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Revision 270, 55.7 kB (checked in by smidl, 16 years ago)

Changes in the very root classes!
* rv and rvc are no longer compulsory,
* samplecond does not return ll
* BM has drv

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19<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
20<a name="l00013"></a>00013 <span class="preprocessor">#ifndef KF_H</span>
21<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define KF_H</span>
22<a name="l00015"></a>00015 <span class="preprocessor"></span>
23<a name="l00016"></a>00016
24<a name="l00017"></a>00017 <span class="preprocessor">#include "../stat/libFN.h"</span>
25<a name="l00018"></a>00018 <span class="preprocessor">#include "../stat/libEF.h"</span>
26<a name="l00019"></a>00019 <span class="preprocessor">#include "../math/chmat.h"</span>
27<a name="l00020"></a>00020
28<a name="l00021"></a>00021 <span class="keyword">namespace </span>bdm{
29<a name="l00022"></a>00022
30<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> {
31<a name="l00028"></a>00028 <span class="keyword">protected</span>:
32<a name="l00029"></a>00029         <span class="keywordtype">int</span> dimx, dimy, dimu;
33<a name="l00030"></a>00030         mat A, B, C, D, R, Q;
34<a name="l00031"></a>00031
35<a name="l00032"></a>00032         <span class="comment">//cache</span>
36<a name="l00033"></a>00033         mat _Pp, _Ry, _iRy, _K;
37<a name="l00034"></a>00034 <span class="keyword">public</span>:
38<a name="l00035"></a>00035         <span class="comment">//posterior</span>
39<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>;
40<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>;
41<a name="l00040"></a>00040
42<a name="l00041"></a>00041         <span class="keywordtype">bool</span> evalll;
43<a name="l00042"></a>00042         <span class="keywordtype">double</span> ll;
44<a name="l00043"></a>00043 <span class="keyword">public</span>:
45<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 );
46<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 );
47<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 );
48<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>(){};
49<a name="l00052"></a>00052 };
50<a name="l00053"></a>00053
51<a name="l00054"></a>00054
52<a name="l00062"></a>00062 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
53<a name="l00063"></a>00063
54<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> {
55<a name="l00065"></a>00065 <span class="keyword">protected</span>:
56<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>;
57<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>;
58<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>;
59<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>;
60<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>;
61<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>;
62<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>;
63<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>;
64<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>;
65<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>;
66<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>;
67<a name="l00088"></a>00088
68<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>;
69<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>;
70<a name="l00093"></a>00093
71<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>;
72<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>;
73<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>;
74<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>;
75<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>;
76<a name="l00104"></a>00104
77<a name="l00105"></a>00105 <span class="keyword">public</span>:
78<a name="l00107"></a>00107         <a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4" title="Default constructor.">Kalman</a> ( );
79<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 );
80<a name="l00111"></a>00111         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1Kalman.html#94eb8cc31731210089db0ba4e1a08a6c" 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;R0,<span class="keyword">const</span> sq_T &amp;Q0 );
81<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 ) {
82<a name="l00114"></a>00114                 sq_T pom(<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>);
83<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 );
84<a name="l00116"></a>00116                 P0.mult_sym(<a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>,pom);
85<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 );
86<a name="l00118"></a>00118         };
87<a name="l00119"></a>00119
88<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 );
