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[224]1\hypertarget{classEKFCh__cond}{
2\section{EKFCh\_\-cond Class Reference}
3\label{classEKFCh__cond}\index{EKFCh\_\-cond@{EKFCh\_\-cond}}
[172]4}
[255]5Extended Kalman filter with unknown {\tt Q}. 
[33]6
7
[224]8Inheritance diagram for EKFCh\_\-cond:\nopagebreak
[33]9\begin{figure}[H]
10\begin{center}
11\leavevmode
[255]12\includegraphics[width=124pt]{classEKFCh__cond__inherit__graph}
[33]13\end{center}
14\end{figure}
[224]15Collaboration diagram for EKFCh\_\-cond:\nopagebreak
[33]16\begin{figure}[H]
17\begin{center}
18\leavevmode
[224]19\includegraphics[width=400pt]{classEKFCh__cond__coll__graph}
[33]20\end{center}
21\end{figure}
22\subsection*{Public Member Functions}
23\begin{CompactItemize}
24\item 
[224]25\hypertarget{classEKFCh__cond_727af5503bba3063c115994dd6adb059}{
[255]26\hyperlink{classEKFCh__cond_727af5503bba3063c115994dd6adb059}{EKFCh\_\-cond} (\hyperlink{classbdm_1_1RV}{RV} rx, \hyperlink{classbdm_1_1RV}{RV} ry, \hyperlink{classbdm_1_1RV}{RV} ru, \hyperlink{classbdm_1_1RV}{RV} rC)}
[224]27\label{classEKFCh__cond_727af5503bba3063c115994dd6adb059}
[33]28
29\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
[224]30\hypertarget{classEKFCh__cond_93ccb6731b68f28e0fb43393d8c66e1d}{
31void \hyperlink{classEKFCh__cond_93ccb6731b68f28e0fb43393d8c66e1d}{condition} (const vec \&val)}
32\label{classEKFCh__cond_93ccb6731b68f28e0fb43393d8c66e1d}
[33]33
34\begin{CompactList}\small\item\em Substitute {\tt val} for {\tt rvc}. \item\end{CompactList}\item 
[255]35\hypertarget{classbdm_1_1EKFCh_50f9fbffad721f35e5ccb75d0f6b842a}{
36void \hyperlink{classbdm_1_1EKFCh_50f9fbffad721f35e5ccb75d0f6b842a}{set\_\-parameters} (diffbifn $\ast$\hyperlink{classbdm_1_1EKFCh_e1e895f994398a55bc425551fc275ba3}{pfxu}, diffbifn $\ast$\hyperlink{classbdm_1_1EKFCh_6b34c69641826322467b704e8252f317}{phxu}, const \hyperlink{classchmat}{chmat} Q0, const \hyperlink{classchmat}{chmat} R0)}
37\label{classbdm_1_1EKFCh_50f9fbffad721f35e5ccb75d0f6b842a}
[33]38
39\begin{CompactList}\small\item\em Set nonlinear functions for mean values and covariance matrices. \item\end{CompactList}\item 
[255]40\hypertarget{classbdm_1_1KalmanCh_ab3a87ba1831e53f193a9dfbaf56a879}{
41void \hyperlink{classbdm_1_1KalmanCh_ab3a87ba1831e53f193a9dfbaf56a879}{set\_\-parameters} (const mat \&A0, const mat \&B0, const mat \&C0, const mat \&D0, const \hyperlink{classchmat}{chmat} \&R0, const \hyperlink{classchmat}{chmat} \&Q0)}
42\label{classbdm_1_1KalmanCh_ab3a87ba1831e53f193a9dfbaf56a879}
[33]43
44\begin{CompactList}\small\item\em Set parameters with check of relevance. \item\end{CompactList}\item 
[255]45\hypertarget{classbdm_1_1EKFCh_4c8609c37290b158f88a31dae4047225}{
46void \hyperlink{classbdm_1_1EKFCh_4c8609c37290b158f88a31dae4047225}{bayes} (const vec \&dt)}
47\label{classbdm_1_1EKFCh_4c8609c37290b158f88a31dae4047225}
[33]48
[172]49\begin{CompactList}\small\item\em Here dt = \mbox{[}yt;ut\mbox{]} of appropriate dimensions. \item\end{CompactList}\item 
