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16<h1>work/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
17<a name="l00013"></a>00013 <span class="preprocessor">#ifndef KF_H</span>
18<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define KF_H</span>
19<a name="l00015"></a>00015 <span class="preprocessor"></span>
20<a name="l00016"></a>00016 <span class="preprocessor">#include &lt;itpp/itbase.h&gt;</span>
21<a name="l00017"></a>00017 <span class="preprocessor">#include "../stat/libFN.h"</span>
22<a name="l00018"></a>00018 <span class="preprocessor">#include "../math/libDC.h"</span>
23<a name="l00019"></a>00019
24<a name="l00020"></a>00020
25<a name="l00021"></a>00021 <span class="keyword">using namespace </span>itpp;
26<a name="l00022"></a>00022
27<a name="l00026"></a><a class="code" href="classKalmanFull.html">00026</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> : <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> {
28<a name="l00027"></a>00027         <span class="keywordtype">int</span> dimx, dimy, dimu;
29<a name="l00028"></a>00028         mat A, B, C, D, R, Q;
30<a name="l00029"></a>00029         
31<a name="l00030"></a>00030         <span class="comment">//cache </span>
32<a name="l00031"></a>00031         mat _Pp, _Ry, _iRy, _K;
33<a name="l00032"></a>00032 <span class="keyword">public</span>:
34<a name="l00033"></a>00033         <span class="comment">//posterior </span>
35<a name="l00035"></a><a class="code" href="classKalmanFull.html#fb5aec635e2720cc5ac31bc01c18a68a">00035</a> <span class="comment"></span>        vec <a class="code" href="classKalmanFull.html#fb5aec635e2720cc5ac31bc01c18a68a" title="Mean value of the posterior density.">mu</a>;
36<a name="l00037"></a><a class="code" href="classKalmanFull.html#b75dc059e84fa8ffc076203b30f926cc">00037</a>         mat <a class="code" href="classKalmanFull.html#b75dc059e84fa8ffc076203b30f926cc" title="Variance of the posterior density.">P</a>;
37<a name="l00038"></a>00038
38<a name="l00039"></a>00039 <span class="keyword">public</span>:
39<a name="l00041"></a>00041         <a class="code" href="classKalmanFull.html#7197ab6e7380790006394eabd3b97043" title="Full constructor.">KalmanFull</a> ( mat A, mat B, mat C, mat D, mat R, mat Q, mat P0, vec mu0);
40<a name="l00043"></a>00043         <span class="keywordtype">void</span> <a class="code" href="classKalmanFull.html#048b13739b94c331cda08249b278552b" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a>(<span class="keyword">const</span> vec &amp;dt, <span class="keywordtype">bool</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>);
41<a name="l00044"></a>00044
42<a name="l00045"></a>00045         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( 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 );
43<a name="l00046"></a>00046
44<a name="l00047"></a>00047 };
45<a name="l00048"></a>00048
46<a name="l00049"></a>00049
47<a name="l00053"></a>00053 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
48<a name="l00054"></a><a class="code" href="classKalman.html">00054</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> {
49<a name="l00055"></a>00055 <span class="keyword">protected</span>:
50<a name="l00056"></a>00056         <span class="keywordtype">int</span> dimx, dimy, dimu;
51<a name="l00057"></a>00057         mat A, B, C, D;
52<a name="l00058"></a>00058         sq_T R, Q;
53<a name="l00059"></a>00059         
54<a name="l00060"></a>00060         <span class="comment">//cache</span>
55<a name="l00061"></a>00061         mat _K;
56<a name="l00062"></a>00062         vec _yp;
57<a name="l00063"></a>00063         sq_T _Ry,_iRy;
58<a name="l00064"></a>00064 <span class="keyword">public</span>:
59<a name="l00065"></a>00065         <span class="comment">//posterior </span>
60<a name="l00067"></a><a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed">00067</a> <span class="comment"></span>        vec <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>;
61<a name="l00069"></a><a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3">00069</a>         sq_T <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>;
62<a name="l00070"></a>00070
63<a name="l00071"></a>00071 <span class="keyword">public</span>:
