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Kalmany funkci, PF nefunkci

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15<h1>work/mixpp/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
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>
20<a name="l00017"></a>00017 <span class="preprocessor">#include "<a class="code" href="libBM_8h.html" title="Bayesian Models (bm) that use Bayes rule to learn from observations.">libBM.h</a>"</span>
21<a name="l00018"></a>00018 <span class="preprocessor">#include "<a class="code" href="libDC_8h.html" title="Matrices in decomposed forms (LDL&amp;#39;, LU, UDU&amp;#39;, etc).">libDC.h</a>"</span>
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;
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 covaraince 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="keywordtype">int</span> dimx, dimy, dimu;
49<a name="l00056"></a>00056         mat A, B, C, D;
50<a name="l00057"></a>00057         sq_T R, Q;
51<a name="l00058"></a>00058         
52<a name="l00059"></a>00059         <span class="comment">//cache</span>
53<a name="l00060"></a>00060         mat _K;
54<a name="l00061"></a>00061         vec _yp;
55<a name="l00062"></a>00062         sq_T _Ry,_iRy;
56<a name="l00063"></a>00063 <span class="keyword">public</span>:
57<a name="l00064"></a>00064         <span class="comment">//posterior </span>
58<a name="l00066"></a><a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed">00066</a> <span class="comment"></span>        vec <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a>;
59<a name="l00068"></a><a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3">00068</a>         sq_T <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>;
60<a name="l00069"></a>00069
61<a name="l00070"></a>00070 <span class="keyword">public</span>:
62<a name="l00072"></a>00072         <a class="code" href="classKalman.html#83118f4bd2ecbc70b03cfd573088ed6f" title="Full constructor.">Kalman</a> ( mat A0, mat B0, mat C0, mat D0, sq_T R0, sq_T Q0, sq_T P0, vec mu0 );
63<a name="l00074"></a>00074         <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>);
64<a name="l00075"></a>00075
65<a name="l00076"></a>00076         <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 );
66<a name="l00077"></a>00077
67<a name="l00078"></a>00078 };
68<a name="l00079"></a>00079
69<a name="l00081"></a>00081
70<a name="l00082"></a>00082 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
71<a name="l00083"></a><a class="code" href="classKalman.html#83118f4bd2ecbc70b03cfd573088ed6f">00083</a> <a class="code" href="classKalman.html#83118f4bd2ecbc70b03cfd573088ed6f" title="Full constructor.">Kalman&lt;sq_T&gt;::Kalman</a>( mat A0, mat B0, mat C0, mat D0, sq_T R0, sq_T Q0, sq_T P0, vec mu0 ) {
72<a name="l00084"></a>00084         dimx = A0.rows();
73<a name="l00085"></a>00085         dimu = B0.cols();
74<a name="l00086"></a>00086         dimy = C0.rows();
75<a name="l00087"></a>00087
76<a name="l00088"></a>00088         it_assert_debug( A0.cols()==dimx, <span class="stringliteral">"Kalman: A is not square"</span> );
77<a name="l00089"></a>00089         it_assert_debug( B0.rows()==dimx, <span class="stringliteral">"Kalman: B is not compatible"</span> );
78<a name="l00090"></a>00090         it_assert_debug( C0.cols()==dimx, <span class="stringliteral">"Kalman: C is not square"</span> );
79<a name="l00091"></a>00091         it_assert_debug(( D0.rows()==dimy ) || ( D0.cols()==dimu ),     <span class="stringliteral">"Kalman: D is not compatible"</span> );
80<a name="l00092"></a>00092         it_assert_debug(( R0.cols()==dimy ) || ( R0.rows()==dimy ), <span class="stringliteral">"Kalman: R is not compatible"</span> );
81<a name="l00093"></a>00093         it_assert_debug(( Q0.cols()==dimx ) || ( Q0.rows()==dimx ), <span class="stringliteral">"Kalman: Q is not compatible"</span> );
82<a name="l00094"></a>00094
83<a name="l00095"></a>00095         A = A0;
84<a name="l00096"></a>00096         B = B0;
