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Andreas Fabri 2003-11-16 21:42:52 +00:00
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48
Packages/Spatial_searching/doc_tex/Spatial_searching/intro.tex
View File

@ -2,9 +2,9 @@
\section{Introduction}
The {\bf spatial searching} package implements
exact and approximate distance browsing
by providing implementations of algorithms supporting
The spatial searching package implements exact and approximate
distance browsing by providing implementations of algorithms
supporting
\begin{itemize}
@ -90,7 +90,7 @@ neighbor must not be smaller than $r/(1+\epsilon)$. Obviously, for
$\epsilon=0$ we get the exact result, and the larger $\epsilon$ is,
the less exact the result.
\subsection{Range searching}
\subsection{Range Searching}
{\bf Exact range searching} and {\bf approximate range searching} is
supported using exact or fuzzy $d$-dimensional objects enclosing a
@ -218,7 +218,7 @@ coordinates.
\ccIncludeExampleCode{../../examples/Spatial_searching/Fuzzy_range_query.C}
\subsection{Example of Distance Browsing}
\subsection{Example of Incremental Searching}
This example program illustrates distance browsing for $4$-dimensional
points with a positive first coordinate using orthogonal priority
@ -241,7 +241,7 @@ in this file as well.
\ccIncludeExampleCode{../../examples/Spatial_searching/User_defined_point_and_distance.C}
\section{Software design}
\section{Software Design}
\begin{ccTexOnly}
\begin{figure}[t]
@ -331,7 +331,7 @@ as implemented by \ccc{CGAL::Plane_separator<NT>}.
\ccc{CGAL::Sliding_Fair<SpatialPoint, PointContainer, SpatialSeparator>}.
\item
\ccc{TreeTraits} denoting a traits class for the construction of a tree.
\ccc{PointTraits} denoting a traits class for the construction of a tree.
\ccc{CGAL::Kd_tree_traits_point< SpatialPoint, Splitter >} provides an implementation.
\item
@ -360,18 +360,18 @@ a weighted Minkowski metric.
\item
\ccc{SpatialTree} denoting a tree supporting spatial searching
\ccc{CGAL::Kd_tree<TreeTraits>} provides an implementation of $k$-$d$ trees.
\ccc{CGAL::Kd_tree<PointTraits>} provides an implementation of $k$-$d$ trees.
\end{itemize}
\subsection{Neighbor search}
\subsection{Neighbor Search}
The four classes implementing neighbor searching algorithms are
\begin{itemize}
\item
The class \ccc{CGAL::General_standard_search<TreeTraits, Distance, QueryItem, SpatialTree>}
The class \ccc{CGAL::K_neighbor_search<PointTraits, Distance, QueryItem, SpatialTree>}
implementing the standard search strategy for general distances
like the Manhattan distance for iso-rectangles.
Requires no use of extended nodes in the $k$-$d$ tree and supports only $k$
@ -379,21 +379,22 @@ neighbor searching for queries defined by points or spatial objects.
\item
The class \ccc{CGAL::General_priority_search<TreeTraits, GeneralDistance, QueryItem, SpatialTree>} implementing the priority search
strategy for general distances
like the Manhattan distance for iso-rectangles.
Requires no use of extended nodes in the $k$-$d$ tree and supports incremental
neighbor searching and distance browsing for queries defined by points or spatial objects.
The class \ccc{CGAL::Incremental_neighbor_search<PointTraits,
GeneralDistance, QueryItem, SpatialTree>} implementing the incremental
search strategy for general distances like the Manhattan distance for
iso-rectangles. Requires no use of extended nodes in the $k$-$d$ tree
and supports incremental neighbor searching and distance browsing for
queries defined by points or spatial objects.
\item
The class \ccc{CGAL::Orthogonal_standard_search<TreeTraits, OrthogonalDistance, SpatialTree>} implementing the standard search strategy for
orthogonal distances
The class \ccc{CGAL::Orthogonal_k_neighbor_search<PointTraits, OrthogonalDistance, SpatialTree>}
implementing the standard search strategy for orthogonal distances
like a weighted Minkowski distance. Requires the use of extended nodes in the $k$-$d$ tree and supports
only $k$ neighbor searching for point queries.
\item
The class \ccc{Orthogonal_priority_search<TreeTraits, GeneralDistance, QueryItem, SpatialTree>} implementing the priority search strategy for general
distances
The class \ccc{Orthogonal_incremental_neighbor_search<PointTraits, GeneralDistance, QueryItem, SpatialTree>}
implementing the priority search strategy for general distances
like a weighted Minkowski distance. Requires the use of extended nodes in the $k$-$d$ tree and supports incremental
neighbor searching and distance browsing for point queries.
@ -410,7 +411,7 @@ methods to compute bounding boxes
of point sets and methods to split iso-rectangles.
\item
The class \ccc{CGAL::Kd_tree_node<TreeTraits>} implementing $k$-$d$ tree nodes.
The class \ccc{CGAL::Kd_tree_node<PointTraits>} implementing $k$-$d$ tree nodes.
\end{itemize}
@ -511,10 +512,9 @@ generate empty cells.
\section{Backward compability with the Kd-trees package}
\label{Spatial_searching:Backward_compability_Kd-trees}
In release 2.3 of CGAL
$k$-$d$ trees have been implemented by the class \ccc{CGAL::Kdtree_d<TreeTraits>},
that is parameterized with one of the
traits classes
In release 2.3 of \cgal\ $k$-$d$ trees have been implemented by the
class \ccc{CGAL::Kdtree_d<PointTraits>}, that is parameterized with
one of the traits classes
\begin{itemize}