64<a name="l00073"></a>00073         <a class="code" href="classKalman.html#96958a5ebfa966d892137987f265083a" title="Default constructor.">Kalman</a> (<span class="keywordtype">int</span> dimx, <span class="keywordtype">int</span> dimu, <span class="keywordtype">int</span> dimy);
65<a name="l00075"></a>00075         <a class="code" href="classKalman.html#96958a5ebfa966d892137987f265083a" title="Default constructor.">Kalman</a> ( mat A0, mat B0, mat C0, mat D0, sq_T R0, sq_T Q0, sq_T P0, vec mu0 );
66<a name="l00077"></a>00077         <span class="keywordtype">void</span> <a class="code" href="classKalman.html#e945d9205ca14acbd83ba80ea6f72b8e" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a>(<span class="keyword">const</span> vec &amp;dt, <span class="keywordtype">bool</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>);
67<a name="l00078"></a>00078
68<a name="l00079"></a>00079         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( 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 );
69<a name="l00080"></a>00080
70<a name="l00081"></a>00081 };
71<a name="l00082"></a>00082
72<a name="l00088"></a>00088 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
73<a name="l00089"></a><a class="code" href="classEKF.html">00089</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; {
74<a name="l00091"></a>00091         <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables $f(x,u)$.">diffbifn</a>* pfxu;
75<a name="l00093"></a>00093         <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables $f(x,u)$.">diffbifn</a>* phxu;
76<a name="l00094"></a>00094 <span class="keyword">public</span>:
77<a name="l00096"></a>00096         <a class="code" href="classEKF.html#003687c6cf2a01be90a00e2c99e3863e" title="Default constructor.">EKF</a> (<a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables $f(x,u)$.">diffbifn</a>* pfxu, <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables $f(x,u)$.">diffbifn</a>* phxu, sq_T Q0, sq_T R0, vec mu0, mat P0);
78<a name="l00098"></a>00098         <span class="keywordtype">void</span> <a class="code" href="classEKF.html#fb0a08463f14e5584344ea2df99fe747" title="Here dt = [yt;ut] of appropriate dimensions.">bayes</a>(<span class="keyword">const</span> vec &amp;dt, <span class="keywordtype">bool</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>);   
79<a name="l00099"></a>00099 };
80<a name="l00100"></a>00100
81<a name="l00102"></a>00102
82<a name="l00103"></a>00103 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
83<a name="l00104"></a><a class="code" href="classKalman.html#96958a5ebfa966d892137987f265083a">00104</a> <a class="code" href="classKalman.html#96958a5ebfa966d892137987f265083a" title="Default constructor.">Kalman&lt;sq_T&gt;::Kalman</a>( <span class="keywordtype">int</span> dx, <span class="keywordtype">int</span> du, <span class="keywordtype">int</span> dy): <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a>(), dimx(dx),dimy(dy),dimu(du){
84<a name="l00105"></a>00105         A = mat(dimx,dimx);
85<a name="l00106"></a>00106         B = mat(dimx,dimu);
86<a name="l00107"></a>00107         C = mat(dimy,dimx);
87<a name="l00108"></a>00108         D = mat(dimy,dimu);
88<a name="l00109"></a>00109
89<a name="l00110"></a>00110         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> = vec(dimx);
90<a name="l00111"></a>00111         <span class="comment">//TODO Initialize the rest?</span>
91<a name="l00112"></a>00112 };
92<a name="l00113"></a>00113
93<a name="l00114"></a>00114 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
94<a name="l00115"></a><a class="code" href="classKalman.html#83118f4bd2ecbc70b03cfd573088ed6f">00115</a> <a class="code" href="classKalman.html#96958a5ebfa966d892137987f265083a" title="Default constructor.">Kalman&lt;sq_T&gt;::Kalman</a>(<span class="keyword">const</span>  mat A0,<span class="keyword">const</span>  mat B0, <span class="keyword">const</span> mat C0, <span class="keyword">const</span> mat D0, <span class="keyword">const</span> sq_T R0, <span class="keyword">const</span> sq_T Q0, <span class="keyword">const</span> sq_T P0, <span class="keyword">const</span> vec mu0 ): <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a>() {
95<a name="l00116"></a>00116         dimx = A0.rows();
96<a name="l00117"></a>00117         dimu = B0.cols();
97<a name="l00118"></a>00118         dimy = C0.rows();
98<a name="l00119"></a>00119
99<a name="l00120"></a>00120         it_assert_debug( A0.cols()==dimx, <span class="stringliteral">"Kalman: A is not square"</span> );
