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59<h1>libKF.h</h1><a href="libKF_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001
60<a name="l00013"></a>00013 <span class="preprocessor">#ifndef KF_H</span>
61<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define KF_H</span>
62<a name="l00015"></a>00015 <span class="preprocessor"></span>
63<a name="l00016"></a>00016
64<a name="l00017"></a>00017 <span class="preprocessor">#include "../stat/libFN.h"</span>
65<a name="l00018"></a>00018 <span class="preprocessor">#include "../stat/libEF.h"</span>
66<a name="l00019"></a>00019 <span class="preprocessor">#include "../math/chmat.h"</span>
67<a name="l00020"></a>00020
68<a name="l00021"></a>00021 <span class="keyword">namespace </span>bdm{
69<a name="l00022"></a>00022
70<a name="l00027"></a><a class="code" href="classbdm_1_1KalmanFull.html">00027</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1KalmanFull.html" title="Basic Kalman filter with full matrices (education purpose only)! Will be deleted...">KalmanFull</a> {
71<a name="l00028"></a>00028 <span class="keyword">protected</span>:
72<a name="l00029"></a>00029         <span class="keywordtype">int</span> dimx, dimy, dimu;
73<a name="l00030"></a>00030         mat A, B, C, D, R, Q;
74<a name="l00031"></a>00031
75<a name="l00032"></a>00032         <span class="comment">//cache</span>
76<a name="l00033"></a>00033         mat _Pp, _Ry, _iRy, _K;
77<a name="l00034"></a>00034 <span class="keyword">public</span>:
78<a name="l00035"></a>00035         <span class="comment">//posterior</span>
79<a name="l00037"></a><a class="code" href="classbdm_1_1KalmanFull.html#2defb75e58892615c5f95fd844f3a666">00037</a> <span class="comment"></span>        vec <a class="code" href="classbdm_1_1KalmanFull.html#2defb75e58892615c5f95fd844f3a666" title="Mean value of the posterior density.">mu</a>;
80<a name="l00039"></a><a class="code" href="classbdm_1_1KalmanFull.html#acacd228e100c3e937de575ad2d7cd9c">00039</a>         mat <a class="code" href="classbdm_1_1KalmanFull.html#acacd228e100c3e937de575ad2d7cd9c" title="Variance of the posterior density.">P</a>;
81<a name="l00040"></a>00040
82<a name="l00041"></a>00041         <span class="keywordtype">bool</span> evalll;
83<a name="l00042"></a>00042         <span class="keywordtype">double</span> ll;
84<a name="l00043"></a>00043 <span class="keyword">public</span>:
85<a name="l00045"></a>00045         <a class="code" href="classbdm_1_1KalmanFull.html#bdcc98c8b18c1cbdebdf218ae838fd11" title="For EKFfull;.">KalmanFull</a> ( mat A, mat B, mat C, mat D, mat R, mat Q, mat P0, vec mu0 );
86<a name="l00047"></a>00047         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1KalmanFull.html#081924bc97f453f674bb982b7951d053" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
87<a name="l00049"></a>00049         <span class="keyword">friend</span> std::ostream &amp;<a class="code" href="classbdm_1_1KalmanFull.html#86ba216243ed95bb46d80d88775d16af" title="print elements of KF">operator&lt;&lt; </a>( std::ostream &amp;os, <span class="keyword">const</span> <a class="code" href="classbdm_1_1KalmanFull.html" title="Basic Kalman filter with full matrices (education purpose only)! Will be deleted...">KalmanFull</a> &amp;kf );
88<a name="l00051"></a><a class="code" href="classbdm_1_1KalmanFull.html#bdcc98c8b18c1cbdebdf218ae838fd11">00051</a>         <a class="code" href="classbdm_1_1KalmanFull.html#bdcc98c8b18c1cbdebdf218ae838fd11" title="For EKFfull;.">KalmanFull</a>(){};
89<a name="l00052"></a>00052 };
90<a name="l00053"></a>00053
91<a name="l00054"></a>00054
92<a name="l00062"></a>00062 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
93<a name="l00063"></a>00063
94<a name="l00064"></a><a class="code" href="classbdm_1_1Kalman.html">00064</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</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> {
95<a name="l00065"></a>00065 <span class="keyword">protected</span>:
96<a name="l00067"></a><a class="code" href="classbdm_1_1Kalman.html#3fe475a1e920b20b63bb342c0e1571f7">00067</a>         <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classbdm_1_1Kalman.html#3fe475a1e920b20b63bb342c0e1571f7" title="Indetifier of output rv.">rvy</a>;
97<a name="l00069"></a><a class="code" href="classbdm_1_1Kalman.html#149e27424fd1a7cc1c998ea088618a94">00069</a>         <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classbdm_1_1Kalman.html#149e27424fd1a7cc1c998ea088618a94" title="Indetifier of exogeneous rv.">rvu</a>;
98<a name="l00071"></a><a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa">00071</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a>;
99<a name="l00073"></a><a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f">00073</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>;
100<a name="l00075"></a><a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b">00075</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b" title="cache of rvu.count()">dimu</a>;
101<a name="l00077"></a><a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace">00077</a>         mat <a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a>;
