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[19]15<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
[8]16<a name="l00013"></a>00013 <span class="preprocessor">#ifndef KF_H</span>
17<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define KF_H</span>
18<a name="l00015"></a>00015 <span class="preprocessor"></span>
19<a name="l00016"></a>00016 <span class="preprocessor">#include &lt;itpp/itbase.h&gt;</span>
[22]20<a name="l00017"></a>00017 <span class="preprocessor">#include "../stat/libFN.h"</span>
[19]21<a name="l00018"></a>00018 <span class="preprocessor">#include "../math/libDC.h"</span>
[8]22<a name="l00019"></a>00019
23<a name="l00020"></a>00020
24<a name="l00021"></a>00021 <span class="keyword">using namespace </span>itpp;
25<a name="l00022"></a>00022
26<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> {
27<a name="l00027"></a>00027         <span class="keywordtype">int</span> dimx, dimy, dimu;
28<a name="l00028"></a>00028         mat A, B, C, D, R, Q;
29<a name="l00029"></a>00029         
30<a name="l00030"></a>00030         <span class="comment">//cache </span>
31<a name="l00031"></a>00031         mat _Pp, _Ry, _iRy, _K;
32<a name="l00032"></a>00032 <span class="keyword">public</span>:
33<a name="l00033"></a>00033         <span class="comment">//posterior </span>
34<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>;
35<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>;
36<a name="l00038"></a>00038
37<a name="l00039"></a>00039 <span class="keyword">public</span>:
38<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);
39<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> evalll=<span class="keyword">true</span>);
40<a name="l00044"></a>00044
41<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 );
42<a name="l00046"></a>00046
43<a name="l00047"></a>00047 };
44<a name="l00048"></a>00048
45<a name="l00049"></a>00049
46<a name="l00053"></a>00053 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
[22]47<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> {
48<a name="l00055"></a>00055 <span class="keyword">protected</span>:
49<a name="l00056"></a>00056         <span class="keywordtype">int</span> dimx, dimy, dimu;
50<a name="l00057"></a>00057         mat A, B, C, D;
51<a name="l00058"></a>00058         sq_T R, Q;
52<a name="l00059"></a>00059         
53<a name="l00060"></a>00060         <span class="comment">//cache</span>
54<a name="l00061"></a>00061         mat _K;
55<a name="l00062"></a>00062         vec _yp;
56<a name="l00063"></a>00063         sq_T _Ry,_iRy;
57<a name="l00064"></a>00064 <span class="keyword">public</span>:
58<a name="l00065"></a>00065         <span class="comment">//posterior </span>
59<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>;
60<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>;
61<a name="l00070"></a>00070
62<a name="l00071"></a>00071 <span class="keyword">public</span>:
63<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);
64<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 );
65<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> evalll=<span class="keyword">true</span>);
66<a name="l00078"></a>00078
67<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 );
68<a name="l00080"></a>00080
69<a name="l00081"></a>00081 };
70<a name="l00082"></a>00082
71<a name="l00088"></a>00088 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
72<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; {
73<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> fxu;
74<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> hxu;
75<a name="l00094"></a>00094 <span class="keyword">public</span>:
76<a name="l00096"></a>00096         <a class="code" href="classEKF.html#ec441d41529eeae4a1309426386b4a10" title="Default constructor.">EKF</a> (<span class="keyword">const</span> <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables $f(x,u)$.">diffbifn</a> fxu, <span class="keyword">const</span> <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables $f(x,u)$.">diffbifn</a> hxu);
77<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> evalll=<span class="keyword">true</span>);   
78<a name="l00099"></a>00099 };
79<a name="l00100"></a>00100
80<a name="l00102"></a>00102
81<a name="l00103"></a>00103 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
82<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){
83<a name="l00105"></a>00105         A = mat(dimx,dimx);
84<a name="l00106"></a>00106         B = mat(dimx,dimu);
85<a name="l00107"></a>00107         C = mat(dimy,dimx);
86<a name="l00108"></a>00108         D = mat(dimy,dimu);
87<a name="l00109"></a>00109
88<a name="l00110"></a>00110         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> = vec(dimx);
