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[261]65<h1>libPF.h</h1><a href="libPF_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001
[8]66<a name="l00013"></a>00013 <span class="preprocessor">#ifndef PF_H</span>
67<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define PF_H</span>
68<a name="l00015"></a>00015 <span class="preprocessor"></span>
[261]69<a name="l00016"></a>00016
[32]70<a name="l00017"></a>00017 <span class="preprocessor">#include "../stat/libEF.h"</span>
[261]71<a name="l00018"></a>00018
[271]72<a name="l00019"></a>00019 <span class="keyword">namespace </span>bdm {
[261]73<a name="l00020"></a>00020
[269]74<a name="l00027"></a><a class="code" href="classbdm_1_1PF.html">00027</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1PF.html" title="Trivial particle filter with proposal density equal to parameter evolution model...">PF</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> {
[261]75<a name="l00028"></a>00028 <span class="keyword">protected</span>:
76<a name="l00030"></a><a class="code" href="classbdm_1_1PF.html#eeafaf9b8ad75fe62ee9fd6369e3f7fe">00030</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1PF.html#eeafaf9b8ad75fe62ee9fd6369e3f7fe" title="number of particles;">n</a>;
77<a name="l00032"></a><a class="code" href="classbdm_1_1PF.html#dc049265b9086cad7071f98d00a2b9af">00032</a>         <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> <a class="code" href="classbdm_1_1PF.html#dc049265b9086cad7071f98d00a2b9af" title="posterior density">est</a>;
78<a name="l00034"></a><a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3">00034</a>         vec &amp;<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a>;
79<a name="l00036"></a><a class="code" href="classbdm_1_1PF.html#914bd66025692c4018dbd482cb3c47c1">00036</a>         Array&lt;vec&gt; &amp;<a class="code" href="classbdm_1_1PF.html#914bd66025692c4018dbd482cb3c47c1" title="pointer into eEmp ">_samples</a>;
[271]80<a name="l00038"></a><a class="code" href="classbdm_1_1PF.html#521e9621d3b5e1274275f323691afdaf">00038</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_1PF.html#521e9621d3b5e1274275f323691afdaf" title="Parameter evolution model.">par</a>;
81<a name="l00040"></a><a class="code" href="classbdm_1_1PF.html#d6e7a62fba1e0a0d73c9b87f4fb683ec">00040</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_1PF.html#d6e7a62fba1e0a0d73c9b87f4fb683ec" title="Observation model.">obs</a>;
[287]82<a name="l00041"></a>00041
83<a name="l00043"></a><a class="code" href="classbdm_1_1PF.html#9932c7c5865954ef9a438afcbe944e52">00043</a>         RESAMPLING_METHOD <a class="code" href="classbdm_1_1PF.html#9932c7c5865954ef9a438afcbe944e52" title="which resampling method will be used">resmethod</a>;
84<a name="l00044"></a>00044
85<a name="l00047"></a>00047
86<a name="l00049"></a><a class="code" href="classbdm_1_1PF.html#98ef9ff80c394fafd28680b7a3f831b1">00049</a>         <span class="keywordtype">bool</span> <a class="code" href="classbdm_1_1PF.html#98ef9ff80c394fafd28680b7a3f831b1" title="Log all samples.">opt_L_smp</a>;
87<a name="l00051"></a><a class="code" href="classbdm_1_1PF.html#5a49463a88ee80771a464861df845ff6">00051</a>         <span class="keywordtype">bool</span> <a class="code" href="classbdm_1_1PF.html#5a49463a88ee80771a464861df845ff6" title="Log all samples.">opt_L_wei</a>;
88<a name="l00053"></a>00053
89<a name="l00054"></a>00054 <span class="keyword">public</span>:
90<a name="l00057"></a>00057         <a class="code" href="classbdm_1_1PF.html" title="Trivial particle filter with proposal density equal to parameter evolution model...">PF</a> ( ) :<a class="code" href="classbdm_1_1PF.html#dc049265b9086cad7071f98d00a2b9af" title="posterior density">est</a>(), <a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a> ( <a class="code" href="classbdm_1_1PF.html#dc049265b9086cad7071f98d00a2b9af" title="posterior density">est</a>.<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a>() ),<a class="code" href="classbdm_1_1PF.html#914bd66025692c4018dbd482cb3c47c1" title="pointer into eEmp ">_samples</a> ( <a class="code" href="classbdm_1_1PF.html#dc049265b9086cad7071f98d00a2b9af" title="posterior density">est</a>.<a class="code" href="classbdm_1_1PF.html#914bd66025692c4018dbd482cb3c47c1" title="pointer into eEmp ">_samples</a>() ), <a class="code" href="classbdm_1_1PF.html#98ef9ff80c394fafd28680b7a3f831b1" title="Log all samples.">opt_L_smp</a> ( false ), <a class="code" href="classbdm_1_1PF.html#5a49463a88ee80771a464861df845ff6" title="Log all samples.">opt_L_wei</a> ( false ) {<a class="code" href="classbdm_1_1BM.html#109c1a626a69031658e3a44e9e500cca" title="IDs of storages in loggers.">LIDs</a>.set_size ( 5 );};