102<a name="l00079"></a><a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c">00079</a>         mat <a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c" title="Matrix B.">B</a>;
103<a name="l00081"></a><a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177">00081</a>         mat <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>;
104<a name="l00083"></a><a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456">00083</a>         mat <a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456" title="Matrix D.">D</a>;
105<a name="l00085"></a><a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee">00085</a>         sq_T <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a>;
106<a name="l00087"></a><a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7">00087</a>         sq_T <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a>;
107<a name="l00088"></a>00088
108<a name="l00090"></a><a class="code" href="classbdm_1_1Kalman.html#383f329ff18bbe219254c8b3b916f40d">00090</a>         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1Kalman.html#383f329ff18bbe219254c8b3b916f40d" title="posterior density on $x_t$">est</a>;
109<a name="l00092"></a><a class="code" href="classbdm_1_1Kalman.html#ba555c394c429f6831c9bbabfa2c944c">00092</a>         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1Kalman.html#ba555c394c429f6831c9bbabfa2c944c" title="preditive density on $y_t$">fy</a>;
110<a name="l00093"></a>00093
111<a name="l00095"></a><a class="code" href="classbdm_1_1Kalman.html#bd69dfb802465f22dd84d73a180d5c92">00095</a>         mat <a class="code" href="classbdm_1_1Kalman.html#bd69dfb802465f22dd84d73a180d5c92" title="placeholder for Kalman gain">_K</a>;
112<a name="l00097"></a><a class="code" href="classbdm_1_1Kalman.html#c249d45258c8578b13858ad3e7b729b1">00097</a>         vec&amp; <a class="code" href="classbdm_1_1Kalman.html#c249d45258c8578b13858ad3e7b729b1" title="cache of fy.mu">_yp</a>;
113<a name="l00099"></a><a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a">00099</a>         sq_T&amp; <a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a" title="cache of fy.R">_Ry</a>;
114<a name="l00101"></a><a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0">00101</a>         vec&amp; <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a>;
115<a name="l00103"></a><a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed">00103</a>         sq_T&amp; <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>;
116<a name="l00104"></a>00104
117<a name="l00105"></a>00105 <span class="keyword">public</span>:
118<a name="l00107"></a>00107         <a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4" title="Default constructor.">Kalman</a> ( );
119<a name="l00109"></a>00109         <a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4" title="Default constructor.">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 );
120<a name="l00111"></a>00111         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1Kalman.html#3c7fb87fb6b87d08deb6a5a7862da957" title="Set parameters with check of relevance.">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 );
121<a name="l00113"></a><a class="code" href="classbdm_1_1Kalman.html#9264fc6b173ecb803d2684b883f32c68">00113</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1Kalman.html#9264fc6b173ecb803d2684b883f32c68" title="Set estimate values, used e.g. in initialization.">set_est</a> ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;P0 ) {
122<a name="l00114"></a>00114                 sq_T pom(<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>);
123<a name="l00115"></a>00115                 <a class="code" href="classbdm_1_1Kalman.html#383f329ff18bbe219254c8b3b916f40d" title="posterior density on $x_t$">est</a>.set_parameters ( mu0,P0 );
124<a name="l00116"></a>00116                 P0.mult_sym(<a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>,pom);
125<a name="l00117"></a>00117                 <a class="code" href="classbdm_1_1Kalman.html#ba555c394c429f6831c9bbabfa2c944c" title="preditive density on $y_t$">fy</a>.set_parameters ( <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>*mu0, pom );
126<a name="l00118"></a>00118         };
127<a name="l00119"></a>00119
128<a name="l00121"></a>00121         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1Kalman.html#4a39330c14eff8d13179e868a1d1aa8c" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
129<a name="l00123"></a><a class="code" href="classbdm_1_1Kalman.html#f75e487ff6c129d7012d702030f8c890">00123</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_1Kalman.html#f75e487ff6c129d7012d702030f8c890" title="access function">posterior</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1Kalman.html#383f329ff18bbe219254c8b3b916f40d" title="posterior density on $x_t$">est</a>;}
130<a name="l00124"></a>00124         <span class="keyword">const</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* _e()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &amp;<a class="code" href="classbdm_1_1Kalman.html#383f329ff18bbe219254c8b3b916f40d" title="posterior density on $x_t$">est</a>;}
