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% | Reference manual page: Kd_tree.tex
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% | 1.07.2001   Johan W.H. Tangelder
% | Package: ASPAS
% | 
\RCSdef{\RCSKdtreeRev}{$Id$}
\RCSdefDate{\RCSKdtreeDate}{$Date$}
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\begin{ccRefClass}{Kd_tree<Traits, Splitter, UseExtendedNode>}  %% add template arg's if necessary

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\ccDefinition  
The class \ccRefName\ defines a $k$-$d$ tree. 

\ccInclude{CGAL/Kd_tree.h}

\ccParameters

Expects for the first template argument a model of the concept
\ccc{SearchTraits}, for example \ccc{CGAL::Search_traits_2<CGAL::Cartesian<double> >}.

Expects for the second template argument a model for the concept \ccc{Splitter}.
It defaults  to \ccc{Sliding_midpoint<Traits>}.

Expects for the third template argument \ccc{CGAL::Tag_true}, if the 
tree shall be built with extended nodes, and \ccc{CGAL::Tag_false} otherwise.

\ccTypes

\ccTypedef{Traits::Point_d Point_d;} {Point class.}
\ccTypedef{Traits::FT FT;}{Number type.}
\ccNestedType{Splitter}{Splitter type.}

\ccNestedType{iterator}{Bidirectional const iterator with value type \ccc{Point_d} that allows
	to enumerate all points in the tree.}

\begin{ccAdvanced}
\ccNestedType{Node_handle}{A handle with value type \ccc{Kd_tree_node<Traits,Splitter>}.}
\ccNestedType{Node_const_handle}{A const handle with value type \ccc{Kd_tree_node<Traits,Splitter>}.}
\ccNestedType{Point_d_iterator} {Random access const iterator 
with value type \ccc{Point_d*}.}
\end{ccAdvanced}

\ccCreation
\ccCreationVariable{tree}

\ccConstructor{Kd_tree(Splitter s=Splitter(),Traits t=Traits());}{Constructs an empty $k$-$d$ tree.}

\ccConstructor
{template <class InputIterator> Kd_tree(InputIterator first, InputIterator beyond, Splitter s=Splitter(),Traits t=Traits());}
{
Constructs a $k$-$d$ tree on the elements from the sequence 
\ccc{[first, beyond)} using the splitting rule implemented by \ccc{s}. 
The value type of the \ccc{InputIterator} must be \ccc{Point_d}.
}

% -----------------------------------
\ccOperations

\ccMethod{void insert(Point_d p);}
{Inserts the point \ccc{p} in the $k$-$d$ tree.}

\ccMethod{template <class InputIterator> void insert(InputIterator first, InputIterator beyond);}
{Inserts the elements from the sequence \ccc{[first, beyond)} in the $k$-$d$ tree.
The value type of the \ccc{InputIterator} must be \ccc{Point_d}.}

\ccMethod{
template <class OutputIterator, class FuzzyQueryItem>
OutputIterator search(OutputIterator it, FuzzyQueryItem q) const;}
{Reports the points that are approximately contained by~\ccc{q}.
The types \ccc{FuzzyQueryItem::Point_d} and \ccc{Point_d} must be equivalent.
To use this function \ccc{Traits} must be a model of the concept \ccc{RangeSearchTraits}.
} 

\ccMethod{iterator begin() const;}{Returns a const iterator to the first point in the tree.}
\ccMethod{iterator end() const;}{Returns the corresponding past-the-end const iterator.}

\ccMethod{void clear();}{Removes all points from the $k$-$d$ tree.}


\ccMethod{unsigned int size() const;}
{Returns the number of points that are stored in the tree.}

\ccMethod{Traits traits() const;}{return the instance of the traits used to construct the tree.}

\begin{ccAdvanced}

\ccMethod{Node_handle root();}
{Returns a handle to the root node of the tree.}

\ccMethod{Node_const_handle root() const;}
{Returns a const handle to the root node of the tree.}

\ccMethod{const Kd_tree_rectangle<FT>& bounding_box() const;}{returns a const reference
to the bounding box of the root node of the tree.}

\ccMethod{std::ostream& statistics(std::ostream& s) const;}
{
Inserts statistics of the tree into the output stream \ccc{s}.
}

\end{ccAdvanced}

% -----------------------------------

\ccSeeAlso
Tree.
\ccc{CGAL::Kd_tree_node<Traits>},\\
\ccc{CGAL::Search_traits_2<Kernel>},\\
\ccc{CGAL::Search_traits_3<Kernel>},\\
\ccc{CGAL::Search_traits<FT_,Point,CartesianIterator,ConstructCartesianIterator>}.

\end{ccRefClass}

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