91<a name="l00058"></a>00058         <span class="comment">/*      PF ( mpdf *par0, mpdf *obs0, epdf *epdf0, int n0 ) :</span>
92<a name="l00059"></a>00059 <span class="comment">                                est ( ),_w ( est._w() ),_samples ( est._samples() ),opt_L_smp(false), opt_L_wei(false)</span>
93<a name="l00060"></a>00060 <span class="comment">                { set_parameters ( par0,obs0,n0 ); set_statistics ( ones ( n0 ),epdf0 ); };*/</span>
94<a name="l00061"></a>00061         <span class="keywordtype">void</span> set_parameters ( <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> *par0, <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> *obs0, <span class="keywordtype">int</span> n0, RESAMPLING_METHOD rm=SYSTEMATIC )
95<a name="l00062"></a>00062         { <a class="code" href="classbdm_1_1PF.html#521e9621d3b5e1274275f323691afdaf" title="Parameter evolution model.">par</a> = par0; <a class="code" href="classbdm_1_1PF.html#d6e7a62fba1e0a0d73c9b87f4fb683ec" title="Observation model.">obs</a>=obs0; <a class="code" href="classbdm_1_1PF.html#eeafaf9b8ad75fe62ee9fd6369e3f7fe" title="number of particles;">n</a>=n0; <a class="code" href="classbdm_1_1PF.html#9932c7c5865954ef9a438afcbe944e52" title="which resampling method will be used">resmethod</a>= rm;};
96<a name="l00063"></a>00063         <span class="keywordtype">void</span> set_statistics ( <span class="keyword">const</span> vec w0, epdf *epdf0 ) {<a class="code" href="classbdm_1_1PF.html#dc049265b9086cad7071f98d00a2b9af" title="posterior density">est</a>.set_statistics ( w0,epdf0 );};
97<a name="l00066"></a>00066 <span class="comment">//      void set_est ( const epdf &amp;epdf0 );</span>
98<a name="l00067"></a><a class="code" href="classbdm_1_1PF.html#bf104b869b5df8dd4a14bbe430d40488">00067</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1PF.html#bf104b869b5df8dd4a14bbe430d40488">set_options</a> ( <span class="keyword">const</span> <span class="keywordtype">string</span> &amp;opt ) {
[290]99<a name="l00068"></a>00068                 <a class="code" href="classbdm_1_1PF.html#bf104b869b5df8dd4a14bbe430d40488">BM::set_options</a>(opt);
[287]100<a name="l00069"></a>00069                 <a class="code" href="classbdm_1_1PF.html#5a49463a88ee80771a464861df845ff6" title="Log all samples.">opt_L_wei</a>= ( opt.find ( <span class="stringliteral">"logweights"</span> ) !=string::npos );
101<a name="l00070"></a>00070                 <a class="code" href="classbdm_1_1PF.html#98ef9ff80c394fafd28680b7a3f831b1" title="Log all samples.">opt_L_smp</a>= ( opt.find ( <span class="stringliteral">"logsamples"</span> ) !=string::npos );
102<a name="l00071"></a>00071         }
103<a name="l00072"></a>00072         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1PF.html#638946eea22d4964bf9350286bb4efd8" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
104<a name="l00074"></a><a class="code" href="classbdm_1_1PF.html#78a9f6809827be1d9bfe215d03b1c6ed">00074</a>         vec* <a class="code" href="classbdm_1_1PF.html#78a9f6809827be1d9bfe215d03b1c6ed" title="access function">__w</a>() {<span class="keywordflow">return</span> &amp;<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a>;}
105<a name="l00075"></a>00075 };
106<a name="l00076"></a>00076
107<a name="l00083"></a>00083 <span class="keyword">template</span>&lt;<span class="keyword">class</span> BM_T&gt;
