diff --git a/.gitattributes b/.gitattributes
index 34e67a2b662..5548b1375e2 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -440,7 +440,7 @@ Jet_fitting_3/demo/Jet_fitting_3/data/venus.off -text
Jet_fitting_3/doc_tex/AIDE -text
Jet_fitting_3/doc_tex/Jet_fitting_3/Maple_formula.mw -text
Jet_fitting_3/doc_tex/Jet_fitting_3/jet_fitting_basis.eps -text
-Jet_fitting_3/doc_tex/Jet_fitting_3/jet_fitting_basis.jpg -text svneol=unset#unset
+Jet_fitting_3/doc_tex/Jet_fitting_3/jet_fitting_basis.gif -text
Jet_fitting_3/doc_tex/Jet_fitting_3/jet_fitting_basis.pdf -text svneol=unset#unset
Jet_fitting_3/doc_tex/Jet_fitting_3_ref/template_dependence.eps -text
Jet_fitting_3/doc_tex/Jet_fitting_3_ref/template_dependence.jpg -text
diff --git a/Jet_fitting_3/doc_tex/Jet_fitting_3/Jet_fitting_3_user.tex b/Jet_fitting_3/doc_tex/Jet_fitting_3/Jet_fitting_3_user.tex
index 82811470326..985cff6bce9 100644
--- a/Jet_fitting_3/doc_tex/Jet_fitting_3/Jet_fitting_3_user.tex
+++ b/Jet_fitting_3/doc_tex/Jet_fitting_3/Jet_fitting_3_user.tex
@@ -172,6 +172,7 @@ $O(h)$ of the point where the calculation is carried out.
The following theorem, proved in \cite{cgal:cp-edqpf-05}, provides the
asymptotic error estimates ---which are the best known to date:
%%
+
\begin{theorem}
A polynomial fitting of degree $d$ estimates any $k^{th}$-order
differential quantity to accuracy $O(h^{d-k+1})$~:
@@ -284,7 +285,7 @@ $(f_x,f_y,f_z)$, the monge-basis $(d_1,d_2,n)$.
\begin{ccHtmlOnly}
-
+
\end{ccHtmlOnly}
\end{figure}
@@ -325,19 +326,13 @@ significantly smaller.
\paragraph{Implementation details.}
-Given a symmetric matrix $M$, we assume the following function:
+We assume a \ccc{eigen_symm_algo} function is provided by the traits
+\ccc{LinAlgTraits}.
%%
-\begin{verbatim}
-void eigen_symm_algo(const LAMatrix& M, LAVector& eigen_vals, LAMatrix& eigen_vecs)
-\end{verbatim}
-%%
-This function computes the eigenvalues and eigenvectors of matrix $M$.
-The eigenvalues are stored in the vector eigen\_vals and are in
-decreasing order. The corresponding eigenvectors are stored in the
-columns of the matrix eigen\_vecs. For example, the eigenvector in the
-first column corresponds to the first (and largest) eigenvalue. The
-eigenvectors are guaranteed to be mutually orthogonal and normalised
-to unit magnitude.
+This function computes the eigenvalues and eigenvectors of a real
+symmetric matrix. Eigen values are sorted in ascending order, eigen
+vectors are sorted in accordance. The eigenvectors are guaranteed to
+be mutually orthogonal and normalised to unit magnitude.
\subsection{Solving the interpolation / approximation problem}
\label{sec:solving}
@@ -352,14 +347,13 @@ translation ($-p$) and multiplication by $ P_{W\rightarrow F}$.
We solve the system $MA=Z$, in the least square sense for
-approximation, with a function {\tt solve\_ls\_svd}. There is a
-preconditioning of the matrix $M$ so as to improve the condition
-number. Assuming the $\{x_i\}$, $\{y_i\}$ are of order $h$, the
-pre-conditioning consists of performing a column scaling by dividing
-each monomial $x_i^ky_i^l$ by $h^{k+l}$ ---refer to
-Eq. (\ref{eq:fit-linalg}). Practically, the parameter $h$ is chosen as
-the mean value of the $\{x_i\}$ and $\{y_i\}$. In other words, the new
-system is $M'Y=(MD^{-1}(DA)=Z$ with $D$ the diagonal matrix
+approximation. There is a preconditioning of the matrix $M$ so as to
+improve the condition number. Assuming the $\{x_i\}$, $\{y_i\}$ are of
+order $h$, the pre-conditioning consists of performing a column
+scaling by dividing each monomial $x_i^ky_i^l$ by $h^{k+l}$ ---refer
+to Eq. (\ref{eq:fit-linalg}). Practically, the parameter $h$ is chosen
+as the mean value of the $\{x_i\}$ and $\{y_i\}$. In other words, the
+new system is $M'Y=(MD^{-1}(DA)=Z$ with $D$ the diagonal matrix
$D=(1,h,h,h^2,\ldots,h^d,h^d)$, so that the solution $A$ of the
original system is $A=D^{-1}Y$.
@@ -397,22 +391,15 @@ that is the smallest singular value is zero. Then, an exception is
raised.
