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64<h1>kalman.h</h1><a href="kalman_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001
65<a name="l00013"></a>00013 <span class="preprocessor">#ifndef KF_H</span>
66<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define KF_H</span>
67<a name="l00015"></a>00015 <span class="preprocessor"></span>
68<a name="l00016"></a>00016
69<a name="l00017"></a>00017 <span class="preprocessor">#include "../math/functions.h"</span>
70<a name="l00018"></a>00018 <span class="preprocessor">#include "../stat/exp_family.h"</span>
71<a name="l00019"></a>00019 <span class="preprocessor">#include "../math/chmat.h"</span>
72<a name="l00020"></a>00020 <span class="preprocessor">#include "../base/user_info.h"</span>
73<a name="l00021"></a>00021
74<a name="l00022"></a>00022 <span class="keyword">namespace </span>bdm {
75<a name="l00023"></a>00023
76<a name="l00028"></a><a class="code" href="classbdm_1_1KalmanFull.html">00028</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> {
77<a name="l00029"></a>00029 <span class="keyword">protected</span>:
78<a name="l00030"></a>00030         <span class="keywordtype">int</span> dimx, dimy, dimu;
79<a name="l00031"></a>00031         mat A, B, C, D, R, Q;
80<a name="l00032"></a>00032
81<a name="l00033"></a>00033         <span class="comment">//cache</span>
82<a name="l00034"></a>00034         mat _Pp, _Ry, _iRy, _K;
83<a name="l00035"></a>00035 <span class="keyword">public</span>:
84<a name="l00036"></a>00036         <span class="comment">//posterior</span>
85<a name="l00038"></a><a class="code" href="classbdm_1_1KalmanFull.html#2defb75e58892615c5f95fd844f3a666">00038</a> <span class="comment"></span>        vec <a class="code" href="classbdm_1_1KalmanFull.html#2defb75e58892615c5f95fd844f3a666" title="Mean value of the posterior density.">mu</a>;
86<a name="l00040"></a><a class="code" href="classbdm_1_1KalmanFull.html#acacd228e100c3e937de575ad2d7cd9c">00040</a>         mat <a class="code" href="classbdm_1_1KalmanFull.html#acacd228e100c3e937de575ad2d7cd9c" title="Variance of the posterior density.">P</a>;
87<a name="l00041"></a>00041
88<a name="l00042"></a>00042         <span class="keywordtype">bool</span> evalll;
89<a name="l00043"></a>00043         <span class="keywordtype">double</span> ll;
90<a name="l00044"></a>00044 <span class="keyword">public</span>:
91<a name="l00046"></a>00046         <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 );
92<a name="l00048"></a>00048         <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 );
93<a name="l00050"></a>00050         <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 );
94<a name="l00052"></a><a class="code" href="classbdm_1_1KalmanFull.html#bdcc98c8b18c1cbdebdf218ae838fd11">00052</a>         <a class="code" href="classbdm_1_1KalmanFull.html#bdcc98c8b18c1cbdebdf218ae838fd11" title="For EKFfull;.">KalmanFull</a>() {};
95<a name="l00053"></a>00053 };
96<a name="l00054"></a>00054
97<a name="l00055"></a>00055
98<a name="l00063"></a>00063 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
99<a name="l00064"></a>00064
100<a name="l00065"></a><a class="code" href="classbdm_1_1Kalman.html">00065</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> {
101<a name="l00066"></a>00066 <span class="keyword">protected</span>:
102<a name="l00068"></a><a class="code" href="classbdm_1_1Kalman.html#3fe475a1e920b20b63bb342c0e1571f7">00068</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>;
103<a name="l00070"></a><a class="code" href="classbdm_1_1Kalman.html#149e27424fd1a7cc1c998ea088618a94">00070</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>;
104<a name="l00072"></a><a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa">00072</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a>;
105<a name="l00074"></a><a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f">00074</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>;
106<a name="l00076"></a><a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b">00076</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b" title="cache of rvu.count()">dimu</a>;
107<a name="l00078"></a><a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace">00078</a>         mat <a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a>;
108<a name="l00080"></a><a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c">00080</a>         mat <a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c" title="Matrix B.">B</a>;
109<a name="l00082"></a><a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177">00082</a>         mat <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>;
110<a name="l00084"></a><a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456">00084</a>         mat <a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456" title="Matrix D.">D</a>;
111<a name="l00086"></a><a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee">00086</a>         sq_T <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a>;
112<a name="l00088"></a><a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7">00088</a>         sq_T <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a>;
113<a name="l00089"></a>00089
114<a name="l00091"></a><a class="code" href="classbdm_1_1Kalman.html#383f329ff18bbe219254c8b3b916f40d">00091</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>;
115<a name="l00093"></a><a class="code" href="classbdm_1_1Kalman.html#ba555c394c429f6831c9bbabfa2c944c">00093</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>;
116<a name="l00094"></a>00094
117<a name="l00096"></a><a class="code" href="classbdm_1_1Kalman.html#bd69dfb802465f22dd84d73a180d5c92">00096</a>         mat <a class="code" href="classbdm_1_1Kalman.html#bd69dfb802465f22dd84d73a180d5c92" title="placeholder for Kalman gain">_K</a>;
118<a name="l00098"></a><a class="code" href="classbdm_1_1Kalman.html#c249d45258c8578b13858ad3e7b729b1">00098</a>         vec&amp; <a class="code" href="classbdm_1_1Kalman.html#c249d45258c8578b13858ad3e7b729b1" title="cache of fy.mu">_yp</a>;