[255]50\hypertarget{classbdm_1_1KalmanCh_f559387dd38bd6002be490cc62987290}{
51void \hyperlink{classbdm_1_1KalmanCh_f559387dd38bd6002be490cc62987290}{set\_\-est} (const vec \&mu0, const \hyperlink{classchmat}{chmat} \&P0)}
52\label{classbdm_1_1KalmanCh_f559387dd38bd6002be490cc62987290}
[33]53
54\begin{CompactList}\small\item\em Set estimate values, used e.g. in initialization. \item\end{CompactList}\item 
[255]55\hypertarget{classbdm_1_1Kalman_93b5936ba397f13c05f52885c545f42d}{
56const epdf \& \hyperlink{classbdm_1_1Kalman_93b5936ba397f13c05f52885c545f42d}{\_\-epdf} () const }
57\label{classbdm_1_1Kalman_93b5936ba397f13c05f52885c545f42d}
[33]58
59\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
[255]60\hypertarget{classbdm_1_1Kalman_c34989b1e53c7d4ecdaea63a95ddbd77}{
61const enorm$<$ \hyperlink{classchmat}{chmat} $>$ $\ast$ \hyperlink{classbdm_1_1Kalman_c34989b1e53c7d4ecdaea63a95ddbd77}{\_\-e} () const }
62\label{classbdm_1_1Kalman_c34989b1e53c7d4ecdaea63a95ddbd77}
[210]63
[255]64\begin{CompactList}\small\item\em Returns a pointer to the epdf representing posterior density on parameters. Use with care! \item\end{CompactList}\item 
65\hypertarget{classbdm_1_1Kalman_c788ec6e6c6f5f5861ae8a56d8ede277}{
66mat \& \hyperlink{classbdm_1_1Kalman_c788ec6e6c6f5f5861ae8a56d8ede277}{\_\-\_\-K} ()}
67\label{classbdm_1_1Kalman_c788ec6e6c6f5f5861ae8a56d8ede277}
[79]68
69\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
[255]70\hypertarget{classbdm_1_1Kalman_a250d1dbe7bba861dba2a324520cfa48}{
71vec \hyperlink{classbdm_1_1Kalman_a250d1dbe7bba861dba2a324520cfa48}{\_\-dP} ()}
72\label{classbdm_1_1Kalman_a250d1dbe7bba861dba2a324520cfa48}
[79]73
74\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
[255]75\hypertarget{classbdm_1_1BM_1dee3fddaf021e62d925289660a707dc}{
76virtual void \hyperlink{classbdm_1_1BM_1dee3fddaf021e62d925289660a707dc}{bayesB} (const mat \&Dt)}
77\label{classbdm_1_1BM_1dee3fddaf021e62d925289660a707dc}
[33]78
[172]79\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
[255]80virtual double \hyperlink{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}{logpred} (const vec \&dt) const
[172]81\item 
[255]82\hypertarget{classbdm_1_1BM_0e8ebe61fb14990abe1254bd3dda5fae}{
83vec \hyperlink{classbdm_1_1BM_0e8ebe61fb14990abe1254bd3dda5fae}{logpred\_\-m} (const mat \&dt) const }
84\label{classbdm_1_1BM_0e8ebe61fb14990abe1254bd3dda5fae}
[180]85
86\begin{CompactList}\small\item\em Matrix version of logpred. \item\end{CompactList}\item 
[255]87\hypertarget{classbdm_1_1BM_710e7d69c0d8791fb41a7cd4683cca2c}{
88virtual epdf $\ast$ \hyperlink{classbdm_1_1BM_710e7d69c0d8791fb41a7cd4683cca2c}{predictor} (const RV \&\hyperlink{classbdm_1_1BM_18d6db4af8ee42077741d9e3618153ca}{rv}) const }
89\label{classbdm_1_1BM_710e7d69c0d8791fb41a7cd4683cca2c}
[180]90
91\begin{CompactList}\small\item\em Constructs a predictive density (marginal density on data). \item\end{CompactList}\item 
[255]92\hypertarget{classbdm_1_1BM_40a3c891996391e3135518053a917793}{