131<a name="l00126"></a><a class="code" href="classbdm_1_1Kalman.html#c788ec6e6c6f5f5861ae8a56d8ede277">00126</a>         mat&amp; <a class="code" href="classbdm_1_1Kalman.html#c788ec6e6c6f5f5861ae8a56d8ede277" title="access function">__K</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1Kalman.html#bd69dfb802465f22dd84d73a180d5c92" title="placeholder for Kalman gain">_K</a>;}
132<a name="l00128"></a><a class="code" href="classbdm_1_1Kalman.html#a250d1dbe7bba861dba2a324520cfa48">00128</a>         vec <a class="code" href="classbdm_1_1Kalman.html#a250d1dbe7bba861dba2a324520cfa48" title="access function">_dP</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>-&gt;getD();}
133<a name="l00129"></a>00129 };
134<a name="l00130"></a>00130
135<a name="l00137"></a><a class="code" href="classbdm_1_1KalmanCh.html">00137</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1KalmanCh.html" title="Kalman filter in square root form.">KalmanCh</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;chmat&gt;{
136<a name="l00138"></a>00138 <span class="keyword">protected</span>:
137<a name="l00140"></a><a class="code" href="classbdm_1_1KalmanCh.html#48611c8582706cfa62e832be0972e75d">00140</a> mat <a class="code" href="classbdm_1_1KalmanCh.html#48611c8582706cfa62e832be0972e75d" title="pre array (triangular matrix)">preA</a>;
138<a name="l00142"></a><a class="code" href="classbdm_1_1KalmanCh.html#bcbd68f51d4b57246e7784ca5900171f">00142</a> mat <a class="code" href="classbdm_1_1KalmanCh.html#bcbd68f51d4b57246e7784ca5900171f" title="post array (triangular matrix)">postA</a>;
139<a name="l00143"></a>00143
140<a name="l00144"></a>00144 <span class="keyword">public</span>:
141<a name="l00146"></a><a class="code" href="classbdm_1_1KalmanCh.html#830486554e1a2c7652541dbc9dcd3fb3">00146</a>         <a class="code" href="classbdm_1_1KalmanCh.html#830486554e1a2c7652541dbc9dcd3fb3" title="Default constructor.">KalmanCh</a> ():<a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;<a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&gt;(),<a class="code" href="classbdm_1_1KalmanCh.html#48611c8582706cfa62e832be0972e75d" title="pre array (triangular matrix)">preA</a>(),<a class="code" href="classbdm_1_1KalmanCh.html#bcbd68f51d4b57246e7784ca5900171f" title="post array (triangular matrix)">postA</a>(){};
142<a name="l00148"></a>00148         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1KalmanCh.html#20a4d4c664e8ac8a3f1bb7b0d11c6d87" title="Set parameters with check of relevance.">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> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> &amp;Q0,<span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> &amp;R0 );
143<a name="l00149"></a>00149         <span class="keywordtype">void</span> set_statistics ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> &amp;P0 ) {
144<a name="l00150"></a>00150                 <a class="code" href="classbdm_1_1Kalman.html#383f329ff18bbe219254c8b3b916f40d" title="posterior density on $x_t$">est</a>.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( mu0,P0 );
145<a name="l00151"></a>00151         };
146<a name="l00152"></a>00152         
147<a name="l00153"></a>00153         
148<a name="l00167"></a>00167         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1KalmanCh.html#b41fe5540548100b08e1684c3be767b6" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
149<a name="l00168"></a>00168 };
150<a name="l00169"></a>00169
151<a name="l00175"></a><a class="code" href="classbdm_1_1EKFfull.html">00175</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1EKFfull.html" title="Extended Kalman Filter in full matrices.">EKFfull</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1KalmanFull.html" title="Basic Kalman filter with full matrices (education purpose only)! Will be deleted...">KalmanFull</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> {
152<a name="l00176"></a>00176
153<a name="l00178"></a>00178         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu;
154<a name="l00180"></a>00180         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu;
155<a name="l00181"></a>00181         
156<a name="l00182"></a>00182         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;fsqmat&gt;</a> E;
157<a name="l00183"></a>00183 <span class="keyword">public</span>:
158<a name="l00185"></a>00185         <a class="code" href="classbdm_1_1EKFfull.html#6939c345389abb8b2481457b4cfe1165" title="Default constructor.">EKFfull</a> ( );
159<a name="l00187"></a>00187         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKFfull.html#78748da361ba61fef162b0d8956d1743" title="Set nonlinear functions for mean values and covariance matrices.">set_parameters</a> ( <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu, <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu, <span class="keyword">const</span> mat Q0, <span class="keyword">const</span> mat R0 );
160<a name="l00189"></a>00189         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKFfull.html#f149ae8e9ce14d9931a7bb2850736699" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
