3 | | \item\contentsline{section}{{\bf ARX} (Linear Autoregressive model with Gaussian noise )}{\pageref{classARX}}{} |
4 | | \item\contentsline{section}{{\bf AssertXercesIsAlive} (Class initializing Xerces library )}{\pageref{classAssertXercesIsAlive}}{} |
5 | | \item\contentsline{section}{{\bf Attribute} (Abstract class declaring general properties of a frame for data binding )}{\pageref{classAttribute}}{} |
6 | | \item\contentsline{section}{{\bf bilinfn} (Class representing function $f(x,u) = Ax+Bu$ )}{\pageref{classbilinfn}}{} |
7 | | \item\contentsline{section}{{\bf BindingFrame} (Abstract class declaring general properties of a frame for data binding )}{\pageref{classBindingFrame}}{} |
8 | | \item\contentsline{section}{{\bf BM} (Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities )}{\pageref{classBM}}{} |
9 | | \item\contentsline{section}{{\bf BMcond} (Conditional Bayesian Filter )}{\pageref{classBMcond}}{} |
10 | | \item\contentsline{section}{{\bf chmat} (Symmetric matrix stored in square root decomposition using upper cholesky )}{\pageref{classchmat}}{} |
11 | | \item\contentsline{section}{{\bf CompoundUserInfo$<$ T $>$} (The main userinfo template class. You should derive this class whenever you need a new userinfo of a class which is compound from smaller elements (all having its own userinfo class prepared) )}{\pageref{classCompoundUserInfo}}{} |
12 | | \item\contentsline{section}{{\bf CompoundUserInfo$<$ T $>$::BindedElement$<$ U $>$} (Templated class binding inner element with its XML tag and automating data transfers in both directions )}{\pageref{classCompoundUserInfo_1_1BindedElement}}{} |
13 | | \item\contentsline{section}{{\bf constfn} (Class representing function $f(x) = a$, here {\tt rv} is empty )}{\pageref{classconstfn}}{} |
14 | | \item\contentsline{section}{{\bf diffbifn} (Class representing a differentiable function of two variables $f(x,u)$ )}{\pageref{classdiffbifn}}{} |
15 | | \item\contentsline{section}{{\bf dirfilelog} (Logging into dirfile with buffer in memory )}{\pageref{classdirfilelog}}{} |
16 | | \item\contentsline{section}{{\bf DS} (Abstract class for discrete-time sources of data )}{\pageref{classDS}}{} |
17 | | \item\contentsline{section}{{\bf eEF} (General conjugate exponential family posterior density )}{\pageref{classeEF}}{} |
18 | | \item\contentsline{section}{{\bf eEmp} (Weighted empirical density )}{\pageref{classeEmp}}{} |
19 | | \item\contentsline{section}{{\bf egamma} (Gamma posterior density )}{\pageref{classegamma}}{} |
20 | | \item\contentsline{section}{{\bf egiw} (Gauss-inverse-Wishart density stored in LD form )}{\pageref{classegiw}}{} |
21 | | \item\contentsline{section}{{\bf EKF$<$ sq\_\-T $>$} (Extended \doxyref{Kalman}{p.}{classKalman} Filter )}{\pageref{classEKF}}{} |
22 | | \item\contentsline{section}{{\bf EKF\_\-unQ} (Extended \doxyref{Kalman}{p.}{classKalman} filter with unknown {\tt Q} )}{\pageref{classEKF__unQ}}{} |
23 | | \item\contentsline{section}{{\bf EKFCh} (Extended \doxyref{Kalman}{p.}{classKalman} Filter in Square root )}{\pageref{classEKFCh}}{} |
24 | | \item\contentsline{section}{{\bf EKFfixed} (Extended \doxyref{Kalman}{p.}{classKalman} Filter with full matrices in fixed point arithmetic )}{\pageref{classEKFfixed}}{} |
25 | | \item\contentsline{section}{{\bf EKFful\_\-unQR} (Extended \doxyref{Kalman}{p.}{classKalman} filter with unknown {\tt Q} and {\tt R} )}{\pageref{classEKFful__unQR}}{} |
26 | | \item\contentsline{section}{{\bf EKFfull} (Extended \doxyref{Kalman}{p.}{classKalman} Filter in full matrices )}{\pageref{classEKFfull}}{} |