93const RV \& \hyperlink{classbdm_1_1BM_40a3c891996391e3135518053a917793}{\_\-rv} () const }
94\label{classbdm_1_1BM_40a3c891996391e3135518053a917793}
[172]95
[33]96\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
[255]97\hypertarget{classbdm_1_1BM_5be65d37dedfe33a3671e7065f523a70}{
98double \hyperlink{classbdm_1_1BM_5be65d37dedfe33a3671e7065f523a70}{\_\-ll} () const }
99\label{classbdm_1_1BM_5be65d37dedfe33a3671e7065f523a70}
[33]100
101\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
[255]102\hypertarget{classbdm_1_1BM_236b3abbcc93594fc97cd86d82c1a83f}{
103void \hyperlink{classbdm_1_1BM_236b3abbcc93594fc97cd86d82c1a83f}{set\_\-evalll} (bool evl0)}
104\label{classbdm_1_1BM_236b3abbcc93594fc97cd86d82c1a83f}
[33]105
[172]106\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
[255]107virtual BM $\ast$ \hyperlink{classbdm_1_1BM_3efb3098172f1f67564a312fe732473e}{\_\-copy\_\-} (bool changerv=false)
[172]108\item 
[255]109\hypertarget{classbdm_1_1BMcond_7506910f93250b44fea505ec4ffb19dc}{
110const RV \& \hyperlink{classbdm_1_1BMcond_7506910f93250b44fea505ec4ffb19dc}{\_\-rvc} () const }
111\label{classbdm_1_1BMcond_7506910f93250b44fea505ec4ffb19dc}
[172]112
[33]113\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
114\subsection*{Protected Attributes}
115\begin{CompactItemize}
116\item 
[255]117\hypertarget{classbdm_1_1EKFCh_e1e895f994398a55bc425551fc275ba3}{
118diffbifn $\ast$ \hyperlink{classbdm_1_1EKFCh_e1e895f994398a55bc425551fc275ba3}{pfxu}}
119\label{classbdm_1_1EKFCh_e1e895f994398a55bc425551fc275ba3}
[224]120
121\begin{CompactList}\small\item\em Internal Model f(x,u). \item\end{CompactList}\item 
[255]122\hypertarget{classbdm_1_1EKFCh_6b34c69641826322467b704e8252f317}{
123diffbifn $\ast$ \hyperlink{classbdm_1_1EKFCh_6b34c69641826322467b704e8252f317}{phxu}}
124\label{classbdm_1_1EKFCh_6b34c69641826322467b704e8252f317}
[224]125
126\begin{CompactList}\small\item\em Observation Model h(x,u). \item\end{CompactList}\item 
[255]127\hypertarget{classbdm_1_1KalmanCh_48611c8582706cfa62e832be0972e75d}{
128mat \hyperlink{classbdm_1_1KalmanCh_48611c8582706cfa62e832be0972e75d}{preA}}
129\label{classbdm_1_1KalmanCh_48611c8582706cfa62e832be0972e75d}
[79]130
131\begin{CompactList}\small\item\em pre array (triangular matrix) \item\end{CompactList}\item 
[255]132\hypertarget{classbdm_1_1KalmanCh_bcbd68f51d4b57246e7784ca5900171f}{
133mat \hyperlink{classbdm_1_1KalmanCh_bcbd68f51d4b57246e7784ca5900171f}{postA}}
134\label{classbdm_1_1KalmanCh_bcbd68f51d4b57246e7784ca5900171f}
[79]135
136\begin{CompactList}\small\item\em post array (triangular matrix) \item\end{CompactList}\item 
[255]137\hypertarget{classbdm_1_1Kalman_3fe475a1e920b20b63bb342c0e1571f7}{
138RV \hyperlink{classbdm_1_1Kalman_3fe475a1e920b20b63bb342c0e1571f7}{rvy}}
139\label{classbdm_1_1Kalman_3fe475a1e920b20b63bb342c0e1571f7}
[33]140
141\begin{CompactList}\small\item\em Indetifier of output rv. \item\end{CompactList}\item 
