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17<h1>work/git/mixpp/bdm/estim/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
18<a name="l00013"></a>00013 <span class="preprocessor">#ifndef KF_H</span>
19<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define KF_H</span>
20<a name="l00015"></a>00015 <span class="preprocessor"></span>
21<a name="l00016"></a>00016 <span class="preprocessor">#include &lt;itpp/itbase.h&gt;</span>
22<a name="l00017"></a>00017 <span class="preprocessor">#include "../stat/libFN.h"</span>
23<a name="l00018"></a>00018 <span class="preprocessor">#include "../stat/libEF.h"</span>
24<a name="l00019"></a>00019 <span class="preprocessor">#include "../math/chmat.h"</span>
25<a name="l00020"></a>00020
26<a name="l00021"></a>00021 <span class="keyword">using namespace </span>itpp;
27<a name="l00022"></a>00022
28<a name="l00027"></a><a class="code" href="classKalmanFull.html">00027</a> <span class="keyword">class </span><a class="code" href="classKalmanFull.html" title="Basic Kalman filter with full matrices (education purpose only)! Will be deleted...">KalmanFull</a> {
29<a name="l00028"></a>00028 <span class="keyword">protected</span>:
30<a name="l00029"></a>00029         <span class="keywordtype">int</span> dimx, dimy, dimu;
31<a name="l00030"></a>00030         mat A, B, C, D, R, Q;
32<a name="l00031"></a>00031
33<a name="l00032"></a>00032         <span class="comment">//cache</span>
34<a name="l00033"></a>00033         mat _Pp, _Ry, _iRy, _K;
35<a name="l00034"></a>00034 <span class="keyword">public</span>:
36<a name="l00035"></a>00035         <span class="comment">//posterior</span>
37<a name="l00037"></a><a class="code" href="classKalmanFull.html#fb5aec635e2720cc5ac31bc01c18a68a">00037</a> <span class="comment"></span>        vec <a class="code" href="classKalmanFull.html#fb5aec635e2720cc5ac31bc01c18a68a" title="Mean value of the posterior density.">mu</a>;
38<a name="l00039"></a><a class="code" href="classKalmanFull.html#b75dc059e84fa8ffc076203b30f926cc">00039</a>         mat <a class="code" href="classKalmanFull.html#b75dc059e84fa8ffc076203b30f926cc" title="Variance of the posterior density.">P</a>;
39<a name="l00040"></a>00040
40<a name="l00041"></a>00041         <span class="keywordtype">bool</span> evalll;
41<a name="l00042"></a>00042         <span class="keywordtype">double</span> ll;
42<a name="l00043"></a>00043 <span class="keyword">public</span>:
43<a name="l00045"></a>00045         <a class="code" href="classKalmanFull.html#9d7cc2235b643d9662cd9c8b8469747d" title="For EKFfull;.">KalmanFull</a> ( mat A, mat B, mat C, mat D, mat R, mat Q, mat P0, vec mu0 );
44<a name="l00047"></a>00047         <span class="keywordtype">void</span> <a class="code" href="classKalmanFull.html#13a041cd98ff157703766be275a657bb" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
45<a name="l00049"></a>00049         <span class="keyword">friend</span> std::ostream &amp;<a class="code" href="classKalmanFull.html#86ba216243ed95bb46d80d88775d16af" title="print elements of KF">operator&lt;&lt; </a>( std::ostream &amp;os, <span class="keyword">const</span> <a class="code" href="classKalmanFull.html" title="Basic Kalman filter with full matrices (education purpose only)! Will be deleted...">KalmanFull</a> &amp;kf );
46<a name="l00051"></a><a class="code" href="classKalmanFull.html#9d7cc2235b643d9662cd9c8b8469747d">00051</a>         <a class="code" href="classKalmanFull.html#9d7cc2235b643d9662cd9c8b8469747d" title="For EKFfull;.">KalmanFull</a>(){};
47<a name="l00052"></a>00052 };
48<a name="l00053"></a>00053
49<a name="l00054"></a>00054
50<a name="l00062"></a>00062 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
51<a name="l00063"></a>00063
52<a name="l00064"></a><a class="code" href="classKalman.html">00064</a> <span class="keyword">class </span><a class="code" href="classKalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a> : <span class="keyword">public</span> <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> {
53<a name="l00065"></a>00065 <span class="keyword">protected</span>:
54<a name="l00067"></a><a class="code" href="classKalman.html#7501230c2fafa3655887d2da23b3184c">00067</a>         <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classKalman.html#7501230c2fafa3655887d2da23b3184c" title="Indetifier of output rv.">rvy</a>;
55<a name="l00069"></a><a class="code" href="classKalman.html#44a16ffd5ac1e6e39bae34fea9e1e498">00069</a>         <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classKalman.html#44a16ffd5ac1e6e39bae34fea9e1e498" title="Indetifier of exogeneous rv.">rvu</a>;
56<a name="l00071"></a><a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb">00071</a>         <span class="keywordtype">int</span> <a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>;
57<a name="l00073"></a><a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a">00073</a>         <span class="keywordtype">int</span> <a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>;
58<a name="l00075"></a><a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce">00075</a>         <span class="keywordtype">int</span> <a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a>;
59<a name="l00077"></a><a class="code" href="classKalman.html#5e02efe86ee91e9c74b93b425fe060b9">00077</a>         mat <a class="code" href="classKalman.html#5e02efe86ee91e9c74b93b425fe060b9" title="Matrix A.">A</a>;
60<a name="l00079"></a><a class="code" href="classKalman.html#dc87704284a6c0bca13bf51f4345a50a">00079</a>         mat <a class="code" href="classKalman.html#dc87704284a6c0bca13bf51f4345a50a" title="Matrix B.">B</a>;
61<a name="l00081"></a><a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13">00081</a>         mat <a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>;
62<a name="l00083"></a><a class="code" href="classKalman.html#d69f774ba3335c970c1c5b1d182f4dd1">00083</a>         mat <a class="code" href="classKalman.html#d69f774ba3335c970c1c5b1d182f4dd1" title="Matrix D.">D</a>;
63<a name="l00085"></a><a class="code" href="classKalman.html#9b69015c800eb93f3ee49da23a6f55d9">00085</a>         sq_T <a class="code" href="classKalman.html#9b69015c800eb93f3ee49da23a6f55d9" title="Matrix Q in square-root form.">Q</a>;
64<a name="l00087"></a><a class="code" href="classKalman.html#11d171dc0e0ab111c56a70f98b97b3ec">00087</a>         sq_T <a class="code" href="classKalman.html#11d171dc0e0ab111c56a70f98b97b3ec" title="Matrix R in square-root form.">R</a>;
