>00075 <span class="keyword">public</span>:
63<a name="l00077"></a>00077         mlnorm( <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, mat &amp;A, sq_T &amp;R );
64<a name="l00079"></a>00079         vec samplecond( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik );
65<a name="l00080"></a>00080         mat samplecond( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n );
66<a name="l00081"></a>00081         <span class="keywordtype">void</span> condition( vec &amp;cond );
67<a name="l00082"></a>00082 };
68<a name="l00083"></a>00083
69<a name="l00085"></a>00085
70<a name="l00086"></a>00086 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
71<a name="l00087"></a>00087 <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, vec &amp;mu0, sq_T &amp;R0 ) {
72<a name="l00088"></a>00088         dim = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>();
73<a name="l00089"></a>00089         mu = mu0;
74<a name="l00090"></a>00090         R = R0;
75<a name="l00091"></a>00091 };
76<a name="l00092"></a>00092
77<a name="l00093"></a>00093 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
78<a name="l00094"></a>00094 <span class="keywordtype">void</span> <a class="code" href="classenorm.html#d1b0faf61260de09cf63bf823add5b32" title="dupdate used in KF">enorm&lt;sq_T&gt;::dupdate</a>( mat &amp;v, <span class="keywordtype">double</span> nu ) {
79<a name="l00095"></a>00095         <span class="comment">//</span>
80<a name="l00096"></a>00096 };
81<a name="l00097"></a>00097
82<a name="l00098"></a>00098 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
83<a name="l00099"></a><a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a">00099</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#2a1a522504c7788dfd7fb733157ee39e" title="tupdate used in KF">enorm&lt;sq_T&gt;::tupdate</a>( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ) {
84<a name="l00100"></a>00100         <span class="comment">//</span>
85<a name="l00101"></a>00101 };
86<a name="l00102"></a>00102
87<a name="l00103"></a>00103 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
88<a name="l00104"></a><a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023">00104</a> vec <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">enorm&lt;sq_T&gt;::sample</a>() {
89<a name="l00105"></a>00105         vec x( dim );
90<a name="l00106"></a>00106         RNG.sample_vector( dim,x );
91<a name="l00107"></a>00107         vec smp = R.sqrt_mult( x );
92<a name="l00108"></a>00108
93<a name="l00109"></a>00109         smp += mu;
94<a name="l00110"></a>00110         <span class="keywordflow">return</span> smp;
95<a name="l00111"></a>00111 };
96<a name="l00112"></a>00112
97<a name="l00113"></a>00113 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
98<a name="l00114"></a>00114 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 ) {
99<a name="l00115"></a>00115         mat X( dim,N );
100<a name="l00116"></a>00116         vec x( dim );
101<a name="l00117"></a>00117         vec pom;
102<a name="l00118"></a>00118         <span class="keywordtype">int</span> i;
103<a name="l00119"></a>00119         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
104<a name="l00120"></a>00120                 RNG.sample_vector( dim,x );
105<a name="l00121"></a>00121                 pom = R.sqrt_mult( x );
106<a name="l00122"></a>00122                 pom +=mu;
107<a name="l00123"></a>00123                 X.set_col( i, pom);
108<a name="l00124"></a>00124         }
109<a name="l00125"></a>00125         <span class="keywordflow">return</span> X;
110<a name="l00126"></a>00126 };
111<a name="l00127"></a>00127
112<a name="l00128"></a>00128 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
113<a name="l00129"></a><a class="code" href="classenorm.html#93107f05a8e9b34b64853767200121a4">00129</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 ) {
114<a name="l00130"></a>00130         <span class="comment">//</span>
115<a name="l00131"></a>00131 };
116<a name="l00132"></a>00132
117<a name="l00133"></a>00133
118<a name="l00134"></a>00134 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
119<a name="l00135"></a>00135 <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::enorm</a>() {};
120<a name="l00136"></a>00136
121<a name="l00137"></a>00137 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
122<a name="l00138"></a>00138 mlnorm&lt;sq_T&gt;::mlnorm( <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, mat &amp;A, sq_T &amp;R ) {
123<a name="l00139"></a>00139         <span class="keywordtype">int</span> dim = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>();
124<a name="l00140"></a>00140         vec mu( dim );
125<a name="l00141"></a>00141
126<a name="l00142"></a>00142         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> = <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>( rv,mu,R );
127<a name="l00143"></a>00143 }
128<a name="l00144"></a>00144
129<a name="l00145"></a>00145 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
130<a name="l00146"></a>00146 vec mlnorm&lt;sq_T&gt;::samplecond( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik ) {
131<a name="l00147"></a>00147         this-&gt;condition( cond );
132<a name="l00148"></a>00148         vec smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample();
133<a name="l00149"></a>00149         lik = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval( smp );
134<a name="l00150"></a>00150         <span class="keywordflow">return</span> smp;
135<a name="l00151"></a>00151 }
136<a name="l00152"></a>00152
137<a name="l00153"></a>00153 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
138<a name="l00154"></a>00154 mat mlnorm&lt;sq_T&gt;::samplecond( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n ) {
139<a name="l00155"></a>00155         <span class="keywordtype">int</span> i;
140<a name="l00156"></a>00156         <span class="keywordtype">int</span> dim = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>();
141<a name="l00157"></a>00157         mat Smp( dim,n );
142<a name="l00158"></a>00158         vec smp( dim );
143<a name="l00159"></a>00159         this-&gt;condition( cond );
144<a name="l00160"></a>00160         <span class="keywordflow">for</span> ( i=0; i&lt;dim; i++ ) {
145<a name="l00161"></a>00161                 smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample();
146<a name="l00162"></a>00162                 lik( i ) = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval( smp );
147<a name="l00163"></a>00163                 Smp.set_col( i ,smp );
148<a name="l00164"></a>00164         }
149<a name="l00165"></a>00165         <span class="keywordflow">return</span> Smp;
150<a name="l00166"></a>00166 }
151<a name="l00167"></a>00167
152<a name="l00168"></a>00168 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
153<a name="l00169"></a>00169 <span class="keywordtype">void</span> mlnorm&lt;sq_T&gt;::condition( vec &amp;cond ) {
154<a name="l00170"></a>00170         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.mu = A*cond;
155<a name="l00171"></a>00171 <span class="comment">//R is already assigned;</span>
156<a name="l00172"></a>00172 }
157<a name="l00173"></a>00173
158<a name="l00174"></a>00174 <span class="preprocessor">#endif //EF_H</span>
159</pre></div><hr size="1"><address style="text-align: right;"><small>Generated on Sun Feb 17 16:14:14 2008 for mixpp by&nbsp;
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161<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.3 </small></address>
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