27 | | \item\contentsline{section}{{\bf emix} (Mixture of epdfs )}{\pageref{classemix}}{} |
28 | | \item\contentsline{section}{{\bf enorm$<$ sq\_\-T $>$} (Gaussian density with positive definite (decomposed) covariance matrix )}{\pageref{classenorm}}{} |
29 | | \item\contentsline{section}{{\bf epdf} (Probability density function with numerical statistics, e.g. posterior density )}{\pageref{classepdf}}{} |
30 | | \item\contentsline{section}{{\bf euni} (Uniform distributed density on a rectangular support )}{\pageref{classeuni}}{} |
31 | | \item\contentsline{section}{{\bf fnc} (Class representing function $f(x)$ of variable $x$ represented by {\tt rv} )}{\pageref{classfnc}}{} |
32 | | \item\contentsline{section}{{\bf fsqmat} (Fake \doxyref{sqmat}{p.}{classsqmat}. This class maps \doxyref{sqmat}{p.}{classsqmat} operations to operations on full matrix )}{\pageref{classfsqmat}}{} |
33 | | \item\contentsline{section}{{\bf itpp::Gamma\_\-RNG} (Gamma distribution )}{\pageref{classitpp_1_1Gamma__RNG}}{} |
34 | | \item\contentsline{section}{{\bf IMpmsm} (State evolution model for a PMSM drive and its derivative with respect to $x$\$ )}{\pageref{classIMpmsm}}{} |
35 | | \item\contentsline{section}{{\bf IMpmsmStat} (State evolution model for a PMSM drive and its derivative with respect to $x$, equation for $\omega$ is omitted.\$ )}{\pageref{classIMpmsmStat}}{} |
36 | | \item\contentsline{section}{{\bf Kalman$<$ sq\_\-T $>$} (\doxyref{Kalman}{p.}{classKalman} filter with covariance matrices in square root form )}{\pageref{classKalman}}{} |
37 | | \item\contentsline{section}{{\bf KalmanCh} (\doxyref{Kalman}{p.}{classKalman} filter in square root form )}{\pageref{classKalmanCh}}{} |
38 | | \item\contentsline{section}{{\bf KalmanFull} (Basic \doxyref{Kalman}{p.}{classKalman} filter with full matrices (education purpose only)! Will be deleted soon! )}{\pageref{classKalmanFull}}{} |
39 | | \item\contentsline{section}{{\bf KFcondQR} (\doxyref{Kalman}{p.}{classKalman} Filter with conditional diagonal matrices R and Q )}{\pageref{classKFcondQR}}{} |
40 | | \item\contentsline{section}{{\bf KFcondR} (\doxyref{Kalman}{p.}{classKalman} Filter with conditional diagonal matrices R and Q )}{\pageref{classKFcondR}}{} |
41 | | \item\contentsline{section}{{\bf ldmat} (Matrix stored in LD form, (typically known as UD) )}{\pageref{classldmat}}{} |
42 | | \item\contentsline{section}{{\bf linfn} (Class representing function $f(x) = Ax+B$ )}{\pageref{classlinfn}}{} |
43 | | \item\contentsline{section}{{\bf logger} (Class for storing results (and semi-results) of an experiment )}{\pageref{classlogger}}{} |
44 | | \item\contentsline{section}{{\bf mEF} (Exponential family model )}{\pageref{classmEF}}{} |
45 | | \item\contentsline{section}{{\bf MemDS} (Class representing off-line data stored in memory )}{\pageref{classMemDS}}{} |
46 | | \item\contentsline{section}{{\bf memlog} (Logging into matrices in data format in memory )}{\pageref{classmemlog}}{} |
47 | | \item\contentsline{section}{{\bf mepdf} (Unconditional \doxyref{mpdf}{p.}{classmpdf}, allows using \doxyref{epdf}{p.}{classepdf} in the role of \doxyref{mpdf}{p.}{classmpdf} )}{\pageref{classmepdf}}{} |
48 | | \item\contentsline{section}{{\bf merger} (Function for general combination of pdfs )}{\pageref{classmerger}}{} |
49 | | \item\contentsline{section}{{\bf mgamma} (Gamma random walk )}{\pageref{classmgamma}}{} |
50 | | \item\contentsline{section}{{\bf mgamma\_\-fix} (Gamma random walk around a fixed point )}{\pageref{classmgamma__fix}}{} |