108<a name="l00084"></a><a class="code" href="classbdm_1_1MPF.html">00084</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1MPF.html" title="Marginalized Particle filter.">MPF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1PF.html" title="Trivial particle filter with proposal density equal to parameter evolution model...">PF</a> {
109<a name="l00085"></a>00085         Array&lt;BM_T*&gt; BMs;
110<a name="l00086"></a>00086
111<a name="l00088"></a>00088
112<a name="l00089"></a>00089 <span class="keyword">class </span>mpfepdf : <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>  {
113<a name="l00090"></a>00090         <span class="keyword">protected</span>:
114<a name="l00091"></a>00091                 <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> &amp;E;
115<a name="l00092"></a>00092                 vec &amp;<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a>;
116<a name="l00093"></a>00093                 Array&lt;const epdf*&gt; Coms;
117<a name="l00094"></a>00094         <span class="keyword">public</span>:
118<a name="l00095"></a>00095                 mpfepdf ( <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> &amp;E0 ) :
119<a name="l00096"></a>00096                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ), E ( E0 ),  <a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a> ( E._w() ),
120<a name="l00097"></a>00097                                 Coms ( <a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a>.length() ) {
121<a name="l00098"></a>00098                 };
122<a name="l00100"></a>00100                 <span class="keywordtype">void</span> read_statistics ( Array&lt;BM_T*&gt; &amp;A ) {
123<a name="l00101"></a>00101                         dim = E.dimension() +A ( 0 )-&gt;posterior().dimension();
124<a name="l00102"></a>00102                         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0; i&lt;<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a>.length() ;i++ ) {Coms ( i ) = A ( i )-&gt;_e();}
125<a name="l00103"></a>00103                 }
126<a name="l00105"></a>00105                 <span class="keywordtype">void</span> set_elements ( <span class="keywordtype">int</span> &amp;i, <span class="keywordtype">double</span> wi, <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>* ep )
127<a name="l00106"></a>00106                 {<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a> ( i ) =wi; Coms ( i ) =ep;};
[271]128<a name="l00107"></a>00107
[287]129<a name="l00108"></a>00108                 <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1PF.html#eeafaf9b8ad75fe62ee9fd6369e3f7fe" title="number of particles;">n</a> ) {
130<a name="l00109"></a>00109                         E.set_parameters ( n, <span class="keyword">false</span> );
131<a name="l00110"></a>00110                         Coms.set_length ( n );
132<a name="l00111"></a>00111                 }
133<a name="l00112"></a>00112                 vec mean()<span class="keyword"> const </span>{
134<a name="l00113"></a>00113                         <span class="comment">// ugly</span>
135<a name="l00114"></a>00114                         vec pom=zeros ( Coms ( 0 )-&gt;dimension() );
136<a name="l00115"></a>00115                         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0; i&lt;<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a>.length(); i++ ) {pom += Coms ( i )-&gt;mean() * <a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a> ( i );}
137<a name="l00116"></a>00116                         <span class="keywordflow">return</span> concat ( E.mean(),pom );
138<a name="l00117"></a>00117                 }
139<a name="l00118"></a>00118                 vec variance()<span class="keyword"> const </span>{
140<a name="l00119"></a>00119                         <span class="comment">// ugly</span>
141<a name="l00120"></a>00120                         vec pom=zeros ( Coms ( 0 )-&gt;dimension() );
142<a name="l00121"></a>00121                         vec pom2=zeros ( Coms ( 0 )-&gt;dimension() );
143<a name="l00122"></a>00122                         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0; i&lt;<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a>.length(); i++ ) {