65<a name="l00088"></a>00088
66<a name="l00090"></a><a class="code" href="classKalman.html#5568c74bac67ae6d3b1061dba60c9424">00090</a>         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classKalman.html#5568c74bac67ae6d3b1061dba60c9424" title="posterior density on $x_t$">est</a>;
67<a name="l00092"></a><a class="code" href="classKalman.html#e580ab06483952bd03f2e651763e184f">00092</a>         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classKalman.html#e580ab06483952bd03f2e651763e184f" title="preditive density on $y_t$">fy</a>;
68<a name="l00093"></a>00093
69<a name="l00095"></a><a class="code" href="classKalman.html#d422f51467c7a06174af2476d2826132">00095</a>         mat <a class="code" href="classKalman.html#d422f51467c7a06174af2476d2826132" title="placeholder for Kalman gain">_K</a>;
70<a name="l00097"></a><a class="code" href="classKalman.html#764bbc95238eda11fc81c5ebd0b1dcfd">00097</a>         vec&amp; <a class="code" href="classKalman.html#764bbc95238eda11fc81c5ebd0b1dcfd" title="cache of fy.mu">_yp</a>;
71<a name="l00099"></a><a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904">00099</a>         sq_T&amp; <a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904" title="cache of fy.R">_Ry</a>;
72<a name="l00101"></a><a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061">00101</a>         vec&amp; <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>;
73<a name="l00103"></a><a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf">00103</a>         sq_T&amp; <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a>;
74<a name="l00104"></a>00104
75<a name="l00105"></a>00105 <span class="keyword">public</span>:
76<a name="l00107"></a>00107         <a class="code" href="classKalman.html#3d56b0a97b8c1e25fdd3b10eef3c2ad3" title="Default constructor.">Kalman</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvx0, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvy0, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvu0 );
77<a name="l00109"></a>00109         <a class="code" href="classKalman.html#3d56b0a97b8c1e25fdd3b10eef3c2ad3" title="Default constructor.">Kalman</a> ( <span class="keyword">const</span> <a class="code" href="classKalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman&lt;sq_T&gt;</a> &amp;K0 );
78<a name="l00111"></a>00111         <span class="keywordtype">void</span> <a class="code" href="classKalman.html#239b28a0380946f5749b2f8d2807f93a" 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 );
79<a name="l00113"></a><a class="code" href="classKalman.html#80bcf29466d9a9dd2b8f74699807d0c0">00113</a>         <span class="keywordtype">void</span> <a class="code" href="classKalman.html#80bcf29466d9a9dd2b8f74699807d0c0" 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 ) {
80<a name="l00114"></a>00114                 sq_T pom(<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>);
81<a name="l00115"></a>00115                 <a class="code" href="classKalman.html#5568c74bac67ae6d3b1061dba60c9424" title="posterior density on $x_t$">est</a>.set_parameters ( mu0,P0 );
82<a name="l00116"></a>00116                 P0.mult_sym(<a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>,pom);
83<a name="l00117"></a>00117                 <a class="code" href="classKalman.html#e580ab06483952bd03f2e651763e184f" title="preditive density on $y_t$">fy</a>.set_parameters ( <a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>*mu0, pom );
84<a name="l00118"></a>00118         };
85<a name="l00119"></a>00119
86<a name="l00121"></a>00121         <span class="keywordtype">void</span> <a class="code" href="classKalman.html#7750ffd73f261828a32c18aaeb65c75c" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
87<a name="l00123"></a><a class="code" href="classKalman.html#67cccaf1c4dcdcd1df110e15ef326bfe">00123</a>         <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>&amp; <a class="code" href="classKalman.html#67cccaf1c4dcdcd1df110e15ef326bfe" title="access function">_epdf</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classKalman.html#5568c74bac67ae6d3b1061dba60c9424" title="posterior density on $x_t$">est</a>;}
88<a name="l00124"></a><a class="code" href="classKalman.html#11f82ef04e3dbc54bd1d3d89edb6aa07">00124</a>         <span class="keyword">const</span> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* <a class="code" href="classKalman.html#11f82ef04e3dbc54bd1d3d89edb6aa07" title="Returns a pointer to the epdf representing posterior density on parameters. Use with...">_e</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &amp;<a class="code" href="classKalman.html#5568c74bac67ae6d3b1061dba60c9424" title="posterior density on $x_t$">est</a>;}
89<a name="l00126"></a><a class="code" href="classKalman.html#980fcd41c6c548c5da7b8b67c8e6da79">00126</a>         mat&amp; <a class="code" href="classKalman.html#980fcd41c6c548c5da7b8b67c8e6da79" title="access function">__K</a>() {<span class="keywordflow">return</span> <a class="code" href="classKalman.html#d422f51467c7a06174af2476d2826132" title="placeholder for Kalman gain">_K</a>;}
90<a name="l00128"></a><a class="code" href="classKalman.html#ac9540f3850b74d89a5fe4db6fc358ce">00128</a>         vec <a class="code" href="classKalman.html#ac9540f3850b74d89a5fe4db6fc358ce" title="access function">_dP</a>() {<span class="keywordflow">return</span> <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a>-&gt;getD();}
91<a name="l00129"></a>00129 };
92<a name="l00130"></a>00130
93<a name="l00133"></a><a class="code" href="classKalmanCh.html">00133</a> <span class="keyword">class </span><a class="code" href="classKalmanCh.html" title="Kalman filter in square root form.">KalmanCh</a> : <span class="keyword">public</span> <a class="code" href="classKalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;chmat&gt;{
94<a name="l00134"></a>00134 <span class="keyword">protected</span>:
95<a name="l00136"></a><a class="code" href="classKalmanCh.html#94ee9da75b0e0f632e4a354988ca3798">00136</a> mat <a class="code" href="classKalmanCh.html#94ee9da75b0e0f632e4a354988ca3798" title="pre array (triangular matrix)">preA</a>;
96<a name="l00138"></a><a class="code" href="classKalmanCh.html#0d31a26dc72b5846cfe5af3ccb63ac87">00138</a> mat <a class="code" href="classKalmanCh.html#0d31a26dc72b5846cfe5af3ccb63ac87" title="post array (triangular matrix)">postA</a>;