161<a name="l00191"></a><a class="code" href="classbdm_1_1EKFfull.html#7562b3d3c17241dab3baf70258742eb2">00191</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;};
162<a name="l00193"></a><a class="code" href="classbdm_1_1EKFfull.html#7e9a69f36a0a0615c9abb806772ef36d">00193</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;};
163<a name="l00194"></a>00194         <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;};
164<a name="l00195"></a>00195         <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>;}
165<a name="l00196"></a>00196 };
166<a name="l00197"></a>00197
167<a name="l00203"></a>00203 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
168<a name="l00204"></a><a class="code" href="classbdm_1_1EKF.html">00204</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1EKF.html" title="Extended Kalman Filter.">EKF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;fsqmat&gt; {
169<a name="l00206"></a>00206         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu;
170<a name="l00208"></a>00208         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu;
171<a name="l00209"></a>00209 <span class="keyword">public</span>:
172<a name="l00211"></a>00211         <a class="code" href="classbdm_1_1EKF.html#d087a8bb408d26ac4f5c542746b81059" title="Default constructor.">EKF</a> ( <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvx, <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classbdm_1_1Kalman.html#3fe475a1e920b20b63bb342c0e1571f7" title="Indetifier of output rv.">rvy</a>, <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classbdm_1_1Kalman.html#149e27424fd1a7cc1c998ea088618a94" title="Indetifier of exogeneous rv.">rvu</a> );
173<a name="l00213"></a>00213         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKF.html#00fec1a0a6a467eb83fb36c65eba7bcb" title="Set nonlinear functions for mean values and covariance matrices.">set_parameters</a> ( <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu, <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu, <span class="keyword">const</span> sq_T Q0, <span class="keyword">const</span> sq_T R0 );
174<a name="l00215"></a>00215         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKF.html#3fb182ecc29b10ca1163cecbf3bcccfa" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
175<a name="l00216"></a>00216 };
176<a name="l00217"></a>00217
177<a name="l00224"></a><a class="code" href="classbdm_1_1EKFCh.html">00224</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1EKFCh.html" title="Extended Kalman Filter in Square root.">EKFCh</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1KalmanCh.html" title="Kalman filter in square root form.">KalmanCh</a> {
178<a name="l00225"></a>00225         <span class="keyword">protected</span>:
179<a name="l00227"></a><a class="code" href="classbdm_1_1EKFCh.html#e1e895f994398a55bc425551fc275ba3">00227</a>         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* <a class="code" href="classbdm_1_1EKFCh.html#e1e895f994398a55bc425551fc275ba3" title="Internal Model f(x,u).">pfxu</a>;
180<a name="l00229"></a><a class="code" href="classbdm_1_1EKFCh.html#6b34c69641826322467b704e8252f317">00229</a>         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* <a class="code" href="classbdm_1_1EKFCh.html#6b34c69641826322467b704e8252f317" title="Observation Model h(x,u).">phxu</a>;
181<a name="l00230"></a>00230 <span class="keyword">public</span>:
182<a name="l00232"></a>00232         <a class="code" href="classbdm_1_1EKFCh.html#8b3228a594532b6a0db0fdc065bc5b9f" title="Default constructor.">EKFCh</a> ();
183<a name="l00234"></a>00234         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKFCh.html#50f9fbffad721f35e5ccb75d0f6b842a" title="Set nonlinear functions for mean values and covariance matrices.">set_parameters</a> ( <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* <a class="code" href="classbdm_1_1EKFCh.html#e1e895f994398a55bc425551fc275ba3" title="Internal Model f(x,u).">pfxu</a>, <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* <a class="code" href="classbdm_1_1EKFCh.html#6b34c69641826322467b704e8252f317" title="Observation Model h(x,u).">phxu</a>, <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> Q0, <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> R0 );
184<a name="l00236"></a>00236         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKFCh.html#4c8609c37290b158f88a31dae4047225" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
185<a name="l00237"></a>00237 };
186<a name="l00238"></a>00238
187<a name="l00243"></a><a class="code" href="classbdm_1_1KFcondQR.html">00243</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1KFcondQR.html" title="Kalman Filter with conditional diagonal matrices R and Q.">KFcondQR</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;ldmat&gt;, <span class="keyword">public</span> <a class="code" href="classbdm_1_1BMcond.html" title="Conditional Bayesian Filter.">BMcond</a> {
188<a name="l00244"></a>00244 <span class="comment">//protected:</span>
189<a name="l00245"></a>00245 <span class="keyword">public</span>:
