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This commit is contained in:
Andreas Fabri 2003-11-16 21:11:08 +00:00
parent dad9db39a5
commit eb33c92fca
8 changed files with 24 additions and 26 deletions
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8
Packages/Spatial_searching/doc_tex/Spatial_searching_ref/General_priority_search.tex → Packages/Spatial_searching/doc_tex/Spatial_searching_ref/Incremental_neighbor_search.tex
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@ -1,5 +1,5 @@
% +------------------------------------------------------------------------+
% | Reference manual page: General_priority_search.tex
% | Reference manual page: Incremental_neighbor_search.tex
% +------------------------------------------------------------------------+
% | 1.07.2001 Johan W.H. Tangelder
% | Package: ASPAS
@ -11,7 +11,7 @@
% +------------------------------------------------------------------------+
\begin{ccRefClass}{General_priority_search<PointTraits, GeneralDistance, Splitter, SpatialTree>}
\begin{ccRefClass}{Incremental_neighbor_search<PointTraits, GeneralDistance, Splitter, SpatialTree>}
%% \ccHtmlCrossLink{} %% add further rules for cross referencing links
%% \ccHtmlIndexC[class]{} %% add further index entries
@ -22,7 +22,7 @@ The class \ccRefName\ implements incremental nearest and furthest neighbor searc
using priority search on a tree. It is not required that the tree is
built with extended nodes.
\ccInclude{CGAL/General_priority_search.h}
\ccInclude{CGAL/Incremental_neighbor_search.h}
\ccParameters
@ -56,7 +56,7 @@ template argument \ccc{CGAL::Tag_false} makes that the tree is built with unexte
\def\ccLongParamLayout{\ccTrue}
\ccConstructor{General_priority_search(Tree& tree, QueryItem q, NT eps=NT(0.0),
\ccConstructor{Incremental_neighbor_search(Tree& tree, QueryItem q, NT eps=NT(0.0),
bool search_nearest=true, GeneralDistance d=GeneralDistance());}
{Constructor for incremental neighbor searching of the query item \ccc{q}
in the points stored \ccc{tree} using a distance \ccc{d} and approximation factor \ccc{eps}. }

17
Packages/Spatial_searching/doc_tex/Spatial_searching_ref/General_standard_search.tex → Packages/Spatial_searching/doc_tex/Spatial_searching_ref/K_neighbor_search.tex
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@ -1,17 +1,17 @@
% +------------------------------------------------------------------------+
% | Reference manual page: General_standard_search.tex
% | Reference manual page: K_neighbor_search.tex
% +------------------------------------------------------------------------+
% | 1.07.2001 Johan W.H. Tangelder
% | Package: ASPAS
% |
\RCSdef{\RCSGeneralstandardsearchRev}{$Revision$}
\RCSdefDate{\RCSGeneralstandardsearchDate}{$Date$}
\RCSdef{\RCSKneighborsearchRev}{$Revision$}
\RCSdefDate{\RCSKneighborsearchDate}{$Date$}
% |
%%RefPage: end of header, begin of main body
% +------------------------------------------------------------------------+
\begin{ccRefClass}{General_standard_search<PointTraits, Splitter, GeneralDistance, SpatialTree>}
\begin{ccRefClass}{K_neighbor_search<PointTraits, Splitter, GeneralDistance, SpatialTree>}
%% \ccHtmlCrossLink{} %% add further rules for cross referencing links
%% \ccHtmlIndexC[class]{} %% add further index entries
@ -19,10 +19,9 @@
\ccDefinition
The class \ccRefName\ implements approximate $k$-nearest and $k$-furthest neighbor searching
using standard search on a tree using a general distance
class.
using standard search on a tree using a general distance class.
\ccInclude{CGAL/General_standard_search.h}
\ccInclude{CGAL/K_neighbor_search.h}
\ccParameters
@ -54,8 +53,8 @@ template argument \ccc{CGAL::Tag_false} makes that the tree is built with unexte
\def\ccLongParamLayout{\ccTrue}
\ccConstructor{General_standard_search(Tree& tree, Query_item q, int k=1, NT eps=NT(0.0),
bool search_nearest=true, GeneralDistance d=GeneralDistance());}
\ccConstructor{K_neighbor_search(Tree& tree, Query_item q, int k=1, NT eps=NT(0.0),
bool search_nearest=true, GeneralDistance d=GeneralDistance());}
{Constructor for searching approximately $k$ neighbors of the query item \ccc{q}
in the points stored in \ccc{tree} using
distance class $d$ and approximation factor \ccc{eps}.}