100<a name="l00121"></a>00121         it_assert_debug( B0.rows()==dimx, <span class="stringliteral">"Kalman: B is not compatible"</span> );
101<a name="l00122"></a>00122         it_assert_debug( C0.cols()==dimx, <span class="stringliteral">"Kalman: C is not square"</span> );
102<a name="l00123"></a>00123         it_assert_debug(( D0.rows()==dimy ) || ( D0.cols()==dimu ),     <span class="stringliteral">"Kalman: D is not compatible"</span> );
103<a name="l00124"></a>00124         it_assert_debug(( R0.cols()==dimy ) || ( R0.rows()==dimy ), <span class="stringliteral">"Kalman: R is not compatible"</span> );
104<a name="l00125"></a>00125         it_assert_debug(( Q0.cols()==dimx ) || ( Q0.rows()==dimx ), <span class="stringliteral">"Kalman: Q is not compatible"</span> );
105<a name="l00126"></a>00126
106<a name="l00127"></a>00127         A = A0;
107<a name="l00128"></a>00128         B = B0;
108<a name="l00129"></a>00129         C = C0;
109<a name="l00130"></a>00130         D = D0;
110<a name="l00131"></a>00131         R = R0;
111<a name="l00132"></a>00132         Q = Q0;
112<a name="l00133"></a>00133         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> = mu0;
113<a name="l00134"></a>00134         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a> = P0;
114<a name="l00135"></a>00135
115<a name="l00136"></a>00136 <span class="comment">//Fixme should we assign cache??</span>
116<a name="l00137"></a>00137         _iRy = eye(dimy); <span class="comment">// needed in inv(_iRy)</span>
117<a name="l00138"></a>00138         _Ry = eye(dimy); <span class="comment">// needed in inv(_iRy)</span>
118<a name="l00139"></a>00139 }
119<a name="l00140"></a>00140
120<a name="l00141"></a>00141 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
121<a name="l00142"></a><a class="code" href="classKalman.html#e945d9205ca14acbd83ba80ea6f72b8e">00142</a> <span class="keywordtype">void</span> <a class="code" href="classKalman.html#e945d9205ca14acbd83ba80ea6f72b8e" title="Here dt = [yt;ut] of appropriate dimensions.">Kalman&lt;sq_T&gt;::bayes</a>( <span class="keyword">const</span> vec &amp;dt , <span class="keywordtype">bool</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>) {
122<a name="l00143"></a>00143         it_assert_debug( dt.length()==( dimy+dimu ),<span class="stringliteral">"KalmanFull::bayes wrong size of dt"</span> );
123<a name="l00144"></a>00144
124<a name="l00145"></a>00145         vec u = dt.get( dimy,dimy+dimu-1 );
125<a name="l00146"></a>00146         vec y = dt.get( 0,dimy-1 );
126<a name="l00147"></a>00147         <span class="comment">//Time update</span>
127<a name="l00148"></a>00148         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> = A*<a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> + B*u;
128<a name="l00149"></a>00149         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
129<a name="l00150"></a>00150         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>.mult_sym( A );
130<a name="l00151"></a>00151         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>+=Q;
131<a name="l00152"></a>00152
132<a name="l00153"></a>00153         <span class="comment">//Data update</span>
133<a name="l00154"></a>00154         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
134<a name="l00155"></a>00155         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>.mult_sym( C, _Ry);
135<a name="l00156"></a>00156         _Ry+=R;
136<a name="l00157"></a>00157
137<a name="l00158"></a>00158         mat Pfull = <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>.to_mat();
138<a name="l00159"></a>00159         
139<a name="l00160"></a>00160         _Ry.inv( _iRy ); <span class="comment">// result is in _iRy;</span>
140<a name="l00161"></a>00161         _K = Pfull*C.transpose()*(_iRy.to_mat());
141<a name="l00162"></a>00162         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a> -= _K*C*Pfull; <span class="comment">// P = P -KCP;</span>
142<a name="l00163"></a>00163         _yp = y-C*<a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>-D*u; <span class="comment">//y prediction</span>
143<a name="l00164"></a>00164         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> += _K*( _yp );
144<a name="l00165"></a>00165         
145<a name="l00166"></a>00166         <span class="keywordflow">if</span> (evalll==<span class="keyword">true</span>) {