144<a name="l00123"></a>00123                                 pom += Coms ( i )-&gt;mean() * <a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a> ( i );
145<a name="l00124"></a>00124                                 pom2 += ( Coms ( i )-&gt;variance() + pow ( Coms ( i )-&gt;mean(),2 ) ) * <a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a> ( i );
146<a name="l00125"></a>00125                         }
147<a name="l00126"></a>00126                         <span class="keywordflow">return</span> concat ( E.variance(),pom2-pow ( pom,2 ) );
[280]148<a name="l00127"></a>00127                 }
[287]149<a name="l00128"></a>00128                 <span class="keywordtype">void</span> qbounds ( vec &amp;lb, vec &amp;ub, <span class="keywordtype">double</span> perc=0.95 )<span class="keyword"> const </span>{
150<a name="l00129"></a>00129                         <span class="comment">//bounds on particles</span>
151<a name="l00130"></a>00130                         vec lbp;
152<a name="l00131"></a>00131                         vec ubp;
153<a name="l00132"></a>00132                         E.qbounds ( lbp,ubp );
154<a name="l00133"></a>00133
155<a name="l00134"></a>00134                         <span class="comment">//bounds on Components</span>
156<a name="l00135"></a>00135                         <span class="keywordtype">int</span> dimC=Coms ( 0 )-&gt;dimension();
157<a name="l00136"></a>00136                         <span class="keywordtype">int</span> j;
158<a name="l00137"></a>00137                         <span class="comment">// temporary</span>
159<a name="l00138"></a>00138                         vec lbc(dimC);
160<a name="l00139"></a>00139                         vec ubc(dimC);
161<a name="l00140"></a>00140                         <span class="comment">// minima and maxima</span>
162<a name="l00141"></a>00141                         vec Lbc(dimC);
163<a name="l00142"></a>00142                         vec Ubc(dimC);
164<a name="l00143"></a>00143                         Lbc = std::numeric_limits&lt;double&gt;::infinity();
165<a name="l00144"></a>00144                         Ubc = -std::numeric_limits&lt;double&gt;::infinity();
166<a name="l00145"></a>00145
167<a name="l00146"></a>00146                         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a>.length();i++ ) {
168<a name="l00147"></a>00147                                 <span class="comment">// check Coms</span>
169<a name="l00148"></a>00148                                 Coms ( i )-&gt;qbounds ( lbc,ubc );
170<a name="l00149"></a>00149                                 <span class="keywordflow">for</span> ( j=0;j&lt;dimC; j++ ) {
171<a name="l00150"></a>00150                                         <span class="keywordflow">if</span> ( lbc ( j ) &lt;Lbc ( j ) ) {Lbc ( j ) =lbc ( j );}
172<a name="l00151"></a>00151                                         <span class="keywordflow">if</span> ( ubc ( j ) &gt;Ubc ( j ) ) {Ubc ( j ) =ubc ( j );}
173<a name="l00152"></a>00152                                 }
174<a name="l00153"></a>00153                         }
175<a name="l00154"></a>00154                         lb=concat(lbp,Lbc);
176<a name="l00155"></a>00155                         ub=concat(ubp,Ubc);
177<a name="l00156"></a>00156                 }
178<a name="l00157"></a>00157
179<a name="l00158"></a>00158                 vec sample()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
180<a name="l00159"></a>00159
181<a name="l00160"></a>00160                 <span class="keywordtype">double</span> evallog ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"not implemented"</span> ); <span class="keywordflow">return</span> 0.0;}
182<a name="l00161"></a>00161         };
183<a name="l00162"></a>00162
184<a name="l00164"></a>00164         mpfepdf jest;
185<a name="l00165"></a>00165
186<a name="l00167"></a>00167         <span class="keywordtype">bool</span> opt_L_mea;
187<a name="l00168"></a>00168
188<a name="l00169"></a>00169 <span class="keyword">public</span>:
