|
|
|
|
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)