[255]142\hypertarget{classbdm_1_1Kalman_149e27424fd1a7cc1c998ea088618a94}{
143RV \hyperlink{classbdm_1_1Kalman_149e27424fd1a7cc1c998ea088618a94}{rvu}}
144\label{classbdm_1_1Kalman_149e27424fd1a7cc1c998ea088618a94}
[33]145
146\begin{CompactList}\small\item\em Indetifier of exogeneous rv. \item\end{CompactList}\item 
[255]147\hypertarget{classbdm_1_1Kalman_ba7699cdb3b1382a54d3e28b9b7517fa}{
148int \hyperlink{classbdm_1_1Kalman_ba7699cdb3b1382a54d3e28b9b7517fa}{dimx}}
149\label{classbdm_1_1Kalman_ba7699cdb3b1382a54d3e28b9b7517fa}
[33]150
151\begin{CompactList}\small\item\em cache of rv.count() \item\end{CompactList}\item 
[255]152\hypertarget{classbdm_1_1Kalman_d2c36ba01760bf207b985bf321b7817f}{
153int \hyperlink{classbdm_1_1Kalman_d2c36ba01760bf207b985bf321b7817f}{dimy}}
154\label{classbdm_1_1Kalman_d2c36ba01760bf207b985bf321b7817f}
[33]155
156\begin{CompactList}\small\item\em cache of rvy.count() \item\end{CompactList}\item 
[255]157\hypertarget{classbdm_1_1Kalman_c5136ef617f6ac0e426bea222755d92b}{
158int \hyperlink{classbdm_1_1Kalman_c5136ef617f6ac0e426bea222755d92b}{dimu}}
159\label{classbdm_1_1Kalman_c5136ef617f6ac0e426bea222755d92b}
[33]160
161\begin{CompactList}\small\item\em cache of rvu.count() \item\end{CompactList}\item 
[255]162\hypertarget{classbdm_1_1Kalman_0a2072e2090c10fac74ad30a023a4ace}{
163mat \hyperlink{classbdm_1_1Kalman_0a2072e2090c10fac74ad30a023a4ace}{A}}
164\label{classbdm_1_1Kalman_0a2072e2090c10fac74ad30a023a4ace}
[33]165
166\begin{CompactList}\small\item\em Matrix A. \item\end{CompactList}\item 
[255]167\hypertarget{classbdm_1_1Kalman_5977b2c81857948a35105f0e7840203c}{
168mat \hyperlink{classbdm_1_1Kalman_5977b2c81857948a35105f0e7840203c}{B}}
169\label{classbdm_1_1Kalman_5977b2c81857948a35105f0e7840203c}
[33]170
171\begin{CompactList}\small\item\em Matrix B. \item\end{CompactList}\item 
[255]172\hypertarget{classbdm_1_1Kalman_818eba63a23972786a4579ad30294177}{
173mat \hyperlink{classbdm_1_1Kalman_818eba63a23972786a4579ad30294177}{C}}
174\label{classbdm_1_1Kalman_818eba63a23972786a4579ad30294177}
[33]175
176\begin{CompactList}\small\item\em Matrix C. \item\end{CompactList}\item 
[255]177\hypertarget{classbdm_1_1Kalman_7b56ac423d0654b5755e4f852a870456}{
178mat \hyperlink{classbdm_1_1Kalman_7b56ac423d0654b5755e4f852a870456}{D}}
179\label{classbdm_1_1Kalman_7b56ac423d0654b5755e4f852a870456}
[33]180
181\begin{CompactList}\small\item\em Matrix D. \item\end{CompactList}\item 
[255]182\hypertarget{classbdm_1_1Kalman_70f8bf19e81b532c60fd3a7a152425ee}{
183\hyperlink{classchmat}{chmat} \hyperlink{classbdm_1_1Kalman_70f8bf19e81b532c60fd3a7a152425ee}{Q}}
184\label{classbdm_1_1Kalman_70f8bf19e81b532c60fd3a7a152425ee}
[33]185
186\begin{CompactList}\small\item\em Matrix Q in square-root form. \item\end{CompactList}\item 
[255]187\hypertarget{classbdm_1_1Kalman_475b088287cdfbba4dc60a3d027728b7}{
188\hyperlink{classchmat}{chmat} \hyperlink{classbdm_1_1Kalman_475b088287cdfbba4dc60a3d027728b7}{R}}
189\label{classbdm_1_1Kalman_475b088287cdfbba4dc60a3d027728b7}