97<a name="l00139"></a>00139
98<a name="l00140"></a>00140 <span class="keyword">public</span>:
99<a name="l00142"></a><a class="code" href="classKalmanCh.html#d11f110cccaa66177514632d37b086bb">00142</a>         <a class="code" href="classKalmanCh.html#d11f110cccaa66177514632d37b086bb" title="Default constructor.">KalmanCh</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvx0, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvy0, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvu0 ):<a class="code" href="classKalman.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;(rvx0,rvy0,rvu0),<a class="code" href="classKalmanCh.html#94ee9da75b0e0f632e4a354988ca3798" title="pre array (triangular matrix)">preA</a>(<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>+<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>+<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>,<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>+<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>),<a class="code" href="classKalmanCh.html#0d31a26dc72b5846cfe5af3ccb63ac87" title="post array (triangular matrix)">postA</a>(<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>+<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>,<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>+<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>){};
100<a name="l00144"></a>00144         <span class="keywordtype">void</span> <a class="code" href="classKalmanCh.html#92fb227287af05c9f0078d523c7c9793" 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 );
101<a name="l00145"></a><a class="code" href="classKalmanCh.html#b261b20f6210d4c85131d33302df0adc">00145</a>         <span class="keywordtype">void</span> <a class="code" href="classKalmanCh.html#b261b20f6210d4c85131d33302df0adc" 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 ) {
102<a name="l00146"></a>00146                 <a class="code" href="classKalman.html#5568c74bac67ae6d3b1061dba60c9424" title="posterior density on $x_t$">est</a>.<a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">set_parameters</a> ( mu0,P0 );
103<a name="l00147"></a>00147         };
104<a name="l00148"></a>00148         
105<a name="l00149"></a>00149         
106<a name="l00163"></a>00163         <span class="keywordtype">void</span> <a class="code" href="classKalmanCh.html#cca758192846940409822b9bd778d4e1" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
107<a name="l00164"></a>00164 };
108<a name="l00165"></a>00165
109<a name="l00171"></a><a class="code" href="classEKFfull.html">00171</a> <span class="keyword">class </span><a class="code" href="classEKFfull.html" title="Extended Kalman Filter in full matrices.">EKFfull</a> : <span class="keyword">public</span> <a class="code" href="classKalmanFull.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="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> {
110<a name="l00172"></a>00172
111<a name="l00174"></a>00174         <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu;
112<a name="l00176"></a>00176         <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu;
113<a name="l00177"></a>00177         
114<a name="l00178"></a>00178         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;fsqmat&gt;</a> E;
115<a name="l00179"></a>00179 <span class="keyword">public</span>:
116<a name="l00181"></a>00181         <a class="code" href="classEKFfull.html#67ac4de96fd025197da767fe0472c7f7" title="Default constructor.">EKFfull</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvx, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvy, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvu );
117<a name="l00183"></a>00183         <span class="keywordtype">void</span> <a class="code" href="classEKFfull.html#fc753106e0d4cf68e4f2160fd54458c0" title="Set nonlinear functions for mean values and covariance matrices.">set_parameters</a> ( <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu, <a class="code" href="classdiffbifn.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 );
118<a name="l00185"></a>00185         <span class="keywordtype">void</span> <a class="code" href="classEKFfull.html#8ca46f177e395fa714bbd8bd29ea43e0" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
119<a name="l00187"></a><a class="code" href="classEKFfull.html#7bb76ea74c144ea0b36db99f94750b7b">00187</a>         <span class="keywordtype">void</span> <a class="code" href="classEKFfull.html#7bb76ea74c144ea0b36db99f94750b7b" title="set estimates">set_est</a> (vec mu0, mat P0){<a class="code" href="classKalmanFull.html#fb5aec635e2720cc5ac31bc01c18a68a" title="Mean value of the posterior density.">mu</a>=mu0;<a class="code" href="classKalmanFull.html#b75dc059e84fa8ffc076203b30f926cc" title="Variance of the posterior density.">P</a>=P0;};
120<a name="l00189"></a><a class="code" href="classEKFfull.html#170a748ad944bdebb0b3073463876abe">00189</a>         <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>&amp; <a class="code" href="classEKFfull.html#170a748ad944bdebb0b3073463876abe" title="dummy!">_epdf</a>()<span class="keyword">const</span>{<span class="keywordflow">return</span> E;};
121<a name="l00190"></a><a class="code" href="classEKFfull.html#820987401e922a03c7d36013e42d8c48">00190</a>         <span class="keyword">const</span> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;fsqmat&gt;</a>* <a class="code" href="classEKFfull.html#820987401e922a03c7d36013e42d8c48" title="Returns a pointer to the epdf representing posterior density on parameters. Use with...">_e</a>()<span class="keyword">const</span>{<span class="keywordflow">return</span> &amp;E;};
122<a name="l00191"></a>00191 };
123<a name="l00192"></a>00192
124<a name="l00198"></a>00198 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
125<a name="l00199"></a><a class="code" href="classEKF.html">00199</a> <span class="keyword">class </span><a class="code" href="classEKF.html" title="Extended Kalman Filter.">EKF</a> : <span class="keyword">public</span> <a class="code" href="classKalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;fsqmat&gt; {
126<a name="l00201"></a>00201         <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu;
127<a name="l00203"></a>00203         <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu;
