Merge branch 'doxygencorrections' of http://emmanuel@melaneum.com/otb-lib-repo...

Merge branch 'doxygencorrections' of http://emmanuel@melaneum.com/otb-lib-repo into doxygencorrections

Code/Learning/otbChangeProfileKernelFunctor.h

@@ -35,10 +35,10 @@ public:
 	 * \subdoxygen{otb}{KullbackLeiblerProfileImageFilter}.
 	 *
 	 * It is implemented as a polynomial kernel:
-	 * $\exp - \left( \gamma \left( \max_i | x_i - y_i | \right)^\text{degree} + \text{coef} \right)$.
+	 * $\exp - \left( \gamma \left( \max_i | x_i - y_i | \right)^\textrm{degree} + \textrm{coef} \right)$.
 	 *
-	 * The parameters $\gamma$, $\text{coef}$ and $\text{degree}$ are 
-	 * to be fixed through \code{SetValue} with keywords: Gamma (def 1.0),
+	 * The parameters $\gamma$, $\textrm{coef}$ and $\textrm{degree}$ are 
+	 * to be fixed through \code SetValue \endcode with keywords: Gamma (def 1.0),
 	 * Coef (def 1.0) and Degree (def 1.0).
 	 */
 	double operator() ( const svm_node * x, const svm_node * y,
@@ -47,7 +47,7 @@ public:
 	ChangeProfileKernelFunctor ();
 	virtual ~ChangeProfileKernelFunctor () { }
 
-	/** Specific implementation of \code{Update} to split m_MapParameters
+	/** Specific implementation of \code Update \endcode to split m_MapParameters
 	 * into specific variables to speed up kernel evaluations */
 	void Update ();
 

Code/Learning/otbCzihoSOMLearningBehaviorFunctor.h

@@ -36,21 +36,21 @@ namespace Functor {
 	*	PhD dissertation, University of Rennes I, Rennes, France. May 5th, 1999.
 	*
 	*	Its behavior is decomposed into two steps depending on the number of iterations:
-	*	\f{equation*}
-			\beta = \begn{cases}
-				\beta_0 \left( 1 - \frac{t}{t_0} \right) & \text{ if $t < t_0$} \\
-				\beta_{\text{end}} \left( 1- \frac{t-t_O}{t_{\text{end}}-t_0} \right) 
-					& \text{ if $ t_0 \leqslant t < t_{\text{end}}$}
-			\end{cases}
-		\f{equation*}
-		where \f$ t_0 \f$ stands for IterationThreshold.
+	*	\f[
+                \beta = 
+                \begin{cases} \beta_0 \left( 1 - \frac{t}{t_0} \right) & \textrm{ if $t < t_0$} 
+                \\
+                \beta_{\textrm{end}} \left( 1- \frac{t-t_O}{t_{\textrm{end}}-t_0} \right) & \textrm{ if $ t_0 \leqslant t < t_{\textrm{end}}$}
+                \end{cases}
+                \f]
+        * where \f$ t_0 \f$ stands for IterationThreshold.
 	*
 	*	CzihoSOMLearningBehaviorFunctor uses some parameters of the SOM class such as:
 	*	BetaInit, BetaEnd, NumberOfIterations, but also NeighborhoodSizeInit which may be 
 	*	(surprisingly) required for the IterationThreshold.
 	*
-	*	The functor function uses \code{NumberOfIterations}, \code{BetaInit}, \code{BetaEnd} parameters, that is 
-	*	why it is necessary to call a specific method for \code{IterationThreshold} intialisation.
+	*	The functor function uses \code NumberOfIterations \endcode, \code BetaInit \endcode, \code BetaEnd \endcode parameters, that is 
+	*	why it is necessary to call a specific method for \code IterationThreshold \endcode intialisation.
 	*
 	*	\sa SOM
 	*/

Code/Learning/otbCzihoSOMNeighborhoodBehaviorFunctor.h

@@ -34,14 +34,14 @@ namespace Functor {
 	*	"Compression d'images et analyse de contenu par quantification vectorielle"
 	*	PhD dissertation, University of Rennes I, Rennes, France. May 5th, 1999.
 	*
-	*	The behavior of the radius r (\em ie. \code{SizeType} component 0, 1,...) is given by:
-	*	\f{equation*}
-			r = r_{\text{init}} \left( 1 - \frac{t}{t_{\text{end}}} \right)^2
-		\f{equation*}
+	*	The behavior of the radius r (\em ie. \code SizeType \endcode component 0, 1,...) is given by:
+	*	\f$
+			r = r_{\textrm{init}} \left( 1 - \frac{t}{t_{\textrm{end}}} \right)^2
+		\f$
 	*
 	*	CzihoSOMNeighborhoodBehaviorFunctor uses some parameters of the SOM class such as:
-	*	\code{NeighborhoodSizeInit}, \code{NumberOfIterations} which are parameters of the functor function.
-	*	\code{operator()} is templated with the dimension of the neighborhoodSize.
+	*	\code NeighborhoodSizeInit \endcode, \code NumberOfIterations \endcode which are parameters of the functor function.
+	*	\code operator() \endcode is templated with the dimension of the neighborhoodSize.
 	*
 	*	\sa SOM
 	*/

