cgal/SearchStructures/doc_tex/SearchStructures_ref/Segment_tree_d.tex

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\begin{ccRefClass} {Segment_tree_d<Data, Window, Traits>}
\ccTypes
\ccNestedType{Data}{container \ccStyle{Data}.}
\ccNestedType{Window}{container \ccStyle{Window}.}
\ccNestedType{Traits}{class \ccStyle{Traits}.}
\ccCreation
\ccCreationVariable{s}
%\renewcommand{\ccAlternateThreeColumn}{\ccFalse}
\ccInclude{CGAL/Segment_tree_d.h}\\
\ccConstructor{Segment_tree_d<Data, Window,
Traits> s(Tree_base<Data, Window> sublayer_tree)}%
{A segment tree is defined, such that the subtree of each
vertex is of the same type prototype
\ccStyle{sublayer\_tree} is.\\
We assume that the dimension of the tree is $d$. This means, that
\ccStyle{ sublayer_tree} is a prototype of a $d-1$-dimensional
tree. All data items of the $d$-dimensional segment tree
have container type \ccStyle{Data}. The query window of the
tree has container type
\ccStyle{Window}. \ccStyle{Traits}
provides access to the corresponding data slots of container
\ccStyle{Data} and \ccStyle{Window} for the $d$-th
dimension. The traits class \ccStyle{Traits}
must at least provide all functions and type definitions
described, for example, in the reference page for
\ccc{tree_point_traits}.
The template class
described there is fully generic and should fulfill the most
requirements one can have.
In order
to generate a one-dimensional segment tree instantiate %
\ccStyle{Tree\_anchor<Data, Window> sublayer\_tree} %
with the same template parameters \ccStyle{Data} and
\ccStyle{Window} \ccStyle{Segment\_tree\_d} is defined. In
order to construct a two-dimensional segment tree, create
\ccStyle{Segment\_tree\_d} with
a one-dimensional \ccStyle{Segment\_tree\_d} with the
corresponding \ccStyle{Traits} of the first dimension.\\
\ccStyle{Precondition: Traits::Data==Data and
Traits::Window==Window.}}
\ccOperations
%\renewcommand{\ccAlternateThreeColumn}{\ccTrue}
\ccMethod{bool make_tree(In_it first, In_it last);}
{The tree is constructed according to the data items in the
sequence between the element pointed by iterator \ccStyle{first} and
iterator \ccStyle{last}.}
\ccPrecond{This function can only be called once. If it is the
first call the tree is build and \ccStyle{true} is
returned. Otherwise, nothing is done but a \ccStyle{CGAL
warning} is given and \ccStyle{false}
returned.}
\ccMethod{OutputIterator window_query(Window win, OutputIterator result);}
{ \ccStyle{
win}$=[a_1,b_1),\ldots , [a_d,b_d)$, $a_i,b_i\in T_i$, $1\le
i\le d$. All elements that
intersect the associated $d$-dimensional interval of
\ccStyle{win} are placed in the
associated sequence container of \ccStyle{OutputIterator} and
returns an
output iterator that points
to the last location the function wrote to.
In order to perform an inverse range query, a range query of
$\epsilon$ width has to be performed.
}
\ccMethod{OutputIterator enclosing_query(Window win, OutputIterator result);}%
{ All elements that
enclose the associated $d$-dimensional interval of
\ccStyle{win} are placed in the
associated sequence container of \ccStyle{OutputIterator} and returns an output iterator that points
to the last location the function wrote to.}
\ccMethod{bool is_valid();}{The tree structure is checked. For each
vertex either the
sublayer tree is a tree anchor, or it stores a (possibly empty)
list of data items. In the first case, the sublayer tree of the
vertex is checked on being valid. In the second case, each data
item is checked weather it contains the associated interval of
the vertex and does not contain the associated interval of the
parent vertex or not. True is returned if the tree structure is valid,
false otherwise.}
{\bf Protected Operations}
\ccMethod{bool is_inside(Window win,
Data object);}{returns true, if the
interval of \ccStyle{object} is contained in the
interval of \ccStyle{win}. False otherwise.}
\ccMethod{bool is_anchor();}{returns false.}
\ccImplementation
A $d$-dimensional segment tree is constructed in ${O}(n\log n^d)$ time.
An inverse range query is performed in time ${O}(k+{\log}^d n )$, where $k$
is the number of reported intervals.
The tree uses ${O}(n\log n^d)$ storage.
\end{ccRefClass}