190<a name="l00247"></a><a class="code" href="classbdm_1_1KFcondQR.html#b586ac962751a6af76b2e0fd7e066194">00247</a>         <a class="code" href="classbdm_1_1KFcondQR.html#b586ac962751a6af76b2e0fd7e066194" title="Default constructor.">KFcondQR</a> ( ) : <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;<a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&gt; ( ),<a class="code" href="classbdm_1_1BMcond.html" title="Conditional Bayesian Filter.">BMcond</a> ( ) {};
191<a name="l00248"></a>00248
192<a name="l00249"></a>00249         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1KFcondQR.html#0288d47032757774a525f196ac3da21d" title="Substitute val for rvc.">condition</a> ( <span class="keyword">const</span> vec &amp;RQ );
193<a name="l00250"></a>00250 };
194<a name="l00251"></a>00251
195<a name="l00256"></a><a class="code" href="classbdm_1_1KFcondR.html">00256</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1KFcondR.html" title="Kalman Filter with conditional diagonal matrices R and Q.">KFcondR</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;ldmat&gt;, <span class="keyword">public</span> <a class="code" href="classbdm_1_1BMcond.html" title="Conditional Bayesian Filter.">BMcond</a> {
196<a name="l00257"></a>00257 <span class="comment">//protected:</span>
197<a name="l00258"></a>00258 <span class="keyword">public</span>:
198<a name="l00260"></a><a class="code" href="classbdm_1_1KFcondR.html#f11639d79f10b1e7dad16a0d8233450d">00260</a>         <a class="code" href="classbdm_1_1KFcondR.html#f11639d79f10b1e7dad16a0d8233450d" title="Default constructor.">KFcondR</a> ( ) : <a class="code" href="classbdm_1_1Kalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;<a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&gt; ( ),<a class="code" href="classbdm_1_1BMcond.html" title="Conditional Bayesian Filter.">BMcond</a> ( ) {};
199<a name="l00261"></a>00261
200<a name="l00262"></a>00262         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1KFcondR.html#6086f02541f8f3bc8351990abf5cd538" title="Substitute val for rvc.">condition</a> ( <span class="keyword">const</span> vec &amp;<a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a> );
201<a name="l00263"></a>00263 };
202<a name="l00264"></a>00264
203<a name="l00266"></a>00266
204<a name="l00267"></a>00267 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
205<a name="l00268"></a><a class="code" href="classbdm_1_1Kalman.html#8b22c45cffa949d70b8e5ac92ed5ce25">00268</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 ),
206<a name="l00269"></a>00269                 dimx ( K0.dimx ), dimy ( K0.dimy ),dimu ( K0.dimu ),
207<a name="l00270"></a>00270                 A ( K0.A ), B ( K0.B ), C ( K0.C ), D ( K0.D ),
208<a name="l00271"></a>00271                 Q(K0.Q), R(K0.R),
209<a name="l00272"></a>00272                 est ( K0.est ), fy ( K0.fy ), _yp(fy._mu()),_Ry(fy._R()), _mu(est._mu()), _P(est._R()) {
210<a name="l00273"></a>00273
211<a name="l00274"></a>00274 <span class="comment">// copy values in pointers</span>
212<a name="l00275"></a>00275 <span class="comment">//      _mu = K0._mu;</span>
213<a name="l00276"></a>00276 <span class="comment">//      _P = K0._P;</span>
214<a name="l00277"></a>00277 <span class="comment">//      _yp = K0._yp;</span>
215<a name="l00278"></a>00278 <span class="comment">//      _Ry = K0._Ry;</span>
216<a name="l00279"></a>00279
217<a name="l00280"></a>00280 }
218<a name="l00281"></a>00281
219<a name="l00282"></a>00282 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
220<a name="l00283"></a><a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4">00283</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()) {
221<a name="l00284"></a>00284 };
222<a name="l00285"></a>00285
223<a name="l00286"></a>00286 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
224<a name="l00287"></a><a class="code" href="classbdm_1_1Kalman.html#3c7fb87fb6b87d08deb6a5a7862da957">00287</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 ) {
225<a name="l00288"></a>00288         <a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a> = A0.rows();
226<a name="l00289"></a>00289         <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> = C0.rows();
227<a name="l00290"></a>00290         <a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b" title="cache of rvu.count()">dimu</a> = B0.cols();
228<a name="l00291"></a>00291         
229<a name="l00292"></a>00292         it_assert_debug ( A0.cols() ==<a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a>, <span class="stringliteral">"Kalman: A is not square"</span> );
230<a name="l00293"></a>00293         it_assert_debug ( B0.rows() ==<a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a>, <span class="stringliteral">"Kalman: B is not compatible"</span> );
231<a name="l00294"></a>00294         it_assert_debug ( C0.cols() ==<a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a>, <span class="stringliteral">"Kalman: C is not square"</span> );
232<a name="l00295"></a>00295         it_assert_debug ( ( D0.rows() ==<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> ) || ( D0.cols() ==<a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b" title="cache of rvu.count()">dimu</a> ), <span class="stringliteral">"Kalman: D is not compatible"</span> );