[33]190
191\begin{CompactList}\small\item\em Matrix R in square-root form. \item\end{CompactList}\item 
[255]192\hypertarget{classbdm_1_1Kalman_383f329ff18bbe219254c8b3b916f40d}{
193enorm$<$ \hyperlink{classchmat}{chmat} $>$ \hyperlink{classbdm_1_1Kalman_383f329ff18bbe219254c8b3b916f40d}{est}}
194\label{classbdm_1_1Kalman_383f329ff18bbe219254c8b3b916f40d}
[33]195
196\begin{CompactList}\small\item\em posterior density on \$x\_\-t\$ \item\end{CompactList}\item 
[255]197\hypertarget{classbdm_1_1Kalman_ba555c394c429f6831c9bbabfa2c944c}{
198enorm$<$ \hyperlink{classchmat}{chmat} $>$ \hyperlink{classbdm_1_1Kalman_ba555c394c429f6831c9bbabfa2c944c}{fy}}
199\label{classbdm_1_1Kalman_ba555c394c429f6831c9bbabfa2c944c}
[33]200
201\begin{CompactList}\small\item\em preditive density on \$y\_\-t\$ \item\end{CompactList}\item 
[255]202\hypertarget{classbdm_1_1Kalman_bd69dfb802465f22dd84d73a180d5c92}{
203mat \hyperlink{classbdm_1_1Kalman_bd69dfb802465f22dd84d73a180d5c92}{\_\-K}}
204\label{classbdm_1_1Kalman_bd69dfb802465f22dd84d73a180d5c92}
[33]205
[255]206\begin{CompactList}\small\item\em placeholder for Kalman gain \item\end{CompactList}\item 
207\hypertarget{classbdm_1_1Kalman_c249d45258c8578b13858ad3e7b729b1}{
208vec \& \hyperlink{classbdm_1_1Kalman_c249d45258c8578b13858ad3e7b729b1}{\_\-yp}}
209\label{classbdm_1_1Kalman_c249d45258c8578b13858ad3e7b729b1}
[33]210
211\begin{CompactList}\small\item\em cache of fy.mu \item\end{CompactList}\item 
[255]212\hypertarget{classbdm_1_1Kalman_2dd268f2d7fbe6382cb8825a1114192a}{
213\hyperlink{classchmat}{chmat} \& \hyperlink{classbdm_1_1Kalman_2dd268f2d7fbe6382cb8825a1114192a}{\_\-Ry}}
214\label{classbdm_1_1Kalman_2dd268f2d7fbe6382cb8825a1114192a}
[33]215
216\begin{CompactList}\small\item\em cache of fy.R \item\end{CompactList}\item 
[255]217\hypertarget{classbdm_1_1Kalman_fa172078091e45561343fa513dd573b0}{
218vec \& \hyperlink{classbdm_1_1Kalman_fa172078091e45561343fa513dd573b0}{\_\-mu}}
219\label{classbdm_1_1Kalman_fa172078091e45561343fa513dd573b0}
[33]220
221\begin{CompactList}\small\item\em cache of est.mu \item\end{CompactList}\item 
[255]222\hypertarget{classbdm_1_1Kalman_00c27b0bf324f0018497921ca23c71ed}{
223\hyperlink{classchmat}{chmat} \& \hyperlink{classbdm_1_1Kalman_00c27b0bf324f0018497921ca23c71ed}{\_\-P}}
224\label{classbdm_1_1Kalman_00c27b0bf324f0018497921ca23c71ed}
[33]225
226\begin{CompactList}\small\item\em cache of est.R \item\end{CompactList}\item 
[255]227\hypertarget{classbdm_1_1BM_18d6db4af8ee42077741d9e3618153ca}{
228RV \hyperlink{classbdm_1_1BM_18d6db4af8ee42077741d9e3618153ca}{rv}}
229\label{classbdm_1_1BM_18d6db4af8ee42077741d9e3618153ca}
[33]230
231\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
[255]232\hypertarget{classbdm_1_1BM_4064b6559d962633e4372b12f4cd204a}{
233double \hyperlink{classbdm_1_1BM_4064b6559d962633e4372b12f4cd204a}{ll}}
234\label{classbdm_1_1BM_4064b6559d962633e4372b12f4cd204a}
[33]235
236\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