128<a name="l00204"></a>00204 <span class="keyword">public</span>:
129<a name="l00206"></a>00206         <a class="code" href="classEKF.html#ea4f3254cacf0a92d2a820b1201d049e" title="Default constructor.">EKF</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvx, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classKalman.html#7501230c2fafa3655887d2da23b3184c" title="Indetifier of output rv.">rvy</a>, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classKalman.html#44a16ffd5ac1e6e39bae34fea9e1e498" title="Indetifier of exogeneous rv.">rvu</a> );
130<a name="l00208"></a>00208         <span class="keywordtype">void</span> <a class="code" href="classEKF.html#28d058ae4d24d992d2f055419a06ee66" title="Set nonlinear functions for mean values and covariance matrices.">set_parameters</a> ( <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu, <a class="code" href="classdiffbifn.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 );
131<a name="l00210"></a>00210         <span class="keywordtype">void</span> <a class="code" href="classEKF.html#c79c62c9b3e0b56b3aaa1b6f1d9a7af7" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
132<a name="l00211"></a>00211 };
133<a name="l00212"></a>00212
134<a name="l00219"></a><a class="code" href="classEKFCh.html">00219</a> <span class="keyword">class </span><a class="code" href="classEKFCh.html" title="Extended Kalman Filter in Square root.">EKFCh</a> : <span class="keyword">public</span> <a class="code" href="classKalmanCh.html" title="Kalman filter in square root form.">KalmanCh</a> {
135<a name="l00221"></a>00221         <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu;
136<a name="l00223"></a>00223         <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu;
137<a name="l00224"></a>00224 <span class="keyword">public</span>:
138<a name="l00226"></a>00226         <a class="code" href="classEKFCh.html#e9e39a9204db3dda88d06e47c1e19064" title="Default constructor.">EKFCh</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvx, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classKalman.html#7501230c2fafa3655887d2da23b3184c" title="Indetifier of output rv.">rvy</a>, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classKalman.html#44a16ffd5ac1e6e39bae34fea9e1e498" title="Indetifier of exogeneous rv.">rvu</a> );
139<a name="l00228"></a>00228         <span class="keywordtype">void</span> <a class="code" href="classEKFCh.html#0216bed270df59fe65d0d62d41f8257c" title="Set nonlinear functions for mean values and covariance matrices.">set_parameters</a> ( <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu, <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* phxu, <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 );
140<a name="l00230"></a>00230         <span class="keywordtype">void</span> <a class="code" href="classEKFCh.html#96f6edda324a0b7ef8b4e86cc7af60c1" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
141<a name="l00231"></a>00231 };
142<a name="l00232"></a>00232
143<a name="l00237"></a><a class="code" href="classKFcondQR.html">00237</a> <span class="keyword">class </span><a class="code" href="classKFcondQR.html" title="Kalman Filter with conditional diagonal matrices R and Q.">KFcondQR</a> : <span class="keyword">public</span> <a class="code" href="classKalman.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="classBMcond.html" title="Conditional Bayesian Filter.">BMcond</a> {
144<a name="l00238"></a>00238 <span class="comment">//protected:</span>
145<a name="l00239"></a>00239 <span class="keyword">public</span>:
146<a name="l00241"></a><a class="code" href="classKFcondQR.html#3f3968f92c7bbe4b0902d5e14ecc1cb4">00241</a>         <a class="code" href="classKFcondQR.html#3f3968f92c7bbe4b0902d5e14ecc1cb4" title="Default constructor.">KFcondQR</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvx, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classKalman.html#7501230c2fafa3655887d2da23b3184c" title="Indetifier of output rv.">rvy</a>, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classKalman.html#44a16ffd5ac1e6e39bae34fea9e1e498" title="Indetifier of exogeneous rv.">rvu</a>, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvRQ ) : <a class="code" href="classKalman.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; ( rvx, rvy,rvu ),<a class="code" href="classBMcond.html" title="Conditional Bayesian Filter.">BMcond</a> ( rvRQ ) {};
147<a name="l00242"></a>00242
148<a name="l00243"></a>00243         <span class="keywordtype">void</span> <a class="code" href="classKFcondQR.html#c9ecf292a85327aa6309c9fd70ceb606" title="Substitute val for rvc.">condition</a> ( <span class="keyword">const</span> vec &amp;RQ );
149<a name="l00244"></a>00244 };
150<a name="l00245"></a>00245
151<a name="l00250"></a><a class="code" href="classKFcondR.html">00250</a> <span class="keyword">class </span><a class="code" href="classKFcondR.html" title="Kalman Filter with conditional diagonal matrices R and Q.">KFcondR</a> : <span class="keyword">public</span> <a class="code" href="classKalman.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="classBMcond.html" title="Conditional Bayesian Filter.">BMcond</a> {
152<a name="l00251"></a>00251 <span class="comment">//protected:</span>
153<a name="l00252"></a>00252 <span class="keyword">public</span>:
154<a name="l00254"></a><a class="code" href="classKFcondR.html#d2acbb8e66c7ee592b1a9da5b429a69e">00254</a>         <a class="code" href="classKFcondR.html#d2acbb8e66c7ee592b1a9da5b429a69e" title="Default constructor.">KFcondR</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvx, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classKalman.html#7501230c2fafa3655887d2da23b3184c" title="Indetifier of output rv.">rvy</a>, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classKalman.html#44a16ffd5ac1e6e39bae34fea9e1e498" title="Indetifier of exogeneous rv.">rvu</a>, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvR ) : <a class="code" href="classKalman.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; ( rvx, rvy,rvu ),<a class="code" href="classBMcond.html" title="Conditional Bayesian Filter.">BMcond</a> ( rvR ) {};
155<a name="l00255"></a>00255
