Salvador Fandiño García > Math-Vector-Real-kdTree > Math::Vector::Real::kdTree

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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 kd-Tree containing the given points.

$t2 = $t->clone

Creates a duplicate of the tree. The two trees will share internal read only data so this method is more efficient in terms of memory usage than others performing a deep copy.

my $ix = $t->insert($p0, $p1, ...)

Inserts the given points into the kd-Tree.

Returns the index assigned to the first point inserted.

$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, $max_d, @but_ix)
($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

Optionally, a list of point indexes to be excluded from the search can be passed or, alternatively, a reference to a hash containing the indexes of the points to be excluded.

@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

Math::Vector::Real

COPYRIGHT AND LICENSE ^

Copyright (C) 2011-2013 by Salvador Fandiño <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.

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