89<a name="l00123"></a><a class="code" href="classbdm_1_1Kalman.html#93b5936ba397f13c05f52885c545f42d">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#93b5936ba397f13c05f52885c545f42d" title="access function">_epdf</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>;}
90<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>;}
91<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>;}
92<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();}
93<a name="l00129"></a>00129 };
94<a name="l00130"></a>00130
95<a name="l00133"></a><a class="code" href="classbdm_1_1KalmanCh.html">00133</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;{
96<a name="l00134"></a>00134 <span class="keyword">protected</span>:
97<a name="l00136"></a><a class="code" href="classbdm_1_1KalmanCh.html#48611c8582706cfa62e832be0972e75d">00136</a> mat <a class="code" href="classbdm_1_1KalmanCh.html#48611c8582706cfa62e832be0972e75d" title="pre array (triangular matrix)">preA</a>;
98<a name="l00138"></a><a class="code" href="classbdm_1_1KalmanCh.html#bcbd68f51d4b57246e7784ca5900171f">00138</a> mat <a class="code" href="classbdm_1_1KalmanCh.html#bcbd68f51d4b57246e7784ca5900171f" title="post array (triangular matrix)">postA</a>;
99<a name="l00139"></a>00139
100<a name="l00140"></a>00140 <span class="keyword">public</span>:
101<a name="l00142"></a><a class="code" href="classbdm_1_1KalmanCh.html#830486554e1a2c7652541dbc9dcd3fb3">00142</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>(){};
102<a name="l00144"></a>00144         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1KalmanCh.html#ab3a87ba1831e53f193a9dfbaf56a879" 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;R0,<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 );
103<a name="l00145"></a><a class="code" href="classbdm_1_1KalmanCh.html#f559387dd38bd6002be490cc62987290">00145</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1KalmanCh.html#f559387dd38bd6002be490cc62987290" 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> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> &amp;P0 ) {
104<a name="l00146"></a>00146                 <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 );
105<a name="l00147"></a>00147         };
106<a name="l00148"></a>00148         
107<a name="l00149"></a>00149         
108<a name="l00163"></a>00163         <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 );
109<a name="l00164"></a>00164 };
110<a name="l00165"></a>00165
111<a name="l00171"></a><a class="code" href="classbdm_1_1EKFfull.html">00171</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> {
112<a name="l00172"></a>00172
113<a name="l00174"></a>00174         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu;
114<a name="l00176"></a>00176         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu;
115<a name="l00177"></a>00177         
116<a name="l00178"></a>00178         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;fsqmat&gt;</a> E;
117<a name="l00179"></a>00179 <span class="keyword">public</span>:
118<a name="l00181"></a>00181         <a class="code" href="classbdm_1_1EKFfull.html#6939c345389abb8b2481457b4cfe1165" title="Default constructor.">EKFfull</a> ( );
119<a name="l00183"></a>00183         <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 );
120<a name="l00185"></a>00185         <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 );
121<a name="l00187"></a><a class="code" href="classbdm_1_1EKFfull.html#7562b3d3c17241dab3baf70258742eb2">00187</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;};
122<a name="l00189"></a><a class="code" href="classbdm_1_1EKFfull.html#6ccc4fa7da522d1c2257156f72291a8a">00189</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#6ccc4fa7da522d1c2257156f72291a8a" title="dummy!">_epdf</a>()<span class="keyword">const</span>{<span class="keywordflow">return</span> E;};
123<a name="l00190"></a>00190         <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;};
124<a name="l00191"></a>00191         <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>;}
125<a name="l00192"></a>00192 };
126<a name="l00193"></a>00193
127<a name="l00199"></a>00199 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
128<a name="l00200"></a><a class="code" href="classbdm_1_1EKF.html">00200</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; {
129<a name="l00202"></a>00202         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu;
130<a name="l00204"></a>00204         <a class="code" href="classbdm_1_1diffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu;
131<a name="l00205"></a>00205 <span class="keyword">public</span>:
132<a name="l00207"></a>00207         <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> );
133<a name="l00209"></a>00209         <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 );