146<a name="l00167"></a>00167         <a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>+= -0.5*(_Ry.cols()*0.79817986835811504957 \
147<a name="l00168"></a>00168         +_Ry.logdet() +_iRy.qform(_yp));
148<a name="l00169"></a>00169         }
149<a name="l00170"></a>00170 };
150<a name="l00171"></a>00171
151<a name="l00172"></a>00172 <span class="comment">//TODO why not const pointer??</span>
152<a name="l00173"></a>00173
153<a name="l00174"></a>00174 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
154<a name="l00175"></a><a class="code" href="classEKF.html#003687c6cf2a01be90a00e2c99e3863e">00175</a> <a class="code" href="classEKF.html#003687c6cf2a01be90a00e2c99e3863e" title="Default constructor.">EKF&lt;sq_T&gt;::EKF</a>( <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables $f(x,u)$.">diffbifn</a>* pfxu0,  <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables $f(x,u)$.">diffbifn</a>* phxu0, sq_T Q0, sq_T R0, vec mu0, mat P0): pfxu(pfxu0), phxu(phxu0), <a class="code" href="classKalman.html" title="Kalman filter with covariance matrices in square root form.">Kalman</a>&lt;<a class="code" href="classfsqmat.html" title="Fake sqmat. This class maps sqmat operations to operations on full matrix.">fsqmat</a>&gt;(pfxu0-&gt;_dimx(),pfxu0-&gt;_dimu(),phxu0-&gt;_dimy()) {
155<a name="l00176"></a>00176                 
156<a name="l00177"></a>00177                 <span class="comment">//initialize matrices A C, later, these will be only updated!</span>
157<a name="l00178"></a>00178                 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#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>,zeros(dimu),A,<span class="keyword">true</span>);
158<a name="l00179"></a>00179                 pfxu-&gt;<a class="code" href="classdiffbifn.html#1978bafd7909d15c139a08c495c24aa0" title="Evaluates  and writes result into A .">dfdu_cond</a>(<a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>,zeros(dimu),B,<span class="keyword">true</span>);
159<a name="l00180"></a>00180                 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#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>,zeros(dimu),C,<span class="keyword">true</span>);
160<a name="l00181"></a>00181                 phxu-&gt;<a class="code" href="classdiffbifn.html#1978bafd7909d15c139a08c495c24aa0" title="Evaluates  and writes result into A .">dfdu_cond</a>(<a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>,zeros(dimu),D,<span class="keyword">true</span>);
161<a name="l00182"></a>00182                 
162<a name="l00183"></a>00183
163<a name="l00184"></a>00184                 R = R0;
164<a name="l00185"></a>00185                 Q = Q0;
165<a name="l00186"></a>00186                 <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> = mu0;
166<a name="l00187"></a>00187                 <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a> = P0;
167<a name="l00188"></a>00188
168<a name="l00189"></a>00189         <span class="keyword">using</span> std::cout;
169<a name="l00190"></a>00190         cout&lt;&lt;A&lt;&lt;std::endl;
170<a name="l00191"></a>00191         cout&lt;&lt;B&lt;&lt;std::endl;
171<a name="l00192"></a>00192         cout&lt;&lt;C&lt;&lt;std::endl;
172<a name="l00193"></a>00193         cout&lt;&lt;D&lt;&lt;std::endl;
173<a name="l00194"></a>00194
174<a name="l00195"></a>00195 }
175<a name="l00196"></a>00196
176<a name="l00197"></a>00197 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
177<a name="l00198"></a><a class="code" href="classEKF.html#fb0a08463f14e5584344ea2df99fe747">00198</a> <span class="keywordtype">void</span> <a class="code" href="classEKF.html#fb0a08463f14e5584344ea2df99fe747" title="Here dt = [yt;ut] of appropriate dimensions.">EKF&lt;sq_T&gt;::bayes</a>( <span class="keyword">const</span> vec &amp;dt , <span class="keywordtype">bool</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>) {
178<a name="l00199"></a>00199         it_assert_debug( dt.length()==( dimy+dimu ),<span class="stringliteral">"KalmanFull::bayes wrong size of dt"</span> );
179<a name="l00200"></a>00200
180<a name="l00201"></a>00201         vec u = dt.get( dimy,dimy+dimu-1 );
181<a name="l00202"></a>00202         vec y = dt.get( 0,dimy-1 );
182<a name="l00203"></a>00203         <span class="comment">//Time update</span>