\paragraph{Implementation details.}
-We assume function:
-\begin{verbatim}
-void solve_ls_svd_algo(const LAMatrix& M, const LAVector& B, Vector& X, double& cond_nb)
-\end{verbatim}
- %%
-This function first factorizes the m-by-n matrix M into the singular
-value decomposition $M = U S V^T$ for $m \geq n$. Then it solves the
-system $MX = B$ in the least square sense using the singular value
-decomposition (U, S, V) of M. The condition number of the matrix M
-which is the ratio of the largest and the smallest singular values is
-stored in $cond_{nb}$.
+We assume a \ccc{solve_ls_svd_algo} function is provided by the traits
+\ccc{LinAlgTraits}. This function solves the system MX=B (in the least square sense
+if M is not square) using a Singular Value Decomposition and gives the
+condition number of M.
\medskip
Remark: as an alternative, other methods may be used to solve the
system. A $QR$ decomposition can be substituted to the $SVD$. One can
-also use the normal equation $M^TMA=MTZ$ and apply methods for square
+also use the normal equation $M^TMX=MTB$ and apply methods for square
systems such as $LU$, $QR$ or Cholesky since $M^TM$ is symmetric
definite positive when $M$ has full rank.
%LU suitable for any square M
@@ -551,7 +538,7 @@ four, that is, we assume $d' \leq 4$.
\medskip
Regarding interpolation versus approximation, we provide a single
-function {\tt Monge\_via\_jet\_fitting} with parameters $d,d'$ and a
+function \ccc{Monge_via_jet_fitting} with parameters $d,d'$ and a
range iterator. If $N=N_d$ then interpolation is performed, else
$N > N_d$ and approximation is used.
@@ -610,31 +597,31 @@ The following picture illustrates the template dependencies.
\begin{ccHtmlOnly}
-
+
\end{ccHtmlOnly}
\end{figure}
More details are given in the reference manual.
-\subsubsection{Template class {\tt Data\_Kernel}}
+\subsubsection{Template class \ccc{Data_Kernel}}
%%%%%%%%%%%
This class provides the types for the input sample points, together
with $3d$ vectors and a number type. It is used as template for the
-Monge\_rep. Typically, one can use {\tt CGAL::Cartesian}.
+\ccc{Monge_rep}. Typically, one can use \ccc{CGAL::Cartesian}.
-\subsubsection{Template class {\tt Local\_Kernel}}
+\subsubsection{Template class \ccc{Local_Kernel}}
%%%%%%%%%%%
This class defines the vector and number types used (i)\ for local
computations (ii)\ to store the Monge\_info class members. Input
-points of type Data\_Kernel::Point\_3 are converted to
-Local\_Kernel::Point\_3. For output of the Monge\_rep class, these
-types are converted back to Data\_Kernel ones. Typically, one can use
-{\tt CGAL::Cartesian}.
+points of type \ccc{Data_Kernel::Point_3} are converted to
+\ccc{Local_Kernel::Point_3}. For output of the \ccc{Monge_rep} class, these
+types are converted back to \ccc{Data_Kernel} ones. Typically, one can use
+\ccc{CGAL::Cartesian}.
-\subsubsection{Template class {\tt Linalg\_traits.}}
+\subsubsection{Template class \ccc{Linalg_traits.}}
%%%%%%%%%%%
This class provides the matrix algebra operations required by the
@@ -673,8 +660,9 @@ file to output the results, the degrees $d$ and $d'$.
\ccIncludeExampleCode{Jet_fitting_3/blind_1pt.C}
\paragraph{On a mesh.}
-The second example illustrates the computation of local differential
-quantities for all vertices of a given mesh. Results are twofold:
+The second example (cf blind.C in the exemple directory) illustrates
+the computation of local differential quantities for all vertices of a
+given mesh. Results are twofold:
\begin{itemize}
\item
a human readable text file featuring the Monge\_rep and the Monge\_info data;
@@ -683,4 +671,4 @@ another text file which may be visualised with the demo program
visu.exe displaying the Monge basis at each vertex of the mesh.
\end{itemize}
-\ccIncludeExampleCode{Jet_fitting_3/blind.C} %too long to be included?
+%\ccIncludeExampleCode{Jet_fitting_3/blind.C} %too long to be included?