89<a name="l00111"></a>00111         <span class="comment">//TODO Initialize the rest?</span>
90<a name="l00112"></a>00112 };
91<a name="l00113"></a>00113
92<a name="l00114"></a>00114 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
93<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>() {
94<a name="l00116"></a>00116         dimx = A0.rows();
95<a name="l00117"></a>00117         dimu = B0.cols();
96<a name="l00118"></a>00118         dimy = C0.rows();
97<a name="l00119"></a>00119
98<a name="l00120"></a>00120         it_assert_debug( A0.cols()==dimx, <span class="stringliteral">"Kalman: A is not square"</span> );
99<a name="l00121"></a>00121         it_assert_debug( B0.rows()==dimx, <span class="stringliteral">"Kalman: B is not compatible"</span> );
100<a name="l00122"></a>00122         it_assert_debug( C0.cols()==dimx, <span class="stringliteral">"Kalman: C is not square"</span> );
101<a name="l00123"></a>00123         it_assert_debug(( D0.rows()==dimy ) || ( D0.cols()==dimu ),     <span class="stringliteral">"Kalman: D is not compatible"</span> );
102<a name="l00124"></a>00124         it_assert_debug(( R0.cols()==dimy ) || ( R0.rows()==dimy ), <span class="stringliteral">"Kalman: R is not compatible"</span> );
103<a name="l00125"></a>00125         it_assert_debug(( Q0.cols()==dimx ) || ( Q0.rows()==dimx ), <span class="stringliteral">"Kalman: Q is not compatible"</span> );
104<a name="l00126"></a>00126
105<a name="l00127"></a>00127         A = A0;
106<a name="l00128"></a>00128         B = B0;
107<a name="l00129"></a>00129         C = C0;
108<a name="l00130"></a>00130         D = D0;
109<a name="l00131"></a>00131         R = R0;
110<a name="l00132"></a>00132         Q = Q0;
111<a name="l00133"></a>00133         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> = mu0;
112<a name="l00134"></a>00134         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a> = P0;
113<a name="l00135"></a>00135
114<a name="l00136"></a>00136 <span class="comment">//Fixme should we assign cache??</span>
115<a name="l00137"></a>00137         _iRy = eye(dimy); <span class="comment">// needed in inv(_iRy)</span>
116<a name="l00138"></a>00138 }
[8]117<a name="l00139"></a>00139
[22]118<a name="l00140"></a>00140 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
119<a name="l00141"></a><a class="code" href="classKalman.html#e945d9205ca14acbd83ba80ea6f72b8e">00141</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> evalll) {
120<a name="l00142"></a>00142         it_assert_debug( dt.length()==( dimy+dimu ),<span class="stringliteral">"KalmanFull::bayes wrong size of dt"</span> );
121<a name="l00143"></a>00143
122<a name="l00144"></a>00144         vec u = dt.get( dimy,dimy+dimu-1 );
123<a name="l00145"></a>00145         vec y = dt.get( 0,dimy-1 );
124<a name="l00146"></a>00146         <span class="comment">//Time update</span>
125<a name="l00147"></a>00147         <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;
126<a name="l00148"></a>00148         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
127<a name="l00149"></a>00149         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>.mult_sym( A );
128<a name="l00150"></a>00150         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>+=Q;
129<a name="l00151"></a>00151
130<a name="l00152"></a>00152         <span class="comment">//Data update</span>
131<a name="l00153"></a>00153         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
132<a name="l00154"></a>00154         _Ry.mult_sym( C, <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>);
133<a name="l00155"></a>00155         _Ry+=R;
134<a name="l00156"></a>00156
135<a name="l00157"></a>00157         mat Pfull = <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>.to_mat();
136<a name="l00158"></a>00158         
137<a name="l00159"></a>00159         _Ry.inv( _iRy ); <span class="comment">// result is in _iRy;</span>
138<a name="l00160"></a>00160         _K = Pfull*C.transpose()*(_iRy.to_mat());
139<a name="l00161"></a>00161         <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>
140<a name="l00162"></a>00162         _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>
141<a name="l00163"></a>00163         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> += _K*( _yp );
142<a name="l00164"></a>00164         
143<a name="l00165"></a>00165         <span class="keywordflow">if</span> (evalll==<span class="keyword">true</span>) {
144<a name="l00166"></a>00166         <a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>+= -0.5*(_Ry.cols()*0.79817986835811504957 \
145<a name="l00167"></a>00167         +_Ry.logdet() +_iRy.qform(_yp));
146<a name="l00168"></a>00168         }
147<a name="l00169"></a>00169 };
148<a name="l00170"></a>00170
149<a name="l00171"></a>00171
