Math::Vector::Real::kdTree(3pm) 			User Contributed Perl Documentation			   Math::Vector::Real::kdTree(3pm)

NAME

       Math::Vector::Real::kdTree - kd-Tree implementation on top of Math::Vector::Real

SYNOPSIS

	 use Math::Vector::Real::kdTree;

	 use Math::Vector::Real;
	 use Math::Vector::Real::Random;

	 my @v = map Math::Vector::Real->random_normal(4), 1..1000;

	 my $tree = Math::Vector::Real::kdTree->new(@v);

	 my $ix = $tree->find_nearest_neighbor(V(0, 0, 0, 0));

	 say "nearest neighbor is $ix, $v[$ix]";

DESCRIPTION

       This module implements a kd-Tree data structure in Perl and some related algorithms.

       The following methods are provided:

       $t = Math::Vector::Real::kdTree->new(@points)
	   Creates a new kdTree containing the gived points.

       $t->insert($p)
	   Inserts the given point into the kdTree.

       $s = $t->size
	   Returns the number of points inside the tree.

       $p = $t->at($ix)
	   Returns the point at the given index inside the tree.

       $t->move($ix, $p)
	   Moves the point at index $ix to the new given position readjusting the tree structure accordingly.

       ($ix, $d) = $t->find_nearest_neighbor($p, $max_d, $but_ix)
	   Find the nearest neighbor for the given point $p and returns its index and the distance between the two points (in scalar context the
	   index is returned).

	   If $max_d is defined, the search is limited to the points within that distance

	   If $but_ix is defined, the point with the given index is not considered.

       @ix = $t->find_nearest_neighbor_all_internal
	   Returns the index of the nearest neighbor for every point inside the tree.

	   It is equivalent to (though, internally, it uses a better algorithm):

	     @ix = map {
		       scalar $t->nearest_neighbor($t->at($_), undef, $_)
		   } 0..($t->size - 1);

       @ix = $t->find_in_ball($z, $d, $but)
       $n = $t->find_in_ball($z, $d, $but)
	   Finds the points inside the tree contained in the hypersphere with center $z and radius $d.

	   In scalar context returns the number of points found. In list context returns the indexes of the points.

	   If the extra argument $but is provided. The point with that index is ignored.

       @ix = $t->ordered_by_proximity
	   Returns the indexes of the points in an ordered where is likely that the indexes of near vectors are also in near positions in the
	   list.

SEE ALSO

       http://en.wikipedia.org/wiki/K-d_tree <http://en.wikipedia.org/wiki/K-d_tree>

       Math::Vector::Real

COPYRIGHT AND LICENSE

       Copyright (C) 2011, 2012 by Salvador FandiA~Xo <sfandino@yahoo.com>

       This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself, either Perl version 5.12.3 or,
       at your option, any later version of Perl 5 you may have available.

perl v5.14.2							    2012-06-18					   Math::Vector::Real::kdTree(3pm)