156<a name="l00256"></a>00256         <span class="keywordtype">void</span> <a class="code" href="classKFcondR.html#8c0721e47879bb8840d829db7a174a7f" title="Substitute val for rvc.">condition</a> ( <span class="keyword">const</span> vec &amp;<a class="code" href="classKalman.html#11d171dc0e0ab111c56a70f98b97b3ec" title="Matrix R in square-root form.">R</a> );
157<a name="l00257"></a>00257 };
158<a name="l00258"></a>00258
159<a name="l00260"></a>00260
160<a name="l00261"></a>00261 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
161<a name="l00262"></a><a class="code" href="classKalman.html#ce38e31810aea4db45a83ad05eaba009">00262</a> <a class="code" href="classKalman.html#3d56b0a97b8c1e25fdd3b10eef3c2ad3" title="Default constructor.">Kalman&lt;sq_T&gt;::Kalman</a> ( <span class="keyword">const</span> <a class="code" href="classKalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman&lt;sq_T&gt;</a> &amp;K0 ) : <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> ( K0.rv ),rvy ( K0.rvy ),rvu ( K0.rvu ),
162<a name="l00263"></a>00263                 dimx ( rv.count() ), dimy ( rvy.count() ),dimu ( rvu.count() ),
163<a name="l00264"></a>00264                 A ( dimx,dimx ), B ( dimx,dimu ), C ( dimy,dimx ), D ( dimy,dimu ),
164<a name="l00265"></a>00265                 Q(dimx), R(dimy),
165<a name="l00266"></a>00266                 est ( rv ), fy ( rvy ), _yp(fy._mu()),_Ry(fy._R()), _mu(est._mu()), _P(est._R()) {
166<a name="l00267"></a>00267
167<a name="l00268"></a>00268         this-&gt;<a class="code" href="classKalman.html#239b28a0380946f5749b2f8d2807f93a" title="Set parameters with check of relevance.">set_parameters</a> ( K0.<a class="code" href="classKalman.html#5e02efe86ee91e9c74b93b425fe060b9" title="Matrix A.">A</a>, K0.<a class="code" href="classKalman.html#dc87704284a6c0bca13bf51f4345a50a" title="Matrix B.">B</a>, K0.<a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>, K0.<a class="code" href="classKalman.html#d69f774ba3335c970c1c5b1d182f4dd1" title="Matrix D.">D</a>, K0.<a class="code" href="classKalman.html#11d171dc0e0ab111c56a70f98b97b3ec" title="Matrix R in square-root form.">R</a>, K0.<a class="code" href="classKalman.html#9b69015c800eb93f3ee49da23a6f55d9" title="Matrix Q in square-root form.">Q</a> );
168<a name="l00269"></a>00269
169<a name="l00270"></a>00270 <span class="comment">// copy values in pointers</span>
170<a name="l00271"></a>00271         <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a> = K0.<a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>;
171<a name="l00272"></a>00272         <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a> = K0.<a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a>;
172<a name="l00273"></a>00273         <a class="code" href="classKalman.html#764bbc95238eda11fc81c5ebd0b1dcfd" title="cache of fy.mu">_yp</a> = K0.<a class="code" href="classKalman.html#764bbc95238eda11fc81c5ebd0b1dcfd" title="cache of fy.mu">_yp</a>;
173<a name="l00274"></a>00274         <a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904" title="cache of fy.R">_Ry</a> = K0.<a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904" title="cache of fy.R">_Ry</a>;
174<a name="l00275"></a>00275
175<a name="l00276"></a>00276 }
176<a name="l00277"></a>00277
177<a name="l00278"></a>00278 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
178<a name="l00279"></a><a class="code" href="classKalman.html#3d56b0a97b8c1e25fdd3b10eef3c2ad3">00279</a> <a class="code" href="classKalman.html#3d56b0a97b8c1e25fdd3b10eef3c2ad3" title="Default constructor.">Kalman&lt;sq_T&gt;::Kalman</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvx, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvy0, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvu0 ) : <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> ( rvx ),<a class="code" href="classKalman.html#7501230c2fafa3655887d2da23b3184c" title="Indetifier of output rv.">rvy</a> ( rvy0 ),<a class="code" href="classKalman.html#44a16ffd5ac1e6e39bae34fea9e1e498" title="Indetifier of exogeneous rv.">rvu</a> ( rvu0 ),
179<a name="l00280"></a>00280                 <a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a> ( rvx.count() ), <a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a> ( <a class="code" href="classKalman.html#7501230c2fafa3655887d2da23b3184c" title="Indetifier of output rv.">rvy</a>.count() ),<a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a> ( <a class="code" href="classKalman.html#44a16ffd5ac1e6e39bae34fea9e1e498" title="Indetifier of exogeneous rv.">rvu</a>.count() ),
180<a name="l00281"></a>00281                 <a class="code" href="classKalman.html#5e02efe86ee91e9c74b93b425fe060b9" title="Matrix A.">A</a> ( <a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>,<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a> ), <a class="code" href="classKalman.html#dc87704284a6c0bca13bf51f4345a50a" title="Matrix B.">B</a> ( <a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>,<a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a> ), <a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a> ( <a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>,<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a> ), <a class="code" href="classKalman.html#d69f774ba3335c970c1c5b1d182f4dd1" title="Matrix D.">D</a> ( <a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>,<a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a> ),
181<a name="l00282"></a>00282                 <a class="code" href="classKalman.html#9b69015c800eb93f3ee49da23a6f55d9" title="Matrix Q in square-root form.">Q</a>(<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>), <a class="code" href="classKalman.html#11d171dc0e0ab111c56a70f98b97b3ec" title="Matrix R in square-root form.">R</a> (<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>),
182<a name="l00283"></a>00283                 <a class="code" href="classKalman.html#5568c74bac67ae6d3b1061dba60c9424" title="posterior density on $x_t$">est</a> ( rvx ), <a class="code" href="classKalman.html#e580ab06483952bd03f2e651763e184f" title="preditive density on $y_t$">fy</a> ( <a class="code" href="classKalman.html#7501230c2fafa3655887d2da23b3184c" title="Indetifier of output rv.">rvy</a> ),  <a class="code" href="classKalman.html#764bbc95238eda11fc81c5ebd0b1dcfd" title="cache of fy.mu">_yp</a>(<a class="code" href="classKalman.html#e580ab06483952bd03f2e651763e184f" title="preditive density on $y_t$">fy</a>.<a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>()),<a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904" title="cache of fy.R">_Ry</a>(<a class="code" href="classKalman.html#e580ab06483952bd03f2e651763e184f" title="preditive density on $y_t$">fy</a>._R()),<a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>(<a class="code" href="classKalman.html#5568c74bac67ae6d3b1061dba60c9424" title="posterior density on $x_t$">est</a>.<a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>()), <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a>(<a class="code" href="classKalman.html#5568c74bac67ae6d3b1061dba60c9424" title="posterior density on $x_t$">est</a>._R()) {