189<a name="l00171"></a><a class="code" href="classbdm_1_1MPF.html#0068e7ca53d90fa5911eb31a0d657f26">00171</a>         <a class="code" href="classbdm_1_1MPF.html#0068e7ca53d90fa5911eb31a0d657f26" title="Default constructor.">MPF</a> () : <a class="code" href="classbdm_1_1PF.html" title="Trivial particle filter with proposal density equal to parameter evolution model...">PF</a> (), jest ( <a class="code" href="classbdm_1_1PF.html#dc049265b9086cad7071f98d00a2b9af" title="posterior density">est</a> ) {};
190<a name="l00172"></a>00172         <span class="keywordtype">void</span> set_parameters ( <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> *par0, <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> *obs0, <span class="keywordtype">int</span> n0, RESAMPLING_METHOD rm=SYSTEMATIC ) {
191<a name="l00173"></a>00173                 PF::set_parameters ( par0, obs0, n0, rm );
192<a name="l00174"></a>00174                 jest.set_parameters ( n0 );<span class="comment">//duplication of rm</span>
193<a name="l00175"></a>00175                 BMs.set_length ( n0 );
194<a name="l00176"></a>00176         }
195<a name="l00177"></a>00177         <span class="keywordtype">void</span> set_statistics ( epdf *epdf0, <span class="keyword">const</span> BM_T* BMcond0 ) {
196<a name="l00178"></a>00178
197<a name="l00179"></a>00179                 PF::set_statistics ( ones ( <a class="code" href="classbdm_1_1PF.html#eeafaf9b8ad75fe62ee9fd6369e3f7fe" title="number of particles;">n</a> ) /<a class="code" href="classbdm_1_1PF.html#eeafaf9b8ad75fe62ee9fd6369e3f7fe" title="number of particles;">n</a>, epdf0 );
198<a name="l00180"></a>00180                 <span class="comment">// copy</span>
199<a name="l00181"></a>00181                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1PF.html#eeafaf9b8ad75fe62ee9fd6369e3f7fe" title="number of particles;">n</a>;i++ ) { BMs ( i ) = <span class="keyword">new</span> BM_T ( *BMcond0 ); BMs ( i )-&gt;condition ( <a class="code" href="classbdm_1_1PF.html#914bd66025692c4018dbd482cb3c47c1" title="pointer into eEmp ">_samples</a> ( i ) );}
[280]200<a name="l00182"></a>00182
[287]201<a name="l00183"></a>00183                 jest.read_statistics ( BMs );
202<a name="l00184"></a>00184                 <span class="comment">//options</span>
203<a name="l00185"></a>00185         };
204<a name="l00186"></a>00186
205<a name="l00187"></a>00187         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1MPF.html#286d040770d08bd7ff416cea617b1b14" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
206<a name="l00188"></a>00188         <span class="keyword">const</span> epdf&amp; posterior()<span class="keyword"> const </span>{<span class="keywordflow">return</span> jest;}
207<a name="l00189"></a>00189         <span class="keyword">const</span> epdf* _e()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &amp;jest;} <span class="comment">//Fixme: is it useful?</span>
208<a name="l00191"></a>00191 <span class="comment"></span>        <span class="comment">/*      void set_est ( const epdf&amp; epdf0 ) {</span>
209<a name="l00192"></a>00192 <span class="comment">                        PF::set_est ( epdf0 );  // sample params in condition</span>
210<a name="l00193"></a>00193 <span class="comment">                        // copy conditions to BMs</span>
211<a name="l00194"></a>00194 <span class="comment"></span>
212<a name="l00195"></a>00195 <span class="comment">                        for ( int i=0;i&lt;n;i++ ) {BMs(i)-&gt;condition ( _samples ( i ) );}</span>
213<a name="l00196"></a>00196 <span class="comment">                }*/</span>
214<a name="l00197"></a><a class="code" href="classbdm_1_1MPF.html#2e95498dec734088ab9f4878ff404144">00197</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1MPF.html#2e95498dec734088ab9f4878ff404144" title="Set postrior of rvc to samples from epdf0. Statistics of BMs are not re-computed!...">set_options</a> ( <span class="keyword">const</span> <span class="keywordtype">string</span> &amp;opt ) {
[290]215<a name="l00198"></a>00198                 <a class="code" href="classbdm_1_1MPF.html#2e95498dec734088ab9f4878ff404144" title="Set postrior of rvc to samples from epdf0. Statistics of BMs are not re-computed!...">PF::set_options</a> ( opt );
