cgal/Packages/kdtree/doc_tex/SearchStructures/kd-tree.tex

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%--------------------------------------------------------
% kd-tree.tex
% Specification of kd-tree,
%
% by Eyal Flato, Dan Halperin, Sariel Har-Peled
%--------------------------------------------------------
% History
% Version 1.0, 26/4/97
% All about kd-trees.
% Adapted by Hans Tangelder using reference pages
%--------------------------------------------------------
%\documentclass[12pt]{book}
%\usepackage{epsfig,epsf}
%\usepackage{cprog}
%\pagestyle{empty}
%\textwidth 15.6cm
%\textheight 23 cm
%\topmargin -14mm
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%\oddsidemargin 3mm
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%\def\keyword#1{{\em #1\/}}
\renewcommand{\Re}{{\rm I\!\hspace{-0.025em} R}}
%\newcommand{\Section}[1]{Section~{\ref{#1}}}
%\newcommand{\Chapter}[1]{Chapter~{\ref{#1}}}
\newcommand{\new}[1]{\marginpar{\sf #1}}
\newcommand{\kdt}{kd-tree}
\newcommand{\kdts}{kd-trees}
\newcommand{\brc}[1]{\left\{ {#1} \right\}}
\def\Ipe#1{\def\IPEfile{#1}\input{#1}}
\parindent0em
\setlength{\parskip}{1ex minus 0.9ex}
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%\begin{document}
%\tableofcontents
\clearpage
\section{Kd-Trees}
\label{KDT_section}
%This is a simple, but nevertheless efficient, data structure for orthogonal
%range searching. In particular, {\cgal} {\kdt} seems to perform better
%than the theoreticly superior range trees, as implemented in \cgal.
%See Section \ref{KDT_sec:appendix}, for comparative results. Of
%course, the {\cgal} range-tree implementation is more flexible than the
%{\kdt} implementation by enabling to layer together range-trees and
%segment-trees in the same data structure, see \Chapter{Range:Trees} for the
%details.
%The implementation of the {\kdt} is independent of the implementation of
%the rest of {\cgal} and can be used separately from \cgal.
%In \Section{KDT_sec:intro}, we present the {\kdt} structure and its
%performance. \Section{KDT_sec:class:main} presents the class
%\ccStyle{Kdtree_d<I>}.
Kd-trees are implemented by
the class \ccc{Kdtree_d}, that is parameterized with a traits class defining
the interface between the class and the geometric primitives used.
For the description of the traits classes \ccc{Kd_Interface}, \ccc{Kd_Interface_2d}
and \ccc{Kd_Interface_3d} provided by {\cgal} please refer to the reference pages.
Also refer to the reference pages, for the description of the formal requirements for a class to be
a kd-tree traits class by the concept \ccc{Kdtree_d_traits}.
% {\cgal} provides ready-made interface classes that are presented in
% Section~\ref{KDT_sec:def:interface}.
% The formal requirements for a class to be a {\kdt} traits class is
% described in Section~\ref{KDT_sec:def:interface}.
% \section{Introduction}
% \label{KDT_sec:intro}
For a given set $S = \brc{ p_1, \ldots, p_n }$ on $n$ points in
$\Re^d$, it is sometimes useful to be able to answer {\em orthogonal
range-searching} on $S$; namely, given an axis parallel query box
$B$ in $\Re^d$, one would like to ``quickly'' determine the subset of
points of $S$ lying inside $B$.
%Orthogonal range-searching is being widely used in databases,
%computational-geometry, and vision {\large \bf (references?)}.
Several data structures were suggested for that problem.
Foremost among those, at least theoreticly, is the range-tree data structure
with $O(n \log^{d-1} n)$ preprocessing time, $O(n\log^{d-1} n)$ space,
and $O(\log^{d} n + k)$ query time, where $k$ is the output size of
the query.
%See \Chapter{Range:Trees} for the the {\cgal}implementation of range-trees.
A theoreticaly inferior data structure is the \kdt, which offers $O(n
\log{n})$ preprocessing time, $O(n)$ space, and $O(n^{1-1/d})$ query
time. The {\kdt} is a binary tree constructed as follows: we compute
the point $p_i$ of $S$ such that its first coordinate is the median
value among $p_1^1, \ldots, p_n^1$, where $p_i^k$ denote the $k$-th
coordinate of the $i$-th point of $S$. Let $S_1$ denote all the points
of $S$ with first coordinate smaller than $p_i$'s first coordinate, and
let $S_2 = S \setminus S_1$. Let $T_1,T_2$ be the {\kdts} constructed
recursively for $S_1, S_2$, respectively. The {\kdt} of $S$ is simply
the binary tree having $T_1$ as its left subtree and $T_2$ as its
right subtree. We apply this algorithm recursively, splitting the sets
in the $i$-level of the {\kdt} using the median point in the $k$-th
coordinate, where $k=(i \;\;mod \;\;n) + 1$. See Figure
\ref{KDT_fig:kdtree} for an illustration.
