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  • library/doc/html/classbdm_1_1KalmanCh.html

    r614 r616  
    7474<p><a class="el" href="classbdm_1_1Kalman.html" title="Common abstract base for Kalman filters.">Kalman</a> filter in square <a class="el" href="classbdm_1_1root.html" title="Root class of BDM objects.">root</a> form.   
    7575<a href="#_details">More...</a></p> 
     76<hr/><a name="_details"></a><h2>Detailed Description</h2> 
     77<p><a class="el" href="classbdm_1_1Kalman.html" title="Common abstract base for Kalman filters.">Kalman</a> filter in square <a class="el" href="classbdm_1_1root.html" title="Root class of BDM objects.">root</a> form. </p> 
     78<p>Trivial example: </p> 
     79<div class="fragment"><pre class="fragment"><span class="preprocessor">#include &quot;<a class="code" href="kalman_8h.html" title="Bayesian Filtering for linear Gaussian models (Kalman Filter) and extensions.">estim/kalman.h</a>&quot;</span> 
     80<span class="keyword">using namespace </span>bdm; 
     81 
     82<span class="comment">// estimation of AR(0) model</span> 
     83<span class="keywordtype">int</span> main() { 
     84        <span class="comment">//dimensions</span> 
     85        <span class="keywordtype">int</span> dx = 3, dy = 3, du = 1; 
     86        <span class="comment">// matrices</span> 
     87        mat A = eye ( dx ); 
     88        mat B = zeros ( dx, du ); 
     89        mat C = eye ( dx ); 
     90        mat D = zeros ( dy, du ); 
     91        mat Q = eye ( dx ); 
     92        mat R = 0.1 * eye ( dy ); 
     93        <span class="comment">//prior</span> 
     94        mat P0 = 100 * eye ( dx ); 
     95        vec mu0 = zeros ( dx ); 
     96        <span class="comment">// Estimator</span> 
     97        KalmanCh KF; 
     98        KF.set_parameters ( A, B, C, D,<span class="comment">/*covariances*/</span> Q, R ); 
     99        KF.set_statistics ( mu0, P0 ); 
     100        <span class="comment">// Estimation loop</span> 
     101        <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i = 0; i &lt; 100; i++ ) { 
     102                KF.bayes ( randn ( dx + du ) ); 
     103        } 
     104        <span class="comment">//print results</span> 
     105        cout &lt;&lt; <span class="stringliteral">&quot;Posterior estimate of x is: &quot;</span>  &lt;&lt; endl; 
     106        cout &lt;&lt; <span class="stringliteral">&quot;mean: &quot;</span> &lt;&lt; KF.posterior().mean() &lt;&lt; endl; 
     107        cout &lt;&lt; <span class="stringliteral">&quot;variance: &quot;</span> &lt;&lt; KF.posterior().variance() &lt;&lt; endl; 
     108} 
     109</pre></div><p>Complete constructor: </p> 
    76110 
    77111<p><code>#include &lt;<a class="el" href="kalman_8h_source.html">kalman.h</a>&gt;</code></p> 
    … …  
    258292<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Flags for logging - same size as LIDs, each entry correspond to the same in LIDs. <br/></td></tr> 
    259293</table> 
    260 <hr/><a name="_details"></a><h2>Detailed Description</h2> 
    261 <p><a class="el" href="classbdm_1_1Kalman.html" title="Common abstract base for Kalman filters.">Kalman</a> filter in square <a class="el" href="classbdm_1_1root.html" title="Root class of BDM objects.">root</a> form. </p> 
    262 <p>Trivial example: </p> 
    263 <div class="fragment"><pre class="fragment"><span class="preprocessor">#include &quot;<a class="code" href="kalman_8h.html" title="Bayesian Filtering for linear Gaussian models (Kalman Filter) and extensions.">estim/kalman.h</a>&quot;</span> 
    264 <span class="keyword">using namespace </span>bdm; 
    265  
    266 <span class="comment">// estimation of AR(0) model</span> 
    267 <span class="keywordtype">int</span> main() { 
    268         <span class="comment">//dimensions</span> 
    269         <span class="keywordtype">int</span> dx = 3, dy = 3, du = 1; 
    270         <span class="comment">// matrices</span> 
    271         mat A = eye ( dx ); 
    272         mat B = zeros ( dx, du ); 
    273         mat C = eye ( dx ); 
    274         mat D = zeros ( dy, du ); 
    275         mat Q = eye ( dx ); 
    276         mat R = 0.1 * eye ( dy ); 
    277         <span class="comment">//prior</span> 
    278         mat P0 = 100 * eye ( dx ); 
    279         vec mu0 = zeros ( dx ); 
    280         <span class="comment">// Estimator</span> 
    281         KalmanCh KF; 
    282         KF.set_parameters ( A, B, C, D,<span class="comment">/*covariances*/</span> Q, R ); 
    283         KF.set_statistics ( mu0, P0 ); 
    284         <span class="comment">// Estimation loop</span> 
    285         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i = 0; i &lt; 100; i++ ) { 
    286                 KF.bayes ( randn ( dx + du ) ); 
    287         } 
    288         <span class="comment">//print results</span> 
    289         cout &lt;&lt; <span class="stringliteral">&quot;Posterior estimate of x is: &quot;</span>  &lt;&lt; endl; 
    290         cout &lt;&lt; <span class="stringliteral">&quot;mean: &quot;</span> &lt;&lt; KF.posterior().mean() &lt;&lt; endl; 
    291         cout &lt;&lt; <span class="stringliteral">&quot;variance: &quot;</span> &lt;&lt; KF.posterior().variance() &lt;&lt; endl; 
    292 } 
    293 </pre></div><p>Complete constructor: </p> 
    294294<hr/><h2>Member Function Documentation</h2> 
    295295<a class="anchor" id="ab41fe5540548100b08e1684c3be767b6"></a><!-- doxytag: member="bdm::KalmanCh::bayes" ref="ab41fe5540548100b08e1684c3be767b6" args="(const vec &amp;dt)" --> 
    … …  
    354354</ul> 
    355355</div> 
    356 <hr size="1"/><address style="text-align: right;"><small>Generated on Sun Sep 13 22:40:43 2009 for mixpp by&nbsp; 
     356<hr size="1"/><address style="text-align: right;"><small>Generated on Sun Sep 13 23:08:56 2009 for mixpp by&nbsp; 
    357357<a href="http://www.doxygen.org/index.html"> 
    358358<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.6.1 </small></address>