[287]216<a name="l00199"></a>00199                 opt_L_mea = ( opt.find ( <span class="stringliteral">"logmeans"</span> ) !=string::npos );
217<a name="l00200"></a>00200         }
218<a name="l00201"></a>00201
219<a name="l00203"></a><a class="code" href="classbdm_1_1MPF.html#82b5a34d9ed0e78452f98d2ecbf1e93c">00203</a>         <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_1MPF.html#82b5a34d9ed0e78452f98d2ecbf1e93c" title="Access function.">_BM</a> ( <span class="keywordtype">int</span> i ) {<span class="keywordflow">return</span> BMs ( i );}
220<a name="l00204"></a>00204 };
[280]221<a name="l00205"></a>00205
[287]222<a name="l00206"></a>00206 <span class="keyword">template</span>&lt;<span class="keyword">class</span> BM_T&gt;
[290]223<a name="l00207"></a><a class="code" href="classbdm_1_1MPF.html#286d040770d08bd7ff416cea617b1b14">00207</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1MPF.html#286d040770d08bd7ff416cea617b1b14" title="Incremental Bayes rule.">MPF&lt;BM_T&gt;::bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
[287]224<a name="l00208"></a>00208         <span class="keywordtype">int</span> i;
225<a name="l00209"></a>00209         vec lls ( n );
226<a name="l00210"></a>00210         vec llsP ( n );
227<a name="l00211"></a>00211         ivec ind;
228<a name="l00212"></a>00212         <span class="keywordtype">double</span> mlls=-std::numeric_limits&lt;double&gt;::infinity();
229<a name="l00213"></a>00213
230<a name="l00214"></a>00214 <span class="preprocessor">#pragma omp parallel for</span>
231<a name="l00215"></a>00215 <span class="preprocessor"></span>        <span class="keywordflow">for</span> ( i=0;i&lt;n;i++ ) {
232<a name="l00216"></a>00216                 <span class="comment">//generate new samples from paramater evolution model;</span>
233<a name="l00217"></a>00217                 <a class="code" href="classbdm_1_1PF.html#914bd66025692c4018dbd482cb3c47c1" title="pointer into eEmp ">_samples</a> ( i ) = <a class="code" href="classbdm_1_1PF.html#521e9621d3b5e1274275f323691afdaf" title="Parameter evolution model.">par</a>-&gt;<a class="code" href="classbdm_1_1mpdf.html#f0c1db6fcbb3aae2dd6123884457a367" title="Returns a sample from the density conditioned on cond, .">samplecond</a> ( <a class="code" href="classbdm_1_1PF.html#914bd66025692c4018dbd482cb3c47c1" title="pointer into eEmp ">_samples</a> ( i ) );
234<a name="l00218"></a>00218                 llsP ( i ) = <a class="code" href="classbdm_1_1PF.html#521e9621d3b5e1274275f323691afdaf" title="Parameter evolution model.">par</a>-&gt;<a class="code" href="classbdm_1_1mpdf.html#05e843fd11c410a99dad2b88c55aca80">_e</a>()-&gt;<a class="code" href="classbdm_1_1epdf.html#deab266d63c236c277538867d5c3f249" title="Compute log-probability of argument val.">evallog</a> ( <a class="code" href="classbdm_1_1PF.html#914bd66025692c4018dbd482cb3c47c1" title="pointer into eEmp ">_samples</a> ( i ) );
235<a name="l00219"></a>00219                 BMs ( i )-&gt;condition ( <a class="code" href="classbdm_1_1PF.html#914bd66025692c4018dbd482cb3c47c1" title="pointer into eEmp ">_samples</a> ( i ) );
236<a name="l00220"></a>00220                 BMs ( i )-&gt;bayes ( dt );
237<a name="l00221"></a>00221                 lls ( i ) = BMs ( i )-&gt;_ll(); <span class="comment">// lls above is also in proposal her must be lls(i) =, not +=!!</span>
238<a name="l00222"></a>00222                 <span class="keywordflow">if</span> ( lls ( i ) &gt;mlls ) mlls=lls ( i ); <span class="comment">//find maximum likelihood (for numerical stability)</span>
239<a name="l00223"></a>00223         }
240<a name="l00224"></a>00224
241<a name="l00225"></a>00225         <span class="keywordtype">double</span> sum_w=0.0;
242<a name="l00226"></a>00226         <span class="comment">// compute weights</span>
243<a name="l00227"></a>00227 <span class="preprocessor">#pragma omp parallel for</span>
244<a name="l00228"></a>00228 <span class="preprocessor"></span>        <span class="keywordflow">for</span> ( i=0;i&lt;n;i++ ) {
245<a name="l00229"></a>00229                 <a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a> ( i ) *= exp ( lls ( i ) - mlls ); <span class="comment">// multiply w by likelihood</span>