51 | | \item\contentsline{section}{{\bf mlnorm$<$ sq\_\-T $>$} (Normal distributed linear function with linear function of mean value; )}{\pageref{classmlnorm}}{} |
52 | | \item\contentsline{section}{{\bf mmix} (Mixture of mpdfs with constant weights, all mpdfs are of equal type )}{\pageref{classmmix}}{} |
53 | | \item\contentsline{section}{{\bf mpdf} (Conditional probability density, e.g. modeling some dependencies )}{\pageref{classmpdf}}{} |
54 | | \item\contentsline{section}{{\bf MPF$<$ BM\_\-T $>$} (Marginalized Particle filter )}{\pageref{classMPF}}{} |
55 | | \item\contentsline{section}{{\bf mprod} (Chain rule decomposition of \doxyref{epdf}{p.}{classepdf} )}{\pageref{classmprod}}{} |
56 | | \item\contentsline{section}{{\bf OMpmsm} (Observation model for PMSM drive and its derivative with respect to $x$ )}{\pageref{classOMpmsm}}{} |
57 | | \item\contentsline{section}{{\bf PF} (Trivial particle filter with proposal density equal to parameter evolution model )}{\pageref{classPF}}{} |
58 | | \item\contentsline{section}{{\bf RootElement} (This class serves to load and/or save DOMElements into/from files stored on a hard-disk )}{\pageref{classRootElement}}{} |
59 | | \item\contentsline{section}{{\bf RV} (Class representing variables, most often random variables )}{\pageref{classRV}}{} |
60 | | \item\contentsline{section}{{\bf sqmat} (Virtual class for representation of double symmetric matrices in square-root form )}{\pageref{classsqmat}}{} |
61 | | \item\contentsline{section}{{\bf str} (Structure of \doxyref{RV}{p.}{classRV} (used internally) )}{\pageref{classstr}}{} |
62 | | \item\contentsline{section}{{\bf TypedUserInfo$<$ T $>$} (TypeUserInfo is still an abstract class, but contrary to the \doxyref{UserInfo}{p.}{classUserInfo} class it is already templated. It serves as a bridge to non-abstract classes CompoundUserInfo$<$T$>$ or ValuedUserInfo$<$T$>$ )}{\pageref{classTypedUserInfo}}{} |
63 | | \item\contentsline{section}{{\bf UserInfo} (\doxyref{UserInfo}{p.}{classUserInfo} is an abstract is for internal purposes only. Use CompoundUserInfo$<$T$>$ or ValuedUserInfo$<$T$>$ instead. The raison d'etre of this class is to allow pointers to its templated descendants )}{\pageref{classUserInfo}}{} |
64 | | \item\contentsline{section}{{\bf ValuedUserInfo$<$ T $>$} (The main userinfo template class. It should be derived whenever you need a new userinfo of a class which does not contain any subelements. It is the case of basic classes(or types) like int, string, double, etc )}{\pageref{classValuedUserInfo}}{} |
| 3 | \item\contentsline{section}{\hyperlink{classARX}{ARX} (Linear Autoregressive model with Gaussian noise )}{\pageref{classARX}}{} |
| 4 | \item\contentsline{section}{\hyperlink{classAssertXercesIsAlive}{AssertXercesIsAlive} (Class initializing Xerces library )}{\pageref{classAssertXercesIsAlive}}{} |
| 5 | \item\contentsline{section}{\hyperlink{classAttribute}{Attribute} (Abstract class declaring general properties of a frame for data binding )}{\pageref{classAttribute}}{} |
| 6 | \item\contentsline{section}{\hyperlink{classbilinfn}{bilinfn} (Class representing function $f(x,u) = Ax+Bu$ )}{\pageref{classbilinfn}}{} |
| 7 | \item\contentsline{section}{\hyperlink{classBindingFrame}{BindingFrame} (Abstract class declaring general properties of a frame for data binding )}{\pageref{classBindingFrame}}{} |
| 8 | \item\contentsline{section}{\hyperlink{classBM}{BM} (Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities )}{\pageref{classBM}}{} |
