use ingroup instead of addtogroup

release/4.3a0
Varun Agrawal 2022-07-26 17:37:50 -04:00
parent fbe4bf867e
commit efdbee2a76
7 changed files with 7 additions and 7 deletions

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@ -27,7 +27,7 @@ namespace gtsam {
/**
* A BayesNet is a tree of conditionals, stored in elimination order.
* @addtogroup inference
* @ingroup inference
*/
template <class CONDITIONAL>
class BayesNet : public FactorGraph<CONDITIONAL> {

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@ -30,7 +30,7 @@ namespace gtsam {
/**
* @brief DotWriter is a helper class for writing graphviz .dot files.
* @addtogroup inference
* @ingroup inference
*/
struct GTSAM_EXPORT DotWriter {
double figureWidthInches; ///< The figure width on paper in inches

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@ -30,7 +30,7 @@ namespace gtsam {
/**
* GaussianBayesNet is a Bayes net made from linear-Gaussian conditionals.
* @addtogroup linear
* @ingroup linear
*/
class GTSAM_EXPORT GaussianBayesNet: public BayesNet<GaussianConditional>
{

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@ -35,7 +35,7 @@ namespace gtsam {
* A GaussianConditional functions as the node in a Bayes network.
* It has a set of parents y,z, etc. and implements a probability density on x.
* The negative log-probability is given by \f$ \frac{1}{2} |Rx - (d - Sy - Tz - ...)|^2 \f$
* @addtogroup linear
* @ingroup linear
*/
class GTSAM_EXPORT GaussianConditional :
public JacobianFactor,

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@ -27,7 +27,7 @@ namespace gtsam {
* A GaussianDensity is a GaussianConditional without parents.
* The negative log-probability is given by \f$ |Rx - d|^2 \f$
* with \f$ \Lambda = \Sigma^{-1} = R^T R \f$ and \f$ \mu = R^{-1} d \f$
* @addtogroup linear
* @ingroup linear
*/
class GTSAM_EXPORT GaussianDensity : public GaussianConditional {

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@ -69,7 +69,7 @@ namespace gtsam {
* which is a view on the underlying data structure.
*
* This class is additionally used in gradient descent and dog leg to store the gradient.
* @addtogroup linear
* @ingroup linear
*/
class GTSAM_EXPORT VectorValues {
protected:

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@ -27,7 +27,7 @@ namespace gtsam {
/**
* A SymbolicBayesNet is a Bayes Net of purely symbolic conditionals.
* @addtogroup symbolic
* @ingroup symbolic
*/
class SymbolicBayesNet : public BayesNet<SymbolicConditional> {
public: