Index: doc/html/libKF_8h-source.html
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@@ -2,8 +2,8 @@
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 <title>mixpp: libKF.h Source File</title>
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@@ -159,5 +165,5 @@
 <a name="l00185"></a>00185         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu;
 <a name="l00186"></a>00186 
-<a name="l00187"></a>00187         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;fsqmat&gt;</a> E;
+<a name="l00187"></a>00187         <a class="code" href="classbdm_1_1enorm.html">enorm&lt;fsqmat&gt;</a> E;
 <a name="l00188"></a>00188 <span class="keyword">public</span>:
 <a name="l00190"></a>00190         <a class="code" href="classbdm_1_1EKFfull.html#6939c345389abb8b2481457b4cfe1165" title="Default constructor.">EKFfull</a> ( );
@@ -166,5 +172,5 @@
 <a name="l00196"></a><a class="code" href="classbdm_1_1EKFfull.html#7562b3d3c17241dab3baf70258742eb2">00196</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKFfull.html#7562b3d3c17241dab3baf70258742eb2" title="set estimates">set_est</a> ( vec mu0, mat P0 ) {<a class="code" href="classbdm_1_1KalmanFull.html#2defb75e58892615c5f95fd844f3a666" title="Mean value of the posterior density.">mu</a>=mu0;<a class="code" href="classbdm_1_1KalmanFull.html#acacd228e100c3e937de575ad2d7cd9c" title="Variance of the posterior density.">P</a>=P0;};
 <a name="l00198"></a><a class="code" href="classbdm_1_1EKFfull.html#7e9a69f36a0a0615c9abb806772ef36d">00198</a>         <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; <a class="code" href="classbdm_1_1EKFfull.html#7e9a69f36a0a0615c9abb806772ef36d" title="dummy!">posterior</a>()<span class="keyword"> const</span>{<span class="keywordflow">return</span> E;};
-<a name="l00199"></a>00199         <span class="keyword">const</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;fsqmat&gt;</a>* _e()<span class="keyword"> const</span>{<span class="keywordflow">return</span> &amp;E;};
+<a name="l00199"></a>00199         <span class="keyword">const</span> <a class="code" href="classbdm_1_1enorm.html">enorm&lt;fsqmat&gt;</a>* _e()<span class="keyword"> const</span>{<span class="keywordflow">return</span> &amp;E;};
 <a name="l00200"></a>00200         <span class="keyword">const</span> mat _R() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1KalmanFull.html#acacd228e100c3e937de575ad2d7cd9c" title="Variance of the posterior density.">P</a>;}
 <a name="l00201"></a>00201 };
@@ -222,5 +228,5 @@
 <a name="l00285"></a>00285 
 <a name="l00286"></a>00286 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00287"></a><a class="code" href="classbdm_1_1Kalman.html#8b22c45cffa949d70b8e5ac92ed5ce25">00287</a> <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman&lt;sq_T&gt;::Kalman</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman&lt;sq_T&gt;</a> &amp;K0 ) : <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( ),rvy ( K0.rvy ),rvu ( K0.rvu ),
+<a name="l00287"></a><a class="code" href="classbdm_1_1Kalman.html#8b22c45cffa949d70b8e5ac92ed5ce25">00287</a> <a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4" title="Default constructor.">Kalman&lt;sq_T&gt;::Kalman</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman&lt;sq_T&gt;</a> &amp;K0 ) : <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( ),rvy ( K0.rvy ),rvu ( K0.rvu ),
 <a name="l00288"></a>00288                 dimx ( K0.dimx ), dimy ( K0.dimy ),dimu ( K0.dimu ),
 <a name="l00289"></a>00289                 A ( K0.A ), B ( K0.B ), C ( K0.C ), D ( K0.D ),
@@ -237,9 +243,9 @@
 <a name="l00300"></a>00300 
 <a name="l00301"></a>00301 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00302"></a><a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4">00302</a> <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman&lt;sq_T&gt;::Kalman</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> (), est ( ), fy (),  _yp ( fy._mu() ), _Ry ( fy._R() ), _mu ( est._mu() ), _P ( est._R() ) {
+<a name="l00302"></a><a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4">00302</a> <a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4" title="Default constructor.">Kalman&lt;sq_T&gt;::Kalman</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> (), est ( ), fy (),  _yp ( fy._mu() ), _Ry ( fy._R() ), _mu ( est._mu() ), _P ( est._R() ) {
 <a name="l00303"></a>00303 };
 <a name="l00304"></a>00304 
 <a name="l00305"></a>00305 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00306"></a><a class="code" href="classbdm_1_1Kalman.html#3c7fb87fb6b87d08deb6a5a7862da957">00306</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0,<span class="keyword">const</span>  mat &amp;B0, <span class="keyword">const</span> mat &amp;C0, <span class="keyword">const</span> mat &amp;D0, <span class="keyword">const</span> sq_T &amp;Q0, <span class="keyword">const</span> sq_T &amp;R0 ) {
+<a name="l00306"></a><a class="code" href="classbdm_1_1Kalman.html#3c7fb87fb6b87d08deb6a5a7862da957">00306</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1Kalman.html#3c7fb87fb6b87d08deb6a5a7862da957" title="Set parameters with check of relevance.">Kalman&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0,<span class="keyword">const</span>  mat &amp;B0, <span class="keyword">const</span> mat &amp;C0, <span class="keyword">const</span> mat &amp;D0, <span class="keyword">const</span> sq_T &amp;Q0, <span class="keyword">const</span> sq_T &amp;R0 ) {