183<a name="l00284"></a>00284 };
184<a name="l00285"></a>00285
185<a name="l00286"></a>00286 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
186<a name="l00287"></a><a class="code" href="classKalman.html#239b28a0380946f5749b2f8d2807f93a">00287</a> <span class="keywordtype">void</span> <a class="code" href="classKalman.html#239b28a0380946f5749b2f8d2807f93a" 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;R0, <span class="keyword">const</span> sq_T &amp;Q0 ) {
187<a name="l00288"></a>00288         it_assert_debug ( A0.cols() ==<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>, <span class="stringliteral">"Kalman: A is not square"</span> );
188<a name="l00289"></a>00289         it_assert_debug ( B0.rows() ==<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>, <span class="stringliteral">"Kalman: B is not compatible"</span> );
189<a name="l00290"></a>00290         it_assert_debug ( C0.cols() ==<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a>, <span class="stringliteral">"Kalman: C is not square"</span> );
190<a name="l00291"></a>00291         it_assert_debug ( ( D0.rows() ==<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a> ) || ( D0.cols() ==<a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a> ), <span class="stringliteral">"Kalman: D is not compatible"</span> );
191<a name="l00292"></a>00292         it_assert_debug ( ( R0.cols() ==<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a> ) || ( R0.rows() ==<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a> ), <span class="stringliteral">"Kalman: R is not compatible"</span> );
192<a name="l00293"></a>00293         it_assert_debug ( ( Q0.cols() ==<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a> ) || ( Q0.rows() ==<a class="code" href="classKalman.html#39c8c403b46fa3b8c7da77cb2e3729eb" title="cache of rv.count()">dimx</a> ), <span class="stringliteral">"Kalman: Q is not compatible"</span> );
193<a name="l00294"></a>00294
194<a name="l00295"></a>00295         <a class="code" href="classKalman.html#5e02efe86ee91e9c74b93b425fe060b9" title="Matrix A.">A</a> = A0;
195<a name="l00296"></a>00296         <a class="code" href="classKalman.html#dc87704284a6c0bca13bf51f4345a50a" title="Matrix B.">B</a> = B0;
196<a name="l00297"></a>00297         <a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a> = C0;
197<a name="l00298"></a>00298         <a class="code" href="classKalman.html#d69f774ba3335c970c1c5b1d182f4dd1" title="Matrix D.">D</a> = D0;
198<a name="l00299"></a>00299         <a class="code" href="classKalman.html#11d171dc0e0ab111c56a70f98b97b3ec" title="Matrix R in square-root form.">R</a> = R0;
199<a name="l00300"></a>00300         <a class="code" href="classKalman.html#9b69015c800eb93f3ee49da23a6f55d9" title="Matrix Q in square-root form.">Q</a> = Q0;
200<a name="l00301"></a>00301 }
201<a name="l00302"></a>00302
202<a name="l00303"></a>00303 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
203<a name="l00304"></a><a class="code" href="classKalman.html#7750ffd73f261828a32c18aaeb65c75c">00304</a> <span class="keywordtype">void</span> <a class="code" href="classKalman.html#7750ffd73f261828a32c18aaeb65c75c" title="Here dt = [yt;ut] of appropriate dimensions.">Kalman&lt;sq_T&gt;::bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
204<a name="l00305"></a>00305         it_assert_debug ( dt.length() == ( <a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>+<a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a> ),<span class="stringliteral">"KalmanFull::bayes wrong size of dt"</span> );
205<a name="l00306"></a>00306
206<a name="l00307"></a>00307         sq_T iRy(<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>);
207<a name="l00308"></a>00308         vec u = dt.get ( <a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>,<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>+<a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a>-1 );
208<a name="l00309"></a>00309         vec y = dt.get ( 0,<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>-1 );
209<a name="l00310"></a>00310         <span class="comment">//Time update</span>
210<a name="l00311"></a>00311         <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a> = <a class="code" href="classKalman.html#5e02efe86ee91e9c74b93b425fe060b9" title="Matrix A.">A</a>* <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a> + <a class="code" href="classKalman.html#dc87704284a6c0bca13bf51f4345a50a" title="Matrix B.">B</a>*u;
211<a name="l00312"></a>00312         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
212<a name="l00313"></a>00313         <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a>.mult_sym ( <a class="code" href="classKalman.html#5e02efe86ee91e9c74b93b425fe060b9" title="Matrix A.">A</a> );
213<a name="l00314"></a>00314         <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a>  +=<a class="code" href="classKalman.html#9b69015c800eb93f3ee49da23a6f55d9" title="Matrix Q in square-root form.">Q</a>;
214<a name="l00315"></a>00315
215<a name="l00316"></a>00316         <span class="comment">//Data update</span>
216<a name="l00317"></a>00317         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
217<a name="l00318"></a>00318         <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a>.mult_sym ( <a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>, <a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904" title="cache of fy.R">_Ry</a> );
218<a name="l00319"></a>00319         <a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904" title="cache of fy.R">_Ry</a>  +=<a class="code" href="classKalman.html#11d171dc0e0ab111c56a70f98b97b3ec" title="Matrix R in square-root form.">R</a>;
219<a name="l00320"></a>00320