| 9 | \item\contentsline{section}{\hyperlink{classBMcond}{BMcond} (Conditional Bayesian Filter )}{\pageref{classBMcond}}{} |
| 10 | \item\contentsline{section}{\hyperlink{classBMEF}{BMEF} (Estimator for Exponential family )}{\pageref{classBMEF}}{} |
| 11 | \item\contentsline{section}{\hyperlink{classchmat}{chmat} (Symmetric matrix stored in square root decomposition using upper cholesky )}{\pageref{classchmat}}{} |
| 12 | \item\contentsline{section}{\hyperlink{classCompoundUserInfo}{CompoundUserInfo$<$ T $>$} (The main userinfo template class. You should derive this class whenever you need a new userinfo of a class which is compound from smaller elements (all having its own userinfo class prepared) )}{\pageref{classCompoundUserInfo}}{} |
| 13 | \item\contentsline{section}{\hyperlink{classCompoundUserInfo_1_1BindedElement}{CompoundUserInfo$<$ T $>$::BindedElement$<$ U $>$} (Templated class binding inner element with its XML tag and automating data transfers in both directions )}{\pageref{classCompoundUserInfo_1_1BindedElement}}{} |
| 14 | \item\contentsline{section}{\hyperlink{classconstfn}{constfn} (Class representing function $f(x) = a$, here {\tt rv} is empty )}{\pageref{classconstfn}}{} |
| 15 | \item\contentsline{section}{\hyperlink{classdiffbifn}{diffbifn} (Class representing a differentiable function of two variables $f(x,u)$ )}{\pageref{classdiffbifn}}{} |
| 16 | \item\contentsline{section}{\hyperlink{classdirfilelog}{dirfilelog} (Logging into dirfile with buffer in memory )}{\pageref{classdirfilelog}}{} |
| 17 | \item\contentsline{section}{\hyperlink{classDS}{DS} (Abstract class for discrete-time sources of data )}{\pageref{classDS}}{} |
| 18 | \item\contentsline{section}{\hyperlink{classeDirich}{eDirich} (Dirichlet posterior density )}{\pageref{classeDirich}}{} |
| 19 | \item\contentsline{section}{\hyperlink{classeEF}{eEF} (General conjugate exponential family posterior density )}{\pageref{classeEF}}{} |
| 20 | \item\contentsline{section}{\hyperlink{classeEmp}{eEmp} (Weighted empirical density )}{\pageref{classeEmp}}{} |
| 21 | \item\contentsline{section}{\hyperlink{classegamma}{egamma} (Gamma posterior density )}{\pageref{classegamma}}{} |
| 22 | \item\contentsline{section}{\hyperlink{classegiw}{egiw} (Gauss-inverse-Wishart density stored in LD form )}{\pageref{classegiw}}{} |
| 23 | \item\contentsline{section}{\hyperlink{classEKF}{EKF$<$ sq\_\-T $>$} (Extended \hyperlink{classKalman}{Kalman} Filter )}{\pageref{classEKF}}{} |
| 24 | \item\contentsline{section}{\hyperlink{classEKF__unQ}{EKF\_\-unQ} (Extended \hyperlink{classKalman}{Kalman} filter with unknown {\tt Q} )}{\pageref{classEKF__unQ}}{} |
| 25 | \item\contentsline{section}{\hyperlink{classEKFCh}{EKFCh} (Extended \hyperlink{classKalman}{Kalman} Filter in Square root )}{\pageref{classEKFCh}}{} |
| 26 | \item\contentsline{section}{\hyperlink{classEKFfixed}{EKFfixed} (Extended \hyperlink{classKalman}{Kalman} Filter with full matrices in fixed point arithmetic )}{\pageref{classEKFfixed}}{} |
| 27 | \item\contentsline{section}{\hyperlink{classEKFful__unQR}{EKFful\_\-unQR} (Extended \hyperlink{classKalman}{Kalman} filter with unknown {\tt Q} and {\tt R} )}{\pageref{classEKFful__unQR}}{} |
| 28 | \item\contentsline{section}{\hyperlink{classEKFfull}{EKFfull} (Extended \hyperlink{classKalman}{Kalman} Filter in full matrices )}{\pageref{classEKFfull}}{} |
| 29 | \item\contentsline{section}{\hyperlink{classemix}{emix} (Mixture of epdfs )}{\pageref{classemix}}{} |