119<a name="l00100"></a><a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a">00100</a>         sq_T&amp; <a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a" title="cache of fy.R">_Ry</a>;
120<a name="l00102"></a><a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0">00102</a>         vec&amp; <a class="code" href="classbdm_1_1Kalman.html#fa172078091e45561343fa513dd573b0" title="cache of est.mu">_mu</a>;
121<a name="l00104"></a><a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed">00104</a>         sq_T&amp; <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>;
122<a name="l00105"></a>00105
123<a name="l00106"></a>00106 <span class="keyword">public</span>:
124<a name="l00108"></a>00108         <a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4" title="Default constructor.">Kalman</a> ( );
125<a name="l00110"></a>00110         <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 );
126<a name="l00112"></a>00112         <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 );
127<a name="l00114"></a><a class="code" href="classbdm_1_1Kalman.html#9264fc6b173ecb803d2684b883f32c68">00114</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 ) {
128<a name="l00115"></a>00115                 sq_T pom ( <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> );
129<a name="l00116"></a>00116                 <a class="code" href="classbdm_1_1Kalman.html#383f329ff18bbe219254c8b3b916f40d" title="posterior density on $x_t$">est</a>.set_parameters ( mu0, P0 );
130<a name="l00117"></a>00117                 P0.mult_sym ( <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>, pom );
131<a name="l00118"></a>00118                 <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 );
132<a name="l00119"></a>00119         };
133<a name="l00120"></a>00120
134<a name="l00122"></a>00122         <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 );
135<a name="l00124"></a><a class="code" href="classbdm_1_1Kalman.html#f75e487ff6c129d7012d702030f8c890">00124</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>{
136<a name="l00125"></a>00125                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1Kalman.html#383f329ff18bbe219254c8b3b916f40d" title="posterior density on $x_t$">est</a>;
137<a name="l00126"></a>00126         }
138<a name="l00128"></a><a class="code" href="classbdm_1_1Kalman.html#c788ec6e6c6f5f5861ae8a56d8ede277">00128</a>         mat&amp; <a class="code" href="classbdm_1_1Kalman.html#c788ec6e6c6f5f5861ae8a56d8ede277" title="access function">__K</a>() {
139<a name="l00129"></a>00129                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1Kalman.html#bd69dfb802465f22dd84d73a180d5c92" title="placeholder for Kalman gain">_K</a>;
140<a name="l00130"></a>00130         }
141<a name="l00132"></a><a class="code" href="classbdm_1_1Kalman.html#a250d1dbe7bba861dba2a324520cfa48">00132</a>         vec <a class="code" href="classbdm_1_1Kalman.html#a250d1dbe7bba861dba2a324520cfa48" title="access function">_dP</a>() {
142<a name="l00133"></a>00133                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>-&gt;getD();
143<a name="l00134"></a>00134         }
144<a name="l00135"></a>00135 };
145<a name="l00136"></a>00136
146<a name="l00143"></a><a class="code" href="classbdm_1_1KalmanCh.html">00143</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; {
147<a name="l00144"></a>00144 <span class="keyword">protected</span>:
148<a name="l00146"></a><a class="code" href="classbdm_1_1KalmanCh.html#48611c8582706cfa62e832be0972e75d">00146</a>         mat <a class="code" href="classbdm_1_1KalmanCh.html#48611c8582706cfa62e832be0972e75d" title="pre array (triangular matrix)">preA</a>;
149<a name="l00148"></a><a class="code" href="classbdm_1_1KalmanCh.html#bcbd68f51d4b57246e7784ca5900171f">00148</a>         mat <a class="code" href="classbdm_1_1KalmanCh.html#bcbd68f51d4b57246e7784ca5900171f" title="post array (triangular matrix)">postA</a>;
150<a name="l00149"></a>00149
151<a name="l00150"></a>00150 <span class="keyword">public</span>:
152<a name="l00152"></a><a class="code" href="classbdm_1_1KalmanCh.html#24ce65bdaa538d4d5153d709a929b996">00152</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_1KalmanCh.html#24ce65bdaa538d4d5153d709a929b996" title="copy constructor">_copy_</a>()<span class="keyword"> const </span>{
153<a name="l00153"></a>00153                 <a class="code" href="classbdm_1_1KalmanCh.html" title="Kalman filter in square root form.">KalmanCh</a>* K = <span class="keyword">new</span> <a class="code" href="classbdm_1_1KalmanCh.html" title="Kalman filter in square root form.">KalmanCh</a>;
154<a name="l00154"></a>00154                 K-&gt;<a class="code" href="classbdm_1_1KalmanCh.html#20a4d4c664e8ac8a3f1bb7b0d11c6d87" title="Set parameters with check of relevance.">set_parameters</a> ( <a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a>, <a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c" title="Matrix B.">B</a>, <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>, <a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456" title="Matrix D.">D</a>, <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a>, <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a> );
155<a name="l00155"></a>00155                 K-&gt;<a class="code" href="classbdm_1_1KalmanCh.html#6e169272657ed101f3d128b49c59b890">set_statistics</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#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a> );
156<a name="l00156"></a>00156                 <span class="keywordflow">return</span> K;
157<a name="l00157"></a>00157         }
158<a name="l00159"></a>00159         <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="classbdm_1_1chmat.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="classbdm_1_1chmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> &amp;R0 );
159<a name="l00160"></a>00160         <span class="keywordtype">void</span> set_statistics ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> <a class="code" href="classbdm_1_1chmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> &amp;P0 ) {