233<a name="l00296"></a>00296         it_assert_debug ( ( R0.cols() ==<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> ) || ( R0.rows() ==<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> ), <span class="stringliteral">"Kalman: R is not compatible"</span> );
234<a name="l00297"></a>00297         it_assert_debug ( ( Q0.cols() ==<a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a> ) || ( Q0.rows() ==<a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a> ), <span class="stringliteral">"Kalman: Q is not compatible"</span> );
235<a name="l00298"></a>00298
236<a name="l00299"></a>00299         <a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a> = A0;
237<a name="l00300"></a>00300         <a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c" title="Matrix B.">B</a> = B0;
238<a name="l00301"></a>00301         <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a> = C0;
239<a name="l00302"></a>00302         <a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456" title="Matrix D.">D</a> = D0;
240<a name="l00303"></a>00303         <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a> = R0;
241<a name="l00304"></a>00304         <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a> = Q0;
242<a name="l00305"></a>00305 }
243<a name="l00306"></a>00306
244<a name="l00307"></a>00307 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
245<a name="l00308"></a><a class="code" href="classbdm_1_1Kalman.html#4a39330c14eff8d13179e868a1d1aa8c">00308</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 ) {
246<a name="l00309"></a>00309         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> );
247<a name="l00310"></a>00310
248<a name="l00311"></a>00311         sq_T iRy(<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>);
249<a name="l00312"></a>00312         vec u = dt.get ( <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>,<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>-1 );
250<a name="l00313"></a>00313         vec y = dt.get ( 0,<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>-1 );
251<a name="l00314"></a>00314         <span class="comment">//Time update</span>
252<a name="l00315"></a>00315         <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a> = <a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a>* <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a> + <a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c" title="Matrix B.">B</a>*u;
253<a name="l00316"></a>00316         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
254<a name="l00317"></a>00317         <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.mult_sym ( <a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a> );
255<a name="l00318"></a>00318         <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>  +=<a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a>;
256<a name="l00319"></a>00319
257<a name="l00320"></a>00320         <span class="comment">//Data update</span>
258<a name="l00321"></a>00321         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
259<a name="l00322"></a>00322         <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.mult_sym ( <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>, <a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a" title="cache of fy.R">_Ry</a> );
260<a name="l00323"></a>00323         <a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a" title="cache of fy.R">_Ry</a>  +=<a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a>;
261<a name="l00324"></a>00324
262<a name="l00325"></a>00325         mat Pfull = <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.to_mat();
263<a name="l00326"></a>00326
264<a name="l00327"></a>00327         <a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a" title="cache of fy.R">_Ry</a>.inv ( iRy ); <span class="comment">// result is in _iRy;</span>
265<a name="l00328"></a>00328         <a class="code" href="classbdm_1_1Kalman.html#bd69dfb802465f22dd84d73a180d5c92" title="placeholder for Kalman gain">_K</a> = Pfull*<a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>.transpose() * ( iRy.to_mat() );
266<a name="l00329"></a>00329
267<a name="l00330"></a>00330         sq_T pom ( ( <span class="keywordtype">int</span> ) Pfull.rows() );
268<a name="l00331"></a>00331         iRy.mult_sym_t ( <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>*Pfull,pom );
269<a name="l00332"></a>00332         (<a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a> ) -= pom; <span class="comment">// P = P -PC'iRy*CP;</span>
270<a name="l00333"></a>00333         (<a class="code" href="classbdm_1_1Kalman.html#c249d45258c8578b13858ad3e7b729b1" title="cache of fy.mu">_yp</a> ) = <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>* <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a>  +<a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456" title="Matrix D.">D</a>*u; <span class="comment">//y prediction</span>
