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This commit is contained in:
Andreas Fabri 2003-11-19 20:10:07 +00:00
parent c9448250da
commit 86f1dd500a
6 changed files with 22 additions and 18 deletions
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16
Packages/Spatial_searching/doc_tex/Spatial_searching/intro.tex
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@ -178,6 +178,7 @@ example illustrates distance browsing and the last example illustrates
nearest and furthest neighbour searching using a user defined point nearest and furthest neighbour searching using a user defined point
and distance class. and distance class.
\newpage
\subsection{Example of Nearest Neighbor Searching} \subsection{Example of Nearest Neighbor Searching}
The first example illustrates nearest neighbor searching. The random The first example illustrates nearest neighbor searching. The random
@ -189,8 +190,9 @@ computes squared distances instead of the Euclidean distance itself.
Finally, if the Euclidean distance is reported the square root is Finally, if the Euclidean distance is reported the square root is
taken. taken.
\ccIncludeExampleCode{../../examples/Spatial_searching/Nearest_neighbor_searching.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/Nearest_neighbor_searching.C}
\newpage
\subsection{Example of Selecting a Splitting Rule and Setting the Bucket Size.} \subsection{Example of Selecting a Splitting Rule and Setting the Bucket Size.}
This example program illustrates selecting a splitting rule and This example program illustrates selecting a splitting rule and
@ -198,7 +200,7 @@ setting the maximal allowed bucket size. The only differences with
the previous example are the declaration of the Fair the previous example are the declaration of the Fair
splitting rule, needed to set the maximal allowed bucket size. splitting rule, needed to set the maximal allowed bucket size.
\ccIncludeExampleCode{../../examples/Spatial_searching/Using_fair_splitting_rule.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/Using_fair_splitting_rule.C}
\subsection{Example of General Neighbor Searching} \subsection{Example of General Neighbor Searching}
@ -207,7 +209,7 @@ neighbor searching using 4-dimensional Cartesian coordinates. Five
approximate nearest and furthest neighbors of the query rectangle approximate nearest and furthest neighbors of the query rectangle
$[0.1,0.2]^4$ are computed. $[0.1,0.2]^4$ are computed.
\ccIncludeExampleCode{../../examples/Spatial_searching/General_neighbor_searching.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/General_neighbor_searching.C}
\subsection{Example of a Range Query} \subsection{Example of a Range Query}
@ -215,7 +217,7 @@ This example program illustrates approximate range querying for
4-dimensional fuzzy iso-rectangles and spheres using homogeneous 4-dimensional fuzzy iso-rectangles and spheres using homogeneous
coordinates. coordinates.
\ccIncludeExampleCode{../../examples/Spatial_searching/Fuzzy_range_query.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/Fuzzy_range_query.C}
\subsection{Example of Incremental Searching} \subsection{Example of Incremental Searching}
@ -225,7 +227,7 @@ points with a positive first coordinate using orthogonal priority
search. search.
\ccIncludeExampleCode{../../examples/Spatial_searching/Distance_browsing.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/Distance_browsing.C}
\subsection{Example Illustrating Use of User Defined Point and Distance Class} \subsection{Example Illustrating Use of User Defined Point and Distance Class}
@ -236,10 +238,10 @@ implementation of the Euclidean distance.
We first have to introduce a point class. We have put the glue layer We first have to introduce a point class. We have put the glue layer
in this file as well. in this file as well.
\ccIncludeExampleCode{../../examples/Spatial_searching/Point.h} \ccIncludeExampleCode{../../../examples/Spatial_searching/Point.h}
\ccIncludeExampleCode{../../examples/Spatial_searching/User_defined_point_and_distance.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/User_defined_point_and_distance.C}
\section{Software Design} \section{Software Design}

2
Packages/Spatial_searching/doc_tex/Spatial_searching/main.tex
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@ -1,5 +1,5 @@
\chapter{Spatial Searching} \chapter{Spatial Searching}
\label{ChapterUserSpatialSearching} \label{ChapterUserSpatialSearching}
\input{Spatial_searching/intro.tex} \input{intro.tex}
%% EOF %% EOF

2
Packages/Spatial_searching/doc_tex/Spatial_searching_ref/Euclidean_distance.tex
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@ -28,7 +28,7 @@ To optimize distance computations squared distances are used.
\ccParameters \ccParameters
Expects for the template argument a model of the concept Expects for the template argument a model of the concept
\ccc{SearchTraits}, for example \ccc{CGAL::Search_traits_2<Kernel>}. \ccc{SearchTraits}, for example \ccc{CGAL::Search_traits_2<CGAL::Cartesian_double> >}.
\ccIsModel \ccIsModel