The resulting data structure has linear size, $O(n\log{n})$
preprocessing time, and can answer a query in $O(n^{1-1/d} +k)$ time,
where $k$ is the size of the query output. See \cite{bkos-cgaa-97}.
\begin{ccTexOnly}
\begin{figure}[hb]
\begin{center}
\Ipe{kdtree.ipe}
\end{center}
\caption{The partition of a set of points induced by a
{\kdt} of the points}
\label{KDT_fig:kdtree}
\end{figure}
\end{ccTexOnly}
\subsection{Example illustrating orthogonal range-searching}
\ccExample
This example illustrates orthogonal range-searching.
The {\kdt} is built using 2D points from the CGAL kernel.
\begin{verbatim}
#include <CGAL/Cartesian.h>
#include <iostream>
#include <ctime>
#include <cassert>
#include <list>
#include <CGAL/kdtree_d.h>
typedef CGAL::Cartesian<double> K;
typedef K::Point_2 point;
typedef CGAL::Kdtree_interface_2d<point> kd_interface;
typedef CGAL::Kdtree_d<kd_interface> kd_tree;
typedef kd_tree::Box box;
typedef std::list<point> points_list;
int main()
{
CGAL::Kdtree_d<kd_interface> tree(2);
points_list l, res;
std::cout << "Insering evenly 81 points in the square (0,0)-(10,10) ...\n\n";
for (int i=1; i<10; i++)
for (int j=1; j<10; j++)
{
point p(i,j);
l.push_front(p);
}
// building the tree
tree.build( l );
// checking validity
if ( ! tree.is_valid() )
tree.dump();
assert( tree.is_valid() );
// Defining and searching the box r
double lx,ly,rx,ry;
std::cout << "Define your query square.\nEnter left x coordinate: " ;
std::cin >> lx ;
std::cout << "Enter left y coordinate: ";
std::cin >> ly;
std::cout << "Enter right x coordinate: " ;
std::cin >> rx ;
std::cout << "Enter right y coordinate: ";
std::cin >> ry;
std::cout << std::endl;
box r(point(lx,ly), point(rx,ry) ,2);
tree.search( std::back_inserter( res ), r );
std::cout << "Listing of the points in the square: \n" ;
std::copy (res.begin(),res.end(),std::ostream_iterator<point>(std::cout," \n") );
std::cout << std::endl;
tree.delete_all();
return 0;
}
\end{verbatim}
%for html
\lcHtml{\label{KDT_fig:kdtree}}
\begin{ccHtmlOnly}
<P>
<center><img border=0 src="./kdtree.gif" alt=" "><br>
The partition of a set of points induced by a kd-tree of the points</center>
\end{ccHtmlOnly}
\section{Reference Pages for Kd-Trees}
\label{KDT_sec:RefPages}
\begin{ccRefClass}{Kdtree_d<Traits>}
\ccThree{KdtreexdxTraitsx}{}{\hspace*{7.6cm}}
\ccTwo{}{\hspace*{7.6cm}}
%\begin{ccClassTemplate}{Kdtree_d<Traits>}
\ccDefinition An object $T$ of the class
\ccStyle{Kdtree_d<Traits>} is the {\kdt} induced by a set of points
in $d$-dimensions.
\ccInclude{ CGAL/kdtree_d.h}
\ccTypes
\ccNestedType{Box}{represents an axis-parallel box in
$d$-dimensions. The box might be unbounded.}
\ccTypedef{typedef Traits::Point Point;}{}
\ccTypedef{typedef list<Point> List_points;}{}
\ccCreation
\ccCreationVariable{kd_tree}
\ccConstructor{Kdtree_d<Traits>( int dim = 2 );}{construct an
empty {\kdt} of dimension \ccStyle{dim}.}
\ccOperations
\ccThree{bool}{}{\hspace*{7.6cm}}
\ccMethod{bool is_valid(bool verbose = false, int level = 0)
const;} {perform internal consistency checks to verify the
correctness of the \kdt}
\ccMethod{void build( list<Point> &l );}{construct the
{\kdt} from the points stored in \ccStyle{l}.
\ccPrecond{all the points in \ccStyle{l} are of dimension no
smaller than the dimension of {{\ccVar} } itself.} }
\ccMethod{void search( back_insert_iterator<List_points>
result, Box & query_box );}{return into \ccStyle{result} all
the points of the {{\kdt} } that lie inside \ccStyle{query_box}}
%\end{ccClassTemplate}
\end{ccRefClass}
%\ccHeading{\ccStyle{Kdtree_d<Traits>::Box}}
%\begin{ccClassTemplate}{Kdtree_d<I>::Box}
\begin{ccRefClass}[Kdtree_d<Traits>::]{Box}
\ccDefinition An object $B$ of the class
\ccStyle{Box} is a $d$-dimensional box (it may
be unbounded). A $d$-dimensional box is the set defined by the
Cartesian set $[l_1, r_1) \times [l_2, r_2) \times \cdots \times
[l_d,r_d)$.