85<a name="l00097"></a>00097         C = C0;
86<a name="l00098"></a>00098         D = D0;
87<a name="l00099"></a>00099         R = R0;
88<a name="l00100"></a>00100         Q = Q0;
89<a name="l00101"></a>00101         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> = mu0;
90<a name="l00102"></a>00102         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a> = P0;
91<a name="l00103"></a>00103
92<a name="l00104"></a>00104         <a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a> = 0;
93<a name="l00105"></a>00105 <span class="comment">//Fixme should we assign cache??</span>
94<a name="l00106"></a>00106         _iRy = eye(dimy); <span class="comment">// needed in inv(_iRy)</span>
95<a name="l00107"></a>00107 }
96<a name="l00108"></a>00108
97<a name="l00109"></a>00109 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
98<a name="l00110"></a><a class="code" href="classKalman.html#e945d9205ca14acbd83ba80ea6f72b8e">00110</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) {
99<a name="l00111"></a>00111         it_assert_debug( dt.length()==( dimy+dimu ),<span class="stringliteral">"KalmanFull::bayes wrong size of dt"</span> );
100<a name="l00112"></a>00112
101<a name="l00113"></a>00113         vec u = dt.get( dimy,dimy+dimu-1 );
102<a name="l00114"></a>00114         vec y = dt.get( 0,dimy-1 );
103<a name="l00115"></a>00115         <span class="comment">//Time update</span>
104<a name="l00116"></a>00116         <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;
105<a name="l00117"></a>00117         <span class="comment">//P  = A*P*A.transpose() + Q; in sq_T</span>
106<a name="l00118"></a>00118         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>.mult_sym( A );
107<a name="l00119"></a>00119         <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>+=Q;
108<a name="l00120"></a>00120
109<a name="l00121"></a>00121         <span class="comment">//Data update</span>
110<a name="l00122"></a>00122         <span class="comment">//_Ry = C*P*C.transpose() + R; in sq_T</span>
111<a name="l00123"></a>00123         _Ry.mult_sym( C, <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>);
112<a name="l00124"></a>00124         _Ry+=R;
113<a name="l00125"></a>00125
114<a name="l00126"></a>00126         mat Pfull = <a class="code" href="classKalman.html#188cd5ac1c9e496b1a371eb7c57c97d3" title="Mean value of the posterior density.">P</a>.to_mat();
115<a name="l00127"></a>00127         
116<a name="l00128"></a>00128         _Ry.inv( _iRy ); <span class="comment">// result is in _iRy;</span>
117<a name="l00129"></a>00129         _K = Pfull*C.transpose()*(_iRy.to_mat());
118<a name="l00130"></a>00130         <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>
119<a name="l00131"></a>00131         _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>
120<a name="l00132"></a>00132         <a class="code" href="classKalman.html#3063a3f58a74cea672ae889971012eed" title="Mean value of the posterior density.">mu</a> += _K*( _yp );
121<a name="l00133"></a>00133         
122<a name="l00134"></a>00134         <span class="keywordflow">if</span> (evalll==<span class="keyword">true</span>) {
123<a name="l00135"></a>00135         <a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>+= -0.5*(_Ry.cols()*0.79817986835811504957 \
124<a name="l00136"></a>00136         +_Ry.logdet() +_iRy.qform(_yp));
125<a name="l00137"></a>00137         }
126<a name="l00138"></a>00138 };
127<a name="l00139"></a>00139
128<a name="l00140"></a>00140 <span class="comment">//extern template class Kalman&lt;ldmat&gt;; </span>
129<a name="l00141"></a>00141
130<a name="l00142"></a>00142
131<a name="l00143"></a>00143 <span class="preprocessor">#endif // KF_H</span>
132<a name="l00144"></a>00144 <span class="preprocessor"></span>
133<a name="l00145"></a>00145
134</pre></div><hr size="1"><address style="text-align: right;"><small>Generated on Wed Jan 23 11:30:10 2008 for mixpp by&nbsp;
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