160<a name="l00161"></a>00161                 <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 );
161<a name="l00162"></a>00162         };
162<a name="l00163"></a>00163
163<a name="l00164"></a>00164
164<a name="l00178"></a>00178         <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 );
165<a name="l00179"></a>00179 };
166<a name="l00180"></a>00180
167<a name="l00186"></a><a class="code" href="classbdm_1_1EKFfull.html">00186</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> {
168<a name="l00187"></a>00187 <span class="keyword">protected</span>:
169<a name="l00189"></a><a class="code" href="classbdm_1_1EKFfull.html#ee5bfb3f09dfa994596c5bc68306f7d6">00189</a>         <a class="code" href="classbdm_1_1shared__ptr.html">shared_ptr&lt;diffbifn&gt;</a> <a class="code" href="classbdm_1_1EKFfull.html#ee5bfb3f09dfa994596c5bc68306f7d6" title="Internal Model f(x,u).">pfxu</a>;
170<a name="l00190"></a>00190
171<a name="l00192"></a><a class="code" href="classbdm_1_1EKFfull.html#6432c675b1a9b83898b4ff19eab9b45c">00192</a>         <a class="code" href="classbdm_1_1shared__ptr.html">shared_ptr&lt;diffbifn&gt;</a> <a class="code" href="classbdm_1_1EKFfull.html#6432c675b1a9b83898b4ff19eab9b45c" title="Observation Model h(x,u).">phxu</a>;
172<a name="l00193"></a>00193
173<a name="l00194"></a>00194         <a class="code" href="classbdm_1_1enorm.html">enorm&lt;fsqmat&gt;</a> E;
174<a name="l00195"></a>00195 <span class="keyword">public</span>:
175<a name="l00197"></a>00197         <a class="code" href="classbdm_1_1EKFfull.html#6939c345389abb8b2481457b4cfe1165" title="Default constructor.">EKFfull</a> ( );
176<a name="l00198"></a>00198
177<a name="l00200"></a>00200         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKFfull.html#8a68f95124a4b01f9ce23225f3c736a4" title="Set nonlinear functions for mean values and covariance matrices.">set_parameters</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1shared__ptr.html">shared_ptr&lt;diffbifn&gt;</a> &amp;<a class="code" href="classbdm_1_1EKFfull.html#ee5bfb3f09dfa994596c5bc68306f7d6" title="Internal Model f(x,u).">pfxu</a>, <span class="keyword">const</span> <a class="code" href="classbdm_1_1shared__ptr.html">shared_ptr&lt;diffbifn&gt;</a> &amp;<a class="code" href="classbdm_1_1EKFfull.html#6432c675b1a9b83898b4ff19eab9b45c" title="Observation Model h(x,u).">phxu</a>, <span class="keyword">const</span> mat Q0, <span class="keyword">const</span> mat R0 );
178<a name="l00201"></a>00201
179<a name="l00203"></a>00203         <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 );
180<a name="l00205"></a><a class="code" href="classbdm_1_1EKFfull.html#1949a9b1496a855cc7c24e619bc52365">00205</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKFfull.html#1949a9b1496a855cc7c24e619bc52365" title="set estimates">set_statistics</a> ( vec mu0, mat P0 ) {
181<a name="l00206"></a>00206                 <a class="code" href="classbdm_1_1KalmanFull.html#2defb75e58892615c5f95fd844f3a666" title="Mean value of the posterior density.">mu</a> = mu0;
182<a name="l00207"></a>00207                 <a class="code" href="classbdm_1_1KalmanFull.html#acacd228e100c3e937de575ad2d7cd9c" title="Variance of the posterior density.">P</a> = P0;
183<a name="l00208"></a>00208         };
184<a name="l00210"></a><a class="code" href="classbdm_1_1EKFfull.html#7e9a69f36a0a0615c9abb806772ef36d">00210</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>{
185<a name="l00211"></a>00211                 <span class="keywordflow">return</span> E;
186<a name="l00212"></a>00212         };
187<a name="l00213"></a>00213         <span class="keyword">const</span> mat _R() {
188<a name="l00214"></a>00214                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1KalmanFull.html#acacd228e100c3e937de575ad2d7cd9c" title="Variance of the posterior density.">P</a>;
189<a name="l00215"></a>00215         }
190<a name="l00216"></a>00216 };
191<a name="l00217"></a>00217
192<a name="l00223"></a>00223 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
193<a name="l00224"></a><a class="code" href="classbdm_1_1EKF.html">00224</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; {
194<a name="l00226"></a>00226         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu;
195<a name="l00228"></a>00228         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu;
196<a name="l00229"></a>00229 <span class="keyword">public</span>:
197<a name="l00231"></a>00231         <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> );
198<a name="l00233"></a><a class="code" href="classbdm_1_1EKF.html#fe9b2e227255ad32dc73df316b7318f4">00233</a>         <a class="code" href="classbdm_1_1EKF.html" title="Extended Kalman Filter.">EKF&lt;sq_T&gt;</a>* <a class="code" href="classbdm_1_1EKF.html#fe9b2e227255ad32dc73df316b7318f4" title="copy constructor">_copy_</a>()<span class="keyword"> const </span>{
199<a name="l00234"></a>00234                 <span class="keywordflow">return</span> <span class="keyword">new</span> <a class="code" href="classbdm_1_1EKF.html" title="Extended Kalman Filter.">EKF&lt;sq_T&gt;</a> ( this );
200<a name="l00235"></a>00235         }
201<a name="l00237"></a>00237         <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 );
202<a name="l00239"></a>00239         <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 );
203<a name="l00240"></a>00240 };
204<a name="l00241"></a>00241
205<a name="l00248"></a><a class="code" href="classbdm_1_1EKFCh.html">00248</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> {
206<a name="l00249"></a>00249 <span class="keyword">protected</span>:
207<a name="l00251"></a><a class="code" href="classbdm_1_1EKFCh.html#5dc1964a6058057c9e17ea3c2f33bd2a">00251</a>         <a class="code" href="classbdm_1_1shared__ptr.html">shared_ptr&lt;diffbifn&gt;</a> <a class="code" href="classbdm_1_1EKFCh.html#5dc1964a6058057c9e17ea3c2f33bd2a" title="Internal Model f(x,u).">pfxu</a>;
208<a name="l00252"></a>00252