 <a name="l00307"></a>00307         <a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a> = A0.rows();
 <a name="l00308"></a>00308         <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> = C0.rows();
@@ -262,5 +268,5 @@
 <a name="l00325"></a>00325 
 <a name="l00326"></a>00326 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00327"></a><a class="code" href="classbdm_1_1Kalman.html#4a39330c14eff8d13179e868a1d1aa8c">00327</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman&lt;sq_T&gt;::bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
+<a name="l00327"></a><a class="code" href="classbdm_1_1Kalman.html#4a39330c14eff8d13179e868a1d1aa8c">00327</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1Kalman.html#4a39330c14eff8d13179e868a1d1aa8c" title="Here dt = [yt;ut] of appropriate dimensions.">Kalman&lt;sq_T&gt;::bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
 <a name="l00328"></a>00328         it_assert_debug ( dt.length() == ( <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>+<a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b" title="cache of rvu.count()">dimu</a> ),<span class="stringliteral">"KalmanFull::bayes wrong size of dt"</span> );
 <a name="l00329"></a>00329 
@@ -304,8 +310,8 @@
 <a name="l00367"></a>00367 
 <a name="l00368"></a>00368 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00369"></a><a class="code" href="classbdm_1_1EKF.html#d087a8bb408d26ac4f5c542746b81059">00369</a> <a class="code" href="classbdm_1_1EKF.html" title="Extended Kalman Filter.">EKF&lt;sq_T&gt;::EKF</a> ( <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvx0, <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvy0, <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvu0 ) : <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;sq_T&gt; ( rvx0,rvy0,rvu0 ) {}
+<a name="l00369"></a><a class="code" href="classbdm_1_1EKF.html#d087a8bb408d26ac4f5c542746b81059">00369</a> <a class="code" href="classbdm_1_1EKF.html#d087a8bb408d26ac4f5c542746b81059" title="Default constructor.">EKF&lt;sq_T&gt;::EKF</a> ( <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvx0, <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvy0, <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvu0 ) : <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;sq_T&gt; ( rvx0,rvy0,rvu0 ) {}
 <a name="l00370"></a>00370 
 <a name="l00371"></a>00371 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00372"></a><a class="code" href="classbdm_1_1EKF.html#00fec1a0a6a467eb83fb36c65eba7bcb">00372</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKF.html" title="Extended Kalman Filter.">EKF&lt;sq_T&gt;::set_parameters</a> ( <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu0,  <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu0,<span class="keyword">const</span> sq_T Q0,<span class="keyword">const</span> sq_T R0 ) {
+<a name="l00372"></a><a class="code" href="classbdm_1_1EKF.html#00fec1a0a6a467eb83fb36c65eba7bcb">00372</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKF.html#00fec1a0a6a467eb83fb36c65eba7bcb" title="Set nonlinear functions for mean values and covariance matrices.">EKF&lt;sq_T&gt;::set_parameters</a> ( <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu0,  <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu0,<span class="keyword">const</span> sq_T Q0,<span class="keyword">const</span> sq_T R0 ) {
 <a name="l00373"></a>00373         pfxu = pfxu0;
 <a name="l00374"></a>00374         phxu = phxu0;
@@ -324,5 +330,5 @@
 <a name="l00387"></a>00387 
 <a name="l00388"></a>00388 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00389"></a><a class="code" href="classbdm_1_1EKF.html#3fb182ecc29b10ca1163cecbf3bcccfa">00389</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKF.html" title="Extended Kalman Filter.">EKF&lt;sq_T&gt;::bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
+<a name="l00389"></a><a class="code" href="classbdm_1_1EKF.html#3fb182ecc29b10ca1163cecbf3bcccfa">00389</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKF.html#3fb182ecc29b10ca1163cecbf3bcccfa" title="Here dt = [yt;ut] of appropriate dimensions.">EKF&lt;sq_T&gt;::bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
 <a name="l00390"></a>00390         it_assert_debug ( dt.length() == ( <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>+<a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b" title="cache of rvu.count()">dimu</a> ),<span class="stringliteral">"KalmanFull::bayes wrong size of dt"</span> );
 <a name="l00391"></a>00391 
@@ -364,7 +370,7 @@
 <a name="l00427"></a>00427 
 </pre></div></div>
-<hr size="1"><address style="text-align: right;"><small>Generated on Wed Mar 4 18:50:11 2009 for mixpp by&nbsp;
+<hr size="1"><address style="text-align: right;"><small>Generated on Fri Mar 6 15:01:36 2009 for mixpp by&nbsp;
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-<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>
+<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.8 </small></address>
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