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64<h1>arx.h</h1><a href="arx_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001
65<a name="l00013"></a>00013 <span class="preprocessor">#ifndef AR_H</span>
66<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define AR_H</span>
67<a name="l00015"></a>00015 <span class="preprocessor"></span>
68<a name="l00016"></a>00016 <span class="preprocessor">#include "../math/functions.h"</span>
69<a name="l00017"></a>00017 <span class="preprocessor">#include "../stat/exp_family.h"</span>
70<a name="l00018"></a>00018 <span class="preprocessor">#include "../base/user_info.h"</span>
71<a name="l00019"></a>00019
72<a name="l00020"></a>00020 <span class="keyword">namespace </span>bdm {
73<a name="l00021"></a>00021
74<a name="l00041"></a><a class="code" href="classbdm_1_1ARX.html">00041</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1ARX.html" title="Linear Autoregressive model with Gaussian noise.">ARX</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> {
75<a name="l00042"></a>00042 <span class="keyword">protected</span>:
76<a name="l00044"></a><a class="code" href="classbdm_1_1ARX.html#8e68db2a218d54b09304cad6c0a897d9">00044</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1ARX.html#8e68db2a218d54b09304cad6c0a897d9" title="size of output variable (needed in regressors)">dimx</a>;
77<a name="l00047"></a><a class="code" href="classbdm_1_1ARX.html#363aaa55b2ab3eec602510cdf53e84ef">00047</a>         <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classbdm_1_1ARX.html#363aaa55b2ab3eec602510cdf53e84ef">_yrv</a>;
78<a name="l00049"></a><a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026">00049</a>         <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> <a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a>;
79<a name="l00051"></a><a class="code" href="classbdm_1_1ARX.html#de5b7d83ff5d3f5af2f80068db0abdfd">00051</a>         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;<a class="code" href="classbdm_1_1ARX.html#de5b7d83ff5d3f5af2f80068db0abdfd" title="cached value of est.V">V</a>;
80<a name="l00053"></a><a class="code" href="classbdm_1_1ARX.html#740b0582f180ba13cae91d66e9bdb67f">00053</a>         <span class="keywordtype">double</span> &amp;<a class="code" href="classbdm_1_1ARX.html#740b0582f180ba13cae91d66e9bdb67f" title="cached value of est.nu">nu</a>;
81<a name="l00054"></a>00054 <span class="keyword">public</span>:
82<a name="l00057"></a>00057         <a class="code" href="classbdm_1_1ARX.html" title="Linear Autoregressive model with Gaussian noise.">ARX</a> ( <span class="keyword">const</span> <span class="keywordtype">double</span> frg0=1.0 ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( frg0 ),<a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a> (), <a class="code" href="classbdm_1_1ARX.html#de5b7d83ff5d3f5af2f80068db0abdfd" title="cached value of est.V">V</a> ( <a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a>._V() ), <a class="code" href="classbdm_1_1ARX.html#740b0582f180ba13cae91d66e9bdb67f" title="cached value of est.nu">nu</a> ( <a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a>._nu() ) {};
83<a name="l00058"></a>00058         <a class="code" href="classbdm_1_1ARX.html" title="Linear Autoregressive model with Gaussian noise.">ARX</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1ARX.html" title="Linear Autoregressive model with Gaussian noise.">ARX</a> &amp;A0 ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> (),<a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a> (), <a class="code" href="classbdm_1_1ARX.html#de5b7d83ff5d3f5af2f80068db0abdfd" title="cached value of est.V">V</a> ( <a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a>._V() ), <a class="code" href="classbdm_1_1ARX.html#740b0582f180ba13cae91d66e9bdb67f" title="cached value of est.nu">nu</a> ( <a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a>._nu() ) {
84<a name="l00059"></a>00059                 set_statistics ( A0.<a class="code" href="classbdm_1_1ARX.html#8e68db2a218d54b09304cad6c0a897d9" title="size of output variable (needed in regressors)">dimx</a>,A0.<a class="code" href="classbdm_1_1ARX.html#de5b7d83ff5d3f5af2f80068db0abdfd" title="cached value of est.V">V</a>,A0.<a class="code" href="classbdm_1_1ARX.html#740b0582f180ba13cae91d66e9bdb67f" title="cached value of est.nu">nu</a> );
85<a name="l00060"></a>00060                 set_parameters(A0.<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>);
86<a name="l00061"></a>00061         };
87<a name="l00062"></a>00062         ARX* <a class="code" href="classbdm_1_1ARX.html#ca0b54c0997cfd567f49377af5def106" title="Flatten the posterior as if to keep nu0 data.">_copy_</a>() <span class="keyword">const</span>;
88<a name="l00063"></a>00063         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">double</span> frg0 ) {<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>=frg0;}