209<a name="l00254"></a><a class="code" href="classbdm_1_1EKFCh.html#27c29351b2cc8248c011af506208e8d1">00254</a>         <a class="code" href="classbdm_1_1shared__ptr.html">shared_ptr&lt;diffbifn&gt;</a> <a class="code" href="classbdm_1_1EKFCh.html#27c29351b2cc8248c011af506208e8d1" title="Observation Model h(x,u).">phxu</a>;
210<a name="l00255"></a>00255 <span class="keyword">public</span>:
211<a name="l00257"></a><a class="code" href="classbdm_1_1EKFCh.html#1d1d91400e3f177de9fe7962ea17adc4">00257</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_1EKFCh.html#1d1d91400e3f177de9fe7962ea17adc4" title="copy constructor duplicated - calls different set_parameters">_copy_</a>()<span class="keyword"> const </span>{
212<a name="l00258"></a>00258                 <a class="code" href="classbdm_1_1EKFCh.html" title="Extended Kalman Filter in Square root.">EKFCh</a>* E = <span class="keyword">new</span> <a class="code" href="classbdm_1_1EKFCh.html" title="Extended Kalman Filter in Square root.">EKFCh</a>;
213<a name="l00259"></a>00259                 E-&gt;<a class="code" href="classbdm_1_1EKFCh.html#cd1697a0b3ac2e17c0c56a90a4dbfad1" title="Set nonlinear functions for mean values and covariance matrices.">set_parameters</a> ( <a class="code" href="classbdm_1_1EKFCh.html#5dc1964a6058057c9e17ea3c2f33bd2a" title="Internal Model f(x,u).">pfxu</a>, <a class="code" href="classbdm_1_1EKFCh.html#27c29351b2cc8248c011af506208e8d1" title="Observation Model h(x,u).">phxu</a>, <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a>, <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a> );
214<a name="l00260"></a>00260                 E-&gt;<a class="code" href="classbdm_1_1KalmanCh.html#6e169272657ed101f3d128b49c59b890">set_statistics</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#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a> );
215<a name="l00261"></a>00261                 <span class="keywordflow">return</span> E;
216<a name="l00262"></a>00262         }
217<a name="l00264"></a>00264         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKFCh.html#cd1697a0b3ac2e17c0c56a90a4dbfad1" title="Set nonlinear functions for mean values and covariance matrices.">set_parameters</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1shared__ptr.html">shared_ptr&lt;diffbifn&gt;</a> &amp;<a class="code" href="classbdm_1_1EKFCh.html#5dc1964a6058057c9e17ea3c2f33bd2a" title="Internal Model f(x,u).">pfxu</a>, <span class="keyword">const</span> <a class="code" href="classbdm_1_1shared__ptr.html">shared_ptr&lt;diffbifn&gt;</a> &amp;<a class="code" href="classbdm_1_1EKFCh.html#27c29351b2cc8248c011af506208e8d1" title="Observation Model h(x,u).">phxu</a>, <span class="keyword">const</span> <a class="code" href="classbdm_1_1chmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> Q0, <span class="keyword">const</span> <a class="code" href="classbdm_1_1chmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> R0 );
218<a name="l00265"></a>00265
219<a name="l00267"></a>00267         <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 );
220<a name="l00268"></a>00268
221<a name="l00269"></a>00269         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1EKFCh.html#3952e181ef0cf230c325ad3be1f002df" title="This method arrange instance properties according the data stored in the Setting...">from_setting</a> ( <span class="keyword">const</span> Setting &amp;<span class="keyword">set</span> );
222<a name="l00270"></a>00270
223<a name="l00271"></a>00271         <span class="comment">// TODO dodelat void to_setting( Setting &amp;set ) const;</span>
224<a name="l00272"></a>00272
225<a name="l00273"></a>00273         <span class="keyword">const</span> <a class="code" href="classbdm_1_1enorm.html">enorm&lt;chmat&gt;</a>&amp; posterior() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1Kalman.html#383f329ff18bbe219254c8b3b916f40d" title="posterior density on $x_t$">est</a>;}
226<a name="l00274"></a>00274 };
227<a name="l00275"></a>00275
228<a name="l00276"></a>00276 <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a> ( EKFCh );
229<a name="l00277"></a>00277 SHAREDPTR ( EKFCh );
230<a name="l00278"></a>00278
231<a name="l00279"></a>00279
232<a name="l00284"></a><a class="code" href="classbdm_1_1KFcondQR.html">00284</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; {
233<a name="l00285"></a>00285 <span class="comment">//protected:</span>
234<a name="l00286"></a>00286 <span class="keyword">public</span>:
235<a name="l00287"></a><a class="code" href="classbdm_1_1KFcondQR.html#31bc31087ee7ed6c0bfb92d626321b91">00287</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1KFcondQR.html#31bc31087ee7ed6c0bfb92d626321b91" title="Substitute val for rvc.">condition</a> ( <span class="keyword">const</span> vec &amp;QR ) {
236<a name="l00288"></a>00288                 it_assert_debug ( QR.length() == ( <a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a> + <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> ), <span class="stringliteral">"KFcondRQ: conditioning by incompatible vector"</span> );
237<a name="l00289"></a>00289
238<a name="l00290"></a>00290                 <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a>.<a class="code" href="classbdm_1_1ldmat.html#72e61ad4e0653f4fc38e7d60d3cf1d4e" title="Access functions.">setD</a> ( QR ( 0, <a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a> - 1 ) );
239<a name="l00291"></a>00291                 <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a>.<a class="code" href="classbdm_1_1ldmat.html#72e61ad4e0653f4fc38e7d60d3cf1d4e" title="Access functions.">setD</a> ( QR ( <a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a>, -1 ) );
240<a name="l00292"></a>00292         };
241<a name="l00293"></a>00293 };
242<a name="l00294"></a>00294
243<a name="l00299"></a><a class="code" href="classbdm_1_1KFcondR.html">00299</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; {
244<a name="l00300"></a>00300 <span class="comment">//protected:</span>
245<a name="l00301"></a>00301 <span class="keyword">public</span>:
246<a name="l00303"></a><a class="code" href="classbdm_1_1KFcondR.html#f11639d79f10b1e7dad16a0d8233450d">00303</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="classbdm_1_1ldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&gt; ( ) {};
247<a name="l00304"></a>00304
248<a name="l00305"></a><a class="code" href="classbdm_1_1KFcondR.html#7d42a421acbdcf9b610a5682ee5fb9a8">00305</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1KFcondR.html#7d42a421acbdcf9b610a5682ee5fb9a8" title="Substitute val for rvc.">condition</a> ( <span class="keyword">const</span> vec &amp;R0 ) {
249<a name="l00306"></a>00306                 it_assert_debug ( R0.length() == ( <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> ), <span class="stringliteral">"KFcondR: conditioning by incompatible vector"</span> );
250<a name="l00307"></a>00307
251<a name="l00308"></a>00308                 <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a>.<a class="code" href="classbdm_1_1ldmat.html#72e61ad4e0653f4fc38e7d60d3cf1d4e" title="Access functions.">setD</a> ( R0 );
252<a name="l00309"></a>00309         };
253<a name="l00310"></a>00310
254<a name="l00311"></a>00311 };
255<a name="l00312"></a>00312
256<a name="l00314"></a>00314
257<a name="l00315"></a>00315 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
258<a name="l00316"></a><a class="code" href="classbdm_1_1Kalman.html#8b22c45cffa949d70b8e5ac92ed5ce25">00316</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 ),
259<a name="l00317"></a>00317                 dimx ( K0.dimx ), dimy ( K0.dimy ), dimu ( K0.dimu ),
260<a name="l00318"></a>00318                 A ( K0.A ), B ( K0.B ), C ( K0.C ), D ( K0.D ),
261<a name="l00319"></a>00319                 Q ( K0.Q ), R ( K0.R ),
262<a name="l00320"></a>00320                 est ( K0.est ), fy ( K0.fy ), _yp ( fy._mu() ), _Ry ( fy._R() ), _mu ( est._mu() ), _P ( est._R() ) {
263<a name="l00321"></a>00321
264<a name="l00322"></a>00322 <span class="comment">// copy values in pointers</span>
265<a name="l00323"></a>00323 <span class="comment">//      _mu = K0._mu;</span>
266<a name="l00324"></a>00324 <span class="comment">//      _P = K0._P;</span>
267<a name="l00325"></a>00325 <span class="comment">//      _yp = K0._yp;</span>
268<a name="l00326"></a>00326 <span class="comment">//      _Ry = K0._Ry;</span>
269<a name="l00327"></a>00327
270<a name="l00328"></a>00328 }
271<a name="l00329"></a>00329
272<a name="l00330"></a>00330 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
273<a name="l00331"></a><a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4">00331</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() ) {
274<a name="l00332"></a>00332 };
275<a name="l00333"></a>00333
276<a name="l00334"></a>00334 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
277<a name="l00335"></a><a class="code" href="classbdm_1_1Kalman.html#3c7fb87fb6b87d08deb6a5a7862da957">00335</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 ) {
278<a name="l00336"></a>00336         <a class="code" href="classbdm_1_1Kalman.html#ba7699cdb3b1382a54d3e28b9b7517fa" title="cache of rv.count()">dimx</a> = A0.rows();
279<a name="l00337"></a>00337         <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> = C0.rows();
280<a name="l00338"></a>00338         <a class="code" href="classbdm_1_1Kalman.html#c5136ef617f6ac0e426bea222755d92b" title="cache of rvu.count()">dimu</a> = B0.cols();
281<a name="l00339"></a>00339
282<a name="l00340"></a>00340         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> );
283<a name="l00341"></a>00341         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> );
284<a name="l00342"></a>00342         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> );
285<a name="l00343"></a>00343         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> );
286<a name="l00344"></a>00344         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> );
287<a name="l00345"></a>00345         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> );
288<a name="l00346"></a>00346
289<a name="l00347"></a>00347         <a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a> = A0;
290<a name="l00348"></a>00348         <a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c" title="Matrix B.">B</a> = B0;
291<a name="l00349"></a>00349         <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a> = C0;
292<a name="l00350"></a>00350         <a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456" title="Matrix D.">D</a> = D0;
293<a name="l00351"></a>00351         <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a> = R0;
294<a name="l00352"></a>00352         <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a> = Q0;
295<a name="l00353"></a>00353 }
296<a name="l00354"></a>00354
297<a name="l00355"></a>00355 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
298<a name="l00356"></a><a class="code" href="classbdm_1_1Kalman.html#4a39330c14eff8d13179e868a1d1aa8c">00356</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 ) {
299<a name="l00357"></a>00357         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> );
300<a name="l00358"></a>00358
301<a name="l00359"></a>00359         sq_T iRy ( <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> );
302<a name="l00360"></a>00360         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 );
303<a name="l00361"></a>00361         vec y = dt.get ( 0, <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> - 1 );
304<a name="l00362"></a>00362         <span class="comment">//Time update</span>
305<a name="l00363"></a>00363         <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;
306<a name="l00364"></a>00364         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
307<a name="l00365"></a>00365         <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> );
308<a name="l00366"></a>00366         <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>;
309<a name="l00367"></a>00367
310<a name="l00368"></a>00368         <span class="comment">//Data update</span>