271<a name="l00334"></a>00334         (<a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a> ) += <a class="code" href="classbdm_1_1Kalman.html#bd69dfb802465f22dd84d73a180d5c92" title="placeholder for Kalman gain">_K</a>* ( y- <a class="code" href="classbdm_1_1Kalman.html#c249d45258c8578b13858ad3e7b729b1" title="cache of fy.mu">_yp</a>  );
272<a name="l00335"></a>00335
273<a name="l00336"></a>00336
274<a name="l00337"></a>00337         <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a>==<span class="keyword">true</span> ) { <span class="comment">//likelihood of observation y</span>
275<a name="l00338"></a>00338                 <a class="code" href="classbdm_1_1BM.html#4064b6559d962633e4372b12f4cd204a" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classbdm_1_1Kalman.html#ba555c394c429f6831c9bbabfa2c944c" title="preditive density on $y_t$">fy</a>.evallog ( y );
276<a name="l00339"></a>00339         }
277<a name="l00340"></a>00340
278<a name="l00341"></a>00341 <span class="comment">//cout &lt;&lt; "y: " &lt;&lt; y-(*_yp) &lt;&lt;" R: " &lt;&lt; _Ry-&gt;to_mat() &lt;&lt; " iR: " &lt;&lt; _iRy-&gt;to_mat() &lt;&lt; " ll: " &lt;&lt; ll &lt;&lt;endl;</span>
279<a name="l00342"></a>00342
280<a name="l00343"></a>00343 };
281<a name="l00344"></a>00344 
282<a name="l00345"></a>00345
283<a name="l00346"></a>00346
284<a name="l00347"></a>00347 <span class="comment">//TODO why not const pointer??</span>
285<a name="l00348"></a>00348
286<a name="l00349"></a>00349 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
287<a name="l00350"></a><a class="code" href="classbdm_1_1EKF.html#d087a8bb408d26ac4f5c542746b81059">00350</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 ) {}
288<a name="l00351"></a>00351
289<a name="l00352"></a>00352 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
290<a name="l00353"></a><a class="code" href="classbdm_1_1EKF.html#00fec1a0a6a467eb83fb36c65eba7bcb">00353</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 ) {
291<a name="l00354"></a>00354         pfxu = pfxu0;
292<a name="l00355"></a>00355         phxu = phxu0;
293<a name="l00356"></a>00356
294<a name="l00357"></a>00357         <span class="comment">//initialize matrices A C, later, these will be only updated!</span>
295<a name="l00358"></a>00358         pfxu-&gt;<a class="code" href="classbdm_1_1diffbifn.html#651184f808a35f236dbfea21aca1b6ac" title="Evaluates  and writes result into A .">dfdx_cond</a> ( <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a>,zeros ( <a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b" title="cache of rvu.count()">dimu</a> ),<a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a>,<span class="keyword">true</span> );
296<a name="l00359"></a>00359 <span class="comment">//      pfxu-&gt;dfdu_cond ( *_mu,zeros ( dimu ),B,true );</span>
297<a name="l00360"></a>00360         <a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c" title="Matrix B.">B</a>.clear();
298<a name="l00361"></a>00361         phxu-&gt;<a class="code" href="classbdm_1_1diffbifn.html#651184f808a35f236dbfea21aca1b6ac" title="Evaluates  and writes result into A .">dfdx_cond</a> ( <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a>,zeros ( <a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b" title="cache of rvu.count()">dimu</a> ),<a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>,<span class="keyword">true</span> );
299<a name="l00362"></a>00362 <span class="comment">//      phxu-&gt;dfdu_cond ( *_mu,zeros ( dimu ),D,true );</span>
300<a name="l00363"></a>00363         <a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456" title="Matrix D.">D</a>.clear();
301<a name="l00364"></a>00364
302<a name="l00365"></a>00365         <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a> = R0;
303<a name="l00366"></a>00366         <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a> = Q0;
304<a name="l00367"></a>00367 }
305<a name="l00368"></a>00368
306<a name="l00369"></a>00369 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
307<a name="l00370"></a><a class="code" href="classbdm_1_1EKF.html#3fb182ecc29b10ca1163cecbf3bcccfa">00370</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 ) {
308<a name="l00371"></a>00371         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> );
309<a name="l00372"></a>00372
310<a name="l00373"></a>00373         sq_T iRy(<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>,<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>);
311<a name="l00374"></a>00374         vec u = dt.get ( <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>,<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>-1 );
312<a name="l00375"></a>00375         vec y = dt.get ( 0,<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>-1 );
313<a name="l00376"></a>00376         <span class="comment">//Time update</span>
314<a name="l00377"></a>00377         <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a> = pfxu-&gt;<a class="code" href="classbdm_1_1diffbifn.html#188f31066bd72e1bf0ddacd1eb0e6af3" title="Evaluates  (VS: Do we really need common eval? ).">eval</a> ( <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a>, u );