48
Packages/Spatial_searching/doc_tex/basic/Spatial_searching/intro.tex
View File

@ -2,9 +2,9 @@
\section{Introduction}
The {\bf spatial searching} package implements
exact and approximate distance browsing
by providing implementations of algorithms supporting
The spatial searching package implements exact and approximate
distance browsing by providing implementations of algorithms
supporting
\begin{itemize}
@ -90,7 +90,7 @@ neighbor must not be smaller than $r/(1+\epsilon)$. Obviously, for
$\epsilon=0$ we get the exact result, and the larger $\epsilon$ is,
the less exact the result.
\subsection{Range searching}
\subsection{Range Searching}
{\bf Exact range searching} and {\bf approximate range searching} is
supported using exact or fuzzy $d$-dimensional objects enclosing a
@ -218,7 +218,7 @@ coordinates.
\ccIncludeExampleCode{../../examples/Spatial_searching/Fuzzy_range_query.C}
\subsection{Example of Distance Browsing}
\subsection{Example of Incremental Searching}
This example program illustrates distance browsing for $4$-dimensional
points with a positive first coordinate using orthogonal priority
@ -241,7 +241,7 @@ in this file as well.
\ccIncludeExampleCode{../../examples/Spatial_searching/User_defined_point_and_distance.C}
\section{Software design}
\section{Software Design}
\begin{ccTexOnly}
\begin{figure}[t]
@ -331,7 +331,7 @@ as implemented by \ccc{CGAL::Plane_separator<NT>}.
\ccc{CGAL::Sliding_Fair<SpatialPoint, PointContainer, SpatialSeparator>}.
\item
\ccc{TreeTraits} denoting a traits class for the construction of a tree.
\ccc{PointTraits} denoting a traits class for the construction of a tree.
\ccc{CGAL::Kd_tree_traits_point< SpatialPoint, Splitter >} provides an implementation.
\item
@ -360,18 +360,18 @@ a weighted Minkowski metric.
\item
\ccc{SpatialTree} denoting a tree supporting spatial searching
\ccc{CGAL::Kd_tree<TreeTraits>} provides an implementation of $k$-$d$ trees.
\ccc{CGAL::Kd_tree<PointTraits>} provides an implementation of $k$-$d$ trees.
\end{itemize}
\subsection{Neighbor search}
\subsection{Neighbor Search}
The four classes implementing neighbor searching algorithms are
\begin{itemize}
\item
The class \ccc{CGAL::General_standard_search<TreeTraits, Distance, QueryItem, SpatialTree>}
The class \ccc{CGAL::K_neighbor_search<PointTraits, Distance, QueryItem, SpatialTree>}
implementing the standard search strategy for general distances
like the Manhattan distance for iso-rectangles.
Requires no use of extended nodes in the $k$-$d$ tree and supports only $k$
@ -379,21 +379,22 @@ neighbor searching for queries defined by points or spatial objects.
\item
The class \ccc{CGAL::General_priority_search<TreeTraits, GeneralDistance, QueryItem, SpatialTree>} implementing the priority search
strategy for general distances
like the Manhattan distance for iso-rectangles.
Requires no use of extended nodes in the $k$-$d$ tree and supports incremental
neighbor searching and distance browsing for queries defined by points or spatial objects.
The class \ccc{CGAL::Incremental_neighbor_search<PointTraits,
GeneralDistance, QueryItem, SpatialTree>} implementing the incremental
search strategy for general distances like the Manhattan distance for
iso-rectangles. Requires no use of extended nodes in the $k$-$d$ tree
and supports incremental neighbor searching and distance browsing for
queries defined by points or spatial objects.
\item
The class \ccc{CGAL::Orthogonal_standard_search<TreeTraits, OrthogonalDistance, SpatialTree>} implementing the standard search strategy for
orthogonal distances
The class \ccc{CGAL::Orthogonal_k_neighbor_search<PointTraits, OrthogonalDistance, SpatialTree>}
implementing the standard search strategy for orthogonal distances
like a weighted Minkowski distance. Requires the use of extended nodes in the $k$-$d$ tree and supports
only $k$ neighbor searching for point queries.
\item
The class \ccc{Orthogonal_priority_search<TreeTraits, GeneralDistance, QueryItem, SpatialTree>} implementing the priority search strategy for general
distances
The class \ccc{Orthogonal_incremental_neighbor_search<PointTraits, GeneralDistance, QueryItem, SpatialTree>}
implementing the priority search strategy for general distances
like a weighted Minkowski distance. Requires the use of extended nodes in the $k$-$d$ tree and supports incremental
neighbor searching and distance browsing for point queries.
@ -410,7 +411,7 @@ methods to compute bounding boxes
of point sets and methods to split iso-rectangles.
\item
The class \ccc{CGAL::Kd_tree_node<TreeTraits>} implementing $k$-$d$ tree nodes.
The class \ccc{CGAL::Kd_tree_node<PointTraits>} implementing $k$-$d$ tree nodes.
\end{itemize}
@ -511,10 +512,9 @@ generate empty cells.
\section{Backward compability with the Kd-trees package}
\label{Spatial_searching:Backward_compability_Kd-trees}
In release 2.3 of CGAL
$k$-$d$ trees have been implemented by the class \ccc{CGAL::Kdtree_d<TreeTraits>},
that is parameterized with one of the
traits classes
In release 2.3 of \cgal\ $k$-$d$ trees have been implemented by the
class \ccc{CGAL::Kdtree_d<PointTraits>}, that is parameterized with
one of the traits classes
\begin{itemize}