220<a name="l00321"></a>00321         mat Pfull = <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a>.to_mat();
221<a name="l00322"></a>00322
222<a name="l00323"></a>00323         <a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904" title="cache of fy.R">_Ry</a>.inv ( iRy ); <span class="comment">// result is in _iRy;</span>
223<a name="l00324"></a>00324         <a class="code" href="classKalman.html#d422f51467c7a06174af2476d2826132" title="placeholder for Kalman gain">_K</a> = Pfull*<a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>.transpose() * ( iRy.to_mat() );
224<a name="l00325"></a>00325
225<a name="l00326"></a>00326         sq_T pom ( ( <span class="keywordtype">int</span> ) Pfull.rows() );
226<a name="l00327"></a>00327         iRy.mult_sym_t ( <a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>*Pfull,pom );
227<a name="l00328"></a>00328         (<a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a> ) -= pom; <span class="comment">// P = P -PC'iRy*CP;</span>
228<a name="l00329"></a>00329         (<a class="code" href="classKalman.html#764bbc95238eda11fc81c5ebd0b1dcfd" title="cache of fy.mu">_yp</a> ) = <a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>* <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>  +<a class="code" href="classKalman.html#d69f774ba3335c970c1c5b1d182f4dd1" title="Matrix D.">D</a>*u; <span class="comment">//y prediction</span>
229<a name="l00330"></a>00330         (<a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a> ) += <a class="code" href="classKalman.html#d422f51467c7a06174af2476d2826132" title="placeholder for Kalman gain">_K</a>* ( y- <a class="code" href="classKalman.html#764bbc95238eda11fc81c5ebd0b1dcfd" title="cache of fy.mu">_yp</a>  );
230<a name="l00331"></a>00331
231<a name="l00332"></a>00332
232<a name="l00333"></a>00333         <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" 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>
233<a name="l00334"></a>00334                 <a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classKalman.html#e580ab06483952bd03f2e651763e184f" title="preditive density on $y_t$">fy</a>.evalpdflog ( y );
234<a name="l00335"></a>00335         }
235<a name="l00336"></a>00336
236<a name="l00337"></a>00337 <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>
237<a name="l00338"></a>00338
238<a name="l00339"></a>00339 };
239<a name="l00340"></a>00340 
240<a name="l00341"></a>00341
241<a name="l00342"></a>00342
242<a name="l00343"></a>00343 <span class="comment">//TODO why not const pointer??</span>
243<a name="l00344"></a>00344
244<a name="l00345"></a>00345 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
245<a name="l00346"></a><a class="code" href="classEKF.html#ea4f3254cacf0a92d2a820b1201d049e">00346</a> <a class="code" href="classEKF.html#ea4f3254cacf0a92d2a820b1201d049e" title="Default constructor.">EKF&lt;sq_T&gt;::EKF</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvx0, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvy0, <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvu0 ) : <a class="code" href="classKalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;sq_T&gt; ( rvx0,rvy0,rvu0 ) {}
246<a name="l00347"></a>00347
247<a name="l00348"></a>00348 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
248<a name="l00349"></a><a class="code" href="classEKF.html#28d058ae4d24d992d2f055419a06ee66">00349</a> <span class="keywordtype">void</span> <a class="code" href="classEKF.html#28d058ae4d24d992d2f055419a06ee66" title="Set nonlinear functions for mean values and covariance matrices.">EKF&lt;sq_T&gt;::set_parameters</a> ( <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables .">diffbifn</a>* pfxu0,  <a class="code" href="classdiffbifn.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 ) {
249<a name="l00350"></a>00350         pfxu = pfxu0;
250<a name="l00351"></a>00351         phxu = phxu0;
251<a name="l00352"></a>00352
252<a name="l00353"></a>00353         <span class="comment">//initialize matrices A C, later, these will be only updated!</span>
253<a name="l00354"></a>00354         pfxu-&gt;<a class="code" href="classdiffbifn.html#6d217a02d4fa13931258d4bebdd0feb4" title="Evaluates  and writes result into A .">dfdx_cond</a> ( <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>,zeros ( <a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a> ),<a class="code" href="classKalman.html#5e02efe86ee91e9c74b93b425fe060b9" title="Matrix A.">A</a>,<span class="keyword">true</span> );
254<a name="l00355"></a>00355 <span class="comment">//      pfxu-&gt;dfdu_cond ( *_mu,zeros ( dimu ),B,true );</span>
255<a name="l00356"></a>00356         <a class="code" href="classKalman.html#dc87704284a6c0bca13bf51f4345a50a" title="Matrix B.">B</a>.clear();
256<a name="l00357"></a>00357         phxu-&gt;<a class="code" href="classdiffbifn.html#6d217a02d4fa13931258d4bebdd0feb4" title="Evaluates  and writes result into A .">dfdx_cond</a> ( <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>,zeros ( <a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a> ),<a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>,<span class="keyword">true</span> );
257<a name="l00358"></a>00358 <span class="comment">//      phxu-&gt;dfdu_cond ( *_mu,zeros ( dimu ),D,true );</span>
258<a name="l00359"></a>00359         <a class="code" href="classKalman.html#d69f774ba3335c970c1c5b1d182f4dd1" title="Matrix D.">D</a>.clear();
259<a name="l00360"></a>00360
260<a name="l00361"></a>00361         <a class="code" href="classKalman.html#11d171dc0e0ab111c56a70f98b97b3ec" title="Matrix R in square-root form.">R</a> = R0;
261<a name="l00362"></a>00362         <a class="code" href="classKalman.html#9b69015c800eb93f3ee49da23a6f55d9" title="Matrix Q in square-root form.">Q</a> = Q0;
262<a name="l00363"></a>00363 }
263<a name="l00364"></a>00364
264<a name="l00365"></a>00365 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
265<a name="l00366"></a><a class="code" href="classEKF.html#c79c62c9b3e0b56b3aaa1b6f1d9a7af7">00366</a> <span class="keywordtype">void</span> <a class="code" href="classEKF.html#c79c62c9b3e0b56b3aaa1b6f1d9a7af7" title="Here dt = [yt;ut] of appropriate dimensions.">EKF&lt;sq_T&gt;::bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