[255]237\hypertarget{classbdm_1_1BM_faff0ad12556fe7dc0e2807d4fd938ee}{
238bool \hyperlink{classbdm_1_1BM_faff0ad12556fe7dc0e2807d4fd938ee}{evalll}}
239\label{classbdm_1_1BM_faff0ad12556fe7dc0e2807d4fd938ee}
[33]240
[172]241\begin{CompactList}\small\item\em If true, the filter will compute likelihood of the data record and store it in {\tt ll} . Set to false if you want to save computational time. \item\end{CompactList}\item 
[255]242\hypertarget{classbdm_1_1BMcond_9a12750776d977408aada06a70093297}{
243RV \hyperlink{classbdm_1_1BMcond_9a12750776d977408aada06a70093297}{rvc}}
244\label{classbdm_1_1BMcond_9a12750776d977408aada06a70093297}
[33]245
246\begin{CompactList}\small\item\em Identificator of the conditioning variable. \item\end{CompactList}\end{CompactItemize}
247
248
249\subsection{Detailed Description}
[255]250Extended Kalman filter with unknown {\tt Q}.
[234]251
[172]252\subsection{Member Function Documentation}
[255]253\hypertarget{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}{
[224]254\index{EKFCh\_\-cond@{EKFCh\_\-cond}!logpred@{logpred}}
255\index{logpred@{logpred}!EKFCh_cond@{EKFCh\_\-cond}}
[255]256\subsubsection[logpred]{\setlength{\rightskip}{0pt plus 5cm}virtual double bdm::BM::logpred (const vec \& {\em dt}) const\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
257\label{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}
[172]258
259
260Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.
261
[255]262Reimplemented in \hyperlink{classbdm_1_1ARX_080a7e531e3aa06694112863b15bc6a4}{bdm::ARX}, \hyperlink{classbdm_1_1MixEF_da724da464a75e07521941e430929efa}{bdm::MixEF}, and \hyperlink{classbdm_1_1multiBM_e157b607c1e3fa91d42aeea44458e2bf}{bdm::multiBM}.
[180]263
[255]264Referenced by bdm::BM::logpred\_\-m().\hypertarget{classbdm_1_1BM_3efb3098172f1f67564a312fe732473e}{
[224]265\index{EKFCh\_\-cond@{EKFCh\_\-cond}!\_\-copy\_\-@{\_\-copy\_\-}}
266\index{\_\-copy\_\-@{\_\-copy\_\-}!EKFCh_cond@{EKFCh\_\-cond}}
[255]267\subsubsection[\_\-copy\_\-]{\setlength{\rightskip}{0pt plus 5cm}virtual BM$\ast$ bdm::BM::\_\-copy\_\- (bool {\em changerv} = {\tt false})\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
268\label{classbdm_1_1BM_3efb3098172f1f67564a312fe732473e}
[172]269
270
[255]271Copy function required in vectors, Arrays of \hyperlink{classbdm_1_1BM}{BM} etc. Have to be DELETED manually! Prototype: BM$\ast$ \hyperlink{classbdm_1_1BM_3efb3098172f1f67564a312fe732473e}{\_\-copy\_\-()}\{\hyperlink{classbdm_1_1BM}{BM} Tmp$\ast$=new Tmp(this$\ast$); return Tmp; \} 
[172]272
[255]273Reimplemented in \hyperlink{classbdm_1_1ARX_20ff2de8d862f28de7da83444d65bcdb}{bdm::ARX}, and \hyperlink{classbdm_1_1BMEF_5912dbcf28ae711e30b08c2fa766a3e6}{bdm::BMEF}.
[172]274
[255]275The documentation for this class was generated from the following file:\begin{CompactItemize}
[33]276\item 
[224]277work/git/mixpp/pmsm/\hyperlink{mpf__u__weight_8cpp}{mpf\_\-u\_\-weight.cpp}\end{CompactItemize}
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