0
Packages/Spatial_searching/doc_tex/Spatial_searching_ref/Orthogonal_priority_search.tex → Packages/Spatial_searching/doc_tex/Spatial_searching_ref/Orthogonal_incremental_neighbor_search.tex
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0
Packages/Spatial_searching/doc_tex/Spatial_searching_ref/Orthogonal_standard_search.tex → Packages/Spatial_searching/doc_tex/Spatial_searching_ref/Orthogonal_k_neighbor_search.tex
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8
Packages/Spatial_searching/doc_tex/basic/Spatial_searching_ref/General_priority_search.tex → Packages/Spatial_searching/doc_tex/basic/Spatial_searching_ref/Incremental_neighbor_search.tex
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@ -1,5 +1,5 @@
% +------------------------------------------------------------------------+
% | Reference manual page: General_priority_search.tex
% | Reference manual page: Incremental_neighbor_search.tex
% +------------------------------------------------------------------------+
% | 1.07.2001 Johan W.H. Tangelder
% | Package: ASPAS
@ -11,7 +11,7 @@
% +------------------------------------------------------------------------+
\begin{ccRefClass}{General_priority_search<PointTraits, GeneralDistance, Splitter, SpatialTree>}
\begin{ccRefClass}{Incremental_neighbor_search<PointTraits, GeneralDistance, Splitter, SpatialTree>}
%% \ccHtmlCrossLink{} %% add further rules for cross referencing links
%% \ccHtmlIndexC[class]{} %% add further index entries
@ -22,7 +22,7 @@ The class \ccRefName\ implements incremental nearest and furthest neighbor searc
using priority search on a tree. It is not required that the tree is
built with extended nodes.
\ccInclude{CGAL/General_priority_search.h}
\ccInclude{CGAL/Incremental_neighbor_search.h}
\ccParameters
@ -56,7 +56,7 @@ template argument \ccc{CGAL::Tag_false} makes that the tree is built with unexte
\def\ccLongParamLayout{\ccTrue}
\ccConstructor{General_priority_search(Tree& tree, QueryItem q, NT eps=NT(0.0),
\ccConstructor{Incremental_neighbor_search(Tree& tree, QueryItem q, NT eps=NT(0.0),
bool search_nearest=true, GeneralDistance d=GeneralDistance());}
{Constructor for incremental neighbor searching of the query item \ccc{q}
in the points stored \ccc{tree} using a distance \ccc{d} and approximation factor \ccc{eps}. }

17
Packages/Spatial_searching/doc_tex/basic/Spatial_searching_ref/General_standard_search.tex → Packages/Spatial_searching/doc_tex/basic/Spatial_searching_ref/K_neighbor_search.tex
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@ -1,17 +1,17 @@
% +------------------------------------------------------------------------+
% | Reference manual page: General_standard_search.tex
% | Reference manual page: K_neighbor_search.tex
% +------------------------------------------------------------------------+
% | 1.07.2001 Johan W.H. Tangelder
% | Package: ASPAS
% |
\RCSdef{\RCSGeneralstandardsearchRev}{$Revision$}
\RCSdefDate{\RCSGeneralstandardsearchDate}{$Date$}
\RCSdef{\RCSKneighborsearchRev}{$Revision$}
\RCSdefDate{\RCSKneighborsearchDate}{$Date$}
% |
%%RefPage: end of header, begin of main body
% +------------------------------------------------------------------------+
\begin{ccRefClass}{General_standard_search<PointTraits, Splitter, GeneralDistance, SpatialTree>}
\begin{ccRefClass}{K_neighbor_search<PointTraits, Splitter, GeneralDistance, SpatialTree>}
%% \ccHtmlCrossLink{} %% add further rules for cross referencing links
%% \ccHtmlIndexC[class]{} %% add further index entries
@ -19,10 +19,9 @@
\ccDefinition
The class \ccRefName\ implements approximate $k$-nearest and $k$-furthest neighbor searching
using standard search on a tree using a general distance
class.
using standard search on a tree using a general distance class.
\ccInclude{CGAL/General_standard_search.h}
\ccInclude{CGAL/K_neighbor_search.h}
\ccParameters
@ -54,8 +53,8 @@ template argument \ccc{CGAL::Tag_false} makes that the tree is built with unexte
\def\ccLongParamLayout{\ccTrue}
\ccConstructor{General_standard_search(Tree& tree, Query_item q, int k=1, NT eps=NT(0.0),
bool search_nearest=true, GeneralDistance d=GeneralDistance());}
\ccConstructor{K_neighbor_search(Tree& tree, Query_item q, int k=1, NT eps=NT(0.0),
bool search_nearest=true, GeneralDistance d=GeneralDistance());}
{Constructor for searching approximately $k$ neighbors of the query item \ccc{q}
in the points stored in \ccc{tree} using
distance class $d$ and approximation factor \ccc{eps}.}

0
Packages/Spatial_searching/doc_tex/basic/Spatial_searching_ref/Orthogonal_priority_search.tex → Packages/Spatial_searching/doc_tex/basic/Spatial_searching_ref/Orthogonal_incremental_neighbor_search.tex
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0
Packages/Spatial_searching/doc_tex/basic/Spatial_searching_ref/Orthogonal_standard_search.tex → Packages/Spatial_searching/doc_tex/basic/Spatial_searching_ref/Orthogonal_k_neighbor_search.tex
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