| 30 | \item\contentsline{section}{\hyperlink{classenorm}{enorm$<$ sq\_\-T $>$} (Gaussian density with positive definite (decomposed) covariance matrix )}{\pageref{classenorm}}{} |
| 31 | \item\contentsline{section}{\hyperlink{classepdf}{epdf} (Probability density function with numerical statistics, e.g. posterior density )}{\pageref{classepdf}}{} |
| 32 | \item\contentsline{section}{\hyperlink{classeprod}{eprod} (Product of independent epdfs. For dependent pdfs, use \hyperlink{classmprod}{mprod} )}{\pageref{classeprod}}{} |
| 33 | \item\contentsline{section}{\hyperlink{classeuni}{euni} (Uniform distributed density on a rectangular support )}{\pageref{classeuni}}{} |
| 34 | \item\contentsline{section}{\hyperlink{classfnc}{fnc} (Class representing function $f(x)$ of variable $x$ represented by {\tt rv} )}{\pageref{classfnc}}{} |
| 35 | \item\contentsline{section}{\hyperlink{classfsqmat}{fsqmat} (Fake \hyperlink{classsqmat}{sqmat}. This class maps \hyperlink{classsqmat}{sqmat} operations to operations on full matrix )}{\pageref{classfsqmat}}{} |
| 36 | \item\contentsline{section}{\hyperlink{classitpp_1_1Gamma__RNG}{itpp::Gamma\_\-RNG} (Gamma distribution )}{\pageref{classitpp_1_1Gamma__RNG}}{} |
| 37 | \item\contentsline{section}{\hyperlink{classIMpmsm}{IMpmsm} (State evolution model for a PMSM drive and its derivative with respect to $x$\$ )}{\pageref{classIMpmsm}}{} |
| 38 | \item\contentsline{section}{\hyperlink{classIMpmsmStat}{IMpmsmStat} (State evolution model for a PMSM drive and its derivative with respect to $x$, equation for $\omega$ is omitted.\$ )}{\pageref{classIMpmsmStat}}{} |
| 39 | \item\contentsline{section}{\hyperlink{classKalman}{Kalman$<$ sq\_\-T $>$} (\hyperlink{classKalman}{Kalman} filter with covariance matrices in square root form )}{\pageref{classKalman}}{} |
| 40 | \item\contentsline{section}{\hyperlink{classKalmanCh}{KalmanCh} (\hyperlink{classKalman}{Kalman} filter in square root form )}{\pageref{classKalmanCh}}{} |
| 41 | \item\contentsline{section}{\hyperlink{classKalmanFull}{KalmanFull} (Basic \hyperlink{classKalman}{Kalman} filter with full matrices (education purpose only)! Will be deleted soon! )}{\pageref{classKalmanFull}}{} |
| 42 | \item\contentsline{section}{\hyperlink{classKFcondQR}{KFcondQR} (\hyperlink{classKalman}{Kalman} Filter with conditional diagonal matrices R and Q )}{\pageref{classKFcondQR}}{} |
| 43 | \item\contentsline{section}{\hyperlink{classKFcondR}{KFcondR} (\hyperlink{classKalman}{Kalman} Filter with conditional diagonal matrices R and Q )}{\pageref{classKFcondR}}{} |
| 44 | \item\contentsline{section}{\hyperlink{classldmat}{ldmat} (Matrix stored in LD form, (typically known as UD) )}{\pageref{classldmat}}{} |
| 45 | \item\contentsline{section}{\hyperlink{classlinfn}{linfn} (Class representing function $f(x) = Ax+B$ )}{\pageref{classlinfn}}{} |
| 46 | \item\contentsline{section}{\hyperlink{classlogger}{logger} (Class for storing results (and semi-results) of an experiment )}{\pageref{classlogger}}{} |
| 47 | \item\contentsline{section}{\hyperlink{classmEF}{mEF} (Exponential family model )}{\pageref{classmEF}}{} |
| 48 | \item\contentsline{section}{\hyperlink{classMemDS}{MemDS} (Class representing off-line data stored in memory )}{\pageref{classMemDS}}{} |
| 49 | \item\contentsline{section}{\hyperlink{classmemlog}{memlog} (Logging into matrices in data format in memory )}{\pageref{classmemlog}}{} |
| 50 | \item\contentsline{section}{\hyperlink{classmepdf}{mepdf} (Unconditional \hyperlink{classmpdf}{mpdf}, allows using \hyperlink{classepdf}{epdf} in the role of \hyperlink{classmpdf}{mpdf} )}{\pageref{classmepdf}}{} |