134<a name="l00211"></a>00211         <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 );
135<a name="l00212"></a>00212 };
136<a name="l00213"></a>00213
137<a name="l00220"></a><a class="code" href="classbdm_1_1EKFCh.html">00220</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> {
138<a name="l00221"></a>00221         <span class="keyword">protected</span>:
139<a name="l00223"></a><a class="code" href="classbdm_1_1EKFCh.html#e1e895f994398a55bc425551fc275ba3">00223</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>;
140<a name="l00225"></a><a class="code" href="classbdm_1_1EKFCh.html#6b34c69641826322467b704e8252f317">00225</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>;
141<a name="l00226"></a>00226 <span class="keyword">public</span>:
142<a name="l00228"></a>00228         <a class="code" href="classbdm_1_1EKFCh.html#8b3228a594532b6a0db0fdc065bc5b9f" title="Default constructor.">EKFCh</a> ();
143<a name="l00230"></a>00230         <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 );
144<a name="l00232"></a>00232         <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 );
145<a name="l00233"></a>00233 };
146<a name="l00234"></a>00234
147<a name="l00239"></a><a class="code" href="classbdm_1_1KFcondQR.html">00239</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> {
148<a name="l00240"></a>00240 <span class="comment">//protected:</span>
149<a name="l00241"></a>00241 <span class="keyword">public</span>:
150<a name="l00243"></a><a class="code" href="classbdm_1_1KFcondQR.html#b586ac962751a6af76b2e0fd7e066194">00243</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> ( ) {};
151<a name="l00244"></a>00244
152<a name="l00245"></a>00245         <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 );
153<a name="l00246"></a>00246 };
154<a name="l00247"></a>00247
155<a name="l00252"></a><a class="code" href="classbdm_1_1KFcondR.html">00252</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> {
156<a name="l00253"></a>00253 <span class="comment">//protected:</span>
157<a name="l00254"></a>00254 <span class="keyword">public</span>:
158<a name="l00256"></a><a class="code" href="classbdm_1_1KFcondR.html#f11639d79f10b1e7dad16a0d8233450d">00256</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> ( ) {};
159<a name="l00257"></a>00257
160<a name="l00258"></a>00258         <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> );
161<a name="l00259"></a>00259 };
162<a name="l00260"></a>00260
163<a name="l00262"></a>00262
164<a name="l00263"></a>00263 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
165<a name="l00264"></a><a class="code" href="classbdm_1_1Kalman.html#8b22c45cffa949d70b8e5ac92ed5ce25">00264</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 ),
166<a name="l00265"></a>00265                 dimx ( K0.dimx ), dimy ( K0.dimy ),dimu ( K0.dimu ),
167<a name="l00266"></a>00266                 A ( K0.A ), B ( K0.B ), C ( K0.C ), D ( K0.D ),
168<a name="l00267"></a>00267                 Q(K0.Q), R(K0.R),
169<a name="l00268"></a>00268                 est ( K0.est ), fy ( K0.fy ), _yp(fy._mu()),_Ry(fy._R()), _mu(est._mu()), _P(est._R()) {
170<a name="l00269"></a>00269
171<a name="l00270"></a>00270 <span class="comment">// copy values in pointers</span>
172<a name="l00271"></a>00271 <span class="comment">//      _mu = K0._mu;</span>
173<a name="l00272"></a>00272 <span class="comment">//      _P = K0._P;</span>
174<a name="l00273"></a>00273 <span class="comment">//      _yp = K0._yp;</span>
175<a name="l00274"></a>00274 <span class="comment">//      _Ry = K0._Ry;</span>
176<a name="l00275"></a>00275
177<a name="l00276"></a>00276 }
178<a name="l00277"></a>00277
179<a name="l00278"></a>00278 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
180<a name="l00279"></a><a class="code" href="classbdm_1_1Kalman.html#025a0196cbcc2e6adb13311f9d3d52b4">00279</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()) {
181<a name="l00280"></a>00280 };
182<a name="l00281"></a>00281
183<a name="l00282"></a>00282 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
184<a name="l00283"></a><a class="code" href="classbdm_1_1Kalman.html#94eb8cc31731210089db0ba4e1a08a6c">00283</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;R0, <span class="keyword">const</span> sq_T &amp;Q0 ) {
185<a name="l00284"></a>00284         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> );
186<a name="l00285"></a>00285         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> );
187<a name="l00286"></a>00286         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> );
188<a name="l00287"></a>00287         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> );
189<a name="l00288"></a>00288         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> );