150<a name="l00172"></a>00172 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
151<a name="l00173"></a><a class="code" href="classEKF.html#ec441d41529eeae4a1309426386b4a10">00173</a> <a class="code" href="classEKF.html#ec441d41529eeae4a1309426386b4a10" title="Default constructor.">EKF&lt;sq_T&gt;::EKF</a>(<span class="keyword">const</span> <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables $f(x,u)$.">diffbifn</a> fxu0, <span class="keyword">const</span> <a class="code" href="classdiffbifn.html" title="Class representing a differentiable function of two variables $f(x,u)$.">diffbifn</a> hxu0): fxu(fxu0), hxu(hxu0),<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;(fxu0._dimx(),fxu0._dimu(),hxu0._dimy()) {
152<a name="l00174"></a>00174                 
153<a name="l00175"></a>00175                 <span class="comment">//initialize matrices A C, later, these will be only updated!</span>
154<a name="l00176"></a>00176                 fxu.<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>);
155<a name="l00177"></a>00177                 hxu.<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>);
156<a name="l00178"></a>00178 }
157<a name="l00179"></a>00179
158<a name="l00180"></a>00180 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
159<a name="l00181"></a><a class="code" href="classEKF.html#fb0a08463f14e5584344ea2df99fe747">00181</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> evalll) {
160<a name="l00182"></a>00182         it_assert_debug( dt.length()==( dimy+dimu ),<span class="stringliteral">"KalmanFull::bayes wrong size of dt"</span> );
161<a name="l00183"></a>00183
162<a name="l00184"></a>00184         vec u = dt.get( dimy,dimy+dimu-1 );
163<a name="l00185"></a>00185         vec y = dt.get( 0,dimy-1 );
164<a name="l00186"></a>00186         <span class="comment">//Time update</span>
165<a name="l00187"></a>00187         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> = fxu.<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);
166<a name="l00188"></a>00188         fxu.<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>
167<a name="l00189"></a>00189         
168<a name="l00190"></a>00190         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
169<a name="l00191"></a>00191         <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 );
170<a name="l00192"></a>00192         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>+=Q;
171<a name="l00193"></a>00193
172<a name="l00194"></a>00194         <span class="comment">//Data update</span>
173<a name="l00195"></a>00195         hxu.<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>
174<a name="l00196"></a>00196         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
175<a name="l00197"></a>00197         _Ry.<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, <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>);
176<a name="l00198"></a>00198         _Ry+=R;
177<a name="l00199"></a>00199
178<a name="l00200"></a>00200         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>();
179<a name="l00201"></a>00201         
180<a name="l00202"></a>00202         _Ry.<a class="code" href="classfsqmat.html#9fa853e1ca28f2a1a1c43377e798ecb1">inv</a>( _iRy ); <span class="comment">// result is in _iRy;</span>
181<a name="l00203"></a>00203         _K = Pfull*C.transpose()*(_iRy.<a class="code" href="classfsqmat.html#cedf4f048309056f4262c930914dfda8" title="Conversion to full matrix.">to_mat</a>());
182<a name="l00204"></a>00204         <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>
183<a name="l00205"></a>00205         _yp = y-hxu.<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>
184<a name="l00206"></a>00206         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> += _K*( _yp );
185<a name="l00207"></a>00207         
186<a name="l00208"></a>00208         <span class="keywordflow">if</span> (evalll==<span class="keyword">true</span>) {
187<a name="l00209"></a>00209         <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 \
188<a name="l00210"></a>00210         +_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));
189<a name="l00211"></a>00211         }
190<a name="l00212"></a>00212 };
191<a name="l00213"></a>00213
192<a name="l00214"></a>00214
193<a name="l00215"></a>00215 <span class="preprocessor">#endif // KF_H</span>
194<a name="l00216"></a>00216 <span class="preprocessor"></span>
195<a name="l00217"></a>00217
196</pre></div><hr size="1"><address style="text-align: right;"><small>Generated on Sun Feb 17 16:14:14 2008 for mixpp by&nbsp;
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198<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.3 </small></address>
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