315<a name="l00378"></a>00378         pfxu-&gt;<a class="code" href="classbdm_1_1diffbifn.html#651184f808a35f236dbfea21aca1b6ac" title="Evaluates  and writes result into A .">dfdx_cond</a> ( <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a>,u,<a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a>,<span class="keyword">false</span> ); <span class="comment">//update A by a derivative of fx</span>
316<a name="l00379"></a>00379
317<a name="l00380"></a>00380         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
318<a name="l00381"></a>00381         <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.<a class="code" href="classfsqmat.html#5530d2756b5d991de755e6121c9a452e" title="Inplace symmetric multiplication by a SQUARE matrix , i.e. .">mult_sym</a> ( <a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a> );
319<a name="l00382"></a>00382         <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a> +=<a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a>;
320<a name="l00383"></a>00383
321<a name="l00384"></a>00384         <span class="comment">//Data update</span>
322<a name="l00385"></a>00385         phxu-&gt;<a class="code" href="classbdm_1_1diffbifn.html#651184f808a35f236dbfea21aca1b6ac" title="Evaluates  and writes result into A .">dfdx_cond</a> ( <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a>,u,<a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>,<span class="keyword">false</span> ); <span class="comment">//update C by a derivative hx</span>
323<a name="l00386"></a>00386         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
324<a name="l00387"></a>00387         <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.<a class="code" href="classfsqmat.html#5530d2756b5d991de755e6121c9a452e" title="Inplace symmetric multiplication by a SQUARE matrix , i.e. .">mult_sym</a> ( <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>, <a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a" title="cache of fy.R">_Ry</a> );
325<a name="l00388"></a>00388         ( <a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a" title="cache of fy.R">_Ry</a> ) +=<a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a>;
326<a name="l00389"></a>00389
327<a name="l00390"></a>00390         mat Pfull = <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.<a class="code" href="classfsqmat.html#f54fc955e8e3b43d15afa92124bc24b3" title="Conversion to full matrix.">to_mat</a>();
328<a name="l00391"></a>00391
329<a name="l00392"></a>00392         <a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a" title="cache of fy.R">_Ry</a>.<a class="code" href="classfsqmat.html#9fa853e1ca28f2a1a1c43377e798ecb1" title="Matrix inversion preserving the chosen form.">inv</a> ( iRy ); <span class="comment">// result is in _iRy;</span>
330<a name="l00393"></a>00393         <a class="code" href="classbdm_1_1Kalman.html#bd69dfb802465f22dd84d73a180d5c92" title="placeholder for Kalman gain">_K</a> = Pfull*<a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>.transpose() * ( iRy.to_mat() );
331<a name="l00394"></a>00394
332<a name="l00395"></a>00395         sq_T pom ( ( <span class="keywordtype">int</span> ) Pfull.rows() );
333<a name="l00396"></a>00396         iRy.mult_sym_t ( <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>*Pfull,pom );
334<a name="l00397"></a>00397         (<a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a> ) -= pom; <span class="comment">// P = P -PC'iRy*CP;</span>
335<a name="l00398"></a>00398         <a class="code" href="classbdm_1_1Kalman.html#c249d45258c8578b13858ad3e7b729b1" title="cache of fy.mu">_yp</a> = phxu-&gt;<a class="code" href="classbdm_1_1diffbifn.html#188f31066bd72e1bf0ddacd1eb0e6af3" title="Evaluates  (VS: Do we really need common eval? ).">eval</a> ( <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a>,u ); <span class="comment">//y prediction</span>
336<a name="l00399"></a>00399         ( <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a> ) += <a class="code" href="classbdm_1_1Kalman.html#bd69dfb802465f22dd84d73a180d5c92" title="placeholder for Kalman gain">_K</a>* ( y-<a class="code" href="classbdm_1_1Kalman.html#c249d45258c8578b13858ad3e7b729b1" title="cache of fy.mu">_yp</a> );
337<a name="l00400"></a>00400
338<a name="l00401"></a>00401         <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a>==<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1BM.html#4064b6559d962633e4372b12f4cd204a" title="Logarithm of marginalized data likelihood.">ll</a>+=<a class="code" href="classbdm_1_1Kalman.html#ba555c394c429f6831c9bbabfa2c944c" title="preditive density on $y_t$">fy</a>.<a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( y );}
339<a name="l00402"></a>00402 };
340<a name="l00403"></a>00403
341<a name="l00404"></a>00404
342<a name="l00405"></a>00405 }
343<a name="l00406"></a>00406 <span class="preprocessor">#endif // KF_H</span>
344<a name="l00407"></a>00407 <span class="preprocessor"></span>
345<a name="l00408"></a>00408
346</pre></div></div>
347<hr size="1"><address style="text-align: right;"><small>Generated on Sun Feb 15 23:09:23 2009 for mixpp by&nbsp;
348<a href="http://www.doxygen.org/index.html">
349<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>
350</body>
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