89<a name="l00064"></a>00064         <span class="keywordtype">void</span> set_statistics ( <span class="keywordtype">int</span> dimx0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) {<a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a>.set_parameters ( dimx0,V0,nu0 );<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a>.lognc();<a class="code" href="classbdm_1_1ARX.html#8e68db2a218d54b09304cad6c0a897d9" title="size of output variable (needed in regressors)">dimx</a>=dimx0;}
90<a name="l00066"></a>00066
91<a name="l00067"></a>00067 <span class="comment">//      //! Set parameters given by moments, \c mu (mean of theta), \c R (mean of R) and \c C (variance of theta)</span>
92<a name="l00068"></a>00068 <span class="comment">//      void set_parameters ( const vec &amp;mu, const mat &amp;R, const mat &amp;C, double dfm){};</span>
93<a name="l00070"></a>00070 <span class="comment"></span>        <span class="keywordtype">void</span> set_statistics ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55" title="Default constructor (=empty constructor).">BMEF</a>* BM0 );
94<a name="l00071"></a>00071 <span class="comment">//      //! Returns sufficient statistics</span>
95<a name="l00072"></a>00072 <span class="comment">//      void get_parameters ( mat &amp;V0, double &amp;nu0 ) {V0=est._V().to_mat(); nu0=est._nu();}</span>
96<a name="l00075"></a>00075 <span class="comment"></span>
97<a name="l00077"></a>00077         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1ARX.html#17e7fe14654ab3c449846c3f43e66169" title="Weighted Bayes .">bayes</a> ( <span class="keyword">const</span> vec &amp;dt, <span class="keyword">const</span> <span class="keywordtype">double</span> w );
98<a name="l00078"></a><a class="code" href="classbdm_1_1ARX.html#8bdf2974052e8ce74eb0d4f3791c58a3">00078</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1ARX.html#8bdf2974052e8ce74eb0d4f3791c58a3" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {<a class="code" href="classbdm_1_1ARX.html#8bdf2974052e8ce74eb0d4f3791c58a3" title="Incremental Bayes rule.">bayes</a> ( dt,1.0 );};
99<a name="l00079"></a>00079         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1ARX.html#080a7e531e3aa06694112863b15bc6a4">logpred</a> ( <span class="keyword">const</span> vec &amp;dt ) <span class="keyword">const</span>;
100<a name="l00080"></a><a class="code" href="classbdm_1_1ARX.html#e86ab499b116b837d3163ec852961eca">00080</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1ARX.html#e86ab499b116b837d3163ec852961eca" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* B ) {
101<a name="l00081"></a>00081                 <span class="keyword">const</span> <a class="code" href="classbdm_1_1ARX.html" title="Linear Autoregressive model with Gaussian noise.">ARX</a>* A=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classbdm_1_1ARX.html" title="Linear Autoregressive model with Gaussian noise.">ARX</a>*<span class="keyword">&gt;</span> ( B );
102<a name="l00082"></a>00082                 <span class="comment">// nu should be equal to B.nu</span>
103<a name="l00083"></a>00083                 <a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a>.<a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be" title="Power of the density, used e.g. to flatten the density.">pow</a> ( A-&gt;<a class="code" href="classbdm_1_1ARX.html#740b0582f180ba13cae91d66e9bdb67f" title="cached value of est.nu">nu</a>/<a class="code" href="classbdm_1_1ARX.html#740b0582f180ba13cae91d66e9bdb67f" title="cached value of est.nu">nu</a> );
104<a name="l00084"></a>00084                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a>.<a class="code" href="classbdm_1_1egiw.html#41d72ba7b2abc8a9a4209ffa98ed5633" title="logarithm of the normalizing constant, ">lognc</a>();}
105<a name="l00085"></a>00085         }
106<a name="l00087"></a>00087         <a class="code" href="classbdm_1_1enorm.html">enorm&lt;ldmat&gt;</a>* <a class="code" href="classbdm_1_1ARX.html#4cdf5e2a7d3480ec31f6247ed4289b15" title="Predictor for empty regressor.">epredictor</a> ( <span class="keyword">const</span> vec &amp;rgr ) <span class="keyword">const</span>;
107<a name="l00089"></a><a class="code" href="classbdm_1_1ARX.html#4cdf5e2a7d3480ec31f6247ed4289b15">00089</a>         <a class="code" href="classbdm_1_1enorm.html">enorm&lt;ldmat&gt;</a>* <a class="code" href="classbdm_1_1ARX.html#4cdf5e2a7d3480ec31f6247ed4289b15" title="Predictor for empty regressor.">epredictor</a>()<span class="keyword"> const </span>{
108<a name="l00090"></a>00090                 it_assert_debug ( <a class="code" href="classbdm_1_1ARX.html#8e68db2a218d54b09304cad6c0a897d9" title="size of output variable (needed in regressors)">dimx</a>==<a class="code" href="classbdm_1_1ARX.html#de5b7d83ff5d3f5af2f80068db0abdfd" title="cached value of est.V">V</a>.<a class="code" href="classsqmat.html#071e80ced9cc3b8cbb360fa7462eb646" title="Reimplementing common functions of mat: rows().">rows</a>()-1,<span class="stringliteral">"Regressor is not only 1"</span> );