246<a name="l00230"></a>00230                 sum_w+=<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a> ( i );
247<a name="l00231"></a>00231         }
248<a name="l00232"></a>00232
249<a name="l00233"></a>00233         <span class="keywordflow">if</span> ( sum_w  &gt;0.0 ) {
250<a name="l00234"></a>00234                 <a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a> /=sum_w; <span class="comment">//?</span>
251<a name="l00235"></a>00235         }
252<a name="l00236"></a>00236         <span class="keywordflow">else</span> {
253<a name="l00237"></a>00237                 cout&lt;&lt;<span class="stringliteral">"sum(w)==0"</span>&lt;&lt;endl;
254<a name="l00238"></a>00238         }
255<a name="l00239"></a>00239
256<a name="l00240"></a>00240
257<a name="l00241"></a>00241         <span class="keywordtype">double</span> eff = 1.0/ ( <a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a>*<a class="code" href="classbdm_1_1PF.html#f5149d5522d1095d39240c4c607f61a3" title="pointer into eEmp ">_w</a> );
258<a name="l00242"></a>00242         <span class="keywordflow">if</span> ( eff &lt; ( 0.3*n ) ) {
259<a name="l00243"></a>00243                 ind = <a class="code" href="classbdm_1_1PF.html#dc049265b9086cad7071f98d00a2b9af" title="posterior density">est</a>.<a class="code" href="classbdm_1_1eEmp.html#f06ce255de5dbb2313f52ee51f82ba3d" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="classbdm_1_1PF.html#9932c7c5865954ef9a438afcbe944e52" title="which resampling method will be used">resmethod</a> );
260<a name="l00244"></a>00244                 <span class="comment">// Resample Bms!</span>
261<a name="l00245"></a>00245
262<a name="l00246"></a>00246 <span class="preprocessor">#pragma omp parallel for</span>
263<a name="l00247"></a>00247 <span class="preprocessor"></span>                <span class="keywordflow">for</span> ( i=0;i&lt;n;i++ ) {
264<a name="l00248"></a>00248                         <span class="keywordflow">if</span> ( ind ( i ) !=i ) {<span class="comment">//replace the current Bm by a new one</span>
265<a name="l00249"></a>00249                                 <span class="comment">//fixme this would require new assignment operator</span>
266<a name="l00250"></a>00250                                 <span class="comment">// *Bms[i] = *Bms[ind ( i ) ];</span>
267<a name="l00251"></a>00251
268<a name="l00252"></a>00252                                 <span class="comment">// poor-man's solution: replicate constructor here</span>
269<a name="l00253"></a>00253                                 <span class="comment">// copied from MPF::MPF</span>
270<a name="l00254"></a>00254                                 <span class="keyword">delete</span> BMs ( i );
271<a name="l00255"></a>00255                                 BMs ( i ) = <span class="keyword">new</span> BM_T ( *BMs ( ind ( i ) ) ); <span class="comment">//copy constructor</span>
272<a name="l00256"></a>00256                                 <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>&amp; pom=BMs ( i )-&gt;posterior();
273<a name="l00257"></a>00257                                 jest.set_elements ( i,1.0/n,&amp;pom );
274<a name="l00258"></a>00258                         }
275<a name="l00259"></a>00259                 };
276<a name="l00260"></a>00260                 cout &lt;&lt; <span class="charliteral">'.'</span>;
277<a name="l00261"></a>00261         }
278<a name="l00262"></a>00262 }
279<a name="l00263"></a>00263
280<a name="l00264"></a>00264 }
281<a name="l00265"></a>00265 <span class="preprocessor">#endif // KF_H</span>
282<a name="l00266"></a>00266 <span class="preprocessor"></span>
283<a name="l00267"></a>00267
[91]284</pre></div></div>
[296]285<hr size="1"><address style="text-align: right;"><small>Generated on Tue Mar 17 13:51:48 2009 for mixpp by&nbsp;
[8]286<a href="http://www.doxygen.org/index.html">
[290]287<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.8 </small></address>
[8]288</body>
289</html>
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