266<a name="l00367"></a>00367         it_assert_debug ( dt.length() == ( <a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>+<a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a> ),<span class="stringliteral">"KalmanFull::bayes wrong size of dt"</span> );
267<a name="l00368"></a>00368
268<a name="l00369"></a>00369         sq_T iRy(<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>,<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>);
269<a name="l00370"></a>00370         vec u = dt.get ( <a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>,<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>+<a class="code" href="classKalman.html#b0153795a1444b6968a86409c778d9ce" title="cache of rvu.count()">dimu</a>-1 );
270<a name="l00371"></a>00371         vec y = dt.get ( 0,<a class="code" href="classKalman.html#ba17b956df1e38b31fbbc299c8213b6a" title="cache of rvy.count()">dimy</a>-1 );
271<a name="l00372"></a>00372         <span class="comment">//Time update</span>
272<a name="l00373"></a>00373         <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a> = pfxu-&gt;<a class="code" href="classdiffbifn.html#ad7673e16aa1a046b131b24c731c4632" title="Evaluates  (VS: Do we really need common eval? ).">eval</a> ( <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>, u );
273<a name="l00374"></a>00374         pfxu-&gt;<a class="code" href="classdiffbifn.html#6d217a02d4fa13931258d4bebdd0feb4" title="Evaluates  and writes result into A .">dfdx_cond</a> ( <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>,u,<a class="code" href="classKalman.html#5e02efe86ee91e9c74b93b425fe060b9" title="Matrix A.">A</a>,<span class="keyword">false</span> ); <span class="comment">//update A by a derivative of fx</span>
274<a name="l00375"></a>00375
275<a name="l00376"></a>00376         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
276<a name="l00377"></a>00377         <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" 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="classKalman.html#5e02efe86ee91e9c74b93b425fe060b9" title="Matrix A.">A</a> );
277<a name="l00378"></a>00378         <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a> +=<a class="code" href="classKalman.html#9b69015c800eb93f3ee49da23a6f55d9" title="Matrix Q in square-root form.">Q</a>;
278<a name="l00379"></a>00379
279<a name="l00380"></a>00380         <span class="comment">//Data update</span>
280<a name="l00381"></a>00381         phxu-&gt;<a class="code" href="classdiffbifn.html#6d217a02d4fa13931258d4bebdd0feb4" title="Evaluates  and writes result into A .">dfdx_cond</a> ( <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>,u,<a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>,<span class="keyword">false</span> ); <span class="comment">//update C by a derivative hx</span>
281<a name="l00382"></a>00382         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
282<a name="l00383"></a>00383         <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" 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="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>, <a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904" title="cache of fy.R">_Ry</a> );
283<a name="l00384"></a>00384         ( <a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904" title="cache of fy.R">_Ry</a> ) +=<a class="code" href="classKalman.html#11d171dc0e0ab111c56a70f98b97b3ec" title="Matrix R in square-root form.">R</a>;
284<a name="l00385"></a>00385
285<a name="l00386"></a>00386         mat Pfull = <a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a>.<a class="code" href="classfsqmat.html#f54fc955e8e3b43d15afa92124bc24b3" title="Conversion to full matrix.">to_mat</a>();
286<a name="l00387"></a>00387
287<a name="l00388"></a>00388         <a class="code" href="classKalman.html#45c9f928d2d62e0c884900fb3380f904" 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>
288<a name="l00389"></a>00389         <a class="code" href="classKalman.html#d422f51467c7a06174af2476d2826132" title="placeholder for Kalman gain">_K</a> = Pfull*<a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>.transpose() * ( iRy.to_mat() );
289<a name="l00390"></a>00390
290<a name="l00391"></a>00391         sq_T pom ( ( <span class="keywordtype">int</span> ) Pfull.rows() );
291<a name="l00392"></a>00392         iRy.mult_sym_t ( <a class="code" href="classKalman.html#86a805cd6515872d1132ad0d6eb5dc13" title="Matrix C.">C</a>*Pfull,pom );
292<a name="l00393"></a>00393         (<a class="code" href="classKalman.html#9fb808cc94a4c2652e1fb93be9bb7dcf" title="cache of est.R">_P</a> ) -= pom; <span class="comment">// P = P -PC'iRy*CP;</span>
293<a name="l00394"></a>00394         <a class="code" href="classKalman.html#764bbc95238eda11fc81c5ebd0b1dcfd" title="cache of fy.mu">_yp</a> = phxu-&gt;<a class="code" href="classdiffbifn.html#ad7673e16aa1a046b131b24c731c4632" title="Evaluates  (VS: Do we really need common eval? ).">eval</a> ( <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a>,u ); <span class="comment">//y prediction</span>
294<a name="l00395"></a>00395         ( <a class="code" href="classKalman.html#fe803a81d2d847b0b1db3c6b29c18061" title="cache of est.mu">_mu</a> ) += <a class="code" href="classKalman.html#d422f51467c7a06174af2476d2826132" title="placeholder for Kalman gain">_K</a>* ( y-<a class="code" href="classKalman.html#764bbc95238eda11fc81c5ebd0b1dcfd" title="cache of fy.mu">_yp</a> );
295<a name="l00396"></a>00396
296<a name="l00397"></a>00397         <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" 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="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>+=<a class="code" href="classKalman.html#e580ab06483952bd03f2e651763e184f" title="preditive density on $y_t$">fy</a>.<a class="code" href="classeEF.html#6466e8d4aa9dd64698ed288cbb1afc03" title="Evaluate normalized log-probability.">evalpdflog</a> ( y );}
297<a name="l00398"></a>00398 };
298<a name="l00399"></a>00399
299<a name="l00400"></a>00400
300<a name="l00401"></a>00401 <span class="preprocessor">#endif // KF_H</span>
301<a name="l00402"></a>00402 <span class="preprocessor"></span>
302<a name="l00403"></a>00403
303</pre></div></div>
304<hr size="1"><address style="text-align: right;"><small>Generated on Wed Nov 12 20:46:05 2008 for mixpp by&nbsp;
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306<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>
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