311<a name="l00369"></a>00369         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
312<a name="l00370"></a>00370         <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> );
313<a name="l00371"></a>00371         <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>;
314<a name="l00372"></a>00372
315<a name="l00373"></a>00373         mat Pfull = <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.to_mat();
316<a name="l00374"></a>00374
317<a name="l00375"></a>00375         <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>
318<a name="l00376"></a>00376         <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() );
319<a name="l00377"></a>00377
320<a name="l00378"></a>00378         sq_T pom ( ( <span class="keywordtype">int</span> ) Pfull.rows() );
321<a name="l00379"></a>00379         iRy.mult_sym_t ( <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>*Pfull, pom );
322<a name="l00380"></a>00380         ( <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>
323<a name="l00381"></a>00381         ( <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>
324<a name="l00382"></a>00382         ( <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> );
325<a name="l00383"></a>00383
326<a name="l00384"></a>00384
327<a name="l00385"></a>00385         <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>
328<a name="l00386"></a>00386                 <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 );
329<a name="l00387"></a>00387         }
330<a name="l00388"></a>00388
331<a name="l00389"></a>00389 <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>
332<a name="l00390"></a>00390
333<a name="l00391"></a>00391 };
334<a name="l00392"></a>00392
335<a name="l00400"></a><a class="code" href="classbdm_1_1MultiModel.html">00400</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1MultiModel.html" title="(Switching) Multiple Model The model runs several models in parallel and evaluates...">MultiModel</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> {
336<a name="l00401"></a>00401 <span class="keyword">protected</span>:
337<a name="l00403"></a><a class="code" href="classbdm_1_1MultiModel.html#33de5d07ee774070632de8963b5d4c93">00403</a>         Array&lt;EKFCh*&gt; <a class="code" href="classbdm_1_1MultiModel.html#33de5d07ee774070632de8963b5d4c93" title="List of models between which we switch.">Models</a>;
338<a name="l00405"></a><a class="code" href="classbdm_1_1MultiModel.html#ef85ea61575bffa8beac8040869ee47a">00405</a>         vec <a class="code" href="classbdm_1_1MultiModel.html#ef85ea61575bffa8beac8040869ee47a" title="vector of model weights">w</a>;
339<a name="l00407"></a><a class="code" href="classbdm_1_1MultiModel.html#7b4012fc2208ce4ddd5c0d1fe69d7634">00407</a>         vec <a class="code" href="classbdm_1_1MultiModel.html#7b4012fc2208ce4ddd5c0d1fe69d7634" title="cache of model lls">_lls</a>;
340<a name="l00409"></a><a class="code" href="classbdm_1_1MultiModel.html#9b56bcde4664bd53f8995d7ee7ed415c">00409</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1MultiModel.html#9b56bcde4664bd53f8995d7ee7ed415c" title="type of switching policy [1=maximum,2=...]">policy</a>;
341<a name="l00411"></a><a class="code" href="classbdm_1_1MultiModel.html#d665551d045b1a1055eeb9185558ff0b">00411</a>         <a class="code" href="classbdm_1_1enorm.html">enorm&lt;chmat&gt;</a> <a class="code" href="classbdm_1_1MultiModel.html#d665551d045b1a1055eeb9185558ff0b" title="internal statistics">est</a>;
342<a name="l00412"></a>00412 <span class="keyword">public</span>:
343<a name="l00413"></a>00413         <span class="keywordtype">void</span> set_parameters ( Array&lt;EKFCh*&gt; A, <span class="keywordtype">int</span> pol0 = 1 ) {
344<a name="l00414"></a>00414                 <a class="code" href="classbdm_1_1MultiModel.html#33de5d07ee774070632de8963b5d4c93" title="List of models between which we switch.">Models</a> = A;<span class="comment">//TODO: test if evalll is set</span>
345<a name="l00415"></a>00415                 <a class="code" href="classbdm_1_1MultiModel.html#ef85ea61575bffa8beac8040869ee47a" title="vector of model weights">w</a>.set_length ( A.length() );
346<a name="l00416"></a>00416                 <a class="code" href="classbdm_1_1MultiModel.html#7b4012fc2208ce4ddd5c0d1fe69d7634" title="cache of model lls">_lls</a>.set_length ( A.length() );
347<a name="l00417"></a>00417                 <a class="code" href="classbdm_1_1MultiModel.html#9b56bcde4664bd53f8995d7ee7ed415c" title="type of switching policy [1=maximum,2=...]">policy</a> = pol0;
348<a name="l00418"></a>00418
349<a name="l00419"></a>00419                 <a class="code" href="classbdm_1_1MultiModel.html#d665551d045b1a1055eeb9185558ff0b" title="internal statistics">est</a>.<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> ( <span class="stringliteral">"MM"</span>, A ( 0 )-&gt;<a class="code" href="classbdm_1_1MultiModel.html#2af3f8dc10dfdae0ddc8160a9f36aaee" title="posterior density">posterior</a>().dimension(), 0 ) );
350<a name="l00420"></a>00420                 <a class="code" href="classbdm_1_1MultiModel.html#d665551d045b1a1055eeb9185558ff0b" title="internal statistics">est</a>.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( A ( 0 )-&gt;<a class="code" href="classbdm_1_1MultiModel.html#2af3f8dc10dfdae0ddc8160a9f36aaee" title="posterior density">posterior</a>().mean(), A ( 0 )-&gt;<a class="code" href="classbdm_1_1MultiModel.html#2af3f8dc10dfdae0ddc8160a9f36aaee" title="posterior density">posterior</a>()._R() );
351<a name="l00421"></a>00421         }
352<a name="l00422"></a><a class="code" href="classbdm_1_1MultiModel.html#a915deeddb0e94c337d02ebc0abe535e">00422</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1MultiModel.html#a915deeddb0e94c337d02ebc0abe535e" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
353<a name="l00423"></a>00423                 <span class="keywordtype">int</span> n = <a class="code" href="classbdm_1_1MultiModel.html#33de5d07ee774070632de8963b5d4c93" title="List of models between which we switch.">Models</a>.length();