190<a name="l00289"></a>00289         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> );
191<a name="l00290"></a>00290
192<a name="l00291"></a>00291         <a class="code" href="classbdm_1_1Kalman.html#0a2072e2090c10fac74ad30a023a4ace" title="Matrix A.">A</a> = A0;
193<a name="l00292"></a>00292         <a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c" title="Matrix B.">B</a> = B0;
194<a name="l00293"></a>00293         <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a> = C0;
195<a name="l00294"></a>00294         <a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456" title="Matrix D.">D</a> = D0;
196<a name="l00295"></a>00295         <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a> = R0;
197<a name="l00296"></a>00296         <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a> = Q0;
198<a name="l00297"></a>00297 }
199<a name="l00298"></a>00298
200<a name="l00299"></a>00299 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
201<a name="l00300"></a><a class="code" href="classbdm_1_1Kalman.html#4a39330c14eff8d13179e868a1d1aa8c">00300</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 ) {
202<a name="l00301"></a>00301         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> );
203<a name="l00302"></a>00302
204<a name="l00303"></a>00303         sq_T iRy(<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>);
205<a name="l00304"></a>00304         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 );
206<a name="l00305"></a>00305         vec y = dt.get ( 0,<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>-1 );
207<a name="l00306"></a>00306         <span class="comment">//Time update</span>
208<a name="l00307"></a>00307         <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;
209<a name="l00308"></a>00308         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
210<a name="l00309"></a>00309         <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> );
211<a name="l00310"></a>00310         <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>;
212<a name="l00311"></a>00311
213<a name="l00312"></a>00312         <span class="comment">//Data update</span>
214<a name="l00313"></a>00313         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
215<a name="l00314"></a>00314         <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> );
216<a name="l00315"></a>00315         <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>;
217<a name="l00316"></a>00316
218<a name="l00317"></a>00317         mat Pfull = <a class="code" href="classbdm_1_1Kalman.html#00c27b0bf324f0018497921ca23c71ed" title="cache of est.R">_P</a>.to_mat();
219<a name="l00318"></a>00318
220<a name="l00319"></a>00319         <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>
221<a name="l00320"></a>00320         <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() );
222<a name="l00321"></a>00321
223<a name="l00322"></a>00322         sq_T pom ( ( <span class="keywordtype">int</span> ) Pfull.rows() );
224<a name="l00323"></a>00323         iRy.mult_sym_t ( <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>*Pfull,pom );
225<a name="l00324"></a>00324         (<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>
226<a name="l00325"></a>00325         (<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>
227<a name="l00326"></a>00326         (<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>  );
228<a name="l00327"></a>00327
229<a name="l00328"></a>00328
230<a name="l00329"></a>00329         <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>
231<a name="l00330"></a>00330                 <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 );
232<a name="l00331"></a>00331         }
233<a name="l00332"></a>00332
234<a name="l00333"></a>00333 <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>
235<a name="l00334"></a>00334
236<a name="l00335"></a>00335 };
237<a name="l00336"></a>00336 
238<a name="l00337"></a>00337
239<a name="l00338"></a>00338
240<a name="l00339"></a>00339 <span class="comment">//TODO why not const pointer??</span>
241<a name="l00340"></a>00340
242<a name="l00341"></a>00341 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
243<a name="l00342"></a><a class="code" href="classbdm_1_1EKF.html#d087a8bb408d26ac4f5c542746b81059">00342</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 ) {}
244<a name="l00343"></a>00343
245<a name="l00344"></a>00344 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
246<a name="l00345"></a><a class="code" href="classbdm_1_1EKF.html#00fec1a0a6a467eb83fb36c65eba7bcb">00345</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 ) {
247<a name="l00346"></a>00346         pfxu = pfxu0;
248<a name="l00347"></a>00347         phxu = phxu0;
249<a name="l00348"></a>00348