\ccCreation
\ccCreationVariable{box}
\ccConstructor{Box( int d );}{Construct a box corresponding to
the whole $d$-dimensional space}
\ccConstructor{Box( Point left, Right right, int d );}{ Construct
the axis parallel box in the $d$-dimensional space defined by
the points \ccStyle{left}, \ccStyle{right}.}
\ccOperations
\ccMethod{void set_coord_left( int k, Point & left );}{ set
the left endpoint of the $k$-th dimensional interval of \ccVar\ {}
to be the $k$-th coordinate of \ccStyle{left}}
\ccMethod{void set_coord_right( int k, Point & right );}{ set
the right endpoint of the $k$-th dimensional interval of \ccVar\ {}
to be the $k$-th coordinate of \ccStyle{right}}
\ccMethod{bool is_in( Point pnt );}{return \ccStyle{true}
if \ccStyle{pnt} lies inside \ccVar.}
\ccMethod{bool is_coord_in_range( int k, Point pnt );}{ return
\ccStyle{true} if the $k$-th coordinate of \ccStyle{pnt} lies
inside the $k$-th dimensional interval of \ccVar.}
%\end{ccClassTemplate}
%\end{ccClass}
\end{ccRefClass}
%********************************************************
\begin{ccRefConcept}{Kdtree_d_traits}
The \ccStyle{Kdtree_d} class is parameterized with the
interface class \ccStyle{Traits} which defines the abstract interface
between the \ccStyle{Kdtree_d} class and the data (i.e., points). The following
requirement catalog lists the primitives, i.e.,~types, member functions
etc., that must be defined for a class that can be used to
parameterize \kdts. Ready-made implementation are available
by the \ccStyle{Kdtree_d} default traits classes.
\ccThree{static void}{}{\hspace*{7.1cm}}
\ccTwo{}{\hspace*{7.1cm}}
\begin{ccClass} {Traits}
\ccCreationVariable{t}
\ccDefinition
A class \ccClassName\ that satisfies the requirements of an
interface class for a \ccStyle{Kdtree_d} class must provide the
following types and operations.
\ccTypes
\ccNestedType{Point}{A type to hold a input item.}
\ccOperations
% \ccSetTwoOfThreeColumns{4cm}{4cm}
\ccMethod{static Comparison_result compare(
int k,
const Point & p0,
const Point & p1);} {compare the \ccStyle{k}-th coordinate
of $p0$ and $p1$. Return \ccStyle{LARGER} if $p0_k >
p1_k$, \ccStyle{SMALLER} if $p0_k < p1_k$, or else
\ccStyle{EQUAL}. }
\ccMethod{static void copy_coord( int k, Point & dest,
const Point & src );}{Copy the $k$-th coordinate of $src$ to
the $k$-th coordinate of $dest$.}
\ccMethod{static int dimension( const Point & pnt );}
{return the dimension of \ccStyle{pnt}}
\end{ccClass}
\end{ccRefConcept}
\begin{ccRefClass}{Kdtree_Interface<Point>}
{\cgal} contains a default implementation for the Kdtree\_d traits
class, that
may be applied to any
standard class of point provided by \cgal.
The default traits class \ccStyle{Kdtree_Interface<Point>} is
templated with a parameter \ccStyle{Point}, which is required to
supply the following methods:
\begin{itemize}
\item Constructor, and copy constructor.
\item \ccStyle{int dimension();} - return the dimension of the point.
\item \ccStyle{operator[](int dim);} - an operator for accessing
the various coordinates of the given point. Used also to copy
coordinates between points.
\end{itemize}
There are two other default traits classes \ccStyle{Kdtree_Interface_2d<Point>}
and \ccStyle{Kdtree_Interface_3d<Point>} which should be used when using
2D and 3D points from the {\cgal} kernel. This is done since the points in the
kernel do not support changing their coordinates through direct access.
\end{ccRefClass}
%********************************************************
%********************************************************
\begin{ccRefClass}{Kdtree_Interface_2d<Point>}
The default traits class \ccStyle{Kdtree_Interface_2d<Point>}
should be used when using 2D points from the {\cgal} kernel.
It is
templated with a parameter \ccStyle{Point}, which is required to
supply the following methods:
\begin{itemize}
\item Constructor, and copy constructor.
\item \ccStyle{int dimension();} - return the dimension of the point.
\item \ccStyle{operator[](int dim);} - an operator for accessing
the various coordinates of the given point. Used also to copy
coordinates between points.
\end{itemize}
\end{ccRefClass}
%********************************************************
\begin{ccRefClass}{Kdtree_Interface_3d<Point>}
The default traits class \ccStyle{Kdtree_Interface_3d<Point>}
should be used when using 3D points from the {\cgal} kernel.
It is
templated with a parameter \ccStyle{Point}, which is required to
supply the following methods:
\begin{itemize}
\item Constructor, and copy constructor.
\item \ccStyle{int dimension();} - return the dimension of the point.
\item \ccStyle{operator[](int dim);} - an operator for accessing
the various coordinates of the given point. Used also to copy
coordinates between points.
\end{itemize}
\end{ccRefClass}
%*********************************************************
%-------------------------------------------------------------------------
% Bibiliography
%-------------------------------------------------------------------------
%\bibliographystyle{alpha}
%\bibliography{geometry}
%\end{document}
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