354<a name="l00424"></a>00424                 <span class="keywordtype">int</span> i;
355<a name="l00425"></a>00425                 <span class="keywordflow">for</span> ( i = 0; i &lt; n; i++ ) {
356<a name="l00426"></a>00426                         <a class="code" href="classbdm_1_1MultiModel.html#33de5d07ee774070632de8963b5d4c93" title="List of models between which we switch.">Models</a> ( i )-&gt;bayes ( dt );
357<a name="l00427"></a>00427                         <a class="code" href="classbdm_1_1MultiModel.html#7b4012fc2208ce4ddd5c0d1fe69d7634" title="cache of model lls">_lls</a> ( i ) = <a class="code" href="classbdm_1_1MultiModel.html#33de5d07ee774070632de8963b5d4c93" title="List of models between which we switch.">Models</a> ( i )-&gt;_ll();
358<a name="l00428"></a>00428                 }
359<a name="l00429"></a>00429                 <span class="keywordtype">double</span> mlls = max ( <a class="code" href="classbdm_1_1MultiModel.html#7b4012fc2208ce4ddd5c0d1fe69d7634" title="cache of model lls">_lls</a> );
360<a name="l00430"></a>00430                 <a class="code" href="classbdm_1_1MultiModel.html#ef85ea61575bffa8beac8040869ee47a" title="vector of model weights">w</a> = exp ( <a class="code" href="classbdm_1_1MultiModel.html#7b4012fc2208ce4ddd5c0d1fe69d7634" title="cache of model lls">_lls</a> - mlls );
361<a name="l00431"></a>00431                 <a class="code" href="classbdm_1_1MultiModel.html#ef85ea61575bffa8beac8040869ee47a" title="vector of model weights">w</a> /= sum ( <a class="code" href="classbdm_1_1MultiModel.html#ef85ea61575bffa8beac8040869ee47a" title="vector of model weights">w</a> ); <span class="comment">//normalization</span>
362<a name="l00432"></a>00432                 <span class="comment">//set statistics</span>
363<a name="l00433"></a>00433                 <span class="keywordflow">switch</span> ( <a class="code" href="classbdm_1_1MultiModel.html#9b56bcde4664bd53f8995d7ee7ed415c" title="type of switching policy [1=maximum,2=...]">policy</a> ) {
364<a name="l00434"></a>00434                 <span class="keywordflow">case</span> 1: {
365<a name="l00435"></a>00435                         <span class="keywordtype">int</span> mi = max_index ( <a class="code" href="classbdm_1_1MultiModel.html#ef85ea61575bffa8beac8040869ee47a" title="vector of model weights">w</a> );
366<a name="l00436"></a>00436                         <span class="keyword">const</span> <a class="code" href="classbdm_1_1enorm.html">enorm&lt;chmat&gt;</a> &amp;st = <a class="code" href="classbdm_1_1MultiModel.html#33de5d07ee774070632de8963b5d4c93" title="List of models between which we switch.">Models</a> ( mi )-&gt;posterior() ;
367<a name="l00437"></a>00437                         <a class="code" href="classbdm_1_1MultiModel.html#d665551d045b1a1055eeb9185558ff0b" title="internal statistics">est</a>.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( st.<a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600" title="return expected value">mean</a>(), st.<a class="code" href="classbdm_1_1enorm.html#81d81e35e57c9f194bde248e3affcf1f">_R</a>() );
368<a name="l00438"></a>00438                 }
369<a name="l00439"></a>00439                 <span class="keywordflow">break</span>;
370<a name="l00440"></a>00440                 <span class="keywordflow">default</span>:
371<a name="l00441"></a>00441                         it_error ( <span class="stringliteral">"unknown policy"</span> );
372<a name="l00442"></a>00442                 }
373<a name="l00443"></a>00443                 <span class="comment">// copy result to all models</span>
374<a name="l00444"></a>00444                 <span class="keywordflow">for</span> ( i = 0; i &lt; n; i++ ) {
375<a name="l00445"></a>00445                         <a class="code" href="classbdm_1_1MultiModel.html#33de5d07ee774070632de8963b5d4c93" title="List of models between which we switch.">Models</a> ( i )-&gt;set_statistics ( <a class="code" href="classbdm_1_1MultiModel.html#d665551d045b1a1055eeb9185558ff0b" title="internal statistics">est</a>.<a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600" title="return expected value">mean</a>(), <a class="code" href="classbdm_1_1MultiModel.html#d665551d045b1a1055eeb9185558ff0b" title="internal statistics">est</a>.<a class="code" href="classbdm_1_1enorm.html#81d81e35e57c9f194bde248e3affcf1f">_R</a>() );
376<a name="l00446"></a>00446                 }
377<a name="l00447"></a>00447         }
378<a name="l00449"></a><a class="code" href="classbdm_1_1MultiModel.html#2af3f8dc10dfdae0ddc8160a9f36aaee">00449</a>         <span class="keyword">const</span> <a class="code" href="classbdm_1_1enorm.html">enorm&lt;chmat&gt;</a>&amp; <a class="code" href="classbdm_1_1MultiModel.html#2af3f8dc10dfdae0ddc8160a9f36aaee" title="posterior density">posterior</a>()<span class="keyword"> const </span>{
379<a name="l00450"></a>00450                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1MultiModel.html#d665551d045b1a1055eeb9185558ff0b" title="internal statistics">est</a>;
380<a name="l00451"></a>00451         }
381<a name="l00452"></a>00452
382<a name="l00453"></a>00453         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1MultiModel.html#7bd320fa2540883749b3f83cf2261878" title="This method arrange instance properties according the data stored in the Setting...">from_setting</a> ( <span class="keyword">const</span> Setting &amp;<span class="keyword">set</span> );
383<a name="l00454"></a>00454
384<a name="l00455"></a>00455         <span class="comment">// TODO dodelat void to_setting( Setting &amp;set ) const;</span>
385<a name="l00456"></a>00456
386<a name="l00457"></a>00457 };
387<a name="l00458"></a>00458
388<a name="l00459"></a>00459 <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a> ( MultiModel );
389<a name="l00460"></a>00460 SHAREDPTR ( MultiModel );
390<a name="l00461"></a>00461
391<a name="l00462"></a>00462
392<a name="l00463"></a>00463
393<a name="l00464"></a>00464 <span class="comment">//TODO why not const pointer??</span>