| 51 | \item\contentsline{section}{\hyperlink{classmerger}{merger} (Function for general combination of pdfs )}{\pageref{classmerger}}{} |
| 52 | \item\contentsline{section}{\hyperlink{classmgamma}{mgamma} (Gamma random walk )}{\pageref{classmgamma}}{} |
| 53 | \item\contentsline{section}{\hyperlink{classmgamma__fix}{mgamma\_\-fix} (Gamma random walk around a fixed point )}{\pageref{classmgamma__fix}}{} |
| 54 | \item\contentsline{section}{\hyperlink{classMixEF}{MixEF} (Mixture of Exponential Family Densities )}{\pageref{classMixEF}}{} |
| 55 | \item\contentsline{section}{\hyperlink{classmlnorm}{mlnorm$<$ sq\_\-T $>$} (Normal distributed linear function with linear function of mean value; )}{\pageref{classmlnorm}}{} |
| 56 | \item\contentsline{section}{\hyperlink{classmmix}{mmix} (Mixture of mpdfs with constant weights, all mpdfs are of equal type )}{\pageref{classmmix}}{} |
| 57 | \item\contentsline{section}{\hyperlink{classmpdf}{mpdf} (Conditional probability density, e.g. modeling some dependencies )}{\pageref{classmpdf}}{} |
| 58 | \item\contentsline{section}{\hyperlink{classMPF}{MPF$<$ BM\_\-T $>$} (Marginalized Particle filter )}{\pageref{classMPF}}{} |
| 59 | \item\contentsline{section}{\hyperlink{classmprod}{mprod} (Chain rule decomposition of \hyperlink{classepdf}{epdf} )}{\pageref{classmprod}}{} |
| 60 | \item\contentsline{section}{\hyperlink{classmultiBM}{multiBM} (Estimator for Multinomial density )}{\pageref{classmultiBM}}{} |
| 61 | \item\contentsline{section}{\hyperlink{classOMpmsm}{OMpmsm} (Observation model for PMSM drive and its derivative with respect to $x$ )}{\pageref{classOMpmsm}}{} |
| 62 | \item\contentsline{section}{\hyperlink{classPF}{PF} (Trivial particle filter with proposal density equal to parameter evolution model )}{\pageref{classPF}}{} |
| 63 | \item\contentsline{section}{\hyperlink{classRootElement}{RootElement} (This class serves to load and/or save DOMElements into/from files stored on a hard-disk )}{\pageref{classRootElement}}{} |
| 64 | \item\contentsline{section}{\hyperlink{classRV}{RV} (Class representing variables, most often random variables )}{\pageref{classRV}}{} |
| 65 | \item\contentsline{section}{\hyperlink{classsqmat}{sqmat} (Virtual class for representation of double symmetric matrices in square-root form )}{\pageref{classsqmat}}{} |
| 66 | \item\contentsline{section}{\hyperlink{classstr}{str} (Structure of \hyperlink{classRV}{RV} (used internally) )}{\pageref{classstr}}{} |
| 67 | \item\contentsline{section}{\hyperlink{classTypedUserInfo}{TypedUserInfo$<$ T $>$} (TypeUserInfo is still an abstract class, but contrary to the \hyperlink{classUserInfo}{UserInfo} class it is already templated. It serves as a bridge to non-abstract classes CompoundUserInfo$<$T$>$ or ValuedUserInfo$<$T$>$ )}{\pageref{classTypedUserInfo}}{} |
| 68 | \item\contentsline{section}{\hyperlink{classUserInfo}{UserInfo} (\hyperlink{classUserInfo}{UserInfo} is an abstract is for internal purposes only. Use CompoundUserInfo$<$T$>$ or ValuedUserInfo$<$T$>$ instead. The raison d'etre of this class is to allow pointers to its templated descendants )}{\pageref{classUserInfo}}{} |
| 69 | \item\contentsline{section}{\hyperlink{classValuedUserInfo}{ValuedUserInfo$<$ T $>$} (The main userinfo template class. It should be derived whenever you need a new userinfo of a class which does not contain any subelements. It is the case of basic classes(or types) like int, string, double, etc )}{\pageref{classValuedUserInfo}}{} |