diff --git a/Jet_fitting_3/doc_tex/Jet_fitting_3/jet_fitting_basis.gif b/Jet_fitting_3/doc_tex/Jet_fitting_3/jet_fitting_basis.gif
new file mode 100644
index 00000000000..bb565fae5b1
Binary files /dev/null and b/Jet_fitting_3/doc_tex/Jet_fitting_3/jet_fitting_basis.gif differ
diff --git a/Jet_fitting_3/doc_tex/Jet_fitting_3/jet_fitting_basis.jpg b/Jet_fitting_3/doc_tex/Jet_fitting_3/jet_fitting_basis.jpg
deleted file mode 100644
index 461a53945ee..00000000000
Binary files a/Jet_fitting_3/doc_tex/Jet_fitting_3/jet_fitting_basis.jpg and /dev/null differ
diff --git a/Jet_fitting_3/doc_tex/Jet_fitting_3/macro_perso.sty b/Jet_fitting_3/doc_tex/Jet_fitting_3/macro_perso.sty
index e38bc7858bf..36551caf9ad 100644
--- a/Jet_fitting_3/doc_tex/Jet_fitting_3/macro_perso.sty
+++ b/Jet_fitting_3/doc_tex/Jet_fitting_3/macro_perso.sty
@@ -1,2 +1,2 @@
\newtheorem{theorem}{Theorem.}
-\newcommand{\hot}[0]{h.o.t}
\ No newline at end of file
+\newcommand{\hot}{h.o.t}%[0]
diff --git a/Jet_fitting_3/doc_tex/Jet_fitting_3/main.tex b/Jet_fitting_3/doc_tex/Jet_fitting_3/main.tex
index 5fd56d6f0c6..36dbd1f361c 100644
--- a/Jet_fitting_3/doc_tex/Jet_fitting_3/main.tex
+++ b/Jet_fitting_3/doc_tex/Jet_fitting_3/main.tex
@@ -6,4 +6,4 @@ surfaces via polynomial fitting}
\minitoc
\input{Jet_fitting_3/Jet_fitting_3_user.tex}
-
\ No newline at end of file
+
diff --git a/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/LinAlgTraits.tex b/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/LinAlgTraits.tex
index 203caca6e83..7e61a083836 100644
--- a/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/LinAlgTraits.tex
+++ b/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/LinAlgTraits.tex
@@ -41,16 +41,18 @@ the \ccc{LocalKernel} concept~: \ccc{LocalKernel::FT}.
%\ccCreationVariable{a} %% choose variable name
%\ccConstructor{LinAlgTraits();}{default constructor.}
+\ccCreationVariable{matrix} %choose variable name
\ccOperations
The Matrix has classical access to its elements.
-\ccMethod{void set_elt(size_t i, size_t j, const FT value)}{}
+\ccMethod{void set_elt(size_t i, size_t j, const FT value);}{}
\ccGlue
-\ccMethod{FT get_elt(size_t i, size_t j)}{}
+\ccMethod{FT get_elt(size_t i, size_t j);}{}
The LinAlgTraits has an eigenanalysis and a singular value
decomposition algorithm.
+\ccCreationVariable{traits} %choose variable name
\ccMethod{void eigen_symm_algo(Matrix& S, FT* eval, Matrix&
evec);} {Performs an eigenanalysis of a real symmetric matrix. Eigen
diff --git a/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_info.tex b/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_info.tex
index 52979536a65..ab9780c107c 100644
--- a/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_info.tex
+++ b/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_info.tex
@@ -23,7 +23,7 @@ the computations performed by the class \ccc{Monge_via_jet_fitting}.
The \ccc{LocalKernel} template parameter must be the same for the
classes \ccc{Monge_info} and \ccc{Monge_via_jet_fitting}.
-\ccInclude{Monge_via_jet_fitting.h}
+\ccInclude{../include/CGAL/Monge_via_jet_fitting.h}
\ccTypes
% +--------------------------------------------------------------
diff --git a/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_rep.tex b/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_rep.tex
index 6779c63a004..0de6c448ae8 100644
--- a/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_rep.tex
+++ b/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_rep.tex
@@ -23,7 +23,7 @@ The class \ccRefName\ stores the Monge representation. The
same for the classes \ccc{Monge_rep} and
\ccc{Monge_via_jet_fitting}.
-\ccInclude{Monge_via_jet_fitting.h}
+\ccInclude{../include/CGAL/Monge_via_jet_fitting.h}
\ccTypes
% +--------------------------------------------------------------
diff --git a/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_via_jet_fitting.tex b/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_via_jet_fitting.tex
index b42c66233e8..48ddfb07c45 100644
--- a/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_via_jet_fitting.tex
+++ b/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/Monge_via_jet_fitting.tex
@@ -38,7 +38,7 @@ representation.
%\ccc{Monge_rep} and \ccc{Monge_info}.
-\ccInclude{Monge_via_jet_fitting.h}
+\ccInclude{../include/CGAL/Monge_via_jet_fitting.h}
\ccParameters
The class \ccRefName\ has three template parameters. Parameter
diff --git a/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/intro.tex b/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/intro.tex
index 53d0273ffcd..b53867ddaea 100644
--- a/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/intro.tex
+++ b/Jet_fitting_3/doc_tex/Jet_fitting_3_ref/intro.tex
@@ -14,7 +14,7 @@
surfaces via polynomial fitting}
\label{ref_chap:Jet_fitting_3}
-\ccChapterAuthor{Marc Pouget and Frédéric Cazals}
+\ccChapterAuthor{Marc Pouget and Frederic Cazals}
\subsection*{Introduction}
@@ -30,7 +30,7 @@ The following picture illustrates the template dependencies.
\begin{ccHtmlOnly}
-
+
\end{ccHtmlOnly}
\end{figure}