16
Packages/Spatial_searching/doc_tex/basic/Spatial_searching/intro.tex
View File

@ -178,6 +178,7 @@ example illustrates distance browsing and the last example illustrates
nearest and furthest neighbour searching using a user defined point nearest and furthest neighbour searching using a user defined point
and distance class. and distance class.
\newpage
\subsection{Example of Nearest Neighbor Searching} \subsection{Example of Nearest Neighbor Searching}
The first example illustrates nearest neighbor searching. The random The first example illustrates nearest neighbor searching. The random
@ -189,8 +190,9 @@ computes squared distances instead of the Euclidean distance itself.
Finally, if the Euclidean distance is reported the square root is Finally, if the Euclidean distance is reported the square root is
taken. taken.
\ccIncludeExampleCode{../../examples/Spatial_searching/Nearest_neighbor_searching.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/Nearest_neighbor_searching.C}
\newpage
\subsection{Example of Selecting a Splitting Rule and Setting the Bucket Size.} \subsection{Example of Selecting a Splitting Rule and Setting the Bucket Size.}
This example program illustrates selecting a splitting rule and This example program illustrates selecting a splitting rule and
@ -198,7 +200,7 @@ setting the maximal allowed bucket size. The only differences with
the previous example are the declaration of the Fair the previous example are the declaration of the Fair
splitting rule, needed to set the maximal allowed bucket size. splitting rule, needed to set the maximal allowed bucket size.
\ccIncludeExampleCode{../../examples/Spatial_searching/Using_fair_splitting_rule.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/Using_fair_splitting_rule.C}
\subsection{Example of General Neighbor Searching} \subsection{Example of General Neighbor Searching}
@ -207,7 +209,7 @@ neighbor searching using 4-dimensional Cartesian coordinates. Five
approximate nearest and furthest neighbors of the query rectangle approximate nearest and furthest neighbors of the query rectangle
$[0.1,0.2]^4$ are computed. $[0.1,0.2]^4$ are computed.
\ccIncludeExampleCode{../../examples/Spatial_searching/General_neighbor_searching.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/General_neighbor_searching.C}
\subsection{Example of a Range Query} \subsection{Example of a Range Query}
@ -215,7 +217,7 @@ This example program illustrates approximate range querying for
4-dimensional fuzzy iso-rectangles and spheres using homogeneous 4-dimensional fuzzy iso-rectangles and spheres using homogeneous
coordinates. coordinates.
\ccIncludeExampleCode{../../examples/Spatial_searching/Fuzzy_range_query.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/Fuzzy_range_query.C}
\subsection{Example of Incremental Searching} \subsection{Example of Incremental Searching}
@ -225,7 +227,7 @@ points with a positive first coordinate using orthogonal priority
search. search.
\ccIncludeExampleCode{../../examples/Spatial_searching/Distance_browsing.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/Distance_browsing.C}
\subsection{Example Illustrating Use of User Defined Point and Distance Class} \subsection{Example Illustrating Use of User Defined Point and Distance Class}
@ -236,10 +238,10 @@ implementation of the Euclidean distance.
We first have to introduce a point class. We have put the glue layer We first have to introduce a point class. We have put the glue layer
in this file as well. in this file as well.
\ccIncludeExampleCode{../../examples/Spatial_searching/Point.h} \ccIncludeExampleCode{../../../examples/Spatial_searching/Point.h}
\ccIncludeExampleCode{../../examples/Spatial_searching/User_defined_point_and_distance.C} \ccIncludeExampleCode{../../../examples/Spatial_searching/User_defined_point_and_distance.C}
\section{Software Design} \section{Software Design}

2
Packages/Spatial_searching/doc_tex/basic/Spatial_searching/main.tex
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@ -1,5 +1,5 @@
\chapter{Spatial Searching} \chapter{Spatial Searching}
\label{ChapterUserSpatialSearching} \label{ChapterUserSpatialSearching}
\input{Spatial_searching/intro.tex} \input{intro.tex}
%% EOF %% EOF

2
Packages/Spatial_searching/doc_tex/basic/Spatial_searching_ref/Euclidean_distance.tex
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@ -28,7 +28,7 @@ To optimize distance computations squared distances are used.
\ccParameters \ccParameters
Expects for the template argument a model of the concept Expects for the template argument a model of the concept
\ccc{SearchTraits}, for example \ccc{CGAL::Search_traits_2<Kernel>}. \ccc{SearchTraits}, for example \ccc{CGAL::Search_traits_2<CGAL::Cartesian_double> >}.
\ccIsModel \ccIsModel