183<a name="l00204"></a>00204         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> = pfxu-&gt;<a class="code" href="classdiffbifn.html#ad7673e16aa1a046b131b24c731c4632" title="Evaluates $f(x0,u0)$ (VS: Do we really need common eval? ).">eval</a>(<a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>, u);
184<a name="l00205"></a>00205         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#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>,u,A,<span class="keyword">false</span>); <span class="comment">//update A by a derivative of fx</span>
185<a name="l00206"></a>00206         
186<a name="l00207"></a>00207         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
187<a name="l00208"></a>00208         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>.<a class="code" href="classfsqmat.html#acc5d2d0a243f1de6d0106065f01f518" title="Inplace symmetric multiplication by a SQUARE matrix $C$, i.e. $V = C*V*C&amp;#39;$.">mult_sym</a>( A );
188<a name="l00209"></a>00209         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>+=Q;
189<a name="l00210"></a>00210
190<a name="l00211"></a>00211         <span class="comment">//Data update</span>
191<a name="l00212"></a>00212         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#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>,u,C,<span class="keyword">false</span>); <span class="comment">//update C by a derivative hx</span>
192<a name="l00213"></a>00213         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
193<a name="l00214"></a>00214         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>.<a class="code" href="classfsqmat.html#acc5d2d0a243f1de6d0106065f01f518" title="Inplace symmetric multiplication by a SQUARE matrix $C$, i.e. $V = C*V*C&amp;#39;$.">mult_sym</a>( C, _Ry);
194<a name="l00215"></a>00215         _Ry+=R;
195<a name="l00216"></a>00216
196<a name="l00217"></a>00217         mat Pfull = <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>.<a class="code" href="classfsqmat.html#cedf4f048309056f4262c930914dfda8" title="Conversion to full matrix.">to_mat</a>();
197<a name="l00218"></a>00218         
198<a name="l00219"></a>00219         _Ry.<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>
199<a name="l00220"></a>00220         _K = Pfull*C.transpose()*(_iRy.<a class="code" href="classfsqmat.html#cedf4f048309056f4262c930914dfda8" title="Conversion to full matrix.">to_mat</a>());
200<a name="l00221"></a>00221         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a> -= _K*C*Pfull; <span class="comment">// P = P -KCP;</span>
201<a name="l00222"></a>00222         _yp = y-phxu-&gt;<a class="code" href="classdiffbifn.html#ad7673e16aa1a046b131b24c731c4632" title="Evaluates $f(x0,u0)$ (VS: Do we really need common eval? ).">eval</a>(<a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>,u); <span class="comment">//y prediction</span>
202<a name="l00223"></a>00223         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> += _K*( _yp );
203<a name="l00224"></a>00224         
204<a name="l00225"></a>00225         <span class="keywordflow">if</span> (evalll==<span class="keyword">true</span>) {
205<a name="l00226"></a>00226         <a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>+= -0.5*(_Ry.<a class="code" href="classsqmat.html#ecc2e2540f95a04f4449842588170f5b" title="Reimplementing common functions of mat: cols().">cols</a>()*0.79817986835811504957 \
206<a name="l00227"></a>00227         +_Ry.<a class="code" href="classfsqmat.html#bf212272ec195ad2706e2bf4d8e7c9b3" title="Logarithm of a determinant.">logdet</a>() +_iRy.<a class="code" href="classfsqmat.html#6d047b9f7a27dfc093303a13cc9b1fba" title="Evaluates quadratic form $x= v&amp;#39;*V*v$;.">qform</a>(_yp));
207<a name="l00228"></a>00228         }
208<a name="l00229"></a>00229 };
209<a name="l00230"></a>00230
210<a name="l00231"></a>00231
211<a name="l00232"></a>00232 <span class="preprocessor">#endif // KF_H</span>
212<a name="l00233"></a>00233 <span class="preprocessor"></span>
213<a name="l00234"></a>00234
214</pre></div><hr size="1"><address style="text-align: right;"><small>Generated on Mon Feb 18 21:48:39 2008 for mixpp by&nbsp;
215<a href="http://www.doxygen.org/index.html">
216<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.3 </small></address>
217</body>
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