250<a name="l00349"></a>00349         <span class="comment">//initialize matrices A C, later, these will be only updated!</span>
251<a name="l00350"></a>00350         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> );
252<a name="l00351"></a>00351 <span class="comment">//      pfxu-&gt;dfdu_cond ( *_mu,zeros ( dimu ),B,true );</span>
253<a name="l00352"></a>00352         <a class="code" href="classbdm_1_1Kalman.html#5977b2c81857948a35105f0e7840203c" title="Matrix B.">B</a>.clear();
254<a name="l00353"></a>00353         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> );
255<a name="l00354"></a>00354 <span class="comment">//      phxu-&gt;dfdu_cond ( *_mu,zeros ( dimu ),D,true );</span>
256<a name="l00355"></a>00355         <a class="code" href="classbdm_1_1Kalman.html#7b56ac423d0654b5755e4f852a870456" title="Matrix D.">D</a>.clear();
257<a name="l00356"></a>00356
258<a name="l00357"></a>00357         <a class="code" href="classbdm_1_1Kalman.html#475b088287cdfbba4dc60a3d027728b7" title="Matrix R in square-root form.">R</a> = R0;
259<a name="l00358"></a>00358         <a class="code" href="classbdm_1_1Kalman.html#70f8bf19e81b532c60fd3a7a152425ee" title="Matrix Q in square-root form.">Q</a> = Q0;
260<a name="l00359"></a>00359 }
261<a name="l00360"></a>00360
262<a name="l00361"></a>00361 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
263<a name="l00362"></a><a class="code" href="classbdm_1_1EKF.html#3fb182ecc29b10ca1163cecbf3bcccfa">00362</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 ) {
264<a name="l00363"></a>00363         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> );
265<a name="l00364"></a>00364
266<a name="l00365"></a>00365         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>);
267<a name="l00366"></a>00366         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 );
268<a name="l00367"></a>00367         vec y = dt.get ( 0,<a class="code" href="classbdm_1_1Kalman.html#d2c36ba01760bf207b985bf321b7817f" title="cache of rvy.count()">dimy</a>-1 );
269<a name="l00368"></a>00368         <span class="comment">//Time update</span>
270<a name="l00369"></a>00369         <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 );
271<a name="l00370"></a>00370         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>
272<a name="l00371"></a>00371
273<a name="l00372"></a>00372         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
274<a name="l00373"></a>00373         <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> );
275<a name="l00374"></a>00374         <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>;
276<a name="l00375"></a>00375
277<a name="l00376"></a>00376         <span class="comment">//Data update</span>
278<a name="l00377"></a>00377         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>
279<a name="l00378"></a>00378         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
280<a name="l00379"></a>00379         <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> );
281<a name="l00380"></a>00380         ( <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>;
282<a name="l00381"></a>00381
283<a name="l00382"></a>00382         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>();
284<a name="l00383"></a>00383
285<a name="l00384"></a>00384         <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>
286<a name="l00385"></a>00385         <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() );
287<a name="l00386"></a>00386
288<a name="l00387"></a>00387         sq_T pom ( ( <span class="keywordtype">int</span> ) Pfull.rows() );
289<a name="l00388"></a>00388         iRy.mult_sym_t ( <a class="code" href="classbdm_1_1Kalman.html#818eba63a23972786a4579ad30294177" title="Matrix C.">C</a>*Pfull,pom );
290<a name="l00389"></a>00389         (<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>
291<a name="l00390"></a>00390         <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>
292<a name="l00391"></a>00391         ( <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> );
293<a name="l00392"></a>00392
294<a name="l00393"></a>00393         <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 );}
295<a name="l00394"></a>00394 };
296<a name="l00395"></a>00395
297<a name="l00396"></a>00396
298<a name="l00397"></a>00397 }
299<a name="l00398"></a>00398 <span class="preprocessor">#endif // KF_H</span>
300<a name="l00399"></a>00399 <span class="preprocessor"></span>
301<a name="l00400"></a>00400
302</pre></div></div>
303<hr size="1"><address style="text-align: right;"><small>Generated on Wed Feb 11 23:33:55 2009 for mixpp by&nbsp;
304<a href="http://www.doxygen.org/index.html">
305<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>
306</body>
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