394<a name="l00465"></a>00465
395<a name="l00466"></a>00466 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
396<a name="l00467"></a><a class="code" href="classbdm_1_1EKF.html#d087a8bb408d26ac4f5c542746b81059">00467</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 ) {}
397<a name="l00468"></a>00468
398<a name="l00469"></a>00469 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
399<a name="l00470"></a><a class="code" href="classbdm_1_1EKF.html#00fec1a0a6a467eb83fb36c65eba7bcb">00470</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 ) {
400<a name="l00471"></a>00471         pfxu = pfxu0;
401<a name="l00472"></a>00472         phxu = phxu0;
402<a name="l00473"></a>00473
403<a name="l00474"></a>00474         <span class="comment">//initialize matrices A C, later, these will be only updated!</span>
404<a name="l00475"></a>00475         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> );
405<a name="l00476"></a>00476 <span class="comment">//      pfxu-&gt;dfdu_cond ( *_mu,zeros ( dimu ),B,true );</span>
406<a name="l00477"></a>00477         <a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c" title="Matrix B.">B</a>.clear();
407<a name="l00478"></a>00478         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> );
408<a name="l00479"></a>00479 <span class="comment">//      phxu-&gt;dfdu_cond ( *_mu,zeros ( dimu ),D,true );</span>
409<a name="l00480"></a>00480         <a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456" title="Matrix D.">D</a>.clear();
410<a name="l00481"></a>00481
411<a name="l00482"></a>00482         <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a> = R0;
412<a name="l00483"></a>00483         <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a> = Q0;
413<a name="l00484"></a>00484 }
414<a name="l00485"></a>00485
415<a name="l00486"></a>00486 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
416<a name="l00487"></a><a class="code" href="classbdm_1_1EKF.html#3fb182ecc29b10ca1163cecbf3bcccfa">00487</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 ) {
417<a name="l00488"></a>00488         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> );
418<a name="l00489"></a>00489
419<a name="l00490"></a>00490         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> );
420<a name="l00491"></a>00491         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 );
421<a name="l00492"></a>00492         vec y = dt.get ( 0, <a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a> - 1 );
422<a name="l00493"></a>00493         <span class="comment">//Time update</span>
423<a name="l00494"></a>00494         <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 );
424<a name="l00495"></a>00495         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>
425<a name="l00496"></a>00496
426<a name="l00497"></a>00497         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
427<a name="l00498"></a>00498         <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.<a class="code" href="classbdm_1_1fsqmat.html#fca713af3f74677fd7fbafe723590112" 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> );
428<a name="l00499"></a>00499         <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>;
429<a name="l00500"></a>00500
430<a name="l00501"></a>00501         <span class="comment">//Data update</span>
431<a name="l00502"></a>00502         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>
432<a name="l00503"></a>00503         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
433<a name="l00504"></a>00504         <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.<a class="code" href="classbdm_1_1fsqmat.html#fca713af3f74677fd7fbafe723590112" 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> );
434<a name="l00505"></a>00505         ( <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>;
435<a name="l00506"></a>00506
436<a name="l00507"></a>00507         mat Pfull = <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.<a class="code" href="classbdm_1_1fsqmat.html#5c27d47cd0fa7ae686d4519f89a3c9b0" title="Conversion to full matrix.">to_mat</a>();
437<a name="l00508"></a>00508
438<a name="l00509"></a>00509         <a class="code" href="classbdm_1_1Kalman.html#2dd268f2d7fbe6382cb8825a1114192a" title="cache of fy.R">_Ry</a>.<a class="code" href="classbdm_1_1fsqmat.html#d17495888ab35e09ebb9b4e789f586d5" title="Matrix inversion preserving the chosen form.">inv</a> ( iRy ); <span class="comment">// result is in _iRy;</span>
439<a name="l00510"></a>00510         <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() );
440<a name="l00511"></a>00511
441<a name="l00512"></a>00512         sq_T pom ( ( <span class="keywordtype">int</span> ) Pfull.rows() );
442<a name="l00513"></a>00513         iRy.mult_sym_t ( <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>*Pfull, pom );
443<a name="l00514"></a>00514         ( <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>
444<a name="l00515"></a>00515         <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>
445<a name="l00516"></a>00516         ( <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> );
446<a name="l00517"></a>00517
447<a name="l00518"></a>00518         <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> ) {
448<a name="l00519"></a>00519                 <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 );
449<a name="l00520"></a>00520         }
450<a name="l00521"></a>00521 };
451<a name="l00522"></a>00522
452<a name="l00523"></a>00523
453<a name="l00524"></a>00524 }
454<a name="l00525"></a>00525 <span class="preprocessor">#endif // KF_H</span>
455<a name="l00526"></a>00526 <span class="preprocessor"></span>
456<a name="l00527"></a>00527
457</pre></div></div>
458<hr size="1"><address style="text-align: right;"><small>Generated on Sun Aug 16 17:58:18 2009 for mixpp by&nbsp;
459<a href="http://www.doxygen.org/index.html">
460<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.8 </small></address>
461</body>
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