109<a name="l00091"></a>00091                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1ARX.html#4cdf5e2a7d3480ec31f6247ed4289b15" title="Predictor for empty regressor.">epredictor</a> ( vec_1 ( 1.0 ) );
110<a name="l00092"></a>00092         }
111<a name="l00094"></a>00094         <a class="code" href="classbdm_1_1mlnorm.html">mlnorm&lt;ldmat&gt;</a>* <a class="code" href="classbdm_1_1ARX.html#74fe8ae2d88bee8639510fd0eaf73513" title="conditional version of the predictor">predictor</a>() <span class="keyword">const</span>;
112<a name="l00095"></a>00095         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a>* predictor_student() <span class="keyword">const</span>;
113<a name="l00097"></a>00097         ivec <a class="code" href="classbdm_1_1ARX.html#16b02ae03316751664c22d59d90c1e34" title="Brute force structure estimation.">structure_est</a> ( <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> Eg0 );
114<a name="l00099"></a>00099
115<a name="l00102"></a>00102         <span class="keyword">const</span> <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a>* _e()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &amp;<a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a> ;};
116<a name="l00103"></a>00103         <span class="keyword">const</span> egiw&amp; posterior()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1ARX.html#11474a627367f81b76830cb8477cf026" title="Posterior estimate of  in the form of Normal-inverse Wishart density.">est</a>;}
117<a name="l00105"></a>00105
118<a name="l00108"></a>00108         <span class="keywordtype">void</span> set_drv ( <span class="keyword">const</span> RV &amp;drv0 ) {<a class="code" href="classbdm_1_1BM.html#c400357e37d27a4834b2b1d9211009ed" title="Random variable of the data (optional).">drv</a>=drv0;}
119<a name="l00109"></a>00109         RV&amp; get_yrv() {
120<a name="l00110"></a>00110                 <span class="comment">//if yrv is not ready create it</span>
121<a name="l00111"></a>00111                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1ARX.html#363aaa55b2ab3eec602510cdf53e84ef">_yrv</a>._dsize() !=<a class="code" href="classbdm_1_1ARX.html#8e68db2a218d54b09304cad6c0a897d9" title="size of output variable (needed in regressors)">dimx</a> ) {
122<a name="l00112"></a>00112                         <span class="keywordtype">int</span> i=0;
123<a name="l00113"></a>00113                         <span class="keywordflow">while</span> ( <a class="code" href="classbdm_1_1ARX.html#363aaa55b2ab3eec602510cdf53e84ef">_yrv</a>._dsize() &lt;<a class="code" href="classbdm_1_1ARX.html#8e68db2a218d54b09304cad6c0a897d9" title="size of output variable (needed in regressors)">dimx</a> ) {<a class="code" href="classbdm_1_1ARX.html#363aaa55b2ab3eec602510cdf53e84ef">_yrv</a>.add ( <a class="code" href="classbdm_1_1BM.html#c400357e37d27a4834b2b1d9211009ed" title="Random variable of the data (optional).">drv</a> ( vec_1 ( i ) ) );i++;}
124<a name="l00114"></a>00114                 }
125<a name="l00115"></a>00115                 <span class="comment">//yrv should be ready by now</span>
126<a name="l00116"></a>00116                 it_assert_debug ( <a class="code" href="classbdm_1_1ARX.html#363aaa55b2ab3eec602510cdf53e84ef">_yrv</a>._dsize() ==<a class="code" href="classbdm_1_1ARX.html#8e68db2a218d54b09304cad6c0a897d9" title="size of output variable (needed in regressors)">dimx</a>,<span class="stringliteral">"incompatible drv"</span> );
127<a name="l00117"></a>00117                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1ARX.html#363aaa55b2ab3eec602510cdf53e84ef">_yrv</a>;
128<a name="l00118"></a>00118         }
129<a name="l00120"></a>00120
130<a name="l00121"></a>00121         <span class="comment">// TODO dokumentace - aktualizovat</span>
131<a name="l00142"></a>00142 <span class="comment"></span>        <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1ARX.html#9637412df898048bafaefee9dc7e9f6c">from_setting</a>( <span class="keyword">const</span> Setting &amp;<span class="keyword">set</span> );
132<a name="l00143"></a>00143
133<a name="l00144"></a>00144 };
134<a name="l00145"></a>00145
135<a name="l00146"></a>00146 <a class="code" href="user__info_8h.html#4f9de2f17e844047726487b99def99c6" title="Macro for registration of class into map of user-infos, registered class is scriptable...">UIREGISTER</a>(ARX);
136<a name="l00147"></a>00147
137<a name="l00148"></a>00148 }
138<a name="l00149"></a>00149
139<a name="l00150"></a>00150 <span class="preprocessor">#endif // AR_H</span>
140<a name="l00151"></a>00151 <span class="preprocessor"></span>
141<a name="l00152"></a>00152
142</pre></div></div>
143<hr size="1"><address style="text-align: right;"><small>Generated on Wed Aug 5 00:06:46 2009 for mixpp by&nbsp;
144<a href="http://www.doxygen.org/index.html">
145<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.9 </small></address>
146</body>
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