Code/Learning/otbMixturePolyRBFKernelFunctor.h

@@ -35,7 +35,7 @@ public:
 	 * polynomial kernel and 
 	 * $k_2(x,y) = \exp\left( - \gamma_2 \| x-y\-^2 \right)$ an RBF one. 
 	 *
-	 * Variable to be instanciated (through \code{SetValue}) are:
+	 * Variable to be instanciated (through \code SetValue \endcode) are:
 	 * Mixture (def=0.5), GammaPoly (def=1.0), CoefPoly (def=1.0),
 	 * DegreePoly (def=2), GammaRBF (def=1.0) */
 	double operator() ( const svm_node * x, const svm_node * y,
@@ -44,7 +44,7 @@ public:
 	MixturePolyRBFKernelFunctor ();
 	virtual ~MixturePolyRBFKernelFunctor () { }
 
-	/** Specific implementation of \code{Update} to split m_MapParameters
+	/** Specific implementation of \code Update \endcode to split m_MapParameters
 	 * into specific variables to speed up kernel evaluations */
 	void Update ();
 

Code/Learning/otbNonGaussianRBFKernelFunctor.h

@@ -37,7 +37,7 @@ public:
 	 * where $0 \leqslant \alpha \leqslant 1$ and 
 	 * $0 \leqslant \beta \leqslant 2$.
 	 *
-	 * Variables to be instanciated (through \code{SetValue}) are:
+	 * Variables to be instanciated (through \code SetValue \endcode) are:
 	 * Alpha (def=1), Beta (def=2) and Gamma (def 1.0).
 	 * */
 	double operator() ( const svm_node * x, const svm_node * y,
@@ -46,7 +46,7 @@ public:
 	NonGaussianRBFKernelFunctor ();
 	virtual ~NonGaussianRBFKernelFunctor () { }
 
-	/** Specific implementation of \code{Update} to split m_MapParameters
+	/** Specific implementation of \code Update \endcode to split m_MapParameters
 	 * into specific variables to speed up kernel evaluations */
 	void Update ();
 	

Code/Learning/otbPeriodicSOM.h

@@ -38,10 +38,10 @@ namespace otb {
  * computed from an initial radius, and to a certain learning factor, decreasing at each iteration.
  *
  * The behavior of the neighborhood is given by a functor (templated) which parameter is the current
- * iteration. It returns a neighborhood of type \code{SizeType}.
+ * iteration. It returns a neighborhood of type \code SizeType \endcode.
  *
  * The behavior of the learning factor (hold by a beta variable) is given by an other functor 
- * which parameter is the current iteration. It returns a beta value of type \code{double}.
+ * which parameter is the current iteration. It returns a beta value of type \code double \endcode.
  *
  * The SOMMap produced as output can be either initialized with a constant custom value or randomly 
  * generated following a normal law. The seed for the random intialization can be modified.

Code/Learning/otbSOM.h

@@ -38,7 +38,7 @@ namespace otb
  * computed from an initial radius, and to a certain learning factor, decreasing at each iteration.
  *
  * The behavior of the neighborhood is given by a functor (templated) which parameter is the current
- * iteration. It returns a neighborhood of type \code{SizeType}.
+ * iteration. It returns a neighborhood of type \code SizeType \endcode.
  *
  * The behavior of the learning factor (hold by a beta variable) is given by an other functor 
  * which parameter is the current iteration. It returns a beta value of type double.

Code/Learning/otbSOMLearningBehaviorFunctor.h

@@ -29,13 +29,13 @@ namespace Functor {
 	/** \class SOMLearningBehaviorFunctor
 	* \brief Classical Beta behavior over SOM training phase
 	*
-	*	This class implements an standart evolution of the $\beta$ weightening
+	*	This class implements an standart evolution of the \f$ \beta \f$ weightening
 	*	coefficient over the SOM training. 
 	*
-	*	\f{equation*}
-		\beta = \beta_0 + \left( \beta_{\text{end}} - \beta_0 \right)
-			\frac{t}{t_{\text{end}}}
-		\f{equation*} 
+	*	\f$
+		\beta = \beta_0 + \left( \beta_{\textrm{end}} - \beta_0 \right)
+			\frac{t}{t_{\textrm{end}}}
+		\f$ 
 	*
 	*	\sa SOM
 	*/

Code/Learning/otbSpectralAngleKernelFunctor.h

@@ -33,11 +33,11 @@ public:
 	 * Performs an inverse multiquadric kernel evaluation that suit
 	 * spectral data.
 	 * It is of kind:
-	 * $\frac{1}{\sqrt{ c_0 + \cos\text{SAM(x,y)} }}$ where $\text{SAM(x,y)}$ is
+	 * $\frac{1}{\sqrt{ c_0 + \cos\textrm{SAM(x,y)} }}$ where $\textrm{SAM(x,y)}$ is
 	 * Spectral Angle Measure: 
-	 * $\text{SAM(x,y) = \acos\left( \frac{x \cdot y}{\|x\| \|y\|} \right)$.
+	 * $\textrm{SAM(x,y) = \acos\left( \frac{x \cdot y}{\|x\| \|y\|} \right)$.
 	 *
-	 * The only parameter $c_0$ is to be fixed by the \code{SetValue}
+	 * The only parameter $c_0$ is to be fixed by the \code SetValue \endcode
 	 * interface with keyword Coef (def 2.0).
 	 */
 	double operator() ( const svm_node * x, const svm_node * y,
@@ -46,7 +46,7 @@ public:
 	SpectralAngleKernelFunctor ();
 	virtual ~SpectralAngleKernelFunctor () { }
 
-	/** Specific implementation of \code{Update} to split m_MapParameters
+	/** Specific implementation of \code Update \endcode to split m_MapParameters
 	 * into specific variables to speed up kernel evaluations */
 	void Update ();