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17<h1>work/git/mixpp/bdm/stat/merger.h</h1><a href="merger_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001
18<a name="l00013"></a>00013 <span class="preprocessor">#ifndef MER_H</span>
19<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define MER_H</span>
20<a name="l00015"></a>00015 <span class="preprocessor"></span>
21<a name="l00016"></a>00016 <span class="preprocessor">#include &lt;itpp/itbase.h&gt;</span>
22<a name="l00017"></a>00017 <span class="preprocessor">#include "<a class="code" href="libBM_8h.html" title="Bayesian Models (bm) that use Bayes rule to learn from observations.">libBM.h</a>"</span>
23<a name="l00018"></a>00018 <span class="preprocessor">#include "<a class="code" href="libEF_8h.html" title="Probability distributions for Exponential Family models.">libEF.h</a>"</span>
24<a name="l00019"></a>00019 <span class="preprocessor">#include "<a class="code" href="emix_8h.html" title="Probability distributions for Mixtures of pdfs.">emix.h</a>"</span>
25<a name="l00020"></a>00020
26<a name="l00021"></a>00021 <span class="keyword">using namespace </span>itpp;
27<a name="l00022"></a>00022 <span class="keyword">using</span> std::string;
28<a name="l00023"></a>00023
29<a name="l00030"></a><a class="code" href="classmerger.html">00030</a> <span class="keyword">class </span><a class="code" href="classmerger.html" title="Function for general combination of pdfs.">merger</a> : <span class="keyword">public</span> <a class="code" href="classmprod.html" title="Chain rule decomposition of epdf.">mprod</a>{
30<a name="l00031"></a>00031 <span class="keyword">protected</span>:     
31<a name="l00033"></a><a class="code" href="classmerger.html#1712341746811f2844de5499817abb46">00033</a>         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;fsqmat&gt;</a> <a class="code" href="classmerger.html#1712341746811f2844de5499817abb46" title="Additional pdf on the part in condition (if undefined);.">condpdf</a>;
32<a name="l00035"></a><a class="code" href="classmerger.html#11eb177ef6409926b2ba3b4630bceab1">00035</a>         Array&lt;ivec&gt; <a class="code" href="classmerger.html#11eb177ef6409926b2ba3b4630bceab1" title="Find potential overlaps in rv.">overlaps</a>;
33<a name="l00036"></a>00036 <span class="keyword">public</span>:
34<a name="l00038"></a><a class="code" href="classmerger.html#1a6a721294321c2e412ca0084c636b69">00038</a>         <a class="code" href="classmerger.html#1a6a721294321c2e412ca0084c636b69" title="Default constructor.">merger</a> (<span class="keyword">const</span> Array&lt;mpdf*&gt; &amp;in_sources) : <a class="code" href="classmprod.html" title="Chain rule decomposition of epdf.">mprod</a>(in_sources,true), <a class="code" href="classmerger.html#1712341746811f2844de5499817abb46" title="Additional pdf on the part in condition (if undefined);.">condpdf</a>(<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a>), <a class="code" href="classmerger.html#11eb177ef6409926b2ba3b4630bceab1" title="Find potential overlaps in rv.">overlaps</a>(<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.count()) {
35<a name="l00039"></a>00039                 <span class="keywordflow">if</span> (<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>()&gt;0) {
36<a name="l00040"></a>00040                         vec mu = zeros(<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>());
37<a name="l00041"></a>00041                         mat R = 100*eye(<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>());
38<a name="l00042"></a>00042                         <a class="code" href="classmerger.html#1712341746811f2844de5499817abb46" title="Additional pdf on the part in condition (if undefined);.">condpdf</a>.<a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">set_parameters</a>(mu,R);
39<a name="l00043"></a>00043                 }
40<a name="l00044"></a>00044                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>();i++){ <span class="comment">// cycle over rv</span>
41<a name="l00045"></a>00045                         ivec tmp(0);
42<a name="l00046"></a>00046                         <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j=0;j&lt;n;j++){<span class="comment">//cycle over rvinds</span>
43<a name="l00047"></a>00047                                 ivec <span class="keywordtype">id</span>=itpp::find(<a class="code" href="classmprod.html#c8f78fbcdb4e38ecd3197d2958082497" title="Indeces of rvs in common rv.">rvinds</a>(j)==i);
44<a name="l00048"></a>00048                                 tmp=concat(tmp,<span class="keywordtype">id</span>); <span class="comment">// add i to tmp if found</span>
45<a name="l00049"></a>00049                         }
46<a name="l00050"></a>00050                         <a class="code" href="classmerger.html#11eb177ef6409926b2ba3b4630bceab1" title="Find potential overlaps in rv.">overlaps</a>(i)=tmp;
47<a name="l00051"></a>00051                 }
48<a name="l00052"></a>00052         }
49<a name="l00055"></a>00055 <span class="comment">/*      vec sample(double &amp;w, const vec &amp;smp0){</span>
50<a name="l00056"></a>00056 <span class="comment">                // result</span>
51<a name="l00057"></a>00057 <span class="comment">                vec smp=smp0;</span>
52<a name="l00058"></a>00058 <span class="comment">                vec cond=condpdf.sample(); // Just like in samplecond, here it is not given!</span>
53<a name="l00059"></a>00059 <span class="comment">                // temporary</span>
54<a name="l00060"></a>00060 <span class="comment">                mat smpi=zeros(rv.count()+rvc.count(), n);</span>
55<a name="l00061"></a>00061 <span class="comment">                vec condi;</span>
56<a name="l00062"></a>00062 <span class="comment">                vec ll(n);</span>
57<a name="l00063"></a>00063 <span class="comment">                </span>
58<a name="l00064"></a>00064 <span class="comment">                // Consider arithmetic mean as a proposal density</span>
59<a name="l00065"></a>00065 <span class="comment">                ll = 0;</span>
60<a name="l00066"></a>00066 <span class="comment">                for ( int i = ( n - 1 );i &gt;= 0;i-- ) {</span>
61<a name="l00067"></a>00067 <span class="comment">                        if ( rvcinds ( i ).length() &gt; 0 ) {</span>
62<a name="l00068"></a>00068 <span class="comment">                                condi = zeros ( rvcinrv.length() + rvcinds.length() );</span>
63<a name="l00069"></a>00069 <span class="comment">                                // copy data in condition</span>
64<a name="l00070"></a>00070 <span class="comment">                                set_subvector ( condi,rvcinds ( i ), cond );</span>
65<a name="l00071"></a>00071 <span class="comment">                                // copy data in already generated sample</span>
66<a name="l00072"></a>00072 <span class="comment">                                set_subvector ( condi,rvcinrv ( i ), smp );</span>
67<a name="l00073"></a>00073 <span class="comment"></span>
68<a name="l00074"></a>00074 <span class="comment">                                mpdfs ( i )-&gt;condition ( condi );</span>
69<a name="l00075"></a>00075 <span class="comment">                        }</span>
70<a name="l00076"></a>00076 <span class="comment">                        smpi.set_col(i, epdfs ( i )-&gt;sample());</span>
71<a name="l00077"></a>00077 <span class="comment">                        // add ith likelihood contribution</span>
72<a name="l00078"></a>00078 <span class="comment">                        ll+=epdfs(i)-&gt;evalpdflog(smpi);</span>
73<a name="l00079"></a>00079 <span class="comment">                }</span>
74<a name="l00080"></a>00080 <span class="comment">                // Now lets analyze samples smpi</span>
75<a name="l00081"></a>00081 <span class="comment">                for (int i=0;i&lt;rv.count();i++){</span>
76<a name="l00082"></a>00082 <span class="comment">                        // number of components</span>
77<a name="l00083"></a>00083 <span class="comment">                        int noc = overlaps(i).length();</span>
78<a name="l00084"></a>00084 <span class="comment">                        if (noc&lt;2) { // only one sample in this dimension, take it</span>
79<a name="l00085"></a>00085 <span class="comment">                                smp(i) = smpi(i,overlaps(i)(0));</span>
80<a name="l00086"></a>00086 <span class="comment">                        }</span>
81<a name="l00087"></a>00087 <span class="comment">                        else {</span>
82<a name="l00088"></a>00088 <span class="comment">                                //pick dimension</span>
83<a name="l00089"></a>00089 <span class="comment">                                double unis = UniRNG.sample();</span>
84<a name="l00090"></a>00090 <span class="comment">                                dim = 0;</span>
85<a name="l00091"></a>00091 <span class="comment">                                while(unis&lt;(dim+1)/noc) {dim++;} // </span>
86<a name="l00092"></a>00092 <span class="comment">                                //in dim we now have randomly picked dimension</span>
87<a name="l00093"></a>00093 <span class="comment">                                smp(i) = smpi(i,overlaps(i)(dim));</span>
88<a name="l00094"></a>00094 <span class="comment">                        }</span>
89<a name="l00095"></a>00095 <span class="comment">                }</span>
90<a name="l00096"></a>00096 <span class="comment">                </span>
91<a name="l00097"></a>00097 <span class="comment">                // now we have sample how to evaluate weight?</span>
92<a name="l00098"></a>00098 <span class="comment">                return smp;</span>
93<a name="l00099"></a>00099 <span class="comment">                </span>
94<a name="l00100"></a>00100 <span class="comment">                // copied from mprod.sample</span>
95<a name="l00101"></a>00101 <span class="comment">        };*/</span>
96<a name="l00102"></a>00102 <span class="comment">//      project </span>
97<a name="l00104"></a><a class="code" href="classmerger.html#51e425ee1dd46d73353526e6a1c24ca3">00104</a> <span class="comment"></span>        <span class="keyword">virtual</span> <a class="code" href="classmerger.html#51e425ee1dd46d73353526e6a1c24ca3" title="for future use">~merger</a>() {};
98<a name="l00105"></a>00105 };
99<a name="l00106"></a>00106
100<a name="l00107"></a>00107
101<a name="l00108"></a>00108 <span class="preprocessor">#endif // MER_H</span>
102</pre></div></div>
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